fix some lint errors

adapters/src/adapters/composition.py

@@ -152,7 +152,7 @@ def validate_composition(adapter_composition: AdapterCompositionBlock, level=0,
                     f"Models of type {model_type} don't support adapter composition using {block_type.__name__}."
                 )
         for child in adapter_composition:
-            if not type(child) in ALLOWED_NESTINGS[type(adapter_composition)]:
+            if type(child) not in ALLOWED_NESTINGS[type(adapter_composition)]:
                 raise ValueError(f"Adapter setup is invalid. Cannot nest {child} in {adapter_composition}")
             # recursively validate children
             validate_composition(child, level=level + 1)

adapters/src/adapters/head_utils.py

@@ -742,6 +742,8 @@ def get_head_config_and_rename_list(model_class_name, head_name, label2id, num_l
             escaped_name = re.escape(name)
             rename_list.append((rf"{escaped_name}\.(\S+)", f"heads.{head_name}.{i}.{{0}}"))
         i += 1
-    rename_func = lambda k, rename_list=rename_list: _regex_list_rename_func(k, rename_list)
+    def rename_func(k, rename_list=rename_list):
+        return _regex_list_rename_func(k, rename_list)
+
 
     return config, rename_func

adapters/src/adapters/heads/model_mixin.py

@@ -707,7 +707,10 @@ def _load_pretrained_model(
             if len(model.base_model_prefix) > 0 and not any(
                 s.startswith(model.base_model_prefix) for s in loaded_keys
             ):
-                rename_func = lambda x: model.base_model_prefix + "." + x if x not in head_state_dict else x
+                def rename_func(x):
+                    if x not in head_state_dict:
+                        return model.base_model_prefix + "." + x
+                    return x
                 state_dict = {rename_func(k): v for k, v in state_dict.items()}
                 loaded_keys = [rename_func(k) for k in loaded_keys]
 

wtpsplit/evaluation/intrinsic_baselines.py

@@ -152,7 +152,7 @@ class Args:
                         indices[lang][dataset_name][name]["length"] = [metrics.pop("length")]
                         results[lang][dataset_name][name] = metrics
                 except LanguageError as e:
-                    print("Language not supported for", name)
+                    print("Language not supported for", name, e)
                     results[lang][dataset_name][name] = None
 
     json.dump(results, open(Constants.CACHE_DIR / "intrinsic_baselines.json", "w"), indent=4, default=int)

wtpsplit/evaluation/intrinsic_pairwise.py

@@ -9,7 +9,6 @@
 import logging
 
 import h5py
-import skops.io as sio
 import torch
 from datasets import load_dataset
 from tqdm.auto import tqdm
@@ -290,7 +289,7 @@ def main(args):
 
     print(save_str)
     eval_data = torch.load(args.eval_data_path)
-    if "canine" in args.model_path and not "no-adapters" in args.model_path:
+    if "canine" in args.model_path and "no-adapters" not in args.model_path:
         eval_data = split_language_data(eval_data)
     if args.valid_text_path is not None:
         valid_data = load_dataset("parquet", data_files=args.valid_text_path, split="train")

wtpsplit/evaluation/stat_tests/permutation_test_data.py

@@ -63,10 +63,6 @@
                 try:
                     if isinstance(data_list[0], int):
                         data_list = [data_list]
-                except:
-                    print(data_list)
-                    print(lang, dataset, model_type)
-                    raise Exception
 
                 raw_data[lang][dataset][model + "-" + model_type] = data_list
 

wtpsplit/extract_batched.py

@@ -3,7 +3,6 @@
 import logging
 
 import numpy as np
-from tqdm.auto import tqdm
 from transformers import AutoTokenizer
 from tokenizers import AddedToken
 

wtpsplit/models.py

@@ -1,12 +1,10 @@
 import copy
 import math
-import warnings
 from typing import List, Optional, Tuple, Union
 
 import torch
 from torch import Tensor, nn
 from torch.nn import CrossEntropyLoss
-from torchinfo import summary
 from transformers import AutoModel, AutoModelForTokenClassification
 from transformers.modeling_outputs import (
     BaseModelOutputWithPoolingAndCrossAttentions,
@@ -40,7 +38,6 @@
 )
 from transformers.models.xlm_roberta.modeling_xlm_roberta import (
     XLMRobertaEmbeddings,
-    XLMRobertaEncoder,
     XLMRobertaPooler,
     XLMRobertaLayer,
 )
@@ -1395,30 +1392,30 @@ def custom_forward(*inputs):
 AutoModel.register(SubwordXLMConfig, SubwordXLMForTokenClassification)
 AutoModelForTokenClassification.register(SubwordXLMConfig, SubwordXLMForTokenClassification)
 
-if __name__ == "__main__":
-    # test XLM
-    from transformers import AutoConfig, AutoTokenizer
-
-    model_str = "xlm-roberta-base"
-    config = SubwordXLMConfig.from_pretrained(model_str)
-    config.num_labels = 4
-    config.num_hidden_layers = 12
-    config.lookahead = 48
-    config.lookahead_split_layers = 6
-    backbone = SubwordXLMForTokenClassification.from_pretrained(model_str, config=config)
-    print(summary(backbone, depth=4))
-
-    # some sample input
-    text = "A sentence. Now we move on. And on and this is the last sentence. Now, we are starting to move on to the next sentence. This is the last sentence."
-    tokenizer = AutoTokenizer.from_pretrained(model_str)
-
-    tokens = tokenizer(text, return_tensors="pt", add_special_tokens=False, pad_to_multiple_of=512, padding=True)
-    from tokenizers import AddedToken
-
-    tokenizer.add_special_tokens({"additional_special_tokens": [AddedToken("\n")]})
-    print(tokenizer.tokenize(text))
-    print(tokenizer.encode(text))
-    print(tokens)
-
-    # forward pass
-    print(backbone(**tokens))
+# if __name__ == "__main__":
+#     # test XLM
+#     from transformers import AutoTokenizer
+
+#     model_str = "xlm-roberta-base"
+#     config = SubwordXLMConfig.from_pretrained(model_str)
+#     config.num_labels = 4
+#     config.num_hidden_layers = 12
+#     config.lookahead = 48
+#     config.lookahead_split_layers = 6
+#     backbone = SubwordXLMForTokenClassification.from_pretrained(model_str, config=config)
+#     print(summary(backbone, depth=4))
+
+#     # some sample input
+#     text = "A sentence. Now we move on. And on and this is the last sentence. Now, we are starting to move on to the next sentence. This is the last sentence."
+#     tokenizer = AutoTokenizer.from_pretrained(model_str)
+
+#     tokens = tokenizer(text, return_tensors="pt", add_special_tokens=False, pad_to_multiple_of=512, padding=True)
+#     from tokenizers import AddedToken
+
+#     tokenizer.add_special_tokens({"additional_special_tokens": [AddedToken("\n")]})
+#     print(tokenizer.tokenize(text))
+#     print(tokenizer.encode(text))
+#     print(tokens)
+
+#     # forward pass
+#     print(backbone(**tokens))

wtpsplit/train/train.py

@@ -4,7 +4,7 @@
 import random
 import shutil
 import sys
-import time
+# import time
 from collections import Counter, defaultdict
 from dataclasses import dataclass
 from functools import partial
@@ -17,7 +17,7 @@
 import torch_xla.core.xla_model as xm
 import transformers
 from datasets import load_dataset
-from datasets.download import DownloadConfig
+# from datasets.download import DownloadConfig
 from tokenizers import AddedToken
 from torchinfo import summary
 from tqdm.auto import tqdm
@@ -35,6 +35,7 @@
 from wtpsplit.train.evaluate import evaluate_sentence
 from wtpsplit.train.trainer import Trainer
 from wtpsplit.train.utils import Model, cleanup_cache_files
+# from wtpsplit.train.utils import cleanup_cache_files
 from wtpsplit.utils import Constants, LabelArgs, corrupt_training, get_label_dict, get_subword_label_dict
 
 logger = logging.getLogger(__name__)