Chapter5.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Chapter 5: Fine-Tuning with `SFTTrainer`\n",
    "\n",
    "\n",
    "### Spoilers\n",
    "\n",
    "In this chapter, we will:\n",
    "\n",
    "- Understand why it’s often difficult to prevent out-of-memory errors from occurring during training\n",
    "- Explore a range of configuration settings that can help you get the maximum use out of your GPU’s RAM\n",
    "- Use the `SFTTrainer` class from trl to fine-tune a model\n",
    "- Discuss the advantages of memory-efficient attention implementations (such as Flash Attention 2 and PyTorch’s SDPA)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setup\n",
    "\n",
    "For better reproducibility during training, use the pinned versions below, the same versions used in the book:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install transformers==4.46.2 peft==0.13.2 accelerate==1.1.1 trl==0.12.1 bitsandbytes==0.44.1 datasets==3.1.0 huggingface-hub==0.26.2 safetensors==0.4.5 pandas==2.2.2 matplotlib==3.8.0 numpy==1.26.4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "kODUm5BmEQhI",
    "outputId": "5a17ca8b-855a-4e55-b7a2-ab2af98d8906"
   },
   "outputs": [],
   "source": [
    "# If you're running on Colab\n",
    "#!pip install datasets bitsandbytes trl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# If you're running on runpod.io's Jupyter Template\n",
    "#!pip install datasets bitsandbytes trl transformers peft huggingface-hub accelerate safetensors pandas matplotlib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import os\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "from datasets import load_dataset, Dataset\n",
    "from peft import get_peft_model, prepare_model_for_kbit_training, LoraConfig, AutoPeftModelForCausalLM\n",
    "from transformers import Trainer, TrainingArguments, AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig\n",
    "from trl import SFTTrainer, SFTConfig, setup_chat_format, DataCollatorForCompletionOnlyLM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# If you're running on Colab, you need to download the helper functions' Python file\n",
    "!wget https://raw.githubusercontent.com/dvgodoy/FineTuningLLMs/refs/heads/main/helper_functions.py\n",
    "    \n",
    "from helper_functions import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The Goal\n",
    "\n",
    "We pull out all the stops to squeeze the base model, the adapters, the optimizer, and a mini-batch into the GPU's RAM with enough room left to fit the computed activations and the gradients. We manage to make it work by trading computing for memory (gradient checkpointing), deferring parameter updates (gradient accumulation), and using memory-efficient implementations of the attention mechanism (Flash Attention or PyTorch's SDPA)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pre-Reqs\n",
    "\n",
    "![](https://github.com/dvgodoy/FineTuningLLMs/blob/main/images/ch5/forward_pass.png?raw=True)\n",
    "<center>Figure 5.1 - The forward pass</center>\n",
    "\n",
    "![](https://github.com/dvgodoy/FineTuningLLMs/blob/main/images/ch5/backward_pass.png?raw=True)\n",
    "<center>Figure 5.2 - The backward pass</center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training in a Nutshell\n",
    "\n",
    "$$\n",
    "\\Large\n",
    "\\text{memory}_{\\text{total}} = \\underbrace{\\text{memory}_{\\text{model}}}_{\\text{stage}_0} + \n",
    "\\underbrace{\\text{memory}_{\\text{batch}} + \\text{memory}_{\\text{act}}}_{\\text{stage}_1} + \\underbrace{\\text{memory}_{\\text{grad}}}_{\\text{stage}_2} + \\underbrace{\\text{memory}_{\\text{optim}}}_{\\text{stage}_3}\n",
    "$$\n",
    "\n",
    "<center>Equation 5.1 - Total memory used for training a model</center>\n",
    "\n",
    "![](https://github.com/dvgodoy/FineTuningLLMs/blob/main/images/ch5/gpu_ram-no_lora.png?raw=True)\n",
    "<center>Figure 5.3 - Memory used by weights, gradients, and the optimizer</center>\n",
    "\n",
    "![](https://github.com/dvgodoy/FineTuningLLMs/blob/main/images/ch5/gpu_ram-8bit_adam.png?raw=True)\n",
    "<center>Figure 5.4 - Memory used by weights, gradients, and a quantized optimizer</center>\n",
    "\n",
    "![](https://github.com/dvgodoy/FineTuningLLMs/blob/main/images/ch5/gpu_ram-lora.png?raw=True)\n",
    "<center>Figure 5.5 - Memory usage under LoRA</center>\n",
    "\n",
    "![](https://github.com/dvgodoy/FineTuningLLMs/blob/main/images/ch5/gpu_ram-lora_s.png?raw=True)\n",
    "<center>Figure 5.6 - Taking activations into account</center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "![](https://github.com/dvgodoy/FineTuningLLMs/blob/main/images/ch5/gpu_ram-lora_short_s.png?raw=True)\n",
    "<center>Figure 5.5 - Taking activations into account (shorter sequences)</center>\n",
    "\n",
    "$$\n",
    "\\Large\n",
    "\\frac{\\text{memory}_{\\text{act}}^{\\text{eager attn}}}{\\text{memory}_{\\text{model}}} = \\left[3\\frac{s}{h} + \\frac{1}{2}n_{\\text{heads}}\\left(\\frac{s}{h}\\right)^2\\right]\\frac{p_{\\text{comp}}}{p_{\\text{model}}} \n",
    "$$\n",
    "\n",
    "<center>Equation 5.2 - Ratio between activation’s and model’s memory usage</center>\n",
    "\n",
    "****\n",
    "**Popular Models' Configurations**\n",
    "\n",
    "The table below illustrates the characteristics of a few models:\n",
    "\n",
    "| Model | hidden_dim (h)  | max seq (s) | n_heads (n_h)  | n_layers (L) |\n",
    "|---|---|---|---|---|\n",
    "| OPT-350M  | 1024  | 2048  | 16  | 24  |\n",
    "| Phi-3.5 Mini  | 3072  | 4096  | 32  | 32  |\n",
    "| Llama-2 7B  | 4096  | 4096  | 32  | 32  |\n",
    "| Llama-3.2 3B  | 3072  | 8192  | 24  | 28  |\n",
    "| Mistral 8B  | 4096  | 32768  | 32  | 32  |\n",
    "| Qwen-2.5 7B  | 3584  | 32768  | 28  | 28  |\n",
    "| OLMo 7B  | 4096  | 2048  | 32  | 32   |\n",
    "****\n",
    "\n",
    "$$\n",
    "\\Large\n",
    "\\begin{aligned}\n",
    "\\frac{\\text{memory}_{\\text{act}}^{\\text{eager attn}}}{18} &= \\left[3\\frac{500}{2048} + \\frac{1}{2}16\\left(\\frac{500}{2048}\\right)^2\\right]\\frac{16}{8}\n",
    "\\\\\n",
    "&= \\left[\\frac{3}{4}+\\frac{1}{2}\\right]2 = \\frac{3}{2} + 1\n",
    "\\\\\n",
    "\\text{memory}_{\\text{act}}^{\\text{eager attn}} &= 18\\frac{3}{2} + 18 = 27 + 18 = 45\n",
    "\\end{aligned}\n",
    "$$\n",
    "<center>Equation 5.3 - Ratio between activation’s and model’s memory usage</center>\n",
    "\n",
    "$$\n",
    "\\Large\n",
    "\\frac{\\text{memory}_{\\text{act}}^{\\text{flash attn}}}{\\text{memory}_{\\text{model}}} = 3\\frac{s}{h}\\frac{p_{\\text{comp}}}{p_{\\text{model}}}\n",
    "$$\n",
    "<center>Equation 5.4 - Ratio between activation’s and model’s memory usage</center>\n",
    "\n",
    "$$\n",
    "\\Large\n",
    "\\text{batch_size}_{\\text{effective}} = \\text{batch_size}_{\\text{micro}} * \\text{n_accumulation_steps}\n",
    "$$\n",
    "<center>Equation 5.5 - Effective mini-batch suize</center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](https://github.com/dvgodoy/FineTuningLLMs/blob/main/images/ch5/training_loop.png?raw=True)\n",
    "<center>Figure 5.8 - Techniques and their targets in memory</center>\n",
    "\n",
    "$$\n",
    "\\Large\n",
    "\\text{memory}_{\\text{total}} = \\underbrace{\\text{memory}_{\\text{model}}}_{\\text{quantization}} + \n",
    "\\underbrace{\\text{memory}_{\\text{batch}}}_{\\text{grad acc}} + \\underbrace{\\text{memory}_{\\text{act}}}_{\\text{grad checkp \\ flash attn}} + \\underbrace{\\text{memory}_{\\text{grad}} + \\text{memory}_{\\text{optim}}}_{\\text{LoRA \\ 8-bit paged optim \\ grad acc}}\n",
    "$$\n",
    "<center>Equation 5.6 - Techniques and their targets in memory</center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "****\n",
    "**The Formulas For Activation**\n",
    "\n",
    "Are you curious about the origin of the formulas for memory allocation above? Then, this aside is for you! Those formulas are based on the paper [\"Reducing Activation Recomputation in Large Transformer Models\"](https://arxiv.org/abs/2205.05198) by Korthikanti et al.\n",
    "\n",
    "For a typical Transformer model with hidden size $h$ (the model's dimensionality), $L$ \"layers\" (Transformer blocks), and $n_\\text{heads}$ attention heads, the total number of parameters of the model itself and the total number of computed activations for a mini-batch of $b$ sequences of length $s$ is given by:\n",
    "\n",
    "$$\n",
    "\\Large\n",
    "\\begin{align}\n",
    "\\text{memory}_{\\text{model}} = &L 12 h^2\n",
    "\\\\\n",
    "\\text{memory}_{\\text{act}} = &L \\left( \\alpha h b s + \\beta n_{\\text{heads}} b s^2 \\right) \n",
    "\\end{align}\n",
    "$$\n",
    "<center>Equation 5.7 - Formulas for the typical Transformer</center>\n",
    "\n",
    "See the sequence length squared at the end of the formula? That's our friend, eager attention, showing its expensive nature.\n",
    "\n",
    "[QUESTION]\n",
    "\"_Hey, you missed alpha and beta..._\"\n",
    "\n",
    "Good catch! For a plain-vanilla Transformer using eager attention, alpha and beta are 34 and 5, respectively. But, here is the kicker: if we're using Flash Attention (or SDPA, for that matter), beta drops to zero! No more squared sequence length!\n",
    "\n",
    "$$\n",
    "\\Large\n",
    "\\begin{align}\n",
    "\\text{memory}_{\\text{act}}^{\\text{eager attn}} \\underset{\\alpha=34\\\\\\beta=5}{=} &L \\left( 34 h b s + 5 n_{\\text{heads}} b s^2 \\right)\n",
    "\\\\\n",
    "\\text{memory}_{\\text{act}}^{\\text{flash attn}} \\underset{\\alpha=34\\\\\\beta=0}{=} &L \\left( 34 h b s \\right)\n",
    "\\end{align}\n",
    "$$\n",
    "<center>Equation 5.8 - Formulas for eager and Flash attentions</center>\n",
    "\n",
    "We can use the two formulas above to compute the ratio between the memory allocated by the activations and that allocated by the model itself. In this case, we also have to take into account the computation data type (used by the activations) and the data type used by the model's weights:\n",
    "\n",
    "$$\n",
    "\\Large\n",
    "\\begin{align}\n",
    "\\frac{\\text{memory}_{\\text{act}}^{\\text{eager attn}}}{\\text{memory}_{\\text{model}}} &= \\left[\\frac{34bs}{12h} + \\frac{5n_{\\text{heads}}bs^2}{12h^2}\\right]\\frac{p_{\\text{comp}}}{p_{\\text{model}}} \n",
    "\\\\\n",
    "&\\underset{b=1}{\\approx} \\left[3\\frac{s}{h} + \\frac{1}{2}n_{\\text{heads}}\\left(\\frac{s}{h}\\right)^2\\right]\\frac{p_{\\text{comp}}}{p_{\\text{model}}} \n",
    "\\end{align}\n",
    "$$\n",
    "\n",
    "$$\n",
    "\\Large\n",
    "\\begin{align}\n",
    "\\frac{\\text{memory}_{\\text{act}}^{\\text{flash attn}}}{\\text{memory}_{\\text{model}}} &= \\frac{34bs}{12h}\\frac{p_{\\text{comp}}}{p_{\\text{model}}}  \\underset{b=1}{\\approx} 3\\frac{s}{h}\\frac{p_{\\text{comp}}}{p_{\\text{model}}} \n",
    "\\end{align}\n",
    "$$\n",
    "<center>Equation 5.9 - Ratios for eager and Flash attentions</center>\n",
    "\n",
    "There we have it! Assuming a mini-batch of one for simplicity, and rounding the fractions, we arrive at the same formulas introduced earlier.\n",
    "****"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The Road So Far"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n",
      "  return self.fget.__get__(instance, owner)()\n"
     ]
    }
   ],
   "source": [
    "supported = torch.cuda.is_bf16_supported(including_emulation=False)\n",
    "compute_dtype = (torch.bfloat16 if supported else torch.float32)\n",
    "\n",
    "nf4_config = BitsAndBytesConfig(\n",
    "   bnb_4bit_quant_type=\"nf4\",\n",
    "   bnb_4bit_use_double_quant=True,\n",
    "   bnb_4bit_compute_dtype=compute_dtype\n",
    ")\n",
    "\n",
    "model_q4 = AutoModelForCausalLM.from_pretrained(\"facebook/opt-350m\",\n",
    "                                                device_map='cuda:0',\n",
    "                                                quantization_config=nf4_config)\n",
    "\n",
    "model_q4 = prepare_model_for_kbit_training(model_q4)\n",
    "\n",
    "config = LoraConfig(\n",
    "    r=16,\n",
    "    lora_alpha=32,\n",
    "    lora_dropout=0.05,\n",
    "    bias=\"none\",\n",
    "    task_type=\"CAUSAL_LM\",\n",
    ")\n",
    "peft_model = get_peft_model(model_q4, config)\n",
    "\n",
    "tokenizer = AutoTokenizer.from_pretrained(\"facebook/opt-350m\")\n",
    "tokenizer = modify_tokenizer(tokenizer)\n",
    "tokenizer = add_template(tokenizer)\n",
    "\n",
    "peft_model = modify_model(peft_model, tokenizer)\n",
    "\n",
    "dataset = load_dataset(\"dvgodoy/yoda_sentences\", split=\"train\")\n",
    "dataset = dataset.rename_column(\"sentence\", \"prompt\")\n",
    "dataset = dataset.rename_column(\"translation_extra\", \"completion\")\n",
    "dataset = dataset.remove_columns([\"translation\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "o7OblnYAEQhR"
   },
   "source": [
    "### Fine-Tuning with `SFTTrainer`\n",
    "\n",
    "****\n",
    "**Summary of \"Fine-Tuning with `SFTTrainer`\"**\n",
    "- the trainer classes below cover the basic cases where the dataset is in one of the supported formats\n",
    "  - training on both prompts and completions\n",
    "    ```python\n",
    "    trainer = SFTTrainer(\n",
    "        model=model,\n",
    "        processing_class=tokenizer,\n",
    "        train_dataset=train_dataset,\n",
    "        eval_dataset=eval_dataset\n",
    "        peft_config=peft_config,\n",
    "        data_collator=None,\n",
    "        args=SFTConfig(\n",
    "            output_dir=\"./future_name_on_the_hub\",\n",
    "            packing=True,\n",
    "            max_seq_length=max_seq_length,\n",
    "        )\n",
    "    )\n",
    "    ```\n",
    "  - training on completions only\n",
    "    ```python\n",
    "    tokenizer.padding_side='left'\n",
    "    response_template = '...' # according to the tokenizer's chat template\n",
    "    collator_fn=DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)\n",
    "\n",
    "    trainer = SFTTrainer(\n",
    "        model=model,\n",
    "        processing_class=tokenizer,\n",
    "        train_dataset=train_dataset,\n",
    "        eval_dataset=eval_dataset,\n",
    "        peft_config=peft_config,\n",
    "        data_collator=collator_fn,\n",
    "        args=SFTConfig(\n",
    "            output_dir=\"./future_name_on_the_hub\",\n",
    "            packing=False,\n",
    "            max_seq_length=max_seq_length,\n",
    "        )\n",
    "    )\n",
    "    ```\n",
    "- let the trainer class apply LoRA to the model, so it will automatically call both the `get_peft_model()` and the `prepare_model_for_kbit_training()` functions under the hood\n",
    "  - the `model` argument should receive the base model (it may be quantized, but you should not call the `get_peft_model()` function on your own)\n",
    "  - the `peft_config` should receive an instance of `LoraConfig`\n",
    "- use the `formatting_func` argument to specify your custom formatting function (BYOFF, from Chapter 4) if your dataset isn't in one of the supported formats\n",
    "- after creating an instance of the trainer, fetch a mini-batch to ensure the collator is configured properly and your labels are as expected\n",
    "    ```python\n",
    "    dataloader = trainer.get_train_dataloader()\n",
    "    batch = next(iter(dataloader))\n",
    "    labels = batch['labels'][0]\n",
    "    valid = labels >= 0\n",
    "    print(tokenizer.decode(labels[valid]))\n",
    "    ```\n",
    "****"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "UserWarning: You didn't pass a `max_seq_length` argument to the SFTTrainer, this will default to 1024\n",
      "  warnings.warn(\n",
      "WARNING:accelerate.utils.other:Detected kernel version 5.4.0, which is below the recommended minimum of 5.5.0; this can cause the process to hang. It is recommended to upgrade the kernel to the minimum version or higher.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<trl.trainer.sft_trainer.SFTTrainer at 0x7ff51e08f2b0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mvt_trainer = SFTTrainer(\n",
    "    model=peft_model,\n",
    "    tokenizer=tokenizer,\n",
    "    train_dataset=dataset,\n",
    "    args=SFTConfig(\n",
    "        output_dir=\"./future_model_name_on_the_hub\",\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Double-Check the Data Loaders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[50265, 12105, 50118,  1213,   362,    49,  1159,    31,     5,   285,\n",
       "           334,     4,     2, 50118, 50265,  2401, 33388, 50118,  7605,     5,\n",
       "           285,   334,     6,    49,  1159,    51,   362,     4,   289,   338,\n",
       "         41311,     4,     2, 50118,     1,     1,     1,     1,     1,     1,\n",
       "             1,     1],\n",
       "        [50265, 12105, 50118,   250,  9371,    16,    99, 33199,   460,  1733,\n",
       "            11,     4,     2, 50118, 50265,  2401, 33388, 50118,  2264, 33199,\n",
       "           460,  1733,    11,     6,    10,  9371,    16,     4,   854, 47820,\n",
       "          7485,    29,     4,     2, 50118,     1,     1,     1,     1,     1,\n",
       "             1,     1]], device='cuda:0')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mvt_train_dataloader = mvt_trainer.get_train_dataloader()\n",
    "batch = next(iter(mvt_train_dataloader))\n",
    "batch['input_ids'][:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 'right')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokenizer.pad_token_id, tokenizer.padding_side"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The padding tokens should be on the left side. Right-padding a big no-no when training generative language models.\n",
    "\n",
    "![](https://github.com/dvgodoy/FineTuningLLMs/blob/main/images/ch4/packing_flow.png?raw=True)\n",
    "\n",
    "<center>Figure 5.9 - Choosing the right configuration for your data</center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:accelerate.utils.other:Detected kernel version 5.4.0, which is below the recommended minimum of 5.5.0; this can cause the process to hang. It is recommended to upgrade the kernel to the minimum version or higher.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<trl.trainer.sft_trainer.SFTTrainer at 0x7ff41eb4bf40>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pack_trainer = SFTTrainer(\n",
    "    model=peft_model,\n",
    "    tokenizer=tokenizer,\n",
    "    train_dataset=dataset,\n",
    "    data_collator=None,\n",
    "    args=SFTConfig(\n",
    "        output_dir=\"./future_name_on_the_hub\",\n",
    "        packing=True,\n",
    "        max_seq_length=64,\n",
    "        report_to='none'\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[  133,   410, 20072,    51,  1137,    32,  3950,     4,     2, 50118,\n",
       "         50265,  2401, 33388, 50118, 46659,     6,     5,   410, 20072,    51,\n",
       "          1137,    32,     4,     2, 50118,     2, 50265, 12105, 50118,   133,\n",
       "         22032, 15909,  1064,    19,    10,  7337,  2058,     4,     2, 50118,\n",
       "         50265,  2401, 33388, 50118,  3908,    10,  7337,  2058,     6,     5,\n",
       "         22032, 15909,  1064,     4,     2, 50118,     2, 50265, 12105, 50118,\n",
       "           133,  5820,    21, 14166],\n",
       "        [    6,    47,   531,     4,     2, 50118,     2, 50265, 12105, 50118,\n",
       "         27728,  3615,     5,  1836,     9,     5,  1123,  6013,     4,     2,\n",
       "         50118, 50265,  2401, 33388, 50118, 42841,   352,     6,  1591,     5,\n",
       "          1836,     9,     5,  1123,  6013,     6,    47,   531,     4,     2,\n",
       "         50118,     2, 50265, 12105, 50118,   894,  2738,    62,     5, 23819,\n",
       "            13,    10,   200,  3825,     4,     2, 50118, 50265,  2401, 33388,\n",
       "         50118,   725, 28015, 41311]], device='cuda:0')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pack_train_dataloader = pack_trainer.get_train_dataloader()\n",
    "batch = next(iter(pack_train_dataloader))\n",
    "batch['input_ids'][:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
      "To disable this warning, you can either:\n",
      "\t- Avoid using `tokenizers` before the fork if possible\n",
      "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n",
      "UserWarning: You passed a processing_class with `padding_side` not equal to `right` to the SFTTrainer. This might lead to some unexpected behaviour due to overflow issues when training a model in half-precision. You might consider adding `processing_class.padding_side = 'right'` to your code.\n",
      "  warnings.warn(\n",
      "WARNING:accelerate.utils.other:Detected kernel version 5.4.0, which is below the recommended minimum of 5.5.0; this can cause the process to hang. It is recommended to upgrade the kernel to the minimum version or higher.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<trl.trainer.sft_trainer.SFTTrainer at 0x7ff41eaf7130>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokenizer.padding_side='left'\n",
    "\n",
    "response_template = '<|im_start|>assistant\\n'\n",
    "collator_fn=DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)\n",
    "\n",
    "completions_trainer = SFTTrainer(\n",
    "    model=peft_model,\n",
    "    tokenizer=tokenizer,\n",
    "    train_dataset=dataset,\n",
    "    data_collator=collator_fn,\n",
    "    args=SFTConfig(\n",
    "        output_dir=\"./future_name_on_the_hub\",\n",
    "        packing=False,\n",
    "        max_seq_length=64,\n",
    "        report_to='none'\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor([    1,     1,     1,     1,     1,     1,     1,     1, 50265, 12105,\n",
       "         50118,  1213,   362,    49,  1159,    31,     5,   285,   334,     4,\n",
       "             2, 50118, 50265,  2401, 33388, 50118,  7605,     5,   285,   334,\n",
       "             6,    49,  1159,    51,   362,     4,   289,   338, 41311,     4,\n",
       "             2, 50118], device='cuda:0'),\n",
       " tensor([ -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100,\n",
       "          -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100,\n",
       "          -100,  -100,  -100,  -100,  -100,  -100,  7605,     5,   285,   334,\n",
       "             6,    49,  1159,    51,   362,     4,   289,   338, 41311,     4,\n",
       "             2, 50118], device='cuda:0'))"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "completions_train_dataloader = completions_trainer.get_train_dataloader()\n",
    "batch = next(iter(completions_train_dataloader))\n",
    "input_ids = batch['input_ids'][0]\n",
    "labels = batch['labels'][0]\n",
    "input_ids, labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<pad><pad><pad><pad><pad><pad><pad><pad><|im_start|>user\n",
      "They took their kids from the public school.</s>\n",
      "<|im_start|>assistant\n",
      "From the public school, their kids they took. Hrmmm.</s>\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(tokenizer.decode(input_ids))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "From the public school, their kids they took. Hrmmm.</s>\n",
      "\n"
     ]
    }
   ],
   "source": [
    "valid = labels >= 0\n",
    "print(tokenizer.decode(labels[valid]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The Actual Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
      "To disable this warning, you can either:\n",
      "\t- Avoid using `tokenizers` before the fork if possible\n",
      "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n",
      "UserWarning: You passed a processing_class with `padding_side` not equal to `right` to the SFTTrainer. This might lead to some unexpected behaviour due to overflow issues when training a model in half-precision. You might consider adding `processing_class.padding_side = 'right'` to your code.\n",
      "  warnings.warn(\n",
      "WARNING:accelerate.utils.other:Detected kernel version 5.4.0, which is below the recommended minimum of 5.5.0; this can cause the process to hang. It is recommended to upgrade the kernel to the minimum version or higher.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<trl.trainer.sft_trainer.SFTTrainer at 0x7ff42b9740d0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainer = SFTTrainer(\n",
    "    model=peft_model,\n",
    "    tokenizer=tokenizer,\n",
    "    train_dataset=dataset,\n",
    "    data_collator=None,\n",
    "    args=SFTConfig(\n",
    "        output_dir=\"./future_name_on_the_hub\",\n",
    "        packing=True,\n",
    "        max_seq_length=64,\n",
    "        report_to='none'\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='150' max='150' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [150/150 01:19, Epoch 3/3]\n",
       "    </div>\n",
       "    <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       " <tr style=\"text-align: left;\">\n",
       "      <th>Step</th>\n",
       "      <th>Training Loss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table><p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "TrainOutput(global_step=150, training_loss=4.1203271484375, metrics={'train_runtime': 80.1217, 'train_samples_per_second': 14.79, 'train_steps_per_second': 1.872, 'total_flos': 138755718512640.0, 'train_loss': 4.1203271484375, 'epoch': 3.0})"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainer.train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<|im_start|>user\n",
      "There is bacon in this sandwich.</s>\n",
      "<|im_start|>assistant\n",
      "\n",
      "<|im_start|>assistant\n",
      "\n",
      "<|im_start|>assistant\n",
      "\n",
      "<|im_start|>assistant\n",
      "\n",
      "<|im_start|>assistant\n",
      "\n",
      "<|im_start|>assistant\n",
      "\n",
      "<|im_start|>assistant\n",
      "\n",
      "<|im_start|>assistant\n",
      "\n",
      "<|im_start|>assistant\n",
      "\n",
      "<|im_start|>assistant\n",
      "\n",
      "<|im_start|>assistant\n",
      "\n",
      "<|im_start|>assistant\n",
      "\n",
      "<|im_start|>assistant\n",
      "\n",
      "<|im_start|>assistant\n"
     ]
    }
   ],
   "source": [
    "# results may vary, but they're not what we'd like them to be\n",
    "print(generate(peft_model, tokenizer, \"There is bacon in this sandwich.\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2e-05, 8, 3.0)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainer.args.learning_rate, trainer.args.per_device_train_batch_size, trainer.args.num_train_epochs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "accelerate.optimizer.AcceleratedOptimizer"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainer.optimizer.__class__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.optim.adamw.AdamW"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainer.optimizer.optimizer.__class__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "**Simplified Training Loop**\n",
    "\n",
    "```python\n",
    "n_epochs = trainer.args.num_train_epochs\n",
    "# Retrieves the data loader according to the packing and\n",
    "# collator configuration\n",
    "train_dataloader = trainer.get_train_dataloader()\n",
    "# Stage 0\n",
    "trainer.create_optimizer()\n",
    "# Prepare all objects passed in `args` for distributed training\n",
    "# and mixed precision, then return them in the same order.\n",
    "trainer.model, trainer.optimizer = trainer.accelerator.prepare(\n",
    "\ttrainer.model, trainer.optimizer\n",
    ")\n",
    "# Activate gradient checkpointing if needed\n",
    "if trainer.args.gradient_checkpointing:\n",
    "    trainer.model.gradient_checkpointing_enable(\n",
    "    \tgradient_checkpointing_kwargs={'use_reentrant': False}\n",
    "    )\n",
    "trainer.model.zero_grad()\n",
    "\n",
    "for epoch in range(int(n_epochs)):\n",
    "    for step, inputs in enumerate(train_dataloader):\n",
    "        # Ready for gradient accumulation\n",
    "        with trainer.accelerator.accumulate(trainer.model):\n",
    "            trainer.model.train()\n",
    "            # Stage 1\n",
    "            ## recursively walks through lists/tuples/dicts\n",
    "            ## to send every tensor to trainer.args.device\n",
    "            inputs = trainer._prepare_inputs(inputs)\n",
    "            ## Forward pass\n",
    "            outputs = trainer.model(**inputs)\n",
    "            ## Unwrap the micro-batch loss\n",
    "            loss = (outputs[\"loss\"] if isinstance(outputs, dict) else outputs[0])\n",
    "            # Stage 2\n",
    "            ## Backward pass - backpropagation\n",
    "            trainer.accelerator.backward(loss)\n",
    "            \n",
    "        # Stage 3\n",
    "        ## Update parameters\n",
    "        trainer.optimizer.step()\n",
    "        # Stage 4\n",
    "        ## Zero gradients\n",
    "        trainer.model.zero_grad()\n",
    "```\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "eHnCCh1hEQhS"
   },
   "source": [
    "### `SFTConfig`\n",
    "\n",
    "****\n",
    "**Summary of \"`SFTConfig`\"**\n",
    "- the basic configuration below is a good starting point\n",
    "    ```python\n",
    "    lr = ...\n",
    "    num_train_epochs = ...\n",
    "    min_effective_batch_size = 8\n",
    "    max_seq_length = ...\n",
    "    collator_fn = ...\n",
    "    packing = (collator_fn is None)\n",
    "    steps = 50\n",
    "\n",
    "    sft_config = SFTConfig(\n",
    "        output_dir='./future_name_on_the_hub',\n",
    "        # Dataset\n",
    "        packing=packing,\n",
    "        max_seq_length=max_seq_length,\n",
    "        # Gradients / Memory\n",
    "        gradient_checkpointing=True,\n",
    "        gradient_checkpointing_kwargs={'use_reentrant': False},\n",
    "        gradient_accumulation_steps=2,\n",
    "        per_device_train_batch_size=min_effective_batch_size,\n",
    "        auto_find_batch_size=True,\n",
    "        # Training\n",
    "        num_train_epochs=num_train_epochs,\n",
    "        learning_rate=lr,\n",
    "        # Env and Logging\n",
    "        report_to='tensorboard',\n",
    "        logging_dir='./logs',\n",
    "        logging_strategy='steps',\n",
    "        logging_steps=steps,\n",
    "        evaluation_strategy='steps',\n",
    "        evaluation_steps=steps,\n",
    "        save_strategy='steps',\n",
    "        save_steps=steps\n",
    "    )\n",
    "    ```\n",
    "- for mixed-precision training (potentially trading a little extra memory usage for improving speed), include the following arguments:\n",
    "    ```python\n",
    "        fp16: not torch.cuda.is_bf16_supported(),\n",
    "        bf16: torch.cuda.is_bf16_supported(),\n",
    "    ```\n",
    "****\n",
    "\n",
    "#### Memory Usage Arguments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_max_batch_size(trainer, initial_batch_size=None, update_trainer=False):\n",
    "    from copy import deepcopy\n",
    "    from accelerate.utils import find_executable_batch_size as accelerate_find_executable_batch_size\n",
    "\n",
    "    # create a new, dummy, Trainer using the same model\n",
    "    new_trainer = SFTTrainer(\n",
    "        model=trainer.model\n",
    "    )\n",
    "    # replicates the whole configuration at low level\n",
    "    new_trainer.__dict__.update(**trainer.__dict__)\n",
    "    # since we're modifying the SFTConfig, deep-copies it\n",
    "    new_trainer.args = deepcopy(new_trainer.args)\n",
    "    # sets learning rate to 0, so model doesn't get updated\n",
    "    new_trainer.args.learning_rate = 0\n",
    "    # only runs it for 1 step, we just need to check for OOMs\n",
    "    new_trainer.args.max_steps = 1\n",
    "    \n",
    "    # you don't need to set this in the actual Trainer\n",
    "    # if you're using this function\n",
    "    new_trainer.args.auto_find_batch_size = True\n",
    "    # by default, it uses the original `per_device_train_batch_size`\n",
    "    # as starting point, but we can change it\n",
    "    if initial_batch_size is not None:\n",
    "        new_trainer.args.per_device_train_batch_size = initial_batch_size\n",
    "\n",
    "    # this is what's happening under the hood\n",
    "    # it creates a decorated function based on the inner training loop function\n",
    "    func = accelerate_find_executable_batch_size(new_trainer._inner_training_loop)\n",
    "    # then it runs the training loop using the copied arguments\n",
    "    # evert time it raises an exception, it halves the batch size and\n",
    "    # then it tries again, until it goes through with it\n",
    "    # but, since lr=0, it won't change the model\n",
    "    func(args=new_trainer.args)\n",
    "    # the succesful batch size is in the `_train_batch_size` attribute\n",
    "    max_batch_size = new_trainer._train_batch_size\n",
    "    del new_trainer\n",
    "    \n",
    "    # for your convenience, it can update the batch size directly\n",
    "    if update_trainer:\n",
    "        trainer.args.per_device_train_batch_size = max_batch_size\n",
    "    \n",
    "    return max_batch_size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Mixed-Precision Arguments\n",
    "\n",
    "```python\n",
    "def peft_module_casting_to_bf16(model):\n",
    "    for name, module in model.named_modules():\n",
    "        if isinstance(module, torch.nn.LayerNorm) or \"norm\" in name:\n",
    "            module = module.to(torch.float32)\n",
    "        elif any(x in name for x in [\"lm_head\", \"embed_tokens\", \"wte\", \"wpe\"]):\n",
    "            if hasattr(module, \"weight\"):\n",
    "                if module.weight.dtype == torch.float32:\n",
    "                    module = module.to(torch.bfloat16)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The Actual Training (For Real!)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:accelerate.utils.other:Detected kernel version 5.4.0, which is below the recommended minimum of 5.5.0; this can cause the process to hang. It is recommended to upgrade the kernel to the minimum version or higher.\n",
      "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='25' max='25' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [25/25 00:27, Epoch 1/1]\n",
       "    </div>\n",
       "    <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       " <tr style=\"text-align: left;\">\n",
       "      <th>Step</th>\n",
       "      <th>Training Loss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>3.689900</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "UserWarning: Setting `save_embedding_layers` to `True` as the embedding layer has been resized during finetuning.\n",
      "  warnings.warn(\n",
      "UserWarning: Setting `save_embedding_layers` to `True` as the embedding layer has been resized during finetuning.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "TrainOutput(global_step=25, training_loss=3.5384653854370116, metrics={'train_runtime': 28.9579, 'train_samples_per_second': 13.64, 'train_steps_per_second': 0.863, 'total_flos': 46251906170880.0, 'train_loss': 3.5384653854370116, 'epoch': 1.0})"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min_effective_batch_size = 8\n",
    "lr = 3e-4\n",
    "max_seq_length = 64\n",
    "collator_fn = None\n",
    "packing = (collator_fn is None)\n",
    "steps = 20\n",
    "num_train_epochs = 10\n",
    "\n",
    "sft_config = SFTConfig(\n",
    "    output_dir='./future_name_on_the_hub',\n",
    "    # Dataset\n",
    "    packing=packing,\n",
    "    max_seq_length=max_seq_length,\n",
    "    # Gradients / Memory\n",
    "    gradient_checkpointing=True,\n",
    "    gradient_checkpointing_kwargs={'use_reentrant': False},\n",
    "    gradient_accumulation_steps=2,\n",
    "    per_device_train_batch_size=min_effective_batch_size,\n",
    "    auto_find_batch_size=True,\n",
    "    # Training\n",
    "    num_train_epochs=num_train_epochs,\n",
    "    learning_rate=lr,\n",
    "    # Env and Logging\n",
    "    report_to='tensorboard',\n",
    "    logging_dir='./logs',\n",
    "    logging_strategy='steps',\n",
    "    logging_steps=steps,\n",
    "    save_strategy='steps',\n",
    "    save_steps=steps\n",
    ")\n",
    "                              \n",
    "trainer = SFTTrainer(\n",
    "    model=peft_model,\n",
    "    tokenizer=tokenizer,\n",
    "    train_dataset=dataset,\n",
    "    data_collator=collator_fn,\n",
    "    args=sft_config\n",
    ")\n",
    "trainer.train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<|im_start|>user\n",
      "There is bacon in this sandwich.</s>\n",
      "<|im_start|>assistant\n",
      "In this sandwich, bacon is, there is.</s>\n"
     ]
    }
   ],
   "source": [
    "print(generate(peft_model, tokenizer, \"There is bacon in this sandwich.\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Saving the Adapter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainer.save_model('yoda-adapter')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['tokenizer.json',\n",
       " 'README.md',\n",
       " 'vocab.json',\n",
       " 'training_args.bin',\n",
       " 'merges.txt',\n",
       " 'adapter_config.json',\n",
       " 'special_tokens_map.json',\n",
       " 'added_tokens.json',\n",
       " 'tokenizer_config.json',\n",
       " 'adapter_model.safetensors']"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.listdir('yoda-adapter')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Saving the Full Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PeftModelForCausalLM(\n",
       "  (base_model): LoraModel(\n",
       "    (model): OPTForCausalLM(\n",
       "      (model): OPTModel(\n",
       "        (decoder): OPTDecoder(\n",
       "          (embed_tokens): Embedding(50266, 512, padding_idx=1)\n",
       "          (embed_positions): OPTLearnedPositionalEmbedding(2050, 1024)\n",
       "          (project_out): Linear(in_features=1024, out_features=512, bias=False)\n",
       "          (project_in): Linear(in_features=512, out_features=1024, bias=False)\n",
       "          (layers): ModuleList(\n",
       "            (0-23): 24 x OPTDecoderLayer(\n",
       "              (self_attn): OPTAttention(\n",
       "                (k_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (v_proj): lora.Linear(\n",
       "                  (base_layer): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                  (lora_dropout): ModuleDict(\n",
       "                    (default): Dropout(p=0.05, inplace=False)\n",
       "                  )\n",
       "                  (lora_A): ModuleDict(\n",
       "                    (default): Linear(in_features=1024, out_features=16, bias=False)\n",
       "                  )\n",
       "                  (lora_B): ModuleDict(\n",
       "                    (default): Linear(in_features=16, out_features=1024, bias=False)\n",
       "                  )\n",
       "                  (lora_embedding_A): ParameterDict()\n",
       "                  (lora_embedding_B): ParameterDict()\n",
       "                  (lora_magnitude_vector): ModuleDict()\n",
       "                )\n",
       "                (q_proj): lora.Linear(\n",
       "                  (base_layer): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                  (lora_dropout): ModuleDict(\n",
       "                    (default): Dropout(p=0.05, inplace=False)\n",
       "                  )\n",
       "                  (lora_A): ModuleDict(\n",
       "                    (default): Linear(in_features=1024, out_features=16, bias=False)\n",
       "                  )\n",
       "                  (lora_B): ModuleDict(\n",
       "                    (default): Linear(in_features=16, out_features=1024, bias=False)\n",
       "                  )\n",
       "                  (lora_embedding_A): ParameterDict()\n",
       "                  (lora_embedding_B): ParameterDict()\n",
       "                  (lora_magnitude_vector): ModuleDict()\n",
       "                )\n",
       "                (out_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "              )\n",
       "              (activation_fn): ReLU()\n",
       "              (self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
       "              (fc1): Linear(in_features=1024, out_features=4096, bias=True)\n",
       "              (fc2): Linear(in_features=4096, out_features=1024, bias=True)\n",
       "              (final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "      )\n",
       "      (lm_head): Linear(in_features=512, out_features=50266, bias=False)\n",
       "    )\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reloaded_model = AutoPeftModelForCausalLM.from_pretrained('yoda-adapter')\n",
    "reloaded_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OPTForCausalLM(\n",
       "  (model): OPTModel(\n",
       "    (decoder): OPTDecoder(\n",
       "      (embed_tokens): Embedding(50266, 512, padding_idx=1)\n",
       "      (embed_positions): OPTLearnedPositionalEmbedding(2050, 1024)\n",
       "      (project_out): Linear(in_features=1024, out_features=512, bias=False)\n",
       "      (project_in): Linear(in_features=512, out_features=1024, bias=False)\n",
       "      (layers): ModuleList(\n",
       "        (0-23): 24 x OPTDecoderLayer(\n",
       "          (self_attn): OPTAttention(\n",
       "            (k_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "            (v_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "            (q_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "            (out_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "          )\n",
       "          (activation_fn): ReLU()\n",
       "          (self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
       "          (fc1): Linear(in_features=1024, out_features=4096, bias=True)\n",
       "          (fc2): Linear(in_features=4096, out_features=1024, bias=True)\n",
       "          (final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "  )\n",
       "  (lm_head): Linear(in_features=512, out_features=50266, bias=False)\n",
       ")"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_model = reloaded_model.merge_and_unload()\n",
    "merged_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "merged_model.save_pretrained('yoda-merged')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Push To Hub"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from huggingface_hub import login\n",
    "login()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](https://github.com/dvgodoy/FineTuningLLMs/blob/main/images/ch0/hub0.png?raw=True)\n",
    "<center>Figure 5.10 - Logging into the Hugging Face Hub</center>\n",
    "\n",
    "![](https://github.com/dvgodoy/FineTuningLLMs/blob/main/images/ch0/hub1.png?raw=True)\n",
    "<center>Figure 5.11- Successful Login</center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainer.push_to_hub()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Attention\n",
    "\n",
    "$$\n",
    "\\Large\n",
    "\\text{Attention}(Q, K, V) = \\text{softmax}\\left(\\frac{QK^T}{\\sqrt{d_k}}\\right)V\n",
    "$$\n",
    "<center>Equation 5.10 - Attention formula</center>\n",
    "\n",
    "****\n",
    "**Summary of \"Attention\"**\n",
    "- the most popular Transformer models in Hugging Face already support the memory-efficient PyTorch's SDPA implementation, you can easily use them by specifying the `attn_implementation` argument:\n",
    "    ```python\n",
    "    model = AutoModelForCausalLM.from_pretrained(repo_id, attn_implementation=\"sdpa\")\n",
    "    ```\n",
    "- Flash Attention 2 can offer a little performance boost (compared to SDPA), but it comes with strings attached as it only supports 16-bit data types by default (FP16 and BF16):\n",
    "  - you must specify the `torch_dtype` argument accordingly or load a quantized model (which will set the type under the hood)\n",
    "    ```python\n",
    "    model = AutoModelForCausalLM.from_pretrained(\"facebook/opt-350m\", \n",
    "                                                 torch_dtype=torch.bfloat16,\n",
    "                                                 attn_implementation=\"flash_attention_2\")\n",
    "    ```\n",
    "    - training a model in full FP16 may lead to underflow or overflow errors, as previously discussed\n",
    "    - training a model in full BF16 offers its own challenges and may require using an optimizer that implements stochastic rounding (please see more details in the \"Full BF16 Training\" aside in the \"Flash Attention 2\" section)\n",
    "  - if you choose to use FP32 instead, you **must** use mixed-precision training (either `fp16` or `bf16`, but not both, must be `True` in the training configuration) or you'll run into a runtime error\n",
    "****\n",
    "\n",
    "\n",
    "****\n",
    "**The Scaling Factor**\n",
    "```python\n",
    "import math\n",
    "import torch\n",
    "d_k = 1024\n",
    "q_vector = torch.randn(1000, 1, d_k)\n",
    "v_vector = torch.randn(1000, 1, d_k).permute(0, 2, 1)\n",
    "torch.bmm(q_vector, v_vector).squeeze().std(), math.sqrt(d_k)\n",
    "```\n",
    "```\n",
    "(tensor(32.5935), 32.0)\n",
    "```\n",
    "****\n",
    "\n",
    "![](https://github.com/dvgodoy/FineTuningLLMs/blob/main/images/ch5/sdpa.png?raw=True)\n",
    "<center>Figure 5.12 - The attention mechanism</center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$\n",
    "\\Large\n",
    "d_k = \\frac{d_\\text{model}}{n_{\\text{heads}}}\n",
    "$$\n",
    "<center>Equation 5.11 - Dimensionality of each attention’s head</center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "class MultiHeadedAttention(nn.Module):\n",
    "    def __init__(self, n_heads, d_model, dropout=0.1, verbose=False):\n",
    "        super(MultiHeadedAttention, self).__init__()\n",
    "        self.n_heads = n_heads\n",
    "        self.d_model = d_model\n",
    "        self.d_k = int(d_model / n_heads)\n",
    "        self.linear_query = nn.Linear(d_model, d_model)\n",
    "        self.linear_key = nn.Linear(d_model, d_model)\n",
    "        self.linear_value = nn.Linear(d_model, d_model)\n",
    "        self.linear_out = nn.Linear(d_model, d_model)\n",
    "        self.dropout = nn.Dropout(p=dropout)\n",
    "        self.alphas = None\n",
    "        self.verbose = verbose\n",
    "        \n",
    "    def print_sizes(self, name, tensor):\n",
    "        if self.verbose:\n",
    "            print(f'{name:<22} - shape: {str(tensor.shape):<30}'\n",
    "                  f'- nelems: {str(torch.numel(tensor)):>10}')\n",
    "\n",
    "    def make_chunks(self, x):\n",
    "        batch_size, seq_len = x.size(0), x.size(1)\n",
    "        # N, L, D -> N, L, n_heads * d_k\n",
    "        x = x.view(batch_size, seq_len, self.n_heads, self.d_k) \n",
    "        # N, n_heads, L, d_k\n",
    "        x = x.transpose(1, 2)  \n",
    "        return x\n",
    "\n",
    "    def init_keys(self, key):\n",
    "        # N, n_heads, L, d_k\n",
    "        self.proj_key = self.make_chunks(self.linear_key(key))\n",
    "        self.proj_value = self.make_chunks(self.linear_value(key))\n",
    "        \n",
    "    def alignment_function(self, query):\n",
    "        # scaled dot product\n",
    "        # N, n_heads, L, d_k x # N, n_heads, d_k, L -> N, n_heads, L, L\n",
    "        proj_query = self.make_chunks(self.linear_query(query))\n",
    "        dot_products = torch.matmul(proj_query, \n",
    "                                    self.proj_key.transpose(-2, -1))\n",
    "        scores =  dot_products / np.sqrt(self.d_k)\n",
    "\n",
    "        self.print_sizes('Query Projs', proj_query)\n",
    "        self.print_sizes('Key Projs', self.proj_key)\n",
    "        self.print_sizes('Value Projs', self.proj_value)\n",
    "        self.print_sizes('Dot Products', dot_products)\n",
    "        self.print_sizes('Alignments', scores)\n",
    "        return scores\n",
    "            \n",
    "    def attn(self, query, mask=None):\n",
    "        # Query is batch-first: N, L, D\n",
    "        # Alignment function will generate alignments for each head\n",
    "        alignments = self.alignment_function(query) # N, n_heads, L, L\n",
    "        if mask is not None:\n",
    "            alignments = alignments.masked_fill(mask == 0, -1e9)\n",
    "        alphas = F.softmax(alignments, dim=-1) # N, n_heads, L, L\n",
    "        alphas = self.dropout(alphas)\n",
    "        self.alphas = alphas.detach()\n",
    "        \n",
    "        # N, n_heads, L, L x N, n_heads, L, d_k -> N, n_heads, L, d_k\n",
    "        context = torch.matmul(alphas, self.proj_value)\n",
    "\n",
    "        self.print_sizes('Attn Scores / Alphas', alphas)\n",
    "        self.print_sizes('Context Vector (heads)', context)\n",
    "        return context        \n",
    "                                \n",
    "    def output_function(self, contexts):\n",
    "        # N, L, D\n",
    "        out = self.linear_out(contexts) # N, L, D\n",
    "        \n",
    "        self.print_sizes('Output Context Vector', out)\n",
    "        return out\n",
    "        \n",
    "    def forward(self, query, mask=None):\n",
    "        self.init_keys(query)\n",
    "        \n",
    "        if mask is not None:\n",
    "            # N, 1, L, L - every head uses the same mask\n",
    "            mask = mask.unsqueeze(1)\n",
    "\n",
    "        # N, n_heads, L, d_k\n",
    "        context = self.attn(query, mask=mask)\n",
    "        # N, L, n_heads, d_k\n",
    "        context = context.transpose(1, 2).contiguous()\n",
    "        # N, L, n_heads * d_k = N, L, d_model\n",
    "        context = context.view(query.size(0), -1, self.d_model)        \n",
    "        # N, L, d_model\n",
    "        out = self.output_function(context)\n",
    "        return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "bsize = 1      # N\n",
    "seqlen = 256   # L\n",
    "d_model = 1024 # D\n",
    "n_heads = 16   # n_heads\n",
    "# d_k = D / n_heads = 64\n",
    "input_batch = torch.randn(bsize, seqlen, d_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Query Projs            - shape: torch.Size([1, 16, 256, 64])  - nelems:     262144\n",
      "Key Projs              - shape: torch.Size([1, 16, 256, 64])  - nelems:     262144\n",
      "Value Projs            - shape: torch.Size([1, 16, 256, 64])  - nelems:     262144\n",
      "Dot Products           - shape: torch.Size([1, 16, 256, 256]) - nelems:    1048576\n",
      "Alignments             - shape: torch.Size([1, 16, 256, 256]) - nelems:    1048576\n",
      "Attn Scores / Alphas   - shape: torch.Size([1, 16, 256, 256]) - nelems:    1048576\n",
      "Context Vector (heads) - shape: torch.Size([1, 16, 256, 64])  - nelems:     262144\n",
      "Output Context Vector  - shape: torch.Size([1, 256, 1024])    - nelems:     262144\n"
     ]
    }
   ],
   "source": [
    "mha = MultiHeadedAttention(n_heads=n_heads, d_model=d_model, verbose=True)\n",
    "out = mha(input_batch)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](https://github.com/dvgodoy/FineTuningLLMs/blob/main/images/ch5/eager_attn.png?raw=True)\n",
    "\n",
    "<center>Figure 5.13 - Eager attention’s memory operations</center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Flash Attention 2\n",
    "\n",
    "You need to install the [`flash-attn` package](https://github.com/Dao-AILab/flash-attention). Keep in mind, though, that older GPU's are not supported. Flash Attention only works with Ampere, Ada, or Hopper GPUs (e.g., A100, RTX 3090, RTX 4090, H100) which, by the way, are those supporting the BF16 data type.\n",
    "\n",
    "Also, in order to install it, you must have NVCC (NVIDIA CUDA Compiler driver) installed. You can easily check if you already have it by running the following command:\n",
    "\n",
    "```shell\n",
    "nvcc --version\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you get an output like that, you're good to go:\n",
    "\n",
    "```\n",
    "nvcc: NVIDIA (R) Cuda compiler driver\n",
    "Copyright (c) 2005-2024 NVIDIA Corporation\n",
    "Built on Thu_Sep_12_02:18:05_PDT_2024\n",
    "Cuda compilation tools, release 12.6, V12.6.77\n",
    "Build cuda_12.6.r12.6/compiler.34841621_0\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However, if `nvcc` isn't recognized by your system, you may need to install the [CUDA Toolkit](https://developer.nvidia.com/cuda-downloads).\n",
    "\n",
    "The next step is installing the package itself:\n",
    "\n",
    "```shell\n",
    "pip install -U flash-attn\n",
    "```\n",
    "\n",
    "Once installation is complete, double-check it using a `transformers` helper function:\n",
    "\n",
    "```python\n",
    "from transformers.utils import is_flash_attn_2_available\n",
    "\n",
    "is_flash_attn_2_available()\n",
    "```\n",
    "\n",
    "```\n",
    "True\n",
    "```\n",
    "\n",
    "Flash Attention 2 only supports 16-bit data types, including FP16 and BF16."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# this cell will only work properly if your environment\n",
    "# supports Flash Attention 2, and it was installed\n",
    "model = AutoModelForCausalLM.from_pretrained(\"facebook/opt-350m\", \n",
    "                                             device_map='cuda:0',\n",
    "                                             torch_dtype=torch.bfloat16,\n",
    "                                             attn_implementation=\"flash_attention_2\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# this cell will only work properly if your environment\n",
    "# supports Flash Attention 2, and it was installed\n",
    "bnb_config = BitsAndBytesConfig(load_in_4bit=True)\n",
    "\n",
    "model = AutoModelForCausalLM.from_pretrained(\"facebook/opt-350m\", \n",
    "                                             device_map='cuda:0', \n",
    "                                             quantization_config=bnb_config, \n",
    "                                             attn_implementation=\"flash_attention_2\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can use FP32, but you must train the model using mixed precision. We’ve discussed two configuration arguments for that: fp16 and bf16."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# this cell will only work properly if your environment\n",
    "# supports Flash Attention 2, and it was installed\n",
    "model = AutoModelForCausalLM.from_pretrained(\"facebook/opt-350m\", \n",
    "                                             device_map='cuda:0', \n",
    "                                             torch_dtype=torch.float32,\n",
    "                                             attn_implementation=\"flash_attention_2\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```python\n",
    "sft_config = SFTConfig(\n",
    "    ...\n",
    "    # mixed precision\n",
    "    fp16=not torch.cuda.is_bf16_supported(),\n",
    "    bf16=torch.cuda.is_bf16_supported()\n",
    ")\n",
    "```\n",
    "\n",
    "****\n",
    "**Full BF16 Training**\n",
    "\n",
    "```python\n",
    "# !pip install torchao\n",
    "from torchao.prototype.low_bit_optim import _AdamW\n",
    "optim = _AdamW(model_bf16.parameters(), bf16_stochastic_round=True)\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "\n",
    "lr = 5e-3\n",
    "res = rounding_evolution(n_updates=10000, decimals=7, lr=lr)\n",
    "fig, axs = plt.subplots(1, 2, figsize=(10, 4))\n",
    "axs[0].plot(np.array(list(res[False].values())).T)\n",
    "axs[0].legend(res[False].keys(), title='Decimals')\n",
    "axs[0].set_title(f'LR={lr} - Truncating/Rounding Down')\n",
    "axs[0].set_ylabel('Weight')\n",
    "axs[0].set_xlabel('# Updates')\n",
    "axs[1].plot(np.array(list(res[True].values())).T)\n",
    "axs[1].legend(res[True].keys(), title='Decimals')\n",
    "axs[1].set_title(f'LR={lr} - Stochastic Rounding')\n",
    "axs[1].set_ylabel('Weight')\n",
    "axs[1].set_xlabel('# Updates')\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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o3749Fi5ciHXr1hX5c5ZEhw4d4OfnV+LtjB8/nrXcrl07VpK2/fv3g8PhsH7eUopdtczNzWXPU1NTkZ+fjw4dOuD06dNITU2Fra1tsepnZWXFGv/H5/PRvHlzvHjxQrbu1KlTqFq1Kj777DPZOjMzM4wZMwbTpk1jbU8sFuPUqVP44YcfVPa1ceNGbNy4UbZsamqKH3/8kdVF+cSJE3B1dcXgwYNZ5aZMmYLBgwfj0qVLslbXoirsZ3HixAmYmJiweh3weDx88803rJZdbTp37sxqqW/YsCFsbGxkx1MkEuHcuXPo27cv3N3dZeVq1qyJ7t274+jRo8X6bIqkSY2krVrnzp1DXl4evv32W3C58lFJY8aMwc8//4zjx48jKCgIXC4XrVu3xn///QcAePz4MT5+/Ijp06dj//79uHr1Krp06YKwsDDUr19fpSVh0KBBrJYIaWuP4neJEFIydH1QutcHc+fOha+vL9asWYPTp0/j5MmTmDlzJho1aoQdO3agbt26Wt9/8+ZNJCQkYM6cOazxuz179oSvry+OHz+OuXPnIjExEf/99x+mTp2K6tWrs7ahrpu2uvPXwYMHkZaWJkuGqct1gvTv9rFjx+Dv71/iXkeHDh2CWCzGrFmzWOcXTZ9DnTp16rCWGzRogM2bN8PCwgKA7se0OAo7b71//x6RkZGYPn066zqrS5cu8PPzQ2Zmpk77KeznJx1uNnHiRFa5b775xihmmKkMKNiuZMaOHYsBAwYgJycH58+fx8qVK9WOUXJzc4Obm5te9jlkyBBMmzYN586dk51Mnz17hsePH2vs3ipNFpKUlIS8vDzZenNzc9ja2sr+8Ksbv5WTk8M6MajTtm1btGjRAufOndNaLjU1VXbiBiQBo6buX7pSznhaHNLx14rs7e2RnJwsW37+/Dnc3d0Lre/ly5cxe/ZsXL16VWWcT0mC7WrVqqmcEO3t7XHv3j3Z8qtXr+Dj46NSrmbNmirbi4iIQGJiotquT59//jkmT56MvLw8RERE4LfffkNWVhbrBP3q1SvUqlVL5aQtvcB59epV0T8kdPtZvHr1Cm5ubqwMvIDqhYA2yhdNyvtJSEhAdna22mOnbl1xZGRkAACsra0ByI+Z8ufg8/nw9vZmHdN27dphzpw5yM7ORlhYGNzc3NC4cWP4+/sjLCwMXbp0QXh4OOvCV0r5s0svYBSPMSGkZOj6QKI0rw8GDx6MwYMHIy0tDdevX8eWLVuwc+dO9O7dGw8ePNCaBEvT31sA8PX1lWXYlgZz9evX11oXKW1/X6XBti7XCR06dED//v0xd+5cLFu2DB07dkSfPn0wZMgQtTc+CvP8+XNwudwSNU7s378fNjY2SExMxMqVK/Hy5UvWz1/XY1ochZ23pPtWN1yhTp06uH37don3Y2Njg1evXoHL5apce+rruoAUjoLtSqZWrVro3LkzAKBXr17g8XiYPn06OnXqxBrflJ2djdTUVJ226erqWmgZDw8PJCUlyZbFYjEaNGiApUuXaiwPAP369cOlS5dk60eMGIEtW7bITvTv37+XlZV6//69TlkmPTw8EBUVpbXM1KlTWQm0OnToUOL5IdWd6DXdpdWUrEVfU6U8f/4cn376KXx9fbF06VJ4eHiAz+fjxIkTWLZsWYnmVdZUR0ZLUhxtTpw4AU9PT7Un3mrVqsm+1z169ICTkxMmT56MTp06ycaF6cpQP4vC6Pt4FseDBw8AFO8k3bZtW+Tn5+Pq1asICwuT3eVv164dwsLC8OTJEyQmJsrWKzKGz05IRUfXB+x9lOb1gY2NDbp06YIuXbrA1NQUW7duxfXr19GhQwed3q9Phf191fU6gcPhYN++fbh27RqOHj2K06dPY9SoUViyZAmuXbumcrO5LLRv316WN6B3795o0KABhg4dilu3bqnceC8Mh8NRe84p6rWBvs9bdH40fhRsV3IzZ87Ehg0b8Msvv7AyG+/evRtBQUE6baOwX2iGYRATE4NGjRrJ1vn4+ODu3bv49NNPtXYHWrJkCav1Sto9Vjr35M2bN1knznfv3uHNmzcYO3ZsofV+8eJFoYmjfvzxR1ZXaOVEMfoi3a5yorTitrYCkmN8+vRpJCUlabzbfvToUeTm5uLIkSOsu6PqEpSVNJu1OjVq1MCjR4/AMAxr+9HR0Spljx8/jh49eui03XHjxmHZsmX45Zdf0LdvX3A4HNSoUQP37t2DWCxmnWSfPHkiqwtQOj+LGjVqIDQ0FBkZGawLjsIu5orCxcUFZmZmao+dunXFsW3bNnA4HHTp0gWA/JhFRUXB29tbVi4vLw8vX76UXbgDQPPmzcHn8xEWFoawsDDZcID27dtjw4YNCA0NlS0TQgyPrg/K5vqgadOm2Lp1K96/fw9A87lW8e+ttEu9VFRUlOx16d9i6c3RkirKdQIAtGzZEi1btsTChQuxc+dODB06FLt27cLo0aOLdB3h4+MDsViMR48e6WW+cSsrK8yePRtBQUHYs2cPvvzyS52PKSD5+aobulTcawPptp89e6bymj6vDWrUqAGxWIyXL1+yWtH1dV1ACkdTf1VydnZ2GDduHE6fPo3IyEjZeumYLF0eihITE1X2sXbtWiQmJqJbt26ydQMHDsTbt2+xYcMGlfLZ2dmysSpNmjRB586dZQ9pq2a9evXg6+uLv//+m3VXce3ateBwOKwxZurqdOLECdy6dYtVJ3X8/PxY+2/SpInW8sVVo0YN8Hg82ZhWqZLMbdq/f38wDKN2vJH0Akh6R1Txgig1NRWbN29WeY+lpWWRs6YXJjAwEG/fvsWRI0dk63JyclS+F/Hx8bh9+7bO2TNNTEwwbdo0PH78GIcPHwYgafGOi4tjzREtFAqxatUqWFlZyVoUSuNn0aNHDwiFQtZUKSKRCKtWrSr2NpXxeDx07twZhw4dwrt372Tro6OjcfLkyRJvf9GiRThz5gwGDRokO2F37twZfD4fK1euZH2HNm7ciNTUVNbPy8zMDM2aNcO///6L2NhYVst2dnY2Vq5cCR8fH711TyWElAxdH+jv+iArKwtXr15V+5r077O0K7OlpSUA1Ru+TZs2hYuLC9atW8fqIn/y5Ek8fvxY9vfW2dkZ7du3x6ZNmxAbG8vaRnFaO3W9TkhOTlbZvjRIltZXOlZal2uJPn36gMvlYt68eSq97Irbajt06FBUq1ZNlqlb12MKSIJ/aQ8sqbt37+Ly5cvFqoubmxsCAgKwdetWVk+Rs2fP4tGjR8XapjqBgYEAVK9h9Hn9QbSjlu0KZNOmTWrn3S1sSqOpU6di+fLlWLRoEXbt2gWg+GOyatSogUGDBsnmdQ4PD8euXbsQEBCAcePGycoNHz4ce/bswfjx43HhwgW0adMGIpEIT548wZ49e3D69GmVaTuULV68GJ999hm6du2KL7/8Eg8ePMBff/2F0aNHsxKNtG7dGo0aNULTpk1ha2uL27dvY9OmTfDw8MDPP/9c5M9YGmxtbTFgwACsWrUKHA4HPj4+OHbsmGxsWnF06tQJw4cPx8qVK/Hs2TN069YNYrEYYWFh6NSpEyZPnoyuXbuCz+ejd+/eGDduHDIyMrBhwwa4uLjI7rJLNWnSBGvXrsWCBQtQs2ZNuLi4qNwJLqpx48bhr7/+wuDBgzF16lS4ublhx44dsnFr0rvgJ06cgJmZGTp16qTztkeOHIlZs2bh999/R58+fTB27FisX78eI0eOxK1bt+Dp6Yl9+/bh8uXLWL58uWwccmn8LHr37o02bdpg+vTpiImJgZ+fHw4cOKBzV0xdzZkzB2fOnEGbNm0wYcIEiEQi/PXXX6hfvz7rYlkboVCI7du3A5Dc+Hj16hWOHDmCe/fuoVOnTvj7779lZZ2dnTFjxgzMnTsX3bp1w2effYaoqCisWbMGzZo1Y7X6AJLAetGiRbC1tUWDBg0ASFrk69Spg6ioKIwcOVIvx4EQwkbXB4a9PsjKykLr1q3RsmVLdOvWDR4eHkhJScGhQ4cQFhaGPn36yFr3fXx8YGdnh3Xr1sHa2hqWlpZo0aIFvLy88PvvvyMoKAgdOnTA4MGDZdNUeXp64rvvvpPtb+XKlWjbti0aN26MsWPHwsvLCzExMTh+/LjO5wIpXa8Ttm7dijVr1qBv377w8fFBeno6NmzYABsbG1mvNHNzc/j5+WH37t2oXbs2HBwcUL9+fbXjy2vWrImZM2di/vz5aNeuHfr16weBQICIiAi4u7sjJCSkyD8HU1NTTJ06FT/88ANOnTqFbt266XxMR40ahaVLlyIwMBBff/01EhISsG7dOtSrV4+VdK8oQkJC0LNnT7Rt2xajRo1CUlISVq1ahXr16slypJRUkyZN0L9/fyxfvhwfP36UTf319OlTAKXTa5EoKdvk56Q0FDYNx+vXr2VTEShOhaRo5MiRDI/HY00tVByjR49m/Pz8GGtra8bU1JSpWbMm89NPP8mmVVKUl5fH/P7770y9evUYgUDA2NvbM02aNGHmzp3LmgZEm4MHDzIBAQGMQCBgqlWrxvzyyy9MXl4eq8zMmTOZgIAAxtbWljE1NWWqV6/OTJgwgYmLiyvRZy2Mpqm/Jk2apLZ8YmIi079/f8bCwoKxt7dnxo0bxzx48EDtdFPqpmdSN2WUUChkFi9ezPj6+jJ8Pp9xdnZmunfvzppu5MiRI0zDhg0ZMzMzxtPTk/n999+ZTZs2MQCYly9fysrFxcUxPXv2ZKytrRkAss+maeqvevXqqdRReYoMhmGYFy9eMD179pRNyTFt2jRm//79DADm2rVrDMMwzBdffMH06NFD7XHTdkznzJnDqlt8fDwTFBTEODk5MXw+n2nQoIHK9CoMUzo/i48fPzLDhw9nbGxsGFtbW2b48OGyqbR0mfpL3WesUaMGM2LECNa60NBQplGjRgyfz2d8fHyY//3vf8y0adMYMzMztcdIkXQqM+nDwsKC8fT0ZPr378/s27dPZfoVqb/++ovx9fVlTE1NmSpVqjATJkyQTd+l6Pjx4wwApnv37qz1o0ePZgAwGzduZK3X9ncLNGUJIYWi6wPjuD7Iz89nNmzYwPTp04epUaMGIxAIGAsLC6ZRo0bM4sWLmdzcXFb5w4cPM35+frJpGxXPEbt372YaNWrECAQCxsHBgRk6dCjz5s0blX0+ePCA6du3L2NnZ8eYmZkxderUYX799VfZ69JzTWJiIut90u+M4vlfl+uE27dvM4MHD2aqV6/OCAQCxsXFhenVqxdz8+ZN1vavXLnCNGnShOHz+ay/4+rOfQzDMJs2bZJ9Xnt7e6ZDhw7M2bNntR5vTZ+NYRgmNTWVsbW1ZV2f6XpMt2/fznh7ezN8Pp8JCAhgTp8+rXHqL13PW/v372fq1q3LCAQCxs/Pjzlw4IDaayXl9xbl55eZmclMmjSJcXBwYKysrJg+ffowUVFRDABm0aJFqgeQ6BWHYWgEPSHEuCxfvhzfffcd3rx5gypVqsDR0REhISEqU1cQ3fTp0wcPHz5UOzaMEEIIIZVLZGQkGjVqhO3bt2Po0KGGrk6FRmO2CSEGpTh1CiDpurx+/XrUqlULVatWRVJSEr777jv07dvXQDUsX5SP57Nnz3DixAl07NjRMBUihBBCiMEoXxcAkkYNLpdLSUnLALVsE0IMqnv37qhevToCAgKQmpqK7du34+HDh9ixYweGDBli6OqVO25ubhg5cqRsnuu1a9ciNzcXd+7cUTufJyGEEEIqrrlz5+LWrVvo1KkTTExMcPLkSZw8eVKWy4aULgq2CSEGtXz5cvzvf/9DTEwMRCIR/Pz88OOPP2LQoEGGrlq5FBQUhAsXLiAuLg4CgQCtWrXCb7/9hsaNGxu6aoQQQggpY2fPnsXcuXPx6NEjZGRkoHr16hg+fDhmzpwJExPKlV3aKNgmhBBCCCGEEEL0jMZsE0IIIYQQQgghekbBNiGEEEIIIYQQomfUUV+JWCzGu3fvYG1tTRO9E0II0QuGYZCeng53d3dwuXSfW1/onE0IIaQ06Ou8TcG2knfv3sHDw8PQ1SCEEFIBvX79GtWqVTN0NSoMOmcTQggpTSU9b1OwrcTa2hqA5MDa2NgYuDaEEEIqgrS0NHh4eMjOMUQ/6JxNCCGkNOjrvE3BthJpNzQbGxs6cRNCCNEr6uqsX3TOJoQQUppKet6mgWOEEEIIIYQQQoieUbBNCCGEEEIIIYToGQXbhBBCCCGEEEKIntGYbUIIIYQQQgipYEQiEfLz8w1dDaNkamoKHo9X6vuhYJsQQgghhBBCKgiGYRAXF4eUlBRDV8Wo2dnZwdXVtVSTl1KwTQghhBBCCCEVhDTQdnFxgYWFBc2EoYRhGGRlZSEhIQEA4ObmVmr7omCbEEIIIYQQQioAkUgkC7QdHR0NXR2jZW5uDgBISEiAi4tLqXUppwRphBBCCCGEEFIBSMdoW1hYGLgmxk96jEpzXDsF24QQQgghhBBSgVDX8cKVxTGqkMH26tWr4enpCTMzM7Ro0QI3btwo8zpk5QmRJxSX+X4JIYQQQgghhBhehRuzvXv3bgQHB2PdunVo0aIFli9fjsDAQERFRcHFxaVM6iAWM9g5ZgfAUT68HA2Lut5V0bEc6y4Nw3ofewtMIdtV/zqn0Pcy8qccyf84DKOmnCqVrXEU18o/C2s1OKzX2P+ovle1DDR+HI1HnCPdp+Y3aTrqklWFHXvNNB8j7ceYU8jripgi10uH8kXcpPx7pvx9LniVo1xO+tXX/P0vNo6Gn6eWTbO+8hzpt1G+sujHWL4dNdWTbFGXHwOrDAccdd9FXX8PFDBKJSX7Uf97pzuOwjFT93uk+X3sRYUfBiP/XVH8Gcl+HoykzgUlAAAmPA76LOpVlIoTQggpY61CQvE+NQf+HnY4NLE1tewamKenJ7799lt8++23FWI/xVXhgu2lS5dizJgxCAoKAgCsW7cOx48fx6ZNmzB9+vQyqcOLxHTkCdzAcCvc4SX6onvcW7bbIoSo4IlyDF0FQgghhXifKvlbffd1Ct4kZ8PDgcYsqzNy5Ehs3boVAGBiYgIHBwc0bNgQgwcPxsiRI8Hl6qfjc0REBCwtLfWyrfKsQkWDeXl5uHXrFmbMmCFbx+Vy0blzZ1y9elXte3Jzc5GbmytbTktLK3E9ajpb4bHfAzx+nYjc7CyYcvNQzZoLFwsA+dmAKBeMMBeMKBcQ5oIRi2TxkljauCJ9FLTUSTukM2DY63h8MFw+GBM+GJ6pbFnM4wFcARgTEzBcUzAcEzAmpmC4JpLtMgwYMADDgGHEBS08DMSMuGDfknXyMgUPAAwjLigrrWlBvRjJ+2Q1LSgPseRfMafg/bJWJUkdmIKy8u1IlxkwYmn9FOshBhiF7YjF8lYosfyzSN8jluwMkqOmjZZWQ4YDjuy5aou47DWwG9FkaxXLKTf8K2KkZThgOGriaA573xyGvQ11raXydjyOzi2L0mOrrkcCI21RVvhMuuEUtFNKt62uhGIrproDoHCcFZ5JtseR/JwVOjNwANY+peuVcTlc1nu5Cj9RDocD6X/cgn/l66X1kK2F5Keg2PIubZ3lsurNKTgIHIaj0NrMke2bA+lYIo7CdljvlpSVtppzuAXfB/k6haOj/nMxCttkpNuD7Psu+wQcDquNWvr5OOw1rE4WHIV6y8swUNyy7FWOQimFzwzWM+l3RvGYKJdU/g0oOJbSX4KCOiv2gJAcZ4U6KDeKczjgmpROhlJCCCGlg8ulVm1tunXrhs2bN0MkEiE+Ph6nTp3C1KlTsW/fPhw5cgQmJiUPEZ2dnfVQ0/KvQgXbHz58gEgkQpUqVVjrq1SpgidPnqh9T0hICObOnavfinC56P1tMAKFYsw9+hAbr8cCDNDCxQGrhjSCi7WZvCzDAHmZQHYSkJUEZCcXPAqWs5KArA9A1kcgO0X+ek6K5v2LCx4AkKvmdb41YOEAWDoBFk6AhaNk2dwOMHcAzO0lyxaOkufmDgC/fN8dZBjJjQQxxPLn0gfE4IILpuBmg1AshJgRQ8SIwDAMRIxItqxuPcMwsu0qr1Pcj2IdFNeJGJFs38rlpM+l+5TcDFEoCwYisfz9sn8LgmTF7SjWU3pTQ/oe5dcgrVfBsnJdAUAkFsk+r+JDsYzivuT1LjjOjBBiseSLqnjTRfkzKe+bYRgIGaHshovyvkWMyDBfMlLucQpuLHDBlfzL4YLL4cLS1BIXMMHQ1SOEEKKBUMTOk5SVK0RmrhCWAhMkZ+bBxtwUPArAZQQCAVxdXQEAVatWRePGjdGyZUt8+umn2LJlC0aPHo2UlBR8//33OHz4MHJzc9G0aVMsW7YM/v7+su0cPXoU8+bNw/3792FlZYV27drh4MGDAFS7d3M4HKxbtw5Hjx7F+fPnUaNGDWzatAnOzs4YPXo0IiIi4O/vj23btsHHxwcA8Pz5cwQHB+PatWvIzMxE3bp1ERISgs6dO6v9XAzDYO7cudi0aRPi4+Ph6OiIL774AitXrizFo6ldhQq2i2PGjBkIDg6WLaelpcHDw0Mv2+abcLGwbwM093LAzwfu4/rLJPRaGY71w5ugUXV7SSEOBxBYSR521XXfuEhYEJB/LAjIPxYE5dKgXfHfgjI5qQAYIC9d8kh5pfv+TMwlwbeloyT4tnBQ+rcgYLdwkAfoAmuNY0zLGofDAY/DAw/UQlVZKN6QkAbs0mBd8SaB9Ln0PdJ/FW82iBgRxGJ5WcWbK8o3JKQ3GhT3rXhjQPnmg+KNGW03ZDTdnFF5KNykUd6XSCxSuTEkFAtZNziUb6AoH0vFOoIBe71SvcUQQywWsz6f7MaR0o0v5W2AAfu4KB0j2WdS+BkoHv/iUKyD4mZMuabF3iYhhJDSl5XPvtHeZdl/qGIjwNZRzdFteRia1rDHvgmtDVS78uGTTz6Bv78/Dhw4gNGjR2PAgAEwNzfHyZMnYWtri/Xr1+PTTz/F06dP4eDggOPHj6Nv376YOXMm/vnnH+Tl5eHEiRNa9zF//nwsXboUS5cuxU8//YQhQ4bA29sbM2bMQPXq1TFq1ChMnjwZJ0+eBABkZGSgR48eWLhwIQQCAf755x/07t0bUVFRqF5dNW7av38/li1bhl27dqFevXqIi4vD3bt3S+V46apCBdtOTk7g8XiIj49nrY+Pj5fdvVEmEAggEAhKtV6fB1RF/aq2GL/tFp4lZGDg+qv4rkttjG/vU/xuLjwTwMpF8tCVWCQJuLOTJQF4RkJBi3lBQJ6dDGQly1vPpa+JhYAwG0h7I3noXEe+vHXczK4gCLdTajlXbkl3AEz4RT0ahKiQtkqSykfTzQbFmweyGyFKy8rvVbwZQwghxDjl5Kn2aotPy8Xm8BgAwM1XyWVco/LJ19cX9+7dQ3h4OG7cuIGEhARZnPTnn3/i0KFD2LdvH8aOHYuFCxfiyy+/ZPUQVmz1VicoKAgDBw4EAPz0009o1aoVfv31VwQGBgIApk6dKsu7Jd2e4jbnz5+PgwcP4siRI5g8ebLK9mNjY+Hq6orOnTvD1NQU1atXR/PmzYt/QPSgQgXbfD4fTZo0QWhoKPr06QMAEIvFCA0NVfsDKUs+zlY4OKkNvt9zF6cexuGPU1G4E5uCP7/wh61FGbWacHny1mdHH93ewzBAbrpSC/pHpZZzhdbz7IKu8MIcQJQHpL+XPIpCuZu7pbNCi7ojuzXd0kkSyOspmQMhpPyT9mQhhBBSOQjF6ns10djtomEYBhwOB3fv3kVGRgYcHR1Zr2dnZ+P58+cAgMjISIwZM6ZI22/YsKHsuXTYb4MGDVjrcnJykJaWBhsbG2RkZGDOnDk4fvw43r9/D6FQiOzsbMTGxqrd/oABA7B8+XJ4e3ujW7du6NGjB3r37q2XMejFVaGCbQAIDg7GiBEj0LRpUzRv3hzLly9HZmYm6y6JoVgJTLB2WGPsufkavx56iLOP4tFtxX9YO6wJAjzsDF099TgcwMxG8nDw0v19eVnywDwnRWEseorSePSP7HHqjLjo3dw5PHnLuIWTJDC3UOjubuGo0M3d0ei6uBNCCCGEkOITaQi2/72hPigj6j1+/BheXl7IyMiAm5sbLl68qFLGzs4OAGBubl7k7ZuayhsYpVOzqVsnzenz/fff4+zZs/jzzz9Rs2ZNmJub44svvkBeXp7a7Xt4eCAqKgrnzp3D2bNnMXHiRCxevBiXLl1i7acsVbhge9CgQUhMTMSsWbMQFxeHgIAAnDp1SiVpmqFwOBwMalYdvq42mLrrDmI+ZmHguqv4uYcvRrT2rDhzAvItJA+7Iox/F4slgbliUrjMD0BmIrvlXNbKngTkpgKMSFImM1H3fXFNC7q120sCcDMbSRAuDdQtFFvSCwJ1MztJ931CCCGEEGI0NLVsK8rJF8HMlHo9aXL+/Hncv38f3333HapVq4a4uDiYmJjA09NTbfmGDRsiNDS0VBs0L1++jJEjR6Jv374AJGO4Y2JitL7H3NwcvXv3Ru/evTFp0iT4+vri/v37aNy4canVU5sKGTlMnjzZ4N3GC+PvYYej37RF8J67OPsoHnOOPsKt2BQs6FMftuaVNBkPlyvv5o6aur1HmCcJwDM/KAToCsnilJPIZX4ARLmAOB/ITJA8isLMtqD13Em1tVwxaZy0ld3cTtJ9nxBCCCGElAqRuPDcGll5FGxL5ebmIi4ujjX1V0hICHr16oWvvvoKXC4XrVq1Qp8+ffDHH3+gdu3aePfunSwpWtOmTTF79mx8+umn8PHxwZdffgmhUIgTJ07gp59+0ls9a9WqhQMHDqB3797gcDj49ddfZa3e6mzZsgUikQgtWrSAhYUFtm/fDnNzc9SoUUNvdSqqChlslxfWZqb4e3gTbAx/iZCTT3D07jtEvExCSP8G6FSnCInPKjMTPmDtKnnoKi9Laaq1gkztimPSZY8k+euA5N+cVCDpuY4740gCbgtHydhzaaZ2C+Uu7got6RSgE0IIIYToLF9UeMu2UIeAvLI4deoU3NzcYGJiAnt7e/j7+2PlypUYMWIEuAV5kE6cOIGZM2ciKCgIiYmJcHV1Rfv27WW9hTt27Ii9e/di/vz5WLRoEWxsbNC+fXu91nPp0qUYNWoUWrduDScnJ/z0009IS0vTWN7Ozg6LFi1CcHAwRCIRGjRogKNHj6qMPS9LHEY6IS8BIJn6y9bWFqmpqbCxsSmz/d6OTca3uyIRm5QFABjTzgszutelxA7GQiSUB+aZiZIWcuUu7crTrUkD9CLjyLu3K06lJn2uuM6yIIGcuQN1cSfEiBnq3FLR0XElhADAlH/v4Mjdd4WWe7awO0x5FTupbk5ODl6+fAkvLy+YmZkZujpGTdux0tf5ha7OjUTj6vY4/W17/H7qCbZcicGGsJd4EpeOpQMD4GxdulOTER3wTAArZ8nDuY5u7xHly6dQy/ygML2awr+KXdxlLehMQVb3JOBjEepoZisZV27hoNCKrvhQaFGXPudbUaI4QgghhJRrugTaALDzeixGtPYs3coQooCCbSNizudhzmf10Ki6Habvv4+wZx/QfcV/WDm4EVr7OBm6eqSoeKZFnwtdlK+QIE5hKjVZi7lC5nZpEJ+dDICRd3HXNYs7oJQozoHdgm5upzAPurSre0FZU7pTSgghhJDy5WOm+izWhJQWCraN0OcBVWXZyp/EpeOrjTcws2ddjKxI2cqJejxTwLqK5KErxS7u0qnVMhPZU63JAvZkeUt6SRLFmVoWjEN3lCeDs3AELOwLnjuwW9TNbAG+JbWiE0IIIcRgxDpkLSdEnyjYNlJ1XK1xcGIbTD9wD4cj32Hu0UcIf/YBIf0bwMWaWhWJAsUu7rpiGCA/ix2EK445z05RCtAV5kxnxEB+JpCaCaQWYf5KHl9hOjUHdtI4M1v2VGyK061xK/bYKkLKi9WrV2Px4sWIi4uDv78/Vq1ahebNm6ste+DAAfz222+Ijo5Gfn4+atWqhWnTpmH48OGyMhkZGZg+fToOHTqEjx8/wsvLC1OmTMH48ePL6iMRQiqA7DyRzmV1mSKMEH2iYNuImfN5WD4oAAEedgg58QShTxLQY0U4Vg1uhFY+hsuqRyoADkfS0sy3BGyr6f4+sRjITVMYa/5Bniwu84N8nTRYl7aqi4WAKA/IiJM8dK4nV820ag5K6xzZDzNbShZHiJ7t3r0bwcHBWLduHVq0aIHly5cjMDAQUVFRcHFRHSrj4OCAmTNnwtfXF3w+H8eOHUNQUBBcXFwQGBgIAAgODsb58+exfft2eHp64syZM5g4cSLc3d3x2WeflfVHJISUU3dik3Uuq8sUYYToE12RGjkOh4OgNl5o5eOIKf/ewdP4DAz53zWMbe+NH7rWgUkFz6hIjAyXWzCW2w5w9NHtPdJWdFYyuGT53OjSwDwnRWH6tSQgL13Sip5VUE5nHMDMht2KLm0xN3eQT8UmfVg6Sdab8It6NAipNJYuXYoxY8YgKCgIALBu3TocP34cmzZtwvTp01XKd+zYkbU8depUbN26FeHh4bJg+8qVKxgxYoSs7NixY7F+/XrcuHGDgm1CiM6KMnMPtWyTskbBdjnh62qDw5PaYvaRB9hz8w3WX3qBR+/SsGxQAJysKFs5MWKKreh21XV/nzCP3X2dlb1dYYo1Wcb3j0BuQTZ3abK45Je6749vpaa13EGeGE65Bd3CATCh3z1S8eXl5eHWrVuYMWOGbB2Xy0Xnzp1x9erVQt/PMAzOnz+PqKgo/P7777L1rVu3xpEjRzBq1Ci4u7vj4sWLePr0KZYtW1Yqn4MQUjGJizCLcb6IWrZJ2aJguxwx5/Pwxxf+aF/bGT/svYewZx/Qc2UYVg9pjKaeDoauHiH6ZcIHbNwkD11Jp1vLTpEH54rZ22UJ4pLkXd+zkyQt6HkZkkdKEcah860lSeJkXdwdlTK6K82XbuEE8C2KfCgIMaQPHz5AJBKhShV24sYqVargyZMnGt+XmpqKqlWrIjc3FzweD2vWrEGXLl1kr69atQpjx45FtWrVYGJiAi6Xiw0bNqB9+/Yat5mbm4vc3FzZclpaWgk+GSGkIuAXoZdnbj4F26RsUbBdDvVq6I5aLtaYuOMWnidm4su/r2FGj7oIau1ZpK40hFQ4xZluTSyWtJxnJyt0b/8oD9CzlAP1grHpjEjS1T0vHUiO0X1/JmbyQNzMTv7cUqHV3NxB0r1d2qpOieJIOWRtbY3IyEhkZGQgNDQUwcHB8Pb2lnUbX7VqFa5du4YjR46gRo0a+O+//zBp0iS4u7ujc+fOarcZEhKCuXPnluGnIIQYO1tzU9nzK9M/Qfs/LmjsLt66JuU8ImWLgu1yqo6rNQ5NaoPpB+7j+L33mH/sEa6/+IglA/1hbWZa+AYIIRJcrjzxmq7EYkmX9cyPBePOk9nd3GXTrSWzW9dFeYAwB0h/L3noisNV7coumxvdXjXLu5mdZGw6dXMneuDk5AQej4f4+HjW+vj4eLi6ump8H5fLRc2aNQEAAQEBePz4MUJCQtCxY0dkZ2fj559/xsGDB9GzZ08AQMOGDREZGYk///xTY7A9Y8YMBAcHy5bT0tLg4eFR0o9ICCnHpGG1oyUf7nbmiP6tBz77Kxz33qTKynzq64LQJwnIE1LLNilbFGyXY9ZmpvhrcCO08HLA/GOPcOZRPLotD8P64U1Qv6qtoatHSMXF5cpbpFFTt/cwjCSTu2z+82R5i3qWUiu64iMvoyBRXMFyUfCtCgJx6fznBQnhlLu8y+ZKd5D0DiBEAZ/PR5MmTRAaGoo+ffoAAMRiMUJDQzF58mSdtyMWi2VdwPPz85Gfnw+uUo8NHo8HsZZswQKBAAIB3UQihMhJx2FzOJp7dwpMJX9r7r9NxaBmZVItomdr167F2rVrERMTAwCoV68eZs2ahe7duxu2YoWgYLuc43A4+KqVJxpUtcXknXfwNiUb/dZcwS+96mJ4yxpa//AQQsoQhyOZlszMFrAvwvuEuaot51kfJK3msjnRlYL1nIJEcdJx6EWZD11goxSMS1vT7eU3GFiZ3h1oHHolEBwcjBEjRqBp06Zo3rw5li9fjszMTFl28q+++gpVq1ZFSEgIAEl376ZNm8LHxwe5ubk4ceIEtm3bhrVr1wIAbGxs0KFDB/zwww8wNzdHjRo1cOnSJfzzzz9YunSpwT4nIaT8ycyVzLP9IUOez+FT3yqylm3/arZITJe8RmO2y69q1aph0aJFqFWrFhiGwdatW/H555/jzp07qFevnqGrpxEF2xVEo+r2OD6lLb7fexfnHidg1uGHuBmTjEX9G8CCTz9mQsotEwFg7Sp56ErazV05c7t0ujXpetnY9CR5orjcNMmjKOPQTS0KplWzV2gpVwjGpePPFR+mZkU+FMRwBg0ahMTERMyaNQtxcXEICAjAqVOnZEnTYmNjWa3UmZmZmDhxIt68eQNzc3P4+vpi+/btGDRokKzMrl27MGPGDAwdOhRJSUmoUaMGFi5ciPHjx5f55yOElF/SdEXWZvLr3c8D3LHs3FMAwNCWNRD7MQsRMcngUiNUudW7d2/W8sKFC7F27Vpcu3aNgm1SNuws+NjwVVP8L+wlFp16giN33+HR+zSs+DIA9dypWzkhlYZiN3dd50MXi+Td27M+qI5Bz/pY8HoKO9O7OF8yj3p+FpD2Rvc68q0UMrY7qLaWy8agKwTvphaSHgLEICZPnqyx2/jFixdZywsWLMCCBQu0bs/V1RWbN2/WV/UIIZWUNBlaFRv5TVyeYsJgRh6I5ysNUxGLmUqfXJhhGGTniwyyb3NTXrF64YpEIuzduxeZmZlo1apVKdRMfyjYrmA4HA7GtPeGv4cdJu+8jeiEDPRdfQWzevthWMsahq4eIcRYcXmSjOiWjijaOPR0hRbyZPmUaqw50BWC9+wkQCxU6OL+Wvc6mpipaUF3AHu6NaVAXWBLmdwJIaQCExUE2yYKQbNiAC1mGFnwLVLIUr76QjQ2hL3AgQmt4e1sVUa1NT7Z+SL4zTptkH0/mhdYpB649+/fR6tWrZCTkwMrKyscPHgQfn5+pVjDkqNgu4Jq7uWAk1Pb4Yd993D+SQJ+OfQAD9+lYnbvejAz5Rm6eoSQioDDAcxsJA946fYeaaK4zA/qx5tnK3ZxVxirLsvk/k7y0LmOXEl2dsXA3Fxh/DlrLLq9fKw635Ja0QkhpByQtmwrtma7KbRy92zohv233rDKAsDi01EAgJCTT7Dhq6ZlUVVSQnXq1EFkZCRSU1Oxb98+jBgxApcuXTLqgJuC7QrM0UqAjSOaYu2l51h8Ogr/3niNGy+TsOLLRpStnBBiGIqJ4nTFMEBeJrv7OquLe5J8ejXFcerSTO7ZBctFSebOE7DHnn91mIJvQggxQqKCruHKLdsvQ3qAYSTPeTxJDyeRSHX+7Ureixzmpjw8mhdosH0XBZ/Pl00p2aRJE0RERGDFihVYv359aVRPLyjYruA4HA4mdqyJ+u62CN5zF88TM9F3zWX83KMuRrb2pGzlhBDjx+EAAivJw74Iw2GEuUpjzAsCccUp12RTsSXJ14lyJQ9pK7qZLQXahBBipNKyhQDk821LcTgc2Z9uaSCelJVXhjUrHzgcTrlNpqw4paSxKp9HlhRZ+9rOOBfcHj/su4ezj+Ix9+gjPHqXht/6NYApj8YzEkIqIBMBYF1F8tCVSit6kiRoJ4QQYpS+3R0JALKpvtSRjtV+n5qt8lp8Gv2NLw9mzJiB7t27o3r16khPT8fOnTtx8eJFnD5tmPHmuqJguxKxs+Dj7+FNsOlyDH478Rh7b71BzMdMrBnaBM7WAkNXjxBCDK+4reiEEEKMlrSF293WXOU1Jyu6Bi4PEhIS8NVXX+H9+/ewtbVFw4YNcfr0aXTp0sXQVdOKgu1KhsPh4Ou2XvByssDUfyMREZOMnivD8OcAf7Sv7Wzo6hFCCCGEEKJXjpaSgFoxG7mcunXE2GzcuNHQVSgW6j9cSX3iWwWHJrdBTRcrJKTnYuTmG/jj1BMIReLC30wIIYQQQkgpO37vPZaeiQLDlCwglmYqv/kqGS8/ZLJeO/c4QaV8QloO5hx5iOiEjBLtlxAKtisxH2crHJ3cFoOaekDMAGsuPsfA9VfxJjnL0FUjhBBCCCGV3KSdt7HyfDSuPi/KdBKqFDOV91tzGXlC7Y1L3+2JxJYrMei9KrxE+yWEgu1KzpzPw+9fNMSqwY1gLTDB7dgU9FwZjlMP4gxdNUIIIYQQQvAmRTWxmZS/hx0AYGHf+hrLKM7BnZyVr6E7uZw02Vp2vqgItSREFQXbBADQ298dJ6a2g381W6Rm52P89lv47cRj5FO3ckIIIYQQYkDarkcFBbPqOFjwNZYxUZpMW1xIt3SaqYfoCyVIIzIeDhbYO741/jj1BP8Lf4m//3uByNcpWDrQH9XsLQxdPUIIIYQQUogHb1Px9dYIDGleA1M71zJ0dYpNcZz2+5QcjeWEYkkgzlMKqBUpv1ZvNnu6KM/px1G/qg3cbM1x9lG8Sj28ZpwAIGlFPzypjW4fgBBQyzZRwjfh4pdeflg7tDEs+TzceJmEHivCcOzeO0NXjRBCCCGEFOLv/14gPi0Xy849NXRVSiQ9Vyh7npKdp7GctEu4CU9zsF3TxarQ/T14m6YSaANAUqZ833dfpxS6HUIUUbBN1OrewA3Hp7RDgIcd0nKEmLzzDuYfe1ToGBdCCCGEEGI4WXnCwguVA4yOIxmFBdemPK7msMaxBHNpa2sxJ6QwFGwTjTydLLF3fCtM7OgDANgY/hJDNlxDYnqugWtGCCGEEELUSc3ON3QV9ELaPRwAONAc8KZkST4vj1M6QXEJZx0jlRwF20QrUx4XP3bzxarBjWDB5+H6yyT0WhWGGy+TDF01QgghhBCiJCIm2dBV0IuJO27LnmtLaPZWS6Zyfeiy7D/WcuxHmiKX6I6CbaKT3v7uOPpNW9R0sUJ8Wi6+/PsqVoY+o2zlhBBCCCFE764rNOxwtbRau9uaAQDMTLWHNR1qOxerHh8y2D06r774UKztkMqJgm2iMx9nKxye1AZ9G1WFmAGWnn2KAeuulvodRUIIIYQQQtQRFbR6m/N5WssNb1lDL/vLF1G/cmOwaNEicDgcfPvtt4auilYUbJMisRSYYNmgACwb5A8bMxNEvk5Bt+X/4ehdylZOCCGEEGJIqy9EG7oKevHgbSpr+YiG60yGYRCfJml5NtGSIA0AeFqylReFkHp1GlxERATWr1+Phg0bGroqhaJgmxRL30bVZNnK03OE+ObfO5h39BHyhPQHiBBCCCHEEBafjjJ0FfSi16pw1rKmpG8nH8TJnheWNfzVh8ySVwyAvSVfL9shxZORkYGhQ4diw4YNsLe3N3R1CkXBNik2DwcL7BvfCuM7SLKVb7r8EgPWX8XzxAwD14wQQgghhFR0T+PTZc9NCgm2Babau5nryqkE04gZJYYB8jIN8yhGqvdJkyahZ8+e6Ny5cykcDP0zMXQFSPlmwuNiendfNK5uh2l77+Lu6xR0XxGG2b39MKR5dXBKaRoGQgghhBAix1TCOaoUe1QW1rJtJdBP2COd17vCyM8CfnM3zL5/fgfwLXUuvmvXLty+fRsRERGlWCn9opZtohdd67ni9Lft0a6WE/KEYsw8+ADT999HTr7I0FUjhBBCCKnw1MWApxS6WVc0DMNgzcXnsmWTQsZkW5npJ9gevbX8BHoVyevXrzF16lTs2LEDZmZmhq6Ozqhlm+iNu505tgY1x7r/nuPP01HYffM1br5KwtphTVC7irWhq0cIIYQQUmGJ1ETb47ffQsyingaojf6l5+TD2sxUthyjNN+1QyFjqZvW0M/43nwRg8xcISz11FJucKYWkhZmQ+1bR7du3UJCQgIaN24sWycSifDff//hr7/+Qm5uLng8/QwV0Cdq2SZ6xeVyMLFjTWwJag4XawGeJ2ai96pwbLn8slJ2byKEEEIIKQviCnSdFeBhp7JOOQmvcu9JgYn2QMvazBS3fpGP8zXhchC1oBtu/9oFtatYqZRvXF21DlIVqis5hyPpym2IRxGGm3766ae4f/8+IiMjZY+mTZti6NChiIyMNMpAG6Bgm5SS9rWdcXJqO7St6YRcoRhzjj7CV5tu4B3NyU0IIYQQohd5QjHep0qurdS1bANAQlqO2tliXidlGe0sMmk5qtnHP2TksZaFxZjv2lEhuZkpjwuBCQ8Olnx4O6kG22ZaEqppOtak9FhbW6N+/fqsh6WlJRwdHVG/fn1DV08jCrZJqXG0EuCfUc0xp7cf+CZchD37gN6rwnE48q2hq0YIIaSIVq9eDU9PT5iZmaFFixa4ceOGxrIHDhxA06ZNYWdnB0tLSwQEBGDbtm2sMhwOR+1j8eLFpf1RCKkwJu64hdaLzuPB21SINLRsN/8tFD1XhrHWXX/xEe3+uIDx22+VRTWLhGEYvEhUnaYrcPl/rOWImKQS7Uex27m68d6ZuUKN79X2GiGKKNgmpYrL5WBkGy+cmNIO9dxt8DEzD1N3RWLmQUqeRggh5cXu3bsRHByM2bNn4/bt2/D390dgYCASEhLUlndwcMDMmTNx9epV3Lt3D0FBQQgKCsLp06dlZd6/f896bNq0CRwOB/379y+rj0VIuXfucQIYBth36w0YLY3UzxLY07IeuC1p+Dj/RP3vsCHlieQfZGx7b43lhOLitcrvGtsSzTzt8b8RTWXrYpOytLxDVa6R9giobC5evIjly5cbuhpaUbBNykRNFyvsn9AaUz6pCQDYcT0Wn/91GdEJNCc3IYQYu6VLl2LMmDEICgqCn58f1q1bBwsLC2zatElt+Y4dO6Jv376oW7cufHx8MHXqVDRs2BDh4eGyMq6urqzH4cOH0alTJ3h7a764JoSox+FAY8u2OlwjjgAUu7Z7OWmeFoqrMN73l551dd5+S29H7B3fGnXdbGTrfF1VE/lq6ylO3ciJroz4V41t4cKFaN26NSwsLGBnZ6e2TGxsLHr27AkLCwu4uLjghx9+gFBI3TyMhZkpD8Fd62DrqOZwtOQjKj4dfVdfltyNrUBJPQghpCLJy8vDrVu30LmzPLEQl8tF586dcfXq1ULfzzAMQkNDERUVhfbt26stEx8fj+PHj+Prr7/WW70JqUwevUvDlH/v6FT2yvMPOHbvfSnXqPgUW41NlObOjkvNkT1XvHQsjeBXW8K54raqk8qn3ATbeXl5GDBgACZMmKD2dZFIhJ49eyIvLw9XrlzB1q1bsWXLFsyaNauMa0oK06G2M05/1x5Na9gjPVeI7/fexbe7I5FB418IIcTofPjwASKRCFWqVGGtr1KlCuLiNM/hm5qaCisrK/D5fPTs2ROrVq1Cly5d1JbdunUrrK2t0a9fP611yc3NRVpaGutBCAGuv0xCePQHrWU+ZuQCAIZsuI70HPk1l7G10ioG1MpahoTKnitmBHezMy/RPi9Hf1RZ96mvi8byxUnORiqnchNsz507F9999x0aNGig9vUzZ87g0aNH2L59OwICAtC9e3fMnz8fq1evRl5entr3EMNxshJg97hWmNalNrgc4HDkO/RaGYaouHRDV40QQogeWFtbIzIyEhEREVi4cCGCg4Nx8eJFtWU3bdqEoUOHwszMTOs2Q0JCYGtrK3t4eHiUQs0JKf/sLExV1mXmqs+Vkys0rhw6ikG0to6PIoXW5e71XUu0z7dqZssZ28EHneo4y5YV5+muUFN/kVJVboLtwly9ehUNGjRg3XkPDAxEWloaHj58qPF9dJfccHhcDr75tBb2jGsFd1szxHzMQr81l3Hs3jtDV40QQkgBJycn8Hg8xMfHs9bHx8fD1VXzBS6Xy0XNmjUREBCAadOm4YsvvkBISIhKubCwMERFRWH06NGF1mXGjBlITU2VPV6/fl30D0RIJRDcpbbKOk1dn3PzjatLtGJLu/au3JLXhrWsDlNeyUIavpr3WwlMsDmouWy5QTVbeBeMITe23gDEeFWYYDsuLk5tFzfpa5rQXXLDa+rpgGNT2qGVtyMy80SYvPMOZh9+QNnKCSHECPD5fDRp0gShofLum2KxGKGhoWjVqpXO2xGLxcjNzVVZv3HjRjRp0gT+/v6FbkMgEMDGxob1IKSyifmQiSEbriH8meZu4yZqMqD9dT4antOPq6xX7FJuDBTz+GgKaddcjMbyc88AABl6qD/fpPCQ6GNGnmyKsKh46olJdGPQYHv69Oka59mUPp48eVKqdaC75MbBwZKPbV83x/gOPgCArVdfof/aK3ifqtqthxBCSNkKDg7Ghg0bsHXrVjx+/BgTJkxAZmYmgoKCAABfffUVZsyYISsfEhKCs2fP4sWLF3j8+DGWLFmCbdu2YdiwYaztpqWlYe/evTq1ahNCJL759w6uPP+IYRuvayyjnFgMAA7ceau27Dsju9ZSbDXuUNtZbZk/TkXJnh+KLHmPSEsBj7Xcxa+KSpkjd9/h5QfJ/N9JGTRElejGxJA7nzZtGkaOHKm1jK5TgLi6uuLGjRusddIub9q6uQkEAggEAp32QUqXCY+L6d190cLbAdP23MXDd2noveoyNo5oCn8PO0NXjxBCKq1BgwYhMTERs2bNQlxcHAICAnDq1ClZD7LY2FhwFVrSMjMzMXHiRLx58wbm5ubw9fXF9u3bMWjQINZ2d+3aBYZhMHjw4DL9PISUZ2+SC58Tmqcm2NbE2JJ9SacwszYzgbudOR7MDUT92adLdZ+K04hd+L4j3O3U54/oE1AVe2+9AaOxzZ0QNoMG287OznB2Vn/HqqhatWqFhQsXIiEhAS4ukuyBZ8+ehY2NDfz8/PSyD1I2OtVxwZHJbTB66008iUvHwPVX8ccXDfF5QFVDV40QQiqtyZMnY/LkyWpfU058tmDBAixYsKDQbY4dOxZjx47VR/UIqTTydQiOpd2ddWFsCdKkLdvutpIM41aC0g9XFIeGa5vb24IvaQE3thsUxHiVmzHbsbGxiIyMRGxsLEQiESIjIxEZGYmMjAwAQNeuXeHn54fhw4fj7t27OH36NH755RdMmjSJWq7LoWr2Ftg7vhU+8XVBrlCMqbsiMfPgfWTnGdcJgRBCCCGkLOkyVapiS21hYpMKbykvS8M3Snqq6jouWl3m9aLStaX6WYIk7vjrQnSJ90kqh3ITbM+aNQuNGjXC7NmzkZGRgUaNGqFRo0a4efMmAIDH4+HYsWPg8Xho1aoVhg0bhq+++grz5s0zcM1JcVmbmWLDV00xoaNkHPeO67Hos/oyYgrGyxBCCCGEEFWmRWjZLouW45JS7BYvVsoEfnRy21Ldd00XKwDAsW/a4spz1fm4SdmYM2eOSm4vX19fQ1erUMb/21Vgy5Yt2LJli9YyNWrUwIkTJ8qmQqRM8Lgc/NTNF619HPHd7ruIik9H9xVhWNi3Pvo1rmbo6hFCCCGElClTHqfQruQuNtrnrFdUHqax+rKZB3ZcjwUA5ArZU5V5OFiUePva5vM+F9yhxNsn+lGvXj2cO3dOtmxiYvyhbLlp2SaVW7tazjj2TVu08HJAdr4IwXvuYs6Rh8gTGtfckIQQQgghpUlbYCile7s2sO/WG6Tn5Be7PmVBMbt6Vp7+pyoz/tsNBJAE166urrKHk5OToatUKAq2SbnhamuGnWNaYnKnmgCALVdiMPR/15CcSdMvEEIIIaRyKCww9HW1LtL2br5Kxvd77xa/QmXAlCcPWV4ny6cqq1/VRi/b71gwxVidKtqPXTV7c9lzY79BoSuGYZCVn2WQB6PLnSMFz549g7u7O7y9vTF06FDExsaW0lHRH+NveydEAY/LwfeBddCouh2+3R2JiJhk9P4rHBtHNEOdIp5cCCGEEELKm8IClOVfBhQ5oezph/ElqVKp+Lqtl+y5iUKwrdjIsnRggF729UO3OnCw5KNvY+0z33zzSU38tP8+ACA7TwRrs5InZzO0bGE2WuxsYZB9Xx9yHRamug0DaNGiBbZs2YI6derg/fv3mDt3Ltq1a4cHDx7A2tp4YwBq2Sbl0qd1q2D/hNao4WiBN8nZ6L/2Co7efWfoahFCCCGElKrChlj7uuqntddQpC3zneq4qH09s6AbeRUbAWoX0hKtKxdrM8zoUbfQY2dvwZc95xZhLnNSct27d8eAAQPQsGFDBAYG4sSJE0hJScGePXsMXTWtqGWblFu1q1jj4MQ2mLD9Fq6/TMI3/97Bk7g0TOtSh/4AEkIIIYSUQ0/iJFN+KV7KKc5k9suhBwAAE27ZtxmKFXoVKGdFL6/MTcxxfch1g+27uOzs7FC7dm1ERxv3NGzUsk3KNQdLPnaMboFxHbwBAKsvPMfYbTeRml0xxtEQQgghhOiqc90qAABPR0sD16T4rAumIuMoRNid68pbuVOyJNd4PAM0rDSoZid7LirieGNjxeFwYGFqYZAHpwjzwSvLyMjA8+fP4ebmpsejoX8UbJNyz4THxYzudbFkgD/4PC7OPU5A39WX8TQ+3dBVI4QQQggpdRe+74hj37TF8i8DAAD2lnz88UVDnd/v42xEwXlB/FXFRiBb1aSGg0oxEwME21Xt5C2xFaRhu9z4/vvvcenSJcTExODKlSvo27cveDweBg8ebOiqaUXBNqkw+jephv0TWsPd1gwvPmTi878u48DtN4auFiGEEEJIqfJyskT9qrawEshHiLbydtRY3syUHQKY83mlVreiks77XVg3cUO0bAOAuankWFWUbuTlxZs3bzB48GDUqVMHAwcOhKOjI65duwZnZ2dDV00rGrNNKpQG1Wxx5Ju2+G53JMKefUDwnrsIf/YB8/rUZ52ACCGEEEIqMm09dN3tzPEiMVO2/OBtWhnUSDdCabDN0x5MGyrYlu5XRMF2mdq1a5ehq1As1LJNKhwnKwG2BDXHd51rg8sBDtx5i8//Cscz6lZOCCGEkEoiTyjW+Nqc3vXwbedaZVgb3clbttnBdA1H9hRRCem5ZVYnRdJqiSvImG1SuijYJhUSj8vB1M61sHtcK1SxEeB5YiY+++syjt97b+iqEUIIIYQUS2FzbCtSbPldOtBf9vzi9x3RvrYzhrSoLlsnMDGOkIBhGFmwrdxyvXlkM9bygKbVyqxeiqQz3lCwTXRhHL9ZhJSSZp4OOD6lHdrUdER2vgiTdt5GyInHyBWKDF01QgghhJAiKUrPZcVgVbHLs/SZwEQ+TrsESaH1SrGeymO2VZcNU+nsPMk15PJzz/Df00SD1IGUHxRskwrPyUqArUHNMaadFwBg/X8v0G/NFbxOyjJwzQghhBBCdPcxQ7XrtLutmdqylnx5rhonK3lmb2mCL8UkaYqBtyFl58sbQ0xN2ME0T2kMN88A82wDQG5B9/xj997jq003kJNPDThEMwq2SaVgwuNiZk8/rB3aGA6WfDx8l4Zeq8JxOPKtoatGCCGEEKKT5II5pqVaeDlg55iWasvaW/IR4GEHGzMTdKzjjAV96uPnHr5wLQjOBSY89GwomaPY0ZJfuhXXUa7COHMLPjuxrXJLtqFatpWl5eQXXohUWpSemVQq3Ru4oUE1W0zccRv33qRi6q5IXHqaiAV96qv8USeEEEIIMSbKSc/+HOAPDwcLDaWBQ5PayJ4Pa1lD5fWg1p44fu+90Yw/lk6npS6OVh7DbSSxNiFaUcs2qXSq2Vtg/4TWmPJJTXA4wIHbb9F12X+4Ev3B0FUjhBBSTl2J/oDp++8hOTPP0FUhFdjbFPYQOH4JE5vJk32VaDN6I60HV80gcuWWbCO5P4A2i84bugrEiFGwTSolUx4XwV3rYPfYVnC3NcOb5GwM23gdm8JfFinTJyGEEAIAo/+5iV0Rr7H+vxeGrgqpwJ4rzI0NALbmpiXanjSoNZqW7YJ6cNU0W5vz2ePKnxjJlK75IgYvP2QWXpBUShRsk0qtuZcDTn/XHl80qQYxA8w79ghBWyLUJiAhhBBCNMkqyFD8JC7NwDUhlYG7rRlOf9seZqYlS2wmmzPaSJq2ZcG2mi7iAhMeDkxsLVuuamdeVtViGdfeW2VdRo7QADUh5QEF26TSszYzxeIvGuKXnnXB53FxMSoRvVaF43ZssqGrRgghpJwxlqRNpGKSZr7u7FcFdVytS7w9ect2iTelF4yWbuQA0Li6vey5oX7X3NUE+QyM5AASo0PBNiEAOBwORrfzxtFv2sLbyRLvU3MwaP1VbLsaY+iqEUIIMXKKw4+UkzgRoi83Y5Kw6nw0AM3BaFFJN2N03ch1+HyGmq5MXRf33RGvDVCTyuft27cYNmwYHB0dYW5ujgYNGuDmzZuGrpZWFGwToqCOqzWOfNMW3eu7Il/E4NfDD/HD3rs0hyIhhBCNFKcrqmKjfs5jQkrq54P3Zc/vvE7RyzaNrWVbVFARbbH20BbVYWtuii+be5RRrdjU3U/bcT227CtSySQnJ6NNmzYwNTXFyZMn8ejRIyxZsgT29vaFv9mAaK4jQpRYCUywZmhjrLn4HEvORGHvrTeIfJ2C1UMbo3aVknfZIoQQUrEoBtt2FsYxXzGpeBSTcLX0ctDLNqU9MYwlOay2bORSC/s2wNzP6sGEZ5g2Q56eehWQovn999/h4eGBzZs3y9Z5eXkZsEa6oZZtQtTgcDiY1Kkmtn/dAs7WAjxLyEDf1ZdxOPKtoatGCCHEyOQK5b2fjkS+xZbLL40m4RQpf/KEYiw9+xTrLz3Hq4/yAFuo8J0y4ekn4JO20n40kinrGC0J0hQZKtAG1HcjL88YhoE4K8sgj6Lc5Dly5AiaNm2KAQMGwMXFBY0aNcKGDRtK8cjoB7VsE6JF65pOODGlHab8ewdXX3zE1F2RePA2FT918zXoH3pCCClrq1evxuLFixEXFwd/f3+sWrUKzZs3V1v2wIED+O233xAdHY38/HzUqlUL06ZNw/Dhw1nlHj9+jJ9++gmXLl2CUCiEn58f9u/fj+rVq5fFR9KbtGx5JuKYj1mYc/QRsvPFmNDRx4C1IuXVvGMPsf2apFtyyMkneDg3EJYCE1iY8pBZkPWeA/0EfIoBfFaeEBZ8w4YGurRsG1pFa9lmsrMR1biJQfZd5/YtcCwsdCr74sULrF27FsHBwfj5558RERGBKVOmgM/nY8SIEaVc0+KjaIGQQjhbC7B9dAtM6iS5aNoQ9hID11/F25RsA9eMEELKxu7duxEcHIzZs2fj9u3b8Pf3R2BgIBISEtSWd3BwwMyZM3H16lXcu3cPQUFBCAoKwunTp2Vlnj9/jrZt28LX1xcXL17EvXv38Ouvv8LMrPyNeVZs2ZZafSHaADUhFYE00Jb6UDAdaa+G7qW6348Zhm/dliZI4xhxQMtVEz3ZmFH7ZWkTi8Vo3LgxfvvtNzRq1Ahjx47FmDFjsG7dOkNXTSv6ZhCiAx6Xgx8CfVHP3RY/7buH27Ep+GxVOJYOCkCH2s6Grh4hhJSqpUuXYsyYMQgKCgIArFu3DsePH8emTZswffp0lfIdO3ZkLU+dOhVbt25FeHg4AgMDAQAzZ85Ejx498Mcff8jK+fiUz5bgPIUx21LGGyqQ8kba09ZSoP/LdqFI3rJtDN2jtc2zbSwUW93HdfDG+ksv0KSGcSfp0oZjbo46t28ZbN+6cnNzg5+fH2td3bp1sX//fn1XS6+oZZuQIujRwA0npraDn5sNPmbmYcSmG1hyJgpCkeqFFiGEVAR5eXm4desWOnfuLFvH5XLRuXNnXL16tdD3MwyD0NBQREVFoX379gAkLRTHjx9H7dq1ERgYCBcXF7Ro0QKHDh3Suq3c3FykpaWxHsYgI1eosi5dzTpCikMaDj9LSNf7tvMVrl+Wnnmq9+0X1ZG77wAACem5Bq6JZorT+5mbSqYfux2bYqDalByHwwHXwsIgj6L0YGjTpg2ioqJY654+fYoaNWro+5DoFQXbhBSRh4MFDkxsjcHNJWMKV52PxsD1V/E6KcvANSOEEP378OEDRCIRqlSpwlpfpUoVxMXFaXxfamoqrKyswOfz0bNnT6xatQpdunQBACQkJCAjIwOLFi1Ct27dcObMGfTt2xf9+vXDpUuXNG4zJCQEtra2soeHh2Gm/lGWZCTJpUjFoNwl2bQgGZqpQq6YZnrKRu5mK29Z3H/7jV62WRLrL70wdBUKlZUrHzYiHTsvMKGQqrR99913uHbtmiwfyM6dO/H3339j0qRJhq6aVvTNIKQYzEx5COnXACsHN4K1wAS3Y1PQY0UYTj14b+iqEUKIUbC2tkZkZCQiIiKwcOFCBAcH4+LFiwAkLdsA8Pnnn+O7775DQEAApk+fjl69emkdfzdjxgykpqbKHq9fvy6Lj1IoY07mRMqfjnVcWMv8giBbGtAFeNihfS0nvezL1bb85UgwNHtL+fR+DT1sAQDmfJ6hqlNpNGvWDAcPHsS///6L+vXrY/78+Vi+fDmGDh1q6KppRWO2CSmBz/zd0cjDDt/ujsStV8kYv/02hresgZ971KU/vISQCsHJyQk8Hg/x8fGs9fHx8XB1ddX4Pi6Xi5o1awIAAgIC8PjxY4SEhKBjx45wcnKCiYmJ2vF34eHhGrcpEAggEAhK8GlKR66aMduEFJdYaTok6ZKoIFX3F02qGXUCsYpOcTy5dcE4ehFN9VcmevXqhV69ehm6GkVCLduElJCHgwX+HdMS4zp4g8MBtl17hb5rLuNpvP7HVhFCSFnj8/lo0qQJQkNDZevEYjFCQ0PRqlUrnbcjFouRm5sr22azZs3K5fg7dR68TVW7PiEtp4xrQsozsZjB8I3Xceweu5dcRq4QLz9k4swjyQ0virONh7Rrv5iCbaIBBduE6AHfhIsZ3eti04hmcLIS4ElcOvqsvozTDzWPZySEkPIiODgYGzZswNatW/H48WNMmDABmZmZsuzkX331FWbMmCErHxISgrNnz+LFixd4/PgxlixZgm3btmHYsGGyMj/88AN2796NDRs2IDo6Gn/99ReOHj2KiRMnlvnnKykrDVmid1yPVbueEHUevU9D2LMPKut3R7zGrMMPZMt3X6eUYa2IsqaeDrA1N0WAh50sWRrF2kQT6kZOiB518nXBialtMfXfSFx98RHjtt3C+A4++L5rbZjw6N4WIaR8GjRoEBITEzFr1izExcUhICAAp06dkiVNi42NBVdh8tnMzExMnDgRb968gbm5OXx9fbF9+3YMGjRIVqZv375Yt24dQkJCMGXKFNSpUwf79+9H27Zty/zzlZSIUX+lnZ2vOv82IZoINURs+SIxa6hCaU6AIhYzBp0CzMfZEs8TM/HvmJYGq0NhbM1NcXXGJ+DzuHganwFA898AQijYJkTPXKzNsO3r5ph/7BG2Xn2FdZee4+G7VCwfFABHK+Mba0gIIbqYPHkyJk+erPY1aeIzqQULFmDBggWFbnPUqFEYNWqUPqpnUJq6kaubf5uQokrOzJMlSQNKtxu5UMyAX4bBdkJaDvgmXNhZSJKOSWNWnjFPtA3Agi8JoWQt22IGWXlCJGXmoZq9hSGrRowMNbURUgpMeFzM/bw+Vg1uBHNTHsKefUDvVeG49SrZ0FUjhBCiR6nZ+Wq7/gLAlisxZVsZUq6FPo5Xu/5Q5DvWcr6em7ZtzU1lz38+eF+v29YmPScfnf68iB4rwmTrpC3E5aUzoPSegIhh0P6PC2j7+wVEJ2QYtlLEqJSTrzIh5VNvf3ccmNgaXk6WeJeag4Hrr2LDfy/AUHcjQgipEKITKBkm0Y9t115pfE0xIObrORJdN6yJ7Pm+W2U313Z0QgYy80R4l5ojuy6SZvUuL9PpSbvci8QMPmTkAQD+e5poyCoRI0PBNiGlrK6bDY5MboM+Ae4QiRksPPEYk3feQXpOvqGrRgghhBAjIRRpvhFvKZBPJ6rvQLSVj6Net6crxTHq0iBbmtXb2LuRS/EKfhaKbSimJhReETn6NhBSBqzNTLH8y0aY1csPpjwOjt9/j16rwnHvTYqhq0YIIYQQI5CRK9T42tG77zW+Vl7lK+Q0qDnzJH7YexfvUiXT5ZWblu2Ceir+7PbefG2o6hAjRME2IWVoVFsv7BrbElXtzPHqYxb6r72CHddfUbdyQggpp0ozMzSpXKramWt8TTGzfb64dL90wjL6UidnsXv47S3DLuz6YmWmmmv63ptUvEikcdtEgoJtQspYkxoOOD6lLbr6VUG+iMHMgw8wdVckMrXc0SaEEGKcykkDHCkHvmzmoVM5Vxszve/767ZesueapiDTN21dxa3VBLHGyMGSr3b9x8y8Mq4JMVYUbBNiAHYWfKwf3gQ/dqsDEy4HR+6+Q++/whEVR4l2CCGkPCks1qaeS0TfSmM8czNPB9nzsgq2hVpa6PnlaNxzXTcblXXlZcx5eeLp6QkOh6PymDRpkqGrplX5+SYTUsFwOBxM7FgTu8a2hKuNGV4kZqLfmss4fq/ijcsihJCK6vTDOK2vN55/toxqQsq7O69TdCpXGmGcOV+egO1Dem6xt/Pf00T0WhWGay8+ai0XFZeOyTvvaHxdYMLT+JqxUZcc/m1ydtlXpIKLiIjA+/fvZY+zZyV/WwcMGGDgmmlHwTYhBtbUU9KtvJW3IzLzRJi08zbmHHlYZmOmCCGEFN+GsJes5Xa1nFjLyVn5sgzLhGhz/kmCTuV6+7vrfd8BHnay54/epxV7O8F7IvHgbRpmHNA+X/eYf25qfd3ctPwE2+qSua279NwANanYnJ2d4erqKnscO3YMPj4+6NChg6GrphUF24QYAUcrAbZ93RzjO/gAALZcicGXf1/Dm+QsA9eMEEKIriJmdsbGEc1U1ufRzVNSDC9DeqisC6xXBbWqWOt9X7bmpvB2sgQA5Jfg+yqda/rlh0yt5ZIKGdNsZlp+QhR1wXZiCXoHlDWGYZCfKzLIo7jDbPLy8rB9+3aMGjUKHCNPnFEusg/ExMRg/vz5OH/+POLi4uDu7o5hw4Zh5syZ4PPliQnu3buHSZMmISIiAs7Ozvjmm2/w448/GrDmhOjOhMfF9O6+CPCwxbQ9d3HzVTJ6rAjD/D718Zm/u9H/MSGEkMrO2Vqgdn2uUAwzLS11KVl5sBSYwFRdf1RSaak777tY6z85mpSXkyVefMhEbr5+bg4xDFPsa5fydM1T3sdnC/PE+HvqJYPse+yKDjAVFL0Xw6FDh5CSkoKRI0fqv1J6Vi7+qj958gRisRjr16/Hw4cPsWzZMqxbtw4///yzrExaWhq6du2KGjVq4NatW1i8eDHmzJmDv//+24A1J6ToutV3w6lv28Pfww5pOUJM3RWJaXvuIj0nv/A3E0IIKTO6tl5p+/v9NiUbAfPOotfKcH1Vi1RgJWl1Loz0Zs/Lj9pbpXW1Mfylxte0zSle3vDU3BhIKEct2+XRxo0b0b17d7i7639Ihb6Vi5btbt26oVu3brJlb29vREVFYe3atfjzzz8BADt27EBeXh42bdoEPp+PevXqITIyEkuXLsXYsWMNVXVCisXDwQL7xrfC2ovPsfzcUxy48xZ336Rg3bAmpdJ9jBBCSNGdfRSvdv3e8a0wYN1V2XJmrkhtOQA4/UCSYC0qnmajqOzqutngcSHjpePTckpt/y8+SOaGdtQwnVVRLTj+GKPbeetlW8aMq6bpckiL6mVfkWIy4XMxdoVhxj2b8Ive7vvq1SucO3cOBw4cKIUa6V+5aNlWJzU1FQ4O8mkKrl69ivbt27O6lQcGBiIqKgrJycmGqCIhJWLK42LKp7Xw7xhJtvLniZnotSoc/wt7QVPJEEKIEWvm6YCYRT3hbivp8psn1NwaKaa/56RAXTfVm+nV7M1Zyw2r2ZXa/uu72wKg72RRKY7ZtimYH7w8HUIOhwNTAc8gj+IMF9i8eTNcXFzQs2fPUjga+lcug+3o6GisWrUK48aNk62Li4tDlSpVWOWky3FxmqflyM3NRVpaGutBiDFp4e2Io9+0RbtaTsgVirHg+GOM23arQnXBIoSQ8uTco3j0WX0ZPx/UnnE5tyDIfp+qeRogxcDm1cdM7Ln5GkKRGCIxg90RsdgY/hKXnibqp+LE4BLScrDzeiwy1ZzDXyepJkVVHsdfmjGc9LqiPCX3MgaKY7alU6jFJumnKz5hE4vF2Lx5M0aMGAETk3LRQduwwfb06dPVTk6u+Hjy5AnrPW/fvkW3bt0wYMAAjBkzpsR1CAkJga2trezh4eFR4m0Som/O1gL8M6o55n9eD6Y8Ds48ikfvVeF48DbV0FUjhJBKZ/Q/NxGpw5zIHwsyLo/ddktzmQx5VuYOiy/ix3338O+NWJx6EIef9t/H/GOPMGLTDaRkac/eTMqH2Uce4ueD99VODZWhMNygT4BkLKqVgB1Q+DhbllrdzhQMi1Cezk5XlXXK0rBnH2TPpYdAX0nmCNu5c+cQGxuLUaNGGboqOjNosD1t2jQ8fvxY68PbWz7W4927d+jUqRNat26tkvjM1dUV8fHssVPSZVdXV411mDFjBlJTU2WP169f6/ETEqI/HA4Hw1t5Ys+4VnCzNcPLD5nov/YKTt5/b+iqEUJIpfe/r5oW6335ItW2yusvk3DvTQprXWo2JcmsCE4WjNHffu2Vymt25qYAADdbM/zcsy4AoLqjhez1KjYC9GzgVga1LB5hEeaT93Bgd4/nV5BM/LbmkpsjloLy0epa3nTt2hUMw6B27dqGrorOivzNjo2NVTtelGEYxMbGFmlbzs7O8PX11fqQjsF++/YtOnbsiCZNmmDz5s3gKmUjaNWqFf777z/k58tPRmfPnkWdOnVgb2+vsQ4CgQA2NjasByHGrFF1exz7pi061nFGrlCMCTtuY+nZpxAV4SRHCKn49Hm+JoXr7Fel8EJqCMWqLWAcDqdIgQspf9T9dKXfhVm9/GRTfCmOaN04ohlMjDgoLco4ZWuBKWu5lY+jnmtTdqo7yG+IOFlJpv/LFWpOikgqlyL/xnp5eSExUXXsUFJSEry8vPRSKWXSQLt69er4888/kZiYiLi4ONZY7CFDhoDP5+Prr7/Gw4cPsXv3bqxYsQLBwcGlUidCDMnRSoANXzXFqDaS37mVoc8waP3VUs1SSggpXwxxviaa7bv1RvY8TyjG5J238e+NWPxzVbWF8/TDODx6x84hc/1FUqnXkZSdlCzVngrSGyyKY4AVk28JTIw30AYAkZpo+4e9d9WWVZ7CLDuv/AanfIWfi0XBmO1rL5IomS0BUIxgW9ME9RkZGTAzM9NLpZSdPXsW0dHRCA0NRbVq1eDm5iZ7SNna2uLMmTN4+fIlmjRpgmnTpmHWrFk07RepsEx5XMzq7Yc/B/jDSmCCm6+S0XnpJexXuKAjhFRehjhfVwbiYrY4f68QdBy4/QbH7r3HjAPqE6zlCcUq0wn9uP9esfZLyg9pDzUTnvz3VnEKKXc7c5X3GBN1Pez2argmUWwNBoDbsfKZg37t5affipWyz/zlcz3bmstb7J8lZBiiOsTI6DygQNpCzOFw8Ouvv8LCQv5LIhKJcP36dQQEBOi9ggAwcuRIjBw5stByDRs2RFhYWKnUgRBj9UWTamjmaY+JO27j4bs0TNt7Fw/fpWFmz7qsu+OEkMrBkOfrykAf3bulidO0oUaxykdYMH7fROFOS0tvR/w7piXcbM1KfRzw7N5+mHv0EXo00JzrSJui3IhysWHf8PNyspQFp0GtPYu1f0NpX9sZS88+BQBUs5f/vc3JL7+t9UR/dP6tvXPnDgDJnfL79++z5rPm8/nw9/fH999/r/8aEkIKVcPREkcmt8XK0GdYEfoMmy6/xMN3qfhzgD88lO4eE0IqNjpfly595McQqkmKplJGzX7yhGJWl1VS/uXki2BmKul6nFMwztdE6UZ5WY1nlk4zpm1eeG3UdSPXJCuPPfWZYnd5bjlrKFCsreJUftJfYZGYgVAshsCEV6b1om7shSuLY6RzsH3hwgUAQFBQEFasWEGJxAgxMjwuB991qY1aVazww957uP4yCT1WhOHX3n4Y0KSa2u6khJCKh87XpasoAYUyadd+bfNuSylnIweALssu4VxwB5W5l0n5dOTuO0z59w7mfV4Pw1rUwItEydzMhgo2pQHvuccJEImZIveOm3X4gU7l/rkag8OR71jr3O3MEBWfXqT9GQvFyyvF59Lp+rot/w9xaTmImNlZdmOlNJmaSrqyZ2VlwdzcuIceGFpWlmRue+kxKw1F7o+yefPm0qgHIURPejV0R4Oqtgjecxe3XiXjx333cP5xAkL6NYC9Jb/wDRBCKgQ6X5cOkZpW6YFNq6kt+0NgHSw+HSV/r5iBCY8DC37hl1/NPB1Y8/cCwKuPWXidlAVvZ6si1poYoyn/SnqhzDr8EH0aVZWtr13F2iD1ESlkxk9Mz4WrbdFyO5y4H1d4IUg+r6LVQxrD29kS0YkZmNypZpH2aQxqV7GGn5sN0nPz8WWz6lh9QTKHuvTmhbR7fFRcOvw97Eq9PjweD3Z2dkhISAAAWFhYUIOLEoZhkJWVhYSEBNjZ2YHHK72bIEUOtjMzM7Fo0SKEhoYiISEBYqUpK168eKG3yhFCiqeGoyV2j22J9f+9wLKzT3HqYRzuvE7G0oEBaFPTydDVI4SUATpflw51U3XN/ay+2rJj2nmzgu2i9EBXztYsRa3a5ZudhanaTOSKN3HszEuvlU0bHxf5TRxxGXVBXjbIHz0bShIeh/34SZnsU9/MTHk4MbWdbLmeuw0evksDA/awk7LMo+PqKhl3Lw24iXp2dnayY1Vaihxsjx49GpcuXcLw4cPh5uZGd0oIMVImPC4mdaqJ9rWcMXXXHbz4kIlhG69jUsea+LZzLaOeq5MQUnJ0vi4dyVmqyc00jaM25bGP+c1XSTDXsRtpzIcsDdukv93lSUJaDm7HpqCLXxXki8RqA21APkafwzFcN3LFqcX0GWqLxQzmHXuErvWqoLUP+4Y/twL+XUoqSICYlStk3TTLKsPpzTgcDtzc3ODi4oL8fPXfucrO1NS0VFu0pYocbJ88eRLHjx9HmzZtSqM+hBA9a1DNFsentMO8Y4/w741Y/HUhGtdefMSyQQGUPI2QCkzf5+vVq1dj8eLFiIuLg7+/P1atWoXmzZurLXvgwAH89ttviI6ORn5+PmrVqoVp06Zh+PDhsjIjR47E1q1bWe8LDAzEqVOn9FLf0jJkw3WVdZparJRvcEjfO1RhOidN4tJy1K5n9BoGkdL27e5IXHn+ESu+DMCbZM1j9aU9JngGDD7tLeRDzdKy81G1BFONcTjyjPoLTzzGlisx2HIlBjGLeiqVq3jB9vtUye/u2kvP0VqhN+HXWyNwf05gmdaFx+OVSUBJNCvy7VF7e3s4ODiURl0IIaXEnM9DSL8GWPFlAKwL5uTuuTIMx+69K/zNhJBySZ/n6927dyM4OBizZ8/G7du34e/vj8DAQI1dFB0cHDBz5kxcvXoV9+7dQ1BQEIKCgnD69GlWuW7duuH9+/eyx7///quX+pamhPTcEm8jI1dYeCEN9JENvUT+6QPMsQWurZWve7AfuLLKYFUyZleefwQAHLv3Hgduq59zGpBnADfklJ2KuQBK+j3b/nUL2fON4S81litniceL5N6bVNZ0aOk5xf+9J+VXkYPt+fPnY9asWbLsbYSQ8uPzgKo4PqUdGlW3Q1qOEJN33sG3u+4gWYc5Xwkh5Ys+z9dLly7FmDFjEBQUBD8/P6xbtw4WFhbYtGmT2vIdO3ZE3759UbduXfj4+GDq1Klo2LAhwsPDWeUEAgFcXV1lD3t7+xLXtaxZFWPu49z84k2tBBjB/NsvJNnucWq65F+GAfaNAs78AhynKeU04XE4Wudozy0ItpWn/Spr0tbskgbb7gXbsS7k96MidiNXVJLZC0jFoNMZolGjRqxuHtHR0ahSpQo8PT1VUqXfvn1bvzUkhOhVdUcL7B7bCqvOP8PqC9E4FPkOV198xOohjdHUk3qtEFKelcb5Oi8vD7du3cKMGTNk67hcLjp37oyrV68W+n6GYXD+/HlERUXh999/Z7128eJFuLi4wN7eHp988gkWLFgAR0fNcwrn5uYiN1fespyWlqbTZyhNxZmn9dRD3bI2q1NWiat0wjDA/b3y5YgNQM8/DVcfI1bYz/xNsuSmmCFbtgHgbYqkq3tETFKJMmdLbxqkK/XiuB2bzFqu6MH27ojXhq4CMTCdgu0+ffqUcjUIIWWJb8LFtK510MnXBd/vvYsXiZn48u9rmNGjLka18ayQY6gIqQxK43z94cMHiEQiVKlShbW+SpUqePLkicb3paamomrVqsjNzQWPx8OaNWvQpUsX2evdunVDv3794OXlhefPn+Pnn39G9+7dcfXqVY1jDENCQjB37lz9fDA9Gd/BR+vrXE7RspAXxqC9yHOUbm5cXg6cm2OImlQ4d1+nAgDSjKSr8YLjjzG6nXex36/ppkG/NVdYyxW5GzkA1mwEpHLSKdiePXt2adeDEGIAjavb48jktvhp/z0cv/ce8489wpXoD1g8wB8ONCc3IeWOMZ2vra2tERkZiYyMDISGhiI4OBje3t7o2LEjAODLL7+UlW3QoAEaNmwIHx8fXLx4EZ9++qnabc6YMQPBwcGy5bS0NHh4eJTq5yjMl821JzuLmNkZTRacK/b2J3T0QWRsCq6+kIz9NWjL9iKlY02Btt5Ie0g0L8c9zDwdLRDzMQubRjbVuTt8RW/ZVtS9fulOMUWME80fQUglZyUwwV+DG2He5/XAN+Ei9EkCOv15EYcj3xq6aoQQI+Dk5AQej4f4+HjW+vj4eK3zk3K5XNSsWRMBAQGYNm0avvjiC4SEhGgs7+3tDScnJ0RHR2ssIxAIYGNjw3oYGr+QqbgcrQSFltFmbDtvWCqMey1Ot/USy04GHh0u+/1WInkF82y72ZkZuCZFoDTnvHRMup0FX+fu8NwKGIloun8gEjOsqcBI5VDsbOTKD0dHR1StWhUdOnTA5s2bS6OuhJBSwuFw8FUrTxyY0Bq+rtZIzc7H1F2RmL7/HnLyy25eSEKI/ujrfM3n89GkSROEhobK1onFYoSGhqJVq1Y610csFrPGWyt78+YNPn78CDc3N523aQx0ChZK0HjHN+GCbyLfQJleqz85Lmm9/t0T2PNVGe648rlW0HPBGFp67ZCOWhzNmdORGCXJSD/PHnh+HoAkkJRObcbjcGCiYxRdEYetaUoKd+ZRPGrNPIkp/94p4xoRQypysD1r1ixwuVz07NkTc+fOxdy5c9GzZ09wuVxMmjQJtWvXxoQJE7Bhw4bSqC8hpBTVr2qLY9+0xZRPaoLLAXZFvMbnf11GdEKGoatGCCkifZ6vg4ODsWHDBmzduhWPHz/GhAkTkJmZiaCgIADAV199xUqgFhISgrNnz+LFixd4/PgxlixZgm3btmHYsGEAgIyMDPzwww+4du0aYmJiEBoais8//xw1a9ZEYGDZzkNbUroER9JpnYrDgs/DjO51Zctl2o181xAgfFnR3pNe/ORvlVnk6xQAmltFy1Kk2TicFfwIJu6+6ovCPGB1c/nytr4AgI+Z8htpPi5WsDHXLUu/MdxcKLL3d4GD44GLvwOpqjclfgiso/XtR+7StKuVSZHnqwgPD8eCBQswfvx41vr169fjzJkz2L9/Pxo2bIiVK1dizJgxeqsoIaRsmPC4CO5aB828HPDd7ruIik9Hz5VhmPtZPQxq5lEh70ITUhHp83w9aNAgJCYmYtasWYiLi0NAQABOnTolS5oWGxsLrkJLVmZmJiZOnIg3b97A3Nwcvr6+2L59OwYNGgQA4PF4uHfvHrZu3YqUlBS4u7uja9eumD9/PgQCgZ6PhH41qm6HO7EpsmVdusu29nGUzbes7IfAOmjh5YAv1kkyu/NNuLLgvFMdZ3A4HHg4WMDZWoDE9FzjykauzqomwM80DEnKzsIUKVn5rHVzP6uH2Uceqi1fZsGnWAwwIoDHnqWgtY8jIP3x7fsamHyD/b78TNVtCXNlPcq5HO3T4TX3csCNl0myZV55vKZY317+/OJvwJxU1ssdarsAUP/zJZVPkVu2T58+jc6dO6us//TTT3H69GkAQI8ePfDixYuS144QYjDtajnj5NR2aFvTCblCMaYfuI8f991DZq5xZEolhGin7/P15MmT8erVK+Tm5uL69eto0aKF7LWLFy9iy5YtsuUFCxbg2bNnyM7ORlJSEq5cuSILtAHA3Nwcp0+fRkJCAvLy8hATE4O///5bJeO5MXj4LhUXoxJk8w6LldKB6xIcFRYfK25CYMJVeC7Pyi4NStTNf5yanY+zj+KRlaenv89iEfDyP82vj2fPl47R5+XP86gnlCJ1Py+ulhs0ZZade0sPYKkfkJ/NWm0nlgfCnA9ReJuSjdMP4+RDyoRqhoIscEHc7aMAdMiWr/S60d88UvbkeKFFKuI4dFJ8Rf46ODg44OjRoyrrjx49CgcHSQbFzMxMWFtbl7x2hBCDcrYW4J9RzfFjtzrgcIC9t96g058XcTMmqfA3E0IMis7XJZeSlYeeK8MxcnMEThfMkyxUiiZ0admOTtQcgDpZ8VnjsNMVpn5iFCITYUHToWKrutSMA/cw5p+bWHj8caF10cmVVcDW3ppfN7eXP7epClRtLF/27aWfOlQQyjdnAO0BdZnNsx17FchMAHYOZK02Y9jB9MB1VzFu2y2suVCQuFApOJcKuDRap93eULp+0DVrudHYNUT76wyDaivcEGM2BI04z8qmTsSoFbkb+a+//ooJEybgwoULaN5cMmYjIiICJ06cwLp16wAAZ8+eRYcOHfRbU0KIQXC5HEzsWBMB1eww/cB9xCZlYfCGa/gx0Bdft/XSeoeeEGI4dL4uuY+ZebLncak5AOQtlS28HNC1nqtOwZGmbOR8Hhf9G1fDyw9quuYC4ChkVsvOk7Qsqvube+K+5EbAjuuxWNi3QaH1KdS5QqaQMzEHbKoBaW+A+v3YTfNPjpV8/xWISKnldktQM7wv+C4p4nAkPSBKfajW/tHA/b3yZaUeDA6iD6zltymS4DryTUFX6Rx2l2lthrWsju3XYjW+/kWTamjmVX6nOpO5vQ1oNAzISACW1JatPiiYDc+cnQasGDEGRW7ZHjNmDC5dugRLS0scOHAABw4cgIWFBS5duoSvv/4aADBt2jTs3r1b75UlhBhO65pOODG1HXo2dEO+iMHCE48xdXckcoWUrZwQY0Tn65JT7AIsfS5t2f6uS2183dZLp+14OVmqXb9ycABMeFyYKATjVe3MZc/rV5VPbdbJ10VSDy3pyMtu+CsDjLsE9NsAdJpZVjstlxRnx+JygI51XNS2bEuHD5TK/ev3d4HQ+UDae3agrUwkxC+J37NWHeL/guYchR4ThyYUvr/HR4HVLTG6dQ2txRb1awDTEkyLZzSOTAYeHmAF2oRIFbllGwDatGmDNm3a6LsuhBAjJ52Tu6WXA+YefYSjd9/hbXIW1gxtAlfbcjQ3KCGVBJ2vS0YoUuzGLXkunZ2hKN1fGeWBqgWkrZiKW3Kw5MtaExW7lEtb0OccfYQu9VxlQfmKc/KuqnrpkpudXHgZC0dJZN9QoQuyeyPgXcGURmnvAZvyNYVbaclTuDki/XmrG+efky8pF5+meXq8IhPmAgmPgb8Leq+E/am9/J1/VFYFcF9gj2A+puTvkKxIeFT4fndLZh3wXOMBYAc0zX1XZl3m9UWsZVaBfaN02kRNzhskMHZIz8mHtZlp4W8oJ57Gp2Pn9VhM7OQDF2u6HlSk0+2ktLQ01nNtD0JIxcbhcDC8lSe2jmoOazMT3I5NQa9VYbgS/aHwNxNCShWdr/WL3bItZiWItLPg67ydrn6uACTTeCmqXUUyXt7BSr6thtVsZc//DpMnr3ubLB8rO+OAZEqmVx8zsezcU7X1LbbfPbW/3m+D+iZ0e4X3HflGMg/z8e9Vy1UisqRiBb5s5gFAe1K9s4/i9VeB5Q3kgXZh5tgCx77T+LJXvm7jj7lgB6QxZkPhBvWZ+Mvd7Ca5unehV8eXE4tzgh9xz2wsFhzV4aZFOfLD3rvYciUGf5yKMnRVjI5OLdv29vZ4//49XFxcYGdnp/aXg2EYcDgciETUpZSQyqBNTScc+6Ytxm27hSdx6Ri68Tq++aQWpnxSk9UlkhBSduh8rV9ChZYsoZhBrsJ82d4auoarM6xlDVS1M0dAdTs8fp+GlaHPMKlTTVn3chszU/w5wB8pWXlo7uWAHdcl41wVh/vmK7SQ3ngpCV4UW74BwNGqBNOmHRinebz1hCvIdvSGOVegOdWy/2Dg4UHJ8+izkn8jNgA9C2lNLS5Rvsq0VcZG8We2anAjdPGTZNsvs2zVGToG7imvCy2SxxFIjrmCr/J+wj/832XLPIhwV6A6jeBVs28qxtjlTIWbBj3+BE4UfjMpxkySUK1H7m/oxI2UrW8cvRLARj1X0HDuFozpP/dYjzeLKgidgu3z58/LMpdeuHChVCtECCk/ajha4uDENph95AH23HyDlaHPcCc2GauHNoZNBeoeRUh5Qedr/VKclkgsZmTBN4ejffomZTwuB50LAq12tZzRrpazSpkvmlQDIO+mro20y7FKfUvSsn1vl/r1LvVwMisWP576EsP9hqOte1vseLIDIe1CYMOXjylHra7F33dR7fwSeHoS6LMWCCgkO7QBKfY06NHATdZtuszm0tbV3hGFFvHIfwk82M9a95/YHx1yl+KSIBgA8NxseKlUz2isbi5/3miYTsG21AnBz9gvaidbHpS7DxUp2Caa6RRsK2YqpaylhBBF5nwe/vjCH618HPHzgQcIe/YBfVZfxqrBjVDP3bbwDRBC9IbO1/r1MUOejfzqi48Y3KI6gNKdrkhxnm1tPKerzvf7MTMPntOP4+7srrA11/GGZ3ocsKSO+tesXIHui/Djf5MAANsebcO2R9sAAG3+bYP7IyTd2bc/2o46DnXQjG8N5KXrtt/iYhhJoA1IknUZabCdnSdCwLyzsmXFr4zRdZ9+e6vQIkNS/gYiFf6m9PgTOADEMi6lWDEjwyj0BjI1B379KJkK7Nlpnd7enxdWShUzHilZ+fCcfhz//dAJ1R0tDF0do1CsjixhYWEYNmwYWrdujbdv3wIAtm3bhvDwcL1WjhBSfvRtVA17x7eCi7UALxIz8flfl7Ey9Jl+xhASQoqFztclo5jAycXaTJYwrTQTOzkpdAVX3M3rZPXzG6vz8F0RxpZqCrT9BwPfRwFe7TW+NTU3FXui9uD3iN8x6vQopECosazeKCdwY4zzHHPk7lvWsmKAre3r4+dmo/lFQ3t5Sf68uaS7OKNjKGEGSeK3XtyriDEbgn9qnNB79coczwTotbT475+j0CDx4RlwZ7v2JGzlyILjFWtMekkUOdjev38/AgMDYW5ujtu3byM3V/LLk5qait9++03vFSSElB/1q9ri1Lft0dWvCoRiBkvPPsVXm67jdVKWoatGSKVD5+uSU4zjcoVi2c1Dk1IcdKvYst2mppPseVGCMJ3jz3zV+Z5lMhNlTz3jzPDHjk/R6okNeCIGPm8ZgGHQdldbzL82X1auXVXV7vFI1HPCpFPT2ctvbup3+3oi1HKjmaelZbu3v3vRd5b5QRK4/d1Rt/IDtgDTNc9//Rt3HJIZq6LXQ4snZkEI4p3EX/xVAID28dv1un2Dsa2muq5e36Jv56+mwOFJQOSOktfJCORrmaKwsiny2WLBggVYt24dNmzYAFNTeRelNm3a4Pbt23qtHCGk/HGw5GP98CZYMsAfZqZcXI7+iE+XXsKWyy/BGGkLBCEVEZ2vS07xL9a1Fx9lY7ZLs2VbMQ5T7K5elH2Kdf1bK9IyzVTHGbKn3V7+jhfen8E/eT7G3FiJLrErMfOQjsMU9hQ+HrhIos+xl7V9BgN6kZip8TVt3ciL9dWSjh1+dwdIiQXeRbJbTQFgVhLgWAtwqg349QHMbIEx6vM61DKJx4i8n7Tu8l2KvKfFZVE91QITr6usmm26Tes2jZpQ4Xs26Yb2sgO2FH8/ir0HyrE8CrZlihxsR0VFoX171S5Ftra2SElJ0UedCCHlHIfDQf8m1XB8Sju08nZEnlCMOUcf4dvdkUjLyS98A4SQEqPzdckdv/dO9jwjV4glZ55qKa0fioFYVXtz2fPqDrqPf9R59E78Q82v2XuBYRikapju6KNrf9lzfj4DjqadJj4GDozVX3fvLKVppLb01G1u8DK2Mfylllc1H4tiJU+TZoEHgM09Vaf76jgD4PKAb24CkyPkd3QY9QGRXXYs7jE+CMxdhEY561RejxDXRutF52XLI/PVBOYuvsDYi9rrLSpH1wMLFMam21Rlv+bmX/ztipSGXtzfW/xtGZHL0eqne6uMihxsu7q6Ijo6WmV9eHg4vL299VIpQkjF4ONshZ1jWuCXnnVhwuXgcOQ7dF8ehluvjO/CiJCKhs7XJXco8h1r+eSDOABAanbpBgnezpIpwX7t5SdbN6Gjj87v17ll++Iiza9ZOKDhPw3RdldbjUUCnvNRI56PUTdXYtz1lQDDgHGqpVrw3m7g4Hjd6lQchc0NXsYK68VlZsrT+FqJc6elquke3lhD7wINNyn+EUkyy0cx1ZEMG0zLY//szJDHWs6HCb7NmyhfMbggs72LH7S6sUH766+uAm8KT95W5gRKXezf35U/51tL/nVvrNu28rMAYZ7q+tx0Se+EsCXFqyMxGkUOtseMGYOpU6fi+vXr4HA4ePfuHXbs2IHvv/8eEyZMKI06EkLKMQ6Hg9HtvPHv2Jao4WiBtynZ+PLvq9gYTt3KCSlNdL4uPb0auuleOP4RsHs4kKStpZPt/LSOiFnUEwITeVDmbmeOfeNb6fR+nf62pr5ld1ntNJP9ug5RX8uExej+YrFsecLZAKxvOQyo2lS18L1dpZvMzIgSSyl3oa1flT3e3tFSngTPks8OvEtlWjC+hjnh3Ruxlzv9Any+BmHihqzV+8XsHjINuDEqmzokboPnzecBYy8BdbpLVvL42ut1eobm17KSgM3dgP99YlQ/20LVKPgd9emksYhnjsK47PxsIDdNtdCGTyX/hs7TY+WIIegcbL98KTlJTJ8+HUOGDMGnn36KjIwMtG/fHqNHj8a4cePwzTfflFpFCSHlWzNPBxyZ3BY9G7ghX8Rg/rFHmHHgPiXRIETP6Hxd+oo09dfaVsDjI8DKgBLvV9dx28vPPcPT+EKm4No/mr3sP5i1KGbEcExl0PSp7u0yjPUorH64ERgTCgz8R7XAsW+BUz8DcQ903qbOFrrqf5tqBG2+gdFbtSdlyxexbyrwlBLqCUzlyyY89mulMiuYmYbkepZO7OXqLYFGQ4u5Ew6S/IYD7gEKq0rwYdLfy5+LyyDLvb48OyP5t+PPQM3OQE912coVjsvRqZLWbUX5OcAHPScW1CBfJMaq0Ge4+vwjEtNzUX/2aSw9W/rDZSoTnebZBgAfHx/UqFEDnTp1QqdOnfD48WOkp6cjIyMDfn5+sLLSb9ZCQkjFY2tuir+GNELTK/aYf+wRdkW8xv23qfi9f0PUr0pzchOiD3S+1o+ENM2ZupWDp7Kia7vwvTep6LrsP8Qs6qm5UOwV+fPWU1TGoeYIczDg0cqiV1LK73PVdbe2SP69thqY8QYQWBd/+8pEuUDUKaBON/1tU8nzxAxciJJkaX+dlAUPDePokzPl3YKH8c5iWuYFIDdc9nntLeQtvo6WfNawhNDHCQhq46V7pYrQY6JQdtV1KjYvf7ja9W62Zvqri2IviPR3gE01yVRbhqYu0/iUSPnNNM92kn95JsCw/ZLn7gHAhk/Ub+/pSaCLUut1GQ6LOHr3HZacfQo+j4t6VW2QkSvEytBnCO5SW+dtUKOJdjqfLc6fP48RI0bgxYsXGDt2LDw9PfH5559j48aNOH78OOLj40uznoSQCoLD4SCojRfWD28KGzMTPHyXhr5rLmNV6DMI6Q82ISVG52v9SMvR3Jqmc8t2RgJ7+eRPknGYGYnqyxdCKFIfbrf2cSzahpJfsZet3QDFGwgDtmLnk51FrJ0aVeprfi2+GPPwFtYN/d9BRd9mEXxIl2ek1jZuP1coAgA04LzAAtPNsM+KAULkU0Q5Wwvwv6+aIqRfA3xa14X1XsWp33Tqdq+HHhMy9jVUV1mYqqy7pNTNfFwHb0zq5INq9qo3H6SBebvcZfDM2YGGOX/jkqihSjkV69rIn6/wB+YX8TuuT4o/hw7TVV93ULg5oi7poIuabO2K3kSwl4XZ7OVl9Yv9N6Mw0QkZACRDH+7EphRrGxRsa6fzLaKOHTuiY8eOAICcnBxcuXIFFy9exMWLF7F161bk5+fD19cXDx9qyWxZQYhEIuTnl6MMimWMz+eDa6C7/qT86OJXBaHTOuKXQ/dx+mE8lpx9ivDoD1g1pBFcrPV4d5yQSobO1/ohDZjU4fF0DLZP/MBevl6Q2fnPmsAc9Vm+tRFqGLv6c4+66LUqXPcNrVAKdpKeS/6dlQSkv8cjYRpWHf4VY7FC5a2ujiL0X9gFq8efV3kNADwSBHj88THqOtYFhh8E/lSTMA0AbqwHqrfQvc4AkPRC/ty7E/BC/dRVpUUx4bq2Lv05+ZKf01HBL+wXHh2Wtfh39qsCAPjzNLu7sBmfJ8lQfeNv+ZjmYnxXdNZ1IXBmpqSngRqNqtvj/BP2TaPnDLsXxIzudTVufpOoOzaJusuW02CFPMXw4852yfzSNlWB4EJuwDBMKfWzL4RY4W+BlYvmcgDQdb7qOlMz4MeXwNlZuGzRCQhVev3wRNX3KEp9LfkZ9ftbp+oWRZGGxGigbU55UoRgW5GZmRk++eQTtG3bFp06dcLJkyexfv16PHnyRN/1MyoMwyAuLo6mTCkEl8uFl5cX+PxCEmOQSs/ZWoB1w5rgwO23+PXwA1x/mYQeK8KxbJA/2tVyNnT1CCn3Kuv5Wh8+ZqjJEFxApwvU/Gzg0aES1+PI8yOYGT4T81rPgzPaqS1TpPgjWvlKH0DE/4CeSyTTQ9lWw6CtDWCbI1ApNnppOwgKWjqbtbFCxOUMlTI9n/+BgccGAgB+afELBtl7Aclqujo/2A98sakIFQewSiHDc/sf1AfbKwKAKXdKJShTzPKuLZFZUqaG787T0yrd65U3w+VwgB39gRcXC6+Q8lzag3ezW/ebjgI8NWeTBwC0nix5aFDSWMxaYIL0XHYvkS682/KFw5Mk/6a9BY5Pk3wP4+6r31jKK8Des2QVKg7F6eY0JX376ZXkZpBy0jkpCwfg87+Qcu89gNvqy2hzb3eRg22GYeA14wQA4PavXeBgqVp3TUNibr1KQpMaDlq3n5krRL3ZpzW+7jn9OAAgakE3VrLHyqZIwXZeXh6uXbuGCxcu4OLFi7h+/To8PDzQvn17/PXXX+jQoUPhGynHpIG2i4sLLCwsWHNhEgmxWIx3797h/fv3qF69Oh0jUijpnNwB1e0wcfttRMWnY/jGGwjuUhuTO9UEVw93XQmpbCr7+VoftHWN1BaIA5C0wP1ZR3uZDZ8AY9S3DgOAOPEpnpyehpmiGJgIGcy6MgtXv7yjtqyPcxHG4W/vx1r8w8EOcSYmWMIw4HA4uPb+GjgMg2F35kOkdH0sUOhS3Hx4c+TlR+DujXRYCoTIzFW9pFxwfQEGNRoBnFfT2ldUb5WmgPJsI5li6skxSeuoVPJLYHFNYFqU3sf4KgYrFnzNwYPGdr7IHZKbBNJux6lv4f3hPLioBnHByM6RDcyAfRfZ70t+xe7iLdbQ68JZYZxt8BPApghZ8xXM+7weZh2W9Hz5uq03zj1OQNvcFQgXTEVwXtGmcPulV138tP8+PvN3x5G777QXjvgf0GYqsE7DDYIV/kC3RUDLUppNQSyS3HBSlhEnf64p2Da3A6oWPt2XaUGvmLo5m/DYbFTR6vfqqjzbuQ5ik+SJ18Zvu4U9amYzcLRS/3kGb7iOpwu6q31N6r+nunVtP3D7LQY31y0fQEWk81+hTz75BNevX4eXlxc6dOiAcePGYefOnXBzK94vcnkjEolkgbajowHHjZQDzs7OePfuHYRCIUxNVcf6EKKOj7MVDk9ug7lHH+LfG6+x9OxT3HiZhOVfBsDJSrWFhRCiXmU/X+tLnlASbFezN8ebZPYYyra1nFTfkBIrmRO35URJV9PcQrr+KgePSn46PACnTMVYttkfUb6jwKSvhNUIE9yd1RX+8yQZj8e088K3nWtrnbdZm3wA2U9ao1qOHRr+0xD3R9zHmDNjMOfgQMS5sf/ujluleoOm7ahmaDsKEApFWD/5ksrrkkLfSf4tScAtEqpPMFWnu+SRFAO8UuhGn/UBiD4rn4JKT0wVhg9ouxEs1tat9tIfQN+1kufL/NAXQDRvIFaL+gAAmuxrqfqeFQ3lXcmlrdmBIewyHX8GHLwlvQUsHIsdaAPAV6080bOBG0y4XJiaSD7nG8YZnjlFH8c/qFl1dPJ1gbOVQBZsf503DRv5GuaPXt5A+wZPTZcE2wfGSlp7f/0A8PRwrfn+LrC+PeA/RP7zkTIxV3hesl6b1R0l49qzUYzhcpu7FWlIgWJW/FdJmWrL8HnqW7alf/+0UZ7iDgD+91VTjP6Hna1fW36DykDngbVhYWFwdHTEJ598gk8//RRdunSpVCdu6RhtCwv1mSeJnLT7uEikebwbIeqYmfIQ0q8h/viiIcxNeQiP/oBeK8NxJfqDoatGSLlR2c/X+pJbcLFZw1H1vK8yXlcklAQJt7YAq5sDT89o3G48j4cGXtUlj60N8DT5KYRqpjb6TyjGl2FeiPKVtH5xrKdAJBbBxlzeTuJibQZLgeZ2k8KSTn7k8OAkHgMBfwDaPHbHnCtz0OqJLeLcVFsWTbQE9CZKXUQtFBK5T7owBS/8+0nGbxdXnmp3dZag46rrlJPA6YFiDP0sPp2VdRyQjPOP/ZgFkbZgW03L6Q+mewAANTnqx03LZCp0Z1aeo7r995J/6/cHvDtq344OHK0EsLUw1Xm6OW1crM1YPR1viDWP8dbZvd2Sf3cVd6oyJesL5hK/q+aGQiE3xoqCp63Hp211YMAW7RvQcc5xkZjB7dhk2XJyJjvgjUvNQZ5QjBwtuSm0efA2FUciVXsqmKvp8aGYWLAy0jnYTklJwd9//w0LCwv8/vvvcHd3R4MGDTB58mTs27cPiYmlkyXP2FC36MLRMSIlNbCpB45MbgMfZ0vEpeVgyP+uI+TkY8p4SYgO6HytH2ceSbqOmqgZ06gyZjtead7og2NlT1+b8NCkmgcae3gAADp7uKPPVXN8ds0Mv28PwJC9/RC4LxCZ+eyWpxE3psDO5FvWuuMvj7POsUwhk4EN+d912fMHHx4gJZOd6GqJZU3Z8wYpP+HCrf3wT1aahgjAhDWdtO4HAJyt5RfUky99B9N8Sd3+e/MfPj/0OZ45eAATrgC+vbRviGGA1LeAKB/ITgbeRQKhSnUK0CHAytB/1v1Dd97Kno/cHIFG888iK09+o2T01ptov/gCwp5p+R27s03jSx249zS/b21bIF996yQA9d2f9UDd97+k0qHHhitNc4jr4uoa4J/PgTylea6Vu+kfKlrXeW20Do1rMU4ytdgPLzSXKazHTIHWi0Lx4z7590mxFfppfDpaLQrFxB23ZcMFiuLco3j0WhWOUKXEeQA7r4HU/8L1OD1dOaTzb5ClpSW6deuGRYsW4fr16/jw4QP++OMPWFhY4I8//kC1atVQv76WKR6IXnh6emL58uUVZj+EaFKrijUOTWojG+ez/tILfPn3NcRrmfuWEELna32xFki6pgrFYizsyz5eKq19alqmpTa/6YRRN1fi64iVaOnqgXm7fOGKRXBnfsdLnyAE3V6JHiedcSn2Iv67/TcG/10bEYtcwPB9VLa18OxM1jLDAGl5aQCAZYP8Wa9xzV7jvnAJUnNTEREXgcHHB6Pdvk9ZZRyiO7KWBz5UzT4OFBIgFOg5TZ5ZPM3GE1/fXInxV+Xb63ekH1LtqgF1e8vfJMxVnd5qrh2wzA+Y7ySZb/jvDsDNjewyvdXM/z09lr0cvrTQOhfVmovPVda9+igP1MKeSXphHbj9FtNM9mjf2FPVxFLPGS09UOLvA0en6lZRPSrsRz+9u2/xNjwnFeizrnjvVZxS7/5e7WU1TZ/GMJLeAS8uAhcWsl/TY0u2Mm2J9WDjLvnX0hEYsgcYfgjotYxdJreQXh4F4tM0tybvv/UGDAOce1y8G1L7b2vugWFrTsNHlRX7dpWlpSUc/s/efYc1db1xAP9mkLD3RpaggMhQcCBuce/Wval1YrXSWrW2aqtWa617tf5qtWpdddS698aF4hYn4kJcgKDM3N8fIeOSm5BAIKDv53nymNx77s3JleTec88572trC1tbW9jY2EAoFOLmzZv6rFulMmjQIPB4PPB4PBgZGcHJyQktW7bEypUrIdFyyIc2zp8/j6FDhxZfkJAPgIWxEWZ+EoRlfWvDwliI+Idv0HLuMRxJVL2bSgjhRufrkpGl/mrm54i+9TxxeoJizrByY3vL7S24cn4pAOBxkeHUNwRGsEOM/HX/hIV46qWa5sdYPBSp/53F2IsLkZdqgsHO3OmFYg6EsV4vTuqCyPWR6LS9E7rWqoLuYdJcznzjJzDzXgIT41touKEhlh79BVO29cU322siH9IAXml8PizRRuMxMMt8gpHLiu/VBgATB+tiyzTc0BBMQCfFgumOwNwAIG6J9LU2eaUB7sBnxlaAfXXV5WWMa8RVKHMdXwi3a94wVfU7OFy4k72gepH/n3vqA+qVleJGK3rbm+m8TxerwvnKob2B8UnAgB3cBbluqgDA8V+0e6PMVOnNm6lWwPVt7L+vt88Uz+MWs7d7mqDd/ktAeRh5s5wi89bdlL7f1VsDPs0A/47sMu9KP62utFMDlOeCF6VuWguj7Xf7A6R1Y1sikeDcuXOYPXs22rZtC2trazRo0ABLly6Fs7MzlixZgvv3NQx7+Ai0adMGz549Q1JSEvbs2YNmzZphzJgx6NChA/Lz1d/11oWDgwPNGycfnbZBLtgxqiGC3KyQkZ2P6D/PY8auG1oF8CDkY0Pn69L77/JTbC+cj2hUGEBIuc0hG0Y++vBoHPl9DibfvIgmHm7oZu+KIG/paJy4PCsceVJM76aSx7ce4qcNjdHs0a9YsJI7fdBbhwH46exPMPWeB/Dy4PrSCD+tbwrLS8/wMOMhhAIeeMI3MPNehJF7q2Pc3mGIvGGGprt98Ny5LjKchqC2pzuCvT3Qzt272Dp1ndFa66lhfDWBloo2oNfc+afIh3oG7PtW+vziX8W/UVB3ZOdnIyUrRXXd0KMa37ssvM9VnfO6hv8De4FyijPrwqjiFuxe7Ob8i6jPL9IA76HF8QCkQcIMROMcZDVYvbsmNoAHR4Ttzw8DtQcAjb4Cmoxnr0vQMlCbclC+zYOkDW+Z92nqt3OqoXiepzSazqeFalkdKY/Kf1B0JINxkVRuAGDuAPRR6r3PL37+s8YAfQCE6r6rWkh9m62xR1zd38PHnIpb66NtbW2NiIgILFiwAHZ2dpg3bx5u376N5ORkrF69GoMGDYKnp2fxO/qAicViODs7w83NDbVr18a3336Lf//9F3v27MGqVasASOfSff7553BwcIClpSWaN2+Oy5cvs/bz33//oU6dOjA2Noa9vT26du0qX1d0eDePx8Nvv/2GDh06wNTUFAEBAYiLi8Pdu3fRtGlTmJmZoUGDBrh3TzH06d69e+jcuTOcnJxgbm6OOnXq4ODBg2o/F8MwmDp1Kjw8PCAWi+Hq6orRo0fr56ARoiVvezP8MyIC/epLL2RXnHiAbstPI+mlhjlshHyEyup8vWTJEnh5ecHY2Bj16tXDuXPn1JbdunUrwsPDYW1tDTMzM4SGhmLNGvVzVYcPHw4ej1chpi89ev0OX6xXpNg6+0AalMrUSNFjY2EsHSoZuCILAZkz0DJ5LrofDkX/hIX4cnc95DHAxVerdHpfn1fhuOvbDeDxcaNGtNpy+8/+jarpKbDw/x6d7sxBsldXRDz/GR22tsf6c49g7jsLC1d4QmIVg9d2gQhK/wnvjerKtx+/1RHD9zpj2K4wte8BAFWrm8DKk7uHXRfDzyzElO195Q3fXy6o6ZV8mwL8p8W1xaf/Q511ddDyn5Y4/vg4e53IDAhWyjP9g7U0SryeuNuaqCxTHkYOAO34Z1Q3rNEF+LRwKLxsbvU29ihFCxSZNzzuHiDUIhNHi8n6icZdQk/T3xdfqFBNN+n86mb+DuwVXBG+q4RJ73C1mAw0+xYwU/pbVBcwj2GA/MKgdZmp3Ddv8nOkPd3LNKTQ2jQQSHskfb48UrH8HkeOeh2p9CrX6q94LlIzSqB6K8C1MK3Y6+JvlJ6+94pzuSxgYmn6tX/apXlUlLph8g9ffbzXalo3tn/55RfcvHkTT548wdq1azF48GD4+KjOJyJszZs3R0hICLZu3QoA6N69O1JTU7Fnzx7Ex8ejdu3aaNGiBV6/fg0A2LVrF7p27Yp27drh0qVLOHToEOrWravpLTBt2jQMGDAACQkJ8Pf3R58+fTBs2DBMnDgRFy5cAMMwGDVqlLx8ZmYm2rVrh0OHDuHSpUto06YNOnbsiORk7hPSli1bMG/ePPz222+4c+cOtm/fjqCgYlIzEFIGxEIBpncJwm/9w2BpLMSVx+nouPgk9l7j6OEg5CNVFufrjRs3IjY2FlOmTMHFixcREhKC1q1bIzWVe0qHra0tJk2ahLi4OFy5cgXR0dGIjo7Gvn2q81S3bduGM2fOwNXVtVR11JfUt+y4EC8KI+lamRphcY8aSDLug8i1Psh6cQP5VopAXTzzzwEA2TZ9MMdY98/y2i5Qq3LdbyxE2/sL0f8Iu1ds7E4/8Ixe4Zd1zXCjZixrXYGRoscsze07wGoiMu17a3wf7xCO9GYl9NypLsbuls7pthZbcxf6tZi85IWUI7fHHIpRLdBhPvt1camkdPBp7Soqy2SpsWSWijiGPvMFip7s1/el0euLWCBayl5gpuXx926qXbkyYizUPjDbgl61MK61H75qqd3/NcuXGoLHpd4CXiRKb65MdwCWRQJzqnGXPTa7+Pd69xKYXxin4dVdnauqiUrPb8eFgGckENxL802T9Edav8ebd7mcy/MLu5c1pQr0dTTXuO+91zVfb6mLp/eOYwRIqUkkwOMLqgHuKhitG9vDhg1D9erlPxfmQ+Dv74+kpCScPHkS586dw+bNmxEeHo5q1aphzpw5sLa2xj//SIdVzZgxA7169cIPP/yAgIAAhISEYOLEiRr3Hx0djR49eqB69eoYP348kpKS0LdvX7Ru3RoBAQEYM2YMjh49Ki8fEhKCYcOGoWbNmqhWrRqmTZsGHx8f7NjBPWcmOTkZzs7OiIqKgoeHB+rWrYshQ4bo7fgQoqvWgc7YN7Yxwj1t8DY7H8PXxmPazhvFprkh5GNQFufruXPnYsiQIYiOjkaNGjWwfPlymJqaYuXKlZzlmzZtiq5duyIgIAA+Pj4YM2YMgoODcfLkSVa5J0+e4IsvvsC6detgZFQxAusUHTatfGHaYUeo/Pk36z9Ru49qZ/vorT7dv+IOZmdmPIH1+r19DMx9ZuOeT1fO8rqq3lT3GzQ9xqpP6fTeti++3VoTaTlpKE3W3ff57J7UHfd2sOeDGqn2PutFXjYsCrTPcSzn1Uj6r/IQ4fk6BCj8pphIzgbOAKNN8DwZHwdzxDTzhY0ZR0+2X3vNG2v6f11aT5pyT6ZodgBlJ+Zofh9leUV67S1Vb7boqujvC8PjAdG7gU9+07yhrEF5axd7vn/6E5XpEurmZBeNFG6EfLS2SoYrFNMQ5vcMBQA4W3LnATcqJjq9PlLFae1HG+B/LYCfKnZqS/3H8y8jnTp1goeHB4yNjeHi4oL+/fvj6VN2frcrV66gUaNGMDY2hru7O2bP1uLuVTlgGAY8Hg+XL19GZmYm7OzsYG5uLn88ePBAPsw7ISEBLVroNickODhY/tzJyQkAWD3PTk5OyM7ORkaGNGJpZmYmvv76awQEBMDa2hrm5ua4efOm2p7t7t274/3796hatSqGDBmCbdu26W0OOiEl5WJlgr+H1MeQRtJ5h3+cfIA+K87i8ZuKfYeTkMomNzcX8fHxiIqKki/j8/mIiopCXFxcsdszDINDhw4hMTERjRs3li+XSCTo378/xo0bh8BA7Xp1c3JykJGRwXroW9FLxV+fDZIOO53Knk8ZeGUC1HnuHKl2HQC4OWs/gdHe2w6dzb/Vqmz0kZLdZLEQqv5uqp2DrYGDn+aL3tcuQ1D3jiM6+5T8ZtDVF1dZryednIRTT08pFpRV43NhLQyOi4IbZGm9GIiQB4kEuPo4Hd2Xn0YPwRF2XU3qAIMKg56Z2ilWKAfn4vLJCsVzU1vgy6vqy/IMeymvt6Pdc63iubkzdxlT/Y220MqByezXjb8q9S6LBgrTei6zLO3brZ3A0vrS36P1vaWR+/d/V2Sf3DuV5X+XpQy8YzwAv+VMwGnj0egjYA+RT8nIxtM01SkCQoHm/3H1c7b1PGk7uwQ3vgyk0jS2mzVrhk2bNiExMRFbtmzBvXv30K1bN/n6jIwMtGrVCp6enoiPj8cvv/yCqVOn4vfffzdgraVu3rwJb29vZGZmwsXFBQkJCaxHYmIixo0bBwAwMdH9jqxyb4DsjhnXMllU9K+//hrbtm3DTz/9hBMnTiAhIQFBQUHIzeUeduLu7o7ExEQsXboUJiYmGDlyJBo3boy8vNLcmyak9ERCPia1r4FlfWvDVCTAuaTXaDv/BA7e0H9+VUI+Vi9fvkRBQYH8Zq6Mk5MTUlLUDylMT0+Hubk5RCIR2rdvj0WLFqFly5by9T///DOEQqFOMUBmzpwJKysr+cO9MHe1Pin3PBkjB475TznLMUIPrfZXxZOH4YubYuSyZohZ3hwxy5ujy9QW6DZau5RJfKEAVcy1ix4vNhlVfCEO3ac3h4VpWom2LaplFzuN68Ofj8MjSTbQMFZjOU7RezDs4DCVxTvuqolmrU9vpX8Hp4yl6bf+MJqDc+KRMC54i3H/XMb5pDf4SshOQzVFoJSqy1TzcWEJ7sF+be0hTW029BjQ/Dug4VjFOudgGFKEjw6fSxM+H6jVT/q86XjuMoP36+e9tHWuSBui1oBS79LK1EgeYBHgjmbPqUYX1WWJu6X/FommXqCmBa+cHMmdx75O+snoD7QPcmGN5Pluu+oIAVMRd7RxGXUjHfQeq1DPw/vLUqVpbI8dOxb169eHp6cnGjRogAkTJuDMmTPyBt+6deuQm5uLlStXIjAwEL169cLo0aMxd67+8yzq4vDhw7h69So+/fRT1K5dGykpKRAKhfD19WU97O2ld+uCg4Nx6FDpAzBocurUKQwaNAhdu3ZFUFAQnJ2dkZSUpHEbExMTdOzYEQsXLsTRo0cRFxeHq1c13GklpBy1DXLB3jGNUcvDGm9z8vH5XxcwadtVZOeVwRwhQohWLCwskJCQgPPnz2PGjBmIjY2VT2mKj4/HggULsGrVKq2jXQPAxIkTkZ6eLn88eqT9PEZtKV8rWhYNWlUCnSc2g0DIV/mcTjXUz+u2s+OhZmMX9P2xvnSByKLU9dDExNoUAcUHJ9dK9TYhGPBTBD75nDsIn0QgHUK8zNZW/U4cAoBxRQJBjb0BeDbgLL4naY/6fZloeJ8SuiUeiBaCS7DmZaH97vrYm9YRDkiDEy9NXuZwQShq+SsNxecKAqYLYyvANRRoPE4RBAxQP0lWj25NY6cga+hrj+s/tMbZb1vA1VqPw/Y7LgJGXwLCP+Neb1dGsaKsPYDOSzSXqTeCO+WcjsRCAY59o0inp3WPr7Gl1u+RXyQ1VyjvLqrwXrDe64R4bNHNsLB3LdiYKjrrXmWpdsK5Ffn/DnW3Zr1WFyBN78HIjXRPOWcolaaxrez169dYt24dGjRoIO/BjYuLQ+PGjSESKX7MWrdujcTERLx586Zc6pWTk4OUlBQ8efIEFy9exE8//YTOnTujQ4cOGDBgAKKiohAREYEuXbpg//79SEpKwunTpzFp0iRcuHABADBlyhSsX78eU6ZMwc2bN3H16lX8/PPPeq1ntWrVsHXrViQkJODy5cvo06ePxlzgq1atwh9//IFr167h/v37WLt2LUxMTD766POkYvGwM8XGoRGIjvQCAKw7m4yev8XRsHJCSsne3h4CgQDPn7N7Qp4/fw5nZzXDPSEdau7r64vQ0FB89dVX6NatG2bOnAkAOHHiBFJTU+Hh4QGhUAihUIiHDx/iq6++gpeXl9p9isViWFpash765nR+Nv40+hkCFMCP/wj5+XysvP8bTt6UztF+YCTE5xYcgbIkqr81wgLNo2yCHG9xLu81oxma9AmAtWNhqs/+21A1iztrSLTDQI3voSzGuSuGOvZkLZPl0bZ0UwwBN3vH3ZuvLQtbE7iE+6D7GPWBsJZe/V39BTiPD5gV6TG1cpM/DbvvhU/PN1RfgYH/KZ6/f118hYuR9ZJ9U8eYpzqyb7YRe85tTN5omIq0CB5m48V+HdS9+G3MSx8lXhfGRgJW4Kw6XrYwEwvhpGZeb4nx+YBtVc1loqbq9z3H3pAO06/Vjx3JvqjU63p7S3tzRVslL1/LZmhxafGUGtLKPdt1eLewXTwZJ8VjUFAYmK/KK+7pPwI+D2Klnu1XmTkqw96LDiOv7qT4uxAJ+WqHkes9z3bR7zVH0MGKolI1tsePHw8zMzPY2dkhOTkZ//77r3xdSkoK5xA32Tp19Dn/a+/evXBxcYGXlxfatGmDI0eOYOHChfj3338hEAjA4/Gwe/duNG7cGNHR0ahevTp69eqFhw8fyuvatGlTbN68GTt27EBoaCiaN2+uMb1KScydOxc2NjZo0KABOnbsiNatW6N27dpqy1tbW2PFihWIjIxEcHAwDh48iP/++w92dnoaOkSInoiEfEzpGIi/PqsLKxMjXH6cjnYLTmDnldJdOBLyMROJRAgLC2ONupJIJDh06BAiIjSkzylCIpEgJ0ca2bt///64cuUKa0qVq6srxo0bxxmxvDw5XV6CZoLL6MiPgx0ysC5pPt6bOuKyTX/k5POx+9Fm1LqziLVNjHNXDHXtq7KvT79rprJMWaMfRqBnl2SMcPoU0Q4D0MZqFkYu5djGvQ58bOI592EqKP66pQrvIkY6SQOnGfFzMWxhE/T8Lhwxy5vLe9yrNFHEehGamha7T204BrjBKk11uOe4wkBz2zv+BPTZBHgWaTiHDZL+a+cr/bfIvOQ6z8fCIb87+p9sLV/26K1Sg9i7MTDsROkqn3QKmGqF90d+xQ+Li5+S2EzATuP6HsYQaTPv3VXp+qvuMODT/xW/Tb3hgH8H1cjrZehlpiK/c/JrA97Erj8S+OR/wNd3tCs/KUV6rNRRuomDLsvVl9NjCjmh0miEkB/1NDQ+7SGQ+QLYOhSvn0u/CydEY7BZ/KO8iKQwZ/iby7vU7kYsVNTt8Zv3GLaG/btTNEe38rBzaxMjCNTM6b72VL/xNSQbivzeZqfpdf/6VPrxEKUwYcKEYnttb968CX9/6bymcePGYfDgwXj48CF++OEHDBgwADt37tRpCFpRM2fOxA8//FDi7WVWrVolz6WtiYWFBRYuXIiFCzlSQxT65JNP8Mkn3FFOiw73LnqnyMvLS2VZ06ZNWcu8vLxw+PBhVpmYGHb6DOX36dKlC7p06aK2voRUNI2rO2DX6IaIWXcRlx+nY9Tfl3D4Viqmda4JM7FBf/YIqZRiY2MxcOBAhIeHo27dupg/fz6ysrIQHS3NBz1gwAC4ubnJe65nzpyJ8PBw+Pj4ICcnB7t378aaNWuwbNkyAICdnZ3KDVsjIyM4OzvDz68EaYH05a3i5vx80VK8ZUzwl/nf8mV/P5sCFAmanp9bJNezEntP9T3/gHR+uL2rKcCTwFTwFj4mZ9nj2JVUtz2D56+2g8+ToLr5ccS96ocaptIL9QZGK3A6j50lJMpiLjxMLsOEX+Qid+ITCEUC2Fdhjwows1P0UNVqq6cx5QA+WdgdayccR57SUPi3DtEAMxqTry1H14FXpamPZio1emSN7S/igWeXWfOS+QWK42MmaAdAenOm3dZ2uDpQaXqbhVKwtuc3AKcaulV8VTsAgMmxH5Ge+yVQglHg77WZytT6J6Dx19L5t5FfardjI2Og1zrdK1QKDuZipL2T9uhvufgYv/YIKdf3lxOKgeDC3v/vXkhTfanz2T5pFPNe64D93wOni1x7y+aIy2gakh8+uGT15VCigN1jrgALNMzPX6D4/xiOjRjOMehAkvcOgC1uF7io7W41KtKY3l8kBo6JkWK9m7UJRreohibVHfB623h0z90GZqk7RJiO3CI/lL8fv4f+9fU3IpZftGf70TnAv53e9q9PBu3Z/uqrr3Dz5k2Nj6pVFcNJ7O3tUb16dbRs2RIbNmzA7t27cebMGQCAs7Mz5xA32Tp1ymP+FyGk/FWxMcU/IxpgdHNf8HnA1otP0GrecZy486L4jQkhLD179sScOXMwefJkhIaGIiEhAXv37pWPykpOTsazZ4oIy1lZWRg5ciQCAwMRGRmJLVu2YO3atfj8888N9RGKtz0Gj34Nwk2JBfLz+ViSsg1/Pf+bVeSdkerFrlBUGGG97z9o37sEKWgclAKljeLuvQYABHVHI7vViLRdAwfRQ3RymQFfq/MAgFp2uyFCFqu4n9kJ1YY2AIjV59Ht9V04mvfwQI2Oobp8Ao1MHW0wdGVnleWj97dVXyfl+c0uIawI4+Y57FZv39NNwOMKCKU8x3W9huHBWvhNNF+n8lsLpD31fs5F5tr3365a2NIFcAqUzsXWlGfZwJR7MIXlmd5JE6FINTVazDlg+Cnpsfaor1ju15ZdbsgR7nnaX1zkHsofoKF3XEcl6iS08QRaTCnV+9r90x04vQj5UDO94a3mHNqALHo6gzNuC3Aquyvscx6jxZlodM/dBgDgpT/CdYsv8LPwdzggTb7duxyOG09xS6RR1dMf6/xZMjyKZG5KKuVIljJk0C4eBwcHODhouCOlgWyOsWxIWkREBCZNmoS8vDz5PO4DBw7Az88PNjY2avcjFoshFotLVAdCSMVmJOAjtpUfGvja4+vNl/H4zXsMWHkOX7fyw4gmPjrlByXkYzdq1CiMGsUd7VoW+Exm+vTpmD59uk77Ly5QZ5lLWIsdKdILxsPFFFVWLzgXGClNQ+NVDQi8/hpvXuSh03eNi9mykEswMGiXtCdWUwCogE7A1c1qVw9x7oclhfW3ldznLuStuU52VSxhV0X/8+ABIKqrEw5uU3SK5Fq2BbAX44+Px8+NtY9N4/LGmvXagvcJmt9IxaGaN3Hj1Q3UsCvswRYqXdulJQNZr1TngatTULpsK0vypTcXxMIijRqfIlMEWv6IykL5fKn3NE6lYWoLfH4IuH9UOiLCTE16MM8GQI3OwI3CKahOanKd2/lIh/IX/a6Jy+Z7oZOIUdKbc54NgJ917yU2enkD2P8dfla6pzModxxWiX6RvlgWCXxzT2W7AgkDAZ+HZ+nvceDGc1gjE86vzkpXLlKdhmqUl4GewqPoKTwKr2zpDUuVYGvZGcC+wpSG8wKBcffU/99xsEwuEkzaORj5BRL0+v0MarpZYWQzHzha6DmmQAlVijnbZ8+exeLFi5GQkICHDx/i8OHD6N27N3x8fOTzxfr06QORSITBgwfj+vXr2LhxIxYsWIDY2BKkliCEfFDqV7XD/rGN0buuBxgG+GVfIj7/6wLS31P6OkKIVLakZDffwka0Zr1uOjISXac0hUCXPNVeDYuPtOynZohkr/VAR+nw2M8d+6KD1Q/o6TKOu6ymAFBlzDcqQGVZs2ue2P1gN84+OytNbeVeD2jwhdp9nE85j9a3+6ksr/Fcmte8504Nn++XYgJvKct4UmyR0wU1MCuvF+e63MK+rJx8jt48WQ5tl1Agcozq+goqpIoiz3ygq5WGkgZQJVw6FL+4xtqnK4GqzaSN1uKiww8rMj1Eh4ZgmRGKpEOlTaz1tsujklqKF+9ecpaRxb35apM0LkEHwRmt9x/KuwsHcASqnlUkbeO+SVrvk9OjM/jzVBIuPHyDVaeTkJqRU/w25aRSNLZNTU2xdetWtGjRAn5+fhg8eDCCg4Nx7Ngxea+0lZUV9u/fjwcPHiAsLAxfffUVJk+ejKFDhxq49oSQisBUJMTMT4Iwo2tNiIV8HL6Vio6LTuJWin6DdhBCKh+JRII/UreWaNvSxI3RiUAIDFDKJy22AqamSy++wwYCU9Mh5r+Dp0kC+Dw1GUaCuRuH5UEg4GPowiasZX5vpR0in+//HBKxhTSPciv1IyI+2/cZChjV6OJ5pkEcpUvh3Su1q37O64V0l0j0y/sWyws6YVN+E5UyjxnpqE3lYFNywT2k/2/DjumtuuXhy6jq8uexraprKFmBCYTAgO1A6xnFl3UJYQevq2hGnC6zXU9o6896ffy2tBF++p70ezHd6E+t97VdPBnnjdlxoVip62SubNC+gm85sjzEr8KRxFT5y/L6WdZGpWhsBwUF4fDhw3j16hWys7Px4MEDLFu2DG5ubqxywcHBOHHiBLKzs/H48WOMHz/eQDUmhFRUfet5YvPwCLhZmyD59Tt8svQ0dl15VvyGhJAP1r5pf+i8TdP2dvK0WeWmahNg8htg4E5g7DWNRbtYb2EvMLbWS57g0jDSkArrzhvN0aWnxU0DALw0VR3mqmzL7S3qVz6+wH6dk8lKmQRAmkIobqnaXSwr6IS7bdZCUngJ/RyqUxWZwnWCcsiBXV7MlYKLmok+kkCjfTYBoX2Bwdxp9wzKtIwyAk21QjcB+0aQbI5+BP86/hN9W/r3eHiyZNu9vg/MCwJ+5b7Zk68Uu0Fdvm9D+HB+BQghREvBVayxa3RDNPCxw7vcAsT8fRFTd1zH+1wtIscSQj44Tx/oFmratN51BHYMKb9ebWV8PuDdiB0AjINEWGS+4oSHZVgp7cUsb856bfNWOoG023/d1G6TnZ+NTbc3AQAss7kbGUOOdgIATI2birrr6qLJxiYI8vZAslCpYfi/FsCbwuOQfFYaAX1NV/aO4v8Erv3D+R6B2dKbMsoX8gKoGUXwgakwQdHKk7kD0GUp4F7H0DVRxS/9DY88Rnrz627b9azl9gfHsl5vvPAIHRadwHrRDATxk0r0XkbIx/0XmYWvSvi3tLAWkK4+BVv3Rz/Jn1NjmxBCDMzaVITVn9XF0MbSeXyrTieh3cITuKHnXJCEkIov29i9+EKFTtb4DdHR6ucVG1TsTaDuUGDcPQj4PARkr0R8oxXSYcsVVM9rc+TPLz6/iEupl3Du2TlWmYcZihsFAnFTzv0IxIroxO/z3+N1tnS4eXt3V3bBBcGApABY2Ur6+v4Rxbo/2wG7v+bc/1e5w5EFE5XlBwrCWK+/y4uWP6/pVgGCaukJn8+DrZkIfB7gbW9m6OpUer3qaP+bw8nUTho0sQY70n9M7mh2OQ1B+NYWRAEAbO0ci327a0+KuTaq1loa5EyNlUaz8W+CdO43it4ILIV+uRPlz7sLj6MB/xoEKIAuITPKWgWqCiGElC8jAR/ftgvA6s/qwtnSGA9eZqHL0lPYeD5ZJV89IeTjcE2SAACwFSmGNTs1T8Jhr/kwGXgf60dvNFDNtGDpCrT7BTCzh5DPw3sYI9WxkaFrVazuZ6RzYwfuHYgBewZg8P7BOPFYkcrntyu/cW43ZHa9kr3hgcns11OtpI+Hp1SKdsn5EVWz12KLRBHJXcIAo5r5AgAuMdXQJmcWgrNXwCv7b6wtaAkAOPRVE7hYqTbOK7PdoxvhQGwTOFhQFp/SGtxQmsu+xCMGeDyg5xqgx19A91XyxV0Ep7A0XzrK4w+fRUDEF7hhVp9zF7sLpN8fWzPulHM2yIAVMnFENBZjhRyjPUbEKZ5/8pvGIHKNBNeQV1A4CiSzcM61hSvQsEgga4Zhz8mOXy39bt4tEn0cwEWJL05K2FHl/xb9hHvG/Q0z6kgNamwTQj56Tao7YM+YRmjm54DcfAnGb7mKb/65gne5+YauGiGknO2x9cNux3foOX8Ian8hgkXUHXzaPRpbJuzAZxEVOE94EYLCi/h8rhzUBtZjdDXWaztmoCyBr9zIQyORnJGMRZcW4cDDAyr74BfkQmTJ7mFtfk3LwF1xi7WuawLjK5+frcDAVKyYf36L8UAGzGBhrBja6+OgPp95ZeVsZfxBfi5DMCrselXOX15i1dvInz5iHDA7vxe8sv/GI6swgM/HWbcB8vVn2u1F05xf0S93Is4z/nCyFAMC1Wk0Iby7uGQ8HJeNh8Kb/xxjhBwBJJ1qSBvLjb4CTApjFzSbBIi4/0ZEOWnApoHA5oHSBW+fSm8Qyjy/DuydIJ2TfWu3dNl/hT31az9R2d8nuT9C3ZB0GkZOCCEVjI2ZCH8MrINxrf3A5wGb4x+jw6KTuPmMhpUT8qHjW/wPvliI6rY/AADSzUzB5/MREdgQA7oNq1C9JNqSNbYP3eSI3GtgDjXc4WbMTjMUkqw6rLb9tvb4/crviu3STeXPq9tL0wk1jVSUr/62SNRjmQmPSlFbblyDnzijjxPCQdYYLNDHzTClYdkXJYobWbL3eG3hJ182eOsTJDEuOCmRRvA3FQmlubuL+Fc8WWUZp6gpQAulsk2+Ab7lTp335aXWwI3t7IW+UYrnVzcDZ5dLn2/ord37q1GRfrHpV4Fg2bJlCA4OhqWlJSwtLREREYE9e/YYulqElDs+n4eYZr5YO7genC2Ncf9FFjovPoWtFx8bumqEkDL0s6APZtu0w3DJVwAUDdXK7E1WHgDApIJGju4451PW68ZJQ4rd5tMbM+XPLeylDe/A/uyAa0V7yAHgnR6jsFd3MlebZ3r1Z3XhYmWM6V1qcq4nREYWqF6ijylrPB4Q2BXPGFvslETIFz9/my19IrZAbO5wLDAbzYo7IODzsKh3rRLlyWJC+2ou4K7lFA9bb8XzByfUl9Pg+7xBKsusjPUwYkBPqLFNUKVKFcyaNQvx8fG4cOECmjdvjs6dO+P69euGrhohBtHA1x67RjeUDisvkCB202VM/vcaDSsn5IPFwz3GDQUQFL6q/NoHuwAAJBVwGDkACITsi+E8sTWc3qgfotz1AnvueY3eDTnLDT/aCYIiP9WLExYD37/kLK9JXvsFKsv2j20CYyMBrEzY81xFAj4CXa0QN7EF+tX31Pm9yMdFdkNPL41tAOi+ChE57OkRwW7Sm0J1vGyxVdIYe0WtWOvv/dQONQvLIES3nmRep0WaC/Rcp9P+AABPLhRfptC0PEVjf2uBalwKgeS97u9fRqixTdCxY0e0a9cO1apVQ/Xq1TFjxgyYm5vjzJkzhq4aIQZjZy7G/wbWkQfB+SvuITosPIl78tQVhJAPVUWa71da8t6tCqhoGrCut2bA/aUdqj11YS03fS+AUx47NZipozX3Tk2iMOT8AgyPkz58Uhyw5sYaQMAdBEott3DkBPdTu7poYCsRDSEnOhAU/sbkFWhubJ+++xJJL7NK9B6yaQ2yhr3GaXFdl+u2c34xPcfmDsXvo+5QzeuvbOZcnF6lGdYVKIafZ8EE3wm+ZBdKZmc0MCT6ZSAsBQUF2LBhA7KyshAREVH8BoR8wAR8Hr5u7YdV0XXgYmWM+y+z0HHRSWw6r//5f4SQiqOGa+VP2bTn2jMAwNHEFwauiW7a35mMFg8nwC5DEfzMO9VUpRxfqWd88IwwlfUyLR98p3hhpEPKqn5bUKChIeRkpb/0ReTjozxVJb+AO1f71cfp6PO/s+j1e/GdX5o6ArS+eTjpOeAUpLHIkvxO8Mleo93+pqYDU9ORI1DzvStuKPpW7oCUIXeHIBvsiPhrs+pieO6X8td8AQ0j/ygwDIN3ufkGeeiatujq1aswNzeHWCzG8OHDsW3bNtSoUaOMjgwhlUtTP0f8OyoS9bxt8S63AN9suYIvN1xCVg4NKyfkQ/RNa9WAQZVNsXlxK4jhixpzLu9+/ScAQO8zndDo6XTWusZF+gKM7bjnUKtoUSToU+SX3OWmpgMm1ijQcC3VtLoD6nnbyl9n0vmA6MBMrIgjkJ3P3di+/DgNAJCSUfzolJdvc1SW8Qsb9FwhKHqEV1FdaGQMDNiu8X1OSWrKp9to68/w7RidO0p1hZqo5ZqkM6o33mT2SurKn5tbF587vLxUzKgZH4j3eQWoMXmfQd77xo+tpREGteTn54eEhASkp6fjn3/+wcCBA3Hs2DFqcBNSyNHCGH8PqY9lR+9i3sE72J7wFNefZuCPgXXgYaf+x58QUvHZmonwOitX/trN5sPKj1yRCYzUX6t8v60jXji3UFke2L+pzu/DMAx44dHA3vHSBb3+BvzbSxvgfIE0l28R+RLuRhAA8Hg8/N4/HCE/7gcAeNF5gOhAJFD0d+bkFcBcrPo90KXbjCuquYO5tPeXK+Bjc38n7h2Z2QOTXwPgYcy8P7HgbSwG5o7HatHPAIA8Rvemo52DC3ZIGiDCJg+9M/4E6g0D8nMBOx+d99UoRzWOgrJ0kROscp8Dkopz84t6tgkAQCQSwdfXF2FhYZg5cyZCQkKwYIHmP2hCPjYCPg+jmlfDhqH14Wghxp3UTHRachIHb1S81DqEEO3lFelZ+gCCkVcqkTXSOZe/cI7iXM7nq16+muW+0vgewX8FA0KxfGgr/NsX7qywl67pt4BlFWDsDfk26e/yNO5TIFD8oSS9eqexLCHK+HyevMGdw9GznV8gwffbr8lfawp0eDc1E+P+uaKyXGzEl79XUUVjDrArJwD4fNwRVodX9t84JgnBivx2OFIQgnhGy1z2rHpIv2Oz0qKA718AraYD7WYroqAP2lXsPrbw26B1zixkQPNUkPV+C4GY84BjgM71LCvUs12GTIwEuPFja4O9d2lIJBLk5KgOSSGESCN77hjVEMPWXMDlx+n4/K8LGBjhie861ICRgO5hElLZ9I/wxNKj9+SvK2Ne7aJ613XH+nPS+BLZeQUwLuV1QVnyHdYRp8Yc16psuw7cPcgD/+iGpSOOlLwSTcdLH0rmH7rDet0pxJX12piCopFSyC2cq/0ut0BlXcKjNNbrB6+y4OPAPey6+/LTeMNxY0g2MCPtXa7KOltzUbH1a1vTGTcKg6rNyFcfLLA4OXnSz+doIeYu4MWdWUDZXNEwPHlXfITxNyaegIPuNwTKEv1KlCEejwdTkdAgD10uFCZOnIjjx48jKSkJV69excSJE3H06FH07VtM4AJCPmLOVsbYOCwCnzeU5ohcHfcQ3ZbHIZl6NwipdGJbVqyLM334rr1iGlh+BU3/JZOdp9rYUMe7Q33O5TweDyOWNkVYddXIzV6pdiWq193niqBTqz+ri9ndglnrhXRzleiF6vczO4/d252rZl43AM6GNgDYFzZus3JUv1811eSKV9arrkexZbThZCkNJsg1nJ1Th/nAlLSSvVkFvE9KvxIEqampGDBgAPz8/NCiRQucP38e+/btQ8uWLQ1dNUIqNGMjAb7rUAO/9Q+DpbEQlx+lod3CE9h26bGhq0YI0cGH2GhSHmXDNZ+zIsnJl+B6/sNiy3VqqjlQFJ/PR/3YjmjXhR1NvuPN0QCAvALNw8KLYpQaQU2qO1To0QGk8rEvnFPNdTMsr0i8gNJ8h7m21abhKxuGXlpa5RSfqjSVxNJVMcQcABqO1fq9KmLaRhpGTvDHH38YugqEVGqtA50R6GqJLzck4MLDNxi78TLi7r3C5I6BnEFPCCGkrClfTGua71kRRP95DrftHbFPkgUBI0Hom2doImSPNug9uipsa3hptT/vNuGIaQMsGX4YAJAnSQQA3E67jUC7QK3rdfu5+nRKhJSWbN50fpEUc9suPcbYjZdZy47feYGabqq90Zq+27JfAK5GrjZNUo3zunUgawAX+zMUPhhIuQL4NJe+nvwaSH8M2HgCFw5r9V4Vr6lNPduEEKIXVWxMsWFofYxuUQ08HrDpwmO0X3gCVwpTdxBCSHlSvk7WlMKqIpA1agv4fOQKhLhl6aBSRtuGNifjegCAXjt7QcJIewwfZTxC0OogHEo+VPL9ElIKshtiRXueiza0AWD23kTOfbzMUh9fyd/FAoC0Q6AobTqATYwECHW3hquVMaxNjeDvbAFzsVDnaTfynu3iWtsd5gKfHwQERtLXfIG0oQ3tU+u912FKSnmhLhdCCNEToYCP2JbV0cDHDrEbE/Dw1Tt0Xx6HaV1qontYlQ8i6BIhH6qvW1XHnP23DV0NveHxeODxAIYpZvhmBZQhMoZ59ltkSiz0vu8FFxfAx9oHk05OAgB8eeRLXB14VeM2dmbFB5MiRFfCwmj2pYmpwPXVvjujLXg8nryRa8Yxwk6b6xEej4etIxrIJ1MI+DzkF0h0nnYjK16am3525iKkvy9+GkiAs2WxZcob9WwTQoie1a9qhz1fNkZzf0fk5EvwzT9X8MX6S1rfmSWElL8P8WaYQDZ8U31sJYNJf5eHa0+4U34BgOso7kBoJRVx2x8AsPLaSnlDW1sVPcAcqZwE8mHkJf+Cct1IEwr42gcjKwafL220y/ZXkvgWst/W0sw7v/9CNfAhF6Gg4v2OU2ObEELKgJWJEf43IBxft6oOIZ+HnVeeodPik7hZmEaDEFKxOJirSUtTicny61bEYeTNfj2KDotO4khiKuf6IX9dkD+v7phWoveo4fhS/jzk1Qidtr3/QjFfuyIOTdXGuWfnELQ6CPfT7+u0XdDqIAStDkJugWrKKKI/sq/l4zfFp7RSpzLcB5Ld9Cvpz5Dyd7E4NqYVbxQKNbYJIaSM8Pk8jGpeDRuH1YezpTHuv8hC5yWnsPLkgwofsIiQj03X2m7oEV4FC3qFGroqeiPr3KqIvzevs6QNuW0Xn6gtE72wMWKWN0fLHz8p0XuYW2t34R20Ogj/u/o/1rInaYoGkKZh5ONa+wEAtoyIKEENy9bg/YMBAJ23dwajZUvn0dtH8ucjD43Em+w3eJihPlL8/bT7eJv7tnQV/Ug9LfwbE2mRr71jkRzvMkW/233qcafralJdEQchwKV8h1qrm5uurVP3XnEu/+uzuirLGlWzL9F7lCVqbBNCSBkL87TFrtEN0czPAbn5Evy48waiV53Hs/SS380mhOiXkYCP2d1C0DnUzdBV0RuBHoZvlrU8DUNoS9shH/K59ilMF1xcwHqtKa+xsphmvkia1R5hnrY61a28jTo8Cuk56ofty5xPOS9/fvbZWTTe2BgdtnXAjVc35Mt33NuBWn/VwpTTU9D5385osL5BmdT5Q1fHS/o3o83309XKmHN50e9IdUdzznKrP6uLpFntkTSrPfaMaaRbRUtJFo28pCNs1H0XI3zsWK9Xf1a3QqZxrHg1IoSQD5CduRh/DKyDHzsHQiTk49jtF4j69Rg2XXhU/MaEEFICsmHkq04nldl77L76DIsO3dG65xQA3ucqhmXvuZaitlxph7+LLM3gxlf0nFd/6qSxfNDqIGxK3ASGYfAqUzGEugKOwtfZ8cfH0XBDw2LLPX/3nHP5vPh5AICcghxMOjkJ+Uw+tt7ZKl/fbms7/VT0I5LwKA0AcPbBK+y4/BReE3ZhxNp4zrJJr7jnLBeds10R4wvIerZfvJVGTmcYBlN3XEfbBSfw4GXxc7GP3OKealI0p7a+UpXpGzW2CSGknPD5PAyI8MKuLxqitoc1snILKHgaqTSWLFkCLy8vGBsbo169ejh37pzaslu3bkV4eDisra1hZmaG0NBQrFmzhlVm6tSp8Pf3h5mZGWxsbBAVFYWzZ8+W9cf4qLzNlv6urDqdhJT07DJ5j5HrLuLXA7dxPumN1ttsPJ+sVbn0d8VHHy5Og2GR8ufNH36Lfs7dMTJ0JPyeuGJ43AI0vxHKKj/tzDQE/xWMd7ns3+SDDw9i+pnpyJOUvk5lgWEY5EvYde50oTqGxy1A5E1Hrfez5voa8BgGojx2o83RVLqPk49Pcm6nPPycaEd23l9/7hFGr78EQP3NJ3U9tkUb21y5uA3NwlgRDf1dbj4O3HiOVaeTcPNZBjov5v57Unby7kuVZc6Wxio5tS2NjUpb1TJBjW1CCCln1ZwssHl4A4xr7QcBn4f/Lj/Fp0tPa3WHlxBD2LhxI2JjYzFlyhRcvHgRISEhaN26NVJTuXscbG1tMWnSJMTFxeHKlSuIjo5GdHQ09u3bJy9TvXp1LF68GFevXsXJkyfh5eWFVq1a4cWLF+X1sT54YqW5oJk5ZdtITMnQvjGf+Fy7Ob6ahphryzGkKuu1+baGqLbBGs2SxwMAqqdHc24nEgpYr8ceHYuNiRux8dbGUtdJ33IKchD8VzBqranFuhngmhcDAAhKmwSRlrHO3uZmYNiZhfjswkJYv1X8/ey4twMAcDuNnR6PXwF7Uj8kbtYmAABTIwHneuXDv6xvbdTzrnjTGRwtFMEnc/MleKQUEC4ju2QdDdO71FTJFR7gov9UgfpAjW1CCDEAAZ+HmGa+2DQsAo4WYiQ+f4t2C05g0/lHOg3HJKQ8zJ07F0OGDEF0dDRq1KiB5cuXw9TUFCtXruQs37RpU3Tt2hUBAQHw8fHBmDFjEBwcjJMnFb0Yffr0QVRUFKpWrYrAwEDMnTsXGRkZuHLlSnl9rA9eVQfF/M2y/lkp0CG/mLZzyMtqrnniG83DyaXvLQGg2hD4+fzPZVCjkssryEP42nD569pramN+/Hz8fuV3VrnP4hfgyz3NcPzxcc79yCKQByUresGHHe8P3ycMat1V/N/efi1tbJtkM7BP42HaP50xc62fvC6bEjchaHUQvjzyJTJzM3Hg4QG8z/9w4pOkZKXgSPKRcjtPyxqQ6qZUyOphayZC2yCXCpnCUDkNWYGE0UvARhszI5XPWhHnawPU2CYcZs2aBR6Phy+//NLQVSHkgxfmaYMdoxqiflVbvM8rwDdbrmDE2otIf18xhyqSj09ubi7i4+MRFRUlX8bn8xEVFYW4uLhit2cYBocOHUJiYiIaN26s9j1+//13WFlZISQkRO2+cnJykJGRwXoQ9coifdWmC4/Q/NejuJv6lpUf+Gma9j3b2l5rP3iZhfT3eWi74ATmHrjNWcZrwi54TdiFLA1TcXoMraJ13WRm3+4Ii4DvYBEwAQwq7g3QCScmqCz749ofWHRpkcrybOsuiDkUI3/98v1L5EnyWCm+gp8ovn/ptrURlbwQ9V4sgmeKAC/evcDB5IP4dksAhsZNRbeb8/HIvQUe+oyE2XsGsUdjMe3MNADAoeRDiFgfgdijsfjk35JFk6+IWv7TEqOPjOY8vmVBFqlcXdte1givoNOVAYDVKC5gGJ1iMaw8+YBzuYBfeZqwlaempFycP38ev/32G4KDgw1dFUI+Gs5Wxlg7uB7Gt/GHkYCHvddT0G3ZaSTRsHJSAbx8+RIFBQVwcmL3Bjo5OSElRX1wq/T0dJibm0MkEqF9+/ZYtGgRWrZkR4feuXMnzM3NYWxsjHnz5uHAgQOwt1efumXmzJmwsrKSP9zd3Uv34T5wOUpRfHckPNXLPr/55wruv8jCL/sSEf9QMU9brEX6IhltUw99/+81bL34GDefZWDhoTsq65VjXXCtl7EL8dH4PsPjFmDI0U/Vrn/nNpb1+kH6A9xLu6dxn4D0RtOShCX4ZMcnkDClHxLPZf/D/QDDYPL2/hi/qxd4Bdo1ZBqsb4Bmm5qh9praCFsbJl8enMRdvs2Nlmi+uTksMwV47TocOcbs4cr9Exbi6OOjnNs+znysVZ0qkxVXVyBodRA23NpQpu8jCwJWdG62jGxASdFgYRWNrHebYRRD47Xx484bnMsrajA0LtTYJnKZmZno27cvVqxYARsbG0NXh5CPilDAx4imPtgyogGcLMW4k5qJ9gtPYDNFKyeVlIWFBRISEnD+/HnMmDEDsbGxOHr0KKtMs2bNkJCQgNOnT6NNmzbo0aOH2nngADBx4kSkp6fLH48e0fdDW/qOUpySns3qLddlyLexkfrLT+VG+7vcAmTnaddIfauhZ5svEMDPXHODTyBurHWXe6ftndDl3y5Iy07TWK71ltZYfnk57ry5g5GHRmq1b138GPcjAGD4mYVIdQpHum0Epm7viSGH6qHr2eqc27i/NEHQ6iC1ubEfeLXnXG793gUA0PK6X4nr+zDjIfIK8nD1xVUUSPQz0qI8cQ2Hn3F2BvY+2FuiYeVL+tQGANTVMM/atjDHu7rvl0Tes12xG5/KaQjNlQKm6aK6k2JajIAa26QyiomJQfv27VlDBQkh5Su4ijW2x0SirpctsnILMO6fKxj/zxWKVk4Mxt7eHgKBAM+fs1MCPX/+HM7Ozmq34/P58PX1RWhoKL766it069YNM2fOZJUxMzODr68v6tevjz/++ANCoRB//PGH2n2KxWJYWlqyHkQ76nrGNLn3IlNtjtvLj9NxQSkCeUa29lNfNLVpWY0Ghr3fonM9lT9TXjF5saPmDEDnT7hzEMuMO/GdxvVFTTipOoRb2bOsZ/Lnp56c0mnf+ZJ81FlbB5sSN7GW5xXk4WnmU+QW5GLz7c1ofpU9uuOZayQEpn3gJIkBl/Z3ZnEu93gO9DghAnjcTYN021AAgM17L50+h0zQ6iB02NYBtdfWRp/dfRC6JrTYbfYn7Vc7x9wQ6q6ry7l83PFxOJh8UOf9ydqLmhrqJoWB0bLVTAORfQcqeFsbslHfBRJG6xz295SmwQCAsVKQOOrZJlIMA+RmGeah40l1w4YNuHjxosqFECGk/LlYmWD90PoYG1UdPB6w8cIjtFtwgjVkk5DyIhKJEBYWhkOHDsmXSSQSHDp0CBEREVrvRyKRICcnp9RlSMk8ev1Op/In77xEi1+P4Zt/Lqsts/jIXfnzJUeKH1YtoylAkou1sfy5lakRlh1V7HfZMfZ7KO8nTYs4F1Va1UWPwc4YNCUYJrmvVda/FTlieNwC2GWoNsoF+TzYpZuxlqlrQO99sJdzLnXQ6iCth5PXWlML2QXZmHZmmjx4mYSRoPba2mi9pbV86Hf1zK+12p8mPIZBu/sLYCv8RXNBhoEZuBucAGCdKYTFOwYTNwC9jomk16IlDCT26v0rfHXsK8QcilFJaVYRxR6N1XkbfmGDUVPj8/LjNADAwZvcI35i/r4IAHhWRqn99EV5OPzaMw+LLX/izgu0+PUYa9mVx+mK/VWixnbJ+vGJdvLeAT+5Gua9v30KiMyKLwfg0aNHGDNmDA4cOABjY+PiNyCElDkBn4cxUdVQr6otYjcmIPn1O/T4LQ7ftw/AgAivSnWiIZVfbGwsBg4ciPDwcNStWxfz589HVlYWoqOlaZMGDBgANzc3+Q3bmTNnIjw8HD4+PsjJycHu3buxZs0aLFu2DACQlZWFGTNmoFOnTnBxccHLly+xZMkSPHnyBN27dzfY5/zQ+Dtb4FaKdLhwUz/tcy0DwP9O3gcAbE94ivm9aum1XuqGxM7vGYqabpaImivtzewS6sZq0P+yLxExzXw599M6UP0oC2UOdWoAAD5b2Q0JK/bjVLzqpXD36zOwPGIMa9nIE2OQZ+yNA1Wn4Z6TIu/vzvs70aFqB/lrhmEw7vg4te8/cM9ArGm3Ru16TX67/Bvrdb1EB622Cw0RIOGyome0fqIdqj13gXN2K5z0WIn2CYF47Fn8fjpcsEeOWP00w14nTOCY0xupnuGwBjBq1wxUfV0NaxtcwEVf1Zto119eR6B9IOe+lPN2n085j3ou9cBX0+tentqcF6L+HRvM6ZKKTFP2eZhhGNx+cxvfHP8GTd2bYmzYWDV7kZI1QDWlvzpzX/WmkLJHrytHpHflYeQmSj3UdoXD5ItacYI7MJqMQ2E6MZGAj9wCCca38ddTTfWPGtsE8fHxSE1NRe3ateXLCgoKcPz4cSxevBg5OTkQCLjz+xFCylb9qnbYO7Yxvt16FTuvPMPU/27g2O0XmNM9BHbm4uJ3QIge9OzZEy9evMDkyZORkpKC0NBQ7N27Vx40LTk5GXyl6LBZWVkYOXIkHj9+DBMTE/j7+2Pt2rXo2bMnAEAgEODWrVtYvXo1Xr58CTs7O9SpUwcnTpxAYCD3xTfRXc867vjhP2mAIV1zVusjx7U6XEPa3axN0KWWGwCgZQ0nHLjxHGZizZepylGNS3L7MXRIK4R8zmDpiCPFls0z9gYAtL3RD4ud5suXr7y2Ut7YZhgGfXb10bifhBcJmt+nIA9fHPmCc93Sy0tZr2u91m7Ye+SIJkgYflj+OvT1ZMAIeGsEhLz5QauGNgBUyZ+suYD1dCj3v+bbTcJtO6Duix646DtGpfi6m+vwU6OfOHf1Lk8xEmPogaEAgKsDr2pX0TKQkJoAAPDP/BYp3nb45a/FeGr9HDvD0nGpmvSvr+mmpnidLW0c30+/D4ZhEBuuvsdb3T3zOl42OJ/0YY1kk3UQSBh24EZrUyPO8uqGzcvIhpHfntFWTzUsO9TYLktGptIeZkO9t5ZatGiBq1fZP2DR0dHw9/fH+PHjqaFNiIFZGhthUe9aqO1hg5/33sKRxBdos+AElvatjTpe6gOrEKJPo0aNwqhRozjXFQ18Nn36dEyfPl3tvoyNjbF161Z9Vo9wUG7TJr3SLbvBM6VUXiuO38eQxlXxKlM/Q/wfvlId0v46S5F+StYI+XnvLc7tp+28gdvP32JOd0Waql1Xn+HTMN1TfKnLSzw8boG0LvkZWN9gr3x5fmGjW2Zo0FD580//+xR33iiiokfetEVQ2hQAwJaACXhhXXwvZO210o4PUS4w/r9IHPG/jJOBirmrHS64o2ZqJ5i9S8ETt2J3V2LVapjgzo33sLUqwOt07utAC2sh3qbpNsS7wXU+ep0NwQWPy/gr6j/81OgnvHj3AutvrcfwkOEQCaQ9nem56bB4x6CAD7wzlkWyZuT/X8Grg8GAwbGex2BrXPbnwf57+gMAso3tAACXQ6S/hW1v34BvylL8E8mXN7Rl/rz+J8yMzDAsZBjnPmU92w8+gswjsu/0kiN3cez2C/nyey+4P/uzdM3flYoeEE6Z4cdjfMh4POlQbkM8dPgjtLCwQM2aNVkPMzMz2NnZoWbNmmV4gAgh2uLxePisoTe2jYyEr6M5XrzNQc/f4rD48B2dogATQj4eb5WGp77KzNVQUpXy0NYZu28CAObsT9RLvR6/UW1sK0c2T3unfv51br4Ef5x8gBN3XmLfdUXqucO3UpGTX7II157Gz9Sukwgt0fNcD9ayafV+hCBfep21/d525Emk9VVuaAOQN7QBoOcV1V7oyy8uY1rcNHmALOVAWZ/FL8AL1x6omTED0Yeroek1Jxjn8FAl72uk2VTHEzd2zvohv0YiZnlzdIz24vwcPs66NehajY7AyGXN0PvnlpzrB/0ciQGzGiNmeXOt9tf6vATLlvsiOGMebgQMgqnZPPm65pubY8XVFawUZJMOj8OPmyMw8+8w8AqPy3enFMdQlvt89OHRAICk9CS8eKdoxJWFyOuqzabXtjVgJ1yMFpe5O7kWJyxWuz91l+rKUz4aV9duqkBFJ/s92Xbpicq6LI4AsMUNj6fGNiGEkDJRw9US22Mi0SnEFRIGmLP/Nvr97yznxSsh5OMmFCguSEU65MEGuNNzrT+nPtWaLte+5sbcQ0dlNN0+zJcohqAmJKex1uVpmWO6qA7z+6KqifYjEZ/Mt8KQ8/MhyOfh1JNTqL2mNsYdUz9PGwDyjcxRL5HdA9tvdz9sur0JbbdKh8L+euFXzm3FJqPg//ZbNLrlonb/IjPptCKPelU5G8CtvudO6aWJul5/ADCz0m0ak3f+IlwOYQ+PdxI7sIaLs9almSOxem/cDBgIt5fSgbg77u0AAPnNDUB6w+Ll+5fouL0jmm9ujqDVQYh7GqdT3bQVnvq92nURj3rqvD+uBuNfn9XFsMZV0bamM6Z2rIHpnT+MTi9NnQJcjW0uyvOyKfUXqfSOHj2K+fPnG7oahBAO5mIhFvQKxZzuITAVCRB3/xXazj+B3VfV984QQj4+ytfy2qbbkdF1xIynrfbT14rLSexmbaLVfnKKfKaCEja2AaDtvH5a99LKfHbqU/nzvUmKoebNr/kj5lAvlfK1Xk9B02teAIDcAsVIgyeZ0t6+1TdWAwB8nnF/fp9347Wu28hlzdC8oz36T5EOS+cL+PBw0a5RYwTdRkFEz26oU3mZWqfzUe/veqxl119dR9DqINhkKgJnfb7fBEEPpP/Xc+Pn4sqLK6xtmm1qxnotm+Otq9+v/I758fNx5cUVfH3sa9b/EQDkaBiu/sJBGkTQ4h2DSRstMWqnIqJ90Oogzm2UG9sC0zsQO+1A3aoWEAr4WNYvDIMiveU3vSpT45JLVXv1QZuLfo/VsTdX/E1UpsNBjW1CCKmEeDweuoVVwc4vGqK2hzXe5uRj5LqL+OG/6zpfVBNCPkzKF/MXdEgd6DVhl0oqoeIayJra5neev4XXhF3wmrALb7JysfOK5huDmoaIKr/PriI3GBOfv9W4X204MNrftBSIGnEur/52BApMudPi+b8di4TUBGy+vZm1PCtPOsw79r9aaJnEnQtbFzweDwHtg2HpYi1f1nFKK6227fJVbdbrwHp2GsubWoow+NdGMLfWPGKhKO+3ipsVsgADvXZKb1KEPlb06KZUnYFWd6Wp1P689icG7R2EBtcl6HuEB4GaGyy6pgv79sS3WHRpEf649gf67u6LfUn75MHuGIYBX4ubT1PXGWPoqfF45TEN+XYz0PGMopkVtDoIa2+sZaVxU24wmnr+AZHtaay5wY5Uz1OK4l2ZcQVFlHmXy57+MWJtPGc55ZEWmkZdVDTU2CaEkEqsqoM5Ng2LwNDGVQEAf55KQqfFJ3GlMDcnIeTj1bamIh2Wtj3Pb7O550vffp6Jmm6WarfTdDE9Yt1F+fMFh+6oLSejrtcquIqVxveR5RwujU+XqvZIaxLy0J31uv/JqGK36b+nP+6lsXOGp2RJ55+/sx+k0/tbCd6i73jtI/iPXNYMDTpUQf2WqnOB7a3yMeCnBnCsxk4T17B/8UOZjc2MMHBWI4xc1gxD5jdG7ShXNOuvOR2TaZ60p3jqOnPM+ysC3s8UcZvNBN1YZbPM3WD2TvF/3+LeYNjyp6HRddWc6ADQYnOLYuus7L/7/6ksS3yTiAJJAaadmYYp64tPnZdS9WdkmSui1rkx81AnUdHU+vn8z/LnddfV5WwwLry0kPVauUdbU376SF/NN0QMLYkjKKJM0cwHe66lcJYraUwGQ6PGNiGEVHJCAR/ftgvA7/3DYG1qhFspb9FlySn8su9Wpb8bTggpOU87M8z8RDqEVVMjVZm6IZ15BRIEV7FWu52m3StHGs94z92Yn95F0aBTN2TW294MjIaBOy/1EC2d0TGJWOOkwfLnww93gZmgo1bbbb69GWF3rTB6Tzh4EgYZuRkwe6/7ZXmz3j6w9nbSujyPx0OtDtUR9mkQPBzYQag6T24KC1tjlW2EQu2z0vB4PIiMhYjo5o8aka4ay8oapilVZyCxem+0TuKesy7z7c7O0icMgxcOocgTWaD5nXYAgIbXJAhMksA6U/qHWDQyeEl0qNoBww8Ox+bbm/GsqiI4W6cYaa72Rj2rwc5V802ssNfzOJfnFOQobirx1A/bFyg1yAs4vmTuttIpB7Et/TTWoyLT5qdp07AIWJnoNnKioqDGNiGEfCBaBTrjUGwTdCwMnrbkyD0MXn1eb+l6CCGVj5OlNJCVtjfe1DW2n6VnIzFF/TBtdfvPK5CwGtvKjX7li2cjpWBufDWNbYbhbnAory+tfDWfo01rERrWUm0UvRAfUrwwaaayXpM6L35ErnV/TNg7Ei/fv0TnSw1Y6z8ZWQ2DZkVq3Ifkfcl/3wMbsXOHGZuJ1JTUj2ELm6jMi3d4o3q8w29z/w0+cm8ByywGLq8VfzdPXRuizi0GtV79hKaPZuPzuG/w+T7p/OCg1UFqA7ApO/DwgPRJkT+gnfd34syzMyrlq9R0Qszy5ghu5o5ek+sXu391krOk0f15AvaUjUknJ8mDwPGUWmpc3zFZvMDKPKdbmxuBxkZ8GAkqZ7O1ctaaEEIIJztzMRb1roUFvUIhFvJxNPEFWs8/zsprSQj5eAj40ks9dY3IotT1PA/56wLiNcz7VnfBXG3SHtbrR28Uval2So07nlKPskDNfEwJw5T5UNK4+6+w0OItLufdx0LzDKDGe4xY0gQ+XRsiZFgbDJvbAGFeil5T24IqgIQBT83xbdvFBp/PUZ2/bfpe0Vv8xsYfsUdjYcnrzirjEuwOM2sxqrkqGowhYSYQ8hWNUZcmoSX9qKgS7lWi7RzNtE8j9tkvDVG3ozeGzG8MoUi1h3zq5hqs178t80b4q0Vq9zdhRyNM3szOdR72ZiHyRBYoEJog07wKhJY/ydfV+7seHr99rLGOsUdj4fKKwbQtn2L+qrpoGS/BzFVCGOVx/5/qOl94xE7uv9kfL0rzbwvNb6HeLQm+3CaBcQ6DHfd2oPaa2mAYhvVd+OG/Gyr7kDXA1X1nKjLZ/QFNN9AUZXmwEAtZy97lvcOkk5Nw7NGxsqie3lBjmxBCPkCdQ92wbWQkqjma42VmLgb9eQ6LDlFObkI+NgIdAyzllzCit7Y/LZE+irmlc3qEyJ8rtxXU9dIxDJCTp34ceedQzcOWtfH7sfvIEQix38EFOUIjDBjaCnyBopEoNDVG3W8+Ab8wUrWEXx/Dzy7EsLMLVfYVs7w5qrapBbE5O7q4US7Q6TK7R9QhnV2mZUtFaq3mE9rC2+kdmrayRMMhEahTTzHUm6sBqy0jGyv5c4uMh1pvFxal/XE2sRChTntviIwVDSXzvJfy51eDhrPKXwr9UuP+kr2643LIqGLfVzlwWtutbXHu2TmNQf46316IJ25NcMuvL3xyF+Gh36+Y/I/2c+E1YewW4+/ZAji/ZhC9vwBNr7D/hvnip+iS0AEFdjPR/bTi/+Rp1lPWd2H9uWSVfctucvErYYvOvTCOhKa56DKBrpaoV9UOzf0dMayJNEbN71d+x457OzDqcPF/D4ZUCf9rCCGEaKOGqyX++6Ihetf1AMMAvx64jaF/XcCbLN3SuhBCKi/Zxbq2Pdva9DJxKS5auaI+iktPH3tFcCtb5V5uNZ10EobRWD9nS9X5xroq2tC34MgJzufzIRFoHnKt3FgGAOuCVPnz6JOt4JzODlBmk8muu3vzUPlzoUiAdj90QOAn4QCA0H4NEOyXh7bdVIOc6UK5hza8vZfGsuZvFTnWnRqUrhFqYa99mriSWr6UnZN88P7BCP4rmLWMYRi1abkA4ImXtOfZKlOxrFUP7W40eKRfYL0+3mguvt7VBGKLxfDPWiRPZQYAItszeOjZGnlG5qjyro98+YorKzRG5gcUje3KOIxcVmdtbgTyeDwI+DysHFQHE9sGAAD+uPaHfP2bbO2zLZQ3amwTQsgHzNhIgJ+61sQv3YIhEvJx6FYqWs0/jovJFffERAjRH2HhXOi7qZmc699k5WLd2YdIfycdPq5tILWiXml5E+/RG8WQaIHSPG2h0nxMdUNiD958jk0XHnGuA4BbKW+x+nQSsnJ0S/uk7OTdl8UXKkZ49UxU/5Q919rLV9FzzTdpj2xL9vxucYEi0jsfBTCxUZ+XmC/go9HY1qgapb6hqK0GkWJUc32PgN5NNZbLtFBEXTezKt1NjZAaZT/CKj58Iufy9/nvMfX0VNRZWweTTk4CAPg8Ub+fDmeF6HdJkYrN1Ud9RH4A4EkKELO8OV7zVaOX3/VVpDrrf7pl4TMJa674G9sA+fMtd7Zg2eXFat/rWfp7vMyUfu+Ka5RXRLI6y26gPUt/D68Ju0q0ry7/dtFXtfSu0jW2c3JyEBoaCh6Ph4SEBNa6K1euoFGjRjA2Noa7uztmz55tmEoSQkgFwuPx0D3cHVtHNICPgxlevM1Bj+Vx+O3YPa17owghlZNsiKa6jq+f997CpG3XMO/gbVZ5AHCxMoaJEfcw5aoO6huDmpy8o2jMipQa2K5KDThzY/bcTJm8Aga/Hbuvdt/Hbr/AlB3XsfLkgxLVTRdVTZ+qXVcvtpPKsrpj2mncn3eqopd/+LLi04fpS63+kWg1uT14xfSMBgbqLxK0yC+g+EIAen1ft1Tv8/PfLeH2kn2O23V/F7bc2YLsgmx5uq8uN75Ru48qkl9RIFTcKDF2c1Yp026E4qaHubV0xIPQgjslmcz9qp3g+IYB+DlwSGc3xyb/XSCfL/6/aytgZHsMgOq5+tutV+XPK2XPdmFjW3YZEjHzsNbb5hawb+7Zm9jrrV76Vuka29988w1cXVWHcGRkZKBVq1bw9PREfHw8fvnlF0ydOhW///67AWpJCCEVT003K/w7qiHaBTkjX8Jg5p5bGLX+EjLU5NUlhFR+1qbSi38bU+5hzxvOS3uKV51OAsAe0vl7/3D8PaQe53ZL+tTG8n5hrCji+UXy5XLdzFNuE4iEfKwcFI6ZnwShmpOFfHmfeh4IqWIFf2cLjGute0qjc0mlT/tUnLZz+8GNrznwljIjjuHoylzzhsqf6xqAqzw0GhmJZn2rYcBPDYovXAy3OlVVltVpbM16/fncRrBzM8fAmQ0Q0dUH0bMb6vw+D7w7YOixnqh1V/F3+UPcDyrllHNjF0cgVG06eYc4wL2GNGd4v5mNAAA1OoUWu69xu5qBL3yLxjfZ7ZpU78VYsdhG/trYaQ/ETqp5wI8kKgKf2qr5flcUW0eq/t3wtRxG/vOnqiM40nPSYZTPYOCBAgQmSdDHv498edGGuKFVqsb2nj17sH//fsyZM0dl3bp165Cbm4uVK1ciMDAQvXr1wujRozF37lwD1LRymTp1Kng8Huvh7+9v6GoRQsqAuViIJX1qY1rnQAj5POy68gydF5/CzWcZhq4aIaQMyDqPtZ2LLSvn62iOoCpW8LZX7cHuGOKKABdLtKnpjCtTWsuXF00bpjxPfGRTHwDA+zxpZOYQd2sAQHN/J/Su68HaztHCGP+Oaoi9XzZGTDNftb3yMg4W7PnRwhL28uk60qfL0gGs195WrzFwSoia0pWbQMBHjUbunHm4dcXnSOFUp1ct1muxqfTmhLmNMWq39oSppWpjMqyFMzqNDsWwhU0wfElTdByteuyfuUaiWdIkbJqZD79H7P9fHsNg7kqvUnwShU6jQxGzvLn8s4W29Sl2m3s+XWFkdxw5YtUh52fq/8BKiyayPa1xX+rS5VUUtT1skDSrvfy1sRFfq2jky/rWRs86HirL03PS0emcFUzMF6PPua6YGjcV+5L2oeGGhghbG1ahRu1Vmsb28+fPMWTIEKxZswampqqBFeLi4tC4cWOIRIovY+vWrZGYmIg3b2huYnECAwPx7Nkz+ePkyZOGrhIhpIzweDz0j/DC5uERcLM2wYOXWeiy5BR2XlE/LJIQUjnxOKKRSyQMvt58Gd9tv6pS/vFraWou2RxvEUdPnqBI77TMs3R2vmDlxrdsOPqbwrnhYh1y5hY3HzW3SCP/SOILfL76vNb7l8krQST26qJ78uftfu4Gcxc7DaWJzMCZkej6VS2MWNIUwxc1BY/PQ70W0qHALfuq9nwDQEAD9hDuep8GwL2GLYQiAQQCPjxqcB/7d2bOONx0CVo8/AUA4POUQaOrEkRdtsXtgLHycuaZ2o9UKA6Px8PIZc0wdEETNOnopLacyPoCGiZyrx96oAr4RXp9Z+65iRm7bqj0BlfwtraK7DwJrj+V3uTff/25ztsffXwUDrxpAIBH7s3BkzD4+tjX8vWp71LVbVruKkVjm2EYDBo0CMOHD0d4eDhnmZSUFDg5sf9YZa9TUlLU7jsnJwcZGRmsx8dIKBTC2dlZ/rC3r7hzHwgh+lHLwwb/jopEo2r2yMmXYNTflzBu82UaVk7IB0Q2L1J5Lnbc/Vf4J/4x1p5RTSX0zZYrrNfGQtU5251rKYbdKs8VnXfgNquccs5ukyIpqpQDpRXn09pVNK5P58gNfvCm7hfbT9LeF1+oiIjxXQEAVun3iikJtG6veR4vAFjklz5AW2VgbiOGazUb8AV8CIykzZHw7sEYtqgJqjfy4tym+YAaEAoVf29c88y9/dTHEpAIxPBOYdD6/i8IzFwEn/dTWeszzavg87mN4F/binP7Zr2L761WxuPxYCQWwD3MXW0ZQT6Q5NWWc90r92+wcqGt8ifAb8fuY8WJB9hz7Rl7P5Wtta2EK6WZTC0PG87lCy4uYL1uds2a9TozjzsgpCEYtLE9YcIEleHLRR+3bt3CokWL8PbtW0ycyB1ZsDRmzpwJKysr+cPdXf0XQlcMw+Bd3juDPHQdPnHnzh24urqiatWq6Nu3L5KT1f/hE0I+HPbmYqwcVEc+xHNz/GN0XXIKt1I+zhuPhHxoZBfhyh1h73ILOMty5bvl83m48F0UmlRXpJmq783dg5j8mt2AVu59Kzq0W3l/xalX1VbjeuV546VRkqGn5m72+HxmHfRe81mxZd0iip9/3v0XzYHUPnRCNQH5ZD6b2xhN+vhh4MxIzvWNBmoexj9qf0NIBGK168WmRmg2uJbK8k+GeqNGE0+N+1bHUikeAQD0GK/Yv2O65rn8Z+pNhdl76d+lRcC3AKSjON5msyPuV8Zo5MX5NyYSzhyR76+9vMbKow4AfhkTWK/f5r4t07rpgjvcYzn56quvMGjQII1lqlatisOHDyMuLg5iMfvLER4ejr59+2L16tVwdnbG8+fsYQiy187OqpEDZSZOnIjY2Fj564yMDL01uN/nv0e9v7kDi5S1s33OwtRIuzyG9erVw6pVq+Dn54dnz57hhx9+QKNGjXDt2jVYWFgUvwNCSKVmJODjmzb+aOrniNHrL+HeC+mw8qkdA9GzjnuFDNZDCNGOPAiRUkNS3Tc6TyLhXG5vLsbIpj44dlsakEndT8K7XHYDQDZn20IsVOl5K9rTrUnRYeJFiYUC5BWUPN2XjDb5fjnf30a7ayUTe+4eU1YZm+J7vz9mRiIBajZWH9CsuDnld6r1ULuu9+Q6AKRzyu3s+Xj1UvF3Z1tdu/zaXHg8Hjp8EYydi6SjRuw9reXrzHPY1+pGIh7yctl/h6MPj8D2mstx0xPgGb0Gk2ev8n2qzD3bADhH1MniOhTVe1dv+D5nf+eyhezGdaB96XLB65NBe7YdHBzg7++v8SESibBw4UJcvnwZCQkJSEhIwO7duwEAGzduxIwZMwAAEREROH78OPLyFP9ZBw4cgJ+fH2xsuIcgAIBYLIalpSXr8bFp27YtunfvjuDgYLRu3Rq7d+9GWloaNm3aZOiqEULKUV1vW+wa3RCNqtkjO0+CCVuvYtiaeKS9q1iRPQkh2uMaRq6usZydp75RqxyASd2F/b0XWazXssarQMBT2feFJO3j6RR3v09dO+PeC92Gkracd1yn8iURPbM+QmuL0T02EJ/9XJ+1zk1cceaZVmbDFjWBtZnu06FsXRUNuJ4/NmGtE5ur7w3XhmegPXp+Vxefz2vMGv4+dqcLq9xnvzaGjxe7sf3GNgC94qU3CXh86U2lb/5hT/dQl5u+sgieul+n8uY57BtX7o+PAAC6iOriUo+zMOLrL1VdaRm0Z1tbHh7sKHTm5tK7fj4+PqhSRTqPp0+fPvjhhx8wePBgjB8/HteuXcOCBQswb968cq+vjInQBGf7nDXYe5eUtbU1qlevjrt37+qxRoSQysDOXIzV0XXx+4n7mLMvEftvPMf1hSexsHcthHmqv3FJCKmYZNf1EqWebXVDTnPyuYeXA0BwFSsEV7GCu40pjNQEN/ukFrvHUdbYFvJ5OPuAnY7r6pP0Yusu06qGM8ZvUQ3mJtMiwAnbLj1RWf6/E/cx85Ngrd9HWVV7M0Q39C7RtpqY2pgicqhiCHT3L3yxeZH0eqvhcO6h0UQ3QiMBGvQOwu7/3dJ6myHzG7Ne8/g82Bln4VV2yfLJc7Gvojpq4UrQCNZroZEAfk28cS8pibX8mUskgE3g8bljHVSWtnakrx1O3X2FaV1qYu+1Zzh195VO2z/JlH7Pqz5nf+BUt15Y8FCEtHhnnF4SjUbH/gZPoP3ombJUKRrb2rCyssL+/fsRExODsLAw2NvbY/LkyRg6dGjxG5cRHo+n9VDuiiQzMxP37t1D//79DV0VQogB8Pk8DG/ig4a+9hi57iKSX79Dt+WnMaKJD76Mqs4ZnZgQUjHxleZsMwwjnRai5sI8R0PPtlgowI5R3LmOv25VHXP234bYiP3bkF84LF3A55WqMWBjpjmHsKMFd69jfgmii8sc/rppibfVhWOgBz79QgBTB0tYOtLUPX3xCnMBChvbTt6WEL56iicZ3EP0jU14EBmrNolaxjbE7gUXENZaNfVUWfCtLv07dw/3BFYnqRZgGIgd9+HdwxEqqyrLdK91nytGc9TztkUrHUeTHE4+DABwZMaprHt6IxjpLr545tIAgQePwa5189JVVk8q5RWTl5cXGIZBaGgoa3lwcDBOnDiB7OxsPH78GOPHjzdMBSuZr7/+GseOHUNSUhJOnz6Nrl27QiAQoHfv3oauGiHEgGq6WWHn6Ib4pJYbGAZYevQePl12Go9eax9FmBBiWMrDSyUM8D63AD/suM5ZVlPPtsb34EsvJ4s2brMLc2oL+fwyHeaqrqHxLq9kn6e8OQe6UUNbz3g8HkYsbYbBvzZCt/HhyH/MHfi3dhN7DJ7XjHOdnYcN+v/aEjVaFR/YTh/qdvACIO3d/nxeI5X1a+aagy9WHcFRWYl1vHF/580dzD4/G2vncI/eTbf2lT+/ckq3HvOyVCkb20S/Hj9+jN69e8PPzw89evSAnZ0dzpw5AwcH7SOFEkI+TJbGRpjbMxRL+9aGtakRrj5JR9sFJ/BPvP7ykRJCyo5yb3P6+zwsO3YPSa+4b5g9ScvmXF4c2ajyogHG4h9K52Xn5BcgqAp7jqWuF9pFuShFKXaz5g6KdeuZ9lkVzhUZ5k4qPz6fB2Mz6dzdbEd22q6aWccxZH5jRPQu2TSDsmBRRZF2l6un/VSDn+DwzBPg5ei03133d+Hgw4Olrp++mYl1G2A9/cx0AMDJyFnFlhW7qQ+OXd6osU2wYcMGPH36FDk5OXj8+DE2bNgAHx/dcgkSQj5s7YJcsGt0I9T2sEZmTj6+3nwZU3dcl/dcEUIqJlOR4oI2Kycfy4+qzwctkTBwt5X2Gg3WYb6yvGe7SGNbMbebh0ENvFjrFveprfX+uYR72WJ8G38s61sbzQOcOMt42Go/le98EjW2P2StYtnz4X0+/4SzQVtePv0mTGWZ0FRx04jH46HN0JqwtmXX0TvXAhb+U8ATvZAvW9ArVO37pGWnYcKJCRh7dCzyClSDxs2Pn49DDw8BAPYn7UfQ6iBsub1F149TIvbmYvnvTVHzeqqmcLuYelGeBq04fh1V07cZCjW2CSGEaMXN2gSbhkXgi+bSoVqrTieh/cITSEypOPksCSGqLAobFfkShhUoraicfAlcLKUXv7oERJTl0C7asy172cDHTqUXy9OudDFthHweRjT1QdsgF5Uc3jK6pENSDhrX1I9G9n1oHD3Y2YbswspnaLg6zlWLTwPnU9sRUZ+yo5U7ZVYFAJj7/Cpf1jlUfSq0tJw0+fNNtxVZhh69fYSg1UH449of+PLol5hxZga+OvYVAGBq3FRtPoJefNe+Buu1r6M5kma1R9daVTjLhyaxf5d6DFH97OHtvWDnWnFS6FFjmxBCiNaEAj6+auWHPwfVgaOFWJ6Te/25ZDAaLuJJ5bdkyRJ4eXnB2NgY9erVw7lz59SW3bp1K8LDw2FtbQ0zMzOEhoZizZo18vV5eXkYP348goKCYGZmBldXVwwYMABPnz4tj4/y0VE0hiUqvc/K5uxPxLnCHl5dhnnLGrW7rj7D2fuKuZJ/n30IgLvXuLR5gZNeKdKMqdvTwZupyMzRLv+2cnXop+zDFLO8OUJaVEFolDtMSpnKS9/6T4/gXM4UsIMWerypptN+d9zbIX++NGEpACAlKwXttrZjlduQuEGn/epL0VgOd1O50/Vl5mbCKJ9Bh8sNWMtNvdmN8s5ja6Fex6r6rWQpUWObEEKIzpr5O2L3mEZoVM0e7/MKMHHrVXy++gJeZ1FO7g/Rxo0bERsbiylTpuDixYsICQlB69atkZrKnRfY1tYWkyZNQlxcHK5cuYLo6GhER0dj3759AIB3797h4sWL+P7773Hx4kVs3boViYmJ6NSpU3l+rI+GumHeRSlf6Ooyn1K5QTty3UX5c1ne7WfpqnPB1fVGa+tScpr8uYWx+py6Cw/d0Wp/7kpDznPz1UdlJ5Vbw+7VEdlNtwZrebC05x5Obe7LjoQuEbnKnwtM7xe739fZihtdGbnSGAYzz84sSRVZ+u7qizpr65T6Jru2W2+6vQk/rrXAQ8/WrOVmtmYYNCsSbYcFYcj8xqjiV/FSlFJjmxBCSInYF+bkntjWHyIhH4dupaLN/OM4eeeloatG9Gzu3LkYMmQIoqOjUaNGDSxfvhympqZYuXIlZ/mmTZuia9euCAgIgI+PD8aMGYPg4GCcPHkSgDRd54EDB9CjRw/4+fmhfv36WLx4MeLj45GczB01mJScrGGrSyosX0fth2F62ytyEb/ScMNtcR/FPMrS9mwrMxEJcDC2MdYOrqeyTtvsCRZK83cpvSEpD4N/lUYcN7VUn9rO3NYEHgHcDUhTz98hMLuNd3ncf+N5EukcbcssBubvGATaBeLPa3+ipn3NEtVXwkjw8v1LvHj3AldeXkF2QTaC/wrGxBMTcfyxbim8FPvU/JvEMAyCVwdjXvw8PPSbwVnGzFqMqrUcDDoHXxP6NSGEEFJifD4Pw5r4YPvISPg6miP1bQ4GrDyL/524T8PKPxC5ubmIj49HVFSUfBmfz0dUVBTi4uKK3Z5hGBw6dAiJiYlo3Lix2nLp6eng8XiwtrbWR7WJElnDtriebWW6DCMXCbQrG1LFWv5cyNfvJaivowUCXS1VlufmS8AwDCTFfHblY1NJUhaTSs7YzAgxy5sjejZ3/nqZjmPYwb7+mmsOo3zp36upx0oMPzicc7v58fOx48Y/mLfSBQtXOOLGy2uYGz8XCy8tLLZuO+/vVFkW8lcImm1qho2JG1XKxhyKQdDqoGL3W1RxlwmjDo8Co3X/d8VEjW1CCCGlVsPVEju/aIhuYVUgYYDpu25i8OoLePFWtxQlpOJ5+fIlCgoK4OTEjvjs5OSElJQUtdulp6fD3NwcIpEI7du3x6JFi9CyZUvOstnZ2Rg/fjx69+4NS0vVBpNMTk4OMjIyWA9SPFlj+9Nlp7XeRpfeXa5eaq65l3ylcvrs2ZbvU6C6z0O3UuE9cTc6Lj6pEsBN2Uul3ypqa5OK7HTEDIz5z1H++lLqJQDAiIMjELQ6COdTzgMA/rrxFxzTjXE+fCLO1f0OXimqf9lnGm2D+Tvp90Kcy6DhNQlMshlMPDEROQU5kDAS+fBzmd+u/Ka2btPPTMe6m+uQUyD9Pt1Lu4fJpybj1utbarbQ3JBW12PuaP4OQ+arv3lbkVBjmxBCiF4YGwnwS7dg/NApECIhH4dvpaLlvGM4msg9r5d82CwsLJCQkIDz589jxowZiI2NxdGjR1XK5eXloUePHmAYBsuWLdO4z5kzZ8LKykr+cHd3L6Paf1iSC4dSczU2O4a4qiwDtO+tBrjnX0/fdUP+3M5MOkzWyUKMGi6WCK5iBVsz9UNnuSzoFQprU8Xc7BFNVVOUWoiFCFcTRf360ww8eJnFuQ6QDkWXeU8pDUkF9952COt1y39a4uQT6TSdz/Z9Jl9ulquYDtL6qiICu1cKg3ZnJTg4ehVmbAhD+G0JfvzbDs3vRWPJ79Ibq9+e+BZha8MQuT4Sex/s1apeGxM3Yta5Wfhs32e48+YOuvzbBdvubkP3/7qrlM0tyMWFlwcBFB8jwf05+3VguxoVdth4UZWjloQQQioFHo+HgQ28UK+qLWI3XsaNZxmIXnUeX7fyw8imPuDR+MxKx97eHgKBAM+fs692nj9/DmdnZ7Xb8fl8+PpK08SFhobi5s2bmDlzJpo2bSovI2toP3z4EIcPH9bYqw0AEydORGxsrPx1RkYGNbi1EFzFClcep6ssfzCzHXg8Hv67rBoFXpfvKlcvtfLw0CV9pTm1hQI+do1uqPP+AWl6o04hruDxeJBIGFYvuXKdNw+PgPfE3TrtG2DfiPB31vx3SEi5k+QCfMUNKtOcHNi8ZfDGQvo9SMlijzJac0Oa/cE0W3GDyjIvBEAiPFMY9Ikfjte2NfC4MJh3m8RaeOAXDABIdawNYAz2P9wv33bc8XE6VffKiyv4ZMcnGsuErZXmGjf3M0Jm4jQAwNROfjj26BjqONeBqZE0aGGX0xI0utcEd6SnE5hbi+Df1Fun+hgS9WwTQgjRO39nS2yLaYBeddzBMMAv+xLxxfpLeJudZ+iqER2JRCKEhYXh0KFD8mUSiQSHDh1CRAR3uhouEokEOTmKobqyhvadO3dw8OBB2NnZFbsPsVgMS0tL1oMUT10vtb5ufgmLDN/+atNlnLjzQrFeqWHM4/FK/L6y7bga2kXL6KJAwuCEUmBHijdBKpr15uwUdm8tPPDTuuoYeKAArq9U/15nn58NKXHkkAAAKf1JREFUAHDKMJYve28ehJE7C/DZsdp4bcvOb/3SPrgMas2WmZuJ+OfxOJ9yHnkFimsBHl/x/PjLPzHq8ChMOjkJAND0MgNn3iLc8e0mL9NvRgONvwEVDfVsE0IIKRNioQCzPg1GTTcrTN1xHTuvPMO1J+lY1Ls2gqpYGbp6RAexsbEYOHAgwsPDUbduXcyfPx9ZWVmIjo4GAAwYMABubm6YOVOaUmbmzJkIDw+Hj48PcnJysHv3bqxZs0Y+TDwvLw/dunXDxYsXsXPnThQUFMjnf9va2kIk0m2IMdHswsM3Gtfbm4vxMrPk8RWKBjvbcvEx63VZzM/WxMrECOnvtb+x9+epB/gnXlFnF2vuNEyEGMpjEQ8oEnT8csgoOKffw5RN/2HYiAcq27S4CDS5E4TkwuxheSJLwG4x3gnulq4yDIP1s/kAeJDweLDp1A7rBeew1jcFjIabXRHri785e/6NNC/4weSDAIDGSQOQ6sguI9BhiktFULlqS8rMkydP0K9fP9jZ2cHExARBQUG4cOGCoatFCPkA9KvvifVD68PVyhhJr97hk2WnsCYuiXqPKpGePXtizpw5mDx5MkJDQ5GQkIC9e/fKg6YlJyfj2bNn8vJZWVkYOXIkAgMDERkZiS1btmDt2rX4/PPPAUjPOTt27MDjx48RGhoKFxcX+eP0ae2DeBH92DayAev19x1qqCnJraqDmcb1+o48XpzpXXRLbbTx/CPW60ENvPRYG0JKz95cjF+s36ssT7fywaXQLwEAVV4wmLixABaFAc9a3GmPZI8o1W2sfYt/v3SO+A5nJNgwS4IayQIcazwfxxrPw4lGc7HjTRRc7/XE2jmqN0kDkhl8elIC68zizveq87YljASpjuHF1rWio55tgjdv3iAyMhLNmjXDnj174ODggDt37sDGpuIlhieEVE51vGyxe0wjjN9yBfuuP8f3/15H3P1X+LFzTdibiw1dPaKFUaNGYdSoUZzrigY+mz59OqZPn652X15eXnSzpQJxtzWVPw91t8bghrrNhzQqpqepvHu27cy5R0ao+5vLK2Bf6BsbCTjLEWIoJiLN37H+hwpQ70EorgV+joUrpiF6zEske7Qq8ftN/LcRvup/gpUHz41ZhKNNgF4XTuJpkbiK6VY+ONFwDtb//CWyzFyxqf4jpJvxUCttISAApm1ciS8GX1b7fmLHvchLC2Mtu/nqpko5z6qV73qBerYJfv75Z7i7u+PPP/9E3bp14e3tjVatWsHHRzXSJyGElJS1qQjL+4Xh23b+EPJ52H01Be0WnED8w9eGrhohpJAuUchl+MXMky46p7usqbuPwxVlPP7hGyS9esdRmpCKQ1D4HTsmzuZcby38CdcCpSOHztb9HiH3i4/wDQD9p3MP7b5TrTu+2czH0D0M1s4xRpVUxZfqqav6vODHGs/HhbBvEPZ8JOo9nyZffr3GZyplux8vwNo5xqh/UwKR3XGY+cyDcQ6DQQcKYJXJoNeuXirbNP6stlafqyKhxnYZYhgGknfvDPLQpcdgx44dCA8PR/fu3eHo6IhatWphxYoVZXhkCCEfKx6Ph6GNfbB1ZANUczRH6tsc9PjtDBYcvIP8Au0uDgghuon0VQ0+VzRFllvhPOW2QeojzKtTXM91cT3f+lbFhnvO9dM01YaKLrnHCTGULrXcAAD37blHbeSJ2MEi+8a10Gq/lvYmsBVncq7LcJ8PvvVCnIz8GWP2N9ehtsAb2wDkitmxWfhFUg/aGS3GycifEfl4gnzZ7LWhMDZfjB/+qae2vpUNDSMvQ8z790isHVZ8wTLgdzEePFPT4gsCuH//PpYtW4bY2Fh8++23OH/+PEaPHg2RSISBAweWcU0JIR+j4CrW2BYTiW+3XsWOy08x7+BtHL/zAot614IrBSciRK+GNfbBqbuvWMtk6bhkdoyKRMKjNDT1KxKNSAvFjRI3F5fv5aannRnWD6mP80mvMffAbaU1xXdEdAurUnYVI6SEYpr5ItDVCtUczfHJrKMYnKn5PHm/ahet993u+yis/e6MxjL3fLTfnzrLl1iDD1OAx8Ohmo+AwhHhWeZu8jI3CnvAb/n1BXBOvtzH1witxkaWug6GQI1tAolEgvDwcPz0008AgFq1auHatWtYvnw5NbYJIWXGXCzEgl6haObvgMn/Xkf8wzdoM/84ZncLRpuaLoauHiEfDK45yE6WxqzXduZitAhwKtH+i0u3ZYgsPRE+dqjlYc1qbOfkFz96xttec7A3QgzBSMBHyxpOSH+fh9c6tt4iu/ni1D+qEcjtXaUNdit7Redctdp2uHPxlUpZfbgY/qP8uTb5SCyyFM/9/YXgV7Io5DLU2C5DPBMT+F2MN9h7a8vFxQU1arAjjwYEBGDLli36rhYhhLDweDx0rVUFtdxtMGbDJVx+nI7hay9iQIQnvm0XQIGKCNEDPaXTLsX7V4ycuGM2JKBtTRfsvZ6C0esvcZYRCyvnBT35OMimbOwXpaNVrnYpNEOjPODkZYmc9/nwCrLHltkXkHI/A92+VQzV7j4xHE/vpCGkuTvujDyi1X7NLI1Qt7MPakS64sKeJJz9977uH6iQ8ysGKbbsZY1vKD6fkXPJbgRWBNTYLkM8Hk/rodyGFBkZicTERNay27dvw9PT00A1IoR8bLzszfDPiAaYvfcWVpx4gL/iHuLy43T81i8MzlbGxe+AEKJW0aauh235XpsYKh4DVxv/xJ0XahvaAOj3hlRopoU3oB+IRUBu8eUHzZIOvXbxtZYv+/Qb1XRajp6WcPSUzvsOb+OBC3uTi933gFkNwS9s/Ie18SxVYzvmSDdUeXIMZ+sqlnnkTQFT+B12qa1bhoSKhG7fEYwdOxZnzpzBTz/9hLt37+Lvv//G77//jpiYGENXjRDyETES8DGpfQ2s/qwurEyMcPlRGtovPIGjiamGrhohlVrRRufhr5qUcwXK9+0Ub6v6xsVFTq9fVTWYHCEVhaxxmyEAXvAlsLQ3xvAlTdWWN7PWPVVWvS6+sHXWPELWN9RGXhdA2sFYLdwRfD4Pwxbp/vvyxK0xztb9nrWM4SlGtvENMRdFT6ixTVCnTh1s27YN69evR82aNTFt2jTMnz8fffv2NXTVCCEfoSbVHbBjVCT8nS3wKisXg/48j59230SuFvMtCSFc2Beq5Z33Wou4ZGWCq13NFFMZEQ0jJ5XEKssc9J/eAAKlucyWDvoJMNp7KndKMJGYj8huvogaEqKyrtXnNTFiaTMIjQTwClBER+/xrWpP+seEhpETAECHDh3QoUMHQ1eDEEIASKMJb4+JxMzdN7E67iF+P34fF5JeY3Gf2hStnBAdFW1bl/ccaqGBAhtx9WJ/tuqCxm1ozjapjEYubQaJhIFAyMeS4YcBAD2+rVOqfQ5f0hTZmXm4fPgRLu2TDivvN70BTCy4048pazc6DPcvvYDIVAgHD0vOMsMWNcFvXxwrVR0rA2psE0IIqZCMjQT4oXNNRPjY45t/LuNichrazD+OeT1DSxw1mZCPkaQcepa/71AD03be4Fxna1b8xXlZ0LUH38vOFGIhBWUkFduawXXR/49zqO5kLl/G4/Pkf+8xy5ujIF8CQSlvHAkEfJhZidGgqy/qdayKgjwJRCbaNR15PB58aivSCFo7mSDt+XvUaOiCt69z0DK6BoRaBkC1sDHM74e+UGObEEJIhdampjP8nS3k0coHr76AzyK9MaGtPw35JEQL5TEFY3BDbwxu6I3/nbiP6btulvn7lYXZ3VSHxhJS0TSq5oCkWe01liltQ5trf6XZZ98fuIelO9ozSH3Jvik2cGYkVk88JX/db0blzK8tQ1cphBBCKjwvezNsHt4Agxp4AQBWnnqAnr/H4cHLLM0bEkKQZ6Bo4JUNwxhocjkhH6nmI+qxXnefGA5zGzGGL2qKXpPrImZ580odHA2gnm1CCCGVhEjIx9ROgYj0tUfspgRcSk5Dh4UnMLljDfQId68wuXwJqWjKM53VybsvWa87hriW23uXFsWDIKR82bmZo//0CIhMhDA2M5IvFxjxYedqrmHLyoN6tgkhhFQqLWs4YffoRqjnbYus3AKM33IVQ/6KR+rbbENXjZAKqbqTRbm91+M37+XPp3Wpiakda5Tbe3M5/FUTzOhaU6uy7uWcf5wQAljam7Aa2h8aamwTQgipdNxtTfH3kPr4po0fjAQ8HLz5HO0WnMSBG88NXTVCKjR327LtvRUojTDpX98Tdua65/nVp6oO5uhbz9OgdSCEfLyosU0IIaRSEvB5GNnUF//GNISfkwVeZuZgyF8XMG3nDeTTHFVCOJX1tOTKPr+SEEL0iRrbhBBCKrUarpb4d1QkhjauCgD44+QD9Pr9DJ5n0LByQsrbrE+CDF0FTjXduHP9yoxu7ltONSGEfEyosU0IIaTSMzYS4Nt2AVjSpzYsxEJcePgGbeYfx56rzwxdNUIqlLKOIxjibo3TE5ojcXqbsn0jHU3uEKh2XWzL6hjbsno51oYQ8rGgxjYhhJAPRvtgF/w7KhI1XCzx5l0eRqy7iC83XMLb7DxDV42Qj4artQnEQoGhq8EiFKi/y2BpLKRsBoSQMkGNbQIvLy/weDyVR0xMjKGrRgghOqvqYI7tMZGIaeYDHg/YnvAUXZacwq2UDENXjRCDEQull3x+TpqHU3+o+Boa0zn5FOOBEFI2qLFNcP78eTx79kz+OHDgAACge/fuBq4ZIYSUjEjIx7jW/vhneAM4WYpx70UWuiw5hf8uPzV01QgxiOX9wlDXyxaT2gcYuioGIRKov+S1NPlw0w4RQgyLGtsEDg4OcHZ2lj927twJHx8fNGnSxNBVI4SQUgnztMGeMY3RqJo9svMk+GL9JcT8fREvM3MMXTVCylUzf0dsGh4Bb3szQ1fFIMzE6oe1UwB1QkhZERq6Ah8yhmGQn2uYoUlCEb9E849yc3Oxdu1axMbG0vwlQsgHwdZMhD8H1cHcA7ex/Ng97LryDOcfvMa8nqGI9LU3dPUIIeVA0xxyHuh6hxBSNqixXYbycyX4fcwxg7z30AVNYKThLq4627dvR1paGgYNGqT/ShFCiIEIBXx808Yf7YJc8OXGBNxNzUTf/53FkEbeiG3pBxNRxQrmRAjRL3Nj9Ze8Xh9pbz8hpOzRMHLC8scff6Bt27ZwdXU1dFUIIUTvarpZ4b9RDdG7rgcAYMWJB+i0+CRuPKXgaYR8yMzF3I3t/vU9UdfbtpxrQwj5WFDPdhkSivgYusAw856FIt3vozx8+BAHDx7E1q1by6BGhBBSMZiIBJj5SRCiAhwxYetV3EnNRJclp/BdhwD0q+cJPk3gJOSj0T/C09BVIIR8wKhnuwzxeDwYiQUGeZRkvvWff/4JR0dHtG/fvgyOBiGEVCwtApywZ0wjNPd3RG6BBJP/vY7eK87gadp7Q1etQlqyZAm8vLxgbGyMevXq4dy5c2rLbt26FeHh4bC2toaZmRlCQ0OxZs0alTKtWrWCnZ0deDweEhISyvgTEKJKQDfXCCFliBrbBAAgkUjw559/YuDAgRAKacADIeTjYG8uxv8GhGNKxxowFQlw9sFrtF1wAqfvvTR01SqUjRs3IjY2FlOmTMHFixcREhKC1q1bIzU1lbO8ra0tJk2ahLi4OFy5cgXR0dGIjo7Gvn375GWysrLQsGFD/Pzzz+X1MchHzs/JQmWZkBrbhJAyRI1tAgA4ePAgkpOT8dlnnxm6KoQQUq74fB6iI72xe3QjBLlZIf19Hvr97ywWH74DiYQxdPUqhLlz52LIkCGIjo5GjRo1sHz5cpiammLlypWc5Zs2bYquXbsiICAAPj4+GDNmDIKDg3Hy5El5mf79+2Py5MmIiooqr49BPnK7xzRSWUY924SQskSNbQIAaNWqFRiGQfXq1Q1dFUIIMQgvezNsHh6BT2tXgYQB5uy/jaFrLuDVR56TOzc3F/Hx8axGMZ/PR1RUFOLi4ordnmEYHDp0CImJiWjcuHFZVpUQjbga1kI+XQoTQsoO/cIQQgghhYyNBJjTPRg/fxoEkYCPgzdT0X7hSTx6/c7QVTOYly9foqCgAE5OTqzlTk5OSElJUbtdeno6zM3NIRKJ0L59eyxatAgtW7YsVV1ycnKQkZHBehBSGtSzTQgpS5Wmse3l5QUej8d6zJo1i1XmypUraNSoEYyNjeHu7o7Zs2cbqLaEEEIqKx6Ph551PLAtpgGqOpjBx9EMrtYmhq5WpWNhYYGEhAScP38eM2bMQGxsLI4ePVqqfc6cORNWVlbyh7u7u34qSz4aP3YOhJ+TBfycLBAV4Ah7c5Ghq0QI+YBVqkhYP/74I4YMGSJ/bWGhCHSRkZGBVq1aISoqCsuXL8fVq1fx2WefwdraGkOHDjVEdQkhhFRiga5W2DGqIfLyJR9175e9vT0EAgGeP3/OWv78+XM4Ozur3Y7P58PX1xcAEBoaips3b2LmzJlo2rRpiesyceJExMbGyl9nZGRQg5voZECEFwZEeBm6GoSQj0SlamxbWFioPbGvW7cOubm5WLlyJUQiEQIDA5GQkIC5c+dSY5sQQkiJmIuFgNjQtTAskUiEsLAwHDp0CF26dAEgzWBx6NAhjBo1Suv9SCQS5OSUbv67WCyGWPyR/4cQQgipNCrNMHIAmDVrFuzs7FCrVi388ssvyM/Pl6+Li4tD48aNIRIphgO1bt0aiYmJePPmjSGqSwghhHwQYmNjsWLFCqxevRo3b97EiBEjkJWVhejoaADAgAEDMHHiRHn5mTNn4sCBA7h//z5u3ryJX3/9FWvWrEG/fv3kZV6/fo2EhATcuHEDAJCYmIiEhASN88AJIYSQyqTS9GyPHj0atWvXhq2tLU6fPo2JEyfi2bNnmDt3LgAgJSUF3t7erG1kwVxSUlJgY2PDud+cnBzWnfbigq0wDKWBKQ4dI0II+bD07NkTL168wOTJk5GSkoLQ0FDs3btXfp5NTk4GXymqc1ZWFkaOHInHjx/DxMQE/v7+WLt2LXr27Ckvs2PHDnljHQB69eoFAJgyZQqmTp1aPh+MEEIIKUM8xoAtowkTJuDnn3/WWObmzZvw9/dXWb5y5UoMGzYMmZmZEIvFaNWqFby9vfHbb7/Jy9y4cQOBgYG4ceMGAgICOPc/depU/PDDDyrL09PTYWlpKX9dUFCA27dvw9HREXZ2dtp+xI9Seno6nj59Cl9fXxgZGRm6OoQQYnAZGRmwsrJSObeQ0qHjSgghpCzo6/xi0J7tr776CoMGDdJYpmrVqpzL69Wrh/z8fCQlJcHPzw/Ozs6cwVsAaAzgom2wFYFAAGtra6SmpgIATE1NweN9vAFz1JFIJHjx4gVMTU0hFFaagROEEEIIIYQQolcGbQ05ODjAwcGhRNsmJCSAz+fD0dERABAREYFJkyYhLy9P3pt64MAB+Pn5qR1CDugWbEXWaJc1uAk3Pp8PDw8PuhlBCCGEEEII+WhViq7HuLg4nD17Fs2aNYOFhQXi4uIwduxY9OvXT96Q7tOnD3744QcMHjwY48ePx7Vr17BgwQLMmzdPb/Xg8XhwcXGBo6Mj8vLy9LbfD41IJGLN3SOEEEIIIYSQj02laGyLxWJs2LABU6dORU5ODry9vTF27FjW8G8rKyvs378fMTExCAsLg729PSZPnlwmab8EAgEEAoHe90sIIYQQQggh5MNQKRrbtWvXxpkzZ4otFxwcjBMnTpRDjQghhBBCCCGEEPVorC8hhBBCCCGEEKJn1NgmhBBCCCGEEEL0rFIMIy9PsrTjGRkZBq4JIYSQD4XsnCI7xxD9oHM2IYSQsqCv8zY1tot4+/YtAHDm2iaEEEJK4+3bt7CysjJ0NT4YdM4mhBBSlkp73uYxdJudRSKR4OnTp7CwsChVnuiMjAy4u7vj0aNHsLS01GMNPyx0nLRDx0l7dKy0Q8dJO/o6TgzD4O3bt3B1daXUiHpE5+zyRcdJe3SstEPHSTt0nLSjz+Okr/M29WwXwefzUaVKFb3tz9LSkr4UWqDjpB06TtqjY6UdOk7a0cdxoh5t/aNztmHQcdIeHSvt0HHSDh0n7ejrOOnjvE231wkhhBBCCCGEED2jxjYhhBBCCCGEEKJn1NguI2KxGFOmTIFYLDZ0VSo0Ok7aoeOkPTpW2qHjpB06Th8H+n/WDh0n7dGx0g4dJ+3QcdJORTxOFCCNEEIIIYQQQgjRM+rZJoQQQgghhBBC9Iwa24QQQgghhBBCiJ5RY5sQQgghhBBCCNEzamyXkSVLlsDLywvGxsaoV68ezp07Z+gq6cXMmTNRp04dWFhYwNHREV26dEFiYiKrTHZ2NmJiYmBnZwdzc3N8+umneP78OatMcnIy2rdvD1NTUzg6OmLcuHHIz89nlTl69Chq164NsVgMX19frFq1SqU+leU4z5o1CzweD19++aV8GR0nhSdPnqBfv36ws7ODiYkJgoKCcOHCBfl6hmEwefJkuLi4wMTEBFFRUbhz5w5rH69fv0bfvn1haWkJa2trDB48GJmZmawyV65cQaNGjWBsbAx3d3fMnj1bpS6bN2+Gv78/jI2NERQUhN27d5fNh9ZRQUEBvv/+e3h7e8PExAQ+Pj6YNm0alMNufIzH6fjx4+jYsSNcXV3B4/Gwfft21vqKdEy0qQsxjIr+G1kadN7WHZ2zNaNzdvHonM3tozxnM0TvNmzYwIhEImblypXM9evXmSFDhjDW1tbM8+fPDV21UmvdujXz559/MteuXWMSEhKYdu3aMR4eHkxmZqa8zPDhwxl3d3fm0KFDzIULF5j69eszDRo0kK/Pz89natasyURFRTGXLl1idu/ezdjb2zMTJ06Ul7l//z5jamrKxMbGMjdu3GAWLVrECAQCZu/evfIyleU4nzt3jvHy8mKCg4OZMWPGyJfTcZJ6/fo14+npyQwaNIg5e/Ysc//+fWbfvn3M3bt35WVmzZrFWFlZMdu3b2cuX77MdOrUifH29mbev38vL9OmTRsmJCSEOXPmDHPixAnG19eX6d27t3x9eno64+TkxPTt25e5du0as379esbExIT57bff5GVOnTrFCAQCZvbs2cyNGzeY7777jjEyMmKuXr1aPgdDgxkzZjB2dnbMzp07mQcPHjCbN29mzM3NmQULFsjLfIzHaffu3cykSZOYrVu3MgCYbdu2sdZXpGOiTV1I+avov5GlRedt3dA5WzM6Z2uHztncPsZzNjW2y0DdunWZmJgY+euCggLG1dWVmTlzpgFrVTZSU1MZAMyxY8cYhmGYtLQ0xsjIiNm8ebO8zM2bNxkATFxcHMMw0i8an89nUlJS5GWWLVvGWFpaMjk5OQzDMMw333zDBAYGst6rZ8+eTOvWreWvK8Nxfvv2LVOtWjXmwIEDTJMmTeQnbjpOCuPHj2caNmyodr1EImGcnZ2ZX375Rb4sLS2NEYvFzPr16xmGYZgbN24wAJjz58/Ly+zZs4fh8XjMkydPGIZhmKVLlzI2NjbyYyd7bz8/P/nrHj16MO3bt2e9f7169Zhhw4aV7kPqQfv27ZnPPvuMteyTTz5h+vbtyzAMHSeGYVRO3BXpmGhTF2IYFf03Ut/ovK0enbOLR+ds7dA5u3gfyzmbhpHrWW5uLuLj4xEVFSVfxufzERUVhbi4OAPWrGykp6cDAGxtbQEA8fHxyMvLY31+f39/eHh4yD9/XFwcgoKC4OTkJC/TunVrZGRk4Pr16/IyyvuQlZHto7Ic55iYGLRv317ls9BxUtixYwfCw8PRvXt3ODo6olatWlixYoV8/YMHD5CSksL6DFZWVqhXrx7rWFlbWyM8PFxeJioqCnw+H2fPnpWXady4MUQikbxM69atkZiYiDdv3sjLaDqehtSgQQMcOnQIt2/fBgBcvnwZJ0+eRNu2bQHQceJSkY6JNnUh5a8y/EbqG5231aNzdvHonK0dOmfrriIdE32es6mxrWcvX75EQUEB68cWAJycnJCSkmKgWpUNiUSCL7/8EpGRkahZsyYAICUlBSKRCNbW1qyyyp8/JSWF8/jI1mkqk5GRgffv31eK47xhwwZcvHgRM2fOVFlHx0nh/v37WLZsGapVq4Z9+/ZhxIgRGD16NFavXg1A8Vk1fYaUlBQ4Ojqy1guFQtja2urleFaEYzVhwgT06tUL/v7+MDIyQq1atfDll1+ib9++AOg4calIx0SbupDyVxl+I/WJztvq0TlbO3TO1g6ds3VXkY6JPs/ZQp1KE6IkJiYG165dw8mTJw1dlQrn0aNHGDNmDA4cOABjY2NDV6dCk0gkCA8Px08//QQAqFWrFq5du4bly5dj4MCBBq5dxbFp0yasW7cOf//9NwIDA5GQkIAvv/wSrq6udJwIIVqh8zY3Omdrj87Z2qFzNpGhnm09s7e3h0AgUIlQ+fz5czg7OxuoVvo3atQo7Ny5E0eOHEGVKlXky52dnZGbm4u0tDRWeeXP7+zszHl8ZOs0lbG0tISJiUmFP87x8fFITU1F7dq1IRQKIRQKcezYMSxcuBBCoRBOTk50nAq5uLigRo0arGUBAQFITk4GoPismj6Ds7MzUlNTWevz8/Px+vVrvRzPinCsxo0bJ79THhQUhP79+2Ps2LHyXhg6Tqoq0jHRpi6k/FWG30h9ofO2enTO1h6ds7VD52zdVaRjos9zNjW29UwkEiEsLAyHDh2SL5NIJDh06BAiIiIMWDP9YBgGo0aNwrZt23D48GF4e3uz1oeFhcHIyIj1+RMTE5GcnCz//BEREbh69Srry3LgwAFYWlrKf8AjIiJY+5CVke2joh/nFi1a4OrVq0hISJA/wsPD0bdvX/lzOk5SkZGRKmlobt++DU9PTwCAt7c3nJ2dWZ8hIyMDZ8+eZR2rtLQ0xMfHy8scPnwYEokE9erVk5c5fvw48vLy5GUOHDgAPz8/2NjYyMtoOp6G9O7dO/D57J9sgUAAiUQCgI4Tl4p0TLSpCyl/leE3srTovF08Omdrj87Z2qFztu4q0jHR6zlbp3BqRCsbNmxgxGIxs2rVKubGjRvM0KFDGWtra1aEyspqxIgRjJWVFXP06FHm2bNn8se7d+/kZYYPH854eHgwhw8fZi5cuMBEREQwERER8vWy9BitWrViEhISmL179zIODg6c6THGjRvH3Lx5k1myZAlneozKdJyVI5syDB0nmXPnzjFCoZCZMWMGc+fOHWbdunWMqakps3btWnmZWbNmMdbW1sy///7LXLlyhencuTNnKohatWoxZ8+eZU6ePMlUq1aNlQoiLS2NcXJyYvr3789cu3aN2bBhA2NqaqqSCkIoFDJz5sxhbt68yUyZMqXCpBEZOHAg4+bmJk8jsnXrVsbe3p755ptv5GU+xuP09u1b5tKlS8ylS5cYAMzcuXOZS5cuMQ8fPmQYpmIdE23qQspfRf+NLC06b5cMnbO50TlbO3TO5vYxnrOpsV1GFi1axHh4eDAikYipW7cuc+bMGUNXSS8AcD7+/PNPeZn3798zI0eOZGxsbBhTU1Oma9euzLNnz1j7SUpKYtq2bcuYmJgw9vb2zFdffcXk5eWxyhw5coQJDQ1lRCIRU7VqVdZ7yFSm41z0xE3HSeG///5jatasyYjFYsbf35/5/fffWeslEgnz/fffM05OToxYLGZatGjBJCYmssq8evWK6d27N2Nubs5YWloy0dHRzNu3b1llLl++zDRs2JARi8WMm5sbM2vWLJW6bNq0ialevTojEomYwMBAZteuXfr/wCWQkZHBjBkzhvHw8GCMjY2ZqlWrMpMmTWKltvgYj9ORI0c4f5MGDhzIMEzFOiba1IUYRkX/jSwNOm+XDJ2z1aNzdvHonM3tYzxn8xiGYXTrCyeEEEIIIYQQQogmNGebEEIIIYQQQgjRM2psE0IIIYQQQgghekaNbUIIIYQQQgghRM+osU0IIYQQQgghhOgZNbYJIYQQQgghhBA9o8Y2IYQQQgghhBCiZ9TYJoQQQgghhBBC9Iwa24QQQgghhBBCiJ5RY5sQoleDBg1Cly5dDF0NQgghhBSDztmElC1qbBNSib148QIikQhZWVnIy8uDmZkZkpOTNW6j7sR69OhR8Hg8pKWllU1l1UhKSgKPx0NCQkK5vi8hhBBSnuicTcjHhxrbhFRicXFxCAkJgZmZGS5evAhbW1t4eHgYulqEEEIIKYLO2YR8fKixTUgldvr0aURGRgIATp48KX+uD1OnTkVoaCh+++03uLu7w9TUFD169EB6erq8TEFBAWJjY2FtbQ07Ozt88803YBiGtZ+9e/eiYcOG8jIdOnTAvXv35Ou9vb0BALVq1QKPx0PTpk3l6/73v/8hICAAxsbG8Pf3x9KlS+XrcnNzMWrUKLi4uMDY2Bienp6YOXOm3j4/IYQQok90zqZzNvn4CA1dAUKIbpKTkxEcHAwAePfuHQQCAVatWoX379+Dx+PB2toaffr0YZ3kSuru3bvYtGkT/vvvP2RkZGDw4MEYOXIk1q1bBwD49ddfsWrVKqxcuRIBAQH49ddfsW3bNjRv3ly+j6ysLMTGxiI4OBiZmZmYPHkyunbtioSEBPD5fJw7dw5169bFwYMHERgYCJFIBABYt24dJk+ejMWLF6NWrVq4dOkShgwZAjMzMwwcOBALFy7Ejh07sGnTJnh4eODRo0d49OhRqT8zIYQQoi90zqZzNvnIMYSQSiUvL4958OABc/nyZcbIyIi5fPkyc/fuXcbc3Jw5duwY8+DBA+bFixdqtx84cCDTuXNnleVHjhxhADBv3rxhGIZhpkyZwggEAubx48fyMnv27GH4fD7z7NkzhmEYxsXFhZk9ezarblWqVOHcv8yLFy8YAMzVq1cZhmGYBw8eMACYS5cuscr5+Pgwf//9N2vZtGnTmIiICIZhGOaLL75gmjdvzkgkErXvRQghhBgSnbPpnE0+bjSMnJBKRigUwsvLC7du3UKdOnUQHByMlJQUODk5oXHjxvDy8oK9vb1e3svDwwNubm7y1xEREZBIJEhMTER6ejqePXuGevXqseoWHh7O2sedO3fQu3dvVK1aFZaWlvDy8gIAjUFhsrKycO/ePQwePBjm5ubyx/Tp0+XD2QYNGoSEhAT4+flh9OjR2L9/v14+MyGEEKIvdM6mczb5uNEwckIqmcDAQDx8+BB5eXmQSCQwNzdHfn4+8vPzYW5uDk9PT1y/fl3t9paWlnj48KHK8rS0NAgEApiZmem1vh07doSnpydWrFgBV1dXSCQS1KxZE7m5uWq3yczMBACsWLGCdWEAAAKBAABQu3ZtPHjwAHv27MHBgwfRo0cPREVF4Z9//tFr/QkhhJCSonM2nbPJx416tgmpZHbv3o2EhAQ4Oztj7dq1SEhIQM2aNTF//nwkJCRg9+7dGrf38/PD9evXkZOTw1p+8eJFeHt7w8jISL4sOTkZT58+lb8+c+YM+Hw+/Pz8YGVlBRcXF5w9e1a+Pj8/H/Hx8fLXr169QmJiIr777ju0aNECAQEBePPmDet9ZfO9CgoK5MucnJzg6uqK+/fvw9fXl/WQBWcBpBchPXv2xIoVK7Bx40Zs2bIFr1+/1uYwEkIIIWWOztl0ziYfN+rZJqSS8fT0REpKCp4/f47OnTuDx+Ph+vXr+PTTT+Hi4lLs9n379sWPP/6IAQMG4JtvvoGVlRWOHz+O+fPnY/bs2ayyxsbGGDhwIObMmYOMjAyMHj0aPXr0gLOzMwBgzJgxmDVrFqpVqwZ/f3/MnTuXlfPTxsYGdnZ2+P333+Hi4oLk5GRMmDCB9R6Ojo4wMTHB3r17UaVKFRgbG8PKygo//PADRo8eDSsrK7Rp0wY5OTm4cOEC3rx5g9jYWMydOxcuLi6oVasW+Hw+Nm/eDGdnZ1hbW5f6GBNCCCH6QOdsOmeTj5yhJ40TQnS3fv16pmHDhgzDMMzx48cZX19fnbZPTExkunbtyri6ujJmZmZMSEgIs2LFClbgkilTpjAhISHM0qVLGVdXV8bY2Jjp1q0b8/r1a3mZvLw8ZsyYMYylpSVjbW3NxMbGMgMGDGAFWzlw4AATEBDAiMViJjg4mDl69CgDgNm2bZu8zIoVKxh3d3eGz+czTZo0kS9ft24dExoayoj+394dmzAMQ1EUdRo3BoM38QRuvKWHEJ5EeKOXIl2KQOCHBHLOBELN5yIJjWOWZcm2bWmtJUmO48i6rpmmKfM8Z9/39N7f2gcA+DQz28zmf92Spw/2AIbHn53neQ7XdX17KQDAC2Y2/CZvtgEAAKCY2AYAAIBirpEDAABAMSfbAAAAUExsAwAAQDGxDQAAAMXENgAAABQT2wAAAFBMbAMAAEAxsQ0AAADFxDYAAAAUE9sAAABQ7A52XcDdHHi+twAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lr = 5e-5\n",
    "res = rounding_evolution(n_updates=100000, decimals=7, lr=lr)\n",
    "fig, axs = plt.subplots(1, 2, figsize=(10, 4))\n",
    "axs[0].plot(np.array(list(res[False].values())).T)\n",
    "axs[0].legend(res[False].keys(), title='Decimals')\n",
    "axs[0].set_title(f'LR={lr} - Truncating/Rounding Down')\n",
    "axs[0].set_ylabel('Weight')\n",
    "axs[0].set_xlabel('# Updates')\n",
    "axs[1].plot(np.array(list(res[True].values())).T)\n",
    "axs[1].legend(res[True].keys(), title='Decimals')\n",
    "axs[1].set_title(f'LR={lr} - Stochastic Rounding')\n",
    "axs[1].set_ylabel('Weight')\n",
    "axs[1].set_xlabel('# Updates')\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### PyTorch's SDPA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = AutoModelForCausalLM.from_pretrained(\"facebook/opt-350m\", \n",
    "                                             device_map='cuda:0',\n",
    "                                             attn_implementation=\"sdpa\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Showdown"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Adapted from https://colab.research.google.com/drive/1_zuAiiBFoFWpexxeWsTS694tCSlMYydo\n",
    "def benchmark(model, inputs, n_iterations):\n",
    "    torch.cuda.reset_max_memory_allocated(device)\n",
    "    torch.cuda.reset_peak_memory_stats(device)\n",
    "    torch.cuda.empty_cache()\n",
    "    torch.cuda.synchronize()\n",
    "\n",
    "    start_event = torch.cuda.Event(enable_timing=True)\n",
    "    end_event = torch.cuda.Event(enable_timing=True)\n",
    "    start_event.record()\n",
    "\n",
    "    for _ in range(n_iterations):\n",
    "        _ = model(inputs)\n",
    "\n",
    "    end_event.record()\n",
    "    torch.cuda.synchronize()\n",
    "\n",
    "    max_memory = torch.cuda.max_memory_allocated(device)\n",
    "\n",
    "    return start_event.elapsed_time(end_event) / n_iterations, max_memory"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We should restart the kernel after each run so that we can start from a clean slate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OPTAttention(\n",
       "  (k_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "  (v_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "  (q_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "  (out_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "repo_id = 'facebook/opt-350m'\n",
    "device = torch.device(\"cuda:0\")\n",
    "attn_implementation = ['eager', 'sdpa', 'flash_attention_2']\n",
    "# change it to switch to a different implementation\n",
    "i = 0 # runs eager attention\n",
    "# We load the model into the CPU because we're only interested\n",
    "# in loading the attention mechanism into the GPU\n",
    "model = AutoModelForCausalLM.from_pretrained(repo_id, device_map='cpu', \n",
    "                                     torch_dtype=torch.float16, \n",
    "                                     attn_implementation=attn_implementation[i])\n",
    "# Attention mechanism in the first layer\n",
    "attn = model.model.decoder.layers[0].self_attn\n",
    "attn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 2\n",
    "sequence_length = 1024\n",
    "shape = (batch_size, sequence_length, model.config.hidden_size)\n",
    "inputs = torch.randn(shape, device=device).half()\n",
    "\n",
    "# warmup\n",
    "attn.to(device)\n",
    "_ = attn(inputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "benchmark(attn, inputs, 1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](https://github.com/dvgodoy/FineTuningLLMs/blob/main/images/ch5/attn_comparison.png?raw=True)\n",
    "<center>Table 5.1 - Comparing the performance of attention’s implementations</center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Studies, Ablation-Style"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](https://github.com/dvgodoy/FineTuningLLMs/blob/main/images/ch5/mem_16_seq500.png?raw=True)\n",
    "\n",
    "<center>Table 5.2 - Peak memory usage for a 16-bit OPT-350M and sequence length of 500</center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](https://github.com/dvgodoy/FineTuningLLMs/blob/main/images/ch5/mem_16_seq2k.png?raw=True)\n",
    "\n",
    "<center>Table 5.3 - Peak memory usage for a 16-bit OPT-350M and sequence length of 2,000</center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](https://github.com/dvgodoy/FineTuningLLMs/blob/main/images/ch5/mem_8_opt.png?raw=True)\n",
    "\n",
    "<center>Table 5.4 - Peak memory usage for a quantized OPT-350M</center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](https://github.com/dvgodoy/FineTuningLLMs/blob/main/images/ch5/mem_8_falcon.png?raw=True)\n",
    "<center>Table 5.5 - Peak memory usage for a quantized Falcon 7B</center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Coming Up in \"Fine-Tuning LLMs\"\n",
    "\n",
    "Trading compute for memory was one of the key ingredients that led to the successful fine-tuning of the LLMs. They learned how to converse and how to efficiently pay attention to every token they receive. They’re finally ready to be deployed in the real world, where they’ll face their biggest challenge yet: the end user’s environment. Deprived of their GPUs and relying on their wits and quantization alone, they’ll be put to the test. Stay tuned for the final chapter of \"Fine-Tuning LLMs.\""
   ]
  }
 ],
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