From 4480e918b2a060c14680cad648447fd889ac0930 Mon Sep 17 00:00:00 2001 From: Moses Paul R Date: Thu, 2 Jan 2025 23:22:49 +0000 Subject: [PATCH 01/92] dynamic option parsing and updated documentation --- convert.py | 3 +- marker/builders/ocr.py | 8 ++-- marker/config/parser.py | 14 +----- marker/config/printer.py | 68 +++++++++++++++++++++++---- marker/converters/pdf.py | 16 ++++--- marker/processors/blockquote.py | 15 +++++- marker/processors/ignoretext.py | 21 +++++++-- marker/processors/line_numbers.py | 17 +++++++ marker/processors/list.py | 10 ++++ marker/processors/llm/__init__.py | 4 ++ marker/providers/pdf.py | 52 +++++++++++++++++++- marker/renderers/html.py | 16 +++++++ marker/renderers/json.py | 12 +++++ marker/schema/blocks/base.py | 4 +- marker/schema/blocks/sectionheader.py | 4 +- 15 files changed, 223 insertions(+), 41 deletions(-) diff --git a/convert.py b/convert.py index 1c8b2efa..7efda686 100755 --- a/convert.py +++ b/convert.py @@ -13,6 +13,7 @@ from tqdm import tqdm from marker.config.parser import ConfigParser +from marker.config.printer import CustomClickPrinter from marker.converters.pdf import PdfConverter from marker.logger import configure_logging from marker.models import create_model_dict @@ -59,7 +60,7 @@ def process_single_pdf(args): print(traceback.format_exc()) -@click.command() +@click.command(cls=CustomClickPrinter) @click.argument("in_folder", type=str) @ConfigParser.common_options @click.option("--chunk_idx", type=int, default=0, help="Chunk index to convert") diff --git a/marker/builders/ocr.py b/marker/builders/ocr.py index 5a9fb537..5edfe813 100644 --- a/marker/builders/ocr.py +++ b/marker/builders/ocr.py @@ -1,4 +1,4 @@ -from typing import List +from typing import List, Optional from ftfy import fix_text from surya.model.detection.model import EfficientViTForSemanticSegmentation @@ -33,9 +33,9 @@ class OcrBuilder(BaseBuilder): languages (List[str]): A list of languages to use for OCR. Default is None. """ - recognition_batch_size: int | None = None - detection_batch_size: int | None = None - languages: List[str] | None = None + recognition_batch_size: Optional[int] = None + detection_batch_size: Optional[int] = None + languages: Optional[List[str]] = None def __init__(self, detection_model: EfficientViTForSemanticSegmentation, recognition_model: OCREncoderDecoderModel, config=None): super().__init__(config) diff --git a/marker/config/parser.py b/marker/config/parser.py index 62900c2f..c0996d24 100644 --- a/marker/config/parser.py +++ b/marker/config/parser.py @@ -25,17 +25,13 @@ def common_options(fn): fn = click.option("--page_range", type=str, default=None, help="Page range to convert, specify comma separated page numbers or ranges. Example: 0,5-10,20")( fn) - fn = click.option("--force_ocr", is_flag=True, help="Force OCR on the whole document.")(fn) fn = click.option("--processors", type=str, default=None, help="Comma separated list of processors to use. Must use full module path.")(fn) fn = click.option("--config_json", type=str, default=None, help="Path to JSON file with additional configuration.")(fn) fn = click.option("--languages", type=str, default=None, help="Comma separated list of languages to use for OCR.")(fn) fn = click.option("--disable_multiprocessing", is_flag=True, default=False, help="Disable multiprocessing.")(fn) - fn = click.option("--paginate_output", is_flag=True, default=False, help="Paginate output.")(fn) fn = click.option("--disable_image_extraction", is_flag=True, default=False, help="Disable image extraction.")(fn) - fn = click.option("--use_llm", is_flag=True, default=False, help="Enable higher quality processing with LLMs.")(fn) - fn = click.option("--strip_existing_ocr", is_flag=True, default=False, help="Strip existing OCR text from the PDF.")(fn) return fn def generate_config_dict(self) -> Dict[str, any]: @@ -53,8 +49,6 @@ def generate_config_dict(self) -> Dict[str, any]: config["debug_data_folder"] = output_dir case "page_range": config["page_range"] = parse_range_str(v) - case "force_ocr": - config["force_ocr"] = True case "languages": config["languages"] = v.split(",") case "config_json": @@ -62,14 +56,10 @@ def generate_config_dict(self) -> Dict[str, any]: config.update(json.load(f)) case "disable_multiprocessing": config["pdftext_workers"] = 1 - case "paginate_output": - config["paginate_output"] = True case "disable_image_extraction": config["extract_images"] = False - case "use_llm": - config["use_llm"] = True - case "strip_existing_ocr": - config["strip_existing_ocr"] = True + case _: + config[k] = v return config def get_renderer(self): diff --git a/marker/config/printer.py b/marker/config/printer.py index 20eac045..ea1159bd 100644 --- a/marker/config/printer.py +++ b/marker/config/printer.py @@ -1,12 +1,14 @@ import importlib import inspect import pkgutil +from typing import Optional import click from marker.builders import BaseBuilder from marker.converters import BaseConverter from marker.processors import BaseProcessor +from marker.renderers import BaseRenderer def find_subclasses(base_class): @@ -39,16 +41,64 @@ def get_help(self, ctx): click.echo(help_text) def parse_args(self, ctx, args): - if 'config' in args and '--help' in args: - click.echo("Here is a list of all the Builders, Processors, and Converters in Marker along with their attributes:") - base_classes = [BaseBuilder, BaseProcessor, BaseConverter] - for base in base_classes: + display_help = 'config' in args and '--help' in args + if display_help: + click.echo("Here is a list of all the Builders, Processors, Converters and Renderers in Marker along with their attributes:") + + base_classes = [BaseBuilder, BaseProcessor, BaseConverter, BaseRenderer] + for base in base_classes: + if display_help: click.echo(f"{base.__name__.removeprefix('Base')}s:\n") - subclasses = find_subclasses(base) - for class_name, class_type in subclasses.items(): - doc = class_type.__doc__ - if doc and "Attributes:" in doc: - click.echo(f" {class_name}: {doc}") + subclasses = find_subclasses(base) + for class_name, class_type in subclasses.items(): + doc = class_type.__doc__ or "" + if display_help and doc and "Attributes:" in doc: + click.echo(f" {class_name}: {doc}") + parsed_doc = self._parse_indentation_based_tree(doc) + for attr, attr_type in class_type.__annotations__.items(): + if attr in doc and attr_type in [str, int, float, bool, Optional[int], Optional[float], Optional[str]]: + if attr not in [p.name for p in ctx.command.params]: + default = getattr(class_type, attr) + is_flag = attr_type in [bool, Optional[bool]] and not default + ctx.command.params.append( + click.Option([f"--{attr}"], help=" ".join(parsed_doc[attr]), type=attr_type, default=default, is_flag=is_flag) + ) + if display_help: ctx.exit() + super().parse_args(ctx, args) + + + def _parse_indentation_based_tree(self, doc): + stack = [] + tree = [] + + for line in doc.splitlines(): + stripped_line = line.lstrip() + if not stripped_line: + continue + + indent_level = len(line) - len(stripped_line) + + node = {"content": stripped_line, "children": []} + while stack and stack[-1]['indent'] >= indent_level: + stack.pop() + + if stack: + stack[-1]["children"].append(node) + else: + tree.append(node) + + node['indent'] = indent_level + stack.append(node) + + attributes = {} + for node in tree: + if node['content'].startswith("Attributes:"): + for child in node["children"]: + var = child["content"].split(" ")[0] + descs = [c["content"] for c in child["children"] if c] + attributes[var] = descs + + return attributes diff --git a/marker/converters/pdf.py b/marker/converters/pdf.py index 5cf2c918..0894bf10 100644 --- a/marker/converters/pdf.py +++ b/marker/converters/pdf.py @@ -6,11 +6,11 @@ import inspect from collections import defaultdict -from typing import Any, Dict, List, Type +from typing import Any, Dict, List, Optional, Type from marker.builders.document import DocumentBuilder -from marker.builders.llm_layout import LLMLayoutBuilder from marker.builders.layout import LayoutBuilder +from marker.builders.llm_layout import LLMLayoutBuilder from marker.builders.ocr import OcrBuilder from marker.builders.structure import StructureBuilder from marker.converters import BaseConverter @@ -20,12 +20,12 @@ from marker.processors.document_toc import DocumentTOCProcessor from marker.processors.equation import EquationProcessor from marker.processors.footnote import FootnoteProcessor -from marker.processors.llm.llm_form import LLMFormProcessor -from marker.processors.llm.llm_table import LLMTableProcessor -from marker.processors.llm.llm_text import LLMTextProcessor from marker.processors.ignoretext import IgnoreTextProcessor from marker.processors.line_numbers import LineNumbersProcessor from marker.processors.list import ListProcessor +from marker.processors.llm.llm_form import LLMFormProcessor +from marker.processors.llm.llm_table import LLMTableProcessor +from marker.processors.llm.llm_text import LLMTextProcessor from marker.processors.page_header import PageHeaderProcessor from marker.processors.sectionheader import SectionHeaderProcessor from marker.processors.table import TableProcessor @@ -48,11 +48,15 @@ class PdfConverter(BaseConverter): The keys are `BlockTypes` enum values, representing the types of blocks, and the values are corresponding `Block` class implementations to use instead of the defaults. + + use_llm (bool): + Enable higher quality processing with LLMs. + Default is False. """ override_map: Dict[BlockTypes, Type[Block]] = defaultdict() use_llm: bool = False - def __init__(self, artifact_dict: Dict[str, Any], processor_list: List[str] | None = None, renderer: str | None = None, config=None): + def __init__(self, artifact_dict: Dict[str, Any], processor_list: Optional[List[str]] = None, renderer: str | None = None, config=None): super().__init__(config) for block_type, override_block_type in self.override_map.items(): diff --git a/marker/processors/blockquote.py b/marker/processors/blockquote.py index cc71e3ab..4a5fb288 100644 --- a/marker/processors/blockquote.py +++ b/marker/processors/blockquote.py @@ -5,7 +5,20 @@ class BlockquoteProcessor(BaseProcessor): """ - A processor for tagging blockquotes + A processor for tagging blockquotes. + + Attributes: + min_x_indent (float): + The minimum horizontal indentation required to consider a block as part of a blockquote. Expressed as a percentage of the block width. + Default is 0.05 (5%). + + x_start_tolerance (float): + The maximum allowable difference between the starting x-coordinates of consecutive blocks to consider them aligned. Expressed as a percentage of the block width. + Default is 0.01 (1%). + + x_end_tolerance (float): + The maximum allowable difference between the ending x-coordinates of consecutive blocks to consider them aligned. Expressed as a percentage of the block width. + Default is 0.01 (1%). """ block_types = (BlockTypes.Text, BlockTypes.TextInlineMath) min_x_indent = 0.05 # % of block width diff --git a/marker/processors/ignoretext.py b/marker/processors/ignoretext.py index 7873c313..dc72a8e8 100644 --- a/marker/processors/ignoretext.py +++ b/marker/processors/ignoretext.py @@ -13,12 +13,27 @@ class IgnoreTextProcessor(BaseProcessor): """ - A processor for ignoring text blocks that are common elements in the document. + A processor for identifying and ignoring common text blocks in a document. + These blocks often represent repetitive or non-essential elements, such as headers, footers, or page numbers. Attributes: common_element_threshold (float): - The minimum fraction of pages that a block must appear in to be considered a common element. - Default is 0.6. + The fraction of pages a text block must appear on to be considered a common element. + Blocks that meet or exceed this threshold are marked as common elements. + Default is 0.6 (60% of pages). + + common_element_min_blocks (int): + The minimum number of occurrences of a text block within a document to consider it a common element. + This ensures that rare blocks are not mistakenly flagged. + Default is 3. + + max_streak (int): + The maximum number of consecutive occurrences of a text block allowed before it is classified as a common element. Helps to identify patterns like repeated headers or footers. + Default is 3. + + text_match_threshold (int): + The minimum fuzzy match score (0-100) required to classify a text block as similar to a common element. Higher values enforce stricter matching. + Default is 90. """ block_types = ( BlockTypes.Text, BlockTypes.PageHeader, diff --git a/marker/processors/line_numbers.py b/marker/processors/line_numbers.py index eea7be75..7c0a5116 100644 --- a/marker/processors/line_numbers.py +++ b/marker/processors/line_numbers.py @@ -4,6 +4,23 @@ class LineNumbersProcessor(BaseProcessor): + """ + A processor for ignoring line numbers. + Attributes: + strip_numbers_threshold (float): + The fraction of lines or tokens in a block that must be numeric to consider them as line numbers. + Default is 0.6 (60%). + + min_lines_in_block (int): + The minimum number of lines required in a block for it to be considered during processing. + Ensures that small blocks are ignored as they are unlikely to contain meaningful line numbers. + Default is 4. + + min_line_length (int): + The minimum length of a line (in characters) to consider it significant when checking for + numeric prefixes or suffixes. Prevents false positives for short lines. + Default is 10. + """ block_types = (BlockTypes.Text, BlockTypes.TextInlineMath) strip_numbers_threshold: int = .6 min_lines_in_block: int = 4 diff --git a/marker/processors/list.py b/marker/processors/list.py index ff394a4a..59e6c35b 100644 --- a/marker/processors/list.py +++ b/marker/processors/list.py @@ -9,6 +9,16 @@ class ListProcessor(BaseProcessor): """ A processor for merging lists across pages and columns + + Attributes: + ignored_block_types (List[BlockTypes]): + The list of block types to ignore when merging lists. + Default is [BlockTypes.PageHeader, BlockTypes.PageFooter]. + + min_x_indent (float): + The minimum horizontal indentation required to consider a block as a nested list item. + This is expressed as a percentage of the page width and is used to determine hierarchical relationships within a list. + Default is 0.01 (1% of page width). """ block_types = (BlockTypes.ListGroup,) ignored_block_types = (BlockTypes.PageHeader, BlockTypes.PageFooter) diff --git a/marker/processors/llm/__init__.py b/marker/processors/llm/__init__.py index 3af3db90..2ba76b0b 100644 --- a/marker/processors/llm/__init__.py +++ b/marker/processors/llm/__init__.py @@ -29,6 +29,10 @@ class BaseLLMProcessor(BaseProcessor): Default is 3. timeout (int): The timeout for requests to the Gemini model. + Default is 60 seconds. + image_expansion_ratio (float): + The ratio to expand the image by when cropping. + Default is 0.01. gemini_rewriting_prompt (str): The prompt to use for rewriting text. Default is a string containing the Gemini rewriting prompt. diff --git a/marker/providers/pdf.py b/marker/providers/pdf.py index d3601bb7..eff83fb9 100644 --- a/marker/providers/pdf.py +++ b/marker/providers/pdf.py @@ -1,7 +1,7 @@ import atexit import ctypes import re -from typing import List, Set +from typing import List, Optional, Set import pypdfium2 as pdfium import pypdfium2.raw as pdfium_c @@ -19,6 +19,54 @@ class PdfProvider(BaseProvider): + """ + A provider for PDF files. + + Attributes: + filepath (str): + The path to the PDF file. + + page_range (List[int]): + The range of pages to process. + Default is None, which will process all pages. + + pdftext_workers (int): + The number of workers to use for pdftext. + Default is 4. + + flatten_pdf (bool): + Whether to flatten the PDF structure. + Default is True. + + force_ocr (bool): + Whether to force OCR on the whole document. + Default is False. + + ocr_invalid_chars (tuple): + The characters to consider invalid for OCR. + Default is (chr(0xfffd), "�"). + + ocr_space_threshold (float): + The minimum ratio of spaces to non-spaces to detect bad text. + Default is 0.7. + + ocr_newline_threshold (float): + The minimum ratio of newlines to non-newlines to detect bad text. + Default is 0.6. + + ocr_alphanum_threshold (float): + The minimum ratio of alphanumeric characters to non-alphanumeric characters to consider an alphanumeric character. + Default is 0.3. + + image_threshold (float): + The minimum coverage ratio of the image to the page to consider skipping the page. + Default is .65. + + strip_existing_ocr (bool): + Whether to strip existing OCR text from the PDF. + Default is True. + """ + page_range: List[int] | None = None pdftext_workers: int = 4 flatten_pdf: bool = True @@ -57,7 +105,7 @@ def cleanup_pdf_doc(self): if self.doc is not None: self.doc.close() - def font_flags_to_format(self, flags: int | None) -> Set[str]: + def font_flags_to_format(self, flags: Optional[int]) -> Set[str]: if flags is None: return {"plain"} diff --git a/marker/renderers/html.py b/marker/renderers/html.py index b9ea1c3a..24654aeb 100644 --- a/marker/renderers/html.py +++ b/marker/renderers/html.py @@ -24,6 +24,22 @@ class HTMLOutput(BaseModel): class HTMLRenderer(BaseRenderer): + """ + A renderer for HTML output. + + Attributes: + page_blocks (list): + The list of block types to consider as pages. + Default is [BlockTypes.Page]. + + paginate_output (bool): + Whether to paginate the output. + Default is False. + + image_extraction_mode (Literal["lowres", "highres"]): + The mode to use for extracting images. + Default is "highres". + """ page_blocks: list = [BlockTypes.Page] paginate_output: bool = False image_extraction_mode: Literal["lowres", "highres"] = "highres" diff --git a/marker/renderers/json.py b/marker/renderers/json.py index ff3a3843..a11ce8a2 100644 --- a/marker/renderers/json.py +++ b/marker/renderers/json.py @@ -35,6 +35,18 @@ def reformat_section_hierarchy(section_hierarchy): class JSONRenderer(BaseRenderer): + """ + A renderer for JSON output. + + Attributes: + image_blocks (list): + The list of block types to consider as images. + Default is [BlockTypes.Picture, BlockTypes.Figure]. + + page_blocks (list): + The list of block types to consider as pages. + Default is [BlockTypes.Page]. + """ image_blocks: list = [BlockTypes.Picture, BlockTypes.Figure] page_blocks: list = [BlockTypes.Page] diff --git a/marker/schema/blocks/base.py b/marker/schema/blocks/base.py index 4c6b5df9..2b64463d 100644 --- a/marker/schema/blocks/base.py +++ b/marker/schema/blocks/base.py @@ -1,6 +1,6 @@ from __future__ import annotations -from typing import TYPE_CHECKING, List, Literal, Optional, Dict, Sequence +from typing import TYPE_CHECKING, Dict, List, Literal, Optional, Sequence from pydantic import BaseModel, ConfigDict, field_validator @@ -33,7 +33,7 @@ class BlockOutput(BaseModel): class BlockId(BaseModel): page_id: int - block_id: int | None = None + block_id: Optional[int] = None block_type: BlockTypes | None = None def __str__(self): diff --git a/marker/schema/blocks/sectionheader.py b/marker/schema/blocks/sectionheader.py index 1b326492..c104f0b4 100644 --- a/marker/schema/blocks/sectionheader.py +++ b/marker/schema/blocks/sectionheader.py @@ -1,10 +1,12 @@ +from typing import Optional + from marker.schema import BlockTypes from marker.schema.blocks import Block class SectionHeader(Block): block_type: BlockTypes = BlockTypes.SectionHeader - heading_level: int | None = None + heading_level: Optional[int] = None def assemble_html(self, child_blocks, parent_structure): if self.ignore_for_output: From 1dcf4b6f2a13fe738a5ca59b45dd02ea128c1e53 Mon Sep 17 00:00:00 2001 From: Vik Paruchuri Date: Sun, 5 Jan 2025 17:06:20 -0500 Subject: [PATCH 02/92] Fix complex region processor --- marker/schema/blocks/__init__.py | 1 + marker/schema/blocks/complexregion.py | 6 ++--- tests/processors/test_llm_processors.py | 30 ++++++++++++++++++++++++- 3 files changed, 33 insertions(+), 4 deletions(-) diff --git a/marker/schema/blocks/__init__.py b/marker/schema/blocks/__init__.py index 936decb7..2203d044 100644 --- a/marker/schema/blocks/__init__.py +++ b/marker/schema/blocks/__init__.py @@ -17,3 +17,4 @@ from marker.schema.blocks.table import Table from marker.schema.blocks.text import Text from marker.schema.blocks.toc import TableOfContents +from marker.schema.blocks.complexregion import ComplexRegion diff --git a/marker/schema/blocks/complexregion.py b/marker/schema/blocks/complexregion.py index 5e341bdd..139bc1dd 100644 --- a/marker/schema/blocks/complexregion.py +++ b/marker/schema/blocks/complexregion.py @@ -4,11 +4,11 @@ class ComplexRegion(Block): block_type: BlockTypes = BlockTypes.ComplexRegion - markdown: str | None = None + html: str | None = None def assemble_html(self, child_blocks, parent_structure): - if self.markdown: - return self.markdown + if self.html: + return self.html else: template = super().assemble_html(child_blocks, parent_structure) return f"

{template}

" diff --git a/tests/processors/test_llm_processors.py b/tests/processors/test_llm_processors.py index 3b0e20c8..41639b42 100644 --- a/tests/processors/test_llm_processors.py +++ b/tests/processors/test_llm_processors.py @@ -1,6 +1,7 @@ from unittest.mock import MagicMock, Mock import pytest +from marker.processors.llm.llm_complex import LLMComplexRegionProcessor from marker.processors.llm.llm_form import LLMFormProcessor from marker.processors.llm.llm_image_description import LLMImageDescriptionProcessor @@ -9,6 +10,7 @@ from marker.processors.table import TableProcessor from marker.renderers.markdown import MarkdownRenderer from marker.schema import BlockTypes +from marker.schema.blocks import ComplexRegion @pytest.mark.filename("form_1040.pdf") @pytest.mark.config({"page_range": [0]}) @@ -138,4 +140,30 @@ def test_llm_caption_processor(pdf_document, mocker): renderer = MarkdownRenderer({"extract_images": False}) md = renderer(pdf_document).markdown - assert description in md \ No newline at end of file + assert description in md + + +@pytest.mark.filename("A17_FlightPlan.pdf") +@pytest.mark.config({"page_range": [0]}) +def test_llm_complex_region_processor(pdf_document, mocker): + md = "This is some *markdown* for a complex region." + mock_cls = Mock() + mock_cls.return_value.generate_response.return_value = {"corrected_markdown": md * 25} + mocker.patch("marker.processors.llm.GoogleModel", mock_cls) + + # Replace the block with a complex region + old_block = pdf_document.pages[0].children[0] + new_block = ComplexRegion( + **old_block.dict(exclude=["id", "block_id", "block_type"]), + ) + pdf_document.pages[0].replace_block(old_block, new_block) + + # Test processor + processor = LLMComplexRegionProcessor({"use_llm": True, "google_api_key": "test"}) + processor(pdf_document) + + # Ensure the rendering includes the description + renderer = MarkdownRenderer() + rendered_md = renderer(pdf_document).markdown + + assert md in rendered_md \ No newline at end of file From 8d8fa2455158c84846a5633a43c987e45880b6f2 Mon Sep 17 00:00:00 2001 From: Moses Paul R Date: Mon, 6 Jan 2025 09:26:38 +0000 Subject: [PATCH 03/92] use typing Annotations for config --- marker/builders/document.py | 24 +++---- marker/builders/layout.py | 74 ++++++++++--------- marker/builders/llm_layout.py | 93 ++++++++++++------------ marker/builders/ocr.py | 32 ++++----- marker/builders/structure.py | 27 +++---- marker/config/printer.py | 74 +++++++------------ marker/converters/pdf.py | 29 ++++---- marker/processors/blockquote.py | 42 ++++++----- marker/processors/debug.py | 70 +++++++++--------- marker/processors/equation.py | 39 +++++----- marker/processors/footnote.py | 18 +---- marker/processors/ignoretext.py | 55 +++++++------- marker/processors/line_numbers.py | 42 +++++------ marker/processors/list.py | 24 +++---- marker/processors/llm/__init__.py | 78 ++++++++++---------- marker/processors/llm/llm_table.py | 22 +++--- marker/processors/sectionheader.py | 45 ++++++------ marker/processors/table.py | 43 +++++------ marker/processors/text.py | 25 ++++--- marker/providers/pdf.py | 112 ++++++++++++++--------------- marker/renderers/html.py | 33 +++++---- marker/renderers/json.py | 27 ++++--- 22 files changed, 506 insertions(+), 522 deletions(-) diff --git a/marker/builders/document.py b/marker/builders/document.py index 60c42749..8c2f683b 100644 --- a/marker/builders/document.py +++ b/marker/builders/document.py @@ -1,4 +1,5 @@ -from marker.settings import settings +from typing import Annotated + from marker.builders import BaseBuilder from marker.builders.layout import LayoutBuilder from marker.builders.ocr import OcrBuilder @@ -12,18 +13,17 @@ class DocumentBuilder(BaseBuilder): """ Constructs a Document given a PdfProvider, LayoutBuilder, and OcrBuilder. - - Attributes: - lowres_image_dpi (int): - DPI setting for low-resolution page images used for Layout and Line Detection. - Default is 96. - - highres_image_dpi (int): - DPI setting for high-resolution page images used for OCR. - Default is 192. """ - lowres_image_dpi: int = 96 - highres_image_dpi: int = 192 + lowres_image_dpi: Annotated[ + int, + "DPI setting for low-resolution page images used for Layout and Line Detection.", + "Default is 96." + ] = 96 + highres_image_dpi: Annotated[ + int, + "DPI setting for high-resolution page images used for OCR.", + "Default is 192." + ] = 192 def __call__(self, provider: PdfProvider, layout_builder: LayoutBuilder, ocr_builder: OcrBuilder): document = self.build_document(provider) diff --git a/marker/builders/layout.py b/marker/builders/layout.py index d2e8d5ca..43337d0a 100644 --- a/marker/builders/layout.py +++ b/marker/builders/layout.py @@ -1,15 +1,12 @@ -from typing import List +from typing import Annotated, List, Optional import numpy as np from surya.layout import batch_layout_detection -from surya.schema import LayoutResult from surya.model.layout.encoderdecoder import SuryaLayoutModel - -from surya.ocr_error import batch_ocr_error_detection -from surya.schema import OCRErrorDetectionResult from surya.model.ocr_error.model import DistilBertForSequenceClassification +from surya.ocr_error import batch_ocr_error_detection +from surya.schema import LayoutResult, OCRErrorDetectionResult -from marker.settings import settings from marker.builders import BaseBuilder from marker.providers import ProviderOutput, ProviderPageLines from marker.providers.pdf import PdfProvider @@ -18,40 +15,47 @@ from marker.schema.groups.page import PageGroup from marker.schema.polygon import PolygonBox from marker.schema.registry import get_block_class +from marker.settings import settings from marker.util import matrix_intersection_area class LayoutBuilder(BaseBuilder): """ A builder for performing layout detection on PDF pages and merging the results into the document. - - Attributes: - batch_size (int): - The batch size to use for the layout model. - Default is None, which will use the default batch size for the model. - - layout_coverage_min_lines (int): - The minimum number of PdfProvider lines that must be covered by the layout model - to consider the lines from the PdfProvider valid. Default is 1. - - layout_coverage_threshold (float): - The minimum coverage ratio required for the layout model to consider - the lines from the PdfProvider valid. Default is 0.3. - - document_ocr_threshold (float): - The minimum ratio of pages that must pass the layout coverage check - to avoid OCR. Default is 0.8. - - error_model_segment_length (int): - The maximum number of characters to send to the OCR error model. - Default is 1024. """ - batch_size = None - layout_coverage_min_lines = 1 - layout_coverage_threshold = .1 - document_ocr_threshold = .8 - error_model_segment_length = 512 - excluded_for_coverage = (BlockTypes.Figure, BlockTypes.Picture, BlockTypes.Table, BlockTypes.FigureGroup, BlockTypes.TableGroup, BlockTypes.PictureGroup) + batch_size: Annotated[ + Optional[int], + "The batch size to use for the layout model.", + "Default is None, which will use the default batch size for the model." + ] = None + layout_coverage_min_lines: Annotated[ + int, + "The minimum number of PdfProvider lines that must be covered by the layout model", + "to consider the lines from the PdfProvider valid.", + "Default is 1." + ] = 1 + layout_coverage_threshold: Annotated[ + float, + "The minimum coverage ratio required for the layout model to consider", + "the lines from the PdfProvider valid.", + "Default is 0.1." + ] = .1 + document_ocr_threshold: Annotated[ + float, + "The minimum ratio of pages that must pass the layout coverage check", + "to avoid OCR.", + "Default is 0.8." + ] = .8 + error_model_segment_length: Annotated[ + int, + "The maximum number of characters to send to the OCR error model.", + "Default is 512." + ] = 512 + excluded_for_coverage: Annotated[ + List[BlockTypes], + "A list of block types to exclude from the layout coverage check.", + "Default is [BlockTypes.Figure, BlockTypes.Picture, BlockTypes.Table, BlockTypes.FigureGroup, BlockTypes.TableGroup, BlockTypes.PictureGroup]." + ] = (BlockTypes.Figure, BlockTypes.Picture, BlockTypes.Table, BlockTypes.FigureGroup, BlockTypes.TableGroup, BlockTypes.PictureGroup) def __init__(self, layout_model: SuryaLayoutModel, ocr_error_model: DistilBertForSequenceClassification, config=None): self.layout_model = layout_model @@ -81,7 +85,7 @@ def surya_layout(self, pages: List[PageGroup]) -> List[LayoutResult]: ) return layout_results - def surya_ocr_error_detection(self, pages:List[PageGroup], provider_page_lines: ProviderPageLines) -> OCRErrorDetectionResult: + def surya_ocr_error_detection(self, pages: List[PageGroup], provider_page_lines: ProviderPageLines) -> OCRErrorDetectionResult: page_texts = [] for document_page in pages: page_text = '' @@ -102,7 +106,7 @@ def surya_ocr_error_detection(self, pages:List[PageGroup], provider_page_lines: page_texts, self.ocr_error_model, self.ocr_error_model.tokenizer, - batch_size=int(self.get_batch_size()) #TODO Better Multiplier + batch_size=int(self.get_batch_size()) # TODO Better Multiplier ) return ocr_error_detection_results diff --git a/marker/builders/llm_layout.py b/marker/builders/llm_layout.py index 28420fbd..2ad9d1a1 100644 --- a/marker/builders/llm_layout.py +++ b/marker/builders/llm_layout.py @@ -1,13 +1,8 @@ import json -import time -import traceback from concurrent.futures import ThreadPoolExecutor, as_completed -from typing import Optional +from typing import Annotated, Optional -import google.generativeai as genai -import PIL from google.ai.generativelanguage_v1beta.types import content -from google.api_core.exceptions import ResourceExhausted from surya.model.layout.encoderdecoder import SuryaLayoutModel from surya.model.ocr_error.model import DistilBertForSequenceClassification from tqdm import tqdm @@ -26,45 +21,48 @@ class LLMLayoutBuilder(LayoutBuilder): """ A builder for relabelling blocks to improve the quality of the layout. - - Attributes: - google_api_key (str): - The Google API key to use for the Gemini model. - Default is None. - confidence_threshold (float): - The confidence threshold to use for relabeling. - Default is 0.75. - picture_height_threshold (float): - The height threshold for pictures that may actually be complex regions. - model_name (str): - The name of the Gemini model to use. - Default is "gemini-1.5-flash". - max_retries (int): - The maximum number of retries to use for the Gemini model. - Default is 3. - max_concurrency (int): - The maximum number of concurrent requests to make to the Gemini model. - Default is 3. - timeout (int): - The timeout for requests to the Gemini model. - Default is 60 seconds. - topk_relabelling_prompt (str): - The prompt to use for relabelling blocks. - Default is a string containing the Gemini relabelling prompt. - complex_relabeling_prompt (str): - The prompt to use for complex relabelling blocks. - Default is a string containing the complex relabelling prompt. """ - google_api_key: Optional[str] = settings.GOOGLE_API_KEY - confidence_threshold: float = 0.75 - picture_height_threshold: float = 0.8 - model_name: str = "gemini-1.5-flash" - max_retries: int = 3 - max_concurrency: int = 3 - timeout: int = 60 - - topk_relabelling_prompt = """You are a layout expert specializing in document analysis. + google_api_key: Annotated[ + Optional[str], + "The Google API key to use for the Gemini model.", + "Default is None." + ] = settings.GOOGLE_API_KEY + confidence_threshold: Annotated[ + float, + "The confidence threshold to use for relabeling.", + "Default is 0.75." + ] = 0.75 + picture_height_threshold: Annotated[ + float, + "The height threshold for pictures that may actually be complex regions.", + "Default is 0.8." + ] = 0.8 + model_name: Annotated[ + str, + "The name of the Gemini model to use.", + "Default is 'gemini-1.5-flash'." + ] = "gemini-1.5-flash" + max_retries: Annotated[ + int, + "The maximum number of retries to use for the Gemini model.", + "Default is 3." + ] = 3 + max_concurrency: Annotated[ + int, + "The maximum number of concurrent requests to make to the Gemini model.", + "Default is 3." + ] = 3 + timeout: Annotated[ + int, + "The timeout for requests to the Gemini model.", + "Default is 60 seconds." + ] = 60 + topk_relabelling_prompt: Annotated[ + str, + "The prompt to use for relabelling blocks.", + "Default is a string containing the Gemini relabelling prompt." + ] = """You are a layout expert specializing in document analysis. Your task is to relabel layout blocks in images to improve the accuracy of an existing layout model. You will be provided with an image of a layout block and the top k predictions from the current model, along with their confidence scores. Your job is to analyze the image and choose the single most appropriate label from the provided top k predictions. @@ -75,7 +73,11 @@ class LLMLayoutBuilder(LayoutBuilder): Here are the top k predictions from the model followed by the image: """ - complex_relabeling_prompt = """You are a layout expert specializing in document analysis. + complex_relabeling_prompt: Annotated[ + str, + "The prompt to use for complex relabelling blocks.", + "Default is a string containing the complex relabelling prompt." + ] = """You are a layout expert specializing in document analysis. Your task is to relabel layout blocks in images to improve the accuracy of an existing layout model. You will be provided with an image of a layout block and some potential labels. Your job is to analyze the image and choose the single most appropriate label from the provided labels. @@ -140,7 +142,6 @@ def process_block_complex_relabeling(self, page: PageGroup, block: Block): complex_prompt = self.complex_relabeling_prompt return self.process_block_relabeling(page, block, complex_prompt) - def process_block_relabeling(self, page: PageGroup, block: Block, prompt: str): image = self.extract_image(page, block) response_schema = content.Schema( @@ -174,4 +175,4 @@ def extract_image(self, page: PageGroup, image_block: Block, expand: float = 0.0 .rescale(page.polygon.size, page_img.size)\ .expand(expand, expand) cropped = page_img.crop(image_box.bbox) - return cropped \ No newline at end of file + return cropped diff --git a/marker/builders/ocr.py b/marker/builders/ocr.py index 5edfe813..b4aee99f 100644 --- a/marker/builders/ocr.py +++ b/marker/builders/ocr.py @@ -1,4 +1,4 @@ -from typing import List, Optional +from typing import Annotated, List, Optional from ftfy import fix_text from surya.model.detection.model import EfficientViTForSemanticSegmentation @@ -20,22 +20,22 @@ class OcrBuilder(BaseBuilder): """ A builder for performing OCR on PDF pages and merging the results into the document. - - Attributes: - detection_batch_size (int): - The batch size to use for the detection model. - Default is None, which will use the default batch size for the model. - - recognition_batch_size (int): - The batch size to use for the recognition model. - Default is None, which will use the default batch size for the model. - - languages (List[str]): - A list of languages to use for OCR. Default is None. """ - recognition_batch_size: Optional[int] = None - detection_batch_size: Optional[int] = None - languages: Optional[List[str]] = None + recognition_batch_size: Annotated[ + Optional[int], + "The batch size to use for the recognition model.", + "Default is None, which will use the default batch size for the model." + ] = None + detection_batch_size: Annotated[ + Optional[int], + "The batch size to use for the detection model.", + "Default is None, which will use the default batch size for the model." + ] = None + languages: Annotated[ + Optional[List[str]], + "A list of languages to use for OCR.", + "Default is None." + ] = None def __init__(self, detection_model: EfficientViTForSemanticSegmentation, recognition_model: OCREncoderDecoderModel, config=None): super().__init__(config) diff --git a/marker/builders/structure.py b/marker/builders/structure.py index 0e8e3da8..89888cfd 100644 --- a/marker/builders/structure.py +++ b/marker/builders/structure.py @@ -1,3 +1,5 @@ +from typing import Annotated + from marker.builders import BaseBuilder from marker.schema import BlockTypes from marker.schema.document import Document @@ -9,18 +11,17 @@ class StructureBuilder(BaseBuilder): """ A builder for grouping blocks together based on their structure. - - Attributes: - gap_threshold (float): - The minimum gap between blocks to consider them part of the same group. - Default is 0.05. - - list_gap_threshold (float): - The minimum gap between list items to consider them part of the same group. - Default is 0.1. """ - gap_threshold: int = .05 - list_gap_threshold: int = .1 + gap_threshold: Annotated[ + float, + "The minimum gap between blocks to consider them part of the same group.", + "Default is 0.05." + ] = 0.05 + list_gap_threshold: Annotated[ + float, + "The minimum gap between list items to consider them part of the same group.", + "Default is 0.1." + ] = 0.1 def __init__(self, config=None): super().__init__(config) @@ -58,8 +59,8 @@ def group_caption_blocks(self, page: PageGroup): selected_polygons.append(prev_block.polygon) if next_block and \ - next_block.block_type in caption_types and \ - next_block.polygon.minimum_gap(block.polygon) < gap_threshold_px: + next_block.block_type in caption_types and \ + next_block.polygon.minimum_gap(block.polygon) < gap_threshold_px: block_structure.append(next_block.id) selected_polygons.append(next_block.polygon) diff --git a/marker/config/printer.py b/marker/config/printer.py index ea1159bd..570b8622 100644 --- a/marker/config/printer.py +++ b/marker/config/printer.py @@ -1,7 +1,7 @@ import importlib import inspect import pkgutil -from typing import Optional +from typing import Annotated, Optional, Type, get_args, get_origin import click @@ -11,6 +11,15 @@ from marker.renderers import BaseRenderer +def format_type(t: Type) -> str: + """Format a typing type like Optional[int] into a readable string.""" + + if get_origin(t): # Handle Optional and types with origins separately + return f"{t}".removeprefix('typing.') + else: # Regular types like int, str + return t.__name__ + + def find_subclasses(base_class): """ Dynamically find all subclasses of a base class in the module where the base class is defined @@ -43,62 +52,33 @@ def get_help(self, ctx): def parse_args(self, ctx, args): display_help = 'config' in args and '--help' in args if display_help: - click.echo("Here is a list of all the Builders, Processors, Converters and Renderers in Marker along with their attributes:") + click.echo("Here is a list of all the Builders, Processors, Converters and Renderers in Marker along with their attributes:") base_classes = [BaseBuilder, BaseProcessor, BaseConverter, BaseRenderer] for base in base_classes: if display_help: - click.echo(f"{base.__name__.removeprefix('Base')}s:\n") + click.echo(f"{base.__name__.removeprefix('Base')}s:") subclasses = find_subclasses(base) for class_name, class_type in subclasses.items(): doc = class_type.__doc__ or "" - if display_help and doc and "Attributes:" in doc: - click.echo(f" {class_name}: {doc}") - parsed_doc = self._parse_indentation_based_tree(doc) + if display_help and doc and len(class_type.__annotations__): + click.echo(f"\n {class_name}: {doc}") + click.echo(" " * 4 + "Attributes:") for attr, attr_type in class_type.__annotations__.items(): - if attr in doc and attr_type in [str, int, float, bool, Optional[int], Optional[float], Optional[str]]: - if attr not in [p.name for p in ctx.command.params]: - default = getattr(class_type, attr) - is_flag = attr_type in [bool, Optional[bool]] and not default - ctx.command.params.append( - click.Option([f"--{attr}"], help=" ".join(parsed_doc[attr]), type=attr_type, default=default, is_flag=is_flag) - ) + if get_origin(attr_type) is Annotated: + base_attr_type = get_args(attr_type)[0] + default = getattr(class_type, attr) + if display_help: + click.echo(" " * 8 + f"{attr} ({format_type(base_attr_type)}):") + click.echo("\n".join([f'{" " * 12}' + desc for desc in attr_type.__metadata__])) + if base_attr_type in [str, int, float, bool, Optional[int], Optional[float], Optional[str]]: + if attr not in [p.name for p in ctx.command.params]: + is_flag = base_attr_type in [bool, Optional[bool]] and not default + ctx.command.params.append( + click.Option([f"--{attr}"], help=" ".join(attr_type.__metadata__), type=base_attr_type, default=default, is_flag=is_flag) + ) if display_help: ctx.exit() super().parse_args(ctx, args) - - - def _parse_indentation_based_tree(self, doc): - stack = [] - tree = [] - - for line in doc.splitlines(): - stripped_line = line.lstrip() - if not stripped_line: - continue - - indent_level = len(line) - len(stripped_line) - - node = {"content": stripped_line, "children": []} - while stack and stack[-1]['indent'] >= indent_level: - stack.pop() - - if stack: - stack[-1]["children"].append(node) - else: - tree.append(node) - - node['indent'] = indent_level - stack.append(node) - - attributes = {} - for node in tree: - if node['content'].startswith("Attributes:"): - for child in node["children"]: - var = child["content"].split(" ")[0] - descs = [c["content"] for c in child["children"] if c] - attributes[var] = descs - - return attributes diff --git a/marker/converters/pdf.py b/marker/converters/pdf.py index 9379c880..fb0e949e 100644 --- a/marker/converters/pdf.py +++ b/marker/converters/pdf.py @@ -1,10 +1,10 @@ import os -os.environ["TOKENIZERS_PARALLELISM"] = "false" # disables a tokenizers warning +os.environ["TOKENIZERS_PARALLELISM"] = "false" # disables a tokenizers warning import inspect from collections import defaultdict -from typing import Any, Dict, List, Optional, Type +from typing import Annotated, Any, Dict, List, Optional, Type from marker.builders.document import DocumentBuilder from marker.builders.layout import LayoutBuilder @@ -41,20 +41,19 @@ class PdfConverter(BaseConverter): """ A converter for processing and rendering PDF files into Markdown, JSON, HTML and other formats. - - Attributes: - override_map (Dict[BlockTypes, Type[Block]]): - A mapping to override the default block classes for specific block types. - The keys are `BlockTypes` enum values, representing the types of blocks, - and the values are corresponding `Block` class implementations to use - instead of the defaults. - - use_llm (bool): - Enable higher quality processing with LLMs. - Default is False. """ - override_map: Dict[BlockTypes, Type[Block]] = defaultdict() - use_llm: bool = False + override_map: Annotated[ + Dict[BlockTypes, Type[Block]], + "A mapping to override the default block classes for specific block types.", + "The keys are `BlockTypes` enum values, representing the types of blocks,", + "and the values are corresponding `Block` class implementations to use", + "instead of the defaults." + ] = defaultdict() + use_llm: Annotated[ + bool, + "Enable higher quality processing with LLMs.", + "Default is False." + ] = False def __init__(self, artifact_dict: Dict[str, Any], processor_list: Optional[List[str]] = None, renderer: str | None = None, config=None): super().__init__(config) diff --git a/marker/processors/blockquote.py b/marker/processors/blockquote.py index 4a5fb288..3854d27d 100644 --- a/marker/processors/blockquote.py +++ b/marker/processors/blockquote.py @@ -1,3 +1,5 @@ +from typing import Annotated, List + from marker.processors import BaseProcessor from marker.schema import BlockTypes from marker.schema.document import Document @@ -6,24 +8,30 @@ class BlockquoteProcessor(BaseProcessor): """ A processor for tagging blockquotes. - - Attributes: - min_x_indent (float): - The minimum horizontal indentation required to consider a block as part of a blockquote. Expressed as a percentage of the block width. - Default is 0.05 (5%). - - x_start_tolerance (float): - The maximum allowable difference between the starting x-coordinates of consecutive blocks to consider them aligned. Expressed as a percentage of the block width. - Default is 0.01 (1%). - - x_end_tolerance (float): - The maximum allowable difference between the ending x-coordinates of consecutive blocks to consider them aligned. Expressed as a percentage of the block width. - Default is 0.01 (1%). """ - block_types = (BlockTypes.Text, BlockTypes.TextInlineMath) - min_x_indent = 0.05 # % of block width - x_start_tolerance = 0.01 # % of block width - x_end_tolerance = 0.01 # % of block width + block_types: Annotated[ + List[BlockTypes], + "The block types to process.", + "Default is [BlockTypes.Text, BlockTypes.TextInlineMath]." + ] = (BlockTypes.Text, BlockTypes.TextInlineMath) + min_x_indent: Annotated[ + float, + "The minimum horizontal indentation required to consider a block as part of a blockquote.", + "Expressed as a percentage of the block width.", + "Default is 0.05 (5%)." + ] = 0.05 + x_start_tolerance: Annotated[ + float, + "The maximum allowable difference between the starting x-coordinates of consecutive blocks to consider them aligned.", + "Expressed as a percentage of the block width.", + "Default is 0.01 (1%)." + ] = 0.01 + x_end_tolerance: Annotated[ + float, + "The maximum allowable difference between the ending x-coordinates of consecutive blocks to consider them aligned.", + "Expressed as a percentage of the block width.", + "Default is 0.01 (1%)." + ] = 0.01 def __init__(self, config): super().__init__(config) diff --git a/marker/processors/debug.py b/marker/processors/debug.py index 3d46b046..ee02e6bb 100644 --- a/marker/processors/debug.py +++ b/marker/processors/debug.py @@ -1,5 +1,6 @@ import json import os +from typing import Annotated import requests from PIL import Image, ImageDraw, ImageFont @@ -13,39 +14,42 @@ class DebugProcessor(BaseProcessor): """ A processor for debugging the document. - - Attributes: - debug_data_folder (str): - The folder to dump debug data to. - Default is "debug_data". - - debug_layout_images (bool): - Whether to dump layout debug images. - Default is False. - - debug_pdf_images (bool): - Whether to dump PDF debug images. - Default is False. - - debug_json (bool): - Whether to dump block debug data. - Default is False. - - render_font (str): - The path to the font to use for rendering debug images. - Default is "GoNotoCurrent-Regular.ttf" in the FONT_DIR folder. - - font_dl_path (str): - The path to download the font from. - Default is "https://github.com/satbyy/go-noto-universal/releases/download/v7.0". """ - block_types = tuple() - debug_data_folder: str = "debug_data" - debug_layout_images: bool = False - debug_pdf_images: bool = False - debug_json: bool = False - render_font: str = os.path.join(settings.FONT_DIR, "GoNotoCurrent-Regular.ttf") - font_dl_path: str = "https://github.com/satbyy/go-noto-universal/releases/download/v7.0" + block_types: Annotated[ + tuple, + "The block types to process.", + "Default is an empty tuple." + ] = tuple() + debug_data_folder: Annotated[ + str, + "The folder to dump debug data to.", + "Default is 'debug_data'." + ] = "debug_data" + debug_layout_images: Annotated[ + bool, + "Whether to dump layout debug images.", + "Default is False." + ] = False + debug_pdf_images: Annotated[ + bool, + "Whether to dump PDF debug images.", + "Default is False." + ] = False + debug_json: Annotated[ + bool, + "Whether to dump block debug data.", + "Default is False." + ] = False + render_font: Annotated[ + str, + "The path to the font to use for rendering debug images.", + "Default is 'GoNotoCurrent-Regular.ttf' in the FONT_DIR folder." + ] = os.path.join(settings.FONT_DIR, "GoNotoCurrent-Regular.ttf") + font_dl_path: Annotated[ + str, + "The path to download the font from.", + "Default is 'https://github.com/satbyy/go-noto-universal/releases/download/v7.0'." + ] = "https://github.com/satbyy/go-noto-universal/releases/download/v7.0" def __call__(self, document: Document): # Remove extension from doc name @@ -90,7 +94,6 @@ def draw_pdf_debug_images(self, document: Document): debug_file = os.path.join(self.debug_folder, f"pdf_page_{page.page_id}.png") png_image.save(debug_file) - def draw_layout_debug_images(self, document: Document, pdf_mode=False): for page in document.pages: img_size = page.highres_image.size @@ -113,7 +116,6 @@ def draw_layout_debug_images(self, document: Document, pdf_mode=False): debug_file = os.path.join(self.debug_folder, f"layout_page_{page.page_id}.png") png_image.save(debug_file) - def render_layout_boxes(self, page, png_image): layout_bboxes = [] layout_labels = [] diff --git a/marker/processors/equation.py b/marker/processors/equation.py index 5da8436d..e15c4bd0 100644 --- a/marker/processors/equation.py +++ b/marker/processors/equation.py @@ -1,4 +1,4 @@ -from typing import List +from typing import Annotated, List, Optional from texify.inference import batch_inference from texify.model.model import GenerateVisionEncoderDecoderModel @@ -13,24 +13,27 @@ class EquationProcessor(BaseProcessor): """ A processor for recognizing equations in the document. - - Attributes: - model_max_length (int): - The maximum number of tokens to allow for the Texify model. - Default is 384. - - batch_size (int): - The batch size to use for the Texify model. - Default is None, which will use the default batch size for the model. - - token_buffer (int): - The number of tokens to buffer above max for the Texify model. - Default is 256. """ - block_types = (BlockTypes.Equation, ) - model_max_length = 384 - texify_batch_size = None - token_buffer = 256 + block_types: Annotated[ + List[BlockTypes], + "The block types to process.", + "Default is [BlockTypes.Equation]." + ] = (BlockTypes.Equation,) + model_max_length: Annotated[ + int, + "The maximum number of tokens to allow for the Texify model.", + "Default is 384." + ] = 384 + texify_batch_size: Annotated[ + Optional[int], + "The batch size to use for the Texify model.", + "Default is None, which will use the default batch size for the model." + ] = None + token_buffer: Annotated[ + int, + "The number of tokens to buffer above max for the Texify model.", + "Default is 256." + ] = 256 def __init__(self, texify_model: GenerateVisionEncoderDecoderModel, config=None): super().__init__(config) diff --git a/marker/processors/footnote.py b/marker/processors/footnote.py index 6dfd033b..fdf31721 100644 --- a/marker/processors/footnote.py +++ b/marker/processors/footnote.py @@ -1,27 +1,12 @@ -from statistics import mean - from marker.processors import BaseProcessor from marker.schema import BlockTypes -from marker.schema.blocks import Footnote from marker.schema.document import Document - -from rapidfuzz import fuzz - from marker.schema.groups import PageGroup class FootnoteProcessor(BaseProcessor): """ A processor for pushing footnotes to the bottom, and relabeling mislabeled text blocks. - - Attributes: - page_bottom_threshold (float): - The fraction of page height that is considered the bottom. - Default is .8 - - line_height_scaler (float): - The amount to scale line height by to consider a block a footnote. (from N to 1+(1-N)) - Default is .99 """ block_types = (BlockTypes.Footnote,) @@ -29,7 +14,6 @@ def __call__(self, document: Document): for page in document.pages: self.push_footnotes_to_bottom(page, document) - def push_footnotes_to_bottom(self, page: PageGroup, document: Document): footnote_blocks = page.contained_blocks(document, self.block_types) @@ -39,4 +23,4 @@ def push_footnotes_to_bottom(self, page: PageGroup, document: Document): if block.id in page.structure: # Move to bottom if it is page.structure.remove(block.id) - page.add_structure(block) \ No newline at end of file + page.add_structure(block) diff --git a/marker/processors/ignoretext.py b/marker/processors/ignoretext.py index 189e54ac..e7f8daa1 100644 --- a/marker/processors/ignoretext.py +++ b/marker/processors/ignoretext.py @@ -1,7 +1,7 @@ import re from collections import Counter from itertools import groupby -from typing import List +from typing import Annotated, List from rapidfuzz import fuzz @@ -15,33 +15,36 @@ class IgnoreTextProcessor(BaseProcessor): """ A processor for identifying and ignoring common text blocks in a document. These blocks often represent repetitive or non-essential elements, such as headers, footers, or page numbers. - - Attributes: - common_element_threshold (float): - The fraction of pages a text block must appear on to be considered a common element. - Default is 0.6 (60% of pages). - - common_element_min_blocks (int): - The minimum number of occurrences of a text block within a document to consider it a common element. - Default is 3. - - max_streak (int): - The maximum number of consecutive occurrences of a text block allowed before it is classified as a common element. Helps to identify patterns like repeated headers or footers. - Default is 3. - - text_match_threshold (int): - The minimum fuzzy match score (0-100) required to classify a text block as similar to a common element. Higher values enforce stricter matching. - Default is 90. """ block_types = ( - BlockTypes.Text, BlockTypes.PageHeader, + BlockTypes.Text, BlockTypes.PageHeader, BlockTypes.PageFooter, BlockTypes.SectionHeader, BlockTypes.TextInlineMath ) - common_element_threshold = .20 - common_element_min_blocks = 3 - max_streak = 3 # The maximum number of blocks in a row to consider a common element - text_match_threshold = 90 + common_element_threshold: Annotated[ + float, + "The minimum ratio of pages a text block must appear on to be considered a common element.", + "Blocks that meet or exceed this threshold are marked as common elements.", + "Default is 0.6 (60% of pages)." + ] = 0.6 + common_element_min_blocks: Annotated[ + int, + "The minimum number of occurrences of a text block within a document to consider it a common element.", + "This ensures that rare blocks are not mistakenly flagged.", + "Default is 3." + ] = 3 + max_streak: Annotated[ + int, + "The maximum number of consecutive occurrences of a text block allowed before it is classified as a common element.", + "Helps to identify patterns like repeated headers or footers.", + "Default is 3." + ] = 3 + text_match_threshold: Annotated[ + int, + "The minimum fuzzy match score (0-100) required to classify a text block as similar to a common element.", + "Higher values enforce stricter matching.", + "Default is 90." + ] = 90 def __call__(self, document: Document): first_blocks = [] @@ -68,8 +71,8 @@ def __call__(self, document: Document): @staticmethod def clean_text(text): text = text.replace("\n", "").strip() - text = re.sub(r"^\d+\s*", "", text) # remove numbers at the start of the line - text = re.sub(r"\s*\d+$", "", text) # remove numbers at the end of the line + text = re.sub(r"^\d+\s*", "", text) # remove numbers at the start of the line + text = re.sub(r"\s*\d+$", "", text) # remove numbers at the end of the line return text def filter_common_elements(self, document, blocks: List[Block]): @@ -87,7 +90,7 @@ def filter_common_elements(self, document, blocks: List[Block]): common = [ k for k, v in counter.items() if (v >= len(blocks) * self.common_element_threshold or streaks[k] >= self.max_streak) - and v > self.common_element_min_blocks + and v > self.common_element_min_blocks ] if len(common) == 0: return diff --git a/marker/processors/line_numbers.py b/marker/processors/line_numbers.py index 7c0a5116..9afdc806 100644 --- a/marker/processors/line_numbers.py +++ b/marker/processors/line_numbers.py @@ -1,3 +1,5 @@ +from typing import Annotated + from marker.processors import BaseProcessor from marker.schema import BlockTypes from marker.schema.document import Document @@ -6,25 +8,25 @@ class LineNumbersProcessor(BaseProcessor): """ A processor for ignoring line numbers. - Attributes: - strip_numbers_threshold (float): - The fraction of lines or tokens in a block that must be numeric to consider them as line numbers. - Default is 0.6 (60%). - - min_lines_in_block (int): - The minimum number of lines required in a block for it to be considered during processing. - Ensures that small blocks are ignored as they are unlikely to contain meaningful line numbers. - Default is 4. - - min_line_length (int): - The minimum length of a line (in characters) to consider it significant when checking for - numeric prefixes or suffixes. Prevents false positives for short lines. - Default is 10. """ block_types = (BlockTypes.Text, BlockTypes.TextInlineMath) - strip_numbers_threshold: int = .6 - min_lines_in_block: int = 4 - min_line_length: int = 10 + strip_numbers_threshold: Annotated[ + float, + "The fraction of lines or tokens in a block that must be numeric to consider them as line numbers.", + "Default is 0.6 (60%)." + ] = 0.6 + min_lines_in_block: Annotated[ + int, + "The minimum number of lines required in a block for it to be considered during processing.", + "Ensures that small blocks are ignored as they are unlikely to contain meaningful line numbers.", + "Default is 4." + ] = 4 + min_line_length: Annotated[ + int, + "The minimum length of a line (in characters) to consider it significant when checking for", + "numeric prefixes or suffixes. Prevents false positives for short lines.", + "Default is 10." + ] = 10 def __init__(self, config): super().__init__(config) @@ -44,11 +46,10 @@ def ignore_line_number_blocks(self, document: Document): tokens_are_numbers = [token.isdigit() for token in tokens] if all([ sum(tokens_are_numbers) / len(tokens) > self.strip_numbers_threshold, - block.polygon.height > block.polygon.width # Ensure block is taller than it is wide, like vertical page numbers + block.polygon.height > block.polygon.width # Ensure block is taller than it is wide, like vertical page numbers ]): block.ignore_for_output = True - def ignore_line_starts_ends(self, document: Document): for page in document.pages: for block in page.contained_blocks(document, self.block_types): @@ -74,7 +75,7 @@ def ignore_line_starts_ends(self, document: Document): len(raw_text) - len(spans[0].text.strip()) > self.min_line_length ]) - ends= all([ + ends = all([ spans[-1].text.strip().isdigit(), len(raw_text) - len(spans[-1].text.strip()) > self.min_line_length ]) @@ -93,4 +94,3 @@ def ignore_line_starts_ends(self, document: Document): if ends: span = page.get_block(line.structure[-1]) span.ignore_for_output = True - diff --git a/marker/processors/list.py b/marker/processors/list.py index 59e6c35b..10eb9c1e 100644 --- a/marker/processors/list.py +++ b/marker/processors/list.py @@ -1,4 +1,4 @@ -from typing import List +from typing import Annotated, List from marker.processors import BaseProcessor from marker.schema import BlockTypes @@ -9,20 +9,18 @@ class ListProcessor(BaseProcessor): """ A processor for merging lists across pages and columns - - Attributes: - ignored_block_types (List[BlockTypes]): - The list of block types to ignore when merging lists. - Default is [BlockTypes.PageHeader, BlockTypes.PageFooter]. - - min_x_indent (float): - The minimum horizontal indentation required to consider a block as a nested list item. - This is expressed as a percentage of the page width and is used to determine hierarchical relationships within a list. - Default is 0.01 (1% of page width). """ block_types = (BlockTypes.ListGroup,) - ignored_block_types = (BlockTypes.PageHeader, BlockTypes.PageFooter) - min_x_indent = 0.01 # % of page width + ignored_block_types: Annotated[ + List[BlockTypes], + "The list of block types to ignore when merging lists.", + "Default is [BlockTypes.PageHeader, BlockTypes.PageFooter]." + ] = (BlockTypes.PageHeader, BlockTypes.PageFooter) + min_x_indent: Annotated[ + float, "The minimum horizontal indentation required to consider a block as a nested list item.", + "This is expressed as a percentage of the page width and is used to determine hierarchical relationships within a list.", + "Default is 0.01 (1% of page width)." + ] = 0.01 def __init__(self, config): super().__init__(config) diff --git a/marker/processors/llm/__init__.py b/marker/processors/llm/__init__.py index b4ead90f..838c3987 100644 --- a/marker/processors/llm/__init__.py +++ b/marker/processors/llm/__init__.py @@ -1,5 +1,5 @@ from concurrent.futures import ThreadPoolExecutor, as_completed -from typing import Optional +from typing import Annotated, Optional from tqdm import tqdm @@ -14,41 +14,47 @@ class BaseLLMProcessor(BaseProcessor): """ A processor for using LLMs to convert blocks. - Attributes: - google_api_key (str): - The Google API key to use for the Gemini model. - Default is None. - model_name (str): - The name of the Gemini model to use. - Default is "gemini-1.5-flash". - max_retries (int): - The maximum number of retries to use for the Gemini model. - Default is 3. - max_concurrency (int): - The maximum number of concurrent requests to make to the Gemini model. - Default is 3. - timeout (int): - The timeout for requests to the Gemini model. - Default is 60 seconds. - image_expansion_ratio (float): - The ratio to expand the image by when cropping. - Default is 0.01. - gemini_rewriting_prompt (str): - The prompt to use for rewriting text. - Default is a string containing the Gemini rewriting prompt. - use_llm (bool): - Whether to use the LLM model. - Default is False. """ - - google_api_key: Optional[str] = settings.GOOGLE_API_KEY - model_name: str = "gemini-1.5-flash" - use_llm: bool = False - max_retries: int = 3 - max_concurrency: int = 3 - timeout: int = 60 - image_expansion_ratio: float = 0.01 - gemini_rewriting_prompt = None + google_api_key: Annotated[ + Optional[str], + "The Google API key to use for the Gemini model.", + "Default is None." + ] = settings.GOOGLE_API_KEY + model_name: Annotated[ + str, + "The name of the Gemini model to use.", + "Default is 'gemini-1.5-flash'." + ] = "gemini-1.5-flash" + max_retries: Annotated[ + int, + "The maximum number of retries to use for the Gemini model.", + "Default is 3." + ] = 3 + max_concurrency: Annotated[ + int, + "The maximum number of concurrent requests to make to the Gemini model.", + "Default is 3." + ] = 3 + timeout: Annotated[ + int, + "The timeout for requests to the Gemini model.", + "Default is 60 seconds." + ] = 60 + image_expansion_ratio: Annotated[ + float, + "The ratio to expand the image by when cropping.", + "Default is 0.01." + ] = 0.01 + gemini_rewriting_prompt: Annotated[ + str, + "The prompt to use for rewriting text.", + "Default is a string containing the Gemini rewriting prompt." + ] = None + use_llm: Annotated[ + bool, + "Whether to use the LLM model.", + "Default is False." + ] = False block_types = None def __init__(self, config=None): @@ -91,4 +97,4 @@ def extract_image(self, page: PageGroup, image_block: Block): .rescale(page.polygon.size, page_img.size)\ .expand(self.image_expansion_ratio, self.image_expansion_ratio) cropped = page_img.crop(image_box.bbox) - return cropped \ No newline at end of file + return cropped diff --git a/marker/processors/llm/llm_table.py b/marker/processors/llm/llm_table.py index 230cbb09..491a7420 100644 --- a/marker/processors/llm/llm_table.py +++ b/marker/processors/llm/llm_table.py @@ -1,12 +1,11 @@ -from tabled.schema import SpanTableCell +from typing import Annotated, List -from marker.processors.llm import BaseLLMProcessor from bs4 import BeautifulSoup -from typing import List - from google.ai.generativelanguage_v1beta.types import content from tabled.formats import html_format +from tabled.schema import SpanTableCell +from marker.processors.llm import BaseLLMProcessor from marker.schema import BlockTypes from marker.schema.blocks import Block from marker.schema.document import Document @@ -15,8 +14,16 @@ class LLMTableProcessor(BaseLLMProcessor): - block_types = (BlockTypes.Table,) - gemini_rewriting_prompt = """You are a text correction expert specializing in accurately reproducing text from images. + block_types: Annotated[ + List[BlockTypes], + "The block types to process.", + "Default is [BlockTypes.Table]." + ] = (BlockTypes.Table,) + gemini_rewriting_prompt: Annotated[ + str, + "The prompt to use for rewriting text.", + "Default is a string containing the Gemini rewriting prompt." + ] = """You are a text correction expert specializing in accurately reproducing text from images. You will receive an image of a text block and an html representation of the table in the image. Your task is to correct any errors in the html representation. The html representation should be as faithful to the original table as possible. **Instructions:** @@ -92,10 +99,8 @@ def process_rewriting(self, document: Document, page: PageGroup, block: Block): block.update_metadata(llm_error_count=1) return - block.cells = parsed_cells - def parse_html_table(self, html_text: str, block: Block) -> List[SpanTableCell]: soup = BeautifulSoup(html_text, 'html.parser') table = soup.find('table') @@ -151,5 +156,4 @@ def parse_html_table(self, html_text: str, block: Block) -> List[SpanTableCell]: cells.append(cell_obj) cur_col += colspan - return cells diff --git a/marker/processors/sectionheader.py b/marker/processors/sectionheader.py index 881bc6c3..b8bf2e44 100644 --- a/marker/processors/sectionheader.py +++ b/marker/processors/sectionheader.py @@ -1,5 +1,5 @@ import warnings -from typing import Dict, List +from typing import Annotated, Dict, List import numpy as np from sklearn.cluster import KMeans @@ -16,29 +16,28 @@ class SectionHeaderProcessor(BaseProcessor): """ A processor for recognizing section headers in the document. - - Attributes: - level_count (int): - The number of levels to use for headings. - Default is 4. - - merge_threshold (float): - The minimum gap between headings to consider them part of the same group. - Default is 0.25. - - default_level (int): - The default heading level to use if no heading level is detected. - Default is 2. - - height_tolerance (float): - The minimum height of a heading to consider it a heading. - Default is 0.99. """ block_types = (BlockTypes.SectionHeader, ) - level_count = 4 - merge_threshold = .25 - default_level = 2 - height_tolerance = .99 + level_count: Annotated[ + int, + "The number of levels to use for headings.", + "Default is 4." + ] = 4 + merge_threshold: Annotated[ + float, + "The minimum gap between headings to consider them part of the same group.", + "Default is 0.25." + ] = 0.25 + default_level: Annotated[ + int, + "The default heading level to use if no heading level is detected.", + "Default is 2." + ] = 2 + height_tolerance: Annotated[ + float, + "The minimum height of a heading to consider it a heading.", + "Default is 0.99." + ] = 0.99 def __call__(self, document: Document): line_heights: Dict[int, List[float]] = {} @@ -48,7 +47,7 @@ def __call__(self, document: Document): line_heights[block.id] = block.line_height(document) else: line_heights[block.id] = 0 - block.ignore_for_output = True # Don't output an empty section header + block.ignore_for_output = True # Don't output an empty section header flat_line_heights = list(line_heights.values()) heading_ranges = self.bucket_headings(flat_line_heights) diff --git a/marker/processors/table.py b/marker/processors/table.py index 853f1205..a8f38eeb 100644 --- a/marker/processors/table.py +++ b/marker/processors/table.py @@ -1,4 +1,6 @@ +from typing import Annotated + from ftfy import fix_text from surya.input.pdflines import get_page_text_lines from surya.model.detection.model import EfficientViTForSemanticSegmentation @@ -16,29 +18,28 @@ class TableProcessor(BaseProcessor): """ A processor for recognizing tables in the document. - - Attributes: - detect_boxes (bool): - Whether to detect boxes for the table recognition model. - Default is False. - - detector_batch_size (int): - The batch size to use for the table detection model. - Default is None, which will use the default batch size for the model. - - table_rec_batch_size (int): - The batch size to use for the table recognition model. - Default is None, which will use the default batch size for the model. - - recognition_batch_size (int): - The batch size to use for the table recognition model. - Default is None, which will use the default batch size for the model. """ block_types = (BlockTypes.Table, BlockTypes.TableOfContents, BlockTypes.Form) - detect_boxes = False - detector_batch_size = None - table_rec_batch_size = None - recognition_batch_size = None + detect_boxes: Annotated[ + bool, + "Whether to detect boxes for the table recognition model.", + "Default is False." + ] = False + detector_batch_size: Annotated[ + int, + "The batch size to use for the table detection model.", + "Default is None, which will use the default batch size for the model." + ] = None + table_rec_batch_size: Annotated[ + int, + "The batch size to use for the table recognition model.", + "Default is None, which will use the default batch size for the model." + ] = None + recognition_batch_size: Annotated[ + int, + "The batch size to use for the table recognition model.", + "Default is None, which will use the default batch size for the model." + ] = None def __init__( self, diff --git a/marker/processors/text.py b/marker/processors/text.py index e13d699b..46f92d32 100644 --- a/marker/processors/text.py +++ b/marker/processors/text.py @@ -1,5 +1,5 @@ import math -from typing import List +from typing import Annotated, List import regex @@ -12,15 +12,14 @@ class TextProcessor(BaseProcessor): """ A processor for merging text across pages and columns. - - Attributes: - column_gap_ratio (float): - The minimum ratio of the page width to the column gap to consider a column break. - Default is 0.02. """ block_types = (BlockTypes.Text, BlockTypes.TextInlineMath) ignored_block_types = (BlockTypes.PageHeader, BlockTypes.PageFooter) - column_gap_ratio = 0.02 # column gaps are atleast 2% of the current column width + column_gap_ratio: Annotated[ + float, + "The minimum ratio of the page width to the column gap to consider a column break.", + "Default is 0.02." + ] = 0.02 def __init__(self, config): super().__init__(config) @@ -35,14 +34,14 @@ def __call__(self, document: Document): continue next_block = document.get_next_block(block, self.ignored_block_types) - if next_block is None: # we've reached the end of the document + if next_block is None: # we've reached the end of the document continue if next_block.block_type not in self.block_types: - continue # we found a non-text block + continue # we found a non-text block if next_block.structure is None: continue # This is odd though, why do we have text blocks with no structure? if next_block.ignore_for_output: - continue # skip ignored blocks + continue # skip ignored blocks column_gap = block.polygon.width * self.column_gap_ratio @@ -53,7 +52,7 @@ def __call__(self, document: Document): last_line_is_hyphentated = False new_block_lines = [] - if next_block.page_id == block.page_id: # block on the same page + if next_block.page_id == block.page_id: # block on the same page # we check for a column break column_break = ( math.floor(next_block.polygon.y_start) <= math.ceil(block.polygon.y_start) and @@ -63,11 +62,11 @@ def __call__(self, document: Document): page_break = True next_page = document.get_page(next_block.page_id) next_block_in_first_quadrant = (next_block.polygon.x_start < next_page.polygon.width // 2) and \ - (next_block.polygon.y_start < next_page.polygon.height // 2) + (next_block.polygon.y_start < next_page.polygon.height // 2) if not (column_break or page_break): continue - + new_block_lines = next_block.structure_blocks(document) # we check for next_block indentation diff --git a/marker/providers/pdf.py b/marker/providers/pdf.py index eff83fb9..d348bfc2 100644 --- a/marker/providers/pdf.py +++ b/marker/providers/pdf.py @@ -1,7 +1,7 @@ import atexit import ctypes import re -from typing import List, Optional, Set +from typing import Annotated, List, Optional, Set import pypdfium2 as pdfium import pypdfium2.raw as pdfium_c @@ -21,62 +21,58 @@ class PdfProvider(BaseProvider): """ A provider for PDF files. - - Attributes: - filepath (str): - The path to the PDF file. - - page_range (List[int]): - The range of pages to process. - Default is None, which will process all pages. - - pdftext_workers (int): - The number of workers to use for pdftext. - Default is 4. - - flatten_pdf (bool): - Whether to flatten the PDF structure. - Default is True. - - force_ocr (bool): - Whether to force OCR on the whole document. - Default is False. - - ocr_invalid_chars (tuple): - The characters to consider invalid for OCR. - Default is (chr(0xfffd), "�"). - - ocr_space_threshold (float): - The minimum ratio of spaces to non-spaces to detect bad text. - Default is 0.7. - - ocr_newline_threshold (float): - The minimum ratio of newlines to non-newlines to detect bad text. - Default is 0.6. - - ocr_alphanum_threshold (float): - The minimum ratio of alphanumeric characters to non-alphanumeric characters to consider an alphanumeric character. - Default is 0.3. - - image_threshold (float): - The minimum coverage ratio of the image to the page to consider skipping the page. - Default is .65. - - strip_existing_ocr (bool): - Whether to strip existing OCR text from the PDF. - Default is True. """ - page_range: List[int] | None = None - pdftext_workers: int = 4 - flatten_pdf: bool = True - force_ocr: bool = False - ocr_invalid_chars: tuple = (chr(0xfffd), "�") - ocr_space_threshold: float = .7 - ocr_newline_threshold: float = .6 - ocr_alphanum_threshold: float = .3 - image_threshold: float = .65 - strip_existing_ocr: bool = False + page_range: Annotated[ + Optional[List[int]], + "The range of pages to process.", + "Default is None, which will process all pages." + ] = None + pdftext_workers: Annotated[ + int, + "The number of workers to use for pdftext.", + "Default is 4." + ] = 4 + flatten_pdf: Annotated[ + bool, + "Whether to flatten the PDF structure.", + "Default is True." + ] = True + force_ocr: Annotated[ + bool, + "Whether to force OCR on the whole document.", + "Default is False." + ] = False + ocr_invalid_chars: Annotated[ + tuple, + "The characters to consider invalid for OCR.", + "Default is (chr(0xfffd), '�')." + ] = (chr(0xfffd), "�") + ocr_space_threshold: Annotated[ + float, + "The minimum ratio of spaces to non-spaces to detect bad text.", + "Default is 0.7." + ] = .7 + ocr_newline_threshold: Annotated[ + float, + "The minimum ratio of newlines to non-newlines to detect bad text.", + "Default is 0.6." + ] = .6 + ocr_alphanum_threshold: Annotated[ + float, + "The minimum ratio of alphanumeric characters to non-alphanumeric characters to consider an alphanumeric character.", + "Default is 0.3." + ] = .3 + image_threshold: Annotated[ + float, + "The minimum coverage ratio of the image to the page to consider skipping the page.", + "Default is .65." + ] = .65 + strip_existing_ocr: Annotated[ + bool, + "Whether to strip existing OCR text from the PDF.", + "Default is True." + ] = True def __init__(self, filepath: str, config=None): super().__init__(filepath, config) @@ -250,7 +246,7 @@ def check_page(self, page_id: int) -> bool: for text_obj in filter(lambda obj: obj.type == pdfium_c.FPDF_PAGEOBJ_TEXT, page_objs): font = pdfium_c.FPDFTextObj_GetFont(text_obj) font_name = self.get_fontname(font) - + # we also skip pages without embedded fonts and fonts without names non_embedded_fonts.append(pdfium_c.FPDFFont_GetIsEmbedded(font) == 0) empty_fonts.append(not font_name or font_name == "GlyphLessFont") @@ -313,8 +309,8 @@ def get_page_lines(self, idx: int) -> List[ProviderOutput]: def get_fontname(self, font) -> str: font_name = "" - buffer_size = 256 - + buffer_size = 256 + try: font_name_buffer = ctypes.create_string_buffer(buffer_size) length = pdfium_c.FPDFFont_GetBaseFontName(font, font_name_buffer, buffer_size) diff --git a/marker/renderers/html.py b/marker/renderers/html.py index 87b90112..cc49686f 100644 --- a/marker/renderers/html.py +++ b/marker/renderers/html.py @@ -1,4 +1,4 @@ -from typing import Literal +from typing import Annotated, List, Literal from bs4 import BeautifulSoup, MarkupResemblesLocatorWarning from pydantic import BaseModel @@ -26,23 +26,22 @@ class HTMLOutput(BaseModel): class HTMLRenderer(BaseRenderer): """ A renderer for HTML output. - - Attributes: - page_blocks (list): - The list of block types to consider as pages. - Default is [BlockTypes.Page]. - - paginate_output (bool): - Whether to paginate the output. - Default is False. - - image_extraction_mode (Literal["lowres", "highres"]): - The mode to use for extracting images. - Default is "highres". """ - page_blocks: list = [BlockTypes.Page] - paginate_output: bool = False - image_extraction_mode: Literal["lowres", "highres"] = "highres" + page_blocks: Annotated[ + List[BlockTypes], + "The block types to consider as pages.", + "Default is [BlockTypes.Page]." + ] = [BlockTypes.Page] + paginate_output: Annotated[ + bool, + "Whether to paginate the output.", + "Default is False." + ] = False + image_extraction_mode: Annotated[ + Literal["lowres", "highres"], + "The mode to use for extracting images.", + "Default is 'highres'." + ] = "highres" def extract_image(self, document, image_id): image_block = document.get_block(image_id) diff --git a/marker/renderers/json.py b/marker/renderers/json.py index a11ce8a2..1e262851 100644 --- a/marker/renderers/json.py +++ b/marker/renderers/json.py @@ -1,6 +1,4 @@ -from __future__ import annotations - -from typing import Dict, List +from typing import Annotated, Dict, List from pydantic import BaseModel @@ -16,7 +14,7 @@ class JSONBlockOutput(BaseModel): block_type: str html: str polygon: List[List[float]] - children: List[JSONBlockOutput] | None = None + children: List['JSONBlockOutput'] | None = None section_hierarchy: Dict[int, str] | None = None images: dict | None = None @@ -37,18 +35,17 @@ def reformat_section_hierarchy(section_hierarchy): class JSONRenderer(BaseRenderer): """ A renderer for JSON output. - - Attributes: - image_blocks (list): - The list of block types to consider as images. - Default is [BlockTypes.Picture, BlockTypes.Figure]. - - page_blocks (list): - The list of block types to consider as pages. - Default is [BlockTypes.Page]. """ - image_blocks: list = [BlockTypes.Picture, BlockTypes.Figure] - page_blocks: list = [BlockTypes.Page] + image_blocks: Annotated[ + List[BlockTypes], + "The list of block types to consider as images.", + "Default is [BlockTypes.Picture, BlockTypes.Figure]." + ] = [BlockTypes.Picture, BlockTypes.Figure] + page_blocks: Annotated[ + List[BlockTypes], + "The list of block types to consider as pages.", + "Default is [BlockTypes.Page]." + ] = [BlockTypes.Page] def extract_json(self, document: Document, block_output: BlockOutput): cls = get_block_class(block_output.id.block_type) From 4b8cad9442837528179feac832b1a05d2cdf3a8b Mon Sep 17 00:00:00 2001 From: Moses Paul R Date: Mon, 6 Jan 2025 09:50:17 +0000 Subject: [PATCH 04/92] generate defaults unless specified --- marker/builders/document.py | 2 - marker/builders/layout.py | 5 --- marker/builders/llm_layout.py | 7 --- marker/builders/structure.py | 2 - marker/config/printer.py | 7 ++- marker/converters/pdf.py | 69 ++++++++++++++---------------- marker/processors/blockquote.py | 4 -- marker/processors/debug.py | 6 --- marker/processors/equation.py | 3 -- marker/processors/ignoretext.py | 4 -- marker/processors/line_numbers.py | 3 -- marker/processors/list.py | 2 - marker/processors/llm/__init__.py | 9 +--- marker/processors/llm/llm_table.py | 1 - marker/processors/sectionheader.py | 4 -- marker/processors/table.py | 1 - marker/processors/text.py | 1 - marker/providers/pdf.py | 9 ---- marker/renderers/html.py | 5 +-- marker/renderers/json.py | 2 - 20 files changed, 41 insertions(+), 105 deletions(-) diff --git a/marker/builders/document.py b/marker/builders/document.py index 8c2f683b..d79c4990 100644 --- a/marker/builders/document.py +++ b/marker/builders/document.py @@ -17,12 +17,10 @@ class DocumentBuilder(BaseBuilder): lowres_image_dpi: Annotated[ int, "DPI setting for low-resolution page images used for Layout and Line Detection.", - "Default is 96." ] = 96 highres_image_dpi: Annotated[ int, "DPI setting for high-resolution page images used for OCR.", - "Default is 192." ] = 192 def __call__(self, provider: PdfProvider, layout_builder: LayoutBuilder, ocr_builder: OcrBuilder): diff --git a/marker/builders/layout.py b/marker/builders/layout.py index 43337d0a..e43142e2 100644 --- a/marker/builders/layout.py +++ b/marker/builders/layout.py @@ -32,29 +32,24 @@ class LayoutBuilder(BaseBuilder): int, "The minimum number of PdfProvider lines that must be covered by the layout model", "to consider the lines from the PdfProvider valid.", - "Default is 1." ] = 1 layout_coverage_threshold: Annotated[ float, "The minimum coverage ratio required for the layout model to consider", "the lines from the PdfProvider valid.", - "Default is 0.1." ] = .1 document_ocr_threshold: Annotated[ float, "The minimum ratio of pages that must pass the layout coverage check", "to avoid OCR.", - "Default is 0.8." ] = .8 error_model_segment_length: Annotated[ int, "The maximum number of characters to send to the OCR error model.", - "Default is 512." ] = 512 excluded_for_coverage: Annotated[ List[BlockTypes], "A list of block types to exclude from the layout coverage check.", - "Default is [BlockTypes.Figure, BlockTypes.Picture, BlockTypes.Table, BlockTypes.FigureGroup, BlockTypes.TableGroup, BlockTypes.PictureGroup]." ] = (BlockTypes.Figure, BlockTypes.Picture, BlockTypes.Table, BlockTypes.FigureGroup, BlockTypes.TableGroup, BlockTypes.PictureGroup) def __init__(self, layout_model: SuryaLayoutModel, ocr_error_model: DistilBertForSequenceClassification, config=None): diff --git a/marker/builders/llm_layout.py b/marker/builders/llm_layout.py index 2ad9d1a1..7c8f50a4 100644 --- a/marker/builders/llm_layout.py +++ b/marker/builders/llm_layout.py @@ -26,37 +26,30 @@ class LLMLayoutBuilder(LayoutBuilder): google_api_key: Annotated[ Optional[str], "The Google API key to use for the Gemini model.", - "Default is None." ] = settings.GOOGLE_API_KEY confidence_threshold: Annotated[ float, "The confidence threshold to use for relabeling.", - "Default is 0.75." ] = 0.75 picture_height_threshold: Annotated[ float, "The height threshold for pictures that may actually be complex regions.", - "Default is 0.8." ] = 0.8 model_name: Annotated[ str, "The name of the Gemini model to use.", - "Default is 'gemini-1.5-flash'." ] = "gemini-1.5-flash" max_retries: Annotated[ int, "The maximum number of retries to use for the Gemini model.", - "Default is 3." ] = 3 max_concurrency: Annotated[ int, "The maximum number of concurrent requests to make to the Gemini model.", - "Default is 3." ] = 3 timeout: Annotated[ int, "The timeout for requests to the Gemini model.", - "Default is 60 seconds." ] = 60 topk_relabelling_prompt: Annotated[ str, diff --git a/marker/builders/structure.py b/marker/builders/structure.py index 89888cfd..1396984a 100644 --- a/marker/builders/structure.py +++ b/marker/builders/structure.py @@ -15,12 +15,10 @@ class StructureBuilder(BaseBuilder): gap_threshold: Annotated[ float, "The minimum gap between blocks to consider them part of the same group.", - "Default is 0.05." ] = 0.05 list_gap_threshold: Annotated[ float, "The minimum gap between list items to consider them part of the same group.", - "Default is 0.1." ] = 0.1 def __init__(self, config=None): diff --git a/marker/config/printer.py b/marker/config/printer.py index 570b8622..e56b9647 100644 --- a/marker/config/printer.py +++ b/marker/config/printer.py @@ -69,14 +69,19 @@ def parse_args(self, ctx, args): if get_origin(attr_type) is Annotated: base_attr_type = get_args(attr_type)[0] default = getattr(class_type, attr) + default_help_str = "" + if all('Default' not in desc for desc in attr_type.__metadata__): + default_help_str = f"Default is {default}." if display_help: click.echo(" " * 8 + f"{attr} ({format_type(base_attr_type)}):") click.echo("\n".join([f'{" " * 12}' + desc for desc in attr_type.__metadata__])) + if default_help_str: + click.echo(f'{" " * 12}' + default_help_str) if base_attr_type in [str, int, float, bool, Optional[int], Optional[float], Optional[str]]: if attr not in [p.name for p in ctx.command.params]: is_flag = base_attr_type in [bool, Optional[bool]] and not default ctx.command.params.append( - click.Option([f"--{attr}"], help=" ".join(attr_type.__metadata__), type=base_attr_type, default=default, is_flag=is_flag) + click.Option([f"--{attr}"], help=" ".join(attr_type.__metadata__ + (default_help_str,)), type=base_attr_type, default=default, is_flag=is_flag) ) if display_help: ctx.exit() diff --git a/marker/converters/pdf.py b/marker/converters/pdf.py index fb0e949e..c1a80c93 100644 --- a/marker/converters/pdf.py +++ b/marker/converters/pdf.py @@ -1,42 +1,40 @@ +from marker.util import strings_to_classes +from marker.schema.registry import register_block_class +from marker.schema.blocks import Block +from marker.schema import BlockTypes +from marker.renderers.markdown import MarkdownRenderer +from marker.providers.pdf import PdfProvider +from marker.processors.text import TextProcessor +from marker.processors.table import TableProcessor +from marker.processors.sectionheader import SectionHeaderProcessor +from marker.processors.page_header import PageHeaderProcessor +from marker.processors.llm.llm_text import LLMTextProcessor +from marker.processors.llm.llm_table import LLMTableProcessor +from marker.processors.llm.llm_image_description import LLMImageDescriptionProcessor +from marker.processors.llm.llm_form import LLMFormProcessor +from marker.processors.llm.llm_complex import LLMComplexRegionProcessor +from marker.processors.list import ListProcessor +from marker.processors.line_numbers import LineNumbersProcessor +from marker.processors.ignoretext import IgnoreTextProcessor +from marker.processors.footnote import FootnoteProcessor +from marker.processors.equation import EquationProcessor +from marker.processors.document_toc import DocumentTOCProcessor +from marker.processors.debug import DebugProcessor +from marker.processors.code import CodeProcessor +from marker.processors.blockquote import BlockquoteProcessor +from marker.converters import BaseConverter +from marker.builders.structure import StructureBuilder +from marker.builders.ocr import OcrBuilder +from marker.builders.llm_layout import LLMLayoutBuilder +from marker.builders.layout import LayoutBuilder +from marker.builders.document import DocumentBuilder +from typing import Annotated, Any, Dict, List, Optional, Type +from collections import defaultdict +import inspect import os os.environ["TOKENIZERS_PARALLELISM"] = "false" # disables a tokenizers warning -import inspect -from collections import defaultdict -from typing import Annotated, Any, Dict, List, Optional, Type - -from marker.builders.document import DocumentBuilder -from marker.builders.layout import LayoutBuilder -from marker.builders.llm_layout import LLMLayoutBuilder -from marker.builders.ocr import OcrBuilder -from marker.builders.structure import StructureBuilder -from marker.converters import BaseConverter -from marker.processors.blockquote import BlockquoteProcessor -from marker.processors.code import CodeProcessor -from marker.processors.debug import DebugProcessor -from marker.processors.document_toc import DocumentTOCProcessor -from marker.processors.equation import EquationProcessor -from marker.processors.footnote import FootnoteProcessor -from marker.processors.ignoretext import IgnoreTextProcessor -from marker.processors.line_numbers import LineNumbersProcessor -from marker.processors.list import ListProcessor -from marker.processors.llm.llm_complex import LLMComplexRegionProcessor -from marker.processors.llm.llm_form import LLMFormProcessor -from marker.processors.llm.llm_image_description import LLMImageDescriptionProcessor -from marker.processors.llm.llm_table import LLMTableProcessor -from marker.processors.llm.llm_text import LLMTextProcessor -from marker.processors.page_header import PageHeaderProcessor -from marker.processors.sectionheader import SectionHeaderProcessor -from marker.processors.table import TableProcessor -from marker.processors.text import TextProcessor -from marker.providers.pdf import PdfProvider -from marker.renderers.markdown import MarkdownRenderer -from marker.schema import BlockTypes -from marker.schema.blocks import Block -from marker.schema.registry import register_block_class -from marker.util import strings_to_classes - class PdfConverter(BaseConverter): """ @@ -52,7 +50,6 @@ class PdfConverter(BaseConverter): use_llm: Annotated[ bool, "Enable higher quality processing with LLMs.", - "Default is False." ] = False def __init__(self, artifact_dict: Dict[str, Any], processor_list: Optional[List[str]] = None, renderer: str | None = None, config=None): diff --git a/marker/processors/blockquote.py b/marker/processors/blockquote.py index 3854d27d..df140c40 100644 --- a/marker/processors/blockquote.py +++ b/marker/processors/blockquote.py @@ -12,25 +12,21 @@ class BlockquoteProcessor(BaseProcessor): block_types: Annotated[ List[BlockTypes], "The block types to process.", - "Default is [BlockTypes.Text, BlockTypes.TextInlineMath]." ] = (BlockTypes.Text, BlockTypes.TextInlineMath) min_x_indent: Annotated[ float, "The minimum horizontal indentation required to consider a block as part of a blockquote.", "Expressed as a percentage of the block width.", - "Default is 0.05 (5%)." ] = 0.05 x_start_tolerance: Annotated[ float, "The maximum allowable difference between the starting x-coordinates of consecutive blocks to consider them aligned.", "Expressed as a percentage of the block width.", - "Default is 0.01 (1%)." ] = 0.01 x_end_tolerance: Annotated[ float, "The maximum allowable difference between the ending x-coordinates of consecutive blocks to consider them aligned.", "Expressed as a percentage of the block width.", - "Default is 0.01 (1%)." ] = 0.01 def __init__(self, config): diff --git a/marker/processors/debug.py b/marker/processors/debug.py index ee02e6bb..d613e465 100644 --- a/marker/processors/debug.py +++ b/marker/processors/debug.py @@ -23,32 +23,26 @@ class DebugProcessor(BaseProcessor): debug_data_folder: Annotated[ str, "The folder to dump debug data to.", - "Default is 'debug_data'." ] = "debug_data" debug_layout_images: Annotated[ bool, "Whether to dump layout debug images.", - "Default is False." ] = False debug_pdf_images: Annotated[ bool, "Whether to dump PDF debug images.", - "Default is False." ] = False debug_json: Annotated[ bool, "Whether to dump block debug data.", - "Default is False." ] = False render_font: Annotated[ str, "The path to the font to use for rendering debug images.", - "Default is 'GoNotoCurrent-Regular.ttf' in the FONT_DIR folder." ] = os.path.join(settings.FONT_DIR, "GoNotoCurrent-Regular.ttf") font_dl_path: Annotated[ str, "The path to download the font from.", - "Default is 'https://github.com/satbyy/go-noto-universal/releases/download/v7.0'." ] = "https://github.com/satbyy/go-noto-universal/releases/download/v7.0" def __call__(self, document: Document): diff --git a/marker/processors/equation.py b/marker/processors/equation.py index e15c4bd0..bb5b18a9 100644 --- a/marker/processors/equation.py +++ b/marker/processors/equation.py @@ -17,12 +17,10 @@ class EquationProcessor(BaseProcessor): block_types: Annotated[ List[BlockTypes], "The block types to process.", - "Default is [BlockTypes.Equation]." ] = (BlockTypes.Equation,) model_max_length: Annotated[ int, "The maximum number of tokens to allow for the Texify model.", - "Default is 384." ] = 384 texify_batch_size: Annotated[ Optional[int], @@ -32,7 +30,6 @@ class EquationProcessor(BaseProcessor): token_buffer: Annotated[ int, "The number of tokens to buffer above max for the Texify model.", - "Default is 256." ] = 256 def __init__(self, texify_model: GenerateVisionEncoderDecoderModel, config=None): diff --git a/marker/processors/ignoretext.py b/marker/processors/ignoretext.py index e7f8daa1..45b83c6b 100644 --- a/marker/processors/ignoretext.py +++ b/marker/processors/ignoretext.py @@ -25,25 +25,21 @@ class IgnoreTextProcessor(BaseProcessor): float, "The minimum ratio of pages a text block must appear on to be considered a common element.", "Blocks that meet or exceed this threshold are marked as common elements.", - "Default is 0.6 (60% of pages)." ] = 0.6 common_element_min_blocks: Annotated[ int, "The minimum number of occurrences of a text block within a document to consider it a common element.", "This ensures that rare blocks are not mistakenly flagged.", - "Default is 3." ] = 3 max_streak: Annotated[ int, "The maximum number of consecutive occurrences of a text block allowed before it is classified as a common element.", "Helps to identify patterns like repeated headers or footers.", - "Default is 3." ] = 3 text_match_threshold: Annotated[ int, "The minimum fuzzy match score (0-100) required to classify a text block as similar to a common element.", "Higher values enforce stricter matching.", - "Default is 90." ] = 90 def __call__(self, document: Document): diff --git a/marker/processors/line_numbers.py b/marker/processors/line_numbers.py index 9afdc806..d38d9131 100644 --- a/marker/processors/line_numbers.py +++ b/marker/processors/line_numbers.py @@ -13,19 +13,16 @@ class LineNumbersProcessor(BaseProcessor): strip_numbers_threshold: Annotated[ float, "The fraction of lines or tokens in a block that must be numeric to consider them as line numbers.", - "Default is 0.6 (60%)." ] = 0.6 min_lines_in_block: Annotated[ int, "The minimum number of lines required in a block for it to be considered during processing.", "Ensures that small blocks are ignored as they are unlikely to contain meaningful line numbers.", - "Default is 4." ] = 4 min_line_length: Annotated[ int, "The minimum length of a line (in characters) to consider it significant when checking for", "numeric prefixes or suffixes. Prevents false positives for short lines.", - "Default is 10." ] = 10 def __init__(self, config): diff --git a/marker/processors/list.py b/marker/processors/list.py index 10eb9c1e..3f84fbda 100644 --- a/marker/processors/list.py +++ b/marker/processors/list.py @@ -14,12 +14,10 @@ class ListProcessor(BaseProcessor): ignored_block_types: Annotated[ List[BlockTypes], "The list of block types to ignore when merging lists.", - "Default is [BlockTypes.PageHeader, BlockTypes.PageFooter]." ] = (BlockTypes.PageHeader, BlockTypes.PageFooter) min_x_indent: Annotated[ float, "The minimum horizontal indentation required to consider a block as a nested list item.", "This is expressed as a percentage of the page width and is used to determine hierarchical relationships within a list.", - "Default is 0.01 (1% of page width)." ] = 0.01 def __init__(self, config): diff --git a/marker/processors/llm/__init__.py b/marker/processors/llm/__init__.py index 838c3987..0e46f94c 100644 --- a/marker/processors/llm/__init__.py +++ b/marker/processors/llm/__init__.py @@ -18,42 +18,35 @@ class BaseLLMProcessor(BaseProcessor): google_api_key: Annotated[ Optional[str], "The Google API key to use for the Gemini model.", - "Default is None." ] = settings.GOOGLE_API_KEY model_name: Annotated[ str, "The name of the Gemini model to use.", - "Default is 'gemini-1.5-flash'." ] = "gemini-1.5-flash" max_retries: Annotated[ int, "The maximum number of retries to use for the Gemini model.", - "Default is 3." ] = 3 max_concurrency: Annotated[ int, "The maximum number of concurrent requests to make to the Gemini model.", - "Default is 3." ] = 3 timeout: Annotated[ int, "The timeout for requests to the Gemini model.", - "Default is 60 seconds." ] = 60 image_expansion_ratio: Annotated[ float, "The ratio to expand the image by when cropping.", - "Default is 0.01." ] = 0.01 gemini_rewriting_prompt: Annotated[ str, "The prompt to use for rewriting text.", "Default is a string containing the Gemini rewriting prompt." - ] = None + ] = '' use_llm: Annotated[ bool, "Whether to use the LLM model.", - "Default is False." ] = False block_types = None diff --git a/marker/processors/llm/llm_table.py b/marker/processors/llm/llm_table.py index 491a7420..c54d6be2 100644 --- a/marker/processors/llm/llm_table.py +++ b/marker/processors/llm/llm_table.py @@ -17,7 +17,6 @@ class LLMTableProcessor(BaseLLMProcessor): block_types: Annotated[ List[BlockTypes], "The block types to process.", - "Default is [BlockTypes.Table]." ] = (BlockTypes.Table,) gemini_rewriting_prompt: Annotated[ str, diff --git a/marker/processors/sectionheader.py b/marker/processors/sectionheader.py index b8bf2e44..6d5e3d0f 100644 --- a/marker/processors/sectionheader.py +++ b/marker/processors/sectionheader.py @@ -21,22 +21,18 @@ class SectionHeaderProcessor(BaseProcessor): level_count: Annotated[ int, "The number of levels to use for headings.", - "Default is 4." ] = 4 merge_threshold: Annotated[ float, "The minimum gap between headings to consider them part of the same group.", - "Default is 0.25." ] = 0.25 default_level: Annotated[ int, "The default heading level to use if no heading level is detected.", - "Default is 2." ] = 2 height_tolerance: Annotated[ float, "The minimum height of a heading to consider it a heading.", - "Default is 0.99." ] = 0.99 def __call__(self, document: Document): diff --git a/marker/processors/table.py b/marker/processors/table.py index a8f38eeb..f810ac4b 100644 --- a/marker/processors/table.py +++ b/marker/processors/table.py @@ -23,7 +23,6 @@ class TableProcessor(BaseProcessor): detect_boxes: Annotated[ bool, "Whether to detect boxes for the table recognition model.", - "Default is False." ] = False detector_batch_size: Annotated[ int, diff --git a/marker/processors/text.py b/marker/processors/text.py index 46f92d32..da23e17e 100644 --- a/marker/processors/text.py +++ b/marker/processors/text.py @@ -18,7 +18,6 @@ class TextProcessor(BaseProcessor): column_gap_ratio: Annotated[ float, "The minimum ratio of the page width to the column gap to consider a column break.", - "Default is 0.02." ] = 0.02 def __init__(self, config): diff --git a/marker/providers/pdf.py b/marker/providers/pdf.py index d348bfc2..bb03815d 100644 --- a/marker/providers/pdf.py +++ b/marker/providers/pdf.py @@ -31,47 +31,38 @@ class PdfProvider(BaseProvider): pdftext_workers: Annotated[ int, "The number of workers to use for pdftext.", - "Default is 4." ] = 4 flatten_pdf: Annotated[ bool, "Whether to flatten the PDF structure.", - "Default is True." ] = True force_ocr: Annotated[ bool, "Whether to force OCR on the whole document.", - "Default is False." ] = False ocr_invalid_chars: Annotated[ tuple, "The characters to consider invalid for OCR.", - "Default is (chr(0xfffd), '�')." ] = (chr(0xfffd), "�") ocr_space_threshold: Annotated[ float, "The minimum ratio of spaces to non-spaces to detect bad text.", - "Default is 0.7." ] = .7 ocr_newline_threshold: Annotated[ float, "The minimum ratio of newlines to non-newlines to detect bad text.", - "Default is 0.6." ] = .6 ocr_alphanum_threshold: Annotated[ float, "The minimum ratio of alphanumeric characters to non-alphanumeric characters to consider an alphanumeric character.", - "Default is 0.3." ] = .3 image_threshold: Annotated[ float, "The minimum coverage ratio of the image to the page to consider skipping the page.", - "Default is .65." ] = .65 strip_existing_ocr: Annotated[ bool, "Whether to strip existing OCR text from the PDF.", - "Default is True." ] = True def __init__(self, filepath: str, config=None): diff --git a/marker/renderers/html.py b/marker/renderers/html.py index cc49686f..3ee93394 100644 --- a/marker/renderers/html.py +++ b/marker/renderers/html.py @@ -1,3 +1,4 @@ +from PIL import Image from typing import Annotated, List, Literal from bs4 import BeautifulSoup, MarkupResemblesLocatorWarning @@ -13,7 +14,6 @@ warnings.filterwarnings("ignore", category=MarkupResemblesLocatorWarning) # Suppress DecompressionBombError -from PIL import Image Image.MAX_IMAGE_PIXELS = None @@ -30,17 +30,14 @@ class HTMLRenderer(BaseRenderer): page_blocks: Annotated[ List[BlockTypes], "The block types to consider as pages.", - "Default is [BlockTypes.Page]." ] = [BlockTypes.Page] paginate_output: Annotated[ bool, "Whether to paginate the output.", - "Default is False." ] = False image_extraction_mode: Annotated[ Literal["lowres", "highres"], "The mode to use for extracting images.", - "Default is 'highres'." ] = "highres" def extract_image(self, document, image_id): diff --git a/marker/renderers/json.py b/marker/renderers/json.py index 1e262851..6a67b3bf 100644 --- a/marker/renderers/json.py +++ b/marker/renderers/json.py @@ -39,12 +39,10 @@ class JSONRenderer(BaseRenderer): image_blocks: Annotated[ List[BlockTypes], "The list of block types to consider as images.", - "Default is [BlockTypes.Picture, BlockTypes.Figure]." ] = [BlockTypes.Picture, BlockTypes.Figure] page_blocks: Annotated[ List[BlockTypes], "The list of block types to consider as pages.", - "Default is [BlockTypes.Page]." ] = [BlockTypes.Page] def extract_json(self, document: Document, block_output: BlockOutput): From 84afb65ec61f0df67d119945d00b9f2263dcfec0 Mon Sep 17 00:00:00 2001 From: Moses Paul R Date: Mon, 6 Jan 2025 12:53:01 +0000 Subject: [PATCH 05/92] missing provider configs [skip ci] --- marker/config/printer.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/marker/config/printer.py b/marker/config/printer.py index e56b9647..c93b157f 100644 --- a/marker/config/printer.py +++ b/marker/config/printer.py @@ -8,6 +8,7 @@ from marker.builders import BaseBuilder from marker.converters import BaseConverter from marker.processors import BaseProcessor +from marker.providers import BaseProvider from marker.renderers import BaseRenderer @@ -54,7 +55,7 @@ def parse_args(self, ctx, args): if display_help: click.echo("Here is a list of all the Builders, Processors, Converters and Renderers in Marker along with their attributes:") - base_classes = [BaseBuilder, BaseProcessor, BaseConverter, BaseRenderer] + base_classes = [BaseBuilder, BaseProcessor, BaseConverter, BaseProvider, BaseRenderer] for base in base_classes: if display_help: click.echo(f"{base.__name__.removeprefix('Base')}s:") From 93d703883f3ce4b85da792562eee29ffc6e87fb0 Mon Sep 17 00:00:00 2001 From: Moses Paul R Date: Mon, 6 Jan 2025 12:53:55 +0000 Subject: [PATCH 06/92] [skip ci] From dc27d53bca073e781029f72f89d668f912c26ba6 Mon Sep 17 00:00:00 2001 From: Moses Paul R Date: Mon, 6 Jan 2025 13:01:58 +0000 Subject: [PATCH 07/92] fix defaults and ocr stripping logic --- marker/providers/pdf.py | 54 ++++++++++++++++++++--------------------- 1 file changed, 26 insertions(+), 28 deletions(-) diff --git a/marker/providers/pdf.py b/marker/providers/pdf.py index bb03815d..09b9603d 100644 --- a/marker/providers/pdf.py +++ b/marker/providers/pdf.py @@ -63,7 +63,7 @@ class PdfProvider(BaseProvider): strip_existing_ocr: Annotated[ bool, "Whether to strip existing OCR text from the PDF.", - ] = True + ] = False def __init__(self, filepath: str, config=None): super().__init__(filepath, config) @@ -223,35 +223,33 @@ def check_page(self, page_id: int) -> bool: if not any([obj.type == pdfium_c.FPDF_PAGEOBJ_TEXT for obj in page_objs]): return False - if not self.strip_existing_ocr: - return True - - # If any text objects on the page are in invisible render mode, skip this page - for text_obj in filter(lambda obj: obj.type == pdfium_c.FPDF_PAGEOBJ_TEXT, page_objs): - if pdfium_c.FPDFTextObj_GetTextRenderMode(text_obj) in [pdfium_c.FPDF_TEXTRENDERMODE_INVISIBLE, pdfium_c.FPDF_TEXTRENDERMODE_UNKNOWN]: + if self.strip_existing_ocr: + # If any text objects on the page are in invisible render mode, skip this page + for text_obj in filter(lambda obj: obj.type == pdfium_c.FPDF_PAGEOBJ_TEXT, page_objs): + if pdfium_c.FPDFTextObj_GetTextRenderMode(text_obj) in [pdfium_c.FPDF_TEXTRENDERMODE_INVISIBLE, pdfium_c.FPDF_TEXTRENDERMODE_UNKNOWN]: + return False + + non_embedded_fonts = [] + empty_fonts = [] + font_map = {} + for text_obj in filter(lambda obj: obj.type == pdfium_c.FPDF_PAGEOBJ_TEXT, page_objs): + font = pdfium_c.FPDFTextObj_GetFont(text_obj) + font_name = self.get_fontname(font) + + # we also skip pages without embedded fonts and fonts without names + non_embedded_fonts.append(pdfium_c.FPDFFont_GetIsEmbedded(font) == 0) + empty_fonts.append(not font_name or font_name == "GlyphLessFont") + if font_name not in font_map: + font_map[font_name or 'Unknown'] = font + + if all(non_embedded_fonts) or all(empty_fonts): return False - non_embedded_fonts = [] - empty_fonts = [] - font_map = {} - for text_obj in filter(lambda obj: obj.type == pdfium_c.FPDF_PAGEOBJ_TEXT, page_objs): - font = pdfium_c.FPDFTextObj_GetFont(text_obj) - font_name = self.get_fontname(font) - - # we also skip pages without embedded fonts and fonts without names - non_embedded_fonts.append(pdfium_c.FPDFFont_GetIsEmbedded(font) == 0) - empty_fonts.append(not font_name or font_name == "GlyphLessFont") - if font_name not in font_map: - font_map[font_name or 'Unknown'] = font - - if all(non_embedded_fonts) or all(empty_fonts): - return False - - # if we see very large images covering most of the page, we can skip this page - for img_obj in filter(lambda obj: obj.type == pdfium_c.FPDF_PAGEOBJ_IMAGE, page_objs): - img_bbox = PolygonBox.from_bbox(img_obj.get_pos()) - if page_bbox.intersection_pct(img_bbox) >= self.image_threshold: - return False + # if we see very large images covering most of the page, we can skip this page + for img_obj in filter(lambda obj: obj.type == pdfium_c.FPDF_PAGEOBJ_IMAGE, page_objs): + img_bbox = PolygonBox.from_bbox(img_obj.get_pos()) + if page_bbox.intersection_pct(img_bbox) >= self.image_threshold: + return False return True From c39a0b2b53c594197e4314f268044be074f5800d Mon Sep 17 00:00:00 2001 From: Moses Paul R Date: Mon, 6 Jan 2025 15:06:02 +0000 Subject: [PATCH 08/92] switch to Tuple for default attrs --- marker/builders/layout.py | 4 ++-- marker/processors/blockquote.py | 4 ++-- marker/processors/equation.py | 4 ++-- marker/processors/list.py | 4 ++-- marker/processors/llm/llm_table.py | 4 ++-- marker/renderers/html.py | 6 +++--- marker/renderers/json.py | 10 +++++----- 7 files changed, 18 insertions(+), 18 deletions(-) diff --git a/marker/builders/layout.py b/marker/builders/layout.py index e43142e2..47c24d81 100644 --- a/marker/builders/layout.py +++ b/marker/builders/layout.py @@ -1,4 +1,4 @@ -from typing import Annotated, List, Optional +from typing import Annotated, List, Optional, Tuple import numpy as np from surya.layout import batch_layout_detection @@ -48,7 +48,7 @@ class LayoutBuilder(BaseBuilder): "The maximum number of characters to send to the OCR error model.", ] = 512 excluded_for_coverage: Annotated[ - List[BlockTypes], + Tuple[BlockTypes], "A list of block types to exclude from the layout coverage check.", ] = (BlockTypes.Figure, BlockTypes.Picture, BlockTypes.Table, BlockTypes.FigureGroup, BlockTypes.TableGroup, BlockTypes.PictureGroup) diff --git a/marker/processors/blockquote.py b/marker/processors/blockquote.py index df140c40..bc0b5bec 100644 --- a/marker/processors/blockquote.py +++ b/marker/processors/blockquote.py @@ -1,4 +1,4 @@ -from typing import Annotated, List +from typing import Annotated, Tuple from marker.processors import BaseProcessor from marker.schema import BlockTypes @@ -10,7 +10,7 @@ class BlockquoteProcessor(BaseProcessor): A processor for tagging blockquotes. """ block_types: Annotated[ - List[BlockTypes], + Tuple[BlockTypes], "The block types to process.", ] = (BlockTypes.Text, BlockTypes.TextInlineMath) min_x_indent: Annotated[ diff --git a/marker/processors/equation.py b/marker/processors/equation.py index bb5b18a9..4150eba5 100644 --- a/marker/processors/equation.py +++ b/marker/processors/equation.py @@ -1,4 +1,4 @@ -from typing import Annotated, List, Optional +from typing import Annotated, List, Optional, Tuple from texify.inference import batch_inference from texify.model.model import GenerateVisionEncoderDecoderModel @@ -15,7 +15,7 @@ class EquationProcessor(BaseProcessor): A processor for recognizing equations in the document. """ block_types: Annotated[ - List[BlockTypes], + Tuple[BlockTypes], "The block types to process.", ] = (BlockTypes.Equation,) model_max_length: Annotated[ diff --git a/marker/processors/list.py b/marker/processors/list.py index 3f84fbda..9d7105ee 100644 --- a/marker/processors/list.py +++ b/marker/processors/list.py @@ -1,4 +1,4 @@ -from typing import Annotated, List +from typing import Annotated, List, Tuple from marker.processors import BaseProcessor from marker.schema import BlockTypes @@ -12,7 +12,7 @@ class ListProcessor(BaseProcessor): """ block_types = (BlockTypes.ListGroup,) ignored_block_types: Annotated[ - List[BlockTypes], + Tuple[BlockTypes], "The list of block types to ignore when merging lists.", ] = (BlockTypes.PageHeader, BlockTypes.PageFooter) min_x_indent: Annotated[ diff --git a/marker/processors/llm/llm_table.py b/marker/processors/llm/llm_table.py index c54d6be2..c1d5ab07 100644 --- a/marker/processors/llm/llm_table.py +++ b/marker/processors/llm/llm_table.py @@ -1,4 +1,4 @@ -from typing import Annotated, List +from typing import Annotated, List, Tuple from bs4 import BeautifulSoup from google.ai.generativelanguage_v1beta.types import content @@ -15,7 +15,7 @@ class LLMTableProcessor(BaseLLMProcessor): block_types: Annotated[ - List[BlockTypes], + Tuple[BlockTypes], "The block types to process.", ] = (BlockTypes.Table,) gemini_rewriting_prompt: Annotated[ diff --git a/marker/renderers/html.py b/marker/renderers/html.py index 3ee93394..f01ef315 100644 --- a/marker/renderers/html.py +++ b/marker/renderers/html.py @@ -1,5 +1,5 @@ from PIL import Image -from typing import Annotated, List, Literal +from typing import Annotated, Literal, Tuple from bs4 import BeautifulSoup, MarkupResemblesLocatorWarning from pydantic import BaseModel @@ -28,9 +28,9 @@ class HTMLRenderer(BaseRenderer): A renderer for HTML output. """ page_blocks: Annotated[ - List[BlockTypes], + Tuple[BlockTypes], "The block types to consider as pages.", - ] = [BlockTypes.Page] + ] = (BlockTypes.Page,) paginate_output: Annotated[ bool, "Whether to paginate the output.", diff --git a/marker/renderers/json.py b/marker/renderers/json.py index 6a67b3bf..fce9c289 100644 --- a/marker/renderers/json.py +++ b/marker/renderers/json.py @@ -1,4 +1,4 @@ -from typing import Annotated, Dict, List +from typing import Annotated, Dict, List, Tuple from pydantic import BaseModel @@ -37,13 +37,13 @@ class JSONRenderer(BaseRenderer): A renderer for JSON output. """ image_blocks: Annotated[ - List[BlockTypes], + Tuple[BlockTypes], "The list of block types to consider as images.", - ] = [BlockTypes.Picture, BlockTypes.Figure] + ] = (BlockTypes.Picture, BlockTypes.Figure) page_blocks: Annotated[ - List[BlockTypes], + Tuple[BlockTypes], "The list of block types to consider as pages.", - ] = [BlockTypes.Page] + ] = (BlockTypes.Page,) def extract_json(self, document: Document, block_output: BlockOutput): cls = get_block_class(block_output.id.block_type) From 6f0e1a9c322cd417d1c2c09c5ae2f21ba46c7f2e Mon Sep 17 00:00:00 2001 From: Moses Paul R Date: Tue, 7 Jan 2025 13:22:11 +0000 Subject: [PATCH 09/92] refactor crawler --- marker/config/crawler.py | 104 ++++++++++++++++++ marker/config/parser.py | 24 ++-- marker/config/printer.py | 97 ++++++---------- marker/processors/ignoretext.py | 2 +- .../processors/llm/llm_image_description.py | 13 ++- marker/renderers/__init__.py | 10 +- marker/renderers/markdown.py | 9 +- marker/util.py | 11 +- 8 files changed, 182 insertions(+), 88 deletions(-) create mode 100644 marker/config/crawler.py diff --git a/marker/config/crawler.py b/marker/config/crawler.py new file mode 100644 index 00000000..bda42d6e --- /dev/null +++ b/marker/config/crawler.py @@ -0,0 +1,104 @@ +import importlib +import inspect +import pkgutil +from functools import cached_property +from typing import Annotated, Dict, Optional, Type, get_args, get_origin + +from marker.builders import BaseBuilder +from marker.converters import BaseConverter +from marker.processors import BaseProcessor +from marker.providers import BaseProvider +from marker.renderers import BaseRenderer +from marker.util import camel_to_snake + + +class ConfigCrawler: + def __init__(self, base_classes=(BaseBuilder, BaseProcessor, BaseConverter, BaseProvider, BaseRenderer)): + self.base_classes = base_classes + self.class_config_map = {} + + self._crawl_config() + + def _crawl_config(self): + for base in self.base_classes: + base_class_type = base.__name__.removeprefix('Base') + self.class_config_map.setdefault(base_class_type, {}) + for class_name, class_type in self._find_subclasses(base).items(): + self.class_config_map[base_class_type].setdefault(class_name, { + 'class_type': class_type, + 'config': {} + }) + for attr, attr_type in class_type.__annotations__.items(): + default = getattr(class_type, attr) + metadata = (f"Default is {default}.",) + + if get_origin(attr_type) is Annotated: + if any('Default' in desc for desc in attr_type.__metadata__): + metadata = attr_type.__metadata__ + else: + metadata = attr_type.__metadata__ + metadata + attr_type = get_args(attr_type)[0] + + formatted_type = self._format_type(attr_type) + self.class_config_map[base_class_type][class_name]['config'][attr] = (attr_type, formatted_type, default, metadata) + + @cached_property + def attr_counts(self) -> Dict[str, int]: + counts: Dict[str, int] = {} + for base_type_dict in self.class_config_map.values(): + for class_map in base_type_dict.values(): + for attr in class_map['config'].keys(): + counts[attr] = counts.get(attr, 0) + 1 + return counts + + @cached_property + def canonical_attr_map(self) -> Dict[str, str]: + canonical_attr_map: Dict[str, str] = {} + for base_type_dict in self.class_config_map.values(): + for class_name, class_map in base_type_dict.items(): + class_name_snake_case = camel_to_snake(class_name) + for attr in class_map['config'].keys(): + canonical_attr_map[attr] = attr + canonical_attr_map[f"{class_name_snake_case}_{attr}"] = attr + return canonical_attr_map + + def validate_attr(self, attr_name: str, class_name: Optional[str] = None) -> bool: + if class_name is None: + # Look through all classes in all base types + for base_type_dict in self.class_config_map.values(): + for class_map in base_type_dict.values(): + if attr_name in class_map['config']: + return True + return False + else: + # We need to find the class_name in *any* base_type + for base_type_dict in self.class_config_map.values(): + if class_name in base_type_dict: + return attr_name in base_type_dict[class_name]['config'] + return False + + def _find_subclasses(self, base_class): + subclasses = {base_class.__name__: base_class} + module_name = base_class.__module__ + package = importlib.import_module(module_name) + if hasattr(package, '__path__'): + for _, module_name, _ in pkgutil.walk_packages(package.__path__, module_name + "."): + try: + module = importlib.import_module(module_name) + for name, obj in inspect.getmembers(module, inspect.isclass): + if issubclass(obj, base_class) and obj is not base_class: + subclasses[name] = obj + except ImportError: + pass + return subclasses + + def _format_type(self, t: Type) -> str: + """Format a typing type like Optional[int] into a readable string.""" + + if get_origin(t): # Handle Optional and types with origins separately + return f"{t}".removeprefix('typing.') + else: # Regular types like int, str + return t.__name__ + + +crawler = ConfigCrawler() diff --git a/marker/config/parser.py b/marker/config/parser.py index c0996d24..bf5c3c81 100644 --- a/marker/config/parser.py +++ b/marker/config/parser.py @@ -4,11 +4,12 @@ import click +from marker.config.crawler import crawler from marker.renderers.html import HTMLRenderer -from marker.settings import settings -from marker.util import parse_range_str, strings_to_classes, classes_to_strings -from marker.renderers.markdown import MarkdownRenderer from marker.renderers.json import JSONRenderer +from marker.renderers.markdown import MarkdownRenderer +from marker.settings import settings +from marker.util import classes_to_strings, parse_range_str, strings_to_classes class ConfigParser: @@ -22,16 +23,22 @@ def common_options(fn): fn = click.option('--debug', '-d', is_flag=True, help='Enable debug mode.')(fn) fn = click.option("--output_format", type=click.Choice(["markdown", "json", "html"]), default="markdown", help="Format to output results in.")(fn) - fn = click.option("--page_range", type=str, default=None, - help="Page range to convert, specify comma separated page numbers or ranges. Example: 0,5-10,20")( - fn) fn = click.option("--processors", type=str, default=None, help="Comma separated list of processors to use. Must use full module path.")(fn) fn = click.option("--config_json", type=str, default=None, help="Path to JSON file with additional configuration.")(fn) - fn = click.option("--languages", type=str, default=None, help="Comma separated list of languages to use for OCR.")(fn) fn = click.option("--disable_multiprocessing", is_flag=True, default=False, help="Disable multiprocessing.")(fn) fn = click.option("--disable_image_extraction", is_flag=True, default=False, help="Disable image extraction.")(fn) + + # these are options that need a list transformation, i.e splitting/parsing a string + fn = click.option("--page_range", type=str, default=None, + help="Page range to convert, specify comma separated page numbers or ranges. Example: 0,5-10,20")( + fn) + fn = click.option("--languages", type=str, default=None, help="Comma separated list of languages to use for OCR.")(fn) + + # we put common options here + fn = click.option("--google_api_key", type=str, default=None, help="Google API key for using LLMs.")(fn) + fn = click.option("--use_llm", is_flag=True, default=False, help="Enable higher quality processing with LLMs.")(fn) return fn def generate_config_dict(self) -> Dict[str, any]: @@ -59,7 +66,8 @@ def generate_config_dict(self) -> Dict[str, any]: case "disable_image_extraction": config["extract_images"] = False case _: - config[k] = v + if k in crawler.canonical_attr_map.keys(): + config[crawler.canonical_attr_map[k]] = v return config def get_renderer(self): diff --git a/marker/config/printer.py b/marker/config/printer.py index c93b157f..2a7a9747 100644 --- a/marker/config/printer.py +++ b/marker/config/printer.py @@ -1,44 +1,9 @@ -import importlib -import inspect -import pkgutil -from typing import Annotated, Optional, Type, get_args, get_origin +from typing import Optional import click -from marker.builders import BaseBuilder -from marker.converters import BaseConverter -from marker.processors import BaseProcessor -from marker.providers import BaseProvider -from marker.renderers import BaseRenderer - - -def format_type(t: Type) -> str: - """Format a typing type like Optional[int] into a readable string.""" - - if get_origin(t): # Handle Optional and types with origins separately - return f"{t}".removeprefix('typing.') - else: # Regular types like int, str - return t.__name__ - - -def find_subclasses(base_class): - """ - Dynamically find all subclasses of a base class in the module where the base class is defined - and its submodules. - """ - subclasses = {} - module_name = base_class.__module__ - package = importlib.import_module(module_name) - if hasattr(package, '__path__'): - for _, module_name, _ in pkgutil.walk_packages(package.__path__, module_name + "."): - try: - module = importlib.import_module(module_name) - for name, obj in inspect.getmembers(module, inspect.isclass): - if issubclass(obj, base_class) and obj is not base_class: - subclasses[name] = obj - except ImportError: - pass - return subclasses +from marker.config.crawler import crawler +from marker.util import camel_to_snake class CustomClickPrinter(click.Command): @@ -53,37 +18,37 @@ def get_help(self, ctx): def parse_args(self, ctx, args): display_help = 'config' in args and '--help' in args if display_help: - click.echo("Here is a list of all the Builders, Processors, Converters and Renderers in Marker along with their attributes:") + click.echo("Here is a list of all the Builders, Processors, Converters, Providers and Renderers in Marker along with their attributes:") - base_classes = [BaseBuilder, BaseProcessor, BaseConverter, BaseProvider, BaseRenderer] - for base in base_classes: + for base_type, base_type_dict in crawler.class_config_map.items(): if display_help: - click.echo(f"{base.__name__.removeprefix('Base')}s:") - - subclasses = find_subclasses(base) - for class_name, class_type in subclasses.items(): - doc = class_type.__doc__ or "" - if display_help and doc and len(class_type.__annotations__): - click.echo(f"\n {class_name}: {doc}") + click.echo(f"{base_type}s:") + for class_name, class_map in base_type_dict.items(): + if display_help and class_map['config']: + click.echo(f"\n {class_name}: {class_map['class_type'].__doc__ or ''}") click.echo(" " * 4 + "Attributes:") - for attr, attr_type in class_type.__annotations__.items(): - if get_origin(attr_type) is Annotated: - base_attr_type = get_args(attr_type)[0] - default = getattr(class_type, attr) - default_help_str = "" - if all('Default' not in desc for desc in attr_type.__metadata__): - default_help_str = f"Default is {default}." - if display_help: - click.echo(" " * 8 + f"{attr} ({format_type(base_attr_type)}):") - click.echo("\n".join([f'{" " * 12}' + desc for desc in attr_type.__metadata__])) - if default_help_str: - click.echo(f'{" " * 12}' + default_help_str) - if base_attr_type in [str, int, float, bool, Optional[int], Optional[float], Optional[str]]: - if attr not in [p.name for p in ctx.command.params]: - is_flag = base_attr_type in [bool, Optional[bool]] and not default - ctx.command.params.append( - click.Option([f"--{attr}"], help=" ".join(attr_type.__metadata__ + (default_help_str,)), type=base_attr_type, default=default, is_flag=is_flag) - ) + for attr, (attr_type, formatted_type, default, metadata) in class_map['config'].items(): + class_name_attr = camel_to_snake(class_name) + "_" + attr + + if display_help: + click.echo(" " * 8 + f"{attr} ({formatted_type}):") + click.echo("\n".join([f'{" " * 12}' + desc for desc in metadata])) + if attr_type in [str, int, float, bool, Optional[int], Optional[float], Optional[str]]: + is_flag = attr_type in [bool, Optional[bool]] and not default + if crawler.attr_counts.get(attr) > 1: + options = ["--" + class_name_attr] + else: + options = ["--" + attr, "--" + class_name_attr] + ctx.command.params.append( + click.Option( + options, + type=attr_type, + help=" ".join(metadata), + default=default, + is_flag=is_flag, + ) + ) + if display_help: ctx.exit() diff --git a/marker/processors/ignoretext.py b/marker/processors/ignoretext.py index 45b83c6b..45afcea5 100644 --- a/marker/processors/ignoretext.py +++ b/marker/processors/ignoretext.py @@ -25,7 +25,7 @@ class IgnoreTextProcessor(BaseProcessor): float, "The minimum ratio of pages a text block must appear on to be considered a common element.", "Blocks that meet or exceed this threshold are marked as common elements.", - ] = 0.6 + ] = 0.2 common_element_min_blocks: Annotated[ int, "The minimum number of occurrences of a text block within a document to consider it a common element.", diff --git a/marker/processors/llm/llm_image_description.py b/marker/processors/llm/llm_image_description.py index 837e5fcf..90547f62 100644 --- a/marker/processors/llm/llm_image_description.py +++ b/marker/processors/llm/llm_image_description.py @@ -7,11 +7,20 @@ from marker.schema.document import Document from marker.schema.groups.page import PageGroup +from typing import Annotated + class LLMImageDescriptionProcessor(BaseLLMProcessor): block_types = (BlockTypes.Picture, BlockTypes.Figure,) - extract_images: bool = True - image_description_prompt = """You are a document analysis expert who specializes in creating text descriptions for images. + extract_images: Annotated[ + bool, + "Extract images from the document." + ] = True + image_description_prompt: Annotated[ + str, + "The prompt to use for generating image descriptions.", + "Default is a string containing the Gemini prompt." + ] = """You are a document analysis expert who specializes in creating text descriptions for images. You will receive an image of a picture or figure. Your job will be to create a short description of the image. **Instructions:** 1. Carefully examine the provided image. diff --git a/marker/renderers/__init__.py b/marker/renderers/__init__.py index f1172910..d0166c9d 100644 --- a/marker/renderers/__init__.py +++ b/marker/renderers/__init__.py @@ -2,7 +2,7 @@ import io import re from collections import Counter -from typing import Optional +from typing import Annotated, Optional, Tuple from bs4 import BeautifulSoup from pydantic import BaseModel @@ -15,9 +15,9 @@ class BaseRenderer: - remove_blocks: list = [BlockTypes.PageHeader, BlockTypes.PageFooter] - image_blocks: list = [BlockTypes.Picture, BlockTypes.Figure] - extract_images: bool = True + remove_blocks: Annotated[Tuple[BlockTypes, ...], "The block types to ignore while rendering."] = (BlockTypes.PageHeader, BlockTypes.PageFooter) + image_blocks: Annotated[Tuple[BlockTypes, ...], "The block types to consider as images."] = (BlockTypes.Picture, BlockTypes.Figure) + extract_images: Annotated[bool, "Extract images from the document."] = True def __init__(self, config: Optional[BaseModel | dict] = None): assign_config(self, config) @@ -71,7 +71,7 @@ def generate_page_stats(self, document: Document, document_output): return page_stats def generate_document_metadata(self, document: Document, document_output): - metadata = { + metadata = { "table_of_contents": document.table_of_contents, "page_stats": self.generate_page_stats(document, document_output), } diff --git a/marker/renderers/markdown.py b/marker/renderers/markdown.py index c5fc3bcb..46bb62ba 100644 --- a/marker/renderers/markdown.py +++ b/marker/renderers/markdown.py @@ -1,5 +1,5 @@ import re -from typing import List +from typing import Annotated, Tuple import regex from markdownify import MarkdownConverter @@ -62,7 +62,6 @@ def convert_th(self, el, text, convert_as_inline): return super().convert_th(el, text, convert_as_inline) - class MarkdownOutput(BaseModel): markdown: str images: dict @@ -70,9 +69,9 @@ class MarkdownOutput(BaseModel): class MarkdownRenderer(HTMLRenderer): - page_separator: str = "-" * 48 - inline_math_delimiters: List[str] = ["$", "$"] - block_math_delimiters: List[str] = ["$$", "$$"] + page_separator: Annotated[str, "The separator to use between pages.", "Default is '-' * 48."] = "-" * 48 + inline_math_delimiters: Annotated[Tuple[str], "The delimiters to use for inline math."] = ("$", "$") + block_math_delimiters: Annotated[Tuple[str], "The delimiters to use for block math."] = ("$$", "$$") def __call__(self, document: Document) -> MarkdownOutput: document_output = document.render() diff --git a/marker/util.py b/marker/util.py index 23af5ca8..a4c7ef1e 100644 --- a/marker/util.py +++ b/marker/util.py @@ -1,4 +1,5 @@ import inspect +import re from importlib import import_module from typing import List @@ -56,7 +57,7 @@ def parse_range_str(range_str: str) -> List[int]: page_lst += list(range(int(start), int(end) + 1)) else: page_lst.append(int(i)) - page_lst = sorted(list(set(page_lst))) # Deduplicate page numbers and sort in order + page_lst = sorted(list(set(page_lst))) # Deduplicate page numbers and sort in order return page_lst @@ -79,3 +80,11 @@ def matrix_intersection_area(boxes1: List[List[float]], boxes2: List[List[float] height = np.maximum(0, max_y - min_y) return width * height # Shape: (N, M) + + +def camel_to_snake(name: str) -> str: + # Split consecutive uppercase letters when followed by a capital+lowercase block + s1 = re.sub(r'([A-Z]+)([A-Z][a-z])', r'\1_\2', name) + # Insert underscores between a lowercase/digit and an uppercase + s2 = re.sub(r'([a-z0-9])([A-Z])', r'\1_\2', s1) + return s2.lower() From c51567ae9d64a08284c2b00e63bb7d882db36588 Mon Sep 17 00:00:00 2001 From: Moses Paul R Date: Tue, 7 Jan 2025 13:46:14 +0000 Subject: [PATCH 10/92] fix formatting (pdf converter) [skip ci] --- marker/converters/pdf.py | 68 +++++++++++++++++++++------------------- 1 file changed, 35 insertions(+), 33 deletions(-) diff --git a/marker/converters/pdf.py b/marker/converters/pdf.py index c1a80c93..310ce277 100644 --- a/marker/converters/pdf.py +++ b/marker/converters/pdf.py @@ -1,40 +1,42 @@ -from marker.util import strings_to_classes -from marker.schema.registry import register_block_class -from marker.schema.blocks import Block -from marker.schema import BlockTypes -from marker.renderers.markdown import MarkdownRenderer -from marker.providers.pdf import PdfProvider -from marker.processors.text import TextProcessor -from marker.processors.table import TableProcessor -from marker.processors.sectionheader import SectionHeaderProcessor -from marker.processors.page_header import PageHeaderProcessor -from marker.processors.llm.llm_text import LLMTextProcessor -from marker.processors.llm.llm_table import LLMTableProcessor -from marker.processors.llm.llm_image_description import LLMImageDescriptionProcessor -from marker.processors.llm.llm_form import LLMFormProcessor -from marker.processors.llm.llm_complex import LLMComplexRegionProcessor -from marker.processors.list import ListProcessor -from marker.processors.line_numbers import LineNumbersProcessor -from marker.processors.ignoretext import IgnoreTextProcessor -from marker.processors.footnote import FootnoteProcessor -from marker.processors.equation import EquationProcessor -from marker.processors.document_toc import DocumentTOCProcessor -from marker.processors.debug import DebugProcessor -from marker.processors.code import CodeProcessor -from marker.processors.blockquote import BlockquoteProcessor -from marker.converters import BaseConverter -from marker.builders.structure import StructureBuilder -from marker.builders.ocr import OcrBuilder -from marker.builders.llm_layout import LLMLayoutBuilder -from marker.builders.layout import LayoutBuilder -from marker.builders.document import DocumentBuilder -from typing import Annotated, Any, Dict, List, Optional, Type -from collections import defaultdict -import inspect import os os.environ["TOKENIZERS_PARALLELISM"] = "false" # disables a tokenizers warning +import inspect +from collections import defaultdict +from typing import Annotated, Any, Dict, List, Optional, Type + +from marker.builders.document import DocumentBuilder +from marker.builders.layout import LayoutBuilder +from marker.builders.llm_layout import LLMLayoutBuilder +from marker.builders.ocr import OcrBuilder +from marker.builders.structure import StructureBuilder +from marker.converters import BaseConverter +from marker.processors.blockquote import BlockquoteProcessor +from marker.processors.code import CodeProcessor +from marker.processors.debug import DebugProcessor +from marker.processors.document_toc import DocumentTOCProcessor +from marker.processors.equation import EquationProcessor +from marker.processors.footnote import FootnoteProcessor +from marker.processors.ignoretext import IgnoreTextProcessor +from marker.processors.line_numbers import LineNumbersProcessor +from marker.processors.list import ListProcessor +from marker.processors.llm.llm_complex import LLMComplexRegionProcessor +from marker.processors.llm.llm_form import LLMFormProcessor +from marker.processors.llm.llm_image_description import LLMImageDescriptionProcessor +from marker.processors.llm.llm_table import LLMTableProcessor +from marker.processors.llm.llm_text import LLMTextProcessor +from marker.processors.page_header import PageHeaderProcessor +from marker.processors.sectionheader import SectionHeaderProcessor +from marker.processors.table import TableProcessor +from marker.processors.text import TextProcessor +from marker.providers.pdf import PdfProvider +from marker.renderers.markdown import MarkdownRenderer +from marker.schema import BlockTypes +from marker.schema.blocks import Block +from marker.schema.registry import register_block_class +from marker.util import strings_to_classes + class PdfConverter(BaseConverter): """ From 5a7ba01aa98821fd9cd4ace037ed3081a95dfb9a Mon Sep 17 00:00:00 2001 From: Moses Paul R Date: Tue, 7 Jan 2025 13:48:17 +0000 Subject: [PATCH 11/92] cleanup [skip ci] --- marker/config/crawler.py | 15 --------------- 1 file changed, 15 deletions(-) diff --git a/marker/config/crawler.py b/marker/config/crawler.py index bda42d6e..eea62ab7 100644 --- a/marker/config/crawler.py +++ b/marker/config/crawler.py @@ -62,21 +62,6 @@ def canonical_attr_map(self) -> Dict[str, str]: canonical_attr_map[f"{class_name_snake_case}_{attr}"] = attr return canonical_attr_map - def validate_attr(self, attr_name: str, class_name: Optional[str] = None) -> bool: - if class_name is None: - # Look through all classes in all base types - for base_type_dict in self.class_config_map.values(): - for class_map in base_type_dict.values(): - if attr_name in class_map['config']: - return True - return False - else: - # We need to find the class_name in *any* base_type - for base_type_dict in self.class_config_map.values(): - if class_name in base_type_dict: - return attr_name in base_type_dict[class_name]['config'] - return False - def _find_subclasses(self, base_class): subclasses = {base_class.__name__: base_class} module_name = base_class.__module__ From 696d68dfadfa1535234eff3619ae46de5d5000d0 Mon Sep 17 00:00:00 2001 From: Moses Paul R Date: Tue, 7 Jan 2025 14:38:26 +0000 Subject: [PATCH 12/92] remove snake case in cli args --- marker/config/crawler.py | 14 ++++++-------- marker/config/parser.py | 4 ++-- marker/config/printer.py | 3 +-- marker/util.py | 8 -------- 4 files changed, 9 insertions(+), 20 deletions(-) diff --git a/marker/config/crawler.py b/marker/config/crawler.py index eea62ab7..401b5905 100644 --- a/marker/config/crawler.py +++ b/marker/config/crawler.py @@ -2,14 +2,13 @@ import inspect import pkgutil from functools import cached_property -from typing import Annotated, Dict, Optional, Type, get_args, get_origin +from typing import Annotated, Dict, Set, Type, get_args, get_origin from marker.builders import BaseBuilder from marker.converters import BaseConverter from marker.processors import BaseProcessor from marker.providers import BaseProvider from marker.renderers import BaseRenderer -from marker.util import camel_to_snake class ConfigCrawler: @@ -52,15 +51,14 @@ def attr_counts(self) -> Dict[str, int]: return counts @cached_property - def canonical_attr_map(self) -> Dict[str, str]: - canonical_attr_map: Dict[str, str] = {} + def attr_set(self) -> Set[str]: + attr_set: Set[str] = set() for base_type_dict in self.class_config_map.values(): for class_name, class_map in base_type_dict.items(): - class_name_snake_case = camel_to_snake(class_name) for attr in class_map['config'].keys(): - canonical_attr_map[attr] = attr - canonical_attr_map[f"{class_name_snake_case}_{attr}"] = attr - return canonical_attr_map + attr_set.add(attr) + attr_set.add(f"{class_name}_{attr}") + return attr_set def _find_subclasses(self, base_class): subclasses = {base_class.__name__: base_class} diff --git a/marker/config/parser.py b/marker/config/parser.py index bf5c3c81..ef627983 100644 --- a/marker/config/parser.py +++ b/marker/config/parser.py @@ -66,8 +66,8 @@ def generate_config_dict(self) -> Dict[str, any]: case "disable_image_extraction": config["extract_images"] = False case _: - if k in crawler.canonical_attr_map.keys(): - config[crawler.canonical_attr_map[k]] = v + if k in crawler.attr_set: + config[k] = v return config def get_renderer(self): diff --git a/marker/config/printer.py b/marker/config/printer.py index 2a7a9747..9cd35cdd 100644 --- a/marker/config/printer.py +++ b/marker/config/printer.py @@ -3,7 +3,6 @@ import click from marker.config.crawler import crawler -from marker.util import camel_to_snake class CustomClickPrinter(click.Command): @@ -28,7 +27,7 @@ def parse_args(self, ctx, args): click.echo(f"\n {class_name}: {class_map['class_type'].__doc__ or ''}") click.echo(" " * 4 + "Attributes:") for attr, (attr_type, formatted_type, default, metadata) in class_map['config'].items(): - class_name_attr = camel_to_snake(class_name) + "_" + attr + class_name_attr = class_name + "_" + attr if display_help: click.echo(" " * 8 + f"{attr} ({formatted_type}):") diff --git a/marker/util.py b/marker/util.py index a4c7ef1e..3dbde5f8 100644 --- a/marker/util.py +++ b/marker/util.py @@ -80,11 +80,3 @@ def matrix_intersection_area(boxes1: List[List[float]], boxes2: List[List[float] height = np.maximum(0, max_y - min_y) return width * height # Shape: (N, M) - - -def camel_to_snake(name: str) -> str: - # Split consecutive uppercase letters when followed by a capital+lowercase block - s1 = re.sub(r'([A-Z]+)([A-Z][a-z])', r'\1_\2', name) - # Insert underscores between a lowercase/digit and an uppercase - s2 = re.sub(r'([a-z0-9])([A-Z])', r'\1_\2', s1) - return s2.lower() From 133096537fbf912db7e3924f81a74ea71b14d2b2 Mon Sep 17 00:00:00 2001 From: Moses Paul R Date: Tue, 7 Jan 2025 14:48:22 +0000 Subject: [PATCH 13/92] fix superclass bug --- marker/config/crawler.py | 18 +++++++++++++++++- 1 file changed, 17 insertions(+), 1 deletion(-) diff --git a/marker/config/crawler.py b/marker/config/crawler.py index 401b5905..7640484c 100644 --- a/marker/config/crawler.py +++ b/marker/config/crawler.py @@ -27,7 +27,7 @@ def _crawl_config(self): 'class_type': class_type, 'config': {} }) - for attr, attr_type in class_type.__annotations__.items(): + for attr, attr_type in self._gather_super_annotations(class_type).items(): default = getattr(class_type, attr) metadata = (f"Default is {default}.",) @@ -41,6 +41,22 @@ def _crawl_config(self): formatted_type = self._format_type(attr_type) self.class_config_map[base_class_type][class_name]['config'][attr] = (attr_type, formatted_type, default, metadata) + def _gather_super_annotations(self, cls: Type) -> Dict[str, Type]: + """ + Collect all annotated attributes from `cls` and its superclasses, bottom-up. + Subclass attributes overwrite superclass attributes with the same name. + """ + # We'll walk the MRO from base -> derived so subclass attributes overwrite + # the same attribute name from superclasses. + annotations = {} + for base in reversed(cls.__mro__): + if base is object: + continue + if hasattr(base, "__annotations__"): + for name, annotation in base.__annotations__.items(): + annotations[name] = annotation + return annotations + @cached_property def attr_counts(self) -> Dict[str, int]: counts: Dict[str, int] = {} From 0ee94e01cc29b0f548f7873b75b4fda3e38d17f4 Mon Sep 17 00:00:00 2001 From: Moses Paul R Date: Tue, 7 Jan 2025 15:17:30 +0000 Subject: [PATCH 14/92] remove base classes from crawler --- marker/config/crawler.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/marker/config/crawler.py b/marker/config/crawler.py index 7640484c..dec47b8c 100644 --- a/marker/config/crawler.py +++ b/marker/config/crawler.py @@ -23,6 +23,9 @@ def _crawl_config(self): base_class_type = base.__name__.removeprefix('Base') self.class_config_map.setdefault(base_class_type, {}) for class_name, class_type in self._find_subclasses(base).items(): + if class_name.startswith('Base'): + continue + self.class_config_map[base_class_type].setdefault(class_name, { 'class_type': class_type, 'config': {} @@ -77,7 +80,7 @@ def attr_set(self) -> Set[str]: return attr_set def _find_subclasses(self, base_class): - subclasses = {base_class.__name__: base_class} + subclasses = {} module_name = base_class.__module__ package = importlib.import_module(module_name) if hasattr(package, '__path__'): From 3a29cd71a366551d626f33301f67e5c3ed3d90da Mon Sep 17 00:00:00 2001 From: Moses Paul R Date: Tue, 7 Jan 2025 16:22:24 +0000 Subject: [PATCH 15/92] fix bug --- marker/config/printer.py | 1 + 1 file changed, 1 insertion(+) diff --git a/marker/config/printer.py b/marker/config/printer.py index 9cd35cdd..e9b37b42 100644 --- a/marker/config/printer.py +++ b/marker/config/printer.py @@ -38,6 +38,7 @@ def parse_args(self, ctx, args): options = ["--" + class_name_attr] else: options = ["--" + attr, "--" + class_name_attr] + options.append(class_name_attr) ctx.command.params.append( click.Option( options, From 3a7bb50b8f12de43a638b3f8de604c68a2447881 Mon Sep 17 00:00:00 2001 From: Vik Paruchuri Date: Tue, 7 Jan 2025 13:47:43 -0500 Subject: [PATCH 16/92] Small bugfix --- README.md | 1 + marker/processors/llm/llm_table_merge.py | 0 marker/processors/llm/utils.py | 2 +- marker/processors/sectionheader.py | 7 ++----- 4 files changed, 4 insertions(+), 6 deletions(-) create mode 100644 marker/processors/llm/llm_table_merge.py diff --git a/README.md b/README.md index 5d30ce23..1f83abd4 100644 --- a/README.md +++ b/README.md @@ -104,6 +104,7 @@ marker_single /path/to/file.pdf Options: - `--output_dir PATH`: Directory where output files will be saved. Defaults to the value specified in settings.OUTPUT_DIR. - `--output_format [markdown|json|html]`: Specify the format for the output results. +- `--paginate_output`: Paginates the output, using `\n\n{PAGE_NUMBER}` followed by `-` * 48, then `\n\n` - `--use_llm`: Uses an LLM to improve accuracy. You must set your Gemini API key using the `GOOGLE_API_KEY` env var. - `--disable_image_extraction`: Don't extract images from the PDF. If you also specify `--use_llm`, then images will be replaced with a description. - `--page_range TEXT`: Specify which pages to process. Accepts comma-separated page numbers and ranges. Example: `--page_range "0,5-10,20"` will process pages 0, 5 through 10, and page 20. diff --git a/marker/processors/llm/llm_table_merge.py b/marker/processors/llm/llm_table_merge.py new file mode 100644 index 00000000..e69de29b diff --git a/marker/processors/llm/utils.py b/marker/processors/llm/utils.py index 27c47db6..c66c590a 100644 --- a/marker/processors/llm/utils.py +++ b/marker/processors/llm/utils.py @@ -35,7 +35,7 @@ def generate_response( while tries < max_retries: try: responses = self.model.generate_content( - [prompt, image], + [image, prompt], # According to gemini docs, it performs better if the image is the first element stream=False, generation_config={ "temperature": 0, diff --git a/marker/processors/sectionheader.py b/marker/processors/sectionheader.py index 881bc6c3..fa5ac15c 100644 --- a/marker/processors/sectionheader.py +++ b/marker/processors/sectionheader.py @@ -54,11 +54,8 @@ def __call__(self, document: Document): heading_ranges = self.bucket_headings(flat_line_heights) for page in document.pages: - for block in page.children: - if block.block_type not in self.block_types: - continue - - block_height = line_heights[block.id] + for block in page.contained_blocks(document, self.block_types): + block_height = line_heights.get(block.id, 0) if block_height > 0: for idx, (min_height, max_height) in enumerate(heading_ranges): if block_height >= min_height * self.height_tolerance: From e8435c13928e1725721564387af7dbe74ab25dbd Mon Sep 17 00:00:00 2001 From: Vik Paruchuri Date: Wed, 8 Jan 2025 12:31:12 -0500 Subject: [PATCH 17/92] Align with surya refactor --- marker/builders/layout.py | 20 ++---- marker/builders/llm_layout.py | 6 +- marker/builders/ocr.py | 14 ++-- marker/models.py | 105 ++++++++++-------------------- marker/processors/equation.py | 7 +- marker/processors/table.py | 13 ++-- pyproject.toml | 2 +- tests/builders/test_blank_page.py | 2 +- tests/conftest.py | 58 +++++++---------- tests/utils.py | 5 -- 10 files changed, 82 insertions(+), 150 deletions(-) diff --git a/marker/builders/layout.py b/marker/builders/layout.py index 47c24d81..a590a7cd 100644 --- a/marker/builders/layout.py +++ b/marker/builders/layout.py @@ -1,11 +1,10 @@ from typing import Annotated, List, Optional, Tuple import numpy as np -from surya.layout import batch_layout_detection -from surya.model.layout.encoderdecoder import SuryaLayoutModel -from surya.model.ocr_error.model import DistilBertForSequenceClassification -from surya.ocr_error import batch_ocr_error_detection -from surya.schema import LayoutResult, OCRErrorDetectionResult +from surya.layout import LayoutPredictor +from surya.layout.schema import LayoutResult +from surya.ocr_error import OCRErrorPredictor +from surya.ocr_error.schema import OCRErrorDetectionResult from marker.builders import BaseBuilder from marker.providers import ProviderOutput, ProviderPageLines @@ -52,7 +51,7 @@ class LayoutBuilder(BaseBuilder): "A list of block types to exclude from the layout coverage check.", ] = (BlockTypes.Figure, BlockTypes.Picture, BlockTypes.Table, BlockTypes.FigureGroup, BlockTypes.TableGroup, BlockTypes.PictureGroup) - def __init__(self, layout_model: SuryaLayoutModel, ocr_error_model: DistilBertForSequenceClassification, config=None): + def __init__(self, layout_model: LayoutPredictor, ocr_error_model: OCRErrorPredictor, config=None): self.layout_model = layout_model self.ocr_error_model = ocr_error_model @@ -71,11 +70,8 @@ def get_batch_size(self): return 6 def surya_layout(self, pages: List[PageGroup]) -> List[LayoutResult]: - processor = self.layout_model.processor - layout_results = batch_layout_detection( + layout_results = self.layout_model( [p.lowres_image for p in pages], - self.layout_model, - processor, batch_size=int(self.get_batch_size()) ) return layout_results @@ -97,10 +93,8 @@ def surya_ocr_error_detection(self, pages: List[PageGroup], provider_page_lines: page_texts.append(page_text) - ocr_error_detection_results = batch_ocr_error_detection( + ocr_error_detection_results = self.ocr_error_model( page_texts, - self.ocr_error_model, - self.ocr_error_model.tokenizer, batch_size=int(self.get_batch_size()) # TODO Better Multiplier ) return ocr_error_detection_results diff --git a/marker/builders/llm_layout.py b/marker/builders/llm_layout.py index 7c8f50a4..e6ed8899 100644 --- a/marker/builders/llm_layout.py +++ b/marker/builders/llm_layout.py @@ -3,8 +3,8 @@ from typing import Annotated, Optional from google.ai.generativelanguage_v1beta.types import content -from surya.model.layout.encoderdecoder import SuryaLayoutModel -from surya.model.ocr_error.model import DistilBertForSequenceClassification +from surya.layout import LayoutPredictor +from surya.ocr_error import OCRErrorPredictor from tqdm import tqdm from marker.builders.layout import LayoutBuilder @@ -91,7 +91,7 @@ class LLMLayoutBuilder(LayoutBuilder): Here is the image of the layout block: """ - def __init__(self, layout_model: SuryaLayoutModel, ocr_error_model: DistilBertForSequenceClassification, config=None): + def __init__(self, layout_model: LayoutPredictor, ocr_error_model: OCRErrorPredictor, config=None): super().__init__(layout_model, ocr_error_model, config) self.model = GoogleModel(self.google_api_key, self.model_name) diff --git a/marker/builders/ocr.py b/marker/builders/ocr.py index b4aee99f..fbfb6e0e 100644 --- a/marker/builders/ocr.py +++ b/marker/builders/ocr.py @@ -1,9 +1,8 @@ from typing import Annotated, List, Optional from ftfy import fix_text -from surya.model.detection.model import EfficientViTForSemanticSegmentation -from surya.model.recognition.encoderdecoder import OCREncoderDecoderModel -from surya.ocr import run_ocr +from surya.detection import DetectionPredictor +from surya.recognition import RecognitionPredictor from marker.builders import BaseBuilder from marker.providers import ProviderOutput, ProviderPageLines @@ -37,7 +36,7 @@ class OcrBuilder(BaseBuilder): "Default is None." ] = None - def __init__(self, detection_model: EfficientViTForSemanticSegmentation, recognition_model: OCREncoderDecoderModel, config=None): + def __init__(self, detection_model: DetectionPredictor, recognition_model: RecognitionPredictor, config=None): super().__init__(config) self.detection_model = detection_model @@ -65,13 +64,10 @@ def get_detection_batch_size(self): def ocr_extraction(self, document: Document, provider: PdfProvider) -> ProviderPageLines: page_list = [page for page in document.pages if page.text_extraction_method == "surya"] - recognition_results = run_ocr( + recognition_results = self.recognition_model( images=[page.lowres_image for page in page_list], langs=[self.languages] * len(page_list), - det_model=self.detection_model, - det_processor=self.detection_model.processor, - rec_model=self.recognition_model, - rec_processor=self.recognition_model.processor, + det_predictor=self.detection_model, detection_batch_size=int(self.get_detection_batch_size()), recognition_batch_size=int(self.get_recognition_batch_size()), highres_images=[page.highres_image for page in page_list] diff --git a/marker/models.py b/marker/models.py index e6344952..908fb863 100644 --- a/marker/models.py +++ b/marker/models.py @@ -1,86 +1,49 @@ import os -os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1" # Transformers uses .isin for a simple op, which is not supported on MPS - -from surya.model.detection.model import load_model as load_detection_model, load_processor as load_detection_processor -from surya.model.layout.model import load_model as load_layout_model -from surya.model.layout.processor import load_processor as load_layout_processor -from texify.model.model import load_model as load_texify_model -from texify.model.processor import load_processor as load_texify_processor from marker.settings import settings -from surya.model.recognition.model import load_model as load_recognition_model -from surya.model.recognition.processor import load_processor as load_recognition_processor -from surya.model.table_rec.model import load_model as load_table_model -from surya.model.table_rec.processor import load_processor as load_table_processor -from surya.model.ocr_error.model import load_model as load_ocr_error_model -from surya.model.ocr_error.model import load_tokenizer as load_ocr_error_tokenizer - -from texify.model.model import GenerateVisionEncoderDecoderModel -from surya.model.layout.encoderdecoder import SuryaLayoutModel -from surya.model.detection.model import EfficientViTForSemanticSegmentation -from surya.model.recognition.encoderdecoder import OCREncoderDecoderModel -from surya.model.table_rec.encoderdecoder import TableRecEncoderDecoderModel -from surya.model.ocr_error.model import DistilBertForSequenceClassification - -def setup_table_rec_model(device=None, dtype=None) -> TableRecEncoderDecoderModel: - if device: - table_model = load_table_model(device=device, dtype=dtype) - else: - table_model = load_table_model() - table_model.processor = load_table_processor() - return table_model - - -def setup_recognition_model(device=None, dtype=None) -> OCREncoderDecoderModel: - if device: - rec_model = load_recognition_model(device=device, dtype=dtype) - else: - rec_model = load_recognition_model() - rec_model.processor = load_recognition_processor() - return rec_model +os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1" # Transformers uses .isin for a simple op, which is not supported on MPS +from typing import List +from PIL import Image -def setup_detection_model(device=None, dtype=None) -> EfficientViTForSemanticSegmentation: - if device: - model = load_detection_model(device=device, dtype=dtype) - else: - model = load_detection_model() - model.processor = load_detection_processor() - return model +from surya.detection import DetectionPredictor +from surya.layout import LayoutPredictor +from surya.ocr_error import OCRErrorPredictor +from surya.recognition import RecognitionPredictor +from surya.table_rec import TableRecPredictor +from texify.model.model import load_model as load_texify_model +from texify.model.processor import load_processor as load_texify_processor +from texify.inference import batch_inference -def setup_texify_model(device=None, dtype=None) -> GenerateVisionEncoderDecoderModel: - if device: - texify_model = load_texify_model(checkpoint=settings.TEXIFY_MODEL_NAME, device=device, dtype=dtype) - else: - texify_model = load_texify_model(checkpoint=settings.TEXIFY_MODEL_NAME, device=settings.TORCH_DEVICE_MODEL, dtype=settings.TEXIFY_DTYPE) - texify_model.processor = load_texify_processor() - return texify_model +class TexifyPredictor: + def __init__(self, device=None, dtype=None): + if not device: + device = settings.TORCH_DEVICE_MODEL + if not dtype: + dtype = settings.TEXIFY_DTYPE + self.model = load_texify_model(checkpoint=settings.TEXIFY_MODEL_NAME, device=device, dtype=dtype) + self.processor = load_texify_processor() + self.device = device + self.dtype = dtype -def setup_layout_model(device=None, dtype=None) -> SuryaLayoutModel: - if device: - model = load_layout_model(device=device, dtype=dtype) - else: - model = load_layout_model() - model.processor = load_layout_processor() - return model + def __call__(self, batch_images: List[Image.Image], max_tokens: int): + return batch_inference( + batch_images, + self.model, + self.processor, + max_tokens=max_tokens + ) -def setup_ocr_error_model(device=None, dtype=None) -> DistilBertForSequenceClassification: - if device: - model = load_ocr_error_model(device=device, dtype=dtype) - else: - model = load_ocr_error_model() - model.tokenizer = load_ocr_error_tokenizer() - return model def create_model_dict(device=None, dtype=None) -> dict: return { - "layout_model": setup_layout_model(device, dtype), - "texify_model": setup_texify_model(device, dtype), - "recognition_model": setup_recognition_model(device, dtype), - "table_rec_model": setup_table_rec_model(device, dtype), - "detection_model": setup_detection_model(device, dtype), - "ocr_error_model": setup_ocr_error_model(device,dtype) + "layout_model": LayoutPredictor(device=device, dtype=dtype), + "texify_model": TexifyPredictor(device=device, dtype=dtype), + "recognition_model": RecognitionPredictor(device=device, dtype=dtype), + "table_rec_model": TableRecPredictor(device=device, dtype=dtype), + "detection_model": DetectionPredictor(device=device, dtype=dtype), + "ocr_error_model": OCRErrorPredictor(device=device, dtype=dtype) } \ No newline at end of file diff --git a/marker/processors/equation.py b/marker/processors/equation.py index 4150eba5..82243a2a 100644 --- a/marker/processors/equation.py +++ b/marker/processors/equation.py @@ -4,6 +4,7 @@ from texify.model.model import GenerateVisionEncoderDecoderModel from tqdm import tqdm +from marker.models import TexifyPredictor from marker.processors import BaseProcessor from marker.schema import BlockTypes from marker.schema.document import Document @@ -32,7 +33,7 @@ class EquationProcessor(BaseProcessor): "The number of tokens to buffer above max for the Texify model.", ] = 256 - def __init__(self, texify_model: GenerateVisionEncoderDecoderModel, config=None): + def __init__(self, texify_model: TexifyPredictor, config=None): super().__init__(config) self.texify_model = texify_model @@ -92,10 +93,8 @@ def get_latex_batched(self, equation_data: List[dict]): batch_images = [eq["image"] for eq in batch_equations] - model_output = batch_inference( + model_output = self.texify_model( batch_images, - self.texify_model, - self.texify_model.processor, max_tokens=max_length ) diff --git a/marker/processors/table.py b/marker/processors/table.py index f810ac4b..04aef9cd 100644 --- a/marker/processors/table.py +++ b/marker/processors/table.py @@ -2,10 +2,9 @@ from typing import Annotated from ftfy import fix_text -from surya.input.pdflines import get_page_text_lines -from surya.model.detection.model import EfficientViTForSemanticSegmentation -from surya.model.recognition.encoderdecoder import OCREncoderDecoderModel -from surya.model.table_rec.encoderdecoder import TableRecEncoderDecoderModel +from surya.detection import DetectionPredictor +from surya.recognition import RecognitionPredictor +from surya.table_rec import TableRecPredictor from tabled.assignment import assign_rows_columns from tabled.inference.recognition import get_cells, recognize_tables @@ -42,9 +41,9 @@ class TableProcessor(BaseProcessor): def __init__( self, - detection_model: EfficientViTForSemanticSegmentation, - recognition_model: OCREncoderDecoderModel, - table_rec_model: TableRecEncoderDecoderModel, + detection_model: DetectionPredictor, + recognition_model: RecognitionPredictor, + table_rec_model: TableRecPredictor, config=None ): super().__init__(config) diff --git a/pyproject.toml b/pyproject.toml index 07f1682f..3f9c4750 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "marker-pdf" -version = "1.2.3" +version = "1.3.0" description = "Convert PDF to markdown with high speed and accuracy." authors = ["Vik Paruchuri "] readme = "README.md" diff --git a/tests/builders/test_blank_page.py b/tests/builders/test_blank_page.py index 3fff42dd..18b067c1 100644 --- a/tests/builders/test_blank_page.py +++ b/tests/builders/test_blank_page.py @@ -1,4 +1,4 @@ -from surya.schema import LayoutResult +from surya.layout.schema import LayoutResult from marker.builders.document import DocumentBuilder from marker.builders.layout import LayoutBuilder diff --git a/tests/conftest.py b/tests/conftest.py index cf2e5da9..8b7abed2 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -9,9 +9,7 @@ from marker.builders.layout import LayoutBuilder from marker.builders.ocr import OcrBuilder from marker.converters.pdf import PdfConverter -from marker.models import setup_detection_model, setup_layout_model, \ - setup_recognition_model, setup_table_rec_model, \ - setup_texify_model, setup_ocr_error_model +from marker.models import create_model_dict from marker.schema import BlockTypes from marker.schema.blocks import Block from marker.renderers.markdown import MarkdownRenderer @@ -19,46 +17,42 @@ from marker.schema.registry import register_block_class from marker.util import classes_to_strings +@pytest.fixture(scope="session") +def model_dict(): + model_dict = create_model_dict() + yield model_dict + del model_dict + @pytest.fixture(scope="session") -def layout_model(): - layout_m = setup_layout_model() - yield layout_m - del layout_m +def layout_model(model_dict): + yield model_dict["layout_model"] @pytest.fixture(scope="session") -def detection_model(): - detection_m = setup_detection_model() - yield detection_m - del detection_m +def detection_model(model_dict): + yield model_dict["detection_model"] @pytest.fixture(scope="session") -def texify_model(): - texify_m = setup_texify_model() - yield texify_m - del texify_m +def texify_model(model_dict): + yield model_dict["texify_model"] @pytest.fixture(scope="session") -def recognition_model(): - ocr_m = setup_recognition_model() - yield ocr_m - del ocr_m +def recognition_model(model_dict): + yield model_dict["recognition_model"] @pytest.fixture(scope="session") -def table_rec_model(): - table_rec_m = setup_table_rec_model() - yield table_rec_m - del table_rec_m +def table_rec_model(model_dict): + yield model_dict["table_rec_model"] + @pytest.fixture(scope="session") -def ocr_error_model(): - ocr_error_m = setup_ocr_error_model() - yield ocr_error_m - del ocr_error_m +def ocr_error_model(model_dict): + yield model_dict["ocr_error_model"] + @pytest.fixture(scope="function") def config(request): @@ -101,15 +95,7 @@ def pdf_document(request, config, pdf_provider, layout_model, ocr_error_model, r @pytest.fixture(scope="function") -def pdf_converter(request, config, layout_model, texify_model, recognition_model, table_rec_model, detection_model, ocr_error_model, renderer): - model_dict = { - "layout_model": layout_model, - "texify_model": texify_model, - "recognition_model": recognition_model, - "table_rec_model": table_rec_model, - "detection_model": detection_model, - "ocr_error_model": ocr_error_model - } +def pdf_converter(request, config, model_dict, renderer): yield PdfConverter( artifact_dict=model_dict, processor_list=None, diff --git a/tests/utils.py b/tests/utils.py index 4fc3bc7b..e5b577b1 100644 --- a/tests/utils.py +++ b/tests/utils.py @@ -2,11 +2,6 @@ import tempfile import datasets -from marker.models import setup_layout_model, setup_recognition_model, setup_detection_model -from marker.builders.document import DocumentBuilder -from marker.builders.layout import LayoutBuilder -from marker.builders.ocr import OcrBuilder -from marker.schema.document import Document def setup_pdf_provider( From eeba30301eb3fa1aff8c11213b2b811034ea4e9b Mon Sep 17 00:00:00 2001 From: Vik Paruchuri Date: Wed, 8 Jan 2025 14:55:29 -0500 Subject: [PATCH 18/92] Strip out tabled --- marker/processors/llm/llm_form.py | 14 +-- marker/processors/llm/llm_table.py | 28 +++-- marker/processors/table.py | 154 +++++++++++++++++------ marker/schema/__init__.py | 1 + marker/schema/blocks/__init__.py | 1 + marker/schema/blocks/base.py | 6 +- marker/schema/blocks/basetable.py | 36 ++++++ marker/schema/blocks/caption.py | 4 +- marker/schema/blocks/code.py | 2 +- marker/schema/blocks/complexregion.py | 4 +- marker/schema/blocks/equation.py | 4 +- marker/schema/blocks/figure.py | 2 +- marker/schema/blocks/footnote.py | 4 +- marker/schema/blocks/form.py | 18 +-- marker/schema/blocks/handwriting.py | 4 +- marker/schema/blocks/inlinemath.py | 4 +- marker/schema/blocks/listitem.py | 4 +- marker/schema/blocks/pagefooter.py | 4 +- marker/schema/blocks/pageheader.py | 4 +- marker/schema/blocks/picture.py | 2 +- marker/schema/blocks/sectionheader.py | 4 +- marker/schema/blocks/table.py | 17 +-- marker/schema/blocks/tablecell.py | 16 +++ marker/schema/blocks/text.py | 4 +- marker/schema/blocks/toc.py | 13 +- marker/schema/groups/list.py | 4 +- marker/schema/groups/page.py | 2 +- marker/schema/registry.py | 4 +- marker/schema/text/span.py | 2 +- pyproject.toml | 1 - tests/processors/test_table_processor.py | 10 +- 31 files changed, 240 insertions(+), 137 deletions(-) create mode 100644 marker/schema/blocks/basetable.py create mode 100644 marker/schema/blocks/tablecell.py diff --git a/marker/processors/llm/llm_form.py b/marker/processors/llm/llm_form.py index 80eb5131..77fca14c 100644 --- a/marker/processors/llm/llm_form.py +++ b/marker/processors/llm/llm_form.py @@ -3,7 +3,6 @@ from marker.processors.llm import BaseLLMProcessor from google.ai.generativelanguage_v1beta.types import content -from tabled.formats import markdown_format from marker.schema import BlockTypes from marker.schema.blocks import Block @@ -42,12 +41,15 @@ class LLMFormProcessor(BaseLLMProcessor): """ def process_rewriting(self, document: Document, page: PageGroup, block: Block): - cells = block.cells - if cells is None: + children = block.contained_blocks(document, (BlockTypes.TableCell,)) + if not children: # Happens if table/form processors didn't run return - prompt = self.gemini_rewriting_prompt + '```markdown\n`' + markdown_format(cells) + '`\n```\n' + block_html = block.render(document).html + table_md = markdown2.markdown(block_html) + + prompt = self.gemini_rewriting_prompt + '```markdown\n`' + table_md + '`\n```\n' image = self.extract_image(page, block) response_schema = content.Schema( type=content.Type.OBJECT, @@ -72,10 +74,8 @@ def process_rewriting(self, document: Document, page: PageGroup, block: Block): if "no corrections" in corrected_markdown.lower(): return - orig_cell_text = "".join([cell.text for cell in cells]) - # Potentially a partial response - if len(corrected_markdown) < len(orig_cell_text) * .5: + if len(corrected_markdown) < len(table_md) * .33: block.update_metadata(llm_error_count=1) return diff --git a/marker/processors/llm/llm_table.py b/marker/processors/llm/llm_table.py index c1d5ab07..25565b6f 100644 --- a/marker/processors/llm/llm_table.py +++ b/marker/processors/llm/llm_table.py @@ -2,12 +2,10 @@ from bs4 import BeautifulSoup from google.ai.generativelanguage_v1beta.types import content -from tabled.formats import html_format -from tabled.schema import SpanTableCell from marker.processors.llm import BaseLLMProcessor from marker.schema import BlockTypes -from marker.schema.blocks import Block +from marker.schema.blocks import Block, TableCell from marker.schema.document import Document from marker.schema.groups.page import PageGroup from marker.schema.polygon import PolygonBox @@ -55,12 +53,14 @@ class LLMTableProcessor(BaseLLMProcessor): """ def process_rewriting(self, document: Document, page: PageGroup, block: Block): - cells = block.cells - if cells is None: + children = block.contained_blocks(document, (BlockTypes.TableCell,)) + if not children: # Happens if table/form processors didn't run return - prompt = self.gemini_rewriting_prompt + '```html\n`' + html_format(cells) + '`\n```\n' + block_html = block.render(document).html + + prompt = self.gemini_rewriting_prompt + '```html\n`' + block_html + '`\n```\n' image = self.extract_image(page, block) response_schema = content.Schema( type=content.Type.OBJECT, @@ -91,8 +91,7 @@ def process_rewriting(self, document: Document, page: PageGroup, block: Block): return parsed_cell_text = "".join([cell.text for cell in parsed_cells]) - orig_cell_text = "".join([cell.text for cell in cells]) - + orig_cell_text = "".join([cell.text for cell in children]) # Potentially a partial response if len(parsed_cell_text) < len(orig_cell_text) * .5: block.update_metadata(llm_error_count=1) @@ -100,7 +99,7 @@ def process_rewriting(self, document: Document, page: PageGroup, block: Block): block.cells = parsed_cells - def parse_html_table(self, html_text: str, block: Block) -> List[SpanTableCell]: + def parse_html_table(self, html_text: str, block: Block) -> List[TableCell]: soup = BeautifulSoup(html_text, 'html.parser') table = soup.find('table') @@ -146,11 +145,14 @@ def parse_html_table(self, html_text: str, block: Block) -> List[SpanTableCell]: ] cell_polygon = PolygonBox.from_bbox(cell_bbox) - cell_obj = SpanTableCell( + cell_obj = TableCell( text=cell_text, - row_ids=cell_rows, - col_ids=cell_cols, - bbox=cell_polygon.bbox + row_id=i, + col_id=cur_col, + rowspan=rowspan, + colspan=colspan, + is_header=cell.name == 'th', + polygon=cell_polygon ) cells.append(cell_obj) cur_col += colspan diff --git a/marker/processors/table.py b/marker/processors/table.py index 04aef9cd..803267af 100644 --- a/marker/processors/table.py +++ b/marker/processors/table.py @@ -1,17 +1,20 @@ - -from typing import Annotated +from collections import defaultdict +from typing import Annotated, List from ftfy import fix_text from surya.detection import DetectionPredictor -from surya.recognition import RecognitionPredictor +from surya.recognition import RecognitionPredictor, OCRResult from surya.table_rec import TableRecPredictor -from tabled.assignment import assign_rows_columns -from tabled.inference.recognition import get_cells, recognize_tables +from surya.table_rec.schema import TableResult, TableCell as SuryaTableCell +from pdftext.extraction import table_output from marker.processors import BaseProcessor from marker.schema import BlockTypes +from marker.schema.blocks.tablecell import TableCell from marker.schema.document import Document +from marker.schema.polygon import PolygonBox from marker.settings import settings +from marker.util import matrix_intersection_area class TableProcessor(BaseProcessor): @@ -61,48 +64,127 @@ def __call__(self, document: Document): image_poly = block.polygon.rescale((page.polygon.width, page.polygon.height), page.highres_image.size) image = page.highres_image.crop(image_poly.bbox).convert("RGB") - if block.text_extraction_method == "surya": - text_lines = None - else: - text_lines = get_page_text_lines( - filepath, - [page.page_id], - [page.highres_image.size], - flatten_pdf=True - )[0] - table_data.append({ "block_id": block.id, + "page_id": page.page_id, "table_image": image, "table_bbox": image_poly.bbox, - "text_lines": text_lines, - "img_size": page.highres_image.size + "img_size": page.highres_image.size, + "ocr_block": block.text_extraction_method == "surya", }) - lst_format = [[t[key] for t in table_data] for key in ["table_image", "table_bbox", "img_size", "text_lines"]] + extract_blocks = [t for t in table_data if not t["ocr_block"]] + self.assign_pdftext_lines(extract_blocks, filepath) # Handle tables where good text exists in the PDF - cells, needs_ocr = get_cells( - *lst_format, - [self.detection_model, self.detection_model.processor], - detect_boxes=self.detect_boxes, - detector_batch_size=self.get_detector_batch_size() - ) + ocr_blocks = [t for t in table_data if t["ocr_block"]] + self.assign_ocr_lines(ocr_blocks) # Handle tables where OCR is needed + assert all("table_text_lines" in t for t in table_data), "All table data must have table cells" - tables = recognize_tables( + tables: List[TableResult] = self.table_rec_model( [t["table_image"] for t in table_data], - cells, - needs_ocr, - [self.table_rec_model, self.table_rec_model.processor, self.recognition_model, self.recognition_model.processor], - table_rec_batch_size=self.get_table_rec_batch_size(), - ocr_batch_size=self.get_recognition_batch_size() + batch_size=self.get_table_rec_batch_size() ) + self.assign_text_to_cells(tables, table_data) + + # Assign table cells to the table + table_idx = 0 + for page in document.pages: + for block in page.contained_blocks(document, self.block_types): + block.structure = [] # Remove any existing lines, spans, etc. + cells: List[SuryaTableCell] = tables[table_idx].cells + for cell in cells: + # Rescale the cell polygon to the page size + cell_polygon = PolygonBox(polygon=cell.polygon).rescale(page.highres_image.size, page.polygon.size) + cell_block = TableCell( + polygon=cell_polygon, + text=cell.text or "", # Cells can be blank (no text) + rowspan=cell.rowspan, + colspan=cell.colspan, + row_id=cell.row_id, + col_id=cell.col_id, + is_header=cell.is_header, + page_id=page.page_id, + ) + page.add_full_block(cell_block) + block.add_structure(cell_block) + table_idx += 1 + + def assign_text_to_cells(self, tables: List[TableResult], table_data: list): + for table_result, table_page_data in zip(tables, table_data): + table_text_lines = table_page_data["table_text_lines"] + table_cells: List[SuryaTableCell] = table_result.cells + text_line_bboxes = [t["bbox"] for t in table_text_lines] + table_cell_bboxes = [c.bbox for c in table_cells] + + intersection_matrix = matrix_intersection_area(text_line_bboxes, table_cell_bboxes) + + cell_text = defaultdict(list) + for text_line_idx, table_text_line in enumerate(table_text_lines): + intersections = intersection_matrix[text_line_idx] + if intersections.sum() == 0: + continue + + table_text_line["text"] = fix_text(table_text_line["text"]) + max_intersection = intersections.argmax() + if not table_cells[max_intersection].text: + table_cells[max_intersection].text = [] + + cell_text[max_intersection].append(table_text_line) + + for k in cell_text: + # TODO: see if the text needs to be sorted (based on rotation) + table_cells[k].text = "\n".join([ct["text"] for ct in cell_text[k]]) + + def assign_pdftext_lines(self, extract_blocks: list, filepath: str): + table_inputs = [] + unique_pages = list(set([t["page_id"] for t in extract_blocks])) + if len(unique_pages) == 0: + return + + for page in unique_pages: + tables = [] + img_size = None + for block in extract_blocks: + if block["page_id"] == page: + tables.append(block["table_bbox"]) + img_size = block["img_size"] + + table_inputs.append({ + "tables": tables, + "img_size": img_size + }) + cell_text = table_output(filepath, table_inputs, page_range=unique_pages) + assert len(cell_text) == len(unique_pages), "Number of pages and table inputs must match" + + for pidx, (page_tables, pnum) in enumerate(zip(cell_text, unique_pages)): + table_idx = 0 + for block in extract_blocks: + if block["page_id"] == pnum: + block["table_text_lines"]: List[TableCell] = page_tables[table_idx] + table_idx += 1 + assert table_idx == len(page_tables), "Number of tables and table inputs must match" + + def assign_ocr_lines(self, ocr_blocks: list): + det_images = [t["table_image"] for t in ocr_blocks] + ocr_results: List[OCRResult] = self.recognition_model(det_images, [None] * len(det_images), self.detection_model, recognition_batch_size=self.get_recognition_batch_size(), detection_batch_size=self.get_detector_batch_size()) + + for block, ocr_res in zip(ocr_blocks, ocr_results): + table_cells = [] + for line in ocr_res.text_lines: + bbox = line.bbox + # Correct back to image size + bbox = [ + bbox[0] + block["table_bbox"][0], + bbox[1] + block["table_bbox"][1], + bbox[2] + block["table_bbox"][0], + bbox[3] + block["table_bbox"][1] + ] + table_cells.append({ + "bbox": bbox, + "text": line.text + }) + block["table_text_lines"] = table_cells - for table_d, table_res in zip(table_data, tables): - block = document.get_block(table_d["block_id"]) - cells = assign_rows_columns(table_res, table_d["img_size"]) - for cell in cells: - cell.text = fix_text(cell.text) - block.cells = cells def get_detector_batch_size(self): if self.detector_batch_size is not None: diff --git a/marker/schema/__init__.py b/marker/schema/__init__.py index 473e6216..91cb936c 100644 --- a/marker/schema/__init__.py +++ b/marker/schema/__init__.py @@ -27,6 +27,7 @@ class BlockTypes(str, Enum): TableOfContents = auto() Document = auto() ComplexRegion = auto() + TableCell = auto() def __str__(self): return self.name diff --git a/marker/schema/blocks/__init__.py b/marker/schema/blocks/__init__.py index 2203d044..50ba2d02 100644 --- a/marker/schema/blocks/__init__.py +++ b/marker/schema/blocks/__init__.py @@ -18,3 +18,4 @@ from marker.schema.blocks.text import Text from marker.schema.blocks.toc import TableOfContents from marker.schema.blocks.complexregion import ComplexRegion +from marker.schema.blocks.tablecell import TableCell diff --git a/marker/schema/blocks/base.py b/marker/schema/blocks/base.py index 2b64463d..c66e80a6 100644 --- a/marker/schema/blocks/base.py +++ b/marker/schema/blocks/base.py @@ -161,7 +161,7 @@ def raw_text(self, document: Document) -> str: text += "\n" return text - def assemble_html(self, child_blocks: List[BlockOutput], parent_structure: Optional[List[str]] = None): + def assemble_html(self, document: Document, child_blocks: List[BlockOutput], parent_structure: Optional[List[str]] = None): if self.ignore_for_output: return "" @@ -199,7 +199,7 @@ def replace_block(self, block: Block, new_block: Block): self.structure[i] = new_block.id break - def render(self, document: Document, parent_structure: Optional[List[str]], section_hierarchy=None): + def render(self, document: Document, parent_structure: Optional[List[str]] = None, section_hierarchy: dict | None = None): child_content = [] if section_hierarchy is None: section_hierarchy = {} @@ -213,7 +213,7 @@ def render(self, document: Document, parent_structure: Optional[List[str]], sect child_content.append(rendered) return BlockOutput( - html=self.assemble_html(child_content, parent_structure), + html=self.assemble_html(document, child_content, parent_structure), polygon=self.polygon, id=self.id, children=child_content, diff --git a/marker/schema/blocks/basetable.py b/marker/schema/blocks/basetable.py new file mode 100644 index 00000000..d1241373 --- /dev/null +++ b/marker/schema/blocks/basetable.py @@ -0,0 +1,36 @@ +from typing import List + +from marker.schema import BlockTypes +from marker.schema.blocks import Block +from marker.schema.blocks.tablecell import TableCell + + +class BaseTable(Block): + block_type: BlockTypes | None = None + html: str | None = None + + def format_cells(self, document, child_blocks): + child_cells: List[TableCell] = [document.get_block(c.id) for c in child_blocks] + unique_rows = sorted(list(set([c.row_id for c in child_cells]))) + html_repr = "" + for row_id in unique_rows: + row_cells = sorted([c for c in child_cells if c.row_id == row_id], key=lambda x: x.col_id) + html_repr += "" + for cell in row_cells: + html_repr += cell.assemble_html(document, child_blocks, None) + html_repr += "" + html_repr += "
" + return html_repr + + + def assemble_html(self, document, child_blocks, parent_structure=None): + if self.html: + # LLM processor + return self.html + elif len(child_blocks) > 0 and child_blocks[0].block_type == BlockTypes.TableCell: + # Table processor + return self.format_cells(document, child_blocks) + else: + # Default text lines and spans + template = super().assemble_html(document, child_blocks, parent_structure) + return f"

{template}

" diff --git a/marker/schema/blocks/caption.py b/marker/schema/blocks/caption.py index acb45ea9..6e424747 100644 --- a/marker/schema/blocks/caption.py +++ b/marker/schema/blocks/caption.py @@ -5,7 +5,7 @@ class Caption(Block): block_type: BlockTypes = BlockTypes.Caption - def assemble_html(self, child_blocks, parent_structure): - template = super().assemble_html(child_blocks, parent_structure) + def assemble_html(self, document, child_blocks, parent_structure): + template = super().assemble_html(document, child_blocks, parent_structure) template = template.replace("\n", " ") return f"

{template}

" diff --git a/marker/schema/blocks/code.py b/marker/schema/blocks/code.py index 9d795c44..ff65966c 100644 --- a/marker/schema/blocks/code.py +++ b/marker/schema/blocks/code.py @@ -8,7 +8,7 @@ class Code(Block): block_type: BlockTypes = BlockTypes.Code code: str | None = None - def assemble_html(self, child_blocks, parent_structure): + def assemble_html(self, document, child_blocks, parent_structure): code = self.code or "" return (f"
"
                 f"{html.escape(code)}"
diff --git a/marker/schema/blocks/complexregion.py b/marker/schema/blocks/complexregion.py
index 139bc1dd..cdfe8179 100644
--- a/marker/schema/blocks/complexregion.py
+++ b/marker/schema/blocks/complexregion.py
@@ -6,9 +6,9 @@ class ComplexRegion(Block):
     block_type: BlockTypes = BlockTypes.ComplexRegion
     html: str | None = None
 
-    def assemble_html(self, child_blocks, parent_structure):
+    def assemble_html(self, document, child_blocks, parent_structure):
         if self.html:
             return self.html
         else:
-            template = super().assemble_html(child_blocks, parent_structure)
+            template = super().assemble_html(document, child_blocks, parent_structure)
             return f"
{template}
"
diff --git a/marker/schema/blocks/equation.py b/marker/schema/blocks/equation.py
index 3881814b..b82f3c7d 100644
--- a/marker/schema/blocks/equation.py
+++ b/marker/schema/blocks/equation.py
@@ -8,7 +8,7 @@ class Equation(Block):
     block_type: BlockTypes = BlockTypes.Equation
     latex: str | None = None
 
-    def assemble_html(self, child_blocks, parent_structure=None):
+    def assemble_html(self, document, child_blocks, parent_structure=None):
         if self.latex:
             html_out = f"
"
 
@@ -31,7 +31,7 @@ def assemble_html(self, child_blocks, parent_structure=None):
             html_out += "
"
             return html_out
         else:
-            template = super().assemble_html(child_blocks, parent_structure)
+            template = super().assemble_html(document, child_blocks, parent_structure)
             return f"
{template}
"
 
     @staticmethod
diff --git a/marker/schema/blocks/figure.py b/marker/schema/blocks/figure.py
index a4c21c01..74270bbe 100644
--- a/marker/schema/blocks/figure.py
+++ b/marker/schema/blocks/figure.py
@@ -6,7 +6,7 @@ class Figure(Block):
     block_type: BlockTypes = BlockTypes.Figure
     description: str | None = None
 
-    def assemble_html(self, child_blocks, parent_structure):
+    def assemble_html(self, document, child_blocks, parent_structure):
         if self.description:
             return f"
Image {self.id} description: {self.description}
"
         else:
diff --git a/marker/schema/blocks/footnote.py b/marker/schema/blocks/footnote.py
index c476aa7d..289727fe 100644
--- a/marker/schema/blocks/footnote.py
+++ b/marker/schema/blocks/footnote.py
@@ -19,8 +19,8 @@ def superscript(child_blocks):
 class Footnote(Block):
     block_type: BlockTypes = BlockTypes.Footnote
 
-    def assemble_html(self, child_blocks, parent_structure):
-        template = super().assemble_html(child_blocks, parent_structure)
+    def assemble_html(self, document, child_blocks, parent_structure):
+        template = super().assemble_html(document, child_blocks, parent_structure)
         template = template.replace("\n", " ")
 
         # Add superscripts to start
diff --git a/marker/schema/blocks/form.py b/marker/schema/blocks/form.py
index 6a57f962..4185520d 100644
--- a/marker/schema/blocks/form.py
+++ b/marker/schema/blocks/form.py
@@ -1,20 +1,8 @@
 from typing import List
 
-from tabled.formats import html_format
-from tabled.schema import SpanTableCell
-
 from marker.schema import BlockTypes
-from marker.schema.blocks import Block
-
-
-class Form(Block):
-    block_type: str = BlockTypes.Form
-    cells: List[SpanTableCell] | None = None
-    html: str | None = None
+from marker.schema.blocks.basetable import BaseTable
 
-    def assemble_html(self, child_blocks, parent_structure=None):
-        # Some processors convert the form to html
-        if self.html is not None:
-            return self.html
 
-        return str(html_format(self.cells))
+class Form(BaseTable):
+    block_type: BlockTypes = BlockTypes.Form
diff --git a/marker/schema/blocks/handwriting.py b/marker/schema/blocks/handwriting.py
index 14a146e6..540369ae 100644
--- a/marker/schema/blocks/handwriting.py
+++ b/marker/schema/blocks/handwriting.py
@@ -5,7 +5,7 @@
 class Handwriting(Block):
     block_type: BlockTypes = BlockTypes.Handwriting
 
-    def assemble_html(self, child_blocks, parent_structure):
-        template = super().assemble_html(child_blocks, parent_structure)
+    def assemble_html(self, document, child_blocks, parent_structure):
+        template = super().assemble_html(document, child_blocks, parent_structure)
         template = template.replace("\n", " ")
         return f"
{template}
"
diff --git a/marker/schema/blocks/inlinemath.py b/marker/schema/blocks/inlinemath.py
index 1b446ae7..6e415745 100644
--- a/marker/schema/blocks/inlinemath.py
+++ b/marker/schema/blocks/inlinemath.py
@@ -8,11 +8,11 @@ class InlineMath(Block):
     blockquote: bool = False
     blockquote_level: int = 0
 
-    def assemble_html(self, child_blocks, parent_structure):
+    def assemble_html(self, document, child_blocks, parent_structure):
         if self.ignore_for_output:
             return ""
 
-        template = super().assemble_html(child_blocks, parent_structure)
+        template = super().assemble_html(document, child_blocks, parent_structure)
         template = template.replace("\n", " ")
 
         el_attr = f" block-type='{self.block_type}'"
diff --git a/marker/schema/blocks/listitem.py b/marker/schema/blocks/listitem.py
index fef515a4..91ab539d 100644
--- a/marker/schema/blocks/listitem.py
+++ b/marker/schema/blocks/listitem.py
@@ -20,8 +20,8 @@ class ListItem(Block):
     block_type: BlockTypes = BlockTypes.ListItem
     list_indent_level: int = 0
 
-    def assemble_html(self, child_blocks, parent_structure):
-        template = super().assemble_html(child_blocks, parent_structure)
+    def assemble_html(self, document, child_blocks, parent_structure):
+        template = super().assemble_html(document, child_blocks, parent_structure)
         template = template.replace("\n", " ")
         # Remove the first bullet character
         replace_bullets(child_blocks)
diff --git a/marker/schema/blocks/pagefooter.py b/marker/schema/blocks/pagefooter.py
index 774474b5..e1127a6c 100644
--- a/marker/schema/blocks/pagefooter.py
+++ b/marker/schema/blocks/pagefooter.py
@@ -5,10 +5,10 @@
 class PageFooter(Block):
     block_type: str = BlockTypes.PageFooter
 
-    def assemble_html(self, child_blocks, parent_structure):
+    def assemble_html(self, document, child_blocks, parent_structure):
         if self.ignore_for_output:
             return ""
 
-        template = super().assemble_html(child_blocks, parent_structure)
+        template = super().assemble_html(document, child_blocks, parent_structure)
         template = template.replace("\n", " ")
         return f"
{template}
"
diff --git a/marker/schema/blocks/pageheader.py b/marker/schema/blocks/pageheader.py
index d304490e..3b648c3b 100644
--- a/marker/schema/blocks/pageheader.py
+++ b/marker/schema/blocks/pageheader.py
@@ -5,10 +5,10 @@
 class PageHeader(Block):
     block_type: BlockTypes = BlockTypes.PageHeader
 
-    def assemble_html(self, child_blocks, parent_structure):
+    def assemble_html(self, document, child_blocks, parent_structure):
         if self.ignore_for_output:
             return ""
 
-        template = super().assemble_html(child_blocks, parent_structure)
+        template = super().assemble_html(document, child_blocks, parent_structure)
         template = template.replace("\n", " ")
         return f"
{template}
"
diff --git a/marker/schema/blocks/picture.py b/marker/schema/blocks/picture.py
index d98d101e..a2be8394 100644
--- a/marker/schema/blocks/picture.py
+++ b/marker/schema/blocks/picture.py
@@ -6,7 +6,7 @@ class Picture(Block):
     block_type: BlockTypes = BlockTypes.Picture
     description: str | None = None
 
-    def assemble_html(self, child_blocks, parent_structure):
+    def assemble_html(self, document, child_blocks, parent_structure):
         if self.description:
             return f"
Image {self.id} description: {self.description}
"
         else:
diff --git a/marker/schema/blocks/sectionheader.py b/marker/schema/blocks/sectionheader.py
index c104f0b4..2a104f24 100644
--- a/marker/schema/blocks/sectionheader.py
+++ b/marker/schema/blocks/sectionheader.py
@@ -8,11 +8,11 @@ class SectionHeader(Block):
     block_type: BlockTypes = BlockTypes.SectionHeader
     heading_level: Optional[int] = None
 
-    def assemble_html(self, child_blocks, parent_structure):
+    def assemble_html(self, document, child_blocks, parent_structure):
         if self.ignore_for_output:
             return ""
 
-        template = super().assemble_html(child_blocks, parent_structure)
+        template = super().assemble_html(document, child_blocks, parent_structure)
         template = template.replace("\n", " ")
         tag = f"h{self.heading_level}" if self.heading_level else "h2"
         return f"<{tag}>{template}"
diff --git a/marker/schema/blocks/table.py b/marker/schema/blocks/table.py
index d389bc9d..812fd57d 100644
--- a/marker/schema/blocks/table.py
+++ b/marker/schema/blocks/table.py
@@ -1,19 +1,6 @@
-from typing import List
-
-from tabled.formats import html_format
-from tabled.schema import SpanTableCell
-
 from marker.schema import BlockTypes
-from marker.schema.blocks import Block
+from marker.schema.blocks.basetable import BaseTable
 
 
-class Table(Block):
+class Table(BaseTable):
     block_type: BlockTypes = BlockTypes.Table
-    cells: List[SpanTableCell] | None = None
-
-    def assemble_html(self, child_blocks, parent_structure=None):
-        if self.cells:
-            return str(html_format(self.cells))
-        else:
-            template = super().assemble_html(child_blocks, parent_structure)
-            return f"
{template}
"
diff --git a/marker/schema/blocks/tablecell.py b/marker/schema/blocks/tablecell.py
new file mode 100644
index 00000000..276def77
--- /dev/null
+++ b/marker/schema/blocks/tablecell.py
@@ -0,0 +1,16 @@
+from marker.schema import BlockTypes
+from marker.schema.blocks import Block
+
+
+class TableCell(Block):
+    block_type: BlockTypes = BlockTypes.TableCell
+    rowspan: int
+    colspan: int
+    row_id: int
+    col_id: int
+    is_header: bool
+    text: str = ""
+
+    def assemble_html(self, document, child_blocks, parent_structure=None):
+        tag = "th" if self.is_header else "td"
+        return f"<{tag} rowspan={self.rowspan} colspan={self.colspan}>{self.text}"
diff --git a/marker/schema/blocks/text.py b/marker/schema/blocks/text.py
index 4c2dea86..edb25969 100644
--- a/marker/schema/blocks/text.py
+++ b/marker/schema/blocks/text.py
@@ -8,11 +8,11 @@ class Text(Block):
     blockquote: bool = False
     blockquote_level: int = 0
 
-    def assemble_html(self, child_blocks, parent_structure):
+    def assemble_html(self, document, child_blocks, parent_structure):
         if self.ignore_for_output:
             return ""
 
-        template = super().assemble_html(child_blocks, parent_structure)
+        template = super().assemble_html(document, child_blocks, parent_structure)
         template = template.replace("\n", " ")
 
         el_attr = f" block-type='{self.block_type}'"
diff --git a/marker/schema/blocks/toc.py b/marker/schema/blocks/toc.py
index 158fe5b4..3e458372 100644
--- a/marker/schema/blocks/toc.py
+++ b/marker/schema/blocks/toc.py
@@ -1,15 +1,6 @@
-from typing import List
-
-from tabled.formats import html_format
-from tabled.schema import SpanTableCell
-
 from marker.schema import BlockTypes
-from marker.schema.blocks import Block
+from marker.schema.blocks.basetable import BaseTable
 
 
-class TableOfContents(Block):
+class TableOfContents(BaseTable):
     block_type: str = BlockTypes.TableOfContents
-    cells: List[SpanTableCell] | None = None
-
-    def assemble_html(self, child_blocks, parent_structure=None):
-        return str(html_format(self.cells))
diff --git a/marker/schema/groups/list.py b/marker/schema/groups/list.py
index 8e8ee3ab..e45bdd15 100644
--- a/marker/schema/groups/list.py
+++ b/marker/schema/groups/list.py
@@ -6,8 +6,8 @@ class ListGroup(Group):
     block_type: BlockTypes = BlockTypes.ListGroup
     has_continuation: bool = False
 
-    def assemble_html(self, child_blocks, parent_structure):
-        template = super().assemble_html(child_blocks, parent_structure)
+    def assemble_html(self, document, child_blocks, parent_structure):
+        template = super().assemble_html(document, child_blocks, parent_structure)
 
         el_attr = f" block-type='{self.block_type}'"
         if self.has_continuation:
diff --git a/marker/schema/groups/page.py b/marker/schema/groups/page.py
index 3089f4ce..6eaf2511 100644
--- a/marker/schema/groups/page.py
+++ b/marker/schema/groups/page.py
@@ -77,7 +77,7 @@ def get_block(self, block_id: BlockId) -> Block | None:
         assert block.block_id == block_id.block_id
         return block
 
-    def assemble_html(self, child_blocks, parent_structure=None):
+    def assemble_html(self, document, child_blocks, parent_structure=None):
         template = ""
         for c in child_blocks:
             template += f""
diff --git a/marker/schema/registry.py b/marker/schema/registry.py
index 250934eb..78a9f8db 100644
--- a/marker/schema/registry.py
+++ b/marker/schema/registry.py
@@ -6,8 +6,7 @@
     Footnote, Form, Handwriting, InlineMath, \
     ListItem, PageFooter, PageHeader, Picture, \
     SectionHeader, Table, TableOfContents, \
-    Text
-from marker.schema.blocks.complexregion import ComplexRegion
+    Text, ComplexRegion, TableCell
 from marker.schema.document import Document
 from marker.schema.groups import FigureGroup, ListGroup, PageGroup, \
     PictureGroup, TableGroup
@@ -51,6 +50,7 @@ def get_block_class(block_type: BlockTypes) -> Type[Block]:
 register_block_class(BlockTypes.Text, Text)
 register_block_class(BlockTypes.TableOfContents, TableOfContents)
 register_block_class(BlockTypes.ComplexRegion, ComplexRegion)
+register_block_class(BlockTypes.TableCell, TableCell)
 register_block_class(BlockTypes.Document, Document)
 
 assert len(BLOCK_REGISTRY) == len(BlockTypes)
diff --git a/marker/schema/text/span.py b/marker/schema/text/span.py
index d066ccbe..7cae6aff 100644
--- a/marker/schema/text/span.py
+++ b/marker/schema/text/span.py
@@ -35,7 +35,7 @@ def italic(self):
     def math(self):
         return 'math' in self.formats
 
-    def assemble_html(self, child_blocks, parent_structure):
+    def assemble_html(self, document, child_blocks, parent_structure):
         if self.ignore_for_output:
             return ""
 
diff --git a/pyproject.toml b/pyproject.toml
index 3f9c4750..e92d2f8f 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -36,7 +36,6 @@ rapidfuzz = "^3.8.1"
 surya-ocr = "~0.8.3"
 regex = "^2024.4.28"
 pdftext = "~0.4.1"
-tabled-pdf = "~0.2.0"
 markdownify = "^0.13.1"
 click = "^8.1.7"
 google-generativeai = "^0.8.3"
diff --git a/tests/processors/test_table_processor.py b/tests/processors/test_table_processor.py
index c968c312..c356aea4 100644
--- a/tests/processors/test_table_processor.py
+++ b/tests/processors/test_table_processor.py
@@ -1,9 +1,8 @@
 import pytest
 
-from tabled.schema import SpanTableCell
-
 from marker.schema import BlockTypes
 from marker.processors.table import TableProcessor
+from marker.schema.blocks import TableCell
 
 
 @pytest.mark.config({"page_range": [5]})
@@ -13,6 +12,7 @@ def test_table_processor(pdf_document, detection_model, recognition_model, table
 
     for block in pdf_document.pages[0].children:
         if block.block_type == BlockTypes.Table:
-            assert block.cells is not None
-            assert len(block.cells) > 0
-            assert isinstance(block.cells[0], SpanTableCell)
+            children = block.contained_blocks(pdf_document, (BlockTypes.TableCell,))
+            assert children
+            assert len(children) > 0
+            assert isinstance(children[0], TableCell)

From cf9aa06a84f4c213171a47dad07e8a15f2dbbced Mon Sep 17 00:00:00 2001
From: Vik Paruchuri 
Date: Wed, 8 Jan 2025 15:27:08 -0500
Subject: [PATCH 19/92] Fix markdown output

---
 marker/builders/llm_layout.py     |  2 +-
 marker/processors/llm/__init__.py |  2 +-
 marker/renderers/markdown.py      | 63 +++++++++++++++++++++++++++----
 marker/schema/blocks/basetable.py |  6 +--
 marker/settings.py                |  2 +-
 5 files changed, 62 insertions(+), 13 deletions(-)

diff --git a/marker/builders/llm_layout.py b/marker/builders/llm_layout.py
index e6ed8899..4ed75a1c 100644
--- a/marker/builders/llm_layout.py
+++ b/marker/builders/llm_layout.py
@@ -24,7 +24,7 @@ class LLMLayoutBuilder(LayoutBuilder):
     """
 
     google_api_key: Annotated[
-        Optional[str],
+        str,
         "The Google API key to use for the Gemini model.",
     ] = settings.GOOGLE_API_KEY
     confidence_threshold: Annotated[
diff --git a/marker/processors/llm/__init__.py b/marker/processors/llm/__init__.py
index 0e46f94c..57ede428 100644
--- a/marker/processors/llm/__init__.py
+++ b/marker/processors/llm/__init__.py
@@ -16,7 +16,7 @@ class BaseLLMProcessor(BaseProcessor):
     A processor for using LLMs to convert blocks.
     """
     google_api_key: Annotated[
-        Optional[str],
+        str,
         "The Google API key to use for the Gemini model.",
     ] = settings.GOOGLE_API_KEY
     model_name: Annotated[
diff --git a/marker/renderers/markdown.py b/marker/renderers/markdown.py
index 46bb62ba..98765099 100644
--- a/marker/renderers/markdown.py
+++ b/marker/renderers/markdown.py
@@ -53,13 +53,62 @@ def convert_math(self, el, text, convert_as_inline):
         else:
             return "\n" + self.block_math_delimiters[0] + text + self.block_math_delimiters[1] + "\n"
 
-    def convert_td(self, el, text, convert_as_inline):
-        text = text.replace("|", " ").replace("\n", " ")
-        return super().convert_td(el, text, convert_as_inline)
-
-    def convert_th(self, el, text, convert_as_inline):
-        text = text.replace("|", " ").replace("\n", " ")
-        return super().convert_th(el, text, convert_as_inline)
+    def convert_table(self, el, text, convert_as_inline):
+        total_rows = len(el.find_all('tr'))
+        colspans = []
+        for row in el.find_all('tr'):
+            row_cols = 0
+            for cell in row.find_all(['td', 'th']):
+                colspan = int(cell.get('colspan', 1))
+                row_cols += colspan
+            colspans.append(row_cols)
+        total_cols = max(colspans)
+
+        grid = [[None for _ in range(total_cols)] for _ in range(total_rows)]
+
+        for row_idx, tr in enumerate(el.find_all('tr')):
+            col_idx = 0
+            for cell in tr.find_all(['td', 'th']):
+                # Skip filled positions
+                while col_idx < total_cols and grid[row_idx][col_idx] is not None:
+                    col_idx += 1
+
+                # Fill in grid
+                value = cell.get_text(strip=True).replace("\n", " ").replace("|", " ")
+                rowspan = int(cell.get('rowspan', 1))
+                colspan = int(cell.get('colspan', 1))
+
+                for r in range(rowspan):
+                    for c in range(colspan):
+                        if r == 0 and c == 0:
+                            grid[row_idx][col_idx] = value
+                        else:
+                            grid[row_idx + r][col_idx + c] = ''
+
+                col_idx += colspan
+
+        markdown_lines = []
+        col_widths = [0] * total_cols
+        for row in grid:
+            for col_idx, cell in enumerate(row):
+                if cell is not None:
+                    col_widths[col_idx] = max(col_widths[col_idx], len(str(cell)))
+
+        # Generate header and separator
+        markdown_lines.append('|' + '|'.join(f" {' ' * width} " for width in col_widths) + '|')
+        markdown_lines.append('|' + '|'.join('-' * (width + 2) for width in col_widths) + '|')
+
+        # Generate markdown rows
+        for row in grid:
+            line = []
+            for col_idx, cell in enumerate(row):
+                if cell is None:
+                    cell = ''
+                padding = col_widths[col_idx] - len(str(cell))
+                line.append(f" {cell}{' ' * padding} ")
+            markdown_lines.append('|' + '|'.join(line) + '|')
+
+        return '\n'.join(markdown_lines)
 
 
 class MarkdownOutput(BaseModel):
diff --git a/marker/schema/blocks/basetable.py b/marker/schema/blocks/basetable.py
index d1241373..706dcf7a 100644
--- a/marker/schema/blocks/basetable.py
+++ b/marker/schema/blocks/basetable.py
@@ -1,7 +1,7 @@
 from typing import List
 
 from marker.schema import BlockTypes
-from marker.schema.blocks import Block
+from marker.schema.blocks import Block, BlockOutput
 from marker.schema.blocks.tablecell import TableCell
 
 
@@ -23,11 +23,11 @@ def format_cells(self, document, child_blocks):
         return html_repr
 
 
-    def assemble_html(self, document, child_blocks, parent_structure=None):
+    def assemble_html(self, document, child_blocks: List[BlockOutput], parent_structure=None):
         if self.html:
             # LLM processor
             return self.html
-        elif len(child_blocks) > 0 and child_blocks[0].block_type == BlockTypes.TableCell:
+        elif len(child_blocks) > 0 and child_blocks[0].id.block_type == BlockTypes.TableCell:
             # Table processor
             return self.format_cells(document, child_blocks)
         else:
diff --git a/marker/settings.py b/marker/settings.py
index 0a3a0ef7..2d416b90 100644
--- a/marker/settings.py
+++ b/marker/settings.py
@@ -19,7 +19,7 @@ class Settings(BaseSettings):
     OUTPUT_IMAGE_FORMAT: str = "JPEG"
 
     # LLM
-    GOOGLE_API_KEY: Optional[str] = None
+    GOOGLE_API_KEY: Optional[str] = ""
 
     # General models
     TORCH_DEVICE: Optional[str] = None  # Note: MPS device does not work for text detection, and will default to CPU

From 31498995a1ed79da7be0ce426fb1dd1b6ac9b062 Mon Sep 17 00:00:00 2001
From: Vik Paruchuri 
Date: Wed, 8 Jan 2025 16:19:46 -0500
Subject: [PATCH 20/92] Add table converter

---
 convert.py                               |  4 ++-
 convert_single.py                        |  3 +-
 marker/builders/document.py              |  7 +++-
 marker/config/parser.py                  | 13 +++++++
 marker/converters/pdf.py                 | 45 +++++++++++++-----------
 marker/converters/table.py               | 44 +++++++++++++++++++++++
 marker/renderers/markdown.py             |  3 +-
 tests/processors/test_table_processor.py |  5 +++
 8 files changed, 99 insertions(+), 25 deletions(-)
 create mode 100644 marker/converters/table.py

diff --git a/convert.py b/convert.py
index 7efda686..35f70589 100755
--- a/convert.py
+++ b/convert.py
@@ -45,8 +45,10 @@ def process_single_pdf(args):
     if cli_options.get('skip_existing') and output_exists(out_folder, base_name):
         return
 
+    converter_cls = config_parser.get_converter_cls()
+
     try:
-        converter = PdfConverter(
+        converter = converter_cls(
             config=config_parser.generate_config_dict(),
             artifact_dict=model_refs,
             processor_list=config_parser.get_processors(),
diff --git a/convert_single.py b/convert_single.py
index c08b304f..271833b6 100755
--- a/convert_single.py
+++ b/convert_single.py
@@ -25,7 +25,8 @@ def main(fpath: str, **kwargs):
     start = time.time()
     config_parser = ConfigParser(kwargs)
 
-    converter = PdfConverter(
+    converter_cls = config_parser.get_converter_cls()
+    converter = converter_cls(
         config=config_parser.generate_config_dict(),
         artifact_dict=models,
         processor_list=config_parser.get_processors(),
diff --git a/marker/builders/document.py b/marker/builders/document.py
index d79c4990..c7fb3968 100644
--- a/marker/builders/document.py
+++ b/marker/builders/document.py
@@ -22,11 +22,16 @@ class DocumentBuilder(BaseBuilder):
         int,
         "DPI setting for high-resolution page images used for OCR.",
     ] = 192
+    disable_ocr: Annotated[
+        bool,
+        "Disable OCR processing.",
+    ] = False
 
     def __call__(self, provider: PdfProvider, layout_builder: LayoutBuilder, ocr_builder: OcrBuilder):
         document = self.build_document(provider)
         layout_builder(document, provider)
-        ocr_builder(document, provider)
+        if not self.disable_ocr:
+            ocr_builder(document, provider)
         return document
 
     def build_document(self, provider: PdfProvider):
diff --git a/marker/config/parser.py b/marker/config/parser.py
index ef627983..ac0dcd30 100644
--- a/marker/config/parser.py
+++ b/marker/config/parser.py
@@ -5,6 +5,7 @@
 import click
 
 from marker.config.crawler import crawler
+from marker.converters.pdf import PdfConverter
 from marker.renderers.html import HTMLRenderer
 from marker.renderers.json import JSONRenderer
 from marker.renderers.markdown import MarkdownRenderer
@@ -39,6 +40,7 @@ def common_options(fn):
         # we put common options here
         fn = click.option("--google_api_key", type=str, default=None, help="Google API key for using LLMs.")(fn)
         fn = click.option("--use_llm", is_flag=True, default=False, help="Enable higher quality processing with LLMs.")(fn)
+        fn = click.option("--converter_cls", type=str, default=None, help="Converter class to use.  Defaults to PDF converter.")(fn)
         return fn
 
     def generate_config_dict(self) -> Dict[str, any]:
@@ -95,6 +97,17 @@ def get_processors(self):
 
         return processors
 
+    def get_converter_cls(self):
+        converter_cls = self.cli_options.get("converter_cls", None)
+        if converter_cls is not None:
+            try:
+                return strings_to_classes([converter_cls])[0]
+            except Exception as e:
+                print(f"Error loading converter: {converter_cls} with error: {e}")
+                raise
+
+        return PdfConverter
+
     def get_output_folder(self, filepath: str):
         output_dir = self.cli_options.get("output_dir", settings.OUTPUT_DIR)
         fname_base = os.path.splitext(os.path.basename(filepath))[0]
diff --git a/marker/converters/pdf.py b/marker/converters/pdf.py
index 310ce277..a0500744 100644
--- a/marker/converters/pdf.py
+++ b/marker/converters/pdf.py
@@ -1,10 +1,12 @@
 import os
 
+from marker.processors import BaseProcessor
+
 os.environ["TOKENIZERS_PARALLELISM"] = "false"  # disables a tokenizers warning
 
 import inspect
 from collections import defaultdict
-from typing import Annotated, Any, Dict, List, Optional, Type
+from typing import Annotated, Any, Dict, List, Optional, Type, Tuple
 
 from marker.builders.document import DocumentBuilder
 from marker.builders.layout import LayoutBuilder
@@ -53,6 +55,26 @@ class PdfConverter(BaseConverter):
         bool,
         "Enable higher quality processing with LLMs.",
     ] = False
+    default_processors: Tuple[BaseProcessor, ...] = (
+        BlockquoteProcessor,
+        CodeProcessor,
+        DocumentTOCProcessor,
+        EquationProcessor,
+        FootnoteProcessor,
+        IgnoreTextProcessor,
+        LineNumbersProcessor,
+        ListProcessor,
+        PageHeaderProcessor,
+        SectionHeaderProcessor,
+        TableProcessor,
+        LLMTableProcessor,
+        LLMFormProcessor,
+        TextProcessor,
+        LLMTextProcessor,
+        LLMComplexRegionProcessor,
+        LLMImageDescriptionProcessor,
+        DebugProcessor,
+    )
 
     def __init__(self, artifact_dict: Dict[str, Any], processor_list: Optional[List[str]] = None, renderer: str | None = None, config=None):
         super().__init__(config)
@@ -63,26 +85,7 @@ def __init__(self, artifact_dict: Dict[str, Any], processor_list: Optional[List[
         if processor_list:
             processor_list = strings_to_classes(processor_list)
         else:
-            processor_list = [
-                BlockquoteProcessor,
-                CodeProcessor,
-                DocumentTOCProcessor,
-                EquationProcessor,
-                FootnoteProcessor,
-                IgnoreTextProcessor,
-                LineNumbersProcessor,
-                ListProcessor,
-                PageHeaderProcessor,
-                SectionHeaderProcessor,
-                TableProcessor,
-                LLMTableProcessor,
-                LLMFormProcessor,
-                TextProcessor,
-                LLMTextProcessor,
-                LLMComplexRegionProcessor,
-                LLMImageDescriptionProcessor,
-                DebugProcessor,
-            ]
+            processor_list = self.default_processors
 
         if renderer:
             renderer = strings_to_classes([renderer])[0]
diff --git a/marker/converters/table.py b/marker/converters/table.py
new file mode 100644
index 00000000..da29cdd6
--- /dev/null
+++ b/marker/converters/table.py
@@ -0,0 +1,44 @@
+from typing import Tuple, List
+
+from marker.builders.document import DocumentBuilder
+from marker.builders.ocr import OcrBuilder
+from marker.converters.pdf import PdfConverter
+from marker.processors import BaseProcessor
+from marker.processors.llm.llm_complex import LLMComplexRegionProcessor
+from marker.processors.llm.llm_form import LLMFormProcessor
+from marker.processors.llm.llm_table import LLMTableProcessor
+from marker.processors.table import TableProcessor
+from marker.providers.pdf import PdfProvider
+from marker.schema import BlockTypes
+
+
+class TableConverter(PdfConverter):
+    default_processors: Tuple[BaseProcessor, ...] = (
+        TableProcessor,
+        LLMTableProcessor,
+        LLMFormProcessor,
+        LLMComplexRegionProcessor,
+    )
+    converter_block_types: List[BlockTypes] = (BlockTypes.Table, BlockTypes.Form, BlockTypes.TableOfContents)
+
+    def build_document(self, filepath: str):
+        pdf_provider = PdfProvider(filepath, self.config)
+        layout_builder = self.resolve_dependencies(self.layout_builder_class)
+        ocr_builder = self.resolve_dependencies(OcrBuilder)
+        document_builder = DocumentBuilder(self.config)
+        document_builder.disable_ocr = True
+        document = document_builder(pdf_provider, layout_builder, ocr_builder)
+
+        for page in document.pages:
+            page.structure = [p for p in page.structure if p.block_type in self.converter_block_types]
+
+        for processor_cls in self.processor_list:
+            processor = self.resolve_dependencies(processor_cls)
+            processor(document)
+
+        return document
+
+    def __call__(self, filepath: str):
+        document = self.build_document(filepath)
+        renderer = self.resolve_dependencies(self.renderer)
+        return renderer(document)
\ No newline at end of file
diff --git a/marker/renderers/markdown.py b/marker/renderers/markdown.py
index 98765099..1871ba19 100644
--- a/marker/renderers/markdown.py
+++ b/marker/renderers/markdown.py
@@ -108,7 +108,8 @@ def convert_table(self, el, text, convert_as_inline):
                 line.append(f" {cell}{' ' * padding} ")
             markdown_lines.append('|' + '|'.join(line) + '|')
 
-        return '\n'.join(markdown_lines)
+        table_md = '\n'.join(markdown_lines)
+        return "\n\n" + table_md + "\n\n"
 
 
 class MarkdownOutput(BaseModel):
diff --git a/tests/processors/test_table_processor.py b/tests/processors/test_table_processor.py
index c356aea4..41334611 100644
--- a/tests/processors/test_table_processor.py
+++ b/tests/processors/test_table_processor.py
@@ -1,5 +1,6 @@
 import pytest
 
+from marker.renderers.markdown import MarkdownRenderer
 from marker.schema import BlockTypes
 from marker.processors.table import TableProcessor
 from marker.schema.blocks import TableCell
@@ -16,3 +17,7 @@ def test_table_processor(pdf_document, detection_model, recognition_model, table
             assert children
             assert len(children) > 0
             assert isinstance(children[0], TableCell)
+
+    renderer = MarkdownRenderer()
+    table_output = renderer(pdf_document)
+    assert "Schedule" in table_output

From 27ae366b488d90e1f54c8005ba0657c042092756 Mon Sep 17 00:00:00 2001
From: Vik Paruchuri 
Date: Wed, 8 Jan 2025 16:31:19 -0500
Subject: [PATCH 21/92] Fix config printer

---
 marker/config/printer.py | 6 +-----
 1 file changed, 1 insertion(+), 5 deletions(-)

diff --git a/marker/config/printer.py b/marker/config/printer.py
index e9b37b42..726cff74 100644
--- a/marker/config/printer.py
+++ b/marker/config/printer.py
@@ -34,11 +34,7 @@ def parse_args(self, ctx, args):
                         click.echo("\n".join([f'{" " * 12}' + desc for desc in metadata]))
                     if attr_type in [str, int, float, bool, Optional[int], Optional[float], Optional[str]]:
                         is_flag = attr_type in [bool, Optional[bool]] and not default
-                        if crawler.attr_counts.get(attr) > 1:
-                            options = ["--" + class_name_attr]
-                        else:
-                            options = ["--" + attr, "--" + class_name_attr]
-                        options.append(class_name_attr)
+                        options = ["--" + attr, "--" + class_name_attr, class_name_attr]
                         ctx.command.params.append(
                             click.Option(
                                 options,

From 4f5a4a924c721fc1a77c14b9390ee23e17917f55 Mon Sep 17 00:00:00 2001
From: Moses Paul R 
Date: Thu, 9 Jan 2025 10:49:48 +0000
Subject: [PATCH 22/92] initial pdf link merging impl

---
 marker/providers/pdf.py      | 104 +++++++++++++++++++++++++++++++++++
 marker/schema/groups/page.py |  15 ++---
 marker/schema/text/span.py   |   9 ++-
 3 files changed, 115 insertions(+), 13 deletions(-)

diff --git a/marker/providers/pdf.py b/marker/providers/pdf.py
index 09b9603d..32ee513e 100644
--- a/marker/providers/pdf.py
+++ b/marker/providers/pdf.py
@@ -1,5 +1,6 @@
 import atexit
 import ctypes
+import math
 import re
 from typing import Annotated, List, Optional, Set
 
@@ -7,6 +8,7 @@
 import pypdfium2.raw as pdfium_c
 from ftfy import fix_text
 from pdftext.extraction import dictionary_output
+from pdftext.schema import Bbox
 from PIL import Image
 
 from marker.providers import BaseProvider, ProviderOutput, ProviderPageLines
@@ -16,6 +18,7 @@
 from marker.schema.registry import get_block_class
 from marker.schema.text.line import Line
 from marker.schema.text.span import Span
+from marker.util import matrix_intersection_area
 
 
 class PdfProvider(BaseProvider):
@@ -196,9 +199,36 @@ def pdftext_extraction(self) -> ProviderPageLines:
                         )
                     )
             if self.check_line_spans(lines):
+                self.merge_links(lines, page_id)
                 page_lines[page_id] = lines
         return page_lines
 
+    def merge_links(self, lines, page_id):
+        links = self.get_links(page_id)
+
+        spans = [span for line in lines for span in line.spans]
+        span_bboxes = [span.polygon.bbox for span in spans]
+        link_bboxes = [link['bbox'] for link in links]
+        intersection_matrix = matrix_intersection_area(span_bboxes, link_bboxes)
+        max_intersections = {}
+
+        for span_idx, span in enumerate(spans):
+            intersection_span = intersection_matrix[span_idx]
+            if intersection_span.sum() == 0:
+                continue
+
+            max_intersection = intersection_span.argmax()
+            if intersection_matrix[span_idx, max_intersection] > 0:
+                max_intersections[span_idx] = (
+                    intersection_matrix[span_idx, max_intersection],
+                    links[max_intersection]
+                )
+
+        for span_idx, span in enumerate(spans):
+            if span_idx in max_intersections:
+                link = max_intersections[span_idx][1]
+                span.url = link['url']
+
     def check_line_spans(self, page_lines: List[ProviderOutput]) -> bool:
         page_spans = [span for line in page_lines for span in line.spans]
         if len(page_spans) == 0:
@@ -313,3 +343,77 @@ def get_fontname(self, font) -> str:
             pass
 
         return font_name
+
+    def get_links(self, page_idx):
+        urls = []
+        page = self.doc[page_idx]
+        page_bbox: List[float] = page.get_bbox()
+        page_width = math.ceil(abs(page_bbox[2] - page_bbox[0]))
+        page_height = math.ceil(abs(page_bbox[1] - page_bbox[3]))
+        page_rotation = 0
+        try:
+            page_rotation = page.get_rotation()
+        except:
+            pass
+
+        annot_count = pdfium_c.FPDFPage_GetAnnotCount(page)
+        for i in range(annot_count):
+            url = {
+                'bbox': [],
+                'url': '',
+                'page': page_idx,
+            }
+            annot = pdfium_c.FPDFPage_GetAnnot(page, i)
+            if pdfium_c.FPDFAnnot_GetSubtype(annot) == pdfium_c.FPDF_ANNOT_LINK:
+                fs_rect = pdfium_c.FS_RECTF()
+                success = pdfium_c.FPDFAnnot_GetRect(annot, ctypes.byref(fs_rect))
+                if not success:
+                    continue
+
+                cx_start, cy_start, cx_end, cy_end = [fs_rect.left, fs_rect.top, fs_rect.right, fs_rect.bottom]
+
+                cx_start -= page_bbox[0]
+                cx_end -= page_bbox[0]
+                cy_start -= page_bbox[1]
+                cy_end -= page_bbox[1]
+
+                ty_start = page_height - cy_start
+                ty_end = page_height - cy_end
+
+                bbox = [cx_start, min(ty_start, ty_end), cx_end, max(ty_start, ty_end)]
+                url['bbox'] = Bbox(bbox).rotate(page_width, page_height, page_rotation).bbox
+
+                link_obj = pdfium_c.FPDFAnnot_GetLink(annot)
+
+                action = pdfium_c.FPDFLink_GetAction(link_obj)
+                a_type = pdfium_c.FPDFAction_GetType(action)
+
+                if a_type == pdfium_c.PDFACTION_UNSUPPORTED:
+                    continue
+
+                elif a_type == pdfium_c.PDFACTION_GOTO:
+                    # Goto a page
+                    dest = pdfium_c.FPDFAction_GetDest(self.doc, action)
+                    if dest:
+                        tgt_page = pdfium_c.FPDFDest_GetDestPageIndex(self.doc, dest)
+                        url['url'] = f"#page-{tgt_page}"
+
+                # elif a_type == pdfium_c.PDFACTION_LAUNCH:
+                #     # Typically opens a file/app
+                #     path_len = pdfium_c.FPDFAction_GetFilePath(action, None, 0)
+                #     if path_len > 0:
+                #         buf = ctypes.create_string_buffer(path_len)
+                #         pdfium_c.FPDFAction_GetFilePath(action, buf, path_len)
+                #         filepath = buf.raw[:path_len].decode('utf-8', errors='replace').rstrip('\x00')
+
+                elif a_type == pdfium_c.PDFACTION_URI:
+                    # External link
+                    needed_len = pdfium_c.FPDFAction_GetURIPath(self.doc, action, None, 0)
+                    if needed_len > 0:
+                        buf = ctypes.create_string_buffer(needed_len)
+                        pdfium_c.FPDFAction_GetURIPath(self.doc, action, buf, needed_len)
+                        uri = buf.raw[:needed_len].decode('utf-8', errors='replace').rstrip('\x00')
+                        url["url"] = uri
+
+                urls.append(url)
+        return urls
diff --git a/marker/schema/groups/page.py b/marker/schema/groups/page.py
index 3089f4ce..2c5c30c3 100644
--- a/marker/schema/groups/page.py
+++ b/marker/schema/groups/page.py
@@ -19,9 +19,9 @@ class PageGroup(Group):
     lowres_image: Image.Image | None = None
     highres_image: Image.Image | None = None
     children: List[Union[Any, Block]] | None = None
-    layout_sliced: bool = False # Whether the layout model had to slice the image (order may be wrong)
+    layout_sliced: bool = False  # Whether the layout model had to slice the image (order may be wrong)
     excluded_block_types: Sequence[BlockTypes] = (BlockTypes.Line, BlockTypes.Span,)
-    maximum_assignment_distance: float = 20 # pixels
+    maximum_assignment_distance: float = 20  # pixels
 
     def incr_block_id(self):
         if self.block_id is None:
@@ -38,7 +38,7 @@ def add_child(self, block: Block):
     def get_next_block(self, block: Optional[Block] = None, ignored_block_types: Optional[List[BlockTypes]] = None):
         if ignored_block_types is None:
             ignored_block_types = []
-        
+
         structure_idx = 0
         if block is not None:
             structure_idx = self.structure.index(block.id) + 1
@@ -78,7 +78,7 @@ def get_block(self, block_id: BlockId) -> Block | None:
         return block
 
     def assemble_html(self, child_blocks, parent_structure=None):
-        template = ""
+        template = f""
         for c in child_blocks:
             template += f""
         return template
@@ -119,7 +119,6 @@ def replace_block(self, block: Block, new_block: Block):
         for child in self.children:
             child.replace_block(block, new_block)
 
-
     def identify_missing_blocks(
             self,
             provider_line_idxs: List[int],
@@ -134,7 +133,7 @@ def identify_missing_blocks(
 
             # if the unassociated line is a new line with minimal area, we can skip it
             if provider_outputs[line_idx].line.polygon.area <= 1 and \
-                provider_outputs[line_idx].raw_text == "\n":
+                    provider_outputs[line_idx].raw_text == "\n":
                 continue
 
             if new_block is None:
@@ -181,7 +180,6 @@ def create_missing_blocks(
             else:
                 self.structure.append(block.id)
 
-
     def add_initial_blocks(
             self,
             block_lines: Dict[BlockId, LINE_MAPPING_TYPE],
@@ -202,7 +200,6 @@ def add_initial_blocks(
                     self.add_full_block(span)
                     line.add_structure(span)
 
-
     def merge_blocks(
         self,
         provider_outputs: List[ProviderOutput],
@@ -254,5 +251,3 @@ def aggregate_block_metadata(self) -> BlockMetadata:
             if block.metadata is not None:
                 self.metadata = self.metadata.merge(block.metadata)
         return self.metadata
-
-
diff --git a/marker/schema/text/span.py b/marker/schema/text/span.py
index d066ccbe..1131df09 100644
--- a/marker/schema/text/span.py
+++ b/marker/schema/text/span.py
@@ -22,6 +22,7 @@ class Span(Block):
     minimum_position: int
     maximum_position: int
     formats: List[Literal['plain', 'math', 'chemical', 'bold', 'italic']]
+    url: str = ''
 
     @property
     def bold(self):
@@ -59,9 +60,11 @@ def assemble_html(self, child_blocks, parent_structure):
         text = cleanup_text(text)
 
         if self.italic:
-            return f"{text}"
+            text = f"{text}"
         elif self.bold:
-            return f"{text}"
+            text = f"{text}"
         elif self.math:
-            return f"{text}"
+            text = f"{text}"
+        elif self.url:
+            text = f"{text}"
         return text

From 6f0166e51d0e300e66c6722aa17b1a996e732b89 Mon Sep 17 00:00:00 2001
From: Moses Paul R 
Date: Fri, 10 Jan 2025 09:27:15 +0000
Subject: [PATCH 23/92] add support for refs and fix markdown conversion etc

---
 marker/providers/pdf.py      | 251 +++++++++++++++++++++++++++--------
 marker/renderers/markdown.py |   8 ++
 marker/schema/groups/page.py |   2 +-
 marker/schema/text/span.py   |   9 +-
 4 files changed, 212 insertions(+), 58 deletions(-)

diff --git a/marker/providers/pdf.py b/marker/providers/pdf.py
index 32ee513e..47701574 100644
--- a/marker/providers/pdf.py
+++ b/marker/providers/pdf.py
@@ -2,8 +2,9 @@
 import ctypes
 import math
 import re
-from typing import Annotated, List, Optional, Set
+from typing import Annotated, List, Optional, Set, Tuple
 
+import numpy as np
 import pypdfium2 as pdfium
 import pypdfium2.raw as pdfium_c
 from ftfy import fix_text
@@ -67,12 +68,17 @@ class PdfProvider(BaseProvider):
         bool,
         "Whether to strip existing OCR text from the PDF.",
     ] = False
+    disable_links: Annotated[
+        bool,
+        "Whether to disable links.",
+    ] = False
 
     def __init__(self, filepath: str, config=None):
         super().__init__(filepath, config)
 
         self.doc: pdfium.PdfDocument = pdfium.PdfDocument(self.filepath)
         self.page_lines: ProviderPageLines = {i: [] for i in range(len(self.doc))}
+        self.refs = {}
 
         if self.page_range is None:
             self.page_range = range(len(self.doc))
@@ -151,7 +157,7 @@ def pdftext_extraction(self) -> ProviderPageLines:
         page_char_blocks = dictionary_output(
             self.filepath,
             page_range=self.page_range,
-            keep_chars=False,
+            keep_chars=True,
             workers=self.pdftext_workers,
             flatten_pdf=self.flatten_pdf,
             quote_loosebox=False
@@ -160,6 +166,14 @@ def pdftext_extraction(self) -> ProviderPageLines:
 
         SpanClass: Span = get_block_class(BlockTypes.Span)
         LineClass: Line = get_block_class(BlockTypes.Line)
+        for page in page_char_blocks:
+            if not self.disable_links:
+                self.merge_links(page)
+
+        for page in page_char_blocks:
+            if not self.disable_links:
+                self.merge_refs(page)
+
         for page in page_char_blocks:
             page_id = page["page"]
             lines: List[ProviderOutput] = []
@@ -188,7 +202,9 @@ def pdftext_extraction(self) -> ProviderPageLines:
                                 maximum_position=span["char_end_idx"],
                                 formats=list(font_formats),
                                 page_id=page_id,
-                                text_extraction_method="pdftext"
+                                text_extraction_method="pdftext",
+                                url=span.get("url"),
+                                anchor=span.get("anchor"),
                             )
                         )
                     polygon = PolygonBox.from_bbox(line["bbox"], ensure_nonzero_area=True)
@@ -199,15 +215,17 @@ def pdftext_extraction(self) -> ProviderPageLines:
                         )
                     )
             if self.check_line_spans(lines):
-                self.merge_links(lines, page_id)
                 page_lines[page_id] = lines
+
         return page_lines
 
-    def merge_links(self, lines, page_id):
+    def merge_links(self, page):
+        page_id = page["page"]
+
         links = self.get_links(page_id)
 
-        spans = [span for line in lines for span in line.spans]
-        span_bboxes = [span.polygon.bbox for span in spans]
+        spans = [span for block in page['blocks'] for line in block['lines'] for span in line['spans'] if span['text']]
+        span_bboxes = [span['bbox'] for span in spans]
         link_bboxes = [link['bbox'] for link in links]
         intersection_matrix = matrix_intersection_area(span_bboxes, link_bboxes)
         max_intersections = {}
@@ -224,10 +242,98 @@ def merge_links(self, lines, page_id):
                     links[max_intersection]
                 )
 
+        span_replace_map = {}
         for span_idx, span in enumerate(spans):
             if span_idx in max_intersections:
                 link = max_intersections[span_idx][1]
-                span.url = link['url']
+                if link['dest_page'] is not None:
+                    dest_page = link['dest_page']
+                    link['url'] = f"#page-{dest_page}"
+                    self.refs.setdefault(dest_page, [])
+                    if link['dest_bbox']:
+                        dest_box = "-".join(map(str, link['dest_bbox']))                        
+                    else:
+                        dest_box = "0.0-0.0-1.0-1.0"
+                    if dest_box not in self.refs[dest_page]:
+                        self.refs[dest_page].append(dest_box)
+                    link['url'] += f"-{self.refs[dest_page].index(dest_box)}"
+                span_replace_map[span_idx] = self.break_spans(span, link)
+            span_idx += 1
+
+        span_idx = 0
+        for block in page["blocks"]:
+            for line in block["lines"]:
+                spans = []
+                for span in line["spans"]:
+                    if not span["text"]:
+                        continue
+                    if span_idx in span_replace_map:
+                        spans.extend(span_replace_map[span_idx])
+                    else:
+                        spans.append(span)
+                    span_idx += 1
+                line['spans'] = spans
+
+    def merge_refs(self, page):
+        page_id = page["page"]
+
+        refs = self.refs.get(page_id, [])
+        if not refs:
+            return
+
+        spans = [span for block in page['blocks'] for line in block['lines'] for span in line['spans'] if span['text']]
+
+        span_starts = np.array([span['bbox'][:2] for span in spans])
+        ref_bboxes = np.array([list(map(float, ref.split("-"))) for ref in refs])
+        ref_starts = np.array([bbox[:2] for bbox in ref_bboxes])
+
+        distances = np.linalg.norm(span_starts[:, np.newaxis, :] - ref_starts[np.newaxis, :, :], axis=2)
+
+        assigned_refs = set()
+        for ref_idx, ref_center in enumerate(ref_starts):
+            if ref_idx in assigned_refs:
+                continue
+
+            span_indices = np.argsort(distances[:, ref_idx])
+            for span_idx in span_indices:
+                if spans[span_idx].get('anchor') is None:
+                    spans[span_idx]['anchor'] = f"page-{page_id}-{ref_idx}"
+                    assigned_refs.add(ref_idx)
+                    break
+
+    def break_spans(self, orig_span, link):
+        spans = []
+        span = None
+        link_bbox = Bbox(link['bbox'])
+
+        for char in orig_span['chars']:
+            char_bbox = Bbox(char['bbox'])
+            char_in_link = bool(link_bbox.intersection_pct(char_bbox) > 0)
+
+            if not span or (char_in_link != span['char_in_link']):
+                span = {
+                    "bbox": char_bbox,
+                    "text": char["char"],
+                    "rotation": char["rotation"],
+                    "font": char["font"],
+                    "char_start_idx": char["char_idx"],
+                    "char_end_idx": char["char_idx"],
+                    "chars": [char],
+                    "url": link['url'] if char_in_link else '',
+                    "char_in_link": char_in_link
+                }
+                spans.append(span)
+            else:
+                span['text'] += char['char']
+                span['char_end_idx'] = char['char_idx']
+                span['bbox'] = span['bbox'].merge(char_bbox)
+                span['chars'].append(char)
+
+        for span in spans:
+            span['bbox'] = span['bbox'].bbox
+            del span['char_in_link']
+
+        return spans
 
     def check_line_spans(self, page_lines: List[ProviderOutput]) -> bool:
         page_spans = [span for line in page_lines for span in line.spans]
@@ -344,6 +450,44 @@ def get_fontname(self, font) -> str:
 
         return font_name
 
+    def get_dest_position(self, dest) -> Optional[Tuple[float, float]]:
+        has_x = ctypes.c_int()
+        has_y = ctypes.c_int()
+        has_zoom = ctypes.c_int()
+        x_coord = ctypes.c_float()
+        y_coord = ctypes.c_float()
+        zoom_level = ctypes.c_float()
+        success = pdfium_c.FPDFDest_GetLocationInPage(
+            dest,
+            ctypes.byref(has_x),
+            ctypes.byref(has_y),
+            ctypes.byref(has_zoom),
+            ctypes.byref(x_coord),
+            ctypes.byref(y_coord),
+            ctypes.byref(zoom_level)
+        )
+        if success:
+            if has_x.value and has_y.value:
+                return x_coord.value, y_coord.value
+        else:
+            return None
+
+    def rect_to_scaled_bbox(self, rect, page_bbox, page_height, page_width, page_rotation) -> List[float]:
+        cx_start, cy_start, cx_end, cy_end = rect
+        cx_start -= page_bbox[0]
+        cx_end -= page_bbox[0]
+        cy_start -= page_bbox[1]
+        cy_end -= page_bbox[1]
+
+        ty_start = page_height - cy_start
+        ty_end = page_height - cy_end
+
+        bbox = [cx_start, min(ty_start, ty_end), cx_end, max(ty_start, ty_end)]
+        return Bbox(bbox).rotate(page_width, page_height, page_rotation).bbox
+
+    def xy_to_scaled_bbox(self, x, y, page_bbox, page_height, page_width, page_rotation, expand_by=1) -> List[float]:
+        return self.rect_to_scaled_bbox([x - expand_by, y - expand_by, x + expand_by, y + expand_by], page_bbox, page_height, page_width, page_rotation)
+
     def get_links(self, page_idx):
         urls = []
         page = self.doc[page_idx]
@@ -358,10 +502,12 @@ def get_links(self, page_idx):
 
         annot_count = pdfium_c.FPDFPage_GetAnnotCount(page)
         for i in range(annot_count):
-            url = {
-                'bbox': [],
-                'url': '',
+            link = {
+                'bbox': None,
                 'page': page_idx,
+                'dest_page': None,
+                'dest_bbox': None,
+                'url': None,
             }
             annot = pdfium_c.FPDFPage_GetAnnot(page, i)
             if pdfium_c.FPDFAnnot_GetSubtype(annot) == pdfium_c.FPDF_ANNOT_LINK:
@@ -369,51 +515,46 @@ def get_links(self, page_idx):
                 success = pdfium_c.FPDFAnnot_GetRect(annot, ctypes.byref(fs_rect))
                 if not success:
                     continue
-
-                cx_start, cy_start, cx_end, cy_end = [fs_rect.left, fs_rect.top, fs_rect.right, fs_rect.bottom]
-
-                cx_start -= page_bbox[0]
-                cx_end -= page_bbox[0]
-                cy_start -= page_bbox[1]
-                cy_end -= page_bbox[1]
-
-                ty_start = page_height - cy_start
-                ty_end = page_height - cy_end
-
-                bbox = [cx_start, min(ty_start, ty_end), cx_end, max(ty_start, ty_end)]
-                url['bbox'] = Bbox(bbox).rotate(page_width, page_height, page_rotation).bbox
+                link['bbox'] = self.rect_to_scaled_bbox(
+                    [fs_rect.left, fs_rect.top, fs_rect.right, fs_rect.bottom],
+                    page_bbox, page_height, page_width, page_rotation
+                )
 
                 link_obj = pdfium_c.FPDFAnnot_GetLink(annot)
 
-                action = pdfium_c.FPDFLink_GetAction(link_obj)
-                a_type = pdfium_c.FPDFAction_GetType(action)
-
-                if a_type == pdfium_c.PDFACTION_UNSUPPORTED:
-                    continue
-
-                elif a_type == pdfium_c.PDFACTION_GOTO:
-                    # Goto a page
-                    dest = pdfium_c.FPDFAction_GetDest(self.doc, action)
-                    if dest:
-                        tgt_page = pdfium_c.FPDFDest_GetDestPageIndex(self.doc, dest)
-                        url['url'] = f"#page-{tgt_page}"
-
-                # elif a_type == pdfium_c.PDFACTION_LAUNCH:
-                #     # Typically opens a file/app
-                #     path_len = pdfium_c.FPDFAction_GetFilePath(action, None, 0)
-                #     if path_len > 0:
-                #         buf = ctypes.create_string_buffer(path_len)
-                #         pdfium_c.FPDFAction_GetFilePath(action, buf, path_len)
-                #         filepath = buf.raw[:path_len].decode('utf-8', errors='replace').rstrip('\x00')
-
-                elif a_type == pdfium_c.PDFACTION_URI:
-                    # External link
-                    needed_len = pdfium_c.FPDFAction_GetURIPath(self.doc, action, None, 0)
-                    if needed_len > 0:
-                        buf = ctypes.create_string_buffer(needed_len)
-                        pdfium_c.FPDFAction_GetURIPath(self.doc, action, buf, needed_len)
-                        uri = buf.raw[:needed_len].decode('utf-8', errors='replace').rstrip('\x00')
-                        url["url"] = uri
-
-                urls.append(url)
+                dest = pdfium_c.FPDFLink_GetDest(self.doc, link_obj)
+                if dest:
+                    tgt_page = pdfium_c.FPDFDest_GetDestPageIndex(self.doc, dest)
+                    link['dest_page'] = tgt_page
+                    dest_position = self.get_dest_position(dest)
+                    if dest_position:
+                        link['dest_bbox'] = self.xy_to_scaled_bbox(*dest_position, page_bbox, page_height, page_width, page_rotation)
+
+                else:
+                    action = pdfium_c.FPDFLink_GetAction(link_obj)
+                    a_type = pdfium_c.FPDFAction_GetType(action)
+
+                    if a_type == pdfium_c.PDFACTION_UNSUPPORTED:
+                        continue
+
+                    elif a_type == pdfium_c.PDFACTION_GOTO:
+                        # Goto a page
+                        dest = pdfium_c.FPDFAction_GetDest(self.doc, action)
+                        if dest:
+                            tgt_page = pdfium_c.FPDFDest_GetDestPageIndex(self.doc, dest)
+                            link['dest_page'] = tgt_page
+                            dest_position = self.get_dest_position(dest)
+                            if dest_position:
+                                link['dest_bbox'] = self.xy_to_scaled_bbox(*dest_position, page_bbox, page_height, page_width, page_rotation)
+
+                    elif a_type == pdfium_c.PDFACTION_URI:
+                        # External link
+                        needed_len = pdfium_c.FPDFAction_GetURIPath(self.doc, action, None, 0)
+                        if needed_len > 0:
+                            buf = ctypes.create_string_buffer(needed_len)
+                            pdfium_c.FPDFAction_GetURIPath(self.doc, action, buf, needed_len)
+                            uri = buf.raw[:needed_len].decode('utf-8', errors='replace').rstrip('\x00')
+                            link["url"] = uri
+
+                urls.append(link)
         return urls
diff --git a/marker/renderers/markdown.py b/marker/renderers/markdown.py
index 46bb62ba..dd386a33 100644
--- a/marker/renderers/markdown.py
+++ b/marker/renderers/markdown.py
@@ -61,6 +61,14 @@ def convert_th(self, el, text, convert_as_inline):
         text = text.replace("|", " ").replace("\n", " ")
         return super().convert_th(el, text, convert_as_inline)
 
+    def convert_a(self, el, text, convert_as_inline):
+        text = self.escape(text)
+        text = re.sub(r"([\[\]])", r"\\\1", text)
+        return super().convert_a(el, self.escape(text), convert_as_inline)
+
+    def convert_span(self, el, text, convert_as_inline):
+        return str(el)
+
 
 class MarkdownOutput(BaseModel):
     markdown: str
diff --git a/marker/schema/groups/page.py b/marker/schema/groups/page.py
index 2c5c30c3..00282899 100644
--- a/marker/schema/groups/page.py
+++ b/marker/schema/groups/page.py
@@ -78,7 +78,7 @@ def get_block(self, block_id: BlockId) -> Block | None:
         return block
 
     def assemble_html(self, child_blocks, parent_structure=None):
-        template = f""
+        template = ""
         for c in child_blocks:
             template += f""
         return template
diff --git a/marker/schema/text/span.py b/marker/schema/text/span.py
index 1131df09..66015e6d 100644
--- a/marker/schema/text/span.py
+++ b/marker/schema/text/span.py
@@ -1,6 +1,6 @@
 import html
 import re
-from typing import List, Literal
+from typing import List, Literal, Optional
 
 from marker.schema import BlockTypes
 from marker.schema.blocks import Block
@@ -22,7 +22,8 @@ class Span(Block):
     minimum_position: int
     maximum_position: int
     formats: List[Literal['plain', 'math', 'chemical', 'bold', 'italic']]
-    url: str = ''
+    url: Optional[str] = None
+    anchor: Optional[str] = None
 
     @property
     def bold(self):
@@ -65,6 +66,10 @@ def assemble_html(self, child_blocks, parent_structure):
             text = f"{text}"
         elif self.math:
             text = f"{text}"
+        elif self.url and self.anchor:
+            text = f"{text}"
         elif self.url:
             text = f"{text}"
+        elif self.anchor:
+            text = f"{text}"
         return text

From 8448dd8035ee2b00bb2c4c7d43076cb3c19c245a Mon Sep 17 00:00:00 2001
From: Vik Paruchuri 
Date: Fri, 10 Jan 2025 09:36:06 -0500
Subject: [PATCH 24/92] Fix header row issues

---
 marker/processors/table.py               | 31 ++++++++++++++----------
 marker/renderers/markdown.py             | 15 +++++++++---
 tests/converters/test_table_converter.py | 31 ++++++++++++++++++++++++
 3 files changed, 60 insertions(+), 17 deletions(-)
 create mode 100644 tests/converters/test_table_converter.py

diff --git a/marker/processors/table.py b/marker/processors/table.py
index 803267af..6c9e8783 100644
--- a/marker/processors/table.py
+++ b/marker/processors/table.py
@@ -1,3 +1,4 @@
+import re
 from collections import defaultdict
 from typing import Annotated, List
 
@@ -61,7 +62,7 @@ def __call__(self, document: Document):
         table_data = []
         for page in document.pages:
             for block in page.contained_blocks(document, self.block_types):
-                image_poly = block.polygon.rescale((page.polygon.width, page.polygon.height), page.highres_image.size)
+                image_poly = block.polygon.rescale((page.polygon.width, page.polygon.height), page.highres_image.size).expand(.01, .01)
                 image = page.highres_image.crop(image_poly.bbox).convert("RGB")
 
                 table_data.append({
@@ -70,7 +71,7 @@ def __call__(self, document: Document):
                     "table_image": image,
                     "table_bbox": image_poly.bbox,
                     "img_size": page.highres_image.size,
-                    "ocr_block": block.text_extraction_method == "surya",
+                    "ocr_block": page.text_extraction_method == "surya",
                 })
 
         extract_blocks = [t for t in table_data if not t["ocr_block"]]
@@ -133,7 +134,11 @@ def assign_text_to_cells(self, tables: List[TableResult], table_data: list):
 
             for k in cell_text:
                 # TODO: see if the text needs to be sorted (based on rotation)
-                table_cells[k].text = "\n".join([ct["text"] for ct in cell_text[k]])
+                text = "\n".join([ct["text"] for ct in cell_text[k]])
+                # Replace . . . etc with ...
+                text = re.sub(r"(\s\.){3,}", "...", text) # Replace . . .
+                text = re.sub(r"\.{3,}", "...", text) # Replace ..., like in table of contents
+                table_cells[k].text = text
 
     def assign_pdftext_lines(self, extract_blocks: list, filepath: str):
         table_inputs = []
@@ -166,21 +171,21 @@ def assign_pdftext_lines(self, extract_blocks: list, filepath: str):
 
     def assign_ocr_lines(self, ocr_blocks: list):
         det_images = [t["table_image"] for t in ocr_blocks]
-        ocr_results: List[OCRResult] = self.recognition_model(det_images, [None] * len(det_images), self.detection_model, recognition_batch_size=self.get_recognition_batch_size(), detection_batch_size=self.get_detector_batch_size())
+        ocr_results: List[OCRResult] = self.recognition_model(
+            det_images,
+            [None] * len(det_images),
+            self.detection_model,
+            recognition_batch_size=self.get_recognition_batch_size(),
+            detection_batch_size=self.get_detector_batch_size()
+        )
 
         for block, ocr_res in zip(ocr_blocks, ocr_results):
             table_cells = []
             for line in ocr_res.text_lines:
-                bbox = line.bbox
-                # Correct back to image size
-                bbox = [
-                    bbox[0] + block["table_bbox"][0],
-                    bbox[1] + block["table_bbox"][1],
-                    bbox[2] + block["table_bbox"][0],
-                    bbox[3] + block["table_bbox"][1]
-                ]
+                # Don't need to correct back to image size
+                # Table rec boxes are relative to the table
                 table_cells.append({
-                    "bbox": bbox,
+                    "bbox": line.bbox,
                     "text": line.text
                 })
             block["table_text_lines"] = table_cells
diff --git a/marker/renderers/markdown.py b/marker/renderers/markdown.py
index 1871ba19..2944d861 100644
--- a/marker/renderers/markdown.py
+++ b/marker/renderers/markdown.py
@@ -56,12 +56,14 @@ def convert_math(self, el, text, convert_as_inline):
     def convert_table(self, el, text, convert_as_inline):
         total_rows = len(el.find_all('tr'))
         colspans = []
+        is_header_row = []
         for row in el.find_all('tr'):
             row_cols = 0
             for cell in row.find_all(['td', 'th']):
                 colspan = int(cell.get('colspan', 1))
                 row_cols += colspan
             colspans.append(row_cols)
+            is_header_row.append(len(row.find_all('th')) == row_cols)
         total_cols = max(colspans)
 
         grid = [[None for _ in range(total_cols)] for _ in range(total_rows)]
@@ -94,12 +96,13 @@ def convert_table(self, el, text, convert_as_inline):
                 if cell is not None:
                     col_widths[col_idx] = max(col_widths[col_idx], len(str(cell)))
 
-        # Generate header and separator
-        markdown_lines.append('|' + '|'.join(f" {' ' * width} " for width in col_widths) + '|')
-        markdown_lines.append('|' + '|'.join('-' * (width + 2) for width in col_widths) + '|')
+        add_header_line = lambda: markdown_lines.append('|' + '|'.join('-' * (width + 2) for width in col_widths) + '|')
 
         # Generate markdown rows
-        for row in grid:
+        for i, row in enumerate(grid):
+            if i == 1:
+                add_header_line()
+
             line = []
             for col_idx, cell in enumerate(row):
                 if cell is None:
@@ -108,6 +111,10 @@ def convert_table(self, el, text, convert_as_inline):
                 line.append(f" {cell}{' ' * padding} ")
             markdown_lines.append('|' + '|'.join(line) + '|')
 
+        # Handle one row tables
+        if total_rows == 1:
+            add_header_line()
+
         table_md = '\n'.join(markdown_lines)
         return "\n\n" + table_md + "\n\n"
 
diff --git a/tests/converters/test_table_converter.py b/tests/converters/test_table_converter.py
new file mode 100644
index 00000000..8f5cdecc
--- /dev/null
+++ b/tests/converters/test_table_converter.py
@@ -0,0 +1,31 @@
+import pytest
+from marker.converters.table import TableConverter
+from marker.renderers.markdown import MarkdownOutput
+from marker.util import classes_to_strings
+
+def _table_converter(config, model_dict, renderer, temp_pdf):
+    converter = TableConverter(
+        artifact_dict=model_dict,
+        processor_list=None,
+        renderer=classes_to_strings([renderer])[0],
+        config=config
+    )
+
+    markdown_output: MarkdownOutput = converter(temp_pdf.name)
+    markdown = markdown_output.markdown
+
+    breakpoint()
+    assert len(markdown) > 0
+    assert "cyclic" in markdown
+
+
+@pytest.mark.output_format("markdown")
+@pytest.mark.config({"page_range": [5]})
+def test_table_converter(config, model_dict, renderer, temp_pdf):
+    _table_converter(config, model_dict, renderer, temp_pdf)
+
+@pytest.mark.output_format("markdown")
+@pytest.mark.config({"page_range": [5], "force_ocr": True})
+def test_table_converter(config, model_dict, renderer, temp_pdf):
+    _table_converter(config, model_dict, renderer, temp_pdf)
+

From 5ef415564f681d3651e8348ba48d9cce5b21e177 Mon Sep 17 00:00:00 2001
From: Vik Paruchuri 
Date: Fri, 10 Jan 2025 10:15:02 -0500
Subject: [PATCH 25/92] Add image provider and tests

---
 README.md                              |  31 ++++-
 marker/converters/pdf.py               |   4 +-
 marker/converters/table.py             |   4 +-
 marker/providers/__init__.py           |   3 +-
 marker/providers/image.py              |  45 +++++++
 marker/providers/registry.py           |  12 ++
 poetry.lock                            | 166 +++++++++++--------------
 pyproject.toml                         |   2 +
 tests/conftest.py                      |  12 ++
 tests/providers/test_image_provider.py |  17 +++
 10 files changed, 198 insertions(+), 98 deletions(-)
 create mode 100644 marker/providers/image.py
 create mode 100644 marker/providers/registry.py
 create mode 100644 tests/providers/test_image_provider.py

diff --git a/README.md b/README.md
index 1f83abd4..c987cdc9 100644
--- a/README.md
+++ b/README.md
@@ -1,6 +1,6 @@
 # Marker
 
-Marker converts PDFs to markdown, JSON, and HTML quickly and accurately.
+Marker converts PDFs and images to markdown, JSON, and HTML quickly and accurately.
 
 - Supports a wide range of documents
 - Supports all languages
@@ -63,11 +63,11 @@ There's a hosted API for marker available [here](https://www.datalab.to/):
 PDF is a tricky format, so marker will not always work perfectly.  Here are some known limitations that are on the roadmap to address:
 
 - Marker will only convert block equations
-- Tables are not always formatted 100% correctly - multiline cells are sometimes split into multiple rows.
+- Tables are not always formatted 100% correctly
 - Forms are not converted optimally
 - Very complex layouts, with nested tables and forms, may not work
 
-Note: Passing the `--use_llm` flag will mostly solve all of these issues.
+Note: Passing the `--use_llm` flag will mostly solve these issues.
 
 # Installation
 
@@ -84,7 +84,7 @@ pip install marker-pdf
 First, some configuration:
 
 - Your torch device will be automatically detected, but you can override this.  For example, `TORCH_DEVICE=cuda`.
-- Some PDFs, even digital ones, have bad text in them.  Set the `force_ocr` flag on the CLI or via configuration to ensure your PDF runs through OCR.
+- Some PDFs, even digital ones, have bad text in them.  Set the `force_ocr` flag on the CLI or via configuration to ensure your PDF runs through OCR, or the `strip_existing_ocr` to keep all digital text, and only strip out any existing OCR text.
 
 ## Interactive App
 
@@ -101,6 +101,8 @@ marker_gui
 marker_single /path/to/file.pdf
 ```
 
+You can pass in PDFs or images.
+
 Options:
 - `--output_dir PATH`: Directory where output files will be saved. Defaults to the value specified in settings.OUTPUT_DIR.
 - `--output_format [markdown|json|html]`: Specify the format for the output results.
@@ -115,6 +117,7 @@ Options:
 - `--config_json PATH`: Path to a JSON configuration file containing additional settings.
 - `--languages TEXT`: Optionally specify which languages to use for OCR processing. Accepts a comma-separated list. Example: `--languages "en,fr,de"` for English, French, and German.
 - `config --help`: List all available builders, processors, and converters, and their associated configuration.  These values can be used to build a JSON configuration file for additional tweaking of marker defaults.
+- `--converter_cls`: One of `marker.converters.pdf.PdfConverter` (default) or `marker.converters.table.TableConverter`.  The `PdfConverter` will convert the whole PDF, the `TableConverter` will only extract and convert tables.
 
 The list of supported languages for surya OCR is [here](https://github.com/VikParuchuri/surya/blob/master/surya/languages.py).  If you don't need OCR, marker can work with any language.
 
@@ -180,7 +183,7 @@ rendered = converter("FILEPATH")
 
 ### Extract blocks
 
-Each document consists of one or more pages.  Pages contain blocks, which can themselves contain other blocks.  It's possible to programatically manipulate these blocks.  
+Each document consists of one or more pages.  Pages contain blocks, which can themselves contain other blocks.  It's possible to programmatically manipulate these blocks.  
 
 Here's an example of extracting all forms from a document:
 
@@ -198,6 +201,24 @@ forms = document.contained_blocks((BlockTypes.Form,))
 
 Look at the processors for more examples of extracting and manipulating blocks.
 
+### Custom converters
+
+You can also use custom converters to define your own conversion pipelines.  For example, the `TableConverter` will only extract tables:
+
+```python
+from marker.converters.table import TableConverter
+from marker.models import create_model_dict
+from marker.output import text_from_rendered
+
+converter = TableConverter(
+    artifact_dict=create_model_dict(),
+)
+rendered = converter("FILEPATH")
+text, _, images = text_from_rendered(rendered)
+```
+
+This takes all the same configuration as the PdfConverter.
+
 # Output Formats
 
 ## Markdown
diff --git a/marker/converters/pdf.py b/marker/converters/pdf.py
index a0500744..a31bdf5a 100644
--- a/marker/converters/pdf.py
+++ b/marker/converters/pdf.py
@@ -1,6 +1,7 @@
 import os
 
 from marker.processors import BaseProcessor
+from marker.providers.registry import provider_from_filepath
 
 os.environ["TOKENIZERS_PARALLELISM"] = "false"  # disables a tokenizers warning
 
@@ -120,7 +121,8 @@ def resolve_dependencies(self, cls):
         return cls(**resolved_kwargs)
 
     def build_document(self, filepath: str):
-        pdf_provider = PdfProvider(filepath, self.config)
+        provider_cls = provider_from_filepath(filepath)
+        pdf_provider = provider_cls(filepath, self.config)
         layout_builder = self.resolve_dependencies(self.layout_builder_class)
         ocr_builder = self.resolve_dependencies(OcrBuilder)
         document = DocumentBuilder(self.config)(pdf_provider, layout_builder, ocr_builder)
diff --git a/marker/converters/table.py b/marker/converters/table.py
index da29cdd6..61804f5d 100644
--- a/marker/converters/table.py
+++ b/marker/converters/table.py
@@ -9,6 +9,7 @@
 from marker.processors.llm.llm_table import LLMTableProcessor
 from marker.processors.table import TableProcessor
 from marker.providers.pdf import PdfProvider
+from marker.providers.registry import provider_from_filepath
 from marker.schema import BlockTypes
 
 
@@ -22,7 +23,8 @@ class TableConverter(PdfConverter):
     converter_block_types: List[BlockTypes] = (BlockTypes.Table, BlockTypes.Form, BlockTypes.TableOfContents)
 
     def build_document(self, filepath: str):
-        pdf_provider = PdfProvider(filepath, self.config)
+        provider_cls = provider_from_filepath(filepath)
+        pdf_provider = provider_cls(filepath, self.config)
         layout_builder = self.resolve_dependencies(self.layout_builder_class)
         ocr_builder = self.resolve_dependencies(OcrBuilder)
         document_builder = DocumentBuilder(self.config)
diff --git a/marker/providers/__init__.py b/marker/providers/__init__.py
index 6b389065..9f403d9d 100644
--- a/marker/providers/__init__.py
+++ b/marker/providers/__init__.py
@@ -3,6 +3,7 @@
 from PIL import Image
 from pydantic import BaseModel
 
+from marker.schema.polygon import PolygonBox
 from marker.schema.text import Span
 from marker.schema.text.line import Line
 from marker.util import assign_config
@@ -29,7 +30,7 @@ def __len__(self):
     def get_images(self, idxs: List[int], dpi: int) -> List[Image.Image]:
         pass
 
-    def get_page_bbox(self, idx: int) -> List[float]:
+    def get_page_bbox(self, idx: int) -> PolygonBox | None:
         pass
 
     def get_page_lines(self, idx: int) -> List[Line]:
diff --git a/marker/providers/image.py b/marker/providers/image.py
new file mode 100644
index 00000000..cda94cec
--- /dev/null
+++ b/marker/providers/image.py
@@ -0,0 +1,45 @@
+from typing import List, Annotated, Optional
+from PIL import Image
+
+from marker.providers import ProviderPageLines, BaseProvider
+from marker.schema.polygon import PolygonBox
+from marker.schema.text import Line
+
+
+class ImageProvider(BaseProvider):
+    page_range: Annotated[
+        Optional[List[int]],
+        "The range of pages to process.",
+        "Default is None, which will process all pages."
+    ] = None
+
+    image_count: int = 1
+
+    def __init__(self, filepath: str, config=None):
+        super().__init__(filepath, config)
+
+        self.images = [Image.open(filepath)]
+        self.page_lines: ProviderPageLines = {i: [] for i in range(self.image_count)}
+
+        if self.page_range is None:
+            self.page_range = range(self.image_count)
+
+        assert max(self.page_range) < self.image_count and min(self.page_range) >= 0, \
+            f"Invalid page range, values must be between 0 and {len(self.doc) - 1}.  Min of provided page range is {min(self.page_range)} and max is {max(self.page_range)}."
+
+        self.page_bboxes = {i: [0, 0, self.images[i].size[0], self.images[i].size[1]] for i in self.page_range}
+
+    def __len__(self):
+        return self.image_count
+
+    def get_images(self, idxs: List[int], dpi: int) -> List[Image.Image]:
+        return [self.images[i] for i in idxs]
+
+    def get_page_bbox(self, idx: int) -> PolygonBox | None:
+        bbox = self.page_bboxes[idx]
+        if bbox:
+            return PolygonBox.from_bbox(bbox)
+
+
+    def get_page_lines(self, idx: int) -> List[Line]:
+        return self.page_lines[idx]
\ No newline at end of file
diff --git a/marker/providers/registry.py b/marker/providers/registry.py
new file mode 100644
index 00000000..8f6d86fb
--- /dev/null
+++ b/marker/providers/registry.py
@@ -0,0 +1,12 @@
+import filetype
+
+from marker.providers.image import ImageProvider
+from marker.providers.pdf import PdfProvider
+
+
+def provider_from_filepath(filepath: str):
+     kind = filetype.image_match(filepath)
+     if kind is not None:
+        return ImageProvider
+
+     return PdfProvider
\ No newline at end of file
diff --git a/poetry.lock b/poetry.lock
index 21fa61d2..4f3cbb0d 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -4194,41 +4194,41 @@ torch = ["safetensors[numpy]", "torch (>=1.10)"]
 
 [[package]]
 name = "scikit-learn"
-version = "1.6.0"
+version = "1.6.1"
 description = "A set of python modules for machine learning and data mining"
 optional = false
 python-versions = ">=3.9"
 files = [
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+    {file = "scipy-1.15.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:161f80a98047c219c257bf5ce1777c574bde36b9d962a46b20d0d7e531f86863"},
+    {file = "scipy-1.15.0-cp312-cp312-win_amd64.whl", hash = "sha256:327163ad73e54541a675240708244644294cb0a65cca420c9c79baeb9648e479"},
+    {file = "scipy-1.15.0-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:0fcb16eb04d84670722ce8d93b05257df471704c913cb0ff9dc5a1c31d1e9422"},
+    {file = "scipy-1.15.0-cp313-cp313-macosx_12_0_arm64.whl", hash = "sha256:767e8cf6562931f8312f4faa7ddea412cb783d8df49e62c44d00d89f41f9bbe8"},
+    {file = "scipy-1.15.0-cp313-cp313-macosx_14_0_arm64.whl", hash = "sha256:37ce9394cdcd7c5f437583fc6ef91bd290014993900643fdfc7af9b052d1613b"},
+    {file = "scipy-1.15.0-cp313-cp313-macosx_14_0_x86_64.whl", hash = "sha256:6d26f17c64abd6c6c2dfb39920f61518cc9e213d034b45b2380e32ba78fde4c0"},
+    {file = "scipy-1.15.0-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:1e2448acd79c6374583581a1ded32ac71a00c2b9c62dfa87a40e1dd2520be111"},
+    {file = "scipy-1.15.0-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:36be480e512d38db67f377add5b759fb117edd987f4791cdf58e59b26962bee4"},
+    {file = "scipy-1.15.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:ccb6248a9987193fe74363a2d73b93bc2c546e0728bd786050b7aef6e17db03c"},
+    {file = "scipy-1.15.0-cp313-cp313-win_amd64.whl", hash = "sha256:952d2e9eaa787f0a9e95b6e85da3654791b57a156c3e6609e65cc5176ccfe6f2"},
+    {file = "scipy-1.15.0-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:b1432102254b6dc7766d081fa92df87832ac25ff0b3d3a940f37276e63eb74ff"},
+    {file = "scipy-1.15.0-cp313-cp313t-macosx_12_0_arm64.whl", hash = "sha256:4e08c6a36f46abaedf765dd2dfcd3698fa4bd7e311a9abb2d80e33d9b2d72c34"},
+    {file = "scipy-1.15.0-cp313-cp313t-macosx_14_0_arm64.whl", hash = "sha256:ec915cd26d76f6fc7ae8522f74f5b2accf39546f341c771bb2297f3871934a52"},
+    {file = "scipy-1.15.0-cp313-cp313t-macosx_14_0_x86_64.whl", hash = "sha256:351899dd2a801edd3691622172bc8ea01064b1cada794f8641b89a7dc5418db6"},
+    {file = "scipy-1.15.0-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e9baff912ea4f78a543d183ed6f5b3bea9784509b948227daaf6f10727a0e2e5"},
+    {file = "scipy-1.15.0-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:cd9d9198a7fd9a77f0eb5105ea9734df26f41faeb2a88a0e62e5245506f7b6df"},
+    {file = "scipy-1.15.0-cp313-cp313t-win_amd64.whl", hash = "sha256:129f899ed275c0515d553b8d31696924e2ca87d1972421e46c376b9eb87de3d2"},
+    {file = "scipy-1.15.0.tar.gz", hash = "sha256:300742e2cc94e36a2880ebe464a1c8b4352a7b0f3e36ec3d2ac006cdbe0219ac"},
 ]
 
 [package.dependencies]
-numpy = ">=1.23.5,<2.3"
+numpy = ">=1.23.5,<2.5"
 
 [package.extras]
 dev = ["cython-lint (>=0.12.2)", "doit (>=0.36.0)", "mypy (==1.10.0)", "pycodestyle", "pydevtool", "rich-click", "ruff (>=0.0.292)", "types-psutil", "typing_extensions"]
-doc = ["jupyterlite-pyodide-kernel", "jupyterlite-sphinx (>=0.13.1)", "jupytext", "matplotlib (>=3.5)", "myst-nb", "numpydoc", "pooch", "pydata-sphinx-theme (>=0.15.2)", "sphinx (>=5.0.0,<=7.3.7)", "sphinx-design (>=0.4.0)"]
-test = ["Cython", "array-api-strict (>=2.0)", "asv", "gmpy2", "hypothesis (>=6.30)", "meson", "mpmath", "ninja", "pooch", "pytest", "pytest-cov", "pytest-timeout", "pytest-xdist", "scikit-umfpack", "threadpoolctl"]
+doc = ["intersphinx_registry", "jupyterlite-pyodide-kernel", "jupyterlite-sphinx (>=0.16.5)", "jupytext", "matplotlib (>=3.5)", "myst-nb", "numpydoc", "pooch", "pydata-sphinx-theme (>=0.15.2)", "sphinx (>=5.0.0,<8.0.0)", "sphinx-copybutton", "sphinx-design (>=0.4.0)"]
+test = ["Cython", "array-api-strict (>=2.0,<2.1.1)", "asv", "gmpy2", "hypothesis (>=6.30)", "meson", "mpmath", "ninja", "pooch", "pytest", "pytest-cov", "pytest-timeout", "pytest-xdist", "scikit-umfpack", "threadpoolctl"]
 
 [[package]]
 name = "send2trash"
@@ -4489,27 +4496,6 @@ mpmath = ">=1.1.0,<1.4"
 [package.extras]
 dev = ["hypothesis (>=6.70.0)", "pytest (>=7.1.0)"]
 
-[[package]]
-name = "tabled-pdf"
-version = "0.2.0"
-description = "Detect and recognize tables in PDFs and images."
-optional = false
-python-versions = "<4.0,>=3.10"
-files = [
-    {file = "tabled_pdf-0.2.0-py3-none-any.whl", hash = "sha256:7f055907d92e4a3322d8c23190eaf552d90dedb4da7f0833eb070c578a6ffe8f"},
-    {file = "tabled_pdf-0.2.0.tar.gz", hash = "sha256:0751227326944bcbf3a6589746d648e802df91bab1545a8a7f0608e8b6c84913"},
-]
-
-[package.dependencies]
-click = ">=8.1.7,<9.0.0"
-pydantic = ">=2.9.2,<3.0.0"
-pydantic-settings = ">=2.5.2,<3.0.0"
-pypdfium2 = ">=4.30.0,<5.0.0"
-python-dotenv = ">=1.0.1,<2.0.0"
-scikit-learn = ">=1.5.2,<2.0.0"
-surya-ocr = ">=0.8.0,<0.9.0"
-tabulate = ">=0.9.0,<0.10.0"
-
 [[package]]
 name = "tabulate"
 version = "0.9.0"
@@ -5320,4 +5306,4 @@ propcache = ">=0.2.0"
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.10"
-content-hash = "f10872bc2f59616bf1093839e7065c82f7d78a88d8db91fa3c05c2ec5f857e7f"
+content-hash = "8118fd027892740419e08b6c5e0c1ff1231f6dc5ccd1b20e82d5ea2de358cb59"
diff --git a/pyproject.toml b/pyproject.toml
index e92d2f8f..dc966837 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -40,6 +40,8 @@ markdownify = "^0.13.1"
 click = "^8.1.7"
 google-generativeai = "^0.8.3"
 markdown2 = "^2.5.2"
+filetype = "^1.2.0"
+scikit-learn = "^1.6.1"
 
 [tool.poetry.group.dev.dependencies]
 jupyter = "^1.0.0"
diff --git a/tests/conftest.py b/tests/conftest.py
index 8b7abed2..7d7f70ef 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -2,6 +2,8 @@
 import tempfile
 from typing import Dict, Type
 
+from PIL import Image, ImageDraw
+
 import datasets
 import pytest
 
@@ -116,3 +118,13 @@ def renderer(request, config):
             raise ValueError(f"Unknown output format: {output_format}")
     else:
         return MarkdownRenderer
+
+@pytest.fixture(scope="function")
+def temp_image():
+    img = Image.new("RGB", (512, 512), color="white")
+    draw = ImageDraw.Draw(img)
+    draw.text((10, 10), "Hello, World!", fill="black")
+    with tempfile.NamedTemporaryFile(suffix=".png") as f:
+        img.save(f.name)
+        f.flush()
+        yield f
diff --git a/tests/providers/test_image_provider.py b/tests/providers/test_image_provider.py
new file mode 100644
index 00000000..d42522b9
--- /dev/null
+++ b/tests/providers/test_image_provider.py
@@ -0,0 +1,17 @@
+from marker.providers.image import ImageProvider
+from marker.renderers.markdown import MarkdownOutput
+
+
+def test_image_provider(config, temp_image):
+    provider = ImageProvider(temp_image.name, config)
+    assert len(provider) == 1
+    assert provider.get_images([0], 72)[0].size == (512, 512)
+
+    page_lines = provider.get_page_lines(0)
+    assert len(page_lines) == 0
+
+def test_image_provider_conversion(pdf_converter, temp_image):
+    markdown_output: MarkdownOutput = pdf_converter(temp_image.name)
+    assert "Hello, World!" in markdown_output.markdown
+
+

From 602cad14e9845d16ee4f313feeef882ececf8d92 Mon Sep 17 00:00:00 2001
From: Moses Paul R 
Date: Fri, 10 Jan 2025 16:03:36 +0000
Subject: [PATCH 26/92] support multiple anchors per span and put the anchors
 at the beginning

---
 marker/providers/pdf.py      | 38 ++++++++++++++++++------------------
 marker/renderers/markdown.py |  2 +-
 marker/schema/text/span.py   |  9 ++++-----
 3 files changed, 24 insertions(+), 25 deletions(-)

diff --git a/marker/providers/pdf.py b/marker/providers/pdf.py
index 47701574..5b73f9d0 100644
--- a/marker/providers/pdf.py
+++ b/marker/providers/pdf.py
@@ -204,7 +204,7 @@ def pdftext_extraction(self) -> ProviderPageLines:
                                 page_id=page_id,
                                 text_extraction_method="pdftext",
                                 url=span.get("url"),
-                                anchor=span.get("anchor"),
+                                anchors=span.get("anchors"),
                             )
                         )
                     polygon = PolygonBox.from_bbox(line["bbox"], ensure_nonzero_area=True)
@@ -248,15 +248,15 @@ def merge_links(self, page):
                 link = max_intersections[span_idx][1]
                 if link['dest_page'] is not None:
                     dest_page = link['dest_page']
-                    link['url'] = f"#page-{dest_page}"
                     self.refs.setdefault(dest_page, [])
-                    if link['dest_bbox']:
-                        dest_box = "-".join(map(str, link['dest_bbox']))                        
+                    link['url'] = f"#page-{dest_page}"
+                    if link['dest_pos']:
+                        dest_pos = link['dest_pos']
                     else:
-                        dest_box = "0.0-0.0-1.0-1.0"
-                    if dest_box not in self.refs[dest_page]:
-                        self.refs[dest_page].append(dest_box)
-                    link['url'] += f"-{self.refs[dest_page].index(dest_box)}"
+                        dest_pos = [0.0, 0.0]
+                    if dest_pos not in self.refs[dest_page]:
+                        self.refs[dest_page].append(dest_pos)
+                    link['url'] += f"-{self.refs[dest_page].index(dest_pos)}"
                 span_replace_map[span_idx] = self.break_spans(span, link)
             span_idx += 1
 
@@ -284,8 +284,8 @@ def merge_refs(self, page):
         spans = [span for block in page['blocks'] for line in block['lines'] for span in line['spans'] if span['text']]
 
         span_starts = np.array([span['bbox'][:2] for span in spans])
-        ref_bboxes = np.array([list(map(float, ref.split("-"))) for ref in refs])
-        ref_starts = np.array([bbox[:2] for bbox in ref_bboxes])
+        ref_pos = np.array([ref for ref in refs])
+        ref_starts = np.array([pos for pos in ref_pos])
 
         distances = np.linalg.norm(span_starts[:, np.newaxis, :] - ref_starts[np.newaxis, :, :], axis=2)
 
@@ -296,10 +296,10 @@ def merge_refs(self, page):
 
             span_indices = np.argsort(distances[:, ref_idx])
             for span_idx in span_indices:
-                if spans[span_idx].get('anchor') is None:
-                    spans[span_idx]['anchor'] = f"page-{page_id}-{ref_idx}"
-                    assigned_refs.add(ref_idx)
-                    break
+                spans[span_idx].setdefault('anchors', [])
+                spans[span_idx]['anchors'].append(f"page-{page_id}-{ref_idx}")
+                assigned_refs.add(ref_idx)
+                break
 
     def break_spans(self, orig_span, link):
         spans = []
@@ -485,8 +485,8 @@ def rect_to_scaled_bbox(self, rect, page_bbox, page_height, page_width, page_rot
         bbox = [cx_start, min(ty_start, ty_end), cx_end, max(ty_start, ty_end)]
         return Bbox(bbox).rotate(page_width, page_height, page_rotation).bbox
 
-    def xy_to_scaled_bbox(self, x, y, page_bbox, page_height, page_width, page_rotation, expand_by=1) -> List[float]:
-        return self.rect_to_scaled_bbox([x - expand_by, y - expand_by, x + expand_by, y + expand_by], page_bbox, page_height, page_width, page_rotation)
+    def xy_to_scaled_pos(self, x, y, page_bbox, page_height, page_width, page_rotation, expand_by=1) -> List[float]:
+        return self.rect_to_scaled_bbox([x - expand_by, y - expand_by, x + expand_by, y + expand_by], page_bbox, page_height, page_width, page_rotation)[:2]
 
     def get_links(self, page_idx):
         urls = []
@@ -506,7 +506,7 @@ def get_links(self, page_idx):
                 'bbox': None,
                 'page': page_idx,
                 'dest_page': None,
-                'dest_bbox': None,
+                'dest_pos': None,
                 'url': None,
             }
             annot = pdfium_c.FPDFPage_GetAnnot(page, i)
@@ -528,7 +528,7 @@ def get_links(self, page_idx):
                     link['dest_page'] = tgt_page
                     dest_position = self.get_dest_position(dest)
                     if dest_position:
-                        link['dest_bbox'] = self.xy_to_scaled_bbox(*dest_position, page_bbox, page_height, page_width, page_rotation)
+                        link['dest_pos'] = self.xy_to_scaled_pos(*dest_position, page_bbox, page_height, page_width, page_rotation)
 
                 else:
                     action = pdfium_c.FPDFLink_GetAction(link_obj)
@@ -545,7 +545,7 @@ def get_links(self, page_idx):
                             link['dest_page'] = tgt_page
                             dest_position = self.get_dest_position(dest)
                             if dest_position:
-                                link['dest_bbox'] = self.xy_to_scaled_bbox(*dest_position, page_bbox, page_height, page_width, page_rotation)
+                                link['dest_pos'] = self.xy_to_scaled_pos(*dest_position, page_bbox, page_height, page_width, page_rotation)
 
                     elif a_type == pdfium_c.PDFACTION_URI:
                         # External link
diff --git a/marker/renderers/markdown.py b/marker/renderers/markdown.py
index dd386a33..65d0a27d 100644
--- a/marker/renderers/markdown.py
+++ b/marker/renderers/markdown.py
@@ -67,7 +67,7 @@ def convert_a(self, el, text, convert_as_inline):
         return super().convert_a(el, self.escape(text), convert_as_inline)
 
     def convert_span(self, el, text, convert_as_inline):
-        return str(el)
+        return f''
 
 
 class MarkdownOutput(BaseModel):
diff --git a/marker/schema/text/span.py b/marker/schema/text/span.py
index 66015e6d..86fe24bc 100644
--- a/marker/schema/text/span.py
+++ b/marker/schema/text/span.py
@@ -23,7 +23,7 @@ class Span(Block):
     maximum_position: int
     formats: List[Literal['plain', 'math', 'chemical', 'bold', 'italic']]
     url: Optional[str] = None
-    anchor: Optional[str] = None
+    anchors: Optional[List[str]] = None
 
     @property
     def bold(self):
@@ -66,10 +66,9 @@ def assemble_html(self, child_blocks, parent_structure):
             text = f"{text}"
         elif self.math:
             text = f"{text}"
-        elif self.url and self.anchor:
-            text = f"{text}"
         elif self.url:
             text = f"{text}"
-        elif self.anchor:
-            text = f"{text}"
+
+        if self.anchors:
+            text = "".join(f"" for anchor in self.anchors) + text
         return text

From cb773fb9b52d6d12f501b5a54801345e7f698de9 Mon Sep 17 00:00:00 2001
From: Tarun Menta 
Date: Sat, 11 Jan 2025 00:39:31 +0530
Subject: [PATCH 27/92] Add new table benchmark

Still needs some minor fixes
---
 benchmarks/table/scoring.py | 133 ++++++++++++++++++++++++++
 benchmarks/table/table.py   |  88 ++++++++++++++++++
 poetry.lock                 | 181 +++++++++++++++++++++++++++++++++++-
 pyproject.toml              |   3 +
 4 files changed, 403 insertions(+), 2 deletions(-)
 create mode 100644 benchmarks/table/scoring.py
 create mode 100644 benchmarks/table/table.py

diff --git a/benchmarks/table/scoring.py b/benchmarks/table/scoring.py
new file mode 100644
index 00000000..ea14f3e5
--- /dev/null
+++ b/benchmarks/table/scoring.py
@@ -0,0 +1,133 @@
+'''
+TEDS Code Adapter from https://github.com/ibm-aur-nlp/EDD
+'''
+
+from concurrent.futures import ThreadPoolExecutor, as_completed
+from typing import List
+
+from tqdm import tqdm
+import distance
+from apted import APTED, Config
+from apted.helpers import Tree
+from lxml import html
+from collections import deque
+import numpy as np
+
+def wrap_table_html(table_html:str)->str:
+    return f'{table_html}'
+
+class TableTree(Tree):
+    def __init__(self, tag, colspan=None, rowspan=None, content=None, *children):
+        self.tag = tag
+        self.colspan = colspan
+        self.rowspan = rowspan
+        self.content = content
+        self.children = list(children)
+
+    def bracket(self):
+        """Show tree using brackets notation"""
+        if self.tag == 'td':
+            result = '"tag": %s, "colspan": %d, "rowspan": %d, "text": %s' % \
+                     (self.tag, self.colspan, self.rowspan, self.content)
+        else:
+            result = '"tag": %s' % self.tag
+        for child in self.children:
+            result += child.bracket()
+        return "{{{}}}".format(result)
+
+class CustomConfig(Config):
+    @staticmethod
+    def maximum(*sequences):
+        """Get maximum possible value
+        """
+        return max(map(len, sequences))
+
+    def normalized_distance(self, *sequences):
+        """Get distance from 0 to 1
+        """
+        return float(distance.levenshtein(*sequences)) / self.maximum(*sequences)
+
+    def rename(self, node1, node2):
+        """Compares attributes of trees"""
+        if (node1.tag != node2.tag) or (node1.colspan != node2.colspan) or (node1.rowspan != node2.rowspan):
+            return 1.
+        if node1.tag == 'td':
+            if node1.content or node2.content:
+                return self.normalized_distance(node1.content, node2.content)
+        return 0.
+
+def tokenize(node):
+    ''' Tokenizes table cells
+    '''
+    global __tokens__
+    __tokens__.append('<%s>' % node.tag)
+    if node.text is not None:
+        __tokens__ += list(node.text)
+    for n in node.getchildren():
+        tokenize(n)
+    if node.tag != 'unk':
+        __tokens__.append('' % node.tag)
+    if node.tag != 'td' and node.tail is not None:
+            __tokens__ += list(node.tail)
+
+def tree_convert_html(node, convert_cell=False, parent=None):
+    ''' Converts HTML tree to the format required by apted
+    '''
+    global __tokens__
+    if node.tag == 'td':
+        if convert_cell:
+            __tokens__ = []
+            tokenize(node)
+            cell = __tokens__[1:-1].copy()
+        else:
+            cell = []
+        new_node = TableTree(node.tag,
+                             int(node.attrib.get('colspan', '1')),
+                             int(node.attrib.get('rowspan', '1')),
+                             cell, *deque())
+    else:
+        new_node = TableTree(node.tag, None, None, None, *deque())
+    if parent is not None:
+        parent.children.append(new_node)
+    if node.tag != 'td':
+        for n in node.getchildren():
+            tree_convert_html(n, convert_cell, new_node)
+    if parent is None:
+        return new_node
+
+def similarity_eval_html(pred, true, structure_only=False):
+    ''' Computes TEDS score between the prediction and the ground truth of a
+        given samples
+    '''
+    if pred.xpath('body/table') and true.xpath('body/table'):
+        pred = pred.xpath('body/table')[0]
+        true = true.xpath('body/table')[0]
+        n_nodes_pred = len(pred.xpath(".//*"))
+        n_nodes_true = len(true.xpath(".//*"))
+        tree_pred = tree_convert_html(pred, convert_cell=not structure_only)
+        tree_true = tree_convert_html(true, convert_cell=not structure_only)
+        n_nodes = max(n_nodes_pred, n_nodes_true)
+        distance = APTED(tree_pred, tree_true, CustomConfig()).compute_edit_distance()
+        return 1.0 - (float(distance) / n_nodes)
+    else:
+        return 0.0
+
+def TEDS(prediction, ground_truth):
+    prediction, ground_truth = wrap_table_html(prediction), wrap_table_html(ground_truth)
+    if prediction:
+        return similarity_eval_html(
+            html.fromstring(prediction),
+            html.fromstring(ground_truth)
+        )
+    else:
+        return 0.
+
+def batched_TEDS(gts: List[str], preds: List[str], n_jobs:int=16):
+    with ThreadPoolExecutor(max_workers=n_jobs) as pool:
+        futures = [pool.submit(TEDS, pred, gt) for pred, gt in zip(preds, gts)]
+
+    teds_scores = []
+    for future in futures:
+        teds_scores.append(future.result())
+
+    return teds_scores
\ No newline at end of file
diff --git a/benchmarks/table/table.py b/benchmarks/table/table.py
new file mode 100644
index 00000000..77e8674e
--- /dev/null
+++ b/benchmarks/table/table.py
@@ -0,0 +1,88 @@
+import os
+import time
+import datasets
+from tqdm import tqdm
+import tempfile
+import click
+from tabulate import tabulate
+import json
+from bs4 import BeautifulSoup
+
+os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"  # Transformers uses .isin for a simple op, which is not supported on MPS
+
+from marker.config.parser import ConfigParser
+from marker.converters.table import TableConverter
+from marker.models import create_model_dict
+from marker.output import save_output
+
+from scoring import batched_TEDS
+
+
+@click.command(help="Benchmark Table to HTML Conversion")
+@click.argument("out_file", type=str)
+@click.option("--dataset", type=str, default="tarun-menta/fintabnet-html-test", help="Dataset to use")
+@click.option("--max", type=int, default=None, help="Max number of tables to process")
+def main(out_file, dataset, max):
+    models = create_model_dict()
+    config_parser = ConfigParser({})
+    start = time.time()
+
+    converter = TableConverter(
+        config=config_parser.generate_config_dict(),
+        artifact_dict=models,
+        processor_list=config_parser.get_processors(),
+        renderer='marker.renderers.html.HTMLRenderer'
+    )
+
+    dataset = datasets.load_dataset(dataset, split='train')
+    dataset = dataset.shuffle(seed=0)
+
+    iterations = len(dataset)
+    if max is not None:
+        iterations = min(max, len(dataset))
+
+    results = []
+    for i in tqdm(range(iterations), desc='Converting Tables'):
+        row = dataset[i]
+        table_img = row['highres_table_img']
+        with tempfile.NamedTemporaryFile(suffix=".png", mode="wb+") as temp_img_file:
+            table_img.save(temp_img_file)
+            temp_img_file.seek(0)
+            filename = temp_img_file.name
+
+            marker_table_html = converter(filename).html
+            marker_table_soup = BeautifulSoup(marker_table_html, 'html.parser')
+
+            #marker wraps the table in  which fintabnet data doesn't
+            marker_table_soup.find('tbody').unwrap()    
+
+            #Fintabnet doesn't use th tags, need to be replaced for fair comparison
+            for th_tag in marker_table_soup.find_all('th'):
+                th_tag.name = 'td'
+
+            marker_table_html = str(marker_table_soup)
+
+        results.append({
+            "marker_table": marker_table_html,
+            "gt_table": row['orig_html']
+        })
+
+    scores = batched_TEDS([r['gt_table'] for r in results], [r['marker_table'] for r in results])
+    for result, score in zip(results, scores):
+        result.update({'score': score})
+
+    avg_score = sum([r["score"] for r in results]) / len(results)
+
+    total_time = time.time() - start
+    print(f"Total time: {time.time() - start}")
+    headers = ["Avg score", "Time per table", "Total tables"]
+    data = [f"{avg_score:.3f}", f"{total_time / iterations:.3f}", iterations]
+    table = tabulate([data], headers=headers, tablefmt="github")
+    print(table)
+    print("Avg score computed by comparing tabled predicted HTML with original HTML")
+
+    with open(out_file, "w+") as f:
+        json.dump(results, f, indent=2)
+
+if __name__ == '__main__':
+    main()
\ No newline at end of file
diff --git a/poetry.lock b/poetry.lock
index 4f3cbb0d..41e9e387 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand.
 
 [[package]]
 name = "aiohappyeyeballs"
@@ -191,6 +191,17 @@ files = [
     {file = "appnope-0.1.4.tar.gz", hash = "sha256:1de3860566df9caf38f01f86f65e0e13e379af54f9e4bee1e66b48f2efffd1ee"},
 ]
 
+[[package]]
+name = "apted"
+version = "1.0.3"
+description = "APTED algorithm for the Tree Edit Distance"
+optional = false
+python-versions = "*"
+files = [
+    {file = "apted-1.0.3-py3-none-any.whl", hash = "sha256:74193369d023649d335269e67c4df07f922959e5ac2597de1b79af4e694150e8"},
+    {file = "apted-1.0.3.tar.gz", hash = "sha256:befa5181e2d4457fa88e54995a82604ee048bb2fbc781ea97d8e1856b4715ce9"},
+]
+
 [[package]]
 name = "argon2-cffi"
 version = "23.1.0"
@@ -750,6 +761,16 @@ files = [
 graph = ["objgraph (>=1.7.2)"]
 profile = ["gprof2dot (>=2022.7.29)"]
 
+[[package]]
+name = "distance"
+version = "0.1.3"
+description = "Utilities for comparing sequences"
+optional = false
+python-versions = "*"
+files = [
+    {file = "Distance-0.1.3.tar.gz", hash = "sha256:60807584f5b6003f5c521aa73f39f51f631de3be5cccc5a1d67166fcbf0d4551"},
+]
+
 [[package]]
 name = "exceptiongroup"
 version = "1.2.2"
@@ -1881,6 +1902,160 @@ files = [
     {file = "jupyterlab_widgets-3.0.13.tar.gz", hash = "sha256:a2966d385328c1942b683a8cd96b89b8dd82c8b8f81dda902bb2bc06d46f5bed"},
 ]
 
+[[package]]
+name = "lxml"
+version = "5.3.0"
+description = "Powerful and Pythonic XML processing library combining libxml2/libxslt with the ElementTree API."
+optional = false
+python-versions = ">=3.6"
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+    {file = "lxml-5.3.0.tar.gz", hash = "sha256:4e109ca30d1edec1ac60cdbe341905dc3b8f55b16855e03a54aaf59e51ec8c6f"},
+]
+
+[package.extras]
+cssselect = ["cssselect (>=0.7)"]
+html-clean = ["lxml-html-clean"]
+html5 = ["html5lib"]
+htmlsoup = ["BeautifulSoup4"]
+source = ["Cython (>=3.0.11)"]
+
 [[package]]
 name = "markdown-it-py"
 version = "3.0.0"
@@ -3066,6 +3241,8 @@ files = [
     {file = "psutil-6.1.1-cp27-cp27m-manylinux2010_x86_64.whl", hash = "sha256:8df0178ba8a9e5bc84fed9cfa61d54601b371fbec5c8eebad27575f1e105c0d4"},
     {file = "psutil-6.1.1-cp27-cp27mu-manylinux2010_i686.whl", hash = "sha256:1924e659d6c19c647e763e78670a05dbb7feaf44a0e9c94bf9e14dfc6ba50468"},
     {file = "psutil-6.1.1-cp27-cp27mu-manylinux2010_x86_64.whl", hash = "sha256:018aeae2af92d943fdf1da6b58665124897cfc94faa2ca92098838f83e1b1bca"},
+    {file = "psutil-6.1.1-cp27-none-win32.whl", hash = "sha256:6d4281f5bbca041e2292be3380ec56a9413b790579b8e593b1784499d0005dac"},
+    {file = "psutil-6.1.1-cp27-none-win_amd64.whl", hash = "sha256:c777eb75bb33c47377c9af68f30e9f11bc78e0f07fbf907be4a5d70b2fe5f030"},
     {file = "psutil-6.1.1-cp36-abi3-macosx_10_9_x86_64.whl", hash = "sha256:fc0ed7fe2231a444fc219b9c42d0376e0a9a1a72f16c5cfa0f68d19f1a0663e8"},
     {file = "psutil-6.1.1-cp36-abi3-macosx_11_0_arm64.whl", hash = "sha256:0bdd4eab935276290ad3cb718e9809412895ca6b5b334f5a9111ee6d9aff9377"},
     {file = "psutil-6.1.1-cp36-abi3-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:b6e06c20c05fe95a3d7302d74e7097756d4ba1247975ad6905441ae1b5b66003"},
@@ -5306,4 +5483,4 @@ propcache = ">=0.2.0"
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.10"
-content-hash = "8118fd027892740419e08b6c5e0c1ff1231f6dc5ccd1b20e82d5ea2de358cb59"
+content-hash = "14d7c7764a6152887a987d8e1790e1e271d137ab88f5952e3043a45806334b6e"
diff --git a/pyproject.toml b/pyproject.toml
index dc966837..7be48f06 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -52,6 +52,9 @@ uvicorn = "^0.32.0"
 python-multipart = "^0.0.16"
 pytest = "^8.3.3"
 pytest-mock = "^3.14.0"
+apted = "1.0.3"
+distance = "0.1.3"
+lxml = "5.3.0"
 
 [tool.poetry.scripts]
 marker = "convert:main"

From ebbb8e71ecfefe3d65750b74e32556e6abd993d6 Mon Sep 17 00:00:00 2001
From: Vik Paruchuri 
Date: Fri, 10 Jan 2025 14:15:34 -0500
Subject: [PATCH 28/92] Refactor image handling

---
 marker/builders/layout.py                     |  2 +-
 marker/builders/llm_layout.py                 | 25 ++++++++-----------
 marker/builders/ocr.py                        |  4 +--
 marker/processors/debug.py                    |  4 +--
 marker/processors/equation.py                 |  3 +--
 marker/processors/llm/__init__.py             |  9 ++-----
 marker/processors/llm/llm_complex.py          |  2 +-
 marker/processors/llm/llm_form.py             |  2 +-
 .../processors/llm/llm_image_description.py   |  2 +-
 marker/processors/llm/llm_table.py            |  2 +-
 marker/processors/llm/llm_text.py             |  2 +-
 marker/processors/table.py                    |  8 +++---
 marker/renderers/__init__.py                  | 16 ++++++------
 marker/renderers/html.py                      |  9 +------
 marker/renderers/markdown.py                  |  4 +++
 marker/schema/blocks/base.py                  | 20 ++++++++++++++-
 marker/schema/groups/page.py                  |  3 +++
 17 files changed, 63 insertions(+), 54 deletions(-)

diff --git a/marker/builders/layout.py b/marker/builders/layout.py
index a590a7cd..f712a500 100644
--- a/marker/builders/layout.py
+++ b/marker/builders/layout.py
@@ -71,7 +71,7 @@ def get_batch_size(self):
 
     def surya_layout(self, pages: List[PageGroup]) -> List[LayoutResult]:
         layout_results = self.layout_model(
-            [p.lowres_image for p in pages],
+            [p.get_image(highres=False) for p in pages],
             batch_size=int(self.get_batch_size())
         )
         return layout_results
diff --git a/marker/builders/llm_layout.py b/marker/builders/llm_layout.py
index 4ed75a1c..51db83e2 100644
--- a/marker/builders/llm_layout.py
+++ b/marker/builders/llm_layout.py
@@ -114,10 +114,10 @@ def relabel_blocks(self, document: Document):
                         confidence = block.top_k.get(block.block_type)
                         # Case when the block is detected as a different type with low confidence
                         if confidence < self.confidence_threshold:
-                            futures.append(executor.submit(self.process_block_topk_relabeling, page, block))
+                            futures.append(executor.submit(self.process_block_topk_relabeling, document, page, block))
                         # Case when the block is detected as a picture or figure, but is actually complex
                         elif block.block_type in (BlockTypes.Picture, BlockTypes.Figure, BlockTypes.SectionHeader) and block.polygon.height > page.polygon.height * self.picture_height_threshold:
-                            futures.append(executor.submit(self.process_block_complex_relabeling, page, block))
+                            futures.append(executor.submit(self.process_block_complex_relabeling, document, page, block))
 
             for future in as_completed(futures):
                 future.result()  # Raise exceptions if any occurred
@@ -125,18 +125,18 @@ def relabel_blocks(self, document: Document):
 
         pbar.close()
 
-    def process_block_topk_relabeling(self, page: PageGroup, block: Block):
+    def process_block_topk_relabeling(self, document: Document, page: PageGroup, block: Block):
         topk = {str(k): round(v, 3) for k, v in block.top_k.items()}
 
         prompt = self.topk_relabelling_prompt + '```json' + json.dumps(topk) + '```\n'
-        return self.process_block_relabeling(page, block, prompt)
+        return self.process_block_relabeling(document, page, block, prompt)
 
-    def process_block_complex_relabeling(self, page: PageGroup, block: Block):
+    def process_block_complex_relabeling(self, document: Document, page: PageGroup, block: Block):
         complex_prompt = self.complex_relabeling_prompt
-        return self.process_block_relabeling(page, block, complex_prompt)
+        return self.process_block_relabeling(document, page, block, complex_prompt)
 
-    def process_block_relabeling(self, page: PageGroup, block: Block, prompt: str):
-        image = self.extract_image(page, block)
+    def process_block_relabeling(self, document: Document, page: PageGroup, block: Block, prompt: str):
+        image = self.extract_image(document, block)
         response_schema = content.Schema(
             type=content.Type.OBJECT,
             enum=[],
@@ -162,10 +162,5 @@ def process_block_relabeling(self, page: PageGroup, block: Block, prompt: str):
             )
             page.replace_block(block, generated_block)
 
-    def extract_image(self, page: PageGroup, image_block: Block, expand: float = 0.01):
-        page_img = page.lowres_image
-        image_box = image_block.polygon\
-            .rescale(page.polygon.size, page_img.size)\
-            .expand(expand, expand)
-        cropped = page_img.crop(image_box.bbox)
-        return cropped
+    def extract_image(self, document: Document, image_block: Block, expand: float = 0.01):
+        return image_block.get_image(document, highres=False, expansion=(expand, expand))
diff --git a/marker/builders/ocr.py b/marker/builders/ocr.py
index fbfb6e0e..7ef4744c 100644
--- a/marker/builders/ocr.py
+++ b/marker/builders/ocr.py
@@ -65,12 +65,12 @@ def get_detection_batch_size(self):
     def ocr_extraction(self, document: Document, provider: PdfProvider) -> ProviderPageLines:
         page_list = [page for page in document.pages if page.text_extraction_method == "surya"]
         recognition_results = self.recognition_model(
-            images=[page.lowres_image for page in page_list],
+            images=[page.get_image(highres=False) for page in page_list],
             langs=[self.languages] * len(page_list),
             det_predictor=self.detection_model,
             detection_batch_size=int(self.get_detection_batch_size()),
             recognition_batch_size=int(self.get_recognition_batch_size()),
-            highres_images=[page.highres_image for page in page_list]
+            highres_images=[page.get_image(highres=True) for page in page_list]
         )
 
         page_lines = {}
diff --git a/marker/processors/debug.py b/marker/processors/debug.py
index d613e465..4fe87601 100644
--- a/marker/processors/debug.py
+++ b/marker/processors/debug.py
@@ -68,7 +68,7 @@ def __call__(self, document: Document):
 
     def draw_pdf_debug_images(self, document: Document):
         for page in document.pages:
-            png_image = page.highres_image.copy()
+            png_image = page.get_image(highres=True).copy()
 
             line_bboxes = []
             span_bboxes = []
@@ -90,7 +90,7 @@ def draw_pdf_debug_images(self, document: Document):
 
     def draw_layout_debug_images(self, document: Document, pdf_mode=False):
         for page in document.pages:
-            img_size = page.highres_image.size
+            img_size = page.get_image(highres=True).size
             png_image = Image.new("RGB", img_size, color="white")
 
             line_bboxes = []
diff --git a/marker/processors/equation.py b/marker/processors/equation.py
index 82243a2a..d2481944 100644
--- a/marker/processors/equation.py
+++ b/marker/processors/equation.py
@@ -43,8 +43,7 @@ def __call__(self, document: Document):
 
         for page in document.pages:
             for block in page.contained_blocks(document, self.block_types):
-                image_poly = block.polygon.rescale((page.polygon.width, page.polygon.height), page.lowres_image.size)
-                image = page.lowres_image.crop(image_poly.bbox).convert("RGB")
+                image = block.get_image(document, highres=False).convert("RGB")
                 raw_text = block.raw_text(document)
                 token_count = self.get_total_texify_tokens(raw_text)
 
diff --git a/marker/processors/llm/__init__.py b/marker/processors/llm/__init__.py
index 57ede428..54b27207 100644
--- a/marker/processors/llm/__init__.py
+++ b/marker/processors/llm/__init__.py
@@ -84,10 +84,5 @@ def rewrite_blocks(self, document: Document):
 
         pbar.close()
 
-    def extract_image(self, page: PageGroup, image_block: Block):
-        page_img = page.lowres_image
-        image_box = image_block.polygon\
-            .rescale(page.polygon.size, page_img.size)\
-            .expand(self.image_expansion_ratio, self.image_expansion_ratio)
-        cropped = page_img.crop(image_box.bbox)
-        return cropped
+    def extract_image(self, document: Document, image_block: Block):
+        return image_block.get_image(document, highres=False, expansion=(self.image_expansion_ratio, self.image_expansion_ratio))
diff --git a/marker/processors/llm/llm_complex.py b/marker/processors/llm/llm_complex.py
index 2ac5dab0..ffc2cac6 100644
--- a/marker/processors/llm/llm_complex.py
+++ b/marker/processors/llm/llm_complex.py
@@ -49,7 +49,7 @@ def process_rewriting(self, document: Document, page: PageGroup, block: Block):
         text = block.raw_text(document)
 
         prompt = self.gemini_rewriting_prompt + '```text\n`' + text + '`\n```\n'
-        image = self.extract_image(page, block)
+        image = self.extract_image(document, block)
         response_schema = content.Schema(
             type=content.Type.OBJECT,
             enum=[],
diff --git a/marker/processors/llm/llm_form.py b/marker/processors/llm/llm_form.py
index 77fca14c..62f051fd 100644
--- a/marker/processors/llm/llm_form.py
+++ b/marker/processors/llm/llm_form.py
@@ -50,7 +50,7 @@ def process_rewriting(self, document: Document, page: PageGroup, block: Block):
         table_md = markdown2.markdown(block_html)
 
         prompt = self.gemini_rewriting_prompt + '```markdown\n`' + table_md + '`\n```\n'
-        image = self.extract_image(page, block)
+        image = self.extract_image(document, block)
         response_schema = content.Schema(
             type=content.Type.OBJECT,
             enum=[],
diff --git a/marker/processors/llm/llm_image_description.py b/marker/processors/llm/llm_image_description.py
index 90547f62..80e89e8e 100644
--- a/marker/processors/llm/llm_image_description.py
+++ b/marker/processors/llm/llm_image_description.py
@@ -45,7 +45,7 @@ def process_rewriting(self, document: Document, page: PageGroup, block: Block):
             return
 
         prompt = self.image_description_prompt + '```text\n`' + block.raw_text(document) + '`\n```\n'
-        image = self.extract_image(page, block)
+        image = self.extract_image(document, block)
         response_schema = content.Schema(
             type=content.Type.OBJECT,
             enum=[],
diff --git a/marker/processors/llm/llm_table.py b/marker/processors/llm/llm_table.py
index 25565b6f..81658ffc 100644
--- a/marker/processors/llm/llm_table.py
+++ b/marker/processors/llm/llm_table.py
@@ -61,7 +61,7 @@ def process_rewriting(self, document: Document, page: PageGroup, block: Block):
         block_html = block.render(document).html
 
         prompt = self.gemini_rewriting_prompt + '```html\n`' + block_html + '`\n```\n'
-        image = self.extract_image(page, block)
+        image = self.extract_image(document, block)
         response_schema = content.Schema(
             type=content.Type.OBJECT,
             enum=[],
diff --git a/marker/processors/llm/llm_text.py b/marker/processors/llm/llm_text.py
index 33862eda..9796a653 100644
--- a/marker/processors/llm/llm_text.py
+++ b/marker/processors/llm/llm_text.py
@@ -74,7 +74,7 @@ def process_rewriting(self, document: Document, page: PageGroup, block: Block):
         extracted_lines = [line.formatted_text(document) for line in text_lines]
 
         prompt = self.gemini_rewriting_prompt + '```json\n`' + json.dumps({"extracted_lines": extracted_lines}, indent=2) + '`\n```\n'
-        image = self.extract_image(page, block)
+        image = self.extract_image(document, block)
         response_schema = content.Schema(
             type=content.Type.OBJECT,
             enum=[],
diff --git a/marker/processors/table.py b/marker/processors/table.py
index 6c9e8783..a7d586ac 100644
--- a/marker/processors/table.py
+++ b/marker/processors/table.py
@@ -62,15 +62,15 @@ def __call__(self, document: Document):
         table_data = []
         for page in document.pages:
             for block in page.contained_blocks(document, self.block_types):
-                image_poly = block.polygon.rescale((page.polygon.width, page.polygon.height), page.highres_image.size).expand(.01, .01)
-                image = page.highres_image.crop(image_poly.bbox).convert("RGB")
+                image = block.get_image(document, highres=True, expansion=(.01, .01))
+                image_poly = block.polygon.rescale((page.polygon.width, page.polygon.height), page.get_image(highres=True).size)
 
                 table_data.append({
                     "block_id": block.id,
                     "page_id": page.page_id,
                     "table_image": image,
                     "table_bbox": image_poly.bbox,
-                    "img_size": page.highres_image.size,
+                    "img_size": page.get_image(highres=True).size,
                     "ocr_block": page.text_extraction_method == "surya",
                 })
 
@@ -95,7 +95,7 @@ def __call__(self, document: Document):
                 cells: List[SuryaTableCell] = tables[table_idx].cells
                 for cell in cells:
                     # Rescale the cell polygon to the page size
-                    cell_polygon = PolygonBox(polygon=cell.polygon).rescale(page.highres_image.size, page.polygon.size)
+                    cell_polygon = PolygonBox(polygon=cell.polygon).rescale(page.get_image(highres=True).size, page.polygon.size)
                     cell_block = TableCell(
                         polygon=cell_polygon,
                         text=cell.text or "", # Cells can be blank (no text)
diff --git a/marker/renderers/__init__.py b/marker/renderers/__init__.py
index d0166c9d..5c3852d6 100644
--- a/marker/renderers/__init__.py
+++ b/marker/renderers/__init__.py
@@ -2,7 +2,7 @@
 import io
 import re
 from collections import Counter
-from typing import Annotated, Optional, Tuple
+from typing import Annotated, Optional, Tuple, Literal
 
 from bs4 import BeautifulSoup
 from pydantic import BaseModel
@@ -18,6 +18,11 @@ class BaseRenderer:
     remove_blocks: Annotated[Tuple[BlockTypes, ...], "The block types to ignore while rendering."] = (BlockTypes.PageHeader, BlockTypes.PageFooter)
     image_blocks: Annotated[Tuple[BlockTypes, ...], "The block types to consider as images."] = (BlockTypes.Picture, BlockTypes.Figure)
     extract_images: Annotated[bool, "Extract images from the document."] = True
+    image_extraction_mode: Annotated[
+        Literal["lowres", "highres"],
+        "The mode to use for extracting images.",
+    ] = "highres"
+
 
     def __init__(self, config: Optional[BaseModel | dict] = None):
         assign_config(self, config)
@@ -26,13 +31,10 @@ def __call__(self, document):
         # Children are in reading order
         raise NotImplementedError
 
-    @staticmethod
-    def extract_image(document: Document, image_id, to_base64=False):
+    def extract_image(self, document: Document, image_id, to_base64=False):
         image_block = document.get_block(image_id)
-        page = document.get_page(image_block.page_id)
-        page_img = page.highres_image
-        image_box = image_block.polygon.rescale(page.polygon.size, page_img.size)
-        cropped = page_img.crop(image_box.bbox)
+        cropped = image_block.get_image(document, highres=self.image_extraction_mode == "highres")
+
         if to_base64:
             image_buffer = io.BytesIO()
             cropped.save(image_buffer, format=settings.OUTPUT_IMAGE_FORMAT)
diff --git a/marker/renderers/html.py b/marker/renderers/html.py
index f01ef315..4f3a27c7 100644
--- a/marker/renderers/html.py
+++ b/marker/renderers/html.py
@@ -35,17 +35,10 @@ class HTMLRenderer(BaseRenderer):
         bool,
         "Whether to paginate the output.",
     ] = False
-    image_extraction_mode: Annotated[
-        Literal["lowres", "highres"],
-        "The mode to use for extracting images.",
-    ] = "highres"
 
     def extract_image(self, document, image_id):
         image_block = document.get_block(image_id)
-        page = document.get_page(image_block.page_id)
-        page_img = page.lowres_image if self.image_extraction_mode == "lowres" else page.highres_image
-        image_box = image_block.polygon.rescale(page.polygon.size, page_img.size)
-        cropped = page_img.crop(image_box.bbox)
+        cropped = image_block.get_image(document, highres=self.image_extraction_mode == "highres")
         return cropped
 
     def extract_html(self, document, document_output, level=0):
diff --git a/marker/renderers/markdown.py b/marker/renderers/markdown.py
index 2944d861..aa45294c 100644
--- a/marker/renderers/markdown.py
+++ b/marker/renderers/markdown.py
@@ -80,6 +80,10 @@ def convert_table(self, el, text, convert_as_inline):
                 rowspan = int(cell.get('rowspan', 1))
                 colspan = int(cell.get('colspan', 1))
 
+                if col_idx >= total_cols:
+                    # Skip this cell if we're out of bounds
+                    continue
+
                 for r in range(rowspan):
                     for c in range(colspan):
                         if r == 0 and c == 0:
diff --git a/marker/schema/blocks/base.py b/marker/schema/blocks/base.py
index c66e80a6..04171dab 100644
--- a/marker/schema/blocks/base.py
+++ b/marker/schema/blocks/base.py
@@ -1,8 +1,9 @@
 from __future__ import annotations
 
-from typing import TYPE_CHECKING, Dict, List, Literal, Optional, Sequence
+from typing import TYPE_CHECKING, Dict, List, Literal, Optional, Sequence, Tuple
 
 from pydantic import BaseModel, ConfigDict, field_validator
+from PIL import Image
 
 from marker.schema import BlockTypes
 from marker.schema.polygon import PolygonBox
@@ -79,6 +80,8 @@ class Block(BaseModel):
     source: Literal['layout', 'heuristics', 'processor'] = 'layout'
     top_k: Optional[Dict[BlockTypes, float]] = None
     metadata: BlockMetadata | None = None
+    lowres_image: Image.Image | None = None
+    highres_image: Image.Image | None = None
 
     model_config = ConfigDict(arbitrary_types_allowed=True)
 
@@ -95,6 +98,21 @@ def from_block(cls, block: Block) -> Block:
         block_attrs = block.model_dump(exclude=["id", "block_id", "block_type"])
         return cls(**block_attrs)
 
+    def get_image(self, document: Document, highres: bool = False, expansion: Tuple[float, float] | None = None) -> Image.Image | None:
+        image = self.highres_image if highres else self.lowres_image
+        if image is None:
+            page = document.get_page(self.page_id)
+            page_image = page.highres_image if highres else page.lowres_image
+
+            # Scale to the image size
+            bbox = self.polygon.rescale((page.polygon.width, page.polygon.height), page_image.size)
+            if expansion:
+                bbox = bbox.expand(*expansion)
+            bbox = bbox.bbox
+            image = page_image.crop(bbox)
+        return image
+
+
     def structure_blocks(self, document_page: Document | PageGroup) -> List[Block]:
         if self.structure is None:
             return []
diff --git a/marker/schema/groups/page.py b/marker/schema/groups/page.py
index 6eaf2511..389584f7 100644
--- a/marker/schema/groups/page.py
+++ b/marker/schema/groups/page.py
@@ -35,6 +35,9 @@ def add_child(self, block: Block):
         else:
             self.children.append(block)
 
+    def get_image(self, *args, highres: bool = False, **kwargs):
+        return self.highres_image if highres else self.lowres_image
+
     def get_next_block(self, block: Optional[Block] = None, ignored_block_types: Optional[List[BlockTypes]] = None):
         if ignored_block_types is None:
             ignored_block_types = []

From 2e48bd8813f2b9e1276ed8efc4bb18efbb36cbb3 Mon Sep 17 00:00:00 2001
From: Moses Paul R 
Date: Fri, 10 Jan 2025 19:20:10 +0000
Subject: [PATCH 29/92] fix bugs and remove defaults

---
 marker/config/printer.py | 1 -
 marker/providers/pdf.py  | 2 ++
 2 files changed, 2 insertions(+), 1 deletion(-)

diff --git a/marker/config/printer.py b/marker/config/printer.py
index e9b37b42..1f4991f8 100644
--- a/marker/config/printer.py
+++ b/marker/config/printer.py
@@ -44,7 +44,6 @@ def parse_args(self, ctx, args):
                                 options,
                                 type=attr_type,
                                 help=" ".join(metadata),
-                                default=default,
                                 is_flag=is_flag,
                             )
                         )
diff --git a/marker/providers/pdf.py b/marker/providers/pdf.py
index 5b73f9d0..ba8dbdb6 100644
--- a/marker/providers/pdf.py
+++ b/marker/providers/pdf.py
@@ -282,6 +282,8 @@ def merge_refs(self, page):
             return
 
         spans = [span for block in page['blocks'] for line in block['lines'] for span in line['spans'] if span['text']]
+        if not spans:
+            return
 
         span_starts = np.array([span['bbox'][:2] for span in spans])
         ref_pos = np.array([ref for ref in refs])

From 662b3b37be0acc536009856b8a8f39c66d129390 Mon Sep 17 00:00:00 2001
From: Vik Paruchuri 
Date: Fri, 10 Jan 2025 18:12:27 -0500
Subject: [PATCH 30/92] Merge tables across pages

---
 marker/builders/llm_layout.py            |   2 +-
 marker/config/printer.py                 |  28 ++-
 marker/converters/pdf.py                 |   2 +
 marker/converters/table.py               |   3 +-
 marker/processors/llm/__init__.py        |   1 +
 marker/processors/llm/llm_complex.py     |   1 +
 marker/processors/llm/llm_form.py        |  75 +++++---
 marker/processors/llm/llm_table.py       |  16 +-
 marker/processors/llm/llm_table_merge.py | 227 +++++++++++++++++++++++
 marker/processors/llm/utils.py           |   7 +-
 10 files changed, 316 insertions(+), 46 deletions(-)

diff --git a/marker/builders/llm_layout.py b/marker/builders/llm_layout.py
index 51db83e2..0b574b76 100644
--- a/marker/builders/llm_layout.py
+++ b/marker/builders/llm_layout.py
@@ -30,7 +30,7 @@ class LLMLayoutBuilder(LayoutBuilder):
     confidence_threshold: Annotated[
         float,
         "The confidence threshold to use for relabeling.",
-    ] = 0.75
+    ] = 0.7
     picture_height_threshold: Annotated[
         float,
         "The height threshold for pictures that may actually be complex regions.",
diff --git a/marker/config/printer.py b/marker/config/printer.py
index 726cff74..6902b836 100644
--- a/marker/config/printer.py
+++ b/marker/config/printer.py
@@ -6,18 +6,20 @@
 
 
 class CustomClickPrinter(click.Command):
-    def get_help(self, ctx):
-        additional_help = (
-            "\n\nTip: Use 'config --help' to display all the attributes of the Builders, Processors, and Converters in Marker."
-        )
-        help_text = super().get_help(ctx)
-        help_text = help_text + additional_help
-        click.echo(help_text)
-
     def parse_args(self, ctx, args):
         display_help = 'config' in args and '--help' in args
         if display_help:
-            click.echo("Here is a list of all the Builders, Processors, Converters, Providers and Renderers in Marker along with their attributes:")
+            click.echo(
+                "Here is a list of all the Builders, Processors, Converters, Providers and Renderers in Marker along with their attributes:")
+
+        shared_attrs = {}
+
+        for base_type, base_type_dict in crawler.class_config_map.items():
+            for class_name, class_map in base_type_dict.items():
+                for attr in class_map['config'].keys():
+                    if attr not in shared_attrs:
+                        shared_attrs[attr] = []
+                    shared_attrs[attr].append(class_name)
 
         for base_type, base_type_dict in crawler.class_config_map.items():
             if display_help:
@@ -32,9 +34,15 @@ def parse_args(self, ctx, args):
                     if display_help:
                         click.echo(" " * 8 + f"{attr} ({formatted_type}):")
                         click.echo("\n".join([f'{" " * 12}' + desc for desc in metadata]))
+
                     if attr_type in [str, int, float, bool, Optional[int], Optional[float], Optional[str]]:
                         is_flag = attr_type in [bool, Optional[bool]] and not default
-                        options = ["--" + attr, "--" + class_name_attr, class_name_attr]
+
+                        # Only include the generic --attr option if it's unique
+                        options = ["--" + class_name_attr, class_name_attr]
+                        if len(shared_attrs[attr]) == 1:
+                            options.insert(0, "--" + attr)
+
                         ctx.command.params.append(
                             click.Option(
                                 options,
diff --git a/marker/converters/pdf.py b/marker/converters/pdf.py
index a31bdf5a..d1fdb3ad 100644
--- a/marker/converters/pdf.py
+++ b/marker/converters/pdf.py
@@ -1,6 +1,7 @@
 import os
 
 from marker.processors import BaseProcessor
+from marker.processors.llm.llm_table_merge import LLMTableMergeProcessor
 from marker.providers.registry import provider_from_filepath
 
 os.environ["TOKENIZERS_PARALLELISM"] = "false"  # disables a tokenizers warning
@@ -68,6 +69,7 @@ class PdfConverter(BaseConverter):
         PageHeaderProcessor,
         SectionHeaderProcessor,
         TableProcessor,
+        LLMTableMergeProcessor,
         LLMTableProcessor,
         LLMFormProcessor,
         TextProcessor,
diff --git a/marker/converters/table.py b/marker/converters/table.py
index 61804f5d..dfeabd48 100644
--- a/marker/converters/table.py
+++ b/marker/converters/table.py
@@ -7,8 +7,8 @@
 from marker.processors.llm.llm_complex import LLMComplexRegionProcessor
 from marker.processors.llm.llm_form import LLMFormProcessor
 from marker.processors.llm.llm_table import LLMTableProcessor
+from marker.processors.llm.llm_table_merge import LLMTableMergeProcessor
 from marker.processors.table import TableProcessor
-from marker.providers.pdf import PdfProvider
 from marker.providers.registry import provider_from_filepath
 from marker.schema import BlockTypes
 
@@ -16,6 +16,7 @@
 class TableConverter(PdfConverter):
     default_processors: Tuple[BaseProcessor, ...] = (
         TableProcessor,
+        LLMTableMergeProcessor,
         LLMTableProcessor,
         LLMFormProcessor,
         LLMComplexRegionProcessor,
diff --git a/marker/processors/llm/__init__.py b/marker/processors/llm/__init__.py
index 54b27207..8cf6bede 100644
--- a/marker/processors/llm/__init__.py
+++ b/marker/processors/llm/__init__.py
@@ -1,3 +1,4 @@
+import traceback
 from concurrent.futures import ThreadPoolExecutor, as_completed
 from typing import Annotated, Optional
 
diff --git a/marker/processors/llm/llm_complex.py b/marker/processors/llm/llm_complex.py
index ffc2cac6..0f8cc8fa 100644
--- a/marker/processors/llm/llm_complex.py
+++ b/marker/processors/llm/llm_complex.py
@@ -79,4 +79,5 @@ def process_rewriting(self, document: Document, page: PageGroup, block: Block):
             return
 
         # Convert LLM markdown to html
+        corrected_markdown = corrected_markdown.strip().lstrip("```markdown").rstrip("```").strip()
         block.html = markdown2.markdown(corrected_markdown)
\ No newline at end of file
diff --git a/marker/processors/llm/llm_form.py b/marker/processors/llm/llm_form.py
index 62f051fd..96f0ed35 100644
--- a/marker/processors/llm/llm_form.py
+++ b/marker/processors/llm/llm_form.py
@@ -13,29 +13,51 @@
 class LLMFormProcessor(BaseLLMProcessor):
     block_types = (BlockTypes.Form,)
     gemini_rewriting_prompt = """You are a text correction expert specializing in accurately reproducing text from images.
-You will receive an image of a text block and a markdown representation of the form in the image.
-Your task is to correct any errors in the markdown representation, and format it properly.
-Values and labels should appear in markdown tables, with the labels on the left side, and values on the right.  The headers should be "Labels" and "Values".  Other text in the form can appear between the tables.
+You will receive an image of a text block and an html representation of the form in the image.
+Your task is to correct any errors in the htmlrepresentation, and format it properly.
+Values and labels should appear in html tables, with the labels on the left side, and values on the right.  The headers should be "Labels" and "Values".  Other text in the form can appear between the tables.  Only use the tags `table, p, span, i, b, th, td, tr, and div`.
 **Instructions:**
 1. Carefully examine the provided form block image.
-2. Analyze the markdown representation of the form.
-3. If the markdown representation is largely correct, then write "No corrections needed."
-4. If the markdown representation contains errors, generate the corrected markdown representation.
-5. Output only either the corrected markdown representation or "No corrections needed."
+2. Analyze the html representation of the form.
+3. If the html representation is largely correct, then write "No corrections needed."
+4. If the html representation contains errors, generate the corrected html representation.
+5. Output only either the corrected html representation or "No corrections needed."
 **Example:**
 Input:
-```markdown
-| Label 1 | Label 2 | Label 3 |
-|----------|----------|----------|
-| Value 1  | Value 2  | Value 3  |
+```html
+
+    
+        
+        
+        
+    
+    
+        
+        
+        
+    
+
Label 1Label 2Label 3
Value 1Value 2Value 3
 
 ```
 Output:
-```markdown
-| Labels | Values |
-|--------|--------|
-| Label 1 | Value 1 |
-| Label 2 | Value 2 |
-| Label 3 | Value 3 |
+```html
+
+    
+        
+        
+    
+    
+        
+        
+    
+    
+        
+        
+    
+    
+        
+        
+    
+
LabelsValues
Label 1Value 1
Label 2Value 2
Label 3Value 3

 ```
 **Input:**
 """
@@ -47,16 +69,15 @@ def process_rewriting(self, document: Document, page: PageGroup, block: Block):
             return
 
         block_html = block.render(document).html
-        table_md = markdown2.markdown(block_html)
 
-        prompt = self.gemini_rewriting_prompt + '```markdown\n`' + table_md + '`\n```\n'
+        prompt = self.gemini_rewriting_prompt + '```html\n`' + block_html + '`\n```\n'
         image = self.extract_image(document, block)
         response_schema = content.Schema(
             type=content.Type.OBJECT,
             enum=[],
-            required=["corrected_markdown"],
+            required=["corrected_html"],
             properties={
-                "corrected_markdown": content.Schema(
+                "corrected_html": content.Schema(
                     type=content.Type.STRING
                 )
             },
@@ -64,20 +85,20 @@ def process_rewriting(self, document: Document, page: PageGroup, block: Block):
 
         response = self.model.generate_response(prompt, image, block, response_schema)
 
-        if not response or "corrected_markdown" not in response:
+        if not response or "corrected_html" not in response:
             block.update_metadata(llm_error_count=1)
             return
 
-        corrected_markdown = response["corrected_markdown"]
+        corrected_html = response["corrected_html"]
 
         # The original table is okay
-        if "no corrections" in corrected_markdown.lower():
+        if "no corrections" in corrected_html.lower():
             return
 
         # Potentially a partial response
-        if len(corrected_markdown) < len(table_md) * .33:
+        if len(corrected_html) < len(block_html) * .33:
             block.update_metadata(llm_error_count=1)
             return
 
-        # Convert LLM markdown to html
-        block.html = markdown2.markdown(corrected_markdown)
\ No newline at end of file
+        corrected_html = corrected_html.strip().lstrip("```html").rstrip("```").strip()
+        block.html = corrected_html
\ No newline at end of file
diff --git a/marker/processors/llm/llm_table.py b/marker/processors/llm/llm_table.py
index 81658ffc..558cd672 100644
--- a/marker/processors/llm/llm_table.py
+++ b/marker/processors/llm/llm_table.py
@@ -15,7 +15,7 @@ class LLMTableProcessor(BaseLLMProcessor):
     block_types: Annotated[
         Tuple[BlockTypes],
         "The block types to process.",
-    ] = (BlockTypes.Table,)
+    ] = (BlockTypes.Table, BlockTypes.TableOfContents)
     gemini_rewriting_prompt: Annotated[
         str,
         "The prompt to use for rewriting text.",
@@ -85,7 +85,8 @@ def process_rewriting(self, document: Document, page: PageGroup, block: Block):
         if "no corrections" in corrected_html.lower():
             return
 
-        parsed_cells = self.parse_html_table(corrected_html, block)
+        corrected_html = corrected_html.strip().lstrip("```html").rstrip("```").strip()
+        parsed_cells = self.parse_html_table(corrected_html, block, page)
         if len(parsed_cells) <= 1:
             block.update_metadata(llm_error_count=1)
             return
@@ -97,9 +98,13 @@ def process_rewriting(self, document: Document, page: PageGroup, block: Block):
             block.update_metadata(llm_error_count=1)
             return
 
-        block.cells = parsed_cells
+        block.structure = []
+        for cell in parsed_cells:
+            page.add_full_block(cell)
+            block.add_structure(cell)
 
-    def parse_html_table(self, html_text: str, block: Block) -> List[TableCell]:
+
+    def parse_html_table(self, html_text: str, block: Block, page: PageGroup) -> List[TableCell]:
         soup = BeautifulSoup(html_text, 'html.parser')
         table = soup.find('table')
 
@@ -152,7 +157,8 @@ def parse_html_table(self, html_text: str, block: Block) -> List[TableCell]:
                     rowspan=rowspan,
                     colspan=colspan,
                     is_header=cell.name == 'th',
-                    polygon=cell_polygon
+                    polygon=cell_polygon,
+                    page_id=page.page_id,
                 )
                 cells.append(cell_obj)
                 cur_col += colspan
diff --git a/marker/processors/llm/llm_table_merge.py b/marker/processors/llm/llm_table_merge.py
index e69de29b..bf62c423 100644
--- a/marker/processors/llm/llm_table_merge.py
+++ b/marker/processors/llm/llm_table_merge.py
@@ -0,0 +1,227 @@
+from concurrent.futures import ThreadPoolExecutor, as_completed
+from typing import Annotated, List, Tuple, Literal
+
+from google.ai.generativelanguage_v1beta.types import content
+from tqdm import tqdm
+from PIL import Image
+
+from marker.processors.llm import BaseLLMProcessor
+from marker.schema import BlockTypes
+from marker.schema.blocks import Block, TableCell
+from marker.schema.document import Document
+
+
+class LLMTableMergeProcessor(BaseLLMProcessor):
+    block_types: Annotated[
+        Tuple[BlockTypes],
+        "The block types to process.",
+    ] = (BlockTypes.Table, BlockTypes.TableOfContents)
+    table_height_threshold: Annotated[
+        float,
+        "The minimum height ratio relative to the page for the first table in a pair to be considered for merging.",
+    ] = 0.6
+    table_start_threshold: Annotated[
+        float,
+        "The maximum percentage down the page the second table can start to be considered for merging."
+    ] = 0.2
+    gemini_table_merge_prompt: Annotated[
+        str,
+        "The prompt to use for rewriting text.",
+        "Default is a string containing the Gemini rewriting prompt."
+    ] = """You're a text correction expert specializing in accurately reproducing tables from PDFs.
+You'll receive two images of tables from successive pages of a PDF.  Table 1 is from the first page, and Table 2 is from the second page.  Both tables may actually be part of the same larger table. Your job is to decide if Table 2 should be merged with Table 1, and how they should be joined.  The should only be merged if they're both part of the same larger table.
+
+You'll specify your judgement in json format - first whether Table 2 should be merged with Table 1, then the direction of the merge, either bottom or right.  Table 2 should be merged at the bottom of Table 1 if they have similar headers, and the rows have similar values.  Table2  should be merged to the right of Table 1 if each row in Table 2 matches a row in Table 1.
+**Instructions:**
+1. Carefully examine the provided table images.  Table 1 is the first image, and Table 2 is the second image.
+2. Examine the provided html representations of Table 1 and Table 2.
+3. Write a description of Table 1.
+4. Write a description of Table 2.
+5. Analyze whether Table 2 should be merged into Table 1, and write an explanation.
+6. Output your decision on whether they should be merged, and merge direction.
+**Example:**
+Input:
+Table 1
+```html
+
+    
+        
+        
+        
+        
+    
+    
+        
+        
+        
+        
+    
+```
+Table 2
+```html
+
NameAgeCityState
John25ChicagoIL

+    
+        
+        
+        
+        
+    
+    
+        
+        
+        
+        
+    
+```
+Output:
+```json
+{
+    "table1_description": "The first table has 4 headers, and 1 row.  The headers are Name, Age, City, and State.",
+    "table2_description": "The second table has 4 headers, and 1 row.  The headers are Name, Age, City, and State.",
+    "explanation": "The tables should be merged, as they have the same headers.  The second table should be merged to the bottom of the first table.",
+    "merge": "true",
+    "direction": "bottom"
+}
+```
+**Input:**
+Table 1
+```html
+{{table1}}
+Table 2
+```html
+{{table2}}
+```
+"""
+
+    def rewrite_blocks(self, document: Document):
+        pbar = tqdm(desc=f"{self.__class__.__name__} running")
+        table_runs = []
+        table_run = []
+        prev_block = None
+        for page in document.pages:
+            for block in page.contained_blocks(document, self.block_types):
+                if prev_block is not None and \
+                    prev_block.page_id == block.page_id - 1 and \
+                    max(prev_block.polygon.height / page.polygon.height, block.polygon.height / page.polygon.height) > self.table_height_threshold and\
+                    block.polygon.y_start / page.polygon.height < self.table_start_threshold:
+                    if prev_block not in table_run:
+                        table_run.append(prev_block)
+                    table_run.append(block)
+                else:
+                    if table_run:
+                        table_runs.append(table_run)
+                    table_run = []
+                prev_block = block
+
+        if table_run:
+            table_runs.append(table_run)
+
+        with ThreadPoolExecutor(max_workers=self.max_concurrency) as executor:
+            for future in as_completed([
+                executor.submit(self.process_rewriting, document, blocks)
+                for blocks in table_runs
+            ]):
+                future.result()  # Raise exceptions if any occurred
+                pbar.update(1)
+
+        pbar.close()
+
+    def process_rewriting(self, document: Document, blocks: List[Block]):
+        start_block = blocks[0]
+        for i in range(1, len(blocks)):
+            curr_block = blocks[i]
+            children = start_block.contained_blocks(document, (BlockTypes.TableCell,))
+            children_curr = curr_block.contained_blocks(document, (BlockTypes.TableCell,))
+            if not children or not children_curr:
+                # Happens if table/form processors didn't run
+                continue
+
+            start_image = start_block.get_image(document, highres=False)
+            curr_image = curr_block.get_image(document, highres=False)
+            start_html = start_block.render(document).html
+            curr_html = curr_block.render(document).html
+
+            prompt = self.gemini_table_merge_prompt.replace("{{table1}}", start_html).replace("{{table2}}", curr_html)
+
+            response_schema = content.Schema(
+                type=content.Type.OBJECT,
+                enum=[],
+                required=["table1_description", "table2_description", "explanation", "merge", "direction"],
+                properties={
+                    "table1_description": content.Schema(
+                        type=content.Type.STRING
+                    ),
+                    "table2_description": content.Schema(
+                        type=content.Type.STRING
+                    ),
+                    "explanation": content.Schema(
+                        type=content.Type.STRING
+                    ),
+                    "merge": content.Schema(
+                        type=content.Type.STRING,
+                        enum=["true", "false"]
+                    ),
+                    "direction": content.Schema(
+                        type=content.Type.STRING,
+                        enum=["bottom", "right"]
+                    ),
+                },
+            )
+
+            response = self.model.generate_response(
+                prompt,
+                [start_image, curr_image],
+                curr_block,
+                response_schema
+            )
+
+            if not response or ("direction" not in response or "merge" not in response):
+                curr_block.update_metadata(llm_error_count=1)
+                return
+
+            merge = response["merge"]
+
+            # The original table is okay
+            if "true" not in merge:
+                start_block = curr_block
+                return
+
+            # Merge the cells and images of the tables
+            direction = response["direction"]
+            merged_image = self.join_images(start_image, curr_image, direction)
+            merged_cells = self.join_cells(children, children_curr, direction)
+            curr_block.structure = []
+            start_block.structure = [b.id for b in merged_cells]
+            start_block.lowres_image = merged_image
+
+    @staticmethod
+    def join_cells(cells1: List[TableCell], cells2: List[TableCell], direction: Literal['right', 'bottom'] = 'right') -> List[TableCell]:
+        if direction == 'right':
+            new_cells = cells1 + cells2
+        else:
+            # Shift rows up
+            row_count = len(cells1)
+            for cell in cells2:
+                cell.row_id += row_count
+            new_cells = cells1 + cells2
+        return new_cells
+
+    @staticmethod
+    def join_images(image1: Image.Image, image2: Image.Image, direction: Literal['right', 'bottom'] = 'right') -> Image.Image:
+        # Get dimensions
+        w1, h1 = image1.size
+        w2, h2 = image2.size
+
+        if direction == 'right':
+            new_height = max(h1, h2)
+            new_width = w1 + w2
+            new_img = Image.new('RGB', (new_width, new_height), 'white')
+            new_img.paste(image1, (0, 0))
+            new_img.paste(image2, (w1, 0))
+        else:
+            new_width = max(w1, w2)
+            new_height = h1 + h2
+            new_img = Image.new('RGB', (new_width, new_height), 'white')
+            new_img.paste(image1, (0, 0))
+            new_img.paste(image2, (0, h1))
+        return new_img
\ No newline at end of file
diff --git a/marker/processors/llm/utils.py b/marker/processors/llm/utils.py
index c66c590a..da7be67f 100644
--- a/marker/processors/llm/utils.py
+++ b/marker/processors/llm/utils.py
@@ -1,5 +1,6 @@
 import json
 import time
+from typing import List
 
 import PIL
 import google.generativeai as genai
@@ -25,17 +26,19 @@ def configure_google_model(self):
     def generate_response(
             self,
             prompt: str,
-            image: PIL.Image.Image,
+            image: PIL.Image.Image | List[PIL.Image.Image],
             block: Block,
             response_schema: content.Schema,
             max_retries: int = 3,
             timeout: int = 60
     ):
+        if not isinstance(image, list):
+            image = [image]
         tries = 0
         while tries < max_retries:
             try:
                 responses = self.model.generate_content(
-                    [image, prompt], # According to gemini docs, it performs better if the image is the first element
+                    image + [prompt], # According to gemini docs, it performs better if the image is the first element
                     stream=False,
                     generation_config={
                         "temperature": 0,

From 0a0a7f2bcd184eaa786aa5d55989ade878c07a5a Mon Sep 17 00:00:00 2001
From: Moses Paul R 
Date: Mon, 13 Jan 2025 07:51:35 +0000
Subject: [PATCH 31/92] bugfix bbox

---
 marker/providers/pdf.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/marker/providers/pdf.py b/marker/providers/pdf.py
index ba8dbdb6..5b1b340f 100644
--- a/marker/providers/pdf.py
+++ b/marker/providers/pdf.py
@@ -484,7 +484,7 @@ def rect_to_scaled_bbox(self, rect, page_bbox, page_height, page_width, page_rot
         ty_start = page_height - cy_start
         ty_end = page_height - cy_end
 
-        bbox = [cx_start, min(ty_start, ty_end), cx_end, max(ty_start, ty_end)]
+        bbox = [min(cx_start, cx_end), min(ty_start, ty_end), max(cx_start, cx_end), max(ty_start, ty_end)]
         return Bbox(bbox).rotate(page_width, page_height, page_rotation).bbox
 
     def xy_to_scaled_pos(self, x, y, page_bbox, page_height, page_width, page_rotation, expand_by=1) -> List[float]:

From baf7a3ac055a915bf7d2054ba9ce3669f4144960 Mon Sep 17 00:00:00 2001
From: Moses Paul R 
Date: Mon, 13 Jan 2025 11:11:18 +0000
Subject: [PATCH 32/92] fix multiple links associated with a single span

---
 marker/providers/pdf.py | 80 ++++++++++++++++++++---------------------
 1 file changed, 39 insertions(+), 41 deletions(-)

diff --git a/marker/providers/pdf.py b/marker/providers/pdf.py
index 5b1b340f..64a4aaf9 100644
--- a/marker/providers/pdf.py
+++ b/marker/providers/pdf.py
@@ -224,51 +224,42 @@ def merge_links(self, page):
 
         links = self.get_links(page_id)
 
-        spans = [span for block in page['blocks'] for line in block['lines'] for span in line['spans'] if span['text']]
+        spans = [span for block in page['blocks'] for line in block['lines'] for span in line['spans']]
         span_bboxes = [span['bbox'] for span in spans]
         link_bboxes = [link['bbox'] for link in links]
-        intersection_matrix = matrix_intersection_area(span_bboxes, link_bboxes)
-        max_intersections = {}
+        intersection_matrix = matrix_intersection_area(link_bboxes, span_bboxes)
 
-        for span_idx, span in enumerate(spans):
-            intersection_span = intersection_matrix[span_idx]
-            if intersection_span.sum() == 0:
+        span_link_map = {}
+        for link_idx, link in enumerate(links):
+            intersection_link = intersection_matrix[link_idx]
+            if intersection_link.sum() == 0:
                 continue
 
-            max_intersection = intersection_span.argmax()
-            if intersection_matrix[span_idx, max_intersection] > 0:
-                max_intersections[span_idx] = (
-                    intersection_matrix[span_idx, max_intersection],
-                    links[max_intersection]
-                )
+            max_intersection = intersection_link.argmax()
+            span = spans[max_intersection]
 
-        span_replace_map = {}
-        for span_idx, span in enumerate(spans):
-            if span_idx in max_intersections:
-                link = max_intersections[span_idx][1]
-                if link['dest_page'] is not None:
-                    dest_page = link['dest_page']
-                    self.refs.setdefault(dest_page, [])
-                    link['url'] = f"#page-{dest_page}"
-                    if link['dest_pos']:
-                        dest_pos = link['dest_pos']
-                    else:
-                        dest_pos = [0.0, 0.0]
-                    if dest_pos not in self.refs[dest_page]:
-                        self.refs[dest_page].append(dest_pos)
-                    link['url'] += f"-{self.refs[dest_page].index(dest_pos)}"
-                span_replace_map[span_idx] = self.break_spans(span, link)
-            span_idx += 1
+            if link['dest_page'] is not None:
+                dest_page = link['dest_page']
+                self.refs.setdefault(dest_page, [])
+                link['url'] = f"#page-{dest_page}"
+                if link['dest_pos']:
+                    dest_pos = link['dest_pos']
+                else:
+                    dest_pos = [0.0, 0.0]
+                if dest_pos not in self.refs[dest_page]:
+                    self.refs[dest_page].append(dest_pos)
+                link['url'] += f"-{self.refs[dest_page].index(dest_pos)}"
+
+            span_link_map.setdefault(max_intersection, [])
+            span_link_map[max_intersection].append(link)
 
         span_idx = 0
         for block in page["blocks"]:
             for line in block["lines"]:
                 spans = []
                 for span in line["spans"]:
-                    if not span["text"]:
-                        continue
-                    if span_idx in span_replace_map:
-                        spans.extend(span_replace_map[span_idx])
+                    if span_idx in span_link_map:
+                        spans.extend(self.break_spans(span, span_link_map[span_idx]))
                     else:
                         spans.append(span)
                     span_idx += 1
@@ -303,16 +294,25 @@ def merge_refs(self, page):
                 assigned_refs.add(ref_idx)
                 break
 
-    def break_spans(self, orig_span, link):
+    def break_spans(self, orig_span, links):
         spans = []
         span = None
-        link_bbox = Bbox(link['bbox'])
+        link_bboxes = [Bbox(link['bbox']) for link in links]
 
         for char in orig_span['chars']:
             char_bbox = Bbox(char['bbox'])
-            char_in_link = bool(link_bbox.intersection_pct(char_bbox) > 0)
-
-            if not span or (char_in_link != span['char_in_link']):
+            intersections = []
+            for i, link_bbox in enumerate(link_bboxes):
+                area = link_bbox.intersection_area(char_bbox)
+                if area > 0:
+                    intersections.append((area, links[i]))
+
+            current_url = ''
+            if intersections:
+                intersections.sort(key=lambda x: x[0], reverse=True)
+                current_url = intersections[0][1]['url']
+
+            if not span or current_url != span['url']:
                 span = {
                     "bbox": char_bbox,
                     "text": char["char"],
@@ -321,8 +321,7 @@ def break_spans(self, orig_span, link):
                     "char_start_idx": char["char_idx"],
                     "char_end_idx": char["char_idx"],
                     "chars": [char],
-                    "url": link['url'] if char_in_link else '',
-                    "char_in_link": char_in_link
+                    "url": current_url
                 }
                 spans.append(span)
             else:
@@ -333,7 +332,6 @@ def break_spans(self, orig_span, link):
 
         for span in spans:
             span['bbox'] = span['bbox'].bbox
-            del span['char_in_link']
 
         return spans
 

From 59a18b8e004310d70e5689bda89f8877790cd045 Mon Sep 17 00:00:00 2001
From: Tarun Menta 
Date: Mon, 13 Jan 2025 17:19:17 +0530
Subject: [PATCH 33/92] Update to PDF benchmark - Skips OCR

---
 benchmarks/table/scoring.py | 23 ++------------
 benchmarks/table/table.py   | 60 ++++++++++++++++++++++---------------
 2 files changed, 38 insertions(+), 45 deletions(-)

diff --git a/benchmarks/table/scoring.py b/benchmarks/table/scoring.py
index ea14f3e5..81715182 100644
--- a/benchmarks/table/scoring.py
+++ b/benchmarks/table/scoring.py
@@ -1,8 +1,7 @@
 '''
-TEDS Code Adapter from https://github.com/ibm-aur-nlp/EDD
+TEDS Code Adapted from https://github.com/ibm-aur-nlp/EDD
 '''
 
-from concurrent.futures import ThreadPoolExecutor, as_completed
 from typing import List
 
 from tqdm import tqdm
@@ -99,6 +98,7 @@ def similarity_eval_html(pred, true, structure_only=False):
     ''' Computes TEDS score between the prediction and the ground truth of a
         given samples
     '''
+    pred, true = html.fromstring(pred), html.fromstring(true)
     if pred.xpath('body/table') and true.xpath('body/table'):
         pred = pred.xpath('body/table')[0]
         true = true.xpath('body/table')[0]
@@ -112,22 +112,3 @@ def similarity_eval_html(pred, true, structure_only=False):
     else:
         return 0.0
 
-def TEDS(prediction, ground_truth):
-    prediction, ground_truth = wrap_table_html(prediction), wrap_table_html(ground_truth)
-    if prediction:
-        return similarity_eval_html(
-            html.fromstring(prediction),
-            html.fromstring(ground_truth)
-        )
-    else:
-        return 0.
-
-def batched_TEDS(gts: List[str], preds: List[str], n_jobs:int=16):
-    with ThreadPoolExecutor(max_workers=n_jobs) as pool:
-        futures = [pool.submit(TEDS, pred, gt) for pred, gt in zip(preds, gts)]
-
-    teds_scores = []
-    for future in futures:
-        teds_scores.append(future.result())
-
-    return teds_scores
\ No newline at end of file
diff --git a/benchmarks/table/table.py b/benchmarks/table/table.py
index 77e8674e..95f7e555 100644
--- a/benchmarks/table/table.py
+++ b/benchmarks/table/table.py
@@ -1,3 +1,4 @@
+import base64
 import os
 import time
 import datasets
@@ -7,21 +8,28 @@
 from tabulate import tabulate
 import json
 from bs4 import BeautifulSoup
+from concurrent.futures import ThreadPoolExecutor
 
 os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"  # Transformers uses .isin for a simple op, which is not supported on MPS
 
 from marker.config.parser import ConfigParser
 from marker.converters.table import TableConverter
 from marker.models import create_model_dict
-from marker.output import save_output
 
-from scoring import batched_TEDS
+from scoring import wrap_table_html, similarity_eval_html
+
+def update_teds_score(result):
+    prediction, ground_truth = result['marker_table'], result['gt_table']
+    prediction, ground_truth = wrap_table_html(prediction), wrap_table_html(ground_truth)
+    score = similarity_eval_html(prediction, ground_truth)
+    result.update({'score':score})
+    return result
 
 
 @click.command(help="Benchmark Table to HTML Conversion")
 @click.argument("out_file", type=str)
-@click.option("--dataset", type=str, default="tarun-menta/fintabnet-html-test", help="Dataset to use")
-@click.option("--max", type=int, default=None, help="Max number of tables to process")
+@click.option("--dataset", type=str, default="datalab-to/fintabnet-test", help="Dataset to use")
+@click.option("--max", type=int, default=None, help="Maximum number of PDFs to process")
 def main(out_file, dataset, max):
     models = create_model_dict()
     config_parser = ConfigParser({})
@@ -44,42 +52,46 @@ def main(out_file, dataset, max):
     results = []
     for i in tqdm(range(iterations), desc='Converting Tables'):
         row = dataset[i]
-        table_img = row['highres_table_img']
-        with tempfile.NamedTemporaryFile(suffix=".png", mode="wb+") as temp_img_file:
-            table_img.save(temp_img_file)
-            temp_img_file.seek(0)
-            filename = temp_img_file.name
+        pdf_binary = base64.b64decode(row['pdf'])
+        gt_tables = row['tables']       #Already sorted by reading order, which is what marker returns
+        with tempfile.NamedTemporaryFile(suffix=".pdf", mode="wb+") as temp_pdf_file:
+            temp_pdf_file.write(pdf_binary)
+            temp_pdf_file.seek(0)
+            filename = temp_pdf_file.name
 
             marker_table_html = converter(filename).html
-            marker_table_soup = BeautifulSoup(marker_table_html, 'html.parser')
 
+        marker_table_soup = BeautifulSoup(marker_table_html, 'html.parser')
+        marker_detected_tables = marker_table_soup.find_all('table')
+        if len(marker_detected_tables)==0:
+            print(f'No tables detected, skipping...')
+        
+        for marker_table_soup, gt_table in zip(marker_detected_tables, gt_tables):
+            gt_table_html = gt_table['html']
+            
             #marker wraps the table in  which fintabnet data doesn't
-            marker_table_soup.find('tbody').unwrap()    
-
+            marker_table_soup.find('tbody').unwrap()
             #Fintabnet doesn't use th tags, need to be replaced for fair comparison
             for th_tag in marker_table_soup.find_all('th'):
                 th_tag.name = 'td'
-
             marker_table_html = str(marker_table_soup)
 
-        results.append({
-            "marker_table": marker_table_html,
-            "gt_table": row['orig_html']
-        })
-
-    scores = batched_TEDS([r['gt_table'] for r in results], [r['marker_table'] for r in results])
-    for result, score in zip(results, scores):
-        result.update({'score': score})
+            results.append({
+                "marker_table": marker_table_html,
+                "gt_table": gt_table_html
+            })
+    total_time = time.time() - start
 
+    with ThreadPoolExecutor(max_workers=16) as executor:
+        results = list(tqdm(executor.map(update_teds_score, results), desc='Computing alignment scores', total=len(results)))
     avg_score = sum([r["score"] for r in results]) / len(results)
 
-    total_time = time.time() - start
     print(f"Total time: {time.time() - start}")
     headers = ["Avg score", "Time per table", "Total tables"]
-    data = [f"{avg_score:.3f}", f"{total_time / iterations:.3f}", iterations]
+    data = [f"{avg_score:.3f}", f"{total_time / len(results):.3f}", len(results)]
     table = tabulate([data], headers=headers, tablefmt="github")
     print(table)
-    print("Avg score computed by comparing tabled predicted HTML with original HTML")
+    print("Avg score computed by comparing marker predicted HTML with original HTML")
 
     with open(out_file, "w+") as f:
         json.dump(results, f, indent=2)

From 5a136e15b6f4c64ccf48520201c213fd53e9c8d5 Mon Sep 17 00:00:00 2001
From: Tarun Menta 
Date: Mon, 13 Jan 2025 20:40:13 +0530
Subject: [PATCH 34/92] Cleanup for multiple tables on a page

---
 benchmarks/table/table.py | 74 +++++++++++++++++++++------------------
 1 file changed, 40 insertions(+), 34 deletions(-)

diff --git a/benchmarks/table/table.py b/benchmarks/table/table.py
index 95f7e555..e056b769 100644
--- a/benchmarks/table/table.py
+++ b/benchmarks/table/table.py
@@ -9,6 +9,7 @@
 import json
 from bs4 import BeautifulSoup
 from concurrent.futures import ThreadPoolExecutor
+from pypdfium2._helpers.misc import PdfiumError
 
 os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"  # Transformers uses .isin for a simple op, which is not supported on MPS
 
@@ -35,12 +36,6 @@ def main(out_file, dataset, max):
     config_parser = ConfigParser({})
     start = time.time()
 
-    converter = TableConverter(
-        config=config_parser.generate_config_dict(),
-        artifact_dict=models,
-        processor_list=config_parser.get_processors(),
-        renderer='marker.renderers.html.HTMLRenderer'
-    )
 
     dataset = datasets.load_dataset(dataset, split='train')
     dataset = dataset.shuffle(seed=0)
@@ -51,35 +46,46 @@ def main(out_file, dataset, max):
 
     results = []
     for i in tqdm(range(iterations), desc='Converting Tables'):
-        row = dataset[i]
-        pdf_binary = base64.b64decode(row['pdf'])
-        gt_tables = row['tables']       #Already sorted by reading order, which is what marker returns
-        with tempfile.NamedTemporaryFile(suffix=".pdf", mode="wb+") as temp_pdf_file:
-            temp_pdf_file.write(pdf_binary)
-            temp_pdf_file.seek(0)
-            filename = temp_pdf_file.name
-
-            marker_table_html = converter(filename).html
-
-        marker_table_soup = BeautifulSoup(marker_table_html, 'html.parser')
-        marker_detected_tables = marker_table_soup.find_all('table')
-        if len(marker_detected_tables)==0:
-            print(f'No tables detected, skipping...')
-        
-        for marker_table_soup, gt_table in zip(marker_detected_tables, gt_tables):
-            gt_table_html = gt_table['html']
+        try:
+            row = dataset[i]
+            pdf_binary = base64.b64decode(row['pdf'])
+            gt_tables = row['tables']       #Already sorted by reading order, which is what marker returns
+
+            converter = TableConverter(
+                config=config_parser.generate_config_dict(),
+                artifact_dict=models,
+                processor_list=config_parser.get_processors(),
+                renderer='marker.renderers.html.HTMLRenderer'
+            )
+
+            with tempfile.NamedTemporaryFile(suffix=".pdf", mode="wb") as temp_pdf_file:
+                temp_pdf_file.write(pdf_binary)
+                temp_pdf_file.seek(0)
+                marker_table_html = converter(temp_pdf_file.name).html
+
+            marker_table_soup = BeautifulSoup(marker_table_html, 'html.parser')
+            marker_detected_tables = marker_table_soup.find_all('table')
+            if len(marker_detected_tables)==0:
+                print(f'No tables detected, skipping...')
             
-            #marker wraps the table in  which fintabnet data doesn't
-            marker_table_soup.find('tbody').unwrap()
-            #Fintabnet doesn't use th tags, need to be replaced for fair comparison
-            for th_tag in marker_table_soup.find_all('th'):
-                th_tag.name = 'td'
-            marker_table_html = str(marker_table_soup)
-
-            results.append({
-                "marker_table": marker_table_html,
-                "gt_table": gt_table_html
-            })
+            for marker_table_soup, gt_table in zip(marker_detected_tables, gt_tables):
+                gt_table_html = gt_table['html']
+                
+                #marker wraps the table in  which fintabnet data doesn't
+                marker_table_soup.find('tbody').unwrap()
+                #Fintabnet doesn't use th tags, need to be replaced for fair comparison
+                for th_tag in marker_table_soup.find_all('th'):
+                    th_tag.name = 'td'
+                marker_table_html = str(marker_table_soup)
+
+                results.append({
+                    "marker_table": marker_table_html,
+                    "gt_table": gt_table_html
+                })
+        except PdfiumError:
+            print('Broken PDF, Skipping...')
+            continue
+
     total_time = time.time() - start
 
     with ThreadPoolExecutor(max_workers=16) as executor:

From b7e32ef156ad302cd88073d65eb66ecbd2bc9ce3 Mon Sep 17 00:00:00 2001
From: Tarun Menta 
Date: Mon, 13 Jan 2025 21:26:00 +0530
Subject: [PATCH 35/92] Update readme with new benchmark details

---
 README.md                 | 18 +++++++++++++++++-
 benchmarks/table/table.py |  7 +++----
 2 files changed, 20 insertions(+), 5 deletions(-)

diff --git a/README.md b/README.md
index c987cdc9..7f67e3fe 100644
--- a/README.md
+++ b/README.md
@@ -370,7 +370,7 @@ There are some settings that you may find useful if things aren't working the wa
 Pass the `debug` option to activate debug mode.  This will save images of each page with detected layout and text, as well as output a json file with additional bounding box information.
 
 # Benchmarks
-
+## Overall PDF Conversion
 Benchmarking PDF extraction quality is hard.  I've created a test set by finding books and scientific papers that have a pdf version and a latex source.  I convert the latex to text, and compare the reference to the output of text extraction methods.  It's noisy, but at least directionally correct.
 
 **Speed**
@@ -393,6 +393,13 @@ Marker takes about 6GB of VRAM on average per task, so you can convert 8 documen
 
 ![Benchmark results](data/images/per_doc.png)
 
+## Table Conversion
+Marker can extract tables from your PDFs using `marker.converters.table.TableConverter`. The table extraction performance is measured by comparing the extracted HTML representation of tables against the original HTML representations using the test split of [FinTabNet](https://developer.ibm.com/exchanges/data/all/fintabnet/). The HTML representations are compared using a [tree edit distance] based metric to judge both structure and content. Marker detects and identifies the structure of all tables in a PDF page and achieves an average score of `0.65` via this approach.
+
+|   Avg score |   Total tables |
+|-------------|----------------|
+|        0.65 |           1149 |
+
 ## Running your own benchmarks
 
 You can benchmark the performance of marker on your machine. Install marker manually with:
@@ -402,12 +409,21 @@ git clone https://github.com/VikParuchuri/marker.git
 poetry install
 ```
 
+### Overall PDF Conversion
+
 Download the benchmark data [here](https://drive.google.com/file/d/1ZSeWDo2g1y0BRLT7KnbmytV2bjWARWba/view?usp=sharing) and unzip. Then run the overall benchmark like this:
 
 ```shell
 python benchmarks/overall.py data/pdfs data/references report.json
 ```
 
+### Table Conversion
+The processed FinTabNet dataset is hosted [here](https://huggingface.co/datasets/datalab-to/fintabnet-test) and is automatically downloaded. Run the benchmark with:
+
+```shell
+python benchmarks/table/table.py table_report.json --max 1000
+```
+
 # Thanks
 
 This work would not have been possible without amazing open source models and datasets, including (but not limited to):
diff --git a/benchmarks/table/table.py b/benchmarks/table/table.py
index e056b769..a11c0cf7 100644
--- a/benchmarks/table/table.py
+++ b/benchmarks/table/table.py
@@ -86,15 +86,14 @@ def main(out_file, dataset, max):
             print('Broken PDF, Skipping...')
             continue
 
-    total_time = time.time() - start
+    print(f"Total time: {time.time() - start}")
 
     with ThreadPoolExecutor(max_workers=16) as executor:
         results = list(tqdm(executor.map(update_teds_score, results), desc='Computing alignment scores', total=len(results)))
     avg_score = sum([r["score"] for r in results]) / len(results)
 
-    print(f"Total time: {time.time() - start}")
-    headers = ["Avg score", "Time per table", "Total tables"]
-    data = [f"{avg_score:.3f}", f"{total_time / len(results):.3f}", len(results)]
+    headers = ["Avg score", "Total tables"]
+    data = [f"{avg_score:.3f}", len(results)]
     table = tabulate([data], headers=headers, tablefmt="github")
     print(table)
     print("Avg score computed by comparing marker predicted HTML with original HTML")

From d09993090b18df812b8612ede96409d80173ccaf Mon Sep 17 00:00:00 2001
From: Tarun Menta 
Date: Mon, 13 Jan 2025 21:53:03 +0530
Subject: [PATCH 36/92] PdfProvider context manager support

---
 marker/converters/pdf.py | 7 +++++--
 marker/providers/pdf.py  | 6 ++++++
 2 files changed, 11 insertions(+), 2 deletions(-)

diff --git a/marker/converters/pdf.py b/marker/converters/pdf.py
index 310ce277..0e07eadb 100644
--- a/marker/converters/pdf.py
+++ b/marker/converters/pdf.py
@@ -5,6 +5,7 @@
 import inspect
 from collections import defaultdict
 from typing import Annotated, Any, Dict, List, Optional, Type
+from functools import cache
 
 from marker.builders.document import DocumentBuilder
 from marker.builders.layout import LayoutBuilder
@@ -116,11 +117,13 @@ def resolve_dependencies(self, cls):
 
         return cls(**resolved_kwargs)
 
+    @cache
     def build_document(self, filepath: str):
-        pdf_provider = PdfProvider(filepath, self.config)
         layout_builder = self.resolve_dependencies(self.layout_builder_class)
         ocr_builder = self.resolve_dependencies(OcrBuilder)
-        document = DocumentBuilder(self.config)(pdf_provider, layout_builder, ocr_builder)
+
+        with PdfProvider(filepath, self.config) as pdf_provider:
+            document = DocumentBuilder(self.config)(pdf_provider, layout_builder, ocr_builder)
         StructureBuilder(self.config)(document)
 
         for processor_cls in self.processor_list:
diff --git a/marker/providers/pdf.py b/marker/providers/pdf.py
index 09b9603d..149c6069 100644
--- a/marker/providers/pdf.py
+++ b/marker/providers/pdf.py
@@ -85,6 +85,12 @@ def __init__(self, filepath: str, config=None):
 
         atexit.register(self.cleanup_pdf_doc)
 
+    def __enter__(self):
+        return self
+
+    def __exit__(self, exc_type, exc_value, traceback):
+        self.cleanup_pdf_doc()
+
     def __len__(self) -> int:
         return len(self.doc)
 

From 646769b34a70ce7c17a88b7177bfb46e6c79611c Mon Sep 17 00:00:00 2001
From: Moses Paul R 
Date: Mon, 13 Jan 2025 17:35:14 +0000
Subject: [PATCH 37/92] fix footnotes [skip ci]

---
 marker/processors/footnote.py    | 12 ++++++++++++
 marker/schema/blocks/footnote.py | 16 ----------------
 marker/schema/text/span.py       |  9 +++++++--
 3 files changed, 19 insertions(+), 18 deletions(-)

diff --git a/marker/processors/footnote.py b/marker/processors/footnote.py
index fdf31721..41f350a2 100644
--- a/marker/processors/footnote.py
+++ b/marker/processors/footnote.py
@@ -1,3 +1,5 @@
+import re
+
 from marker.processors import BaseProcessor
 from marker.schema import BlockTypes
 from marker.schema.document import Document
@@ -13,6 +15,7 @@ class FootnoteProcessor(BaseProcessor):
     def __call__(self, document: Document):
         for page in document.pages:
             self.push_footnotes_to_bottom(page, document)
+            self.assign_superscripts(page, document)
 
     def push_footnotes_to_bottom(self, page: PageGroup, document: Document):
         footnote_blocks = page.contained_blocks(document, self.block_types)
@@ -24,3 +27,12 @@ def push_footnotes_to_bottom(self, page: PageGroup, document: Document):
                 # Move to bottom if it is
                 page.structure.remove(block.id)
                 page.add_structure(block)
+
+    def assign_superscripts(self, page: PageGroup, document: Document):
+        footnote_blocks = page.contained_blocks(document, self.block_types)
+
+        for block in footnote_blocks:
+            for span in block.contained_blocks(document, (BlockTypes.Span,)):
+                if re.match(r"^[0-9\W]+", span.text):
+                    span.has_superscript = True
+                break
diff --git a/marker/schema/blocks/footnote.py b/marker/schema/blocks/footnote.py
index c476aa7d..71d3580b 100644
--- a/marker/schema/blocks/footnote.py
+++ b/marker/schema/blocks/footnote.py
@@ -1,21 +1,7 @@
-import re
-
 from marker.schema import BlockTypes
 from marker.schema.blocks import Block
 
 
-def superscript(child_blocks):
-    # Superscript leading symbol or digit sequence
-    first_block = None
-    while len(child_blocks) > 0:
-        first_block = child_blocks[0]
-        child_blocks = first_block.children
-
-    if first_block is not None and first_block.id.block_type == BlockTypes.Line:
-        digit_start = r"^([0-9\W]+)(.*)"
-        first_block.html = re.sub(digit_start, r"\1\2", first_block.html.lstrip())
-
-
 class Footnote(Block):
     block_type: BlockTypes = BlockTypes.Footnote
 
@@ -23,6 +9,4 @@ def assemble_html(self, child_blocks, parent_structure):
         template = super().assemble_html(child_blocks, parent_structure)
         template = template.replace("\n", " ")
 
-        # Add superscripts to start
-        superscript(child_blocks)
         return f"
{template}
"
diff --git a/marker/schema/text/span.py b/marker/schema/text/span.py
index 86fe24bc..1b6e18f2 100644
--- a/marker/schema/text/span.py
+++ b/marker/schema/text/span.py
@@ -22,6 +22,7 @@ class Span(Block):
     minimum_position: int
     maximum_position: int
     formats: List[Literal['plain', 'math', 'chemical', 'bold', 'italic']]
+    has_superscript: bool = False
     url: Optional[str] = None
     anchors: Optional[List[str]] = None
 
@@ -60,14 +61,18 @@ def assemble_html(self, child_blocks, parent_structure):
         text = html.escape(text)
         text = cleanup_text(text)
 
+        if self.has_superscript:
+            text = re.sub(r"^([0-9\W]+)(.*)", r"\1\2", text)
+
+        if self.url:
+            text = f"{text}"
+
         if self.italic:
             text = f"{text}"
         elif self.bold:
             text = f"{text}"
         elif self.math:
             text = f"{text}"
-        elif self.url:
-            text = f"{text}"
 
         if self.anchors:
             text = "".join(f"" for anchor in self.anchors) + text

From cb2c26f5ac4f0ebf49085275570e3008eb3852d6 Mon Sep 17 00:00:00 2001
From: Moses Paul R 
Date: Mon, 13 Jan 2025 18:45:53 +0000
Subject: [PATCH 38/92] more cleanup [skip ci]

---
 marker/providers/pdf.py | 49 ++++++++++++++++++++---------------------
 1 file changed, 24 insertions(+), 25 deletions(-)

diff --git a/marker/providers/pdf.py b/marker/providers/pdf.py
index 64a4aaf9..cc31c100 100644
--- a/marker/providers/pdf.py
+++ b/marker/providers/pdf.py
@@ -238,17 +238,24 @@ def merge_links(self, page):
             max_intersection = intersection_link.argmax()
             span = spans[max_intersection]
 
-            if link['dest_page'] is not None:
-                dest_page = link['dest_page']
-                self.refs.setdefault(dest_page, [])
-                link['url'] = f"#page-{dest_page}"
-                if link['dest_pos']:
-                    dest_pos = link['dest_pos']
-                else:
-                    dest_pos = [0.0, 0.0]
-                if dest_pos not in self.refs[dest_page]:
-                    self.refs[dest_page].append(dest_pos)
-                link['url'] += f"-{self.refs[dest_page].index(dest_pos)}"
+            if link['dest_page'] is None:
+                continue
+
+            dest_page = link['dest_page']
+            self.refs.setdefault(dest_page, [])
+            link['url'] = f"#page-{dest_page}"
+            if link['dest_pos']:
+                dest_pos = link['dest_pos']
+            else:
+                # Don't link to self if there is no dest_pos
+                if dest_page == page_id:
+                    continue
+                dest_pos = [0.0, 0.0]
+
+            if dest_pos not in self.refs[dest_page]:
+                self.refs[dest_page].append(dest_pos)
+
+            link['url'] += f"-{self.refs[dest_page].index(dest_pos)}"
 
             span_link_map.setdefault(max_intersection, [])
             span_link_map[max_intersection].append(link)
@@ -272,27 +279,19 @@ def merge_refs(self, page):
         if not refs:
             return
 
-        spans = [span for block in page['blocks'] for line in block['lines'] for span in line['spans'] if span['text']]
+        spans = [span for block in page['blocks'] for line in block['lines'] for span in line['spans']]
         if not spans:
             return
 
         span_starts = np.array([span['bbox'][:2] for span in spans])
-        ref_pos = np.array([ref for ref in refs])
-        ref_starts = np.array([pos for pos in ref_pos])
+        ref_starts = np.array(refs)
 
         distances = np.linalg.norm(span_starts[:, np.newaxis, :] - ref_starts[np.newaxis, :, :], axis=2)
 
-        assigned_refs = set()
-        for ref_idx, ref_center in enumerate(ref_starts):
-            if ref_idx in assigned_refs:
-                continue
-
-            span_indices = np.argsort(distances[:, ref_idx])
-            for span_idx in span_indices:
-                spans[span_idx].setdefault('anchors', [])
-                spans[span_idx]['anchors'].append(f"page-{page_id}-{ref_idx}")
-                assigned_refs.add(ref_idx)
-                break
+        for ref_idx in range(len(ref_starts)):
+            span_idx = np.argmin(distances[:, ref_idx])
+            spans[span_idx].setdefault('anchors', [])
+            spans[span_idx]['anchors'].append(f"page-{page_id}-{ref_idx}")
 
     def break_spans(self, orig_span, links):
         spans = []

From 18eea949cbe8a774e93492e96fa094bda20dd9dd Mon Sep 17 00:00:00 2001
From: Moses Paul R 
Date: Tue, 14 Jan 2025 09:14:00 +0000
Subject: [PATCH 39/92] cleanup [skip ci]

---
 marker/providers/pdf.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/marker/providers/pdf.py b/marker/providers/pdf.py
index cc31c100..dba6785e 100644
--- a/marker/providers/pdf.py
+++ b/marker/providers/pdf.py
@@ -166,12 +166,12 @@ def pdftext_extraction(self) -> ProviderPageLines:
 
         SpanClass: Span = get_block_class(BlockTypes.Span)
         LineClass: Line = get_block_class(BlockTypes.Line)
-        for page in page_char_blocks:
-            if not self.disable_links:
+
+        if not self.disable_links:
+            for page in page_char_blocks:
                 self.merge_links(page)
 
-        for page in page_char_blocks:
-            if not self.disable_links:
+            for page in page_char_blocks:
                 self.merge_refs(page)
 
         for page in page_char_blocks:

From 152727bb5e18c3df83aa0e334c97a84cd2057198 Mon Sep 17 00:00:00 2001
From: Moses Paul R 
Date: Tue, 14 Jan 2025 10:14:52 +0000
Subject: [PATCH 40/92] more minor cleanup [skip ci]

---
 marker/providers/pdf.py | 131 +++++++++++++++++++++++-----------------
 1 file changed, 75 insertions(+), 56 deletions(-)

diff --git a/marker/providers/pdf.py b/marker/providers/pdf.py
index dba6785e..d9d344e4 100644
--- a/marker/providers/pdf.py
+++ b/marker/providers/pdf.py
@@ -220,6 +220,10 @@ def pdftext_extraction(self) -> ProviderPageLines:
         return page_lines
 
     def merge_links(self, page):
+        """
+        Merges links with spans. Some spans can also have multiple links associated with them.
+        We break up the spans and reconstruct them taking the links into account.
+        """
         page_id = page["page"]
 
         links = self.get_links(page_id)
@@ -266,13 +270,17 @@ def merge_links(self, page):
                 spans = []
                 for span in line["spans"]:
                     if span_idx in span_link_map:
-                        spans.extend(self.break_spans(span, span_link_map[span_idx]))
+                        spans.extend(self._reconstruct_spans(span, span_link_map[span_idx]))
                     else:
                         spans.append(span)
                     span_idx += 1
                 line['spans'] = spans
 
     def merge_refs(self, page):
+        """
+        We associate each reference to the nearest span.
+        """
+
         page_id = page["page"]
 
         refs = self.refs.get(page_id, [])
@@ -293,7 +301,10 @@ def merge_refs(self, page):
             spans[span_idx].setdefault('anchors', [])
             spans[span_idx]['anchors'].append(f"page-{page_id}-{ref_idx}")
 
-    def break_spans(self, orig_span, links):
+    def _reconstruct_spans(self, orig_span: dict, links: List[dict]):
+        """
+        Reconstructs the spans by breaking them up into smaller spans based on the links.
+        """
         spans = []
         span = None
         link_bboxes = [Bbox(link['bbox']) for link in links]
@@ -369,7 +380,7 @@ def check_page(self, page_id: int) -> bool:
             font_map = {}
             for text_obj in filter(lambda obj: obj.type == pdfium_c.FPDF_PAGEOBJ_TEXT, page_objs):
                 font = pdfium_c.FPDFTextObj_GetFont(text_obj)
-                font_name = self.get_fontname(font)
+                font_name = self._get_fontname(font)
 
                 # we also skip pages without embedded fonts and fonts without names
                 non_embedded_fonts.append(pdfium_c.FPDFFont_GetIsEmbedded(font) == 0)
@@ -431,7 +442,8 @@ def get_page_bbox(self, idx: int) -> PolygonBox | None:
     def get_page_lines(self, idx: int) -> List[ProviderOutput]:
         return self.page_lines[idx]
 
-    def get_fontname(self, font) -> str:
+    @staticmethod
+    def _get_fontname(font) -> str:
         font_name = ""
         buffer_size = 256
 
@@ -449,7 +461,8 @@ def get_fontname(self, font) -> str:
 
         return font_name
 
-    def get_dest_position(self, dest) -> Optional[Tuple[float, float]]:
+    @staticmethod
+    def _get_dest_position(dest) -> Optional[Tuple[float, float]]:
         has_x = ctypes.c_int()
         has_y = ctypes.c_int()
         has_zoom = ctypes.c_int()
@@ -471,7 +484,11 @@ def get_dest_position(self, dest) -> Optional[Tuple[float, float]]:
         else:
             return None
 
-    def rect_to_scaled_bbox(self, rect, page_bbox, page_height, page_width, page_rotation) -> List[float]:
+    @staticmethod
+    def _rect_to_scaled_bbox(rect, page_bbox, page_rotation) -> List[float]:
+        page_width = math.ceil(abs(page_bbox[2] - page_bbox[0]))
+        page_height = math.ceil(abs(page_bbox[1] - page_bbox[3]))
+
         cx_start, cy_start, cx_end, cy_end = rect
         cx_start -= page_bbox[0]
         cx_end -= page_bbox[0]
@@ -484,15 +501,14 @@ def rect_to_scaled_bbox(self, rect, page_bbox, page_height, page_width, page_rot
         bbox = [min(cx_start, cx_end), min(ty_start, ty_end), max(cx_start, cx_end), max(ty_start, ty_end)]
         return Bbox(bbox).rotate(page_width, page_height, page_rotation).bbox
 
-    def xy_to_scaled_pos(self, x, y, page_bbox, page_height, page_width, page_rotation, expand_by=1) -> List[float]:
-        return self.rect_to_scaled_bbox([x - expand_by, y - expand_by, x + expand_by, y + expand_by], page_bbox, page_height, page_width, page_rotation)[:2]
+    @staticmethod
+    def _xy_to_scaled_pos(x, y, page_bbox, page_rotation, expand_by=1) -> List[float]:
+        return PdfProvider._rect_to_scaled_bbox([x - expand_by, y - expand_by, x + expand_by, y + expand_by], page_bbox, page_rotation)[:2]
 
     def get_links(self, page_idx):
         urls = []
         page = self.doc[page_idx]
         page_bbox: List[float] = page.get_bbox()
-        page_width = math.ceil(abs(page_bbox[2] - page_bbox[0]))
-        page_height = math.ceil(abs(page_bbox[1] - page_bbox[3]))
         page_rotation = 0
         try:
             page_rotation = page.get_rotation()
@@ -509,51 +525,54 @@ def get_links(self, page_idx):
                 'url': None,
             }
             annot = pdfium_c.FPDFPage_GetAnnot(page, i)
-            if pdfium_c.FPDFAnnot_GetSubtype(annot) == pdfium_c.FPDF_ANNOT_LINK:
-                fs_rect = pdfium_c.FS_RECTF()
-                success = pdfium_c.FPDFAnnot_GetRect(annot, ctypes.byref(fs_rect))
-                if not success:
+            if pdfium_c.FPDFAnnot_GetSubtype(annot) != pdfium_c.FPDF_ANNOT_LINK:
+                continue
+
+            fs_rect = pdfium_c.FS_RECTF()
+            success = pdfium_c.FPDFAnnot_GetRect(annot, ctypes.byref(fs_rect))
+            if not success:
+                continue
+
+            link['bbox'] = self._rect_to_scaled_bbox(
+                [fs_rect.left, fs_rect.top, fs_rect.right, fs_rect.bottom],
+                page_bbox, page_rotation
+            )
+
+            link_obj = pdfium_c.FPDFAnnot_GetLink(annot)
+
+            dest = pdfium_c.FPDFLink_GetDest(self.doc, link_obj)
+            if dest:
+                tgt_page = pdfium_c.FPDFDest_GetDestPageIndex(self.doc, dest)
+                link['dest_page'] = tgt_page
+                dest_position = self._get_dest_position(dest)
+                if dest_position:
+                    link['dest_pos'] = self._xy_to_scaled_pos(*dest_position, page_bbox, page_rotation)
+
+            else:
+                action = pdfium_c.FPDFLink_GetAction(link_obj)
+                a_type = pdfium_c.FPDFAction_GetType(action)
+
+                if a_type == pdfium_c.PDFACTION_UNSUPPORTED:
                     continue
-                link['bbox'] = self.rect_to_scaled_bbox(
-                    [fs_rect.left, fs_rect.top, fs_rect.right, fs_rect.bottom],
-                    page_bbox, page_height, page_width, page_rotation
-                )
-
-                link_obj = pdfium_c.FPDFAnnot_GetLink(annot)
-
-                dest = pdfium_c.FPDFLink_GetDest(self.doc, link_obj)
-                if dest:
-                    tgt_page = pdfium_c.FPDFDest_GetDestPageIndex(self.doc, dest)
-                    link['dest_page'] = tgt_page
-                    dest_position = self.get_dest_position(dest)
-                    if dest_position:
-                        link['dest_pos'] = self.xy_to_scaled_pos(*dest_position, page_bbox, page_height, page_width, page_rotation)
-
-                else:
-                    action = pdfium_c.FPDFLink_GetAction(link_obj)
-                    a_type = pdfium_c.FPDFAction_GetType(action)
-
-                    if a_type == pdfium_c.PDFACTION_UNSUPPORTED:
-                        continue
-
-                    elif a_type == pdfium_c.PDFACTION_GOTO:
-                        # Goto a page
-                        dest = pdfium_c.FPDFAction_GetDest(self.doc, action)
-                        if dest:
-                            tgt_page = pdfium_c.FPDFDest_GetDestPageIndex(self.doc, dest)
-                            link['dest_page'] = tgt_page
-                            dest_position = self.get_dest_position(dest)
-                            if dest_position:
-                                link['dest_pos'] = self.xy_to_scaled_pos(*dest_position, page_bbox, page_height, page_width, page_rotation)
-
-                    elif a_type == pdfium_c.PDFACTION_URI:
-                        # External link
-                        needed_len = pdfium_c.FPDFAction_GetURIPath(self.doc, action, None, 0)
-                        if needed_len > 0:
-                            buf = ctypes.create_string_buffer(needed_len)
-                            pdfium_c.FPDFAction_GetURIPath(self.doc, action, buf, needed_len)
-                            uri = buf.raw[:needed_len].decode('utf-8', errors='replace').rstrip('\x00')
-                            link["url"] = uri
-
-                urls.append(link)
+
+                elif a_type == pdfium_c.PDFACTION_GOTO:
+                    # Goto a page
+                    dest = pdfium_c.FPDFAction_GetDest(self.doc, action)
+                    if dest:
+                        tgt_page = pdfium_c.FPDFDest_GetDestPageIndex(self.doc, dest)
+                        link['dest_page'] = tgt_page
+                        dest_position = self._get_dest_position(dest)
+                        if dest_position:
+                            link['dest_pos'] = self._xy_to_scaled_pos(*dest_position, page_bbox, page_rotation)
+
+                elif a_type == pdfium_c.PDFACTION_URI:
+                    # External link
+                    needed_len = pdfium_c.FPDFAction_GetURIPath(self.doc, action, None, 0)
+                    if needed_len > 0:
+                        buf = ctypes.create_string_buffer(needed_len)
+                        pdfium_c.FPDFAction_GetURIPath(self.doc, action, buf, needed_len)
+                        uri = buf.raw[:needed_len].decode('utf-8', errors='replace').rstrip('\x00')
+                        link["url"] = uri
+
+            urls.append(link)
         return urls

From 5ac8b0db8e76009623ff9d2935c84a8831f86f5e Mon Sep 17 00:00:00 2001
From: Moses Paul R 
Date: Tue, 14 Jan 2025 12:31:53 +0000
Subject: [PATCH 41/92] add test for pdf link and reference

---
 tests/builders/test_pdf_links.py | 32 ++++++++++++++++++++++++++++++++
 1 file changed, 32 insertions(+)
 create mode 100644 tests/builders/test_pdf_links.py

diff --git a/tests/builders/test_pdf_links.py b/tests/builders/test_pdf_links.py
new file mode 100644
index 00000000..00bff7ee
--- /dev/null
+++ b/tests/builders/test_pdf_links.py
@@ -0,0 +1,32 @@
+import pytest
+
+from marker.converters.pdf import PdfConverter
+from marker.renderers.markdown import MarkdownOutput
+from marker.schema import BlockTypes
+from marker.schema.document import Document
+
+
+@pytest.mark.filename("arxiv_test.pdf")
+@pytest.mark.output_format("markdown")
+@pytest.mark.config({"page_range": [1]})
+def test_pdf_links(pdf_document: Document, pdf_converter: PdfConverter, temp_pdf):
+    first_page = pdf_document.pages[0]
+
+    for section_header_span in first_page.contained_blocks(pdf_document, (BlockTypes.Span,)):
+        if section_header_span.text == " II.":
+            assert section_header_span.url == "#page-1-0"
+            break
+    else:
+        raise ValueError("Could not find II. in the first page")
+
+    section_header_block = first_page.contained_blocks(pdf_document, (BlockTypes.SectionHeader,))[0]
+    assert section_header_block.raw_text(pdf_document) == 'II. THEORETICAL FRAMEWORK\n'
+
+    section_header_span = section_header_block.contained_blocks(pdf_document, (BlockTypes.Span,))[0]
+    assert section_header_span.anchors == ['page-1-0']
+
+    markdown_output: MarkdownOutput = pdf_converter(temp_pdf.name)
+    markdown = markdown_output.markdown
+
+    assert '[II.](#page-1-0)' in markdown
+    assert 'II. THEORETICAL FRAMEWORK' in markdown

From 2049068e8e2cc5a8a03f3d6b30773ee1bef764d7 Mon Sep 17 00:00:00 2001
From: Moses Paul R 
Date: Tue, 14 Jan 2025 12:39:32 +0000
Subject: [PATCH 42/92] more cleanup

---
 marker/providers/pdf.py | 28 ++++++++++++++++------------
 1 file changed, 16 insertions(+), 12 deletions(-)

diff --git a/marker/providers/pdf.py b/marker/providers/pdf.py
index d9d344e4..ab441c5f 100644
--- a/marker/providers/pdf.py
+++ b/marker/providers/pdf.py
@@ -78,7 +78,6 @@ def __init__(self, filepath: str, config=None):
 
         self.doc: pdfium.PdfDocument = pdfium.PdfDocument(self.filepath)
         self.page_lines: ProviderPageLines = {i: [] for i in range(len(self.doc))}
-        self.refs = {}
 
         if self.page_range is None:
             self.page_range = range(len(self.doc))
@@ -168,11 +167,15 @@ def pdftext_extraction(self) -> ProviderPageLines:
         LineClass: Line = get_block_class(BlockTypes.Line)
 
         if not self.disable_links:
+            refs = {}
+
+            # we first go through the entire document and merge links and collect refs
             for page in page_char_blocks:
-                self.merge_links(page)
+                self.merge_links(page, refs)
 
+            # we can now merge the collected refs for each page
             for page in page_char_blocks:
-                self.merge_refs(page)
+                self.merge_refs(page, refs)
 
         for page in page_char_blocks:
             page_id = page["page"]
@@ -219,7 +222,7 @@ def pdftext_extraction(self) -> ProviderPageLines:
 
         return page_lines
 
-    def merge_links(self, page):
+    def merge_links(self, page, refs):
         """
         Merges links with spans. Some spans can also have multiple links associated with them.
         We break up the spans and reconstruct them taking the links into account.
@@ -246,7 +249,7 @@ def merge_links(self, page):
                 continue
 
             dest_page = link['dest_page']
-            self.refs.setdefault(dest_page, [])
+            refs.setdefault(dest_page, [])
             link['url'] = f"#page-{dest_page}"
             if link['dest_pos']:
                 dest_pos = link['dest_pos']
@@ -254,12 +257,13 @@ def merge_links(self, page):
                 # Don't link to self if there is no dest_pos
                 if dest_page == page_id:
                     continue
+                # if we don't have a dest pos, we just link to the top of the page
                 dest_pos = [0.0, 0.0]
 
-            if dest_pos not in self.refs[dest_page]:
-                self.refs[dest_page].append(dest_pos)
+            if dest_pos not in refs[dest_page]:
+                refs[dest_page].append(dest_pos)
 
-            link['url'] += f"-{self.refs[dest_page].index(dest_pos)}"
+            link['url'] += f"-{refs[dest_page].index(dest_pos)}"
 
             span_link_map.setdefault(max_intersection, [])
             span_link_map[max_intersection].append(link)
@@ -276,15 +280,15 @@ def merge_links(self, page):
                     span_idx += 1
                 line['spans'] = spans
 
-    def merge_refs(self, page):
+    def merge_refs(self, page, refs):
         """
         We associate each reference to the nearest span.
         """
 
         page_id = page["page"]
 
-        refs = self.refs.get(page_id, [])
-        if not refs:
+        page_refs = refs.get(page_id, [])
+        if not page_refs:
             return
 
         spans = [span for block in page['blocks'] for line in block['lines'] for span in line['spans']]
@@ -292,7 +296,7 @@ def merge_refs(self, page):
             return
 
         span_starts = np.array([span['bbox'][:2] for span in spans])
-        ref_starts = np.array(refs)
+        ref_starts = np.array(page_refs)
 
         distances = np.linalg.norm(span_starts[:, np.newaxis, :] - ref_starts[np.newaxis, :, :], axis=2)
 

From acf2aa4a65d49bf099169546ba12297a023cf1a3 Mon Sep 17 00:00:00 2001
From: Vik Paruchuri 
Date: Tue, 14 Jan 2025 09:58:30 -0500
Subject: [PATCH 43/92] Add way to bypass table detection

---
 marker/builders/layout.py    | 32 +++++++++++++++++++++++++--
 marker/config/parser.py      |  4 ++++
 marker/config/printer.py     | 43 +++++++++++++++++++++++++++---------
 marker/renderers/markdown.py | 21 +++++++++++++-----
 4 files changed, 82 insertions(+), 18 deletions(-)

diff --git a/marker/builders/layout.py b/marker/builders/layout.py
index f712a500..3539a984 100644
--- a/marker/builders/layout.py
+++ b/marker/builders/layout.py
@@ -2,7 +2,7 @@
 
 import numpy as np
 from surya.layout import LayoutPredictor
-from surya.layout.schema import LayoutResult
+from surya.layout.schema import LayoutResult, LayoutBox
 from surya.ocr_error import OCRErrorPredictor
 from surya.ocr_error.schema import OCRErrorDetectionResult
 
@@ -50,6 +50,10 @@ class LayoutBuilder(BaseBuilder):
         Tuple[BlockTypes],
         "A list of block types to exclude from the layout coverage check.",
     ] = (BlockTypes.Figure, BlockTypes.Picture, BlockTypes.Table, BlockTypes.FigureGroup, BlockTypes.TableGroup, BlockTypes.PictureGroup)
+    force_layout_block: Annotated[
+        str,
+        "Skip layout and force every page to be treated as a specific block type.",
+    ] = None
 
     def __init__(self, layout_model: LayoutPredictor, ocr_error_model: OCRErrorPredictor, config=None):
         self.layout_model = layout_model
@@ -58,7 +62,11 @@ def __init__(self, layout_model: LayoutPredictor, ocr_error_model: OCRErrorPredi
         super().__init__(config)
 
     def __call__(self, document: Document, provider: PdfProvider):
-        layout_results = self.surya_layout(document.pages)
+        if self.force_layout_block is not None:
+            # Assign the full content of every page to a single layout type
+            layout_results = self.forced_layout(document.pages)
+        else:
+            layout_results = self.surya_layout(document.pages)
         self.add_blocks_to_pages(document.pages, layout_results)
         self.merge_blocks(document.pages, provider.page_lines)
 
@@ -69,6 +77,26 @@ def get_batch_size(self):
             return 6
         return 6
 
+    def forced_layout(self, pages: List[PageGroup]) -> List[LayoutResult]:
+        layout_results = []
+        for page in pages:
+            layout_results.append(
+                LayoutResult(
+                    image_bbox=page.polygon.bbox,
+                    bboxes=[
+                        LayoutBox(
+                            label=self.force_layout_block,
+                            position=0,
+                            top_k={self.force_layout_block: 1},
+                            polygon=page.polygon.polygon,
+                        ),
+                    ],
+                    sliced=False
+                )
+            )
+        return layout_results
+
+
     def surya_layout(self, pages: List[PageGroup]) -> List[LayoutResult]:
         layout_results = self.layout_model(
             [p.get_image(highres=False) for p in pages],
diff --git a/marker/config/parser.py b/marker/config/parser.py
index ac0dcd30..ce9d7005 100644
--- a/marker/config/parser.py
+++ b/marker/config/parser.py
@@ -11,6 +11,7 @@
 from marker.renderers.markdown import MarkdownRenderer
 from marker.settings import settings
 from marker.util import classes_to_strings, parse_range_str, strings_to_classes
+from marker.schema import BlockTypes
 
 
 class ConfigParser:
@@ -41,6 +42,9 @@ def common_options(fn):
         fn = click.option("--google_api_key", type=str, default=None, help="Google API key for using LLMs.")(fn)
         fn = click.option("--use_llm", is_flag=True, default=False, help="Enable higher quality processing with LLMs.")(fn)
         fn = click.option("--converter_cls", type=str, default=None, help="Converter class to use.  Defaults to PDF converter.")(fn)
+
+        # enum options
+        fn = click.option("--force_layout_block", type=click.Choice(choices=[t.name for t in BlockTypes]), default=None,)(fn)
         return fn
 
     def generate_config_dict(self) -> Dict[str, any]:
diff --git a/marker/config/printer.py b/marker/config/printer.py
index 6902b836..fd37396e 100644
--- a/marker/config/printer.py
+++ b/marker/config/printer.py
@@ -3,24 +3,51 @@
 import click
 
 from marker.config.crawler import crawler
+from marker.schema import BlockTypes
 
 
 class CustomClickPrinter(click.Command):
     def parse_args(self, ctx, args):
+
         display_help = 'config' in args and '--help' in args
         if display_help:
             click.echo(
                 "Here is a list of all the Builders, Processors, Converters, Providers and Renderers in Marker along with their attributes:")
 
+        # Keep track of shared attributes and their types
         shared_attrs = {}
 
+        # First pass: identify shared attributes and verify compatibility
         for base_type, base_type_dict in crawler.class_config_map.items():
             for class_name, class_map in base_type_dict.items():
-                for attr in class_map['config'].keys():
+                for attr, (attr_type, formatted_type, default, metadata) in class_map['config'].items():
                     if attr not in shared_attrs:
-                        shared_attrs[attr] = []
-                    shared_attrs[attr].append(class_name)
+                        shared_attrs[attr] = {
+                            'classes': [],
+                            'type': attr_type,
+                            'is_flag': attr_type in [bool, Optional[bool]] and not default,
+                            'metadata': metadata,
+                            'default': default
+                        }
+                    shared_attrs[attr]['classes'].append(class_name)
+
+        # These are the types of attrs that can be set from the command line
+        attr_types = [str, int, float, bool, Optional[int], Optional[float], Optional[str]]
 
+        # Add shared attribute options first
+        for attr, info in shared_attrs.items():
+            if info['type'] in attr_types:
+                ctx.command.params.append(
+                    click.Option(
+                        ["--" + attr],
+                        type=info['type'],
+                        help=" ".join(info['metadata']) + f" (Applies to: {', '.join(info['classes'])})",
+                        default=info['default'],
+                        is_flag=info['is_flag'],
+                    )
+                )
+
+        # Second pass: create class-specific options
         for base_type, base_type_dict in crawler.class_config_map.items():
             if display_help:
                 click.echo(f"{base_type}s:")
@@ -35,17 +62,13 @@ def parse_args(self, ctx, args):
                         click.echo(" " * 8 + f"{attr} ({formatted_type}):")
                         click.echo("\n".join([f'{" " * 12}' + desc for desc in metadata]))
 
-                    if attr_type in [str, int, float, bool, Optional[int], Optional[float], Optional[str]]:
+                    if attr_type in attr_types:
                         is_flag = attr_type in [bool, Optional[bool]] and not default
 
-                        # Only include the generic --attr option if it's unique
-                        options = ["--" + class_name_attr, class_name_attr]
-                        if len(shared_attrs[attr]) == 1:
-                            options.insert(0, "--" + attr)
-
+                        # Only add class-specific options
                         ctx.command.params.append(
                             click.Option(
-                                options,
+                                ["--" + class_name_attr, class_name_attr],
                                 type=attr_type,
                                 help=" ".join(metadata),
                                 default=default,
diff --git a/marker/renderers/markdown.py b/marker/renderers/markdown.py
index aa45294c..96dd2508 100644
--- a/marker/renderers/markdown.py
+++ b/marker/renderers/markdown.py
@@ -1,4 +1,5 @@
 import re
+from collections import defaultdict
 from typing import Annotated, Tuple
 
 import regex
@@ -57,11 +58,14 @@ def convert_table(self, el, text, convert_as_inline):
         total_rows = len(el.find_all('tr'))
         colspans = []
         is_header_row = []
-        for row in el.find_all('tr'):
-            row_cols = 0
+        rowspan_cols = defaultdict(int)
+        for i, row in enumerate(el.find_all('tr')):
+            row_cols = rowspan_cols[i]
             for cell in row.find_all(['td', 'th']):
                 colspan = int(cell.get('colspan', 1))
                 row_cols += colspan
+                for r in range(int(cell.get('rowspan', 1)) - 1):
+                    rowspan_cols[i + r] += colspan # Add the colspan to the next rows, so they get the correct number of columns
             colspans.append(row_cols)
             is_header_row.append(len(row.find_all('th')) == row_cols)
         total_cols = max(colspans)
@@ -86,10 +90,15 @@ def convert_table(self, el, text, convert_as_inline):
 
                 for r in range(rowspan):
                     for c in range(colspan):
-                        if r == 0 and c == 0:
-                            grid[row_idx][col_idx] = value
-                        else:
-                            grid[row_idx + r][col_idx + c] = ''
+                        try:
+                            if r == 0 and c == 0:
+                                grid[row_idx][col_idx] = value
+                            else:
+                                grid[row_idx + r][col_idx + c] = ''
+                        except IndexError:
+                            # Sometimes the colspan/rowspan predictions can overflow
+                            print(f"Overflow in columns: {col_idx + c} >= {total_cols}")
+                            continue
 
                 col_idx += colspan
 

From a818baf4d91a1f8ec63f9b63a07da4a63eae1cfa Mon Sep 17 00:00:00 2001
From: Vik Paruchuri 
Date: Tue, 14 Jan 2025 11:55:28 -0500
Subject: [PATCH 44/92] heuristics to split tables

---
 marker/processors/table.py | 88 ++++++++++++++++++++++++++++++++++----
 marker/providers/pdf.py    | 15 ++++++-
 marker/renderers/html.py   |  4 +-
 3 files changed, 96 insertions(+), 11 deletions(-)

diff --git a/marker/processors/table.py b/marker/processors/table.py
index a7d586ac..3d231138 100644
--- a/marker/processors/table.py
+++ b/marker/processors/table.py
@@ -1,5 +1,6 @@
 import re
 from collections import defaultdict
+from copy import deepcopy
 from typing import Annotated, List
 
 from ftfy import fix_text
@@ -86,6 +87,7 @@ def __call__(self, document: Document):
             batch_size=self.get_table_rec_batch_size()
         )
         self.assign_text_to_cells(tables, table_data)
+        self.split_combined_rows(tables) # Split up rows that were combined
 
         # Assign table cells to the table
         table_idx = 0
@@ -98,7 +100,7 @@ def __call__(self, document: Document):
                     cell_polygon = PolygonBox(polygon=cell.polygon).rescale(page.get_image(highres=True).size, page.polygon.size)
                     cell_block = TableCell(
                         polygon=cell_polygon,
-                        text=cell.text or "", # Cells can be blank (no text)
+                        text="\n".join([self.normalize_spaces(fix_text(t["text"])) for t in cell.text_lines]) if cell.text_lines else "", # Cells can be blank (no text)
                         rowspan=cell.rowspan,
                         colspan=cell.colspan,
                         row_id=cell.row_id,
@@ -110,6 +112,71 @@ def __call__(self, document: Document):
                     block.add_structure(cell_block)
                 table_idx += 1
 
+    @staticmethod
+    def normalize_spaces(text):
+        space_chars = [
+            '\u2003',  # em space
+            '\u2002',  # en space
+            '\u00A0',  # non-breaking space
+            '\u200B',  # zero-width space
+            '\u3000',  # ideographic space
+        ]
+        for space in space_chars:
+            text = text.replace(space, ' ')
+        return text
+
+    def split_combined_rows(self, tables: List[TableResult]):
+        for table in tables:
+            unique_rows = sorted(list(set([c.row_id for c in table.cells])))
+            new_cells = []
+            shift_up = 0
+            max_cell_id = max([c.cell_id for c in table.cells])
+            new_cell_count = 0
+            for row in unique_rows:
+                # Cells in this row
+                # Deepcopy is because we do an in-place mutation later, and that can cause rows to shift to match rows in unique_rows
+                # making them be processed twice
+                row_cells = deepcopy([c for c in table.cells if c.row_id == row and c.cell_id])
+                rowspans = [c.rowspan for c in row_cells]
+                line_lens = [len(c.text_lines) if isinstance(c.text_lines, list) else 1 for c in row_cells]
+
+                # Other cells that span into this row
+                rowspan_cells = [c for c in table.cells if c.row_id != row and c.row_id + c.rowspan > row > c.row_id]
+                should_split = all([
+                    len(row_cells) > 0,
+                    len(rowspan_cells) == 0,
+                    all([r == 1 for r in rowspans]),
+                    all([l > 1 for l in line_lens]),
+                    all([l == line_lens[0] for l in line_lens])
+                ])
+                if should_split:
+                    for i in range(0, line_lens[0]):
+                        for cell in row_cells:
+                            line = cell.text_lines[i]
+                            cell_id = max_cell_id + new_cell_count
+                            new_cells.append(
+                                SuryaTableCell(
+                                    polygon=line["bbox"],
+                                    text_lines=[line],
+                                    rowspan=1,
+                                    colspan=cell.colspan,
+                                    row_id=cell.row_id + shift_up + i,
+                                    col_id=cell.col_id,
+                                    is_header=cell.is_header,
+                                    within_row_id=cell.within_row_id,
+                                    cell_id=cell_id
+                                )
+                            )
+                            new_cell_count += 1
+
+                    # For each new row we add, shift up subsequent rows
+                    shift_up += line_lens[0] - 1
+                else:
+                    for cell in row_cells:
+                        cell.row_id += shift_up
+                        new_cells.append(cell)
+            table.cells = new_cells
+
     def assign_text_to_cells(self, tables: List[TableResult], table_data: list):
         for table_result, table_page_data in zip(tables, table_data):
             table_text_lines = table_page_data["table_text_lines"]
@@ -127,18 +194,21 @@ def assign_text_to_cells(self, tables: List[TableResult], table_data: list):
 
                 table_text_line["text"] = fix_text(table_text_line["text"])
                 max_intersection = intersections.argmax()
-                if not table_cells[max_intersection].text:
-                    table_cells[max_intersection].text = []
+                if not table_cells[max_intersection].text_lines:
+                    table_cells[max_intersection].text_lines = []
 
                 cell_text[max_intersection].append(table_text_line)
 
             for k in cell_text:
                 # TODO: see if the text needs to be sorted (based on rotation)
-                text = "\n".join([ct["text"] for ct in cell_text[k]])
-                # Replace . . . etc with ...
-                text = re.sub(r"(\s\.){3,}", "...", text) # Replace . . .
-                text = re.sub(r"\.{3,}", "...", text) # Replace ..., like in table of contents
-                table_cells[k].text = text
+                text = cell_text[k]
+                for item in text:
+                    item["text"] = re.sub(r"(\s\.){3,}", "...", item["text"]) # Replace . . .
+                    item["text"] = re.sub(r"\.{3,}", "...", item["text"]) # Replace ..., like in table of contents
+
+                assert all("text" in t for t in text), "All text lines must have text"
+                assert all("bbox" in t for t in text), "All text lines must have a bbox"
+                table_cells[k].text_lines = text
 
     def assign_pdftext_lines(self, extract_blocks: list, filepath: str):
         table_inputs = []
@@ -165,7 +235,7 @@ def assign_pdftext_lines(self, extract_blocks: list, filepath: str):
             table_idx = 0
             for block in extract_blocks:
                 if block["page_id"] == pnum:
-                    block["table_text_lines"]: List[TableCell] = page_tables[table_idx]
+                    block["table_text_lines"] = page_tables[table_idx]
                     table_idx += 1
             assert table_idx == len(page_tables), "Number of tables and table inputs must match"
 
diff --git a/marker/providers/pdf.py b/marker/providers/pdf.py
index 09b9603d..f275cc41 100644
--- a/marker/providers/pdf.py
+++ b/marker/providers/pdf.py
@@ -143,6 +143,19 @@ def font_names_to_format(self, font_name: str | None) -> Set[str]:
             formats.add("italic")
         return formats
 
+    @staticmethod
+    def normalize_spaces(text):
+        space_chars = [
+            '\u2003',  # em space
+            '\u2002',  # en space
+            '\u00A0',  # non-breaking space
+            '\u200B',  # zero-width space
+            '\u3000',  # ideographic space
+        ]
+        for space in space_chars:
+            text = text.replace(space, ' ')
+        return text
+
     def pdftext_extraction(self) -> ProviderPageLines:
         page_lines: ProviderPageLines = {}
         page_char_blocks = dictionary_output(
@@ -177,7 +190,7 @@ def pdftext_extraction(self) -> ProviderPageLines:
                         spans.append(
                             SpanClass(
                                 polygon=polygon,
-                                text=fix_text(span["text"]),
+                                text=self.normalize_spaces(fix_text(span["text"])),
                                 font=font_name,
                                 font_weight=font_weight,
                                 font_size=font_size,
diff --git a/marker/renderers/html.py b/marker/renderers/html.py
index 4f3a27c7..6f31a738 100644
--- a/marker/renderers/html.py
+++ b/marker/renderers/html.py
@@ -78,10 +78,12 @@ def extract_html(self, document, document_output, level=0):
                 images.update(sub_images)
                 ref.replace_with(BeautifulSoup(f"{content}", 'html.parser'))
 
-        output = str(soup)
         if level == 0:
+            output = soup.prettify()
             output = self.merge_consecutive_tags(output, 'b')
             output = self.merge_consecutive_tags(output, 'i')
+        else:
+            output = str(soup)
 
         return output, images
 

From 0dc0e948ef6236a4b43e7cda2db812691560eb72 Mon Sep 17 00:00:00 2001
From: Moses Paul R 
Date: Tue, 14 Jan 2025 17:06:31 +0000
Subject: [PATCH 45/92] move link code to pdftext

---
 marker/providers/pdf.py          | 263 +------------------------------
 tests/builders/test_pdf_links.py |   2 +-
 2 files changed, 4 insertions(+), 261 deletions(-)

diff --git a/marker/providers/pdf.py b/marker/providers/pdf.py
index ab441c5f..8232e742 100644
--- a/marker/providers/pdf.py
+++ b/marker/providers/pdf.py
@@ -1,15 +1,12 @@
 import atexit
 import ctypes
-import math
 import re
-from typing import Annotated, List, Optional, Set, Tuple
+from typing import Annotated, List, Optional, Set
 
-import numpy as np
 import pypdfium2 as pdfium
 import pypdfium2.raw as pdfium_c
 from ftfy import fix_text
 from pdftext.extraction import dictionary_output
-from pdftext.schema import Bbox
 from PIL import Image
 
 from marker.providers import BaseProvider, ProviderOutput, ProviderPageLines
@@ -19,7 +16,6 @@
 from marker.schema.registry import get_block_class
 from marker.schema.text.line import Line
 from marker.schema.text.span import Span
-from marker.util import matrix_intersection_area
 
 
 class PdfProvider(BaseProvider):
@@ -159,24 +155,14 @@ def pdftext_extraction(self) -> ProviderPageLines:
             keep_chars=True,
             workers=self.pdftext_workers,
             flatten_pdf=self.flatten_pdf,
-            quote_loosebox=False
+            quote_loosebox=False,
+            disable_links=self.disable_links
         )
         self.page_bboxes = {i: [0, 0, page["width"], page["height"]] for i, page in zip(self.page_range, page_char_blocks)}
 
         SpanClass: Span = get_block_class(BlockTypes.Span)
         LineClass: Line = get_block_class(BlockTypes.Line)
 
-        if not self.disable_links:
-            refs = {}
-
-            # we first go through the entire document and merge links and collect refs
-            for page in page_char_blocks:
-                self.merge_links(page, refs)
-
-            # we can now merge the collected refs for each page
-            for page in page_char_blocks:
-                self.merge_refs(page, refs)
-
         for page in page_char_blocks:
             page_id = page["page"]
             lines: List[ProviderOutput] = []
@@ -222,133 +208,6 @@ def pdftext_extraction(self) -> ProviderPageLines:
 
         return page_lines
 
-    def merge_links(self, page, refs):
-        """
-        Merges links with spans. Some spans can also have multiple links associated with them.
-        We break up the spans and reconstruct them taking the links into account.
-        """
-        page_id = page["page"]
-
-        links = self.get_links(page_id)
-
-        spans = [span for block in page['blocks'] for line in block['lines'] for span in line['spans']]
-        span_bboxes = [span['bbox'] for span in spans]
-        link_bboxes = [link['bbox'] for link in links]
-        intersection_matrix = matrix_intersection_area(link_bboxes, span_bboxes)
-
-        span_link_map = {}
-        for link_idx, link in enumerate(links):
-            intersection_link = intersection_matrix[link_idx]
-            if intersection_link.sum() == 0:
-                continue
-
-            max_intersection = intersection_link.argmax()
-            span = spans[max_intersection]
-
-            if link['dest_page'] is None:
-                continue
-
-            dest_page = link['dest_page']
-            refs.setdefault(dest_page, [])
-            link['url'] = f"#page-{dest_page}"
-            if link['dest_pos']:
-                dest_pos = link['dest_pos']
-            else:
-                # Don't link to self if there is no dest_pos
-                if dest_page == page_id:
-                    continue
-                # if we don't have a dest pos, we just link to the top of the page
-                dest_pos = [0.0, 0.0]
-
-            if dest_pos not in refs[dest_page]:
-                refs[dest_page].append(dest_pos)
-
-            link['url'] += f"-{refs[dest_page].index(dest_pos)}"
-
-            span_link_map.setdefault(max_intersection, [])
-            span_link_map[max_intersection].append(link)
-
-        span_idx = 0
-        for block in page["blocks"]:
-            for line in block["lines"]:
-                spans = []
-                for span in line["spans"]:
-                    if span_idx in span_link_map:
-                        spans.extend(self._reconstruct_spans(span, span_link_map[span_idx]))
-                    else:
-                        spans.append(span)
-                    span_idx += 1
-                line['spans'] = spans
-
-    def merge_refs(self, page, refs):
-        """
-        We associate each reference to the nearest span.
-        """
-
-        page_id = page["page"]
-
-        page_refs = refs.get(page_id, [])
-        if not page_refs:
-            return
-
-        spans = [span for block in page['blocks'] for line in block['lines'] for span in line['spans']]
-        if not spans:
-            return
-
-        span_starts = np.array([span['bbox'][:2] for span in spans])
-        ref_starts = np.array(page_refs)
-
-        distances = np.linalg.norm(span_starts[:, np.newaxis, :] - ref_starts[np.newaxis, :, :], axis=2)
-
-        for ref_idx in range(len(ref_starts)):
-            span_idx = np.argmin(distances[:, ref_idx])
-            spans[span_idx].setdefault('anchors', [])
-            spans[span_idx]['anchors'].append(f"page-{page_id}-{ref_idx}")
-
-    def _reconstruct_spans(self, orig_span: dict, links: List[dict]):
-        """
-        Reconstructs the spans by breaking them up into smaller spans based on the links.
-        """
-        spans = []
-        span = None
-        link_bboxes = [Bbox(link['bbox']) for link in links]
-
-        for char in orig_span['chars']:
-            char_bbox = Bbox(char['bbox'])
-            intersections = []
-            for i, link_bbox in enumerate(link_bboxes):
-                area = link_bbox.intersection_area(char_bbox)
-                if area > 0:
-                    intersections.append((area, links[i]))
-
-            current_url = ''
-            if intersections:
-                intersections.sort(key=lambda x: x[0], reverse=True)
-                current_url = intersections[0][1]['url']
-
-            if not span or current_url != span['url']:
-                span = {
-                    "bbox": char_bbox,
-                    "text": char["char"],
-                    "rotation": char["rotation"],
-                    "font": char["font"],
-                    "char_start_idx": char["char_idx"],
-                    "char_end_idx": char["char_idx"],
-                    "chars": [char],
-                    "url": current_url
-                }
-                spans.append(span)
-            else:
-                span['text'] += char['char']
-                span['char_end_idx'] = char['char_idx']
-                span['bbox'] = span['bbox'].merge(char_bbox)
-                span['chars'].append(char)
-
-        for span in spans:
-            span['bbox'] = span['bbox'].bbox
-
-        return spans
-
     def check_line_spans(self, page_lines: List[ProviderOutput]) -> bool:
         page_spans = [span for line in page_lines for span in line.spans]
         if len(page_spans) == 0:
@@ -464,119 +323,3 @@ def _get_fontname(font) -> str:
             pass
 
         return font_name
-
-    @staticmethod
-    def _get_dest_position(dest) -> Optional[Tuple[float, float]]:
-        has_x = ctypes.c_int()
-        has_y = ctypes.c_int()
-        has_zoom = ctypes.c_int()
-        x_coord = ctypes.c_float()
-        y_coord = ctypes.c_float()
-        zoom_level = ctypes.c_float()
-        success = pdfium_c.FPDFDest_GetLocationInPage(
-            dest,
-            ctypes.byref(has_x),
-            ctypes.byref(has_y),
-            ctypes.byref(has_zoom),
-            ctypes.byref(x_coord),
-            ctypes.byref(y_coord),
-            ctypes.byref(zoom_level)
-        )
-        if success:
-            if has_x.value and has_y.value:
-                return x_coord.value, y_coord.value
-        else:
-            return None
-
-    @staticmethod
-    def _rect_to_scaled_bbox(rect, page_bbox, page_rotation) -> List[float]:
-        page_width = math.ceil(abs(page_bbox[2] - page_bbox[0]))
-        page_height = math.ceil(abs(page_bbox[1] - page_bbox[3]))
-
-        cx_start, cy_start, cx_end, cy_end = rect
-        cx_start -= page_bbox[0]
-        cx_end -= page_bbox[0]
-        cy_start -= page_bbox[1]
-        cy_end -= page_bbox[1]
-
-        ty_start = page_height - cy_start
-        ty_end = page_height - cy_end
-
-        bbox = [min(cx_start, cx_end), min(ty_start, ty_end), max(cx_start, cx_end), max(ty_start, ty_end)]
-        return Bbox(bbox).rotate(page_width, page_height, page_rotation).bbox
-
-    @staticmethod
-    def _xy_to_scaled_pos(x, y, page_bbox, page_rotation, expand_by=1) -> List[float]:
-        return PdfProvider._rect_to_scaled_bbox([x - expand_by, y - expand_by, x + expand_by, y + expand_by], page_bbox, page_rotation)[:2]
-
-    def get_links(self, page_idx):
-        urls = []
-        page = self.doc[page_idx]
-        page_bbox: List[float] = page.get_bbox()
-        page_rotation = 0
-        try:
-            page_rotation = page.get_rotation()
-        except:
-            pass
-
-        annot_count = pdfium_c.FPDFPage_GetAnnotCount(page)
-        for i in range(annot_count):
-            link = {
-                'bbox': None,
-                'page': page_idx,
-                'dest_page': None,
-                'dest_pos': None,
-                'url': None,
-            }
-            annot = pdfium_c.FPDFPage_GetAnnot(page, i)
-            if pdfium_c.FPDFAnnot_GetSubtype(annot) != pdfium_c.FPDF_ANNOT_LINK:
-                continue
-
-            fs_rect = pdfium_c.FS_RECTF()
-            success = pdfium_c.FPDFAnnot_GetRect(annot, ctypes.byref(fs_rect))
-            if not success:
-                continue
-
-            link['bbox'] = self._rect_to_scaled_bbox(
-                [fs_rect.left, fs_rect.top, fs_rect.right, fs_rect.bottom],
-                page_bbox, page_rotation
-            )
-
-            link_obj = pdfium_c.FPDFAnnot_GetLink(annot)
-
-            dest = pdfium_c.FPDFLink_GetDest(self.doc, link_obj)
-            if dest:
-                tgt_page = pdfium_c.FPDFDest_GetDestPageIndex(self.doc, dest)
-                link['dest_page'] = tgt_page
-                dest_position = self._get_dest_position(dest)
-                if dest_position:
-                    link['dest_pos'] = self._xy_to_scaled_pos(*dest_position, page_bbox, page_rotation)
-
-            else:
-                action = pdfium_c.FPDFLink_GetAction(link_obj)
-                a_type = pdfium_c.FPDFAction_GetType(action)
-
-                if a_type == pdfium_c.PDFACTION_UNSUPPORTED:
-                    continue
-
-                elif a_type == pdfium_c.PDFACTION_GOTO:
-                    # Goto a page
-                    dest = pdfium_c.FPDFAction_GetDest(self.doc, action)
-                    if dest:
-                        tgt_page = pdfium_c.FPDFDest_GetDestPageIndex(self.doc, dest)
-                        link['dest_page'] = tgt_page
-                        dest_position = self._get_dest_position(dest)
-                        if dest_position:
-                            link['dest_pos'] = self._xy_to_scaled_pos(*dest_position, page_bbox, page_rotation)
-
-                elif a_type == pdfium_c.PDFACTION_URI:
-                    # External link
-                    needed_len = pdfium_c.FPDFAction_GetURIPath(self.doc, action, None, 0)
-                    if needed_len > 0:
-                        buf = ctypes.create_string_buffer(needed_len)
-                        pdfium_c.FPDFAction_GetURIPath(self.doc, action, buf, needed_len)
-                        uri = buf.raw[:needed_len].decode('utf-8', errors='replace').rstrip('\x00')
-                        link["url"] = uri
-
-            urls.append(link)
-        return urls
diff --git a/tests/builders/test_pdf_links.py b/tests/builders/test_pdf_links.py
index 00bff7ee..72a97070 100644
--- a/tests/builders/test_pdf_links.py
+++ b/tests/builders/test_pdf_links.py
@@ -13,7 +13,7 @@ def test_pdf_links(pdf_document: Document, pdf_converter: PdfConverter, temp_pdf
     first_page = pdf_document.pages[0]
 
     for section_header_span in first_page.contained_blocks(pdf_document, (BlockTypes.Span,)):
-        if section_header_span.text == " II.":
+        if "II." in section_header_span.text:
             assert section_header_span.url == "#page-1-0"
             break
     else:

From 54ab2ccb25d9f83c151b83021ddd03569dfaff4a Mon Sep 17 00:00:00 2001
From: Moses Paul R 
Date: Tue, 14 Jan 2025 17:26:16 +0000
Subject: [PATCH 46/92] keep_chars=False [skip ci]

---
 marker/providers/pdf.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/marker/providers/pdf.py b/marker/providers/pdf.py
index 8232e742..f568cb42 100644
--- a/marker/providers/pdf.py
+++ b/marker/providers/pdf.py
@@ -152,7 +152,7 @@ def pdftext_extraction(self) -> ProviderPageLines:
         page_char_blocks = dictionary_output(
             self.filepath,
             page_range=self.page_range,
-            keep_chars=True,
+            keep_chars=False,
             workers=self.pdftext_workers,
             flatten_pdf=self.flatten_pdf,
             quote_loosebox=False,

From 14e9773ce3100a115095aee42197d3627f6eab0e Mon Sep 17 00:00:00 2001
From: Vik Paruchuri 
Date: Tue, 14 Jan 2025 12:34:23 -0500
Subject: [PATCH 47/92] Cleanup text handling

---
 marker/processors/table.py | 18 ++++++++----------
 1 file changed, 8 insertions(+), 10 deletions(-)

diff --git a/marker/processors/table.py b/marker/processors/table.py
index 3d231138..e943ab47 100644
--- a/marker/processors/table.py
+++ b/marker/processors/table.py
@@ -100,7 +100,7 @@ def __call__(self, document: Document):
                     cell_polygon = PolygonBox(polygon=cell.polygon).rescale(page.get_image(highres=True).size, page.polygon.size)
                     cell_block = TableCell(
                         polygon=cell_polygon,
-                        text="\n".join([self.normalize_spaces(fix_text(t["text"])) for t in cell.text_lines]) if cell.text_lines else "", # Cells can be blank (no text)
+                        text=self.finalize_cell_text(cell),
                         rowspan=cell.rowspan,
                         colspan=cell.colspan,
                         row_id=cell.row_id,
@@ -112,6 +112,12 @@ def __call__(self, document: Document):
                     block.add_structure(cell_block)
                 table_idx += 1
 
+    def finalize_cell_text(self, cell: SuryaTableCell):
+        text = "\n".join([t["text"].strip() for t in cell.text_lines]) if cell.text_lines else ""
+        text = re.sub(r"(\s\.){3,}", "...", text)  # Replace . . .
+        text = re.sub(r"\.{3,}", "...", text)  # Replace ..., like in table of contents
+        return self.normalize_spaces(fix_text(text))
+
     @staticmethod
     def normalize_spaces(text):
         space_chars = [
@@ -162,7 +168,7 @@ def split_combined_rows(self, tables: List[TableResult]):
                                     colspan=cell.colspan,
                                     row_id=cell.row_id + shift_up + i,
                                     col_id=cell.col_id,
-                                    is_header=cell.is_header,
+                                    is_header=cell.is_header and i == 0, # Only first line is header
                                     within_row_id=cell.within_row_id,
                                     cell_id=cell_id
                                 )
@@ -192,20 +198,12 @@ def assign_text_to_cells(self, tables: List[TableResult], table_data: list):
                 if intersections.sum() == 0:
                     continue
 
-                table_text_line["text"] = fix_text(table_text_line["text"])
                 max_intersection = intersections.argmax()
-                if not table_cells[max_intersection].text_lines:
-                    table_cells[max_intersection].text_lines = []
-
                 cell_text[max_intersection].append(table_text_line)
 
             for k in cell_text:
                 # TODO: see if the text needs to be sorted (based on rotation)
                 text = cell_text[k]
-                for item in text:
-                    item["text"] = re.sub(r"(\s\.){3,}", "...", item["text"]) # Replace . . .
-                    item["text"] = re.sub(r"\.{3,}", "...", item["text"]) # Replace ..., like in table of contents
-
                 assert all("text" in t for t in text), "All text lines must have text"
                 assert all("bbox" in t for t in text), "All text lines must have a bbox"
                 table_cells[k].text_lines = text

From f91c2ea6871f68e56831425f873753cc89cbf437 Mon Sep 17 00:00:00 2001
From: Vik Paruchuri 
Date: Tue, 14 Jan 2025 13:59:15 -0500
Subject: [PATCH 48/92] Merge tables on same page

---
 marker/processors/ignoretext.py          |  4 +---
 marker/processors/llm/llm_table_merge.py | 21 ++++++++++++++++++---
 marker/renderers/markdown.py             | 14 ++++++++++----
 3 files changed, 29 insertions(+), 10 deletions(-)

diff --git a/marker/processors/ignoretext.py b/marker/processors/ignoretext.py
index 45afcea5..d52ffcd8 100644
--- a/marker/processors/ignoretext.py
+++ b/marker/processors/ignoretext.py
@@ -17,8 +17,7 @@ class IgnoreTextProcessor(BaseProcessor):
     These blocks often represent repetitive or non-essential elements, such as headers, footers, or page numbers.
     """
     block_types = (
-        BlockTypes.Text, BlockTypes.PageHeader,
-        BlockTypes.PageFooter, BlockTypes.SectionHeader,
+        BlockTypes.Text, BlockTypes.SectionHeader,
         BlockTypes.TextInlineMath
     )
     common_element_threshold: Annotated[
@@ -47,7 +46,6 @@ def __call__(self, document: Document):
         last_blocks = []
         for page in document.pages:
             initial_block = None
-            block = None
             last_block = None
             for block in page.contained_blocks(document, self.block_types):
                 if block.structure is not None:
diff --git a/marker/processors/llm/llm_table_merge.py b/marker/processors/llm/llm_table_merge.py
index bf62c423..84b22886 100644
--- a/marker/processors/llm/llm_table_merge.py
+++ b/marker/processors/llm/llm_table_merge.py
@@ -100,10 +100,25 @@ def rewrite_blocks(self, document: Document):
         prev_block = None
         for page in document.pages:
             for block in page.contained_blocks(document, self.block_types):
+                if prev_block is None:
+                    subsequent_page_table = False
+                    same_page_vertical_table = False
+                else:
+                    subsequent_page_table = all([
+                        prev_block.page_id == block.page_id - 1, # Subsequent pages
+                        max(prev_block.polygon.height / page.polygon.height,
+                            block.polygon.height / page.polygon.height) > self.table_height_threshold, # Take up most of the page height
+                        block.polygon.y_start / page.polygon.height < self.table_start_threshold
+                        ])
+
+                    same_page_vertical_table = all([
+                        prev_block.page_id == block.page_id, # On the same page
+                        .75 < prev_block.polygon.height / block.polygon.height < 1.25, # Similar height
+                        abs(block.polygon.x_start - prev_block.polygon.x_end) < 20, # Close together
+                    ])
+
                 if prev_block is not None and \
-                    prev_block.page_id == block.page_id - 1 and \
-                    max(prev_block.polygon.height / page.polygon.height, block.polygon.height / page.polygon.height) > self.table_height_threshold and\
-                    block.polygon.y_start / page.polygon.height < self.table_start_threshold:
+                        (subsequent_page_table or same_page_vertical_table):
                     if prev_block not in table_run:
                         table_run.append(prev_block)
                     table_run.append(block)
diff --git a/marker/renderers/markdown.py b/marker/renderers/markdown.py
index 96dd2508..67524b03 100644
--- a/marker/renderers/markdown.py
+++ b/marker/renderers/markdown.py
@@ -57,7 +57,6 @@ def convert_math(self, el, text, convert_as_inline):
     def convert_table(self, el, text, convert_as_inline):
         total_rows = len(el.find_all('tr'))
         colspans = []
-        is_header_row = []
         rowspan_cols = defaultdict(int)
         for i, row in enumerate(el.find_all('tr')):
             row_cols = rowspan_cols[i]
@@ -67,7 +66,6 @@ def convert_table(self, el, text, convert_as_inline):
                 for r in range(int(cell.get('rowspan', 1)) - 1):
                     rowspan_cols[i + r] += colspan # Add the colspan to the next rows, so they get the correct number of columns
             colspans.append(row_cols)
-            is_header_row.append(len(row.find_all('th')) == row_cols)
         total_cols = max(colspans)
 
         grid = [[None for _ in range(total_cols)] for _ in range(total_rows)]
@@ -112,9 +110,12 @@ def convert_table(self, el, text, convert_as_inline):
         add_header_line = lambda: markdown_lines.append('|' + '|'.join('-' * (width + 2) for width in col_widths) + '|')
 
         # Generate markdown rows
+        added_header = False
         for i, row in enumerate(grid):
-            if i == 1:
-                add_header_line()
+            is_empty_line = all(not cell for cell in row)
+            if is_empty_line and not added_header:
+                # Skip leading blank lines
+                continue
 
             line = []
             for col_idx, cell in enumerate(row):
@@ -124,6 +125,11 @@ def convert_table(self, el, text, convert_as_inline):
                 line.append(f" {cell}{' ' * padding} ")
             markdown_lines.append('|' + '|'.join(line) + '|')
 
+            if not added_header:
+                # Skip empty lines when adding the header row
+                add_header_line()
+                added_header = True
+
         # Handle one row tables
         if total_rows == 1:
             add_header_line()

From f455262720c30cfa765476e493c6f2229d6f9b41 Mon Sep 17 00:00:00 2001
From: Vik Paruchuri 
Date: Tue, 14 Jan 2025 15:49:07 -0500
Subject: [PATCH 49/92] Enhance table merging

---
 marker/processors/llm/llm_table_merge.py | 31 ++++++++++++++++++------
 1 file changed, 24 insertions(+), 7 deletions(-)

diff --git a/marker/processors/llm/llm_table_merge.py b/marker/processors/llm/llm_table_merge.py
index 84b22886..369f9701 100644
--- a/marker/processors/llm/llm_table_merge.py
+++ b/marker/processors/llm/llm_table_merge.py
@@ -24,14 +24,24 @@ class LLMTableMergeProcessor(BaseLLMProcessor):
         float,
         "The maximum percentage down the page the second table can start to be considered for merging."
     ] = 0.2
+    vertical_table_height_threshold: Annotated[
+        float,
+        "The height tolerance for 2 adjacent tables to be merged into one."
+    ] = 0.25
+    vertical_table_distance_threshold: Annotated[
+        int,
+        "The maximum distance between table edges for adjacency."
+    ] = 20
     gemini_table_merge_prompt: Annotated[
         str,
         "The prompt to use for rewriting text.",
         "Default is a string containing the Gemini rewriting prompt."
     ] = """You're a text correction expert specializing in accurately reproducing tables from PDFs.
-You'll receive two images of tables from successive pages of a PDF.  Table 1 is from the first page, and Table 2 is from the second page.  Both tables may actually be part of the same larger table. Your job is to decide if Table 2 should be merged with Table 1, and how they should be joined.  The should only be merged if they're both part of the same larger table.
+You'll receive two images of tables from successive pages of a PDF.  Table 1 is from the first page, and Table 2 is from the second page.  Both tables may actually be part of the same larger table. Your job is to decide if Table 2 should be merged with Table 1, and how they should be joined.  The should only be merged if they're both part of the same larger table, and Table 2 cannot be interpreted without merging.
+
+You'll specify your judgement in json format - first whether Table 2 should be merged with Table 1, then the direction of the merge, either bottom or right.  Table 2 should be merged at the bottom of Table 1 if Table 2 has no headers, and the rows have similar values, meaning that Table 2 continues Table 1. Table2  should be merged to the right of Table 1 if each row in Table 2 matches a row in Table 1, meaning that Table 2 contains additional columns from Table 1.
 
-You'll specify your judgement in json format - first whether Table 2 should be merged with Table 1, then the direction of the merge, either bottom or right.  Table 2 should be merged at the bottom of Table 1 if they have similar headers, and the rows have similar values.  Table2  should be merged to the right of Table 1 if each row in Table 2 matches a row in Table 1.
+In general, you should only merge Table 1 and Table 2 if Table 2 cannot effectively be interpreted without merging.
 **Instructions:**
 1. Carefully examine the provided table images.  Table 1 is the first image, and Table 2 is the second image.
 2. Examine the provided html representations of Table 1 and Table 2.
@@ -98,8 +108,10 @@ def rewrite_blocks(self, document: Document):
         table_runs = []
         table_run = []
         prev_block = None
+        prev_page_block_count = None
         for page in document.pages:
-            for block in page.contained_blocks(document, self.block_types):
+            page_blocks = page.contained_blocks(document, self.block_types)
+            for block in page_blocks:
                 if prev_block is None:
                     subsequent_page_table = False
                     same_page_vertical_table = False
@@ -108,13 +120,13 @@ def rewrite_blocks(self, document: Document):
                         prev_block.page_id == block.page_id - 1, # Subsequent pages
                         max(prev_block.polygon.height / page.polygon.height,
                             block.polygon.height / page.polygon.height) > self.table_height_threshold, # Take up most of the page height
-                        block.polygon.y_start / page.polygon.height < self.table_start_threshold
+                            (len(page_blocks) == 1 or prev_page_block_count == 1) # Only table on the page
                         ])
 
                     same_page_vertical_table = all([
                         prev_block.page_id == block.page_id, # On the same page
-                        .75 < prev_block.polygon.height / block.polygon.height < 1.25, # Similar height
-                        abs(block.polygon.x_start - prev_block.polygon.x_end) < 20, # Close together
+                        (1 - self.vertical_table_height_threshold) < prev_block.polygon.height / block.polygon.height < (1 + self.vertical_table_height_threshold), # Similar height
+                        abs(block.polygon.x_start - prev_block.polygon.x_end) < self.vertical_table_distance_threshold, # Close together in x
                     ])
 
                 if prev_block is not None and \
@@ -127,6 +139,7 @@ def rewrite_blocks(self, document: Document):
                         table_runs.append(table_run)
                     table_run = []
                 prev_block = block
+            prev_page_block_count = len(page_blocks)
 
         if table_run:
             table_runs.append(table_run)
@@ -142,6 +155,10 @@ def rewrite_blocks(self, document: Document):
         pbar.close()
 
     def process_rewriting(self, document: Document, blocks: List[Block]):
+        if len(blocks) < 2:
+            # Can't merge single tables
+            return
+
         start_block = blocks[0]
         for i in range(1, len(blocks)):
             curr_block = blocks[i]
@@ -199,7 +216,7 @@ def process_rewriting(self, document: Document, blocks: List[Block]):
             # The original table is okay
             if "true" not in merge:
                 start_block = curr_block
-                return
+                continue
 
             # Merge the cells and images of the tables
             direction = response["direction"]

From 0c926147577435c81fc77aa8487739a19cf5c274 Mon Sep 17 00:00:00 2001
From: Vik Paruchuri 
Date: Tue, 14 Jan 2025 15:54:37 -0500
Subject: [PATCH 50/92] Patch section header issue

---
 marker/processors/sectionheader.py | 11 +++++++----
 marker/schema/blocks/base.py       |  2 +-
 pyproject.toml                     |  2 +-
 3 files changed, 9 insertions(+), 6 deletions(-)

diff --git a/marker/processors/sectionheader.py b/marker/processors/sectionheader.py
index 6d5e3d0f..08ae5a8f 100644
--- a/marker/processors/sectionheader.py
+++ b/marker/processors/sectionheader.py
@@ -36,9 +36,12 @@ class SectionHeaderProcessor(BaseProcessor):
     ] = 0.99
 
     def __call__(self, document: Document):
-        line_heights: Dict[int, List[float]] = {}
+        line_heights: Dict[int, float] = {}
         for page in document.pages:
-            for block in page.contained_blocks(document, self.block_types):
+            # Iterate children to grab all section headers
+            for block in page.children:
+                if block.block_type not in self.block_types:
+                    continue
                 if block.structure is not None:
                     line_heights[block.id] = block.line_height(document)
                 else:
@@ -49,11 +52,11 @@ def __call__(self, document: Document):
         heading_ranges = self.bucket_headings(flat_line_heights)
 
         for page in document.pages:
+            # Iterate children to grab all section headers
             for block in page.children:
                 if block.block_type not in self.block_types:
                     continue
-
-                block_height = line_heights[block.id]
+                block_height = line_heights.get(block.id, 0)
                 if block_height > 0:
                     for idx, (min_height, max_height) in enumerate(heading_ranges):
                         if block_height >= min_height * self.height_tolerance:
diff --git a/marker/schema/blocks/base.py b/marker/schema/blocks/base.py
index 2b64463d..a6ad98b8 100644
--- a/marker/schema/blocks/base.py
+++ b/marker/schema/blocks/base.py
@@ -220,7 +220,7 @@ def render(self, document: Document, parent_structure: Optional[List[str]], sect
             section_hierarchy=section_hierarchy
         )
 
-    def line_height(self, document: Document):
+    def line_height(self, document: Document) -> float:
         lines = self.contained_blocks(document, (BlockTypes.Line,))
         if len(lines) == 0:
             return 0
diff --git a/pyproject.toml b/pyproject.toml
index 07f1682f..7e8b1071 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "marker-pdf"
-version = "1.2.3"
+version = "1.2.4"
 description = "Convert PDF to markdown with high speed and accuracy."
 authors = ["Vik Paruchuri "]
 readme = "README.md"

From 8317db4d84b1fa36a4e4e1703c69a219705ae6f3 Mon Sep 17 00:00:00 2001
From: Vik Paruchuri 
Date: Wed, 15 Jan 2025 10:48:06 -0500
Subject: [PATCH 51/92] Fix various table bugs

---
 marker/converters/pdf.py                 |  2 +-
 marker/converters/table.py               |  2 +-
 marker/processors/llm/llm_complex.py     |  2 +-
 marker/processors/llm/llm_form.py        |  2 +-
 marker/processors/llm/llm_table.py       |  8 ++-
 marker/processors/llm/llm_table_merge.py | 84 +++++++++++++++++++-----
 marker/processors/llm/llm_text.py        |  2 +-
 marker/processors/table.py               |  7 +-
 marker/providers/pdf.py                  |  7 +-
 9 files changed, 89 insertions(+), 27 deletions(-)

diff --git a/marker/converters/pdf.py b/marker/converters/pdf.py
index d1fdb3ad..a52d6a6d 100644
--- a/marker/converters/pdf.py
+++ b/marker/converters/pdf.py
@@ -69,8 +69,8 @@ class PdfConverter(BaseConverter):
         PageHeaderProcessor,
         SectionHeaderProcessor,
         TableProcessor,
-        LLMTableMergeProcessor,
         LLMTableProcessor,
+        LLMTableMergeProcessor,
         LLMFormProcessor,
         TextProcessor,
         LLMTextProcessor,
diff --git a/marker/converters/table.py b/marker/converters/table.py
index dfeabd48..c2ee3854 100644
--- a/marker/converters/table.py
+++ b/marker/converters/table.py
@@ -16,8 +16,8 @@
 class TableConverter(PdfConverter):
     default_processors: Tuple[BaseProcessor, ...] = (
         TableProcessor,
-        LLMTableMergeProcessor,
         LLMTableProcessor,
+        LLMTableMergeProcessor,
         LLMFormProcessor,
         LLMComplexRegionProcessor,
     )
diff --git a/marker/processors/llm/llm_complex.py b/marker/processors/llm/llm_complex.py
index 0f8cc8fa..5d2e1425 100644
--- a/marker/processors/llm/llm_complex.py
+++ b/marker/processors/llm/llm_complex.py
@@ -14,7 +14,7 @@ class LLMComplexRegionProcessor(BaseLLMProcessor):
     block_types = (BlockTypes.ComplexRegion,)
     gemini_rewriting_prompt = """You are a text correction expert specializing in accurately reproducing text from images.
 You will receive an image of a text block and the text that can be extracted from the image.
-Your task is to correct any errors in the text, and format it properly.
+Your task is to correct any errors in the text, and format it properly.  Do not omit any text from the block - make sure everything is included in the markdown representation.  The markdown representation should be as faithful to the original text as possible.
 
 Formatting should be in markdown, with the following rules:
 - * for italics, ** for bold, and ` for inline code.
diff --git a/marker/processors/llm/llm_form.py b/marker/processors/llm/llm_form.py
index 96f0ed35..15c6deeb 100644
--- a/marker/processors/llm/llm_form.py
+++ b/marker/processors/llm/llm_form.py
@@ -15,7 +15,7 @@ class LLMFormProcessor(BaseLLMProcessor):
     gemini_rewriting_prompt = """You are a text correction expert specializing in accurately reproducing text from images.
 You will receive an image of a text block and an html representation of the form in the image.
 Your task is to correct any errors in the htmlrepresentation, and format it properly.
-Values and labels should appear in html tables, with the labels on the left side, and values on the right.  The headers should be "Labels" and "Values".  Other text in the form can appear between the tables.  Only use the tags `table, p, span, i, b, th, td, tr, and div`.
+Values and labels should appear in html tables, with the labels on the left side, and values on the right.  The headers should be "Labels" and "Values".  Other text in the form can appear between the tables.  Only use the tags `table, p, span, i, b, th, td, tr, and div`.  Do not omit any text from the form - make sure everything is included in the html representation.  It should be as faithful to the original form as possible.
 **Instructions:**
 1. Carefully examine the provided form block image.
 2. Analyze the html representation of the form.
diff --git a/marker/processors/llm/llm_table.py b/marker/processors/llm/llm_table.py
index 558cd672..f905f4b4 100644
--- a/marker/processors/llm/llm_table.py
+++ b/marker/processors/llm/llm_table.py
@@ -103,6 +103,12 @@ def process_rewriting(self, document: Document, page: PageGroup, block: Block):
             page.add_full_block(cell)
             block.add_structure(cell)
 
+    @staticmethod
+    def get_cell_text(element, keep_tags=('br',)):
+        for tag in element.find_all(True):
+            if tag.name not in keep_tags:
+                tag.unwrap()
+        return element.decode_contents().replace("
", "\n")
 
     def parse_html_table(self, html_text: str, block: Block, page: PageGroup) -> List[TableCell]:
         soup = BeautifulSoup(html_text, 'html.parser')
@@ -128,7 +134,7 @@ def parse_html_table(self, html_text: str, block: Block, page: PageGroup) -> Lis
                     print("Table parsing warning: too many columns found")
                     break
 
-                cell_text = cell.text.strip()
+                cell_text = self.get_cell_text(cell).strip()
                 rowspan = min(int(cell.get('rowspan', 1)), len(rows) - i)
                 colspan = min(int(cell.get('colspan', 1)), max_cols - cur_col)
                 cell_rows = list(range(i, i + rowspan))
diff --git a/marker/processors/llm/llm_table_merge.py b/marker/processors/llm/llm_table_merge.py
index 369f9701..d1561b76 100644
--- a/marker/processors/llm/llm_table_merge.py
+++ b/marker/processors/llm/llm_table_merge.py
@@ -37,11 +37,14 @@ class LLMTableMergeProcessor(BaseLLMProcessor):
         "The prompt to use for rewriting text.",
         "Default is a string containing the Gemini rewriting prompt."
     ] = """You're a text correction expert specializing in accurately reproducing tables from PDFs.
-You'll receive two images of tables from successive pages of a PDF.  Table 1 is from the first page, and Table 2 is from the second page.  Both tables may actually be part of the same larger table. Your job is to decide if Table 2 should be merged with Table 1, and how they should be joined.  The should only be merged if they're both part of the same larger table, and Table 2 cannot be interpreted without merging.
+You'll receive two images of tables from successive pages of a PDF.  Table 1 is from the first page, and Table 2 is from the second page.  Both tables may actually be part of the same larger table. Your job is to decide if Table 2 should be merged with Table 1, and how they should be joined.  The should only be merged if they're part of the same larger table, and Table 2 cannot be interpreted without merging.
 
-You'll specify your judgement in json format - first whether Table 2 should be merged with Table 1, then the direction of the merge, either bottom or right.  Table 2 should be merged at the bottom of Table 1 if Table 2 has no headers, and the rows have similar values, meaning that Table 2 continues Table 1. Table2  should be merged to the right of Table 1 if each row in Table 2 matches a row in Table 1, meaning that Table 2 contains additional columns from Table 1.
+You'll specify your judgement in json format - first whether Table 2 should be merged with Table 1, then the direction of the merge, either `bottom` or `right`.  A bottom merge means that the rows of Table 2 are joined to the rows of Table 1. A right merge means that the columns of Table 2 are joined to the columns of Table 1.  (bottom merge is equal to np.vstack, right merge is equal to np.hstack)
+
+Table 2 should be merged at the bottom of Table 1 if Table 2 has no headers, and the rows have similar values, meaning that Table 2 continues Table 1. Table 2 should be merged to the right of Table 1 if each row in Table 2 matches a row in Table 1, meaning that Table 2 contains additional columns that augment Table 1.
+
+Only merge Table 1 and Table 2 if Table 2 cannot be interpreted without merging.
 
-In general, you should only merge Table 1 and Table 2 if Table 2 cannot effectively be interpreted without merging.
 **Instructions:**
 1. Carefully examine the provided table images.  Table 1 is the first image, and Table 2 is the second image.
 2. Examine the provided html representations of Table 1 and Table 2.
@@ -70,12 +73,6 @@ class LLMTableMergeProcessor(BaseLLMProcessor):
 Table 2
 ```html
 
NameAgeCityState
Jane30Los AngelesCA

-    
-        
-        
-        
-        
-    
@@ -86,9 +83,9 @@ class LLMTableMergeProcessor(BaseLLMProcessor):
 Output:
 ```json
 {
-    "table1_description": "The first table has 4 headers, and 1 row.  The headers are Name, Age, City, and State.",
-    "table2_description": "The second table has 4 headers, and 1 row.  The headers are Name, Age, City, and State.",
-    "explanation": "The tables should be merged, as they have the same headers.  The second table should be merged to the bottom of the first table.",
+    "table1_description": "Table 1 has 4 headers, and 1 row.  The headers are Name, Age, City, and State.",
+    "table2_description": "Table 2 has no headers, but the values appear to represent a person's name, age, city, and state.",
+    "explanation": "The values in Table 2 match the headers in Table 1, and Table 2 has no headers. Table 2 should be merged to the bottom of Table 1.",
     "merge": "true",
     "direction": "bottom"
 }
@@ -103,6 +100,30 @@ class LLMTableMergeProcessor(BaseLLMProcessor):
 ```
 """
 
+    @staticmethod
+    def get_row_count(cells: List[TableCell]):
+        max_rows = None
+        for col_id in set([cell.col_id for cell in cells]):
+            col_cells = [cell for cell in cells if cell.col_id == col_id]
+            rows = 0
+            for cell in col_cells:
+                rows += cell.rowspan
+            if max_rows is None or rows > max_rows:
+                max_rows = rows
+        return max_rows
+
+    @staticmethod
+    def get_column_count(cells: List[TableCell]):
+        max_cols = None
+        for row_id in set([cell.row_id for cell in cells]):
+            row_cells = [cell for cell in cells if cell.row_id == row_id]
+            cols = 0
+            for cell in row_cells:
+                cols += cell.colspan
+            if max_cols is None or cols > max_cols:
+                max_cols = cols
+        return max_cols
+
     def rewrite_blocks(self, document: Document):
         pbar = tqdm(desc=f"{self.__class__.__name__} running")
         table_runs = []
@@ -116,17 +137,24 @@ def rewrite_blocks(self, document: Document):
                     subsequent_page_table = False
                     same_page_vertical_table = False
                 else:
+                    prev_cells = prev_block.contained_blocks(document, (BlockTypes.TableCell,))
+                    curr_cells = block.contained_blocks(document, (BlockTypes.TableCell,))
+                    row_match = abs(self.get_row_count(prev_cells) - self.get_row_count(curr_cells)) < 5, # Similar number of rows
+                    col_match = abs(self.get_column_count(prev_cells) - self.get_column_count(curr_cells)) < 2
+
                     subsequent_page_table = all([
                         prev_block.page_id == block.page_id - 1, # Subsequent pages
                         max(prev_block.polygon.height / page.polygon.height,
                             block.polygon.height / page.polygon.height) > self.table_height_threshold, # Take up most of the page height
-                            (len(page_blocks) == 1 or prev_page_block_count == 1) # Only table on the page
+                            (len(page_blocks) == 1 or prev_page_block_count == 1), # Only table on the page
+                            (row_match or col_match)
                         ])
 
                     same_page_vertical_table = all([
                         prev_block.page_id == block.page_id, # On the same page
                         (1 - self.vertical_table_height_threshold) < prev_block.polygon.height / block.polygon.height < (1 + self.vertical_table_height_threshold), # Similar height
                         abs(block.polygon.x_start - prev_block.polygon.x_end) < self.vertical_table_distance_threshold, # Close together in x
+                        row_match
                     ])
 
                 if prev_block is not None and \
@@ -166,7 +194,7 @@ def process_rewriting(self, document: Document, blocks: List[Block]):
             children_curr = curr_block.contained_blocks(document, (BlockTypes.TableCell,))
             if not children or not children_curr:
                 # Happens if table/form processors didn't run
-                continue
+                break
 
             start_image = start_block.get_image(document, highres=False)
             curr_image = curr_block.get_image(document, highres=False)
@@ -209,7 +237,7 @@ def process_rewriting(self, document: Document, blocks: List[Block]):
 
             if not response or ("direction" not in response or "merge" not in response):
                 curr_block.update_metadata(llm_error_count=1)
-                return
+                break
 
             merge = response["merge"]
 
@@ -220,19 +248,39 @@ def process_rewriting(self, document: Document, blocks: List[Block]):
 
             # Merge the cells and images of the tables
             direction = response["direction"]
+            if not self.validate_merge(children, children_curr, direction):
+                start_block = curr_block
+                continue
+
             merged_image = self.join_images(start_image, curr_image, direction)
             merged_cells = self.join_cells(children, children_curr, direction)
             curr_block.structure = []
             start_block.structure = [b.id for b in merged_cells]
             start_block.lowres_image = merged_image
 
-    @staticmethod
-    def join_cells(cells1: List[TableCell], cells2: List[TableCell], direction: Literal['right', 'bottom'] = 'right') -> List[TableCell]:
+    def validate_merge(self, cells1: List[TableCell], cells2: List[TableCell], direction: Literal['right', 'bottom'] = 'right'):
+        if direction == "right":
+            # Check if the number of rows is the same
+            cells1_row_count = self.get_row_count(cells1)
+            cells2_row_count = self.get_row_count(cells2)
+            return abs(cells1_row_count - cells2_row_count) < 5
+        elif direction == "bottom":
+            # Check if the number of columns is the same
+            cells1_col_count = self.get_column_count(cells1)
+            cells2_col_count = self.get_column_count(cells2)
+            return abs(cells1_col_count - cells2_col_count) < 2
+
+
+    def join_cells(self, cells1: List[TableCell], cells2: List[TableCell], direction: Literal['right', 'bottom'] = 'right') -> List[TableCell]:
         if direction == 'right':
+            # Shift columns right
+            col_count = self.get_column_count(cells1)
+            for cell in cells2:
+                cell.col_id += col_count
             new_cells = cells1 + cells2
         else:
             # Shift rows up
-            row_count = len(cells1)
+            row_count = self.get_row_count(cells1)
             for cell in cells2:
                 cell.row_id += row_count
             new_cells = cells1 + cells2
diff --git a/marker/processors/llm/llm_text.py b/marker/processors/llm/llm_text.py
index 9796a653..fda2cd77 100644
--- a/marker/processors/llm/llm_text.py
+++ b/marker/processors/llm/llm_text.py
@@ -16,7 +16,7 @@ class LLMTextProcessor(BaseLLMProcessor):
     gemini_rewriting_prompt = """You are a text correction expert specializing in accurately reproducing text from images.
 You will receive an image of a text block and a set of extracted lines corresponding to the text in the image.
 Your task is to correct any errors in the extracted lines, including math, formatting, and other inaccuracies, and output the corrected lines in a JSON format.
-The number of output lines MUST match the number of input lines.
+The number of output lines MUST match the number of input lines.  Stay as faithful to the original text as possible.
 
 **Instructions:**
 
diff --git a/marker/processors/table.py b/marker/processors/table.py
index e943ab47..45c6bcc8 100644
--- a/marker/processors/table.py
+++ b/marker/processors/table.py
@@ -142,7 +142,7 @@ def split_combined_rows(self, tables: List[TableResult]):
                 # Cells in this row
                 # Deepcopy is because we do an in-place mutation later, and that can cause rows to shift to match rows in unique_rows
                 # making them be processed twice
-                row_cells = deepcopy([c for c in table.cells if c.row_id == row and c.cell_id])
+                row_cells = deepcopy([c for c in table.cells if c.row_id == row])
                 rowspans = [c.rowspan for c in row_cells]
                 line_lens = [len(c.text_lines) if isinstance(c.text_lines, list) else 1 for c in row_cells]
 
@@ -181,7 +181,10 @@ def split_combined_rows(self, tables: List[TableResult]):
                     for cell in row_cells:
                         cell.row_id += shift_up
                         new_cells.append(cell)
-            table.cells = new_cells
+
+            # Only update the cells if we added new cells
+            if len(new_cells) > len(table.cells):
+                table.cells = new_cells
 
     def assign_text_to_cells(self, tables: List[TableResult], table_data: list):
         for table_result, table_page_data in zip(tables, table_data):
diff --git a/marker/providers/pdf.py b/marker/providers/pdf.py
index f275cc41..54db1901 100644
--- a/marker/providers/pdf.py
+++ b/marker/providers/pdf.py
@@ -8,6 +8,7 @@
 from ftfy import fix_text
 from pdftext.extraction import dictionary_output
 from PIL import Image
+from pypdfium2 import PdfiumError
 
 from marker.providers import BaseProvider, ProviderOutput, ProviderPageLines
 from marker.providers.utils import alphanum_ratio
@@ -230,7 +231,11 @@ def check_line_spans(self, page_lines: List[ProviderOutput]) -> bool:
     def check_page(self, page_id: int) -> bool:
         page = self.doc.get_page(page_id)
         page_bbox = PolygonBox.from_bbox(page.get_bbox())
-        page_objs = list(page.get_objects(filter=[pdfium_c.FPDF_PAGEOBJ_TEXT, pdfium_c.FPDF_PAGEOBJ_IMAGE]))
+        try:
+            page_objs = list(page.get_objects(filter=[pdfium_c.FPDF_PAGEOBJ_TEXT, pdfium_c.FPDF_PAGEOBJ_IMAGE]))
+        except PdfiumError:
+            # Happens when pdfium fails to get the number of page objects
+            return False
 
         # if we do not see any text objects in the pdf, we can skip this page
         if not any([obj.type == pdfium_c.FPDF_PAGEOBJ_TEXT for obj in page_objs]):

From 9324f10940cfdf3eee9bc99ec67690c168f8b3b7 Mon Sep 17 00:00:00 2001
From: Vik Paruchuri 
Date: Wed, 15 Jan 2025 12:12:53 -0500
Subject: [PATCH 52/92] Fix whitespace inside tags

---
 marker/converters/table.py   | 1 -
 marker/renderers/__init__.py | 9 +++++----
 marker/renderers/html.py     | 4 +---
 3 files changed, 6 insertions(+), 8 deletions(-)

diff --git a/marker/converters/table.py b/marker/converters/table.py
index ac555d87..a664fa49 100644
--- a/marker/converters/table.py
+++ b/marker/converters/table.py
@@ -25,7 +25,6 @@ class TableConverter(PdfConverter):
 
     def build_document(self, filepath: str):
         provider_cls = provider_from_filepath(filepath)
-        pdf_provider = provider_cls(filepath, self.config)
         layout_builder = self.resolve_dependencies(self.layout_builder_class)
         ocr_builder = self.resolve_dependencies(OcrBuilder)
         document_builder = DocumentBuilder(self.config)
diff --git a/marker/renderers/__init__.py b/marker/renderers/__init__.py
index 5a53432c..e45744e7 100644
--- a/marker/renderers/__init__.py
+++ b/marker/renderers/__init__.py
@@ -47,7 +47,11 @@ def merge_consecutive_tags(html, tag):
             return html
 
         def replace_whitespace(match):
-            return match.group(1)
+            whitespace = match.group(1)
+            if len(whitespace) == 0:
+                return ""
+            else:
+                return " "
 
         pattern = fr'(\s*)<{tag}>'
 
@@ -57,9 +61,6 @@ def replace_whitespace(match):
                 break
             html = new_merged
 
-        # Replace consecutive whitespace
-        html = re.sub(r'\s+', ' ', html)
-
         return html
 
     def generate_page_stats(self, document: Document, document_output):
diff --git a/marker/renderers/html.py b/marker/renderers/html.py
index 6f31a738..4f3a27c7 100644
--- a/marker/renderers/html.py
+++ b/marker/renderers/html.py
@@ -78,12 +78,10 @@ def extract_html(self, document, document_output, level=0):
                 images.update(sub_images)
                 ref.replace_with(BeautifulSoup(f"{content}", 'html.parser'))
 
+        output = str(soup)
         if level == 0:
-            output = soup.prettify()
             output = self.merge_consecutive_tags(output, 'b')
             output = self.merge_consecutive_tags(output, 'i')
-        else:
-            output = str(soup)
 
         return output, images
 

From 04bb7ad25bd808630c6878420a4f47ad43ca0d85 Mon Sep 17 00:00:00 2001
From: Vik Paruchuri 
Date: Wed, 15 Jan 2025 13:01:50 -0500
Subject: [PATCH 53/92] Minor cleanups

---
 README.md                   |  2 +-
 benchmarks/table/scoring.py | 33 ++++++++++++++-------------------
 benchmarks/table/table.py   | 18 +++++++++---------
 3 files changed, 24 insertions(+), 29 deletions(-)

diff --git a/README.md b/README.md
index 7f67e3fe..418f0395 100644
--- a/README.md
+++ b/README.md
@@ -421,7 +421,7 @@ python benchmarks/overall.py data/pdfs data/references report.json
 The processed FinTabNet dataset is hosted [here](https://huggingface.co/datasets/datalab-to/fintabnet-test) and is automatically downloaded. Run the benchmark with:
 
 ```shell
-python benchmarks/table/table.py table_report.json --max 1000
+python benchmarks/table/table.py table_report.json --max_rows 1000
 ```
 
 # Thanks
diff --git a/benchmarks/table/scoring.py b/benchmarks/table/scoring.py
index 81715182..940bd6e4 100644
--- a/benchmarks/table/scoring.py
+++ b/benchmarks/table/scoring.py
@@ -1,16 +1,12 @@
-'''
+""""
 TEDS Code Adapted from https://github.com/ibm-aur-nlp/EDD
-'''
+"""
 
-from typing import List
-
-from tqdm import tqdm
 import distance
 from apted import APTED, Config
 from apted.helpers import Tree
 from lxml import html
 from collections import deque
-import numpy as np
 
 def wrap_table_html(table_html:str)->str:
     return f'{table_html}'
@@ -21,7 +17,9 @@ def __init__(self, tag, colspan=None, rowspan=None, content=None, *children):
         self.colspan = colspan
         self.rowspan = rowspan
         self.content = content
-        self.children = list(children)
+
+        # Sets self.name and self.children
+        super().__init__(tag, *children)
 
     def bracket(self):
         """Show tree using brackets notation"""
@@ -37,17 +35,12 @@ def bracket(self):
 class CustomConfig(Config):
     @staticmethod
     def maximum(*sequences):
-        """Get maximum possible value
-        """
         return max(map(len, sequences))
 
     def normalized_distance(self, *sequences):
-        """Get distance from 0 to 1
-        """
         return float(distance.levenshtein(*sequences)) / self.maximum(*sequences)
 
     def rename(self, node1, node2):
-        """Compares attributes of trees"""
         if (node1.tag != node2.tag) or (node1.colspan != node2.colspan) or (node1.rowspan != node2.rowspan):
             return 1.
         if node1.tag == 'td':
@@ -56,8 +49,9 @@ def rename(self, node1, node2):
         return 0.
 
 def tokenize(node):
-    ''' Tokenizes table cells
-    '''
+    """
+    Tokenizes table cells
+    """
     global __tokens__
     __tokens__.append('<%s>' % node.tag)
     if node.text is not None:
@@ -70,8 +64,9 @@ def tokenize(node):
             __tokens__ += list(node.tail)
 
 def tree_convert_html(node, convert_cell=False, parent=None):
-    ''' Converts HTML tree to the format required by apted
-    '''
+    """
+    Converts HTML tree to the format required by apted
+    """
     global __tokens__
     if node.tag == 'td':
         if convert_cell:
@@ -95,9 +90,9 @@ def tree_convert_html(node, convert_cell=False, parent=None):
         return new_node
 
 def similarity_eval_html(pred, true, structure_only=False):
-    ''' Computes TEDS score between the prediction and the ground truth of a
-        given samples
-    '''
+    """
+    Computes TEDS score between the prediction and the ground truth of a given samples
+    """
     pred, true = html.fromstring(pred), html.fromstring(true)
     if pred.xpath('body/table') and true.xpath('body/table'):
         pred = pred.xpath('body/table')[0]
diff --git a/benchmarks/table/table.py b/benchmarks/table/table.py
index a11c0cf7..a6e4bd82 100644
--- a/benchmarks/table/table.py
+++ b/benchmarks/table/table.py
@@ -1,5 +1,7 @@
-import base64
 import os
+os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"  # Transformers uses .isin for a simple op, which is not supported on MPS
+
+import base64
 import time
 import datasets
 from tqdm import tqdm
@@ -11,8 +13,6 @@
 from concurrent.futures import ThreadPoolExecutor
 from pypdfium2._helpers.misc import PdfiumError
 
-os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"  # Transformers uses .isin for a simple op, which is not supported on MPS
-
 from marker.config.parser import ConfigParser
 from marker.converters.table import TableConverter
 from marker.models import create_model_dict
@@ -30,10 +30,10 @@ def update_teds_score(result):
 @click.command(help="Benchmark Table to HTML Conversion")
 @click.argument("out_file", type=str)
 @click.option("--dataset", type=str, default="datalab-to/fintabnet-test", help="Dataset to use")
-@click.option("--max", type=int, default=None, help="Maximum number of PDFs to process")
-def main(out_file, dataset, max):
+@click.option("--max_rows", type=int, default=None, help="Maximum number of PDFs to process")
+def main(out_file: str, dataset: str, max_rows: int):
     models = create_model_dict()
-    config_parser = ConfigParser({})
+    config_parser = ConfigParser({'output_format': 'html'})
     start = time.time()
 
 
@@ -41,8 +41,8 @@ def main(out_file, dataset, max):
     dataset = dataset.shuffle(seed=0)
 
     iterations = len(dataset)
-    if max is not None:
-        iterations = min(max, len(dataset))
+    if max_rows is not None:
+        iterations = min(max_rows, len(dataset))
 
     results = []
     for i in tqdm(range(iterations), desc='Converting Tables'):
@@ -55,7 +55,7 @@ def main(out_file, dataset, max):
                 config=config_parser.generate_config_dict(),
                 artifact_dict=models,
                 processor_list=config_parser.get_processors(),
-                renderer='marker.renderers.html.HTMLRenderer'
+                renderer=config_parser.get_renderer()
             )
 
             with tempfile.NamedTemporaryFile(suffix=".pdf", mode="wb") as temp_pdf_file:

From dbe1fc4c811a36711dacfcefa2e727b13511cd8d Mon Sep 17 00:00:00 2001
From: Vik Paruchuri 
Date: Wed, 15 Jan 2025 15:03:03 -0500
Subject: [PATCH 54/92] Update layout prompts

---
 benchmarks/table/table.py                | 105 ++++++++++++++++++++---
 convert_single.py                        |   1 -
 marker/builders/llm_layout.py            |  61 +++++++++----
 marker/converters/table.py               |   2 +
 marker/processors/llm/llm_table_merge.py |  23 +++--
 marker/processors/table.py               |   5 +-
 marker/renderers/json.py                 |   3 +
 marker/schema/blocks/base.py             |   1 +
 marker/schema/blocks/caption.py          |   1 +
 marker/schema/blocks/code.py             |   1 +
 marker/schema/blocks/complexregion.py    |   1 +
 marker/schema/blocks/equation.py         |   1 +
 marker/schema/blocks/figure.py           |   1 +
 marker/schema/blocks/footnote.py         |   1 +
 marker/schema/blocks/form.py             |   1 +
 marker/schema/blocks/handwriting.py      |   1 +
 marker/schema/blocks/inlinemath.py       |   1 +
 marker/schema/blocks/listitem.py         |   1 +
 marker/schema/blocks/pagefooter.py       |   1 +
 marker/schema/blocks/pageheader.py       |   1 +
 marker/schema/blocks/picture.py          |   1 +
 marker/schema/blocks/sectionheader.py    |   1 +
 marker/schema/blocks/table.py            |   1 +
 marker/schema/blocks/tablecell.py        |   1 +
 marker/schema/blocks/text.py             |   1 +
 marker/schema/blocks/toc.py              |   1 +
 marker/schema/groups/figure.py           |   1 +
 marker/schema/groups/list.py             |   1 +
 marker/schema/groups/page.py             |   1 +
 marker/schema/groups/picture.py          |   1 +
 marker/schema/groups/table.py            |   1 +
 marker/schema/text/line.py               |   1 +
 marker/schema/text/span.py               |   1 +
 33 files changed, 186 insertions(+), 40 deletions(-)

diff --git a/benchmarks/table/table.py b/benchmarks/table/table.py
index a6e4bd82..a5a0ef24 100644
--- a/benchmarks/table/table.py
+++ b/benchmarks/table/table.py
@@ -1,4 +1,10 @@
 import os
+from typing import List
+
+import numpy as np
+
+from marker.renderers.json import JSONOutput, JSONBlockOutput
+
 os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"  # Transformers uses .isin for a simple op, which is not supported on MPS
 
 import base64
@@ -10,8 +16,9 @@
 from tabulate import tabulate
 import json
 from bs4 import BeautifulSoup
-from concurrent.futures import ThreadPoolExecutor
+from concurrent.futures import ThreadPoolExecutor, ProcessPoolExecutor
 from pypdfium2._helpers.misc import PdfiumError
+from marker.util import matrix_intersection_area
 
 from marker.config.parser import ConfigParser
 from marker.converters.table import TableConverter
@@ -27,13 +34,24 @@ def update_teds_score(result):
     return result
 
 
+def extract_tables(children: List[JSONBlockOutput]):
+    tables = []
+    for child in children:
+        if child.block_type == 'Table':
+            tables.append(child)
+        elif child.children:
+            tables.extend(extract_tables(child.children))
+    return tables
+
+
 @click.command(help="Benchmark Table to HTML Conversion")
 @click.argument("out_file", type=str)
 @click.option("--dataset", type=str, default="datalab-to/fintabnet-test", help="Dataset to use")
 @click.option("--max_rows", type=int, default=None, help="Maximum number of PDFs to process")
-def main(out_file: str, dataset: str, max_rows: int):
+@click.option("--max_workers", type=int, default=16, help="Maximum number of workers to use")
+def main(out_file: str, dataset: str, max_rows: int, max_workers: int):
     models = create_model_dict()
-    config_parser = ConfigParser({'output_format': 'html'})
+    config_parser = ConfigParser({'output_format': 'json'})
     start = time.time()
 
 
@@ -45,6 +63,7 @@ def main(out_file: str, dataset: str, max_rows: int):
         iterations = min(max_rows, len(dataset))
 
     results = []
+    total_unaligned = 0
     for i in tqdm(range(iterations), desc='Converting Tables'):
         try:
             row = dataset[i]
@@ -61,19 +80,74 @@ def main(out_file: str, dataset: str, max_rows: int):
             with tempfile.NamedTemporaryFile(suffix=".pdf", mode="wb") as temp_pdf_file:
                 temp_pdf_file.write(pdf_binary)
                 temp_pdf_file.seek(0)
-                marker_table_html = converter(temp_pdf_file.name).html
+                tqdm.disable = True
+                marker_json = converter(temp_pdf_file.name).children
+                tqdm.disable = False
 
-            marker_table_soup = BeautifulSoup(marker_table_html, 'html.parser')
-            marker_detected_tables = marker_table_soup.find_all('table')
-            if len(marker_detected_tables)==0:
+            if len(marker_json) == 0 or len(gt_tables) == 0:
                 print(f'No tables detected, skipping...')
+                total_unaligned += len(gt_tables)
+                continue
+
+            marker_tables = extract_tables(marker_json)
+            marker_table_boxes = [table.bbox for table in marker_tables]
+            page_bbox = marker_json[0].bbox
+
+            # Normalize the bboxes
+            for bbox in marker_table_boxes:
+                bbox[0] = bbox[0] / page_bbox[2]
+                bbox[1] = bbox[1] / page_bbox[3]
+                bbox[2] = bbox[2] / page_bbox[2]
+                bbox[3] = bbox[3] / page_bbox[3]
+
+            gt_boxes = [table['normalized_bbox'] for table in gt_tables]
+            gt_areas = [(bbox[2] - bbox[0]) * (bbox[3] - bbox[1]) for bbox in gt_boxes]
+            marker_areas = [(bbox[2] - bbox[0]) * (bbox[3] - bbox[1]) for bbox in marker_table_boxes]
+            table_alignments = matrix_intersection_area(gt_boxes, marker_table_boxes)
+
+            aligned_tables = []
+            used_tables = set()
+            unaligned_tables = set()
+            for table_idx, alignment in enumerate(table_alignments):
+                try:
+                    max_area = np.max(alignment)
+                    aligned_idx = np.argmax(alignment)
+                except ValueError:
+                    # No alignment found
+                    unaligned_tables.add(table_idx)
+                    continue
+
+                if aligned_idx in used_tables:
+                    # Marker table already aligned with another gt table
+                    unaligned_tables.add(table_idx)
+                    continue
+
+                # Gt table doesn't align well with any marker table
+                gt_table_pct = gt_areas[table_idx] / max_area
+                if not .75 < gt_table_pct < 1.25:
+                    unaligned_tables.add(table_idx)
+                    continue
+
+                # Marker table doesn't align with gt table
+                marker_table_pct = marker_areas[aligned_idx] / max_area
+                if not .75 < marker_table_pct < 1.25:
+                    unaligned_tables.add(table_idx)
+                    continue
+
+                aligned_tables.append(
+                    (marker_tables[aligned_idx], gt_tables[table_idx])
+                )
+                used_tables.add(aligned_idx)
+
+            total_unaligned += len(unaligned_tables)
             
-            for marker_table_soup, gt_table in zip(marker_detected_tables, gt_tables):
+            for marker_table, gt_table in aligned_tables:
                 gt_table_html = gt_table['html']
-                
+
                 #marker wraps the table in  which fintabnet data doesn't
-                marker_table_soup.find('tbody').unwrap()
                 #Fintabnet doesn't use th tags, need to be replaced for fair comparison
+                marker_table_soup = BeautifulSoup(marker_table.html, 'html.parser')
+                marker_table_soup.find('tbody').unwrap()
                 for th_tag in marker_table_soup.find_all('th'):
                     th_tag.name = 'td'
                 marker_table_html = str(marker_table_soup)
@@ -86,10 +160,15 @@ def main(out_file: str, dataset: str, max_rows: int):
             print('Broken PDF, Skipping...')
             continue
 
-    print(f"Total time: {time.time() - start}")
+    print(f"Total time: {time.time() - start}.")
+    print(f"Could not align {total_unaligned} tables from fintabnet.")
 
-    with ThreadPoolExecutor(max_workers=16) as executor:
-        results = list(tqdm(executor.map(update_teds_score, results), desc='Computing alignment scores', total=len(results)))
+    with ProcessPoolExecutor(max_workers=max_workers) as executor:
+        results = list(
+            tqdm(
+                executor.map(update_teds_score, results), desc='Computing alignment scores', total=len(results)
+            )
+        )
     avg_score = sum([r["score"] for r in results]) / len(results)
 
     headers = ["Avg score", "Total tables"]
diff --git a/convert_single.py b/convert_single.py
index 271833b6..637e1b10 100755
--- a/convert_single.py
+++ b/convert_single.py
@@ -9,7 +9,6 @@
 
 from marker.config.parser import ConfigParser
 from marker.config.printer import CustomClickPrinter
-from marker.converters.pdf import PdfConverter
 from marker.logger import configure_logging
 from marker.models import create_model_dict
 from marker.output import save_output
diff --git a/marker/builders/llm_layout.py b/marker/builders/llm_layout.py
index 0b574b76..17a007d3 100644
--- a/marker/builders/llm_layout.py
+++ b/marker/builders/llm_layout.py
@@ -30,7 +30,7 @@ class LLMLayoutBuilder(LayoutBuilder):
     confidence_threshold: Annotated[
         float,
         "The confidence threshold to use for relabeling.",
-    ] = 0.7
+    ] = 0.75
     picture_height_threshold: Annotated[
         float,
         "The height threshold for pictures that may actually be complex regions.",
@@ -57,14 +57,22 @@ class LLMLayoutBuilder(LayoutBuilder):
         "Default is a string containing the Gemini relabelling prompt."
     ] = """You are a layout expert specializing in document analysis.
 Your task is to relabel layout blocks in images to improve the accuracy of an existing layout model.
-You will be provided with an image of a layout block and the top k predictions from the current model, along with their confidence scores.
+You will be provided with an image of a layout block and the top k predictions from the current model, along with the per-label confidence scores.
 Your job is to analyze the image and choose the single most appropriate label from the provided top k predictions.
 Do not invent any new labels. 
-Carefully examine the image and consider the provided predictions. 
-Choose the label you believe is the most accurate representation of the layout block.
+Carefully examine the image and consider the provided predictions.  Take the model confidence scores into account.  If the existing label is the most appropriate, you should not change it.
+**Instructions**
+1. Analyze the image and consider the provided top k predictions.
+2. Write a short description of the image, and which of the potential labels you believe is the most accurate representation of the layout block.
+3. Choose the single most appropriate label from the provided top k predictions.
 
-Here are the top k predictions from the model followed by the image:
+Here are descriptions of the layout blocks you can choose from:
 
+{potential_labels}
+
+Here are the top k predictions from the model:
+
+{top_k}
 """
     complex_relabeling_prompt: Annotated[
         str,
@@ -72,23 +80,19 @@ class LLMLayoutBuilder(LayoutBuilder):
         "Default is a string containing the complex relabelling prompt."
     ] = """You are a layout expert specializing in document analysis.
 Your task is to relabel layout blocks in images to improve the accuracy of an existing layout model.
-You will be provided with an image of a layout block and some potential labels.
+You will be provided with an image of a layout block and some potential labels that might be appropriate.
 Your job is to analyze the image and choose the single most appropriate label from the provided labels.
 Do not invent any new labels. 
-Carefully examine the image and consider the provided predictions. 
-Choose the label you believe is the most accurate representation of the layout block.
+**Instructions**
+1. Analyze the image and consider the potential labels.
+2. Write a short description of the image, and which of the potential labels you believe is the most accurate representation of the layout block.
+3. Choose the single most appropriate label from the provided labels.
 
 Potential labels:
 
-- Picture
-- Table
-- Form
-- Figure - A graph or diagram with text.
-- ComplexRegion - a complex region containing multiple text and other elements.
+{potential_labels}
 
 Respond only with one of `Figure`, `Picture`, `ComplexRegion`, `Table`, or `Form`.
-
-Here is the image of the layout block:
 """
 
     def __init__(self, layout_model: LayoutPredictor, ocr_error_model: OCRErrorPredictor, config=None):
@@ -126,13 +130,29 @@ def relabel_blocks(self, document: Document):
         pbar.close()
 
     def process_block_topk_relabeling(self, document: Document, page: PageGroup, block: Block):
-        topk = {str(k): round(v, 3) for k, v in block.top_k.items()}
+        topk_types = list(block.top_k.keys())
+        potential_labels = ""
+        for block_type in topk_types:
+            label_cls = get_block_class(block_type)
+            potential_labels += f"- `{block_type}` - {label_cls.block_description}\n"
+
+        topk = ""
+        for k,v in block.top_k.items():
+            topk += f"- `{k}` - Confidence {round(v, 3)}\n"
+
+        prompt = self.topk_relabelling_prompt.replace("{potential_labels}", potential_labels).replace("{top_k}", topk)
+        print(prompt)
 
-        prompt = self.topk_relabelling_prompt + '```json' + json.dumps(topk) + '```\n'
         return self.process_block_relabeling(document, page, block, prompt)
 
     def process_block_complex_relabeling(self, document: Document, page: PageGroup, block: Block):
-        complex_prompt = self.complex_relabeling_prompt
+        potential_labels = ""
+        for block_type in [BlockTypes.Figure, BlockTypes.Picture, BlockTypes.ComplexRegion, BlockTypes.Table, BlockTypes.Form]:
+            label_cls = get_block_class(block_type)
+            potential_labels += f"- `{block_type}` - {label_cls.block_description}\n"
+
+        complex_prompt = self.complex_relabeling_prompt.replace("{potential_labels}", potential_labels)
+        print(complex_prompt)
         return self.process_block_relabeling(document, page, block, complex_prompt)
 
     def process_block_relabeling(self, document: Document, page: PageGroup, block: Block, prompt: str):
@@ -140,8 +160,11 @@ def process_block_relabeling(self, document: Document, page: PageGroup, block: B
         response_schema = content.Schema(
             type=content.Type.OBJECT,
             enum=[],
-            required=["label"],
+            required=["image_description", "label"],
             properties={
+                "image_description": content.Schema(
+                    type=content.Type.STRING,
+                ),
                 "label": content.Schema(
                     type=content.Type.STRING,
                 ),
diff --git a/marker/converters/table.py b/marker/converters/table.py
index a664fa49..1a56fc82 100644
--- a/marker/converters/table.py
+++ b/marker/converters/table.py
@@ -1,3 +1,4 @@
+from functools import cache
 from typing import Tuple, List
 
 from marker.builders.document import DocumentBuilder
@@ -23,6 +24,7 @@ class TableConverter(PdfConverter):
     )
     converter_block_types: List[BlockTypes] = (BlockTypes.Table, BlockTypes.Form, BlockTypes.TableOfContents)
 
+    @cache
     def build_document(self, filepath: str):
         provider_cls = provider_from_filepath(filepath)
         layout_builder = self.resolve_dependencies(self.layout_builder_class)
diff --git a/marker/processors/llm/llm_table_merge.py b/marker/processors/llm/llm_table_merge.py
index d1561b76..4476c31e 100644
--- a/marker/processors/llm/llm_table_merge.py
+++ b/marker/processors/llm/llm_table_merge.py
@@ -32,6 +32,10 @@ class LLMTableMergeProcessor(BaseLLMProcessor):
         int,
         "The maximum distance between table edges for adjacency."
     ] = 20
+    column_gap_threshold: Annotated[
+        int,
+        "The maximum gap between columns to merge tables"
+    ] = 50
     gemini_table_merge_prompt: Annotated[
         str,
         "The prompt to use for rewriting text.",
@@ -133,10 +137,8 @@ def rewrite_blocks(self, document: Document):
         for page in document.pages:
             page_blocks = page.contained_blocks(document, self.block_types)
             for block in page_blocks:
-                if prev_block is None:
-                    subsequent_page_table = False
-                    same_page_vertical_table = False
-                else:
+                merge_condition = False
+                if prev_block is not None:
                     prev_cells = prev_block.contained_blocks(document, (BlockTypes.TableCell,))
                     curr_cells = block.contained_blocks(document, (BlockTypes.TableCell,))
                     row_match = abs(self.get_row_count(prev_cells) - self.get_row_count(curr_cells)) < 5, # Similar number of rows
@@ -154,11 +156,20 @@ def rewrite_blocks(self, document: Document):
                         prev_block.page_id == block.page_id, # On the same page
                         (1 - self.vertical_table_height_threshold) < prev_block.polygon.height / block.polygon.height < (1 + self.vertical_table_height_threshold), # Similar height
                         abs(block.polygon.x_start - prev_block.polygon.x_end) < self.vertical_table_distance_threshold, # Close together in x
+                        abs(block.polygon.y_start - prev_block.polygon.y_start) < self.vertical_table_distance_threshold, # Close together in y
                         row_match
                     ])
 
-                if prev_block is not None and \
-                        (subsequent_page_table or same_page_vertical_table):
+                    same_page_new_column = all([
+                        prev_block.page_id == block.page_id, # On the same page
+                        abs(block.polygon.x_start - prev_block.polygon.x_end) < self.column_gap_threshold,
+                        block.y_start < prev_block.y_end,
+                        block.polygon.width * (1 - self.vertical_table_height_threshold) < prev_block.polygon.width  < block.polygon.width * (1 + self.vertical_table_height_threshold), # Similar width
+                        col_match
+                    ])
+                    merge_condition = any([subsequent_page_table, same_page_vertical_table, same_page_new_column])
+
+                if prev_block is not None and merge_condition:
                     if prev_block not in table_run:
                         table_run.append(prev_block)
                     table_run.append(block)
diff --git a/marker/processors/table.py b/marker/processors/table.py
index 45c6bcc8..100090ed 100644
--- a/marker/processors/table.py
+++ b/marker/processors/table.py
@@ -105,7 +105,7 @@ def __call__(self, document: Document):
                         colspan=cell.colspan,
                         row_id=cell.row_id,
                         col_id=cell.col_id,
-                        is_header=cell.is_header,
+                        is_header=bool(cell.is_header),
                         page_id=page.page_id,
                     )
                     page.add_full_block(cell_block)
@@ -133,6 +133,9 @@ def normalize_spaces(text):
 
     def split_combined_rows(self, tables: List[TableResult]):
         for table in tables:
+            if len(table.cells) == 0:
+                # Skip empty tables
+                continue
             unique_rows = sorted(list(set([c.row_id for c in table.cells])))
             new_cells = []
             shift_up = 0
diff --git a/marker/renderers/json.py b/marker/renderers/json.py
index fce9c289..116bc557 100644
--- a/marker/renderers/json.py
+++ b/marker/renderers/json.py
@@ -14,6 +14,7 @@ class JSONBlockOutput(BaseModel):
     block_type: str
     html: str
     polygon: List[List[float]]
+    bbox: List[float]
     children: List['JSONBlockOutput'] | None = None
     section_hierarchy: Dict[int, str] | None = None
     images: dict | None = None
@@ -52,6 +53,7 @@ def extract_json(self, document: Document, block_output: BlockOutput):
             return JSONBlockOutput(
                 html=html,
                 polygon=block_output.polygon.polygon,
+                bbox=block_output.polygon.bbox,
                 id=str(block_output.id),
                 block_type=str(block_output.id.block_type),
                 images=images,
@@ -66,6 +68,7 @@ def extract_json(self, document: Document, block_output: BlockOutput):
             return JSONBlockOutput(
                 html=block_output.html,
                 polygon=block_output.polygon.polygon,
+                bbox=block_output.polygon.bbox,
                 id=str(block_output.id),
                 block_type=str(block_output.id.block_type),
                 children=children,
diff --git a/marker/schema/blocks/base.py b/marker/schema/blocks/base.py
index 0fbafe0e..b2a03fa0 100644
--- a/marker/schema/blocks/base.py
+++ b/marker/schema/blocks/base.py
@@ -71,6 +71,7 @@ def to_path(self):
 
 class Block(BaseModel):
     polygon: PolygonBox
+    block_description: str
     block_type: Optional[BlockTypes] = None
     block_id: Optional[int] = None
     page_id: Optional[int] = None
diff --git a/marker/schema/blocks/caption.py b/marker/schema/blocks/caption.py
index 6e424747..3ca7544a 100644
--- a/marker/schema/blocks/caption.py
+++ b/marker/schema/blocks/caption.py
@@ -4,6 +4,7 @@
 
 class Caption(Block):
     block_type: BlockTypes = BlockTypes.Caption
+    block_description: str = "A text caption that is directly above or below an image or table. Only used for text describing the image or table.  "
 
     def assemble_html(self, document, child_blocks, parent_structure):
         template = super().assemble_html(document, child_blocks, parent_structure)
diff --git a/marker/schema/blocks/code.py b/marker/schema/blocks/code.py
index ff65966c..337ca04e 100644
--- a/marker/schema/blocks/code.py
+++ b/marker/schema/blocks/code.py
@@ -7,6 +7,7 @@
 class Code(Block):
     block_type: BlockTypes = BlockTypes.Code
     code: str | None = None
+    block_description: str = "A programming code block."
 
     def assemble_html(self, document, child_blocks, parent_structure):
         code = self.code or ""
diff --git a/marker/schema/blocks/complexregion.py b/marker/schema/blocks/complexregion.py
index cdfe8179..7b4f6e67 100644
--- a/marker/schema/blocks/complexregion.py
+++ b/marker/schema/blocks/complexregion.py
@@ -5,6 +5,7 @@
 class ComplexRegion(Block):
     block_type: BlockTypes = BlockTypes.ComplexRegion
     html: str | None = None
+    block_description: str = "A complex region that can consist of multiple different types of blocks mixed with images. This block is chosen when it is difficult to categorize the region as a single block type."
 
     def assemble_html(self, document, child_blocks, parent_structure):
         if self.html:
diff --git a/marker/schema/blocks/equation.py b/marker/schema/blocks/equation.py
index b82f3c7d..e4ab59eb 100644
--- a/marker/schema/blocks/equation.py
+++ b/marker/schema/blocks/equation.py
@@ -7,6 +7,7 @@
 class Equation(Block):
     block_type: BlockTypes = BlockTypes.Equation
     latex: str | None = None
+    block_description: str = "A block math equation."
 
     def assemble_html(self, document, child_blocks, parent_structure=None):
         if self.latex:
diff --git a/marker/schema/blocks/figure.py b/marker/schema/blocks/figure.py
index 74270bbe..4eade60d 100644
--- a/marker/schema/blocks/figure.py
+++ b/marker/schema/blocks/figure.py
@@ -5,6 +5,7 @@
 class Figure(Block):
     block_type: BlockTypes = BlockTypes.Figure
     description: str | None = None
+    block_description: str = "A chart or other image that contains data."
 
     def assemble_html(self, document, child_blocks, parent_structure):
         if self.description:
diff --git a/marker/schema/blocks/footnote.py b/marker/schema/blocks/footnote.py
index f58983fb..1cd1978d 100644
--- a/marker/schema/blocks/footnote.py
+++ b/marker/schema/blocks/footnote.py
@@ -4,6 +4,7 @@
 
 class Footnote(Block):
     block_type: BlockTypes = BlockTypes.Footnote
+    block_description: str = "A footnote that explains a term or concept in the document."
 
     def assemble_html(self, document, child_blocks, parent_structure):
         template = super().assemble_html(document, child_blocks, parent_structure)
diff --git a/marker/schema/blocks/form.py b/marker/schema/blocks/form.py
index 4185520d..30731a97 100644
--- a/marker/schema/blocks/form.py
+++ b/marker/schema/blocks/form.py
@@ -6,3 +6,4 @@
 
 class Form(BaseTable):
     block_type: BlockTypes = BlockTypes.Form
+    block_description: str = "A form, such as a tax form, that contains fields and labels."
diff --git a/marker/schema/blocks/handwriting.py b/marker/schema/blocks/handwriting.py
index 540369ae..4eafa6f3 100644
--- a/marker/schema/blocks/handwriting.py
+++ b/marker/schema/blocks/handwriting.py
@@ -4,6 +4,7 @@
 
 class Handwriting(Block):
     block_type: BlockTypes = BlockTypes.Handwriting
+    block_description: str = "A region that contains handwriting."
 
     def assemble_html(self, document, child_blocks, parent_structure):
         template = super().assemble_html(document, child_blocks, parent_structure)
diff --git a/marker/schema/blocks/inlinemath.py b/marker/schema/blocks/inlinemath.py
index 6e415745..d669406a 100644
--- a/marker/schema/blocks/inlinemath.py
+++ b/marker/schema/blocks/inlinemath.py
@@ -7,6 +7,7 @@ class InlineMath(Block):
     has_continuation: bool = False
     blockquote: bool = False
     blockquote_level: int = 0
+    block_description: str = "A text block that contains inline math.  This is not used for italic text or references - only for text that contains math."
 
     def assemble_html(self, document, child_blocks, parent_structure):
         if self.ignore_for_output:
diff --git a/marker/schema/blocks/listitem.py b/marker/schema/blocks/listitem.py
index 91ab539d..d8c45e6e 100644
--- a/marker/schema/blocks/listitem.py
+++ b/marker/schema/blocks/listitem.py
@@ -19,6 +19,7 @@ def replace_bullets(child_blocks):
 class ListItem(Block):
     block_type: BlockTypes = BlockTypes.ListItem
     list_indent_level: int = 0
+    block_description: str = "A list item that is part of a list.  This block is used to represent a single item in a list."
 
     def assemble_html(self, document, child_blocks, parent_structure):
         template = super().assemble_html(document, child_blocks, parent_structure)
diff --git a/marker/schema/blocks/pagefooter.py b/marker/schema/blocks/pagefooter.py
index e1127a6c..945199e5 100644
--- a/marker/schema/blocks/pagefooter.py
+++ b/marker/schema/blocks/pagefooter.py
@@ -4,6 +4,7 @@
 
 class PageFooter(Block):
     block_type: str = BlockTypes.PageFooter
+    block_description: str = "Text that appears at the bottom of a page, like a page number."
 
     def assemble_html(self, document, child_blocks, parent_structure):
         if self.ignore_for_output:
diff --git a/marker/schema/blocks/pageheader.py b/marker/schema/blocks/pageheader.py
index 3b648c3b..b27e0b30 100644
--- a/marker/schema/blocks/pageheader.py
+++ b/marker/schema/blocks/pageheader.py
@@ -4,6 +4,7 @@
 
 class PageHeader(Block):
     block_type: BlockTypes = BlockTypes.PageHeader
+    block_description: str = "Text that appears at the top of a page, like a page title."
 
     def assemble_html(self, document, child_blocks, parent_structure):
         if self.ignore_for_output:
diff --git a/marker/schema/blocks/picture.py b/marker/schema/blocks/picture.py
index a2be8394..5d0d633b 100644
--- a/marker/schema/blocks/picture.py
+++ b/marker/schema/blocks/picture.py
@@ -5,6 +5,7 @@
 class Picture(Block):
     block_type: BlockTypes = BlockTypes.Picture
     description: str | None = None
+    block_description: str = "An image block that represents a picture."
 
     def assemble_html(self, document, child_blocks, parent_structure):
         if self.description:
diff --git a/marker/schema/blocks/sectionheader.py b/marker/schema/blocks/sectionheader.py
index 2a104f24..32468433 100644
--- a/marker/schema/blocks/sectionheader.py
+++ b/marker/schema/blocks/sectionheader.py
@@ -7,6 +7,7 @@
 class SectionHeader(Block):
     block_type: BlockTypes = BlockTypes.SectionHeader
     heading_level: Optional[int] = None
+    block_description: str = "The header of a section of text or other blocks."
 
     def assemble_html(self, document, child_blocks, parent_structure):
         if self.ignore_for_output:
diff --git a/marker/schema/blocks/table.py b/marker/schema/blocks/table.py
index 812fd57d..3f45cdb7 100644
--- a/marker/schema/blocks/table.py
+++ b/marker/schema/blocks/table.py
@@ -4,3 +4,4 @@
 
 class Table(BaseTable):
     block_type: BlockTypes = BlockTypes.Table
+    block_description: str = "A table of data, like a results table."
diff --git a/marker/schema/blocks/tablecell.py b/marker/schema/blocks/tablecell.py
index 276def77..09eaaa75 100644
--- a/marker/schema/blocks/tablecell.py
+++ b/marker/schema/blocks/tablecell.py
@@ -10,6 +10,7 @@ class TableCell(Block):
     col_id: int
     is_header: bool
     text: str = ""
+    block_description: str = "A cell in a table."
 
     def assemble_html(self, document, child_blocks, parent_structure=None):
         tag = "th" if self.is_header else "td"
diff --git a/marker/schema/blocks/text.py b/marker/schema/blocks/text.py
index edb25969..853a73a4 100644
--- a/marker/schema/blocks/text.py
+++ b/marker/schema/blocks/text.py
@@ -7,6 +7,7 @@ class Text(Block):
     has_continuation: bool = False
     blockquote: bool = False
     blockquote_level: int = 0
+    block_description: str = "A paragraph or line of text."
 
     def assemble_html(self, document, child_blocks, parent_structure):
         if self.ignore_for_output:
diff --git a/marker/schema/blocks/toc.py b/marker/schema/blocks/toc.py
index 3e458372..e5a043ae 100644
--- a/marker/schema/blocks/toc.py
+++ b/marker/schema/blocks/toc.py
@@ -4,3 +4,4 @@
 
 class TableOfContents(BaseTable):
     block_type: str = BlockTypes.TableOfContents
+    block_description: str = "A table of contents."
diff --git a/marker/schema/groups/figure.py b/marker/schema/groups/figure.py
index ce916cc6..e3517eb4 100644
--- a/marker/schema/groups/figure.py
+++ b/marker/schema/groups/figure.py
@@ -4,3 +4,4 @@
 
 class FigureGroup(Group):
     block_type: BlockTypes = BlockTypes.FigureGroup
+    block_description: str = "A group that contains a figure and associated captions."
diff --git a/marker/schema/groups/list.py b/marker/schema/groups/list.py
index e45bdd15..6e4304dc 100644
--- a/marker/schema/groups/list.py
+++ b/marker/schema/groups/list.py
@@ -5,6 +5,7 @@
 class ListGroup(Group):
     block_type: BlockTypes = BlockTypes.ListGroup
     has_continuation: bool = False
+    block_description: str = "A group of list items that should be rendered together."
 
     def assemble_html(self, document, child_blocks, parent_structure):
         template = super().assemble_html(document, child_blocks, parent_structure)
diff --git a/marker/schema/groups/page.py b/marker/schema/groups/page.py
index 4051cb55..6094faa9 100644
--- a/marker/schema/groups/page.py
+++ b/marker/schema/groups/page.py
@@ -22,6 +22,7 @@ class PageGroup(Group):
     layout_sliced: bool = False  # Whether the layout model had to slice the image (order may be wrong)
     excluded_block_types: Sequence[BlockTypes] = (BlockTypes.Line, BlockTypes.Span,)
     maximum_assignment_distance: float = 20  # pixels
+    block_description: str = "A single page in the document."
 
     def incr_block_id(self):
         if self.block_id is None:
diff --git a/marker/schema/groups/picture.py b/marker/schema/groups/picture.py
index 36097dc2..c233ce27 100644
--- a/marker/schema/groups/picture.py
+++ b/marker/schema/groups/picture.py
@@ -4,3 +4,4 @@
 
 class PictureGroup(Group):
     block_type: BlockTypes = BlockTypes.PictureGroup
+    block_description: str = "A picture along with associated captions."
diff --git a/marker/schema/groups/table.py b/marker/schema/groups/table.py
index 374f2a3e..86c13890 100644
--- a/marker/schema/groups/table.py
+++ b/marker/schema/groups/table.py
@@ -4,3 +4,4 @@
 
 class TableGroup(Group):
     block_type: BlockTypes = BlockTypes.TableGroup
+    block_description: str = "A table along with associated captions."
diff --git a/marker/schema/text/line.py b/marker/schema/text/line.py
index 70469757..30525a38 100644
--- a/marker/schema/text/line.py
+++ b/marker/schema/text/line.py
@@ -35,6 +35,7 @@ def strip_trailing_hyphens(line_text, next_line_text, line_html) -> str:
 
 class Line(Block):
     block_type: BlockTypes = BlockTypes.Line
+    block_description: str = "A line of text."
 
     def formatted_text(self, document):
         text = ""
diff --git a/marker/schema/text/span.py b/marker/schema/text/span.py
index 4ac5ebca..185059b3 100644
--- a/marker/schema/text/span.py
+++ b/marker/schema/text/span.py
@@ -14,6 +14,7 @@ def cleanup_text(full_text):
 
 class Span(Block):
     block_type: BlockTypes = BlockTypes.Span
+    block_description: str = "A span of text inside a line."
 
     text: str
     font: str

From 230a29962ea08c129d15a63e1eda360eef7fcc82 Mon Sep 17 00:00:00 2001
From: Vik Paruchuri 
Date: Wed, 15 Jan 2025 15:21:23 -0500
Subject: [PATCH 55/92] Update llm processors

---
 marker/builders/llm_layout.py      | 18 ++++++++----------
 marker/processors/llm/llm_table.py | 14 +++++++++++---
 2 files changed, 19 insertions(+), 13 deletions(-)

diff --git a/marker/builders/llm_layout.py b/marker/builders/llm_layout.py
index 17a007d3..65fb5d5e 100644
--- a/marker/builders/llm_layout.py
+++ b/marker/builders/llm_layout.py
@@ -1,6 +1,5 @@
-import json
 from concurrent.futures import ThreadPoolExecutor, as_completed
-from typing import Annotated, Optional
+from typing import Annotated
 
 from google.ai.generativelanguage_v1beta.types import content
 from surya.layout import LayoutPredictor
@@ -30,7 +29,7 @@ class LLMLayoutBuilder(LayoutBuilder):
     confidence_threshold: Annotated[
         float,
         "The confidence threshold to use for relabeling.",
-    ] = 0.75
+    ] = 0.8
     picture_height_threshold: Annotated[
         float,
         "The height threshold for pictures that may actually be complex regions.",
@@ -55,12 +54,12 @@ class LLMLayoutBuilder(LayoutBuilder):
         str,
         "The prompt to use for relabelling blocks.",
         "Default is a string containing the Gemini relabelling prompt."
-    ] = """You are a layout expert specializing in document analysis.
+    ] = """You're a layout expert specializing in document analysis.
 Your task is to relabel layout blocks in images to improve the accuracy of an existing layout model.
 You will be provided with an image of a layout block and the top k predictions from the current model, along with the per-label confidence scores.
 Your job is to analyze the image and choose the single most appropriate label from the provided top k predictions.
 Do not invent any new labels. 
-Carefully examine the image and consider the provided predictions.  Take the model confidence scores into account.  If the existing label is the most appropriate, you should not change it.
+Carefully examine the image and consider the provided predictions.  Take the model confidence scores into account.  The confidence is reported on a 0-1 scale, with 1 being 100% confident.  If the existing label is the most appropriate, you should not change it.
 **Instructions**
 1. Analyze the image and consider the provided top k predictions.
 2. Write a short description of the image, and which of the potential labels you believe is the most accurate representation of the layout block.
@@ -78,7 +77,7 @@ class LLMLayoutBuilder(LayoutBuilder):
         str,
         "The prompt to use for complex relabelling blocks.",
         "Default is a string containing the complex relabelling prompt."
-    ] = """You are a layout expert specializing in document analysis.
+    ] = """You're a layout expert specializing in document analysis.
 Your task is to relabel layout blocks in images to improve the accuracy of an existing layout model.
 You will be provided with an image of a layout block and some potential labels that might be appropriate.
 Your job is to analyze the image and choose the single most appropriate label from the provided labels.
@@ -134,14 +133,13 @@ def process_block_topk_relabeling(self, document: Document, page: PageGroup, blo
         potential_labels = ""
         for block_type in topk_types:
             label_cls = get_block_class(block_type)
-            potential_labels += f"- `{block_type}` - {label_cls.block_description}\n"
+            potential_labels += f"- `{block_type}` - {label_cls.model_fields['block_description'].default}\n"
 
         topk = ""
         for k,v in block.top_k.items():
             topk += f"- `{k}` - Confidence {round(v, 3)}\n"
 
         prompt = self.topk_relabelling_prompt.replace("{potential_labels}", potential_labels).replace("{top_k}", topk)
-        print(prompt)
 
         return self.process_block_relabeling(document, page, block, prompt)
 
@@ -149,10 +147,9 @@ def process_block_complex_relabeling(self, document: Document, page: PageGroup,
         potential_labels = ""
         for block_type in [BlockTypes.Figure, BlockTypes.Picture, BlockTypes.ComplexRegion, BlockTypes.Table, BlockTypes.Form]:
             label_cls = get_block_class(block_type)
-            potential_labels += f"- `{block_type}` - {label_cls.block_description}\n"
+            potential_labels += f"- `{block_type}` - {label_cls.model_fields['block_description'].default}\n"
 
         complex_prompt = self.complex_relabeling_prompt.replace("{potential_labels}", potential_labels)
-        print(complex_prompt)
         return self.process_block_relabeling(document, page, block, complex_prompt)
 
     def process_block_relabeling(self, document: Document, page: PageGroup, block: Block, prompt: str):
@@ -172,6 +169,7 @@ def process_block_relabeling(self, document: Document, page: PageGroup, block: B
         )
 
         response = self.model.generate_response(prompt, image, block, response_schema)
+        print(response)
         generated_label = None
         if response and "label" in response:
             generated_label = response["label"]
diff --git a/marker/processors/llm/llm_table.py b/marker/processors/llm/llm_table.py
index f905f4b4..6bdc3941 100644
--- a/marker/processors/llm/llm_table.py
+++ b/marker/processors/llm/llm_table.py
@@ -117,9 +117,17 @@ def parse_html_table(self, html_text: str, block: Block, page: PageGroup) -> Lis
         # Initialize grid
         rows = table.find_all('tr')
         cells = []
-        max_cols = max(len(row.find_all(['td', 'th'])) for row in rows)
-        if max_cols == 0:
-            return []
+
+        # Find maximum number of columns in colspan-aware way
+        max_cols = 0
+        for row in rows:
+            row_tds = row.find_all(['td', 'th'])
+            curr_cols = 0
+            for cell in row_tds:
+                colspan = int(cell.get('colspan', 1))
+                curr_cols += colspan
+            if curr_cols > max_cols:
+                max_cols = curr_cols
 
         grid = [[True] * max_cols for _ in range(len(rows))]
 

From 2c48a703b97b1083975f9b1b0c18ea4ee23e8fe6 Mon Sep 17 00:00:00 2001
From: Vik Paruchuri 
Date: Wed, 15 Jan 2025 15:46:35 -0500
Subject: [PATCH 56/92] Test table merge

---
 benchmarks/table/table.py                |  5 ++--
 marker/builders/llm_layout.py            |  1 -
 marker/processors/llm/llm_table_merge.py |  2 +-
 tests/processors/test_table_merge.py     | 29 ++++++++++++++++++++++++
 4 files changed, 33 insertions(+), 4 deletions(-)
 create mode 100644 tests/processors/test_table_merge.py

diff --git a/benchmarks/table/table.py b/benchmarks/table/table.py
index a5a0ef24..d6ebf64c 100644
--- a/benchmarks/table/table.py
+++ b/benchmarks/table/table.py
@@ -49,9 +49,10 @@ def extract_tables(children: List[JSONBlockOutput]):
 @click.option("--dataset", type=str, default="datalab-to/fintabnet-test", help="Dataset to use")
 @click.option("--max_rows", type=int, default=None, help="Maximum number of PDFs to process")
 @click.option("--max_workers", type=int, default=16, help="Maximum number of workers to use")
-def main(out_file: str, dataset: str, max_rows: int, max_workers: int):
+@click.option("--use_llm", is_flag=True, help="Use LLM for improving table recognition.")
+def main(out_file: str, dataset: str, max_rows: int, max_workers: int, use_llm: bool):
     models = create_model_dict()
-    config_parser = ConfigParser({'output_format': 'json'})
+    config_parser = ConfigParser({'output_format': 'json', "use_llm": use_llm})
     start = time.time()
 
 
diff --git a/marker/builders/llm_layout.py b/marker/builders/llm_layout.py
index 65fb5d5e..d3d4e42d 100644
--- a/marker/builders/llm_layout.py
+++ b/marker/builders/llm_layout.py
@@ -169,7 +169,6 @@ def process_block_relabeling(self, document: Document, page: PageGroup, block: B
         )
 
         response = self.model.generate_response(prompt, image, block, response_schema)
-        print(response)
         generated_label = None
         if response and "label" in response:
             generated_label = response["label"]
diff --git a/marker/processors/llm/llm_table_merge.py b/marker/processors/llm/llm_table_merge.py
index 4476c31e..a5efaacc 100644
--- a/marker/processors/llm/llm_table_merge.py
+++ b/marker/processors/llm/llm_table_merge.py
@@ -163,7 +163,7 @@ def rewrite_blocks(self, document: Document):
                     same_page_new_column = all([
                         prev_block.page_id == block.page_id, # On the same page
                         abs(block.polygon.x_start - prev_block.polygon.x_end) < self.column_gap_threshold,
-                        block.y_start < prev_block.y_end,
+                        block.polygon.y_start < prev_block.polygon.y_end,
                         block.polygon.width * (1 - self.vertical_table_height_threshold) < prev_block.polygon.width  < block.polygon.width * (1 + self.vertical_table_height_threshold), # Similar width
                         col_match
                     ])
diff --git a/tests/processors/test_table_merge.py b/tests/processors/test_table_merge.py
new file mode 100644
index 00000000..f8522249
--- /dev/null
+++ b/tests/processors/test_table_merge.py
@@ -0,0 +1,29 @@
+from unittest.mock import Mock
+
+import pytest
+
+from marker.processors.llm.llm_table_merge import LLMTableMergeProcessor
+from marker.processors.table import TableProcessor
+from marker.schema import BlockTypes
+
+
+@pytest.mark.filename("table_ex2.pdf")
+def test_llm_table_processor_nomerge(pdf_document, detection_model, table_rec_model, recognition_model, mocker):
+    mock_cls = Mock()
+    mock_cls.return_value.generate_response.return_value = {
+        "merge": "true",
+        "direction": "right"
+    }
+    mocker.patch("marker.processors.llm.GoogleModel", mock_cls)
+
+    cell_processor = TableProcessor(detection_model, recognition_model, table_rec_model)
+    cell_processor(pdf_document)
+
+    tables = pdf_document.contained_blocks((BlockTypes.Table,))
+    assert len(tables) == 3
+
+    processor = LLMTableMergeProcessor({"use_llm": True, "google_api_key": "test"})
+    processor(pdf_document)
+
+    tables = pdf_document.contained_blocks((BlockTypes.Table,))
+    assert len(tables) == 3
\ No newline at end of file

From f3601d7d396892e96adc1f89ce7625cdae747320 Mon Sep 17 00:00:00 2001
From: Vik Paruchuri 
Date: Wed, 15 Jan 2025 16:01:49 -0500
Subject: [PATCH 57/92] Clean up table output

---
 README.md                         | 5 ++++-
 benchmarks/table/table.py         | 1 +
 marker/processors/table.py        | 4 ++--
 marker/schema/blocks/tablecell.py | 9 +++++++--
 4 files changed, 14 insertions(+), 5 deletions(-)

diff --git a/README.md b/README.md
index 418f0395..a4533fc7 100644
--- a/README.md
+++ b/README.md
@@ -394,12 +394,15 @@ Marker takes about 6GB of VRAM on average per task, so you can convert 8 documen
 ![Benchmark results](data/images/per_doc.png)
 
 ## Table Conversion
-Marker can extract tables from your PDFs using `marker.converters.table.TableConverter`. The table extraction performance is measured by comparing the extracted HTML representation of tables against the original HTML representations using the test split of [FinTabNet](https://developer.ibm.com/exchanges/data/all/fintabnet/). The HTML representations are compared using a [tree edit distance] based metric to judge both structure and content. Marker detects and identifies the structure of all tables in a PDF page and achieves an average score of `0.65` via this approach.
+Marker can extract tables from PDFs using `marker.converters.table.TableConverter`. The table extraction performance is measured by comparing the extracted HTML representation of tables against the original HTML representations using the test split of [FinTabNet](https://developer.ibm.com/exchanges/data/all/fintabnet/). The HTML representations are compared using a tree edit distance based metric to judge both structure and content. Marker detects and identifies the structure of all tables in a PDF page and achieves these scores:
 
 |   Avg score |   Total tables |
 |-------------|----------------|
 |        0.65 |           1149 |
 
+
+We filter out tables that we cannot align with the ground truth, since fintabnet and our layout model have slightly different detection methods (this results in some tables being split/merged).
+
 ## Running your own benchmarks
 
 You can benchmark the performance of marker on your machine. Install marker manually with:
diff --git a/benchmarks/table/table.py b/benchmarks/table/table.py
index d6ebf64c..cd5b8817 100644
--- a/benchmarks/table/table.py
+++ b/benchmarks/table/table.py
@@ -152,6 +152,7 @@ def main(out_file: str, dataset: str, max_rows: int, max_workers: int, use_llm:
                 for th_tag in marker_table_soup.find_all('th'):
                     th_tag.name = 'td'
                 marker_table_html = str(marker_table_soup)
+                marker_table_html = marker_table_html.replace("\n", " ") # Fintabnet uses spaces instead of newlines
 
                 results.append({
                     "marker_table": marker_table_html,
diff --git a/marker/processors/table.py b/marker/processors/table.py
index 100090ed..d86e6775 100644
--- a/marker/processors/table.py
+++ b/marker/processors/table.py
@@ -114,8 +114,8 @@ def __call__(self, document: Document):
 
     def finalize_cell_text(self, cell: SuryaTableCell):
         text = "\n".join([t["text"].strip() for t in cell.text_lines]) if cell.text_lines else ""
-        text = re.sub(r"(\s\.){3,}", "...", text)  # Replace . . .
-        text = re.sub(r"\.{3,}", "...", text)  # Replace ..., like in table of contents
+        text = re.sub(r"(\s\.){2,}", "", text)  # Replace . . .
+        text = re.sub(r"\.{2,}", "", text)  # Replace ..., like in table of contents
         return self.normalize_spaces(fix_text(text))
 
     @staticmethod
diff --git a/marker/schema/blocks/tablecell.py b/marker/schema/blocks/tablecell.py
index 09eaaa75..02778954 100644
--- a/marker/schema/blocks/tablecell.py
+++ b/marker/schema/blocks/tablecell.py
@@ -13,5 +13,10 @@ class TableCell(Block):
     block_description: str = "A cell in a table."
 
     def assemble_html(self, document, child_blocks, parent_structure=None):
-        tag = "th" if self.is_header else "td"
-        return f"<{tag} rowspan={self.rowspan} colspan={self.colspan}>{self.text}"
+        tag_cls = "th" if self.is_header else "td"
+        tag = f"<{tag_cls}"
+        if self.rowspan > 1:
+            tag += f" rowspan={self.rowspan}"
+        if self.colspan > 1:
+            tag += f" colspan={self.colspan}"
+        return f"{tag}>{self.text}"

From d91dcb8d300b1ad6d8d8acbd64095234a5d8ec2c Mon Sep 17 00:00:00 2001
From: Vik Paruchuri 
Date: Thu, 16 Jan 2025 14:05:10 -0500
Subject: [PATCH 58/92] clean up html output

---
 README.md                                     |  18 +-
 benchmarks/table/table.py                     |   5 +-
 chunk_convert.py                              |  22 +--
 convert.py                                    | 119 +-----------
 convert_single.py                             |  43 +----
 marker/builders/llm_layout.py                 |   6 +-
 marker/config/printer.py                      |   1 -
 marker/renderers/html.py                      |  15 ++
 marker/schema/blocks/form.py                  |   2 +-
 marker/schema/blocks/table.py                 |   2 +-
 marker/scripts/__init__.py                    |   5 +
 marker/scripts/chunk_convert.py               |  20 ++
 .../scripts/chunk_convert.sh                  |   0
 marker/scripts/convert.py                     | 114 ++++++++++++
 marker/scripts/convert_single.py              |  39 ++++
 .../scripts/run_streamlit_app.py              |   8 +-
 marker/scripts/server.py                      | 170 +++++++++++++++++
 marker/scripts/streamlit_app.py               | 167 +++++++++++++++++
 marker_app.py                                 | 169 +----------------
 marker_server.py                              | 174 +-----------------
 pyproject.toml                                |  18 +-
 21 files changed, 569 insertions(+), 548 deletions(-)
 create mode 100644 marker/scripts/__init__.py
 create mode 100644 marker/scripts/chunk_convert.py
 rename chunk_convert.sh => marker/scripts/chunk_convert.sh (100%)
 create mode 100644 marker/scripts/convert.py
 create mode 100644 marker/scripts/convert_single.py
 rename run_marker_app.py => marker/scripts/run_streamlit_app.py (65%)
 create mode 100644 marker/scripts/server.py
 create mode 100644 marker/scripts/streamlit_app.py

diff --git a/README.md b/README.md
index a4533fc7..8f851d7c 100644
--- a/README.md
+++ b/README.md
@@ -201,9 +201,13 @@ forms = document.contained_blocks((BlockTypes.Form,))
 
 Look at the processors for more examples of extracting and manipulating blocks.
 
-### Custom converters
+## Other converters
 
-You can also use custom converters to define your own conversion pipelines.  For example, the `TableConverter` will only extract tables:
+You can also use other converters that define different conversion pipelines:
+
+### Extract tables
+
+The `TableConverter` will only convert and extract tables:
 
 ```python
 from marker.converters.table import TableConverter
@@ -217,7 +221,7 @@ rendered = converter("FILEPATH")
 text, _, images = text_from_rendered(rendered)
 ```
 
-This takes all the same configuration as the PdfConverter.
+This takes all the same configuration as the PdfConverter.  You can specify the configuration `force_layout_block=Table` to avoid layout detection and instead assume every page is a table.
 
 # Output Formats
 
@@ -396,10 +400,12 @@ Marker takes about 6GB of VRAM on average per task, so you can convert 8 documen
 ## Table Conversion
 Marker can extract tables from PDFs using `marker.converters.table.TableConverter`. The table extraction performance is measured by comparing the extracted HTML representation of tables against the original HTML representations using the test split of [FinTabNet](https://developer.ibm.com/exchanges/data/all/fintabnet/). The HTML representations are compared using a tree edit distance based metric to judge both structure and content. Marker detects and identifies the structure of all tables in a PDF page and achieves these scores:
 
-|   Avg score |   Total tables |
-|-------------|----------------|
-|        0.65 |           1149 |
+| Avg score | Total tables | use_llm |
+|-----------|--------------|---------|
+| 0.824     | 54           | False   |
+| 0.873     | 54           | True    |
 
+The `--use_llm` flag can significantly improve table recognition performance, as you can see.
 
 We filter out tables that we cannot align with the ground truth, since fintabnet and our layout model have slightly different detection methods (this results in some tables being split/merged).
 
diff --git a/benchmarks/table/table.py b/benchmarks/table/table.py
index cd5b8817..f540d723 100644
--- a/benchmarks/table/table.py
+++ b/benchmarks/table/table.py
@@ -50,9 +50,10 @@ def extract_tables(children: List[JSONBlockOutput]):
 @click.option("--max_rows", type=int, default=None, help="Maximum number of PDFs to process")
 @click.option("--max_workers", type=int, default=16, help="Maximum number of workers to use")
 @click.option("--use_llm", is_flag=True, help="Use LLM for improving table recognition.")
-def main(out_file: str, dataset: str, max_rows: int, max_workers: int, use_llm: bool):
+@click.option("--table_rec_batch_size", type=int, default=None, help="Batch size for table recognition.")
+def main(out_file: str, dataset: str, max_rows: int, max_workers: int, use_llm: bool, table_rec_batch_size: int | None):
     models = create_model_dict()
-    config_parser = ConfigParser({'output_format': 'json', "use_llm": use_llm})
+    config_parser = ConfigParser({'output_format': 'json', "use_llm": use_llm, "table_rec_batch_size": table_rec_batch_size})
     start = time.time()
 
 
diff --git a/chunk_convert.py b/chunk_convert.py
index b62b751b..bb976c56 100755
--- a/chunk_convert.py
+++ b/chunk_convert.py
@@ -1,22 +1,4 @@
-import argparse
-import subprocess
-import pkg_resources
-
-
-def main():
-    parser = argparse.ArgumentParser(description="Convert a folder of PDFs to a folder of markdown files in chunks.")
-    parser.add_argument("in_folder", help="Input folder with pdfs.")
-    parser.add_argument("out_folder", help="Output folder")
-    args = parser.parse_args()
-
-    script_path = pkg_resources.resource_filename(__name__, 'chunk_convert.sh')
-
-    # Construct the command
-    cmd = f"{script_path} {args.in_folder} {args.out_folder}"
-
-    # Execute the shell script
-    subprocess.run(cmd, shell=True, check=True)
-
+from marker.scripts import chunk_convert_cli
 
 if __name__ == "__main__":
-    main()
\ No newline at end of file
+    chunk_convert_cli()
\ No newline at end of file
diff --git a/convert.py b/convert.py
index 35f70589..73e2a770 100755
--- a/convert.py
+++ b/convert.py
@@ -1,119 +1,4 @@
-import os
-
-os.environ["GRPC_VERBOSITY"] = "ERROR"
-os.environ["GLOG_minloglevel"] = "2"
-os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1" # Transformers uses .isin for a simple op, which is not supported on MPS
-os.environ["IN_STREAMLIT"] = "true" # Avoid multiprocessing inside surya
-
-import math
-import traceback
-
-import click
-import torch.multiprocessing as mp
-from tqdm import tqdm
-
-from marker.config.parser import ConfigParser
-from marker.config.printer import CustomClickPrinter
-from marker.converters.pdf import PdfConverter
-from marker.logger import configure_logging
-from marker.models import create_model_dict
-from marker.output import output_exists, save_output
-from marker.settings import settings
-
-configure_logging()
-
-
-def worker_init(model_dict):
-    if model_dict is None:
-        model_dict = create_model_dict()
-
-    global model_refs
-    model_refs = model_dict
-
-
-def worker_exit():
-    global model_refs
-    del model_refs
-
-
-def process_single_pdf(args):
-    fpath, cli_options = args
-    config_parser = ConfigParser(cli_options)
-
-    out_folder = config_parser.get_output_folder(fpath)
-    base_name = config_parser.get_base_filename(fpath)
-    if cli_options.get('skip_existing') and output_exists(out_folder, base_name):
-        return
-
-    converter_cls = config_parser.get_converter_cls()
-
-    try:
-        converter = converter_cls(
-            config=config_parser.generate_config_dict(),
-            artifact_dict=model_refs,
-            processor_list=config_parser.get_processors(),
-            renderer=config_parser.get_renderer()
-        )
-        rendered = converter(fpath)
-        out_folder = config_parser.get_output_folder(fpath)
-        save_output(rendered, out_folder, base_name)
-    except Exception as e:
-        print(f"Error converting {fpath}: {e}")
-        print(traceback.format_exc())
-
-
-@click.command(cls=CustomClickPrinter)
-@click.argument("in_folder", type=str)
-@ConfigParser.common_options
-@click.option("--chunk_idx", type=int, default=0, help="Chunk index to convert")
-@click.option("--num_chunks", type=int, default=1, help="Number of chunks being processed in parallel")
-@click.option("--max_files", type=int, default=None, help="Maximum number of pdfs to convert")
-@click.option("--workers", type=int, default=5, help="Number of worker processes to use.")
-@click.option("--skip_existing", is_flag=True, default=False, help="Skip existing converted files.")
-def main(in_folder: str, **kwargs):
-    in_folder = os.path.abspath(in_folder)
-    files = [os.path.join(in_folder, f) for f in os.listdir(in_folder)]
-    files = [f for f in files if os.path.isfile(f)]
-
-    # Handle chunks if we're processing in parallel
-    # Ensure we get all files into a chunk
-    chunk_size = math.ceil(len(files) / kwargs["num_chunks"])
-    start_idx = kwargs["chunk_idx"] * chunk_size
-    end_idx = start_idx + chunk_size
-    files_to_convert = files[start_idx:end_idx]
-
-    # Limit files converted if needed
-    if kwargs["max_files"]:
-        files_to_convert = files_to_convert[:kwargs["max_files"]]
-
-    # Disable nested multiprocessing 
-    kwargs["disable_multiprocessing"] = True
-
-    total_processes = min(len(files_to_convert), kwargs["workers"])
-
-    try:
-        mp.set_start_method('spawn') # Required for CUDA, forkserver doesn't work
-    except RuntimeError:
-        raise RuntimeError("Set start method to spawn twice. This may be a temporary issue with the script. Please try running it again.")
-
-    if settings.TORCH_DEVICE == "mps" or settings.TORCH_DEVICE_MODEL == "mps":
-        model_dict = None
-    else:
-        model_dict = create_model_dict()
-        for k, v in model_dict.items():
-            v.share_memory()
-
-    print(f"Converting {len(files_to_convert)} pdfs in chunk {kwargs['chunk_idx'] + 1}/{kwargs['num_chunks']} with {total_processes} processes and saving to {kwargs['output_dir']}")
-    task_args = [(f, kwargs) for f in files_to_convert]
-
-    with mp.Pool(processes=total_processes, initializer=worker_init, initargs=(model_dict,)) as pool:
-        list(tqdm(pool.imap(process_single_pdf, task_args), total=len(task_args), desc="Processing PDFs", unit="pdf"))
-
-        pool._worker_handler.terminate = worker_exit
-
-    # Delete all CUDA tensors
-    del model_dict
-
+from marker.scripts import convert_cli
 
 if __name__ == "__main__":
-    main()
\ No newline at end of file
+    convert_cli()
\ No newline at end of file
diff --git a/convert_single.py b/convert_single.py
index 637e1b10..093fee50 100755
--- a/convert_single.py
+++ b/convert_single.py
@@ -1,43 +1,4 @@
-import os
-
-os.environ["GRPC_VERBOSITY"] = "ERROR"
-os.environ["GLOG_minloglevel"] = "2"
-os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"  # Transformers uses .isin for a simple op, which is not supported on MPS
-
-import time
-import click
-
-from marker.config.parser import ConfigParser
-from marker.config.printer import CustomClickPrinter
-from marker.logger import configure_logging
-from marker.models import create_model_dict
-from marker.output import save_output
-
-configure_logging()
-
-
-@click.command(cls=CustomClickPrinter, help="Convert a single PDF to markdown.")
-@click.argument("fpath", type=str)
-@ConfigParser.common_options
-def main(fpath: str, **kwargs):
-    models = create_model_dict()
-    start = time.time()
-    config_parser = ConfigParser(kwargs)
-
-    converter_cls = config_parser.get_converter_cls()
-    converter = converter_cls(
-        config=config_parser.generate_config_dict(),
-        artifact_dict=models,
-        processor_list=config_parser.get_processors(),
-        renderer=config_parser.get_renderer()
-    )
-    rendered = converter(fpath)
-    out_folder = config_parser.get_output_folder(fpath)
-    save_output(rendered, out_folder, config_parser.get_base_filename(fpath))
-
-    print(f"Saved markdown to {out_folder}")
-    print(f"Total time: {time.time() - start}")
-
+from marker.scripts import convert_single_cli
 
 if __name__ == "__main__":
-    main()
+    convert_single_cli()
diff --git a/marker/builders/llm_layout.py b/marker/builders/llm_layout.py
index d3d4e42d..b061ea48 100644
--- a/marker/builders/llm_layout.py
+++ b/marker/builders/llm_layout.py
@@ -28,11 +28,11 @@ class LLMLayoutBuilder(LayoutBuilder):
     ] = settings.GOOGLE_API_KEY
     confidence_threshold: Annotated[
         float,
-        "The confidence threshold to use for relabeling.",
-    ] = 0.8
+        "The confidence threshold to use for relabeling (anything below is relabeled).",
+    ] = 0.7
     picture_height_threshold: Annotated[
         float,
-        "The height threshold for pictures that may actually be complex regions.",
+        "The height threshold for pictures that may actually be complex regions. (anything above this ratio against the page is relabeled)",
     ] = 0.8
     model_name: Annotated[
         str,
diff --git a/marker/config/printer.py b/marker/config/printer.py
index d2b6af2f..2c728553 100644
--- a/marker/config/printer.py
+++ b/marker/config/printer.py
@@ -3,7 +3,6 @@
 import click
 
 from marker.config.crawler import crawler
-from marker.schema import BlockTypes
 
 
 class CustomClickPrinter(click.Command):
diff --git a/marker/renderers/html.py b/marker/renderers/html.py
index 4f3a27c7..ccebb2a1 100644
--- a/marker/renderers/html.py
+++ b/marker/renderers/html.py
@@ -1,3 +1,5 @@
+import textwrap
+
 from PIL import Image
 from typing import Annotated, Literal, Tuple
 
@@ -82,12 +84,25 @@ def extract_html(self, document, document_output, level=0):
         if level == 0:
             output = self.merge_consecutive_tags(output, 'b')
             output = self.merge_consecutive_tags(output, 'i')
+            output = textwrap.dedent(f"""
+            
+            
+                
+                    
+                
+                
+                    {output}
+                
+            
+""")
 
         return output, images
 
     def __call__(self, document) -> HTMLOutput:
         document_output = document.render()
         full_html, images = self.extract_html(document, document_output)
+        soup = BeautifulSoup(full_html, 'html.parser')
+        full_html = soup.prettify() # Add indentation to the HTML
         return HTMLOutput(
             html=full_html,
             images=images,
diff --git a/marker/schema/blocks/form.py b/marker/schema/blocks/form.py
index 30731a97..af3e747e 100644
--- a/marker/schema/blocks/form.py
+++ b/marker/schema/blocks/form.py
@@ -6,4 +6,4 @@
 
 class Form(BaseTable):
     block_type: BlockTypes = BlockTypes.Form
-    block_description: str = "A form, such as a tax form, that contains fields and labels."
+    block_description: str = "A form, such as a tax form, that contains fields and labels.  It most likely doesn't have a table structure."
diff --git a/marker/schema/blocks/table.py b/marker/schema/blocks/table.py
index 3f45cdb7..f72cb4c2 100644
--- a/marker/schema/blocks/table.py
+++ b/marker/schema/blocks/table.py
@@ -4,4 +4,4 @@
 
 class Table(BaseTable):
     block_type: BlockTypes = BlockTypes.Table
-    block_description: str = "A table of data, like a results table."
+    block_description: str = "A table of data, like a results table.  It will be in a tabular format."
diff --git a/marker/scripts/__init__.py b/marker/scripts/__init__.py
new file mode 100644
index 00000000..a3f361f5
--- /dev/null
+++ b/marker/scripts/__init__.py
@@ -0,0 +1,5 @@
+from marker.scripts.convert_single import convert_single_cli
+from marker.scripts.convert import convert_cli
+from marker.scripts.server import server_cli
+from marker.scripts.run_streamlit_app import streamlit_app_cli
+from marker.scripts.chunk_convert import chunk_convert_cli
\ No newline at end of file
diff --git a/marker/scripts/chunk_convert.py b/marker/scripts/chunk_convert.py
new file mode 100644
index 00000000..1b9b1928
--- /dev/null
+++ b/marker/scripts/chunk_convert.py
@@ -0,0 +1,20 @@
+import argparse
+import os
+import subprocess
+import pkg_resources
+
+
+def chunk_convert_cli():
+    parser = argparse.ArgumentParser(description="Convert a folder of PDFs to a folder of markdown files in chunks.")
+    parser.add_argument("in_folder", help="Input folder with pdfs.")
+    parser.add_argument("out_folder", help="Output folder")
+    args = parser.parse_args()
+
+    cur_dir = os.path.dirname(os.path.abspath(__file__))
+    script_path = os.path.join(cur_dir, "chunk_convert.sh")
+
+    # Construct the command
+    cmd = f"{script_path} {args.in_folder} {args.out_folder}"
+
+    # Execute the shell script
+    subprocess.run(cmd, shell=True, check=True)
\ No newline at end of file
diff --git a/chunk_convert.sh b/marker/scripts/chunk_convert.sh
similarity index 100%
rename from chunk_convert.sh
rename to marker/scripts/chunk_convert.sh
diff --git a/marker/scripts/convert.py b/marker/scripts/convert.py
new file mode 100644
index 00000000..0a4051a8
--- /dev/null
+++ b/marker/scripts/convert.py
@@ -0,0 +1,114 @@
+import os
+
+os.environ["GRPC_VERBOSITY"] = "ERROR"
+os.environ["GLOG_minloglevel"] = "2"
+os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1" # Transformers uses .isin for a simple op, which is not supported on MPS
+os.environ["IN_STREAMLIT"] = "true" # Avoid multiprocessing inside surya
+
+import math
+import traceback
+
+import click
+import torch.multiprocessing as mp
+from tqdm import tqdm
+
+from marker.config.parser import ConfigParser
+from marker.config.printer import CustomClickPrinter
+from marker.logger import configure_logging
+from marker.models import create_model_dict
+from marker.output import output_exists, save_output
+from marker.settings import settings
+
+configure_logging()
+
+
+def worker_init(model_dict):
+    if model_dict is None:
+        model_dict = create_model_dict()
+
+    global model_refs
+    model_refs = model_dict
+
+
+def worker_exit():
+    global model_refs
+    del model_refs
+
+
+def process_single_pdf(args):
+    fpath, cli_options = args
+    config_parser = ConfigParser(cli_options)
+
+    out_folder = config_parser.get_output_folder(fpath)
+    base_name = config_parser.get_base_filename(fpath)
+    if cli_options.get('skip_existing') and output_exists(out_folder, base_name):
+        return
+
+    converter_cls = config_parser.get_converter_cls()
+
+    try:
+        converter = converter_cls(
+            config=config_parser.generate_config_dict(),
+            artifact_dict=model_refs,
+            processor_list=config_parser.get_processors(),
+            renderer=config_parser.get_renderer()
+        )
+        rendered = converter(fpath)
+        out_folder = config_parser.get_output_folder(fpath)
+        save_output(rendered, out_folder, base_name)
+    except Exception as e:
+        print(f"Error converting {fpath}: {e}")
+        print(traceback.format_exc())
+
+
+@click.command(cls=CustomClickPrinter)
+@click.argument("in_folder", type=str)
+@ConfigParser.common_options
+@click.option("--chunk_idx", type=int, default=0, help="Chunk index to convert")
+@click.option("--num_chunks", type=int, default=1, help="Number of chunks being processed in parallel")
+@click.option("--max_files", type=int, default=None, help="Maximum number of pdfs to convert")
+@click.option("--workers", type=int, default=5, help="Number of worker processes to use.")
+@click.option("--skip_existing", is_flag=True, default=False, help="Skip existing converted files.")
+def convert_cli(in_folder: str, **kwargs):
+    in_folder = os.path.abspath(in_folder)
+    files = [os.path.join(in_folder, f) for f in os.listdir(in_folder)]
+    files = [f for f in files if os.path.isfile(f)]
+
+    # Handle chunks if we're processing in parallel
+    # Ensure we get all files into a chunk
+    chunk_size = math.ceil(len(files) / kwargs["num_chunks"])
+    start_idx = kwargs["chunk_idx"] * chunk_size
+    end_idx = start_idx + chunk_size
+    files_to_convert = files[start_idx:end_idx]
+
+    # Limit files converted if needed
+    if kwargs["max_files"]:
+        files_to_convert = files_to_convert[:kwargs["max_files"]]
+
+    # Disable nested multiprocessing
+    kwargs["disable_multiprocessing"] = True
+
+    total_processes = min(len(files_to_convert), kwargs["workers"])
+
+    try:
+        mp.set_start_method('spawn') # Required for CUDA, forkserver doesn't work
+    except RuntimeError:
+        raise RuntimeError("Set start method to spawn twice. This may be a temporary issue with the script. Please try running it again.")
+
+    if settings.TORCH_DEVICE == "mps" or settings.TORCH_DEVICE_MODEL == "mps":
+        model_dict = None
+    else:
+        model_dict = create_model_dict()
+        for k, v in model_dict.items():
+            v.share_memory()
+
+    print(f"Converting {len(files_to_convert)} pdfs in chunk {kwargs['chunk_idx'] + 1}/{kwargs['num_chunks']} with {total_processes} processes and saving to {kwargs['output_dir']}")
+    task_args = [(f, kwargs) for f in files_to_convert]
+
+    with mp.Pool(processes=total_processes, initializer=worker_init, initargs=(model_dict,)) as pool:
+        list(tqdm(pool.imap(process_single_pdf, task_args), total=len(task_args), desc="Processing PDFs", unit="pdf"))
+
+        pool._worker_handler.terminate = worker_exit
+
+    # Delete all CUDA tensors
+    del model_dict
\ No newline at end of file
diff --git a/marker/scripts/convert_single.py b/marker/scripts/convert_single.py
new file mode 100644
index 00000000..bb6babee
--- /dev/null
+++ b/marker/scripts/convert_single.py
@@ -0,0 +1,39 @@
+import os
+
+os.environ["GRPC_VERBOSITY"] = "ERROR"
+os.environ["GLOG_minloglevel"] = "2"
+os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"  # Transformers uses .isin for a simple op, which is not supported on MPS
+
+import time
+import click
+
+from marker.config.parser import ConfigParser
+from marker.config.printer import CustomClickPrinter
+from marker.logger import configure_logging
+from marker.models import create_model_dict
+from marker.output import save_output
+
+configure_logging()
+
+
+@click.command(cls=CustomClickPrinter, help="Convert a single PDF to markdown.")
+@click.argument("fpath", type=str)
+@ConfigParser.common_options
+def convert_single_cli(fpath: str, **kwargs):
+    models = create_model_dict()
+    start = time.time()
+    config_parser = ConfigParser(kwargs)
+
+    converter_cls = config_parser.get_converter_cls()
+    converter = converter_cls(
+        config=config_parser.generate_config_dict(),
+        artifact_dict=models,
+        processor_list=config_parser.get_processors(),
+        renderer=config_parser.get_renderer()
+    )
+    rendered = converter(fpath)
+    out_folder = config_parser.get_output_folder(fpath)
+    save_output(rendered, out_folder, config_parser.get_base_filename(fpath))
+
+    print(f"Saved markdown to {out_folder}")
+    print(f"Total time: {time.time() - start}")
\ No newline at end of file
diff --git a/run_marker_app.py b/marker/scripts/run_streamlit_app.py
similarity index 65%
rename from run_marker_app.py
rename to marker/scripts/run_streamlit_app.py
index 03f32d7f..5c9ab327 100644
--- a/run_marker_app.py
+++ b/marker/scripts/run_streamlit_app.py
@@ -2,12 +2,8 @@
 import os
 
 
-def run():
+def streamlit_app_cli():
     cur_dir = os.path.dirname(os.path.abspath(__file__))
-    app_path = os.path.join(cur_dir, "marker_app.py")
+    app_path = os.path.join(cur_dir, "streamlit_app.py")
     cmd = ["streamlit", "run", app_path]
     subprocess.run(cmd, env={**os.environ, "IN_STREAMLIT": "true"})
-
-
-if __name__ == "__main__":
-    run()
\ No newline at end of file
diff --git a/marker/scripts/server.py b/marker/scripts/server.py
new file mode 100644
index 00000000..54a44496
--- /dev/null
+++ b/marker/scripts/server.py
@@ -0,0 +1,170 @@
+import traceback
+
+import click
+import os
+
+import uvicorn
+from pydantic import BaseModel, Field
+from starlette.responses import HTMLResponse
+
+from marker.config.parser import ConfigParser
+from marker.output import text_from_rendered
+
+import base64
+from contextlib import asynccontextmanager
+from typing import Optional, Annotated
+import io
+
+from fastapi import FastAPI, Form, File, UploadFile
+from marker.converters.pdf import PdfConverter
+from marker.models import create_model_dict
+from marker.settings import settings
+
+app_data = {}
+
+
+UPLOAD_DIRECTORY = "./uploads"
+os.makedirs(UPLOAD_DIRECTORY, exist_ok=True)
+
+
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    app_data["models"] = create_model_dict()
+
+    yield
+
+    if "models" in app_data:
+        del app_data["models"]
+
+
+app = FastAPI(lifespan=lifespan)
+
+
+@app.get("/")
+async def root():
+    return HTMLResponse(
+        """
+
Marker API

+
+"""
+    )
+
+
+class CommonParams(BaseModel):
+    filepath: Annotated[
+        Optional[str], Field(description="The path to the PDF file to convert.")
+    ]
+    page_range: Annotated[
+        Optional[str],
+        Field(description="Page range to convert, specify comma separated page numbers or ranges.  Example: 0,5-10,20", example=None)
+    ] = None
+    languages: Annotated[
+        Optional[str],
+        Field(description="Comma separated list of languages to use for OCR. Must be either the names or codes from from https://github.com/VikParuchuri/surya/blob/master/surya/languages.py.", example=None)
+    ] = None
+    force_ocr: Annotated[
+        bool,
+        Field(
+            description="Force OCR on all pages of the PDF.  Defaults to False.  This can lead to worse results if you have good text in your PDFs (which is true in most cases)."
+        ),
+    ] = False
+    paginate_output: Annotated[
+        bool,
+        Field(
+            description="Whether to paginate the output.  Defaults to False.  If set to True, each page of the output will be separated by a horizontal rule that contains the page number (2 newlines, {PAGE_NUMBER}, 48 - characters, 2 newlines)."
+        ),
+    ] = False
+    output_format: Annotated[
+        str,
+        Field(description="The format to output the text in.  Can be 'markdown', 'json', or 'html'.  Defaults to 'markdown'.")
+    ] = "markdown"
+
+
+async def _convert_pdf(params: CommonParams):
+    assert params.output_format in ["markdown", "json", "html"], "Invalid output format"
+    try:
+        options = params.model_dump()
+        print(options)
+        config_parser = ConfigParser(options)
+        config_dict = config_parser.generate_config_dict()
+        config_dict["pdftext_workers"] = 1
+        converter = PdfConverter(
+            config=config_dict,
+            artifact_dict=app_data["models"],
+            processor_list=config_parser.get_processors(),
+            renderer=config_parser.get_renderer()
+        )
+        rendered = converter(params.filepath)
+        text, _, images = text_from_rendered(rendered)
+        metadata = rendered.metadata
+    except Exception as e:
+        traceback.print_exc()
+        return {
+            "success": False,
+            "error": str(e),
+        }
+
+    encoded = {}
+    for k, v in images.items():
+        byte_stream = io.BytesIO()
+        v.save(byte_stream, format=settings.OUTPUT_IMAGE_FORMAT)
+        encoded[k] = base64.b64encode(byte_stream.getvalue()).decode(settings.OUTPUT_ENCODING)
+
+    return {
+        "format": params.output_format,
+        "output": text,
+        "images": encoded,
+        "metadata": metadata,
+        "success": True,
+    }
+
+@app.post("/marker")
+async def convert_pdf(
+    params: CommonParams
+):
+    return await _convert_pdf(params)
+
+
+
+@app.post("/marker/upload")
+async def convert_pdf_upload(
+    page_range: Optional[str] = Form(default=None),
+    languages: Optional[str] = Form(default=None),
+    force_ocr: Optional[bool] = Form(default=False),
+    paginate_output: Optional[bool] = Form(default=False),
+    output_format: Optional[str] = Form(default="markdown"),
+    file: UploadFile = File(
+        ..., description="The PDF file to convert.", media_type="application/pdf"
+    ),
+):
+    upload_path = os.path.join(UPLOAD_DIRECTORY, file.filename)
+    with open(upload_path, "wb+") as upload_file:
+        file_contents = await file.read()
+        upload_file.write(file_contents)
+
+    params = CommonParams(
+        filepath=upload_path,
+        page_range=page_range,
+        languages=languages,
+        force_ocr=force_ocr,
+        paginate_output=paginate_output,
+        output_format=output_format,
+    )
+    results = await _convert_pdf(params)
+    os.remove(upload_path)
+    return results
+
+
+@click.command()
+@click.option("--port", type=int, default=8000, help="Port to run the server on")
+@click.option("--host", type=str, default="127.0.0.1", help="Host to run the server on")
+def server_cli(port: int, host: str):
+    # Run the server
+    uvicorn.run(
+        app,
+        host=host,
+        port=port,
+    )
\ No newline at end of file
diff --git a/marker/scripts/streamlit_app.py b/marker/scripts/streamlit_app.py
new file mode 100644
index 00000000..ad6e89c2
--- /dev/null
+++ b/marker/scripts/streamlit_app.py
@@ -0,0 +1,167 @@
+import os
+
+from marker.settings import settings
+
+os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"
+os.environ["IN_STREAMLIT"] = "true"
+
+import base64
+import io
+import re
+import tempfile
+from typing import Any, Dict
+
+import pypdfium2
+import streamlit as st
+from PIL import Image
+
+from marker.converters.pdf import PdfConverter
+from marker.models import create_model_dict
+from marker.config.parser import ConfigParser
+from marker.output import text_from_rendered
+
+@st.cache_resource()
+def load_models():
+    return create_model_dict()
+
+
+def convert_pdf(fname: str, config_parser: ConfigParser) -> (str, Dict[str, Any], dict):
+    config_dict = config_parser.generate_config_dict()
+    config_dict["pdftext_workers"] = 1
+    converter = PdfConverter(
+        config=config_dict,
+        artifact_dict=model_dict,
+        processor_list=config_parser.get_processors(),
+        renderer=config_parser.get_renderer()
+    )
+    return converter(fname)
+
+
+def open_pdf(pdf_file):
+    stream = io.BytesIO(pdf_file.getvalue())
+    return pypdfium2.PdfDocument(stream)
+
+
+def img_to_html(img, img_alt):
+    img_bytes = io.BytesIO()
+    img.save(img_bytes, format=settings.OUTPUT_IMAGE_FORMAT)
+    img_bytes = img_bytes.getvalue()
+    encoded = base64.b64encode(img_bytes).decode()
+    img_html = f'{img_alt}'
+    return img_html
+
+
+def markdown_insert_images(markdown, images):
+    image_tags = re.findall(r'(!\[(?P[^\]]*)\]\((?P[^\)"\s]+)\s*([^\)]*)\))', markdown)
+
+    for image in image_tags:
+        image_markdown = image[0]
+        image_alt = image[1]
+        image_path = image[2]
+        if image_path in images:
+            markdown = markdown.replace(image_markdown, img_to_html(images[image_path], image_alt))
+    return markdown
+
+
+@st.cache_data()
+def get_page_image(pdf_file, page_num, dpi=96):
+    doc = open_pdf(pdf_file)
+    renderer = doc.render(
+        pypdfium2.PdfBitmap.to_pil,
+        page_indices=[page_num],
+        scale=dpi / 72,
+    )
+    png = list(renderer)[0]
+    png_image = png.convert("RGB")
+    return png_image
+
+
+@st.cache_data()
+def page_count(pdf_file):
+    doc = open_pdf(pdf_file)
+    return len(doc) - 1
+
+
+st.set_page_config(layout="wide")
+col1, col2 = st.columns([.5, .5])
+
+model_dict = load_models()
+
+
+st.markdown("""
+# Marker Demo
+
+This app will let you try marker, a PDF -> Markdown converter. It works with any languages, and extracts images, tables, equations, etc.
+
+Find the project [here](https://github.com/VikParuchuri/marker).
+""")
+
+in_file = st.sidebar.file_uploader("PDF file:", type=["pdf"])
+
+if in_file is None:
+    st.stop()
+
+filetype = in_file.type
+
+with col1:
+    page_count = page_count(in_file)
+    page_number = st.number_input(f"Page number out of {page_count}:", min_value=0, value=0, max_value=page_count)
+    pil_image = get_page_image(in_file, page_number)
+
+    st.image(pil_image, caption="PDF file (preview)", use_container_width=True)
+
+page_range = st.sidebar.text_input("Page range to parse, comma separated like 0,5-10,20", value=f"{page_number}-{page_number}")
+output_format = st.sidebar.selectbox("Output format", ["markdown", "json", "html"], index=0)
+run_marker = st.sidebar.button("Run Marker")
+
+use_llm = st.sidebar.checkbox("Use LLM", help="Use LLM for higher quality processing", value=False)
+force_ocr = st.sidebar.checkbox("Force OCR", help="Force OCR on all pages", value=False)
+strip_existing_ocr = st.sidebar.checkbox("Strip existing OCR", help="Strip existing OCR text from the PDF and re-OCR.", value=False)
+debug = st.sidebar.checkbox("Debug", help="Show debug information", value=False)
+
+if not run_marker:
+    st.stop()
+
+# Run Marker
+with tempfile.NamedTemporaryFile(suffix=".pdf", mode="wb+") as temp_pdf:
+    temp_pdf.write(in_file.getvalue())
+    temp_pdf.seek(0)
+    filename = temp_pdf.name
+    cli_options = {
+        "output_format": output_format,
+        "page_range": page_range,
+        "force_ocr": force_ocr,
+        "debug": debug,
+        "output_dir": settings.DEBUG_DATA_FOLDER if debug else None,
+        "use_llm": use_llm,
+        "strip_existing_ocr": strip_existing_ocr
+    }
+    config_parser = ConfigParser(cli_options)
+    rendered = convert_pdf(
+        filename,
+        config_parser
+    )
+    page_range = config_parser.generate_config_dict()["page_range"]
+    first_page = page_range[0] if page_range else 0
+
+text, ext, images = text_from_rendered(rendered)
+with col2:
+    if output_format == "markdown":
+        text = markdown_insert_images(text, images)
+        st.markdown(text, unsafe_allow_html=True)
+    elif output_format == "json":
+        st.json(text)
+    elif output_format == "html":
+        st.html(text)
+
+if debug:
+    with col1:
+        debug_data_path = rendered.metadata.get("debug_data_path")
+        if debug_data_path:
+            pdf_image_path = os.path.join(debug_data_path, f"pdf_page_{first_page}.png")
+            img = Image.open(pdf_image_path)
+            st.image(img, caption="PDF debug image", use_container_width=True)
+            layout_image_path = os.path.join(debug_data_path, f"layout_page_{first_page}.png")
+            img = Image.open(layout_image_path)
+            st.image(img, caption="Layout debug image", use_container_width=True)
+
diff --git a/marker_app.py b/marker_app.py
index ad6e89c2..f3e6e9dc 100644
--- a/marker_app.py
+++ b/marker_app.py
@@ -1,167 +1,4 @@
-import os
-
-from marker.settings import settings
-
-os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"
-os.environ["IN_STREAMLIT"] = "true"
-
-import base64
-import io
-import re
-import tempfile
-from typing import Any, Dict
-
-import pypdfium2
-import streamlit as st
-from PIL import Image
-
-from marker.converters.pdf import PdfConverter
-from marker.models import create_model_dict
-from marker.config.parser import ConfigParser
-from marker.output import text_from_rendered
-
-@st.cache_resource()
-def load_models():
-    return create_model_dict()
-
-
-def convert_pdf(fname: str, config_parser: ConfigParser) -> (str, Dict[str, Any], dict):
-    config_dict = config_parser.generate_config_dict()
-    config_dict["pdftext_workers"] = 1
-    converter = PdfConverter(
-        config=config_dict,
-        artifact_dict=model_dict,
-        processor_list=config_parser.get_processors(),
-        renderer=config_parser.get_renderer()
-    )
-    return converter(fname)
-
-
-def open_pdf(pdf_file):
-    stream = io.BytesIO(pdf_file.getvalue())
-    return pypdfium2.PdfDocument(stream)
-
-
-def img_to_html(img, img_alt):
-    img_bytes = io.BytesIO()
-    img.save(img_bytes, format=settings.OUTPUT_IMAGE_FORMAT)
-    img_bytes = img_bytes.getvalue()
-    encoded = base64.b64encode(img_bytes).decode()
-    img_html = f'{img_alt}'
-    return img_html
-
-
-def markdown_insert_images(markdown, images):
-    image_tags = re.findall(r'(!\[(?P[^\]]*)\]\((?P[^\)"\s]+)\s*([^\)]*)\))', markdown)
-
-    for image in image_tags:
-        image_markdown = image[0]
-        image_alt = image[1]
-        image_path = image[2]
-        if image_path in images:
-            markdown = markdown.replace(image_markdown, img_to_html(images[image_path], image_alt))
-    return markdown
-
-
-@st.cache_data()
-def get_page_image(pdf_file, page_num, dpi=96):
-    doc = open_pdf(pdf_file)
-    renderer = doc.render(
-        pypdfium2.PdfBitmap.to_pil,
-        page_indices=[page_num],
-        scale=dpi / 72,
-    )
-    png = list(renderer)[0]
-    png_image = png.convert("RGB")
-    return png_image
-
-
-@st.cache_data()
-def page_count(pdf_file):
-    doc = open_pdf(pdf_file)
-    return len(doc) - 1
-
-
-st.set_page_config(layout="wide")
-col1, col2 = st.columns([.5, .5])
-
-model_dict = load_models()
-
-
-st.markdown("""
-# Marker Demo
-
-This app will let you try marker, a PDF -> Markdown converter. It works with any languages, and extracts images, tables, equations, etc.
-
-Find the project [here](https://github.com/VikParuchuri/marker).
-""")
-
-in_file = st.sidebar.file_uploader("PDF file:", type=["pdf"])
-
-if in_file is None:
-    st.stop()
-
-filetype = in_file.type
-
-with col1:
-    page_count = page_count(in_file)
-    page_number = st.number_input(f"Page number out of {page_count}:", min_value=0, value=0, max_value=page_count)
-    pil_image = get_page_image(in_file, page_number)
-
-    st.image(pil_image, caption="PDF file (preview)", use_container_width=True)
-
-page_range = st.sidebar.text_input("Page range to parse, comma separated like 0,5-10,20", value=f"{page_number}-{page_number}")
-output_format = st.sidebar.selectbox("Output format", ["markdown", "json", "html"], index=0)
-run_marker = st.sidebar.button("Run Marker")
-
-use_llm = st.sidebar.checkbox("Use LLM", help="Use LLM for higher quality processing", value=False)
-force_ocr = st.sidebar.checkbox("Force OCR", help="Force OCR on all pages", value=False)
-strip_existing_ocr = st.sidebar.checkbox("Strip existing OCR", help="Strip existing OCR text from the PDF and re-OCR.", value=False)
-debug = st.sidebar.checkbox("Debug", help="Show debug information", value=False)
-
-if not run_marker:
-    st.stop()
-
-# Run Marker
-with tempfile.NamedTemporaryFile(suffix=".pdf", mode="wb+") as temp_pdf:
-    temp_pdf.write(in_file.getvalue())
-    temp_pdf.seek(0)
-    filename = temp_pdf.name
-    cli_options = {
-        "output_format": output_format,
-        "page_range": page_range,
-        "force_ocr": force_ocr,
-        "debug": debug,
-        "output_dir": settings.DEBUG_DATA_FOLDER if debug else None,
-        "use_llm": use_llm,
-        "strip_existing_ocr": strip_existing_ocr
-    }
-    config_parser = ConfigParser(cli_options)
-    rendered = convert_pdf(
-        filename,
-        config_parser
-    )
-    page_range = config_parser.generate_config_dict()["page_range"]
-    first_page = page_range[0] if page_range else 0
-
-text, ext, images = text_from_rendered(rendered)
-with col2:
-    if output_format == "markdown":
-        text = markdown_insert_images(text, images)
-        st.markdown(text, unsafe_allow_html=True)
-    elif output_format == "json":
-        st.json(text)
-    elif output_format == "html":
-        st.html(text)
-
-if debug:
-    with col1:
-        debug_data_path = rendered.metadata.get("debug_data_path")
-        if debug_data_path:
-            pdf_image_path = os.path.join(debug_data_path, f"pdf_page_{first_page}.png")
-            img = Image.open(pdf_image_path)
-            st.image(img, caption="PDF debug image", use_container_width=True)
-            layout_image_path = os.path.join(debug_data_path, f"layout_page_{first_page}.png")
-            img = Image.open(layout_image_path)
-            st.image(img, caption="Layout debug image", use_container_width=True)
+from marker.scripts import streamlit_app_cli
 
+if __name__ == "__main__":
+    streamlit_app_cli()
\ No newline at end of file
diff --git a/marker_server.py b/marker_server.py
index 8092b3ba..434395e5 100644
--- a/marker_server.py
+++ b/marker_server.py
@@ -1,174 +1,4 @@
-import traceback
-
-import click
-import os
-
-import uvicorn
-from pydantic import BaseModel, Field
-from starlette.responses import HTMLResponse
-
-from marker.config.parser import ConfigParser
-from marker.output import text_from_rendered
-
-import base64
-from contextlib import asynccontextmanager
-from typing import Optional, Annotated
-import io
-
-from fastapi import FastAPI, Form, File, UploadFile
-from marker.converters.pdf import PdfConverter
-from marker.models import create_model_dict
-from marker.settings import settings
-
-app_data = {}
-
-
-UPLOAD_DIRECTORY = "./uploads"
-os.makedirs(UPLOAD_DIRECTORY, exist_ok=True)
-
-
-@asynccontextmanager
-async def lifespan(app: FastAPI):
-    app_data["models"] = create_model_dict()
-
-    yield
-
-    if "models" in app_data:
-        del app_data["models"]
-
-
-app = FastAPI(lifespan=lifespan)
-
-
-@app.get("/")
-async def root():
-    return HTMLResponse(
-        """
-
Marker API

-
-"""
-    )
-
-
-class CommonParams(BaseModel):
-    filepath: Annotated[
-        Optional[str], Field(description="The path to the PDF file to convert.")
-    ]
-    page_range: Annotated[
-        Optional[str],
-        Field(description="Page range to convert, specify comma separated page numbers or ranges.  Example: 0,5-10,20", example=None)
-    ] = None
-    languages: Annotated[
-        Optional[str],
-        Field(description="Comma separated list of languages to use for OCR. Must be either the names or codes from from https://github.com/VikParuchuri/surya/blob/master/surya/languages.py.", example=None)
-    ] = None
-    force_ocr: Annotated[
-        bool,
-        Field(
-            description="Force OCR on all pages of the PDF.  Defaults to False.  This can lead to worse results if you have good text in your PDFs (which is true in most cases)."
-        ),
-    ] = False
-    paginate_output: Annotated[
-        bool,
-        Field(
-            description="Whether to paginate the output.  Defaults to False.  If set to True, each page of the output will be separated by a horizontal rule that contains the page number (2 newlines, {PAGE_NUMBER}, 48 - characters, 2 newlines)."
-        ),
-    ] = False
-    output_format: Annotated[
-        str,
-        Field(description="The format to output the text in.  Can be 'markdown', 'json', or 'html'.  Defaults to 'markdown'.")
-    ] = "markdown"
-
-
-async def _convert_pdf(params: CommonParams):
-    assert params.output_format in ["markdown", "json", "html"], "Invalid output format"
-    try:
-        options = params.model_dump()
-        print(options)
-        config_parser = ConfigParser(options)
-        config_dict = config_parser.generate_config_dict()
-        config_dict["pdftext_workers"] = 1
-        converter = PdfConverter(
-            config=config_dict,
-            artifact_dict=app_data["models"],
-            processor_list=config_parser.get_processors(),
-            renderer=config_parser.get_renderer()
-        )
-        rendered = converter(params.filepath)
-        text, _, images = text_from_rendered(rendered)
-        metadata = rendered.metadata
-    except Exception as e:
-        traceback.print_exc()
-        return {
-            "success": False,
-            "error": str(e),
-        }
-
-    encoded = {}
-    for k, v in images.items():
-        byte_stream = io.BytesIO()
-        v.save(byte_stream, format=settings.OUTPUT_IMAGE_FORMAT)
-        encoded[k] = base64.b64encode(byte_stream.getvalue()).decode(settings.OUTPUT_ENCODING)
-
-    return {
-        "format": params.output_format,
-        "output": text,
-        "images": encoded,
-        "metadata": metadata,
-        "success": True,
-    }
-
-@app.post("/marker")
-async def convert_pdf(
-    params: CommonParams
-):
-    return await _convert_pdf(params)
-
-
-
-@app.post("/marker/upload")
-async def convert_pdf_upload(
-    page_range: Optional[str] = Form(default=None),
-    languages: Optional[str] = Form(default=None),
-    force_ocr: Optional[bool] = Form(default=False),
-    paginate_output: Optional[bool] = Form(default=False),
-    output_format: Optional[str] = Form(default="markdown"),
-    file: UploadFile = File(
-        ..., description="The PDF file to convert.", media_type="application/pdf"
-    ),
-):
-    upload_path = os.path.join(UPLOAD_DIRECTORY, file.filename)
-    with open(upload_path, "wb+") as upload_file:
-        file_contents = await file.read()
-        upload_file.write(file_contents)
-
-    params = CommonParams(
-        filepath=upload_path,
-        page_range=page_range,
-        languages=languages,
-        force_ocr=force_ocr,
-        paginate_output=paginate_output,
-        output_format=output_format,
-    )
-    results = await _convert_pdf(params)
-    os.remove(upload_path)
-    return results
-
-
-@click.command()
-@click.option("--port", type=int, default=8000, help="Port to run the server on")
-@click.option("--host", type=str, default="127.0.0.1", help="Host to run the server on")
-def main(port: int, host: str):
-    # Run the server
-    uvicorn.run(
-        app,
-        host=host,
-        port=port,
-    )
-
+from marker.scripts import server_cli
 
 if __name__ == "__main__":
-    main()
+    server_cli()
diff --git a/pyproject.toml b/pyproject.toml
index 7be48f06..3152bd28 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -11,13 +11,7 @@ packages = [
     {include = "marker"}
 ]
 include = [
-    "convert.py",
-    "convert_single.py",
-    "chunk_convert.sh",
-    "chunk_convert.py",
-    "marker_app.py",
-    "run_marker_app.py",
-    "marker_server.py",
+    "marker/scripts/*.sh"
 ]
 
 [tool.poetry.dependencies]
@@ -57,11 +51,11 @@ distance = "0.1.3"
 lxml = "5.3.0"
 
 [tool.poetry.scripts]
-marker = "convert:main"
-marker_single = "convert_single:main"
-marker_chunk_convert = "chunk_convert:main"
-marker_gui = "run_marker_app:run"
-marker_server = "marker_server:main"
+marker = "marker.scripts.convert:convert_cli"
+marker_single = "marker.scripts.convert_single:convert_single_cli"
+marker_chunk_convert = "marker.scripts.chunk_convert:chunk_convert_cli"
+marker_gui = "marker.scripts.run_streamlit_app:streamlit_app_cli"
+marker_server = "marker.scripts.server:server_cli"
 
 [build-system]
 requires = ["poetry-core"]

From 505ec2aa5b792fa679ed8f4534ea988217b37952 Mon Sep 17 00:00:00 2001
From: Moses Paul R 
Date: Fri, 17 Jan 2025 05:09:41 +0000
Subject: [PATCH 59/92] improved links!

---
 marker/builders/document.py         |  3 +-
 marker/converters/pdf.py            |  4 ++-
 marker/processors/reference.py      | 55 +++++++++++++++++++++++++++++
 marker/providers/pdf.py             |  9 +++--
 marker/renderers/__init__.py        |  1 -
 marker/renderers/html.py            |  6 ++--
 marker/schema/__init__.py           |  1 +
 marker/schema/blocks/__init__.py    |  1 +
 marker/schema/blocks/base.py        |  6 ++++
 marker/schema/blocks/caption.py     |  6 +---
 marker/schema/blocks/equation.py    | 14 ++++----
 marker/schema/blocks/figure.py      |  7 ++--
 marker/schema/blocks/footnote.py    |  7 +---
 marker/schema/blocks/handwriting.py |  6 +---
 marker/schema/blocks/pagefooter.py  | 10 ++----
 marker/schema/blocks/pageheader.py  | 10 ++----
 marker/schema/blocks/reference.py   | 11 ++++++
 marker/schema/blocks/table.py       |  8 ++---
 marker/schema/groups/page.py        |  2 ++
 marker/schema/registry.py           |  3 +-
 marker/schema/text/span.py          |  3 --
 tests/builders/test_pdf_links.py    | 11 +++---
 22 files changed, 122 insertions(+), 62 deletions(-)
 create mode 100644 marker/processors/reference.py
 create mode 100644 marker/schema/blocks/reference.py

diff --git a/marker/builders/document.py b/marker/builders/document.py
index d79c4990..99f58221 100644
--- a/marker/builders/document.py
+++ b/marker/builders/document.py
@@ -38,7 +38,8 @@ def build_document(self, provider: PdfProvider):
                 page_id=p,
                 lowres_image=lowres_images[i],
                 highres_image=highres_images[i],
-                polygon=provider.get_page_bbox(p)
+                polygon=provider.get_page_bbox(p),
+                refs=provider.get_page_refs(p)
             ) for i, p in enumerate(provider.page_range)
         ]
         DocumentClass: Document = get_block_class(BlockTypes.Document)
diff --git a/marker/converters/pdf.py b/marker/converters/pdf.py
index 0e07eadb..16c4b2f7 100644
--- a/marker/converters/pdf.py
+++ b/marker/converters/pdf.py
@@ -4,8 +4,8 @@
 
 import inspect
 from collections import defaultdict
-from typing import Annotated, Any, Dict, List, Optional, Type
 from functools import cache
+from typing import Annotated, Any, Dict, List, Optional, Type
 
 from marker.builders.document import DocumentBuilder
 from marker.builders.layout import LayoutBuilder
@@ -28,6 +28,7 @@
 from marker.processors.llm.llm_table import LLMTableProcessor
 from marker.processors.llm.llm_text import LLMTextProcessor
 from marker.processors.page_header import PageHeaderProcessor
+from marker.processors.reference import ReferenceProcessor
 from marker.processors.sectionheader import SectionHeaderProcessor
 from marker.processors.table import TableProcessor
 from marker.processors.text import TextProcessor
@@ -82,6 +83,7 @@ def __init__(self, artifact_dict: Dict[str, Any], processor_list: Optional[List[
                 LLMTextProcessor,
                 LLMComplexRegionProcessor,
                 LLMImageDescriptionProcessor,
+                ReferenceProcessor,
                 DebugProcessor,
             ]
 
diff --git a/marker/processors/reference.py b/marker/processors/reference.py
new file mode 100644
index 00000000..f3eb2d06
--- /dev/null
+++ b/marker/processors/reference.py
@@ -0,0 +1,55 @@
+import numpy as np
+
+from marker.processors import BaseProcessor
+from marker.schema import BlockTypes
+from marker.schema.blocks import Reference
+from marker.schema.document import Document
+from marker.schema.groups.list import ListGroup
+from marker.schema.groups.table import TableGroup
+from marker.schema.registry import get_block_class
+from marker.schema.groups.picture import PictureGroup
+from marker.schema.groups.figure import FigureGroup
+
+
+class ReferenceProcessor(BaseProcessor):
+    """
+    A processor for adding references to the document.
+    """
+
+    def __init__(self, config):
+        super().__init__(config)
+
+    def __call__(self, document: Document):
+        ReferenceClass: Reference = get_block_class(BlockTypes.Reference)
+
+        for page in document.pages:
+            refs = page.refs
+            ref_starts = np.array([ref.coord for ref in refs])
+
+            blocks = []
+            for block_id in page.structure:
+                block = page.get_block(block_id)
+                if isinstance(block, (ListGroup, FigureGroup, TableGroup)):
+                    blocks.extend([page.get_block(b) for b in block.structure])
+                else:
+                    blocks.append(block)
+            blocks = [b for b in blocks if not b.ignore_for_output]
+
+            block_starts = np.array([block.polygon.bbox[:2] for block in blocks])
+
+            if not (len(refs) and len(block_starts)):
+                continue
+
+            distances = np.linalg.norm(block_starts[:, np.newaxis, :] - ref_starts[np.newaxis, :, :], axis=2)
+            for ref_idx in range(len(ref_starts)):
+                block_idx = np.argmin(distances[:, ref_idx])
+                block = blocks[block_idx]
+
+                ref_block = page.add_full_block(ReferenceClass(
+                    ref=refs[ref_idx].ref,
+                    polygon=block.polygon,
+                    page_id=page.page_id
+                ))
+                if block.structure is None:
+                    block.structure = []
+                block.structure.insert(0, ref_block.id)
diff --git a/marker/providers/pdf.py b/marker/providers/pdf.py
index feb8b35b..a72c2adf 100644
--- a/marker/providers/pdf.py
+++ b/marker/providers/pdf.py
@@ -1,12 +1,13 @@
 import atexit
 import ctypes
 import re
-from typing import Annotated, List, Optional, Set
+from typing import Annotated, Dict, List, Optional, Set
 
 import pypdfium2 as pdfium
 import pypdfium2.raw as pdfium_c
 from ftfy import fix_text
 from pdftext.extraction import dictionary_output
+from pdftext.schema import Reference
 from PIL import Image
 
 from marker.providers import BaseProvider, ProviderOutput, ProviderPageLines
@@ -74,6 +75,7 @@ def __init__(self, filepath: str, config=None):
 
         self.doc: pdfium.PdfDocument = pdfium.PdfDocument(self.filepath)
         self.page_lines: ProviderPageLines = {i: [] for i in range(len(self.doc))}
+        self.page_refs: Dict[int, List[Reference]] = {i: [] for i in range(len(self.doc))}
 
         if self.page_range is None:
             self.page_range = range(len(self.doc))
@@ -199,7 +201,6 @@ def pdftext_extraction(self) -> ProviderPageLines:
                                 page_id=page_id,
                                 text_extraction_method="pdftext",
                                 url=span.get("url"),
-                                anchors=span.get("anchors"),
                             )
                         )
                     polygon = PolygonBox.from_bbox(line["bbox"], ensure_nonzero_area=True)
@@ -211,6 +212,7 @@ def pdftext_extraction(self) -> ProviderPageLines:
                     )
             if self.check_line_spans(lines):
                 page_lines[page_id] = lines
+            self.page_refs[page_id] = page["refs"]
 
         return page_lines
 
@@ -311,6 +313,9 @@ def get_page_bbox(self, idx: int) -> PolygonBox | None:
     def get_page_lines(self, idx: int) -> List[ProviderOutput]:
         return self.page_lines[idx]
 
+    def get_page_refs(self, idx: int):
+        return self.page_refs[idx]
+
     @staticmethod
     def _get_fontname(font) -> str:
         font_name = ""
diff --git a/marker/renderers/__init__.py b/marker/renderers/__init__.py
index d0166c9d..9c17d528 100644
--- a/marker/renderers/__init__.py
+++ b/marker/renderers/__init__.py
@@ -15,7 +15,6 @@
 
 
 class BaseRenderer:
-    remove_blocks: Annotated[Tuple[BlockTypes, ...], "The block types to ignore while rendering."] = (BlockTypes.PageHeader, BlockTypes.PageFooter)
     image_blocks: Annotated[Tuple[BlockTypes, ...], "The block types to consider as images."] = (BlockTypes.Picture, BlockTypes.Figure)
     extract_images: Annotated[bool, "Extract images from the document."] = True
 
diff --git a/marker/renderers/html.py b/marker/renderers/html.py
index f01ef315..d68647dd 100644
--- a/marker/renderers/html.py
+++ b/marker/renderers/html.py
@@ -65,14 +65,12 @@ def extract_html(self, document, document_output, level=0):
                     ref_block_id: BlockId = item.id
                     break
 
-            if ref_block_id.block_type in self.remove_blocks:
-                ref.replace_with('')
-            elif ref_block_id.block_type in self.image_blocks:
+            if ref_block_id.block_type in self.image_blocks:
                 if self.extract_images:
                     image = self.extract_image(document, ref_block_id)
                     image_name = f"{ref_block_id.to_path()}.{settings.OUTPUT_IMAGE_FORMAT.lower()}"
                     images[image_name] = image
-                    ref.replace_with(BeautifulSoup(f"
", 'html.parser'))
+                    ref.replace_with(BeautifulSoup(f"
{content}
", 'html.parser'))
                 else:
                     # This will be the image description if using llm mode, or empty if not
                     ref.replace_with(BeautifulSoup(f"{content}", 'html.parser'))
diff --git a/marker/schema/__init__.py b/marker/schema/__init__.py
index 473e6216..19f3ea02 100644
--- a/marker/schema/__init__.py
+++ b/marker/schema/__init__.py
@@ -27,6 +27,7 @@ class BlockTypes(str, Enum):
     TableOfContents = auto()
     Document = auto()
     ComplexRegion = auto()
+    Reference = auto()
 
     def __str__(self):
         return self.name
diff --git a/marker/schema/blocks/__init__.py b/marker/schema/blocks/__init__.py
index 2203d044..de1980ac 100644
--- a/marker/schema/blocks/__init__.py
+++ b/marker/schema/blocks/__init__.py
@@ -18,3 +18,4 @@
 from marker.schema.blocks.text import Text
 from marker.schema.blocks.toc import TableOfContents
 from marker.schema.blocks.complexregion import ComplexRegion
+from marker.schema.blocks.reference import Reference
diff --git a/marker/schema/blocks/base.py b/marker/schema/blocks/base.py
index a6ad98b8..e71294cd 100644
--- a/marker/schema/blocks/base.py
+++ b/marker/schema/blocks/base.py
@@ -11,6 +11,7 @@
     from marker.schema.document import Document
     from marker.schema.groups.page import PageGroup
 
+
 class BlockMetadata(BaseModel):
     llm_request_count: int = 0
     llm_error_count: int = 0
@@ -76,6 +77,7 @@ class Block(BaseModel):
     text_extraction_method: Optional[Literal['pdftext', 'surya', 'gemini']] = None
     structure: List[BlockId] | None = None  # The top-level page structure, which is the block ids in order
     ignore_for_output: bool = False  # Whether this block should be ignored in output
+    replace_output_newlines: bool = False  # Whether to replace newlines with spaces in output
     source: Literal['layout', 'heuristics', 'processor'] = 'layout'
     top_k: Optional[Dict[BlockTypes, float]] = None
     metadata: BlockMetadata | None = None
@@ -168,6 +170,10 @@ def assemble_html(self, child_blocks: List[BlockOutput], parent_structure: Optio
         template = ""
         for c in child_blocks:
             template += f""
+
+        if self.replace_output_newlines:
+            template = "
" + template.replace("\n", " ") + "
"
+
         return template
 
     def assign_section_hierarchy(self, section_hierarchy):
diff --git a/marker/schema/blocks/caption.py b/marker/schema/blocks/caption.py
index acb45ea9..40827ecd 100644
--- a/marker/schema/blocks/caption.py
+++ b/marker/schema/blocks/caption.py
@@ -4,8 +4,4 @@
 
 class Caption(Block):
     block_type: BlockTypes = BlockTypes.Caption
-
-    def assemble_html(self, child_blocks, parent_structure):
-        template = super().assemble_html(child_blocks, parent_structure)
-        template = template.replace("\n", " ")
-        return f"
{template}
"
+    replace_output_newlines: bool = True
diff --git a/marker/schema/blocks/equation.py b/marker/schema/blocks/equation.py
index 3881814b..e2ae288f 100644
--- a/marker/schema/blocks/equation.py
+++ b/marker/schema/blocks/equation.py
@@ -10,7 +10,9 @@ class Equation(Block):
 
     def assemble_html(self, child_blocks, parent_structure=None):
         if self.latex:
-            html_out = f"
"
+            child_ref_blocks = [block for block in child_blocks if block.id.block_type == BlockTypes.Reference]
+            html_out = super().assemble_html(child_ref_blocks, parent_structure)
+            html_out += f"
"
 
             try:
                 latex = self.parse_latex(html.escape(self.latex))
@@ -43,9 +45,9 @@ def parse_latex(text: str):
             ("$$", "block"),
             ("$", "inline")
         ]
-        
-        text = text.replace("\n", "
") # we can't handle \n's inside 
 properly if we don't do this
-       
+
+        text = text.replace("\n", "
")  # we can't handle \n's inside 
 properly if we don't do this
+
         i = 0
         stack = []
         result = []
@@ -72,7 +74,7 @@ def parse_latex(text: str):
             else:  # No delimiter match
                 buffer += text[i]
                 i += 1
-                
+
         if buffer:
             result.append({"class": "text", "content": buffer})
-        return result
\ No newline at end of file
+        return result
diff --git a/marker/schema/blocks/figure.py b/marker/schema/blocks/figure.py
index a4c21c01..6dfb0593 100644
--- a/marker/schema/blocks/figure.py
+++ b/marker/schema/blocks/figure.py
@@ -7,7 +7,8 @@ class Figure(Block):
     description: str | None = None
 
     def assemble_html(self, child_blocks, parent_structure):
+        child_ref_blocks = [block for block in child_blocks if block.id.block_type == BlockTypes.Reference]
+        html = super().assemble_html(child_ref_blocks, parent_structure)
         if self.description:
-            return f"
Image {self.id} description: {self.description}
"
-        else:
-            return ""
+            html += f"
Image {self.id} description: {self.description}
"
+        return html
diff --git a/marker/schema/blocks/footnote.py b/marker/schema/blocks/footnote.py
index 71d3580b..0bad0a89 100644
--- a/marker/schema/blocks/footnote.py
+++ b/marker/schema/blocks/footnote.py
@@ -4,9 +4,4 @@
 
 class Footnote(Block):
     block_type: BlockTypes = BlockTypes.Footnote
-
-    def assemble_html(self, child_blocks, parent_structure):
-        template = super().assemble_html(child_blocks, parent_structure)
-        template = template.replace("\n", " ")
-
-        return f"
{template}
"
+    replace_output_newlines: bool = True
diff --git a/marker/schema/blocks/handwriting.py b/marker/schema/blocks/handwriting.py
index 14a146e6..98753928 100644
--- a/marker/schema/blocks/handwriting.py
+++ b/marker/schema/blocks/handwriting.py
@@ -4,8 +4,4 @@
 
 class Handwriting(Block):
     block_type: BlockTypes = BlockTypes.Handwriting
-
-    def assemble_html(self, child_blocks, parent_structure):
-        template = super().assemble_html(child_blocks, parent_structure)
-        template = template.replace("\n", " ")
-        return f"
{template}
"
+    replace_output_newlines: bool = True
diff --git a/marker/schema/blocks/pagefooter.py b/marker/schema/blocks/pagefooter.py
index 774474b5..6054464a 100644
--- a/marker/schema/blocks/pagefooter.py
+++ b/marker/schema/blocks/pagefooter.py
@@ -4,11 +4,5 @@
 
 class PageFooter(Block):
     block_type: str = BlockTypes.PageFooter
-
-    def assemble_html(self, child_blocks, parent_structure):
-        if self.ignore_for_output:
-            return ""
-
-        template = super().assemble_html(child_blocks, parent_structure)
-        template = template.replace("\n", " ")
-        return f"
{template}
"
+    replace_output_newlines: bool = True
+    ignore_for_output: bool = True
diff --git a/marker/schema/blocks/pageheader.py b/marker/schema/blocks/pageheader.py
index d304490e..2587294a 100644
--- a/marker/schema/blocks/pageheader.py
+++ b/marker/schema/blocks/pageheader.py
@@ -4,11 +4,5 @@
 
 class PageHeader(Block):
     block_type: BlockTypes = BlockTypes.PageHeader
-
-    def assemble_html(self, child_blocks, parent_structure):
-        if self.ignore_for_output:
-            return ""
-
-        template = super().assemble_html(child_blocks, parent_structure)
-        template = template.replace("\n", " ")
-        return f"
{template}
"
+    replace_output_newlines: bool = True
+    ignore_for_output: bool = True
diff --git a/marker/schema/blocks/reference.py b/marker/schema/blocks/reference.py
new file mode 100644
index 00000000..43e66011
--- /dev/null
+++ b/marker/schema/blocks/reference.py
@@ -0,0 +1,11 @@
+from marker.schema import BlockTypes
+from marker.schema.blocks import Block
+
+
+class Reference(Block):
+    block_type: BlockTypes = BlockTypes.Reference
+    ref: str
+
+    def assemble_html(self, child_blocks, parent_structure=None):
+        template = super().assemble_html(child_blocks, parent_structure)
+        return f"{template}"
diff --git a/marker/schema/blocks/table.py b/marker/schema/blocks/table.py
index d389bc9d..da4148e0 100644
--- a/marker/schema/blocks/table.py
+++ b/marker/schema/blocks/table.py
@@ -12,8 +12,8 @@ class Table(Block):
     cells: List[SpanTableCell] | None = None
 
     def assemble_html(self, child_blocks, parent_structure=None):
+        child_ref_blocks = [block for block in child_blocks if block.id.block_type == BlockTypes.Reference]
+        template = super().assemble_html(child_ref_blocks, parent_structure)
         if self.cells:
-            return str(html_format(self.cells))
-        else:
-            template = super().assemble_html(child_blocks, parent_structure)
-            return f"
{template}
"
+            return template + str(html_format(self.cells))
+        return f"
{template}
"
diff --git a/marker/schema/groups/page.py b/marker/schema/groups/page.py
index 00282899..4bef286d 100644
--- a/marker/schema/groups/page.py
+++ b/marker/schema/groups/page.py
@@ -3,6 +3,7 @@
 
 from PIL import Image
 
+from pdftext.schema import Reference
 from marker.providers import ProviderOutput
 from marker.schema import BlockTypes
 from marker.schema.blocks import Block, BlockId, Text
@@ -22,6 +23,7 @@ class PageGroup(Group):
     layout_sliced: bool = False  # Whether the layout model had to slice the image (order may be wrong)
     excluded_block_types: Sequence[BlockTypes] = (BlockTypes.Line, BlockTypes.Span,)
     maximum_assignment_distance: float = 20  # pixels
+    refs: List[Reference] | None = None
 
     def incr_block_id(self):
         if self.block_id is None:
diff --git a/marker/schema/registry.py b/marker/schema/registry.py
index 250934eb..dbf5beff 100644
--- a/marker/schema/registry.py
+++ b/marker/schema/registry.py
@@ -6,7 +6,7 @@
     Footnote, Form, Handwriting, InlineMath, \
     ListItem, PageFooter, PageHeader, Picture, \
     SectionHeader, Table, TableOfContents, \
-    Text
+    Text, Reference
 from marker.schema.blocks.complexregion import ComplexRegion
 from marker.schema.document import Document
 from marker.schema.groups import FigureGroup, ListGroup, PageGroup, \
@@ -51,6 +51,7 @@ def get_block_class(block_type: BlockTypes) -> Type[Block]:
 register_block_class(BlockTypes.Text, Text)
 register_block_class(BlockTypes.TableOfContents, TableOfContents)
 register_block_class(BlockTypes.ComplexRegion, ComplexRegion)
+register_block_class(BlockTypes.Reference, Reference)
 register_block_class(BlockTypes.Document, Document)
 
 assert len(BLOCK_REGISTRY) == len(BlockTypes)
diff --git a/marker/schema/text/span.py b/marker/schema/text/span.py
index 1b6e18f2..06f32249 100644
--- a/marker/schema/text/span.py
+++ b/marker/schema/text/span.py
@@ -24,7 +24,6 @@ class Span(Block):
     formats: List[Literal['plain', 'math', 'chemical', 'bold', 'italic']]
     has_superscript: bool = False
     url: Optional[str] = None
-    anchors: Optional[List[str]] = None
 
     @property
     def bold(self):
@@ -74,6 +73,4 @@ def assemble_html(self, child_blocks, parent_structure):
         elif self.math:
             text = f"{text}"
 
-        if self.anchors:
-            text = "".join(f"" for anchor in self.anchors) + text
         return text
diff --git a/tests/builders/test_pdf_links.py b/tests/builders/test_pdf_links.py
index 72a97070..c639c59e 100644
--- a/tests/builders/test_pdf_links.py
+++ b/tests/builders/test_pdf_links.py
@@ -1,3 +1,5 @@
+import re
+
 import pytest
 
 from marker.converters.pdf import PdfConverter
@@ -8,9 +10,8 @@
 
 @pytest.mark.filename("arxiv_test.pdf")
 @pytest.mark.output_format("markdown")
-@pytest.mark.config({"page_range": [1]})
 def test_pdf_links(pdf_document: Document, pdf_converter: PdfConverter, temp_pdf):
-    first_page = pdf_document.pages[0]
+    first_page = pdf_document.pages[1]
 
     for section_header_span in first_page.contained_blocks(pdf_document, (BlockTypes.Span,)):
         if "II." in section_header_span.text:
@@ -22,11 +23,13 @@ def test_pdf_links(pdf_document: Document, pdf_converter: PdfConverter, temp_pdf
     section_header_block = first_page.contained_blocks(pdf_document, (BlockTypes.SectionHeader,))[0]
     assert section_header_block.raw_text(pdf_document) == 'II. THEORETICAL FRAMEWORK\n'
 
-    section_header_span = section_header_block.contained_blocks(pdf_document, (BlockTypes.Span,))[0]
-    assert section_header_span.anchors == ['page-1-0']
+    assert first_page.refs[0].ref == "page-1-0"
 
     markdown_output: MarkdownOutput = pdf_converter(temp_pdf.name)
     markdown = markdown_output.markdown
 
     assert '[II.](#page-1-0)' in markdown
     assert 'II. THEORETICAL FRAMEWORK' in markdown
+
+    for ref in set([f'' for m in re.findall(r'\]\(#page-(\d+)-(\d+)\)', markdown)]):
+        assert ref in markdown, f"Reference {ref} not found in markdown"

From 9ad7dc37fcb4d3454f5cf5f2b23bff00d09dd2f7 Mon Sep 17 00:00:00 2001
From: Vik Paruchuri 
Date: Fri, 17 Jan 2025 11:55:32 -0500
Subject: [PATCH 60/92] Add LLM handwriting processor

---
 marker/converters/pdf.py                      |  7 +-
 marker/processors/llm/__init__.py             |  5 --
 marker/processors/llm/llm_complex.py          | 25 +++---
 marker/processors/llm/llm_equation.py         | 82 ++++++++++++++++++
 marker/processors/llm/llm_form.py             | 11 +--
 marker/processors/llm/llm_handwriting.py      | 86 +++++++++++++++++++
 .../processors/llm/llm_image_description.py   |  5 +-
 marker/processors/llm/llm_table.py            | 16 +++-
 marker/processors/llm/llm_table_merge.py      |  4 +-
 marker/processors/llm/llm_text.py             |  9 +-
 marker/schema/blocks/handwriting.py           | 10 ++-
 11 files changed, 227 insertions(+), 33 deletions(-)
 create mode 100644 marker/processors/llm/llm_equation.py
 create mode 100644 marker/processors/llm/llm_handwriting.py

diff --git a/marker/converters/pdf.py b/marker/converters/pdf.py
index 6496c003..90fcb750 100644
--- a/marker/converters/pdf.py
+++ b/marker/converters/pdf.py
@@ -1,4 +1,7 @@
 import os
+
+from marker.processors.llm.llm_handwriting import LLMHandwritingProcessor
+
 os.environ["TOKENIZERS_PARALLELISM"] = "false"  # disables a tokenizers warning
 
 import inspect
@@ -33,7 +36,7 @@
 from marker.processors.sectionheader import SectionHeaderProcessor
 from marker.processors.table import TableProcessor
 from marker.processors.text import TextProcessor
-from marker.providers.pdf import PdfProvider
+from marker.processors.llm.llm_equation import LLMEquationProcessor
 from marker.renderers.markdown import MarkdownRenderer
 from marker.schema import BlockTypes
 from marker.schema.blocks import Block
@@ -75,6 +78,8 @@ class PdfConverter(BaseConverter):
         LLMTextProcessor,
         LLMComplexRegionProcessor,
         LLMImageDescriptionProcessor,
+        LLMEquationProcessor,
+        LLMHandwritingProcessor,
         DebugProcessor,
     )
 
diff --git a/marker/processors/llm/__init__.py b/marker/processors/llm/__init__.py
index 8cf6bede..87baeb71 100644
--- a/marker/processors/llm/__init__.py
+++ b/marker/processors/llm/__init__.py
@@ -40,11 +40,6 @@ class BaseLLMProcessor(BaseProcessor):
         float,
         "The ratio to expand the image by when cropping.",
     ] = 0.01
-    gemini_rewriting_prompt: Annotated[
-        str,
-        "The prompt to use for rewriting text.",
-        "Default is a string containing the Gemini rewriting prompt."
-    ] = ''
     use_llm: Annotated[
         bool,
         "Whether to use the LLM model.",
diff --git a/marker/processors/llm/llm_complex.py b/marker/processors/llm/llm_complex.py
index 5d2e1425..52c46364 100644
--- a/marker/processors/llm/llm_complex.py
+++ b/marker/processors/llm/llm_complex.py
@@ -12,9 +12,9 @@
 
 class LLMComplexRegionProcessor(BaseLLMProcessor):
     block_types = (BlockTypes.ComplexRegion,)
-    gemini_rewriting_prompt = """You are a text correction expert specializing in accurately reproducing text from images.
+    complex_region_prompt = """You are a text correction expert specializing in accurately reproducing text from images.
 You will receive an image of a text block and the text that can be extracted from the image.
-Your task is to correct any errors in the text, and format it properly.  Do not omit any text from the block - make sure everything is included in the markdown representation.  The markdown representation should be as faithful to the original text as possible.
+Your task is to generate markdown to properly represent the content of the image.  Do not omit any text present in the image - make sure everything is included in the markdown representation.  The markdown representation should be as faithful to the original image as possible.
 
 Formatting should be in markdown, with the following rules:
 - * for italics, ** for bold, and ` for inline code.
@@ -29,26 +29,31 @@ class LLMComplexRegionProcessor(BaseLLMProcessor):
 
 **Instructions:**
 1. Carefully examine the provided block image.
-2. Analyze the text representation
-3. If the text representation is largely correct, then write "No corrections needed."
-4. If the text representation contains errors, generate the corrected markdown representation.
-5. Output only either the corrected markdown representation or "No corrections needed."
+2. Analyze the existing text representation.
+3. Generate the markdown representation of the content in the image.
 **Example:**
 Input:
 ```text
-This is an example text block.
+Table 1: Car Sales
 ```
 Output:
 ```markdown
-No corrections needed.
+## Table 1: Car Sales
+
+| Car | Sales |
+| --- | --- |
+| Honda | 100 |
+| Toyota | 200 |
 ```
 **Input:**
+```text
+{extracted_text}
+```
 """
 
     def process_rewriting(self, document: Document, page: PageGroup, block: Block):
         text = block.raw_text(document)
-
-        prompt = self.gemini_rewriting_prompt + '```text\n`' + text + '`\n```\n'
+        prompt = self.complex_region_prompt.replace("{extracted_text}", text)
         image = self.extract_image(document, block)
         response_schema = content.Schema(
             type=content.Type.OBJECT,
diff --git a/marker/processors/llm/llm_equation.py b/marker/processors/llm/llm_equation.py
new file mode 100644
index 00000000..93b13c34
--- /dev/null
+++ b/marker/processors/llm/llm_equation.py
@@ -0,0 +1,82 @@
+from marker.processors.llm import BaseLLMProcessor
+
+from google.ai.generativelanguage_v1beta.types import content
+
+from marker.schema import BlockTypes
+from marker.schema.blocks import Equation
+from marker.schema.document import Document
+from marker.schema.groups.page import PageGroup
+
+from typing import Annotated
+
+
+class LLMEquationProcessor(BaseLLMProcessor):
+    block_types = (BlockTypes.Equation,)
+    min_equation_height: Annotated[
+        float,
+        "The minimum ratio between equation height and page height to consider for processing.",
+     ] = 0.1
+    equation_latex_prompt: Annotated[
+        str,
+        "The prompt to use for generating LaTeX from equations.",
+        "Default is a string containing the Gemini prompt."
+    ] = """You're an expert mathematician who is good at writing LaTeX code for equations'.
+You will receive an image of a math block that may contain one or more equations. Your job is to write the LaTeX code for the equation, along with markdown for any other text.
+
+Some guidelines:
+- Keep the LaTeX code simple and concise.
+- Make it KaTeX compatible.
+- Use $$ as a block equation delimiter and $ for inline equations.  Block equations should also be on their own line.  Do not use any other delimiters.
+- You can include text in between equation blocks as needed.  Try to put long text segments into plain text and not inside the equations.
+
+**Instructions:**
+1. Carefully examine the provided image.
+2. Analyze the existing markdown, which may include LaTeX code.
+3. If the markdown and LaTeX are correct, write "No corrections needed."
+4. If the markdown and LaTeX are incorrect, generate the corrected markdown and LaTeX.
+5. Output only the corrected text or "No corrections needed."
+**Example:**
+Input:
+```markdown
+Equation 1: 
+$$x^2 + y^2 = z2$$
+```
+Output:
+```markdown
+Equation 1: 
+$$x^2 + y^2 = z^2$$
+```
+**Input:**
+```markdown
+{equation}
+```
+"""
+
+    def process_rewriting(self, document: Document, page: PageGroup, block: Equation):
+        text = block.latex if block.latex else block.raw_text(document)
+        prompt = self.equation_latex_prompt.replace("{equation}", text)
+
+        image = self.extract_image(document, block)
+        response_schema = content.Schema(
+            type=content.Type.OBJECT,
+            enum=[],
+            required=["markdown_equation"],
+            properties={
+                "markdown_equation": content.Schema(
+                    type=content.Type.STRING
+                )
+            },
+        )
+
+        response = self.model.generate_response(prompt, image, block, response_schema)
+
+        if not response or "markdown_equation" not in response:
+            block.update_metadata(llm_error_count=1)
+            return
+
+        markdown_equation = response["markdown_equation"]
+        if len(markdown_equation) < len(text) * .5:
+            block.update_metadata(llm_error_count=1)
+            return
+
+        block.latex = markdown_equation
diff --git a/marker/processors/llm/llm_form.py b/marker/processors/llm/llm_form.py
index 15c6deeb..a9a4f339 100644
--- a/marker/processors/llm/llm_form.py
+++ b/marker/processors/llm/llm_form.py
@@ -1,5 +1,3 @@
-import markdown2
-
 from marker.processors.llm import BaseLLMProcessor
 
 from google.ai.generativelanguage_v1beta.types import content
@@ -12,9 +10,9 @@
 
 class LLMFormProcessor(BaseLLMProcessor):
     block_types = (BlockTypes.Form,)
-    gemini_rewriting_prompt = """You are a text correction expert specializing in accurately reproducing text from images.
+    form_rewriting_prompt = """You are a text correction expert specializing in accurately reproducing text from images.
 You will receive an image of a text block and an html representation of the form in the image.
-Your task is to correct any errors in the htmlrepresentation, and format it properly.
+Your task is to correct any errors in the html representation, and format it properly.
 Values and labels should appear in html tables, with the labels on the left side, and values on the right.  The headers should be "Labels" and "Values".  Other text in the form can appear between the tables.  Only use the tags `table, p, span, i, b, th, td, tr, and div`.  Do not omit any text from the form - make sure everything is included in the html representation.  It should be as faithful to the original form as possible.
 **Instructions:**
 1. Carefully examine the provided form block image.
@@ -60,6 +58,9 @@ class LLMFormProcessor(BaseLLMProcessor):
 
NameAgeCityState

     

         Jane
         30

 ```
 **Input:**
+```html
+{block_html}
+```
 """
 
     def process_rewriting(self, document: Document, page: PageGroup, block: Block):
@@ -69,8 +70,8 @@ def process_rewriting(self, document: Document, page: PageGroup, block: Block):
             return
 
         block_html = block.render(document).html
+        prompt = self.form_rewriting_prompt.replace("{block_html}", block_html)
 
-        prompt = self.gemini_rewriting_prompt + '```html\n`' + block_html + '`\n```\n'
         image = self.extract_image(document, block)
         response_schema = content.Schema(
             type=content.Type.OBJECT,
diff --git a/marker/processors/llm/llm_handwriting.py b/marker/processors/llm/llm_handwriting.py
new file mode 100644
index 00000000..cb00e298
--- /dev/null
+++ b/marker/processors/llm/llm_handwriting.py
@@ -0,0 +1,86 @@
+import markdown2
+
+from marker.processors.llm import BaseLLMProcessor
+
+from google.ai.generativelanguage_v1beta.types import content
+
+from marker.schema import BlockTypes
+from marker.schema.blocks import Equation
+from marker.schema.document import Document
+from marker.schema.groups.page import PageGroup
+
+from typing import Annotated
+
+
+class LLMHandwritingProcessor(BaseLLMProcessor):
+    block_types = (BlockTypes.Equation,)
+    min_handwriting_height: Annotated[
+        float,
+        "The minimum ratio between handwriting height and page height to consider for processing.",
+     ] = 0.1
+    handwriting_generation_prompt: Annotated[
+        str,
+        "The prompt to use for OCRing handwriting.",
+        "Default is a string containing the Gemini prompt."
+    ] = """You are an expert editor specializing in accurately reproducing text from images.
+You will receive an image of a text block, along with the text that can be extracted. Your task is to generate markdown to properly represent the content of the image.  Do not omit any text present in the image - make sure everything is included in the markdown representation.  The markdown representation should be as faithful to the original image as possible.
+
+Formatting should be in markdown, with the following rules:
+- * for italics, ** for bold, and ` for inline code.
+- Headers should be formatted with #, with one # for the largest header, and up to 6 for the smallest.
+- Lists should be formatted with either - or 1. for unordered and ordered lists, respectively.
+- Links should be formatted with [text](url).
+- Use ``` for code blocks.
+- Inline math should be formatted with math expression.
+- Display math should be formatted with math expression.
+- Values and labels should be extracted from forms, and put into markdown tables, with the labels on the left side, and values on the right.  The headers should be "Labels" and "Values".  Other text in the form can appear between the tables.
+- Tables should be formatted with markdown tables, with the headers bolded.
+
+**Instructions:**
+1. Carefully examine the provided block image.
+2. Analyze the existing text representation.
+3. Output the markdown representing the content of the image.
+**Example:**
+Input:
+```text
+This i sm handwritting.
+```
+Output:
+```markdown
+This is some *handwriting*.
+```
+**Input:**
+```text
+{extracted_text}
+```
+"""
+
+    def process_rewriting(self, document: Document, page: PageGroup, block: Equation):
+        text = block.raw_text(document)
+        prompt = self.handwriting_generation_prompt.replace("{handwriting_text}", text)
+
+        image = self.extract_image(document, block)
+        response_schema = content.Schema(
+            type=content.Type.OBJECT,
+            enum=[],
+            required=["markdown"],
+            properties={
+                "markdown": content.Schema(
+                    type=content.Type.STRING
+                )
+            },
+        )
+
+        response = self.model.generate_response(prompt, image, block, response_schema)
+
+        if not response or "markdown" not in response:
+            block.update_metadata(llm_error_count=1)
+            return
+
+        markdown = response["markdown"]
+        if len(markdown) < len(text) * .5:
+            block.update_metadata(llm_error_count=1)
+            return
+
+        markdown = markdown.strip().lstrip("```markdown").rstrip("```").strip()
+        block.html = markdown2.markdown(markdown)
diff --git a/marker/processors/llm/llm_image_description.py b/marker/processors/llm/llm_image_description.py
index 80e89e8e..a08e0dc9 100644
--- a/marker/processors/llm/llm_image_description.py
+++ b/marker/processors/llm/llm_image_description.py
@@ -36,6 +36,9 @@ class LLMImageDescriptionProcessor(BaseLLMProcessor):
 Output:
 In this figure, a bar chart titled "Fruit Preference Survey" is showing the number of people who prefer different types of fruits.  The x-axis shows the types of fruits, and the y-axis shows the number of people.  The bar chart shows that most people prefer apples, followed by bananas and oranges.  20 people prefer apples, 15 people prefer bananas, and 10 people prefer oranges.
 **Input:**
+```text
+{raw_text}
+```
 """
 
     def process_rewriting(self, document: Document, page: PageGroup, block: Block):
@@ -44,7 +47,7 @@ def process_rewriting(self, document: Document, page: PageGroup, block: Block):
             # Since this processor replaces images with descriptions
             return
 
-        prompt = self.image_description_prompt + '```text\n`' + block.raw_text(document) + '`\n```\n'
+        prompt = self.image_description_prompt.replace("{raw_text}", block.raw_text(document))
         image = self.extract_image(document, block)
         response_schema = content.Schema(
             type=content.Type.OBJECT,
diff --git a/marker/processors/llm/llm_table.py b/marker/processors/llm/llm_table.py
index 6bdc3941..12208546 100644
--- a/marker/processors/llm/llm_table.py
+++ b/marker/processors/llm/llm_table.py
@@ -16,18 +16,25 @@ class LLMTableProcessor(BaseLLMProcessor):
         Tuple[BlockTypes],
         "The block types to process.",
     ] = (BlockTypes.Table, BlockTypes.TableOfContents)
-    gemini_rewriting_prompt: Annotated[
+    table_rewriting_prompt: Annotated[
         str,
         "The prompt to use for rewriting text.",
         "Default is a string containing the Gemini rewriting prompt."
     ] = """You are a text correction expert specializing in accurately reproducing text from images.
 You will receive an image of a text block and an html representation of the table in the image.
 Your task is to correct any errors in the html representation.  The html representation should be as faithful to the original table as possible.
+
+Some guidelines:
+- Make sure to reproduce the original values as faithfully as possible.
+- If you see any math in a table cell, fence it with the  tag.  Block math should be fenced with .
+- Replace any images with a description, like "Image: [description]".
+- Only use the tags th, td, tr, span, i, b, math, and table.  Only use the attributes display, style, colspan, and rowspan if necessary.
+
 **Instructions:**
 1. Carefully examine the provided text block image.
 2. Analyze the html representation of the table.
 3. If the html representation is largely correct, then write "No corrections needed."
-4. If the html representation contains errors, generate the corrected html representation.  Only use the tags th, td, tr, and table.  Only use the attributes colspan and rowspan if necessary.
+4. If the html representation contains errors, generate the corrected html representation.  
 5. Output only either the corrected html representation or "No corrections needed."
 **Example:**
 Input:
@@ -50,6 +57,9 @@ class LLMTableProcessor(BaseLLMProcessor):
 No corrections needed.
 ```
 **Input:**
+```html
+{block_html}
+```
 """
 
     def process_rewriting(self, document: Document, page: PageGroup, block: Block):
@@ -59,8 +69,8 @@ def process_rewriting(self, document: Document, page: PageGroup, block: Block):
             return
 
         block_html = block.render(document).html
+        prompt = self.table_rewriting_prompt.replace("{block_html}", block_html)
 
-        prompt = self.gemini_rewriting_prompt + '```html\n`' + block_html + '`\n```\n'
         image = self.extract_image(document, block)
         response_schema = content.Schema(
             type=content.Type.OBJECT,
diff --git a/marker/processors/llm/llm_table_merge.py b/marker/processors/llm/llm_table_merge.py
index a5efaacc..785ecad9 100644
--- a/marker/processors/llm/llm_table_merge.py
+++ b/marker/processors/llm/llm_table_merge.py
@@ -36,7 +36,7 @@ class LLMTableMergeProcessor(BaseLLMProcessor):
         int,
         "The maximum gap between columns to merge tables"
     ] = 50
-    gemini_table_merge_prompt: Annotated[
+    table_merge_prompt: Annotated[
         str,
         "The prompt to use for rewriting text.",
         "Default is a string containing the Gemini rewriting prompt."
@@ -212,7 +212,7 @@ def process_rewriting(self, document: Document, blocks: List[Block]):
             start_html = start_block.render(document).html
             curr_html = curr_block.render(document).html
 
-            prompt = self.gemini_table_merge_prompt.replace("{{table1}}", start_html).replace("{{table2}}", curr_html)
+            prompt = self.table_merge_prompt.replace("{{table1}}", start_html).replace("{{table2}}", curr_html)
 
             response_schema = content.Schema(
                 type=content.Type.OBJECT,
diff --git a/marker/processors/llm/llm_text.py b/marker/processors/llm/llm_text.py
index fda2cd77..ba9bd54d 100644
--- a/marker/processors/llm/llm_text.py
+++ b/marker/processors/llm/llm_text.py
@@ -1,4 +1,5 @@
 import json
+import textwrap
 
 from marker.processors.llm import BaseLLMProcessor
 from bs4 import BeautifulSoup
@@ -13,7 +14,7 @@
 
 class LLMTextProcessor(BaseLLMProcessor):
     block_types = (BlockTypes.TextInlineMath, BlockTypes.Handwriting)
-    gemini_rewriting_prompt = """You are a text correction expert specializing in accurately reproducing text from images.
+    text_math_rewriting_prompt = """You are a text correction expert specializing in accurately reproducing text from images.
 You will receive an image of a text block and a set of extracted lines corresponding to the text in the image.
 Your task is to correct any errors in the extracted lines, including math, formatting, and other inaccuracies, and output the corrected lines in a JSON format.
 The number of output lines MUST match the number of input lines.  Stay as faithful to the original text as possible.
@@ -64,7 +65,9 @@ class LLMTextProcessor(BaseLLMProcessor):
 ```
 
 **Input:**
-
+```json
+{extracted_lines}
+```
 """
 
     def process_rewriting(self, document: Document, page: PageGroup, block: Block):
@@ -73,7 +76,7 @@ def process_rewriting(self, document: Document, page: PageGroup, block: Block):
         text_lines = block.contained_blocks(document, (BlockTypes.Line,))
         extracted_lines = [line.formatted_text(document) for line in text_lines]
 
-        prompt = self.gemini_rewriting_prompt + '```json\n`' + json.dumps({"extracted_lines": extracted_lines}, indent=2) + '`\n```\n'
+        prompt = self.text_math_rewriting_prompt.replace("{extracted_lines}", json.dumps({"extracted_lines": extracted_lines}, indent=2))
         image = self.extract_image(document, block)
         response_schema = content.Schema(
             type=content.Type.OBJECT,
diff --git a/marker/schema/blocks/handwriting.py b/marker/schema/blocks/handwriting.py
index 4eafa6f3..cf844c1a 100644
--- a/marker/schema/blocks/handwriting.py
+++ b/marker/schema/blocks/handwriting.py
@@ -5,8 +5,12 @@
 class Handwriting(Block):
     block_type: BlockTypes = BlockTypes.Handwriting
     block_description: str = "A region that contains handwriting."
+    html: str | None = None
 
     def assemble_html(self, document, child_blocks, parent_structure):
-        template = super().assemble_html(document, child_blocks, parent_structure)
-        template = template.replace("\n", " ")
-        return f"
{template}
"
+        if self.html:
+            return self.html
+        else:
+            template = super().assemble_html(document, child_blocks, parent_structure)
+            template = template.replace("\n", " ")
+            return f"
{template}
"

From bd4ee4ecd0bdf14e17cdda3f5b191121c1867701 Mon Sep 17 00:00:00 2001
From: Vik Paruchuri 
Date: Mon, 20 Jan 2025 15:13:31 -0500
Subject: [PATCH 61/92] Cleanup post-merge

---
 marker/schema/blocks/basetable.py   | 4 ++--
 marker/schema/blocks/caption.py     | 5 -----
 marker/schema/blocks/equation.py    | 2 +-
 marker/schema/blocks/footnote.py    | 7 -------
 marker/schema/blocks/handwriting.py | 5 +----
 marker/schema/blocks/pagefooter.py  | 8 --------
 marker/schema/blocks/pageheader.py  | 8 --------
 marker/schema/blocks/reference.py   | 5 +++--
 8 files changed, 7 insertions(+), 37 deletions(-)

diff --git a/marker/schema/blocks/basetable.py b/marker/schema/blocks/basetable.py
index c3a7d422..941c6efb 100644
--- a/marker/schema/blocks/basetable.py
+++ b/marker/schema/blocks/basetable.py
@@ -25,8 +25,8 @@ def format_cells(self, document, child_blocks):
 
     def assemble_html(self, document, child_blocks: List[BlockOutput], parent_structure=None):
         # Filter out the table cells, so they don't render twice
-        selected_blocks = [b for b in child_blocks if b.id.block_type != BlockTypes.TableCell]
-        template = super().assemble_html(document, selected_blocks, parent_structure)
+        child_ref_blocks = [block for block in child_blocks if block.id.block_type == BlockTypes.Reference]
+        template = super().assemble_html(document, child_ref_blocks, parent_structure)
 
         if self.html:
             # LLM processor
diff --git a/marker/schema/blocks/caption.py b/marker/schema/blocks/caption.py
index 341664c1..15741388 100644
--- a/marker/schema/blocks/caption.py
+++ b/marker/schema/blocks/caption.py
@@ -7,8 +7,3 @@ class Caption(Block):
     block_description: str = "A text caption that is directly above or below an image or table. Only used for text describing the image or table.  "
     replace_output_newlines: bool = True
 
-    def assemble_html(self, document, child_blocks, parent_structure):
-        template = super().assemble_html(document, child_blocks, parent_structure)
-        template = template.replace("\n", " ")
-        return f"
{template}
"
-
diff --git a/marker/schema/blocks/equation.py b/marker/schema/blocks/equation.py
index d87f31e9..d01c349b 100644
--- a/marker/schema/blocks/equation.py
+++ b/marker/schema/blocks/equation.py
@@ -12,7 +12,7 @@ class Equation(Block):
     def assemble_html(self, document, child_blocks, parent_structure=None):
         if self.latex:
             child_ref_blocks = [block for block in child_blocks if block.id.block_type == BlockTypes.Reference]
-            html_out = super().assemble_html(child_ref_blocks, parent_structure)
+            html_out = super().assemble_html(document, child_ref_blocks, parent_structure)
             html_out += f"
"
 
             try:
diff --git a/marker/schema/blocks/footnote.py b/marker/schema/blocks/footnote.py
index ee994f8a..b80c83e5 100644
--- a/marker/schema/blocks/footnote.py
+++ b/marker/schema/blocks/footnote.py
@@ -6,10 +6,3 @@ class Footnote(Block):
     block_type: BlockTypes = BlockTypes.Footnote
     block_description: str = "A footnote that explains a term or concept in the document."
     replace_output_newlines: bool = True
-
-    def assemble_html(self, document, child_blocks, parent_structure):
-        template = super().assemble_html(document, child_blocks, parent_structure)
-        template = template.replace("\n", " ")
-
-        return f"
{template}
"
-
diff --git a/marker/schema/blocks/handwriting.py b/marker/schema/blocks/handwriting.py
index 2d6b2f3a..423b75ef 100644
--- a/marker/schema/blocks/handwriting.py
+++ b/marker/schema/blocks/handwriting.py
@@ -12,7 +12,4 @@ def assemble_html(self, document, child_blocks, parent_structure):
         if self.html:
             return self.html
         else:
-            template = super().assemble_html(document, child_blocks, parent_structure)
-            template = template.replace("\n", " ")
-            return f"
{template}
"
-
+            return super().assemble_html(document, child_blocks, parent_structure)
diff --git a/marker/schema/blocks/pagefooter.py b/marker/schema/blocks/pagefooter.py
index 4b540c01..ef402f92 100644
--- a/marker/schema/blocks/pagefooter.py
+++ b/marker/schema/blocks/pagefooter.py
@@ -8,11 +8,3 @@ class PageFooter(Block):
     replace_output_newlines: bool = True
     ignore_for_output: bool = True
 
-    def assemble_html(self, document, child_blocks, parent_structure):
-        if self.ignore_for_output:
-            return ""
-
-        template = super().assemble_html(document, child_blocks, parent_structure)
-        template = template.replace("\n", " ")
-        return f"
{template}
"
-
diff --git a/marker/schema/blocks/pageheader.py b/marker/schema/blocks/pageheader.py
index add0c0f6..d484899c 100644
--- a/marker/schema/blocks/pageheader.py
+++ b/marker/schema/blocks/pageheader.py
@@ -8,11 +8,3 @@ class PageHeader(Block):
     replace_output_newlines: bool = True
     ignore_for_output: bool = True
 
-    def assemble_html(self, document, child_blocks, parent_structure):
-        if self.ignore_for_output:
-            return ""
-
-        template = super().assemble_html(document, child_blocks, parent_structure)
-        template = template.replace("\n", " ")
-        return f"
{template}
"
-
diff --git a/marker/schema/blocks/reference.py b/marker/schema/blocks/reference.py
index 43e66011..bb0181c6 100644
--- a/marker/schema/blocks/reference.py
+++ b/marker/schema/blocks/reference.py
@@ -5,7 +5,8 @@
 class Reference(Block):
     block_type: BlockTypes = BlockTypes.Reference
     ref: str
+    block_description: str = "A reference to this block from another block."
 
-    def assemble_html(self, child_blocks, parent_structure=None):
-        template = super().assemble_html(child_blocks, parent_structure)
+    def assemble_html(self, document, child_blocks, parent_structure=None):
+        template = super().assemble_html(document, child_blocks, parent_structure)
         return f"{template}"

From 355d11b8eb01fe9a529f700b8b02b561bedb20ff Mon Sep 17 00:00:00 2001
From: Vik Paruchuri 
Date: Mon, 20 Jan 2025 15:40:03 -0500
Subject: [PATCH 62/92] Fix tests

---
 README.md                                | 6 ++----
 marker/providers/__init__.py             | 5 +++++
 marker/providers/image.py                | 6 +++++-
 marker/providers/pdf.py                  | 2 +-
 marker/renderers/markdown.py             | 2 +-
 marker/schema/blocks/base.py             | 3 ++-
 marker/schema/blocks/picture.py          | 8 +++++---
 tests/converters/test_table_converter.py | 1 -
 tests/processors/test_llm_processors.py  | 9 ++++-----
 tests/processors/test_table_processor.py | 2 +-
 10 files changed, 26 insertions(+), 18 deletions(-)

diff --git a/README.md b/README.md
index 8f851d7c..f1d387d9 100644
--- a/README.md
+++ b/README.md
@@ -2,12 +2,10 @@
 
 Marker converts PDFs and images to markdown, JSON, and HTML quickly and accurately.
 
-- Supports a wide range of documents
-- Supports all languages
+- Supports a range of documents in all languages
 - Removes headers/footers/other artifacts
-- Formats tables, forms, and code blocks
+- Formats tables, forms, equations, links, and code blocks
 - Extracts and saves images along with the markdown
-- Converts equations to latex
 - Easily extensible with your own formatting and logic
 - Optionally boost accuracy with an LLM
 - Works on GPU, CPU, or MPS
diff --git a/marker/providers/__init__.py b/marker/providers/__init__.py
index 3817001d..5230a410 100644
--- a/marker/providers/__init__.py
+++ b/marker/providers/__init__.py
@@ -3,6 +3,8 @@
 from PIL import Image
 from pydantic import BaseModel
 
+from pdftext.schema import Reference
+
 from marker.schema.polygon import PolygonBox
 from marker.schema.text import Span
 from marker.schema.text.line import Line
@@ -36,6 +38,9 @@ def get_page_bbox(self, idx: int) -> PolygonBox | None:
     def get_page_lines(self, idx: int) -> List[Line]:
         pass
 
+    def get_page_refs(self, idx: int) -> List[Reference]:
+        pass
+
     def __enter__(self):
         return self
 
diff --git a/marker/providers/image.py b/marker/providers/image.py
index 8b001256..45aa9ad7 100644
--- a/marker/providers/image.py
+++ b/marker/providers/image.py
@@ -4,6 +4,7 @@
 from marker.providers import ProviderPageLines, BaseProvider
 from marker.schema.polygon import PolygonBox
 from marker.schema.text import Line
+from pdftext.schema import Reference
 
 
 class ImageProvider(BaseProvider):
@@ -45,4 +46,7 @@ def get_page_bbox(self, idx: int) -> PolygonBox | None:
 
 
     def get_page_lines(self, idx: int) -> List[Line]:
-        return self.page_lines[idx]
\ No newline at end of file
+        return self.page_lines[idx]
+
+    def get_page_refs(self, idx: int) -> List[Reference]:
+        return []
\ No newline at end of file
diff --git a/marker/providers/pdf.py b/marker/providers/pdf.py
index ad2019e7..2b805f00 100644
--- a/marker/providers/pdf.py
+++ b/marker/providers/pdf.py
@@ -328,7 +328,7 @@ def get_page_bbox(self, idx: int) -> PolygonBox | None:
     def get_page_lines(self, idx: int) -> List[ProviderOutput]:
         return self.page_lines[idx]
 
-    def get_page_refs(self, idx: int):
+    def get_page_refs(self, idx: int) -> List[Reference]:
         return self.page_refs[idx]
 
     @staticmethod
diff --git a/marker/renderers/markdown.py b/marker/renderers/markdown.py
index 91fee52d..082bfc1f 100644
--- a/marker/renderers/markdown.py
+++ b/marker/renderers/markdown.py
@@ -14,7 +14,7 @@
 def cleanup_text(full_text):
     full_text = re.sub(r'\n{3,}', '\n\n', full_text)
     full_text = re.sub(r'(\n\s){3,}', '\n\n', full_text)
-    return full_text
+    return full_text.strip()
 
 
 class Markdownify(MarkdownConverter):
diff --git a/marker/schema/blocks/base.py b/marker/schema/blocks/base.py
index f15642c1..b9abe11e 100644
--- a/marker/schema/blocks/base.py
+++ b/marker/schema/blocks/base.py
@@ -191,7 +191,8 @@ def assemble_html(self, document: Document, child_blocks: List[BlockOutput], par
             template += f""
 
         if self.replace_output_newlines:
-            template = "
" + template.replace("\n", " ") + "
"
+            template = template.replace("\n", " ")
+            template = "
" + template + "
"
 
         return template
 
diff --git a/marker/schema/blocks/picture.py b/marker/schema/blocks/picture.py
index 5d0d633b..6f815516 100644
--- a/marker/schema/blocks/picture.py
+++ b/marker/schema/blocks/picture.py
@@ -8,7 +8,9 @@ class Picture(Block):
     block_description: str = "An image block that represents a picture."
 
     def assemble_html(self, document, child_blocks, parent_structure):
+        child_ref_blocks = [block for block in child_blocks if block.id.block_type == BlockTypes.Reference]
+        html = super().assemble_html(document, child_ref_blocks, parent_structure)
+
         if self.description:
-            return f"
Image {self.id} description: {self.description}
"
-        else:
-            return ""
+            return html + f"
Image {self.id} description: {self.description}
"
+        return html
diff --git a/tests/converters/test_table_converter.py b/tests/converters/test_table_converter.py
index 8f5cdecc..f388d5a7 100644
--- a/tests/converters/test_table_converter.py
+++ b/tests/converters/test_table_converter.py
@@ -14,7 +14,6 @@ def _table_converter(config, model_dict, renderer, temp_pdf):
     markdown_output: MarkdownOutput = converter(temp_pdf.name)
     markdown = markdown_output.markdown
 
-    breakpoint()
     assert len(markdown) > 0
     assert "cyclic" in markdown
 
diff --git a/tests/processors/test_llm_processors.py b/tests/processors/test_llm_processors.py
index 41639b42..63adf878 100644
--- a/tests/processors/test_llm_processors.py
+++ b/tests/processors/test_llm_processors.py
@@ -35,13 +35,11 @@ def test_llm_form_processor_no_cells(pdf_document):
 @pytest.mark.filename("form_1040.pdf")
 @pytest.mark.config({"page_range": [0]})
 def test_llm_form_processor(pdf_document, detection_model, table_rec_model, recognition_model, mocker):
-    corrected_markdown = "*This is corrected markdown.*\n" * 100
-
     corrected_html = "This is corrected markdown.\n" * 100
     corrected_html = "
" + corrected_html.strip() + "
\n"
 
     mock_cls = Mock()
-    mock_cls.return_value.generate_response.return_value = {"corrected_markdown": corrected_markdown}
+    mock_cls.return_value.generate_response.return_value = {"corrected_html": corrected_html}
     mocker.patch("marker.processors.llm.GoogleModel", mock_cls)
 
     cell_processor = TableProcessor(detection_model, recognition_model, table_rec_model)
@@ -51,7 +49,7 @@ def test_llm_form_processor(pdf_document, detection_model, table_rec_model, reco
     processor(pdf_document)
 
     forms = pdf_document.contained_blocks((BlockTypes.Form,))
-    assert forms[0].html == corrected_html
+    assert forms[0].html == corrected_html.strip()
 
 
 
@@ -92,7 +90,8 @@ def test_llm_table_processor(pdf_document, detection_model, table_rec_model, rec
     processor(pdf_document)
 
     tables = pdf_document.contained_blocks((BlockTypes.Table,))
-    assert tables[0].cells[0].text == "Column 1"
+    table_cells = tables[0].contained_blocks(pdf_document, (BlockTypes.TableCell,))
+    assert table_cells[0].text == "Column 1"
 
 
 @pytest.mark.filename("adversarial.pdf")
diff --git a/tests/processors/test_table_processor.py b/tests/processors/test_table_processor.py
index 41334611..0b0e808a 100644
--- a/tests/processors/test_table_processor.py
+++ b/tests/processors/test_table_processor.py
@@ -20,4 +20,4 @@ def test_table_processor(pdf_document, detection_model, recognition_model, table
 
     renderer = MarkdownRenderer()
     table_output = renderer(pdf_document)
-    assert "Schedule" in table_output
+    assert "Schedule" in table_output.markdown

From a5df6a2aaa99bf787e0b986b5435395c1e6a45f9 Mon Sep 17 00:00:00 2001
From: Vik Paruchuri 
Date: Tue, 21 Jan 2025 09:50:25 -0500
Subject: [PATCH 63/92] Fix bug with table outputs

---
 marker/schema/blocks/basetable.py        | 5 +++--
 marker/scripts/server.py                 | 3 ++-
 marker/scripts/streamlit_app.py          | 3 ++-
 tests/processors/test_table_processor.py | 3 +++
 4 files changed, 10 insertions(+), 4 deletions(-)

diff --git a/marker/schema/blocks/basetable.py b/marker/schema/blocks/basetable.py
index 941c6efb..b3473f44 100644
--- a/marker/schema/blocks/basetable.py
+++ b/marker/schema/blocks/basetable.py
@@ -10,7 +10,7 @@ class BaseTable(Block):
     html: str | None = None
 
     def format_cells(self, document, child_blocks):
-        child_cells: List[TableCell] = [document.get_block(c.id) for c in child_blocks]
+        child_cells: List[TableCell] = [document.get_block(c.id) for c in child_blocks if c.id.block_type == BlockTypes.TableCell]
         unique_rows = sorted(list(set([c.row_id for c in child_cells])))
         html_repr = ""
         for row_id in unique_rows:
@@ -28,10 +28,11 @@ def assemble_html(self, document, child_blocks: List[BlockOutput], parent_struct
         child_ref_blocks = [block for block in child_blocks if block.id.block_type == BlockTypes.Reference]
         template = super().assemble_html(document, child_ref_blocks, parent_structure)
 
+        child_block_types = set([c.id.block_type for c in child_blocks])
         if self.html:
             # LLM processor
             return template + self.html
-        elif len(child_blocks) > 0 and child_blocks[0].id.block_type == BlockTypes.TableCell:
+        elif len(child_blocks) > 0 and BlockTypes.TableCell in child_block_types:
             # Table processor
             return template + self.format_cells(document, child_blocks)
         else:
diff --git a/marker/scripts/server.py b/marker/scripts/server.py
index 54a44496..9fbf9268 100644
--- a/marker/scripts/server.py
+++ b/marker/scripts/server.py
@@ -91,7 +91,8 @@ async def _convert_pdf(params: CommonParams):
         config_parser = ConfigParser(options)
         config_dict = config_parser.generate_config_dict()
         config_dict["pdftext_workers"] = 1
-        converter = PdfConverter(
+        converter_cls = PdfConverter
+        converter = converter_cls(
             config=config_dict,
             artifact_dict=app_data["models"],
             processor_list=config_parser.get_processors(),
diff --git a/marker/scripts/streamlit_app.py b/marker/scripts/streamlit_app.py
index ad6e89c2..7d7fe555 100644
--- a/marker/scripts/streamlit_app.py
+++ b/marker/scripts/streamlit_app.py
@@ -28,7 +28,8 @@ def load_models():
 def convert_pdf(fname: str, config_parser: ConfigParser) -> (str, Dict[str, Any], dict):
     config_dict = config_parser.generate_config_dict()
     config_dict["pdftext_workers"] = 1
-    converter = PdfConverter(
+    converter_cls = PdfConverter
+    converter = converter_cls(
         config=config_dict,
         artifact_dict=model_dict,
         processor_list=config_parser.get_processors(),
diff --git a/tests/processors/test_table_processor.py b/tests/processors/test_table_processor.py
index 0b0e808a..1e83916b 100644
--- a/tests/processors/test_table_processor.py
+++ b/tests/processors/test_table_processor.py
@@ -1,4 +1,5 @@
 import pytest
+from marker.renderers.json import JSONRenderer
 
 from marker.renderers.markdown import MarkdownRenderer
 from marker.schema import BlockTypes
@@ -18,6 +19,8 @@ def test_table_processor(pdf_document, detection_model, recognition_model, table
             assert len(children) > 0
             assert isinstance(children[0], TableCell)
 
+    assert len(pdf_document.contained_blocks((BlockTypes.Table,))) == 2
+
     renderer = MarkdownRenderer()
     table_output = renderer(pdf_document)
     assert "Schedule" in table_output.markdown

From a093a4571dafa0cc9959e7fef5ca3436458e5d5f Mon Sep 17 00:00:00 2001
From: Vik Paruchuri 
Date: Tue, 21 Jan 2025 10:13:10 -0500
Subject: [PATCH 64/92] Avoid double OCR for tables

---
 marker/builders/ocr.py                   |  6 ++--
 marker/schema/groups/page.py             | 19 +++++++++++--
 marker/scripts/streamlit_app.py          | 35 ++++++++++++++----------
 tests/processors/test_table_processor.py | 16 +++++++++++
 4 files changed, 57 insertions(+), 19 deletions(-)

diff --git a/marker/builders/ocr.py b/marker/builders/ocr.py
index 7ef4744c..0bea3f1c 100644
--- a/marker/builders/ocr.py
+++ b/marker/builders/ocr.py
@@ -64,13 +64,15 @@ def get_detection_batch_size(self):
 
     def ocr_extraction(self, document: Document, provider: PdfProvider) -> ProviderPageLines:
         page_list = [page for page in document.pages if page.text_extraction_method == "surya"]
+
+        # Remove tables because we re-OCR them later with the table processor
         recognition_results = self.recognition_model(
-            images=[page.get_image(highres=False) for page in page_list],
+            images=[page.get_image(highres=False, remove_tables=True) for page in page_list],
             langs=[self.languages] * len(page_list),
             det_predictor=self.detection_model,
             detection_batch_size=int(self.get_detection_batch_size()),
             recognition_batch_size=int(self.get_recognition_batch_size()),
-            highres_images=[page.get_image(highres=True) for page in page_list]
+            highres_images=[page.get_image(highres=True, remove_tables=True) for page in page_list]
         )
 
         page_lines = {}
diff --git a/marker/schema/groups/page.py b/marker/schema/groups/page.py
index fcea1ee3..48e360ca 100644
--- a/marker/schema/groups/page.py
+++ b/marker/schema/groups/page.py
@@ -1,7 +1,7 @@
 from collections import defaultdict
 from typing import Any, Dict, List, Optional, Sequence, Tuple, Union
 
-from PIL import Image
+from PIL import Image, ImageDraw
 
 from pdftext.schema import Reference
 from marker.providers import ProviderOutput
@@ -38,8 +38,21 @@ def add_child(self, block: Block):
         else:
             self.children.append(block)
 
-    def get_image(self, *args, highres: bool = False, **kwargs):
-        return self.highres_image if highres else self.lowres_image
+    def get_image(self, *args, highres: bool = False, remove_tables: bool = False, **kwargs):
+        image = self.highres_image if highres else self.lowres_image
+
+        # Avoid double OCR for tables
+        if remove_tables:
+            image = image.copy()
+            draw = ImageDraw.Draw(image)
+            table_blocks = [block for block in self.children if block.block_type in (BlockTypes.Table, BlockTypes.Form, BlockTypes.TableOfContents)]
+            for table_block in table_blocks:
+                poly = table_block.polygon.rescale(self.polygon.size, image.size).polygon
+                poly = [(int(p[0]), int(p[1])) for p in poly]
+                draw.polygon(poly, fill='white')
+
+        return image
+
 
     def get_next_block(self, block: Optional[Block] = None, ignored_block_types: Optional[List[BlockTypes]] = None):
         if ignored_block_types is None:
diff --git a/marker/scripts/streamlit_app.py b/marker/scripts/streamlit_app.py
index 7d7fe555..11955453 100644
--- a/marker/scripts/streamlit_app.py
+++ b/marker/scripts/streamlit_app.py
@@ -1,6 +1,7 @@
 import os
 
 from marker.settings import settings
+from streamlit.runtime.uploaded_file_manager import UploadedFile
 
 os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"
 os.environ["IN_STREAMLIT"] = "true"
@@ -66,21 +67,27 @@ def markdown_insert_images(markdown, images):
 
 @st.cache_data()
 def get_page_image(pdf_file, page_num, dpi=96):
-    doc = open_pdf(pdf_file)
-    renderer = doc.render(
-        pypdfium2.PdfBitmap.to_pil,
-        page_indices=[page_num],
-        scale=dpi / 72,
-    )
-    png = list(renderer)[0]
-    png_image = png.convert("RGB")
+    if "pdf" in pdf_file.type:
+        doc = open_pdf(pdf_file)
+        renderer = doc.render(
+            pypdfium2.PdfBitmap.to_pil,
+            page_indices=[page_num],
+            scale=dpi / 72,
+        )
+        png = list(renderer)[0]
+        png_image = png.convert("RGB")
+    else:
+        png_image = Image.open(in_file).convert("RGB")
     return png_image
 
 
 @st.cache_data()
-def page_count(pdf_file):
-    doc = open_pdf(pdf_file)
-    return len(doc) - 1
+def page_count(pdf_file: UploadedFile):
+    if "pdf" in pdf_file.type:
+        doc = open_pdf(pdf_file)
+        return len(doc) - 1
+    else:
+        return 1
 
 
 st.set_page_config(layout="wide")
@@ -92,12 +99,12 @@ def page_count(pdf_file):
 st.markdown("""
 # Marker Demo
 
-This app will let you try marker, a PDF -> Markdown converter. It works with any languages, and extracts images, tables, equations, etc.
+This app will let you try marker, a PDF or image -> Markdown, HTML, JSON converter. It works with any language, and extracts images, tables, equations, etc.
 
 Find the project [here](https://github.com/VikParuchuri/marker).
 """)
 
-in_file = st.sidebar.file_uploader("PDF file:", type=["pdf"])
+in_file: UploadedFile = st.sidebar.file_uploader("PDF or image file:", type=["pdf", "png", "jpg", "jpeg", "gif"])
 
 if in_file is None:
     st.stop()
@@ -109,7 +116,7 @@ def page_count(pdf_file):
     page_number = st.number_input(f"Page number out of {page_count}:", min_value=0, value=0, max_value=page_count)
     pil_image = get_page_image(in_file, page_number)
 
-    st.image(pil_image, caption="PDF file (preview)", use_container_width=True)
+    st.image(pil_image, caption="File preview", use_container_width=True)
 
 page_range = st.sidebar.text_input("Page range to parse, comma separated like 0,5-10,20", value=f"{page_number}-{page_number}")
 output_format = st.sidebar.selectbox("Output format", ["markdown", "json", "html"], index=0)
diff --git a/tests/processors/test_table_processor.py b/tests/processors/test_table_processor.py
index 1e83916b..fed89f7d 100644
--- a/tests/processors/test_table_processor.py
+++ b/tests/processors/test_table_processor.py
@@ -24,3 +24,19 @@ def test_table_processor(pdf_document, detection_model, recognition_model, table
     renderer = MarkdownRenderer()
     table_output = renderer(pdf_document)
     assert "Schedule" in table_output.markdown
+
+
+@pytest.mark.filename("table_ex.pdf")
+@pytest.mark.config({"page_range": [0], "force_ocr": True})
+def test_avoid_double_ocr(pdf_document, detection_model, recognition_model, table_rec_model):
+    tables = pdf_document.contained_blocks((BlockTypes.Table,))
+    lines = tables[0].contained_blocks(pdf_document, (BlockTypes.Line,))
+    assert len(lines) == 0
+
+    processor = TableProcessor(detection_model, recognition_model, table_rec_model, config={"force_ocr": True})
+    processor(pdf_document)
+
+    renderer = MarkdownRenderer()
+    table_output = renderer(pdf_document)
+    assert "Participants" in table_output.markdown
+

From b30843e4cd95464abf245852714d34d1ea7f5573 Mon Sep 17 00:00:00 2001
From: Vik Paruchuri 
Date: Tue, 21 Jan 2025 10:34:02 -0500
Subject: [PATCH 65/92] Clean blocks out of tables

---
 marker/processors/llm/llm_table_merge.py | 18 +++++++++++++++++-
 marker/processors/table.py               | 14 ++++++++++++++
 2 files changed, 31 insertions(+), 1 deletion(-)

diff --git a/marker/processors/llm/llm_table_merge.py b/marker/processors/llm/llm_table_merge.py
index 785ecad9..b6d8dbc1 100644
--- a/marker/processors/llm/llm_table_merge.py
+++ b/marker/processors/llm/llm_table_merge.py
@@ -32,6 +32,14 @@ class LLMTableMergeProcessor(BaseLLMProcessor):
         int,
         "The maximum distance between table edges for adjacency."
     ] = 20
+    horizontal_table_width_threshold: Annotated[
+        float,
+        "The width tolerance for 2 adjacent tables to be merged into one."
+    ] = 0.25
+    horizontal_table_distance_threshold: Annotated[
+        int,
+        "The maximum distance between table edges for adjacency."
+    ] = 20
     column_gap_threshold: Annotated[
         int,
         "The maximum gap between columns to merge tables"
@@ -160,6 +168,14 @@ def rewrite_blocks(self, document: Document):
                         row_match
                     ])
 
+                    same_page_horizontal_table = all([
+                        prev_block.page_id == block.page_id, # On the same page
+                        (1 - self.horizontal_table_width_threshold) < prev_block.polygon.width / block.polygon.width < (1 + self.horizontal_table_width_threshold), # Similar width
+                        abs(block.polygon.y_start - prev_block.polygon.y_end) < self.horizontal_table_distance_threshold, # Close together in y
+                        abs(block.polygon.x_start - prev_block.polygon.x_start) < self.horizontal_table_distance_threshold, # Close together in x
+                        col_match
+                    ])
+
                     same_page_new_column = all([
                         prev_block.page_id == block.page_id, # On the same page
                         abs(block.polygon.x_start - prev_block.polygon.x_end) < self.column_gap_threshold,
@@ -167,7 +183,7 @@ def rewrite_blocks(self, document: Document):
                         block.polygon.width * (1 - self.vertical_table_height_threshold) < prev_block.polygon.width  < block.polygon.width * (1 + self.vertical_table_height_threshold), # Similar width
                         col_match
                     ])
-                    merge_condition = any([subsequent_page_table, same_page_vertical_table, same_page_new_column])
+                    merge_condition = any([subsequent_page_table, same_page_vertical_table, same_page_new_column, same_page_horizontal_table])
 
                 if prev_block is not None and merge_condition:
                     if prev_block not in table_run:
diff --git a/marker/processors/table.py b/marker/processors/table.py
index d86e6775..9e55424f 100644
--- a/marker/processors/table.py
+++ b/marker/processors/table.py
@@ -43,6 +43,10 @@ class TableProcessor(BaseProcessor):
         "The batch size to use for the table recognition model.",
         "Default is None, which will use the default batch size for the model."
     ] = None
+    contained_block_types: Annotated[
+        List[BlockTypes],
+        "Block types to remove if they're contained inside the tables."
+    ] = (BlockTypes.Text, BlockTypes.TextInlineMath)
 
     def __init__(
         self,
@@ -112,6 +116,16 @@ def __call__(self, document: Document):
                     block.add_structure(cell_block)
                 table_idx += 1
 
+        # Clean out other blocks inside the table
+        # This can happen with stray text blocks inside the table post-merging
+        for page in document.pages:
+            child_contained_blocks = page.contained_blocks(document, self.contained_block_types)
+            for block in page.contained_blocks(document, self.block_types):
+                intersections = matrix_intersection_area([c.polygon.bbox for c in child_contained_blocks], [block.polygon.bbox])
+                for child, intersection in zip(child_contained_blocks, intersections):
+                    if intersection > 0.95 and child.id in page.structure:
+                        page.structure.remove(child.id)
+
     def finalize_cell_text(self, cell: SuryaTableCell):
         text = "\n".join([t["text"].strip() for t in cell.text_lines]) if cell.text_lines else ""
         text = re.sub(r"(\s\.){2,}", "", text)  # Replace . . .

From 45459325a471b862c3aa82458ffb71a704681f6c Mon Sep 17 00:00:00 2001
From: Vik Paruchuri 
Date: Tue, 21 Jan 2025 10:44:16 -0500
Subject: [PATCH 66/92] Fix debug output

---
 marker/processors/debug.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/marker/processors/debug.py b/marker/processors/debug.py
index 4fe87601..e44a611b 100644
--- a/marker/processors/debug.py
+++ b/marker/processors/debug.py
@@ -140,7 +140,7 @@ def dump_block_debug_data(self, document: Document):
         debug_file = os.path.join(self.debug_folder, f"blocks.json")
         debug_data = []
         for page in document.pages:
-            page_data = page.model_dump(exclude=["lowres_image", "highres_image"])
+            page_data = page.model_dump(exclude={"lowres_image": True, "highres_image": True, "children": {"__all__": {"lowres_image": True, "highres_image": True}}})
             debug_data.append(page_data)
 
         with open(debug_file, "w+") as f:

From 849827cc533742e97c696842580fda1637bffd67 Mon Sep 17 00:00:00 2001
From: Vik Paruchuri 
Date: Tue, 21 Jan 2025 11:20:42 -0500
Subject: [PATCH 67/92] Fix handwriting block

---
 marker/processors/llm/__init__.py        |  5 +++
 marker/processors/llm/llm_handwriting.py | 41 +++++++++---------------
 marker/schema/blocks/complexregion.py    |  4 ++-
 marker/schema/blocks/text.py             |  7 ++++
 4 files changed, 30 insertions(+), 27 deletions(-)

diff --git a/marker/processors/llm/__init__.py b/marker/processors/llm/__init__.py
index 87baeb71..17196443 100644
--- a/marker/processors/llm/__init__.py
+++ b/marker/processors/llm/__init__.py
@@ -68,6 +68,11 @@ def process_rewriting(self, document: Document, page: PageGroup, block: Block):
         raise NotImplementedError()
 
     def rewrite_blocks(self, document: Document):
+        # Don't show progress if there are no blocks to process
+        total_blocks = sum(len(page.contained_blocks(document, self.block_types)) for page in document.pages)
+        if total_blocks == 0:
+            return
+
         pbar = tqdm(desc=f"{self.__class__.__name__} running")
         with ThreadPoolExecutor(max_workers=self.max_concurrency) as executor:
             for future in as_completed([
diff --git a/marker/processors/llm/llm_handwriting.py b/marker/processors/llm/llm_handwriting.py
index cb00e298..d3e9b9f3 100644
--- a/marker/processors/llm/llm_handwriting.py
+++ b/marker/processors/llm/llm_handwriting.py
@@ -5,7 +5,7 @@
 from google.ai.generativelanguage_v1beta.types import content
 
 from marker.schema import BlockTypes
-from marker.schema.blocks import Equation
+from marker.schema.blocks import Handwriting, Text
 from marker.schema.document import Document
 from marker.schema.groups.page import PageGroup
 
@@ -13,17 +13,13 @@
 
 
 class LLMHandwritingProcessor(BaseLLMProcessor):
-    block_types = (BlockTypes.Equation,)
-    min_handwriting_height: Annotated[
-        float,
-        "The minimum ratio between handwriting height and page height to consider for processing.",
-     ] = 0.1
+    block_types = (BlockTypes.Handwriting, BlockTypes.Text)
     handwriting_generation_prompt: Annotated[
         str,
         "The prompt to use for OCRing handwriting.",
         "Default is a string containing the Gemini prompt."
     ] = """You are an expert editor specializing in accurately reproducing text from images.
-You will receive an image of a text block, along with the text that can be extracted. Your task is to generate markdown to properly represent the content of the image.  Do not omit any text present in the image - make sure everything is included in the markdown representation.  The markdown representation should be as faithful to the original image as possible.
+You will receive an image of a text block. Your task is to generate markdown to properly represent the content of the image.  Do not omit any text present in the image - make sure everything is included in the markdown representation.  The markdown representation should be as faithful to the original image as possible.
 
 Formatting should be in markdown, with the following rules:
 - * for italics, ** for bold, and ` for inline code.
@@ -38,26 +34,19 @@ class LLMHandwritingProcessor(BaseLLMProcessor):
 
 **Instructions:**
 1. Carefully examine the provided block image.
-2. Analyze the existing text representation.
-3. Output the markdown representing the content of the image.
-**Example:**
-Input:
-```text
-This i sm handwritting.
-```
-Output:
-```markdown
-This is some *handwriting*.
-```
-**Input:**
-```text
-{extracted_text}
-```
+2. Output the markdown representing the content of the image.
 """
 
-    def process_rewriting(self, document: Document, page: PageGroup, block: Equation):
-        text = block.raw_text(document)
-        prompt = self.handwriting_generation_prompt.replace("{handwriting_text}", text)
+    def process_rewriting(self, document: Document, page: PageGroup, block: Handwriting | Text):
+        raw_text = block.raw_text(document)
+
+        # Don't process text blocks that contain lines already
+        if block.block_type == BlockTypes.Text:
+            lines = block.contained_blocks(document, (BlockTypes.Line,))
+            if len(lines) > 0 or len(raw_text.strip()) > 0:
+                return
+
+        prompt = self.handwriting_generation_prompt
 
         image = self.extract_image(document, block)
         response_schema = content.Schema(
@@ -78,7 +67,7 @@ def process_rewriting(self, document: Document, page: PageGroup, block: Equation
             return
 
         markdown = response["markdown"]
-        if len(markdown) < len(text) * .5:
+        if len(markdown) < len(raw_text) * .5:
             block.update_metadata(llm_error_count=1)
             return
 
diff --git a/marker/schema/blocks/complexregion.py b/marker/schema/blocks/complexregion.py
index 7b4f6e67..cc6b3c1a 100644
--- a/marker/schema/blocks/complexregion.py
+++ b/marker/schema/blocks/complexregion.py
@@ -9,7 +9,9 @@ class ComplexRegion(Block):
 
     def assemble_html(self, document, child_blocks, parent_structure):
         if self.html:
-            return self.html
+            child_ref_blocks = [block for block in child_blocks if block.id.block_type == BlockTypes.Reference]
+            html = super().assemble_html(document, child_ref_blocks, parent_structure)
+            return html + self.html
         else:
             template = super().assemble_html(document, child_blocks, parent_structure)
             return f"
{template}
"
diff --git a/marker/schema/blocks/text.py b/marker/schema/blocks/text.py
index 853a73a4..4b56867a 100644
--- a/marker/schema/blocks/text.py
+++ b/marker/schema/blocks/text.py
@@ -7,12 +7,19 @@ class Text(Block):
     has_continuation: bool = False
     blockquote: bool = False
     blockquote_level: int = 0
+    html: str | None = None
     block_description: str = "A paragraph or line of text."
 
     def assemble_html(self, document, child_blocks, parent_structure):
         if self.ignore_for_output:
             return ""
 
+        # This happens when we used an llm processor
+        if self.html:
+            child_ref_blocks = [block for block in child_blocks if block.id.block_type == BlockTypes.Reference]
+            html = super().assemble_html(document, child_ref_blocks, parent_structure)
+            return html + self.html
+
         template = super().assemble_html(document, child_blocks, parent_structure)
         template = template.replace("\n", " ")
 

From 3a972d50d1fd335e52e06573b18933f68c2f5ae6 Mon Sep 17 00:00:00 2001
From: Vik Paruchuri 
Date: Tue, 21 Jan 2025 12:01:49 -0500
Subject: [PATCH 68/92] Add max row count

---
 marker/processors/llm/llm_table.py | 9 +++++++++
 1 file changed, 9 insertions(+)

diff --git a/marker/processors/llm/llm_table.py b/marker/processors/llm/llm_table.py
index 12208546..7d71e423 100644
--- a/marker/processors/llm/llm_table.py
+++ b/marker/processors/llm/llm_table.py
@@ -16,6 +16,10 @@ class LLMTableProcessor(BaseLLMProcessor):
         Tuple[BlockTypes],
         "The block types to process.",
     ] = (BlockTypes.Table, BlockTypes.TableOfContents)
+    max_row_count: Annotated[
+        int,
+        "If the table has more rows than this, don't run LLM processor. (LLMs can be inaccurate with a lot of rows)",
+    ] = 75
     table_rewriting_prompt: Annotated[
         str,
         "The prompt to use for rewriting text.",
@@ -68,6 +72,11 @@ def process_rewriting(self, document: Document, page: PageGroup, block: Block):
             # Happens if table/form processors didn't run
             return
 
+        # LLMs don't handle tables with a lot of rows very well
+        row_count = len(set([cell.row_id for cell in children]))
+        if row_count > self.max_row_count:
+            return
+
         block_html = block.render(document).html
         prompt = self.table_rewriting_prompt.replace("{block_html}", block_html)
 

From 62e4f1a76e0d9b1b386ea81d994550997b81d38a Mon Sep 17 00:00:00 2001
From: Tarun Menta 
Date: Wed, 22 Jan 2025 17:48:36 +0530
Subject: [PATCH 69/92] Update input method to OCR error detection model

---
 marker/builders/layout.py | 19 +++----------------
 1 file changed, 3 insertions(+), 16 deletions(-)

diff --git a/marker/builders/layout.py b/marker/builders/layout.py
index 3539a984..ff4af17d 100644
--- a/marker/builders/layout.py
+++ b/marker/builders/layout.py
@@ -42,10 +42,6 @@ class LayoutBuilder(BaseBuilder):
         "The minimum ratio of pages that must pass the layout coverage check",
         "to avoid OCR.",
     ] = .8
-    error_model_segment_length: Annotated[
-        int,
-        "The maximum number of characters to send to the OCR error model.",
-    ] = 512
     excluded_for_coverage: Annotated[
         Tuple[BlockTypes],
         "A list of block types to exclude from the layout coverage check.",
@@ -104,26 +100,17 @@ def surya_layout(self, pages: List[PageGroup]) -> List[LayoutResult]:
         )
         return layout_results
 
-    def surya_ocr_error_detection(self, pages: List[PageGroup], provider_page_lines: ProviderPageLines) -> OCRErrorDetectionResult:
+    def surya_ocr_error_detection(self, pages:List[PageGroup], provider_page_lines: ProviderPageLines) -> OCRErrorDetectionResult:
         page_texts = []
         for document_page in pages:
             page_text = ''
             provider_lines = provider_page_lines.get(document_page.page_id, [])
-            for line in provider_lines:
-                page_text += ' '.join([s.text for s in line.spans])
-
-            # Sample text from the middle
-            if len(page_text) > 0:
-                page_text_middle = len(page_text) // 2
-                page_text_start = max(0, page_text_middle - self.error_model_segment_length // 2)
-                page_text_end = page_text_start + self.error_model_segment_length
-                page_text = page_text[page_text_start:page_text_end]
-
+            page_text = '\n'.join(' '.join(s.text for s in line.spans) for line in provider_lines)
             page_texts.append(page_text)
 
         ocr_error_detection_results = self.ocr_error_model(
             page_texts,
-            batch_size=int(self.get_batch_size())  # TODO Better Multiplier
+            batch_size=int(self.get_batch_size())       #TODO Better Multiplier
         )
         return ocr_error_detection_results
 

From ebd03c95e191b1429c1a3c03f63c86e1bbe11333 Mon Sep 17 00:00:00 2001
From: Vik Paruchuri 
Date: Wed, 22 Jan 2025 10:05:07 -0500
Subject: [PATCH 70/92] Fix table slicing issues

---
 README.md                                |  4 ++--
 marker/processors/llm/llm_form.py        |  2 +-
 marker/processors/llm/llm_table.py       |  2 +-
 marker/processors/llm/llm_table_merge.py |  2 +-
 marker/processors/table.py               | 29 ++++++++++++++++++------
 marker/scripts/streamlit_app.py          |  5 ++--
 6 files changed, 29 insertions(+), 15 deletions(-)

diff --git a/README.md b/README.md
index f1d387d9..a25894d4 100644
--- a/README.md
+++ b/README.md
@@ -400,8 +400,8 @@ Marker can extract tables from PDFs using `marker.converters.table.TableConverte
 
 | Avg score | Total tables | use_llm |
 |-----------|--------------|---------|
-| 0.824     | 54           | False   |
-| 0.873     | 54           | True    |
+| 0.82      | 54           | False   |
+| 0.887     | 54           | True    |
 
 The `--use_llm` flag can significantly improve table recognition performance, as you can see.
 
diff --git a/marker/processors/llm/llm_form.py b/marker/processors/llm/llm_form.py
index a9a4f339..fc66f155 100644
--- a/marker/processors/llm/llm_form.py
+++ b/marker/processors/llm/llm_form.py
@@ -17,7 +17,7 @@ class LLMFormProcessor(BaseLLMProcessor):
 **Instructions:**
 1. Carefully examine the provided form block image.
 2. Analyze the html representation of the form.
-3. If the html representation is largely correct, then write "No corrections needed."
+3. If the html representation is largely correct, or you cannot read the image properly, then write "No corrections needed."
 4. If the html representation contains errors, generate the corrected html representation.
 5. Output only either the corrected html representation or "No corrections needed."
 **Example:**
diff --git a/marker/processors/llm/llm_table.py b/marker/processors/llm/llm_table.py
index 7d71e423..550c5f71 100644
--- a/marker/processors/llm/llm_table.py
+++ b/marker/processors/llm/llm_table.py
@@ -37,7 +37,7 @@ class LLMTableProcessor(BaseLLMProcessor):
 **Instructions:**
 1. Carefully examine the provided text block image.
 2. Analyze the html representation of the table.
-3. If the html representation is largely correct, then write "No corrections needed."
+3. If the html representation is largely correct, or you cannot read the image properly, then write "No corrections needed."
 4. If the html representation contains errors, generate the corrected html representation.  
 5. Output only either the corrected html representation or "No corrections needed."
 **Example:**
diff --git a/marker/processors/llm/llm_table_merge.py b/marker/processors/llm/llm_table_merge.py
index b6d8dbc1..e2012998 100644
--- a/marker/processors/llm/llm_table_merge.py
+++ b/marker/processors/llm/llm_table_merge.py
@@ -55,7 +55,7 @@ class LLMTableMergeProcessor(BaseLLMProcessor):
 
 Table 2 should be merged at the bottom of Table 1 if Table 2 has no headers, and the rows have similar values, meaning that Table 2 continues Table 1. Table 2 should be merged to the right of Table 1 if each row in Table 2 matches a row in Table 1, meaning that Table 2 contains additional columns that augment Table 1.
 
-Only merge Table 1 and Table 2 if Table 2 cannot be interpreted without merging.
+Only merge Table 1 and Table 2 if Table 2 cannot be interpreted without merging.  Only merge Table 1 and Table 2 if you can read both images properly.
 
 **Instructions:**
 1. Carefully examine the provided table images.  Table 1 is the first image, and Table 2 is the second image.
diff --git a/marker/processors/table.py b/marker/processors/table.py
index 9e55424f..1aca6656 100644
--- a/marker/processors/table.py
+++ b/marker/processors/table.py
@@ -2,6 +2,8 @@
 from collections import defaultdict
 from copy import deepcopy
 from typing import Annotated, List
+from collections import Counter
+from PIL import ImageDraw
 
 from ftfy import fix_text
 from surya.detection import DetectionPredictor
@@ -67,7 +69,7 @@ def __call__(self, document: Document):
         table_data = []
         for page in document.pages:
             for block in page.contained_blocks(document, self.block_types):
-                image = block.get_image(document, highres=True, expansion=(.01, .01))
+                image = block.get_image(document, highres=True)
                 image_poly = block.polygon.rescale((page.polygon.width, page.polygon.height), page.get_image(highres=True).size)
 
                 table_data.append({
@@ -165,22 +167,35 @@ def split_combined_rows(self, tables: List[TableResult]):
 
                 # Other cells that span into this row
                 rowspan_cells = [c for c in table.cells if c.row_id != row and c.row_id + c.rowspan > row > c.row_id]
-                should_split = all([
-                    len(row_cells) > 0,
+                should_split_entire_row = all([
+                    len(row_cells) > 1,
                     len(rowspan_cells) == 0,
                     all([r == 1 for r in rowspans]),
                     all([l > 1 for l in line_lens]),
                     all([l == line_lens[0] for l in line_lens])
                 ])
+                line_lens_counter = Counter(line_lens)
+                counter_keys = sorted(list(line_lens_counter.keys()))
+                should_split_partial_row = all([
+                    len(row_cells) > 3, # Only split if there are more than 3 cells
+                    len(rowspan_cells) == 0,
+                    all([r == 1 for r in rowspans]),
+                    len(line_lens_counter) == 2 and counter_keys[0] <= 1 and counter_keys[1] > 1 and line_lens_counter[counter_keys[0]] == 1, # Allow a single column with a single line - keys are the line lens, values are the counts
+                ])
+                should_split = should_split_entire_row or should_split_partial_row
                 if should_split:
-                    for i in range(0, line_lens[0]):
+                    for i in range(0, max(line_lens)):
                         for cell in row_cells:
-                            line = cell.text_lines[i]
+                            # Calculate height based on number of splits
+                            split_height = cell.bbox[3] - cell.bbox[1]
+                            current_bbox = [cell.bbox[0], cell.bbox[1] + i * split_height, cell.bbox[2], cell.bbox[1] + (i + 1) * split_height]
+
+                            line = [cell.text_lines[i]] if cell.text_lines and i < len(cell.text_lines) else None
                             cell_id = max_cell_id + new_cell_count
                             new_cells.append(
                                 SuryaTableCell(
-                                    polygon=line["bbox"],
-                                    text_lines=[line],
+                                    polygon=current_bbox,
+                                    text_lines=line,
                                     rowspan=1,
                                     colspan=cell.colspan,
                                     row_id=cell.row_id + shift_up + i,
diff --git a/marker/scripts/streamlit_app.py b/marker/scripts/streamlit_app.py
index 11955453..86b46fae 100644
--- a/marker/scripts/streamlit_app.py
+++ b/marker/scripts/streamlit_app.py
@@ -1,11 +1,10 @@
 import os
+os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"
+os.environ["IN_STREAMLIT"] = "true"
 
 from marker.settings import settings
 from streamlit.runtime.uploaded_file_manager import UploadedFile
 
-os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"
-os.environ["IN_STREAMLIT"] = "true"
-
 import base64
 import io
 import re

From b76453fe696b47662e62788f6c8f36e65789039b Mon Sep 17 00:00:00 2001
From: Vik Paruchuri 
Date: Wed, 22 Jan 2025 12:40:10 -0500
Subject: [PATCH 71/92] Add gemini to table bench

---
 README.md                  |  2 +-
 benchmarks/table/gemini.py | 49 ++++++++++++++++++++++++++
 benchmarks/table/table.py  | 72 +++++++++++++++++++++++++++++---------
 3 files changed, 106 insertions(+), 17 deletions(-)
 create mode 100644 benchmarks/table/gemini.py

diff --git a/README.md b/README.md
index a25894d4..5a45b4b8 100644
--- a/README.md
+++ b/README.md
@@ -400,7 +400,7 @@ Marker can extract tables from PDFs using `marker.converters.table.TableConverte
 
 | Avg score | Total tables | use_llm |
 |-----------|--------------|---------|
-| 0.82      | 54           | False   |
+| 0.822     | 54           | False   |
 | 0.887     | 54           | True    |
 
 The `--use_llm` flag can significantly improve table recognition performance, as you can see.
diff --git a/benchmarks/table/gemini.py b/benchmarks/table/gemini.py
new file mode 100644
index 00000000..9e2591ee
--- /dev/null
+++ b/benchmarks/table/gemini.py
@@ -0,0 +1,49 @@
+import json
+from PIL import Image
+import google.generativeai as genai
+from google.ai.generativelanguage_v1beta.types import content
+from marker.settings import settings
+
+prompt = """
+You're an expert document analyst who is good at turning tables in documents into HTML.  Analyze the provided image, and convert it to a faithful HTML representation.
+ 
+Guidelines:
+- Keep the HTML simple and concise.
+- Only include the 
 tag and contents.
+- Only use 
, , and , , or  which fintabnet data doesn't
@@ -154,10 +174,12 @@ def main(out_file: str, dataset: str, max_rows: int, max_workers: int, use_llm:
                     th_tag.name = 'td'
                 marker_table_html = str(marker_table_soup)
                 marker_table_html = marker_table_html.replace("\n", " ") # Fintabnet uses spaces instead of newlines
+                gemini_table_html = gemini_table.replace("\n", " ") # Fintabnet uses spaces instead of newlines
 
                 results.append({
                     "marker_table": marker_table_html,
-                    "gt_table": gt_table_html
+                    "gt_table": gt_table_html,
+                    "gemini_table": gemini_table_html
                 })
         except PdfiumError:
             print('Broken PDF, Skipping...')
@@ -167,19 +189,37 @@ def main(out_file: str, dataset: str, max_rows: int, max_workers: int, use_llm:
     print(f"Could not align {total_unaligned} tables from fintabnet.")
 
     with ProcessPoolExecutor(max_workers=max_workers) as executor:
-        results = list(
+        marker_results = list(
             tqdm(
                 executor.map(update_teds_score, results), desc='Computing alignment scores', total=len(results)
             )
         )
-    avg_score = sum([r["score"] for r in results]) / len(results)
 
+    avg_score = sum([r["marker_score"] for r in marker_results]) / len(marker_results)
     headers = ["Avg score", "Total tables"]
-    data = [f"{avg_score:.3f}", len(results)]
+    data = [f"{avg_score:.3f}", len(marker_results)]
+    gemini_results = None
+    if use_gemini:
+        with ProcessPoolExecutor(max_workers=max_workers) as executor:
+            gemini_results = list(
+                tqdm(
+                    executor.map(update_teds_score, results, repeat("gemini")), desc='Computing Gemini scores',
+                    total=len(results)
+                )
+            )
+        avg_gemini_score = sum([r["gemini_score"] for r in gemini_results]) / len(gemini_results)
+        headers.append("Avg Gemini score")
+        data.append(f"{avg_gemini_score:.3f}")
+
     table = tabulate([data], headers=headers, tablefmt="github")
     print(table)
     print("Avg score computed by comparing marker predicted HTML with original HTML")
 
+    results = {
+        "marker": marker_results,
+        "gemini": gemini_results
+    }
+
     with open(out_file, "w+") as f:
         json.dump(results, f, indent=2)
 

From 918ae373b977f1de0d1aeb475c057a2175d555f2 Mon Sep 17 00:00:00 2001
From: Vik Paruchuri 
Date: Wed, 22 Jan 2025 12:54:54 -0500
Subject: [PATCH 72/92] Adjust params

---
 marker/processors/llm/llm_table.py | 8 +++++---
 1 file changed, 5 insertions(+), 3 deletions(-)

diff --git a/marker/processors/llm/llm_table.py b/marker/processors/llm/llm_table.py
index 550c5f71..ffa56b5b 100644
--- a/marker/processors/llm/llm_table.py
+++ b/marker/processors/llm/llm_table.py
@@ -16,9 +16,9 @@ class LLMTableProcessor(BaseLLMProcessor):
         Tuple[BlockTypes],
         "The block types to process.",
     ] = (BlockTypes.Table, BlockTypes.TableOfContents)
-    max_row_count: Annotated[
+    max_rows_per_batch: Annotated[
         int,
-        "If the table has more rows than this, don't run LLM processor. (LLMs can be inaccurate with a lot of rows)",
+        "If the table has more rows than this, chunk the table. (LLMs can be inaccurate with a lot of rows)",
     ] = 75
     table_rewriting_prompt: Annotated[
         str,
@@ -74,7 +74,9 @@ def process_rewriting(self, document: Document, page: PageGroup, block: Block):
 
         # LLMs don't handle tables with a lot of rows very well
         row_count = len(set([cell.row_id for cell in children]))
-        if row_count > self.max_row_count:
+
+        # TODO: eventually chunk the table and inference each chunk
+        if row_count > self.max_rows_per_batch:
             return
 
         block_html = block.render(document).html

From a1bd429a37d0dacf6b6e7e3148cbf44361386289 Mon Sep 17 00:00:00 2001
From: Vik Paruchuri 
Date: Wed, 22 Jan 2025 13:50:12 -0500
Subject: [PATCH 73/92] Update surya dep

---
 README.md   |  7 ++++++-
 poetry.lock | 19 +++++++++----------
 2 files changed, 15 insertions(+), 11 deletions(-)

diff --git a/README.md b/README.md
index 5a45b4b8..f2f21f57 100644
--- a/README.md
+++ b/README.md
@@ -219,7 +219,12 @@ rendered = converter("FILEPATH")
 text, _, images = text_from_rendered(rendered)
 ```
 
-This takes all the same configuration as the PdfConverter.  You can specify the configuration `force_layout_block=Table` to avoid layout detection and instead assume every page is a table.
+This takes all the same configuration as the PdfConverter.  You can specify the configuration `--force_layout_block=Table` to avoid layout detection and instead assume every page is a table.
+
+You can also run this via the CLI with 
+```shell
+python convert_single.py FILENAME --use_llm --force_layout_block Table --converter_cls marker.converters.table.TableConverter
+```
 
 # Output Formats
 
diff --git a/poetry.lock b/poetry.lock
index 4352b206..392c8ad8 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -2923,16 +2923,17 @@ testing = ["docopt", "pytest"]
 
 [[package]]
 name = "pdftext"
-version = "0.4.1"
+version = "0.5.0"
 description = "Extract structured text from pdfs quickly"
 optional = false
 python-versions = "<4.0,>=3.10"
 files = [
-    {file = "pdftext-0.4.1-py3-none-any.whl", hash = "sha256:c25514f7a9ded34f68c8d28511fd78d7586a43d0cf5ef7d6bc33c476fa55fd1f"},
-    {file = "pdftext-0.4.1.tar.gz", hash = "sha256:ae06f3c0844e7cc631af86b844f4af06b72da2b67d7450441ead258a64e98660"},
+    {file = "pdftext-0.5.0-py3-none-any.whl", hash = "sha256:e14179c5039c711dc5c490ecb1bc15c92ab920e5f7715034b7ae5a387b3b2787"},
+    {file = "pdftext-0.5.0.tar.gz", hash = "sha256:f6487d170abc97867d7539774fecdb0a17599965ba88287b3b89731f5cd7d612"},
 ]
 
 [package.dependencies]
+click = ">=8.1.8,<9.0.0"
 pydantic = ">=2.7.1,<3.0.0"
 pydantic-settings = ">=2.2.1,<3.0.0"
 pypdfium2 = "4.30.0"
@@ -4638,26 +4639,24 @@ snowflake = ["snowflake-connector-python (>=2.8.0)", "snowflake-snowpark-python[
 
 [[package]]
 name = "surya-ocr"
-version = "0.8.3"
+version = "0.9.0"
 description = "OCR, layout, reading order, and table recognition in 90+ languages"
 optional = false
 python-versions = "<4.0,>=3.10"
 files = [
-    {file = "surya_ocr-0.8.3-py3-none-any.whl", hash = "sha256:b2a0e07de8741d2f1b68a1b9f33b6864779648619607ee09dcbeabc31ee79289"},
-    {file = "surya_ocr-0.8.3.tar.gz", hash = "sha256:13d9ab7d5d971f16e37bffe48767b80df6999cccd3c7eb7c154f33f440ac02e3"},
+    {file = "surya_ocr-0.9.0-py3-none-any.whl", hash = "sha256:1180f504ff9aea3a9992b3ae64eb638d72ed69237baa0550ccb0f62766d3f4e6"},
+    {file = "surya_ocr-0.9.0.tar.gz", hash = "sha256:cd70b55b4d320443ff1b974899e8495279881c4fa7406cc7f243d49b6c73b87d"},
 ]
 
 [package.dependencies]
+click = ">=8.1.8,<9.0.0"
 filetype = ">=1.2.0,<2.0.0"
-ftfy = ">=6.1.3,<7.0.0"
 opencv-python = ">=4.9.0.80,<5.0.0.0"
-pdftext = ">=0.4.1,<0.5.0"
 pillow = ">=10.2.0,<11.0.0"
 pydantic = ">=2.5.3,<3.0.0"
 pydantic-settings = ">=2.1.0,<3.0.0"
 pypdfium2 = "4.30.0"
 python-dotenv = ">=1.0.0,<2.0.0"
-tabulate = ">=0.9.0,<0.10.0"
 torch = ">=2.4.1,<3.0.0"
 transformers = ">=4.41.0,<5.0.0"
 
@@ -5488,4 +5487,4 @@ propcache = ">=0.2.0"
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.10"
-content-hash = "58505c3f91b4bc225c36c0901cd4d6162e2892b47850dd8e11c4c12568ab19d3"
+content-hash = "f8d6cc52210b2d55a576c13c73deafebd70392f1d48d73a905867e082301ae40"

From d7b7d6f1788b414ff8711d45818235f4668c8689 Mon Sep 17 00:00:00 2001
From: Vik Paruchuri 
Date: Wed, 22 Jan 2025 14:48:07 -0500
Subject: [PATCH 74/92] Fix tests

---
 marker/builders/ocr.py             |  8 ++++++--
 marker/schema/blocks/base.py       |  3 ++-
 marker/scripts/convert.py          |  2 +-
 tests/builders/test_garbled_pdf.py | 16 ++++++++++++----
 4 files changed, 21 insertions(+), 8 deletions(-)

diff --git a/marker/builders/ocr.py b/marker/builders/ocr.py
index 0bea3f1c..43308c5b 100644
--- a/marker/builders/ocr.py
+++ b/marker/builders/ocr.py
@@ -35,6 +35,10 @@ class OcrBuilder(BaseBuilder):
         "A list of languages to use for OCR.",
         "Default is None."
     ] = None
+    enable_table_ocr: Annotated[
+        bool,
+        "Whether to skip OCR on tables.  The TableProcessor will re-OCR them.  Only enable if the TableProcessor is not running.",
+    ] = False
 
     def __init__(self, detection_model: DetectionPredictor, recognition_model: RecognitionPredictor, config=None):
         super().__init__(config)
@@ -67,12 +71,12 @@ def ocr_extraction(self, document: Document, provider: PdfProvider) -> ProviderP
 
         # Remove tables because we re-OCR them later with the table processor
         recognition_results = self.recognition_model(
-            images=[page.get_image(highres=False, remove_tables=True) for page in page_list],
+            images=[page.get_image(highres=False, remove_tables=not self.enable_table_ocr) for page in page_list],
             langs=[self.languages] * len(page_list),
             det_predictor=self.detection_model,
             detection_batch_size=int(self.get_detection_batch_size()),
             recognition_batch_size=int(self.get_recognition_batch_size()),
-            highres_images=[page.get_image(highres=True, remove_tables=True) for page in page_list]
+            highres_images=[page.get_image(highres=True, remove_tables=not self.enable_table_ocr) for page in page_list]
         )
 
         page_lines = {}
diff --git a/marker/schema/blocks/base.py b/marker/schema/blocks/base.py
index b9abe11e..69952f70 100644
--- a/marker/schema/blocks/base.py
+++ b/marker/schema/blocks/base.py
@@ -167,9 +167,10 @@ def remove_structure_items(self, block_ids: List[BlockId]):
     def raw_text(self, document: Document) -> str:
         from marker.schema.text.line import Line
         from marker.schema.text.span import Span
+        from marker.schema.blocks.tablecell import TableCell
 
         if self.structure is None:
-            if isinstance(self, Span):
+            if isinstance(self, (Span, TableCell)):
                 return self.text
             else:
                 return ""
diff --git a/marker/scripts/convert.py b/marker/scripts/convert.py
index 0a4051a8..b859f9f9 100644
--- a/marker/scripts/convert.py
+++ b/marker/scripts/convert.py
@@ -100,7 +100,7 @@ def convert_cli(in_folder: str, **kwargs):
     else:
         model_dict = create_model_dict()
         for k, v in model_dict.items():
-            v.share_memory()
+            v.model.share_memory()
 
     print(f"Converting {len(files_to_convert)} pdfs in chunk {kwargs['chunk_idx'] + 1}/{kwargs['num_chunks']} with {total_processes} processes and saving to {kwargs['output_dir']}")
     task_args = [(f, kwargs) for f in files_to_convert]
diff --git a/tests/builders/test_garbled_pdf.py b/tests/builders/test_garbled_pdf.py
index b848148e..cb62bf4b 100644
--- a/tests/builders/test_garbled_pdf.py
+++ b/tests/builders/test_garbled_pdf.py
@@ -2,10 +2,11 @@
 
 from marker.builders.document import DocumentBuilder
 from marker.builders.layout import LayoutBuilder
+from marker.processors.table import TableProcessor
 from marker.schema import BlockTypes
 
 @pytest.mark.filename("water_damage.pdf")
-def test_garbled_pdf(pdf_document):
+def test_garbled_pdf(pdf_document, detection_model, recognition_model, table_rec_model):
     assert pdf_document.pages[0].structure[0] == '/page/0/Table/0'
 
     table_block = pdf_document.pages[0].get_block(pdf_document.pages[0].structure[0])
@@ -16,9 +17,16 @@ def test_garbled_pdf(pdf_document):
     assert table_cell.block_type == BlockTypes.Line
     assert table_cell.structure[0] == "/page/0/Span/2"
 
-    span = pdf_document.pages[0].get_block(table_cell.structure[0])
+    span = pdf_document.pages[0].contained_blocks(pdf_document, (BlockTypes.Span,))[0]
     assert span.block_type == BlockTypes.Span
-    assert "комплекс" in span.text
+    assert len(span.text.strip()) == 0
+
+    # We don't OCR in the initial pass, only with the TableProcessor
+    processor = TableProcessor(detection_model, recognition_model, table_rec_model)
+    processor(pdf_document)
+
+    table = pdf_document.pages[0].contained_blocks(pdf_document, (BlockTypes.Table,))[0]
+    assert "варіант" in table.raw_text(pdf_document)
 
 
 @pytest.mark.filename("hindi_judgement.pdf")
@@ -30,7 +38,7 @@ def test_garbled_builder(config, pdf_provider, layout_model, ocr_error_model):
 
     bad_ocr_results = layout_builder.surya_ocr_error_detection(document.pages, pdf_provider.page_lines)
     assert len(bad_ocr_results.labels) == 2
-    assert all([l == "bad" for l in bad_ocr_results.labels])
+    assert any([l == "bad" for l in bad_ocr_results.labels])
 
 
 @pytest.mark.filename("adversarial.pdf")

From 0d0e5d7c21f1cc93d803f0294b68177a516139b1 Mon Sep 17 00:00:00 2001
From: "github-actions[bot]"
 <41898282+github-actions[bot]@users.noreply.github.com>
Date: Thu, 23 Jan 2025 17:01:17 +0000
Subject: [PATCH 75/92] @jazzido has signed the CLA in VikParuchuri/marker#502

---
 signatures/version1/cla.json | 8 ++++++++
 1 file changed, 8 insertions(+)

diff --git a/signatures/version1/cla.json b/signatures/version1/cla.json
index aeba1b2d..a210857d 100644
--- a/signatures/version1/cla.json
+++ b/signatures/version1/cla.json
@@ -143,6 +143,14 @@
       "created_at": "2025-01-05T16:23:12Z",
       "repoId": 712111618,
       "pullRequestNo": 464
+    },
+    {
+      "name": "jazzido",
+      "id": 27584,
+      "comment_id": 2610428000,
+      "created_at": "2025-01-23T17:01:02Z",
+      "repoId": 712111618,
+      "pullRequestNo": 502
     }
   ]
 }
\ No newline at end of file

From 6a43860f99b44544a7110fc7b6733d2c03bf1885 Mon Sep 17 00:00:00 2001
From: Vik Paruchuri 
Date: Thu, 23 Jan 2025 14:02:37 -0500
Subject: [PATCH 76/92] Chunk rows in large tables

---
 benchmarks/table/table.py          |  4 +-
 marker/processors/llm/__init__.py  |  2 +-
 marker/processors/llm/llm_table.py | 73 ++++++++++++++++++++++++------
 marker/processors/llm/llm_text.py  |  2 +-
 marker/processors/table.py         |  6 +++
 marker/schema/blocks/basetable.py  |  8 ++--
 6 files changed, 76 insertions(+), 19 deletions(-)

diff --git a/benchmarks/table/table.py b/benchmarks/table/table.py
index c6fce062..3c15fc5b 100644
--- a/benchmarks/table/table.py
+++ b/benchmarks/table/table.py
@@ -169,7 +169,9 @@ def main(
                 #marker wraps the table in  which fintabnet data doesn't
                 #Fintabnet doesn't use th tags, need to be replaced for fair comparison
                 marker_table_soup = BeautifulSoup(marker_table.html, 'html.parser')
-                marker_table_soup.find('tbody').unwrap()
+                tbody = marker_table_soup.find('tbody')
+                if tbody:
+                    tbody.unwrap()
                 for th_tag in marker_table_soup.find_all('th'):
                     th_tag.name = 'td'
                 marker_table_html = str(marker_table_soup)
diff --git a/marker/processors/llm/__init__.py b/marker/processors/llm/__init__.py
index 17196443..c41853ac 100644
--- a/marker/processors/llm/__init__.py
+++ b/marker/processors/llm/__init__.py
@@ -86,4 +86,4 @@ def rewrite_blocks(self, document: Document):
         pbar.close()
 
     def extract_image(self, document: Document, image_block: Block):
-        return image_block.get_image(document, highres=False, expansion=(self.image_expansion_ratio, self.image_expansion_ratio))
+        return image_block.get_image(document, highres=True, expansion=(self.image_expansion_ratio, self.image_expansion_ratio))
diff --git a/marker/processors/llm/llm_table.py b/marker/processors/llm/llm_table.py
index ffa56b5b..3f31bb8e 100644
--- a/marker/processors/llm/llm_table.py
+++ b/marker/processors/llm/llm_table.py
@@ -2,10 +2,11 @@
 
 from bs4 import BeautifulSoup
 from google.ai.generativelanguage_v1beta.types import content
+from PIL import Image
 
 from marker.processors.llm import BaseLLMProcessor
 from marker.schema import BlockTypes
-from marker.schema.blocks import Block, TableCell
+from marker.schema.blocks import Block, TableCell, Table
 from marker.schema.document import Document
 from marker.schema.groups.page import PageGroup
 from marker.schema.polygon import PolygonBox
@@ -19,7 +20,15 @@ class LLMTableProcessor(BaseLLMProcessor):
     max_rows_per_batch: Annotated[
         int,
         "If the table has more rows than this, chunk the table. (LLMs can be inaccurate with a lot of rows)",
-    ] = 75
+    ] = 60
+    max_table_rows: Annotated[
+        int,
+        "The maximum number of rows in a table to process with the LLM processor.  Beyond this will be skipped.",
+    ] = 175
+    table_image_expansion_ratio: Annotated[
+        float,
+        "The ratio to expand the image by when cropping.",
+    ] = 0
     table_rewriting_prompt: Annotated[
         str,
         "The prompt to use for rewriting text.",
@@ -66,23 +75,64 @@ class LLMTableProcessor(BaseLLMProcessor):
 ```
 """
 
-    def process_rewriting(self, document: Document, page: PageGroup, block: Block):
-        children = block.contained_blocks(document, (BlockTypes.TableCell,))
+    def process_rewriting(self, document: Document, page: PageGroup, block: Table):
+        children: List[TableCell] = block.contained_blocks(document, (BlockTypes.TableCell,))
         if not children:
             # Happens if table/form processors didn't run
             return
 
         # LLMs don't handle tables with a lot of rows very well
-        row_count = len(set([cell.row_id for cell in children]))
+        unique_rows = set([cell.row_id for cell in children])
+        row_count = len(unique_rows)
+        row_idxs = sorted(list(unique_rows))
 
-        # TODO: eventually chunk the table and inference each chunk
-        if row_count > self.max_rows_per_batch:
+        if row_count > self.max_table_rows:
             return
 
-        block_html = block.render(document).html
+        # Inference by chunk to handle long tables better
+        parsed_cells = []
+        row_shift = 0
+        block_image = self.extract_image(document, block)
+        block_rescaled_bbox = block.polygon.rescale(page.polygon.size, page.get_image(highres=True).size).bbox
+        for i in range(0, row_count, self.max_rows_per_batch):
+            batch_row_idxs = row_idxs[i:i + self.max_rows_per_batch]
+            batch_cells = [cell for cell in children if cell.row_id in batch_row_idxs]
+            batch_cell_bboxes = [cell.polygon.rescale(page.polygon.size, page.get_image(highres=True).size).bbox for cell in batch_cells]
+            # bbox relative to the block
+            batch_bbox = [
+                min([bbox[0] for bbox in batch_cell_bboxes]) - block_rescaled_bbox[0],
+                min([bbox[1] for bbox in batch_cell_bboxes]) - block_rescaled_bbox[1],
+                max([bbox[2] for bbox in batch_cell_bboxes]) - block_rescaled_bbox[0],
+                max([bbox[3] for bbox in batch_cell_bboxes]) - block_rescaled_bbox[1]
+            ]
+            if i == 0:
+                # Ensure first image starts from the beginning
+                batch_bbox[0] = 0
+                batch_bbox[1] = 0
+            elif i > row_count - self.max_rows_per_batch + 1:
+                # Ensure final image grabs the entire height and width
+                batch_bbox[2] = block_image.size[0]
+                batch_bbox[3] = block_image.size[1]
+
+            batch_image = block_image.crop(batch_bbox)
+            block_html = block.format_cells(document, [], batch_cells)
+            batch_parsed_cells = self.rewrite_single_chunk(page, block, block_html, batch_cells, batch_image)
+            if batch_parsed_cells is None:
+                return # Error occurred or no corrections needed
+
+            for cell in batch_parsed_cells:
+                cell.row_id += row_shift
+                parsed_cells.append(cell)
+            row_shift += max([cell.row_id for cell in batch_parsed_cells])
+
+        block.structure = []
+        for cell in parsed_cells:
+            page.add_full_block(cell)
+            block.add_structure(cell)
+
+    def rewrite_single_chunk(self, page: PageGroup, block: Block, block_html: str, children: List[TableCell], image: Image.Image):
         prompt = self.table_rewriting_prompt.replace("{block_html}", block_html)
 
-        image = self.extract_image(document, block)
         response_schema = content.Schema(
             type=content.Type.OBJECT,
             enum=[],
@@ -119,10 +169,7 @@ def process_rewriting(self, document: Document, page: PageGroup, block: Block):
             block.update_metadata(llm_error_count=1)
             return
 
-        block.structure = []
-        for cell in parsed_cells:
-            page.add_full_block(cell)
-            block.add_structure(cell)
+        return parsed_cells
 
     @staticmethod
     def get_cell_text(element, keep_tags=('br',)):
diff --git a/marker/processors/llm/llm_text.py b/marker/processors/llm/llm_text.py
index ba9bd54d..8a71b54e 100644
--- a/marker/processors/llm/llm_text.py
+++ b/marker/processors/llm/llm_text.py
@@ -13,7 +13,7 @@
 
 
 class LLMTextProcessor(BaseLLMProcessor):
-    block_types = (BlockTypes.TextInlineMath, BlockTypes.Handwriting)
+    block_types = (BlockTypes.TextInlineMath,)
     text_math_rewriting_prompt = """You are a text correction expert specializing in accurately reproducing text from images.
 You will receive an image of a text block and a set of extracted lines corresponding to the text in the image.
 Your task is to correct any errors in the extracted lines, including math, formatting, and other inaccuracies, and output the corrected lines in a JSON format.
diff --git a/marker/processors/table.py b/marker/processors/table.py
index 1aca6656..6094f70d 100644
--- a/marker/processors/table.py
+++ b/marker/processors/table.py
@@ -104,6 +104,12 @@ def __call__(self, document: Document):
                 for cell in cells:
                     # Rescale the cell polygon to the page size
                     cell_polygon = PolygonBox(polygon=cell.polygon).rescale(page.get_image(highres=True).size, page.polygon.size)
+
+                    # Rescale cell polygon to be relative to the page instead of the table
+                    for corner in cell_polygon.polygon:
+                        corner[0] += block.polygon.bbox[0]
+                        corner[1] += block.polygon.bbox[1]
+
                     cell_block = TableCell(
                         polygon=cell_polygon,
                         text=self.finalize_cell_text(cell),
diff --git a/marker/schema/blocks/basetable.py b/marker/schema/blocks/basetable.py
index b3473f44..a18b09ab 100644
--- a/marker/schema/blocks/basetable.py
+++ b/marker/schema/blocks/basetable.py
@@ -9,8 +9,11 @@ class BaseTable(Block):
     block_type: BlockTypes | None = None
     html: str | None = None
 
-    def format_cells(self, document, child_blocks):
-        child_cells: List[TableCell] = [document.get_block(c.id) for c in child_blocks if c.id.block_type == BlockTypes.TableCell]
+    @staticmethod
+    def format_cells(document, child_blocks, child_cells: List[TableCell] | None = None):
+        if child_cells is None:
+            child_cells: List[TableCell] = [document.get_block(c.id) for c in child_blocks if c.id.block_type == BlockTypes.TableCell]
+
         unique_rows = sorted(list(set([c.row_id for c in child_cells])))
         html_repr = "
 tags.  Only use the colspan and rowspan attributes if necessary.  Do not use 
 tags.
+- Make sure the table is as faithful to the image as possible with the given tags.
+
+**Instructions**
+1. Analyze the image, and determine the table structure.
+2. Convert the table image to HTML, following the guidelines above.
+3. Output only the HTML for the table, starting with the  tag and ending with the 
 tag.
+""".strip()
+
+genai.configure(api_key=settings.GOOGLE_API_KEY)
+
+def gemini_table_rec(image: Image.Image):
+    schema = content.Schema(
+        type=content.Type.OBJECT,
+        required=["table_html"],
+        properties={
+            "table_html": content.Schema(
+                type=content.Type.STRING,
+            )
+        }
+    )
+
+    model = genai.GenerativeModel("gemini-1.5-flash")
+
+    responses = model.generate_content(
+        [image, prompt],  # According to gemini docs, it performs better if the image is the first element
+        stream=False,
+        generation_config={
+            "temperature": 0,
+            "response_schema": schema,
+            "response_mime_type": "application/json",
+        },
+        request_options={'timeout': 60}
+    )
+
+    output = responses.candidates[0].content.parts[0].text
+    return json.loads(output)["table_html"]
\ No newline at end of file
diff --git a/benchmarks/table/table.py b/benchmarks/table/table.py
index f540d723..c6fce062 100644
--- a/benchmarks/table/table.py
+++ b/benchmarks/table/table.py
@@ -1,12 +1,11 @@
 import os
-from typing import List
-
-import numpy as np
-
-from marker.renderers.json import JSONOutput, JSONBlockOutput
+from itertools import repeat
+from tkinter import Image
 
 os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"  # Transformers uses .isin for a simple op, which is not supported on MPS
 
+from typing import List
+import numpy as np
 import base64
 import time
 import datasets
@@ -16,21 +15,24 @@
 from tabulate import tabulate
 import json
 from bs4 import BeautifulSoup
-from concurrent.futures import ThreadPoolExecutor, ProcessPoolExecutor
+from concurrent.futures import ProcessPoolExecutor
 from pypdfium2._helpers.misc import PdfiumError
+import pypdfium2 as pdfium
 from marker.util import matrix_intersection_area
+from marker.renderers.json import JSONOutput, JSONBlockOutput
 
 from marker.config.parser import ConfigParser
 from marker.converters.table import TableConverter
 from marker.models import create_model_dict
 
 from scoring import wrap_table_html, similarity_eval_html
+from gemini import gemini_table_rec
 
-def update_teds_score(result):
-    prediction, ground_truth = result['marker_table'], result['gt_table']
+def update_teds_score(result, prefix: str = "marker"):
+    prediction, ground_truth = result[f'{prefix}_table'], result['gt_table']
     prediction, ground_truth = wrap_table_html(prediction), wrap_table_html(ground_truth)
     score = similarity_eval_html(prediction, ground_truth)
-    result.update({'score':score})
+    result.update({f'{prefix}_score':score})
     return result
 
 
@@ -51,7 +53,16 @@ def extract_tables(children: List[JSONBlockOutput]):
 @click.option("--max_workers", type=int, default=16, help="Maximum number of workers to use")
 @click.option("--use_llm", is_flag=True, help="Use LLM for improving table recognition.")
 @click.option("--table_rec_batch_size", type=int, default=None, help="Batch size for table recognition.")
-def main(out_file: str, dataset: str, max_rows: int, max_workers: int, use_llm: bool, table_rec_batch_size: int | None):
+@click.option("--use_gemini", is_flag=True, help="Evaluate Gemini for table recognition.")
+def main(
+        out_file: str,
+        dataset: str,
+        max_rows: int,
+        max_workers: int,
+        use_llm: bool,
+        table_rec_batch_size: int | None,
+        use_gemini: bool = False
+):
     models = create_model_dict()
     config_parser = ConfigParser({'output_format': 'json', "use_llm": use_llm, "table_rec_batch_size": table_rec_batch_size})
     start = time.time()
@@ -86,6 +97,9 @@ def main(out_file: str, dataset: str, max_rows: int, max_workers: int, use_llm:
                 marker_json = converter(temp_pdf_file.name).children
                 tqdm.disable = False
 
+                doc = pdfium.PdfDocument(temp_pdf_file.name)
+                page_image = doc[0].render(scale=92/72).to_pil()
+
             if len(marker_json) == 0 or len(gt_tables) == 0:
                 print(f'No tables detected, skipping...')
                 total_unaligned += len(gt_tables)
@@ -94,6 +108,8 @@ def main(out_file: str, dataset: str, max_rows: int, max_workers: int, use_llm:
             marker_tables = extract_tables(marker_json)
             marker_table_boxes = [table.bbox for table in marker_tables]
             page_bbox = marker_json[0].bbox
+            w_scaler, h_scaler = page_image.width / page_bbox[2], page_image.height / page_bbox[3]
+            table_images = [page_image.crop([bbox[0] * w_scaler, bbox[1] * h_scaler, bbox[2] * w_scaler, bbox[3] * h_scaler]) for bbox in marker_table_boxes]
 
             # Normalize the bboxes
             for bbox in marker_table_boxes:
@@ -136,14 +152,18 @@ def main(out_file: str, dataset: str, max_rows: int, max_workers: int, use_llm:
                     unaligned_tables.add(table_idx)
                     continue
 
+                gemini_html = ""
+                if use_gemini:
+                    gemini_html = gemini_table_rec(table_images[aligned_idx])
+
                 aligned_tables.append(
-                    (marker_tables[aligned_idx], gt_tables[table_idx])
+                    (marker_tables[aligned_idx], gt_tables[table_idx], gemini_html)
                 )
                 used_tables.add(aligned_idx)
 
             total_unaligned += len(unaligned_tables)
             
-            for marker_table, gt_table in aligned_tables:
+            for marker_table, gt_table, gemini_table in aligned_tables:
                 gt_table_html = gt_table['html']
 
                 #marker wraps the table in 
"
         for row_id in unique_rows:
@@ -22,7 +25,6 @@ def format_cells(self, document, child_blocks):
         html_repr += "
"
         return html_repr
 
-
     def assemble_html(self, document, child_blocks: List[BlockOutput], parent_structure=None):
         # Filter out the table cells, so they don't render twice
         child_ref_blocks = [block for block in child_blocks if block.id.block_type == BlockTypes.Reference]

From f634f8c9c61ea726b60c28940260a1596f08abd1 Mon Sep 17 00:00:00 2001
From: Vik Paruchuri 
Date: Thu, 23 Jan 2025 16:49:15 -0500
Subject: [PATCH 77/92] Bump surya version

---
 poetry.lock    | 162 +++++++++++++++++++++++++------------------------
 pyproject.toml |   2 +-
 2 files changed, 83 insertions(+), 81 deletions(-)

diff --git a/poetry.lock b/poetry.lock
index 392c8ad8..b6a94987 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -801,23 +801,23 @@ tests = ["asttokens (>=2.1.0)", "coverage", "coverage-enable-subprocess", "ipyth
 
 [[package]]
 name = "fastapi"
-version = "0.115.6"
+version = "0.115.7"
 description = "FastAPI framework, high performance, easy to learn, fast to code, ready for production"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "fastapi-0.115.6-py3-none-any.whl", hash = "sha256:e9240b29e36fa8f4bb7290316988e90c381e5092e0cbe84e7818cc3713bcf305"},
-    {file = "fastapi-0.115.6.tar.gz", hash = "sha256:9ec46f7addc14ea472958a96aae5b5de65f39721a46aaf5705c480d9a8b76654"},
+    {file = "fastapi-0.115.7-py3-none-any.whl", hash = "sha256:eb6a8c8bf7f26009e8147111ff15b5177a0e19bb4a45bc3486ab14804539d21e"},
+    {file = "fastapi-0.115.7.tar.gz", hash = "sha256:0f106da6c01d88a6786b3248fb4d7a940d071f6f488488898ad5d354b25ed015"},
 ]
 
 [package.dependencies]
 pydantic = ">=1.7.4,<1.8 || >1.8,<1.8.1 || >1.8.1,<2.0.0 || >2.0.0,<2.0.1 || >2.0.1,<2.1.0 || >2.1.0,<3.0.0"
-starlette = ">=0.40.0,<0.42.0"
+starlette = ">=0.40.0,<0.46.0"
 typing-extensions = ">=4.8.0"
 
 [package.extras]
-all = ["email-validator (>=2.0.0)", "fastapi-cli[standard] (>=0.0.5)", "httpx (>=0.23.0)", "itsdangerous (>=1.1.0)", "jinja2 (>=2.11.2)", "orjson (>=3.2.1)", "pydantic-extra-types (>=2.0.0)", "pydantic-settings (>=2.0.0)", "python-multipart (>=0.0.7)", "pyyaml (>=5.3.1)", "ujson (>=4.0.1,!=4.0.2,!=4.1.0,!=4.2.0,!=4.3.0,!=5.0.0,!=5.1.0)", "uvicorn[standard] (>=0.12.0)"]
-standard = ["email-validator (>=2.0.0)", "fastapi-cli[standard] (>=0.0.5)", "httpx (>=0.23.0)", "jinja2 (>=2.11.2)", "python-multipart (>=0.0.7)", "uvicorn[standard] (>=0.12.0)"]
+all = ["email-validator (>=2.0.0)", "fastapi-cli[standard] (>=0.0.5)", "httpx (>=0.23.0)", "itsdangerous (>=1.1.0)", "jinja2 (>=3.1.5)", "orjson (>=3.2.1)", "pydantic-extra-types (>=2.0.0)", "pydantic-settings (>=2.0.0)", "python-multipart (>=0.0.18)", "pyyaml (>=5.3.1)", "ujson (>=4.0.1,!=4.0.2,!=4.1.0,!=4.2.0,!=4.3.0,!=5.0.0,!=5.1.0)", "uvicorn[standard] (>=0.12.0)"]
+standard = ["email-validator (>=2.0.0)", "fastapi-cli[standard] (>=0.0.5)", "httpx (>=0.23.0)", "jinja2 (>=3.1.5)", "python-multipart (>=0.0.18)", "uvicorn[standard] (>=0.12.0)"]
 
 [[package]]
 name = "fastjsonschema"
@@ -1135,13 +1135,13 @@ uritemplate = ">=3.0.1,<5"
 
 [[package]]
 name = "google-auth"
-version = "2.37.0"
+version = "2.38.0"
 description = "Google Authentication Library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google_auth-2.37.0-py2.py3-none-any.whl", hash = "sha256:42664f18290a6be591be5329a96fe30184be1a1badb7292a7f686a9659de9ca0"},
-    {file = "google_auth-2.37.0.tar.gz", hash = "sha256:0054623abf1f9c83492c63d3f47e77f0a544caa3d40b2d98e099a611c2dd5d00"},
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 [package.dependencies]
@@ -1214,85 +1214,85 @@ grpc = ["grpcio (>=1.44.0,<2.0.0.dev0)"]
 
 [[package]]
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+    {file = "grpcio-1.70.0-cp38-cp38-manylinux_2_17_aarch64.whl", hash = "sha256:0ba0a173f4feacf90ee618fbc1a27956bfd21260cd31ced9bc707ef551ff7dc7"},
+    {file = "grpcio-1.70.0-cp38-cp38-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:558c386ecb0148f4f99b1a65160f9d4b790ed3163e8610d11db47838d452512d"},
+    {file = "grpcio-1.70.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:412faabcc787bbc826f51be261ae5fa996b21263de5368a55dc2cf824dc5090e"},
+    {file = "grpcio-1.70.0-cp38-cp38-musllinux_1_1_i686.whl", hash = "sha256:3b0f01f6ed9994d7a0b27eeddea43ceac1b7e6f3f9d86aeec0f0064b8cf50fdb"},
+    {file = "grpcio-1.70.0-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:7385b1cb064734005204bc8994eed7dcb801ed6c2eda283f613ad8c6c75cf873"},
+    {file = "grpcio-1.70.0-cp38-cp38-win32.whl", hash = "sha256:07269ff4940f6fb6710951116a04cd70284da86d0a4368fd5a3b552744511f5a"},
+    {file = "grpcio-1.70.0-cp38-cp38-win_amd64.whl", hash = "sha256:aba19419aef9b254e15011b230a180e26e0f6864c90406fdbc255f01d83bc83c"},
+    {file = "grpcio-1.70.0-cp39-cp39-linux_armv7l.whl", hash = "sha256:4f1937f47c77392ccd555728f564a49128b6a197a05a5cd527b796d36f3387d0"},
+    {file = "grpcio-1.70.0-cp39-cp39-macosx_10_14_universal2.whl", hash = "sha256:0cd430b9215a15c10b0e7d78f51e8a39d6cf2ea819fd635a7214fae600b1da27"},
+    {file = "grpcio-1.70.0-cp39-cp39-manylinux_2_17_aarch64.whl", hash = "sha256:e27585831aa6b57b9250abaf147003e126cd3a6c6ca0c531a01996f31709bed1"},
+    {file = "grpcio-1.70.0-cp39-cp39-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:c1af8e15b0f0fe0eac75195992a63df17579553b0c4af9f8362cc7cc99ccddf4"},
+    {file = "grpcio-1.70.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:cbce24409beaee911c574a3d75d12ffb8c3e3dd1b813321b1d7a96bbcac46bf4"},
+    {file = "grpcio-1.70.0-cp39-cp39-musllinux_1_1_i686.whl", hash = "sha256:ff4a8112a79464919bb21c18e956c54add43ec9a4850e3949da54f61c241a4a6"},
+    {file = "grpcio-1.70.0-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:5413549fdf0b14046c545e19cfc4eb1e37e9e1ebba0ca390a8d4e9963cab44d2"},
+    {file = "grpcio-1.70.0-cp39-cp39-win32.whl", hash = "sha256:b745d2c41b27650095e81dea7091668c040457483c9bdb5d0d9de8f8eb25e59f"},
+    {file = "grpcio-1.70.0-cp39-cp39-win_amd64.whl", hash = "sha256:a31d7e3b529c94e930a117b2175b2efd179d96eb3c7a21ccb0289a8ab05b645c"},
+    {file = "grpcio-1.70.0.tar.gz", hash = "sha256:8d1584a68d5922330025881e63a6c1b54cc8117291d382e4fa69339b6d914c56"},
 ]
 
 [package.extras]
-protobuf = ["grpcio-tools (>=1.69.0)"]
+protobuf = ["grpcio-tools (>=1.70.0)"]
 
 [[package]]
 name = "grpcio-status"
-version = "1.69.0"
+version = "1.70.0"
 description = "Status proto mapping for gRPC"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "grpcio_status-1.69.0-py3-none-any.whl", hash = "sha256:d6b2a3c9562c03a817c628d7ba9a925e209c228762d6d7677ae5c9401a542853"},
-    {file = "grpcio_status-1.69.0.tar.gz", hash = "sha256:595ef84e5178d6281caa732ccf68ff83259241608d26b0e9c40a5e66eee2a2d2"},
+    {file = "grpcio_status-1.70.0-py3-none-any.whl", hash = "sha256:fc5a2ae2b9b1c1969cc49f3262676e6854aa2398ec69cb5bd6c47cd501904a85"},
+    {file = "grpcio_status-1.70.0.tar.gz", hash = "sha256:0e7b42816512433b18b9d764285ff029bde059e9d41f8fe10a60631bd8348101"},
 ]
 
 [package.dependencies]
 googleapis-common-protos = ">=1.5.5"
-grpcio = ">=1.69.0"
+grpcio = ">=1.70.0"
 protobuf = ">=5.26.1,<6.0dev"
 
 [[package]]
@@ -3248,6 +3248,8 @@ files = [
     {file = "psutil-6.1.1-cp27-cp27m-manylinux2010_x86_64.whl", hash = "sha256:8df0178ba8a9e5bc84fed9cfa61d54601b371fbec5c8eebad27575f1e105c0d4"},
     {file = "psutil-6.1.1-cp27-cp27mu-manylinux2010_i686.whl", hash = "sha256:1924e659d6c19c647e763e78670a05dbb7feaf44a0e9c94bf9e14dfc6ba50468"},
     {file = "psutil-6.1.1-cp27-cp27mu-manylinux2010_x86_64.whl", hash = "sha256:018aeae2af92d943fdf1da6b58665124897cfc94faa2ca92098838f83e1b1bca"},
+    {file = "psutil-6.1.1-cp27-none-win32.whl", hash = "sha256:6d4281f5bbca041e2292be3380ec56a9413b790579b8e593b1784499d0005dac"},
+    {file = "psutil-6.1.1-cp27-none-win_amd64.whl", hash = "sha256:c777eb75bb33c47377c9af68f30e9f11bc78e0f07fbf907be4a5d70b2fe5f030"},
     {file = "psutil-6.1.1-cp36-abi3-macosx_10_9_x86_64.whl", hash = "sha256:fc0ed7fe2231a444fc219b9c42d0376e0a9a1a72f16c5cfa0f68d19f1a0663e8"},
     {file = "psutil-6.1.1-cp36-abi3-macosx_11_0_arm64.whl", hash = "sha256:0bdd4eab935276290ad3cb718e9809412895ca6b5b334f5a9111ee6d9aff9377"},
     {file = "psutil-6.1.1-cp36-abi3-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:b6e06c20c05fe95a3d7302d74e7097756d4ba1247975ad6905441ae1b5b66003"},
@@ -4587,20 +4589,20 @@ tests = ["cython", "littleutils", "pygments", "pytest", "typeguard"]
 
 [[package]]
 name = "starlette"
-version = "0.41.3"
+version = "0.45.2"
 description = "The little ASGI library that shines."
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 files = [
-    {file = "starlette-0.41.3-py3-none-any.whl", hash = "sha256:44cedb2b7c77a9de33a8b74b2b90e9f50d11fcf25d8270ea525ad71a25374ff7"},
-    {file = "starlette-0.41.3.tar.gz", hash = "sha256:0e4ab3d16522a255be6b28260b938eae2482f98ce5cc934cb08dce8dc3ba5835"},
+    {file = "starlette-0.45.2-py3-none-any.whl", hash = "sha256:4daec3356fb0cb1e723a5235e5beaf375d2259af27532958e2d79df549dad9da"},
+    {file = "starlette-0.45.2.tar.gz", hash = "sha256:bba1831d15ae5212b22feab2f218bab6ed3cd0fc2dc1d4442443bb1ee52260e0"},
 ]
 
 [package.dependencies]
-anyio = ">=3.4.0,<5"
+anyio = ">=3.6.2,<5"
 
 [package.extras]
-full = ["httpx (>=0.22.0)", "itsdangerous", "jinja2", "python-multipart (>=0.0.7)", "pyyaml"]
+full = ["httpx (>=0.27.0,<0.29.0)", "itsdangerous", "jinja2", "python-multipart (>=0.0.18)", "pyyaml"]
 
 [[package]]
 name = "streamlit"
@@ -4639,13 +4641,13 @@ snowflake = ["snowflake-connector-python (>=2.8.0)", "snowflake-snowpark-python[
 
 [[package]]
 name = "surya-ocr"
-version = "0.9.0"
+version = "0.9.1"
 description = "OCR, layout, reading order, and table recognition in 90+ languages"
 optional = false
 python-versions = "<4.0,>=3.10"
 files = [
-    {file = "surya_ocr-0.9.0-py3-none-any.whl", hash = "sha256:1180f504ff9aea3a9992b3ae64eb638d72ed69237baa0550ccb0f62766d3f4e6"},
-    {file = "surya_ocr-0.9.0.tar.gz", hash = "sha256:cd70b55b4d320443ff1b974899e8495279881c4fa7406cc7f243d49b6c73b87d"},
+    {file = "surya_ocr-0.9.1-py3-none-any.whl", hash = "sha256:837c43382208ff8cbe10976cb80c0e63039b86db5a7244f15dcc23467530908f"},
+    {file = "surya_ocr-0.9.1.tar.gz", hash = "sha256:0a3d084455d880151b43884fd7a4d2942d7a88741fd7c21f88e49f4f4af9f9e1"},
 ]
 
 [package.dependencies]
@@ -5487,4 +5489,4 @@ propcache = ">=0.2.0"
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.10"
-content-hash = "f8d6cc52210b2d55a576c13c73deafebd70392f1d48d73a905867e082301ae40"
+content-hash = "958f917c22da2ff290f6196257c3867f878c2848925b382ad4990f7fb7e6a40c"
diff --git a/pyproject.toml b/pyproject.toml
index 6257f60c..ef148121 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -26,7 +26,7 @@ tqdm = "^4.66.1"
 ftfy = "^6.1.1"
 texify = "^0.2.1"
 rapidfuzz = "^3.8.1"
-surya-ocr = "~0.9.0"
+surya-ocr = "~0.9.1"
 regex = "^2024.4.28"
 pdftext = "~0.5.0"
 markdownify = "^0.13.1"

From 98aaababe3b49177135c5b0a9c614a5c348a00ef Mon Sep 17 00:00:00 2001
From: Vik Paruchuri 
Date: Thu, 23 Jan 2025 21:02:12 -0500
Subject: [PATCH 78/92] Bump surya

---
 poetry.lock    | 14 +++++++-------
 pyproject.toml |  2 +-
 2 files changed, 8 insertions(+), 8 deletions(-)

diff --git a/poetry.lock b/poetry.lock
index b6a94987..1a782467 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -3383,13 +3383,13 @@ files = [
 
 [[package]]
 name = "pydantic"
-version = "2.10.5"
+version = "2.10.6"
 description = "Data validation using Python type hints"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "pydantic-2.10.5-py3-none-any.whl", hash = "sha256:4dd4e322dbe55472cb7ca7e73f4b63574eecccf2835ffa2af9021ce113c83c53"},
-    {file = "pydantic-2.10.5.tar.gz", hash = "sha256:278b38dbbaec562011d659ee05f63346951b3a248a6f3642e1bc68894ea2b4ff"},
+    {file = "pydantic-2.10.6-py3-none-any.whl", hash = "sha256:427d664bf0b8a2b34ff5dd0f5a18df00591adcee7198fbd71981054cef37b584"},
+    {file = "pydantic-2.10.6.tar.gz", hash = "sha256:ca5daa827cce33de7a42be142548b0096bf05a7e7b365aebfa5f8eeec7128236"},
 ]
 
 [package.dependencies]
@@ -4641,13 +4641,13 @@ snowflake = ["snowflake-connector-python (>=2.8.0)", "snowflake-snowpark-python[
 
 [[package]]
 name = "surya-ocr"
-version = "0.9.1"
+version = "0.9.2"
 description = "OCR, layout, reading order, and table recognition in 90+ languages"
 optional = false
 python-versions = "<4.0,>=3.10"
 files = [
-    {file = "surya_ocr-0.9.1-py3-none-any.whl", hash = "sha256:837c43382208ff8cbe10976cb80c0e63039b86db5a7244f15dcc23467530908f"},
-    {file = "surya_ocr-0.9.1.tar.gz", hash = "sha256:0a3d084455d880151b43884fd7a4d2942d7a88741fd7c21f88e49f4f4af9f9e1"},
+    {file = "surya_ocr-0.9.2-py3-none-any.whl", hash = "sha256:95866f38a05d97c7faad7d82fb7d95f96df6cf9471617b7a6fa01ba5b1367622"},
+    {file = "surya_ocr-0.9.2.tar.gz", hash = "sha256:ae57c7de6b4507ef4db30c18cee387d2d6e69d15e6708789b0ce2a4412713984"},
 ]
 
 [package.dependencies]
@@ -5489,4 +5489,4 @@ propcache = ">=0.2.0"
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.10"
-content-hash = "958f917c22da2ff290f6196257c3867f878c2848925b382ad4990f7fb7e6a40c"
+content-hash = "f38cc17855cc95babd721161782ec64728a1061602236fe2845519d027966482"
diff --git a/pyproject.toml b/pyproject.toml
index ef148121..ef1fd92a 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -26,7 +26,7 @@ tqdm = "^4.66.1"
 ftfy = "^6.1.1"
 texify = "^0.2.1"
 rapidfuzz = "^3.8.1"
-surya-ocr = "~0.9.1"
+surya-ocr = "~0.9.2"
 regex = "^2024.4.28"
 pdftext = "~0.5.0"
 markdownify = "^0.13.1"

From fb4da2bb368dbdf30cdbfe9493b0ad003ad5cf3d Mon Sep 17 00:00:00 2001
From: Vik Paruchuri 
Date: Thu, 23 Jan 2025 21:56:30 -0500
Subject: [PATCH 79/92] Update examples, fix table cells with lines

---
 data/examples/json/multicolcnn.json           |   8266 +-
 data/examples/json/switch_trans.json          |  33140 +++-
 data/examples/json/thinkpython.json           | 132304 +++++++++++----
 .../multicolcnn/_page_1_Figure_0.jpeg         |    Bin 78234 -> 79399 bytes
 .../multicolcnn/_page_2_Picture_0.jpeg        |    Bin 18804 -> 20280 bytes
 .../multicolcnn/_page_6_Figure_0.jpeg         |    Bin 65096 -> 61254 bytes
 .../multicolcnn/_page_7_Figure_0.jpeg         |    Bin 49597 -> 50878 bytes
 .../markdown/multicolcnn/multicolcnn.md       |    283 +-
 .../multicolcnn/multicolcnn_meta.json         |    367 +-
 .../_page_11_Figure_4.jpeg                    |    Bin 45263 -> 45263 bytes
 .../_page_12_Figure_4.jpeg                    |    Bin 40329 -> 38988 bytes
 .../_page_13_Figure_2.jpeg                    |    Bin 46945 -> 48371 bytes
 .../_page_18_Figure_1.jpeg                    |    Bin 66250 -> 65245 bytes
 .../_page_18_Figure_3.jpeg                    |    Bin 23361 -> 23236 bytes
 .../_page_20_Figure_1.jpeg                    |    Bin 58052 -> 55091 bytes
 .../_page_20_Figure_4.jpeg                    |    Bin 55167 -> 54404 bytes
 .../_page_27_Figure_1.jpeg                    |    Bin 60047 -> 59468 bytes
 .../_page_29_Figure_1.jpeg                    |    Bin 40407 -> 39968 bytes
 .../switch_transformers/_page_2_Figure_3.jpeg |    Bin 45653 -> 45353 bytes
 .../_page_30_Figure_1.jpeg                    |    Bin 33076 -> 32204 bytes
 .../_page_31_Figure_3.jpeg                    |    Bin 41477 -> 42257 bytes
 .../switch_transformers/_page_4_Figure_1.jpeg |    Bin 57501 -> 57016 bytes
 .../switch_transformers/_page_5_Figure_3.jpeg |    Bin 59356 -> 60424 bytes
 .../switch_transformers/switch_trans.md       |    963 +-
 .../switch_trans_meta.json                    |   1249 +-
 .../thinkpython/_page_109_Figure_1.jpeg       |    Bin 15859 -> 16169 bytes
 .../thinkpython/_page_115_Figure_1.jpeg       |    Bin 0 -> 6755 bytes
 .../thinkpython/_page_116_Figure_3.jpeg       |    Bin 10173 -> 10238 bytes
 .../thinkpython/_page_127_Figure_1.jpeg       |    Bin 17303 -> 17231 bytes
 .../thinkpython/_page_127_Figure_1.png        |    Bin 18013 -> 17970 bytes
 .../thinkpython/_page_128_Figure_1.jpeg       |    Bin 24178 -> 23877 bytes
 .../thinkpython/_page_128_Figure_1.png        |    Bin 26715 -> 26727 bytes
 .../thinkpython/_page_167_Figure_1.jpeg       |    Bin 11569 -> 10958 bytes
 .../thinkpython/_page_169_Figure_1.jpeg       |    Bin 16406 -> 15668 bytes
 .../thinkpython/_page_173_Figure_1.jpeg       |    Bin 0 -> 6383 bytes
 .../thinkpython/_page_190_Figure_1.jpeg       |    Bin 13664 -> 13421 bytes
 .../thinkpython/_page_195_Figure_1.jpeg       |    Bin 5754 -> 5960 bytes
 .../thinkpython/_page_205_Figure_1.jpeg       |    Bin 38877 -> 43006 bytes
 .../thinkpython/_page_205_Figure_1.png        |    Bin 60822 -> 60784 bytes
 .../thinkpython/_page_230_Figure_1.jpeg       |    Bin 3394 -> 30653 bytes
 .../thinkpython/_page_230_Figure_1.png        |    Bin 1434 -> 1433 bytes
 .../thinkpython/_page_230_Figure_2.jpeg       |    Bin 6738 -> 0 bytes
 .../thinkpython/_page_230_Figure_2.png        |    Bin 1530 -> 0 bytes
 .../thinkpython/_page_230_Picture_11.png      |    Bin 1581 -> 0 bytes
 .../thinkpython/_page_233_Figure_1.jpeg       |    Bin 6562 -> 6609 bytes
 .../thinkpython/_page_233_Figure_1.png        |    Bin 9286 -> 9292 bytes
 .../thinkpython/_page_233_Figure_3.jpeg       |    Bin 5379 -> 5194 bytes
 .../thinkpython/_page_233_Figure_3.png        |    Bin 5131 -> 5132 bytes
 .../thinkpython/_page_234_Figure_1.jpeg       |    Bin 11855 -> 12497 bytes
 .../thinkpython/_page_235_Figure_1.jpeg       |    Bin 17302 -> 17479 bytes
 .../thinkpython/_page_236_Figure_1.jpeg       |    Bin 16641 -> 16112 bytes
 .../thinkpython/_page_236_Figure_1.png        |    Bin 20896 -> 20895 bytes
 .../thinkpython/_page_236_Figure_3.jpeg       |    Bin 16898 -> 16862 bytes
 .../thinkpython/_page_237_Figure_1.jpeg       |    Bin 9650 -> 9570 bytes
 .../thinkpython/_page_238_Figure_1.jpeg       |    Bin 34091 -> 33789 bytes
 .../thinkpython/_page_23_Figure_1.jpeg        |    Bin 12758 -> 13269 bytes
 .../thinkpython/_page_23_Figure_1.png         |    Bin 11356 -> 11349 bytes
 .../thinkpython/_page_23_Figure_3.jpeg        |    Bin 19270 -> 19277 bytes
 .../thinkpython/_page_33_Figure_1.jpeg        |    Bin 11440 -> 11426 bytes
 .../thinkpython/_page_33_Figure_1.png         |    Bin 18383 -> 18391 bytes
 .../thinkpython/_page_46_Figure_1.jpeg        |    Bin 26387 -> 24148 bytes
 .../thinkpython/_page_60_Figure_1.jpeg        |    Bin 24112 -> 24024 bytes
 .../thinkpython/_page_60_Figure_3.jpeg        |    Bin 12400 -> 12476 bytes
 .../thinkpython/_page_67_Figure_1.jpeg        |    Bin 13096 -> 13205 bytes
 .../thinkpython/_page_71_Figure_1.jpeg        |    Bin 10185 -> 10500 bytes
 .../thinkpython/_page_71_Figure_1.png         |    Bin 19807 -> 19814 bytes
 .../thinkpython/_page_78_Figure_1.jpeg        |    Bin 28594 -> 30640 bytes
 .../thinkpython/_page_78_Figure_1.png         |    Bin 33842 -> 33830 bytes
 .../thinkpython/_page_85_Figure_1.jpeg        |    Bin 3068 -> 2386 bytes
 .../thinkpython/_page_85_Figure_1.png         |    Bin 3992 -> 4002 bytes
 .../thinkpython/_page_94_Figure_1.jpeg        |    Bin 0 -> 9288 bytes
 .../thinkpython/_page_99_Figure_178.jpeg      |    Bin 0 -> 7348 bytes
 .../markdown/thinkpython/thinkpython.md       |   3743 +-
 .../thinkpython/thinkpython_meta.json         |   7190 +-
 marker/processors/llm/llm_table.py            |      2 +-
 marker/processors/table.py                    |     16 +-
 marker/renderers/markdown.py                  |      5 +-
 marker/schema/blocks/tablecell.py             |     13 +-
 78 files changed, 142626 insertions(+), 44915 deletions(-)
 create mode 100644 data/examples/markdown/thinkpython/_page_115_Figure_1.jpeg
 create mode 100644 data/examples/markdown/thinkpython/_page_173_Figure_1.jpeg
 delete mode 100644 data/examples/markdown/thinkpython/_page_230_Figure_2.jpeg
 delete mode 100644 data/examples/markdown/thinkpython/_page_230_Figure_2.png
 delete mode 100644 data/examples/markdown/thinkpython/_page_230_Picture_11.png
 create mode 100644 data/examples/markdown/thinkpython/_page_94_Figure_1.jpeg
 create mode 100644 data/examples/markdown/thinkpython/_page_99_Figure_178.jpeg

diff --git a/data/examples/json/multicolcnn.json b/data/examples/json/multicolcnn.json
index 98824999..fcd7904c 100644
--- a/data/examples/json/multicolcnn.json
+++ b/data/examples/json/multicolcnn.json
@@ -1,9 +1,9 @@
 {
   "children": [
     {
-      "id": "/page/0/Page/248",
+      "id": "/page/0/Page/277",
       "block_type": "Page",
-      "html": "",
+      "html": "",
       "polygon": [
         [
           0.0,
@@ -22,353 +22,397 @@
           792.0
         ]
       ],
+      "bbox": [
+        0.0,
+        0.0,
+        612.0,
+        792.0
+      ],
       "children": [
         {
-          "id": "/page/0/PageHeader/0",
+          "id": "/page/0/PageHeader/14",
           "block_type": "PageHeader",
-          "html": "
arXiv:1804.07821v1 [cs.CV] 20 Apr 2018
",
+          "html": "",
           "polygon": [
             [
-              18.34000015258789,
-              207.474609375
+              18.119998931884766,
+              211.199951171875
             ],
             [
-              36.755859375,
-              207.474609375
+              36.2599983215332,
+              211.199951171875
             ],
             [
-              36.755859375,
-              559.2599945068359
+              36.2599983215332,
+              559.2799987792969
             ],
             [
-              18.34000015258789,
-              559.2599945068359
+              18.119998931884766,
+              559.2799987792969
             ]
           ],
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An Aggregated Multicolumn Dilated Convolution Network for Perspective-Free Counting
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Diptodip Deb Georgia Institute of Technology diptodipdeb@gatech.edu
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Jonathan Ventura University of Colorado Colorado Springs
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jventura@uccs.edu
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Abstract
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We propose the use of dilated filters to construct an aggregation module in a multicolumn convolutional neural network for perspective-free counting. Counting is a common problem in computer vision (e.g. traffic on the street or pedestrians in a crowd). Modern approaches to the counting problem involve the production of a density map via regression whose integral is equal to the number of objects in the image. However, objects in the image can occur at different scales (e.g. due to perspective effects) which can make it difficult for a learning agent to learn the proper density map. While the use of multiple columns to extract multiscale information from images has been shown before, our approach aggregates the multiscale information gathered by the multicolumn convolutional neural network to improve performance. Our experiments show that our proposed network outperforms the state-of-the-art on many benchmark datasets, and also that using our aggregation module in combination with a higher number of columns is beneficial for multiscale counting.
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1. Introduction
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Learning to count the number of objects in an image is a deceptively difficult problem with many interesting applications, such as surveillance [20], traffic monitoring [14] and medical image analysis [22]. In many of these application areas, the objects to be counted vary widely in appearance, size and shape, and labeled training data is typically sparse. These factors pose a significant computer vision and machine learning challenge.
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Learning to count the number of objects in an image is a deceptively difficult problem with many interesting applications, such as surveillance [20], traffic monitoring [14] and medical image analysis [22]. In many of these application areas, the objects to be counted vary widely in appearance, size and shape, and labeled training data is typically sparse. These factors pose a significant computer vision and machine learning challenge.
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Lempitsky et al. [15] showed that it is possible to learn to count without learning to explicitly detect and localize individual objects. Instead, they propose learning to predict a density map whose integral over the image equals the number of objects in the image. This approach has been adopted by many later works (Cf. [18, 28]).
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Lempitsky et al. [15] showed that it is possible to learn to count without learning to explicitly detect and localize individual objects. Instead, they propose learning to predict a density map whose integral over the image equals the number of objects in the image. This approach has been adopted by many later works (Cf. [18, 28]).
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However, in many counting problems, such as those
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counting cells in a microscope image, pedestrians in a crowd, or vehicles in a traffic jam, regressors trained on a single image scale are not reliable [18]. This is due to a variety of challenges including overlap of objects and perspective effects which cause significant variance in object shape, size and appearance.
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Jonathan Ventura University of Colorado Colorado Springs jventura@uccs.edu
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counting cells in a microscope image, pedestrians in a crowd, or vehicles in a traffic jam, regressors trained on a single image scale are not reliable [18]. This is due to a variety of challenges including overlap of objects and perspective effects which cause significant variance in object shape, size and appearance.
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The most successful recent approaches address this issue by explicitly incorporating multi-scale information in the network [18,28]. These approaches either combine multiple networks which take input patches of different sizes [18] or combine multiple filtering paths (\"columns\") which have different size filters [28].
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The most successful recent approaches address this issue by explicitly incorporating multi-scale information in the network [18,28]. These approaches either combine multiple networks which take input patches of different sizes [18] or combine multiple filtering paths (\"columns\") which have different size filters [28].
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Following on the intuition that multiscale integration is key to achieving good counting performance, we propose to incorporate dilated filters [25] into a multicolumn convolutional neural network design [28]. Dilated filters exponentially increase the network's receptive field without an exponential increase in parameters, allowing for efficient use of multiscale information. Convolutional neural networks with dilated filters have proven to provide competitive performance in image segmentation where multiscale analysis is also critical [25, 26]. By incorporating dilated filters into the multicolumn network design, we greatly increase the ability of the network to selectively aggregate multiscale information, without the need for explicit perspective maps during training and testing. We propose the \"aggregated multicolumn dilated convolution network\" or AMDCN which uses dilations to aggregate multiscale information. Our extensive experimental evaluation shows that this proposed network outperforms previous methods on many benchmark datasets.
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Following on the intuition that multiscale integration is key to achieving good counting performance, we propose to incorporate dilated filters [25] into a multicolumn convolutional neural network design [28]. Dilated filters exponentially increase the network's receptive field without an exponential increase in parameters, allowing for efficient use of multiscale information. Convolutional neural networks with dilated filters have proven to provide competitive performance in image segmentation where multiscale analysis is also critical [25, 26]. By incorporating dilated filters into the multicolumn network design, we greatly increase the ability of the network to selectively aggregate multiscale information, without the need for explicit perspective maps during training and testing. We propose the \"aggregated multicolumn dilated convolution network\" or AMDCN which uses dilations to aggregate multiscale information. Our extensive experimental evaluation shows that this proposed network outperforms previous methods on many benchmark datasets.
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2. Related Work
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Counting using a supervised regressor to formulate a density map was first shown by [15]. In this paper, Lempitsky et al. show that the minimal annotation of a single dot blurred by a Gaussian kernel produces a sufficient density map to train a network to count. All of the counting methods that we examine as well as the method we use in
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Figure 1. Fully convolutional architecture diagram (not to scale). Arrows show separate columns that all take the same input. At the end of the columns, the feature maps are merged (concatenated) together and passed to another series of dilated convolutions: the aggregator, which can aggregate the multiscale information collected by the columns [25]. The input image is I with C channels. The output single channel density map is D, and integrating over this map (summing the pixels) results in the final count. Initial filter sizes are labeled with brackets or lines. Convolution operations are shown as flat rectangles, feature maps are shown as prisms. The number below each filter represents the dilation rate (1 means no dilation).
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Figure 1. Fully convolutional architecture diagram (not to scale). Arrows show separate columns that all take the same input. At the end of the columns, the feature maps are merged (concatenated) together and passed to another series of dilated convolutions: the aggregator, which can aggregate the multiscale information collected by the columns [25]. The input image is I with C channels. The output single channel density map is D, and integrating over this map (summing the pixels) results in the final count. Initial filter sizes are labeled with brackets or lines. Convolution operations are shown as flat rectangles, feature maps are shown as prisms. The number below each filter represents the dilation rate (1 means no dilation).
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our paper follow this method of producing a density map via regression. This is particularly advantageous because a sufficiently accurate regressor can also locate the objects in the image via this method. However, the Lempitsky paper ignores the issue of perspective scaling and other scaling issues. The work of [27] introduces CNNs (convolutional neural networks) for the purposes of crowd counting, but performs regression on similarly scaled image patches.
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our paper follow this method of producing a density map via regression. This is particularly advantageous because a sufficiently accurate regressor can also locate the objects in the image via this method. However, the Lempitsky paper ignores the issue of perspective scaling and other scaling issues. The work of [27] introduces CNNs (convolutional neural networks) for the purposes of crowd counting, but performs regression on similarly scaled image patches.
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These issues are addressed by the work of [18]. Rubio et al. show that a fully convolutional neural network can be used to produce a supervised regressor that produces density maps as in [15]. They further demonstrate a method dubbed HydraCNN which essentially combines multiple convolutional networks that take in differently scaled image patches in order to incorporate multiscale, global information from the image. The premise of this method is that a single regressor will fail to accurately represent the difference in values of the features of an image caused by perspective shifts (scaling effects) [18].
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These issues are addressed by the work of [18]. Rubio et al. show that a fully convolutional neural network can be used to produce a supervised regressor that produces density maps as in [15]. They further demonstrate a method dubbed HydraCNN which essentially combines multiple convolutional networks that take in differently scaled image patches in order to incorporate multiscale, global information from the image. The premise of this method is that a single regressor will fail to accurately represent the difference in values of the features of an image caused by perspective shifts (scaling effects) [18].
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However, the architectures of both [18] and [27] are not fully convolutional due to requiring multiple image patches and, as discussed in [25], the experiments of [11, 17] and [9, 12, 16] leave it unclear as to whether rescaling patches
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However, the architectures of both [18] and [27] are not fully convolutional due to requiring multiple image patches and, as discussed in [25], the experiments of [11, 17] and [9, 12, 16] leave it unclear as to whether rescaling patches
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of the image is truly necessary in order to solve dense prediction problems via convolutional neural networks. Moreover, these approaches seem to saturate in performance at three columns, which means the network is extracting information from fewer scales. The work of [25] proposes the use of dilated convolutions as a simpler alternative that does not require sampling of rescaled image patches to provide global, scale-aware information to the network. A fully convolutional approach to multiscale counting has been proposed by [28], in which a multicolumn convolutional network gathers features of different scales by using convolutions of increasing kernel sizes from column to column instead of scaling image patches. Further, DeepLab has used dilated convolutions in multiple columns to extract scale information for segmentation [8]. We build on these approaches with our aggregator module as described in Section 3.1, which should allow for extracting information from more scales.
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of the image is truly necessary in order to solve dense prediction problems via convolutional neural networks. Moreover, these approaches seem to saturate in performance at three columns, which means the network is extracting information from fewer scales. The work of [25] proposes the use of dilated convolutions as a simpler alternative that does not require sampling of rescaled image patches to provide global, scale-aware information to the network. A fully convolutional approach to multiscale counting has been proposed by [28], in which a multicolumn convolutional network gathers features of different scales by using convolutions of increasing kernel sizes from column to column instead of scaling image patches. Further, DeepLab has used dilated convolutions in multiple columns to extract scale information for segmentation [8]. We build on these approaches with our aggregator module as described in Section 3.1, which should allow for extracting information from more scales.
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It should be noted that other methods of counting exist, including training a network to recognize deep object features via only providing the counts of the objects of interest in an image [21] and using CNNs (convolutional neural networks) along with boosting in order to improve the results
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It should be noted that other methods of counting exist, including training a network to recognize deep object features via only providing the counts of the objects of interest in an image [21] and using CNNs (convolutional neural networks) along with boosting in order to improve the results
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Figure 2. UCF sample results. Left: input counting image. Middle: Ground truth density map. Right: AMDCN prediction of density map on test image. The network never saw these images during training. All density maps are one channel only (i.e. grayscale), but are colored here for clarity.
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of regression for production of density maps [24]. In the same spirit, [4] combines deep and shallow convolutions within the same network, providing accurate counting of dense objects (e.g. the UCF50 crowd dataset).
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of regression for production of density maps [24]. In the same spirit, [4] combines deep and shallow convolutions within the same network, providing accurate counting of dense objects (e.g. the UCF50 crowd dataset).
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In this paper, however, we aim to apply the dilated convolution method of [25], which has shown to be able to incorporate multiscale perspective information without using multiple inputs or a complicated network architecture, as well as the multicolumn approach of [8, 28] to aggregate multiscale information for the counting problem.
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In this paper, however, we aim to apply the dilated convolution method of [25], which has shown to be able to incorporate multiscale perspective information without using multiple inputs or a complicated network architecture, as well as the multicolumn approach of [8, 28] to aggregate multiscale information for the counting problem.
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3. Method
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3.1. Dilated Convolutions for Multicolumn Networks
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3.1. Dilated Convolutions for Multicolumn Networks
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We propose the use of dilated convolutions as an attractive alternative to the architecture of the HydraCNN [18], which seems to saturate in performance at 3 or more columns. We refer to our proposed network as the aggregated multicolumn dilated convolution network1 , henceforth shortened as the AMDCN. The architecture of the AMDCN is inspired by the multicolumn counting network of [28]. Extracting features from multiple scales is a good idea when attempting to perform perspective-free counting and increasing the convolution kernel size across columns is an efficient method of doing so. However, the number of parameters increases exponentially as larger kernels are used in these columns to extract features at larger scales. Therefore, we propose using dilated convolutions rather than larger kernels.
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We propose the use of dilated convolutions as an attractive alternative to the architecture of the HydraCNN [18], which seems to saturate in performance at 3 or more columns. We refer to our proposed network as the aggregated multicolumn dilated convolution network1 , henceforth shortened as the AMDCN. The architecture of the AMDCN is inspired by the multicolumn counting network of [28]. Extracting features from multiple scales is a good idea when attempting to perform perspective-free counting and increasing the convolution kernel size across columns is an efficient method of doing so. However, the number of parameters increases exponentially as larger kernels are used in these columns to extract features at larger scales. Therefore, we propose using dilated convolutions rather than larger kernels.
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Dilated convolutions, as discussed in [25], allow for the exponential increase of the receptive field with a linear increase in the number of parameters with respect to each hidden layer.
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Dilated convolutions, as discussed in [25], allow for the exponential increase of the receptive field with a linear increase in the number of parameters with respect to each hidden layer.
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In a traditional 2D convolution, we define a real valued function F : Z 2 → R, an input Ωr = [−r, r] 2 ∈ Z 2 , and a filter function k : Ωr → R. In this case, a convolution
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operation as defined in [25] is given by
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operation as defined in [25] is given by
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$$(F*k)({\\bf p})=\\sum_{{\\bf s}+{\\bf t}={\\bf p}}F({\\bf s})k({\\bf t}).\\tag{1}$$
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(F*k)(\\mathbf{p}) = \\sum_{\\mathbf{s}+\\mathbf{t}=\\mathbf{p}} F(\\mathbf{s})k(\\mathbf{t}). \\quad (1)
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A dilated convolution is essentially a generalization of the traditional 2D convolution that allows the operation to skip some inputs. This enables an increase in the size of the filter (i.e. the size of the receptive field) without losing resolution. Formally, we define from [25] the dilated convolution as
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A dilated convolution is essentially a generalization of the traditional 2D convolution that allows the operation to skip some inputs. This enables an increase in the size of the filter (i.e. the size of the receptive field) without losing resolution. Formally, we define from [25] the dilated convolution as
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$$(F*_{l}k)({\\bf p})=\\sum_{{\\bf s}+l{\\bf t}={\\bf p}}F({\\bf s})k({\\bf t})\\tag{2}$$
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(F*_{l}k)(\\mathbf{p})=\\sum_{\\mathbf{s}+l\\mathbf{t}=\\mathbf{p}}F(\\mathbf{s})k(\\mathbf{t})\\tag{2}
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Using dilations to construct the aggregator in combination with the multicolumn idea will allow for the construction of a network with more than just 3 or 4 columns as in [28] and [8], because the aggregator should prevent the saturation of performance with increasing numbers of columns. Therefore the network will be able to extract useful features from more scales. We take advantage of dilations within the columns as well to provide large receptive fields with fewer parameters.
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Looking at more scales should allow for more accurate regression of the density map. However, because not all scales will be relevant, we extend the network beyond a simple 1 × 1 convolution after the merged columns. Instead, we construct a second part of the network, the aggregator, which sets our method apart from [28], [8], and other multicolumn networks. This aggregator is another series of dilated convolutions that should appropriately consolidate the multiscale information collected by the columns. This is a capability of dilated convolutions observed by [25]. While papers such as [28] and [8] have shown that multiple columns and dilated columns are useful in extracting multiscale information, we argue in this paper that the simple aggregator module built using dilated convolutions is able to effectively make use multiscale information from multiple columns. We show compelling evidence for these claims in Section 4.5.
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Looking at more scales should allow for more accurate regression of the density map. However, because not all scales will be relevant, we extend the network beyond a simple 1 × 1 convolution after the merged columns. Instead, we construct a second part of the network, the aggregator, which sets our method apart from [28], [8], and other multicolumn networks. This aggregator is another series of dilated convolutions that should appropriately consolidate the multiscale information collected by the columns. This is a capability of dilated convolutions observed by [25]. While papers such as [28] and [8] have shown that multiple columns and dilated columns are useful in extracting multiscale information, we argue in this paper that the simple aggregator module built using dilated convolutions is able to effectively make use multiscale information from multiple columns. We show compelling evidence for these claims in Section 4.5.
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The network as shown in Figure 1 contains 5 columns. Note that dilations allow us to use more columns for counting than [28] or [8]. Each column looks at a larger scale than the previous (the exact dilations can also be seen in Figure 1). There are 32 feature maps for each convolution, and all inputs are zero padded prior to each convolution in order to maintain the same data shape from input to output. That is, an image input to this network will result in a density map of the same dimensions. All activations in the specified network are ReLUs. Our input pixel values are floating point 32 bit values from 0 to 1. We center our inputs at 0 by subtracting the per channel mean from each channel. When
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The network as shown in Figure 1 contains 5 columns. Note that dilations allow us to use more columns for counting than [28] or [8]. Each column looks at a larger scale than the previous (the exact dilations can also be seen in Figure 1). There are 32 feature maps for each convolution, and all inputs are zero padded prior to each convolution in order to maintain the same data shape from input to output. That is, an image input to this network will result in a density map of the same dimensions. All activations in the specified network are ReLUs. Our input pixel values are floating point 32 bit values from 0 to 1. We center our inputs at 0 by subtracting the per channel mean from each channel. When
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1 Implementation available on https://github.com/ diptodip/counting.
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1 Implementation available on https://github.com/ diptodip/counting.
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training, we use a scaled mean absolute error for our loss function:
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$$L=\\frac{1}{n}\\sum_{i=1}^{n}|\\hat{y}_{i}-\\gamma y_{i}|\\tag{3}$$
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L = \\frac{1}{n} \\sum_{i=1}^{n} |\\hat{y}_i - \\gamma y_i| \\qquad (3)
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where γ is the scale factor, yˆi is the prediction, yi is the true value, and n is the number of pixels. We use a scaled mean absolute error because the target values are so small that it is numerically unstable to regress to these values. At testing time, when retrieving the output density map from the network, we scale the pixel values by γ −1 to obtain the correct value. This approach is more numerically stable and avoids having the network learn to output only zeros by weighting the nonzero values highly. For all our datasets, we set γ = 255.
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where γ is the scale factor, \\hat{y}_i is the prediction, y_i is the true value, and n is the number of pixels. We use a scaled mean absolute error because the target values are so small that it is numerically unstable to regress to these values. At testing time, when retrieving the output density map from the network, we scale the pixel values by γ^{-1} to obtain the correct value. This approach is more numerically stable and avoids having the network learn to output only zeros by weighting the nonzero values highly. For all our datasets, we set γ = 255. 
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3.2. Experiments
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3.2. Experiments
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We evaluated the performance of dilated convolutions against various counting methods on a variety of common counting datasets: UCF50 crowd data, TRANCOS traffic data [18], UCSD crowd data [5], and WorldExpo crowd data [27]. For each of these data sets, we used labels given by the corresponding density map for each image. An example of this is shown in Figure 2. We have performed experiments on the four different splits of the UCSD data as used in [18] and the split of the UCSD data as used in [28] (which we call the original split). We also evaluated the performance of our network on the TRANCOS traffic dataset [14]. We have also experimented with higher density datasets for crowd counting, namely WorldExpo and UCF.
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We evaluated the performance of dilated convolutions against various counting methods on a variety of common counting datasets: UCF50 crowd data, TRANCOS traffic data [18], UCSD crowd data [5], and WorldExpo crowd data [27]. For each of these data sets, we used labels given by the corresponding density map for each image. An example of this is shown in Figure 2. We have performed experiments on the four different splits of the UCSD data as used in [18] and the split of the UCSD data as used in [28] (which we call the original split). We also evaluated the performance of our network on the TRANCOS traffic dataset [14]. We have also experimented with higher density datasets for crowd counting, namely WorldExpo and UCF.
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We have observed that multicolumn dilations produce density maps (and therefore counts) that often have lower loss than those of HydraCNN [18] and [28]. We measure density map regression loss via a scaled mean absolute error loss during training. We compare accuracy of the counts via mean absolute error for the crowd datasets and the GAME metric in the TRANCOS dataset as explained in Section 3.2.2. Beyond the comparison to HydraCNN, we will also compare to other recent convolutional counting methods, especially those of [21], [24], and [4] where possible.
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We have observed that multicolumn dilations produce density maps (and therefore counts) that often have lower loss than those of HydraCNN [18] and [28]. We measure density map regression loss via a scaled mean absolute error loss during training. We compare accuracy of the counts via mean absolute error for the crowd datasets and the GAME metric in the TRANCOS dataset as explained in Section 3.2.2. Beyond the comparison to HydraCNN, we will also compare to other recent convolutional counting methods, especially those of [21], [24], and [4] where possible.
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For all datasets, we generally use patched input images and ground truth density maps produced by summing a Gaussian of a fixed size (σ) for each object for training. This size varies from dataset to dataset, but remains constant within a dataset with the exception of cases in which a perspective map is used. This is explained per dataset. All experiments were performed using Keras with the Adam optimizer [10]. The learning rates used are detailed per dataset. For testing, we also use patches that can either be directly pieced together or overlapped and averaged except in the case of UCF, for which we run our network on the full image.
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For all datasets, we generally use patched input images and ground truth density maps produced by summing a Gaussian of a fixed size (σ) for each object for training. This size varies from dataset to dataset, but remains constant within a dataset with the exception of cases in which a perspective map is used. This is explained per dataset. All experiments were performed using Keras with the Adam optimizer [10]. The learning rates used are detailed per dataset. For testing, we also use patches that can either be directly pieced together or overlapped and averaged except in the case of UCF, for which we run our network on the full image.
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Furthermore, we performed a set of experiments in which we varied the number of columns from 1 to 5 (simply by including or not including the columns as specified in Figure 1, starting with the smallest filter column and adding larger filter columns one by one). Essentially, the network is allowed to extract information at larger and larger scales in addition to the smaller scales as we include each column. We then performed the same set of experiments, varying the number of columns, but with the aggregator module removed. We perform these experiments on the original split of UCSD as specified in Section 3.2.3 and [5], the TRAN-COS dataset, and the WorldExpo dataset because these are relatively large and well defined datasets. We limit the number of epochs to 10 for all of these sets of experiments in order to control for the effect of learning time, and also compare all results using MAE for consistency. These experiments are key to determining the efficacy of the aggregator in effectively combining multiscale information and in providing evidence to support the use of multiple columns to extract multiscale information from images. We report the results of these ablation studies in Section 4.5.
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Furthermore, we performed a set of experiments in which we varied the number of columns from 1 to 5 (simply by including or not including the columns as specified in Figure 1, starting with the smallest filter column and adding larger filter columns one by one). Essentially, the network is allowed to extract information at larger and larger scales in addition to the smaller scales as we include each column. We then performed the same set of experiments, varying the number of columns, but with the aggregator module removed. We perform these experiments on the original split of UCSD as specified in Section 3.2.3 and [5], the TRAN-COS dataset, and the WorldExpo dataset because these are relatively large and well defined datasets. We limit the number of epochs to 10 for all of these sets of experiments in order to control for the effect of learning time, and also compare all results using MAE for consistency. These experiments are key to determining the efficacy of the aggregator in effectively combining multiscale information and in providing evidence to support the use of multiple columns to extract multiscale information from images. We report the results of these ablation studies in Section 4.5.
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3.2.1 UCF50 Crowd Counting
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UCF is a particularly challenging crowd counting dataset. There are only 50 images in the whole dataset and they are all of varying sizes and from different scenes. The number of people also varies between images from less than 100 to the thousands. The average image has on the order of 1000 people. The difficulty is due to the combination of the very low number of images in the dataset and the fact that the images are all of varying scenes, making high quality generalization crucial. Furthermore, perspective effects are particularly noticeable for many images in this dataset. Despite this, there is no perspective information available for this dataset.
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We take 1600 random patches of size 150 × 150 for the training. For testing, we do not densely scan the image as in [18] but instead test on the whole image. In order to standardize the image sizes, we pad each image out with zeros until all images are 1024 × 1024. We then suppress output in the regions where we added padding when testing. This provides a cleaner resulting density map for these large crowds. The ground truth density maps are produced by annotating each object with a Gaussian of σ = 15.
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We take 1600 random patches of size 150 × 150 for the training. For testing, we do not densely scan the image as in [18] but instead test on the whole image. In order to standardize the image sizes, we pad each image out with zeros until all images are 1024 × 1024. We then suppress output in the regions where we added padding when testing. This provides a cleaner resulting density map for these large crowds. The ground truth density maps are produced by annotating each object with a Gaussian of σ = 15.
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3.2.2 TRANCOS Traffic Counting
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3.2.2 TRANCOS Traffic Counting
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TRANCOS is a traffic counting dataset that comes with its own metric [14]. This metric is known as GAME, which stands for Grid Average Mean absolute Error. GAME splits a given density map into 4 L grids, or subarrays, and obtains a mean absolute error within each grid separately. The value of L is a parameter chosen by the user. These
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TRANCOS is a traffic counting dataset that comes with its own metric [14]. This metric is known as GAME, which stands for Grid Average Mean absolute Error. GAME splits a given density map into 4 L grids, or subarrays, and obtains a mean absolute error within each grid separately. The value of L is a parameter chosen by the user. These
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individual errors are summed to obtain the final error for a particular image. The intuition behind this metric is that it is desirable to penalize a density map whose overall count might match the ground truth, but whose shape does not match the ground truth [14]. More formally, we define
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individual errors are summed to obtain the final error for a particular image. The intuition behind this metric is that it is desirable to penalize a density map whose overall count might match the ground truth, but whose shape does not match the ground truth [14]. More formally, we define
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$$GAME(L)=\\frac{1}{N}\\cdot\\sum_{n=1}^{N}\\left(\\sum_{l=1}^{4^{L}}\\lvert e_{n}^{l}-t_{n}^{l}\\rvert\\right)\\tag{4}$$
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GAME(L) = \\frac{1}{N} \\cdot \\sum_{n=1}^{N} \\left( \\sum_{l=1}^{4^L} |e_n^l - t_n^l| \\right) \\tag{4}
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where N refers to the number of images, L is the level parameter for GAME, e l n is the predicted or estimated count in region l of image n and t l n is the ground truth count in region l of image n [14].
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where N refers to the number of images, L is the level parameter for GAME, e l n is the predicted or estimated count in region l of image n and t l n is the ground truth count in region l of image n [14].
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For training this dataset, we take 1600 randomly sampled patches of size 80 × 80. For testing this dataset, we take 80 × 80 non-overlapping patches which we can stitch back together into the full-sized 640 × 480 images. We trained the AMDCN network with density maps produced with a Gaussian of σ = 15 as specified in [18].
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3.2.3 UCSD Crowd Counting
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3.2.3 UCSD Crowd Counting
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The UCSD crowd counting dataset consists of frames of video of a sidewalk. There are relatively few people in view at any given time (approximately 25 on average). Furthermore, because the dataset comes from a video, there are many nearly identical images in the dataset. For this dataset, there have been two different ways to split the data into train and test sets. Therefore, we report results using both methods of splitting the data. The first method consists of four different splits: maximal, downscale, upscale, and minimal. Minimal is particularly challenging as the train set contains only 10 images. Moreover, upscale appears to be the easiest for the majority of methods [18]. The second method of splitting this data is much more succinct, leaving 1200 images in the testing set and 800 images in the training set [28]. This split comes from the original paper, so we call it the original split [5].
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The UCSD crowd counting dataset consists of frames of video of a sidewalk. There are relatively few people in view at any given time (approximately 25 on average). Furthermore, because the dataset comes from a video, there are many nearly identical images in the dataset. For this dataset, there have been two different ways to split the data into train and test sets. Therefore, we report results using both methods of splitting the data. The first method consists of four different splits: maximal, downscale, upscale, and minimal. Minimal is particularly challenging as the train set contains only 10 images. Moreover, upscale appears to be the easiest for the majority of methods [18]. The second method of splitting this data is much more succinct, leaving 1200 images in the testing set and 800 images in the training set [28]. This split comes from the original paper, so we call it the original split [5].
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For this dataset, each object is annotated with a 2D Gaussian of covariance Σ = 8 · 12×2. The ground truth map is produced by summing these. When we make use of the perspective maps provided, we divide Σ by the perspective map value at that pixel x, represented by M(x). The provided perspective map for UCSD contains both a horizontal and vertical direction so we take the square root of the provided combined value. For training, we take 1600 random 79 × 119 pixel patches and for testing, we split each test image up into quadrants (which have dimension 79 × 119). There are two different ways to split the dataset into training and testing sets. We have experimented on the split that gave [18] the best results as well as the split used in [28].
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For this dataset, each object is annotated with a 2D Gaussian of covariance Σ = 8 \n· 12x2. The ground truth map is produced by summing these. When we make use of the perspective maps provided, we divide Σ by the perspective map value at that pixel x, represented by M(x). The provided perspective map for UCSD contains both a horizontal and vertical direction so we take the square root of the provided combined value. For training, we take 1600 random 79 × 119 pixel patches and for testing, we split each test image up into quadrants (which have dimension 79 × 119). There are two different ways to split the dataset into training and testing sets. We have experimented on the split that gave [18] the best results as well as the split used in [28].
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First, we split the dataset into four separate groups of training and testing sets as used in [18] and originally defined by [20]. These groups are \"upscale,\" \"maximal,\"
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First, we split the dataset into four separate groups of training and testing sets as used in [18] and originally defined by [20]. These groups are \"upscale,\" \"maximal,\"
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\"minimal,\" and \"downscale.\" We see in Table 3 that the \"upscale\" split and \"downscale\" split give us state of the art results on counting for this dataset. For this experiment, we sampled 1600 random patches of size 119 × 79 pixels (width and height respectively) for the training set and split the test set images into 119 × 79 quadrants that could be reconstructed by piecing them together without overlap. We also added left-right flips of each image to our training data.
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\"minimal,\" and \"downscale.\" We see in Table 3 that the \"upscale\" split and \"downscale\" split give us state of the art results on counting for this dataset. For this experiment, we sampled 1600 random patches of size 119 × 79 pixels (width and height respectively) for the training set and split the test set images into 119 × 79 quadrants that could be reconstructed by piecing them together without overlap. We also added left-right flips of each image to our training data.
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We then evaluate the original split. For this experiment, we similarly sampled 1600 random patches of size 119×79 pixels (width and height respectively) for the training set and split the test set images into 119 × 79 quadrants that could be reconstructed by piecing them together without overlap.
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We then evaluate the original split. For this experiment, we similarly sampled 1600 random patches of size 119 × 79 pixels (width and height respectively) for the training set and split the test set images into 119 × 79 quadrants that could be reconstructed by piecing them together without overlap.
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3.2.4 WorldExpo '10 Crowd Counting
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The WorldExpo dataset [27] contains a larger number of people (approximately 50 on average, which is double that of UCSD) and contains images from multiple locations. Perspective effects are also much more noticeable in this dataset as compared to UCSD. These qualities of the dataset serve to increase the difficulty of counting. Like UCSD, the WorldExpo dataset was constructed from frames of video recordings of crowds. This means that, unlike UCF, this dataset contains a relatively large number of training and testing images. We experiment on this dataset with and without perspective information.
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The WorldExpo dataset [27] contains a larger number of people (approximately 50 on average, which is double that of UCSD) and contains images from multiple locations. Perspective effects are also much more noticeable in this dataset as compared to UCSD. These qualities of the dataset serve to increase the difficulty of counting. Like UCSD, the WorldExpo dataset was constructed from frames of video recordings of crowds. This means that, unlike UCF, this dataset contains a relatively large number of training and testing images. We experiment on this dataset with and without perspective information.
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Without perspective maps, we generate label density maps for this dataset in the same manner as previously described: a 2D Gaussian with σ = 15. We take 16000 150 × 150 randomly sampled patches for training. For testing, we densely scan the image, producing 150 × 150 patches at a stride of 100.
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When perspective maps are used, however, we follow the procedure as described in [27], which involves estimating a \"crowd density distribution kernel\" as the sum of two 2D Gaussians: a symmetric Gaussian for the head and an ellipsoid Gaussian for the body. These are scaled by the perspective map M provided, where M(x) gives the number of pixels that represents a meter at pixel x [27]. Note that the meaning of this perspective map is distinct from the meaning of the perspective map provided for the UCSD dataset. Using this information, the density contribution from a person with head pixel x is given by the following sum of normalized Gaussians:
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When perspective maps are used, however, we follow the procedure as described in [27], which involves estimating a \"crowd density distribution kernel\" as the sum of two 2D Gaussians: a symmetric Gaussian for the head and an ellipsoid Gaussian for the body. These are scaled by the perspective map M provided, where M(x) gives the number of pixels that represents a meter at pixel x [27]. Note that the meaning of this perspective map is distinct from the meaning of the perspective map provided for the UCSD dataset. Using this information, the density contribution from a person with head pixel x is given by the following sum of normalized Gaussians:
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$$D_{\\bf x}=\\frac{1}{||Z||}({\\cal N}_{h}({\\bf x},\\sigma_{h})+{\\cal N}_{b}({\\bf x}_{b},\\Sigma_{b}))\\qquad\\qquad(5)$$
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D_{\\bf x}=\\frac{1}{||Z||}\\left(\\mathcal{N}_{h}(\\bf x,\\sigma_{h})+\\mathcal{N}_{b}(\\bf x_{b},\\Sigma_{b})\\right)\\tag{5}
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where xb is the center of the body, which is 0.875 meters down from the head on average, and can be determined from the perspective map M and the head center x [27]. We sum these Gaussians for each person to pro-
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where $x_b$ is the center of the body, which is 0.875 meters down from the head on average, and can be determined from the perspective map $M$ and the head center x [27]. We sum these Gaussians for each person to produce
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Method      MAE   
AMDCN       290.82
Hydra2s [18]333.73
MCNN [28]   377.60
[27]        467.00
[23]        295.80
[3]         318.10
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Table 1. Mean absolute error of various methods on UCF crowds
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MethodMAE
AMDCN290.82
Hydra2s [18]333.73
MCNN [28]377.60
[27]467.00
[23]295.80
[3]318.10
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Table 1. Mean absolute error of various methods on UCF crowds
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duce the final density map. We set σ = 0.2M(x) for Nh and σx = 0.2M(x), σy = 0.5M(x) for Σb in Nb.
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duce the final density map. We set σ = 0.2M(x) for Nh and σx = 0.2M(x), σy = 0.5M(x) for Σb in Nb.
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4. Results
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4.1. UCF Crowd Counting
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4.1. UCF Crowd Counting
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The UCF dataset is particularly challenging due to the large number of people in the images, the variety of the scenes, as well as the low number of training images. We see in Figure 2 that because the UCF dataset has over 1000 people on average in each image, the shapes output by the network in the density map are not as well defined or separated as in the UCSD dataset.
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The UCF dataset is particularly challenging due to the large number of people in the images, the variety of the scenes, as well as the low number of training images. We see in Figure 2 that because the UCF dataset has over 1000 people on average in each image, the shapes output by the network in the density map are not as well defined or separated as in the UCSD dataset.
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We report a state of the art result on this dataset in Table 1, following the standard protocol of 5-fold cross validation. Our MAE on the dataset is 290.82, which is approximately 5 lower than the previous state of the art, HydraCNN [18]. This is particularly indicative of the power of an aggregated multicolumn dilation network. Despite not making use of perspective information, the AMDCN is still able to produce highly accurate density maps for UCF.
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We report a state of the art result on this dataset in Table 1, following the standard protocol of 5-fold cross validation. Our MAE on the dataset is 290.82, which is approximately 5 lower than the previous state of the art, HydraCNN [18]. This is particularly indicative of the power of an aggregated multicolumn dilation network. Despite not making use of perspective information, the AMDCN is still able to produce highly accurate density maps for UCF.
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4.2. TRANCOS Traffic Counting
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4.2. TRANCOS Traffic Counting
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Our network performs very well on the TRANCOS dataset. Indeed, as confirmed by the GAME score, AMDCN produces the most accurate count and shape combined as compared to other methods. Table 2 shows that we achieve state of the art results as measured by the GAME metric [14] across all levels.
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Our network performs very well on the TRANCOS dataset. Indeed, as confirmed by the GAME score, AMDCN produces the most accurate count and shape combined as compared to other methods. Table 2 shows that we achieve state of the art results as measured by the GAME metric [14] across all levels.
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4.3. UCSD Crowd Counting
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4.3. UCSD Crowd Counting
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Results are shown in Table 3 and Figure 3. We see that the \"original\" split as defined by the creators of the dataset in [5] and used in [28] gives us somewhat worse results for counting on this dataset. Results were consistent over multiple trainings. Again, including the perspective map does not seem to increase performance on this dataset. Despite this, we see in Table 3 and Figure 3 that the results are comparable to the state of the art. In fact, for two of the splits, our proposed network beats the state of the art. For the upscale split, the AMDCN is the state of the art by a large relative margin. This is compelling because it shows that accurate perspective-free counting can be achieved without
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Results are shown in Table 3 and Figure 3. We see that the \"original\" split as defined by the creators of the dataset in [5] and used in [28] gives us somewhat worse results for counting on this dataset. Results were consistent over multiple trainings. Again, including the perspective map does not seem to increase performance on this dataset. Despite this, we see in Table 3 and Figure 3 that the results are comparable to the state of the art. In fact, for two of the splits, our proposed network beats the state of the art. For the upscale split, the AMDCN is the state of the art by a large relative margin. This is compelling because it shows that accurate perspective-free counting can be achieved without
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Table 2. Mean absolute error of various methods on TRANCOS traffic
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Table 2. Mean absolute error of various methods on TRANCOS traffic
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creating image pyramids or requiring perspective maps as labels using the techniques presented by the AMDCN.
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4.4. WorldExpo '10 Crowd Counting
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4.4. WorldExpo '10 Crowd Counting
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Our network performs reasonably well on the more challenging WorldExpo dataset. While it does not beat the state of the art, our results are comparable. What is more, we do not need to use the perspective maps to obtain these results. As seen in Table 4, the AMDCN is capable of incorporating the perspective effects without scaling the Gaussians with perspective information. This shows that it is possible to achieve counting results that approach the state of the art with much simpler labels for the counting training data.
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Our network performs reasonably well on the more challenging WorldExpo dataset. While it does not beat the state of the art, our results are comparable. What is more, we do not need to use the perspective maps to obtain these results. As seen in Table 4, the AMDCN is capable of incorporating the perspective effects without scaling the Gaussians with perspective information. This shows that it is possible to achieve counting results that approach the state of the art with much simpler labels for the counting training data.
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4.5. Ablation Studies
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4.5. Ablation Studies
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We report the results of the ablation studies in Figure 4. We note from these plots that while there is variation in performance, a few trends stand out. Most importantly, the lowest errors are consistently with a combination of a larger number of columns and including the aggregator module. Notably for the TRANCOS dataset, including the aggregator consistently improves performance. Generally, the aggregator tends to decrease the variance in performance of the network. Some of the variance that we see in the plots can be explained by: (1) for lower numbers of columns, including an aggregator is not as likely to help as there is not much separation of multiscale information across columns and (2) for the UCSD dataset, there is less of a perspective effect than TRANCOS and WorldExpo so a simpler network is more likely to perform comparably to a larger network. These results verify the notion that using more columns increases accuracy, and also support our justification for the use of the aggregator module.
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We report the results of the ablation studies in Figure 4. We note from these plots that while there is variation in performance, a few trends stand out. Most importantly, the lowest errors are consistently with a combination of a larger number of columns and including the aggregator module. Notably for the TRANCOS dataset, including the aggregator consistently improves performance. Generally, the aggregator tends to decrease the variance in performance of the network. Some of the variance that we see in the plots can be explained by: (1) for lower numbers of columns, including an aggregator is not as likely to help as there is not much separation of multiscale information across columns and (2) for the UCSD dataset, there is less of a perspective effect than TRANCOS and WorldExpo so a simpler network is more likely to perform comparably to a larger network. These results verify the notion that using more columns increases accuracy, and also support our justification for the use of the aggregator module.
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+                "/page/6/Figure/0": 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Figure 3. UCSD crowd counting dataset. Both plots show comparisons of predicted and ground truth counts over time. While AMDCN does not beat the state of the art on the original split, the predictions still follow the true counts reasonably. The jump in the original split is due to that testing set including multiple scenes of highly varying counts.
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(a) UCSD upscale split. (b) UCSD original split.
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Method                                 maximal  downscale  upscale  minimal  original  
AMDCN (without perspective information)1.63     1.43       0.63     1.71     1.74      
AMDCN (with perspective information)   1.60     1.24       1.37     1.59     1.72      
[18] (with perspective information)    1.65     1.79       1.11     1.50     -         
[18] (without perspective information) 2.22     1.93       1.37     2.38     -         
[15]                                   1.70     1.28       1.59     2.02     -         
[13]                                   1.70     2.16       1.61     2.20     -         
[19]                                   1.43     1.30       1.59     1.62     -         
[2]                                    1.24     1.31       1.69     1.49     -         
[27]                                   1.70     1.26       1.59     1.52     1.60      
[28]                                   -        -          -        -        1.07      
[1, 28]                                -        -          -        -        2.16      
[7]                                    -        -          -        -        2.25      
[5]                                    -        -          -        -        2.24      
[6]                                    -        -          -        -        2.07      
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Figure 3. UCSD crowd counting dataset. Both plots show comparisons of predicted and ground truth counts over time. While AMDCN does not beat the state of the art on the original split, the predictions still follow the true counts reasonably. The jump in the original split is due to that testing set including multiple scenes of highly varying counts.
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Methodmaximaldownscaleupscaleminimaloriginal
AMDCN (without perspective information)1.631.430.631.711.74
AMDCN (with perspective information)1.601.241.371.591.72
[18] (with perspective information)1.651.791.111.50-
[18] (without perspective information)2.221.931.372.38-
[15]1.701.281.592.02-
[13]1.702.161.612.20-
[19]1.431.301.591.62-
[2]1.241.311.691.49-
[27]1.701.261.591.521.60
[28]----1.07
[1,28]----2.16
[7]----2.25
[5]----2.24
[6]----2.07
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Table 3. Mean absolute error of various methods on UCSD crowds
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Table 3. Mean absolute error of various methods on UCSD crowds
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5. Conclusion
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5.1. Summary
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5.1. Summary
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We have proposed the use of aggregated multicolumn dilated convolutions, the AMDCN, as an alternative to the HydraCNN [18] or multicolumn CNN [28] for the vision task of counting objects in images. Inspired by the multicolumn approach to multiscale problems, we also employ dilations to increase the receptive field of our columns. We
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We have proposed the use of aggregated multicolumn dilated convolutions, the AMDCN, as an alternative to the HydraCNN [18] or multicolumn CNN [28] for the vision task of counting objects in images. Inspired by the multicolumn approach to multiscale problems, we also employ dilations to increase the receptive field of our columns. We
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then aggregate this multiscale information using another series of dilated convolutions to enable a wide network and detect features at more scales. This method takes advantage of the ability of dilated convolutions to provide exponentially increasing receptive fields. We have performed experiments on the challenging UCF crowd counting dataset, the TRANCOS traffic dataset, multiple splits of the UCSD crowd counting dataset, and the WorldExpo crowd counting dataset.
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+                "/page/7/Figure/0": 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Figure 4. Ablation studies on various datasets in which the number of columns is varied and the aggregator is included or not included. The results generally support the use of more columns and an aggregator module.
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Figure 4. Ablation studies on various datasets in which the number of columns is varied and the aggregator is included or not included. The results generally support the use of more columns and an aggregator module.
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Method                             MAE  
AMDCN (without perspective infor   16.6 
mation)                             
AMDCN (with perspective informa    14.9 
tion)                               
LBP+RR [28] (with perspective infor31.0 
mation)                             
MCNN [28] (with perspective informa11.6 
tion)                               
[27] (with perspective information)12.9 
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AMDCN (without perspective information)16.6
AMDCN (with perspective information)14.9
LBP+RR [28] (with perspective information)31.0
MCNN [28] (with perspective information)11.6
[27] (with perspective information)12.9
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Table 4. Mean absolute error of various methods on WorldExpo crowds
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Table 4. Mean absolute error of various methods on WorldExpo crowds
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We obtain superior or comparable results in most of these datasets. The AMDCN is capable of outperforming these approaches completely especially when perspective information is not provided, as in UCF and TRANCOS. These results show that the AMDCN performs surprisingly well and is also robust to scale effects. Further, our ablation study of removing the aggregator network shows that using more columns and an aggregator provides the best accuracy for counting — especially so when there is no perspective information.
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5.2. Future Work
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5.2. Future Work
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In addition to an analysis of performance on counting, a density regressor can also be used to locate objects in the image. As mentioned previously, if the regressor is accurate and precise enough, the resulting density map can be used to locate the objects in the image. We expect that in order to do this, one must regress each object to a single point rather than a region specified by a Gaussian. Perhaps this might be
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Indeed, the method of applying dilated filters to a multicolumn convolutional network in order to enable extracting features of a large number of scales can be applied to various other dense prediction tasks, such as object segmentation at multiple scales or single image depth map prediction. Though we have only conducted experiments on counting and used 5 columns, the architecture presented can be extended and adapted to a variety of tasks that require information at multiple scales.
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Acknowledgment
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    In deep learning, models typically reuse the same parameters for all inputs. Mixture of Experts (MoE) models defy this and instead select different parameters for each incoming example. The result is a sparsely-activated model—with an outrageous number of parameters—but a constant computational cost. However, despite several notable successes of MoE, widespread adoption has been hindered by complexity, communication costs, and training instability. We address these with the introduction of the Switch Transformer. We simplify the MoE routing algorithm and design intuitive improved models with reduced communication and computational costs. Our proposed training techniques mitigate the instabilities, and we show large sparse models may be trained, for the first time, with lower precision (bfloat16) formats. We design models based off T5-Base and T5-Large (Raffel et al., 2019) to obtain up to 7x increases in pre-training speed with the same computational resources. These improvements extend into multilingual settings where we measure gains over the mT5-Base version across all 101 languages. Finally, we advance the current scale of language models by pre-training up to trillion parameter models on the \"Colossal Clean Crawled Corpus\", and achieve a 4x speedup over the T5-XXL model.12
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    In deep learning, models typically reuse the same parameters for all inputs. Mixture of Experts (MoE) models defy this and instead select different parameters for each incoming example. The result is a sparsely-activated model—with an outrageous number of parameters—but a constant computational cost. However, despite several notable successes of MoE, widespread adoption has been hindered by complexity, communication costs, and training instability. We address these with the introduction of the Switch Transformer. We simplify the MoE routing algorithm and design intuitive improved models with reduced communication and computational costs. Our proposed training techniques mitigate the instabilities, and we show large sparse models may be trained, for the first time, with lower precision (bfloat16) formats. We design models based off T5-Base and T5-Large (Raffel et al., 2019) to obtain up to 7x increases in pre-training speed with the same computational resources. These improvements extend into multilingual settings where we measure gains over the mT5-Base version across all 101 languages. Finally, we advance the current scale of language models by pre-training up to trillion parameter models on the \"Colossal Clean Crawled Corpus\", and achieve a 4x speedup over the T5-XXL model.12
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    Keywords: mixture-of-experts, natural language processing, sparsity, large-scale machine learning, distributed computing
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    ∗. Equal contribution.
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    ©2022 William Fedus, Barret Zoph and Noam Shazeer.
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    1. JAX code for Switch Transformer and all model checkpoints are available at https://github.com/ google-research/t5x
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    ∗. Equal contribution.
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    ©2022 William Fedus, Barret Zoph and Noam Shazeer.
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    License: CC-BY 4.0, see https://creativecommons.org/licenses/by/4.0/. Attribution requirements are provided at http://jmlr.org/papers/v23/21-0998.html.
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    Contents
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    Contents
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    1  Introduction       3  
    2  Switch Transformer 4  
     2.1               Simplifying Sparse Routing                               5  
     2.2               Efficient Sparse Routing                                 6  
     2.3               Putting It All Together: The Switch Transformer          8  
     2.4               Improved Training and Fine-Tuning Techniques             8  
    3   Scaling Properties                                       11 
     3.1               Scaling Results on a Step-Basis                          12 
     3.2               Scaling Results on a Time-Basis                          13 
     3.3               Scaling Versus a Larger Dense Model                      13 
    4   Downstream Results                                       14 
     4.1               Fine-Tuning                                              14 
     4.2               Distillation                                             16 
     4.3               Multilingual Learning                                    17 
    5   Designing Models with Data, Model, and Expert-Parallelism18 
     5.1               Data Parallelism                                         20 
     5.2               Model Parallelism                                        20 
     5.3               Model and Data Parallelism                               21 
     5.4               Expert and Data Parallelism                              22 
     5.5               Expert, Model and Data Parallelism                       22 
     5.6               Towards Trillion Parameter Models                        22 
    6   Related Work                                             24 
    7  Discussion         25 
    8  Future Work        26 
    9  Conclusion         27 
    A   Switch for Attention                                     27 
    B   Preventing Token Dropping with No-Token-Left-Behind      29 
    C   Encouraging Exploration Across Experts                   29 
    D   Switch Transformers in Lower Compute Regimes             29 
    E   Relation of Upstream to Downstream Model Performance     32 
    F   Pseudo Code for Switch Transformers                      33 
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    1Introduction3
    2Switch Transformer
    2.1 Simplifying Sparse Routing
    2.2 Efficient Sparse Routing
    2.3 Putting It All Together: The Switch Transformer
    2.4 Improved Training and Fine-Tuning Techniques    		4
    5
    6
    8
    3Scaling Properties
    3.1 Scaling Results on a Step-Basis
    3.2 Scaling Results on a Time-Basis
    3.3 Scaling Versus a Larger Dense Model    		11
    12
    13
    13
    4Downstream Results
    4.1 Fine-Tuning
    4.2 Distillation
    4.3 Multilingual Learning    		14
    14
    16
    17
    5Designing Models with Data, Model, and Expert-Parallelism
    5.1 Data Parallelism
    5.2 Model Parallelism
    5.3 Model and Data Parallelism
    5.4 Expert and Data Parallelism
    5.5 Expert, Model and Data Parallelism
    5.6 Towards Trillion Parameter Models    		18
    20
    21
    22
    22
    22
    24
    6Related Work24
    7Discussion25
    8Future Work26
    9Conclusion27
    ASwitch for Attention27
    BPreventing Token Dropping with No-Token-Left-Behind29
    CEncouraging Exploration Across Experts29
    DSwitch Transformers in Lower Compute Regimes29
    ERelation of Upstream to Downstream Model Performance32
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    1. Introduction
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    Large scale training has been an effective path towards flexible and powerful neural language models (Radford et al., 2018; Kaplan et al., 2020; Brown et al., 2020). Simple architectures backed by a generous computational budget, data set size and parameter count—surpass more complicated algorithms (Sutton, 2019). An approach followed in Radford et al. (2018); Raffel et al. (2019); Brown et al. (2020) expands the model size of a densely-activated Transformer (Vaswani et al., 2017). While effective, it is also extremely computationally intensive (Strubell et al., 2019). Inspired by the success of model scale, but seeking greater computational efficiency, we instead propose a sparsely-activated expert model: the Switch Transformer. In our case the sparsity comes from activating a subset of the neural network weights for each incoming example.
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    Figure 1: Scaling and sample efficiency of Switch Transformers. Left Plot: Scaling properties for increasingly sparse (more experts) Switch Transformers. Right Plot: Negative log perplexity comparing Switch Transformers to T5 (Raffel et al., 2019) models using the same compute budget.
    ",
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+              "html": "Introduction",
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    Sparse training is an active area of research and engineering (Gray et al., 2017; Gale et al., 2020), but as of today, machine learning libraries and hardware accelerators still cater to dense matrix multiplications. To have an efficient sparse algorithm, we start with the Mixture-of-Expert (MoE) paradigm (Jacobs et al., 1991; Jordan and Jacobs, 1994; Shazeer et al., 2017), and simplify it to yield training stability and computational benefits. MoE models have had notable successes in machine translation (Shazeer et al., 2017, 2018; Lepikhin et al., 2020), however, widespread adoption is hindered by complexity, communication costs, and training instabilities.
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    We address these issues, and then go beyond translation, to find that these class of algorithms are broadly valuable in natural language. We measure superior scaling on a diverse set of natural language tasks and across three regimes in NLP: pre-training, finetuning and multi-task training. While this work focuses on scale, we also show that the Switch Transformer architecture not only excels in the domain of supercomputers, but is
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  •  The Switch Transformer architecture, which simplifies and improves over Mixture of Experts.
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  •  Scaling properties and a benchmark against the strongly tuned T5 model (Raffel et al., 2019) where we measure 7x+ pre-training speedups while still using the same FLOPS per token. We further show the improvements hold even with limited computational resources, using as few as two experts.
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  •  Successful distillation of sparse pre-trained and specialized fine-tuned models into small dense models. We reduce the model size by up to 99% while preserving 30% of the quality gains of the large sparse teacher.
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    2.1 Simplifying Sparse Routing
    2.2 Efficient Sparse Routing
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  •  Improved pre-training and fine-tuning techniques: (1) selective precision training that enables training with lower bfloat16 precision (2) an initialization scheme that allows for scaling to a larger number of experts and (3) increased expert regularization that improves sparse model fine-tuning and multi-task training.
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  •  A measurement of the pre-training benefits on multilingual data where we find a universal improvement across all 101 languages and with 91% of languages benefiting from 4x+ speedups over the mT5 baseline (Xue et al., 2020).
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  •  An increase in the scale of neural language models achieved by efficiently combining data, model, and expert-parallelism to create models with up to a trillion parameters. These models improve the pre-training speed of a strongly tuned T5-XXL baseline by 4x.
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    3.2 Scaling Results on a Time-Basis
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    2. Switch Transformer
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    The guiding design principle for Switch Transformers is to maximize the parameter count of a Transformer model (Vaswani et al., 2017) in a simple and computationally efficient way. The benefit of scale was exhaustively studied in Kaplan et al. (2020) which uncovered powerlaw scaling with model size, data set size and computational budget. Importantly, this work advocates training large models on relatively small amounts of data as the computationally optimal approach.
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    Heeding these results, we investigate a fourth axis: increase the parameter count while keeping the floating point operations (FLOPs) per example constant. Our hypothesis is that the parameter count, independent of total computation performed, is a separately important axis on which to scale. We achieve this by designing a sparsely activated model that efficiently uses hardware designed for dense matrix multiplications such as GPUs and TPUs. Our work here focuses on TPU architectures, but these class of models may be similarly trained on GPU clusters. In our distributed training setup, our sparsely activated layers split unique weights on different devices. Therefore, the weights of the model increase with the number of devices, all while maintaining a manageable memory and computational footprint on each device.
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  • Figure 2: Illustration of a Switch Transformer encoder block. We replace the dense feed forward network (FFN) layer present in the Transformer with a sparse Switch FFN layer (light blue). The layer operates independently on the tokens in the sequence. We diagram two tokens (x1 = \"More\" and x2 = \"Parameters\" below) being routed (solid lines) across four FFN experts, where the router independently routes each token. The switch FFN layer returns the output of the selected FFN multiplied by the router gate value (dotted-line).
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    2.1 Simplifying Sparse Routing
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    Mixture of Expert Routing. Shazeer et al. (2017) proposed a natural language Mixtureof-Experts (MoE) layer which takes as an input a token representation x and then routes this to the best determined top-k experts, selected from a set {Ei(x)} N i=1 of N experts. The router variable Wr produces logits h(x) = Wr · x which are normalized via a softmax distribution over the available N experts at that layer. The gate-value for expert i is given by,
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    4.1 Fine-Tuning
    4.2 Distillation
    4.3 Multilingual Learning",
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    5.1 Data Parallelism
    5.2 Model Parallelism
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    5.4 Expert and Data Parallelism
    5.5 Expert, Model and Data Parallelism
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    1. Introduction
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    Large scale training has been an effective path towards flexible and powerful neural language models (Radford et al., 2018; Kaplan et al., 2020; Brown et al., 2020). Simple architectures backed by a generous computational budget, data set size and parameter count—surpass more complicated algorithms (Sutton, 2019). An approach followed in Radford et al. (2018); Raffel et al. (2019); Brown et al. (2020) expands the model size of a densely-activated Transformer (Vaswani et al., 2017). While effective, it is also extremely computationally intensive (Strubell et al., 2019). Inspired by the success of model scale, but seeking greater computational efficiency, we instead propose a sparsely-activated expert model: the Switch Transformer. In our case the sparsity comes from activating a subset of the neural network weights for each incoming example.
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    Figure 1: Scaling and sample efficiency of Switch Transformers. Left Plot: Scaling properties for increasingly sparse (more experts) Switch Transformers. Right Plot: Negative log perplexity comparing Switch Transformers to T5 (Raffel et al., 2019) models using the same compute budget.
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    Sparse training is an active area of research and engineering (Gray et al., 2017; Gale et al., 2020), but as of today, machine learning libraries and hardware accelerators still cater to dense matrix multiplications. To have an efficient sparse algorithm, we start with the Mixture-of-Expert (MoE) paradigm (Jacobs et al., 1991; Jordan and Jacobs, 1994; Shazeer et al., 2017), and simplify it to yield training stability and computational benefits. MoE models have had notable successes in machine translation (Shazeer et al., 2017, 2018; Lepikhin et al., 2020), however, widespread adoption is hindered by complexity, communication costs, and training instabilities.
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    We address these issues, and then go beyond translation, to find that these class of algorithms are broadly valuable in natural language. We measure superior scaling on a diverse set of natural language tasks and across three regimes in NLP: pre-training, finetuning and multi-task training. While this work focuses on scale, we also show that the Switch Transformer architecture not only excels in the domain of supercomputers, but is
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    beneficial even with only a few computational cores. Further, our large sparse models can be distilled (Hinton et al., 2015) into small dense versions while preserving 30% of the sparse model quality gain. Our contributions are the following:
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  •  The Switch Transformer architecture, which simplifies and improves over Mixture of Experts.
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  •  Scaling properties and a benchmark against the strongly tuned T5 model (Raffel et al., 2019) where we measure 7x+ pre-training speedups while still using the same FLOPS per token. We further show the improvements hold even with limited computational resources, using as few as two experts.
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  •  Successful distillation of sparse pre-trained and specialized fine-tuned models into small dense models. We reduce the model size by up to 99% while preserving 30% of the quality gains of the large sparse teacher.
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  •  Improved pre-training and fine-tuning techniques: (1) selective precision training that enables training with lower bfloat16 precision (2) an initialization scheme that allows for scaling to a larger number of experts and (3) increased expert regularization that improves sparse model fine-tuning and multi-task training.
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  •  A measurement of the pre-training benefits on multilingual data where we find a universal improvement across all 101 languages and with 91% of languages benefiting from 4x+ speedups over the mT5 baseline (Xue et al., 2020).
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  •  An increase in the scale of neural language models achieved by efficiently combining data, model, and expert-parallelism to create models with up to a trillion parameters. These models improve the pre-training speed of a strongly tuned T5-XXL baseline by 4x.
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    2. Switch Transformer
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    The guiding design principle for Switch Transformers is to maximize the parameter count of a Transformer model (Vaswani et al., 2017) in a simple and computationally efficient way. The benefit of scale was exhaustively studied in Kaplan et al. (2020) which uncovered powerlaw scaling with model size, data set size and computational budget. Importantly, this work advocates training large models on relatively small amounts of data as the computationally optimal approach.
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    Heeding these results, we investigate a fourth axis: increase the parameter count while keeping the floating point operations (FLOPs) per example constant. Our hypothesis is that the parameter count, independent of total computation performed, is a separately important axis on which to scale. We achieve this by designing a sparsely activated model that efficiently uses hardware designed for dense matrix multiplications such as GPUs and TPUs. Our work here focuses on TPU architectures, but these class of models may be similarly trained on GPU clusters. In our distributed training setup, our sparsely activated layers split unique weights on different devices. Therefore, the weights of the model increase with the number of devices, all while maintaining a manageable memory and computational footprint on each device.
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+              }
+            },
+            {
+              "id": "/page/4/Caption/335",
+              "block_type": "Caption",
+              "html": "
    Figure 2: Illustration of a Switch Transformer encoder block. We replace the dense feed forward network (FFN) layer present in the Transformer with a sparse Switch FFN layer (light blue). The layer operates independently on the tokens in the sequence. We diagram two tokens (x1 = \"More\" and x2 = \"Parameters\" below) being routed (solid lines) across four FFN experts, where the router independently routes each token. The switch FFN layer returns the output of the selected FFN multiplied by the router gate value (dotted-line).
    ",
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    2.1 Simplifying Sparse Routing
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    Mixture of Expert Routing. Shazeer et al. (2017) proposed a natural language Mixtureof-Experts (MoE) layer which takes as an input a token representation x and then routes this to the best determined top-k experts, selected from a set {Ei(x)}N of N experts. The router variable Wr produces logits h(x) = Wr \n· x which are normalized via a softmax distribution over the available N experts at that layer. The gate-value for expert i is given by,
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    p_{i}(x) = \\frac{e^{h(x)_{i}}}{\\\\\\sum_{j}^{N}e^{h(x)_{j}}}.\\tag{1}
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    The top-k gate values are selected for routing the token x. If T is the set of selected top-k indices then the output computation of the layer is the linearly weighted combination of each expert's computation on the token by the gate value,
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    y = \\sum_{i \\in T} p_i(x) E_i(x) \\quad (2)
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    Switch Routing: Rethinking Mixture-of-Experts. Shazeer et al. (2017) conjectured that routing to k > 1 experts was necessary in order to have non-trivial gradients to the routing functions. The authors intuited that learning to route would not work without the ability to compare at least two experts. Ramachandran and Le (2018) went further to
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    study the top-k decision and found that higher k-values in lower layers in the model were important for models with many routing layers. Contrary to these ideas, we instead use a simplified strategy where we route to only a single expert. We show this simplification preserves model quality, reduces routing computation and performs better. This k = 1 routing strategy is later referred to as a Switch layer. Note that for both MoE and Switch Routing, the gate value pi(x) in Equation 2 permits differentiability of the router.
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    The benefits for the Switch layer are three-fold: (1) The router computation is reduced as we are only routing a token to a single expert. (2) The batch size (expert capacity) of each expert can be at least halved since each token is only being routed to a single expert.3 (3) The routing implementation is simplified and communication costs are reduced. Figure 3 shows an example of routing with different expert capacity factors.
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  • Figure 3: Illustration of token routing dynamics. Each expert processes a fixed batch-size of tokens modulated by the capacity factor. Each token is routed to the expert with the highest router probability, but each expert has a fixed batch size of (total tokens / num experts) × capacity factor. If the tokens are unevenly dispatched then certain experts will overflow (denoted by dotted red lines), resulting in these tokens not being processed by this layer. A larger capacity factor alleviates this overflow issue, but also increases computation and communication costs (depicted by padded white/empty slots).
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    2.2 Efficient Sparse Routing
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    We use Mesh-Tensorflow (MTF) (Shazeer et al., 2018) which is a library, with similar semantics and API to Tensorflow (Abadi et al., 2016) that facilitates efficient distributed data and model parallel architectures. It does so by abstracting the physical set of cores to a logical mesh of processors. Tensors and computations may then be sharded per named dimensions, facilitating easy partitioning of models across dimensions. We design our model with TPUs in mind, which require statically declared sizes. Below we describe our distributed Switch Transformer implementation.
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    3. See Section 2.2 for a technical description.
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    Distributed Switch Implementation. All of our tensor shapes are statically determined at compilation time, but our computation is dynamic due to the routing decisions at training and inference. Because of this, one important technical consideration is how to set the expert capacity. The expert capacity—the number of tokens each expert computes—is set by evenly dividing the number of tokens in the batch across the number of experts, and then further expanding by a capacity factor,
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    \\text{expert capacity} = \\left(\\frac{\\text{tokens per batch}}{\\text{number of experts}}\\right) \\times \\text{capacity factor}. \\tag{3}
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    A capacity factor greater than 1.0 creates additional buffer to accommodate for when tokens are not perfectly balanced across experts. If too many tokens are routed to an expert (referred to later as dropped tokens), computation is skipped and the token representation is passed directly to the next layer through the residual connection. Increasing the expert capacity is not without drawbacks, however, since high values will result in wasted computation and memory. This trade-off is explained in Figure 3. Empirically we find ensuring lower rates of dropped tokens are important for the scaling of sparse expert-models. Throughout our experiments we didn't notice any dependency on the number of experts for the number of tokens dropped (typically < 1%). Using the auxiliary load balancing loss (next section) with a high enough coefficient ensured good load balancing. We study the impact that these design decisions have on model quality and speed in Table 1.
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    A Differentiable Load Balancing Loss. To encourage a balanced load across experts we add an auxiliary loss (Shazeer et al., 2017, 2018; Lepikhin et al., 2020). As in Shazeer et al. (2018); Lepikhin et al. (2020), Switch Transformers simplifies the original design in Shazeer et al. (2017) which had separate load-balancing and importance-weighting losses. For each Switch layer, this auxiliary loss is added to the total model loss during training. Given N experts indexed by i = 1 to N and a batch B with T tokens, the auxiliary loss is computed as the scaled dot-product between vectors f and P,
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    \\text{loss} = \\alpha \\cdot N \\cdot \\sum_{i=1}^{N} f_i \\cdot P_i \\tag{4}
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    where fi is the fraction of tokens dispatched to expert i,
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    f_{i} = \\frac{1}{T} \\sum_{x \\in \\mathcal{B}} \\mathbb{1} \\left\\{ \\operatorname*{argmax} p(x) = i \\right\\} \\tag{5}
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    and Pi is the fraction of the router probability allocated for expert i, 2
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    P_{i}=
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    Since we seek uniform routing of the batch of tokens across the N experts, we desire both vectors to have values of 1/N. The auxiliary loss of Equation 4 encourages uniform routing since it is minimized under a uniform distribution. The objective can also be differentiated as
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    2. A potential source of confusion: pi(x) is the probability of routing token x to expert i. Pi is the probability fraction to expert i across all tokens in the batch B.
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    the P-vector is differentiable, but the f-vector is not. The final loss is multiplied by expert count N to keep the loss constant as the number of experts varies since under uniform routing PN i=1(fi · Pi) = PN i=1( 1 N · 1 N ) = 1 N . Finally, a hyper-parameter α is a multiplicative coefficient for these auxiliary losses; throughout this work we use an α = 10−2 which was sufficiently large to ensure load balancing while small enough to not to overwhelm the primary cross-entropy objective. We swept hyper-parameter ranges of α from 10−1 to 10−5 in powers of 10 and found 10−2 balanced load quickly without interfering with training loss.
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    2.3 Putting It All Together: The Switch Transformer
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    Our first test of the Switch Transformer starts with pre-training on the \"Colossal Clean Crawled Corpus\" (C4), introduced in (Raffel et al., 2019). For our pre-training objective, we use a masked language modeling task (Taylor, 1953; Fedus et al., 2018; Devlin et al., 2018) where the model is trained to predict missing tokens. In our pre-training setting, as determined in Raffel et al. (2019) to be optimal, we drop out 15% of tokens and then replace the masked sequence with a single sentinel token. To compare our models, we record the negative log perplexity.4 Throughout all tables in the paper, ↑ indicates that a higher value for that metric is better and vice-versa for ↓. A comparison of all the models studied in this work are in Table 9.
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    A head-to-head comparison of the Switch Transformer and the MoE Transformer is presented in Table 1. Our Switch Transformer model is FLOP-matched to 'T5-Base' (Raffel et al., 2019) (same amount of computation per token is applied). The MoE Transformer, using top-2 routing, has two experts which each apply a separate FFN to each token and thus its FLOPS are larger. All models were trained for the same number of steps on identical hardware. Note that the MoE model going from capacity factor 2.0 to 1.25 actually slows down (840 to 790) in the above experiment setup, which is unexpected.5
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    We highlight three key findings from Table 1: (1) Switch Transformers outperform both carefully tuned dense models and MoE Transformers on a speed-quality basis. For a fixed amount of computation and wall-clock time, Switch Transformers achieve the best result. (2) The Switch Transformer has a smaller computational footprint than the MoE counterpart. If we increase its size to match the training speed of the MoE Transformer, we find this outperforms all MoE and Dense models on a per step basis as well. (3) Switch Transformers perform better at lower capacity factors (1.0, 1.25). Smaller expert capacities are indicative of the scenario in the large model regime where model memory is very scarce and the capacity factor will want to be made as small as possible.
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    2.4 Improved Training and Fine-Tuning Techniques
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    Sparse expert models may introduce training difficulties over a vanilla Transformer. Instability can result because of the hard-switching (routing) decisions at each of these layers. Further, low precision formats like bfloat16 (Wang and Kanwar, 2019) can exacerbate issues
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    4. We use log base-e for this metric so the units are nats.
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    5. Note that speed measurements are both a function of the algorithm and the implementation details. Switch Transformer reduces the necessary computation relative to MoE (algorithm), but the final speed differences are impacted by low-level optimizations (implementation).
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    ModelCapacity
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    T5-Base—-1.731Not achieved†1600
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  • Table 1: Benchmarking Switch versus MoE. Head-to-head comparison measuring per step and per time benefits of the Switch Transformer over the MoE Transformer and T5 dense baselines. We measure quality by the negative log perplexity and the time to reach an arbitrary chosen quality threshold of Neg. Log Perp.=-1.50. All MoE and Switch Transformer models use 128 experts, with experts at every other feed-forward layer. For Switch-Base+, we increase the model size until it matches the speed of the MoE model by increasing the model hidden-size from 768 to 896 and the number of heads from 14 to 16. All models are trained with the same amount of computation (32 cores) and on the same hardware (TPUv3). Further note that all our models required pre-training beyond 100k steps to achieve our level threshold of -1.50. † T5-Base did not achieve this negative log perplexity in the 100k steps the models were trained.
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    The top-k gate values are selected for routing the token x. If T is the set of selected top-k indices then the output computation of the layer is the linearly weighted combination of each expert's computation on the token by the gate value,
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    in the softmax computation for our router. We describe training difficulties here and the methods we use to overcome them to achieve stable and scalable training.
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    Selective precision with large sparse models. Model instability hinders the ability to train using efficient bfloat16 precision, and as a result, Lepikhin et al. (2020) trains with float32 precision throughout their MoE Transformer. However, we show that by instead selectively casting to float32 precision within a localized part of the model, stability may be achieved, without incurring expensive communication cost of float32 tensors. This technique is inline with modern mixed precision training strategies where certain parts of the model and gradient updates are done in higher precision Micikevicius et al. (2017). Table 2 shows that our approach permits nearly equal speed to bfloat16 training while conferring the training stability of float32.
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    Switch Routing: Rethinking Mixture-of-Experts. Shazeer et al. (2017) conjectured that routing to k > 1 experts was necessary in order to have non-trivial gradients to the routing functions. The authors intuited that learning to route would not work without the ability to compare at least two experts. Ramachandran and Le (2018) went further to
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    To achieve this, we cast the router input to float32 precision. The router function takes the tokens as input and produces the dispatch and combine tensors used for the selection and recombination of expert computation (refer to Code Block 15 in the Appendix for details). Importantly, the float32 precision is only used within the body of the router function—on computations local to that device. Because the resulting dispatch and combine tensors are recast to bfloat16 precision at the end of the function, no expensive float32 tensors
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    Table 2: Selective precision. We cast the local routing operations to float32 while preserving bfloat16 precision elsewhere to stabilize our model while achieving nearly equal speed to (unstable) bfloat16-precision training. We measure the quality of a 32 expert model after a fixed step count early in training its speed performance. For both Switch-Base in float32 and with Selective prevision we notice similar learning dynamics.
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    study the top-k decision and found that higher k-values in lower layers in the model were important for models with many routing layers. Contrary to these ideas, we instead use a simplified strategy where we route to only a single expert. We show this simplification preserves model quality, reduces routing computation and performs better. This k = 1 routing strategy is later referred to as a Switch layer. Note that for both MoE and Switch Routing, the gate value pi(x) in Equation 2 permits differentiability of the router.
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    are broadcast through all-to-all communication operations, but we still benefit from the increased stability of float32.
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    Smaller parameter initialization for stability. Appropriate initialization is critical to successful training in deep learning and we especially observe this to be true for Switch Transformer. We initialize our weight matrices by drawing elements from a truncated normal distribution with mean µ = 0 and standard deviation σ = p s/n where s is a scale hyper-parameter and n is the number of input units in the weight tensor (e.g. fan-in).6
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    The benefits for the Switch layer are three-fold: (1) The router computation is reduced as we are only routing a token to a single expert. (2) The batch size (expert capacity) of each expert can be at least halved since each token is only being routed to a single expert.3 (3) The routing implementation is simplified and communication costs are reduced. Figure 3 shows an example of routing with different expert capacity factors.
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    As an additional remedy to the instability, we recommend reducing the default Transformer initialization scale s = 1.0 by a factor of 10. This both improves quality and reduces the likelihood of destabilized training in our experiments. Table 3 measures the improvement of the model quality and reduction of the variance early in training. We find that
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    Model (Initialization scale)Average Quality
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    Switch-Base (1.0x-init)-3.600.68
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  • Figure 3: Illustration of token routing dynamics. Each expert processes a fixed batch-size of tokens modulated by the capacity factor. Each token is routed to the expert with the highest router probability, but each expert has a fixed batch size of (total tokens / num experts) × capacity factor. If the tokens are unevenly dispatched then certain experts will overflow (denoted by dotted red lines), resulting in these tokens not being processed by this layer. A larger capacity factor alleviates this overflow issue, but also increases computation and communication costs (depicted by padded white/empty slots).
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  • Table 3: Reduced initialization scale improves stability. Reducing the initialization scale results in better model quality and more stable training of Switch Transformer. Here we record the average and standard deviation of model quality, measured by the negative log perplexity, of a 32 expert model after 3.5k steps (3 random seeds each).
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    2.2 Efficient Sparse Routing
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    We use Mesh-Tensorflow (MTF) (Shazeer et al., 2018) which is a library, with similar semantics and API to Tensorflow (Abadi et al., 2016) that facilitates efficient distributed data and model parallel architectures. It does so by abstracting the physical set of cores to a logical mesh of processors. Tensors and computations may then be sharded per named dimensions, facilitating easy partitioning of models across dimensions. We design our model with TPUs in mind, which require statically declared sizes. Below we describe our distributed Switch Transformer implementation.
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    the average model quality, as measured by the Neg. Log Perp., is dramatically improved and there is a far reduced variance across runs. Further, this same initialization scheme is broadly effective for models spanning several orders of magnitude. We use the same approach to stably train models as small as our 223M parameter baseline to enormous models in excess of one trillion parameters.
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    Distributed Switch Implementation. All of our tensor shapes are statically determined at compilation time, but our computation is dynamic due to the routing decisions at training and inference. Because of this, one important technical consideration is how to set the expert capacity. The expert capacity—the number of tokens each expert computes—is set by evenly dividing the number of tokens in the batch across the number of experts, and then further expanding by a capacity factor,
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    A capacity factor greater than 1.0 creates additional buffer to accommodate for when tokens are not perfectly balanced across experts. If too many tokens are routed to an expert (referred to later as dropped tokens), computation is skipped and the token representation is passed directly to the next layer through the residual connection. Increasing the expert capacity is not without drawbacks, however, since high values will result in wasted computation and memory. This trade-off is explained in Figure 3. Empirically we find ensuring lower rates of dropped tokens are important for the scaling of sparse expert-models. Throughout our experiments we didn't notice any dependency on the number of experts for the number of tokens dropped (typically < 1%). Using the auxiliary load balancing loss (next section) with a high enough coefficient ensured good load balancing. We study the impact that these design decisions have on model quality and speed in Table 1.
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    A Differentiable Load Balancing Loss. To encourage a balanced load across experts we add an auxiliary loss (Shazeer et al., 2017, 2018; Lepikhin et al., 2020). As in Shazeer et al. (2018); Lepikhin et al. (2020), Switch Transformers simplifies the original design in Shazeer et al. (2017) which had separate load-balancing and importance-weighting losses. For each Switch layer, this auxiliary loss is added to the total model loss during training. Given N experts indexed by i = 1 to N and a batch B with T tokens, the auxiliary loss is computed as the scaled dot-product between vectors f and P,
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    where fi is the fraction of tokens dispatched to expert i,
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    Regularizing large sparse models. Our paper considers the common NLP approach of pre-training on a large corpus followed by fine-tuning on smaller downstream tasks such as summarization or question answering. One issue that naturally arises is overfitting since many fine-tuning tasks have very few examples. During fine-tuning of standard Transformers, Raffel et al. (2019) use dropout (Srivastava et al., 2014) at each layer to prevent overfitting. Our Switch Transformers have significantly more parameters than the FLOP matched dense baseline, which can lead to more severe overfitting on these smaller downstream tasks.
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    Table 4: Fine-tuning regularization results. A sweep of dropout rates while fine-tuning Switch Transformer models pre-trained on 34B tokens of the C4 data set (higher numbers are better). We observe that using a lower standard dropout rate at all non-expert layer, with a much larger dropout rate on the expert feed-forward layers, to perform the best.
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    and Pi is the fraction of the router probability allocated for expert i, 2
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    We thus propose a simple way to alleviate this issue during fine-tuning: increase the dropout inside the experts, which we name as expert dropout. During fine-tuning we simply increase the dropout rate by a significant amount only at the interim feed-forward computation at each expert layer. Table 4 has the results for our expert dropout protocol. We observe that simply increasing the dropout across all layers leads to worse performance. However, setting a smaller dropout rate (0.1) at non-expert layers and a much larger dropout rate (0.4) at expert layers leads to performance improvements on four smaller downstream tasks.
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    $$P_{i}=\\frac{1}{T}\\sum_{x\\in\\mathcal{B}}p_{i}(x).\\tag{6}$$
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    3. Scaling Properties
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    Since we seek uniform routing of the batch of tokens across the N experts, we desire both vectors to have values of 1/N. The auxiliary loss of Equation 4 encourages uniform routing since it is minimized under a uniform distribution. The objective can also be differentiated as
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    We present a study of the scaling properties of the Switch Transformer architecture during pre-training. Per Kaplan et al. (2020), we consider a regime where the model is not bottlenecked by either the computational budget or amount of data. To avoid the data bottleneck, we use the large C4 corpus with over 180B target tokens (Raffel et al., 2019) and we train until diminishing returns are observed.
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    The number of experts is the most efficient dimension for scaling our model. Increasing the experts keeps the computational cost approximately fixed since the model only selects one expert per token, regardless of the number of experts to choose from. The router must compute a probability distribution over more experts, however, this is a lightweight computation of cost O(d_{model} \\times \\text{num experts}) where d_{model} is the embedding dimension of
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    2. A potential source of confusion: pi(x) is the probability of routing token x to expert i. Pi is the probability fraction to expert i across all tokens in the batch B.
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    the P-vector is differentiable, but the f-vector is not. The final loss is multiplied by expert count N to keep the loss constant as the number of experts varies since under uniform routing PN i=1(fi · Pi) = PN i=1( 1 N · 1 N ) = 1 N . Finally, a hyper-parameter α is a multiplicative coefficient for these auxiliary losses; throughout this work we use an α = 10−2 which was sufficiently large to ensure load balancing while small enough to not to overwhelm the primary cross-entropy objective. We swept hyper-parameter ranges of α from 10−1 to 10−5 in powers of 10 and found 10−2 balanced load quickly without interfering with training loss.
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    2.3 Putting It All Together: The Switch Transformer
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    Our first test of the Switch Transformer starts with pre-training on the \"Colossal Clean Crawled Corpus\" (C4), introduced in (Raffel et al., 2019). For our pre-training objective, we use a masked language modeling task (Taylor, 1953; Fedus et al., 2018; Devlin et al., 2018) where the model is trained to predict missing tokens. In our pre-training setting, as determined in Raffel et al. (2019) to be optimal, we drop out 15% of tokens and then replace the masked sequence with a single sentinel token. To compare our models, we record the negative log perplexity.4 Throughout all tables in the paper, ↑ indicates that a higher value for that metric is better and vice-versa for ↓. A comparison of all the models studied in this work are in Table 9.
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    A head-to-head comparison of the Switch Transformer and the MoE Transformer is presented in Table 1. Our Switch Transformer model is FLOP-matched to 'T5-Base' (Raffel et al., 2019) (same amount of computation per token is applied). The MoE Transformer, using top-2 routing, has two experts which each apply a separate FFN to each token and thus its FLOPS are larger. All models were trained for the same number of steps on identical hardware. Note that the MoE model going from capacity factor 2.0 to 1.25 actually slows down (840 to 790) in the above experiment setup, which is unexpected.5
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    tokens passed between the layers. In this section, we consider the scaling properties on a step-basis and a time-basis with a fixed computational budget.
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    We highlight three key findings from Table 1: (1) Switch Transformers outperform both carefully tuned dense models and MoE Transformers on a speed-quality basis. For a fixed amount of computation and wall-clock time, Switch Transformers achieve the best result. (2) The Switch Transformer has a smaller computational footprint than the MoE counterpart. If we increase its size to match the training speed of the MoE Transformer, we find this outperforms all MoE and Dense models on a per step basis as well. (3) Switch Transformers perform better at lower capacity factors (1.0, 1.25). Smaller expert capacities are indicative of the scenario in the large model regime where model memory is very scarce and the capacity factor will want to be made as small as possible.
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    3.1 Scaling Results on a Step-Basis
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    2.4 Improved Training and Fine-Tuning Techniques
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    Figure 4 demonstrates consistent scaling benefits with the number of experts when training all models for a fixed number of steps. We observe a clear trend: when keeping the FLOPS per token fixed, having more parameters (experts) speeds up training. The left Figure demonstrates consistent scaling properties (with fixed FLOPS per token) between sparse model parameters and test loss. This reveals the advantage of scaling along this additional axis of sparse model parameters. Our right Figure measures sample efficiency of a dense model variant and four FLOP-matched sparse variants. We find that increasing the number of experts leads to more sample efficient models. Our Switch-Base 64 expert model achieves the same performance of the T5-Base model at step 60k at step 450k, which is a 7.5x speedup in terms of step time. In addition, consistent with the findings of Kaplan et al. (2020), we find that larger models are also more sample efficient—learning more quickly for a fixed number of observed tokens.
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    Sparse expert models may introduce training difficulties over a vanilla Transformer. Instability can result because of the hard-switching (routing) decisions at each of these layers. Further, low precision formats like bfloat16 (Wang and Kanwar, 2019) can exacerbate issues
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    Figure 4: Scaling properties of the Switch Transformer. Left Plot: We measure the quality improvement, as measured by perplexity, as the parameters increase by scaling the number of experts. The top-left point corresponds to the T5-Base model with 223M parameters. Moving from top-left to bottom-right, we double the number of experts from 2, 4, 8 and so on until the bottom-right point of a 256 expert model with 14.7B parameters. Despite all models using an equal computational budget, we observe consistent improvements scaling the number of experts. Right Plot: Negative log perplexity per step sweeping over the number of experts. The dense baseline is shown with the purple line and we note improved sample efficiency of our Switch-Base models.
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    4. We use log base-e for this metric so the units are nats.
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    5. Note that speed measurements are both a function of the algorithm and the implementation details. Switch Transformer reduces the necessary computation relative to MoE (algorithm), but the final speed differences are impacted by low-level optimizations (implementation).
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    3.2 Scaling Results on a Time-Basis
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    Figure 4 demonstrates that on a step basis, as we increase the number of experts, the performance consistently improves. While our models have roughly the same amount of FLOPS per token as the baseline, our Switch Transformers incurs additional communication costs across devices as well as the extra computation of the routing mechanism. Therefore, the increased sample efficiency observed on a step-basis doesn't necessarily translate to a better model quality as measured by wall-clock. This raises the question:
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    Model       Capacity Factor  Quality after   Time to Quality  Threshold (↓)  Speed (↑)     
      100k steps (↑)   (examples/sec)
      (Neg. Log Perp.)(hours)                          
    T5-Base     —                -1.731          Not achieved†                   1600          
    T5-Large    —                -1.550          131.1                           470           
    MoE-Base    2.0              -1.547          68.7                            840           
    Switch-Base 2.0              -1.554          72.8                            860           
    MoE-Base    1.25             -1.559          80.7                            790           
    Switch-Base 1.25             -1.553          65.0                            910           
    MoE-Base    1.0              -1.572          80.1                            860           
    Switch-Base 1.0              -1.561          62.8                            1000          
    Switch-Base+1.0              -1.534          67.6                            780           
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    For a fixed training duration and computational budget, should one train a dense or a sparse model?
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  • Table 1: Benchmarking Switch versus MoE. Head-to-head comparison measuring per step and per time benefits of the Switch Transformer over the MoE Transformer and T5 dense baselines. We measure quality by the negative log perplexity and the time to reach an arbitrary chosen quality threshold of Neg. Log Perp.=-1.50. All MoE and Switch Transformer models use 128 experts, with experts at every other feed-forward layer. For Switch-Base+, we increase the model size until it matches the speed of the MoE model by increasing the model hidden-size from 768 to 896 and the number of heads from 14 to 16. All models are trained with the same amount of computation (32 cores) and on the same hardware (TPUv3). Further note that all our models required pre-training beyond 100k steps to achieve our level threshold of -1.50. † T5-Base did not achieve this negative log perplexity in the 100k steps the models were trained.
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    Figure 5: Speed advantage of Switch Transformer. All models trained on 32 TPUv3 cores with equal FLOPs per example. For a fixed amount of computation and training time, Switch Transformers significantly outperform the dense Transformer baseline. Our 64 expert Switch-Base model achieves the same quality in one-seventh the time of the T5-Base and continues to improve.
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    in the softmax computation for our router. We describe training difficulties here and the methods we use to overcome them to achieve stable and scalable training.
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    Figures 5 and 6 address this question. Figure 5 measures the pre-training model quality as a function of time. For a fixed training duration and computational budget, Switch Transformers yield a substantial speed-up. In this setting, our Switch-Base 64 expert model trains in one-seventh the time that it would take the T5-Base to get similar perplexity.
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    Selective precision with large sparse models. Model instability hinders the ability to train using efficient bfloat16 precision, and as a result, Lepikhin et al. (2020) trains with float32 precision throughout their MoE Transformer. However, we show that by instead selectively casting to float32 precision within a localized part of the model, stability may be achieved, without incurring expensive communication cost of float32 tensors. This technique is inline with modern mixed precision training strategies where certain parts of the model and gradient updates are done in higher precision Micikevicius et al. (2017). Table 2 shows that our approach permits nearly equal speed to bfloat16 training while conferring the training stability of float32.
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    3.3 Scaling Versus a Larger Dense Model
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    To achieve this, we cast the router input to float32 precision. The router function takes the tokens as input and produces the dispatch and combine tensors used for the selection and recombination of expert computation (refer to Code Block 15 in the Appendix for details). Importantly, the float32 precision is only used within the body of the router function—on computations local to that device. Because the resulting dispatch and combine tensors are recast to bfloat16 precision at the end of the function, no expensive float32 tensors
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    The above analysis shows that a computationally-matched dense model is outpaced by its Switch counterpart. Figure 6 considers a different scenario: what if we instead had allocated our resources to a larger dense model? We do so now, measuring Switch-Base against the next strong baseline, T5-Large. But despite T5-Large applying 3.5x more FLOPs per token,
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    Model                            Quality             Speed             
    (precision)                      (Neg. Log Perp.) (↑)(Examples/sec) (↑)
    Switch-Base (float32)            -1.718              1160              
    Switch-Base (bfloat16)           -3.780 [diverged]   1390              
    Switch-Base (Selective precision)-1.716              1390              
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    Switch-Base is still more sample efficient and yields a 2.5x speedup. Furthermore, more gains can be had simply by designing a new, larger sparse version, Switch-Large, which is FLOP-matched to T5-Large. We do this and demonstrate superior scaling and fine-tuning in the following section.
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    Table 2: Selective precision. We cast the local routing operations to float32 while preserving bfloat16 precision elsewhere to stabilize our model while achieving nearly equal speed to (unstable) bfloat16-precision training. We measure the quality of a 32 expert model after a fixed step count early in training its speed performance. For both Switch-Base in float32 and with Selective prevision we notice similar learning dynamics.
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    are broadcast through all-to-all communication operations, but we still benefit from the increased stability of float32.
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    Figure 6: Scaling Transformer models with Switch layers or with standard dense model scaling. Left Plot: Switch-Base is more sample efficient than both the T5-Base, and T5-Large variant, which applies 3.5x more FLOPS per token. Right Plot: As before, on a wall-clock basis, we find that Switch-Base is still faster, and yields a 2.5x speedup over T5-Large.
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    Smaller parameter initialization for stability. Appropriate initialization is critical to successful training in deep learning and we especially observe this to be true for Switch Transformer. We initialize our weight matrices by drawing elements from a truncated normal distribution with mean µ = 0 and standard deviation σ = p s/n where s is a scale hyper-parameter and n is the number of input units in the weight tensor (e.g. fan-in).6
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    4. Downstream Results
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    As an additional remedy to the instability, we recommend reducing the default Transformer initialization scale s = 1.0 by a factor of 10. This both improves quality and reduces the likelihood of destabilized training in our experiments. Table 3 measures the improvement of the model quality and reduction of the variance early in training. We find that
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    Section 3 demonstrated the superior scaling properties while pre-training, but we now validate that these gains translate to improved language learning abilities on downstream tasks. We begin by fine-tuning on a diverse set of NLP tasks. Next we study reducing the memory footprint of our sparse models by over 90% by distilling into small—and easily deployed—dense baselines. Finally, we conclude this section measuring the improvements in a multi-task, multilingual setting, where we show that Switch Transformers are strong multi-task learners, improving over the multilingual T5-base model across all 101 languages.
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    Model (Initialization scale)  Average Quality  Std. Dev. of Quality  
     (Neg. Log Perp.) (Neg. Log Perp.)      
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    Switch-Base (1.0x-init)       -3.60            0.68                  
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  • Table 3: Reduced initialization scale improves stability. Reducing the initialization scale results in better model quality and more stable training of Switch Transformer. Here we record the average and standard deviation of model quality, measured by the negative log perplexity, of a 32 expert model after 3.5k steps (3 random seeds each).
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    4.1 Fine-Tuning
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    the average model quality, as measured by the Neg. Log Perp., is dramatically improved and there is a far reduced variance across runs. Further, this same initialization scheme is broadly effective for models spanning several orders of magnitude. We use the same approach to stably train models as small as our 223M parameter baseline to enormous models in excess of one trillion parameters.
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    Baseline and Switch models used for fine-tuning. Our baselines are the highly-tuned 223M parameter T5-Base model and the 739M parameter T5-Large model (Raffel et al., 2019). For both versions, we design a FLOP-matched Switch Transformer, with many more parameters, which is summarized in Table 9. 7 Our baselines differ slightly from those in Raffel et al. (2019) because we pre-train on an improved C4 corpus which removes intraexample text duplication and thus increases the efficacy as a pre-training task Lee et al.
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    6. Values greater than two standard deviations from the mean are resampled.
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    7. FLOPS are calculated for the forward pass as done in Kaplan et al. (2020).
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    Regularizing large sparse models. Our paper considers the common NLP approach of pre-training on a large corpus followed by fine-tuning on smaller downstream tasks such as summarization or question answering. One issue that naturally arises is overfitting since many fine-tuning tasks have very few examples. During fine-tuning of standard Transformers, Raffel et al. (2019) use dropout (Srivastava et al., 2014) at each layer to prevent overfitting. Our Switch Transformers have significantly more parameters than the FLOP matched dense baseline, which can lead to more severe overfitting on these smaller downstream tasks.
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    Model (dropout)            GLUE  CNNDM  SQuAD  SuperGLUE  
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    Switch-Base (d=0.1)        84.7  19.1   83.7   73.0       
    Switch-Base (d=0.2)        84.4  19.2   83.9   73.2       
    Switch-Base (d=0.3)        83.9  19.6   83.4   70.7       
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  • Table 4: Fine-tuning regularization results. A sweep of dropout rates while fine-tuning Switch Transformer models pre-trained on 34B tokens of the C4 data set (higher numbers are better). We observe that using a lower standard dropout rate at all non-expert layer, with a much larger dropout rate on the expert feed-forward layers, to perform the best.
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    (2021). In our protocol we pre-train with 220 (1,048,576) tokens per batch for 550k steps amounting to 576B total tokens. We then fine-tune across a diverse set of tasks using a dropout rate of 0.1 for all layers except the Switch layers, which use a dropout rate of 0.4 (see Table 4). We fine-tune using a batch-size of 1M for 16k steps and for each task, we evaluate model quality every 200-steps and report the peak performance as computed on the validation set.
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    We thus propose a simple way to alleviate this issue during fine-tuning: increase the dropout inside the experts, which we name as expert dropout. During fine-tuning we simply increase the dropout rate by a significant amount only at the interim feed-forward computation at each expert layer. Table 4 has the results for our expert dropout protocol. We observe that simply increasing the dropout across all layers leads to worse performance. However, setting a smaller dropout rate (0.1) at non-expert layers and a much larger dropout rate (0.4) at expert layers leads to performance improvements on four smaller downstream tasks.
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    Fine-tuning tasks and data sets. We select tasks probing language capabilities including question answering, summarization and knowledge about the world. The language benchmarks GLUE (Wang et al., 2018) and SuperGLUE (Wang et al., 2019) are handled as composite mixtures with all the tasks blended in proportion to the amount of tokens present in each. These benchmarks consist of tasks requiring sentiment analysis (SST-2), word sense disambiguation (WIC), sentence similarty (MRPC, STS-B, QQP), natural language inference (MNLI, QNLI, RTE, CB), question answering (MultiRC, RECORD, BoolQ), coreference resolution (WNLI, WSC) and sentence completion (COPA) and sentence acceptability (CoLA). The CNNDM (Hermann et al., 2015) and BBC XSum (Narayan et al., 2018) data sets are used to measure the ability to summarize articles. Question answering is probed with the SQuAD data set (Rajpurkar et al., 2016) and the ARC Reasoning Challenge (Clark et al., 2018). And as in Roberts et al. (2020), we evaluate the knowledge of our models by fine-tuning on three closed-book question answering data sets: Natural Questions (Kwiatkowski et al., 2019), Web Questions (Berant et al., 2013) and Trivia QA (Joshi et al., 2017). Closed-book refers to questions posed with no supplemental reference or context material. To gauge the model's common sense reasoning we evaluate it on the Winogrande Schema Challenge (Sakaguchi et al., 2020). And finally, we test our model's natural language inference capabilities on the Adversarial NLI Benchmark (Nie et al., 2019).
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    3. Scaling Properties
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    Fine-tuning metrics. The following evaluation metrics are used throughout the paper: We report the average scores across all subtasks for GLUE and SuperGLUE. The Rouge-2 metric is used both the CNNDM and XSum. In SQuAD and the closed book tasks (Web, Natural, and Trivia Questions) we report the percentage of answers exactly matching the target (refer to Roberts et al. (2020) for further details and deficiency of this measure). Finally, in ARC Easy, ARC Challenge, ANLI, and Winogrande we report the accuracy of the generated responses.
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    We present a study of the scaling properties of the Switch Transformer architecture during pre-training. Per Kaplan et al. (2020), we consider a regime where the model is not bottlenecked by either the computational budget or amount of data. To avoid the data bottleneck, we use the large C4 corpus with over 180B target tokens (Raffel et al., 2019) and we train until diminishing returns are observed.
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    Fine-tuning results. We observe significant downstream improvements across many natural language tasks. Notable improvements come from SuperGLUE, where we find FLOP-matched Switch variants improve by 4.4 and 2 percentage points over the T5-Base and T5-Large baselines, respectively as well as large improvements in Winogrande, closed book Trivia QA, and XSum.8 In our fine-tuning study, the only tasks where we do not observe gains are on the AI2 Reasoning Challenge (ARC) data sets where the T5-Base outperforms Switch-Base on the challenge data set and T5-Large outperforms Switch-Large on the easy data set. Taken as a whole, we observe significant improvements spanning both reasoning and knowledge-heavy tasks. This validates our architecture, not just as one that pre-trains well, but can translate quality improvements to downstream tasks via fine-tuning.
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    The number of experts is the most efficient dimension for scaling our model. Increasing the experts keeps the computational cost approximately fixed since the model only selects one expert per token, regardless of the number of experts to choose from. The router must compute a probability distribution over more experts, however, this is a lightweight computation of cost O(dmodel × num experts) where dmodel is the embedding dimension of
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    tokens passed between the layers. In this section, we consider the scaling properties on a step-basis and a time-basis with a fixed computational budget.
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    Table 5: Fine-tuning results. Fine-tuning results of T5 baselines and Switch models across a diverse set of natural language tests (validation sets; higher numbers are better). We compare FLOP-matched Switch models to the T5-Base and T5-Large baselines. For most tasks considered, we find significant improvements of the Switchvariants. We observe gains across both model sizes and across both reasoning and knowledge-heavy language tasks.
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    3.1 Scaling Results on a Step-Basis
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    4.2 Distillation
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    Figure 4 demonstrates consistent scaling benefits with the number of experts when training all models for a fixed number of steps. We observe a clear trend: when keeping the FLOPS per token fixed, having more parameters (experts) speeds up training. The left Figure demonstrates consistent scaling properties (with fixed FLOPS per token) between sparse model parameters and test loss. This reveals the advantage of scaling along this additional axis of sparse model parameters. Our right Figure measures sample efficiency of a dense model variant and four FLOP-matched sparse variants. We find that increasing the number of experts leads to more sample efficient models. Our Switch-Base 64 expert model achieves the same performance of the T5-Base model at step 60k at step 450k, which is a 7.5x speedup in terms of step time. In addition, consistent with the findings of Kaplan et al. (2020), we find that larger models are also more sample efficient—learning more quickly for a fixed number of observed tokens.
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    Deploying massive neural networks with billions, or trillions, of parameters is inconvenient. To alleviate this, we study distilling (Hinton et al., 2015) large sparse models into small dense models. Future work could additionally study distilling large models into smaller sparse models.
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    Distillation techniques. In Table 6 we study a variety of distillation techniques. These techniques are built off of Sanh et al. (2019), who study distillation methods for BERT models. We find that initializing the dense model with the non-expert weights yields a modest improvement. This is possible since all models are FLOP matched, so non-expert layers will have the same dimensions. Since expert layers are usually only added at every or every other FFN layer in a Transformer, this allows for many of the weights to be initialized with trained parameters. Furthermore, we observe a distillation improvement using a mixture of 0.25 for the teacher probabilities and 0.75 for the ground truth label. By combining both techniques we preserve ≈ 30% of the quality gains from the larger sparse models with only ≈ 1/20th of the parameters. The quality gain refers to the percent of
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  • Figure 4: Scaling properties of the Switch Transformer. Left Plot: We measure the quality improvement, as measured by perplexity, as the parameters increase by scaling the number of experts. The top-left point corresponds to the T5-Base model with 223M parameters. Moving from top-left to bottom-right, we double the number of experts from 2, 4, 8 and so on until the bottom-right point of a 256 expert model with 14.7B parameters. Despite all models using an equal computational budget, we observe consistent improvements scaling the number of experts. Right Plot: Negative log perplexity per step sweeping over the number of experts. The dense baseline is shown with the purple line and we note improved sample efficiency of our Switch-Base models.
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    Figure 4 demonstrates that on a step basis, as we increase the number of experts, the performance consistently improves. While our models have roughly the same amount of FLOPS per token as the baseline, our Switch Transformers incurs additional communication costs across devices as well as the extra computation of the routing mechanism. Therefore, the increased sample efficiency observed on a step-basis doesn't necessarily translate to a better model quality as measured by wall-clock. This raises the question:
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    For a fixed training duration and computational budget, should one train a dense or a sparse model?
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    Figure 5: Speed advantage of Switch Transformer. All models trained on 32 TPUv3 cores with equal FLOPs per example. For a fixed amount of computation and training time, Switch Transformers significantly outperform the dense Transformer baseline. Our 64 expert Switch-Base model achieves the same quality in one-seventh the time of the T5-Base and continues to improve.
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    Figures 5 and 6 address this question. Figure 5 measures the pre-training model quality as a function of time. For a fixed training duration and computational budget, Switch Transformers yield a substantial speed-up. In this setting, our Switch-Base 64 expert model trains in one-seventh the time that it would take the T5-Base to get similar perplexity.
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    3.3 Scaling Versus a Larger Dense Model
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    The above analysis shows that a computationally-matched dense model is outpaced by its Switch counterpart. Figure 6 considers a different scenario: what if we instead had allocated our resources to a larger dense model? We do so now, measuring Switch-Base against the next strong baseline, T5-Large. But despite T5-Large applying 3.5x more FLOPs per token,
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    Switch-Base is still more sample efficient and yields a 2.5x speedup. Furthermore, more gains can be had simply by designing a new, larger sparse version, Switch-Large, which is FLOP-matched to T5-Large. We do this and demonstrate superior scaling and fine-tuning in the following section.
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    Figure 6: Scaling Transformer models with Switch layers or with standard dense model scaling. Left Plot: Switch-Base is more sample efficient than both the T5-Base, and T5-Large variant, which applies 3.5x more FLOPS per token. Right Plot: As before, on a wall-clock basis, we find that Switch-Base is still faster, and yields a 2.5x speedup over T5-Large.
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    4. Downstream Results
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    Section 3 demonstrated the superior scaling properties while pre-training, but we now validate that these gains translate to improved language learning abilities on downstream tasks. We begin by fine-tuning on a diverse set of NLP tasks. Next we study reducing the memory footprint of our sparse models by over 90% by distilling into small—and easily deployed—dense baselines. Finally, we conclude this section measuring the improvements in a multi-task, multilingual setting, where we show that Switch Transformers are strong multi-task learners, improving over the multilingual T5-base model across all 101 languages.
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    4.1 Fine-Tuning
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    Baseline and Switch models used for fine-tuning. Our baselines are the highly-tuned 223M parameter T5-Base model and the 739M parameter T5-Large model (Raffel et al., 2019). For both versions, we design a FLOP-matched Switch Transformer, with many more parameters, which is summarized in Table 9. 7 Our baselines differ slightly from those in Raffel et al. (2019) because we pre-train on an improved C4 corpus which removes intraexample text duplication and thus increases the efficacy as a pre-training task Lee et al.
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    7. FLOPS are calculated for the forward pass as done in Kaplan et al. (2020).
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    (2021). In our protocol we pre-train with 220 (1,048,576) tokens per batch for 550k steps amounting to 576B total tokens. We then fine-tune across a diverse set of tasks using a dropout rate of 0.1 for all layers except the Switch layers, which use a dropout rate of 0.4 (see Table 4). We fine-tune using a batch-size of 1M for 16k steps and for each task, we evaluate model quality every 200-steps and report the peak performance as computed on the validation set.
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    the quality difference between Switch-Base (Teacher) and T5-Base (Student). Therefore, a quality gain of 100% implies the Student equals the performance of the Teacher.
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    Fine-tuning tasks and data sets. We select tasks probing language capabilities including question answering, summarization and knowledge about the world. The language benchmarks GLUE (Wang et al., 2018) and SuperGLUE (Wang et al., 2019) are handled as composite mixtures with all the tasks blended in proportion to the amount of tokens present in each. These benchmarks consist of tasks requiring sentiment analysis (SST-2), word sense disambiguation (WIC), sentence similarty (MRPC, STS-B, QQP), natural language inference (MNLI, QNLI, RTE, CB), question answering (MultiRC, RECORD, BoolQ), coreference resolution (WNLI, WSC) and sentence completion (COPA) and sentence acceptability (CoLA). The CNNDM (Hermann et al., 2015) and BBC XSum (Narayan et al., 2018) data sets are used to measure the ability to summarize articles. Question answering is probed with the SQuAD data set (Rajpurkar et al., 2016) and the ARC Reasoning Challenge (Clark et al., 2018). And as in Roberts et al. (2020), we evaluate the knowledge of our models by fine-tuning on three closed-book question answering data sets: Natural Questions (Kwiatkowski et al., 2019), Web Questions (Berant et al., 2013) and Trivia QA (Joshi et al., 2017). Closed-book refers to questions posed with no supplemental reference or context material. To gauge the model's common sense reasoning we evaluate it on the Winogrande Schema Challenge (Sakaguchi et al., 2020). And finally, we test our model's natural language inference capabilities on the Adversarial NLI Benchmark (Nie et al., 2019).
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  • Table 6: Distilling Switch Transformers for Language Modeling. Initializing T5-Base with the non-expert weights from Switch-Base and using a loss from a mixture of teacher and ground-truth labels obtains the best performance. We can distill 30% of the performance improvement of a large sparse model with 100x more parameters back into a small dense model. For a final baseline, we find no improvement of T5-Base initialized with the expert weights, but trained normally without distillation.
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    Fine-tuning metrics. The following evaluation metrics are used throughout the paper: We report the average scores across all subtasks for GLUE and SuperGLUE. The Rouge-2 metric is used both the CNNDM and XSum. In SQuAD and the closed book tasks (Web, Natural, and Trivia Questions) we report the percentage of answers exactly matching the target (refer to Roberts et al. (2020) for further details and deficiency of this measure). Finally, in ARC Easy, ARC Challenge, ANLI, and Winogrande we report the accuracy of the generated responses.
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    Achievable compression rates. Using our best distillation technique described in Table 6, we distill a wide variety of sparse models into dense models. We distill Switch-Base versions, sweeping over an increasing number of experts, which corresponds to varying between 1.1B to 14.7B parameters. Through distillation, we can preserve 37% of the quality gain of the 1.1B parameter model while compressing 82%. At the extreme, where we compress the model 99%, we are still able to maintain 28% of the teacher's model quality improvement.
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    Fine-tuning results. We observe significant downstream improvements across many natural language tasks. Notable improvements come from SuperGLUE, where we find FLOP-matched Switch variants improve by 4.4 and 2 percentage points over the T5-Base and T5-Large baselines, respectively as well as large improvements in Winogrande, closed book Trivia QA, and XSum.8 In our fine-tuning study, the only tasks where we do not observe gains are on the AI2 Reasoning Challenge (ARC) data sets where the T5-Base outperforms Switch-Base on the challenge data set and T5-Large outperforms Switch-Large on the easy data set. Taken as a whole, we observe significant improvements spanning both reasoning and knowledge-heavy tasks. This validates our architecture, not just as one that pre-trains well, but can translate quality improvements to downstream tasks via fine-tuning.
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    Distilling a fine-tuned model. We conclude this with a study of distilling a finetuned sparse model into a dense model. Table 8 shows results of distilling a 7.4B parameter Switch-Base model, fine-tuned on the SuperGLUE task, into the 223M T5-Base. Similar to our pre-training results, we find we are able to preserve 30% of the gains of the sparse model when distilling into a FLOP matched dense variant. One potential future avenue, not considered here, may examine the specific experts being used for fine-tuning tasks and extracting them to achieve better model compression.
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    8. Our T5 and Switch models were pre-trained with 220 tokens per batch for 550k steps on a revised C4 data set for fair comparisons.
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    4.3 Multilingual Learning
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    Model       GLUE     SQuAD        SuperGLUE   Winogrande (XL)  
    T5-Base     84.3     85.5         75.1        66.6             
    Switch-Base 86.7     87.2         79.5        73.3             
    T5-Large    87.8     88.1         82.7        79.1             
    Switch-Large88.5     88.6         84.7        83.0             
    Model       XSum     ANLI (R3)    ARC Easy    ARC Chal.        
    T5-Base     18.7     51.8         56.7        35.5             
    Switch-Base 20.3     54.0         61.3        32.8             
    T5-Large    20.9     56.6         68.8        35.5             
    Switch-Large22.3     58.6         66.0        35.5             
    Model       CB Web QACB Natural QACB Trivia QA 
    T5-Base     26.6     25.8         24.5         
    Switch-Base 27.4     26.8         30.7         
    T5-Large    27.7     27.6         29.5         
    Switch-Large31.3     29.5         36.9         
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    Table 5: Fine-tuning results. Fine-tuning results of T5 baselines and Switch models across a diverse set of natural language tests (validation sets; higher numbers are better). We compare FLOP-matched Switch models to the T5-Base and T5-Large baselines. For most tasks considered, we find significant improvements of the Switchvariants. We observe gains across both model sizes and across both reasoning and knowledge-heavy language tasks.
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    In our final set of downstream experiments, we measure the model quality and speed tradeoffs while pre-training on a mixture of 101 different languages. We build and benchmark off the recent work of mT5 (Xue et al., 2020), a multilingual extension to T5. We pre-train on the multilingual variant of the Common Crawl data set (mC4) spanning 101 languages introduced in mT5, but due to script variants within certain languages, the mixture contains 107 tasks.
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    4.2 Distillation
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    Deploying massive neural networks with billions, or trillions, of parameters is inconvenient. To alleviate this, we study distilling (Hinton et al., 2015) large sparse models into small dense models. Future work could additionally study distilling large models into smaller sparse models.
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    Distillation techniques. In Table 6 we study a variety of distillation techniques. These techniques are built off of Sanh et al. (2019), who study distillation methods for BERT models. We find that initializing the dense model with the non-expert weights yields a modest improvement. This is possible since all models are FLOP matched, so non-expert layers will have the same dimensions. Since expert layers are usually only added at every or every other FFN layer in a Transformer, this allows for many of the weights to be initialized with trained parameters. Furthermore, we observe a distillation improvement using a mixture of 0.25 for the teacher probabilities and 0.75 for the ground truth label. By combining both techniques we preserve ≈ 30% of the quality gains from the larger sparse models with only ≈ 1/20th of the parameters. The quality gain refers to the percent of
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    In Figure 7 we plot the quality improvement in negative log perplexity for all languages of a FLOP-matched Switch model, mSwitch-Base to the T5 base variant, mT5-Base. After
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    Switch-Base                              3,800M       -1.444       
    Distillation                             223M        (3%) -1.631       
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    Pre-trained Neg. Log Perp. (↑)-1.636-1.505-1.474-1.444-1.432-1.427
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    Table 7: Distillation compression rates. We measure the quality when distilling large sparse models into a dense baseline. Our baseline, T5-Base, has a -1.636 Neg. Log Perp. quality. In the right columns, we then distill increasingly large sparse models into this same architecture. Through a combination of weight-initialization and a mixture of hard and soft losses, we can shrink our sparse teachers by 95%+ while preserving 30% of the quality gain. However, for significantly better and larger pre-trained teachers, we expect larger student models would be necessary to achieve these compression rates.
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    Table 6: Distilling Switch Transformers for Language Modeling. Initializing T5-Base with the non-expert weights from Switch-Base and using a loss from a mixture of teacher and ground-truth labels obtains the best performance. We can distill 30% of the performance improvement of a large sparse model with 100x more parameters back into a small dense model. For a final baseline, we find no improvement of T5-Base initialized with the expert weights, but trained normally without distillation.
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  • Table 8: Distilling a fine-tuned SuperGLUE model. We distill a Switch-Base model finetuned on the SuperGLUE tasks into a T5-Base model. We observe that on smaller data sets our large sparse model can be an effective teacher for distillation. We find that we again achieve 30% of the teacher's performance on a 97% compressed model.
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    Achievable compression rates. Using our best distillation technique described in Table 6, we distill a wide variety of sparse models into dense models. We distill Switch-Base versions, sweeping over an increasing number of experts, which corresponds to varying between 1.1B to 14.7B parameters. Through distillation, we can preserve 37% of the quality gain of the 1.1B parameter model while compressing 82%. At the extreme, where we compress the model 99%, we are still able to maintain 28% of the teacher's model quality improvement.
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    Distilling a fine-tuned model. We conclude this with a study of distilling a finetuned sparse model into a dense model. Table 8 shows results of distilling a 7.4B parameter Switch-Base model, fine-tuned on the SuperGLUE task, into the 223M T5-Base. Similar to our pre-training results, we find we are able to preserve 30% of the gains of the sparse model when distilling into a FLOP matched dense variant. One potential future avenue, not considered here, may examine the specific experts being used for fine-tuning tasks and extracting them to achieve better model compression.
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    pre-training both versions for 1M steps, we find that on all 101 languages considered, Switch Transformer increases the final negative log perplexity over the baseline. In Figure 8, we present a different view and now histogram the per step speed-up of using Switch Transformer over the mT5-Base.9 We find a mean speed-up over mT5-Base of 5x and that 91% of languages achieve at least a 4x speedup. This presents evidence that Switch Transformers are effective multi-task and multi-lingual learners.
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    4.3 Multilingual Learning
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    5. Designing Models with Data, Model, and Expert-Parallelism
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    In our final set of downstream experiments, we measure the model quality and speed tradeoffs while pre-training on a mixture of 101 different languages. We build and benchmark off the recent work of mT5 (Xue et al., 2020), a multilingual extension to T5. We pre-train on the multilingual variant of the Common Crawl data set (mC4) spanning 101 languages introduced in mT5, but due to script variants within certain languages, the mixture contains 107 tasks.
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    Arbitrarily increasing the number of experts is subject to diminishing returns (Figure 4). Here we describe complementary scaling strategies. The common way to scale a Transformer is to increase dimensions in tandem, like dmodel or df f . This increases both the parameters
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    In Figure 7 we plot the quality improvement in negative log perplexity for all languages of a FLOP-matched Switch model, mSwitch-Base to the T5 base variant, mT5-Base. After
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    9. The speedup on a step basis is computed as the ratio of the number of steps for the baseline divided by the number of steps required by our model to reach that same quality.
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     Dense    Sparse    
    Parameters                    223M   1.1B  2.0B  3.8B    7.4B  14.7B 
    Pre-trained Neg. Log Perp. (↑)-1.636 -1.505-1.474-1.444  -1.432-1.427
    Distilled Neg. Log Perp. (↑)  —      -1.587-1.585-1.579  -1.582-1.578
    Percent of Teacher Performance—      37%   32%   30 %    27 %  28 %  
    Compression Percent           —      82 %  90 %  95 %    97 %  99 %  
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  • Table 7: Distillation compression rates. We measure the quality when distilling large sparse models into a dense baseline. Our baseline, T5-Base, has a -1.636 Neg. Log Perp. quality. In the right columns, we then distill increasingly large sparse models into this same architecture. Through a combination of weight-initialization and a mixture of hard and soft losses, we can shrink our sparse teachers by 95%+ while preserving 30% of the quality gain. However, for significantly better and larger pre-trained teachers, we expect larger student models would be necessary to achieve these compression rates.
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    Model            Parameters  FLOPS  SuperGLUE (↑)   
    T5-Base          223M        124B    74.6
    Switch-Base      7410M       124B    81.3
    Distilled T5-Base223M        124B   (30%)          76.6
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    Figure 7: Multilingual pre-training on 101 languages. Improvements of Switch T5 Base model over dense baseline when multi-task training on 101 languages. We observe Switch Transformers to do quite well in the multi-task training setup and yield improvements on all 101 languages.
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  • Table 8: Distilling a fine-tuned SuperGLUE model. We distill a Switch-Base model finetuned on the SuperGLUE tasks into a T5-Base model. We observe that on smaller data sets our large sparse model can be an effective teacher for distillation. We find that we again achieve 30% of the teacher's performance on a 97% compressed model.
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    Figure 8: Multilingual pre-training on 101 languages. We histogram for each language, the step speedup of Switch Transformers over the FLOP matched T5 dense baseline to reach the same quality. Over all 101 languages, we achieve a mean step speedup over mT5-Base of 5x and, for 91% of languages, we record a 4x, or greater, speedup to reach the final perplexity of mT5-Base.
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    pre-training both versions for 1M steps, we find that on all 101 languages considered, Switch Transformer increases the final negative log perplexity over the baseline. In Figure 8, we present a different view and now histogram the per step speed-up of using Switch Transformer over the mT5-Base.9 We find a mean speed-up over mT5-Base of 5x and that 91% of languages achieve at least a 4x speedup. This presents evidence that Switch Transformers are effective multi-task and multi-lingual learners.
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    and computation performed and is ultimately limited by the memory per accelerator. Once it exceeds the size of the accelerator's memory, single program multiple data (SPMD) modelparallelism can be employed. This section studies the trade-offs of combining data, model, and expert-parallelism.
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    5. Designing Models with Data, Model, and Expert-Parallelism
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    Reviewing the Feed-Forward Network (FFN) Layer. We use the FFN layer as an example of how data, model and expert-parallelism works in Mesh TensorFlow (Shazeer et al., 2018) and review it briefly here. We assume B tokens in the batch, each of dimension
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    Arbitrarily increasing the number of experts is subject to diminishing returns (Figure 4). Here we describe complementary scaling strategies. The common way to scale a Transformer is to increase dimensions in tandem, like dmodel or df f . This increases both the parameters
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    9. The speedup on a step basis is computed as the ratio of the number of steps for the baseline divided by the number of steps required by our model to reach that same quality.
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    dmodel. Both the input (x) and output (y) of the FFN are of size [B, dmodel] and the intermediate (h) is of size [B, dff] where dff is typically several times larger than dmodel. In the FFN, the intermediate is h = xWin and then the output of the layer is y = ReLU(h)Wout. Thus Win and Wout are applied independently to each token and have sizes [dmodel, dff] and [dff, dmodel].
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    We describe two aspects of partitioning: how the weights and batches of data divide over cores, depicted in Figure 9. We denote all cores available as N which Mesh Tensorflow may then remap into a logical multidimensional mesh of processors. Here we create a two-dimensional logical mesh, with one dimension representing the number of ways for data-parallel sharding (n) and the other, the model-parallel sharding (m). The total cores must equal the ways to shard across both data and model-parallelism, e.g. N = n × m. To shard the layer across cores, the tensors containing that batch of B tokens are sharded across n data-parallel cores, so each core contains B/n tokens. Tensors and variables with df f are then sharded across m model-parallel cores. For the variants with experts-layers, we consider E experts, each of which can process up to C tokens.
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    TermDescription
    BNumber of tokens in the batch.
    NNumber of total cores.
    nNumber of ways for data-parallelism sharding.
    mNumber of ways for model-parallelism sharding.
    ENumber of experts in Switch layers.
    CExpert capacity, the batch size of each expert.
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k0e1AXUEgcZpI5oy8i7lyG6Z9hQA8keeoxztPP4iiUgIMjI3L/MUzz4zMAGU4UknPTpRJPH5e4MrAMuefcUATUyEgwIVGBtGBR50W3d5i49c0yOeMQRlmVcqOM0APQjzJMDnIz+QoYjzkyOcHFMWaPznQsobIA568UGaPz0XcpOCevSgB8xAiO4ZHFPqGSaPY2GViOoz708TREEiRSB1OaACEgxDaMDmhSPOfA5wM0yKeMwhtygfWkE8QkbLqBgYOetAEjkeZHkc5OPyomIEDlhkbTkUySaNZIgWXJJ7+1E08axuNyk7SduevFAE1MiIKHaMDc38zQJYyxUOu4dRmmQzxsjEMowzZ59zQA8EeewxztHP4miQjdHkfxcfkajE8RlY71wFHzZ9zSyTxjy23KQW659jQBJJgRPkZG05pV+6MelRyzRrGxLrypwM9aUTR/KpdQSOmaAFiIO/Ax8xzRkefjHzbev40yKaMs67lBDHjNAmjM5AZTheTmgB8pAC5GfmFZPiXxDb+HNOFzNG0ryNsjjU43HGeT2Fack8W0MHUjcMnPSszxBo9h4h00W91KUCtvjkQjKtz/nFXDl5lzbEyvb3dyHwt4mt/EllI0ULQSwELJEW3Yz0IPccH8q36wfDehaf4bsWWCUs8+GkkkIycDgfQZ/Wt4EEZBoqcvM+TYIc3L724UUUVBQUUUUAMmhiuYJIJ4klhkUo8bqGV1IwQQeoI7ViS+DPDxglW20bT7OZ42RLi3tI0kjJBGVIHB5reooAx10zUXuGvJ7+3+2LA0Fu8dsQkYYgsxUuSSSq8ZGMU288MWd1cJOklzC/2kXEmy5lCuw/2Q4APTkDtW1RQBieKv+QTb/8AYSsP/SqKtusTxV/yCbf/ALCVh/6VRVt0AMiJMKFuG2jNCE75M9MjH5URAiFA3XaM0JnfJnpkY/IUADE+agHTBzRKSIzt68UNnzUx0wc0S5MZ29eKAH0yIkxjd1p9MiBEY3daABSfNcHpgYocnfHjpk5/Khc+a+emBih8+ZHjpk5/KgAlJETleWwcU+mSgmFwvXBxT6AGRElDuHO4/wAzQCfOYfw7Rj680RZCHd13N/M0DPnMe20fzNAA5IZMf3ufpg0smRG2OuDikfO5Mf3ufyNLJny2x1wcUAKv3Rn0psZJ3bh/EcfSnL90Z9KbGCN2f7xxQAZPnYx8u39aJCQF2j+IZ+lGD52f4dv9aJASFx/eGaAHH7px6UkeTGhbrgZpW+6fpSR5ESZ64GaAEQktJkfxcfTAoJPnKP4dpz9eKEB3SZ/vcfkKCD5yn+Hac/mKACUkJ8o53D+Yp9MlBKfL13D+Yp9ADIiTEhbg45oUnzJM9MjH5URAiFA3XHNCZ8yTPTIx+VAAxPmoAOOc0SkiM7Rk0MD5qY6c5olyYzt60APpkRJjG7rz/On0yLIjG7rz/OgAUnzXBHGBj9aHJ3x46Z5/I0LnzXz0wMfrQ+d8eOmTn8jQASkiFyv3tpxT6ZKCYXC9dpxT6AGRklTu67j+WaAT5xH8O0f1oiyFOeu5v5mgZ85j22j+ZoAHJBTA/i5/KlfPltjrg4pJM5TH97n8qV8+W2OuDigBVztGeuKbGSd2R/EcU5c7Rnrimx5+fP8AeOKADJ87H8O39aJCQF2j+Ifzo584Htt/rRJkhcf3h/OgBx6GkjJMSFuu0Zpx6GmxZESbuu0ZoARCS0mRxu4+mBQSfOUfw7Tn9KEB3SZ6buPyFBB85T22nP6UAEpIT5euR/On0yXJT5euR/On0AMiJMSluuOaFJ8x89OMflRFkRLu645oXPmPnpxj8qABifMTA45zRKSImKjJ7UMD5qY6c5olBMTbetAD6ZESY/mGDk/zp9MiBEfzdcn+dAACfNcHpgY/Whyd8eBxu5/I0KD5zntgY/Whwd8eOm7n8jQASkiFyvLbTin0yXJhcL12nFPoAZGSVO4fxH8s0AnziMfLtH50RAhTu/vH+dAz5xP8O0fzNABISCmB/Fz9Kc+djY644psgOUx/e5pXzsbHXFACpnYueuKbGSd+f73H0pyZ2Lnrimx5+fP944oAMnzgP4dpokJCjb/eH5Zo584HttP86JMlRj+8P50AP7UyIkxIW+9tGaf2pkWRCm7rtGaABCd8megbj8hQSfNUfw4Of0oTO+TPTdx+QoOfOT0wc/pQASkhPl65H86fTJclPl65H86fQAyIkxKW645oUnzHz04xRFkRLu645oUHzHz04xQAMT5iY6c5olJETFeuOKGz5iY6c5olyYm29ccUAPpkRJT5uuT/ADp9MiyE+brk/wA6AAE+cw/h2jH60OTujx0Lc/TBoAPnOe20Y/Whwd0eOm7n8jQAspIicr12nFOHSmS5MT7eu04p46CgBkZJDbv7x/LNGT5xH8O39aI8gNn+8f50c+cT22/1oAJCRtx/eGfpTmzsOOuKbICdmP7wzTmzsbHXFACJkxrnrgZpIySXyP4uPpSpny1z1wM0kecvn+9xQAEnzgMfLtP58USEhRtHO4flmgg+cD/DtP8AMUSglRt67h/OgB9MiJMKFuG2jNPpkORCgbrtGaABCd8mRxnj8hQxPmoAOMHP6UJnfJnpnj8hQwPmpjpg5/SgAlJEZ2jnI/nT6ZKCYzt65H86fQAyIkxqW60KT5rg9OMURZES7utCg+a+enGKABifMTHTJz+VEpIiYryccUNnzI8dMnP5US5MT7euOKAH0yIkp83XJ/nT6ZFkJ83Xcf5mgABPnMP4dox9ef8A61Dkho8dN3P0waBnzmPbaMfmaHzujx03c/kaAFkyInK9cHFKOgpJMmJ8ddpxSjoKAGxkkNuH8RxRk+dj+Hb+tEeQGz/eOKOfOz22/wBaACQkbcf3hn6U5vunHXFNkz8uP7wzTm+6cdcUAJHkxrnrgZpEJJfP97j6YFLHny1z1wM0iZy+f73H5CgAJPnKP4dpz9eKJCQo29dy/lkUHPnKe20/zFEuSg29dy/zFAD6ZESYULcNgZp9MiyIUDddozQAITvkz0yMflQxPmoB0wc0JnfJnpkY/Khs+amOmDmgAlJEZ29afTJQTGdvXin0AMiJMY3daFJ81wRxgYoiyIxu60KD5r56YGKAByd8eBxk5/KiUkQuVGWwcUOD5keOmTn8qJsmF9vXBxQA+mRElDuHO5v5mn0yIEId3Xc38zQAAnzmGPl2jH15ockFMD+Ln6YNAB85j/DtH8zRIDujx/e5/I0ALJkRtjrg4pV+6M9cUkmfLbHXBxSr90Z64oAbGSd2f7xx9KMnzsY+Xb+tEYI3Z/vHFGD52f4dv9aACQkBdv8AeGfpTj904psmSFx/eGacfun6UAJHkxoW64GadTY8iJM9cDNOoAKKKKACiiigAooqvf3ken2E95MGMcMZchRknA6D3PSgCxRWbZ6lcPffYr60W2naIzRBJfMV1BAbnAwQWXI6fMME1Wm19l1CW3t7VJkhlEUh88LIzYBby0x84UMM8juBnFAB4q/5BNv/ANhKw/8ASqKtusTxV/yCbf8A7CVh/wClUVbdADIRiFBnOFHNCDDyHOckfhwKIQBBGAcjaMGhAPMkweSRn8hQAMP3qHPQHiiUZjIzjpz+NDAechzzg4H5UTAGIgnA4/nQA+mRDEQGc+9PpkIAiAByKABR+9c56gcUOMyRnOME/jxQoHnOc84GRQ4HmR5PIJx+VABMMwuM4yDzT6ZMAYXBOBtOTT6AGRDCEZz8zfzNAH79jn+EcfiaIgAhwcjc38zQAPPY552jj8TQASDLR84w358GlkGYnGccHmkkALR5OPm4/I0smDE4JwNpzQAq/dH0psYwG5z8xpy/dH0psQADYOfmNABj9/nP8PT8aJBkLzj5hRgefnPO3p+NEoBC5OPmFADm+6fpSRjESDOcKOaVvun6UkQAiQA5G0YoASMYaTnOWz9OBQR+/U5/hPH4iiMANJg5y3P5CggeepzztPH4igAlGUxnHzL/ADFPpkoBQZOBuX+Yp9ADIRiFBnOB1oQYkkOepH4cUQgCFApyMDBoQDzJMHkkZ/KgAYZlQ56Z4olGYiM496GA81CTzzgUTAGJgTgUAPpkQxGBnPXn8afTIQBEMHI5/nQAKP3rnPUDj86HGXjOcYJ/Hg0KB5znPOBkfnQ4HmR5PIJx+RoAJhmFxnGVPNPpkwBgkBOBtOTT6AGRDCnnPzN/M0Afv2Of4Rx+JoiACHBz8zfzNAA89jnnaOPxNABIMlOcYb+hpZBmNhnHBpJACY8nHzcfkaWTBjbPAwaAFXhB9KbGMb+c/Macv3Fx6U2MAb8HPzHNABj9+Dn+Hp+NEgyF5x8w/nRgeeDnnb0/GiUAhcnHzD+dADj0NNiGIkGc4Uc049DTYgBCgByNowaABBhpOc5b8uBQR++Q5/hPH5URgBpMHOW5/IUEDz0OedrYH5UAEoymM45H86fTJQDHycDI/mKfQAyEYiUZzx1oUYkkOeuOPTiiEAQoAcjHWhAPNkweeM/lQAMMyoc9M8UTDMTDOPehgPNjJPPOKJgDEwJwPWgB9MiGI8Zzyf50+mQgCMYORk/zoAFH75znqBx+dDjLxnOMN+fBoUDznOecDI/OhwC8WTyG4/I0AEwzDIM4yp59OKfTJgDBICcDacn8KfQAyIYU85+Yn9aAP35Of4Rx+JoiACtg5+Zv50ADzyc87Rx+JoAJBkpzjDUrjKMM44NJIASmTj5uKV8eW2emDQAqcIo9qbGMb+c5Y0qY2LjpikjAG/Bz8xzQAY/fg5/hPH40SjKrzj5h/OjA88HPO08fjRKAVXJx8w/nQA89KZEMQoM5wo5p56UyEAQxgHI2jB/CgAQYeTnOW/LgUEfvkOex4/KhAA8uDyW59uBQwHnIc87TgflQASjMeM45H86fTJQDHycDI/nT6AGRDESjOeOtCjErnPXHFEIAiUA5GOtCgebIQeeM0ADDMkZz0zx68UTDMTDOOOtDgebGSeecflRMAYWBOBjrQA+mRDCYznk/zp9MiAEfByMn+dAAB++c56qOPzocZePnGG/Pg0ADz3OedoyPzokALxZPRuPyNABKMwuM4yp5p46CmSgGFwTgbTk08dBQAyIYDc5+Y/zox+/Jz/D0/GiIABsHPzH+dGB55OedvT8aACQZ2c4wwpz8ow9qbIAdmTj5hinP9xs9MUAIgxGozngUkYwX5zlqWPAjXHTApIwAZMHPzc0ABH78HP8ACePxFEoyo5x8y/zoIHnqc87Tx+IolAKjJx8y/wAxQA+mQjEMYznCjn1p9MhAEEYU5G0YP4UACDDyHOcsPw4FDD98hz0B4/KhABJJg8lhn8hQwHnIc84OB+VABKMxkZxyOfxp9MmAMZBOBkfzp9ADIhiJRnPvQoxK5z1xxRCAIlAORQoHmyEHnjNAA4zJGc9Cfx4omGYXGcZHWhwPMjyecnH5UTAGFwTgY5NAD6ZEMJjOfmP8zT6ZEAE4ORub+ZoAAP37HP8ACOPxNDjLR84w358GgAee5zztXI/E0SAFo8no3H5GgBZBmJxnGVPNKOgpsoBicE4G05NOH3R9KAGxjAbnPzGjH77Of4en40RAANg5+Y0YHn5zzt6fjQASDO3nHzCnN90/SmygHZk4+YU5vunPpQAkYxGozngc0kYwZOc5b8uBSx4ESAHIwMUkYAaTBz83P5CgAI/fqc/wnj8RRKMoOcfMv8xQQPPU552nj8RRKAUGTgbl/mKAH0yEYhQZzhRzT6ZCAIEAORtGDQAIMSSHOckfhxQw/eoc9AeKEA8yTB5JGfyFDAechzzg4FABKMxkZx05p9MmAMRBOBx/On0AMiGIwM5680KP3rnPUDiiEARAA5HP86FA85yDzgZFAA4zJGc4wT+PFEwzC4zjKnmhwPMjyeQTj8qJgDA4Y4G05NAD6ZEMIec/M38zT6ZEAEODkbm/maAAD9+xz/COPxNEgy0fOMN+fBoAHnsc87Rx+JokALR5OMNx+RoAWQZicZxweaVfuj6UkmDE4JwNpzSr90fSgBsYxu5z8xox+/zn+Hp+NEQA34OfmNGB5+c87en40AEgyF5x8wrE8V6ldabpiPana0km0yYztGP61tygELk4+YUTRRzQtHKiuhHKsMg/hUVIuUWk7F05KM1KSujn/CGqXmo2k63RLiEqEkIxnOePwwPzro6htYYbe3SOCNEQDICKAKmopxcYpSd2OrOM5uUVZBRRRVmYUUUUAMmjaWCSNJXhZ1KiRACyEjqNwIyOvII9jWBeeHdQuLKeI+I9QuC0bBI7iO3EZbHyltkStgHB4I6V0VFAGHs1OfUBqTWCRyW1q8MUDTj96zshY7gCAo8tcHqcnIFULvQJydWtobGFzqE/nR3pcZgJC8nPO5WBZccZxyOa6uigDl/Fuk6fJFa6g9pE14uoWKiYr8wH2mIYz9Ca6XyY9u3YMelY/ir/AJBNv/2ErD/0qirboAhiiiaCP5AV2jGRQsURkc7BkEdvYU+EgwIQMAqOKEIMkgA6EZ9+BQAxoovPQlBuwcHFEsUQVmKDJIyQPensR5yDHJB5/KiYgREkZHH86AAQxqCAgAPWmRRRGEAINpzxipqZCQYgQMD0oAjEMJd12DgDjH1pZIoi8YZATnjj2p6kec4xyAOaHIEkYI6k49uKAGTRReW7Mg4B5xTxFGG3BBn1omIELkjICnin0AQwxRbCVQcswPHuaRYYhK6hByoyMe5qSIgoSBj5m/maAR57DHO0c/iaAGSRRfu1KDG7gY9jSyxRGJtyDAB7e1OkIDR5Gctx7cGlkIETkjICnigBoijO1tgyBwabFFFlmCDO4jOKlX7o+lNiIIbAx8xoAYI4hcHCDcV5496SSGJVA2DBYdBUmR5+Mc7ev40SkALkZ+YUAIYYvLK7Bt9KakUTQoNgK4GMipW+6fpSREGJCBgFRxQBGkUTO52DIb09hQYovtCkoN2044+lPjILSYGMNz78CgkeeoxztPP4igBkkcSjcUGSy8ge4p4hjXOEAz1olICAkZ+Zf5in0AQwxRG3UKg2kdMUiwwlnXYMAjIx7VJCQYUIGAQOKEIMkgA5BGffigBjxRGSMMgJwccUTRRbCzIOO+PensQJYxjk55omIETEjI9KADyow27YM+tMhii8vKoOc9vepqZCQYwQMDJ/nQBGsUQkkUIOgyMfWh4osxqUGN3Ax7GpFI85xjkAc/nQ5AkjBGck49uDQAyaKLyG3INoU9vanmKMkEoMjoaJiBBISMgKcin0AQxRRYLKgzuYZx7mhY4hcMQo3FQT+Zp8RBQ4GPmb+ZoBHnsMc7Rz+JoAjeKJdg2AAt2Hsac8MQhYFBtwTjFOkIBjyM5b+hpZDiNiRkYNADBDEY1GwYxwKRIom3fIOGPUVKvKL9KbGQd+Bj5jQAwxxG4BKDdt64okiiBDFBksOce9PyPPAxzt6/jRKQAuRn5h/OgBPJjUHCAZHNNiii8hQEG0qOMVKehpsRBhQgYBUcUARpDCTIoQYDcjHfApWiiMqKUB+U44+n/1qfGQXkwMYbn34FBI89Bjna3P5UAMmjiwGZRkMOfxp/lRht2wZ9aJSBHkjPI/mKfQBDBFEIlKoOR6UJFEHkUIO2Rj2p8JBhQgYGOlCEGSQY5BGfyoAjaGHeilB3wMUs0UXkncg2j2p7ECWMY5OcGiYgRMSMj0oADFGSCUGR0pkUURUMEHUjOKmpkJBjBAwMn+dADFjjFw5CjdgEn86RoolZBsAy3Yex/+vUikec4xyAOfzocgPECM5bj24NADJYolt5AUG3aSQBTvJjKhSgwOgpZiBBISMgKcj14p9AEEcUTKcIOGPUe9L5cRuCdg3BRzj3NPiIKtgY+Zv50AjzyMc7Rz+JoAY8UQdGKDJbrilaKJUc7AAQc4p0hAKZGctSucRsTzwaAGJDF5QAQbSOlJHFEQ6hBjd0x3qROUUj0pIyCXwMYY0AMaKIzKpQEhTjj6USxx/KzKMhhg/jT8jzwMc7Tz+NEpAVcjPzD+dAB5Med2wZx1pkMUQiRlQDKjtUx6UyEgwxkDAKjj8KAGRxRAyqqD73Ix7CkMMXmIuwdDgY+lSIQXlwMYbn34FDEecgxyVPP5UAMmii8rDIMAjHHvTzFGSCUBI6USkCPJGeR/On0AQxRRFFYIOnBxQkcYnkIUbuMmnwkGJSBgY6UKQZZBjkYyaAIzDEsiAIBnPb2pZYolgcFBtxyAKe5AljGOTnH5UTECFiRkY6UABhjKhSgwO1RxwxNGPkBAJxke9T0yIgx5Axyf50AMEcRuGOwbgAc4+tDxRCSM7BuLcHHsaeCPPcY5Cjn86JCA8WRnLce3BoAZJFEsUh2AAqc4FOEMYXAQYPallIELkjICninjoKAIY4oijKEG3ceMe9IYYTNtKAkL6VJEQQ2Bj5j/ADoyPPIxzt6/jQAyWOLcjMozuGDSvFHhmKDOOv4U6QgbMjOWFOfhGPtQBHHFGEDKgGQKSKKIeYFQY3c8e1SRnMakDAwKSMgmTAxhqAIzDD5oXYPuk4x7illii8tQyDAYY496eSPPUY52nn8RRKQFGRn5l/mKAAwxkglAcdKZFFEYo2CD7owSO2KmpkJBgjIGAVGB+FADI44vNkYKN27k/gKQxRLMoCAEg9qkQgvIAMYYZ9+BQxHnIMckHn8qAGSRRLCQUG3IyAPcU/yYyoXYMDtRMQIySMjI/nT6AIY4omiX5AQOmRQI4jO52DcMc4p8JBiUgYHpQpBlkGORjJoAY0UQmQ7BuOcHHtRLFEsUhKAAjnAp7kCSMY5JOPyomIELkjIA6UAAhjAICDB6imRRRGLAQbdx4I9zU1MiIKZAx8zfzNAEfkwmZgUGQq9vrSyRxb4iUGd3HHsaeCPPcY52rz+JokIDR5Gctx7cGgBksUWx2KD7pycdsU5Yox8wQZPellIETkjICninD7o+lAEUUUQVwqDBY54pPJh80rsH3emPepIiCGwMfMaMjz8Y529fxoAZLFFtQFBjcMDFOeKMrkoDgUshA2ZGfmFOb7p+lAEaRRFEYIPujBpI44t8jBRu3cn8BUkZBiQgYGBxSRkFpMDGG5/IUAM8qJZwAgBKnt9KJIoljwUG0svAHuKeSPPUY52nn8RRKQEGRn5l/mKADyY9u3YMelMjiieCP5ARtGMipqZCQYEIGAVHFADFjiMztsG4MOce1BiiE6HYNxB7U9CDJIAOhGffgUMR5yDHJB5oAZJFEqMSg564HvTxDGAQEGD1omIERJGRxx+NPoAhiiiMIAQbfQikEMJkddg4A7VJCQYgQMDnihSPOcY5AHNADJIojLFlBnJxx7UTRReW7Mg+6cnHbFPcgSRgjkk49uKJiBA5IyAp4oABFGGLBBk9TTIYovLbagwWYHj3NTUyIgoSBj5m/maAIxDD5rKEH3RkY9zSyRRfu1KDG7gY9jTwR57DHO0c/iaJCA0eRnLce3BoAbLFEY2LICAp7UoijO1tgzjg06QgROSMjaeKVfuj6UARRRRZdlQZ3HJxQIohOQEAJXninxEHfgY+Y0ZHn4xzt6/jQBDMtvEih9iqzgDPGTUrQxeWVKDb6YrzT4iaRrN5rcU8FvcXNqYwsYiUsEPcEDpn1/wruvDtveWnhuyg1Bi1ykWHyckcnA/AYH4VtOkowUk9zOM25ONi+kUTQp8gI2jGR7VMAAOKZEQYkIGAVHFPrE0CiiigAooooAKKKjuJ4rW3luJ5FjhiQu7t0VQMkmgCSiqNlqtvfTNCizxTKok8ueJo2KHowBHI/Ud8VHNrtlBdPA/nYidY5ZViYxxu2MBmxgfeX6ZGcUAVvFX/ACCbf/sJWH/pVFW3WJ4q/wCQTb/9hKw/9Koq26AGREmFCwwdoyKEJLyZGACMe/FEOfJTdjO0ZxQmd8mcYyMfkKABifNQY4wcmiUkRkgZPHFDZ81MYxg5olz5Z24zx1+tAD6ZESYwSMH0p9Miz5Y3Yz7UACk+a4I4wMGhyQ8YAyCTn24oXPmvnGMDFD58yPGMZOfyoAJSRE5UZODgU+mTZ8l9uM4OM0+gBkRJQ5GDuP8AM0AnzmGONo5/OiLOw7sZ3N0+poGfPbpjaP5mgAckMmBnLc+3BpZCRGxAycHApJM7o8Y+9z+RpZM+U+OuDigBV+6PpTYyTuyMfMcU5fujPpTY84bOPvHFABk+djHG3rRISAuBn5hmjnz+23b/AFokzhcY+8M0AOP3T9KSMkxoSMHAyKVvunHpSR58pM9dozQAiElpMjGG49+BQSfOUY42nn8qI87pM4+9x+QoOfPXpt2nP5igAlJCcDJ3D+Yp9Mlzs+XGdy9fqKfQAyIkxIWGDjkUKSZJMjgEY9+KIc+Sm7Gcc4oTPmSZxjIx+VAAxPmoAOOcmiUkRkgZPpQ2fNTGMc5olz5R24z70APpkRJjBIweePxp9Miz5Y3Yzz0+tAApPmuMcYGDQ5IePAyCefbg0LnzX6YwMfrQ+d8eMYyc/kaACUkQuVGSFOB60+mTZ8l9uM7TjNPoAZGSVORg7j/OgE+cRjjaOfzoiztOcZ3N0+poGfPbpjaP5mgAckFMDOW5pXyI2IGTg8UkmcpjH3ufyNLJny2x1waAFX7oz1xTYyTuyMfMcU5fuDPXFNjz8+cfeOKADJ87GONvWiQkBcDPzD+dHPnDpjb/AFokzhcY+8Ov1oAeehpsZJiQkYO0ZFKehpsWfKTPXaM4oAEJLSZGMNx78Cgk+coxxtOT+VCZ3SZxjdx+QoOfOTpjac/pQASkhOBk5H86fTJc7OMZyOv1p9ADIiTEpYYOORQpPmOCOBjB9eKIc+Uu7Gcc4oXPmSZxjjH5UADE+YgA4OcmiUkRMVGT6UNnzUxjHOaJs+U23GfegB9MiJMeSMHJ/nT6ZFny/mxnJ6fWgABPmuMcYGD+dDkh48DILc+3BoXPnP0xgY/Wh8748Yxu5/I0AEpIhcqMkKcD1p9Mmz5Mm3Gdpxn6U+gBkZJU5GPmP86MnziMcbRzRFnac4+8en1oGfPPTG0fzNABISCmBn5uac+QjYGTimyZymMfe5pXzsbHXBoAVMlFz1xTYyTvyMfNxTkzsXPXFNjz8+cfeOKADJ84DHG080SEhRgZ+Yfzo584dMbT/OiXO1cY+8Ov1oAf2pkRJiQkYJUZHpTz0pkWfJTdjO0ZxQAISXkyMANx78Cgk+aoxxg5P5UJnfJnGN3H5Cg585OmMHP6UAEpITIGTkfzp9Mlz5fGM5HX60+gBkRJiUsMHHShSfMcEcDGDRFnyl3YzjtQufMfOMcYoAGJ8xABwc5PpRKSImIGTjpQ2fMjxjHOfyolz5TbcZxxmgB9MiJKcjByf50+mRZ2c4zk9PrQAAnzmGONowfzockNHgZBbn24NAz5z9MbRj9aHzvjxjG7n8jQAspIicgZIU4FOHSmS58l8ddpxmnjoKAGRkkNkY+Y/wA6MnziMcbetEWcNnH3j/Ojnzj0xt/rQASEjZgZ+YZpzZ2HHXFNkz8mMfeGac+djY64oARMmNSRg4FJGSS+RjDcUqZ8tc9cCkjzl84+9xQAEnzgMcbTz+VEhIUYGfmH86Dnzx0xtP8AMUS52jGPvL1+tAD6ZESYULDBKjI9KfTIc+THuxnaM4oAEJLyZGADx78ChifNQY4wcn8qEzvkzjG4Y/IUNnzk6Ywc/pQASkiMkDJyOPxp9Mlz5Z24zkdfrT6AGREmNSRg+lCk+a4I4GMH1oiz5S7sZ9qFz5r5xjjFAAxPmIAOCTn24olJETFRk44FD58yPGMZOfyomz5L7cZxxmgB9MiJKcjByf50+mRZ2c4zuPT6mgABPnMMcbRg/nQ5IaPAzlufbg0DPnN0xtGPzND53R4x97n8jQAshIicgZODgUo6Ckkz5T467TilHQUANjJIbIx8xoyfOxjjb1ojzhs4+8aOfP7Y2/1oAJCRtwM/MM05vunHpTZM/LjH3hmnN9049KAEjJMakjBwMikQkl8jGG49+BSx58tc9cDNJHnMmcfe4/IUABJ85RjjaefyokJCjAydy/zFBz569MbT/MUS52DGM7l6/UUAPpkRJhQsMHAyKfTIc+Sm7GdozigAQkvJkYAIx78UMT5qADjByaEz5kmcYyMflQ2fNTGMYOaACUkRkgZPpT6ZLnyztxnjrT6AGREmMEjB9KFJ81wRxgYNEWfLG7GeelC58184xgYoAHJDxgDIJOfbiiUkQuVGTg4FD58yPGMZOfyomz5L7cZ2nGaAH0yIkocjB3N/M0+mRZ2HdjO5un1NAACfOYY42jn86HJBTAzlufbg0DPnt0xtH8zRJndHjH3ufyNACyEiNiBk4OBSr90Z9KSTPlvjrg4pV+6M+lADYyTuyMfMcUZPnYxxt60R5+bOPvHFeVN4910eJSoC+QJ/L+yeUM4zjGcZz+PWtaVGVS/L0InUULXPVZCQFwM/MKcfummyZwuMfeFOP3T9KyLEjJMaEjBwMinU2PPlJnrtGadQAUUUUAFc/ql/e2+oOmmSzXdxgbrI2waNeP8Anr8oQnr8zN7LW+xIUkDJxwM9a4lra6tbvUCt1cEjZPcBtVEfk5jUc5Tp8p+b29qAOmtbu+u7a4jltRY3qLgF/wB9FuI4IIKlgD1HynjtkGsrU9O8SXGl3UU1/ptyhjY+RBYPE8hAyFDNMwGSAOQfw61taSIho1iIVKxfZ02AsWIG0Y5PJ+pq5QBz5vDcaympxWl79ntLKWNw1s6O7O8ZCqrAFsCM5xxyKytSs5nutTOzUBfSXKSW8EUTG2m2hdhcgbcHADbiDx7Cu1ooA5fxbYO8Vrdi/u1UahYj7OrL5R/0mIZIxnPOevaul8s7ceY/1yM1j+Kv+QTb/wDYSsP/AEqirboAhjibyUBlfIAyQetCxt5jkyvjIwAfanwgCCMA5AUYNCACSQg9SM+3AoAY0bGZD5j4wcjNEkbbWIkbk9M8DmnsB5yHPIB4/KiYAxEE4HH86AARkAjzHOfU0yKJhCFMrk+uampkIAiAByPWgBgibew818cd+e/tQ8bF48SOADzz14p6gec5zyQOKHAMkZJ5BOPfigBk0bMjESODg454p4jO7PmP9M0TAGFwTgFTzT6AIYo2CnMjnJPfpyaFiYSN+9cgqO/Tk0+IAIcHPzN/M0ADz2Oedo4/E0AMeJsIBK4+bk556GlkjJiIEjg4POadIAWjycYbj8jSyAGJwTgbTzQA0Rn5T5j8ds9abHEwLEyueTxmpV+6PpTYgAGwc/MaAGCNhOT5jEFemfeh4m2gCV/vDkmn4Hn5zzt6fjRKAQuTj5hQAhjOwjzHz65pEjPlIDI+QBkg9akb7p+lJEAIkAOQFHNAEaxtucmV/vcAH2FBjbz1bzGxtPGfpT4wA0mDnLc+3AoIHnqc87Tx+IoAZJG2MiRvvDjPHUU8RkA/vHOfU0SgFACcfMv8xT6AIYomECqZXJx1zQsTZcGV+oxzz0+lPhAEKAHIA60IAJJCDySM+3FADHjYyJiRwBnOD1oljYqSJGB+vFPYDzYznkZwKJgDEwJwPWgA8s7s+Y/0zTIo2CcyOTz396mpkIAjAByMn+dADFiYO+ZXIIGOelDxNlAJX68nPPQ09QPOc55IHH50OAZIyT0Jx78GgBk0TGFgJXBwec08xkkHe4x2z1omAMEgJwCpyafQBDHEwyTK55PGfelEbCdj5jEFRxnpzTogAhwc/M38zQAPPY552jj8TQAxom+UCV/vdz7GleM+UyiR84POadIATHk4w3H5GlkGY2BOBg0ANEZ8tR5j5A6g9aakTc5lf7x6GpV4RfpTYwBvwc/MaAGGNvPDeY2NvTNDxtkESP8AeHGeOtPwPPBzzt6fjRKAQuTj5h/OgBPLIB/eOc+p6UkcZEKgyOTgc5qQ9DTYgBCgByAo5oAYkTfODK/3uDnnoPahomMifvXACnOD16U+MAPJg5y3PtwKCB56HPO1uPyoAbLGxAIkYcjjPHWneWd2fMf6ZolAMeCccj+Yp9AEMMbLGMyOTj1oSJg0gMrnOMc9OKfCAIUAORjrQgAkkOeSRn8qAGNE25AJXxznnmiWJjEQJXB9c09gDLGc8jOBRMAYmBOB60ABjJIPmOMds0yOJguTK5696mpkIAjAByMn+dADVjYTu3mMQQMDPSkaJgyYlfrzk+xp6gec5zyQOPzocAvEScYbj34NADJI28h1Er5wcHNO8slQPMf65pZgDBICcAqcn8KfQBCkTbeZX+8eh96Xy288t5jY2jjNOiACtg5+Zv50ADzyc87Rx+JoAY8bb1Ikf73TPFKYyEb94+SD1PSnSAEpk4w1K4zGwPHBoAYkZEYBkcnHXNIkTbWBlf73XPNSJwi49KSMAb8HOWNADDExlX964AXsevSlkjYlSJGGGHGeDzTsDzwc87Tx+NEoBVcnHzD+dAB5ZznzH6dM0yGNljXMjk7R3qY9KZCAIYwDkBRg/hQAxI2BkBlc5bjnpwKDE29R5r4we/Pb2p6AB5cHOW59uBQwHnIc87Tx+VADJY2MYAlcEEc596eYySD5jjHoetEoBjwTgZH86fQBDHG2xSZXJx60qRkTOxkYg4wM9KdCAIlAORjrQoAlkOeTjIoAYYmDr+9fHOcn2okjbyWUSvn1Jp7gGWM55GcflRMAYWBOBjrQAGM7QPMf65FMSJtgzK+QT0PvU1MiAEeAc8n+dADfLbz2bzGxgcZ+tI0beYhEj43cjPsaeAPPc552jj86JAC8WTjDce/BoAa8beXJiR8lTjJ6UojIXHmOffNLKAYXBOAVPNPHQUAQxxNsIMr/AHjzn3oMTGX/AFrgbexp8QADYOfmP86MDzyc87en40ANkjYshEjDDDIz1pWjOCRI/TpnjpSyAHZk4wwpz8owPpQBHHGQozI547mkjjYbwZXOW456VJGMRqAcjApIwAZMHOW5oAZ5TeYB5r42nvz1FEsTFFAlcEMOc9eaeQPPU552nj8RRKAVGTj5l/mKAAxkkfvHGPQ0yKNhGmZHJCjPPB4qamQgCCMA5AUYP4UANSNhLIxkYgngZ6cCk8phKv71yMHqaegAkkIOcsM+3AoYDzkOeQDx+VADJIm8ogSvnI5J9xT/ACztx5j/AFyKJgDGQTgZH86fQBCkTeWoMr5HcHrSiNvOZvMbHGBmnQgCJQDketCgebIc8nGRQAxo281CJHxzkZ9qJI28t8SPkjjJ6U9wDJGc8gnH5UTAGFwTgY60AAjIBHmOc980yOJvKwZXzuPOfc1NTIgAmAc/M38zQAzymMp/evjaOh+vtSyRsXjIkYANyM9eDTgB57nPO1ePxNEgBaPJxhuPfg0ANkjJRiJHB2kDnjpSrGRz5jnPbNLKAYnBOAVPNOH3R9KAIoo2CsDK5yx5z0o8pvMx5r42+vPX6U+IABsHPzGjA8/OedvT8aAGSRsVQCVxhhznrTnjJXiRxgetLIAdmTj5hTm+6fpQBGkZ2qTI+QB34ojjYO5MjHLcDPA6U+MARIAcjApIwA0mDnLc/kKAGeUwmB81yNp4J+lDxN5eBK+dw5J9xTyB56nPO08fiKJQCgycfMv8xQAeWduPMf655piRMYUBlfIAyQetTUyEAQIAcgKOaAGLG3muxkbGRgZ46UGNvOU+Y+MHIzT0AEkhB6kZ9uBQwHnIc8gHigBkkbbGxI+T6npzTxGQCPMc575omAMRBOBxz+NPoAhjjbyQplcn1zQImLt+9fGB0NPhAEQAORzzQoHnOc8kDIoAY8bGSMiRgAeRnrRLGxjciRwduBzx0p7gGSMk8gnHvxRMAYHBOAVPNAAEIYne/PbPFMiiZUIMrkljznpyalZgqlmICgZJPaq1jd2l3EzWl1DcKrtkxOGAJJOODRYCQRMJG/evgqO/Tk+1c7rvi/S9Evo7K4luXmGGk8lQdgI4zkj1BrpQB57HPO0cfia43xN4CTW9Y+3xX3kNNtWVWTdyBjI5HYDitaKpuX7zYio5W93c6uOSO9skuLedmikjDow7gjg9KZf3cOmafLfXMriGFNzY7+w9yeK5PxhqF34T8PadYaWSiEGIzsoJAUDj0ycn8jUvhS8n8X+Fry01cGRd3leaoClhgEe2QcfpVex932n2b/Mn2mvL1J/DXjKz17UJbRUnhm5eNXYEMo/r7VtjRbAap9u+zRG5xnzSg3Z9c46+/Wsbw14Is9Av5L0XL3MwBRNyhQgPX1yf84rqMDz85529Pxqargpfu9ioKTXv7nJ+I/EN3pl+tpbfwqHZ35Jz2HtW/pdy2paRBdEujSLkjPcEg/hRqOkWGotG93ArupADZIOPTgirqRpFCIo1CIq4VQMACuSEKiqNyeh1TnSdOMYxs1uxqxkxIDI+QBkg9eKlAwOuabEAIkAOQFHNOrYwCiiigAri9ftbfU9fVZYbu4lgbyrZoBGqwyiPzsfMf3jEIDhhsGV75Ndm2dp2gE44BOK4LWTdf2r5kkaQXKESyixu7hwDsKBn227BDsJGeDjHPAwAdpptybzS7W5LhzLErlghTJI5+Uk4+mTirVVdMWJNKtFhWJYhCgQQuXQDAxtY/eHoe9WqACimpIkhcI6sUbawBztOAcH0OCPzqCTULKK8S0kvLdLl+UhaVQ7fRc5PQ0AZvir/AJBNv/2ErD/0qirbrE8Vf8gm3/7CVh/6VRVt0AMhx5Cbfu7RihMeZJjrkZ/IUQkGCMgYG0YFCEGSTA5BGfyFAA2POTPXBx+lE2PKO7px/OhiPOQY5wcH8qJiBESRkcfzoAfTIceUNvSn0yEgxAgYFAAuPOfHXAzQ+PMjz1ycflQpHnOMc4GTQ5HmR5HJJx+VABNjyX3dNpzT6ZMQIXJGRtORT6AGRY2Hb03N/M0DHnt/e2jP5miIgocDA3N/M0Ajz2GOdo5/E0AEmN0ef73H5Glkx5T56bTmkkIDR5Gctx+RpZCBE5IyNpzQAq/dGPSmxYw2P7xzTl+6PpTYiCGwMfMaADjz/wDa2/pmiXGFz/eGPrRkefjHO3r+NEpAC5GfmFADm+6c+lJFjykx02jFK33T9KSIgxIQMDaMUAJHjdJj+9z9cCg489f7204+mRRGQWkwMYbn8hQSPPUY52nn8RQAS42Dd03L/MU+mSkBBkZG5f5in0AMhx5KbemBihMeZJjrkZ/KiEgwoQMDHAoQjzJMDkEZ/KgAbHmx5684omx5TbulDEebGCOecGiYgRMSMigB9Mhx5Y29Mn+dPpkJBiBAwOf50AC4858dcDP60PjzI89cnH5GhSPOcY5wMn86HIEkeRyScfkaACbHkSbvu7Tmn0yYgQSEjI2nIp9ADIsbDt6bm/maBjz2/vbR/M0REFDgY+Zv5mgEeewxztHP4mgAkxmPP97j8jSyY8ts9MHNJIQDHkZ+bj8jSyYEbZ5GDQAq42Ljpimx4+fH945py/cXHpTYyDvwMfMaADjzx/e2/wBaJcYXP94fzoyPPAxzt6/jRKQAuRn5h/OgBx6GmxY8lNvTaMU49DTYiDChAwNowKACPG+THXdz+QoOPPT+9tbH6URkF5MDGG5/IUEjz0GOdrc/lQAS48v5umR/MU+mSkCPkZGR/MU+gBkOPJTb0xxQmPMkx1yM/lRCQYUIGBjpQhHmyADnjP5UADY82PPXnFE2PKbd0oYjzYwRzziiYgRMSMj0oAfTIceWNvTJ/nT6ZCQYwQMDJ/nQALjznx1wM/rQ+N8Weu7j8jQpHnOMc4GT+dDkB4sjktx7cGgAmx5Em77u05/Kn0yYgQSEjICnI/Cn0AMixtbb/eb880DHnn+9tH5ZNERBVsDHzN/OgEeeRjnaOfxNABJjKZ/vcfWlfHltnpg5pJCAUyM/NxSvgRtnpg0ACY2Ljpikjxl8f3jmlT7i46YpIyCXwMfMc0AHHnj+9tP86JcbVz/eH86MjzwMc7Tz+NEpAVcjPzD+dADz0pkOPJj29Nox+VPPSmQkGGMgYG0YH4UACY3y467ufyFDY85PXacfpQhBeXA5Dc+/AoYjzkGOdpwfyoAJceX83TI/nT6ZKQI+RkZH86fQAyHHlLt6Y4oXHmyY68ZohIMKkDAx0oUjzZABzxmgAfHmx5684/KibHktu6Y5ocjzYwRzzj8qJiBCxIyMdKAH0yLHl/L0yf50+mREGPgYGT/OgAGPPf8AvbRn9aJMb4s9d3H5GgEee4xztHP50SEB4sjq3HtwaACXHkvu6bTmnjoKZKQIXJGRtORTx0FADIsYbH94/wA6OPPP97b/AFoiIIbAx8x/nRkeeRjnb1/GgAkx8mf7wxTnxsbPTFNkIGzIz8wxTn+42emKAEjx5a46YGKSPGZMf3ufrSx4Ma46YFJGQTJgY+bmgAOPPX+9tP5ZFEuNo3dNy/nkUEjz1GOdp5/EUSkBRkZ+Zf5igB9Mhx5Ee37u0Y/Kn0yEgwRkDAKjA/CgATHmSY67hn8hQ2POTPXBx+lCEF5MDkMM+/AoYjzkGOcHB/KgAmx5Z3dMj+dPpkxAjJIyMj+dPoAZDjyl29KFx5smOvGaISDEpAwKFI82QAc8ZNAA+PMjz1ycflRNjyX3dMc0OR5keRyScflRMQIXJGRjpQA+mRY2fL03N/M0+mREFOBgbm/maAAY89/721c/maJMbo89d3H5GgEee4xztXn8TRIQGjyM5bj8jQAS48p89NpzTh90fSmykCJyRkbTkU4fdH0oAbFjDY/vHNHHn/7W3+tERBDYGPmNGR5+Mc7ev40AEmPkz/eGKc33TnpimyEDZkZ+YU5vun6UAJHjykx0wMUkeN0mP73P5CljwYkIGBgUkZBaTAxhufyFAAceev8Ae2nH5iiXGwbum5f5igkeeoxztPP4iiUgIMjPzL/MUAPpkOPITb02jFPpkJBgQgYG0YFAAmPMkx1yM/kKGx5yZ64OKEIMkmByCM/kKGI85BjnBwaAMbxF4m03QI41vTK0kvKxxKC2B35IAFaOmana6vYR3tm++GTpkYII6gj1rnfGPhAeIXhuorkQzxL5Z3LkMucj6YyfzrY8O6JH4f0iOxjkMpDF3cjG5j7du1bSVP2aafvGac+d32NKHHlDb05oXHnPjrgZpnnRw2jTN8saKWb2A61w+kfEZNQ1+OzksPKhuHEccgfLAngZGO5/L3qYUpTTcVsVKcYtJmlrXjvTNI1gWMkM8rxH948eMKSOnPU81qar4i0rTdMiurq5xFcrmHaCTICM5A+hH51h694AtNX1sXwvJIPOb97GqBskDqDnjp70/wAXeDjq2nWSWMyQmxj8tEkJ2lMDuM8jFapUHyq/qZ3qK+noXdYceKvB1z/Ys4kMoAXnbkgglTnofr/I1z3w88PatpupXN3eQtbwGIx7GPLtuHb2weff611PhPQG8OaN9jkmEsryGWQqPlBIAwPbgVsxEFDgYG5v5mpdXkUqcdmNQ5mpy3OOs/iJpt1r4s1t5lilYRJcEjBOeMr2HPWuxkxujz/e4+uDXHWfw80y118XizzNFEwlSAgYBycAnuBjpXYyEBo8jOW4/I0q3s7r2Y6fP9sivbW2vLR4buGOaIjJV1DD68061toLS2SG2hSGJR8qIoUD8BUkhAickZG05pV+6PpWV3axpYbFj58f3jn61R/tjTf7T+yfa0+0fc29s56Z6Z9qvREHfgY+Y1xB8H3h1r/XJ5BfzPM3fNjP86wrTqRtyK50UIU539pKx28uMLn+8MU5vun6U2UgBcjPzCnN90/StjnGxY8pMdNoxT6bEQYkIGBtGKdQAUUUUAFcncXlzba9qsFpqmmWbOUkMd9GSxYxqu9SHGVwoGMdVPrXWVxWuOi+IV+3NcxxtNsTybbeGiMDfNkIdz+ZgY5xheOeQDf0i3vrHQ0g8y0umihAtihaNXwvG5vmxk45A79KLaXXZp1jvtN06G2YEPJBqMkjjg4wphXPOP4hjrVrSTcHR7I3SbLjyE8xdoXDYGRgcD6CrlAHP6GLTSY9by6wW0V8zM0j8LmOMkkk+p71m30sC6R4jt5HjGpSXTeUhI3tIQvkEDqeiYP+yfSuwKIQwKqQ3UY60hhiaVZWjQyKMK5UZA9jQBzXi2PUjFaulzaCx/tCx3RNbsZSftMXR9+MZx/DXS4l2/eTd67Tj+dY/ir/AJBNv/2ErD/0qirboAhj81oIyCikqMgqf8aF80yPgoACM/KeePrT4cmFN3XaM0JnfJnpkY/IUAMbzfPQZTBB/hP+NEnmBWOVK54G056/WntnzUx0wc0S5EZ29eP50AAEuDlkJ7YU/wCNMi80wjlA3P8ACf8AGpqZFkxjd1oAYPNLsMoCAOdp56+9Enm74wCnJ5JU+n1p658189MDFD58yPHTJz+VADJvNEbkFCADxtOT+tPAk3csm302nP8AOiUkQuV64OKfQBDD5pUklB8zYG0+p96F83zXBKZ2jB2n1PvT4slDu67m/maBnzmHbaP5mgBj+aPLGULFuu04HB96WXzRESGTIBz8p54+tOfO6PH97n8jSyZ8tsdcHFADQJDtIZAO42n/ABpsfmktygG4jG0/41Kv3Rn0psefmz/eOKAGDzPPIJXG3IO0+v1ofzQoyUJLDGFPH60/J87H8O3+tEmcLj+8M0AIRL5Z+ZN3+6f8aRPNMKEMgOBnKn/GpG+6celJHnykz1wM0ARp5pd8FAA3OVPPA96D5v2hRlMbT/Cfb3p8ed0mf73H5Cg585fTac/mKAGSeYBklSu4cbTnqPengS4OWT2wp/xolyEG3ruH8xT6AIYfNMCnKBiP7p4/WhfNLOMoCCOdpwePrT4cmFN3XHNCZ8yTPTIx+VADH83zIwCnIOSVP+NE3mBSQUK+m05/nT2z5qY6c5olyIzt60AGJd3LJj02n/GmQ+aUySg64G0+v1qamRZMY3def50AMXzfMcEpnAwdp/xofzQYxlCxPXacDg+9PXPmvnpgY/Wh8748dMnP5GgBk3miBiChIU5G088fWnkSZGGTHf5T/jRNkQvt67Tin0AQxeaQTlANzDG0+p96F8z7QwJXG0EfKfU+9PiyVOeu5v5mgZ85vTaP5mgBj+aNuShJbjCnjg+9K/miFvmQtg/wn/GnSZymP73P5Ur58tsdcHFADAJTGuGQHHPyn/GkTzTuwUGGOcqef1qVfujPXFNjz8+f7xxQAw+Z9oABTbt/un/GiTzQQcoV3Djac9frT+fOA7bf60SZwuP7w/nQAmJQDlkPphT/AI02LzTApJQEqMfKeP1qU9DTYsmJM9dozQBGnmnzBlAQ3XaeeB70reb5qAFM7TklT7e9PTO6TPTdx+QoOfOT02nP6UAMl8wAEFSu4ZG3nr9afiXd95Mf7p/xolyE465H86fQBDD5pjBJQZHA2nj9aE83fICUyMYO0+n1p8WTEu7rjmhc+Y+enGPyoAY3mh0GUyc87TgfrRN5ohJBQkdflPP609s+amOnOaJciJtvWgAIlyMMmO/yn/GmReaVBygHPG0/41NTIsmP5uuT/OgBi+Z57glduBj5fr70N5oZAShJbghTxwfenqT5zjtgY/Wh8748dN3P5GgBkvmi3c5QsFJ+6ef1p2JdowyZ7/Kf8aWXIhfb12nFPoAhj80qcFB8xzlTzz9a4K3+Jfn68lv9jRbKSQRiTJ34JwGI/pXoEWSpz/eP865uHwPosOu/2ikMm5SJFi3/ALsPnOQOv4ZxW1J01fnRnNTduU6F/NDpyhUt/dOR+tK3mhHJZDwcAKf8adJnKY/vc0r52Njrg1iaDEEpiGWTOOPlP+NInmkPygYN12nB/WpUzsXPXFNjz8+f7xxQAxvN81QCmdpySp9veiXzBtIKldwyNvPX60/nzgO20/zokyFXH94fzoAMS5+8mMdNp/xpkPmmJSSgBUYG08frUx6UyLJhTd12jNADI/NJlBKZDcHafQe9B83zEGUyQedpx296emd8mem7j8hQSfOQdsHP6UAMl80RDBQnIz8p9frTyJcjDJjvlT/jRLkR/L1yP50+gCGLzSinKAY6bT/jQnmedJkrtGMfLz/OnxZMS7uuKFz5j56cYoAYfNDoCUOc4IU8cfWiXzRAxyhbH90/409s+YmOnOaJciJtvXHFAARLtGGTPc7T/jTI/NMYIKDk5yp9frU1MiyU+brk/wA6AGDzPtDAFQoA/hPPX3ofzRJHyhUtj7p44PvTxnzn9Nox+tD53R46bufyNADJPNEUhJQ/KcAKf8acBLt5ZM9vlP8AjSy5ET7eu04p46CgCGPzSh5QNuP8J9frQfN83AKZ28kqf8afHnDZ/vH+dHPnEdtv9aAGSeYGTBUjcM/Kf8ac3mgMdyEY6bT6fWlkz8mP7wzTmzsbHXFAEcfmlQSUwQMDaeP1pI/NPmZKZDcfKfT6024uorKwe6uX2RRR73b0AFYOgeNdN12/ezhWaKY5aMSgDeB9Cee9UoSacktES5JOzOgPm+YBlM7Sc7TjqPeiXzQi4KE7gD8p9frTznzgO20/zFEuQox/eH86koCJcjDJ75U/40yLzTHGcoAVHG0+n1qamQ5MKbuu0ZoAYnmGWTJXaG/unJ4HvR+9EqglDkHBCnj9aemd8memePyFDZ85MdMHP6UAMk80QnJQtkfwn1HvT8S7fvJu9dp/xrkPGvjG48PXNvaWdvE8sieazSglQuSAAARzwa2fDmurregx6jMiwEbllGflBHUgntWjpSUFN7MhTi5cvU04/NaJSCi+oKn/ABoHmGdwCoUY/hOT+tNsrqC8thLBPFMucFonDDP1FVbXW9Nu9RltIL63knX+BXBJx1x61HK+xV0XG80SpyhU5/hPHH1ok81YnJKNxwAp/wAaytS8U6PpmpR2V1erHN/Gu0nbkcZIHFa8jZgZkIOVyCOhocWldoE09hQJcHLJnt8p/wAaZH5pi6oG3H+E+p96mqONsRlnwMFs/TJpDG/vTKwBQEKMkqeevvXC+KfHl7o2vPp9ra27rAFLtKCSxIB4wRjg1asfiNZXmvLZC1kSCVxFHOW6nOASuOAc+tber+F9I1e9iuby0Dy52swYruAB64PNdEIqlL97HQyk3OP7tlyxvX1DSYL5QqrNCJNhU5BI6Zz61bUSdSyY7Daf8agup7TTNPLSSQ20Ea7E3EKo44A/wqW2uYLu3Se3mjliYcPGwIP4isGuq2NF2CLzSr5KZ3HHyn/Gj975hGUztznacdfrTbW5huVkMU0cm1yDsYNj64pv222/tD7L9ph8/Znyt43/AJdaLMdySTzQqYKE7hn5T/jTnEu3hk6c5U/41zviLxpp+gXsdpKks0xw7iMD5V/E9faq3ijxc1h4ctNQ0sLJ9sOEkdchBjnj17fga0jRm7abkOpFX12OqTzSqHKAYHG05/nSR+YXkyVChv7pz0HvXNeBfEl34gsbkXiL5tuyjzEXAcEHt68fqK6iPOXz/e4/IVE4OEnFjjJSV0M/e+cAShypwQp46e9EnmiPkoTuXHyn1HvTznzlHbaf5ivPfiF4h1bTNTtrWzme3gMQk3qOXbJ7+2Bx7/SqpU3UlyoU5qCuz0LEu37ybvXacfzqEyNHaCZ5I40RNzllOFAGT3+tYGneKRa+C7XWNZ3CR8r8i4aU5IGBwMkDPpVjR9b07xdpF1bxeauYzFOjABgGBGRjI9aHSkrtrRApp7bkGkeNNM1jVmsbaR1dj8hkiwJMDnBz6DuK1bjVrO21KK0nv7VJmHEbMA2T07965bw98Pv7J11b6e9EyW5zEqpgscdTzxjPSsbX/Aut3via5nt1SWC5lMgmaQDYCehB546cA1v7OjKdlKysZ89RRu1qenSeaEYkoR2AU+v1rC8ZvqsXhm4bT2bzdy7zCCGCdyOfp+Ga3FRobNI95dkVVLN1bGOanrmjLlkmbNXVjzj4bTatc/bUmeR7DYMGbLDfnoM+2c/hW5p/gbS9O1g30CuZIzujSQ7kQnuB149ya6iLJjG7rQufNfPTAxWk60pSbjpciNNJJPWwx/NEkYBTBPPyn0+tE3miNyChAU8bTk8fWnvnzI8dMnP5UTZEL7eu04rE0ACTccsm3sNvP86ZD5pRslAdzY+U+p96mqG2mSaMskiOAzDKkHufSgBB53msuUztBztPqfeq17qVvZSQx3F3BHIx6N6c89eBUMXiDTZtTNolypc4ReDgtk8A1heI/Dd/fawbq2CyRy7QQWA2EDHftxnisKlVqN6ep00qKc+Wq+VHQ6pqkOmWizTyph+FVVyXOO3NVpdc87QZ9RsCsnlrjYyHKnjrz2zmq+p+GmvdFs7YTgT2iYDHo3AyP0FXNA0UaRYvE7iWSU7nOOOnSles52tZWHy0Iw5r3lfbyMPwvr2o32ptbXDiVGUsTsA2kd+K60eb5xBKfd4IU/41HZ2Vta+YYLeKIsxzsQDI/Cp8nzsdtv8AWrowlCNpO7M69SFSfNBWQx/NCjJQksP4Tx+tOYS7D8ybv904/nSyZwuP7wpx+6fpWpiRJ5piQhkBwM5U+n1qYZxzTY8+Umeu0Zp1ABRRRQBFdM6WkzRZ8wRsVwM844ritIv9N0y+sJIYtQjEli4vWNncHfNmPaWyvLf6zn6+1d1WLc6zqC3t5BY6O94lrgM4uFTcxUNtAPfBHtyOaANeGVZ4Y5k3bHUMu5SpweeQeR9DT6zNG1hdWhLG3eCQRpKFZgwaNxlWBHY4I5wcg8Vp0AFFFFAGJ4q/5BNv/wBhKw/9Koq26xPFX/IJt/8AsJWH/pVFW3QAyIEQoGOW2jNCBt8mTxkY/KiEbYUGc4Uc0IMSSHPUj8OBQAMD5qEHjBzRKCYztODxQwzMhz0B4/KiYZiIzjpz+NAD6ZECIwGOTT6ZCMRAZz70ACg+a5J4wMUODvjweMnP5UKMSuc9QOKHGZIznoT+PFABKCYnCnDYOKfTJhuhcZxlTzT6AGRBgh3HJ3H+ZoAPnMc/LtGPrzREMIRnPzN/M0Afv2Of4QMfiaABwSUwf4ufpg0smTGwHXBxSSDLR84w2frwaWQZicZxlTzQAq/dGeuKbGGG7cf4jj6U5fuj6U2IYDc5yxNABhvOzn5dv60SBiF2n+IZ+lGP3+c/w4x+NEoyF5xhgaAHH7px6UkeRGobrgZpW+6fpSRDESDOcKOaAEQMGfJ4LcfTAoIbzlOfl2nP14ojGGk5zls/TgUEfv1OeikY/EUAEoYp8pwdw/mKfTJRlAM4+Zf5in0AMiDCJAxy2OaFB3vk8ZGPyohG2FBnOAOaEGJJDnqR+HFAAwbzUIPHOaJQTGQp5oYZlQ56Z4omGYiM496AH0yIERjccnn+dPpkIxGBnPJ5/GgAUHzXJPGBihwd8eDwDz+RoUfvnOeoHH50OMyRnPQn8eDQAShjC4U4bacU+mTDdBIM4yp5p9ADIwwU7jzuP5ZoAPnE5+XaPz5oiGFPOfmY/qaAP37HP8IGPxNAA4JKYP8AFz9MUr5MbY64OKSQZMfOMNn9DSyDMbDOODQAq52jPXFNjBG7J/iOPpTl4RR7U2MY385yxNABg+cDn5dv60SAkLtP8Q/KjH78HP8ADjH40SjIXnHzA/rQA89DTYwREgbrtGaU9DTYhiFBnOFHNAAgYNJk8FuPpgUEN5ynPy7Tn9KIxhpOc5bP04FBH79Dnop4/KgAlBKfKcHI/nT6ZKMx4zjkfzFPoAZECIlDHJxzQoPmPk8cY/KiEbYUGc4HWhBiSQ56kcfhQAMG8xCDxzmiUMYmCnB7UMMyxnPTPFEw3RMM496AH0yIMI/mOTk/zp9MhG2MDOeT/OgAAbzXJPy4GP1ocMXjweA3P5GhR++c56gcfnQ4y8ZzjDfnwaACUEwuFOG2nH1p9ZutavYaPZGS/uBCsmUTgkscdgKn07UrPVrNbqynWaFjjcOMH0IPINPldua2grq9ixGGCncedx/LNGG84nPy7R+dEQwrc5+Yn9aAP35Of4QMfiaQwkDEpg/xc/SnPnY2OuOKbIMlOcYbNK4zGw6cGgBVzsXPXFNjBG/J/i4+lKgwij2pIxgvznLE0AGD5wOfl2n86JASBtP8Q/LNGP34Of4SMfjWF4h8V6ZoMsMF20rTPh9kShiFz1OSPQ1UYuTtFCbSV2dB2pkQIiQMcttGaisr231Gyiu7WQSQSruVhUsIxDGM5wo5/CpatoxggbfJk8FuPyFBB81SD8uDn9KEGHkOc5bP04FeWa3461q08UXEULRpb20zRCExghwDjJOM84zwa1pUZVXaJE6igrs9TlBKfKcHI/nT6rTzxJaLJPIsCttP7xguDxxzWJ45j1GfwxIumeYzl1MgizuaPnOMe+PwzUQjzSSHKVlc6GIERKGOTjmhQfMck8cYrgfhlBqkMd4bhJUsSB5ayAgF88lQfbr+FdZZa/pN9qMtpa38Ms4/gU9cDnB6H8KupScZNLWwoTUkm9DSYN5iYPHOaJQTEwU4OOKwNU8Y6NperJZXM7+ah+cohZUyO5/wzW85WW3LKwKsuQwPBFQ4Sik2tylJPRElMiDBPmOTk/zrN8RawNB0Wa/8rzWTComcAknHPtXP+DfGc2uTXNreW0aSxRtOrQg4K5GRgk88iqVKUoOa2RLnFS5XudkAfOY5+XaMfrQ4YtHg8Bufpg15tpXxFvrzxJDDLawi2uZViCrncoJwDnPPXnj8qk8W+N9T0zxE1lZpEkVsVJ3rkyErnn0HPatPqtTm5SfbQtc3fHHie48O2lstpEjTXJcb35ChcZ49fmFRaN40Nz4Su9Xv4AHtG2MIuA5ONuM9OSAfzrWktLHxR4ftZtQtFZZYlmCsSChIzwRg/wCNTpoGmJo0mkx2qJZyAhkBPJ9c9c8Dn2FHNTUFGS1vqFpuV09DC8KeN18QX8llLa+RLtMkZD7gRnp06/54rlLfxvr7+KEVmOxrgRGz2DAG7G3pnPvXdeH/AAhpnh+4luLUzSTOCm+Vgdoz0GAPStUadZDUTeC0txclf9b5a7/rnrVe0oxk+WN0yeSo0rvU5jxR47XQtUWwhtPPkQK8pL7QM8hRx6Y5pvibxhPb+GLHUNLXYb04Ejrny8Dkemc/yNaWveDdL168ju7kzRzABGaJgN47ZyDWo+j6e2krpr2sb2kaBVjcZAx357+9JSopRdtepTjUbevocx4U1O48YeHr+y1UbsYjMyKF3Aj8sjH8qk8NeA4tC1U38l4biSMFYh5ewDIwSeTngkV1NhY2unWiW9pBHDEOdqDAJ9fc1NGMF+c5bNRKs7tQ0T6DVNaOWrR53efEqa38QPCLOM2MUhjbk7yAcFh27dMVJ4+8UatpOqwWdjL9ni8oSFwoJc5Pr2GK6Obwdok+tf2i9rmYnzGTcdrPn7xH+RWpqGnWWoJGLy2gmCMCvmoGxyOmfWtPaUVJNR9SeSo002cZrGv6xL8OrPUoS0NxM+2eSIYKrlhkemSBz7+9M8EanrmoaDq6tNNO8cZFrLKcnzCrcZPXnb9Pxr0ARosYjVFEYG0KBwB6YpltGkVtGiBQqqANowKn20eRxUeo/ZvmTueU+B11weLFLC6EeW+1+bux0P3s984x3rptT+Iljp+vNY/ZpZIoXMc0qsBhu+B3x9RXZIMPIc5yw/DgVyWo/D3TNQ1xr5p5o1lcySwrjDN3weoz3+var9rTqT5qitoTyThG0GbOs6DpniC3ia6hEpXBjkViCAcdx2rO8T+HpJfCB0vRYhGsTKwhU43qDkjJ75559K6N0VYAi4VRgADsBUtYRqSi1Z7GrgnfzPPfAfhzVbKHUWvVktI7mHykU/e3c/Njtj+tZ/h7wJrNj4mguLkJFb20gcyrID5mOwHXn3xxXp8I2xKM596FGJXOeuOK0eJneT7kKjGy8jz3xF8Pr7U/EMt5a3UIguG3N5hO5D36Dn2/L3ru4LX7HpkNpEzEQwrGrHqcDGf0qdxmSM56E/yqK+uLe2tXe5uIoIzxvlcKM/U1Eqs5pRfQqMIxbaM3xXqN3pXhu7u7Ifv0AAbGdmSATiuW+HviDVdVv7u0vpmuIFi8wO68q24DGfcE8e1egK0VzCGRklikXgghlYH+YqCxsrWxhZLW3hhVmJIiQKCc+1ONSKpuDWvcTi3JST0OZsvh7pdnri3yyTNHGwkjhYjAbJPXuBx/WutcEtHg8Bufpg0Afv3OeqqMfiaJBlo+cYbP14NROcp/EyoxUdjkfiBoGoa3ZWjWA8xrdmLw7sbgccjPHGP1qnpHhjWLLwHqViW8u9uSWjiDjgcZGemWAI/Ku6lGYnGcZU804fdFWq8lBQ6Il0k5cx5p8PtB1mw1uW5urea2txGyOJBjzDxgAd/XNZqeDvEQ8VBjG+RceabzI243Z3Z9e+OtetxDAbnPzE0Y/f5z/DjH41p9alzOVtyfYRskcp4n8CweINRS+S7a2lICSDy94YDoeowcVsp4f05NBi0iSATWsS4Ak6k/3sjock9PWtKQZ2c4wwNOblT9KxdWbSjfY0UIpt23MabSf7O8MXVnokK28xhbYE4JfHXJ5z7n2rg/AGn61b+JvMkguYbYKwufNUqG4OBz1O7B/OvVIxiJBnOAKSMYaTnOWz9OBVRrOMZR3uTKmm0+x5FqnibxHF4vmSO4nV47gxx2ozsIzgDb0ORjnvmvV72yt72JUuLeGcK6kCVAwHIz1pzW8TXiTmNDKqEBio3Y479aklGUAzj5lP6iipVU7cqtYIQcb3dzK8R+H4vEOkfYWlMGxw8bKuQpAI6emCapeEfCieGoZma4M88+Nx27QoHYfnXS0yEbYUGc4Uc1HtJcvJfQrkjzc3UEB3yZPGRj8qGB81CDxg5oQYkkOepH4cChhmZDnoDxUFBKCYztODT6ZMMxEZx05qtqeow6VYvdTAkAgBV6sT2pNqKuxxi5Oy3LMQIjAY5NChvNck8YGKxdD8RW+pwzKY2heFS7AnIK+oNUtP8AGEV3q4t2tmjjmYIj7snPbI96y+sU9Hfc2+rVbtW23Ne91vT7K9jt57pUkzllwTjI4zgcVl+LtXvLCK2SzfYs2SZAM5xjgH8aq614Tur7V3uYJohHMQTvJBU45+tdK1hB/ZcdnKiSpFGFBkUHoMZ5rN+1qc0Xp2ZqvYUuSa97ujAtp9R17wdcDObndsBHy+YowT/UfhUHg7TNQtL2ea4ikhhKbdrjG5sjHH5812EcccMaxxIqIvAVRgD8KSIYQjOfmY/qapYf3oyk9US8T7soRVkzj7TwXPBq6Stcxm2jcOMZ3kA5A6Y/WuwcMSmD/Fz9MGgD9+xz/CBj8TRIMtHzjDZ+vBrSnSjTuomVWvOq05vYWTJjYDrg4pV+6M9cUkgzE4zjKnmlXhR9K0MhsYYbtx/iOPpRhvOzn5dv60RjG/nOWJox+/zn+HGPxoAJASF2n+IZ+lOPQ02UZC84+YGnN90/SgBI8iNQ3XAzTqbEMRIM5wo5p1ABRRRQAVyuoWN5qWrX/wDZjfZ1iZY7n/Snj+0PsVsYUHb8rKN457Y+UV1JIUEkgAckmsC8bwxfzmee/tPMZQrmO98vzFHQPtYbhyeDnrQBZ0gWMGjrqVvbyRRzW6SmNQ0rKipwiqMkgDoqjk5wMnlbbX7O/nW1hj1OOSQEK8umXESrwTnc8YUdO568VL4fnNz4b0udtm6W0ic7FCrygPAHAH0rRoAxfDySxPq0U11Ncsl8wEkxG7HlxnHygAAZ7CqOo2iz6pLHavNPqbTxyCZWIWyjG3gnOOQCdvVt3IxyN6TTrSWC5heEGO5bdMMkbzgDP5AflVc6DYG6kuQtwkkj+Y/l3UqKzcclQwHYdqAMnxbc3qxWsC2G61OoWJNz5yjB+0xcbevXA/Gul3Sbc+Xz6bqx/FX/ACCbf/sJWH/pVFW3QBBEzC3j2JkbR/FSqzCV9qZyRu+bpwKfDgwR7RgbRihMeZJgc5GfyFADGZvPT5Oxx81ErMVYMmFyOc+9PbHnJkc4OP0omwIjuGRx/OgADSEHMeP+BVHCzCAbUz1wN1T0yHBiG0YFAEYZhI5EeWIGRu6daWRm8yL5OcnHze1PXHnPgc4GaHx5keRzk4/KgBk7N5bgp8mDls9KeGfdjy+PXNE2BC+4ZG05p9AEMLNsbCcbm/i75NIrN5rny/m2jjd7n/69SRYKHaMDc38zQMee3HO0Z/M0ARyM37smP5t3A3expZWfyWzHwQc/N04p8mN0eR/Fx+RpZMeU+em05oAaGcbQEyMdc02JmywCcbjk7u9Sr90Y9KbFjDYH8RzQAwM32g5TB28c9RmkkZiq7o8EMMDd1qTjz8Y+bb/WiXGFyP4higBGZ/LJMfPpupsbMIY9seflH8VSt9059KSLHlJjptGKAI0Zg77Uzlvm+bocCgs32lfk52nHP0/+tT48bpMD+Ln8hQceevHzbTj8xQAyVmIwyYXcvOfcU8NIc5jx6fNRLgINwyNy/wAxT6AIIWYW67Y8gDj5utCswaQiPLEjI3dOKkhwYU2jAwMUJjzJMDnIz+VAEbs3mxnZlsHjd9KWZm2EFPl7nPvT2x5seRzziibAibcMigA3Pux5fHrmmQs3l4Ccc4O7rzU1MhwYhtGBk/zoAjRm8yQ+X82Bxu+tDs2YyY/m3cDd7GpFx5z4HOBn9aHx5keRzk4/I0ARzs32d8x8FTn5unFSFnyAI8juc9KJsCCTcMjac0+gCCJmAIVMrubnd3yf60qs32h/kwdo79smnxY2HaONzfzNAx57cc7R/M0ARuzHYWjwQ3A3deDTnZzC2Y8cHI3U6TGY8j+Lj8jSyY8ts9MHNADFZxGuI88dN1IjMN21M5Y5+bpUq42Ljpimx4+fA/iOaAGFm+0j5OdvrXmnxHl1ddYgCmdLPYPJ8snaXyc9P4q9O488cc7f60S4wuR/EP51rSqezlzWuROHOrXMrw2+ot4cszqSP9q8v5y5+YjJxn3xjOa0ombyFxH0UY+brSXl5bWFq9xdzJDCvV3OBVS31rTpdHfUILlJLWFCzsnJXAyRjrn2qWnJ8yQ1ZaXLaMwMhEeWLcjd04H/ANah2bzkIjy208bvpXL+H/HdjrerNYi1lt3lJMTMwIfA7+hwK6w489OOdrY/SicJQdpIIyUldDJmbABT5dw5z70/c+7Hl8euagv72zsoVa8uYYFZgFMsgXJz7kVmeKPEI0HQhfwIk7SuqRc5UkgnJx2wDSjByaS6jckldmvAzeUoCfKBwd3Wmxy7pZgFBcEblDDjiuS8LeKb3xLp+oWvlRW97FCfJePO3JBA6k4wcd65fwhouvW/i6GSS2uYBGx+0SSKQpXuM98/j61ssPbm5nZozdXayvc9WZm3xkx/Nzgbq4X4j6tq1iLOK1kltreQEvJE2CzDtkdOPzzXU33iXRdP1KOzu76OO4/ukEhc9MkcD8a0rqOKa2ZZo1kjPJVhkGopv2clKS0KkudNJmB4K1DUtQ8OQzX6s772VZWOC6DoT69x+Fb0TMFAVMrk87qyPF99eaX4ZuLjTxtlXau5RnYpOCcVy/w81rWNQmvraeZ54Uh3pJLzskJ4GfQ8n8Kt03OLqrREqai1BnoCsftEnydh3+tRTXMccsKzMkbs37tWkALHB6V5P4etfEK+NYWeO7WcTD7Uzg4KZ+bcehGM4/DFXPG3h7W7zxS88NtNcwz7RCychQB0P93nP86v6tFT5XLoT7Z8t0jqfG3hq58RWtvLbukUttu+WQ8Mpxnkd+Kt+EdBn8OaM1vMyyzSSGR9h+VeAMDPXp+ta1lFNb6JBDeP5k0duqytnOWC8896q+J7S8vvDV9bWBP2mRAFAOCRkEj8RkfjWSqSaVK+ly+VJ89tS9bzrIjGDbIu9gxVwcHPIp+5vtJwnO0Z57ZNedfDnR9XstWuLi5t57a28sowlUrvbIxgH055/wAa9IGPPIx820fzNTVgoS5U7jhJyV2rGXqev6bpdxDDf3McDsQygtk46ZIA4Huaq+I/FMGg6bFcPD55uOIVSQYcYyTnnjkevUVzfjPwXqWq699vsnjdJwqMrvjyyBj8uM/nW/c+D7W88L2el3srtJaJ8sydQ2OcZ7e3sK1UKSUW3fuRzVG2kifwx4li8R6fJLDbtFJCQrxFwcehzgcdfyrYjZgHKx5O7kbqzdB0Ow8M6Y6QudrfvJZpSATgd+wAqXSte0rVpporC8jlkQksoyDj1Geo9xWU0nJuC0Li2klLcsNeQrfLB5kX2koT5XmjdjjtXL+LvBba9qMV7DcCB9ojcOMgjPBHvzXJ3fhPxFJ4tkKRSF3nMq3eflA3ZDZ7Y9Oteuy42rkfxD+dayXsWpQle5K/eJqSKGi6aNF0i306MF0hU/OTjcSSScduSadeXktjotxdRQ7zBbtIgz97C5FYnxBGpHw5/wAS/wA3HmDz/Kzu2YPp2zjNUPhmNS/s27F4Jfsm5fs/m5687tue3T2/Wp9neDqt9Q5rS5EjK8I+M9Z1DxIlpdOs8VxuJURquwhScgge2Oa7ybS7CfUI72XToHuxyHZQTxjBzjqO1SWOlabZXM8tpYwQSscM0cYB6A4+lXGx5ycc7Tj9KVWpGUrwVhwg0rSdzzz4jaPq+oXdpc21vLPbJHsMcfzFHyecD1GPyrp/CNnqGm+G7W2vkJlXJ2lhlFJ4H+fpW5LgR/MMjI/nT6JVnKmqfYFTSk5FOWJrnT5bZdyrLEyeYG5GQRmvPPDvgTVbDxLDcXLRpBayB96Pkv6AD+ea9MhwYV2jAxQuPNkwOeM0oVpQTS6jlTUmm+h5/rnw+m1PXpLyG6SGK4fe6OCWBxzj1/HFR/EGx1KPTdNt7SKY6fAnlsqEtyAApbHsOPxr0R8ebHkc84/KibAhbcMjHNXHETTTetiXSi07dTk/Cmn3l14N+xa3bPLHIxCRzEhhHxjPccgkfhWponh/T9CV20+1IeXh3Z8nAPTntW3TIsGP5RgZP86zlUlJvsy1BK3kYtt4Z0e01lr6DTkW4HzBgxwpOeQvQVLqOhaZqd7DPfadFLKpwrk9eCcHHUfWtUY89+Odoz+tEmN8WRzu4/I1PPK97hyxtaxG2RA6GIIgQgYPbFPVn258vnsN1LLgQvu5G05p46CpKII2YIxWPJ3HI3e9BZhNkR5bbyN3SpIsYbA/iP8AOjjzzxzt/rQAyVm3RgpxuHOaV2fDAx/Ljru9qdJj5Mj+IYpz42NnpigCONm2AbPlAGDnrTYmb94RHzu6bu+Kljx5a46YGKSPGZMD+LmgCMs3mg+X8+0/Lu7ZFEzN5a5T+JeN3fNSHHnrx820/wAxRLjaNw43L/MUABZwRiPPr83SmQswijCplNowd3tU1MhwYI9owNox+VADI2PnSgJ/EMnPsKQsxmUtHhgDgbutSJjzJMDncM/kKGx5yZHODj9KAI5WYwncmORxu9xUm6Tbny+fTdRNgRncMjI/nT6AII2YRLsj3D/epVYid8JnOMnPSnw4MS7RgULjzZMDnjNADGZjNHlMcnBz7VxPxD0TVdWjtJrOFpYoNweNWGRnHzY79K7l8eZHkc5OPyomwIX3DIxzV06jpyUkTOKkrM5zwRpeoaN4d+z3yEStK0ixlgdikDj8wT+Nb8TMIsrHn5jxu9zU5IAJJwB3qC1ngniLQSI6hiCUYHnNKc+aTb6jjG0bINzCZiI8sVXI3dOv/wBelkZt8WU/i459jVGHXdMm1JrWO5UzHCjg4YjPAPSmal4g07TruO3uJG8wEMQq52ZHesnVgle+hqqNRvl5Xc0ZWby3BT5dpyd3tTlZ+nl8djnrVHUtZsbCxS4mkLRzD92EGS4I7UttrVjc6Y9+kpWCMfPuGCp9MUe0he19ReynbmtoWoWba+E/iP8AF3o3N5pPl/Pt+7u7ZrP0jX7HVZpIbfzFkGW2yLjI9RyazR4ztTq3k/Z28kt5fn7vfrj0/GpdemknfctYeq20o6o6KVm2oTHzuHG7vT3Z9v8Aq8jHPzdK57XvFA0u+W1jtxKy4dyzYx7CpNS8TLb6Hb31vCGa54VX6LjrnHWk69NNq+w1hqrUXbfY3I2YIgVMrtHOaSNm8yQBON3Jz7CsHSvErXejXlzJbgS2i5Kpna3Bx9OlU/D3ie6v9WFrcRx7ZtxBQEbSBn16cUvrNO8Vfcr6rVtJ2+Hc6ncxnBKYYKcDd16f/WokZjF80ePmXA3d8jFcZeeLr2HW5AsUfkxOY/LI5Izzz68Vc8Wa7e6feRWtqRGuwSFyoJJz79uKn61Ts32KWCq80Y9zq90m3Pl8+m6o4mYQR7I9w2j+L2rnbvxBer4Sg1CNFS4lfyy23IHX5gPw/WmeHNTv9X06/tpHHmpHtilA27SQQM4qvrEHJRW7Vyfqs1Bzeydjo4p0e5lSMo7BhvAcEr+FUbnXtPttRW2nnVJV4PPAJ9TjArmPDmjanb68kkkLwpCT5jN0Ix096tan4QubnWXlinjEU7lyWzle5479eKy9tVlDmjHW5r9Xoxqcsp6WJPGOpahayQQws8ELqWLoeWOemfbj86u6dHL4g8MCPUUYsxO2QEAnB4P9K25beH7GsMqCSNABhxnpU4AAAAwB0ArVUXzuUno+hk66VNRirNdTD0TQoNKikdA00ko2sWIA2+lRWfhixstR+1RRyu6HcsbOCEP5c1vw4MQ2jA5oXHnPgc4GapUaaSVtiHXqNt82+4yRm82L5O5xz7GiZmMThk+Xacnd7U98eZHkc5OPyomwIH3DI2nNamIBn3EGPj1zUcLN5bYj/ib+Lvk/1qemRYKHaMDc38zQBGGbzWPl/PtHy7u2T/8AXokZv3ZKc7uBu74NSDHntx820Z/M0SY3R5H8XH5GgBkrN5TZj4KnPzdKcGcbQEyuOuadJjynz02nNKv3RjpigCKJmy4Ccbjk570gZjOSUw23gbvepIsfPgfxHNHHn9Pm2/1oAjkZiqlo8HcMDd1p7M/lk+Xz6bqWXGFyP4hinN90/SgCJGYQx7Y8jaP4vaphnHIxTYseUmOm0Yp1ABRRRQAEAjB5FVXsbFEZ2s4MKMnEQP8ASrVclqiRf2rqBv7vWoN4UWosmm2ldgyVCAjfu3DB7AHHOSAdRbSRTWsMsAIidAyAoUIUjj5SAR9CKlrB8NLfxxSRXhuSqxxZ+0EkrKVzIqseWUHbg5PJIzxW9QAUUUUAYnir/kE2/wD2ErD/ANKoq26xPFX/ACCbf/sJWH/pVFW3QAyE7oEIGMqOKEOZJBjoR+PAoiJMKFhhtozQhbfJkcAjH5UADHEyDHUHn8qJjiInGen86GLeagA4wc0SkiM7Rk8UAPpkJzEDjHtT6ZESYwWGDQAKcyuMdAOaHOJIxjqT+HFCk+a4I4wMUMW3pgcEnP5UAEx2wucZwp4p9MlJETlRlsHFPoAZEcoTjHzMP1NAP79hj+EHP4miIsUO4YO4/wAzQCfOYY+XaMfXmgAkOGj4zlsfoaWQ4ic4zhTxSOWDJgfxc/TBpZMiNiOuDigBV+6PpTYjkNxj5iKcv3Rnrimxlju3D+I4+lABn9/jH8Oc/jRKcBeM/MBRlvOxj5dvX3qlqWsWGlmJby8hgaRhtDtgkev0ppN6ITdty+33T9KSI5iQ4xlRxVe61C0tLE3k9zFHb7QRKWG056YPfNJp2oWupWaXFpcRzIeCyHODjofQ0WdrhdbFiM5aTjGGx9eBWPP4q0W31hdPlvY1uBlDwcK3HBbGB+f1rYQsWfI4DcfTArzfUfhxe3fiKWaO6hFnPK0pds71BOSMY5PPHPNa0Y05N87sRUcl8KPSJThAcZ+Zf5in1G4KRgLk4Kj8MipKxNBkJ3QoQMZA4oQ5kkGOhH48U0SiO182Zgiqm5y3YAc5rA0nxrpGr6q1jbvKJGP7oyJhZMDnHPt3xVKEpJtLYTkk7M6B3UTxqcZbOKWY4iYkZ9q8j8UaP4hn8XzyJb3UrPLm3ljB2hc/Lg9Bj9OtdP4w8V6h4ejsbOFI2uZIQ8szLkZHHH4g1u8O3yqLu2Ze13urWO4pkJzGDjHX+dcPc+J9T1L4eS6naRGG7V/LlaIH5VB5ZfwI+nPpVf4b6pq99c3kd5PPcWqICJJmLFXz0BPqM8e1T9XkoOT6D9quZJdT0BT++cY6Ac/nVC/1zTNPvIbe7vYIpXOQrsAQMHk+lV7PxRo19qjWNvfxyT9FUBsMR1wSMH8K4zxX4I1fUvEst3ZhJYbkg7mcDy8ADnPbjtminSTlao7DnNpXhqdl4j8S2Ph61ja6DyPNkRxxgEtjqeeMcj86taLrVpr2ni8sy2zcVZXGGVh2P5isPXvBi6vomn25umS5sYRGsm3cJMKAcjPtWr4b8PxeHNL+xxymZmcySSEYyTgcDsMAUmqXs9H7wJz5tdjViOUPGPmYfqa8+s/iUbnxAkBsVWzlkESvn5xk4DH8+legxlip3DB3H8s1z0HgrRbfXP7RjtmDqRIibvkV8nkD+nSik6aT516BNTduVmD4u8c3+ka81hZQw7YArO0qlt5IzxzwMGl8ZXup6r4Q029so5Ut5hvuUjzkccZx/D1/Suq1Xw7pWr3MM97YpNKp278spx74Iz+NaYRYbfy4kCqibUVRgAAcAVaqwiouMdUS4Sd03ozjvAMt9aeGrifVXeO0jbdC02cqgHJ5/h9Pxrb0XxPpOt3EsFlcbpUy21kKll9RkVb1fTRrGi3Fg7mPz48bh/CeoP5iuT8I+BrrRNYN/e3MTmMMsaRZ5zxk5A7dqTdOopTk7PsNc0WorVHUHXtKXVxp5voBdY27N3Oc/dz6+1aEpwF4z8wH615bL8PdYfxIzCRfsrTGT7Vv5A3Z6dd3+c16lIWAXaP4hn6VNWEI25HccJSd+ZWOc8b6Dda/oyRWbL50MnmBGOA/BGPrz/OqHgvwpcafo9/DqgXbfqFMKtnauCM5Hc7u3oK7U9DTYyTEhbg7Rmkq0lDkWw3Ti5cxyfh/wLY6Lqz3y3Es7xErEHAAXI6nHU4OKL34g6TZa21i8czCJjHJMqjarZGfcgd/p3rq0LFpMjgNx9MCuKv/AIc2d7r7Xv2uVIJnMskITPOckBs8Zz6VcJxnJusyZRlFWpoq/EHw7qmrX1reWMfnwrEIyisPlO4nPPrkdPSt7Q/Dm3wfBpGsxrL1Z492dmSSACO49q6BxsiAQdCAAPTIqSpdeTgodhqmlJy7mXoeiadotqVsLfy/NwzsWLM3pkmtFDmSQY6Y/lREWMSlhg45oUnzHBHHGPyrJtt3ZaSWiPN/EHw/1C/8SS3NtcQ+RdSFy0jHKeoxjn2r0KOEWmnxwAl1ijVAW6nAA5qZi3mIAOOc0SlhExUZParnVlNJPoTGCi211HEBgQQCDwQahs4YYINsEMcKbidsahRnPtU9MiLGP5hg5P8AOsywU/vnGOgHP50OcPEMZy35cGgFvNcEfLgY/WhywePA4Lc/kaACY7YJDjOFJx68U+mSkiFyoy204+tPoAZEcq3GPmYfrQD+/Ix/CDn8TRGWKncMHcfyzQC3nEY+XaPzoAJDgpxnLYpXOI2PXg0khYFMD+Ln6U587Gx1xxQBQ1ewbVtCubFJPKaeLareh7fhXF+DPBeo6Trpv7140SEMiqj53kjH5c/yr0Nc7Fz1xTYyTvyP4uPpWsasoxcFsyJU1KSk+gZ/fgY/hJz+NEpwq8Z+YD9aMt5wGPl2n86JCwUbR/EPyzWRY89KZCcwxnGMqOPwp/amREmJCww20ZoAEOXlGMYbH14FDH98gx1U8/lQhYvJkcBuPyFBLeaoA+XBz+lABKcR5xnkfzp9MlLBPlGTkfzp/SgBkJzEpxjjpQpzLIMdMc0kD+ZCrZByOoOaRJA00qBgSuOAelADnOJYxjrn+VEx2wscZ46VXuNQtbe6igluYkkb+FnAPtTr68gsrcyTzJEDwpZsZPtS5l3K5ZaablmmRHMecY5P86ie+tY7I3jTp9nAz5gORj8KraZrFlqUchgmDNHksCCCBng80ueN7XDklZu2iLwP79xjoo5/OiQ4eMYzlsfTg1zlp4xtrrVRa+Q6xSMESQnqc9x2zTda8WHTdTNpFbCQRY8xmbGcjoPzrJ4iko819DZYWs5cttdzpZTiFzjOFPFPHQVy3iPXrm30yxmsSY1ugWLkZKjA4/X9KTT9ev5PCt3euokuIG2q+3r05I9s0PEQUnHsrjWFqOCn3dv0OniOQ3GPmI/WjP78jH8Oc/jXI+Fdd1C/1GS2upPOQoX3bQCpz7duayYYda/4SVSVn+0+b8zYO3bn/wBBqHilyqUU3c0WCalKMpJWVz0C5uYbcx+dLGm5wF3sBn6Zpt/fW2n2pmupQidBnnJ9AK5HxXo2pXerieCJ54nVVXb/AAe39c+9aF/4eu73w7ZWzTL9qtlyQx4Ptx6cD8KbrVG5JR22JVCklCUp77+RsQatZTaa97HMGgjX5yByuB0xVHSPE1nql41skckUjZZN+PmA+neotE8OG00m6trxwxuhhgh4UY469+aNF8LRaXftdNO0rplYwV2gZHXrzwcUKVduLt6g44dKavd9BJvF9hDqptjHIURjG0wxgHP8qTxF4lfSrqO1hgSR8B2Z+g56D34pk/g60m1c3BlkELkyNEB3z0z6VpavodnqrRyTxt5ikLuQ4O3PQ/rU2xDjJbPoVfCxlF2bVtSjf+JHi8N2+pW8IEk7bAH5CnnP16GotB8Q3N7pl880SNJaR712DAbgnBH4VunTLI6etgbdTbKMBD/j6+9Jp1hbWFosdvAsQYAsBnJPuTzV+zq86blpb8SHUo8jio63/A5Tw/4l1G81pLe4KyRzE8BANvGcjH071BqvibVLfXJkjZUjhkKLGUByAe/fmuxtNOtLW4mlgtIonJxuVcHGBT5bK2lvY53tYnkAP7woCRjGOaz9hV5OXn1uafWKPtObk0sc34r1q/s3tYrfMKSR+YzYBJOfu8+n9adJrWpP4NW+UYuC+xpQP4c43Y/SukvLaG5h2zQRzAEEK6BsVMI0WMRhFCAY2gcY9MVo6M3KT5tGjNV6ahFcmqf3nI+E9T1G5hvI5C06xR7oy/8Ae7Ln3/pWboeratN4hiR5pZfMfEsbHgDvx2xXeWsSQwKscSxj+6q4FKiKs8rCNQTj5gOTUrDztH3ti3ioXm+RanBeI7zVY/ELqss6bSPIVCQCPb1rR8WXGppp1gSXjVk/f+XwN+BwcdutdcxbzEwOMnP5USlhExUZbHFDwz973txLFpOHuL3fxOVtIdU1LwXLE7OZ2b91vOC6DBxk/jUfg/Sr61ubia5jeKFozHtY4LNnrj2wfzrsaZESU+YYOT/OqWHXNGTeqJeKlyygkkmzjbLwdcW+tI73EZghdZARncwzwMdulWte8LSajqou4bhU84hXDg8YHUfgK6gFvOYY+XaMfXn/AOtQ5IaPA4Lc/TBo+q0uXltoDxlbmU767GDrHhtLzSbWCKYo9mhCMwyGGBnP5CnWPhmKDQZ9PkmLNcfM7gYweMYHtit2QkROV64OKUdBV+wp83NbyI+sVOXlvpe5zfh/wyNLvHupJxK65RAFwB708eE9OGr/AGj95tB8zysjbnPTp09q34yxDbh/EcfSjJ87GPl2/rSVCmko22B4mq5OV9WZWr+H7HVJ455xIsmQpaNsZHvkGrNzpFjPpq2UkAMEY+RQcFcdwfWrkhI24H8Qz9Kc2dpx1xV+zhdu25HtZ2SvtsUtO0yzsbHyIIQI5Blw3zbsjvmmafpGn2M8strarHITtLZJ44PGelX0yY1J64GaRCxL5H8XH0wKahFW02E6k3fXcoyaTp8mppdPaRmfBbf7gjnHTPvU1/ZWt2ifabeOXawC7hyMkVYJPnKMfLtOfrxRIWCjaMncv5ZGaOSPYXPLR32GtbQPb/Z2hjMONvllRtx6YpllbwW9qi28KRKw3FUAHNWKZESYULDDYGadle4uZ2sCHMkgx0I/HgUMcTIMdQeaELb5MjgEY/Khi3moAOMHNMQTHEROM9OKfTJSRGSoyafQAyE5iBxjrQpzM4x0A5oiJMYLDBoUt5rgjjAxQAOcSRjHUn8OKJjtgckZwp4octvjwOMnP5USlhC5UZbBxQA+mRHKE4x8zD9TT6ZGWKHcMHc35ZOKAAH9+wx/CDn8TRIcNHxnLY+nBoBbzmGPl2jH15ocsCmB/Fz9MGgBZDiJzjOFPFKvKj6UkmRGxHXBxSrnaM9cUANiOd/GMMRRn9/jH8Oc/jRGWO7cP4jj6UZbzsY+Xb196ACU4C8Z+YCnN90/SmyEgLtH8Qz9KcehxQAkRzEhxjKjinU2MkxqW64GadQAUUUUAISACSQAOpNcJrFzZvryXLxW2pRGbKul3B8kRgZDHh3GMud3Gc556AV3TkBGJBIA5AGc/hXCNNa605/tDSL+ysAcC0i0qbzJh/00dU4H+yp+pOSKAOt05xZeH7aS9uoiIbZWmuGlymAuSxc9R33H61DbeJtC1CdbXT9b0y5unB8uKK7R2YgE9AcngE/QVoWghFnALeLyofLXy4/LMexccDaQNuB2xxU1AGToVzfXB1FL+SJ5Ybxo18pdqquxCAO/8XU1W1LVLo6raQWThLeK6SK6faDvZgf3Yz6DBJ91HrjRGmqkV8sNxNC93IZWkTG5G2qvy5GOijrmq954a0e+nSebTrXzlmExkECbnYf3iRzQBm+LdTgjitbExXZlbULFg62shi/4+YjzIF2g8dM+1dL5q7d2Hx/uHNY/ir/kE2//AGErD/0qirboAhilCwRlg+dozhDQsmJHBDckYwp9KfCMQoCckKOaEGHkOc5I/DgUAMaT9+gw2MHPymiSTKsqhtwI6qcdfWnsP3qHPQHiiUZjIBx05/GgAEqkE4fj1QimRSjyQSH7/wABqamRDEYBOfegBglAdmIfaQMfIc96JJMPHw2M8/IfSnqP3rnPUDiszUPEGk6dfw2t3fxQzE52MTxkcZ7D8aai3ohNpbmhNJ+7dQG3EHHynH508SqW24fP+4cUS/NA+D1Xg0SyxwRPLLIscaDczscAD1JpDGQyZUghshm/gPqaFlBldsPt2j+A56mq2l6rYanHJ9ivIrjYx3bG5GScZFXAP3zHPG0cfiabTTswTvsMklH7tgHxu5+Q56GsbxR4ot/DtjHI8LyzTkrFGPlzgDJJI4AyPzrckGWj5xhvz4NZHiTw3aeI7JIrhnjkhJaOROq5HI+nA/Kqp8vMufYmfNy+7uN8N+JbbxDYefFFJFIjbJIyN20/UdvyrWjk5YENncSPkPSsO0sdP8D+G7mZTJIifvJGP3nY4AHt2FZ/hXxxHr2ovYy2ht5GDPGQ+4H1HQc4q5U+a8qa91EqdrRludYJM3B4bAX+6fWvPvG/hXU9Y1hb+wQzRsioyMdpQjPTOMj6d81dPxIsf7f+y/ZZPsu/yvtG8euN23HT8en5V28gyF5x8wpx9pQkpWB8tVWucLrHhS+uPBGn6dBKZbmzO5lIID5zwMjtnAzjik8NaPqvhjwzqtwyE3s0e6GFQX2FQcHjjPPT2Fd633T9KSMYiQE5+Uc0e3ly8r2vcPZRvdHlfgXX9auvEywT3Vxc28qs0wclwmBwR6c4H416gZP9IUYbG0/wn2oggjieZkRFLvlioxngdaeR+/U542nj8RU1qiqSulYdODirN3GSSZG1Q2Qw6qcdR3qtqWs2Wk2L3d47RxKQOUOWJ6ADua5rxp4zm0C7hsrS3jklZBK7yZwBngAAj0qe5t08f+DYHUm1lL+YoPzAOuVIPqDk040mkpT+FidS94x3L2ma1pnijR7mGF5GUxmKdChDqGBHb15rn9A8BDSdbS/nvGmjgbMSLCQWOOC3pitXwb4Tbw5FPLPcLNPOADsB2qB9etdOgxJIc5yR+HFOVTkbjTejEo8yTmtRjy4kjOHxg5+Q1Q1nSdO1mFIr6084ocoSGBX1+YYrSYfvUOemePWiUZiIBx71im07o0aT0ZBZwWthbJaWsHlQxjCoqHApESN7N7fayKwZTsQjrnpxVumRDEYBOevP40Xe47Hmui+AL2y8Qw3U9whtraUShkVtz4OQMEcdOf616O8ozGwD43c/Ic9DT1H71znqBx+dDjLxnOME/jwaupVlUd5EQgoKyGTSjyGwHyVOPkPpWZ4n1WfSfD91d2qFp0UbSUJC5YDJ+nWtaYZhcA4JU8+lK6LIjI6hkYYZWGQR6VEWk02U1dWR574C8U6rqmqT2d/IbiLyjIH8sAocj0HQ5rvlkzcMMNgKP4T6modP06z09HFpawwb2JbykC55OM49KsgfvmOeNo4/E1dWcZyvFWRMIuKs3cY8obaVD4Dc5Q+hpXlBhYgP0P8AAadIMlOcYb+lK4zGwzjg81mWMEoEakh+nZDSJJt3bg/LHGEJqVeFH0psYxv5z8xoAYZMXAGGxt/umiSTJCqGyGGcqcdafj98Dnjb0/GiQZC4OPmH86AE81SDgPwO6EU2KUeQpIfIUZ+Q1KehpsX+pTnPyjn1oAjSXHmMQ+C3HyHPQUrSgSocPt2noh9qIJY5JJgkqOQ3IVs7eB1qnNrOnQ6klrJeIs2CpQ5xk4xk9BUuUVq2UoSbskXJpOAoDZLD+E46+tP81d2MPn/cNUdU1ay01EW5n2M5BUAEkgHnpUz6pZJp4v2uF+zEZDjv+HXPtRzxu1fYfs52TtuSwyfuwCGyBz8poSXLyNh8cY+Q56VV03VbK/tGkgnDCIfvMjBX3Oaqaf4k0+/1F7aJ5A7H5C64DYHb/wCvil7WGmu4/ZVNdNtzVaX50YB9vOfkOaJpf3JID5PT5DWTfeJtOstSW2leQshw5VcqufX/AOtmpda1200uCPzCzvMMoI+ePX6UvawV3fYao1G0rb7GoZVBAw/P+waZFJhQrBs8n7hxVH+37E6MdTDMYRxtx8270x61BoniG21RJUCvHJEC7BucrnqKPawulfcPY1LOVtEaqyZuJBhsYH8J96Gl3MhAfAbnKEdjXN2PjGK61dbc2zJFMwjR92SDnjI9807WfFi6dqgtUtjIIiC7F8ZyOg496j6zS5ea+hf1StzcttdzopZQbeQqHztIGUPWneaoUHD8/wCwa5Xxbd3U2lWVxZmVbSUbpCvHUDAbH40acNWufBl0uZTOT+5LH5inGQPwzik8R77glsrlLDP2am3a7sdPHMApzuOWPRSe9HmgXRXn7o42n1Nch4Ms9Qgv5nkilit9hDBwQC2eOD361n2um60viNGaKbzhMGebB24zyc9MY/wqPrMuWL5dy/qkeaUedaI7+SZd6L8wO4dVIzTnmXy3PzDAPJUiuJ8W2GpT6wJEhmmgZQIvLUkL6jjvmruuWOqy+GrCLEkskYHnovLHjj64qniJXkuXYlYaLUHzr3vwOoW4QW/mEkKFySVOPrVWy1Wyu2lEFwsrK33UBJx64rB0LStQk8N3tpcF4PO/1KyZBHr9Af8AGo/DXhy/sdV+1XQWNIwygBwd5Ix27d6FWqNxtHR7+QOhSip3nqtvM2X8SaYmpi2ac7h8hOw4DZ6Uax4hs9KkjhlEjSNhiqL0Gff6GsabwU8mrNILlBau5cjneBnp6fjmtHXvDK6vdJcpceS+AjArkEZ6/Wp58Q4vTXoVyYZSj72ltS1d+IrK20xL4eZJHJxGFX7x9M9OOah0/wASW95pVxcJHJ5lrHueMjrx6++KfP4btptDh0zzHAh+ZJO+7nJx6cnin6HoUGkWsiBzM8wHmMw4I9MenJq/3zmu1vxI/wBnUHve/wCBi6H4rur3Vhb3MUeybJHlqcqQP16VR1PxNqkOuTLE+yOKQosRTggHvnnmutsdF0+wupJ7e2VJCcA5JwMDpk8VZksraS9juHt4WlUHDlAW7Y5qPY1nCznrc09vQVRyUNLHK+L9T1CGW1SB5beF495K5Uls8jPtx+dPnfVNU8GRfLK9wzZbCnMiAn/631xXV3ESSxbXVWGQfmGe9S1boNyk3LRmaxKUIpRV073OM8K2epW0F8JEmgEkeIg6EfPg/NVDQdL1WDxBE7wTxBH/AHrspwR3Ge+a7+IYiUE5460KMSOc9ccVKwsbRV3oU8ZK8nZe8cLrXh7UbzWpp4EMkUrbg7fLt9sHnj2zV7xJo15e2lk1tvmNvH5bqwKk9Pm5rrGGZEOemePWiUZiYA4460/qsPe8xfXKl4vT3TmLXQZv+EUfT55GSaR/MX5SQh44P5frS+HNAk0ieW4unDtIhjCxqxGMgnPHtXU0yIYTBOeT/OrWHgmn2IeKqNSjfRnO2vhWwtdXFwGnZUYPHGVyAcnqcdBVnUtA0/U79J5Y5hIThyuQHAHfj6dK2QP3znPG0cfnQ4y8fOMN+fBpqjTS5baEvEVXLm5tStNb20tibY24aJEwqPGcDA4xkVJEsEdt5UcOyIDGwRED8sVLKMwuAcZU808dBWlkZcz2uUrOC1tEcwWyxbmOfLiwTzx0FTmUCXOH2leyHNPjGA2Tn5j/ADox++Jzxt6fjQklogbb1YyWT5kADcsP4TTmlGGUB84/uHHSlkGdnOMMKc3KMPamIjjkBUAhsgDPymkilz5hIfG7j5D6VIgxGozngUkYIL85y1ADDL+8DYfbtPGw5zkUSy/u1ID8sP4D608j98DnjaePxFEoJUYOPmX+dAAZVBHD8+iGmRSYjjVg27aM4U46VNTIRiGME5IUc+tADI5P3sgIb73Hyn0FHm7pVID4AOcoRT0BDyHOct+XAoYHzkOeADx+VADJZcwkqH6jqh9RT/NUKGw+P9w0SgmMgHHI5/Gn0AQxyhYl3ByT6ITQJMTuCGwcYwpp8QxEoJz70KD5rnPXHHpQAxpMzIAG4zn5T6USSbonChsgd0Ip7jMkZz0J/HiiUZhcA4460AAlUgnD8f7BpkUoEWWD/eP8B9TU1MiGEwTn5j/M0AM80CViQ+0qMYQ570SSYeIAN97n5T6GngfvmOeNo4/E0OMtHzjDfnwaAGySDY6gPu2nHyHHSlWQH5cPkf7JxTpBmJwDjKnmlHQUARRSgq5Ifhj/AAGjzR5m7D7dvTYc9afGMBuc/MaMfvs5429PxoAZLKNqEB/vD+A055QF6Pkjj5DSyDO3nHzCnN90/SgCNJAFRSGzgdFOKSOT55FIb73Hyn0FSRjEajOeBzSRjBfnOW/LgUAM8zMwID4CnOUPtRJKDHlQ/DL1Q+op5H75TnjaePxFEoygwcfMv8xQAeau3dh8f7hzTI5QsEe4OTtGcIT2qamQjEKAnJCjmgBiyYmcENyRjCn0oMmZ0ADYwc/Iaegw8hznJH4cUMP3qHPQHigBkkm5GChsj1U+tPEqkE4fj/YNEozGQDjpzT6AIYpQIQWD/wDfBoEuJGYh9pAxhDmnxDEYBOevNCg+a5z1A4oAZJJiWLAbGTn5T6UTSfu3VQ27acfIcdKe4JkjOcYJ/HiiYEwuAcEqeaAASAsVw+R/snFMhlzG2Q/DN/AfU1NTIgQhBOfmb+ZoAjEv7xmw+3aP4DnOTSyS/wCrYB8bufkOehp4B89jnjaOPxNEgy0fOMN+fBoAbLKBG2A+SDj5DSiQDapD5x/dOKdIMxsM44PNKv3R9KAIopPmdSGzuP8ACcUCTM54bAXuhp8YI3ZOfmNGD52c8ben40AMklBUFQ/DDOUNOaUeWTh/++DSyDIXnHzCnH7p+lAESShYU3B87R0QntUwORTYxiJBnOFHNOoAKKKKACiiigAooooAKKKKAMTxV/yCbf8A7CVh/wClUVbdYnir/kE2/wD2ErD/ANKoq26AGQgCCMA5G0YNCACSTB5JGfyFEOPIj29NoxQmPMkx1yM/kKABgPOQ55wcD8qJgDEQTgcfzobHnJnrg4/SsTxJ4p0/w+sUd2JZJZeVjiAJAB6nJHFVGLk7RE2krs3qZCAIgAciuX1rxZt8HHWdIBYyMIwXXmIk8lh6jp6cjrWX4G8UatqyX9vcgXDwwmSJ9gX5uynGBz/Q1oqE+Rz7Ee1jzKJ3igec5zzgZFeb+KPAuqaj4kku7SSJ4bpgcyPgxnHIPqOO1ZfhnxD4guPF8EclzcTedLtnhc/KF7nb0XHJ4x0r1x8eZHnrk4/KtGp4aWj3JXLWjqZ0ktnoehwQXt4kccUSwiSQ4LkLj8Txmq+vWY8S+F54NPuYm84AxyBsq2GBxkfTH1rG+IHhy/1qG1nsQJDbhg0RYLwccjPHb+VafgrRbrQtA+zXpXznlMpQHOzIAxn8M8etRaMYKope9fYd25ODWhheAvCmpaPqE99fhYlMZiWMOGLHI5OOMcV3gA89jnnaOPxNJEVEZI6bm6/U5rk7T4g6Td66LJIpgsrCJJyBtJyccdQDnrSk6lZuVhrlppRudbIAWjycYbj8jSyAGJwTgbTXlXj+DWpPFAZI7l7fCfZfLBIBwM4x/Fuz79K9H0sXS6DajUCTciAednrnHOfeidLlgpX3CM+aTVtixd2UGoWElpcpvglTaw9RWJ4f8HaZoF3LdWzTSzEFA0rA7B7YA5966JfujHpTYsYbH945+tZqcknFPRluKbuzk/8AhX2j/wBu/bt0u3f5v2fI2Zz9OntXWSgELk4+YUcef/tbf60S4wuf7wx9acpyn8TFGKjsOb7p+lJEAIkAORtGDSt9059KSLHlJjptGKgoSMANJg5y3P5CggeepzztPH4iiPG6TH97n64FBx56/wB7acfmKAMjXfDWl675Ul9EfMjwqyI204J6e4rTsrK306zitLSIRQRDCIO1SS42Dd03L/MU+qcpNcreguVXuMhAEKAHIwMGhAPMkweSRn8qIceSm3pgYoTHmSY65GfyqRgwHmxknnnAomAMTAnAobHmpnrziibHlNu6UAPpkIAjAByMn+dEsscETSyuqRqMszHAFVrG/s7u3Z7adHRM7sH7v1pXV7D5W1exZUDznOecDI/OhwDJHk8gnH5GsKy8V6fd6p9mRZVMhCJIw4Y9vpmq2seLF0/VPs0dr5ohPzsXxkkdB+dZPEU1HmvobLC1XLl5dTppgDBICcDacmn1zuveIxY6fbPbwiQ3ce9d/AVSB1x35pI/ErzeGbjUlgAnibYV6rk45+nNDr01JxvtqCw1RxUraN2OgiACHBz8zfzNV1vLRr9oFuYjPtAMYcZ4z2rnvCuv3eo3ktrchXG0yB1XBHPI+nNZdp4Y1SLXY94ASOQSGfeORnr654rN4hySlTjdM1WFUZSjVlZpfebWueKU03URaJbea0RDOS23qOg49DU+reJIrTSLa6iiMjXanYrHGBjnP0zUuq+HNP1O7S4n81JDhSYyBu+uRVi/0WxvNNS0ljKxQj92UPK4FNxr3lZ+glLDWhdPzKOn+JorjQ7i+khKvbDDIDnJ7YPvVfw/4nbUr9rSaBY2fc6FST+B/Ctey0axs9NezjjLQyj95vOS2R3/APrVFpWg2GmTyTW6uZCSu52ztHoKFGveN36g54e07L0OOluNa/4SYgNN9q83Cpk7dufT+7V/xnNqEeowhXlS22gx7CQC3f8AGu1488f3tv8AWiXGFz/eH86n6q+Vx5nqV9cXPGXItEcpqkOrXXg+0DLK0+QZkGdxXnGR37ZpPDVlqQ0LUIm8yISri3D8YODkj0B45rrj0NNix5Kbem0Yq/q651K/SxH1p8jgkt7nDeF9H1K21wSywvDFFuWQtwGyOnv2NSX/AIQvLjWpHSaLyZpDJuY8qCcnjuea7WPG6THXdz9cCg489P721sfpUrCU+TkZTx1Tn51ppY5vxL4cm1OaG4t5kVkQRESE8jPByB71JN4ZaTwzFpgnHnRv5gc52luePpzXQS48v5um4fzFPrR4em25W3M1iaiio32Ob8P+G2sLa6F1Kkn2lPLIjJwF5zye/NQaV4QFjqouZLoSJC2UULgk9s11EOPJTb0xxQmPMkx1yM/lSWHpq2mwPFVW5O++5y+p+D1vNWa4S6EaTMWdSuSD3xVzW/DcGo29vsmMDW6bFJG4FfQ1uNjzY89ecUTY8pt3Sn9Xp66bh9aq6O+2xkReG7VNCbS2dmVjvaTGDu9cfhTdB8OwaQZZRMZ5JAU3FcALnpj8K3KZDjyxt6ZP86aowTTtsS69Rpxb0e5iWnhXTbXU/tSGQlCHSNmyqnt2zT9S8NafqN8tzMZUkYgMEYAPgd+Pathcec+OuBn9aHxviz13cfkaPY07cttA+sVebm5tRhijiszCgCRrHtAHYAYqamTY8iTd93ac/lT61MRkQAVsHPzN/OgAeeTnnaOPxNEWNrbf7zfnmgY88/3to/LJoAJACUycfNxSvgxtnpg0kmMpn+9x9aV8eW2emDQAJ9xcdMUkYAL4OfmOaVMbFx0wKSPHz4/vHNABgeeDnnaePxolAKrk4+Yfzo488f3tp/nRLjau7+8P50APPSmQgCGMA5G0YP4U89KZDjyY9vTaMflQAIAHlwerc+3AoYDzkOedpwPyoTG+XHXdz+QobHnJ67Tj9KACYAx8nAyP50+mS48v5umR/On0AMhAEKgHIx1oUDzZCDzxmiHHlLt6Y4oXHmyY68ZoAHA82Mk8jOPyomAMLAnAx1ofHmx5684/KibHktu6Y5oAfTIgBHwcjJ/nT6ZFjy/l6ZP86AAAee5zztHH50SAF4snGG49+DQMee/rtGf1okxviz13cfkaACUAwuCcDacmnjoKZLjyX3dNpzTx0FADIgAGwc/Mf50YHnk5529Pxoixhsf3j/Ojjzz/AHtv9aACQA7MnHzCnP8AcbPTFNkx8mf7wxTnxsbPTFACR4EagcjApIwAZMHOW5pY8eWuOmBikjxmTH97n60ABA89TnnaePxFEoBUZOPmX+YoOPPX+9tP8xRLjaN3Tcv8xQA+mQgCCMA5AUYP4U+mQ48iPb93aMflQAIAJJMHksM+3AoYDzkOecHA/KhMeZJjruGfyFDY85M9cHH6UAEwBjIJwMj+dPpk2PLO7pkfzp9ADIQBEoByPWhQPNkIPPGRRDjyl29KFx5smOvGaABwPMjJPIJx+VEwBhcE4GOTQ+PMjz1ycflRNjyX3dMc0APpkQATg5G5v5mn0yLGz5em5v5mgAAHnuc87V4/E0SAFo8nGG4/I0DHnv8A3tq5/M0SY3R567uPyNABKAYnBOBtOTTh90fSmy48p89NpzTh90fSgBsQADYOfmNGB5+c87en40RYw2P7xzRx5/8Atbf60AEgB2ZOPmFOb7p+lNkx8mf7wxTm+6c9MUAJGAIkAORgUkYAaTBzlufyFLHjykx0wMUkeN0mP73P5CgAIHnqc87Tx+IolAKDJx8y/wAxQceev97acfmKJcbBu6bl/mKAH0yEAQIAcjaMGn0yHHkJt6bRigAQASSYPJIz+QoYDzkOecHAoTHmSY65GfyFDY85M9cHFABMAYiCcDj+dPpk2PKO7pxT6AGQgCIAHI5oUDznOecDIohx5Q29OaFx5z464GaABwDJGSeQTj8qJgDA4JwNpyaHx5keeuTj8qJseQ+77u05oAfTIgAhwcjc38zT6ZFjYdvTc38zQAADz2Oedo4/E0SAFo8nGG4/I0DHnt/e2jP5miTG6PP97j8jQAsgBicE4G00q/dH0pJMeU+em05pV+6MdMUANiAG/Bz8xowPPznnb0/GiLHz4/vHP1o48/8A2tv9aACUAhcnHzCnN90/Smy4wuf7wxTm+6fpQAkQAiQA5G0YNOpkWPKTHTaMU+gAooooAKKKKACkYlUJCliBkKMZPtzTZolngkhcuFkUqSjlGAIxwykEH3BBFZ1n4fsrG6S4im1JnTOBNqdzKnIxyryFT17j3oAdo+pT6kL0z2ptWt7kwiNmDNgKpBJBIyd3Y1mT+JJP7UngglslS3nWAxTMQ8xOM4bO1OSQAQdxBHFacVjdWiak9vJCZ7mczReYpKr8irhsc/wnpVC50K6ddTs4GtlstSkMkshyJI9yqr4GMMTtyDkYJ6HFADfFl/ZpaWto13AtydRsSITIA5H2qI529egNdD5iYzvXHrmsLxXbwnT7aYwxmUalYAOVG7/j6i71vbVxjaMfSgCld3htdGmuYVV5I4GeOPP3iFyBXnXhLxfrV74nht7qfz4bgkOhQDbxwRgcYxXp8QDQoWQA7RxjpVe206ytrmaWCxt4ZGPMkcQUt35I681tTqRjFpq9zOUW2mmcDrXxEvbLxFNb29rAba2lMRDg7nwcE5zx044/OtvxR4YtvFMdtex3X2edIwOV3ZUnIBGeCCa1L3wvo19qqXtxp6STHlmJIDEYxkA4P41rygKhKqCeO3vVSqxXK6as0JQbup6oytF0Kx0nQhpe5LmI5MpkAIcnrkenH6Vb02z0/T7YrZQQ26MctsAGT7nvV3ao/hH5UyIAxDKAe2KxcpO93uaKKWxGn2dbiWRVjV2A3OMAt+NPeSMSR5ZeScHPtSqAZHBQYAGOKHA3xjaCMnt04qRiTSRrE+WU/KeM9af5iZxvXPpmmzACJ2CgsAccU/auc4GfpQBFFJG8bAFcbmyM+5rjLL4faba6+L1b13t4WEqQEDg54BbPIGPSu1iAKElADubt7mgAecy7Bt2jnHuauFSUL8r3JlCMtxJJYx5ZLKQW656cGllkjETEsuMHv1qjf6tp+nzRQ3M8aOxztwTgc88dPxo1XVrLTLJZpjvEn+rVACW46j2rF1Iq93saqnN2st9i+JEwo3KCRwM02KWM7gGUHceM1ljX7STQ5tTgQuIRgowwQ3GAfzFZ3h7xNLqmoG1ubeJSylkaMHt2OSah14KSjfc0WHqOMpW23OlEiG4IBGQvJz70SSx7VO5SNw79K8883Wf+Elxmb7V5v3eduM+n92r/AIwstQl1SNo4ZZLfYBH5akgHv071l9a91yUXob/U/fjFyWqudpLcQxwtJJLGqAcszAD86bBcwS2ySRyxmMjgqwIrmdS0rU7zwrZREF7mI7pI88kc4/EDFGg6JfxaHfwz/unuVxEjH7pweT6ZyB+FX7afPbl0sZ+whyOXPre39fmdFbX9ncSSrDcwuytyFcHsOaik1fT4tRS1e6jExBGM9CccZ9a5jw14f1G01lbi5i8qKIMGywO4kYwMfXP4Uy98I30+tSMjoYJZDJ5hP3QTnkevNR7as4KShrc0+r0FUcXPSx1Wo6tYaeqi6uFUswwvJPB64Gakl1Wxhsftj3KeRgYYHOc+1YXibw5calcxXNq6EhBGyucd+v61LL4ZeTwzDpvnqJ428zfj5S3PHrjmqc6vNJKOi2M1TocsW5at6mpp2r2N9aNLBMoWIfOG4KfWq1j4k0y+vXt4ZSHJ+UuuA+B2/wDr1T0Hw01lZXSXcis10mwhM/KuD69+ar6R4Rey1YXE1wrxwnKBVILHHf0pKdf3dPUp08Ouf3ttitqni67ttYkighiMMLlMMOW9Tn+VSeLtUvUSz+zSSQwSpvJQ4Jb0OPSugutF065vkuJrNHk7sc846ZHQ/jV2eNGiOY1f2IzR7GrJSUpb7Aq9GLi4w23OXgivde8IeTNMBc78oXbBkUdM/r+Qo8K6JPZPcTXjIqyJ5flBwcjPJOPp+tdYFUdFH5UyIAxglQCc9verWHjzKT1aIeKlyyhFWTZzNh4TtLTVRc/bDJHCwdEIAwe2Tnt9Ku6l4f0vUL5LqYuHc4fY4AbA7/ljjFbKgea42ADA7daHADRgICCeeOnBqlQppcttCXiarlzOWpn6npenXtgkU6DZCv7rY2CvHQfpU9rYWNlZfYokTyWzlGOd+euc9aszACFyEBIU4GKftX0H5VfJG97amftJ25b6FHTrLT7EP9khjiLMd2DknBP6VaEiG4ZQRkKM8+5pYwCpJUA7iOnuaAB57DaMbQc49zTSSVkS5OTuxHljyh3Kfm656cGleSPymbcpGD3ocAFAEGC3PHsaV1AjbCg8HjFMQLInlqdygEcc02OWP5vmUfMe/WnqoKLlR06YpqAHdlRwxxxQAhkT7QFyudvrRJInyjcpO4cZ96XA88fKMbeuKJABtIUE7h296AF8xCDh1OOvNNikjMCMGUDaO/SnlVAOFH5U2IAwplQMqMjFADUljzIdygbsZyOeBStLGJkyy8qcHP0pUAJkBQABuOOvAoYDzkGwEbTzjp0oASaRAuCQTuHGfen+Ymcb1z6ZpsoAXIUE5Hb3p+1c5wPyoAjhkjaJdrKOOmelCSxl5MMvGMnPtSxAGJSVAJHIxQgG+QbABx268UANaWPzIzuXvznpSzSxiEkspH160rAeYg2DBznjpRKAIjhASOgxQA4yICAXXJ6c0yKWMoAGUcnjPvUm1fQflTIgCgJQA5Pb3oARZEM7qCNwAzz9aHljLx/Mp+brkccGlUDzn+UDgc469aHADxgIMFuePY0AJLLH9nkbcpXae/Wn+YgAO9ceuabKAIHwgOFJAx1p+1cY2j8qAI45Y9p+ZR8x79eaPMT7QVyN20d/c0sYDKcoBhj296MDzz8oxtHOPc0AJJJHuQblJ3dM9KVpEKN8ynAORmiQAFCFBy3PFKygIxCjOD2oARJIzGCGXGPXpSRyx4c7lA3dc9acijYPlAyORikjAO8FAAG44oAQyxiYZZeVPOfpRLIg2gkElhgZ96UgecBsGNp5xRIAApCgncO3vQA7zEzjeufTNMhkjaFNrKPlHGelSbV67Rn6UyIAxISoBKjIxQAkcsZaUhl4bk59hQZY/NQ7l6HnI9qVAN0g2ADdxx14FBA81F2DaQc8fSgBJpYxFkspBI7+9PMiAgF156c02UAR8IDyOMe9P2r6D8qAI4pYzGuGUcdM0JIhmkUEbhjPNLEAY1JUA49KFH71/lA6c460AI0sZkTDKevOenFEssfkM25SMetKwAkQBBg5zx7USgCFsIDx0xQA7zExneuPXNMilj8sHcoBJ7j1qTauMbRj6UyMBk+ZADk9vegBBIn2hlyA20d+vWh5I/MjG5Sd3r04NKADM2VHCjBx9aHADx4UctycdODQAkkkflSfMrYU5ANOEiFch1x9aSQBYnKqCdp4x1pwVcfdH5UAMjlj2Mdygbj396PNjE3LKPl65FLGAVbKAfMeMe9GAZsbBjb1xQAkkiAoCQSWGBmnPIm1huUnHTNJIAChCg/MO3SnMoCsQozj0oAbHIhRcMvQcZ6UkcsZ8whlwG5OfanIBsU7QCQM8UkYHzgoAN3p1oAQyx+cPmX7p+bPuKJZYwikspBZcc+9KQPNC7Bt2k5x7iiUAIMID8w4x70AOMiDGXUZ96ZDJGYY8MoyowM+1SbVPYflTIgDEjMoDbRnjpQAiSIZZFBGQwzz14FBljMyAMpODznp0pUA8yT5QMNwcdeBQQBKoCDBBycfSgBJZY/KLblIyO/uKf5iYzvXHrmmygCM4QHkcY96ftXGNox9KAI4pY/KU7lUHoCaFkTznXIDcd+tLEA0SlkAPpihRmV8qOMYOKAEaRPNjG5Scnv04olkj8p/mVsDkZpWAEiYUck5OPaiUBYnKoCcdMdaAHCRCMh1wO+aZFLH5e7coG49/c1JtX+6PypkQBj5QD5jxj3NACCWMTN8yj5V5yOetEkiB4gSMluOfY0oAMzAoMBRg4+tDgbo/lB+brjpwaAElkjEbgsp+UnGfanLIh4DLn0zRIAI3IUE7Tjj2pQoxnaM/SgBkUsZVyGXAY55pPNj877y/d6596dGBhsoB8x7UYHm7dg27fT3oASWWMBCWUgsMHNOeRApyy9PXrSSAALhAfmHanMo2n5QcD0oAbHImxBuUZUcZpI5ELyAEAhuefYU5ACikqAcDtSRgFnJUD5uDjrwKAE82MzgBlJ2nnPTpRJLH5edykbl7+4pcATABBgqcnH0okACDCA/MvGPcUAO8xMZ3rj1zTIpY/IjO5VBUYBNSbVxjaMfSmRANChZADtHGOlACJInmyLlQ2QOvXihpI/OQblJwe/SlUAyPlRwRg49qGAEqYUcg5OKAElkj8tvmU4xkZ96eJEIyHXA96bKAsZKqCfp70/av90flQBHFLGYQwZQOe9IJY/Nf5lHA5yOadEAYhlAPbFAAMjgoMADHFACPIgkiBIyScc+1E0kYifLKflJxnrSuB5kfyg8nnHTiiUAROQgJCnAxQA7ehJAZcjtmmQyxshIZQAzZ59zUm0ZzgZ+lMiAKHKAfM3GPc0ANEsZmb5l4UZOR6mlkljHlkspBbg59jSgDzWXYNu0HOPc0SAAxgICN3p04NABLJGsTZZfunjPWlWRMKNyg46ZokA8tiFBIBwMUqqCASozj0oAZFJGS4BUHceM0CSMz4DKSF6596WMA7iVAO49qMATY2DG3rigBJJY9qncpG4d+lOaRNhO9cfWkkAAXCA5YZ4pxVdp+UfTFADI5IxEh3KoKjGT7VKDnpTI1DRJlRnA4x0p9ABRRRQAUUUUAFFFFABRRRQBieKv+QTb/wDYSsP/AEqirbrE8Vf8gm3/AOwlYf8ApVFUPjLXp/D2iC5to1aaSURKXGQuQTn9KqMXKSihSairs3oc+Sm7GdozihM75M4xkY/IVyPgPxNea/BdQ3qqZLfaRKoxuBz19+K65AQ8mTwSMe3FOcHCXKxRkpK6Bs+amMYwc0S58s7cZ46/WhgfNQg8YORRKCYyFODxzUFD6ZFnyxuxn2p9MiBEYDHJ9aABc+a+cYwMVyes+LZ7HV2toLeNo4Thy+cscc49K6iOeJ7qWNZkZ1AygbJX6iqF94f06/vUuZ4MyZ+fDEbsDjOKxrKco/u3Zm9CVOEv3quihr/iSSysbN7WJSbqPzAZBnauB29eaIvEk83hafUhConifyyB93PHP61r6jpdpqFoIp4FYRj931G36YqWCwtbaz+yRQILfBBQjIOfXPWp5KvO3zaWLVSioJcut/wOc8La/e6ldzWt0VfCGRXCgY5HBx9axdPstZXxJGzxziYSgyyEHG3PJJ7jFd1Yada2COLeBIyzHJUcnk4qyAfOY542jj86j6vKUY88tUX9ajGUnTjozkvEPhe81DVjd20kZWbaCHbG0gY/LitDUvDS3mjWlr55WW0TCuRkHgZH6Ct1wSyYOMNz78GlkBMbAHBwcGtPq9O8nbcy+tVbRSfw7GVo+gw6bpslrIwn87mUkYB4xjFSaZotjpsksltAFdiV3FiSF9K0l+6M+lNjBG7Jz8xxWipQjay2M5VZyvd77hz5/bbt/rRJnC4x94ZowfOznjb0okBIXBx8wzVmY5vunHpSR58pM9dozSn7p+lJGCI1BOTgZNACR53SZx97j8hQc+evTG05/MUICGkyc5bj24FBB85Tnjacj8qACXOzjGdy9fqKfTJQSnynB3D+Yp9ADIc+Sm7Gcc4oTPmSZxjIx+VEQIiQMcnHJoUEO+TwSMe3FAA2fNTGMc5olz5R24z70MD5qEHjnIolBMZCnB9aAH0yLPljdjPPT60+mRAiMBjk88/jQALnzX6YwMfrQ+d8eMYyc/kaFB81yTxgYFDgl48HAB59+DQATZ8l9uM7TjNPpkoJhcKcEqcGn0AMiztOcZ3N0+poGfPbpjaP5miMEKcnJ3H+dAB84nPG0cfnQASZymMfe5/I0smfLbHXBpHBJTBxhufypXyY2AODg4oAVfuDPXFNjz8+cfeOKcv3Rnrimxgjdk5+Y4oAOfOHTG3+tEmcLjH3h1+tGD52c8belEgJC4OPmH86AHHoabFnykz12jOKeehpsYIiQE5O0ZNACJndJnGN3H5Cg585OmNpz+lCAhpMnILce3AoIPnKc8bTkflQAS52cYzkdfrT6ZKCU4ODkfzp9ADIc+Uu7Gcc4oXPmSZxjjH5URAiJQxyccmhQfMck8HGB6cUADZ81MYxzmibPlNtxn3oYHzEIPAzkUSgmJgpwfWgB9Miz5fzYzk9PrT6ZECI8McnJ/nQALnzn6YwMfrQ+d8eMY3c/kaAD5rnPGBgfnQ4JePBwA3PvwaACbPkybcZ2nGfpT6ZKCYXCnBKnB9KfQAyLO05x949PrQM+eemNo/maIwQpyc/Mf50YPnE542jigAkzlMY+9zSvnY2OuDSSAkpg4+bmnPko2Dg4oAEzsXPXFNjz8+cfeOKcuQi564psYI35Ofm4oAOfOHTG0/zolztXGPvDr9aMHzgc8bTxRICVGDj5h/OgB56UyLPkpuxnaM4p/amRAiJAxyQoyaABM75M4xu4/IUHPnJ0xg5/ShAQ8mTkFuPbgUEHzUOeMHI/KgAlz5fGM5HX60+mSglPlODkfzp9ADIs+Uu7GcdqFz5j5xjjFEQIiUMcnHWhQfMck8cYFAA2fMjxjHOfyolz5TbcZxxmhgfMQg8DOR60SgmJgpwccGgB9Mizs5xnJ6fWn0yIEJycnJ/nQADPnP0xtGP1ofO+PGMbufyNAB85jnjaMD86HBLR4OAG59+DQAS58l8ddpxmnjoKbKCYnAODtODTh0oAZFnDZx94/zo5849Mbf60RggNk5+Y/zowfOJzxt6UAEmfkxj7wzTnzsbHXFNkBO3Bx8wzTmyUOOuKAETPlrnrgUkecvnH3uKVMhFycnAzSRggvk5+bigAOfPHTG0/wAxRLnaMY+8vX60EHzgc8bTx+VEgJUYODuH86AH0yHPkx7sZ2jOKfTIgRCgY5IUZPrQAJnfJnGNwx+QobPnJ0xg5/ShAQ8mTkE8e3AoYHzUIPGDkflQAS58s7cZyOv1p9MlBMZCnByOfxp9ADIs+Uu7Gfahc+a+cY4xRECI1DHJ9aFB81yTwcYFAA+fMjxjGTn8qJs+S+3GccZoYHzEIPAJz78USgmJgpwccGgB9Mizs5xncen1NPpkQITk5OT/ADoABnzm6Y2jH5mh87o8Y+9z+RoAPnMc8bRgfnQ4JaPBxhuffg0ALJnynx12nFKOgpJATE4BwcHBpR0FADY84bOPvHpRz5/bG3+tEYIDZOfmNGD52c8belABJn5cY+8M05vunHpTZATtwcfMM05vunHpQAkefLXPXAzSR5zJnH3uPyFLGCI1BOTgZNIgIL5Octx7cCgAOfPXpjaf5iiXOwYxncvX6igg+cpzxtPH5USAlRg4O5f5igB9Mhz5KbsZ2jOKfTIgRCgY5OBk0ACZ8yTOMZGPyobPmpjGMHNCAh5MngkY9uKGB81CDxg5FABLnyztxnjrT6ZKCYyFOD60+gBkWfLG7GeelC58184xgYoiBEYDHJ9aFB81yTxgYFAA+fMjxjGTn8qJs+S+3GdpxmhwS8eDgAnPvxRKCYXCnBwcGgB9MizsO7GdzdPqafVW4vINOsZbq8mWOGMks7Z4GeP6ChK+iAnGfPbpjaP5miTO6PGPvc/kazNH1/TtckmawuhL5YAZCCpHJ5wa03BJTBxhuffg02nF2Yk09ULJny3x1wcUq/dGfSkkBMbAHBwcGsaXX5I/tMkWnT3FpaOY55kZd2VA3FU6sBnnoeDgHjKGbEefmzj7xxRz5/bG3+tRR3MOMm4jIeVo0+ccsM5Ue4wePY1Q1DX7HTJ2e6uI0gXy495lXG9324xnIx1Ptn0NAGpJnC4x94Zpx+6fpVOXUbL7TDa/brcXEhDJF5o3uOvAzkjFRabrlhqsMfkXMQneISG38xTIgPqoOR1FAGhHnykz12jNOpsYIjQE5OBk06gAooooAKKKKACkZgiFjnAGTgZps0bSwSRpK8LOpUSIAWQkdRuBGR15BHsazrPSr22ukml8QaldoucwzR2wRuMc7IlbjrwR0oAm0zVYNWS4e3SVVgmMJ82MoSQAc4POOe9VrjX0gubhfsk729tKkM9wpXCOwUjgnJADrnA78ZpbWG7sRq8wtTM8t0ZYY1dQZB5aDqTgcqevpWLqGi3M93fv/Z00l/LOslrfLKojiChdhZdwPykHI2nP48AGr4q/5BNv/wBhKw/9Koq1LyyttQtXtruFJoX+8jDiue8W6XayRWt+wm+0LqNioIncLj7TEPuZ29/Sul8lNu35sf7xoTtqgKek6VY6TZiGwt0iRvmYr1Y+pPOauIAJJDnOSM+3ApkUUbQRgbtu0Y+Y0LFGZXPzZBGfmPoKbbbuwStsObHnR5bBwcD16Vna1rVnpSIlyZC8nIWNQTgd+SK4/WtP1Z/EUrLFO7NJmF0BwF7YPbArotc8N/2okM5nEdwihXJBIb/DkmuT21SakoR1R2qhSg4upLR9ifUfECReH/7SsR5m9gi7h9098iqPhnW7zVUubSbaJFjLJKq4xnjn861tP0O1stK+wSDz0bmQt0Y/TtU1hpllZQFbWERq5+YgnJ/HrVKFVyUm7K2qIdSjGEopXd9GcZouh6rB4gid4njEMgaSQngjvz3z0/Gu+cAyRnPQnHvxUYijLuvzcAfxH3pZIoy8ed2c8fMfSqo0VSVkRXryrNOSHzAGFwTgFTzT6hmijMbs277pz8xp4iQNu5z/ALxrYwCIAIQDn5m/maAB57HPO0cfiaZDFGFJXdyzZ+Y+ppFijErgbslRn5j6mgCSQAtHk4w358GlkGYnBOAVPNRyRR/u1O7G7j5j6GllijMTBt2AD/EfSgCRfuj6U2IABsHPzE0giQ7W54HHzGmxRR5YjdncR940APwPPznnbjH40SgELk4+YGmCOMXBIzuK/wB4+tI8UYUA7sFh/EaAJm+6fpSRACJADkBRzTTEnllfmx/vGmpFGYUA3bcDHzGgB8YAaTBzlsn24FBA89TnnaRj8RTEijLuRuyG5+Y+goMUf2hSd27acfMfagB8oBQAnHzL/MU+oZI4wNxzyy/xH1FPEKAHG7n/AGjQAQgCFADkADmhABJIc9SM+3FMhij+zqF3bSP7xpFhj3SKN3UZ+Y+lAEjAGWM55GeKJgDEwJwPWmPFGZIwd2QDj5jRNFGULNu/76PrQBNTIQBEADkZPP40eUgbdzn/AHjTIYowmV3YOf4j60APUDznOeSBx+dDgGSMk4wTj34NMWKMSSAbuQM/Mfeh4o8xqd33uPmPoaAHzAGCQE4BU80+oZoozAwbdtCn+I+lPMSkgnOR/tGgAiACHBz8zfzNAA89jnnaOPxNMiijwSu77zfxH1NCxoLhiM7ioz8x9TQA+QAmPJxhv6GlkGY2BOODUTxRjYPm5b+8fQ054oxCwO7bg5+Y0ASLwi/SmxgDfg5yxpoiQxqPmwBx8xpEijO7G7hj/EaAH4Hng5529PxolAIXJx8w/nTDFGbgH5t23+8aJIowQx3Z3D+I+tAEp6GmxACFADkBRzSeUgBxu56/MabFFH5Chd20qP4jQA+MANJg5y2T7cCggeehzyFbj8qjSGPMijd97n5j1wKVoozKgO7IU4+Y+1AD5QDHgnHI/mKfUM0aEBmzww/iPrT/ACk3buc/7xoAIQBCgByMdaEAEshzySOPTimQRRiJSu7BHdjQkUYeQDd2z8x9KAHsAZYznkZ4omAMTAnA9ajaKPeind3x8xpZoozCQ27aP9o0ATUyEARgA5GT/OgxISCc5H+0ajiij2gru6n+I0ASKB5znPJA4/OhwC8RJxhuPfg0xY0FxIRncQM/MfekaKNWQfNy3HzH0NAEkwBgkBOAVPP4U+oZYoxbyA7tu05+Y07yUKgfNgf7RoAWIAK2Dn5mP60ADzyc87QMfiajjijKnG7hj/EfWl8uM3BPO7aP4j6mgB8gBKZOMNmlcZjYE44NRvFGHRjuzu4+Y0rRIqP97BBz8xoAegwij2pIwAXwc5Y01IkEQA3YI/vGmxxRkOo3Y3c/MetAEmB54Oedp4/GiUAquTj5h/Oo2ijMyg7shTj5j7UssafKzZyGGPmPrQBMelMhAEMYByAo5/CjykznnOMfeNMhijESFd2Co6saAHoAHlwc5bn24FDAechzyFPH5UyOKMGUDd97n5j6CkMMfmIvzdDj5j7UASSgGPBOOR/On1DNFH5WG3YBH8R9aeYkJBOeP9o0AEIAiUA5GOtCgCWQ55OOKZFFGUVhu6cfMaEjQTSMM7uM/MaAHuAZYznkZ4/CiYAwsCcDHWozDGsiD5snP8R9KWWKMQODu245+Y0ATUyIAR4Bzyf50GFCoX5sD/aNRxwxmMY3YBP8R9aAJAB57nPJUcfnRIAXiycYbj34NMEaG4Y87gB/Efeh4oxJGx3Z3cfMfQ0APlAMLgnAKnmnjoKhkijWKQndgqc/MacIkC4G7B/2jQAsQADYOfmP86MDzyc87en40yOKMoyjdjcf4j60hhjM2DuyF/vGgCSQA7MnGGFOflGHtUUsaFkLZyGGPmNK0SYZju6f3j6UAPjGI1AOeBSRgAvg5y2abHEgQFd3IH8RpIoox5gXd97n5j6UAPIHnqc87SMfiKJQCoycfMp/UVGYY/NC/Nnaf4j6illij8tQ27AYY+Y+tAE1MhAEEYByAo59eKDEhIJzx/tGmRRRmKNhuxtGPmPTFAD0AEkhBzlhn24FDAechzyAePypkcaCWRhndu5+Y+gpDFGJl+9kg/xGgCSYAxkE4GR/On1DLFGISDu25H8R9RT/ACUKhfmx/vGgAhAESgHI9aFAEshzyccUyOKNolxuwOnzGgRxmd253DH8RoAe4BkjOehP48UTAGFwTgEdaY0UYmQ/Nu5x8x9KJYoxFITuwRz8xoAmpkQATAOfmb+ZoEKAEfNg/wC0aZFFGYto3Y3H+I+poAeAPPc55Krx+JokALR5OMNx78Go/JjMzD5shV/iPvSyRoXiJzkNx8x9DQA+UAxOCcAqeacPuiopYo9jsd33Tn5j0xTliQcjPP8AtGgBYgAGwc/MaMDz85529PxpkUUYVwu7BY5+Y0nkx+aV+bO3+8fWgCSQA7MnHzCnN90/SopYo9qA7sbhj5jTniQrzu4H940AOjGIkAORgc0kYAaTBzlvy4FNSJCiMN33Rj5jSRxpvkYZzu5+Y+goAeQPPU552nj8RRKAUGTj5l/mKZ5UYnH3slT/ABH2okijEeDuwWX+I+ooAmpkIAgQA5AUc0eSm3b82P8AeNMjijaCPG7aFGPmNAD0AEkhz1Iz7cChgPOQ55APFMWKMzO3O4MM/MfSgxRidD827Bx8xoAfMAYiCcDjn8afUMkUYRid2D1+Y+tPEKAEDdg/7RoAIQBEADkc80KB5znPJA4pkUUZhAG7b/vGkEMZkcfNkAfxGgCRwDJGScYJx78UTAGBwTgFTzTJI4zLETnIJx8x9KJoo/Ldm3Y2nPzH0oAmrE8QaIdd0CewjmEcjOXRj03BicHHbtWwIlDFucn/AGjTIYoxGwXdgs2fmPqacZOLuhNJqzOR8FeD7nw9e3F1eTxNM8flqkRJAUnOTkDn5a7GQAtHk4w2R78GoxDH5rKN2doz8x9TSyRR/u1O7G7j5j6GqqVJTlzSFGKirIkkAMTgnAKnmuSv7C7dr1otJuU1J2b7PfWE6wpJ/wA8zJ84yVGAdysDjgHpXVSxRmNt27AU/wARqidY0VLsWz6pZrchgnktdKHDem3Oc+1QUc/caFqT3tyyxrLHbt9vtirKoe6+TKgZ45R8k8fvj6GmnQ9QiTzPs5knMNrNKqOuWlF0ZpQMkepxnjpzXXxRR7nZd2dxz8xoEUYnON2Sv940Ac8ttdRPNbSaUZzPqC3QuHZNm3erAt827cgAUAA/dXnGcOsdHuLbTvDcX2ZY3s5We4AK/IGhlBPXnLsvT61vSRRhVB3YLD+I09ok8sr82P8AeNACxACJADkBRzT6hSKNoU+9gKMfMfSpgMCgAooooAKKKKACiiigAooooAxPFX/IJt/+wlYf+lUVat1dwWVu09zKscS9WNZXir/kE2//AGErD/0qiqXxFpMmsaaIIpFSRHDru6HgjH61M3JRbirsumouaUnZEkGrW1xpM1zYus3kRk7ACDkDIGOvauY8P+ItSutcSCdxLHOTuUIBt46jH0rY8LaJPpUM0tw6l5guEXsB6+/Na1tZWlvcTSQ2sUTk4LIgBPANc6jVqcsm7d0dLnRp88Eua+zLDEecgxzg4P5UTECIkjI4/nQx/eoMdQeaJTiMnGenH411HGPpkJBiGBgU+mRHMQOMe1AApHnOAOcDJocjzI8jnJx+VCn964x0A5oc4kjGM5J/DigAmIELkjI2nIp9MmOIXOM4B4p9ADIiChwMDc38zQCPPYY52jn8TREcoTjHzN/M0A/v2GP4Rz+JoAJCA0eRn5uPyNLJgRPkZG05pJDho+M5b8uDSyHETnGeDxQAq/dH0psRBDYGPmNOX7o+lNjOQ3GPmNABkefjHO3r+NEpAC5GfmFGf3+Mfw9fxokOAvGfmFADm+6fpSRYMSEDA2jFK33T9KSM5iQ4xlRxQAkZBaTAxhufyFBI89RjnaefxFEZy0nGMNj68Cgn9+ox/CefxFABKQEGRkbl/mKfTJThAcZ+Zf5in0AMhIMKFRgYGBQhHmSYHORn8qITmFDjGR0oQ5kkGOhH48UADEebGCOecGiYgRNkZFDHEqDHXPNEpxETjPtQA+mQkGMYGBk/zp9MiOYwcY68fjQAKR5zjHOBk/nQ5HmR5HOTj8jQp/fOMdAOfzoc4eMYzkn8ODQATECCQsMjacin0yY4hc4zhTxT6AGREFDgY+Zv5mgEeewxztHP4miI5U8Y+Zv5mgH9+wx/COfxNABIQDHkZ+bj8jSyYEbZ5GDSSHBTjOW/oaWQ4jY4zwaAFX7i46YpsZB34GPmOacvKD6U2M538YwxoAMjzwMc7ev40SkALkZ+Yfzoz+/Ax/D1/GiU4C8Z+YfzoAcehpsRBhQgYG0YFOPQ02I5iQ4xlRxQARkF5MDo3P5CgkeegxztbB/KiM5aTjGG/PgUE/vkGP4Tz+VABKQI+RkZH8xT6ZKcJnGeR/On0AMhIMKEDAx0oQjzJMDnIz+VEJzEpxjjpQpzJIMdMc/hQAMR5sYI55xRMQImJGR6UMcSoMdc80THETHGfagB9MhIMYwMDJ/nT6ZEcxg4xyf50ACkec4xzgZP50OQHiyOS3H5GhT++cY6Ac/nQ5w8YxnLflwaACYgQSFhkbTkfhT6ZMcQyHGcKePXin0AMiIKtgY+Zv50AjzyMc7Rz+JoiOVPGPmI/WgH9+Rj+Ec/iaACQgFMjPzcUr48ts9MGkkOCnGctSucIxxng0ACY2LjpikjIO/Ax8xzTk5RT7U2M538YwxoAMjzwMc7Tz+NEpAVcjPzD+dGf34GP4Tz+NEpwq8Z+YfzoAeelMhIMMZAwNowPwp56UyI5hQ4xlRxQAIQXlwOQ3P5ChiPOQY52nB/KhDl5OMYb8+BQT++QY6g8/lQASkCPkZGR/On0yU4jzjPI/nT6AGQkGJSBgY6UKR5smBzxmiE5iU4xx0oU5lcY6Y59aAByPNjyOecflRMQIWJGRjpQxxJGMdc8+nFExxExxnjpQA+mREGPgYGT/On0yI5jzjHJ/nQAAjz3GOdoyfzokIDxZHJbj8jQD++cY/hHP50OcPHxnLflwaACUgQuSMjacinjoKZKcQucZwp4p46CgBkRBDYGPmP86MjzyMc7ev40RHIbjHzH+dGf35GP4ev40AEhA2ZGfmGKc+NjZ6Ypshxs4zlhTn4Rj7UAJHgxrjpgUkZBMmBj5uaVDmNTjHApIzkvxjDUABI89RjnaefxFEpAUZGfmX+YoJ/fgY/hPP4iiU4UcZ+Zf50APpkJBgjKjA2jA/Cn0yE5hjOMZUcelAAhHmSYHIYZ/IUMR5yDHODg/lQhy8gxjDD8eBQx/fIMdQefyoAJiBGSRkZH86fTJTiMnGeRx+NPoAZCQYlIGBQpHmyADnjNERzEpxj2oU5lcY6Y5oAHI8yPI5ycflRMQIXJGRjkUOcSRjHUn8OKJjiFzjOB0oAfTIiCnAwNzfzNPpkRymcY+Y/zNAACPPcY52rk/iaJCA0eR1bj8jQD+/YY/hHP4mhzho+M5b8uDQASkCJyRkbTmnD7o+lJKcROcZwp4pR0FADYiCGwMfMaMjz8Y529fxojOQ3GPmNGf3+Mfw9fxoAJCBsyM/MMU5vunPpTZDjbxn5hTm+6fpQAkeDEmBgYGKSMgtJgY+bn8hSxnManGOBxSRnLScYw358CgAJHnqMc7Tz+IolICDIyNy/zFBP79Rj+E8/iKJThBxn5l/mKAH0yEgwIQMDaMCn0yE5hQ4xlRxQAIR5kmBzkZ/IUMR5yAjnBxQhzJIMYwR+PFDH96gx1B5oAJiBEcjI4/nT6ZKcRk4z04p9ADISDEMDA5oUjznAHOBk0RHMQOMdeKFOZXGOgHNAA5HmR5HJJx+VExAgcsMjacihziSMY6k/hxRMcQucZwp4oAfWNry3Uug3C2IbzNxyE+8Ru5xWzTIjlCcY+Zv5mplHmi4lQlyyUuxx/gmG9iubnzUkS3KDhwRls8Yz+NdhIQGjyM/Nx+RoB/fsMfwjn8TRIcNHxnLflwamlT9nDlvcuvV9rNztYWTAickZG05rl7K/0mC21uLUri2WFryQPFKw+ZSq8bT1z6d66iQ4ic4zgHio1trfzBMIIhKed+wbvzrQyPPbS/1iDTJRPP8AZ7qwtoAiy3Wz5vKUlnTaTJucsp6/dwuDzWxFPdoseotfXDSnVZLfyy3yeT9paLZt74GDnrkDnHFdQIIZpBLJBG0kbnYzKCV+h7U/anm7fLXGN3TvnP8APmgDgJNYvrSwRxfNPPPbrJ9oS4DxMPMjVpsMMwlVdjt2lR3zjnpNBnuDe39tLMskMccTqpuTOyM27IL7RwQFIGSRz0BFbH2eCFmeK3iVpXHmFUALe59afFBDbRGOCGOJOTtRQoz9BQA+LBiQgYG0Yp1NjOYkOMZUcU6gAooooAKKKKACiiigAooooAw/Fm4aJHIsUsgivrOVlhiaRtq3MbMQqgk4AJ4Hal/4SvTf+eOq/wDgouv/AI3W3Ve9vI7GASOkjlmCIka5ZmPQAUAY1r4x0q4tIZo11KRJEDK6aVdMrAjqCI8EUReMdKkmnRV1JmjYKyrpV0SpKg4I8vjgg/jVjw5dWraZFZ2VtcwQWkESRC4wGZNvykcnIwOvrn0NMtNR0y01OdJy9jfXziRobogbyFVBtIJU8AcAmgCJ/GOlLdQxFdSDuGKodKustjGcDy+cZ/Wi68Y6VBbtJIupRqCMs+lXQAyQOpjqxcXEMuspLHp97czWW6LzItoRS4UsPmYZ4C9Kdrt1pqpFYapIIoLnLCQuFGUZWAz7/lx7igCP/hK9N/546r/4KLr/AON1Da+MdKnt1kjXUpFOcMmlXRB59RHWx9tjksvtVqDdoRlRAVJfnHBJA/WqWhyQxRPp0NndWotgG23G3JDljkEMc8g0AVI/GOlPczRBdSLoFLKNKuiVznGR5fGcUS+MdKjngjZdSVpGIVW0q6BbAJ4Hl89M1Y024huNQkvbfT70Leombl9ojZVB2kDduGc+ncUmpXdsL+KZrO8uDpzGR5YAu2IshByCQWO1s4UE8igCC68Y6Vb2k00i6lGiIWZ30q6VVA7kmPgVN/wlem/88dV/8FF1/wDG6m1qS1ubBtPlguLpb6J18u3xuMeAGOSQAPmHfuKstqCxae13Pbzw448pgC5OcAAAkZJxjnvQBlW3jHSp4i8a6k4EjrlNKuiMqxBHEfUEEH0INC+MdKN5JCF1IusauUGlXW4AlgCR5ecHB59j6VZ0W5s0aaxtYLiJRNPKGlwRIxlYy7cEnh2I5x2xkc00XMUGuNcyabfRyXIjtTO2zy8KzlejZGS57c5AoAgn8Y6VE8CuupIZJNihtKuhuOCcD93yeCcexp0/i/SoreWR01NFRCxZtJugAAOpPl8Cp9Unge9t0+xXl3LZOLn/AEcLiMlWUZ3MN3Bb5Rk9OOmU1TVdOezitZ1u5oNSiKK1vbSSAoyMeqqccA8dfbFAEaeLdMaNWEWqEEAgjSbrB/8AIdR2/jHSphIUXUn2yMh26VdHBHUHEfX2rRt9Qtk0yW4EdzFbWqkEzQOjFVUHIVgGPp05INU9EvrVrqe2t7K8gMzzXDNOFALhwrgck9T/AJyMgEX/AAmOlfbPJ26lv8vfs/sq63YzjOPLzj3ouPGOlQiMuupJukVBu0q6GSTwBmPr7UNq1iNe89LS8ecbbMzBQIwDKVzyR0cEZx29xm1q00E1xDaNZ3d1LA0d1tg2jZhjtJLEDkqePagCF/FumKjMYtUAAySdJuuP/IdMt/GGlTW0UqJqbq6Bgy6TdEMCOoIj5FaN/fxW+nLJcQTnzxsECKGkJKkkcHGQAT17VW0jULNNHYRwz21rYRqn7/G7YqAhuCcjH8jQBWg8Y6VK86oupMY5NjBdKuiVO0HB/d8HBBx7ihvGOlC8jhK6kHaNnCHSrrcQCoJA8vOBuGT7j1q3pbKtzdOmm3tuLqT7RJJPswW2qvADEjhRxiqqavpt3qMd+lrqbXMCyWqD7DMAQzIW6rgcovJIxzQAlz4x0qCEO66kil0XL6VdAZLAAcx9SSAPc1N/wlem/wDPHVf/AAUXX/xurGtGBrZI7y2uZLVZEmeWEbvLMbq65AO4jKjoD3zUsWrW15YG805hfpx8ts6k/qQAcHOCc0AZlr4x0q4tIpo11KRHUMrppV0ysPUER8iiLxjpUk88arqTNGwDKulXRK5API8vjrmptOvrCy0dbbTo7iWOw2QPB5bebGOBypGTxzx17UaTe2U9xJcwW19FNfhZX8+3dBkIABkjHQetAEMnjHSkuYYiupB3DFVOlXQLYxnA8vmi68Y6VBbvLIupRquMs+lXQA59THSX+rWMOq2kt1aXCXMKEx7pYVwHAyCC4J6Y+oOM1Pq13a3aT6ZJa3FxltrpC6BvlCPnlgcfOv8AnGQA/wCEr03/AJ46r/4KLr/43UNr4x0qe3WSNdSkUkjcmlXRBwSOojq9c60lpbRTS2lwPMDEoWiDIAQMnLgdx0J61D4du7KW0e1sY5EjgJb55Y3Pzszfwse+euKAK6eMdKa6liC6kXRVLINKuty5zjI8vjOD+VEvjHSo5oEZdSVpHKqraVdAsQpOB+75OAT9Aamtr/T4/EEscsc9rqV2ir5UoB3qm4ggqSvc98+1LqV1bfb4ZGsry5bTnMrSQBdsRZGU5BILHaxOFBPI9RQBBdeMdKt7OaaRdSjSNGZnfSrpVUAZySY8AVN/wlem/wDPHVf/AAUXX/xurOsCK90ia1a0uLy2vImicWxUHYy4JyzDsafFq0L6dLeyRTQLCWV45FG8EdsAnk8YHfI9aAM228Y6VPGzIupOBI6EppV0QCrEEcR9QQQR2IoHjHSjePCF1IusauU/sq63AEkA48vOODz7GrejyQwGSyWzurSRnkuQlwVJffIWcgqxGNzdM5GR7Uxbqyg14y3MU9reXKrbRmUZSVVLMu0qSM/M3BIPtQBXn8Y6VE0IddSQySbFDaVdDccE4H7vk8dPanT+L9LigkkdNTRVUsWbSboAADqT5dT6pPBJeW8Rsru7ls3W6/0fbiM4ZQTuYZ4LcDJ9ulO1bUbIaG0kiTXFvdwsFSADc6FCxxkgD5QTyR+dAFePxdpbxIyx6owYAgjSbog/+Q6ZB4x0qbzdi6k+yQodulXRwR2P7vg1oW1/aw6bO+2WKGwUpIHXLKqqGzxnOVIPHr68UzSdqSXCpp15bLNI1wzzlMFjjIGGJH5UAUv+Ex0r7YIdupbzGX2f2VdbsZxnHl5x70XHjHSoVQuupIGkVAX0q6GSTgAZj6n0qwlxDPrX2uHT72V0DWhuF2iMAP8ANwWBOGXrjtxmn6vLDNLDaGzuruWJ47rZblRs2tlSSzAclTxnJwaAIW8WaYFJMWqgAdTpN1/8bqO28YaVPawyompujoGV00q6KsCM5BEfIq9davZRaXDeysRaTlVLn5doboSDz7HuPwNMsbqyttHhi0oNfW9qiQKtu6uwCgAZJIHTHegCnD4x0qWS4VV1JjHJsYLpV0Sp2qcH93wcEHHoRQ3jHSlvIoSupB3R2CHSrrcQCoJA8vJA3DP1HrT9G1Kwm1K8jtoZUmupPtEu+aJvmCInRXJHCL2qS/vdPstWhvdRSa1MKPbw3MmPKYSFCeQTjlF+9jvQBBc+MdKgh3yLqUa71Xc+lXQGSwAHMfUk4Huam/4SvTf+eOq/+Ci6/wDjdLrF7p81pHDMstzHJ5dwn2cg7isiGPBzj5n2gev0yasy6zbw6T/aMqvHCHCSB8Bozv2NnnHBznBPTjNAGfa+MdKuLWOWNdSkR1yHTSrplP0Ij5oi8Y6U9xPGq6kzRkBlGlXRK5GeR5fFW9NudPtdISDSWN7DaqsYjgkV2A7ckgfrVfTdQs31mdY7WeK5vAJH8yWI5CDbkBXJxxjp1oAZJ4x0pLmGJl1JWk3bVOlXQLYHOB5fNF14x0qC2eWRdSjRRks+lXSgfUmOrF3cQy6vG8en3t1NYMV3w7Qqs6gkHcwz8pB49an1pYptPe3ubW4mtZRiVoMEoBg5Izk/8BBNAFb/AISvTf8Anjqv/gouv/jdQ23jHSp4BJGupSKWYbk0q6I4JB5Eftirsev2NxZtd2cgu4UUs7RMvyY7NuI2nr19DUegXFoYZrK1glhFu7F1lkRiGdixB2scck9cUAVk8Y6U13LCF1IuiqzINKutyg5wSPL4zg/kaJvGOlRzW6MupK0rlVDaVdAsdpOB+75OAT9Aamt7/T4/EMiTRz2upXaLGI5QCJFj3EEFSQPvN1IPtT9XlSO5triTTL64+xOZ45LfYQGKMh4LAn5XbjFAFa68Y6Vb2c00i6lGkcbOzvpV0qqAMkkmPAFSjxZpmP8AU6r/AOCi6/8AjdS61dWEujS296JGtby2kEnlckRbCXbI7AHt3IxViLVYTp891LFNb/Z8iWKRQXQgA4wpIJIIIwT1HegDNtvGOlTI7IupOFkdCU0q6IBDEEcR9QRgigeMdKN40O3Ut4jDlP7KutwBJGceXnHHX2q3o8kMLy2a2d1ZySPJdBLgqd+9yzEFWI+83TORkcUy3uYZ9Ye9g0+9cuotTcYUR7UducFs4yW5xzQBXn8Y6VEYQ66km+QIu7Srobjg8D93yeOlPm8XaXHDI7R6oqqpJZtJugAMdSfLqfVZoJLuCE2V3dy2rrdYt9o8v7wUncwzn5uBk8dOlO1HUrH+y4XmEslpfjyw8SE4VkZtxHUDaD24oArReLtLkhR1j1RlZQQy6TdEEe37umQeMdKlMuxdSfZIUbbpV0dp44P7vg+1aFtfWUGnTmJz9lsECmQfMCgjV8jHUbWFR6SVSa5CadeWwnka4d59mCxwMDDEjp6dqAKZ8Y6ULxYdupbzGXCf2VdbiAQM48vOOetFx4x0qFEZ11JA0ioC+lXQySQAOY+pzgCnyalYjXBcm2ujJGjWwnAXy9pkVW4znhwATj9MmrGrywTyR2bWd1dSRPFdbbfaNhV8oSWIHLIePY0AQnxZpgBPlar/AOCi6/8AjdRWvjHSp7SGaNdTkSSNWV00q6KsCMggiPkVq3N+sFnHJJbzl5iESBQDIWIJx1x0BPXHHWotHVbbSYbSKyubeK0iWGKOcqWKqoA5DEdu9AGfD4x0qSW4RV1JmikCsF0q6JU7VOD+74OCDz2IofxjpS3cUJXUg7ozKh0q63MBtyQPLyQMjP1FWNKvLCa/u2sI53e5kE105XCxSBETY2ejYRcqMkY5xkZTULmK21OO+m02+c2qPEtwmzywrlCxI3ZwNg5I4waAILnxjpUEBkkXUo13KNz6VdAZJAAyY+5OKm/4SvTf+eOq/wDgouv/AI3TNev7UQta39lc/Z/MRhIJIkVmVg4wWcHqBVqbX7SDTre9kjuTHOyoohhMxBLBefL3DqfX6ZPFAFK18Y6VPbRyxrqUiMMhk0q6YH6ER0R+MdKe4mjVdSZo9u5RpV0SuRxkeXxVrS7mytNONva29+ttaIMGW0lDNknhQV3MR3wO4qHStR0+6vPtdpbagJNRVHdpbSVEUBCQSzKFHHHBOSRQBHJ4x0pLiGNl1JWkJ2qdKugWwM8Dy+aLrxjpUFrJLIupRooyXfSrpQPqTHRq2q2NvqSSPZ3txPYB2BhUbVJQFgSSM/Kyn8fY1a12W3lthps1pdXQvEYFLbbkKMZJJIwORz70AR/8JXpv/PHVf/BRdf8AxuobbxjpU8O+NdSkXcy7k0q6IyGIIyI+xGKkHiux+zGaS2v4yHVDH9mZ3+ZimcLn+IEHuCMdSM2NGltEje0soLxIkZpS1xbyRgs7sxA3gZ5J6dOKAKa+MdKa7lhC6kXREYoNKutwBLYJHl5AODj6H0om8Y6VFLboy6kplkKKG0q6BY7WOB+75OATgdgfSptO1LR7nVJriK5RL+dVt5IWkG792z4AH1duR1pNWvbcX8O/T7+6fTm+1BrZVKqxR0wfmBJ2s3HuDQBHc+MNKgtZpZF1ONERmZ30q6CqAM5JMfAqQeLNMKgiLVTkf9Ai6/8AjdMv9YsLywFje2moLFqMLx4jtmkyjKcndHuHIz0Oe9XDq8cdibiOyvWhjO0l4xEQAAdxEhUgdsn09OaAKNv4x0qZHKLqThZGQlNKujgg4IOI+o9KB4x0r7YYdupbxGH2f2VdbsZIzjy84460ujarp5W5hsoZd7PJcmMzROzszFmC7XPc98Cl/tOwTxMwMEy3jKLXcZosbQxIO3fu756Z9qAGz+MdKh8reupJvkCLu0q6G4nsP3fJ9qfL4u0tIndo9UUKpJJ0m6AH/kOpdYurP7RBBNb3E80EkU8aQ4GXJZUGSQOzn0AU5xxma61C2m0mGTyp5o79AkUUa/O4ZC2OSAPlBPJHSgCnD4v0uWCORY9UZWUMGXSboggjqD5dNg8Y6VK0wRdScxyFG26VdHacA4P7vg89K0LS4Sz0Yu9rcW0NpFgJMV3bEXrkMR0Hc9qzrLVrSyupEbTtRtTeyfaGeeMYydqZ4YkDO0HjjPOKAFPjHSheJDt1Le0bOE/sq63EAgZx5eccjn3FFz4x0qGNWddSQGREBfSroAksABzH1JOAPU1Nc3+n2uvwS3sc9vcsrWtvI4BSUMyk42k45UfexU+stAYo1vLW5e2SSOcywjcEaNw65AO48qOgPvigCD/hK9N/546r/wCCi6/+N1DaeMdKuLOCaNdSkSSNXV00q6ZWBGQQRHgitG41myt9Mi1HzVezkKYlU8bWOA305pmkS6Xb6NDb6dcxy2djCsIZX37VVcDJ9cCgCjD4x0qSa4RV1JmjcKyrpV0Sp2g4P7vg4IP0IofxjpS3UURXUg7qxVDpV1uYDGSB5fOMj8xVrS5Y3u7meHTr2EXrCd5ZtgUkIqjjdkfKo4xTNSuYrbUo76bTb5/siOizx7NgV9u4kbs4G0c44waAILrxjpUEBkkXUo1BA3PpV0ByQOpj96m/4SvTf+eOq/8Agouv/jdO1+6sxAtldQXM4lZG2W68jEi7ckkAZbaP8mrceqRvpX9oGCZYxnchUF0AbByAT0wSR1GDxnigDMtfGOlT2ySxrqUiN0ZNKuiDz6iOiPxjpT3M0QXUi0e3co0q6JXPTI8viruhSWf2EQackpsoeIp2+7LkkkqerD36HPBNQaTqWj3d7JcW1ygvbwKJIGkBYFAeMe3PI4PWgCGXxjpUc8EbLqStISFVtKugWwCeB5fNF14x0q3tZZpF1KNEUszvpV0qqPUkx8Uuqalax6gJpNPv5zpjFjNCq7FLJznLDorZPp1o1LVLHUNMe0ntbporstbGPckbsRwwAZhyDkcfhkc0AS/8JXpv/PHVf/BRdf8AxuobbxjpU8JeNdSkXe67k0q6IyGII4j6ggg+4rQudYistPW8urW8jDOE8tYTI4JOBkJuwP8APXio9Gls0SS0soLxIw7zM1xbyRjdI7OQC4GeWPToKAKa+MdKa8khC6kXSNHKDSrrcASwBI8vODtOD7H0om8Y6VFJArrqSmSTYobSroFjtJwP3fJwCcegNSWmpaZNfzahEsy3cwhtZI3GG2q77Gx/dJkbkZ9OoIp+q3dob61R7a5uLi1nWSFIcDMjRyDHzEAnYXPX9cCgCG48YaVDbSyumpoiIWZn0q6AUAdSTHwKevi3TGRSItVIIzkaTdf/ABuptUvbW50iOIwXF1DqUZiVLcDcUZCSeSMDHftkU201+zNvMHgurb7NGzFJo/mKpgNjBOSMjjryPUUAV7fxjpUyyFF1J9sjIdmlXRwQeQcR9faj/hMdK+2eTt1Lf5e/Z/ZV1uxnGceXnHvV3SNsZnjTT7y2WSR52acpgsxycYYnvVI6tY/28Zo7S8ef5bNpgoEYBlK55I/jDDOO3HUZAC48Y6VCI966km+QIN2lXQyT2GY+T7VJJ4t0xI2Yx6oAASSdJugB/wCQ6j1rVbOOSBNQs7mNYrgSQu0sSB3U8EbnGR7Vp315BFpnnXivFbyLtlZsfulYcliDwB0J5A69MmgDOg8X6XLbxyImpuroGDLpN0QQR1B8vkU2DxjpUrzhF1JzHJsYLpV0dpwDg/u+DyOPerNrqEFnoayW9ndtZ2yiOMkKpaNV+/8AMR8vHU4JxnGMGo9IvIPt0saaffWr3xN0WuVUBiFRSBhiQcBePrQBE3jHSheJCV1IO0bOEOlXW4gFQSB5ecDI59xRc+MdKgiV3XUkBkRMvpV0BlmAA5j6kkADuSKsG4hn1pLuHT72eSBZLUTptEYDMu/7zAnBQcgdjS65c2hEFncW9xPmeCbEO0bGWZDGWJI4LheO+D70AM/4SvTf+eOq/wDgouv/AI3UNr4x0q4tIZo11KRHQMrppV0ysCOoIjwRWk2rQDT47wRzMJG2LEqZkL5wVx6gg55xwaqeHbyz/sqO1tbe4t7azgjEf2nAJj2/K3U8YHX6+hoAgi8Y6VJNOirqTNGwVlXSrolSVBwR5fHBz+ND+MdKS6hiK6kHcMVQ6VdAtjGcDy+cZqTStQspL+4khtbuF72cbnmC7WcQoy4AJIzHg8gdDnBqPUtWsYNXSVrS8nnsUkAeJRsUEIXySR0BTnoM+xwAF14x0qC3aSRdSjUYyz6VdADJx1MdTf8ACV6b/wA8dV/8FF1/8bqbW3t5YV0+a2ubk3ALiO3AzhGUkkkgDkr371ba98vTzdy208YUZeMqC6jOCcKTnA54zx78UAZFr4x0qe3WSNdSkU5wyaVdEHnHUR0J4x0p7qaILqRdApZRpV0Suc4yPL4zirmhS2ZsvI01JGsoj+7nb7ku4knYerAZ69Dngnmora5ij1tnfTb63mvtsZkl2FDsViBwxwcZoAgl8Y6VHPBGy6krSMQqtpV0CxCk4A8vngZouvGOlW9pNNIupRoiFmd9KulVQB1JMeAKsX1xDLqcJSwvLqawkLBoAoVHZCMHcwydr549RVjWkjuNLmtbi0uJ7a5jaKbyMbkUjBOM5P8AwHJ9qAKv/CV6b/zx1X/wUXX/AMbqG28Y6VPEXRdScCR0ymlXRGVYgjiPqCCD6EGtK11iy1G1kn06VbwxjmKJgHB9CGI2ng9cVV0i+0qGR9Mt5TBdebJM1rcfLJukdpGwD1GWbpkUAV18Y6UbySELqRdY1coNKutwBLAEjy84ODz7GifxjpUTwK66khkk2KG0q6G44JwP3fJ4Jx7Gpob20OrvfC0u1W4VLQXbBfKba77QBncMs7AMRg5HPIyms39kl9aRS2t5czW0onjFumcPsfAPIydu84+ntQAyfxfpcVvLI6amiohYs2k3QAAHUny+BTk8W6Y8asItUIIBBGk3RB/8h0uq6tbHR3N1aXBsbm3/AHkqyRKiq4xjcXAzg9RxVuz1C3GkNdFJIbe2Vg28qxAQc/dJzjBHrkUAZ9v4x0qYSbF1J9kjIdulXRwR2OI+vtR/wmOlfbPJ26lv8vfs/sq63YzjOPLzj3qxptxDbPcwpp97BNMZLsRy7CZeRu24YgcleCR1FMhvba4155obK5kniH2SSVHjKIN2eQHyOfbPtQBFceMdKhEZddSTdIqDfpV0MkngDMfX2qR/FumKjMYtVAAzk6Tdcf8AkOm6lfxXF0LOXSdRme2kW5UwhCPlb5W+90JBwDycH0qe61uL7HbOLC9mivUGzyghOWBO3G7rgEnHAweeKAK9v4w0qa2ilRNTdHQMrJpV0QwI6giPkVrWGoQalbmaBbhUDbcT28kLZ/3XUHHPXGKr6HNCbBbSC2uLdbILbiO4xuAVRjoTngjmtKgAooooAKKKKACiiigAooooAKo6jZz3sXlxywoAVZTJEzFWBBByrqf8/hV6igDC03Sb3SnkZZrWaN1wyiOVGUAHaFLSPhQTjaAAMkj0NDw9DaywiFppGNzEftED2DRbyRzudhuPfqxPua6yigDibyD7Bq9xFbpeGFpEMt6b25McBKj/AFi7vmOAOQcAEbtoAJ2dbSebVLGCKKWZWimZo0u2txwUwSV5JGTx7k9q3aq3mm2OoBPttlb3PlklPOiV9ueuMjigCheiS18NlTG8TAqrf6RI5jBcAsXBDEAEseRwMZAqhoMAunv0lE6iWOJw/nzCVFJcrGzM5IYLtYgY+/yPXpILeG1gWC3hjhiT7scahVH0AqSgDjtFWSxfR4pbe7hkuDlf9Knkh2GJmxtZiFYEAbW/DOOIvEV0kWrXMf2qK1RxtmgeZkNziMsG4PAOPLyvJI/2QK7amtGjsrMisy8qSMkfSgDmPEUzx3FhJLFbQ2qoxWR9RktZFc7flwinIxnjkcDpgZkW6D+DLictDKnzjJkknXG/H3nwzkegxkjA7VN4ggupt8ghjSCOF4zN9q2Eq4G4EGNsYIUgjnKjtkVZs9LmfRZLW+dlnmdnkeJgSrFsgqdoAxxjj8zzQBW8PQQxyyGOSRyI1Qb7GSDaoJ4BfrycnHfk1nxmRZ7eW9trkyy3imFvtEzwkeZjlC2FYDkZ44BHoOks7OW1LGTULq6BGAJhH8v02qP1q3QByevysNYeGK4+xSPEoeYysqtGA7MzAYwoxt3Ag5ccjjNrVbC51XwvbRWlssZMIJtHcoBmMgL0/hJBwePl+ldFRQBgvaakmh6gkVnbSTXLOyWk1w2xFZcFd23uckgYHzHnvS2Gl3kF/JcmC2tnMDIrC7lufnO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    Figure 7: Multilingual pre-training on 101 languages. Improvements of Switch T5 Base model over dense baseline when multi-task training on 101 languages. We observe Switch Transformers to do quite well in the multi-task training setup and yield improvements on all 101 languages.
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    Figure 8: Multilingual pre-training on 101 languages. We histogram for each language, the step speedup of Switch Transformers over the FLOP matched T5 dense baseline to reach the same quality. Over all 101 languages, we achieve a mean step speedup over mT5-Base of 5x and, for 91% of languages, we record a 4x, or greater, speedup to reach the final perplexity of mT5-Base.
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    and computation performed and is ultimately limited by the memory per accelerator. Once it exceeds the size of the accelerator's memory, single program multiple data (SPMD) modelparallelism can be employed. This section studies the trade-offs of combining data, model, and expert-parallelism.
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    Reviewing the Feed-Forward Network (FFN) Layer. We use the FFN layer as an example of how data, model and expert-parallelism works in Mesh TensorFlow (Shazeer et al., 2018) and review it briefly here. We assume B tokens in the batch, each of dimension
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    dmodel. Both the input (x) and output (y) of the FFN are of size [B, dmodel] and the intermediate (h) is of size [B, df f ] where df f is typically several times larger than dmodel. In the FFN, the intermediate is h = xWin and then the output of the layer is y = ReLU(h)Wout. Thus Win and Wout are applied independently to each token and have sizes [dmodel, df f ] and [df f , dmodel].
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    We describe two aspects of partitioning: how the weights and batches of data divide over cores, depicted in Figure 9. We denote all cores available as N which Mesh Tensorflow may then remap into a logical multidimensional mesh of processors. Here we create a two-dimensional logical mesh, with one dimension representing the number of ways for data-parallel sharding (n) and the other, the model-parallel sharding (m). The total cores must equal the ways to shard across both data and model-parallelism, e.g. N = n × m. To shard the layer across cores, the tensors containing that batch of B tokens are sharded across n data-parallel cores, so each core contains B/n tokens. Tensors and variables with df f are then sharded across m model-parallel cores. For the variants with experts-layers, we consider E experts, each of which can process up to C tokens.
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    Term  Description                                    
    B     Number of tokens in the batch.                 
    N     Number of total cores.                         
    n     Number of ways for data-parallelism sharding.  
    m     Number of ways for model-parallelism sharding. 
    E     Number of experts in Switch layers.            
    C     Expert capacity, the batch size of each expert.
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    5.1 Data Parallelism
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    5.1 Data Parallelism
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    When training data parallel models, which is the standard for distributed training, then all cores are allocated to the data-parallel dimension or n = N, m = 1. This has the advantage that no communication is needed until the entire forward and backward pass is finished and the gradients need to be then aggregated across all cores. This corresponds to the left-most column of Figure 9.
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    When training data parallel models, which is the standard for distributed training, then all cores are allocated to the data-parallel dimension or n = N, m = 1. This has the advantage that no communication is needed until the entire forward and backward pass is finished and the gradients need to be then aggregated across all cores. This corresponds to the left-most column of Figure 9.
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    5.2 Model Parallelism
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+            "/page/20/Figure/1": 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kxkgoAy55wRg8+tT/ABytYb698GWlynmQT6n5ciZI3KdoI49qAPV7a/s73d9lu4J9vXypA2PyqxXhHxE8JaR8NrnQPEnhSGTT7kX6wSxpM7LKhGSCGJ9MfjW94z+IHijSNb1CG0k8O6XY2cQkiOqz5mvDjOERWz7cgfWgD1mivnnxl4n1zxbongLW4YrC3W5vgEjO8kXCuV55/wBXwOOvWu18X+M/Fmj6rFYRS+G9LijtFllvdSnISeXHzLEgbfjPqD9aAPUaK5D4a+MLjxv4QTVbu3iguFmeGRYc7CVxyM8gEEV19ABRRRQAUUUUAFFFFABRRRQAUVxnij4oeG/CGrrpeqvdC6aISgQwFxtOcc/hV3wp4+8O+NPOXRrwyTQDMkMsZR1Hrg9R9KAOmrh/il4O1Pxr4ctrDSp7SG4hu0uN10zKuACP4VJzyO1dxRQB5He/D/x34wuLG18Za3pI0a2lEr2+mq+6UjoDuUfTOe/SpdY+HniL/hYd5rukjQ7i1vYEhB1ONpGs9oAzGo4zxxz3r1esS78V6XZeKrLw5M8o1C8iaaIBMrtGc5Pb7poA8zT4Ra8Phc3hlr/Thfx6n9uilDOY3GMYb5QQfoDVvV/AfjG78Wab4k2+HL68WyFtcQXiyNBC4J+eMEZP44Oc16F4Z8U6Z4tsJrzS2laGGdoHMibTuXGf51sSOI42dvuqCTQBwvwz8F6r4Mh1mLVLmzuTe3huY5LYsMgjnKlRt+gJrvK40fE7w43hdPESvdHT3uvsgIgO7zPp6e9dirBlDDoRmgBaKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKwvFfi3SvBulJqOrvKtu8oiBij3ncQT0/A0AbtFcHonxh8Ha7qsOm297NDcznbEtzA0YduwB6Z+td5QB4vcfCDXpfDmtWkV9p8eoXOs/wBo2cokfai8ghjsyDg9gfrWxpfwwvdP8UeGr0zWbWGmaSbG4QO293YNuKjbjBLdyD7V6hRQB41F8PvHujaDqHhHRr7Rn0C8d9lzceYJ4Y3+8uAMZx9fw7Xrz4YatoN14c1XwfdWjahpFqbSSO+3KlwpySfl6HLNx9OeK7/xN4l07wnozarqjSLaq6oTGm45Y4HFQweL9KuPFI8OxvL/AGgbUXeDH8vlnGOfXnpQBwmnfDHXr7WfFF94nv8AT3Gu2Itz9i35hfKkYVh91doxzk4qDRfh94ztP7O0q4PheDTrJgHv4bFZbm4jHRSHQgHHf9T39grA/wCEx0n/AISO/wBB3zfbrG2+1TDy/lCYB4Pc8jigDI8L+D9Q0Tx74o124mtWtdVaMwJGzF1C9dwKgD8Ca7asnw54isPFOjR6rpplNtIzKvmpsbIODxWtQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAHjt58JdbuNO8VJHe2Ed3qOqR6hp8gkfERVmPznZwcN2zV7SvhdqOm634NuvtFk1vo1nJDeAO+6SR9xJT5eRlu5FeqUUAePReAfHXh3T9X8O+HL3R30PUZHZJbsuJrdXGGAAGDx9fwqa6+FOp6JB4YvvCl5atq2iRtG4vAVjuAxJbpyOWbj0PXivVrq5js7Sa6mJEUMbSPgZOAMmvOI/jx4Hdl3XN9GhON7WjbR+WaAKVp8M/EOreIfEOp+KL7Tius6WbMrZb8wPuQrgMOVGzrnJNQaL8PvGdp/Z2lXB8LwadZMA9/DYrLc3EY6KQ6EA47/qe/rNle22o2UN7ZzJPbToHjkQ5DKehFT0AcT4X8H6honj3xRrtxNata6q0ZgSNmLqF67gVAH4E1Q+I3gnWfEWuaHrGkDTbltNL77LUwxhk3Y+bABz/APqr0WmyOI42dvuqCTQB4/bfC3xKE8YG6u9HMuvWiRxfZ98aROCONu04UdAQSeBxzWp4g+HOr6r4A8L6DBc2K3WlTQvO7u4RgikHaQpJ69wK21+KPhptAtdaEtz9jurz7FGfJO7zfcenvXZ0AeYT+DPF2g+NtX1fwldaUbTWsNcR3+/MD/3lC9epP44x3ra+GHhDUfBXhu707VLm2uLia+kuRJbliCrKo5yBg/KeP1ra8QeKtM8Mvp6ai0qm/uBbQeWm7Lnpn0FLY+KNN1DxNqGgQGb7dYIrzBoyEwcYw3fqKAOK8VeCfGFx8SI/Fnhi80eFksxbBb5pM988KhHf1p/h74d66/ia78T+LtYt7rV3tmtrZbJSI7dSCMjIHPJ7dzzXplFAHgY+DvjBvCV14cD+G4ovO81b3Y/2i5+bIDvtO0D6HoB712l74H8Rr490PxNpl1pY+y2KWV3DcGQ/KPvGMgcnBOM4r0iigDwv/hUfi1NO13RoZfDxt7+Z5l1KeN2unyQQhbHyjjnr1OM5r17wxptxo/hbS9MuzEbi0to4ZDExZSVGMgkA449K1qKACiiigAooooAKKKKACiiigAooooAK86+IngvxF4h8Q6BrHh2502G40pncfbmcAsSMcKpyOPavRaKAOD0S2+KUWqxPrV74amsAG8yO281XY4O3BKcc4z7VyU3wn8W3GpP4qbW7JfFv24TRFZJPsywAY8vOzPt06cd69pooA8Pvh4mPx5vJNDOnLqqaRG0sNyXaCQYXcoYAN15Bx2q4Pg3qWqeGtbbWdSth4h1S8W98yFSYYmXOF5GSDuOeOOOuOd/W/if4H8OeKru3vbeUavABFNPFZbnK4BA3jkjpXU+F/F+ieMbB7zRbwTxxtskUqVeM+hBoA5Hwz4U8Wp4gtbzW4/DVjZ2iY8rTLJS9w/Zi7JlPX5SPpWp8OfB2oeEbLWINQmtpGvb+S5jNuzMArAYByo5/Ou2ooA4H4a+D9e8F/wBp2F9c6dcaXNO1xbtBv87cx535AAGAOmeaz/F3gXxBffEO38TaQmi3sf2UWxt9WVmSE5PzqAOf/wBdenVzs3jbRoNd1PRneb7Xptoby4AjO0RgA8HucHpQB5sPhD4j/wCEJ13Q3vtLkub/AFNL2OYO6qVHXcNh2n2GR711/iTwVqOs+KfB+p281osGiuWuVkdgzj5fuAKQeh6kV1Ph/XrHxNolvq+nM7WlwCUMi7TwSDx9RVjVNRt9I0q61G7LC3tYmlkKjJ2gZOBQB5XB8PvGugrruieH7/SBoWrSSSCW63+db7xggADB44zz613Xw/8AD134U8D6Zol9JBJc2quHeBiUO6RmGCQD0YdqjHxB0I2mhXIa5MWuPssyITycgfN/d611NAHk134H8e6f8QNc8ReGr/QYo9S2rtvDIWCgDsEIByPU1Npfws1QaX4lutZ1mO48R65btA1zEpEcKnsOAcHAB4HA6V6nRQB4ZF8KPGE+l6HZyr4atE0i6jlxbq6vchTy8j7TlvQY7nNdTN4D8Qx/ELW9Zsb/AE9NN1q1+z3G/f58XybQUwMZ3AHk9K9KooA8D/4U94wm8Ir4fJ8NW8dvcCVblEk866+Yn94+04AB9Owr3iBWS3iVwA6oA205GcdqkooAKKKKACiiigAooooA5rx/4eu/FXgnUdFsZII7m5VQjTsQgwwPJAJ7elcdpuh/GDStMtdPttT8J+RbRLFHuExO1RgZOyvVqKAPKfFvw01/x3rTTa5qVpDp9tZlLGG1kckXBAzI4KgYz6E8ACuc8e2Hiez0fwFp+rXlm2sw6mIoruBmkRsbdjNuVTnpn1x717zVa60+yvnhe7s7e4aB98TSxK5jb1XI4PuKAPObDwL4l13xsviLxnNpqrbWj2ttbaeXKkMCCxLdOGP/ANbFYun/AAz8Y6Jbf2Fpz+GW04Tl49TubMSXSRk5K7WUqT9fzFe00UAcJp3gjULH4pN4lNxatp/9mJZhBlZSwxztChQOOx/Cq1z4P8Q2XxSuvFml3mlixu7VYLhLreJECqOVwMdVByT68V6JXMeL/Hmh+CVtDrLzgXZYRCKIvnGM5/MUAR/D3UNb1PwwbnXriC5uvtMqJPbptjljU4Vl4GVODg45rF1z4e3us/EK+1t5rT+zrrR308xlm8wOwIzjbjHPr+FaXhn4o+FPFepDTdOvJEvCpKQXELRlwOu3PB+nWuzoA8Ph+DPiBPA/9lS3+nSam+qRXckxlk2GKNNqqDszkemMe9dDd+DfF3h3xhquu+DptKli1dVNzbagXHlyD+JSvXqT+PSvT6KAPID8Grp/h/cabJqcR8QT339pNdAHyvO/u9M7cE846npU0ngvx54h8U+G9a8Q3WiRx6TNlre1aTLLxlskHLHHTgDFd94g8VaZ4ZfT01FpVN/cC2g8tN2XPTPoKNL8VaZq+v6poto0pvNMKi4DJhRnpg96APM4vhl4r0CW/wBP0AeG7jTru4M0V3qNtvuLUE8gAqQfxz+FdKPAWpR+PvDeuC7s5LXS7Braf5PKeRyG+ZURdgGW6ZFehVy4+IGhFNfYPcEaE229xEcg8/d/vdDQBW+JnhO+8Z+D5NL0+5ihuBMkyiYkJJt/hbHb/CuV0n4eeKB420LxFfr4etIbGJ4XstOV41RSpAI+U7id2TkjpXp+k6nb6zpNrqVpv+z3MYkj3rtOD0yO1XKAPL9G+G+sad8L/EHhma5sWvdRlleGRHcxqG243ErkdOwNVJfhv4k0k+F9Y0G605tb0iyFncRXDP5My89CAD/Eewr1uigDzrwX4M8SaV471nxNr95p076lbqhS0LjYwK8YYfdAGAckmpvid4M1vxb/AGHNodxYQ3Om3RuM3jOFJ4x91Tnke1d/RQB5T/wr7xj4n8Qabe+OdY0yWw06TzorPTkbEj/7W5R6D1/DNVL74a+Jx4t8RXdi+hTWutZIvL6NpLi1BBG1BjAPOM57D0xXsNFAHjsnwr8RjwB4b0m3vdLXVdFvmulZ3kaGTLFhztDdxxj8amv/AIf+Ln8cz+IIT4eunv7WOKZ72N5BaOFAZolx6g4ye/NeuUUAcV8MPCOo+CvC8ulalNazSm6eZZLdmIKsB1yowePeu1oooAKKKKACiiigAooooAKKKKAPDfGWpalpP7QNjd6To7atdrpmFtFmERYHfk7iD061JZ6d4kt9b8U/EDXbeHwssmnmGEbhOyN8o3kL1PHfGSRxXpkvgzTpvHUHi5proahDbm3WMMvlFTnkjbnPJ71o69oln4j0O70jUFY2t0mx9hww7gg+oIBoA+fdO1fWNJ8QeEby01DxY0epXaR3M+rygQXYYjJjjyTjB6n2rqPC1rrvij4ieJvtPinUoNM0fVBItqszFXwzYQ5PCYXoOOfauktvg3pMT6ZLc67rt3NpkqyWjTXKsIgpyEClSAvA6c8da6bQvBum+H9T1q/tZbmWTWJvOuFmZSqnnhcKCB8x6k0AeCa9rWqSabq/iHT9e8V31xBd4j1CBvs+nRLuA2BSxLdccAdRXbT3Ut98ZPAt3OczT6GZHIGMsyOT+prZPwQ0JrO709tY1z+zJ5DKlkt0BFC5OdwXbgn65/Pmujj8A6XHr+jaz9qvWutJs/scAZ02umCMuAvJwexA9qAPD9M8San4e+El0mkyywXN/wCIJLYzQ/6xFKgnb6McYFdV4W/4SrS/F0UNvaeLH0C5t3S7/t0iTy5NpIdSCcDIH5967iH4U+HI/Cl34dka8ntLm6N35kki+ZFKe6FVAGMdwaXQfhlp+j6t/at3q2q6vfrCYIZtQn8zyUIxhRjrgmgDx+x1TUNJ+AdrcabfXNnM2vFDJbytGxU5yMg9K6D4gXWvL4yupNTk8TroEdsn2SXQJeIX2jLSge+epB9K7o/CXQT4Oj8Mfa9S+xR3n2wSeZH5m/0zsxj8M+9O1n4XWOo67PrOn61q+jXlygjuWsJwgmAGORjrx/8AWoA881XxJqt34O8D/wBn+LryeS61M2s19BuhkYZA2yLk5IB75B681YtNB1N/ifrfg0eL/EQ0tLNbzd9szMXwvG8jgZY5xjOBmu8T4T+HotL0bT4pL6OLSbv7ZGyyLullyCTISpznA6YrZh8HadD40vPFSzXRvru2Fs8ZZfKCjHIG3OflHegDmfgprGo6x4GkOp3kt3NbXkkCzTMWcqMEZJ5PU16PXP8AhHwhp/gvSpdO06a5lhlnadmuGVm3NjIG1RxxXQUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABXk37QZI8B2ZUbiNRjwPX5Wr1mue8YeDtP8baVFp2pTXUUMU6zq1syq24AgD5lPHNAHmWr2HjX4jar4eivPCK6HZadcrcSXct2sjFRjgYAPboAecVg+K9Vvr698T6lZa54pvpbCVhBNprfZ7Gz2/wALktliPYDPvmvopFCIqDoowM151N8GtEmuNTA1XWodP1F2lm0+G6Cw+Yf4sY5weQDnoOtAHGazrfiXWU+G0Fpr93Y3Or2zLczQuQGJCgsVBAYgZIz3qbxJbT2niGw8LDxH4q1X7FZbms9I+WdnJJEk0pbGOR2OOPWvQofhrpEMvhqQXeoM3h5StpudPnB/v4Tn8MUzXfhpp+teJzr8eq6rpt3LEIbn7BceWJ0HYnGR0A49KAPHbrWtT1j4Aan/AGpdz3MtrrCQI9w++QKCpwzd8ZPNdTNfvpXxivdQjTe9t4W85VPcqgI/lXXp8IfD8XhO88NJd6mun3V2Ltv3qF0cY4UlOnA65PvWyngXSk8Vt4haS5kuWsRYNC7KYmjxjkbc5/HHtQB4XpuoeNtU0a38RaY3jG71uWbzN6lW0903HKBM9OPTHt3rsrG4nb4zeKLl1a3uT4eWQqDho38tDj2INb6/BbQ1lWD+1daOjLN540k3X+jhs56Yzj8c+9dEvgTSk8TajryS3S3N/Z/YpIgy+WkeAPlG3IOAOpI9qAPIINV8Y6p8IdAns7zVr3zL6Uag9rcE3kkQbgIxyx79M9u1afhDxARpni+1s/EWtt5GnSTQ2GsKRd2rBfvCTPIz6Y7cV2T/AAg0A+GtO0aG81OFtNmee0vUmUTxsxyeQoGM47dqs6T8MNM046pPdanqmpX+p2xtZ727mDyLGRjC8YHbrnpQB5BdS+IbT4eeFvF0fi7XG1K7vBbMsl0XiCEsB8p6n5ed2c5ruPD66l4Z+Nv/AAjo13VNR0+6043DrfzmUiT1HYdO3rXUz/C/RLjwjpXhp7rUBZ6bcC4hcSJ5jMCxwx2YI+Y9AK1X8Hae/jiPxaZrr7fHbfZhGGXytvPONuc8+tAHQ0UUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAGb4h/5FrVP+vSX/0A18/+DNQ8XX/wlk8NaP4N+32l350S6g90ioCzHPyEDkfWvou8tUvrG4tJSwjnjaNivUBhg49+ayvCXhax8G6BFo2nS3EttG7OGuGVnyxyeVAH6UAeQ6rYXfh+x8J+DrjWtaaaK0Z7jTNAT9/KxZjkyEgBB05z0JqhpniXxBH8MPHcUmp6ok2l3UcdrJcXJNzAC+CpkU9eOx9a9Z8T/Dux8Sa9a62up6npmo28fk+fYTCNnj54JwcdTyPWqNt8I9AtNE1vSYrzUxa6wyPcbpkZlKnIKsUz9d2aAOJ1CLVtD+Hdrqmo+ONW+3a6bbiGNpJcbSfKgG4YJyMtkZx703wPqWq2PxKvNCabxBHpkulvOLXXJxJMGH8WB93PPFeka/8ADzS/EHhrTdGmubyAaZsNpdQyASxlBgHOMdB6flVbR/hhpej+IRrw1TV7vUTbtbzS3dwsnnBupbK5z0xggcDigDxKH/kjfh//ALGY10/iPWda8S/EXX9O3eKTZ6XiK2g0Bwmx8ffkyRnnP/1q9AX4SaAnhqz0EXepfZbS++3xv5ke8yehOzG38M+9TeIPhlp2ta3NrNpqmq6PfXEflXMmnT+WJ16fMMHnFAHl2sXXiO58NeB08U29zFqEGvrEGuV2ySINpVj6nnGfaumOseIpPiL8QLLTdQneW205WsYJpj5UUhC8qrHaDyfxrrG+Fnh7+yNG02JryGDSboXcRSRd0sncyEqc5x2xVib4c6HdaxrupXJupm1uAW91C7r5YUYwVwoIPyg8k0AeQ+FtZvdM8TaGmq654q0rU5pgt5HrAaa0u8nohyNuexIIGRzWpcQ6t4j8UfESCXxNrVrbaSpmtoLa7ZVDBWIHsvHQY/Suy0/4PaZa3envea5repWenSCSzsbu5DRREdOAB09sVu2vgPS7TUfEV9HcXhl15Cl0GdcICCPk+XjqeuaAPFJr7xDa/Dzw343PinWJdRlvVt2he4/cGMFhgp3J28k5zmvpNDuRT6jNcRN8LNDn8Gaf4Wa61AWNjP8AaIpBInmlsscMdmMfMegFduo2qAOwxQAtFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQB4JFrWtaL8c/Fs2ieHX1uaSJFeJLgQ+WuEO7JBzzxRFpmveFfC/i3xJrN2vhm61q6j8qO2XzpIwWYkKFP3jnAOeOTxXrOm+DNO0vxhqniaCa6a91FAk0bspjUDH3QFBH3R1JqXxd4T0/wAZ6E+k6i0yRl1kSSFsPG46EZBHrQB4/wCEdS1bSfinommR3HiePT9QtnaWHXpgzSkKx3qgJ2jIHvVz4dx65rt1q+u6l4t1BLLR9Qn8u2klZ4zhTkvk8qBjC+xrttL+Fmm6d4g0/XpdZ1q+1SyBUTXdyJPMUgjaQV4ABOACOtaugeBdJ8O6XqunW73M9vqc0ktwtw6k5cYYDaowMfjQB4FqOu6xZ6PD4k0/W/Ft1cm+CtqU7eTYyqSflSIsSRx9OvFdlfMX+K3jhz1bw0Sf+/a10Q+B2hvpf9mXGt6/NZRyb7aA3S7Lc5ydq7SuT6kd66Nvh9pT67qesNdXxudRsPsEw3ptCbQuVG3huPce1AHi8XiHVbP4ceBNB02XUIo9SeY3B00gXEirIfkjJIwTk1sWb+KLLSfF1hdWviH/AIR2TSZpLd9bw0sUgXldwJ4OTx7V6HP8KtBm8KaboInv0GmOZLO9SULcRMTkkMFA6+3YUaZ8LtKsrXVhdajqeoXuqwG2uL67mDzCMjGFJGB26g9KAPO7XVdQ0/wx8JobK+ubaK5uNk6QysiyrvHDAHkcng1R8WX+v23iXXLnX77xVZQpORpt7pL7rOBAeN6gjtjPIPXNesH4Z6MbPw3a/ab/AGeH5PMtD5iZc5B+f5OenbFZuofB/TLm+1Cay1zW9MtdScveWdncBYpSevBB65Pr1oA5fxFq+qarqvw7tLLxTeiDVInS4u7BzD5/QFtvQN16jg1l6fpGrXWueNvDzeL/ABAtjokZuLYi7PmM5Ukb3xkgY6DAPWvU/wDhXGhrd+HJ4WuoBoAItIo3Xa2eu/Kkn14I61Pa+BdMtNZ1/VI57wz65H5dyrOu1BjHyfLkde5NAGf8JNYvtc+G+mXupXD3F188bSucswVyASe5x3rt6xfCvhiy8IeH4NG0+W4ltoSxVrhgzncSTkgAd/StqgAooooAKKKKACiiigAooooAKKKKACiiigAooooAK8a+N1xcWviTwRcWlqbu4ivWeO3D7TKwKYXPbPTNey1zviPwZp3ifVNH1C9muo5tJn8+BYWUKzZBw2VOR8o6YoA88j03xb4t+I2keJtY8Ox+HrHR43ZnluVlaTgnHGDj8MAZ5rg9Z1zV49FufEun674svLpLzA1Ld5Gn7d2NixliW9McD2r6emhS4gkhlXdHIpRl9QRgivM/+FHaE+mzaXLrWvPppfzILX7WBHA2ckqu3BPUZIPU9+aAMPxBJ4g8R/FbTtBtPEd/pdreaMk0/wBnkYDuSVXOAxwBnriqXiuS5uPFV7pMGueKdROmWSIltox8lbdwg+eeUtgk4yeO/XivUoPA+mweLLTxILm8e9tbIWSKzJsZAMZIC53fQge1ZWp/CvStR8SXusJqmr2f9oKBe2tpc+XHcYGMNgZwe4zQB5TNrOoa74D+Ht5qdzJc3I10xmWQ5ZgrYGT3OO9asuu3XhvxL8VtVscC6hEIiYjO1mO3dj2zmu/g+EuhW+jaTpa3upm30u9N9blpI9xcnO1js5X9fetKP4e6IupeIbyb7Rcf28oW8hmdfLAH93ABH5mgDyHQp/Gdtc6Dq+lw+MryaZ0fUf7QYSWk8bYyUAJwMHj+lXLW7ubBPjBdWdxLb3EU26OWJyrIdzcgjkV3WmfCDSLDULKa41fWdQtNPffZWN3choYT2wAB0/zmtL/hW2jeV4lj+03+PELbrv8AeJ8nJP7v5eOvfNAHmet3/ii88NeDJjLr13o8mniTUDpE/wDpckmPvMeWI6HnjrRb+JrofCDxU+n+KdUup7OVPJN0GivLRS4G1nDHP1B9fpXoF/8ACjSbm30j7HqWqade6Vbi2t761mCymMdmOMHqegHWlt/hRokXhzVtImvdSuH1d1e9vpZlaeQqcjkrjr7d6APOL201yw1jwM0PjDXjJ4jhVLxnudwUFVzsGMKcNweSCM9a6z4bz6hpvxG8V+GJtWv9QsLIRyQG+mMrqTjPzH6/pXW3XgDSru58NzyXF4G8PgC1CuuHwFHz/Lz90dMVZ0zwdp+k+LNV8RwTXTXmpqqzJIymNQuMbQFBHTuTQB0NFFFABRRRQAUUUUAFFFFABRRRQAUVnazruneH7WK41Kdoo5ZRDHsieRmcgkAKgJ6A9u1Zf/CfeH/+emof+Cq6/wDjdAHS0VzX/CfeH/8AnpqH/gquv/jdH/CfeH/+emof+Cq6/wDjdAHS0VzX/CfeH/8AnpqH/gquv/jdH/CfeH/+emof+Cq6/wDjdAHS0VzP/CfeH8483UP/AAV3X/xul/4T7w//AM9NQ/8ABVdf/G6AOlormv8AhPvD/wDz01D/AMFV1/8AG6P+E+8P/wDPTUP/AAVXX/xugDpaK5r/AIT7w/8A89NQ/wDBVdf/ABuj/hPvD/8Az01D/wAFV1/8boA6Wiua/wCE+8P/APPTUP8AwVXX/wAbo/4T7w//AM9NQ/8ABVdf/G6AOlormv8AhPvD/wDz01D/AMFV1/8AG6P+E+8P/wDPTUP/AAVXX/xugDpaK5r/AIT7w/8A89NQ/wDBVdf/ABuj/hPvD/8Az01D/wAFV1/8boA6WiuZ/wCE+8P5x5uocf8AULuv/jdL/wAJ94f/AOemof8Agquv/jdAHS0VzX/CfeH/APnpqH/gquv/AI3R/wAJ94f/AOemof8Agquv/jdAHS0VzX/CfeH/APnpqH/gquv/AI3R/wAJ94f/AOemof8Agquv/jdAHS0VS0rVbLW9Mh1HTpvOtZt2xyjJnDFTwwBGCCOR2q7QAUUUUAFFFQXlyLOxuLplLCGNpCo74GcfpQBPRXJWXibxFf2FveQeEHMNxEsqbtRiB2sARn8DVj+2/E//AEKDf+DKKgDpaK5r+2/E/wD0KDf+DKKj+2/E/wD0KDf+DKKgDpaK5r+2/E//AEKDf+DKKq1/4p17TNPuL+88KmK1t42llkOoxnaqjJOAM9KAOuormf7c8TEf8ig3/gyipf7b8T/9Cg3/AIMoqAOlormv7b8T/wDQoN/4MoqP7b8T/wDQoN/4MoqAOlormv7b8T/9Cg3/AIMoqhuPE2v2iI9x4WESvIsSl9ThGXY4VR7kkAUAdXRXNf234n/6FBv/AAZRUf234n/6FBv/AAZRUAdLRXNf234n/wChQb/wZRUf234n/wChQb/wZRUAdLRXNf234n/6FBv/AAZRVWn8Va7bXdrazeFSs90zLCn9ox/OVUse3oCeaAOuormv7b8T/wDQoN/4MoqP7b8T/wDQoN/4MoqAOlormv7b8T/9Cg3/AIMoqP7b8T/9Cg3/AIMoqAOlormv7b8T/wDQoN/4Moqm0bxBeahrN3pWoaQ+n3NvbxXODcLKHR2dRyvTmNqAN+iiigAooooAKKK5qbxe41S/sbPw5rN+bGUQzTWwtwm8or4G+VT9117UAdLRXNf8JVqH/Ql+IfztP/j9H/CVah/0JfiH87T/AOP0AdLRXNf8JVqH/Ql+IfztP/j9H/CVah/0JfiH87T/AOP0AdLRXNf8JVqH/Ql+IfztP/j9RxeMLudC8Pg/X5FDFSUa0IBBwR/r+oIIP0oA6miua/4SrUP+hL8Q/naf/H6P+Eq1D/oS/EP52n/x+gDpaK5r/hKtQ/6EvxD+dp/8fo/4SrUP+hL8Q/naf/H6AOlormv+Eq1D/oS/EP52n/x+o4fGF3cQpND4P1+SJxlXRrQqw9QRPzQB1NFc1/wlWof9CX4h/O0/+P0f8JVqH/Ql+IfztP8A4/QB0tFc1/wlWof9CX4h/O0/+P0f8JVqH/Ql+IfztP8A4/QB0tFcpdeNLmytZLm68I69DBEu55JGtAqj1J8+pv8AhKtQ/wChL8Q/naf/AB+gDpaK5r/hKtQ/6EvxD+dp/wDH6P8AhKtQ/wChL8Q/naf/AB+gDpaK5r/hKtQ/6EvxD+dp/wDH6P8AhKtQ/wChL8Q/naf/AB+gDpaK5ceMpIryyt73wzrdkt3cLbxzTi3KB2zjOyZiOh7V1FABRRRQAUUUUAFFY+teJLTQrizt57e9uJ7vf5UVpbtMxCAFiQvQciqX/CaQ/wDQC8Q/+CyT/CgDpaK5r/hNIf8AoBeIf/BZJ/hR/wAJpD/0AvEP/gsk/wAKAOlormv+E0h/6AXiH/wWSf4Uf8JpD/0AvEP/AILJP8KAOlorlv8AhOrTzxB/Y2v+aV3iP+zZN23OM49M1J/wmkP/AEAvEP8A4LJP8KAOlormv+E0h/6AXiH/AMFkn+FH/CaQ/wDQC8Q/+CyT/CgDpaK5r/hNIf8AoBeIf/BZJ/hR/wAJpD/0AvEP/gsk/wAKAOlormf+E1gJI/sLxDkf9QyT/Cl/4TSH/oBeIf8AwWSf4UAdLRXNf8JpD/0AvEP/AILJP8KP+E0h/wCgF4h/8Fkn+FAHS0VzX/CaQ/8AQC8Q/wDgsk/wo/4TSH/oBeIf/BZJ/hQB0tFctH46tJt3laNr77GKNt02Q4YdQfepP+E0h/6AXiH/AMFkn+FAHS0VzX/CaQ/9ALxD/wCCyT/Cj/hNIf8AoBeIf/BZJ/hQB0tFc1/wmkP/AEAvEP8A4LJP8Ka/je3jjaR9D8QhVBJP9lycAfhQB09FVtOvoNU0y01C2LG3uoUniLDBKsAwyO3BqzQAUUUUAFFFZviDV10HQL3VWgacWsRk8pWAL+wJ6UAaVFc1/bfif/oUG/8ABlFR/bfif/oUG/8ABlFQB0tFc1/bfif/AKFBv/BlFR/bfif/AKFBv/BlFQB0tFc1/bfif/oUG/8ABlFUNx4m1+0WNrjwsIhJIsSF9ThG52OFUe5PFAHV0VzX9t+J/wDoUG/8GUVH9t+J/wDoUG/8GUVAHS0VzX9t+J/+hQb/AMGUVH9t+J/+hQb/AMGUVAHS0VzX9t+J/wDoUG/8GUVQy+Jtfgmghl8LBJLhykKNqcIMjBSxA9TtUn6A0AdXRXNf234n/wChQb/wZRUf234n/wChQb/wZRUAdLRXNf234n/6FBv/AAZRUf234n/6FBv/AAZRUAdLRXNf234n/wChQb/wZRVW/wCEq13+0v7P/wCEVP2vyfP8v+0Y87M7c5xjrQB11Fc1/bfif/oUG/8ABlFR/bfif/oUG/8ABlFQB0tFc1/bfif/AKFBv/BlFR/bfif/AKFBv/BlFQB0tFc1/bfif/oUG/8ABlFVrw/rs+sTajb3enNYXVhOsMkbTLJndGsgIK8dHFAG3RRRQAUUUUAc14t/4/vC/wD2GU/9EzV0tc14t/4/vC//AGGU/wDRM1dLQAUVzv2i9v5ZpVvZbeJZXjSOJU6KxXJLA8kg0vlXv/QXvPyi/wDiK19k+5n7RHQ0Vzxjv1GU1a53DpvSMj8RtFa+m3bX2m29y6hXkQFgOgPfFTKDirlRkmVNQs7SfXdHuZrsRXNu0xghLAGYsmG46nA54rVrK1BdNOvaObp2F8DN9jUZwTs+fOOPu+tajsERmPQDNQULRXNW8uoX1vHdPqM0PnKHEcKptQHkDlST9ak8q9/6C95+UX/xFa+xfcz9oux0NFc1PJqFlBJcpqU0piUuY5lQqwHJHCgj65ro43EkSOOAwBH41MoOJUZXM4WI/wCEoOofbefsQg+yf8DLb+v4dPxrTrKFtYf8JY10Lhv7S+wiMwZGPK8wkNjGc7sjr+FW9SujY6bcXSqGaKMsqnoT2qUruxT0LVFc8I79hmTVrkOeojSML+AKn+dHlXv/AEF7z8ov/iK09k+5n7RdjoaK5w3F7YPFM19LcRGVEkjmVOjMFyCoGCM5ro6mUHEqMrmVp1naQa3rNzBdiW4uZImuIgwPklYwqggcjKgHn1rVrK05dNGt6ybR2N6ZYvtoOcK3lrsxnj7mOlTavdy2lkDBtEskixIzDIUscZx3wM1KV3Ybdlcv0Vz3lXvfV7zPssX/AMRR5V7/ANBe8/KL/wCIrT2T7ke0XY6GisG2uby11G2hlu3uYbhjH+9VQyMFLAgqBx8pFb1RKLiyoyuc14B/5FGH/r6u/wD0plrpa5rwD/yKMP8A19Xf/pTLXS1JQUUUUAFUNc/5F/Uv+vWX/wBANX6oa5/yL+pf9esv/oBoAg8Lf8ihov8A14Qf+i1rWrJ8Lf8AIoaL/wBeEH/ota1qACis6/1VbWQW0Efn3bDIjBwFHqx7D9TWc8N1dfNe3srZ/wCWUDGJB+XJ/E1pGm3qyHNLQ6KsrxLNp9v4X1SbVoWm05LWRrmJRkvGFO4Dkds9xWd/ZFgetqjH1bJP61na/baXpfh7UL+XSorqO3t3laDH+sABO3v1qvZLuT7R9jtx0ormf7H07/n0i/Kj+x9P/wCfSL8qfsl3D2j7HTUVzP8AY+n/APPpF+VH9j6f/wA+kX5UeyXcPaPsdNWVr39m/ZbT+1C3lfbrfyduf9d5i+X07b8e3rWd/Y+n/wDPpF+VZus2miWUFq93pqSrJeQQoAoO2RnAVuT2JBpeyXcPaPsdxRXM/wBj6f8A8+kX5Uf2Pp//AD6RflT9ku4e0fY6aiuZ/sfT/wDn0i/Kj+x9P/59Ivyo9ku4e0fY6asrUZtOTW9GjuoWe8kklFm4HCMI2LE891yO9Z39j6f/AM+kX5Vm31tpdtq+l2raVFI908irLj/VbULE/jjH40vZLuHtH2O4ormf7H0//n0i/KlGmwR827TW7esMrL+mcH8RR7Jdw9o+x0tFYMepXmn/APH7/pVsOsyLiRPdlHBHuMfStyORJY1kjdXRhlWU5BFZyg47lqSY6uatv+Snap/2BrP/ANHXNdLXNW3/ACU7VP8AsDWf/o65qSjpaKKKACiiigArmvC//Ic8X/8AYWX/ANJLeulrmvC//Ic8X/8AYWX/ANJLegDpaKiubiO0tpLiZsRxqWY1gn7XqH727nlhRuVt4XKbR/tMOSfxxVxg5EylY6Oiub/s23/vXH/gTJ/8VR/Ztv8A3rj/AMCZP/iqv2S7k+08jpKyvD0Gn2+nSppk7TQG8uXZmOcStM5kXoOjlh+HfrVD+zbf+9cf+BMn/wAVWbolnpE9hI+nJcxwC6uFZTcSDMglcSH73dwx/Gj2S7h7TyO2orm/7Nt/71x/4Eyf/FUf2bb/AN64/wDAmT/4qj2S7h7TyOkormxYmI7rW7uoJB0PnM4/FWJBrU0vUHuhLBcqq3UON4X7rA9GHscH6EGplTsrocZ30NCsrwzBp9t4Z02HSZ2n09LdFt5WOS6Y4J4H8q1ayvDMmmy+GNMk0eJotNa3Q2yNnKx4+UHJJ6e9ZlmrRVDVL9rKKNIUElzM22JW6e7H2H+A71lGzeY7ru8uZnPXErRqPoqkD+daRp3VyJTtodJRXN/2bb/3rj/wJk/+Ko/s23/vXH/gTJ/8VVeyXcXtPIv+Jf7NPhrUP7YLDTvJP2jbnOzv05/KtWuI8Q2+l2Ph+9udQhubm0jiLSw+e53r6YLYrT/s23/vXH/gTJ/8VR7Jdw9p5HSUVzf9m2/964/8CZP/AIqj+zbf+9cf+BMn/wAVR7Jdw9p5HSUVzgju7H95ZXErgcm3nkLq49ATkqfxx7VuWd3HfWkdzFnY4zg9QehB9wciolBx1KjK5g+MPveHv+wzb/yeulrmvGH3vD3/AGGbf+T10tQUFFFFABRRRQBzWr/8j94Z/wCuF7/KKulrmtX/AOR+8M/9cL3+UVdLQAUVy9qbnUbWO8mvbpWmUOEik2KgPIAA9Kl+xv8A8/8Aff8AgQa29j5mftPI6OiuYniubaCSeDULvzI1LqJJN6nAzgg9q6O3l8+2imxjzEDY9MjNROHKVGVzOaDTz4sjuDO39pixdFhzwYTIpLdOu4Ade9atZTSab/wlkcZib+1PsLMsnOPJ8xcjrjO7Hart/cNaafc3CgFoo2cA9yBWbdlcosUVzQjuGGZL+6LnqVk2jPsB0pfKl/5/bz/v8ajnfYz5/I6SiuaMtxZMk6XU7gOoZJX3BgSAevQ810tVGVyoyuZVhZ2kPiDV7qG7ElzcCHz4QwJi2qQvHUZBzzWrWVYLpo1/V2tXY358n7YpzgfKdmM8dM9Kt38zw2uYjtd3VA2M4yQM0Skoxcn0LSu7Fqisv7M3e6uT/wBtTR9mb/n6uf8Av6a5/rPkaez8zUorLQy211ABPJIkr7GWRt3YkEH8K1K2p1FUVyJR5TK0KDT4E1D+zp2lD30zz7jnbMT869BwDWrWVoUmmyR3502Jo1W+mWfdn5pg3znkngmpNVmlU21vDIYjO5DOvUKATx7mnUmqceZhGPM7I0aKwvsj/wDP7ef9/wA0fZH/AOf28/7/AJrn+sy/l/E19h5m7VbUP+Qbdf8AXF/5GqFjJNBqS2rTyTRSxM48w5KlSvf0O79Kv6h/yDbr/ri/8jW1Kp7RXtaxnOHK7GX4K/5EPw7/ANgy2/8ARS1u1heCv+RD8O/9gy2/9FLW7WpAUUUUAFc18Qv+RA1r/r3P8xXS1zXxC/5EDWv+vc/zFAHS0UVlapfTLOljaMEmdd8kuM+WmccD1Jzj6GnGLk7ITdlc1aK5k6VaPzMjTv3eZy5P50f2Pp//AD6RflWvsl3M/aPsdNWVr39m+RZf2mW2fboPI25/128eX07bse1Z39j6f/z6RflWbrFnollFaNd6akqy3kMMYCg7ZGYBW5PY80eyXcPaPsdxRXM/2Pp//PpF+VH9j6f/AM+kX5U/ZLuHtH2Omormf7H0/wD59IvypQs+mDz7J5GjXl7VmLKw77c/dP04pey7MftO6OlrK1T+zf7X0T7aW+1/aZPsOM48zyZN2ccf6vf1/nWjBNHc28c8Tbo5FDKfUHkVnandWMGr6JDc2xluLi5kS1kwD5TiGRi3tlAy8etYmhq0UVgXd3NfzyRxSvFaxsUJQ4aRh157AHjjrUylYTlY36K5b+zrQ9YFJ9TyaP7Os/8An3T8qjnfYjnfY6msoTad/wAJYYPJb+0/sIcy448nzCNvXru56Vl/2dZ/8+6flWcE0/8A4SM2H9mx+YLQTfaMdt5Gz+vWnzvsHO+x3VFct/Z1n/z7p+VH9nWf/Pun5Uud9g532Oporl/7PtByIVU+q5B/MVbsryazuY7eeVpbeU7EdzlkbsCe4P55pqfdDU+5u1zXh3/ka/F//X9B/wCksNdLXNeHf+Rr8X/9f0H/AKSw1oWdLRRRQAUUUUAc14t/4/vC/wD2GU/9EzV0tc14t/4/vC//AGGU/wDRM1dLQBzen/6ib/r5n/8ARrVbqpp/+pnHcXM+f+/jVbrrZzhU/h//AJAVp/un+ZqCp/D/APyAbT3TP6ms6nwlQ+IZf3NhFr2kQXFu0l5MZvssoAIjwmXyc8ZHHQ1oz/8AHvJ/un+VUL6++z65pVp9i837T5v7/wD54bVz6d+nUfjWhMMwSAddp/lWCNjntL/5BNl/1wT/ANBFW6qaUQdIsiD/AMsE/wDQRVuux7nMtitqP/IMu/8Ari//AKCa3bX/AI84f+ua/wAqwdSIGl3ZJwPJf/0E1vWoxaQg9di/yrKrsjSnuZ4bTf8AhLHUI39q/YQS/OPJ8w4HXGd2e2af4g/5AF7/ANcjTBe2h8WPYi0H20WKzG52jJjMhATPXqCfTmn+IP8AkAXv/XImsofEjSWzIKKKK6TAqal/x6r/ANd4f/Ri10lc3qX/AB6oO5nhA/7+LXSVlV2RpT6mVptzYTa1rMNrbtHdQyxLdyEACVjEpUjnnCkDt0o1/wD49bX/AK+o/wCdO0+++06vq9t9i8n7LLGnnf8APfdGrZ6Dpnb1PT8KZr//AB62x7faov51FP4kVP4WMoooroMSvL/yE9L/AOvg/wDop66Guek/5Cmljv8AaGP/AJCeuhrGrujSnszmvAP/ACKMP/X1d/8ApTLXS1zXgH/kUYf+vq7/APSmWulrI0CiiigAqhrn/Iv6l/16y/8AoBq/VDXP+Rf1L/r1l/8AQDQBB4W/5FDRf+vCD/0WtWtTvTYWTSooeViEiQ/xOeAPp3PsDVXwt/yKGi/9eEH/AKLWotUbzdatIT92GJ5iP9okKP03fnVwV5akydkRWtt9njO5jJM53SyHq7ev+A7Cp6KK6DEKoa3dXdjod9dWFt9pvIYHeGDBPmOBwuBzyav1Q1s6iNDvjpIU6j5LfZg2MeZj5c546+tIC/RRRTAKKKKACs/V717GG1dLT7SZLuGEr/cDOAX6H7uc/hWhVDVZ7+CK2NhAsrtdRJKGGdsRcB26jouTSAv0UUUwCiiigAqheXV3DqmnQQW3mW87SC4lwT5QCEqc9snA5q/VC8Oo/wBqacLUL9j3SfaycZA2HZjPP3sdKQF+iiimAVDp8n9m6gtr0tLonyx2jk6kD2YZP1HvU1U9UDf2dLIn+shHnJ/vL8w/lRa+gXtqdLXNW3/JTtU/7A1n/wCjrmujRxJGrr0YAiuctv8Akp2qf9gaz/8AR1zXIdB0tFFFABRRRQAVzXhf/kOeL/8AsLL/AOklvXS1zXhf/kOeL/8AsLL/AOklvQBoeIObCJD917mIMPUbwf6VHUmv/wDHnb/9fUX/AKFUddFP4TGfxBRRRVkhWZoN5b3+mvNbWq20YuriMxqAMskzqzcf3mUt+NadUNHvLq+sGmvLVraUTzRiNlIyiSsqNz/eUBvxpAX6KKKYBUdr8viKHH8drID74ZMfzP51JUdt/wAjFbf9es3/AKFHSezGt0btZXhq+ttS8MaZfWdotnbXFskkVuoAESkZC4HHHtWrWdoN7d6joGn3t/aNaXc8CSTW7KVMTkZKkHkYPrXKblS/+bxBHn+C1OPbL8/yFPpl9/yMI/69B/6GafXVH4UYPdhRRRTEZ+u3r6dod3dx2n2too9wgH/LT26H+VaFUNanv7bRrqbTIFnvUTMMbDIZvTqP51fpAFFFFMAp2gcRXy9lu3wPTIUn9SabTtB+7qH/AF9t/wCgJUT+FlQ+Io+MPveHv+wzb/yeulrmvGH3vD3/AGGbf+T10tc5sFFFFABRRRQBzWr/API/eGf+uF7/ACirpT0Nc1q//I/eGf8Arhe/yirpT0oA5nR/+QLZf9cE/kKu1R0c/wDEmsx6QqD7EDBFXq7JbnMtiC9/48Lj/rk38jWxp/8AyDbX/rin8hWNfsF0+5ZiABE2SfpWzYqV0+2VgQREoIPbgVlV+FGlPcpG+th4rSw+yL9qNi0wucDIQOAUz16kH8Kn1r/kB33/AFwf+VMN7djxGtj9kb7EbQzG52nAk3gbM9OhJ9eKfrILaJfADJ8h/wCRrmn8LNJbGdRSKQyhgcgjIIpazMSve/8AHt/wNP8A0IV09cxe/wCoA7mRAB6ncK6eqhuy4dTKsLmwl1/V4Le3ZLyHyftUpAxJlMpg55wOOgqxqf8Ax7x/9do//QhUNjffaNb1W0+xeV9mMX7/AP577kz6dunU/hU2qcWqE9BNGSfT5hSrfw5ehtD4kFFFFcJuQzf8fFp/12H/AKC1adZc3NzZjv52cf8AAWrUrqw20vX9DKp0MrQr62vo75ra0W2EN9NDIAAPMdWwz8ep5pdV/wCP3T/99/8A0A0/SL27vY7tru0a2MV3LDGCpG+NWwr8+o59Kj1bi708ngeY4z77DTxX8P5r80Oj8f8AXYWiiisDoIov+Q5a/wDXCX+aVoah/wAg26/64v8AyNZ8POu2wHJWCUn2yUrQ1D/kG3X/AFxf+RrXDfa9f0RhW3XoZfgr/kQ/Dv8A2DLb/wBFLW7WF4K/5EPw7/2DLb/0UtbtdRiFFFFABXNfEL/kQNa/69z/ADFdLXNfEL/kQNa/69z/ADFAHS1gNzrmoE9R5Sj6bc/1Nb9c+f8AkN6j9Y//AEAVrS3ZnU2J6KKK2MwrP1e9eyjtGS0+0+bdxQkf3AzYL9D061oVQ1We/gS1NhAsrPdRJMGGdsRb526joKQF+iiimAUUUUATeHv+QJAOwZ1H0DsBS6hfPbappFutmZ1u53jab/ngBE77uncrt7fe/Ck8Pf8AIFh/35P/AEY1Ov59Qi1PSorSBZLWaZ1vHI5jQROVI57uFHfrXNP4mbR+FGjXLadzp1ue5QE/U11Nctpv/INtv+uYrCfxIme6LVFFFIgKoC6u/wC3mtPs3+hC1Eonwf8AWbiNuenTBq/VAHUP7eYEL/Zn2UYPGfO3HPv93HtQBfooooAKr33FqT3DoR9QwqxVe+/49G/3l/8AQhUy2YmdPXNeHf8Aka/F/wD1/Qf+ksNdLXNeHf8Aka/F/wD1/Qf+ksNdB0HS0UUUAFFFFAHNeLf+P7wv/wBhlP8A0TNXS1yXju5ezPhueO0nu3TWEIgg273/AHM3A3Mo/Miti71i5ttMt7tNC1O5kmxutYfJ82LIz826QLx04Y0AST6LazzvMGnhdzl/JlKhj6kdM1H/AGBb/wDP1e/+BBp99qlxZy2iRaNqF2Jz872/lYg6ff3SKe/8O7oadPqc8Orw2K6RfzRSKC17H5Xkx9eGy4fPHZT1FXzy7k8sexCfD9qww8946nqrXDYI9DWpGiRRrHGoVFAVVAwAB2qjHqc76zJYHSL9IUXIvm8ryH4BwMPvzzjlR0NJY6nPd3t1byaRf2iQEhZ5/K2Tc4ym12Pv8wHWk5N7jSS2C8ub+LWNNgt7dXs5vN+1SkEmPC5TBzxk8d60a5katrl5dW91Fomo2lnAsxubW4+z+bOdmYwm2Rh97I5ZffitFdYuW0V9QOhamsytgWJ8nz25AyP3mzHOeW6A9+KkYraBaF2Mb3MKsSdkUzKoJ64Hb8KT+wLf/n6vf/Ag0XGsXMGkwXqaFqc8srANZxeT50XB5bMgTHHZj1Hvh19q1xZraGLRNRvPP++Lfyf3HT7+6RfX+Hd0Ptm/aS7k8kew1dAs9ymWS5mUHOyWZmU/Ud61azrrU57fVLezTSL+4ilALXcXleVFkkYbc4btnhT1FA1Oc602n/2RfiEDP24+V5B4zj7+/Pb7vX25qXJvcaSWwC+uD4kewNowthaLMLnBwXLkFM9OgB/Gr7osiMjqGRhhlIyCKxLLUtWuvFd3ay6Vc22lQ2+I7iby8SyhyCV2uTtK4IyAeDnFWbHV7m8trqWTQ9StGgGUiuPJ3T8E4TbIw7Y+YjqKQxo8P2qjEc94iDoq3DYA9BS/2Bb/APP1e/8AgQaItYuZdHlvm0LU45kbAsn8nzn6cjEhTHPdh0NEusXMejxXy6Fqckztg2SeT5ydeTmQJjjsx6ir9pLuTyR7D4NEtYZ0mZp5njOU86UsFPqB0zWlWXfavc2dtazR6HqV204y8Vv5O6DgHD7pFHfHyk9DT7zU57XUba1j0i/uo5iN1zD5Xlw5OPn3OG468KeKlyb3GklsFjc382qapDdW6x2sEqLaSAEGVTGpYnnnDFhxjpVy4t4bu3eCdA8TjDKaxbS/1hPFeoWV3p13Jp0jo1ndp5XlRoIl3BvmD5Lhv4T1Har1tqc9xqlxZvpF/bxRAlbuXyvKlwQMLtct3zyo6GkMj/sC37XV7j/r4aj+wLf/AJ+r3/wINOsdVuLxbsy6JqNn9n+4Ljyf3/X7m2RvT+Lb1HvhtvrFzPpM96+hanBLExC2cvk+dLwOVxIUxz3YdD7Zv2ku5PJHsTWmkW1pP56mWSXG0PNIXKjvjPSr9ZTaxcroqagNC1NpmbBsR5PnryRk/vNmOM8N0I78UXusXNpYW1zHoWp3UkwBa2g8nzIcjOH3SBeOnyk8/nUtt7jSS2M/wD/yKMP/AF9Xf/pTLXS1zPgH/kUYf+vq7/8ASmWumpDCiiigAqhrn/Iv6l/16y/+gGr9UNc/5F/Uv+vWX/0A0AQeFv8AkUNF/wCvCD/0WtQXv/IxH/r0X/0NqqeDRrX/AAhdl9pfTzIbGL7H5SuAB5Yx5mTz2zjHeq04177XKJ3006n9mXyyiSeTjeeoJ3Zxnv6VpS3IqbGxRWZMNc/sqIQvp39o5/eF1fycc9ADn07+tLejWzDbfYX08S4/0jz1cqTgfcwfr19q2MjSqhrdte3mh3ttp1wLe9lhZIJiSNjkcHI5ouhq39oW5tGshZceeJlcyHnnaQcdPWs7xBY6pqNtfWbX1jZ6VPbtGZyredESv3s7guAf0oYzoKKoWw1b+0pzdNZGx58kRK4lHPG4k46Z6UyyGtBLr7c+nl/+XbyFcAdfv5P06e9AjSorMgGuf2XMLh9OOoZPlGNX8rHGNwJz69Pahxrn9kIEfTv7Tz85ZX8nGT0Gd3THf1oA06oaqmoPFbDTpFRxdRGYtjmEMN45Hdc1HeDXDZW32F9OF3gfaDOrlCcc7cHPXPXtUGuWes3tzp66ddW9vaxXMUtzuLiR1VwSoK8YIyMGgZtUVQcat/a6FGsv7Mx84ZX87OD0OdvXHb1ogGrf2pMbhrI6fg+UI1fzc8Y3EnHr09qBF+is+yGsh7r7c1gU/wCXbyFcEdfv5P06e9Mthrn9mzi6fTjfZPkmJXEXTjcCc9c9KANOqF3bXsuq6dPBcCO1haQ3MeSPMBXC/XB5qMjXP7IAD6d/aeeTtfycZ9M7unv1rP1ay1iebRrm3vdPi1C3MheKUP5UxZMHaAc8daBnR0Vn3g1gzW32FrERZ/0jz1cseR9zB+vX2pZhq39qxGFrL+zsfvA6v52eehBx6dvWgRfqvf8A/IOuv+uT/wAjUEY1b+1pDK1l/ZuP3YVX87OB1OdvXPb0quw1cQal9uaxNv5b/ZxArhwMH7+TjpjpTW4M6my/48Lf/rkv8qwbb/kp2qf9gaz/APR1zVrSBr39hN9sfTTd+UPsxhRxGPl435OTz6Vk6GNRHxC1P+1GtWuv7HtMm1DBMefc4+8Sa5XubrY7GiiikMKKKKACua8L/wDIc8X/APYWX/0kt66WuM8OSaoPGfiiOK1tG006mpmna4YTK32SDAWPYQR93ksOp445AN7X/wDjzt/+vqL/ANCqOqWry6w10I7mzso9OFxH5M0d0zysd4xuQxgLxnox6D14jspdYc3X26zsYQv/AB7+TdNJv6/fzGu3t03dT6c9FP4TGfxGjRWZbza42mzvc2NhHfgnyYo7t3jYYGNzmMFec9FNBm1z+yFkFjYHUs8wG7fyQM/89PLz0/2ev51RJp1Q0dtRewJ1RFS58+YALjHl+a3lng45TYf581Hdza4lhbvZ2NhLeMB58ct26RpxztYRktz6qKp2aeJbOC3heOxvC1xM080t06FI2lYoFAjO4iMgckcjGe9AzfoqhLLqw1aOOK0s204j95M1ywlU89E2EHt/EO9EUurHVZY5bSzXTgP3cy3LGVjx1TYAO/8AEe1Ai/Udt/yMVt/16zf+hR1Ts5dXe4uRfWdlFCp/0doblpGcZP3gUXbxjoTVfTZtcbUt91Y6fHerazeRFFeO8bnMeNzmMFefRTQ9mNbnY1n6C2pPoGntrMax6mbdDdIuMLLj5gMEjrnoaiE+v/2K0hsNOGq54gF4/k4z/wA9PK3dOfudePeqloniew8K6bAY7DUNYjjSO7ee7eNHIXlwwjYkk442jqfTnlNyS+/5GEf9eg/9DNPrM1qXV08QWn2Kzspd1qPtHnXLR7Bv/hwjbu/XbUk8urLqkKW9pZvp5A82aS5ZZVPOdqBCD26sOp/Hpj8KMHuy/RVBZdWOrvG1pZjTQPlnFyxmJwOsezA5z/F0/Kizl1Z765W9tLOK0UnyJIblndxnjcpQBePQn+tMQa0moSaNdJpUix35T9y7YwGz7gir9c/dw+ItT8PajbSpZ6ffOmLaS2vJHGevLeWpX04B61dSbXDpMkkljYDUgfkgW7cxEZHWTy8jjP8ACaBmnRWZNNrg0qKSCxsH1An97C926xKOejiMk9v4R1NLeza0kVqbGysZZGH+kCa6eMJ0+4RG27v129BQI0qdoP3dQ/6+2/8AQErOupdWXUYEtLSzksjjzpZblkkTnnagQhuPVhTtCl1b+1r6NbSzOl/aWLTm5bzg2xeBHsxjOOd/4VM/hZUfiF8Yfe8Pf9hm3/k9dLXE+JpdYfVdHS8s7KKwXWYPIliumeR/vY3IYwF4z0Y121c5sFFFFABRRRQBzWr/API/eGf+uF7/ACirpa43xVc3tr4z8NSWGnm+m8q8Hk+csfGI+ctxXQX17qdvJaLaaQbpZf8AXt9pVPJ6dj97qenpQAsuhadNK8rQMrOct5croCfXCkDNM/4R7Tf+ec3/AIEyf/FVJPeajHq0NtDpRlsnUGS7+0KvlnnjYeT0H50iXmotrMlq+lFbFRlb37Qp3HA42dRzkfhVc8u5PLHsImgaajq/kM5U5Akmdxn6EkVpVnWV5qM99dRXWlG1t4yRDP8AaFfzhnAO0crxzzUdlf6tPYXU11optbiNSYYPtSP5xxkDcOF545pOTe7GklsSFtS/4SNUEa/2V9kJL8Z87eMDrnG3PbFaJGRg9K5lZ/ErXLam2mBUW2MQ0z7WpZpfMHz7sbQNue/bFaNxf6tHpMFzDopmvXYCS0+1IvljnneeD0HT1pDJDoWnEkiBl9kldQPoAcCk/sLT/wDnlL/3/k/+KpL6+1S3S0Npo5uml/16/aUTyenr97qenpT7q81GLVLe3t9KM9o4Hm3X2hV8rk5+U8njB49ankj2FyrsOg0ixt5lljhJdful3Z8fTJOKvVni81H+2janSiLDbkXv2heTjONnXrxSWl5qM2pXMFzpRt7WPPlXP2hX83nj5RyvHPNNJLYEktgs7m/l1jUoLi3WOzhMX2WUAgyZXLZOecHjtV90WRGR1DKwwQRwawNIuvEct3rE2p6b5EC7DYQefGxbCncNy9MsB971q5Df6s+kS3MuiGO9VsJZ/akO8cc7+g6n8qYyz/ZVp/ckH0lf/Gj+yrT+5J/3+f8AxqtLf6smjxXMeil75mw9n9qQbBk87+h7fnS31/qsFrayWmim6mkAM0X2pE8k4HGTw3ORx6Vn7Gn/ACr7iueXcuQWNvbvvjj+fGNzMWOPxNWKz7y81GDULaG20o3NtIR5tx9oVPK5wflPLcc8UNeaiNaW1GlE2BXJvftC8HGcbOvXj8auMVFWSsJtvcNIbUmju/7TjVHF3KINuOYd37s8HrjHXmrdxbw3UJinjDoexrF0F9ei1DULbVbTNt9pmltrv7QrZjL/ACJs6jCn9KuWN9qdx9r+16ObXyv9R/pKP5/X0+70HX19qGk1ZiTtqh39h2H/ADzl/wDAiT/4qj+w7D/nnL/4ESf/ABVR21/q0ulT3E+imC8QkR2n2pG8wYGDvHA6nr6UNf6sNFW6Gik35bBsvtScDJGd/Tpg/jWX1aj/ACL7kX7Wfdl21sbay3eRFtLfeYksT+J5pNQ/5Bt1/wBcX/kap3l/q0Gn201ropubmQDzrf7UieUcZPzHhueOKj1281GCDy7XSjdQyRMJpvtCp5Ix1weW4yePStIxjFWirIltt3ZH4K/5EPw7/wBgy2/9FLW7WF4K/wCRD8O/9gy2/wDRS1u1QgooooAK5r4hf8iBrX/Xuf5iulrmfiHn/hX2t7cZ+zHGfqKAOmrnz/yG9R+sf/oAq3ENf/seUTPpn9qbv3ZRJPJxx1BO7PXv6VgyjXvNvBG+m/2luj80sj+Tjb2Gd3p1PrWtLdkVNkbVFZt4NbNvbfYX08TY/wBI89XKk4H3MHPXPX2p90NW/tC3Nm1kLLjzxMrmQ887SDjp61qZF+qGqpqDpa/2dIqMLqIz7scw5+ccj0oI1b+1wQ1l/ZmOQVfzs49c7evt0qheWWv3l/GDeWcFjFcxzL5IkErorAlGOcEEZBoGb1FZ1kNaC3X257At/wAu3kK4A6/fyfp096bANc/suYXD6cdQyfKMav5WOMbgTn16e1AjTorMYa5/ZCBH07+08/OSj+TjJ6DO7pjv1zReDXDY232J9OF3gfaDMrmMnHO3Bz19e1AGz4e/5AsP+/J/6ManX6ak2p6U1nIq2iTOb1TjLp5bBQMj+/tPGOlZmmDWTZWH2BrAWnmP9oE6uXI8052YOOmevep7+x1y48UaZcw3ltFpFq7vNCC4llJjZcHB2kAkHB9K55/EzaPwo3a5bTf+Qbbf9cxWxANZ/teY3DWB0zB8oRq/nZ4+8SduOvQelcxoQ1cWR+2NYmPYPs3kq4I6/fyfp096xnuiZ7o2aKzLYa5/Z04un0833PkmJXEY443AnPXPSjGuf2Rjfp39pZ67X8nGfTO7p79akg06oC2vP7ea6NwPsJtRGIMnIk3ElsdOhAqO5Guf2fALR9OF7x5xlVzGeOdoBz19ap3dlqn/AAk0N9Y3llzarFcW04YkKHJ3rg98kcjtQBv0VQmGrf2pEYGshp+P3gdX83PPQg49OvvRGNW/taQyNZf2bj5Aqv52cDqc7eue3pQBfqvff8ejf7y/+hCoLMasLu5+2tZG2yfs4hVw4GeN2TjpjpVNhrY0e6+2vp5usjyDArhAcjG7Jz19KUtmDO6rmvDv/I1+L/8Ar+g/9JYa0FGv/wBiuHfTP7V3fIwSTyMZHUZ3Zxnv1xWP4R+1jX/FX24wm6+2QeYYAQmfssPTPOK3NzraKKKACiiigDmvFv8Ax/eF/wDsMp/6Jmrpa5rxb/x/eF/+wyn/AKJmrpaACiuYWM6jJPcXM05/fSIiJMyKiqxUYCkemc0/+zbf+9cf+BMn/wAVW3su7M/aeR0lFc0dNhA+SW6RuzLcyZB/E1saRcyXek208xBkZPmIGMkcZqZw5VccZ3dhl3Z3k2s6bdQ3RjtbcS+fCGIEu5QF46HByea0Ky76xgn1/SbuS8EU1sJvKgJGZtygHvnjGePWtKRtkbNjO0E1mWOorlbW3+22kN1dTXEk0yCRiJ3UDIzgAEAAVN/Ztv8A3rj/AMCZP/iq29l5mXtPI6SiuVurUWlrLc209xHNEhdSZ3YZAzggkgiunhfzIY5MY3KDj61E4cpcZXKKwagPEck5nX+zDaKixZ5E29iW6dNpUde1aNZq2Mg8Syah9sJiazSEWuTgMHZt/XvnHTtU2rXMlppN1cRECRIyVJGcHsalK7sU3bUuUVzQ02IjMk11I/d2uZMk/gcUv9m2/wDeuP8AwJk/+KrX2S7mftPI6SiuYkjOnNFc2004ImjVkaZnV1ZgpBDE+vWunqJw5SoyuZ9hZ3lvqeqXFxdGWC5mR7eIsT5KiNVIweBlgzcetaFZem2MFtrGtXMV2s0t1PG8sQIzARCihTz3Chuf71O1ueWGxRYZDG00qRF16qCece+KmKu7DbsrmlRXN/2bB3e5J9Tcyf8AxVH9m2/964/8CZP/AIqtfZLuR7TyOkornbYNYapZrDLMYrh2jkjklZx9xmBG4nB+X9a6KonHlZUZXOa8A/8AIow/9fV3/wClMtdLXNeAf+RRh/6+rv8A9KZa6WoKCiiigAqhrn/Iv6l/16y/+gGr9UNc/wCRf1L/AK9Zf/QDQBB4W/5FDRf+vCD/ANFrUF7/AMjEf+vRf/Q2qfwt/wAihov/AF4Qf+i1qC9/5GI/9ei/+htWlLcipsSUUUVuZBWZ4igsbrw7qEGpztBYyQMs8qnBRCOSOD/KtOszxE+np4d1BtWjaTTxC32hFzkp3HBB/KkwRp0UUUwCiiigArM1qztryKzW6ultxFewyxliBvdWBVOfU8etadZmtJpzxWQ1GRkUXsJh255m3fIOB6/hSYGnRRRTAKKKKACsy+gsZNb0qW4nZLuIym2jB4kymGzx2HPatOsy+fThrmlLcxs16xl+yMM4X5Pnzzjp60mBp0UUUwCq9/8A8g66/wCuT/yNWKr3/wDyDrr/AK5P/I0LcGbVl/x4W/8A1yX+QrBtv+Snap/2BrP/ANHXNb1l/wAeFv8A9cl/kKwbb/kp2qf9gaz/APR1zXK9zdbHS0UUUhhRRRQAVzXhf/kOeL/+wsv/AKSW9dLXNeF/+Q54v/7Cy/8ApJb0AaGv/wDHnb/9fUX/AKFUdSa//wAedv8A9fUX/oVR10U/hMZ/EFFFFWSFZ2iWRsNN8g3n2s+fPJ5v+/K77ep+7u2/8B7dK0azNAtrK10rytPuDcW/2id95IPztM7OOAOjlh+FIDTooopgFR23/IxW3/XrN/6FHUlR23/IxW3/AF6zf+hR0nsxrc3D0NZ3h6xOmeHNNsWvPtpt7aOI3P8Az2woG/qevXqa0WxtOTgY5rM8N21jZ+GNLtdMuDc2EVpGlvMSCZIwoCtkAA5GD0rlNyvff8jCP+vQf+hmn0y+/wCRhH/XoP8A0M0+uqPwowe7CiiimIzPENnbahoF5a3l0trbyph5mIAQZHOTxWnWZ4hTT30G7XVZGjsSo85lzkDI9AT1xWnSAKKKKYBTtB+7qH/X23/oCU2naD93UP8Ar7b/ANASon8LKh8RR8Yfe8Pf9hm3/k9dLXNeMPveHv8AsM2/8nrpa5zYKKKKACiiigDmtX/5H7wz/wBcL3+UVdLXNav/AMj94Z/64Xv8oq6U8CgAoriLS0h1CzhvLxPPnnUSO7knk84HoB6VP/ZNh/z6R/lWnIu5fKdhRXFXGn21vbyzW8fkTRoWSSM4KkDIrr7SVp7OCVsbnjVjj1IzUyjYTVil9hP/AAk41D7bx9jMH2T/AIHu39fw6fjWnWV9msP+Es+1/aT/AGj9h8vyMjHleZndjGfvcdam1u5kstCv7mE4lit3dD6EKcGpbsKKu0kX6K4oaLYEAywCZ/4pJSWZj6kml/sTTP8Anyh/75rPnfY6vq0f5vw/4J2lFcPNaQaWI7yxTyJo5U5jJAYFgCpHcEE13FVGVzKrS5LWdzOsrO8g1fU7me6MttcNGbeEsT5QVMNx0GTzxWjWXp9jBb65q91HdiWW5eIywgjMJVAADz3HPNN8RSSJpiRxSPGZ7iGBnQ4YKzgNg9jgnmqMTWorA/4RrRe+nQH3IyT+NH/CNaL/ANA23/75oA36K5l7G30fU9Nl0+P7OJ7jyJY0JCOpRjyOmQVGDXTUAZmi2JsIbxTe/avNvJp93/PPc5Ozqfu9Pw6CtOsrQbawtYL1bC5M6SX1xJKSQdsrOS68AdGyKzPFkZv9U0DRZZJFsr65k+1JG5QyqkTOEJHO0sBkd8Y6E0AdRRXM/wDCvfCH/QvWP/ful/4V74Q/6F6x/wC/dAHS1W1D/kG3X/XF/wCRrl7PTbXw146sLDSUNtYahYXMk1qrExiSJ4Qrqp+6SJWBx1wPSuo1D/kG3X/XF/5GgDL8Ff8AIh+Hf+wZbf8Aopa3awvBX/Ih+Hf+wZbf+ilrdoAKKKKACua+IX/Iga1/17n+Yrpa5r4hf8iBrX/Xuf5igDpa58/8hvUfrH/6AK6CufP/ACG9R+sf/oArWluzOpsT0UUVsZhWZrVnbXiWIurpbcRXsU0ZYgb3U5VOfX8606zNaTTnWx/tGRkAvYjBtzzNn5BwPX8KTA06KKKYBRRRQBN4e/5AsP8Avyf+jGpupWVrc6zotxPdrFPazSPBEWAMzGJ1IAPJwpLcelO8Pf8AIFh/35P/AEY1N1JNNbWtFa8kZbxZpTZKM4Z/KYNnAx9zcecVzT+Jm0fhRq1y2m/8g22/65iuprltN/5Btt/1zFYT+JEz3RaooopEBWYsFj/wk8lwJ2/tA2ao0OeBFvYhsY/vZHXtWnWYr6f/AMJPIgjb+0hZoWfnHk72wOuPvbu2aANOiiigAqvff8ejf7y/+hCrFV77/j0b/eX/ANCFTLZiZ09c14d/5Gvxf/1/Qf8ApLDXS1zXh3/ka/F//X9B/wCksNdB0HS0UUUAFFFFAHNeLf8Aj+8L/wDYZT/0TNXS1zXi3/j+8L/9hlP/AETNXS0Ac3p/+om/6+Z//RrVbqpp5AW5jJ+dLqbcvcZkYj9CDVuutnOFT+H/APkBWn+6f5mq7MqIWdgqgZJJwAKsaApXQrPIIzHkZ9Ccis6nwlQ+IZfRac3iHSZLmVlv0Wf7IgzhwVXfnjsMelaM/wDx7yf7p/lWdfTacniHSYriFmv5FnNrIBwgAXfnnuCOxrSlBaF1HUqQKwRsc9pf/IJsv+uCf+girdU9JZW0m0wc7YlU+xAwR+dXK7Hucy2K2o/8gy7/AOuL/wDoJrdtf+POH/rmv8qwNUdU0u6LEDMTAe5IwBXQW6lLaJWGCEAI/CsquyNKe7M5LSyHiya8F1m/axSJrfcOIhI5D468ksPwp/iD/kAXv/XI0xP7N/4Sybbu/tX7DHv648nzH2+2d273qTXlZtBvQoJPlE4HtWUPiRpLZleimo6yIrowZWGQQeCKdXSYFTUv+PVf+u8P/oxa6Sua1Egwwxj78lxEFXuT5in+QJrpayq7I0p9TK0yLTk1jW3s5We7kuIzeqc4R/JQKBx/cCHv1o1//j1tf+vqP+dGmTadJq+tpaQsl1FcxreORxJIYYypHP8AcKDt0pPEB22MEh4RLmNmbsBnGT+dRT+JFT+FjKKKK6DEry/8hPS/+vg/+inroa55yG1fTIwcuJmcj0URuM/mR+ddDWNXdGlPZnNeAf8AkUYf+vq7/wDSmWulrmvAP/Iow/8AX1d/+lMtdLWRoFFFFABVDXP+Rf1L/r1l/wDQDV+qGuf8i/qX/XrL/wCgGgCDwt/yKGi/9eEH/otagvf+RiP/AF6L/wChtU/hb/kUNF/68IP/AEWtQXv/ACMR/wCvRf8A0Nq0pbkVNiSiiitzIKzPEV5a6f4dv7u9tVuraKFmkgYAiRfTB4rTqhrd5c6fot3d2dq11cQxlo4FBJkPpgc0gL9FFFMAooooAKzNansYI7I38DSq97CkIUZ2ylvkbqOhrTrO1i9WySzLWn2nzryKED/nmWON/Q9Ov9aTA0aKKKYBRRRQAVmX15aw65pVtLarJcXHm+TMVBMW1ctg9RkccVp1Qu7y5h1bT7WK1aSC48zzZgDiLauVyegyeOaQF+iiimAVXv8A/kHXX/XJ/wCRqxVe/wD+Qddf9cn/AJGhbgzasv8Ajwt/+uS/yFYNt/yU7VP+wNZ/+jrmt6y/48Lf/rkv8hWDbf8AJTtU/wCwNZ/+jrmuV7m62OlooopDCiiigArmvC//ACHPF/8A2Fl/9JLeulrmvC//ACHPF/8A2Fl/9JLegDQ1/wD48oD2F1Fn/voCo60b20S+spbaQkLIuNw6qexHuDg1gm8ay/c6mPIkHHmkYjk9w3QfQ81vSd1YymrO5doqp/aunf8AP/a/9/l/xo/tXTv+f+1/7/L/AI1rZmd0W6zNAOnHSQdLVltfPn4bOd/mv5nXn7+6rH9q6d/z/wBr/wB/l/xrN0PXNGudLEtrJbWkXnTL5RkRfmErBmwD/EwLfjSsx3N2iqn9q6d/z/2v/f5f8aP7V07/AJ/7X/v8v+NOzFdFuo7bnxFBjtay5/76jqsdVsidsM63Eh6RwHzGP4CtLSbKaN5by7ULcTAKIwc+Wg6DPrySf/rVE/dWpUdXoab42Nu6Y5rM8NnTj4Y0o6QrLphtIjahs5EW0bc556Y681pOQsbEjIAJIrO8OXdpf+GNKu7C2FrZz2kUkEAAHlIVBVcDgYGBxXMbla+48Qr72vH4P/8AXFPqfVrGW4EVzagG5gJ2qTgSKfvLntnAI9wKzBqlop2XEotpR1juPkYfn1/CumDvFWMJaMuUVU/tXTv+f+1/7/L/AI0f2rp3/P8A2v8A3+X/ABq7Mm6K/iGexttBu5tTgaezVR5sajJYZHuO+O9adYeueINOsdGubkta3uwD/RxKp38ge/16dq0f7V07/n/tf+/y/wCNKzHdFuiqn9q6d/z/ANr/AN/l/wAaP7V07/n/ALX/AL/L/jTsxXRbp2g/cvz2N22P++VFUBfC6PlaaBdSngMvMae7N0/DrW5p9mthZR24YuwyXc9WYnJP4kms6jtGxcFd3MTxh97w9/2Gbf8Ak9dLXNeMPveHv+wzb/yeulrnNgooooAKKKKAOa1f/kfvDP8A1wvf5RV0h6Gub1f/AJH7wz/1wvf5RV0p6UAcdpH/ACB7L/rin8qu1n6XNFHpsEMkiJLEgjkRmAKsOCCKufaIP+e0f/fQrd7mrGXv/Hjcf9cm/lXRad/yDLT/AK4p/wCgiuYvrqAWM481CWQqqhskkjAAHc11NlG0VjbxuMMkSqR6EConsTLYo503/hLMbW/tX7DndzjyfM/LO78aPE3/ACK+qf8AXrJ/6CaPtln/AMJYbL7KPt32HzftG0f6vzMbM9evPpUmvwyXHh3UYYkLyPbSBVHUnaeKylswp/GvUy6KrxX9pNEsiXERVhkfOKf9qt/+e8X/AH2KxPRsyvqv/Hgf+ukf/oa12VcVqE8U8MdvFIkk0s0aoisCT84J/IAmu1q4dTmxOy+f6GVp0WnJrmsSWsrNeu8X2xDnCERjZjjuuD3pniP/AI87T/r+t/8A0YtP06bTpNc1mO1hZLyOSIXbkcOxjBXHPZcDtTPEvyaXHOQfLguoJZCBnaiyKWP0A5rQ5C9RVcX9myhlu4CDyCJBzS/brT/n6g/7+CgCnqv/AB+aP/1/D/0XJW7XO31zBdarpFvbypLKt15zKjA7UEbgscdBkgfU10VAGVoJ00294dLVlj+3XAm3Z5m8w+Z17bs+3pWfr3/I4+E/+u9z/wCiGrQ0G8s722vHsrUW6R31xFIoUDfIsjK78f3mBPrzWV4qni0/X/DGpXbrDZQ3cscs7nCRmSF1TcewLYGTxkj1oA6qiqX9saX/ANBKz/7/AK/40f2xpn/QRs/+/wCv+NAGPqX/ACUfw9/2Dr//ANDtq3NQ/wCQbdf9cX/ka5t7221T4kaZ9gnjuVsNNuvtLRMGWIyvBsBI4BPlucegrpNQ/wCQbdf9cX/kaAMvwV/yIfh3/sGW3/opa3awvBX/ACIfh3/sGW3/AKKWt2gAooooAK5r4hf8iBrX/Xuf5iulrmviF/yIGtf9e5/mKAOlrAbjXdQB6kRMPptx/Q1v1l6pYzPMl7ZgNOi7HjJwJU64z2IPT6n1rSm0nqRNXRFRVM6nbp8s4mgfuksTAj9MH8KT+1rH/nv/AOOn/CuizMbou1mazPYwLYfboGlD3sSQ7RnbKT8rdR0qb+1rH/nv/wCOn/Cs/VfEFjaiyzELnzbuOLlT+7zn5+h6UmmO6N2iqX9rWP8Az3/8dP8AhR/a1j/z3/8AHT/hTsxXRdoql/a1l/z3/wDHT/hTh9o1QeTaRyxQtw9zIhQAd9oPJPv0FJq2499jR8Pf8gSA9mLsPoXYim6lcafFrWjRXVu0l3NLKLSQDiNhExYnnjKhh361pQwx28EcMShY41Cqo7AcCs/UL5bbWdItTZecbqSRVm/54bY2bPTvjb1HWuWTu2zdKysadctp3GnQA9QgB+o4rqawbuymsriSWCJpbaRi7Kgy0bHrgdwTzxzWU1syZrqJRVb7fbjq7A+hRgf5Ufb7b/np/wCOn/Cs+ZdzK5ZrMW8tj4nlshaqLtbNJTcYGShdgEz14IJ9Oatfb7b/AJ6f+On/AAqiutZ12SzMRFqtssq3GxsFyzAp0xwAD+NPmXcdzXoqt9vtv+en/jp/wo+323/PT/x0/wCFLmXcVyzVe95tio6s6Ae5LCk+3W54VmY+ioxJ/DFXLGymurmO4uImigiO6ON/vO3YkdgPT1o+LRDSvojcrmvDv/I1+L/+v6D/ANJYa6Wua8O/8jX4v/6/oP8A0lhroNzpaKKKACiiigDmvFv/AB/eF/8AsMp/6Jmrpa5Lx2bwHw2bBYGuv7YTyxcMVQnyZupAJ/Sti7bXxplubOHTGvzjz1mlkEQ452kKSecdQKALNzpVheS+bcWcMkmMbmQZx9ah/sDSf+gfB/3zRfNrYltP7Pi09oyf9JNxK6kdPuYU579cdqWdtZGrwrbxWB0zaPNeSRxMDz91Qu0jp1PrVc0l1FyrsC6DpKsGGn2+QcjKA1o1nRtrP9syLJFYDS9v7t1lczk4HVdu3Gc9/SiybWTeXQv4bBLUE/Z2gldnYZ43gqAOMdCaTbe4JJbDL2+ig8QaVZvZiSW4ScpcYGYdoXI6fxZH5VqVzK33iyO8gtrjTdNJmjnIkgllaNGVV2B2KjbliR0PA4rRV9f/ALFdnt9M/tXd8kYnk8gjI6ts3Zxn+HrikMnm0bTbiVpZbGBpGOWYoMk+9R/2BpP/AED4P++aZcPr40mBra20xtSLDzo5Z5FhAwc7WCFic46gd6dfPrqraf2fb6dIx/4+vtE7pt6fcwh3fxdcdqrnl3FyrsSQ6LpkEqyxWMCyKcq2wZB9RV+s+6fWRqlutpBYNpxA8+SWZ1lU5OdqhCDxjqR3oD6z/bTIbew/srbxKJ38/OP7mzbjP+10pNt7gklsRJdWJ8WT2a2xGoLYxyvcYHMRkcKuevBDH8a1awLHUNWfxfe6fe6daR2kdss0F3DI7M4LsArZUAEBSSATjI65q3Yya81tdG/tdNjnA/0Zbe5kdWOD98lAV5x0B70hj20HSmYsdPt8k5OEApP7A0n/AKB8H/fNMik186PK81ppi6mG/dxJdSGEjjq5jDA9f4T2oll18aPE8NppjamW/eRPdSCEDno4jLE9P4R3queXcXKuxZttK0+zl823s4Y5MYDqgyPxq5WXfS68ttamwtNNlnIH2lZ7qRFQ4H3CI2Lc56gdqfeSayuo2y2VrYSWJI+0STXDpIvPOxQhDcerDmk23uNK2wzTL6K61bWreOzEL2lykckoA/fkwxuG6dgwXv8AdrSdFkRkdQysMFWGQRWJaX3iCfxPf20mnWkejW8qpHcvK6zSAxKxKrtKsN7Fc7h0PGRzdtpNZbVLhLu1sE04A+RLFcu8rHIxuQoAvGejHtSAb/YGk/8AQPt/wSj+wNJ/6B8H/fNJYy6463f9oWenRFf+PX7PdPJv6/fzGu3+Hpu6n05bbza+2kzvc2OmpqQY+TDFeSNCwwMbnMQIOc9FPQevFc8u4uVdi3a6dZWJY2trFEzcEooBP41arKabXxoqSLY6adVLfPAbxxCBk9JPK3E4x/B1yPei9m19LC2exsNOlvGA+0RzXjxxocc7GETFufVV4/Kk23uO1jP8A/8AIow/9fV3/wClMtdLXM+Af+RRh/6+rv8A9KZa6akAUUUUAFUNc/5F/Uv+vWX/ANANX6oa5/yL+pf9esv/AKAaAIPC3/IoaL/14Qf+i1qC9/5GI/8AXov/AKG1VPBumXUHguyR9Zvp2uLGIxvIsWbbMYxswgBxn+Ld0HWq0+lXkV3LZNrt/LO9spF66Q+anzngARhO3dT1NaUtyKmxsUVmTaXeSaVFZprd9FOhy14qQmV+vBBQp37KOgpb3Tby5htkh1q9tGhGHkiSEmbgfe3IQOn8IHWtjI0qoa0+oR6LdvpUayX4jJgRsYLdupAourC6uNQt7mLVrq2hixvto0iKS85+YshYenBFUNb0rWblbqbS9cuIJWiKw2zLEIVbGMlthf36/pQM3qKoW1hdQalPcy6tdXEEmdlrIkQSLn+EqgY+nJPWmWWnXtsl0s+tXd2Zv9W0scKmDr93agz1H3s9B70CNKisyDTb6LS5rWTXLya4ckpePFCJIxxwAECHoeqnr9KH02+bSEs11y8W5U5N6IofMYZJwVKbO+OF7UAadUNUub22W0NlbiYyXUccuQTsjJ+ZuPQVHeabf3FlbQwa5d2ssQAkuI4oWaY4xlgyFRzz8oFQa1aazLeadPpeoyQxJcRi6twkRWSLd85JZSwOOOCKBm1RVB7G8bV0u11a4W1UYayEcXlscEZLFd/vw3aiCxvItUmupNWuJrZwQlm0UYSM8chgoY9D1J60CL9FZ1lYXts901xrFxdiX/VLJFEog6/d2qM9R97PT602206/h02e3l1u5uLiQnZdPDEHi47BUCn15B60AadULp9QXVtPS3jVrJvM+1McZX5RsxznrnpUZ07UP7IFp/bdz9qzn7b5MW/Gc427dnTjpVPUtK1y4fS47PXZbZIVcXc4jjLzHaNp2lCvXOcAdaBm/RWfe2N9cTWz2+rz2qRH97GkUbCbp1LKSOh6Y60s1leyarFdR6rNFaoMNZiKMo555LFdw6joe1Ai/Ve//wCQddf9cn/kagjsr1NWkun1WZ7VhhbMxRhEOByGC7j36nvVdrK9t4NSluNVmuopY3McLxRqIeDwCqgn05z0prcGdTZf8eFv/wBcl/kKwbb/AJKdqn/YGs//AEdc1a0jTtSg0JoZ9cnuJ5Yh5Vw0ESmDK8YCqAcHnkGsnQ7W6tPiFqcV5qEl/MNHtCZ5I0Qkefc8YQAfpXK9zdbHY0UUUhhRRRQAVzXhf/kOeL/+wsv/AKSW9dLXGeHLa/fxn4ouI9REdimpqJLTyAfMb7JBzvzkdRx7e9AHZ0EZGDWdb2mpR6tPcT6oJrJwRHa/Z1UxnIwd+cnoe3em2NnqsBuzeast0JP9QBarH5PX0PzdR19PegDQ8mL/AJ5p/wB8ijyYv+eaf98is22sdXi0u4guNZSe8cnyroWiqIxgY+QHDc5796DY6wdFW2GsoNQDZN59kXBGc48vOOnHWgDS8mL/AJ5p/wB8is7Q7htR0sXNxp4tJDNMnlFccLKyhuQPvBQ340l5Y6xNp1tDa6ylvdIB51wbQOJeOfl3Dbzz1qlZQ+Jb21s57m+isJUmlFxB9lEnmxiVgmDu+XKAevX8KAN/yYv+eaf98ijyYv8Anmn/AHyKoy2upvrEVxFqaR2CjD2htgS5558zOR27dqIrXU01iW4l1OOSwZcJaC2Csh458zdz37d6ANBVVfuqB9BS1nWNpqkNzdPeapHcwyH9xGtqIzEMnqdx3cY9OlR2dlrMWnXMN1rEVxduD5NwtmEEXHGU3Hdzz1FAGm5KxswGSATj1qjod5NqOgade3FqbSa4to5ZLcggxMyglOQDwTj8Kgaz1pdCeAavC2pZyt39jAXrnHl7vTjr71WtIfEd74U0vzb+Gw1hoY3vGe1EoDlfmTaGAGCeoPb3oA6CkZVYYZQR7is6+tNWma0NlqsNsI/+PgPaeZ53Tp8w29D69faluLXVJNWgnt9SiisFAEtq1ruaQ5OSH3Db27Hp70AXvJi/55p/3yKPJi/55p/3yKorbaoNae4bUoW04jC2gtcOpwOfM3c85P3e+KLK21SK/uZbzUobi1ckwQJa+W0QzwC247uOOgoAZrklzZaNcXGm2UdzeIB5cRTO75gDwMHpk1o+TF/zzT/vkVz91aeKYPD9+kGq211qj7fssi2giWPkZyCzBuM+lXo7PWl0aSCTV7ZtRLZS7FlhFGRwY9/PGR97vQBpeTF/zzT/AL5FHkxf880/75FZs9prb6RDBBq9tHqCsDJdNZbkcc8CPeMdv4j096W+tNZmitFstVtraRB/pDSWZkEvT7o3jZ39evtQBqAADAGBRWdd22qyanby2upW8FkmPOt3tC7yc84feNvHH3TR9m1X+2vP/tG3/s3bj7J9kPmZx18zf68/d9vegDL8Yfe8Pf8AYZt/5PXS1xPia21WPVdHlutRt57J9Zg8m3S0KPH97GX3ndxx90V21ABRRRQAUUUUAc1q/wDyP3hn/rhe/wAoq6WuN8VJqUnjPw0NKntILnyrz57qFpUxiPPyq6nP410F9HrbyWh0+70+JF/4+RcWzyF+n3CJF29+u7t6cgFqbT7K4k8ya0t5X/vPGGP5kVH/AGTpv/QPtP8Avyv+FRTx6ydWhe3ubBdNAHmxSW7tMx5ztcOFA6dVPehI9ZGsyPJc2B0or8kK27icHA6vv2kZz/COMU7sLliLTrGCQSRWdvG46MkSgj8cVZrOso9ZW+umv7mwktCT9nSC3dJFGeN7FyGOMdAOajso9fWwulv7vTJLwqfs7wW0iRqccb1MhLDOOhHFICQX03/CSGw+xnyBZib7Vg43byNnTHTnrWjXMxnxh9pltpJdKJ+zB47pbVxEJfM5Up5m4/J79SPpWjcR6+dJgW2utMXUgw86SS2kMJHOdqhwwPTqx70AWZNJ06WRpJNPtXdjks0Kkn8cU3+xdK/6Bln/AN+F/wAKivo9dZLT+z7nTo3H/H0bi3dw3T7mHG3v1z2p10msnVLdrSewXTwB56Swu0rHJztYMAOMdQaVkVzy7liDT7K1cvb2dvCxGC0cQU/oKs1nBNZ/tosZ7D+yscR+S/n5x/f3bcZ/2elLaJrI1K5a9nsHsTnyEhhdZV543MWIPHoBTE23uR6ffRXGtaxapZiGS1kiWSYAfvi0YYHp2Bx36VqEZGDXPaPc+JJrnWbbU0sVMDItlPFBIiSZTcSdzHcASBwR0NXIU18aRKs9xph1Pd+6dIJBCBx95S+4nr0PpQInOi6UxJOmWRJ6kwL/AIUn9h6T/wBAuy/8B0/wqGVNf/seJYp9MGqbv3jvDIYSMnou7cDjHf1pb5NfNraiwn01bkAfaTPDIyMcDOwBgRznqTQBdtrK0swwtbWGDd18qMLn8qnrOvF1k6hbGxlsFsgR9oWeN2kIzzsIYAceoPNDLrP9tKyy2H9lbfmQxv5+cdm3bcZx26UAGjX01/b3Lz2ZtWjvJ4VUgjeqSFVfkfxAA/jV6WGOeJ4po0kjcYZHUEMPQg9axdDk8STX18+rrZR2InmS1SNGExQSERsx3FSCnPQHkVasV1wfa/7Ql05s/wDHr9njdcdfv5Y5/h6Y70ARf8Il4a/6F7Sf/AKP/wCJo/4RLw1/0L2k/wDgFH/8TT7ZdfGlTi6l0w6jk+S0UcghAwMbgWyec9D6UMuv/wBiqFk0z+1d3zMY5PIxk9BndnGO/WgC7ZWFnp0HkWNpBaxZz5cEYRc+uAKTUP8AkG3X/XF/5Gqd4uvnT7YWMmmC9wPtBnSQxk452AHI59e1Ra6NZMH+gPYC28tvtInVy5GOdmDgcZ60AM8Ff8iH4d/7Blt/6KWt2sLwV/yIfh3/ALBlt/6KWt2gAooooAK5r4hf8iBrX/Xuf5iulrmfiGM/D7WwCQTbHkduRQB01FZUWk3kejy2Ta9qEk7tlb50g85OnAAjCdu6nqaJtJvJNHisk17UIrhGy16qQGWQc8EGMp37KOgoA1aKy77Sry6t7WKHXb+zeEYklhSEtPwOW3xkDpn5QOtSXmnXdzqNtcw6xeWsMWN9rEkRjmwc/MWQsM9PlIoA0KztWur61+xfYbYT+bdxxT5BOyI53Nx6cUp0+7OtC+GsXgtgMGw2ReUTjGc7N/Xn73X24rOvNO16HVIbuy1m5ngkuk86zmjgEcUOcttIQOTxjlj1oA6Gisyx0y9tVuxPrl7eGb/VGaOFfs/X7uxBnqPvZ6D3y230u/h0me0k1++nuZGJS9eKASRDjgBYwh6Hqp6n2oA1aKyn0vUG0VLJdfvVulbJvxDB5rDJOCpj2dCBwvb1ovdL1C5sLW3g1+9tJogBJcxQwM85xjLBoyoyeflA/KgDVrOvbq+h1XTILa2ElrO8gupcE+UoQlTntlgBz60Xun3tze2s1vrN1ZxREGS3iiiZZxnOGLoWHHHykdaqXtnrf/CUabdWmpS/2VlxeWZji242HaQSu/JbHRu1AG5RWdBYXsWrzXcmsXM1q6kJZNFEI4zxyGCBz0PVj1+lJY6ffWst21xrV1eLMf3SSxRKIOv3dqAnqPvZ6fWgDSorKtdM1GDTLi2m167ubiTPl3bwQq8XGOFVApx15B60f2ZqP9i/Y/7euvte7P2/yIfMxnONuzZ04+7+tAGrWcr6l/wkcsbRr/ZYtEZJOMmbe24dc427O3eo7vTNRn023toNeura4jx5l0kELPNxjlWQqM9eAKqXela7ceI47mDXZbTTI7aNWhjjjYyyh2LFtyHAKlRlSD1oA6Cis6exvpNXhu49XnhtEXD2SwxlJDzyWK7h1HQ9qWOxvk1mS7fV53tGXC2JhjCIcDkMF3noTye9AGhRWdY2F9bXl1Nc6xPdwykmKB4Y1EIyTgFVBPHHJPSo7LTdSt7C5guNduLqeUERXLwRK0JxgEBVCnB55BoA1a5rw7/yNfi//r+g/wDSWGtBdN1NdFezOu3DXjNlb428W9RkHGzbs6ZHTvWP4Rhmt9f8VRXF011Mt5AHmdFUufssPJCgAfhQB1tFFFABRRRQBzXi3/j+8L/9hlP/AETNXS1zXi3/AI/vC/8A2GU/9EzV0tADJJooseZIiZ6bmApn2u2/5+Iv++xXOW9tBdvc3NzDHNK1xKpaRQxAVyoAz0AAFT/2fZf8+dv/AN+l/wAK39ku5l7R9jdW5t3YKs8RJ6AOKlrnH0yxdCps4MH0jANaeiSyTaLaPK5d9mCx6nHHNROCiroqM7uwl0dS/tvThbBf7O2TfaycZDYXy8d/73StGs66tb6XW9PuYbkJZQpMLiHJ/eMwXYcdDjDfnV+RikTsOoUkVmWNe4hjba80an0ZgKb9rtv+fiL/AL7Fc1p9lbTafbzTQRSzSxrJJJIgZmYjJJJqz/Z9l/z52/8A36X/AArf2S7mXtH2N5LiGRtqTRs3orA1JXK39jax2E8sVvFFLHGzpJGgVlYDIIIrpoHMlvG7dWQE/lUThy6oqMrlJJ9QPiOa3aBRpq2kbxzY5Mxdwy5z0ChD071o1nJHqQ8RzyPIv9lm0jWOPjIm3vvPTONpTv2p2tSyQaLeSRMUcRHaw6j3FQld2KbsrlprmBGKtPGrDqC4FJ9rtv8An4i/77FYSaZYogUWcGB3MYJP40v9n2X/AD52/wD36X/CtvZLuZ+0fY345opSRHIj467WBp9ctdW0Fp5NzbQxwzRzxgNGoU4LhSDjqCCa6ms5w5di4yuZ+nnUjqOq/bQotRcJ9hxjJi8pN2cc/wCs8zr/ACxWhWdp9rfQahqst1ciW3uLhXtIwSfKjESKV9surtx/eqPxASdPjiyQs08cb4OMqTyPx6VMVd2G3ZXL5u7YHBuIs/74o+123/PxF/32Kwxp1iBgWdvj/rkv+FH9n2X/AD52/wD36X/CtvZLuZ+0fY6BJY5RmN1cDupzTq5qKCKz1ewe2jSIyyNFIEG0Muxm5x15UV0tZzjysuMro5rwD/yKMP8A19Xf/pTLXS1zXgH/AJFGH/r6u/8A0plrpagoKKKKACqGuf8AIv6l/wBesv8A6Aav1Q1z/kX9S/69Zf8A0A0AQeFv+RQ0X/rwg/8ARa1Be/8AIxH/AK9F/wDQ2qfwt/yKGi/9eEH/AKLWoL3/AJGI/wDXov8A6G1aUtyKmxJRRRW5kFZ+u2T6jod3aR3ZtHlj2icf8s/fqP51oVmeIbazvPD97b6hcG2tJI8SyggbBnrk8UmCNOiiimAUUUUAFUNUtbu6W0Fpc+QYrqOWXkjfGD8y8etX6zdYsor0WIluxb+TeRTLkj94yk4Tr3pAaVFFFMAooooAKz7uyebWdOuhd+WtuJQ0H/PXcAB37Yz0PWtCsy9trOXXtLuJrgpdwrN9niyP3gKgNx3wMfnSA06KKKYBVe//AOQddf8AXJ/5GrFV7/8A5B11/wBcn/kaFuDNqy/48Lf/AK5L/IVg23/JTtU/7A1n/wCjrmt6y/48Lf8A65L/ACFYNt/yU7VP+wNZ/wDo65rle5utjpaKKKQwooooAK5rwv8A8hzxf/2Fl/8ASS3rpa5rwv8A8hzxf/2Fl/8ASS3oA6Wq91f2liAbq4ii3dA7YJ+g70zU7w2GnyzqoaQYWNT/ABOSAo/Misi2tFhJlkbzbp+ZJm+8x/oPQVpCHNqyJStojQ/4SDSv+fxf++T/AIUf8JBpX/P4v/fJ/wAKgorT2cSOeRP/AMJBpX/P4v8A3yf8KztE1uODS1TVdSjmu/NlJdQcbDIxQdB0QqPwq1WfotnNYaYtvPdm6kEkreaSTkNIzAck9AQPwpeziPnZr/8ACQaV/wA/i/8AfJ/wo/4SDSv+fxf++T/hUFFP2cRc8i1DrWm3EoijvIjI33VJ2k/TPWr9Yc0MVxGY5o1kQ9VYZFP0eeSG5l06V2dVQSQMxydmcFSe+Djn0IqJU0ldFRnd2ZryBjE4Q4cqdp96p6LFfw6Fp8WqSrLqKW0a3Ui9HlCjeRwOrZ7Crcql4nUNtJUgH0qnolnLp+gadZT3Ru5re1iikuCSTKyqAX5yeSM/jWRoX6pXGsadaymKa8iWQdUDZI+oHSqus3ErSQ2EEjRtMC8sinBWMYzj0JJAz9ahgt4raIRwxqiDsorWFNNXZnKdnZFv/hINK/5/F/75P+FH/CQaV/z+L/3yf8Kgoq/ZxJ55FXXNVt7/AEa4ttP1ZbW6fbsm+YbcMCeQM9AR+NaP/CQaV/z+L/3yf8KxtfsotQ0S4tZrsWkb7czEgbcMD3I64x+NaVL2cR87J/8AhINK/wCfxf8Avk/4Uf8ACQaV/wA/i/8AfJ/wqCin7OIueRoWuo2d9kW1zFKV6qrcj6jqKs1z1zaR3GHyY505jmThkPsf6dDWppV497Yq8oAmRjHKB03KcHHsev41nOFldFxlfRmP4w+94e/7DNv/ACeulrmvGH3vD3/YZt/5PXS1mWFFFFABRRRQBzWr/wDI/eGf+uF7/KKulrmtX/5H7wz/ANcL3+UVdIeATQBn3fiDRbCcwXmsafbzL1jmuURh+BOag/4S3w3/ANDDpP8A4Gx/41ynhi3hfw3YTvEjTXEKzTSMoLO7DczE9ySa1/s8P/PGP/vkV50swUZNcv4kc5s23iPQrydYLXWtOnmc4WOK6Rmb6AHNadcJrdlbTaJeq8EZxA7KdoBUgEgg9iDzmuw0qV59IsppW3SSQRszHuSoJrpw+IVZN2tYpO5CItR/4SNpjMv9mfZAoi7+dvJLdOm3A61o1nCxmHiRr/7YfINmIRa5OAwcnf1x0OOnaq/i2eW18H6zPA7RypZSsjqcFTtPIroGSTeJ9At5mhn1zTIpUOGR7uNWU+hBNM/4S3w3/wBDDpP/AIGx/wCNFrptlZ20dvb2sMcUahVUIOBUv2aD/njH/wB8ivGecL+T8f8AgGns/MfZa7pGozeTY6rY3UuM7ILhHbHrgGtCuS8UW0EWkLdxxIlxbXEEkMiqAyHzVBwfcEg+oNdbXfhMSsRBzSsTJWZnWJ1I6rqguwoshJH9iIxkr5a7845+/u61oMwVSzEAAZJPas+xtb6HVdUnubkSWs8kbWseSfKURqGHtlgTx61gfE+WSPwNdKjlRLLFG+DjKlxkfQ9K6JS5YthCPPJR7mwfFvhtSQfEOkgjgg3sf+NJ/wAJd4Z/6GLSf/A2P/4qvnzYoGAo/Kl2L/dH5Vw/XH2PT/s+P8x9HWGsaZqm/wDs/UbS72fe+zzrJt+uCcVdrwDwS7QeOdHeI7GeVo228blKNkH1HAP4Cvf66qNX2kbnFiKPsZcqdzO0eLUYbe4GpzLLK13O0RXtCZGMa9ByE2j/ABq9LLHBE0s0iRxqMs7tgAe5NUdFsZrC2uY57w3TSXk8ysSTsV5GZU5J+6CF/CszxWBLcaTbyDdC87syHoxVCRn1wea3iruxx1Z8kHI0P+Ek0L/oNad/4FJ/jR/wkmhf9BrTv/ApP8axfJi/55p/3yKPJi/55p/3yK19mjk+tT7I6e1vbS+iMtndQ3EYOC0MgcZ+opuof8g26/64v/I1zOlosHi2DylCCe0l80KMB9rR7SfpuP5102of8g26/wCuL/yNZyjZnVRqOpG7MvwV/wAiH4d/7Blt/wCilrdrC8Ff8iH4d/7Blt/6KWt2pNQooooAK5r4hf8AIga1/wBe5/mK6Wua+IX/ACIGtf8AXuf5igDpaQkKCSQAOpNLWNqLG8v/ALGx/wBHhUPKv99j0B9hjOPcVlVqezjfd9C4Q5nYsnW7AEhJXlx3iidx+YBFJ/bdn6XP/gLJ/wDE1GAAAAMAdAKWub2tbuvuf+Zv7KHn/XyH/wBt2fpc/wDgLJ/8TWdq2oG7+w/Y7i6g8m7jlm/0eUeZGM7k4XnOR+VXqzNYsorw6f5t2LfyLyOZMkfvGAOE698/pR7Wt3X3P/MPZQ8/6+Rrf23Z+lz/AOAsn/xNH9t2fpc/+Asn/wATTKKPa1u6+7/gh7KHn/XyH/23Z+lz/wCAsn/xNWLW/tbzcIJlZl+8pyGH1B5FVKr3Nv5oEkZ8u4j5jkHVT/Ueoo9tWWrs/lb9QdGD2Nys69tL2fVtMuILryra3eQ3MW4jzQUIUY6HDEHmrNjc/bLGG427S65K+h7j86p6hYxXGt6RdPeCKS1eUxwkj99ujKkdewOe9dsZKSUlszmas7M1Kgury2sYDPdTxwxDjc7YGfT61PXHRP8A2rdtqc/zgsy2qnpHGDgED1bGSfcDtSlKxpSpc712Rsf8JRpf8Ju2HqtnMQf/AB2j/hKNN9Lz/wAApv8A4mqdFRzyOn2FPz+//gFz/hKNN9Lz/wAApv8A4ms1NasF8Sz6h9rvTG9nHALX7FNhSru2/wC733AdP4etTVmpbWY8TT3S3Gb1rOON4Mj5Yw7lWx15JYfhRzyF7Cn5/f8A8A3v+Eo030vP/AKb/wCJo/4SjTfS8/8AAKb/AOJqnRRzyH7Cn5/f/wAAuf8ACUaX/E10g/vPZygD8dtaltdQXkCz20yTRN0dGBBrn6qq/wDZWoRX0PyxSyLHdIOjBjtD49QSOfTNCm+pMsPFr3dzr65rw7/yNfi//r+g/wDSWGulrmvDv/I1+L/+v6D/ANJYa1OM6WiiigAooooA5rxb/wAf3hf/ALDKf+iZq6Wua8W/8f3hf/sMp/6JmrpaAOb0/wD1E3/XzP8A+jWq3WdDdW9jJcW13PHBKs8rYlYLuVnLAjPUYNTf2rp3/P8A2v8A3+X/ABrsszmuW6n8P/8AICtP90/zNZjatpyqT9ut29lkDE/QDk1raJDJBotpHKhRwmSp6jPODWdVWiXT3I7uxWbxDpl6b3y2t451Fv8A89t+znr/AA7fQ/e7Vfn/AOPeT/dP8qzry30+TxJpU89wyX8UVwLaIHh1OzeSMdsL3HWtORS8TqOpUiudGxzul/8AIJsv+uCf+girdZdjf2lvYQW9xcxQTwxrHJHK4VlYDB4NWf7V07/n/tf+/wAv+NdjTucyasO1H/kGXf8A1xf/ANBNbtr/AMecP/XNf5VzF7qNnPZTwQXMU80qMiRxOGZmIwAAK6iBDHbxo3VVAP5VlV0SNKe7KSWV0viOe/N2xtHtI4VtsnCuruWfHTJDKPX5aTxB/wAgC9/65GmRWVoviy6v1u1N5JYwwvbbhlY1kkKvjrglmHp8tT61DJcaLeRRKXkaI7VHUn0rKHxI0lsypRVNdW05lBN7bqe6vIFI+oPIpf7V07/n/tf+/wAv+NdVmc90Gpf8eq/9d4f/AEYtdJXK3F3b3xhtbSeOeZ5oztiYNgK4Yk46AAGuqrGr0NafUzNMsVtdS1mdbzzzd3SytF/zwIhjTZ1PXbu7fe/Et1//AI9bX/r6j/nRpVvp8Op63JZ3DSXE12r3iE5EcghjUKOOPkCHv1/Cl19WNhHKqsywzpI+0ZIUHk49utRT+JFT+FkVFUxq2nEZ+32v/f5f8aX+1dO/5/7X/v8AL/jXTZmF0Pl/5Cel/wDXwf8A0U9dDXNQ3EN/q1gtpKkwhkaWRozuCrsZeSO5LCulrCtujWnsc14B/wCRRh/6+rv/ANKZa6Wua8A/8ijD/wBfV3/6Uy10tZGgUUUUAFUNc/5F/Uv+vWX/ANANX6oa5/yL+pf9esv/AKAaAIPC3/IoaL/14Qf+i1qC9/5GI/8AXov/AKG1T+Fv+RQ0X/rwg/8ARa1Be/8AIxH/AK9F/wDQ2rSluRU2JKKKK3MgrM8Q/wBnf2Bef2tu+wbP323OduR6c1p1meIbqzstAvLm/tvtNrGmZIcA7xkcYPFJgjTooopgFFFFABWZrMVhKLD7fM0ey9iaDB+9KM7V6d+a06zNZk0+P+z/ALfE0m69jW3xn5Zedp6/WkwNOiiimAUUUUAFZl5/Z39vaX9o3fbts32XGcYwu/PbpjrWnWZeXVnFr+l201tvupkmME2B+7Chd3PUZyPypMDTooopgFV7/wD5B11/1yf+RqxVe/8A+Qddf9cn/kaFuDNqy/48Lf8A65L/ACFYNt/yU7VP+wNZ/wDo65resv8Ajwt/+uS/yFYNt/yU7VP+wNZ/+jrmuV7m62OlooopDCiiigArmvC//Ic8X/8AYWX/ANJLeulrmvC//Ic8X/8AYWX/ANJLegDQ1/8A487f/r6i/wDQhUdSa/8A8edv/wBfUX/oVR10U/hMZ/EFFFFWSFZnh+0tLLR0gsbkXMAllYSgg5LSMzDj0YkfhWnWZ4fGnDR4xpTM1n5ku0tnO7zG39efvbqQGnRRRTAKitv+Rit/+vWX/wBCjqWo7b/kYrb/AK9Zv/Qo6T2Y1ubM6q8Eiu21SpBPoMVR8P2trY+GtKtLG4FzaQWcMUE4IPmxqgCtkcHIAPHrV6fZ9nl83/V7Du+mOapeHxYL4b0saUWbThaRC1LZyYtg2Zzz93HXmuU3Kl9/yMI/69B/6GafTL7/AJGEf9eg/wDQzT66o/CjB7sKKKKYjM8QRWE+h3EepzNDZsU8x1PI+cY7HvitOszxBJp8WiTvqkTS2YKb0XOSd67ehHfFadIAooopgFO0H7t//wBfbf8AoC02naD93UP+vtv/AEBKifwsqHxFHxh97w9/2Gbf+T10tc14w+94e/7DNv8AyeulrnNgooooAKKKKAOa1f8A5H7wz/1wvf5RV0jfdP0rm9X/AOR+8M/9cL3+UVdKRkEUAcD4W/5FPSf+vSL/ANBFa1c7pWpW+i6Ta6XqRktru0jWGRHibkqMblOMFTjII9auf8JLpH/P3/5Df/Cvn6lKpzv3WZWLWrf8ga+/695P/QTXTaL/AMgHTv8Ar1j/APQRXD32uWd7p9xaWJlurqeJo4oYoWJZiMDtgDnkngV3un27WmmWts5BeKFI2I6ZAAr0MBCUYy5lYqBTWzsx4se9F0PtxsViNvuHEYkYh8depI9OKr+NP+RI1z/rxl/9BNWFGm/8JZIQzf2r9hUMvOPJ8xse2d273pfEljNqfhjVLG3AM9xayRxgnGWKkAfnXoFk1FYUfi7R/LXz55LabHzwzQuroe4IxTv+Eu0L/n/X/v2/+FfHvD1V9l/cdF0L4s/5F2b/AK6w/wDo1K6iuJ1XVrTXrRNM0tpLq4nni+5E22NVkVmZmIwAAD9TgCu2r3srhKFFqStr/kZTd2Zmn2K22savci9843UsbtD/AM8NsSrjr3xu6DrXP/FL/kSJv+viD/0YK3tNt9Pi1nWpbW4aS6mmjN3GTxG4iQKBxxlAp79ay/iHp11qfg26hsoWmmR45REgyzBXBIA7nGeO9d9VXgyqLSqRb7nh1FB3qSGhmBHUGJgR+lJlv+ecv/fpv8K8azPfujb8H/8AI7aL/wBfJ/8AQGr6ArwnwHpt5feMNOnhtpvs9rI0s0zRlVUbGAGT3JI4/wAK92r0sImoank49p1FbsZWgWdnZWd0lldC5je+uZXYMDtkeZmdOP7rEr+FUfE//IQ0f/rrL/6LNXtAGmizuv7KZmhN9cmXdn/Xec/m9e2/d7enFU/FMUq/2ferE8kVtMxl8tSzKrIRuwOSAcZrth8R5WIX7t/11KtFZ/8Aben/APPx/wCON/hR/ben/wDPx/443+Fb2Z5vNHuXrD/kbbH/AK9Lj/0KKuk1D/kG3X/XF/5Gua0MnUPEMd5Ajm1t7aRGlZCqszsmAM9cBTn8K6XUP+Qbdf8AXF/5Gsam56GF+BvzMvwV/wAiH4d/7Blt/wCilrdrC8Ff8iH4d/7Blt/6KWt2szpCiiigArmviF/yIGtf9e5/mK6Wua+IX/Iga1/17n+YoA6WsX/mL3/1j/8AQa2qxf8AmL3/ANY//Qa5cV9j1/Rm9Dd+n6omooorI2CszWYrCVtO+3zNGUvY3t9p+9KAdqnjp1rTrL1mTT42037fE0he9RbfGflmw20nnpjNNAzUooopAFFFFAD9D/5BMX+/J/6G1R6hDp0mu6PJdTMl7G8ptEB4cmMh88dlye1S6H/yCYv9+T/0Nqi1CXTk13R47qJmvXab7I4zhCE+fPPdeO9dOG/gw9F+Ry1fjfqatcXon/IDsv8Ariv8q7SuL0T/AJAdl/1xX+VVPdHRhvhl8v1L9FFFQdAVmx/2f/wk1xt3f2l9ji8zrjyd8m32+9v960qzY7m0bxNcWi2+L1LOKR58D5oy8gVc9eCrH8aBM0qKKKBhVLVv+QbJ/vJ/6EKu1S1b/kGyf7yf+hCkxx3R2Vc14d/5Gvxf/wBf0H/pLDXS1zXh3/ka/F//AF/Qf+ksNdB5Z0tFFFABRRRQBzXi3/j+8L/9hlP/AETNXS1yXjuO6lPhtLK4jt7k6wmyWSLzFU+TN1XIz+YrYu7TW5NMt4rXVrSG9THnXD2JdJOOcJ5g288/eNAGoyK33lB+opvkx/8APNP++RVC+ttXlltDY6nbW0aH/SFkszKZen3TvXZ39evtTp7bVn1eGaDUbePT1UCW2a0LO555Em8Y7fwnp70AXhHGpyEUH1Ap1Z8dtqy6zJPJqNu2mlcJai0IkU4HJk3885P3R1pLG21aK9unvdSt7i2cnyIo7QxtEM8Bm3ndxgdBQBHePpo8SaWlxGx1Fobg2rjOFQbPMzzjnKdR2rVrlQ2o2urWmnajd2V3qt1b3LWV8lh5YtQoQMCpkJbJZSQGXO3Faq2mtjRXgbVrQ6kWyt2LEiNRkcGLzOeMj73fPtQBptGjHLIpPuKTyY/+eaf98is24tNbfSYIbfVrSLUFYGW5exLo4wcgR+YNvb+I9PenX1rrMq2gsdUtbYp/x8GSzMvm9Pu/vF2d/wC919uQDRWNFOVRQfYU6s+6ttWk1S3mttRt4bFQPOt3tC7yHJziTeNvGP4T0oFtq39tNOdRtzpu3AtPsh8wHHXzd/rz93296AIok03/AISy6dJGOqmxhEqc4EPmS7D0xnd5nfPH0rVrmdOEyePdUjubqGab7DDIoSz8spC0sojUyeYdxBWT+Efez7VpWNrrUVtdLe6ra3Ezj/R5I7IxiI4P3l8w7+cdx0oA0jGjHJRSfUik8mP/AJ5p/wB8is2K01tdHlgl1a0fUWbMd0tiVjUccGPzDnv/ABDr7US2mtto8UEWrWiairZkumsS0bDngR+YMdv4j096ANNUVfuqB9BTqy7611qW2tUstVtbeZAPtEklkZBKcD7q+YNvOe560+8ttWl1G2ls9Rt4LNCPPgktDI8nPOH3jbxx0NAEekvpralrYsY2W4W8UXpOcNL5MRBGT/zzMY4x0rVrmdKlu7vxVrLW01rBY2t2IbmAWf7yeT7PGwcyh/R0HKk4XHpWtbW2rR6pcTXOo281gwPk2yWhR4zkYzJvO7jP8I60AXfKjJyY1/KjyY/+eaf98is+xtdZiW7+3apa3Jf/AI9zHZmLyuv3v3jb+393ofXhtvaa2mkzw3GrWkuoMxMVyliURBgYBj8w7u/8Q6+1AGoqqowoAHsKWsprTWzoqQLq1oNSDZa7NiTGRk8eV5nHGB97tn2ovbTW5bC2js9WtLe7QDz5pLEyLKcclU8wbeeep9KAM/wD/wAijD/19Xf/AKUy10tcz4B/5FGH/r6u/wD0plrpqACiiigAqhrn/Iv6l/16y/8AoBq/VDXP+Rf1L/r1l/8AQDQBB4W/5FDRf+vCD/0WtQXv/IxH/r0X/wBDaqng3RYLbwXZRrdag4vLGIuZLyRzHmMf6slv3fXjbjHHpVafQbeC7l01bvUmiktlYyvfStMPnJ4kLbgOOx9fWtKW5FTY2KKzJtDgm0qLTmu9RWOM5EqXsqzHr1kDbj17n09KW90WC+htopLrUIxbjCmC9ljZ+B98qwL9O+e/rWxkaVZ+uXsmn6LdXcVobuSNQRAP4+QMdDS3Wkw3eoW969zeo8GNscN3JHG2Dn5kBCt+INUNa0+5g+0avpj3k2pIgEVs11J5DHgcxbgvTJ+vNDGb1FULbSYbXUp75Li9eSbO6OW7keJcnPyoTtXp2FMstFgsUukjutQkFz94z3kkpTr9wsx2de2O3pQI0qKzINDgg0ubT1u9RaOUkmWS9laVen3ZC25enY+vrQ+hwPpCaabvUREhyJVvZRMeSeZN249fX0oA06zNYvILP+z/AD7UXHnXkcKZAPlsc4fn0xReaHBe2Vtavd6jGluAFeC9ljdsDHzMrAt+JPNVPEFpK99pN1AL52W7jjdILmVEVCSSzqhAYDA+8COcUMZvUVQfSYX1dNSNzeiVBgRLdyCE8Ecx52nr6elEGkw2+qTagtzetJKCDFJdyNEvT7sZO1enYevrQIv0VnWWjw2L3Tx3N/Ibn7wnvJJAnX7gYnZ17Y7elNttDgtdNnsUu9ReOYktJLeyvKuRj5XLbl6djQBp1n3V7JDrWn2i2hkjuElZp8f6raFwOnfP6Uw6HAdIGm/a9R8oHPm/bZfO65/1m7d+vTiqGp6TcXFxpGmxy3405EkM9xFeypKCoUJudWDNnLZznpQM6Kis+90eG+mtpZLm+jNucqILuSNW5H3wpAfp3z39aWbSYZtVi1Frm9WSMYESXciwnr1jB2nr3Hp6UCL9V7//AJB11/1yf+RqCPSYYtWk1EXN6ZJBgxNdyGEcAcRk7R07D1qu2kw2MGpXEdzfSNcRuWWe7kkReCfkViQvXtimtwZ1Nl/x4W//AFyX+QrBtv8Akp2qf9gaz/8AR1zVrSNCgtNCa0S71J0uYhuea+lkkTK4+R2YlP8AgJGKydD06PS/iFqdrFNdTKNHtDvurh5n5nuf4nJP61yvc3Wx2NFFFIYUUUUAFc14X/5Dni//ALCy/wDpJb10tcZ4c0wS+NPFGo/bLxDFqap9nSYiF/8ARIOWToTz19h6UAb2v/8AHnb/APX1F/6FUdUtX0kW10NQF9fyGa4jH2eWctCmXHKp2PH6mo7LSRZG6Iv7+f7R/wA95y/l9fuf3ev6Cuin8JjP4jRorMttGFtpk9l/aOoyCUk+fLcFpUyAPlbt0/U0HRgdIXTv7R1EYOftAuP3x5zjfj8PpVEmnWZ4fubK70aObT7c29sZJQsZAGCJGDHgnqwJ/Gi70b7XYW9p/aWow+SAPOhn2yPgY+Zsc1R0+3OuWNlfs99ppikkH2aCbar7ZCMuMDOduf8AgVAzoaKoS6X5urRX/wBuvU8sY+zpLiFuvJXHPX9BRFpnlatLf/b71/MGPs7y5hXpyFxx09e5oEX6jtv+Ritv+vWb/wBCjqnZ6V9juLmX+0L6fzznZPNuWPkn5Bjjr+lV9N0Y22pfZP7T1Gbz7WcefLOGkjyYx8pxx60PZjW51twyJbStIu5AhLD1GOapaBPZ3XhzS7jT4Db2UtpE9vCQAY4ygKrgZAwMCq8+jmHw7NY/2lqchAL/AGgzjzzg5xu29OMdOlVdO09tY8G6MpvNT09mtopj5U4Eq5TOxm284zjoOg6Vym5Pff8AIwj/AK9B/wChmn1ma1pZvPEFpjUb6DyLUH9xKF835/4+Dnp7dTUk+mGfVIb0ahexCIAfZ45AIn68suMnr69hXTH4UYPdl+iqC6Yy6u+of2hekMMfZTIPJHAGQuM9s9epos9Ma0vrm5OoXs4mJIhmkDRx5OcKABj06nimIZr95Bp+iz3Nxai6iRkBhIBDZdQOvoSD+FaVc7e2V7o3h+++x3epajcy7VTznDum5gpKbVGMAk9D0q/Ho7ppElh/auosznP2ppV85eQcA7cdvTuaBmnRWZNo7zaVFYjVdRjaM5NykiiV+vDHbjv6dhS3ukveRWqLqmoW/kDloJFBl6ffypz07Y6mgRpU7Qfu6h/19t/6AlZ11pr3OowXY1G9hWLGbeJ1EcmDn5gVJPpwRTtC0xzq19qH9pXoQXLL9kDr5J+RRkjbnP41M/hZUfiF8Yfe8Pf9hm3/AJPXS1xPibS3tdV0e8bU7+dZtZgxbTOpijzuPygKCPTkmu2rnNgooooAKKKKAOa1f/kfvDP/AFwvf5RV0tcb4qtbu88Z+GorLUZLCXyrw+dHEjnGI+MOCK6C+sNQuZLRrbWZrRYv9cqQRv5/TruU46HpjrQBpUVnz2N/Lq0N1Fq80NoigPZrDGVkPPJYjcOo6HtSJY366zJdtq8z2bDC2JhjCKcAZ3Y3HkE8nvQBo0VnWVjf299dTXOrzXcEpJit3hjUQjOcAqATxxyTUdlpup29hdQXOuz3c8qkRXD28StCcYBAVQDg88g0AC3VgfFktoLYjURYpI0+BgxGRgFznP3gT071q1y8UeqC9uNEbULhrv7Ik66wbeIMMyEeXs2bOAv/AI9+Nadxpupy6TBaxa7PDdowL3i28RaQc8FSu0dR0HagDVorMvtP1G5S0Ftrc9o0P+uZIIn8/p13KdvQ9MdafdWN/NqlvcwavNb2sYHmWiwxssvJ6sQWHpwR0oA0KKzxY3/9tG8/teY2e3H2HyY9gOMZ343deetJaWN/BqVzcT6vNc20mfLtWhjVYecjDKAxwOOSaAI9NfTW1nWls42W8WeMXrHOGfykK4ycfc2jjFatct4ejn/tHxLaNqMst1FPHG1y1tCh3GFWDfIo3YDKPmz930rUh03U49IltJNdnlu3bKXpt4g0Y44Chdp6HqO9AGrRWVLpupvo8Vomuzx3itlr0W8Rdxk8FSu0dR0HalvtO1K5tLWK21ye0liAEsyW8TmY4HJDKQO54x1oA1KKz7yxv7jULae31ea1t4iDLbJDGyzc5OWYFhkccEUjWN+daW8GrzCzAwbHyY9hOMZ343deevagCPQLqxu7K5k0+2NvEt9dRyKQBulWZ1kbgn7zhj+Natc54a/tC6uL6/nupo7U3VzBHYNDGqrsmZRIGChjuCluSfvd+taFjp+o232v7TrU9353+p3wRp5HXptUbuo+9np9aANOisq203U4dKntZtdnnupCTHeNbxK0QwOAoXaehPI70NpupnRVsxrs4vA2TffZ4t7DJONm3b046dqANWq2of8AINuv+uL/AMjVO803U7jT7aC312e1uIgBLcpbxM03GCSrKVGTzwBUWu2N/cQebbavNaRRRMZYUhjYTjHQlgSOMjjHWgBngr/kQ/Dv/YMtv/RS1u1heCv+RD8O/wDYMtv/AEUtbtABRRRQAVzXxC/5EDWv+vc/zFdLXM/EMBvh9ranobYjg+4oA6asX/mL3/1j/wDQali8O6dDo8ulJ9r+yytubdfTNJnjpIX3joOh/nWNJ4e0+U3Wlt9r+yxNGy7b2YSZ295A+89T1P8AKuXE/Y9f0ZvQ3fp+qNqisy90Gxv7e1gnN3sthiPyr2aM9APmKsC3QfezUl3o1ne6hb30xufPt8eX5d3LGnBz8yKwVvxBrLQ2L9ZmsXsFm+mia0FwZ71IYyQD5TEMQ/I7YP51IdGszrA1Um5+1AbQPtcvl4xj/V7tnT/Z9+tZ91Zf2VqsF/YW93PNe3Kw3O+5mkjjjYElwhYouCo5AHX3poTN+is2y0Oy09bpYGvCLr/WebezS46/d3MdnU/dx29BSQaBZW2lzadG979nmJZi99M8mTjpIXLr0HQjv6mloPU06Ky20CyfR00syXv2ZDuDC+mEuck8y79569z7UXmgWd9Y21nNJfLFbgBDFfTRucDHzOrBm/4ET60aBqbGh/8AIJi/35P/AENqjv76C313SLSS0Es1yZhFMQMw7UyccdxxxVKx0W11C2sbuaS8WS2kcoIbyWJDiQn5kRgr9P4gfSjULJl8Y6TdxQ30gkEqzOt1MIYgqHbmMN5fJOMkfqK6cN/Bh6L8jlq/G/U6GuL0T/kB2X/XFf5V0kGjW1vq82ppLemeYFWR7yV4h0+7GWKL07Ad/U1xnhrSbez0mOWOW7ZrmJS4lu5JAvH8IZiE6/w47elVPdHRhvhl8v1N6is220S1tdPnso575ops7mkvZXkGRj5XZiy/gRR/Ylt/ZP8AZvn3/k5zv+2y+b1z/rN2/wDXpxUG+ppVQS8kbxBPZG1IjjtY5Rc/3izuCnTttB6/xUy50S2utPgsnuL9Y4cbXivpUkOBj5nVgzfiTVS506e+8Rqs5u47C1tongkgu5It8pd96uFYBwAsf3gep9aBO5u0VRm0qGbVItQae8WWMYEaXcixHr1jDbT17j09KE0qGPVZNRE96ZXGDG13IYRwBxGW2jp2HrQPUvVS1b/kGyf7yf8AoQpLPSobK6uLiO4vZGnJLLNdySIvOflViQvXsBWddaLBY6Fd20dzfyJMyhmnvZZXX5gPlZmJX8CKGON7o9ErmvDv/I1+L/8Ar+g/9JYa0F0C3TRX0sXmpmJ23GY38xnHIPEu7eBx69MisfwjapY6/wCKraOSaRY7yBQ88rSOf9Fh6sxJJ+prc8w62iiigAooooA5rxb/AMf3hf8A7DKf+iZq6Wua8W/8f3hf/sMp/wCiZq6WgClc6tYWkxhnuo0kAyU6kfXHSov+Eg0r/n8X/vk/4Vm6eMpcOfvPdTFj64kYD9ABVuuj2UTLnZONf0okD7bGM92yB+ZrSBBGRyKxWUMpVgCCMEHvVjQCToNnkk4jwM+gOBUTgkrocZNuzFub26i13T7KO0Z7a4imeW4AOImTZtBPQbtzdf7taNZ9y+pDXrBII1OmtDMbpzjKuNnlgc55y/QdquykrC5BwQpIrI0Kcut6bBK0T3kYdDhgMnB9Dimf8JBpX/P4v/fJ/wAKzNKULpFngYzCjH3JGSfzq5XR7KKMfaMsxa3pk0qxJeR72OFByMn05rQrm9TVX0q7DAEeSx/ECt+3YtbRMxySgJP4VnOCjqi4yb3KMV8r+KLvT/sW1orKGY3X98M8q7On8OzPX+PoO+n0rPiur5vEV1aPbAaelpDJFPg5aVnkDrnOOAqHp/FSa8Sug32CRmIjj3qEruxTdlcQ6/pQJH22M44yuSPzFH/CQaV/z+L/AN8n/Cq6qqKFUAKBgAdAKWt/ZxMudly21fT7uYQwXUbSHkJ0J+metXa5rURiCJx95LiEqfQ+Yo/kTXS1nOCjsXCTe5nabe3V3earFcWjQR2t0IYHII85PKjfeM9fmZl44+Wr8kiRRtJI6oijLMxwAPeqOmvqT3eqC/jVIFugLIjHzQ+VGcnB67zIOcdKh8QDdZQIeVe5iDDsRnP9KmKu7FN2VyT/AISDSv8An8Q/QH/Cj/hINK/5/F/75P8AhUFFbeziZc7L9pqVlfOyW1ykjqMlQeQPXFWq55xjVtLccN5zLn2Mbkj9B+VdDWU4qL0NIyujmvAP/Iow/wDX1d/+lMtdLXNeAf8AkUYf+vq7/wDSmWulqCgooooAKoa5/wAi/qX/AF6y/wDoBq/VDXP+Rf1L/r1l/wDQDQBB4W/5FDRf+vCD/wBFrUF7x4i+toMf99n/ABqfwt/yKGi/9eEH/otaZrS+Td2V7/ACYJD6B8bT/wB9AD/gVaUviInsFFFFbmQVQ1qPUJdIuE0qRYr4geU7YwDkZ6g9s1fqhrdnPqGj3Fra3RtZpAAswJBTkHtz2xSAv0UUUwCiiigAqhqZ1EGy/s9VYG6QXOccQ4O4jP4dOav1Q1O3vrhrL7FcCER3SST5JG+IA5Xp3yPypAX6KKKYBRRRQAVQuY9QbWbB4JFWxVJRcocZZjt2Y47fN+dX6oXNnPLrVhdpdGOCBJVkgycSltu0+nGD+dIC/RRRTAKragQNNuieghf+RqzVPUFNxHHZL9+6cRf8B6sf++QaFuDN+zBWygB6iNR+lYFt/wAlO1T/ALA1n/6Oua6Wuatv+Snap/2BrP8A9HXNcjOg6WiiigAooooAK5rwv/yHPF//AGFl/wDSS3rpa5rwv/yHPF//AGFl/wDSS3oA0Nf/AOPO3/6+ov8A0Ko6k1//AI87f/r6i/8AQqjrop/CYz+IKKKKskKoaNcX11pccupQLBdFnDRqMAAOQvc9VAP41fqhoy6imlxrqrq95ufey4xjedvQAfd20gL9FFFMAqO2/wCRitv+vWb/ANCjqSo7b/kYrb/r1m/9CjpPZjW5sztIlvK0S7pAhKKe5xwKraRNeXOi2E+oQrBfSW8b3ESjASQqCygZPQ5HU1ZuBKbaUQECXYdhP97HH61X0hb5dFsF1N1fUBbxi5ZcYMu0byMcdc9K5Tcz77/kYR/16D/0M0+mX3/Iwj/r0H/oZp9dUfhRg92FFFFMRQ1ltRXS5TpSq15uTYGxjG8buvH3c1fqhrNvfXWlyQ6dcLb3RZCsjEgABwW6A9VBH41fpAFFFFMAp2g/d1D/AK+2/wDQEptO0H7uof8AX23/AKAlRP4WVD4ij4w+94e/7DNv/J66Wua8Yfe8Pf8AYZt/5PXS1zmwUUUUAFFFFAHNav8A8j94Z/64Xv8AKKulrmtX/wCR+8M/9cL3+UVdIehoA5m9+IPhnT7yW1m1FmliYq4igkkCkdRlVIz+NQf8LN8K/wDP9P8A+AU3/wARXiVmSbKEk5JQEn1NT10+xiaciParf4jeFrm4SBdRZGc7VMtvJGufdmUAfia6qvme4GbaUH+4f5V9F6OxfQ9PZiSxtoySe52is6kFHYmUbEa3GoHxHLbNbqNNFokiz4OTKXYMuc9AoU9O/WtBmCqWYgKBkk9qz1XUv+EjldpF/sr7IgROM+dvbcemcbdvfFVvF7FfButFSQfsUvI/3TWTJKMnxD8MJIyC/kfBxujtpWU/QhcH8Kb/AMLG8M/8/k//AIBzf/E15IAAAB0FLWHtWY+0Z7Jp3jbw/ql6lpbXx8+Q4RZYXj3H0BYAE+1dBXz9CSLy1YHBW4iIPod4r6BrSEnLc0jK5nWF7dXOp6rbz2jQw2syRwSkECZTEjFhng4ZmXj+7Vy4uIbS3kuLiVYoY13O7nAAqpYPqTalqq3karaJOgsmGMvH5SFicH+/vHOOlY/xAJ/4ROUdjPECPX5xSrVPZ05T7I3oU/a1Y031aQ7/AIT7w5ni8mPuLWX/AOJo/wCE/wDDv/P3N/4CS/8AxNeXUV81/btf+Vfj/mfUf2Dhv5pfh/kewaX4o0fWbg29ld7pgN3lvG0ZI9QGAz+Fa9eN+GiR4r0ojg+eRn6o1eyV7WX4uWKpc8lZ3seHmWDhhaqhB3TV9TP0a41C6s5X1K3WCZbqeNFUEZiWVljbknqgU/j26VYvb+2062NxdyiOIEDJBJJPQADkn2FV9GXUkspRqsivcfapyhXHEJlbyhwB0j2D19eaz/EPOp6KDyPOkbHuI25/U12ydlc4qcVKVmS/8JVpv9y//wDACf8A+Io/4SrTf7l//wCAE/8A8RTaKz55HR7Kn2f3/wDAL+n6xY6m0i2srGSPBeOSNo3UHodrAHHvUuof8g26/wCuL/yNYUHHi6xI4LWdwD7gNFit3UP+Qbdf9cX/AJGtIttamFWCg9DL8Ff8iH4d/wCwZbf+ilrdrC8Ff8iH4d/7Blt/6KWt2qMgooooAK5r4hf8iBrX/Xuf5iulrmviF/yIGtf9e5/mKAOlrF/5i9/9Y/8A0GtqsX/mL3/1j/8AQa5cV9j1/Rm9Dd+n6omooorI2CqGptqIew/s9VZTdKLrOOIcNkjPvt6c1fqhqdvfXElgbK4WFYrpZLgEkeZEFYFenqV/KhAy/RRRQAUUUUASaH/yCYv9+T/0NqS8bUhrGmi1RTYHzfthOMj5fkxnn73pS6H/AMgmL/fk/wDQ2pLy2v5dY02e3uFjs4fN+1REkGTK4TAxzg89q6cN/Bh6L8jlq/G/U0a4vRP+QHZf9cV/lXaVxeif8gOy/wCuK/yqp7o6MN8Mvl+pfoooqDoCqMcd+NeuJHkU6cbaNYo+MiUM+89M8qY+/ar1UI7OddeuL03RNvJaxRLb5OEZWkJfHTkMo/4DQJl+iiigYVS1b/kGyf7yf+hCrtUtW/5Bsn+8n/oQpMcd0dlXNeHf+Rr8X/8AX9B/6Sw10tc14d/5Gvxf/wBf0H/pLDXQeWdLRRRQAUUUUAc14t/4/vC//YZT/wBEzV0tc14t/wCP7wv/ANhlP/RM1dLQBzen/wCom/6+Z/8A0a1W6pKZNOlngntrkgzSSI8ULSKysxYcqDg84wfSnf2hH/z73v8A4By//E1176o5y3U/h/8A5AVp/un+ZrNN+CMJaXzt2X7LIM/iQAK19JtpLPSraCXAkRBuAOcHqRWdX4S4bkdzBqD69YTwzqunxwzLcxE8u5KeWRx2w/cde9XZ/wDj3k/3T/KqNzYvL4h06+F4Y0t4J42tv+epcx4br/DtPY/e7d9B13xsucbgRWBqc7pf/IJsv+uCf+girdZ9rNJY2kNrc2l2ssKCMlLd3VsDGQVBGDUv9oR/8+97/wCAcv8A8TXY9zmQ7Uf+QZd/9cX/APQTW7a/8ecP/XNf5VzdzO95ay21vaXbSyoUXfbuijIxklgABXTRJ5cKR5ztUDP0rKrsjSnuUohqX/CRXRkK/wBlfZYfIHGfO3SeZ7/d8r2/Wm+IP+QBe/8AXI06K0vE8RXd491usZLWGKK33H5JFaQu2OnIZB/wGpNVtnvNKureLHmSRkLnoT2rKOkkaS2ZSoqoL8AYktL1H7r9lkbB+oBB/Cj+0I/+fe9/8A5f/ia6bGAal/x6r/13h/8ARi10lcw7Sai0VvBbXIzLG7vLC0aqqsGPLAZ6YwK6esqvRGlPqZ+mwahDd6o97OssMt3vtFBz5cPlRjaeOu8SHv1qLX/+PW1/6+o/50/S7F7O71aV7w3AurzzlT/ngPKjTZ1PdC3b73TuV1q3lnsVMKGR4pUl2DqwB5A98VnD4kVL4WV6Kqf2gne2vQfT7JL/APE0f2hH/wA+97/4By//ABNdNjEfL/yE9L/6+D/6KeuhrnbYSX2p2bx286RW7tI8ksTRj7jKAAwBJ+b9K6Ksau6NKexzXgH/AJFGH/r6u/8A0plrpa5rwD/yKMP/AF9Xf/pTLXS1kaBRRRQAVQ1z/kX9S/69Zf8A0A1fqhrn/Iv6l/16y/8AoBoAg8Lf8ihov/XhB/6LWtG5t4ru2kt5l3RyKVYVneFv+RQ0X/rwg/8ARa1rUAc5G8tnOLG9b97/AMspT0mX1/3vUfjVqtO5tYLyAw3ESyRnsf5j0PvWU+kXtv8A8ed4ssfaO6BJH/Axz+YNdEaie5i4NbDqzPENpa3+hXVte3QtbeQLvmJAC/MCOTx1AH41e8rVxwbG3Putzx+q1l+IopDoNz/a9nGlj8vmstwcj5hjopPXFVddxWZs0VF5Wrf9A+H/AMCf/saPK1b/AKB8P/gT/wDY0XXcVmS0VF5Wrf8AQPh/8Cf/ALGjytW/6B8P/gT/APY0XXcLMlrO1ay+2Np5+2fZvIu0mx/z1wG+TqOuffp0q55Wrf8AQPh/8Cf/ALGsvWbSed9N+3W8cJjvUe3AugPMlAbC/dPbP5UXXcdmbNFReVq3/QPh/wDAn/7GjytW/wCgfD/4E/8A2NF13FZktFR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"
+            "/page/20/Figure/4": 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siE41tbhNl1575RfMYBVGcADGeB1rsV02z8efGO803xSZJ7Sy0uKS0tWlZFZmVCzjaQc/MT/+qvT5fAvhufwtF4Zk07do8TBkt/PkGCCW+9u3dSe9QeIPh14X8TTW0+p6bvmtkEcckcrxtsH8JKkZH1oA4f4GQQ2t94zt7adp4ItSCRys24uo3gEnv9al8bSxw/HnwXJLIsaLbyZZjgD7/evQvD3hLQvCouhomnrZrdOHmVZHYMR0wGJx16DFVPEvgDwx4vuobnXdM+1zQp5cbefJHtXOcYRh3oA8+8dX1prfxk8E2mkzxXF3aSNLcPCwby48g4JHsrHHv71wHiK0tNZ0fxR4kstJnu0jvCBreo6jskjYMPkihUdORjPb6V9B+HPAnhjwlJJLomkRWssg2tJvaRyPTc5JA9qypPhF4Hmvbu6l0RXa63GRDNIEBPUqobCn3HTtigDzC70XTdW8c/DmbUYPPfVNNSS8d5GzMyxjaSc9eB0qn4rs7LxFqvjTUrXR5tT+wFo5NR1DUfJSzZQflijUZbpxk9vevatQ+HfhTVbPTrS+0kTRaagS0zPIGjUYwNwbJHA6k1VvPhV4Mv8AWLjVLrRkkuLjJlBlcIzHq20HGff1560AO+FdzPefDHQZrmV5ZTbkF3OSQGIHP0ArsKoaNo1h4f0qDTNMgMFnACI4zIz7QTnqxJ6n1q/QAUUUUAFFFFABRRRQAUUUUAFfOvg3TddvvEfi9tI8ZxeH0XVHEkbwJJ5p3Ng/MRjFfRVcNefB3wHf3s95c6F5k88jSSP9rnG5ick4D4HNAGN4x0S2uPhFdp4l8RRahd2O6aLVI0VCJc/IAqnvkLjvmuQ+GUi+JLXxR4h1u8a58RQWDW6RSrtMMPlYDgf7Xr/jXqKfC3wbHo66QukEaeLj7T5H2ubaZMY3H5+eB0PFWNT8EaNJc6hrFlp4TWZrJ7ZZY5nQOChUKVDBD26jsPSgDwqy8MaE3wGbxS1xKNctpD5FwLlgYmEuBGq5wODnpnnNWrmTWPG/jexsdU0lda8nRoJo7C4vzaIWaNS0mR1bJP8AkV3Hgn4M6LH4c01/FGiqdXgZjKguCUf5yVLBW2txj+RrtfEnw/8ADPitoJNV01XmgXZHLE7ROq/3cqRkexoA8O1W31TT/grqtheXlrLDBq0QtUt75Lr7OpJzGWU8YPY4rU+JXgHRfDnw80+7hE9xez38TTXNxMWZi6/P7c7R+Veun4feFm8Mr4d/slBpSyCXyFkdSzj+IsGDE/U1o614b0jxFpH9latZLc2QKkRszDBHQgggg/jQA+xtbHQvDsdvaJ9nsrWA7ApLbFAzxnJNfL2tWkMvg2fxTp+kzrvv8xa7falm6lbd0WJcAfjX1NpukWOkaRBpVjbiOxgj8uOIsXAX0yxJP41yK/BzwGPtAbQ1dZzkq1xLhOc/J83y9O306UAcPNoVl4s+OC2WsCSe1fQ4ZZYhKyCU4X7xUgkZOceoFck19faB8PfGuk6bczxWUGuJaLhyTFCxYMAe2dqg/X3r6HtfCmi2euLrUFmV1FbZbQTmZ2PlDGFwWx2HOM+9V4vA/huG11a2XS0MOrSGW9R5HcStyc8k7eT2xQB5B4g8IeHfC3i/4cnQmZDdX8LSp57OJcMmJcEnBOT04r1f4k/8k28Q/wDXjJ/KqemfCbwXpN1a3VtpB+02syzwSvcysyMpyv8AFyB6Hiur1LTrTV9NuNOvovNtLlDHLHuK7lPUZBBH4UAeWaVrmkaf+zzEt9eWwLaXJEITINzO24BQvXOSK4mC2SXwL4D8P31lf6jd3bzXEGlpci3imQuSDI5GQMcjHqa9Yt/g18P7WdJo/DsZdDkCS5mdfxVnIP4itnxJ4G8O+LIrWPVrASfZP9Q0btGyD0BUjjgcUAeE6NpcRsviTo11ZxQWtlbC5isYbp5YreZQ2NrE5JHerz2mjWnwh8Jaeltey3Gs3Su9naXIiF5IOMSO2dq/d6V7Jpnw+8K6Ob37BpEcK3sAt7lBI5SSPGMFS2PxxnrzVGT4T+Cn0U6SujBLTz/tAC3Em5ZMAEhixI4A46UAeffDC2m0X4x6zpKWEOlQfYBI+n2921xHG2Uxlj1bk/TNe61zWh+APDHhvUl1DSdLFrdiHyDIs0h3JnJ3AtgnPcjNdLQAUUUUAFFFFABRRRQB4f8AEi3vbr43eHodO1hdIumsG2XrRq4j5fPB45HH412fh3QtSaPUbPxL4zt/EVndW5jNv5Ecewd2ypPb8q1vEnw98LeLr2O81zS/tdxFH5aP9oljwuScYRgOpNUtP+E3gnSpJnsdGaB54WgkZbyfJRvvDO/jPtQB4r4ISx1b4g2HhfUNWe68OabdTyaUsi4S5cEYUnuO/wD+uum0XwroPiv4n+PbfXi0kUEweODz2jUdQZMAjJX34Ga9Pb4ceEm0/TrH+yFSDTZDLaeXPIjRMTkneGDHn1JrjLP4Twax408VXvifTN9ld3CyWMqXG1yOd33WyAeODQB5odc1mT4V6fpC3M02nya69mHaby/MhABWMv2Ukn6fhXVaLoOqeHPFl6lvY2Gh2NxpcwuNLj1hbl3xGxWQITu6gc49a9efwT4bk8ML4cfSYDpK/dt+eD/eDZ3bvfOaqaD8OPC3hsXR07Tdsl1GYZZZJXdyh4KhicgfTFAHkngbwJpGo/CG98Q6j9ours2VykSPKRHEqEsAoH+0u6u2+DMejaL8M9OvGlt7Sa/ZjK8s23zXDMB944zgdBXdaV4b0jRNC/sTT7NYtNw6+Qzs4IbO4EsSTnJ71yOp/CXw/wD2bb6fo2lw2kZvo7uSRrmUmMr1KqSQSR8uDgDOe1AHcar/AMgi9/64Sf8AoJr5d8M6o3g/w9Ya6mVXUdOv7Ekf89Vbcn6kV9VSxJPC8Ug3I6lWGcZB4Ncs/wANPCEmiWmjvo4awtJmngiNxL8jnqd27J+hOKAPENLS+8H6j4qXT9yXtp4bgO5eqMwiLt9RuY/hVrWfDmi+HvhPofjLR7mWPxG8kMv2xbhi80jH51Izjjnt25r3iPwpocWt3usLYKb69hFvcSM7MJIwANpQnbjAHQVg2Xwj8E6fqqajBow82N/MjR5naNG9QhOKAOGGnWvj34y3uneLFeS3s9MiktbIytGu5lQswwQc5Y/l7VwgvLjRPBXjW30i8laCTW4rVrkSfM0X7zq/vgAn3r6E8S/D7wz4tvIbzV9O825hG1ZY5WjYr/dJUjIpbL4feFNO03UNOtdGhSy1AhrmBndlcjpgEnbj2xQB5T4a8Oan4d8faFPYadp2g288Wy5tRraztexkffCNgk9+P0rqPgN/yKGqf9hWb+S11Hh34a+FPC1+b/S9M2XW0osssryFFPZdxOPw5rX0Hw3pPhizltNHtPs0EsrTOvmO+XPU5Yk9qAPKPH1rZ+JfigdLTSLjXbmzsd72txfi1tLcHnfkDcx5GcH09K4FZp5vgc1u87bIfEaxRbZCwjXbnCk9skkV9A698OvC3iXWI9V1XTBNdooUuJXQOo6BgpAYfWmD4aeEF0mTSl0gLYyXQu2hW4lC+aBgMPm4+g49qAPOb3w9Y+CfjF4SGgpNCb63l+1AzO5nYKeW3E5JNc9o+gaT4m+G/izxdrdxK3iCOaf/AEg3DK0OANqYzjBzjBHTgV77e+GdH1DW9P1m6s/M1DTwRay+a48sHrwDg/iDWDe/CfwVqGrz6lc6MrTXBJlUTOqOx6ttBAzz19eetAB8JP8Aklfh/wD64H/0Nq5PwBeW1p8WfiC1zcQwqbhMGRwueW9a9T0jSbHQtKt9M02DyLO3XbFHvZtoznqxJPJ7muX1P4S+B9Z1O41K/wBE867uXMksn2qZdzHvgOAPwFAHmMGrWUnjP4k+Iba7uI9HWyELXVkRuaQlVyhPGSQ3PvmsG2006Hr3gS/s9EGji9ukxOdSM9xeRsVyzqMKoIPQDvX0La+DvD1l4em0C20q3j0udSssAB+fPcnOSffOeKxLH4R+CdPaB4NHPm28yzRStcy70YdMHdnA9OlAHnfhbSdC0T4gfEG7MT2yaREWtpIWJeBWRtxUE8n65rir2xGnaJ4f8Q6do8lgbjUFaHVbjUzLdXWWOSY1AVRxz3/OvoybwD4XuNfm12XSIm1GdGjll3vh1ZdrArnacjjpWRB8HPAkEbINE3AuJFLXMu5CDkBTuyB9OvegDux0opAAqgDOAMcnNLQAUUUUAFFFFABUN3I0NnPKn3kjZhn1Aqaq2of8g26/64v/ACNAHiWgeLfilr/hK78T2V9oRtLQyb4JoWV2CDJxgY6e4rttK+LGhnwNpfiDXp0sJb3cggRWkLOpw20AE4/x615t8Lfh0PF3giWa48S63Z2kl1JFJY2lxthcDGSVORk59K2PHIsvBOveE9Fsng0TTbeCQprMtkLqWI5OVXIOCTyTj+KgD0ez+I/hO/8AD93rkGrKbCzIFy5hcNEScDKbd36U2H4l+EJ7PULyPWYja6eENxNscKu/7oHGWPHQZNeD28wk8L/E8m6uLhpGt5BLdR+XLKDIfnK4GM5B6dxXceLvs/h74J6A9jpFiUmFr580tqJVhymTKVx8xye+etAHoXh34jeF/Fd5JZaRqXmXaIX8mSF42KjuNwGfwrzc/FLxKPhtNrnm2321db+xA+SNvlY6Y9fes3wvqEd98cNIuV1651qN9PlRb2e1FsrkK3yxrgfKP55rnW/5Ircf9jR/SgD33xD8QvDPhSSC31nUhFcyoHEMcbSMF9SFBwPrWB44+IT2fhnQ9Z8L3tvcW9/qEduZSm4FDnIweQeO/NcF4heXw98XNXvtV8QXWgW19aJ9lvUshcLKoVQY+QcdO3pzWVf6da2Pw10aawl1GS0u/EUc0bX1ssBb5cFlVWI2nHt9KAPaLfxHOPiRqulXGsaf9gtbIT/ZPKdZouFJdnK7NvJ6MetJpnxW8F6vq8el2esq1zK+yLfC6JI3orFQD/WuE1COKb4yeNo57a5uYX0Ha8NtjzXUqmQue/pXEaZrf2VvDthpGrReIrdLtRFoeoaZie0OeSHAIyPUN+FAHuerfFTwbol3d2l7q+26tJBHNCtvIzK34LyPccU1Piz4Il1C1so9dieW62+WwifZlugLYwp56Hp3xXCaNGj+Lvi2XRWP2dhyM8bH/wAK5e/ijX9njwqyooY6qDkDnO+TmgD6Xopsf+qT6CnUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABXkereJvHWpfFPU/C3hu+0u2itLdJwbyEnghc8gE5y3pXrleD3vh2XxL+0Dr1nDrWo6SyWUcnn2EpjdvljG0kduf0oA6vwb8Q9Q+2eItK8aNY29zoQDzXlvkRMh46Hv06dc9M1vaB8T/CPiXVF03TNU33bgmOOSF4/MA/ulgAf51xfinwdYfDr4b65eafbSaze3jxm6n1IeeSN33iMAEAnPPfrmuQ0zUor/wCJ3gadPEM+sRJmLzmshawQtt/1UYCgHGRnr1FAHsyfEvwjLqyaXHq6vfPdG0EIicESDqDlcYHr096pr8XvA76oNPGtr5hk8oSeTJ5Rbpjft2/jnHvXF/DazthcfEPUv7NhvL2C/mEQeMMzY3kKD1GT6V5nq2tLqXw+t4/7YIkjvBJJotnpogt7MbiNzOF5J4xk9/agD3S+8Y6tB8UtQ0CN4fsEGjteIPLBbzAMg59Pajwb8RIpfhnb+J/Fl/Bbl5ZI2kVCAxDEAKoyScDtXNXTB/jbqLKQVPhhiCO421wf2O7b4UeC9TV5otPsdSma6nihEpgy4xIVPBxg9f60Ae52/wARdD1/w7rF34e1FJruxtZJjFJGyMhCkglWAJGRXMWPjzWbrwT4R1WfV9OsrrU7wxT+fbuwmG8jagRW2tgdTge9cvpSadrGt+ItZsvEOq65PDo08c962npBbsDGcKWBBLD/AHe3Xisxv+SZ/DH/ALC//tQ0Aey658UPCHhzVW0zUtWCXUePNSOF5BFnpuKggfSruqeO/DGjWun3V/q8MVvqClrWUKzrIAAc5UEDqOuK8P8AFV/Z6N4s8VtY6/c6Hd3DE3Om6nYCeG/6/cYBvlPbI/i61oX80t9b/CeS70uHT2e8b/RY49iAb1wQp6A9ce9AHoy/GXwI1nLc/wBtYET7DGbeQSE+oXbkjjr0Heut0bWdP8QaVDqel3S3NnMMpIoIzjggg8g+xryzSYIj8b/HhMSH/iWL1Ud0TP51qfAT/kmUQ/6e5v5igD06iiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKbISsbMOoBNOpkv+pf/dNAHh/h3xX8UvFGiajrWm3+hi2sppEMM8JV32DdxgY6H1FdfoHxZ0ifwDY+IvEM0enyTyPAYo1Z98innYoycdD7Z615x8L/AAA3i7w5qcsniXWrC2N9JDJaWdxsikGBklehJzjp2ra8eW1j4Dn8IaLp4h0vTomkb+2bi0F1JbtkE7QQRuJ56emMYoA9K0n4h+F9a0i+1Oy1MPbWCb7rdE6vEvqVIz2PQGorL4l+ENRF01rrMTx2lsLqeQo6rHGSAMkgc8jjrz0rxfQbhZpPifJ9uubwzaZ5iXF1F5Uk64Pz7MDAOQRx0IrogkGh/s32t9ZaLZ3U00CGbzbcODmTl3H8WOOvHSgD0PQPif4R8S6oum6Zqm+7cExxyQvH5gH90sAD/OvP5/if4kj8BeItYWW2+12OsizhPkjAjzjkdz71zmmalFf/ABO8DTp4hn1iJMxec1kLWCFtv+qjAUA4yM9eoqhdf8km8Y/9jIP/AEKgD3fWviD4d8L21l/bmorDc3MSyLDHG0jkY5O1QcDr1rA8c/EUWvgOz8Q+FL63uI572ODzCm4YOcgqcEHgda4PxGZtC+Kg1TUtdudBs7vTIltdRjsxcKcIoaPkHHQ9P61j6pYWdt8J5rzT7jUp7W916KVZL21SAOcNlkVWPyn8OnSgD2uHxDdt8TW0Z9WsfsgsBP8AYfJkE4bj59+3Zt9t2famR/FrwRLq40xNbTzjJ5SuYnERfpgPjb+Oce9cjKA3x/vg0UkwPh4gxx/efgcD3NeaLq0Ol6PBYaNq/wDaUAvePC+r6XulR9x6MARn6FetAH0FrfxL8JeHr+5sNS1URXlsqtJAIJGbBGRjC4PB7HiqTfGHwIptf+J6hFyAVIhkITJx8/y/KfY/XpXLaGom+PHiVpoFRzo0eUPOzKx5H9K4a2hjH7NOqsI13f2tnOOfvIP5UAfTSsrqGUgqRkEdCKWs/Qf+Rd0z/r0i/wDQBWhQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAVieL9TudG8H6vqdmVFza2ryxlhkbgOMituuY+Iv/JOfEP8A14y/+g0AeZR+Mfihb+BYvG0l1oNzpmwSvbGJlk27tvYAdfQ131z8VPDGmaRpV5qt6babUbZLlLZI2lcKwzyFBwOvJ64rgPAvwqg8UeAtIudT8S661jOm86clziAYY8BSCAOKTxXqkGhfE5NLW7j8NWdtpqRW99BpwuLi5XAAjViDj049PegD1D/hYPhb/hGV8Rf2sh0oyCLzxE5w5/hKhdwP1FQH4m+EBpV5qf8AbEf2K0n+zySiNyGkxnaoxlvwzXgcRB+A+txMX3rr67lk4ccD7w7HrXo3xOktvDfhvwtBZWFhZWj3Me+/exWYWeFGHCYwW69R2oA663+JGg6/oWsT+HtQWW9sbSScxSxMjLhSQdrAZGcdK5C1+JHiGbRvAN08lv5ut3jw3mIRgqHCjb6cVyei3iXfj3xZcDVrnVFn8OzGO9uYBAZ8KOVTA+XggcdqbYf8i18JP+wlJ/6NFAHsmu/E7wh4b1Q6bqerLHdLjzEjieTy89NxUED6dazPEvjW7tfF/gu00i5t5dM1qRvNcKH3p8uCp7da8nmdfD3iTxnZeIPEl7oj3lw8ghj05bj7fExbG1mHofUDnrxWvDpselat8KraE33k/aJnjF9EI5QrMCMqGIHXjmgD0rQ/F3/E18Vtq+u6dJYaVMAFiikRrVctxIWUBjwPuk1b8P8AxL8J+J9S/s7S9U8y7ILJFJC8ZkA7ruAz/OvJTDaTj4rx31tfT2xvoy4sQDKmHY7gDwQCMn2qPwhrs0vj3wzZ2mrWviy2VdiPLpxiuNOjx/fx2/3iOPcUAeqX/wAXfA+nSyxT61+9hmaCVFt5SUYHBz8vT36elWLT4oeDb/XIdHtdailu5yFi2xvsdj0UPjbn8fbrXlWkRRt4H+LLMik/a5eSPQsR+tGrxRx+DfhKURVP2uLkDHUqT+tAH0JRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFeM2PiX4j+J/FniTT9A1DRoINJujEFu4TkrlgvIBz92vZq+ffC3hCbxV8QPHKx+IdW0lYb8hhp85j80Fn+964x+poA7fwd8U4rvwrqWoeLXttPn0u7NpcSRBikjdtqjJJ4PAz0zXReHfiJ4X8Uyzw6VqXmTwIZJIpInjcIP4gGAyPpXnXjzw/p/w38C6dYaXapLBPqSvc6jfwC5aBsf60rjBPpx+prI8K3yXvxkmuRrdzq6TaNKEvri3EHnYH8CYHyjBHTsaAPWdN+JnhDWL2G0sNZjmlljklA8t0CogJYsWA29D1qtp3xa8Fapq0em2usgzyv5cReCREkbpgMVxXAfDa2t7H4HazqsGkW17fZucq8W4ygDG09yuO31rhrnWI9R03wgw157sQahEZbKHT1trawy2du4KMt179ATQB6nefEDXYb74hxJJBt0KJXssxDgn+9610dn8QtM0vwJomt+JtQjgnv7dH2pGS0jkZO1FBP9K8z1H/kKfGH/AK90rO1OK5sP+Fea5cahcaZpcelLD/aMVsJxbSYPVSCOcjt9OlAHqmv/ABHsbn4b6v4i8KahDcTWaqPnjOY2LAYZGAPQmmQeLNSl1fwXDJq1hANVsxNcWrwSGSdiucxlVKqPqRXmEttYXHgrx7rWn6pqeprcRxRzXlxZJbwzuJFO5NpyT65Udfet1/8AkePhRxn/AIlnQd/3dAHoF38WfBNjrD6Zca0izxyeU7CF2jR/QuBt/XFaOueP/DHhu7jtdV1VIJ5YfPjTynfenqCoIOcdM5r55vdTtdFs9XtNJ1qWJXvS0vhjWdNErSvuHRgCP/QTxXoOXuPjV4HkubJbSU6Jua2A4hbY/wAoB6Y6UAdafjL4DFlFdnWxskYrsFvIXXHUsoXIHPU9e1drZ3lvqFlDeWkyzW86CSORDkMp5BFeC6PBCPDvxfIiTi4lA+UcAFyB+deo/Cv/AJJf4f8A+vUfzNAHYUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFMkijlAEiK4ByAwzg+tPooAKRlV1KuoZSMEEZBpaKAEVVRQqqFUDAAGAKWiigBkkUcy7ZY0dc5wygjNPoooAKjWCFJWlWKNZG+84UAn6mpKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigBCAQQRkHqDSJGkSBI0VFHRVGAKdRQAUxIo4yxRFUscsVGMn1NPooAKCAQQRkHqKKKAGRxRwpsijVFH8KjAp9FFAEckEMrK0kUbsv3SygkfSpKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigApskUcq7ZEV1znDDIzTqKACkIBBBGQeoNLRQA1I0iQJGioo6KowBTqKKAGSRRzJsljV1/usMingADAGAKKKACo/Ih87zvKj83GN+0bvzqSigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigApjRRu6uyKzJypIyR9KfRQAU2SNJUKSIrqeqsMg06igAAAAAGAO1FFFADHhilZWkjRyhypZQcH2p9FFABUccEMJYxRRoWOWKqBn61JRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUANdEkQo6qyngqwyDSgBVCqAAOAB2paKACmxxRxAiNFQE5IUY5p1FABTXRZEKOoZTwVYZBp1FACIixoERQqjgADAFLRRQBGYIWmEzRRmUdHKjI/GpKKKACiiigAooooAKKKKACiiigAooooA5ifxPqTa5qOm6b4dmvlsGjSSYXUcYLOgfADHPAYU7+3fEv/AEJ03/gwh/xo8P8A/I3eLf8Ar7t//SaOuloA5r+3fEv/AEJ03/gwh/xo/t3xL/0J03/gwh/xrpaKAOa/t3xL/wBCdN/4MIf8aP7d8S/9CdN/4MIf8a6WigDkbXxXrl6ZxbeFJJDbymCXGoQ/K4wSOvuKs/274l/6E6b/AMGEP+NaGiS6dK2p/wBnwtGUvpFud2fmmAXcRyeMYrVoA5r+3fEv/QnTf+DCH/Gj+3fEv/QnTf8Agwh/xrpaKAOa/t3xL/0J03/gwh/xo/t3xL/0J03/AIMIf8a6WigDlLbxPrt7bpcWvhRpoXztkj1KAqeccHNTf274l/6E6b/wYQ/41oeGxpo8P2g0dmaww3kls5xuOevPXNatAHNf274l/wChOm/8GEP+NH9u+Jf+hOm/8GEP+NdLRQBzX9u+Jf8AoTpv/BhD/jUN14m1+ytJrq48JSxwQo0kjnUIflUDJPX0FdXVHWpLSLQtQk1CMyWS20jXCL1aMKdwH4ZoAxYvEXiKeFJovCErRyKGVv7Qh5B5B60/+3fEv/QnTf8Agwh/xresTC1hbNbqVgMSmNT2XAwPyqegDmv7d8S/9CdN/wCDCH/Gj+3fEv8A0J03/gwh/wAa6WigDmv7d8S/9CdN/wCDCH/GoJvFer2M9kNQ8Lz21vc3UVr5wvIn2NIwUEgHJGT2rrK5rxr/AMeWkf8AYZsv/Ry0AdLRRRQAUUUUAFc94j1bVbPUdI0/SI7Jp7+SVWe73bUCIW42854xXQ1zWu/8jn4U/wCut1/6JNABjxx/e8PflN/jRjxx/e8PflN/jXS1Wn1GxtpPLnvLeJ+u2SVVP5E00m9gMPHjj+94e/Kb/GjHjj+94e/Kb/Gtj+2dL/6CVn/3/X/GnR6pp80gjiv7V3bgKsykn8M0cr7CujFx44/veHvym/xqjJqnjGPXLfSW/sH7RPby3KEJNt2xsitk565kXHHrXZ1ly30aeKbTTzZhpJbKeYXX9wK8QKdP4t4PX+D8kMzseOP73h78pv8AGjHjj+94e/Kb/GulqpJqmnwyGOW/tUdeCrTKCPwzTSb2C9jFx44/veHvym/xox44/veHvym/xrY/tnS/+glZ/wDf9f8AGpINRsrmTy4Ly3lfrtjlVj+QNHK+wrow8eOP73h78pv8aqQ6j4vn1O606OTw+bm1jjklXZPgLJu289/uNXYVlWl3ZS+JtUtIrXZewwW7Tz7R+8VjJsGepxtbr/epDM/Hjj+94e/Kb/GjHjj+94e/Kb/GulqkdX0xSQdRtAR1BnX/ABppN7CvYx8eOP73h78pv8aMeOP73h78pv8AGtj+2dL/AOglZ/8Af9f8amt720uyRbXUMxXqI5A2Pyo5WugXRg48cf3vD35Tf41R0/VPGOpyXyQf2CDZ3LW0m9Jhlwqscc9MMK7OsvR76O9l1RUsxbG3vWhYj/lsQqnf0HXOO/SkMzseOP73h78pv8aMeOP73h78pv8AGukd1RC7sFUDJJOAKp/2zpf/AEErP/v+v+NNJvYV0jHx44/veHvym/xox44/veHvym/xrY/tnS/+glZ/9/1/xqxBdW90he3nimUHBMbhgPyocWtwujn8eOP73h78pv8AGr/hjVpNd8LaVq00aRy3lrHOyJnCllBwM1rVzXw7/wCSceHP+wdB/wCgCkM6WiiigAooooAy/EuqSaH4X1XVoY1kksrSW4VH6MVUkA47cVmr/wAJwyg58PcjPSb/ABp/xB/5Jz4k/wCwZcf+i2roU/1a/QUAc5jxx/e8PflN/jRjxx/e8PflN/jXQT3NvaoHuJ4oVJwDI4UfrVf+2dL/AOglZ/8Af9f8aaTewrox8eOP73h78pv8aMeOP73h78pv8a2P7Z0v/oJWf/f9f8auI6yIHRgykZBByDQ01uF0zkNR1HxfpdstxdyeH1jaaKAEJOfnkdUUcf7TCrePHH97w9+U3+NaHiG7srLTopb+1+0wteW0SptBxI8yKjc/3WKt+FatIZzWPHH97w9+U3+NGPHH97w9+U3+Nb1xe2toQLm5hhLdPMkC5/Oof7Z0v/oJWf8A3/X/ABp8rfQV0Y+PHH97w9+U3+NGPHH97w9+U3+NbA1jTGIA1G0JPQCdf8au9aGmtwumcfe6j4vsJbOK4k8Pq15P9nhASc5faz49uEarePHH97w9+U3+NaGsXdla3ejpd2vnyXF8IrZtoPlSeVI2/np8qsOP71atIZzWPHH97w9+U3+NGPHH97w9+U3+NdG0saHDuqn3OKb9oh/57R/99Ci4HPY8cf3vD35Tf40Y8cf3vD35Tf410SzRMcLIhPoGFPoA4xtU8YrrkWkH+wftEls9yDsm27FZVPOeuXHb1q9jxx/e8PflN/jWi99GvimHT/sYMj2Ukwuv7oDouzp33A9f4a1KAOax44/veHvym/xox44/veHvym/xrommiQ4aRFPoWApPtEP/AD2j/wC+hRcDnseOP73h78pv8aMeOP73h78pv8a6JZY3OEdWPsc0+gDnvDurateanq+navHZLNYPEFe037XDpu53c5FdDXNaJ/yO3ir/AHrX/wBFV0tABRRRQAUUUUAcsvjRprm8is/DWuXiWtw9s80KQBGdDhtu6UHGfapP+Ervf+hO8Q/9823/AMeo8F/8e2s/9hm8/wDRhrpaAOa/4Su9/wChO8Q/9823/wAeo/4Su9/6E7xD/wB823/x6ulooA5r/hK73/oTvEP/AHzbf/HqP+Ervf8AoTvEP/fNt/8AHq6WigDlLbxrNeQCe28Ka9NESQHj+ysCQSDyJ+xBH4VN/wAJXe/9Cd4h/wC+bb/49Wh4cGmjQ4RpDM1l5kuwtnO7zG39efvbq1aAOa/4Su9/6E7xD/3zbf8Ax6j/AISu9/6E7xD/AN823/x6ulooA5r/AISu9/6E7xD/AN823/x6mt4uu0Rnfwf4gVVGSxFsAB/3+rp6q6mlvLpV5HdyGO2aB1lcHlUKnJ/LNAGEni67kRXTwh4gZGGVZRbEEeo/fU7/AISu9/6E7xD/AN823/x6trS47eHSLKKzkMlqkCLC5OSyBRtP5Yq3QBzX/CV3v/QneIf++bb/AOPUf8JXe/8AQneIf++bb/49XS0UAc1/wld7/wBCd4h/75tv/j1Q3PjWazgae58Ka9DCpALyfZVAycDkz+pArq6yvEg006FONXZlsd0e8rnOfMXb05+9toAz/wDhK73/AKE7xD/3zbf/AB6j/hK73/oTvEP/AHzbf/Hq6WigDmv+Ervf+hO8Q/8AfNt/8eo/4Su9/wChO8Q/9823/wAerpaKAOa/4Su9/wChO8Q/9823/wAeotvF5k1iw0278P6xYPfM6Qy3KQ+WWVGcglJGI4U9q6Wua8Q/8jX4Q/6/p/8A0lmoA6WiiigAooooAK5jWL3XJvFdto+k31lZo1jJdSSXFm05JV1UAASJj73v0rp65qX/AJKda/8AYGm/9HR0AH9m+M/+hm0j/wAEr/8AyRR/ZvjP/oZtI/8ABK//AMkV0tZ8muaZFI0b3sW5ThgDnB9OKaTewm0tzK/s3xn/ANDNpH/glf8A+SKP7N8Z/wDQzaR/4JX/APkitP8A4SDSv+f2P9akg1nTbiZYYryJpG4Vc4J+mafJLsHMu5kf2b4z/wChm0j/AMEr/wDyRVG3fxZc6ve6YniTSxPZpFJIToj7SJN23B+0/wCwc/hXZ1l2d9HN4h1OyWzEb28UDNcf89t+/A6fw7T3P3qkZnf2b4z/AOhm0j/wSv8A/JFH9m+M/wDoZtI/8Er/APyRXSEhQSSABySazjr+lZ/4/oj7g5H501FvYTaW5mf2b4z/AOhm0j/wSv8A/JFH9m+M/wDoZtI/8Er/APyRWn/wkGlf8/sf61Na6rYXkpit7qOSTGdgPOPpTcZLoHMu5jf2b4z/AOhm0j/wSv8A/JFUbB/Fmo3GoQw+JNLDWFz9ml3aI4Bfy0k4/wBJ5GJF/HNdnWXpN9HeXerxpZi3Nre+S7j/AJbnyo239B2YL3+7UjM7+zfGf/QzaR/4JX/+SKP7N8Z/9DNpH/glf/5IrpGYKpZiABySe1Vf7V07/n/tf+/y/wCNJtLcLmL/AGb4z/6GbSP/AASv/wDJFH9m+M/+hm0j/wAEr/8AyRW1/aunf8/9r/3+X/GpobmC5UtBNHKB1KMGx+VCaYro5/8As3xn/wBDNpH/AIJX/wDkis7Q5PF+uaPb6lF4h0qNJgxCPor5GGI/5+Pau2rO0K+uNS0W2u7q0a0nkBLQMCCmGI789Bn8aYzL/s3xn/0M2kf+CV//AJIo/s3xn/0M2kf+CV//AJIro5JY4Yy8rqiDqzHAFVv7V07/AJ/7X/v8v+NJtLcV0Yv9m+M/+hm0j/wSv/8AJFH9m+M/+hm0j/wSv/8AJFbX9q6d/wA/9r/3+X/GrEU0U8YeGRJEPRkYEfpQmnsF0cdrf/CZ6PoOo6p/wkGkTfY7WW48v+x3XfsUtjP2g4zjGcGuvtZGmtIZWxueNWOPUisfxt/yIXiL/sGXP/opq1dP/wCQba/9cU/kKYyxRRRQBzXh/wD5G7xb/wBfdv8A+k0ddLXNeH/+Ru8W/wDX3b/+k0ddLQBBd3kFjbmed9qDgADJY9gB3NY8lzqN/wAmQ2MB6JHgyke7dB9B+dRJIdTuzfvzCpK2qnoF7v8AVv5Y96t10Rgo77mMpN7FM6XaPzKskzf3pZWc/qaT+yNP/wCfVP1q7RWl2RZGBpNtpd8b8RaZHB9nvHgb/powAy345/StL+yNP/59U/Wm6Zd3d2b37VbGDybp4osgjzIxjD8+uT+VX6Sk+47Ipf2Rp/8Az6p+tH9kaf8A8+qfrV2inzMVkUv7I0//AJ9U/Wj+yNP/AOfVP1q7RRzMLIwNBs9Ev9Etbqx08Q20gJSNhgryR2J75rS/sjT/APn1T9aj0K9Oo6LbXbWn2QyKT5H9zkj0H8q0aSk+47Ipf2Rp/wDz6p+tH9kaf/z6p+tXaKfMxWRS/sjT/wDn1T9ap6tY6ZZaPfXcmnxzxwW8kjRf3wqklfxxitmqup3E9rpN5cWsJnuIoHeKIAnzHCkhcDnk4FHM+4WRWtNM06azglWyRFeNWCf3QRnFTDSrJeUiMZ9Y3ZT+YNWLZ3ltYpJU2SMgZl/ukjkVLRdhZFdH1Cy5t7g3UY6w3Byfwfr+ea17DUIdQiZo9yuh2yROMMh9CP696oVVuRJbSrqFspM0Q+dR/wAtU7r9e496iUFL1LUmjo65rxr/AMeWkf8AYZsv/Ry10UMyXEEc0Tbo5FDKR3B5Fc741/48tI/7DNl/6OWuY2OlooooAKKKKACua13/AJHPwp/11uv/AESa6Wua13/kc/Cn/XW6/wDRJoA6WuW0uKN7BJnRWklJd2IyWJJ5NdTXM6R/yCrf/d/rW1LZmVTdFryYv+eaf98iobu1gktJVeJCCh/h9qs1HP8A8e8n+4f5VqmQaemSPNpNnLIxZ3gRmJ7kqM1BJd3q+I7azS2zYPaSyyXG0/LKrxhEz05DOf8AgNSaP/yBLD/r2j/9BFMk/tL/AISO22bf7K+yS+d0z52+PZ742+Z7fpXLLdm62JdUkeHSb2WNirpA7KR2IU4rItbWCK1iRIUACj+GtXWP+QJf/wDXtJ/6CaoQ/wCoj/3R/KtqXwmdTcPJi/55p/3yKpapFHHYvMiKskRDowGCpBHIrQqlq/8AyCrj/d/qK1W5m9jpqzbW+lm1/UbJrMxxW8MDpcYP70vvyvT+HaO/8VaVZ1tNqL67qEM8Krp8cUJtpAOXc7/MB57YTt3rjOkb4gJ/saVQSA8kUbY7q0igj8QSKqrbwqoVYowB0AUVZ8Q/8gg/9d4P/RqVFXRT+Exn8QzyYv8Anmn/AHyKrTRpDe2E0aKkguFTcowdpBBH0q5Va7/19h/19x/1qyToqztKu726k1AXlt5Cw3bRW52keZEFUh+euSWHHpWjWdpX9pb9Q/tHbt+1t9l24/1O1ducd87uvNch0EGvgSf2fA4zFJc/Op6NhGYA/iAfwqPyYv8Anmn/AHyKl1z/AI+NM/6+W/8ART02umHwoxl8QzyYv+eaf98io7dFh1+1MShDLFIr7RjcBgjP0/rU9Qx/8h6w/wByX+QpvZkrdG/XNfDv/knHhz/sHQf+gCulrmvh3/yTjw5/2DoP/QBXKdB0tFFFABRRRQBznxB/5Jz4k/7Blx/6LauhT/Vr9BXPfEH/AJJz4k/7Blx/6LauhT/Vr9BQBg3KLN4guTKofyoY1TcM7c7icfXj8qk8mL/nmn/fIpsv/Ievv+ucX/s1S11LZHO92M8mL/nmn/fIqTQQI31CBBtijuBsUdFyik4/Ek/jSUuif8fWp/8AXdf/AEWlKfwsqPxE2t30un2MU0Nmbtnu7eExgH5VeVUL9D90MW/DtWlWfrM2owWcLaZCs05uoEdWGcRGVRI3UdELH8O9aFcxsc3FGkt/qE0iK8n2gpuYZIUAYH0qx5MX/PNP++RUNt/x83//AF9P/IVZrrOcjMELAhooyD1BUVZ8Pk/2QiZJEcssa57KsjAD8AAKiqXw/wD8gs/9fE//AKNaoqfCVD4h2qX0tnc6VHHZm4F1eeS7gH9wvlyNv6eqhe33q0qz9Tm1GK50tbGFZIpLvZdkj/Vw+XIdw567wg79a0K5zY5O3ghuYzcTxJLLIxZndQT1Pr2qb7Faf8+sP/fsU2w/48o/x/mas1zRSsjnRVlsbUxNiCNTjIZVAIPqCOldBp0rz6ZaSyHLvCjMfUlQTWO/+rb6GtXSf+QNY/8AXvH/AOgitIK0tC4bkTXd6PEcVmLbNg1o8rXG08Sh1ATPTkFj+FWr6RobC5lQ4dImZT7gGqrf2l/wkce3b/ZX2R9/TPnb12++Nu72qfU/+QVef9cH/wDQTWktmaPY5+GytjChaCN2IBZnUEsfUk1J9itP+fWH/v2Kkh/1Ef8Auj+VPrBRVjCxRu4Iba3a4giSKWL50dFAII+ldbXLaj/yDp/9yupq6ejZpT6nNaJ/yO3ir/etf/RVdLXNaJ/yO3ir/etf/RVdLWpoFFFFABRRRQBzXgv/AI9tZ/7DN5/6MNdLXNeC/wDj21n/ALDN5/6MNdHI6xRtI7BUUFmJ7AUAQ3l7BYQebO2BnCqBlmPYAdzWQ91qV7zvFjCeioA0pHuTkD6AH61FAXv5/wC0ZwQWGIIz/wAs0/8Aij1P5dqt10Rgo77mLk3sVDYI3MlzeSH1a6kH6AgUf2bB/wA9Lr/wKl/+Kq3RV3ZJg6Bb6XeaNFPp8V1b2zPIFj+0yLgh2DHAbHJBP41p/wBmwf8APS6/8Cpf/iqh0O9OoaRFctafZCzOPJ/u4dhnoOuM9O9aNCbCxU/s2D/npdf+BUv/AMVR/ZsH/PS6/wDAqX/4qrdFF2Fip/ZsH/PS6/8AAqX/AOKqtqFhYR6bdPdm6a2WFzKv2qU5TByPvema1Kq6lNFbaVeTzxCWKKB3eMgEOoUkjn1FF2Firp9hYSabavaG6W2aFDEv2qUYTAwPvemKs/2bB/z0uv8AwKl/+Kp2nTRXGmWk0MQiikhR0jAACKVBA49Ks0XYWKn9mwf89Lr/AMCpf/iqP7Ng/wCel1/4FS//ABVW6KLsLFT+zYP+el1/4FS//FVma/b6XZ6NLPqEV1cWyvGGj+0yNkl1C8FscEg/hW9Wdrl6dP0mW5W0+1lXjXyfXc6rnoemc9O1DbCxN/ZsH/PS6/8AAqX/AOKoFiEOYbq9iPqLl2/RiRVuii4EUd/qFlzP/psA6sqhZV98DhvwwfrWzbXMN5brPbyB426MKy6q+Z/ZV59sTi2lYC5TsM8CT6jv7fSolBPbcqM2tzo65rxD/wAjX4Q/6/p//SWaulrmvEP/ACNfhD/r+n/9JZq5zY6WiiigAooooAK5qX/kp1r/ANgab/0dHXS1zUv/ACU61/7A03/o6OgDc1BmTTLp1JDLC5BHY4NY9iix2FuiKFURrgD6Vr6n/wAgq8/64P8A+gmsq0/484P+ua/yrel8JlU3Jqo6wP8AiUXLfxIhdT6Ecg/nV6qOsf8AIGvP+uTfyrWO6M3sdPWda3d7LrmoWs1tss4Y4Wt5tp/eM27eM9DjC9PWtGs+1/tL+29Q+0bf7O2Q/ZMYzuw3mZ7/AN3rXGdIzxD/AMgScdmKKfcF1BH5GoAAAABgDoBU3iH/AJAsv+/H/wCjFqKuin8JjP4gqreDE1i44dbqMA9xk4P6E1aqre/fs/8Ar7i/9CFWiTo6ztMu725udTS8tvJjgu/Ktm2kebF5aNv56/Mzjj+7WjWfpn9pfaNT/tDb5X2s/Y8Y/wBT5adcd9+/rz+lch0EOvfNb2sZ5SS4AdezAKxwfxAqp5Uf9xfyq3rv3LL/AK+R/wCgPVasX8TMZbjfLj/uL+VFqoi1q1MYCmRXV8DG4Yzz+Ip1JB/yGbL/ALaf+g0uwludBWdocmozaLbSavEsV+QfNRcYBycdCe2K0az9Ch1G30W2i1WZZ75VPmyKchjk47DtjtW5uVdaAk1CyjcbkCSPtPTcNoB/U/nUHlx/3F/Kp9X/AOQpZ/8AXGX+aVFWL+JmMviY3y4/7i/lUmkgRavOiAKrwhmA6EgkZptP03/kNSf9e4/9CpLdAt0J42/5ELxF/wBgy5/9FNWrp/8AyDbX/rin8hWV42/5ELxF/wBgy5/9FNWrp/8AyDbX/rin8hW5sWKKKKAOa8P/API3eLf+vu3/APSaOtTXZWi0W52Eh3AiUjsXIX+tZfh//kbvFv8A192//pNHWj4g/wCQWv8A18Qf+jVqofEiZfCyBEWONY0GFUAAegFOoorpMQooooAoaWdRP23+0FVcXTi3xjmHjaTj8evNX6oaXb3tv9s+23Am8y6eSDBJ2RHG1eR25q/SAKKKKYBRRRQBQ0We+utHtptSgWC8dSZY1GApyfc9sd6v1Q0VdRTR7ZdWdXvwv75lxgnJ9AB0xV+kAUUUUwCqmpm8Gk3h08Br0QP9nBxgybTt68dcdat1U1SG6uNJvYLKYQ3ckDpBKTgI5UhTkehwaQE9v5ptovPAE2wb8f3sc/rUlR26yJbRJK26RUAdvU45NSUwCiiigB/h9ttnPbdre4eNfZThgPyYCqHjX/jy0j/sM2X/AKOWr2h/67U/+vkf+ikqj41/48tI/wCwzZf+jlrnqfEzaHwnS0UUVBQUUUUAFc1rv/I5+FP+ut1/6JNdLXNa7/yOfhT/AK63X/ok0AdLXJ2t1Fp9utpdsYZoiVIZTzzwQe4NdZRVwnykyjc5n+1rD/n4X8jTJdTtZIXSF2mlZSFjjQksfTpXU0VftV2I9m+5W0+F7bTbWB8b4oURseoAFV5LS9bxHb3i3OLBLSWKS33H5pWeMq+OnAVx/wACrRrLksY28U22oG8Cyx2U0Ite7hnjYv17bAOn8VYt31NS3qED3Om3UEeN8sLoufUggVz8WqWscSJM7QyqoDRyIQVPp0rqaK0hPlViJRucz/a1h/z8L+RqG6uotRt2tLRjNNKQoCqcDnkk9gK6yir9suiJ9n5hWfbR6iuu6hJPKrae8UItYxjKuN/mE8d8p37VoVnWtjcQ67qN6920kFxHCsduScRFN+446fNuH/fNYGoa5DJPpMqwoXdXjk2r1IV1Yge+Aayv7Wse84B9GUgj8MV01FaQqcqtYiULu5zP9rWH/Pwv5GkSdNRvbOO13SeXMJZGCnaijPU/piunoqnVXRC9n5hWdpVpe2r6gb2588TXbywDcT5cRCgJz0wQfzrRrL0axjsn1Ix3guftF68zAf8ALIkKNnU9Me3WsTQbrqOEs7lUZ0t598gQZIUoy5wOuCwrP/taw/5+F/I101FaxqWVmiJQu7nM/wBrWH/Pwv5GpbCQX2sQTW4ZoYI33ybSFy2AACep610NFN1U1ZIShruFc18O/wDknHhz/sHQf+gCulrmvh3/AMk48Of9g6D/ANAFYmh0tFFFABRRRQBznxB/5Jz4k/7Blx/6LauhT/Vr9BXPfEH/AJJz4k/7Blx/6LauhT/Vr9BQBgag4sdZmmnDLDPGgSTaSuVzkEjoeRUX9rWH/Pwv5GumorZVUlZozcNdzmf7WsP+fhfyNaGhI7LeXJRkSeYNGHGCVCKucHpkg1rUUpVLqyQ4ws7mfrMeoy2kK6XKscwuoGkLY5hEqmUcg8lNw/HtWhWdrNjcX9pBFbXbWrpd28zOCRuRJVdk4/vAFfxrRrIs5mSdNOvryO63RiSYyxuVO11IHQ+o54o/taw/5+F/I101FbKquqM/Z+ZzP9rWPacE+gUkn9K1dDhkh0pBKhjd3kk2N1UM5YA++CK0aKmdTmVkhxhZ3M7U4tRkutLNhKqQpd7rwHHzw+XIMDjrvMZ7dK0aztTsbi7utKkgu2gS1u/OmQEjzk8qRdhx15dW5/u1o1mWcnbzw2sZt7iVIpY2KsrsFPU889ql+3Wn/P1B/wB/BXStGjnLIrfUZpPJi/55J/3yKyVNrS5n7NnMyX1r5bbbiN2IwFRgxJ9AB1roNOieDTLSGQYdIUVh6EKAamWKNTlUUH1Ap9VGLTuxxjYzmtL0+I47wXOLBbRomt9x5lLqQ+OnABH41avY2msLmJBl3iZVHuQapNYxnxTHqH2wCRbJ4fsvqC6tv69sY6d61Ktq5ZysN9aiFFeeNHUAMjsFZT6EGn/brT/n6g/7+CulaKNjlkUn1IpPJi/55J/3yKy5H3M+R9zlbqeG6t2t7eVJZpfkREYMST9O1dbTVjRDlUVT7DFOqoxtqyoxsc1on/I7eKv961/9FV0tc1on/I7eKv8Aetf/AEVXS1ZQUUUUAFFFFAHNeC/+PbWf+wzef+jDWh4gbOnpbf8APzMkR/3c5Yf98gis/wAF/wDHtrP/AGGbz/0Yava79/Tf+vv/ANpyVdP4kTP4RtFFFdBiFFFFAFDRp7650qKXUoFgu2Zw8ajAADEL3PUAH8av1Q0ZdRTSol1V1e9BfzGXGCNx29AB93FX6QBRRRTAKr388ttp1zPBEZpoondIwCS7AEgcepqxVe/a5XTrprNQ10InMKnGC+DtHPvikAtlNJcWFvNNEYpZIld4yMFGIBI59KnqCyM7WNubpQtwYlMoHQPjn9c1PTAKKKKACqGsz31tpckumwLPdB4wsbDIILqGPUdFJP4VfqhrK6i+mOulOqXm+PazYxt3rv6gj7u6kBfooopgFNljWaJ4nGUdSrD1Bp1FAFnQ5nn0a3Mh3SIDExPcqSpP6VleIf8Aka/CH/X9P/6SzVoeH/8AkHSf9fM//oxqz/EP/I1+EP8Ar+n/APSWauaekmbR+FHS0UUVJQUUUUAFc1L/AMlOtf8AsDTf+jo66Wual/5Kda/9gab/ANHR0Ab93CbizngBAMkbICe2Riudgu/IgjhuLa7jlRQrKLd2GR6FQQRXUUVcJ8uhMo3Ob/tGH/nld/8AgJL/APE1DdyNqNrJZ21vcmSYbMvA6KoPUksAOK6qir9r2RPs/MKzrW0vYtb1G5mud9nMkIt4dx/dlQ2846DOR09K0ay7Oxjg8QapeLeCR7hIFa3/AOeO0Ng9f4s+g6ViaE2s20t3pU8UK7pPlZVzjcVYNj8cVk/2jH/FBeK3dTaScfktdJRWkanKrEShd3Ob/tGH/nld/wDgJL/8TSBn1G6tY4ILgKkyyySSQtGqhTn+IDJPTiuloqva9kL2YVnaZaXttcam95c+ck92ZbZdxPlReWi7OenzKx4/vVo1l6RYx2dzqzpeC4NzemZ1H/LE+XGuzqeyg9vvViaC65G7W0EqIziGYSOFGTtwRnH41lf2ha/89h+RrqKKhwbd0yHC7ucv/aFr/wA9h+RqfTz9r1WCSEM0UIYu+0gZIwBnua6GilyPqxKAVm6BYy6bodrZzXhvJIlIa4JPz8k55J9fWtKsrw1aWVj4dsrbTrr7VaRoRFNuB3jJ5yOK0NCPWlaO5tboqxiRXR2UZ252kE47fKaof2ha/wDPYfka6iis3B3umQ4Xdzl/7Qtf+ew/I1c0cGe/mukVvJEQjDkEBjnJxW5RQoO92wULO5heNv8AkQvEX/YMuf8A0U1aun/8g21/64p/IVleNv8AkQvEX/YMuf8A0U1aun/8g21/64p/IVoWWKKKKAOM0b+1f+Fg+Jvs5s/7P+02/n+YG83P2aP7uOPTrV7W/wC2fNfzjYf2X58Hl7A/nZ8xOuflx1/Sjw//AMjd4t/6+7f/ANJo60fEH/IMX/r4g/8ARq1dP4kTL4WZVn/a/wBpuftxsfIyfs/kB92Mn7+eOmOlMtP7d+wXH2w6d9swfI8kP5eccbs89fStOitzEzB/bv8AZDbjp39p5+XAfycZ7/xdM/jRcf27/ZkH2Y6d9vyPO80P5WMHO3HPXHX3rTooA521stZsvM+wXlhcia8kmufPD/u9235EwT056+3Fadx/a39qQfZjZf2fged5gfzc5OduOOmOvvUejW9jbi/+wztMJL2WSbJB2SkjcvQdK06BlBf7W/td9xsv7Mx8uA/nZwOv8PXP4UWn9rfbrj7abL7Hk+R5IfzMZ43Z46ener9FAjMs/wC3Pstz9uOnfacH7P5AfZnHG/PPXHSiP+3P7Jk8w6d/aWfk2h/Jxkdf4umf0rTooAwLGz8Q2HhiztftVnc6rHxNNcNIyOMnnP3ienX3q7e/235Nt9hOn+bj/SPPD7e33Mfj19qTw9Z2+n6BZ2lpdLdW8Ue1J1IIcZPORxWnQMoXX9rf2jb/AGQ2X2Ljz/ND+Z152446etH/ABNv7X62X9mY9H87OP8Avnr+lX6KBFC2/tb+0Z/tZsvsPPk+UH83rxuzx0z0qjcW2sXmh6va6jcWFuZreSOCa33jy8qw3NuPbIPHoa3ao63DaXGg6jDfzGGzktZUnlU4KRlSGYHB6DJ6UDK1jHrcWhGOZ9Pe/UAQsm/yiABgtnnPXp7VI/8Abv8AZCbDp39p5+fcH8nGT0/i6Y/Wr1sqJawrE26MIApPcY4qWgRmXn9ufY7b7CdO+1YH2jzw+zOOduOeuetSXn9rfbbb7EbL7JkfaPPD+ZjPO3HHT171fooAo6P/AGv/AG/feUbH+y/PHmbg/nbvKXp/DjOOvvVDxb/bP2nT/tJsP7N/tey8nyw/nZ89Mbs/Ljr09q39D/1+p/8AXyP/AEUlUfGv/HlpH/YZsv8A0ctYVPiNobHS0UUVBQUUUUAFcf4utrq68TeF4rO+eynM1zidI1kK/uTnhgRzXYVzWu/8jn4U/wCut1/6JNAGlfafqVxHaLa61LaNF/rmW3jfz+nXcPl6Hp6066stQm1O3uINWkt7WPHmWogRhLzzliNw4449K0azptc0+GZ4mlkd0OG8qF5AD6EqCM00m9hNpbh9i1D+2vtf9rSfYsY+w+Qm3OMZ343deetLaWWoQalcXFxq0lzbSZ8q1aBFEXPGGAyeOOaj/wCEh0/1uf8AwEl/+JpR4g03cA0ssYJxukt5EUfUlQBT5Jdhc0e4ljp+p28N2l1rUt28o/cu1vGnkcHoFGG6jr6Vkro7pqsaz+I55NfNlMsE5t4xthMkRY7Au04ZVGT/AHj+HVVlSSab/wAJZbRvEx1Q2MrRyc4EPmR7x1xnds7dqkoJNO1R9HjtU1yVL1Wy16LaMs4yeNhG0dQOB2ovtP1O4s7WK11yW0miAEsy28bmY4HJDDC85PHrWrWWfEGm5IWWWQA43R28jqfoQpBpqLeyE2luPvbLULjULae21aS1t4yDLbrAjiYZyQWIyMjjihrLUDrSXY1aRbIDDWPkJtY4Izvxu64PXtTP+Eh0/wBbn/wEl/8AiafDrmnzzJEssiO5wvmwvGCfQFgBmnyS7C5o9xbayv4tWnuZtWkntHUiO0MCKsZyOQwG49D1PeszR9Iez13XLh9dN7e3UcKshijVrZV8zZwvXO44yOdtdHWVZLpo8R6q1u7HUTHB9rU5wFw/l47f3ulSUFtp2qQ6VPbT65LPdyEmO7NtGpiGBwFA2noTyO9Dadqh0VbQa5KL0Nk332aPcRnONmNvTjp2rTkkSGNpJHVEUZZmOABWb/wkOnHkPcMPVbWUg/jtpqLeyE2luJeadqk+nW0FvrkttcRgebcrbRsZuMHKkYGTzxUl9Y6hcXVrJa6vJaRREGaJYEcTcjglhle449aZ/wAJDp/rc/8AgJL/APE1Na6vZXk4hilYSkZCSRNGSPbcBn8KbhJdBcy7jJbLUH1iK6j1aSOzUYeyECFXPPO8jcOo6HtWZoOmrb6zq11Za1JPaS3chnszEmI5yF3fNjdwAOOnNdJWVokmmyNqf9nRNGVvpFud2fmmwu4jJ6dKkofY2OoW9xdPdavJdxyn9zG0EaCHk9Coy3br6Uy007VINOuYLjXJbm5kz5Vy1tGpi4wMKBg8881oXNzDaQNNcSCONerGqH/CQ6f63P8A4CS//E1SjJ7ITkluINO1QaKbQ65Kb0tkX32aPcBnONmNvTjpRdadqk2l29tBrktvdRkeZdi2jYy8HqpG0djx6Uv/AAkOn+tz/wCAkv8A8TVmz1O0v2ZLeUl0GWR0ZGA9cMAcUOElq0Ckn1IL6w1G4e0NrrMloIv9cFgjfz+nXcPl6Hp61mfDv/knHhz/ALB0H/oArpa5r4d/8k48Of8AYOg/9AFSM6WiiigAooooA5z4g/8AJOfEn/YMuP8A0W1XLPTtUi0aW2m1yWa7k5juzbxqYhgcBQNp6HqO9U/iD/yTnxJ/2DLj/wBFtXQp/q1+goAzG07VDoqWi65Kt6Gy199mj3MMk42Y29MDp2ovdO1S40+2gttcltbiMAS3C20bmY4wSVIwMnnirV5qVpYMi3EpDvyqIjOxHrhQTiq3/CQ6f63P/gJL/wDE1ShJ6pCckuo++stQuLy1ltdXktIYiDLCsCOJhkcEsMjjjj1okstQbWY7tNWkSyUYayECFXODzvxuHY9e1M/4SHT/AFuf/ASX/wCJq/bXUN5As1vIJI26Efy9jQ4yW6BST2Of1nRpLq6jm1HxA0dh9stpYbVoo0USpMjIofG4lmULjPO76Vp2NjqNvNdvdaxJdpKf3KNBGnkdehUfN1HX0pmvrprWdqNUdlh+3WxiK55mEq+UOO2/b7etatSMyrTTtUh024t7jXJbi5kz5V01tGpi44woGDzzzR/Z2qf2L9k/tyX7buz9u+zR7sZzjZjb046VPdavZWc3kyysZQMlI42kIHvtBx+NQ/8ACQ6f63P/AICS/wDxNUoSeyFzLuJdadqk2mW9vBrktvdR48y6W2jYy8c5UjaOeePSn31jqNxLaNa6xJaLF/rlWCN/P6ddw+XoenrTf+Eh04dXuAPU2soA/wDHa0YpUmiWWJ1eNxlWU5BFJxa3QJp7GBrWkm88Q6JeT639mhtrnfBZGNMTTeXKDhj82djNwOyk471pR2WoLrMl2+rSPZMuFsjAgVTgc78bjzk9e9Rauumtf6L9vdlnF6TZAZ+abyZcg47bPM68ceuK1aQzOsbLULe9uprrVpLqCUkxQNAiCEZzgFRk8cc+lR2Wnapb2F1Dc65LdXEoIiuGto0MJxgEKowcHnmrM+q2FtKYpbqNZB1XOSPrUf8Abmmf8/kf61PPHuLmREmnaouivaNrkrXrNlb77NGGUZBxsxtPGR070XGnapLpMFtDrksN2jAyXgto2aQc8FSNo6joO1TprOmu4VbyLJOBk4/nV6mmnsCaexzF/pZufE1jLba9LaarFZMkirAj+dDvXJIYYX5gOnrWtdWWoTapb3MGrSW9rGB5loIEYS8nOWI3DsOPSo2k03/hLEjMTf2p9hZlk5x5PmLkdcZ3Y7Vq0xmcLLUBrRuzq0hsiMCx8hNoOMZ343deetFpZahBqNzPcatJc20mfKtmgRRFzkYYDJwOOae+s6dG7I15FuU4IBzg/hTf7c0z/n8j/Wp549xcy7kdjp+p29vdJda3LdySj9zI1vGhh4PQKMN26+lJDp2qJo8trJrksl47ZS8NtGGQccbANp6HqO9WYdVsLiURRXUbO3Rc4J+lXKaaewJ3OR8KwXFt4p8Sw3V213Optd07IqF/3Z7LwPTiuurmtE/5HbxV/vWv/oqulpjCiiigAooooA4zwVNqn27W4ms7Uab/AGteEXAuW80t5h48vZjHvu/Crd/PrEl9bLfWFnBbLeH7PJDdtI0g8uTG5TGoXjHQtT/Bf/HtrP8A2Gbz/wBGGr2u/f0z/r7/APaclXT+JEz2Mq0uNbexuHvNOsobtQfIiivGkRzjjcxjBXn0BoW41s6Q0radZDUgfltxeMYiMjrJ5eRxk/d9vetOitzEzLi41tdMgkt9OspL9iPNge8ZI0GDnDiMlucfwjqfSnXs+sR/ZfsNhZz7v+Pjzrto/L6fdxG27v129B68aNFAGDp0Ov6ctnZSJbX0A3G4vJbtxKMuxwEKHdhSvVh+lXxNqv8Aa7RNZWg03Hy3Aum80nH/ADz2Y65H3vf2qPw/Z2+n6Jb21pdLdQIX2zKQQ2XJPTjgkj8K06EMoWk2qvf3CXllaQ2ik+RLFdNI7jPG5CgC8ejGmWc+tSW1y17p9lDOoPkJFeNIshwfvMY12846A1pUUCMyO41s6TJLLp1kuog/u7dbxjEw46yeWCO/8J6VDqD6/L4eYWdpZx6rIrIUN2wjiyCAyv5eWI4ONorZqrqUButLu7dZ/IaWF0Ev/PPKkbvw60AUnk16202wWCzs7u68tRdma7aIK2BkqRG27nPUCrF1NqqahbpaWVpLZtjzppLpkdOedqBCG4/2hViyiNvYW8Jl80xxKhk/v4GM/jU9AFAzar/a4iFlaf2bjm4+1N5ucf8APPZjrx9739qLabVX1KeO5srSKxUHyZo7pnkfkY3IUAXjP8Rq/RQBnWU+syJdG9sLOBl/49xFdtIJOv3sxrt7dN3U+nNC/i8Raj4buIxDa2OqeahiEN67IVV1Y5k8tSMgMMbT+tdBWZr9nb3+jvb3V0trCZYmMrEAArIrAc+pAH40DB7jWxpCSpp1kdSJ+e3N4wiAyeknl5PGD90f1ovLjW47K2ez06ymumA8+KW8aNIzjnawjYtzxyB/StOigRQvJtVjvLZLKytJrZiPPklumjeMZ52qEYNxnqRQ82qjV0iSytG04j57g3TCUHB6R7MHnH8Q61fooAqeGptVaa6imsrRNNE85juFumaVm8w8GPYAB153HoOOeM3UJ9Xl8ZeGV1Kxs7aFb+fyHgu2maQfZZ+WBjXbxjoT1Ppz0Ph//kHSf9fU/wD6Mas/xD/yNfhD/r+n/wDSWasKnxM2h8KOloooqCgooooAK4zV9Pk1H4k2KR6jeWJTSZXL2pQFx50fyncrcfTn3rs65qX/AJKda/8AYGm/9HR0Aal3pct1qdveLqt/bpDjNtCyCKXBz8wKk+3BFL/Zcv8AbX9of2rf+Xtx9i3J5HTGcbd2e/3utaBOBk9Kyf8AhILVuYYLqZO0kcJKt9Ceo96ai3sJtLcltNLlttTuLxtVv7hJs4tpmQxRZOflAUN7ck0yx0iaziu0fWdRujcD5XuGjJh6/c2oPXvnoKb/AG/F/wA+V9/35/8Ar05NetWkRJYrmAMQoeWIhcnoCe341Xs5dhc8e4kGjTQ6RNYtrWpyySMSLuRo/Oj6cKQgXHHdT1NZVr4ftftOpWtt4h1X+0iIPtVwHj84Ab9n/LPbyCe3YV1VZVjJpreIdWS2iZb9Vg+1uc4YFW2Y5xwM9KgoL7RpryytbdNa1O1aAANNA0YebjGX3IR78Acmpb3S5bu/trpNVv7ZYCC1vAyCObBzh9yk89OCOKuzTR28LzSuEjQFmY9AKzP+EggPK2l6ynoRARn86pRb2E5Jbkz6XK2tJqA1W/WNVwbIMnkNwRkjbuzzn73UCi30uWDVp75tVv5o5VIFpIyeTH05UBQ2eO7Hqah/t+L/AJ8r7/vz/wDXqW21q2uLhICk8Mj/AHBNGVDH0B6Z9qbhJdBcy7iWOky2TXZfV9QuvtH3RcNGRD1+5tQevfPQVmaFotpbWWs2dlreoXE814xurqRkM0U3loCAQgX7oQ9D1rpaytGk02S41cafEySLfMt2Tn5pvLTJGT027OmOlQUB0aY6KNP/ALa1MSBs/bQ0fnnnOM7NuO33elF5o011p1taJrWp27w43XMLRiWXAx8+UI9+AK0Z54raFppnCRr1Y1R/t2w/vz/+A0n/AMTSckt2JtLcW+0qW8uLWVNW1C1EBy0cDRhZuQfn3IT27EdTRNpcsusRX41W/jjjGDZoyeS/Xkgruzz2YdBSf27Yf3p//AaT/wCJqxa6ja3rskEpLqMlWQq2PXBAOKSlF7MOZPqQRaXLFrEt+dVv5I3GBZuyeSnTkALuzx3Y9TWX4P0yytNISfStV1C602dM28VyV2wrk/c+QN3/AIieldLWV4aurG98OWNzptsbazkj3RQkAbBk8YHFUMLPRprTTrm0fWtTuXmBC3EzRmSLIx8mEA9+QeaBo0w0VtP/ALa1MyFs/bS0fnjnOAdm3Hb7vSr11dwWcQkuJAik4HBJJ9AByaqf27Yf3p//AAGk/wDialyit2JtLcZc6NNcaVb2S61qcDxEE3UTR+bJgEYYlCvfPAHQU6+0ma9NoU1fUbX7P94W7Rjz+n39yH07Y6ml/t2w/vT/APgNJ/8AE1atb23vUZreTdtOGBBUj6g8ihSi9mCknscz4+0uW48Ma3fLqt/DHFpVwDaRMnkyfu35YFS2eezDoK6bT/8AkG2v/XFP5Csrxt/yIXiL/sGXP/opq1dP/wCQba/9cU/kKoZYooooA5rw/wD8jd4t/wCvu3/9Jo60fEH/ACDF/wCviD/0atZ3h/8A5G7xb/192/8A6TR1o+IP+QYv/XxB/wCjVq6fxImXwsioooroMQooooAzNFbTmW//ALORlAvZRPuzzNn5zz/TitOszRbu1u1vja2otxFeyxSYAG+RThn49a06SBhRRRTAKKKKAMzw8mnx+H7JNKkaSwEf7l2zkrn3ANadZnh2awuPD9jNpkLQ2LxAwxsMFV9Op/nWnSQBRRRTAKoa21kugai2pKWsBaym5Vc5Me07gMc9M9Kv1Q1y5t7Lw/qV1d24uLaG1lklhIBEiBCWXB4OQCOaQFu38v7NF5QxHsGwe2OKkqOBle3idF2qyAhfQY6VJTAKKKKAHaH/AK/U/wDr5H/opKo+Nf8Ajy0j/sM2X/o5avaH/r9T/wCvkf8AopKo+Nf+PLSP+wzZf+jlrnqfEbQ2OloooqCgooooAK5rXf8Akc/Cn/XW6/8ARJrpa5rXf+Rz8Kf9dbr/ANEmgDpa5nSP+QXAe5BJ9ySc101czpH/ACCrf/d/rW1LZmVTdF2orgBrWVWAIKEEH6VLUc//AB7yf7h/lWqINHSCW0axJOSbeMkn/dFVpL62XxZb2BtFN09jLMtzgZVFeMFM9eSyn/gNWNH/AOQJYf8AXtH/AOgio3vbtfEcNiLRjZPaSTNc7ThZA6AJnpyGY46/LXLLdm62JdXJXRb8g4It5CCP901nW6hbaJVAACAAD6Voax/yBL//AK9pP/QTVCH/AFEf+6P5VtS+EznuPqlq/wDyC5z3ABHsQRirtUtX/wCQVcf7v9RWsdzN7HTVlWVzYSeI9Vt4Ldkvoo4DcykDEisH2AHPOMN2HWtWsyzvvP1/U7P7F5X2ZIW+0f8APbcG46fw49T17VxnSJ4h50dx2M0II9QZVzUVS+If+QQf+u8H/o1Kirop/AYz+IKq3X/Hzp7dxdJg/XIq1Va7/wBfYf8AX3H/AFqyToqytEvra9OpfZrRbfyL6SGXAA81xjLnHrkdfStWs/Sr27vft32u0a38m7khiypHmRjGH565yenHFch0FfXOZtMU9Dck4+kb4pKdrn/Hxpn/AF8t/wCinptdMPhRjL4mFQx8a/YkdTHKD9MLU1Qx/wDIesP9yX+QpvZkrdG/XNfDv/knHhz/ALB0H/oArpa5r4d/8k48Of8AYOg/9AFcp0HS0UUUAFFFFAHOfEH/AJJz4k/7Blx/6LauhT/Vr9BXPfEH/knPiT/sGXH/AKLauhT/AFa/QUAYUvPiC9J6iKID2HzVNUUv/Ievv+ucX/s1S11LZGD3Cl0Pi41NRwPtCnHuY0zSUuif8fWp/wDXdf8A0WlKfwscfiQ/X7mwtbS1bUbdp43vraONVAO2VpVEbckcBip/DvWrWZrl9/Z9tayfYvtfmXtvBs/ub5FXf0P3c7vw6jrWnXMbHO23/H3qDdzdNk/QAVZqtbf8fN//ANfT/wAhVmus5wqXw9xpIUdFnmAHoPNaoql8P/8AILP/AF8T/wDo1qip8JUPiE1a5sINQ0WO8t2lmnvClowAPlS+TKxY88fIrjv978a1azNUvvsmoaPD9i+0fa7tovM/598QyPv6H+5t7fe69jp1zmxy9hzaK38TszMfUknmrNVrD/jyj/H+ZqzXPHZHOthkyq8LqwBUqQQa19LZn0iyZiSzQIST3O0Vkv8A6tvoa1dJ/wCQNY/9e8f/AKCKuPxFw3KxvrYeK0sPsi/ajYtMLnAyE3gbM9epB/CrmoMyabdMpIZYXII7HBqub27/AOEjWx+yN9iNoZjc7TgSbwNmenTJ9an1P/kFXn/XB/8A0E1pLZmj2MW2VUtolUAKEGAPpUtMh/1Ef+6P5U+sVsYlXUeLCVv4lG5T6EHINdTXLaj/AMg6f/crqaqG7LhuzmtE/wCR28Vf71r/AOiq6Wua0T/kdvFX+9a/+iq6WtTQKKKKACiiigDmvBf/AB7az/2Gbz/0Yava79/TP+vv/wBpyVR8F/8AHtrP/YZvP/Rhq9rv39M/6+//AGnJV0/iRM9htFFFdBiFFFFAGZ4eTT49Dt10qRpLIbvLZs5PzHPUDvmtOszw9NYXGh20umQtDZtu8uNhgj5jnue+a06SAKKKKYBVTVYrefR72G6l8q2kt5FlkzjYhUgn8BmrdU9WNqNGvjfZ+x/Z5PPxnPl7Tu6c9M0gJLGOKLT7aOB/MhSJVR8/eUAYP5VYqCx8n7BbfZs+R5S+Xn+7gY6+1T0AFFFFMArM8QJp8mkOuqSNHaebCSy5zu8xdnQHq20Vp1ma/NYW+kNJqULTWvnQqUUZO4yqEPUdGKn8KTBGnRRRTAKKKKAJPD//ACDpP+vqf/0Y1Z/iH/ka/CH/AF/T/wDpLNWh4f8A+QdJ/wBfU/8A6Mas/wAQ/wDI1+EP+v6f/wBJZq56nxM2h8KOloooqCgooooAK5qX/kp1r/2Bpv8A0dHXS1zUv/JTrX/sDTf+jo6ANvUuNKvCP+eD/wDoJrKtBiygA6eWv8q1dT/5BV5/1wf/ANBNZVp/x5wf9c1/lW9L4TKpuTVR1j/kDXf/AFyar1UdY/5A15/1yb+Vax3Rm9jp6yrG+tp/EGrWkdosdxbLCZZwBmXcpK5PXjBHPrWrWdaXt3PrOpWk1o0dtbiIwTlSBLuUlsHocEAcetcZ0jPEP/IEm/34/wD0NaiqXxD/AMgWX/fj/wDRi1FXRT+Exn8QVVvfv2X/AF9xf+hCrVVb379n/wBfcX/oQq0SdHWVo19bXk+rLb2i27W980MzAAec4RDvOOvDAc/3a1aztLvbu8l1Fbq0a3W3u2hgJUjzYwqkOM9eWYcccVyHQRa7/q7IdjcjP/fLGq1Wdd+5Zf8AXyP/AEB6rVi/iZjLcKSDjWbIjqRIPw20tJB/yGbL/tp/6DSYludBWb4fvn1LQbO9kszZPNHuNuf+WfPToP5VpVnaFPqF1odnPqsCwX7xgzxKMBW9Op/nW5uVtX51SyB7RSn8cpUVS6v/AMhSz/64y/zSoqxfxMxl8TCn6ZxrUuO9uM+/zGmU/Tf+Q1J/17j/ANCo6oFuhPG3/IheIv8AsGXP/opq1dP/AOQba/8AXFP5Csrxt/yIXiL/ALBlz/6KatXT/wDkG2v/AFxT+QrY2LFFFFAHNeH/APkbvFv/AF92/wD6TR1o+IP+QYv/AF8Qf+jVrO8P/wDI3eLf+vu3/wDSaOtHxB/yDF/6+IP/AEatXT+JEy+FkVFFFdBiFFFFAFDSrye8W7Nxam3MV1JCgII3opwH59av1Q0uTUJEu/7QjWNlupFg245hB+Q8HuKv0gCiiimAUUUUAZugXqajoFleR2gtEmiDC3H/ACz9ug/lWlVDRbq8vdFtLnULf7NeSRhpYcEbG9MHmr9JAFFFFMAqnq91NY6Lf3dvbm5ngt5JY4ACTKyqSF455Ix+NXKp6u97Hot9JpsYkv1t5DbI2MNKFO0HOO+KQFmJi8KOy7WZQSvp7U+mRFzChkGH2jcPfvT6YBRRRQA7Q/8AX6n/ANfI/wDRSVR8a/8AHlpH/YZsv/Ry1e0P/X6n/wBfI/8ARSVR8a/8eWkf9hmy/wDRy1z1PiNobHS0UUVBQUUUUAFc1rv/ACOfhT/rrdf+iTXS1zWu/wDI5+FP+ut1/wCiTQB0tcxAl1p0QtJLG5k8skLJEoZXXPB68fSunoq4T5SZRuc79ql/6B19/wB+f/r0ySa6miaKHTbvzHBVd6BVBPqSeldLRV+18ifZ+ZBZQG1sbe3LbjFEqZ9cDFVXbUv+EjhRY1/sr7I5kfjIm3ptHXONu/titGs5rfUD4jiuVuFGmraPG8GTkyl1KtjHQKGHXv0rF6mhavbc3dhcWwbaZYmjz6ZGKwY5rqGNY5tNuxIgAbYgZSfYg8iuloq4z5dCZRuc79ql/wCgdff9+f8A69RTpdajF9kjsbiPzCA0kqhVRc8nrz9K6eir9r5E+z8wrOtLm/k1vUbee3VLKFYjbSgHMhYNvyc84IHYda0azrRdSGtaibllOnlYvsijGQcHzM4567etYmg/V7WW80ySKEAyhkdQTgEqwbH44xWX9qnH3tNvge48oH+RroqK0jU5VYiULu5zv2qX/oHX3/fn/wCvRHHc315agWk8MUMoleSYBemcADOSSa6Kim6vkL2YVnaS2pN9u/tKNUxdyC2245h42E4PXr15rRrO0m31C3F79vuFm8y7kkg2knZEcbVPA5HP+NZGg3WbaaaO2mt4/Ne3m8wxggFgVZTjPf5s/hVD7VN/0Dr7/vz/APXroqK0jUsrWIcLu5zv2qX/AKB19/35/wDr1LYQ3FzqkVy9tLBDAjAGUAM7NjoPQAda3aKbq3VrCUNQrmvh3/yTjw5/2DoP/QBXS1zXw7/5Jx4c/wCwdB/6AKyNDpaKKKACiiigDnPiD/yTnxJ/2DLj/wBFtXQp/q1+grnviD/yTnxJ/wBgy4/9FtXQp/q1+goAxL+G4ttUkuktpJ4Zo0U+UAWRlz1HoQf0qL7VL/0Dr7/vz/8AXroqK1VWytYhw1Od+1Tf9A6+/wC/P/16v6NbTxJcz3EZie4l3iMkEqoUKM478Z/GtOilKpdWsChZ3M7WLm/tbe2bT7dZ5Hu4I5VIJ2xNIBI3BHRST+HetGs7WF1JoLb+y3VZBdwmbdjmHePMHPfbn39K0azLOeljubG9us2k00U0plR4QG6gZBGcg5FJ9ql/6B19/wB+f/r10VFaqr5GfsznftU3bTb4nsPKx/M1qaRay2mmpHOAJWZ5GUHIUsxbGfbOKvUUpVOZWHGFncz9Rub+C+0qO0t1lgnuWS7cgnyo/KkYMOePnVBzn71aFZ+oLqRv9KNi6i2FyxvgcZMXlPgDP/TTy+nP4ZrQrMs5eMSWKm3mgn3IxAZIWdWGeCCAad9qX/njdf8AgNJ/8TXTUVl7NrZmfIcw07OhSK2unkIwq+Q65P1IAFdBYwNbWFtAxBaKJUJHqABU9FVGNncqMbGcW1L/AISMII1/sr7ISX4z528YHXONue2Kt3cJuLOeFSAZI2QE+4xVT7PqH/CRi5+0L/Zv2Qx+Rk583fndjHTbx1/CtGrepRy8czRRrHNbXKSKAGAgdhn2IBBp32pf+eN1/wCA0n/xNdNRWfI+5nyeZy0wkvojbQQXG6T5dzwsqqO5JIFdTRRVRjYqMbHNaJ/yO3ir/etf/RVdLXNaJ/yO3ir/AHrX/wBFV0tUUFFFFABRRRQBzXgv/j21n/sM3n/ow1e137+mf9ff/tOSqPgv/j21n/sM3n/ow1e137+mf9ff/tOSrp/EiZ7DaKKK6DEKKKKAM3Qb1NQ0S2uo7QWiSBsQD+HDEeg9M9O9aVUNFury80i3uNQt/s104PmRYI28kDg89MVfpIAooopgFU9WngttGvp7qHzreK3keWLAO9ApJHPqMirlVdTuHtNJvLmOA3DwwPIsI/5aEKSF/HpSAfZSRy2FvJCnlxPErIn90EDAqeobWRprOCVo/LZ41Yp/dJHSpqYBRRRQAVm67epp+lNcSWgu1E0KeUe5aVVB6HoTu/CtKqGsXV5Z6cZrC3+0XHmxKI8E/K0iq549FLH8KTAv0UUUwCiiigCTw/8A8g6T/r6n/wDRjVn+If8Aka/CH/X9P/6SzVoeH/8AkHSf9fU//oxqz/EP/I1+EP8Ar+n/APSWauep8TNofCjpaKKKgoKKKKACual/5Kda/wDYGm/9HR10tc1L/wAlOtf+wNN/6OjoA6G4hFxbSwE4EiFCR2yMVgRR6nbxJC+mSStGoXfFLHtbHcbmB/Sujoq4zcSZRTOf36h/0CLn/v5F/wDF1FPa6hqELWh0+S3SX5XllkQhV74CsSTiuloqvbPsT7NBWdaNqR1nUluo1Fgoi+yMMZb5Tvzg56461o1nWdvqEes6lPcXCvZS+V9liBOY8KQ+RjjJx3NZGhLqlo99ps1vGwWRsFS3TIIIz7ZFZW7URw2kT577ZYiPwywroaKuNRxViZQT1Of36h/0CLn/AL+Rf/F0Ja317dW4ls3toYpVlZpHQk7eQAFJ74610FFV7V9ifZoKztLbUml1H+0Y1RBdsLTGPmh2rgnB67t3XmtGs7S7fULeTUTfXCzLLdtJbBST5cRVQFPA5yGPfr1rI0F1i2muLaJoE3vDKJNmcFhggge/NZfmT/8APhd/9+//AK9dJRUOF3chwu7nN+ZP/wA+N3/37qewt7ibUYp3t5IYoQ3MgwWJGMAVu0UuTuwUArO0JNSj0OzTWJFk1ERgXDrjBb8ABWjWdoVldaboVnZ3t213cwxBJLhiSZD65PNaFker287TW91DE0oiDo6L97DY5Hr939aoeZP/AM+N3/37rpKKhwu73IcLu5zfmT/8+N3/AN+6uaVbzm8lu5YWhQxiNFf7x5yTjtWxRQoa3uChZ3MLxt/yIXiL/sGXP/opq1dP/wCQba/9cU/kKyvG3/IheIv+wZc/+imrV0//AJBtr/1xT+QqyyxRRRQBxmjWNzL8QfE11HqlzDBFc24e0RIzHL/o0fLEoWH4MOlXtb0+8jle8fWLuS2eeALYtHEIkPmJyGCB+x6sev0o8P8A/I3eLf8Ar7t//SaOtHxB/wAgxf8Ar4g/9GrV0/iRMvhZlWdhd21zcyz6vdXaSkmOKWOJVh5PClUBPp8xPSmWmm31vYXFvNrl5czSg+XcyRQh4eMfKFQKcdfmBrTorcxMwabfDSGszrl4bonIvjFD5gGc427NnTj7vf1ouNNv5tMgtotcu4LiMgvdpFCXl4PBDIVHUHgDpWnRRYDn7fStcdf9I1+4hKXUrjyY4m82IsNituQ4wAfu46n2rRuLG8l1SC6i1a4gt4wA9okcRSQ5PJYqWHUdCOlJpNlJZR3ayXZuTLdyzKT/AABmyE6np0rQoGUFsbwau12dWuGtSMCyMUflqcAZ3bd/Xn73ei0sby3vriefVri6hlJMdvJFGqw85wCqhjgcck1fooEZlnp1/b2tzFPrdzdSygiOaSGJWhODyAqAH15B6UR6dqCaTJaNrdy90xyt6YYg6DI4ChdvYjkd606KAMGxsdduPC9jBfatLa6sFDXNxFFCxJ5yuCpXHI6DtVy90+/uIbVLfWbi1eIfvXSGJjN06hlIHQ9MdafolreWWiWdtqFz9pvIogs02Sd7dzk81foGULqyvZtQt7iHVZre3jx5lskUbLLz3ZlLD04Io+xXv9r/AGv+1ZvsmMfYvKj2ZxjO7bu689av0UCKFtZXsOoz3E2qzXFvJny7Voo1WLnswAY+nJPWqU2m61Ho2qxQ61Jc308DizeaKOMQSbW2n5V5GSOuelblUdZtHv8AQtQs4rk2slxbSRLcD/lkWUgN1HTOevagZBa6fqcWjyW0+tTS3j8rdmCMNH04Chdp6HqO9OfT9RbSEtV1qZbtTlrzyIyzDJ427dvTA6dq0Y1KRIpbcQoGfWnUCMy80/UZ7O2it9amtZowBLOsEbGY4xkhhgc88VJeWd9Pe201vqkltBGQZYFhRhKM5wSwyOOOKv0UAUdHs75tfvrpNUkSzWcB7IQoVc+UvO8jcOoPB7VQ8W2V/Fc6fczatJNaPq9kI7MwIqxnz05DAbj0PU9639D/ANfqf/XyP/RSVR8a/wDHlpH/AGGbL/0ctYVPiNobHS0UUVBQUUUUAFcf4u0631XxN4XtLkziJprkkwXDwtxCTwyEMPzrsK5rXf8Akc/Cn/XW6/8ARJoA0r7QLHUY7SOdr0LajEXk300RPT7xRwX6D72e/qafdaLaXmp2+oSvdie3xsEd5LHGcHPzRqwVuvcGtCsl9dUyOttZXNyisV8xNiqSODjcwz9apRcthNpbk39i2n9tf2tvu/tONu37ZL5WMY/1W7Z0/wBnrz1otNFtLLUri/ie8M1xnest5LJGMnPyxsxVf+AgVX/tub/oE3f/AH3F/wDF0HXzGN8+m3cUQ5Z8o20epCsT+Qp+zkLniSWOgWenw3UUEt8y3QxIZr6aUjgj5S7kp1P3cdvSsuPwhaQ6hsTUtQ+zPaSRNbvfztKSzo28SmTeuNmMD+8fU56dWV0V1IKsMgjuKy3tbA+LIrs3JGorYvGsGRgxGRSWxjPDBR171BQSeH7OXR49LabUBbxtuV1v51mPJPMofeevc+npRfeH7TULO1tZZ9QSO2ACGC/midsAD5mVgX6fxE1qO6ojO5CqoySewrIGvmQb4NNu5Yjyr5Rdw9cMwP5iqUXLYTkluWL3Rra+1C2vZZr1JbcgosN5LFG2Dn5kVgr/APAgeOKRtGt21pNVM98J1G0Ri8lEJ4I5i3bD19OvPWoP7bm/6BN3/wB9xf8AxdOTXVEiLc2VzbIzBRI+xlBPAztY4+tP2chc8Sa20a3tdWn1FJ75pp1KtHJeSvEMkH5Y2Yop47Ad/U1R0nw9Lpup6vK2oXs9neiPyopryWUwkBt+0sx25LD7uOg9BW9WXY2MEHiDVbuO8Es1ysIkgBGYdqkDvnnOefSoKEttAtrXSp9OS61JopyS0kuoTPKuQB8sjMWXp2I7+tDaBbNoq6V9r1MQq24SjUJhP1zzLu3kc9M9OOlaFzcxWltJcTNtjQZJxmsz+3JTyuk3hHbLRj9N9UoN7Ccktx95oFte6dbWUl3qccduAFeDUJo5GwMfO6sGf8SeeakvtGgv7q1uJLm/ja2IKrBeyxI/IPzqrAP0/iz3qD+25v8AoE3f/fcX/wAXUttrKTXKQT2s9s8nEZk2lWOM4ypPPXrTdOSFzofLo8M2sRambm/WWNdoiS8kWE9esQbYTz3Hp6Vn6L4ej0/VdQ1E6peXUtxcyv5RuXMMQYg7PL3FcjHXANdBWVodrYWy6h9guTOJb6WSfJB2Sk/MvA7VBQ+x0WGwuLqaO61CVrk5Zbi8klVOSfkViQnXtjt6Uy00CCy065sUvNTkS4zukmvpZJVyMfK7MWX8DVy9vYrCDzZdxywVUQZZ2PQAVQ/tyb/oE3f/AH3F/wDF1ShJ6olyS3HjQIBop0v7bqflFt3nG+l8/rn/AFu7dj8enFF1oMF3pdvYNe6nHHAQRLFfSpK2AR80gO5uvc0z+25v+gTd/wDfcX/xdWLLVY7uc27wTW84XeElx8w7kEEg4yPzpuEkrgpJjb7RYb97RnvNQi+zfdFveSRB+n39pG/p3z1PrWZ8O/8AknHhz/sHQf8AoArpa5r4d/8AJOPDn/YOg/8AQBUFHS0UUUAFFFFAHOfEH/knPiT/ALBlx/6Larln4esLXRpdNia9+zz/ADOXv53kyQB8sjOXXoOAR39TVP4g/wDJOfEn/YMuP/RbV0Kf6tfoKAMxvD1g2ippJe++yo24EX84lzknmXfvPJ7t7dKL3w9Y39hbWU0l8IbYARmK/nic4GPmdXDP/wACJ9atXeowWkixMJJJWG4JGm449T6CoP7aj/59Lz/v1/8AXrGWIpxdm9S1Tm1dIW+0W01C8tbqd7xZLYgxiG8liQ4IPzKjBX6fxA0smi2susx6q0l4LiMbVVbyURdCOYg2w9e4/lTf7aj/AOfS8/79f/Xq5a3UV5D5sLErkggjBUjqCD0NOFanN2i9QlCUVdoxNR8MtLqCahp9/e2901zDJMGvpjE0SurOgj3bBuC44A6n1NX7HQ7XT5ruWGW+Zro5kE17NKB1+4HYhOv8OO3oKbrtjBf29mlxdi1WK+t5kYkDe6SBlTk/xEY/GtStSDKtPD9nZabcWEU+oNDcZ3tLqE0kgyMfLIzll/Aij/hH7T+xf7K+0aj9n3bvM/tCbzuuf9bv3/r046VPcarBbztCEmlkXG4RJu256ZNR/wBtR/8APpef9+v/AK9YvEUk7ORapzavYZdeH7S80y3sJLjUUigxseLUJo5TgY+aRWDN17k0++0S21CW0kluL+M2v3BBeyxBun3wrAP0/iz39TR/bUQ5NreAdz5NX4Zo7iFJonDxuMqw71UK0Ju0WKUJR3RjanoM154i0rVbe/u4VtpD9pgW6lWKaPY4A8sHYTvZTkjouKuR6NBHrMmqC4vjM42mJryUwjgDiItsB46gevrUeqWMF1quizy3awyWl08sMRI/fsYZEKjnsGLcZ+7WpWhJnWWjQWF7dXcdxfyPcklknvZZUXJz8iMxVOv8IHHFR2WgW1hYXVnHd6nIlyCrPPqE0si5GPkdmLIf90jnmoJfEsfnSJa2F3dIjFDLHsVSQcEDcwJwe/Sm/wDCSS/9AW+/77h/+LqedGvsKnYspoFsmivpYu9TMLtuMrX8xnHIPEu7eBx2PTI70XGgW9zpMGnNeamkULBlljv5kmbGfvSBtzDnufT0qqfE4jBefSr6KIctJ+7baPXCuT+QrdjdJY1kjYMjAMrDoQe9NNPYmVOUPiRz174Ziv8AX7W5k1a/jS3tBEttBeSRM5DZ3uysC3pyDmtO60eG71S31B7q/SSAALFFdyJE2CT80YO1uvcVF9lsP+Es+1/aT/aP2Hy/IyMeV5md2MZ+9x1rUZlRGdiFVRkk9AKZBnjR4RrR1T7Vf+aV2+SbuTyOmP8AVZ25/DrzRZ6PDZajc3qXV/I9xndHNdySRLk5+VGJVfwFUR4nEo32+l3s0J5WQeWoYeoDMDj6il/4SSX/AKAt9/33D/8AF1POjb2FTsWrHQoLC3uoUvNSlFyMM1xeySsnBHyFiSnXtjtSQ6DBDo8umi91No5W3GZ76Rpl6fdkJ3Acdj6+tQJ4mjEqLdafd2sbsF82TYygngZ2sSOe+MVuU009iJQlD4jkfCtmmn+KfEtqks8qxm1Ae4laVz+7J5ZiSfxrrq5rRP8AkdvFX+9a/wDoqulpkBRRRQAUUUUAcZ4KttQF9rdwdQU2J1a8AtPs4yG8w/Nvzn8MVbv7TVIL62kvNUS5gkvCYIlthGYh5cnBbJ3cYHbpT/Bf/HtrP/YZvP8A0Yava79/TP8Ar7/9pyVdP4kTPYyrSz1aKxuIrrVo7i5cHyZxaBBEccZXcd3PPUULZ6sNIa3OrRm/Jyt39kG0DI42bvTI69606K3MTMuLPVpNMghg1aKK8Ugy3JtAwkGDkBNwx279qde2uqTfZfsepxW3l/6/daiTzenT5ht6H16+1aNFAGDptt4jkjsJr7UYYmVf9Kt/sysZDuPRwwA429j0q+LbUhq7TnUYzYEYFr9m+YHHXzN3rk9KNFtbyy0i3t9QuftN0gIklyTuOSep56Yq/QMoWltqUV/cS3WoxT2rk+TAttsMfPGW3Hdxx0FMs7TV4ra5S71WG4mcHyZEtPLERweo3HdzjuOlaVFAjMjtNYXSZIJNVge/Jyl0LPCqOODHv579+9QanDr0fhqQWN/C+rRRs4k+y/LMQCQoTf8ALk4GcmtqquppdyaVeJYOEvGgcQOeiybTtP54oGU5bfWriwsfI1GC0uVRTcl7TzA7YGcDeNvOfWp7q31OTULeW11CGC0THnQPbb2k55w+4beOOhq1bCVbWETkNMEUOR3bHP61LQIoG31P+1xONQh/s/GDa/Zvnzjr5m715+77UW1vqcepTy3OoQTWTA+Vbra7Gj5GMvvO7jPYVfooAzrK21aJLoXmpW9wz/6gx2hj8rr975zu7enT3qjeW3iWLw9NFb6jbT6s00flTraiNEj3puyjOc4Xeeoz2wa36oaxa3l5p3k2Fz9nn86F/MyR8qyKzjj1UMPxosMje11k6QkKaparqAOWujZEowyePL8zjjA+92ovLXWZLK2js9UtYLpABPLJZGRZDjkhfMG3nnqa06KLCKF5b6pLeWz2eoW8FshHnxSWpkaQZ5CtvG3jjoaHg1Q6ukyX9uunAfPbG1JkY4PIk34HOD909Kv0UAVPDUGqLNdTTX9s+nNPOI7ZbUrIjeYeTJvII68bR19uc3ULfVofGXhltR1C1uonv5zAkNoYTGPss/DEyNu4x2HT346Hw/8A8g6T/r6n/wDRjVn+If8Aka/CH/X9P/6SzVhU+Jm0PhR0tFFFQUFFFFABXGavo+n6x8SbGLULVLhItJlkQNn5WE0fPH1rs65qX/kp1r/2Bpv/AEdHQBqXehaZfanb6jc2iSXltjyZSTlMHIxz6ml/sLTP7a/tj7In9obdvn5OcYx646VfJCgkkADkk1mDWvMG6CwuZYz91xsUMPUAsDis51YQ+JlRhKWw+00LTLHU7jUba0SO8uc+bKCcvk5PfHUUyx8O6TpsV3FZ2SRJdjE4DE7+vqfc/nR/a8v/AEDLr/vuP/4qlGshWX7RZ3ECEgeY20qCfXDHH1qPrNLv+D/yK9lPsNg8N6RbaRNpUNkiWMzFpIQzYY8d857Csy28GaSjahZybZtPlaJkst7YtyoPfdnkkntXUVlWFrYRa/q9xb3JkvJjD9piJBEeEwvGOMjmtzML7w3pGo2VrZ3dmstvagLChdhsAGB0PoB1qW90PTdRv7a+u7YSXNqQ0L72Gwg5HAODz61cnnjtoHmlbbGgyxrP/tiQ8rpt0QehJjH6FqynWhB2k/1/IuMJS2JH0PTX1pNYa2Bv0Xas29uBgjpnHQntSW+h6baatPqkFtsvZ1KyS72O4HB6E47DtTP7Xl/6Bl1/33H/APFVJDqyyTpDNbTW7SHCGTaQx9MqTg/WpWJpt2v+D/yG6U10G2Ph/S9Na7a0tfLN3/r/AN4x39fU8fePSs3Q/CmnaZp2racjiezvLt5WiDN+7BVRszuJ425zx1rpKytFtbG2k1Q2NyZmmvnkuASD5cpVQV4HGAF/OtzMD4b0k6KNINqfsAbcIvNfrnP3s56+9F54b0m/0620+5tS9rbYESCVxtwMDkHJ49TVnUtRj0y1E0iPIzMEjjTq7HoBn8fyrK/4SDUP+gOv/gUP/ialyS3NI0pyV0jQvtB03Uri1uLu3MktqcwsJXXbyD0BGeg60s2h6dPrEWqyQE3sS7Uk81wAOf4c4PU9qzv+Eg1D/oDr/wCBQ/8AiatafrhurwWlzaNazMpaP5w6uB1AI7jPTFCmmN0JpXaJotD06DWJdWjgYXso2vJ5rkEcfw52joO1ZnhDwxo2g6NCNKl+0pLCqm6ExdZgM4I5Kjr/AA10lZXhpNNj8NacmjyNJpogX7O7ZyydicgH8xVGQWfhvSrDTrmwtrd0trkESqZ5GLZGDyWJHHoRQPDmlLoraQLd/sLNuMfnyZznP3t27qPWpNU1ZdNEKLC89xMT5cSkDIHUknoBkfmKz/8AhINQ/wCgOv8A4FD/AOJqXJI1jRnJXSLlz4c0u80q302eCRrS3IMaCeRSCAR94NuPU9TTr7QNN1I2huoZHNp/qds8ibenXaw3fdHXNUf+Eg1D/oDr/wCBQ/8AiavaXq41CSWCW3e3uYgGaNmDAqc4II6jg0KSYSozirtGJ4+0PT7vwxrepzRSNdwaVcLG4mdQAI3PKg7T1PUV02n/APINtf8Arin8hWV42/5ELxF/2DLn/wBFNWrp/wDyDbX/AK4p/IVRkWKKKKAOa8P/API3eLf+vu3/APSaOtHxB/yDF/6+IP8A0atZ3h//AJG7xb/192//AKTR1o+IP+QYv/XxB/6NWrp/EiZfCyKiiiugxCiiigDM0W1s7WK9Fnc+esl7NLIcg7JGYll49DxWnWZon9neTef2cW2fbZvO3Z/1287+v+1n2rTpIAooopgFFFFAGb4fso9O8P2FnFdi7jhhVFuB/wAtAB97qf51pVmeHYbC38OafDpczTWCQKIJGOSyY4PQfyrTpIAooopgFZ2v29reeHdTtr64+z2c1rLHPNkDy0KEM2TwMDJrRrO1/wCwf8I7qf8Aam7+z/ssv2rbnPlbTuxjnpnpSYF+IBYkVTlQoAPqKdTY9vlps+7gY+lOpgFFFFADtD/1+p/9fI/9FJVHxr/x5aR/2GbL/wBHLV7Q/wDX6n/18j/0UlUfGv8Ax5aR/wBhmy/9HLXPU+I2hsdLRRRUFBRRRQAVzWu/8jn4U/663X/ok10tc1rv/I5+FP8Arrdf+iTQB0tczpH/ACCrf/d/rXTVzOkf8gq3/wB3+tbUtmZVN0Xajn/495f9w/yqSo5/+PeT/cP8q1INDR/+QJYf9e0f/oIqux03/hLIgyt/av2Fyp5x5PmLu9s7tvvVjR/+QJYf9e0f/oIqu15ZjxZHZG1BvjYvKLjaOIxIoKZ68kg/hXLLdm62LGsf8gS//wCvaT/0E1Qh/wBRH/uj+VX9Y/5Al/8A9e0n/oJqhD/qI/8AdH8q2pfCZz3H1S1f/kFXH+6P5irtUtX/AOQVcf7v9RWsdzN7HTVlWMWnL4g1aS2lZr5xD9rQ5wmFOzHHcZ9a1ayrCbTn8QavFbQst9GIftchHD5U7Mc9hnsK4zpF8Q/8ghv+u8H/AKNSoql8Q/8AIIP/AF3g/wDRqVFXRT+Axn8QVVuv9fYf9faf1q1Va7/19h/19x/1qyToqytDOmlNQ/sxWA+3TC43Z5mz8/Xtn04rVrK0K8s7xL82dqLcRX00UoCgb5FbDPx6n8a5DoGa5/x8aZ/18t/6Kekp2uf8fGmf9fLf+inptdMPhRjL4mFQx/8AIesP9yX+QqaoY/8AkPWH+5L/ACFN7Mlbo365r4d/8k48Of8AYOg/9AFdLXNfDv8A5Jx4c/7B0H/oArlOg6WiiigAooooA5z4g/8AJOfEn/YMuP8A0W1dCn+rX6Cue+IP/JOfEn/YMuP/AEW1dCn+rX6CgDIf/kNXn/XOL/2apaif/kNXn/XOL/2apa86PX1f5s7VsvRBRpH/AB8aiP8Apuv/AKLWijSf+PnUf+uy/wDotaqP8SHr+jJn8D/rqJr0WnSwWQ1KVo0W+t2hK5+aYODGOh4LYrVrK16bToYLI6lC0qNfQJCFH3Zi48tuo4DYNatd5yGHbf8AHxe/9fLfyFWKr23+vvf+vlv5CrFebT+H7/zO5hT9E/5BuPSab/0Y1Mp+if8AIOP/AF3m/wDRjVpS/jL0f5ozq/B8/wDMj1SLTpNW0R7yVkuo7qRrJRnDyeRIGB4/55lz26Vq1lapNp0er6JHeQs91JcyLZOBxHIIZCxPP9wOO/WtWu45TjNH/wCQVD/wL/0I1eqjo/8AyCof+Bf+hGr1c62PUl8TGS/6l/8AdNa/h/8A5FvS/wDr0i/9AFZEv+pf/dNa/h//AJFvS/8Ar0i/9AFXDc58R8HzI86b/wAJYRtb+1fsOc848nzPyzu/Gp9a/wCQFqP/AF7Sf+gmoBeWf/CWGy+yj7cLES/aNo/1fmEbM9evNT61/wAgHUf+vaT/ANBNaPY5YfEjDtf+PSH/AHF/lUtRW3/HpD/uL/KpaxPRe5Q1r/kC3f8A1zNdpXF61/yBbv8A65mu0q4bs58T8Mfn+hzWif8AI7eKv961/wDRVdLXNaJ/yO3ir/etf/RVdLWhyBRRRQAUUUUAc14L/wCPbWf+wzef+jDV7Xfv6Z/19/8AtOSqPgv/AI9tZ/7DN5/6MNXtd+/pn/X3/wC05Kun8SJnsNoooroMQooooAzdAso9P0O1tIrsXaRqQJx/HyTnqf51pVmeHobC30Czi0yZprJUPlSMclhk+w757Vp0kAUUUUwCqmqW0t5pF7awTm3mmgeNJgSDGxUgNx6E5q3VHWbeC70PULa6nEFvNbSRyzEgCNCpBbJ44GTSAs20bRWsMbvvZECl/wC8QOtS1FaokVpDHG+9FjVVb1AHBqWmAUUUUAFZuu2Ud/pf2eS7FqvnwP5p9VlRgvUfeKhfxrSrM1+Gwn0ry9SmaG2+0W7b1ODvEyGMdD1cKPxpMDTooopgFFFFAEnh/wD5B0n/AF9T/wDoxqz/ABD/AMjX4Q/6/p//AElmrQ8P/wDIOk/6+p//AEY1Z/iH/ka/CH/X9P8A+ks1c9T4mbQ+FHS0UUVBQUUUUAFc1L/yU61/7A03/o6Oulrmpf8Akp1r/wBgab/0dHQBuah/yDbr/ri/8jWfbf8AHpD/ALi/yrQ1D/kG3X/XF/5Gs+2/49Yf9xf5VxVv4q9P1Omj8LJap6r/AMgq6/65n+VXKp6r/wAgm6/65N/KsK38OXozaPxI6CsqwOmnXtXFqrC+Bh+2E5wfk+TGePu+latZWn3lnPr2sW0FqI7m3aEXE20DzdyZXnqcDjmvUOAk1z/kEy/78f8A6GtMqTXP+QTL/vx/+hrUdcNX+M/RfmzqpfB8/wDIKrXf3rX/AK+Yv/QhVmq13961/wCvmL/0IVlU+Bmi3N2srRTppk1T+zlYML5xdbs8zbV3Yz2xt6cVq1laJeWd2+qC0tRAYb6SKchQPMkAXL8dcgjrzxXpnCV/E33dL/6/R/6LkqvVjxN93S/+v0f+i5Kr1jLc76P8NBUUP/IxaX9Zf/QDUtRQ/wDIxaX9Zf8A0A0luW/hfo/yOqrK8M3Gn3XhnTrjSrdrfT5IFa3iYYKIRwCMn+datZnhy+XU/Dmn3yWX2JZ4FkFt/wA8sj7vQdPoK3PNKGuf8h/Tv+vef/0KKmU/XP8AkP6d/wBe0/8A6FFTKxl8TPQp/wAOP9dWFLpP/IzS/wDXmP8A0OkpdJ/5GaX/AK8x/wCh0LdDn8DJ/G3/ACIXiL/sGXP/AKKatXT/APkG2v8A1xT+QrK8bf8AIheIv+wZc/8Aopq1dP8A+Qba/wDXFP5CtjzixRRRQBzXh/8A5G7xb/192/8A6TR1o+IP+QYv/XxB/wCjVrO8P/8AI3eLf+vu3/8ASaOtHxB/yDF/6+IP/Rq1dP4kTL4WRUUUV0GIUUUUAZmiXNndQ3jWVuYFS9njkGAN0iuQ7cepya06z9IvXvobp3tPsxiu5oQv98K5AfoPvYz+NaFJAFFFFMAooooAzPDsmny+HNOk0mNotPa3Q26NnKpjgHJJ6e9adZnh28t9R8OadeWlqtpbz26SRwKABGpGQuBxxWnSQBRRRTAKzvEFzaWfhzU7m/t/tFnFayPPCAD5iBSWXB45GRWjVDXLx9P0HULyO1+1vBbySLbj/lqQpO3oevToaQF1CDGpUYGBgU6kU5QHGMjpS0wCiiigB2h/6/U/+vkf+ikqj41/48tI/wCwzZf+jlq9of8Ar9T/AOvkf+ikqj41/wCPLSP+wzZf+jlrnqfEbQ2OloooqCgooooAK5rXf+Rz8Kf9dbr/ANEmulrmtd/5HPwp/wBdbr/0SaAOlrno7DUrFfs8NvFcQqT5b+btO3PAII6iuhoqozcSZRTMDy9V/wCgfH/4ED/CmvbatPG0QtIYd42+Y0+4L74A5roaKv2r7E+zRFbQLa2sNuhJWJFQE+gGKpm+mHiRLD7GTAbNpjdYOAwcDZ0x0OevatGs4y6j/wAJGsIhX+zPshYy9/O3gBevTbk9KyNC5cwLdWk1u5IWVGQkehGKxEttWhRYjaQylBjzFn2hvfBHFdBRVxm4kyimYHl6r/0D4/8AwIH+FNksNRvlFvNbxW8LEeY/m7ztByQAB1NdDRVe1YvZoKy7G+in17VrRLMRSW3k758D99uUkdu3TvWpWdZ3d7NrGpW09r5drB5X2ebaR5u5ctz0ODxxWRZNqdmb/T5LdXCOSrIxGQGVgwz7ZArL8vVhwbCIn1W44/lW/RVxm4qxLinqYHl6r/0D4/8AwIH+FOhsL65u7d7mGKCGF/MIEm9nIBwOnA5zW7RTdVi9mgrO0e+mvkvDNZm2MN5LCgII8xVOA/I79a0aztIl1GaO7OpQrEy3cqwBf4oQ3yN1PJFZli6rZS3cUL25QTwSeYgc4VuCpBPbgnmqHl6r/wBA+P8A8CB/hW/RVxqNKxLgm7mB5eq/9A+P/wACB/hU9jY3j6gl3dpHCsSMscaPvJLYyScDsOnvWxRTdVtWEoJBXNfDv/knHhz/ALB0H/oArpa5r4d/8k48Of8AYOg/9AFZlnS0UUUAFFFFAHOfEH/knPiT/sGXH/otq6FP9Wv0Fc98Qf8AknPiT/sGXH/otq3Ibm3e281Z4mjUYZw4IH1NAFC8t7qK/e5ggM6SoqsqsAykZ55IBHP6VFvvv+gXcf8AfyP/AOKrW+0wCETefH5R6PvG386HuYI0V3mjVG+6xYAH6VzPCpttSa+7/I2VZpWsZO++/wCgXcf9/I//AIqrml200CTyzgLLPJvKA52gAADPrxVt54o2VXlRWb7oZgCfpSmaISiIyIJD0TcMn8KcMOoyUnJu3p+iQpVXJWsZ2uX0NjDZNNZi6Et7BCoIH7tmcAPyD93rWpWXq2o3Fr9lSwhW5le7iimQcmOJmAd8Dpgc56VopNFIWCSIxX7wDA4+tdBkZElvd2t3cGK1a4ilfzAUdQVJAyCGI9KTfff9Au4/7+R//FVrrPE6F1lRkXqwYECl86Ly/M81Nn97cMfnXL9VV9JNfd/kbKu7bIx99920ufPvJHj/ANCrQ022e0sUikIMhZnfb0yzFiB+dWDNEqB2kQKejFhg0rSxoQGdQW6ZPWrp0FCXNdt+dv0SJnVclaxm6lfQ22raNbyWYme6uJI45SB+4Ihkct07hSvb71alZmoX9zbarpNrbwCWK5mdLl8HMSCJ2De2WVV59a0fMQvs3ruH8Oea3Mzl10nVdP3W9vbQ3NurMYn87Y20nOCCOozjI6077NrX/QMi/wDAof4V0wdGJCspI6gHpQJEYEh1IHUg9KjkR0fWZdUjl3sdbmRolsreEuMeY9xuC++AOfpXR2VstlY29ohJSCJY1J6kKMf0qXehXduXb654o3qFDFhg9801FIipVc1ZlAX03/CSGw+xnyBZib7Vg43byNnTHTnrVy6t1u7Sa2ckJLG0bEdcEYql9ovx4iMLRoNL+yB1l4yZt5BXr024PStEsAcEjPpVGSdtTlY7DW7aNYTZ28/lgKJVn2hgO+COPpT/ALNrX/QMi/8AAof4V0+RnGRn0oyCcZqORHR9Zl2Rysmk6rqCfZZ7aG2gcgSyedvO3PIAA6muroyPWimopGdSq57nNaJ/yO3ir/etf/RVdLXM6J/yO3in/etf/RVdNVGYUUUUAFFFFAHNeC/+PbWf+wzef+jDV7Xfv6Z/19/+05Ko+C/+PbWf+wzef+jDV7Xfv6Z/19/+05Kun8SJnsNoooroMQooooAzPD0mny6BZvpUbR2JTMKNnIGT6k1p1meHry31DQLO7tbVbWCWPckCgAIMnjjitOkgCiiimAVR1oWZ0LUBqLFbE20n2hlzkR7TuIxz0z0q9VHWprW20HUZ76EzWcdrI88QGS8YUllwSOoyKQFm1EYtIRCSYti7Cf7uOKlqK2ZHtYWiXbGUUqvoMcCpaYBRRRQAVma/Jp8elhtTjaS2+0242rnPmGZBGeCOjlT+FadZmvXlvYaYs91arcxm5t4/LYAjc8yKrc/3WYN+FJgadFFFMAooooAk8P8A/IOk/wCvqf8A9GNWf4h/5Gvwh/1/T/8ApLNWh4f/AOQdJ/19T/8Aoxqz/EP/ACNfhD/r+n/9JZq56nxM2h8KOloooqCgooooAK5qX/kp1r/2Bpv/AEdHXS1yGpalYaZ8S7KS/vba0STSZURp5VjDN50fAJPJ9qAOsmiWaCSJs7XUqcehGKxo7fUoI1h+zRS7BtEiy7dwHfBHFaFxq2m2l7DZXOoWkN3PjyoJJlWSTJwNqk5PPpR/a2nf2l/Zv9oWv2/GfsvnL5uMZzsznpz0rGpRVRp3szSFRx0KWzUv+fFP+/4/wpktnf3sZt5II4Yn4d/N3Hb3wMda0INV066vZrK31C1mu4c+bBHMrPHg4O5Qcjn1pLXWdLvUne01KzuEt/8AXNFOriLr97B46Hr6Vm8ImrOTa+X+RXt5dEXazrK+muNY1S1kszFFbNEI5iD++3Jk9ux4p8WtaVPYSX8Wp2UllEcSXCTqY0PHBbOB1H51nWuvx+dqF7eXtjHooeNbG889BHLlfn+bdg4YEfhXUYmtf2pvLKWANtZgCrYzgg5H6is7ZqY4NlET3Kz8H9KuXOs6XZ28NxdalZwQTgGGSWdVWQEZ+Uk4PHPFPuNU0+zuobW5vraC4nIEUUsyq8hJwNoJyeeOKwqUFOXNdp/13NIVHFWKOzUv+fFP+/4/woSzvbm4hNxFHDFE4kOJNzMR0HTjmr7anp66guntfWwvWGVtjKvmEYzkLnPQE/hRFqdhPfSWMN9bSXcQzJAkqmRBxyVByOo/Oo+qrrJv7v8AIr2z7Fqs7Sb6a9bUBNZm28i8eFCQR5qgDD8jvk/lU1tqunXhnFrf2s5t/wDXCKZW8vr97B46Hr6Gs3RNdW9tNTvLq7svslveSJFPFMhQQgKQWYEgHk9cV1GJd1jTm1K0jWKQRzwyCWJmGV3AEYI9CCR+NZP9m69/zz03/v8Av/8AEVtf2vpgsBfnUbT7GTgXHnr5ZOcfezjrxRPq+m21rFdT6haRW82PLlkmVUfIzwScHj0qXFM1hWlBWRi/2brv/PLTf+/7/wDxFWdO0e9XUY72/eAGFWEUUBLDLcEkkDtxjHetO41KwtJYYrm9toZJuIkklVTJ/ugnnqOlK+o2Md6lk95brduMpA0qh2HsucnofyoUEVKvJqxZrP0G6vr3QbG61O2FtfSwq88ABHluRyMHJqdNRsZL17JLy3a7QZaBZVLqPdc5HUfnWd4a1GW50W2h1G+tZ9XhhUXywyo2yTGGyF4HINUYEms6XPeyW9zaSRpcwblAlztdWxkHHI5UHPtWf/Zuvf8APPTf+/7/APxFbcOq6dc20tzBf2ssEX+slSZWVOM8kHA4oGqaebI3ov7U2gODP5y+WDnH3s468VLgm7m0a8oqxif2brv/ADy03/v+/wD8RV7SNKuLW6mvL2SJp5EEapDnaigk9TySSfQdKuy6pp8NrHdS31tHbyHCTPMoRvoc4PQ06fUbG1MIuLy3i87/AFXmSqu/p93J56jp60KCQSrykrGX42/5ELxF/wBgy5/9FNWrp/8AyDbX/rin8hWF46v7OHwbr9rLdwJcyaXclIWkAdh5T9B1PQ/lW7p//INtf+uKfyFUYliiiigDjNG0uO4+IPia/a5vEeC5twIo7l1ib/Ro/vIDtbr3FXtb0eKKV9SF3ftJJPAphe7doR+8TpGTtB47D19aPD//ACN3i3/r7t//AEmjrR8Qf8goHsLiAn/v6tXT+JEy+FmVZ6TFZXNzOl1fSm4JLLPdPIqck/IpOF69qZaaJDZ2FxaLeajIs4IMk15I8i5GPlcnK/hWnRW9jEzBokI0htN+2aiY2OfON5J5w5zxJnd+vTii40SG50yCxa81FEhIIlivJElbAI+Zwdzde59K06KLAc/babdarEG1KS9tXtLqZIBbXUkXmxB8Iz4b58qoPPqfWtG40qO41OG/a6vUeEACKO6dImwSfmQHa3XuPT0o0pNQSG4GoyK8hupjEVxxCXPljgdlxV+gZQXSo11d9S+1XpkYY8k3TmEcAcR52jp6deaLTSo7O+uLtLq9kackmOa6eSNMnPyqThfw7VfooEZlnokNla3Nul5qMq3AIZ57ySR04x8jE5Xr2oj0SGPSZNOF5qJjc5MzXkhmHIPEmdw6evrWnRRYDndK0+TVvB2mR376lZztEksqi7lSZHIyVZ87zjPQ+g9K0L3RYr6G1je81CIW4wpgu5I2fp98g/N07+9P0RtRfQ7FtXRU1EwIblVxgSY+YDBI6+lX6LDKF1pUd3qFvetdXsbwYxHFdOkbYOfmQHDfjR/ZUf8Aa/8AaX2q98zGPJ+1P5PTH+rzt/TrzV+iiwihbaVHa6jPerdXsjzZzHLdO8S5OflQnC9OwrOvdMuNJ0DWJNJlvru+lt3aCO5upJf3gU7QuW+XJPYjt6V0FUdaTUJNDvk0qRY9RaBxbO2MLJg7ScgjrjrQMht9Eig0mWw+2aiyync0r3kjSqePuuTuA47H19aV9EhfSE043moiNDkTLeSCY8k8yZ3Hr6+laQzgZ60tFhGZeaJDe2dtbPeajEtuAFeC8kjd8DHzMDlvxqS80qO9vba6e6vY2gIKpBdPHG+Dn5lBw349qv0UWAo6PpMUmv32pG6vlkjnCiFbpxC37pRkx52k89SOw9KoeLdHitrnT9RW7v3km1eyUwyXTvCuZ0+7GTtU8dh3PrW/ofMupnt9qA/8hpVHxr/x5aR/2GbL/wBHLWFT4jaGx0tFFFQUFFFFABXH+LtMsdX8TeF7PUrOC7tXmuS0M6B1JEJIyD712Fc1rv8AyOfhT/rrdf8Aok0AaV94c0TU47SO+0qzuUs+LZZYVYQ9Pu5HHQdPQU660HSL7U7fU7vTbWe+tsCG4kiDSR4ORtY8jkk1o1hjWby5LSWdrAbfJCPNKQXwcZwFOBVRi5bCckty3/YOk/21/bP9m2v9pkbftflDzcY243denFFpoOk2GpXGpWmm2sF9c58+4jiCvJk5O4jk881V/tHVv+fWy/7/AD//ABNI2ralCpklsrZ41GWEUzbse2VwTVeykT7SJYsfDeiaZDdw2Ok2dtFdjFwkUKqJRgj5gBz1PX1rNi8KWdveSafBp1hH4amtmE1gsKhHnLqdxXHPyrj8BXRwypPDHNGcpIoZT6gjIqibGY+JFv8A7YfIFmYfsuTgtvB39cdBjpWZZHJ4Z0KbR49Hk0iyfTYm3JaNCpiU5JyFxgck/nRfeGtD1KztbS+0iyuba0AW3ilhVliAAACgjjgAfhWlNKkEEk0hwkalmPoAMmsZdW1KZRJFZWyRsMqJZm3Y98LgGqjBy2Jckty3e6DpGo6hbX97ptrcXlqQYJ5YgzxEHIKk8jnmhtB0l9aTWW021OpoNq3ZiHmgYK4DdehI/Gqv9o6t/wA+tl/3+f8A+JpP7ZvLcq95awC33AO8MpJTJxnBUZFV7KQueJcttB0mz1afVbbTbWHULhSs1ykQEkgJBILdTyB+QrM0Xw9Bpms675ek2NrY3hi2GGFFM3ynfvxyfmY9fU10dZ1na30WsalcXFyJLScxfZosk+VhcNxjjJ54rMsjtvDOhWelT6XbaRZQ2FwSZraOFRHISACSuMHgD8qG8M6E2irozaRZHTFbctoYV8oHO7O3GOpJq7fXaWFnJcurMEwAq9WJOAB9SQKzP7S1U8/Y7Nc9jOxI/wDHauMHLVEuSW5NeeGdC1DTrbT7zSLK4s7UAQQSwqyRADA2gjA44qS+0DSNTurW5vtMtLme0INvJLEGaIgg/KT05A/Kqv8AaOrf8+tl/wB/n/8AiafBq1yt1DDe20SJM2xJIpCwDYyAQQOuOtN05C50WJdB0mfWItXl021fUol2x3TRAyKORgN1HU/nWdofhyKy1TUNWvLOzOqXFzMUu44lEhgLfIrMACcAAfhXQ1naPYzWMV2s94bky3k0ykkny1ZshOT/AAjisywsfD+j6ZcXVxY6ZaW012c3EkUQVpTkn5iOvJP51HaeGdC0/TrnTrPSLKCyuc+fbxwqqSZGDuAGDxxVnUb/AOwQoyxGWWVxHHHnG5sE8nsMAn8Kof2jq3/PrZf9/wB//iatQk1dEuSWhMPDOhDRTow0iyGmFtxtPJXyic7s7cY680XXhnQr3S7fTLrSLKawtyDDbSQq0cZAIG1cYHBP51D/AGjq3/PrZf8Af5//AImp7LVJpbwWl3bpFI6l42jcsrAYyOQCDyKHTklcFNMdfeHtG1NrRr7S7S5az/49jLCreT0+7np90dPQVmfDv/knHhz/ALB0H/oArpa5r4d/8k48Of8AYOg/9AFQUdLRRRQAUUUUAc58Qf8AknPiT/sGXH/otq0bbQtIt9LfT4NKsYrKb5pbZLdFjkJABLKBg9B19BWd8Qf+Sc+JP+wZcf8Aotq6FP8AVr9BQBRbQdHfS10ttJsTp6nK2ht08oHOchMY6knp3outB0e+s4LO70mwuLW3AEMMtsjpEAMAKpGBxxxS3epGC4+zwW7TyhQzDcFCg9Mn1ODUP9p3v/QOX/v+P8KwliacXZ/k3+hoqU2rosXejaXf3Nvc3mm2dxcWxBglmgV2iIOQVJGV5APFD6PpcuqJqkmm2b6hGNqXbQKZVGCMB8ZHBPfvVf8AtO9/6By/9/x/hVyyvFvYmYI0bo2x0bqrf/qIP404V4TfKt/Rr8xSpyirsytR0dbe/g1PRtMsU1Ka6iW7uhBGsrwbh5mXIyeAO/YVftNE0nT5bmWy0uytpLrm4eG3RDL1+8QPm6nr6mm6va311HaCwuRA0d3FLMSSN8Stl14HccVo1sQZ1t4f0WzsJ7C10fT4LO4z51vFbIscmRg7lAweOOaT/hHtF/sn+yv7H0/+zc7vsf2ZPJznOdmNvXnp1on1R1uJIba1M/lnDsXCqDjOB60z+073/oHL/wB/x/hWDxNNO2v3P/I0VGbVx9z4e0S80+DT7rR9PnsrfHk20tsjRx4GBtUjA4JHFOvNC0jUZLaS90qyuXtebdprdHMPT7hI+XoOnoKi/tS8HJ04Y9pxn+VX7W5jvLZJ4s7W7MMEEcEH3BqoV4Tdo7+jX5ilTlFXZjaroqXHifRdTh0uxlkildbm7kgjM0cQjfYFcjcPnK9PU+9aCaJpUerPqyaZZrqUg2vdrAolYYAwXxk8ADr2pt/a30+p6VNbXIitoJna6jJI81DGyqOnOGKnn0rRrUgoWeiaTp15c3llplnbXNyS080MCo8pJySxAyeSTzUdn4c0TT7K5srLR7C2tLoFZ4IbdESUEYIZQMHjjms8+IL64d3sbK3NsGKpJPMVL4OMgBTgccc0f2zrP/PlYf8AgQ//AMRUc6N/q9Q0F8OaGmkPpCaPYLprnc9oLdBExyDkpjHUA/hRceG9DutLh0u40ewl0+AhorV7dTHGRnBVSMDqfzNZza7qsKmSXT7V41GWWK4beR7ZUAn8RXQW88d1bRXER3RyoHQ+oIyKpST2InSlDcwbzwzb6l4gt5L/AE+wudKtLQJawzQI5hm38lcj5RtCjg9hWlc6HpN7qcGpXWm2k1/bgCG5khVpIwCSNrEZHJP50gsZh4ke/wDthMBs1hFrk4DByd/XHQ46dqvTSpBBJNI22ONSzH0AGTTMykNC0kaydYGm2g1MjabvyV80jGMbsZ6cUWmhaTYajcahZ6baQXtznz7iKFVeTJydzAZPPNZSa/qlwglh0+1SJxlBNOwfHbICkA+2TTv7Z1n/AJ8rD/wIf/4io50b/V6n9Mv2XhvRNNt7q3sdJsraG7GLiOKBVWUYI+YAc8E9fWki8M6FBpEukQ6RZR6dM26S1WBRG545K4weg/KqH/CQ31qVkvrK3FtkB3hmLMgJxuwVGR6810lUpJ7Gc6cofEcj4VsLTS/FPiWysLaK2tYjaiOGFAqrmMngDpyTXXVzWif8jt4q/wB61/8ARVdLTICiiigAooooA4zwVp0gvtbv/wC0bwxnVrxPsZZPJH7w/Njbuz/wKrd/pctnfW076rf3Sz3hKwzshSH93IcJtUHvjknpT/Bf/HtrP/YZvP8A0Yava797TT/09/8AtOSrp/EiZ/CZVppE1rY3Fs2sajcNMCBPM0ZkiyMfLhAPfkGhdImXSGsP7Y1EyMc/bC0fnDnOAdm32+70NadFb2MTMuNImn0yCzXWNRheIgm6jaPzZODwxKFe/YDoKde6XLd/Zdmq39t5H3vIZB53T7+5T6dsdTWjRRYDB02G+1eHTtWu7m/0+bywZdPTasZOT94MpbPPqOgq+NNkGrtf/wBp3pjIx9jLJ5I4xkDbu9/vdaNFbUX0a0bVkVL8p+/VcYDfgSKv0DKFppslrf3Fy2p3twsxJEEzIY4snPy4UH25JplnpMtpbXML6vqFyZgQss7RlouCMphAO+eQelaVFAjMj0iaPSZLE6xqLu5yLtmj85OnAITb29O5qlr1lLbeDrtRf6jM9pBJMXRo/OuNqsdhJQrz04X0roKp6tNd22j309hCJryK3keCIgkPIFJVcAjqcCgZVl0yW/sLEJqmoWZiRSxgaMNJwPv5Qjt2A6mp7rTZLnULe6XU723SHGbeFkEcuDn5sqT7cEVchZ2gjaRdrlQWHocc0+gRQOmyHVxf/wBp3ojAx9j3J5J4xnG3d7/e60W2myW+pT3banezpKCBbSsnlR8g/KAoPbHJPWr9FAGdZaVLaJdK+rahc+f90zshMPX7mEHr3z0FZ2o2moaX4dkgtb7Ur64luYU85ijTRo8iIxXamMKpZuQe+eK6KqGsNqK2KnS0V7j7RCGDYx5Xmr5h5PZNx/lzQMjfSJm0hLAaxqKyKcm7DR+c3JOCdm3vj7vQCi80ia7sra3TWNRtmhABngaMSS4GMtlCPfgDmtOiiwiheabJd3ltcJqd7bLCQTDAyBJec4bKk+3BFD6bI+rpfDU71Y1GDaKyeS3BGSNu7vn73YVfooAqeGtNkimur86nfSRyTzqLR2TyU/eHlQF3Z47seprN1DTJbDxl4Zlk1S/vBNfzlY7lkKxf6LPwm1Qcc989BXQ+H/8AkGyHsbmfH/fxqz/EP/I1+EP+v6f/ANJZqwqfEzaPwo6WiiioKCiiigArlrqCGf4m2gmiSQDR5iA6g4PnR8811Nc1L/yU61/7A03/AKOjoA6B7aCSVZXhjaRPuuygkfQ0fZoPtHn+TH53TzNg3fn1p7uscbSOcKoJJ9AKyU1O/nQSxWkCxsMqJJSGx2yAvFZVK0KbSkXGnKWxppbQRzNNHBGsr/edUALfU0kdpbQq6xW8SCT74VAN319az/t2p/8APvaf9/m/+Jprard248y6tofIB+dopSSo9cEDIrP61TW9/uZXsZmitlapA0C20Kwty0YjAU/UdKzrTTZhfalHdx28mmM8ZsrfYpEYCfPxju2T361sVnWNjNb6vql1JeGWK6eNo4STiEKgUjr3Iz+NdJkWpbK0njSOW1hkjj4RXjBC/QdqWW0tppUllt4pJIzlHdASvfg9qLu5SztZLhwSqDOB1J7AfjWd9v1I8/ZbVc9jMxI/8drGpXhB8r39C405SV0aJtLY3IuTbxG4AwJSg3Dt160JZ20dw9wlvCs7jDSKgDN9T1PSs/7dqf8Az72n/f5v/iaWLU7hLiKO7t4lSVtivFIWwx6AggdalYqnfr9zKdGZeisrSAyGG1hjMv8ArNkYG/6469TWbo2lS2sGpW95DbGCa7keGOONQvkkDaCAAM8Hrmtms7SLGayN/wCdeG58+8kmTJJ8pTjCcntj9a6DIsHT7I2gtTZ2/wBmBz5PlDZnOemMdaJdPsp4I4JrO3khj+5G8SlV7cAjioNY1FtNtEaKISTzSCKJWOBuIJyT6AAn8Kyf7S13+/p3/fl//i6lySNYUZTV0b01jaXEkck9rBK8XMbPGGKfQnpStY2j3S3T2sDXKDCzGMFwPY9e5rA/tLXf7+nf9+X/APi6s6drF42ox2V/HATMrGKSAEDK8lSCT25zntQpoqVCaVzWWxtEu2u0tYFuWGGmEYDke7dewrO8N6KdH0S0guhBLqAgVLu6ROZ3A5YnGTk5PPrWxWX4bsYtM8M6ZYwXgvIbe2jjS5BBEoCgBuCevXrVGBah02wt7eS3hsraOGX/AFkaRKFftyAMGgaZYCzNmLG2+yk5MHlLsJzn7uMdap6xqk1lJb21pHG9zPuYGQnairjJOOT94DHvWf8A2lrv9/Tv+/L/APxdS5pOxtGhKSubkumafPax2stjbSW8ZykTxKUX6DGB1NLPp1jdGE3FlbzGH/VeZEreX0+7kcdB09Kwv7S13+/p3/fl/wD4ur2karcXVzNZ3scS3EaCRXiztdSSOh5BBH6ihTTCVCUVco+OtPspvBuv3ctpbvcx6XchJmjBdR5T9GxkdT+db2n/APINtf8Arin8hWV42/5ELxF/2DLn/wBFNWrp/wDyDbX/AK4p/IVRiWKKKKAOa8P/API3eLf+vu3/APSaOtrVLVr3TLm3Q4d0Ow+jDkfqBWL4f/5G7xb/ANfdv/6TR10tNOzuDV9DAtbgXVrHMBjeuSD2PcfgeKmqG/gbS7qS6RSbKY7pQB/qX7t/unv6HnualVldQykFSMgg8Guq6aujntbRi0UUUAUNJs7myguEubprhpLqaZGJJ2IzllTn+6CB+FX6zNDs7aytrpLW6W4WS9uJnZSDtdpGZk4/ukkevFadJAFFFFMAoopDyDQBR0S3vrXQ7G31Odbi/jgRbiVSSHcD5iCQOp9hV+s/QrM6doGn2Ru/thgt0jNz/wA9cKBu6nr16mtCkAUUUUwCqGt2dzqGh31nZ3TWtzPA8cU6kgxsQQGBHPB54q/WZ4is7bUPDepWd7dLa2s9tJHLcMwAjUqQWJPAwPWkBpjoKKKKYBTXdY42kcgKoJJPYU6qsMX9sziNObCNv3snaUj+Aeoz1P4etG2rDyNHQoWj0xZZFKyXDtOwPbccgfgMCszxr/x5aR/2GbL/ANHLXS1zXjX/AI8tI/7DNl/6OWuWTu7m6VlY6WiiikMKKKKACua13/kc/Cn/AF1uv/RJrpa5rXf+Rz8Kf9dbr/0SaAOlrmdI/wCQVb/7v9a6auZ0j/kFW/8Au/1ralszKpui7Uc//HvJ/uH+VSVHP/x7yf7h/lWpBoaP/wAgSw/69o//AEEVXNnZ/wDCWLe/ah9uFiYvs+4f6vzAd+OvXj0qxo//ACBLD/r2j/8AQRVfGm/8JYDub+1fsJG3nHk+Z19M7vxrlluzdbFjWP8AkCX/AP17Sf8AoJqhD/qI/wDdH8qv6x/yBL//AK9pP/QTVCH/AFEf+6P5VtS+EznuPqlq/wDyCrj/AHf6irtUtX/5BVx/u/1Fax3M3sdNWZY2K2+t6tdi981rloiYP+eG1Mevfr0FadZWn2+nx67rE1tcM95K0P2qMniMhAExx3Xnqa4zpF8Q/wDIIP8A13g/9GpUVS+If+QQf+u8H/o1Kirop/AYz+IKrXf+vsP+vuP+tWarXf8Ar7D/AK+4/wCtWSdFWVoVnZ2cN6tldC4WW+nlkIYHZIzksnHoePWtWsrQRpogvf7MZmQ305n3Z4m3nzBz23Z9q5DoG65/x8aZ/wBfLf8Aop6bTtc/4+NM/wCvlv8A0U9Nrph8KMZfEwqGP/kPWH+5L/IVNUMf/IesP9yX+QpvZkrdG/XNfDv/AJJx4c/7B0H/AKAK6Wua+Hf/ACTjw5/2DoP/AEAVynQdLRRRQAUUUUAc58Qf+Sc+JP8AsGXH/otq6FP9Wv0Fc98Qf+Sc+JP+wZcf+i2roU/1a/QUAZD/APIavP8ArnF/7NUtRP8A8hq8/wCucX/s1S150evq/wA2dq2Xogo0n/j51H/rsv8A6LWijSf+PnUf+uy/+i1qo/xIev6MmfwP+uoutWK3yWIa9+y+Tewzg/8APQq2dnUden9K06ytdt9PuE08ahcNCEvoZINpxvmB+Reh4J//AF1q13nIYdt/r73/AK+W/kKsVXtv9fe/9fLfyFWK82n8P3/mdzCn6J/yDj/13m/9GNTKfon/ACDj/wBd5v8A0Y1aUv4y9H+aM6vwfP8AzG6jYrc6vo9yb3yTazSOsP8Az3zE67evbdu79PxrTrK1K30+XWdFlurho7qGaRrSMHiRzE4YHjnCFj26Vq13HKcZo/8AyCof+Bf+hGr1UdH/AOQVD/wL/wBCNXq51sepL4mMl/1L/wC6a1/D/wDyLel/9ekX/oArIl/1L/7prX8P/wDIt6X/ANekX/oAq4bnPiPg+ZGtnZjxZJei6BvjYrEbfcOIxIxD469SR6cVPrX/ACAdR/69pP8A0E1Ao03/AISyQhm/tX7CgZeceT5jYPpndu96n1r/AJAOo/8AXtJ/6Ca0exyw+JGHbf8AHpD/ALi/yqWorb/j0h/3F/lUtYnovcoa1/yBbv8A65mu0ri9a/5At3/1zNdpVw3Zz4n4Y/P9DmtE/wCR28Vf71r/AOiq6Wua0T/kdvFX+9a/+iq6WtDkCiiigAooooA5rwX/AMe2s/8AYZvP/RhrS16Nm0wzIpZrZ1nAHcKfm/8AHc1m+C/+PbWf+wzef+jDXSkAjBGRTi7O4mrqxiqyugZSCrDII7ilqoYzpFwLWTi0kb/RpD0X/pmfT29Rx2q3XV5owCiiigChotvfWui2kGpzrPepGBNKpJDN65IH8qv1naDZHTtCsrM3n2wwxBftH/PT36n+daNJAFFFFMAqnqwvW0a+GmMFvzbyC2LYwJdp2Zzx1x1q5VPVra5vNGvrWzuDb3U1vJHDMCQY3KkK2RyMEg8UgLMO/wAmPzTmTaN31xzT6ZErJCiu25goBPqafTAKKKKACqGr299c2SR6fOsE4uIXZ2JGY1kUyL0PVAw/HtV+s7WrI39jHCLv7LtuYJfM9dkqts6j723b+PfpSA0aKKKYBUVxOltbSTv92NSx96lqvaRHV7tHA/0CB927tM46Aeqg857ke1F0tWG+iNTSLZ7TSbaGTiQLuf8A3jyf1JrH8Q/8jX4Q/wCv6f8A9JZq6Wua8Q/8jX4Q/wCv6f8A9JZq5W7u5ulZWOlooopDCiiigArmpf8Akp1r/wBgab/0dHXS1zUv/JTrX/sDTf8Ao6OgDc1D/kG3X/XF/wCRrPtv+PWH/cX+VaGof8g26/64v/I1n23/AB6w/wC4v8q4q38Ven6nTR+FktU9V/5BN1/1yb+VXKp6r/yCbr/rk38qwrfw5ejNo/EjoKytOs7ODXNZuILoS3NxJEbiLcD5JWMBRjqMqAefWtWsrThpo1vWTaMxvTJF9tBzgN5a7MZ4+7jpXqHAS65/yCZf9+P/ANDWo6k1z/kEy/78f/oa1HXDV/jP0X5s6qXwfP8AyCq13961/wCvmL/0IVZqtd/etf8Ar5i/9CFZVPgZotzdrK0Ozs7T+0vsd0Ljzr6WWbDA+XIcbk49MD3rVrK0MaaBqP8AZrMc30p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  • Figure 9: Data and weight partitioning strategies. Each 4×4 dotted-line grid represents 16 cores and the shaded squares are the data contained on that core (either model weights or batch of tokens). We illustrate both how the model weights and the data tensors are split for each strategy. First Row: illustration of how model weights are split across the cores. Shapes of different sizes in this row represent larger weight matrices in the Feed Forward Network (FFN) layers (e.g larger df f sizes). Each color of the shaded squares identifies a unique weight matrix. The number of parameters per core is fixed, but larger weight matrices will apply more computation to each token. Second Row: illustration of how the data batch is split across cores. Each core holds the same number of tokens which maintains a fixed memory usage across all strategies. The partitioning strategies have different properties of allowing each core to either have the same tokens or different tokens across cores, which is what the different colors symbolize.
    • ",
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  • Figure 9: Data and weight partitioning strategies. Each 4×4 dotted-line grid represents 16 cores and the shaded squares are the data contained on that core (either model weights or batch of tokens). We illustrate both how the model weights and the data tensors are split for each strategy. First Row: illustration of how model weights are split across the cores. Shapes of different sizes in this row represent larger weight matrices in the Feed Forward Network (FFN) layers (e.g larger df f sizes). Each color of the shaded squares identifies a unique weight matrix. The number of parameters per core is fixed, but larger weight matrices will apply more computation to each token. Second Row: illustration of how the data batch is split across cores. Each core holds the same number of tokens which maintains a fixed memory usage across all strategies. The partitioning strategies have different properties of allowing each core to either have the same tokens or different tokens across cores, which is what the different colors symbolize.
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    5.3 Model and Data Parallelism
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    5.3 Model and Data Parallelism
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    It is common to mix both model and data parallelism for large scale models, which was done in the largest T5 models (Raffel et al., 2019; Xue et al., 2020) and in GPT-3 (Brown et al., 2020). With a total of N = n × m cores, now each core will be responsible for B/n tokens and df f /m of both the weights and intermediate activation. In the forward and backward pass each core communicates a tensor of size [B/n, dmodel] in an all-reduce operation.
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    It is common to mix both model and data parallelism for large scale models, which was done in the largest T5 models (Raffel et al., 2019; Xue et al., 2020) and in GPT-3 (Brown et al., 2020). With a total of N = n × m cores, now each core will be responsible for B/n tokens and df f /m of both the weights and intermediate activation. In the forward and backward pass each core communicates a tensor of size [B/n, dmodel] in an all-reduce operation.
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    5.4 Expert and Data Parallelism
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    5.4 Expert and Data Parallelism
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    Next we describe the partitioning strategy for expert and data parallelism. Switch Transformers will allocate all of their cores to the data partitioning dimension n, which will also correspond to the number of experts in the model. For each token per core a router locally computes assignments to the experts. The output is a binary matrix of size [n, B/n, E, C] which is partitioned across the first dimension and determines expert assignment. This binary matrix is then used to do a gather via matrix multiplication with the input tensor of [n, B/n, dmodel].
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    Next we describe the partitioning strategy for expert and data parallelism. Switch Transformers will allocate all of their cores to the data partitioning dimension \"n\", which will also correspond to the number of experts in the model. For each token per core a router locally computes assignments to the experts. The output is a binary matrix of size [\"n\", \"B/n\", \"E\", \"C\"] which is partitioned across the first dimension and determines expert assignment. This binary matrix is then used to do a gather via matrix multiplication with the input tensor of [\"n\", \"B/n\", \"dmodel\"].
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    resulting in the final tensor of shape [n, E, C, dmodel], which is sharded across the first dimension. Because each core has its own expert, we do an all-to-all communication of size [E, C, dmodel] to now shard the E dimension instead of the n-dimension. There are additional communication costs of bfloat16 tensors of size E×C ×dmodel in the forward pass to analogusly receive the tokens from each expert located on different cores. See Appendix F for a detailed analysis of the expert partitioning code.
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    resulting in the final tensor of shape [n, E, C, dmodel], which is sharded across the first dimension. Because each core has its own expert, we do an all-to-all communication of size [E, C, dmodel] to now shard the E dimension instead of the n-dimension. There are additional communication costs of bfloat16 tensors of size E \n× C \n× dmodel in the forward pass to analogously receive the tokens from each expert located on different cores. See Appendix F for a detailed analysis of the expert partitioning code.
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    5.5 Expert, Model and Data Parallelism
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    5.5 Expert, Model and Data Parallelism
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    In the design of our best model, we seek to balance the FLOPS per token and the parameter count. When we scale the number of experts, we increase the number of parameters, but do not change the FLOPs per token. In order to increase FLOPs, we must also increase the df f dimension (which also increases parameters, but at a slower rate). This presents a trade-off: as we increase df f we will run out of memory per core, which then necessitates increasing m. But since we have a fixed number of cores N, and N = n × m, we must decrease n, which forces use of a smaller batch-size (in order to hold tokens per core constant).
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    In the design of our best model, we seek to balance the FLOPS per token and the parameter count. When we scale the number of experts, we increase the number of parameters, but do not change the FLOPs per token. In order to increase FLOPs, we must also increase the d_{ff} dimension (which also increases parameters, but at a slower rate). This presents a trade-off: as we increase d_{ff} we will run out of memory per core, which then necessitates increasing m. But since we have a fixed number of cores N, and N = n \\times m, we must decrease n, which forces use of a smaller batch-size (in order to hold tokens per core constant).
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    When combining both model and expert-parallelism, we will have all-to-all communication costs from routing the tokens to the correct experts along with the internal all-reduce communications from the model parallelism. Balancing the FLOPS, communication costs and memory per core becomes quite complex when combining all three methods where the best mapping is empirically determined. See our further analysis in section 5.6 for how the number of experts effects the downstream performance as well.
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    5.6 Towards Trillion Parameter Models
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    Combining expert, model and data parallelism, we design two large Switch Transformer models, one with 395 billion and 1.6 trillion parameters, respectively. We study how these models perform on both up-stream pre-training as language models and their downstream fine-tuning performance. The parameters, FLOPs per sequence and hyper-parameters of the two different models are listed below in Table 9. Standard hyper-parameters of the Transformer, including $d_{model}$, $d_{ff}$, $d_{kv}$, number of heads and number of layers are described, as well as a less common feature, FFNGEGLU, which refers to a variation of the FFN layer where the expansion matrix is substituted with two sets of weights which are non-linearly combined (Shazeer, 2020).
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    The Switch-C model is designed using only expert-parallelism, and no model-parallelism, as described earlier in Section 5.4. As a result, the hyper-parameters controlling the width,
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    5.6 Towards Trillion Parameter Models
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    ModelParametersFLOPs/seqdmodelF F NGEGLUdf fdkvNum. Heads
    T5-Base0.2B124B768X20486412
    T5-Large0.7B425B1024X28166416
    T5-XXL11B6.3T4096X102406464
    Switch-Base7B124B768X20486412
    Switch-Large26B425B1024X28166416
    Switch-XXL395B6.3T4096X102406464
    Switch-C1571B890B208061446432
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    T5-Base–12–-1.599-1.556
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    Switch-Base1/212128-1.370-1.306
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    Combining expert, model and data parallelism, we design two large Switch Transformer models, one with 395 billion and 1.6 trillion parameters, respectively. We study how these models perform on both up-stream pre-training as language models and their downstream fine-tuning performance. The parameters, FLOPs per sequence and hyper-parameters of the two different models are listed below in Table 9. Standard hyper-parameters of the Transformer, including dmodel, df f , dkv, number of heads and number of layers are described, as well as a less common feature, F F NGEGLU , which refers to a variation of the FFN layer where the expansion matrix is substituted with two sets of weights which are non-linearly combined (Shazeer, 2020).
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  • Table 9: Switch model design and pre-training performance. We compare the hyperparameters and pre-training performance of the T5 models to our Switch Transformer variants. The last two columns record the pre-training model quality on the C4 data set after 250k and 500k steps, respectively. We observe that the Switch-C Transformer variant is 4x faster to a fixed perplexity (with the same compute budget) than the T5-XXL model, with the gap increasing as training progresses.
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    The Switch-C model is designed using only expert-parallelism, and no model-parallelism, as described earlier in Section 5.4. As a result, the hyper-parameters controlling the width,
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    depth, number of heads, and so on, are all much smaller than the T5-XXL model. In contrast, the Switch-XXL is FLOP-matched to the T5-XXL model, which allows for larger dimensions of the hyper-parameters, but at the expense of additional communication costs induced by model-parallelism (see Section 5.5 for more details).
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    Model       Parameters  FLOPs/seq  dmodel     F F NGEGLU          df f                 dkv  Num. Heads  
    T5-Base     0.2B        124B       768        X                   2048                 64   12          
    T5-Large    0.7B        425B       1024       X                   2816                 64   16          
    T5-XXL      11B         6.3T       4096       X                   10240                64   64          
    Switch-Base 7B          124B       768        X                   2048                 64   12          
    Switch-Large26B         425B       1024       X                   2816                 64   16          
    Switch-XXL  395B        6.3T       4096       X                   10240                64   64          
    Switch-C    1571B       890B       2080        6144                 64   32          
    Model       Expert Freq.Num. LayersNum ExpertsNeg. Log Perp. @250kNeg. Log Perp. @ 500k  
    T5-Base     –           12         –          -1.599              -1.556                 
    T5-Large    –           24         –          -1.402              -1.350                 
    T5-XXL      –           24         –          -1.147              -1.095                 
    Switch-Base 1/2         12         128        -1.370              -1.306                 
    Switch-Large1/2         24         128        -1.248              -1.177                 
    Switch-XXL  1/2         24         64         -1.086              -1.008                 
    Switch-C    1           15         2048       -1.096              -1.043                 
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    Table 9: Switch model design and pre-training performance. We compare the hyperparameters and pre-training performance of the T5 models to our Switch Transformer variants. The last two columns record the pre-training model quality on the C4 data set after 250k and 500k steps, respectively. We observe that the Switch-C Transformer variant is 4x faster to a fixed perplexity (with the same compute budget) than the T5-XXL model, with the gap increasing as training progresses.
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    depth, number of heads, and so on, are all much smaller than the T5-XXL model. In contrast, the Switch-XXL is FLOP-matched to the T5-XXL model, which allows for larger dimensions of the hyper-parameters, but at the expense of additional communication costs induced by model-parallelism (see Section 5.5 for more details).
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    Sample efficiency versus T5-XXL. In the final two columns of Table 9 we record the negative log perplexity on the C4 corpus after 250k and 500k steps, respectively. After 250k steps, we find both Switch Transformer variants to improve over the T5-XXL version's negative log perplexity by over 0.061.10 To contextualize the significance of a gap of 0.061, we note that the T5-XXL model had to train for an additional 250k steps to increase 0.052. The gap continues to increase with additional training, with the Switch-XXL model out-performing the T5-XXL by 0.087 by 500k steps.
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    Sample efficiency versus T5-XXL. In the final two columns of Table 9 we record the negative log perplexity on the C4 corpus after 250k and 500k steps, respectively. After 250k steps, we find both Switch Transformer variants to improve over the T5-XXL version's negative log perplexity by over 0.061.10 To contextualize the significance of a gap of 0.061, we note that the T5-XXL model had to train for an additional 250k steps to increase 0.052. The gap continues to increase with additional training, with the Switch-XXL model out-performing the T5-XXL by 0.087 by 500k steps.
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    Training instability. However, as described in the introduction, large sparse models can be unstable, and as we increase the scale, we encounter some sporadic issues. We find that the larger Switch-C model, with 1.6T parameters and 2048 experts, exhibits no training instability at all. Instead, the Switch XXL version, with nearly 10x larger FLOPs per sequence, is sometimes unstable. As a result, though this is our better model on a step-basis, we do not pre-train for a full 1M steps, in-line with the final reported results of T5 (Raffel et al., 2019).
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    Training instability. However, as described in the introduction, large sparse models can be unstable, and as we increase the scale, we encounter some sporadic issues. We find that the larger Switch-C model, with 1.6T parameters and 2048 experts, exhibits no training instability at all. Instead, the Switch XXL version, with nearly 10x larger FLOPs per sequence, is sometimes unstable. As a result, though this is our better model on a step-basis, we do not pre-train for a full 1M steps, in-line with the final reported results of T5 (Raffel et al., 2019).
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    10. This reported quality difference is a lower bound, and may actually be larger. The T5-XXL was pretrained on an easier C4 data set which included duplicated, and thus easily copied, snippets within examples.
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    10. This reported quality difference is a lower bound, and may actually be larger. The T5-XXL was pretrained on an easier C4 data set which included duplicated, and thus easily copied, snippets within examples.
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    Reasoning fine-tuning performance. As a preliminary assessment of the model quality, we use a Switch-XXL model partially pre-trained on 503B tokens, or approximately half the text used by the T5-XXL model. Using this checkpoint, we conduct multi-task training for efficiency, where all tasks are learned jointly, rather than individually fine-tuned. We find that SQuAD accuracy on the validation set increases to 89.7 versus state-of-the-art of 91.3. Next, the average SuperGLUE test score is recorded at 87.5 versus the T5 version obtaining a score of 89.3 compared to the state-of-the-art of 90.0 (Wang et al., 2019). On ANLI (Nie et al., 2019), Switch XXL improves over the prior state-of-the-art to get a 65.7 accuracy versus the prior best of 49.4 (Yang et al., 2020). We note that while the Switch-XXL has state-of-the-art Neg. Log Perp. on the upstream pre-training task, its gains have not yet fully translated to SOTA downstream performance. We study this issue more in Appendix E.
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    Reasoning fine-tuning performance. As a preliminary assessment of the model quality, we use a Switch-XXL model partially pre-trained on 503B tokens, or approximately half the text used by the T5-XXL model. Using this checkpoint, we conduct multi-task training for efficiency, where all tasks are learned jointly, rather than individually fine-tuned. We find that SQuAD accuracy on the validation set increases to 89.7 versus state-of-the-art of 91.3. Next, the average SuperGLUE test score is recorded at 87.5 versus the T5 version obtaining a score of 89.3 compared to the state-of-the-art of 90.0 (Wang et al., 2019). On ANLI (Nie et al., 2019), Switch XXL improves over the prior state-of-the-art to get a 65.7 accuracy versus the prior best of 49.4 (Yang et al., 2020). We note that while the Switch-XXL has state-of-the-art Neg. Log Perp. on the upstream pre-training task, its gains have not yet fully translated to SOTA downstream performance. We study this issue more in Appendix E.
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    Knowledge-based fine-tuning performance. Finally, we also conduct an early examination of the model's knowledge with three closed-book knowledge-based tasks: Natural Questions, WebQuestions and TriviaQA, without additional pre-training using Salient Span Masking (Guu et al., 2020). In all three cases, we observe improvements over the prior stateof-the-art T5-XXL model (without SSM). Natural Questions exact match increases to 34.4 versus the prior best of 32.8, Web Questions increases to 41.0 over 37.2, and TriviaQA increases to 47.5 versus 42.9.
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    Knowledge-based fine-tuning performance. Finally, we also conduct an early examination of the model's knowledge with three closed-book knowledge-based tasks: Natural Questions, WebQuestions and TriviaQA, without additional pre-training using Salient Span Masking (Guu et al., 2020). In all three cases, we observe improvements over the prior stateof-the-art T5-XXL model (without SSM). Natural Questions exact match increases to 34.4 versus the prior best of 32.8, Web Questions increases to 41.0 over 37.2, and TriviaQA increases to 47.5 versus 42.9.
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    Summing up, despite training on less than half the data of other models, we already find comparable, and sometimes state-of-the-art, model quality. Currently, the Switch Transformer translates substantial upstream gains better to knowledge-based tasks, than reasoning-tasks (see Appendix E). Extracting stronger fine-tuning performance from large expert models is an active research question, and the pre-training perplexity indicates future improvements should be possible.
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    Summing up, despite training on less than half the data of other models, we already find comparable, and sometimes state-of-the-art, model quality. Currently, the Switch Transformer translates substantial upstream gains better to knowledge-based tasks, than reasoning-tasks (see Appendix E). Extracting stronger fine-tuning performance from large expert models is an active research question, and the pre-training perplexity indicates future improvements should be possible.
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    6. Related Work
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    6. Related Work
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    The importance of scale in neural networks is widely recognized and several approaches have been proposed. Recent works have scaled models to billions of parameters through using model parallelism (e.g. splitting weights and tensors across multiple cores) (Shazeer et al., 2018; Rajbhandari et al., 2019; Raffel et al., 2019; Brown et al., 2020; Shoeybi et al., 2019). Alternatively, Harlap et al. (2018); Huang et al. (2019) propose using pipeline based model parallelism, where different layers are split across devices and micro-batches are pipelined to the different layers. Finally, Product Key networks (Lample et al., 2019) were proposed to scale up the capacity of neural networks by doing a lookup for learnable embeddings based on the incoming token representations to a given layer.
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    The importance of scale in neural networks is widely recognized and several approaches have been proposed. Recent works have scaled models to billions of parameters through using model parallelism (e.g. splitting weights and tensors across multiple cores) (Shazeer et al., 2018; Rajbhandari et al., 2019; Raffel et al., 2019; Brown et al., 2020; Shoeybi et al., 2019). Alternatively, Harlap et al. (2018); Huang et al. (2019) propose using pipeline based model parallelism, where different layers are split across devices and micro-batches are pipelined to the different layers. Finally, Product Key networks (Lample et al., 2019) were proposed to scale up the capacity of neural networks by doing a lookup for learnable embeddings based on the incoming token representations to a given layer.
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    Our work studies a specific model in a class of methods that do conditional computation, where computation decisions are made dynamically based on the input. Cho and Bengio (2014) proposed adaptively selecting weights based on certain bit patterns occuring in the model hidden-states. Eigen et al. (2013) built stacked expert layers with dense matrix multiplications and ReLU activations and showed promising results on jittered MNIST and monotone speech. In computer vision Puigcerver et al. (2020) manually route tokens based on semantic classes during upstream pre-training and then select the relevant experts to be used according to the downstream task.
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    Our work studies a specific model in a class of methods that do conditional computation, where computation decisions are made dynamically based on the input. Cho and Bengio (2014) proposed adaptively selecting weights based on certain bit patterns occuring in the model hidden-states. Eigen et al. (2013) built stacked expert layers with dense matrix multiplications and ReLU activations and showed promising results on jittered MNIST and monotone speech. In computer vision Puigcerver et al. (2020) manually route tokens based on semantic classes during upstream pre-training and then select the relevant experts to be used according to the downstream task.
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    Mixture of Experts (MoE), in the context of modern deep learning architectures, was proven effective in Shazeer et al. (2017). That work added an MoE layer which was stacked between LSTM (Hochreiter and Schmidhuber, 1997) layers, and tokens were separately routed to combinations of experts. This resulted in state-of-the-art results in language modeling and machine translation benchmarks. The MoE layer was reintroduced into the Transformer architecture by the Mesh Tensorflow library (Shazeer et al., 2018) where MoE layers were introduced as a substitute of the FFN layers, however, there were no accompanying NLP results. More recently, through advances in machine learning infrastructure, GShard (Lepikhin et al., 2020), which extended the XLA compiler, used the MoE Transformer to dramatically improve machine translation across 100 languages. Finally Fan et al. (2021) chooses a different deterministic MoE strategy to split the model parameters into non-overlapping groups of languages.
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    Mixture of Experts (MoE), in the context of modern deep learning architectures, was proven effective in Shazeer et al. (2017). That work added an MoE layer which was stacked between LSTM (Hochreiter and Schmidhuber, 1997) layers, and tokens were separately routed to combinations of experts. This resulted in state-of-the-art results in language modeling and machine translation benchmarks. The MoE layer was reintroduced into the Transformer architecture by the Mesh Tensorflow library (Shazeer et al., 2018) where MoE layers were introduced as a substitute of the FFN layers, however, there were no accompanying NLP results. More recently, through advances in machine learning infrastructure, GShard (Lepikhin et al., 2020), which extended the XLA compiler, used the MoE Transformer to dramatically improve machine translation across 100 languages. Finally Fan et al. (2021) chooses a different deterministic MoE strategy to split the model parameters into non-overlapping groups of languages.
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    Sparsity along the sequence length dimension (L) in the Transformer attention patterns has been a successful technique to reduce the attention complexity from O(L 2 ) (Child et al., 2019; Correia et al., 2019; Sukhbaatar et al., 2019; Kitaev et al., 2020; Zaheer et al., 2020; Beltagy et al., 2020). This has enabled learning longer sequences than previously possible. This version of the Switch Transformer does not employ attention sparsity, but these techniques are complimentary, and, as future work, these could be combined to potentially improve learning on tasks requiring long contexts.
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    Sparsity along the sequence length dimension (L) in the Transformer attention patterns has been a successful technique to reduce the attention complexity from O(L 2 ) (Child et al., 2019; Correia et al., 2019; Sukhbaatar et al., 2019; Kitaev et al., 2020; Zaheer et al., 2020; Beltagy et al., 2020). This has enabled learning longer sequences than previously possible. This version of the Switch Transformer does not employ attention sparsity, but these techniques are complimentary, and, as future work, these could be combined to potentially improve learning on tasks requiring long contexts.
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    7. Discussion
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    7. Discussion
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    We pose and discuss questions about the Switch Transformer, and sparse expert models generally, where sparsity refers to weights, not on attention patterns.
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    Isn't Switch Transformer better due to sheer parameter count? Yes, and by design! Parameters, independent of the total FLOPs used, are a useful axis to scale neural language models. Large models have been exhaustively shown to perform better (Kaplan et al., 2020). But in this case, our model is more sample efficient and faster while using the same computational resources.
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    Isn't Switch Transformer better due to sheer parameter count? Yes, and by design! Parameters, independent of the total FLOPs used, are a useful axis to scale neural language models. Large models have been exhaustively shown to perform better (Kaplan et al., 2020). But in this case, our model is more sample efficient and faster while using the same computational resources.
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    I don't have access to a supercomputer—is this still useful for me? Though this work has focused on extremely large models, we also find that models with as few as two experts improves performance while easily fitting within memory constraints of commonly available GPUs or TPUs (details in Appendix D). We therefore believe our techniques are useful in small-scale settings.
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    Do sparse models outperform dense models on the speed-accuracy Pareto curve? Yes. Across a wide variety of different models sizes, sparse models outperform dense models per step and on wall clock time. Our controlled experiments show for a fixed amount of computation and time, sparse models outperform dense models.
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    I can't deploy a trillion parameter model—can we shrink these models? We cannot fully preserve the model quality, but compression rates of 10 to 100x are achievable by distilling our sparse models into dense models while achieving ≈30% of the quality gain of the expert model.
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    Why use Switch Transformer instead of a model-parallel dense model? On a time basis, Switch Transformers can be far more efficient than dense-models with sharded parameters (Figure 6). Also, we point out that this decision is not mutually exclusive—we
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    Why aren't sparse models widely used already? The motivation to try sparse models has been stymied by the massive success of scaling dense models (the success of which is partially driven by co-adaptation with deep learning hardware as argued in Hooker (2020)). Further, sparse models have been subject to multiple issues including (1) model complexity, (2) training difficulties, and (3) communication costs. Switch Transformer makes strides to alleviate these issues.
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    8. Future Work
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    8. Future Work
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    This paper lays out a simplified architecture, improved training procedures, and a study of how sparse models scale. However, there remain many open future directions which we briefly describe here:
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  • 1. A significant challenge is further improving training stability for the largest models. While our stability techniques were effective for our Switch-Base, Switch-Large and Switch-C models (no observed instability), they were not sufficient for Switch-XXL. We have taken early steps towards stabilizing these models, which we think may be generally useful for large models, including using regularizers for improving stability and adapted forms of gradient clipping, but this remains unsolved.
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  • 2. Generally we find that improved pre-training quality leads to better downstream results (Appendix E), though we sometimes encounter striking anomalies. For instance, despite similar perplexities modeling the C4 data set, the 1.6T parameter Switch-C achieves only an 87.7 exact match score in SQuAD, which compares unfavorably to 89.6 for the smaller Switch-XXL model. One notable difference is that the Switch-XXL model applies ≈10x the FLOPS per token than the Switch-C model, even though it has ≈4x less unique parameters (395B vs 1.6T). This suggests a poorly understood dependence between fine-tuning quality, FLOPS per token and number of parameters.
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  • 2. Generally we find that improved pre-training quality leads to better downstream results (Appendix E), though we sometimes encounter striking anomalies. For instance, despite similar perplexities modeling the C4 data set, the 1.6T parameter Switch-C achieves only an 87.7 exact match score in SQuAD, which compares unfavorably to 89.6 for the smaller Switch-XXL model. One notable difference is that the Switch-XXL model applies ≈10x the FLOPS per token than the Switch-C model, even though it has ≈4x less unique parameters (395B vs 1.6T). This suggests a poorly understood dependence between fine-tuning quality, FLOPS per token and number of parameters.
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  • 3. Perform a comprehensive study of scaling relationships to guide the design of architectures blending data, model and expert-parallelism. Ideally, given the specs of a hardware configuration (computation, memory, communication) one could more rapidly design an optimal model. And, vice versa, this may also help in the design of future hardware.
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  • 4. Our work falls within the family of adaptive computation algorithms. Our approach always used identical, homogeneous experts, but future designs (facilitated by more flexible infrastructure) could support heterogeneous experts. This would enable more flexible adaptation by routing to larger experts when more computation is desired perhaps for harder examples.
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  • 5. Investigating expert layers outside the FFN layer of the Transformer. We find preliminary evidence that this similarly can improve model quality. In Appendix A, we report quality improvement adding these inside Self-Attention layers, where our
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  • 6. Examining Switch Transformer in new and across different modalities. We have thus far only considered language, but we believe that model sparsity can similarly provide advantages in new modalities, as well as multi-modal networks.
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    This list could easily be extended, but we hope this gives a flavor for the types of challenges that we are thinking about and what we suspect are promising future directions.
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    9. Conclusion
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    Switch Transformers are scalable and effective natural language learners. We simplify Mixture of Experts to produce an architecture that is easy to understand, stable to train and vastly more sample efficient than equivalently-sized dense models. We find that these models excel across a diverse set of natural language tasks and in different training regimes, including pre-training, fine-tuning and multi-task training. These advances make it possible to train models with hundreds of billion to trillion parameters and which achieve substantial speedups relative to dense T5 baselines. We hope our work motivates sparse models as an effective architecture and that this encourages researchers and practitioners to consider these flexible models in natural language tasks, and beyond.
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    Acknowledgments
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    Acknowledgments
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    The authors would like to thank Margaret Li who provided months of key insights into algorithmic improvements and suggestions for empirical studies. Hugo Larochelle for sage advising and clarifying comments on the draft, Irwan Bello for detailed comments and careful revisions, Colin Raffel and Adam Roberts for timely advice on neural language models and the T5 code-base, Yoshua Bengio for advising and encouragement on research in adaptive computation, Jascha Sohl-dickstein for interesting new directions for stabilizing new large scale models and paper revisions, and the Google Brain Team for useful discussions on the paper. Blake Hechtman who provided invaluable help in profiling and improving the training performance of our models.
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    A. Switch for Attention
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    Shazeer et al. (2018); Lepikhin et al. (2020) designed MoE Transformers (Shazeer et al., 2017) by adding MoE layers into the dense feedfoward network (FFN) computations of the Transformer. Similarly, our work also replaced the FFN layer in the Transformer, but we briefly explore here an alternate design. We add Switch layers into the Transformer Self-Attention layers. To do so, we replace the trainable weight matrices that produce the queries, keys and values with Switch layers as seen in Figure 10.
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  • Figure 10: Switch layers in attention. We diagram how to incorporate the Switch layer into the Self-Attention transformer block. For each token (here we show two tokens, x1 = \"More\" and x2 = \"Parameters\"), one set of weights produces the query and the other set of unique weights produces the shared keys and values. We experimented with each expert being a linear operation, as well as a FFN, as was the case throughout this work. While we found quality improvements using this, we found this to be more unstable when used with low precision number formats, and thus leave it for future work.
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    Figure 10: Switch layers in attention. We diagram how to incorporate the Switch layer into the Self-Attention transformer block. For each token (here we show two tokens, x1 = \"More\" and x2 = \"Parameters\"), one set of weights produces the query and the other set of unique weights produces the shared keys and values. We experimented with each expert being a linear operation, as well as a FFN, as was the case throughout this work. While we found quality improvements using this, we found this to be more unstable when used with low precision number formats, and thus leave it for future work.
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    However, when these layers do train stably, we believe the preliminary positive results  
    suggests a future promising direction.                                                   
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    Model                 Precision  Quality        Quality   Speed       
      @100k Steps (↑)@16H (↑)  (ex/sec) (↑)
    Experts FF            float32    -1.548         -1.614    1480        
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    Expert Attention      bfloat16   [diverges]     [diverges]–           
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    Expert FF + Attention bfloat16   [diverges]     [diverges]–           
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    Table 10: Switch attention layer results. All models have 32 experts and train with 524k tokens per batch. Experts FF is when experts replace the FFN in the Transformer, which is our standard setup throughout the paper. Experts FF + Attention is when experts are used to replace both the FFN and the Self-Attention layers. When training with bfloat16 precision the models that have experts attention diverge.
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  • Table 10: Switch attention layer results. All models have 32 experts and train with 524k tokens per batch. Experts FF is when experts replace the FFN in the Transformer, which is our standard setup throughout the paper. Experts FF + Attention is when experts are used to replace both the FFN and the Self-Attention layers. When training with bfloat16 precision the models that have experts attention diverge.
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    B. Preventing Token Dropping with No-Token-Left-Behind
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    B. Preventing Token Dropping with No-Token-Left-Behind
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    Due to software constraints on TPU accelerators, the shapes of our Tensors must be statically sized. As a result, each expert has a finite and fixed capacity to process token representations. This, however, presents an issue for our model which dynamically routes tokens at run-time that may result in an uneven distribution over experts. If the number of tokens sent to an expert is less than the expert capacity, then the computation may simply be padded – an inefficient use of the hardware, but mathematically correct. However, when the number of tokens sent to an expert is larger than its capacity (expert overflow), a protocol is needed to handle this. Lepikhin et al. (2020) adapts a Mixture-of-Expert model and addresses expert overflow by passing its representation to the next layer without processing through a residual connection which we also follow.
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    Due to software constraints on TPU accelerators, the shapes of our Tensors must be statically sized. As a result, each expert has a finite and fixed capacity to process token representations. This, however, presents an issue for our model which dynamically routes tokens at run-time that may result in an uneven distribution over experts. If the number of tokens sent to an expert is less than the expert capacity, then the computation may simply be padded – an inefficient use of the hardware, but mathematically correct. However, when the number of tokens sent to an expert is larger than its capacity (expert overflow), a protocol is needed to handle this. Lepikhin et al. (2020) adapts a Mixture-of-Expert model and addresses expert overflow by passing its representation to the next layer without processing through a residual connection which we also follow.
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    We suspected that having no computation applied to tokens could be very wasteful, especially since if there is overflow on one expert, that means another expert will have extra capacity. With this intuition we create No-Token-Left-Behind, which iteratively reroutes any tokens that are at first routed to an expert that is overflowing. Figure 11 shows a graphical description of this method, which will allow us to guarantee almost no tokens will be dropped during training and inference. We hypothesised that this could improve performance and further stabilize training, but we found no empirical benefits. We suspect that once the network learns associations between different tokens and experts, if this association is changed (e.g. sending a token to its second highest expert) then performance could be degraded.
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    We suspected that having no computation applied to tokens could be very wasteful, especially since if there is overflow on one expert, that means another expert will have extra capacity. With this intuition we create No-Token-Left-Behind, which iteratively reroutes any tokens that are at first routed to an expert that is overflowing. Figure 11 shows a graphical description of this method, which will allow us to guarantee almost no tokens will be dropped during training and inference. We hypothesised that this could improve performance and further stabilize training, but we found no empirical benefits. We suspect that once the network learns associations between different tokens and experts, if this association is changed (e.g. sending a token to its second highest expert) then performance could be degraded.
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    C. Encouraging Exploration Across Experts
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    C. Encouraging Exploration Across Experts
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    At each expert-layer, the router determines to which expert to send the token. This is a discrete decision over the available experts, conditioned on information about the token's representation. Based on the incoming token representation, the router determines the best expert, however, it receives no counterfactual information about how well it would have done selecting an alternate expert. As in reinforcement learning, a classic explorationexploitation dilemma arises (Sutton and Barto, 2018). These issues have been similarly noted and addressed differently by Rosenbaum et al. (2017) which demonstrated success in multi-task learning. This particular setting most closely matches that of a contextual bandit (Robbins, 1952). Deterministically selecting the top expert always amounts to an exploitative strategy – we consider balancing exploration to seek better expert assignment.
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    At each expert-layer, the router determines to which expert to send the token. This is a discrete decision over the available experts, conditioned on information about the token's representation. Based on the incoming token representation, the router determines the best expert, however, it receives no counterfactual information about how well it would have done selecting an alternate expert. As in reinforcement learning, a classic explorationexploitation dilemma arises (Sutton and Barto, 2018). These issues have been similarly noted and addressed differently by Rosenbaum et al. (2017) which demonstrated success in multi-task learning. This particular setting most closely matches that of a contextual bandit (Robbins, 1952). Deterministically selecting the top expert always amounts to an exploitative strategy – we consider balancing exploration to seek better expert assignment.
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    To introduce exploration, we consider several approaches: 1) deterministic or argmax 2) sampling from the softmax distribution 3) input dropout on the incoming representation 4) multiplicative jitter noise on the incoming representation. The resulting impact on model quality is reported in Table 11. Throughout this work, we use input jitter to inject noise as we have found it to empirically perform the best.
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    D. Switch Transformers in Lower Compute Regimes
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    D. Switch Transformers in Lower Compute Regimes
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  • Figure 11: Diagram of the No-Token-Left-Behind Routing. Stage 1 is equivalent to Switch routing where tokens are routed to the expert with the highest probability from the router. In Stage 2 we look at all tokens that have overflowed and route them to the expert with which has the second highest probability. Tokens can still be overflowed if their second highest expert has too many tokens, but this allows most of the tokens to be routed. This process can be iterated to guarantee virtually no tokens are dropped at all.
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  • Table 11: Router Exploration Strategies. Quality of the Switch Transformer, measured by the negative log perplexity, under different randomness-strategies for selecting the expert (lower is better). There is no material speed performance difference between the variants.
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  • Figure 11: Diagram of the No-Token-Left-Behind Routing. Stage 1 is equivalent to Switch routing where tokens are routed to the expert with the highest probability from the router. In Stage 2 we look at all tokens that have overflowed and route them to the expert with which has the second highest probability. Tokens can still be overflowed if their second highest expert has too many tokens, but this allows most of the tokens to be routed. This process can be iterated to guarantee virtually no tokens are dropped at all.
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    at the scale of 10B+ parameter models, but we show in Figure 12 as few as 2 experts produce compelling gains over a FLOP-matched counterpart. Even if a super computer is not readily available, training Switch Transformers with 2, 4, or 8 experts (as we typically recommend one expert per core) results in solid improvements over T5 dense baselines.
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    Model         Quality (Neg. Log Perp.) (↑)  
    Argmax        -1.471                        
    Sample softmax-1.570                        
    Input dropout -1.480                        
    Input jitter  -1.468                        
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  • Table 11: Router Exploration Strategies. Quality of the Switch Transformer, measured by the negative log perplexity, under different randomness-strategies for selecting the expert (lower is better). There is no material speed performance difference between the variants.
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    Figure 12: Switch Transformer with few experts. Switch Transformer improves over the baseline even with very few experts. Here we show scaling properties at very small scales, where we improve over the T5-Base model using 2, 4, and 8 experts.
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    E. Relation of Upstream to Downstream Model Performance
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    There is no guarantee that a model's quality on a pre-training objective will translate to downstream task results. Figure 13 presents the correlation of the upstream model quality, for both dense and Switch models, on the C4 pre-training task with two downstream task measures: average SuperGLUE performance and TriviaQA score. We choose these two tasks as one probes the model's reasoning and the other factual knowledge.
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+                "/page/31/Figure/3": 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    Figure 12: Switch Transformer with few experts. Switch Transformer improves over the baseline even with very few experts. Here we show scaling properties at very small scales, where we improve over the T5-Base model using 2, 4, and 8 experts.
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    Figure 13: Upstream pre-trained quality to downstream model quality. We correlate the upstream performance with downstream quality on both SuperGLUE and TriviaQA (SOTA recorded without SSM), reasoning and knowledge-heavy benchmarks, respectively (validation sets). We find that, as with the baseline, the Switch model scales with improvements in the upstream pre-training task. For SuperGLUE, we find a loosely linear relation between negative log perplexity and the average SuperGLUE score. However, the dense model often performs better for a fixed perplexity, particularly in the large-scale regime. Conversely, on the knowledge-heavy task, TriviaQA, we find that the Switch Transformer may follow an improved scaling relationship – for a given upstream perplexity, it does better than a dense counterpart. Further statistics (expensive to collect and left to future work) would be necessary to confirm these observations.
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    We find a consistent correlation, indicating that for both baseline and Switch models, improved pre-training leads to better downstream results. Additionally, for a fixed upstream perplexity we find that both Switch and dense models perform similarly in the small to medium model size regime. However, in the largest model regime (T5-11B/T5-XXL) our largest Switch models, as mentioned in Section 5.6, do not always translate their upstream perplexity well to downstream fine-tuning on the SuperGLUE task. This warrants future investigation and study to fully realize the potential of sparse models. Understanding the fine-tuning dynamics with expert-models is very complicated and is dependent on regularization, load-balancing, and fine-tuning hyper-parameters.
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    E. Relation of Upstream to Downstream Model Performance
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    F. Pseudo Code for Switch Transformers
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    There is no guarantee that a model's quality on a pre-training objective will translate to downstream task results. Figure 13 presents the correlation of the upstream model quality, for both dense and Switch models, on the C4 pre-training task with two downstream task measures: average SuperGLUE performance and TriviaQA score. We choose these two tasks as one probes the model's reasoning and the other factual knowledge.
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    Pseudocode for Switch Transformers in Mesh Tensorflow (Shazeer et al., 2018). No model parallelism is being used for the below code (see 5.4 for more details).
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    import mesh tensorflow as mtf\ndef load balance loss(router probs, expert mask):\n   \"\"\"Calculate load−balancing loss to ensure diverse expert routing.\"\"\"\n   # router probs is the probability assigned for each expert per token.\n   # router probs shape: [num cores, tokens per core, num experts]\n   # expert index contains the expert with the highest router probability in one−hot format.\n   # expert mask shape: [num cores, tokens per core, num experts]\n   # For each core, get the fraction of tokens routed to each expert.\n   # density 1 shape: [num cores, num experts]\n   density 1 = mtf.reduce mean(expert mask, reduced dim=tokens per core)\n   # For each core, get fraction of probability mass assigned to each expert\n   # from the router across all tokens.\n   # density 1 proxy shape: [num cores, num experts]\n   density 1 proxy = mtf.reduce mean(router probs, reduced dim=tokens per core)\n   # density l for a single core: vector of length num experts that sums to 1.\n   # density l proxy for a single core: vector of length num experts that sums to 1.\n   # Want both vectors to have uniform allocation (1/num experts) across all num expert elements.\n   # The two vectors will be pushed towards uniform allocation when the dot product is minimized.\n   loss = mtf.reduce mean(density 1 proxy ∗ density 1) ∗ (num experts ˆ 2)\n   return loss
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  • Figure 13: Upstream pre-trained quality to downstream model quality. We correlate the upstream performance with downstream quality on both SuperGLUE and TriviaQA (SOTA recorded without SSM), reasoning and knowledge-heavy benchmarks, respectively (validation sets). We find that, as with the baseline, the Switch model scales with improvements in the upstream pre-training task. For SuperGLUE, we find a loosely linear relation between negative log perplexity and the average SuperGLUE score. However, the dense model often performs better for a fixed perplexity, particularly in the large-scale regime. Conversely, on the knowledge-heavy task, TriviaQA, we find that the Switch Transformer may follow an improved scaling relationship – for a given upstream perplexity, it does better than a dense counterpart. Further statistics (expensive to collect and left to future work) would be necessary to confirm these observations.
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    We find a consistent correlation, indicating that for both baseline and Switch models, improved pre-training leads to better downstream results. Additionally, for a fixed upstream perplexity we find that both Switch and dense models perform similarly in the small to medium model size regime. However, in the largest model regime (T5-11B/T5-XXL) our largest Switch models, as mentioned in Section 5.6, do not always translate their upstream perplexity well to downstream fine-tuning on the SuperGLUE task. This warrants future investigation and study to fully realize the potential of sparse models. Understanding the fine-tuning dynamics with expert-models is very complicated and is dependent on regularization, load-balancing, and fine-tuning hyper-parameters.
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  • Figure 14: Pseudo code for the load balance loss for Switch Transformers in Mesh Tensorflow.
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    import mesh tensorflow as mtf
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    def router(inputs, capacity factor):\n   \"\"\"Produce the combine and dispatch tensors used for sending and\n   receiving tokens from their highest probability expert. \"\"\"\n   # Core layout is split across num cores for all tensors and operations.\n   # inputs shape: [num cores, tokens per core, d model]
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    F. Pseudo Code for Switch Transformers
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    router weights = mtf.Variable(shape=[d model, num experts])
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    Pseudocode for Switch Transformers in Mesh Tensorflow (Shazeer et al., 2018). No model parallelism is being used for the below code (see 5.4 for more details).
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    # router logits shape: [num cores, tokens per core, num experts]\nrouter logits = mtf.einsum([inputs, router weights], reduced dim=d model)
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    if is training:
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    import mesh tensorflow as mtf\ndef load balance loss(router probs, expert mask):\n   \"\"\"Calculate load−balancing loss to ensure diverse expert routing.\"\"\"\n   # router probs is the probability assigned for each expert per token.\n   # router probs shape: [num cores, tokens per core, num experts]\n   # expert index contains the expert with the highest router probability in one−hot format.\n   # expert mask shape: [num cores, tokens per core, num experts]\n   # For each core, get the fraction of tokens routed to each expert.\n   # density 1 shape: [num cores, num experts]\n   density 1 = mtf.reduce mean(expert mask, reduced dim=tokens per core)\n   # For each core, get fraction of probability mass assigned to each expert\n   # from the router across all tokens.\n   # density 1 proxy shape: [num cores, num experts]\n   density 1 proxy = mtf.reduce mean(router probs, reduced dim=tokens per core)\n   # density l for a single core: vector of length num experts that sums to 1.\n   # density l proxy for a single core: vector of length num experts that sums to 1.\n   # Want both vectors to have uniform allocation (1/num experts) across all num expert elements.\n   # The two vectors will be pushed towards uniform allocation when the dot product is minimized.\n   loss = mtf.reduce mean(density 1 proxy ∗ density 1) ∗ (num experts ˆ 2)\n   return loss
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    # Add noise for exploration across experts. router logits += mtf.random uniform(shape=router logits.shape, minval=1−eps, maxval=1+eps)
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    Figure 14: Pseudo code for the load balance loss for Switch Transformers in Mesh Tensorflow.
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    # Convert input to softmax operation from bfloat16 to float32 for stability. router logits = mtf.to float32(router logits)
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    # Probabilities for each token of what expert it should be sent to.\nrouter probs = mtf.softmax(router logits, axis=−1)
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    # Get the top−1 expert for each token. expert gate is the top−1 probability\n# from the router for each token. expert index is what expert each token\n# is going to be routed to.\n# expert gate shape: [num cores, tokens per core]\n# expert index shape: [num cores, tokens per core]\nexpert gate, expert index = mtf.top 1(router probs, reduced dim=num experts)\n# expert mask shape: [num cores, tokens per core, num experts]\nexpert mask = mtf.one hot(expert index, dimension=num experts)\n# Compute load balancing loss.\naux loss = load balance loss(router probs, expert mask)\n# Experts have a fixed capacity, ensure we do not exceed it. Construct\n# the batch indices, to each expert, with position in expert\n# make sure that not more that expert capacity examples can be routed to\n# each expert.\nposition in expert = mtf.cumsum(expert mask, dimension=tokens per core) ∗ expert mask\n# Keep only tokens that fit within expert capacity.\nexpert mask ∗= mtf.less(position in expert, expert capacity)\nexpert mask flat = mtf.reduce sum(expert mask, reduced dim=experts dim)\n# Mask out the experts that have overflowed the expert capacity.\nexpert gate ∗= expert mask flat\n# combine tensor used for combining expert outputs and scaling with router probability.\n# combine tensor shape: [num cores, tokens per core, num experts, expert capacity]\ncombine tensor = (\n   expert gate ∗ expert mask flat ∗\n   mtf.one hot(expert index, dimension=num experts) ∗\n   mtf.one hot(position in expert, dimension=expert capacity))\n# Cast back outputs to bfloat16 for the rest of the layer.\ncombine tensor = mtf.to bfloat16(combine tensor)\n# Create binary dispatch tensor that is 1 if the token gets routed to the corresponding expert.\n# dispatch tensor shape: [num cores, tokens per core, num experts, expert capacity]\ndispatch tensor = mtf.cast(combine tensor, tf.bool)
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    import mesh tensorflow as mtf\ndef router(inputs, capacity factor):\n   \"\"\"Produce the combine and dispatch tensors used for sending and\n   receiving tokens from their highest probability expert. \"\"\"\n   # Core layout is split across num cores for all tensors and operations.\n   # inputs shape: [num cores, tokens per core, d model]\n   router weights = mtf.Variable(shape=[d model, num experts])\n   # router logits shape: [num cores, tokens per core, num experts]\n   router logits = mtf.einsum([inputs, router weights], reduced dim=d model)\n   if is training:\n       # Add noise for exploration across experts.\n       router logits += mtf.random uniform(shape=router logits.shape, minval=1−eps, maxval=1+eps)\n   # Convert input to softmax operation from bfloat16 to float32 for stability.\n   router logits = mtf.to float32(router logits)\n   # Probabilities for each token of what expert it should be sent to.\n   router probs = mtf.softmax(router logits, axis=−1)\n   # Get the top−1 expert for each token. expert gate is the top−1 probability\n   # from the router for each token. expert index is what expert each token\n   # is going to be routed to.\n   # expert gate shape: [num cores, tokens per core]\n   # expert index shape: [num cores, tokens per core]\n   expert gate, expert index = mtf.top 1(router probs, reduced dim=num experts)\n   # expert mask shape: [num cores, tokens per core, num experts]\n   expert mask = mtf.one hot(expert index, dimension=num experts)\n   # Compute load balancing loss.\n   aux loss = load balance loss(router probs, expert mask)\n   # Experts have a fixed capacity, ensure we do not exceed it. Construct\n   # the batch indices, to each expert, with position in expert\n   # make sure that not more that expert capacity examples can be routed to\n   # each expert.\n   position in expert = mtf.cumsum(expert mask, dimension=tokens per core) ∗ expert mask\n   # Keep only tokens that fit within expert capacity.\n   expert mask ∗= mtf.less(position in expert, expert capacity)\n   expert mask flat = mtf.reduce sum(expert mask, reduced dim=experts dim)\n   # Mask out the experts that have overflowed the expert capacity.\n   expert gate ∗= expert mask flat\n   # combine tensor used for combining expert outputs and scaling with router probability.\n   # combine tensor shape: [num cores, tokens per core, num experts, expert capacity]\n   combine tensor = (\n       expert gate ∗ expert mask flat ∗\n       mtf.one hot(expert index, dimension=num experts) ∗\n       mtf.one hot(position in expert, dimension=expert capacity))\n   # Cast back outputs to bfloat16 for the rest of the layer.\n   combine tensor = mtf.to bfloat16(combine tensor)\n   # Create binary dispatch tensor that is 1 if the token gets routed to the corresponding expert.\n   # dispatch tensor shape: [num cores, tokens per core, num experts, expert capacity]\n   dispatch tensor = mtf.cast(combine tensor, tf.bool)\n   return dispatch tensor, combine tensor, aux loss
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    Figure 15: Pseudo code for the router for Switch Transformers in Mesh Tensorflow.
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    Figure 15: Pseudo code for the router for Switch Transformers in Mesh Tensorflow.
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    import mesh tensorflow as mtf\ndef switch layer(inputs, n, capacity factor, num experts):\n   \"\"\"Distributed switch transformer feed−forward layer.\"\"\"\n   # num cores (n) = total cores for training the model (scalar).\n   # d model = model hidden size (scalar).\n   # num experts = total number of experts.\n   # capacity factor = extra buffer for each expert.\n   # inputs shape: [batch, seq len, d model]\n   batch, seq len, d model = inputs.get shape()\n   # Each core will route tokens per core tokens to the correct experts.\n   tokens per core = batch ∗ seq len / num cores\n   # Each expert will have shape [num cores, expert capacity, d model].\n   # Each core is responsible for sending expert capacity tokens\n   # to each expert.\n   expert capacity = tokens per core ∗ capacity factor / num experts\n   # Reshape to setup per core expert dispatching.\n   # shape: [batch, seq len, d model] −> [num cores, tokens per core, d model]\n   # Core layout: [n, 1, 1] −> [n, 1, 1]\n   inputs = mtf.reshape(inputs, [num cores, tokens per core, d model])\n   # Core Layout: [n, 1, 1] −> [n, 1, 1, 1], [n, 1, 1, 1]\n   # dispatch tensor (boolean) shape: [num cores, tokens per core, num experts, expert capacity]\n   # dispatch tensor is used for routing tokens to the correct expert.\n   # combine tensor (float) shape: [num cores, tokens per core, num experts, expert capacity]\n   # combine tensor used for combining expert outputs and scaling with router\n   # probability.\n   dispatch tensor, combine tensor, aux loss = router(inputs, expert capacity)\n   # Matmul with large boolean tensor to assign tokens to the correct expert.\n   # Core Layout: [n, 1, 1], −> [1, n, 1, 1]\n   # expert inputs shape: [num experts, num cores, expert capacity, d model]\n   expert inputs = mtf.einsum([inputs, dispatch tensor], reduce dims=[tokens per core])\n   # All−to−All communication. Cores split across num cores and now we want to split\n   # across num experts. This sends tokens, routed locally, to the correct expert now\n   # split across different cores.\n   # Core layout: [1, n, 1, 1] −> [n, 1, 1, 1]\n   expert inputs = mtf.reshape(expert inputs, [num experts, num cores, expert capacity, d model])\n   # Standard feed forward computation, where each expert will have its own\n   # unique set of parameters.\n   # Total unique parameters created: num experts ∗ (d model ∗ d ff ∗ 2).\n   # expert outputs shape: [num experts, num cores, expert capacity, d model]\n   expert outputs = feed forward(expert inputs)\n   # All−to−All communication. Cores are currently split across the experts\n   # dimension, which needs to be switched back to being split across num cores.\n   # Core Layout: [n, 1, 1, 1] −> [1, n, 1, 1]\n   expert outputs = mtf.reshape(expert outputs, [num experts, num cores, expert capacity, d model])\n   # Convert back to input shape and multiply outputs of experts by the routing probability.\n   # expert outputs shape: [num experts, num cores, tokens per core, d model]\n   # expert outputs combined shape: [num cores, tokens per core, d model]\n   # Core Layout: [1, n, 1, 1] −> [n, 1, 1]\n   expert outputs combined = mtf.einsum([expert outputs, combine tensor], reduce dims=[tokens per core])\n   # Remove tokens per core shapes used for local routing dispatching to match input shape.\n   # Core Layout: [n, 1, 1] −> [n, 1, 1]\n   outputs = mtf.reshape(expert outputs combined, [batch, seq len, d model])\n   return outputs, aux loss
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    import mesh tensorflow as mtf
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    def switch layer(inputs, n, capacity factor, num experts): \"\"\"Distributed switch transformer feed−forward layer.\"\"\"
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    # num cores (n) = total cores for training the model (scalar).\n# d model = model hidden size (scalar).\n# num experts = total number of experts.\n# capacity factor = extra buffer for each expert.\n# inputs shape: [batch, seq len, d model]\nbatch, seq len, d model = inputs.get shape()\n# Each core will route tokens per core tokens to the correct experts.\ntokens per core = batch ∗ seq len / num cores\n# Each expert will have shape [num cores, expert capacity, d model].\n# Each core is responsible for sending expert capacity tokens\n# to each expert.\nexpert capacity = tokens per core ∗ capacity factor / num experts\n# Reshape to setup per core expert dispatching.\n# shape: [batch, seq len, d model] −> [num cores, tokens per core, d model]\n# Core layout: [n, 1, 1] −> [n, 1, 1]\ninputs = mtf.reshape(inputs, [num cores, tokens per core, d model])\n# Core Layout: [n, 1, 1] −> [n, 1, 1, 1], [n, 1, 1, 1]\n# dispatch tensor (boolean) shape: [num cores, tokens per core, num experts, expert capacity]\n# dispatch tensor is used for routing tokens to the correct expert.\n# combine tensor (float) shape: [num cores, tokens per core, num experts, expert capacity]\n# combine tensor used for combining expert outputs and scaling with router\n# probability.\ndispatch tensor, combine tensor, aux loss = router(inputs, expert capacity)\n# Matmul with large boolean tensor to assign tokens to the correct expert.\n# Core Layout: [n, 1, 1], −> [1, n, 1, 1]\n# expert inputs shape: [num experts, num cores, expert capacity, d model]\nexpert inputs = mtf.einsum([inputs, dispatch tensor], reduce dims=[tokens per core])\n# All−to−All communication. Cores split across num cores and now we want to split\n# across num experts. This sends tokens, routed locally, to the correct expert now\n# split across different cores.\n# Core layout: [1, n, 1, 1] −> [n, 1, 1, 1]\nexpert inputs = mtf.reshape(expert inputs, [num experts, num cores, expert capacity, d model])\n# Standard feed forward computation, where each expert will have its own\n# unique set of parameters.\n# Total unique parameters created: num experts ∗ (d model ∗ d ff ∗ 2).\n# expert outputs shape: [num experts, num cores, expert capacity, d model]\nexpert outputs = feed forward(expert inputs)\n# All−to−All communication. Cores are currently split across the experts\n# dimension, which needs to be switched back to being split across num cores.\n# Core Layout: [n, 1, 1, 1] −> [1, n, 1, 1]\nexpert outputs = mtf.reshape(expert outputs, [num experts, num cores, expert capacity, d model])\n# Convert back to input shape and multiply outputs of experts by the routing probability.\n# expert outputs shape: [num experts, num cores, tokens per core, d model]\n# expert outputs combined shape: [num cores, tokens per core, d model]\n# Core Layout: [1, n, 1, 1] −> [n, 1, 1]\nexpert outputs combined = mtf.einsum([expert outputs, combine tensor], reduce dims=[tokens per core])\n# Remove tokens per core shapes used for local routing dispatching to match input shape.\n# Core Layout: [n, 1, 1] −> [n, 1, 1]\noutputs = mtf.reshape(expert outputs combined, [batch, seq len, d model])\nreturn outputs, aux loss
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    Figure 16: Pseudo code of the Switch Transformer layer in Mesh Tensorflow.
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    References
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  • Mart´ın Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, et al. Tensorflow: A system for large-scale machine learning. In 12th {USENIX} symposium on operating systems design and implementation ({OSDI} 16), pages 265–283, 2016.
    • ",
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  • Iz Beltagy, Matthew E Peters, and Arman Cohan. Longformer: The long-document transformer. arXiv preprint arXiv:2004.05150, 2020.
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  • Jonathan Berant, Andrew Chou, Roy Frostig, and Percy Liang. Semantic parsing on freebase from question-answer pairs. In Proceedings of the 2013 conference on empirical methods in natural language processing, pages 1533–1544, 2013.
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  • Manzil Zaheer, Guru Guruganesh, Avinava Dubey, Joshua Ainslie, Chris Alberti, Santiago Ontanon, Philip Pham, Anirudh Ravula, Qifan Wang, Li Yang, et al. Big bird: Transformers for longer sequences. arXiv preprint arXiv:2007.14062, 2020.
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    Preface
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    The strange history of this book
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    In January 1999 I was preparing to teach an introductory programming class in Java. I had taught it three times and I was getting frustrated. The failure rate in the class was too high and, even for students who succeeded, the overall level of achievement was too low.
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    One of the problems I saw was the books. They were too big, with too much unnecessary detail about Java, and not enough high-level guidance about how to program. And they all suffered from the trap door effect: they would start out easy, proceed gradually, and then somewhere around Chapter 5 the bottom would fall out. The students would get too much new material, too fast, and I would spend the rest of the semester picking up the pieces.
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    Two weeks before the first day of classes, I decided to write my own book. My goals were:
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  •  Be careful with vocabulary. I tried to minimize the jargon and define each term at first use.
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    I needed a title, so on a whim I chose How to Think Like a Computer Scientist.
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    My first version was rough, but it worked. Students did the reading, and they understood enough that I could spend class time on the hard topics, the interesting topics and (most important) letting the students practice.
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    What happened next is the cool part. Jeff Elkner, a high school teacher in Virginia, adopted my book and translated it into Python. He sent me a copy of his translation, and I had the unusual experience of learning Python by reading my own book. As Green Tea Press, I published the first Python version in 2001.
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    I hope you enjoy working with this book, and that it helps you learn to program and think, at least a little bit, like a computer scientist.
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    Allen B. Downey Needham MA
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    Acknowledgments
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    Acknowledgments
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    Many thanks to Jeff Elkner, who translated my Java book into Python, which got this project started and introduced me to what has turned out to be my favorite language.
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    Thanks also to Chris Meyers, who contributed several sections to How to Think Like a Computer Scientist.
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    Thanks to the Free Software Foundation for developing the GNU Free Documentation License, which helped make my collaboration with Jeff and Chris possible, and Creative Commons for the license I am using now.
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    Thanks to the editors at Lulu who worked on How to Think Like a Computer Scientist.
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  •  Michael Schmitt sent in a correction to the chapter on files and exceptions.
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  •  Robin Shaw pointed out an error in Section 13.1, where the printTime function was used in an example without being defined.
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  •  Ian Thomas and his students are using the text in a programming course. They are the first ones to test the chapters in the latter half of the book, and they have made numerous corrections and suggestions.
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  •  Chris Wrobel made corrections to the code in the chapter on file I/O and exceptions.
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  •  Moshe Zadka has made invaluable contributions to this project. In addition to writing the first draft of the chapter on Dictionaries, he provided continual guidance in the early stages of the book.
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  •  Gregor Lingl is teaching Python at a high school in Vienna, Austria. He is working on a German translation of the book, and he caught a couple of bad errors in Chapter 5.
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  •  Louis Cordier noticed a spot in Chapter 16 where the code didn't match the text.
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    x Chapter 0. Preface
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  •  Daryl Hammond and Sarah Zimmerman pointed out that I served up math.pi too early. And Zim spotted a typo.
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  •  Brian Bingham suggested Exercise 11.10.
    • ",
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  •  Brian Bingham suggested Exercise 11.10.
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  •  Joe Funke spotted a typo.
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  •  Chao-chao Chen found an inconsistency in the Fibonacci example.
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  •  Lubos Pintes sent in a typo.
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  •  Gregg Lind and Abigail Heithoff suggested Exercise 14.4.
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  •  Gregg Lind and Abigail Heithoff suggested Exercise 14.4.
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  •  Max Hailperin has sent in a number of corrections and suggestions. Max is one of the authors of the extraordinary Concrete Abstractions, which you might want to read when you are done with this book.
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  •  Stanislaw Antol sent a list of very helpful suggestions.
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  •  Eric Pashman sent a number of corrections for Chapters 4–11.
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  •  Martin Zuther sent a long list of suggestions.
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  •  Adam Zimmerman found an inconsistency in my instance of an \"instance\" and several other errors.
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    xii Chapter 0. Preface
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    Contents
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    Preface    v  
    1        The way of the program 1  
    1.1       The Python programming language      1  
    1.2       What is a program?                   3  
    1.3       What is debugging?                   3  
    1.4       Formal and natural languages         5  
    1.5       The first program                    6  
    1.6       Debugging                            7  
    1.7       Glossary                             7  
    1.8       Exercises                            9  
    2         Variables, expressions and statements11 
    2.1       Values and types                     11 
    2.2       Variables                            12 
    2.3       Variable names and keywords          12 
    2.4       Operators and operands               13 
    2.5       Expressions and statements           14 
    2.6       Interactive mode and script mode     14 
    2.7       Order of operations                  15 
    2.8       String operations                    15 
    2.9       Comments                             16 
    2.10      Debugging                            16 
    2.11      Glossary                             17 
    2.12      Exercises                            18 
    ",
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    Preface
    1The way of the program1
    1.1The Python programming language1
    1.2What is a program?3
    1.3What is debugging?3
    1.4Formal and natural languages5
    1.5The first program6
    1.6Debugging7
    1.7Glossary7
    1.8Exercises9
    2Variables, expressions and statements
    2.1Values and types11
    2.2Variables12
    2.3Variable names and keywords12
    2.4Operators and operands13
    2.5Expressions and statements14
    2.6Interactive mode and script mode14
    2.7Order of operations15
    2.8String operations15
    2.9Comments16
    2.10Debugging16
    2.11Glossary17
    2.12Exercises18
    3Functions19
    3.1Function calls19
    3.2Type conversion functions19
    3.3Math functions20
    3.4Composition21
    3.5Adding new functions21
    3.6Definitions and uses22
    3.7Flow of execution23
    3.8Parameters and arguments23
    3.9Variables and parameters are local24
    3.10Stack diagrams25
    3.11Fruitful functions and void functions26
    3.12Why functions?26
    3.13Importing with from27
    3.14Debugging27
    3.15Glossary28
    3.16Exercises29
    4Case study: interface design31
    4.1TurtleWorld31
    4.2Simple repetition32
    4.3Exercises33
    4.4Encapsulation34
    4.5Generalization34
    4.6Interface design35
    4.7Refactoring36
    4.8A development plan37
    4.9docstring37
    4.10Debugging38
    4.11Glossary38
    4.12Exercises39
    5Conditionals and recursion41
    5.1Modulus operator41
    5.2Boolean expressions41
    5.3Logical operators42
    5.4Conditional execution42
    5.5Alternative execution43
    5.6Chained conditionals43
    5.7Nested conditionals43
    5.8Recursion44
    5.9Stack diagrams for recursive functions45
    5.10Infinite recursion46
    5.11Keyboard input46
    5.12Debugging47
    5.13Glossary48
    5.14Exercises49
    6Fruitful functions51
    6.1Return values51
    6.2Incremental development52
    6.3Composition54
    6.4Boolean functions54
    6.5More recursion55
    6.6Leap of faith57
    6.7One more example57
    6.8Checking types58
    6.9Debugging59
    6.10Glossary60
    7Iteration6
    7.1Multiple assignment6
    7.2Updating variables6
    7.3The while statement6
    7.4break6
    7.5Square roots6
    7.6Algorithms6
    7.7Debugging6
    7.8Glossary6
    7.9Exercises6
    8Strings7
    8.1A string is a sequence7
    8.2len7
    8.3Traversal with a for loop7
    8.4String slices7
    8.5Strings are immutable7
    8.6Searching7
    8.7Looping and counting7
    8.8String methods7
    8.9The in operator7
    8.10String comparison7
    8.11Debugging7
    8.12Glossary7
    8.13Exercises7
    9Case study: word play8
    9.1Reading word lists8
    9.2Exercises8
    9.3Search8
    9.4Looping with indices8
    9.5Debugging8
    9.6Glossary8
    9.7Exercises8
    10 Lists87
    10.1A list is a sequence87
    10.2Lists are mutable87
    10.3Traversing a list89
    10.4List operations89
    10.5List slices89
    10.6List methods90
    10.7Map, filter and reduce91
    10.8Deleting elements92
    10.9Lists and strings93
    10.10Objects and values93
    10.11Aliasing94
    10.12List arguments95
    10.13Debugging96
    10.14Glossary97
    10.15Exercises98
    11 Dictionaries101
    11.1Dictionary as a set of counters102
    11.2Looping and dictionaries103
    11.3Reverse lookup104
    11.4Dictionaries and lists105
    11.5Memos106
    11.6Global variables108
    11.7Long integers109
    11.8Debugging109
    11.9Glossary110
    11.10Exercises111
    12 Tuples113
    12.1Tuples are immutable113
    12.2Tuple assignment114
    12.3Tuples as return values115
    12.4Variable-length argument tuples115
    12.5Lists and tuples116
    12.6Dictionaries and tuples117
    12.7Comparing tuples118
    12.8Sequences of sequences119
    12.9Debugging120
    12.10Glossary121
    12.11Exercises121
    13 Case study: data structure selection123
    13.1Word frequency analysis123
    13.2Random numbers124
    13.3Word histogram125
    13.4Most common words126
    13.5Optional parameters126
    13.6Dictionary subtraction127
    13.7Random words127
    13.8Markov analysis128
    13.9Data structures129
    13.10Debugging131
    13.11Glossary132
    13.12Exercises132
    14 Files133
    14.1Persistence133
    14.2Reading and writing133
    14.3Format operator134
    14.4Filenames and paths135
    14.5Catching exceptions136
    14.6Databases137
    14.7Pickling137
    14.8Pipes138
    14.9Writing modules139
    14.10Debugging140
    14.11Glossary141
    14.12Exercises141
    15Classes and objects
    15.1User-defined types143
    15.2Attributes144
    15.3Rectangles145
    15.4Instances as return values146
    15.5Objects are mutable146
    15.6Copying147
    15.7Debugging148
    15.8Glossary149
    15.9Exercises149
    16Classes and functions
    16.1Time151
    16.2Pure functions151
    16.3Modifiers153
    16.4Prototyping versus planning154
    16.5Debugging155
    16.6Glossary155
    16.7Exercises156
    17 Classes and methods157
    17.1Object-oriented features157
    17.2Printing objects158
    17.3Another example159
    17.4A more complicated example160
    17.5The init method160
    17.6The __str__ method161
    17.7Operator overloading161
    17.8Type-based dispatch162
    17.9Polymorphism163
    17.10Debugging164
    17.11Interface and implementation164
    17.12Glossary165
    17.13Exercises165
    18 Inheritance167
    18.1Card objects167
    18.2Class attributes168
    18.3Comparing cards169
    18.4Decks170
    18.5Printing the deck171
    18.6Add, remove, shuffle and sort171
    18.7Inheritance172
    18.8Class diagrams173
    18.9Debugging174
    18.10Data encapsulation175
    18.11Glossary176
    19 Case study: Tkinter179
    19.1GUI179
    19.2Buttons and callbacks180
    19.3Canvas widgets181
    19.4Coordinate sequences182
    19.5More widgets182
    19.6Packing widgets183
    19.7Menus and Callables185
    19.8Binding186
    19.9Debugging188
    19.10Glossary189
    19.11Exercises190
    ADebugging193
    A.1Syntax errors193
    A.2Runtime errors195
    A.3Semantic errors198
    BAnalysis of Algorithms201
    B.1Order of growth202
    B.2Analysis of basic Python operations204
    B.3Analysis of search algorithms205
    B.4Hashtables206
    CLumpy211
    C.1State diagram211
    C.2Stack diagram212
    C.3Object diagrams213
    C.4Function and class objects215
    C.5Class Diagrams216
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    xiv Contents
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    3  Functions   19  
     3.1        Function calls                       19  
     3.2        Type conversion functions            19  
     3.3        Math functions                       20  
     3.4        Composition                          21  
     3.5        Adding new functions                 21  
     3.6        Definitions and uses                 22  
     3.7        Flow of execution                    23  
     3.8        Parameters and arguments             23  
     3.9        Variables and parameters are local   24  
     3.10       Stack diagrams                       25  
     3.11       Fruitful functions and void functions26  
     3.12       Why functions?                       26  
     3.13       Importing with from                  27  
     3.14       Debugging                            27  
     3.15       Glossary                             28  
     3.16       Exercises                            29  
    4   Case study: interface design         31  
     4.1        TurtleWorld                          31  
     4.2        Simple repetition                    32  
     4.3        Exercises                            33  
     4.4        Encapsulation                        34  
     4.5        Generalization                       34  
     4.6        Interface design                     35  
     4.7        Refactoring                          36  
     4.8        A development plan                   37  
     4.9        docstring                            37  
     4.10       Debugging                            38  
     4.11       Glossary                             38  
     4.12       Exercises                            39  
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     Contents                   xv  
    5  Conditionals and recursion 41  
    5.1 Modulus operator                      41  
    5.2 Boolean expressions                   41  
    5.3 Logical operators                     42  
    5.4 Conditional execution                 42  
    5.5 Alternative execution                 43  
    5.6 Chained conditionals                  43  
    5.7 Nested conditionals                   43  
    5.8 Recursion                             44  
    5.9 Stack diagrams for recursive functions45  
     5.10                      Infinite recursion                    46  
     5.11                      Keyboard input                        46  
     5.12                      Debugging                             47  
     5.13                      Glossary                              48  
     5.14                      Exercises                             49  
    6   Fruitful functions                    51  
    6.1 Return values                         51  
    6.2 Incremental development               52  
    6.3 Composition                           54  
    6.4 Boolean functions                     54  
    6.5 More recursion                        55  
    6.6 Leap of faith                         57  
    6.7One more example           57  
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  • 6.9 Debugging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
    • ",
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    6.10 Glossary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
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    xvi Contents
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    7  Iteration   63  
     7.1        Multiple assignment      63  
     7.2        Updating variables       64  
     7.3        The while statement      64  
     7.4        break                    65  
     7.5        Square roots             66  
     7.6        Algorithms               67  
     7.7        Debugging                68  
     7.8        Glossary                 68  
     7.9        Exercises                69  
    8  Strings     71  
     8.1        A string is a sequence   71  
     8.2        len                      71  
     8.3        Traversal with a for loop72  
     8.4        String slices            73  
     8.5        Strings are immutable    74  
     8.6        Searching                74  
     8.7        Looping and counting     75  
     8.8        String methods           75  
     8.9        The in operator          76  
     8.10       String comparison        76  
     8.11       Debugging                77  
     8.12       Glossary                 78  
     8.13       Exercises                79  
    9   Case study: word play    81  
     9.1        Reading word lists       81  
     9.2        Exercises                82  
     9.3        Search                   82  
     9.4        Looping with indices     83  
     9.5        Debugging                85  
     9.6        Glossary                 85  
     9.7        Exercises                86  
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    Contents        xvii  
    10 Lists        87    
    10.1           A list is a sequence           87    
    10.2           Lists are mutable              87    
    10.3           Traversing a list              89    
    10.4           List operations                89    
    10.5           List slices                    89    
    10.6           List methods                   90    
    10.7           Map, filter and reduce         91    
    10.8           Deleting elements              92    
    10.9           Lists and strings              93    
    10.10          Objects and values             93    
    10.11          Aliasing                       94    
    10.12          List arguments                 95    
    10.13          Debugging                      96    
    10.14          Glossary                       97    
    10.15          Exercises                      98    
    11 Dictionaries 101   
    11.1           Dictionary as a set of counters102   
    11.2           Looping and dictionaries       103   
    11.3           Reverse lookup                 104   
    11.4           Dictionaries and lists         105   
    11.5           Memos                          106   
    11.6           Global variables               108   
    11.7           Long integers                  109   
    11.8           Debugging                      109   
    11.9           Glossary                       110   
    11.10          Exercises                      111   
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    xviii Contents
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    12 Tuples   113  
    12.1       Tuples are immutable                   113  
    12.2       Tuple assignment                       114  
    12.3       Tuples as return values                115  
    12.4       Variable-length argument tuples        115  
    12.5       Lists and tuples                       116  
    12.6       Dictionaries and tuples                117  
    12.7       Comparing tuples                       118  
    12.8       Sequences of sequences                 119  
    12.9       Debugging                              120  
    12.10      Glossary                               121  
    12.11      Exercises                              121  
     13 Case study: data structure selection123  
    13.1       Word frequency analysis                123  
    13.2       Random numbers                         124  
    13.3       Word histogram                         125  
    13.4       Most common words                      126  
    13.5       Optional parameters                    126  
    13.6       Dictionary subtraction                 127  
    13.7       Random words                           127  
    13.8       Markov analysis                        128  
    13.9       Data structures                        129  
    13.10      Debugging                              131  
    13.11      Glossary                               132  
    13.12      Exercises                              132  
    14 Files    133  
    14.1       Persistence                            133  
    14.2       Reading and writing                    133  
    14.3       Format operator                        134  
    14.4       Filenames and paths                    135  
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    Contents   xix  
    14.5      Catching exceptions        136  
    14.6      Databases                  137  
    14.7      Pickling                   137  
    14.8      Pipes                      138  
    14.9      Writing modules            139  
    14.10     Debugging                  140  
    14.11     Glossary                   141  
    14.12     Exercises                  141  
     15 Classes and objects     143  
    15.1      User-defined types         143  
    15.2      Attributes                 144  
    15.3      Rectangles                 145  
    15.4      Instances as return values 146  
    15.5      Objects are mutable        146  
    15.6      Copying                    147  
    15.7      Debugging                  148  
    15.8      Glossary                   149  
    15.9      Exercises                  149  
     16 Classes and functions   151  
    16.1      Time                       151  
    16.2      Pure functions             151  
    16.3      Modifiers                  153  
    16.4      Prototyping versus planning154  
    16.5      Debugging                  155  
    16.6      Glossary                   155  
    16.7      Exercises                  156  
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    17 Classes and methods   157  
    17.1                    Object-oriented features     157  
    17.2                    Printing objects             158  
    17.3                    Another example              159  
    17.4                    A more complicated example   160  
    17.5                    The init method              160  
    17.6                    The __str__ method           161  
    17.7                    Operator overloading         161  
    17.8                    Type-based dispatch          162  
    17.9                    Polymorphism                 163  
    17.10                   Debugging                    164  
    17.11                   Interface and implementation 164  
    17.12                   Glossary                     165  
    17.13                   Exercises                    165  
    18 Inheritance           167  
    18.1                    Card objects                 167  
    18.2                    Class attributes             168  
    18.3                    Comparing cards              169  
    18.4                    Decks                        170  
    18.5                    Printing the deck            171  
    18.6                    Add, remove, shuffle and sort171  
    18.7                    Inheritance                  172  
    18.8                    Class diagrams               173  
    18.9                    Debugging                    174  
    18.10                   Data encapsulation           175  
    18.11                   Glossary                     176  
    18.12                   Exercises                    177  
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    Contents    xxi  
     19 Case study: Tkinter 179  
    19.1       GUI                                179  
    19.2       Buttons and callbacks              180  
    19.3       Canvas widgets                     181  
    19.4       Coordinate sequences               182  
    19.5       More widgets                       182  
    19.6       Packing widgets                    183  
    19.7       Menus and Callables                185  
    19.8       Binding                            186  
    19.9       Debugging                          188  
     19.10                 Glossary                           189  
     19.11                 Exercises                          190  
    A         Debugging              193  
    A.1        Syntax errors                      193  
    A.2        Runtime errors                     195  
    A.3        Semantic errors                    198  
    B          Analysis of Algorithms             201  
    B.1        Order of growth                    202  
    B.2        Analysis of basic Python operations204  
    B.3        Analysis of search algorithms      205  
    B.4        Hashtables                         206  
    C         Lumpy                  211  
    C.1        State diagram                      211  
    C.2        Stack diagram                      212  
    C.3        Object diagrams                    213  
    C.4        Function and class objects         215  
    C.5        Class Diagrams                     216  
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    Chapter 1
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    The goal of this book is to teach you to think like a computer scientist. This way of thinking combines some of the best features of mathematics, engineering, and natural science. Like mathematicians, computer scientists use formal languages to denote ideas (specifically computations). Like engineers, they design things, assembling components into systems and evaluating tradeoffs among alternatives. Like scientists, they observe the behavior of complex systems, form hypotheses, and test predictions.
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    The programming language you will learn is Python. Python is an example of a high-level language; other high-level languages you might have heard of are C, C++, Perl, and Java.
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    The advantages are enormous. First, it is much easier to program in a high-level language. Programs written in a high-level language take less time to write, they are shorter and easier to read, and they are more likely to be correct. Second, high-level languages are portable, meaning that they can run on different kinds of computers with few or no modifications. Low-level programs can run on only one kind of computer and have to be rewritten to run on another.
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+        },
+        {
+          "id": "/page/23/Text/4",
+          "block_type": "Text",
+          "html": "
    Figure 1.2: A compiler translates source code into object code, which is run by a hardware executor.
    ",
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    Figure 1.2: A compiler translates source code into object code, which is run by a hardware executor.
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    For example, Linux is an operating system that contains thousands of lines of code, but it started out as a simple program Linus Torvalds used to explore the Intel 80386 chip. According to Larry Greenfield, \"One of Linus's earlier projects was a program that would switch between printing AAAA and BBBB. This later evolved to Linux.\" (The Linux Users' Guide Beta Version 1).
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    Later chapters will make more suggestions about debugging and other programming practices.
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    1.4. Formal and natural languages 5
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    Formal languages are languages that are designed by people for specific applications. For example, the notation that mathematicians use is a formal language that is particularly good at denoting relationships among numbers and symbols. Chemists use a formal language to represent the chemical structure of molecules. And most importantly:
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    Formal languages tend to have strict rules about syntax. For example, 3 + 3 = 6 is a syntactically correct mathematical statement, but 3+ = 3$6 is not. H2O is a syntactically correct chemical formula, but 2Zz is not.
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    Programming, and especially debugging, sometimes brings out strong emotions. If you are struggling with a difficult bug, you might feel angry, despondent or embarrassed.
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-              "section_hierarchy": {
-                "1": "/page/32/SectionHeader/1",
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-                "/page/33/Figure/1": 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Neg+JdQm0rwxqeoW23z7a2klj3jI3BSRkVw+kXuoeIfixba2mj39tpCadJBBPcQFN53Akn+7kngHriux8YQTXXgzWYLeJ5ZpLOVUjjUszEqcAAck0AcVPrvjt/BieLo7rTIII7Zbg6e1uWMiAckvngnqAOnHNaur+LtVvJdB0nw9Fbx6nq1sLt5bkFktosAk4HU5OBUl1YXjfBhrBbSc3n9kCP7OIz5m/ywNu3rn2rEubbUPDd/4U8TnTbu5toNLWxv4YYi0sIKghtnXg9fTFAGtZa34l0XxRbeHvEFxZ3g1GGRrG/ghMeJFGSrpnH5f/AKs8ePtW/wCFay6l5UB8QJenT/KCfKZvMwPlz/d561JFeTeOvHujahY2F5BpGjiSVrm6hMXmyMMBUB5OO9Z7eGtUX4uC2WznOgyXY1cz+WfLEojK7d2MZ3c4oA0pPHeo3Pgzw1d2Kwf2tq11HasrISqsCRIcZ7Y/WvQLpbhrOVbWRI7goRG7ruVWxwSO4zXlXhfwzqkHxNnt7qynTRtKmuLuymeMiN2m24CnGDjnpXrdAHkXgTU7/wAN+FPEutajc281ja3lyTDHEVd5ww53ZPynoBjjNaU2p/EOy8P/APCUTzaXJAsYuZNJWAgrF1IEmc7gPwqHTfC+o6n8NvFGkPbS211dahcyQLOhTf8AMGU89jjr0pt543utQ8IP4fh8P6t/wkM1t9ka2a1YIjFdpcueNvU5oA0te8a6sbzwxF4cgt5v7cgkdBcA4Q7VKkkdlySR3xXZaNFqUGlQx6vcw3N8AfNlhTYjc8YH0xXDJ4dvdL8R+ALZbeaaLTrWaK4njjJRG8sDlugyc4zXpFAHnl94h8Q61421Lw/o2qadpK6eqc3MXmy3DMM/KpI+Ue1Wbrxbr3h/wPLfa7pcQ1lbj7JBFEw8u4djhHGCSAeuOvHasbxXc6LP4ivbTxn4UllgXb/Z+oWdtI7SJjlS6HIYHtWZb+HvEF/8P7toIL9ks9US90m0vyfPMKH7pzyM84B/rQBvajq/jfwhaQa3rd3puoad5iLeW1vAY2t1Y4yjZ+bBPerur+IPEVx47Tw7ob2UUM2nrdfaZ4y/lfMQWABG7jAA461jeJvE0vjvQh4b0fRtUS9vXRblrm1aNLVQwLFmPGeO1bttpl1b/FpbgW05sk0RYBcGM7Cwk+7u6ZxzigDJ0zV/Hmo67qnhj7dpkdxpxV5NU+zE7kYZVRFnGeuTnirekeO7208I6/feIEhlvNEuXtna3G1Z2GNuB2ySBV/w/Y3cPxL8V3ctrPHbTpbCKZ4yEkwhztPQ49q5tvCupax4b8ead9llhmu9Tea185CgmxtYYJ6g4xnpQBbutY8faTov/CSXdzo9xAiCefSo4irJH1IWTOSwHrx9aqeJJtX1X4geDr7TL+0ihu4JZbPzbcsYwYwW3Yb5s9umPesYw+FptNjs7D4d3L+I2Cxm0ntJFjR+hZnyBt6nP8q6fxMo8PeJPBN7LZyLY2Mc0MptIXkSItGAAAMnGelAFTGvTfFzxFa6JLa20klpbGa7njMgiAX+FMjJJPc44NaWmeJtdit/Fml6y1nc32jQebHcRRbUmVkLDcn4Ve0Szuk+KPiS9e1nS1ntbYRTNGQjkA5APQkd6zJdMvz4h+IMosbkx3VhGlu/lNiZhEwIQ4+Y544oAjufGWuw6R4KOnW1m91rSFZI2QqgbYCCMdFBOT7CrsGreKdC8aaVpOuX1jqNpqqyBHgt/JaF0XdjGTkfWsu20nUVj+GYbT7ofYw32nMLfuP3QHz8fLz61v8Aieyu7jx94PuYbWaSC3kuDNKkZKx5TA3EcDPvQBSTWfFfirWdTXw5d2Gn6Zps5tvNuIDK1zKv3u/Cjp61d8BeJtX8QT65DrEEEE+n3Qt/LhBwCF+bk9RnkexrE0XVZPh/qes6VqmmajLbXN693ZXNpbNMsof+D5ejA+tWPhdPc3eqeL7q7tmtppdSDNCxyU+X7px3AxmgDe8ZeI73Rk0/T9Jhim1bVJ/IthNnYmBlnbHJAFUbC78ZaN4gs7PWmt9W027Vt15aWxjNqwGfnAyNp6ZpPH1lfQ6hoPiSxs5b3+ybhmnt4RmRonXDFR3I9KZbeL73xRr1nY6Hp10ml7XOo3V7atGFGOETOPmz9aAKWnav418YQXOr6Fd6dp2mpK8dnDcQGRrgKcbmbPygkdqW6+IV+/wwuvEEFtFDqlpOLa4gYblWQSBWA9iDx9ao+GfEcngHR5PDes6Pqj3NpLILV7W1aVLpGYlSrDjPPeqdz4b1mP4RaqJrCdtT1O/F61pEhd4w0qnbgc5AGT6UAaWu6/478N/2ZqVxJpd3FqE62/8AZyRFPJdx8v7zOWx3OPwq9Hq/izQvGWjadrd7YX9nq3mKBb25iMDqu7AOTuH1q14/sby8sfDi2trPO0WrW0kgijLFFGcscdAPWn+K7G7ufHHg64gtZ5YLe4nM0iRlljBTALEcDPvQBi6VrvjjxXeaxb6Zc6dp9vp17LALmWAyNLg/KoXOBgdT71R8QeI7/Wvhh4qsNYgih1bS5EgufJzsfLqVdc9iKg8H+L18NXviWPUNN1CSyk1edobm1tzKN+fmRsdD0Iz60l1pOs6r4J8a6y+lXUVxrc0TW1j5RM3lxkBSVHOSCTj2oA6nWfEWqx3mj+GvD/2WPUbm0FxLc3QJS3iAAzgdST0qPT9e8Sab4lTw3rdzYXUt9bySafqEERUb1HKyJn8eP/1Y3jDw/bxeIdH1/VtCm1bSRYC0u4ooy727DlX2jkjkg+lWPCNjot54vju/D3hFbHTbWJi2o3MDxO0h4CxgnkYPJIoAr/D+71HRLLxZqmp3ltJYWl9cvPHHCVdpVwSwOeAegH61bTV/H9zof/CTRXOjxwmP7QmktESxixnBkznfj8Kq6JZPqNh448KSxXFvfXl3dTRGSFghRsbW3YxycVg21t4YtNDjsLr4fXcviaKMRG2NpIUlkAxv3g42nrmgDqNT1iHxBr/w51aBSkd1JNIFJyVPl8j8DkVv+G/E1zcX3iOw1lokn0m4J3Iu0NbkbkbGfQGsW60O8ttV+H8cWliFLN5TcpaRs0NuWTpnnAySMk1k/E+x1C18U2jaTw3iO3/sufB6EMDu/wC+SR9KAO48DazqPiHw9/a2oLGi3M0jWqIuNsIOFz6k4zWf4l8Qa3P4ptvC3ho20N40Burq7uULrBHnAwvck11mn2UOm6dbWNuu2G3iWJB7AYrhfEL3XhT4hx+KDYXV5pV3ZC0umtYzI8DK2VYqOcGgDLt28QR/GLQrPxA1rPLDZXBhu7ZCgmRh3U9GBHb1FVNd1XVde+D+uXQezt1hu5opkSDHmRq4AAweGzgk1p2moX3ib4qaNrFvo+oQaNb2s8KXNxbsm9iMknPQdAM9cGqUWj6oPg34ksTpt4Lya8naK38hvMcGUEELjJBHPFAEXjC18RroPhAT6lYOTqFuE22rABznYT83IA6jv7V0up694juNeg8LaRc2EV/BarPqGoTREomeAETPU9eT0pPGGn3t1o/hJLezuJXg1K0eZY4mYxqByWwOAO5NYvi/QdOsfH8uua9oM2q6Ne2yRtJDE0ptZU4yVXnBHegDf0LxJrVt4hu/DfiBrS4u0tTd2t5aqVWZAcEMvZgfSqXgvxJ4p8RafBr+oyWNpo0CS+cixkyTlc5YdlUYH1wah8G6fptxrd/qGieE10zTI7Yxw3c0TxzTueu1SfufhWl4C0e5/wCFWRaTfQTWk8sU8TJNGUZdzNyQee+aAM2y1nxz4j0xvEGl3ek2Vm+57TT54i7TIDwXfPyk47U7UPiLfy+D9A1nSrOM3N/fLaTWshyA3zAqD2+YdfSuS0zT/DOiaONL8QeBry41+2BjHlWryLdnPysrg4weM10t9oN9D4Z8HQxaGtpLHq8VxcWtjGzpbgliSeuMcZOcZoAv3OreL/DfiDRRrF9p1/Y6pci1eKC3MRt3IyNpydw+tUvD9vro+MfiAyX9myLDA1wFtyC8ZB2KvzcEcZPOfatzx3ZXd5qHhVra1mnWDV45JTFGWEaAHLNjoPc1Ss7n+zPjJq63VvcqupWtutrKIWZHKqd2WAwMe9AHY6TpMOkQTRxOztPM88jsACzMeeAAAOg/Cr9FFABRRRQAUUUUAFFFFABRRRQAUUUUAFc74j8NXOrXtnqWmavNpepWgZUlWMSxurdVdCQD/SuiooA5bRfCV1a682u63rDarqQhMELCBYY4UJyQqgnk+pNdTRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAcdc+ENZttWvbzQPFD6fFev5k1vPaLcqr92Qkjb9Ola3hjw3D4Z06S3S4lurieZp7m5lxumkbqxA6fStuigAooooAKKKKACiiigDB8L+HD4cj1NTdC4+230l3wm3Zvx8vU5xjrW9RRQAUUUUAFFFFABXLReEbmbxkviDVdYe9S23iwtPIWNbcN1yQfmOOMmupooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiivKfC/h0eLrjxEda1HUJrS31aeK3t47p41Q55Y4OT1AAPAx05oA9Worifhfc3cnh29tLq6luvsOoT2sUszbnKKRgE9+tdhd3UdlayXEqysiDJEMLyufoqAsfwFAE1FYX/CW6b/z7a1/4JLz/AONUf8Jbpv8Az7a1/wCCS8/+NUAbtFYX/CW6b/z7a1/4JLz/AONVT1HxLHcQoLCXWLSVWzubw9dyqw9CPLBx9CDQB1NFee3fxEutEhM2p6dLcW69ZodPvLZv++JYtv8A5EroPDPjTTfFcQksLfUYwRn/AEizdF/77wU/8eoA6Kuc8VeNNK8KWJluZ4pbouqR2iygSOSQOnU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    Figure 2.1: State diagram.
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    >>> 1,000,000
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    Figure 2.1: State diagram.
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    One of the most powerful features of a programming language is the ability to manipulate variables. A variable is a name that refers to a value.
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    An assignment statement creates new variables and gives them values:
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    This example makes three assignments. The first assigns a string to a new variable named message; the second gives the integer 17 to n; the third assigns the (approximate) value of π to pi.
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    This example makes three assignments. The first assigns a string to a new variable named message; the second gives the integer 17 to n; the third assigns the (approximate) value of π to pi.
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    A common way to represent variables on paper is to write the name with an arrow pointing to the variable's value. This kind of figure is called a state diagram because it shows what state each of the variables is in (think of it as the variable's state of mind). Figure 2.1 shows the result of the previous example.
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    A common way to represent variables on paper is to write the name with an arrow pointing to the variable's value. This kind of figure is called a state diagram because it shows what state each of the variables is in (think of it as the variable's state of mind). Figure 2.1 shows the result of the previous example.
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    The type of a variable is the type of the value it refers to.
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    >>> type(message) <type 'str'> >>> type(n) <type 'int'> >>> type(pi) <type 'float'>
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    2.3 Variable names and keywords
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    Programmers generally choose names for their variables that are meaningful—they document what the variable is used for.
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    If you give a variable an illegal name, you get a syntax error:
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    2.5 Expressions and statements
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  •  Parentheses have the highest precedence and can be used to force an expression to evaluate in the order you want. Since expressions in parentheses are evaluated first, 2 * (3-1) is 4, and (1+1)**(5-2) is 8. You can also use parentheses to make an expression easier to read, as in (minute * 100) / 60, even if it doesn't change the result.
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  •  Parentheses have the highest precedence and can be used to force an expression to evaluate in the order you want. Since expressions in parentheses are evaluated first, 2 * (3-1) is 4, and (1+1)**(5-2) is 8. You can also use parentheses to make an expression easier to read, as in (minute * 100) / 60, even if it doesn't change the result.
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  •  Exponentiation has the next highest precedence, so 2**1+1 is 3, not 4, and 3*1**3 is 3, not 27.
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  •  Exponentiation has the next highest precedence, so 2**1+1 is 3, not 4, and 3*1**3 is 3, not 27.
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  •  Multiplication and Division have the same precedence, which is higher than Addition and Subtraction, which also have the same precedence. So 2*3-1 is 5, not 4, and 6+4/2 is 8, not 5.
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  •  Multiplication and Division have the same precedence, which is higher than Addition and Subtraction, which also have the same precedence. So 2*3-1 is 5, not 4, and 6+4/2 is 8, not 5.
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  •  Operators with the same precedence are evaluated from left to right (except exponentiation). So in the expression degrees / 2 * pi, the division happens first and the result is multiplied by pi. To divide by 2π, you can use parentheses or write degrees / 2 / pi.
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  •  Operators with the same precedence are evaluated from left to right (except exponentiation). So in the expression degrees / 2 * pi, the division happens first and the result is multiplied by pi. To divide by 2π, you can use parentheses or write degrees / 2 / pi.
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    I don't work very hard to remember rules of precedence for other operators. If I can't tell by looking at the expression, I use parentheses to make it obvious.
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    2.8 String operations
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    2.8 String operations
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    In general, you can't perform mathematical operations on strings, even if the strings look like numbers, so the following are illegal:
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    '2'-'1' 'eggs'/'easy' 'third'*'a charm'
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    '2' - '1' 'eggs' / 'easy' 'third' * 'a charm'
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    16 Chapter 2. Variables, expressions and statements
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    The + operator works with strings, but it might not do what you expect: it performs concatenation, which means joining the strings by linking them end-to-end. For example:
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    first = 'throat' second = 'warbler' print first + second
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    The output of this program is throatwarbler.
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    first = 'throat'\nsecond = 'warbler'\nprint first + second\nThe output of this program is throatwarbler.
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    The * operator also works on strings; it performs repetition. For example, 'Spam'*3 is 'SpamSpamSpam'. If one of the operands is a string, the other has to be an integer.
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    This use of + and * makes sense by analogy with addition and multiplication. Just as 4*3 is equivalent to 4+4+4, we expect 'Spam'*3 to be the same as 'Spam'+'Spam'+'Spam', and it is. On the other hand, there is a significant way in which string concatenation and repetition are different from integer addition and multiplication. Can you think of a property that addition has that string concatenation does not?
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    2.9 Comments
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    For this reason, it is a good idea to add notes to your programs to explain in natural language what the program is doing. These notes are called comments, and they start with the # symbol:
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    2.12 Exercises
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  • 2. Suppose the cover price of a book is $24.95, but bookstores get a 40% discount. Shipping costs $3 for the first copy and 75 cents for each additional copy. What is the total wholesale cost for 60 copies?
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  • 3. If I leave my house at 6:52 am and run 1 mile at an easy pace (8:15 per mile), then 3 miles at tempo (7:12 per mile) and 1 mile at easy pace again, what time do I get home for breakfast?
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    Functions
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    3.2 Type conversion functions
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    20 Chapter 3. Functions
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    22 Chapter 3. Functions
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    Once you have defined a function, you can use it inside another function. For example, to repeat the previous refrain, we could write a function called repeat_lyrics:
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    def repeat_lyrics():\n    print_lyrics()\n    print_lyrics()\nAnd then call repeat_lyrics:
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    def repeat_lyrics():\n    print_lyrics()\n    print_lyrics()
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    >>> repeat_lyrics()
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    And then call repeat_lyrics:
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    I'm a lumberjack, and I'm okay. I sleep all night and I work all day. I'm a lumberjack, and I'm okay. I sleep all night and I work all day.
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    But that's not really how the song goes.
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    >>> repeat_lyrics()\nI'm a lumberjack, and I'm okay.\nI sleep all night and I work all day.\nI'm a lumberjack, and I'm okay.\nI sleep all night and I work all day.\nBut that's not really how the song goes.
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    3.6 Definitions and uses
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    3.6 Definitions and uses
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    Pulling together the code fragments from the previous section, the whole program looks like this:
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    def print_lyrics():\n    print \"I'm a lumberjack, and I'm okay.\"\n    print \"I sleep all night and I work all day.\"\ndef repeat_lyrics():\n    print_lyrics()\n    print_lyrics()
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    repeat_lyrics()
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    repeat_lyrics()
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    This program contains two function definitions: print_lyrics and repeat_lyrics. Function definitions get executed just like other statements, but the effect is to create function objects. The statements inside the function do not get executed until the function is called, and the function definition generates no output.
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    3.7. Flow of execution 23
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    As you might expect, you have to create a function before you can execute it. In other words, the function definition has to be executed before the first time it is called. Exercise 3.1. Move the last line of this program to the top, so the function call appears before the definitions. Run the program and see what error message you get.
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    As you might expect, you have to create a function before you can execute it. In other words, the function definition has to be executed before the first time it is called.
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    Exercise 3.1. Move the last line of this program to the top, so the function call appears before the definitions. Run the program and see what error message you get.
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    Exercise 3.2. Move the function call back to the bottom and move the definition of print_lyrics after the definition of repeat_lyrics. What happens when you run this program?
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    3.7 Flow of execution
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    3.7 Flow of execution
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    Execution always begins at the first statement of the program. Statements are executed one at a time, in order from top to bottom.
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    Function definitions do not alter the flow of execution of the program, but remember that statements inside the function are not executed until the function is called.
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    3.8 Parameters and arguments
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    3.8 Parameters and arguments
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    24 Chapter 3. Functions
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+                "/page/46/Figure/1": 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    Figure 3.1: Stack diagram.
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    3.10 Stack diagrams
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    3.10 Stack diagrams
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    Each function is represented by a frame. A frame is a box with the name of a function beside it and the parameters and variables of the function inside it. The stack diagram for the previous example is shown in Figure 3.1.
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    26 Chapter 3. Functions
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    3.11 Fruitful functions and void functions
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    The advantage of importing everything from the math module is that your code can be more concise. The disadvantage is that there might be conflicts between names defined in different modules, or between a name from a module and one of your variables.
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    28 Chapter 3. Functions
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    Write a function named right_justify that takes a string named s as a parameter and prints the string with enough leading spaces so that the last letter of the string is in column 70 of the display.
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    Here's an example that uses do_twice to call a function named print_spam twice.
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  • def print_spam(): print 'spam'
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  • 1. Type this example into a script and test it.
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  • 3. Write a more general version of print_spam, called print_twice, that takes a string as a parameter and prints it twice.
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  • 4. Use the modified version of do_twice to call print_twice twice, passing 'spam' as an argument.
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  • 5. Define a new function called do_four that takes a function object and a value and calls the function four times, passing the value as a parameter. There should be only two statements in the body of this function, not four.
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    Solution: http: // thinkpython. com/ code/ do_ four. py .
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  • 1. Write a function that draws a grid like the following:
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    30 Chapter 3. Functions
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+            "4": "/page/50/SectionHeader/4"
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           "images": {}
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-          "id": "/page/51/TextInlineMath/1",
-          "block_type": "TextInlineMath",
-          "html": "
    + - - - - + - - - - + | | | | | | | | | | | | + - - - - + - - - - + | | | | | | | | | | | | + - - - - + - - - - +
    ",
+          "id": "/page/51/Code/1",
+          "block_type": "Code",
+          "html": "
    + - - - - + - - - - +\n| | |\n| | |\n| | |\n| | |\n+ - - - - + - - - - +\n| | |\n| | |\n| | |\n| | |\n+ - - - - + - - - - +
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+            "2": "/page/48/SectionHeader/3",
+            "4": "/page/50/SectionHeader/4"
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           "images": {}
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    Hint: to print more than one value on a line, you can print a comma-separated sequence:
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           "polygon": [
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+              110.56640625,
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-            "3": "/page/50/SectionHeader/4"
+            "2": "/page/48/SectionHeader/3",
+            "4": "/page/50/SectionHeader/4"
           },
           "images": {}
         },
         {
-          "id": "/page/51/TextInlineMath/3",
-          "block_type": "TextInlineMath",
-          "html": "
    print '+', '-'
    ",
+          "id": "/page/51/Code/3",
+          "block_type": "Code",
+          "html": "
    print '+', '-'
    ",
           "polygon": [
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+              109.5205078125,
+              249.626953125
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-            "3": "/page/50/SectionHeader/4"
+            "2": "/page/48/SectionHeader/3",
+            "4": "/page/50/SectionHeader/4"
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           "images": {}
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@@ -23237,123 +63055,151 @@
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    If the sequence ends with a comma, Python leaves the line unfinished, so the value printed next appears on the same line.
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+              483.50390625,
+              270.896484375
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+            "2": "/page/48/SectionHeader/3",
+            "4": "/page/50/SectionHeader/4"
           },
           "images": {}
         },
         {
-          "id": "/page/51/Text/5",
-          "block_type": "Text",
-          "html": "
    print '+', print '-'
    ",
+          "id": "/page/51/Code/5",
+          "block_type": "Code",
+          "html": "
    print '+',\nprint '-'
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+              109.74462890625,
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+            "2": "/page/48/SectionHeader/3",
+            "4": "/page/50/SectionHeader/4"
           },
           "images": {}
         },
         {
           "id": "/page/51/Text/6",
           "block_type": "Text",
-          "html": "
    The output of these statements is '+ -'.
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+          "html": "
    The output of these statements is '+ -'.
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           "block_type": "Text",
-          "html": "
    A print statement all by itself ends the current line and goes to the next line.
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+          "html": "
    A print statement all by itself ends the current line and goes to the next line.
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+            "4": "/page/50/SectionHeader/4"
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-          "id": "/page/51/Text/8",
-          "block_type": "Text",
-          "html": "
    2. Write a function that draws a similar grid with four rows and four columns.
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+          "id": "/page/51/ListItem/8",
+          "block_type": "ListItem",
+          "html": "
  • 2. Write a function that draws a similar grid with four rows and four columns.
    • ",
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    Solution: http: // thinkpython. com/ code/ grid. py . Credit: This exercise is based on an exercise in Oualline, Practical C Programming, Third Edition, O'Reilly Media, 1997.
    ",
+          "html": "
    Solution: http: // thinkpython. com/ code/ grid. py . Credit: This exercise is based on an exercise in Oualline, Practical C Programming, Third Edition, O'Reilly Media, 1997.
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           "section_hierarchy": {
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-            "3": "/page/50/SectionHeader/4"
+            "2": "/page/48/SectionHeader/3",
+            "4": "/page/50/SectionHeader/4"
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           "images": {}
         }
       ],
       "section_hierarchy": {
         "1": "/page/40/SectionHeader/1",
-        "3": "/page/50/SectionHeader/4"
+        "2": "/page/48/SectionHeader/3",
+        "4": "/page/50/SectionHeader/4"
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       "images": null
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     {
-      "id": "/page/52/Page/114",
+      "id": "/page/52/Page/115",
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-      "html": "",
+      "html": "",
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    Chapter 4
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+          "html": "
    Chapter 4
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    Case study: interface design
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           "block_type": "Text",
-          "html": "
    Code examples from this chapter are available from http://thinkpython.com/code/ polygon.py.
    ",
+          "html": "
    Code examples from this chapter are available from http://thinkpython.com/code/ polygon.py.
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@@ -23523,58 +63408,70 @@
         {
           "id": "/page/52/SectionHeader/3",
           "block_type": "SectionHeader",
-          "html": "
    4.1 TurtleWorld
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+          "html": "
    4.1 TurtleWorld
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+            "4": "/page/52/SectionHeader/3"
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-          "html": "
    To accompany this book, I have written a package called Swampy. You can download Swampy from http://thinkpython.com/swampy; follow the instructions there to install Swampy on your system.
    ",
+          "html": "
    To accompany this book, I have written a package called Swampy. You can download Swampy from http://thinkpython.com/swampy; follow the instructions there to install Swampy on your system.
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-            "3": "/page/52/SectionHeader/3"
+            "4": "/page/52/SectionHeader/3"
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@@ -23584,26 +63481,32 @@
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    A package is a collection of modules; one of the modules in Swampy is TurtleWorld, which provides a set of functions for drawing lines by steering turtles around the screen.
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-            "3": "/page/52/SectionHeader/3"
+            "4": "/page/52/SectionHeader/3"
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@@ -23613,55 +63516,67 @@
           "html": "
    If Swampy is installed as a package on your system, you can import TurtleWorld like this:
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-            "3": "/page/52/SectionHeader/3"
+            "4": "/page/52/SectionHeader/3"
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         },
         {
-          "id": "/page/52/Text/7",
-          "block_type": "Text",
-          "html": "
    from swampy.TurtleWorld import *
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+          "id": "/page/52/Code/7",
+          "block_type": "Code",
+          "html": "
    from swampy.TurtleWorld import *
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-            "3": "/page/52/SectionHeader/3"
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@@ -23671,26 +63586,32 @@
           "html": "
    If you downloaded the Swampy modules but did not install them as a package, you can either work in the directory that contains the Swampy files, or add that directory to Python's search path. Then you can import TurtleWorld like this:
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@@ -23700,55 +63621,67 @@
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    from TurtleWorld import *
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    The details of the installation process and setting Python's search path depend on your system, so rather than include those details here, I will try to maintain current information for several systems at http://thinkpython.com/swampy
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    The details of the installation process and setting Python's search path depend on your system, so rather than include those details here, I will try to maintain current information for several systems at http://thinkpython.com/swampy
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-          "block_type": "Text",
-          "html": "
    from swampy.TurtleWorld import *
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+          "id": "/page/52/Code/12",
+          "block_type": "Code",
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    from swampy.TurtleWorld import *\nworld = TurtleWorld()\nbob = Turtle()\nprint bob
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    wait_for_user()
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    32 Chapter 4. Case study: interface design
    ",
+          "html": "",
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    TurtleWorld provides several turtle-steering functions: fd and bk for forward and backward, and lt and rt for left and right turns. Also, each Turtle is holding a pen, which is either down or up; if the pen is down, the Turtle leaves a trail when it moves. The functions pu and pd stand for \"pen up\" and \"pen down.\"
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    To draw a right angle, add these lines to the program (after creating bob and before calling wait_for_user):
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    Now modify the program to draw a square. Don't go on until you've got it working!
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    4.2 Simple repetition
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    You should see something like this:
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    32
    \n",
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    4.3. Exercises 33
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    Hello!
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    This is the simplest use of the for statement; we will see more later. But that should be enough to let you rewrite your square-drawing program. Don't go on until you do.
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    Here is a for statement that draws a square:
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    for i in range(4):\n    fd(bob, 100)\n    lt(bob)
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    The syntax of a for statement is similar to a function definition. It has a header that ends with a colon and an indented body. The body can contain any number of statements.
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    A for statement is sometimes called a loop because the flow of execution runs through the body and then loops back to the top. In this case, it runs the body four times.
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    This version is actually a little different from the previous square-drawing code because it makes another turn after drawing the last side of the square. The extra turn takes a little more time, but it simplifies the code if we do the same thing every time through the loop. This version also has the effect of leaving the turtle back in the starting position, facing in the starting direction.
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    4.3 Exercises
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    4.3 Exercises
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    The following is a series of exercises using TurtleWorld. They are meant to be fun, but they have a point, too. While you are working on them, think about what the point is.
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  • 1. Write a function called square that takes a parameter named t, which is a turtle. It should use the turtle to draw a square.
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    Write a function call that passes bob as an argument to square, and then run the program again.
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    34 Chapter 4. Case study: interface design
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  • 5. Make a more general version of circle called arc that takes an additional parameter angle, which determines what fraction of a circle to draw. angle is in units of degrees, so when angle=360, arc should draw a complete circle.
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    4.4 Encapsulation
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    4.4 Encapsulation
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    36 Chapter 4. Case study: interface design
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    4.7 Refactoring
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    4.7 Refactoring
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    4.8. A development plan 37
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+                "/page/60/Figure/1": 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"
+                "/page/60/Figure/3": 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    4.12 Exercises
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    4.12 Exercises
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  • 2. Draw a stack diagram that shows the state of the program while executing circle(bob, radius). You can do the arithmetic by hand or add print statements to the code.
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  • 3. The version of arc in Section 4.7 is not very accurate because the linear approximation of the circle is always outside the true circle. As a result, the turtle ends up a few units away from the correct destination. My solution shows a way to reduce the effect of this error. Read the code and see if it makes sense to you. If you draw a diagram, you might see how it works.
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    Solution: http: // thinkpython. com/ code/ pie. py .
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    Exercise 4.4. The letters of the alphabet can be constructed from a moderate number of basic elements, like vertical and horizontal lines and a few curves. Design a font that can be drawn with a minimal number of basic elements and then write functions that draw letters of the alphabet.
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    Exercise 4.5. Read about spirals at http: // en. wikipedia. org/ wiki/ Spiral ; then write a program that draws an Archimedian spiral (or one of the other kinds). Solution: http: // thinkpython. com/ code/ spiral. py .
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    Chapter 5
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    5.1 Modulus operator
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    Although these operations are probably familiar to you, the Python symbols are different from the mathematical symbols. A common error is to use a single equal sign (=) instead of a double equal sign (==). Remember that = is an assignment operator and == is a relational operator. There is no such thing as =< or =>.
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    5.3 Logical operators
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    5.3 Logical operators
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    There are three logical operators: and, or, and not. The semantics (meaning) of these operators is similar to their meaning in English. For example, x > 0 and x < 10 is true only if x is greater than 0 and less than 10.
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-          "html": "
    n%2 == 0 or n%3 == 0 is true if either of the conditions is true, that is, if the number is divisible by 2 or 3.
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    n%2 == 0 or n%3 == 0 is true if either of the conditions is true, that is, if the number is divisible by 2 or 3.
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    Finally, the not operator negates a boolean expression, so not (x > y) is true if x > y is false, that is, if x is less than or equal to y.
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    Strictly speaking, the operands of the logical operators should be boolean expressions, but Python is not very strict. Any nonzero number is interpreted as \"true.\"
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    >>> 17 and True
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    >>> 17 and True
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    True
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@@ -29144,44 +71565,50 @@
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    This flexibility can be useful, but there are some subtleties to it that might be confusing. You might want to avoid it (unless you know what you are doing).
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           "block_type": "SectionHeader",
-          "html": "
    5.4 Conditional execution
    ",
+          "html": "
    5.4 Conditional execution
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@@ -29189,6 +71616,12 @@
               487.25201416015625
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@@ -29202,22 +71635,28 @@
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    In order to write useful programs, we almost always need the ability to check conditions and change the behavior of the program accordingly. Conditional statements give us this ability. The simplest form is the if statement:
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@@ -29228,53 +71667,30 @@
         {
           "id": "/page/63/Code/14",
           "block_type": "Code",
-          "html": "
    if x > 0:
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+          "html": "
    if x > 0:\n    print 'x is positive'
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    print 'x is positive'
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@@ -29284,27 +71700,33 @@
           "images": {}
         },
         {
-          "id": "/page/63/Text/16",
+          "id": "/page/63/Text/15",
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           "html": "
    The boolean expression after if is called the condition. If it is true, then the indented statement gets executed. If not, nothing happens.
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@@ -29313,27 +71735,33 @@
           "images": {}
         },
         {
-          "id": "/page/63/Text/17",
+          "id": "/page/63/Text/16",
           "block_type": "Text",
           "html": "
    if statements have the same structure as function definitions: a header followed by an indented body. Statements like this are called compound statements.
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           "images": {}
         },
         {
-          "id": "/page/63/Text/18",
+          "id": "/page/63/Text/17",
           "block_type": "Text",
           "html": "
    There is no limit on the number of statements that can appear in the body, but there has to be at least one. Occasionally, it is useful to have a body with no statements (usually as a place keeper for code you haven't written yet). In that case, you can use the pass statement, which does nothing.
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           "images": {}
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         {
-          "id": "/page/63/Text/19",
-          "block_type": "Text",
-          "html": "
    if x < 0: pass # need to handle negative values!
    ",
+          "id": "/page/63/Code/247",
+          "block_type": "Code",
+          "html": "
    if x < 0:\n   pass # need to handle negative values!
    ",
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@@ -29407,9 +71847,9 @@
       "images": null
     },
     {
-      "id": "/page/64/Page/184",
+      "id": "/page/64/Page/191",
       "block_type": "Page",
-      "html": "",
+      "html": "",
       "polygon": [
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+        612.0,
+        792.0
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           "block_type": "PageHeader",
-          "html": "
    5.5. Alternative execution 43
    ",
+          "html": "",
           "polygon": [
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@@ -29461,25 +71913,31 @@
         {
           "id": "/page/64/PageHeader/14",
           "block_type": "PageHeader",
-          "html": "
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+          "html": "",
           "polygon": [
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+            69.99609375
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         {
           "id": "/page/64/SectionHeader/1",
           "block_type": "SectionHeader",
-          "html": "
    5.5 Alternative execution
    ",
+          "html": "
    5.5 Alternative execution
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@@ -29522,44 +71987,51 @@
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    A second form of the if statement is alternative execution, in which there are two possibilities and the condition determines which one gets executed. The syntax looks like this:
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-          "block_type": "Code",
-          "html": "
    if x%2 == 0:\n    print 'x is even'\nelse:\n    print 'x is odd'
    ",
+          "id": "/page/64/TextInlineMath/3",
+          "block_type": "TextInlineMath",
+          "html": "
    if x%2 == 0: print 'x is even' else: print 'x is odd'
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+            "3": "/page/63/SectionHeader/12",
+            "4": "/page/64/SectionHeader/1"
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@@ -29580,51 +72059,64 @@
           "html": "
    If the remainder when x is divided by 2 is 0, then we know that x is even, and the program displays a message to that effect. If the condition is false, the second set of statements is executed. Since the condition must be true or false, exactly one of the alternatives will be executed. The alternatives are called branches, because they are branches in the flow of execution.
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-          "html": "
    5.6 Chained conditionals
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+          "html": "
    5.6 Chained conditionals
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    5.7 Nested conditionals
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    5.7 Nested conditionals
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@@ -29872,9 +72412,9 @@
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    44 Chapter 5. Conditionals and recursion
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+          "html": "",
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           "block_type": "PageHeader",
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@@ -29953,27 +72511,33 @@
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-          "block_type": "Code",
-          "html": "
    print 'x is less than y'\nelse:\n    print 'x is greater than y'
    ",
+          "id": "/page/65/TextInlineMath/1",
+          "block_type": "TextInlineMath",
+          "html": "
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-          "block_type": "TextInlineMath",
-          "html": "
    if 0 < x: if x < 10: print 'x is a positive single-digit number.'
    ",
+          "id": "/page/65/Code/5",
+          "block_type": "Code",
+          "html": "
    if 0 < x:\n    if x < 10:\n        print 'x is a positive single-digit number.'
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    It is legal for one function to call another; it is also legal for a function to call itself. It may not be obvious why that is a good thing, but it turns out to be one of the most magical things a program can do. For example, look at the following function:
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    What happens if we call this function like this?
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    A function that calls itself is recursive; the process is called recursion.
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    5.9 Stack diagrams for recursive functions
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    5.9 Stack diagrams for recursive functions
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    In Section 3.10, we used a stack diagram to represent the state of a program during a function call. The same kind of diagram can help interpret a recursive function.
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    In Section 3.10, we used a stack diagram to represent the state of a program during a function call. The same kind of diagram can help interpret a recursive function.
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    Every time a function gets called, Python creates a new function frame, which contains the function's local variables and parameters. For a recursive function, there might be more than one frame on the stack at the same time.
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    Figure 5.1 shows a stack diagram for countdown called with n = 3.
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    Figure 5.1 shows a stack diagram for countdown called with n = 3.
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    As usual, the top of the stack is the frame for __main__. It is empty because we did not create any variables in __main__ or pass any arguments to it.
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    The four countdown frames have different values for the parameter n. The bottom of the stack, where n=0, is called the base case. It does not make a recursive call, so there are no more frames.
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    Exercise 5.1. Draw a stack diagram for print_n called with s = 'Hello' and n=2.
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    Exercise 5.1. Draw a stack diagram for print_n called with s = 'Hello' and n=2. Exercise 5.2. Write a function called do_n that takes a function object and a number, n, as arguments, and that calls the given function n times.
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    46 Chapter 5. Conditionals and recursion
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+                "/page/67/Figure/1": 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    Figure 5.1: Stack diagram.
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    Figure 5.1: Stack diagram.
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    5.10 Infinite recursion
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    5.10 Infinite recursion
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    If you expect the user to type an integer, you can try to convert the return value to int:
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    chained conditional: A conditional statement with a series of alternative branches.
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    branch: One of the alternative sequences of statements in a conditional statement.
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    5.14. Exercises 49
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    nested conditional: A conditional statement that appears in one of the branches of another conditional statement.
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  • nested conditional: A conditional statement that appears in one of the branches of another conditional statement.
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    5.14 Exercises
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    5.14 Exercises
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  • 1. Write a function named check_fermat that takes four parameters—a, b, c and n—and that checks to see if Fermat's theorem holds. If n is greater than 2 and it turns out to be true that
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  • 2. Write a function that prompts the user to input values for a, b, c and n, converts them to integers, and uses check_fermat to check whether they violate Fermat's theorem.
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    Exercise 5.4. If you are given three sticks, you may or may not be able to arrange them in a triangle. For example, if one of the sticks is 12 inches long and the other two are one inch long, it is clear that you will not be able to get the short sticks to meet in the middle. For any three lengths, there is a simple test to see if it is possible to form a triangle:
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  • 1. Write a function named is_triangle that takes three integers as arguments, and that prints either \"Yes\" or \"No,\" depending on whether you can or cannot form a triangle from sticks with the given lengths.
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    The following exercises use TurtleWorld from Chapter 4:
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    The following exercises use TurtleWorld from Chapter 4:
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    Exercise 5.5. Read the following function and see if you can figure out what it does. Then run it (see the examples in Chapter 4).
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    Exercise 5.5. Read the following function and see if you can figure out what it does. Then run it (see the examples in Chapter 4).
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    50 Chapter 5. Conditionals and recursion
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+                "/page/71/Figure/1": 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    Figure 5.2: A Koch curve.
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    Figure 5.2: A Koch curve.
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    Exercise 5.6. The Koch curve is a fractal that looks something like Figure 5.2. To draw a Koch curve with length x, all you have to do is
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    Exercise 5.6. The Koch curve is a fractal that looks something like Figure 5.2. To draw a Koch curve with length x, all you have to do is
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  • 1. Write a function called koch that takes a turtle and a length as parameters, and that uses the turtle to draw a Koch curve with the given length.
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  • 2. Write a function called snowflake that draws three Koch curves to make the outline of a snowflake.
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    Solution: http: // thinkpython. com/ code/ koch. py .
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    Solution: http: // thinkpython. com/ code/ koch. py .
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  • 3. The Koch curve can be generalized in several ways. See http: // en. wikipedia. org/ wiki/ Koch_ snowflake for examples and implement your favorite.
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  • 3. The Koch curve can be generalized in several ways. See http: // en. wikipedia. org/ wiki/ Koch_ snowflake for examples and implement your favorite.
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    Chapter 6
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    Chapter 6
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    Fruitful functions
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    6.1 Return values
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    52 Chapter 6. Fruitful functions
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    In a fruitful function, it is a good idea to ensure that every possible path through the program hits a return statement. For example:
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    This function is incorrect because if x happens to be 0, neither condition is true, and the function ends without hitting a return statement. If the flow of execution gets to the end of a function, the return value is None, which is not the absolute value of 0.
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    6.2 Incremental development
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    6.2. Incremental development 53
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    When you start out, you should add only a line or two of code at a time. As you gain more experience, you might find yourself writing and debugging bigger chunks. Either way, incremental development can save you a lot of debugging time.
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    The key aspects of the process are:
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    54 Chapter 6. Fruitful functions
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  • 2. Use temporary variables to hold intermediate values so you can display and check them.
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  • 3. Once the program is working, you might want to remove some of the scaffolding or consolidate multiple statements into compound expressions, but only if it does not make the program difficult to read.
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    6.3 Composition
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    return x % y == 0
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    Boolean functions are often used in conditional statements:
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    It might be tempting to write something like:
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    if is_divisible(x, y) == True:
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    But the extra comparison is unnecessary.
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    Exercise 6.3. Write a function is_between(x, y, z) that returns True if x ≤ y ≤ z or False otherwise.
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    6.5 More recursion
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    6.5 More recursion
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    Proving that claim is a nontrivial exercise first accomplished by Alan Turing, one of the first computer scientists (some would argue that he was a mathematician, but a lot of early computer scientists started as mathematicians). Accordingly, it is known as the Turing Thesis. For a more complete (and accurate) discussion of the Turing Thesis, I recommend Michael Sipser's book Introduction to the Theory of Computation.
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    To give you an idea of what you can do with the tools you have learned so far, we'll evaluate a few recursively defined mathematical functions. A recursive definition is similar to a circular definition, in the sense that the definition contains a reference to the thing being defined. A truly circular definition is not very useful:
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    vorpal: An adjective used to describe something that is vorpal.
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    If you saw that definition in the dictionary, you might be annoyed. On the other hand, if you looked up the definition of the factorial function, denoted with the symbol !, you might get something like this:
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    0! = 1 n! = n(n − 1)!
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    56 Chapter 6. Fruitful functions
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    This definition says that the factorial of 0 is 1, and the factorial of any other value, n, is n multiplied by the factorial of n − 1.
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    Otherwise, and this is the interesting part, we have to make a recursive call to find the factorial of n − 1 and then multiply it by n:
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    The flow of execution for this program is similar to the flow of countdown in Section 5.8. If we call factorial with the value 3:
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+                "/page/78/Figure/1": 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"
               }
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    Figure 6.1: Stack diagram.
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    Figure 6.1: Stack diagram.
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    6.6 Leap of faith
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    6.6 Leap of faith
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    Following the flow of execution is one way to read programs, but it can quickly become labyrinthine. An alternative is what I call the \"leap of faith.\" When you come to a function call, instead of following the flow of execution, you assume that the function works correctly and returns the right result.
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    In fact, you are already practicing this leap of faith when you use built-in functions. When you call math.cos or math.exp, you don't examine the bodies of those functions. You just assume that they work because the people who wrote the built-in functions were good programmers.
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    The same is true when you call one of your own functions. For example, in Section 6.4, we wrote a function called is_divisible that determines whether one number is divisible by another. Once we have convinced ourselves that this function is correct—by examining the code and testing—we can use the function without looking at the body again.
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    The same is true when you call one of your own functions. For example, in Section 6.4, we wrote a function called is_divisible that determines whether one number is divisible by another. Once we have convinced ourselves that this function is correct—by examining the code and testing—we can use the function without looking at the body again.
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    The same is true of recursive programs. When you get to the recursive call, instead of following the flow of execution, you should assume that the recursive call works (yields the correct result) and then ask yourself, \"Assuming that I can find the factorial of n − 1, can I compute the factorial of n?\" In this case, it is clear that you can, by multiplying by n.
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    The same is true of recursive programs. When you get to the recursive call, instead of following the flow of execution, you should assume that the recursive call works (yields the correct result) and then ask yourself, \"Assuming that I can find the factorial of n − 1, can I compute the factorial of n?\" In this case, it is clear that you can, by multiplying by n.
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    Of course, it's a bit strange to assume that the function works correctly when you haven't finished writing it, but that's why it's called a leap of faith!
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    6.7 One more example
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    6.7 One more example
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    After factorial, the most common example of a recursively defined mathematical function is fibonacci, which has the following definition (see http://en.wikipedia.org/ wiki/Fibonacci_number):
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    After factorial, the most common example of a recursively defined mathematical function is fibonacci, which has the following definition (see http://en.wikipedia.org/ wiki/Fibonacci_number):
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    fibonacci(0) = 0 fibonacci(1) = 1 fibonacci(n) = fibonacci(n-1) + fibonacci(n-2)
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    fibonacci(0) = 0\nfibonacci(1) = 1\nfibonacci(n) = fibonacci(n − 1) + fibonacci(n − 2)
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    Translated into Python, it looks like this:
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    58 Chapter 6. Fruitful functions
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    If you try to follow the flow of execution here, even for fairly small values of n, your head explodes. But according to the leap of faith, if you assume that the two recursive calls work correctly, then it is clear that you get the right result by adding them together.
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    6.8 Checking types
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    6.8 Checking types
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    What happens if we call factorial and give it 1.5 as an argument?
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    >>> factorial(1.5)\nRuntimeError: Maximum recursion depth exceeded
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    6.9. Debugging 59
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    6.10 Glossary
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  • None: A special value returned by functions that have no return statement or a return statement without an argument.
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  • guardian: A programming pattern that uses a conditional statement to check for and handle circumstances that might cause an error.
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    Exercise 6.6. A palindrome is a word that is spelled the same backward and forward, like \"noon\" and \"redivider\". Recursively, a word is a palindrome if the first and last letters are the same and the middle is a palindrome.
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    We'll see how they work in Chapter 8.
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    We'll see how they work in Chapter 8.
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  • 1. Type these functions into a file named palindrome.py and test them out. What happens if you call middle with a string with two letters? One letter? What about the empty string, which is written '' and contains no letters?
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  • 2. Write a function called is_palindrome that takes a string argument and returns True if it is a palindrome and False otherwise. Remember that you can use the built-in function len to check the length of a string.
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    Solution: http: // thinkpython. com/ code/ palindrome_ soln. py .
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    Exercise 6.7. A number, a, is a power of b if it is divisible by b and a/b is a power of b. Write a function called is_power that takes parameters a and b and returns True if a is a power of b. Note: you will have to think about the base case.
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    Exercise 6.8. The greatest common divisor (GCD) of a and b is the largest number that divides both of them with no remainder.
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    One way to find the GCD of two numbers is based on the observation that if r is the remainder when a is divided by b, then gcd(a, b) = gcd(b,r). As a base case, we can use gcd(a, 0) = a.
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    One way to find the GCD of two numbers is based on the observation that if r is the remainder when a is divided by b, then gcd(a, b) = gcd(b, r). As a base case, we can use gcd(a, 0) = a. 
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    62 Chapter 6. Fruitful functions
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    Write a function called gcd that takes parameters a and b and returns their greatest common divisor.
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    Write a function called gcd that takes parameters a and b and returns their greatest common divisor.
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    Credit: This exercise is based on an example from Abelson and Sussman's Structure and Interpretation of Computer Programs.
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    Credit: This exercise is based on an example from Abelson and Sussman's Structure and Interpretation of Computer Programs.
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    Chapter 7
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    Chapter 7
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    Iteration
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    7.1 Multiple assignment
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    7.1 Multiple assignment
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    As you may have discovered, it is legal to make more than one assignment to the same variable. A new assignment makes an existing variable refer to a new value (and stop referring to the old value).
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    bruce = 5 print bruce, bruce = 7 print bruce
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    The output of this program is 5 7, because the first time bruce is printed, its value is 5, and the second time, its value is 7. The comma at the end of the first print statement suppresses the newline, which is why both outputs appear on the same line.
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    Figure 7.1 shows what multiple assignment looks like in a state diagram.
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  • 2. If the condition is false, exit the while statement and continue execution at the next statement.
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  • 3. If the condition is true, execute the body and then go back to step 1.
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    This type of flow is called a loop because the third step loops back around to the top.
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    The body of the loop should change the value of one or more variables so that eventually the condition becomes false and the loop terminates. Otherwise the loop will repeat forever, which is called an infinite loop. An endless source of amusement for computer scientists is the observation that the directions on shampoo, \"Lather, rinse, repeat,\" are an infinite loop.
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    In the case of countdown, we can prove that the loop terminates because we know that the value of n is finite, and we can see that the value of n gets smaller each time through the loop, so eventually we have to get to 0. In other cases, it is not so easy to tell:
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    def sequence(n):\n   while n != 1:\n       print n,\n       if n%2 == 0: # n is even\n          n = n/2\n       else: # n is odd\n          n = n*3+1
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    def sequence(n):
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    while n != 1:\n   print n,\n   if n%2 == 0: # n is even\n      n = n/2\n   else: # n is odd\n      n = n*3+1
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    The condition for this loop is n != 1, so the loop will continue until n is 1, which makes the condition false.
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    Each time through the loop, the program outputs the value of n and then checks whether it is even or odd. If it is even, n is divided by 2. If it is odd, the value of n is replaced with n*3+1. For example, if the argument passed to sequence is 3, the resulting sequence is 3, 10, 5, 16, 8, 4, 2, 1.
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    Since n sometimes increases and sometimes decreases, there is no obvious proof that n will ever reach 1, or that the program terminates. For some particular values of n, we can prove termination. For example, if the starting value is a power of two, then the value of n will be even each time through the loop until it reaches 1. The previous example ends with such a sequence, starting with 16.
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    The hard question is whether we can prove that this program terminates for all positive values of n. So far, no one has been able to prove it or disprove it! (See http: //en.wikipedia.org/wiki/Collatz_conjecture.)
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    The hard question is whether we can prove that this program terminates for all positive values of n. So far, no one has been able to prove it or disprove it! (See http: //en.wikipedia.org/wiki/Collatz_conjecture.)
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    Exercise 7.1. Rewrite the function print_n from Section 5.8 using iteration instead of recursion.
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    Exercise 7.1. Rewrite the function print_n from Section 5.8 using iteration instead of recursion.
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    7.4 break
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    7.4 break
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    Sometimes you don't know it's time to end a loop until you get half way through the body. In that case you can use the break statement to jump out of the loop.
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    For example, suppose you want to take input from the user until they type done. You could write:
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    66 Chapter 7. Iteration
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    while True:\n    line = raw_input('> ')\n    if line == 'done':\n        break\n    print line
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    Each time through, it prompts the user with an angle bracket. If the user types done, the break statement exits the loop. Otherwise the program echoes whatever the user types and goes back to the top of the loop. Here's a sample run:
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    > not done not done > done Done!
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    This way of writing while loops is common because you can check the condition anywhere in the loop (not just at the top) and you can express the stop condition affirmatively (\"stop when this happens\") rather than negatively (\"keep going until that happens.\").
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    7.5 Square roots
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    7.5 Square roots
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    Loops are often used in programs that compute numerical results by starting with an approximate answer and iteratively improving it.
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    For example, one way of computing square roots is Newton's method. Suppose that you want to know the square root of a. If you start with almost any estimate, x, you can compute a better estimate with the following formula:
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    For example, one way of computing square roots is Newton's method. Suppose that you want to know the square root of a. If you start with almost any estimate, x, you can compute a better estimate with the following formula:
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    $$y={\\frac{x+a/x}{2}}$$
    \n",
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    For example, if a is 4 and x is 3:
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    For example, if a is 4 and x is 3:
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    Which is closer to the correct answer (√ 4 = 2). If we repeat the process with the new estimate, it gets even closer:
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    Which is closer to the correct answer (√ 4 = 2). If we repeat the process with the new estimate, it gets even closer:
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    >>> x = y\n>>> y = (x + a/x) / 2\n>>> print y\n2.00641025641
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    After a few more updates, the estimate is almost exact:
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    7.6. Algorithms 67
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    >>> x = y >>> y = (x + a/x) / 2 >>> print y 2.00001024003 >>> x = y >>> y = (x + a/x) / 2 >>> print y 2.00000000003
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    When y == x, we can stop. Here is a loop that starts with an initial estimate, x, and improves it until it stops changing:
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    7.6 Algorithms
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    68 Chapter 7. Iteration
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    But if you were \"lazy,\" you probably cheated by learning a few tricks. For example, to find the product of n and 9, you can write n − 1 as the first digit and 10 − n as the second digit. This trick is a general solution for multiplying any single-digit number by 9. That's an algorithm!
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    Similarly, the techniques you learned for addition with carrying, subtraction with borrowing, and long division are all algorithms. One of the characteristics of algorithms is that they do not require any intelligence to carry out. They are mechanical processes in which each step follows from the last according to a simple set of rules.
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    Some of the things that people do naturally, without difficulty or conscious thought, are the hardest to express algorithmically. Understanding natural language is a good example. We all do it, but so far no one has been able to explain how we do it, at least not in the form of an algorithm.
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    Exercise 7.5. The mathematician Srinivasa Ramanujan found an infinite series that can be used to generate a numerical approximation of 1/π:
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    Exercise 7.5. The mathematician Srinivasa Ramanujan found an infinite series that can be used to generate a numerical approximation of 1/π:
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    $${\\frac{1}{\\pi}}={\\frac{2{\\sqrt{2}}}{9801}}\\sum_{k=0}^{\\infty}{\\frac{(4k)!(1103+26390k)}{(k!)^{4}396^{4k}}}$$
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    \\frac{1}{\\pi} = \\frac{2\\sqrt{2}}{9801} \\sum_{k=0}^{\\infty} \\frac{(4k)!(1103 + 26390k)}{(k!)^4 396^{4k}}
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    70 Chapter 7. Iteration
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    Write a function called estimate_pi that uses this formula to compute and return an estimate of π. It should use a while loop to compute terms of the summation until the last term is smaller than 1e-15 (which is Python notation for 10−15). You can check the result by comparing it to math.pi.
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    Write a function called estimate_pi that uses this formula to compute and return an estimate of π. It should use a while loop to compute terms of the summation until the last term is smaller than 1e-15 (which is Python notation for 10−15). You can check the result by comparing it to math.pi.
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    Solution: http: // thinkpython. com/ code/ pi. py .
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    Solution: http: // thinkpython. com/ code/ pi. py .
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    Chapter 8
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    Chapter 8
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    8.1 A string is a sequence
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    8.1 A string is a sequence
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    >>> letter = fruit[1]
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    The expression in brackets is called an index. The index indicates which character in the sequence you want (hence the name).
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    So b is the 0th letter (\"zero-eth\") of 'banana', a is the 1th letter (\"one-eth\"), and n is the 2th (\"two-eth\") letter.
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    You can use any expression, including variables and operators, as an index, but the value of the index has to be an integer. Otherwise you get:
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    8.2 len
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    8.2 len
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    len is a built-in function that returns the number of characters in a string:
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    72 Chapter 8. Strings
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    >>> fruit = 'banana'\n>>> len(fruit)\n6\nTo get the last letter of a string, you might be tempted to try something like this:\n>>> length = len(fruit)\n>>> last = fruit[length]\nIndexError: string index out of range\nThe reason for the IndexError is that there is no letter in 'banana' with the index 6. Since\nwe started counting at zero, the six letters are numbered 0 to 5. To get the last character,\nyou have to subtract 1 from length:\n>>> last = fruit[length-1]\n>>> print last\na
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    To get the last letter of a string, you might be tempted to try something like this:
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    >>> length = len(fruit)\n>>> last = fruit[length]\nIndexError: string index out of range
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    The reason for the IndexError is that there is no letter in 'banana' with the index 6. Since we started counting at zero, the six letters are numbered 0 to 5. To get the last character, you have to subtract 1 from length:
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    >>> last = fruit[length-1]\n>>> print last\na
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    Alternatively, you can use negative indices, which count backward from the end of the string. The expression fruit[-1] yields the last letter, fruit[-2] yields the second to last, and so on.
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    8.3 Traversal with a for loop
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    8.3 Traversal with a for loop
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    A lot of computations involve processing a string one character at a time. Often they start at the beginning, select each character in turn, do something to it, and continue until the end. This pattern of processing is called a traversal. One way to write a traversal is with a while loop:
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    index = 0\nwhile index < len(fruit):\n    letter = fruit[index]\n    print letter\n    index = index + 1
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    This loop traverses the string and displays each letter on a line by itself. The loop condition is index < len(fruit), so when index is equal to the length of the string, the condition is false, and the body of the loop is not executed. The last character accessed is the one with the index len(fruit)-1, which is the last character in the string.
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    Exercise 8.1. Write a function that takes a string as an argument and displays the letters backward, one per line.
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    Another way to write a traversal is with a for loop:
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    Figure 8.1: Slice indices.
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    The operator [n:m] returns the part of the string from the \"n-eth\" character to the \"m-eth\" character, including the first but excluding the last. This behavior is counterintuitive, but it might help to imagine the indices pointing between the characters, as in Figure 8.1.
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    The operator [n:m] returns the part of the string from the \"n-eth\" character to the \"m-eth\" character, including the first but excluding the last. This behavior is counterintuitive, but it might help to imagine the indices pointing between the characters, as in Figure 8.1.
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    If you omit the first index (before the colon), the slice starts at the beginning of the string. If you omit the second index, the slice goes to the end of the string:
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    If the first index is greater than or equal to the second the result is an empty string, represented by two quotation marks:
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    >>> fruit = 'banana'\n>>> fruit[3:3]\n''
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    74 Chapter 8. Strings
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    An empty string contains no characters and has length 0, but other than that, it is the same as any other string.
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    Exercise 8.3. Given that fruit is a string, what does fruit[:] mean?
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    8.5 Strings are immutable
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    >>> greeting = 'Hello, world!' >>> greeting[0] = 'J'
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    The \"object\" in this case is the string and the \"item\" is the character you tried to assign. For now, an object is the same thing as a value, but we will refine that definition later. An item is one of the values in a sequence.
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    now, an object is the same thing as a value, but we will refine that definition later. An item is one of the values in a sequence.
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@@ -45610,36 +91626,43 @@
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    The reason for the error is that strings are immutable, which means you can't change an existing string. The best you can do is create a new string that is a variation on the original:
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    What does the following function do?
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    8.7. Looping and counting 75
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    8.7 Looping and counting
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    8.7 Looping and counting
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    8.8 String methods
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    8.8 String methods
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    Instead of the function syntax upper(word), it uses the method syntax word.upper().
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    >>> word = 'banana'\n>>> new_word = word.upper()\n>>> print new_word\nBANANA
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    76 Chapter 8. Strings
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    It can take as a second argument the index where it should start: >>> word.find('na', 3) 4 And as a third argument the index where it should stop: >>> name = 'bob' >>> name.find('b', 1, 2) -1
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    It can take as a second argument the index where it should start:
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    >>> word.find('na', 3)\n4
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    And as a third argument the index where it should stop:
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    This search fails because b does not appear in the index range from 1 to 2 (not including 2). Exercise 8.7. There is a string method called count that is similar to the function in the previous exercise. Read the documentation of this method and write an invocation that counts the number of as in 'banana'.
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    This search fails because b does not appear in the index range from 1 to 2 (not including 2). Exercise 8.7. There is a string method called count that is similar to the function in the previous exercise. Read the documentation of this method and write an invocation that counts the number of as in 'banana'.
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    Exercise 8.8. Read the documentation of the string methods at http: // docs. python. org/ 2/ library/ stdtypes. html# string-methods . You might want to experiment with some of them to make sure you understand how they work. strip and replace are particularly useful.
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    Exercise 8.8. Read the documentation of the string methods at http: // docs. python. org/ 2/ library/ stdtypes. html# string-methods . You might want to experiment with some of them to make sure you understand how they work. strip and replace are particularly useful.
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    8.9 The in operator
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    False
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    For example, the following function prints all the letters from word1 that also appear in word2:
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    def in_both(word1, word2):\n    for letter in word1:\n        if letter in word2:\n            print letter
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           "block_type": "Text",
           "html": "
    With well-chosen variable names, Python sometimes reads like English. You could read this loop, \"for (each) letter in (the first) word, if (the) letter (appears) in (the second) word, print (the) letter.\"
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           "html": "
    Here's what you get if you compare apples and oranges:
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    >>> in_both('apples', 'oranges')\na\ne\ns
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               635.9633026123047
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+          "id": "/page/97/SectionHeader/17",
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-          "html": "
    8.10 String comparison
    ",
+          "html": "
    8.10 String comparison
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    The relational operators work on strings. To see if two strings are equal:
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       "images": null
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    8.11. Debugging 77
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-          "block_type": "Code",
-          "html": "
    if word == 'banana':\n    print 'All right, bananas.'
    ",
+          "id": "/page/98/TextInlineMath/1",
+          "block_type": "TextInlineMath",
+          "html": "
    if word == 'banana': print 'All right, bananas.'
    ",
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@@ -47063,26 +93674,33 @@
           "html": "
    Other relational operations are useful for putting words in alphabetical order:
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@@ -47092,26 +93710,33 @@
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    if word < 'banana':\n    print 'Your word,' + word + ', comes before banana.'\nelif word > 'banana':\n    print 'Your word,' + word + ', comes after banana.'\nelse:\n    print 'All right, bananas.'
    ",
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+            "4": "/page/97/SectionHeader/17"
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           "images": {}
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@@ -47121,55 +93746,69 @@
           "html": "
    Python does not handle uppercase and lowercase letters the same way that people do. All the uppercase letters come before all the lowercase letters, so:
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-          "block_type": "TextInlineMath",
-          "html": "
    Your word, Pineapple, comes before banana.
    ",
+          "id": "/page/98/Text/5",
+          "block_type": "Text",
+          "html": "
    Your word, Pineapple, comes before banana.
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@@ -47179,55 +93818,69 @@
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    A common way to address this problem is to convert strings to a standard format, such as all lowercase, before performing the comparison. Keep that in mind in case you have to defend yourself against a man armed with a Pineapple.
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         },
         {
           "id": "/page/98/SectionHeader/7",
           "block_type": "SectionHeader",
-          "html": "
    8.11 Debugging
    ",
+          "html": "
    8.11 Debugging
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    The first if statement checks whether the words are the same length. If not, we can return False immediately and then, for the rest of the function, we can assume that the words are the same length. This is an example of the guardian pattern in Section 6.8.
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    The first if statement checks whether the words are the same length. If not, we can return False immediately and then, for the rest of the function, we can assume that the words are the same length. This is an example of the guardian pattern in Section 6.8.
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    i and j are indices: i traverses word1 forward while j traverses word2 backward. If we find two letters that don't match, we can return False immediately. If we get through the whole loop and all the letters match, we return True.
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    If we test this function with the words \"pots\" and \"stop\", we expect the return value True, but we get an IndexError:
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    >>> is_reverse('pots', 'stop')\n...
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    >>> is_reverse('pots', 'stop') ...
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    78 Chapter 8. Strings
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           "html": "
    For debugging this kind of error, my first move is to print the values of the indices immediately before the line where the error appears.
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@@ -47676,26 +94398,33 @@
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    while j > 0:\n   print i, j # print here\n   if word1[i] != word2[j]:\n       return False\n   i = i+1\n   j = j-1
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@@ -47705,84 +94434,105 @@
           "html": "
    Now when I run the program again, I get more information:
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           "polygon": [
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-          "block_type": "TextInlineMath",
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    >>> is_reverse('pots', 'stop') 0 4 ... IndexError: string index out of range
    ",
+          "id": "/page/99/Code/7",
+          "block_type": "Code",
+          "html": "
    >>> is_reverse('pots', 'stop')\n0 4\n...\nIndexError: string index out of range
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    The first time through the loop, the value of j is 4, which is out of range for the string 'pots'. The index of the last character is 3, so the initial value for j should be len(word2)-1.
    ",
+          "id": "/page/99/Text/8",
+          "block_type": "Text",
+          "html": "
    The first time through the loop, the value of j is 4, which is out of range for the string 'pots'. The index of the last character is 3, so the initial value for j should be len(word2)-1.
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@@ -47792,26 +94542,33 @@
           "html": "
    If I fix that error and run the program again, I get:
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    >>> is_reverse('pots', 'stop')\n0 3\n1 2\n2 1\nTrue
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-          "html": "
    This time we get the right answer, but it looks like the loop only ran three times, which is suspicious. To get a better idea of what is happening, it is useful to draw a state diagram. During the first iteration, the frame for is_reverse is shows in Figure 8.2.
    ",
+          "html": "
    This time we get the right answer, but it looks like the loop only ran three times, which is suspicious. To get a better idea of what is happening, it is useful to draw a state diagram. During the first iteration, the frame for is_reverse is shows in Figure 8.2.
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    I took a little license by arranging the variables in the frame and adding dotted lines to show that the values of i and j indicate characters in word1 and word2. Exercise 8.9. Starting with this diagram, execute the program on paper, changing the values of i and j during each iteration. Find and fix the second error in this function.
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    I took a little license by arranging the variables in the frame and adding dotted lines to show that the values of i and j indicate characters in word1 and word2.
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+          "block_type": "Text",
+          "html": "
    Exercise 8.9. Starting with this diagram, execute the program on paper, changing the values of i and j during each iteration. Find and fix the second error in this function.
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    8.12 Glossary
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-          "block_type": "Text",
-          "html": "
    sequence: An ordered set; that is, a set of values where each value is identified by an integer index.
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+          "id": "/page/100/ListItem/1",
+          "block_type": "ListItem",
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+        {
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    slice: A part of a string specified by a range of indices.
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+          "block_type": "ListItem",
+          "html": "
  • empty string: A string with no characters and length 0, represented by two quotation marks.
    • ",
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+        {
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+          "block_type": "Text",
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    immutable: The property of a sequence whose items cannot be assigned.
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+          "images": {}
+        },
+        {
+          "id": "/page/100/ListItem/7",
+          "block_type": "ListItem",
+          "html": "
  • traverse: To iterate through the items in a sequence, performing a similar operation on each.
    • ",
+          "polygon": [
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         },
         {
-          "id": "/page/100/Text/4",
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           "block_type": "Text",
-          "html": "
    slice: A part of a string specified by a range of indices.
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    search: A pattern of traversal that stops when it finds what it is looking for.
    ",
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         {
-          "id": "/page/100/ListGroup/176",
-          "block_type": "ListGroup",
-          "html": "
    ",
+          "id": "/page/100/ListItem/9",
+          "block_type": "ListItem",
+          "html": "
  • counter: A variable used to count something, usually initialized to zero and then incremented.
    • ",
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  • empty string: A string with no characters and length 0, represented by two quotation marks.
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  • immutable: The property of a sequence whose items cannot be assigned.
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-              "id": "/page/100/ListItem/7",
-              "block_type": "ListItem",
-              "html": "
  • traverse: To iterate through the items in a sequence, performing a similar operation on each.
    • ",
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  • search: A pattern of traversal that stops when it finds what it is looking for.
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-              "id": "/page/100/ListItem/9",
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    invocation: A statement that calls a method.
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    8.13 Exercises
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    8.13 Exercises
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    Exercise 8.10. A string slice can take a third index that specifies the \"step size;\" that is, the number of spaces between successive characters. A step size of 2 means every other character; 3 means every third, etc.
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    Use this idiom to write a one-line version of is_palindrome from Exercise 6.6.
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    80 Chapter 8. Strings
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+          "html": "",
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    Exercise 8.12. ROT13 is a weak form of encryption that involves \"rotating\" each letter in a word by 13 places. To rotate a letter means to shift it through the alphabet, wrapping around to the beginning if necessary, so 'A' shifted by 3 is 'D' and 'Z' shifted by 1 is 'A'.
    ",
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    Exercise 8.12. ROT13 is a weak form of encryption that involves \"rotating\" each letter in a word by 13 places. To rotate a letter means to shift it through the alphabet, wrapping around to the beginning if necessary, so 'A' shifted by 3 is 'D' and 'Z' shifted by 1 is 'A'.
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    Write a function called rotate_word that takes a string and an integer as parameters, and that returns a new string that contains the letters from the original string \"rotated\" by the given amount.
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-          "html": "
    For example, \"cheer\" rotated by 7 is \"jolly\" and \"melon\" rotated by -10 is \"cubed\".
    ",
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    For example, \"cheer\" rotated by 7 is \"jolly\" and \"melon\" rotated by -10 is \"cubed\".
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    You might want to use the built-in functions ord, which converts a character to a numeric code, and chr, which converts numeric codes to characters.
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    You might want to use the built-in functions ord, which converts a character to a numeric code, and chr, which converts numeric codes to characters.
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    Potentially offensive jokes on the Internet are sometimes encoded in ROT13. If you are not easily offended, find and decode some of them. Solution: http: // thinkpython. com/ code/ rotate. py .
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    Potentially offensive jokes on the Internet are sometimes encoded in ROT13. If you are not easily offended, find and decode some of them. Solution: http: // thinkpython. com/ code/ rotate. py .
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    Chapter 9
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    Chapter 9
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    Case study: word play
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    Case study: word play
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    9.1 Reading word lists
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    9.1 Reading word lists
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    For the exercises in this chapter we need a list of English words. There are lots of word lists available on the Web, but the one most suitable for our purpose is one of the word lists collected and contributed to the public domain by Grady Ward as part of the Moby lexicon project (see http://wikipedia.org/wiki/Moby_Project). It is a list of 113,809 official crosswords; that is, words that are considered valid in crossword puzzles and other word games. In the Moby collection, the filename is 113809of.fic; you can download a copy, with the simpler name words.txt, from http://thinkpython.com/code/words.txt.
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    For the exercises in this chapter we need a list of English words. There are lots of word lists available on the Web, but the one most suitable for our purpose is one of the word lists collected and contributed to the public domain by Grady Ward as part of the Moby lexicon project (see http://wikipedia.org/wiki/Moby_Project). It is a list of 113,809 official crosswords; that is, words that are considered valid in crossword puzzles and other word games. In the Moby collection, the filename is 113809of.fic; you can download a copy, with the simpler name words.txt, from http://thinkpython.com/code/words.txt.
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    This file is in plain text, so you can open it with a text editor, but you can also read it from Python. The built-in function open takes the name of the file as a parameter and returns a file object you can use to read the file.
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    >>> print fin
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    >>> fin.readline()
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    The next word is \"aah,\" which is a perfectly legitimate word, so stop looking at me like that. Or, if it's the whitespace that's bothering you, we can get rid of it with the string method strip:
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    82 Chapter 9. Case study: word play
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    ",
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    >>> line = fin.readline()\n>>> word = line.strip()\n>>> print word
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    aahed
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    You can also use a file object as part of a for loop. This program reads words.txt and prints each word, one per line:
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    >>> line = fin.readline()\n>>> word = line.strip()\n>>> print word\naahed\nYou can also use a file object as part of a for loop. This program reads words.txt and\nprints each word, one per line:\nfin = open('words.txt')\nfor line in fin:\n    word = line.strip()\n    print word\nExercise 9.1. Write a program that reads words.txt and prints only the words with more than 20
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    characters (not counting whitespace).
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    Exercise 9.1. Write a program that reads words.txt and prints only the words with more than 20 characters (not counting whitespace).
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    9.2 Exercises
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    9.2 Exercises
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    There are solutions to these exercises in the next section. You should at least attempt each one before you read the solutions.
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    Exercise 9.2. In 1939 Ernest Vincent Wright published a 50,000 word novel called Gadsby that does not contain the letter \"e.\" Since \"e\" is the most common letter in English, that's not easy to do.
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    In fact, it is difficult to construct a solitary thought without using that most common symbol. It is slow going at first, but with caution and hours of training you can gradually gain facility.
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    All right, I'll stop now.
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    Write a function called has_no_e that returns True if the given word doesn't have the letter \"e\" in it.
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    Write a function called has_no_e that returns True if the given word doesn't have the letter \"e\" in it.
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    Exercise 9.4. Write a function named uses_only that takes a word and a string of letters, and that returns True if the word contains only letters in the list. Can you make a sentence using only the letters acefhlo? Other than \"Hoe alfalfa?\"
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    Exercise 9.6. Write a function called is_abecedarian that returns True if the letters in a word appear in alphabetical order (double letters are ok). How many abecedarian words are there?
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    9.3 Search
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    9.3 Search
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    All of the exercises in the previous section have something in common; they can be solved with the search pattern we saw in Section 8.6. The simplest example is:
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    All of the exercises in the previous section have something in common; they can be solved with the search pattern we saw in Section 8.6. The simplest example is:
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    82
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    If we get to the end of the loop without finding a fault, then the word passes the test. To convince yourself that the loop ends correctly, consider an example like 'flossy'. The length of the word is 6, so the last time the loop runs is when i is 4, which is the index of the second-to-last character. On the last iteration, it compares the second-to-last character to the last, which is what we want.
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    Here is a version of is_palindrome (see Exercise 6.6) that uses two indices; one starts at the beginning and goes up; the other starts at the end and goes down.
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    Or, if you noticed that this is an instance of a previously-solved problem, you might have written:
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    9.5 Debugging
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    9.5 Debugging
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    Taking has_no_e as an example, there are two obvious cases to check: words that have an 'e' should return False; words that don't should return True. You should have no trouble coming up with one of each.
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    Program testing can be used to show the presence of bugs, but never to show their absence!
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    86 Chapter 9. Case study: word play
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+                "/page/109/Figure/1": 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    The in operator also works on lists.
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-      "html": "",
+      "html": "",
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-          "html": "
    10.3. Traversing a list 89
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           "block_type": "SectionHeader",
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    10.3 Traversing a list
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    10.3 Traversing a list
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@@ -53041,26 +101055,32 @@
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    The most common way to traverse the elements of a list is with a for loop. The syntax is the same as for strings:
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    for cheese in cheeses: print cheese
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-            "3": "/page/110/SectionHeader/1"
+            "4": "/page/110/SectionHeader/1"
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@@ -53099,26 +101125,32 @@
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@@ -53128,55 +101160,67 @@
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    for i in range(len(numbers)):\n    numbers[i] = numbers[i] * 2
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           "block_type": "Text",
-          "html": "
    This loop traverses the list and updates each element. len returns the number of elements in the list. range returns a list of indices from 0 to n − 1, where n is the length of the list. Each time through the loop i gets the index of the next element. The assignment statement in the body uses i to read the old value of the element and to assign the new value.
    ",
+          "html": "
    This loop traverses the list and updates each element. len returns the number of elements in the list. range returns a list of indices from 0 to n − 1, where n is the length of the list. Each time through the loop i gets the index of the next element. The assignment statement in the body uses i to read the old value of the element and to assign the new value.
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@@ -53186,26 +101230,32 @@
           "html": "
    A for loop over an empty list never executes the body:
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@@ -53215,26 +101265,32 @@
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-            "3": "/page/110/SectionHeader/1"
+            "4": "/page/110/SectionHeader/1"
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@@ -53244,26 +101300,32 @@
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@@ -53273,55 +101335,67 @@
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         {
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           "block_type": "SectionHeader",
-          "html": "
    10.4 List operations
    ",
+          "html": "
    10.4 List operations
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@@ -53331,55 +101405,67 @@
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    The + operator concatenates lists:
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+          "id": "/page/110/Code/191",
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    90 Chapter 10. Lists
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    >>> t = ['a', 'b', 'c', 'd', 'e', 'f']\n>>> t[1:3]\n['b', 'c']\n>>> t[:4]\n['a', 'b', 'c', 'd']\n>>> t[3:]\n['d', 'e', 'f']
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    92 Chapter 10. Lists
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    Most common list operations can be expressed as a combination of map, filter and reduce. Because these operations are so common, Python provides language features to support them, including the built-in function map and an operator called a \"list comprehension.\"
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    10.8 Deleting elements
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    10.10 Objects and values
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    10.10 Objects and values
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    Figure 10.3: State diagram.
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    But when you create two lists, you get two objects:
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    So the state diagram looks like Figure 10.3.
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    The state diagram looks like Figure 10.4.
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+                "/page/116/Figure/3": 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    10.12 List arguments
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    10.12 List arguments
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    Here's how it is used:
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    The parameter t and the variable letters are aliases for the same object. The stack diagram looks like Figure 10.5.
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    The parameter t and the variable letters are aliases for the same object. The stack diagram looks like Figure 10.5.
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    Since the list is shared by two frames, I drew it between them.
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    It is important to distinguish between operations that modify lists and operations that create new lists. For example, the append method modifies a list, but the + operator creates a new list:
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    96 Chapter 10. Lists
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    def bad_delete_head(t): t = t[1:]  # WRONG!
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    Before using list methods and operators, you should read the documentation carefully and then test them in interactive mode. The methods and operators that lists share with other sequences (like strings) are documented at http://docs.python. org/2/library/stdtypes.html#typesseq. The methods and operators that only apply to mutable sequences are documented at http://docs.python.org/2/library/ stdtypes.html#typesseq-mutable.
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  • 2. Pick an idiom and stick with it.
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  • 1. Write a function called has_duplicates that takes a list and returns True if there is any element that appears more than once. It should not modify the original list.
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  • 2. If there are 23 students in your class, what are the chances that two of you have the same birthday? You can estimate this probability by generating random samples of 23 birthdays and checking for matches. Hint: you can generate random birthdays with the randint function in the random module.
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    You can read about this problem at http: // en. wikipedia. org/ wiki/ Birthday_ paradox , and you can download my solution from http: // thinkpython. com/ code/ birthday. py . Exercise 10.9. Write a function called remove_duplicates that takes a list and returns a new list with only the unique elements from the original. Hint: they don't have to be in the same order. Exercise 10.10. Write a function that reads the file words.txt and builds a list with one element per word. Write two versions of this function, one using the append method and the other using the idiom t = t + [x]. Which one takes longer to run? Why?
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    You can read about this problem at http: // en. wikipedia. org/ wiki/ Birthday_ paradox , and you can download my solution from http: // thinkpython. com/ code/ birthday. py . Exercise 10.9. Write a function called remove_duplicates that takes a list and returns a new list with only the unique elements from the original. Hint: they don't have to be in the same order. Exercise 10.10. Write a function that reads the file words.txt and builds a list with one element per word. Write two versions of this function, one using the append method and the other using the idiom t = t + [x]. Which one takes longer to run? Why?
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    Hint: use the time module to measure elapsed time. Solution: http: // thinkpython. com/ code/ wordlist. py .
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    Hint: use the time module to measure elapsed time. Solution: http: // thinkpython. com/ code/ wordlist. py .
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    Exercise 10.11. To check whether a word is in the word list, you could use the in operator, but it would be slow because it searches through the words in order.
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    Exercise 10.11. To check whether a word is in the word list, you could use the in operator, but it would be slow because it searches through the words in order.
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    Because the words are in alphabetical order, we can speed things up with a bisection search (also known as binary search), which is similar to what you do when you look a word up in the dictionary.
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    10.15. Exercises 99
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    Either way, you cut the remaining search space in half. If the word list has 113,809 words, it will take about 17 steps to find the word or conclude that it's not there.
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    Write a function called bisect that takes a sorted list and a target value and returns the index of the value in the list, if it's there, or None if it's not.
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    Or you could read the documentation of the bisect module and use that! Solution: http: // thinkpython. com/ code/ inlist. py .
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    Or you could read the documentation of the bisect module and use that! Solution: http: // thinkpython. com/ code/ inlist. py .
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    Exercise 10.12. Two words are a \"reverse pair\" if each is the reverse of the other. Write a program that finds all the reverse pairs in the word list. Solution: http: // thinkpython. com/ code/ reverse_ pair. py .
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    Exercise 10.13. Two words \"interlock\" if taking alternating letters from each forms a new word. For example, \"shoe\" and \"cold\" interlock to form \"schooled.\" Solution: http: // thinkpython. com/ code/ interlock. py . Credit: This exercise is inspired by an example at http: // puzzlers. org .
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  • 1. Write a program that finds all pairs of words that interlock. Hint: don't enumerate all pairs!
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  • 2. Can you find any words that are three-way interlocked; that is, every third letter forms a word, starting from the first, second or third?
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    100 Chapter 10. Lists
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    Chapter 11
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    Chapter 11
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    Dictionaries
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    You can think of a dictionary as a mapping between a set of indices (which are called keys) and a set of values. Each key maps to a value. The association of a key and a value is called a key-value pair or sometimes an item.
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    As an example, we'll build a dictionary that maps from English to Spanish words, so the keys and the values are all strings.
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    The function dict creates a new dictionary with no items. Because dict is the name of a built-in function, you should avoid using it as a variable name.
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    The squiggly-brackets, {}, represent an empty dictionary. To add items to the dictionary, you can use square brackets:
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    >>> eng2sp['one'] = 'uno'
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    This line creates an item that maps from the key 'one' to the value 'uno'. If we print the dictionary again, we see a key-value pair with a colon between the key and value:
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    This output format is also an input format. For example, you can create a new dictionary with three items:
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    But if you print eng2sp, you might be surprised:
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+          "id": "/page/122/Code/14",
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    102 Chapter 11. Dictionaries
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    The in operator works on dictionaries; it tells you whether something appears as a key in the dictionary (appearing as a value is not good enough).
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    Exercise 11.1. Write a function that reads the words in words.txt and stores them as keys in a dictionary. It doesn't matter what the values are. Then you can use the in operator as a fast way to check whether a string is in the dictionary.
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    If you did Exercise 10.11, you can compare the speed of this implementation with the list in operator and the bisection search.
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    An implementation is a way of performing a computation; some implementations are better than others. For example, an advantage of the dictionary implementation is that we don't have to know ahead of time which letters appear in the string and we only have to make room for the letters that do appear.
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    Here is what the code might look like:
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    The first line of the function creates an empty dictionary. The for loop traverses the string. Each time through the loop, if the character c is not in the dictionary, we create a new item with key c and the initial value 1 (since we have seen this letter once). If c is already in the dictionary we increment d[c].
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    104 Chapter 11. Dictionaries
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    Again, the keys are in no particular order.
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    Exercise 11.3. Dictionaries have a method called keys that returns the keys of the dictionary, in no particular order, as a list.
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    Modify print_hist to print the keys and their values in alphabetical order.
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    11.3 Reverse lookup
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    11.3 Reverse lookup
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    Given a dictionary d and a key k, it is easy to find the corresponding value v = d[k]. This operation is called a lookup.
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    But what if you have v and you want to find k? You have two problems: first, there might be more than one key that maps to the value v. Depending on the application, you might be able to pick one, or you might have to make a list that contains all of them. Second, there is no simple syntax to do a reverse lookup; you have to search.
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    Here is a function that takes a value and returns the first key that maps to that value:
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    This function is yet another example of the search pattern, but it uses a feature we haven't seen before, raise. The raise statement causes an exception; in this case it causes a ValueError, which generally indicates that there is something wrong with the value of a parameter.
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    Here is an example of a successful reverse lookup:
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    And an unsuccessful one:
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    104
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    11.4. Dictionaries and lists 105
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    Lists can appear as values in a dictionary. For example, if you were given a dictionary that maps from letters to frequencies, you might want to invert it; that is, create a dictionary that maps from frequencies to letters. Since there might be several letters with the same frequency, each value in the inverted dictionary should be a list of letters.
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    106 Chapter 11. Dictionaries
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+                "/page/127/Figure/1": 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    Figure 11.1: State diagram.
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    Figure 11.1: State diagram.
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    Figure 11.1 is a state diagram showing hist and inverse. A dictionary is represented as a box with the type dict above it and the key-value pairs inside. If the values are integers, floats or strings, I usually draw them inside the box, but I usually draw lists outside the box, just to keep the diagram simple.
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    Figure 11.1 is a state diagram showing hist and inverse. A dictionary is represented as a box with the type dict above it and the key-value pairs inside. If the values are integers, floats or strings, I usually draw them inside the box, but I usually draw lists outside the box, just to keep the diagram simple.
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    Lists can be values in a dictionary, as this example shows, but they cannot be keys. Here's what happens if you try:
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    >>> t = [1, 2, 3] >>> d = dict() >>> d[t] = 'oops' Traceback (most recent call last): File \"<stdin>\", line 1, in ? TypeError: list objects are unhashable
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    This system works fine if the keys are immutable. But if the keys are mutable, like lists, bad things happen. For example, when you create a key-value pair, Python hashes the key and stores it in the corresponding location. If you modify the key and then hash it again, it would go to a different location. In that case you might have two entries for the same key, or you might not be able to find a key. Either way, the dictionary wouldn't work correctly.
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    That's why the keys have to be hashable, and why mutable types like lists aren't. The simplest way to get around this limitation is to use tuples, which we will see in the next chapter.
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    Since lists and dictionaries are mutable, they can't be used as keys, but they can be used as values.
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    Since lists and dictionaries are mutable, they can't be used as keys, but they can be used as values.
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    Exercise 11.5. Read the documentation of the dictionary method setdefault and use it to write a more concise version of invert_dict. Solution: http: // thinkpython. com/ code/ invert_ dict. py .
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    11.5 Memos
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    11.5 Memos
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    If you played with the fibonacci function from Section 6.7, you might have noticed that the bigger the argument you provide, the longer the function takes to run. Furthermore,
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    If you played with the fibonacci function from Section 6.7, you might have noticed that the bigger the argument you provide, the longer the function takes to run. Furthermore,
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+                "/page/128/Figure/1": 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    Figure 11.2: Call graph.
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    Figure 11.2: Call graph.
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    the run time increases very quickly.
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    To understand why, consider Figure 11.2, which shows the call graph for fibonacci with n=4:
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    To understand why, consider Figure 11.2, which shows the call graph for fibonacci with n=4:
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    A call graph shows a set of function frames, with lines connecting each frame to the frames of the functions it calls. At the top of the graph, fibonacci with n=4 calls fibonacci with n=3 and n=2. In turn, fibonacci with n=3 calls fibonacci with n=2 and n=1. And so on.
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    Count how many times fibonacci(0) and fibonacci(1) are called. This is an inefficient solution to the problem, and it gets worse as the argument gets bigger.
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    One solution is to keep track of values that have already been computed by storing them in a dictionary. A previously computed value that is stored for later use is called a memo. Here is a \"memoized\" version of fibonacci:
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    known = {0:0, 1:1}
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    known = {0:0, 1:1}\ndef fibonacci(n):\n    if n in known:\n        return known[n]\n    res = fibonacci(n-1) + fibonacci(n-2)\n    known[n] = res\n    return res
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    known is a dictionary that keeps track of the Fibonacci numbers we already know. It starts with two items: 0 maps to 0 and 1 maps to 1.
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    Whenever fibonacci is called, it checks known. If the result is already there, it can return immediately. Otherwise it has to compute the new value, add it to the dictionary, and return it.
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    Exercise 11.6. Run this version of fibonacci and the original with a range of parameters and compare their run times.
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    Exercise 11.7. Memoize the Ackermann function from Exercise 6.5 and see if memoization makes it possible to evaluate the function with bigger arguments. Hint: no. Solution: http: // thinkpython. com/ code/ ackermann_ memo. py .
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    Exercise 11.7. Memoize the Ackermann function from Exercise 6.5 and see if memoization makes it possible to evaluate the function with bigger arguments. Hint: no. Solution: http: // thinkpython. com/ code/ ackermann_ memo. py .
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    108 Chapter 11. Dictionaries
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    11.6 Global variables
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    11.6 Global variables
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    In the previous example, known is created outside the function, so it belongs to the special frame called __main__. Variables in __main__ are sometimes called global because they can be accessed from any function. Unlike local variables, which disappear when their function ends, global variables persist from one function call to the next.
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    It is common to use global variables for flags; that is, boolean variables that indicate (\"flag\") whether a condition is true. For example, some programs use a flag named verbose to control the level of detail in the output:
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    verbose = True\ndef example1():\n    if verbose:\n         print 'Running example1'\nIf you try to reassign a global variable, you might be surprised. The following example is\nsupposed to keep track of whether the function has been called:\nbeen_called = False
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    verbose = True\ndef example1():\n    if verbose:\n        print 'Running example1'
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    def example2(): been_called = True # WRONG
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    If you try to reassign a global variable, you might be surprised. The following example is supposed to keep track of whether the function has been called:
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    been_called = False\ndef example2():\n   been_called = True # WRONG
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    But if you run it you will see that the value of been_called doesn't change. The problem is that example2 creates a new local variable named been_called. The local variable goes away when the function ends, and has no effect on the global variable.
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    To reassign a global variable inside a function you have to declare the global variable before you use it:
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    been_called = False\ndef example2():\n    global been_called\n    been_called = True
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    The global statement tells the interpreter something like, \"In this function, when I say been_called, I mean the global variable; don't create a local one.\"
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    Here's an example that tries to update a global variable:
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    count = 0\ndef example3():\n    count = count + 1 # WRONG\nIf you run it you get:\nUnboundLocalError: local variable 'count' referenced before assignment\nPython assumes that count is local, which means that you are reading it before writing it.\nThe solution, again, is to declare count global.\ndef example3():
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    count = 0\ndef example3():\n   count = count + 1 # WRONG
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    If you run it you get:
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    11.7. Long integers 109
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    known = {0:0, 1:1} def example4(): known[2] = 1
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    11.7 Long integers
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    11.7 Long integers
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    If you compute fibonacci(50), you get:
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    As you work with bigger datasets it can become unwieldy to debug by printing and checking data by hand. Here are some suggestions for debugging large datasets:
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  • Scale down the input: If possible, reduce the size of the dataset. For example if the program reads a text file, start with just the first 10 lines, or with the smallest example you can find. You can either edit the files themselves, or (better) modify the program so it reads only the first n lines.
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    110 Chapter 11. Dictionaries
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  • Check summaries and types: Instead of printing and checking the entire dataset, consider printing summaries of the data: for example, the number of items in a dictionary or the total of a list of numbers.
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    A common cause of runtime errors is a value that is not the right type. For debugging this kind of error, it is often enough to print the type of a value.
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  • Write self-checks: Sometimes you can write code to check for errors automatically. For example, if you are computing the average of a list of numbers, you could check that the result is not greater than the largest element in the list or less than the smallest. This is called a \"sanity check\" because it detects results that are \"insane.\"
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    Another kind of check compares the results of two different computations to see if they are consistent. This is called a \"consistency check.\"
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  • Pretty print the output: Formatting debugging output can make it easier to spot an error. We saw an example in Section 6.9. The pprint module provides a pprint function that displays built-in types in a more human-readable format.
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  • Pretty print the output: Formatting debugging output can make it easier to spot an error. We saw an example in Section 6.9. The pprint module provides a pprint function that displays built-in types in a more human-readable format.
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    Again, time you spend building scaffolding can reduce the time you spend debugging.
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    11.9 Glossary
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    11.9 Glossary
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  • hashtable: The algorithm used to implement Python dictionaries.
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  • hash function: A function used by a hashtable to compute the location for a key.
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    11.10 Exercises
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    Exercise 11.9. If you did Exercise 10.8, you already have a function named has_duplicates that takes a list as a parameter and returns True if there is any object that appears more than once in the list.
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    Exercise 11.9. If you did Exercise 10.8, you already have a function named has_duplicates that takes a list as a parameter and returns True if there is any object that appears more than once in the list.
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    Use a dictionary to write a faster, simpler version of has_duplicates. Solution: http: // thinkpython. com/ code/ has_ duplicates. py .
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    Exercise 11.10. Two words are \"rotate pairs\" if you can rotate one of them and get the other (see rotate_word in Exercise 8.12).
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    Exercise 11.10. Two words are \"rotate pairs\" if you can rotate one of them and get the other (see rotate_word in Exercise 8.12).
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    Exercise 11.11. Here's another Puzzler from Car Talk (http: // www. cartalk. com/ content/ puzzlers ):
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    This was sent in by a fellow named Dan O'Leary. He came upon a common one-syllable, five-letter word recently that has the following unique property. When you remove the first letter, the remaining letters form a homophone of the original word, that is a word that sounds exactly the same. Replace the first letter, that is, put it back and remove the second letter and the result is yet another homophone of the original word. And the question is, what's the word?
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    Now I'm going to give you an example that doesn't work. Let's look at the five-letter word, 'wrack.' W-R-A-C-K, you know like to 'wrack with pain.' If I remove the first letter, I am left with a four-letter word, 'R-A-C-K.' As in, 'Holy cow, did you see the rack on that buck! It must have been a nine-pointer!' It's a perfect homophone. If you put the 'w' back, and remove the 'r,' instead, you're left with the word, 'wack,' which is a real word, it's just not a homophone of the other two words.
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    You can use the dictionary from Exercise 11.1 to check whether a string is in the word list.
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    You can use the dictionary from Exercise 11.1 to check whether a string is in the word list.
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    To check whether two words are homophones, you can use the CMU Pronouncing Dictionary. You can download it from http: // www. speech. cs. cmu. edu/ cgi-bin/ cmudict or from http: // thinkpython. com/ code/ c06d and you can also download http: // thinkpython. com/ code/ pronounce. py , which provides a function named read_dictionary that reads the pronouncing dictionary and returns a Python dictionary that maps from each word to a string that describes its primary pronunciation.
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    To check whether two words are homophones, you can use the CMU Pronouncing Dictionary. You can download it from http: // www. speech. cs. cmu. edu/ cgi-bin/ cmudict or from http: // thinkpython. com/ code/ c06d and you can also download http: // thinkpython. com/ code/ pronounce. py , which provides a function named read_dictionary that reads the pronouncing dictionary and returns a Python dictionary that maps from each word to a string that describes its primary pronunciation.
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    Write a program that lists all the words that solve the Puzzler. Solution: http: // thinkpython. com/ code/ homophone. py .
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    112 Chapter 11. Dictionaries
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    Chapter 12
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    Chapter 12
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    114 Chapter 12. Tuples
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-          "block_type": "Code",
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    >>> print t[1:3]\n('b', 'c')
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+          "block_type": "TextInlineMath",
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    >>> print t[1:3] ('b', 'c')
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    >>> t[0] = 'A'\nTypeError: object doesn't support item assignment
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    Strictly speaking, a function can only return one value, but if the value is a tuple, the effect is the same as returning multiple values. For example, if you want to divide two integers and compute the quotient and remainder, it is inefficient to compute x/y and then x%y. It is better to compute them both at the same time.
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    The built-in function divmod takes two arguments and returns a tuple of two values, the quotient and remainder. You can store the result as a tuple:
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@@ -66972,26 +118568,32 @@
           "html": "
    >>> t = divmod(7, 3)\n>>> print t\n(2, 1)\nOr use tuple assignment to store the elements separately:\n>>> quot, rem = divmod(7, 3)\n>>> print quot\n2\n>>> print rem\n1
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    Here is an example of a function that returns a tuple:
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    return min(t), max(t)
    ",
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    def min_max(t):\n    return min(t), max(t)
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    12.4 Variable-length argument tuples
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    12.4 Variable-length argument tuples
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    Functions can take a variable number of arguments. A parameter name that begins with * gathers arguments into a tuple. For example, printall takes any number of arguments and prints them:
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    def printall(*args):
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    def printall(*args):\n    print args
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    print args
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    The gather parameter can have any name you like, but args is conventional. Here's how the function works:
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    The gather parameter can have any name you like, but args is conventional. Here's how the function works:
    ",
+          "id": "/page/136/Code/12",
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    >>> printall(1, 2.0, '3')\n(1, 2.0, '3')
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    >>> printall(1, 2.0, '3')\n(1, 2.0, '3')
    ",
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+          "block_type": "Text",
+          "html": "
    The complement of gather is scatter. If you have a sequence of values and you want to pass it to a function as multiple arguments, you can use the * operator. For example, divmod takes exactly two arguments; it doesn't work with a tuple:
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    The complement of gather is scatter. If you have a sequence of values and you want to pass it to a function as multiple arguments, you can use the * operator. For example, divmod takes exactly two arguments; it doesn't work with a tuple:
    ",
+          "id": "/page/136/Code/14",
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    >>> t = (7, 3)\n>>> divmod(t)\nTypeError: divmod expected 2 arguments, got 1
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    >>> t = (7, 3)\n>>> divmod(t)\nTypeError: divmod expected 2 arguments, got 1
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+          "block_type": "Text",
+          "html": "
    TypeError: init() expected 2 arguments, got 3
    \n",
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+            "4": "/page/136/SectionHeader/8"
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         {
-          "id": "/page/136/Text/17",
+          "id": "/page/136/Text/16",
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           "html": "
    But if you scatter the tuple, it works:
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         {
-          "id": "/page/136/Text/18",
-          "block_type": "Text",
-          "html": "
    >>> divmod(*t) (2, 1)
    ",
+          "id": "/page/136/TextInlineMath/17",
+          "block_type": "TextInlineMath",
+          "html": "
    >>> divmod(*t)
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+            "4": "/page/136/SectionHeader/8"
+          },
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+        },
+        {
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+          "block_type": "Text",
+          "html": "
    (2, 1)
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-          "html": "
    Exercise 12.1. Many of the built-in functions use variable-length argument tuples. For example, max and min can take any number of arguments:
    ",
+          "html": "
    Exercise 12.1. Many of the built-in functions use variable-length argument tuples. For example, max and min can take any number of arguments:
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    116 Chapter 12. Tuples
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    12.6. Dictionaries and tuples 117
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    The output of this loop is: 0 a 1 b 2 c
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    The output of this loop is:
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    Again.
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    0 a 1 b 2 c Again.
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    12.6 Dictionaries and tuples
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    12.6 Dictionaries and tuples
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    >>> d = {'a':0, 'b':1, 'c':2}\n>>> t = d.items()\n>>> print t\n[('a', 0), ('c', 2), ('b', 1)]
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    Going in the other direction, you can use a list of tuples to initialize a new dictionary:
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    >>> t = [('a', 0), ('c', 2), ('b', 1)]\n>>> d = dict(t)\n>>> print d\n{'a': 0, 'c': 2, 'b': 1}\nCombining dict with zip yields a concise way to create a dictionary:\n>>> d = dict(zip('abc', range(3)))\n>>> print d\n{'a': 0, 'c': 2, 'b': 1}
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    >>> t = [('a', 0), ('c', 2), ('b', 1)]\n>>> d = dict(t)\n>>> print d\n{'a': 0, 'c': 2, 'b': 1}
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    Combining dict with zip yields a concise way to create a dictionary:
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    >>> d = dict(zip('abc', range(3)))\n>>> print d\n{'a': 0, 'c': 2, 'b': 1}
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    The dictionary method update also takes a list of tuples and adds them, as key-value pairs, to an existing dictionary.
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    Combining items, tuple assignment and for, you get the idiom for traversing the keys and values of a dictionary:
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    for key, val in d.items():\n    print val, key
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    The output of this loop is:
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    0 a 2 c 1 b Again.
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    0 a\n2 c\n1 b
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    It is common to use tuples as keys in dictionaries (primarily because you can't use lists). For example, a telephone directory might map from last-name, first-name pairs to telephone numbers. Assuming that we have defined last, first and number, we could write:
    ",
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    Again.
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    directory[last,first] = number
    ",
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    It is common to use tuples as keys in dictionaries (primarily because you can't use lists). For example, a telephone directory might map from last-name, first-name pairs to telephone numbers. Assuming that we have defined last, first and number, we could write:
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    directory[last,first] = number
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    118 Chapter 12. Tuples
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+                    "2": "/page/137/SectionHeader/2",
+                    "4": "/page/138/SectionHeader/3"
+                  },
+                  "images": {}
+                },
+                {
+                  "id": "/page/139/TableCell/198",
+                  "block_type": "TableCell",
+                  "html": "",
+                  "polygon": [
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+                  "children": null,
+                  "section_hierarchy": {
+                    "1": "/page/134/SectionHeader/1",
+                    "2": "/page/137/SectionHeader/2",
+                    "4": "/page/138/SectionHeader/3"
+                  },
+                  "images": {}
+                },
+                {
+                  "id": "/page/139/TableCell/199",
+                  "block_type": "TableCell",
+                  "html": "('Palin', 'Michael') → '08700 100 222'",
+                  "polygon": [
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+                    [
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+                    "2": "/page/137/SectionHeader/2",
+                    "4": "/page/138/SectionHeader/3"
+                  },
+                  "images": {}
+                }
+              ],
+              "section_hierarchy": {
+                "1": "/page/134/SectionHeader/1",
+                "2": "/page/137/SectionHeader/2",
+                "4": "/page/138/SectionHeader/3"
+              },
+              "images": null
+            },
+            {
+              "id": "/page/139/Caption/5",
+              "block_type": "Caption",
+              "html": "
    Figure 12.2: State diagram.
    ",
+              "polygon": [
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+                [
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+                [
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+              "children": null,
+              "section_hierarchy": {
+                "1": "/page/134/SectionHeader/1",
+                "2": "/page/137/SectionHeader/2",
+                "4": "/page/138/SectionHeader/3"
+              },
+              "images": {}
+            }
+          ],
           "section_hierarchy": {
             "1": "/page/134/SectionHeader/1",
-            "3": "/page/138/SectionHeader/3",
-            "4": "/page/139/SectionHeader/3"
+            "2": "/page/137/SectionHeader/2",
+            "4": "/page/138/SectionHeader/3"
           },
-          "images": {}
+          "images": null
         },
         {
-          "id": "/page/139/TextInlineMath/6",
-          "block_type": "TextInlineMath",
-          "html": "
    for last, first in directory:
    ",
+          "id": "/page/139/Code/6",
+          "block_type": "Code",
+          "html": "
    for last, first in directory:\n    print first, last, directory[last,first]
    ",
           "polygon": [
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-            "4": "/page/139/SectionHeader/3"
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-          "images": {}
-        },
-        {
-          "id": "/page/139/TextInlineMath/7",
-          "block_type": "TextInlineMath",
-          "html": "
    print first, last, directory[last,first]
    ",
-          "polygon": [
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-            [
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           ],
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           "section_hierarchy": {
             "1": "/page/134/SectionHeader/1",
-            "3": "/page/138/SectionHeader/3",
-            "4": "/page/139/SectionHeader/3"
+            "2": "/page/137/SectionHeader/2",
+            "4": "/page/138/SectionHeader/3"
           },
           "images": {}
         },
         {
-          "id": "/page/139/Text/8",
+          "id": "/page/139/Text/7",
           "block_type": "Text",
           "html": "
    This loop traverses the keys in directory, which are tuples. It assigns the elements of each tuple to last and first, then prints the name and corresponding telephone number.
    ",
           "polygon": [
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             [
@@ -68823,89 +121465,107 @@
               367.201904296875
             ]
           ],
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             "1": "/page/134/SectionHeader/1",
-            "3": "/page/138/SectionHeader/3",
-            "4": "/page/139/SectionHeader/3"
+            "2": "/page/137/SectionHeader/2",
+            "4": "/page/138/SectionHeader/3"
           },
           "images": {}
         },
         {
-          "id": "/page/139/Text/9",
+          "id": "/page/139/Text/8",
           "block_type": "Text",
-          "html": "
    There are two ways to represent tuples in a state diagram. The more detailed version shows the indices and elements just as they appear in a list. For example, the tuple ('Cleese', 'John') would appear as in Figure 12.1.
    ",
+          "html": "
    There are two ways to represent tuples in a state diagram. The more detailed version shows the indices and elements just as they appear in a list. For example, the tuple ('Cleese', 'John') would appear as in Figure 12.1.
    ",
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-            "4": "/page/139/SectionHeader/3"
+            "2": "/page/137/SectionHeader/2",
+            "4": "/page/138/SectionHeader/3"
           },
           "images": {}
         },
         {
-          "id": "/page/139/Text/10",
+          "id": "/page/139/Text/9",
           "block_type": "Text",
-          "html": "
    But in a larger diagram you might want to leave out the details. For example, a diagram of the telephone directory might appear as in Figure 12.2.
    ",
+          "html": "
    But in a larger diagram you might want to leave out the details. For example, a diagram of the telephone directory might appear as in Figure 12.2.
    ",
           "polygon": [
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           "section_hierarchy": {
             "1": "/page/134/SectionHeader/1",
-            "3": "/page/138/SectionHeader/3",
-            "4": "/page/139/SectionHeader/3"
+            "2": "/page/137/SectionHeader/2",
+            "4": "/page/138/SectionHeader/3"
           },
           "images": {}
         },
         {
-          "id": "/page/139/Text/11",
+          "id": "/page/139/Text/10",
           "block_type": "Text",
           "html": "
    Here the tuples are shown using Python syntax as a graphical shorthand.
    ",
           "polygon": [
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+              453.234375
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             ],
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@@ -68913,200 +121573,248 @@
               464.3238830566406
             ]
           ],
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           "section_hierarchy": {
             "1": "/page/134/SectionHeader/1",
-            "3": "/page/138/SectionHeader/3",
-            "4": "/page/139/SectionHeader/3"
+            "2": "/page/137/SectionHeader/2",
+            "4": "/page/138/SectionHeader/3"
           },
           "images": {}
         },
         {
-          "id": "/page/139/Text/12",
+          "id": "/page/139/Text/11",
           "block_type": "Text",
           "html": "
    The telephone number in the diagram is the complaints line for the BBC, so please don't call it.
    ",
           "polygon": [
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           "section_hierarchy": {
             "1": "/page/134/SectionHeader/1",
-            "3": "/page/138/SectionHeader/3",
-            "4": "/page/139/SectionHeader/3"
+            "2": "/page/137/SectionHeader/2",
+            "4": "/page/138/SectionHeader/3"
           },
           "images": {}
         },
         {
-          "id": "/page/139/SectionHeader/13",
+          "id": "/page/139/SectionHeader/12",
           "block_type": "SectionHeader",
-          "html": "
    12.7 Comparing tuples
    ",
+          "html": "
    12.7 Comparing tuples
    ",
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             "1": "/page/134/SectionHeader/1",
-            "3": "/page/139/SectionHeader/13"
+            "2": "/page/137/SectionHeader/2",
+            "3": "/page/139/SectionHeader/12"
           },
           "images": {}
         },
         {
-          "id": "/page/139/Text/14",
+          "id": "/page/139/Text/13",
           "block_type": "Text",
           "html": "
    The relational operators work with tuples and other sequences; Python starts by comparing the first element from each sequence. If they are equal, it goes on to the next elements, and so on, until it finds elements that differ. Subsequent elements are not considered (even if they are really big).
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         {
-          "id": "/page/139/Code/15",
+          "id": "/page/139/Code/14",
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           "html": "
    >>> (0, 1, 2) < (0, 3, 4)\nTrue\n>>> (0, 1, 2000000) < (0, 3, 4)\nTrue
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             "1": "/page/134/SectionHeader/1",
-            "3": "/page/139/SectionHeader/13"
+            "2": "/page/137/SectionHeader/2",
+            "3": "/page/139/SectionHeader/12"
           },
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-          "id": "/page/139/Text/16",
+          "id": "/page/139/Text/15",
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           "html": "
    The sort function works the same way. It sorts primarily by first element, but in the case of a tie, it sorts by second element, and so on.
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             "1": "/page/134/SectionHeader/1",
-            "3": "/page/139/SectionHeader/13"
+            "2": "/page/137/SectionHeader/2",
+            "3": "/page/139/SectionHeader/12"
           },
           "images": {}
         },
         {
-          "id": "/page/139/Text/17",
+          "id": "/page/139/Text/16",
           "block_type": "Text",
-          "html": "
    This feature lends itself to a pattern called DSU for
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+          "html": "
    This feature lends itself to a pattern called DSU for
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+            "2": "/page/137/SectionHeader/2",
+            "3": "/page/139/SectionHeader/12"
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-        "3": "/page/139/SectionHeader/13"
+        "2": "/page/137/SectionHeader/2",
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       "images": null
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+      "id": "/page/140/Page/180",
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-      "html": "",
+      "html": "",
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           "block_type": "PageHeader",
-          "html": "
    12.8. Sequences of sequences 119
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         },
         {
           "id": "/page/140/PageHeader/16",
           "block_type": "PageHeader",
-          "html": "
    ",
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         {
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-          "block_type": "Text",
-          "html": "
    Decorate a sequence by building a list of tuples with one or more sort keys preceding the elements from the sequence,
    ",
+          "id": "/page/140/ListItem/1",
+          "block_type": "ListItem",
+          "html": "
  • Decorate a sequence by building a list of tuples with one or more sort keys preceding the elements from the sequence,
    • ",
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    Here's the result for a simple list:
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  • shape (of a data structure): A summary of the type, size and composition of a data structure.
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    12.11 Exercises
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    12.11 Exercises
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    Exercise 12.3. Write a function called most_frequent that takes a string and prints the letters in decreasing order of frequency. Find text samples from several different languages and see how letter frequency varies between languages. Compare your results with the tables at http: // en. wikipedia. org/ wiki/ Letter_ frequencies . Solution: http: // thinkpython. com/ code/ most_ frequent. py . Exercise 12.4. More anagrams!
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    Exercise 12.4. More anagrams!
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  • 1. Write a program that reads a word list from a file (see Section 9.1) and prints all the sets of words that are anagrams.
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  • 1. Write a program that reads a word list from a file (see Section 9.1) and prints all the sets of words that are anagrams.
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    Here is an example of what the output might look like:
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    ['deltas', 'desalt', 'lasted', 'salted', 'slated', 'staled']\n['retainers', 'ternaries']\n['generating', 'greatening']\n['resmelts', 'smelters', 'termless']
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    ['deltas', 'desalt', 'lasted', 'salted', 'slated', 'staled'] ['retainers', 'ternaries'] ['generating', 'greatening'] ['resmelts', 'smelters', 'termless']
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    Hint: you might want to build a dictionary that maps from a set of letters to a list of words that can be spelled with those letters. The question is, how can you represent the set of letters in a way that can be used as a key?
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  • 2. Modify the previous program so that it prints the largest set of anagrams first, followed by the second largest set, and so on.
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  • 3. In Scrabble a \"bingo\" is when you play all seven tiles in your rack, along with a letter on the board, to form an eight-letter word. What set of 8 letters forms the most possible bingos? Hint: there are seven.
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    Solution: http: // thinkpython. com/ code/ anagram_ sets. py .
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    Solution: http: // thinkpython. com/ code/ anagram_ sets. py .
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    122 Chapter 12. Tuples
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    Exercise 12.5. Two words form a \"metathesis pair\" if you can transform one into the other by swapping two letters; for example, \"converse\" and \"conserve.\" Write a program that finds all of the metathesis pairs in the dictionary. Hint: don't test all pairs of words, and don't test all possible swaps. Solution: http: // thinkpython. com/ code/ metathesis. py . Credit: This exercise is inspired by an example at http: // puzzlers. org .
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    Exercise 12.5. Two words form a \"metathesis pair\" if you can transform one into the other by swapping two letters; for example, \"converse\" and \"conserve.\" Write a program that finds all of the metathesis pairs in the dictionary. Hint: don't test all pairs of words, and don't test all possible swaps. Solution: http: // thinkpython. com/ code/ metathesis. py . Credit: This exercise is inspired by an example at http: // puzzlers. org .
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    Exercise 12.6. Here's another Car Talk Puzzler (http: // www. cartalk. com/ content/ puzzlers ):
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    Exercise 12.6. Here's another Car Talk Puzzler (http: // www. cartalk. com/ content/ puzzlers ):
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    What is the longest English word, that remains a valid English word, as you remove its letters one at a time?
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    Now, letters can be removed from either end, or the middle, but you can't rearrange any of the letters. Every time you drop a letter, you wind up with another English word. If you do that, you're eventually going to wind up with one letter and that too is going to be an English word—one that's found in the dictionary. I want to know what's the longest word and how many letters does it have?
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    I'm going to give you a little modest example: Sprite. Ok? You start off with sprite, you take a letter off, one from the interior of the word, take the r away, and we're left with the word spite, then we take the e off the end, we're left with spit, we take the s off, we're left with pit, it, and I.
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    This exercise is a little more challenging than most, so here are some suggestions:
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  • 1. You might want to write a function that takes a word and computes a list of all the words that can be formed by removing one letter. These are the \"children\" of the word.
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  • 2. Recursively, a word is reducible if any of its children are reducible. As a base case, you can consider the empty string reducible.
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  • 3. The wordlist I provided, words.txt, doesn't contain single letter words. So you might want to add \"I\", \"a\", and the empty string.
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  • 4. To improve the performance of your program, you might want to memoize the words that are known to be reducible.
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    Solution: http: // thinkpython. com/ code/ reducible. py .
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    Solution: http: // thinkpython. com/ code/ reducible. py .
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    Chapter 13
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    Chapter 13
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    Case study: data structure selection
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    13.1 Word frequency analysis
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    13.1 Word frequency analysis
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    Hint: The string module provides strings named whitespace, which contains space, tab, newline, etc., and punctuation which contains the punctuation characters. Let's see if we can make Python swear:
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    Also, you might consider using the string methods strip, replace and translate. Exercise 13.2. Go to Project Gutenberg (http: // gutenberg. org ) and download your favorite out-of-copyright book in plain text format.
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    Also, you might consider using the string methods strip, replace and translate. Exercise 13.2. Go to Project Gutenberg (http: // gutenberg. org ) and download your favorite out-of-copyright book in plain text format.
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    Modify your program from the previous exercise to read the book you downloaded, skip over the header information at the beginning of the file, and process the rest of the words as before.
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    Then modify the program to count the total number of words in the book, and the number of times each word is used.
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    Print the number of different words used in the book. Compare different books by different authors, written in different eras. Which author uses the most extensive vocabulary?
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    Exercise 13.3. Modify the program from the previous exercise to print the 20 most frequently-used words in the book.
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    Exercise 13.4. Modify the previous program to read a word list (see Section 9.1) and then print all the words in the book that are not in the word list. How many of them are typos? How many of them are common words that should be in the word list, and how many of them are really obscure?
    ",
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    Exercise 13.4. Modify the previous program to read a word list (see Section 9.1) and then print all the words in the book that are not in the word list. How many of them are typos? How many of them are common words that should be in the word list, and how many of them are really obscure?
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       "images": null
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    124 Chapter 13. Case study: data structure selection
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    13.2 Random numbers
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    13.2 Random numbers
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    Given the same inputs, most computer programs generate the same outputs every time, so they are said to be deterministic. Determinism is usually a good thing, since we expect the same calculation to yield the same result. For some applications, though, we want the computer to be unpredictable. Games are an obvious example, but there are more.
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    Making a program truly nondeterministic turns out to be not so easy, but there are ways to make it at least seem nondeterministic. One of them is to use algorithms that generate pseudorandom numbers. Pseudorandom numbers are not truly random because they are generated by a deterministic computation, but just by looking at the numbers it is all but impossible to distinguish them from random.
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    The random module provides functions that generate pseudorandom numbers (which I will simply call \"random\" from here on).
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    The function random returns a random float between 0.0 and 1.0 (including 0.0 but not 1.0). Each time you call random, you get the next number in a long series. To see a sample, run this loop:
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    import random
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    import random
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    for i in range(10):\n    x = random.random()\n    print x
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    The function randint takes parameters low and high and returns an integer between low and high (including both).
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@@ -71808,26 +125273,33 @@
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    To choose an element from a sequence at random, you can use choice:
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    >>> t = [1, 2, 3]\n>>> random.choice(t)\n2\n>>> random.choice(t)\n3
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    Exercise 13.5. Write a function named choose_from_hist that takes a histogram as defined in Section 11.1 and returns a random value from the histogram, chosen with probability in proportion to frequency. For example, for this histogram:
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    your function should return 'a' with probability 2/3 and 'b' with probability 1/3.
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    your function should return 'a' with probability 2/3 and 'b' with probability 1/3.
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    13.3. Word histogram 125
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    13.3 Word histogram
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    You should attempt the previous exercises before you go on. You can download my solution from http://thinkpython.com/code/analyze_book.py. You will also need http: //thinkpython.com/code/emma.txt.
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    You should attempt the previous exercises before you go on. You can download my solution from http://thinkpython.com/code/analyze_book.py. You will also need http: //thinkpython.com/code/emma.txt.
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    import string
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    import string\ndef process_file(filename):\n    hist = dict()\n    fp = open(filename)\n    for line in fp:\n        process_line(line, hist)\n    return hist\ndef process_line(line, hist):\n    line = line.replace('-', ' ')\n    for word in line.split():\n        word = word.strip(string.punctuation + string.whitespace)\n        word = word.lower()\n        hist[word] = hist.get(word, 0) + 1
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    process_line uses the string method replace to replace hyphens with spaces before using split to break the line into a list of strings. It traverses the list of words and uses strip and lower to remove punctuation and convert to lower case. (It is a shorthand to say that strings are \"converted;\" remember that string are immutable, so methods like strip and lower return new strings.)
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    def total_words(hist):
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           "children": null,
           "section_hierarchy": {
             "1": "/page/144/SectionHeader/1",
-            "3": "/page/146/SectionHeader/1"
+            "3": "/page/144/SectionHeader/2",
+            "4": "/page/146/SectionHeader/1"
           },
           "images": {}
         },
         {
           "id": "/page/146/Text/14",
           "block_type": "Text",
-          "html": "
    def different_words(hist):
    ",
+          "html": "
    return len(hist)
    ",
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+            "3": "/page/144/SectionHeader/2",
+            "4": "/page/146/SectionHeader/1"
           },
           "images": {}
         },
         {
           "id": "/page/146/Text/15",
           "block_type": "Text",
-          "html": "
    return len(hist)
    ",
+          "html": "
    Here is some code to print the results:
    ",
+          "polygon": [
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+            [
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+            "3": "/page/144/SectionHeader/2",
+            "4": "/page/146/SectionHeader/1"
+          },
+          "images": {}
+        },
+        {
+          "id": "/page/146/Code/16",
+          "block_type": "Code",
+          "html": "
    print 'Total number of words:', total_words(hist)\nprint 'Number of different words:', different_words(hist)
    ",
+          "polygon": [
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+            "3": "/page/144/SectionHeader/2",
+            "4": "/page/146/SectionHeader/1"
+          },
+          "images": {}
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+      "section_hierarchy": {
+        "1": "/page/144/SectionHeader/1",
+        "3": "/page/144/SectionHeader/2",
+        "4": "/page/146/SectionHeader/1"
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+      "images": null
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+    {
+      "id": "/page/147/Page/162",
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+          "id": "/page/147/PageHeader/0",
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+          "html": "",
+          "polygon": [
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+          "section_hierarchy": {
+            "1": "/page/144/SectionHeader/1",
+            "3": "/page/144/SectionHeader/2",
+            "4": "/page/146/SectionHeader/1"
+          },
+          "images": {}
+        },
+        {
+          "id": "/page/147/PageHeader/15",
+          "block_type": "PageHeader",
+          "html": "",
+          "polygon": [
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+          "section_hierarchy": {
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+            "3": "/page/144/SectionHeader/2",
+            "4": "/page/146/SectionHeader/1"
+          },
+          "images": {}
+        },
+        {
+          "id": "/page/147/Text/1",
+          "block_type": "Text",
+          "html": "
    And the results:
    ",
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-              646.4197845458984
+              86.0625,
+              88.41357421875
             ],
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-              646.4197845458984
+              156.45700073242188,
+              88.41357421875
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             [
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+              86.0625,
+              98.79791259765625
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           ],
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           "children": null,
           "section_hierarchy": {
             "1": "/page/144/SectionHeader/1",
-            "3": "/page/146/SectionHeader/1"
+            "3": "/page/144/SectionHeader/2",
+            "4": "/page/146/SectionHeader/1"
           },
           "images": {}
         },
         {
-          "id": "/page/146/Text/16",
+          "id": "/page/147/Text/2",
           "block_type": "Text",
-          "html": "
    Here is some code to print the results:
    ",
+          "html": "
    Total number of words: 161080 Number of different words: 7214
    ",
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-            "3": "/page/146/SectionHeader/1"
+            "3": "/page/144/SectionHeader/2",
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           },
           "images": {}
         },
         {
-          "id": "/page/146/Text/17",
-          "block_type": "Text",
-          "html": "
    print 'Total number of words:', total_words(hist) print 'Number of different words:', different_words(hist)
    ",
+          "id": "/page/147/SectionHeader/3",
+          "block_type": "SectionHeader",
+          "html": "
    13.4 Most common words
    ",
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+            "3": "/page/144/SectionHeader/2",
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-      "section_hierarchy": {
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-          "block_type": "PageHeader",
-          "html": "
    126 Chapter 13. Case study: data structure selection
    ",
+          "id": "/page/147/Text/4",
+          "block_type": "Text",
+          "html": "
    To find the most common words, we can apply the DSU pattern; most_common takes a histogram and returns a list of word-frequency tuples, sorted in reverse order by frequency:
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    ",
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    def most_common(hist):\n    t = []\n    for key, value in hist.items():\n        t.append((value, key))\n    t.sort(reverse=True)\n    return t
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         {
-          "id": "/page/147/Text/1",
+          "id": "/page/147/Text/6",
           "block_type": "Text",
-          "html": "
    And the results: Total number of words: 161080 Number of different words: 7214
    ",
+          "html": "
    Here is a loop that prints the ten most common words:
    ",
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-          "html": "
    13.4 Most common words
    ",
+          "id": "/page/147/Code/7",
+          "block_type": "Code",
+          "html": "
    t = most_common(hist)\nprint 'The most common words are:'\nfor freq, word in t[0:10]:\n    print word, '\\t', freq
    ",
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         {
-          "id": "/page/147/Text/3",
+          "id": "/page/147/Text/8",
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-          "html": "
    To find the most common words, we can apply the DSU pattern; most_common takes a histogram and returns a list of word-frequency tuples, sorted in reverse order by frequency:
    ",
+          "html": "
    And here are the results from Emma:
    ",
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    def most_common(hist):\n    t = []\n    for key, value in hist.items():\n        t.append((value, key))\n    t.sort(reverse=True)\n    return t\nHere is a loop that prints the ten most common words:\nt = most_common(hist)\nprint 'The most common words are:'\nfor freq, word in t[0:10]:\n    print word, '\\t', freq\nAnd here are the results from Emma:\nThe most common words are:\nto 5242\nthe 5205\nand 4897\nof 4295\ni 3191\na 3130\nit 2529\nher 2483\nwas 2400\nshe 2364
    ",
+          "id": "/page/147/Text/9",
+          "block_type": "Text",
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    The most common words are: to 5242 the 5205 and 4897 of 4295 i 3191 a 3130 it 2529 her 2483 was 2400 she 2364
    ",
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             "1": "/page/144/SectionHeader/1",
-            "3": "/page/147/SectionHeader/2"
+            "3": "/page/144/SectionHeader/2",
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           },
           "images": {}
         },
         {
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+          "id": "/page/147/SectionHeader/10",
           "block_type": "SectionHeader",
-          "html": "
    13.5 Optional parameters
    ",
+          "html": "
    13.5 Optional parameters
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           "html": "
    We have seen built-in functions and methods that take a variable number of arguments. It is possible to write user-defined functions with optional arguments, too. For example, here is a function that prints the most common words in a histogram
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    13.6. Dictionary subtraction 127
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    print_most_common(hist) num gets the default value. If you provide two arguments: print_most_common(hist, 20) num gets the value of the argument instead. In other words, the optional argument overrides the default value.
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    13.6 Dictionary subtraction
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    13.6 Dictionary subtraction
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    Finding the words from the book that are not in the word list from words.txt is a problem you might recognize as set subtraction; that is, we want to find all the words from one set (the words in the book) that are not in another set (the words in the list).
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    ",
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         {
-          "id": "/page/148/Text/7",
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           "block_type": "Text",
-          "html": "
    a histogram for words.txt, and then subtract: words = process_file('words.txt')
    ",
+          "html": "
    To find the words in the book that are not in words.txt, we can use process_file to build a histogram for words.txt, and then subtract:
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         {
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-          "block_type": "Text",
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    diff = subtract(hist, words)
    ",
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         {
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-          "block_type": "Text",
-          "html": "
    print \"The words in the book that aren't in the word list are:\" for word in diff.keys():
    ",
+          "id": "/page/148/Code/12",
+          "block_type": "Code",
+          "html": "
    print \"The words in the book that aren't in the word list are:\"\nfor word in diff.keys():
    ",
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             "1": "/page/144/SectionHeader/1",
-            "3": "/page/148/SectionHeader/3"
+            "3": "/page/144/SectionHeader/2",
+            "4": "/page/148/SectionHeader/6"
           },
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         },
         {
-          "id": "/page/148/Text/10",
+          "id": "/page/148/Text/13",
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    print word,
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             "1": "/page/144/SectionHeader/1",
-            "3": "/page/148/SectionHeader/3"
+            "3": "/page/144/SectionHeader/2",
+            "4": "/page/148/SectionHeader/6"
           },
           "images": {}
         },
         {
-          "id": "/page/148/Text/11",
+          "id": "/page/148/Text/14",
           "block_type": "Text",
           "html": "
    Here are some of the results from Emma:
    ",
           "polygon": [
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    128 Chapter 13. Case study: data structure selection
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  • 1. Use keys to get a list of the words in the book.
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  • 2. Build a list that contains the cumulative sum of the word frequencies (see Exercise 10.3). The last item in this list is the total number of words in the book, n.
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  • 3. Choose a random number from 1 to n. Use a bisection search (See Exercise 10.11) to find the index where the random number would be inserted in the cumulative sum.
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  • 1. Write a program to read a text from a file and perform Markov analysis. The result should be a dictionary that maps from prefixes to a collection of possible suffixes. The collection might be a list, tuple, or dictionary; it is up to you to make an appropriate choice. You can test your program with prefix length two, but you should write the program in a way that makes it easy to try other lengths.
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  • 2. Add a function to the previous program to generate random text based on the Markov analysis. Here is an example from Emma with prefix length 2:
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    13.9 Data structures
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    13.9 Data structures
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    Using Markov analysis to generate random text is fun, but there is also a point to this exercise: data structure selection. In your solution to the previous exercises, you had to choose:
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  •  How to represent the prefixes.
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    130 Chapter 13. Case study: data structure selection
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    For the collection of suffixes, the operations we need to perform include adding a new suffix (or increasing the frequency of an existing one), and choosing a random suffix.
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    Adding a new suffix is equally easy for the list implementation or the histogram. Choosing a random element from a list is easy; choosing from a histogram is harder to do efficiently (see Exercise 13.7).
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    Adding a new suffix is equally easy for the list implementation or the histogram. Choosing a random element from a list is easy; choosing from a histogram is harder to do efficiently (see Exercise 13.7).
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    So far we have been talking mostly about ease of implementation, but there are other factors to consider in choosing data structures. One is run time. Sometimes there is a theoretical reason to expect one data structure to be faster than other; for example, I mentioned that the in operator is faster for dictionaries than for lists, at least when the number of elements is large.
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    But often you don't know ahead of time which implementation will be faster. One option is to implement both of them and see which is better. This approach is called benchmarking. A practical alternative is to choose the data structure that is easiest to implement, and then see if it is fast enough for the intended application. If so, there is no need to go on. If not, there are tools, like the profile module, that can identify the places in a program that take the most time.
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    The other factor to consider is storage space. For example, using a histogram for the collection of suffixes might take less space because you only have to store each word once, no matter how many times it appears in the text. In some cases, saving space can also make your program run faster, and in the extreme, your program might not run at all if you run out of memory. But for many applications, space is a secondary consideration after run time.
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    One final thought: in this discussion, I have implied that we should use one data structure for both analysis and generation. But since these are separate phases, it would also be possible to use one structure for analysis and then convert to another structure for generation. This would be a net win if the time saved during generation exceeded the time spent in conversion.
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  • reading: Examine your code, read it back to yourself, and check that it says what you meant to say.
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  • running: Experiment by making changes and running different versions. Often if you display the right thing at the right place in the program, the problem becomes obvious, but sometimes you have to spend some time to build scaffolding.
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  • ruminating: Take some time to think! What kind of error is it: syntax, runtime, semantic? What information can you get from the error messages, or from the output of the program? What kind of error could cause the problem you're seeing? What did you change last, before the problem appeared?
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  • retreating: At some point, the best thing to do is back off, undoing recent changes, until you get back to a program that works and that you understand. Then you can start rebuilding.
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    Beginning programmers sometimes get stuck on one of these activities and forget the others. Each activity comes with its own failure mode.
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    For example, reading your code might help if the problem is a typographical error, but not if the problem is a conceptual misunderstanding. If you don't understand what your program does, you can read it 100 times and never see the error, because the error is in your head.
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    Running experiments can help, especially if you run small, simple tests. But if you run experiments without thinking or reading your code, you might fall into a pattern I call \"random walk programming,\" which is the process of making random changes until the program does the right thing. Needless to say, random walk programming can take a long time.
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    You have to take time to think. Debugging is like an experimental science. You should have at least one hypothesis about what the problem is. If there are two or more possibilities, try to think of a test that would eliminate one of them.
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    Taking a break helps with the thinking. So does talking. If you explain the problem to someone else (or even yourself), you will sometimes find the answer before you finish asking the question.
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    But even the best debugging techniques will fail if there are too many errors, or if the code you are trying to fix is too big and complicated. Sometimes the best option is to retreat, simplifying the program until you get to something that works and that you understand.
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    Beginning programmers are often reluctant to retreat because they can't stand to delete a line of code (even if it's wrong). If it makes you feel better, copy your program into another file before you start stripping it down. Then you can paste the pieces back in a little bit at a time.
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    Finding a hard bug requires reading, running, ruminating, and sometimes retreating. If you get stuck on one of these activities, try the others.
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    132 Chapter 13. Case study: data structure selection
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    13.11 Glossary
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    13.11 Glossary
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    deterministic: Pertaining to a program that does the same thing each time it runs, given the same inputs.
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  • pseudorandom: Pertaining to a sequence of numbers that appear to be random, but are generated by a deterministic program.
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  • deterministic: Pertaining to a program that does the same thing each time it runs, given the same inputs.
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  • pseudorandom: Pertaining to a sequence of numbers that appear to be random, but are generated by a deterministic program.
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    default value: The value given to an optional parameter if no argument is provided.
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  • benchmarking: The process of choosing between data structures by implementing alternatives and testing them on a sample of the possible inputs.
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    13.12 Exercises
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    Exercise 13.9. The \"rank\" of a word is its position in a list of words sorted by frequency: the most common word has rank 1, the second most common has rank 2, etc.
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    Zipf's law describes a relationship between the ranks and frequencies of words in natural languages (http: // en. wikipedia. org/ wiki/ Zipf's_ law ). Specifically, it predicts that the frequency, f , of the word with rank r is:
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    Zipf's law describes a relationship between the ranks and frequencies of words in natural languages (http: // en. wikipedia. org/ wiki/ Zipf's_ law ). Specifically, it predicts that the frequency, f , of the word with rank r is:
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    f = cr−s
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    f = cr^{-s} 
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    where s and c are parameters that depend on the language and the text. If you take the logarithm of both sides of this equation, you get:
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    log f = log c − slog r
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    \\log f = \\log c - s \\log r
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    So if you plot log f versus log r, you should get a straight line with slope −s and intercept log c.
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    So if you plot log f versus log r, you should get a straight line with slope −s and intercept log c.
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    Write a program that reads a text from a file, counts word frequencies, and prints one line for each word, in descending order of frequency, with log f and log r. Use the graphing program of your choice to plot the results and check whether they form a straight line. Can you estimate the value of s?
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    Write a program that reads a text from a file, counts word frequencies, and prints one line for each word, in descending order of frequency, with log f and log r. Use the graphing program of your choice to plot the results and check whether they form a straight line. Can you estimate the value of s?
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    Solution: http: // thinkpython. com/ code/ zipf. py . To make the plots, you might have to install matplotlib (see http: // matplotlib. sourceforge. net/ ).
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    Solution: http: // thinkpython. com/ code/ zipf. py . To make the plots, you might have to install matplotlib (see http: // matplotlib. sourceforge. net/ ).
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    Chapter 14
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    Chapter 14
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    Files
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    14.1 Persistence
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    14.1 Persistence
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    Most of the programs we have seen so far are transient in the sense that they run for a short time and produce some output, but when they end, their data disappears. If you run the program again, it starts with a clean slate.
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    Other programs are persistent: they run for a long time (or all the time); they keep at least some of their data in permanent storage (a hard drive, for example); and if they shut down and restart, they pick up where they left off.
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    Examples of persistent programs are operating systems, which run pretty much whenever a computer is on, and web servers, which run all the time, waiting for requests to come in on the network.
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    14.2 Reading and writing
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    14.2 Reading and writing
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    A text file is a sequence of characters stored on a permanent medium like a hard drive, flash memory, or CD-ROM. We saw how to open and read a file in Section 9.1.
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    A text file is a sequence of characters stored on a permanent medium like a hard drive, flash memory, or CD-ROM. We saw how to open and read a file in Section 9.1.
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    To write a file, you have to open it with mode 'w' as a second parameter:
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    If the file already exists, opening it in write mode clears out the old data and starts fresh, so be careful! If the file doesn't exist, a new one is created.
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    The write method puts data into the file.
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    134 Chapter 14. Files
    ",
+          "html": "",
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         },
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    ",
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    >>> line1 = \"This here's the wattle,\\n\"\n>>> fout.write(line1)\nAgain, the file object keeps track of where it is, so if you call write again, it adds the new\ndata to the end.\n>>> line2 = \"the emblem of our land.\\n\"\n>>> fout.write(line2)\nWhen you are done writing, you have to close the file.
    ",
+          "id": "/page/155/TextInlineMath/1",
+          "block_type": "TextInlineMath",
+          "html": "
    >>> line1 = \"This here's the wattle,\\n\" >>> fout.write(line1)
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+            "4": "/page/154/SectionHeader/8"
+          },
+          "images": {}
+        },
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+          "block_type": "Text",
+          "html": "
    Again, the file object keeps track of where it is, so if you call write again, it adds the new data to the end.
    ",
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+            "1": "/page/154/SectionHeader/1",
+            "4": "/page/154/SectionHeader/8"
+          },
+          "images": {}
+        },
+        {
+          "id": "/page/155/Text/3",
+          "block_type": "Text",
+          "html": "
    >>> line2 = \"the emblem of our land.\\n\" >>> fout.write(line2)
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+            "4": "/page/154/SectionHeader/8"
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    When you are done writing, you have to close the file.
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-          "html": "
    >>> fout.close()
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+          "html": "
    >>> fout.close()
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    14.3 Format operator
    ",
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    14.3 Format operator
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    The argument of write has to be a string, so if we want to put other values in a file, we have to convert them to strings. The easiest way to do that is with str:
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    >>> x = 52\n>>> fout.write(str(x))
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    An alternative is to use the format operator, %. When applied to integers, % is the modulus operator. But when the first operand is a string, % is the format operator.
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    The first operand is the format string, which contains one or more format sequences, which specify how the second operand is formatted. The result is a string.
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    For example, the format sequence '%d' means that the second operand should be formatted as an integer (d stands for \"decimal\"):
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    >>> camels = 42\n>>> '%d' % camels\n'42'
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    The result is the string '42', which is not to be confused with the integer value 42.
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    A format sequence can appear anywhere in the string, so you can embed a value in a sentence:
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+          "id": "/page/155/Code/15",
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    >>> camels = 42\n>>> 'I have spotted %d camels.' % camels\n'I have spotted 42 camels.'
    ",
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-            "3": "/page/155/SectionHeader/3"
+            "4": "/page/155/SectionHeader/6"
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    If there is more than one format sequence in the string, the second argument has to be a tuple. Each format sequence is matched with an element of the tuple, in order.
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    14.4. Filenames and paths 135
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    14.4 Filenames and paths
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    14.4 Filenames and paths
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    >>> os.path.abspath('memo.txt')\n'/home/dinsdale/memo.txt'
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         {
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           "block_type": "Text",
-          "html": "
    for name in os.listdir(dirname): path = os.path.join(dirname, name)
    ",
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    os.path.exists checks whether a file or directory exists:
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    >>> os.path.exists('memo.txt')
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+          "block_type": "Text",
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    True
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+            "4": "/page/156/SectionHeader/3"
+          },
+          "images": {}
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+        {
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    If it exists, os.path.isdir checks whether it's a directory:
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+            "1": "/page/154/SectionHeader/1",
+            "4": "/page/156/SectionHeader/3"
+          },
+          "images": {}
+        },
+        {
+          "id": "/page/156/Code/15",
+          "block_type": "Code",
+          "html": "
    >>> os.path.isdir('memo.txt')\nFalse\n>>> os.path.isdir('music')\nTrue
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+            "4": "/page/156/SectionHeader/3"
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+          "images": {}
+        },
+        {
+          "id": "/page/156/Text/16",
+          "block_type": "Text",
+          "html": "
    Similarly, os.path.isfile checks whether it's a file.
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+          "section_hierarchy": {
+            "1": "/page/154/SectionHeader/1",
+            "4": "/page/156/SectionHeader/3"
+          },
+          "images": {}
+        },
+        {
+          "id": "/page/156/Text/17",
+          "block_type": "Text",
+          "html": "
    os.listdir returns a list of the files (and other directories) in the given directory:
    ",
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+            "4": "/page/156/SectionHeader/3"
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+          "images": {}
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+        {
+          "id": "/page/156/Code/18",
+          "block_type": "Code",
+          "html": "
    >>> os.listdir(cwd)\n['music', 'photos', 'memo.txt']
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    To demonstrate these functions, the following example \"walks\" through a directory, prints the names of all the files, and calls itself recursively on all the directories.
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+          "html": "
    def walk(dirname):\n    for name in os.listdir(dirname):\n        path = os.path.join(dirname, name)
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    136 Chapter 14. Files
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               71.13372802734375
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-            "3": "/page/156/SectionHeader/3"
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    if os.path.isfile(path):\n    print path\nelse:\n    walk(path)
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    os.path.join takes a directory and a file name and joins them into a complete path. Exercise 14.1. The os module provides a function called walk that is similar to this one but more versatile. Read the documentation and use it to print the names of the files in a given directory and its subdirectories.
    ",
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    os.path.join takes a directory and a file name and joins them into a complete path. Exercise 14.1. The os module provides a function called walk that is similar to this one but more versatile. Read the documentation and use it to print the names of the files in a given directory and its subdirectories.
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    Solution: http: // thinkpython. com/ code/ walk. py .
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    Solution: http: // thinkpython. com/ code/ walk. py .
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    14.5 Catching exceptions
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    14.5 Catching exceptions
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    A lot of things can go wrong when you try to read and write files. If you try to open a file that doesn't exist, you get an IOError:
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    >>> fin = open('bad_file')\nIOError: [Errno 2] No such file or directory: 'bad_file'\nIf you don't have permission to access a file:\n>>> fout = open('/etc/passwd', 'w')\nIOError: [Errno 13] Permission denied: '/etc/passwd'\nAnd if you try to open a directory for reading, you get\n>>> fin = open('/home')\nIOError: [Errno 21] Is a directory\nTo avoid these errors, you could use functions like os.path.exists and os.path.isfile,\nbut it would take a lot of time and code to check all the possibilities (if \"Errno 21\" is any
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    >>> fin = open('bad_file')\nIOError: [Errno 2] No such file or directory: 'bad_file'
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+            "1": "/page/154/SectionHeader/1",
+            "2": "/page/157/SectionHeader/4"
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+        {
+          "id": "/page/157/Text/7",
+          "block_type": "Text",
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    If you don't have permission to access a file:
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+          "polygon": [
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+          "children": null,
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+            "1": "/page/154/SectionHeader/1",
+            "2": "/page/157/SectionHeader/4"
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+          "images": {}
+        },
+        {
+          "id": "/page/157/Text/8",
+          "block_type": "Text",
+          "html": "
    >>> fout = open('/etc/passwd', 'w') IOError: [Errno 13] Permission denied: '/etc/passwd' And if you try to open a directory for reading, you get
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-            "3": "/page/157/SectionHeader/4"
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    indication, there are at least 21 things that can go wrong).
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    >>> fin = open('/home') IOError: [Errno 21] Is a directory
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             "1": "/page/154/SectionHeader/1",
-            "3": "/page/157/SectionHeader/4"
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-          "html": "
    It is better to go ahead and try—and deal with problems if they happen—which is exactly what the try statement does. The syntax is similar to an if statement:
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    To avoid these errors, you could use functions like os.path.exists and os.path.isfile, but it would take a lot of time and code to check all the possibilities (if \"Errno 21\" is any indication, there are at least 21 things that can go wrong).
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    try:\n    fin = open('bad_file')\n    for line in fin:\n        print line\n    fin.close()\nexcept:
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    It is better to go ahead and try—and deal with problems if they happen—which is exactly what the try statement does. The syntax is similar to an if statement:
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-          "id": "/page/157/Text/9",
-          "block_type": "Text",
-          "html": "
    print 'Something went wrong.'
    ",
+          "id": "/page/157/Code/12",
+          "block_type": "Code",
+          "html": "
    try:\n    fin = open('bad_file')\n    for line in fin:\n        print line\n    fin.close()\nexcept:\n    print 'Something went wrong.'
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             "1": "/page/154/SectionHeader/1",
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           },
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-          "id": "/page/157/Text/11",
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    Handling an exception with a try statement is called catching an exception. In this example, the except clause prints an error message that is not very helpful. In general, catching an exception gives you a chance to fix the problem, or try again, or at least end the program gracefully.
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             "1": "/page/154/SectionHeader/1",
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-          "id": "/page/157/Text/12",
+          "id": "/page/157/Text/15",
           "block_type": "Text",
           "html": "
    Exercise 14.2. Write a function called sed that takes as arguments a pattern string, a replacement string, and two filenames; it should read the first file and write the contents into the second file (creating it if necessary). If the pattern string appears anywhere in the file, it should be replaced with the replacement string.
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         "1": "/page/154/SectionHeader/1",
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       "block_type": "Page",
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+      "html": "",
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           792.0
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           "block_type": "PageHeader",
-          "html": "
    14.6. Databases 137
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+          "html": "",
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    If an error occurs while opening, reading, writing or closing files, your program should catch the exception, print an error message, and exit. Solution: http: // thinkpython. com/ code/ sed. py .
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    If an error occurs while opening, reading, writing or closing files, your program should catch the exception, print an error message, and exit. Solution: http: // thinkpython. com/ code/ sed. py .
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    14.6 Databases
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    14.6 Databases
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           "html": "
    A database is a file that is organized for storing data. Most databases are organized like a dictionary in the sense that they map from keys to values. The biggest difference is that the database is on disk (or other permanent storage), so it persists after the program ends.
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@@ -78319,26 +134028,33 @@
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    The module anydbm provides an interface for creating and updating database files. As an example, I'll create a database that contains captions for image files.
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-            "3": "/page/158/SectionHeader/2"
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@@ -78348,26 +134064,33 @@
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    Opening a database is similar to opening other files:
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           "polygon": [
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@@ -78377,26 +134100,33 @@
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@@ -78406,55 +134136,69 @@
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-          "block_type": "Text",
-          "html": "
    >>> db['cleese.png'] = 'Photo of John Cleese.'
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+          "id": "/page/158/TextInlineMath/8",
+          "block_type": "TextInlineMath",
+          "html": "
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@@ -78464,26 +134208,33 @@
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@@ -78493,26 +134244,33 @@
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@@ -78538,10 +134296,17 @@
               422.5107421875
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@@ -78560,17 +134325,24 @@
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-            "3": "/page/158/SectionHeader/2"
+            "2": "/page/157/SectionHeader/4",
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@@ -78580,36 +134352,43 @@
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    Many dictionary methods, like keys and items, also work with database objects. So does iteration with a for statement.
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    for key in db:\n    print key
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    for key in db: print key
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    14.7 Pickling
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    14.7 Pickling
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    138 Chapter 14. Files
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    >>> import pickle\n>>> t = [1, 2, 3]\n>>> pickle.dumps(t)\n'(lp0\\nI1\\naI2\\naI3\\na.'
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    The format isn't obvious to human readers; it is meant to be easy for pickle to interpret. pickle.loads (\"load string\") reconstitutes the object:
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+          },
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+        },
+        {
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+          "block_type": "Code",
+          "html": "
    >>> t1 = [1, 2, 3]\n>>> s = pickle.dumps(t1)\n>>> t2 = pickle.loads(s)\n>>> print t2\n[1, 2, 3]
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    Although the new object has the same value as the old, it is not (in general) the same object:
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    >>> t1 == t2\nTrue\n>>> t1 is t2\nFalse
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    In other words, pickling and then unpickling has the same effect as copying the object.
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    You can use pickle to store non-strings in a database. In fact, this combination is so common that it has been encapsulated in a module called shelve.
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    Exercise 14.3. If you download my solution to Exercise 12.4 from http: // thinkpython. com/ code/ anagram_ sets. py , you'll see that it creates a dictionary that maps from a sorted string of letters to the list of words that can be spelled with those letters. For example, 'opst' maps to the list ['opts', 'post', 'pots', 'spot', 'stop', 'tops'].
    ",
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    Exercise 14.3. If you download my solution to Exercise 12.4 from http: // thinkpython. com/ code/ anagram_ sets. py , you'll see that it creates a dictionary that maps from a sorted string of letters to the list of words that can be spelled with those letters. For example, 'opst' maps to the list ['opts', 'post', 'pots', 'spot', 'stop', 'tops'].
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    Write a module that imports anagram_sets and provides two new functions: store_anagrams should store the anagram dictionary in a \"shelf;\" read_anagrams should look up a word and return a list of its anagrams. Solution: http: // thinkpython. com/ code/ anagram_ db. py
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    Write a module that imports anagram_sets and provides two new functions: store_anagrams should store the anagram dictionary in a \"shelf;\" read_anagrams should look up a word and return a list of its anagrams. Solution: http: // thinkpython. com/ code/ anagram_ db. py
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    14.8 Pipes
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    14.8 Pipes
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    Most operating systems provide a command-line interface, also known as a shell. Shells usually provide commands to navigate the file system and launch applications. For example, in Unix you can change directories with cd, display the contents of a directory with ls, and launch a web browser by typing (for example) firefox.
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    Any program that you can launch from the shell can also be launched from Python using a pipe. A pipe is an object that represents a running program.
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    For example, the Unix command ls -l normally displays the contents of the current directory (in long format). You can launch ls with os.popen1 :
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    For example, the Unix command ls -l normally displays the contents of the current directory (in long format). You can launch ls with os.popen1 :
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    >>> cmd = 'ls -l' >>> fp = os.popen(cmd)
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    >>> cmd = 'ls -l'\n>>> fp = os.popen(cmd)
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    The argument is a string that contains a shell command. The return value is an object that behaves just like an open file. You can read the output from the ls process one line at a time with readline or get the whole thing at once with read:
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    1popen is deprecated now, which means we are supposed to stop using it and start using the subprocess module. But for simple cases, I find subprocess more complicated than necessary. So I am going to keep using popen until they take it away.
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    1popen is deprecated now, which means we are supposed to stop using it and start using the subprocess module. But for simple cases, I find subprocess more complicated than necessary. So I am going to keep using popen until they take it away.
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    14.9. Writing modules 139
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    >>> res = fp.read() When you are done, you close the pipe like a file: >>> stat = fp.close() >>> print stat None The return value is the final status of the ls process; None means that it ended normally (with no errors).
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    >>> res = fp.read()
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    When you are done, you close the pipe like a file:
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    >>> stat = fp.close()\n>>> print stat\nNone
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    The return value is the final status of the ls process; None means that it ended normally (with no errors).
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    For example, most Unix systems provide a command called md5sum that reads the contents of a file and computes a \"checksum.\" You can read about MD5 at http://en.wikipedia. org/wiki/Md5. This command provides an efficient way to check whether two files have the same contents. The probability that different contents yield the same checksum is very small (that is, unlikely to happen before the universe collapses).
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    You can use a pipe to run md5sum from Python and get the result:
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    >>> filename = 'book.tex'\n>>> cmd = 'md5sum ' + filename\n>>> fp = os.popen(cmd)\n>>> res = fp.read()\n>>> stat = fp.close()\n>>> print res\n1e0033f0ed0656636de0d75144ba32e0 book.tex\n>>> print stat\nNone
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    Exercise 14.4. In a large collection of MP3 files, there may be more than one copy of the same song, stored in different directories or with different file names. The goal of this exercise is to search for duplicates.
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    Exercise 14.4. In a large collection of MP3 files, there may be more than one copy of the same song, stored in different directories or with different file names. The goal of this exercise is to search for duplicates.
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  • 1. Write a program that searches a directory and all of its subdirectories, recursively, and returns a list of complete paths for all files with a given suffix (like .mp3). Hint: os.path provides several useful functions for manipulating file and path names.
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  • 2. To recognize duplicates, you can use md5sum to compute a \"checksum\" for each files. If two files have the same checksum, they probably have the same contents.
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  • 3. To double-check, you can use the Unix command diff.
    • ",
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  • 3. To double-check, you can use the Unix command diff.
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    Solution: http: // thinkpython. com/ code/ find_ duplicates. py .
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    Solution: http: // thinkpython. com/ code/ find_ duplicates. py .
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    14.9 Writing modules
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    14.9 Writing modules
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    Any file that contains Python code can be imported as a module. For example, suppose you have a file named wc.py with the following code:
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    140 Chapter 14. Files
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    If you run this program, it reads itself and prints the number of lines in the file, which is 7. You can also import it like this:
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    Now you have a module object wc:
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    That provides a function called linecount:
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    The only problem with this example is that when you import the module it executes the test code at the bottom. Normally when you import a module, it defines new functions but it doesn't execute them.
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    Programs that will be imported as modules often use the following idiom:
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    if __name__ == '__main__': print linecount('wc.py')
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    Exercise 14.5. Type this example into a file named wc.py and run it as a script. Then run the Python interpreter and import wc. What is the value of __name__ when the module is being imported?
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    14.10 Debugging
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    >>> s = '1 2\\t 3\\n 4'\n>>> print s\n1 2 3\n 4
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    The built-in function repr can help. It takes any object as an argument and returns a string representation of the object. For strings, it represents whitespace characters with backslash sequences:
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    14.11. Glossary 141
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    This can be helpful for debugging.
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    One other problem you might run into is that different systems use different characters to indicate the end of a line. Some systems use a newline, represented \\n. Others use a return character, represented \\r. Some use both. If you move files between different systems, these inconsistencies might cause problems.
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    14.11 Glossary
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    14.11 Glossary
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    14.12 Exercises
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    Exercise 14.6. The urllib module provides methods for manipulating URLs and downloading information from the web. The following example downloads and prints a secret message from thinkpython.com:
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    Exercise 14.6. The urllib module provides methods for manipulating URLs and downloading information from the web. The following example downloads and prints a secret message from thinkpython.com:
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    142 Chapter 14. Files
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    Figure 15.1: Object diagram.
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-                "3": "/page/164/SectionHeader/3"
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+            "4": "/page/164/SectionHeader/3"
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+          "images": {}
         },
         {
-          "id": "/page/165/TextInlineMath/3",
-          "block_type": "TextInlineMath",
-          "html": "
    >>> print Point <class '__main__.Point'>
    ",
+          "id": "/page/165/Code/1",
+          "block_type": "Code",
+          "html": "
    x\n              y\n                       3.0\n                       4.0\nblank\n            Point
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           "polygon": [
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-              86.0625,
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+          "images": {}
+        },
+        {
+          "id": "/page/165/Caption/2",
+          "block_type": "Caption",
+          "html": "
    Figure 15.1: Object diagram.
    ",
+          "polygon": [
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+          },
+          "images": {}
+        },
+        {
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+          "block_type": "Code",
+          "html": "
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    Because Point is defined at the top level, its \"full name\" is __main__.Point.
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+            "4": "/page/164/SectionHeader/3"
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    >>> blank = Point()\n>>> print blank\n<__main__.Point instance at 0xb7e9d3ac>
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    15.2 Attributes
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+          "html": "
    15.2 Attributes
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               192.77703857421875,
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-              85.9130859375,
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@@ -81926,22 +138910,28 @@
           "html": "
    You can assign values to an instance using dot notation:
    ",
           "polygon": [
             [
-              86.13720703125,
-              390.392578125
+              85.763671875,
+              391.166015625
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-              390.392578125
+              331.69921875,
+              391.166015625
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+            331.69921875,
+            402.1918029785156
+          ],
           "children": null,
           "section_hierarchy": {
             "1": "/page/164/SectionHeader/1",
@@ -81955,22 +138945,28 @@
           "html": "
    >>> blank.x = 3.0\n>>> blank.y = 4.0
    ",
           "polygon": [
             [
-              85.6142578125,
-              405.28125
+              85.3154296875,
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-              85.6142578125,
-              430.8046875
+              85.3154296875,
+              429.2578125
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+          "bbox": [
+            85.3154296875,
+            406.0546875,
+            175.8603515625,
+            429.2578125
+          ],
           "children": null,
           "section_hierarchy": {
             "1": "/page/164/SectionHeader/1",
@@ -81984,22 +138980,28 @@
           "html": "
    This syntax is similar to the syntax for selecting a variable from a module, such as math.pi or string.whitespace. In this case, though, we are assigning values to named elements of an object. These elements are called attributes.
    ",
           "polygon": [
             [
-              85.46484375,
-              432.73828125
+              85.6142578125,
+              433.51171875
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-              432.73828125
+              482.4085388183594,
+              433.51171875
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-              483.50390625,
-              468.31640625
+              482.4085388183594,
+              468.2088317871094
             ],
             [
-              85.46484375,
-              468.31640625
+              85.6142578125,
+              468.2088317871094
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+          "bbox": [
+            85.6142578125,
+            433.51171875,
+            482.4085388183594,
+            468.2088317871094
+          ],
           "children": null,
           "section_hierarchy": {
             "1": "/page/164/SectionHeader/1",
@@ -82013,22 +139015,28 @@
           "html": "
    As a noun, \"AT-trib-ute\" is pronounced with emphasis on the first syllable, as opposed to \"a-TRIB-ute,\" which is a verb.
    ",
           "polygon": [
             [
-              85.763671875,
-              474.890625
+              85.6142578125,
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-              474.890625
+              482.4033203125,
+              475.27734375
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-              482.90625,
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             ],
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-              85.763671875,
+              85.6142578125,
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+          "bbox": [
+            85.6142578125,
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+            482.4033203125,
+            499.0918273925781
+          ],
           "children": null,
           "section_hierarchy": {
             "1": "/page/164/SectionHeader/1",
@@ -82039,25 +139047,31 @@
         {
           "id": "/page/165/Text/14",
           "block_type": "Text",
-          "html": "
    The following diagram shows the result of these assignments. A state diagram that shows an object and its attributes is called an object diagram; see Figure 15.1.
    ",
+          "html": "
    The following diagram shows the result of these assignments. A state diagram that shows an object and its attributes is called an object diagram; see Figure 15.1.
    ",
           "polygon": [
             [
-              85.763671875,
-              506.98828125
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+              506.6015625
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-              483.802734375,
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+              482.40325927734375,
+              506.6015625
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             [
-              483.802734375,
-              530.19140625
+              482.40325927734375,
+              529.9748229980469
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             [
-              85.763671875,
-              530.19140625
+              85.46484375,
+              529.9748229980469
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           ],
+          "bbox": [
+            85.46484375,
+            506.6015625,
+            482.40325927734375,
+            529.9748229980469
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           "children": null,
           "section_hierarchy": {
             "1": "/page/164/SectionHeader/1",
@@ -82071,22 +139085,28 @@
           "html": "
    The variable blank refers to a Point object, which contains two attributes. Each attribute refers to a floating-point number.
    ",
           "polygon": [
             [
-              86.361328125,
+              85.46484375,
               537.5390625
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             [
-              483.50390625,
+              482.90625,
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             [
-              483.50390625,
+              482.90625,
               560.8578186035156
             ],
             [
-              86.361328125,
+              85.46484375,
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           ],
+          "bbox": [
+            85.46484375,
+            537.5390625,
+            482.90625,
+            560.8578186035156
+          ],
           "children": null,
           "section_hierarchy": {
             "1": "/page/164/SectionHeader/1",
@@ -82100,22 +139120,28 @@
           "html": "
    You can read the value of an attribute using the same syntax:
    ",
           "polygon": [
             [
-              85.39013671875,
-              568.08984375
+              85.53955078125,
+              569.25
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             [
-              354.111328125,
-              568.08984375
+              353.74627685546875,
+              569.25
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             [
-              354.111328125,
+              353.74627685546875,
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             [
-              85.39013671875,
+              85.53955078125,
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           ],
+          "bbox": [
+            85.53955078125,
+            569.25,
+            353.74627685546875,
+            579.5468139648438
+          ],
           "children": null,
           "section_hierarchy": {
             "1": "/page/164/SectionHeader/1",
@@ -82129,22 +139155,28 @@
           "html": "
    >>> print blank.y\n4.0\n>>> x = blank.x\n>>> print x\n3.0
    ",
           "polygon": [
             [
-              84.64306640625,
-              583.9453125
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+              584.1516571044922
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-              645.046875
+              84.8671875,
+              642.8912658691406
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+          "bbox": [
+            84.8671875,
+            584.1516571044922,
+            175.31614685058594,
+            642.8912658691406
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           "section_hierarchy": {
             "1": "/page/164/SectionHeader/1",
@@ -82158,22 +139190,28 @@
           "html": "
    The expression blank.x means, \"Go to the object blank refers to and get the value of x.\" In this case, we assign that value to a variable named x. There is no conflict between the variable x and the attribute x.
    ",
           "polygon": [
             [
-              85.763671875,
-              646.20703125
+              86.0625,
+              646.98046875
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-              646.20703125
+              482.4050598144531,
+              646.98046875
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-              85.763671875,
+              86.0625,
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+          "bbox": [
+            86.0625,
+            646.98046875,
+            482.4050598144531,
+            682.1468200683594
+          ],
           "children": null,
           "section_hierarchy": {
             "1": "/page/164/SectionHeader/1",
@@ -82191,46 +139229,23 @@
               690.29296875
             ],
             [
-              370.845703125,
+              370.248046875,
               690.29296875
             ],
             [
-              370.845703125,
-              701.12109375
+              370.248046875,
+              700.8348159790039
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-              701.12109375
+              700.8348159790039
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           ],
-          "children": null,
-          "section_hierarchy": {
-            "1": "/page/164/SectionHeader/1",
-            "3": "/page/165/SectionHeader/9"
-          },
-          "images": {}
-        },
-        {
-          "id": "/page/165/Text/20",
-          "block_type": "Text",
-          "html": "
    ",
-          "polygon": [
-            [
-              84.7177734375,
-              59.4580078125
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-            [
-              100.40625,
-              59.4580078125
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-            [
-              100.40625,
-              69.22265625
-            ],
-            [
-              84.7177734375,
-              69.22265625
-            ]
+          "bbox": [
+            86.39995574951172,
+            690.29296875,
+            370.248046875,
+            700.8348159790039
           ],
           "children": null,
           "section_hierarchy": {
@@ -82247,9 +139262,9 @@
       "images": null
     },
     {
-      "id": "/page/166/Page/179",
+      "id": "/page/166/Page/185",
       "block_type": "Page",
-      "html": "",
+      "html": "",
       "polygon": [
         [
           0.0,
@@ -82268,29 +139283,41 @@
           792.0
         ]
       ],
+      "bbox": [
+        0.0,
+        0.0,
+        612.0,
+        792.0
+      ],
       "children": [
         {
           "id": "/page/166/PageHeader/0",
           "block_type": "PageHeader",
-          "html": "
    15.3. Rectangles 145
    ",
+          "html": "",
           "polygon": [
             [
-              128.197265625,
-              60.71484375
+              128.6455078125,
+              61.1015625
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               525.6033935546875,
-              60.71484375
+              61.1015625
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               525.6033935546875,
               71.13372802734375
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-              128.197265625,
+              128.6455078125,
               71.13372802734375
             ]
           ],
+          "bbox": [
+            128.6455078125,
+            61.1015625,
+            525.6033935546875,
+            71.13372802734375
+          ],
           "children": null,
           "section_hierarchy": {
             "1": "/page/164/SectionHeader/1",
@@ -82301,25 +139328,31 @@
         {
           "id": "/page/166/PageHeader/18",
           "block_type": "PageHeader",
-          "html": "
    ",
+          "html": "",
           "polygon": [
             [
               510.3984375,
-              60.37646484375
+              60.908203125
             ],
             [
               525.9375,
-              60.37646484375
+              60.908203125
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             [
               525.9375,
-              69.94775390625
+              70.2861328125
             ],
             [
               510.3984375,
-              69.94775390625
+              70.2861328125
             ]
           ],
+          "bbox": [
+            510.3984375,
+            60.908203125,
+            525.9375,
+            70.2861328125
+          ],
           "children": null,
           "section_hierarchy": {
             "1": "/page/164/SectionHeader/1",
@@ -82333,22 +139366,28 @@
           "html": "
    >>> print '(%g, %g)' % (blank.x, blank.y)\n(3.0, 4.0)\n>>> distance = math.sqrt(blank.x**2 + blank.y**2)\n>>> print distance\n5.0
    ",
           "polygon": [
             [
-              129.60000610351562,
-              88.51025390625
+              128.6455078125,
+              88.31689453125
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-              388.177734375,
-              88.51025390625
+              385.9077453613281,
+              88.31689453125
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-              388.177734375,
-              154.6875
+              385.9077453613281,
+              147.42529296875
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-              129.60000610351562,
-              154.6875
+              128.6455078125,
+              147.42529296875
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+          "bbox": [
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+            88.31689453125,
+            385.9077453613281,
+            147.42529296875
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           "section_hierarchy": {
             "1": "/page/164/SectionHeader/1",
@@ -82362,22 +139401,28 @@
           "html": "
    You can pass an instance as an argument in the usual way. For example:
    ",
           "polygon": [
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-              153.646240234375
+              152.8505859375
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+              163.60888671875
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             [
-              129.2431640625,
-              165.0322265625
+              128.794921875,
+              163.60888671875
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           ],
+          "bbox": [
+            128.794921875,
+            152.8505859375,
+            445.8515625,
+            163.60888671875
+          ],
           "children": null,
           "section_hierarchy": {
             "1": "/page/164/SectionHeader/1",
@@ -82386,12 +139431,12 @@
           "images": {}
         },
         {
-          "id": "/page/166/Text/3",
-          "block_type": "Text",
-          "html": "
    def print_point(p): print '(%g, %g)' % (p.x, p.y)
    ",
+          "id": "/page/166/TextInlineMath/3",
+          "block_type": "TextInlineMath",
+          "html": "
    def print_point(p): print '(%g, %g)' % (p.x, p.y)
    ",
           "polygon": [
             [
-              129.60000610351562,
+              128.57080078125,
               169.52972412109375
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@@ -82403,10 +139448,16 @@
               191.686279296875
             ],
             [
-              129.60000610351562,
+              128.57080078125,
               191.686279296875
             ]
           ],
+          "bbox": [
+            128.57080078125,
+            169.52972412109375,
+            302.1754150390625,
+            191.686279296875
+          ],
           "children": null,
           "section_hierarchy": {
             "1": "/page/164/SectionHeader/1",
@@ -82420,22 +139471,28 @@
           "html": "
    print_point takes a point as an argument and displays it in mathematical notation. To invoke it, you can pass blank as an argument:
    ",
           "polygon": [
             [
-              128.49609375,
-              197.1298828125
+              128.197265625,
+              196.83984375
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             [
-              526.53515625,
-              197.1298828125
+              525.638671875,
+              196.83984375
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             [
-              526.53515625,
+              525.638671875,
               220.06390380859375
             ],
             [
-              128.49609375,
+              128.197265625,
               220.06390380859375
             ]
           ],
+          "bbox": [
+            128.197265625,
+            196.83984375,
+            525.638671875,
+            220.06390380859375
+          ],
           "children": null,
           "section_hierarchy": {
             "1": "/page/164/SectionHeader/1",
@@ -82444,27 +139501,33 @@
           "images": {}
         },
         {
-          "id": "/page/166/Code/5",
-          "block_type": "Code",
-          "html": "
    >>> print_point(blank)\n(3.0, 4.0)
    ",
+          "id": "/page/166/Text/5",
+          "block_type": "Text",
+          "html": "
    >>> print_point(blank) (3.0, 4.0)
    ",
           "polygon": [
             [
-              128.72021484375,
-              225.650390625
+              128.197265625,
+              225.84375
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-              225.650390625
+              225.84375
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               248.142333984375
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-              128.72021484375,
+              128.197265625,
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+          "bbox": [
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+            225.84375,
+            244.67799377441406,
+            248.142333984375
+          ],
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           "section_hierarchy": {
             "1": "/page/164/SectionHeader/1",
@@ -82494,6 +139557,12 @@
               288.5412292480469
             ]
           ],
+          "bbox": [
+            128.794921875,
+            254.2137451171875,
+            525.6033325195312,
+            288.5412292480469
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           "section_hierarchy": {
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@@ -82504,29 +139573,36 @@
         {
           "id": "/page/166/SectionHeader/7",
           "block_type": "SectionHeader",
-          "html": "
    15.3 Rectangles
    ",
+          "html": "
    15.3 Rectangles
    ",
           "polygon": [
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-              316.142578125
+              127.82373046875,
+              317.8707580566406
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-              316.142578125
+              239.96533203125,
+              317.8707580566406
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               332.21697998046875
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-              127.37548828125,
+              127.82373046875,
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+          "bbox": [
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+            317.8707580566406,
+            239.96533203125,
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             "1": "/page/164/SectionHeader/1",
-            "3": "/page/166/SectionHeader/7"
+            "3": "/page/165/SectionHeader/9",
+            "4": "/page/166/SectionHeader/7"
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           "images": {}
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@@ -82536,26 +139612,33 @@
           "html": "
    Sometimes it is obvious what the attributes of an object should be, but other times you have to make decisions. For example, imagine you are designing a class to represent rectangles. What attributes would you use to specify the location and size of a rectangle? You can ignore angle; to keep things simple, assume that the rectangle is either vertical or horizontal.
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-              343.40625
+              128.9443359375,
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+              344.4303283691406
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+              390.9759216308594
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-              128.49609375,
-              391.359375
+              128.9443359375,
+              390.9759216308594
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+          "bbox": [
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+            344.4303283691406,
+            525.9375,
+            390.9759216308594
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             "1": "/page/164/SectionHeader/1",
-            "3": "/page/166/SectionHeader/7"
+            "3": "/page/165/SectionHeader/9",
+            "4": "/page/166/SectionHeader/7"
           },
           "images": {}
         },
@@ -82565,26 +139648,33 @@
           "html": "
    There are at least two possibilities:
    ",
           "polygon": [
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-              411.85546875
+              128.197265625,
+              412.2421875
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-              281.197265625,
-              411.85546875
+              280.7326354980469,
+              412.2421875
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+              280.7326354980469,
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-              128.49609375,
+              128.197265625,
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+          "bbox": [
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+            412.2421875,
+            280.7326354980469,
+            423.1749267578125
+          ],
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             "1": "/page/164/SectionHeader/1",
-            "3": "/page/166/SectionHeader/7"
+            "3": "/page/165/SectionHeader/9",
+            "4": "/page/166/SectionHeader/7"
           },
           "images": {}
         },
@@ -82594,22 +139684,28 @@
           "html": "
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           "polygon": [
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-              143.2880859375,
-              435.83203125
+              143.48800659179688,
+              436.60546875
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-              525.603515625,
-              435.83203125
+              525.9375,
+              436.60546875
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"
+                "/page/167/Figure/1": 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    The expression box.corner.x means, \"Go to the object box refers to and select the attribute named corner; then go to that object and select the attribute named x.\"
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    Figure 15.2 shows the state of this object. An object that is an attribute of another object is embedded.
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    Figure 15.2 shows the state of this object. An object that is an attribute of another object is embedded.
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    15.4 Instances as return values
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+          "html": "
    15.4 Instances as return values
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    Functions can return instances. For example, find_center takes a Rectangle as an argument and returns a Point that contains the coordinates of the center of the Rectangle:
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+            "3": "/page/165/SectionHeader/9",
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@@ -83208,26 +140445,33 @@
           "html": "
    Here is an example that passes box as an argument and assigns the resulting Point to center:
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    >>> center = find_center(box)\n>>> print_point(center)\n(50.0, 100.0)
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-          "html": "
    15.5 Objects are mutable
    ",
+          "html": "
    15.5 Objects are mutable
    ",
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@@ -83295,26 +140553,33 @@
           "html": "
    You can change the state of an object by making an assignment to one of its attributes. For example, to change the size of a rectangle without changing its position, you can modify the values of width and height:
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-            "3": "/page/167/SectionHeader/11"
+            "3": "/page/165/SectionHeader/9",
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@@ -83324,26 +140589,33 @@
           "html": "
    box.width = box.width + 50\nbox.height = box.width + 100
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+            "3": "/page/165/SectionHeader/9",
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@@ -83353,26 +140625,33 @@
           "html": "
    You can also write functions that modify objects. For example, grow_rectangle takes a Rectangle object and two numbers, dwidth and dheight, and adds the numbers to the width and height of the rectangle:
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+            "3": "/page/165/SectionHeader/9",
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@@ -83382,38 +140661,46 @@
           "html": "
    def grow_rectangle(rect, dwidth, dheight):\n    rect.width += dwidth\n    rect.height += dheight
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       ],
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         "1": "/page/164/SectionHeader/1",
-        "3": "/page/167/SectionHeader/11"
+        "3": "/page/165/SectionHeader/9",
+        "4": "/page/167/SectionHeader/11"
       },
       "images": null
     },
     {
-      "id": "/page/168/Page/229",
+      "id": "/page/168/Page/230",
       "block_type": "Page",
       "html": "",
       "polygon": [
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           792.0
         ]
       ],
+      "bbox": [
+        0.0,
+        0.0,
+        612.0,
+        792.0
+      ],
       "children": [
         {
           "id": "/page/168/PageHeader/0",
           "block_type": "PageHeader",
-          "html": "
    15.6. Copying 147
    ",
+          "html": "",
           "polygon": [
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               71.13372802734375
             ],
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             "1": "/page/164/SectionHeader/1",
-            "3": "/page/167/SectionHeader/11"
+            "3": "/page/165/SectionHeader/9",
+            "4": "/page/167/SectionHeader/11"
           },
           "images": {}
         },
         {
           "id": "/page/168/PageHeader/12",
           "block_type": "PageHeader",
-          "html": "
    ",
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           "polygon": [
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             "1": "/page/164/SectionHeader/1",
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+            "3": "/page/165/SectionHeader/9",
+            "4": "/page/167/SectionHeader/11"
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@@ -83499,26 +140806,33 @@
           "html": "
    Here is an example that demonstrates the effect:
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           "polygon": [
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+            "4": "/page/167/SectionHeader/11"
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@@ -83528,7 +140842,7 @@
           "html": "
    >>> print box.width\n100.0\n>>> print box.height\n200.0\n>>> grow_rectangle(box, 50, 100)\n>>> print box.width\n150.0\n>>> print box.height\n300.0
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-          "html": "
    Inside the function, rect is an alias for box, so if the function modifies rect, box changes. Exercise 15.2. Write a function named move_rectangle that takes a Rectangle and two numbers named dx and dy. It should change the location of the rectangle by adding dx to the x coordinate of corner and adding dy to the y coordinate of corner.
    ",
+          "html": "
    Inside the function, rect is an alias for box, so if the function modifies rect, box changes. Exercise 15.2. Write a function named move_rectangle that takes a Rectangle and two numbers named dx and dy. It should change the location of the rectangle by adding dx to the x coordinate of corner and adding dy to the y coordinate of corner.
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         {
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           "block_type": "SectionHeader",
-          "html": "
    15.6 Copying
    ",
+          "html": "
    15.6 Copying
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+            "3": "/page/165/SectionHeader/9",
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@@ -83615,26 +140950,33 @@
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    Aliasing can make a program difficult to read because changes in one place might have unexpected effects in another place. It is hard to keep track of all the variables that might refer to a given object.
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"
+                "/page/169/Figure/1": 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"
               }
             },
             {
               "id": "/page/169/Caption/2",
               "block_type": "Caption",
-              "html": "
    Figure 15.3: Object diagram.
    ",
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    Figure 15.3: Object diagram.
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    >>> box2 = copy.copy(box)\n>>> box2 is box\nFalse\n>>> box2.corner is box.corner\nTrue\nFigure 15.3 shows what the object diagram looks like. This operation is called a shallow
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    >>> box2 = copy.copy(box)\n>>> box2 is box\nFalse\n>>> box2.corner is box.corner\nTrue
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    copy because it copies the object and any references it contains, but not the embedded objects.
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    Figure 15.3 shows what the object diagram looks like. This operation is called a shallow copy because it copies the object and any references it contains, but not the embedded objects.
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@@ -84054,26 +141494,33 @@
           "html": "
    For most applications, this is not what you want. In this example, invoking grow_rectangle on one of the Rectangles would not affect the other, but invoking move_rectangle on either would affect both! This behavior is confusing and error-prone.
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           "html": "
    Fortunately, the copy module contains a method named deepcopy that copies not only the object but also the objects it refers to, and the objects they refer to, and so on. You will not be surprised to learn that this operation is called a deep copy.
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    >>> box3 = copy.deepcopy(box)\n>>> box3 is box\nFalse\n>>> box3.corner is box.corner\nFalse\nbox3 and box are completely separate objects.\nExercise 15.3. Write a version of move_rectangle that creates and returns a new Rectangle
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    >>> box3 = copy.deepcopy(box)\n>>> box3 is box\nFalse\n>>> box3.corner is box.corner\nFalse
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    instead of modifying the old one.
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    box3 and box are completely separate objects.
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    Exercise 15.3. Write a version of move_rectangle that creates and returns a new Rectangle instead of modifying the old one.
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+          "id": "/page/169/SectionHeader/10",
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    15.7 Debugging
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    15.7 Debugging
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    When you start working with objects, you are likely to encounter some new exceptions. If you try to access an attribute that doesn't exist, you get an AttributeError:
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+          "id": "/page/169/Code/12",
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-          "html": "
    >>> p = Point()\n>>> print p.z\nAttributeError: Point instance has no attribute 'z'\nIf you are not sure what type an object is, you can ask:\n>>> type(p)\n<type '__main__.Point'>\nIf you are not sure whether an object has a particular attribute, you can use the built-in\nfunction hasattr:\n>>> hasattr(p, 'x')\nTrue\n>>> hasattr(p, 'z')\nFalse
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    >>> p = Point()\n>>> print p.z\nAttributeError: Point instance has no attribute 'z'
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+            "3": "/page/165/SectionHeader/9",
+            "4": "/page/169/SectionHeader/10"
+          },
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+        },
+        {
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+          "block_type": "Text",
+          "html": "
    If you are not sure what type an object is, you can ask:
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+            "4": "/page/169/SectionHeader/10"
+          },
+          "images": {}
+        },
+        {
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+          "block_type": "Code",
+          "html": "
    >>> type(p)\n<type '__main__.Point'>
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+              600.0967102050781
+            ],
+            [
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+          "section_hierarchy": {
+            "1": "/page/164/SectionHeader/1",
+            "3": "/page/165/SectionHeader/9",
+            "4": "/page/169/SectionHeader/10"
+          },
+          "images": {}
+        },
+        {
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+          "block_type": "Text",
+          "html": "
    If you are not sure whether an object has a particular attribute, you can use the built-in function hasattr:
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+          },
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+        },
+        {
+          "id": "/page/169/Code/16",
+          "block_type": "Code",
+          "html": "
    >>> hasattr(p, 'x')\nTrue\n>>> hasattr(p, 'z')\nFalse
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           "images": {}
         }
       ],
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         "1": "/page/164/SectionHeader/1",
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+        "3": "/page/165/SectionHeader/9",
+        "4": "/page/169/SectionHeader/10"
       },
       "images": null
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+      "id": "/page/170/Page/197",
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-      "html": "",
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-          "html": "
    15.8. Glossary 149
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+            "4": "/page/169/SectionHeader/10"
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    The first argument can be any object; the second argument is a string that contains the name of the attribute.
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    15.8 Glossary
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    15.8 Glossary
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  • shallow copy: To copy the contents of an object, including any references to embedded objects; implemented by the copy function in the copy module.
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    15.9 Exercises
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    15.9 Exercises
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    Exercise 15.4. Swampy (see Chapter 4) provides a module named World, which defines a userdefined type also called World. You can import it like this:
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    Exercise 15.4. Swampy (see Chapter 4) provides a module named World, which defines a userdefined type also called World. You can import it like this:
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@@ -84752,26 +142540,33 @@
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    Or, depending on how you installed Swampy, like this:
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    The following code creates a World object and calls the mainloop method, which waits for the user. world = World()
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    The following code creates a World object and calls the mainloop method, which waits for the user.
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    world.mainloop()
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    A window should appear with a title bar and an empty square. We will use this window to draw Points, Rectangles and other shapes. Add the following lines before calling mainloop and run the program again.
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    bbox = [[-150,-100], [150, 100]]
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    canvas = world.ca(width=500, height=500, background='white')\nbbox = [[-150,-100], [150, 100]]\ncanvas.rectangle(bbox, outline='black', width=2, fill='green4')
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    I have written a small program that lists the available colors; you can download it from http: // thinkpython. com/ code/ color_ list. py .
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    Chapter 16
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    Chapter 16
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    Classes and functions
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    Code examples from this chapter are available from http://thinkpython.com/code/ Time1.py.
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    16.1 Time
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    16.1 Time
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    class Time(object):
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    attributes: hour, minute, second \"\"\"
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    We can create a new Time object and assign attributes for hours, minutes, and seconds:
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    The state diagram for the Time object looks like Figure 16.1.
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    The state diagram for the Time object looks like Figure 16.1.
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    Exercise 16.1. Write a function called print_time that takes a Time object and prints it in the form hour:minute:second. Hint: the format sequence '%.2d' prints an integer using at least two digits, including a leading zero if necessary.
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    Exercise 16.1. Write a function called print_time that takes a Time object and prints it in the form hour:minute:second. Hint: the format sequence '%.2d' prints an integer using at least two digits, including a leading zero if necessary.
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    16.2 Pure functions
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    16.2 Pure functions
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    In the next few sections, we'll write two functions that add time values. They demonstrate two kinds of functions: pure functions and modifiers. They also demonstrate a development plan I'll call prototype and patch, which is a way of tackling a complex problem by starting with a simple prototype and incrementally dealing with the complications.
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    152 Chapter 16. Classes and functions
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    59\n                        30\n             hour\n           minute\n          second\n                        11\n         Time\ntime
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    Figure 16.1: Object diagram.
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    Figure 16.1: Object diagram.
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    Here is a simple prototype of add_time:
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    def add_time(t1, t2):\n    sum = Time()\n    sum.hour = t1.hour + t2.hour\n    sum.minute = t1.minute + t2.minute\n    sum.second = t1.second + t2.second\n    return sum
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    The function creates a new Time object, initializes its attributes, and returns a reference to the new object. This is called a pure function because it does not modify any of the objects passed to it as arguments and it has no effect, like displaying a value or getting user input, other than returning a value.
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    To test this function, I'll create two Time objects: start contains the start time of a movie, like Monty Python and the Holy Grail, and duration contains the run time of the movie, which is one hour 35 minutes.
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    The result, 10:80:00 might not be what you were hoping for. The problem is that this function does not deal with cases where the number of seconds or minutes adds up to more than sixty. When that happens, we have to \"carry\" the extra seconds into the minute column or the extra minutes into the hour column.
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    Here's an improved version:
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    16.3. Modifiers 153
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    return sum
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    Although this function is correct, it is starting to get big. We will see a shorter alternative later.
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    16.3 Modifiers
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    16.3 Modifiers
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    increment, which adds a given number of seconds to a Time object, can be written naturally as a modifier. Here is a rough draft:
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    def increment(time, seconds):\n    time.second += seconds
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    def increment(time, seconds):\n    time.second += seconds\n    if time.second >= 60:\n        time.second -= 60\n        time.minute += 1\n    if time.minute >= 60:\n        time.minute -= 60\n        time.hour += 1
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    The first line performs the basic operation; the remainder deals with the special cases we saw before.
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    Is this function correct? What happens if the parameter seconds is much greater than sixty?
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    In that case, it is not enough to carry once; we have to keep doing it until time.second is less than sixty. One solution is to replace the if statements with while statements. That would make the function correct, but not very efficient.
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    Exercise 16.3. Write a correct version of increment that doesn't contain any loops.
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    Anything that can be done with modifiers can also be done with pure functions. In fact, some programming languages only allow pure functions. There is some evidence that programs that use pure functions are faster to develop and less error-prone than programs that use modifiers. But modifiers are convenient at times, and functional programs tend to be less efficient.
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    In general, I recommend that you write pure functions whenever it is reasonable and resort to modifiers only if there is a compelling advantage. This approach might be called a functional programming style.
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    Exercise 16.4. Write a \"pure\" version of increment that creates and returns a new Time object rather than modifying the parameter.
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    154 Chapter 16. Classes and functions
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    16.4 Prototyping versus planning
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    The development plan I am demonstrating is called \"prototype and patch.\" For each function, I wrote a prototype that performed the basic calculation and then tested it, patching errors along the way.
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    This approach can be effective, especially if you don't yet have a deep understanding of the problem. But incremental corrections can generate code that is unnecessarily complicated—since it deals with many special cases—and unreliable—since it is hard to know if you have found all the errors.
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    An alternative is planned development, in which high-level insight into the problem can make the programming much easier. In this case, the insight is that a Time object is really a three-digit number in base 60 (see http://en.wikipedia.org/wiki/Sexagesimal.)! The second attribute is the \"ones column,\" the minute attribute is the \"sixties column,\" and the hour attribute is the \"thirty-six hundreds column.\"
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    An alternative is planned development, in which high-level insight into the problem can make the programming much easier. In this case, the insight is that a Time object is really a three-digit number in base 60 (see http://en.wikipedia.org/wiki/Sexagesimal.)! The second attribute is the \"ones column,\" the minute attribute is the \"sixties column,\" and the hour attribute is the \"thirty-six hundreds column.\"
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    Here is a function that converts Times to integers:
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    This version is shorter than the original, and easier to verify. Exercise 16.5. Rewrite increment using time_to_int and int_to_time.
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    This version is shorter than the original, and easier to verify. Exercise 16.5. Rewrite increment using time_to_int and int_to_time.
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    A Time object is well-formed if the values of minute and second are between 0 and 60 (including 0 but not 60) and if hour is positive. hour and minute should be integral values, but we might allow second to have a fraction part.
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    16.6 Glossary
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    156 Chapter 16. Classes and functions
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  • modifier: A function that changes one or more of the objects it receives as arguments. Most modifiers are fruitless.
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  • functional programming style: A style of program design in which the majority of functions are pure.
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  • invariant: A condition that should always be true during the execution of a program.
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    Exercise 16.7. The datetime module provides date and time objects that are similar to the Date and Time objects in this chapter, but they provide a rich set of methods and operators. Read the documentation at http: // docs. python. org/ 2/ library/ datetime. html .
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  • 1. Use the datetime module to write a program that gets the current date and prints the day of the week.
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  • 2. Write a program that takes a birthday as input and prints the user's age and the number of days, hours, minutes and seconds until their next birthday.
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  • 3. For two people born on different days, there is a day when one is twice as old as the other. That's their Double Day. Write a program that takes two birthdays and computes their Double Day.
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  • 4. For a little more challenge, write the more general version that computes the day when one person is n times older than the other.
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    Chapter 17
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    Chapter 17
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    Classes and methods
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    Code examples from this chapter are available from http://thinkpython.com/code/ Time2.py.
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    17.1 Object-oriented features
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    So far, we have not taken advantage of the features Python provides to support objectoriented programming. These features are not strictly necessary; most of them provide alternative syntax for things we have already done. But in many cases, the alternative is more concise and more accurately conveys the structure of the program.
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    This observation is the motivation for methods; a method is a function that is associated with a particular class. We have seen methods for strings, lists, dictionaries and tuples. In this chapter, we will define methods for user-defined types.
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    158 Chapter 17. Classes and methods
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    In the next few sections, we will take the functions from the previous two chapters and transform them into methods. This transformation is purely mechanical; you can do it simply by following a sequence of steps. If you are comfortable converting from one form to another, you will be able to choose the best form for whatever you are doing.
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    This version assumes that time_to_int is written as a method, as in Exercise 17.1. Also, note that it is a pure function, not a modifier.
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    This version assumes that time_to_int is written as a method, as in Exercise 17.1. Also, note that it is a pure function, not a modifier.
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    Here's how you would invoke increment:
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    This mechanism can be confusing, especially if you make an error. For example, if you invoke increment with two arguments, you get:
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    >>> end = start.increment(1337, 460)
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    >>> end = start.increment(1337, 460)\nTypeError: increment() takes exactly 2 arguments (3 given)
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    The error message is initially confusing, because there are only two arguments in parentheses. But the subject is also considered an argument, so all together that's three.
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    160 Chapter 17. Classes and methods
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    17.4 A more complicated example
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    17.4 A more complicated example
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    is_after (from Exercise 16.2) is slightly more complicated because it takes two Time objects as parameters. In this case it is conventional to name the first parameter self and the second parameter other:
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    # inside class Time:
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    def is_after(self, other):\n    return self.time_to_int() > other.time_to_int()
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    To use this method, you have to invoke it on one object and pass the other as an argument: >>> end.is_after(start)
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    True
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    One nice thing about this syntax is that it almost reads like English: \"end is after start?\"
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    17.5 The init method
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    17.5 The init method
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    # inside class Time:
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    The init method (short for \"initialization\") is a special method that gets invoked when an object is instantiated. Its full name is __init__ (two underscore characters, followed by init, and then two more underscores). An init method for the Time class might look like this:
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    # inside class Time:
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    # inside class Time:
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    def __init__(self, hour=0, minute=0, second=0):\n        self.hour = hour\n        self.minute = minute\n        self.second = second\nIt is common for the parameters of __init__ to have the same names as the attributes. The
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    def __init__(self, hour=0, minute=0, second=0):\n    self.hour = hour\n    self.minute = minute\n    self.second = second
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    statement
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    It is common for the parameters of __init__ to have the same names as the attributes. The statement
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    self.hour = hour
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    stores the value of the parameter hour as an attribute of self.
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    The parameters are optional, so if you call Time with no arguments, you get the default values.
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    >>> time = Time() >>> time.print_time() 00:00:00
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    >>> time = Time()\n>>> time.print_time()\n00:00:00
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    If you provide one argument, it overrides hour:
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    >>> time = Time (9)\n>>> time.print_time()\n09:00:00
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    If you provide two arguments, they override hour and minute.
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    >>> time = Time(9, 45)\n>>> time.print_time()\n09:45:00
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    And if you provide three arguments, they override all three default values.
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    And if you provide three arguments, they override all three default values. Exercise 17.2. Write an init method for the Point class that takes x and y as optional parameters and assigns them to the corresponding attributes.
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    Exercise 17.2. Write an init method for the Point class that takes x and y as optional parameters and assigns them to the corresponding attributes.
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    17.6. The __str__ method 161
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    Exercise 17.4. Write an add method for the Point class.
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    162 Chapter 17. Classes and methods
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+            "2": "/page/182/SectionHeader/1",
+            "4": "/page/182/SectionHeader/9"
+          },
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    17.8 Type-based dispatch
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    17.8 Type-based dispatch
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    In the previous section we added two Time objects, but you also might want to add an integer to a Time object. The following is a version of __add__ that checks the type of other and invokes either add_time or increment:
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    # inside class Time:
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+          "id": "/page/183/Code/3",
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    # inside class Time:
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@@ -91698,26 +150874,33 @@
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    def __add__(self, other):\n    if isinstance(other, Time):\n        return self.add_time(other)\n    else:\n        return self.increment(other)\ndef add_time(self, other):\n    seconds = self.time_to_int() + other.time_to_int()\n    return int_to_time(seconds)\ndef increment(self, seconds):\n    seconds += self.time_to_int()\n    return int_to_time(seconds)
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+            "2": "/page/182/SectionHeader/1",
+            "4": "/page/183/SectionHeader/1"
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@@ -91727,26 +150910,33 @@
           "html": "
    The built-in function isinstance takes a value and a class object, and returns True if the value is an instance of the class.
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@@ -91756,26 +150946,33 @@
           "html": "
    If other is a Time object, __add__ invokes add_time. Otherwise it assumes that the parameter is a number and invokes increment. This operation is called a type-based dispatch because it dispatches the computation to different methods based on the type of the arguments.
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@@ -91785,7 +150982,7 @@
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    Here are examples that use the + operator with different types:
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+            "2": "/page/182/SectionHeader/1",
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@@ -91814,26 +151018,33 @@
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    >>> start = Time(9, 45)\n>>> duration = Time(1, 35)\n>>> print start + duration\n11:20:00\n>>> print start + 1337\n10:07:17
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             "1": "/page/178/SectionHeader/1",
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+            "2": "/page/182/SectionHeader/1",
+            "4": "/page/183/SectionHeader/1"
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@@ -91843,185 +151054,264 @@
           "html": "
    Unfortunately, this implementation of addition is not commutative. If the integer is the first operand, you get
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           "section_hierarchy": {
             "1": "/page/178/SectionHeader/1",
-            "3": "/page/183/SectionHeader/1"
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           },
           "images": {}
         },
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-          "block_type": "TextInlineMath",
-          "html": "
    >>> print 1337 + start TypeError: unsupported operand type(s) for +: 'int' and 'instance'
    ",
+          "id": "/page/183/Code/10",
+          "block_type": "Code",
+          "html": "
    >>> print 1337 + start
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             "1": "/page/178/SectionHeader/1",
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+            "4": "/page/183/SectionHeader/1"
           },
           "images": {}
         },
         {
           "id": "/page/183/Text/11",
           "block_type": "Text",
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    TypeError: unsupported operand type(s) for +: 'int' and 'instance'
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+            "4": "/page/183/SectionHeader/1"
+          },
+          "images": {}
+        },
+        {
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+          "block_type": "Text",
           "html": "
    The problem is, instead of asking the Time object to add an integer, Python is asking an integer to add a Time object, and it doesn't know how to do that. But there is a clever solution for this problem: the special method __radd__, which stands for \"right-side add.\" This method is invoked when a Time object appears on the right side of the + operator. Here's the definition:
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+            "2": "/page/182/SectionHeader/1",
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           "images": {}
         },
         {
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-          "block_type": "Text",
-          "html": "
    # inside class Time:
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    # inside class Time:
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    def __radd__(self, other):\n        return self.__add__(other)\nAnd here's how it's used:
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    def __radd__(self, other):\n    return self.__add__(other)
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         },
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    ",
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    And here's how it's used:
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             "1": "/page/178/SectionHeader/1",
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         }
       ],
       "section_hierarchy": {
         "1": "/page/178/SectionHeader/1",
-        "3": "/page/183/SectionHeader/1"
+        "2": "/page/182/SectionHeader/1",
+        "4": "/page/183/SectionHeader/1"
       },
       "images": null
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+      "id": "/page/184/Page/184",
       "block_type": "Page",
-      "html": "",
+      "html": "",
       "polygon": [
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       "children": [
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           "id": "/page/184/PageHeader/0",
           "block_type": "PageHeader",
-          "html": "
    17.9. Polymorphism 163
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+          "html": "",
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             "1": "/page/178/SectionHeader/1",
-            "3": "/page/183/SectionHeader/1"
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+            "4": "/page/183/SectionHeader/1"
           },
           "images": {}
         },
         {
           "id": "/page/184/PageHeader/15",
           "block_type": "PageHeader",
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           "polygon": [
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           "section_hierarchy": {
             "1": "/page/178/SectionHeader/1",
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+            "2": "/page/182/SectionHeader/1",
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         {
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-          "block_type": "Text",
-          "html": "
    >>> print 1337 + start 10:07:17 Exercise 17.5. Write an add method for Points that works with either a Point object or a tuple:
    ",
+          "id": "/page/184/Code/182",
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    Functions that can work with several types are called polymorphic. Polymorphism can facilitate code reuse. For example, the built-in function sum, which adds the elements of a sequence, works as long as the elements of the sequence support addition.
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    164 Chapter 17. Classes and methods
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    17.10 Debugging
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    For purposes of debugging, you might find it useful to keep this function handy: def print_attributes(obj): for attr in obj.__dict__:
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    For purposes of debugging, you might find it useful to keep this function handy:
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    17.11 Interface and implementation
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    One of the goals of object-oriented design is to make software more maintainable, which means that you can keep the program working when other parts of the system change, and modify the program to meet new requirements.
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    We could implement those methods in several ways. The details of the implementation depend on how we represent time. In this chapter, the attributes of a Time object are hour, minute, and second.
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    Keeping the interface separate from the implementation means that you have to hide the attributes. Code in other parts of the program (outside the class definition) should use methods to read and modify the state of the object. They should not access the attributes directly. This principle is called information hiding; see http://en.wikipedia.org/wiki/ Information_hiding.
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    Keeping the interface separate from the implementation means that you have to hide the attributes. Code in other parts of the program (outside the class definition) should use methods to read and modify the state of the object. They should not access the attributes directly. This principle is called information hiding; see http://en.wikipedia.org/wiki/ Information_hiding.
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  • object-oriented language: A language that provides features, such as user-defined classes and method syntax, that facilitate object-oriented programming.
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  • information hiding: The principle that the interface provided by an object should not depend on its implementation, in particular the representation of its attributes.
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    17.13 Exercises
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    Exercise 17.7. This exercise is a cautionary tale about one of the most common, and difficult to find, errors in Python. Write a definition for a class named Kangaroo with the following methods:
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  • 1. An __init__ method that initializes an attribute named pouch_contents to an empty list.
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  • 2. A method named put_in_pouch that takes an object of any type and adds it to pouch_contents.
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  • 2. A method named put_in_pouch that takes an object of any type and adds it to pouch_contents.
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  • 3. A __str__ method that returns a string representation of the Kangaroo object and the contents of the pouch.
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    Test your code by creating two Kangaroo objects, assigning them to variables named kanga and roo, and then adding roo to the contents of kanga's pouch.
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    166 Chapter 17. Classes and methods
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    Download http: // thinkpython. com/ code/ BadKangaroo. py . It contains a solution to the previous problem with one big, nasty bug. Find and fix the bug.
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    Download http: // thinkpython. com/ code/ BadKangaroo. py . It contains a solution to the previous problem with one big, nasty bug. Find and fix the bug.
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    If you get stuck, you can download http: // thinkpython. com/ code/ GoodKangaroo. py , which explains the problem and demonstrates a solution.
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    If you get stuck, you can download http: // thinkpython. com/ code/ GoodKangaroo. py , which explains the problem and demonstrates a solution.
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    Exercise 17.8. Visual is a Python module that provides 3-D graphics. It is not always included in a Python installation, so you might have to install it from your software repository or, if it's not there, from http: // vpython. org .
    ",
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    Exercise 17.8. Visual is a Python module that provides 3-D graphics. It is not always included in a Python installation, so you might have to install it from your software repository or, if it's not there, from http: // vpython. org .
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    The following example creates a 3-D space that is 256 units wide, long and high, and sets the \"center\" to be the point (128, 128, 128). Then it draws a blue sphere.
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    from visual import *
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    from visual import *
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    scene.range = (256, 256, 256)\nscene.center = (128, 128, 128)
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    scene.range = (256, 256, 256) scene.center = (128, 128, 128)
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    color = (0.1, 0.1, 0.9) # mostly blue\nsphere(pos=scene.center, radius=128, color=color)
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    color is an RGB tuple; that is, the elements are Red-Green-Blue levels between 0.0 and 1.0 (see http: // en. wikipedia. org/ wiki/ RGB_ color_ model ).
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+          "block_type": "TextInlineMath",
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    The following loop creates a cube of spheres:
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    The following loop creates a cube of spheres:
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  • 1. Put this code in a script and make sure it works for you.
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  • 2. Modify the program so that each sphere in the cube has the color that corresponds to its position in RGB space. Notice that the coordinates are in the range 0–255, but the RGB tuples are in the range 0.0–1.0.
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  • 3. Download http: // thinkpython. com/ code/ color_ list. py and use the function read_colors to generate a list of the available colors on your system, their names and RGB values. For each named color draw a sphere in the position that corresponds to its RGB values.
    • ",
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  • 3. Download http: // thinkpython. com/ code/ color_ list. py and use the function read_colors to generate a list of the available colors on your system, their names and RGB values. For each named color draw a sphere in the position that corresponds to its RGB values.
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    You can see my solution at http: // thinkpython. com/ code/ color_ space. py .
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    You can see my solution at http: // thinkpython. com/ code/ color_ space. py .
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    Chapter 18
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    Chapter 18
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    Inheritance
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    In this chapter I present classes to represent playing cards, decks of cards, and poker hands. If you don't play poker, you can read about it at http://en.wikipedia.org/wiki/Poker, but you don't have to; I'll tell you what you need to know for the exercises. Code examples from this chapter are available from http://thinkpython.com/code/Card.py.
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    In this chapter I present classes to represent playing cards, decks of cards, and poker hands. If you don't play poker, you can read about it at http://en.wikipedia.org/wiki/Poker, but you don't have to; I'll tell you what you need to know for the exercises. Code examples from this chapter are available from http://thinkpython.com/code/Card.py.
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    There are fifty-two cards in a deck, each of which belongs to one of four suits and one of thirteen ranks. The suits are Spades, Hearts, Diamonds, and Clubs (in descending order in bridge). The ranks are Ace, 2, 3, 4, 5, 6, 7, 8, 9, 10, Jack, Queen, and King. Depending on the game that you are playing, an Ace may be higher than King or lower than 2.
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    168 Chapter 18. Inheritance
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    The mapping for ranks is fairly obvious; each of the numerical ranks maps to the corresponding integer, and for face cards:
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    is the 2 of Clubs.
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    I am using the 7→ symbol to make it clear that these mappings are not part of the Python program. They are part of the program design, but they don't appear explicitly in the code.
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    The class definition for Card looks like this:
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    The class definition for Card looks like this:
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    18.2 Class attributes
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    18.2 Class attributes
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    In order to print Card objects in a way that people can easily read, we need a mapping from the integer codes to the corresponding ranks and suits. A natural way to do that is with lists of strings. We assign these lists to class attributes:
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    suit_names = ['Clubs', 'Diamonds', 'Hearts', 'Spades']\nrank_names = [None, 'Ace', '2', '3', '4', '5', '6', '7',\n          '8', '9', '10', 'Jack', 'Queen', 'King']\ndef __str__(self):\n    return '%s of %s' % (Card.rank_names[self.rank],\n                         Card.suit_names[self.suit])
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    18.3. Comparing cards 169
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+                "/page/190/Figure/1": 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    Figure 18.1: Object diagram.
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    Figure 18.1: Object diagram.
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    Putting it all together, the expression Card.rank_names[self.rank] means \"use the attribute rank from the object self as an index into the list rank_names from the class Card, and select the appropriate string.\"
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    The first element of rank_names is None because there is no card with rank zero. By including None as a place-keeper, we get a mapping with the nice property that the index 2 maps to the string '2', and so on. To avoid this tweak, we could have used a dictionary instead of a list.
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    With the methods we have so far, we can create and print cards:
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    >>> card1 = Card(2, 11)\n>>> print card1\nJack of Hearts
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    Figure 18.1 is a diagram of the Card class object and one Card instance. Card is a class object, so it has type type. card1 has type Card. (To save space, I didn't draw the contents of suit_names and rank_names).
    ",
+          "html": "
    Figure 18.1 is a diagram of the Card class object and one Card instance. Card is a class object, so it has type type. card1 has type Card. (To save space, I didn't draw the contents of suit_names and rank_names).
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    With that decided, we can write __cmp__:
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    170 Chapter 18. Inheritance
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    You can write this more concisely using tuple comparison:
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    should provide __lt__, which returns True if self is less than other. You can implement\n__lt__ using tuples and the < operator.\nExercise 18.1. Write a __cmp__ method for Time objects. Hint: you can use tuple comparison, but\nyou also might consider using integer subtraction.
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    18.4 Decks
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    18.4 Decks
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    def __init__(self):\n    self.cards = []\n    for suit in range(4):\n        for rank in range(1, 14):\n            card = Card(suit, rank)\n            self.cards.append(card)
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    The easiest way to populate the deck is with a nested loop. The outer loop enumerates the suits from 0 to 3. The inner loop enumerates the ranks from 1 to 13. Each iteration creates a new Card with the current suit and rank, and appends it to self.cards.
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    18.5. Printing the deck 171
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    18.5 Printing the deck
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    18.5 Printing the deck
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    Here is a __str__ method for Deck: #inside class Deck:
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    Here is a __str__ method for Deck: #inside class Deck: def __str__(self): res = [] for card in self.cards: res.append(str(card)) return '\\n'.join(res)
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    def __str__(self):\n    res = []\n    for card in self.cards:\n        res.append(str(card))\n    return '\\n'.join(res)
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    This method demonstrates an efficient way to accumulate a large string: building a list of strings and then using join. The built-in function str invokes the __str__ method on each card and returns the string representation.
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    Since we invoke join on a newline character, the cards are separated by newlines. Here's what the result looks like:
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    >>> deck = Deck()\n>>> print deck\nAce of Clubs\n2 of Clubs\n3 of Clubs\n...\n10 of Spades\nJack of Spades\nQueen of Spades\nKing of Spades
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    Even though the result appears on 52 lines, it is one long string that contains newlines.
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    18.6 Add, remove, shuffle and sort
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    To deal cards, we would like a method that removes a card from the deck and returns it. The list method pop provides a convenient way to do that:
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    #inside class Deck:
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    def pop_card(self): return self.cards.pop()
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    def pop_card(self):\n    return self.cards.pop()
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    Since pop removes the last card in the list, we are dealing from the bottom of the deck. In real life \"bottom dealing\" is frowned upon, but in this context it's ok.
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    To add a card, we can use the list method append:
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    def add_card(self, card): self.cards.append(card)
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    A method like this that uses another function without doing much real work is sometimes called a veneer. The metaphor comes from woodworking, where it is common to glue a thin layer of good quality wood to the surface of a cheaper piece of wood.
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    172 Chapter 18. Inheritance
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    As another example, we can write a Deck method named shuffle using the function shuffle from the random module:
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    # inside class Deck:
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    def shuffle(self):\n    random.shuffle(self.cards)
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    Don't forget to import random.
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    Don't forget to import random.
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    # inside class Deck:
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    Exercise 18.2. Write a Deck method named sort that uses the list method sort to sort the cards in a Deck. sort uses the __cmp__ method we defined to determine sort order.
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    Exercise 18.2. Write a Deck method named sort that uses the list method sort to sort the cards in a Deck. sort uses the __cmp__ method we defined to determine sort order.
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    18.7 Inheritance
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    18.7 Inheritance
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    The language feature most often associated with object-oriented programming is inheritance. Inheritance is the ability to define a new class that is a modified version of an existing class.
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    It is called \"inheritance\" because the new class inherits the methods of the existing class. Extending this metaphor, the existing class is called the parent and the new class is called the child.
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    As an example, let's say we want a class to represent a \"hand,\" that is, the set of cards held by one player. A hand is similar to a deck: both are made up of a set of cards, and both require operations like adding and removing cards.
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    A hand is also different from a deck; there are operations we want for hands that don't make sense for a deck. For example, in poker we might compare two hands to see which one wins. In bridge, we might compute a score for a hand in order to make a bid.
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    This relationship between classes—similar, but different—lends itself to inheritance.
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    The definition of a child class is like other class definitions, but the name of the parent class appears in parentheses:
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    class Hand(Deck):
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    \"\"\"Represents a hand of playing cards.\"\"\"
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    This definition indicates that Hand inherits from Deck; that means we can use methods like pop_card and add_card for Hands as well as Decks.
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    But the other methods are inherited from Deck, so we can use pop_card and add_card to deal a card:
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    for i in range(num):\n    hand.add_card(self.pop_card())
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    174 Chapter 18. Inheritance
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+                "/page/195/Figure/1": 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    Figure 18.2: Class diagram.
    ",
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    Figure 18.2: Class diagram.
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    A class diagram is a graphical representation of these relationships. For example, Figure 18.2 shows the relationships between Card, Deck and Hand.
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    A class diagram is a graphical representation of these relationships. For example, Figure 18.2 shows the relationships between Card, Deck and Hand.
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    The standard arrow head represents a HAS-A relationship; in this case a Deck has references to Card objects.
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    The star (*) near the arrow head is a multiplicity; it indicates how many Cards a Deck has. A multiplicity can be a simple number, like 52, a range, like 5..7 or a star, which indicates that a Deck can have any number of Cards.
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    A more detailed diagram might show that a Deck actually contains a list of Cards, but built-in types like list and dict are usually not included in class diagrams. Exercise 18.4. Read TurtleWorld.py, World.py and Gui.py and draw a class diagram that shows the relationships among the classes defined there.
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    So the shuffle method for this Hand is the one in Deck.
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    Here's an example:
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    >>> hand = Hand()\n>>> print find_defining_class(hand, 'shuffle')\n<class 'Card.Deck'>
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    So the shuffle method for this Hand is the one in Deck.
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    Here's a program design suggestion: whenever you override a method, the interface of the new method should be the same as the old. It should take the same parameters, return the same type, and obey the same preconditions and postconditions. If you obey this rule, you will find that any function designed to work with an instance of a superclass, like a Deck, will also work with instances of subclasses like a Hand or PokerHand.
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    If you violate this rule, your code will collapse like (sorry) a house of cards.
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    18.10 Data encapsulation
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    18.10 Data encapsulation
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    Chapter 16 demonstrates a development plan we might call \"object-oriented design.\" We identified objects we needed—Time, Point and Rectangle—and defined classes to represent them. In each case there is an obvious correspondence between the object and some entity in the real world (or at least a mathematical world).
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    Chapter 16 demonstrates a development plan we might call \"object-oriented design.\" We identified objects we needed—Time, Point and Rectangle—and defined classes to represent them. In each case there is an obvious correspondence between the object and some entity in the real world (or at least a mathematical world).
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    But sometimes it is less obvious what objects you need and how they should interact. In that case you need a different development plan. In the same way that we discovered function interfaces by encapsulation and generalization, we can discover class interfaces by data encapsulation.
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    Markov analysis, from Section 13.8, provides a good example. If you download my code from http://thinkpython.com/code/markov.py, you'll see that it uses two global variables—suffix_map and prefix—that are read and written from several functions.
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    Markov analysis, from Section 13.8, provides a good example. If you download my code from http://thinkpython.com/code/markov.py, you'll see that it uses two global variables—suffix_map and prefix—that are read and written from several functions.
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    suffix_map = {}\nprefix = ()
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    suffix_map = {} prefix = ()
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    Because these variables are global we can only run one analysis at a time. If we read two texts, their prefixes and suffixes would be added to the same data structures (which makes for some interesting generated text).
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    To run multiple analyses, and keep them separate, we can encapsulate the state of each analysis in an object. Here's what that looks like:
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    176 Chapter 18. Inheritance
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    class Markov(object):\n    def __init__(self):\n        self.suffix_map = {}\n        self.prefix = ()\nNext, we transform the functions into methods. For example, here's process_word:\n    def process_word(self, word, order=2):\n        if len(self.prefix) < order:\n            self.prefix += (word,)\n            return\n        try:\n            self.suffix_map[self.prefix].append(word)\n        except KeyError:\n            # if there is no entry for this prefix, make one\n            self.suffix_map[self.prefix] = [word]\n        self.prefix = shift(self.prefix, word)
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    class Markov(object):
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    Next, we transform the functions into methods. For example, here's process_word:
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    Transforming a program like this—changing the design without changing the function—is another example of refactoring (see Section 4.7).
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    Transforming a program like this—changing the design without changing the function—is another example of refactoring (see Section 4.7).
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    This example suggests a development plan for designing objects and methods:
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  • 1. Start by writing functions that read and write global variables (when necessary).
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  • 3. Encapsulate related variables as attributes of an object.
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    18.11 Glossary
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  • encode: To represent one set of values using another set of values by constructing a mapping between them.
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  • class attribute: An attribute associated with a class object. Class attributes are defined inside a class definition but outside any method.
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  • instance attribute: An attribute associated with an instance of a class.
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  • veneer: A method or function that provides a different interface to another function without doing much computation.
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  • inheritance: The ability to define a new class that is a modified version of a previously defined class.
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    Exercise 18.6. The following are the possible hands in poker, in increasing order of value (and decreasing order of probability):
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    The goal of these exercises is to estimate the probability of drawing these various hands.
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  • 1. Download the following files from http: // thinkpython. com/ code :
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  • 1. Download the following files from http: // thinkpython. com/ code :
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    Card.py : A complete version of the Card, Deck and Hand classes in this chapter.
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  • PokerHand.py : An incomplete implementation of a class that represents a poker hand, and some code that tests it.
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  • 2. If you run PokerHand.py, it deals seven 7-card poker hands and checks to see if any of them contains a flush. Read this code carefully before you go on.
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  • 3. Add methods to PokerHand.py named has_pair, has_twopair, etc. that return True or False according to whether or not the hand meets the relevant criteria. Your code should work correctly for \"hands\" that contain any number of cards (although 5 and 7 are the most common sizes).
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  • 3. Add methods to PokerHand.py named has_pair, has_twopair, etc. that return True or False according to whether or not the hand meets the relevant criteria. Your code should work correctly for \"hands\" that contain any number of cards (although 5 and 7 are the most common sizes).
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  • 4. Write a method named classify that figures out the highest-value classification for a hand and sets the label attribute accordingly. For example, a 7-card hand might contain a flush and a pair; it should be labeled \"flush\".
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    178 Chapter 18. Inheritance
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  • 5. When you are convinced that your classification methods are working, the next step is to estimate the probabilities of the various hands. Write a function in PokerHand.py that shuffles a deck of cards, divides it into hands, classifies the hands, and counts the number of times various classifications appear.
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  • 6. Print a table of the classifications and their probabilities. Run your program with larger and larger numbers of hands until the output values converge to a reasonable degree of accuracy. Compare your results to the values at http: // en. wikipedia. org/ wiki/ Hand_ rankings .
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  • 6. Print a table of the classifications and their probabilities. Run your program with larger and larger numbers of hands until the output values converge to a reasonable degree of accuracy. Compare your results to the values at http: // en. wikipedia. org/ wiki/ Hand_ rankings .
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    Solution: http: // thinkpython. com/ code/ PokerHandSoln. py .
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    Solution: http: // thinkpython. com/ code/ PokerHandSoln. py .
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    Exercise 18.7. This exercise uses TurtleWorld from Chapter 4. You will write code that makes Turtles play tag. If you are not familiar with the rules of tag, see http: // en. wikipedia. org/ wiki/ Tag_ ( game) .
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    Exercise 18.7. This exercise uses TurtleWorld from Chapter 4. You will write code that makes Turtles play tag. If you are not familiar with the rules of tag, see http: // en. wikipedia. org/ wiki/ Tag_ ( game) .
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  • 1. Download http: // thinkpython. com/ code/ Wobbler. py and run it. You should see a TurtleWorld with three Turtles. If you press the Run button, the Turtles wander at random.
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  • 1. Download http: // thinkpython. com/ code/ Wobbler. py and run it. You should see a TurtleWorld with three Turtles. If you press the Run button, the Turtles wander at random.
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  • 2. Read the code and make sure you understand how it works. The Wobbler class inherits from Turtle, which means that the Turtle methods lt, rt, fd and bk work on Wobblers.
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  • 4. Add a steer method to Tagger to override the one in Wobbler. As a starting place, write a version that always points the Turtle toward the origin. Hint: use the math function atan2 and the Turtle attributes x, y and heading.
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  • 6. Modify steer so that each Turtle points toward its nearest neighbor. Hint: Turtles have an attribute, world, that is a reference to the TurtleWorld they live in, and the TurtleWorld has an attribute, animals, that is a list of all Turtles in the world.
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  • 7. Modify steer so the Turtles play tag. You can add methods to Tagger and you can override steer and __init__, but you may not modify or override step, wobble or move. Also, steer is allowed to change the heading of the Turtle but not the position.
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    Solution: http: // thinkpython. com/ code/ Tagger. py .
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    Chapter 19
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    Chapter 19
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    Case study: Tkinter
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    19.1 GUI
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    19.1 GUI
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    Most of the programs we have seen so far are text-based, but many programs use graphical user interfaces, also known as GUIs.
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    There are several books and web pages about Tkinter. One of the best online resources is An Introduction to Tkinter by Fredrik Lundh.
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    180 Chapter 19. Case study: Tkinter
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-            "3": "/page/203/SectionHeader/3"
+            "4": "/page/203/SectionHeader/3"
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@@ -103000,26 +165427,32 @@
           "html": "
    oval takes a bounding box and draws an oval within the specified rectangle:
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           "polygon": [
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-            "3": "/page/203/SectionHeader/3"
+            "4": "/page/203/SectionHeader/3"
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@@ -103029,26 +165462,32 @@
           "html": "
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-            "3": "/page/203/SectionHeader/3"
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-            "3": "/page/203/SectionHeader/3"
+            "4": "/page/203/SectionHeader/3"
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           "html": "
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-            "3": "/page/203/SectionHeader/3"
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@@ -103132,68 +165583,86 @@
               405.2769775390625
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+          "bbox": [
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-            "3": "/page/203/SectionHeader/3"
+            "4": "/page/203/SectionHeader/3"
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-          "block_type": "TextInlineMath",
-          "html": "
    canvas.polygon([[0, 100], [100, 200], [200, 100]], fill='red', outline='orange', width=10)
    ",
+          "id": "/page/203/Text/12",
+          "block_type": "Text",
+          "html": "
    canvas.polygon([[0, 100], [100, 200], [200, 100]], fill='red', outline='orange', width=10)
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-            "3": "/page/203/SectionHeader/3"
+            "4": "/page/203/SectionHeader/3"
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         {
           "id": "/page/203/SectionHeader/13",
           "block_type": "SectionHeader",
-          "html": "
    19.5 More widgets
    ",
+          "html": "
    19.5 More widgets
    ",
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+            "4": "/page/203/SectionHeader/13"
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           "polygon": [
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@@ -103219,39 +165688,51 @@
               516.0209655761719
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+          "bbox": [
+            85.763671875,
+            493.06640625,
+            482.40350341796875,
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             "1": "/page/200/SectionHeader/1",
-            "3": "/page/203/SectionHeader/13"
+            "4": "/page/203/SectionHeader/13"
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         {
-          "id": "/page/203/Code/15",
-          "block_type": "Code",
-          "html": "
    en creates a new Entry:
    ",
+          "id": "/page/203/Text/15",
+          "block_type": "Text",
+          "html": "
    en creates a new Entry:
    ",
           "polygon": [
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-            "3": "/page/203/SectionHeader/13"
+            "4": "/page/203/SectionHeader/13"
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@@ -103261,26 +165742,32 @@
           "html": "
    entry = g.en(text='Default text.')
    ",
           "polygon": [
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-            "3": "/page/203/SectionHeader/13"
+            "4": "/page/203/SectionHeader/13"
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@@ -103306,53 +165793,240 @@
               583.0149688720703
             ]
           ],
+          "bbox": [
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-            "3": "/page/203/SectionHeader/13"
+            "4": "/page/203/SectionHeader/13"
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         {
           "id": "/page/203/Code/18",
           "block_type": "Code",
-          "html": "
    >>> entry.get()\n'Default text.'\nte creates a Text widget:\ntext = g.te(width=100, height=5)\nwidth and height are the dimensions of the widget in characters and lines.\ninsert puts text into the Text widget:\ntext.insert(END, 'A line of text.')
    ",
+          "html": "
    >>> entry.get()\n'Default text.'
    ",
           "polygon": [
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+              85.9130859375,
+              589.359375
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+            "4": "/page/203/SectionHeader/13"
+          },
+          "images": {}
+        },
+        {
+          "id": "/page/203/Text/19",
+          "block_type": "Text",
+          "html": "
    te creates a Text widget:
    ",
+          "polygon": [
+            [
+              85.68896484375,
+              618.36328125
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+            [
+              195.43359375,
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+          "children": null,
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+            "4": "/page/203/SectionHeader/13"
+          },
+          "images": {}
+        },
+        {
+          "id": "/page/203/Text/20",
+          "block_type": "Text",
+          "html": "
    text = g.te(width=100, height=5)
    ",
+          "polygon": [
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+            "1": "/page/200/SectionHeader/1",
+            "4": "/page/203/SectionHeader/13"
+          },
+          "images": {}
+        },
+        {
+          "id": "/page/203/Text/21",
+          "block_type": "Text",
+          "html": "
    width and height are the dimensions of the widget in characters and lines.
    ",
+          "polygon": [
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+            "4": "/page/203/SectionHeader/13"
+          },
+          "images": {}
+        },
+        {
+          "id": "/page/203/Text/22",
+          "block_type": "Text",
+          "html": "
    insert puts text into the Text widget:
    ",
+          "polygon": [
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+              673.27734375
+            ],
+            [
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+          ],
+          "children": null,
+          "section_hierarchy": {
+            "1": "/page/200/SectionHeader/1",
+            "4": "/page/203/SectionHeader/13"
+          },
+          "images": {}
+        },
+        {
+          "id": "/page/203/Code/23",
+          "block_type": "Code",
+          "html": "
    text.insert(END, 'A line of text.')
    ",
+          "polygon": [
+            [
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+              690.6796875
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+            [
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             "1": "/page/200/SectionHeader/1",
-            "3": "/page/203/SectionHeader/13"
+            "4": "/page/203/SectionHeader/13"
           },
           "images": {}
         }
       ],
       "section_hierarchy": {
         "1": "/page/200/SectionHeader/1",
-        "3": "/page/203/SectionHeader/13"
+        "4": "/page/203/SectionHeader/13"
       },
       "images": null
     },
     {
-      "id": "/page/204/Page/182",
+      "id": "/page/204/Page/202",
       "block_type": "Page",
-      "html": "",
+      "html": "",
       "polygon": [
         [
           0.0,
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           792.0
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       ],
+      "bbox": [
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+        792.0
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       "children": [
         {
           "id": "/page/204/PageHeader/0",
           "block_type": "PageHeader",
-          "html": "
    19.6. Packing widgets 183
    ",
+          "html": "",
           "polygon": [
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+              128.794921875,
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             "1": "/page/200/SectionHeader/1",
-            "3": "/page/203/SectionHeader/13"
+            "4": "/page/203/SectionHeader/13"
           },
           "images": {}
         },
         {
-          "id": "/page/204/PageHeader/21",
+          "id": "/page/204/PageHeader/19",
           "block_type": "PageHeader",
-          "html": "
    ",
+          "html": "",
           "polygon": [
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+          "bbox": [
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           "children": null,
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             "1": "/page/200/SectionHeader/1",
-            "3": "/page/203/SectionHeader/13"
+            "4": "/page/203/SectionHeader/13"
           },
           "images": {}
         },
@@ -103436,26 +166128,32 @@
           "html": "
    END is a special index that indicates the last character in the Text widget.
    ",
           "polygon": [
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+              127.599609375,
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             "1": "/page/200/SectionHeader/1",
-            "3": "/page/203/SectionHeader/13"
+            "4": "/page/203/SectionHeader/13"
           },
           "images": {}
         },
@@ -103465,55 +166163,67 @@
           "html": "
    You can also specify a character using a dotted index, like 1.1, which has the line number before the dot and the column number after. The following example adds the letters 'nother' after the first character of the first line.
    ",
           "polygon": [
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+            "4": "/page/203/SectionHeader/13"
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-          "id": "/page/204/TextInlineMath/3",
-          "block_type": "TextInlineMath",
-          "html": "
    >>> text.insert(1.1, 'nother')
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+          "id": "/page/204/Text/3",
+          "block_type": "Text",
+          "html": "
    >>> text.insert(1.1, 'nother')
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-            "3": "/page/203/SectionHeader/13"
+            "4": "/page/203/SectionHeader/13"
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    The get method reads the text in the widget; it takes a start and end index as arguments. The following example returns all the text in the widget, including the newline character:
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    >>> text.get(0.0, END)
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-          "id": "/page/204/Text/6",
-          "block_type": "Text",
-          "html": "
    'Another line of text.\\n'
    ",
+          "id": "/page/204/Code/5",
+          "block_type": "Code",
+          "html": "
    >>> text.get(0.0, END)\n'Another line of text.\\n'
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+            "4": "/page/203/SectionHeader/13"
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+          "id": "/page/204/Text/6",
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    The delete method removes text from the widget; the following example deletes all but the first two characters:
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               237.57696533203125
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    >>> text.delete(1.2, END) >>> text.get(0.0, END) 'An\\n'
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+          "id": "/page/204/Code/7",
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    >>> text.delete(1.2, END)\n>>> text.get(0.0, END)\n'An\\n'
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               276.5823974609375
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    Exercise 19.3. Modify your solution to Exercise 19.2 by adding an Entry widget and a second button. When the user presses the second button, it should read a color name from the Entry and use it to change the fill color of the circle. Use config to modify the existing circle; don't create a new one.
    ",
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    Exercise 19.3. Modify your solution to Exercise 19.2 by adding an Entry widget and a second button. When the user presses the second button, it should read a color name from the Entry and use it to change the fill color of the circle. Use config to modify the existing circle; don't create a new one.
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    You can see my solution at http: // thinkpython. com/ code/ circle_ demo. py .
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    You can see my solution at http: // thinkpython. com/ code/ circle_ demo. py .
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    19.6 Packing widgets
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    19.6 Packing widgets
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    So far we have been stacking widgets in a single column, but in most GUIs the layout is more complicated. For example, Figure 19.1 shows a simplified version of TurtleWorld (see Chapter 4).
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    So far we have been stacking widgets in a single column, but in most GUIs the layout is more complicated. For example, Figure 19.1 shows a simplified version of TurtleWorld (see Chapter 4).
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    This section presents the code that creates this GUI, broken into a series of steps. You can download the complete example from http://thinkpython.com/code/ SimpleTurtleWorld.py.
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    This section presents the code that creates this GUI, broken into a series of steps. You can download the complete example from http://thinkpython.com/code/ SimpleTurtleWorld.py.
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    At the top level, this GUI contains two widgets—a Canvas and a Frame—arranged in a row. So the first step is to create the row.
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    class SimpleTurtleWorld(TurtleWorld): \"\"\"This class is identical to TurtleWorld, but the code that lays out the GUI is simplified for explanatory purposes.\"\"\"
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    Here is the code that creates the Canvas and the column Frame that hold the other widgets:
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    184 Chapter 19. Case study: Tkinter
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    Figure 19.1: TurtleWorld after running the snowflake code.
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    Finally, drop restores the original color of the item:
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    19.10. Glossary 189
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    19.11 Exercises
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    The first line imports Image and gives it the local name PIL. The second line imports ImageTk, which can translate a PIL image into a Tkinter PhotoImage. Here's an example:
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  • 1. Download image_demo.py, danger.gif and allen.png from http: // thinkpython. com/ code . Run image_demo.py. You might have to install PIL and ImageTk. They are probably in your software repository, but if not you can get them from http: // pythonware. com/ products/ pil .
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  • 1. Download image_demo.py, danger.gif and allen.png from http: // thinkpython. com/ code . Run image_demo.py. You might have to install PIL and ImageTk. They are probably in your software repository, but if not you can get them from http: // pythonware. com/ products/ pil .
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  • 2. In image_demo.py change the name of the second PhotoImage from photo2 to photo and run the program again. You should see the second PhotoImage but not the first.
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  • 2. In image_demo.py change the name of the second PhotoImage from photo2 to photo and run the program again. You should see the second PhotoImage but not the first.
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    Appendix A
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    Debugging
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  •  Syntax errors are produced by Python when it is translating the source code into byte code. They usually indicate that there is something wrong with the syntax of the program. Example: Omitting the colon at the end of a def statement yields the somewhat redundant message SyntaxError: invalid syntax.
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  •  Semantic errors are problems with a program that runs without producing error messages but doesn't do the right thing. Example: An expression may not be evaluated in the order you expect, yielding an incorrect result.
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    The first step in debugging is to figure out which kind of error you are dealing with. Although the following sections are organized by error type, some techniques are applicable in more than one situation.
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    A.1 Syntax errors
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    A.1 Syntax errors
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    Syntax errors are usually easy to fix once you figure out what they are. Unfortunately, the error messages are often not helpful. The most common messages are SyntaxError: invalid syntax and SyntaxError: invalid token, neither of which is very informative.
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    On the other hand, the message does tell you where in the program the problem occurred. Actually, it tells you where Python noticed a problem, which is not necessarily where the error is. Sometimes the error is prior to the location of the error message, often on the preceding line.
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    194 Appendix A. Debugging
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    If you are building the program incrementally, you should have a good idea about where the error is. It will be in the last line you added.
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    If you are copying code from a book, start by comparing your code to the book's code very carefully. Check every character. At the same time, remember that the book might be wrong, so if you see something that looks like a syntax error, it might be.
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    Here are some ways to avoid the most common syntax errors:
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  • 2. Check that you have a colon at the end of the header of every compound statement, including for, while, if, and def statements.
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  • 3. Make sure that any strings in the code have matching quotation marks.
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  • 4. If you have multiline strings with triple quotes (single or double), make sure you have terminated the string properly. An unterminated string may cause an invalid token error at the end of your program, or it may treat the following part of the program as a string until it comes to the next string. In the second case, it might not produce an error message at all!
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  • 5. An unclosed opening operator—(, {, or [—makes Python continue with the next line as part of the current statement. Generally, an error occurs almost immediately in the next line.
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  • 6. Check for the classic = instead of == inside a conditional.
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  • 7. Check the indentation to make sure it lines up the way it is supposed to. Python can handle space and tabs, but if you mix them it can cause problems. The best way to avoid this problem is to use a text editor that knows about Python and generates consistent indentation.
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    A.1.1 I keep making changes and it makes no difference.
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    A.3 Semantic errors
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  •  Is a section of code producing an effect that is not what you expected? Make sure that you understand the code in question, especially if it involves invocations to functions or methods in other Python modules. Read the documentation for the functions you invoke. Try them out by writing simple test cases and checking the results.
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    200 Appendix A. Debugging
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  •  Superstitious beliefs (\"the computer hates me\") and magical thinking (\"the program only works when I wear my hat backward\").
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    If you find yourself suffering from any of these symptoms, get up and go for a walk. When you are calm, think about the program. What is it doing? What are some possible causes of that behavior? When was the last time you had a working program, and what did you do next?
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    Sometimes it just takes time to find a bug. I often find bugs when I am away from the computer and let my mind wander. Some of the best places to find bugs are trains, showers, and in bed, just before you fall asleep.
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    Before you bring someone else in, make sure you are prepared. Your program should be as simple as possible, and you should be working on the smallest input that causes the error. You should have print statements in the appropriate places (and the output they produce should be comprehensible). You should understand the problem well enough to describe it concisely.
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    When you bring someone in to help, be sure to give them the information they need:
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  •  What was the last thing you did before this error occurred? What were the last lines of code that you wrote, or what is the new test case that fails?
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    When you find the bug, take a second to think about what you could have done to find it faster. Next time you see something similar, you will be able to find the bug more quickly.
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    Remember, the goal is not just to make the program work. The goal is to learn how to make the program work.
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    Appendix B
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    Appendix B
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    Analysis of Algorithms
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    This appendix is an edited excerpt from Think Complexity, by Allen B. Downey, also published by O'Reilly Media (2011). When you are done with this book, you might want to move on to that one.
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    Analysis of algorithms is a branch of computer science that studies the performance of algorithms, especially their run time and space requirements. See http://en.wikipedia. org/wiki/Analysis_of_algorithms.
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    The practical goal of algorithm analysis is to predict the performance of different algorithms in order to guide design decisions.
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    During the 2008 United States Presidential Campaign, candidate Barack Obama was asked to perform an impromptu analysis when he visited Google. Chief executive Eric Schmidt jokingly asked him for \"the most efficient way to sort a million 32-bit integers.\" Obama had apparently been tipped off, because he quickly replied, \"I think the bubble sort would be the wrong way to go.\" See http://www.youtube.com/watch?v=k4RRi_ntQc8.
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    During the 2008 United States Presidential Campaign, candidate Barack Obama was asked to perform an impromptu analysis when he visited Google. Chief executive Eric Schmidt jokingly asked him for \"the most efficient way to sort a million 32-bit integers.\" Obama had apparently been tipped off, because he quickly replied, \"I think the bubble sort would be the wrong way to go.\" See http://www.youtube.com/watch?v=k4RRi_ntQc8.
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    This is true: bubble sort is conceptually simple but slow for large datasets. The answer Schmidt was probably looking for is \"radix sort\" (http://en.wikipedia.org/wiki/ Radix_sort) 1 .
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    This is true: bubble sort is conceptually simple but slow for large datasets. The answer Schmidt was probably looking for is \"radix sort\" (http://en.wikipedia.org/wiki/ Radix_sort) 1 .
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    The goal of algorithm analysis is to make meaningful comparisons between algorithms, but there are some problems:
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  •  The relative performance of the algorithms might depend on characteristics of the hardware, so one algorithm might be faster on Machine A, another on Machine B. The general solution to this problem is to specify a machine model and analyze the number of steps, or operations, an algorithm requires under a given model.
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  •  Relative performance might depend on the details of the dataset. For example, some sorting algorithms run faster if the data are already partially sorted; other algorithms
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    1 But if you get a question like this in an interview, I think a better answer is, \"The fastest way to sort a million integers is to use whatever sort function is provided by the language I'm using. Its performance is good enough for the vast majority of applications, but if it turned out that my application was too slow, I would use a profiler to see where the time was being spent. If it looked like a faster sort algorithm would have a significant effect on performance, then I would look around for a good implementation of radix sort.\"
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    1 But if you get a question like this in an interview, I think a better answer is, \"The fastest way to sort a million integers is to use whatever sort function is provided by the language I'm using. Its performance is good enough for the vast majority of applications, but if it turned out that my application was too slow, I would use a profiler to see where the time was being spent. If it looked like a faster sort algorithm would have a significant effect on performance, then I would look around for a good implementation of radix sort.\"
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    202 Appendix B. Analysis of Algorithms
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    The good thing about this kind of comparison that it lends itself to simple classification of algorithms. For example, if I know that the run time of Algorithm A tends to be proportional to the size of the input, n, and Algorithm B tends to be proportional to n 2 , then I expect A to be faster than B for large values of n.
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    The good thing about this kind of comparison that it lends itself to simple classification of algorithms. For example, if I know that the run time of Algorithm A tends to be proportional to the size of the input, n, and Algorithm B tends to be proportional to n 2 , then I expect A to be faster than B for large values of n.
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    This kind of analysis comes with some caveats, but we'll get to that later.
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    B.1 Order of growth
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    Suppose you have analyzed two algorithms and expressed their run times in terms of the size of the input: Algorithm A takes 100n + 1 steps to solve a problem with size n; Algorithm B takes n 2 + n + 1 steps.
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    At n = 10, Algorithm A looks pretty bad; it takes almost 10 times longer than Algorithm B. But for n = 100 they are about the same, and for larger values A is much better.
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    The fundamental reason is that for large values of n, any function that contains an n 2 term will grow faster than a function whose leading term is n. The leading term is the term with the highest exponent.
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    For Algorithm A, the leading term has a large coefficient, 100, which is why B does better than A for small n. But regardless of the coefficients, there will always be some value of n where an2 > bn.
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    The same argument applies to the non-leading terms. Even if the run time of Algorithm A were n + 1000000, it would still be better than Algorithm B for sufficiently large n.
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    The same argument applies to the non-leading terms. Even if the run time of Algorithm A were n + 1000000, it would still be better than Algorithm B for sufficiently large n.
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    In general, we expect an algorithm with a smaller leading term to be a better algorithm for large problems, but for smaller problems, there may be a crossover point where another algorithm is better. The location of the crossover point depends on the details of the algorithms, the inputs, and the hardware, so it is usually ignored for purposes of algorithmic analysis. But that doesn't mean you can forget about it.
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    B.1. Order of growth 203
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    An order of growth is a set of functions whose asymptotic growth behavior is considered equivalent. For example, 2n, 100n and n + 1 belong to the same order of growth, which is written O(n) in Big-Oh notation and often called linear because every function in the set grows linearly with n.
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    An order of growth is a set of functions whose asymptotic growth behavior is considered equivalent. For example, 2n, 100n and n + 1 belong to the same order of growth, which is written O(n) in Big-Oh notation and often called linear because every function in the set grows linearly with n.
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    All functions with the leading term n 2 belong to O(n 2 ); they are quadratic, which is a fancy word for functions with the leading term n 2 .
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    For the logarithmic terms, the base of the logarithm doesn't matter; changing bases is the equivalent of multiplying by a constant, which doesn't change the order of growth. Similarly, all exponential functions belong to the same order of growth regardless of the base of the exponent. Exponential functions grow very quickly, so exponential algorithms are only useful for small problems.
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    Exercise B.1. Read the Wikipedia page on Big-Oh notation at http: // en. wikipedia. org/ wiki/ Big_ O_ notation and answer the following questions:
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  • 1. What is the order of growth of n3 + n 2? What about 1000000n 3 + n 2? What about n3 + 1000000n 2?
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  • 1. What is the order of growth of n3 + n 2? What about 1000000n 3 + n 2? What about n3 + 1000000n 2?
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  • 2. What is the order of growth of (n 2 + n) · (n + 1)? Before you start multiplying, remember that you only need the leading term.
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    But if you keep those caveats in mind, algorithmic analysis is a useful tool. At least for large problems, the \"better\" algorithms is usually better, and sometimes it is much better. The difference between two algorithms with the same order of growth is usually a constant factor, but the difference between a good algorithm and a bad algorithm is unbounded!
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    But if you keep those caveats in mind, algorithmic analysis is a useful tool. At least for large problems, the “better” algorithms is usually better, and sometimes it is much better. The difference between two algorithms with the same order of growth is usually a constant factor, but the difference between a good algorithm and a bad algorithm is unbounded!
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    204 Appendix B. Analysis of Algorithms
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    The performance of dictionaries is one of the minor miracles of computer science. We will see how they work in Section B.4.
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  • 1. What is a \"comparison sort?\" What is the best worst-case order of growth for a comparison sort? What is the best worst-case order of growth for any sort algorithm?
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  • 2. What is the order of growth of bubble sort, and why does Barack Obama think it is \"the wrong way to go?\"
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  • 3. What is the order of growth of radix sort? What preconditions do we need to use it?
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  • 4. What is a stable sort and why might it matter in practice?
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  • 5. What is the worst sorting algorithm (that has a name)?
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  • 7. Many of the non-comparison sorts are linear, so why does does Python use an O(n log n) comparison sort?
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  • 7. Many of the non-comparison sorts are linear, so why does does Python use an O(n log n) comparison sort?
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    206 Appendix B. Analysis of Algorithms
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    Exercise B.3. Write a function called bisection that takes a sorted list and a target value and returns the index of the value in the list, if it's there, or None if it's not.
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    There is another data structure, called a hashtable that is even faster—it can do a search in constant time—and it doesn't require the items to be sorted. Python dictionaries are implemented using hashtables, which is why most dictionary operations, including the in operator, are constant time.
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    I use Python to demonstrate these implementations, but in real life you wouldn't write code like this in Python; you would just use a dictionary! So for the rest of this chapter, you have to imagine that dictionaries don't exist and you want to implement a data structure that maps from keys to values. The operations you have to implement are:
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  • get(target): Look up and return the value that corresponds to key target. With a Python dictionary, d, this operation is written d[target] or d.get(target).
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  • get(target): Look up and return the value that corresponds to key target. With a Python dictionary, d, this operation is written d[target] or d.get(target).
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    add appends a key-value tuple to the list of items, which takes constant time.
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    get uses a for loop to search the list: if it finds the target key it returns the corresponding value; otherwise it raises a KeyError. So get is linear.
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    An alternative is to keep the list sorted by key. Then get could use a bisection search, which is O(log n). But inserting a new item in the middle of a list is linear, so this might
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    An alternative is to keep the list sorted by key. Then get could use a bisection search, which is O(log n). But inserting a new item in the middle of a list is linear, so this might
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    B.4. Hashtables 207
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    not be the best option. There are other data structures (see http://en.wikipedia.org/ wiki/Red-black_tree) that can implement add and get in log time, but that's still not as good as constant time, so let's move on.
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    not be the best option. There are other data structures (see http://en.wikipedia.org/ wiki/Red-black_tree) that can implement add and get in log time, but that's still not as good as constant time, so let's move on.
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    One way to improve LinearMap is to break the list of key-value pairs into smaller lists. Here's an implementation called BetterMap, which is a list of 100 LinearMaps. As we'll see in a second, the order of growth for get is still linear, but BetterMap is a step on the path toward hashtables:
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    def __init__(self, n=100):\n    self.maps = []\n    for i in range(n):\n        self.maps.append(LinearMap())\ndef find_map(self, k):\n    index = hash(k) % len(self.maps)\n    return self.maps[index]\ndef add(self, k, v):\n    m = self.find_map(k)\n    m.add(k, v)\ndef get(self, k):\n    m = self.find_map(k)\n    return m.get(k)
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    def __init__(self, n=100):\n        self.maps = []\n        for i in range(n):\n            self.maps.append(LinearMap())\n    def find_map(self, k):\n        index = hash(k) % len(self.maps)\n        return self.maps[index]\n    def add(self, k, v):\n        m = self.find_map(k)\n        m.add(k, v)\n    def get(self, k):\n        m = self.find_map(k)\n        return m.get(k)\n__init__ makes a list of n LinearMaps.
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    Hashable objects that are considered equal return the same hash value, but the converse is not necessarily true: two different objects can return the same hash value.
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    Since the run time of LinearMap.get is proportional to the number of items, we expect BetterMap to be about 100 times faster than LinearMap. The order of growth is still linear, but the leading coefficient is smaller. That's nice, but still not as good as a hashtable.
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    Here (finally) is the crucial idea that makes hashtables fast: if you can keep the maximum length of the LinearMaps bounded, LinearMap.get is constant time. All you have to do is keep track of the number of items and when the number of items per LinearMap exceeds a threshold, resize the hashtable by adding more LinearMaps.
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    Here is an implementation of a hashtable:
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    208 Appendix B. Analysis of Algorithms
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    class HashMap(object):\n    def __init__(self):\n        self.maps = BetterMap(2)\n        self.num = 0\n    def get(self, k):\n        return self.maps.get(k)\n    def add(self, k, v):\n        if self.num == len(self.maps.maps):\n            self.resize()\n        self.maps.add(k, v)\n        self.num += 1\n    def resize(self):\n        new_maps = BetterMap(self.num * 2)\n        for m in self.maps.maps:\n            for k, v in m.items:\n                new_maps.add(k, v)\n        self.maps = new_maps
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    class HashMap(object):\n    def __init__(self):\n        self.maps = BetterMap(2)\n        self.num = 0\n    def get(self, k):\n        return self.maps.get(k)\n    def add(self, k, v):\n        if self.num == len(self.maps.maps):\n            self.resize()\n        self.maps.add(k, v)\n        self.num += 1\n    def resize(self):\n        new_maps = BetterMap(self.num * 2)\n        for m in self.maps.maps:\n            for k, v in m.items:\n                new_maps.add(k, v)
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    Each HashMap contains a BetterMap; __init__ starts with just 2 LinearMaps and initializes num, which keeps track of the number of items.
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    get just dispatches to BetterMap. The real work happens in add, which checks the number of items and the size of the BetterMap: if they are equal, the average number of items per LinearMap is 1, so it calls resize.
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    resize make a new BetterMap, twice as big as the previous one, and then \"rehashes\" the items from the old map to the new.
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    Rehashing is necessary because changing the number of LinearMaps changes the denominator of the modulus operator in find_map. That means that some objects that used to wrap into the same LinearMap will get split up (which is what we wanted, right?).
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    Rehashing is linear, so resize is linear, which might seem bad, since I promised that add would be constant time. But remember that we don't have to resize every time, so add is usually constant time and only occasionally linear. The total amount of work to run add n times is proportional to n, so the average time of each add is constant time!
    ",
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    Rehashing is linear, so resize is linear, which might seem bad, since I promised that add would be constant time. But remember that we don't have to resize every time, so add is usually constant time and only occasionally linear. The total amount of work to run add n times is proportional to n, so the average time of each add is constant time!
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    To see how this works, think about starting with an empty HashTable and adding a sequence of items. We start with 2 LinearMaps, so the first 2 adds are fast (no resizing required). Let's say that they take one unit of work each. The next add requires a resize, so we have to rehash the first two items (let's call that 2 more units of work) and then add the third item (one more unit). Adding the next item costs 1 unit, so the total so far is 6 units of work for 4 items.
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+                "/page/230/Figure/1": 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               }
             },
             {
-              "id": "/page/230/Caption/3",
+              "id": "/page/230/Caption/2",
               "block_type": "Caption",
-              "html": "
    Figure B.1: The cost of a hashtable add.
    ",
+              "html": "
    Figure B.1: The cost of a hashtable add.
    ",
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    The next add costs 9 units, but then we can add 7 more before the next resize, so the total is 30 units for the first 16 adds.
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    After 32 adds, the total cost is 62 units, and I hope you are starting to see a pattern. After n adds, where n is a power of two, the total cost is 2n − 2 units, so the average work per add is a little less than 2 units. When n is a power of two, that's the best case; for other values of n the average work is a little higher, but that's not important. The important thing is that it is O(1).
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    After 32 adds, the total cost is 62 units, and I hope you are starting to see a pattern. After n adds, where n is a power of two, the total cost is 2n − 2 units, so the average work per add is a little less than 2 units. When n is a power of two, that's the best case; for other values of n the average work is a little higher, but that's not important. The important thing is that it is O(1).
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    Figure B.1 shows how this works graphically. Each block represents a unit of work. The columns show the total work for each add in order from left to right: the first two adds cost 1 units, the third costs 3 units, etc.
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    Figure B.1 shows how this works graphically. Each block represents a unit of work. The columns show the total work for each add in order from left to right: the first two adds cost 1 units, the third costs 3 units, etc.
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    The extra work of rehashing appears as a sequence of increasingly tall towers with increasing space between them. Now if you knock over the towers, amortizing the cost of resizing over all adds, you can see graphically that the total cost after n adds is 2n − 2.
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    The extra work of rehashing appears as a sequence of increasingly tall towers with increasing space between them. Now if you knock over the towers, amortizing the cost of resizing over all adds, you can see graphically that the total cost after n adds is 2n − 2.
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    An important feature of this algorithm is that when we resize the HashTable it grows geometrically; that is, we multiply the size by a constant. If you increase the size arithmetically—adding a fixed number each time—the average time per add is linear.
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    You can download my implementation of HashMap from http://thinkpython/code/ Map.py, but remember that there is no reason to use it; if you want a map, just use a Python dictionary.
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    You can download my implementation of HashMap from http://thinkpython/code/ Map.py, but remember that there is no reason to use it; if you want a map, just use a Python dictionary.
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    210 Appendix B. Analysis of Algorithms
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    210 Appendix B. Analysis of Algorithms
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    Appendix C
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    Appendix C
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    Lumpy
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    Throughout the book, I have used diagrams to represent the state of running programs.
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    In Section 2.2, we used a state diagram to show the names and values of variables. In Section 3.10 I introduced a stack diagram, which shows one frame for each function call. Each frame shows the parameters and local variables for the function or method. Stack diagrams for recursive functions appear in Section 5.9 and Section 6.5.
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    In Section 2.2, we used a state diagram to show the names and values of variables. In Section 3.10 I introduced a stack diagram, which shows one frame for each function call. Each frame shows the parameters and local variables for the function or method. Stack diagrams for recursive functions appear in Section 5.9 and Section 6.5.
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    Section 10.2 shows what a list looks like in a state diagram, Section 11.4 shows what a dictionary looks like, and Section 12.6 shows two ways to represent tuples.
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    Section 15.2 introduces object diagrams, which show the state of an object's attributes, and their attributes, and so on. Section 15.3 has object diagrams for Rectangles and their embedded Points. Section 16.1 shows the state of a Time object. Section 18.2 has a diagram that includes a class object and an instance, each with their own attributes.
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    Finally, Section 18.8 introduces class diagrams, which show the classes that make up a program and the relationships between them.
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    Finally, Section 18.8 introduces class diagrams, which show the classes that make up a program and the relationships between them.
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    Figure C.2: Stack diagram.
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    Figure C.2: Stack diagram.
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    C.2 Stack diagram
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+                "/page/234/Figure/1": 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    Figure C.3: Object diagram.
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    Figure C.3: Object diagram.
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    from swampy.Lumpy import Lumpy
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    from swampy.Lumpy import Lumpy\ndef countdown(n):\n    if n <= 0:\n        print 'Blastoff!'\n        lumpy.object_diagram()\n    else:\n        print n\n        countdown(n-1)\nlumpy = Lumpy()\nlumpy.make_reference()
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    def countdown(n):\n    if n <= 0:\n        print 'Blastoff!'\n        lumpy.object_diagram()\n    else:\n        print n\n        countdown(n-1)
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-          "block_type": "TextInlineMath",
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    lumpy = Lumpy() lumpy.make_reference() countdown(3)
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+          "id": "/page/234/Text/4",
+          "block_type": "Text",
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    countdown(3)
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+          "id": "/page/234/Text/5",
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    Figure C.2 shows the result. Each frame is represented with a box that has the function's name outside and variables inside. Since this function is recursive, there is one frame for each level of recursion.
    ",
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    Figure C.2 shows the result. Each frame is represented with a box that has the function's name outside and variables inside. Since this function is recursive, there is one frame for each level of recursion.
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    Remember that a stack diagram shows the state of the program at a particular point in its execution. To get the diagram you want, sometimes you have to think about where to invoke object_diagram.
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    In this case I invoke object_diagram after executing the base case of the recursion; that way the stack diagram shows each level of the recursion. You can call object_diagram more than once to get a series of snapshots of the program's execution.
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    C.3 Object diagrams
    ",
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    C.3 Object diagrams
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    214 Appendix C. Lumpy
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    Figure C.4: Object diagram.
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    Figure C.4: Object diagram.
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    numbers = [17, 123] empty = []
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    numbers = [17, 123]\nempty = []
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    lumpy.object_diagram()
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    lumpy.object_diagram()
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    Figure C.3 shows the result. Lists are represented by a box that shows the indices mapping to the elements. This representation is slightly misleading, since indices are not actually part of the list, but I think they make the diagram easier to read. The empty list is represented by an empty box.
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    Figure C.3 shows the result. Lists are represented by a box that shows the indices mapping to the elements. This representation is slightly misleading, since indices are not actually part of the list, but I think they make the diagram easier to read. The empty list is represented by an empty box.
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    And here's an example showing the dictionaries from Section 11.4. You can download it from http://thinkpython.com/code/lumpy_demo4.py.
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    And here's an example showing the dictionaries from Section 11.4. You can download it from http://thinkpython.com/code/lumpy_demo4.py.
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+                "/page/236/Figure/1": 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V1UUiSxJJHIsiMAVdSCGHqCKVkVwA6hgDkZGeadQBnXi/atRgs5WIgMbSMoOPMIIGPpzmoZ4l0u7tjZRMRMxR7dG4bjO4Z4BGP1rQubSK7VRIGDKco6HDKfY0y3sIoJTMXkllIxvlbcQPQelcFTDzlUbS1bTUuqWmm3/A118+mNWKil5bdGY/iS8uW8LauDps6g2UwJLpx8h96d4evLlfDWlAabOwFnCAQ6c/IPerfif/AJFPWf8Arxn/APQDTvDn/IsaT/15Q/8AoAr0vZT+q/G/i8u3oY88eb4V+P8AmS/brr/oGT/99p/8VR9uuv8AoGT/APfaf/FVeork9jP/AJ+P/wAl/wAiueP8q/H/ADKP266/6Bk//faf/FVheGr8xPrW+zuudTmJKJuA4Xjg11dYHhf72t/9hWb+S1LpVFJWm/ml+iR00px9jU93t37mh/bFkP8AWSPEfSSNl/mKmj1Cyl/1d3A3sJBVmoZLS2l/1lvE/wDvIDVWrrqn8mv1ZzXpvo/v/wCASghhkEEeopay7rw9pl2m14HiGc5t53gP5owNU28IWRGE1DW4x6Lq1x/VzVKVVL3o/c/80iWodH+H/Dmrqk32bSrucTiAxwuwlOPkIHXnj86wbDWHt5bU3Oqm9W4057woFjzldv8AqwoBIO49c9OvWrNv4YazuFng1jUmZDkLc3csq/ipfB/EVo7dVTo9nL9VZD/M1PtpLeD/AAf5NlezXSS/H/I5WDxHOs88UmsQ7ZLGW4jJuIZHiZcEZ2xqoOCTglulS6fq16t7bebrZuY2lgQoyxAOJYi2cqoP3hxg/nXSLPfxDH9mxkdf3Uw/kQKX+0mX/WWF4v0QMP8Ax0mj6zT63Xqmv0D2MulvvRzWl63qlxe2hnvLdJJZtk1pJcRDZ1yqoFD7h7sc/jx2lZr6np7bvNd4mZdpLxvG2PrgY/OqMel6JL/q9Svm9hrNzn/0ZVRxNGTspr70J0qi3izoKKxx4csCMi41Qj1/ta6/+OVrIgjRUBYhQANzEn8SeTWxmZkdtFqN1dNdr5gik8tImPyqABzj1OetNhmmsry4tIIJbiFArKA4/d5/hyx6Vcn0+KabzlklhlIwzxPtLD37Gpba1itIykQPJ3MzHJY+pPevPWGnzprR3bclu1rpt+eitp5dTqx5e/l0RzXiS7uGm0Pdp8y41OMjLp8x2Px1rd+3XX/QMn/77T/4qs7xP/rtB/7CsX/oD1v1tGlPmf7x/h/kaVpx9jT91de/f1KP266/6Bk//faf/FUfbrr/AKBk/wD32n/xVXqKr2M/+fj/APJf8jm54/yr8f8AMqRXdxJKqvYTRqersyED8jUE+jW8/iG01lifPtYJIFGOCHKkn/x39a0qK1hFxVnK/wB36JESaeyscRc2tpqHirV7O2j0n7Y6x/v3kAuIW28sqhckjg/eFaWoai8V9eRT60NO+zxqYEKx/v8AK5LHcCWG75cLg8e4rpaQqrEEqCQcjI6VZJzOp6vBA2iTX2oW9nO7o0trM0YA3Icn5huGDxkEVJfXzza79lg1z7NF9hNzsi8picHhssp+XH9Onfo6KAOPe8+2zaDqUuttZxXFi5Z4zEIy5EZOC6nrzxnt9a29W0uz8S6CbOScSW0xjcSoQ27awYEY45xWmyK4w6hh1wRmnUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFYHjn/kQvEH/YOn/9Fmt+sDxz/wAiF4g/7B0//os10YT/AHiHqvzFLZmvp/8AyDbX/rin8hViq+n/APINtf8Arin8hVisZ/ExhRRRUgFFFFABRRRQAVk63eSWxs0+1/YoJpCst1hfk+UkDLAqMnjJB9OpFa1IQGBBAIPUGgDidQ1QNHZCTVo/LTVBHDflUy6eU24jA2nBJG7G0Y5HBrc0u4mvft9vHqb3MMbKIr2NY92SMsuQuwkcdu+OoraAAGAAB6VHLEXt2iilaAkYDxhcr9AQR+lAGboYkifUreSdphDdEK7Iik5RGOdiqCcsecVb1SZ4NPkeNtjEqu/+6CQCfwzTrGxSxjkVZJJXkcySSykFnYgDJwAOgAwABxVh0WWNkdQyMMEHoRWdWLnCUYuzaKg1GSbMu8022tLGa5gDR3ESF1m3HcSBnknrn3qWPULt4kY6ZPkqCcOn9TTl0mAFQ0k8kSnKwvISg9OO/wCNX65aeHmpNx9xdlb79v66m86sWrP3vW/+ZxnhW7uF1TxSRp8zFtWJIDp8p8iHjrXS/brr/oGT/wDfaf8AxVYvhL/kK+K/+wuf/RENdRXo42lP238R7R7fyryMITjb4V+P+ZR+3XX/AEDJ/wDvtP8A4qj7ddf9Ayf/AL7T/wCKq9RXJ7Gf/Px/+S/5F88f5V+P+ZzbyyS+OdNaSB4W+wXI2sQT9+Lng10lctreoW2leMdMu7xnjg+xXEe9Y2cbi0ZA+UH0NWv+E10D/n9k/wDAaX/4mnCUYXUpa+fodlbD1qsKcqcG1y9E39pknjH/AJEzWf8Arzk/9BNa8H/HvF/uD+Vcd4n8V6NfeF9Ttba5lknmtnSNBbS/MxGAPu12MHFvH/uj+VVGSlN2fRfqRWo1KWGgqkWnzS3VukSSiiitThCuXk/5Knb/APYFl/8AR6V1FcvJ/wAlTt/+wLL/AOj0rqwu8/8AC/yJkdRRRRXKUFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAZXif/kU9Z/68Z//AEA07w5/yLGk/wDXlD/6AKb4n/5FPWf+vGf/ANANO8Of8ixpP/XlD/6AK6v+YX/t79CftGnRRRXKUFcbpWvWGj3es298bmOR9RlkULaSuCpC4IKqR2NdlRUSi200dFGrCEZRmm0+ztt8mYH/AAmmh/8APa7/APACf/4ij/hNND/57Xf/AIAT/wDxFb9FFp9193/BK58L/JL/AMCX/wAiYH/CaaH/AM9rv/wAn/8AiKP+E00P/ntd/wDgBP8A/EVv0UWn3X3f8EOfC/yS/wDAl/8AImB/wmmh/wDPa7/8AJ//AIij/hNND/57Xf8A4AT/APxFb9FFp9193/BDnwv8kv8AwJf/ACJgf8Jpof8Az2u//ACf/wCIo/4TTQ/+e13/AOAE/wD8RW/RRafdfd/wQ58L/JL/AMCX/wAiYH/CaaH/AM9rv/wAn/8AiKhk8U+G5f8AWLM/+9psx/8AaddLRUyhKStKz+X/AARqphltCX/gS/8AkTkjrnhMnIS5Q+sdjcJ/JKT/AISDQV/1eo6vH7C2uCP/AB5DXXUVj9Uh0SXorfky/rNHtL/wJf8AyJyP/CUWC/6vWL4j/pppcrfyQUf8JlAnS5Ev1066Q/8AoBrrqKPqzW0mvm/1bH9Yw3Wm/vj/APIHBah4k/ti+0eC1s5pJIr9JTtilUYCsOrooHX1rrv7Sdf9ZYXi/RAw/wDHSav0U40KsdfaO/oiK2IpTUYxhZLzu/0/Iof2xZD/AFkjxH0kjZf5ipo9Qspf9XdwN7CQZqzUMlpbS/6y3if/AHkBqrV11T+TX6swvTfR/f8A8AlBDDIII9RS1ROj6eTkWyofWMlP5Un9lov+ru7yP2ExI/8AHs0c9dbxXyf+aQctPo393/BL9FUPsd6v+r1JyPSSJW/lijbqqdHs5R7qyH+Zo9vJbwf4P8mHs10kvx/yL9FUPtOop9/T0f3jnH9QKP7Sdf8AWWF4v0QMP/HSaPrNPrdeqa/QPYy6W+9F+iqH9sWQ/wBZI8R9JI2X+YqaPULKX/V3cDewkGaqOJoydlNfehOlUW8WWaKQEMMggj1FLWxmFFFFABRRRQAUUUUAFFFFABRRRQAVgeOf+RC8Qf8AYOn/APRZrfrA8c/8iF4g/wCwdP8A+izXRhP94h6r8xS2Zr6f/wAg21/64p/IVYqvp/8AyDbX/rin8hVisZ/ExhRRRUgFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAcv4S/5Cviv/sLn/0RDXUVy/hL/kK+K/8AsLn/ANEQ11FdWM/i/KP/AKSiY7BRRRXKUFFFFABRRRQAUUUUAFcvJ/yVO3/7Asv/AKPSuorl5P8Akqdv/wBgWX/0eldWF3n/AIX+RMjqKKKK5SgooooAKKKKACiiigAooooAKKKKACiiigDK8T/8inrP/XjP/wCgGneHP+RY0n/ryh/9AFN8T/8AIp6z/wBeM/8A6Aad4c/5FjSf+vKH/wBAFdX/ADC/9vfoT9o06KKK5SgoorD10X8t7p9rayIkMzv5n7x0Y4UkDcuCB9DQBuUViXWqXFjHfIscTCzERUu7EsrcHJPfikuNWvoL26Agtza20sUbHe29g+3pxgY3e+fagDcorJOoX81xObSC2MFvMIXEshVm6biOMDAPAPX2pNJuNTuLq8F01u0Edw8abAdwAxgenegDXorMt55V1nUUmyVSKJ0CuSNpL/wnoeO3Xj0qC11e8lNo8sNuIr5C8ASQlkwu4BuOeO46HjnrQBtUVi6Rqt5ePai7ggQXVr9oTynJ2Y25ByOfvDn/APXW1QAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABUMlrby/wCst4n/AN5AamopSipK0lcabWxROj6eTkWyofWMlP5Un9lov+ru7yP2ExI/XNX6Kx+q0ekUvTT8i/bVO5Q+x3q/6vUnI9JIlb+WKNuqp0ezl+qsh/mav0Uvq8Vs2vm/1bH7V9UvuRQ+06in39PR/eOcf1Ao/tJ1/wBZYXi/RAw/Qmr9FHsqi2qP5pf5IOePWK/H/Mo/2xZD/WSPEfSSJl/mKlj1Cyl/1d3A3sJBmrNQyWtvL/rLeJ/95AaLV11T+TX6sL030f3/APAJQQwyCCPUUtUTo+nk5FsqH1jJX+VJ/ZaL/q7u8j9hMSP1zRz11vFfJ/5pBy0+kn93/BL9FUPsd6v+r1JyPSSJW/lijbqqdHs5R7qyH+Zo9tJbwf4P8mHs10kvx/yL9YHjn/kQvEH/AGDp/wD0Wa0PtOop9/T0f3jnH9QKwvGt+7eBteSSxuoy2nzjJUFR8h7gmunCYmH1imndardNdfQmVKXK9vvR0mn/APINtf8Arin8hVismw1eyXTrYSSPGREmfMjZew9RV2PULKX/AFd3Ax9BIM1lLEUZTaU196D2VRK7iyzRSAhhkEEeopa0ICiiigAooooAKKKKACiiigAooooAKKKKAOX8Jf8AIV8V/wDYXP8A6IhrqK5fwl/yFfFf/YXP/oiGuorqxn8X5R/9JRMdgooorlKCiiigAooooAKKKKACuXk/5Knb/wDYFl/9HpXUVy8n/JU4P+wLL/6PSurC7z/wv8iZHUUVSstShvluZI5IGhhkKCSOZXBAAJJx93qRg+nvTrXVdOvZTFaX9rcSBdxSKZXOPXAPTkVylFuiqkeq6dLdNax39q9wpIMSzKXBHXjOeKg07X9L1V3js763llR3UxrKpb5TgnAPT3oA0qKqpqdhJeGzS+tmulyDAJVLjHX5c5pqavpr3QtU1C0a4LFREJlLkjqMZzng/lQBcorNj1/SpNTm04X9uLuJlQxGVQxYjOAM5JqaXUIo9Tt7EPC0sqszKZlDqoHBCdSD7dKALlFVbjU7C0nSC5vraGZ8bI5JVVmycDAJ5qvPr+lWup/2dcX1vDc7A+2SVV4JwByc5oA0qKq3epWNhs+2Xtvbb/u+dKqbvpk81ZBDAEEEHkEUALRVe6u47RV3hndzhI0GWY+wplvfpNN5LxSwTYyElAG4exGQaydamp8jepapycea2hV8T/8AIp6z/wBeM/8A6Aad4c/5FjSf+vKH/wBAFN8T/wDIp6z/ANeM/wD6Aad4c/5FjSf+vKH/ANAFd/8AzC/9vfoZfaNOiiiuUoK4lNLvfEuoahcTX1sq2l7JbwLJp0UpRVx0ZhnvXbVgeF/va3/2FZv5LWc1dpHZh5clOpNJXVt0n180yldeEr++kEl3rME7hduZdLhbj05pX8J38nnb9Zt284q0u7S4TvK/dLcc47Z6V1dFP2a8/vZP1yp2j/4DH/I5SXwnqE90LmXWoZJwQRI2mQlsjoc+3b0p3/CLal9t+2f25F9pznzf7Ni3ZxjOfXHGa6mij2a8/vYfXKnaP/gMf8jlI/CV9Ddm7i1e2S5JyZl0qAOf+BYz3P50ReE9Qgkkki1qBHlBV2XTIQWB6g+x711EsscETyzSLHGgLM7nAUDuSelQWupWF67paXttcNH98RSq5X64PFHs15/ew+uVO0f/AAGP+RgQ+FtSt2haDW4ImhQxxFNLhUxoeqrjoPYVa/sjxB/0NDf+AEdaUWsaZOszQ6jZyLCu6UpOpCD1bB4H1pLfWtKu5khttTs5pXztSOdWZsdcAHmj2a8/vYfXKnaP/gMf8jO/sjxB/wBDQ3/gBHR/ZHiD/oaG/wDACOtWPU7CW8a0jvrZ7lc7oVlUuMdcrnNWqPZrz+9h9cqdo/8AgMf8jA/sjxB/0NDf+AEdH9keIP8AoaG/8AI636KPZrz+9h9cqdo/+Ax/yMD+yPEH/Q0N/wCAEdH9keIP+hob/wAAI61J9QSKcwRwyzyqMssQB2/UkgVJbXUd2jNHuBU7XRxhlPoRWSlSlLkUtfVlvEVkuZxjb/DH/I5u7/t3SL3Smm1z7XDc3qW8kRtETKlWPUc/w11dYHif/XaD/wBhWL/0B6360grNoWJlz06c2km77JLr5WCiiitDjCiisyfWBB4ks9H+zSMbm3knEwI2qEKggj1O4UAadFU5dQij1O3sQ8LSyqzMpmUOqgcEJ1IPt0on1fTbWfyLjULSKbIHlyTKrc9OCe9AFyiqt3qdhYMi3l9bWxcEqJpVTdj0yarXPiDSbO/jsrnULaKWSIyr5kyqNuQO5754/GgDToqrd6lY2AQ3l7bW4f7pmlVN30yeaS51TT7PZ9qv7WDeu5fNmVdw9Rk8jkfnQBborNvNf0qwe0W5v7eMXefKdpVCkBSc5J6cdfcVNealb2mmPfedAYtm6NnmVEc44G88DPrQBcoqCW8t7a0FzdTxQRYBLySAKM+54qrd69pdnpyX8t9bm1eRY1lWVSrEtt4OcHHf6GgDRoqsmoWUtmbyO8t3tVyTMsqlBjr82cU61vbW+iMtpcw3EYOC8MgcZ9MigCeiszQNYGu6RHqAtpLcO8ieXIQSNjle3+7WnQAUUUUAFFFFABWB45/5ELxB/wBg6f8A9Fmt+sDxz/yIXiD/ALB0/wD6LNdGE/3iHqvzFLZmtp//ACDbX/rin8hT5LW3l/1kET/7yA03T/8AkG2v/XFP5Cufvtsl5fx382rxSlsWpsvO27NowV2fKTu3Z3fyxWFWKlJpq5SbWxtHR9PJyLZUPrGSn8qT+y0X/V3V5H7CYkfrmsXUWlFzffaG1VbkAfYBbebsI2DH3PkJ35zv7e1RX0d1Lc6pJI+qrNDYRvEtuZxGZsNnYF4c52/KM/TrXP8AVaPSCXpp+Rp7ap/Mb/2O9X/V6k5HpJErfyxSFtSjkWPzrKRmBKqwZCQOvc+o/OsHXJp5pIpFm1CMi1DCJIrhFkZs9Gi6Px0ZTjjjrU2o20TX2k316upRoLV45PKmmyjHYQG8o9eG5xyR9KX1eK2bXzf6th7V9UvuRtfadRT7+no/vHOP6gUf2k6/6ywvF+iBx+hNZWvzCSHS2hfVolaYFmtYZywiwd28BTjt94ZHbB5qS52afPplxG2omzIkWTLTyt8y5Xcpy2c9CRkdOKPZVFtUfzS/yQc8esV+P+Zo/wBsWQ/1kjxH0kiZf5inrqdg23F7b/McAeYMk+lc7BNO2l2X2karHZi4nE5ImEwXc3l7j/rNuMcj2ycZqLUNJsLvTIJWTU5raK+EhMxmEioeDjo5Uepz3PSnauuqfya/VivT7P7/APgHYghhkEEeopa5O70/TmsLN7O2vUsln23DRrMs2zBwefnI3bckc49qqXNnIuh6vJaXOurAB/oW152lLbewILld3r79sU1Kt1ivk/8AgILQ6P8AD/gnb0VgaRYTrbma21bUXL43/b4nzkdwrhdv4ACp786lDZOXngMXAd40ZXVSQCRyR0rOpiXTi5Sg7L0/zKjSUmkpLX1/yNUTRNIY1kQuOqhhkfhT6yr+ysrfSpJYoo42iQvFInDBgOOfc/nWjbtI9tE0q7ZGQFl9DjkVVOrJzcJrW19P6/4cU4RUeaJzfhL/AJCviv8A7C5/9EQ11Fcv4S/5Cviv/sLn/wBEQ11FeljP4vyj/wCkowjsFFFFcpQUUUUAFFFFABRRRQAVy8uf+FowbQCf7ElwCcf8tkrqK5eT/kqdv/2BZf8A0eldWF3n/hf5EyL+mw6tBeX8tza2SpcSeavl3TuQQiqAQYx/d6/pVfTtJv7SLQ0eGzX7GjrP5czHquPl+QZyeTnH410NFcpRzen6RqMGo20jQ29vFG7vKqXLSoxYH/Vo65Q5OeCO45zV7T4dQsriWA29u1s9xJL53nkNh2LY27eozjr+PataigDljo+syaja3EpjlMF0ZXke+kCsnzABYgm1SAw9+OvOanOk6j9nuVWCyEkmppdA+c3KBlPJ2fewuMcj3roqKAMkwahbavcz21vbzRXIjyZJzGUKjB4CnNN1KDVZdUs5rW2sngt2Zsy3TIzZUr0EZHf1rYooA5jXtI1fUzeRRMjRTRbIN15JCkR24O5UX58n1JGOw76E8GoxahDe29vbyubbyZI2nKBWznIOw5HXsD7Vr0UAc5qWjXs2qNfw5YzQJFJEl40OwqSeCFO4Hd6Dp3zWxplp9h0y2tQgQRRhNokLgY7BiAT+Qq3RQBn3hNtqEN4yM8IjaNyq5KZIO7HpxioJnGrXVuLV5VjhYu1wq7ccEbQSOevNa9Fcs8M5Nq/ut3a66W6/Lt8zaNWyTtqtP6RzviSwkXwtq7HULtsWUxwSuD8h/wBmneHtPkbw1pTf2hdrmzhOAVwPkH+zVvxP/wAinrP/AF4z/wDoBp3hz/kWNJ/68of/AEAV2/Vqf1W2vxd329SPay5v+AiX+zpP+glef99J/wDE0f2dJ/0Erz/vpP8A4mr1Fcn1an5/e/8AMr20/wCkij/Z0n/QSvP++k/+JrN8KjB1sZJxqk3J+i10FYHhf72t/wDYVm/ktONKMJLl/Nm8JuVCpfy/M36KKK3OMr3l7FYQiWZJ2UnGILd5m/75QE/jiqH/AAktj/zw1X/wU3X/AMbrXooAxZtYh1G3ms7SC9+0TRskf2iwuIY8kH7ztHgCootL1KFrIwraQmDTntiyyE7JDt24GwZUbe+OvSt+igDko9E1j7S0zJCS1nLbsZ9QkkLO23DYKYUZXoAOvTtU9voV9FPDIUtF2yW7tslbOEjKMB8nbOR6+1dNRQByVj4eurA2sc6vNb2cnmrL9skOcZ5EQT73PIzzk884rW/4SSx/54ar/wCCq6/+N1r0UAZH/CSWP/PDVf8AwVXX/wAbrVRxIiuAwDAEblIP4g8inUUAZcdwmm3V0t0GVJZPMSXaSGyBxkdCMUyG2kvry4vEmuLaJwqoFAUvjPzEEH14rXork+q3aUn7qbaWz1v1v5m/trapav8ArY5TxJYyJNoeb+6bdqcY+Yrx8j8j5etbv9nSf9BK8/76T/4ms7xP/rtB/wCwrF/6A9b9OOGp8z3+9/5m9arL2NP59F3KP9nSf9BK8/76T/4mj+zpP+glef8AfSf/ABNXqKr6tT8/vf8Amc3tp/0kVIrJ4pVc311IB/C5XB/JaslELhyqlwCA2OQD1/kKdRWsKcYK0SJSctWY+pQarLqlnNa21k8FuzNmW6ZGbKlegjI7+tQ3ul31xc6xIkNpi7slt4WaVs7hvzu+Thfn7Z6dK3qKsk5vUdK1G4ZXjt4PN+yrCJUvHjIbnIYbCrp0xkevHNW5bTULe5sbmCK3uJIrZoJFLmIZJQ7hhTx8p4962aKAMLVLLVbyaIoFMJhw0KXrwhZO5LKu5xjjHH054j0/StQ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+                "/page/236/Figure/3": 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    Figure C.6: Object diagram.
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    Figure C.6: Object diagram.
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    lumpy = Lumpy()\nlumpy.make_reference()\nbox = Rectangle()\nbox.width = 100.0\nbox.height = 200.0\nbox.corner = Point()\nbox.corner.x = 0.0\nbox.corner.y = 0.0
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    lumpy = Lumpy()\nlumpy.make_reference()\nbox = Rectangle()\nbox.width = 100.0\nbox.height = 200.0\nbox.corner = Point()\nbox.corner.x = 0.0\nbox.corner.y = 0.0\nbox2 = copy.copy(box)
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    box2 = copy.copy(box)
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    lumpy.object_diagram()
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    lumpy.object_diagram()
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    Figure C.5 shows the result. copy.copy make a shallow copy, so box and box2 have their own width and height, but they share the same embedded Point object. This kind of sharing is usually fine with immutable objects, but with mutable types, it is highly errorprone.
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    Figure C.5 shows the result. copy.copy make a shallow copy, so box and box2 have their own width and height, but they share the same embedded Point object. This kind of sharing is usually fine with immutable objects, but with mutable types, it is highly errorprone.
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    C.4 Function and class objects
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    C.4 Function and class objects
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+                "/page/237/Figure/1": 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    Figure C.7: Class diagram.
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    Figure C.7: Class diagram.
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    But if you are passing functions and classes as parameters, you might want them to appear. This example shows what that looks like; you can download it from http://thinkpython. com/code/lumpy_demo6.py.
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    But if you are passing functions and classes as parameters, you might want them to appear. This example shows what that looks like; you can download it from http://thinkpython. com/code/lumpy_demo6.py.
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    import copy\nfrom swampy.Lumpy import Lumpy\nlumpy = Lumpy()\nlumpy.make_reference()\nclass Point(object):\n    \"\"\"Represents a point in 2-D space.\"\"\"\nclass Rectangle(object):\n    \"\"\"Represents a rectangle.\"\"\"\ndef instantiate(constructor):\n    \"\"\"Instantiates a new object.\"\"\"\n    obj = constructor()\n    lumpy.object_diagram()\n    return obj
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    Figure C.6 shows the result. Since we invoke object_diagram inside a function, we get a stack diagram with a frame for the module-level variables and for the invocation of instantiate.
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    Figure C.6 shows the result. Since we invoke object_diagram inside a function, we get a stack diagram with a frame for the module-level variables and for the invocation of instantiate.
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+                "/page/238/Figure/1": 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    Figure C.8: Class diagram.
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    Figure C.8: Class diagram.
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    Class diagrams are different. They show the classes that make up a program and the relationships between them. They are timeless in the sense that they describe the program as a whole, not any particular point in time. For example, if an instance of Class A generally contains a reference to an instance of Class B, we say there is a \"HAS-A relationship\" between those classes.
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    Here's an example that shows a HAS-A relationship. You can download it from http: //thinkpython.com/code/lumpy_demo7.py.
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    Here's an example that shows a HAS-A relationship. You can download it from http: //thinkpython.com/code/lumpy_demo7.py.
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    from swampy.Lumpy import Lumpy
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    from swampy.Lumpy import Lumpy
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    lumpy = Lumpy()\nlumpy.make_reference()\nbox = Rectangle()\nbox.width = 100.0\nbox.height = 200.0\nbox.corner = Point()\nbox.corner.x = 0.0\nbox.corner.y = 0.0
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    box = Rectangle()\nbox.width = 100.0\nbox.height = 200.0\nbox.corner = Point()\nbox.corner.x = 0.0\nbox.corner.y = 0.0
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    lumpy.class_diagram()
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    lumpy.class_diagram()
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    Figure C.7 shows the result. Each class is represented with a box that contains the name of the class, any methods the class provides, any class variables, and any instance variables. In this example, Rectangle and Point have instance variables, but no methods or class variables.
    ",
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    Figure C.7 shows the result. Each class is represented with a box that contains the name of the class, any methods the class provides, any class variables, and any instance variables. In this example, Rectangle and Point have instance variables, but no methods or class variables.
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    The arrow from Rectangle to Point shows that Rectangles contain an embedded Point. In addition, Rectangle and Point both inherit from object, which is represented in the diagram with a triangle-headed arrow.
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    218 Appendix C. Lumpy
    ",
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               71.13372802734375
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    ",
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    Here's a more complex example using my solution to Exercise 18.6. You can download the code from http://thinkpython.com/code/lumpy_demo8.py; you will also need http: //thinkpython.com/code/PokerHand.py.
    ",
+          "html": "
    Here's a more complex example using my solution to Exercise 18.6. You can download the code from http://thinkpython.com/code/lumpy_demo8.py; you will also need http: //thinkpython.com/code/PokerHand.py.
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    from swampy.Lumpy import Lumpy
    ",
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    from swampy.Lumpy import Lumpy\nfrom PokerHand import *\nlumpy = Lumpy()\nlumpy.make_reference()\ndeck = Deck()\nhand = PokerHand()\ndeck.move_cards(hand, 7)
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    from PokerHand import *
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    lumpy = Lumpy() lumpy.make_reference() deck = Deck()
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           "block_type": "Code",
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    hand = PokerHand()\ndeck.move_cards(hand, 7)
    ",
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    lumpy.class_diagram()
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    lumpy.class_diagram()
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    Figure C.8 shows the result. PokerHand inherits from Hand, which inherits from Deck. Both Deck and PokerHand have Cards.
    ",
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    Figure C.8 shows the result. PokerHand inherits from Hand, which inherits from Deck. Both Deck and PokerHand have Cards.
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    This diagram does not show that Hand also has cards, because in the program there are no instances of Hand. This example demonstrates a limitation of Lumpy; it only knows about the attributes and HAS-A relationships of objects that are instantiated.
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diff --git a/data/examples/markdown/multicolcnn/multicolcnn.md b/data/examples/markdown/multicolcnn/multicolcnn.md
index 55bd9749..75c952c7 100644
--- a/data/examples/markdown/multicolcnn/multicolcnn.md
+++ b/data/examples/markdown/multicolcnn/multicolcnn.md
@@ -2,236 +2,227 @@
 
 Diptodip Deb Georgia Institute of Technology diptodipdeb@gatech.edu
 
-# Jonathan Ventura University of Colorado Colorado Springs
-
-jventura@uccs.edu
-
 # Abstract
 
 *We propose the use of dilated filters to construct an aggregation module in a multicolumn convolutional neural network for perspective-free counting. Counting is a common problem in computer vision (e.g. traffic on the street or pedestrians in a crowd). Modern approaches to the counting problem involve the production of a density map via regression whose integral is equal to the number of objects in the image. However, objects in the image can occur at different scales (e.g. due to perspective effects) which can make it difficult for a learning agent to learn the proper density map. While the use of multiple columns to extract multiscale information from images has been shown before, our approach aggregates the multiscale information gathered by the multicolumn convolutional neural network to improve performance. Our experiments show that our proposed network outperforms the state-of-the-art on many benchmark datasets, and also that using our aggregation module in combination with a higher number of columns is beneficial for multiscale counting.*
 
 # 1. Introduction
 
-Learning to count the number of objects in an image is a deceptively difficult problem with many interesting applications, such as surveillance [20], traffic monitoring [14] and medical image analysis [22]. In many of these application areas, the objects to be counted vary widely in appearance, size and shape, and labeled training data is typically sparse. These factors pose a significant computer vision and machine learning challenge.
+Learning to count the number of objects in an image is a deceptively difficult problem with many interesting applications, such as surveillance [\[20\]](#page-8-0), traffic monitoring [\[14\]](#page-8-1) and medical image analysis [\[22\]](#page-8-2). In many of these application areas, the objects to be counted vary widely in appearance, size and shape, and labeled training data is typically sparse. These factors pose a significant computer vision and machine learning challenge.
 
-Lempitsky et al. [15] showed that it is possible to learn to count without learning to explicitly detect and localize individual objects. Instead, they propose learning to predict a density map whose integral over the image equals the number of objects in the image. This approach has been adopted by many later works (Cf. [18, 28]).
+Lempitsky et al. [\[15\]](#page-8-3) showed that it is possible to learn to count without learning to explicitly detect and localize individual objects. Instead, they propose learning to predict a density map whose integral over the image equals the number of objects in the image. This approach has been adopted by many later works (Cf. [\[18,](#page-8-4) [28\]](#page-9-0)).
 
 However, in many counting problems, such as those
 
-counting cells in a microscope image, pedestrians in a crowd, or vehicles in a traffic jam, regressors trained on a single image scale are not reliable [18]. This is due to a variety of challenges including overlap of objects and perspective effects which cause significant variance in object shape, size and appearance.
+Jonathan Ventura University of Colorado Colorado Springs jventura@uccs.edu
+
+> counting cells in a microscope image, pedestrians in a crowd, or vehicles in a traffic jam, regressors trained on a single image scale are not reliable [\[18\]](#page-8-4). This is due to a variety of challenges including overlap of objects and perspective effects which cause significant variance in object shape, size and appearance.
 
-The most successful recent approaches address this issue by explicitly incorporating multi-scale information in the network [18,28]. These approaches either combine multiple networks which take input patches of different sizes [18] or combine multiple filtering paths ("columns") which have different size filters [28].
+> The most successful recent approaches address this issue by explicitly incorporating multi-scale information in the network [\[18,](#page-8-4)[28\]](#page-9-0). These approaches either combine multiple networks which take input patches of different sizes [\[18\]](#page-8-4) or combine multiple filtering paths ("columns") which have different size filters [\[28\]](#page-9-0).
 
-Following on the intuition that multiscale integration is key to achieving good counting performance, we propose to incorporate dilated filters [25] into a multicolumn convolutional neural network design [28]. Dilated filters exponentially increase the network's receptive field without an exponential increase in parameters, allowing for efficient use of multiscale information. Convolutional neural networks with dilated filters have proven to provide competitive performance in image segmentation where multiscale analysis is also critical [25, 26]. By incorporating dilated filters into the multicolumn network design, we greatly increase the ability of the network to selectively aggregate multiscale information, without the need for explicit perspective maps during training and testing. We propose the "aggregated multicolumn dilated convolution network" or AMDCN which uses dilations to aggregate multiscale information. Our extensive experimental evaluation shows that this proposed network outperforms previous methods on many benchmark datasets.
+> Following on the intuition that multiscale integration is key to achieving good counting performance, we propose to incorporate dilated filters [\[25\]](#page-8-5) into a multicolumn convolutional neural network design [\[28\]](#page-9-0). Dilated filters exponentially increase the network's receptive field without an exponential increase in parameters, allowing for efficient use of multiscale information. Convolutional neural networks with dilated filters have proven to provide competitive performance in image segmentation where multiscale analysis is also critical [\[25,](#page-8-5) [26\]](#page-8-6). By incorporating dilated filters into the multicolumn network design, we greatly increase the ability of the network to selectively aggregate multiscale information, without the need for explicit perspective maps during training and testing. We propose the "aggregated multicolumn dilated convolution network" or AMDCN which uses dilations to aggregate multiscale information. Our extensive experimental evaluation shows that this proposed network outperforms previous methods on many benchmark datasets.
 
 # 2. Related Work
 
-Counting using a supervised regressor to formulate a density map was first shown by [15]. In this paper, Lempitsky et al. show that the minimal annotation of a single dot blurred by a Gaussian kernel produces a sufficient density map to train a network to count. All of the counting methods that we examine as well as the method we use in
+Counting using a supervised regressor to formulate a density map was first shown by [\[15\]](#page-8-3). In this paper, Lempitsky et al. show that the minimal annotation of a single dot blurred by a Gaussian kernel produces a sufficient density map to train a network to count. All of the counting methods that we examine as well as the method we use in
 
 ![](_page_1_Figure_0.jpeg)
 
-Figure 1. Fully convolutional architecture diagram (not to scale). Arrows show separate columns that all take the same input. At the end of the columns, the feature maps are merged (concatenated) together and passed to another series of dilated convolutions: the aggregator, which can aggregate the multiscale information collected by the columns [25]. The input image is I with C channels. The output single channel density map is D, and integrating over this map (summing the pixels) results in the final count. Initial filter sizes are labeled with brackets or lines. Convolution operations are shown as flat rectangles, feature maps are shown as prisms. The number below each filter represents the dilation rate (1 means no dilation).
+Figure 1. Fully convolutional architecture diagram (not to scale). Arrows show separate columns that all take the same input. At the end of the columns, the feature maps are merged (concatenated) together and passed to another series of dilated convolutions: the aggregator, which can aggregate the multiscale information collected by the columns [\[25\]](#page-8-5). The input image is I with C channels. The output single channel density map is D, and integrating over this map (summing the pixels) results in the final count. Initial filter sizes are labeled with brackets or lines. Convolution operations are shown as flat rectangles, feature maps are shown as prisms. The number below each filter represents the dilation rate (1 means no dilation).
 
-our paper follow this method of producing a density map via regression. This is particularly advantageous because a sufficiently accurate regressor can also locate the objects in the image via this method. However, the Lempitsky paper ignores the issue of perspective scaling and other scaling issues. The work of [27] introduces CNNs (convolutional neural networks) for the purposes of crowd counting, but performs regression on similarly scaled image patches.
+our paper follow this method of producing a density map via regression. This is particularly advantageous because a sufficiently accurate regressor can also locate the objects in the image via this method. However, the Lempitsky paper ignores the issue of perspective scaling and other scaling issues. The work of [\[27\]](#page-8-7) introduces CNNs (convolutional neural networks) for the purposes of crowd counting, but performs regression on similarly scaled image patches.
 
-These issues are addressed by the work of [18]. Rubio et al. show that a fully convolutional neural network can be used to produce a supervised regressor that produces density maps as in [15]. They further demonstrate a method dubbed HydraCNN which essentially combines multiple convolutional networks that take in differently scaled image patches in order to incorporate multiscale, global information from the image. The premise of this method is that a single regressor will fail to accurately represent the difference in values of the features of an image caused by perspective shifts (scaling effects) [18].
+These issues are addressed by the work of [\[18\]](#page-8-4). Rubio et al. show that a fully convolutional neural network can be used to produce a supervised regressor that produces density maps as in [\[15\]](#page-8-3). They further demonstrate a method dubbed HydraCNN which essentially combines multiple convolutional networks that take in differently scaled image patches in order to incorporate multiscale, global information from the image. The premise of this method is that a single regressor will fail to accurately represent the difference in values of the features of an image caused by perspective shifts (scaling effects) [\[18\]](#page-8-4).
 
-However, the architectures of both [18] and [27] are not fully convolutional due to requiring multiple image patches and, as discussed in [25], the experiments of [11, 17] and [9, 12, 16] leave it unclear as to whether rescaling patches of the image is truly necessary in order to solve dense prediction problems via convolutional neural networks. Moreover, these approaches seem to saturate in performance at three columns, which means the network is extracting information from fewer scales. The work of [25] proposes the use of dilated convolutions as a simpler alternative that does not require sampling of rescaled image patches to provide global, scale-aware information to the network. A fully convolutional approach to multiscale counting has been proposed by [28], in which a multicolumn convolutional network gathers features of different scales by using convolutions of increasing kernel sizes from column to column instead of scaling image patches. Further, DeepLab has used dilated convolutions in multiple columns to extract scale information for segmentation [8]. We build on these approaches with our aggregator module as described in Section 3.1, which should allow for extracting information from more scales.
+However, the architectures of both [\[18\]](#page-8-4) and [\[27\]](#page-8-7) are not fully convolutional due to requiring multiple image patches and, as discussed in [\[25\]](#page-8-5), the experiments of [\[11,](#page-8-8) [17\]](#page-8-9) and [\[9,](#page-8-10) [12,](#page-8-11) [16\]](#page-8-12) leave it unclear as to whether rescaling patches of the image is truly necessary in order to solve dense prediction problems via convolutional neural networks. Moreover, these approaches seem to saturate in performance at three columns, which means the network is extracting information from fewer scales. The work of [\[25\]](#page-8-5) proposes the use of dilated convolutions as a simpler alternative that does not require sampling of rescaled image patches to provide global, scale-aware information to the network. A fully convolutional approach to multiscale counting has been proposed by [\[28\]](#page-9-0), in which a multicolumn convolutional network gathers features of different scales by using convolutions of increasing kernel sizes from column to column instead of scaling image patches. Further, DeepLab has used dilated convolutions in multiple columns to extract scale information for segmentation [\[8\]](#page-8-13). We build on these approaches with our aggregator module as described in Section [3.1,](#page-2-0) which should allow for extracting information from more scales.
 
-It should be noted that other methods of counting exist, including training a network to recognize deep object features via only providing the counts of the objects of interest in an image [21] and using CNNs (convolutional neural networks) along with boosting in order to improve the results
+It should be noted that other methods of counting exist, including training a network to recognize deep object features via only providing the counts of the objects of interest in an image [\[21\]](#page-8-14) and using CNNs (convolutional neural networks) along with boosting in order to improve the results
 
 ![](_page_2_Picture_0.jpeg)
 
 Figure 2. UCF sample results. Left: input counting image. Middle: Ground truth density map. Right: AMDCN prediction of density map on test image. The network never saw these images during training. All density maps are one channel only (i.e. grayscale), but are colored here for clarity.
 
-of regression for production of density maps [24]. In the same spirit, [4] combines deep and shallow convolutions within the same network, providing accurate counting of dense objects (e.g. the UCF50 crowd dataset).
+of regression for production of density maps [\[24\]](#page-8-15). In the same spirit, [\[4\]](#page-8-16) combines deep and shallow convolutions within the same network, providing accurate counting of dense objects (e.g. the UCF50 crowd dataset).
 
-In this paper, however, we aim to apply the dilated convolution method of [25], which has shown to be able to incorporate multiscale perspective information without using multiple inputs or a complicated network architecture, as well as the multicolumn approach of [8, 28] to aggregate multiscale information for the counting problem.
+In this paper, however, we aim to apply the dilated convolution method of [\[25\]](#page-8-5), which has shown to be able to incorporate multiscale perspective information without using multiple inputs or a complicated network architecture, as well as the multicolumn approach of [\[8,](#page-8-13) [28\]](#page-9-0) to aggregate multiscale information for the counting problem.
 
 # 3. Method
 
-### 3.1. Dilated Convolutions for Multicolumn Networks
+## 3.1. Dilated Convolutions for Multicolumn Networks
 
-We propose the use of dilated convolutions as an attractive alternative to the architecture of the HydraCNN [18], which seems to saturate in performance at 3 or more columns. We refer to our proposed network as the aggregated multicolumn dilated convolution network1 , henceforth shortened as the AMDCN. The architecture of the AMDCN is inspired by the multicolumn counting network of [28]. Extracting features from multiple scales is a good idea when attempting to perform perspective-free counting and increasing the convolution kernel size across columns is an efficient method of doing so. However, the number of parameters increases exponentially as larger kernels are used in these columns to extract features at larger scales. Therefore, we propose using dilated convolutions rather than larger kernels.
+We propose the use of dilated convolutions as an attractive alternative to the architecture of the HydraCNN [\[18\]](#page-8-4), which seems to saturate in performance at 3 or more columns. We refer to our proposed network as the aggregated multicolumn dilated convolution network[1](#page-2-1) , henceforth shortened as the AMDCN. The architecture of the AMDCN is inspired by the multicolumn counting network of [\[28\]](#page-9-0). Extracting features from multiple scales is a good idea when attempting to perform perspective-free counting and increasing the convolution kernel size across columns is an efficient method of doing so. However, the number of parameters increases exponentially as larger kernels are used in these columns to extract features at larger scales. Therefore, we propose using dilated convolutions rather than larger kernels.
 
-Dilated convolutions, as discussed in [25], allow for the exponential increase of the receptive field with a linear increase in the number of parameters with respect to each hidden layer.
+Dilated convolutions, as discussed in [\[25\]](#page-8-5), allow for the exponential increase of the receptive field with a linear increase in the number of parameters with respect to each hidden layer.
 
-In a traditional 2D convolution, we define a real valued function F : Z 2 → R, an input Ωr = [−r, r] 2 ∈ Z 2 , and a filter function k : Ωr → R. In this case, a convolution operation as defined in [25] is given by
+In a traditional 2D convolution, we define a real valued function F : Z 2 → R, an input Ωr = [−r, r] 2 ∈ Z 2 , and a filter function k : Ωr → R. In this case, a convolution operation as defined in [\[25\]](#page-8-5) is given by
 
-$$(F*k)({\bf p})=\sum_{{\bf s}+{\bf t}={\bf p}}F({\bf s})k({\bf t}).\tag{1}$$
+$$(F*k)(\mathbf{p}) = \sum_{\mathbf{s}+\mathbf{t}=\mathbf{p}} F(\mathbf{s})k(\mathbf{t}). \quad (1)$$
 
-A dilated convolution is essentially a generalization of the traditional 2D convolution that allows the operation to skip some inputs. This enables an increase in the size of the filter (i.e. the size of the receptive field) without losing resolution. Formally, we define from [25] the dilated convolution as
+A dilated convolution is essentially a generalization of the traditional 2D convolution that allows the operation to skip some inputs. This enables an increase in the size of the filter (i.e. the size of the receptive field) without losing resolution. Formally, we define from [\[25\]](#page-8-5) the dilated convolution as
 
-$$(F*_{l}k)({\bf p})=\sum_{{\bf s}+l{\bf t}={\bf p}}F({\bf s})k({\bf t})\tag{2}$$
+$$(F*_{l}k)(\mathbf{p})=\sum_{\mathbf{s}+l\mathbf{t}=\mathbf{p}}F(\mathbf{s})k(\mathbf{t})\tag{2}$$
 
 where l is the index of the current layer of the convolution.
 
-Using dilations to construct the aggregator in combination with the multicolumn idea will allow for the construction of a network with more than just 3 or 4 columns as in [28] and [8], because the aggregator should prevent the saturation of performance with increasing numbers of columns. Therefore the network will be able to extract useful features from more scales. We take advantage of dilations within the columns as well to provide large receptive fields with fewer parameters.
+Using dilations to construct the aggregator in combination with the multicolumn idea will allow for the construction of a network with more than just 3 or 4 columns as in [\[28\]](#page-9-0) and [\[8\]](#page-8-13), because the aggregator should prevent the saturation of performance with increasing numbers of columns. Therefore the network will be able to extract useful features from more scales. We take advantage of dilations within the columns as well to provide large receptive fields with fewer parameters.
 
-Looking at more scales should allow for more accurate regression of the density map. However, because not all scales will be relevant, we extend the network beyond a simple 1 × 1 convolution after the merged columns. Instead, we construct a second part of the network, the aggregator, which sets our method apart from [28], [8], and other multicolumn networks. This aggregator is another series of dilated convolutions that should appropriately consolidate the multiscale information collected by the columns. This is a capability of dilated convolutions observed by [25]. While papers such as [28] and [8] have shown that multiple columns and dilated columns are useful in extracting multiscale information, we argue in this paper that the simple aggregator module built using dilated convolutions is able to effectively make use multiscale information from multiple columns. We show compelling evidence for these claims in Section 4.5.
+Looking at more scales should allow for more accurate regression of the density map. However, because not all scales will be relevant, we extend the network beyond a simple 1 × 1 convolution after the merged columns. Instead, we construct a second part of the network, the aggregator, which sets our method apart from [\[28\]](#page-9-0), [\[8\]](#page-8-13), and other multicolumn networks. This aggregator is another series of dilated convolutions that should appropriately consolidate the multiscale information collected by the columns. This is a capability of dilated convolutions observed by [\[25\]](#page-8-5). While papers such as [\[28\]](#page-9-0) and [\[8\]](#page-8-13) have shown that multiple columns and dilated columns are useful in extracting multiscale information, we argue in this paper that the simple aggregator module built using dilated convolutions is able to effectively make use multiscale information from multiple columns. We show compelling evidence for these claims in Section [4.5.](#page-5-0)
 
-The network as shown in Figure 1 contains 5 columns. Note that dilations allow us to use more columns for counting than [28] or [8]. Each column looks at a larger scale than the previous (the exact dilations can also be seen in Figure 1). There are 32 feature maps for each convolution, and all inputs are zero padded prior to each convolution in order to maintain the same data shape from input to output. That is, an image input to this network will result in a density map of the same dimensions. All activations in the specified network are ReLUs. Our input pixel values are floating point 32 bit values from 0 to 1. We center our inputs at 0 by subtracting the per channel mean from each channel. When
+The network as shown in Figure [1](#page-1-0) contains 5 columns. Note that dilations allow us to use more columns for counting than [\[28\]](#page-9-0) or [\[8\]](#page-8-13). Each column looks at a larger scale than the previous (the exact dilations can also be seen in Figure [1)](#page-1-0). There are 32 feature maps for each convolution, and all inputs are zero padded prior to each convolution in order to maintain the same data shape from input to output. That is, an image input to this network will result in a density map of the same dimensions. All activations in the specified network are ReLUs. Our input pixel values are floating point 32 bit values from 0 to 1. We center our inputs at 0 by subtracting the per channel mean from each channel. When
 
-1 Implementation available on https://github.com/ diptodip/counting.
+1 Implementation available on [https://github.com/](https://github.com/diptodip/counting) [diptodip/counting](https://github.com/diptodip/counting).
 
 training, we use a scaled mean absolute error for our loss function:
 
-$$L=\frac{1}{n}\sum_{i=1}^{n}|\hat{y}_{i}-\gamma y_{i}|\tag{3}$$
+$$L = \frac{1}{n} \sum_{i=1}^{n} |\hat{y}_i - \gamma y_i| \qquad\qquad (3)$$
 
-where γ is the scale factor, yˆi is the prediction, yi is the true value, and n is the number of pixels. We use a scaled mean absolute error because the target values are so small that it is numerically unstable to regress to these values. At testing time, when retrieving the output density map from the network, we scale the pixel values by γ −1 to obtain the correct value. This approach is more numerically stable and avoids having the network learn to output only zeros by weighting the nonzero values highly. For all our datasets, we set γ = 255.
+where γ is the scale factor, $\hat{y}_i $is the prediction, $y_i $is the true value, and $n$ is the number of pixels. We use a scaled mean absolute error because the target values are so small that it is numerically unstable to regress to these values. At testing time, when retrieving the output density map from the network, we scale the pixel values by $γ^{-1} $to obtain the correct value. This approach is more numerically stable and avoids having the network learn to output only zeros by weighting the nonzero values highly. For all our datasets, we set $γ = 255. $
 
-### 3.2. Experiments
+## 3.2. Experiments
 
-We evaluated the performance of dilated convolutions against various counting methods on a variety of common counting datasets: UCF50 crowd data, TRANCOS traffic data [18], UCSD crowd data [5], and WorldExpo crowd data [27]. For each of these data sets, we used labels given by the corresponding density map for each image. An example of this is shown in Figure 2. We have performed experiments on the four different splits of the UCSD data as used in [18] and the split of the UCSD data as used in [28] (which we call the original split). We also evaluated the performance of our network on the TRANCOS traffic dataset [14]. We have also experimented with higher density datasets for crowd counting, namely WorldExpo and UCF.
+We evaluated the performance of dilated convolutions against various counting methods on a variety of common counting datasets: UCF50 crowd data, TRANCOS traffic data [\[18\]](#page-8-4), UCSD crowd data [\[5\]](#page-8-17), and WorldExpo crowd data [\[27\]](#page-8-7). For each of these data sets, we used labels given by the corresponding density map for each image. An example of this is shown in Figure [2.](#page-2-2) We have performed experiments on the four different splits of the UCSD data as used in [\[18\]](#page-8-4) and the split of the UCSD data as used in [\[28\]](#page-9-0) (which we call the original split). We also evaluated the performance of our network on the TRANCOS traffic dataset [\[14\]](#page-8-1). We have also experimented with higher density datasets for crowd counting, namely WorldExpo and UCF.
 
-We have observed that multicolumn dilations produce density maps (and therefore counts) that often have lower loss than those of HydraCNN [18] and [28]. We measure density map regression loss via a scaled mean absolute error loss during training. We compare accuracy of the counts via mean absolute error for the crowd datasets and the GAME metric in the TRANCOS dataset as explained in Section 3.2.2. Beyond the comparison to HydraCNN, we will also compare to other recent convolutional counting methods, especially those of [21], [24], and [4] where possible.
+We have observed that multicolumn dilations produce density maps (and therefore counts) that often have lower loss than those of HydraCNN [\[18\]](#page-8-4) and [\[28\]](#page-9-0). We measure density map regression loss via a scaled mean absolute error loss during training. We compare accuracy of the counts via mean absolute error for the crowd datasets and the GAME metric in the TRANCOS dataset as explained in Section [3.2.2.](#page-3-0) Beyond the comparison to HydraCNN, we will also compare to other recent convolutional counting methods, especially those of [\[21\]](#page-8-14), [\[24\]](#page-8-15), and [\[4\]](#page-8-16) where possible.
 
-For all datasets, we generally use patched input images and ground truth density maps produced by summing a Gaussian of a fixed size (σ) for each object for training. This size varies from dataset to dataset, but remains constant within a dataset with the exception of cases in which a perspective map is used. This is explained per dataset. All experiments were performed using Keras with the Adam optimizer [10]. The learning rates used are detailed per dataset. For testing, we also use patches that can either be directly pieced together or overlapped and averaged except in the case of UCF, for which we run our network on the full image.
+For all datasets, we generally use patched input images and ground truth density maps produced by summing a Gaussian of a fixed size (σ) for each object for training. This size varies from dataset to dataset, but remains constant within a dataset with the exception of cases in which a perspective map is used. This is explained per dataset. All experiments were performed using Keras with the Adam optimizer [\[10\]](#page-8-18). The learning rates used are detailed per dataset. For testing, we also use patches that can either be directly pieced together or overlapped and averaged except in the case of UCF, for which we run our network on the full image.
 
-Furthermore, we performed a set of experiments in which we varied the number of columns from 1 to 5 (simply by including or not including the columns as specified in Figure 1, starting with the smallest filter column and adding larger filter columns one by one). Essentially, the network is allowed to extract information at larger and larger scales in addition to the smaller scales as we include each column. We then performed the same set of experiments, varying the number of columns, but with the aggregator module removed. We perform these experiments on the original split of UCSD as specified in Section 3.2.3 and [5], the TRAN-COS dataset, and the WorldExpo dataset because these are relatively large and well defined datasets. We limit the number of epochs to 10 for all of these sets of experiments in order to control for the effect of learning time, and also compare all results using MAE for consistency. These experiments are key to determining the efficacy of the aggregator in effectively combining multiscale information and in providing evidence to support the use of multiple columns to extract multiscale information from images. We report the results of these ablation studies in Section 4.5.
+Furthermore, we performed a set of experiments in which we varied the number of columns from 1 to 5 (simply by including or not including the columns as specified in Figure [1,](#page-1-0) starting with the smallest filter column and adding larger filter columns one by one). Essentially, the network is allowed to extract information at larger and larger scales in addition to the smaller scales as we include each column. We then performed the same set of experiments, varying the number of columns, but with the aggregator module removed. We perform these experiments on the original split of UCSD as specified in Section [3.2.3](#page-4-0) and [\[5\]](#page-8-17), the TRAN-COS dataset, and the WorldExpo dataset because these are relatively large and well defined datasets. We limit the number of epochs to 10 for all of these sets of experiments in order to control for the effect of learning time, and also compare all results using MAE for consistency. These experiments are key to determining the efficacy of the aggregator in effectively combining multiscale information and in providing evidence to support the use of multiple columns to extract multiscale information from images. We report the results of these ablation studies in Section [4.5.](#page-5-0)
 
 #### 3.2.1 UCF50 Crowd Counting
 
 UCF is a particularly challenging crowd counting dataset. There are only 50 images in the whole dataset and they are all of varying sizes and from different scenes. The number of people also varies between images from less than 100 to the thousands. The average image has on the order of 1000 people. The difficulty is due to the combination of the very low number of images in the dataset and the fact that the images are all of varying scenes, making high quality generalization crucial. Furthermore, perspective effects are particularly noticeable for many images in this dataset. Despite this, there is no perspective information available for this dataset.
 
-We take 1600 random patches of size 150 × 150 for the training. For testing, we do not densely scan the image as in [18] but instead test on the whole image. In order to standardize the image sizes, we pad each image out with zeros until all images are 1024 × 1024. We then suppress output in the regions where we added padding when testing. This provides a cleaner resulting density map for these large crowds. The ground truth density maps are produced by annotating each object with a Gaussian of σ = 15.
+We take 1600 random patches of size 150 × 150 for the training. For testing, we do not densely scan the image as in [\[18\]](#page-8-4) but instead test on the whole image. In order to standardize the image sizes, we pad each image out with zeros until all images are 1024 × 1024. We then suppress output in the regions where we added padding when testing. This provides a cleaner resulting density map for these large crowds. The ground truth density maps are produced by annotating each object with a Gaussian of σ = 15.
 
-#### 3.2.2 TRANCOS Traffic Counting
+#### 3.2.2 TRANCOS Traffic Counting
 
-TRANCOS is a traffic counting dataset that comes with its own metric [14]. This metric is known as GAME, which stands for Grid Average Mean absolute Error. GAME splits a given density map into 4 L grids, or subarrays, and obtains a mean absolute error within each grid separately. The value of L is a parameter chosen by the user. These individual errors are summed to obtain the final error for a particular image. The intuition behind this metric is that it is desirable to penalize a density map whose overall count might match the ground truth, but whose shape does not match the ground truth [14]. More formally, we define
+TRANCOS is a traffic counting dataset that comes with its own metric [\[14\]](#page-8-1). This metric is known as GAME, which stands for Grid Average Mean absolute Error. GAME splits a given density map into 4 L grids, or subarrays, and obtains a mean absolute error within each grid separately. The value of L is a parameter chosen by the user. These individual errors are summed to obtain the final error for a particular image. The intuition behind this metric is that it is desirable to penalize a density map whose overall count might match the ground truth, but whose shape does not match the ground truth [\[14\]](#page-8-1). More formally, we define
 
-$$GAME(L)=\frac{1}{N}\cdot\sum_{n=1}^{N}\left(\sum_{l=1}^{4^{L}}\lvert e_{n}^{l}-t_{n}^{l}\rvert\right)\tag{4}$$
+$$GAME(L) = \frac{1}{N} \cdot \sum_{n=1}^{N} \left( \sum_{l=1}^{4^L} |e_n^l - t_n^l| \right) \tag{4}$$
 
-where N refers to the number of images, L is the level parameter for GAME, e l n is the predicted or estimated count in region l of image n and t l n is the ground truth count in region l of image n [14].
+where N refers to the number of images, L is the level parameter for GAME, e l n is the predicted or estimated count in region l of image n and t l n is the ground truth count in region l of image n [\[14\]](#page-8-1).
 
 For training this dataset, we take 1600 randomly sampled patches of size 80 × 80. For testing this dataset, we take 80 × 80 non-overlapping patches which we can stitch back together into the full-sized 640 × 480 images. We trained the AMDCN network with density maps produced with a Gaussian of σ = 15 as specified in [18].
 
-#### 3.2.3 UCSD Crowd Counting
+#### 3.2.3 UCSD Crowd Counting
 
-The UCSD crowd counting dataset consists of frames of video of a sidewalk. There are relatively few people in view at any given time (approximately 25 on average). Furthermore, because the dataset comes from a video, there are many nearly identical images in the dataset. For this dataset, there have been two different ways to split the data into train and test sets. Therefore, we report results using both methods of splitting the data. The first method consists of four different splits: maximal, downscale, upscale, and minimal. Minimal is particularly challenging as the train set contains only 10 images. Moreover, upscale appears to be the easiest for the majority of methods [18]. The second method of splitting this data is much more succinct, leaving 1200 images in the testing set and 800 images in the training set [28]. This split comes from the original paper, so we call it the original split [5].
+The UCSD crowd counting dataset consists of frames of video of a sidewalk. There are relatively few people in view at any given time (approximately 25 on average). Furthermore, because the dataset comes from a video, there are many nearly identical images in the dataset. For this dataset, there have been two different ways to split the data into train and test sets. Therefore, we report results using both methods of splitting the data. The first method consists of four different splits: maximal, downscale, upscale, and minimal. Minimal is particularly challenging as the train set contains only 10 images. Moreover, upscale appears to be the easiest for the majority of methods [\[18\]](#page-8-4). The second method of splitting this data is much more succinct, leaving 1200 images in the testing set and 800 images in the training set [\[28\]](#page-9-0). This split comes from the original paper, so we call it the original split [\[5\]](#page-8-17).
 
-For this dataset, each object is annotated with a 2D Gaussian of covariance Σ = 8 · 12×2. The ground truth map is produced by summing these. When we make use of the perspective maps provided, we divide Σ by the perspective map value at that pixel x, represented by M(x). The provided perspective map for UCSD contains both a horizontal and vertical direction so we take the square root of the provided combined value. For training, we take 1600 random 79 × 119 pixel patches and for testing, we split each test image up into quadrants (which have dimension 79 × 119). There are two different ways to split the dataset into training and testing sets. We have experimented on the split that gave [18] the best results as well as the split used in [28].
+For this dataset, each object is annotated with a 2D Gaussian of covariance Σ = 8 
+· 12x2. The ground truth map is produced by summing these. When we make use of the perspective maps provided, we divide Σ by the perspective map value at that pixel x, represented by M(x). The provided perspective map for UCSD contains both a horizontal and vertical direction so we take the square root of the provided combined value. For training, we take 1600 random 79 × 119 pixel patches and for testing, we split each test image up into quadrants (which have dimension 79 × 119). There are two different ways to split the dataset into training and testing sets. We have experimented on the split that gave [18] the best results as well as the split used in [28].
 
-First, we split the dataset into four separate groups of training and testing sets as used in [18] and originally defined by [20]. These groups are "upscale," "maximal," "minimal," and "downscale." We see in Table 3 that the "upscale" split and "downscale" split give us state of the art results on counting for this dataset. For this experiment, we sampled 1600 random patches of size 119 × 79 pixels (width and height respectively) for the training set and split the test set images into 119 × 79 quadrants that could be reconstructed by piecing them together without overlap. We also added left-right flips of each image to our training data.
+First, we split the dataset into four separate groups of training and testing sets as used in [\[18\]](#page-8-4) and originally defined by [\[20\]](#page-8-0). These groups are "upscale," "maximal," "minimal," and "downscale." We see in Table [3](#page-6-0) that the "upscale" split and "downscale" split give us state of the art results on counting for this dataset. For this experiment, we sampled 1600 random patches of size 119 × 79 pixels (width and height respectively) for the training set and split the test set images into 119 × 79 quadrants that could be reconstructed by piecing them together without overlap. We also added left-right flips of each image to our training data.
 
-We then evaluate the original split. For this experiment, we similarly sampled 1600 random patches of size 119×79 pixels (width and height respectively) for the training set and split the test set images into 119 × 79 quadrants that could be reconstructed by piecing them together without overlap.
+We then evaluate the original split. For this experiment, we similarly sampled 1600 random patches of size 119 × 79 pixels (width and height respectively) for the training set and split the test set images into 119 × 79 quadrants that could be reconstructed by piecing them together without overlap.
 
 #### 3.2.4 WorldExpo '10 Crowd Counting
 
-The WorldExpo dataset [27] contains a larger number of people (approximately 50 on average, which is double that of UCSD) and contains images from multiple locations. Perspective effects are also much more noticeable in this dataset as compared to UCSD. These qualities of the dataset serve to increase the difficulty of counting. Like UCSD, the WorldExpo dataset was constructed from frames of video recordings of crowds. This means that, unlike UCF, this dataset contains a relatively large number of training and testing images. We experiment on this dataset with and without perspective information.
+The WorldExpo dataset [\[27\]](#page-8-7) contains a larger number of people (approximately 50 on average, which is double that of UCSD) and contains images from multiple locations. Perspective effects are also much more noticeable in this dataset as compared to UCSD. These qualities of the dataset serve to increase the difficulty of counting. Like UCSD, the WorldExpo dataset was constructed from frames of video recordings of crowds. This means that, unlike UCF, this dataset contains a relatively large number of training and testing images. We experiment on this dataset with and without perspective information.
 
 Without perspective maps, we generate label density maps for this dataset in the same manner as previously described: a 2D Gaussian with σ = 15. We take 16000 150 × 150 randomly sampled patches for training. For testing, we densely scan the image, producing 150 × 150 patches at a stride of 100.
 
-When perspective maps are used, however, we follow the procedure as described in [27], which involves estimating a "crowd density distribution kernel" as the sum of two 2D Gaussians: a symmetric Gaussian for the head and an ellipsoid Gaussian for the body. These are scaled by the perspective map M provided, where M(x) gives the number of pixels that represents a meter at pixel x [27]. Note that the meaning of this perspective map is distinct from the meaning of the perspective map provided for the UCSD dataset. Using this information, the density contribution from a person with head pixel x is given by the following sum of normalized Gaussians:
+When perspective maps are used, however, we follow the procedure as described in [\[27\]](#page-8-7), which involves estimating a "crowd density distribution kernel" as the sum of two 2D Gaussians: a symmetric Gaussian for the head and an ellipsoid Gaussian for the body. These are scaled by the perspective map M provided, where M(x) gives the number of pixels that represents a meter at pixel x [\[27\]](#page-8-7). Note that the meaning of this perspective map is distinct from the meaning of the perspective map provided for the UCSD dataset. Using this information, the density contribution from a person with head pixel x is given by the following sum of normalized Gaussians:
 
-$$D_{\bf x}=\frac{1}{||Z||}({\cal N}_{h}({\bf x},\sigma_{h})+{\cal N}_{b}({\bf x}_{b},\Sigma_{b}))\qquad\qquad(5)$$
+$$D_{\bf x} = \frac{1}{||Z||} \left( \mathcal{N}_h(\bf x, \sigma_h) + \mathcal{N}_b(\bf x_b, \Sigma_b) \right) \tag{5}$$
 
-where xb is the center of the body, which is 0.875 meters down from the head on average, and can be determined from the perspective map M and the head center x [27]. We sum these Gaussians for each person to pro-
+where $x_b$ is the center of the body, which is 0.875 meters down from the head on average, and can be determined from the perspective map $M$ and the head center x [27]. We sum these Gaussians for each person to pro-
 
-| Method | MAE |
-| --- | --- |
-| AMDCN | 290.82 |
+| Method       | MAE    |
+|--------------|--------|
+| AMDCN        | 290.82 |
 | Hydra2s [18] | 333.73 |
-| MCNN [28] | 377.60 |
-| [27] | 467.00 |
-| [23] | 295.80 |
-| [3] | 318.10 |
+| MCNN [28]    | 377.60 |
+| [27]         | 467.00 |
+| [23]         | 295.80 |
+| [3]          | 318.10 |
 
-Table 1. Mean absolute error of various methods on UCF crowds
+Table 1. Mean absolute error of various methods on UCF crowds
 
-duce the final density map. We set σ = 0.2M(x) for Nh and σx = 0.2M(x), σy = 0.5M(x) for Σb in Nb.
+duce the final density map. We set σ = 0.2*M*(x) for *Nh* and σx = 0.2*M*(x), σy = 0.5*M*(x) for Σb in *Nb*.
 
 # 4. Results
 
-### 4.1. UCF Crowd Counting
+## 4.1. UCF Crowd Counting
 
-The UCF dataset is particularly challenging due to the large number of people in the images, the variety of the scenes, as well as the low number of training images. We see in Figure 2 that because the UCF dataset has over 1000 people on average in each image, the shapes output by the network in the density map are not as well defined or separated as in the UCSD dataset.
+The UCF dataset is particularly challenging due to the large number of people in the images, the variety of the scenes, as well as the low number of training images. We see in Figure [2](#page-2-2) that because the UCF dataset has over 1000 people on average in each image, the shapes output by the network in the density map are not as well defined or separated as in the UCSD dataset.
 
-We report a state of the art result on this dataset in Table 1, following the standard protocol of 5-fold cross validation. Our MAE on the dataset is 290.82, which is approximately 5 lower than the previous state of the art, HydraCNN [18]. This is particularly indicative of the power of an aggregated multicolumn dilation network. Despite not making use of perspective information, the AMDCN is still able to produce highly accurate density maps for UCF.
+We report a state of the art result on this dataset in Table [1,](#page-5-1) following the standard protocol of 5-fold cross validation. Our MAE on the dataset is 290.82, which is approximately 5 lower than the previous state of the art, HydraCNN [\[18\]](#page-8-4). This is particularly indicative of the power of an aggregated multicolumn dilation network. Despite not making use of perspective information, the AMDCN is still able to produce highly accurate density maps for UCF.
 
-### 4.2. TRANCOS Traffic Counting
+## 4.2. TRANCOS Traffic Counting
 
-Our network performs very well on the TRANCOS dataset. Indeed, as confirmed by the GAME score, AMDCN produces the most accurate count and shape combined as compared to other methods. Table 2 shows that we achieve state of the art results as measured by the GAME metric [14] across all levels.
+Our network performs very well on the TRANCOS dataset. Indeed, as confirmed by the GAME score, AMDCN produces the most accurate count and shape combined as compared to other methods. Table [2](#page-5-2) shows that we achieve state of the art results as measured by the GAME metric [\[14\]](#page-8-1) across all levels.
 
-### 4.3. UCSD Crowd Counting
+## 4.3. UCSD Crowd Counting
 
-Results are shown in Table 3 and Figure 3. We see that the "original" split as defined by the creators of the dataset in [5] and used in [28] gives us somewhat worse results for counting on this dataset. Results were consistent over multiple trainings. Again, including the perspective map does not seem to increase performance on this dataset. Despite this, we see in Table 3 and Figure 3 that the results are comparable to the state of the art. In fact, for two of the splits, our proposed network beats the state of the art. For the upscale split, the AMDCN is the state of the art by a large relative margin. This is compelling because it shows that accurate perspective-free counting can be achieved without
+Results are shown in Table [3](#page-6-0) and Figure [3.](#page-6-1) We see that the "original" split as defined by the creators of the dataset in [\[5\]](#page-8-17) and used in [\[28\]](#page-9-0) gives us somewhat worse results for counting on this dataset. Results were consistent over multiple trainings. Again, including the perspective map does not seem to increase performance on this dataset. Despite this, we see in Table [3](#page-6-0) and Figure [3](#page-6-1) that the results are comparable to the state of the art. In fact, for two of the splits, our proposed network beats the state of the art. For the upscale split, the AMDCN is the state of the art by a large relative margin. This is compelling because it shows that accurate perspective-free counting can be achieved without
 
-| Method |  | GAME | GAME | GAME | GAME |
-| --- | --- | --- | --- | --- | --- |
-|  |  | (L=0) | (L=1) | (L=2) | (L=3) |
-| AMDCN |  | 9.77 | 13.16 | 15.00 | 15.87 |
-| [18] |  | 10.99 | 13.75 | 16.69 | 19.32 |
-| [15] + | SIFT | 13.76 | 16.72 | 20.72 | 24.36 |
-| from [14] |  |  |  |  |  |
-| [13] + | RGB | 17.68 | 19.97 | 23.54 | 25.84 |
-| Norm + Filters |  |  |  |  |  |
-| from [14] |  |  |  |  |  |
-| HOG-2 |  | 13.29 | 18.05 | 23.65 | 28.41 |
-| from [14] |  |  |  |  |  |
+| Method                            | GAME(L=0) | GAME(L=1)  | GAME(L=2)  | GAME(L=3)  |
+|-----------------------------------|-----------|------------|------------|------------|
+| AMDCN[18]                         | 9.7710.99 | 13.1613.75 | 15.0016.69 | 15.8719.32 |
+| [15] + SIFTfrom [14]              | 13.76     | 16.72      | 20.72      | 24.36      |
+| [13] + RGBNorm + Filtersfrom [14] | 17.68     | 19.97      | 23.54      | 25.84      |
+| HOG-2from [14]                    | 13.29     | 18.05      | 23.65      | 28.41      |
 
-Table 2. Mean absolute error of various methods on TRANCOS traffic
+Table 2. Mean absolute error of various methods on TRANCOS traffic
 
 creating image pyramids or requiring perspective maps as labels using the techniques presented by the AMDCN.
 
-### 4.4. WorldExpo '10 Crowd Counting
+## 4.4. WorldExpo '10 Crowd Counting
 
-Our network performs reasonably well on the more challenging WorldExpo dataset. While it does not beat the state of the art, our results are comparable. What is more, we do not need to use the perspective maps to obtain these results. As seen in Table 4, the AMDCN is capable of incorporating the perspective effects without scaling the Gaussians with perspective information. This shows that it is possible to achieve counting results that approach the state of the art with much simpler labels for the counting training data.
+Our network performs reasonably well on the more challenging WorldExpo dataset. While it does not beat the state of the art, our results are comparable. What is more, we do not need to use the perspective maps to obtain these results. As seen in Table [4,](#page-7-1) the AMDCN is capable of incorporating the perspective effects without scaling the Gaussians with perspective information. This shows that it is possible to achieve counting results that approach the state of the art with much simpler labels for the counting training data.
 
-### 4.5. Ablation Studies
+## 4.5. Ablation Studies
 
-We report the results of the ablation studies in Figure 4. We note from these plots that while there is variation in performance, a few trends stand out. Most importantly, the lowest errors are consistently with a combination of a larger number of columns and including the aggregator module. Notably for the TRANCOS dataset, including the aggregator consistently improves performance. Generally, the aggregator tends to decrease the variance in performance of the network. Some of the variance that we see in the plots can be explained by: (1) for lower numbers of columns, including an aggregator is not as likely to help as there is not much separation of multiscale information across columns and (2) for the UCSD dataset, there is less of a perspective effect than TRANCOS and WorldExpo so a simpler network is more likely to perform comparably to a larger network. These results verify the notion that using more columns increases accuracy, and also support our justification for the use of the aggregator module.
+We report the results of the ablation studies in Figure [4.](#page-7-2) We note from these plots that while there is variation in performance, a few trends stand out. Most importantly, the lowest errors are consistently with a combination of a larger number of columns and including the aggregator module. Notably for the TRANCOS dataset, including the aggregator consistently improves performance. Generally, the aggregator tends to decrease the variance in performance of the network. Some of the variance that we see in the plots can be explained by: (1) for lower numbers of columns, including an aggregator is not as likely to help as there is not much separation of multiscale information across columns and (2) for the UCSD dataset, there is less of a perspective effect than TRANCOS and WorldExpo so a simpler network is more likely to perform comparably to a larger network. These results verify the notion that using more columns increases accuracy, and also support our justification for the use of the aggregator module.
 
 ![](_page_6_Figure_0.jpeg)
 
-Figure 3. UCSD crowd counting dataset. Both plots show comparisons of predicted and ground truth counts over time. While AMDCN does not beat the state of the art on the original split, the predictions still follow the true counts reasonably. The jump in the original split is due to that testing set including multiple scenes of highly varying counts.
-
-| Method | maximal | downscale | upscale | minimal | original |
-| --- | --- | --- | --- | --- | --- |
-| AMDCN (without perspective information) | 1.63 | 1.43 | 0.63 | 1.71 | 1.74 |
-| AMDCN (with perspective information) | 1.60 | 1.24 | 1.37 | 1.59 | 1.72 |
-| [18] (with perspective information) | 1.65 | 1.79 | 1.11 | 1.50 | - |
-| [18] (without perspective information) | 2.22 | 1.93 | 1.37 | 2.38 | - |
-| [15] | 1.70 | 1.28 | 1.59 | 2.02 | - |
-| [13] | 1.70 | 2.16 | 1.61 | 2.20 | - |
-| [19] | 1.43 | 1.30 | 1.59 | 1.62 | - |
-| [2] | 1.24 | 1.31 | 1.69 | 1.49 | - |
-| [27] | 1.70 | 1.26 | 1.59 | 1.52 | 1.60 |
-| [28] | - | - | - | - | 1.07 |
-| [1, 28] | - | - | - | - | 2.16 |
-| [7] | - | - | - | - | 2.25 |
-| [5] | - | - | - | - | 2.24 |
-| [6] | - | - | - | - | 2.07 |
-
-Table 3. Mean absolute error of various methods on UCSD crowds
+(a) UCSD upscale split. (b) UCSD original split.
+
+Figure 3. UCSD crowd counting dataset. Both plots show comparisons of predicted and ground truth counts over time. While AMDCN does not beat the state of the art on the original split, the predictions still follow the true counts reasonably. The jump in the original split is due to that testing set including multiple scenes of highly varying counts.
+
+| Method                                  | maximal | downscale | upscale | minimal | original |
+|-----------------------------------------|---------|-----------|---------|---------|----------|
+| AMDCN (without perspective information) | 1.63    | 1.43      | 0.63    | 1.71    | 1.74     |
+| AMDCN (with perspective information)    | 1.60    | 1.24      | 1.37    | 1.59    | 1.72     |
+| [18] (with perspective information)     | 1.65    | 1.79      | 1.11    | 1.50    | -        |
+| [18] (without perspective information)  | 2.22    | 1.93      | 1.37    | 2.38    | -        |
+| [15]                                    | 1.70    | 1.28      | 1.59    | 2.02    | -        |
+| [13]                                    | 1.70    | 2.16      | 1.61    | 2.20    | -        |
+| [19]                                    | 1.43    | 1.30      | 1.59    | 1.62    | -        |
+| [2]                                     | 1.24    | 1.31      | 1.69    | 1.49    | -        |
+| [27]                                    | 1.70    | 1.26      | 1.59    | 1.52    | 1.60     |
+| [28]                                    | -       | -         | -       | -       | 1.07     |
+| [1,28]                                  | -       | -         | -       | -       | 2.16     |
+| [7]                                     | -       | -         | -       | -       | 2.25     |
+| [5]                                     | -       | -         | -       | -       | 2.24     |
+| [6]                                     | -       | -         | -       | -       | 2.07     |
+
+Table 3. Mean absolute error of various methods on UCSD crowds
 
 # 5. Conclusion
 
-### 5.1. Summary
+## 5.1. Summary
 
-We have proposed the use of aggregated multicolumn dilated convolutions, the AMDCN, as an alternative to the HydraCNN [18] or multicolumn CNN [28] for the vision task of counting objects in images. Inspired by the multicolumn approach to multiscale problems, we also employ dilations to increase the receptive field of our columns. We then aggregate this multiscale information using another series of dilated convolutions to enable a wide network and detect features at more scales. This method takes advantage of the ability of dilated convolutions to provide exponentially increasing receptive fields. We have performed experiments on the challenging UCF crowd counting dataset, the TRANCOS traffic dataset, multiple splits of the UCSD crowd counting dataset, and the WorldExpo crowd counting dataset.
+We have proposed the use of aggregated multicolumn dilated convolutions, the AMDCN, as an alternative to the HydraCNN [\[18\]](#page-8-4) or multicolumn CNN [\[28\]](#page-9-0) for the vision task of counting objects in images. Inspired by the multicolumn approach to multiscale problems, we also employ dilations to increase the receptive field of our columns. We then aggregate this multiscale information using another series of dilated convolutions to enable a wide network and detect features at more scales. This method takes advantage of the ability of dilated convolutions to provide exponentially increasing receptive fields. We have performed experiments on the challenging UCF crowd counting dataset, the TRANCOS traffic dataset, multiple splits of the UCSD crowd counting dataset, and the WorldExpo crowd counting dataset.
 
 ![](_page_7_Figure_0.jpeg)
 
-Figure 4. Ablation studies on various datasets in which the number of columns is varied and the aggregator is included or not included. The results generally support the use of more columns and an aggregator module.
+Figure 4. Ablation studies on various datasets in which the number of columns is varied and the aggregator is included or not included. The results generally support the use of more columns and an aggregator module.
 
-| Method | MAE |
-| --- | --- |
-| AMDCN (without perspective infor | 16.6 |
-| mation) |  |
-| AMDCN (with perspective informa | 14.9 |
-| tion) |  |
-| LBP+RR [28] (with perspective infor | 31.0 |
-| mation) |  |
-| MCNN [28] (with perspective informa | 11.6 |
-| tion) |  |
-| [27] (with perspective information) | 12.9 |
+| Method                                     | MAE  |
+|--------------------------------------------|------|
+| AMDCN (without perspective information)    | 16.6 |
+| AMDCN (with perspective information)       | 14.9 |
+| LBP+RR [28] (with perspective information) | 31.0 |
+| MCNN [28] (with perspective information)   | 11.6 |
+| [27] (with perspective information)        | 12.9 |
 
-Table 4. Mean absolute error of various methods on WorldExpo crowds
+Table 4. Mean absolute error of various methods on WorldExpo crowds
 
 We obtain superior or comparable results in most of these datasets. The AMDCN is capable of outperforming these approaches completely especially when perspective information is not provided, as in UCF and TRANCOS. These results show that the AMDCN performs surprisingly well and is also robust to scale effects. Further, our ablation study of removing the aggregator network shows that using more columns and an aggregator provides the best accuracy for counting — especially so when there is no perspective information.
 
-### 5.2. Future Work
+## 5.2. Future Work
 
 In addition to an analysis of performance on counting, a density regressor can also be used to locate objects in the image. As mentioned previously, if the regressor is accurate and precise enough, the resulting density map can be used to locate the objects in the image. We expect that in order to do this, one must regress each object to a single point rather than a region specified by a Gaussian. Perhaps this might be accomplished by applying non-maxima suppression to the final layer activations.
 
@@ -243,37 +234,37 @@ This material is based upon work supported by the National Science Foundation un
 
 # References
 
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-- [3] D. Babu Sam, S. Surya, and R. Venkatesh Babu. Switching convolutional neural network for crowd
+- [1] S. An, W. Liu, and S. Venkatesh. Face recognition using kernel ridge regression. In *Computer Vision and Pattern Recognition, 2007. CVPR'07. IEEE Conference on*, pages 1–7. IEEE, 2007.
+- [2] C. Arteta, V. Lempitsky, J. A. Noble, and A. Zisserman. Interactive object counting. In *European Conference on Computer Vision*, pages 504–518. Springer, 2014.
+- [3] D. Babu Sam, S. Surya, and R. Venkatesh Babu. Switching convolutional neural network for crowd
 
 counting. In *Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition*, pages 5744–5752, 2017.
 
-- [4] L. Boominathan, S. S. Kruthiventi, and R. V. Babu. Crowdnet: A deep convolutional network for dense crowd counting. In *Proceedings of the 2016 ACM on Multimedia Conference*, pages 640–644. ACM, 2016.
-- [5] A. B. Chan, Z.-S. J. Liang, and N. Vasconcelos. Privacy preserving crowd monitoring: Counting people without people models or tracking. In *Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on*, pages 1–7. IEEE, 2008.
-- [6] K. Chen, S. Gong, T. Xiang, and C. Change Loy. Cumulative attribute space for age and crowd density estimation. In *Proceedings of the IEEE conference on computer vision and pattern recognition*, pages 2467– 2474, 2013.
-- [7] K. Chen, C. C. Loy, S. Gong, and T. Xiang. Feature mining for localised crowd counting.
-- [8] L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille. Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. *IEEE Transactions on Pattern Analysis and Machine Intelligence*, 2017.
-- [9] L.-C. Chen, Y. Yang, J. Wang, W. Xu, and A. L. Yuille. Attention to scale: Scale-aware semantic image segmentation. In *Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition*, pages 3640–3649, 2016.
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-- [11] A. Dosovitskiy, P. Fischer, E. Ilg, P. Hausser, C. Hazirbas, V. Golkov, P. van der Smagt, D. Cremers, and T. Brox. Flownet: Learning optical flow with convolutional networks. In *Proceedings of the IEEE International Conference on Computer Vision*, pages 2758– 2766, 2015.
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-- [13] L. Fiaschi, U. Kothe, R. Nair, and F. A. Hamprecht. ¨ Learning to count with regression forest and structured labels. In *Pattern Recognition (ICPR), 2012 21st International Conference on*, pages 2685–2688. IEEE, 2012.
-- [14] R. Guerrero-Gomez-Olmedo, B. Torre-Jim ´ enez, S. M. ´ Lopez-Sastre, Roberto Basc ´ on, and D. O ´ noro Rubio. ˜ Extremely overlapping vehicle counting. In *Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA)*, 2015.
-- [15] V. Lempitsky and A. Zisserman. Learning to count objects in images. In *Advances in Neural Information Processing Systems*, pages 1324–1332, 2010.
-- [16] G. Lin, C. Shen, A. van den Hengel, and I. Reid. Efficient piecewise training of deep structured models for semantic segmentation. In *Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition*, pages 3194–3203, 2016.
-- [17] H. Noh, S. Hong, and B. Han. Learning deconvolution network for semantic segmentation. In *Proceedings of the IEEE International Conference on Computer Vision*, pages 1520–1528, 2015.
-- [18] D. Onoro-Rubio and R. J. Lopez-Sastre. Towards ´ perspective-free object counting with deep learning. In *European Conference on Computer Vision*, pages 615–629. Springer, 2016.
-- [19] V.-Q. Pham, T. Kozakaya, O. Yamaguchi, and R. Okada. Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In *Proceedings of the IEEE International Conference on Computer Vision*, pages 3253–3261, 2015.
-- [20] D. Ryan, S. Denman, C. Fookes, and S. Sridharan. Crowd counting using multiple local features. In *Digital Image Computing: Techniques and Applications, 2009. DICTA'09.*, pages 81–88. IEEE, 2009.
-- [21] S. Segu´ı, O. Pujol, and J. Vitria. Learning to count with deep object features. In *Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops*, pages 90–96, 2015.
-- [22] J. Selinummi, O. Yli-Harja, and J. A. Puhakka. Software for quantification of labeled bacteria from digital microscope images by automated image analysis. *Biotechniques*, 39(6):859, 2005.
-- [23] V. A. Sindagi and V. M. Patel. Generating high-quality crowd density maps using contextual pyramid cnns. In *Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition*, pages 1861–1870, 2017.
-- [24] E. Walach and L. Wolf. Learning to count with cnn boosting. In *European Conference on Computer Vision*, pages 660–676. Springer, 2016.
-- [25] F. Yu and V. Koltun. Multi-scale context aggregation by dilated convolutions. *arXiv preprint arXiv:1511.07122*, 2015.
-- [26] F. Yu, V. Koltun, and T. Funkhouser. Dilated residual networks. *arXiv preprint arXiv:1705.09914*, 2017.
-- [27] C. Zhang, H. Li, X. Wang, and X. Yang. Crossscene crowd counting via deep convolutional neural networks. In *Proceedings of the IEEE Conference on*
+- [4] L. Boominathan, S. S. Kruthiventi, and R. V. Babu. Crowdnet: A deep convolutional network for dense crowd counting. In *Proceedings of the 2016 ACM on Multimedia Conference*, pages 640–644. ACM, 2016.
+- [5] A. B. Chan, Z.-S. J. Liang, and N. Vasconcelos. Privacy preserving crowd monitoring: Counting people without people models or tracking. In *Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on*, pages 1–7. IEEE, 2008.
+- [6] K. Chen, S. Gong, T. Xiang, and C. Change Loy. Cumulative attribute space for age and crowd density estimation. In *Proceedings of the IEEE conference on computer vision and pattern recognition*, pages 2467– 2474, 2013.
+- [7] K. Chen, C. C. Loy, S. Gong, and T. Xiang. Feature mining for localised crowd counting.
+- [8] L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille. Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. *IEEE Transactions on Pattern Analysis and Machine Intelligence*, 2017.
+- [9] L.-C. Chen, Y. Yang, J. Wang, W. Xu, and A. L. Yuille. Attention to scale: Scale-aware semantic image segmentation. In *Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition*, pages 3640–3649, 2016.
+- [10] F. Chollet et al. Keras. [https://github.com/](https://github.com/fchollet/keras) [fchollet/keras](https://github.com/fchollet/keras), 2015.
+- [11] A. Dosovitskiy, P. Fischer, E. Ilg, P. Hausser, C. Hazirbas, V. Golkov, P. van der Smagt, D. Cremers, and T. Brox. Flownet: Learning optical flow with convolutional networks. In *Proceedings of the IEEE International Conference on Computer Vision*, pages 2758– 2766, 2015.
+- [12] C. Farabet, C. Couprie, L. Najman, and Y. Le-Cun. Learning hierarchical features for scene labeling. *IEEE transactions on pattern analysis and machine intelligence*, 35(8):1915–1929, 2013.
+- [13] L. Fiaschi, U. Kothe, R. Nair, and F. A. Hamprecht. ¨ Learning to count with regression forest and structured labels. In *Pattern Recognition (ICPR), 2012 21st International Conference on*, pages 2685–2688. IEEE, 2012.
+- [14] R. Guerrero-Gomez-Olmedo, B. Torre-Jim ´ enez, S. M. ´ Lopez-Sastre, Roberto Basc ´ on, and D. O ´ noro Rubio. ˜ Extremely overlapping vehicle counting. In *Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA)*, 2015.
+- [15] V. Lempitsky and A. Zisserman. Learning to count objects in images. In *Advances in Neural Information Processing Systems*, pages 1324–1332, 2010.
+- [16] G. Lin, C. Shen, A. van den Hengel, and I. Reid. Efficient piecewise training of deep structured models for semantic segmentation. In *Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition*, pages 3194–3203, 2016.
+- [17] H. Noh, S. Hong, and B. Han. Learning deconvolution network for semantic segmentation. In *Proceedings of the IEEE International Conference on Computer Vision*, pages 1520–1528, 2015.
+- [18] D. Onoro-Rubio and R. J. Lopez-Sastre. Towards ´ perspective-free object counting with deep learning. In *European Conference on Computer Vision*, pages 615–629. Springer, 2016.
+- [19] V.-Q. Pham, T. Kozakaya, O. Yamaguchi, and R. Okada. Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation. In *Proceedings of the IEEE International Conference on Computer Vision*, pages 3253–3261, 2015.
+- [20] D. Ryan, S. Denman, C. Fookes, and S. Sridharan. Crowd counting using multiple local features. In *Digital Image Computing: Techniques and Applications, 2009. DICTA'09.*, pages 81–88. IEEE, 2009.
+- [21] S. Segu´ı, O. Pujol, and J. Vitria. Learning to count with deep object features. In *Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops*, pages 90–96, 2015.
+- [22] J. Selinummi, O. Yli-Harja, and J. A. Puhakka. Software for quantification of labeled bacteria from digital microscope images by automated image analysis. *Biotechniques*, 39(6):859, 2005.
+- [23] V. A. Sindagi and V. M. Patel. Generating high-quality crowd density maps using contextual pyramid cnns. In *Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition*, pages 1861–1870, 2017.
+- [24] E. Walach and L. Wolf. Learning to count with cnn boosting. In *European Conference on Computer Vision*, pages 660–676. Springer, 2016.
+- [25] F. Yu and V. Koltun. Multi-scale context aggregation by dilated convolutions. *arXiv preprint arXiv:1511.07122*, 2015.
+- [26] F. Yu, V. Koltun, and T. Funkhouser. Dilated residual networks. *arXiv preprint arXiv:1705.09914*, 2017.
+- [27] C. Zhang, H. Li, X. Wang, and X. Yang. Crossscene crowd counting via deep convolutional neural networks. In *Proceedings of the IEEE Conference on*
 
 *Computer Vision and Pattern Recognition*, pages 833– 841, 2015.
 
-- [28] Y. Zhang, D. Zhou, S. Chen, S. Gao, and Y. Ma. Single-image crowd counting via multi-column convolutional neural network. In *Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition*, pages 589–597, 2016.
+- [28] Y. Zhang, D. Zhou, S. Chen, S. Gao, and Y. Ma. Single-image crowd counting via multi-column convolutional neural network. In *Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition*, pages 589–597, 2016.
\ No newline at end of file
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z+aDRq)^e}ss$G@OfAZV2{@;qSbs-0*m&C<6oSlx4hn~∗. Equal contribution.
-
-1. JAX code for Switch Transformer and all model checkpoints are available at https://github.com/ google-research/t5x
-
-2. Tensorflow code for Switch Transformer is available at https://github.com/tensorflow/mesh/blob/ master/mesh_tensorflow/transformer/moe.py
-
-©2022 William Fedus, Barret Zoph and Noam Shazeer.
-
-License: CC-BY 4.0, see https://creativecommons.org/licenses/by/4.0/. Attribution requirements are provided at http://jmlr.org/papers/v23/21-0998.html.
-
-# Contents
-
-| 1 | Introduction |  | 3 |
-| --- | --- | --- | --- |
-| 2 | Switch Transformer |  | 4 |
-|  | 2.1 | Simplifying Sparse Routing | 5 |
-|  | 2.2 | Efficient Sparse Routing | 6 |
-|  | 2.3 | Putting It All Together: The Switch Transformer | 8 |
-|  | 2.4 | Improved Training and Fine-Tuning Techniques | 8 |
-| 3 |  | Scaling Properties | 11 |
-|  | 3.1 | Scaling Results on a Step-Basis | 12 |
-|  | 3.2 | Scaling Results on a Time-Basis | 13 |
-|  | 3.3 | Scaling Versus a Larger Dense Model | 13 |
-| 4 |  | Downstream Results | 14 |
-|  | 4.1 | Fine-Tuning | 14 |
-|  | 4.2 | Distillation | 16 |
-|  | 4.3 | Multilingual Learning | 17 |
-| 5 |  | Designing Models with Data, Model, and Expert-Parallelism | 18 |
-|  | 5.1 | Data Parallelism | 20 |
-|  | 5.2 | Model Parallelism | 20 |
-|  | 5.3 | Model and Data Parallelism | 21 |
-|  | 5.4 | Expert and Data Parallelism | 22 |
-|  | 5.5 | Expert, Model and Data Parallelism | 22 |
-|  | 5.6 | Towards Trillion Parameter Models | 22 |
-| 6 |  | Related Work | 24 |
-| 7 | Discussion |  | 25 |
-| 8 | Future Work |  | 26 |
-| 9 | Conclusion |  | 27 |
-| A |  | Switch for Attention | 27 |
-| B |  | Preventing Token Dropping with No-Token-Left-Behind | 29 |
-| C |  | Encouraging Exploration Across Experts | 29 |
-| D |  | Switch Transformers in Lower Compute Regimes | 29 |
-| E |  | Relation of Upstream to Downstream Model Performance | 32 |
-| F |  | Pseudo Code for Switch Transformers | 33 |
-
-### 1. Introduction
-
-Large scale training has been an effective path towards flexible and powerful neural language models (Radford et al., 2018; Kaplan et al., 2020; Brown et al., 2020). Simple architectures backed by a generous computational budget, data set size and parameter count—surpass more complicated algorithms (Sutton, 2019). An approach followed in Radford et al. (2018); Raffel et al. (2019); Brown et al. (2020) expands the model size of a densely-activated Transformer (Vaswani et al., 2017). While effective, it is also extremely computationally intensive (Strubell et al., 2019). Inspired by the success of model scale, but seeking greater computational efficiency, we instead propose a sparsely-activated expert model: the Switch Transformer. In our case the sparsity comes from activating a subset of the neural network weights for each incoming example.
+©2022 William Fedus, Barret Zoph and Noam Shazeer.
+
+∗. Equal contribution.
+
+1. JAX code for Switch Transformer and all model checkpoints are available at [https://github.com/](https://github.com/google-research/t5x) [google-research/t5x](https://github.com/google-research/t5x)
+
+2. Tensorflow code for Switch Transformer is available at [https://github.com/tensorflow/mesh/blob/](https://github.com/tensorflow/mesh/blob/master/mesh_tensorflow/transformer/moe.py) [master/mesh_tensorflow/transformer/moe.py](https://github.com/tensorflow/mesh/blob/master/mesh_tensorflow/transformer/moe.py)
+
+## Contents
+
+| 1 | Introduction                                              | 3  |
+|---|-----------------------------------------------------------|----|
+| 2 | Switch Transformer                                        | 4  |
+|   | 2.1 Simplifying Sparse Routing                            | 5  |
+|   | 2.2 Efficient Sparse Routing                              | 6  |
+|   | 2.3 Putting It All Together: The Switch Transformer       | 8  |
+|   | 2.4 Improved Training and Fine-Tuning Techniques          | 8  |
+| 3 | Scaling Properties                                        | 11 |
+|   | 3.1 Scaling Results on a Step-Basis                       | 12 |
+|   | 3.2 Scaling Results on a Time-Basis                       | 13 |
+|   | 3.3 Scaling Versus a Larger Dense Model                   | 13 |
+| 4 | Downstream Results                                        | 14 |
+|   | 4.1 Fine-Tuning                                           | 14 |
+|   | 4.2 Distillation                                          | 16 |
+|   | 4.3 Multilingual Learning                                 | 17 |
+| 5 | Designing Models with Data, Model, and Expert-Parallelism | 18 |
+|   | 5.1 Data Parallelism                                      | 20 |
+|   | 5.2 Model Parallelism                                     | 20 |
+|   | 5.3 Model and Data Parallelism                            | 21 |
+|   | 5.4 Expert and Data Parallelism                           | 22 |
+|   | 5.5 Expert, Model and Data Parallelism                    | 22 |
+|   | 5.6 Towards Trillion Parameter Models                     | 22 |
+| 6 | Related Work                                              | 24 |
+| 7 | Discussion                                                | 25 |
+| 8 | Future Work                                               | 26 |
+| 9 | Conclusion                                                | 27 |
+| A | Switch for Attention                                      | 27 |
+| B | Preventing Token Dropping with No-Token-Left-Behind       | 29 |
+| C | Encouraging Exploration Across Experts                    | 29 |
+| D | Switch Transformers in Lower Compute Regimes              | 29 |
+| E | Relation of Upstream to Downstream Model Performance      | 32 |
+| F | Pseudo Code for Switch Transformers                       | 33 |
+
+### 1. Introduction
+
+Large scale training has been an effective path towards flexible and powerful neural language models [(Radford](#page-37-1) [et](#page-37-1) [al.,](#page-37-1) [2018;](#page-37-1) [Kaplan](#page-36-0) [et](#page-36-0) [al.,](#page-36-0) [2020;](#page-36-0) [Brown](#page-35-0) [et](#page-35-0) [al.,](#page-35-0) [2020)](#page-35-0). Simple architectures backed by a generous computational budget, data set size and parameter count—surpass more complicated algorithms [(Sutton,](#page-38-0) [2019)](#page-38-0). An approach followed in [Radford](#page-37-1) [et](#page-37-1) [al.](#page-37-1) [(2018)](#page-37-1); [Raffel](#page-37-0) [et](#page-37-0) [al.](#page-37-0) [(2019)](#page-37-0); [Brown](#page-35-0) [et](#page-35-0) [al.](#page-35-0) [(2020)](#page-35-0) expands the model size of a densely-activated Transformer [(Vaswani](#page-39-1) [et](#page-39-1) [al.,](#page-39-1) [2017)](#page-39-1). While effective, it is also extremely computationally intensive [(Strubell](#page-38-1) [et](#page-38-1) [al.,](#page-38-1) [2019)](#page-38-1). Inspired by the success of model scale, but seeking greater computational efficiency, we instead propose a sparsely-activated expert model: the Switch Transformer. In our case the sparsity comes from activating a subset of the neural network weights for each incoming example.
 
 ![](_page_2_Figure_3.jpeg)
 
-Figure 1: Scaling and sample efficiency of Switch Transformers. Left Plot: Scaling properties for increasingly sparse (more experts) Switch Transformers. Right Plot: Negative log perplexity comparing Switch Transformers to T5 (Raffel et al., 2019) models using the same compute budget.
+Figure 1: Scaling and sample efficiency of Switch Transformers. Left Plot: Scaling properties for increasingly sparse (more experts) Switch Transformers. Right Plot: Negative log perplexity comparing Switch Transformers to T5 [(Raffel](#page-37-0) [et](#page-37-0) [al.,](#page-37-0) [2019)](#page-37-0) models using the same compute budget.
 
-Sparse training is an active area of research and engineering (Gray et al., 2017; Gale et al., 2020), but as of today, machine learning libraries and hardware accelerators still cater to dense matrix multiplications. To have an efficient sparse algorithm, we start with the Mixture-of-Expert (MoE) paradigm (Jacobs et al., 1991; Jordan and Jacobs, 1994; Shazeer et al., 2017), and simplify it to yield training stability and computational benefits. MoE models have had notable successes in machine translation (Shazeer et al., 2017, 2018; Lepikhin et al., 2020), however, widespread adoption is hindered by complexity, communication costs, and training instabilities.
+Sparse training is an active area of research and engineering [(Gray](#page-35-1) [et](#page-35-1) [al.,](#page-35-1) [2017;](#page-35-1) [Gale](#page-35-2) [et](#page-35-2) [al.,](#page-35-2) [2020)](#page-35-2), but as of today, machine learning libraries and hardware accelerators still cater to dense matrix multiplications. To have an efficient sparse algorithm, we start with the Mixture-of-Expert (MoE) paradigm [(Jacobs](#page-36-1) [et](#page-36-1) [al.,](#page-36-1) [1991;](#page-36-1) [Jordan](#page-36-2) [and](#page-36-2) [Jacobs,](#page-36-2) [1994;](#page-36-2) [Shazeer](#page-38-2) [et](#page-38-2) [al.,](#page-38-2) [2017)](#page-38-2), and simplify it to yield training stability and computational benefits. MoE models have had notable successes in machine translation [(Shazeer](#page-38-2) [et](#page-38-2) [al.,](#page-38-2) [2017,](#page-38-2) [2018;](#page-38-3) [Lep](#page-37-2)[ikhin](#page-37-2) [et](#page-37-2) [al.,](#page-37-2) [2020)](#page-37-2), however, widespread adoption is hindered by complexity, communication costs, and training instabilities.
 
-We address these issues, and then go beyond translation, to find that these class of algorithms are broadly valuable in natural language. We measure superior scaling on a diverse set of natural language tasks and across three regimes in NLP: pre-training, finetuning and multi-task training. While this work focuses on scale, we also show that the Switch Transformer architecture not only excels in the domain of supercomputers, but is beneficial even with only a few computational cores. Further, our large sparse models can be distilled (Hinton et al., 2015) into small dense versions while preserving 30% of the sparse model quality gain. Our contributions are the following:
+We address these issues, and then go beyond translation, to find that these class of algorithms are broadly valuable in natural language. We measure superior scaling on a diverse set of natural language tasks and across three regimes in NLP: pre-training, finetuning and multi-task training. While this work focuses on scale, we also show that the Switch Transformer architecture not only excels in the domain of supercomputers, but is beneficial even with only a few computational cores. Further, our large sparse models can be distilled [(Hinton](#page-36-3) [et](#page-36-3) [al.,](#page-36-3) [2015)](#page-36-3) into small dense versions while preserving 30% of the sparse model quality gain. Our contributions are the following:
 
 - The Switch Transformer architecture, which simplifies and improves over Mixture of Experts.
-- Scaling properties and a benchmark against the strongly tuned T5 model (Raffel et al., 2019) where we measure 7x+ pre-training speedups while still using the same FLOPS per token. We further show the improvements hold even with limited computational resources, using as few as two experts.
+- Scaling properties and a benchmark against the strongly tuned T5 model [(Raffel](#page-37-0) [et](#page-37-0) [al.,](#page-37-0) [2019)](#page-37-0) where we measure 7x+ pre-training speedups while still using the same FLOPS per token. We further show the improvements hold even with limited computational resources, using as few as two experts.
 - Successful distillation of sparse pre-trained and specialized fine-tuned models into small dense models. We reduce the model size by up to 99% while preserving 30% of the quality gains of the large sparse teacher.
 - Improved pre-training and fine-tuning techniques: (1) selective precision training that enables training with lower bfloat16 precision (2) an initialization scheme that allows for scaling to a larger number of experts and (3) increased expert regularization that improves sparse model fine-tuning and multi-task training.
-- A measurement of the pre-training benefits on multilingual data where we find a universal improvement across all 101 languages and with 91% of languages benefiting from 4x+ speedups over the mT5 baseline (Xue et al., 2020).
+- A measurement of the pre-training benefits on multilingual data where we find a universal improvement across all 101 languages and with 91% of languages benefiting from 4x+ speedups over the mT5 baseline [(Xue](#page-39-2) [et](#page-39-2) [al.,](#page-39-2) [2020)](#page-39-2).
 - An increase in the scale of neural language models achieved by efficiently combining data, model, and expert-parallelism to create models with up to a trillion parameters. These models improve the pre-training speed of a strongly tuned T5-XXL baseline by 4x.
 
-### 2. Switch Transformer
+## 2. Switch Transformer
 
-The guiding design principle for Switch Transformers is to maximize the parameter count of a Transformer model (Vaswani et al., 2017) in a simple and computationally efficient way. The benefit of scale was exhaustively studied in Kaplan et al. (2020) which uncovered powerlaw scaling with model size, data set size and computational budget. Importantly, this work advocates training large models on relatively small amounts of data as the computationally optimal approach.
+The guiding design principle for Switch Transformers is to maximize the parameter count of a Transformer model [(Vaswani](#page-39-1) [et](#page-39-1) [al.,](#page-39-1) [2017)](#page-39-1) in a simple and computationally efficient way. The benefit of scale was exhaustively studied in [Kaplan](#page-36-0) [et](#page-36-0) [al.](#page-36-0) [(2020)](#page-36-0) which uncovered powerlaw scaling with model size, data set size and computational budget. Importantly, this work advocates training large models on relatively small amounts of data as the computationally optimal approach.
 
 Heeding these results, we investigate a fourth axis: increase the parameter count while keeping the floating point operations (FLOPs) per example constant. Our hypothesis is that the parameter count, independent of total computation performed, is a separately important axis on which to scale. We achieve this by designing a sparsely activated model that efficiently uses hardware designed for dense matrix multiplications such as GPUs and TPUs. Our work here focuses on TPU architectures, but these class of models may be similarly trained on GPU clusters. In our distributed training setup, our sparsely activated layers split unique weights on different devices. Therefore, the weights of the model increase with the number of devices, all while maintaining a manageable memory and computational footprint on each device.
 
 ![](_page_4_Figure_1.jpeg)
 
-- Figure 2: Illustration of a Switch Transformer encoder block. We replace the dense feed forward network (FFN) layer present in the Transformer with a sparse Switch FFN layer (light blue). The layer operates independently on the tokens in the sequence. We diagram two tokens (x1 = "More" and x2 = "Parameters" below) being routed (solid lines) across four FFN experts, where the router independently routes each token. The switch FFN layer returns the output of the selected FFN multiplied by the router gate value (dotted-line).
-### 2.1 Simplifying Sparse Routing
+Figure 2: Illustration of a Switch Transformer encoder block. We replace the dense feed forward network (FFN) layer present in the Transformer with a sparse Switch FFN layer (light blue). The layer operates independently on the tokens in the sequence. We diagram two tokens (x1 = "More" and x2 = "Parameters" below) being routed (solid lines) across four FFN experts, where the router independently routes each token. The switch FFN layer returns the output of the selected FFN multiplied by the router gate value (dotted-line).
 
-Mixture of Expert Routing. Shazeer et al. (2017) proposed a natural language Mixtureof-Experts (MoE) layer which takes as an input a token representation x and then routes this to the best determined top-k experts, selected from a set {Ei(x)} N i=1 of N experts. The router variable Wr produces logits h(x) = Wr · x which are normalized via a softmax distribution over the available N experts at that layer. The gate-value for expert i is given by,
+#### 2.1 Simplifying Sparse Routing
 
-$$p_{i}(x)=\frac{e^{h(x)_{i}}}{\sum_{j}^{N}e^{h(x)_{j}}}.\tag{1}$$
+Mixture of Expert Routing. Shazeer et al. (2017) proposed a natural language Mixtureof-Experts (MoE) layer which takes as an input a token representation x and then routes this to the best determined top-*k* experts, selected from a set {*Ei*(*x*)}N of *N* experts. The router variable *Wr* produces logits *h*(*x*) = *Wr* 
+· *x* which are normalized via a softmax distribution over the available *N* experts at that layer. The gate-value for expert *i* is given by,
+
+$$p_{i}(x) = \frac{e^{h(x)_{i}}}{\sum_{j}^{N}e^{h(x)_{j}}}.\tag{1}$$
 
 The top-k gate values are selected for routing the token x. If T is the set of selected top-k indices then the output computation of the layer is the linearly weighted combination of each expert's computation on the token by the gate value,
 
-$$y=\sum_{i\in{\cal T}}p_{i}(x)E_{i}(x).\tag{2}$$
+
+$$y = \sum_{i \in T} p_i(x) E_i(x) \quad (2)$$
 
-Switch Routing: Rethinking Mixture-of-Experts. Shazeer et al. (2017) conjectured that routing to k > 1 experts was necessary in order to have non-trivial gradients to the routing functions. The authors intuited that learning to route would not work without the ability to compare at least two experts. Ramachandran and Le (2018) went further to study the top-k decision and found that higher k-values in lower layers in the model were important for models with many routing layers. Contrary to these ideas, we instead use a simplified strategy where we route to only a single expert. We show this simplification preserves model quality, reduces routing computation and performs better. This k = 1 routing strategy is later referred to as a Switch layer. Note that for both MoE and Switch Routing, the gate value pi(x) in Equation 2 permits differentiability of the router.
+Switch Routing: Rethinking Mixture-of-Experts. [Shazeer](#page-38-2) [et](#page-38-2) [al.](#page-38-2) [(2017)](#page-38-2) conjectured that routing to k > 1 experts was necessary in order to have non-trivial gradients to the routing functions. The authors intuited that learning to route would not work without the ability to compare at least two experts. [Ramachandran](#page-37-3) [and](#page-37-3) [Le](#page-37-3) [(2018)](#page-37-3) went further to study the top-k decision and found that higher k-values in lower layers in the model were important for models with many routing layers. Contrary to these ideas, we instead use a simplified strategy where we route to only a single expert. We show this simplification preserves model quality, reduces routing computation and performs better. This k = 1 routing strategy is later referred to as a Switch layer. Note that for both MoE and Switch Routing, the gate value pi(x) in Equation [2](#page-4-1) permits differentiability of the router.
 
-The benefits for the Switch layer are three-fold: (1) The router computation is reduced as we are only routing a token to a single expert. (2) The batch size (expert capacity) of each expert can be at least halved since each token is only being routed to a single expert.3 (3) The routing implementation is simplified and communication costs are reduced. Figure 3 shows an example of routing with different expert capacity factors.
+The benefits for the Switch layer are three-fold: (1) The router computation is reduced as we are only routing a token to a single expert. (2) The batch size (expert capacity) of each expert can be at least halved since each token is only being routed to a single expert.[3](#page-5-1) (3) The routing implementation is simplified and communication costs are reduced. Figure [3](#page-5-2) shows an example of routing with different expert capacity factors.
 
 ![](_page_5_Figure_3.jpeg)
 
-- Figure 3: Illustration of token routing dynamics. Each expert processes a fixed batch-size of tokens modulated by the capacity factor. Each token is routed to the expert with the highest router probability, but each expert has a fixed batch size of (total tokens / num experts) × capacity factor. If the tokens are unevenly dispatched then certain experts will overflow (denoted by dotted red lines), resulting in these tokens not being processed by this layer. A larger capacity factor alleviates this overflow issue, but also increases computation and communication costs (depicted by padded white/empty slots).
-### 2.2 Efficient Sparse Routing
+- Figure 3: Illustration of token routing dynamics. Each expert processes a fixed batch-size of tokens modulated by the capacity factor. Each token is routed to the expert with the highest router probability, but each expert has a fixed batch size of (total tokens / num experts) × capacity factor. If the tokens are unevenly dispatched then certain experts will overflow (denoted by dotted red lines), resulting in these tokens not being processed by this layer. A larger capacity factor alleviates this overflow issue, but also increases computation and communication costs (depicted by padded white/empty slots).
+### 2.2 Efficient Sparse Routing
 
-We use Mesh-Tensorflow (MTF) (Shazeer et al., 2018) which is a library, with similar semantics and API to Tensorflow (Abadi et al., 2016) that facilitates efficient distributed data and model parallel architectures. It does so by abstracting the physical set of cores to a logical mesh of processors. Tensors and computations may then be sharded per named dimensions, facilitating easy partitioning of models across dimensions. We design our model with TPUs in mind, which require statically declared sizes. Below we describe our distributed Switch Transformer implementation.
+We use Mesh-Tensorflow (MTF) [(Shazeer](#page-38-3) [et](#page-38-3) [al.,](#page-38-3) [2018)](#page-38-3) which is a library, with similar semantics and API to Tensorflow [(Abadi](#page-35-3) [et](#page-35-3) [al.,](#page-35-3) [2016)](#page-35-3) that facilitates efficient distributed data and model parallel architectures. It does so by abstracting the physical set of cores to a logical mesh of processors. Tensors and computations may then be sharded per named dimensions, facilitating easy partitioning of models across dimensions. We design our model with TPUs in mind, which require statically declared sizes. Below we describe our distributed Switch Transformer implementation.
 
-3. See Section 2.2 for a technical description.
+3. See Section [2.2](#page-5-0) for a technical description.
 
 Distributed Switch Implementation. All of our tensor shapes are statically determined at compilation time, but our computation is dynamic due to the routing decisions at training and inference. Because of this, one important technical consideration is how to set the expert capacity. The expert capacity—the number of tokens each expert computes—is set by evenly dividing the number of tokens in the batch across the number of experts, and then further expanding by a capacity factor,
 
-$$\text{expert capacity=}\!\left(\frac{\text{tokens per batch}}{\text{number of experts}}\right)\times\text{capacity factor}.\tag{3}$$
+$$\text{expert capacity} = \left(\frac{\text{tokens per batch}}{\text{number of experts}}\right) \times \text{capacity factor}. \tag{3}$$
 
-A capacity factor greater than 1.0 creates additional buffer to accommodate for when tokens are not perfectly balanced across experts. If too many tokens are routed to an expert (referred to later as dropped tokens), computation is skipped and the token representation is passed directly to the next layer through the residual connection. Increasing the expert capacity is not without drawbacks, however, since high values will result in wasted computation and memory. This trade-off is explained in Figure 3. Empirically we find ensuring lower rates of dropped tokens are important for the scaling of sparse expert-models. Throughout our experiments we didn't notice any dependency on the number of experts for the number of tokens dropped (typically < 1%). Using the auxiliary load balancing loss (next section) with a high enough coefficient ensured good load balancing. We study the impact that these design decisions have on model quality and speed in Table 1.
+A capacity factor greater than 1.0 creates additional buffer to accommodate for when tokens are not perfectly balanced across experts. If too many tokens are routed to an expert (referred to later as dropped tokens), computation is skipped and the token representation is passed directly to the next layer through the residual connection. Increasing the expert capacity is not without drawbacks, however, since high values will result in wasted computation and memory. This trade-off is explained in Figure [3.](#page-5-2) Empirically we find ensuring lower rates of dropped tokens are important for the scaling of sparse expert-models. Throughout our experiments we didn't notice any dependency on the number of experts for the number of tokens dropped (typically < 1%). Using the auxiliary load balancing loss (next section) with a high enough coefficient ensured good load balancing. We study the impact that these design decisions have on model quality and speed in Table [1.](#page-8-0)
 
-A Differentiable Load Balancing Loss. To encourage a balanced load across experts we add an auxiliary loss (Shazeer et al., 2017, 2018; Lepikhin et al., 2020). As in Shazeer et al. (2018); Lepikhin et al. (2020), Switch Transformers simplifies the original design in Shazeer et al. (2017) which had separate load-balancing and importance-weighting losses. For each Switch layer, this auxiliary loss is added to the total model loss during training. Given N experts indexed by i = 1 to N and a batch B with T tokens, the auxiliary loss is computed as the scaled dot-product between vectors f and P,
+A Differentiable Load Balancing Loss. To encourage a balanced load across experts we add an auxiliary loss [(Shazeer](#page-38-2) [et](#page-38-2) [al.,](#page-38-2) [2017,](#page-38-2) [2018;](#page-38-3) [Lepikhin](#page-37-2) [et](#page-37-2) [al.,](#page-37-2) [2020)](#page-37-2). As in [Shazeer](#page-38-3) [et](#page-38-3) [al.](#page-38-3) [(2018)](#page-38-3); [Lepikhin](#page-37-2) [et](#page-37-2) [al.](#page-37-2) [(2020)](#page-37-2), Switch Transformers simplifies the original design in [Shazeer](#page-38-2) [et](#page-38-2) [al.](#page-38-2) [(2017)](#page-38-2) which had separate load-balancing and importance-weighting losses. For each Switch layer, this auxiliary loss is added to the total model loss during training. Given N experts indexed by i = 1 to N and a batch B with T tokens, the auxiliary loss is computed as the scaled dot-product between vectors f and P,
 
-$$\text{loss}=\alpha\cdot N\cdot\sum_{i=1}^{N}f_{i}\cdot P_{i}\tag{4}$$
+$$\text{loss} = \alpha \cdot N \cdot \sum_{i=1}^{N} f_i \cdot P_i \quad (4)$$
 
-where fi is the fraction of tokens dispatched to expert i,
+where fi is the fraction of tokens dispatched to expert i,
 
-$$f_{i}=\frac{1}{T}\sum_{x\in\mathcal{B}}1\left\{\operatorname*{argmax}p(x)=i\right\}\tag{5}$$
+$$f_{i} = \frac{1}{T} \sum_{x \in \mathcal{B}} \mathbb{1} \left\{ \operatorname*{argmax} p(x) = i \right\} \tag{5}$$
 
-and Pi is the fraction of the router probability allocated for expert i, 2
+and Pi is the fraction of the router probability allocated for expert i, [2](#page-6-0)
 
-$$P_{i}=\frac{1}{T}\sum_{x\in\mathcal{B}}p_{i}(x).\tag{6}$$
+$$P_{i}=  
+\frac{1}{T}  
+\sum_{x  
+in\mathcal{B}}p_{i}(x).  
+\tag{6}$$
 
-Since we seek uniform routing of the batch of tokens across the N experts, we desire both vectors to have values of 1/N. The auxiliary loss of Equation 4 encourages uniform routing since it is minimized under a uniform distribution. The objective can also be differentiated as
+Since we seek uniform routing of the batch of tokens across the N experts, we desire both vectors to have values of 1/N. The auxiliary loss of Equation [4](#page-6-1) encourages uniform routing since it is minimized under a uniform distribution. The objective can also be differentiated as
 
-2. A potential source of confusion: pi(x) is the probability of routing token x to expert i. Pi is the probability fraction to expert i across all tokens in the batch B.
+2. A potential source of confusion: pi(x) is the probability of routing token x to expert i. Pi is the probability fraction to expert i across all tokens in the batch B.
 
 the P-vector is differentiable, but the f-vector is not. The final loss is multiplied by expert count N to keep the loss constant as the number of experts varies since under uniform routing PN i=1(fi · Pi) = PN i=1( 1 N · 1 N ) = 1 N . Finally, a hyper-parameter α is a multiplicative coefficient for these auxiliary losses; throughout this work we use an α = 10−2 which was sufficiently large to ensure load balancing while small enough to not to overwhelm the primary cross-entropy objective. We swept hyper-parameter ranges of α from 10−1 to 10−5 in powers of 10 and found 10−2 balanced load quickly without interfering with training loss.
 
-### 2.3 Putting It All Together: The Switch Transformer
+#### 2.3 Putting It All Together: The Switch Transformer
 
-Our first test of the Switch Transformer starts with pre-training on the "Colossal Clean Crawled Corpus" (C4), introduced in (Raffel et al., 2019). For our pre-training objective, we use a masked language modeling task (Taylor, 1953; Fedus et al., 2018; Devlin et al., 2018) where the model is trained to predict missing tokens. In our pre-training setting, as determined in Raffel et al. (2019) to be optimal, we drop out 15% of tokens and then replace the masked sequence with a single sentinel token. To compare our models, we record the negative log perplexity.4 Throughout all tables in the paper, ↑ indicates that a higher value for that metric is better and vice-versa for ↓. A comparison of all the models studied in this work are in Table 9.
+Our first test of the Switch Transformer starts with pre-training on the "Colossal Clean Crawled Corpus" (C4), introduced in [(Raffel](#page-37-0) [et](#page-37-0) [al.,](#page-37-0) [2019)](#page-37-0). For our pre-training objective, we use a masked language modeling task [(Taylor,](#page-38-4) [1953;](#page-38-4) [Fedus](#page-35-4) [et](#page-35-4) [al.,](#page-35-4) [2018;](#page-35-4) [Devlin](#page-35-5) [et](#page-35-5) [al.,](#page-35-5) [2018)](#page-35-5) where the model is trained to predict missing tokens. In our pre-training setting, as determined in [Raffel](#page-37-0) [et](#page-37-0) [al.](#page-37-0) [(2019)](#page-37-0) to be optimal, we drop out 15% of tokens and then replace the masked sequence with a single sentinel token. To compare our models, we record the negative log perplexity.[4](#page-7-2) Throughout all tables in the paper, ↑ indicates that a higher value for that metric is better and vice-versa for ↓. A comparison of all the models studied in this work are in Table [9.](#page-22-0)
 
-A head-to-head comparison of the Switch Transformer and the MoE Transformer is presented in Table 1. Our Switch Transformer model is FLOP-matched to 'T5-Base' (Raffel et al., 2019) (same amount of computation per token is applied). The MoE Transformer, using top-2 routing, has two experts which each apply a separate FFN to each token and thus its FLOPS are larger. All models were trained for the same number of steps on identical hardware. Note that the MoE model going from capacity factor 2.0 to 1.25 actually slows down (840 to 790) in the above experiment setup, which is unexpected.5
+A head-to-head comparison of the Switch Transformer and the MoE Transformer is presented in Table [1.](#page-8-0) Our Switch Transformer model is FLOP-matched to 'T5-Base' [(Raffel](#page-37-0) [et](#page-37-0) [al.,](#page-37-0) [2019)](#page-37-0) (same amount of computation per token is applied). The MoE Transformer, using top-2 routing, has two experts which each apply a separate FFN to each token and thus its FLOPS are larger. All models were trained for the same number of steps on identical hardware. Note that the MoE model going from capacity factor 2.0 to 1.25 actually slows down (840 to 790) in the above experiment setup, which is unexpected.[5](#page-7-3)
 
-We highlight three key findings from Table 1: (1) Switch Transformers outperform both carefully tuned dense models and MoE Transformers on a speed-quality basis. For a fixed amount of computation and wall-clock time, Switch Transformers achieve the best result. (2) The Switch Transformer has a smaller computational footprint than the MoE counterpart. If we increase its size to match the training speed of the MoE Transformer, we find this outperforms all MoE and Dense models on a per step basis as well. (3) Switch Transformers perform better at lower capacity factors (1.0, 1.25). Smaller expert capacities are indicative of the scenario in the large model regime where model memory is very scarce and the capacity factor will want to be made as small as possible.
+We highlight three key findings from Table [1:](#page-8-0) (1) Switch Transformers outperform both carefully tuned dense models and MoE Transformers on a speed-quality basis. For a fixed amount of computation and wall-clock time, Switch Transformers achieve the best result. (2) The Switch Transformer has a smaller computational footprint than the MoE counterpart. If we increase its size to match the training speed of the MoE Transformer, we find this outperforms all MoE and Dense models on a per step basis as well. (3) Switch Transformers perform better at lower capacity factors (1.0, 1.25). Smaller expert capacities are indicative of the scenario in the large model regime where model memory is very scarce and the capacity factor will want to be made as small as possible.
 
-#### 2.4 Improved Training and Fine-Tuning Techniques
+#### 2.4 Improved Training and Fine-Tuning Techniques
 
-Sparse expert models may introduce training difficulties over a vanilla Transformer. Instability can result because of the hard-switching (routing) decisions at each of these layers. Further, low precision formats like bfloat16 (Wang and Kanwar, 2019) can exacerbate issues
+Sparse expert models may introduce training difficulties over a vanilla Transformer. Instability can result because of the hard-switching (routing) decisions at each of these layers. Further, low precision formats like bfloat16 [(Wang](#page-39-3) [and](#page-39-3) [Kanwar,](#page-39-3) [2019)](#page-39-3) can exacerbate issues
 
-4. We use log base-e for this metric so the units are nats.
+4. We use log base-e for this metric so the units are nats.
 
-5. Note that speed measurements are both a function of the algorithm and the implementation details. Switch Transformer reduces the necessary computation relative to MoE (algorithm), but the final speed differences are impacted by low-level optimizations (implementation).
+5. Note that speed measurements are both a function of the algorithm and the implementation details. Switch Transformer reduces the necessary computation relative to MoE (algorithm), but the final speed differences are impacted by low-level optimizations (implementation).
 
-| Model | Capacity Factor | Quality after | Time to Quality Threshold (↓) | Speed (↑) |
-| --- | --- | --- | --- | --- |
-|  |  | 100k steps (↑) |  | (examples/sec) |
-|  |  | (Neg. Log Perp.) | (hours) |  |
-| T5-Base | — | -1.731 | Not achieved† | 1600 |
-| T5-Large | — | -1.550 | 131.1 | 470 |
-| MoE-Base | 2.0 | -1.547 | 68.7 | 840 |
-| Switch-Base | 2.0 | -1.554 | 72.8 | 860 |
-| MoE-Base | 1.25 | -1.559 | 80.7 | 790 |
-| Switch-Base | 1.25 | -1.553 | 65.0 | 910 |
-| MoE-Base | 1.0 | -1.572 | 80.1 | 860 |
-| Switch-Base | 1.0 | -1.561 | 62.8 | 1000 |
-| Switch-Base+ | 1.0 | -1.534 | 67.6 | 780 |
+| Model        | Capacity
    Factor | Quality after
    100k steps (↑)
    (Neg. Log Perp.) | Time to Quality
    Threshold (↓)
    (hours) | Speed (↑)
    (examples/sec) |
+|--------------|---------------------|-------------------------------------------------------|-----------------------------------------------|------------------------------|
+| T5-Base      | —                   | -1.731                                                | Not achieved†                                 | 1600                         |
+| T5-Large     | —                   | -1.550                                                | 131.1                                         | 470                          |
+| MoE-Base     | 2.0                 | -1.547                                                | 68.7                                          | 840                          |
+| Switch-Base  | 2.0                 | -1.554                                                | 72.8                                          | 860                          |
+| MoE-Base     | 1.25                | -1.559                                                | 80.7                                          | 790                          |
+| Switch-Base  | 1.25                | -1.553                                                | 65.0                                          | 910                          |
+| MoE-Base     | 1.0                 | -1.572                                                | 80.1                                          | 860                          |
+| Switch-Base  | 1.0                 | -1.561                                                | 62.8                                          | 1000                         |
+| Switch-Base+ | 1.0                 | -1.534                                                | 67.6                                          | 780                          |
 
-- Table 1: Benchmarking Switch versus MoE. Head-to-head comparison measuring per step and per time benefits of the Switch Transformer over the MoE Transformer and T5 dense baselines. We measure quality by the negative log perplexity and the time to reach an arbitrary chosen quality threshold of Neg. Log Perp.=-1.50. All MoE and Switch Transformer models use 128 experts, with experts at every other feed-forward layer. For Switch-Base+, we increase the model size until it matches the speed of the MoE model by increasing the model hidden-size from 768 to 896 and the number of heads from 14 to 16. All models are trained with the same amount of computation (32 cores) and on the same hardware (TPUv3). Further note that all our models required pre-training beyond 100k steps to achieve our level threshold of -1.50. † T5-Base did not achieve this negative log perplexity in the 100k steps the models were trained.
+- Table 1: Benchmarking Switch versus MoE. Head-to-head comparison measuring per step and per time benefits of the Switch Transformer over the MoE Transformer and T5 dense baselines. We measure quality by the negative log perplexity and the time to reach an arbitrary chosen quality threshold of Neg. Log Perp.=-1.50. All MoE and Switch Transformer models use 128 experts, with experts at every other feed-forward layer. For Switch-Base+, we increase the model size until it matches the speed of the MoE model by increasing the model hidden-size from 768 to 896 and the number of heads from 14 to 16. All models are trained with the same amount of computation (32 cores) and on the same hardware (TPUv3). Further note that all our models required pre-training beyond 100k steps to achieve our level threshold of -1.50. † T5-Base did not achieve this negative log perplexity in the 100k steps the models were trained.
 in the softmax computation for our router. We describe training difficulties here and the methods we use to overcome them to achieve stable and scalable training.
 
-Selective precision with large sparse models. Model instability hinders the ability to train using efficient bfloat16 precision, and as a result, Lepikhin et al. (2020) trains with float32 precision throughout their MoE Transformer. However, we show that by instead selectively casting to float32 precision within a localized part of the model, stability may be achieved, without incurring expensive communication cost of float32 tensors. This technique is inline with modern mixed precision training strategies where certain parts of the model and gradient updates are done in higher precision Micikevicius et al. (2017). Table 2 shows that our approach permits nearly equal speed to bfloat16 training while conferring the training stability of float32.
+Selective precision with large sparse models. Model instability hinders the ability to train using efficient bfloat16 precision, and as a result, [Lepikhin](#page-37-2) [et](#page-37-2) [al.](#page-37-2) [(2020)](#page-37-2) trains with float32 precision throughout their MoE Transformer. However, we show that by instead selectively casting to float32 precision within a localized part of the model, stability may be achieved, without incurring expensive communication cost of float32 tensors. This technique is inline with modern mixed precision training strategies where certain parts of the model and gradient updates are done in higher precision [Micikevicius](#page-37-4) [et](#page-37-4) [al.](#page-37-4) [(2017)](#page-37-4). Table [2](#page-9-0) shows that our approach permits nearly equal speed to bfloat16 training while conferring the training stability of float32.
 
-To achieve this, we cast the router input to float32 precision. The router function takes the tokens as input and produces the dispatch and combine tensors used for the selection and recombination of expert computation (refer to Code Block 15 in the Appendix for details). Importantly, the float32 precision is only used within the body of the router function—on computations local to that device. Because the resulting dispatch and combine tensors are recast to bfloat16 precision at the end of the function, no expensive float32 tensors
+To achieve this, we cast the router input to float32 precision. The router function takes the tokens as input and produces the dispatch and combine tensors used for the selection and recombination of expert computation (refer to Code Block [15](#page-33-0) in the Appendix for details). Importantly, the float32 precision is only used within the body of the router function—on computations local to that device. Because the resulting dispatch and combine tensors are recast to bfloat16 precision at the end of the function, no expensive float32 tensors
 
-| Model | Quality | Speed |
-| --- | --- | --- |
-| (precision) | (Neg. Log Perp.) (↑) | (Examples/sec) (↑) |
-| Switch-Base (float32) | -1.718 | 1160 |
-| Switch-Base (bfloat16) | -3.780 [diverged] | 1390 |
-| Switch-Base (Selective precision) | -1.716 | 1390 |
+| Model                             | Quality              | Speed              |
+|-----------------------------------|----------------------|--------------------|
+| (precision)                       | (Neg. Log Perp.) (↑) | (Examples/sec) (↑) |
+| Switch-Base (float32)             | -1.718               | 1160               |
+| Switch-Base (bfloat16)            | -3.780 [diverged]    | 1390               |
+| Switch-Base (Selective precision) | -1.716               | 1390               |
 
-Table 2: Selective precision. We cast the local routing operations to float32 while preserving bfloat16 precision elsewhere to stabilize our model while achieving nearly equal speed to (unstable) bfloat16-precision training. We measure the quality of a 32 expert model after a fixed step count early in training its speed performance. For both Switch-Base in float32 and with Selective prevision we notice similar learning dynamics.
+Table 2: Selective precision. We cast the local routing operations to float32 while preserving bfloat16 precision elsewhere to stabilize our model while achieving nearly equal speed to (unstable) bfloat16-precision training. We measure the quality of a 32 expert model after a fixed step count early in training its speed performance. For both Switch-Base in float32 and with Selective prevision we notice similar learning dynamics.
 
 are broadcast through all-to-all communication operations, but we still benefit from the increased stability of float32.
 
-Smaller parameter initialization for stability. Appropriate initialization is critical to successful training in deep learning and we especially observe this to be true for Switch Transformer. We initialize our weight matrices by drawing elements from a truncated normal distribution with mean µ = 0 and standard deviation σ = p s/n where s is a scale hyper-parameter and n is the number of input units in the weight tensor (e.g. fan-in).6
+Smaller parameter initialization for stability. Appropriate initialization is critical to successful training in deep learning and we especially observe this to be true for Switch Transformer. We initialize our weight matrices by drawing elements from a truncated normal distribution with mean μ = 0 and standard deviation σ = √s/n where s is a scale hyper-parameter and n is the number of input units in the weight tensor (e.g., fan-in).
 
-As an additional remedy to the instability, we recommend reducing the default Transformer initialization scale s = 1.0 by a factor of 10. This both improves quality and reduces the likelihood of destabilized training in our experiments. Table 3 measures the improvement of the model quality and reduction of the variance early in training. We find that
+As an additional remedy to the instability, we recommend reducing the default Transformer initialization scale s = 1.0 by a factor of 10. This both improves quality and reduces the likelihood of destabilized training in our experiments. Table [3](#page-9-2) measures the improvement of the model quality and reduction of the variance early in training. We find that
 
-| Model (Initialization scale) | Average Quality | Std. Dev. of Quality |
-| --- | --- | --- |
-|  | (Neg. Log Perp.) | (Neg. Log Perp.) |
-| Switch-Base (0.1x-init) | -2.72 | 0.01 |
-| Switch-Base (1.0x-init) | -3.60 | 0.68 |
+| Model (Initialization scale) | Average Quality
    (Neg. Log Perp.) | Std. Dev. of Quality
    (Neg. Log Perp.) |
+|------------------------------|--------------------------------------|-------------------------------------------|
+| Switch-Base (0.1x-init)      | -2.72                                | 0.01                                      |
+| Switch-Base (1.0x-init)      | -3.60                                | 0.68                                      |
 
-- Table 3: Reduced initialization scale improves stability. Reducing the initialization scale results in better model quality and more stable training of Switch Transformer. Here we record the average and standard deviation of model quality, measured by the negative log perplexity, of a 32 expert model after 3.5k steps (3 random seeds each).
+- Table 3: Reduced initialization scale improves stability. Reducing the initialization scale results in better model quality and more stable training of Switch Transformer. Here we record the average and standard deviation of model quality, measured by the negative log perplexity, of a 32 expert model after 3.5k steps (3 random seeds each).
 the average model quality, as measured by the Neg. Log Perp., is dramatically improved and there is a far reduced variance across runs. Further, this same initialization scheme is broadly effective for models spanning several orders of magnitude. We use the same approach to stably train models as small as our 223M parameter baseline to enormous models in excess of one trillion parameters.
 
-6. Values greater than two standard deviations from the mean are resampled.
+6. Values greater than two standard deviations from the mean are resampled.
+
+Regularizing large sparse models. Our paper considers the common NLP approach of pre-training on a large corpus followed by fine-tuning on smaller downstream tasks such as summarization or question answering. One issue that naturally arises is overfitting since many fine-tuning tasks have very few examples. During fine-tuning of standard Transformers, [Raffel](#page-37-0) [et](#page-37-0) [al.](#page-37-0) [(2019)](#page-37-0) use dropout [(Srivastava](#page-38-5) [et](#page-38-5) [al.,](#page-38-5) [2014)](#page-38-5) at each layer to prevent overfitting. Our Switch Transformers have significantly more parameters than the FLOP matched dense baseline, which can lead to more severe overfitting on these smaller downstream tasks.
 
-Regularizing large sparse models. Our paper considers the common NLP approach of pre-training on a large corpus followed by fine-tuning on smaller downstream tasks such as summarization or question answering. One issue that naturally arises is overfitting since many fine-tuning tasks have very few examples. During fine-tuning of standard Transformers, Raffel et al. (2019) use dropout (Srivastava et al., 2014) at each layer to prevent overfitting. Our Switch Transformers have significantly more parameters than the FLOP matched dense baseline, which can lead to more severe overfitting on these smaller downstream tasks.
+| Model (dropout)             | GLUE | CNNDM | SQuAD | SuperGLUE |
+|-----------------------------|------|-------|-------|-----------|
+| T5-Base (d=0.1)             | 82.9 | 19.6  | 83.5  | 72.4      |
+| Switch-Base (d=0.1)         | 84.7 | 19.1  | 83.7  | 73.0      |
+| Switch-Base (d=0.2)         | 84.4 | 19.2  | 83.9  | 73.2      |
+| Switch-Base (d=0.3)         | 83.9 | 19.6  | 83.4  | 70.7      |
+| Switch-Base (d=0.1, ed=0.4) | 85.2 | 19.6  | 83.7  | 73.0      |
 
-| Model (dropout) | GLUE | CNNDM | SQuAD | SuperGLUE |
-| --- | --- | --- | --- | --- |
-| T5-Base (d=0.1) | 82.9 | 19.6 | 83.5 | 72.4 |
-| Switch-Base (d=0.1) | 84.7 | 19.1 | 83.7 | 73.0 |
-| Switch-Base (d=0.2) | 84.4 | 19.2 | 83.9 | 73.2 |
-| Switch-Base (d=0.3) | 83.9 | 19.6 | 83.4 | 70.7 |
-| Switch-Base (d=0.1, ed=0.4) | 85.2 | 19.6 | 83.7 | 73.0 |
+Table 4: Fine-tuning regularization results. A sweep of dropout rates while fine-tuning Switch Transformer models pre-trained on 34B tokens of the C4 data set (higher numbers are better). We observe that using a lower standard dropout rate at all non-expert layer, with a much larger dropout rate on the expert feed-forward layers, to perform the best.
 
-- Table 4: Fine-tuning regularization results. A sweep of dropout rates while fine-tuning Switch Transformer models pre-trained on 34B tokens of the C4 data set (higher numbers are better). We observe that using a lower standard dropout rate at all non-expert layer, with a much larger dropout rate on the expert feed-forward layers, to perform the best.
-We thus propose a simple way to alleviate this issue during fine-tuning: increase the dropout inside the experts, which we name as expert dropout. During fine-tuning we simply increase the dropout rate by a significant amount only at the interim feed-forward computation at each expert layer. Table 4 has the results for our expert dropout protocol. We observe that simply increasing the dropout across all layers leads to worse performance. However, setting a smaller dropout rate (0.1) at non-expert layers and a much larger dropout rate (0.4) at expert layers leads to performance improvements on four smaller downstream tasks.
+We thus propose a simple way to alleviate this issue during fine-tuning: increase the dropout inside the experts, which we name as expert dropout. During fine-tuning we simply increase the dropout rate by a significant amount only at the interim feed-forward computation at each expert layer. Table [4](#page-10-1) has the results for our expert dropout protocol. We observe that simply increasing the dropout across all layers leads to worse performance. However, setting a smaller dropout rate (0.1) at non-expert layers and a much larger dropout rate (0.4) at expert layers leads to performance improvements on four smaller downstream tasks.
 
-### 3. Scaling Properties
+### 3. Scaling Properties
 
-We present a study of the scaling properties of the Switch Transformer architecture during pre-training. Per Kaplan et al. (2020), we consider a regime where the model is not bottlenecked by either the computational budget or amount of data. To avoid the data bottleneck, we use the large C4 corpus with over 180B target tokens (Raffel et al., 2019) and we train until diminishing returns are observed.
+We present a study of the scaling properties of the Switch Transformer architecture during pre-training. Per [Kaplan](#page-36-0) [et](#page-36-0) [al.](#page-36-0) [(2020)](#page-36-0), we consider a regime where the model is not bottlenecked by either the computational budget or amount of data. To avoid the data bottleneck, we use the large C4 corpus with over 180B target tokens [(Raffel](#page-37-0) [et](#page-37-0) [al.,](#page-37-0) [2019)](#page-37-0) and we train until diminishing returns are observed.
 
-The number of experts is the most efficient dimension for scaling our model. Increasing the experts keeps the computational cost approximately fixed since the model only selects one expert per token, regardless of the number of experts to choose from. The router must compute a probability distribution over more experts, however, this is a lightweight computation of cost O(dmodel × num experts) where dmodel is the embedding dimension of tokens passed between the layers. In this section, we consider the scaling properties on a step-basis and a time-basis with a fixed computational budget.
+The number of experts is the most efficient dimension for scaling our model. Increasing the experts keeps the computational cost approximately fixed since the model only selects one expert per token, regardless of the number of experts to choose from. The router must compute a probability distribution over more experts, however, this is a lightweight computation of cost $O(d_{model} \times \text{num experts})$ where $d_{model}$ is the embedding dimension of tokens passed between the layers. In this section, we consider the scaling properties on a step-basis and a time-basis with a fixed computational budget.
 
-#### 3.1 Scaling Results on a Step-Basis
+#### 3.1 Scaling Results on a Step-Basis
 
-Figure 4 demonstrates consistent scaling benefits with the number of experts when training all models for a fixed number of steps. We observe a clear trend: when keeping the FLOPS per token fixed, having more parameters (experts) speeds up training. The left Figure demonstrates consistent scaling properties (with fixed FLOPS per token) between sparse model parameters and test loss. This reveals the advantage of scaling along this additional axis of sparse model parameters. Our right Figure measures sample efficiency of a dense model variant and four FLOP-matched sparse variants. We find that increasing the number of experts leads to more sample efficient models. Our Switch-Base 64 expert model achieves the same performance of the T5-Base model at step 60k at step 450k, which is a 7.5x speedup in terms of step time. In addition, consistent with the findings of Kaplan et al. (2020), we find that larger models are also more sample efficient—learning more quickly for a fixed number of observed tokens.
+Figure [4](#page-11-1) demonstrates consistent scaling benefits with the number of experts when training all models for a fixed number of steps. We observe a clear trend: when keeping the FLOPS per token fixed, having more parameters (experts) speeds up training. The left Figure demonstrates consistent scaling properties (with fixed FLOPS per token) between sparse model parameters and test loss. This reveals the advantage of scaling along this additional axis of sparse model parameters. Our right Figure measures sample efficiency of a dense model variant and four FLOP-matched sparse variants. We find that increasing the number of experts leads to more sample efficient models. Our Switch-Base 64 expert model achieves the same performance of the T5-Base model at step 60k at step 450k, which is a 7.5x speedup in terms of step time. In addition, consistent with the findings of [Kaplan](#page-36-0) [et](#page-36-0) [al.](#page-36-0) [(2020)](#page-36-0), we find that larger models are also more sample efficient—learning more quickly for a fixed number of observed tokens.
 
 ![](_page_11_Figure_4.jpeg)
 
-- Figure 4: Scaling properties of the Switch Transformer. Left Plot: We measure the quality improvement, as measured by perplexity, as the parameters increase by scaling the number of experts. The top-left point corresponds to the T5-Base model with 223M parameters. Moving from top-left to bottom-right, we double the number of experts from 2, 4, 8 and so on until the bottom-right point of a 256 expert model with 14.7B parameters. Despite all models using an equal computational budget, we observe consistent improvements scaling the number of experts. Right Plot: Negative log perplexity per step sweeping over the number of experts. The dense baseline is shown with the purple line and we note improved sample efficiency of our Switch-Base models.
-#### 3.2 Scaling Results on a Time-Basis
+Figure 4: Scaling properties of the Switch Transformer. Left Plot: We measure the quality improvement, as measured by perplexity, as the parameters increase by scaling the number of experts. The top-left point corresponds to the T5-Base model with 223M parameters. Moving from top-left to bottom-right, we double the number of experts from 2, 4, 8 and so on until the bottom-right point of a 256 expert model with 14.7B parameters. Despite all models using an equal computational budget, we observe consistent improvements scaling the number of experts. Right Plot: Negative log perplexity per step sweeping over the number of experts. The dense baseline is shown with the purple line and we note improved sample efficiency of our Switch-Base models.
 
-Figure 4 demonstrates that on a step basis, as we increase the number of experts, the performance consistently improves. While our models have roughly the same amount of FLOPS per token as the baseline, our Switch Transformers incurs additional communication costs across devices as well as the extra computation of the routing mechanism. Therefore, the increased sample efficiency observed on a step-basis doesn't necessarily translate to a better model quality as measured by wall-clock. This raises the question:
+#### 3.2 Scaling Results on a Time-Basis
+
+Figure [4](#page-11-1) demonstrates that on a step basis, as we increase the number of experts, the performance consistently improves. While our models have roughly the same amount of FLOPS per token as the baseline, our Switch Transformers incurs additional communication costs across devices as well as the extra computation of the routing mechanism. Therefore, the increased sample efficiency observed on a step-basis doesn't necessarily translate to a better model quality as measured by wall-clock. This raises the question:
 
 For a fixed training duration and computational budget, should one train a dense or a sparse model?
 
 ![](_page_12_Figure_4.jpeg)
 
-Figure 5: Speed advantage of Switch Transformer. All models trained on 32 TPUv3 cores with equal FLOPs per example. For a fixed amount of computation and training time, Switch Transformers significantly outperform the dense Transformer baseline. Our 64 expert Switch-Base model achieves the same quality in one-seventh the time of the T5-Base and continues to improve.
+Figure 5: Speed advantage of Switch Transformer. All models trained on 32 TPUv3 cores with equal FLOPs per example. For a fixed amount of computation and training time, Switch Transformers significantly outperform the dense Transformer baseline. Our 64 expert Switch-Base model achieves the same quality in one-seventh the time of the T5-Base and continues to improve.
 
-Figures 5 and 6 address this question. Figure 5 measures the pre-training model quality as a function of time. For a fixed training duration and computational budget, Switch Transformers yield a substantial speed-up. In this setting, our Switch-Base 64 expert model trains in one-seventh the time that it would take the T5-Base to get similar perplexity.
+Figures [5](#page-12-2) and [6](#page-13-2) address this question. Figure [5](#page-12-2) measures the pre-training model quality as a function of time. For a fixed training duration and computational budget, Switch Transformers yield a substantial speed-up. In this setting, our Switch-Base 64 expert model trains in one-seventh the time that it would take the T5-Base to get similar perplexity.
 
-#### 3.3 Scaling Versus a Larger Dense Model
+#### 3.3 Scaling Versus a Larger Dense Model
 
-The above analysis shows that a computationally-matched dense model is outpaced by its Switch counterpart. Figure 6 considers a different scenario: what if we instead had allocated our resources to a larger dense model? We do so now, measuring Switch-Base against the next strong baseline, T5-Large. But despite T5-Large applying 3.5x more FLOPs per token, Switch-Base is still more sample efficient and yields a 2.5x speedup. Furthermore, more gains can be had simply by designing a new, larger sparse version, Switch-Large, which is FLOP-matched to T5-Large. We do this and demonstrate superior scaling and fine-tuning in the following section.
+The above analysis shows that a computationally-matched dense model is outpaced by its Switch counterpart. Figure [6](#page-13-2) considers a different scenario: what if we instead had allocated our resources to a larger dense model? We do so now, measuring Switch-Base against the next strong baseline, T5-Large. But despite T5-Large applying 3.5x more FLOPs per token, Switch-Base is still more sample efficient and yields a 2.5x speedup. Furthermore, more gains can be had simply by designing a new, larger sparse version, Switch-Large, which is FLOP-matched to T5-Large. We do this and demonstrate superior scaling and fine-tuning in the following section.
 
 ![](_page_13_Figure_2.jpeg)
 
-Figure 6: Scaling Transformer models with Switch layers or with standard dense model scaling. Left Plot: Switch-Base is more sample efficient than both the T5-Base, and T5-Large variant, which applies 3.5x more FLOPS per token. Right Plot: As before, on a wall-clock basis, we find that Switch-Base is still faster, and yields a 2.5x speedup over T5-Large.
+Figure 6: Scaling Transformer models with Switch layers or with standard dense model scaling. Left Plot: Switch-Base is more sample efficient than both the T5-Base, and T5-Large variant, which applies 3.5x more FLOPS per token. Right Plot: As before, on a wall-clock basis, we find that Switch-Base is still faster, and yields a 2.5x speedup over T5-Large.
 
-# 4. Downstream Results
+### 4. Downstream Results
 
-Section 3 demonstrated the superior scaling properties while pre-training, but we now validate that these gains translate to improved language learning abilities on downstream tasks. We begin by fine-tuning on a diverse set of NLP tasks. Next we study reducing the memory footprint of our sparse models by over 90% by distilling into small—and easily deployed—dense baselines. Finally, we conclude this section measuring the improvements in a multi-task, multilingual setting, where we show that Switch Transformers are strong multi-task learners, improving over the multilingual T5-base model across all 101 languages.
+Section [3](#page-10-0) demonstrated the superior scaling properties while pre-training, but we now validate that these gains translate to improved language learning abilities on downstream tasks. We begin by fine-tuning on a diverse set of NLP tasks. Next we study reducing the memory footprint of our sparse models by over 90% by distilling into small—and easily deployed—dense baselines. Finally, we conclude this section measuring the improvements in a multi-task, multilingual setting, where we show that Switch Transformers are strong multi-task learners, improving over the multilingual T5-base model across all 101 languages.
 
-#### 4.1 Fine-Tuning
+### 4.1 Fine-Tuning
 
-Baseline and Switch models used for fine-tuning. Our baselines are the highly-tuned 223M parameter T5-Base model and the 739M parameter T5-Large model (Raffel et al., 2019). For both versions, we design a FLOP-matched Switch Transformer, with many more parameters, which is summarized in Table 9. 7 Our baselines differ slightly from those in Raffel et al. (2019) because we pre-train on an improved C4 corpus which removes intraexample text duplication and thus increases the efficacy as a pre-training task Lee et al.
+Baseline and Switch models used for fine-tuning. Our baselines are the highly-tuned 223M parameter T5-Base model and the 739M parameter T5-Large model [(Raffel](#page-37-0) [et](#page-37-0) [al.,](#page-37-0) [2019)](#page-37-0). For both versions, we design a FLOP-matched Switch Transformer, with many more parameters, which is summarized in Table [9.](#page-22-0) [7](#page-13-3) Our baselines differ slightly from those in [Raffel](#page-37-0) [et](#page-37-0) [al.](#page-37-0) [(2019)](#page-37-0) because we pre-train on an improved C4 corpus which removes intraexample text duplication and thus increases the efficacy as a pre-training task [Lee](#page-37-5) [et](#page-37-5) [al.](#page-37-5)
 
-7. FLOPS are calculated for the forward pass as done in Kaplan et al. (2020).
+7. FLOPS are calculated for the forward pass as done in [Kaplan](#page-36-0) [et](#page-36-0) [al.](#page-36-0) [(2020)](#page-36-0).
 
-(2021). In our protocol we pre-train with 220 (1,048,576) tokens per batch for 550k steps amounting to 576B total tokens. We then fine-tune across a diverse set of tasks using a dropout rate of 0.1 for all layers except the Switch layers, which use a dropout rate of 0.4 (see Table 4). We fine-tune using a batch-size of 1M for 16k steps and for each task, we evaluate model quality every 200-steps and report the peak performance as computed on the validation set.
+[(2021)](#page-37-5). In our protocol we pre-train with 220 (1,048,576) tokens per batch for 550k steps amounting to 576B total tokens. We then fine-tune across a diverse set of tasks using a dropout rate of 0.1 for all layers except the Switch layers, which use a dropout rate of 0.4 (see Table [4)](#page-10-1). We fine-tune using a batch-size of 1M for 16k steps and for each task, we evaluate model quality every 200-steps and report the peak performance as computed on the validation set.
 
-Fine-tuning tasks and data sets. We select tasks probing language capabilities including question answering, summarization and knowledge about the world. The language benchmarks GLUE (Wang et al., 2018) and SuperGLUE (Wang et al., 2019) are handled as composite mixtures with all the tasks blended in proportion to the amount of tokens present in each. These benchmarks consist of tasks requiring sentiment analysis (SST-2), word sense disambiguation (WIC), sentence similarty (MRPC, STS-B, QQP), natural language inference (MNLI, QNLI, RTE, CB), question answering (MultiRC, RECORD, BoolQ), coreference resolution (WNLI, WSC) and sentence completion (COPA) and sentence acceptability (CoLA). The CNNDM (Hermann et al., 2015) and BBC XSum (Narayan et al., 2018) data sets are used to measure the ability to summarize articles. Question answering is probed with the SQuAD data set (Rajpurkar et al., 2016) and the ARC Reasoning Challenge (Clark et al., 2018). And as in Roberts et al. (2020), we evaluate the knowledge of our models by fine-tuning on three closed-book question answering data sets: Natural Questions (Kwiatkowski et al., 2019), Web Questions (Berant et al., 2013) and Trivia QA (Joshi et al., 2017). Closed-book refers to questions posed with no supplemental reference or context material. To gauge the model's common sense reasoning we evaluate it on the Winogrande Schema Challenge (Sakaguchi et al., 2020). And finally, we test our model's natural language inference capabilities on the Adversarial NLI Benchmark (Nie et al., 2019).
+Fine-tuning tasks and data sets. We select tasks probing language capabilities including question answering, summarization and knowledge about the world. The language benchmarks GLUE [(Wang](#page-39-4) [et](#page-39-4) [al.,](#page-39-4) [2018)](#page-39-4) and SuperGLUE [(Wang](#page-39-5) [et](#page-39-5) [al.,](#page-39-5) [2019)](#page-39-5) are handled as composite mixtures with all the tasks blended in proportion to the amount of tokens present in each. These benchmarks consist of tasks requiring sentiment analysis (SST-2), word sense disambiguation (WIC), sentence similarty (MRPC, STS-B, QQP), natural language inference (MNLI, QNLI, RTE, CB), question answering (MultiRC, RECORD, BoolQ), coreference resolution (WNLI, WSC) and sentence completion (COPA) and sentence acceptability (CoLA). The CNNDM [(Hermann](#page-36-4) [et](#page-36-4) [al.,](#page-36-4) [2015)](#page-36-4) and BBC XSum [(Narayan](#page-37-6) [et](#page-37-6) [al.,](#page-37-6) [2018)](#page-37-6) data sets are used to measure the ability to summarize articles. Question answering is probed with the SQuAD data set [(Rajpurkar](#page-37-7) [et](#page-37-7) [al.,](#page-37-7) [2016)](#page-37-7) and the ARC Reasoning Challenge [(Clark](#page-35-6) [et](#page-35-6) [al.,](#page-35-6) [2018)](#page-35-6). And as in [Roberts](#page-38-6) [et](#page-38-6) [al.](#page-38-6) [(2020)](#page-38-6), we evaluate the knowledge of our models by fine-tuning on three closed-book question answering data sets: Natural Questions [(Kwiatkowski](#page-36-5) [et](#page-36-5) [al.,](#page-36-5) [2019)](#page-36-5), Web Questions [(Berant](#page-35-7) [et](#page-35-7) [al.,](#page-35-7) [2013)](#page-35-7) and Trivia QA [(Joshi](#page-36-6) [et](#page-36-6) [al.,](#page-36-6) [2017)](#page-36-6). Closed-book refers to questions posed with no supplemental reference or context material. To gauge the model's common sense reasoning we evaluate it on the Winogrande Schema Challenge [(Sakaguchi](#page-38-7) [et](#page-38-7) [al.,](#page-38-7) [2020)](#page-38-7). And finally, we test our model's natural language inference capabilities on the Adversarial NLI Benchmark [(Nie](#page-37-8) [et](#page-37-8) [al.,](#page-37-8) [2019)](#page-37-8).
 
-Fine-tuning metrics. The following evaluation metrics are used throughout the paper: We report the average scores across all subtasks for GLUE and SuperGLUE. The Rouge-2 metric is used both the CNNDM and XSum. In SQuAD and the closed book tasks (Web, Natural, and Trivia Questions) we report the percentage of answers exactly matching the target (refer to Roberts et al. (2020) for further details and deficiency of this measure). Finally, in ARC Easy, ARC Challenge, ANLI, and Winogrande we report the accuracy of the generated responses.
+Fine-tuning metrics. The following evaluation metrics are used throughout the paper: We report the average scores across all subtasks for GLUE and SuperGLUE. The Rouge-2 metric is used both the CNNDM and XSum. In SQuAD and the closed book tasks (Web, Natural, and Trivia Questions) we report the percentage of answers exactly matching the target (refer to [Roberts](#page-38-6) [et](#page-38-6) [al.](#page-38-6) [(2020)](#page-38-6) for further details and deficiency of this measure). Finally, in ARC Easy, ARC Challenge, ANLI, and Winogrande we report the accuracy of the generated responses.
 
-Fine-tuning results. We observe significant downstream improvements across many natural language tasks. Notable improvements come from SuperGLUE, where we find FLOP-matched Switch variants improve by 4.4 and 2 percentage points over the T5-Base and T5-Large baselines, respectively as well as large improvements in Winogrande, closed book Trivia QA, and XSum.8 In our fine-tuning study, the only tasks where we do not observe gains are on the AI2 Reasoning Challenge (ARC) data sets where the T5-Base outperforms Switch-Base on the challenge data set and T5-Large outperforms Switch-Large on the easy data set. Taken as a whole, we observe significant improvements spanning both reasoning and knowledge-heavy tasks. This validates our architecture, not just as one that pre-trains well, but can translate quality improvements to downstream tasks via fine-tuning.
+Fine-tuning results. We observe significant downstream improvements across many natural language tasks. Notable improvements come from SuperGLUE, where we find FLOP-matched Switch variants improve by 4.4 and 2 percentage points over the T5-Base and T5-Large baselines, respectively as well as large improvements in Winogrande, closed book Trivia QA, and XSum.[8](#page-14-0) In our fine-tuning study, the only tasks where we do not observe gains are on the AI2 Reasoning Challenge (ARC) data sets where the T5-Base outperforms Switch-Base on the challenge data set and T5-Large outperforms Switch-Large on the easy data set. Taken as a whole, we observe significant improvements spanning both reasoning and knowledge-heavy tasks. This validates our architecture, not just as one that pre-trains well, but can translate quality improvements to downstream tasks via fine-tuning.
 
-8. Our T5 and Switch models were pre-trained with 220 tokens per batch for 550k steps on a revised C4 data set for fair comparisons.
+8. Our T5 and Switch models were pre-trained with 220 tokens per batch for 550k steps on a revised C4 data set for fair comparisons.
 
-| Model | GLUE | SQuAD | SuperGLUE | Winogrande (XL) |
-| --- | --- | --- | --- | --- |
-| T5-Base | 84.3 | 85.5 | 75.1 | 66.6 |
-| Switch-Base | 86.7 | 87.2 | 79.5 | 73.3 |
-| T5-Large | 87.8 | 88.1 | 82.7 | 79.1 |
-| Switch-Large | 88.5 | 88.6 | 84.7 | 83.0 |
-| Model | XSum | ANLI (R3) | ARC Easy | ARC Chal. |
-| T5-Base | 18.7 | 51.8 | 56.7 | 35.5 |
-| Switch-Base | 20.3 | 54.0 | 61.3 | 32.8 |
-| T5-Large | 20.9 | 56.6 | 68.8 | 35.5 |
-| Switch-Large | 22.3 | 58.6 | 66.0 | 35.5 |
-| Model | CB Web QA | CB Natural QA | CB Trivia QA |  |
-| T5-Base | 26.6 | 25.8 | 24.5 |  |
-| Switch-Base | 27.4 | 26.8 | 30.7 |  |
-| T5-Large | 27.7 | 27.6 | 29.5 |  |
-| Switch-Large | 31.3 | 29.5 | 36.9 |  |
+| Model        | GLUE      | SQuAD         | SuperGLUE    | Winogrande (XL) |
+|--------------|-----------|---------------|--------------|-----------------|
+| T5-Base      | 84.3      | 85.5          | 75.1         | 66.6            |
+| Switch-Base  | 86.7      | 87.2          | 79.5         | 73.3            |
+| T5-Large     | 87.8      | 88.1          | 82.7         | 79.1            |
+| Switch-Large | 88.5      | 88.6          | 84.7         | 83.0            |
+|              |           |               |              |                 |
+| Model        | XSum      | ANLI (R3)     | ARC Easy     | ARC Chal.       |
+| T5-Base      | 18.7      | 51.8          | 56.7         | 35.5            |
+| Switch-Base  | 20.3      | 54.0          | 61.3         | 32.8            |
+| T5-Large     | 20.9      | 56.6          | 68.8         | 35.5            |
+| Switch-Large | 22.3      | 58.6          | 66.0         | 35.5            |
+|              |           |               |              |                 |
+| Model        | CB Web QA | CB Natural QA | CB Trivia QA |                 |
+| T5-Base      | 26.6      | 25.8          | 24.5         |                 |
+| Switch-Base  | 27.4      | 26.8          | 30.7         |                 |
+| T5-Large     | 27.7      | 27.6          | 29.5         |                 |
+| Switch-Large | 31.3      | 29.5          | 36.9         |                 |
 
 Table 5: Fine-tuning results. Fine-tuning results of T5 baselines and Switch models across a diverse set of natural language tests (validation sets; higher numbers are better). We compare FLOP-matched Switch models to the T5-Base and T5-Large baselines. For most tasks considered, we find significant improvements of the Switchvariants. We observe gains across both model sizes and across both reasoning and knowledge-heavy language tasks.
 
-#### 4.2 Distillation
+#### 4.2 Distillation
 
-Deploying massive neural networks with billions, or trillions, of parameters is inconvenient. To alleviate this, we study distilling (Hinton et al., 2015) large sparse models into small dense models. Future work could additionally study distilling large models into smaller sparse models.
+Deploying massive neural networks with billions, or trillions, of parameters is inconvenient. To alleviate this, we study distilling [(Hinton](#page-36-3) [et](#page-36-3) [al.,](#page-36-3) [2015)](#page-36-3) large sparse models into small dense models. Future work could additionally study distilling large models into smaller sparse models.
 
 Distillation techniques. In Table 6 we study a variety of distillation techniques. These techniques are built off of Sanh et al. (2019), who study distillation methods for BERT models. We find that initializing the dense model with the non-expert weights yields a modest improvement. This is possible since all models are FLOP matched, so non-expert layers will have the same dimensions. Since expert layers are usually only added at every or every other FFN layer in a Transformer, this allows for many of the weights to be initialized with trained parameters. Furthermore, we observe a distillation improvement using a mixture of 0.25 for the teacher probabilities and 0.75 for the ground truth label. By combining both techniques we preserve ≈ 30% of the quality gains from the larger sparse models with only ≈ 1/20th of the parameters. The quality gain refers to the percent of
 
-| Technique | Parameters |  | Quality (↑) |
-| --- | --- | --- | --- |
-| T5-Base | 223M |  | -1.636 |
-| Switch-Base | 3,800M |  | -1.444 |
-| Distillation | 223M | (3%) | -1.631 |
-| + Init. non-expert weights from teacher | 223M | (20%) | -1.598 |
-| + 0.75 mix of hard and soft loss | 223M | (29%) | -1.580 |
-| Initialization Baseline (no distillation) |  |  |  |
-| Init. non-expert weights from teacher | 223M |  | -1.639 |
+| Technique                                 | Parameters | Quality (↑)  |
+|-------------------------------------------|------------|--------------|
+| T5-Base                                   | 223M       | -1.636       |
+| Switch-Base                               | 3,800M     | -1.444       |
+| Distillation                              | 223M       | (3%) -1.631  |
+| + Init. non-expert weights from teacher   | 223M       | (20%) -1.598 |
+| + 0.75 mix of hard and soft loss          | 223M       | (29%) -1.580 |
+| Initialization Baseline (no distillation) |            |              |
+| Init. non-expert weights from teacher     | 223M       | -1.639       |
 
 the quality difference between Switch-Base (Teacher) and T5-Base (Student). Therefore, a quality gain of 100% implies the Student equals the performance of the Teacher.
 
-Table 6: Distilling Switch Transformers for Language Modeling. Initializing T5-Base with the non-expert weights from Switch-Base and using a loss from a mixture of teacher and ground-truth labels obtains the best performance. We can distill 30% of the performance improvement of a large sparse model with 100x more parameters back into a small dense model. For a final baseline, we find no improvement of T5-Base initialized with the expert weights, but trained normally without distillation.
-
-Achievable compression rates. Using our best distillation technique described in Table 6, we distill a wide variety of sparse models into dense models. We distill Switch-Base versions, sweeping over an increasing number of experts, which corresponds to varying between 1.1B to 14.7B parameters. Through distillation, we can preserve 37% of the quality gain of the 1.1B parameter model while compressing 82%. At the extreme, where we compress the model 99%, we are still able to maintain 28% of the teacher's model quality improvement.
+- Table 6: Distilling Switch Transformers for Language Modeling. Initializing T5-Base with the non-expert weights from Switch-Base and using a loss from a mixture of teacher and ground-truth labels obtains the best performance. We can distill 30% of the performance improvement of a large sparse model with 100x more parameters back into a small dense model. For a final baseline, we find no improvement of T5-Base initialized with the expert weights, but trained normally without distillation.
+Achievable compression rates. Using our best distillation technique described in Table [6,](#page-16-1) we distill a wide variety of sparse models into dense models. We distill Switch-Base versions, sweeping over an increasing number of experts, which corresponds to varying between 1.1B to 14.7B parameters. Through distillation, we can preserve 37% of the quality gain of the 1.1B parameter model while compressing 82%. At the extreme, where we compress the model 99%, we are still able to maintain 28% of the teacher's model quality improvement.
 
-Distilling a fine-tuned model. We conclude this with a study of distilling a finetuned sparse model into a dense model. Table 8 shows results of distilling a 7.4B parameter Switch-Base model, fine-tuned on the SuperGLUE task, into the 223M T5-Base. Similar to our pre-training results, we find we are able to preserve 30% of the gains of the sparse model when distilling into a FLOP matched dense variant. One potential future avenue, not considered here, may examine the specific experts being used for fine-tuning tasks and extracting them to achieve better model compression.
+Distilling a fine-tuned model. We conclude this with a study of distilling a finetuned sparse model into a dense model. Table [8](#page-17-1) shows results of distilling a 7.4B parameter Switch-Base model, fine-tuned on the SuperGLUE task, into the 223M T5-Base. Similar to our pre-training results, we find we are able to preserve 30% of the gains of the sparse model when distilling into a FLOP matched dense variant. One potential future avenue, not considered here, may examine the specific experts being used for fine-tuning tasks and extracting them to achieve better model compression.
 
-### 4.3 Multilingual Learning
+#### 4.3 Multilingual Learning
 
-In our final set of downstream experiments, we measure the model quality and speed tradeoffs while pre-training on a mixture of 101 different languages. We build and benchmark off the recent work of mT5 (Xue et al., 2020), a multilingual extension to T5. We pre-train on the multilingual variant of the Common Crawl data set (mC4) spanning 101 languages introduced in mT5, but due to script variants within certain languages, the mixture contains 107 tasks.
+In our final set of downstream experiments, we measure the model quality and speed tradeoffs while pre-training on a mixture of 101 different languages. We build and benchmark off the recent work of mT5 [(Xue](#page-39-2) [et](#page-39-2) [al.,](#page-39-2) [2020)](#page-39-2), a multilingual extension to T5. We pre-train on the multilingual variant of the Common Crawl data set (mC4) spanning 101 languages introduced in mT5, but due to script variants within certain languages, the mixture contains 107 tasks.
 
-In Figure 7 we plot the quality improvement in negative log perplexity for all languages of a FLOP-matched Switch model, mSwitch-Base to the T5 base variant, mT5-Base. After
+In Figure [7](#page-18-0) we plot the quality improvement in negative log perplexity for all languages of a FLOP-matched Switch model, mSwitch-Base to the T5 base variant, mT5-Base. After
 
-|  | Dense |  |  | Sparse |  |  |
-| --- | --- | --- | --- | --- | --- | --- |
-| Parameters | 223M | 1.1B | 2.0B | 3.8B | 7.4B | 14.7B |
+|                                | Dense  | Sparse |        |        |        |        |
+|--------------------------------|--------|--------|--------|--------|--------|--------|
+| Parameters                     | 223M   | 1.1B   | 2.0B   | 3.8B   | 7.4B   | 14.7B  |
 | Pre-trained Neg. Log Perp. (↑) | -1.636 | -1.505 | -1.474 | -1.444 | -1.432 | -1.427 |
-| Distilled Neg. Log Perp. (↑) | — | -1.587 | -1.585 | -1.579 | -1.582 | -1.578 |
-| Percent of Teacher Performance | — | 37% | 32% | 30 % | 27 % | 28 % |
-| Compression Percent | — | 82 % | 90 % | 95 % | 97 % | 99 % |
+| Distilled Neg. Log Perp. (↑)   | —      | -1.587 | -1.585 | -1.579 | -1.582 | -1.578 |
+| Percent of Teacher Performance | —      | 37%    | 32%    | 30 %   | 27 %   | 28 %   |
+| Compression Percent            | —      | 82 %   | 90 %   | 95 %   | 97 %   | 99 %   |
 
-- Table 7: Distillation compression rates. We measure the quality when distilling large sparse models into a dense baseline. Our baseline, T5-Base, has a -1.636 Neg. Log Perp. quality. In the right columns, we then distill increasingly large sparse models into this same architecture. Through a combination of weight-initialization and a mixture of hard and soft losses, we can shrink our sparse teachers by 95%+ while preserving 30% of the quality gain. However, for significantly better and larger pre-trained teachers, we expect larger student models would be necessary to achieve these compression rates.
+Table 7: Distillation compression rates. We measure the quality when distilling large sparse models into a dense baseline. Our baseline, T5-Base, has a -1.636 Neg. Log Perp. quality. In the right columns, we then distill increasingly large sparse models into this same architecture. Through a combination of weight-initialization and a mixture of hard and soft losses, we can shrink our sparse teachers by 95%+ while preserving 30% of the quality gain. However, for significantly better and larger pre-trained teachers, we expect larger student models would be necessary to achieve these compression rates.
 
-| Model | Parameters | FLOPS | SuperGLUE (↑) |  |
-| --- | --- | --- | --- | --- |
-| T5-Base | 223M | 124B |  | 74.6 |
-| Switch-Base | 7410M | 124B |  | 81.3 |
-| Distilled T5-Base | 223M | 124B | (30%) | 76.6 |
+| Model             | Parameters | FLOPS | SuperGLUE (↑) |
+|-------------------|------------|-------|---------------|
+| T5-Base           | 223M       | 124B  | 74.6          |
+| Switch-Base       | 7410M      | 124B  | 81.3          |
+| Distilled T5-Base | 223M       | 124B  | (30%) 76.6    |
 
-- Table 8: Distilling a fine-tuned SuperGLUE model. We distill a Switch-Base model finetuned on the SuperGLUE tasks into a T5-Base model. We observe that on smaller data sets our large sparse model can be an effective teacher for distillation. We find that we again achieve 30% of the teacher's performance on a 97% compressed model.
-pre-training both versions for 1M steps, we find that on all 101 languages considered, Switch Transformer increases the final negative log perplexity over the baseline. In Figure 8, we present a different view and now histogram the per step speed-up of using Switch Transformer over the mT5-Base.9 We find a mean speed-up over mT5-Base of 5x and that 91% of languages achieve at least a 4x speedup. This presents evidence that Switch Transformers are effective multi-task and multi-lingual learners.
+- Table 8: Distilling a fine-tuned SuperGLUE model. We distill a Switch-Base model finetuned on the SuperGLUE tasks into a T5-Base model. We observe that on smaller data sets our large sparse model can be an effective teacher for distillation. We find that we again achieve 30% of the teacher's performance on a 97% compressed model.
+pre-training both versions for 1M steps, we find that on all 101 languages considered, Switch Transformer increases the final negative log perplexity over the baseline. In Figure [8,](#page-18-1) we present a different view and now histogram the per step speed-up of using Switch Transformer over the mT5-Base.[9](#page-17-2) We find a mean speed-up over mT5-Base of 5x and that 91% of languages achieve at least a 4x speedup. This presents evidence that Switch Transformers are effective multi-task and multi-lingual learners.
 
-# 5. Designing Models with Data, Model, and Expert-Parallelism
+### 5. Designing Models with Data, Model, and Expert-Parallelism
 
-Arbitrarily increasing the number of experts is subject to diminishing returns (Figure 4). Here we describe complementary scaling strategies. The common way to scale a Transformer is to increase dimensions in tandem, like dmodel or df f . This increases both the parameters
+Arbitrarily increasing the number of experts is subject to diminishing returns (Figure [4)](#page-11-1). Here we describe complementary scaling strategies. The common way to scale a Transformer is to increase dimensions in tandem, like dmodel or df f . This increases both the parameters
 
-9. The speedup on a step basis is computed as the ratio of the number of steps for the baseline divided by the number of steps required by our model to reach that same quality.
+9. The speedup on a step basis is computed as the ratio of the number of steps for the baseline divided by the number of steps required by our model to reach that same quality.
 
 ![](_page_18_Figure_1.jpeg)
 
-Figure 7: Multilingual pre-training on 101 languages. Improvements of Switch T5 Base model over dense baseline when multi-task training on 101 languages. We observe Switch Transformers to do quite well in the multi-task training setup and yield improvements on all 101 languages.
+Figure 7: Multilingual pre-training on 101 languages. Improvements of Switch T5 Base model over dense baseline when multi-task training on 101 languages. We observe Switch Transformers to do quite well in the multi-task training setup and yield improvements on all 101 languages.
 
 ![](_page_18_Figure_3.jpeg)
 
-Figure 8: Multilingual pre-training on 101 languages. We histogram for each language, the step speedup of Switch Transformers over the FLOP matched T5 dense baseline to reach the same quality. Over all 101 languages, we achieve a mean step speedup over mT5-Base of 5x and, for 91% of languages, we record a 4x, or greater, speedup to reach the final perplexity of mT5-Base.
+Figure 8: Multilingual pre-training on 101 languages. We histogram for each language, the step speedup of Switch Transformers over the FLOP matched T5 dense baseline to reach the same quality. Over all 101 languages, we achieve a mean step speedup over mT5-Base of 5x and, for 91% of languages, we record a 4x, or greater, speedup to reach the final perplexity of mT5-Base.
 
 and computation performed and is ultimately limited by the memory per accelerator. Once it exceeds the size of the accelerator's memory, single program multiple data (SPMD) modelparallelism can be employed. This section studies the trade-offs of combining data, model, and expert-parallelism.
 
-Reviewing the Feed-Forward Network (FFN) Layer. We use the FFN layer as an example of how data, model and expert-parallelism works in Mesh TensorFlow (Shazeer et al., 2018) and review it briefly here. We assume B tokens in the batch, each of dimension dmodel. Both the input (x) and output (y) of the FFN are of size [B, dmodel] and the intermediate (h) is of size [B, df f ] where df f is typically several times larger than dmodel. In the FFN, the intermediate is h = xWin and then the output of the layer is y = ReLU(h)Wout. Thus Win and Wout are applied independently to each token and have sizes [dmodel, df f ] and [df f , dmodel].
+Reviewing the Feed-Forward Network (FFN) Layer. We use the FFN layer as an example of how data, model and expert-parallelism works in Mesh TensorFlow [(Shazeer](#page-38-3) [et](#page-38-3) [al.,](#page-38-3) [2018)](#page-38-3) and review it briefly here. We assume B tokens in the batch, each of dimension dmodel. Both the input (x) and output (y) of the FFN are of size [B, dmodel] and the intermediate (h) is of size [B, dff] where dff is typically several times larger than dmodel. In the FFN, the intermediate is h = xWin and then the output of the layer is y = ReLU(h)Wout. Thus Win and Wout are applied independently to each token and have sizes [dmodel, dff] and [dff, dmodel].
 
-We describe two aspects of partitioning: how the weights and batches of data divide over cores, depicted in Figure 9. We denote all cores available as N which Mesh Tensorflow may then remap into a logical multidimensional mesh of processors. Here we create a two-dimensional logical mesh, with one dimension representing the number of ways for data-parallel sharding (n) and the other, the model-parallel sharding (m). The total cores must equal the ways to shard across both data and model-parallelism, e.g. N = n × m. To shard the layer across cores, the tensors containing that batch of B tokens are sharded across n data-parallel cores, so each core contains B/n tokens. Tensors and variables with df f are then sharded across m model-parallel cores. For the variants with experts-layers, we consider E experts, each of which can process up to C tokens.
+We describe two aspects of partitioning: how the weights and batches of data divide over cores, depicted in Figure [9.](#page-20-1) We denote all cores available as N which Mesh Tensorflow may then remap into a logical multidimensional mesh of processors. Here we create a two-dimensional logical mesh, with one dimension representing the number of ways for data-parallel sharding (n) and the other, the model-parallel sharding (m). The total cores must equal the ways to shard across both data and model-parallelism, e.g. N = n × m. To shard the layer across cores, the tensors containing that batch of B tokens are sharded across n data-parallel cores, so each core contains B/n tokens. Tensors and variables with df f are then sharded across m model-parallel cores. For the variants with experts-layers, we consider E experts, each of which can process up to C tokens.
 
-| Term | Description |
-| --- | --- |
-| B | Number of tokens in the batch. |
-| N | Number of total cores. |
-| n | Number of ways for data-parallelism sharding. |
-| m | Number of ways for model-parallelism sharding. |
-| E | Number of experts in Switch layers. |
-| C | Expert capacity, the batch size of each expert. |
+| Term | Description                                     |
+|------|-------------------------------------------------|
+| B    | Number of tokens in the batch.                  |
+| N    | Number of total cores.                          |
+| n    | Number of ways for data-parallelism sharding.   |
+| m    | Number of ways for model-parallelism sharding.  |
+| E    | Number of experts in Switch layers.             |
+| C    | Expert capacity, the batch size of each expert. |
 
-#### 5.1 Data Parallelism
+### 5.1 Data Parallelism
 
-When training data parallel models, which is the standard for distributed training, then all cores are allocated to the data-parallel dimension or n = N, m = 1. This has the advantage that no communication is needed until the entire forward and backward pass is finished and the gradients need to be then aggregated across all cores. This corresponds to the left-most column of Figure 9.
+When training data parallel models, which is the standard for distributed training, then all cores are allocated to the data-parallel dimension or $n = N, m = 1$. This has the advantage that no communication is needed until the entire forward and backward pass is finished and the gradients need to be then aggregated across all cores. This corresponds to the left-most column of Figure 9.
 
-#### 5.2 Model Parallelism
+#### 5.2 Model Parallelism
 
-We now consider a scenario where all cores are allocated exclusively to the model-parallel dimension and so n = 1, m = N. Now all cores must keep the full B tokens and each core will contain a unique slice of the weights. For each forward and backward pass, a communication cost is now incurred. Each core sends a tensor of [B, dmodel] to compute the second matrix multiplication ReLU(h)Wout because the df f dimension is partitioned and must be summed over. As a general rule, whenever a dimension that is partitioned across cores must be summed, then an all-reduce operation is added for both the forward and backward pass. This contrasts with pure data parallelism where an all-reduce only occurs at the end of the entire forward and backward pass.
+We now consider a scenario where all cores are allocated exclusively to the model-parallel dimension and so $n = 1, m = N$. Now all cores must keep the full $B$ tokens and each core will contain a unique slice of the weights. For each forward and backward pass, a communication cost is now incurred. Each core sends a tensor of $[B, d_{model}]$ to compute the second matrix multiplication $ReLU(h)W_{out}$ because the $d_{ff}$ dimension is partitioned and must be summed over. As a general rule, whenever a dimension that is partitioned across cores must be summed, then an all-reduce operation is added for both the forward and backward pass. This contrasts with pure data parallelism where an all-reduce only occurs at the end of the entire forward and backward pass.
 
 ![](_page_20_Figure_1.jpeg)
 
 #### **How the** *model weights* **are split over cores**
 
-### **How the** *data* **is split over cores**
+#### **How the** *data* **is split over cores**
 
 ![](_page_20_Figure_4.jpeg)
 
-- Figure 9: Data and weight partitioning strategies. Each 4×4 dotted-line grid represents 16 cores and the shaded squares are the data contained on that core (either model weights or batch of tokens). We illustrate both how the model weights and the data tensors are split for each strategy. First Row: illustration of how model weights are split across the cores. Shapes of different sizes in this row represent larger weight matrices in the Feed Forward Network (FFN) layers (e.g larger df f sizes). Each color of the shaded squares identifies a unique weight matrix. The number of parameters per core is fixed, but larger weight matrices will apply more computation to each token. Second Row: illustration of how the data batch is split across cores. Each core holds the same number of tokens which maintains a fixed memory usage across all strategies. The partitioning strategies have different properties of allowing each core to either have the same tokens or different tokens across cores, which is what the different colors symbolize.
-#### 5.3 Model and Data Parallelism
+- Figure 9: Data and weight partitioning strategies. Each 4×4 dotted-line grid represents 16 cores and the shaded squares are the data contained on that core (either model weights or batch of tokens). We illustrate both how the model weights and the data tensors are split for each strategy. First Row: illustration of how model weights are split across the cores. Shapes of different sizes in this row represent larger weight matrices in the Feed Forward Network (FFN) layers (e.g larger df f sizes). Each color of the shaded squares identifies a unique weight matrix. The number of parameters per core is fixed, but larger weight matrices will apply more computation to each token. Second Row: illustration of how the data batch is split across cores. Each core holds the same number of tokens which maintains a fixed memory usage across all strategies. The partitioning strategies have different properties of allowing each core to either have the same tokens or different tokens across cores, which is what the different colors symbolize.
+#### 5.3 Model and Data Parallelism
 
-It is common to mix both model and data parallelism for large scale models, which was done in the largest T5 models (Raffel et al., 2019; Xue et al., 2020) and in GPT-3 (Brown et al., 2020). With a total of N = n × m cores, now each core will be responsible for B/n tokens and df f /m of both the weights and intermediate activation. In the forward and backward pass each core communicates a tensor of size [B/n, dmodel] in an all-reduce operation.
+It is common to mix both model and data parallelism for large scale models, which was done in the largest T5 models [(Raffel](#page-37-0) [et](#page-37-0) [al.,](#page-37-0) [2019;](#page-37-0) [Xue](#page-39-2) [et](#page-39-2) [al.,](#page-39-2) [2020)](#page-39-2) and in GPT-3 [(Brown](#page-35-0) [et](#page-35-0) [al.,](#page-35-0) [2020)](#page-35-0). With a total of N = n × m cores, now each core will be responsible for B/n tokens and df f /m of both the weights and intermediate activation. In the forward and backward pass each core communicates a tensor of size [B/n, dmodel] in an all-reduce operation.
 
-#### 5.4 Expert and Data Parallelism
+#### 5.4 Expert and Data Parallelism
 
-Next we describe the partitioning strategy for expert and data parallelism. Switch Transformers will allocate all of their cores to the data partitioning dimension n, which will also correspond to the number of experts in the model. For each token per core a router locally computes assignments to the experts. The output is a binary matrix of size [n, B/n, E, C] which is partitioned across the first dimension and determines expert assignment. This binary matrix is then used to do a gather via matrix multiplication with the input tensor of [n, B/n, dmodel].
+Next we describe the partitioning strategy for expert and data parallelism. Switch Transformers will allocate all of their cores to the data partitioning dimension n, which will also correspond to the number of experts in the model. For each token per core a router locally computes assignments to the experts. The output is a binary matrix of size [n, B/n, E, C] which is partitioned across the first dimension and determines expert assignment. This binary matrix is then used to do a gather via matrix multiplication with the input tensor of [n, B/n, $d_{model}$].
 
-einsum($[n,B/n,d_{model}],[n,B/n,E,C],$dimension $=[B/n]$) (7)
+$$\text{einsum}([n, B/n, d_{\text{model}}], [n, B/n, E, C], \text{dimension} = [B/n])$$
 
-resulting in the final tensor of shape [n, E, C, dmodel], which is sharded across the first dimension. Because each core has its own expert, we do an all-to-all communication of size [E, C, dmodel] to now shard the E dimension instead of the n-dimension. There are additional communication costs of bfloat16 tensors of size E×C ×dmodel in the forward pass to analogusly receive the tokens from each expert located on different cores. See Appendix F for a detailed analysis of the expert partitioning code.
+resulting in the final tensor of shape [n, E, C, dmodel], which is sharded across the first dimension. Because each core has its own expert, we do an all-to-all communication of size [E, C, dmodel] to now shard the E dimension instead of the n-dimension. There are additional communication costs of bfloat16 tensors of size E 
+× C 
+× dmodel in the forward pass to analogously receive the tokens from each expert located on different cores. See Appendix F for a detailed analysis of the expert partitioning code.
 
-#### 5.5 Expert, Model and Data Parallelism
+#### 5.5 Expert, Model and Data Parallelism
 
-In the design of our best model, we seek to balance the FLOPS per token and the parameter count. When we scale the number of experts, we increase the number of parameters, but do not change the FLOPs per token. In order to increase FLOPs, we must also increase the df f dimension (which also increases parameters, but at a slower rate). This presents a trade-off: as we increase df f we will run out of memory per core, which then necessitates increasing m. But since we have a fixed number of cores N, and N = n × m, we must decrease n, which forces use of a smaller batch-size (in order to hold tokens per core constant).
+In the design of our best model, we seek to balance the FLOPS per token and the parameter count. When we scale the number of experts, we increase the number of parameters, but do not change the FLOPs per token. In order to increase FLOPs, we must also increase the $d_{ff} $dimension (which also increases parameters, but at a slower rate). This presents a trade-off: as we increase $d_{ff}$ we will run out of memory per core, which then necessitates increasing m. But since we have a fixed number of cores N, and $N = n \times m$, we must decrease n, which forces use of a smaller batch-size (in order to hold tokens per core constant).
 
-When combining both model and expert-parallelism, we will have all-to-all communication costs from routing the tokens to the correct experts along with the internal all-reduce communications from the model parallelism. Balancing the FLOPS, communication costs and memory per core becomes quite complex when combining all three methods where the best mapping is empirically determined. See our further analysis in section 5.6 for how the number of experts effects the downstream performance as well.
+When combining both model and expert-parallelism, we will have all-to-all communication costs from routing the tokens to the correct experts along with the internal all-reduce communications from the model parallelism. Balancing the FLOPS, communication costs and memory per core becomes quite complex when combining all three methods where the best mapping is empirically determined. See our further analysis in section [5.6](#page-21-2) for how the number of experts effects the downstream performance as well.
 
-#### 5.6 Towards Trillion Parameter Models
+#### 5.6 Towards Trillion Parameter Models
 
-Combining expert, model and data parallelism, we design two large Switch Transformer models, one with 395 billion and 1.6 trillion parameters, respectively. We study how these models perform on both up-stream pre-training as language models and their downstream fine-tuning performance. The parameters, FLOPs per sequence and hyper-parameters of the two different models are listed below in Table 9. Standard hyper-parameters of the Transformer, including dmodel, df f , dkv, number of heads and number of layers are described, as well as a less common feature, F F NGEGLU , which refers to a variation of the FFN layer where the expansion matrix is substituted with two sets of weights which are non-linearly combined (Shazeer, 2020).
+Combining expert, model and data parallelism, we design two large Switch Transformer models, one with 395 billion and 1.6 trillion parameters, respectively. We study how these models perform on both up-stream pre-training as language models and their downstream fine-tuning performance. The parameters, FLOPs per sequence and hyper-parameters of the two different models are listed below in Table 9. Standard hyper-parameters of the Transformer, including $d_{model}$, $d_{ff}$, $d_{kv}$, number of heads and number of layers are described, as well as a less common feature, FFNGEGLU, which refers to a variation of the FFN layer where the expansion matrix is substituted with two sets of weights which are non-linearly combined (Shazeer, 2020).
 
-The Switch-C model is designed using only expert-parallelism, and no model-parallelism, as described earlier in Section 5.4. As a result, the hyper-parameters controlling the width,
+The Switch-C model is designed using only expert-parallelism, and no model-parallelism, as described earlier in Section [5.4.](#page-21-0) As a result, the hyper-parameters controlling the width,
 
-| Model | Parameters | FLOPs/seq | dmodel | F F NGEGLU | df f | dkv | Num. Heads |
-| --- | --- | --- | --- | --- | --- | --- | --- |
-| T5-Base | 0.2B | 124B | 768 | X | 2048 | 64 | 12 |
-| T5-Large | 0.7B | 425B | 1024 | X | 2816 | 64 | 16 |
-| T5-XXL | 11B | 6.3T | 4096 | X | 10240 | 64 | 64 |
-| Switch-Base | 7B | 124B | 768 | X | 2048 | 64 | 12 |
-| Switch-Large | 26B | 425B | 1024 | X | 2816 | 64 | 16 |
-| Switch-XXL | 395B | 6.3T | 4096 | X | 10240 | 64 | 64 |
-| Switch-C | 1571B | 890B | 2080 |  | 6144 | 64 | 32 |
-| Model | Expert Freq. | Num. Layers | Num Experts | Neg. Log Perp. @250k | Neg. Log Perp. @ 500k |  |  |
-| T5-Base | – | 12 | – | -1.599 | -1.556 |  |  |
-| T5-Large | – | 24 | – | -1.402 | -1.350 |  |  |
-| T5-XXL | – | 24 | – | -1.147 | -1.095 |  |  |
-| Switch-Base | 1/2 | 12 | 128 | -1.370 | -1.306 |  |  |
-| Switch-Large | 1/2 | 24 | 128 | -1.248 | -1.177 |  |  |
-| Switch-XXL | 1/2 | 24 | 64 | -1.086 | -1.008 |  |  |
-| Switch-C | 1 | 15 | 2048 | -1.096 | -1.043 |  |  |
+| Model        | Parameters   | FLOPs/seq   | dmodel      | FFNGEGLU             | dff                   | dkv | Num. Heads |
+|--------------|--------------|-------------|-------------|----------------------|-----------------------|-----|------------|
+| T5-Base      | 0.2B         | 124B        | 768         | ✓                    | 2048                  | 64  | 12         |
+| T5-Large     | 0.7B         | 425B        | 1024        | ✓                    | 2816                  | 64  | 16         |
+| T5-XXL       | 11B          | 6.3T        | 4096        | ✓                    | 10240                 | 64  | 64         |
+| Switch-Base  | 7B           | 124B        | 768         | ✓                    | 2048                  | 64  | 12         |
+| Switch-Large | 26B          | 425B        | 1024        | ✓                    | 2816                  | 64  | 16         |
+| Switch-XXL   | 395B         | 6.3T        | 4096        | ✓                    | 10240                 | 64  | 64         |
+| Switch-C     | 1571B        | 890B        | 2080        |                      | 6144                  | 64  | 32         |
+| Model        | Expert Freq. | Num. Layers | Num Experts | Neg. Log Perp. @250k | Neg. Log Perp. @ 500k |     |            |
+| T5-Base      | –            | 12          | –           | -1.599               | -1.556                |     |            |
+| T5-Large     | –            | 24          | –           | -1.402               | -1.350                |     |            |
+| T5-XXL       | –            | 24          | –           | -1.147               | -1.095                |     |            |
+| Switch-Base  | 1/2          | 12          | 128         | -1.370               | -1.306                |     |            |
+| Switch-Large | 1/2          | 24          | 128         | -1.248               | -1.177                |     |            |
+| Switch-XXL   | 1/2          | 24          | 64          | -1.086               | -1.008                |     |            |
+| Switch-C     | 1            | 15          | 2048        | -1.096               | -1.043                |     |            |
 
-Table 9: Switch model design and pre-training performance. We compare the hyperparameters and pre-training performance of the T5 models to our Switch Transformer variants. The last two columns record the pre-training model quality on the C4 data set after 250k and 500k steps, respectively. We observe that the Switch-C Transformer variant is 4x faster to a fixed perplexity (with the same compute budget) than the T5-XXL model, with the gap increasing as training progresses.
+- Table 9: Switch model design and pre-training performance. We compare the hyperparameters and pre-training performance of the T5 models to our Switch Transformer variants. The last two columns record the pre-training model quality on the C4 data set after 250k and 500k steps, respectively. We observe that the Switch-C Transformer variant is 4x faster to a fixed perplexity (with the same compute budget) than the T5-XXL model, with the gap increasing as training progresses.
+depth, number of heads, and so on, are all much smaller than the T5-XXL model. In contrast, the Switch-XXL is FLOP-matched to the T5-XXL model, which allows for larger dimensions of the hyper-parameters, but at the expense of additional communication costs induced by model-parallelism (see Section [5.5](#page-21-1) for more details).
 
-depth, number of heads, and so on, are all much smaller than the T5-XXL model. In contrast, the Switch-XXL is FLOP-matched to the T5-XXL model, which allows for larger dimensions of the hyper-parameters, but at the expense of additional communication costs induced by model-parallelism (see Section 5.5 for more details).
+Sample efficiency versus T5-XXL. In the final two columns of Table [9](#page-22-0) we record the negative log perplexity on the C4 corpus after 250k and 500k steps, respectively. After 250k steps, we find both Switch Transformer variants to improve over the T5-XXL version's negative log perplexity by over 0.061.[10](#page-22-1) To contextualize the significance of a gap of 0.061, we note that the T5-XXL model had to train for an additional 250k steps to increase 0.052. The gap continues to increase with additional training, with the Switch-XXL model out-performing the T5-XXL by 0.087 by 500k steps.
 
-Sample efficiency versus T5-XXL. In the final two columns of Table 9 we record the negative log perplexity on the C4 corpus after 250k and 500k steps, respectively. After 250k steps, we find both Switch Transformer variants to improve over the T5-XXL version's negative log perplexity by over 0.061.10 To contextualize the significance of a gap of 0.061, we note that the T5-XXL model had to train for an additional 250k steps to increase 0.052. The gap continues to increase with additional training, with the Switch-XXL model out-performing the T5-XXL by 0.087 by 500k steps.
+Training instability. However, as described in the introduction, large sparse models can be unstable, and as we increase the scale, we encounter some sporadic issues. We find that the larger Switch-C model, with 1.6T parameters and 2048 experts, exhibits no training instability at all. Instead, the Switch XXL version, with nearly 10x larger FLOPs per sequence, is sometimes unstable. As a result, though this is our better model on a step-basis, we do not pre-train for a full 1M steps, in-line with the final reported results of T5 [(Raffel](#page-37-0) [et](#page-37-0) [al.,](#page-37-0) [2019)](#page-37-0).
 
-Training instability. However, as described in the introduction, large sparse models can be unstable, and as we increase the scale, we encounter some sporadic issues. We find that the larger Switch-C model, with 1.6T parameters and 2048 experts, exhibits no training instability at all. Instead, the Switch XXL version, with nearly 10x larger FLOPs per sequence, is sometimes unstable. As a result, though this is our better model on a step-basis, we do not pre-train for a full 1M steps, in-line with the final reported results of T5 (Raffel et al., 2019).
+10. This reported quality difference is a lower bound, and may actually be larger. The T5-XXL was pretrained on an easier C4 data set which included duplicated, and thus easily copied, snippets within examples.
 
-10. This reported quality difference is a lower bound, and may actually be larger. The T5-XXL was pretrained on an easier C4 data set which included duplicated, and thus easily copied, snippets within examples.
+Reasoning fine-tuning performance. As a preliminary assessment of the model quality, we use a Switch-XXL model partially pre-trained on 503B tokens, or approximately half the text used by the T5-XXL model. Using this checkpoint, we conduct multi-task training for efficiency, where all tasks are learned jointly, rather than individually fine-tuned. We find that SQuAD accuracy on the validation set increases to 89.7 versus state-of-the-art of 91.3. Next, the average SuperGLUE test score is recorded at 87.5 versus the T5 version obtaining a score of 89.3 compared to the state-of-the-art of 90.0 [(Wang](#page-39-5) [et](#page-39-5) [al.,](#page-39-5) [2019)](#page-39-5). On ANLI [(Nie](#page-37-8) [et](#page-37-8) [al.,](#page-37-8) [2019)](#page-37-8), Switch XXL improves over the prior state-of-the-art to get a 65.7 accuracy versus the prior best of 49.4 [(Yang](#page-39-6) [et](#page-39-6) [al.,](#page-39-6) [2020)](#page-39-6). We note that while the Switch-XXL has state-of-the-art Neg. Log Perp. on the upstream pre-training task, its gains have not yet fully translated to SOTA downstream performance. We study this issue more in Appendix [E.](#page-31-0)
 
-Reasoning fine-tuning performance. As a preliminary assessment of the model quality, we use a Switch-XXL model partially pre-trained on 503B tokens, or approximately half the text used by the T5-XXL model. Using this checkpoint, we conduct multi-task training for efficiency, where all tasks are learned jointly, rather than individually fine-tuned. We find that SQuAD accuracy on the validation set increases to 89.7 versus state-of-the-art of 91.3. Next, the average SuperGLUE test score is recorded at 87.5 versus the T5 version obtaining a score of 89.3 compared to the state-of-the-art of 90.0 (Wang et al., 2019). On ANLI (Nie et al., 2019), Switch XXL improves over the prior state-of-the-art to get a 65.7 accuracy versus the prior best of 49.4 (Yang et al., 2020). We note that while the Switch-XXL has state-of-the-art Neg. Log Perp. on the upstream pre-training task, its gains have not yet fully translated to SOTA downstream performance. We study this issue more in Appendix E.
+Knowledge-based fine-tuning performance. Finally, we also conduct an early examination of the model's knowledge with three closed-book knowledge-based tasks: Natural Questions, WebQuestions and TriviaQA, without additional pre-training using Salient Span Masking [(Guu](#page-36-7) [et](#page-36-7) [al.,](#page-36-7) [2020)](#page-36-7). In all three cases, we observe improvements over the prior stateof-the-art T5-XXL model (without SSM). Natural Questions exact match increases to 34.4 versus the prior best of 32.8, Web Questions increases to 41.0 over 37.2, and TriviaQA increases to 47.5 versus 42.9.
 
-Knowledge-based fine-tuning performance. Finally, we also conduct an early examination of the model's knowledge with three closed-book knowledge-based tasks: Natural Questions, WebQuestions and TriviaQA, without additional pre-training using Salient Span Masking (Guu et al., 2020). In all three cases, we observe improvements over the prior stateof-the-art T5-XXL model (without SSM). Natural Questions exact match increases to 34.4 versus the prior best of 32.8, Web Questions increases to 41.0 over 37.2, and TriviaQA increases to 47.5 versus 42.9.
+Summing up, despite training on less than half the data of other models, we already find comparable, and sometimes state-of-the-art, model quality. Currently, the Switch Transformer translates substantial upstream gains better to knowledge-based tasks, than reasoning-tasks (see Appendix [E)](#page-31-0). Extracting stronger fine-tuning performance from large expert models is an active research question, and the pre-training perplexity indicates future improvements should be possible.
 
-Summing up, despite training on less than half the data of other models, we already find comparable, and sometimes state-of-the-art, model quality. Currently, the Switch Transformer translates substantial upstream gains better to knowledge-based tasks, than reasoning-tasks (see Appendix E). Extracting stronger fine-tuning performance from large expert models is an active research question, and the pre-training perplexity indicates future improvements should be possible.
+### 6. Related Work
 
-### 6. Related Work
+The importance of scale in neural networks is widely recognized and several approaches have been proposed. Recent works have scaled models to billions of parameters through using model parallelism (e.g. splitting weights and tensors across multiple cores) [(Shazeer](#page-38-3) [et](#page-38-3) [al.,](#page-38-3) [2018;](#page-38-3) [Rajbhandari](#page-37-9) [et](#page-37-9) [al.,](#page-37-9) [2019;](#page-37-9) [Raffel](#page-37-0) [et](#page-37-0) [al.,](#page-37-0) [2019;](#page-37-0) [Brown](#page-35-0) [et](#page-35-0) [al.,](#page-35-0) [2020;](#page-35-0) [Shoeybi](#page-38-10) [et](#page-38-10) [al.,](#page-38-10) [2019)](#page-38-10). Alternatively, [Harlap](#page-36-8) [et](#page-36-8) [al.](#page-36-8) [(2018)](#page-36-8); [Huang](#page-36-9) [et](#page-36-9) [al.](#page-36-9) [(2019)](#page-36-9) propose using pipeline based model parallelism, where different layers are split across devices and micro-batches are pipelined to the different layers. Finally, Product Key networks [(Lample](#page-37-10) [et](#page-37-10) [al.,](#page-37-10) [2019)](#page-37-10) were proposed to scale up the capacity of neural networks by doing a lookup for learnable embeddings based on the incoming token representations to a given layer.
 
-The importance of scale in neural networks is widely recognized and several approaches have been proposed. Recent works have scaled models to billions of parameters through using model parallelism (e.g. splitting weights and tensors across multiple cores) (Shazeer et al., 2018; Rajbhandari et al., 2019; Raffel et al., 2019; Brown et al., 2020; Shoeybi et al., 2019). Alternatively, Harlap et al. (2018); Huang et al. (2019) propose using pipeline based model parallelism, where different layers are split across devices and micro-batches are pipelined to the different layers. Finally, Product Key networks (Lample et al., 2019) were proposed to scale up the capacity of neural networks by doing a lookup for learnable embeddings based on the incoming token representations to a given layer.
+Our work studies a specific model in a class of methods that do conditional computation, where computation decisions are made dynamically based on the input. [Cho](#page-35-8) [and](#page-35-8) [Bengio](#page-35-8) [(2014)](#page-35-8) proposed adaptively selecting weights based on certain bit patterns occuring in the model hidden-states. [Eigen](#page-35-9) [et](#page-35-9) [al.](#page-35-9) [(2013)](#page-35-9) built stacked expert layers with dense matrix multiplications and ReLU activations and showed promising results on jittered MNIST and monotone speech. In computer vision [Puigcerver](#page-37-11) [et](#page-37-11) [al.](#page-37-11) [(2020)](#page-37-11) manually route tokens based on semantic classes during upstream pre-training and then select the relevant experts to be used according to the downstream task.
 
-Our work studies a specific model in a class of methods that do conditional computation, where computation decisions are made dynamically based on the input. Cho and Bengio (2014) proposed adaptively selecting weights based on certain bit patterns occuring in the model hidden-states. Eigen et al. (2013) built stacked expert layers with dense matrix multiplications and ReLU activations and showed promising results on jittered MNIST and monotone speech. In computer vision Puigcerver et al. (2020) manually route tokens based on semantic classes during upstream pre-training and then select the relevant experts to be used according to the downstream task.
+Mixture of Experts (MoE), in the context of modern deep learning architectures, was proven effective in [Shazeer](#page-38-2) [et](#page-38-2) [al.](#page-38-2) [(2017)](#page-38-2). That work added an MoE layer which was stacked between LSTM [(Hochreiter](#page-36-10) [and](#page-36-10) [Schmidhuber,](#page-36-10) [1997)](#page-36-10) layers, and tokens were separately routed to combinations of experts. This resulted in state-of-the-art results in language modeling and machine translation benchmarks. The MoE layer was reintroduced into the Transformer architecture by the Mesh Tensorflow library [(Shazeer](#page-38-3) [et](#page-38-3) [al.,](#page-38-3) [2018)](#page-38-3) where MoE layers were introduced as a substitute of the FFN layers, however, there were no accompanying NLP results. More recently, through advances in machine learning infrastructure, GShard [(Lepikhin](#page-37-2) [et](#page-37-2) [al.,](#page-37-2) [2020)](#page-37-2), which extended the XLA compiler, used the MoE Transformer to dramatically improve machine translation across 100 languages. Finally [Fan](#page-35-10) [et](#page-35-10) [al.](#page-35-10) [(2021)](#page-35-10) chooses a different deterministic MoE strategy to split the model parameters into non-overlapping groups of languages.
 
-Mixture of Experts (MoE), in the context of modern deep learning architectures, was proven effective in Shazeer et al. (2017). That work added an MoE layer which was stacked between LSTM (Hochreiter and Schmidhuber, 1997) layers, and tokens were separately routed to combinations of experts. This resulted in state-of-the-art results in language modeling and machine translation benchmarks. The MoE layer was reintroduced into the Transformer architecture by the Mesh Tensorflow library (Shazeer et al., 2018) where MoE layers were introduced as a substitute of the FFN layers, however, there were no accompanying NLP results. More recently, through advances in machine learning infrastructure, GShard (Lepikhin et al., 2020), which extended the XLA compiler, used the MoE Transformer to dramatically improve machine translation across 100 languages. Finally Fan et al. (2021) chooses a different deterministic MoE strategy to split the model parameters into non-overlapping groups of languages.
+Sparsity along the sequence length dimension (L) in the Transformer attention patterns has been a successful technique to reduce the attention complexity from O(L 2 ) [(Child](#page-35-11) [et](#page-35-11) [al.,](#page-35-11) [2019;](#page-35-11) [Correia](#page-35-12) [et](#page-35-12) [al.,](#page-35-12) [2019;](#page-35-12) [Sukhbaatar](#page-38-11) [et](#page-38-11) [al.,](#page-38-11) [2019;](#page-38-11) [Kitaev](#page-36-11) [et](#page-36-11) [al.,](#page-36-11) [2020;](#page-36-11) [Zaheer](#page-39-7) [et](#page-39-7) [al.,](#page-39-7) [2020;](#page-39-7) [Beltagy](#page-35-13) [et](#page-35-13) [al.,](#page-35-13) [2020)](#page-35-13). This has enabled learning longer sequences than previously possible. This version of the Switch Transformer does not employ attention sparsity, but these techniques are complimentary, and, as future work, these could be combined to potentially improve learning on tasks requiring long contexts.
 
-Sparsity along the sequence length dimension (L) in the Transformer attention patterns has been a successful technique to reduce the attention complexity from O(L 2 ) (Child et al., 2019; Correia et al., 2019; Sukhbaatar et al., 2019; Kitaev et al., 2020; Zaheer et al., 2020; Beltagy et al., 2020). This has enabled learning longer sequences than previously possible. This version of the Switch Transformer does not employ attention sparsity, but these techniques are complimentary, and, as future work, these could be combined to potentially improve learning on tasks requiring long contexts.
-
-### 7. Discussion
+### 7. Discussion
 
 We pose and discuss questions about the Switch Transformer, and sparse expert models generally, where sparsity refers to weights, not on attention patterns.
 
-Isn't Switch Transformer better due to sheer parameter count? Yes, and by design! Parameters, independent of the total FLOPs used, are a useful axis to scale neural language models. Large models have been exhaustively shown to perform better (Kaplan et al., 2020). But in this case, our model is more sample efficient and faster while using the same computational resources.
+Isn't Switch Transformer better due to sheer parameter count? Yes, and by design! Parameters, independent of the total FLOPs used, are a useful axis to scale neural language models. Large models have been exhaustively shown to perform better [(Kaplan](#page-36-0) [et](#page-36-0) [al.,](#page-36-0) [2020)](#page-36-0). But in this case, our model is more sample efficient and faster while using the same computational resources.
 
-I don't have access to a supercomputer—is this still useful for me? Though this work has focused on extremely large models, we also find that models with as few as two experts improves performance while easily fitting within memory constraints of commonly available GPUs or TPUs (details in Appendix D). We therefore believe our techniques are useful in small-scale settings.
+I don't have access to a supercomputer—is this still useful for me? Though this work has focused on extremely large models, we also find that models with as few as two experts improves performance while easily fitting within memory constraints of commonly available GPUs or TPUs (details in Appendix [D)](#page-28-2). We therefore believe our techniques are useful in small-scale settings.
 
 Do sparse models outperform dense models on the speed-accuracy Pareto curve? Yes. Across a wide variety of different models sizes, sparse models outperform dense models per step and on wall clock time. Our controlled experiments show for a fixed amount of computation and time, sparse models outperform dense models.
 
 I can't deploy a trillion parameter model—can we shrink these models? We cannot fully preserve the model quality, but compression rates of 10 to 100x are achievable by distilling our sparse models into dense models while achieving ≈30% of the quality gain of the expert model.
 
-Why use Switch Transformer instead of a model-parallel dense model? On a time basis, Switch Transformers can be far more efficient than dense-models with sharded parameters (Figure 6). Also, we point out that this decision is not mutually exclusive—we can, and do, use model-parallelism in Switch Transformers, increasing the FLOPs per token, but incurring the slowdown of conventional model-parallelism.
+Why use Switch Transformer instead of a model-parallel dense model? On a time basis, Switch Transformers can be far more efficient than dense-models with sharded parameters (Figure [6)](#page-13-2). Also, we point out that this decision is not mutually exclusive—we can, and do, use model-parallelism in Switch Transformers, increasing the FLOPs per token, but incurring the slowdown of conventional model-parallelism.
 
-Why aren't sparse models widely used already? The motivation to try sparse models has been stymied by the massive success of scaling dense models (the success of which is partially driven by co-adaptation with deep learning hardware as argued in Hooker (2020)). Further, sparse models have been subject to multiple issues including (1) model complexity, (2) training difficulties, and (3) communication costs. Switch Transformer makes strides to alleviate these issues.
+Why aren't sparse models widely used already? The motivation to try sparse models has been stymied by the massive success of scaling dense models (the success of which is partially driven by co-adaptation with deep learning hardware as argued in [Hooker](#page-36-12) [(2020)](#page-36-12)). Further, sparse models have been subject to multiple issues including (1) model complexity, (2) training difficulties, and (3) communication costs. Switch Transformer makes strides to alleviate these issues.
 
-### 8. Future Work
+### 8. Future Work
 
 This paper lays out a simplified architecture, improved training procedures, and a study of how sparse models scale. However, there remain many open future directions which we briefly describe here:
 
 - 1. A significant challenge is further improving training stability for the largest models. While our stability techniques were effective for our Switch-Base, Switch-Large and Switch-C models (no observed instability), they were not sufficient for Switch-XXL. We have taken early steps towards stabilizing these models, which we think may be generally useful for large models, including using regularizers for improving stability and adapted forms of gradient clipping, but this remains unsolved.
-- 2. Generally we find that improved pre-training quality leads to better downstream results (Appendix E), though we sometimes encounter striking anomalies. For instance, despite similar perplexities modeling the C4 data set, the 1.6T parameter Switch-C achieves only an 87.7 exact match score in SQuAD, which compares unfavorably to 89.6 for the smaller Switch-XXL model. One notable difference is that the Switch-XXL model applies ≈10x the FLOPS per token than the Switch-C model, even though it has ≈4x less unique parameters (395B vs 1.6T). This suggests a poorly understood dependence between fine-tuning quality, FLOPS per token and number of parameters.
+- 2. Generally we find that improved pre-training quality leads to better downstream results (Appendix [E)](#page-31-0), though we sometimes encounter striking anomalies. For instance, despite similar perplexities modeling the C4 data set, the 1.6T parameter Switch-C achieves only an 87.7 exact match score in SQuAD, which compares unfavorably to 89.6 for the smaller Switch-XXL model. One notable difference is that the Switch-XXL model applies ≈10x the FLOPS per token than the Switch-C model, even though it has ≈4x less unique parameters (395B vs 1.6T). This suggests a poorly understood dependence between fine-tuning quality, FLOPS per token and number of parameters.
 - 3. Perform a comprehensive study of scaling relationships to guide the design of architectures blending data, model and expert-parallelism. Ideally, given the specs of a hardware configuration (computation, memory, communication) one could more rapidly design an optimal model. And, vice versa, this may also help in the design of future hardware.
 - 4. Our work falls within the family of adaptive computation algorithms. Our approach always used identical, homogeneous experts, but future designs (facilitated by more flexible infrastructure) could support heterogeneous experts. This would enable more flexible adaptation by routing to larger experts when more computation is desired perhaps for harder examples.
-- 5. Investigating expert layers outside the FFN layer of the Transformer. We find preliminary evidence that this similarly can improve model quality. In Appendix A, we report quality improvement adding these inside Self-Attention layers, where our
+- 5. Investigating expert layers outside the FFN layer of the Transformer. We find preliminary evidence that this similarly can improve model quality. In Appendix [A,](#page-26-1) we report quality improvement adding these inside Self-Attention layers, where our
 
 layer replaces the weight matrices which produce Q, K, V. However, due to training instabilities with the bfloat16 format, we instead leave this as an area for future work.
 
 - 6. Examining Switch Transformer in new and across different modalities. We have thus far only considered language, but we believe that model sparsity can similarly provide advantages in new modalities, as well as multi-modal networks.
 This list could easily be extended, but we hope this gives a flavor for the types of challenges that we are thinking about and what we suspect are promising future directions.
 
-### 9. Conclusion
+### 9. Conclusion
 
 Switch Transformers are scalable and effective natural language learners. We simplify Mixture of Experts to produce an architecture that is easy to understand, stable to train and vastly more sample efficient than equivalently-sized dense models. We find that these models excel across a diverse set of natural language tasks and in different training regimes, including pre-training, fine-tuning and multi-task training. These advances make it possible to train models with hundreds of billion to trillion parameters and which achieve substantial speedups relative to dense T5 baselines. We hope our work motivates sparse models as an effective architecture and that this encourages researchers and practitioners to consider these flexible models in natural language tasks, and beyond.
 
-# Acknowledgments
+### Acknowledgments
 
 The authors would like to thank Margaret Li who provided months of key insights into algorithmic improvements and suggestions for empirical studies. Hugo Larochelle for sage advising and clarifying comments on the draft, Irwan Bello for detailed comments and careful revisions, Colin Raffel and Adam Roberts for timely advice on neural language models and the T5 code-base, Yoshua Bengio for advising and encouragement on research in adaptive computation, Jascha Sohl-dickstein for interesting new directions for stabilizing new large scale models and paper revisions, and the Google Brain Team for useful discussions on the paper. Blake Hechtman who provided invaluable help in profiling and improving the training performance of our models.
 
-# A. Switch for Attention
+### A. Switch for Attention
 
-Shazeer et al. (2018); Lepikhin et al. (2020) designed MoE Transformers (Shazeer et al., 2017) by adding MoE layers into the dense feedfoward network (FFN) computations of the Transformer. Similarly, our work also replaced the FFN layer in the Transformer, but we briefly explore here an alternate design. We add Switch layers into the Transformer Self-Attention layers. To do so, we replace the trainable weight matrices that produce the queries, keys and values with Switch layers as seen in Figure 10.
+[Shazeer](#page-38-3) [et](#page-38-3) [al.](#page-38-3) [(2018)](#page-38-3); [Lepikhin](#page-37-2) [et](#page-37-2) [al.](#page-37-2) [(2020)](#page-37-2) designed MoE Transformers [(Shazeer](#page-38-2) [et](#page-38-2) [al.,](#page-38-2) [2017)](#page-38-2) by adding MoE layers into the dense feedfoward network (FFN) computations of the Transformer. Similarly, our work also replaced the FFN layer in the Transformer, but we briefly explore here an alternate design. We add Switch layers into the Transformer Self-Attention layers. To do so, we replace the trainable weight matrices that produce the queries, keys and values with Switch layers as seen in Figure [10.](#page-27-0)
 
-Table 10 records the quality after a fixed number of steps as well as training time for several variants. Though we find improvements, we also found these layers to be more unstable when using bfloat16 precision and thus we did not include them in the final variant.
+Table [10](#page-27-1) records the quality after a fixed number of steps as well as training time for several variants. Though we find improvements, we also found these layers to be more unstable when using bfloat16 precision and thus we did not include them in the final variant.
 
 ![](_page_27_Figure_1.jpeg)
 
-Figure 10: Switch layers in attention. We diagram how to incorporate the Switch layer into the Self-Attention transformer block. For each token (here we show two tokens, x1 = "More" and x2 = "Parameters"), one set of weights produces the query and the other set of unique weights produces the shared keys and values. We experimented with each expert being a linear operation, as well as a FFN, as was the case throughout this work. While we found quality improvements using this, we found this to be more unstable when used with low precision number formats, and thus leave it for future work.
+- Figure 10: Switch layers in attention. We diagram how to incorporate the Switch layer into the Self-Attention transformer block. For each token (here we show two tokens, x1 = "More" and x2 = "Parameters"), one set of weights produces the query and the other set of unique weights produces the shared keys and values. We experimented with each expert being a linear operation, as well as a FFN, as was the case throughout this work. While we found quality improvements using this, we found this to be more unstable when used with low precision number formats, and thus leave it for future work.
 
 | However, when these layers do train stably, we believe the preliminary positive results |
-| --- |
-| suggests a future promising direction. |
-
-| Model | Precision | Quality | Quality | Speed |
-| --- | --- | --- | --- | --- |
-|  |  | @100k Steps (↑) | @16H (↑) | (ex/sec) (↑) |
-| Experts FF | float32 | -1.548 | -1.614 | 1480 |
-| Expert Attention | float32 | -1.524 | -1.606 | 1330 |
-| Expert Attention | bfloat16 | [diverges] | [diverges] | – |
-| Experts FF + Attention | float32 | -1.513 | -1.607 | 1240 |
-| Expert FF + Attention | bfloat16 | [diverges] | [diverges] | – |
+|-----------------------------------------------------------------------------------------|
+| suggests a future promising direction.                                                  |
 
-Table 10: Switch attention layer results. All models have 32 experts and train with 524k tokens per batch. Experts FF is when experts replace the FFN in the Transformer, which is our standard setup throughout the paper. Experts FF + Attention is when experts are used to replace both the FFN and the Self-Attention layers. When training with bfloat16 precision the models that have experts attention diverge.
+| Model                  | Precision | Quality
    @100k Steps (↑) | Quality
    @16H (↑) | Speed
    (ex/sec) (↑) |
+|------------------------|-----------|-----------------------------|----------------------|------------------------|
+| Experts FF             | float32   | -1.548                      | -1.614               | 1480                   |
+| Expert Attention       | float32   | -1.524                      | -1.606               | 1330                   |
+| Expert Attention       | bfloat16  | [diverges]                  | [diverges]           | -                      |
+| Experts FF + Attention | float32   | -1.513                      | -1.607               | 1240                   |
+| Expert FF + Attention  | bfloat16  | [diverges]                  | [diverges]           | -                      |
 
-# B. Preventing Token Dropping with No-Token-Left-Behind
+- Table 10: Switch attention layer results. All models have 32 experts and train with 524k tokens per batch. Experts FF is when experts replace the FFN in the Transformer, which is our standard setup throughout the paper. Experts FF + Attention is when experts are used to replace both the FFN and the Self-Attention layers. When training with bfloat16 precision the models that have experts attention diverge.
+### B. Preventing Token Dropping with No-Token-Left-Behind
 
-Due to software constraints on TPU accelerators, the shapes of our Tensors must be statically sized. As a result, each expert has a finite and fixed capacity to process token representations. This, however, presents an issue for our model which dynamically routes tokens at run-time that may result in an uneven distribution over experts. If the number of tokens sent to an expert is less than the expert capacity, then the computation may simply be padded – an inefficient use of the hardware, but mathematically correct. However, when the number of tokens sent to an expert is larger than its capacity (expert overflow), a protocol is needed to handle this. Lepikhin et al. (2020) adapts a Mixture-of-Expert model and addresses expert overflow by passing its representation to the next layer without processing through a residual connection which we also follow.
+Due to software constraints on TPU accelerators, the shapes of our Tensors must be statically sized. As a result, each expert has a finite and fixed capacity to process token representations. This, however, presents an issue for our model which dynamically routes tokens at run-time that may result in an uneven distribution over experts. If the number of tokens sent to an expert is less than the expert capacity, then the computation may simply be padded – an inefficient use of the hardware, but mathematically correct. However, when the number of tokens sent to an expert is larger than its capacity (expert overflow), a protocol is needed to handle this. [Lepikhin](#page-37-2) [et](#page-37-2) [al.](#page-37-2) [(2020)](#page-37-2) adapts a Mixture-of-Expert model and addresses expert overflow by passing its representation to the next layer without processing through a residual connection which we also follow.
 
-We suspected that having no computation applied to tokens could be very wasteful, especially since if there is overflow on one expert, that means another expert will have extra capacity. With this intuition we create No-Token-Left-Behind, which iteratively reroutes any tokens that are at first routed to an expert that is overflowing. Figure 11 shows a graphical description of this method, which will allow us to guarantee almost no tokens will be dropped during training and inference. We hypothesised that this could improve performance and further stabilize training, but we found no empirical benefits. We suspect that once the network learns associations between different tokens and experts, if this association is changed (e.g. sending a token to its second highest expert) then performance could be degraded.
+We suspected that having no computation applied to tokens could be very wasteful, especially since if there is overflow on one expert, that means another expert will have extra capacity. With this intuition we create No-Token-Left-Behind, which iteratively reroutes any tokens that are at first routed to an expert that is overflowing. Figure [11](#page-29-0) shows a graphical description of this method, which will allow us to guarantee almost no tokens will be dropped during training and inference. We hypothesised that this could improve performance and further stabilize training, but we found no empirical benefits. We suspect that once the network learns associations between different tokens and experts, if this association is changed (e.g. sending a token to its second highest expert) then performance could be degraded.
 
-### C. Encouraging Exploration Across Experts
+### C. Encouraging Exploration Across Experts
 
-At each expert-layer, the router determines to which expert to send the token. This is a discrete decision over the available experts, conditioned on information about the token's representation. Based on the incoming token representation, the router determines the best expert, however, it receives no counterfactual information about how well it would have done selecting an alternate expert. As in reinforcement learning, a classic explorationexploitation dilemma arises (Sutton and Barto, 2018). These issues have been similarly noted and addressed differently by Rosenbaum et al. (2017) which demonstrated success in multi-task learning. This particular setting most closely matches that of a contextual bandit (Robbins, 1952). Deterministically selecting the top expert always amounts to an exploitative strategy – we consider balancing exploration to seek better expert assignment.
+At each expert-layer, the router determines to which expert to send the token. This is a discrete decision over the available experts, conditioned on information about the token's representation. Based on the incoming token representation, the router determines the best expert, however, it receives no counterfactual information about how well it would have done selecting an alternate expert. As in reinforcement learning, a classic explorationexploitation dilemma arises [(Sutton](#page-38-12) [and](#page-38-12) [Barto,](#page-38-12) [2018)](#page-38-12). These issues have been similarly noted and addressed differently by [Rosenbaum](#page-38-13) [et](#page-38-13) [al.](#page-38-13) [(2017)](#page-38-13) which demonstrated success in multi-task learning. This particular setting most closely matches that of a contextual bandit [(Robbins,](#page-37-12) [1952)](#page-37-12). Deterministically selecting the top expert always amounts to an exploitative strategy – we consider balancing exploration to seek better expert assignment.
 
-To introduce exploration, we consider several approaches: 1) deterministic or argmax 2) sampling from the softmax distribution 3) input dropout on the incoming representation 4) multiplicative jitter noise on the incoming representation. The resulting impact on model quality is reported in Table 11. Throughout this work, we use input jitter to inject noise as we have found it to empirically perform the best.
+To introduce exploration, we consider several approaches: 1) deterministic or argmax 2) sampling from the softmax distribution 3) input dropout on the incoming representation 4) multiplicative jitter noise on the incoming representation. The resulting impact on model quality is reported in Table [11.](#page-29-1) Throughout this work, we use input jitter to inject noise as we have found it to empirically perform the best.
 
-### D. Switch Transformers in Lower Compute Regimes
+### D. Switch Transformers in Lower Compute Regimes
 
 Switch Transformer is also an effective architecture at small scales as well as in regimes with thousands of cores and trillions of parameters. Many of our prior experiments were
 
 ![](_page_29_Figure_1.jpeg)
 
-- Figure 11: Diagram of the No-Token-Left-Behind Routing. Stage 1 is equivalent to Switch routing where tokens are routed to the expert with the highest probability from the router. In Stage 2 we look at all tokens that have overflowed and route them to the expert with which has the second highest probability. Tokens can still be overflowed if their second highest expert has too many tokens, but this allows most of the tokens to be routed. This process can be iterated to guarantee virtually no tokens are dropped at all.
+- Figure 11: Diagram of the No-Token-Left-Behind Routing. Stage 1 is equivalent to Switch routing where tokens are routed to the expert with the highest probability from the router. In Stage 2 we look at all tokens that have overflowed and route them to the expert with which has the second highest probability. Tokens can still be overflowed if their second highest expert has too many tokens, but this allows most of the tokens to be routed. This process can be iterated to guarantee virtually no tokens are dropped at all.
 
-| Model | Quality (Neg. Log Perp.) (↑) |
-| --- | --- |
-| Argmax | -1.471 |
-| Sample softmax | -1.570 |
-| Input dropout | -1.480 |
-| Input jitter | -1.468 |
+| Model          | Quality (Neg. Log Perp.) (↑) |
+|----------------|------------------------------|
+| Argmax         | -1.471                       |
+| Sample softmax | -1.570                       |
+| Input dropout  | -1.480                       |
+| Input jitter   | -1.468                       |
 
-- Table 11: Router Exploration Strategies. Quality of the Switch Transformer, measured by the negative log perplexity, under different randomness-strategies for selecting the expert (lower is better). There is no material speed performance difference between the variants.
-at the scale of 10B+ parameter models, but we show in Figure 12 as few as 2 experts produce compelling gains over a FLOP-matched counterpart. Even if a super computer is not readily available, training Switch Transformers with 2, 4, or 8 experts (as we typically recommend one expert per core) results in solid improvements over T5 dense baselines.
+- Table 11: Router Exploration Strategies. Quality of the Switch Transformer, measured by the negative log perplexity, under different randomness-strategies for selecting the expert (lower is better). There is no material speed performance difference between the variants.
+at the scale of 10B+ parameter models, but we show in Figure [12](#page-30-0) as few as 2 experts produce compelling gains over a FLOP-matched counterpart. Even if a super computer is not readily available, training Switch Transformers with 2, 4, or 8 experts (as we typically recommend one expert per core) results in solid improvements over T5 dense baselines.
 
 ![](_page_30_Figure_1.jpeg)
 
-Figure 12: Switch Transformer with few experts. Switch Transformer improves over the baseline even with very few experts. Here we show scaling properties at very small scales, where we improve over the T5-Base model using 2, 4, and 8 experts.
+Figure 12: Switch Transformer with few experts. Switch Transformer improves over the baseline even with very few experts. Here we show scaling properties at very small scales, where we improve over the T5-Base model using 2, 4, and 8 experts.
 
-### E. Relation of Upstream to Downstream Model Performance
+### E. Relation of Upstream to Downstream Model Performance
 
-There is no guarantee that a model's quality on a pre-training objective will translate to downstream task results. Figure 13 presents the correlation of the upstream model quality, for both dense and Switch models, on the C4 pre-training task with two downstream task measures: average SuperGLUE performance and TriviaQA score. We choose these two tasks as one probes the model's reasoning and the other factual knowledge.
+There is no guarantee that a model's quality on a pre-training objective will translate to downstream task results. Figure [13](#page-31-1) presents the correlation of the upstream model quality, for both dense and Switch models, on the C4 pre-training task with two downstream task measures: average SuperGLUE performance and TriviaQA score. We choose these two tasks as one probes the model's reasoning and the other factual knowledge.
 
 ![](_page_31_Figure_3.jpeg)
 
-- Figure 13: Upstream pre-trained quality to downstream model quality. We correlate the upstream performance with downstream quality on both SuperGLUE and TriviaQA (SOTA recorded without SSM), reasoning and knowledge-heavy benchmarks, respectively (validation sets). We find that, as with the baseline, the Switch model scales with improvements in the upstream pre-training task. For SuperGLUE, we find a loosely linear relation between negative log perplexity and the average SuperGLUE score. However, the dense model often performs better for a fixed perplexity, particularly in the large-scale regime. Conversely, on the knowledge-heavy task, TriviaQA, we find that the Switch Transformer may follow an improved scaling relationship – for a given upstream perplexity, it does better than a dense counterpart. Further statistics (expensive to collect and left to future work) would be necessary to confirm these observations.
-We find a consistent correlation, indicating that for both baseline and Switch models, improved pre-training leads to better downstream results. Additionally, for a fixed upstream perplexity we find that both Switch and dense models perform similarly in the small to medium model size regime. However, in the largest model regime (T5-11B/T5-XXL) our largest Switch models, as mentioned in Section 5.6, do not always translate their upstream perplexity well to downstream fine-tuning on the SuperGLUE task. This warrants future investigation and study to fully realize the potential of sparse models. Understanding the fine-tuning dynamics with expert-models is very complicated and is dependent on regularization, load-balancing, and fine-tuning hyper-parameters.
+Figure 13: Upstream pre-trained quality to downstream model quality. We correlate the upstream performance with downstream quality on both SuperGLUE and TriviaQA (SOTA recorded without SSM), reasoning and knowledge-heavy benchmarks, respectively (validation sets). We find that, as with the baseline, the Switch model scales with improvements in the upstream pre-training task. For SuperGLUE, we find a loosely linear relation between negative log perplexity and the average SuperGLUE score. However, the dense model often performs better for a fixed perplexity, particularly in the large-scale regime. Conversely, on the knowledge-heavy task, TriviaQA, we find that the Switch Transformer may follow an improved scaling relationship – for a given upstream perplexity, it does better than a dense counterpart. Further statistics (expensive to collect and left to future work) would be necessary to confirm these observations.
 
-# F. Pseudo Code for Switch Transformers
+We find a consistent correlation, indicating that for both baseline and Switch models, improved pre-training leads to better downstream results. Additionally, for a fixed upstream perplexity we find that both Switch and dense models perform similarly in the small to medium model size regime. However, in the largest model regime (T5-11B/T5-XXL) our largest Switch models, as mentioned in Section [5.6,](#page-21-2) do not always translate their upstream perplexity well to downstream fine-tuning on the SuperGLUE task. This warrants future investigation and study to fully realize the potential of sparse models. Understanding the fine-tuning dynamics with expert-models is very complicated and is dependent on regularization, load-balancing, and fine-tuning hyper-parameters.
 
-Pseudocode for Switch Transformers in Mesh Tensorflow (Shazeer et al., 2018). No model parallelism is being used for the below code (see 5.4 for more details).
+### F. Pseudo Code for Switch Transformers
+
+Pseudocode for Switch Transformers in Mesh Tensorflow [(Shazeer](#page-38-3) [et](#page-38-3) [al.,](#page-38-3) [2018)](#page-38-3). No model parallelism is being used for the below code (see [5.4](#page-21-0) for more details).
 
 ```
 import mesh tensorflow as mtf
@@ -584,178 +589,194 @@ def load balance loss(router probs, expert mask):
    loss = mtf.reduce mean(density 1 proxy ∗ density 1) ∗ (num experts ˆ 2)
    return loss
 ```
-Figure 14: Pseudo code for the load balance loss for Switch Transformers in Mesh Tensorflow.
+- Figure 14: Pseudo code for the load balance loss for Switch Transformers in Mesh Tensorflow.
 
 ```
 import mesh tensorflow as mtf
+```
+
+```
 def router(inputs, capacity factor):
    """Produce the combine and dispatch tensors used for sending and
    receiving tokens from their highest probability expert. """
    # Core layout is split across num cores for all tensors and operations.
    # inputs shape: [num cores, tokens per core, d model]
-   router weights = mtf.Variable(shape=[d model, num experts])
-   # router logits shape: [num cores, tokens per core, num experts]
-   router logits = mtf.einsum([inputs, router weights], reduced dim=d model)
-   if is training:
-       # Add noise for exploration across experts.
-       router logits += mtf.random uniform(shape=router logits.shape, minval=1−eps, maxval=1+eps)
-   # Convert input to softmax operation from bfloat16 to float32 for stability.
-   router logits = mtf.to float32(router logits)
-   # Probabilities for each token of what expert it should be sent to.
-   router probs = mtf.softmax(router logits, axis=−1)
-   # Get the top−1 expert for each token. expert gate is the top−1 probability
-   # from the router for each token. expert index is what expert each token
-   # is going to be routed to.
-   # expert gate shape: [num cores, tokens per core]
-   # expert index shape: [num cores, tokens per core]
-   expert gate, expert index = mtf.top 1(router probs, reduced dim=num experts)
-   # expert mask shape: [num cores, tokens per core, num experts]
-   expert mask = mtf.one hot(expert index, dimension=num experts)
-   # Compute load balancing loss.
-   aux loss = load balance loss(router probs, expert mask)
-   # Experts have a fixed capacity, ensure we do not exceed it. Construct
-   # the batch indices, to each expert, with position in expert
-   # make sure that not more that expert capacity examples can be routed to
-   # each expert.
-   position in expert = mtf.cumsum(expert mask, dimension=tokens per core) ∗ expert mask
-   # Keep only tokens that fit within expert capacity.
-   expert mask ∗= mtf.less(position in expert, expert capacity)
-   expert mask flat = mtf.reduce sum(expert mask, reduced dim=experts dim)
-   # Mask out the experts that have overflowed the expert capacity.
-   expert gate ∗= expert mask flat
-   # combine tensor used for combining expert outputs and scaling with router probability.
-   # combine tensor shape: [num cores, tokens per core, num experts, expert capacity]
-   combine tensor = (
-       expert gate ∗ expert mask flat ∗
-       mtf.one hot(expert index, dimension=num experts) ∗
-       mtf.one hot(position in expert, dimension=expert capacity))
-   # Cast back outputs to bfloat16 for the rest of the layer.
-   combine tensor = mtf.to bfloat16(combine tensor)
-   # Create binary dispatch tensor that is 1 if the token gets routed to the corresponding expert.
-   # dispatch tensor shape: [num cores, tokens per core, num experts, expert capacity]
-   dispatch tensor = mtf.cast(combine tensor, tf.bool)
-   return dispatch tensor, combine tensor, aux loss
 ```
-Figure 15: Pseudo code for the router for Switch Transformers in Mesh Tensorflow.
+#### router weights = mtf.Variable(shape=[d model, num experts])
+
+```
+# router logits shape: [num cores, tokens per core, num experts]
+router logits = mtf.einsum([inputs, router weights], reduced dim=d model)
+```
+#### if is training:
+
+# Add noise for exploration across experts. router logits += mtf.random uniform(shape=router logits.shape, minval=1−eps, maxval=1+eps)
+
+# Convert input to softmax operation from bfloat16 to float32 for stability. router logits = mtf.to float32(router logits)
+
+```
+# Probabilities for each token of what expert it should be sent to.
+router probs = mtf.softmax(router logits, axis=−1)
+```
+
+```
+# Get the top−1 expert for each token. expert gate is the top−1 probability
+# from the router for each token. expert index is what expert each token
+# is going to be routed to.
+# expert gate shape: [num cores, tokens per core]
+# expert index shape: [num cores, tokens per core]
+expert gate, expert index = mtf.top 1(router probs, reduced dim=num experts)
+# expert mask shape: [num cores, tokens per core, num experts]
+expert mask = mtf.one hot(expert index, dimension=num experts)
+# Compute load balancing loss.
+aux loss = load balance loss(router probs, expert mask)
+# Experts have a fixed capacity, ensure we do not exceed it. Construct
+# the batch indices, to each expert, with position in expert
+# make sure that not more that expert capacity examples can be routed to
+# each expert.
+position in expert = mtf.cumsum(expert mask, dimension=tokens per core) ∗ expert mask
+# Keep only tokens that fit within expert capacity.
+expert mask ∗= mtf.less(position in expert, expert capacity)
+expert mask flat = mtf.reduce sum(expert mask, reduced dim=experts dim)
+# Mask out the experts that have overflowed the expert capacity.
+expert gate ∗= expert mask flat
+# combine tensor used for combining expert outputs and scaling with router probability.
+# combine tensor shape: [num cores, tokens per core, num experts, expert capacity]
+combine tensor = (
+   expert gate ∗ expert mask flat ∗
+   mtf.one hot(expert index, dimension=num experts) ∗
+   mtf.one hot(position in expert, dimension=expert capacity))
+# Cast back outputs to bfloat16 for the rest of the layer.
+combine tensor = mtf.to bfloat16(combine tensor)
+# Create binary dispatch tensor that is 1 if the token gets routed to the corresponding expert.
+# dispatch tensor shape: [num cores, tokens per core, num experts, expert capacity]
+dispatch tensor = mtf.cast(combine tensor, tf.bool)
+```
+
+```
+return dispatch tensor, combine tensor, aux loss
+```
+Figure 15: Pseudo code for the router for Switch Transformers in Mesh Tensorflow.
 
 ```
 import mesh tensorflow as mtf
-def switch layer(inputs, n, capacity factor, num experts):
-   """Distributed switch transformer feed−forward layer."""
-   # num cores (n) = total cores for training the model (scalar).
-   # d model = model hidden size (scalar).
-   # num experts = total number of experts.
-   # capacity factor = extra buffer for each expert.
-   # inputs shape: [batch, seq len, d model]
-   batch, seq len, d model = inputs.get shape()
-   # Each core will route tokens per core tokens to the correct experts.
-   tokens per core = batch ∗ seq len / num cores
-   # Each expert will have shape [num cores, expert capacity, d model].
-   # Each core is responsible for sending expert capacity tokens
-   # to each expert.
-   expert capacity = tokens per core ∗ capacity factor / num experts
-   # Reshape to setup per core expert dispatching.
-   # shape: [batch, seq len, d model] −> [num cores, tokens per core, d model]
-   # Core layout: [n, 1, 1] −> [n, 1, 1]
-   inputs = mtf.reshape(inputs, [num cores, tokens per core, d model])
-   # Core Layout: [n, 1, 1] −> [n, 1, 1, 1], [n, 1, 1, 1]
-   # dispatch tensor (boolean) shape: [num cores, tokens per core, num experts, expert capacity]
-   # dispatch tensor is used for routing tokens to the correct expert.
-   # combine tensor (float) shape: [num cores, tokens per core, num experts, expert capacity]
-   # combine tensor used for combining expert outputs and scaling with router
-   # probability.
-   dispatch tensor, combine tensor, aux loss = router(inputs, expert capacity)
-   # Matmul with large boolean tensor to assign tokens to the correct expert.
-   # Core Layout: [n, 1, 1], −> [1, n, 1, 1]
-   # expert inputs shape: [num experts, num cores, expert capacity, d model]
-   expert inputs = mtf.einsum([inputs, dispatch tensor], reduce dims=[tokens per core])
-   # All−to−All communication. Cores split across num cores and now we want to split
-   # across num experts. This sends tokens, routed locally, to the correct expert now
-   # split across different cores.
-   # Core layout: [1, n, 1, 1] −> [n, 1, 1, 1]
-   expert inputs = mtf.reshape(expert inputs, [num experts, num cores, expert capacity, d model])
-   # Standard feed forward computation, where each expert will have its own
-   # unique set of parameters.
-   # Total unique parameters created: num experts ∗ (d model ∗ d ff ∗ 2).
-   # expert outputs shape: [num experts, num cores, expert capacity, d model]
-   expert outputs = feed forward(expert inputs)
-   # All−to−All communication. Cores are currently split across the experts
-   # dimension, which needs to be switched back to being split across num cores.
-   # Core Layout: [n, 1, 1, 1] −> [1, n, 1, 1]
-   expert outputs = mtf.reshape(expert outputs, [num experts, num cores, expert capacity, d model])
-   # Convert back to input shape and multiply outputs of experts by the routing probability.
-   # expert outputs shape: [num experts, num cores, tokens per core, d model]
-   # expert outputs combined shape: [num cores, tokens per core, d model]
-   # Core Layout: [1, n, 1, 1] −> [n, 1, 1]
-   expert outputs combined = mtf.einsum([expert outputs, combine tensor], reduce dims=[tokens per core])
-   # Remove tokens per core shapes used for local routing dispatching to match input shape.
-   # Core Layout: [n, 1, 1] −> [n, 1, 1]
-   outputs = mtf.reshape(expert outputs combined, [batch, seq len, d model])
-   return outputs, aux loss
+```
+def switch layer(inputs, n, capacity factor, num experts): """Distributed switch transformer feed−forward layer."""
+
+```
+# num cores (n) = total cores for training the model (scalar).
+# d model = model hidden size (scalar).
+# num experts = total number of experts.
+# capacity factor = extra buffer for each expert.
+# inputs shape: [batch, seq len, d model]
+batch, seq len, d model = inputs.get shape()
+# Each core will route tokens per core tokens to the correct experts.
+tokens per core = batch ∗ seq len / num cores
+# Each expert will have shape [num cores, expert capacity, d model].
+# Each core is responsible for sending expert capacity tokens
+# to each expert.
+expert capacity = tokens per core ∗ capacity factor / num experts
+# Reshape to setup per core expert dispatching.
+# shape: [batch, seq len, d model] −> [num cores, tokens per core, d model]
+# Core layout: [n, 1, 1] −> [n, 1, 1]
+inputs = mtf.reshape(inputs, [num cores, tokens per core, d model])
+# Core Layout: [n, 1, 1] −> [n, 1, 1, 1], [n, 1, 1, 1]
+# dispatch tensor (boolean) shape: [num cores, tokens per core, num experts, expert capacity]
+# dispatch tensor is used for routing tokens to the correct expert.
+# combine tensor (float) shape: [num cores, tokens per core, num experts, expert capacity]
+# combine tensor used for combining expert outputs and scaling with router
+# probability.
+dispatch tensor, combine tensor, aux loss = router(inputs, expert capacity)
+# Matmul with large boolean tensor to assign tokens to the correct expert.
+# Core Layout: [n, 1, 1], −> [1, n, 1, 1]
+# expert inputs shape: [num experts, num cores, expert capacity, d model]
+expert inputs = mtf.einsum([inputs, dispatch tensor], reduce dims=[tokens per core])
+# All−to−All communication. Cores split across num cores and now we want to split
+# across num experts. This sends tokens, routed locally, to the correct expert now
+# split across different cores.
+# Core layout: [1, n, 1, 1] −> [n, 1, 1, 1]
+expert inputs = mtf.reshape(expert inputs, [num experts, num cores, expert capacity, d model])
+# Standard feed forward computation, where each expert will have its own
+# unique set of parameters.
+# Total unique parameters created: num experts ∗ (d model ∗ d ff ∗ 2).
+# expert outputs shape: [num experts, num cores, expert capacity, d model]
+expert outputs = feed forward(expert inputs)
+# All−to−All communication. Cores are currently split across the experts
+# dimension, which needs to be switched back to being split across num cores.
+# Core Layout: [n, 1, 1, 1] −> [1, n, 1, 1]
+expert outputs = mtf.reshape(expert outputs, [num experts, num cores, expert capacity, d model])
+# Convert back to input shape and multiply outputs of experts by the routing probability.
+# expert outputs shape: [num experts, num cores, tokens per core, d model]
+# expert outputs combined shape: [num cores, tokens per core, d model]
+# Core Layout: [1, n, 1, 1] −> [n, 1, 1]
+expert outputs combined = mtf.einsum([expert outputs, combine tensor], reduce dims=[tokens per core])
+# Remove tokens per core shapes used for local routing dispatching to match input shape.
+# Core Layout: [n, 1, 1] −> [n, 1, 1]
+outputs = mtf.reshape(expert outputs combined, [batch, seq len, d model])
+return outputs, aux loss
 ```
 Figure 16: Pseudo code of the Switch Transformer layer in Mesh Tensorflow.
 
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-- Shibo Wang and Pankaj Kanwar. Bfloat16: The secret to high performance on cloud tpus. Google Cloud Blog, 2019.
-- Linting Xue, Noah Constant, Adam Roberts, Mihir Kale, Rami Al-Rfou, Aditya Siddhant, Aditya Barua, and Colin Raffel. mt5: A massively multilingual pre-trained text-to-text transformer. arXiv preprint arXiv:2010.11934, 2020.
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\ No newline at end of file
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diff --git a/data/examples/markdown/thinkpython/_page_71_Figure_1.png b/data/examples/markdown/thinkpython/_page_71_Figure_1.png
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diff --git a/data/examples/markdown/thinkpython/thinkpython.md b/data/examples/markdown/thinkpython/thinkpython.md
index 682181fb..c5e3109b 100644
--- a/data/examples/markdown/thinkpython/thinkpython.md
+++ b/data/examples/markdown/thinkpython/thinkpython.md
@@ -1,12 +1,12 @@
 ## Think Python
 
-How to Think Like a Computer Scientist
+## How to Think Like a Computer Scientist
 
 Version 2.0.17
 
-## Think Python
+# Think Python
 
-How to Think Like a Computer Scientist
+## How to Think Like a Computer Scientist
 
 Version 2.0.17
 
@@ -20,15 +20,15 @@ Copyright © 2012 Allen Downey.
 
 Green Tea Press 9 Washburn Ave Needham MA 02492
 
-Permission is granted to copy, distribute, and/or modify this document under the terms of the Creative Commons Attribution-NonCommercial 3.0 Unported License, which is available at http: //creativecommons.org/licenses/by-nc/3.0/.
+Permission is granted to copy, distribute, and/or modify this document under the terms of the Creative Commons Attribution-NonCommercial 3.0 Unported License, which is available at [http:](http://creativecommons.org/licenses/by-nc/3.0/) [//creativecommons.org/licenses/by-nc/3.0/](http://creativecommons.org/licenses/by-nc/3.0/).
 
 The original form of this book is LATEX source code. Compiling this LATEX source has the effect of generating a device-independent representation of a textbook, which can be converted to other formats and printed.
 
-The LATEX source for this book is available from http://www.thinkpython.com
+The LATEX source for this book is available from 
 
-## **Preface**
+# **Preface**
 
-### **The strange history of this book**
+## **The strange history of this book**
 
 In January 1999 I was preparing to teach an introductory programming class in Java. I had taught it three times and I was getting frustrated. The failure rate in the class was too high and, even for students who succeeded, the overall level of achievement was too low.
 
@@ -57,7 +57,7 @@ The result is this book, now with the less grandiose title *Think Python*. Some
 
 - I added a section about debugging at the end of each chapter. These sections present general techniques for finding and avoiding bugs, and warnings about Python pitfalls.
 - I added more exercises, ranging from short tests of understanding to a few substantial projects. And I wrote solutions for most of them.
-- I added a series of case studies—longer examples with exercises, solutions, and discussion. Some are based on Swampy, a suite of Python programs I wrote for use in my classes. Swampy, code examples, and some solutions are available from http://thinkpython.com.
+- I added a series of case studies—longer examples with exercises, solutions, and discussion. Some are based on Swampy, a suite of Python programs I wrote for use in my classes. Swampy, code examples, and some solutions are available from .
 - I expanded the discussion of program development plans and basic design patterns.
 - I added appendices about debugging, analysis of algorithms, and UML diagrams with Lumpy.
 
@@ -67,7 +67,7 @@ Allen B. Downey Needham MA
 
 Allen Downey is a Professor of Computer Science at the Franklin W. Olin College of Engineering.
 
-### **Acknowledgments**
+## **Acknowledgments**
 
 Many thanks to Jeff Elkner, who translated my Java book into Python, which got this project started and introduced me to what has turned out to be my favorite language.
 
@@ -79,7 +79,7 @@ Thanks to the editors at Lulu who worked on *How to Think Like a Computer Scient
 
 Thanks to all the students who worked with earlier versions of this book and all the contributors (listed below) who sent in corrections and suggestions.
 
-### **Contributor List**
+## **Contributor List**
 
 More than 100 sharp-eyed and thoughtful readers have sent in suggestions and corrections over the past few years. Their contributions, and enthusiasm for this project, have been a huge help.
 
@@ -162,13 +162,13 @@ If you include at least part of the sentence the error appears in, that makes it
 - Adam Hobart fixed a problem with floor division in arc.
 - Daryl Hammond and Sarah Zimmerman pointed out that I served up math.pi too early. And Zim spotted a typo.
 - George Sass found a bug in a Debugging section.
-- Brian Bingham suggested Exercise 11.10.
+- Brian Bingham suggested Exercise [11.10.](#page-132-0)
 - Leah Engelbert-Fenton pointed out that I used tuple as a variable name, contrary to my own advice. And then found a bunch of typos and a "use before def."
 - Joe Funke spotted a typo.
 - Chao-chao Chen found an inconsistency in the Fibonacci example.
 - Jeff Paine knows the difference between space and spam.
 - Lubos Pintes sent in a typo.
-- Gregg Lind and Abigail Heithoff suggested Exercise 14.4.
+- Gregg Lind and Abigail Heithoff suggested Exercise [14.4.](#page-160-0)
 - Max Hailperin has sent in a number of corrections and suggestions. Max is one of the authors of the extraordinary *Concrete Abstractions*, which you might want to read when you are done with this book.
 - Chotipat Pornavalai found an error in an error message.
 - Stanislaw Antol sent a list of very helpful suggestions.
@@ -218,287 +218,261 @@ If you include at least part of the sentence the error appears in, that makes it
 - Brian McGhie suggested a clarification.
 - Andrea Zanella translated the book into Italian, and sent a number of corrections along the way.
 
-## **Contents**
-
-| Preface |  |  | v |
-| --- | --- | --- | --- |
-| 1 | The way of the program |  | 1 |
-| 1.1 |  | The Python programming language | 1 |
-| 1.2 |  | What is a program? | 3 |
-| 1.3 |  | What is debugging? | 3 |
-| 1.4 |  | Formal and natural languages | 5 |
-| 1.5 |  | The first program | 6 |
-| 1.6 |  | Debugging | 7 |
-| 1.7 |  | Glossary | 7 |
-| 1.8 |  | Exercises | 9 |
-| 2 |  | Variables, expressions and statements | 11 |
-| 2.1 |  | Values and types | 11 |
-| 2.2 |  | Variables | 12 |
-| 2.3 |  | Variable names and keywords | 12 |
-| 2.4 |  | Operators and operands | 13 |
-| 2.5 |  | Expressions and statements | 14 |
-| 2.6 |  | Interactive mode and script mode | 14 |
-| 2.7 |  | Order of operations | 15 |
-| 2.8 |  | String operations | 15 |
-| 2.9 |  | Comments | 16 |
-| 2.10 |  | Debugging | 16 |
-| 2.11 |  | Glossary | 17 |
-| 2.12 |  | Exercises | 18 |
-
-| 3 | Functions |  | 19 |
-| --- | --- | --- | --- |
-|  | 3.1 | Function calls | 19 |
-|  | 3.2 | Type conversion functions | 19 |
-|  | 3.3 | Math functions | 20 |
-|  | 3.4 | Composition | 21 |
-|  | 3.5 | Adding new functions | 21 |
-|  | 3.6 | Definitions and uses | 22 |
-|  | 3.7 | Flow of execution | 23 |
-|  | 3.8 | Parameters and arguments | 23 |
-|  | 3.9 | Variables and parameters are local | 24 |
-|  | 3.10 | Stack diagrams | 25 |
-|  | 3.11 | Fruitful functions and void functions | 26 |
-|  | 3.12 | Why functions? | 26 |
-|  | 3.13 | Importing with from | 27 |
-|  | 3.14 | Debugging | 27 |
-|  | 3.15 | Glossary | 28 |
-|  | 3.16 | Exercises | 29 |
-| 4 |  | Case study: interface design | 31 |
-|  | 4.1 | TurtleWorld | 31 |
-|  | 4.2 | Simple repetition | 32 |
-|  | 4.3 | Exercises | 33 |
-|  | 4.4 | Encapsulation | 34 |
-|  | 4.5 | Generalization | 34 |
-|  | 4.6 | Interface design | 35 |
-|  | 4.7 | Refactoring | 36 |
-|  | 4.8 | A development plan | 37 |
-|  | 4.9 | docstring | 37 |
-|  | 4.10 | Debugging | 38 |
-|  | 4.11 | Glossary | 38 |
-|  | 4.12 | Exercises | 39 |
-
-|  | Contents |  | xv |
-| --- | --- | --- | --- |
-| 5 | Conditionals and recursion |  | 41 |
-| 5.1 |  | Modulus operator | 41 |
-| 5.2 |  | Boolean expressions | 41 |
-| 5.3 |  | Logical operators | 42 |
-| 5.4 |  | Conditional execution | 42 |
-| 5.5 |  | Alternative execution | 43 |
-| 5.6 |  | Chained conditionals | 43 |
-| 5.7 |  | Nested conditionals | 43 |
-| 5.8 |  | Recursion | 44 |
-| 5.9 |  | Stack diagrams for recursive functions | 45 |
-|  | 5.10 | Infinite recursion | 46 |
-|  | 5.11 | Keyboard input | 46 |
-|  | 5.12 | Debugging | 47 |
-|  | 5.13 | Glossary | 48 |
-|  | 5.14 | Exercises | 49 |
-| 6 |  | Fruitful functions | 51 |
-| 6.1 |  | Return values | 51 |
-| 6.2 |  | Incremental development | 52 |
-| 6.3 |  | Composition | 54 |
-| 6.4 |  | Boolean functions | 54 |
-| 6.5 |  | More recursion | 55 |
-| 6.6 |  | Leap of faith | 57 |
-| 6.7 | One more example |  | 57 |
-
-- 6.8 Checking types . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
-- 6.9 Debugging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
-- 
-
-6.10 Glossary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
-
-|
-|  |
-
-| 7 | Iteration |  | 63 |
-| --- | --- | --- | --- |
-|  | 7.1 | Multiple assignment | 63 |
-|  | 7.2 | Updating variables | 64 |
-|  | 7.3 | The while statement | 64 |
-|  | 7.4 | break | 65 |
-|  | 7.5 | Square roots | 66 |
-|  | 7.6 | Algorithms | 67 |
-|  | 7.7 | Debugging | 68 |
-|  | 7.8 | Glossary | 68 |
-|  | 7.9 | Exercises | 69 |
-| 8 | Strings |  | 71 |
-|  | 8.1 | A string is a sequence | 71 |
-|  | 8.2 | len | 71 |
-|  | 8.3 | Traversal with a for loop | 72 |
-|  | 8.4 | String slices | 73 |
-|  | 8.5 | Strings are immutable | 74 |
-|  | 8.6 | Searching | 74 |
-|  | 8.7 | Looping and counting | 75 |
-|  | 8.8 | String methods | 75 |
-|  | 8.9 | The in operator | 76 |
-|  | 8.10 | String comparison | 76 |
-|  | 8.11 | Debugging | 77 |
-|  | 8.12 | Glossary | 78 |
-|  | 8.13 | Exercises | 79 |
-| 9 |  | Case study: word play | 81 |
-|  | 9.1 | Reading word lists | 81 |
-|  | 9.2 | Exercises | 82 |
-|  | 9.3 | Search | 82 |
-|  | 9.4 | Looping with indices | 83 |
-|  | 9.5 | Debugging | 85 |
-|  | 9.6 | Glossary | 85 |
-|  | 9.7 | Exercises | 86 |
-
-| Contents |  | xvii |
-| --- | --- | --- |
-| 10 Lists |  | 87 |
-| 10.1 | A list is a sequence | 87 |
-| 10.2 | Lists are mutable | 87 |
-| 10.3 | Traversing a list | 89 |
-| 10.4 | List operations | 89 |
-| 10.5 | List slices | 89 |
-| 10.6 | List methods | 90 |
-| 10.7 | Map, filter and reduce | 91 |
-| 10.8 | Deleting elements | 92 |
-| 10.9 | Lists and strings | 93 |
-| 10.10 | Objects and values | 93 |
-| 10.11 | Aliasing | 94 |
-| 10.12 | List arguments | 95 |
-| 10.13 | Debugging | 96 |
-| 10.14 | Glossary | 97 |
-| 10.15 | Exercises | 98 |
-| 11 Dictionaries |  | 101 |
-| 11.1 | Dictionary as a set of counters | 102 |
-| 11.2 | Looping and dictionaries | 103 |
-| 11.3 | Reverse lookup | 104 |
-| 11.4 | Dictionaries and lists | 105 |
-| 11.5 | Memos | 106 |
-| 11.6 | Global variables | 108 |
-| 11.7 | Long integers | 109 |
-| 11.8 | Debugging | 109 |
-| 11.9 | Glossary | 110 |
-| 11.10 | Exercises | 111 |
-
-| 12 Tuples |  | 113 |
-| --- | --- | --- |
-| 12.1 | Tuples are immutable | 113 |
-| 12.2 | Tuple assignment | 114 |
-| 12.3 | Tuples as return values | 115 |
-| 12.4 | Variable-length argument tuples | 115 |
-| 12.5 | Lists and tuples | 116 |
-| 12.6 | Dictionaries and tuples | 117 |
-| 12.7 | Comparing tuples | 118 |
-| 12.8 | Sequences of sequences | 119 |
-| 12.9 | Debugging | 120 |
-| 12.10 | Glossary | 121 |
-| 12.11 | Exercises | 121 |
-|  | 13 Case study: data structure selection | 123 |
-| 13.1 | Word frequency analysis | 123 |
-| 13.2 | Random numbers | 124 |
-| 13.3 | Word histogram | 125 |
-| 13.4 | Most common words | 126 |
-| 13.5 | Optional parameters | 126 |
-| 13.6 | Dictionary subtraction | 127 |
-| 13.7 | Random words | 127 |
-| 13.8 | Markov analysis | 128 |
-| 13.9 | Data structures | 129 |
-| 13.10 | Debugging | 131 |
-| 13.11 | Glossary | 132 |
-| 13.12 | Exercises | 132 |
-| 14 Files |  | 133 |
-| 14.1 | Persistence | 133 |
-| 14.2 | Reading and writing | 133 |
-| 14.3 | Format operator | 134 |
-| 14.4 | Filenames and paths | 135 |
-
-| Contents |  | xix |
-| --- | --- | --- |
-| 14.5 | Catching exceptions | 136 |
-| 14.6 | Databases | 137 |
-| 14.7 | Pickling | 137 |
-| 14.8 | Pipes | 138 |
-| 14.9 | Writing modules | 139 |
-| 14.10 | Debugging | 140 |
-| 14.11 | Glossary | 141 |
-| 14.12 | Exercises | 141 |
-|  | 15 Classes and objects | 143 |
-| 15.1 | User-defined types | 143 |
-| 15.2 | Attributes | 144 |
-| 15.3 | Rectangles | 145 |
-| 15.4 | Instances as return values | 146 |
-| 15.5 | Objects are mutable | 146 |
-| 15.6 | Copying | 147 |
-| 15.7 | Debugging | 148 |
-| 15.8 | Glossary | 149 |
-| 15.9 | Exercises | 149 |
-|  | 16 Classes and functions | 151 |
-| 16.1 | Time | 151 |
-| 16.2 | Pure functions | 151 |
-| 16.3 | Modifiers | 153 |
-| 16.4 | Prototyping versus planning | 154 |
-| 16.5 | Debugging | 155 |
-| 16.6 | Glossary | 155 |
-| 16.7 | Exercises | 156 |
-
-| 17 Classes and methods |  | 157 |
-| --- | --- | --- |
-| 17.1 | Object-oriented features | 157 |
-| 17.2 | Printing objects | 158 |
-| 17.3 | Another example | 159 |
-| 17.4 | A more complicated example | 160 |
-| 17.5 | The init method | 160 |
-| 17.6 | The __str__ method | 161 |
-| 17.7 | Operator overloading | 161 |
-| 17.8 | Type-based dispatch | 162 |
-| 17.9 | Polymorphism | 163 |
-| 17.10 | Debugging | 164 |
-| 17.11 | Interface and implementation | 164 |
-| 17.12 | Glossary | 165 |
-| 17.13 | Exercises | 165 |
-| 18 Inheritance |  | 167 |
-| 18.1 | Card objects | 167 |
-| 18.2 | Class attributes | 168 |
-| 18.3 | Comparing cards | 169 |
-| 18.4 | Decks | 170 |
-| 18.5 | Printing the deck | 171 |
-| 18.6 | Add, remove, shuffle and sort | 171 |
-| 18.7 | Inheritance | 172 |
-| 18.8 | Class diagrams | 173 |
-| 18.9 | Debugging | 174 |
-| 18.10 | Data encapsulation | 175 |
-| 18.11 | Glossary | 176 |
-| 18.12 | Exercises | 177 |
-
-| Contents |  |  | xxi |
-| --- | --- | --- | --- |
-|  | 19 Case study: Tkinter |  | 179 |
-| 19.1 |  | GUI | 179 |
-| 19.2 |  | Buttons and callbacks | 180 |
-| 19.3 |  | Canvas widgets | 181 |
-| 19.4 |  | Coordinate sequences | 182 |
-| 19.5 |  | More widgets | 182 |
-| 19.6 |  | Packing widgets | 183 |
-| 19.7 |  | Menus and Callables | 185 |
-| 19.8 |  | Binding | 186 |
-| 19.9 |  | Debugging | 188 |
-|  | 19.10 | Glossary | 189 |
-|  | 19.11 | Exercises | 190 |
-| A | Debugging |  | 193 |
-| A.1 |  | Syntax errors | 193 |
-| A.2 |  | Runtime errors | 195 |
-| A.3 |  | Semantic errors | 198 |
-| B |  | Analysis of Algorithms | 201 |
-| B.1 |  | Order of growth | 202 |
-| B.2 |  | Analysis of basic Python operations | 204 |
-| B.3 |  | Analysis of search algorithms | 205 |
-| B.4 |  | Hashtables | 206 |
-| C | Lumpy |  | 211 |
-| C.1 |  | State diagram | 211 |
-| C.2 |  | Stack diagram | 212 |
-| C.3 |  | Object diagrams | 213 |
-| C.4 |  | Function and class objects | 215 |
-| C.5 |  | Class Diagrams | 216 |
-
-**xxii Contents**
-
-## **Chapter 1**
+# **Contents**
+
+|    | Preface                               |                                         |     |  |
+|----|---------------------------------------|-----------------------------------------|-----|--|
+| 1  |                                       | The way of the program                  | 1   |  |
+|    | 1.1                                   | The Python programming language         | 1   |  |
+|    | 1.2                                   | What is a program?                      | 3   |  |
+|    | 1.3                                   | What is debugging?                      | 3   |  |
+|    | 1.4                                   | Formal and natural languages            | 5   |  |
+|    | 1.5                                   | The first program                       | 6   |  |
+|    | 1.6                                   | Debugging                               | 7   |  |
+|    | 1.7                                   | Glossary                                | 7   |  |
+|    | 1.8                                   | Exercises                               | 9   |  |
+| 2  | Variables, expressions and statements |                                         |     |  |
+|    | 2.1                                   | Values and types                        | 11  |  |
+|    | 2.2                                   | Variables                               | 12  |  |
+|    | 2.3                                   | Variable names and keywords             | 12  |  |
+|    | 2.4                                   | Operators and operands                  | 13  |  |
+|    | 2.5                                   | Expressions and statements              | 14  |  |
+|    | 2.6                                   | Interactive mode and script mode        | 14  |  |
+|    | 2.7                                   | Order of operations                     | 15  |  |
+|    | 2.8                                   | String operations                       | 15  |  |
+|    | 2.9                                   | Comments                                | 16  |  |
+|    | 2.10                                  | Debugging                               | 16  |  |
+|    | 2.11                                  | Glossary                                | 17  |  |
+|    | 2.12                                  | Exercises                               | 18  |  |
+| 3  | Functions                             |                                         | 19  |  |
+|    | 3.1                                   | Function calls                          | 19  |  |
+|    | 3.2                                   | Type conversion functions               | 19  |  |
+|    | 3.3                                   | Math functions                          | 20  |  |
+|    | 3.4                                   | Composition                             | 21  |  |
+|    | 3.5                                   | Adding new functions                    | 21  |  |
+|    | 3.6                                   | Definitions and uses                    | 22  |  |
+|    | 3.7                                   | Flow of execution                       | 23  |  |
+|    | 3.8                                   | Parameters and arguments                | 23  |  |
+|    | 3.9                                   | Variables and parameters are local      | 24  |  |
+|    | 3.10                                  | Stack diagrams                          | 25  |  |
+|    | 3.11                                  | Fruitful functions and void functions   | 26  |  |
+|    | 3.12                                  | Why functions?                          | 26  |  |
+|    | 3.13                                  | Importing with from                     | 27  |  |
+|    | 3.14                                  | Debugging                               | 27  |  |
+|    | 3.15                                  | Glossary                                | 28  |  |
+|    | 3.16                                  | Exercises                               | 29  |  |
+| 4  | Case study: interface design          |                                         | 31  |  |
+|    | 4.1                                   | TurtleWorld                             | 31  |  |
+|    | 4.2                                   | Simple repetition                       | 32  |  |
+|    | 4.3                                   | Exercises                               | 33  |  |
+|    | 4.4                                   | Encapsulation                           | 34  |  |
+|    | 4.5                                   | Generalization                          | 34  |  |
+|    | 4.6                                   | Interface design                        | 35  |  |
+|    | 4.7                                   | Refactoring                             | 36  |  |
+|    | 4.8                                   | A development plan                      | 37  |  |
+|    | 4.9                                   | docstring                               | 37  |  |
+|    | 4.10                                  | Debugging                               | 38  |  |
+|    | 4.11                                  | Glossary                                | 38  |  |
+|    | 4.12                                  | Exercises                               | 39  |  |
+| 5  |                                       | Conditionals and recursion              | 41  |  |
+|    | 5.1                                   | Modulus operator                        | 41  |  |
+|    | 5.2                                   | Boolean expressions                     | 41  |  |
+|    | 5.3                                   | Logical operators                       | 42  |  |
+|    | 5.4                                   | Conditional execution                   | 42  |  |
+|    | 5.5                                   | Alternative execution                   | 43  |  |
+|    | 5.6                                   | Chained conditionals                    | 43  |  |
+|    | 5.7                                   | Nested conditionals                     | 43  |  |
+|    | 5.8                                   | Recursion                               | 44  |  |
+|    | 5.9                                   | Stack diagrams for recursive functions  | 45  |  |
+|    | 5.10                                  | Infinite recursion                      | 46  |  |
+|    | 5.11                                  | Keyboard input                          | 46  |  |
+|    | 5.12                                  | Debugging                               | 47  |  |
+|    | 5.13                                  | Glossary                                | 48  |  |
+|    | 5.14                                  | Exercises                               | 49  |  |
+| 6  |                                       | Fruitful functions                      | 51  |  |
+|    | 6.1                                   | Return values                           | 51  |  |
+|    | 6.2                                   | Incremental development                 | 52  |  |
+|    | 6.3                                   | Composition                             | 54  |  |
+|    | 6.4                                   | Boolean functions                       | 54  |  |
+|    | 6.5                                   | More recursion                          | 55  |  |
+|    | 6.6                                   | Leap of faith                           | 57  |  |
+|    | 6.7                                   | One more example                        | 57  |  |
+|    | 6.8                                   | Checking types                          | 58  |  |
+|    | 6.9                                   | Debugging                               | 59  |  |
+|    | 6.10                                  | Glossary                                | 60  |  |
+| 7  | Iteration                             |                                         | 6   |  |
+|    | 7.1                                   | Multiple assignment                     | 6   |  |
+|    | 7.2                                   | Updating variables                      | 6   |  |
+|    | 7.3                                   | The while statement                     | 6   |  |
+|    | 7.4                                   | break                                   | 6   |  |
+|    | 7.5                                   | Square roots                            | 6   |  |
+|    | 7.6                                   | Algorithms                              | 6   |  |
+|    | 7.7                                   | Debugging                               | 6   |  |
+|    | 7.8                                   | Glossary                                | 6   |  |
+|    | 7.9                                   | Exercises                               | 6   |  |
+| 8  | Strings                               |                                         | 7   |  |
+|    | 8.1                                   | A string is a sequence                  | 7   |  |
+|    | 8.2                                   | len                                     | 7   |  |
+|    | 8.3                                   | Traversal with a for loop               | 7   |  |
+|    | 8.4                                   | String slices                           | 7   |  |
+|    | 8.5                                   | Strings are immutable                   | 7   |  |
+|    | 8.6                                   | Searching                               | 7   |  |
+|    | 8.7                                   | Looping and counting                    | 7   |  |
+|    | 8.8                                   | String methods                          | 7   |  |
+|    | 8.9                                   | The in operator                         | 7   |  |
+|    | 8.10                                  | String comparison                       | 7   |  |
+|    | 8.11                                  | Debugging                               | 7   |  |
+|    | 8.12                                  | Glossary                                | 7   |  |
+|    | 8.13                                  | Exercises                               | 7   |  |
+| 9  | Case study: word play                 |                                         | 8   |  |
+|    | 9.1                                   | Reading word lists                      | 8   |  |
+|    | 9.2                                   | Exercises                               | 8   |  |
+|    | 9.3                                   | Search                                  | 8   |  |
+|    | 9.4                                   | Looping with indices                    | 8   |  |
+|    | 9.5                                   | Debugging                               | 8   |  |
+|    | 9.6                                   | Glossary                                | 8   |  |
+|    | 9.7                                   | Exercises                               | 8   |  |
+|    | 10 Lists                              |                                         | 87  |  |
+|    | 10.1                                  | A list is a sequence                    | 87  |  |
+|    | 10.2                                  | Lists are mutable                       | 87  |  |
+|    | 10.3                                  | Traversing a list                       | 89  |  |
+|    | 10.4                                  | List operations                         | 89  |  |
+|    | 10.5                                  | List slices                             | 89  |  |
+|    | 10.6                                  | List methods                            | 90  |  |
+|    | 10.7                                  | Map, filter and reduce                  | 91  |  |
+|    | 10.8                                  | Deleting elements                       | 92  |  |
+|    | 10.9                                  | Lists and strings                       | 93  |  |
+|    | 10.10                                 | Objects and values                      | 93  |  |
+|    | 10.11                                 | Aliasing                                | 94  |  |
+|    | 10.12                                 | List arguments                          | 95  |  |
+|    | 10.13                                 | Debugging                               | 96  |  |
+|    | 10.14                                 | Glossary                                | 97  |  |
+|    | 10.15                                 | Exercises                               | 98  |  |
+|    |                                       |                                         |     |  |
+|    |                                       | 11 Dictionaries                         | 101 |  |
+|    | 11.1                                  | Dictionary as a set of counters         | 102 |  |
+|    | 11.2                                  | Looping and dictionaries                | 103 |  |
+|    | 11.3                                  | Reverse lookup                          | 104 |  |
+|    | 11.4                                  | Dictionaries and lists                  | 105 |  |
+|    | 11.5                                  | Memos                                   | 106 |  |
+|    | 11.6                                  | Global variables                        | 108 |  |
+|    | 11.7                                  | Long integers                           | 109 |  |
+|    | 11.8                                  | Debugging                               | 109 |  |
+|    | 11.9                                  | Glossary                                | 110 |  |
+|    | 11.10                                 | Exercises                               | 111 |  |
+|    | 12 Tuples                             |                                         | 113 |  |
+|    | 12.1                                  | Tuples are immutable                    | 113 |  |
+|    | 12.2                                  | Tuple assignment                        | 114 |  |
+|    | 12.3                                  | Tuples as return values                 | 115 |  |
+|    | 12.4                                  | Variable-length argument tuples         | 115 |  |
+|    | 12.5                                  | Lists and tuples                        | 116 |  |
+|    | 12.6                                  | Dictionaries and tuples                 | 117 |  |
+|    | 12.7                                  | Comparing tuples                        | 118 |  |
+|    | 12.8                                  | Sequences of sequences                  | 119 |  |
+|    | 12.9                                  | Debugging                               | 120 |  |
+|    | 12.10                                 | Glossary                                | 121 |  |
+|    | 12.11                                 | Exercises                               | 121 |  |
+|    |                                       | 13 Case study: data structure selection | 123 |  |
+|    | 13.1                                  | Word frequency analysis                 | 123 |  |
+|    | 13.2                                  | Random numbers                          | 124 |  |
+|    | 13.3                                  | Word histogram                          | 125 |  |
+|    | 13.4                                  | Most common words                       | 126 |  |
+|    | 13.5                                  | Optional parameters                     | 126 |  |
+|    | 13.6                                  | Dictionary subtraction                  | 127 |  |
+|    | 13.7                                  | Random words                            | 127 |  |
+|    | 13.8                                  | Markov analysis                         | 128 |  |
+|    | 13.9                                  | Data structures                         | 129 |  |
+|    | 13.10                                 | Debugging                               | 131 |  |
+|    | 13.11                                 | Glossary                                | 132 |  |
+|    | 13.12                                 | Exercises                               | 132 |  |
+|    |                                       |                                         |     |  |
+|    | 14 Files                              |                                         | 133 |  |
+|    | 14.1                                  | Persistence                             | 133 |  |
+|    | 14.2                                  | Reading and writing                     | 133 |  |
+|    | 14.3                                  | Format operator                         | 134 |  |
+|    | 14.4                                  | Filenames and paths                     | 135 |  |
+|    |                                       |                                         |     |  |
+|    | 14.5                                  | Catching exceptions                     | 136 |  |
+|    | 14.6                                  | Databases                               | 137 |  |
+|    | 14.7                                  | Pickling                                | 137 |  |
+|    | 14.8                                  | Pipes                                   | 138 |  |
+|    | 14.9                                  | Writing modules                         | 139 |  |
+|    | 14.10                                 | Debugging                               | 140 |  |
+|    | 14.11                                 | Glossary                                | 141 |  |
+|    | 14.12                                 | Exercises                               | 141 |  |
+| 15 | Classes and objects                   |                                         |     |  |
+|    | 15.1                                  | User-defined types                      | 143 |  |
+|    | 15.2                                  | Attributes                              | 144 |  |
+|    | 15.3                                  | Rectangles                              | 145 |  |
+|    | 15.4                                  | Instances as return values              | 146 |  |
+|    | 15.5                                  | Objects are mutable                     | 146 |  |
+|    | 15.6                                  | Copying                                 | 147 |  |
+|    | 15.7                                  | Debugging                               | 148 |  |
+|    | 15.8                                  | Glossary                                | 149 |  |
+|    | 15.9                                  | Exercises                               | 149 |  |
+| 16 | Classes and functions                 |                                         |     |  |
+|    | 16.1                                  | Time                                    | 151 |  |
+|    | 16.2                                  | Pure functions                          | 151 |  |
+|    | 16.3                                  | Modifiers                               | 153 |  |
+|    | 16.4                                  | Prototyping versus planning             | 154 |  |
+|    | 16.5                                  | Debugging                               | 155 |  |
+|    | 16.6                                  | Glossary                                | 155 |  |
+|    | 16.7                                  | Exercises                               | 156 |  |
+|    |                                       | 17 Classes and methods                  | 157 |  |
+|    | 17.1                                  | Object-oriented features                | 157 |  |
+|    | 17.2                                  | Printing objects                        | 158 |  |
+|    | 17.3                                  | Another example                         | 159 |  |
+|    | 17.4                                  | A more complicated example              | 160 |  |
+|    | 17.5                                  | The init method                         | 160 |  |
+|    | 17.6                                  | The __str__ method                      | 161 |  |
+|    | 17.7                                  | Operator overloading                    | 161 |  |
+|    | 17.8                                  | Type-based dispatch                     | 162 |  |
+|    | 17.9                                  | Polymorphism                            | 163 |  |
+|    | 17.10                                 | Debugging                               | 164 |  |
+|    | 17.11                                 | Interface and implementation            | 164 |  |
+|    | 17.12                                 | Glossary                                | 165 |  |
+|    | 17.13                                 | Exercises                               | 165 |  |
+|    |                                       | 18 Inheritance                          | 167 |  |
+|    | 18.1                                  | Card objects                            | 167 |  |
+|    | 18.2                                  | Class attributes                        | 168 |  |
+|    | 18.3                                  | Comparing cards                         | 169 |  |
+|    | 18.4                                  | Decks                                   | 170 |  |
+|    | 18.5                                  | Printing the deck                       | 171 |  |
+|    | 18.6                                  | Add, remove, shuffle and sort           | 171 |  |
+|    | 18.7                                  | Inheritance                             | 172 |  |
+|    | 18.8                                  | Class diagrams                          | 173 |  |
+|    | 18.9                                  | Debugging                               | 174 |  |
+|    | 18.10                                 | Data encapsulation                      | 175 |  |
+|    | 18.11                                 | Glossary                                | 176 |  |
+|    |                                       | 19 Case study: Tkinter                  | 179 |  |
+|    | 19.1                                  | GUI                                     | 179 |  |
+|    | 19.2                                  | Buttons and callbacks                   | 180 |  |
+|    | 19.3                                  | Canvas widgets                          | 181 |  |
+|    | 19.4                                  | Coordinate sequences                    | 182 |  |
+|    | 19.5                                  | More widgets                            | 182 |  |
+|    | 19.6                                  | Packing widgets                         | 183 |  |
+|    | 19.7                                  | Menus and Callables                     | 185 |  |
+|    | 19.8                                  | Binding                                 | 186 |  |
+|    | 19.9                                  | Debugging                               | 188 |  |
+|    | 19.10                                 | Glossary                                | 189 |  |
+|    | 19.11                                 | Exercises                               | 190 |  |
+| A  | Debugging                             |                                         | 193 |  |
+|    | A.1                                   | Syntax errors                           | 193 |  |
+|    | A.2                                   | Runtime errors                          | 195 |  |
+|    | A.3                                   | Semantic errors                         | 198 |  |
+| B  | Analysis of Algorithms                |                                         | 201 |  |
+|    | B.1                                   | Order of growth                         | 202 |  |
+|    | B.2                                   | Analysis of basic Python operations     | 204 |  |
+|    | B.3                                   | Analysis of search algorithms           | 205 |  |
+|    | B.4                                   | Hashtables                              | 206 |  |
+| C  | Lumpy                                 |                                         | 211 |  |
+|    | C.1                                   | State diagram                           | 211 |  |
+|    | C.2                                   | Stack diagram                           | 212 |  |
+|    | C.3                                   | Object diagrams                         | 213 |  |
+|    | C.4                                   | Function and class objects              | 215 |  |
+|    | C.5                                   | Class Diagrams                          | 216 |  |
+
+## **Chapter 1**
 
 # **The way of the program**
 
@@ -508,7 +482,7 @@ The single most important skill for a computer scientist is **problem solving**.
 
 On one level, you will be learning to program, a useful skill by itself. On another level, you will use programming as a means to an end. As we go along, that end will become clearer.
 
-### **1.1 The Python programming language**
+### **1.1 The Python programming language**
 
 The programming language you will learn is Python. Python is an example of a **high-level language**; other high-level languages you might have heard of are C, C++, Perl, and Java.
 
@@ -520,15 +494,15 @@ The advantages are enormous. First, it is much easier to program in a high-level
 
 Figure 1.1: An interpreter processes the program a little at a time, alternately reading lines and performing computations.
 
-![](_page_23_Figure_3.jpeg)
+![](_page_23_Figure_3.jpeg)
 
-Figure 1.2: A compiler translates source code into object code, which is run by a hardware executor.
+Figure 1.2: A compiler translates source code into object code, which is run by a hardware executor.
 
 Due to these advantages, almost all programs are written in high-level languages. Lowlevel languages are used only for a few specialized applications.
 
-Two kinds of programs process high-level languages into low-level languages: **interpreters** and **compilers**. An interpreter reads a high-level program and executes it, meaning that it does what the program says. It processes the program a little at a time, alternately reading lines and performing computations. Figure 1.1 shows the structure of an interpreter.
+Two kinds of programs process high-level languages into low-level languages: **interpreters** and **compilers**. An interpreter reads a high-level program and executes it, meaning that it does what the program says. It processes the program a little at a time, alternately reading lines and performing computations. Figure [1.1](#page-23-0) shows the structure of an interpreter.
 
-A compiler reads the program and translates it completely before the program starts running. In this context, the high-level program is called the **source code**, and the translated program is called the **object code** or the **executable**. Once a program is compiled, you can execute it repeatedly without further translation. Figure 1.2 shows the structure of a compiler.
+A compiler reads the program and translates it completely before the program starts running. In this context, the high-level program is called the **source code**, and the translated program is called the **object code** or the **executable**. Once a program is compiled, you can execute it repeatedly without further translation. Figure [1.2](#page-23-1) shows the structure of a compiler.
 
 Python is considered an interpreted language because Python programs are executed by an interpreter. There are two ways to use the interpreter: **interactive mode** and **script mode**. In interactive mode, you type Python programs and the interpreter displays the result:
 
@@ -540,11 +514,11 @@ The chevron, >>>, is the **prompt** the interpreter uses to indicate that it is
 
 Alternatively, you can store code in a file and use the interpreter to execute the contents of the file, which is called a **script**. By convention, Python scripts have names that end with .py.
 
-To execute the script, you have to tell the interpreter the name of the file. If you have a script named dinsdale.py and you are working in a UNIX command window, you type python dinsdale.py. In other development environments, the details of executing scripts are different. You can find instructions for your environment at the Python website http: //python.org.
+To execute the script, you have to tell the interpreter the name of the file. If you have a script named dinsdale.py and you are working in a UNIX command window, you type python dinsdale.py. In other development environments, the details of executing scripts are different. You can find instructions for your environment at the Python website [http:](http://python.org) [//python.org](http://python.org).
 
 Working in interactive mode is convenient for testing small pieces of code because you can type and execute them immediately. But for anything more than a few lines, you should save your code as a script so you can modify and execute it in the future.
 
-### **1.2 What is a program?**
+#### **1.2 What is a program?**
 
 A **program** is a sequence of instructions that specifies how to perform a computation. The computation might be something mathematical, such as solving a system of equations or finding the roots of a polynomial, but it can also be a symbolic computation, such as searching and replacing text in a document or (strangely enough) compiling a program.
 
@@ -564,7 +538,7 @@ Believe it or not, that's pretty much all there is to it. Every program you've e
 
 That may be a little vague, but we will come back to this topic when we talk about **algorithms**.
 
-### **1.3 What is debugging?**
+#### **1.3 What is debugging?**
 
 Programming is error-prone. For whimsical reasons, programming errors are called **bugs** and the process of tracking them down is called **debugging**.
 
@@ -602,7 +576,7 @@ For example, Linux is an operating system that contains thousands of lines of co
 
 Later chapters will make more suggestions about debugging and other programming practices.
 
-### **1.4 Formal and natural languages**
+#### **1.4 Formal and natural languages**
 
 **Natural languages** are the languages people speak, such as English, Spanish, and French. They were not designed by people (although people try to impose some order on them); they evolved naturally.
 
@@ -610,9 +584,9 @@ Later chapters will make more suggestions about debugging and other programming
 
 #### **Programming languages are formal languages that have been designed to express computations.**
 
-Formal languages tend to have strict rules about syntax. For example, 3 + 3 = 6 is a syntactically correct mathematical statement, but 3+ = 3$6 is not. H2O is a syntactically correct chemical formula, but 2Zz is not.
+Formal languages tend to have strict rules about syntax. For example, 3 + 3 = 6 is a syntactically correct mathematical statement, but 3+ = 3$6 is not. *H*2*O* is a syntactically correct chemical formula, but 2*Zz* is not.
 
-Syntax rules come in two flavors, pertaining to **tokens** and structure. Tokens are the basic elements of the language, such as words, numbers, and chemical elements. One of the problems with 3+ = 3$6 is that $ is not a legal token in mathematics (at least as far as I know). Similarly, 2Zz is not legal because there is no element with the abbreviation Zz.
+Syntax rules come in two flavors, pertaining to **tokens** and structure. Tokens are the basic elements of the language, such as words, numbers, and chemical elements. One of the problems with 3+ = 3$6 is that $ is not a legal token in mathematics (at least as far as I know). Similarly, 2*Zz* is not legal because there is no element with the abbreviation *Zz*.
 
 The second type of syntax rule pertains to the structure of a statement; that is, the way the tokens are arranged. The statement 3+ = 3 is illegal because even though + and = are legal tokens, you can't have one right after the other. Similarly, in a chemical formula the subscript comes after the element name, not before.
 
@@ -636,12 +610,13 @@ People who grow up speaking a natural language—everyone—often have a hard ti
 
 Here are some suggestions for reading programs (and other formal languages). First, remember that formal languages are much more dense than natural languages, so it takes longer to read them. Also, the structure is very important, so it is usually not a good idea to read from top to bottom, left to right. Instead, learn to parse the program in your head, identifying the tokens and interpreting the structure. Finally, the details matter. Small errors in spelling and punctuation, which you can get away with in natural languages, can make a big difference in a formal language.
 
-### **1.5 The first program**
+#### **1.5 The first program**
 
 Traditionally, the first program you write in a new language is called "Hello, World!" because all it does is display the words "Hello, World!". In Python, it looks like this:
 
+```
 print 'Hello, World!'
-
+```
 This is an example of a **print statement**, which doesn't actually print anything on paper. It displays a value on the screen. In this case, the result is the words
 
 Hello, World!
@@ -650,13 +625,14 @@ The quotation marks in the program mark the beginning and end of the text to be
 
 In Python 3, the syntax for printing is slightly different:
 
+```
 print('Hello, World!')
-
-The parentheses indicate that print is a function. We'll get to functions in Chapter 3.
+```
+The parentheses indicate that print is a function. We'll get to functions in Chapter [3.](#page-40-0)
 
 For the rest of this book, I'll use the print statement. If you are using Python 3, you will have to translate. But other than that, there are very few differences we have to worry about.
 
-### **1.6 Debugging**
+### **1.6 Debugging**
 
 It is a good idea to read this book in front of a computer so you can try out the examples as you go. You can run most of the examples in interactive mode, but if you put the code in a script, it is easier to try out variations.
 
@@ -674,7 +650,7 @@ Your job is to be a good manager: find ways to take advantage of the strengths a
 
 Learning to debug can be frustrating, but it is a valuable skill that is useful for many activities beyond programming. At the end of each chapter there is a debugging section, like this one, with my thoughts about debugging. I hope they help!
 
-### **1.7 Glossary**
+#### **1.7 Glossary**
 
 **problem solving:** The process of formulating a problem, finding a solution, and expressing the solution.
 
@@ -688,39 +664,31 @@ Learning to debug can be frustrating, but it is a valuable skill that is useful
 
 **object code:** The output of the compiler after it translates the program.
 
-**executable:** Another name for object code that is ready to be executed.
-
+- **executable:** Another name for object code that is ready to be executed.
 - **prompt:** Characters displayed by the interpreter to indicate that it is ready to take input from the user.
 - **script:** A program stored in a file (usually one that will be interpreted).
 - **interactive mode:** A way of using the Python interpreter by typing commands and expressions at the prompt.
 - **script mode:** A way of using the Python interpreter to read and execute statements in a script.
-
-**program:** A set of instructions that specifies a computation.
-
-**algorithm:** A general process for solving a category of problems.
+- **program:** A set of instructions that specifies a computation.
+- **algorithm:** A general process for solving a category of problems.
 
 **bug:** An error in a program.
 
 - **debugging:** The process of finding and removing any of the three kinds of programming errors.
-**syntax:** The structure of a program.
-
+- **syntax:** The structure of a program.
 - **syntax error:** An error in a program that makes it impossible to parse (and therefore impossible to interpret).
 - **exception:** An error that is detected while the program is running.
-
-**semantics:** The meaning of a program.
-
+- **semantics:** The meaning of a program.
 - **semantic error:** An error in a program that makes it do something other than what the programmer intended.
-**natural language:** Any one of the languages that people speak that evolved naturally.
-
+- **natural language:** Any one of the languages that people speak that evolved naturally.
 - **formal language:** Any one of the languages that people have designed for specific purposes, such as representing mathematical ideas or computer programs; all programming languages are formal languages.
 - **token:** One of the basic elements of the syntactic structure of a program, analogous to a word in a natural language.
-
-**parse:** To examine a program and analyze the syntactic structure.
-
+- **parse:** To examine a program and analyze the syntactic structure.
 - **print statement:** An instruction that causes the Python interpreter to display a value on the screen.
-### **1.8 Exercises**
 
-**Exercise 1.2.** *Use a web browser to go to the Python website* http: // python. org *. This page contains information about Python and links to Python-related pages, and it gives you the ability to search the Python documentation.*
+#### **1.8 Exercises**
+
+**Exercise 1.2.** *Use a web browser to go to the Python website* [http:](http://python.org) [//](http://python.org) [python.](http://python.org) [org](http://python.org) *. This page contains information about Python and links to Python-related pages, and it gives you the ability to search the Python documentation.*
 
 *For example, if you enter* print *in the search window, the first link that appears is the documentation of the* print *statement. At this point, not all of it will make sense to you, but it is good to know where it is.*
 
@@ -728,15 +696,17 @@ Learning to debug can be frustrating, but it is a valuable skill that is useful
 
 *If this example doesn't work, you may need to install additional Python documentation or set an environment variable; the details depend on your operating system and version of Python.*
 
-**Exercise 1.4.** *Start the Python interpreter and use it as a calculator. Python's syntax for math operations is almost the same as standard mathematical notation. For example, the symbols* +, - and / *denote addition, subtraction and division, as you would expect. The symbol for multiplication is* *.
+**Exercise 1.4.** *Start the Python interpreter and use it as a calculator. Python's syntax for math operations is almost the same as standard mathematical notation. For example, the symbols* +*,* - *and* / *denote addition, subtraction and division, as you would expect. The symbol for multiplication is* **.*
 
 *If you run a 10 kilometer race in 43 minutes 30 seconds, what is your average time per mile? What is your average speed in miles per hour? (Hint: there are 1.61 kilometers in a mile).*
 
-### **Chapter 2**
+**10 Chapter 1. The way of the program**
+
+## **Chapter 2**
 
 # **Variables, expressions and statements**
 
-### **2.1 Values and types**
+#### **2.1 Values and types**
 
 A **value** is one of the basic things a program works with, like a letter or a number. The values we have seen so far are 1, 2, and 'Hello, World!'.
 
@@ -744,8 +714,12 @@ These values belong to different **types**: 2 is an integer, and 'Hello, World!'
 
 If you are not sure what type a value has, the interpreter can tell you.
 
->>> type('Hello, World!')  >>> type(17) 
-
+```
+>>> type('Hello, World!')
+
+>>> type(17)
+
+```
 Not surprisingly, strings belong to the type str and integers belong to the type int. Less obviously, numbers with a decimal point belong to a type called float, because these numbers are represented in a format called **floating-point**.
 
 ```
@@ -759,21 +733,20 @@ What about values like '17' and '3.2'? They look like numbers, but they are in q
 
 >>> type('3.2')
 
-They're strings.
 ```
-When you type a large integer, you might be tempted to use commas between groups of three digits, as in 1,000,000. This is not a legal integer in Python, but it is legal:
+They're strings.
 
-![](_page_33_Figure_1.jpeg)
+When you type a large integer, you might be tempted to use commas between groups of three digits, as in 1,000,000. This is not a legal integer in Python, but it is legal:
 
-Figure 2.1: State diagram.
+message n pi 17 'And now for something completely different' 3.1415926535897932
 
->>> 1,000,000
+Figure 2.1: State diagram.
 
-(1, 0, 0)
+>>> 1,000,000 (1, 0, 0)
 
 Well, that's not what we expected at all! Python interprets 1,000,000 as a commaseparated sequence of integers. This is the first example we have seen of a semantic error: the code runs without producing an error message, but it doesn't do the "right" thing.
 
-### **2.2 Variables**
+### **2.2 Variables**
 
 One of the most powerful features of a programming language is the ability to manipulate **variables**. A variable is a name that refers to a value.
 
@@ -784,15 +757,21 @@ An **assignment statement** creates new variables and gives them values:
 >>> n = 17
 >>> pi = 3.1415926535897932
 ```
-This example makes three assignments. The first assigns a string to a new variable named message; the second gives the integer 17 to n; the third assigns the (approximate) value of π to pi.
+This example makes three assignments. The first assigns a string to a new variable named message; the second gives the integer 17 to n; the third assigns the (approximate) value of *π* to pi.
 
-A common way to represent variables on paper is to write the name with an arrow pointing to the variable's value. This kind of figure is called a **state diagram** because it shows what state each of the variables is in (think of it as the variable's state of mind). Figure 2.1 shows the result of the previous example.
+A common way to represent variables on paper is to write the name with an arrow pointing to the variable's value. This kind of figure is called a **state diagram** because it shows what state each of the variables is in (think of it as the variable's state of mind). Figure [2.1](#page-33-2) shows the result of the previous example.
 
 The type of a variable is the type of the value it refers to.
 
->>> type(message)  >>> type(n)  >>> type(pi) 
-
-### **2.3 Variable names and keywords**
+```
+>>> type(message)
+
+>>> type(n)
+
+>>> type(pi)
+
+```
+#### **2.3 Variable names and keywords**
 
 Programmers generally choose names for their variables that are meaningful—they document what the variable is used for.
 
@@ -816,29 +795,28 @@ It turns out that class is one of Python's **keywords**. The interpreter uses ke
 
 Python 2 has 31 keywords:
 
-| and | del | from | not | while |
-| --- | --- | --- | --- | --- |
-| as | elif | global | or | with |
-| assert | else | if | pass | yield |
-| break | except | import | print |  |
-| class | exec | in | raise |  |
-| continue | finally | is | return |  |
-| def | for | lambda | try |  |
+| and      | del     | from   | not    | while |
+|----------|---------|--------|--------|-------|
+| as       | elif    | global | or     | with  |
+| assert   | else    | if     | pass   | yield |
+| break    | except  | import | print  |       |
+| class    | exec    | in     | raise  |       |
+| continue | finally | is     | return |       |
+| def      | for     | lambda | try    |       |
 
 In Python 3, exec is no longer a keyword, but nonlocal is.
 
 You might want to keep this list handy. If the interpreter complains about one of your variable names and you don't know why, see if it is on this list.
 
-### **2.4 Operators and operands**
+#### **2.4 Operators and operands**
 
 **Operators** are special symbols that represent computations like addition and multiplication. The values the operator is applied to are called **operands**.
 
 The operators +, -, *, / and ** perform addition, subtraction, multiplication, division and exponentiation, as in the following examples:
 
-```
 20+32 hour-1 hour*60+minute minute/60 5**2 (5+9)*(15-7)
-```
-In some other languages, ^ is used for exponentiation, but in Python it is a bitwise operator called XOR. I won't cover bitwise operators in this book, but you can read about them at http://wiki.python.org/moin/BitwiseOperators.
+
+In some other languages, ^ is used for exponentiation, but in Python it is a bitwise operator called XOR. I won't cover bitwise operators in this book, but you can read about them at .
 
 In Python 2, the division operator might not do what you expect:
 
@@ -855,18 +833,17 @@ If either of the operands is a floating-point number, Python performs floating-p
 
 >>> minute/60.0 0.98333333333333328
 
-### **2.5 Expressions and statements**
+#### **2.5 Expressions and statements**
 
 An **expression** is a combination of values, variables, and operators. A value all by itself is considered an expression, and so is a variable, so the following are all legal expressions (assuming that the variable x has been assigned a value):
 
-17
+17 x x + 17
 
-- x x + 17
 A **statement** is a unit of code that the Python interpreter can execute. We have seen two kinds of statement: print and assignment.
 
 Technically an expression is also a statement, but it is probably simpler to think of them as different things. The important difference is that an expression has a value; a statement does not.
 
-### **2.6 Interactive mode and script mode**
+#### **2.6 Interactive mode and script mode**
 
 One of the benefits of working with an interpreted language is that you can test bits of code in interactive mode before you put them in a script. But there are differences between interactive mode and script mode that can be confusing.
 
@@ -891,66 +868,63 @@ A script usually contains a sequence of statements. If there is more than one st
 
 For example, the script
 
-print 1 x = 2 print x produces the output 1 2
-
-The assignment statement produces no output.
-
-**Exercise 2.1.** *Type the following statements in the Python interpreter to see what they do:*
-
-5
-
-x = 5
+print 1 x = 2 print x produces the output 1 2 The assignment statement produces no output. **Exercise 2.1.** *Type the following statements in the Python interpreter to see what they do:* 5 x = 5
 
 x + 1
 
 *Now put the same statements into a script and run it. What is the output? Modify the script by transforming each expression into a print statement and then run it again.*
 
-### **2.7 Order of operations**
+## **2.7 Order of operations**
 
 When more than one operator appears in an expression, the order of evaluation depends on the **rules of precedence**. For mathematical operators, Python follows mathematical convention. The acronym **PEMDAS** is a useful way to remember the rules:
 
-- Parentheses have the highest precedence and can be used to force an expression to evaluate in the order you want. Since expressions in parentheses are evaluated first, 2 * (3-1) is 4, and (1+1)**(5-2) is 8. You can also use parentheses to make an expression easier to read, as in (minute * 100) / 60, even if it doesn't change the result.
-- Exponentiation has the next highest precedence, so 2**1+1 is 3, not 4, and 3*1**3 is 3, not 27.
-- Multiplication and Division have the same precedence, which is higher than Addition and Subtraction, which also have the same precedence. So 2*3-1 is 5, not 4, and 6+4/2 is 8, not 5.
-- Operators with the same precedence are evaluated from left to right (except exponentiation). So in the expression degrees / 2 * pi, the division happens first and the result is multiplied by pi. To divide by 2π, you can use parentheses or write degrees / 2 / pi.
+- **P**arentheses have the highest precedence and can be used to force an expression to evaluate in the order you want. Since expressions in parentheses are evaluated first, 2 * (3-1) is 4, and (1+1)**(5-2) is 8. You can also use parentheses to make an expression easier to read, as in (minute * 100) / 60, even if it doesn't change the result.
+- **E**xponentiation has the next highest precedence, so 2**1+1 is 3, not 4, and 3*1**3 is 3, not 27.
+- **M**ultiplication and **D**ivision have the same precedence, which is higher than **A**ddition and **S**ubtraction, which also have the same precedence. So 2*3-1 is 5, not 4, and 6+4/2 is 8, not 5.
+- Operators with the same precedence are evaluated from left to right (except exponentiation). So in the expression degrees / 2 * pi, the division happens first and the result is multiplied by pi. To divide by 2*π*, you can use parentheses or write degrees / 2 / pi.
 
 I don't work very hard to remember rules of precedence for other operators. If I can't tell by looking at the expression, I use parentheses to make it obvious.
 
-### **2.8 String operations**
+### **2.8 String operations**
 
 In general, you can't perform mathematical operations on strings, even if the strings look like numbers, so the following are illegal:
 
-'2'-'1' 'eggs'/'easy' 'third'*'a charm'
+'2' - '1' 'eggs' / 'easy' 'third' * 'a charm'
 
 The + operator works with strings, but it might not do what you expect: it performs **concatenation**, which means joining the strings by linking them end-to-end. For example:
 
-first = 'throat' second = 'warbler' print first + second
-
+```
+first = 'throat'
+second = 'warbler'
+print first + second
 The output of this program is throatwarbler.
-
+```
 The * operator also works on strings; it performs repetition. For example, 'Spam'*3 is 'SpamSpamSpam'. If one of the operands is a string, the other has to be an integer.
 
 This use of + and * makes sense by analogy with addition and multiplication. Just as 4*3 is equivalent to 4+4+4, we expect 'Spam'*3 to be the same as 'Spam'+'Spam'+'Spam', and it is. On the other hand, there is a significant way in which string concatenation and repetition are different from integer addition and multiplication. Can you think of a property that addition has that string concatenation does not?
 
-### **2.9 Comments**
+### **2.9 Comments**
 
 As programs get bigger and more complicated, they get more difficult to read. Formal languages are dense, and it is often difficult to look at a piece of code and figure out what it is doing, or why.
 
 For this reason, it is a good idea to add notes to your programs to explain in natural language what the program is doing. These notes are called **comments**, and they start with the # symbol:
 
-# compute the percentage of the hour that has elapsed percentage = (minute * 100) / 60
-
+```
+# compute the percentage of the hour that has elapsed
+percentage = (minute * 100) / 60
+```
 In this case, the comment appears on a line by itself. You can also put comments at the end of a line:
 
+```
 percentage = (minute * 100) / 60 # percentage of an hour
-
+```
 Everything from the # to the end of the line is ignored—it has no effect on the program.
 
-Comments are most useful when they document non-obvious features of the code. It is reasonable to assume that the reader can figure out *what* the code does; it is much more useful to explain why.
+Comments are most useful when they document non-obvious features of the code. It is reasonable to assume that the reader can figure out *what* the code does; it is much more useful to explain *why*.
 
 This comment is redundant with the code and useless:
 
-$$\begin{array}{l l l l}{\mathbf{v}}&{=}&{\mathbf{5}}&{\quad\quad\mathbf{\#\ \ a s s i g n\ \ 5\ \to\ v}}\end{array}$$
+v = 5 # assign 5 to v
 
 This comment contains useful information that is not in the code:
 
@@ -958,7 +932,7 @@ v = 5 # velocity in meters/second.
 
 Good variable names can reduce the need for comments, but long names can make complex expressions hard to read, so there is a tradeoff.
 
-### **2.10 Debugging**
+#### **2.10 Debugging**
 
 At this point the syntax error you are most likely to make is an illegal variable name, like class and yield, which are keywords, or odd~job and US$, which contain illegal characters.
 
@@ -977,18 +951,18 @@ NameError: name 'principle' is not defined
 ```
 Variables names are case sensitive, so LaTeX is not the same as latex.
 
-At this point the most likely cause of a semantic error is the order of operations. For example, to evaluate 1 2π , you might be tempted to write
+At this point the most likely cause of a semantic error is the order of operations. For example, to evaluate 1 2*π* , you might be tempted to write
 
 >>> 1.0 / 2.0 * pi
 
-But the division happens first, so you would get π/2, which is not the same thing! There is no way for Python to know what you meant to write, so in this case you don't get an error message; you just get the wrong answer.
+But the division happens first, so you would get *π*/2, which is not the same thing! There is no way for Python to know what you meant to write, so in this case you don't get an error message; you just get the wrong answer.
 
-### **2.11 Glossary**
+#### **2.11 Glossary**
 
 **value:** One of the basic units of data, like a number or string, that a program manipulates.
 
 - **type:** A category of values. The types we have seen so far are integers (type int), floatingpoint numbers (type float), and strings (type str).
-**integer:** A type that represents whole numbers.
+- **integer:** A type that represents whole numbers.
 
 **floating-point:** A type that represents numbers with fractional parts.
 
@@ -1015,43 +989,45 @@ But the division happens first, so you would get π/2, which is not the same thi
 **concatenate:** To join two operands end-to-end.
 
 - **comment:** Information in a program that is meant for other programmers (or anyone reading the source code) and has no effect on the execution of the program.
-### **2.12 Exercises**
+#### **2.12 Exercises**
 
 **Exercise 2.2.** *Assume that we execute the following assignment statements:*
 
-width = 17 height = 12.0 delimiter = '.'
-
+```
+width = 17
+height = 12.0
+delimiter = '.'
+```
 *For each of the following expressions, write the value of the expression and the type (of the value of the expression).*
 
-- 1. width/2
-- 2. width/2.0
-- 3. height/3
-- 4. 1 + 2 * 5
-- 5. delimiter * 5
-
+```
+1. width/2
+2. width/2.0
+3. height/3
+4. 1 + 2 * 5
+5. delimiter * 5
+```
 *Use the Python interpreter to check your answers.* **Exercise 2.3.** *Practice using the Python interpreter as a calculator:*
 
-- *1. The volume of a sphere with radius r is* 4 3 πr 3 *. What is the volume of a sphere with radius 5? Hint: 392.7 is wrong!*
+- *1. The volume of a sphere with radius r is* 4 3 *πr* 3 *. What is the volume of a sphere with radius 5? Hint: 392.7 is wrong!*
 - *2. Suppose the cover price of a book is $24.95, but bookstores get a 40% discount. Shipping costs $3 for the first copy and 75 cents for each additional copy. What is the total wholesale cost for 60 copies?*
 - *3. If I leave my house at 6:52 am and run 1 mile at an easy pace (8:15 per mile), then 3 miles at tempo (7:12 per mile) and 1 mile at easy pace again, what time do I get home for breakfast?*
 
-## **Chapter 3**
+## **Chapter 3**
 
 # **Functions**
 
-### **3.1 Function calls**
+#### **3.1 Function calls**
 
 In the context of programming, a **function** is a named sequence of statements that performs a computation. When you define a function, you specify the name and the sequence of statements. Later, you can "call" the function by name. We have already seen one example of a **function call**:
 
-```
->>> type(32)
-
-```
+>>> type(32) 
+
 The name of the function is type. The expression in parentheses is called the **argument** of the function. The result, for this function, is the type of the argument.
 
 It is common to say that a function "takes" an argument and "returns" a result. The result is called the **return value**.
 
-### **3.2 Type conversion functions**
+### **3.2 Type conversion functions**
 
 Python provides built-in functions that convert values from one type to another. The int function takes any value and converts it to an integer, if it can, or complains otherwise:
 
@@ -1060,8 +1036,10 @@ Python provides built-in functions that convert values from one type to another.
 32
 >>> int('Hello')
 ValueError: invalid literal for int(): Hello
-int can convert floating-point values to integers, but it doesn't round off; it chops off the
-fraction part:
+```
+int can convert floating-point values to integers, but it doesn't round off; it chops off the fraction part:
+
+```
 >>> int(3.99999)
 3
 >>> int(-2.3)
@@ -1080,18 +1058,21 @@ Finally, str converts its argument to a string:
 >>> str(3.14159)
 '3.14159'
 ```
-### **3.3 Math functions**
+### **3.3 Math functions**
 
 Python has a math module that provides most of the familiar mathematical functions. A **module** is a file that contains a collection of related functions.
 
 Before we can use the module, we have to import it:
 
+```
 >>> import math
-
+```
 This statement creates a **module object** named math. If you print the module object, you get some information about it:
 
->>> print math 
-
+```
+>>> print math
+
+```
 The module object contains the functions and variables defined in the module. To access one of the functions, you have to specify the name of the module and the name of the function, separated by a dot (also known as a period). This format is called **dot notation**.
 
 ```
@@ -1102,7 +1083,7 @@ The module object contains the functions and variables defined in the module. To
 ```
 The first example uses log10 to compute a signal-to-noise ratio in decibels (assuming that signal_power and noise_power are defined). The math module also provides log, which computes logarithms base e.
 
-The second example finds the sine of radians. The name of the variable is a hint that sin and the other trigonometric functions (cos, tan, etc.) take arguments in radians. To convert from degrees to radians, divide by 360 and multiply by 2π:
+The second example finds the sine of radians. The name of the variable is a hint that sin and the other trigonometric functions (cos, tan, etc.) take arguments in radians. To convert from degrees to radians, divide by 360 and multiply by 2*π*:
 
 ```
 >>> degrees = 45
@@ -1110,15 +1091,13 @@ The second example finds the sine of radians. The name of the variable is a hint
 >>> math.sin(radians)
 0.707106781187
 ```
-The expression math.pi gets the variable pi from the math module. The value of this variable is an approximation of π, accurate to about 15 digits.
+The expression math.pi gets the variable pi from the math module. The value of this variable is an approximation of *π*, accurate to about 15 digits.
 
 If you know your trigonometry, you can check the previous result by comparing it to the square root of two divided by two:
 
-```
->>> math.sqrt(2) / 2.0
-0.707106781187
-```
-### **3.4 Composition**
+>>> math.sqrt(2) / 2.0 0.707106781187
+
+#### **3.4 Composition**
 
 So far, we have looked at the elements of a program—variables, expressions, and statements—in isolation, without talking about how to combine them.
 
@@ -1129,13 +1108,16 @@ x = math.sin(degrees / 360.0 * 2 * math.pi)
 ```
 And even function calls:
 
-x = math.exp(math.log(x+1))
+x = math.exp(math.log(x + 1))
 
 Almost anywhere you can put a value, you can put an arbitrary expression, with one exception: the left side of an assignment statement has to be a variable name. Any other expression on the left side is a syntax error (we will see exceptions to this rule later).
 
->>> minutes = hours * 60 # right >>> hours * 60 = minutes # wrong! SyntaxError: can't assign to operator
-
-### **3.5 Adding new functions**
+```
+>>> minutes = hours * 60 # right
+>>> hours * 60 = minutes # wrong!
+SyntaxError: can't assign to operator
+```
+#### **3.5 Adding new functions**
 
 So far, we have only been using the functions that come with Python, but it is also possible to add new functions. A **function definition** specifies the name of a new function and the sequence of statements that execute when the function is called.
 
@@ -1150,11 +1132,11 @@ def is a keyword that indicates that this is a function definition. The name of
 
 The empty parentheses after the name indicate that this function doesn't take any arguments.
 
-The first line of the function definition is called the **header**; the rest is called the **body**. The header has to end with a colon and the body has to be indented. By convention, the indentation is always four spaces (see Section 3.14). The body can contain any number of statements.
+The first line of the function definition is called the **header**; the rest is called the **body**. The header has to end with a colon and the body has to be indented. By convention, the indentation is always four spaces (see Section [3.14)](#page-48-1). The body can contain any number of statements.
 
 The strings in the print statements are enclosed in double quotes. Single quotes and double quotes do the same thing; most people use single quotes except in cases like this where a single quote (which is also an apostrophe) appears in the string.
 
-If you type a function definition in interactive mode, the interpreter prints ellipses (...) to let you know that the definition isn't complete:
+If you type a function definition in interactive mode, the interpreter prints ellipses (*...*) to let you know that the definition isn't complete:
 
 ```
 >>> def print_lyrics():
@@ -1186,15 +1168,18 @@ Once you have defined a function, you can use it inside another function. For ex
 def repeat_lyrics():
     print_lyrics()
     print_lyrics()
+```
 And then call repeat_lyrics:
+
 ```
 >>> repeat_lyrics()
-
-I'm a lumberjack, and I'm okay. I sleep all night and I work all day. I'm a lumberjack, and I'm okay. I sleep all night and I work all day.
-
+I'm a lumberjack, and I'm okay.
+I sleep all night and I work all day.
+I'm a lumberjack, and I'm okay.
+I sleep all night and I work all day.
 But that's not really how the song goes.
-
-### **3.6 Definitions and uses**
+```
+#### **3.6 Definitions and uses**
 
 Pulling together the code fragments from the previous section, the whole program looks like this:
 
@@ -1206,15 +1191,19 @@ def repeat_lyrics():
     print_lyrics()
     print_lyrics()
 ```
-repeat_lyrics()
 
+```
+repeat_lyrics()
+```
 This program contains two function definitions: print_lyrics and repeat_lyrics. Function definitions get executed just like other statements, but the effect is to create function objects. The statements inside the function do not get executed until the function is called, and the function definition generates no output.
 
-As you might expect, you have to create a function before you can execute it. In other words, the function definition has to be executed before the first time it is called. **Exercise 3.1.** *Move the last line of this program to the top, so the function call appears before the definitions. Run the program and see what error message you get.*
+As you might expect, you have to create a function before you can execute it. In other words, the function definition has to be executed before the first time it is called.
+
+**Exercise 3.1.** *Move the last line of this program to the top, so the function call appears before the definitions. Run the program and see what error message you get.*
 
 **Exercise 3.2.** *Move the function call back to the bottom and move the definition of* print_lyrics *after the definition of* repeat_lyrics*. What happens when you run this program?*
 
-### **3.7 Flow of execution**
+#### **3.7 Flow of execution**
 
 In order to ensure that a function is defined before its first use, you have to know the order in which statements are executed, which is called the **flow of execution**.
 
@@ -1230,14 +1219,17 @@ Fortunately, Python is good at keeping track of where it is, so each time a func
 
 What's the moral of this sordid tale? When you read a program, you don't always want to read from top to bottom. Sometimes it makes more sense if you follow the flow of execution.
 
-### **3.8 Parameters and arguments**
+#### **3.8 Parameters and arguments**
 
 Some of the built-in functions we have seen require arguments. For example, when you call math.sin you pass a number as an argument. Some functions take more than one argument: math.pow takes two, the base and the exponent.
 
 Inside the function, the arguments are assigned to variables called **parameters**. Here is an example of a user-defined function that takes an argument:
 
-def print_twice(bruce): print bruce print bruce
-
+```
+def print_twice(bruce):
+    print bruce
+    print bruce
+```
 This function assigns the argument to a parameter named bruce. When the function is called, it prints the value of the parameter (whatever it is) twice.
 
 This function works with any value that can be printed.
@@ -1275,7 +1267,7 @@ Eric, the half a bee.
 ```
 The name of the variable we pass as an argument (michael) has nothing to do with the name of the parameter (bruce). It doesn't matter what the value was called back home (in the caller); here in print_twice, we call everybody bruce.
 
-### **3.9 Variables and parameters are local**
+#### **3.9 Variables and parameters are local**
 
 When you create a variable inside a function, it is **local**, which means that it only exists inside the function. For example:
 
@@ -1301,15 +1293,15 @@ NameError: name 'cat' is not defined
 ```
 ![](_page_46_Figure_1.jpeg)
 
-Figure 3.1: Stack diagram.
+Figure 3.1: Stack diagram.
 
 Parameters are also local. For example, outside print_twice, there is no such thing as bruce.
 
-### **3.10 Stack diagrams**
+#### **3.10 Stack diagrams**
 
 To keep track of which variables can be used where, it is sometimes useful to draw a **stack diagram**. Like state diagrams, stack diagrams show the value of each variable, but they also show the function each variable belongs to.
 
-Each function is represented by a **frame**. A frame is a box with the name of a function beside it and the parameters and variables of the function inside it. The stack diagram for the previous example is shown in Figure 3.1.
+Each function is represented by a **frame**. A frame is a box with the name of a function beside it and the parameters and variables of the function inside it. The stack diagram for the previous example is shown in Figure [3.1.](#page-46-1)
 
 The frames are arranged in a stack that indicates which function called which, and so on. In this example, print_twice was called by cat_twice, and cat_twice was called by __main__, which is a special name for the topmost frame. When you create a variable outside of any function, it belongs to __main__.
 
@@ -1333,17 +1325,20 @@ This list of functions is called a **traceback**. It tells you what program file
 
 The order of the functions in the traceback is the same as the order of the frames in the stack diagram. The function that is currently running is at the bottom.
 
-### **3.11 Fruitful functions and void functions**
+### **3.11 Fruitful functions and void functions**
 
 Some of the functions we are using, such as the math functions, yield results; for lack of a better name, I call them **fruitful functions**. Other functions, like print_twice, perform an action but don't return a value. They are called **void functions**.
 
 When you call a fruitful function, you almost always want to do something with the result; for example, you might assign it to a variable or use it as part of an expression:
 
-x = math.cos(radians) golden = (math.sqrt(5) + 1) / 2 When you call a function in interactive mode, Python displays the result:
-
->>> math.sqrt(5)
+```
+x = math.cos(radians)
+golden = (math.sqrt(5) + 1) / 2
+```
+When you call a function in interactive mode, Python displays the result:
 
 ```
+>>> math.sqrt(5)
 2.2360679774997898
 ```
 But in a script, if you call a fruitful function all by itself, the return value is lost forever!
@@ -1365,12 +1360,11 @@ The value None is not the same as the string 'None'. It is a special value that
 
 ```
 >>> print type(None)
-```
 
-
+```
 The functions we have written so far are all void. We will start writing fruitful functions in a few chapters.
 
-### **3.12 Why functions?**
+#### **3.12 Why functions?**
 
 It may not be clear why it is worth the trouble to divide a program into functions. There are several reasons:
 
@@ -1379,7 +1373,7 @@ It may not be clear why it is worth the trouble to divide a program into functio
 - Dividing a long program into functions allows you to debug the parts one at a time and then assemble them into a working whole.
 - Well-designed functions are often useful for many programs. Once you write and debug one, you can reuse it.
 
-### **3.13 Importing with** from
+## **3.13 Importing with** from
 
 Python provides two ways to import modules; we have already seen one:
 
@@ -1399,13 +1393,18 @@ But if you try to access pi directly, you get an error.
 Traceback (most recent call last):
   File "", line 1, in 
 NameError: name 'pi' is not defined
+```
 As an alternative, you can import an object from a module like this:
+
+```
 >>> from math import pi
+```
 Now you can access pi directly, without dot notation.
->>> print pi
+
 ```
+>>> print pi
 3.14159265359
-
+```
 Or you can use the star operator to import *everything* from the module:
 
 ```
@@ -1415,7 +1414,7 @@ Or you can use the star operator to import *everything* from the module:
 ```
 The advantage of importing everything from the math module is that your code can be more concise. The disadvantage is that there might be conflicts between names defined in different modules, or between a name from a module and one of your variables.
 
-### **3.14 Debugging**
+#### **3.14 Debugging**
 
 If you are using a text editor to write your scripts, you might run into problems with spaces and tabs. The best way to avoid these problems is to use spaces exclusively (no tabs). Most text editors that know about Python do this by default, but some don't.
 
@@ -1427,7 +1426,7 @@ Debugging can take a long time if you keep running the same, incorrect, program
 
 Make sure that the code you are looking at is the code you are running. If you're not sure, put something like print 'hello' at the beginning of the program and run it again. If you don't see hello, you're not running the right program!
 
-### **3.15 Glossary**
+#### **3.15 Glossary**
 
 - **function:** A named sequence of statements that performs some useful operation. Functions may or may not take arguments and may or may not produce a result.
 - **function definition:** A statement that creates a new function, specifying its name, parameters, and the statements it executes.
@@ -1446,13 +1445,15 @@ Make sure that the code you are looking at is the code you are running. If you'r
 - **module object:** A value created by an import statement that provides access to the values defined in a module.
 - **dot notation:** The syntax for calling a function in another module by specifying the module name followed by a dot (period) and the function name.
 - **composition:** Using an expression as part of a larger expression, or a statement as part of a larger statement.
-- **flow of execution:** The order in which statements are executed during a program run.
+
+**flow of execution:** The order in which statements are executed during a program run.
+
 - **stack diagram:** A graphical representation of a stack of functions, their variables, and the values they refer to.
 - **frame:** A box in a stack diagram that represents a function call. It contains the local variables and parameters of the function.
 
 **traceback:** A list of the functions that are executing, printed when an exception occurs.
 
-### **3.16 Exercises**
+#### **3.16 Exercises**
 
 **Exercise 3.3.** *Python provides a built-in function called* len *that returns the length of a string, so the value of* len('allen') *is 5.*
 
@@ -1472,65 +1473,82 @@ def do_twice(f):
 ```
 *Here's an example that uses* do_twice *to call a function named* print_spam *twice.*
 
-- def print_spam(): print 'spam'
-do_twice(print_spam)
+```
+def print_spam():
+    print 'spam'
+```
 
+```
+do_twice(print_spam)
+```
 - *1. Type this example into a script and test it.*
 - *2. Modify* do_twice *so that it takes two arguments, a function object and a value, and calls the function twice, passing the value as an argument.*
 - *3. Write a more general version of* print_spam*, called* print_twice*, that takes a string as a parameter and prints it twice.*
 - *4. Use the modified version of* do_twice *to call* print_twice *twice, passing* 'spam' *as an argument.*
 - *5. Define a new function called* do_four *that takes a function object and a value and calls the function four times, passing the value as a parameter. There should be only two statements in the body of this function, not four.*
 
-*Solution:* http: // thinkpython. com/ code/ do_ four. py .
-
-**Exercise 3.5.** *This exercise can be done using only the statements and other features we have learned so far.*
+*Solution:* [http:](http://thinkpython.com/code/do_four.py) [//](http://thinkpython.com/code/do_four.py) [thinkpython.](http://thinkpython.com/code/do_four.py) [com/](http://thinkpython.com/code/do_four.py) [code/](http://thinkpython.com/code/do_four.py) [do_](http://thinkpython.com/code/do_four.py) [four.](http://thinkpython.com/code/do_four.py) [py](http://thinkpython.com/code/do_four.py) *.* **Exercise 3.5.** *This exercise can be done using only the statements and other features we have learned so far.*
 
 - *1. Write a function that draws a grid like the following:*
-+ - - - - + - - - - + | | | | | | | | | | | | + - - - - + - - - - + | | | | | | | | | | | | + - - - - + - - - - +
 
+```
++ - - - - + - - - - +
+| | |
+| | |
+| | |
+| | |
++ - - - - + - - - - +
+| | |
+| | |
+| | |
+| | |
++ - - - - + - - - - +
+```
 *Hint: to print more than one value on a line, you can print a comma-separated sequence:*
 
+```
 print '+', '-'
-
+```
 *If the sequence ends with a comma, Python leaves the line unfinished, so the value printed next appears on the same line.*
 
-print '+', print '-'
-
-*The output of these statements is* '+ -'.
-
-A print *statement all by itself ends the current line and goes to the next line.*
+```
+print '+',
+print '-'
+```
+*The output of these statements is* '+ -'*.*
 
-*2. Write a function that draws a similar grid with four rows and four columns.*
+*A* print *statement all by itself ends the current line and goes to the next line.*
 
-*Solution:* http: // thinkpython. com/ code/ grid. py *. Credit: This exercise is based on an exercise in Oualline,* Practical C Programming, Third Edition*, O'Reilly Media, 1997.*
+- *2. Write a function that draws a similar grid with four rows and four columns.*
+*Solution:* [http:](http://thinkpython.com/code/grid.py) [//](http://thinkpython.com/code/grid.py) [thinkpython.](http://thinkpython.com/code/grid.py) [com/](http://thinkpython.com/code/grid.py) [code/](http://thinkpython.com/code/grid.py) [grid.](http://thinkpython.com/code/grid.py) [py](http://thinkpython.com/code/grid.py) *. Credit: This exercise is based on an exercise in Oualline,* Practical C Programming, Third Edition*, O'Reilly Media, 1997.*
 
-## **Chapter 4**
+## **Chapter 4**
 
 # **Case study: interface design**
 
-Code examples from this chapter are available from http://thinkpython.com/code/ polygon.py.
+Code examples from this chapter are available from [http://thinkpython.com/code/](http://thinkpython.com/code/polygon.py) [polygon.py](http://thinkpython.com/code/polygon.py).
 
-### **4.1 TurtleWorld**
+#### **4.1 TurtleWorld**
 
-To accompany this book, I have written a package called Swampy. You can download Swampy from http://thinkpython.com/swampy; follow the instructions there to install Swampy on your system.
+To accompany this book, I have written a package called Swampy. You can download Swampy from ; follow the instructions there to install Swampy on your system.
 
 A **package** is a collection of modules; one of the modules in Swampy is TurtleWorld, which provides a set of functions for drawing lines by steering turtles around the screen.
 
 If Swampy is installed as a package on your system, you can import TurtleWorld like this:
 
+```
 from swampy.TurtleWorld import *
-
+```
 If you downloaded the Swampy modules but did not install them as a package, you can either work in the directory that contains the Swampy files, or add that directory to Python's search path. Then you can import TurtleWorld like this:
 
 from TurtleWorld import *
 
-The details of the installation process and setting Python's search path depend on your system, so rather than include those details here, I will try to maintain current information for several systems at http://thinkpython.com/swampy
+The details of the installation process and setting Python's search path depend on your system, so rather than include those details here, I will try to maintain current information for several systems at 
 
 Create a file named mypolygon.py and type in the following code:
 
-from swampy.TurtleWorld import *
-
 ```
+from swampy.TurtleWorld import *
 world = TurtleWorld()
 bob = Turtle()
 print bob
@@ -1559,7 +1577,7 @@ When you run this program, you should see bob move east and then north, leaving
 
 Now modify the program to draw a square. Don't go on until you've got it working!
 
-### **4.2 Simple repetition**
+#### **4.2 Simple repetition**
 
 Chances are you wrote something like this (leaving out the code that creates TurtleWorld and waits for the user):
 
@@ -1571,31 +1589,34 @@ lt(bob)
 fd(bob, 100)
 lt(bob)
 fd(bob, 100)
-We can do the same thing more concisely with a for statement. Add this example to
-mypolygon.py and run it again:
+```
+We can do the same thing more concisely with a for statement. Add this example to mypolygon.py and run it again:
+
+```
 for i in range(4):
     print 'Hello!'
 ```
 You should see something like this:
 
-Hello! Hello! Hello! Hello!
+32
+
+Hello! Hello! Hello!
+
+Hello!
 
 This is the simplest use of the for statement; we will see more later. But that should be enough to let you rewrite your square-drawing program. Don't go on until you do.
 
 Here is a for statement that draws a square:
 
-```
-for i in range(4):
-    fd(bob, 100)
-    lt(bob)
-```
+for i in range(4): fd(bob, 100) lt(bob)
+
 The syntax of a for statement is similar to a function definition. It has a header that ends with a colon and an indented body. The body can contain any number of statements.
 
 A for statement is sometimes called a **loop** because the flow of execution runs through the body and then loops back to the top. In this case, it runs the body four times.
 
 This version is actually a little different from the previous square-drawing code because it makes another turn after drawing the last side of the square. The extra turn takes a little more time, but it simplifies the code if we do the same thing every time through the loop. This version also has the effect of leaving the turtle back in the starting position, facing in the starting direction.
 
-### **4.3 Exercises**
+#### **4.3 Exercises**
 
 The following is a series of exercises using TurtleWorld. They are meant to be fun, but they have a point, too. While you are working on them, think about what the point is.
 
@@ -1607,7 +1628,7 @@ Write a function call that passes bob as an argument to square, and then run the
 - 2. Add another parameter, named length, to square. Modify the body so length of the sides is length, and then modify the function call to provide a second argument. Run the program again. Test your program with a range of values for length.
 - 3. The functions lt and rt make 90-degree turns by default, but you can provide a second argument that specifies the number of degrees. For example, lt(bob, 45) turns bob 45 degrees to the left.
 
-Make a copy of square and change the name to polygon. Add another parameter named n and modify the body so it draws an n-sided regular polygon. Hint: The exterior angles of an n-sided regular polygon are 360/n degrees.
+Make a copy of square and change the name to polygon. Add another parameter named n and modify the body so it draws an n-sided regular polygon. Hint: The exterior angles of an n-sided regular polygon are 360/*n* degrees.
 
 - 4. Write a function called circle that takes a turtle, t, and radius, r, as parameters and that draws an approximate circle by invoking polygon with an appropriate length and number of sides. Test your function with a range of values of r.
 Hint: figure out the circumference of the circle and make sure that length * n = circumference.
@@ -1615,15 +1636,18 @@ Hint: figure out the circumference of the circle and make sure that length * n =
 Another hint: if bob is too slow for you, you can speed him up by changing bob.delay, which is the time between moves, in seconds. bob.delay = 0.01 ought to get him moving.
 
 - 5. Make a more general version of circle called arc that takes an additional parameter angle, which determines what fraction of a circle to draw. angle is in units of degrees, so when angle=360, arc should draw a complete circle.
-### **4.4 Encapsulation**
+### **4.4 Encapsulation**
 
 The first exercise asks you to put your square-drawing code into a function definition and then call the function, passing the turtle as a parameter. Here is a solution:
 
 ```
 def square(t):
-    for i in range(4):
-        fd(t, 100)
-        lt(t)
+```
+
+```
+for i in range(4):
+    fd(t, 100)
+    lt(t)
 ```
 square(bob)
 
@@ -1631,11 +1655,13 @@ The innermost statements, fd and lt are indented twice to show that they are ins
 
 Inside the function, t refers to the same turtle bob refers to, so lt(t) has the same effect as lt(bob). So why not call the parameter bob? The idea is that t can be any turtle, not just bob, so you could create a second turtle and pass it as an argument to square:
 
-ray = Turtle() square(ray)
-
+```
+ray = Turtle()
+square(ray)
+```
 Wrapping a piece of code up in a function is called **encapsulation**. One of the benefits of encapsulation is that it attaches a name to the code, which serves as a kind of documentation. Another advantage is that if you re-use the code, it is more concise to call a function twice than to copy and paste the body!
 
-### **4.5 Generalization**
+#### **4.5 Generalization**
 
 The next step is to add a length parameter to square. Here is a solution:
 
@@ -1658,17 +1684,20 @@ def polygon(t, n, length):
         fd(t, length)
         lt(t, angle)
 ```
-polygon(bob, 7, 70)
 
+```
+polygon(bob, 7, 70)
+```
 This draws a 7-sided polygon with side length 70. If you have more than a few numeric arguments, it is easy to forget what they are, or what order they should be in. It is legal, and sometimes helpful, to include the names of the parameters in the argument list:
 
+```
 polygon(bob, n=7, length=70)
-
+```
 These are called **keyword arguments** because they include the parameter names as "keywords" (not to be confused with Python keywords like while and def).
 
 This syntax makes the program more readable. It is also a reminder about how arguments and parameters work: when you call a function, the arguments are assigned to the parameters.
 
-### **4.6 Interface design**
+#### **4.6 Interface design**
 
 The next step is to write circle, which takes a radius, r, as a parameter. Here is a simple solution that uses polygon to draw a 50-sided polygon:
 
@@ -1679,7 +1708,7 @@ def circle(t, r):
     length = circumference / n
     polygon(t, n, length)
 ```
-The first line computes the circumference of a circle with radius r using the formula 2πr. Since we use math.pi, we have to import math. By convention, import statements are usually at the beginning of the script.
+The first line computes the circumference of a circle with radius r using the formula 2*πr*. Since we use math.pi, we have to import math. By convention, import statements are usually at the beginning of the script.
 
 n is the number of line segments in our approximation of a circle, so length is the length of each segment. Thus, polygon draws a 50-sides polygon that approximates a circle with radius r.
 
@@ -1687,7 +1716,7 @@ One limitation of this solution is that n is a constant, which means that for ve
 
 The **interface** of a function is a summary of how it is used: what are the parameters? What does the function do? And what is the return value? An interface is "clean" if it is "as simple as possible, but not simpler. (Einstein)"
 
-In this example, r belongs in the interface because it specifies the circle to be drawn. n is less appropriate because it pertains to the details of how the circle should be rendered.
+In this example, r belongs in the interface because it specifies the circle to be drawn. n is less appropriate because it pertains to the details of *how* the circle should be rendered.
 
 Rather than clutter up the interface, it is better to choose an appropriate value of n depending on circumference:
 
@@ -1700,7 +1729,7 @@ def circle(t, r):
 ```
 Now the number of segments is (approximately) circumference/3, so the length of each segment is (approximately) 3, which is small enough that the circles look good, but big enough to be efficient, and appropriate for any size circle.
 
-### **4.7 Refactoring**
+### **4.7 Refactoring**
 
 When I wrote circle, I was able to re-use polygon because a many-sided polygon is a good approximation of a circle. But arc is not as cooperative; we can't use polygon or circle to draw an arc.
 
@@ -1723,10 +1752,16 @@ def polyline(t, n, length, angle):
     for i in range(n):
         fd(t, length)
         lt(t, angle)
+```
 Now we can rewrite polygon and arc to use polyline:
+
+```
 def polygon(t, n, length):
     angle = 360.0 / n
     polyline(t, n, length, angle)
+```
+
+```
 def arc(t, r, angle):
     arc_length = 2 * math.pi * r * angle / 360
     n = int(arc_length / 3) + 1
@@ -1742,7 +1777,7 @@ This process—rearranging a program to improve function interfaces and facilita
 
 If we had planned ahead, we might have written polyline first and avoided refactoring, but often you don't know enough at the beginning of a project to design all the interfaces. Once you start coding, you understand the problem better. Sometimes refactoring is a sign that you have learned something.
 
-### **4.8 A development plan**
+### **4.8 A development plan**
 
 A **development plan** is a process for writing programs. The process we used in this case study is "encapsulation and generalization." The steps of this process are:
 
@@ -1754,7 +1789,7 @@ A **development plan** is a process for writing programs. The process we used in
 
 This process has some drawbacks—we will see alternatives later—but it can be useful if you don't know ahead of time how to divide the program into functions. This approach lets you design as you go along.
 
-### **4.9 docstring**
+#### **4.9 docstring**
 
 A **docstring** is a string at the beginning of a function that explains the interface ("doc" is short for "documentation"). Here is an example:
 
@@ -1773,7 +1808,7 @@ It is terse, but it contains the essential information someone would need to use
 
 Writing this kind of documentation is an important part of interface design. A welldesigned interface should be simple to explain; if you are having a hard time explaining one of your functions, that might be a sign that the interface could be improved.
 
-### **4.10 Debugging**
+#### **4.10 Debugging**
 
 An interface is like a contract between a function and a caller. The caller agrees to provide certain parameters and the function agrees to do certain work.
 
@@ -1783,7 +1818,7 @@ These requirements are called **preconditions** because they are supposed to be
 
 Preconditions are the responsibility of the caller. If the caller violates a (properly documented!) precondition and the function doesn't work correctly, the bug is in the caller, not the function.
 
-### **4.11 Glossary**
+#### **4.11 Glossary**
 
 - **instance:** A member of a set. The TurtleWorld in this chapter is a member of the set of TurtleWorlds.
 - **loop:** A part of a program that can execute repeatedly.
@@ -1795,48 +1830,48 @@ Preconditions are the responsibility of the caller. If the caller violates a (pr
 
 ![](_page_60_Figure_1.jpeg)
 
-Figure 4.1: Turtle flowers.
+Figure 4.1: Turtle flowers.
 
 ![](_page_60_Figure_3.jpeg)
 
-Figure 4.2: Turtle pies.
+Figure 4.2: Turtle pies.
 
 **development plan:** A process for writing programs.
 
 - **docstring:** A string that appears in a function definition to document the function's interface.
-- **precondition:** A requirement that should be satisfied by the caller before a function starts.
+**precondition:** A requirement that should be satisfied by the caller before a function starts.
 
 **postcondition:** A requirement that should be satisfied by the function before it ends.
 
-### **4.12 Exercises**
+#### **4.12 Exercises**
 
-**Exercise 4.1.** *Download the code in this chapter from* http: // thinkpython. com/ code/ polygon. py .
+**Exercise 4.1.** *Download the code in this chapter from* [http:](http://thinkpython.com/code/polygon.py) [//](http://thinkpython.com/code/polygon.py) [thinkpython.](http://thinkpython.com/code/polygon.py) [com/](http://thinkpython.com/code/polygon.py) [code/](http://thinkpython.com/code/polygon.py) [polygon.](http://thinkpython.com/code/polygon.py) [py](http://thinkpython.com/code/polygon.py) *.*
 
-- *1. Write appropriate docstrings for* polygon, arc and circle.
+- *1. Write appropriate docstrings for* polygon*,* arc *and* circle*.*
 - *2. Draw a stack diagram that shows the state of the program while executing* circle(bob, radius)*. You can do the arithmetic by hand or add* print *statements to the code.*
-- *3. The version of* arc *in Section 4.7 is not very accurate because the linear approximation of the circle is always outside the true circle. As a result, the turtle ends up a few units away from the correct destination. My solution shows a way to reduce the effect of this error. Read the code and see if it makes sense to you. If you draw a diagram, you might see how it works.*
+- *3. The version of* arc *in Section [4.7](#page-57-0) is not very accurate because the linear approximation of the circle is always outside the true circle. As a result, the turtle ends up a few units away from the correct destination. My solution shows a way to reduce the effect of this error. Read the code and see if it makes sense to you. If you draw a diagram, you might see how it works.*
 
-**Exercise 4.2.** *Write an appropriately general set of functions that can draw flowers as in Figure 4.1.*
+**Exercise 4.2.** *Write an appropriately general set of functions that can draw flowers as in Figure [4.1.](#page-60-1)*
 
-*Solution:* http: // thinkpython. com/ code/ flower. py *, also requires* http: // thinkpython. com/ code/ polygon. py .
+*Solution:* [http:](http://thinkpython.com/code/flower.py) [//](http://thinkpython.com/code/flower.py) [thinkpython.](http://thinkpython.com/code/flower.py) [com/](http://thinkpython.com/code/flower.py) [code/](http://thinkpython.com/code/flower.py) [flower.](http://thinkpython.com/code/flower.py) [py](http://thinkpython.com/code/flower.py) *, also requires* [http:](http://thinkpython.com/code/polygon.py) [//](http://thinkpython.com/code/polygon.py) [thinkpython.](http://thinkpython.com/code/polygon.py) [com/](http://thinkpython.com/code/polygon.py) [code/](http://thinkpython.com/code/polygon.py) [polygon.](http://thinkpython.com/code/polygon.py) [py](http://thinkpython.com/code/polygon.py) *.*
 
-**Exercise 4.3.** *Write an appropriately general set of functions that can draw shapes as in Figure 4.2.*
+**Exercise 4.3.** *Write an appropriately general set of functions that can draw shapes as in Figure [4.2.](#page-60-2)*
 
-*Solution:* http: // thinkpython. com/ code/ pie. py .
+*Solution:* [http:](http://thinkpython.com/code/pie.py) [//](http://thinkpython.com/code/pie.py) [thinkpython.](http://thinkpython.com/code/pie.py) [com/](http://thinkpython.com/code/pie.py) [code/](http://thinkpython.com/code/pie.py) [pie.](http://thinkpython.com/code/pie.py) [py](http://thinkpython.com/code/pie.py) *.*
 
 **Exercise 4.4.** *The letters of the alphabet can be constructed from a moderate number of basic elements, like vertical and horizontal lines and a few curves. Design a font that can be drawn with a minimal number of basic elements and then write functions that draw letters of the alphabet.*
 
-*You should write one function for each letter, with names* draw_a, draw_b*, etc., and put your functions in a file named* letters.py*. You can download a "turtle typewriter" from* http: // thinkpython. com/ code/ typewriter. py *to help you test your code.*
+*You should write one function for each letter, with names* draw_a*,* draw_b*, etc., and put your functions in a file named* letters.py*. You can download a "turtle typewriter" from* [http:](http://thinkpython.com/code/typewriter.py) [//](http://thinkpython.com/code/typewriter.py) [thinkpython.](http://thinkpython.com/code/typewriter.py) [com/](http://thinkpython.com/code/typewriter.py) [code/](http://thinkpython.com/code/typewriter.py) [typewriter.](http://thinkpython.com/code/typewriter.py) [py](http://thinkpython.com/code/typewriter.py) *to help you test your code.*
 
-*Solution:* http: // thinkpython. com/ code/ letters. py *, also requires* http: // thinkpython. com/ code/ polygon. py .
+*Solution:* [http:](http://thinkpython.com/code/letters.py) [//](http://thinkpython.com/code/letters.py) [thinkpython.](http://thinkpython.com/code/letters.py) [com/](http://thinkpython.com/code/letters.py) [code/](http://thinkpython.com/code/letters.py) [letters.](http://thinkpython.com/code/letters.py) [py](http://thinkpython.com/code/letters.py) *, also requires* [http:](http://thinkpython.com/code/polygon.py) [//](http://thinkpython.com/code/polygon.py) [thinkpython.](http://thinkpython.com/code/polygon.py) [com/](http://thinkpython.com/code/polygon.py) [code/](http://thinkpython.com/code/polygon.py) [polygon.](http://thinkpython.com/code/polygon.py) [py](http://thinkpython.com/code/polygon.py) *.*
 
-**Exercise 4.5.** *Read about spirals at* http: // en. wikipedia. org/ wiki/ Spiral *; then write a program that draws an Archimedian spiral (or one of the other kinds). Solution:* http: // thinkpython. com/ code/ spiral. py .
+**Exercise 4.5.** *Read about spirals at* [http:](http://en.wikipedia.org/wiki/Spiral) [//](http://en.wikipedia.org/wiki/Spiral) [en.](http://en.wikipedia.org/wiki/Spiral) [wikipedia.](http://en.wikipedia.org/wiki/Spiral) [org/](http://en.wikipedia.org/wiki/Spiral) [wiki/](http://en.wikipedia.org/wiki/Spiral) [Spiral](http://en.wikipedia.org/wiki/Spiral) *; then write a program that draws an Archimedian spiral (or one of the other kinds). Solution:* [http:](http://thinkpython.com/code/spiral.py) [//](http://thinkpython.com/code/spiral.py) [thinkpython.](http://thinkpython.com/code/spiral.py) [com/](http://thinkpython.com/code/spiral.py) [code/](http://thinkpython.com/code/spiral.py) [spiral.](http://thinkpython.com/code/spiral.py) [py](http://thinkpython.com/code/spiral.py) *.*
 
-## **Chapter 5**
+## **Chapter 5**
 
 # **Conditionals and recursion**
 
-### **5.1 Modulus operator**
+#### **5.1 Modulus operator**
 
 The **modulus operator** works on integers and yields the remainder when the first operand is divided by the second. In Python, the modulus operator is a percent sign (%). The syntax is the same as for other operators:
 
@@ -1853,7 +1888,7 @@ The modulus operator turns out to be surprisingly useful. For example, you can c
 
 Also, you can extract the right-most digit or digits from a number. For example, x % 10 yields the right-most digit of x (in base 10). Similarly x % 100 yields the last two digits.
 
-### **5.2 Boolean expressions**
+#### **5.2 Boolean expressions**
 
 A **boolean expression** is an expression that is either true or false. The following examples use the operator ==, which compares two operands and produces True if they are equal and False otherwise:
 
@@ -1862,7 +1897,10 @@ A **boolean expression** is an expression that is either true or false. The foll
 True
 >>> 5 == 6
 False
+```
 True and False are special values that belong to the type bool; they are not strings:
+
+```
 >>> type(True)
 
 >>> type(False)
@@ -1870,36 +1908,39 @@ True and False are special values that belong to the type bool; they are not str
 ```
 The == operator is one of the **relational operators**; the others are:
 
-x != y # x is not equal to y x > y # x is greater than y x < y # x is less than y x >= y # x is greater than or equal to y x <= y # x is less than or equal to y
+|  | x != y |  |  |  | # x is not equal to y             |
+|--|--------|--|--|--|-----------------------------------|
+|  | x > y  |  |  |  | # x is greater than y             |
+|  | x < y  |  |  |  | # x is less than y                |
+|  | x >= y |  |  |  | # x is greater than or equal to y |
+|  | x <= y |  |  |  | # x is less than or equal to y    |
 
 Although these operations are probably familiar to you, the Python symbols are different from the mathematical symbols. A common error is to use a single equal sign (=) instead of a double equal sign (==). Remember that = is an assignment operator and == is a relational operator. There is no such thing as =< or =>.
 
-### **5.3 Logical operators**
+#### **5.3 Logical operators**
 
-There are three **logical operators**: and, or, and not. The semantics (meaning) of these operators is similar to their meaning in English. For example, x > 0 and x < 10 is true only if x is greater than 0 and less than 10.
+There are three **logical operators**: and, or, and not. The semantics (meaning) of these operators is similar to their meaning in English. For example, x > 0 and x < 10 is true only if x is greater than 0 *and* less than 10.
 
-n%2 == 0 or n%3 == 0 is true if *either* of the conditions is true, that is, if the number is divisible by 2 or 3.
+n%2 == 0 or n%3 == 0 is true if *either* of the conditions is true, that is, if the number is divisible by 2 *or* 3.
 
 Finally, the not operator negates a boolean expression, so not (x > y) is true if x > y is false, that is, if x is less than or equal to y.
 
 Strictly speaking, the operands of the logical operators should be boolean expressions, but Python is not very strict. Any nonzero number is interpreted as "true."
 
+```
 >>> 17 and True
-
+```
 True
 
 This flexibility can be useful, but there are some subtleties to it that might be confusing. You might want to avoid it (unless you know what you are doing).
 
-### **5.4 Conditional execution**
+### **5.4 Conditional execution**
 
 In order to write useful programs, we almost always need the ability to check conditions and change the behavior of the program accordingly. **Conditional statements** give us this ability. The simplest form is the if statement:
 
 ```
 if x > 0:
-```
-
-```
-print 'x is positive'
+    print 'x is positive'
 ```
 The boolean expression after if is called the **condition**. If it is true, then the indented statement gets executed. If not, nothing happens.
 
@@ -1907,21 +1948,19 @@ if statements have the same structure as function definitions: a header followed
 
 There is no limit on the number of statements that can appear in the body, but there has to be at least one. Occasionally, it is useful to have a body with no statements (usually as a place keeper for code you haven't written yet). In that case, you can use the pass statement, which does nothing.
 
-if x < 0: pass # need to handle negative values!
-
-### **5.5 Alternative execution**
+```
+if x < 0:
+   pass # need to handle negative values!
+```
+#### **5.5 Alternative execution**
 
 A second form of the if statement is **alternative execution**, in which there are two possibilities and the condition determines which one gets executed. The syntax looks like this:
 
-```
-if x%2 == 0:
-    print 'x is even'
-else:
-    print 'x is odd'
-```
+if x%2 == 0: print 'x is even' else: print 'x is odd'
+
 If the remainder when x is divided by 2 is 0, then we know that x is even, and the program displays a message to that effect. If the condition is false, the second set of statements is executed. Since the condition must be true or false, exactly one of the alternatives will be executed. The alternatives are called **branches**, because they are branches in the flow of execution.
 
-### **5.6 Chained conditionals**
+### **5.6 Chained conditionals**
 
 Sometimes there are more than two possibilities and we need more than two branches. One way to express a computation like that is a **chained conditional**:
 
@@ -1945,7 +1984,7 @@ elif choice == 'c':
 ```
 Each condition is checked in order. If the first is false, the next is checked, and so on. If one of them is true, the corresponding branch executes, and the statement ends. Even if more than one condition is true, only the first true branch executes.
 
-### **5.7 Nested conditionals**
+### **5.7 Nested conditionals**
 
 One conditional can also be nested within another. We could have written the trichotomy example like this:
 
@@ -1955,27 +1994,26 @@ if x == y:
 else:
     if x < y:
 ```
+print 'x is less than y' else: print 'x is greater than y'
 
-```
-print 'x is less than y'
-else:
-    print 'x is greater than y'
-```
 The outer conditional contains two branches. The first branch contains a simple statement. The second branch contains another if statement, which has two branches of its own. Those two branches are both simple statements, although they could have been conditional statements as well.
 
 Although the indentation of the statements makes the structure apparent, **nested conditionals** become difficult to read very quickly. In general, it is a good idea to avoid them when you can.
 
 Logical operators often provide a way to simplify nested conditional statements. For example, we can rewrite the following code using a single conditional:
 
-if 0 < x: if x < 10: print 'x is a positive single-digit number.'
-
+```
+if 0 < x:
+    if x < 10:
+        print 'x is a positive single-digit number.'
+```
 The print statement is executed only if we make it past both conditionals, so we can get the same effect with the and operator:
 
 ```
 if 0 < x and x < 10:
     print 'x is a positive single-digit number.'
 ```
-### **5.8 Recursion**
+#### **5.8 Recursion**
 
 It is legal for one function to call another; it is also legal for a function to call itself. It may not be obvious why that is a good thing, but it turns out to be one of the most magical things a program can do. For example, look at the following function:
 
@@ -2010,45 +2048,48 @@ The countdown that got n=3 returns.
 
 And then you're back in __main__. So, the total output looks like this:
 
+```
 3
-
 2
-
-1 Blastoff!
-
+1
+Blastoff!
+```
 A function that calls itself is **recursive**; the process is called **recursion**.
 
 As another example, we can write a function that prints a string n times.
 
-def print_n(s, n): if n <= 0: return print s print_n(s, n-1)
-
+```
+def print_n(s, n):
+    if n <= 0:
+        return
+    print s
+    print_n(s, n-1)
+```
 If n <= 0 the return statement exits the function. The flow of execution immediately returns to the caller, and the remaining lines of the function are not executed.
 
-The rest of the function is similar to countdown: if n is greater than 0, it displays s and then calls itself to display s n − 1 additional times. So the number of lines of output is 1 + (n - 1), which adds up to n.
+The rest of the function is similar to countdown: if n is greater than 0, it displays s and then calls itself to display s *n* − 1 additional times. So the number of lines of output is 1 + (n - 1), which adds up to n.
 
 For simple examples like this, it is probably easier to use a for loop. But we will see examples later that are hard to write with a for loop and easy to write with recursion, so it is good to start early.
 
-### **5.9 Stack diagrams for recursive functions**
+#### **5.9 Stack diagrams for recursive functions**
 
-In Section 3.10, we used a stack diagram to represent the state of a program during a function call. The same kind of diagram can help interpret a recursive function.
+In Section [3.10,](#page-46-0) we used a stack diagram to represent the state of a program during a function call. The same kind of diagram can help interpret a recursive function.
 
 Every time a function gets called, Python creates a new function frame, which contains the function's local variables and parameters. For a recursive function, there might be more than one frame on the stack at the same time.
 
-Figure 5.1 shows a stack diagram for countdown called with n = 3.
+Figure [5.1](#page-67-2) shows a stack diagram for countdown called with n = 3.
 
 As usual, the top of the stack is the frame for __main__. It is empty because we did not create any variables in __main__ or pass any arguments to it.
 
 The four countdown frames have different values for the parameter n. The bottom of the stack, where n=0, is called the **base case**. It does not make a recursive call, so there are no more frames.
 
-**Exercise 5.1.** *Draw a stack diagram for* print_n *called with* s = 'Hello' and n=2.
-
-**Exercise 5.2.** *Write a function called* do_n *that takes a function object and a number,* n*, as arguments, and that calls the given function* n *times.*
+**Exercise 5.1.** *Draw a stack diagram for* print_n *called with* s = 'Hello' *and* n=2*.* **Exercise 5.2.** *Write a function called* do_n *that takes a function object and a number,* n*, as arguments, and that calls the given function* n *times.*
 
 ![](_page_67_Figure_1.jpeg)
 
-Figure 5.1: Stack diagram.
+Figure 5.1: Stack diagram.
 
-### **5.10 Infinite recursion**
+#### **5.10 Infinite recursion**
 
 If a recursion never reaches a base case, it goes on making recursive calls forever, and the program never terminates. This is known as **infinite recursion**, and it is generally not a good idea. Here is a minimal program with an infinite recursion:
 
@@ -2060,18 +2101,17 @@ In most programming environments, a program with infinite recursion does not rea
 
 ```
 File "", line 2, in recurse
-File "", line 2, in recurse
-File "", line 2, in recurse
-                .
-                .
-                .
-File "", line 2, in recurse
-```
+  File "", line 2, in recurse
+  File "", line 2, in recurse
+                  .
+                  .
+                  .
+  File "", line 2, in recurse
 RuntimeError: Maximum recursion depth exceeded
-
+```
 This traceback is a little bigger than the one we saw in the previous chapter. When the error occurs, there are 1000 recurse frames on the stack!
 
-### **5.11 Keyboard input**
+#### **5.11 Keyboard input**
 
 The programs we have written so far are a bit rude in the sense that they accept no input from the user. They just do the same thing every time.
 
@@ -2115,7 +2155,7 @@ ValueError: invalid literal for int() with base 10
 ```
 We will see how to handle this kind of error later.
 
-### **5.12 Debugging**
+#### **5.12 Debugging**
 
 The traceback Python displays when an error occurs contains a lot of information, but it can be overwhelming, especially when there are many frames on the stack. The most useful parts are usually:
 
@@ -2137,7 +2177,7 @@ In this example, the problem is that the second line is indented by one space. B
 
 The same is true of runtime errors.
 
-Suppose you are trying to compute a signal-to-noise ratio in decibels. The formula is SNRdb = 10 log10(P*signal*/P*noise*). In Python, you might write something like this:
+Suppose you are trying to compute a signal-to-noise ratio in decibels. The formula is *SNRdb* = 10 log10(*Psignal*/*Pnoise*). In Python, you might write something like this:
 
 ```
 import math
@@ -2146,7 +2186,10 @@ noise_power = 10
 ratio = signal_power / noise_power
 decibels = 10 * math.log10(ratio)
 print decibels
+```
 But when you run it in Python 2, you get an error message.
+
+```
 Traceback (most recent call last):
   File "snr.py", line 5, in ?
     decibels = 10 * math.log10(ratio)
@@ -2158,7 +2201,7 @@ In general, error messages tell you where the problem was discovered, but that i
 
 In Python 3, this example does not cause an error; the division operator performs floatingpoint division even with integer operands.
 
-### **5.13 Glossary**
+#### **5.13 Glossary**
 
 - **modulus operator:** An operator, denoted with a percent sign (%), that works on integers and yields the remainder when one number is divided by another.
 - **boolean expression:** An expression whose value is either True or False.
@@ -2167,32 +2210,32 @@ In Python 3, this example does not cause an error; the division operator perform
 - **conditional statement:** A statement that controls the flow of execution depending on some condition.
 - **condition:** The boolean expression in a conditional statement that determines which branch is executed.
 - **compound statement:** A statement that consists of a header and a body. The header ends with a colon (:). The body is indented relative to the header.
-- **branch:** One of the alternative sequences of statements in a conditional statement.
 
-**chained conditional:** A conditional statement with a series of alternative branches.
+**branch:** One of the alternative sequences of statements in a conditional statement.
 
-**nested conditional:** A conditional statement that appears in one of the branches of another conditional statement.
+**chained conditional:** A conditional statement with a series of alternative branches.
 
+- **nested conditional:** A conditional statement that appears in one of the branches of another conditional statement.
 **recursion:** The process of calling the function that is currently executing.
 
 **base case:** A conditional branch in a recursive function that does not make a recursive call.
 
 - **infinite recursion:** A recursion that doesn't have a base case, or never reaches it. Eventually, an infinite recursion causes a runtime error.
-### **5.14 Exercises**
+#### **5.14 Exercises**
 
 **Exercise 5.3.** *Fermat's Last Theorem says that there are no positive integers a, b, and c such that*
 
-$$a^{n}+b^{n}=c^{n}$$
+$$a^n + b^n = c^n$$
 
 *for any values of n greater than 2.*
 
-- *1. Write a function named* check_fermat *that takes four parameters—*a, b, c and n*—and that checks to see if Fermat's theorem holds. If n is greater than 2 and it turns out to be true that*
+- *1. Write a function named* check_fermat *that takes four parameters—*a*,* b*,* c *and* n*—and that checks to see if Fermat's theorem holds. If n is greater than 2 and it turns out to be true that*
 
-$$a^{n}+b^{n}=c^{n}$$
+$$a^n + b^n = c^n$$
 
 *the program should print, "Holy smokes, Fermat was wrong!" Otherwise the program should print, "No, that doesn't work."*
 
-- *2. Write a function that prompts the user to input values for* a, b, c and n*, converts them to integers, and uses* check_fermat *to check whether they violate Fermat's theorem.*
+- *2. Write a function that prompts the user to input values for* a*,* b*,* c *and* n*, converts them to integers, and uses* check_fermat *to check whether they violate Fermat's theorem.*
 **Exercise 5.4.** *If you are given three sticks, you may or may not be able to arrange them in a triangle. For example, if one of the sticks is 12 inches long and the other two are one inch long, it is clear that you will not be able to get the short sticks to meet in the middle. For any three lengths, there is a simple test to see if it is possible to form a triangle:*
 
 > *If any of the three lengths is greater than the sum of the other two, then you cannot form a triangle. Otherwise, you can. (If the sum of two lengths equals the third, they form what is called a "degenerate" triangle.)*
@@ -2200,13 +2243,13 @@ $$a^{n}+b^{n}=c^{n}$$
 - *1. Write a function named* is_triangle *that takes three integers as arguments, and that prints either "Yes" or "No," depending on whether you can or cannot form a triangle from sticks with the given lengths.*
 - *2. Write a function that prompts the user to input three stick lengths, converts them to integers, and uses* is_triangle *to check whether sticks with the given lengths can form a triangle.*
 
-The following exercises use TurtleWorld from Chapter 4:
+The following exercises use TurtleWorld from Chapter [4:](#page-52-0)
 
-**Exercise 5.5.** *Read the following function and see if you can figure out what it does. Then run it (see the examples in Chapter 4).*
+**Exercise 5.5.** *Read the following function and see if you can figure out what it does. Then run it (see the examples in Chapter [4)](#page-52-0).*
 
 ![](_page_71_Figure_1.jpeg)
 
-Figure 5.2: A Koch curve.
+Figure 5.2: A Koch curve.
 
 ```
 def draw(t, length, n):
@@ -2221,36 +2264,34 @@ def draw(t, length, n):
     lt(t, angle)
     bk(t, length*n)
 ```
-**Exercise 5.6.** *The Koch curve is a fractal that looks something like Figure 5.2. To draw a Koch curve with length x, all you have to do is*
+**Exercise 5.6.** *The Koch curve is a fractal that looks something like Figure [5.2.](#page-71-0) To draw a Koch curve with length x, all you have to do is*
 
-- *1. Draw a Koch curve with length x*/3.
+- *1. Draw a Koch curve with length x*/3*.*
 - *2. Turn left 60 degrees.*
-- *3. Draw a Koch curve with length x*/3.
+- *3. Draw a Koch curve with length x*/3*.*
 - *4. Turn right 120 degrees.*
-- *5. Draw a Koch curve with length x*/3.
+- *5. Draw a Koch curve with length x*/3*.*
 - *6. Turn left 60 degrees.*
-- *7. Draw a Koch curve with length x*/3.
+- *7. Draw a Koch curve with length x*/3*.*
 
 *The exception is if x is less than 3: in that case, you can just draw a straight line with length x.*
 
 - *1. Write a function called* koch *that takes a turtle and a length as parameters, and that uses the turtle to draw a Koch curve with the given length.*
 - *2. Write a function called* snowflake *that draws three Koch curves to make the outline of a snowflake.*
 
-*Solution:* http: // thinkpython. com/ code/ koch. py .
+*Solution:* [http:](http://thinkpython.com/code/koch.py) [//](http://thinkpython.com/code/koch.py) [thinkpython.](http://thinkpython.com/code/koch.py) [com/](http://thinkpython.com/code/koch.py) [code/](http://thinkpython.com/code/koch.py) [koch.](http://thinkpython.com/code/koch.py) [py](http://thinkpython.com/code/koch.py) *.*
 
-- *3. The Koch curve can be generalized in several ways. See* http: // en. wikipedia. org/ wiki/ Koch_ snowflake *for examples and implement your favorite.*
-## **Chapter 6**
+- *3. The Koch curve can be generalized in several ways. See* [http:](http://en.wikipedia.org/wiki/Koch_snowflake) [//](http://en.wikipedia.org/wiki/Koch_snowflake) [en.](http://en.wikipedia.org/wiki/Koch_snowflake) [wikipedia.](http://en.wikipedia.org/wiki/Koch_snowflake) [org/](http://en.wikipedia.org/wiki/Koch_snowflake) [wiki/](http://en.wikipedia.org/wiki/Koch_snowflake) [Koch_](http://en.wikipedia.org/wiki/Koch_snowflake) [snowflake](http://en.wikipedia.org/wiki/Koch_snowflake) *for examples and implement your favorite.*
+## **Chapter 6**
 
 # **Fruitful functions**
 
-### **6.1 Return values**
+#### **6.1 Return values**
 
 Some of the built-in functions we have used, such as the math functions, produce results. Calling the function generates a value, which we usually assign to a variable or use as part of an expression.
 
-```
-e = math.exp(1.0)
-height = radius * math.sin(radians)
-```
+e = math.exp(1.0) height = radius * math.sin(radians)
+
 All of the functions we have written so far are void; they print something or move turtles around, but their return value is None.
 
 In this chapter, we are (finally) going to write fruitful functions. The first example is area, which returns the area of a circle with the given radius:
@@ -2262,8 +2303,10 @@ def area(radius):
 ```
 We have seen the return statement before, but in a fruitful function the return statement includes an expression. This statement means: "Return immediately from this function and use the following expression as a return value." The expression can be arbitrarily complicated, so we could have written this function more concisely:
 
-def area(radius): return math.pi * radius**2
-
+```
+def area(radius):
+    return math.pi * radius**2
+```
 On the other hand, **temporary variables** like temp often make debugging easier.
 
 Sometimes it is useful to have multiple return statements, one in each branch of a conditional:
@@ -2281,25 +2324,30 @@ As soon as a return statement executes, the function terminates without executin
 
 In a fruitful function, it is a good idea to ensure that every possible path through the program hits a return statement. For example:
 
-def absolute_value(x): if x < 0: return -x if x > 0: return x
-
+```
+def absolute_value(x):
+    if x < 0:
+        return -x
+    if x > 0:
+        return x
+```
 This function is incorrect because if x happens to be 0, neither condition is true, and the function ends without hitting a return statement. If the flow of execution gets to the end of a function, the return value is None, which is not the absolute value of 0.
 
 ```
 >>> print absolute_value(0)
 None
 ```
-By the way, Python provides a built-in function called abs that computes absolute values. **Exercise 6.1.** *Write a* compare *function that returns* 1 if x > y, 0 if x == y*, and* -1 if x < y.
+By the way, Python provides a built-in function called abs that computes absolute values. **Exercise 6.1.** *Write a* compare *function that returns* 1 *if* x > y*,* 0 *if* x == y*, and* -1 *if* x < y*.*
 
-### **6.2 Incremental development**
+#### **6.2 Incremental development**
 
 As you write larger functions, you might find yourself spending more time debugging.
 
-To deal with increasingly complex programs, you might want to try a process called in**cremental development**. The goal of incremental development is to avoid long debugging sessions by adding and testing only a small amount of code at a time.
+To deal with increasingly complex programs, you might want to try a process called **incremental development**. The goal of incremental development is to avoid long debugging sessions by adding and testing only a small amount of code at a time.
 
-As an example, suppose you want to find the distance between two points, given by the coordinates (x1, y1) and (x2, y2). By the Pythagorean theorem, the distance is:
+As an example, suppose you want to find the distance between two points, given by the coordinates (*x*1, *y*1) and (*x*2, *y*2). By the Pythagorean theorem, the distance is:
 
-$${\mathrm{distance}}={\sqrt{(x_{2}-x_{1})^{2}+(y_{2}-y_{1})^{2}}}$$
+$$\mathrm{distance} = \sqrt{(x_2 - x_1)^2 + (y_2 - y_1)^2}$$
 
 The first step is to consider what a distance function should look like in Python. In other words, what are the inputs (parameters) and what is the output (return value)?
 
@@ -2319,7 +2367,7 @@ To test the new function, call it with sample arguments:
 
 I chose these values so that the horizontal distance is 3 and the vertical distance is 4; that way, the result is 5 (the hypotenuse of a 3-4-5 triangle). When testing a function, it is useful to know the right answer.
 
-At this point we have confirmed that the function is syntactically correct, and we can start adding code to the body. A reasonable next step is to find the differences x2 − x1 and y2 − y1. The next version stores those values in temporary variables and prints them.
+At this point we have confirmed that the function is syntactically correct, and we can start adding code to the body. A reasonable next step is to find the differences *x*2 − *x*1 and *y*2 − *y*1. The next version stores those values in temporary variables and prints them.
 
 ```
 def distance(x1, y1, x2, y2):
@@ -2365,7 +2413,7 @@ The key aspects of the process are:
 
 **Exercise 6.2.** *Use incremental development to write a function called* hypotenuse *that returns the length of the hypotenuse of a right triangle given the lengths of the two legs as arguments. Record each stage of the development process as you go.*
 
-### **6.3 Composition**
+#### **6.3 Composition**
 
 As you should expect by now, you can call one function from within another. This ability is called **composition**.
 
@@ -2377,8 +2425,9 @@ radius = distance(xc, yc, xp, yp)
 
 The next step is to find the area of a circle with that radius; we just wrote that, too:
 
+```
 result = area(radius)
-
+```
 Encapsulating these steps in a function, we get:
 
 ```
@@ -2389,9 +2438,11 @@ def circle_area(xc, yc, xp, yp):
 ```
 The temporary variables radius and result are useful for development and debugging, but once the program is working, we can make it more concise by composing the function calls:
 
-def circle_area(xc, yc, xp, yp): return area(distance(xc, yc, xp, yp))
-
-### **6.4 Boolean functions**
+```
+def circle_area(xc, yc, xp, yp):
+    return area(distance(xc, yc, xp, yp))
+```
+#### **6.4 Boolean functions**
 
 Functions can return booleans, which is often convenient for hiding complicated tests inside functions. For example:
 
@@ -2404,36 +2455,33 @@ def is_divisible(x, y):
 ```
 It is common to give boolean functions names that sound like yes/no questions; is_divisible returns either True or False to indicate whether x is divisible by y.
 
-```
 Here is an example:
+
+```
 >>> is_divisible(6, 4)
 False
 >>> is_divisible(6, 3)
 True
-The result of the == operator is a boolean, so we can write the function more concisely by
-returning it directly:
-def is_divisible(x, y):
 ```
-return x % y == 0
+The result of the == operator is a boolean, so we can write the function more concisely by returning it directly:
 
+```
+def is_divisible(x, y):
+    return x % y == 0
+```
 Boolean functions are often used in conditional statements:
 
-```
 if is_divisible(x, y):
-    print 'x is divisible by y'
-```
-It might be tempting to write something like:
 
-```
-if is_divisible(x, y) == True:
-```
 print 'x is divisible by y'
 
-But the extra comparison is unnecessary.
+It might be tempting to write something like:
+
+if is_divisible(x, y) == True: print 'x is divisible by y'
 
-**Exercise 6.3.** *Write a function* is_between(x, y, z) *that returns* True *if x* ≤ y ≤ *z or* False *otherwise.*
+But the extra comparison is unnecessary. **Exercise 6.3.** *Write a function* is_between(x, y, z) *that returns* True *if x* ≤ *y* ≤ *z or* False *otherwise.*
 
-### **6.5 More recursion**
+#### **6.5 More recursion**
 
 We have only covered a small subset of Python, but you might be interested to know that this subset is a *complete* programming language, which means that anything that can be computed can be expressed in this language. Any program ever written could be rewritten using only the language features you have learned so far (actually, you would need a few commands to control devices like the keyboard, mouse, disks, etc., but that's all).
 
@@ -2445,21 +2493,25 @@ To give you an idea of what you can do with the tools you have learned so far, w
 
 If you saw that definition in the dictionary, you might be annoyed. On the other hand, if you looked up the definition of the factorial function, denoted with the symbol !, you might get something like this:
 
-> 0! = 1 n! = n(n − 1)!
+$$\begin{array}{l}{{0!=1}}\\ {{n!=n(n-1)!!}}\end{array}$$
 
-This definition says that the factorial of 0 is 1, and the factorial of any other value, n, is n multiplied by the factorial of n − 1.
+This definition says that the factorial of 0 is 1, and the factorial of any other value, *n*, is *n* multiplied by the factorial of *n* − 1.
 
 So 3! is 3 times 2!, which is 2 times 1!, which is 1 times 0!. Putting it all together, 3! equals 3 times 2 times 1 times 1, which is 6.
 
 If you can write a recursive definition of something, you can usually write a Python program to evaluate it. The first step is to decide what the parameters should be. In this case it should be clear that factorial takes an integer:
 
+```
 def factorial(n):
-
+```
 If the argument happens to be 0, all we have to do is return 1:
 
-def factorial(n): if n == 0: return 1
-
-Otherwise, and this is the interesting part, we have to make a recursive call to find the factorial of n − 1 and then multiply it by n:
+```
+def factorial(n):
+    if n == 0:
+        return 1
+```
+Otherwise, and this is the interesting part, we have to make a recursive call to find the factorial of *n* − 1 and then multiply it by *n*:
 
 ```
 def factorial(n):
@@ -2470,7 +2522,7 @@ def factorial(n):
         result = n * recurse
         return result
 ```
-The flow of execution for this program is similar to the flow of countdown in Section 5.8. If we call factorial with the value 3:
+The flow of execution for this program is similar to the flow of countdown in Section [5.8.](#page-65-0) If we call factorial with the value 3:
 
 Since 3 is not 0, we take the second branch and calculate the factorial of n-1...
 
@@ -2478,15 +2530,15 @@ Since 2 is not 0, we take the second branch and calculate the factorial of n-1..
 
 Since 1 is not 0, we take the second branch and calculate the factorial of n-1...
 
-> Since 0 is 0, we take the first branch and return 1 without making any more recursive calls.
+> Since 0 *is* 0, we take the first branch and return 1 without making any more recursive calls.
 
-The return value (1) is multiplied by n, which is 1, and the result is returned.
+The return value (1) is multiplied by *n*, which is 1, and the result is returned.
 
-The return value (1) is multiplied by n, which is 2, and the result is returned.
+The return value (1) is multiplied by *n*, which is 2, and the result is returned.
 
-The return value (2) is multiplied by n, which is 3, and the result, 6, becomes the return value of the function call that started the whole process.
+The return value (2) is multiplied by *n*, which is 3, and the result, 6, becomes the return value of the function call that started the whole process.
 
-Figure 6.1 shows what the stack diagram looks like for this sequence of function calls.
+Figure [6.1](#page-78-2) shows what the stack diagram looks like for this sequence of function calls.
 
 The return values are shown being passed back up the stack. In each frame, the return value is the value of result, which is the product of n and recurse.
 
@@ -2494,26 +2546,29 @@ In the last frame, the local variables recurse and result do not exist, because
 
 ![](_page_78_Figure_1.jpeg)
 
-Figure 6.1: Stack diagram.
+Figure 6.1: Stack diagram.
 
-### **6.6 Leap of faith**
+#### **6.6 Leap of faith**
 
 Following the flow of execution is one way to read programs, but it can quickly become labyrinthine. An alternative is what I call the "leap of faith." When you come to a function call, instead of following the flow of execution, you *assume* that the function works correctly and returns the right result.
 
 In fact, you are already practicing this leap of faith when you use built-in functions. When you call math.cos or math.exp, you don't examine the bodies of those functions. You just assume that they work because the people who wrote the built-in functions were good programmers.
 
-The same is true when you call one of your own functions. For example, in Section 6.4, we wrote a function called is_divisible that determines whether one number is divisible by another. Once we have convinced ourselves that this function is correct—by examining the code and testing—we can use the function without looking at the body again.
+The same is true when you call one of your own functions. For example, in Section [6.4,](#page-75-1) we wrote a function called is_divisible that determines whether one number is divisible by another. Once we have convinced ourselves that this function is correct—by examining the code and testing—we can use the function without looking at the body again.
 
-The same is true of recursive programs. When you get to the recursive call, instead of following the flow of execution, you should assume that the recursive call works (yields the correct result) and then ask yourself, "Assuming that I can find the factorial of n − 1, can I compute the factorial of n?" In this case, it is clear that you can, by multiplying by n.
+The same is true of recursive programs. When you get to the recursive call, instead of following the flow of execution, you should assume that the recursive call works (yields the correct result) and then ask yourself, "Assuming that I can find the factorial of *n* − 1, can I compute the factorial of *n*?" In this case, it is clear that you can, by multiplying by *n*.
 
 Of course, it's a bit strange to assume that the function works correctly when you haven't finished writing it, but that's why it's called a leap of faith!
 
-### **6.7 One more example**
-
-After factorial, the most common example of a recursively defined mathematical function is fibonacci, which has the following definition (see http://en.wikipedia.org/ wiki/Fibonacci_number):
+### **6.7 One more example**
 
-fibonacci(0) = 0 fibonacci(1) = 1 fibonacci(n) = fibonacci(n-1) + fibonacci(n-2)
+After factorial, the most common example of a recursively defined mathematical function is fibonacci, which has the following definition (see [http://en.wikipedia.org/](http://en.wikipedia.org/wiki/Fibonacci_number) [wiki/Fibonacci_number](http://en.wikipedia.org/wiki/Fibonacci_number)):
 
+```
+fibonacci(0) = 0
+fibonacci(1) = 1
+fibonacci(n) = fibonacci(n − 1) + fibonacci(n − 2)
+```
 Translated into Python, it looks like this:
 
 ```
@@ -2525,16 +2580,17 @@ def fibonacci (n):
     else:
         return fibonacci(n-1) + fibonacci(n-2)
 ```
-If you try to follow the flow of execution here, even for fairly small values of n, your head explodes. But according to the leap of faith, if you assume that the two recursive calls work correctly, then it is clear that you get the right result by adding them together.
+If you try to follow the flow of execution here, even for fairly small values of *n*, your head explodes. But according to the leap of faith, if you assume that the two recursive calls work correctly, then it is clear that you get the right result by adding them together.
 
-### **6.8 Checking types**
+#### **6.8 Checking types**
 
 What happens if we call factorial and give it 1.5 as an argument?
 
 ```
 >>> factorial(1.5)
-RuntimeError: Maximum recursion depth exceeded
 ```
+RuntimeError: Maximum recursion depth exceeded
+
 It looks like an infinite recursion. But how can that be? There is a base case—when n == 0. But if n is not an integer, we can *miss* the base case and recurse forever.
 
 In the first recursive call, the value of n is 0.5. In the next, it is -0.5. From there, it gets smaller (more negative), but it will never be 0.
@@ -2566,13 +2622,13 @@ None
 Factorial is not defined for negative integers.
 None
 ```
-If we get past both checks, then we know that n is positive or zero, so we can prove that the recursion terminates.
+If we get past both checks, then we know that *n* is positive or zero, so we can prove that the recursion terminates.
 
 This program demonstrates a pattern sometimes called a **guardian**. The first two conditionals act as guardians, protecting the code that follows from values that might cause an error. The guardians make it possible to prove the correctness of the code.
 
-In Section 11.3 we will see a more flexible alternative to printing an error message: raising an exception.
+In Section [11.3](#page-125-0) we will see a more flexible alternative to printing an error message: raising an exception.
 
-### **6.9 Debugging**
+#### **6.9 Debugging**
 
 Breaking a large program into smaller functions creates natural checkpoints for debugging. If a function is not working, there are three possibilities to consider:
 
@@ -2582,7 +2638,7 @@ Breaking a large program into smaller functions creates natural checkpoints for
 
 To rule out the first possibility, you can add a print statement at the beginning of the function and display the values of the parameters (and maybe their types). Or you can write code that checks the preconditions explicitly.
 
-If the parameters look good, add a print statement before each return statement that displays the return value. If possible, check the result by hand. Consider calling the function with values that make it easy to check the result (as in Section 6.2).
+If the parameters look good, add a print statement before each return statement that displays the return value. If possible, check the result by hand. Consider calling the function with values that make it easy to check the result (as in Section [6.2)](#page-73-0).
 
 If the function seems to be working, look at the function call to make sure the return value is being used correctly (or used at all!).
 
@@ -2619,18 +2675,18 @@ returning 1
 ```
 If you are confused about the flow of execution, this kind of output can be helpful. It takes some time to develop effective scaffolding, but a little bit of scaffolding can save a lot of debugging.
 
-### **6.10 Glossary**
+## **6.10 Glossary**
 
 - **temporary variable:** A variable used to store an intermediate value in a complex calculation.
 - **dead code:** Part of a program that can never be executed, often because it appears after a return statement.
-- None: A special value returned by functions that have no return statement or a return statement without an argument.
+- None**:** A special value returned by functions that have no return statement or a return statement without an argument.
 - **incremental development:** A program development plan intended to avoid debugging by adding and testing only a small amount of code at a time.
 - **scaffolding:** Code that is used during program development but is not part of the final version.
 - **guardian:** A programming pattern that uses a conditional statement to check for and handle circumstances that might cause an error.
 
-### **6.11 Exercises**
+#### **6.11 Exercises**
 
-**Exercise 6.4.** *Draw a stack diagram for the following program. What does the program print? Solution:* http: // thinkpython. com/ code/ stack_ diagram. py .
+**Exercise 6.4.** *Draw a stack diagram for the following program. What does the program print? Solution:* [http:](http://thinkpython.com/code/stack_diagram.py) [//](http://thinkpython.com/code/stack_diagram.py) [thinkpython.](http://thinkpython.com/code/stack_diagram.py) [com/](http://thinkpython.com/code/stack_diagram.py) [code/](http://thinkpython.com/code/stack_diagram.py) [stack_](http://thinkpython.com/code/stack_diagram.py) [diagram.](http://thinkpython.com/code/stack_diagram.py) [py](http://thinkpython.com/code/stack_diagram.py) *.*
 
 ```
 def b(z):
@@ -2638,49 +2694,68 @@ def b(z):
     print z, prod
     return prod
 def a(x, y):
-    x = x + 1
-    return x * y
 ```
-def c(x, y, z): total = x + y + z square = b(total)**2 return square x = 1 y = x + 1
 
-print c(x, y+3, x+y) **Exercise 6.5.** *The Ackermann function, A*(m, n)*, is defined:*
+```
+x = x + 1
+return x * y
+```
 
-$$A(m,n)=\begin{cases}n+1&\text{if}m=0\\ A(m-1,1)&\text{if}m>0\text{and}n=0\\ A(m-1,A(m,n-1))&\text{if}m>0\text{and}n>0.\end{cases}$$
+```
+def c(x, y, z):
+    total = x + y + z
+    square = b(total)**2
+    return square
+x = 1
+y = x + 1
+print c(x, y+3, x+y)
+Exercise 6.5. The Ackermann function, A(m, n), is defined:
+```
+
+$$A(m,n) = \begin{cases} n+1 & \text{if } m = 0\\ A(m-1,1) & \text{if } m > 0 \text{ and } n = 0\\ A(m-1, A(m, n-1)) & \text{if } m > 0 \text{ and } n > 0. \end{cases}$$
 
-See http: // en. wikipedia. org/ wiki/ Ackermann_ function *. Write a function named* ack *that evaluates Ackermann's function. Use your function to evaluate* ack(3, 4)*, which should be 125. What happens for larger values of* m and n*? Solution:* http: // thinkpython. com/ code/ ackermann. py .
+*See* [http:](http://en.wikipedia.org/wiki/Ackermann_function) [//](http://en.wikipedia.org/wiki/Ackermann_function) [en.](http://en.wikipedia.org/wiki/Ackermann_function) [wikipedia.](http://en.wikipedia.org/wiki/Ackermann_function) [org/](http://en.wikipedia.org/wiki/Ackermann_function) [wiki/](http://en.wikipedia.org/wiki/Ackermann_function) [Ackermann_](http://en.wikipedia.org/wiki/Ackermann_function) [function](http://en.wikipedia.org/wiki/Ackermann_function) *. Write a function named* ack *that evaluates Ackermann's function. Use your function to evaluate* ack(3, 4)*, which should be 125. What happens for larger values of* m *and* n*? Solution:* [http:](http://thinkpython.com/code/ackermann.py) [//](http://thinkpython.com/code/ackermann.py) [thinkpython.](http://thinkpython.com/code/ackermann.py) [com/](http://thinkpython.com/code/ackermann.py) [code/](http://thinkpython.com/code/ackermann.py) [ackermann.](http://thinkpython.com/code/ackermann.py) [py](http://thinkpython.com/code/ackermann.py) *.*
 
-**Exercise 6.6.** *A palindrome is a word that is spelled the same backward and forward, like "noon" and "redivider". Recursively, a word is a palindrome if the first and last letters are the same and the middle is a palindrome.*
+**Exercise 6.6.** *A palindrome is a word that is spelled the same backward and forward, like "noon" and "redivider". Recursively, a word is a palindrome if the first and last letters are the same and the middle is a palindrome.*
 
 *The following are functions that take a string argument and return the first, last, and middle letters:*
 
-def first(word): return word[0]
-
-def last(word): return word[-1]
+```
+def first(word):
+    return word[0]
+```
 
-def middle(word): return word[1:-1]
+```
+def last(word):
+    return word[-1]
+```
 
-*We'll see how they work in Chapter 8.*
+```
+def middle(word):
+    return word[1:-1]
+```
+*We'll see how they work in Chapter [8.](#page-92-0)*
 
 - *1. Type these functions into a file named* palindrome.py *and test them out. What happens if you call* middle *with a string with two letters? One letter? What about the empty string, which is written* '' *and contains no letters?*
 - *2. Write a function called* is_palindrome *that takes a string argument and returns* True *if it is a palindrome and* False *otherwise. Remember that you can use the built-in function* len *to check the length of a string.*
 
-*Solution:* http: // thinkpython. com/ code/ palindrome_ soln. py .
+*Solution:* [http:](http://thinkpython.com/code/palindrome_soln.py) [//](http://thinkpython.com/code/palindrome_soln.py) [thinkpython.](http://thinkpython.com/code/palindrome_soln.py) [com/](http://thinkpython.com/code/palindrome_soln.py) [code/](http://thinkpython.com/code/palindrome_soln.py) [palindrome_](http://thinkpython.com/code/palindrome_soln.py) [soln.](http://thinkpython.com/code/palindrome_soln.py) [py](http://thinkpython.com/code/palindrome_soln.py) *.*
 
-**Exercise 6.7.** *A number, a, is a power of b if it is divisible by b and a*/*b is a power of b. Write a function called* is_power *that takes parameters* a and b *and returns* True if a *is a power of* b*. Note: you will have to think about the base case.*
+**Exercise 6.7.** *A number, a, is a power of b if it is divisible by b and a*/*b is a power of b. Write a function called* is_power *that takes parameters* a *and* b *and returns* True *if* a *is a power of* b*. Note: you will have to think about the base case.*
 
 **Exercise 6.8.** *The greatest common divisor (GCD) of a and b is the largest number that divides both of them with no remainder.*
 
-*One way to find the GCD of two numbers is based on the observation that if r is the remainder when a is divided by b, then gcd*(a, b) = gcd(b,r)*. As a base case, we can use gcd*(a, 0) = a.
+*One way to find the GCD of two numbers is based on the observation that if r is the remainder when a is divided by b, then gcd(a, b) = gcd(b, r). As a base case, we can use gcd(a, 0) = a.* 
 
-*Write a function called* gcd *that takes parameters* a and b *and returns their greatest common divisor.*
+*Write a function called* gcd *that takes parameters* a *and* b *and returns their greatest common divisor.*
 
-*Credit: This exercise is based on an example from Abelson and Sussman's* Structure and Interpretation of Computer Programs.
+*Credit: This exercise is based on an example from Abelson and Sussman's* Structure and Interpretation of Computer Programs*.*
 
-## **Chapter 7**
+## **Chapter 7**
 
 # **Iteration**
 
-### **7.1 Multiple assignment**
+#### **7.1 Multiple assignment**
 
 As you may have discovered, it is legal to make more than one assignment to the same variable. A new assignment makes an existing variable refer to a new value (and stop referring to the old value).
 
@@ -2688,13 +2763,13 @@ bruce = 5 print bruce, bruce = 7 print bruce
 
 The output of this program is 5 7, because the first time bruce is printed, its value is 5, and the second time, its value is 7. The comma at the end of the first print statement suppresses the newline, which is why both outputs appear on the same line.
 
-Figure 7.1 shows what **multiple assignment** looks like in a state diagram.
+Figure [7.1](#page-85-2) shows what **multiple assignment** looks like in a state diagram.
 
 With multiple assignment it is especially important to distinguish between an assignment operation and a statement of equality. Because Python uses the equal sign (=) for assignment, it is tempting to interpret a statement like a = b as a statement of equality. It is not!
 
-First, equality is a symmetric relation and assignment is not. For example, in mathematics, if a = 7 then 7 = a. But in Python, the statement a = 7 is legal and 7 = a is not.
+First, equality is a symmetric relation and assignment is not. For example, in mathematics, if *a* = 7 then 7 = *a*. But in Python, the statement a = 7 is legal and 7 = a is not.
 
-Furthermore, in mathematics, a statement of equality is either true or false, for all time. If a = b now, then a will always equal b. In Python, an assignment statement can make two variables equal, but they don't have to stay that way:
+Furthermore, in mathematics, a statement of equality is either true or false, for all time. If *a* = *b* now, then *a* will always equal *b*. In Python, an assignment statement can make two variables equal, but they don't have to stay that way:
 
 a = 5 b = a # a and b are now equal a = 3 # a and b are no longer equal
 
@@ -2702,15 +2777,15 @@ The third line changes the value of a but does not change the value of b, so the
 
 Although multiple assignment is frequently helpful, you should use it with caution. If the values of variables change frequently, it can make the code difficult to read and debug.
 
-![](_page_85_Figure_1.jpeg)
+![](_page_85_Figure_1.jpeg)
 
 Figure 7.1: State diagram.
 
-### **7.2 Updating variables**
+### **7.2 Updating variables**
 
 One of the most common forms of multiple assignment is an **update**, where the new value of the variable depends on the old.
 
-x = x+1
+x = x + 1
 
 This means "get the current value of x, add one, and then update x with the new value."
 
@@ -2722,15 +2797,15 @@ NameError: name 'x' is not defined
 ```
 Before you can update a variable, you have to **initialize** it, usually with a simple assignment:
 
->>> x = 0 >>> x = x+1
+>>> x = 0 >>> x = x + 1
 
 Updating a variable by adding 1 is called an **increment**; subtracting 1 is called a **decrement**.
 
-### **7.3 The** while **statement**
+## **7.3 The** while **statement**
 
 Computers are often used to automate repetitive tasks. Repeating identical or similar tasks without making errors is something that computers do well and people do poorly.
 
-We have seen two programs, countdown and print_n, that use recursion to perform repetition, which is also called **iteration**. Because iteration is so common, Python provides several language features to make it easier. One is the for statement we saw in Section 4.2. We'll get back to that later.
+We have seen two programs, countdown and print_n, that use recursion to perform repetition, which is also called **iteration**. Because iteration is so common, Python provides several language features to make it easier. One is the for statement we saw in Section [4.2.](#page-53-0) We'll get back to that later.
 
 Another is the while statement. Here is a version of countdown that uses a while statement:
 
@@ -2757,12 +2832,15 @@ In the case of countdown, we can prove that the loop terminates because we know
 
 ```
 def sequence(n):
-   while n != 1:
-       print n,
-       if n%2 == 0: # n is even
-          n = n/2
-       else: # n is odd
-          n = n*3+1
+```
+
+```
+while n != 1:
+   print n,
+   if n%2 == 0: # n is even
+      n = n/2
+   else: # n is odd
+      n = n*3+1
 ```
 The condition for this loop is n != 1, so the loop will continue until n is 1, which makes the condition false.
 
@@ -2770,11 +2848,11 @@ Each time through the loop, the program outputs the value of n and then checks w
 
 Since n sometimes increases and sometimes decreases, there is no obvious proof that n will ever reach 1, or that the program terminates. For some particular values of n, we can prove termination. For example, if the starting value is a power of two, then the value of n will be even each time through the loop until it reaches 1. The previous example ends with such a sequence, starting with 16.
 
-The hard question is whether we can prove that this program terminates for *all positive values* of n. So far, no one has been able to prove it or disprove it! (See http: //en.wikipedia.org/wiki/Collatz_conjecture.)
+The hard question is whether we can prove that this program terminates for *all positive values* of n. So far, no one has been able to prove it *or* disprove it! (See [http:](http://en.wikipedia.org/wiki/Collatz_conjecture) [//en.wikipedia.org/wiki/Collatz_conjecture](http://en.wikipedia.org/wiki/Collatz_conjecture).)
 
-**Exercise 7.1.** *Rewrite the function* print_n *from Section 5.8 using iteration instead of recursion.*
+**Exercise 7.1.** *Rewrite the function* print_n *from Section [5.8](#page-65-0) using iteration instead of recursion.*
 
-### 7.4 break
+## **7.4** break
 
 Sometimes you don't know it's time to end a loop until you get half way through the body. In that case you can use the break statement to jump out of the loop.
 
@@ -2793,19 +2871,23 @@ The loop condition is True, which is always true, so the loop runs until it hits
 
 Each time through, it prompts the user with an angle bracket. If the user types done, the break statement exits the loop. Otherwise the program echoes whatever the user types and goes back to the top of the loop. Here's a sample run:
 
-> not done not done > done Done!
-
+```
+> not done
+not done
+> done
+Done!
+```
 This way of writing while loops is common because you can check the condition anywhere in the loop (not just at the top) and you can express the stop condition affirmatively ("stop when this happens") rather than negatively ("keep going until that happens.").
 
-### **7.5 Square roots**
+#### **7.5 Square roots**
 
 Loops are often used in programs that compute numerical results by starting with an approximate answer and iteratively improving it.
 
-For example, one way of computing square roots is Newton's method. Suppose that you want to know the square root of a. If you start with almost any estimate, x, you can compute a better estimate with the following formula:
+For example, one way of computing square roots is Newton's method. Suppose that you want to know the square root of *a*. If you start with almost any estimate, *x*, you can compute a better estimate with the following formula:
 
-$$y={\frac{x+a/x}{2}}$$
+$$y = \frac{x + a/x}{2}$$
 
-For example, if a is 4 and x is 3:
+For example, if *a* is 4 and *x* is 3:
 
 ```
 >>> a = 4.0
@@ -2824,8 +2906,16 @@ Which is closer to the correct answer (√ 4 = 2). If we repeat the process with
 ```
 After a few more updates, the estimate is almost exact:
 
->>> x = y >>> y = (x + a/x) / 2 >>> print y 2.00001024003 >>> x = y >>> y = (x + a/x) / 2 >>> print y 2.00000000003
-
+```
+>>> x = y
+>>> y = (x + a/x) / 2
+>>> print y
+2.00001024003
+>>> x = y
+>>> y = (x + a/x) / 2
+>>> print y
+2.00000000003
+```
 In general we don't know ahead of time how many steps it takes to get to the right answer, but we know when we get there because the estimate stops changing:
 
 ```
@@ -2856,15 +2946,15 @@ Rather than checking whether x and y are exactly equal, it is safer to use the b
 if abs(y-x) < epsilon:
     break
 ```
-Where epsilon has a value like 0.0000001 that determines how close is close enough. **Exercise 7.2.** *Encapsulate this loop in a function called* square_root *that takes* a *as a parameter, chooses a reasonable value of* x*, and returns an estimate of the square root of* a.
+Where epsilon has a value like 0.0000001 that determines how close is close enough. **Exercise 7.2.** *Encapsulate this loop in a function called* square_root *that takes* a *as a parameter, chooses a reasonable value of* x*, and returns an estimate of the square root of* a*.*
 
-### **7.6 Algorithms**
+### **7.6 Algorithms**
 
 Newton's method is an example of an **algorithm**: it is a mechanical process for solving a category of problems (in this case, computing square roots).
 
 It is not easy to define an algorithm. It might help to start with something that is not an algorithm. When you learned to multiply single-digit numbers, you probably memorized the multiplication table. In effect, you memorized 100 specific solutions. That kind of knowledge is not algorithmic.
 
-But if you were "lazy," you probably cheated by learning a few tricks. For example, to find the product of n and 9, you can write n − 1 as the first digit and 10 − n as the second digit. This trick is a general solution for multiplying any single-digit number by 9. That's an algorithm!
+But if you were "lazy," you probably cheated by learning a few tricks. For example, to find the product of *n* and 9, you can write *n* − 1 as the first digit and 10 − *n* as the second digit. This trick is a general solution for multiplying any single-digit number by 9. That's an algorithm!
 
 Similarly, the techniques you learned for addition with carrying, subtraction with borrowing, and long division are all algorithms. One of the characteristics of algorithms is that they do not require any intelligence to carry out. They are mechanical processes in which each step follows from the last according to a simple set of rules.
 
@@ -2872,9 +2962,9 @@ In my opinion, it is embarrassing that humans spend so much time in school learn
 
 On the other hand, the process of designing algorithms is interesting, intellectually challenging, and a central part of what we call programming.
 
-Some of the things that people do naturally, without difficulty or conscious thought, are the hardest to express algorithmically. Understanding natural language is a good example. We all do it, but so far no one has been able to explain how we do it, at least not in the form of an algorithm.
+Some of the things that people do naturally, without difficulty or conscious thought, are the hardest to express algorithmically. Understanding natural language is a good example. We all do it, but so far no one has been able to explain *how* we do it, at least not in the form of an algorithm.
 
-### **7.7 Debugging**
+## **7.7 Debugging**
 
 As you start writing bigger programs, you might find yourself spending more time debugging. More code means more chances to make an error and more place for bugs to hide.
 
@@ -2888,7 +2978,7 @@ Every time you perform a check like this, you halve the number of lines you have
 
 In practice it is not always clear what the "middle of the program" is and not always possible to check it. It doesn't make sense to count lines and find the exact midpoint. Instead, think about places in the program where there might be errors and places where it is easy to put a check. Then choose a spot where you think the chances are about the same that the bug is before or after the check.
 
-### **7.8 Glossary**
+## **7.8 Glossary**
 
 **multiple assignment:** Making more than one assignment to the same variable during the execution of a program.
 
@@ -2900,10 +2990,11 @@ In practice it is not always clear what the "middle of the program" is and not a
 
 **decrement:** An update that decreases the value of a variable.
 
-- **iteration:** Repeated execution of a set of statements using either a recursive function call or a loop.
+**iteration:** Repeated execution of a set of statements using either a recursive function call or a loop.
+
 **infinite loop:** A loop in which the terminating condition is never satisfied.
 
-### **7.9 Exercises**
+#### **7.9 Exercises**
 
 **Exercise 7.3.** *To test the square root algorithm in this chapter, you could compare it with* math.sqrt*. Write a function named* test_square_root *that prints a table like this:*
 
@@ -2918,7 +3009,7 @@ In practice it is not always clear what the "middle of the program" is and not a
 8.0 2.82842712475 2.82842712475 4.4408920985e-16
 9.0 3.0 3.0 0.0
 ```
-*The first column is a number, a; the second column is the square root of a computed with the function from Section 7.5; the third column is the square root computed by* math.sqrt*; the fourth column is the absolute value of the difference between the two estimates.*
+*The first column is a number, a; the second column is the square root of a computed with the function from Section [7.5;](#page-87-0) the third column is the square root computed by* math.sqrt*; the fourth column is the absolute value of the difference between the two estimates.*
 
 **Exercise 7.4.** *The built-in function* eval *takes a string and evaluates it using the Python interpreter. For example:*
 
@@ -2935,25 +3026,23 @@ In practice it is not always clear what the "middle of the program" is and not a
 
 *It should continue until the user enters* 'done'*, and then return the value of the last expression it evaluated.*
 
-**Exercise 7.5.** *The mathematician Srinivasa Ramanujan found an infinite series that can be used to generate a numerical approximation of* 1/π:
+**Exercise 7.5.** *The mathematician Srinivasa Ramanujan found an infinite series that can be used to generate a numerical approximation of* 1/*π:*
 
-$${\frac{1}{\pi}}={\frac{2{\sqrt{2}}}{9801}}\sum_{k=0}^{\infty}{\frac{(4k)!(1103+26390k)}{(k!)^{4}396^{4k}}}$$
+$$\frac{1}{\pi} = \frac{2\sqrt{2}}{9801} \sum_{k=0}^{\infty} \frac{(4k)!(1103 + 26390k)}{(k!)^4 396^{4k}}$$
 
-*Write a function called* estimate_pi *that uses this formula to compute and return an estimate of* π*. It should use a* while *loop to compute terms of the summation until the last term is smaller than* 1e-15 *(which is Python notation for* 10−15*). You can check the result by comparing it to* math.pi.
+*Write a function called* estimate_pi *that uses this formula to compute and return an estimate of π. It should use a* while *loop to compute terms of the summation until the last term is smaller than* 1e-15 *(which is Python notation for* 10−15*). You can check the result by comparing it to* math.pi*.*
 
-*Solution:* http: // thinkpython. com/ code/ pi. py .
+*Solution:* [http:](http://thinkpython.com/code/pi.py) [//](http://thinkpython.com/code/pi.py) [thinkpython.](http://thinkpython.com/code/pi.py) [com/](http://thinkpython.com/code/pi.py) [code/](http://thinkpython.com/code/pi.py) [pi.](http://thinkpython.com/code/pi.py) [py](http://thinkpython.com/code/pi.py) *.*
 
-## **Chapter 8**
+## **Chapter 8**
 
 # **Strings**
 
-### **8.1 A string is a sequence**
+#### **8.1 A string is a sequence**
 
 A string is a **sequence** of characters. You can access the characters one at a time with the bracket operator:
 
->>> fruit = 'banana'
-
->>> letter = fruit[1]
+>>> fruit = 'banana' >>> letter = fruit[1]
 
 The second statement selects character number 1 from fruit and assigns it to letter.
 
@@ -2961,9 +3050,7 @@ The expression in brackets is called an **index**. The index indicates which cha
 
 But you might not get what you expect:
 
->>> print letter
-
-a
+>>> print letter a
 
 For most people, the first letter of 'banana' is b, not a. But for computer scientists, the index is an offset from the beginning of the string, and the offset of the first letter is zero.
 
@@ -2980,7 +3067,7 @@ You can use any expression, including variables and operators, as an index, but
 >>> letter = fruit[1.5]
 TypeError: string indices must be integers, not float
 ```
-### 8.2 len
+## **8.2** len
 
 len is a built-in function that returns the number of characters in a string:
 
@@ -2988,20 +3075,24 @@ len is a built-in function that returns the number of characters in a string:
 >>> fruit = 'banana'
 >>> len(fruit)
 6
+```
 To get the last letter of a string, you might be tempted to try something like this:
+
+```
 >>> length = len(fruit)
 >>> last = fruit[length]
 IndexError: string index out of range
-The reason for the IndexError is that there is no letter in 'banana' with the index 6. Since
-we started counting at zero, the six letters are numbered 0 to 5. To get the last character,
-you have to subtract 1 from length:
+```
+The reason for the IndexError is that there is no letter in 'banana' with the index 6. Since we started counting at zero, the six letters are numbered 0 to 5. To get the last character, you have to subtract 1 from length:
+
+```
 >>> last = fruit[length-1]
 >>> print last
 a
 ```
 Alternatively, you can use negative indices, which count backward from the end of the string. The expression fruit[-1] yields the last letter, fruit[-2] yields the second to last, and so on.
 
-### **8.3 Traversal with a** for **loop**
+## **8.3 Traversal with a** for **loop**
 
 A lot of computations involve processing a string one character at a time. Often they start at the beginning, select each character in turn, do something to it, and continue until the end. This pattern of processing is called a **traversal**. One way to write a traversal is with a while loop:
 
@@ -3018,34 +3109,36 @@ This loop traverses the string and displays each letter on a line by itself. The
 
 Another way to write a traversal is with a for loop:
 
-for char in fruit: print char
-
+```
+for char in fruit:
+    print char
+```
 Each time through the loop, the next character in the string is assigned to the variable char. The loop continues until no characters are left.
 
 The following example shows how to use concatenation (string addition) and a for loop to generate an abecedarian series (that is, in alphabetical order). In Robert McCloskey's book *Make Way for Ducklings*, the names of the ducklings are Jack, Kack, Lack, Mack, Nack, Ouack, Pack, and Quack. This loop outputs these names in order:
 
-| fruit | ' |  | b a n | a |  | n | a ' |
-| --- | --- | --- | --- | --- | --- | --- | --- |
-|  | index | 0 | 1 2 3 |  | 4 | 5 | 6 |
+![](_page_94_Figure_1.jpeg)
 
-Figure 8.1: Slice indices.
+Figure 8.1: Slice indices.
 
 ```
 prefixes = 'JKLMNOPQ'
 suffix = 'ack'
-```
-
-```
 for letter in prefixes:
     print letter + suffix
-```
 The output is:
-
-Jack Kack Lack Mack Nack Oack Pack Qack
-
-Of course, that's not quite right because "Ouack" and "Quack" are misspelled. **Exercise 8.2.** *Modify the program to fix this error.*
-
-### **8.4 String slices**
+Jack
+Kack
+Lack
+Mack
+Nack
+Oack
+Pack
+Qack
+Of course, that's not quite right because "Ouack" and "Quack" are misspelled.
+Exercise 8.2. Modify the program to fix this error.
+```
+#### **8.4 String slices**
 
 A segment of a string is called a **slice**. Selecting a slice is similar to selecting a character:
 
@@ -3056,7 +3149,7 @@ Monty
 >>> print s[6:12]
 Python
 ```
-The operator [n:m] returns the part of the string from the "n-eth" character to the "m-eth" character, including the first but excluding the last. This behavior is counterintuitive, but it might help to imagine the indices pointing *between* the characters, as in Figure 8.1.
+The operator [n:m] returns the part of the string from the "n-eth" character to the "m-eth" character, including the first but excluding the last. This behavior is counterintuitive, but it might help to imagine the indices pointing *between* the characters, as in Figure [8.1.](#page-94-1)
 
 If you omit the first index (before the colon), the slice starts at the beginning of the string. If you omit the second index, the slice goes to the end of the string:
 
@@ -3078,23 +3171,30 @@ An empty string contains no characters and has length 0, but other than that, it
 
 **Exercise 8.3.** *Given that* fruit *is a string, what does* fruit[:] *mean?*
 
-### **8.5 Strings are immutable**
+### **8.5 Strings are immutable**
 
 It is tempting to use the [] operator on the left side of an assignment, with the intention of changing a character in a string. For example:
 
->>> greeting = 'Hello, world!' >>> greeting[0] = 'J'
-
+```
+>>> greeting = 'Hello, world!'
+>>> greeting[0] = 'J'
 TypeError: 'str' object does not support item assignment
+```
+The "object" in this case is the string and the "item" is the character you tried to assign. For
 
-The "object" in this case is the string and the "item" is the character you tried to assign. For now, an **object** is the same thing as a value, but we will refine that definition later. An **item** is one of the values in a sequence.
+now, an **object** is the same thing as a value, but we will refine that definition later. An **item** is one of the values in a sequence.
 
 The reason for the error is that strings are **immutable**, which means you can't change an existing string. The best you can do is create a new string that is a variation on the original:
 
->>> greeting = 'Hello, world!' >>> new_greeting = 'J' + greeting[1:] >>> print new_greeting Jello, world!
-
+```
+>>> greeting = 'Hello, world!'
+>>> new_greeting = 'J' + greeting[1:]
+>>> print new_greeting
+Jello, world!
+```
 This example concatenates a new first letter onto a slice of greeting. It has no effect on the original string.
 
-### **8.6 Searching**
+#### **8.6 Searching**
 
 What does the following function do?
 
@@ -3117,7 +3217,7 @@ This pattern of computation—traversing a sequence and returning when we find w
 
 **Exercise 8.4.** *Modify* find *so that it has a third parameter, the index in* word *where it should start looking.*
 
-### **8.7 Looping and counting**
+#### **8.7 Looping and counting**
 
 The following program counts the number of times the letter a appears in a string:
 
@@ -3135,7 +3235,7 @@ This program demonstrates another pattern of computation called a **counter**. T
 
 **Exercise 8.6.** *Rewrite this function so that instead of traversing the string, it uses the threeparameter version of* find *from the previous section.*
 
-### **8.8 String methods**
+#### **8.8 String methods**
 
 A **method** is similar to a function—it takes arguments and returns a value—but the syntax is different. For example, the method upper takes a string and returns a new string with all uppercase letters:
 
@@ -3167,15 +3267,26 @@ Actually, the find method is more general than our function; it can find substri
 >>> word.find('na')
 2
 ```
-It can take as a second argument the index where it should start: >>> word.find('na', 3) 4 And as a third argument the index where it should stop: >>> name = 'bob' >>> name.find('b', 1, 2) -1
+It can take as a second argument the index where it should start:
+
+```
+>>> word.find('na', 3)
+4
+```
+And as a third argument the index where it should stop:
 
-This search fails because b does not appear in the index range from 1 to 2 (not including 2). **Exercise 8.7.** *There is a string method called* count *that is similar to the function in the previous exercise. Read the documentation of this method and write an invocation that counts the number of* a*s in* 'banana'.
+```
+>>> name = 'bob'
+>>> name.find('b', 1, 2)
+-1
+```
+This search fails because b does not appear in the index range from 1 to 2 (not including 2). **Exercise 8.7.** *There is a string method called* count *that is similar to the function in the previous exercise. Read the documentation of this method and write an invocation that counts the number of* a*s in* 'banana'*.*
 
-**Exercise 8.8.** *Read the documentation of the string methods at* http: // docs. python. org/ 2/ library/ stdtypes. html# string-methods *. You might want to experiment with some of them to make sure you understand how they work.* strip and replace *are particularly useful.*
+**Exercise 8.8.** *Read the documentation of the string methods at* [http:](http://docs.python.org/2/library/stdtypes.html#string-methods) [//](http://docs.python.org/2/library/stdtypes.html#string-methods) [docs.](http://docs.python.org/2/library/stdtypes.html#string-methods) [python.](http://docs.python.org/2/library/stdtypes.html#string-methods) [org/](http://docs.python.org/2/library/stdtypes.html#string-methods) [2/](http://docs.python.org/2/library/stdtypes.html#string-methods) [library/](http://docs.python.org/2/library/stdtypes.html#string-methods) [stdtypes.](http://docs.python.org/2/library/stdtypes.html#string-methods) [html#](http://docs.python.org/2/library/stdtypes.html#string-methods) [string-methods](http://docs.python.org/2/library/stdtypes.html#string-methods) *. You might want to experiment with some of them to make sure you understand how they work.* strip *and* replace *are particularly useful.*
 
 *The documentation uses a syntax that might be confusing. For example, in* find(sub[, start[, end]])*, the brackets indicate optional arguments. So* sub *is required, but* start *is optional, and if you include* start*, then* end *is optional.*
 
-### **8.9 The** in **operator**
+## **8.9 The** in **operator**
 
 The word in is a boolean operator that takes two strings and returns True if the first appears as a substring in the second:
 
@@ -3183,13 +3294,16 @@ The word in is a boolean operator that takes two strings and returns True if the
 >>> 'a' in 'banana'
 True
 >>> 'seed' in 'banana'
+```
 False
-For example, the following function prints all the letters from word1 that also appear in
-word2:
+
+For example, the following function prints all the letters from word1 that also appear in word2:
+
+```
 def in_both(word1, word2):
     for letter in word1:
         if letter in word2:
-             print letter
+            print letter
 ```
 With well-chosen variable names, Python sometimes reads like English. You could read this loop, "for (each) letter in (the first) word, if (the) letter (appears) in (the second) word, print (the) letter."
 
@@ -3201,14 +3315,12 @@ a
 e
 s
 ```
-### **8.10 String comparison**
+#### **8.10 String comparison**
 
 The relational operators work on strings. To see if two strings are equal:
 
-```
-if word == 'banana':
-    print 'All right, bananas.'
-```
+if word == 'banana': print 'All right, bananas.'
+
 Other relational operations are useful for putting words in alphabetical order:
 
 ```
@@ -3225,7 +3337,7 @@ Your word, Pineapple, comes before banana.
 
 A common way to address this problem is to convert strings to a standard format, such as all lowercase, before performing the comparison. Keep that in mind in case you have to defend yourself against a man armed with a Pineapple.
 
-### **8.11 Debugging**
+#### **8.11 Debugging**
 
 When you use indices to traverse the values in a sequence, it is tricky to get the beginning and end of the traversal right. Here is a function that is supposed to compare two words and return True if one of the words is the reverse of the other, but it contains two errors:
 
@@ -3240,25 +3352,25 @@ def is_reverse(word1, word2):
             return False
         i = i+1
         j = j-1
+    return True
 ```
-return True
-
-The first if statement checks whether the words are the same length. If not, we can return False immediately and then, for the rest of the function, we can assume that the words are the same length. This is an example of the guardian pattern in Section 6.8.
+The first if statement checks whether the words are the same length. If not, we can return False immediately and then, for the rest of the function, we can assume that the words are the same length. This is an example of the guardian pattern in Section [6.8.](#page-79-0)
 
 i and j are indices: i traverses word1 forward while j traverses word2 backward. If we find two letters that don't match, we can return False immediately. If we get through the whole loop and all the letters match, we return True.
 
 If we test this function with the words "pots" and "stop", we expect the return value True, but we get an IndexError:
 
-```
->>> is_reverse('pots', 'stop')
-...
-```
-![](_page_99_Figure_1.jpeg)
+>>> is_reverse('pots', 'stop') ...
 
-Figure 8.2: State diagram.
+![](_page_99_Figure_178.jpeg)
 
-File "reverse.py", line 15, in is_reverse if word1[i] != word2[j]: IndexError: string index out of range
+Figure 8.2: State diagram.
 
+```
+File "reverse.py", line 15, in is_reverse
+    if word1[i] != word2[j]:
+IndexError: string index out of range
+```
 For debugging this kind of error, my first move is to print the values of the indices immediately before the line where the error appears.
 
 ```
@@ -3271,8 +3383,12 @@ while j > 0:
 ```
 Now when I run the program again, I get more information:
 
->>> is_reverse('pots', 'stop') 0 4 ... IndexError: string index out of range
-
+```
+>>> is_reverse('pots', 'stop')
+0 4
+...
+IndexError: string index out of range
+```
 The first time through the loop, the value of j is 4, which is out of range for the string 'pots'. The index of the last character is 3, so the initial value for j should be len(word2)-1.
 
 If I fix that error and run the program again, I get:
@@ -3284,16 +3400,17 @@ If I fix that error and run the program again, I get:
 2 1
 True
 ```
-This time we get the right answer, but it looks like the loop only ran three times, which is suspicious. To get a better idea of what is happening, it is useful to draw a state diagram. During the first iteration, the frame for is_reverse is shows in Figure 8.2.
+This time we get the right answer, but it looks like the loop only ran three times, which is suspicious. To get a better idea of what is happening, it is useful to draw a state diagram. During the first iteration, the frame for is_reverse is shows in Figure [8.2.](#page-99-1)
 
-I took a little license by arranging the variables in the frame and adding dotted lines to show that the values of i and j indicate characters in word1 and word2. **Exercise 8.9.** *Starting with this diagram, execute the program on paper, changing the values of* i and j *during each iteration. Find and fix the second error in this function.*
+I took a little license by arranging the variables in the frame and adding dotted lines to show that the values of i and j indicate characters in word1 and word2.
 
-### **8.12 Glossary**
+**Exercise 8.9.** *Starting with this diagram, execute the program on paper, changing the values of* i *and* j *during each iteration. Find and fix the second error in this function.*
 
-**object:** Something a variable can refer to. For now, you can use "object" and "value" interchangeably.
+#### **8.12 Glossary**
 
-**sequence:** An ordered set; that is, a set of values where each value is identified by an integer index.
+**object:** Something a variable can refer to. For now, you can use "object" and "value" interchangeably.
 
+- **sequence:** An ordered set; that is, a set of values where each value is identified by an integer index.
 **item:** One of the values in a sequence.
 
 **index:** An integer value used to select an item in a sequence, such as a character in a string.
@@ -3301,16 +3418,17 @@ I took a little license by arranging the variables in the frame and adding dotte
 **slice:** A part of a string specified by a range of indices.
 
 - **empty string:** A string with no characters and length 0, represented by two quotation marks.
-- **immutable:** The property of a sequence whose items cannot be assigned.
+**immutable:** The property of a sequence whose items cannot be assigned.
+
 - **traverse:** To iterate through the items in a sequence, performing a similar operation on each.
-- **search:** A pattern of traversal that stops when it finds what it is looking for.
-- **counter:** A variable used to count something, usually initialized to zero and then incremented.
+**search:** A pattern of traversal that stops when it finds what it is looking for.
 
+- **counter:** A variable used to count something, usually initialized to zero and then incremented.
 **method:** A function that is associated with an object and called using dot notation.
 
 **invocation:** A statement that calls a method.
 
-### **8.13 Exercises**
+#### **8.13 Exercises**
 
 **Exercise 8.10.** *A string slice can take a third index that specifies the "step size;" that is, the number of spaces between successive characters. A step size of 2 means every other character; 3 means every third, etc.*
 
@@ -3321,9 +3439,9 @@ I took a little license by arranging the variables in the frame and adding dotte
 ```
 *A step size of -1 goes through the word backwards, so the slice* [::-1] *generates a reversed string.*
 
-*Use this idiom to write a one-line version of* is_palindrome *from Exercise 6.6.*
+*Use this idiom to write a one-line version of* is_palindrome *from Exercise [6.6.](#page-82-0)* **Exercise 8.11.** *The following functions are all* intended *to check whether a string contains any*
 
-**Exercise 8.11.** *The following functions are all* intended *to check whether a string contains any lowercase letters, but at least some of them are wrong. For each function, describe what the function actually does (assuming that the parameter is a string).*
+*lowercase letters, but at least some of them are wrong. For each function, describe what the function actually does (assuming that the parameter is a string).*
 
 ```
 def any_lowercase1(s):
@@ -3356,78 +3474,77 @@ def any_lowercase5(s):
             return False
     return True
 ```
-**Exercise 8.12.** *ROT13 is a weak form of encryption that involves "rotating" each letter in a word by 13 places. To rotate a letter means to shift it through the alphabet, wrapping around to the beginning if necessary, so 'A' shifted by 3 is 'D' and 'Z' shifted by 1 is 'A'.*
+**Exercise 8.12.** *ROT13 is a weak form of encryption that involves "rotating" each letter in a word by 13 places. To rotate a letter means to shift it through the alphabet, wrapping around to the beginning if necessary, so 'A' shifted by 3 is 'D' and 'Z' shifted by 1 is 'A'.*
 
 *Write a function called* rotate_word *that takes a string and an integer as parameters, and that returns a new string that contains the letters from the original string "rotated" by the given amount.*
 
 *For example, "cheer" rotated by 7 is "jolly" and "melon" rotated by -10 is "cubed".*
 
-*You might want to use the built-in functions* ord*, which converts a character to a numeric code,* and chr*, which converts numeric codes to characters.*
+*You might want to use the built-in functions* ord*, which converts a character to a numeric code, and* chr*, which converts numeric codes to characters.*
 
-*Potentially offensive jokes on the Internet are sometimes encoded in ROT13. If you are not easily offended, find and decode some of them. Solution:* http: // thinkpython. com/ code/ rotate. py .
+*Potentially offensive jokes on the Internet are sometimes encoded in ROT13. If you are not easily offended, find and decode some of them. Solution:* [http:](http://thinkpython.com/code/rotate.py) [//](http://thinkpython.com/code/rotate.py) [thinkpython.](http://thinkpython.com/code/rotate.py) [com/](http://thinkpython.com/code/rotate.py) [code/](http://thinkpython.com/code/rotate.py) [rotate.](http://thinkpython.com/code/rotate.py) [py](http://thinkpython.com/code/rotate.py) *.*
 
-### **Chapter 9**
+## **Chapter 9**
 
-## **Case study: word play**
+# **Case study: word play**
 
-### **9.1 Reading word lists**
+#### **9.1 Reading word lists**
 
-For the exercises in this chapter we need a list of English words. There are lots of word lists available on the Web, but the one most suitable for our purpose is one of the word lists collected and contributed to the public domain by Grady Ward as part of the Moby lexicon project (see http://wikipedia.org/wiki/Moby_Project). It is a list of 113,809 official crosswords; that is, words that are considered valid in crossword puzzles and other word games. In the Moby collection, the filename is 113809of.fic; you can download a copy, with the simpler name words.txt, from http://thinkpython.com/code/words.txt.
+For the exercises in this chapter we need a list of English words. There are lots of word lists available on the Web, but the one most suitable for our purpose is one of the word lists collected and contributed to the public domain by Grady Ward as part of the Moby lexicon project (see ). It is a list of 113,809 official crosswords; that is, words that are considered valid in crossword puzzles and other word games. In the Moby collection, the filename is 113809of.fic; you can download a copy, with the simpler name words.txt, from .
 
 This file is in plain text, so you can open it with a text editor, but you can also read it from Python. The built-in function open takes the name of the file as a parameter and returns a **file object** you can use to read the file.
 
+```
 >>> fin = open('words.txt')
-
 >>> print fin
-
 
-
+```
 fin is a common name for a file object used for input. Mode 'r' indicates that this file is open for reading (as opposed to 'w' for writing).
 
 The file object provides several methods for reading, including readline, which reads characters from the file until it gets to a newline and returns the result as a string:
 
 ```
 >>> fin.readline()
-```
 'aa\r\n'
-
+```
 The first word in this particular list is "aa," which is a kind of lava. The sequence \r\n represents two whitespace characters, a carriage return and a newline, that separate this word from the next.
 
 The file object keeps track of where it is in the file, so if you call readline again, you get the next word:
 
 ```
 >>> fin.readline()
-```
 'aah\r\n'
-
+```
 The next word is "aah," which is a perfectly legitimate word, so stop looking at me like that. Or, if it's the whitespace that's bothering you, we can get rid of it with the string method strip:
 
 ```
 >>> line = fin.readline()
 >>> word = line.strip()
 >>> print word
+```
 aahed
-You can also use a file object as part of a for loop. This program reads words.txt and
-prints each word, one per line:
+
+You can also use a file object as part of a for loop. This program reads words.txt and prints each word, one per line:
+
+```
 fin = open('words.txt')
 for line in fin:
     word = line.strip()
     print word
-Exercise 9.1. Write a program that reads words.txt and prints only the words with more than 20
 ```
-#### *characters (not counting whitespace).*
+**Exercise 9.1.** *Write a program that reads* words.txt *and prints only the words with more than 20 characters (not counting whitespace).*
 
-### **9.2 Exercises**
+## **9.2 Exercises**
 
 There are solutions to these exercises in the next section. You should at least attempt each one before you read the solutions.
 
-**Exercise 9.2.** *In 1939 Ernest Vincent Wright published a 50,000 word novel called* Gadsby *that does not contain the letter "e." Since "e" is the most common letter in English, that's not easy to* do.
+**Exercise 9.2.** *In 1939 Ernest Vincent Wright published a 50,000 word novel called* Gadsby *that does not contain the letter "e." Since "e" is the most common letter in English, that's not easy to do.*
 
 *In fact, it is difficult to construct a solitary thought without using that most common symbol. It is slow going at first, but with caution and hours of training you can gradually gain facility.*
 
 *All right, I'll stop now.*
 
-*Write a function called* has_no_e *that returns* True *if the given word doesn't have the letter "e" in* it.
+*Write a function called* has_no_e *that returns* True *if the given word doesn't have the letter "e" in it.*
 
 *Modify your program from the previous section to print only the words that have no "e" and compute the percentage of the words in the list have no "e."*
 
@@ -3437,13 +3554,15 @@ There are solutions to these exercises in the next section. You should at least
 
 **Exercise 9.4.** *Write a function named* uses_only *that takes a word and a string of letters, and that returns* True *if the word contains only letters in the list. Can you make a sentence using only the letters* acefhlo*? Other than "Hoe alfalfa?"*
 
-**Exercise 9.5.** *Write a function named* uses_all *that takes a word and a string of required letters, and that returns* True *if the word uses all the required letters at least once. How many words are there that use all the vowels* aeiou*? How about* aeiouy?
+**Exercise 9.5.** *Write a function named* uses_all *that takes a word and a string of required letters, and that returns* True *if the word uses all the required letters at least once. How many words are there that use all the vowels* aeiou*? How about* aeiouy*?*
 
 **Exercise 9.6.** *Write a function called* is_abecedarian *that returns* True *if the letters in a word appear in alphabetical order (double letters are ok). How many abecedarian words are there?*
 
-### **9.3 Search**
+### **9.3 Search**
+
+All of the exercises in the previous section have something in common; they can be solved with the search pattern we saw in Section [8.6.](#page-95-1) The simplest example is:
 
-All of the exercises in the previous section have something in common; they can be solved with the search pattern we saw in Section 8.6. The simplest example is:
+82
 
 ```
 def has_no_e(word):
@@ -3495,7 +3614,7 @@ def uses_all(word, required):
 ```
 This is an example of a program development method called **problem recognition**, which means that you recognize the problem you are working on as an instance of a previouslysolved problem, and apply a previously-developed solution.
 
-### **9.4 Looping with indices**
+#### **9.4 Looping with indices**
 
 I wrote the functions in the previous section with for loops because I only needed the characters in the strings; I didn't have to do anything with the indices.
 
@@ -3506,32 +3625,38 @@ def is_abecedarian(word):
     previous = word[0]
     for c in word:
         if c < previous:
-             return False
+            return False
         previous = c
     return True
+```
 An alternative is to use recursion:
+
+```
 def is_abecedarian(word):
     if len(word) <= 1:
         return True
     if word[0] > word[1]:
         return False
     return is_abecedarian(word[1:])
+```
 Another option is to use a while loop:
+
+```
 def is_abecedarian(word):
     i = 0
     while i < len(word)-1:
         if word[i+1] < word[i]:
-             return False
+            return False
         i = i+1
     return True
 ```
-The loop starts at i=0 and ends when i=len(word)-1. Each time through the loop, it compares the ith character (which you can think of as the current character) to the i + 1th character (which you can think of as the next).
+The loop starts at i=0 and ends when i=len(word)-1. Each time through the loop, it compares the *i*th character (which you can think of as the current character) to the *i* + 1th character (which you can think of as the next).
 
 If the next character is less than (alphabetically before) the current one, then we have discovered a break in the abecedarian trend, and we return False.
 
 If we get to the end of the loop without finding a fault, then the word passes the test. To convince yourself that the loop ends correctly, consider an example like 'flossy'. The length of the word is 6, so the last time the loop runs is when i is 4, which is the index of the second-to-last character. On the last iteration, it compares the second-to-last character to the last, which is what we want.
 
-Here is a version of is_palindrome (see Exercise 6.6) that uses two indices; one starts at the beginning and goes up; the other starts at the end and goes down.
+Here is a version of is_palindrome (see Exercise [6.6)](#page-82-0) that uses two indices; one starts at the beginning and goes up; the other starts at the end and goes down.
 
 ```
 def is_palindrome(word):
@@ -3550,9 +3675,9 @@ Or, if you noticed that this is an instance of a previously-solved problem, you
 def is_palindrome(word):
     return is_reverse(word, word)
 ```
-Assuming you did Exercise 8.9.
+Assuming you did Exercise [8.9.](#page-99-2)
 
-### **9.5 Debugging**
+#### **9.5 Debugging**
 
 Testing programs is hard. The functions in this chapter are relatively easy to test because you can check the results by hand. Even so, it is somewhere between difficult and impossible to choose a set of words that test for all possible errors.
 
@@ -3570,20 +3695,20 @@ Program testing can be used to show the presence of bugs, but never to show thei
 
 — Edsger W. Dijkstra
 
-### **9.6 Glossary**
+#### **9.6 Glossary**
 
 **file object:** A value that represents an open file.
 
 - **problem recognition:** A way of solving a problem by expressing it as an instance of a previously-solved problem.
 - **special case:** A test case that is atypical or non-obvious (and less likely to be handled correctly).
 
-### **9.7 Exercises**
+#### **9.7 Exercises**
 
-**Exercise 9.7.** *This question is based on a Puzzler that was broadcast on the radio program* Car Talk (http: // www. cartalk. com/ content/ puzzlers ):
+**Exercise 9.7.** *This question is based on a Puzzler that was broadcast on the radio program* Car Talk *(*[http:](http://www.cartalk.com/content/puzzlers) [//](http://www.cartalk.com/content/puzzlers) [www.](http://www.cartalk.com/content/puzzlers) [cartalk.](http://www.cartalk.com/content/puzzlers) [com/](http://www.cartalk.com/content/puzzlers) [content/](http://www.cartalk.com/content/puzzlers) [puzzlers](http://www.cartalk.com/content/puzzlers) *):*
 
 > *Give me a word with three consecutive double letters. I'll give you a couple of words that almost qualify, but don't. For example, the word committee, c-o-m-m-i-t-t-e-e. It would be great except for the 'i' that sneaks in there. Or Mississippi: M-i-s-s-i-s-s-ip-p-i. If you could take out those i's it would work. But there is a word that has three consecutive pairs of letters and to the best of my knowledge this may be the only word. Of course there are probably 500 more but I can only think of one. What is the word?*
 
-*Write a program to find it. Solution:* http: // thinkpython. com/ code/ cartalk1. py . **Exercise 9.8.** *Here's another* Car Talk *Puzzler (*http: // www. cartalk. com/ content/ puzzlers ):
+*Write a program to find it. Solution:* [http:](http://thinkpython.com/code/cartalk1.py) [//](http://thinkpython.com/code/cartalk1.py) [thinkpython.](http://thinkpython.com/code/cartalk1.py) [com/](http://thinkpython.com/code/cartalk1.py) [code/](http://thinkpython.com/code/cartalk1.py) [cartalk1.](http://thinkpython.com/code/cartalk1.py) [py](http://thinkpython.com/code/cartalk1.py) *.* **Exercise 9.8.** *Here's another* Car Talk *Puzzler (*[http:](http://www.cartalk.com/content/puzzlers) [//](http://www.cartalk.com/content/puzzlers) [www.](http://www.cartalk.com/content/puzzlers) [cartalk.](http://www.cartalk.com/content/puzzlers) [com/](http://www.cartalk.com/content/puzzlers) [content/](http://www.cartalk.com/content/puzzlers) [puzzlers](http://www.cartalk.com/content/puzzlers) *):*
 
 > *"I was driving on the highway the other day and I happened to notice my odometer. Like most odometers, it shows six digits, in whole miles only. So, if my car had 300,000 miles, for example, I'd see 3-0-0-0-0-0.*
 
@@ -3593,7 +3718,7 @@ Program testing can be used to show the presence of bugs, but never to show thei
 
 *"The question is, what was on the odometer when I first looked?"*
 
-*Write a Python program that tests all the six-digit numbers and prints any numbers that satisfy these requirements. Solution:* http: // thinkpython. com/ code/ cartalk2. py . **Exercise 9.9.** *Here's another* Car Talk *Puzzler you can solve with a search (*http: // www. cartalk. com/ content/ puzzlers ):
+*Write a Python program that tests all the six-digit numbers and prints any numbers that satisfy these requirements. Solution:* [http:](http://thinkpython.com/code/cartalk2.py) [//](http://thinkpython.com/code/cartalk2.py) [thinkpython.](http://thinkpython.com/code/cartalk2.py) [com/](http://thinkpython.com/code/cartalk2.py) [code/](http://thinkpython.com/code/cartalk2.py) [cartalk2.](http://thinkpython.com/code/cartalk2.py) [py](http://thinkpython.com/code/cartalk2.py) *.* **Exercise 9.9.** *Here's another* Car Talk *Puzzler you can solve with a search (*[http:](http://www.cartalk.com/content/puzzlers) [//](http://www.cartalk.com/content/puzzlers) [www.](http://www.cartalk.com/content/puzzlers) [cartalk.](http://www.cartalk.com/content/puzzlers) [com/](http://www.cartalk.com/content/puzzlers) [content/](http://www.cartalk.com/content/puzzlers) [puzzlers](http://www.cartalk.com/content/puzzlers) *):*
 
 > *"Recently I had a visit with my mom and we realized that the two digits that make up my age when reversed resulted in her age. For example, if she's 73, I'm 37. We wondered how often this has happened over the years but we got sidetracked with other topics and we never came up with an answer.*
 
@@ -3601,22 +3726,22 @@ Program testing can be used to show the presence of bugs, but never to show thei
 
 *Write a Python program that searches for solutions to this Puzzler. Hint: you might find the string method* zfill *useful.*
 
-*Solution:* http: // thinkpython. com/ code/ cartalk3. py .
+*Solution:* [http:](http://thinkpython.com/code/cartalk3.py) [//](http://thinkpython.com/code/cartalk3.py) [thinkpython.](http://thinkpython.com/code/cartalk3.py) [com/](http://thinkpython.com/code/cartalk3.py) [code/](http://thinkpython.com/code/cartalk3.py) [cartalk3.](http://thinkpython.com/code/cartalk3.py) [py](http://thinkpython.com/code/cartalk3.py) *.*
 
-### **Chapter 10**
+## **Chapter 10**
 
 # **Lists**
 
-### **10.1 A list is a sequence**
+#### **10.1 A list is a sequence**
 
 Like a string, a **list** is a sequence of values. In a string, the values are characters; in a list, they can be any type. The values in a list are called **elements** or sometimes **items**.
 
 There are several ways to create a new list; the simplest is to enclose the elements in square brackets ([ and ]):
 
-```
 [10, 20, 30, 40]
+
 ['crunchy frog', 'ram bladder', 'lark vomit']
-```
+
 The first example is a list of four integers. The second is a list of three strings. The elements of a list don't have to be the same type. The following list contains a string, a float, an integer, and (lo!) another list:
 
 ['spam', 2.0, 5, [10, 20]]
@@ -3634,7 +3759,7 @@ As you might expect, you can assign list values to variables:
 >>> print cheeses, numbers, empty
 ['Cheddar', 'Edam', 'Gouda'] [17, 123] []
 ```
-### **10.2 Lists are mutable**
+#### **10.2 Lists are mutable**
 
 The syntax for accessing the elements of a list is the same as for accessing the characters of a string—the bracket operator. The expression inside the brackets specifies the index. Remember that the indices start at 0:
 
@@ -3644,7 +3769,7 @@ Cheddar
 ```
 ![](_page_109_Figure_1.jpeg)
 
-Figure 10.1: State diagram.
+Figure 10.1: State diagram.
 
 Unlike strings, lists are mutable. When the bracket operator appears on the left side of an assignment, it identifies the element of the list that will be assigned.
 
@@ -3656,7 +3781,7 @@ Unlike strings, lists are mutable. When the bracket operator appears on the left
 ```
 The one-eth element of numbers, which used to be 123, is now 5.
 
-You can think of a list as a relationship between indices and elements. This relationship is called a **mapping**; each index "maps to" one of the elements. Figure 10.1 shows the state diagram for cheeses, numbers and empty:
+You can think of a list as a relationship between indices and elements. This relationship is called a **mapping**; each index "maps to" one of the elements. Figure [10.1](#page-109-0) shows the state diagram for cheeses, numbers and empty:
 
 Lists are represented by boxes with the word "list" outside and the elements of the list inside. cheeses refers to a list with three elements indexed 0, 1 and 2. numbers contains two elements; the diagram shows that the value of the second element has been reassigned from 123 to 5. empty refers to a list with no elements.
 
@@ -3675,7 +3800,7 @@ True
 >>> 'Brie' in cheeses
 False
 ```
-### **10.3 Traversing a list**
+#### **10.3 Traversing a list**
 
 The most common way to traverse the elements of a list is with a for loop. The syntax is the same as for strings:
 
@@ -3687,7 +3812,7 @@ This works well if you only need to read the elements of the list. But if you wa
 for i in range(len(numbers)):
     numbers[i] = numbers[i] * 2
 ```
-This loop traverses the list and updates each element. len returns the number of elements in the list. range returns a list of indices from 0 to n − 1, where n is the length of the list. Each time through the loop i gets the index of the next element. The assignment statement in the body uses i to read the old value of the element and to assign the new value.
+This loop traverses the list and updates each element. len returns the number of elements in the list. range returns a list of indices from 0 to *n* − 1, where *n* is the length of the list. Each time through the loop i gets the index of the next element. The assignment statement in the body uses i to read the old value of the element and to assign the new value.
 
 A for loop over an empty list never executes the body:
 
@@ -3700,7 +3825,7 @@ Although a list can contain another list, the nested list still counts as a sing
 ```
 ['spam', 1, ['Brie', 'Roquefort', 'Pol le Veq'], [1, 2, 3]]
 ```
-### **10.4 List operations**
+#### **10.4 List operations**
 
 The + operator concatenates lists:
 
@@ -3713,15 +3838,11 @@ The + operator concatenates lists:
 ```
 Similarly, the * operator repeats a list a given number of times:
 
-```
->>> [0] * 4
-[0, 0, 0, 0]
->>> [1, 2, 3] * 3
-[1, 2, 3, 1, 2, 3, 1, 2, 3]
-```
+>>> [0] * 4 [0, 0, 0, 0] >>> [1, 2, 3] * 3 [1, 2, 3, 1, 2, 3, 1, 2, 3]
+
 The first example repeats [0] four times. The second example repeats the list [1, 2, 3] three times.
 
-### **10.5 List slices**
+#### **10.5 List slices**
 
 The slice operator also works on lists:
 
@@ -3750,7 +3871,7 @@ A slice operator on the left side of an assignment can update multiple elements:
 >>> print t
 ['a', 'x', 'y', 'd', 'e', 'f']
 ```
-### **10.6 List methods**
+#### **10.6 List methods**
 
 Python provides methods that operate on lists. For example, append adds a new element to the end of a list:
 
@@ -3781,12 +3902,17 @@ sort arranges the elements of the list from low to high:
 ```
 List methods are all void; they modify the list and return None. If you accidentally write t = t.sort(), you will be disappointed with the result.
 
-### **10.7 Map, filter and reduce**
+#### **10.7 Map, filter and reduce**
 
 To add up all the numbers in a list, you can use a loop like this:
 
-def add_all(t): total = 0 for x in t: total += x return total
-
+```
+def add_all(t):
+    total = 0
+    for x in t:
+        total += x
+    return total
+```
 total is initialized to 0. Each time through the loop, x gets one element from the list. The += operator provides a short way to update a variable. This **augmented assignment statement**:
 
 total += x
@@ -3799,25 +3925,37 @@ As the loop executes, total accumulates the sum of the elements; a variable used
 
 Adding up the elements of a list is such a common operation that Python provides it as a built-in function, sum:
 
->>> t = [1, 2, 3] >>> sum(t) 6
-
+```
+>>> t = [1, 2, 3]
+>>> sum(t)
+6
+```
 An operation like this that combines a sequence of elements into a single value is sometimes called **reduce**.
 
 **Exercise 10.1.** *Write a function called* nested_sum *that takes a nested list of integers and add up the elements from all of the nested lists.*
 
 Sometimes you want to traverse one list while building another. For example, the following function takes a list of strings and returns a new list that contains capitalized strings:
 
-def capitalize_all(t): res = [] for s in t: res.append(s.capitalize()) return res
-
+```
+def capitalize_all(t):
+    res = []
+    for s in t:
+        res.append(s.capitalize())
+    return res
+```
 res is initialized with an empty list; each time through the loop, we append the next element. So res is another kind of accumulator.
 
-An operation like capitalize_all is sometimes called a map because it "maps" a function (in this case the method capitalize) onto each of the elements in a sequence.
+An operation like capitalize_all is sometimes called a **map** because it "maps" a function (in this case the method capitalize) onto each of the elements in a sequence.
 
-**Exercise 10.2.** Use capitalize_all *to write a function named* capitalize_nested *that takes a nested list of strings and returns a new nested list with all strings capitalized.*
+**Exercise 10.2.** *Use* capitalize_all *to write a function named* capitalize_nested *that takes a nested list of strings and returns a new nested list with all strings capitalized.*
 
 Another common operation is to select some of the elements from a list and return a sublist. For example, the following function takes a list of strings and returns a list that contains only the uppercase strings:
 
-def only_upper(t): res = [] for s in t:
+```
+def only_upper(t):
+    res = []
+    for s in t:
+```
 
 ```
 if s.isupper():
@@ -3828,11 +3966,11 @@ isupper is a string method that returns True if the string contains only upper c
 
 An operation like only_upper is called a **filter** because it selects some of the elements and filters out the others.
 
-Most common list operations can be expressed as a combination of map, filter and reduce. Because these operations are so common, Python provides language features to support them, including the built-in function map and an operator called a "list comprehension."
+Most common list operations can be expressed as a combination of map, filter and reduce. Because these operations are so common, Python provides language features to support them, including the built-in function map and an operator called a "list comprehension." **Exercise 10.3.** *Write a function that takes a list of numbers and returns the cumulative sum; that*
 
-**Exercise 10.3.** *Write a function that takes a list of numbers and returns the cumulative sum; that is, a new list where the ith element is the sum of the first i* + 1 *elements from the original list. For example, the cumulative sum of* [1, 2, 3] is [1, 3, 6].
+*is, a new list where the ith element is the sum of the first i* + 1 *elements from the original list. For example, the cumulative sum of* [1, 2, 3] *is* [1, 3, 6]*.*
 
-### **10.8 Deleting elements**
+#### **10.8 Deleting elements**
 
 There are several ways to delete elements from a list. If you know the index of the element you want, you can use pop:
 
@@ -3848,12 +3986,11 @@ pop modifies the list and returns the element that was removed. If you don't pro
 
 If you don't need the removed value, you can use the del operator:
 
-```
->>> t = ['a', 'b', 'c']
->>> del t[1]
->>> print t
-['a', 'c']
+>>> t = ['a', 'b', 'c'] >>> del t[1] >>> print t ['a', 'c']
+
 If you know the element you want to remove (but not the index), you can use remove:
+
+```
 >>> t = ['a', 'b', 'c']
 >>> t.remove('b')
 >>> print t
@@ -3861,18 +3998,19 @@ If you know the element you want to remove (but not the index), you can use remo
 ```
 The return value from remove is None.
 
-To remove more than one element, you can use del with a slice index: >>> t = ['a', 'b', 'c', 'd', 'e', 'f']
+To remove more than one element, you can use del with a slice index:
 
 ```
+>>> t = ['a', 'b', 'c', 'd', 'e', 'f']
 >>> del t[1:5]
 >>> print t
 ['a', 'f']
 ```
-As usual, the slice selects all the elements up to, but not including, the second index. **Exercise 10.4.** *Write a function called* middle *that takes a list and returns a new list that contains all but the first and last elements. So* middle([1,2,3,4]) *should return* [2,3].
+As usual, the slice selects all the elements up to, but not including, the second index. **Exercise 10.4.** *Write a function called* middle *that takes a list and returns a new list that contains all but the first and last elements. So* middle([1,2,3,4]) *should return* [2,3]*.*
 
-**Exercise 10.5.** *Write a function called* chop *that takes a list, modifies it by removing the first and last elements, and returns* None.
+**Exercise 10.5.** *Write a function called* chop *that takes a list, modifies it by removing the first and last elements, and returns* None*.*
 
-### **10.9 Lists and strings**
+#### **10.9 Lists and strings**
 
 A string is a sequence of characters and a list is a sequence of values, but a list of characters is not the same as a string. To convert from a string to a list of characters, you can use list:
 
@@ -3910,7 +4048,7 @@ join is the inverse of split. It takes a list of strings and concatenates the el
 ```
 In this case the delimiter is a space character, so join puts a space between words. To concatenate strings without spaces, you can use the empty string, '', as a delimiter.
 
-### **10.10 Objects and values**
+#### **10.10 Objects and values**
 
 If we execute these assignment statements:
 
@@ -3918,39 +4056,43 @@ If we execute these assignment statements:
 a = 'banana'
 b = 'banana'
 ```
-We know that a and b both refer to a string, but we don't know whether they refer to the *same* string. There are two possible states, shown in Figure 10.2.
+We know that a and b both refer to a string, but we don't know whether they refer to the *same* string. There are two possible states, shown in Figure [10.2.](#page-115-1)
 
 In one case, a and b refer to two different objects that have the same value. In the second case, they refer to the same object.
 
 To check whether two variables refer to the same object, you can use the is operator.
 
-| a | 'banana' | a | 'banana' |
-| --- | --- | --- | --- |
-| b | 'banana' | b |  |
+![](_page_115_Figure_1.jpeg)
 
 Figure 10.2: State diagram.
 
-| a | [ 1, 2, 3 ] |
-| --- | --- |
-| b | [ 1, 2, 3 ] |
+
 
-Figure 10.3: State diagram.
+| a | [1, 2, 3] |
+|---|-----------|
+| b | [1, 2, 3] |
 
->>> a = 'banana' >>> b = 'banana' >>> a is b True
+Figure 10.3: State diagram.
 
+```
+>>> a = 'banana'
+>>> b = 'banana'
+>>> a is b
+True
+```
 In this example, Python only created one string object, and both a and b refer to it.
 
 But when you create two lists, you get two objects:
 
 >>> a = [1, 2, 3] >>> b = [1, 2, 3] >>> a is b False
 
-So the state diagram looks like Figure 10.3.
+So the state diagram looks like Figure [10.3.](#page-115-2)
 
 In this case we would say that the two lists are **equivalent**, because they have the same elements, but not **identical**, because they are not the same object. If two objects are identical, they are also equivalent, but if they are equivalent, they are not necessarily identical.
 
 Until now, we have been using "object" and "value" interchangeably, but it is more precise to say that an object has a value. If you execute [1,2,3], you get a list object whose value is a sequence of integers. If another list has the same elements, we say it has the same value, but it is not the same object.
 
-### **10.11 Aliasing**
+#### **10.11 Aliasing**
 
 If a refers to an object and you assign b = a, then both variables refer to the same object:
 
@@ -3960,7 +4102,7 @@ If a refers to an object and you assign b = a, then both variables refer to the
 >>> b is a
 True
 ```
-The state diagram looks like Figure 10.4.
+The state diagram looks like Figure [10.4.](#page-116-1)
 
 The association of a variable with an object is called a **reference**. In this example, there are two references to the same object.
 
@@ -3968,13 +4110,13 @@ An object with more than one reference has more than one name, so we say that th
 
 If the aliased object is mutable, changes made with one alias affect the other:
 
-![](_page_116_Figure_1.jpeg)
+\(\begin{array}{c} a \\ b \end{array}\) \rightarrow [1, 2, 3]
 
 Figure 10.4: State diagram.
 
 ![](_page_116_Figure_3.jpeg)
 
-Figure 10.5: Stack diagram.
+Figure 10.5: Stack diagram.
 
 >>> b[0] = 17 >>> print a [17, 2, 3]
 
@@ -3982,13 +4124,11 @@ Although this behavior can be useful, it is error-prone. In general, it is safer
 
 For immutable objects like strings, aliasing is not as much of a problem. In this example:
 
-a = 'banana'
-
-b = 'banana'
+a = 'banana' b = 'banana'
 
 It almost never makes a difference whether a and b refer to the same string or not.
 
-### **10.12 List arguments**
+#### **10.12 List arguments**
 
 When you pass a list to a function, the function gets a reference to the list. If the function modifies a list parameter, the caller sees the change. For example, delete_head removes the first element from a list:
 
@@ -4004,7 +4144,7 @@ Here's how it is used:
 >>> print letters
 ['b', 'c']
 ```
-The parameter t and the variable letters are aliases for the same object. The stack diagram looks like Figure 10.5.
+The parameter t and the variable letters are aliases for the same object. The stack diagram looks like Figure [10.5.](#page-116-2)
 
 Since the list is shared by two frames, I drew it between them.
 
@@ -4019,26 +4159,20 @@ It is important to distinguish between operations that modify lists and operatio
 None
 >>> t3 = t1 + [4]
 >>> print t3
-```
-
-```
 [1, 2, 3, 4]
 ```
 This difference is important when you write functions that are supposed to modify lists. For example, this function *does not* delete the head of a list:
 
-```
-def bad_delete_head(t):
-```
-t = t[1:] # WRONG!
+def bad_delete_head(t): t = t[1:] # WRONG!
 
 The slice operator creates a new list and the assignment makes t refer to it, but none of that has any effect on the list that was passed as an argument.
 
 An alternative is to write a function that creates and returns a new list. For example, tail returns all but the first element of a list:
 
+```
 def tail(t):
-
-return t[1:]
-
+    return t[1:]
+```
 This function leaves the original list unmodified. Here's how it is used:
 
 ```
@@ -4047,7 +4181,7 @@ This function leaves the original list unmodified. Here's how it is used:
 >>> print rest
 ['b', 'c']
 ```
-### **10.13 Debugging**
+#### **10.13 Debugging**
 
 Careless use of lists (and other mutable objects) can lead to long hours of debugging. Here are some common pitfalls and ways to avoid them:
 
@@ -4062,7 +4196,7 @@ t = t.sort() # WRONG!
 
 Because sort returns None, the next operation you perform with t is likely to fail.
 
-Before using list methods and operators, you should read the documentation carefully and then test them in interactive mode. The methods and operators that lists share with other sequences (like strings) are documented at http://docs.python. org/2/library/stdtypes.html#typesseq. The methods and operators that only apply to mutable sequences are documented at http://docs.python.org/2/library/ stdtypes.html#typesseq-mutable.
+Before using list methods and operators, you should read the documentation carefully and then test them in interactive mode. The methods and operators that lists share with other sequences (like strings) are documented at [http://docs.python.](http://docs.python.org/2/library/stdtypes.html#typesseq) [org/2/library/stdtypes.html#typesseq](http://docs.python.org/2/library/stdtypes.html#typesseq). The methods and operators that only apply to mutable sequences are documented at [http://docs.python.org/2/library/](http://docs.python.org/2/library/stdtypes.html#typesseq-mutable) [stdtypes.html#typesseq-mutable](http://docs.python.org/2/library/stdtypes.html#typesseq-mutable).
 
 - 2. Pick an idiom and stick with it.
 Part of the problem with lists is that there are too many ways to do things. For example, to remove an element from a list, you can use pop, remove, del, or even a slice assignment.
@@ -4075,11 +4209,11 @@ t = t + [x]
 ```
 And these are wrong:
 
-| t.append([x]) | # WRONG! |
-| --- | --- |
-| t = t.append(x) | # WRONG! |
-| t + [x] | # WRONG! |
-| t = t + x | # WRONG! |
+| t.append([x])   |  | # WRONG! |
+|-----------------|--|----------|
+| t = t.append(x) |  | # WRONG! |
+| t + [x]         |  | # WRONG! |
+| t = t + x       |  | # WRONG! |
 
 Try out each of these examples in interactive mode to make sure you understand what they do. Notice that only the last one causes a runtime error; the other three are legal, but they do the wrong thing.
 
@@ -4090,7 +4224,7 @@ orig = t[:] t.sort()
 
 In this example you could also use the built-in function sorted, which returns a new, sorted list and leaves the original alone. But in that case you should avoid using sorted as a variable name!
 
-### **10.14 Glossary**
+#### **10.14 Glossary**
 
 **list:** A sequence of values.
 
@@ -4103,8 +4237,7 @@ In this example you could also use the built-in function sorted, which returns a
 **list traversal:** The sequential accessing of each element in a list.
 
 - **mapping:** A relationship in which each element of one set corresponds to an element of another set. For example, a list is a mapping from indices to elements.
-**accumulator:** A variable used in a loop to add up or accumulate a result.
-
+- **accumulator:** A variable used in a loop to add up or accumulate a result.
 - **augmented assignment:** A statement that updates the value of a variable using an operator like +=.
 - **reduce:** A processing pattern that traverses a sequence and accumulates the elements into a single result.
 - **map:** A processing pattern that traverses a sequence and performs an operation on each element.
@@ -4120,22 +4253,22 @@ In this example you could also use the built-in function sorted, which returns a
 
 **delimiter:** A character or string used to indicate where a string should be split.
 
-### **10.15 Exercises**
+#### **10.15 Exercises**
 
-**Exercise 10.6.** *Write a function called* is_sorted *that takes a list as a parameter and returns* True *if the list is sorted in ascending order and* False *otherwise. You can assume (as a precondition) that the elements of the list can be compared with the relational operators* <, >*, etc.*
+**Exercise 10.6.** *Write a function called* is_sorted *that takes a list as a parameter and returns* True *if the list is sorted in ascending order and* False *otherwise. You can assume (as a precondition) that the elements of the list can be compared with the relational operators* <*,* >*, etc.*
 
-*For example,* is_sorted([1,2,2]) *should return* True and is_sorted(['b','a']) *should return* False.
+*For example,* is_sorted([1,2,2]) *should return* True *and* is_sorted(['b','a']) *should return* False*.*
 
-**Exercise 10.7.** *Two words are anagrams if you can rearrange the letters from one to spell the other. Write a function called* is_anagram *that takes two strings and returns* True *if they are anagrams.* **Exercise 10.8.** *The (so-called) Birthday Paradox:*
+**Exercise 10.7.** *Two words are anagrams if you can rearrange the letters from one to spell the other. Write a function called* is_anagram *that takes two strings and returns* True *if they are anagrams.* **Exercise 10.8.** *The (so-called) Birthday Paradox:*
 
 - *1. Write a function called* has_duplicates *that takes a list and returns* True *if there is any element that appears more than once. It should not modify the original list.*
 - *2. If there are 23 students in your class, what are the chances that two of you have the same birthday? You can estimate this probability by generating random samples of 23 birthdays and checking for matches. Hint: you can generate random birthdays with the* randint *function in the* random *module.*
 
-*You can read about this problem at* http: // en. wikipedia. org/ wiki/ Birthday_ paradox , *and you can download my solution from* http: // thinkpython. com/ code/ birthday. py . **Exercise 10.9.** *Write a function called* remove_duplicates *that takes a list and returns a new list with only the unique elements from the original. Hint: they don't have to be in the same order.* **Exercise 10.10.** *Write a function that reads the file* words.txt *and builds a list with one element per word. Write two versions of this function, one using the* append *method and the other using the idiom* t = t + [x]*. Which one takes longer to run? Why?*
+*You can read about this problem at* [http:](http://en.wikipedia.org/wiki/Birthday_paradox) [//](http://en.wikipedia.org/wiki/Birthday_paradox) [en.](http://en.wikipedia.org/wiki/Birthday_paradox) [wikipedia.](http://en.wikipedia.org/wiki/Birthday_paradox) [org/](http://en.wikipedia.org/wiki/Birthday_paradox) [wiki/](http://en.wikipedia.org/wiki/Birthday_paradox) [Birthday_](http://en.wikipedia.org/wiki/Birthday_paradox) [paradox](http://en.wikipedia.org/wiki/Birthday_paradox) *, and you can download my solution from* [http:](http://thinkpython.com/code/birthday.py) [//](http://thinkpython.com/code/birthday.py) [thinkpython.](http://thinkpython.com/code/birthday.py) [com/](http://thinkpython.com/code/birthday.py) [code/](http://thinkpython.com/code/birthday.py) [birthday.](http://thinkpython.com/code/birthday.py) [py](http://thinkpython.com/code/birthday.py) *.* **Exercise 10.9.** *Write a function called* remove_duplicates *that takes a list and returns a new list with only the unique elements from the original. Hint: they don't have to be in the same order.* **Exercise 10.10.** *Write a function that reads the file* words.txt *and builds a list with one element per word. Write two versions of this function, one using the* append *method and the other using the idiom* t = t + [x]*. Which one takes longer to run? Why?*
 
-*Hint: use the* time *module to measure elapsed time. Solution:* http: // thinkpython. com/ code/ wordlist. py .
+*Hint: use the* time *module to measure elapsed time. Solution:* [http:](http://thinkpython.com/code/wordlist.py) [//](http://thinkpython.com/code/wordlist.py) [thinkpython.](http://thinkpython.com/code/wordlist.py) [com/](http://thinkpython.com/code/wordlist.py) [code/](http://thinkpython.com/code/wordlist.py) [wordlist.](http://thinkpython.com/code/wordlist.py) [py](http://thinkpython.com/code/wordlist.py) *.*
 
-**Exercise 10.11.** *To check whether a word is in the word list, you could use the* in *operator, but it would be slow because it searches through the words in order.*
+**Exercise 10.11.** *To check whether a word is in the word list, you could use the* in *operator, but it would be slow because it searches through the words in order.*
 
 *Because the words are in alphabetical order, we can speed things up with a bisection search (also known as binary search), which is similar to what you do when you look a word up in the dictionary.* *You start in the middle and check to see whether the word you are looking for comes before the word in the middle of the list. If so, then you search the first half of the list the same way. Otherwise you search the second half.*
 
@@ -4143,16 +4276,16 @@ In this example you could also use the built-in function sorted, which returns a
 
 *Write a function called* bisect *that takes a sorted list and a target value and returns the index of the value in the list, if it's there, or* None *if it's not.*
 
-*Or you could read the documentation of the* bisect *module and use that! Solution:* http: // thinkpython. com/ code/ inlist. py .
+*Or you could read the documentation of the* bisect *module and use that! Solution:* [http:](http://thinkpython.com/code/inlist.py) [//](http://thinkpython.com/code/inlist.py) [thinkpython.](http://thinkpython.com/code/inlist.py) [com/](http://thinkpython.com/code/inlist.py) [code/](http://thinkpython.com/code/inlist.py) [inlist.](http://thinkpython.com/code/inlist.py) [py](http://thinkpython.com/code/inlist.py) *.*
 
-**Exercise 10.12.** *Two words are a "reverse pair" if each is the reverse of the other. Write a program that finds all the reverse pairs in the word list. Solution:* http: // thinkpython. com/ code/ reverse_ pair. py .
+**Exercise 10.12.** *Two words are a "reverse pair" if each is the reverse of the other. Write a program that finds all the reverse pairs in the word list. Solution:* [http:](http://thinkpython.com/code/reverse_pair.py) [//](http://thinkpython.com/code/reverse_pair.py) [thinkpython.](http://thinkpython.com/code/reverse_pair.py) [com/](http://thinkpython.com/code/reverse_pair.py) [code/](http://thinkpython.com/code/reverse_pair.py) [reverse_](http://thinkpython.com/code/reverse_pair.py) [pair.](http://thinkpython.com/code/reverse_pair.py) [py](http://thinkpython.com/code/reverse_pair.py) *.*
 
-**Exercise 10.13.** *Two words "interlock" if taking alternating letters from each forms a new word. For example, "shoe" and "cold" interlock to form "schooled." Solution:* http: // thinkpython. com/ code/ interlock. py *. Credit: This exercise is inspired by an example at* http: // puzzlers. org .
+**Exercise 10.13.** *Two words "interlock" if taking alternating letters from each forms a new word. For example, "shoe" and "cold" interlock to form "schooled." Solution:* [http:](http://thinkpython.com/code/interlock.py) [//](http://thinkpython.com/code/interlock.py) [thinkpython.](http://thinkpython.com/code/interlock.py) [com/](http://thinkpython.com/code/interlock.py) [code/](http://thinkpython.com/code/interlock.py) [interlock.](http://thinkpython.com/code/interlock.py) [py](http://thinkpython.com/code/interlock.py) *. Credit: This exercise is inspired by an example at* [http:](http://puzzlers.org) [//](http://puzzlers.org) [puzzlers.](http://puzzlers.org) [org](http://puzzlers.org) *.*
 
 - *1. Write a program that finds all pairs of words that interlock. Hint: don't enumerate all pairs!*
 - *2. Can you find any words that are three-way interlocked; that is, every third letter forms a word, starting from the first, second or third?*
 
-## **Chapter 11**
+## **Chapter 11**
 
 # **Dictionaries**
 
@@ -4178,26 +4311,27 @@ This line creates an item that maps from the key 'one' to the value 'uno'. If we
 
 ```
 >>> print eng2sp
-```
 {'one': 'uno'}
-
+```
 This output format is also an input format. For example, you can create a new dictionary with three items:
 
+```
 >>> eng2sp = {'one': 'uno', 'two': 'dos', 'three': 'tres'}
-
+```
 But if you print eng2sp, you might be surprised:
 
 ```
 >>> print eng2sp
-```
 {'one': 'uno', 'three': 'tres', 'two': 'dos'}
-
+```
 The order of the key-value pairs is not the same. In fact, if you type the same example on your computer, you might get a different result. In general, the order of items in a dictionary is unpredictable.
 
 But that's not a problem because the elements of a dictionary are never indexed with integer indices. Instead, you use the keys to look up the corresponding values:
 
->>> print eng2sp['two'] 'dos'
-
+```
+>>> print eng2sp['two']
+'dos'
+```
 The key 'two' always maps to the value 'dos' so the order of the items doesn't matter.
 
 If the key isn't in the dictionary, you get an exception:
@@ -4212,7 +4346,7 @@ The len function works on dictionaries; it returns the number of key-value pairs
 >>> len(eng2sp)
 3
 ```
-The in operator works on dictionaries; it tells you whether something appears as a key in the dictionary (appearing as a value is not good enough).
+The in operator works on dictionaries; it tells you whether something appears as a *key* in the dictionary (appearing as a value is not good enough).
 
 ```
 >>> 'one' in eng2sp
@@ -4227,13 +4361,13 @@ To see whether something appears as a value in a dictionary, you can use the met
 >>> 'uno' in vals
 True
 ```
-The in operator uses different algorithms for lists and dictionaries. For lists, it uses a search algorithm, as in Section 8.6. As the list gets longer, the search time gets longer in direct proportion. For dictionaries, Python uses an algorithm called a **hashtable** that has a remarkable property: the in operator takes about the same amount of time no matter how many items there are in a dictionary. I won't explain how that's possible, but you can read more about it at http://en.wikipedia.org/wiki/Hash_table.
+The in operator uses different algorithms for lists and dictionaries. For lists, it uses a search algorithm, as in Section [8.6.](#page-95-1) As the list gets longer, the search time gets longer in direct proportion. For dictionaries, Python uses an algorithm called a **hashtable** that has a remarkable property: the in operator takes about the same amount of time no matter how many items there are in a dictionary. I won't explain how that's possible, but you can read more about it at .
 
-**Exercise 11.1.** *Write a function that reads the words in* words.txt *and stores them as keys in a dictionary. It doesn't matter what the values are. Then you can use the* in *operator as a fast way to check whether a string is in the dictionary.*
+**Exercise 11.1.** *Write a function that reads the words in* words.txt *and stores them as keys in a dictionary. It doesn't matter what the values are. Then you can use the* in *operator as a fast way to check whether a string is in the dictionary.*
 
-*If you did Exercise 10.11, you can compare the speed of this implementation with the list* in *operator and the bisection search.*
+*If you did Exercise [10.11,](#page-119-1) you can compare the speed of this implementation with the list* in *operator and the bisection search.*
 
-### **11.1 Dictionary as a set of counters**
+#### **11.1 Dictionary as a set of counters**
 
 Suppose you are given a string and you want to count how many times each letter appears. There are several ways you could do it:
 
@@ -4267,30 +4401,31 @@ Here's how it works:
 >>> h = histogram('brontosaurus')
 >>> print h
 {'a': 1, 'b': 1, 'o': 2, 'n': 1, 's': 2, 'r': 2, 'u': 2, 't': 1}
-The histogram indicates that the letters 'a' and 'b' appear once; 'o' appears twice, and
-so on.
-Exercise 11.2. Dictionaries have a method called get that takes a key and a default value. If the
-key appears in the dictionary, get returns the corresponding value; otherwise it returns the default
-value. For example:
+```
+The histogram indicates that the letters 'a' and 'b' appear once; 'o' appears twice, and so on.
+
+**Exercise 11.2.** *Dictionaries have a method called* get *that takes a key and a default value. If the key appears in the dictionary,* get *returns the corresponding value; otherwise it returns the default value. For example:*
+
+```
 >>> h = histogram('a')
 >>> print h
 {'a': 1}
+>>> h.get('a', 0)
+1
+>>> h.get('b', 0)
+0
 ```
->>> h.get('a', 0) 1 >>> h.get('b', 0)
+*Use* get *to write* histogram *more concisely. You should be able to eliminate the* if *statement.*
 
-0
+#### **11.2 Looping and dictionaries**
 
-Use get *to write* histogram *more concisely. You should be able to eliminate the* if *statement.*
+If you use a dictionary in a for statement, it traverses the keys of the dictionary. For example, print_hist prints each key and the corresponding value:
 
-### **11.2 Looping and dictionaries**
+def print_hist(h): for c in h: print c, h[c]
 
-If you use a dictionary in a for statement, it traverses the keys of the dictionary. For example, print_hist prints each key and the corresponding value:
+Here's what the output looks like:
 
 ```
-def print_hist(h):
-    for c in h:
-         print c, h[c]
-Here's what the output looks like:
 >>> h = histogram('parrot')
 >>> print_hist(h)
 a 1
@@ -4305,7 +4440,7 @@ Again, the keys are in no particular order.
 
 *Modify* print_hist *to print the keys and their values in alphabetical order.*
 
-### **11.3 Reverse lookup**
+#### **11.3 Reverse lookup**
 
 Given a dictionary d and a key k, it is easy to find the corresponding value v = d[k]. This operation is called a **lookup**.
 
@@ -4334,6 +4469,8 @@ r
 ```
 And an unsuccessful one:
 
+104
+
 ```
 >>> k = reverse_lookup(h, 3)
 Traceback (most recent call last):
@@ -4345,17 +4482,13 @@ The result when you raise an exception is the same as when Python raises one: it
 
 The raise statement takes a detailed error message as an optional argument. For example:
 
-```
->>> raise ValueError('value does not appear in the dictionary')
-Traceback (most recent call last):
-  File "", line 1, in ?
-ValueError: value does not appear in the dictionary
-```
+>>> raise ValueError('value does not appear in the dictionary') Traceback (most recent call last): File "", line 1, in ? ValueError: value does not appear in the dictionary
+
 A reverse lookup is much slower than a forward lookup; if you have to do it often, or if the dictionary gets big, the performance of your program will suffer.
 
 **Exercise 11.4.** *Modify* reverse_lookup *so that it builds and returns a list of* all *keys that map to* v*, or an empty list if there are none.*
 
-### **11.4 Dictionaries and lists**
+#### **11.4 Dictionaries and lists**
 
 Lists can appear as values in a dictionary. For example, if you were given a dictionary that maps from letters to frequencies, you might want to invert it; that is, create a dictionary that maps from frequencies to letters. Since there might be several letters with the same frequency, each value in the inverted dictionary should be a list of letters.
 
@@ -4386,14 +4519,20 @@ Here is an example:
 ```
 ![](_page_127_Figure_1.jpeg)
 
-Figure 11.1: State diagram.
+Figure 11.1: State diagram.
 
-Figure 11.1 is a state diagram showing hist and inverse. A dictionary is represented as a box with the type dict above it and the key-value pairs inside. If the values are integers, floats or strings, I usually draw them inside the box, but I usually draw lists outside the box, just to keep the diagram simple.
+Figure [11.1](#page-127-1) is a state diagram showing hist and inverse. A dictionary is represented as a box with the type dict above it and the key-value pairs inside. If the values are integers, floats or strings, I usually draw them inside the box, but I usually draw lists outside the box, just to keep the diagram simple.
 
 Lists can be values in a dictionary, as this example shows, but they cannot be keys. Here's what happens if you try:
 
->>> t = [1, 2, 3] >>> d = dict() >>> d[t] = 'oops' Traceback (most recent call last): File "", line 1, in ? TypeError: list objects are unhashable
-
+```
+>>> t = [1, 2, 3]
+>>> d = dict()
+>>> d[t] = 'oops'
+Traceback (most recent call last):
+  File "", line 1, in ?
+TypeError: list objects are unhashable
+```
 I mentioned earlier that a dictionary is implemented using a hashtable and that means that the keys have to be **hashable**.
 
 A **hash** is a function that takes a value (of any kind) and returns an integer. Dictionaries use these integers, called hash values, to store and look up key-value pairs.
@@ -4402,21 +4541,21 @@ This system works fine if the keys are immutable. But if the keys are mutable, l
 
 That's why the keys have to be hashable, and why mutable types like lists aren't. The simplest way to get around this limitation is to use tuples, which we will see in the next chapter.
 
-Since lists and dictionaries are mutable, they can't be used as keys, but they can be used as values.
+Since lists and dictionaries are mutable, they can't be used as keys, but they *can* be used as values.
 
-**Exercise 11.5.** *Read the documentation of the dictionary method* setdefault *and use it to write a more concise version of* invert_dict*. Solution:* http: // thinkpython. com/ code/ invert_ dict. py .
+**Exercise 11.5.** *Read the documentation of the dictionary method* setdefault *and use it to write a more concise version of* invert_dict*. Solution:* [http:](http://thinkpython.com/code/invert_dict.py) [//](http://thinkpython.com/code/invert_dict.py) [thinkpython.](http://thinkpython.com/code/invert_dict.py) [com/](http://thinkpython.com/code/invert_dict.py) [code/](http://thinkpython.com/code/invert_dict.py) [invert_](http://thinkpython.com/code/invert_dict.py) [dict.](http://thinkpython.com/code/invert_dict.py) [py](http://thinkpython.com/code/invert_dict.py) *.*
 
-### **11.5 Memos**
+#### **11.5 Memos**
 
-If you played with the fibonacci function from Section 6.7, you might have noticed that the bigger the argument you provide, the longer the function takes to run. Furthermore,
+If you played with the fibonacci function from Section [6.7,](#page-78-1) you might have noticed that the bigger the argument you provide, the longer the function takes to run. Furthermore,
 
 ![](_page_128_Figure_1.jpeg)
 
-Figure 11.2: Call graph.
+Figure 11.2: Call graph.
 
 the run time increases very quickly.
 
-To understand why, consider Figure 11.2, which shows the **call graph** for fibonacci with n=4:
+To understand why, consider Figure [11.2,](#page-128-0) which shows the **call graph** for fibonacci with n=4:
 
 A call graph shows a set of function frames, with lines connecting each frame to the frames of the functions it calls. At the top of the graph, fibonacci with n=4 calls fibonacci with n=3 and n=2. In turn, fibonacci with n=3 calls fibonacci with n=2 and n=1. And so on.
 
@@ -4426,9 +4565,6 @@ One solution is to keep track of values that have already been computed by stori
 
 ```
 known = {0:0, 1:1}
-```
-
-```
 def fibonacci(n):
     if n in known:
         return known[n]
@@ -4442,9 +4578,9 @@ Whenever fibonacci is called, it checks known. If the result is already there, i
 
 **Exercise 11.6.** *Run this version of* fibonacci *and the original with a range of parameters and compare their run times.*
 
-**Exercise 11.7.** *Memoize the Ackermann function from Exercise 6.5 and see if memoization makes it possible to evaluate the function with bigger arguments. Hint: no. Solution:* http: // thinkpython. com/ code/ ackermann_ memo. py .
+**Exercise 11.7.** *Memoize the Ackermann function from Exercise [6.5](#page-82-1) and see if memoization makes it possible to evaluate the function with bigger arguments. Hint: no. Solution:* [http:](http://thinkpython.com/code/ackermann_memo.py) [//](http://thinkpython.com/code/ackermann_memo.py) [thinkpython.](http://thinkpython.com/code/ackermann_memo.py) [com/](http://thinkpython.com/code/ackermann_memo.py) [code/](http://thinkpython.com/code/ackermann_memo.py) [ackermann_](http://thinkpython.com/code/ackermann_memo.py) [memo.](http://thinkpython.com/code/ackermann_memo.py) [py](http://thinkpython.com/code/ackermann_memo.py) *.*
 
-### **11.6 Global variables**
+#### **11.6 Global variables**
 
 In the previous example, known is created outside the function, so it belongs to the special frame called __main__. Variables in __main__ are sometimes called **global** because they can be accessed from any function. Unlike local variables, which disappear when their function ends, global variables persist from one function call to the next.
 
@@ -4454,13 +4590,15 @@ It is common to use global variables for **flags**; that is, boolean variables t
 verbose = True
 def example1():
     if verbose:
-         print 'Running example1'
-If you try to reassign a global variable, you might be surprised. The following example is
-supposed to keep track of whether the function has been called:
-been_called = False
+        print 'Running example1'
 ```
-def example2(): been_called = True # WRONG
+If you try to reassign a global variable, you might be surprised. The following example is supposed to keep track of whether the function has been called:
 
+```
+been_called = False
+def example2():
+   been_called = True # WRONG
+```
 But if you run it you will see that the value of been_called doesn't change. The problem is that example2 creates a new local variable named been_called. The local variable goes away when the function ends, and has no effect on the global variable.
 
 To reassign a global variable inside a function you have to **declare** the global variable before you use it:
@@ -4478,19 +4616,28 @@ Here's an example that tries to update a global variable:
 ```
 count = 0
 def example3():
-    count = count + 1 # WRONG
+   count = count + 1 # WRONG
+```
+
+```
 If you run it you get:
+```
 UnboundLocalError: local variable 'count' referenced before assignment
-Python assumes that count is local, which means that you are reading it before writing it.
-The solution, again, is to declare count global.
+
+Python assumes that count is local, which means that you are reading it before writing it. The solution, again, is to declare count global.
+
+```
 def example3():
+    global count
+    count += 1
 ```
-global count count += 1
-
 If the global value is mutable, you can modify it without declaring it:
 
-known = {0:0, 1:1} def example4(): known[2] = 1
-
+```
+known = {0:0, 1:1}
+def example4():
+    known[2] = 1
+```
 So you can add, remove and replace elements of a global list or dictionary, but if you want to reassign the variable, you have to declare it:
 
 ```
@@ -4498,7 +4645,7 @@ def example5():
     global known
     known = dict()
 ```
-### **11.7 Long integers**
+#### **11.7 Long integers**
 
 If you compute fibonacci(50), you get:
 
@@ -4520,9 +4667,11 @@ Any time the result of a computation is too big to be represented with an intege
 >>> 100000 * 100000
 10000000000L
 ```
-In the first case the result has type int; in the second case it is long. **Exercise 11.8.** *Exponentiation of large integers is the basis of common algorithms for public-key encryption. Read the Wikipedia page on the RSA algorithm (*http: // en. wikipedia. org/ wiki/ RSA_ ( algorithm) *) and write functions to encode and decode messages.*
+In the first case the result has type int; in the second case it is long.
 
-### **11.8 Debugging**
+**Exercise 11.8.** *Exponentiation of large integers is the basis of common algorithms for public-key encryption. Read the Wikipedia page on the RSA algorithm (*[http:](http://en.wikipedia.org/wiki/RSA_(algorithm)) [//](http://en.wikipedia.org/wiki/RSA_(algorithm)) [en.](http://en.wikipedia.org/wiki/RSA_(algorithm)) [wikipedia.](http://en.wikipedia.org/wiki/RSA_(algorithm)) [org/](http://en.wikipedia.org/wiki/RSA_(algorithm)) [wiki/](http://en.wikipedia.org/wiki/RSA_(algorithm)) [RSA_](http://en.wikipedia.org/wiki/RSA_(algorithm)) [(](http://en.wikipedia.org/wiki/RSA_(algorithm)) [algorithm)](http://en.wikipedia.org/wiki/RSA_(algorithm)) *) and write functions to encode and decode messages.*
+
+#### **11.8 Debugging**
 
 As you work with bigger datasets it can become unwieldy to debug by printing and checking data by hand. Here are some suggestions for debugging large datasets:
 
@@ -4535,45 +4684,54 @@ A common cause of runtime errors is a value that is not the right type. For debu
 - **Write self-checks:** Sometimes you can write code to check for errors automatically. For example, if you are computing the average of a list of numbers, you could check that the result is not greater than the largest element in the list or less than the smallest. This is called a "sanity check" because it detects results that are "insane."
 Another kind of check compares the results of two different computations to see if they are consistent. This is called a "consistency check."
 
-- **Pretty print the output:** Formatting debugging output can make it easier to spot an error. We saw an example in Section 6.9. The pprint module provides a pprint function that displays built-in types in a more human-readable format.
+- **Pretty print the output:** Formatting debugging output can make it easier to spot an error. We saw an example in Section [6.9.](#page-80-0) The pprint module provides a pprint function that displays built-in types in a more human-readable format.
 Again, time you spend building scaffolding can reduce the time you spend debugging.
 
-### **11.9 Glossary**
+#### **11.9 Glossary**
 
 **dictionary:** A mapping from a set of keys to their corresponding values.
 
-- **key-value pair:** The representation of the mapping from a key to a value.
-- **item:** Another name for a key-value pair.
-- **key:** An object that appears in a dictionary as the first part of a key-value pair.
+**key-value pair:** The representation of the mapping from a key to a value.
+
+**item:** Another name for a key-value pair.
+
+**key:** An object that appears in a dictionary as the first part of a key-value pair.
+
 - **value:** An object that appears in a dictionary as the second part of a key-value pair. This is more specific than our previous use of the word "value."
-- **implementation:** A way of performing a computation.
-- **hashtable:** The algorithm used to implement Python dictionaries.
-- **hash function:** A function used by a hashtable to compute the location for a key.
-- **hashable:** A type that has a hash function. Immutable types like integers, floats and strings are hashable; mutable types like lists and dictionaries are not.
-- **lookup:** A dictionary operation that takes a key and finds the corresponding value.
+**implementation:** A way of performing a computation.
+
+**hashtable:** The algorithm used to implement Python dictionaries.
+
+**hash function:** A function used by a hashtable to compute the location for a key.
+
+**hashable:** A type that has a hash function. Immutable types like integers, floats and strings are hashable; mutable types like lists and dictionaries are not.
+
+**lookup:** A dictionary operation that takes a key and finds the corresponding value.
+
 - **reverse lookup:** A dictionary operation that takes a value and finds one or more keys that map to it.
-- **singleton:** A list (or other sequence) with a single element.
+**singleton:** A list (or other sequence) with a single element.
+
 - **call graph:** A diagram that shows every frame created during the execution of a program, with an arrow from each caller to each callee.
-- **histogram:** A set of counters.
-- **memo:** A computed value stored to avoid unnecessary future computation.
+**histogram:** A set of counters.
 
-**global variable:** A variable defined outside a function. Global variables can be accessed from any function.
+**memo:** A computed value stored to avoid unnecessary future computation.
 
-**flag:** A boolean variable used to indicate whether a condition is true.
+- **global variable:** A variable defined outside a function. Global variables can be accessed from any function.
+- **flag:** A boolean variable used to indicate whether a condition is true.
 
 **declaration:** A statement like global that tells the interpreter something about a variable.
 
-### **11.10 Exercises**
+#### **11.10 Exercises**
 
-**Exercise 11.9.** *If you did Exercise 10.8, you already have a function named* has_duplicates *that takes a list as a parameter and returns* True *if there is any object that appears more than once in the list.*
+**Exercise 11.9.** *If you did Exercise [10.8,](#page-119-2) you already have a function named* has_duplicates *that takes a list as a parameter and returns* True *if there is any object that appears more than once in the list.*
 
-*Use a dictionary to write a faster, simpler version of* has_duplicates*. Solution:* http: // thinkpython. com/ code/ has_ duplicates. py .
+*Use a dictionary to write a faster, simpler version of* has_duplicates*. Solution:* [http:](http://thinkpython.com/code/has_duplicates.py) [//](http://thinkpython.com/code/has_duplicates.py) [thinkpython.](http://thinkpython.com/code/has_duplicates.py) [com/](http://thinkpython.com/code/has_duplicates.py) [code/](http://thinkpython.com/code/has_duplicates.py) [has_](http://thinkpython.com/code/has_duplicates.py) [duplicates.](http://thinkpython.com/code/has_duplicates.py) [py](http://thinkpython.com/code/has_duplicates.py) *.*
 
-**Exercise 11.10.** *Two words are "rotate pairs" if you can rotate one of them and get the other (see* rotate_word *in Exercise 8.12).*
+**Exercise 11.10.** *Two words are "rotate pairs" if you can rotate one of them and get the other (see* rotate_word *in Exercise [8.12)](#page-101-0).*
 
-*Write a program that reads a wordlist and finds all the rotate pairs. Solution:* http: // thinkpython. com/ code/ rotate_ pairs. py .
+*Write a program that reads a wordlist and finds all the rotate pairs. Solution:* [http:](http://thinkpython.com/code/rotate_pairs.py) [//](http://thinkpython.com/code/rotate_pairs.py) [thinkpython.](http://thinkpython.com/code/rotate_pairs.py) [com/](http://thinkpython.com/code/rotate_pairs.py) [code/](http://thinkpython.com/code/rotate_pairs.py) [rotate_](http://thinkpython.com/code/rotate_pairs.py) [pairs.](http://thinkpython.com/code/rotate_pairs.py) [py](http://thinkpython.com/code/rotate_pairs.py) *.*
 
-**Exercise 11.11.** *Here's another Puzzler from* Car Talk (http: // www. cartalk. com/ content/ puzzlers ):
+**Exercise 11.11.** *Here's another Puzzler from* Car Talk *(*[http:](http://www.cartalk.com/content/puzzlers) [//](http://www.cartalk.com/content/puzzlers) [www.](http://www.cartalk.com/content/puzzlers) [cartalk.](http://www.cartalk.com/content/puzzlers) [com/](http://www.cartalk.com/content/puzzlers) [content/](http://www.cartalk.com/content/puzzlers) [puzzlers](http://www.cartalk.com/content/puzzlers) *):*
 
 > *This was sent in by a fellow named Dan O'Leary. He came upon a common one-syllable, five-letter word recently that has the following unique property. When you remove the first letter, the remaining letters form a homophone of the original word, that is a word that sounds exactly the same. Replace the first letter, that is, put it back and remove the second letter and the result is yet another homophone of the original word. And the question is, what's the word?*
 
@@ -4581,17 +4739,17 @@ Again, time you spend building scaffolding can reduce the time you spend debuggi
 
 > *But there is, however, at least one word that Dan and we know of, which will yield two homophones if you remove either of the first two letters to make two, new four-letter words. The question is, what's the word?*
 
-*You can use the dictionary from Exercise 11.1 to check whether a string is in the word list.*
+*You can use the dictionary from Exercise [11.1](#page-123-1) to check whether a string is in the word list.*
 
-*To check whether two words are homophones, you can use the CMU Pronouncing Dictionary. You can download it from* http: // www. speech. cs. cmu. edu/ cgi-bin/ cmudict *or from* http: // thinkpython. com/ code/ c06d *and you can also download* http: // thinkpython. com/ code/ pronounce. py *, which provides a function named* read_dictionary *that reads the pronouncing dictionary and returns a Python dictionary that maps from each word to a string that describes its primary pronunciation.*
+*To check whether two words are homophones, you can use the CMU Pronouncing Dictionary. You can download it from* [http:](http://www.speech.cs.cmu.edu/cgi-bin/cmudict) [//](http://www.speech.cs.cmu.edu/cgi-bin/cmudict) [www.](http://www.speech.cs.cmu.edu/cgi-bin/cmudict) [speech.](http://www.speech.cs.cmu.edu/cgi-bin/cmudict) [cs.](http://www.speech.cs.cmu.edu/cgi-bin/cmudict) [cmu.](http://www.speech.cs.cmu.edu/cgi-bin/cmudict) [edu/](http://www.speech.cs.cmu.edu/cgi-bin/cmudict) [cgi-bin/](http://www.speech.cs.cmu.edu/cgi-bin/cmudict) [cmudict](http://www.speech.cs.cmu.edu/cgi-bin/cmudict) *or from* [http:](http://thinkpython.com/code/c06d) [//](http://thinkpython.com/code/c06d) [thinkpython.](http://thinkpython.com/code/c06d) [com/](http://thinkpython.com/code/c06d) [code/](http://thinkpython.com/code/c06d) [c06d](http://thinkpython.com/code/c06d) *and you can also download* [http:](http://thinkpython.com/code/pronounce.py) [//](http://thinkpython.com/code/pronounce.py) [thinkpython.](http://thinkpython.com/code/pronounce.py) [com/](http://thinkpython.com/code/pronounce.py) [code/](http://thinkpython.com/code/pronounce.py) [pronounce.](http://thinkpython.com/code/pronounce.py) [py](http://thinkpython.com/code/pronounce.py) *, which provides a function named* read_dictionary *that reads the pronouncing dictionary and returns a Python dictionary that maps from each word to a string that describes its primary pronunciation.*
 
-*Write a program that lists all the words that solve the Puzzler. Solution:* http: // thinkpython. com/ code/ homophone. py .
+*Write a program that lists all the words that solve the Puzzler. Solution:* [http:](http://thinkpython.com/code/homophone.py) [//](http://thinkpython.com/code/homophone.py) [thinkpython.](http://thinkpython.com/code/homophone.py) [com/](http://thinkpython.com/code/homophone.py) [code/](http://thinkpython.com/code/homophone.py) [homophone.](http://thinkpython.com/code/homophone.py) [py](http://thinkpython.com/code/homophone.py) *.*
 
-### **Chapter 12**
+## **Chapter 12**
 
 # **Tuples**
 
-### **12.1 Tuples are immutable**
+#### **12.1 Tuples are immutable**
 
 A tuple is a sequence of values. The values can be any type, and they are indexed by integers, so in that respect tuples are a lot like lists. The important difference is that tuples are immutable.
 
@@ -4601,8 +4759,9 @@ Syntactically, a tuple is a comma-separated list of values:
 
 Although it is not necessary, it is common to enclose tuples in parentheses:
 
+```
 >>> t = ('a', 'b', 'c', 'd', 'e')
-
+```
 To create a tuple with a single element, you have to include a final comma:
 
 ```
@@ -4611,12 +4770,18 @@ To create a tuple with a single element, you have to include a final comma:
 
 A value in parentheses is not a tuple:
 >>> t2 = ('a')
+```
+
+```
 >>> type(t2)
 
-Another way to create a tuple is the built-in function tuple. With no argument, it creates
-an empty tuple:
+```
+Another way to create a tuple is the built-in function tuple. With no argument, it creates an empty tuple:
+
+```
 >>> t = tuple()
 >>> print t
+()
 ```
 If the argument is a sequence (string, list or tuple), the result is a tuple with the elements of the sequence:
 
@@ -4625,8 +4790,6 @@ If the argument is a sequence (string, list or tuple), the result is a tuple wit
 >>> print t
 ('l', 'u', 'p', 'i', 'n', 's')
 ```
-()
-
 Because tuple is the name of a built-in function, you should avoid using it as a variable name.
 
 Most list operators also work on tuples. The bracket operator indexes an element:
@@ -4638,16 +4801,12 @@ Most list operators also work on tuples. The bracket operator indexes an element
 ```
 And the slice operator selects a range of elements.
 
-```
->>> print t[1:3]
-('b', 'c')
-```
+>>> print t[1:3] ('b', 'c')
+
 But if you try to modify one of the elements of the tuple, you get an error:
 
-```
->>> t[0] = 'A'
-TypeError: object doesn't support item assignment
-```
+>>> t[0] = 'A' TypeError: object doesn't support item assignment
+
 You can't modify the elements of a tuple, but you can replace one tuple with another:
 
 ```
@@ -4655,7 +4814,7 @@ You can't modify the elements of a tuple, but you can replace one tuple with ano
 >>> print t
 ('A', 'b', 'c', 'd', 'e')
 ```
-### **12.2 Tuple assignment**
+#### **12.2 Tuple assignment**
 
 It is often useful to swap the values of two variables. With conventional assignments, you have to use a temporary variable. For example, to swap a and b:
 
@@ -4666,9 +4825,8 @@ It is often useful to swap the values of two variables. With conventional assign
 ```
 This solution is cumbersome; **tuple assignment** is more elegant:
 
-```
 >>> a, b = b, a
-```
+
 The left side is a tuple of variables; the right side is a tuple of expressions. Each value is assigned to its respective variable. All the expressions on the right side are evaluated before any of the assignments.
 
 The number of variables on the left and the number of values on the right have to be the same:
@@ -4691,7 +4849,7 @@ monty
 >>> print domain
 python.org
 ```
-### **12.3 Tuples as return values**
+#### **12.3 Tuples as return values**
 
 Strictly speaking, a function can only return one value, but if the value is a tuple, the effect is the same as returning multiple values. For example, if you want to divide two integers and compute the quotient and remainder, it is inefficient to compute x/y and then x%y. It is better to compute them both at the same time.
 
@@ -4710,21 +4868,20 @@ Or use tuple assignment to store the elements separately:
 ```
 Here is an example of a function that returns a tuple:
 
+```
 def min_max(t):
-
-```
-return min(t), max(t)
+    return min(t), max(t)
 ```
 max and min are built-in functions that find the largest and smallest elements of a sequence. min_max computes both and returns a tuple of two values.
 
-### **12.4 Variable-length argument tuples**
+#### **12.4 Variable-length argument tuples**
 
 Functions can take a variable number of arguments. A parameter name that begins with * **gathers** arguments into a tuple. For example, printall takes any number of arguments and prints them:
 
+```
 def printall(*args):
-
-print args
-
+    print args
+```
 The gather parameter can have any name you like, but args is conventional. Here's how the function works:
 
 ```
@@ -4738,11 +4895,15 @@ The complement of gather is **scatter**. If you have a sequence of values and yo
 >>> divmod(t)
 TypeError: divmod expected 2 arguments, got 1
 ```
+TypeError: **init**() expected 2 arguments, got 3
+
 But if you scatter the tuple, it works:
 
->>> divmod(*t) (2, 1)
+>>> divmod(*t)
 
-**Exercise 12.1.** *Many of the built-in functions use variable-length argument tuples. For example,* max and min *can take any number of arguments:*
+(2, 1)
+
+**Exercise 12.1.** *Many of the built-in functions use variable-length argument tuples. For example,* max *and* min *can take any number of arguments:*
 
 ```
 >>> max(1,2,3)
@@ -4752,7 +4913,7 @@ But sum does not.
 TypeError: sum expected at most 2 arguments, got 3
 Write a function called sumall that takes any number of arguments and returns their sum.
 ```
-### **12.5 Lists and tuples**
+## **12.5 Lists and tuples**
 
 zip is a built-in function that takes two or more sequences and "zips" them into a list of tuples where each tuple contains one element from each sequence. In Python 3, zip returns an iterator of tuples, but for most purposes, an iterator behaves like a list.
 
@@ -4763,15 +4924,13 @@ This example zips a string and a list:
 >>> t = [0, 1, 2]
 >>> zip(s, t)
 [('a', 0), ('b', 1), ('c', 2)]
-The result is a list of tuples where each tuple contains a character from the string and the
-corresponding element from the list.
 ```
+The result is a list of tuples where each tuple contains a character from the string and the corresponding element from the list.
+
 If the sequences are not the same length, the result has the length of the shorter one.
 
-```
->>> zip('Anne', 'Elk')
-[('A', 'E'), ('n', 'l'), ('n', 'k')]
-```
+>>> zip('Anne', 'Elk') [('A', 'E'), ('n', 'l'), ('n', 'k')]
+
 You can use tuple assignment in a for loop to traverse a list of tuples:
 
 ```
@@ -4781,9 +4940,8 @@ for letter, number in t:
 ```
 Each time through the loop, Python selects the next tuple in the list and assigns the elements to letter and number. The output of this loop is:
 
-0 a 1 b 2 c
-
-> If you combine zip, for and tuple assignment, you get a useful idiom for traversing two (or more) sequences at the same time. For example, has_match takes two sequences, t1 and t2, and returns True if there is an index i such that t1[i] == t2[i]:
+- 0 a 1 b 2 c
+If you combine zip, for and tuple assignment, you get a useful idiom for traversing two (or more) sequences at the same time. For example, has_match takes two sequences, t1 and t2, and returns True if there is an index i such that t1[i] == t2[i]:
 
 ```
 def has_match(t1, t2):
@@ -4798,11 +4956,11 @@ If you need to traverse the elements of a sequence and their indices, you can us
 for index, element in enumerate('abc'):
     print index, element
 ```
-The output of this loop is: 0 a 1 b 2 c
+The output of this loop is:
 
-Again.
+0 a 1 b 2 c Again.
 
-### **12.6 Dictionaries and tuples**
+#### **12.6 Dictionaries and tuples**
 
 Dictionaries have a method called items that returns a list of tuples, where each tuple is a key-value pair.
 
@@ -4821,7 +4979,10 @@ Going in the other direction, you can use a list of tuples to initialize a new d
 >>> d = dict(t)
 >>> print d
 {'a': 0, 'c': 2, 'b': 1}
+```
 Combining dict with zip yields a concise way to create a dictionary:
+
+```
 >>> d = dict(zip('abc', range(3)))
 >>> print d
 {'a': 0, 'c': 2, 'b': 1}
@@ -4836,50 +4997,52 @@ for key, val in d.items():
 ```
 The output of this loop is:
 
-0 a 2 c 1 b Again.
+```
+0 a
+2 c
+1 b
+```
+Again.
 
 It is common to use tuples as keys in dictionaries (primarily because you can't use lists). For example, a telephone directory might map from last-name, first-name pairs to telephone numbers. Assuming that we have defined last, first and number, we could write:
 
+```
 directory[last,first] = number
-
+```
 The expression in brackets is a tuple. We could use tuple assignment to traverse this dictionary.
 
-```
-0
-   1
-            'Cleese'
-            'John'
 tuple
-```
-Figure 12.1: State diagram.
 
-#### dict
+0 1 'Cleese' 'John'
 
-| ('Cleese', 'John') | '08700 100 222' |
-| --- | --- |
-| ('Chapman', 'Graham') | '08700 100 222' |
-| ('Idle', 'Eric') | '08700 100 222' |
-| ('Gilliam', 'Terry') | '08700 100 222' |
-| ('Jones', 'Terry') | '08700 100 222' |
-| ('Palin', 'Michael') | '08700 100 222' |
+Figure 12.1: State diagram.
 
-Figure 12.2: State diagram.
+|  | dict                                    |
+|--|-----------------------------------------|
+|  | ('Cleese', 'John') → '08700 100 222'    |
+|  | ('Chapman', 'Graham') → '08700 100 222' |
+|  | ('Idle', 'Eric') → '08700 100 222'      |
+|  | ('Gilliam', 'Terry') → '08700 100 222'  |
+|  | ('Jones', 'Terry') → '08700 100 222'    |
+|  | ('Palin', 'Michael') → '08700 100 222'  |
 
-for last, first in directory:
-
-print first, last, directory[last,first]
+Figure 12.2: State diagram.
 
+```
+for last, first in directory:
+    print first, last, directory[last,first]
+```
 This loop traverses the keys in directory, which are tuples. It assigns the elements of each tuple to last and first, then prints the name and corresponding telephone number.
 
-There are two ways to represent tuples in a state diagram. The more detailed version shows the indices and elements just as they appear in a list. For example, the tuple ('Cleese', 'John') would appear as in Figure 12.1.
+There are two ways to represent tuples in a state diagram. The more detailed version shows the indices and elements just as they appear in a list. For example, the tuple ('Cleese', 'John') would appear as in Figure [12.1.](#page-139-1)
 
-But in a larger diagram you might want to leave out the details. For example, a diagram of the telephone directory might appear as in Figure 12.2.
+But in a larger diagram you might want to leave out the details. For example, a diagram of the telephone directory might appear as in Figure [12.2.](#page-139-2)
 
 Here the tuples are shown using Python syntax as a graphical shorthand.
 
 The telephone number in the diagram is the complaints line for the BBC, so please don't call it.
 
-### **12.7 Comparing tuples**
+### **12.7 Comparing tuples**
 
 The relational operators work with tuples and other sequences; Python starts by comparing the first element from each sequence. If they are equal, it goes on to the next elements, and so on, until it finds elements that differ. Subsequent elements are not considered (even if they are really big).
 
@@ -4891,10 +5054,9 @@ True
 ```
 The sort function works the same way. It sorts primarily by first element, but in the case of a tie, it sorts by second element, and so on.
 
-This feature lends itself to a pattern called DSU for
-
-**Decorate** a sequence by building a list of tuples with one or more sort keys preceding the elements from the sequence,
+This feature lends itself to a pattern called **DSU** for
 
+- **Decorate** a sequence by building a list of tuples with one or more sort keys preceding the elements from the sequence,
 **Sort** the list of tuples, and
 
 **Undecorate** by extracting the sorted elements of the sequence.
@@ -4918,9 +5080,9 @@ sort compares the first element, length, first, and only considers the second el
 
 The second loop traverses the list of tuples and builds a list of words in descending order of length.
 
-**Exercise 12.2.** *In this example, ties are broken by comparing words, so words with the same length appear in reverse alphabetical order. For other applications you might want to break ties at random. Modify this example so that words with the same length appear in random order. Hint: see the* random *function in the* random *module. Solution:* http: // thinkpython. com/ code/ unstable_ sort. py .
+**Exercise 12.2.** *In this example, ties are broken by comparing words, so words with the same length appear in reverse alphabetical order. For other applications you might want to break ties at random. Modify this example so that words with the same length appear in random order. Hint: see the* random *function in the* random *module. Solution:* [http:](http://thinkpython.com/code/unstable_sort.py) [//](http://thinkpython.com/code/unstable_sort.py) [thinkpython.](http://thinkpython.com/code/unstable_sort.py) [com/](http://thinkpython.com/code/unstable_sort.py) [code/](http://thinkpython.com/code/unstable_sort.py) [unstable_](http://thinkpython.com/code/unstable_sort.py) [sort.](http://thinkpython.com/code/unstable_sort.py) [py](http://thinkpython.com/code/unstable_sort.py) *.*
 
-### **12.8 Sequences of sequences**
+#### **12.8 Sequences of sequences**
 
 I have focused on lists of tuples, but almost all of the examples in this chapter also work with lists of lists, tuples of tuples, and tuples of lists. To avoid enumerating the possible combinations, it is sometimes easier to talk about sequences of sequences.
 
@@ -4936,11 +5098,11 @@ Lists are more common than tuples, mostly because they are mutable. But there ar
 
 Because tuples are immutable, they don't provide methods like sort and reverse, which modify existing lists. But Python provides the built-in functions sorted and reversed, which take any sequence as a parameter and return a new list with the same elements in a different order.
 
-### **12.9 Debugging**
+### **12.9 Debugging**
 
 Lists, dictionaries and tuples are known generically as **data structures**; in this chapter we are starting to see compound data structures, like lists of tuples, and dictionaries that contain tuples as keys and lists as values. Compound data structures are useful, but they are prone to what I call **shape errors**; that is, errors caused when a data structure has the wrong type, size or composition. For example, if you are expecting a list with one integer and I give you a plain old integer (not in a list), it won't work.
 
-To help debug these kinds of errors, I have written a module called structshape that provides a function, also called structshape, that takes any kind of data structure as an argument and returns a string that summarizes its shape. You can download it from http://thinkpython.com/code/structshape.py
+To help debug these kinds of errors, I have written a module called structshape that provides a function, also called structshape, that takes any kind of data structure as an argument and returns a string that summarizes its shape. You can download it from 
 
 Here's the result for a simple list:
 
@@ -4968,44 +5130,41 @@ And here's a dictionary with 3 items that map integers to strings.
 >>> d = dict(lt)
 >>> print structshape(d)
 dict of 3 int->str
-If you are having trouble keeping track of your data structures, structshape can help.
 ```
-### **12.10 Glossary**
+If you are having trouble keeping track of your data structures, structshape can help.
+
+#### **12.10 Glossary**
 
 **tuple:** An immutable sequence of elements.
 
 - **tuple assignment:** An assignment with a sequence on the right side and a tuple of variables on the left. The right side is evaluated and then its elements are assigned to the variables on the left.
-**gather:** The operation of assembling a variable-length argument tuple.
-
-**scatter:** The operation of treating a sequence as a list of arguments.
-
+- **gather:** The operation of assembling a variable-length argument tuple.
+- **scatter:** The operation of treating a sequence as a list of arguments.
 - **DSU:** Abbreviation of "decorate-sort-undecorate," a pattern that involves building a list of tuples, sorting, and extracting part of the result.
 - **data structure:** A collection of related values, often organized in lists, dictionaries, tuples, etc.
 - **shape (of a data structure):** A summary of the type, size and composition of a data structure.
 
-### **12.11 Exercises**
+#### **12.11 Exercises**
 
-**Exercise 12.3.** *Write a function called* most_frequent *that takes a string and prints the letters in decreasing order of frequency. Find text samples from several different languages and see how letter frequency varies between languages. Compare your results with the tables at* http: // en. wikipedia. org/ wiki/ Letter_ frequencies *. Solution:* http: // thinkpython. com/ code/ most_ frequent. py . **Exercise 12.4.** *More anagrams!*
+**Exercise 12.3.** *Write a function called* most_frequent *that takes a string and prints the letters in decreasing order of frequency. Find text samples from several different languages and see how letter frequency varies between languages. Compare your results with the tables at* [http:](http://en.wikipedia.org/wiki/Letter_frequencies) [//](http://en.wikipedia.org/wiki/Letter_frequencies) [en.](http://en.wikipedia.org/wiki/Letter_frequencies) [wikipedia.](http://en.wikipedia.org/wiki/Letter_frequencies) [org/](http://en.wikipedia.org/wiki/Letter_frequencies) [wiki/](http://en.wikipedia.org/wiki/Letter_frequencies) [Letter_](http://en.wikipedia.org/wiki/Letter_frequencies) [frequencies](http://en.wikipedia.org/wiki/Letter_frequencies) *. Solution:* [http:](http://thinkpython.com/code/most_frequent.py) [//](http://thinkpython.com/code/most_frequent.py) [thinkpython.](http://thinkpython.com/code/most_frequent.py) [com/](http://thinkpython.com/code/most_frequent.py) [code/](http://thinkpython.com/code/most_frequent.py) [most_](http://thinkpython.com/code/most_frequent.py) [frequent.](http://thinkpython.com/code/most_frequent.py) [py](http://thinkpython.com/code/most_frequent.py) *.*
 
-- *1. Write a program that reads a word list from a file (see Section 9.1) and prints all the sets of words that are anagrams.*
+**Exercise 12.4.** *More anagrams!*
+
+- *1. Write a program that reads a word list from a file (see Section [9.1)](#page-102-1) and prints all the sets of words that are anagrams.*
 *Here is an example of what the output might look like:*
 
-```
-['deltas', 'desalt', 'lasted', 'salted', 'slated', 'staled']
-['retainers', 'ternaries']
-['generating', 'greatening']
-['resmelts', 'smelters', 'termless']
-```
+['deltas', 'desalt', 'lasted', 'salted', 'slated', 'staled'] ['retainers', 'ternaries'] ['generating', 'greatening'] ['resmelts', 'smelters', 'termless']
+
 *Hint: you might want to build a dictionary that maps from a set of letters to a list of words that can be spelled with those letters. The question is, how can you represent the set of letters in a way that can be used as a key?*
 
 - *2. Modify the previous program so that it prints the largest set of anagrams first, followed by the second largest set, and so on.*
 - *3. In Scrabble a "bingo" is when you play all seven tiles in your rack, along with a letter on the board, to form an eight-letter word. What set of 8 letters forms the most possible bingos? Hint: there are seven.*
 
-*Solution:* http: // thinkpython. com/ code/ anagram_ sets. py .
+*Solution:* [http:](http://thinkpython.com/code/anagram_sets.py) [//](http://thinkpython.com/code/anagram_sets.py) [thinkpython.](http://thinkpython.com/code/anagram_sets.py) [com/](http://thinkpython.com/code/anagram_sets.py) [code/](http://thinkpython.com/code/anagram_sets.py) [anagram_](http://thinkpython.com/code/anagram_sets.py) [sets.](http://thinkpython.com/code/anagram_sets.py) [py](http://thinkpython.com/code/anagram_sets.py) *.*
 
-**Exercise 12.5.** *Two words form a "metathesis pair" if you can transform one into the other by swapping two letters; for example, "converse" and "conserve." Write a program that finds all of the metathesis pairs in the dictionary. Hint: don't test all pairs of words, and don't test all possible swaps. Solution:* http: // thinkpython. com/ code/ metathesis. py *. Credit: This exercise is inspired by an example at* http: // puzzlers. org .
+**Exercise 12.5.** *Two words form a "metathesis pair" if you can transform one into the other by swapping two letters; for example, "converse" and "conserve." Write a program that finds all of the metathesis pairs in the dictionary. Hint: don't test all pairs of words, and don't test all possible swaps. Solution:* [http:](http://thinkpython.com/code/metathesis.py) [//](http://thinkpython.com/code/metathesis.py) [thinkpython.](http://thinkpython.com/code/metathesis.py) [com/](http://thinkpython.com/code/metathesis.py) [code/](http://thinkpython.com/code/metathesis.py) [metathesis.](http://thinkpython.com/code/metathesis.py) [py](http://thinkpython.com/code/metathesis.py) *. Credit: This exercise is inspired by an example at* [http:](http://puzzlers.org) [//](http://puzzlers.org) [puzzlers.](http://puzzlers.org) [org](http://puzzlers.org) *.*
 
-**Exercise 12.6.** *Here's another Car Talk Puzzler (*http: // www. cartalk. com/ content/ puzzlers ):
+**Exercise 12.6.** *Here's another Car Talk Puzzler (*[http:](http://www.cartalk.com/content/puzzlers) [//](http://www.cartalk.com/content/puzzlers) [www.](http://www.cartalk.com/content/puzzlers) [cartalk.](http://www.cartalk.com/content/puzzlers) [com/](http://www.cartalk.com/content/puzzlers) [content/](http://www.cartalk.com/content/puzzlers) [puzzlers](http://www.cartalk.com/content/puzzlers) *):*
 
 > *What is the longest English word, that remains a valid English word, as you remove its letters one at a time?*
 
@@ -5022,24 +5181,21 @@ If you are having trouble keeping track of your data structures, structshape can
 - *3. The wordlist I provided,* words.txt*, doesn't contain single letter words. So you might want to add "I", "a", and the empty string.*
 - *4. To improve the performance of your program, you might want to memoize the words that are known to be reducible.*
 
-*Solution:* http: // thinkpython. com/ code/ reducible. py .
+*Solution:* [http:](http://thinkpython.com/code/reducible.py) [//](http://thinkpython.com/code/reducible.py) [thinkpython.](http://thinkpython.com/code/reducible.py) [com/](http://thinkpython.com/code/reducible.py) [code/](http://thinkpython.com/code/reducible.py) [reducible.](http://thinkpython.com/code/reducible.py) [py](http://thinkpython.com/code/reducible.py) *.*
 
-### **Chapter 13**
+## **Chapter 13**
 
 # **Case study: data structure selection**
 
-### **13.1 Word frequency analysis**
+### **13.1 Word frequency analysis**
 
 As usual, you should at least attempt the following exercises before you read my solutions. **Exercise 13.1.** *Write a program that reads a file, breaks each line into words, strips whitespace and punctuation from the words, and converts them to lowercase.*
 
 *Hint: The* string *module provides strings named* whitespace*, which contains space, tab, newline, etc., and* punctuation *which contains the punctuation characters. Let's see if we can make Python swear:*
 
-```
->>> import string
->>> print string.punctuation
-!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~
-```
-*Also, you might consider using the string methods* strip, replace and translate. **Exercise 13.2.** *Go to Project Gutenberg (*http: // gutenberg. org *) and download your favorite out-of-copyright book in plain text format.*
+>>> import string >>> print string.punctuation !"#$%&'()*+,-./:;<=>?@[\]^_`{|}~
+
+*Also, you might consider using the string methods* strip*,* replace *and* translate*.* **Exercise 13.2.** *Go to Project Gutenberg (*[http:](http://gutenberg.org) [//](http://gutenberg.org) [gutenberg.](http://gutenberg.org) [org](http://gutenberg.org) *) and download your favorite out-of-copyright book in plain text format.*
 
 *Modify your program from the previous exercise to read the book you downloaded, skip over the header information at the beginning of the file, and process the rest of the words as before.*
 
@@ -5049,9 +5205,9 @@ As usual, you should at least attempt the following exercises before you read my
 
 **Exercise 13.3.** *Modify the program from the previous exercise to print the 20 most frequently-used words in the book.*
 
-**Exercise 13.4.** *Modify the previous program to read a word list (see Section 9.1) and then print all the words in the book that are not in the word list. How many of them are typos? How many of them are common words that* should *be in the word list, and how many of them are really obscure?*
+**Exercise 13.4.** *Modify the previous program to read a word list (see Section [9.1)](#page-102-1) and then print all the words in the book that are not in the word list. How many of them are typos? How many of them are common words that* should *be in the word list, and how many of them are really obscure?*
 
-### **13.2 Random numbers**
+#### **13.2 Random numbers**
 
 Given the same inputs, most computer programs generate the same outputs every time, so they are said to be **deterministic**. Determinism is usually a good thing, since we expect the same calculation to yield the same result. For some applications, though, we want the computer to be unpredictable. Games are an obvious example, but there are more.
 
@@ -5061,7 +5217,9 @@ The random module provides functions that generate pseudorandom numbers (which I
 
 The function random returns a random float between 0.0 and 1.0 (including 0.0 but not 1.0). Each time you call random, you get the next number in a long series. To see a sample, run this loop:
 
+```
 import random
+```
 
 ```
 for i in range(10):
@@ -5087,7 +5245,7 @@ To choose an element from a sequence at random, you can use choice:
 ```
 The random module also provides functions to generate random values from continuous distributions including Gaussian, exponential, gamma, and a few more.
 
-**Exercise 13.5.** *Write a function named* choose_from_hist *that takes a histogram as defined in Section 11.1 and returns a random value from the histogram, chosen with probability in proportion to frequency. For example, for this histogram:*
+**Exercise 13.5.** *Write a function named* choose_from_hist *that takes a histogram as defined in Section [11.1](#page-123-0) and returns a random value from the histogram, chosen with probability in proportion to frequency. For example, for this histogram:*
 
 ```
 >>> t = ['a', 'a', 'b']
@@ -5095,17 +5253,16 @@ The random module also provides functions to generate random values from continu
 >>> print hist
 {'a': 2, 'b': 1}
 ```
-*your function should return* 'a' *with probability* 2/3 and 'b' *with probability* 1/3.
+*your function should return* 'a' *with probability* 2/3 *and* 'b' *with probability* 1/3*.*
 
-### **13.3 Word histogram**
+#### **13.3 Word histogram**
 
-You should attempt the previous exercises before you go on. You can download my solution from http://thinkpython.com/code/analyze_book.py. You will also need http: //thinkpython.com/code/emma.txt.
+You should attempt the previous exercises before you go on. You can download my solution from . You will also need [http:](http://thinkpython.com/code/emma.txt) [//thinkpython.com/code/emma.txt](http://thinkpython.com/code/emma.txt).
 
 Here is a program that reads a file and builds a histogram of the words in the file:
 
-import string
-
 ```
+import string
 def process_file(filename):
     hist = dict()
     fp = open(filename)
@@ -5118,8 +5275,9 @@ def process_line(line, hist):
         word = word.strip(string.punctuation + string.whitespace)
         word = word.lower()
         hist[word] = hist.get(word, 0) + 1
-hist = process_file('emma.txt')
 ```
+hist = process_file('emma.txt')
+
 This program reads emma.txt, which contains the text of *Emma* by Jane Austen.
 
 process_file loops through the lines of the file, passing them one at a time to process_line. The histogram hist is being used as an accumulator.
@@ -5132,9 +5290,8 @@ To count the total number of words in the file, we can add up the frequencies in
 
 ```
 def total_words(hist):
+    return sum(hist.values())
 ```
-return sum(hist.values())
-
 The number of different words is just the number of items in the dictionary:
 
 def different_words(hist):
@@ -5143,9 +5300,15 @@ return len(hist)
 
 Here is some code to print the results:
 
-print 'Total number of words:', total_words(hist) print 'Number of different words:', different_words(hist) And the results: Total number of words: 161080 Number of different words: 7214
+```
+print 'Total number of words:', total_words(hist)
+print 'Number of different words:', different_words(hist)
+```
+And the results:
+
+Total number of words: 161080 Number of different words: 7214
 
-### **13.4 Most common words**
+#### **13.4 Most common words**
 
 To find the most common words, we can apply the DSU pattern; most_common takes a histogram and returns a list of word-frequency tuples, sorted in reverse order by frequency:
 
@@ -5156,25 +5319,20 @@ def most_common(hist):
         t.append((value, key))
     t.sort(reverse=True)
     return t
+```
 Here is a loop that prints the ten most common words:
+
+```
 t = most_common(hist)
 print 'The most common words are:'
 for freq, word in t[0:10]:
     print word, '\t', freq
-And here are the results from Emma:
-The most common words are:
-to 5242
-the 5205
-and 4897
-of 4295
-i 3191
-a 3130
-it 2529
-her 2483
-was 2400
-she 2364
-```
-### **13.5 Optional parameters**
+```
+And here are the results from *Emma*:
+
+The most common words are: to 5242 the 5205 and 4897 of 4295 i 3191 a 3130 it 2529 her 2483 was 2400 she 2364
+
+#### **13.5 Optional parameters**
 
 We have seen built-in functions and methods that take a variable number of arguments. It is possible to write user-defined functions with optional arguments, too. For example, here is a function that prints the most common words in a histogram
 
@@ -5189,11 +5347,19 @@ The first parameter is required; the second is optional. The **default value** o
 
 If you only provide one argument:
 
-print_most_common(hist) num gets the default value. If you provide two arguments: print_most_common(hist, 20) num gets the value of the argument instead. In other words, the optional argument **overrides** the default value.
+```
+print_most_common(hist)
+```
+num gets the default value. If you provide two arguments:
+
+```
+print_most_common(hist, 20)
+```
+num gets the value of the argument instead. In other words, the optional argument **overrides** the default value.
 
 If a function has both required and optional parameters, all the required parameters have to come first, followed by the optional ones.
 
-### **13.6 Dictionary subtraction**
+#### **13.6 Dictionary subtraction**
 
 Finding the words from the book that are not in the word list from words.txt is a problem you might recognize as set subtraction; that is, we want to find all the words from one set (the words in the book) that are not in another set (the words in the list).
 
@@ -5204,29 +5370,38 @@ def subtract(d1, d2):
     res = dict()
     for key in d1:
         if key not in d2:
-             res[key] = None
+            res[key] = None
     return res
-To find the words in the book that are not in words.txt, we can use process_file to build
 ```
-a histogram for words.txt, and then subtract: words = process_file('words.txt')
+To find the words in the book that are not in words.txt, we can use process_file to build a histogram for words.txt, and then subtract:
 
+```
+words = process_file('words.txt')
 diff = subtract(hist, words)
+```
 
-print "The words in the book that aren't in the word list are:" for word in diff.keys():
-
+```
+print "The words in the book that aren't in the word list are:"
+for word in diff.keys():
+```
 print word,
 
 Here are some of the results from *Emma*:
 
-The words in the book that aren't in the word list are: rencontre jane's blanche woodhouses disingenuousness
-
+```
+The words in the book that aren't in the word list are:
+ rencontre jane's blanche woodhouses disingenuousness
 friend's venice apartment ...
-
+```
 Some of these words are names and possessives. Others, like "rencontre," are no longer in common use. But a few are common words that should really be in the list!
 
-**Exercise 13.6.** *Python provides a data structure called* set *that provides many common set operations. Read the documentation at* http: // docs. python. org/ 2/ library/ stdtypes. html# types-set *and write a program that uses set subtraction to find words in the book that are not in the word list. Solution:* http: // thinkpython. com/ code/ analyze_ book2. py .
-
-### **13.7 Random words**
+```
+Exercise 13.6. Python provides a data structure called set that provides many common set opera-
+tions. Read the documentation at http: // docs. python. org/ 2/ library/ stdtypes. html#
+types-set and write a program that uses set subtraction to find words in the book that are not in
+the word list. Solution: http: // thinkpython. com/ code/ analyze_ book2. py .
+```
+#### **13.7 Random words**
 
 To choose a random word from the histogram, the simplest algorithm is to build a list with multiple copies of each word, according to the observed frequency, and then choose from the list:
 
@@ -5236,24 +5411,22 @@ def random_word(h):
     for word, freq in h.items():
         t.extend([word] * freq)
 ```
-
-```
 return random.choice(t)
-```
-The expression [word] * freq creates a list with freq copies of the string word. The extend method is similar to append except that the argument is a sequence. **Exercise 13.7.** *This algorithm works, but it is not very efficient; each time you choose a random*
 
-*word, it rebuilds the list, which is as big as the original book. An obvious improvement is to build the list once and then make multiple selections, but the list is still big.*
+The expression [word] * freq creates a list with freq copies of the string word. The extend method is similar to append except that the argument is a sequence.
+
+**Exercise 13.7.** *This algorithm works, but it is not very efficient; each time you choose a random word, it rebuilds the list, which is as big as the original book. An obvious improvement is to build the list once and then make multiple selections, but the list is still big.*
 
 *An alternative is:*
 
 - *1. Use* keys *to get a list of the words in the book.*
-- *2. Build a list that contains the cumulative sum of the word frequencies (see Exercise 10.3). The last item in this list is the total number of words in the book, n.*
-- *3. Choose a random number from 1 to n. Use a bisection search (See Exercise 10.11) to find the index where the random number would be inserted in the cumulative sum.*
+- *2. Build a list that contains the cumulative sum of the word frequencies (see Exercise [10.3)](#page-113-1). The last item in this list is the total number of words in the book, n.*
+- *3. Choose a random number from 1 to n. Use a bisection search (See Exercise [10.11)](#page-119-1) to find the index where the random number would be inserted in the cumulative sum.*
 - *4. Use the index to find the corresponding word in the word list.*
 
-*Write a program that uses this algorithm to choose a random word from the book. Solution:* http: // thinkpython. com/ code/ analyze_ book3. py .
+*Write a program that uses this algorithm to choose a random word from the book. Solution:* [http:](http://thinkpython.com/code/analyze_book3.py) [//](http://thinkpython.com/code/analyze_book3.py) [thinkpython.](http://thinkpython.com/code/analyze_book3.py) [com/](http://thinkpython.com/code/analyze_book3.py) [code/](http://thinkpython.com/code/analyze_book3.py) [analyze_](http://thinkpython.com/code/analyze_book3.py) [book3.](http://thinkpython.com/code/analyze_book3.py) [py](http://thinkpython.com/code/analyze_book3.py) *.*
 
-### **13.8 Markov analysis**
+#### **13.8 Markov analysis**
 
 If you choose words from the book at random, you can get a sense of the vocabulary, you probably won't get a sentence:
 
@@ -5289,16 +5462,16 @@ In this example the length of the prefix is always two, but you can do Markov an
 - *3. Once your program is working, you might want to try a mash-up: if you analyze text from two or more books, the random text you generate will blend the vocabulary and phrases from the sources in interesting ways.*
 *Credit: This case study is based on an example from Kernighan and Pike,* The Practice of Programming*, Addison-Wesley, 1999.*
 
-You should attempt this exercise before you go on; then you can can download my solution from http://thinkpython.com/code/markov.py. You will also need http:// thinkpython.com/code/emma.txt.
+You should attempt this exercise before you go on; then you can can download my solution from . You will also need [http://](http://thinkpython.com/code/emma.txt) [thinkpython.com/code/emma.txt](http://thinkpython.com/code/emma.txt).
 
-### **13.9 Data structures**
+#### **13.9 Data structures**
 
 Using Markov analysis to generate random text is fun, but there is also a point to this exercise: data structure selection. In your solution to the previous exercises, you had to choose:
 
 - How to represent the prefixes.
-• How to represent the collection of possible suffixes.
-
+- How to represent the collection of possible suffixes.
 - How to represent the mapping from each prefix to the collection of possible suffixes.
+
 Ok, the last one is easy; the only mapping type we have seen is a dictionary, so it is the natural choice.
 
 For the prefixes, the most obvious options are string, list of strings, or tuple of strings. For the suffixes, one option is a list; another is a histogram (dictionary).
@@ -5307,14 +5480,15 @@ How should you choose? The first step is to think about the operations you will
 
 Your first choice might be a list, since it is easy to add and remove elements, but we also need to be able to use the prefixes as keys in a dictionary, so that rules out lists. With tuples, you can't append or remove, but you can use the addition operator to form a new tuple:
 
-- def shift(prefix, word):
-return prefix[1:] + (word,)
-
+```
+def shift(prefix, word):
+    return prefix[1:] + (word,)
+```
 shift takes a tuple of words, prefix, and a string, word, and forms a new tuple that has all the words in prefix except the first, and word added to the end.
 
 For the collection of suffixes, the operations we need to perform include adding a new suffix (or increasing the frequency of an existing one), and choosing a random suffix.
 
-Adding a new suffix is equally easy for the list implementation or the histogram. Choosing a random element from a list is easy; choosing from a histogram is harder to do efficiently (see Exercise 13.7).
+Adding a new suffix is equally easy for the list implementation or the histogram. Choosing a random element from a list is easy; choosing from a histogram is harder to do efficiently (see Exercise [13.7)](#page-149-1).
 
 So far we have been talking mostly about ease of implementation, but there are other factors to consider in choosing data structures. One is run time. Sometimes there is a theoretical reason to expect one data structure to be faster than other; for example, I mentioned that the in operator is faster for dictionaries than for lists, at least when the number of elements is large.
 
@@ -5324,7 +5498,7 @@ The other factor to consider is storage space. For example, using a histogram fo
 
 One final thought: in this discussion, I have implied that we should use one data structure for both analysis and generation. But since these are separate phases, it would also be possible to use one structure for analysis and then convert to another structure for generation. This would be a net win if the time saved during generation exceeded the time spent in conversion.
 
-### **13.10 Debugging**
+#### **13.10 Debugging**
 
 When you are debugging a program, and especially if you are working on a hard bug, there are four things to try:
 
@@ -5349,39 +5523,39 @@ Beginning programmers are often reluctant to retreat because they can't stand to
 
 Finding a hard bug requires reading, running, ruminating, and sometimes retreating. If you get stuck on one of these activities, try the others.
 
-### **13.11 Glossary**
-
-**deterministic:** Pertaining to a program that does the same thing each time it runs, given the same inputs.
+#### **13.11 Glossary**
 
+- **deterministic:** Pertaining to a program that does the same thing each time it runs, given the same inputs.
 - **pseudorandom:** Pertaining to a sequence of numbers that appear to be random, but are generated by a deterministic program.
+
 **default value:** The value given to an optional parameter if no argument is provided.
 
 **override:** To replace a default value with an argument.
 
 - **benchmarking:** The process of choosing between data structures by implementing alternatives and testing them on a sample of the possible inputs.
-### **13.12 Exercises**
+#### **13.12 Exercises**
 
 **Exercise 13.9.** *The "rank" of a word is its position in a list of words sorted by frequency: the most common word has rank 1, the second most common has rank 2, etc.*
 
-*Zipf's law describes a relationship between the ranks and frequencies of words in natural languages* (http: // en. wikipedia. org/ wiki/ Zipf's_ law *). Specifically, it predicts that the frequency, f , of the word with rank r is:*
+*Zipf's law describes a relationship between the ranks and frequencies of words in natural languages (*[http:](http://en.wikipedia.org/wiki/Zipf) [//](http://en.wikipedia.org/wiki/Zipf) [en.](http://en.wikipedia.org/wiki/Zipf) [wikipedia.](http://en.wikipedia.org/wiki/Zipf) [org/](http://en.wikipedia.org/wiki/Zipf) [wiki/](http://en.wikipedia.org/wiki/Zipf) [Zipf's_](http://en.wikipedia.org/wiki/Zipf) [law](http://en.wikipedia.org/wiki/Zipf) *). Specifically, it predicts that the frequency, f , of the word with rank r is:*
 
-f = cr−s
+$f = cr^{-s} $
 
 *where s and c are parameters that depend on the language and the text. If you take the logarithm of both sides of this equation, you get:*
 
-log f = log c − slog r
+$$\log f = \log c - s \log r$$
 
 *So if you plot log f versus log r, you should get a straight line with slope* −*s and intercept log c.*
 
-*Write a program that reads a text from a file, counts word frequencies, and prints one line for each word, in descending order of frequency, with log f and log r. Use the graphing program of your choice to plot the results and check whether they form a straight line. Can you estimate the value of* s?
+*Write a program that reads a text from a file, counts word frequencies, and prints one line for each word, in descending order of frequency, with log f and log r. Use the graphing program of your choice to plot the results and check whether they form a straight line. Can you estimate the value of s?*
 
-*Solution:* http: // thinkpython. com/ code/ zipf. py *. To make the plots, you might have to install matplotlib (see* http: // matplotlib. sourceforge. net/ ).
+*Solution:* [http:](http://thinkpython.com/code/zipf.py) [//](http://thinkpython.com/code/zipf.py) [thinkpython.](http://thinkpython.com/code/zipf.py) [com/](http://thinkpython.com/code/zipf.py) [code/](http://thinkpython.com/code/zipf.py) [zipf.](http://thinkpython.com/code/zipf.py) [py](http://thinkpython.com/code/zipf.py) *. To make the plots, you might have to install matplotlib (see* [http:](http://matplotlib.sourceforge.net/) [//](http://matplotlib.sourceforge.net/) [matplotlib.](http://matplotlib.sourceforge.net/) [sourceforge.](http://matplotlib.sourceforge.net/) [net/](http://matplotlib.sourceforge.net/) *).*
 
-### **Chapter 14**
+## **Chapter 14**
 
 # **Files**
 
-### **14.1 Persistence**
+#### **14.1 Persistence**
 
 Most of the programs we have seen so far are transient in the sense that they run for a short time and produce some output, but when they end, their data disappears. If you run the program again, it starts with a clean slate.
 
@@ -5393,9 +5567,9 @@ One of the simplest ways for programs to maintain their data is by reading and w
 
 An alternative is to store the state of the program in a database. In this chapter I will present a simple database and a module, pickle, that makes it easy to store program data.
 
-### **14.2 Reading and writing**
+#### **14.2 Reading and writing**
 
-A text file is a sequence of characters stored on a permanent medium like a hard drive, flash memory, or CD-ROM. We saw how to open and read a file in Section 9.1.
+A text file is a sequence of characters stored on a permanent medium like a hard drive, flash memory, or CD-ROM. We saw how to open and read a file in Section [9.1.](#page-102-1)
 
 To write a file, you have to open it with mode 'w' as a second parameter:
 
@@ -5408,20 +5582,17 @@ If the file already exists, opening it in write mode clears out the old data and
 
 The write method puts data into the file.
 
-```
->>> line1 = "This here's the wattle,\n"
->>> fout.write(line1)
-Again, the file object keeps track of where it is, so if you call write again, it adds the new
-data to the end.
->>> line2 = "the emblem of our land.\n"
->>> fout.write(line2)
+>>> line1 = "This here's the wattle,\n" >>> fout.write(line1)
+
+Again, the file object keeps track of where it is, so if you call write again, it adds the new data to the end.
+
+>>> line2 = "the emblem of our land.\n" >>> fout.write(line2)
+
 When you are done writing, you have to close the file.
-```
 
-```
 >>> fout.close()
-```
-### **14.3 Format operator**
+
+#### **14.3 Format operator**
 
 The argument of write has to be a string, so if we want to put other values in a file, we have to convert them to strings. The easiest way to do that is with str:
 
@@ -5467,9 +5638,9 @@ TypeError: illegal argument type for built-in operation
 ```
 In the first example, there aren't enough elements; in the second, the element is the wrong type.
 
-The format operator is powerful, but it can be difficult to use. You can read more about it at http://docs.python.org/2/library/stdtypes.html#string-formatting.
+The format operator is powerful, but it can be difficult to use. You can read more about it at .
 
-### **14.4 Filenames and paths**
+#### **14.4 Filenames and paths**
 
 Files are organized into **directories** (also called "folders"). Every running program has a "current directory," which is the default directory for most operations. For example, when you open a file for reading, Python looks for it in the current directory.
 
@@ -5490,23 +5661,37 @@ The paths we have seen so far are simple filenames, so they are relative to the
 ```
 >>> os.path.abspath('memo.txt')
 '/home/dinsdale/memo.txt'
+```
 os.path.exists checks whether a file or directory exists:
+
+```
 >>> os.path.exists('memo.txt')
+```
 True
+
 If it exists, os.path.isdir checks whether it's a directory:
+
+```
 >>> os.path.isdir('memo.txt')
 False
 >>> os.path.isdir('music')
 True
+```
 Similarly, os.path.isfile checks whether it's a file.
+
 os.listdir returns a list of the files (and other directories) in the given directory:
+
+```
 >>> os.listdir(cwd)
 ['music', 'photos', 'memo.txt']
-To demonstrate these functions, the following example "walks" through a directory, prints
-the names of all the files, and calls itself recursively on all the directories.
+```
+To demonstrate these functions, the following example "walks" through a directory, prints the names of all the files, and calls itself recursively on all the directories.
+
+```
 def walk(dirname):
+    for name in os.listdir(dirname):
+        path = os.path.join(dirname, name)
 ```
-for name in os.listdir(dirname): path = os.path.join(dirname, name)
 
 ```
 if os.path.isfile(path):
@@ -5514,27 +5699,25 @@ if os.path.isfile(path):
 else:
     walk(path)
 ```
-os.path.join takes a directory and a file name and joins them into a complete path. **Exercise 14.1.** The os *module provides a function called* walk *that is similar to this one but more versatile. Read the documentation and use it to print the names of the files in a given directory and its subdirectories.*
+os.path.join takes a directory and a file name and joins them into a complete path. **Exercise 14.1.** *The* os *module provides a function called* walk *that is similar to this one but more versatile. Read the documentation and use it to print the names of the files in a given directory and its subdirectories.*
 
-*Solution:* http: // thinkpython. com/ code/ walk. py .
+*Solution:* [http:](http://thinkpython.com/code/walk.py) [//](http://thinkpython.com/code/walk.py) [thinkpython.](http://thinkpython.com/code/walk.py) [com/](http://thinkpython.com/code/walk.py) [code/](http://thinkpython.com/code/walk.py) [walk.](http://thinkpython.com/code/walk.py) [py](http://thinkpython.com/code/walk.py) *.*
 
-### **14.5 Catching exceptions**
+## **14.5 Catching exceptions**
 
 A lot of things can go wrong when you try to read and write files. If you try to open a file that doesn't exist, you get an IOError:
 
 ```
 >>> fin = open('bad_file')
 IOError: [Errno 2] No such file or directory: 'bad_file'
-If you don't have permission to access a file:
->>> fout = open('/etc/passwd', 'w')
-IOError: [Errno 13] Permission denied: '/etc/passwd'
-And if you try to open a directory for reading, you get
->>> fin = open('/home')
-IOError: [Errno 21] Is a directory
-To avoid these errors, you could use functions like os.path.exists and os.path.isfile,
-but it would take a lot of time and code to check all the possibilities (if "Errno 21" is any
 ```
-indication, there are at least 21 things that can go wrong).
+If you don't have permission to access a file:
+
+>>> fout = open('/etc/passwd', 'w') IOError: [Errno 13] Permission denied: '/etc/passwd' And if you try to open a directory for reading, you get
+
+>>> fin = open('/home') IOError: [Errno 21] Is a directory
+
+To avoid these errors, you could use functions like os.path.exists and os.path.isfile, but it would take a lot of time and code to check all the possibilities (if "Errno 21" is any indication, there are at least 21 things that can go wrong).
 
 It is better to go ahead and try—and deal with problems if they happen—which is exactly what the try statement does. The syntax is similar to an if statement:
 
@@ -5545,18 +5728,17 @@ try:
         print line
     fin.close()
 except:
+    print 'Something went wrong.'
 ```
-print 'Something went wrong.'
-
 Python starts by executing the try clause. If all goes well, it skips the except clause and proceeds. If an exception occurs, it jumps out of the try clause and executes the except clause.
 
 Handling an exception with a try statement is called **catching** an exception. In this example, the except clause prints an error message that is not very helpful. In general, catching an exception gives you a chance to fix the problem, or try again, or at least end the program gracefully.
 
 **Exercise 14.2.** *Write a function called* sed *that takes as arguments a pattern string, a replacement string, and two filenames; it should read the first file and write the contents into the second file (creating it if necessary). If the pattern string appears anywhere in the file, it should be replaced with the replacement string.*
 
-*If an error occurs while opening, reading, writing or closing files, your program should catch the exception, print an error message, and exit. Solution:* http: // thinkpython. com/ code/ sed. py .
+*If an error occurs while opening, reading, writing or closing files, your program should catch the exception, print an error message, and exit. Solution:* [http:](http://thinkpython.com/code/sed.py) [//](http://thinkpython.com/code/sed.py) [thinkpython.](http://thinkpython.com/code/sed.py) [com/](http://thinkpython.com/code/sed.py) [code/](http://thinkpython.com/code/sed.py) [sed.](http://thinkpython.com/code/sed.py) [py](http://thinkpython.com/code/sed.py) *.*
 
-### **14.6 Databases**
+#### **14.6 Databases**
 
 A **database** is a file that is organized for storing data. Most databases are organized like a dictionary in the sense that they map from keys to values. The biggest difference is that the database is on disk (or other permanent storage), so it persists after the program ends.
 
@@ -5587,16 +5769,14 @@ Photo of John Cleese doing a silly walk.
 ```
 Many dictionary methods, like keys and items, also work with database objects. So does iteration with a for statement.
 
-```
-for key in db:
-    print key
-```
+for key in db: print key
+
 As with other files, you should close the database when you are done:
 
 ```
 >>> db.close()
 ```
-### **14.7 Pickling**
+#### **14.7 Pickling**
 
 A limitation of anydbm is that the keys and values have to be strings. If you try to use any other type, you get an error.
 
@@ -5609,14 +5789,19 @@ pickle.dumps takes an object as a parameter and returns a string representation
 >>> t = [1, 2, 3]
 >>> pickle.dumps(t)
 '(lp0\nI1\naI2\naI3\na.'
-The format isn't obvious to human readers; it is meant to be easy for pickle to interpret.
-pickle.loads ("load string") reconstitutes the object:
+```
+The format isn't obvious to human readers; it is meant to be easy for pickle to interpret. pickle.loads ("load string") reconstitutes the object:
+
+```
 >>> t1 = [1, 2, 3]
 >>> s = pickle.dumps(t1)
 >>> t2 = pickle.loads(s)
 >>> print t2
 [1, 2, 3]
+```
 Although the new object has the same value as the old, it is not (in general) the same object:
+
+```
 >>> t1 == t2
 True
 >>> t1 is t2
@@ -5626,27 +5811,38 @@ In other words, pickling and then unpickling has the same effect as copying the
 
 You can use pickle to store non-strings in a database. In fact, this combination is so common that it has been encapsulated in a module called shelve.
 
-**Exercise 14.3.** *If you download my solution to Exercise 12.4 from* http: // thinkpython. com/ code/ anagram_ sets. py *, you'll see that it creates a dictionary that maps from a sorted string of letters to the list of words that can be spelled with those letters. For example,* 'opst' *maps to the list* ['opts', 'post', 'pots', 'spot', 'stop', 'tops'].
+**Exercise 14.3.** *If you download my solution to Exercise [12.4](#page-142-2) from* [http:](http://thinkpython.com/code/anagram_sets.py) [//](http://thinkpython.com/code/anagram_sets.py) [thinkpython.](http://thinkpython.com/code/anagram_sets.py) [com/](http://thinkpython.com/code/anagram_sets.py) [code/](http://thinkpython.com/code/anagram_sets.py) [anagram_](http://thinkpython.com/code/anagram_sets.py) [sets.](http://thinkpython.com/code/anagram_sets.py) [py](http://thinkpython.com/code/anagram_sets.py) *, you'll see that it creates a dictionary that maps from a sorted string of letters to the list of words that can be spelled with those letters. For example,* 'opst' *maps to the list* ['opts', 'post', 'pots', 'spot', 'stop', 'tops']*.*
 
-*Write a module that imports* anagram_sets *and provides two new functions:* store_anagrams *should store the anagram dictionary in a "shelf;"* read_anagrams *should look up a word and return a list of its anagrams. Solution:* http: // thinkpython. com/ code/ anagram_ db. py
+*Write a module that imports* anagram_sets *and provides two new functions:* store_anagrams *should store the anagram dictionary in a "shelf;"* read_anagrams *should look up a word and return a list of its anagrams. Solution:* [http:](http://thinkpython.com/code/anagram_db.py) [//](http://thinkpython.com/code/anagram_db.py) [thinkpython.](http://thinkpython.com/code/anagram_db.py) [com/](http://thinkpython.com/code/anagram_db.py) [code/](http://thinkpython.com/code/anagram_db.py) [anagram_](http://thinkpython.com/code/anagram_db.py) [db.](http://thinkpython.com/code/anagram_db.py) [py](http://thinkpython.com/code/anagram_db.py)
 
-### **14.8 Pipes**
+### **14.8 Pipes**
 
 Most operating systems provide a command-line interface, also known as a **shell**. Shells usually provide commands to navigate the file system and launch applications. For example, in Unix you can change directories with cd, display the contents of a directory with ls, and launch a web browser by typing (for example) firefox.
 
 Any program that you can launch from the shell can also be launched from Python using a **pipe**. A pipe is an object that represents a running program.
 
-For example, the Unix command ls -l normally displays the contents of the current directory (in long format). You can launch ls with os.popen1 :
-
->>> cmd = 'ls -l' >>> fp = os.popen(cmd)
+For example, the Unix command ls -l normally displays the contents of the current directory (in long format). You can launch ls with os.popen[1](#page-159-1) :
 
+```
+>>> cmd = 'ls -l'
+>>> fp = os.popen(cmd)
+```
 The argument is a string that contains a shell command. The return value is an object that behaves just like an open file. You can read the output from the ls process one line at a time with readline or get the whole thing at once with read:
 
-1popen is deprecated now, which means we are supposed to stop using it and start using the subprocess module. But for simple cases, I find subprocess more complicated than necessary. So I am going to keep using popen until they take it away.
+1popen is deprecated now, which means we are supposed to stop using it and start using the subprocess module. But for simple cases, I find subprocess more complicated than necessary. So I am going to keep using popen until they take it away.
 
->>> res = fp.read() When you are done, you close the pipe like a file: >>> stat = fp.close() >>> print stat None The return value is the final status of the ls process; None means that it ended normally (with no errors).
+>>> res = fp.read()
 
-For example, most Unix systems provide a command called md5sum that reads the contents of a file and computes a "checksum." You can read about MD5 at http://en.wikipedia. org/wiki/Md5. This command provides an efficient way to check whether two files have the same contents. The probability that different contents yield the same checksum is very small (that is, unlikely to happen before the universe collapses).
+When you are done, you close the pipe like a file:
+
+```
+>>> stat = fp.close()
+>>> print stat
+None
+```
+The return value is the final status of the ls process; None means that it ended normally (with no errors).
+
+For example, most Unix systems provide a command called md5sum that reads the contents of a file and computes a "checksum." You can read about MD5 at [http://en.wikipedia.](http://en.wikipedia.org/wiki/Md5) [org/wiki/Md5](http://en.wikipedia.org/wiki/Md5). This command provides an efficient way to check whether two files have the same contents. The probability that different contents yield the same checksum is very small (that is, unlikely to happen before the universe collapses).
 
 You can use a pipe to run md5sum from Python and get the result:
 
@@ -5661,15 +5857,15 @@ You can use a pipe to run md5sum from Python and get the result:
 >>> print stat
 None
 ```
-**Exercise 14.4.** *In a large collection of MP3 files, there may be more than one copy of the same song, stored in different directories or with different file names. The goal of this exercise is to search for duplicates.*
+**Exercise 14.4.** *In a large collection of MP3 files, there may be more than one copy of the same song, stored in different directories or with different file names. The goal of this exercise is to search for duplicates.*
 
 - *1. Write a program that searches a directory and all of its subdirectories, recursively, and returns a list of complete paths for all files with a given suffix (like* .mp3*). Hint:* os.path *provides several useful functions for manipulating file and path names.*
 - *2. To recognize duplicates, you can use* md5sum *to compute a "checksum" for each files. If two files have the same checksum, they probably have the same contents.*
-- *3. To double-check, you can use the Unix command* diff.
+- *3. To double-check, you can use the Unix command* diff*.*
 
-*Solution:* http: // thinkpython. com/ code/ find_ duplicates. py .
+*Solution:* [http:](http://thinkpython.com/code/find_duplicates.py) [//](http://thinkpython.com/code/find_duplicates.py) [thinkpython.](http://thinkpython.com/code/find_duplicates.py) [com/](http://thinkpython.com/code/find_duplicates.py) [code/](http://thinkpython.com/code/find_duplicates.py) [find_](http://thinkpython.com/code/find_duplicates.py) [duplicates.](http://thinkpython.com/code/find_duplicates.py) [py](http://thinkpython.com/code/find_duplicates.py) *.*
 
-### **14.9 Writing modules**
+#### **14.9 Writing modules**
 
 Any file that contains Python code can be imported as a module. For example, suppose you have a file named wc.py with the following code:
 
@@ -5687,10 +5883,16 @@ If you run this program, it reads itself and prints the number of lines in the f
 ```
 >>> import wc
 7
+```
 Now you have a module object wc:
+
+```
 >>> print wc
 
+```
 That provides a function called linecount:
+
+```
 >>> wc.linecount('wc.py')
 7
 ```
@@ -5700,8 +5902,10 @@ The only problem with this example is that when you import the module it execute
 
 Programs that will be imported as modules often use the following idiom:
 
-if __name__ == '__main__': print linecount('wc.py')
-
+```
+if __name__ == '__main__':
+    print linecount('wc.py')
+```
 __name__ is a built-in variable that is set when the program starts. If the program is running as a script, __name__ has the value __main__; in that case, the test code is executed. Otherwise, if the module is being imported, the test code is skipped.
 
 **Exercise 14.5.** *Type this example into a file named* wc.py *and run it as a script. Then run the Python interpreter and* import wc*. What is the value of* __name__ *when the module is being imported?*
@@ -5710,7 +5914,7 @@ __name__ is a built-in variable that is set when the program starts. If the prog
 
 *If you want to reload a module, you can use the built-in function* reload*, but it can be tricky, so the safest thing to do is restart the interpreter and then import the module again.*
 
-### **14.10 Debugging**
+### **14.10 Debugging**
 
 When you are reading and writing files, you might run into problems with whitespace. These errors can be hard to debug because spaces, tabs and newlines are normally invisible:
 
@@ -5730,9 +5934,9 @@ This can be helpful for debugging.
 
 One other problem you might run into is that different systems use different characters to indicate the end of a line. Some systems use a newline, represented \n. Others use a return character, represented \r. Some use both. If you move files between different systems, these inconsistencies might cause problems.
 
-For most systems, there are applications to convert from one format to another. You can find them (and read more about this issue) at http://en.wikipedia.org/wiki/Newline. Or, of course, you could write one yourself.
+For most systems, there are applications to convert from one format to another. You can find them (and read more about this issue) at . Or, of course, you could write one yourself.
 
-### **14.11 Glossary**
+#### **14.11 Glossary**
 
 - **persistent:** Pertaining to a program that runs indefinitely and keeps at least some of its data in permanent storage.
 - **format operator:** An operator, %, that takes a format string and a tuple and generates a string that includes the elements of the tuple formatted as specified by the format string.
@@ -5746,9 +5950,9 @@ For most systems, there are applications to convert from one format to another.
 - **catch:** To prevent an exception from terminating a program using the try and except statements.
 - **database:** A file whose contents are organized like a dictionary with keys that correspond to values.
 
-### **14.12 Exercises**
+#### **14.12 Exercises**
 
-**Exercise 14.6.** The urllib *module provides methods for manipulating URLs and downloading information from the web. The following example downloads and prints a secret message from* thinkpython.com:
+**Exercise 14.6.** *The* urllib *module provides methods for manipulating URLs and downloading information from the web. The following example downloads and prints a secret message from* thinkpython.com*:*
 
 ```
 import urllib
@@ -5758,21 +5962,20 @@ import urllib
 conn = urllib.urlopen('http://thinkpython.com/secret.html')
 for line in conn:
     print line.strip()
-Run this code and follow the instructions you see there. Solution: http: // thinkpython. com/
 ```
-code/ zip_ code. py .
+*Run this code and follow the instructions you see there. Solution:* [http:](http://thinkpython.com/code/zip_code.py) [//](http://thinkpython.com/code/zip_code.py) [thinkpython.](http://thinkpython.com/code/zip_code.py) [com/](http://thinkpython.com/code/zip_code.py) [code/](http://thinkpython.com/code/zip_code.py) [zip_](http://thinkpython.com/code/zip_code.py) [code.](http://thinkpython.com/code/zip_code.py) [py](http://thinkpython.com/code/zip_code.py) *.*
 
-## **Chapter 15**
+## **Chapter 15**
 
 # **Classes and objects**
 
-Code examples from this chapter are available from http://thinkpython.com/code/ Point1.py; solutions to the exercises are available from http://thinkpython.com/code/ Point1_soln.py.
+Code examples from this chapter are available from [http://thinkpython.com/code/](http://thinkpython.com/code/Point1.py) [Point1.py](http://thinkpython.com/code/Point1.py); solutions to the exercises are available from [http://thinkpython.com/code/](http://thinkpython.com/code/Point1_soln.py) [Point1_soln.py](http://thinkpython.com/code/Point1_soln.py).
 
-### **15.1 User-defined types**
+#### **15.1 User-defined types**
 
 We have used many of Python's built-in types; now we are going to define a new type. As an example, we will create a type called Point that represents a point in two-dimensional space.
 
-In mathematical notation, points are often written in parentheses with a comma separating the coordinates. For example, (0, 0) represents the origin, and (x, y) represents the point x units to the right and y units up from the origin.
+In mathematical notation, points are often written in parentheses with a comma separating the coordinates. For example, (0, 0) represents the origin, and (*x*, *y*) represents the point *x* units to the right and *y* units up from the origin.
 
 There are several ways we might represent points in Python:
 
@@ -5794,12 +5997,20 @@ The body is a docstring that explains what the class is for. You can define vari
 
 Defining a class named Point creates a class object.
 
-![](_page_165_Figure_1.jpeg)
-
-Figure 15.1: Object diagram.
-
->>> print Point 
+```
+x
+              y
+                       3.0
+                       4.0
+blank
+            Point
+```
+Figure 15.1: Object diagram.
 
+```
+>>> print Point
+
+```
 Because Point is defined at the top level, its "full name" is __main__.Point.
 
 The class object is like a factory for creating objects. To create a Point, you call Point as if it were a function.
@@ -5813,7 +6024,7 @@ The return value is a reference to a Point object, which we assign to blank. Cre
 
 When you print an instance, Python tells you what class it belongs to and where it is stored in memory (the prefix 0x means that the following number is in hexadecimal).
 
-### **15.2 Attributes**
+### **15.2 Attributes**
 
 You can assign values to an instance using dot notation:
 
@@ -5825,7 +6036,7 @@ This syntax is similar to the syntax for selecting a variable from a module, suc
 
 As a noun, "AT-trib-ute" is pronounced with emphasis on the first syllable, as opposed to "a-TRIB-ute," which is a verb.
 
-The following diagram shows the result of these assignments. A state diagram that shows an object and its attributes is called an **object diagram**; see Figure 15.1.
+The following diagram shows the result of these assignments. A state diagram that shows an object and its attributes is called an **object diagram**; see Figure [15.1.](#page-165-1)
 
 The variable blank refers to a Point object, which contains two attributes. Each attribute refers to a floating-point number.
 
@@ -5855,13 +6066,11 @@ def print_point(p): print '(%g, %g)' % (p.x, p.y)
 
 print_point takes a point as an argument and displays it in mathematical notation. To invoke it, you can pass blank as an argument:
 
-```
->>> print_point(blank)
-(3.0, 4.0)
-```
+>>> print_point(blank) (3.0, 4.0)
+
 Inside the function, p is an alias for blank, so if the function modifies p, blank changes. **Exercise 15.1.** *Write a function called* distance_between_points *that takes two Points as arguments and returns the distance between them.*
 
-### **15.3 Rectangles**
+#### **15.3 Rectangles**
 
 Sometimes it is obvious what the attributes of an object should be, but other times you have to make decisions. For example, imagine you are designing a class to represent rectangles. What attributes would you use to specify the location and size of a rectangle? You can ignore angle; to keep things simple, assume that the rectangle is either vertical or horizontal.
 
@@ -5888,7 +6097,7 @@ box = Rectangle() box.width = 100.0 box.height = 200.0
 
 ![](_page_167_Figure_1.jpeg)
 
-Figure 15.2: Object diagram.
+Figure 15.2: Object diagram.
 
 ```
 box.corner = Point()
@@ -5897,9 +6106,9 @@ box.corner.y = 0.0
 ```
 The expression box.corner.x means, "Go to the object box refers to and select the attribute named corner; then go to that object and select the attribute named x."
 
-Figure 15.2 shows the state of this object. An object that is an attribute of another object is **embedded**.
+Figure [15.2](#page-167-2) shows the state of this object. An object that is an attribute of another object is **embedded**.
 
-### **15.4 Instances as return values**
+#### **15.4 Instances as return values**
 
 Functions can return instances. For example, find_center takes a Rectangle as an argument and returns a Point that contains the coordinates of the center of the Rectangle:
 
@@ -5917,7 +6126,7 @@ Here is an example that passes box as an argument and assigns the resulting Poin
 >>> print_point(center)
 (50.0, 100.0)
 ```
-### **15.5 Objects are mutable**
+#### **15.5 Objects are mutable**
 
 You can change the state of an object by making an assignment to one of its attributes. For example, to change the size of a rectangle without changing its position, you can modify the values of width and height:
 
@@ -5945,9 +6154,9 @@ Here is an example that demonstrates the effect:
 >>> print box.height
 300.0
 ```
-Inside the function, rect is an alias for box, so if the function modifies rect, box changes. **Exercise 15.2.** *Write a function named* move_rectangle *that takes a Rectangle and two numbers named* dx and dy*. It should change the location of the rectangle by adding* dx *to the* x *coordinate of* corner *and adding* dy *to the* y *coordinate of* corner.
+Inside the function, rect is an alias for box, so if the function modifies rect, box changes. **Exercise 15.2.** *Write a function named* move_rectangle *that takes a Rectangle and two numbers named* dx *and* dy*. It should change the location of the rectangle by adding* dx *to the* x *coordinate of* corner *and adding* dy *to the* y *coordinate of* corner*.*
 
-### **15.6 Copying**
+#### **15.6 Copying**
 
 Aliasing can make a program difficult to read because changes in one place might have unexpected effects in another place. It is hard to keep track of all the variables that might refer to a given object.
 
@@ -5978,7 +6187,7 @@ If you use copy.copy to duplicate a Rectangle, you will find that it copies the
 
 ![](_page_169_Figure_1.jpeg)
 
-Figure 15.3: Object diagram.
+Figure 15.3: Object diagram.
 
 ```
 >>> box2 = copy.copy(box)
@@ -5986,9 +6195,8 @@ Figure 15.3: Object diagram.
 False
 >>> box2.corner is box.corner
 True
-Figure 15.3 shows what the object diagram looks like. This operation is called a shallow
 ```
-**copy** because it copies the object and any references it contains, but not the embedded objects.
+Figure [15.3](#page-169-1) shows what the object diagram looks like. This operation is called a **shallow copy** because it copies the object and any references it contains, but not the embedded objects.
 
 For most applications, this is not what you want. In this example, invoking grow_rectangle on one of the Rectangles would not affect the other, but invoking move_rectangle on either would affect both! This behavior is confusing and error-prone.
 
@@ -6000,12 +6208,12 @@ Fortunately, the copy module contains a method named deepcopy that copies not on
 False
 >>> box3.corner is box.corner
 False
-box3 and box are completely separate objects.
-Exercise 15.3. Write a version of move_rectangle that creates and returns a new Rectangle
 ```
-*instead of modifying the old one.*
+box3 and box are completely separate objects.
+
+**Exercise 15.3.** *Write a version of* move_rectangle *that creates and returns a new Rectangle instead of modifying the old one.*
 
-### **15.7 Debugging**
+#### **15.7 Debugging**
 
 When you start working with objects, you are likely to encounter some new exceptions. If you try to access an attribute that doesn't exist, you get an AttributeError:
 
@@ -6013,11 +6221,16 @@ When you start working with objects, you are likely to encounter some new except
 >>> p = Point()
 >>> print p.z
 AttributeError: Point instance has no attribute 'z'
+```
 If you are not sure what type an object is, you can ask:
+
+```
 >>> type(p)
 
-If you are not sure whether an object has a particular attribute, you can use the built-in
-function hasattr:
+```
+If you are not sure whether an object has a particular attribute, you can use the built-in function hasattr:
+
+```
 >>> hasattr(p, 'x')
 True
 >>> hasattr(p, 'z')
@@ -6025,7 +6238,7 @@ False
 ```
 The first argument can be any object; the second argument is a *string* that contains the name of the attribute.
 
-### **15.8 Glossary**
+#### **15.8 Glossary**
 
 **class:** A user-defined type. A class definition creates a new class object.
 
@@ -6040,9 +6253,9 @@ The first argument can be any object; the second argument is a *string* that con
 - **deep copy:** To copy the contents of an object as well as any embedded objects, and any objects embedded in them, and so on; implemented by the deepcopy function in the copy module.
 - **object diagram:** A diagram that shows objects, their attributes, and the values of the attributes.
 
-### **15.9 Exercises**
+#### **15.9 Exercises**
 
-**Exercise 15.4.** *Swampy (see Chapter 4) provides a module named* World*, which defines a userdefined type also called* World*. You can import it like this:*
+**Exercise 15.4.** *Swampy (see Chapter [4)](#page-52-0) provides a module named* World*, which defines a userdefined type also called* World*. You can import it like this:*
 
 from swampy.World import World
 
@@ -6050,18 +6263,19 @@ from swampy.World import World
 
 from World import World
 
-*The following code creates a World object and calls the* mainloop *method, which waits for the user.* world = World()
+*The following code creates a World object and calls the* mainloop *method, which waits for the user.*
 
+```
+world = World()
 world.mainloop()
-
+```
 *A window should appear with a title bar and an empty square. We will use this window to draw Points, Rectangles and other shapes. Add the following lines before calling* mainloop *and run the program again.*
 
+```
 canvas = world.ca(width=500, height=500, background='white')
-
 bbox = [[-150,-100], [150, 100]]
-
 canvas.rectangle(bbox, outline='black', width=2, fill='green4')
-
+```
 *You should see a green rectangle with a black outline. The first line creates a Canvas, which appears in the window as a white square. The Canvas object provides methods like* rectangle *for drawing various shapes.*
 
 bbox *is a list of lists that represents the "bounding box" of the rectangle. The first pair of coordinates is the lower-left corner of the rectangle; the second pair is the upper-right corner.*
@@ -6072,35 +6286,33 @@ canvas.circle([-25,0], 70, outline=None, fill='red')
 
 *The first parameter is the coordinate pair for the center of the circle; the second parameter is the radius.*
 
-*If you add this line to the program, the result should resemble the national flag of Bangladesh (see* http: // en. wikipedia. org/ wiki/ Gallery_ of_ sovereign-state_ flags ).
+*If you add this line to the program, the result should resemble the national flag of Bangladesh (see* [http:](http://en.wikipedia.org/wiki/Gallery_of_sovereign-state_flags) [//](http://en.wikipedia.org/wiki/Gallery_of_sovereign-state_flags) [en.](http://en.wikipedia.org/wiki/Gallery_of_sovereign-state_flags) [wikipedia.](http://en.wikipedia.org/wiki/Gallery_of_sovereign-state_flags) [org/](http://en.wikipedia.org/wiki/Gallery_of_sovereign-state_flags) [wiki/](http://en.wikipedia.org/wiki/Gallery_of_sovereign-state_flags) [Gallery_](http://en.wikipedia.org/wiki/Gallery_of_sovereign-state_flags) [of_](http://en.wikipedia.org/wiki/Gallery_of_sovereign-state_flags) [sovereign-state_](http://en.wikipedia.org/wiki/Gallery_of_sovereign-state_flags) [flags](http://en.wikipedia.org/wiki/Gallery_of_sovereign-state_flags) *).*
 
 - *1. Write a function called* draw_rectangle *that takes a Canvas and a Rectangle as arguments and draws a representation of the Rectangle on the Canvas.*
-- *2. Add an attribute named* color *to your Rectangle objects and modify* draw_rectangle so *that it uses the color attribute as the fill color.*
+- *2. Add an attribute named* color *to your Rectangle objects and modify* draw_rectangle *so that it uses the color attribute as the fill color.*
 - *3. Write a function called* draw_point *that takes a Canvas and a Point as arguments and draws a representation of the Point on the Canvas.*
 - *4. Define a new class called Circle with appropriate attributes and instantiate a few Circle objects. Write a function called* draw_circle *that draws circles on the canvas.*
 - *5. Write a program that draws the national flag of the Czech Republic. Hint: you can draw a polygon like this:*
 
 points = [[-150,-100], [150, 100], [150, -100]] canvas.polygon(points, fill='blue')
 
-*I have written a small program that lists the available colors; you can download it from* http: // thinkpython. com/ code/ color_ list. py .
+*I have written a small program that lists the available colors; you can download it from* [http:](http://thinkpython.com/code/color_list.py) [//](http://thinkpython.com/code/color_list.py) [thinkpython.](http://thinkpython.com/code/color_list.py) [com/](http://thinkpython.com/code/color_list.py) [code/](http://thinkpython.com/code/color_list.py) [color_](http://thinkpython.com/code/color_list.py) [list.](http://thinkpython.com/code/color_list.py) [py](http://thinkpython.com/code/color_list.py) *.*
 
-## **Chapter 16**
+## **Chapter 16**
 
 # **Classes and functions**
 
-Code examples from this chapter are available from http://thinkpython.com/code/ Time1.py.
+Code examples from this chapter are available from [http://thinkpython.com/code/](http://thinkpython.com/code/Time1.py) [Time1.py](http://thinkpython.com/code/Time1.py).
 
-### **16.1 Time**
+#### **16.1 Time**
 
 As another example of a user-defined type, we'll define a class called Time that records the time of day. The class definition looks like this:
 
-class Time(object):
-
 ```
-"""Represents the time of day.
+class Time(object):
+    """Represents the time of day.
+    attributes: hour, minute, second
 ```
-attributes: hour, minute, second """
-
 We can create a new Time object and assign attributes for hours, minutes, and seconds:
 
 ```
@@ -6109,27 +6321,21 @@ time.hour = 11
 time.minute = 59
 time.second = 30
 ```
-The state diagram for the Time object looks like Figure 16.1.
+"""
+
+The state diagram for the Time object looks like Figure [16.1.](#page-173-0)
 
-**Exercise 16.1.** *Write a function called* print_time *that takes a Time object and prints it in the form* hour:minute:second*. Hint: the format sequence* '%.2d' *prints an integer using at least two digits, including a leading zero if necessary.*
+**Exercise 16.1.** *Write a function called* print_time *that takes a Time object and prints it in the form* hour:minute:second*. Hint: the format sequence* '%.2d' *prints an integer using at least two digits, including a leading zero if necessary.*
 
-**Exercise 16.2.** *Write a boolean function called* is_after *that takes two Time objects,* t1 and t2, *and returns* True if t1 *follows* t2 *chronologically and* False *otherwise. Challenge: don't use an* if *statement.*
+**Exercise 16.2.** *Write a boolean function called* is_after *that takes two Time objects,* t1 *and* t2*, and returns* True *if* t1 *follows* t2 *chronologically and* False *otherwise. Challenge: don't use an* if *statement.*
 
-### **16.2 Pure functions**
+#### **16.2 Pure functions**
 
 In the next few sections, we'll write two functions that add time values. They demonstrate two kinds of functions: pure functions and modifiers. They also demonstrate a development plan I'll call **prototype and patch**, which is a way of tackling a complex problem by starting with a simple prototype and incrementally dealing with the complications.
 
-```
-59
-                        30
-             hour
-           minute
-          second
-                        11
-         Time
-time
-```
-Figure 16.1: Object diagram.
+![](_page_173_Figure_1.jpeg)
+
+Figure 16.1: Object diagram.
 
 Here is a simple prototype of add_time:
 
@@ -6184,7 +6390,7 @@ return sum
 
 Although this function is correct, it is starting to get big. We will see a shorter alternative later.
 
-### **16.3 Modifiers**
+#### **16.3 Modifiers**
 
 Sometimes it is useful for a function to modify the objects it gets as parameters. In that case, the changes are visible to the caller. Functions that work this way are called **modifiers**.
 
@@ -6193,11 +6399,13 @@ increment, which adds a given number of seconds to a Time object, can be written
 ```
 def increment(time, seconds):
     time.second += seconds
+    if time.second >= 60:
+        time.second -= 60
+        time.minute += 1
+    if time.minute >= 60:
+        time.minute -= 60
+        time.hour += 1
 ```
-if time.second >= 60: time.second -= 60 time.minute += 1
-
-if time.minute >= 60: time.minute -= 60 time.hour += 1
-
 The first line performs the basic operation; the remainder deals with the special cases we saw before.
 
 Is this function correct? What happens if the parameter seconds is much greater than sixty?
@@ -6212,13 +6420,13 @@ In general, I recommend that you write pure functions whenever it is reasonable
 
 **Exercise 16.4.** *Write a "pure" version of* increment *that creates and returns a new Time object rather than modifying the parameter.*
 
-### **16.4 Prototyping versus planning**
+### **16.4 Prototyping versus planning**
 
 The development plan I am demonstrating is called "prototype and patch." For each function, I wrote a prototype that performed the basic calculation and then tested it, patching errors along the way.
 
 This approach can be effective, especially if you don't yet have a deep understanding of the problem. But incremental corrections can generate code that is unnecessarily complicated—since it deals with many special cases—and unreliable—since it is hard to know if you have found all the errors.
 
-An alternative is **planned development**, in which high-level insight into the problem can make the programming much easier. In this case, the insight is that a Time object is really a three-digit number in base 60 (see http://en.wikipedia.org/wiki/Sexagesimal.)! The second attribute is the "ones column," the minute attribute is the "sixties column," and the hour attribute is the "thirty-six hundreds column."
+An alternative is **planned development**, in which high-level insight into the problem can make the programming much easier. In this case, the insight is that a Time object is really a three-digit number in base 60 (see .)! The second attribute is the "ones column," the minute attribute is the "sixties column," and the hour attribute is the "thirty-six hundreds column."
 
 When we wrote add_time and increment, we were effectively doing addition in base 60, which is why we had to carry from one column to the next.
 
@@ -6250,7 +6458,7 @@ def add_time(t1, t2):
     seconds = time_to_int(t1) + time_to_int(t2)
     return int_to_time(seconds)
 ```
-This version is shorter than the original, and easier to verify. **Exercise 16.5.** *Rewrite* increment *using* time_to_int and int_to_time.
+This version is shorter than the original, and easier to verify. **Exercise 16.5.** *Rewrite* increment *using* time_to_int *and* int_to_time*.*
 
 In some ways, converting from base 60 to base 10 and back is harder than just dealing with times. Base conversion is more abstract; our intuition for dealing with time values is better.
 
@@ -6260,7 +6468,7 @@ It is also easier to add features later. For example, imagine subtracting two Ti
 
 Ironically, sometimes making a problem harder (or more general) makes it easier (because there are fewer special cases and fewer opportunities for error).
 
-### **16.5 Debugging**
+#### **16.5 Debugging**
 
 A Time object is well-formed if the values of minute and second are between 0 and 60 (including 0 but not 60) and if hour is positive. hour and minute should be integral values, but we might allow second to have a fraction part.
 
@@ -6295,39 +6503,38 @@ def add_time(t1, t2):
 ```
 assert statements are useful because they distinguish code that deals with normal conditions from code that checks for errors.
 
-### **16.6 Glossary**
+#### **16.6 Glossary**
 
 **prototype and patch:** A development plan that involves writing a rough draft of a program, testing, and correcting errors as they are found.
 
-**planned development:** A development plan that involves high-level insight into the problem and more planning than incremental development or prototype development.
-
+- **planned development:** A development plan that involves high-level insight into the problem and more planning than incremental development or prototype development.
 - **pure function:** A function that does not modify any of the objects it receives as arguments. Most pure functions are fruitful.
 - **modifier:** A function that changes one or more of the objects it receives as arguments. Most modifiers are fruitless.
 - **functional programming style:** A style of program design in which the majority of functions are pure.
 - **invariant:** A condition that should always be true during the execution of a program.
 
-### **16.7 Exercises**
+#### **16.7 Exercises**
 
-Code examples from this chapter are available from http://thinkpython.com/code/ Time1.py; solutions to these exercises are available from http://thinkpython.com/code/ Time1_soln.py.
+Code examples from this chapter are available from [http://thinkpython.com/code/](http://thinkpython.com/code/Time1.py) [Time1.py](http://thinkpython.com/code/Time1.py); solutions to these exercises are available from [http://thinkpython.com/code/](http://thinkpython.com/code/Time1_soln.py) [Time1_soln.py](http://thinkpython.com/code/Time1_soln.py).
 
 **Exercise 16.6.** *Write a function called* mul_time *that takes a Time object and a number and returns a new Time object that contains the product of the original Time and the number.*
 
 *Then use* mul_time *to write a function that takes a Time object that represents the finishing time in a race, and a number that represents the distance, and returns a Time object that represents the average pace (time per mile).*
 
-**Exercise 16.7.** The datetime *module provides* date and time *objects that are similar to the Date and Time objects in this chapter, but they provide a rich set of methods and operators. Read the documentation at* http: // docs. python. org/ 2/ library/ datetime. html .
+**Exercise 16.7.** *The* datetime *module provides* date *and* time *objects that are similar to the Date and Time objects in this chapter, but they provide a rich set of methods and operators. Read the documentation at* [http:](http://docs.python.org/2/library/datetime.html) [//](http://docs.python.org/2/library/datetime.html) [docs.](http://docs.python.org/2/library/datetime.html) [python.](http://docs.python.org/2/library/datetime.html) [org/](http://docs.python.org/2/library/datetime.html) [2/](http://docs.python.org/2/library/datetime.html) [library/](http://docs.python.org/2/library/datetime.html) [datetime.](http://docs.python.org/2/library/datetime.html) [html](http://docs.python.org/2/library/datetime.html) *.*
 
 - *1. Use the* datetime *module to write a program that gets the current date and prints the day of the week.*
 - *2. Write a program that takes a birthday as input and prints the user's age and the number of days, hours, minutes and seconds until their next birthday.*
 - *3. For two people born on different days, there is a day when one is twice as old as the other. That's their Double Day. Write a program that takes two birthdays and computes their Double Day.*
 - *4. For a little more challenge, write the more general version that computes the day when one person is n times older than the other.*
 
-## **Chapter 17**
+## **Chapter 17**
 
 # **Classes and methods**
 
-Code examples from this chapter are available from http://thinkpython.com/code/ Time2.py.
+Code examples from this chapter are available from [http://thinkpython.com/code/](http://thinkpython.com/code/Time2.py) [Time2.py](http://thinkpython.com/code/Time2.py).
 
-### **17.1 Object-oriented features**
+#### **17.1 Object-oriented features**
 
 Python is an **object-oriented programming language**, which means that it provides features that support object-oriented programming.
 
@@ -6336,7 +6543,7 @@ It is not easy to define object-oriented programming, but we have already seen s
 - Programs are made up of object definitions and function definitions, and most of the computation is expressed in terms of operations on objects.
 - Each object definition corresponds to some object or concept in the real world, and the functions that operate on that object correspond to the ways real-world objects interact.
 
-For example, the Time class defined in Chapter 16 corresponds to the way people record the time of day, and the functions we defined correspond to the kinds of things people do with times. Similarly, the Point and Rectangle classes correspond to the mathematical concepts of a point and a rectangle.
+For example, the Time class defined in Chapter [16](#page-172-0) corresponds to the way people record the time of day, and the functions we defined correspond to the kinds of things people do with times. Similarly, the Point and Rectangle classes correspond to the mathematical concepts of a point and a rectangle.
 
 So far, we have not taken advantage of the features Python provides to support objectoriented programming. These features are not strictly necessary; most of them provide alternative syntax for things we have already done. But in many cases, the alternative is more concise and more accurately conveys the structure of the program.
 
@@ -6351,9 +6558,9 @@ Methods are semantically the same as functions, but there are two syntactic diff
 
 In the next few sections, we will take the functions from the previous two chapters and transform them into methods. This transformation is purely mechanical; you can do it simply by following a sequence of steps. If you are comfortable converting from one form to another, you will be able to choose the best form for whatever you are doing.
 
-### **17.2 Printing objects**
+### **17.2 Printing objects**
 
-In Chapter 16, we defined a class named Time and in Exercise 16.1, you wrote a function named print_time:
+In Chapter [16,](#page-172-0) we defined a class named Time and in Exercise [16.1,](#page-172-3) you wrote a function named print_time:
 
 ```
 class Time(object):
@@ -6363,10 +6570,17 @@ class Time(object):
 ```
 def print_time(time):
     print '%.2d:%.2d:%.2d' % (time.hour, time.minute, time.second)
-To call this function, you have to pass a Time object as an argument:
 ```
->>> start = Time() >>> start.hour = 9 >>> start.minute = 45 >>> start.second = 00 >>> print_time(start) 09:45:00
+To call this function, you have to pass a Time object as an argument:
 
+```
+>>> start = Time()
+>>> start.hour = 9
+>>> start.minute = 45
+>>> start.second = 00
+>>> print_time(start)
+09:45:00
+```
 To make print_time a method, all we have to do is move the function definition inside the class definition. Notice the change in indentation.
 
 ```
@@ -6406,11 +6620,11 @@ The reason for this convention is an implicit metaphor:
 
 This change in perspective might be more polite, but it is not obvious that it is useful. In the examples we have seen so far, it may not be. But sometimes shifting responsibility from the functions onto the objects makes it possible to write more versatile functions, and makes it easier to maintain and reuse code.
 
-**Exercise 17.1.** *Rewrite* time_to_int *(from Section 16.4) as a method. It is probably not appropriate to rewrite* int_to_time *as a method; what object you would invoke it on?*
+**Exercise 17.1.** *Rewrite* time_to_int *(from Section [16.4)](#page-175-0) as a method. It is probably not appropriate to rewrite* int_to_time *as a method; what object you would invoke it on?*
 
-### **17.3 Another example**
+#### **17.3 Another example**
 
-Here's a version of increment (from Section 16.3) rewritten as a method:
+Here's a version of increment (from Section [16.3)](#page-174-0) rewritten as a method:
 
 ```
 # inside class Time:
@@ -6421,7 +6635,7 @@ def increment(self, seconds):
     seconds += self.time_to_int()
     return int_to_time(seconds)
 ```
-This version assumes that time_to_int is written as a method, as in Exercise 17.1. Also, note that it is a pure function, not a modifier.
+This version assumes that time_to_int is written as a method, as in Exercise [17.1.](#page-180-1) Also, note that it is a pure function, not a modifier.
 
 Here's how you would invoke increment:
 
@@ -6436,17 +6650,15 @@ The subject, start, gets assigned to the first parameter, self. The argument, 13
 
 This mechanism can be confusing, especially if you make an error. For example, if you invoke increment with two arguments, you get:
 
+```
 >>> end = start.increment(1337, 460)
-
 TypeError: increment() takes exactly 2 arguments (3 given)
-
+```
 The error message is initially confusing, because there are only two arguments in parentheses. But the subject is also considered an argument, so all together that's three.
 
-### **17.4 A more complicated example**
-
-is_after (from Exercise 16.2) is slightly more complicated because it takes two Time objects as parameters. In this case it is conventional to name the first parameter self and the second parameter other:
+#### **17.4 A more complicated example**
 
-# inside class Time:
+is_after (from Exercise [16.2)](#page-172-4) is slightly more complicated because it takes two Time objects as parameters. In this case it is conventional to name the first parameter self and the second parameter other:
 
 ```
 def is_after(self, other):
@@ -6458,29 +6670,36 @@ True
 
 One nice thing about this syntax is that it almost reads like English: "end is after start?"
 
-### **17.5 The init method**
+### **17.5 The init method**
+
+# inside class Time:
 
 The init method (short for "initialization") is a special method that gets invoked when an object is instantiated. Its full name is __init__ (two underscore characters, followed by init, and then two more underscores). An init method for the Time class might look like this:
 
+```
 # inside class Time:
+```
 
 ```
 def __init__(self, hour=0, minute=0, second=0):
-        self.hour = hour
-        self.minute = minute
-        self.second = second
-It is common for the parameters of __init__ to have the same names as the attributes. The
+    self.hour = hour
+    self.minute = minute
+    self.second = second
 ```
-statement
+It is common for the parameters of __init__ to have the same names as the attributes. The statement
 
+```
 self.hour = hour
-
+```
 stores the value of the parameter hour as an attribute of self.
 
 The parameters are optional, so if you call Time with no arguments, you get the default values.
 
->>> time = Time() >>> time.print_time() 00:00:00
-
+```
+>>> time = Time()
+>>> time.print_time()
+00:00:00
+```
 If you provide one argument, it overrides hour:
 
 ```
@@ -6495,9 +6714,11 @@ If you provide two arguments, they override hour and minute.
 >>> time.print_time()
 09:45:00
 ```
-And if you provide three arguments, they override all three default values. **Exercise 17.2.** *Write an init method for the* Point *class that takes* x and y *as optional parameters and assigns them to the corresponding attributes.*
+And if you provide three arguments, they override all three default values.
+
+**Exercise 17.2.** *Write an init method for the* Point *class that takes* x *and* y *as optional parameters and assigns them to the corresponding attributes.*
 
-### **17.6 The** __str__ **method**
+## **17.6 The** __str__ **method**
 
 __str__ is a special method, like __init__, that is supposed to return a string representation of an object.
 
@@ -6505,11 +6726,8 @@ For example, here is a str method for Time objects:
 
 ```
 # inside class Time:
-```
-
-```
-def __str__(self):
-    return '%.2d:%.2d:%.2d' % (self.hour, self.minute, self.second)
+    def __str__(self):
+        return '%.2d:%.2d:%.2d' % (self.hour, self.minute, self.second)
 ```
 When you print an object, Python invokes the str method:
 
@@ -6522,7 +6740,7 @@ When I write a new class, I almost always start by writing __init__, which makes
 
 **Exercise 17.3.** *Write a* str *method for the* Point *class. Create a Point object and print it.*
 
-### **17.7 Operator overloading**
+#### **17.7 Operator overloading**
 
 By defining other special methods, you can specify the behavior of operators on userdefined types. For example, if you define a method named __add__ for the Time class, you can use the + operator on Time objects.
 
@@ -6536,19 +6754,25 @@ Here is what the definition might look like:
 ```
 And here is how you could use it:
 
->>> start = Time(9, 45) >>> duration = Time(1, 35) >>> print start + duration 11:20:00
-
+```
+>>> start = Time(9, 45)
+>>> duration = Time(1, 35)
+>>> print start + duration
+11:20:00
+```
 When you apply the + operator to Time objects, Python invokes __add__. When you print the result, Python invokes __str__. So there is quite a lot happening behind the scenes!
 
-Changing the behavior of an operator so that it works with user-defined types is called op**erator overloading**. For every operator in Python there is a corresponding special method, like __add__. For more details, see http://docs.python.org/2/reference/datamodel. html#specialnames.
+Changing the behavior of an operator so that it works with user-defined types is called **operator overloading**. For every operator in Python there is a corresponding special method, like __add__. For more details, see [http://docs.python.org/2/reference/datamodel.](http://docs.python.org/2/reference/datamodel.html#specialnames) [html#specialnames](http://docs.python.org/2/reference/datamodel.html#specialnames).
 
 **Exercise 17.4.** *Write an* add *method for the Point class.*
 
-### **17.8 Type-based dispatch**
+#### **17.8 Type-based dispatch**
 
 In the previous section we added two Time objects, but you also might want to add an integer to a Time object. The following is a version of __add__ that checks the type of other and invokes either add_time or increment:
 
+```
 # inside class Time:
+```
 
 ```
 def __add__(self, other):
@@ -6579,27 +6803,36 @@ Here are examples that use the + operator with different types:
 ```
 Unfortunately, this implementation of addition is not commutative. If the integer is the first operand, you get
 
->>> print 1337 + start TypeError: unsupported operand type(s) for +: 'int' and 'instance'
+```
+>>> print 1337 + start
+```
+TypeError: unsupported operand type(s) for +: 'int' and 'instance'
 
 The problem is, instead of asking the Time object to add an integer, Python is asking an integer to add a Time object, and it doesn't know how to do that. But there is a clever solution for this problem: the special method __radd__, which stands for "right-side add." This method is invoked when a Time object appears on the right side of the + operator. Here's the definition:
 
+```
 # inside class Time:
+```
 
 ```
 def __radd__(self, other):
-        return self.__add__(other)
-And here's how it's used:
+    return self.__add__(other)
 ```
->>> print 1337 + start 10:07:17 **Exercise 17.5.** *Write an* add *method for Points that works with either a Point object or a tuple:*
+And here's how it's used:
 
+```
+>>> print 1337 + start
+10:07:17
+Exercise 17.5. Write an add method for Points that works with either a Point object or a tuple:
+```
 - *If the second operand is a Point, the method should return a new Point whose x coordinate is the sum of the x coordinates of the operands, and likewise for the y coordinates.*
 - *If the second operand is a tuple, the method should add the first element of the tuple to the x coordinate and the second element to the y coordinate, and return a new Point with the result.*
 
-### **17.9 Polymorphism**
+#### **17.9 Polymorphism**
 
 Type-based dispatch is useful when it is necessary, but (fortunately) it is not always necessary. Often you can avoid it by writing functions that work correctly for arguments with different types.
 
-Many of the functions we wrote for strings will actually work for any kind of sequence. For example, in Section 11.1 we used histogram to count the number of times each letter appears in a word.
+Many of the functions we wrote for strings will actually work for any kind of sequence. For example, in Section [11.1](#page-123-0) we used histogram to count the number of times each letter appears in a word.
 
 ```
 def histogram(s):
@@ -6634,25 +6867,31 @@ In general, if all of the operations inside a function work with a given type, t
 
 The best kind of polymorphism is the unintentional kind, where you discover that a function you already wrote can be applied to a type you never planned for.
 
-### **17.10 Debugging**
+## **17.10 Debugging**
 
 It is legal to add attributes to objects at any point in the execution of a program, but if you are a stickler for type theory, it is a dubious practice to have objects of the same type with different attribute sets. It is usually a good idea to initialize all of an object's attributes in the init method.
 
-If you are not sure whether an object has a particular attribute, you can use the built-in function hasattr (see Section 15.7).
+If you are not sure whether an object has a particular attribute, you can use the built-in function hasattr (see Section [15.7)](#page-169-0).
 
 Another way to access the attributes of an object is through the special attribute __dict__, which is a dictionary that maps attribute names (as strings) and values:
 
->>> p = Point(3, 4) >>> print p.__dict__ {'y': 4, 'x': 3}
-
-For purposes of debugging, you might find it useful to keep this function handy: def print_attributes(obj): for attr in obj.__dict__:
-
-print attr, getattr(obj, attr)
+```
+>>> p = Point(3, 4)
+>>> print p.__dict__
+{'y': 4, 'x': 3}
+```
+For purposes of debugging, you might find it useful to keep this function handy:
 
+```
+def print_attributes(obj):
+    for attr in obj.__dict__:
+        print attr, getattr(obj, attr)
+```
 print_attributes traverses the items in the object's dictionary and prints each attribute name and its corresponding value.
 
 The built-in function getattr takes an object and an attribute name (as a string) and returns the attribute's value.
 
-### **17.11 Interface and implementation**
+## **17.11 Interface and implementation**
 
 One of the goals of object-oriented design is to make software more maintainable, which means that you can keep the program working when other parts of the system change, and modify the program to meet new requirements.
 
@@ -6666,11 +6905,11 @@ As an alternative, we could replace these attributes with a single integer repre
 
 After you deploy a new class, you might discover a better implementation. If other parts of the program are using your class, it might be time-consuming and error-prone to change the interface.
 
-But if you designed the interface carefully, you can change the implementation without changing the interface, which means that other parts of the program don't have to change. Keeping the interface separate from the implementation means that you have to hide the attributes. Code in other parts of the program (outside the class definition) should use methods to read and modify the state of the object. They should not access the attributes directly. This principle is called **information hiding**; see http://en.wikipedia.org/wiki/ Information_hiding.
+But if you designed the interface carefully, you can change the implementation without changing the interface, which means that other parts of the program don't have to change. Keeping the interface separate from the implementation means that you have to hide the attributes. Code in other parts of the program (outside the class definition) should use methods to read and modify the state of the object. They should not access the attributes directly. This principle is called **information hiding**; see [http://en.wikipedia.org/wiki/](http://en.wikipedia.org/wiki/Information_hiding) [Information_hiding](http://en.wikipedia.org/wiki/Information_hiding).
 
-**Exercise 17.6.** *Download the code from this chapter (*http: // thinkpython. com/ code/ Time2. py *). Change the attributes of* Time *to be a single integer representing seconds since midnight. Then modify the methods (and the function* int_to_time*) to work with the new implementation. You should not have to modify the test code in* main*. When you are done, the output should be the same as before. Solution:* http: // thinkpython. com/ code/ Time2_ soln. py
+**Exercise 17.6.** *Download the code from this chapter (http://thinkpython.com/code/ Time2.py). Change the attributes of* Time *to be a single integer representing seconds since mid night. Then modify the methods (and the function* int_to_time*) to work with the new implemen tation. You should not have to modify the test code in* main*. When you are done, the output should be the same as before. Solution:* http://thinkpython.com/code/Time2_soln.py
 
-### **17.12 Glossary**
+#### **17.12 Glossary**
 
 - **object-oriented language:** A language that provides features, such as user-defined classes and method syntax, that facilitate object-oriented programming.
 - **object-oriented programming:** A style of programming in which data and the operations that manipulate it are organized into classes and methods.
@@ -6681,38 +6920,31 @@ But if you designed the interface carefully, you can change the implementation w
 - **polymorphic:** Pertaining to a function that can work with more than one type.
 - **information hiding:** The principle that the interface provided by an object should not depend on its implementation, in particular the representation of its attributes.
 
-### **17.13 Exercises**
+#### **17.13 Exercises**
 
 **Exercise 17.7.** *This exercise is a cautionary tale about one of the most common, and difficult to find, errors in Python. Write a definition for a class named* Kangaroo *with the following methods:*
 
 - *1. An* __init__ *method that initializes an attribute named* pouch_contents *to an empty list.*
-- *2. A method named* put_in_pouch *that takes an object of any type and adds it to* pouch_contents.
+- *2. A method named* put_in_pouch *that takes an object of any type and adds it to* pouch_contents*.*
 - *3. A* __str__ *method that returns a string representation of the Kangaroo object and the contents of the pouch.*
 
-*Test your code by creating two* Kangaroo *objects, assigning them to variables named* kanga and roo*, and then adding* roo *to the contents of* kanga*'s pouch.*
+*Test your code by creating two* Kangaroo *objects, assigning them to variables named* kanga *and* roo*, and then adding* roo *to the contents of* kanga*'s pouch.*
 
-*Download* http: // thinkpython. com/ code/ BadKangaroo. py *. It contains a solution to the previous problem with one big, nasty bug. Find and fix the bug.*
+*Download* [http:](http://thinkpython.com/code/BadKangaroo.py) [//](http://thinkpython.com/code/BadKangaroo.py) [thinkpython.](http://thinkpython.com/code/BadKangaroo.py) [com/](http://thinkpython.com/code/BadKangaroo.py) [code/](http://thinkpython.com/code/BadKangaroo.py) [BadKangaroo.](http://thinkpython.com/code/BadKangaroo.py) [py](http://thinkpython.com/code/BadKangaroo.py) *. It contains a solution to the previous problem with one big, nasty bug. Find and fix the bug.*
 
-*If you get stuck, you can download* http: // thinkpython. com/ code/ GoodKangaroo. py , *which explains the problem and demonstrates a solution.*
+*If you get stuck, you can download* [http:](http://thinkpython.com/code/GoodKangaroo.py) [//](http://thinkpython.com/code/GoodKangaroo.py) [thinkpython.](http://thinkpython.com/code/GoodKangaroo.py) [com/](http://thinkpython.com/code/GoodKangaroo.py) [code/](http://thinkpython.com/code/GoodKangaroo.py) [GoodKangaroo.](http://thinkpython.com/code/GoodKangaroo.py) [py](http://thinkpython.com/code/GoodKangaroo.py) *, which explains the problem and demonstrates a solution.*
 
-**Exercise 17.8.** *Visual is a Python module that provides 3-D graphics. It is not always included in a Python installation, so you might have to install it from your software repository or, if it's not there, from* http: // vpython. org .
+**Exercise 17.8.** *Visual is a Python module that provides 3-D graphics. It is not always included in a Python installation, so you might have to install it from your software repository or, if it's not there, from* [http:](http://vpython.org) [//](http://vpython.org) [vpython.](http://vpython.org) [org](http://vpython.org) *.*
 
 *The following example creates a 3-D space that is 256 units wide, long and high, and sets the "center" to be the point* (128, 128, 128)*. Then it draws a blue sphere.*
 
-```
 from visual import *
-```
 
-```
-scene.range = (256, 256, 256)
-scene.center = (128, 128, 128)
-```
+scene.range = (256, 256, 256) scene.center = (128, 128, 128)
 
-```
-color = (0.1, 0.1, 0.9) # mostly blue
-sphere(pos=scene.center, radius=128, color=color)
-```
-color *is an RGB tuple; that is, the elements are Red-Green-Blue levels between 0.0 and 1.0 (see* http: // en. wikipedia. org/ wiki/ RGB_ color_ model ).
+color = (0.1, 0.1, 0.9) # mostly blue sphere(pos=scene.center, radius=128, color=color)
+
+color *is an RGB tuple; that is, the elements are Red-Green-Blue levels between 0.0 and 1.0 (see* http://en.wikipedia.org/wiki/RGB_color_model*).* 
 
 *If you run this code, you should see a window with a black background and a blue sphere. If you drag the middle button up and down, you can zoom in and out. You can also rotate the scene by dragging the right button, but with only one sphere in the world, it is hard to tell the difference.*
 
@@ -6726,22 +6958,21 @@ for x in t:
             pos = x, y, z
             sphere(pos=pos, radius=10, color=color)
 ```
-*1. Put this code in a script and make sure it works for you.*
-
+- *1. Put this code in a script and make sure it works for you.*
 - *2. Modify the program so that each sphere in the cube has the color that corresponds to its position in RGB space. Notice that the coordinates are in the range 0–255, but the RGB tuples are in the range 0.0–1.0.*
-- *3. Download* http: // thinkpython. com/ code/ color_ list. py *and use the function* read_colors *to generate a list of the available colors on your system, their names and RGB values. For each named color draw a sphere in the position that corresponds to its RGB values.*
+- *3. Download* [http:](http://thinkpython.com/code/color_list.py) [//](http://thinkpython.com/code/color_list.py) [thinkpython.](http://thinkpython.com/code/color_list.py) [com/](http://thinkpython.com/code/color_list.py) [code/](http://thinkpython.com/code/color_list.py) [color_](http://thinkpython.com/code/color_list.py) [list.](http://thinkpython.com/code/color_list.py) [py](http://thinkpython.com/code/color_list.py) *and use the function* read_colors *to generate a list of the available colors on your system, their names and RGB values. For each named color draw a sphere in the position that corresponds to its RGB values.*
 
-*You can see my solution at* http: // thinkpython. com/ code/ color_ space. py .
+*You can see my solution at* [http:](http://thinkpython.com/code/color_space.py) [//](http://thinkpython.com/code/color_space.py) [thinkpython.](http://thinkpython.com/code/color_space.py) [com/](http://thinkpython.com/code/color_space.py) [code/](http://thinkpython.com/code/color_space.py) [color_](http://thinkpython.com/code/color_space.py) [space.](http://thinkpython.com/code/color_space.py) [py](http://thinkpython.com/code/color_space.py) *.*
 
-## **Chapter 18**
+## **Chapter 18**
 
 # **Inheritance**
 
-In this chapter I present classes to represent playing cards, decks of cards, and poker hands. If you don't play poker, you can read about it at http://en.wikipedia.org/wiki/Poker, but you don't have to; I'll tell you what you need to know for the exercises. Code examples from this chapter are available from http://thinkpython.com/code/Card.py.
+In this chapter I present classes to represent playing cards, decks of cards, and poker hands. If you don't play poker, you can read about it at , but you don't have to; I'll tell you what you need to know for the exercises. Code examples from this chapter are available from .
 
-If you are not familiar with Anglo-American playing cards, you can read about them at http://en.wikipedia.org/wiki/Playing_cards.
+If you are not familiar with Anglo-American playing cards, you can read about them at .
 
-### **18.1 Card objects**
+#### **18.1 Card objects**
 
 There are fifty-two cards in a deck, each of which belongs to one of four suits and one of thirteen ranks. The suits are Spades, Hearts, Diamonds, and Clubs (in descending order in bridge). The ranks are Ace, 2, 3, 4, 5, 6, 7, 8, 9, 10, Jack, Queen, and King. Depending on the game that you are playing, an Ace may be higher than King or lower than 2.
 
@@ -6751,7 +6982,11 @@ An alternative is to use integers to **encode** the ranks and suits. In this con
 
 For example, this table shows the suits and the corresponding integer codes:
 
-Spades 7→ 3 Hearts 7→ 2 Diamonds 7→ 1 Clubs 7→ 0
+| Spades   | → | 3 |
+|----------|---|---|
+| Hearts   | → | 2 |
+| Diamonds | → | 1 |
+| Clubs    | → | 0 |
 
 This code makes it easy to compare cards; because higher suits map to higher numbers, we can compare suits by comparing their codes.
 
@@ -6759,8 +6994,6 @@ The mapping for ranks is fairly obvious; each of the numerical ranks maps to the
 
 Jack 7→ 11 Queen 7→ 12 King 7→ 13
 
-is the 2 of Clubs.
-
 I am using the 7→ symbol to make it clear that these mappings are not part of the Python program. They are part of the program design, but they don't appear explicitly in the code.
 
 The class definition for Card looks like this:
@@ -6771,17 +7004,21 @@ class Card(object):
     def __init__(self, suit=0, rank=2):
         self.suit = suit
         self.rank = rank
-As usual, the init method takes an optional parameter for each attribute. The default card
 ```
+As usual, the init method takes an optional parameter for each attribute. The default card is the 2 of Clubs.
+
 To create a Card, you call Card with the suit and rank of the card you want.
 
+```
 queen_of_diamonds = Card(1, 12)
-
-### **18.2 Class attributes**
+```
+#### **18.2 Class attributes**
 
 In order to print Card objects in a way that people can easily read, we need a mapping from the integer codes to the corresponding ranks and suits. A natural way to do that is with lists of strings. We assign these lists to **class attributes**:
 
+```
 # inside class Card:
+```
 
 ```
 suit_names = ['Clubs', 'Diamonds', 'Hearts', 'Spades']
@@ -6801,7 +7038,7 @@ Every card has its own suit and rank, but there is only one copy of suit_names a
 
 ![](_page_190_Figure_1.jpeg)
 
-Figure 18.1: Object diagram.
+Figure 18.1: Object diagram.
 
 Putting it all together, the expression Card.rank_names[self.rank] means "use the attribute rank from the object self as an index into the list rank_names from the class Card, and select the appropriate string."
 
@@ -6814,9 +7051,9 @@ With the methods we have so far, we can create and print cards:
 >>> print card1
 Jack of Hearts
 ```
-Figure 18.1 is a diagram of the Card class object and one Card instance. Card is a class object, so it has type type. card1 has type Card. (To save space, I didn't draw the contents of suit_names and rank_names).
+Figure [18.1](#page-190-1) is a diagram of the Card class object and one Card instance. Card is a class object, so it has type type. card1 has type Card. (To save space, I didn't draw the contents of suit_names and rank_names).
 
-### **18.3 Comparing cards**
+#### **18.3 Comparing cards**
 
 For built-in types, there are relational operators (<, >, ==, etc.) that compare values and determine when one is greater than, less than, or equal to another. For user-defined types, we can override the behavior of the built-in operators by providing a method named __cmp__.
 
@@ -6839,25 +7076,26 @@ With that decided, we can write __cmp__:
         if self.rank < other.rank: return -1
         # ranks are the same... it's a tie
         return 0
+```
 You can write this more concisely using tuple comparison:
+
+```
 # inside class Card:
-    def __cmp__(self, other):
-        t1 = self.suit, self.rank
-        t2 = other.suit, other.rank
-        return cmp(t1, t2)
-The built-in function cmp has the same interface as the method __cmp__: it takes two values
-and returns a positive number if the first is larger, a negative number if the second is larger,
-and 0 if they are equal.
-In Python 3, cmp no longer exists, and the __cmp__ method is not supported. Instead you
 ```
 
 ```
-should provide __lt__, which returns True if self is less than other. You can implement
-__lt__ using tuples and the < operator.
-Exercise 18.1. Write a __cmp__ method for Time objects. Hint: you can use tuple comparison, but
-you also might consider using integer subtraction.
+def __cmp__(self, other):
+    t1 = self.suit, self.rank
+    t2 = other.suit, other.rank
+    return cmp(t1, t2)
 ```
-### **18.4 Decks**
+The built-in function cmp has the same interface as the method __cmp__: it takes two values and returns a positive number if the first is larger, a negative number if the second is larger, and 0 if they are equal.
+
+In Python 3, cmp no longer exists, and the __cmp__ method is not supported. Instead you should provide __lt__, which returns True if self is less than other. You can implement __lt__ using tuples and the < operator.
+
+**Exercise 18.1.** *Write a* __cmp__ *method for Time objects. Hint: you can use tuple comparison, but you also might consider using integer subtraction.*
+
+#### **18.4 Decks**
 
 Now that we have Cards, the next step is to define Decks. Since a deck is made up of cards, it is natural for each Deck to contain a list of cards as an attribute.
 
@@ -6877,17 +7115,10 @@ def __init__(self):
 ```
 The easiest way to populate the deck is with a nested loop. The outer loop enumerates the suits from 0 to 3. The inner loop enumerates the ranks from 1 to 13. Each iteration creates a new Card with the current suit and rank, and appends it to self.cards.
 
-### **18.5 Printing the deck**
+#### **18.5 Printing the deck**
 
-Here is a __str__ method for Deck: #inside class Deck:
+Here is a __str__ method for Deck: #inside class Deck: def __str__(self): res = [] for card in self.cards: res.append(str(card)) return '\n'.join(res)
 
-```
-def __str__(self):
-    res = []
-    for card in self.cards:
-        res.append(str(card))
-    return '\n'.join(res)
-```
 This method demonstrates an efficient way to accumulate a large string: building a list of strings and then using join. The built-in function str invokes the __str__ method on each card and returns the string representation.
 
 Since we invoke join on a newline character, the cards are separated by newlines. Here's what the result looks like:
@@ -6906,21 +7137,21 @@ King of Spades
 ```
 Even though the result appears on 52 lines, it is one long string that contains newlines.
 
-### **18.6 Add, remove, shuffle and sort**
+#### **18.6 Add, remove, shuffle and sort**
 
 To deal cards, we would like a method that removes a card from the deck and returns it. The list method pop provides a convenient way to do that:
 
 #inside class Deck:
 
-def pop_card(self): return self.cards.pop()
-
+```
+def pop_card(self):
+    return self.cards.pop()
+```
 Since pop removes the *last* card in the list, we are dealing from the bottom of the deck. In real life "bottom dealing" is frowned upon, but in this context it's ok.
 
 To add a card, we can use the list method append:
 
-#inside class Deck:
-
-def add_card(self, card): self.cards.append(card)
+#inside class Deck: def add_card(self, card): self.cards.append(card)
 
 A method like this that uses another function without doing much real work is sometimes called a **veneer**. The metaphor comes from woodworking, where it is common to glue a thin layer of good quality wood to the surface of a cheaper piece of wood.
 
@@ -6928,17 +7159,17 @@ In this case we are defining a "thin" method that expresses a list operation in
 
 As another example, we can write a Deck method named shuffle using the function shuffle from the random module:
 
-# inside class Deck:
-
 ```
 def shuffle(self):
     random.shuffle(self.cards)
 ```
 Don't forget to import random.
 
-**Exercise 18.2.** *Write a Deck method named* sort *that uses the list method* sort *to sort the cards in a* Deck. sort *uses the* __cmp__ *method we defined to determine sort order.*
+# inside class Deck:
+
+**Exercise 18.2.** *Write a Deck method named* sort *that uses the list method* sort *to sort the cards in a* Deck*.* sort *uses the* __cmp__ *method we defined to determine sort order.*
 
-### **18.7 Inheritance**
+### **18.7 Inheritance**
 
 The language feature most often associated with object-oriented programming is **inheritance**. Inheritance is the ability to define a new class that is a modified version of an existing class.
 
@@ -6952,8 +7183,9 @@ This relationship between classes—similar, but different—lends itself to inh
 
 The definition of a child class is like other class definitions, but the name of the parent class appears in parentheses:
 
+```
 class Hand(Deck):
-
+```
 """Represents a hand of playing cards."""
 
 This definition indicates that Hand inherits from Deck; that means we can use methods like pop_card and add_card for Hands as well as Decks.
@@ -6977,21 +7209,24 @@ So when you create a Hand, Python invokes this init method:
 []
 >>> print hand.label
 new hand
-But the other methods are inherited from Deck, so we can use pop_card and add_card to
-deal a card:
+```
+But the other methods are inherited from Deck, so we can use pop_card and add_card to deal a card:
+
+```
 >>> deck = Deck()
 >>> card = deck.pop_card()
 >>> hand.add_card(card)
 >>> print hand
 King of Spades
+```
 A natural next step is to encapsulate this code in a method called move_cards:
+
 #inside class Deck:
-```
-def move_cards(self, hand, num):
 
 ```
-for i in range(num):
-    hand.add_card(self.pop_card())
+def move_cards(self, hand, num):
+    for i in range(num):
+        hand.add_card(self.pop_card())
 ```
 move_cards takes two arguments, a Hand object and the number of cards to deal. It modifies both self and hand, and returns None.
 
@@ -7003,7 +7238,7 @@ Inheritance is a useful feature. Some programs that would be repetitive without
 
 On the other hand, inheritance can make programs difficult to read. When a method is invoked, it is sometimes not clear where to find its definition. The relevant code may be scattered among several modules. Also, many of the things that can be done using inheritance can be done as well or better without it.
 
-### **18.8 Class diagrams**
+#### **18.8 Class diagrams**
 
 So far we have seen stack diagrams, which show the state of a program, and object diagrams, which show the attributes of an object and their values. These diagrams represent a snapshot in the execution of a program, so they change as the program runs.
 
@@ -7011,7 +7246,7 @@ They are also highly detailed; for some purposes, too detailed. A class diagram
 
 ![](_page_195_Figure_1.jpeg)
 
-Figure 18.2: Class diagram.
+Figure 18.2: Class diagram.
 
 There are several kinds of relationship between classes:
 
@@ -7019,7 +7254,7 @@ There are several kinds of relationship between classes:
 - One class might inherit from another. This relationship is called **IS-A**, as in, "a Hand is a kind of a Deck."
 - One class might depend on another in the sense that changes in one class would require changes in the other.
 
-A **class diagram** is a graphical representation of these relationships. For example, Figure 18.2 shows the relationships between Card, Deck and Hand.
+A **class diagram** is a graphical representation of these relationships. For example, Figure [18.2](#page-195-1) shows the relationships between Card, Deck and Hand.
 
 The arrow with a hollow triangle head represents an IS-A relationship; in this case it indicates that Hand inherits from Deck.
 
@@ -7027,9 +7262,11 @@ The standard arrow head represents a HAS-A relationship; in this case a Deck has
 
 The star (*) near the arrow head is a **multiplicity**; it indicates how many Cards a Deck has. A multiplicity can be a simple number, like 52, a range, like 5..7 or a star, which indicates that a Deck can have any number of Cards.
 
-A more detailed diagram might show that a Deck actually contains a *list* of Cards, but built-in types like list and dict are usually not included in class diagrams. **Exercise 18.4.** *Read* TurtleWorld.py, World.py and Gui.py *and draw a class diagram that shows the relationships among the classes defined there.*
+A more detailed diagram might show that a Deck actually contains a *list* of Cards, but built-in types like list and dict are usually not included in class diagrams.
 
-### **18.9 Debugging**
+**Exercise 18.4.** *Read* TurtleWorld.py*,* World.py *and* Gui.py *and draw a class diagram that shows the relationships among the classes defined there.*
+
+### **18.9 Debugging**
 
 Inheritance can make debugging a challenge because when you invoke a method on an object, you might not know which method will be invoked.
 
@@ -7039,12 +7276,11 @@ Any time you are unsure about the flow of execution through your program, the si
 
 As an alternative, you could use this function, which takes an object and a method name (as a string) and returns the class that provides the definition of the method:
 
-```
-def find_defining_class(obj, meth_name):
-    for ty in type(obj).mro():
-        if meth_name in ty.__dict__:
-            return ty
+def find_defining_class(obj, meth_name): for ty in type(obj).mro(): if meth_name in ty.__dict__: return ty
+
 Here's an example:
+
+```
 >>> hand = Hand()
 >>> print find_defining_class(hand, 'shuffle')
 
@@ -7057,40 +7293,44 @@ Here's a program design suggestion: whenever you override a method, the interfac
 
 If you violate this rule, your code will collapse like (sorry) a house of cards.
 
-### **18.10 Data encapsulation**
+#### **18.10 Data encapsulation**
 
-Chapter 16 demonstrates a development plan we might call "object-oriented design." We identified objects we needed—Time, Point and Rectangle—and defined classes to represent them. In each case there is an obvious correspondence between the object and some entity in the real world (or at least a mathematical world).
+Chapter [16](#page-172-0) demonstrates a development plan we might call "object-oriented design." We identified objects we needed—Time, Point and Rectangle—and defined classes to represent them. In each case there is an obvious correspondence between the object and some entity in the real world (or at least a mathematical world).
 
 But sometimes it is less obvious what objects you need and how they should interact. In that case you need a different development plan. In the same way that we discovered function interfaces by encapsulation and generalization, we can discover class interfaces by **data encapsulation**.
 
-Markov analysis, from Section 13.8, provides a good example. If you download my code from http://thinkpython.com/code/markov.py, you'll see that it uses two global variables—suffix_map and prefix—that are read and written from several functions.
+Markov analysis, from Section [13.8,](#page-149-0) provides a good example. If you download my code from , you'll see that it uses two global variables—suffix_map and prefix—that are read and written from several functions.
+
+suffix_map = {} prefix = ()
 
-```
-suffix_map = {}
-prefix = ()
-```
 Because these variables are global we can only run one analysis at a time. If we read two texts, their prefixes and suffixes would be added to the same data structures (which makes for some interesting generated text).
 
 To run multiple analyses, and keep them separate, we can encapsulate the state of each analysis in an object. Here's what that looks like:
 
 ```
 class Markov(object):
-    def __init__(self):
-        self.suffix_map = {}
-        self.prefix = ()
+```
+
+```
+def __init__(self):
+    self.suffix_map = {}
+    self.prefix = ()
+```
 Next, we transform the functions into methods. For example, here's process_word:
-    def process_word(self, word, order=2):
-        if len(self.prefix) < order:
-            self.prefix += (word,)
-            return
-        try:
-            self.suffix_map[self.prefix].append(word)
-        except KeyError:
-            # if there is no entry for this prefix, make one
-            self.suffix_map[self.prefix] = [word]
-        self.prefix = shift(self.prefix, word)
-```
-Transforming a program like this—changing the design without changing the function—is another example of refactoring (see Section 4.7).
+
+```
+def process_word(self, word, order=2):
+    if len(self.prefix) < order:
+        self.prefix += (word,)
+        return
+    try:
+        self.suffix_map[self.prefix].append(word)
+    except KeyError:
+        # if there is no entry for this prefix, make one
+        self.suffix_map[self.prefix] = [word]
+    self.prefix = shift(self.prefix, word)
+```
+Transforming a program like this—changing the design without changing the function—is another example of refactoring (see Section [4.7)](#page-57-0).
 
 This example suggests a development plan for designing objects and methods:
 
@@ -7099,9 +7339,9 @@ This example suggests a development plan for designing objects and methods:
 - 3. Encapsulate related variables as attributes of an object.
 - 4. Transform the associated functions into methods of the new class.
 
-**Exercise 18.5.** *Download my code from Section 13.8 (*http: // thinkpython. com/ code/ markov. py *), and follow the steps described above to encapsulate the global variables as attributes of a new class called* Markov*. Solution:* http: // thinkpython. com/ code/ Markov. py *(note the capital M).*
+**Exercise 18.5.** *Download my code from Section [13.8](#page-149-0) (*[http:](http://thinkpython.com/code/markov.py) [//](http://thinkpython.com/code/markov.py) [thinkpython.](http://thinkpython.com/code/markov.py) [com/](http://thinkpython.com/code/markov.py) [code/](http://thinkpython.com/code/markov.py) [markov.](http://thinkpython.com/code/markov.py) [py](http://thinkpython.com/code/markov.py) *), and follow the steps described above to encapsulate the global variables as attributes of a new class called* Markov*. Solution:* [http:](http://thinkpython.com/code/Markov.py) [//](http://thinkpython.com/code/Markov.py) [thinkpython.](http://thinkpython.com/code/Markov.py) [com/](http://thinkpython.com/code/Markov.py) [code/](http://thinkpython.com/code/Markov.py) [Markov.](http://thinkpython.com/code/Markov.py) [py](http://thinkpython.com/code/Markov.py) *(note the capital M).*
 
-### **18.11 Glossary**
+#### **18.11 Glossary**
 
 - **encode:** To represent one set of values using another set of values by constructing a mapping between them.
 - **class attribute:** An attribute associated with a class object. Class attributes are defined inside a class definition but outside any method.
@@ -7117,9 +7357,9 @@ This example suggests a development plan for designing objects and methods:
 - **class diagram:** A diagram that shows the classes in a program and the relationships between them.
 - **multiplicity:** A notation in a class diagram that shows, for a HAS-A relationship, how many references there are to instances of another class.
 
-### **18.12 Exercises**
+#### **18.12 Exercises**
 
-**Exercise 18.6.** *The following are the possible hands in poker, in increasing order of value (and decreasing order of probability):*
+**Exercise 18.6.** *The following are the possible hands in poker, in increasing order of value (and decreasing order of probability):*
 
 **pair:** *two cards with the same rank*
 
@@ -7127,8 +7367,9 @@ This example suggests a development plan for designing objects and methods:
 
 **three of a kind:** *three cards with the same rank*
 
-- **straight:** *five cards with ranks in sequence (aces can be high or low, so* Ace-2-3-4-5 *is a straight and so is* 10-Jack-Queen-King-Ace*, but* Queen-King-Ace-2-3 *is not.)*
-- **flush:** *five cards with the same suit*
+**straight:** *five cards with ranks in sequence (aces can be high or low, so* Ace-2-3-4-5 *is a straight and so is* 10-Jack-Queen-King-Ace*, but* Queen-King-Ace-2-3 *is not.)*
+
+**flush:** *five cards with the same suit*
 
 **full house:** *three cards with one rank, two cards with another*
 
@@ -7138,40 +7379,40 @@ This example suggests a development plan for designing objects and methods:
 
 *The goal of these exercises is to estimate the probability of drawing these various hands.*
 
-- *1. Download the following files from* http: // thinkpython. com/ code :
-Card.py *: A complete version of the* Card, Deck and Hand *classes in this chapter.*
+- *1. Download the following files from* [http:](http://thinkpython.com/code) [//](http://thinkpython.com/code) [thinkpython.](http://thinkpython.com/code) [com/](http://thinkpython.com/code) [code](http://thinkpython.com/code) *:*
+Card.py *: A complete version of the* Card*,* Deck *and* Hand *classes in this chapter.*
 
 - PokerHand.py *: An incomplete implementation of a class that represents a poker hand, and some code that tests it.*
 - *2. If you run* PokerHand.py*, it deals seven 7-card poker hands and checks to see if any of them contains a flush. Read this code carefully before you go on.*
-- *3. Add methods to* PokerHand.py *named* has_pair, has_twopair*, etc. that return True or False according to whether or not the hand meets the relevant criteria. Your code should work correctly for "hands" that contain any number of cards (although 5 and 7 are the most common sizes).*
+- *3. Add methods to* PokerHand.py *named* has_pair*,* has_twopair*, etc. that return True or False according to whether or not the hand meets the relevant criteria. Your code should work correctly for "hands" that contain any number of cards (although 5 and 7 are the most common sizes).*
 - *4. Write a method named* classify *that figures out the highest-value classification for a hand and sets the* label *attribute accordingly. For example, a 7-card hand might contain a flush and a pair; it should be labeled "flush".*
 - *5. When you are convinced that your classification methods are working, the next step is to estimate the probabilities of the various hands. Write a function in* PokerHand.py *that shuffles a deck of cards, divides it into hands, classifies the hands, and counts the number of times various classifications appear.*
-- *6. Print a table of the classifications and their probabilities. Run your program with larger and larger numbers of hands until the output values converge to a reasonable degree of accuracy. Compare your results to the values at* http: // en. wikipedia. org/ wiki/ Hand_ rankings .
+- *6. Print a table of the classifications and their probabilities. Run your program with larger and larger numbers of hands until the output values converge to a reasonable degree of accuracy. Compare your results to the values at* [http:](http://en.wikipedia.org/wiki/Hand_rankings) [//](http://en.wikipedia.org/wiki/Hand_rankings) [en.](http://en.wikipedia.org/wiki/Hand_rankings) [wikipedia.](http://en.wikipedia.org/wiki/Hand_rankings) [org/](http://en.wikipedia.org/wiki/Hand_rankings) [wiki/](http://en.wikipedia.org/wiki/Hand_rankings) [Hand_](http://en.wikipedia.org/wiki/Hand_rankings) [rankings](http://en.wikipedia.org/wiki/Hand_rankings) *.*
 
-#### *Solution:* http: // thinkpython. com/ code/ PokerHandSoln. py .
+*Solution:* [http:](http://thinkpython.com/code/PokerHandSoln.py) [//](http://thinkpython.com/code/PokerHandSoln.py) [thinkpython.](http://thinkpython.com/code/PokerHandSoln.py) [com/](http://thinkpython.com/code/PokerHandSoln.py) [code/](http://thinkpython.com/code/PokerHandSoln.py) [PokerHandSoln.](http://thinkpython.com/code/PokerHandSoln.py) [py](http://thinkpython.com/code/PokerHandSoln.py) *.*
 
-**Exercise 18.7.** *This exercise uses TurtleWorld from Chapter 4. You will write code that makes Turtles play tag. If you are not familiar with the rules of tag, see* http: // en. wikipedia. org/ wiki/ Tag_ ( game) .
+**Exercise 18.7.** *This exercise uses TurtleWorld from Chapter [4.](#page-52-0) You will write code that makes Turtles play tag. If you are not familiar with the rules of tag, see* [http:](http://en.wikipedia.org/wiki/Tag_(game)) [//](http://en.wikipedia.org/wiki/Tag_(game)) [en.](http://en.wikipedia.org/wiki/Tag_(game)) [wikipedia.](http://en.wikipedia.org/wiki/Tag_(game)) [org/](http://en.wikipedia.org/wiki/Tag_(game)) [wiki/](http://en.wikipedia.org/wiki/Tag_(game)) [Tag_](http://en.wikipedia.org/wiki/Tag_(game)) [(](http://en.wikipedia.org/wiki/Tag_(game)) [game)](http://en.wikipedia.org/wiki/Tag_(game)) *.*
 
-- *1. Download* http: // thinkpython. com/ code/ Wobbler. py *and run it. You should see a TurtleWorld with three Turtles. If you press the* Run *button, the Turtles wander at random.*
-- *2. Read the code and make sure you understand how it works. The* Wobbler *class inherits from* Turtle*, which means that the* Turtle *methods* lt, rt, fd and bk *work on Wobblers.*
+- *1. Download* [http:](http://thinkpython.com/code/Wobbler.py) [//](http://thinkpython.com/code/Wobbler.py) [thinkpython.](http://thinkpython.com/code/Wobbler.py) [com/](http://thinkpython.com/code/Wobbler.py) [code/](http://thinkpython.com/code/Wobbler.py) [Wobbler.](http://thinkpython.com/code/Wobbler.py) [py](http://thinkpython.com/code/Wobbler.py) *and run it. You should see a TurtleWorld with three Turtles. If you press the* Run *button, the Turtles wander at random.*
+- *2. Read the code and make sure you understand how it works. The* Wobbler *class inherits from* Turtle*, which means that the* Turtle *methods* lt*,* rt*,* fd *and* bk *work on Wobblers.*
 
-The step *method gets invoked by TurtleWorld. It invokes* steer*, which turns the Turtle in the desired direction,* wobble*, which makes a random turn in proportion to the Turtle's clumsiness, and* move*, which moves forward a few pixels, depending on the Turtle's speed.*
+*The* step *method gets invoked by TurtleWorld. It invokes* steer*, which turns the Turtle in the desired direction,* wobble*, which makes a random turn in proportion to the Turtle's clumsiness, and* move*, which moves forward a few pixels, depending on the Turtle's speed.*
 
 - *3. Create a file named* Tagger.py*. Import everything from* Wobbler*, then define a class named* Tagger *that inherits from* Wobbler*. Call* make_world *passing the* Tagger *class object as an argument.*
-- *4. Add a* steer *method to* Tagger *to override the one in* Wobbler*. As a starting place, write a version that always points the Turtle toward the origin. Hint: use the math function* atan2 *and the Turtle attributes* x, y and heading.
+- *4. Add a* steer *method to* Tagger *to override the one in* Wobbler*. As a starting place, write a version that always points the Turtle toward the origin. Hint: use the math function* atan2 *and the Turtle attributes* x*,* y *and* heading*.*
 - *5. Modify* steer *so that the Turtles stay in bounds. For debugging, you might want to use the* Step *button, which invokes* step *once on each Turtle.*
 - *6. Modify* steer *so that each Turtle points toward its nearest neighbor. Hint: Turtles have an attribute,* world*, that is a reference to the TurtleWorld they live in, and the TurtleWorld has an attribute,* animals*, that is a list of all Turtles in the world.*
-- *7. Modify* steer *so the Turtles play tag. You can add methods to* Tagger *and you can override* steer and __init__*, but you may not modify or override* step, wobble or move*. Also,* steer *is allowed to change the heading of the Turtle but not the position.*
+- *7. Modify* steer *so the Turtles play tag. You can add methods to* Tagger *and you can override* steer *and* __init__*, but you may not modify or override* step*,* wobble *or* move*. Also,* steer *is allowed to change the heading of the Turtle but not the position.*
 
 *Adjust the rules and your* steer *method for good quality play; for example, it should be possible for the slow Turtle to tag the faster Turtles eventually.*
 
-*Solution:* http: // thinkpython. com/ code/ Tagger. py .
+*Solution:* [http:](http://thinkpython.com/code/Tagger.py) [//](http://thinkpython.com/code/Tagger.py) [thinkpython.](http://thinkpython.com/code/Tagger.py) [com/](http://thinkpython.com/code/Tagger.py) [code/](http://thinkpython.com/code/Tagger.py) [Tagger.](http://thinkpython.com/code/Tagger.py) [py](http://thinkpython.com/code/Tagger.py) *.*
 
-## **Chapter 19**
+## **Chapter 19**
 
 # **Case study: Tkinter**
 
-### **19.1 GUI**
+#### **19.1 GUI**
 
 Most of the programs we have seen so far are text-based, but many programs use **graphical user interfaces**, also known as **GUIs**.
 
@@ -7216,7 +7457,7 @@ This Gui doesn't do much because it doesn't have any **widgets**. Widgets are th
 
 The empty gray square you see when you create a Gui is a Frame. When you create a new widget, it is added to this Frame.
 
-### **19.2 Buttons and callbacks**
+#### **19.2 Buttons and callbacks**
 
 The method bu creates a Button widget:
 
@@ -7256,29 +7497,27 @@ The challenge of event-driven programming is to construct a set of widgets and c
 
 **Exercise 19.1.** *Write a program that creates a GUI with a single button. When the button is pressed it should create a second button. When* that *button is pressed, it should create a label that says, "Nice job!".*
 
-*What happens if you press the buttons more than once? Solution:* http: // thinkpython. com/ code/ button_ demo. py
+*What happens if you press the buttons more than once? Solution:* [http:](http://thinkpython.com/code/button_demo.py) [//](http://thinkpython.com/code/button_demo.py) [thinkpython.](http://thinkpython.com/code/button_demo.py) [com/](http://thinkpython.com/code/button_demo.py) [code/](http://thinkpython.com/code/button_demo.py) [button_](http://thinkpython.com/code/button_demo.py) [demo.](http://thinkpython.com/code/button_demo.py) [py](http://thinkpython.com/code/button_demo.py)
 
-### **19.3 Canvas widgets**
+#### **19.3 Canvas widgets**
 
-One of the most versatile widgets is the Canvas, which creates a region for drawing lines, circles and other shapes. If you did Exercise 15.4 you are already familiar with canvases.
+One of the most versatile widgets is the Canvas, which creates a region for drawing lines, circles and other shapes. If you did Exercise [15.4](#page-170-2) you are already familiar with canvases.
 
 The method ca creates a new Canvas:
 
+```
 canvas = g.ca(width=500, height=500)
-
+```
 width and height are the dimensions of the canvas in pixels.
 
 After you create a widget, you can still change the values of the options with the config method. For example, the bg option changes the background color:
 
+```
 canvas.config(bg='white')
-
+```
 The value of bg is a string that names a color. The set of legal color names is different for different implementations of Python, but all implementations provide at least:
 
-white black
-
-red green blue
-
-cyan yellow magenta
+white black red green blue cyan yellow magenta
 
 Shapes on a Canvas are called **items**. For example, the Canvas method circle draws (you guessed it) a circle:
 
@@ -7286,15 +7525,15 @@ item = canvas.circle([0,0], 100, fill='red')
 
 The first argument is a coordinate pair that specifies the center of the circle; the second is the radius.
 
-Gui.py provides a standard Cartesian coordinate system with the origin at the center of the Canvas and the positive y axis pointing up. This is different from some other graphics systems where the origin is in the upper left corner, with the y axis pointing down.
+Gui.py provides a standard Cartesian coordinate system with the origin at the center of the Canvas and the positive *y* axis pointing up. This is different from some other graphics systems where the origin is in the upper left corner, with the *y* axis pointing down.
 
 The fill option specifies that the circle should be filled in with red.
 
 The return value from circle is an Item object that provides methods for modifying the item on the canvas. For example, you can use config to change any of the circle's options: item.config(fill='yellow', outline='orange', width=10)
 
-width is the thickness of the outline in pixels; outline is the color. **Exercise 19.2.** *Write a program that creates a Canvas and a Button. When the user presses the Button, it should draw a circle on the canvas.*
+width is the thickness of the outline in pixels; outline is the color. **Exercise 19.2.** *Write a program that creates a Canvas and a Button. When the user presses the Button, it should draw a circle on the canvas.*
 
-### **19.4 Coordinate sequences**
+#### **19.4 Coordinate sequences**
 
 The rectangle method takes a sequence of coordinates that specify opposite corners of the rectangle. This example draws a blue rectangle with the lower left corner at the origin and the upper right corner at (200, 100):
 
@@ -7316,13 +7555,12 @@ polygon takes the same arguments, but it draws the last leg of the polygon (if n
 
 canvas.polygon([[0, 100], [100, 200], [200, 100]], fill='red', outline='orange', width=10)
 
-### **19.5 More widgets**
+#### **19.5 More widgets**
 
 Tkinter provides two widgets that let users type text: an Entry, which is a single line, and a Text widget, which has multiple lines.
 
-```
 en creates a new Entry:
-```
+
 entry = g.en(text='Default text.')
 
 The text option allows you to put text into the entry when it is created. The get method returns the contents of the Entry (which may have been changed by the user):
@@ -7330,10 +7568,16 @@ The text option allows you to put text into the entry when it is created. The ge
 ```
 >>> entry.get()
 'Default text.'
+```
 te creates a Text widget:
+
 text = g.te(width=100, height=5)
+
 width and height are the dimensions of the widget in characters and lines.
+
 insert puts text into the Text widget:
+
+```
 text.insert(END, 'A line of text.')
 ```
 END is a special index that indicates the last character in the Text widget.
@@ -7344,32 +7588,39 @@ You can also specify a character using a dotted index, like 1.1, which has the l
 
 The get method reads the text in the widget; it takes a start and end index as arguments. The following example returns all the text in the widget, including the newline character:
 
+```
 >>> text.get(0.0, END)
-
 'Another line of text.\n'
-
+```
 The delete method removes text from the widget; the following example deletes all but the first two characters:
 
->>> text.delete(1.2, END) >>> text.get(0.0, END) 'An\n'
-
-**Exercise 19.3.** *Modify your solution to Exercise 19.2 by adding an Entry widget and a second button. When the user presses the second button, it should read a color name from the Entry and use it to change the fill color of the circle. Use* config *to modify the existing circle; don't create a new one.*
+```
+>>> text.delete(1.2, END)
+>>> text.get(0.0, END)
+'An\n'
+```
+**Exercise 19.3.** *Modify your solution to Exercise [19.2](#page-203-2) by adding an Entry widget and a second button. When the user presses the second button, it should read a color name from the Entry and use it to change the fill color of the circle. Use* config *to modify the existing circle; don't create a new one.*
 
 *Your program should handle the case where the user tries to change the color of a circle that hasn't been created, and the case where the color name is invalid.*
 
-*You can see my solution at* http: // thinkpython. com/ code/ circle_ demo. py .
+*You can see my solution at* [http:](http://thinkpython.com/code/circle_demo.py) [//](http://thinkpython.com/code/circle_demo.py) [thinkpython.](http://thinkpython.com/code/circle_demo.py) [com/](http://thinkpython.com/code/circle_demo.py) [code/](http://thinkpython.com/code/circle_demo.py) [circle_](http://thinkpython.com/code/circle_demo.py) [demo.](http://thinkpython.com/code/circle_demo.py) [py](http://thinkpython.com/code/circle_demo.py) *.*
 
-### **19.6 Packing widgets**
+#### **19.6 Packing widgets**
 
-So far we have been stacking widgets in a single column, but in most GUIs the layout is more complicated. For example, Figure 19.1 shows a simplified version of TurtleWorld (see Chapter 4).
+So far we have been stacking widgets in a single column, but in most GUIs the layout is more complicated. For example, Figure [19.1](#page-205-0) shows a simplified version of TurtleWorld (see Chapter [4)](#page-52-0).
 
-This section presents the code that creates this GUI, broken into a series of steps. You can download the complete example from http://thinkpython.com/code/ SimpleTurtleWorld.py.
+This section presents the code that creates this GUI, broken into a series of steps. You can download the complete example from [http://thinkpython.com/code/](http://thinkpython.com/code/SimpleTurtleWorld.py) [SimpleTurtleWorld.py](http://thinkpython.com/code/SimpleTurtleWorld.py).
 
 At the top level, this GUI contains two widgets—a Canvas and a Frame—arranged in a row. So the first step is to create the row.
 
-class SimpleTurtleWorld(TurtleWorld): """This class is identical to TurtleWorld, but the code that lays out the GUI is simplified for explanatory purposes."""
-
-> def setup(self): self.row() ...
-
+```
+class SimpleTurtleWorld(TurtleWorld):
+    """This class is identical to TurtleWorld, but the code that
+    lays out the GUI is simplified for explanatory purposes."""
+    def setup(self):
+        self.row()
+        ...
+```
 setup is the function that creates and arranges the widgets. Arranging widgets in a GUI is called **packing**.
 
 row creates a row Frame and makes it the "current Frame." Until this Frame is closed or another Frame is created, all subsequent widgets are packed in a row.
@@ -7378,7 +7629,7 @@ Here is the code that creates the Canvas and the column Frame that hold the othe
 
 ![](_page_205_Figure_1.jpeg)
 
-Figure 19.1: TurtleWorld after running the snowflake code.
+Figure 19.1: TurtleWorld after running the snowflake code.
 
 ```
 self.canvas = self.ca(width=400, height=400, bg='white')
@@ -7408,7 +7659,7 @@ self.endrow()
 ```
 The first argument to row is a list of weights that determines how extra space is allocated between widgets. The list [0,1] means that all extra space is allocated to the second widget, which is the Entry. If you run this code and resize the window, you will see that the Entry grows and the Button doesn't.
 
-The option pady "pads" this row in the y direction, adding 30 pixels of space above and below.
+The option pady "pads" this row in the *y* direction, adding 30 pixels of space above and below.
 
 endrow ends this row of widgets, so subsequent widgets are packed in the column Frame. Gui.py keeps a stack of Frames:
 
@@ -7446,11 +7697,11 @@ Unfortunately, the details of widget layout are different in other languages, an
 
 Fortunately, most of the concepts in this section apply to other GUI modules and other languages.
 
-### **19.7 Menus and Callables**
+#### **19.7 Menus and Callables**
 
 A Menubutton is a widget that looks like a button, but when pressed it pops up a menu. After the user selects an item, the menu disappears.
 
-Here is code that creates a color selection Menubutton (you can download it from http: //thinkpython.com/code/menubutton_demo.py):
+Here is code that creates a color selection Menubutton (you can download it from [http:](http://thinkpython.com/code/menubutton_demo.py) [//thinkpython.com/code/menubutton_demo.py](http://thinkpython.com/code/menubutton_demo.py)):
 
 ```
 g = Gui()
@@ -7477,18 +7728,19 @@ def set_color(color):
     mb.config(text=color)
     print color
 ```
-When the user selects a menu item and set_color is called, it configures the Menubutton to display the newly-selected color. It also print the color; if you try this example, you can confirm that set_color is called when you select an item (and not called when you create the Callable object).
+When the user selects a menu item and set_color is called, it configures the Menubutton to display the newly-selected color. It also print the color; if you try this example, you can confirm that set_color is called when you select an item (and *not* called when you create the Callable object).
 
-### **19.8 Binding**
+#### **19.8 Binding**
 
 A **binding** is an association between a widget, an event and a callback: when an event (like a button press) happens on a widget, the callback is invoked.
 
 Many widgets have default bindings. For example, when you press a button, the default binding changes the relief of the button to make it look depressed. When you release the button, the binding restores the appearance of the button and invokes the callback specified with the command option.
 
-You can use the bind method to override these default bindings or to add new ones. For example, this code creates a binding for a canvas (you can download the code in this section from http://thinkpython.com/code/draggable_demo.py):
+You can use the bind method to override these default bindings or to add new ones. For example, this code creates a binding for a canvas (you can download the code in this section from ):
 
+```
 ca.bind('', make_circle)
-
+```
 The first argument is an event string; this event is triggered when the user presses the left mouse button. Other mouse events include ButtonMotion, ButtonRelease and Double-Button.
 
 The second argument is an event handler. An event handler is a function or bound method, like a callback, but an important difference is that an event handler takes an Event object as a parameter. Here is an example:
@@ -7506,9 +7758,10 @@ For Entry widgets, it is common to bind the  event, which is triggered w
 bu = g.bu('Make text item:', make_text)
 en = g.en()
 en.bind('', make_text)
-make_text is called when the Button is pressed or when the user hits Return while typing
-in the Entry. To make this work, we need a function that can be called as a command (with
-no arguments) or as an event handler (with an Event as an argument):
+```
+make_text is called when the Button is pressed or when the user hits Return while typing in the Entry. To make this work, we need a function that can be called as a command (with no arguments) or as an event handler (with an Event as an argument):
+
+```
 def make_text(event=None):
     text = en.get()
     item = ca.text([0,0], text)
@@ -7517,7 +7770,9 @@ make_text gets the contents of the Entry and displays it as a Text item in the C
 
 It is also possible to create bindings for Canvas items. The following is a class definition for Draggable, which is a child class of Item that provides bindings that implement dragand-drop capability.
 
+```
 class Draggable(Item):
+```
 
 ```
 def __init__(self, item):
@@ -7551,9 +7806,6 @@ def drag(self, event):
 dy = event.y - self.dragy
 self.dragx = event.x
 self.dragy = event.y
-```
-
-```
 self.move(dx, dy)
 ```
 This computation is done in pixel coordinates; there is no need to convert to Canvas coordinates.
@@ -7574,7 +7826,7 @@ def make_circle(event):
 ```
 This example demonstrates one of the benefits of inheritance: you can modify the capabilities of a parent class without modifying its definition. This is particularly useful if you want to change behavior defined in a module you did not write.
 
-### **19.9 Debugging**
+#### **19.9 Debugging**
 
 One of the challenges of GUI programming is keeping track of which things happen while the GUI is being built and which things happen later in response to user events.
 
@@ -7590,7 +7842,7 @@ If you run this code, you will see that it calls the_callback immediately, and *
 
 Another challenge of GUI programming is that you don't have control of the flow of execution. Which parts of the program execute and their order are determined by user actions. That means that you have to design your program to work correctly for any possible sequence of events.
 
-For example, the GUI in Exercise 19.3 has two widgets: one creates a Circle item and the other changes the color of the Circle. If the user creates the circle and then changes its color, there's no problem. But what if the user changes the color of a circle that doesn't exist yet? Or creates more than one circle?
+For example, the GUI in Exercise [19.3](#page-204-1) has two widgets: one creates a Circle item and the other changes the color of the Circle. If the user creates the circle and then changes its color, there's no problem. But what if the user changes the color of a circle that doesn't exist yet? Or creates more than one circle?
 
 As the number of widgets grows, it is increasingly difficult to imagine all possible sequences of events. One way to manage this complexity is to encapsulate the state of the system in an object and then consider:
 
@@ -7603,7 +7855,7 @@ You might also find it useful to define, and check, invariants that should hold
 
 This approach to GUI programming can help you write correct code without taking the time to test every possible sequence of user events!
 
-### **19.10 Glossary**
+#### **19.10 Glossary**
 
 **GUI:** A graphical user interface.
 
@@ -7617,34 +7869,42 @@ This approach to GUI programming can help you write correct code without taking
 - **event loop:** An infinite loop that waits for user actions and responds.
 - **item:** A graphical element on a Canvas widget.
 - **bounding box:** A rectangle that encloses a set of items, usually specified by two opposing corners.
-
-**pack:** To arrange and display the elements of a GUI.
-
-**geometry manager:** A system for packing widgets.
-
+- **pack:** To arrange and display the elements of a GUI.
+- **geometry manager:** A system for packing widgets.
 - **binding:** An association between a widget, an event, and an event handler. The event handler is called when the event occurs in the widget.
-### **19.11 Exercises**
 
-**Exercise 19.4.** *For this exercise, you will write an image viewer. Here is a simple example:*
+### **19.11 Exercises**
 
-g = Gui() canvas = g.ca(width=300) photo = PhotoImage(file='danger.gif') canvas.image([0,0], image=photo) g.mainloop()
+**Exercise 19.4.** *For this exercise, you will write an image viewer. Here is a simple example:*
 
+```
+g = Gui()
+canvas = g.ca(width=300)
+photo = PhotoImage(file='danger.gif')
+canvas.image([0,0], image=photo)
+g.mainloop()
+```
 PhotoImage *reads a file and returns a* PhotoImage *object that Tkinter can display.* Canvas.image *puts the image on the canvas, centered on the given coordinates. You can also put images on labels, buttons, and some other widgets:*
 
-g.la(image=photo) g.bu(image=photo)
-
+```
+g.la(image=photo)
+g.bu(image=photo)
+```
 *PhotoImage can only handle a few image formats, like GIF and PPM, but we can use the Python Imaging Library (PIL) to read other files.*
 
 *The name of the PIL module is* Image*, but Tkinter defines an object with the same name. To avoid the conflict, you can use* import...as *like this:*
 
 import Image as PIL import ImageTk
 
-*The first line imports* Image *and gives it the local name* PIL*. The second line imports* ImageTk, *which can translate a PIL image into a Tkinter PhotoImage. Here's an example:*
-
-image = PIL.open('allen.png') photo2 = ImageTk.PhotoImage(image) g.la(image=photo2)
+*The first line imports* Image *and gives it the local name* PIL*. The second line imports* ImageTk*, which can translate a PIL image into a Tkinter PhotoImage. Here's an example:*
 
-- *1. Download* image_demo.py, danger.gif and allen.png *from* http: // thinkpython. com/ code *. Run* image_demo.py*. You might have to install* PIL and ImageTk*. They are probably in your software repository, but if not you can get them from* http: // pythonware. com/ products/ pil .
-- *2. In* image_demo.py *change the name of the second PhotoImage from* photo2 to photo and *run the program again. You should see the second PhotoImage but not the first.*
+```
+image = PIL.open('allen.png')
+photo2 = ImageTk.PhotoImage(image)
+g.la(image=photo2)
+```
+- *1. Download* image_demo.py*,* danger.gif *and* allen.png *from* [http:](http://thinkpython.com/code) [//](http://thinkpython.com/code) [thinkpython.](http://thinkpython.com/code) [com/](http://thinkpython.com/code) [code](http://thinkpython.com/code) *. Run* image_demo.py*. You might have to install* PIL *and* ImageTk*. They are probably in your software repository, but if not you can get them from* [http:](http://pythonware.com/products/pil) [//](http://pythonware.com/products/pil) [pythonware.](http://pythonware.com/products/pil) [com/](http://pythonware.com/products/pil) [products/](http://pythonware.com/products/pil) [pil](http://pythonware.com/products/pil) *.*
+- *2. In* image_demo.py *change the name of the second PhotoImage from* photo2 *to* photo *and run the program again. You should see the second PhotoImage but not the first.*
 
 *The problem is that when you reassign* photo *it overwrites the reference to the first PhotoImage, which then disappears. The same thing happens if you assign a PhotoImage to a local variable; it disappears when the function ends.*
 
@@ -7655,20 +7915,20 @@ image = PIL.open('allen.png') photo2 = ImageTk.PhotoImage(image) g.la(image=phot
 - *3. Starting with this example, write a program that takes the name of a directory and loops through all the files, displaying any files that PIL recognizes as images. You can use a* try *statement to catch the files PIL doesn't recognize.*
 *When the user clicks on the image, the program should display the next one.*
 
-- *4. PIL provides a variety of methods for manipulating images. You can read about them at* http: // pythonware. com/ library/ pil/ handbook *. As a challenge, choose a few of these methods and provide a GUI for applying them to images.*
-*Solution:* http: // thinkpython. com/ code/ ImageBrowser. py .
-
-**Exercise 19.5.** *A vector graphics editor is a program that allows users to draw and edit shapes on the screen and generate output files in vector graphics formats like Postscript and SVG.*
+- *4. PIL provides a variety of methods for manipulating images. You can read about them at* [http:](http://pythonware.com/library/pil/handbook) [//](http://pythonware.com/library/pil/handbook) [pythonware.](http://pythonware.com/library/pil/handbook) [com/](http://pythonware.com/library/pil/handbook) [library/](http://pythonware.com/library/pil/handbook) [pil/](http://pythonware.com/library/pil/handbook) [handbook](http://pythonware.com/library/pil/handbook) *. As a challenge, choose a few of these methods and provide a GUI for applying them to images.*
+*Solution:* [http:](http://thinkpython.com/code/ImageBrowser.py) [//](http://thinkpython.com/code/ImageBrowser.py) [thinkpython.](http://thinkpython.com/code/ImageBrowser.py) [com/](http://thinkpython.com/code/ImageBrowser.py) [code/](http://thinkpython.com/code/ImageBrowser.py) [ImageBrowser.](http://thinkpython.com/code/ImageBrowser.py) [py](http://thinkpython.com/code/ImageBrowser.py) *.* **Exercise 19.5.** *A vector graphics editor is a program that allows users to draw and edit shapes on the screen and generate output files in vector graphics formats like Postscript and SVG.*
 
 *Write a simple vector graphics editor using Tkinter. At a minimum, it should allow users to draw lines, circles and rectangles, and it should use* Canvas.dump *to generate a Postscript description of the contents of the Canvas.*
 
 *As a challenge, you could allow users to select and resize items on the Canvas.* **Exercise 19.6.** *Use Tkinter to write a basic web browser. It should have a Text widget where the user can enter a URL and a Canvas to display the contents of the page.*
 
-*You can use the* urllib *module to download files (see Exercise 14.6) and the* HTMLParser *module to parse the HTML tags (see* http: // docs. python. org/ 2/ library/ htmlparser. html ).
+*You can use the* urllib *module to download files (see Exercise [14.6)](#page-162-2) and the* HTMLParser *module to parse the HTML tags (see* [http:](http://docs.python.org/2/library/htmlparser.html) [//](http://docs.python.org/2/library/htmlparser.html) [docs.](http://docs.python.org/2/library/htmlparser.html) [python.](http://docs.python.org/2/library/htmlparser.html) [org/](http://docs.python.org/2/library/htmlparser.html) [2/](http://docs.python.org/2/library/htmlparser.html) [library/](http://docs.python.org/2/library/htmlparser.html) [htmlparser.](http://docs.python.org/2/library/htmlparser.html) [html](http://docs.python.org/2/library/htmlparser.html) *).*
 
 *At a minimum your browser should handle plain text and hyperlinks. As a challenge you could handle background colors, text formatting tags and images.*
 
-## **Appendix A**
+**192 Chapter 19. Case study: Tkinter**
+
+## **Appendix A**
 
 # **Debugging**
 
@@ -7680,7 +7940,7 @@ Different kinds of errors can occur in a program, and it is useful to distinguis
 
 The first step in debugging is to figure out which kind of error you are dealing with. Although the following sections are organized by error type, some techniques are applicable in more than one situation.
 
-### **A.1 Syntax errors**
+#### **A.1 Syntax errors**
 
 Syntax errors are usually easy to fix once you figure out what they are. Unfortunately, the error messages are often not helpful. The most common messages are SyntaxError: invalid syntax and SyntaxError: invalid token, neither of which is very informative.
 
@@ -7718,7 +7978,7 @@ There are a few likely culprits:
 
 If you get stuck and you can't figure out what is going on, one approach is to start again with a new program like "Hello, World!," and make sure you can get a known program to run. Then gradually add the pieces of the original program to the new one.
 
-### **A.2 Runtime errors**
+#### **A.2 Runtime errors**
 
 Once your program is syntactically correct, Python can compile it and at least start running it. What could possibly go wrong?
 
@@ -7780,17 +8040,18 @@ The traceback identifies the function that is currently running, and then the fu
 The first step is to examine the place in the program where the error occurred and see if you can figure out what happened. These are some of the most common runtime errors:
 
 - **NameError:** You are trying to use a variable that doesn't exist in the current environment. Remember that local variables are local. You cannot refer to them from outside the function where they are defined.
-- **TypeError:** There are several possible causes:
-	- You are trying to use a value improperly. Example: indexing a string, list, or tuple with something other than an integer.
-	- There is a mismatch between the items in a format string and the items passed for conversion. This can happen if either the number of items does not match or an invalid conversion is called for.
-	- You are passing the wrong number of arguments to a function or method. For methods, look at the method definition and check that the first parameter is self. Then look at the method invocation; make sure you are invoking the method on an object with the right type and providing the other arguments correctly.
+**TypeError:** There are several possible causes:
+
+- You are trying to use a value improperly. Example: indexing a string, list, or tuple with something other than an integer.
+- There is a mismatch between the items in a format string and the items passed for conversion. This can happen if either the number of items does not match or an invalid conversion is called for.
+- You are passing the wrong number of arguments to a function or method. For methods, look at the method definition and check that the first parameter is self. Then look at the method invocation; make sure you are invoking the method on an object with the right type and providing the other arguments correctly.
 - **KeyError:** You are trying to access an element of a dictionary using a key that the dictionary does not contain.
 - **AttributeError:** You are trying to access an attribute or method that does not exist. Check the spelling! You can use dir to list the attributes that do exist.
 
 If an AttributeError indicates that an object has NoneType, that means that it is None. One common cause is forgetting to return a value from a function; if you get to the end of a function without hitting a return statement, it returns None. Another common cause is using the result from a list method, like sort, that returns None.
 
 - **IndexError:** The index you are using to access a list, string, or tuple is greater than its length minus one. Immediately before the site of the error, add a print statement to display the value of the index and the length of the array. Is the array the right size? Is the index the right value?
-The Python debugger (pdb) is useful for tracking down Exceptions because it allows you to examine the state of the program immediately before the error. You can read about pdb at http://docs.python.org/2/library/pdb.html.
+The Python debugger (pdb) is useful for tracking down Exceptions because it allows you to examine the state of the program immediately before the error. You can read about pdb at .
 
 #### **A.2.4 I added so many** print **statements I get inundated with output.**
 
@@ -7806,7 +8067,7 @@ Often the process of finding the minimal test case leads you to the bug. If you
 
 Similarly, rewriting a piece of code can help you find subtle bugs. If you make a change that you think shouldn't affect the program, and it does, that can tip you off.
 
-### **A.3 Semantic errors**
+### **A.3 Semantic errors**
 
 In some ways, semantic errors are the hardest to debug, because the interpreter provides no information about what is wrong. Only you know what the program is supposed to do.
 
@@ -7834,8 +8095,9 @@ Writing complex expressions is fine as long as they are readable, but they can b
 
 For example:
 
+```
 self.hands[i].addCard(self.hands[self.findNeighbor(i)].popCard())
-
+```
 This can be rewritten as:
 
 ```
@@ -7845,11 +8107,11 @@ self.hands[i].addCard(pickedCard)
 ```
 The explicit version is easier to read because the variable names provide additional documentation, and it is easier to debug because you can check the types of the intermediate variables and display their values.
 
-Another problem that can occur with big expressions is that the order of evaluation may not be what you expect. For example, if you are translating the expression x 2π into Python, you might write:
+Another problem that can occur with big expressions is that the order of evaluation may not be what you expect. For example, if you are translating the expression *x* 2*π* into Python, you might write:
 
 y = x / 2 * math.pi
 
-That is not correct because multiplication and division have the same precedence and are evaluated from left to right. So this expression computes xπ/2.
+That is not correct because multiplication and division have the same precedence and are evaluated from left to right. So this expression computes *xπ*/2.
 
 A good way to debug expressions is to add parentheses to make the order of evaluation explicit:
 
@@ -7861,17 +8123,15 @@ Whenever you are not sure of the order of evaluation, use parentheses. Not only
 
 If you have a return statement with a complex expression, you don't have a chance to print the return value before returning. Again, you can use a temporary variable. For example, instead of:
 
+```
 return self.hands[i].removeMatches()
-
 you could write:
-
 count = self.hands[i].removeMatches()
-
 return count
-
+```
 Now you have the opportunity to display the value of count before returning.
 
-### **A.3.4 I'm really, really stuck and I need help.**
+#### **A.3.4 I'm really, really stuck and I need help.**
 
 First, try getting away from the computer for a few minutes. Computers emit waves that affect the brain, causing these symptoms:
 
@@ -7883,7 +8143,7 @@ If you find yourself suffering from any of these symptoms, get up and go for a w
 
 Sometimes it just takes time to find a bug. I often find bugs when I am away from the computer and let my mind wander. Some of the best places to find bugs are trains, showers, and in bed, just before you fall asleep.
 
-### **A.3.5 No, I really need help.**
+#### **A.3.5 No, I really need help.**
 
 It happens. Even the best programmers occasionally get stuck. Sometimes you work on a program so long that you can't see the error. A fresh pair of eyes is just the thing.
 
@@ -7899,93 +8159,91 @@ When you find the bug, take a second to think about what you could have done to
 
 Remember, the goal is not just to make the program work. The goal is to learn how to make the program work.
 
-## **Appendix B**
+## **Appendix B**
 
 # **Analysis of Algorithms**
 
 This appendix is an edited excerpt from *Think Complexity*, by Allen B. Downey, also published by O'Reilly Media (2011). When you are done with this book, you might want to move on to that one.
 
-**Analysis of algorithms** is a branch of computer science that studies the performance of algorithms, especially their run time and space requirements. See http://en.wikipedia. org/wiki/Analysis_of_algorithms.
+**Analysis of algorithms** is a branch of computer science that studies the performance of algorithms, especially their run time and space requirements. See [http://en.wikipedia.](http://en.wikipedia.org/wiki/Analysis_of_algorithms) [org/wiki/Analysis_of_algorithms](http://en.wikipedia.org/wiki/Analysis_of_algorithms).
 
 The practical goal of algorithm analysis is to predict the performance of different algorithms in order to guide design decisions.
 
-During the 2008 United States Presidential Campaign, candidate Barack Obama was asked to perform an impromptu analysis when he visited Google. Chief executive Eric Schmidt jokingly asked him for "the most efficient way to sort a million 32-bit integers." Obama had apparently been tipped off, because he quickly replied, "I think the bubble sort would be the wrong way to go." See http://www.youtube.com/watch?v=k4RRi_ntQc8.
+During the 2008 United States Presidential Campaign, candidate Barack Obama was asked to perform an impromptu analysis when he visited Google. Chief executive Eric Schmidt jokingly asked him for "the most efficient way to sort a million 32-bit integers." Obama had apparently been tipped off, because he quickly replied, "I think the bubble sort would be the wrong way to go." See .
 
-This is true: bubble sort is conceptually simple but slow for large datasets. The answer Schmidt was probably looking for is "radix sort" (http://en.wikipedia.org/wiki/ Radix_sort) 1 .
+This is true: bubble sort is conceptually simple but slow for large datasets. The answer Schmidt was probably looking for is "radix sort" ([http://en.wikipedia.org/wiki/](http://en.wikipedia.org/wiki/Radix_sort) [Radix_sort](http://en.wikipedia.org/wiki/Radix_sort)) [1](#page-222-1) .
 
 The goal of algorithm analysis is to make meaningful comparisons between algorithms, but there are some problems:
 
 - The relative performance of the algorithms might depend on characteristics of the hardware, so one algorithm might be faster on Machine A, another on Machine B. The general solution to this problem is to specify a **machine model** and analyze the number of steps, or operations, an algorithm requires under a given model.
 - Relative performance might depend on the details of the dataset. For example, some sorting algorithms run faster if the data are already partially sorted; other algorithms
 
-1 But if you get a question like this in an interview, I think a better answer is, "The fastest way to sort a million integers is to use whatever sort function is provided by the language I'm using. Its performance is good enough for the vast majority of applications, but if it turned out that my application was too slow, I would use a profiler to see where the time was being spent. If it looked like a faster sort algorithm would have a significant effect on performance, then I would look around for a good implementation of radix sort."
+1 But if you get a question like this in an interview, I think a better answer is, "The fastest way to sort a million integers is to use whatever sort function is provided by the language I'm using. Its performance is good enough for the vast majority of applications, but if it turned out that my application was too slow, I would use a profiler to see where the time was being spent. If it looked like a faster sort algorithm would have a significant effect on performance, then I would look around for a good implementation of radix sort."
 
 run slower in this case. A common way to avoid this problem is to analyze the **worst case** scenario. It is sometimes useful to analyze average case performance, but that's usually harder, and it might not be obvious what set of cases to average over.
 
 - Relative performance also depends on the size of the problem. A sorting algorithm that is fast for small lists might be slow for long lists. The usual solution to this problem is to express run time (or number of operations) as a function of problem size, and to compare the functions **asymptotically** as the problem size increases.
-The good thing about this kind of comparison that it lends itself to simple classification of algorithms. For example, if I know that the run time of Algorithm A tends to be proportional to the size of the input, n, and Algorithm B tends to be proportional to n 2 , then I expect A to be faster than B for large values of n.
+The good thing about this kind of comparison that it lends itself to simple classification of algorithms. For example, if I know that the run time of Algorithm A tends to be proportional to the size of the input, *n*, and Algorithm B tends to be proportional to *n* 2 , then I expect A to be faster than B for large values of *n*.
 
 This kind of analysis comes with some caveats, but we'll get to that later.
 
-### **B.1 Order of growth**
+### **B.1 Order of growth**
 
-Suppose you have analyzed two algorithms and expressed their run times in terms of the size of the input: Algorithm A takes 100n + 1 steps to solve a problem with size n; Algorithm B takes n 2 + n + 1 steps.
+Suppose you have analyzed two algorithms and expressed their run times in terms of the size of the input: Algorithm A takes 100*n* + 1 steps to solve a problem with size *n*; Algorithm B takes *n* 2 + *n* + 1 steps.
 
 The following table shows the run time of these algorithms for different problem sizes:
 
-| Input | Run time of | Run time of |
-| --- | --- | --- |
-| size | Algorithm A | Algorithm B |
-| 10 | 1 001 | 111 |
-| 100 | 10 001 | 10 101 |
-| 1 000 | 100 001 | 1 001 001 |
-| 10 000 | 1 000 001 | > 1010 |
+| Inputsize | Run time ofAlgorithm A | Run time ofAlgorithm B |
+|-----------|------------------------|------------------------|
+| 10        | 1 001                  | 111                    |
+| 100       | 10 001                 | 10 101                 |
+| 1 000     | 100 001                | 1 001 001              |
+| 10 000    | 1 000 001              | > 1010                 |
 
-At n = 10, Algorithm A looks pretty bad; it takes almost 10 times longer than Algorithm B. But for n = 100 they are about the same, and for larger values A is much better.
+At *n* = 10, Algorithm A looks pretty bad; it takes almost 10 times longer than Algorithm B. But for *n* = 100 they are about the same, and for larger values A is much better.
 
-The fundamental reason is that for large values of n, any function that contains an n 2 term will grow faster than a function whose leading term is n. The **leading term** is the term with the highest exponent.
+The fundamental reason is that for large values of *n*, any function that contains an *n* 2 term will grow faster than a function whose leading term is *n*. The **leading term** is the term with the highest exponent.
 
-For Algorithm A, the leading term has a large coefficient, 100, which is why B does better than A for small n. But regardless of the coefficients, there will always be some value of n where an2 > bn.
+For Algorithm A, the leading term has a large coefficient, 100, which is why B does better than A for small *n*. But regardless of the coefficients, there will always be some value of *n* where *an*2 > *bn*.
 
-The same argument applies to the non-leading terms. Even if the run time of Algorithm A were n + 1000000, it would still be better than Algorithm B for sufficiently large n.
+The same argument applies to the non-leading terms. Even if the run time of Algorithm A were *n* + 1000000, it would still be better than Algorithm B for sufficiently large *n*.
 
 In general, we expect an algorithm with a smaller leading term to be a better algorithm for large problems, but for smaller problems, there may be a **crossover point** where another algorithm is better. The location of the crossover point depends on the details of the algorithms, the inputs, and the hardware, so it is usually ignored for purposes of algorithmic analysis. But that doesn't mean you can forget about it.
 
 If two algorithms have the same leading order term, it is hard to say which is better; again, the answer depends on the details. So for algorithmic analysis, functions with the same leading term are considered equivalent, even if they have different coefficients.
 
-An **order of growth** is a set of functions whose asymptotic growth behavior is considered equivalent. For example, 2n, 100n and n + 1 belong to the same order of growth, which is written O(n) in **Big-Oh notation** and often called **linear** because every function in the set grows linearly with n.
+An **order of growth** is a set of functions whose asymptotic growth behavior is considered equivalent. For example, 2*n*, 100*n* and *n* + 1 belong to the same order of growth, which is written *O*(*n*) in **Big-Oh notation** and often called **linear** because every function in the set grows linearly with *n*.
 
-All functions with the leading term n 2 belong to O(n 2 ); they are **quadratic**, which is a fancy word for functions with the leading term n 2 .
+All functions with the leading term *n*2 belong to *O*(*n*2) ; they are **quadratic**, which is a fancy word for functions with the leading term *n*2. 
 
 The following table shows some of the orders of growth that appear most commonly in algorithmic analysis, in increasing order of badness.
 
-| Order of |  | Name |
-| --- | --- | --- |
-| growth |  |  |
-| O(1) |  | constant |
-| O(logb | n) | logarithmic (for any b) |
-| O(n) |  | linear |
-| O(n logb | n) | "en log en" |
-| O(n | 2 ) | quadratic |
-| O(n | 3 ) | cubic |
-| O(c | n ) | exponential (for any c) |
+| Order ofgrowth | Name                    |
+|----------------|-------------------------|
+| O(1)           | constant                |
+| O(logbn)       | logarithmic (for any b) |
+| O(n)           | linear                  |
+| O(nlogbn)      | "en log en"             |
+| O(n2)          | quadratic               |
+| O(n3)          | cubic                   |
+| O(cn)          | exponential (for any c) |
 
 For the logarithmic terms, the base of the logarithm doesn't matter; changing bases is the equivalent of multiplying by a constant, which doesn't change the order of growth. Similarly, all exponential functions belong to the same order of growth regardless of the base of the exponent. Exponential functions grow very quickly, so exponential algorithms are only useful for small problems.
 
-**Exercise B.1.** *Read the Wikipedia page on Big-Oh notation at* http: // en. wikipedia. org/ wiki/ Big_ O_ notation *and answer the following questions:*
+**Exercise B.1.** *Read the Wikipedia page on Big-Oh notation at* http://en.wikipedia.org/ wiki/Big_O_notation *and answer the following questions:* 
 
-- *1. What is the order of growth of n*3 + n 2*? What about* 1000000n 3 + n 2*? What about n*3 + 1000000n 2?
-- *2. What is the order of growth of* (n 2 + n) · (n + 1)*? Before you start multiplying, remember that you only need the leading term.*
-- *3. If f is in O*(g)*, for some unspecified function g, what can we say about a f* + b?
-- *4. If f*1 *and f*2 *are in O*(g)*, what can we say about f*1 + f2?
-- *5. If f*1 *is in O*(g) *and f*2 *is in O*(h)*, what can we say about f*1 + f2?
-- *6. If f*1 *is in O*(g) *and f*2 *is O*(h)*, what can we say about f*1 · f2?
+- *1. What is the order of growth of n*3 + *n* 2*? What about* 1000000*n* 3 + *n* 2*? What about n*3 + 1000000*n* 2*?*
+- *2. What is the order of growth of* (*n* 2 + *n*) · (*n* + 1)*? Before you start multiplying, remember that you only need the leading term.*
+- *3. If f is in O*(*g*)*, for some unspecified function g, what can we say about a f* + *b?*
+- *4. If f*1 *and f*2 *are in O*(*g*)*, what can we say about f*1 + *f*2*?*
+- *5. If f*1 *is in O*(*g*) *and f*2 *is in O*(*h*)*, what can we say about f*1 + *f*2*?*
+- *6. If f*1 *is in O*(*g*) *and f*2 *is O*(*h*)*, what can we say about f*1 · *f*2*?*
 
 Programmers who care about performance often find this kind of analysis hard to swallow. They have a point: sometimes the coefficients and the non-leading terms make a real difference. Sometimes the details of the hardware, the programming language, and the characteristics of the input make a big difference. And for small problems asymptotic behavior is irrelevant.
 
-But if you keep those caveats in mind, algorithmic analysis is a useful tool. At least for large problems, the "better" algorithms is usually better, and sometimes it is *much* better. The difference between two algorithms with the same order of growth is usually a constant factor, but the difference between a good algorithm and a bad algorithm is unbounded!
+But if you keep those caveats in mind, algorithmic analysis is a useful tool. At least for large problems, the “better” algorithms is usually better, and sometimes it is *much* better. The difference between two algorithms with the same order of growth is usually a constant factor, but the difference between a good algorithm and a bad algorithm is unbounded!
 
-### **B.2 Analysis of basic Python operations**
+## **B.2 Analysis of basic Python operations**
 
 Most arithmetic operations are constant time; multiplication usually takes longer than addition and subtraction, and division takes even longer, but these run times don't depend on the magnitude of the operands. Very large integers are an exception; in that case the run time increases with the number of digits.
 
@@ -7993,17 +8251,20 @@ Indexing operations—reading or writing elements in a sequence or dictionary—
 
 A for loop that traverses a sequence or dictionary is usually linear, as long as all of the operations in the body of the loop are constant time. For example, adding up the elements of a list is linear:
 
-> total = 0 for x in t: total += x
-
+```
+total = 0
+for x in t:
+    total += x
+```
 The built-in function sum is also linear because it does the same thing, but it tends to be faster because it is a more efficient implementation; in the language of algorithmic analysis, it has a smaller leading coefficient.
 
 If you use the same loop to "add" a list of strings, the run time is quadratic because string concatenation is linear.
 
 The string method join is usually faster because it is linear in the total length of the strings.
 
-As a rule of thumb, if the body of a loop is in O(n a ) then the whole loop is in O(n a+1 ). The exception is if you can show that the loop exits after a constant number of iterations. If a loop runs k times regardless of n, then the loop is in O(n a ), even for large k.
+As a rule of thumb, if the body of a loop is in *O*(*n a* ) then the whole loop is in *O*(*n a*+1 ). The exception is if you can show that the loop exits after a constant number of iterations. If a loop runs *k* times regardless of *n*, then the loop is in *O*(*n a* ), even for large *k*.
 
-Multiplying by k doesn't change the order of growth, but neither does dividing. So if the body of a loop is in O(n a ) and it runs n/k times, the loop is in O(n a+1 ), even for large k.
+Multiplying by *k* doesn't change the order of growth, but neither does dividing. So if the body of a loop is in *O*(*n a* ) and it runs *n*/*k* times, the loop is in *O*(*n a*+1 ), even for large *k*.
 
 Most string and tuple operations are linear, except indexing and len, which are constant time. The built-in functions min and max are linear. The run-time of a slice operation is proportional to the length of the output, but independent of the size of the input.
 
@@ -8011,9 +8272,9 @@ All string methods are linear, but if the lengths of the strings are bounded by
 
 Most list methods are linear, but there are some exceptions:
 
-- Adding an element to the end of a list is constant time on average; when it runs out of room it occasionally gets copied to a bigger location, but the total time for n operations is O(n), so we say that the "amortized" time for one operation is O(1).
+- Adding an element to the end of a list is constant time on average; when it runs out of room it occasionally gets copied to a bigger location, but the total time for *n* operations is *O*(*n*), so we say that the "amortized" time for one operation is *O*(1).
 - Removing an element from the end of a list is constant time.
-- Sorting is O(n log n).
+- Sorting is *O*(*n* log *n*).
 
 Most dictionary operations and methods are constant time, but there are some exceptions:
 
@@ -8021,9 +8282,9 @@ Most dictionary operations and methods are constant time, but there are some exc
 - The run time of update is proportional to the size of the dictionary passed as a parameter, not the dictionary being updated.
 - keys, values and items are linear because they return new lists; iterkeys, itervalues and iteritems are constant time because they return iterators. But if you loop through the iterators, the loop will be linear. Using the "iter" functions saves some overhead, but it doesn't change the order of growth unless the number of items you access is bounded.
 
-The performance of dictionaries is one of the minor miracles of computer science. We will see how they work in Section B.4.
+The performance of dictionaries is one of the minor miracles of computer science. We will see how they work in Section [B.4.](#page-227-0)
 
-**Exercise B.2.** *Read the Wikipedia page on sorting algorithms at* http: // en. wikipedia. org/ wiki/ Sorting_ algorithm *and answer the following questions:*
+**Exercise B.2.** *Read the Wikipedia page on sorting algorithms at* [http:](http://en.wikipedia.org/wiki/Sorting_algorithm) [//](http://en.wikipedia.org/wiki/Sorting_algorithm) [en.](http://en.wikipedia.org/wiki/Sorting_algorithm) [wikipedia.](http://en.wikipedia.org/wiki/Sorting_algorithm) [org/](http://en.wikipedia.org/wiki/Sorting_algorithm) [wiki/](http://en.wikipedia.org/wiki/Sorting_algorithm) [Sorting_](http://en.wikipedia.org/wiki/Sorting_algorithm) [algorithm](http://en.wikipedia.org/wiki/Sorting_algorithm) *and answer the following questions:*
 
 - *1. What is a "comparison sort?" What is the best worst-case order of growth for a comparison sort? What is the best worst-case order of growth for any sort algorithm?*
 - *2. What is the order of growth of bubble sort, and why does Barack Obama think it is "the wrong way to go?"*
@@ -8031,9 +8292,9 @@ The performance of dictionaries is one of the minor miracles of computer science
 - *4. What is a stable sort and why might it matter in practice?*
 - *5. What is the worst sorting algorithm (that has a name)?*
 - *6. What sort algorithm does the C library use? What sort algorithm does Python use? Are these algorithms stable? You might have to Google around to find these answers.*
-- *7. Many of the non-comparison sorts are linear, so why does does Python use an O*(n log n) *comparison sort?*
+- *7. Many of the non-comparison sorts are linear, so why does does Python use an O*(*n* log *n*) *comparison sort?*
 
-### **B.3 Analysis of search algorithms**
+#### **B.3 Analysis of search algorithms**
 
 A **search** is an algorithm that takes a collection and a target item and determines whether the target is in the collection, often returning the index of the target.
 
@@ -8041,7 +8302,7 @@ The simplest search algorithm is a "linear search," which traverses the items of
 
 The in operator for sequences uses a linear search; so do string methods like find and count.
 
-If the elements of the sequence are in order, you can use a **bisection search**, which is O(log n). Bisection search is similar to the algorithm you probably use to look a word up in a dictionary (a real dictionary, not the data structure). Instead of starting at the beginning and checking each item in order, you start with the item in the middle and check whether the word you are looking for comes before or after. If it comes before, then you search the first half of the sequence. Otherwise you search the second half. Either way, you cut the number of remaining items in half.
+If the elements of the sequence are in order, you can use a **bisection search**, which is *O*(log *n*). Bisection search is similar to the algorithm you probably use to look a word up in a dictionary (a real dictionary, not the data structure). Instead of starting at the beginning and checking each item in order, you start with the item in the middle and check whether the word you are looking for comes before or after. If it comes before, then you search the first half of the sequence. Otherwise you search the second half. Either way, you cut the number of remaining items in half.
 
 If the sequence has 1,000,000 items, it will take about 20 steps to find the word or conclude that it's not there. So that's about 50,000 times faster than a linear search.
 
@@ -8053,34 +8314,35 @@ Bisection search can be much faster than linear search, but it requires the sequ
 
 There is another data structure, called a **hashtable** that is even faster—it can do a search in constant time—and it doesn't require the items to be sorted. Python dictionaries are implemented using hashtables, which is why most dictionary operations, including the in operator, are constant time.
 
-### **B.4 Hashtables**
+#### **B.4 Hashtables**
 
 To explain how hashtables work and why their performance is so good, I start with a simple implementation of a map and gradually improve it until it's a hashtable.
 
 I use Python to demonstrate these implementations, but in real life you wouldn't write code like this in Python; you would just use a dictionary! So for the rest of this chapter, you have to imagine that dictionaries don't exist and you want to implement a data structure that maps from keys to values. The operations you have to implement are:
 
-- add(k, v): Add a new item that maps from key k to value v. With a Python dictionary, d, this operation is written d[k] = v.
-- get(target): Look up and return the value that corresponds to key target. With a Python dictionary, d, this operation is written d[target] or d.get(target).
+- add(k, v)**:** Add a new item that maps from key k to value v. With a Python dictionary, d, this operation is written d[k] = v.
+- get(target)**:** Look up and return the value that corresponds to key target. With a Python dictionary, d, this operation is written d[target] or d.get(target).
 
 For now, I assume that each key only appears once. The simplest implementation of this interface uses a list of tuples, where each tuple is a key-value pair.
 
-```
 class LinearMap(object):
-    def __init__(self):
-        self.items = []
-    def add(self, k, v):
-        self.items.append((k, v))
-    def get(self, k):
-        for key, val in self.items:
-            if key == k:
-                return val
-        raise KeyError
+
+```
+def __init__(self):
+    self.items = []
+def add(self, k, v):
+    self.items.append((k, v))
+def get(self, k):
+    for key, val in self.items:
+        if key == k:
+            return val
+    raise KeyError
 ```
 add appends a key-value tuple to the list of items, which takes constant time.
 
 get uses a for loop to search the list: if it finds the target key it returns the corresponding value; otherwise it raises a KeyError. So get is linear.
 
-An alternative is to keep the list sorted by key. Then get could use a bisection search, which is O(log n). But inserting a new item in the middle of a list is linear, so this might not be the best option. There are other data structures (see http://en.wikipedia.org/ wiki/Red-black_tree) that can implement add and get in log time, but that's still not as good as constant time, so let's move on.
+An alternative is to keep the list sorted by key. Then get could use a bisection search, which is *O*(log *n*). But inserting a new item in the middle of a list is linear, so this might not be the best option. There are other data structures (see [http://en.wikipedia.org/](http://en.wikipedia.org/wiki/Red-black_tree) [wiki/Red-black_tree](http://en.wikipedia.org/wiki/Red-black_tree)) that can implement add and get in log time, but that's still not as good as constant time, so let's move on.
 
 One way to improve LinearMap is to break the list of key-value pairs into smaller lists. Here's an implementation called BetterMap, which is a list of 100 LinearMaps. As we'll see in a second, the order of growth for get is still linear, but BetterMap is a step on the path toward hashtables:
 
@@ -8088,28 +8350,27 @@ class BetterMap(object):
 
 ```
 def __init__(self, n=100):
-    self.maps = []
-    for i in range(n):
-        self.maps.append(LinearMap())
-def find_map(self, k):
-    index = hash(k) % len(self.maps)
-    return self.maps[index]
-def add(self, k, v):
-    m = self.find_map(k)
-    m.add(k, v)
-def get(self, k):
-    m = self.find_map(k)
-    return m.get(k)
-```
+        self.maps = []
+        for i in range(n):
+            self.maps.append(LinearMap())
+    def find_map(self, k):
+        index = hash(k) % len(self.maps)
+        return self.maps[index]
+    def add(self, k, v):
+        m = self.find_map(k)
+        m.add(k, v)
+    def get(self, k):
+        m = self.find_map(k)
+        return m.get(k)
 __init__ makes a list of n LinearMaps.
-
+```
 find_map is used by add and get to figure out which map to put the new item in, or which map to search.
 
 find_map uses the built-in function hash, which takes almost any Python object and returns an integer. A limitation of this implementation is that it only works with hashable keys. Mutable types like lists and dictionaries are unhashable.
 
 Hashable objects that are considered equal return the same hash value, but the converse is not necessarily true: two different objects can return the same hash value.
 
-find_map uses the modulus operator to wrap the hash values into the range from 0 to len(self.maps), so the result is a legal index into the list. Of course, this means that many different hash values will wrap onto the same index. But if the hash function spreads things out pretty evenly (which is what hash functions are designed to do), then we expect n/100 items per LinearMap.
+find_map uses the modulus operator to wrap the hash values into the range from 0 to len(self.maps), so the result is a legal index into the list. Of course, this means that many different hash values will wrap onto the same index. But if the hash function spreads things out pretty evenly (which is what hash functions are designed to do), then we expect *n*/100 items per LinearMap.
 
 Since the run time of LinearMap.get is proportional to the number of items, we expect BetterMap to be about 100 times faster than LinearMap. The order of growth is still linear, but the leading coefficient is smaller. That's nice, but still not as good as a hashtable.
 
@@ -8134,7 +8395,10 @@ class HashMap(object):
         for m in self.maps.maps:
             for k, v in m.items:
                 new_maps.add(k, v)
-        self.maps = new_maps
+```
+
+```
+self.maps = new_maps
 ```
 Each HashMap contains a BetterMap; __init__ starts with just 2 LinearMaps and initializes num, which keeps track of the number of items.
 
@@ -8144,7 +8408,7 @@ resize make a new BetterMap, twice as big as the previous one, and then "rehashe
 
 Rehashing is necessary because changing the number of LinearMaps changes the denominator of the modulus operator in find_map. That means that some objects that used to wrap into the same LinearMap will get split up (which is what we wanted, right?).
 
-Rehashing is linear, so resize is linear, which might seem bad, since I promised that add would be constant time. But remember that we don't have to resize every time, so add is usually constant time and only occasionally linear. The total amount of work to run add n times is proportional to n, so the average time of each add is constant time!
+Rehashing is linear, so resize is linear, which might seem bad, since I promised that add would be constant time. But remember that we don't have to resize every time, so add is usually constant time and only occasionally linear. The total amount of work to run add *n* times is proportional to *n*, so the average time of each add is constant time!
 
 To see how this works, think about starting with an empty HashTable and adding a sequence of items. We start with 2 LinearMaps, so the first 2 adds are fast (no resizing required). Let's say that they take one unit of work each. The next add requires a resize, so we have to rehash the first two items (let's call that 2 more units of work) and then add the third item (one more unit). Adding the next item costs 1 unit, so the total so far is 6 units of work for 4 items.
 
@@ -8152,59 +8416,57 @@ The next add costs 5 units, but the next three are only one unit each, so the to
 
 ![](_page_230_Figure_1.jpeg)
 
-![](_page_230_Figure_2.jpeg)
-
-Figure B.1: The cost of a hashtable add.
+Figure B.1: The cost of a hashtable add.
 
 The next add costs 9 units, but then we can add 7 more before the next resize, so the total is 30 units for the first 16 adds.
 
-After 32 adds, the total cost is 62 units, and I hope you are starting to see a pattern. After n adds, where n is a power of two, the total cost is 2n − 2 units, so the average work per add is a little less than 2 units. When n is a power of two, that's the best case; for other values of n the average work is a little higher, but that's not important. The important thing is that it is O(1).
+After 32 adds, the total cost is 62 units, and I hope you are starting to see a pattern. After *n* adds, where *n* is a power of two, the total cost is 2*n* − 2 units, so the average work per add is a little less than 2 units. When *n* is a power of two, that's the best case; for other values of *n* the average work is a little higher, but that's not important. The important thing is that it is *O*(1).
 
-Figure B.1 shows how this works graphically. Each block represents a unit of work. The columns show the total work for each add in order from left to right: the first two adds cost 1 units, the third costs 3 units, etc.
+Figure [B.1](#page-230-0) shows how this works graphically. Each block represents a unit of work. The columns show the total work for each add in order from left to right: the first two adds cost 1 units, the third costs 3 units, etc.
 
-The extra work of rehashing appears as a sequence of increasingly tall towers with increasing space between them. Now if you knock over the towers, amortizing the cost of resizing over all adds, you can see graphically that the total cost after n adds is 2n − 2.
+The extra work of rehashing appears as a sequence of increasingly tall towers with increasing space between them. Now if you knock over the towers, amortizing the cost of resizing over all adds, you can see graphically that the total cost after *n* adds is 2*n* − 2.
 
 An important feature of this algorithm is that when we resize the HashTable it grows geometrically; that is, we multiply the size by a constant. If you increase the size arithmetically—adding a fixed number each time—the average time per add is linear.
 
-You can download my implementation of HashMap from http://thinkpython/code/ Map.py, but remember that there is no reason to use it; if you want a map, just use a Python dictionary.
+You can download my implementation of HashMap from [http://thinkpython/code/](http://thinkpython/code/Map.py) [Map.py](http://thinkpython/code/Map.py), but remember that there is no reason to use it; if you want a map, just use a Python dictionary.
+
+**210 Appendix B. Analysis of Algorithms**
 
-## **Appendix C**
+## **Appendix C**
 
 # **Lumpy**
 
 Throughout the book, I have used diagrams to represent the state of running programs.
 
-In Section 2.2, we used a state diagram to show the names and values of variables. In Section 3.10 I introduced a stack diagram, which shows one frame for each function call. Each frame shows the parameters and local variables for the function or method. Stack diagrams for recursive functions appear in Section 5.9 and Section 6.5.
+In Section [2.2,](#page-33-0) we used a state diagram to show the names and values of variables. In Section [3.10](#page-46-0) I introduced a stack diagram, which shows one frame for each function call. Each frame shows the parameters and local variables for the function or method. Stack diagrams for recursive functions appear in Section [5.9](#page-66-0) and Section [6.5.](#page-76-0)
 
-Section 10.2 shows what a list looks like in a state diagram, Section 11.4 shows what a dictionary looks like, and Section 12.6 shows two ways to represent tuples.
+Section [10.2](#page-108-2) shows what a list looks like in a state diagram, Section [11.4](#page-126-0) shows what a dictionary looks like, and Section [12.6](#page-138-0) shows two ways to represent tuples.
 
-Section 15.2 introduces object diagrams, which show the state of an object's attributes, and their attributes, and so on. Section 15.3 has object diagrams for Rectangles and their embedded Points. Section 16.1 shows the state of a Time object. Section 18.2 has a diagram that includes a class object and an instance, each with their own attributes.
+Section [15.2](#page-165-0) introduces object diagrams, which show the state of an object's attributes, and their attributes, and so on. Section [15.3](#page-166-0) has object diagrams for Rectangles and their embedded Points. Section [16.1](#page-172-1) shows the state of a Time object. Section [18.2](#page-189-0) has a diagram that includes a class object and an instance, each with their own attributes.
 
-Finally, Section 18.8 introduces class diagrams, which show the classes that make up a program and the relationships between them.
+Finally, Section [18.8](#page-194-0) introduces class diagrams, which show the classes that make up a program and the relationships between them.
 
 These diagrams are based on the Unified Modeling Language (UML), which is a standardized graphical language used by software engineers to communicate about program design, especially for object-oriented programs.
 
 UML is a rich language with many kinds of diagrams that represent many kinds of relationship between objects and classes. What I presented in this book is a small subset of the language, but it is the subset most commonly used in practice.
 
-The purpose of this appendix is to review the diagrams presented in the previous chapters, and to introduce Lumpy. Lumpy, which stands for "UML in Python," with some of the letters rearranged, is part of Swampy, which you already installed if you worked on the case study in Chapter 4 or Chapter 19, or if you did Exercise 15.4,
+The purpose of this appendix is to review the diagrams presented in the previous chapters, and to introduce Lumpy. Lumpy, which stands for "UML in Python," with some of the letters rearranged, is part of Swampy, which you already installed if you worked on the case study in Chapter [4](#page-52-0) or Chapter [19,](#page-200-0) or if you did Exercise [15.4,](#page-170-2)
 
 Lumpy uses Python's inspect module to examine the state of a running program and generate object diagrams (including stack diagrams) and class diagrams.
 
-### **C.1 State diagram**
+#### **C.1 State diagram**
 
 Here's an example that uses Lumpy to generate a state diagram.
 
 ![](_page_233_Figure_1.jpeg)
 
-Figure C.1: State diagram generated by Lumpy.
+Figure C.1: State diagram generated by Lumpy.
 
 ![](_page_233_Figure_3.jpeg)
 
-Figure C.2: Stack diagram.
+Figure C.2: Stack diagram.
 
-```
 from swampy.Lumpy import Lumpy
-```
 
 ```
 lumpy = Lumpy()
@@ -8222,32 +8484,28 @@ lumpy.object_diagram()
 ```
 The first line imports the Lumpy class from swampy.Lumpy. If you don't have Swampy installed as a package, make sure the Swampy files are in Python's search path and use this import statement instead:
 
-```
 from Lumpy import Lumpy
-```
+
 The next lines create a Lumpy object and make a "reference" point, which means that Lumpy records the objects that have been defined so far.
 
 Next we define new variables and invoke object_diagram, which draws the objects that have been defined since the reference point, in this case message, n and pi.
 
-Figure C.1 shows the result. The graphical style is different from what I showed earlier; for example, each reference is represented by a circle next to the variable name and a line to the value. And long strings are truncated. But the information conveyed by the diagram is the same.
+Figure [C.1](#page-233-1) shows the result. The graphical style is different from what I showed earlier; for example, each reference is represented by a circle next to the variable name and a line to the value. And long strings are truncated. But the information conveyed by the diagram is the same.
 
 The variable names are in a frame labeled , which indicates that these are modulelevel variables, also known as global.
 
-You can download this example from http://thinkpython.com/code/lumpy_demo1.py. Try adding some additional assignments and see what the diagram looks like.
+You can download this example from . Try adding some additional assignments and see what the diagram looks like.
 
-### **C.2 Stack diagram**
+## **C.2 Stack diagram**
 
-Here's an example that uses Lumpy to generate a stack diagram. You can download it from http://thinkpython.com/code/lumpy_demo2.py.
+Here's an example that uses Lumpy to generate a stack diagram. You can download it from .
 
 ![](_page_234_Figure_1.jpeg)
 
-Figure C.3: Object diagram.
+Figure C.3: Object diagram.
 
 ```
 from swampy.Lumpy import Lumpy
-```
-
-```
 def countdown(n):
     if n <= 0:
         print 'Blastoff!'
@@ -8255,22 +8513,22 @@ def countdown(n):
     else:
         print n
         countdown(n-1)
+lumpy = Lumpy()
+lumpy.make_reference()
 ```
-lumpy = Lumpy() lumpy.make_reference() countdown(3)
+countdown(3)
 
-Figure C.2 shows the result. Each frame is represented with a box that has the function's name outside and variables inside. Since this function is recursive, there is one frame for each level of recursion.
+Figure [C.2](#page-233-2) shows the result. Each frame is represented with a box that has the function's name outside and variables inside. Since this function is recursive, there is one frame for each level of recursion.
 
 Remember that a stack diagram shows the state of the program at a particular point in its execution. To get the diagram you want, sometimes you have to think about where to invoke object_diagram.
 
 In this case I invoke object_diagram after executing the base case of the recursion; that way the stack diagram shows each level of the recursion. You can call object_diagram more than once to get a series of snapshots of the program's execution.
 
-### **C.3 Object diagrams**
+#### **C.3 Object diagrams**
 
-This example generates an object diagram showing the lists from Section 10.1. You can download it from http://thinkpython.com/code/lumpy_demo3.py.
+This example generates an object diagram showing the lists from Section [10.1.](#page-108-1) You can download it from .
 
-```
 from swampy.Lumpy import Lumpy
-```
 
 ```
 lumpy = Lumpy()
@@ -8279,15 +8537,19 @@ cheeses = ['Cheddar', 'Edam', 'Gouda']
 ```
 ![](_page_235_Figure_1.jpeg)
 
-Figure C.4: Object diagram.
+Figure C.4: Object diagram.
 
-numbers = [17, 123] empty = []
+```
+numbers = [17, 123]
+empty = []
+```
 
+```
 lumpy.object_diagram()
+```
+Figure [C.3](#page-234-1) shows the result. Lists are represented by a box that shows the indices mapping to the elements. This representation is slightly misleading, since indices are not actually part of the list, but I think they make the diagram easier to read. The empty list is represented by an empty box.
 
-Figure C.3 shows the result. Lists are represented by a box that shows the indices mapping to the elements. This representation is slightly misleading, since indices are not actually part of the list, but I think they make the diagram easier to read. The empty list is represented by an empty box.
-
-And here's an example showing the dictionaries from Section 11.4. You can download it from http://thinkpython.com/code/lumpy_demo4.py.
+And here's an example showing the dictionaries from Section [11.4.](#page-126-0) You can download it from .
 
 from swampy.Lumpy import Lumpy
 
@@ -8297,19 +8559,19 @@ hist = histogram('parrot') inverse = invert_dict(hist)
 
 lumpy.object_diagram()
 
-Figure C.4 shows the result. hist is a dictionary that maps from characters (single-letter strings) to integers; inverse maps from integers to lists of strings.
+Figure [C.4](#page-235-0) shows the result. hist is a dictionary that maps from characters (single-letter strings) to integers; inverse maps from integers to lists of strings.
 
-This example generates an object diagram for Point and Rectangle objects, as in Section 15.6. You can download it from http://thinkpython.com/code/lumpy_demo5.py.
+This example generates an object diagram for Point and Rectangle objects, as in Section [15.6.](#page-168-0) You can download it from .
 
 import copy from swampy.Lumpy import Lumpy
 
 ![](_page_236_Figure_1.jpeg)
 
-Figure C.5: Object diagram.
+Figure C.5: Object diagram.
 
 ![](_page_236_Figure_3.jpeg)
 
-Figure C.6: Object diagram.
+Figure C.6: Object diagram.
 
 ```
 lumpy = Lumpy()
@@ -8320,22 +8582,23 @@ box.height = 200.0
 box.corner = Point()
 box.corner.x = 0.0
 box.corner.y = 0.0
-```
 box2 = copy.copy(box)
+```
 
+```
 lumpy.object_diagram()
+```
+Figure [C.5](#page-236-1) shows the result. copy.copy make a shallow copy, so box and box2 have their own width and height, but they share the same embedded Point object. This kind of sharing is usually fine with immutable objects, but with mutable types, it is highly errorprone.
 
-Figure C.5 shows the result. copy.copy make a shallow copy, so box and box2 have their own width and height, but they share the same embedded Point object. This kind of sharing is usually fine with immutable objects, but with mutable types, it is highly errorprone.
-
-### **C.4 Function and class objects**
+#### **C.4 Function and class objects**
 
 When I use Lumpy to make object diagrams, I usually define the functions and classes before I make the reference point. That way, function and class objects don't appear in the diagram.
 
 ![](_page_237_Figure_1.jpeg)
 
-Figure C.7: Class diagram.
+Figure C.7: Class diagram.
 
-But if you are passing functions and classes as parameters, you might want them to appear. This example shows what that looks like; you can download it from http://thinkpython. com/code/lumpy_demo6.py.
+But if you are passing functions and classes as parameters, you might want them to appear. This example shows what that looks like; you can download it from [http://thinkpython.](http://thinkpython.com/code/lumpy_demo6.py) [com/code/lumpy_demo6.py](http://thinkpython.com/code/lumpy_demo6.py).
 
 ```
 import copy
@@ -8356,29 +8619,30 @@ def instantiate(constructor):
 ```
 point = instantiate(Point)
 ```
-Figure C.6 shows the result. Since we invoke object_diagram inside a function, we get a stack diagram with a frame for the module-level variables and for the invocation of instantiate.
+Figure [C.6](#page-236-2) shows the result. Since we invoke object_diagram inside a function, we get a stack diagram with a frame for the module-level variables and for the invocation of instantiate.
 
 At the module level, Point and Rectangle refer to class objects (which have type type); instantiate refers to a function object.
 
 This diagram might clarify two points of common confusion: (1) the difference between the class object, Point, and the instance of Point, obj, and (2) the difference between the function object created when instantiate is defined, and the frame created with it is called.
 
-### **C.5 Class Diagrams**
+## **C.5 Class Diagrams**
 
 Although I distinguish between state diagrams, stack diagrams and object diagrams, they are mostly the same thing: they show the state of a running program at a point in time.
 
 ![](_page_238_Figure_1.jpeg)
 
-Figure C.8: Class diagram.
+Figure C.8: Class diagram.
 
 Class diagrams are different. They show the classes that make up a program and the relationships between them. They are timeless in the sense that they describe the program as a whole, not any particular point in time. For example, if an instance of Class A generally contains a reference to an instance of Class B, we say there is a "HAS-A relationship" between those classes.
 
-Here's an example that shows a HAS-A relationship. You can download it from http: //thinkpython.com/code/lumpy_demo7.py.
+Here's an example that shows a HAS-A relationship. You can download it from [http:](http://thinkpython.com/code/lumpy_demo7.py) [//thinkpython.com/code/lumpy_demo7.py](http://thinkpython.com/code/lumpy_demo7.py).
 
+```
 from swampy.Lumpy import Lumpy
+```
+lumpy = Lumpy() lumpy.make_reference()
 
 ```
-lumpy = Lumpy()
-lumpy.make_reference()
 box = Rectangle()
 box.width = 100.0
 box.height = 200.0
@@ -8386,28 +8650,29 @@ box.corner = Point()
 box.corner.x = 0.0
 box.corner.y = 0.0
 ```
-lumpy.class_diagram()
 
-Figure C.7 shows the result. Each class is represented with a box that contains the name of the class, any methods the class provides, any class variables, and any instance variables. In this example, Rectangle and Point have instance variables, but no methods or class variables.
+```
+lumpy.class_diagram()
+```
+Figure [C.7](#page-237-1) shows the result. Each class is represented with a box that contains the name of the class, any methods the class provides, any class variables, and any instance variables. In this example, Rectangle and Point have instance variables, but no methods or class variables.
 
 The arrow from Rectangle to Point shows that Rectangles contain an embedded Point. In addition, Rectangle and Point both inherit from object, which is represented in the diagram with a triangle-headed arrow.
 
-Here's a more complex example using my solution to Exercise 18.6. You can download the code from http://thinkpython.com/code/lumpy_demo8.py; you will also need http: //thinkpython.com/code/PokerHand.py.
+Here's a more complex example using my solution to Exercise [18.6.](#page-198-1) You can download the code from ; you will also need [http:](http://thinkpython.com/code/PokerHand.py) [//thinkpython.com/code/PokerHand.py](http://thinkpython.com/code/PokerHand.py).
 
 ```
 from swampy.Lumpy import Lumpy
-```
 from PokerHand import *
-
-lumpy = Lumpy() lumpy.make_reference() deck = Deck()
-
-```
+lumpy = Lumpy()
+lumpy.make_reference()
+deck = Deck()
 hand = PokerHand()
 deck.move_cards(hand, 7)
 ```
-lumpy.class_diagram()
-
-Figure C.8 shows the result. PokerHand inherits from Hand, which inherits from Deck. Both Deck and PokerHand have Cards.
 
-This diagram does not show that Hand also has cards, because in the program there are no instances of Hand. This example demonstrates a limitation of Lumpy; it only knows about the attributes and HAS-A relationships of objects that are instantiated.
+```
+lumpy.class_diagram()
+```
+Figure [C.8](#page-238-0) shows the result. PokerHand inherits from Hand, which inherits from Deck. Both Deck and PokerHand have Cards.
 
+This diagram does not show that Hand also has cards, because in the program there are no instances of Hand. This example demonstrates a limitation of Lumpy; it only knows about the attributes and HAS-A relationships of objects that are instantiated.
\ No newline at end of file
diff --git a/data/examples/markdown/thinkpython/thinkpython_meta.json b/data/examples/markdown/thinkpython/thinkpython_meta.json
index d66ac112..eb4c8c16 100644
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       "page_id": 206,
@@ -13522,8 +15262,17 @@
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@@ -13543,7 +15292,7 @@
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@@ -13554,10 +15303,15 @@
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@@ -13577,13 +15331,18 @@
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@@ -13591,7 +15350,7 @@
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@@ -13603,7 +15362,7 @@
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-          5
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           "PageHeader",
@@ -13612,8 +15371,17 @@
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@@ -13629,11 +15397,11 @@
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           "PageHeader",
@@ -13646,8 +15414,17 @@
         [
           "SectionHeader",
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+          1
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@@ -13655,7 +15432,7 @@
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@@ -13663,7 +15440,11 @@
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           "Text",
-          14
+          10
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+        [
+          "Code",
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           "ListItem",
@@ -13671,7 +15452,7 @@
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-          1
+          2
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           "SectionHeader",
@@ -13680,8 +15461,17 @@
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           "ListGroup",
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@@ -13689,7 +15479,7 @@
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-          6
+          5
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         [
           "PageHeader",
@@ -13707,15 +15497,24 @@
           "ListItem",
           1
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+      "block_metadata": {
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+        "llm_error_count": 0,
+        "llm_tokens_used": 0
+      }
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     {
       "page_id": 213,
       "text_extraction_method": "pdftext",
       "block_counts": [
+        [
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           "PageHeader",
-          2
+          1
         ],
         [
           "Line",
@@ -13725,7 +15524,12 @@
           "Span",
           1
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+      "block_metadata": {
+        "llm_request_count": 0,
+        "llm_error_count": 0,
+        "llm_tokens_used": 0
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     {
       "page_id": 214,
@@ -13751,11 +15555,20 @@
           "ListItem",
           3
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+        [
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+      "block_metadata": {
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     {
       "page_id": 215,
@@ -13789,7 +15602,12 @@
           "SectionHeader",
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+      "block_metadata": {
+        "llm_request_count": 0,
+        "llm_error_count": 0,
+        "llm_tokens_used": 0
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     {
       "page_id": 216,
@@ -13822,8 +15640,17 @@
         [
           "ListGroup",
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+          1
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+      "block_metadata": {
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     {
       "page_id": 217,
@@ -13853,7 +15680,12 @@
           "Code",
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-      ]
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+      "block_metadata": {
+        "llm_request_count": 0,
+        "llm_error_count": 0,
+        "llm_tokens_used": 0
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     {
       "page_id": 218,
@@ -13868,12 +15700,12 @@
           40
         ],
         [
-          "ListItem",
-          8
+          "Text",
+          7
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-          "Text",
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+          "ListItem",
+          7
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         [
           "PageHeader",
@@ -13887,7 +15719,12 @@
           "ListGroup",
           1
         ]
-      ]
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+      "block_metadata": {
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+        "llm_error_count": 0,
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     {
       "page_id": 219,
@@ -13920,8 +15757,17 @@
         [
           "ListGroup",
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+      "block_metadata": {
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+        "llm_error_count": 0,
+        "llm_tokens_used": 0
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@@ -13929,7 +15775,7 @@
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@@ -13937,7 +15783,11 @@
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         [
           "Text",
-          19
+          12
+        ],
+        [
+          "Code",
+          3
         ],
         [
           "PageHeader",
@@ -13948,10 +15798,15 @@
           2
         ],
         [
-          "Code",
-          1
+          "TextInlineMath",
+          2
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+      "block_metadata": {
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+        "llm_error_count": 0,
+        "llm_tokens_used": 1794
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       "page_id": 221,
@@ -13985,7 +15840,12 @@
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     {
       "page_id": 222,
@@ -14011,6 +15871,10 @@
           "ListItem",
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+        [
+          "Reference",
+          2
+        ],
         [
           "Footnote",
           1
@@ -14019,7 +15883,12 @@
           "ListGroup",
           1
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+      "block_metadata": {
+        "llm_request_count": 0,
+        "llm_error_count": 0,
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     },
     {
       "page_id": 223,
@@ -14033,9 +15902,13 @@
           "Line",
           47
         ],
+        [
+          "TableCell",
+          33
+        ],
         [
           "Text",
-          10
+          11
         ],
         [
           "PageHeader",
@@ -14050,14 +15923,19 @@
           1
         ],
         [
-          "TextInlineMath",
+          "Table",
           1
         ],
         [
-          "Table",
+          "Reference",
           1
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+      ],
+      "block_metadata": {
+        "llm_request_count": 1,
+        "llm_error_count": 0,
+        "llm_tokens_used": 882
+      }
     },
     {
       "page_id": 224,
@@ -14065,27 +15943,31 @@
       "block_counts": [
         [
           "Span",
-          325
+          367
         ],
         [
           "Line",
           60
         ],
         [
-          "Text",
-          6
+          "TableCell",
+          36
         ],
         [
           "ListItem",
           6
         ],
         [
-          "PageHeader",
-          2
+          "Text",
+          4
         ],
         [
           "TextInlineMath",
-          1
+          3
+        ],
+        [
+          "PageHeader",
+          2
         ],
         [
           "Table",
@@ -14095,7 +15977,12 @@
           "ListGroup",
           1
         ]
-      ]
+      ],
+      "block_metadata": {
+        "llm_request_count": 4,
+        "llm_error_count": 0,
+        "llm_tokens_used": 3927
+      }
     },
     {
       "page_id": 225,
@@ -14119,25 +16006,30 @@
         ],
         [
           "PageHeader",
-          1
+          2
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         [
           "SectionHeader",
           1
         ],
         [
-          "TextInlineMath",
+          "Code",
           1
         ],
         [
-          "Caption",
+          "ListGroup",
           1
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-          "ListGroup",
+          "Reference",
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+      ],
+      "block_metadata": {
+        "llm_request_count": 0,
+        "llm_error_count": 0,
+        "llm_tokens_used": 0
+      }
     },
     {
       "page_id": 226,
@@ -14145,7 +16037,7 @@
       "block_counts": [
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           "Span",
-          135
+          148
         ],
         [
           "Line",
@@ -14170,8 +16062,17 @@
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           "SectionHeader",
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+        ],
+        [
+          "Reference",
+          1
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-      ]
+      ],
+      "block_metadata": {
+        "llm_request_count": 0,
+        "llm_error_count": 0,
+        "llm_tokens_used": 0
+      }
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     {
       "page_id": 227,
@@ -14187,7 +16088,7 @@
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           "Text",
-          10
+          11
         ],
         [
           "PageHeader",
@@ -14208,8 +16109,17 @@
         [
           "ListGroup",
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+        ],
+        [
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+          1
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+      ],
+      "block_metadata": {
+        "llm_request_count": 0,
+        "llm_error_count": 0,
+        "llm_tokens_used": 0
+      }
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     {
       "page_id": 228,
@@ -14225,7 +16135,7 @@
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-          11
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         [
           "PageHeader",
@@ -14235,7 +16145,12 @@
           "Code",
           1
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-      ]
+      ],
+      "block_metadata": {
+        "llm_request_count": 0,
+        "llm_error_count": 0,
+        "llm_tokens_used": 0
+      }
     },
     {
       "page_id": 229,
@@ -14259,9 +16174,14 @@
         ],
         [
           "Code",
-          1
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-      ]
+      ],
+      "block_metadata": {
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+        "llm_error_count": 0,
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+      }
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@@ -14269,7 +16189,7 @@
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           "Line",
@@ -14277,7 +16197,7 @@
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           "Text",
-          7
+          6
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           "PageHeader",
@@ -14285,7 +16205,7 @@
         ],
         [
           "Figure",
-          2
+          1
         ],
         [
           "Caption",
@@ -14294,16 +16214,29 @@
         [
           "FigureGroup",
           1
+        ],
+        [
+          "Reference",
+          1
         ]
-      ]
+      ],
+      "block_metadata": {
+        "llm_request_count": 0,
+        "llm_error_count": 0,
+        "llm_tokens_used": 0
+      }
     },
     {
       "page_id": 231,
       "text_extraction_method": "pdftext",
       "block_counts": [
+        [
+          "Text",
+          1
+        ],
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           "PageHeader",
-          2
+          1
         ],
         [
           "Line",
@@ -14313,7 +16246,12 @@
           "Span",
           1
         ]
-      ]
+      ],
+      "block_metadata": {
+        "llm_request_count": 0,
+        "llm_error_count": 0,
+        "llm_tokens_used": 0
+      }
     },
     {
       "page_id": 232,
@@ -14321,7 +16259,7 @@
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-          58
+          86
         ],
         [
           "Line",
@@ -14334,8 +16272,17 @@
         [
           "SectionHeader",
           3
+        ],
+        [
+          "Reference",
+          2
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-      ]
+      ],
+      "block_metadata": {
+        "llm_request_count": 0,
+        "llm_error_count": 0,
+        "llm_tokens_used": 0
+      }
     },
     {
       "page_id": 233,
@@ -14343,7 +16290,7 @@
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+          113
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           "Line",
@@ -14351,11 +16298,15 @@
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         [
           "Text",
-          7
+          10
         ],
         [
           "Code",
-          5
+          3
+        ],
+        [
+          "Reference",
+          3
         ],
         [
           "PageHeader",
@@ -14367,17 +16318,22 @@
         ],
         [
           "Caption",
-          2
+          1
         ],
         [
-          "FigureGroup",
-          2
+          "SectionHeader",
+          1
         ],
         [
-          "SectionHeader",
+          "FigureGroup",
           1
         ]
-      ]
+      ],
+      "block_metadata": {
+        "llm_request_count": 1,
+        "llm_error_count": 0,
+        "llm_tokens_used": 719
+      }
     },
     {
       "page_id": 234,
@@ -14385,34 +16341,34 @@
       "block_counts": [
         [
           "Span",
-          99
+          103
         ],
         [
           "Line",
           39
         ],
-        [
-          "Code",
-          4
-        ],
         [
           "Text",
-          4
+          6
         ],
         [
           "PageHeader",
           2
         ],
         [
-          "Figure",
-          1
+          "Code",
+          2
         ],
         [
-          "Caption",
+          "Reference",
+          2
+        ],
+        [
+          "Figure",
           1
         ],
         [
-          "TextInlineMath",
+          "Caption",
           1
         ],
         [
@@ -14423,7 +16379,12 @@
           "FigureGroup",
           1
         ]
-      ]
+      ],
+      "block_metadata": {
+        "llm_request_count": 0,
+        "llm_error_count": 0,
+        "llm_tokens_used": 0
+      }
     },
     {
       "page_id": 235,
@@ -14431,7 +16392,7 @@
       "block_counts": [
         [
           "Span",
-          95
+          103
         ],
         [
           "Line",
@@ -14439,21 +16400,38 @@
         ],
         [
           "Text",
-          11
+          9
         ],
         [
           "PageHeader",
           2
         ],
+        [
+          "Code",
+          2
+        ],
         [
           "Figure",
           1
         ],
         [
-          "TextInlineMath",
+          "Caption",
+          1
+        ],
+        [
+          "FigureGroup",
+          1
+        ],
+        [
+          "Reference",
           1
         ]
-      ]
+      ],
+      "block_metadata": {
+        "llm_request_count": 0,
+        "llm_error_count": 0,
+        "llm_tokens_used": 0
+      }
     },
     {
       "page_id": 236,
@@ -14461,15 +16439,15 @@
       "block_counts": [
         [
           "Span",
-          115
+          117
         ],
         [
           "Line",
           44
         ],
         [
-          "Text",
-          4
+          "Reference",
+          3
         ],
         [
           "PageHeader",
@@ -14484,18 +16462,27 @@
           2
         ],
         [
-          "FigureGroup",
+          "Code",
           2
         ],
         [
-          "Code",
-          1
+          "Text",
+          2
+        ],
+        [
+          "FigureGroup",
+          2
         ],
         [
           "SectionHeader",
           1
         ]
-      ]
+      ],
+      "block_metadata": {
+        "llm_request_count": 1,
+        "llm_error_count": 0,
+        "llm_tokens_used": 686
+      }
     },
     {
       "page_id": 237,
@@ -14503,7 +16490,7 @@
       "block_counts": [
         [
           "Span",
-          105
+          107
         ],
         [
           "Line",
@@ -14521,6 +16508,10 @@
           "Code",
           2
         ],
+        [
+          "Reference",
+          2
+        ],
         [
           "Figure",
           1
@@ -14537,7 +16528,12 @@
           "FigureGroup",
           1
         ]
-      ]
+      ],
+      "block_metadata": {
+        "llm_request_count": 0,
+        "llm_error_count": 0,
+        "llm_tokens_used": 0
+      }
     },
     {
       "page_id": 238,
@@ -14545,7 +16541,7 @@
       "block_counts": [
         [
           "Span",
-          131
+          133
         ],
         [
           "Line",
@@ -14553,7 +16549,11 @@
         ],
         [
           "Text",
-          6
+          5
+        ],
+        [
+          "Code",
+          3
         ],
         [
           "PageHeader",
@@ -14568,14 +16568,19 @@
           1
         ],
         [
-          "Code",
+          "FigureGroup",
           1
         ],
         [
-          "FigureGroup",
+          "Reference",
           1
         ]
-      ]
+      ],
+      "block_metadata": {
+        "llm_request_count": 0,
+        "llm_error_count": 0,
+        "llm_tokens_used": 0
+      }
     },
     {
       "page_id": 239,
@@ -14583,7 +16588,7 @@
       "block_counts": [
         [
           "Span",
-          59
+          63
         ],
         [
           "Line",
@@ -14591,7 +16596,7 @@
         ],
         [
           "Text",
-          6
+          3
         ],
         [
           "PageHeader",
@@ -14601,7 +16606,12 @@
           "Code",
           2
         ]
-      ]
+      ],
+      "block_metadata": {
+        "llm_request_count": 0,
+        "llm_error_count": 0,
+        "llm_tokens_used": 0
+      }
     }
   ],
   "debug_data_path": "debug_data/thinkpython"
diff --git a/marker/processors/llm/llm_table.py b/marker/processors/llm/llm_table.py
index 3f31bb8e..cce504d9 100644
--- a/marker/processors/llm/llm_table.py
+++ b/marker/processors/llm/llm_table.py
@@ -233,7 +233,7 @@ def parse_html_table(self, html_text: str, block: Block, page: PageGroup) -> Lis
                 cell_polygon = PolygonBox.from_bbox(cell_bbox)
 
                 cell_obj = TableCell(
-                    text=cell_text,
+                    text_lines=[cell_text],
                     row_id=i,
                     col_id=cur_col,
                     rowspan=rowspan,
diff --git a/marker/processors/table.py b/marker/processors/table.py
index 6094f70d..bd191ac2 100644
--- a/marker/processors/table.py
+++ b/marker/processors/table.py
@@ -112,7 +112,7 @@ def __call__(self, document: Document):
 
                     cell_block = TableCell(
                         polygon=cell_polygon,
-                        text=self.finalize_cell_text(cell),
+                        text_lines=self.finalize_cell_text(cell),
                         rowspan=cell.rowspan,
                         colspan=cell.colspan,
                         row_id=cell.row_id,
@@ -135,10 +135,16 @@ def __call__(self, document: Document):
                         page.structure.remove(child.id)
 
     def finalize_cell_text(self, cell: SuryaTableCell):
-        text = "\n".join([t["text"].strip() for t in cell.text_lines]) if cell.text_lines else ""
-        text = re.sub(r"(\s\.){2,}", "", text)  # Replace . . .
-        text = re.sub(r"\.{2,}", "", text)  # Replace ..., like in table of contents
-        return self.normalize_spaces(fix_text(text))
+        fixed_text = []
+        text_lines = cell.text_lines if cell.text_lines else []
+        for line in text_lines:
+            text = line["text"].strip()
+            if not text or text == ".":
+                continue
+            text = re.sub(r"(\s\.){2,}", "", text)  # Replace . . .
+            text = re.sub(r"\.{2,}", "", text)  # Replace ..., like in table of contents
+            fixed_text.append(self.normalize_spaces(fix_text(text)))
+        return fixed_text
 
     @staticmethod
     def normalize_spaces(text):
diff --git a/marker/renderers/markdown.py b/marker/renderers/markdown.py
index 082bfc1f..bf907c6a 100644
--- a/marker/renderers/markdown.py
+++ b/marker/renderers/markdown.py
@@ -16,6 +16,9 @@ def cleanup_text(full_text):
     full_text = re.sub(r'(\n\s){3,}', '\n\n', full_text)
     return full_text.strip()
 
+def get_text_with_br(element):
+    return ''.join(str(content) if content.name == 'br' else content.strip() for content in element.contents)
+
 
 class Markdownify(MarkdownConverter):
     def __init__(self, paginate_output, page_separator, inline_math_delimiters, block_math_delimiters, **kwargs):
@@ -78,7 +81,7 @@ def convert_table(self, el, text, convert_as_inline):
                     col_idx += 1
 
                 # Fill in grid
-                value = cell.get_text(strip=True).replace("\n", " ").replace("|", " ")
+                value = get_text_with_br(cell).replace("\n", " ").replace("|", " ")
                 rowspan = int(cell.get('rowspan', 1))
                 colspan = int(cell.get('colspan', 1))
 
diff --git a/marker/schema/blocks/tablecell.py b/marker/schema/blocks/tablecell.py
index 02778954..e6f279a1 100644
--- a/marker/schema/blocks/tablecell.py
+++ b/marker/schema/blocks/tablecell.py
@@ -1,3 +1,5 @@
+from typing import List
+
 from marker.schema import BlockTypes
 from marker.schema.blocks import Block
 
@@ -9,9 +11,13 @@ class TableCell(Block):
     row_id: int
     col_id: int
     is_header: bool
-    text: str = ""
+    text_lines: List[str] | None = None
     block_description: str = "A cell in a table."
 
+    @property
+    def text(self):
+        return "\n".join(self.text_lines)
+
     def assemble_html(self, document, child_blocks, parent_structure=None):
         tag_cls = "th" if self.is_header else "td"
         tag = f"<{tag_cls}"
@@ -19,4 +25,7 @@ def assemble_html(self, document, child_blocks, parent_structure=None):
             tag += f" rowspan={self.rowspan}"
         if self.colspan > 1:
             tag += f" colspan={self.colspan}"
-        return f"{tag}>{self.text}"
+        if self.text_lines is None:
+            self.text_lines = []
+        text = "
    ".join(self.text_lines)
+        return f"{tag}>{text}"

From 7ab4e927f3c599a8b93eda57241d7045f9599841 Mon Sep 17 00:00:00 2001
From: Vik Paruchuri 
Date: Thu, 23 Jan 2025 22:04:29 -0500
Subject: [PATCH 80/92] Replace br tags

---
 benchmarks/table/table.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/benchmarks/table/table.py b/benchmarks/table/table.py
index 3c15fc5b..3116274d 100644
--- a/benchmarks/table/table.py
+++ b/benchmarks/table/table.py
@@ -175,6 +175,7 @@ def main(
                 for th_tag in marker_table_soup.find_all('th'):
                     th_tag.name = 'td'
                 marker_table_html = str(marker_table_soup)
+                marker_table_html = marker_table_html.replace("
    ", " ") # Fintabnet uses spaces instead of newlines
                 marker_table_html = marker_table_html.replace("\n", " ") # Fintabnet uses spaces instead of newlines
                 gemini_table_html = gemini_table.replace("\n", " ") # Fintabnet uses spaces instead of newlines
 

From 989c697d209919c49053644eb89f85e850a10b1f Mon Sep 17 00:00:00 2001
From: Vik Paruchuri 
Date: Thu, 23 Jan 2025 22:26:36 -0500
Subject: [PATCH 81/92] Update examples

---
 README.md                                     |   6 +-
 .../markdown/multicolcnn/multicolcnn.md       |  16 +-
 .../multicolcnn/multicolcnn_meta.json         |   4 +-
 .../thinkpython/_page_109_Figure_1.png        | Bin 23207 -> 0 bytes
 .../thinkpython/_page_116_Figure_1.jpeg       | Bin 3467 -> 0 bytes
 .../thinkpython/_page_116_Figure_1.png        | Bin 4054 -> 0 bytes
 .../thinkpython/_page_116_Figure_3.png        | Bin 14121 -> 0 bytes
 .../thinkpython/_page_127_Figure_1.png        | Bin 17970 -> 0 bytes
 .../thinkpython/_page_128_Figure_1.png        | Bin 26727 -> 0 bytes
 .../thinkpython/_page_165_Figure_1.jpeg       | Bin 4633 -> 0 bytes
 .../thinkpython/_page_165_Figure_1.png        | Bin 6125 -> 0 bytes
 .../thinkpython/_page_167_Figure_1.png        | Bin 17628 -> 0 bytes
 .../thinkpython/_page_169_Figure_1.png        | Bin 19740 -> 0 bytes
 .../thinkpython/_page_190_Figure_1.png        | Bin 18545 -> 0 bytes
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 .../thinkpython/_page_78_Figure_1.png         | Bin 33830 -> 0 bytes
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 .../thinkpython/_page_99_Figure_1.jpeg        | Bin 6566 -> 0 bytes
 .../thinkpython/_page_99_Figure_1.png         | Bin 9341 -> 0 bytes
 .../markdown/thinkpython/thinkpython.md       | 533 +++++++++---------
 .../thinkpython/thinkpython_meta.json         |  60 +-
 40 files changed, 310 insertions(+), 309 deletions(-)
 delete mode 100644 data/examples/markdown/thinkpython/_page_109_Figure_1.png
 delete mode 100644 data/examples/markdown/thinkpython/_page_116_Figure_1.jpeg
 delete mode 100644 data/examples/markdown/thinkpython/_page_116_Figure_1.png
 delete mode 100644 data/examples/markdown/thinkpython/_page_116_Figure_3.png
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 delete mode 100644 data/examples/markdown/thinkpython/_page_169_Figure_1.png
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 delete mode 100644 data/examples/markdown/thinkpython/_page_195_Figure_1.png
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 delete mode 100644 data/examples/markdown/thinkpython/_page_99_Figure_1.png

diff --git a/README.md b/README.md
index f2f21f57..880dba9e 100644
--- a/README.md
+++ b/README.md
@@ -3,9 +3,9 @@
 Marker converts PDFs and images to markdown, JSON, and HTML quickly and accurately.
 
 - Supports a range of documents in all languages
-- Removes headers/footers/other artifacts
-- Formats tables, forms, equations, links, and code blocks
+- Formats tables, forms, equations, links, references, and code blocks
 - Extracts and saves images along with the markdown
+- Removes headers/footers/other artifacts
 - Easily extensible with your own formatting and logic
 - Optionally boost accuracy with an LLM
 - Works on GPU, CPU, or MPS
@@ -16,7 +16,7 @@ Marker is a pipeline of deep learning models:
 
 - Extract text, OCR if necessary (heuristics, [surya](https://github.com/VikParuchuri/surya))
 - Detect page layout and find reading order ([surya](https://github.com/VikParuchuri/surya))
-- Clean and format each block (heuristics, [texify](https://github.com/VikParuchuri/texify). [tabled](https://github.com/VikParuchuri/tabled))
+- Clean and format each block (heuristics, [texify](https://github.com/VikParuchuri/texify), [surya](https://github.com/VikParuchuri/surya))
 - Optionally use an LLM to improve quality
 - Combine blocks and postprocess complete text
 
diff --git a/data/examples/markdown/multicolcnn/multicolcnn.md b/data/examples/markdown/multicolcnn/multicolcnn.md
index 75c952c7..37fa7c33 100644
--- a/data/examples/markdown/multicolcnn/multicolcnn.md
+++ b/data/examples/markdown/multicolcnn/multicolcnn.md
@@ -123,9 +123,9 @@ Without perspective maps, we generate label density maps for this dataset in the
 
 When perspective maps are used, however, we follow the procedure as described in [\[27\]](#page-8-7), which involves estimating a "crowd density distribution kernel" as the sum of two 2D Gaussians: a symmetric Gaussian for the head and an ellipsoid Gaussian for the body. These are scaled by the perspective map M provided, where M(x) gives the number of pixels that represents a meter at pixel x [\[27\]](#page-8-7). Note that the meaning of this perspective map is distinct from the meaning of the perspective map provided for the UCSD dataset. Using this information, the density contribution from a person with head pixel x is given by the following sum of normalized Gaussians:
 
-$$D_{\bf x} = \frac{1}{||Z||} \left( \mathcal{N}_h(\bf x, \sigma_h) + \mathcal{N}_b(\bf x_b, \Sigma_b) \right) \tag{5}$$
+$$D_{\bf x}=\frac{1}{||Z||}\left(\mathcal{N}_{h}(\bf x,\sigma_{h}) + \mathcal{N}_{b}(\bf x_{b},\Sigma_{b})\right)\tag{5}$$
 
-where $x_b$ is the center of the body, which is 0.875 meters down from the head on average, and can be determined from the perspective map $M$ and the head center x [27]. We sum these Gaussians for each person to pro-
+where $x_b$ is the center of the body, which is 0.875 meters down from the head on average, and can be determined from the perspective map $M$ and the head center x [27]. We sum these Gaussians for each person to produce
 
 | Method       | MAE    |
 |--------------|--------|
@@ -156,12 +156,12 @@ Our network performs very well on the TRANCOS dataset. Indeed, as confirmed by t
 
 Results are shown in Table [3](#page-6-0) and Figure [3.](#page-6-1) We see that the "original" split as defined by the creators of the dataset in [\[5\]](#page-8-17) and used in [\[28\]](#page-9-0) gives us somewhat worse results for counting on this dataset. Results were consistent over multiple trainings. Again, including the perspective map does not seem to increase performance on this dataset. Despite this, we see in Table [3](#page-6-0) and Figure [3](#page-6-1) that the results are comparable to the state of the art. In fact, for two of the splits, our proposed network beats the state of the art. For the upscale split, the AMDCN is the state of the art by a large relative margin. This is compelling because it shows that accurate perspective-free counting can be achieved without
 
-| Method                            | GAME(L=0) | GAME(L=1)  | GAME(L=2)  | GAME(L=3)  |
-|-----------------------------------|-----------|------------|------------|------------|
-| AMDCN[18]                         | 9.7710.99 | 13.1613.75 | 15.0016.69 | 15.8719.32 |
-| [15] + SIFTfrom [14]              | 13.76     | 16.72      | 20.72      | 24.36      |
-| [13] + RGBNorm + Filtersfrom [14] | 17.68     | 19.97      | 23.54      | 25.84      |
-| HOG-2from [14]                    | 13.29     | 18.05      | 23.65      | 28.41      |
+| Method                                      | GAME
    (L=0) | GAME
    (L=1)  | GAME
    (L=2)  | GAME
    (L=3)  |
+|---------------------------------------------|----------------|-----------------|-----------------|-----------------|
+| AMDCN
    [18]                              | 9.77
    10.99 | 13.16
    13.75 | 15.00
    16.69 | 15.87
    19.32 |
+| [15] + SIFT
    from [14]                   | 13.76          | 16.72           | 20.72           | 24.36           |
+| [13] + RGB
    Norm + Filters
    from [14] | 17.68          | 19.97           | 23.54           | 25.84           |
+| HOG-2
    from [14]                         | 13.29          | 18.05           | 23.65           | 28.41           |
 
 Table 2. Mean absolute error of various methods on TRANCOS traffic
 
diff --git a/data/examples/markdown/multicolcnn/multicolcnn_meta.json b/data/examples/markdown/multicolcnn/multicolcnn_meta.json
index 29bb4fcb..e9eff2fd 100644
--- a/data/examples/markdown/multicolcnn/multicolcnn_meta.json
+++ b/data/examples/markdown/multicolcnn/multicolcnn_meta.json
@@ -712,7 +712,7 @@
       "block_metadata": {
         "llm_request_count": 9,
         "llm_error_count": 1,
-        "llm_tokens_used": 8870
+        "llm_tokens_used": 8867
       }
     },
     {
@@ -763,7 +763,7 @@
       "block_metadata": {
         "llm_request_count": 3,
         "llm_error_count": 0,
-        "llm_tokens_used": 3037
+        "llm_tokens_used": 3041
       }
     },
     {
diff --git a/data/examples/markdown/thinkpython/_page_109_Figure_1.png b/data/examples/markdown/thinkpython/_page_109_Figure_1.png
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