02_single_task_models/Preference/KRBERT_preference_task.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tvtwH9ggGf1G"
   },
   "source": [
    "### <b>Install the required libraries</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "zeHuY4y0ICJ-",
    "outputId": "d4246bc2-1b49-48b9-fb07-7d8e34dabbc7"
   },
   "outputs": [],
   "source": [
    "# Install the transformers library (Allows importing models from Hugging Face)\n",
    "!pip install transformers\n",
    "!pip install datasets "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9z-vERIKbQiT"
   },
   "source": [
    "### <b>Import necessary libraries</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "LH2ySA1QbQzw"
   },
   "outputs": [],
   "source": [
    "import copy \n",
    "import json\n",
    "import logging \n",
    "import os \n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "import logging\n",
    "logging.basicConfig(level=logging.ERROR)\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from datasets import load_dataset\n",
    "from sklearn.model_selection import train_test_split\n",
    "from tqdm import tqdm\n",
    "import torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CUDA Available: True\n",
      "Current GPU Index: 0\n",
      "Current GPU Name: GeForce RTX 2080 Ti\n",
      "GPU Memory Allocated: 0\n",
      "GPU Memory Cached: 0\n"
     ]
    }
   ],
   "source": [
    "# Check if CUDA is available\n",
    "print(\"CUDA Available:\", torch.cuda.is_available())\n",
    "\n",
    "# If CUDA is available, print the current GPU details\n",
    "if torch.cuda.is_available():\n",
    "    print(\"Current GPU Index:\", torch.cuda.current_device())\n",
    "    print(\"Current GPU Name:\", torch.cuda.get_device_name(torch.cuda.current_device()))\n",
    "    print(\"GPU Memory Allocated:\", torch.cuda.memory_allocated())\n",
    "    print(\"GPU Memory Cached:\", torch.cuda.memory_cached())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4vCdR9qwGjei"
   },
   "source": [
    "### <b>Load the model from Hugging Face's model hub</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "plGoHyuyZ79h"
   },
   "outputs": [],
   "source": [
    "import transformers\n",
    "from transformers import AutoConfig, AutoModelForSequenceClassification, AutoTokenizer\n",
    "from transformers import AdamW, get_linear_schedule_with_warmup\n",
    "from transformers import BigBirdConfig, AutoConfig, AutoModel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "XwuHa80eR3dC",
    "outputId": "b0ed45fe-25f7-4f6f-b4e6-7a4b842fcd90"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no truncation.\n"
     ]
    }
   ],
   "source": [
    "from transformers import AutoModel, AutoTokenizer, BigBirdConfig\n",
    "\n",
    "model_path = \"snunlp/KR-Medium\"\n",
    "model = AutoModel.from_pretrained(model_path)\n",
    "\n",
    "tokenizer = AutoTokenizer.from_pretrained(model_path, do_lower_case=False)\n",
    "\n",
    "text = \"한국어 KR-BERT 모델을 공개합니다!\"\n",
    "encoded_input = tokenizer(text, return_tensors='pt', truncation=True)\n",
    "output = model(**encoded_input)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[[ 0.3643,  0.6459, -0.2313,  ...,  0.6451, -0.1159,  0.3841],\n",
      "         [-0.0726,  1.3123,  0.5474,  ...,  1.5355,  0.4892,  0.2628],\n",
      "         [-0.2217,  1.5919, -0.4332,  ...,  0.1309, -0.0770,  0.3442],\n",
      "         ...,\n",
      "         [-0.6567,  0.5202, -0.1853,  ...,  0.4653,  0.0383,  0.7940],\n",
      "         [-0.0108,  0.5530, -0.1801,  ...,  0.4510, -0.4076,  0.3430],\n",
      "         [ 0.1201,  0.7531, -0.3095,  ...,  0.6505,  0.5646,  0.0246]]],\n",
      "       grad_fn=<NativeLayerNormBackward0>)\n",
      "torch.Size([1, 16, 768])\n"
     ]
    }
   ],
   "source": [
    "# Print the values of the final hidden state\n",
    "print(output[0])\n",
    "\n",
    "# Print the shape of the final hidden state\n",
    "print(output[0].shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OBlLlRQJHx1m"
   },
   "source": [
    "### <b>Set hyperparameters for the experiment</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "-OAqKcbuH1Tm"
   },
   "outputs": [],
   "source": [
    "from types import SimpleNamespace\n",
    "\n",
    "# Create a configuration object to store hyperparameters and other settings\n",
    "config = SimpleNamespace()\n",
    "\n",
    "config.task = \"krbert_preference\"\n",
    "config.dataset = \"KoMultiText\"\n",
    "\n",
    "config.cache_dir = \"krbert_preference_cache\" # Temporary directory for current task assets like vocabulary\n",
    "config.output_dir = \"krbert_preference_output\" # Directory where final models and results will be saved\n",
    "\n",
    "config.use_tpu = False\n",
    "config.model_name_or_path = model_path \n",
    "config.data_dir = \"./\" # The input data dir \n",
    "\n",
    "config.train_file = \"train_dataset.csv\" \n",
    "config.predict_file = \"test_dataset.csv\" \n",
    "\n",
    "config.max_seq_length = 512 # Maximum sequence length\n",
    "config.train_batch_size = 8 # Batch size for training\n",
    "config.eval_batch_size = 4 # Batch size for evaluation\n",
    "\n",
    "config.learning_rate = 3e-6 # The initial learning rate for Adam\n",
    "config.num_train_epochs = 20 # Total number of training epochs to perform\n",
    "\n",
    "config.gradient_accumulation_steps = 4 # Number of updates steps to accumulate before performing a backward/update pass.\n",
    "config.threads = 4\n",
    "config.seed = 42 \n",
    "\n",
    "config.device_num = 0 # Device number (changable for multi-GPU setups)\n",
    "\n",
    "config.do_train = True \n",
    "config.do_eval_during_train = True\n",
    "config.do_eval = True \n",
    "\n",
    "config.do_lower_case = False\n",
    "config.weight_decay = 0.0 # Weight decay if we apply some.\n",
    "config.adam_epsilon = 1e-8 # Epsilon for Adam optimizer.\n",
    "config.max_grad_norm = 1.0 # Max gradient norm.\n",
    "config.warmup_proportion = 0.0 # Warmup proportion for linear warmup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <b>Preprocessing the training dataset</b>\n",
    "* Instead of tokenizing the training text every time, we preprocess and <b>save all tokenized results before starting the model training</b>.\n",
    "    * This approach significantly speeds up the process."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "2gJGxD9bYBTK"
   },
   "outputs": [],
   "source": [
    "tokenizer = AutoTokenizer.from_pretrained(config.model_name_or_path, do_lower_case=False, cache_dir=config.cache_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "7QpRhocwKqhl"
   },
   "outputs": [],
   "source": [
    "def train_split(config, texts, labels, is_train):\n",
    "    x_train, y_train, x_label, y_label = train_test_split(\n",
    "        texts, labels, test_size=0.2, random_state=config.seed, stratify=None\n",
    "    )\n",
    "    if is_train:\n",
    "        texts, labels = x_train, x_label\n",
    "    else:\n",
    "        texts, labels = y_train, y_label\n",
    "        \n",
    "    return texts, labels\n",
    "\n",
    "def process_comment_cls(config, data_file, is_train):\n",
    "    df = pd.read_csv(data_file)\n",
    "    df = df[df[\"preference\"]!= -1] # Exclude rows where preference is -1\n",
    "    preferences = df[\"preference\"].astype(int).values.tolist()\n",
    "    labels = preferences\n",
    "    texts = df[\"comment\"].astype(str).values.tolist()\n",
    "    texts, labels = train_split(config, texts, labels, is_train)\n",
    "    \n",
    "    return texts, labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "tAq-i9SuLta-"
   },
   "outputs": [],
   "source": [
    "import torch.utils.data as torch_data\n",
    "\n",
    "def data_pretokenizing(config, tokenizer, is_train=True):\n",
    "    if is_train:\n",
    "        data_file = config.train_file\n",
    "    else:\n",
    "        data_file = config.predict_file\n",
    "\n",
    "    data_path = config.data_dir\n",
    "    if data_file is not None:\n",
    "        data_path = os.path.join(data_path, data_file)\n",
    "    else:\n",
    "        data_path += \"/\"\n",
    "    \n",
    "    # Construct the file name for the tokenized dataset\n",
    "    comps = [\n",
    "        data_path,\n",
    "        config.dataset,\n",
    "        config.model_name_or_path.replace(\"/\", \"_\"),\n",
    "        config.max_seq_length,\n",
    "        \"train\" if is_train else \"dev\",\n",
    "        \"dataset.txt\",\n",
    "    ]\n",
    "    dataset_file = \"_\".join([str(comp) for comp in comps])\n",
    "    print(\"dataset_file:\", dataset_file)\n",
    "    \n",
    "    # Tokenize the dataset and write to the file\n",
    "    with open(dataset_file, \"w\", encoding=\"utf-8\") as writer_file:\n",
    "        cnt = 0\n",
    "        total_data = process_comment_cls(config, data_path, is_train)\n",
    "        for text, label in zip(total_data[0], total_data[1]):\n",
    "            feature = tokenizer(text, max_length=config.max_seq_length, padding=\"max_length\", truncation=True, add_special_tokens=True)\n",
    "            writed_data = {\n",
    "                \"input_ids\": feature[\"input_ids\"],\n",
    "                \"attention_mask\": feature[\"attention_mask\"],\n",
    "                \"preference\": int(label), \n",
    "            }\n",
    "            writer_file.write(json.dumps(writed_data) + \"\\n\")\n",
    "            cnt += 1\n",
    "        print(f\"{cnt} features processed from {data_path}\")\n",
    "\n",
    "    return dataset_file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "WYuakKfzXid0",
    "outputId": "8a212719-1cd8-4ee7-f479-ca0eda77f16a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dataset_file: ./train_dataset.csv_KoMultiText_snunlp_KR-Medium_512_train_dataset.txt\n",
      "30688 features processed from ./train_dataset.csv\n",
      "dataset_file: ./test_dataset.csv_KoMultiText_snunlp_KR-Medium_512_dev_dataset.txt\n",
      "400 features processed from ./test_dataset.csv\n"
     ]
    }
   ],
   "source": [
    "if config.do_train:\n",
    "    train_dataset_file = data_pretokenizing(config, tokenizer=tokenizer)\n",
    "predict_dataset_file = data_pretokenizing(config, tokenizer=tokenizer, is_train=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dH-Uc4zFaZg9"
   },
   "source": [
    "### <b>Initialize the data loader</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "9T_kyo4kV_Cr"
   },
   "outputs": [],
   "source": [
    "class IterableDatasetPad(torch.utils.data.IterableDataset):\n",
    "    def __init__(\n",
    "        self,\n",
    "        dataset: torch.utils.data.IterableDataset,\n",
    "        batch_size: int = 1,\n",
    "        num_devices: int = 1,\n",
    "        seed: int = 0,\n",
    "    ):\n",
    "        self.dataset = dataset\n",
    "        self.batch_size = batch_size\n",
    "        self.seed = seed\n",
    "        self.num_examples = 0\n",
    "\n",
    "        chunk_size = self.batch_size * num_devices\n",
    "        length = len(dataset)\n",
    "        self.length = length + (chunk_size - length % chunk_size)\n",
    "\n",
    "    def __len__(self):\n",
    "        return self.length\n",
    "\n",
    "    def __iter__(self):\n",
    "        self.num_examples = 0\n",
    "        # If dataset has a set epoch method, set the manual seed for reproducibility\n",
    "        if (\n",
    "            not hasattr(self.dataset, \"set_epoch\")\n",
    "            and hasattr(self.dataset, \"generator\")\n",
    "            and isinstance(self.dataset.generator, torch.Generator)\n",
    "        ):\n",
    "            self.dataset.generator.manual_seed(self.seed + self.epoch)\n",
    "\n",
    "        first_batch = None\n",
    "        current_batch = []\n",
    "        for element in self.dataset:\n",
    "            self.num_examples += 1\n",
    "            current_batch.append(element)\n",
    "            # Yield the current batch once it's full\n",
    "            if len(current_batch) == self.batch_size:\n",
    "                for batch in current_batch:\n",
    "                    yield batch\n",
    "                    if first_batch is None:\n",
    "                        first_batch = batch.copy()\n",
    "                current_batch = []\n",
    "                \n",
    "        # Pad the last batch if it's smaller than the batch size\n",
    "        while self.num_examples < self.length:\n",
    "            add_num = self.batch_size - len(current_batch)\n",
    "            self.num_examples += add_num\n",
    "            current_batch += [first_batch] * add_num\n",
    "            for batch in current_batch:\n",
    "                yield batch\n",
    "            current_batch = []"
   ]
  },
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    },
    "id": "ICk8UkNtUT6L",
    "outputId": "659c2c15-4976-4bbf-dae1-d9caacb691cb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading and preparing dataset text/default to /home/dasol/.cache/huggingface/datasets/text/default-20834d509ca6c7cf/0.0.0/cb1e9bd71a82ad27976be3b12b407850fe2837d80c22c5e03a28949843a8ace2...\n"
     ]
    },
    {
     "data": {
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       "model_id": "19bff159fb214693ae3dcf2ece86f7d5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading data files:   0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cebda5f61a3342d98d6281ebd5b63448",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating train split: 0 examples [00:00, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset text downloaded and prepared to /home/dasol/.cache/huggingface/datasets/text/default-20834d509ca6c7cf/0.0.0/cb1e9bd71a82ad27976be3b12b407850fe2837d80c22c5e03a28949843a8ace2. Subsequent calls will reuse this data.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9792eac0583b48cdab6cf93b2005cb9d",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading and preparing dataset text/default to /home/dasol/.cache/huggingface/datasets/text/default-a4bee32796360cd5/0.0.0/cb1e9bd71a82ad27976be3b12b407850fe2837d80c22c5e03a28949843a8ace2...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "364c8d0824ac4dbb860f84b56111bc27",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2d6b9d8fde304ebda46e6e27d60735a9",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating train split: 0 examples [00:00, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset text downloaded and prepared to /home/dasol/.cache/huggingface/datasets/text/default-a4bee32796360cd5/0.0.0/cb1e9bd71a82ad27976be3b12b407850fe2837d80c22c5e03a28949843a8ace2. Subsequent calls will reuse this data.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "86f3b8287e1e4e629ad8c9b5a4633490",
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     },
     "metadata": {},
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    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
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       "version_minor": 0
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def collate_fn(features):\n",
    "    \n",
    "    input_ids = [sample[\"input_ids\"] for sample in features]\n",
    "    attention_mask = [sample[\"attention_mask\"] for sample in features]\n",
    "    \n",
    "    preference = [sample[\"preference\"] for sample in features]\n",
    "\n",
    "    input_ids = torch.tensor(np.array(input_ids).astype(np.int64), dtype=torch.long)\n",
    "    attention_mask = torch.tensor(np.array(attention_mask).astype(np.int8), dtype=torch.long)\n",
    "    \n",
    "    preference = torch.tensor(np.array(preference).astype(np.int64), dtype=torch.long)\n",
    "    inputs = {\n",
    "        \"input_ids\": input_ids,\n",
    "        \"attention_mask\": attention_mask,\n",
    "    }\n",
    "    labels =  preference\n",
    "\n",
    "    return inputs, labels\n",
    "\n",
    "if config.do_train:\n",
    "    train_dataset = load_dataset(\"text\", data_files=train_dataset_file, download_mode=\"force_redownload\")[\"train\"]\n",
    "    train_dataset = train_dataset.map(lambda x: json.loads(x[\"text\"]), batched=False)\n",
    "\n",
    "    train_dataloader = torch_data.DataLoader(\n",
    "        train_dataset,\n",
    "        sampler=torch_data.RandomSampler(train_dataset),\n",
    "        drop_last=False,\n",
    "        batch_size=config.train_batch_size,\n",
    "        collate_fn=(collate_fn),\n",
    "    )\n",
    "\n",
    "predict_dataset = load_dataset(\"text\", data_files=predict_dataset_file, download_mode=\"force_redownload\")[\"train\"]\n",
    "predict_dataset = predict_dataset.map(lambda x: json.loads(x[\"text\"]), batched=False)\n",
    "predict_dataset = IterableDatasetPad(\n",
    "    dataset=predict_dataset,\n",
    "    batch_size=config.eval_batch_size,\n",
    "    num_devices=1,\n",
    "    seed=config.seed,\n",
    ")\n",
    "\n",
    "predict_dataloader = torch_data.DataLoader(\n",
    "    predict_dataset,\n",
    "    sampler=None,\n",
    "    drop_last=False,\n",
    "    batch_size=config.eval_batch_size,\n",
    "    collate_fn=(collate_fn),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fLK23Y3wauv5"
   },
   "source": [
    "### <b>Define the text classification model</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "EzordVjHE52u"
   },
   "outputs": [],
   "source": [
    "from transformers import AutoModel\n",
    "import torch.nn as nn\n",
    "\n",
    "class ClsModel(torch.nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        data_file = os.path.join(config.data_dir, str(config.train_file))  \n",
    "        model_config = AutoConfig.from_pretrained(config.model_name_or_path, num_labels=5)\n",
    "        self.model = AutoModel.from_pretrained(\n",
    "            config.model_name_or_path, config=model_config, cache_dir=config.cache_dir\n",
    "        )\n",
    "        self.classifier = nn.Linear(768, 5) \n",
    "        self.tokenizer = tokenizer\n",
    "\n",
    "    def save_pretrained(self, save_dir):\n",
    "        self.model.save_pretrained(save_dir)\n",
    "        # Remove unnecessary keys from the tokenizer's init_kwargs\n",
    "        for key in [\"special_tokens_map_file\", \"tokenizer_file\"]:\n",
    "            self.tokenizer.init_kwargs.pop(key, None)\n",
    "        self.tokenizer.save_pretrained(save_dir)\n",
    "\n",
    "    def get_optimizer(self): \n",
    "        # Specify parameters that should not undergo weight decay during training\n",
    "        no_decay = [\"bias\", \"LayerNorm.weight\"]\n",
    "        # Group model parameters based on whether they should have weight decay or not\n",
    "        optimizer_grouped_parameters = [\n",
    "            {\n",
    "                \"params\": [p for n, p in self.model.named_parameters() if not any(nd in n for nd in no_decay)],\n",
    "                \"weight_decay\": config.weight_decay,\n",
    "            },\n",
    "            {\n",
    "                \"params\": [p for n, p in self.model.named_parameters() if any(nd in n for nd in no_decay)],\n",
    "                \"weight_decay\": config.weight_decay,\n",
    "            },\n",
    "        ]\n",
    "        \n",
    "        optimizer = AdamW(optimizer_grouped_parameters, lr=config.learning_rate, eps=config.adam_epsilon)\n",
    "        return optimizer\n",
    "\n",
    "    def get_scheduler(self, batch_num, optimizer): \n",
    "        # If no warmup is needed, return None\n",
    "        if config.warmup_proportion == 0.0:\n",
    "            return None\n",
    "        \n",
    "        # Calculate the total number of training steps\n",
    "        t_total = batch_num // config.gradient_accumulation_steps * config.num_train_epochs\n",
    "        \n",
    "        # Define the learning rate scheduler with a linear warmup phase\n",
    "        scheduler = get_linear_schedule_with_warmup(\n",
    "            optimizer,\n",
    "            num_warmup_steps=int(t_total * config.warmup_proportion),\n",
    "            num_training_steps=t_total,\n",
    "        )\n",
    "\n",
    "        return scheduler\n",
    "\n",
    "    def forward(self, inputs):\n",
    "        hidden = self.model(**inputs) # Get the model's hidden states\n",
    "        # Extract [CLS] token from the last layer\n",
    "        cls_token_embeddings = hidden.last_hidden_state[:,0,:] # [batch_size, 768]\n",
    "        output = self.classifier(cls_token_embeddings) # 768 → 5\n",
    "\n",
    "        return output\n",
    "    \n",
    "    def eval_step(self, inputs, labels, outputs):\n",
    "        logits = outputs.detach().cpu()\n",
    "        labels = self.tensor_to_list(labels)\n",
    "        probabilities = torch.nn.functional.softmax(logits, dim=1)\n",
    "        predictions = self.tensor_to_list(torch.argmax(logits, dim=-1))\n",
    "        results = {\"prediction\": predictions, \"label\": labels, \"prob\":probabilities} \n",
    "        \n",
    "        return results\n",
    "\n",
    "    def tensor_to_array(self, tensor):\n",
    "        return tensor.detach().cpu().numpy()\n",
    "    \n",
    "    def tensor_to_list(self, tensor):\n",
    "        return self.tensor_to_array(tensor).tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "WNc_ouz_c2oK"
   },
   "outputs": [],
   "source": [
    "# Set the random seed for reproducibility in both numpy and PyTorch\n",
    "def set_seed(seed):\n",
    "    np.random.seed(seed)\n",
    "    torch.manual_seed(seed)\n",
    "    torch.cuda.manual_seed_all(seed)\n",
    "    torch.backends.cudnn.deterministic = True\n",
    "    torch.backends.cudnn.benchmark = True\n",
    "\n",
    "# Calculate the running average of the loss using a decay factor\n",
    "def cal_running_avg_loss(loss, running_avg_loss, decay=0.99):\n",
    "    if running_avg_loss == 0:\n",
    "        return loss\n",
    "    running_avg_loss = running_avg_loss * decay + (1 - decay) * loss\n",
    "    return running_avg_loss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9ZKdxuz1fT_C"
   },
   "source": [
    "### <b>Define the functions for training and evaluation</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "Dt87LWjVfR4q"
   },
   "outputs": [],
   "source": [
    "from functools import partial\n",
    "import sklearn.metrics as sklearn_metrics\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from collections import Counter\n",
    "from sklearn.metrics import f1_score\n",
    "\n",
    "# Function to compute top 2 accuracy. \n",
    "# This considers a prediction as correct if the true label is among the top 2 predicted labels.\n",
    "def top2_accuracy(labels, prob):\n",
    "    prob_concatenated = torch.cat(prob, dim=0) \n",
    "    top_2_indices = torch.topk(prob_concatenated, 2, dim=1)[1]\n",
    "    match = (top_2_indices == torch.tensor(labels).view(-1, 1)).any(dim=1)\n",
    "    return match.float().mean().item()\n",
    "\n",
    "# Function to compute top 2 F1 score in a macro-average setting. \n",
    "# This treats each class independently then averages the F1 scores.\n",
    "def top2_f1_macro(labels, prob):\n",
    "    prob_concatenated = torch.cat(prob, dim=0)  # Concatenate tensors along dimension 0\n",
    "    top_2_indices = torch.topk(prob_concatenated, 2, dim=1)[1]\n",
    "    labels = np.array(labels)\n",
    "    \n",
    "    # Create a binary matrix where each row is the top-2 predicted classes\n",
    "    top_2_pred = np.zeros((len(labels), prob_concatenated.shape[1]), dtype=int)\n",
    "    for i, indices in enumerate(top_2_indices):\n",
    "        top_2_pred[i, indices] = 1\n",
    "\n",
    "    # Create a binary vector where each element indicates the actual class\n",
    "    y_true = np.zeros((len(labels), prob_concatenated.shape[1]), dtype=int)\n",
    "    y_true[np.arange(len(labels)), labels] = 1\n",
    "    \n",
    "    return f1_score(y_true, top_2_pred, average='macro')\n",
    "\n",
    "# Metrics for multi-classification\n",
    "metrics = {\n",
    "    \"accuracy\": sklearn_metrics.accuracy_score,\n",
    "    \"f1-macro\": partial(sklearn_metrics.f1_score, average=\"macro\")\n",
    "}\n",
    "\n",
    "# Evaluate the performance of the model\n",
    "def eval_cls(predictions, labels, prob, **kwargs):\n",
    "    \n",
    "    predictions = np.array(predictions)\n",
    "\n",
    "    results = {\n",
    "        metric: round(f(labels, predictions) * 100, 2)\n",
    "        for metric, f in metrics.items()\n",
    "    }\n",
    "    \n",
    "    mae = mean_absolute_error(labels, predictions)\n",
    "    top2_acc = top2_accuracy(labels, prob)\n",
    "    top2_f1_macro_score = top2_f1_macro(labels, prob)\n",
    "\n",
    "    return {\n",
    "        \"results\": results,\n",
    "        \"f1-macro\": results[\"f1-macro\"],\n",
    "        \"accuracy\" : results[\"accuracy\"],\n",
    "        \"mae\" : mae,\n",
    "        \"top2_accuracy\" : top2_acc,\n",
    "        \"top2_f1_macro\" : top2_f1_macro_score\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "orQFvXpcfVop"
   },
   "outputs": [],
   "source": [
    "def _run_epoch(model, loader, device=None, context=None, **kwargs):\n",
    "    \n",
    "    config = kwargs[\"config\"]\n",
    "    is_train = kwargs[\"is_train\"]\n",
    "\n",
    "    avg_loss = 0\n",
    "    results = []\n",
    "    batch_num = len(loader)\n",
    "    corrected = 0\n",
    "    total = 0\n",
    "    \n",
    "    # Set the model to train or eval mode based on 'is_train'\n",
    "    if is_train:\n",
    "        model.train()\n",
    "        if config.use_tpu:\n",
    "            optimizer = context.getattr_or(\n",
    "                \"optimizer\",\n",
    "                lambda: model.get_optimizer(),\n",
    "            )\n",
    "            scheduler = context.getattr_or(\n",
    "                \"scheduler\",\n",
    "                lambda: model.get_scheduler(batch_num, optimizer),\n",
    "            )\n",
    "        else:\n",
    "            optimizer = kwargs[\"optimizer\"]\n",
    "            scheduler = kwargs[\"scheduler\"]\n",
    "    else:\n",
    "        model.eval()\n",
    "    \n",
    "    # ProgressBar setup\n",
    "    is_master = True\n",
    "    pbar = tqdm(enumerate(loader), total=batch_num, disable=not is_master, dynamic_ncols=True, position=0, leave=True)\n",
    "    \n",
    "    for i, (inputs, labels) in pbar:\n",
    "        # Move data to the appropriate device (if not using TPU)\n",
    "        if not config.use_tpu:\n",
    "            if isinstance(inputs, torch.Tensor):\n",
    "                inputs = inputs.to(device)\n",
    "            if isinstance(labels, torch.Tensor):\n",
    "                labels = labels.to(device)\n",
    "\n",
    "        outputs = model(inputs)\n",
    "        labels = labels\n",
    "        \n",
    "        # Handling class imbalance using weights\n",
    "        class_percentages = [11, 31, 50, 6, 2]\n",
    "        most_frequent_class_percentage = max(class_percentages)\n",
    "        weights_normalized = [most_frequent_class_percentage / p for p in class_percentages]\n",
    "        weights = torch.tensor(weights_normalized).float().to(device)\n",
    "        \n",
    "        # Compute the loss\n",
    "        loss_function = nn.CrossEntropyLoss(weight=weights)\n",
    "        loss = loss_function(outputs, labels) \n",
    "\n",
    "        total += outputs.shape[0] # Update total samples processed\n",
    "        _, predicted = outputs.max(1)\n",
    "        corrected += predicted.eq(labels).sum().item() # Update correctly classified count\n",
    "\n",
    "        avg_loss = cal_running_avg_loss(loss.item(), avg_loss)\n",
    "        loss /= config.gradient_accumulation_steps\n",
    "        \n",
    "        # Backpropagation and optimization step (for training)\n",
    "        if is_train:\n",
    "            loss.backward()\n",
    "            if i % config.gradient_accumulation_steps == 0 or i == batch_num - 1:\n",
    "                if config.max_grad_norm > 0:\n",
    "                    torch.nn.utils.clip_grad_norm_(model.parameters(), config.max_grad_norm)\n",
    "\n",
    "                optimizer.step()\n",
    "                optimizer.zero_grad()\n",
    "\n",
    "                if scheduler is not None:\n",
    "                    scheduler.step()\n",
    "        # Store evaluation results (for evaluation mode)\n",
    "        else:\n",
    "            result = (model.module if hasattr(model, \"module\") else model).eval_step(inputs, labels, outputs)\n",
    "            results.append(result)\n",
    "            \n",
    "        # Update progress bar description\n",
    "        if is_master:\n",
    "            pbar.set_description(\n",
    "                f\"epoch: {kwargs['epoch'] + 1}, {('train' if is_train else 'valid')} loss: {min(100, round(avg_loss, 4))}\"\n",
    "            )\n",
    "            \n",
    "    if is_train:\n",
    "        epoch_accuracy = (corrected / total) * 100  # Compute accuracy\n",
    "        print(f\"Training accuracy after epoch {kwargs['epoch'] + 1}: {epoch_accuracy:.2f}%\")\n",
    "              \n",
    "    return {\n",
    "        \"loss\": avg_loss,\n",
    "        \"result\": results,\n",
    "    }\n",
    "\n",
    "# Wrapper to run the epoch either on CPU/GPU or TPUs\n",
    "def run_epoch(**kwargs):\n",
    "    model = kwargs.pop(\"model\")\n",
    "    if kwargs[\"config\"].use_tpu:\n",
    "        results = model(_run_epoch, **kwargs)\n",
    "    else:\n",
    "        results = _run_epoch(model, **kwargs)\n",
    "   \n",
    "    if isinstance(results, list):\n",
    "        loss = sum([result[\"loss\"] for result in results]) / len(results)\n",
    "        result = []\n",
    "        for res in results:\n",
    "            result.extend(res[\"result\"])\n",
    "        results = {\"loss\": loss, \"result\": result}\n",
    "\n",
    "    return results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Fc_gQCjxffEY"
   },
   "source": [
    "### <b>Model Initialization and Configuration</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ZOns-W1GfemT",
    "outputId": "5acc4dd2-2f77-43ed-db0a-fb88df3c9a49"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "configuration: namespace(task='krbert_preference', dataset='KoMultiText', cache_dir='krbert_preference_cache', output_dir='krbert_preference_output', use_tpu=False, model_name_or_path='snunlp/KR-Medium', data_dir='./', train_file='train_dataset.csv', predict_file='test_dataset.csv', max_seq_length=512, train_batch_size=8, eval_batch_size=4, learning_rate=3e-06, num_train_epochs=20, gradient_accumulation_steps=4, threads=4, seed=42, device_num=0, do_train=True, do_eval_during_train=True, do_eval=True, do_lower_case=False, weight_decay=0.0, adam_epsilon=1e-08, max_grad_norm=1.0, warmup_proportion=0.0)\n",
      "4 GPU device detected\n",
      "Output directory: krbert_preference_output/krbert_preference/KoMultiText\n"
     ]
    }
   ],
   "source": [
    "set_seed(config.seed) # Set the random seed for reproducibility\n",
    "\n",
    "model = ClsModel()  # Initialize the classification model\n",
    "\n",
    "print(f\"configuration: {str(config)}\")\n",
    "\n",
    "# Check for available GPUs and set the model to run on GPU(s) if available\n",
    "if torch.cuda.is_available():\n",
    "    gpu_count = torch.cuda.device_count()\n",
    "    print(f\"{gpu_count} GPU device detected\")\n",
    "    device_num = config.device_num\n",
    "    devices = [device_num]\n",
    "    \n",
    "    # Use Data Parallelism if more than one GPU is available\n",
    "    model_dp = torch.nn.DataParallel(model, device_ids=devices)\n",
    "    model_dp.to(devices[0])  \n",
    "else: \n",
    "    devices = [\"cpu\"] # If no GPUs are available, use the CPU\n",
    "    model_dp = model\n",
    "\n",
    "if not os.path.exists(config.cache_dir):\n",
    "    os.makedirs(config.cache_dir)\n",
    "\n",
    "# Define and create the output directory\n",
    "output_dir = os.path.join(config.output_dir, config.task, config.dataset)\n",
    "print(\"Output directory:\", output_dir)\n",
    "if not os.path.exists(output_dir):\n",
    "    os.makedirs(output_dir)\n",
    "\n",
    "# Initialize optimizer and scheduler if in training mode\n",
    "optimizer = None\n",
    "scheduler = None\n",
    "if config.do_train: \n",
    "    optimizer = model.get_optimizer()\n",
    "    scheduler = model.get_scheduler(len(train_dataloader), optimizer)\n",
    "\n",
    "params = {\n",
    "    \"config\": config,\n",
    "    \"model\": model_dp,\n",
    "    \"optimizer\": optimizer,\n",
    "    \"scheduler\": scheduler,\n",
    "}\n",
    "if not config.use_tpu:\n",
    "    params[\"device\"] = devices[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "iziDJg23gorw"
   },
   "source": [
    "### <b>Traning the model</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ibdTiq1Ue2D8",
    "outputId": "53383735-c930-44c2-93c4-2e6ff330e766"
   },
   "outputs": [],
   "source": [
    "from collections import defaultdict\n",
    "\n",
    "def do_eval(epoch):\n",
    "    # Ensure no gradient computation during evaluation\n",
    "    with torch.no_grad():\n",
    "        # Run the model on the predict_dataloader and obtain results\n",
    "        results = run_epoch(loader=predict_dataloader, epoch=epoch, is_train=False, **params)[\"result\"]\n",
    "        predictions = [item['prediction'] for item in results]\n",
    "        labels = [item['label'] for item in results]\n",
    "        prob = [item['prob'] for item in results]\n",
    "        predictions = np.array([subitem for sublist in predictions for subitem in sublist])\n",
    "        labels = np.array([subitem for sublist in labels for subitem in sublist])\n",
    "        predictions = np.array(predictions)\n",
    "        labels = np.array(labels)\n",
    "        \n",
    "        # Initialize class-wise counts\n",
    "        class_counts = defaultdict(int)  # Count of each class in ground-truth\n",
    "        class_correct = defaultdict(int)\n",
    "        \n",
    "        for pred, true_label in zip(predictions, labels):\n",
    "            class_counts[true_label] += 1\n",
    "            if pred == true_label:\n",
    "                class_correct[true_label] += 1\n",
    "                \n",
    "        # Compute and print class-wise accuracy\n",
    "        class_accuracies = {}\n",
    "        for cls in range(0, 5):  # Classes are 0, 1, 2, 3, 4\n",
    "            if class_counts[cls] == 0:\n",
    "                class_accuracies[cls] = 0  # Avoid division by zero\n",
    "            else:\n",
    "                class_accuracies[cls] = (class_correct[cls] / class_counts[cls]) * 100\n",
    "                \n",
    "        print(\"\\nClass-wise accuracies:\")\n",
    "        for cls, acc in class_accuracies.items():\n",
    "            print(f\"Class {cls}: {acc:.2f}%\")\n",
    "            \n",
    "        # Compute additional evaluation metrics using the eval_cls function\n",
    "        eval_results = eval_cls(\n",
    "            config=config,\n",
    "            model=model,\n",
    "            loader=predict_dataloader,\n",
    "            tokenizer=model.tokenizer,\n",
    "            predictions=predictions,  \n",
    "            labels=labels, \n",
    "            prob=prob\n",
    "        )\n",
    "        \n",
    "        print(\"\\nEval results for Preference.\")\n",
    "        for k, v in eval_results[\"results\"].items():\n",
    "            print(f\"{k} : {v}\")\n",
    "        print(f\"mae : {eval_results['mae']}\")\n",
    "        print(f\"top2_accuracy : {eval_results['top2_accuracy']}\")\n",
    "        print(f\"top2_f1_macro : {eval_results['top2_f1_macro']}\")\n",
    "\n",
    "    return eval_results[\"f1-macro\"], eval_results[\"accuracy\"], eval_results['mae'], eval_results[\"top2_accuracy\"],eval_results[\"top2_f1_macro\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ibdTiq1Ue2D8",
    "outputId": "53383735-c930-44c2-93c4-2e6ff330e766",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "epoch: 1, train loss: 0.934: 100%|██████████| 3836/3836 [31:55<00:00,  2.00it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training accuracy after epoch 1: 58.91%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "epoch: 1, valid loss: 0.6835: 100%|███████████| 101/101 [00:09<00:00, 10.67it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Class-wise accuracies:\n",
      "Class 0: 86.59%\n",
      "Class 1: 56.41%\n",
      "Class 2: 66.34%\n",
      "Class 3: 84.06%\n",
      "Class 4: 35.14%\n",
      "\n",
      "Eval results for Preference.\n",
      "accuracy : 65.84\n",
      "f1-macro : 64.36\n",
      "mae : 0.3811881188118812\n",
      "top2_accuracy : 0.9207921028137207\n",
      "top2_f1_macro : 0.6296705414358985\n",
      "Checkpoint krbert_preference_output/krbert_preference/KoMultiText/0-64.36-ckpt saved.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "epoch: 2, train loss: 0.7826: 100%|█████████| 3836/3836 [32:56<00:00,  1.94it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training accuracy after epoch 2: 66.63%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "epoch: 2, valid loss: 0.5574: 100%|███████████| 101/101 [00:09<00:00, 10.38it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Class-wise accuracies:\n",
      "Class 0: 86.59%\n",
      "Class 1: 65.38%\n",
      "Class 2: 74.26%\n",
      "Class 3: 81.16%\n",
      "Class 4: 63.51%\n",
      "\n",
      "Eval results for Preference.\n",
      "accuracy : 74.26\n",
      "f1-macro : 74.18\n",
      "mae : 0.29455445544554454\n",
      "top2_accuracy : 0.9331682920455933\n",
      "top2_f1_macro : 0.6375331461076384\n",
      "Checkpoint krbert_preference_output/krbert_preference/KoMultiText/1-74.18-ckpt saved.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "epoch: 3, train loss: 0.7306: 100%|█████████| 3836/3836 [32:52<00:00,  1.94it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training accuracy after epoch 3: 69.20%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "epoch: 3, valid loss: 0.5644: 100%|███████████| 101/101 [00:09<00:00, 10.53it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Class-wise accuracies:\n",
      "Class 0: 79.27%\n",
      "Class 1: 69.23%\n",
      "Class 2: 76.24%\n",
      "Class 3: 78.26%\n",
      "Class 4: 63.51%\n",
      "\n",
      "Eval results for Preference.\n",
      "accuracy : 73.51\n",
      "f1-macro : 73.54\n",
      "mae : 0.30198019801980197\n",
      "top2_accuracy : 0.9232673048973083\n",
      "top2_f1_macro : 0.6356508585003504\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "epoch: 4, train loss: 0.7404: 100%|█████████| 3836/3836 [32:59<00:00,  1.94it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training accuracy after epoch 4: 71.46%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "epoch: 4, valid loss: 0.5709: 100%|███████████| 101/101 [00:09<00:00, 10.46it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Class-wise accuracies:\n",
      "Class 0: 92.68%\n",
      "Class 1: 61.54%\n",
      "Class 2: 67.33%\n",
      "Class 3: 78.26%\n",
      "Class 4: 58.11%\n",
      "\n",
      "Eval results for Preference.\n",
      "accuracy : 71.53\n",
      "f1-macro : 71.35\n",
      "mae : 0.3217821782178218\n",
      "top2_accuracy : 0.9257425665855408\n",
      "top2_f1_macro : 0.6385215691749266\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "epoch: 5, train loss: 0.6875: 100%|█████████| 3836/3836 [32:54<00:00,  1.94it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training accuracy after epoch 5: 73.38%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "epoch: 5, valid loss: 0.6121: 100%|███████████| 101/101 [00:09<00:00, 10.45it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Class-wise accuracies:\n",
      "Class 0: 85.37%\n",
      "Class 1: 65.38%\n",
      "Class 2: 73.27%\n",
      "Class 3: 82.61%\n",
      "Class 4: 56.76%\n",
      "\n",
      "Eval results for Preference.\n",
      "accuracy : 72.77\n",
      "f1-macro : 72.58\n",
      "mae : 0.3094059405940594\n",
      "top2_accuracy : 0.9257425665855408\n",
      "top2_f1_macro : 0.6376890144240223\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "epoch: 6, train loss: 0.5214: 100%|█████████| 3836/3836 [32:59<00:00,  1.94it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training accuracy after epoch 6: 75.44%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "epoch: 6, valid loss: 0.7376: 100%|███████████| 101/101 [00:09<00:00, 10.62it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Class-wise accuracies:\n",
      "Class 0: 87.80%\n",
      "Class 1: 62.82%\n",
      "Class 2: 80.20%\n",
      "Class 3: 71.01%\n",
      "Class 4: 52.70%\n",
      "\n",
      "Eval results for Preference.\n",
      "accuracy : 71.78\n",
      "f1-macro : 71.02\n",
      "mae : 0.3118811881188119\n",
      "top2_accuracy : 0.9158415794372559\n",
      "top2_f1_macro : 0.6368896177169872\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "epoch: 7, train loss: 0.5259: 100%|█████████| 3836/3836 [32:52<00:00,  1.94it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training accuracy after epoch 7: 77.22%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "epoch: 7, valid loss: 0.6901: 100%|███████████| 101/101 [00:09<00:00, 10.42it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Class-wise accuracies:\n",
      "Class 0: 80.49%\n",
      "Class 1: 76.92%\n",
      "Class 2: 79.21%\n",
      "Class 3: 66.67%\n",
      "Class 4: 64.86%\n",
      "\n",
      "Eval results for Preference.\n",
      "accuracy : 74.26\n",
      "f1-macro : 74.16\n",
      "mae : 0.28465346534653463\n",
      "top2_accuracy : 0.9158415794372559\n",
      "top2_f1_macro : 0.6353467589912092\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "epoch: 8, train loss: 0.4253: 100%|█████████| 3836/3836 [32:53<00:00,  1.94it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training accuracy after epoch 8: 78.85%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "epoch: 8, valid loss: 0.7848: 100%|███████████| 101/101 [00:09<00:00, 10.53it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Class-wise accuracies:\n",
      "Class 0: 79.27%\n",
      "Class 1: 73.08%\n",
      "Class 2: 83.17%\n",
      "Class 3: 66.67%\n",
      "Class 4: 64.86%\n",
      "\n",
      "Eval results for Preference.\n",
      "accuracy : 74.26\n",
      "f1-macro : 74.14\n",
      "mae : 0.28217821782178215\n",
      "top2_accuracy : 0.9133663177490234\n",
      "top2_f1_macro : 0.637442958930714\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "epoch: 9, train loss: 0.3913: 100%|█████████| 3836/3836 [32:48<00:00,  1.95it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training accuracy after epoch 9: 81.27%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "epoch: 9, valid loss: 0.8577: 100%|███████████| 101/101 [00:09<00:00, 10.64it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Class-wise accuracies:\n",
      "Class 0: 81.71%\n",
      "Class 1: 70.51%\n",
      "Class 2: 75.25%\n",
      "Class 3: 69.57%\n",
      "Class 4: 59.46%\n",
      "\n",
      "Eval results for Preference.\n",
      "accuracy : 71.78\n",
      "f1-macro : 71.75\n",
      "mae : 0.31683168316831684\n",
      "top2_accuracy : 0.9084158539772034\n",
      "top2_f1_macro : 0.6355594783220537\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "epoch: 10, train loss: 0.3705: 100%|████████| 3836/3836 [32:49<00:00,  1.95it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training accuracy after epoch 10: 82.96%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "epoch: 10, valid loss: 0.9121: 100%|██████████| 101/101 [00:09<00:00, 10.58it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Class-wise accuracies:\n",
      "Class 0: 87.80%\n",
      "Class 1: 60.26%\n",
      "Class 2: 69.31%\n",
      "Class 3: 66.67%\n",
      "Class 4: 55.41%\n",
      "\n",
      "Eval results for Preference.\n",
      "accuracy : 68.32\n",
      "f1-macro : 68.15\n",
      "mae : 0.35148514851485146\n",
      "top2_accuracy : 0.905940592288971\n",
      "top2_f1_macro : 0.6315157144602089\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "epoch: 11, train loss: 0.354: 100%|█████████| 3836/3836 [32:38<00:00,  1.96it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training accuracy after epoch 11: 84.34%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "epoch: 11, valid loss: 0.9956: 100%|██████████| 101/101 [00:09<00:00, 10.47it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Class-wise accuracies:\n",
      "Class 0: 80.49%\n",
      "Class 1: 74.36%\n",
      "Class 2: 71.29%\n",
      "Class 3: 66.67%\n",
      "Class 4: 52.70%\n",
      "\n",
      "Eval results for Preference.\n",
      "accuracy : 69.55\n",
      "f1-macro : 69.48\n",
      "mae : 0.3415841584158416\n",
      "top2_accuracy : 0.8886138796806335\n",
      "top2_f1_macro : 0.6249365629382323\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "epoch: 12, train loss: 0.3502: 100%|████████| 3836/3836 [32:54<00:00,  1.94it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training accuracy after epoch 12: 86.35%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "epoch: 12, valid loss: 1.0419: 100%|██████████| 101/101 [00:09<00:00, 10.62it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Class-wise accuracies:\n",
      "Class 0: 79.27%\n",
      "Class 1: 70.51%\n",
      "Class 2: 74.26%\n",
      "Class 3: 66.67%\n",
      "Class 4: 59.46%\n",
      "\n",
      "Eval results for Preference.\n",
      "accuracy : 70.54\n",
      "f1-macro : 70.64\n",
      "mae : 0.3341584158415842\n",
      "top2_accuracy : 0.8935643434524536\n",
      "top2_f1_macro : 0.6364832216534058\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "epoch: 13, train loss: 0.2881: 100%|████████| 3836/3836 [32:01<00:00,  2.00it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training accuracy after epoch 13: 87.71%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "epoch: 13, valid loss: 1.512: 100%|███████████| 101/101 [00:09<00:00, 10.59it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Class-wise accuracies:\n",
      "Class 0: 75.61%\n",
      "Class 1: 74.36%\n",
      "Class 2: 81.19%\n",
      "Class 3: 66.67%\n",
      "Class 4: 48.65%\n",
      "\n",
      "Eval results for Preference.\n",
      "accuracy : 70.3\n",
      "f1-macro : 69.81\n",
      "mae : 0.33663366336633666\n",
      "top2_accuracy : 0.8613861203193665\n",
      "top2_f1_macro : 0.6150098476538199\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "epoch: 14, train loss: 0.2107:  25%|██▏      | 951/3836 [07:57<24:09,  1.99it/s]\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[20], line 12\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[38;5;66;03m# Start training over a number of epochs\u001b[39;00m\n\u001b[1;32m     11\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m epoch \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(config\u001b[38;5;241m.\u001b[39mnum_train_epochs):\n\u001b[0;32m---> 12\u001b[0m     train_results \u001b[38;5;241m=\u001b[39m \u001b[43mrun_epoch\u001b[49m\u001b[43m(\u001b[49m\u001b[43mloader\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtrain_dataloader\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mepoch\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mepoch\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mis_train\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mparams\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     13\u001b[0m     train_loss \u001b[38;5;241m=\u001b[39m train_results[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mloss\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[1;32m     14\u001b[0m     train_losses\u001b[38;5;241m.\u001b[39mappend(train_loss)\n",
      "Cell \u001b[0;32mIn[17], line 100\u001b[0m, in \u001b[0;36mrun_epoch\u001b[0;34m(**kwargs)\u001b[0m\n\u001b[1;32m     98\u001b[0m     results \u001b[38;5;241m=\u001b[39m model(_run_epoch, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m     99\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 100\u001b[0m     results \u001b[38;5;241m=\u001b[39m \u001b[43m_run_epoch\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    102\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(results, \u001b[38;5;28mlist\u001b[39m):\n\u001b[1;32m    103\u001b[0m     loss \u001b[38;5;241m=\u001b[39m \u001b[38;5;28msum\u001b[39m([result[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mloss\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;28;01mfor\u001b[39;00m result \u001b[38;5;129;01min\u001b[39;00m results]) \u001b[38;5;241m/\u001b[39m \u001b[38;5;28mlen\u001b[39m(results)\n",
      "Cell \u001b[0;32mIn[17], line 34\u001b[0m, in \u001b[0;36m_run_epoch\u001b[0;34m(model, loader, device, context, **kwargs)\u001b[0m\n\u001b[1;32m     31\u001b[0m is_master \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m     32\u001b[0m pbar \u001b[38;5;241m=\u001b[39m tqdm(\u001b[38;5;28menumerate\u001b[39m(loader), total\u001b[38;5;241m=\u001b[39mbatch_num, disable\u001b[38;5;241m=\u001b[39m\u001b[38;5;129;01mnot\u001b[39;00m is_master, dynamic_ncols\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, position\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m, leave\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m---> 34\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i, (inputs, labels) \u001b[38;5;129;01min\u001b[39;00m pbar:\n\u001b[1;32m     35\u001b[0m     \u001b[38;5;66;03m# Move data to the appropriate device (if not using TPU)\u001b[39;00m\n\u001b[1;32m     36\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m config\u001b[38;5;241m.\u001b[39muse_tpu:\n\u001b[1;32m     37\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(inputs, torch\u001b[38;5;241m.\u001b[39mTensor):\n",
      "File \u001b[0;32m/home/bmi-admin/anaconda3/lib/python3.9/site-packages/tqdm/std.py:1195\u001b[0m, in \u001b[0;36mtqdm.__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1192\u001b[0m time \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_time\n\u001b[1;32m   1194\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1195\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m obj \u001b[38;5;129;01min\u001b[39;00m iterable:\n\u001b[1;32m   1196\u001b[0m         \u001b[38;5;28;01myield\u001b[39;00m obj\n\u001b[1;32m   1197\u001b[0m         \u001b[38;5;66;03m# Update and possibly print the progressbar.\u001b[39;00m\n\u001b[1;32m   1198\u001b[0m         \u001b[38;5;66;03m# Note: does not call self.update(1) for speed optimisation.\u001b[39;00m\n",
      "File \u001b[0;32m/home/bmi-admin/anaconda3/lib/python3.9/site-packages/torch/utils/data/dataloader.py:633\u001b[0m, in \u001b[0;36m_BaseDataLoaderIter.__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    630\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_sampler_iter \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    631\u001b[0m     \u001b[38;5;66;03m# TODO(https://github.com/pytorch/pytorch/issues/76750)\u001b[39;00m\n\u001b[1;32m    632\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reset()  \u001b[38;5;66;03m# type: ignore[call-arg]\u001b[39;00m\n\u001b[0;32m--> 633\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_next_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    634\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_num_yielded \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m    635\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_dataset_kind \u001b[38;5;241m==\u001b[39m _DatasetKind\u001b[38;5;241m.\u001b[39mIterable \u001b[38;5;129;01mand\u001b[39;00m \\\n\u001b[1;32m    636\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_IterableDataset_len_called \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \\\n\u001b[1;32m    637\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_num_yielded \u001b[38;5;241m>\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_IterableDataset_len_called:\n",
      "File \u001b[0;32m/home/bmi-admin/anaconda3/lib/python3.9/site-packages/torch/utils/data/dataloader.py:677\u001b[0m, in \u001b[0;36m_SingleProcessDataLoaderIter._next_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    675\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_next_data\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m    676\u001b[0m     index \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_next_index()  \u001b[38;5;66;03m# may raise StopIteration\u001b[39;00m\n\u001b[0;32m--> 677\u001b[0m     data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_dataset_fetcher\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfetch\u001b[49m\u001b[43m(\u001b[49m\u001b[43mindex\u001b[49m\u001b[43m)\u001b[49m  \u001b[38;5;66;03m# may raise StopIteration\u001b[39;00m\n\u001b[1;32m    678\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_pin_memory:\n\u001b[1;32m    679\u001b[0m         data \u001b[38;5;241m=\u001b[39m _utils\u001b[38;5;241m.\u001b[39mpin_memory\u001b[38;5;241m.\u001b[39mpin_memory(data, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_pin_memory_device)\n",
      "File \u001b[0;32m/home/bmi-admin/anaconda3/lib/python3.9/site-packages/torch/utils/data/_utils/fetch.py:49\u001b[0m, in \u001b[0;36m_MapDatasetFetcher.fetch\u001b[0;34m(self, possibly_batched_index)\u001b[0m\n\u001b[1;32m     47\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mauto_collation:\n\u001b[1;32m     48\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdataset, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m__getitems__\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdataset\u001b[38;5;241m.\u001b[39m__getitems__:\n\u001b[0;32m---> 49\u001b[0m         data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdataset\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__getitems__\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpossibly_batched_index\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     50\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m     51\u001b[0m         data \u001b[38;5;241m=\u001b[39m [\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdataset[idx] \u001b[38;5;28;01mfor\u001b[39;00m idx \u001b[38;5;129;01min\u001b[39;00m possibly_batched_index]\n",
      "File \u001b[0;32m/home/bmi-admin/anaconda3/lib/python3.9/site-packages/datasets/arrow_dataset.py:2782\u001b[0m, in \u001b[0;36mDataset.__getitems__\u001b[0;34m(self, keys)\u001b[0m\n\u001b[1;32m   2780\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__getitems__\u001b[39m(\u001b[38;5;28mself\u001b[39m, keys: List) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m List:\n\u001b[1;32m   2781\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Can be used to get a batch using a list of integers indices.\"\"\"\u001b[39;00m\n\u001b[0;32m-> 2782\u001b[0m     batch \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__getitem__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mkeys\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2783\u001b[0m     n_examples \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlen\u001b[39m(batch[\u001b[38;5;28mnext\u001b[39m(\u001b[38;5;28miter\u001b[39m(batch))])\n\u001b[1;32m   2784\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m [{col: array[i] \u001b[38;5;28;01mfor\u001b[39;00m col, array \u001b[38;5;129;01min\u001b[39;00m batch\u001b[38;5;241m.\u001b[39mitems()} \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(n_examples)]\n",
      "File \u001b[0;32m/home/bmi-admin/anaconda3/lib/python3.9/site-packages/datasets/arrow_dataset.py:2778\u001b[0m, in \u001b[0;36mDataset.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2776\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__getitem__\u001b[39m(\u001b[38;5;28mself\u001b[39m, key):  \u001b[38;5;66;03m# noqa: F811\u001b[39;00m\n\u001b[1;32m   2777\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Can be used to index columns (by string names) or rows (by integer index or iterable of indices or bools).\"\"\"\u001b[39;00m\n\u001b[0;32m-> 2778\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_getitem\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/home/bmi-admin/anaconda3/lib/python3.9/site-packages/datasets/arrow_dataset.py:2763\u001b[0m, in \u001b[0;36mDataset._getitem\u001b[0;34m(self, key, **kwargs)\u001b[0m\n\u001b[1;32m   2761\u001b[0m formatter \u001b[38;5;241m=\u001b[39m get_formatter(format_type, features\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_info\u001b[38;5;241m.\u001b[39mfeatures, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mformat_kwargs)\n\u001b[1;32m   2762\u001b[0m pa_subtable \u001b[38;5;241m=\u001b[39m query_table(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_data, key, indices\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_indices \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_indices \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[0;32m-> 2763\u001b[0m formatted_output \u001b[38;5;241m=\u001b[39m \u001b[43mformat_table\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   2764\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpa_subtable\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mformatter\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mformatter\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mformat_columns\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mformat_columns\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moutput_all_columns\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_all_columns\u001b[49m\n\u001b[1;32m   2765\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2766\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m formatted_output\n",
      "File \u001b[0;32m/home/bmi-admin/anaconda3/lib/python3.9/site-packages/datasets/formatting/formatting.py:624\u001b[0m, in \u001b[0;36mformat_table\u001b[0;34m(table, key, formatter, format_columns, output_all_columns)\u001b[0m\n\u001b[1;32m    622\u001b[0m python_formatter \u001b[38;5;241m=\u001b[39m PythonFormatter(features\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[1;32m    623\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m format_columns \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 624\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mformatter\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpa_table\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mquery_type\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mquery_type\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    625\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m query_type \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcolumn\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m    626\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m key \u001b[38;5;129;01min\u001b[39;00m format_columns:\n",
      "File \u001b[0;32m/home/bmi-admin/anaconda3/lib/python3.9/site-packages/datasets/formatting/formatting.py:400\u001b[0m, in \u001b[0;36mFormatter.__call__\u001b[0;34m(self, pa_table, query_type)\u001b[0m\n\u001b[1;32m    398\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mformat_column(pa_table)\n\u001b[1;32m    399\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m query_type \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbatch\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m--> 400\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mformat_batch\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpa_table\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/home/bmi-admin/anaconda3/lib/python3.9/site-packages/datasets/formatting/formatting.py:443\u001b[0m, in \u001b[0;36mPythonFormatter.format_batch\u001b[0;34m(self, pa_table)\u001b[0m\n\u001b[1;32m    441\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlazy:\n\u001b[1;32m    442\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m LazyBatch(pa_table, \u001b[38;5;28mself\u001b[39m)\n\u001b[0;32m--> 443\u001b[0m batch \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpython_arrow_extractor\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mextract_batch\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpa_table\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    444\u001b[0m batch \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpython_features_decoder\u001b[38;5;241m.\u001b[39mdecode_batch(batch)\n\u001b[1;32m    445\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m batch\n",
      "File \u001b[0;32m/home/bmi-admin/anaconda3/lib/python3.9/site-packages/datasets/formatting/formatting.py:150\u001b[0m, in \u001b[0;36mPythonArrowExtractor.extract_batch\u001b[0;34m(self, pa_table)\u001b[0m\n\u001b[1;32m    149\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mextract_batch\u001b[39m(\u001b[38;5;28mself\u001b[39m, pa_table: pa\u001b[38;5;241m.\u001b[39mTable) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mdict\u001b[39m:\n\u001b[0;32m--> 150\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mpa_table\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_pydict\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "train_losses = []\n",
    "val_f1 = []\n",
    "val_acc = []\n",
    "val_mae =[]\n",
    "val_top2_acc = []\n",
    "val_top2_f1 = []\n",
    "\n",
    "if config.do_train:\n",
    "    best_score = 0\n",
    "    # Start training over a number of epochs\n",
    "    for epoch in range(config.num_train_epochs):\n",
    "        train_results = run_epoch(loader=train_dataloader, epoch=epoch, is_train=True, **params)\n",
    "        train_loss = train_results['loss']\n",
    "        train_losses.append(train_loss)\n",
    "        \n",
    "        # Evaluation during training (if configured to do so)\n",
    "        if config.do_eval_during_train:\n",
    "            # Evaluate the model using the evaluation function\n",
    "            f1, accuracy, mae, top2_acc, top2_f1 = do_eval(epoch)\n",
    "            val_f1.append(f1)\n",
    "            val_acc.append(accuracy)\n",
    "            val_mae.append(mae)\n",
    "            val_top2_acc.append(top2_acc)\n",
    "            val_top2_f1.append(top2_f1)\n",
    "            \n",
    "            # Checkpointing: Save the model if the F1-macro is the best till now\n",
    "            if f1 >= best_score:\n",
    "                best_score = f1\n",
    "                output_dir = os.path.join(config.output_dir, config.task, config.dataset, f\"{epoch}-{best_score}-ckpt\")\n",
    "                if not os.path.exists(output_dir):\n",
    "                    os.makedirs(output_dir)\n",
    "                # Save the best model\n",
    "                model_path = os.path.join(output_dir, \"model_weights.pth\")\n",
    "                torch.save(model.state_dict(), model_path)\n",
    "                with open(os.path.join(output_dir, \"finetune_config.json\"), \"w\") as save_config:\n",
    "                    json.dump(vars(config), save_config, sort_keys=True, indent=4)\n",
    "                print(f\"Checkpoint {output_dir} saved.\")   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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cdEpmExERERFJJ26P7z02sAH4EHg1AdeYHji+FHAauBIokmwJ0Xt+Ecm4tIJZRCQIzq4ENrPHzOwv4B0zy2dmX5lZhJkdDHxfIs45P5hZ98D3XcxsiZm9ETj2DzNrlsRjy5jZYjM7ambfmdkYM3svCa+pcuC+h8xsk5m1jPNcczPzBe6x28weDjxeMPA6D5nZATP70cz0/z0iIiIicsmcc2OccwuAUwk4vC4w1Tl33DkX7Zxb55ybc/ZJM7vWzJYG3rfuPLu62czymNm7gffwO8zs6bPvZwPvw38ys+Fmth8YamZZAu/L/zSzfWY23syyBeHli4ikGvqPfBGR4CkC5Me/SqIn/v/NfSfwc0ngJDD6AufXBzYDBYHXgClmZkk49gNgJVAAGAp0TOwLMbNMwJfAPKAw0Bd438wqBg6ZAvRyzuUCqgHfBx5/CNgFFAIuA54EXGLvLyIiIiJyiZYDY8ysjZmVjPuEmZUC5gCj8L9vrQmsDzw9CsgDlAUaA52ArnFOrw/8jv+97ovAK0CFwDXKAcWBwUF4PSIiqYYKZhGR4IkFhjjnTjvnTjrn9jvnZjnnTjjnjuJ/A9r4AufvcM5Ncs7FANOAovjfuCb42MCb57rAYOfcGefcEmB2El5LAyAn8ErgOt8DXwFtA89HAVXMLLdz7qBzbm2cx4sCpZxzUc65H51zKphFREREJDE+D6wsPmRmnyfxGvcCPwLPAH+Y2Xozqxt4rh3wnXNuRuA9637n3HozCwXaAE84544657YDb/LvBRt7nHOjAqMxTuFfWDLQOXcg8J7/pcA1RETSLRXMIiLBE+Gc++fjemaW3cwmBD5adwRYDOQNvHGNz19nv3HOnQh8mzORxxYDDsR5DGBnIl8HgevsdM7FxnlsB/4VGQB3A82BHWa2yMwaBh5/HdgKzDOz383s8STcW0REREQytjudc3kDX3cm5QKBRRCPO+eq4l+0sR5/cW3A5cC2eE4rCGTC/773rLjvgeHf760LAdmBNWcLceDbwOMiIumWCmYRkeA5d6XuQ0BFoL5zLjfQKPD4+cZeJIe9QH4zyx7nscuTcJ09wOXnzE8uCewGcM6tcs7dgX98xufAR4HHjzrnHnLOlQVaAoPM7IYk3F9EREREJFk45yKBN/AvosiPvyS+Ip5DI/F/Iq9UnMf+eQ989nLnHH8SqBqnEM8Td2NwEZH0SAWziEjKyYX/DechM8sPDAn2DZ1zO4DV+DccyRxYWXz7xc4zs6xxv/DPcD4BPGpmmcysSeA6MwPXbW9meZxzUcAR/ONBMLMWZlYusDLkMBBz9jkRERERkUsReB+aFf+CjUyB967x9hxm9qqZVTOzMDPLBfQBtjrn9gPvAzeaWevA8wXMrGZg/NxHwItmliswq3kQEO+G2YFP+00ChptZ4cB9i5vZLcn92kVEUhMVzCIiKWcEkA3/yobl+D8ulxLaAw2B/cALwIfA6QscXxx/ER7363L8hXIz/PnHAp2cc78GzukIbA+M/ugduCdAeeA74BiwDBjrnFuYbK9MRERERDKyefjfq14NTAx83+g8x2YHPgMO4d+UrxT+T9jhnPsT/7i3h4AD+Mdn1Aic1xc4HjhnCf4NtN++QKbH8I+IWx54b/wd/k8xioikW6a9lkREMhYz+xD41TkX9BXUIiIiIiIiIpK+aQWziEg6Z2Z1zewKMwsxs1uBO/DPSRYRERERERERuSRhXgcQEZGgKwJ8ChQAdgF9nHPrvI0kIiIiIiIiIumBRmSIiIiIiIiIiIiISJJoRIaIiIiIiIiIiIiIJIkKZhERERERERERERFJkjQ3g7lgwYKudOnSXscQERERkXRizZo1kc65Ql7nSG563ywiIiIiyel875vTXMFcunRpVq9e7XUMEREREUknzGyH1xmCQe+bRURERCQ5ne99s0ZkiIiIiIiIiIiIiEiSqGAWERERERERERERkSRRwSwiIiIiIiIiIiIiSZLmZjCLiIiIZBRRUVHs2rWLU6dOeR0lXciaNSslSpQgU6ZMXkcRERERkRSg99NJk9j3zSqYRURERFKpXbt2kStXLkqXLo2ZeR0nTXPOsX//fnbt2kWZMmW8jiMiIiIiKUDvpxMvKe+bNSJDREREJJU6deoUBQoU0JvhZGBmFChQQKtXRERERDIQvZ9OvKS8b1bBLCIiIpKK6c1w8tHvUkRERCTj0XvAxEvs70wFs4iIiIjEa//+/dSsWZOaNWtSpEgRihcv/s/PZ86cueC5q1evpl+/fom6X+nSpYmMjLyUyCIiIiIiqUpoaOg/76Fr1qzJ9u3b2b9/P9dffz05c+bkwQcfPO+5X331FbVq1aJGjRpUqVKFCRMmpGDyhNMMZhERERGJV4ECBVi/fj0AQ4cOJWfOnDz88MP/PB8dHU1YWPxvJ+vUqUOdOnVSIqaIiIiISKqVLVu2f95Tn3X8+HGef/55Nm7cyMaNG+M9Lyoqip49e7Jy5UpKlCjB6dOn2b59+yVlcc7hnCMkJHnXHGsFs4iIiIgkWJcuXejduzf169fn0UcfZeXKlTRs2JBatWpx9dVXs3nzZgB++OEHWrRoAfjL6W7dutGkSRPKli3LyJEjE3y/7du307RpU6pXr84NN9zAn3/+CcDHH39MtWrVqFGjBo0aNQJg06ZN1KtXj5o1a1K9enW2bNmSzK9eREREROTS5ciRg2uvvZasWbOe95ijR48SHR1NgQIFAMiSJQsVK1YEYN++fdx1113UqFGDGjVqsHTpUgCGDRtGtWrVqFatGiNGjAD876crVqxIp06dqFatGjt37uT111+nbt26VK9enSFDhlzy69EKZhERERFJlF27drF06VJCQ0M5cuQIP/74I2FhYXz33Xc8+eSTzJo16z/n/PrrryxcuJCjR49SsWJF+vTpQ6ZMmS56r759+9K5c2c6d+7M22+/Tb9+/fj888957rnnmDt3LsWLF+fQoUMAjB8/nv79+9O+fXvOnDlDTExMcr90EREREZFEOXnyJDVr1gSgTJkyfPbZZwk6L3/+/LRs2ZJSpUpxww030KJFC9q2bUtISAj9+vWjcePGfPbZZ8TExHDs2DHWrFnDO++8w4oVK3DOUb9+fRo3bky+fPnYsmUL06ZNo0GDBsybN48tW7awcuVKnHO0bNmSxYsX/7NoIylUMIuIiIikAQMGwDmfrLtkNWtCYGFDotx7772EhoYCcPjwYTp37syWLVswM6KiouI957bbbiNLlixkyZKFwoULs2/fPkqUKHHRey1btoxPP/0UgI4dO/Loo48CcM0119ClSxdat25Nq1atAGjYsCEvvvgiu3btolWrVpQvXz7xL05ERERE0qUB3w5g/V/rk/WaNYvUZMStIy54THwjMhJq8uTJ/PLLL3z33Xe88cYbzJ8/n6lTp/L999/z7rvvAv4Zz3ny5GHJkiXcdddd5MiRA4BWrVrx448//lNSN2jQAIB58+Yxb948atWqBcCxY8fYsmXLJRXMGpGRAIcPw+zZXqcQERERSR3OvmkFeOaZZ7j++uvZuHEjX375JadOnYr3nCxZsvzzfWhoKNHR0ZeUYfz48bzwwgvs3LmTq666iv3799OuXTtmz55NtmzZaN68Od9///0l3UMyrpNRJ/l5388457yOIiIiIhnclVdeycCBA5k/f368nxRMiLjv351zPPHEE6xfv57169ezdetW7rvvvkvKqBXMCTBkCIweDcuWQd26XqcRERGRjCgpK41TwuHDhylevDgAU6dOTfbrX3311cycOZOOHTvy/vvvc9111wGwbds26tevT/369ZkzZw47d+7k8OHDlC1bln79+vHnn3/y888/07Rp02TPJOnf8OXDeer7p6hYoCI9r+pJpxqdKJi9oNexRCSZxcTG8Nh3j7H32F5CLZTQkFD/Xy2UsJCw//85JHU9FhYS9u+8gceyhGbBzLz+tYqkWhdbaZzaHDt2jNWrV9OkSRMA1q9fT6lSpQC44YYbGDduHAMGDPhnRMZ1111Hly5dePzxx3HO8dlnnzF9+vT/XPeWW27hmWeeoX379uTMmZPdu3eTKVMmChcunOSsKpgTYMgQ+OQT6NgR1q6F7Nm9TiQiIiKSOjz66KN07tyZF154gdtuu+2Sr1e9evV/drVu3bo1o0aNomvXrrz++usUKlSId955B4BHHnmELVu24JzjhhtuoEaNGrz66qtMnz6dTJkyUaRIEZ588slLziMZ09q9aymUvRD5s+XnoXkP8cSCJ7inyj30uqoX15W8TgWOSDrx0aaPeHPZm5TKU4oQCyHGxRAdG01MbAwxLoaY2MDPge/PPp9a3VnpTma1nkWI6cPqImlB6dKlOXLkCGfOnOHzzz9n3rx5VKlS5Z/nnXO89tpr9OrVi2zZspEjR45/FnS89dZb9OzZkylTphAaGsq4ceNo2LAhXbp0oV69egB0796dWrVqsX379n/d9+abbyY8PJyGDRsCkDNnTt57771LKpgtrX3sq06dOm716tUpft/vvoObboK+fSERG5+LiIiIJFl4eDiVK1f2Oka6Et/v1MzWOOfqeBTpX8ysIvBhnIfKAoOdcyMCzz8EvAEUcs5FXuhaXr1vTg+qjq1Kufzl+KLNF/yy7xcmrpnI9J+nc/j0YSoVrETP2v5VzQWyF/A6qogkUXRsNFXHViVLaBbW916fqFI21sX+q4ROSDF9vrL6YtdJ6LW3HNjCpLWTGNt8LH3q9gnib04kbdH76aRLzPtmrWBOoBtvhP794a234Pbb/WWziIiIiEhycs5tBmoCmFkosBv4LPDz5cDNwJ9e5csIomKi+G3/b7Ss0BKAKy+7klHNR/HqTa/y0aaPmLhmIoPmDfrXquZrS16rVc0iacyMX2bw2/7f+LT1p4le8RtiIYSEhpCJTEFKl3jOOf449AePzH+EW8vdSpl8ZbyOJCIZiD43kQgvvwyVK0OXLnDggNdpRERERCSduwHY5pzbEfh5OPAokLY+gpjGbDu4jejYaKoUqvKvx7Nnyk6Xml1Yet9SNvTeQPfa3fnyty9pNLURVcdWZcTyERw4qf9IEEkLomKieHbRs9QqUos7K93pdZxkYWZMvn0yIRbCfbPvI9bFeh1JRDIQFcyJkC0bTJ8Of/8NDzzgdRoRERERSefaADMAzOwOYLdzboO3kdI/X4QPgMqFzv9x2uqXVWd089HsGbSHt1u+Te4suRk4dyDF3ixGx886suTPJaS1UYQiGcn0n6ez7eA2nm3ybLr69EGpvKV44+Y3WLh9IeNXj/c6johkICqYE+mqq/yb/s2cCTNmeJ1GRERE0juVVMknLf0uzSwz0BL42MyyA08CgxNwXk8zW21mqyMiIoIdM106WzBXKljposfmyJyDrrW6srz7ctb3Ws99te5j9ubZXPfOdVQbV423lr/FwZMHgx1ZRBLhTMwZnl/8PHWL1aVFhRZex0l2PWr34KayN/Ho/Ef54+AfXscRSRXS0nvA1CKxvzMVzEnw+OPQoAHcfz/s2uV1GhEREUmvsmbNyv79+/WmOBk459i/fz9Zs2b1OkpCNQPWOuf2AVcAZYANZrYdKAGsNbMi557knJvonKvjnKtTqFChFA2cXoRHhlMqTylyZs6ZqPNqFKnBmNvGsGfQHqa0nELOzDkZMHcAxYYVo9Nnnfjpz5/077JIKjB1/VS2H9rOc9c/l65WL59lZkxu6R+V0W12N43KkAxP76cTLynvmy2t/YJTy27YW7dCjRpw9dUwdy6EqKoXERGRZBYVFcWuXbs4deqU11HShaxZs1KiRAkyZfr3pkzn2w3bS2Y2E5jrnHsnnue2A3Wcc5EXukZqed+c1tSaUIsiOYswp/2cS77W+r/WM3HNRN77+T2OnjlK1UJV6XlVTzpW70i+bPmSIa2IJMbp6NOUH1WeErlL8FO3n9JlwXzWpDWT6PlVT0Y3G80D9TTjUzIuvZ9OmsS+b1bBfAkmTIDevWHkSOjb1+s0IiIiIpIUqa1gNrMcwJ9AWefc4Xie344K5qCIiY0h58s5ub/O/bx5y5vJdt1jZ47x4cYPmbBmAqv2rCJrWFZaV21Nr6t60bBEw3RdcomkJmNXjeWBbx5gfsf53Fj2Rq/jBJVzjlvfv5Ulfy7hlz6/UDZfWa8jiUg6cL73zVp3ewl69oTmzeHRRyE83Os0IiIiIpIeOOeOO+cKxFcuB54vfbFyWZJmx+EdnIo+dcEN/pIiZ+ac3Ff7Plb2WMnanmvpUqMLn4V/xjVvX0P18dUZtWIUh04dStZ7isi/nYw6yYs/vsh1Ja/jhjI3eB0n6MyMSbdPItRC6faFRmWISHCpYL4EZjB5MuTIAR07QlSU14lERERERCSpzm7wV6VQlaDdo1bRWoxrMY49D+1hYouJZA3LSr9v+1HszWJ0/aIry3Yu05xIkSCYuGYie47uSbezl+NTMk9Jht0yjEU7FjF21Viv44hIOqaC+RIVLQoTJ8KaNfD8816nERERERGRpAqP8H8ssXLB5F3BHJ+cmXPS46oerOqxijU919CpRic+8X3C1W9fTY3xNRi9crRWNYskkxNRJ3h5yctcX/p6mpRu4nWcFHVfrfu45YpbeOy7x9h2YJvXcUQknQpqwWxmt5rZZjPbamaPx/N8KTNbYGY/m9kPZlYimHmCpVUr6NQJXnoJli/3Oo2IiIiIiCSFL9JHkZxFUnwDvtpFazO+xXj2DNrDhBYTyByamb5z+lLszWJ0+6Iby3ct16pmkUswbtU49h3fx3PXP+d1lBR3dlRGWEgY982+T6MyRCQoglYwm1koMAZoBlQB2prZuZ81ewN41zlXHXgOeDlYeYJt5EgoXtw/KuP4ca/TiIiIiIhIYvkifEEdj3ExubLkoudVPVndczWre6ymQ/UOfLTpIxpOaUjNCTUZu2osh0/FO5pbRM7j2JljvPrTq9x8xc1cW/Jar+N44vI8lzPsZo3KEJHgCeYK5nrAVufc7865M8BM4I5zjqkCfB/4fmE8z6cZefLAtGmwbRs8/LDXaUREREREJDGcc4RHhKfIeIyEuKrYVUy8fSJ7H9rL+NvGE2qhPPDNAxQbVoz7vriPlbtXalWzSAKMWTmGiBMRPNvkWa+jeKpbrW7cWu5WjcoQkaAIZsFcHNgZ5+ddgcfi2gC0Cnx/F5DLzAoEMVNQNWkCgwbB+PEwZ47XaUREREREJKF2H93N0TNHPV3BHJ9cWXLRq04v1vZay6oeq2hXrR0fbvqQ+pPrU2tCLcatGqdVzSLnceT0EV5b+hrNyzenQYkGXsfxVNxRGd1md9OoDBFJVl5v8vcw0NjM1gGNgd1AzLkHmVlPM1ttZqsjIiJSOmOivPACVKsG3bpBZKTXaUREREREJCF8ET6AVFcwx1WnWB0mtZzEnof2MO62cZgZ939zP8WGFaP77O6s2r1Kq5pF4hi5YiQHTh7I8KuXzyqRuwTDbxnO4h2LGbNyjNdxRCQdCWbBvBu4PM7PJQKP/cM5t8c518o5Vwt4KvDYoXMv5Jyb6Jyr45yrU6hQoSBGvnRZs8L06bB/P/TuDXp/JyIiIiKS+oVHhAOkmhEZF5I7S2561+nN2p5rWdl9JW2rtWXGxhnUm1yP2hNrM371eI6cPuJ1TBFPHTp1iDeXvUnLii2pU6yO13FSja41u9KsXDMeX/C4RmWISLIJZsG8CihvZmXMLDPQBpgd9wAzK2hmZzM8AbwdxDwppmZNeP55mDUL3nvP6zQiIiIiInIxvggf+bPlp3COwl5HSTAzo27xukxuOZm9D+1lbPOxOOfo83Ufir1ZjB6ze7BmzxqvY4p4YsTyERw6dYihjYd6HSVVMTMm3j6RTCGZNCpDRJJN0Apm51w08CAwFwgHPnLObTKz58ysZeCwJsBmM/sNuAx4MVh5UtrDD8O118KDD8Kff3qdRkRERERELiQ8MpwqhapgZl5HSZLcWXLTp24f1vVax4ruK/hf1f/xwcYPqDOpDm+vSxfreEQS7MDJAwxfPpxWlVtRq2gtr+OkOnFHZYxeOdrrOCKSDgR1BrNz7hvnXAXn3BXOuRcDjw12zs0OfP+Jc6584JjuzrnTwcyTkkJDYdo0iI2FLl38fxURERERkdTHOcemiE1pYjzGxZgZ9YrXY8odU9gzaA9NyzSl75y+/4wAEckIhi0bxtHTRzV7+QK61OxC8/LNefy7x9l6YKvXcUQkjfN6k790rWxZGDECFi6Et97yOo2IiIiIiMQn4kQEB04eSNUb/CVFnqx5mH7XdLJnyk6bWW04FX3K60giQRd5IpK3VrxF66qtqVa4mtdxUi0zY2KLiWQOzUy3LzQqQ0QujQrmIOvWDVq2hCeegI0bvU4jIiIiIiLnSksb/CVWsVzFmHbnNH7e9zOPzHvE6zgiQff6T69z/MxxhjQe4nWUVK947uKMuHUEP/75I6NWjPI6joikYSqYg8wMJk2C3LmhQwc4c8brRCIiIiIiEpcvwgeQ7lYwn9W8fHMG1B/A6FWjmb159sVPEEmj9h3bx+hVo2l3ZTsqF0p/f2AUDJ1rdOa28rfxxIIn2LJ/i9dxRCSNUsGcAgoX9pfMGzbA0KFepxERERERkbjCI8PJmTknJXKX8DpK0Lxy4yvUKlKLrl90ZdeRXV7HEQmK1356jVPRpxjceLDXUdIMM2NCiwn+URmzNSpDRJJGBXMKueMO/7iMV1+Fn37yOo2IiIiIiJzli/BRuWBlzMzrKEGTJSwLM++Zyeno03T4tAMxsTFeRxJJVnuP7mXs6rF0rN6RCgUqeB0nTSmeuzhv3foWS/5cwsgVI72OIyJpkArmFDRiBJQqBZ06wdGjXqcRERERERHwF8zpdTxGXBUKVGBM8zEs2rGIl358yes4IsnqlSWvEBUTpdXLSdSpRiduK38bTy54UqMyRCTRVDCnoFy54N134Y8/YNAgr9OIiIiIiMihU4fYe2xvutzgLz6danSi/ZXtGbpoKEv+XOJ1HJFksevILiasmUDXml0pm6+s13HSJDNj4u0TyRKWha5fdNWnHEQkUVQwp7Brr4VHH4XJk+HLL71OIyIiIiKSsYVHhAPpd4O/c5kZY28bS5m8ZWg3qx0HTx70OpLIJXv5x5eJdbE81egpr6OkacVyFeOtW9/ip50/aVSGiCSKCmYPPPss1KgB3bvD3397nUZEREREJOPyRfiAjFMwA+TOkpsZd89g77G9dP+yO845ryOJJNmOQzuYtHYS99W6j9J5S3sdJ83rWL0jLSq04Mnvn+S3/b95HUdE0ggVzB7IkgXeew8OHYKePUHv50REREREvBEeGU6W0CwZrpiqW7wuLzV9iU/DP2XimolexxFJshd/fBEz48nrnvQ6SrpgZkxoMYGsYVk1KkNEEkwFs0eqVYOXXoIvvoCpU71OIyIiIiKSMfkifFQqWInQkFCvo6S4h65+iFuuuIUBcwew8e+NXscRSbTfD/7OO+vfoWftnlye53Kv46QbxXIVY+StI1m6cylvrXjL6zgikgaoYPbQwIHQuDH06+ff+E9ERERERFJWeGR4hhqPEVeIhTDtzmnkzpKbNp+04UTUCa8jiSTKC4tfICwkjCeue8LrKOlOh+oduL3C7Tz1/VNsjtzsdRwRSeVUMHsoJASmTQMz6NwZYvTJExERERGRFHP8zHG2H9pO5YKVvY7imctyXsb0u6azKWITD819yOs4Igm2Zf8W3t3wLn3q9KFYrmJex0l3zo7KyBaWTaMyROSiVDB7rFQpGD0afvwR3nzT6zQiIiIiIhnHr5G/Ahlrg7/43HzFzTxy9SOMXzOeWb5ZXscRSZDnFz9P5tDMPHbNY15HSbeK5irKyGYjWbZrGSOWj/A6joikYiqYU4GOHaFVK3j6adiwwes0IiIiIiIZQ3hkOACVC2XcFcxnvdD0BeoWq0v3L7vz5+E/vY4jckG/Rv7K+7+8z4P1HuSynJd5HSdda39le1pWbMnTC5/WqAwROS8VzKmAGUyYAPnz+8vm06e9TiQiIiIikv75InyEhYRRLn85r6N4LnNoZmbcPYOY2BjazWpHdGy015FEzuvZRc+SLSwbj1z9iNdR0j0zY/xt4zUqQ0QuSAVzKlGwIEyZAr/8As8843UaEREREZH0LzwynPL5y5M5NLPXUVKFK/JfwfgW4/lp5088t+g5r+OIxGvj3xv5cOOH9Kvfj0I5CnkdJ0Momqsoo5qNYtmuZQxfPtzrOCKSCqlgTkVuuw169YI33oBFi7xOIyIiIiKSvvkifBqPcY52V7ajc43OvLD4BX7Y/oPXcUT+49lFz5Izc04eaqhNKVNSuyvbcUfFO3j6+6f/mV8vInKWCuZU5o03oGxZ6NwZjhzxOo2IiIiISPp0Ovo0Ww9spUrBjL3BX3xGNx9Nufzl6PBpB/af2O91HJF/bPhrA5/4PmFAgwEUyF7A6zgZipkxvsV4cmTOoVEZIvIfKphTmZw5Yfp02LkT+vf3Oo2IiIiISPq05cAWYl0sVQqpYD5Xzsw5mXnPTP4+/jfdZnfDOed1JBEAhi4aSp4seRjUcJDXUTKkIjmLMKrZKJbvWs6wZcO8jiMiqYgK5lSoYUN48kmYOhU++8zrNCIiIiIi6Y8vwgegERnnUbtobV676TVmb57NmFVjvI4jwpo9a/j81895qOFD5M2a1+s4GVbbam25s9KdPLPwGcIjwr2OIyKphArmVGrwYKhdG3r2hL/+8jqNiIiIiEj64ovwYRgVC1T0Okqq1b9+f24rfxsPz3uYDX9t8DqOZHBDfhhCvqz56N9AH/X1kpkx7rZxGpUhIv+igjmVypTJPyrj2DHo3h30qTQRERERkeQTHhlOmXxlyJYpm9dRUi0z45073iF/tvy0mdWG42eOex1JMqgVu1bw9ZaveeTqR8idJbfXcTK8IjmLMLrZaFbsXsGby970Oo6IpAIqmFOxKlXglVfg669h8mSv04iIiIiIpB++CJ/mLydAoRyFeK/Ve2yO3Ez/b7VyVLwx5IchFMxekAfrPeh1FAloU60Nd1W6i8ELB2tUhoioYE7t+vaFG26AgQNh61av04iIiIiIpH3RsdH8tv83qhRUwZwQTcs05Ylrn2DKuil8uPFDr+NIBvPTnz8xd9tcHr36UXJlyeV1HAk4OyojZ+acdPmiC9Gx0V5HEhEPqWBO5UJC4J13ICwMOnWCaP1vtoiIiIjIJfn94O+ciTmjDf4SYWiToTQo0YCeX/Xkj4N/eB1HMpAhPwyhcI7C3F/3fq+jyDkuy3kZo5uPZuXulby5VKMyRDIyFcxpwOWXw9ixsGwZvPaa12lERERERNI2X4QPQCMyEiFTaCZm3D0DgLaz2hIVE+VxIskIFm1fxII/FvDEtU+QI3MOr+NIPP5X9X+0qtyKwT8M/ud/W0Uk41HBnEa0bQutW8OQIbB2rddpRERERETSrrPzQisVrORxkrSldN7STLp9Eit2r2DID0O8jiPpnHOOwT8MpmjOovS6qpfXceQ8zIyxzceSK3MuunyuURkiGZUK5jTCDMaNg0KFoGNHOHnS60QiIiIiImmTL9JHidwlyJ0lt9dR0pzWVVvTvVZ3XlnyCgt+X+B1nDTlVPQp+n7Tl5YzWnLk9BGv46R63//xPYt3LObJ654kW6ZsXseRC7gs52WMaT6GVXtW8cbSN7yOIyIeUMGchuTP75/H7PPBk096nUZEREREJG0KjwjXeIxL8Fazt6hUsBIdPutAxPEIr+OkCX8c/INr3r6G0atG882Wb7hp+k0cOnXI61ip1tnVyyVyl6B77e5ex5EEaF21NXdXvpshPwxh09+bvI4jIilMBXMac8st8MADMGIELNCCARERERGRRIl1sYRHhlO5oDb4S6rsmbIz856ZHDx5kC5fdCHWxXodKVX7+revqT2xNr8f/J3ZbWYzq/Us1u1dx43v3siBkwe8jpcqzds2j6U7l/LUdU+RNSyr13EkAcyMsbeNJXeW3HT5QqMyRDIaFcxp0GuvQYUK0KULHDrkdRoRERERkbTjz8N/ciLqhFYwX6Lql1XnzZvf5Jst3zByxUiv46RKMbExPLXgKVrMaEGZvGVY03MNt1e8nTsq3cHnbT5n498baTqtKZEnIr2OmqqcXb1cMk9JutXq5nUcSYTCOQozpvkYVu9Zzes/ve51HBFJQSqY06Ds2eG992DvXujb1+s0IiIiIiJpx9kN/lQwX7r7697PHRXv4NH5j7J2r3Yij+vv439z83s389KSl+heqztL71tK2Xxl/3m+efnmzG47m837N3P9tOvZd2yfh2lTl2+2fMPK3St5ptEzZA7N7HUcSaTWVVtzT5V7GLpoKBv/3uh1HBFJISqY06i6deGZZ/xF80cfeZ1GRERERCRt8EX4ADQiIxmYGVNaTuGynJfR5pM2HD191OtIqcJPf/5ErQm1WLpzKW+3fJtJLSfFO+bh5itu5ut2X/P7wd9pMq0Je4/uTfmwqczZ1ctl85Wlc43OXseRJBrTfAy5s+Sm6xddNSpDJINQwZyGPfmkv2ju3Rv27PE6jYiIiIhI6ueL8FE4R2EKZC/gdZR0oUD2Arx313tsO7iNvnMy9scrnXOMWD6CJtOakC0sG8vuW0bXWl0veE7TMk35tv237Dqyi8ZTG7PryK6UCZtKfbH5C9buXcvgRoPJFJrJ6ziSRIVzFGZs87Gs3rOa1356zes4IpICVDCnYZkywfTpcOoUdOsGznmdSEREREQkddMGf8mvcenGPH3d00zbMI33f37f6zieOHL6CP/75H8MnDuQFhVasLrnamoWqZmgc68rdR1zO8xl3/F9NJ7amB2HdgQ3bCoV62IZ8sMQyucvT/vq7b2OI5fo3qr3cm+Vexn6g0ZliGQEKpjTuIoV4Y03YO5cGDfO6zQiIiIiIqmXcw5fhE/zl4PgmcbPcG3Ja+n9dW+2HdjmdZwUtfHvjdSdVJdPwz/ltRtf49PWn5I3a95EXePqy69mfsf5HDh5gMZTG/P7wd+DEzYV+zT8U37e9zNDGg8hLCTM6ziSDMY0H0PerHnp8nkXomKivI4jIkGkgjkd6NMHbrkFHn4YfvvN6zQiIiIiIqnTX8f+4vDpwyqYgyAsJIz3W71PppBMtJnVhjMxZ7yOlCLe//l96k+uz+FTh1nQaQGPXPMIZpaka9UrXo8FnRZw9MxRGk9tzJb9W5I5beoVExvDkB+GUKlgJdpUa+N1HEkmhXIUYuxtY1mzd41GZYikcyqY0wEzePttyJoVOnaEaM3QFxERERH5D23wF1wl85RkSssprN6zmqcWPOV1nKA6HX2a+7++nw6fdaBOsTqs67WOxqUbX/J1axetzcLOCzkVfYrGUxvza+SvyZA29fvY9zG+CB9DGw8lNCTU6ziSjO6pcg+tq7bm2UXP8su+X7yOIyJBEtSC2cxuNbPNZrbVzB6P5/mSZrbQzNaZ2c9m1jyYedKzYsVg/HhYuRJeesnrNCIiIiIiqc/ZglkrmIPnrsp30adOH95Y9gZzt871Ok5Q7Di0g+veuY5xq8fxyNWPsKDTAormKpps169+WXV+6PwDsS6WxlMbp/v5tTGxMQz9YSjVClfj3qr3eh1HgmB0s9H+URlfaFSGSHoVtILZzEKBMUAzoArQ1szOfSf3NPCRc64W0AYYG6w8GUHr1tC+PTz3HKxa5XUaEREREZHUJTwynDxZ8lAkZxGvo6Rrb978JtUKV6PT553469hfXsdJVt9u/ZbaE2uzef9mPm39Ka/d9FpQ5gVXLVyVRV0WERYSxvXTrmfDXxuS/R6pxQe/fMDm/Zt5tsmzhJg+ZJ0eFcpRiHG3jWPt3rW8+tOrXscRkSAI5v961wO2Oud+d86dAWYCd5xzjANyB77PA+wJYp4MYfRoKFrUPyrjxAmv04iIiIiIpB5nN/hL6oxcSZhsmbIx8+6ZHDl9hM6fdybWxXod6ZLFxMYwZOEQmr/fnBK5S7C6x2ruqnxXUO9ZsWBFFnVZRLawbFw/7XrW7FkT1Pt5ITo2mmcXPUvNIjW5s9KdXseRILq7yt38r+r/eG7Rc/y872ev44hIMgtmwVwc2Bnn512Bx+IaCnQws13AN0DfIObJEPLmhalTYfNmeOwxr9OIiIiIiKQe4ZHhGo+RQqoWrsqIW0Ywb9s83lz6ptdxLknkiUiaf9Cc5xY/R+eanVl23zLKFyifIvcul78ci7osIneW3Nzw7g2s2LUiRe6bUqZvmM62g9u0ejmDGN18NPmy5aPL5xqVIZLeeP2/4G2Bqc65EkBzYLrZf/9fxcx6mtlqM1sdERGR4iHTmhtugP79/auZ583zOo2IiIiIJJSZVTSz9XG+jpjZADN73cx+Dexb8pmZ5fU6a1oTeSKSv4//rQ3+UlDPq3pyd+W7efL7J1m5e6XXcZJk+a7l1JpQi0XbFzHp9km83fJtsmfKnqIZyuQrw+KuiymQvQA3Tb+Jn/78KUXvHyxRMVE8t/g5rip6FbdXuN3rOJICCmYvyLjbxrHur3W8suQVr+OISDIKZsG8G7g8zs8lAo/FdR/wEYBzbhmQFSh47oWccxOdc3Wcc3UKFSoUpLjpy8svQ+XK0LUrHDjgdRoRERERSQjn3GbnXE3nXE3gKuAE8BkwH6jmnKsO/AY84V3KtCk8IhzQBn8pycyYdPskiuUqRttZbTly+ojXkRLMOcfolaNp9E4jwkLCWHrfUrrX7u7ZeJWSeUqyuMtiiuYqyi3v3cLiHYs9yZGcpq6fyvZD23nu+uc0tiYDaVW5FW2qteH5xc9rVIZIOhLMgnkVUN7MyphZZvyb+M0+55g/gRsAzKwy/oJZS5STQbZs8N578PffcP/9XqcRERERkSS4AdjmnNvhnJvnnIsOPL4c/+INSYTwSBXMXsiXLR8ftPqAHYd20OfrPjjnvI50UcfOHKPdp+3oO6cvt5S7hbU911K7aG2vY1E8d3F+6PwDJfOU5Nb3bmXB7wu8jpRkp6NP88KPL1C/eH2alWvmdRxJYaOajdKoDJF0JmgFc+AN8IPAXCAc+Mg5t8nMnjOzloHDHgJ6mNkGYAbQxaWFdxxpRO3aMHQofPghzJjhdRoRERERSaQ2+N8jn6sbMCeFs6R5vggf2TNl5/I8l1/8YElW15S8hqFNhvLBLx/w7oZ3vY5zQeER4dSbVI+PNn3ES01f4os2X5AvWz6vY/2jaK6i/NDlB8rlL0eLGS2Yu3Wu15GS5O11b/Pn4T95/vrntXo5AyqYvSDjbxuvURki6YiltT63Tp06bvXq1V7HSDOio6FRIwgPh19+gRJa6yIiIiLyL2a2xjlXx+sccQU+AbgHqOqc2xfn8aeAOkCr+BZmmFlPoCdAyZIlr9qxY0cKJU79bp5+MwdOHmB1T/23hBdiYmO4cfqNrNq9irW91lKhQAWvI/3HzI0z6T67O9kzZWfmPTNpWqap15HOK/JEJDdNvwlfhI9ZrWfRokILryMl2KnoU5QbWc4/W7rLYhXMGVi7We34xPcJq3uupvpl1b2OIyIJcL73zV5v8idBFhYG774LUVHQpQvExnqdSEREREQSoBmw9pxyuQvQAmh/vk/9ae+S8wuPDKdyIW3w55XQkFDeu+s9soZlpc0nbTgdfdrrSP84E3OGfnP60XZWW2oUqcG6XutSdbkM/hWgCzotoPpl1Wn1YSs+//VzryMl2MQ1E9l9dDfPNdHs5YxuVLNR5M+WX6MyRNIBFcwZQLlyMGwYLFgAo0d7nUZEREREEqAtccZjmNmtwKNAS+fcCc9SpVFHTh9h15FdVCmo+cteKp67OO/c8Q7r/lrH49897nUcAHYd2UXjqY0ZtXIUAxsM5IfOP1A8d3GvYyVI/mz5md9xPrWL1ubej+/l400fex3pok5EneDlJS/TpHQTri9zvddxxGMFshdgfAv/qIyXl7zsdRwRuQQqmDOIHj3gttvgscdg0iQ4c8brRCIiIiISHzPLAdwEfBrn4dFALmC+ma03s/GehEujfo38FdAGf6nB7RVvp2+9voxYMYKvf/va0yzzt82n1oRabPp7Ex/f+zHDbhlGptBMnmZKrLxZ8zKv4zzqF69Pm1lt+OCXD7yOdEHjV4/nr2N/8WyTZ72OIqnEnZXupN2V7Xh+8fNs+GuD13FEJIlUMGcQZjB5MtSoAT17QoUKMGGCimYRERGR1MY5d9w5V8A5dzjOY+Wcc5c752oGvnp7mTGt8UX4ADQiI5V47abXqHFZDbp80YU9R/ek+P1jXSzPL3qeW967hctyXMaqHqu4p8o9KZ4jueTOkptvO3zLdSWvo+NnHZm2fprXkeJ1/MxxXlnyCjeWvZFGpRp5HUdSkZG3jqRAtgJ0+UKjMkTSKhXMGUiRIrBsGXzzjf/73r394zPGjYPTqWcEmoiIiIhIsvJF+Mgcmpmy+cp6HUWArGFZmXnPTE5EnaDjZx2JiY1JsXvvP7GfFh+0YPAPg2lfvT0ruq+gYsGKKXb/YMmZOSfftP+GpmWa0vWLrkxeO9nrSP8xZtUYIk5E8FyT57yOIqlMgewFmNBiAuv/Ws9LP77kdRwRSQIVzBmMGTRr5i+a586Fyy+H+++HK67wz2c+dcrrhCIiIiIiySs8MpwKBSoQFhLmdRQJqFSwEqOajeL7P77n1Z9eTZF7rt6zmqsmXsWCPxYw7rZxvHvnu+TInCNF7p0SsmfKzuw2s7ml3C30+LIH41aN8zrSP46ePsprP71Gs3LNaHh5Q6/jSCp0R6U7aH9le1748QXW/7Xe6zgikkgqmDMoM7j5ZliyBObPhzJloG9ff9E8ciScPOl1QhERERGR5OGL8Gn+cirUtWZX2lRrw+CFg1m2c1nQ7uOcY/zq8Vzz9jU4HEu6LqF3nd6YWdDu6ZVsmbLx+f8+5/YKt3P/N/fz1vK3vI4EwMgVI9l/cr9mL8sFjWw2koLZC9Ll8y4p+skGEbl0KpgzODO48UZYvBi+/x7Kl4f+/aFsWRg+HE5oj3IRERERScNORp3kj4N/UKWgCubUxswYf9t4SuYpSdtZbTl06lCy3+P4meN0+rwTfb7uQ9MyTVnbcy11i9dN9vukJlnCsvBJ60+4q9JdDJg7gDeWvuFpnsOnDvPGsje4vcLt6f53L5cmf7b8DLt5GBv2beCr377yOo6IJIIKZgH8RfP118MPP/i/KleGQYP8RfObb8Lx414nFBERERFJvM37N+Nw2uAvlcqTNQ8z7p7B7qO76fllT5xzyXbt3/b/RoMpDXj/5/d5rslzfN3uawpkL5Bs10/NModm5sN7PqR11dY8Mv8RT+fajlg+gkOnDmn1siTIvVXvpVSeUgxbPszrKCKSCCqY5T8aN/avZl68GK68Eh5+2D9C47XX4Ngxr9OJiIiIiCScL8IHoBEZqVj9EvV54foX+Nj3MVPWTUmWa87yzaLOxDrsPbqXuR3m8kzjZwixjPWfv5lCM/F+q/dpf2V7nvr+KYb+MDRZC/yEOHjyIMOWD+OuSndRq2itFL23pE1hIWH0q9+PxTsWs2bPGq/jiEgCZaz/h5VEue46/3zmn36CWrXgscf8RfMrr8DRo16nExERERG5uPCIcEItlPL5y3sdRS7gkWse4cayN9JvTr9//lAgKaJiohg0dxD3fHwPVQpVYV2vddx0xU3JmDRtCQsJY9qd0+hSswvPLnqWp79/OkVL5mHLhnHk9BGGNhmaYveUtO++WveRK3Muhi8f7nUUEUkgFcxyUVdfDXPnwrJlUKcOPPEElC4NL70ER454nU5ERERE5Px8kT6uyH8FWcKyeB1FLiDEQnj3znfJmTknbT5pw6noU4m+xu4ju7l+2vUMXz6cvvX6srjrYi7Pc3kQ0qYtoSGhTGk5hR61e/DSkpd4dP6jKVIy7z+xnxErRnBvlXupfln1oN9P0o88WfPQvXZ3Ptz0IbuP7PY6jogkgApmSbAGDWDOHFixAho2hKee8hfNzz8Phw97nU5ERERE5L98ET6Nx0gjiuYqyrQ7p/HL37/w8LyHE3Xuwj8WUntibdb/tZ4Zd89gZLORZA7NHKSkaU+IhTC+xXgeqPsAbyx7gwHfDgh6yfz60tc5fua4Vi9LkvSr349YF8volaO9jiIiCaCCWRKtXj346itYtQquvRYGD/YXzc8+C4cOeZ1ORERERMTvTMwZth7YSuWC2uAvrWhWvhmDGgxizKoxfPHrFxc9PtbF8sqSV7hx+o0UyFaAVT1W0aZamxRImvaEWAijmo1iQP0BjFw5kge+eYBYFxuUe/19/G9GrRxF2yvb6g94JElK5y1Nq8qtmLBmAsfPHPc6johchApmSbI6dWD2bFi7Fpo0gaFDoVQpf+F84IDX6UREREQko9t6YCvRsdEquNKYl298mauKXkW32d3YdWTXeY87ePIgd868kycWPEHrqq1Z2WMllQvpDxMuxMwYdsswHr36UcatHkfPL3sGpWR+7afXOBV9isGNBif7tSXjGNhgIAdPHWTahmleRxGRi1DBLJesVi347DNYtw5uvNE/MqN0aXj6adi/3+t0IiIiIpJRhUeEA6hgTmMyh2Zmxt0zOBNzhvafticmNuY/x6zbu46rJl7Ft1u/ZVSzUXzQ6gNyZs7pQdq0x8x45cZXePq6p5mybgpdv+ga7+84qfYe3cuYVWPoUL0DFQtWTLbrSsbTsERD6hevz4jlI4K22l5EkocKZkk2NWvCrFnw889w663+TQBLl4Ynn4TISK/TiYiIiEhG44vwAVCxgEqutKZ8gfKMbT6WxTsW8+KPL/7ruSlrp9BwSkOiYqNY3HUxD9Z7EDPzKGnaZGY83/R5nmvyHO9ueJeOn3UkOjY6Wa796k+vEhUTxTONnkmW60nGZWYMbDCQLQe28PVvX3sdR0QuQAWzJLsrr4SPPoJffoHbboNXXvEXzY89BhERXqcTERERkYzCF+mjdN7S5Micw+sokgQda3SkQ/UOPLvoWX7c8SMno07S7YtudP+yO41KNWJtz7U0KNHA65hp2jONn+HlG15mxsYZtJ3VlqiYqEu63u4juxm/ejyda3SmXP5yyZRSMrK7q9xNyTwlGbZ8mNdRROQCVDBL0FStCjNnwsaNcMcd8MYb/qL5kUdg3z6v04mIiIhIehceEa7xGGnc2OZjKZuvLO0/bU/DKQ15Z/07DG40mDnt51AoRyGv46ULj1/7OG/e/Caf+D7h3o/v5XT06SRf6+UlLxPjYni60dPJmFAysrCQMPrW68sP239g3d51XscRkfNQwSxBV6UKvP8++HzQqhUMGwZlysCgQfDXX16nExEREZH0KCY2hl8jf6VyQW36lpblypKLmXfP5K9jf7HzyE6+afcNz17/LKEhoV5HS1cGNRzEqGaj+GLzF9z90d2cij6V6Gv8efhPJq2dxH217qNMvjJBSCkZVffa3cmZOSfDlw/3OoqInIcKZkkxFSvC9OkQHg6tW8PIkf6iecAA2LPH63QiIiIikp5sP7Sd0zGntYI5Hbiq2FWs6L6CX/r8QrPyzbyOk249WO9Bxt82nq+3fM0dM+/gZNTJRJ3/4mL/rOwnr3syGPEkA8ubNS/danZj5saZ7Dmq8kAkNVLBLCmuQgWYOhV+/RXatoXRo6FsWejXD3bv9jqdiIiIiKQHZzf40wrm9KFW0VoUy1XM6xjpXq86vZjScgrzt82nxYwWHD9zPEHn/XHwD95e/zY9avegZJ6SQU4pGVH/Bv2Jjo1mzMoxXkcRkXioYBbPlCsHb78Nv/0GHTrAuHH+ovmBB2DnTq/TJS/nYP9+2LQJFizwjwx5803/POrXXoPDh71OKCIiIpK+/FMwF1LBLJIY3Wp149273uWH7T/Q7P1mHD199KLnvLD4BUItlCeufSIFEkpGVDZfWe6sdCfj14znRNQJr+OIyDnCvA4gUrYsTJ4MTz8NL78Mkyb5v+67D554Akqm4j8AP37cP0f6Yl/79kFUPBsyZ8kCp0/DK6/4Z1L36we5c6f86xARERFJb8IjwymWqxh5s+b1OopImtOhegfCQsLo8GkHbnnvFua0n0OerHniPXbrga1M2zCNB+s9SPHcxVM4qWQkgxoO4rNfP+PdDe/Su05vr+OISBzmnPM6Q6LUqVPHrV692usYEkQ7dvgL1ylT/D937eovmkuXTpn7R0VBRIS/GN6798LF8bFj/z0/JAQKF4YiRS7+lTs3rFsHQ4fCl19C/vzw0EPQty/kypUyr1dERCSjM7M1zrk6XudIbhn9fXO9SfXInSU333X6zusoImnWLN8s2sxqQ+2itfm2/bfky5bvP8d0/rwzH2/6mN/7/06RnEU8SCkZhXOOepPrcfT0UXwP+AgxfShfJKWd732zCmZJtXbuhFdf9a9mjo2Fzp3hySf9K54Tyzk4eDBhq40jI/3Hnytv3oSVxgULQmgSNrVevdpfNH/9NRQoAA8/DA8+CDlzJv5aIiIiknAqmNMf5xy5X8lN15pdGdlspNdxRNK0L379gns/vpcrL7uSeR3mUSB7gX+e+zXyV6qOrcqgBoN4/ebXPUwpGcWMX2bQ7tN2fNX2K26rcJvXcUQyHBXMkmbt3u0vmidOhOho6NTJXzSXKwcnTvjHT1xspfFff51/REXRohcvjS+7DLJmTZnXu3Klv2ieM8dfVj/yiH8udY4cKXN/ERGRjEYFc/qz8/BOSo4oydjmY+lTt4/XcUTSvG+2fEOrD1tRsWBFvuv4HYVyFAKg3ax2zN48mz/6//HPYyLBFBUTRdmRZalYoKI+oSLigfO9b9YMZkn1iheHkSPh8cfh9ddh/Hh4911/4XrkyH+PN/v3iIoqVeIvjYsW9Y+oMEv513Qh9erBN9/A8uX+ovmxx+CNN+DRR+H++yF7dq8TioiIiKRuZzf4q1KoisdJRNKH5uWbM7vtbO6YeQdNpjVhQacF7D+xn5kbZ/LYNY+pXJYUkyk0E33r9eWx7x5jw18bqFGkhteRRAStYJY06K+/YMwYOHr0/CMqwtLRH50sXeovmufP9xfnjz0GvXuraBYREUkuWsGc/oxYPoKBcwfy98N/q/gSSUbf//E9t8+4nctzX06pvKVYunMp2/tv/9fYDJFgO3jyICWGl6B11da8c8c7XscRyVDO975ZE9ElzSlSBJ5/HkaM8K9q7tIFbr0Vatb0P5eeymWAq6+GefPgxx/hyiv9mwBecYX/9Z886XU6ERERkdTHF+GjQLYCKpdFklnTMk35tv237D66m3nb5jGg/gCVy5Li8mXLR7ea3fjglw/469hfXscREVQwi6QZ114L330HixZB5cowcKC/aB45Ek6d8jqdiIiISOrhi/BpPIZIkFxX6jrmd5xPpxqdeOjqh7yOIxlU/wb9iYqJYuyqsV5HERFUMIukOY0awfffw8KFUL489O/vL5pHj1bRLCIiIuKcU8EsEmQNSjRg2p3TyJs1r9dRJIMql78cLSu2ZOyqsZyM0kd7RbymglkkjWrSBH74ARYsgLJloW9fKFcOxo6F06e9TiciIiLijb+P/83BUwepXLCy11FERCSIBjYYyP6T+5n+83Svo4hkeCqYRdIwM2jaFBYv9m8CWKoUPPCAf2Xz+PFw5ozXCUVERERSVnhkOIBWMIuIpHONSjWidtHajFg+glgX63UckQxNBbNIOmAGN94IS5bA3LlQvDj06eMvmidOVNEsIiIiGYcvwgdA5UJawSwikp6ZGYMaDCI8Mpy5W+d6HUckQ1PBLJKOmMHNN8PSpTBnDhQpAr16QcWKMHkyREV5nVBEREQkuHwRPnJlzkXxXMW9jiIiIkF2b9V7KZarGMOXD/c6ikiGpoJZJB0yg1tvheXL4euvoWBB6NHDXzS//baKZhEREUm/wiPDqVKoCmbmdRQREQmyzKGZ6VuvL/N/n88v+37xOo5IhqWCWSQdM4PmzWHlSvjyS8ifH+67DypXhqlTITra64QiIiIiycsX4dN4DBGRDKTnVT3Jnik7I5aP8DqKSIalglkkAzCDFi1g1Sr44gvInRu6dvUXze++q6JZRERE0oeDJw/y17G/qFJQG/yJiGQU+bPlp0uNLrz3y3vsO7bP6zgiGVJQC2Yzu9XMNpvZVjN7PJ7nh5vZ+sDXb2Z2KJh5RDI6M2jZEtasgc8+gxw5oHNnqFoV3nsPYmK8TigiIiKSdOGR4YA2+BMRyWj6N+jPmZgzjFs9zusoIhlS0ApmMwsFxgDNgCpAWzP711IC59xA51xN51xNYBTwabDyiMj/M4M774S1a2HWLMiSBTp29BfNH3ygollERETSJl+ED4AqhbSCWUQkI6lQoAK3V7idsavGcir6lNdxRDKcYK5grgdsdc797pw7A8wE7rjA8W2BGUHMIyLnCAmBVq1g/Xr4+GPIlAnat4crr4SZMyE21uuEIiIiIgkXHhFOtrBslMpTyusoIiKSwgY2GEjEiQje//l9r6NIKhMTG8PLP75M+0/bs/HvjV7HSZeCWTAXB3bG+XlX4LH/MLNSQBng+yDmEZHzCAmBe+6BDRvgww/9K5zbtvUXzR99pKJZRERE0gZfpI+KBSsSGhLqdRQREUlhTUo3oWaRmgxfPhznnNdxJJXYdWQXN7x7A09+/ySzfLOoPq46HT7twNYDW72Olq6klk3+2gCfOOfi/WC+mfU0s9VmtjoiIiKFo4lkHCEh0Lo1/PwzzJjhL5b/9z+oUQM++URFs4iIiKRuvgifxmOIiGRQZsbABgPZFLGJ+b/P9zqOpAKf//o5NcbXYPWe1Uy7cxq7B+3mkasf4dPwT6k0uhI9v+zJzsM7L34huahgFsy7gcvj/Fwi8Fh82nCB8RjOuYnOuTrOuTqFChVKxogiEp/QUGjTBjZuhPffh6gouPdeqFULPv1URbOIiIikPsfOHOPPw39SpaAKZhGRjKpNtTYUzVmUYcuGeR1FPHQy6iQPfP0Ad314F6XzlmZtr7V0qtGJAtkL8OpNr7Kt3zb61OnD1PVTKTeqHAO+HcC+Y/u8jp2mBbNgXgWUN7MyZpYZf4k8+9yDzKwSkA9YFsQsIpIEoaHQrh1s2gTTp8PJk3D33XDVVfD556BPHYmIiEhq8WvkrwBULlTZ4yQiIuKVzKGZeaDuA8zdNpdNf2/yOo54YNPfm6g3uR5jV4/loYYPsey+ZVQoUOFfxxTNVZRRzUexpe8WOlzZgVErR1F2ZFmeXPAkB08e9Ch52ha0gtk5Fw08CMwFwoGPnHObzOw5M2sZ59A2wEynATkiqVZoKHToAD4fTJsGx47BXXf5i+bZs1U0i4iIiPfCI8IBNCJDRCSD61WnF9nCsjFi+Qivo0gKcs4xYfUE6kyqw9/H/2ZO+zm8cfMbZA7NfN5zSuUtxZQ7phD+QDgtK7bk5SUvU+atMryw+AWOnj6agunTvqDOYHbOfeOcq+Ccu8I592LgscHOudlxjhnqnHs8mDlEJHmEhUGnThAeDu+8A4cPwx13QN26MGeO1+lEREQkI/NF+AgLCeOKfFd4HUVERDxUMHtBOtXoxPSfpxNxXPt4ZQQHTh7gno/voffXvWlUqhEbem/g1nK3Jvj8CgUqMOPuGazvtZ5GpRrxzMJnuGLkFQxfNpxT0aeCmDz9SC2b/IlIGhIWBl26wK+/wpQpcOAANG8O33zjdTIRERHJqHyRPioUqECm0ExeRxEREY8NaDCA0zGnGbd6nNdRJMh+3PEjNcfXZPbm2bx+0+vMaT+HIjmLJOlaNYrUYHbb2Sy7bxnVL6vOoHmDKDeyHBNWTyAqJiqZk6cvKphFJMkyZYJu3fwrmitXhvvv94/PEBEREUlp4RHhGo8hIiIAVCpYieblmzNm1RitQE2nomOjefaHZ2kyrQmZQzOztNtSHr76YULs0qvOBiUa8F2n7/i+0/eUzFOS3l/3ptKYSkzfMJ2Y2JhLD58OqWAWkUuWJQtMnAg7dsCQIV6nERERkYzmVPQpth3cRuWC2uBPRET8BjUYxN/H/2bGLzO8jiLJbOfhnTSd1pShi4bS7sp2rOu1jrrF6yb7fa4vcz0/dfuJr9p+Re4suen0eSeqj6/OLN8stJXcv6lgFpFkce210KsXjBgBa9Z4nUZEREQykt/2/0asi9UKZhER+UfTMk2pfll1hi8frjIwHfks/DNqjK/Bur/W8e6d7zL9runkypIraPczM26rcBtreq7ho3s+ItbFcs/H91BnUh3mbJmjf7YCVDCLSLJ55RUoXBh69IDoaK/TiIiISEYRHhEOoBXMIiLyDzNjYIOB/PL3Lyz4Y4HXceQSnYw6SZ+v+tDqo1Zckf8K1vVaR8caHVPs/iEWwr1V7+WXPr/wzh3vcODkAZp/0JxGUxuxeMfiFMuRWqlgFpFkkzcvjBwJ69bBW295nUZEREQyCl+EjxALoUKBCl5HERGRVKRttbZcluMyhi0b5nUUuQQb/95I3Ul1Gb9mPA83fJifuv1EufzlPMkSFhJGl5pd2PzgZsY0H8O2A9toPLUxt7x3C6t2r/IkU2qggllEktU990CLFjB4MGzf7nUaERERyQjCI8Mpm68s2TJl8zqKiIikIlnCsvBA3QeYs3XOP592kbTDOce4VeOoO6kukSci+bb9t7x+8+tkDs3sdTQyh2bm/rr3s7XfVl6/6XXW7FlDvcn1uOvDu9j490av46U4FcwikqzMYMwY/1/79AGNIxIREZFg80X4NB5DRETi1btOb7KGZWXE8hFeR5FEOHDyAK0+asX939xP41KN2dB7A7eUu8XrWP+RPVN2Hr76YX7v/zvPNnmW7//4nurjqtP+0/ZsPbDV63gpRgWziCS7kiXhxRfh22/hww+9TiMiIiLpWXRsNL/t/00b/ImISLwK5ShEx+odeffnd4k8Eel1HEmAxTsWU2N8Db7+7WveuOkNvmn/DZflvMzrWBeUO0tuBjcezO/9fufRax7ls/DPqDS6Ej1m92Dn4Z1exws6FcwiEhQPPgh16kD//nDggNdpREREJL3admAbUbFRKphFROS8BjQYwKnoU4xfPd7rKHIB0bHRDFk4hOunXU/WsKwsvW8pD139ECGWdurLAtkL8MqNr/B7/9+5v+79vPvzu5QbVY7+c/qz79g+r+MFTdr5OyQiaUpoKEyaBPv3w2OPeZ1GREQkbTCzima2Ps7XETMbYGb5zWy+mW0J/DWf11lTC1+ED0AjMkRE5LyqFKrCreVuZcyqMZyOPu11HInHjkM7aDK1Cc8tfo4O1Tuwtuda6hSr43WsJCuSswgjm43ktwd/o8OVHRizagxlR5blyQVPcvDkQa/jJTsVzCISNDVrwqBBMHkyLFrkdRoREZHUzzm32TlX0zlXE7gKOAF8BjwOLHDOlQcWBH4W/Bv8AVQqWMnjJCIikpoNajCIv479xcyNM72OIueY5ZtFzQk12bBvA+/d9R7T7pxGriy5vI6VLErlLcWUO6bge8BHy4oteXnJy5R5qwwvLH6Bo6ePeh0v2ahgFpGgGjIEypSBXr3g1Cmv04iIiKQpNwDbnHM7gDuAaYHHpwF3ehUqtfFF+Lg89+Xp5j9ERUQkOG4seyPVCldj+PLhOO1GnyqciDpBry97cc/H91A+f3nW91pP++rtvY4VFBUKVGDG3TPY0HsDjUs35pmFz1B2ZFmGLRvGyaiTXse7ZCqYRSSocuSAceNg82Z4+WWv04iIiKQpbYAZge8vc87tDXz/F5C6d7pJQb4In+Yvi4jIRZkZAxsMZMO+DSzcvtDrOBnez/t+pu6kukxcO5FHr36UJd2WcEX+K7yOFXTVL6vOF22+YPl9y6lZpCYPzXuI8qPKM2H1BKJioryOl2QqmEUk6G65Bdq39xfMPp/XaURERFI/M8sMtAQ+Pvc55192Fe/SKzPraWarzWx1REREkFN6L9bF8mvkryqYRUQkQdpd2Y7COQozfPlwr6NkWM45xqwcQ71J9dh/Yj/zOszj1ZteJXNoZq+jpaj6Jeozv+N8FnZeSKm8pej9dW8qjanE9A3TiYmN8TpeoqlgFpEUMWwY5MrlH5URG+t1GhERkVSvGbDWOXd2u/F9ZlYUIPDXv+M7yTk30TlXxzlXp1ChQikU1Ts7Du3gZPRJbfAnIiIJkjUsK/fXuZ+vfvuKzZGbvY6T4ew/sZ+7PryLB+c8yPVlrufnPj9z0xU3eR3LU01KN2FJ1yV83e5rcmfJTafPO1F9fHVm+WalqVEuKphFJEUULgxvvAFLlvg3/RMREZELasv/j8cAmA10DnzfGfgixROlQr4I/0ejtIJZREQSqk/dPmQJzcJbK97yOkqGsmj7ImqMr8E3W75h2M3D+Lrd1xTOUdjrWKmCmdG8fHPW9FzDR/d8RKyL5Z6P76HOpDrM2TInTRTNKphFJMV06QJNmsCjj8LevRc7WkREJG0zs2vNrGvg+0JmViaB5+UAbgI+jfPwK8BNZrYFuDHwc4YXHhkOQOVCWsEsIiIJUzhHYTpU78DU9VPZf2K/13HSvejYaAYvHMz1064ne6bsLO++nIENBxJiqiTPFWIh3Fv1Xjb22cjUO6Zy4OQBmn/QnOveuY5F2xd5He+C9HdTRFKMGUyYAKdOQf/+XqcREREJHjMbAjwGPBF4KBPwXkLOdc4dd84VcM4djvPYfufcDc658s65G51zB5I/ddrji/BxWY7LyJ8tv9dRREQkDRnQYAAno08ycc1Er6OkazsO7aDx1MY8v/h5OtfszNpea6ldtLbXsVK90JBQOtfszOYHNzO2+Vh+P/g7TaY14ebpN7Nq9yqv48VLBbOIpKgKFeCZZ+Djj+Grr7xOIyIiEjR34d+k7ziAc24PkMvTROlQeGS4xmOIiEiiVStcjZuvuJlRK0dxJuaM13HSpY83fUyN8TX4Zd8vvN/qfd654x1yZs7pdaw0JXNoZvrU7cO2ftt446Y3WLt3LfUm1+OuD+/ij4N/eB3vX1Qwi0iKe+QRqFoV7r8fjh3zOo2IiEhQnHH+gXkO/hl7IcnIOYcvwqcN/kREJEkGNhjI3mN7+XDjh15HSVeOnzlOzy970vqT1lQsWJH1vdfT7sp2XsdK07JlysZDVz/EH/3/4Lkmz7F051IyhWbyOta/qGAWkRSXOTNMnAg7d/pXM4uIiKRDH5nZBCCvmfUAvgMmeZwpXdlzdA9HTh/RCmYREUmSW664hSqFqjB8+fA0sYlaWrDhrw3UmVSHyWsn8/g1j7Ok6xLK5ivrdax0I1eWXDzT+Bl2DtxJidwlvI7zLyqYRcQTV18NffrAyJGwKnWOEBIREUkSMzPgQ+ATYBZQERjsnBvlabB05uwGfyqYRUQkKcyMAfUHsO6vdSzakbo3UEvtnHOMXjma+pPrc+jUIeZ1nMfLN76c6lbZpheZQzN7HeE/VDCLiGdefhkuuwx69oToaK/TiIiIJI/AaIxvnHPznXOPOOceds7N9zpXeuOL8AFQuZBGZIiISNJ0qN6BgtkLMnz5cK+jpFmRJyK5Y+Yd9J3TlxvK3sDPvX/mxrI3eh1LUpgKZhHxTJ48MGoUrF8PI0Z4nUZERCRZrTWzul6HSM/CI8LJlzUfl+W4zOsoIiKSRmXLlI0+dfrw5eYv2bJ/i9dx0pyFfyykxvgazN02lxG3jOCrtl9RKEchr2OJB1Qwi4inWrWCli1h8GD4I3VtgioiInIp6gPLzGybmf1sZr+Y2c9eh0pPfJE+KheqjH8iiYiISNLcX/d+MoVm4q0Vb3kdJc2Iioni6e+f5oZ3byBn5pwsv285/Rv01/8nZ2AqmEXEU2YwejSEhvpnMmtvBRERSSduAa4AmgK3Ay0Cf5Vk4ovwUaWg5i+LiMilKZKzCO2ubMc769/hwMkDXsdJ9bYf2k7jqY158ccX6VqzK2t6rqFW0VpexxKPqWAWEc9dfjm89BLMnQszZnidRkRE5NI553YAefGXyrcDeQOPSTKIOB5B5IlIbfAnIiLJYmCDgZyIOsGkNZO8jpKqfbjxQ2qMr8GmiE3MuHsGU+6YQs7MOb2OJalAggpmM8thZiGB7yuYWUsz01aQIpJs7r8f6tWDAQPggP7QWERE0jgz6w+8DxQOfL1nZn29TZV+hEeGA9rgT0REkkf1y6pzQ5kbGLVyFFExUV7HSXWOnzlO99ndaTOrDVUKVWF9r/W0qdbG61iSiiR0BfNiIKuZFQfmAR2BqcEKJSIZT2goTJrkL5cfecTrNCIiIpfsPqC+c26wc24w0ADo4XGmdMMX4QPQCmYREUk2gxoOYvfR3Xzs+9jrKKnK+r/Wc9XEq3h73ds8ee2TLO6ymDL5yngdS1KZhBbM5pw7AbQCxjrn7gWqBi+WiGRE1avDww/D22/DwoVepxEREbkkBsTE+Tkm8Jgkg/CIcHJmzsnluS/3OoqIiKQTt5a7lUoFKzFs2TCcNgcC/CMxGk5pyJHTR/iu03e8eMOLZArVQAP5rwQXzGbWEGgPfB14LDQ4kUQkIxs8GMqWhV694NQpr9OIiIgk2TvACjMbamZDgeXAFG8jpR++SB+VClbSbvUiIpJsQiyEAfUHsGbvGpb8ucTrOJ6KdbEMWTiENrPacFXRq1jfez1NyzT1OpakYgktmAcATwCfOec2mVlZQOsLRSTZZc8O48fDli3w4otepxEREUka59wwoCtwIPDV1Tk3wtNQ6Uh4RLjGY4iISLLrWKMjBbIVYNjyYV5H8cyJqBP875P/8dzi5+hSswsLOi2gcI7CXseSVC5BBbNzbpFzrqVz7tXAZn+Rzrl+Qc4mIhnUTTdBx47w6quwaZPXaURERBLPzBoAW5xzI51zI4FtZlbf61zpweFTh9l9dDeVC2qDPxERSV7ZM2Wnd53efPHrF2w7sM3rOClu95HdNHqnEbN8s3j9ptd5u+XbZAnL4nUsSQMSVDCb2QdmltvMcgAbAZ+ZaRsuEQmaN9+E3LmhZ0+IjfU6jYiISKKNA47F+flY4DG5ROGR4YA2+BMRkeB4oO4DhIWE8daKt7yOkqJW7V5F3Ul12bx/M7Pbzubhqx/WKCpJsISOyKjinDsC3AnMAcoAHYMVSkSkUCF/ybx0KUyc6HUaERGRRDMXZ4cg51wsEOZhnnQjPEIFs4iIBE/RXEVpe2Vb3l73NodOHfI6ToqYuXEmjaY2IktYFpZ2W0qLCi28jiRpTEIL5kxmlgl/wTzbORcFaEtNEQmqTp2gaVN47DHYs8frNCIiIonyu5n1M7NMga/+wO9eh0oPfBE+soRmoUzeMl5HERGRdGpgg4EcjzrOpDWTvI4SVGc382s7qy1XFb2KFd1XcOVlV3odS9KghBbME4DtQA5gsZmVAo4EK5SICIAZTJgAZ85AP019FxGRtKU3cDWwO/BVH+jpaaJ0IjwynIoFKxIaEup1FBERSadqFqnJ9aWvZ9TKUUTFRHkdJyi0mZ8kp4Ru8jfSOVfcOdfc+e0Arg9yNhERypWDwYNh1iyYPdvrNCIiIgnjnPvbOdfGOVc48NXOOfe317nSA1+ETxv8iYhI0A1qOIidR3YyK3yW11GS3a4ju7juneu0mZ8km4Ru8pfHzIaZ2erA15v4VzNf7LxbzWyzmW01s8fPc0xrM/OZ2SYz+yCR+UUkA3j4YahWDR54AI4e9TqNiIjI+ZlZDzMrH/jezOxtMztsZj+bWW2v86V1J6JOsP3Qds1fFhGRoGtevjkVClRg2LJhxNlWIc1buXsl9SbV47f9v2kzP0k2CR2R8TZwFGgd+DoCvHOhE8wsFBgDNAOqAG3NrMo5x5QHngCucc5VBQYkJryIZAyZMvk3+tu9G55+2us0IiIiF9Qf/2g5gLZADaAsMAjIWNvRB8HmyM04nApmEREJuhALYUD9Aazas4qlO5d6HSdZzNw4k8ZTG2szP0l2CS2Yr3DODXHO/R74ehb/G+ULqQdsDRx/BpgJ3HHOMT2AMc65g+D/KGFiwotIxtGwIdx/P4waBStXep1GRETkvKIDG2IDtADedc7td859RwI+ASgX5ovwAWhEhoiIpIhONTqRL2s+hi0f5nWUSxJ3M786xeqwsvtKbeYnySqhBfNJM7v27A9mdg1w8iLnFAd2xvl5V+CxuCoAFczsJzNbbma3xnchM+t5djxHREREAiOLSHrz0ktQrBj07AlR6XOfBRERSftizayomWUFbgC+i/NcNo8ypRu+CB+hFkr5AuW9jiIiIhlAjsw56F2nN5//+jm/H/zd6zhJEnczv641u/Jdx+8olKOQ17EknUlowdwbGGNm281sOzAa6JUM9w8DygNN8H+EcJKZ5T33IOfcROdcHedcnUKF9C+BSEaVOzeMHg0bNsDw4V6nERERiddgYDX+MRmznXObAMysMZA2/8s0FQmPDKd8gfJkDs3sdRQREckgHqj7ACEWwsgVI72OkmjnbuY3peUUbeYnQZGggtk5t8E5VwOoDlR3ztUCml7ktN3A5XF+LhF4LK5d+N94Rznn/gB+w184i4jE6847/V9Dh8K2bR6HEREROYdz7iugFFDZOdcjzlOrgf95kyr98EX4NB5DRERSVPHcxWlTrQ1T1k3h8KnDXsdJMG3mJykpoSuYAXDOHXHOHQn8OOgih68CyptZGTPLDLQBZp9zzOf4Vy9jZgXxj8zQyg4RuaBRoyAsDPr0gXS0ma+IiKQTzrnos3uMxHnsuHPumFeZ0oMzMWfYemCrNvgTEZEUN7DBQI6dOcbktZO9jpIgcTfzW3bfMm3mJ0GXqIL5HBf8Yw/nXDTwIDAXCAc+cs5tMrPnzKxl4LC5wH4z8wELgUecc/svIZOIZAAlSsDLL8P8+fD++16nERERkZSwZf8WYlyMVjCLiEiKq120No1LNWbkypFEx0Z7Hee8Yl0sgxcO/tdmftUKV/M6lmQAl1IwX3TdoHPuG+dcBefcFc65FwOPDXbOzQ5875xzg5xzVZxzVzrnZl5CHhHJQHr3hgYNYOBA2K8/lhIREUn3fBE+AK1gFhERTwxsMJA/D//Jp+Gfeh0lXsfPHKf1x615fvHz2sxPUtwFC2YzO2pmR+L5OgoUS6GMIiL/ERoKEyfCoUPw8MNepxEREbk4M6vkdYa0LDwyHMOoWLCi11FERCQDalGhBeXyl2P48tS34/yuI7toNLURn4Z/yhs3vaHN/CTFXbBgds7lcs7ljucrl3MuLKVCiojE58or4ZFHYOpU+P57r9OIiIhc1DyvA6RlvggfpfOWJnum7F5HERGRDCg0JJT+9fuzfNdylu1c5nWcf5y7md9DVz+kzfwkxakkFpE07Zln4KOPoFcv+PlnyJbN60QiIpKRmdnI8z0F5E3BKOlOeGS4xmOIiIinutTswjMLn2H48uE0vLyh13GYuXEmXb/oSpGcRZjXcZ7mLYtnLmUGs4iI57JlgwkTYOtWePFFr9OIiIjQFdgIrDnnazVwxsNcaVp0bDSbIzdrgz8REfFUzsw56Vm7J7PCZ7H90HbPcsTdzK9usbrazE88p4JZRNK8G26Azp3h1Vdh40av04iISAa3CtjonJt27hdw1OtwadUfB//gdMxprWAWERHP9a3flxALYdSKUZ7c/z+b+XXSZn7iPRXMIpIuvPEG5MkDPXpAbKzXaUREJAO7B1gf3xPOuTIpGyX9CI8MB1DBLCIiniuRuwStq7Zm0tpJHDl9JEXvHd9mfplDM6doBpH4qGAWkXShYEEYPhyWL4fx471OIyIiGVhO59wJr0OkN74IHwCVClbyOImIiAgMbDCQo2eO8va6t1Psnit3r6TupLps2b+FL9t+qc38JFVRwSwi6UaHDnDjjfD447B7t9dpREQkg/r87DdmNsvDHOmKL8JH8VzFyZM1j9dRREREqFOsDteVvI63VrxFdGx00O83c+NMGk9tTLawbCy7bxm3Vbgt6PcUSQwVzCKSbpj5Vy9HRUG/fl6nERGRDCruUqKynqVIZ8IjwzUeQ0REUpWBDQay/dB2Pv/186Dd49zN/FZ0X0HVwlWDdj+RpFLBLCLpyhVXwNCh8Omn8PnnXqcREZEMyJ3ne0miWBdLeEQ4lQtW9jqKiIjIP1pWbEnZfGUZvnx4UK4fdzO/bjW7aTM/SdVUMItIujNoEFSvDg8+CEdSds8FERGRGmZ2xMyOAtUD3x8xs6Nmpv9XSoJdR3ZxPOq4VjCLiEiqEhoSSv/6/Vm6cykrdq1I1mvvOrKL6965jk/DP+XNm99kcsvJ2sxPUjUVzCKS7mTKBBMnwp498NRTXqcREZGMxDkX6pzL7ZzL5ZwLC3x/9ufcXudLi85u8Fe5kFYwi4hI6tK1ZlfyZMmTrKuYz27mt/XAVr5s+yWDGg7SZn6S6qlgFpF0qX59/wrmMWNgRfL+YbKIiIikoLMFs1Ywi4hIapMrSy561O7BJ75P+PPwn5d8PW3mJ2mVCmYRSbdefBGKF4cePfwb/4mIiEjaEx4RTqHshSiYvaDXUURERP6jb/2+AIxaMSrJ14h1sTzz/TPazE/SLBXMIpJu5crlX8H8yy/w5ptepxEREZGk8EX6NB5DRERSrZJ5SnJPlXuYuHYiR08fTfT5Zzfze+HHF7SZn6RZKphFJF1r2RJatYJnn4WtW71OIyIiIonhnCM8IpwqBTUeQ0REUq9BDQdx5PQR3ln/TqLO02Z+kl6oYBaRdG/kSMicGXr3Bue8TiMiIiIJte/4Pg6eOqj5yyIikqrVK16Pqy+/mhHLRxATG5Ogc1bsWvHPZn5ftftKm/lJmqaCWUTSveLF4ZVXYMECeO89r9OIiIhIQp3d4E8jMkREJLUb1GAQfxz6g9mbZ1/02Bm/zPjXZn7NyzdPgYQiwaOCWUQyhF694OqrYeBAiIz0Oo2IiIgkRHhEOIBWMIuISKp3Z6U7KZ23NMOWDzvvMWc382v3aTvqFa/Hyh4rtZmfpAsqmEUkQwgJgYkT4cgReOghr9OIiIhIQvgifOTOkpuiOYt6HUVEROSCQkNC6V+/P0v+XMKq3av+83x8m/kVzF7Qg6QiyU8Fs4hkGFWrwqOPwrvvwnffeZ1GRERELsYX6aNKoSqaSSkiImlCt1rdyJU5F8OXD//X42c38/vs18+0mZ+kSyqYRSRDefppKF/ev+HfyZNepxEREZELCY8Ip0pBjccQEZG0IXeW3PSo3YOPfR+z8/BO4N+b+X3Z9ktt5ifpkgpmEclQsmaFCRNg2zZ4/nmv04iIiMj5HDh5gH3H92mDPxERSVP61u9LrItl9MrR2sxPMowwrwOIiKS066+Hrl3h9dehTRuoXt3rRCIiInIubfAnIiJpUem8pbm78t2MWDGCMzFnaFSqEbNaz9K8ZUnXtIJZRDKk11+HvHmhZ0+IifE6jYiIiJzLF+EDoHJBrWAWEZG05eGrHybWxXJfrfuY33G+ymVJ91Qwi0iGVKAAjBgBK1bAuHFepxEREfl/ZpbXzD4xs1/NLNzMGppZTTNbbmbrzWy1mdXzOmew+SJ8ZAvLRqm8pbyOIiIikij1itcj8pFIbeYnGYYKZhHJsNq1g5tvhiefhF27vE4jIiLyj7eAb51zlYAaQDjwGvCsc64mMDjwc7oWHhlO5UKVCTH9J4uIiKQ9ebLm8TqCSIrRuzURybDM/KuXo6Ohb1+v04iIiICZ5QEaAVMAnHNnnHOHAAfkDhyWB9jjScAU5IvwaTyGiIiISBqggllEMrSyZeHZZ+Hzz+Gzz7xOIyIiQhkgAnjHzNaZ2WQzywEMAF43s53AG8ATHmYMuqOnj7LzyE5t8CciIiKSBoR5HUBExGsDBsD778ODD0LTppDHo08yxcbCyZNw/HjCvk6cuPDzN94Ir74KoaHevB4REUmSMKA20Nc5t8LM3gIex79qeaBzbpaZtca/wvnGc082s55AT4CSJUumXOpk9mvkrwAqmEVERETSABXMIpLhZcoEkyZB/frw1FMwevT5j42OTngBnJgy+OwxiWEGOXLE/5UlC7z5JuzdC1On+l+jiIikCbuAXc65FYGfP8FfMF8L9A889jEwOb6TnXMTgYkAderUccGNGjy+CB+ARmSIiIiIpAEqmEVEgLp1oV8/GDkStmw5/0riM2cSd93Mmf2Fb/bs/y6A8+WDEiXOXxBf6OvstbJm9ZfM5/Pqq/D44/7ieuZMf+ksIiKpm3PuLzPbaWYVnXObgRsAH1AWaAz8ADQFtniXMvjCI8PJFJKJK/Jf4XUUEREREbkIFcwiIgHPPw87dsCePf4Ct2jRpBXAcYtgL1cOP/aYP0O/fnDnnTBrlv9nERFJ9foC75tZZuB3oCvwBfCWmYUBpwiMwUivfBE+KhSoQFiI/nNFREREJLXTOzYRkYBcudLfRn99+/pL5R494LbbYPZs/+sUEZHUyzm3HqhzzsNLgKtSPo03fBE+ahet7XUMEREREUmAEK8DiIhIcN13n38Twx9/hJtvhkOHvE4kIiJyfiejTvLHoT+0wZ+IiIhIGqGCWUQkA2jbFj7+GNasgaZNITLS60QiIiLx+23/b8S6WG3wJyIiIpJGqGAWEckg7rrLPyIjPBwaN4a9e71OJCIi8l/hkeEAWsEsIiIikkaoYBYRyUBuvRXmzPFvZtioEfz5p9eJRERE/s0X4SPEQqhQoILXUUREREQkAVQwi4hkME2awHffQUQEXHcdbN3qdSIREZH/54vwcUW+K8gSlsXrKCIiIiKSAEEtmM3sVjPbbGZbzezxeJ7vYmYRZrY+8NU9mHlERMSvQQP4/ns4fty/ktnn8zqRiIiIX3hkuMZjiIiIiKQhQSuYzSwUGAM0A6oAbc0svneKHzrnaga+Jgcrj4iI/Fvt2rBoETjnn8m8fr3XiUREJKOLionit/2/aYM/ERERkTQkmCuY6wFbnXO/O+fOADOBO4J4PxERSaSqVWHxYsiWDa6/Hlas8DqRiIhkZFsPbCU6NlormEVERETSkGAWzMWBnXF+3hV47Fx3m9nPZvaJmV0e34XMrKeZrTaz1REREcHIKiKSYZUvDz/+CAUKwI03+gtnERERL4RHhgOoYBYRERFJQ7ze5O9LoLRzrjowH5gW30HOuYnOuTrOuTqFChVK0YAiIhlBqVL+Yvnyy+HWW2HuXK8TiYhIRuSL8G8KUKlgJY+TiIiIiEhCBbNg3g3EXZFcIvDYP5xz+51zpwM/TgauCmIeERG5gGLF/DOZK1SAli3hiy+8TiQiIhlNeGQ4pfKUIkfmHF5HEREREZEECmbBvAoob2ZlzCwz0AaYHfcAMysa58eWQHgQ84iIyEUUKgQLF0LNmnD33fDhh14nEhGRjMQX4aNyIW3wJyIiIpKWBK1gds5FAw8Cc/EXxx855zaZ2XNm1jJwWD8z22RmG4B+QJdg5RERkYTJlw/mz4drroF27eCdd7xOJCIiGUFMbAy/Rv5KlYKavywiIiKSloQF8+LOuW+Ab855bHCc758AnghmBhERSbzcuWHOHLjrLujWDU6cgAce8DqViIikZzsO7+BU9Clt8CciIiKSxni9yZ+IiKRS2bPD7Nn+ecwPPgivv+51IhERSc/ObvCnERkiIiIiaYsKZhEROa8sWeCTT+B//4NHH4VnnwXnvE4lIiLpUXiEfzuWygVVMIuIiIikJUEdkSEiImlfpkzw/vuQLRsMHQrHj8Orr4KZ18lERCQ98UX6KJKzCPmy5fM6ioiIiIgkggpmERG5qNBQmDLFPzbj9df9M5lHjoQQfQ5GRESSiS/Cp/nLIiIiImmQCmYREUmQkBAYPRpy5PCXzMePw+TJ/vJZRETkUjjnCI8Ip3ONzl5HEREREZFEUsEsIiIJZuYfj5Ejh39cxsmTMH26f4yGiIhIUu0+upujZ45qgz8RERGRNEgFs4iIJIoZDBniH5fx6KNw6hR8+KF/Q0AREZGk8EX4ADQiQ0RERCQN0vRMERFJkkce8Y/M+OILaNnSP5dZREQkKcIjwgEVzCIiIiJpkQpmERFJsgcegLffhu++g2bN4OhRrxOJiEha5IvwkT9bfgplL+R1FBERERFJJBXMIiJySbp2hfffh59+ghtvhIMHvU4kIiJpTXhkOFUKVcHMvI4iIiIiIomkgllERC5ZmzYwaxasXw9Nm0JEhNeJREQkrXDOsSliE5ULaoM/ERERkbRIBbOIiCSLO+6A2bPh11+hcWPYs8frRCIikhZEnIjgwMkDmr8sIiIikkapYBYRkWRzyy3w7bewcyc0agQ7dnidSEREUjtt8CciIiKStqlgFhGRZNW4sX/Tv/374brrYMsWrxOJiEhq5ovwAWhEhoiIiEgapYJZRESSXf36sHAhnDzpX8m8aZPXiUREJLUKjwwnZ+aclMhdwusoIiIiIpIEKphFRCQoataERYvAzL+qee1arxOJiEhq5IvwUaVQFczM6ygiIiIikgQqmEVEJGiqVIHFiyFHDmjaFJYt8zqRiIikNr4In8ZjiIiIiKRhKphFRCSoypWDH3+EQoXgppvghx+8TiQiIqnFoVOH2Htsrzb4ExEREUnDVDCLiEjQlSzpX8lcqhQ0awbffut1IhERSQ3CI8IBbfAnIiIikpapYBYRkRRRtKh/9XKlStCy5f+xd+dhVpf1/8ef71nYB2ZYBGURRFaFAcQ1yz33Lc00SytzqdQsrfz27Wtm9SvLyhZLLbfUcMst18rdckNFkU0EUcBtQPadmfv3xznAoCAMzsznzMzzcV3nOueznHNeh+Py4TX33DfceWfWiSRJWZtYNRHAEcySJElNmAWzJKnRdOsGDz8Mo0bBscfCmDFZJ5IkZWnSnEm0KWlD3/K+WUeRJEnSFrJgliQ1qooK+Ne/YM894cQT4aqrsk4kScrKxKqJDOoyiOKi4qyjSJIkaQtZMEuSGl1ZGdx3H3z60/DVr8Lvf591IklSFibNmeT0GJIkSU2cBbMkKRPt2sFdd8FRR8HZZ8PFF2edSJLUmJasXMKM+TNc4E+SJKmJs2CWJGWmdWu45RY4/ng4/3y44AJIKetUkqTGMHnOZMAF/iRJkpq6kqwDSJJattJSuOGG3IjmH/8YliyBSy6BiKyTSZIa0qQ5kwALZkmSpKbOglmSlLniYvjzn3Ml869/DUuXwmWXQZG/ZyNJzdbEqomUFJWwfefts44iSZKkj8GCWZJUEIqK4He/g/btc/MxL1sGf/kLlPh/KklqlibNmcSAzgMoLS7NOookSZI+Bv/aLkkqGBHws5/lSuYLLsiNZL7hBmjVKutkkqT6NrFqIsO2GpZ1DEmSJH1M/vKxJKmgRMD//V9uHuZbb4VDDoF77oEVK7JOJkmqLytWr+C1919jSNchWUeRJEnSx2TBLEkqSOeeC1dcAWPHwuGHQ7du8PnPw9//nlsIUJLUdE19fyo1qcYF/iRJkpoBC2ZJUsE67TR47z247z447jj45z/h2GNzZfNnPgM33ggLFmSdUpJUVxOrJgIwpJsjmCVJkpo6C2ZJUkFr1QoOPji34N8778DDD8NXvgJPPw1f+AJstRUceihcfTXMnZt1WknS5phYNZEgGNRlUNZRJEmS9DFZMEuSmoySEthnH/jDH2DWLPjPf+Css2DiRDjlFOjeHfbbD/74R3j77azTSpI2ZtKcSWxXsR1tS9tmHUWSJEkfkwWzJKlJKiqCPfbILQY4fTo8/zx873swezZ84xvQsyfsuSf8+tfwxhtZp5Uk1TaxaqLTY0iSJDUTFsySpCYvAkaNgp/+FCZPhgkT4Ec/gsWLc4sF9u0Lo0fDz34Gr76adVpJatlW16zm1bmvMrSrC/xJkiQ1BxbMkqRmZ+hQ+L//g3HjYOpUuPji3PQa3/8+DBoEw4bBD38IL78MKWWdVpJalunzprOyeqUjmCVJkpoJC2ZJUrO2/fbw3e/mFgV880347W+hc2f48Y+hshIGDoTzz4fnnrNslqTGMLFqIgBDuzmCWZIkqTmwYJYktRi9e8PZZ8Njj+UWAbziCthuO/jVr2CXXWDbbeGcc+CJJ6C6Ouu0ktQ8TaqaBMCQro5gliRJag4atGCOiIMiYkpEvBYR53/EecdERIqI0Q2ZR5KkNbp3h9NOgwcfhHffhWuvhREj4PLL4VOfyi0S+LWvwb//DatWZZ1WkpqPiXMm0qtjL8pal2UdRZIkSfWgwQrmiCgGLgMOBoYCJ0TEh34PLiLKgG8CzzRUFkmSPkrnznDyyXD33VBVBTfdlCuZr78eDjgAevSAL38Z7rkHVqzIOq2k5i4iyiPitoiYHBGTImL3/P6z8vsmRMQvss65pSZVTXJ6DEmSpGakIUcw7wK8llKanlJaCdwEHLmB834MXAwsb8AskiRtlrIy+Nzn4JZbcmXzHXfAoYfm7g8/HLp1g89/Hv7+d1iyJOu0kpqp3wIPpJQGA5XApIjYh9y1dGVKaQfgkiwDbqmaVMOkOZMY2tWCWZIkqbloyIK5JzCz1vas/L61ImIU0DuldO9HvVBEnBYRYyNibFVVVf0nlSRpA9q2haOOgr/+Fd57D+6/P1c+/+tfcOyxubL5M5+BG2+EBQuyTiupOYiITsCngKsAUkorU0rzga8BP08prcjvfy+zkB/DmwveZOmqpQzp5vzLkiRJzUVmi/xFRBHwa+DcTZ2bUroypTQ6pTS6W7duDR9OkqQPaNUKDjoI/vzn3AKBjzwCp5wCzzwDX/gCbLVVbqTz1VfD3LlZp5XUhPUDqoBrIuLFiPhLRLQHBgKfjIhnIuKxiNg525hbZs0Cf06RIUmS1Hw0ZME8G+hda7tXft8aZcCOwKMRMQPYDbjbhf4kSYWupAT23ht+/3uYORP++1846yyYODFXOnfvDvvtB3/8Y66MlqQ6KAFGAX9KKY0ElgDn5/d3JnfN/B3gloiIDz650H/zb2LVRACGdHUEsyRJUnPRkAXzc8CAiOgXEa2A44G71xxMKS1IKXVNKfVNKfUFngaOSCmNbcBMkiTVq6Ii2H13uOQSmD4dXngBzj8f3noLvvEN6NkT9twTfvWr3GjnZcuyTiypwM0CZqWU1iyAfRu5wnkWcHvKeRaoAbp+8MmF/pt/E6smslX7rejSrkvWUSRJklRPShrqhVNKqyPiTOBBoBi4OqU0ISIuAsamlO7+6FeQJKlpiYCRI3O3n/wkN6L59ttzCwKed17unOJi2GEHGDUKdtopd6ushHbtss0uqTCklN6JiJkRMSilNAXYD5gITAP2AR6JiIFAK2BOhlG3yKQ5k5weQ5IkqZlpsIIZIKV0H3DfB/ZdsJFz927ILJIkNbahQ3O3H/wAZs2C556D55/PjXK+7z649trcecXFMGTIusJ5Tencvn2m8SVl5yzgxvxvAU4HvkxuqoyrI+IVYCVwckopZZixzlJKTKyayOeHfT7rKJIkSapHDVowS5KknF69crejj85tpwSzZ+cK5zW3Bx6A667LHS8qgsGD1y+dR4yADh0y+wiSGklKaRywoXVJvtDIUerVO4vfYcGKBY5gliRJamYsmCVJykDEutL5yCNz+1LKzd38wgvrSud//xuuv37dcwYPXn96jZEjoawsu88hSZvLBf4kSZKaJwtmSZIKRERuUcCePeHww9ftf/vtdYXzCy/Ao4/CjTeue87AgeuPdB45Ejp2zOQjSNJGrSmYHcEsSZLUvFgwS5JU4LbeGg47LHdb4913159e4/HH4W9/W3d8wID1S+dRo6BTp8bPLklrTJozifI25fTo0CPrKJIkSapHFsySJDVB3bvDIYfkbmu8997602v8979w003rjm+//bqyec19RUXjZ5fUMk2smsiQrkOIiKyjSJIkqR5ZMEuS1ExstRUcdFDutkZV1fql89NPw803rzu+3XYfHuncuXPjZ5fU/E2aM4nDBx6+6RMlSZLUpFgwS5LUjHXrBgcemLutMXfu+qXz2LFw663rjvftu37pvNNO0KVLo0eX1IzMWTqH95a85/zLkiRJzZAFsyRJLUyXLnDAAbnbGu+/nyudaxfPf//7uuPbbrtuao3hw2HwYOjXD0q8kpC0GSZVTQJgSNchGSeRJElSffOvhZIkic6dYf/9c7c15s9fv3B+/nm44451x0tLc4sJDhmSK5zX3AYNgrKyRv8IkgrYpDm5gtkRzJIkSc2PBbMkSdqg8nLYd9/cbY0FC2DyZJg0KXc/eTK88grceSdUV687r2fPDxfPgwfDNtuA63tJLc/Eqom0K21H7069s44iSZKkembBLEmSNlunTrDrrrlbbStXwrRp60rnNbfrroNFi9adV1b24dJ58GDYfnto1apxP4ukxjOxaiJDug6hKIqyjiJJkqR6ZsEsSZI+tlatciOWh3xgetWU4O23P1w8P/IIXH/9uvOKi6F//w2XzxUVjftZJNW/SXMmsU/ffbKOIUmSpAZgwSxJkhpMRG5ajG22WX+qDciNbH711fWL50mT4IEHciOi1+je/cOl85Ah0Ls3FDkYUip4C1csZNbCWS7wJ0mS1ExZMEuSpEyUlcFOO+Vuta1eDTNmfLh4vuUWmDdv3Xlt2+YWFPxg8TxgQO6YpMIwec5kwAX+JEmSmisLZkmSVFBKSnJzMm+/PRx22Lr9KcGcOR8unp95Bm6+OXcccqOm+/b9cPE8eDB07eoig1Jjm1g1EbBgliRJaq4smCVJUpMQAd265W6f/OT6x5Ytg6lT15XOawroRx/NHVujc+d1pfO22+ZGOrdtC+3arXu8oe3a+0pLLamluphYNZFWxa3oV9Ev6yiSJElqABbMkiSpyWvbFoYPz91qq6mBmTM/XDzfey+8++6WvVdR0eaV0h9VUtflOSVeramJmzRnEoO6DKKkyH+YJUmSmiOv8iRJUrNVVJQbqbzttnDggesfq67OjW5ec1u6tG7bGzvn/fc3fLy6ess+Q2npR5fQW20F3/kOVFZ+/D8vqSFMrJrI6G1GZx1DkiRJDcSCWZIktUjFxdChQ+7WGFatqnuJvalSe9EiGDsWxoyBr38dLroIKioa5/NIm2PZqmW8Pu91Thp+UtZRJEmS1EAsmCVJkhpBaSl06pS71af334cLLoA//hFuugn+3/+Dr3wlV6BLWZsydwqJ5AJ/kiRJzVhR1gEkSZK05Tp3hj/8AV54AYYMgdNOg912g2eeyTqZlJseA2BItyEZJ5EkSVJDsWCWJElqBior4bHH4MYb4a23ciXzl7+85YsZSvVhUtUkiqOYAZ0HZB1FkiRJDcSCWZIkqZmIgM9/HiZPhu99L1c2DxwIl16amwNaamwT50ykf+f+tC5pnXUUSZIkNRALZkmSpGamrAx+/nMYPx722AO+9S0YORIefjjrZGppJlZNdP5lSZKkZs6CWZIkqZkaNAjuuw/uuguWLoX99oPjjoM338w6mVqCldUree391xja1YJZkiSpObNgliRJasYi4IgjYMIE+NGP4B//gMGD4ac/heXLs06n5uy1919jdc1qF/iTJElq5iyYJUmSWoC2beGCC3LzMx9yCPzgB7DDDnDPPVknU3M1qWoSgFNkSJIkNXMWzJIkSS3IttvCbbfBv/4FrVrB4YfDoYfC1KlZJ1NzM7FqIkEwuOvgrKNIkiSpAVkwS5IktUD77w8vvQSXXAJPPAE77gjf/z4sWZJ1MjUXE+dMZNvybWlX2i7rKJIkSWpAFsySJEktVKtWcO65MGUKHH88/OxnufmZb74ZUso6nZq6SVWTnB5DkiSpBbBgliRJauG23hquuw7+8x/o1i1XNu+7L4wfn3UyNVXVNdVMnjOZIV1d4E+SJKm5s2CWJEkSAHvsAc89B5dfDi+/DCNHwje/CfPnZ51MTc2M+TNYUb3CEcySJEktgAWzJEmS1iouhtNPh1dfhdNOgz/8AQYOhKuvhpqarNOpqZhYNRHAglmSJKkFsGCWJEnSh3TpAn/8I4wdCwMGwCmnwO67w7PPZp1MTcGagtkpMiRJkpo/C2ZJkiRt1MiR8OST8Ne/wptvwq67wle/Cu+9l3UyFbJJcyaxTdk2dGrTKesokiRJamAWzJIkSfpIEfDFL8KUKXDeebkFAQcOhN//HlavzjqdCtHEqomOXpYkSWohLJglSZK0WTp2hF/+MrcA4C67wNlnw6hR8NhjWSdTIUkpMWnOJOdfliRJaiEsmCVJklQnQ4bAgw/C7bfDwoWw995wwgkwa1bWyVQIZi2cxeKViy2YJUmSWogGLZgj4qCImBIRr0XE+Rs4fkZEjI+IcRHxZER4FSpJktQERMDRR8PEifDDH8Kdd8LgwfDzn8OKFVmnU5Zc4E+SJKllabCCOSKKgcuAg4GhwAkbKJD/llIallIaAfwC+HVD5ZEkSVL9a9cOLrwwVzQfcAD8z//AsGFw331ZJ1NWJs2ZBOAIZkmSpBaiIUcw7wK8llKanlJaCdwEHFn7hJTSwlqb7YHUgHkkSZLUQPr1gzvugAceyI1uPvRQOOIImDYt62RqbBOrJtK1XVe6te+WdRRJkiQ1goYsmHsCM2ttz8rvW09EfCMippEbwXx2A+aRJElSAzvwQBg/Hn7xC3jkEdhhB/i//4OlS7NOpsYysWqi02NIkiS1IJkv8pdSuiyl1B/4HvCDDZ0TEadFxNiIGFtVVdW4ASVJklQnrVrBd74DU6bAscfCT36Sm5/51lsh+ftqzVpKiYlVE50eQ5IkqQVpyIJ5NtC71nav/L6NuQk4akMHUkpXppRGp5RGd+vmr9pJkiQ1BdtsAzfcAI8/Dp07w3HHwf775+ZrVvP03pL3mLd8niOYJUmSWpCGLJifAwZERL+IaAUcD9xd+4SIGFBr81BgagPmkSRJUgY++UkYOxYuuwxefBEqK+Hb34YFC7JOpvrmAn+SJEktT4MVzCml1cCZwIPAJOCWlNKEiLgoIo7In3ZmREyIiHHAt4GTGyqPJEmSslNSAl//Orz6KnzlK3DppTBoEFx3HdTUZJ1O9WViVW54ugWzJElSy9GgczCnlO5LKQ1MKfVPKf00v++ClNLd+cffTCntkFIakVLaJ6U0oSHzSJIkKVtdu8IVV8Bzz0G/fvClL8EnPgHPP591MtWHiVUTKWtVxjZl22QdRZIkSY0k80X+JEmS1PLstBP85z9w7bXw+uuw887w299mnUof16Q5kxjabSgRkXUUSZIkNZKSrANIkiSpZSoqgpNPhqOOgh/9CPbdN+tE+riOGnQUrYpbZR1DkiRJjciCWZIkSZnq1Al+/eusU6g+nLXrWVlHkCRJUiNzigxJkiRJkiRJ0haxYJYkSZIkSZIkbRELZkmSJEmSJEnSFrFgliRJkiRJkiRtEQtmSZIkSZIkSdIWsWCWJEmSCkhElEfEbRExOSImRcTutY6dGxEpIrpmmVGSJElaoyTrAJIkSZLW81vggZTSsRHRCmgHEBG9gU8Db2YZTpIkSarNEcySJElSgYiITsCngKsAUkorU0rz84d/A3wXSNmkkyRJkj7MglmSJEkqHP2AKuCaiHgxIv4SEe0j4khgdkrppYzzSZIkSeuxYJYkSZIKRwkwCvhTSmkksAS4EPg+cMGmnhwRp0XE2IgYW1VV1aBBJUmSJLBgliRJkgrJLGBWSumZ/PZt5ArnfsBLETED6AW8EBE9PvjklNKVKaXRKaXR3bp1a6zMkiRJasEsmCVJkqQCkVJ6B5gZEYPyu/YDXkgpbZVS6ptS6kuuhB6VP1eSJEnKVEnWASRJkiSt5yzgxohoBUwHvpxxHkmSJGmjIqWmtQh1RFQBb2Tw1l2BORm8rzbO76Sw+H0UHr+TwuN3Ulj8PgpPVt/JtimlZjefhNfNqsXvpLD4fRQev5PC4vdRePxOCkuW38cGr5ubXMGclYgYm1IanXUOreN3Ulj8PgqP30nh8TspLH4fhcfvpHnweyw8fieFxe+j8PidFBa/j8Ljd1JYCvH7cA5mSZIkSZIkSdIWsWCWJEmSJEmSJG0RC+bNd2XWAfQhfieFxe+j8PidFB6/k8Li91F4/E6aB7/HwuN3Ulj8PgqP30lh8fsoPH4nhaXgvg/nYJYkSZIkSZIkbRFHMEuSJEmSJEmStogFsyRJkiRJkiRpi1gwb4aIOCgipkTEaxFxftZ5WrKI6B0Rj0TExIiYEBHfzDqTciKiOCJejIh7ss4iiIjyiLgtIiZHxKSI2D3rTC1ZRHwr/9+sVyJiTES0yTpTSxMRV0fEexHxSq19nSPiXxExNX9fkWXGlmQj38cv8//Nejki7oiI8gwjagt4zVxYvG4uTF4zFxavmQuP183Z87q5sDSV62YL5k2IiGLgMuBgYChwQkQMzTZVi7YaODelNBTYDfiG30fB+CYwKesQWuu3wAMppcFAJX43mYmInsDZwOiU0o5AMXB8tqlapGuBgz6w73zgoZTSAOCh/LYax7V8+Pv4F7BjSmk48CrwP40dSlvOa+aC5HVzYfKaubB4zVxAvG4uGNfidXMhuZYmcN1swbxpuwCvpZSmp5RWAjcBR2acqcVKKb2dUnoh/3gRuQuAntmmUkT0Ag4F/pJ1FkFEdAI+BVwFkFJamVKan2kolQBtI6IEaAe8lXGeFiel9Djw/gd2Hwlcl398HXBUY2ZqyTb0faSU/plSWp3ffBro1ejB9HF4zVxgvG4uPF4zFxavmQuW180Z87q5sDSV62YL5k3rCcystT0LL8wKQkT0BUYCz2QcRXAp8F2gJuMcyukHVAHX5H8F8y8R0T7rUC1VSmk2cAnwJvA2sCCl9M9sUymve0rp7fzjd4DuWYbRer4C3J91CNWJ18wFzOvmgnEpXjMXEq+ZC4zXzQXN6+bCVRDXzRbMapIiogPwd+CclNLCrPO0ZBFxGPBeSun5rLNorRJgFPCnlNJIYAn+ClNm8vOTHUnuLzHbAO0j4gvZptIHpZQSkLLOIYiI/yX3q/03Zp1Fag68bi4MXjMXJK+ZC4zXzU2D182Fo5Cumy2YN2020LvWdq/8PmUkIkrJXSTfmFK6Pes84hPAERExg9yvw+4bETdkG6nFmwXMSimtGaV0G7mLZ2Vjf+D1lFJVSmkVcDuwR8aZlPNuRGwNkL9/L+M8LV5EfAk4DDgx/5cXNR1eMxcgr5sLitfMhcdr5sLjdXPh8rq5wBTadbMF86Y9BwyIiH4R0YrcBPN3Z5ypxYqIIDdH1qSU0q+zziNIKf1PSqlXSqkvuX8/Hk4p+VPmDKWU3gFmRsSg/K79gIkZRmrp3gR2i4h2+f+G7YcLyBSKu4GT849PBu7KMEuLFxEHkfvV8SNSSkuzzqM685q5wHjdXFi8Zi48XjMXJK+bC5fXzQWkEK+bLZg3IT9p9pnAg+T+w3ZLSmlCtqlatE8AXyT3E/9x+dshWYeSCtBZwI0R8TIwAvh/2cZpufKjYm4DXgDGk/t/75WZhmqBImIM8BQwKCJmRcQpwM+BAyJiKrkRMz/PMmNLspHv4w9AGfCv/P/fL880pOrEa+aC5HWztGleMxcQr5sLg9fNhaWpXDdHAYyiliRJkiRJkiQ1QY5gliRJkiRJkiRtEQtmSZIkSZIkSdIWsWCWJEmSJEmSJG0RC2ZJkiRJkiRJ0haxYJYkSZIkSZIkbRELZkkqcBFRHRHjat3Or8fX7hsRr9TX60mSJElZ8bpZkrJRknUASdImLUspjcg6hCRJklTgvG6WpAw4glmSmqiImBERv4iI8RHxbERsn9/fNyIejoiXI+KhiOiT3989Iu6IiJfytz3yL1UcEX+OiAkR8c+IaJvZh5IkSZLqmdfNktSwLJglqfC1/cCv+n2u1rEFKaVhwB+AS/P7fg9cl1IaDtwI/C6//3fAYymlSmAUMCG/fwBwWUppB2A+cEyDfhpJkiSpYXjdLEkZiJRS1hkkSR8hIhanlDpsYP8MYN+U0vSIKAXeSSl1iYg5wNYppVX5/W+nlLpGRBXQK6W0otZr9AX+lVIakN/+HlCaUvpJI3w0SZIkqd543SxJ2XAEsyQ1bWkjj+tiRa3H1Tg/vyRJkpofr5slqYFYMEtS0/a5WvdP5R//Fzg+//hE4In844eArwFERHFEdGqskJIkSVLGvG6WpAbiT9skqfC1jYhxtbYfSCmdn39cEREvkxtNcUJ+31nANRHxHaAK+HJ+/zeBKyPiFHIjLr4GvN3Q4SVJkqRG4nWzJGXAOZglqYnKzyU3OqU0J+sskiRJUqHyulmSGpZTZEiSJEmSJEmStogjmCVJkiRJkiRJW8QRzJIkSZIkSZKkLWLBLEmSJEmSJEnaIhbMkiRJkiRJkqQtYsEsSZIkSZIkSdoiFsySJEmSJEmSpC1iwSxJkiRJkiRJ2iIWzJIkSZIkSZKkLWLBLEmSJEmSJEnaIhbMkgpeRDwaEfMionXWWRpKRHSOiH9ExIKIeCsivvsR5/aJiMW1bikiltTa/mQ9ZTo0Ip6MiPkR8U5E/CUiyjbjec3++5IkSVLhiogZEbEyIrp+YP+L+WvnvrX2XZjft+sHzv1SRFR/4Lp7cURs00gfQ5KaDAtmSQUtf/H3SSABRzTye5c04tt9B2gDbA3sAPxnYyemlN5MKXVYc8vvrqy174l6ytQJ+AmwDTAE6An88qOe0IK+L0mSJBW214ET1mxExDCgXe0TIiKAk4D38/cf9FTt6+787a2GDC1JTZEFs6RCdxLwNHAtcHLtAxHROyJuj4iqiJgbEX+odezUiJgUEYsiYmJEjMrvTxGxfa3zro2In+Qf7x0RsyLiexHxDnBNRFRExD3595iXf9yr1vM7R8Q1+VHH8yLizvz+VyLi8FrnlUbEnIgYuZHPuQp4L6W0NKU0L6W00YL5o0REp4j4az7vGxHxg4goyh/7UkT8JyL+kB8pPTki9tvYa6WU/pZSemBNJuDPwCc2EaGlfF+SJEkqbNezfml8MvDXD5zzSXIDPM4Gjo+IVo2UTZKaFQtmSYXuJODG/O3AiOgOEBHFwD3AG0BfcqNrb8of+yxwYf65HcmNpJ27me/XA+gMbAucRu6/k9fkt/sAy4A/1Dr/enIjIXYAtgJ+k9//V+ALtc47BHg7pfTiRt73OeCEiDhlM3NuzO/JjTzeDtiL3J/Bl2sd3xWYBnQFfgjcHhGdN/O1PwVM2MQ5LeX7kiRJUmF7GugYEUPy16LHAzd84JyTgX8At+S3D0eSVGcWzJIKVkTsSa4ovCWl9Dy5YvTz+cO7kJu64TsppSUppeUppSfzx74K/CKl9FzKeS2l9MZmvm0N8MOU0oqU0rKU0tyU0t/zo3gXAT8lV9wSEVsDBwNn5Ecdr0opPZZ/nRuAQyKiY377i+TKzQ19zu2BK4G9gfMj4iv5/a3zc8d12pzgtS6c/yeltCilNAP4Vf6913gPuDSf9WZgCnDoZrz2AeQuwC/4iHNaxPclSZKkJmPNKOYDgEnA7DUHIqId8FngbymlVcBtfHiajN0itx7Jmtu0RsotSU2KBbOkQnYy8M+U0pz89t9YN+1Cb+CNlNLqDTyvN7lyc0tUpZSWr9mIiHYRcUV+uomFwONAeb7M7Q28n58+Yj35udn+AxwTEeXkis0bN/KepwB3p5QeBz4NXJQvmXcDXkopLdjM7F2BUnKjhNd4g9xo4TVmp5TSB45vExGfjHULl6w3SjkidiP3Z39sSunVj3j/lvJ9SZIkqWm4ntyAhy/x4ekxjgZWA/flt28EDo6IbrXOeTqlVF7r1r+hA0tSU+SCSJIKUkS0BY4DivPz6wK0JlcWVgIzgT4RUbKB0nImsLGLv6Wsv7hHD2BWre20/umcCwwCdk0pvRMRI4AXgci/T+eIKE8pzd/Ae11HbnRuCbkFQmZv4Bzyx0sBUkqvR8RBwCPAfOB/NvKcDZlDbi7nbYGJ+X19qDVSA+gZEVGrZO5Drtx+AujAB+TnIL4b+EpK6aGNvXEL+74kSZLUBKSU3oiI18lNf/bBqehOJnf9+2ZEQO56sZRcIf3bxswpSU2dI5glFaqjgGpgKDAifxsCPEHuV9eeBd4Gfh4R7SOiTUSsWYDuL8B5EbFT5GwfEdvmj40DPh8Rxfkid69N5CgjN4/v/PxcxT9ccyCl9DZwP/DH/OJypRHxqVrPvRMYBXyTD4+YqO124HMRcVR+pO1C4CVypevSTeRbK6VUTW7+uJ9GRFn+M3+b9eea2wo4O5/1s+T+TO/78KtBROwIPACclVL6xybe/ihazvclSZKkpuMUYN+U0pJa+3oC+wGHse7atRK4mA9PkyFJ2gQLZkmF6mTgmpTSmymld9bcyC3YdiK5EQaHA9sDb5Ib1fo5gJTSreTm3v0bsIhccbhmIbtv5p83P/86d24ix6VAW3Kjg58mV7jW9kVyo4Ynk5vf+Jw1B1JKy4C/A/3IlcgblFJ6itxIiR8CC8hN6/AocCwwJj+KeHOdBSwBpgNPkvszuLrW8WeAAfnP81Ny015sbEG9c4FuwFUbmz6jlhbzfUmSJKnpSClNSymN/cDuTwLjUkr//MC16++A4fmBFgC717oOXnPbuVE/gCQ1AbH+VJySpPoUERcAA1NKXyiALF8CvppS2jPrLIWqkL4vSZIkSZKaAudglqQGkp+i4RRyo2ZV4Py+JEmSJEmqO6fIkKQGEBGnkltU7v6U0uNZ59FH8/uSJEmSJGnLOEWGJEmSJEmSJGmLOIJZkiRJkiRJkrRFLJglSZIkSZIkSVukyS3y17Vr19S3b9+sY0iSJKmZeP755+eklLplnaO+ed0sSZKk+rSx6+YmVzD37duXsWPHZh1DkiRJzUREvJF1hobgdbMkSZLq08aum50iQ5IkSZIkSZK0RSyYJUmSJEmSJElbxIJZkiRJkiRJkrRFmtwczJIkSVlbtWoVs2bNYvny5VlHUR20adOGXr16UVpamnUUSZIkNVEt4e8Cdb1utmCWJEmqo1mzZlFWVkbfvn2JiKzjaDOklJg7dy6zZs2iX79+WceRJElSE9Xc/y6wJdfNTpEhSZJUR8uXL6dLly7N8oKyuYoIunTp0qxHmkiSJKnhNfe/C2zJdbMFsyRJ0hZorheUzZnfmSRJkupDc7+urOvns2CWJElqou68804igsmTJ2cdRZIkSVIjiQi+8IUvrN1evXo13bp147DDDlvvvKOOOorddtttvX0XXnghPXv2ZMSIEWtv8+fP/1h5LJglSZKaqDFjxrDnnnsyZsyYBnuP6urqBnttSZIkSXXXvn17XnnlFZYtWwbAv/71L3r27LneOfPnz+f5559nwYIFTJ8+fb1j3/rWtxg3btzaW3l5+cfKY8EsSZLUBC1evJgnn3ySq666iptuugnIlcHnnXceO+64I8OHD+f3v/89AM899xx77LEHlZWV7LLLLixatIhrr72WM888c+3rHXbYYTz66KMAdOjQgXPPPZfKykqeeuopLrroInbeeWd23HFHTjvtNFJKALz22mvsv//+VFZWMmrUKKZNm8ZJJ53EnXfeufZ1TzzxRO66667G+UORJEmSWohDDjmEe++9F8gNPDnhhBPWO3777bdz+OGHc/zxx6/9+0JDKWnQV5ckSWruzjkHxo2r39ccMQIuvfQjT7nrrrs46KCDGDhwIF26dOH555/n2WefZcaMGYwbN46SkhLef/99Vq5cyec+9zluvvlmdt55ZxYuXEjbtm0/8rWXLFnCrrvuyq9+9SsAhg4dygUXXADAF7/4Re655x4OP/xwTjzxRM4//3yOPvpoli9fTk1NDaeccgq/+c1vOOqoo1iwYAH//e9/ue666+rjT0WSJEkqLM+fA/PG1e9rVoyAnS7d5GnHH388F110EYcddhgvv/wyX/nKV3jiiSfWHh8zZgwXXHAB3bt355hjjuH73//+2mO/+c1vuOGGG3JvV1HBI4888rEiO4JZkiSpCRozZgzHH388kLu4HDNmDP/+9785/fTTKSnJjSHo3LkzU6ZMYeutt2bnnXcGoGPHjmuPb0xxcTHHHHPM2u1HHnmEXXfdlWHDhvHwww8zYcIEFi1axOzZszn66KMBaNOmDe3atWOvvfZi6tSpVFVVMWbMGI455phNvp8kSZKkuhk+fDgzZsxgzJgxHHLIIesde/fdd5k6dSp77rknAwcOpLS0lFdeeWXt8dpTZHzcchkcwdwi1KyuYfZzs5n24DTeeu4tOg/sTK/detF799507N2x2a98KUlSg9rESOOG8P777/Pwww8zfvx4IoLq6moiYm2JvDlKSkqoqalZu718+fK1j9u0aUNxcfHa/V//+tcZO3YsvXv35sILL1zv3A056aSTuOGGG7jpppu45ppr6vjp1KQ993UoaQ8jf5l1EkmSpIa3GSONG9IRRxzBeeedx6OPPsrcuXPX7r/llluYN28e/fr1A2DhwoWMGTOGn/70pw2SwxHMzdSCmQt44S8vcOtnb+WX3X7J1XtczWMXPca81+fx/BXP8/fj/86l217Kb3r9hluOvYX/XvJf3vzPm6xevjrr6JIkaRNuu+02vvjFL/LGG28wY8YMZs6cSb9+/aisrOSKK65g9erc/8/ff/99Bg0axNtvv81zzz0HwKJFi1i9ejV9+/Zl3Lhx1NTUMHPmTJ599tkNvteaMrlr164sXryY2267DYCysjJ69eq1dr7lFStWsHTpUgC+9KUvcWm+eB86dGhD/TE0ORFxUERMiYjXIuL8DRw/IyLGR8S4iHgyIobm95dGxHX5Y5Mi4n8aP/1mWjIT3ro/6xSSJEktwle+8hV++MMfMmzYsPX2jxkzhgceeIAZM2YwY8YMnn/++Qadh9kRzM3EqqWreOPxN3jtwdeY9uA05kyaA0DZNmUMPnow/Q/sz3b7b0e7Lu2oXlXNuy+/y6ynZjHrqVnMfGomk/4+CYCi0iK2Hrk1vXbvRa/detFr91506tPJUc6SJBWQMWPG8L3vfW+9fccccwyTJk2iT58+DB8+nNLSUk499VTOPPNMbr75Zs466yyWLVtG27Zt+fe//80nPvEJ+vXrx9ChQxkyZAijRo3a4HuVl5dz6qmnsuOOO9KjR4/1Rklff/31nH766VxwwQWUlpZy6623st1229G9e3eGDBnCUUcd1ZB/DE1KRBQDlwEHALOA5yLi7pTSxFqn/S2ldHn+/COAXwMHAZ8FWqeUhkVEO2BiRIxJKc1o1A+xOSoq4e37oXo5FLfJOo0kSVKz1qtXL84+++z19s2YMYM33niD3Xbbbe2+fv360alTJ5555hlg/TmYAe6880769u27xTlizSrgTcXo0aPT2LFjs46RuZQS773yHtMenMa0B6fxxhNvUL2imuLWxfTdqy/9D+xP/wP7021ot80qhxe/u5hZT+cK51lPz+Kt595i1dJVAHTYusPasrn37r3ZeqetKW1b2tAfUZKkgjVp0iSGDBmSdYyCtXTpUoYNG8YLL7xAp06dso6zng19dxHxfEppdEO+b0TsDlyYUjowv/0/ACmln23k/BOAk1JKB+cffx44GugEPAXsllJ6/6PeM5Pr5jdvgyc/CweNhc47Ne57S5IkNYKW8neBulw3O4K5CVk6dynT/zU9Vyr/cxqL3loEQLeh3dj56zvT/8D+bPupbbeo/O3QvQODjxzM4CMHA1C9qpr3xr/HzKdmMvvp2cx8aiaT75gMQFFJET1G9MiNcs6Xzp22dZSzJEmCf//735xyyil861vfKrhyOWM9gZm1tmcBu37wpIj4BvBtoBWwb373bcCRwNtAO+BbmyqXM1NembufN86CWZIkqYWwYC5g1auqmf3M7LXTXrw19i1I0KaiDdvtv11ulPKn+9Opd/3/5a24tJitR23N1qO2hm/k9i15b0lulHN+pPOLV73Is7/PzdfYoce6Uc69du/FNjttQ2k7RzlLktTS7L///rzxxhtZx2iyUkqXAZdFxOeBHwAnA7sA1cA2QAXwRET8O6U0/YPPj4jTgNMA+vTp02i51yrrn1vkb95Ljf/ekiRJyoQFc4GZ9/o8pv0zN+3F6w+9zoqFK4iioOeuPdnrh3ux/YHbs83O21BU3PjrM7bfqj2DjhjEoCMGAVCzuoZ3x7+7bmqNp2Yx+c51o5y7V3ZfO8K51269KO9X7ihnSZLUUs0Getfa7pXftzE3AX/KP/488EBKaRXwXkT8BxgNfKhgTildCVwJuSky6iF33UQRlA/PjWCWJElSi2DBnLGVi1cy49EZa0cpvz8199uOHXt3ZOhxQ9n+wO3pt18/2la0zTjphxWV5BYE3Hrk1uz8tdyCP0uqljD7mdyUGrOemsW4a8bx3B9yq9a336r92hHOvXbrxTajt6FV+1ZZfgRJkqTG8hwwICL6kSuWjydXHK8VEQNSSlPzm4cCax6/SW66jOsjoj2wG3BpY4TeIhUjYMaNkBI4uECSJDVDKaVmPYiyrmv2WTA3spQS77707tpC+c0n36RmVQ0lbUvou3dfdv7Gzmx/4PZ0GdSlSf6D2r5bewYeNpCBhw0EcqOc35vw3toRzjOfmsmUu6YAEMVBj8oeawvnXrv3omK7iib5uTdl9YrVrFi4ghULVuTua91WL1+ddbx6Udy6mK1Hbp1bWLKo+X2HkiR9HCml1RFxJvAgUAxcnVKaEBEXAWNTSncDZ0bE/sAqYB656TEALgOuiYgJQADXpJRebvxPsZkqRsDUP8GSGdChX9ZpJEmS6lWbNm2YO3cuXbo0ze5uU1JKzJ07lzZt2mz2cyyYG8GS95Yw7V/T1i7Ot+TdJQBsNWwrdv3mrmx/4Pb02bMPJW2a39dRVFJEj8oe9KjswegzcotMLp27dN20Gk/P4qXrXuK5y3KjnNt1a7e2bO69e2+22Tm7Uc4pJapXVK9XBi9fsPxDBfGGSuMP7q9eWZ3JZ8hC646t6blrz3U/ONitV0GOwJckqbGllO4D7vvAvgtqPf7mRp63GPhsw6arR7UX+rNgliRJzUyvXr2YNWsWVVVVWUdpMG3atKFXr16bfX7zazQLQPXKamb+dyavPfga0/85nbdfeBuAtl3a0v+A/msX5yvbpizjpNlo16UdAw8dyMBD86Ocq2uomlDFzKdmMvvp3PQar/7jVSA3yrn7sO5rp9bovXtvKvp/9CjnlBKrl6/eZOm7qYJ4+YLl1Kyq2eTnKSotok2nNrTu2HrtrWOvjrQe2nq9fa07fWA7fytpU9IsfuK1YtEKZj87e+1o9Sd+8gSpJvcrFV0Hd11vpHq3od0ymUdckpqDuXPnst9++wHwzjvvUFxcTLdu3QB49tlnadWqbj+YXbp0KZ/97GeZNm0axcXFHH744fz85z/f6PlHHXUU77zzDk8//fSWfwg1b+XDcnMxz3sJeh+ddRpJkqR6VVpaSr9+/hC9NgvmevL+a++vnfZixiMzWLl4JVEc9N69N/v8eB/6H9ifrUdtbam2AUXFRXQf3p3uw7sz+vR1o5xnPzN77Ujnl294mbF/GgtAu665Uc6tO7be6KjizSmGi1sVf6j07di7I206taFVx1YfKoI/WCKvKY1LWvuv0RrdhnRjxMkjgFzh/NZzb639wcGUu6cw7ppxALQqa0WvXXvRc7eeaxeBbNvZUc6StDm6dOnCuHHjALjwwgvp0KED55133sd6zfPOO4999tmHlStXst9++3H//fdz8MEHf+i8+fPn8/zzz9OhQwemT5/Odttt97Hed2NWr15NSYn/f22yStpB2QCYPy7rJJIkSWoEXrlvoRWLVvD6w6/npr14cBrzps8DoLxvOcNOHEb/A/vTb99+tOm0+fOVaJ12Xdox4JABDDhkAJAf5Tyxam3hPPuZ2axesXpt6Vu+bTmtO7amVcdWGy6CNzCC2GK4YbUua02/ffvRb9/cT/VSSrz/2vtrp0aZ9dQsnvx/T64d5dxlUJf1pkfptoOjnCVpcz300EOcd955rF69mp133pk//elPtG7dmr59+3Lcccdx//3307ZtW/72t7+x/fbbr/fcdu3asc8++wDQqlUrRo0axaxZszb4PrfffjuHH3443bt356abbuL73/8+AK+99hpnnHEGVVVVFBcXc+utt9K/f38uvvhibrjhBoqKijj44IP5+c9/zt57780ll1zC6NGjmTNnDqNHj2bGjBlce+213H777SxevJjq6mruvfdejjzySObNm8eqVav4yU9+wpFHHgnAX//6Vy655BIiguHDh/PHP/6R4cOH8+qrr1JaWsrChQuprKxcu60MlI+Auc9knUKSJEmNwIZtM6WaxNsvvr22UJ7535nUrK6htH0p/fbpx27f2o3+B/an8/adm8V0B4WmqLiI7sO6031Yd3Y6daes42gLRARdBnShy4AuVJ6Um5tx5eKVzH5u3Uj1qfdO5aXrXgKgVYdW9Nyl59rpUXrt1ot2Xdpl+REkaYMeOOcB3hn3Tr2+Zo8RPTjo0oM269zly5fzpS99iYceeoiBAwdy0kkn8ac//YlzzjkHgE6dOjF+/Hj++te/cs4553DPPfds9LXmz5/PP/7xD775zQ1OBcyYMWO44IIL6N69O8ccc8zagvnEE0/k/PPP5+ijj2b58uXU1NRw//33c9ddd/HMM8/Qrl073n///U1+lhdeeIGXX36Zzp07s3r1au644w46duzInDlz2G233TjiiCOYOHEiP/nJT/jvf/9L165def/99ykrK2Pvvffm3nvv5aijjuKmm27iM5/5jOVylioq4c2bYeV8aFWedRpJkiQ1IAvmzfDsZc/y2I8eY2nVUiD3l77dz92d/gf2p/cevR0JK22hVh1a0W+ffvTbZ90o53nT5jHr6VnMfGpmbpTzz58kVedGOXce0Dk3pUa+dN5qh60oKnGUs6SWrbq6mn79+jFwYG5tg5NPPpnLLrtsbcF8wgknrL3/1re+tdHXWb16NSeccAJnn332Bqe+ePfdd5k6dSp77rknEUFpaSmvvPIK2267LbNnz+boo3Nz7a5Zbfrf//43X/7yl2nXLvfDwc6dO2/ysxxwwAFrz0sp8f3vf5/HH3+coqIiZs+ezbvvvsvDDz/MZz/7Wbp27bre6371q1/lF7/4BUcddRTXXHMNf/7znzf5fmpAFSNy9/Nfhq0+lWkUSZIkNSyb0c3QoXsH+n86vzjfAf3p0KND1pGkZiki6Lx9Zzpv35nhXxgOwMolK3lr7FtrFw+cev9UXvprbpRzafvStaOc18zl3K6ro5wlNa7NHWmcldq/WRURVFdXs9NOud8GOuKII7jooosAOO200xgwYMDaYvqDbrnlFubNm7d2QZOFCxcyZswYzj///DrlKSkpoaYmt1bC8uXL1zvWvn37tY9vvPFGqqqqeP755yktLaVv374fOr+2T3ziE8yYMYNHH32U6upqdtxxxzrlUj1bUzDPG2fBLEmS1MxZMG+GoccOZeixQ7OOIbVIrdq3ou9efem7V18gN6Jt/uvzcyOc81Nr/Ofi/6wb5bx95/Wm1eg+rLujnCU1a8XFxcyYMYPXXnuN7bffnuuvv5699tpr7fGbb76Z888/n5tvvpndd9+d4uLitYsErvGDH/yABQsW8Je//GWj7zNmzBgeeOABdt99dwBef/119t9/f37605/Sq1cv7rzzTo466ihWrFhBdXU1BxxwABdddBEnnnji2ikyOnfuTN++fXn++efZZZdduO222zb6fgsWLGCrrbaitLSURx55hDfeeAOAfffdl6OPPppvf/vbdOnSZe3rApx00kl8/vOf5//+7/+29I9T9aVND2jdLVcwS5IkqVmzYJbUpEQEFdtVULFdBcNPzI1yXrV0VW6Uc75wnvbPabx8/ctAfpTzzj3puVvPtdNrtO/W/qPeQpKalDZt2nDNNdfw2c9+du0if2ecccba4/PmzWP48OG0bt2aMWPGfOj5s2bN4qc//SmDBw9m1KhRAJx55pl89atfXXvOjBkzeOONN9htt93W7uvXrx+dOnXimWee4frrr+f000/nggsuoLS0lFtvvZWDDjqIcePGMXr0aFq1asUhhxzC//t//4/zzjuP4447jiuvvJJDDz10o5/rxBNP5PDDD2fYsGGMHj2awYMHA7DDDjvwv//7v+y1114UFxczcuRIrr322rXP+cEPfrB2WhBlKCI3inneS1knkSRJUgOLlFLWGepk9OjRaezYsVnHkFTAUkoseGPB2nmcZz01i3fGvUPN6tyvZFf0r6DXbr3WTq3RfbijnCXVzaRJkxgyZEjWMTapb9++jB07du18xc3dbbfdxl133cX111+/0XM29N1FxPMppdENna+xZX7d/OJ3YMrv4LjFUOSCi5IkSU3dxq6bHcEsqdmJCMr7llPet5xhJwwDYNWyVbz9/NtrS+fXH3qd8TeOB6C0XSnbjN5m7bQabSraZBm/3pS0LqF1x9Zrb606tCKKYtNPlKQm6KyzzuL+++/nvvvuyzqK1igfATUrYeEUKHdObEmSpObKgllSi1DatpQ+e/ahz559gPwo5zcX5EY456fWeOrXT1GzqibjpA0ooHVZ6/VK57W3Tpu5r2NrWpe1tqiWmogZM2ZkHaHR/P73v886gj6oojJ3P2+cBbMkSVIzZsEsqUWKCMq3Lad823J2PD73l95Vy1bx7svvsmrpqozT1Y/qFdUsX7CcFQtXrH9bsO7x8gXLWTBzwdr9Kxev3KzXblXWar3SuU2nNrlR0h1bfWjfxkrrVh1aUVTs1CSS1Gx1HARFrWH+S8AXsk4jSZKkBmLBLEl5pW1L6bVrr6xjZKqmuoaVi1dusIyuXUqvebxy4coPF9ULV7By0WYW1R1abXIEdduKtnTs1XHtrcPWHSymVRBSSkQ4mr8paWprjzR5RaW5kcvzxmWdRJIkSQ3IglmStFZRcRFtOrWhTaePNw91qkmsXLxykyOoN7Rv0exFa0vrDRXVURyUbV1Gx97rSufajzv17kSHHh1cuFENqk2bNsydO5cuXbpYMjcRKSXmzp1LmzbNY579JqO8Emb/A1IC/12RJElqliyYJUn1Lopi7UjkjyPVJJbNW8bCWQvX3Wauu3/3pXd59Z5XWb1s9Yfev2ybsg0W0Gsel21dZgmtLdarVy9mzZpFVVVV1lFUB23atKFXr5b9myqNrmIETL8alr0N7bbJOo0kSZIagAWzJKlgRVHQrks72nVpR4/KHhs8J6XE8nnL1xbQC2YuWK+Ifnf8u0y9b+qH5taOoqDD1h3Wjnou61VGp96d1iuiLaG1MaWlpfTr1y/rGFLhq73QnwWzJElSs2TBLElq0iKCtp3b0rZzW7oP777Bc1JKLJ+/fL3iecHMBSyatWjTJXSPDutGQH9gKo41c0IXlxY3xkeVpKanPF8wz38Jeh6SbRZJkiQ1CAtmSVKzFxG0rWhL24q2dB+28RJ6xYIVHxoBveZx1YQqXnvgNVYt2UgJvZGpODr26kjZNmWW0JJapladoH1fF/qTJElqxiyYJUkiV0K3KW9Dm/I2myyh15uKo1YZXTWximkPTmPl4g8sThjQoXsH2lS0WTs3dZtObWjVsdXa7dr7P7ivdafWtOrQiqJip+uQ1ARVjMiNYJYkSVKzZMEsSdJmql1Cb7XjVhs8J6XEioUr1hsBvWDmAhbNXsSKBStYsTB3Wzhr4drHKxet3OBrfVCrDq3WK50/VERvZH/t0rpVmUW1pEZWXgmz7oLVS6CkfdZpJEmSVM8smCVJqkcRQZtObWjTaeMl9AelmsTKxStZsXAFyxcsX1s8r70tWLHRfYtmL1q3b9EKSJt+v9L2pR85WnpTxXXHXh0tqSVtvooRQIL5r0DXXbNOI0mSpHpmwSxJUsaiKNYrb7dU7aK69m2DpfUHiutFby9at28TRXXHXh2p/FIlI78ykop+FVucV1ILUTEidz9vnAWzJElSM2TBLElSM1G7qP44Uk1i5ZKVGyyjl85dypS7pvDET5/giZ88Qb/9+jHylJEMOXoIJW28rJC0Ae23hdJOzsMsSZLUTPk3QUmStJ4oClqXtaZ1WWvo+eHjo08fzYKZCxh37TjGXT2O2z9/O20q2jD8C8MZecpIelT2aPzQkgpXBFRU5kYwS5IkqdmxYJYkSXXWqXcn9vq/vfjU/36K1x9+nRevepHnr3ieZ3//LFvvtDWjvjqKHU/YkTad2mQdVVIhKK+E6VdDqoFwDndJkqTmpNGu7iLimxHxSkRMiIhz8vs6R8S/ImJq/t6JHCVJakKiKNhu/+04ZswxfPutb3PQbw+iZlUN937tXn619a+446Q7mPHYDFLajNUHJTVfFSNg9RJYNC3rJJIkSapnjVIwR8SOwKnALkAlcFhEbA+cDzyUUhoAPJTfliRJTVC7Lu3Y9exdOX3c6Zz63KlUnlTJlLumcN3e1/GHQX/gyZ8/yaK3F2UdU1IWKipz9/PHZRpDkiRJ9a+xRjAPAZ5JKS1NKa0GHgM+AxwJXJc/5zrgqEbKI0mSGkhEsM3obTjs8sM49+1zOeq6oyjbuoyH/uchftP7N9x05E1MuXsKNatrso4qqbF02gGiGOa50J8kSVJz01hzML8C/DQiugDLgEOAsUD3lNLb+XPeAbpv6MkRcRpwGkCfPn0aPq0kSaoXpe1KqTypksqTKpn76lxevPpFXrruJabcPYUOPTpQ+aVKRn5lJF0GdMk6qqSGVNwGOg5xoT9JkqRmKBprTsSIOAX4OrAEmACsAL6UUiqvdc68lNJHzsM8evToNHbs2IaMKkmSGlD1qmqm3jeVF696kan3TSVVJ7b91LaMPGUkQ48dSmm70qwjqoWJiOdTSqOzzlHfCu66+b9fgPceg6NmZp1EkiRJW2Bj182NtshfSumqlNJOKaVPAfOAV4F3I2LrfMCtgfcaK48kScpGcWkxg48czAl3n8C33vwW+/1sPxa9tYg7T76TX239K+752j28NfYtFwaUmpuKEbB0Fiyfk3USSZIk1aNGK5gjYqv8fR9y8y//DbgbODl/ysnAXY2VR5IkZa9smzL2PH9Pznz1TE5+9GQGHTmIl659iT/v/GeuGHkFz/z+GZa9vyzrmJLqQ/mahf6ch1mSJKk5abSCGfh7REwE/gF8I6U0H/g5cEBETAX2z29LkqQWJiLou1dfjv7r0Zz79rkc8sdDKCop4oGzH+BX2/yKv3/+70x/aDqpxlHNUpNVkS+YXehPkiSpWWmsRf5IKX1yA/vmAvs1VgZJklT42pS3Yeev7czOX9uZd156hxevepGXb3iZV8a8Qnm/ckZ8eQQjvzySjr06Zh1VUl202Qrabu1Cf5IkSc1MY45gliRJqpMelT04+HcHc+5b5/KZv32Giu0qePSCR7l020u58ZAbmfj3iVSvrM46ZiZSSix7fxmL3l6UdRRp85WPcIoMSZKkZqbRRjBLkiRtqZI2JQw7YRjDThjGvOnzePGaFxl3zThuPfZW2nVrR+VJlYw8ZSTdhnTLOmq9WFMeL5y1kIUzF+buP/h41kJWLV0FwK7n7Mr+P9+fktZe2qnAVVTCO/+C6hVQ3DrrNJIkSaoH/i1EkiQ1KRXbVbDvj/dl7wv3ZtqD03jxqhd55rfP8NSvnqLX7r0Y9dVR7HDcDrTq0CrrqBuUUmLZ3GVrS+IFMxewcNZCFs1atPbxwlkLWb1s9XrPi+KgY8+OdOzVkR4jejDw8IF07NWRua/O5ZlLn2HGIzM4ZswxzaZkVzNVMQLSalg4KfdYkiRJTZ4FsyRJapKKiosYcMgABhwygCXvLeGl61/ixb+8yN2n3M0D33yAHT63AyNPGUmv3XoREY2SaU15XLsoXjvquNbo49XL1y+Pi0qKKNumjI69O7L1qK0ZdMQgOvbOlcmdeneiY6+OtO/enqLiDc9uNuDQAdz1pbu4cqcrOejSgxh16qhG+8xSnawpleeNs2CWJElqJiKlprUa++jRo9PYsWOzjiFJkgpQSolZT83ihateYMLNE1i1ZBXdhnZj5CkjGf7F4bTv1v5jvfbSOUvXK4oXzFzAolmL1huJXL1i/Tmhi0qKKOtZtrYsLutVtl5xvKnyeHMtensRd558J9P/NZ0hnxnC4X8+nLad236s12wpIuL5lNLorHPUt4K8bq6phlvLYPvTYaffZJ1GkiRJdbCx62YLZkmS1CytWLSCCTdP4MWrXmTW07MoKi1i0BGDGPXVUWx3wHbrFbopJZZWLV2vKN7QnMcbK4/XlsX5Uce1H7ff6uOXx5sr1SSe+s1TPPQ/D9GheweOvuFo+u7Vt1HeuymzYG5kD+4GxW1h/0eyTiJJkqQ6sGCWJEkt1nsT3uPFq17k5etfZumcpXTs3ZHee/Rm8duL15XHKz9QHpcWrZ3zeG1xXKtA7tS7E+23ak8UFd5UFG+/8DZ/P+HvzJ06l09+/5Ps9cO9KC4tzjpWwbJgbmTPng5v3ALHvg9O5SJJktRkWDBLkqQWr3plNVPunsKLV73InClzPjTaeL05jwu0PN5cKxev5IFzHuDFq16k1269+MyNn6Fiu4qsYxUkC+ZGNvVP8NzX4cg3oH2frNNIkiRpM23sutlF/iRJUotR3KqYoccOZeixQ7OO0uBadWjFEX85gv6f7s8/TvsHl4+4nEP/dCjDTxyedTS1dOWVuft54yyYJUmSmoHGmRBQkiRJmdjhuB0446Uz6FHZgzu+cAd3nHQHKxauyDqWWrLyYUDAvJeyTiJJkqR6YMEsSZLUzJVvW87Jj5zM3j/am/E3jueKkVcw+9nZWcdSS1VaBmXbw/xxWSeRJElSPbBgliRJagGKSorY64K9+NLjX6KmuoarP3E1T/zsCWqqa7KOppaovNIRzJIkSc2EBbMkSVIL0ucTfThj3BkMOWYID3//Ya4/4HoWzl6YdSy1NBUjYPE0WOU/e5IkSU2dBbMkSVIL06a8DceMOYYjrzmS2c/O5vLhlzP5zslZx1JLUpFf6G/++GxzSJIk6WOzYJYkSWqBIoIRXxrB6S+eTnm/cm4++mbuOeMeVi1dlXU0tQQVI3L388ZlmUKSJEn1wIJZkiSpBesyoAun/PcU9vjuHjx/xfNcOfpK3nnpnaxjqblr2xNadbZgliRJagYsmCVJklq44lbFHHDxAXzxX19k+fzl/GWXv/DM754hpZR1NDVXEblRzC70J0mS1ORZMEuSJAmA7fbfjjNeOoP+B/bngW8+wJjDxrDkvSVZx1JzVV4JC8ZDzeqsk0iSJOljsGCWJEnSWu27tef4u47n4D8czPSHpvOn4X/itQdfyzqWmqOKEVC9HBZNzTqJJEmSPgYLZkmSJK0nItjlG7tw2tjTaNe1HTcedCMPnvsgq1c40lT1yIX+JEmSmgULZkmSJG3QVjtuxanPncrO39iZp3/9NFftfhVzpszJOlaTFREHRcSUiHgtIs7fwPEzImJ8RIyLiCcjYmitY8Mj4qmImJA/p03jpm8AHQdDUSnMdx5mSZKkpsyCWZIkSRtV2raUQ/5wCMfffTwL3lzAlaOu5IW/vOACgHUUEcXAZcDBwFDghNoFct7fUkrDUkojgF8Av84/twS4ATgjpbQDsDewqpGiN5ziVtBpB0cwS5IkNXEWzJIkSdqkQYcP4msvf41eu/fiH6f+g9uOu41l85ZlHasp2QV4LaU0PaW0ErgJOLL2CSmlhbU22wNrWvxPAy+nlF7Knzc3pVTdCJkbXnmlBbMkSVITZ8EsSZKkzVK2TRlf/OcX2f8X+zP5zslcXnk5bzzxRtaxmoqewMxa27Py+9YTEd+IiGnkRjCfnd89EEgR8WBEvBAR393Ym0TEaRExNiLGVlVV1WP8BlIxApa/C8veyTqJJEmStpAFsyRJkjZbFAWf+M4nOOWpUyhpXcJ1e1/HIxc8Qs3qmqyjNQsppctSSv2B7wE/yO8uAfYETszfHx0R+23k+VemlEanlEZ369atUTJ/LBWVuft5zsMsSZLUVFkwS5Ikqc62Gb0Np71wGpUnVfL4jx/nmk9dw7zX52Udq5DNBnrX2u6V37cxNwFH5R/PAh5PKc1JKS0F7gNGNUTIRleeL5hd6E+SJKnJsmCWJEnSFmld1pojrzmSY8YcQ9WEKq4YcQXjx4zPOlaheg4YEBH9IqIVcDxwd+0TImJArc1Dgan5xw8CwyKiXX7Bv72AiY2QueG17gzt+jgPsyRJUhNmwSxJkqSPZcfjd+SMl85gqx234vbP386dX7qTFYtWZB2roKSUVgNnkiuLJwG3pJQmRMRFEXFE/rQzI2JCRIwDvg2cnH/uPODX5ErqccALKaV7G/kjNJyKSkcwS5IkNWElWQeQJElS01fet5wvPfYlHvvxYzzxkyeY+Z+ZfOZvn6Hnzh9ax67FSindR256i9r7Lqj1+Jsf8dwbgBsaLl2GKkbAW/fC6mVQ0jbrNJIkSaojRzBLkiSpXhSVFLHPj/bh5EdPZvWK1Vy9x9U8efGTpJqUdTQVsvJKSDWwYELWSSRJkrQFLJglSZJUr7b95Lac8dIZDD56MA+d/xDXH3A9i95alHUsFaqKEbl752GWJElqkiyYJUmSVO/aVrTl2JuP5fC/HM6sp2fxp+F/YsrdU7KOpULUoR+UdLBgliRJaqIsmCVJktQgIoJRp4zitBdOo1OfTtx05E3c+417WbVsVdbRVEiiyIX+JEmSmjALZkmSJDWoroO6cspTp7D7ubsz9o9j+fPOf+bd8e9mHUuFpLwS5r2Um4tZkiRJTYoFsyRJkhpcSesSPn3Jp/nCg19g6Zyl/HnnP/PsH54lJRcAFLl5mFcvgiUzsk4iSZKkOrJgliRJUqPp/+n+fO3lr7Hdfttx/1n3c9MRN7GkaknWsZQ1F/qTJElqsiyYJUmS1Kjab9WeE+45gYN+dxDT/jWNy4dfzrR/Tcs6lrLUacfcXMzznIdZkiSpqbFgliRJUqOLCHY9a1dOffZU2nZuyw2fvoFnfvdM1rGUlZK2UDbIEcySJElNkAWzJEmSMtN9eHdOfe5UdjlrF/rt1y/rOMpSRSXMdwSzJElSU2PBLEmSpEyVtivl4N8dzFY7bJV1FGWpYgQseQNWzss6iSRJkurAglmSJElS9sorc/fOwyxJktSkWDBLkiRJyl7FiNy9BbMkSVKTYsEsSZIkKXtte0Cb7jB/XNZJJEmSVAcWzJIkSZIKQ3mlI5glSZKaGAtmSZIkSYWhYgQsmAA1q7JOIkmSpM1kwSxJkiSpMFRUQs1KWDg56ySSJEnaTBbMkiRJkgrD2oX+xmWZQpIkSXXQaAVzRHwrIiZExCsRMSYi2kTEtRHxekSMy99GNFYeSZIkSQWmbCAUtbZgliRJakJKGuNNIqIncDYwNKW0LCJuAY7PH/5OSum2xsghSZIkqYAVlUD5MBf6kyRJakIac4qMEqBtRJQA7YC3GvG9JUmSJDUFFSNg/jhIKeskkiRtmcXT4Z7B8PIFsHpZ1mmkBtcoBXNKaTZwCfAm8DawIKX0z/zhn0bEyxHxm4ho3Rh5JEmSJBWo8kpYMReWOR5FktRETfwFLJoKr/wY7hsGbz2YdSKpQTVKwRwRFcCRQD9gG6B9RHwB+B9gMLAz0Bn43kaef1pEjI2IsVVVVY0RWZIkSVIWXOhPktSULXsHpl8L250C+z4EUQyPHgRPfg6W+sNTNU+NNUXG/sDrKaWqlNIq4HZgj5TS2ylnBXANsMuGnpxSujKlNDqlNLpbt26NFFmSJElSo6sYnruf7zzMkqQmaMrvoGYlDDkPeuwLh7wMw38Ms+7KTZsx+bdQszrrlFK9aqyC+U1gt4hoFxEB7AdMioitAfL7jgJeaaQ8kiRJkgpRaUfosJ0jmCVJTc+qhTD1j9D7M9BxYG5fcWvY8Qdw6ATo9gl44Rx4cBeY82ymUaX61FhzMD8D3Aa8AIzPv++VwI0RMT6/ryvwk8bII0mSJKmAlVfCPEcwS5KamNeuhFULYOgGZoAt6w973wd73gLL34V/7gbPfR1Wzm/0mFJ9K2msN0op/RD44Qd279tY7y9JkiSpiagYAbPuhFWLobRD1mkkSdq06hUw+TfQfR/osvOGz4mAPp+FrQ+Ely+AV38PM/8OI38NfT+fOy41QY01RYYkSZIkbZ6KSiDB/PFZJ5EkafPMuBGWvQVDvrvpc0s7wk6XwoFjoX1feOoL8PD+sHBKQ6eUGoQFsyRJkqTCUjEid+9Cf5KkpiDVwKRf5KZ42vrAzX9e55FwwH9h5z/B+y/AfcPhpf+D1csaLqvUACyYJUmSJBWWdn2gtNyF/iRJTcPsf+RGHw/9bt2nuSgqhgFnwGGToc9xMOEncN+O8NYDDZNVagAWzJIkSZIKS0RumgwX+pMkFbqUYOLFuaku+hy35a/TtjvscT3s+xAUlcKjB8MTn4Wls+stqtRQLJglSZIkFZ6KETD/ZaipzjqJJEkbV/UkzHkKBp8LRSUf//V67AsHvwTDfwJv3QP3DIbJv4Wa1R//taUGYsEsSZIkqfCUV0L1Ulg8LeskkiRt3MSLoXVX6P+V+nvN4taw4//CIa9Atz3hhXPgwZ1hzjP19x5SPbJgliRJklR41iz05zzMkqRCNf8VeOteGHgWlLSr/9cv6w973wd73grL34N/7g7Pfg1Wzqv/95I+BgtmSZIkSYWn01CIEgtmSVLhmvgLKG4HA7/RcO8RAX2OzS0COOibMO3K3LQZr9+Qm/9ZKgAWzJIkSZIKT3Fr6DQE5rvQnySpAC15E94YA9ufCq27NPz7lZbBTr+BA8dC+37w1Bfh4f1gweSGf29pEyyYJUmSJBWm8hGOYJYkFabJv87dD/52475v55Hw6f/CzpfD+y/C/cPhpR/A6mWNm0OqxYJZkiRJUmGqqIRlb8HyqqyTSJK0zoq58NqfYdsToH2fxn//KIIBp+emzehzPEz4Kdy3I7x1f+NnkbBgliRJklSo1iz05zQZkqRC8uplUL0Uhn4n2xxtu8Mef4X9HoaiUnj0EHjis7B0dra51OJYMEuSJEkqTOWVuft5FsySpAKxeim8+nvY5hAoH5Z1mpzu+8DBL8Hwn8Bb9+QWAZx8KdSszjqZWggLZkmSJEmFqU1XaNvTeZglSYVj+jWwYg4M/V7WSdZX3Bp2/F84dAJ0+yS88C14cGeY80zWydQCWDBLkiRJKlwVlU6RIUkqDDWrYdIl0GW3XIlbiDpsB3vfC3vellvD4J+7w7NnwMp5WSdTM2bBLEmSJKlwVYyABZOgennWSSRJLd2bt8KSGbnRyxFZp9m4COhzDBw2CQadA9P+Av8YBK9fDyllnU7NkAWzJEmSpMJVXglpNSyYmHUSSVJLlhJMvBg6DoZeR2SdZvOUlsFOv4aDxuZGNj91Ejy0LyyYnHUyNTMWzJIkSZIKV8WI3L0L/UmSsvT2P3NTNg35DkQTq9MqRsCn/ws7X55b1+D+4fDSD2D1sqyTqZloYv9GSJIkSWpROvSHkvYu9CdJytaki6HtNtD3xKyTbJkoggGnw+FToM/xMOGncO8OMPu+rJOpGbBgliRJklS4ioqh0zAX+pMkZWfuc/DuIzD4W1DcOus0H0+brWCPv8J+D+c+y2OHwhPHwtJZWSdTE2bBLEmSJKmwVYzIjWB2YSJJUhYmXgylnWD707JOUn+67wMHvwSVP4W37oV7hsDk30DN6qyTqQmyYJYkSZJU2CoqYdUCWPpm1kkkSS3Nwldh5u0w4GtQ2jHrNPWruBXs8H04dAJs9Sl44dvwwGiY83TWydTEWDBLkiRJKmxrF/obl2UKSVJLNOkSKGoFg76ZdZKG02E72Ose+OTfYcUc+Oce8OzpsOL9rJOpibBgliRJklTYyocBAfOch1mS1IiWvQOvXwfbnQxte2SdpmFFQO/PwGGTcnNNT7sK7hkM0//qFFXaJAtmSZIkSYWtpD2UDXAEsySpcU35LdSsgsHnZZ2k8ZSWwahfwUHPQ4f+8PTJ8ODO8MYtUFOddToVKAtmSZIkSYVvzUJ/kiQ1hlULYeqfoPcx0HFA1mkaX0UlfPo/sOvVuT+L/3wO7hmY+zNZvSzrdCowFsySJElSI4iIgyJiSkS8FhHnb+D4GRExPiLGRcSTETH0A8f7RMTiiGhBw6hqqaiEJa/DygVZJ5EktQRTr8gtMDv0e1knyU4UQf8vw6GT4JO3Q+tu8NzX4a5tYfyPYcXcrBOqQFgwS5IkSQ0sIoqBy4CDgaHACR8skIG/pZSGpZRGAL8Afv2B478G7m/orAWrfETufv7LmcaQJLUA1Stgym+g+77QZXTWabJXVAy9j4ZPPwX7PwZddoHxF8CdfWDsN2HxjKwTKmMWzJIkSVLD2wV4LaU0PaW0ErgJOLL2CSmlhbU22wNrV9SJiKOA14EJDR+1QFVU5u5d6E+S1NBm3ADL3m7Zo5c3JAK2+hTsfQ8cMh76fBam/hH+sT3850SnsmrBLJglSZKkhtcTmFlre1Z+33oi4hsRMY3cCOaz8/s6AN8DftQIOQtX222gdVeYPy7rJJKk5izVwKRfQsVI6HFA1mkKV/mOsPu1cMR0GHQOzL4b7h8JDx8I7zwEKW3qFdSMWDBLkiRJBSKldFlKqT+5QvkH+d0XAr9JKS3e1PMj4rSIGBsRY6uqqhowaQYioLzSEcySpIY16y5YOAWGfDf3/x59tPa9YdQlcNRMqPwZzH8JHt4fHtwZ3rgFalZnnVCNwIJZkiRJanizgd61tnvl923MTcBR+ce7Ar+IiBnAOcD3I+LMDT0ppXRlSml0Sml0t27dPm7mwlMxAuaP9y+rkqSGkRJMvBja94M+x2adpmlpVQ47nA9HzoBdroRVi+A/n4N7BsGrf4TVS7NOqAZkwSxJkiQ1vOeAARHRLyJaAccDd9c+ISIG1No8FJgKkFL6ZEqpb0qpL3Ap8P9SSn9olNSFpqISalbkRpZJklTf3nsc5j4DQ86FopKs0zRNxW1g+1Ph0InwyduhdTcY+w24a1sYfxGsmJt1QjUAC2ZJkiSpgaWUVgNnAg8Ck4BbUkoTIuKiiDgif9qZETEhIsYB3wZOziZtAasYkbuf7zQZkqQGMPHi3Hz/23056yRNX1Ex9D4aPv0U7P84dNkNxv8Q7uwDY8+GxTOyTqh65I9jJEmSpEaQUroPuO8D+y6o9fibm/EaF9Z/siak42AoapVbpb7v57NOI0lqTua9DG/fD8MugpJ2WadpPiJgq0/mbvMnwORL4LXLYeofoc9xMOQ70Hlk1in1MdVpBHNEHB4RjnqWJEmS1PiKSqHTDi70J0mqf5N+CSXtYeA3sk7SfJXvALtdA0dMh8Hfgtn3wAOj4OFPwzsP5ebAVpNU17L4c8DUiPhFRAxuiECSJEmStFEVI2D+uKxTSJKakyVvwBtjoP+p0Lpz1mmav3a9YOQv4ag3ofJnMP9leHh/eGA0vHGzi/k2QXUqmFNKXwBGAtOAayPiqYg4LSLKGiSdJEmSJNVWXgnL34Nl72SdRJLUXEz6NRAw+NtZJ2lZWpXDDufDkTNglz/D6sXwn+PhHwPh1ctg9dKsE2oz1Xm6i5TSQuA24CZga+Bo4IWIOKues0mSJEnS+tYs9DdvXJYpJEnNxYq5MO0vubn92/fOOk3LVNwGtv8qHDYJPnkHtOkOY8+Eu7aF8RflviMVtLrOwXxERNwBPAqUAruklA4GKoFz6z+eJEmSJNVSMTx3P995mCVJ9eDVP0D1Uhjy3ayTKIqg91Hw6f/C/k9Al91g/A/hzj4w9mxYPCPrhNlICZbMhHf+DVP+AGPPghXvZ51qPSV1PP8Y4Dcppcdr70wpLY2IU+ovliRJkiRtQKsKaL+tI5glSR/f6iXw6u9hm8NyC9CpMETAVnvmbvMnwORL4LXLYeofoc9xMOQ70Hlk1inr3+olsPBVWDgFFk6GRVPyj6fkfgiyRkkZ9D+loOYLr2vBfCHw9pqNiGgLdE8pzUgpPVSfwSRJkiRpgypGWDBLkj6+aVfnpl8Y+r2sk2hjyneA3a6B4T+GKb+FqVfkFmTscQAM/S503y9XSDcVqQaWzs4VyAun5Evk/OOlM2udGNC+L3QcDFvtBR0H5R53HARtehTcZ65rwXwrsEet7er8vp3rLZEkSZIkfZTySpj9j9ziPyXtsk4jSWqKalbB5F9B1z1yI2VV2Nr1gpG/hB3+F167AiZfCg8fABUjc9Ob9DkWiupaczagzR2NXNoRygblS+TB64rksu1zc1M3EXX9ky9JKa1cs5FSWhkRreo5kyRJkiRtXMWI3Aig+a9A112yTiNJaoreuAWWvAE7/TbrJKqLVuW5EeeDzoEZN8CkX8J/T4CX/gcGnwv9v9J4P3z+4Gjk2kXyR45GXlMkF+Zo5C1R14K5KiKOSCndDRARRwJz6j+WJEmSJG1ERWXufv5LFsySpLpLCSb9AjoOgZ6HZ51GW6K4dW4e4u2+nPutpokXw/NnwSsXwsCzYMA3oE3X+nmvtaORPzitxasbGY2897oCuQmORt4SdS2YzwBujIg/AAHMBE6q91SSJEmStDHt++b+Euc8zJKkLfH2gzD/Zdj1aoiirNPo44gi6HVk7vbek7kfHIy/MFc49z8FBn8bOvTb9OukGlg6a900FmtHI0/O7V/3hrnXW1skN7/RyFuiTgVzSmkasFtEdMhvL26QVJIkSZK0MVEE5cNzI5glSaqriRdD257Q98Ssk6g+bbVn7rZgIky6JDdX89Q/Qp/jYMh3oPOoD49GXlskb2w08j7rL7DXAkYjb4k6z34dEYcCOwBtIt/Kp5Qu2oznfQv4KpCA8cCXga2Bm4AuwPPAF2vP8SxJkiQVkojomFJauJFjfVJKbzZ2pharYgRMvzY34sjRZ5KkzTXnWXjvURh5CRS7rFiz1Gko7HY1DP8xTPktTL0c3rgpN8J4+Tu1Tqw9Gnmf9ae1aNO9xY5G3hJ1Kpgj4nKgHbAP8BfgWODZzXheT+BsYGhKaVlE3AIcDxwC/CaldFP+tU8B/lS3jyBJkiQ1mkeBUQAR8VBKab9ax+5cc0yNoLwSVi+Gxa9DWf+s00iSmopJF0NpOWx/WtZJ1NDa9YSRv4Ad/jc3mnnBRCgb4GjkBlDXEcx7pJSGR8TLKaUfRcSvgPvr8F5tI2IVuZL6bWBf4PP549cBF2LBLEmSpMJVeyhL5484poZWMSJ3P2+cBbMkafMsnAIz74Ad/gdKy7JOo8bSqhMM/W7WKZq1uv4u2fL8/dKI2AZYRW6ai4+UUpoNXAK8Sa5YXkBuSoz5KaXV+dNmAT3rmEeSJElqTGkjjze0rYbUaQeIYhf6kyRtvkmXQFErGHh21kmkZqWuI5j/ERHlwC+BF8hdRP95U0+KiArgSKAfMB+4FThoc980Ik4DTgPo06dPHSNLkiRJ9WariPg2udHKax6T3+6WXawWqKRt7tdbXehPkrQ5lr0Nr/8VtvsKtO2edRqpWdnsgjkiioCHUkrzgb9HxD1Am5TSgs14+v7A6ymlqvxr3Q58AiiPiJL8KOZewOwNPTmldCVwJcDo0aMdGSJJkqSs/Bko28BjyK1RosZUPgKqnsg6hSSpKZh8KaTVMOS8rJNIzc5mF8wppZqIuAwYmd9eAazYzKe/CewWEe2AZcB+wFjgEXILBd4EnAzctfnRJUmSpMaVUvrRxo5FxM6NmUVARSW88TdY8T60/uCU2JIk5a1cAK9dDr2Pdd5+qQHUdQ7mhyLimIio0wImKaVngNvITasxPv++VwLfA74dEa8BXYCr6phHkiRJykxEDI2IH+evZ12surGtWejPaTIkSR/ltcth1UIXepMaSF3nYD4d+DawOiKWk5trLqWUOm7qiSmlHwI//MDu6cAudcwgSZIkZSYi+gIn5G+rgG2B0SmlGRnGapnKK3P3816C7vtkm0WSVJiql+emx+i+H3TeKes0UrNUp4I5pVS26bMkSZKk5ikingI6kpvi7ZiU0tSIeN1yOSNtu0ObHjBvXNZJJEmF6vUbYPk7sPtfs04iNVt1Kpgj4lMb2p9Serx+4kiSJEkF7V2gJ9Ad6AZMBVyEOksVIyyYJUkbVlMNk34JFSOhx/5Zp5GarbpOkfGdWo/bkJve4nlg33pLJEmSJBWolNJREdEJ+AxwYUQMAMojYpeU0rMZx2uZKirh3YegeiUUt8o6jSSpkMy+Cxa9Cp+4Ceq2nJikOqjrFBmH196OiN7ApfUZSJIkSSpkKaUFwDXANRHRHTgO+E1E9Ekp9c42XQtUPgJqVsHCSbmyWZIkgJRg4sXQYTvofUzWaaRmrehjPn8WMKQ+gkiSJElNTUrp3ZTS71NKnwD2zDpPi1RRa6E/SZLWeO8xmPssDDkPiur6C/yS6qKuczD/nnVzzBUBI4AX6jmTJEmSVJAi4u5NnHJEowTROmUDobhtfh7mk7JOI0kqFBMvhjZbQb8vZZ1Eavbq+iOcsbUerwbGpJT+U495JEmSpEK2OzATGAM8AzihY9aKiqHTjjDfEcySpLx5L8HbD8Dwn0BJ26zTSM1eXQvm24DlKaVqgIgojoh2KaWl9R9NkiRJKjg9gAOAE4DPA/eSG3QxIdNULV3FCJj599x8my7iJEma+Aso6QADv551EqlFqOsczA8BtX/00xb4d/3FkSRJkgpXSqk6pfRASulkYDfgNeDRiDgz42gtW8UIWPk+LJuddRJJUtYWz4A3b4btT4NWFVmnkVqEuo5gbpNSWrxmI6W0OCLa1XMmSZIkqWBFRGvgUHKjmPsCvwPuyDJTi7d2ob9x0K5XplEkSRmb/CsgYNA5WSeRWoy6jmBeEhGj1mxExE7AsvqNJEmSJBWmiPgr8BQwCvhRSmnnlNKPU0oOnc1S+fDc/bxxmcaQJGVs+RyYdhX0PRHa9846jdRi1HUE8znArRHxFrkFTXoAn6vvUJIkSVKB+gKwBPgmcHasm+83gJRS6phVsBattAw69M8t6iRJarle/QNUL4Oh3806idSi1KlgTik9FxGDgUH5XVNSSqvqP5YkSZJUeFJKdf0NQDWWihGOYJaklmz1Enj199DzcOg0NOs0UotSpwvkiPgG0D6l9EpK6RWgQ0S4JKckSZKkbJVXwuJpsGpR1kkkSVmYdlVuwdeh38s6idTi1HUExqkppflrNlJK84BT6zWRJEmSJNVVxQggwfzxWSeRJDW2mlUw6VfQ7RO5m6RGVdeCuThqTzQXUQy0qt9IkiRJklRHFZW5+/nOwyxJLc4bN8PSN2GIo5elLNR1kb8HgJsj4or89unA/fUbSZIkSZLqqF1vaFXhPMyS1NKkBJN+kZt3ueehWaeRWqS6FszfA04Dzshvvwz0qNdEkiRJklRXEfmF/hzBLEktylv356ZH2u1aCNfilbJQp3/zUko1wDPADGAXYF9gUv3HkiRJkqQ6Kq+E+S9DTXXWSSRJjWXSxdCuF2x7QtZJpBZrs0YwR8RA4IT8bQ5wM0BKaZ+GiyZJkiRJdVAxAqqXwaKp0Glw1mkkSQ1tztPw3uMw8ldQ7BJhUlY2dwTzZHKjlQ9LKe2ZUvo94LAASZIkSYXDhf4kqWWZeDGUlsP2p2adRGrRNrdg/gzwNvBIRPw5IvYDouFiSZIkSVIddRwKRaUu9CdJLcGCyTDrLhj4DSgtyzqN1KJtVsGcUrozpXQ8MBh4BDgH2Coi/hQRn27AfJIkSZK0eYpbQcchLvQnSS3B5EuguDUMOjvrJFKLV9dF/paklP6WUjoc6AW8CHyvQZJJkiRJUl1VjID547JOIUlqSEvfgtevh+2+DG22yjqN1OLVqWCuLaU0L6V0ZUppv/oMJEmSJElbrGIELHsblr+XdRJJUkOZcimk1TDkvKyTSOJjFMySJEmSVHDK8wv9OU2GJDVPK+fD1Muh92ehw3ZZp5GEBbMkSZKk5qRiTcE8LtMYkqQGMvVyWL0Ihjpjq1QoLJglSZIkNR+tu0C7XjDfEcyS1OxUL89Nj9HjAOg8Mus0kvIsmCVJkiQ1L+UjHMEsSc3R63+F5e86elkqMBbMkiRJkpqXikpYODk30k3Nw6pFUFOddQpJWaqphom/hM47Qfd9s04jqRYLZkmSJEnNS8UISNWwYELWSVQfFk+Hu7eDB0fnHktqmWbdAYtfgyHfhYis00iqxYJZkiRJUvNSvmahP+dhbvJWLYbHjoSa1bDkDbh/FMy6O+tUkhpbSjDxYujQH3ofk3UaSR9gwSxJkiQ1gog4KCKmRMRrEXH+Bo6fERHjI2JcRDwZEUPz+w+IiOfzx56PCH8veFPK+kNJe+dhbupSDTx1EiycCHveAgc9nyuXHj8Sxp2fK50ltQzvPQrvj4Uh50FRcdZpJH2ABbMkSZLUwCKiGLgMOBgYCpywpkCu5W8ppWEppRHAL4Bf5/fPAQ5PKQ0DTgaub5zUTVgU5UYxz3cEc5P2yk9yvxI/8hLY+gDo0A8+/R/Y/vTcSMaH94dl72SdUlJjmHgxtNkK+p2cdRJJG2DBLEmSJDW8XYDXUkrTU0orgZuAI2ufkFJaWGuzPZDy+19MKb2V3z8BaBsRrRshc9NWUZkbwZxS1km0JWbeCeN/CP1OgkHnrNtf3AZ2uRx2/yvMfRbuHwnvPZ5VSkmNYd44ePtBGPRNKGmbdRpJG2DBLEmSJDW8nsDMWtuz8vvWExHfiIhp5EYwn72B1zkGeCGltKJBUjYnFSNg1UJYMiPrJKqr+a/AU1+EzjvDLldseDGvfl+EA5+B0jJ4aF+Y+Et/mCA1VxN/ASUdYMDXsk4iaSMsmCVJkqQCkVK6LKXUH/ge8IPaxyJiB+Bi4PSNPT8iTouIsRExtqqqqmHDFjoX+muaVryfm2O5tAw+dUduxPLGlA+Dg8ZCr6Nh3Hfhic/AyvmNFlVSI1j8Orx5c25qnFYVWaeRtBEWzJIkSVLDmw30rrXdK79vY24CjlqzERG9gDuAk1JK0zb2pJTSlSml0Sml0d26dft4iZu68mG5uZhd6K/pqFkN//kcLJ0Fn7wd2n1okP+HlXbMLQA46jcw+x54YLTfudScTPoVRDEM/lbWSSR9BAtmSZIkqeE9BwyIiH4R0Qo4Hri79gkRMaDW5qHA1Pz+cuBe4PyU0n8aJ24zUNIOyga40F9T8uJ34Z1/w86XQ9fdNv95ETD4HNj/UaheBv/cHaZd3VApJTWW5VUw/Wro+4XN+4GTpMxYMEuSJEkNLKW0GjgTeBCYBNySUpoQERdFxBH5086MiAkRMQ74NnDymv3A9sAFETEuf9uqkT9C01Q+wtGsTcX062DKb3KLePX/8pa9RrdPwMEvQtdPwDOnwNOnwOpl9ZtTUuN59fe5HxoN+U7WSSRtQknWASRJkqSWIKV0H3DfB/ZdUOvxNzfyvJ8AP2nYdM1UxYjc3J0r50Or8ozDaKPmPAPPng7d94WRl3y812qzFezzIIy/ECb8BOa9AHveBmX96yWqpEayajG8+gfodSR0GpJ1Gkmb4AhmSZIkSc1TRX6hv/kvZ5tDG7f0LXjiaGi7TW4u5aJ6GANVVAyVP4a97oUlb8ADO8Gsuz7+60pqPNP+AivnwZDvZp1E0mawYJYkSZLUPFWMyN3Pcx7mglS9HJ74DKxaCHvdBa271O/r9zwEDnohNxf340fBi9/LLSQoqbDVrILJv4Zue0K3PbJOI2kzWDBLkiRJap7a9IDW3ZyHuRClBM99DeY+A7v/FcqHNcz7dOgLBzwJ258Bk34BD+8Py95pmPeSVD/euAmWzoSh38s6iaTNZMEsSZIkqXmKyI1itmAuPFN+B9OvhR1/CL0/07DvVdwadvkT7H49zH0O7h8J7z7WsO8pacukBBN/AZ12gG0OyTqNpM1kwSxJkiSp+aqohAUTcr9yrcLwzr/hxXOh11Ew7IJNnl5v+n0BDnwGWnWCh/fLlVgpNd77S9q0t+6DBa/k5l4OKyupqfDfVkmSJEnNV/kIqFkBC6dknUQAi6bBk8dBx8G5qTEau0Aq3xEOfC43anrc93ILDK6c37gZJG3cxIuhXW/oe0LWSSTVgQWzJEmSpOarojJ370J/2Vu1CB4/Egj41F1QWpZNjtIy+MTNMOpSmH0vPDDaaVSkQlD1FFQ9AYO/DUWlWaeRVAeNUjBHxKCIGFfrtjAizomICyNidq39TrAjSZIkqf50HARFrWH+uKyTtGypBp46GRZOhj1vgbL+2eaJgMHfhP0fg+rl8OBuMO2qbDNJLd2ki6FVBfT/atZJJNVRoxTMKaUpKaURKaURwE7AUuCO/OHfrDmWUrqvMfJIkiRJaiGKSnPTIjiCOVuv/Bhm3QEjfwU99ss6zTrd9oCDX4Bue8IzX4WnvwKrl2WdSmp5FkyCWXfBwDOhtEPWaSTVURZTZOwHTEspvZHBe0uSJElqacorc1MguKBbNmbeAeMvhO2+BIPOzjrNh7XZCvZ5EHb4AUy/Bv65Oyx6LetUUssy6ZdQ3BYGnpV1EklbIIuC+XhgTK3tMyPi5Yi4OiIqNvSEiDgtIsZGxNiqqqrGSSlJkiSpeagYASuqYNnbWSdpeeaPh6e+CF12hZ3/lJuaohAVFUPlj2Hv+2DpTHhgJ5h5Z9appJZh6SyYcQNs9xVo0y3rNJK2QKMWzBHRCjgCuDW/609Af2AE8Dbwqw09L6V0ZUppdEppdLdu/sdGkiRJUh2sWehvvtNkNKoVc+GxI6G0I3zydihuk3WiTdvm4NyUGWUD4Ymj4cXvQM3qrFNJzduU30KqhiHfzjqJpC3U2COYDwZeSCm9C5BSejelVJ1SqgH+DOzSyHkkSZIkNXfl+YJ53rhMY7QoNavhyc/BstnwyTug3TZZJ9p87beFA56EAV+DSZfAQ/s6+l1qKCvnw9QroM9x0GG7rNNI2kKNXTCfQK3pMSJi61rHjgZeaeQ8kiRJkpq7Vp2gfV8X+mtML34H3n0IdrkSuu6adZq6K24NO/8Rdr8B3n8e7h8J7z6adSqp+Zn6J1i9CIZ8N+skkj6GRiuYI6I9cABwe63dv4iI8RHxMrAP8K3GyiNJkiSpBakYAfPHZZ2iZZh+LUy5FAadA9udnHGYj6nfiXDgs9CqHB7eDyZeDKkm61RS81C9PDc9Ro9PQ+eRWaeR9DGUNNYbpZSWAF0+sO+LjfX+kiRJklqwihEw6y5YvQRK2medpvma8ww8ezp03w9G/jLrNPWjfAc48Dl45qsw7nyo+i/sfi202uAa9ZI21/TrYPm7MPR7WSeR9DE19hQZkiRJktT4yiuBBPOdla/BLH0rtzBeu16w581Q1GjjmRpeaRl84ibY6bfw1n1w/07w/otZp5Karprq3BznnUdD932yTiPpY2pG/8fXZpk6Fbp1g/LyrJNIakhz5sD06VmnqB/dukG/flmnkCQ1dRUjcvfzX2qacwIXuurl8MRnYNVC2Oef0LrLpp/T1ETAoLOh887wn+Pgn7vDzpdB/1OyTiY1PbNuh8WvwZ635v7dktSkWTC3JPfdB4cemnu8zTawww7r34YOhU6dss0o6eNZvBh++Uu45BJYujTrNPXni1+En/4UevfOOokkqalqvy2UdoJ547JO0vykBM+eAXOfgU/eDuU7Zp2oYXXbHQ56Af57Ym7ajKr/wOg/QEm7rJNJTUNKufnMywZAr6OzTiOpHlgwtxQ1NXD++bDddnDaaTBhQu52xRWwbNm683r12nDxXFaWXXZJm7Z6NVxzDfzf/8G778LnPgdf+AIUNYOZkB5/HC69FG69Fb71rdx/yzp2zDqVJKmpiYCKSgvmhjDlt/D6dTDsR9C7hZRFbbrB3vfDKxfBKz+G95+HT/4dyrbPOlnDSwlWVMHKBdBxQNZp1BS9+3Du35ldroCi4qzTSKoHFswtxZgxMH587v7449ftr66GGTPWFc5rbo89BsuXrzuvT58NF8/tXSBFylRK8MAD8J3v5P7d3WMPuPNO2G23rJPVn0MOga99DX7wA/jZz+Avf4ELL4RTT4XS0qzTSZKakvJKmH41pBqIZvBD2ELwzr/hxXOh92dgxx9knaZxFRXD8B9B191zo5kf2Al2u7b5lOzVK2DxNFg4GRZOyd/yj1fNz52z77+gx/6ZxlQT9P/bu+/wKKv0jePfk4QSekekCEhHeqTYQEHEtaCyim3Xwm/tiCIiiqugooKCgGVXxYpdcFXUlaLY6VUghCZVEZAWkZJyfn+cySY0ISEz552Z+3Ndc03mnZQ7vBBOnnne5ywZCsWrQp2/+04iIoXEWGt9Z8iXlJQUO3v2bN8xosu+fdC4sev4mzPn6Doas7Lc/Na8ReclS2DpUti7N/f9atc+uPDcuDGU0OVhImG3YAH06wdTpsCJJ8LQoXDJJbE9w2z2bPc9f/01NGoEw4bB+efH9vcsImFnjJljrU3xnaOwad18CCtfhhm94Pxl6rwsDOkrYeLJkFwduk6DIqV8J/Jn1xr49lLYOgsa94MWj0JCFLwQbi3s2QTpaQcXknf95F6MyZFcHco0DN0auc71hGLwlwXR8b1KMGydB5+3hhaPQdMBvtOISD4dbt2sDuZ4MGaMKxZ/9tnRXy6fmAj167vbRRflHs/MPLjwvHgxTJ7sCtngCj116hxceG7UCJKTC/3bE4k7Gza4URivvgrly7vxETffDEWL+k4WfikpMHUqTJgA/fvDhRdCp05u5nSbNr7TiYhI0JVv4e63L1CB+VhlpMM33QEDHT+K7+IyuBnfZ38Lc++C1Cdhyww49R0ocbzvZE7WXkhfESokH1BMzulGBkgsDqUbQIU2UPtKKN0QyjZyx4ocMDaxZB345kJIexoa943otyNRLHUYJJWG+jf5TiIihUgdzLFu1y7X2diwIXz1Vfi6/DIzYcWKgwvPaWnuOXDF7bp1Dy48N2wIxYuHJ5dILElPz93ALysLeveGgQNdkTkeZWTAiy/Cgw/Cli1u5vSQIW6kj4hIPqiDOY5k7YH3SkGTAdDiEd9popfNhm97wIYJcOZEOK6z70TBsvotmPEPV5A99R2o2ikyX/eQ3cih+yN1I+cUkkvUPPrxMdbCV+fB5u/ggmWQfFx4vi+JHb+vggn1oVFfaPWE7zQiUgDqYI5Xo0a5Db8++CC8l5AnJbkO5UaNoEeP3OMZGbB8+cGF508/3b/wXK/ewYXnBg2gWLHwZRaJFpmZ8PLL8MADuRv4Pfqoe8EmnhUpArfcAlddBY8/Dk89BePGaSNAERE5vMTiUKYxbFvgO0l0+/EhWP8htBml4vKh1L4Syrd0RfgvO0PzIdCkf+HN/d6vG/mAsRYZO3LfL7G4KxzndCOXaeQKyofqRi4IY9zfgc9Ogvn3QIfXjv1zSmxLHQ4mERre4TuJiBQydTDHst9+cwWoTp3go498p9nfvn2wbNnBhecVK1xnJuSO6Tiw8Fy/fnyMAhA5cAO/U0913cuxtIFfYVq71nV0v/EGVK6sjQBF5KipgznO/HA1bPoaLlrnO0l0WveBK5zWvQ7avaR9EP5MRrrrZF77LlS/wBVgix7llWc53cg7lx481uKw3cg5BeQCdCMfi/n3wpLH4ezvofIp4f96Ep32bIKPToATroT2L/lOIyIFdLh1swrMsax/f1eMWrgQTjrJd5qjs3evG6txYOF55UrIDi2ikpJcd/N558Ftt+lyeIlN8+e7wvKUKa7Df+hQuPhi/RJ3NObMgbvuchsBNmzoNgK84AL92YnIYanAHGdSn4R5d0OPLVCsou800WX7jzCpA5RtBl2+gkRdbXhE1sKyZ2DeXZBcA04fBxVa5z5/yG7k0P2hupHzjrUozG7kY5HxO3zSCIpXhXNmQkKi3zwSTAv+CYuHwHlL3AsgIhKVVGCONxs2uKLUpZfC66/7TnPs9uyBpUtdsXnJEpg3DyZNcs/99a/Qty+0bes3o0hh2LAB7r8fXnvNzVZ+8EG46SZ17eeXtfDJJ65In5amjQBF5E+pwBxnfpkMU7vCWV/AcWf5ThM99v4Gn58M2Xuh22xIruY7UXTZMh2+uxT2bIY6f4fdG/6kG7nRwYXkSHUjF9Tqd+CHK+Dkf0P9G32nkaDJ+B0+qgVVOsIZ//GdRkSOgWYwx5uHHnKjJgYP9p2kcBQvDi1buluONWvg6afdJl/vvuvGB9x5J1x0kRuvIRJN0tNdp+3w4e7fbr9+cN99UK6c72TRyRjXtdytG4wZ4wr1KSnaCFBERKB8C3e/bb4KzEcrOxO+uwx2/wxdvlFxuSAqtYdu82D6tbD6TShdPzQb+arcYnIQupEL6oSesOLfsHAg1LoUilXwnUiCZOWLsG8bNLnHdxIRCZMAvwQqBbZsGbz0kut6rFPHd5rwOeEE15G4fj2MHAk//+y6mevXd4937vSdUOTIMjPhhRfc39tHHoHu3V23/rBhKi4XhiJF4Oab3Xz3++5zmwA2aAD33gs7dhz540VEJPYUr+IKpNu10d9Rm9cPfv0S2r4AlXTVYIEVrwSdPoHLfoe/zIfT3oXmg90GfBXaRG9xGUIb/o12RcSF//SdRoIkax8sHQFVznAvtIhITFKBORb985+u43fgQN9JIqN0aejTB5Yvh/HjoXp118lcs6abw7pmje+EIgezFj77DFq0gBtvdAXm6dPh7bdj+4UhX8qUcZ3LaWlw2WXw+ONujNCzz0JGhu90IiISaeVaug5mObKVr0DaKGjUF+r+3Xea2BCr+0KUbw71b3WdzPr3JTnWvA1/rIfG6l4WiWUqMMeauXPhvffcTOKqVX2niazERLjkEvj2W5g5020COGoU1K3rCkrTpvlOKOLMnw9nn+3+ju7bBx98AN98A+3a+U4W+2rVcnPp58xxm5/edpu7/+gjV/QXEZH4UL4l7Ex1nXVyeFumw6yb4LizoeVQ32kkGjQfDEUrwOzeWluJmy+e+gSUawbHn+s7jYiEkQrMsea++6BCBde5G89OPhneegt++snNsp00CU45BTp0gPffd2MJRCJt/Xq47jpo3dptVDlqlNu48uKLY7eTJahat4Yvv4QJEyAhwc1u79QJtBmWiEh8KN8CsjNg5xLfSYLrjw3wzcVuc7lT34EEbd8jR6FoeWjxGGz+Dla/5TuN+PbzZ7BjMTTur993RGKcCsyxZOpUmDjRFZnLlvWdJhhq1oShQ11h7+mnYfNm181cr57bTE0zWCUS0tPd6JoGDdwLH/36wcqVcPvtULSo73Txyxg4/3z48Ud47jlITXUvTl19tUbriIjEuvIt3f02zWE+pKw9rric+Tuc8ZE2bJP8OfF6qHAyzL8bMtJ9pxGflgyFErXcJpAiEtNUYI4V1rpNq2rUgFtu8Z0meEqVcpfCp6XBhx+6DQL79XN/Xnfc4TqdpWCysmDGDBgzxt3v06Wm/5OZCc8/717QeOQR1yWblqYN/IImKWn/jQDHj4eGDWHAAL0IJSISq0rVg8RkzYk9FGthxg2wdRac8gaUa+o7kUQbkwApT8PuX2DRw77TiC+bf3Cd7I36QkIR32lEJMxUYI4VH33kinuDBkFysu80wZWYCN27w9dfu0vhu3d3m3zVqwc9esD332tW2NFYvx5efhl69oQqVaB9e/jHP9x9mTJw6qmugD9uHGzY4Dtt5OXdwO+mm1zn8owZrnu5dm3f6eRwcjYCXLbM/d0eOtT9bHjmGW0EKCISaxISoVxzFZgPJW0krB4LzR6CGt19p5FoVakd1L3O/X3asdR3GvFhyVA3j7ve//lOIiIRoAJzLMjKgoEDXcfdNdf4ThM92rSBN96A1auhf383YuS009xGa++8o4JSXrt3uznWd93lNkSrWRN69YLvvnNF+nfecZ2548a5TnFwRblLL3Vd4rVquYLdU0/B9Omwd6/f7yecDreBX9u2vpPJ0apZE157zW0E2KwZ9O6tjQBFRGJR+RawfYF+tuf1y2SY1w9q9oCTBvpOI9GuxWOQWALm9NG/s3izYwls+Bga3AZJJX2nEZEIMDbKftCnpKTY2dqEaX+vvQbXXuuKez16+E4TvXbtcn+WI0fC8uWuMHr77a4zN97GGVjr5tFOnOhuX38Ne/ZAsWJwxhlwzjnu1rTp4Tdr2LfPFVunTcu9rV3rnita1BX427d3Gy926OD+vKPZ+vVw//3w+utuo80HH4Qbb9SM5WhnLXz6Kdx9Nyxd6v7+P/mkm9UsIjHDGDPHWpviO0dh07r5CJb/C2bdAt3XQMlavtP4l74CJraFEjXg7B+gSCnfiSQWLB0Fc++A0/8DNS/ynUYiZfp1sOZd6L4WilfynUZECtHh1s0qMEe7vXvd5fdVqsDMmdqZtTBkZ7vxBiNGuK7mkiXh+uuhTx848UTf6cJn2zaYMsUVlCdNgnXr3PFGjVwxuVs3V1wrUaLgX+Pnn10Hc07Bec4cV7gGV2DOW3Bu3doVtIMuPd2NUhgxwl1NcMcdbh56vL0oEesyM92c8QcecJuFXnklPPqom+cuIlFPBeY4tXkaTD4FzvgYalzgO41fGekwqT3s3gjdZkOpOr4TSazIzoD/toLMXXDeEkjSOMeY98d6+Lgu1LvRzeIWkZiiAnOsGjXKFbQmT4YuXXyniT3z57uxDm+/7QpM3btD375ulEa0F/OzsmDWrNwu5RkzXHG9bFn3dymnS7lWGDt69u2DBQtyC87Tp7uRJeA6f1u33r/oXLNm+LLkV2YmvPSSKzhu2gRXXOEKjpqxHNt27nSbNA4f7rqb+/RxGwOWLes7mYgcAxWY41TG7/B+GWg2GJr903caf2w2fHsJbPgEzpwEx53lO5HEml+nwhdnQbNB0OxB32kk3ObeBWmj4IIVUKq27zQiUshUYI5F6elQt67bSGzKFN9pYtsvv7jNAP/1L9i61Y13uPNOuOwyKBJFO+KuX59bUJ4yxXUtG+PmA+cUlNu2haQkfxl/+SW3y3n6dFcEz+lyrl794C7n4sUjmy9nA7+773ZjRE4/3Y1M0Izl+LJunRuJMnasG4kyaJAbiRJNPw9E5H9UYI5jExq4zf5OH+c7iT8LH4RFD0Gb0dCwt+80Equ+6+lm8p6XqqJjLNu3DT6sBdUvhFPf9J1GRMJABeZYNHiwK2rMnKl5oJHyxx+uoDRypJvHWr2629TuhhtckSlodu92G8zlFJWXLHHHq1fPLSh36RLM7DkyMlyXc97RGj/95J4rUgRatcotOLdv7zquw9VdPm8e9OsHX34J9eu7Ttbu3aO/m10Kbu5c93di6lT9nRCJYiowx7FvL4Vt8+DCFb6T+LF2PHz3VzixF7R9Uf9/SfjsWgefNILju8Hp432nkXBZ/CgsGAjnzncbqYpIzFGBOdZs3uzmAXft6jb3k8jKzobPP3fjM6ZMcXOJr73WXS7foIG/XNa6InJOQfmbb/K/OV802LjRjfTIKTjPmuWK6QDVqu1fcG7TBpKPcdbb+vUwcKC6VeXQDtXVPny4XvgTiSIqMMexRUNg4f1w6U4oUtp3msjattDNoC7XHDpPhcQo2PtColtO8fHMSVDtbN9ppLBl7oaPa0P51nDmf32nEZEwUYE51vTt6+YvL1oEjRv7ThPfFi50Hc1vvum6bc8/352fjh0jU8TdunX/zfnWr3fHGzfOLSgf6+Z8QZeRAT/+mFtwnjYNVq1yzxUpAi1b5hacO3RwG7Mdzbk51LxdbeAnh6O53CJRSwXmOLbhE/j6Ajj7O6h8qu80kbNnC0w82W3A1m0WJFfznUjiQdZe+LQpJBSBcxdAYlHfiaQwLf83zLrZvWBVtZPvNCISJiowx5K1a92l2Fdf7YoZEgwbN7oZzc89B1u2uKJm377Qs6fbsK6wZGbuvznfzJmuo7pcudzN+bp2De/mfNFg06b9x2rMmuVGnAAcd9z+Xc4pKft3OWdmwpgx8OCD7vNceSUMGaJCoRwdvTAhEnVUYI5jf6yHD2tCyjPQ4FbfaSIjOwOmngObf4Czv4WKuuJGImjDp/D1+dDqSWh8l+80Uliys+CThlCsInSdHt1Xy4rIn1KBOZZcfz289RYsW6YiYhDt3u26mZ96yo2rqFbNzWm+8UaoWLFgn3Pduv0359u+HRIS9t+c7+ST/W7OF3SZmft3OU+fDitC8xaTktwLAu3bQ8OGbkPHpUs16kCOzfr1biPA1193o1UefBBuvln/TkUCSAXmOGYtjK8ENXtAuxd8p4mM2X1g2Wjo8DrU+ZvvNBKPvjofNn0NFyxT93ysWPMefN8TThsHtXr4TiMiYaQCc6xYsgSaNYM77nCFLwkua93IihEj3H1yMlxzjTt3DRv++cfu3g1ff51bVE5NdcdzNufr1g06dw725nzRYPPm3C7n6dNdN/iuXdqsTQpX3s0hzzwT3n4bqlb1nUpE8lCBOc590Rky0qHbTN9Jwm/lyzCjFzS6C1o/6TuNxKv0FW5URq2ecMrrvtPIsbIWPk+BzHQ4LxUSEn0nEpEwUoE5VlxyietgXbUKKlXynUaO1uLFbk7z2LGwdy+cdx7ceSecdZYrYFrr3ifv5nx790Lx4vtvztekiQqe4ZSZCT/95EZhaAM/KUzWwmuvuQ7mChXg/ffhlFN8pxKREBWY49zcu2D5c3BpOiTE8FUmm6fBF52gSifo9Glsf68SfAsGuk3/4m3+eSzaOAW+PBvavgD1/uE7jYiE2eHWzQk+wkgBzZwJ//mP64RTcTm6NG0KL77o5mcPHuzmAXfp4sYyXHst1KzpOtP79YMNG+CWW+Dzz90GfhMnulnOTZuquBxuSUmue1nFZSlsxrh/69OmuasZOnaE0aNd4VlERPwq1wKy9kD6ct9JwueP9fDtxVCiFpz2jorL4l/T+6BEDZh9m5vfK9FryVAofpxG1WlPjQAAJQtJREFU7ojEORWYo4W1MGAAVK7sOl8lOlWpAg88AGvWwMsvu/P68cdus7kxY1wBevFiN1bjnHP233hORKJfy5Ywezace67b/O+KK+D3332nEhGJb+VbuvttC7zGCJvM3fDNxZC5Czp+BEXL+04kAkkl3UZ/2+bDyhd9p5GC2jrHdTA3ugMSi/tOIyIeqcAcLaZMgalT3YZRpUv7TiPHqnhxuO46WLjQdSm//z706uU6mUUktpUrBx9+CI895v7tt22bO2ddREQir0wjSCgC2+f7TlL4sjNh5o2wdTac8iaUbeI7kUiuWpe5kS0LBsLe33ynkYJY8gQUKQP1bvKdREQ8U4E5GmRnw733wgknwI03+k4jIiLHKiHBXZUyaRJs2eKKzO+95zuViEh8SiwKZZvGVgdz5h+w7Dn4pCGsHgvNH4YaF/pOJbI/YyDlacjYAQvu951G8it9Jax73xWXi5b1nUZEPFOBORqMHw9z5sBDD0GxYr7TiIhIYencGebOdTPYe/Z0I5AyMnynEhGJP+VauEv1o93e3+DHh+CjE2D2rVCsCpz+H2g60HcykUMrdxLUvxVWPA9b5/lOI/mxdDiYJDceQ0TingrMQZeZ6cZiNG0KV13lO42IiBS2GjXgq6+gd28YORLOPBN+/tl3KhGR+FK+JezZCLt/9Z2kYH5fDbNvhw9rwY8PQsX20OVb6PoD1LxIG0VLsDUfDMUquQ3/tAFydNizCVa9AnX+DsnVfKcRkQBQgTnoXn0Vli2DIUMgMdF3GhERCYeiRWH0aHjrLZg3D1q1ckVnEYkpxphuxpg0Y8wKY8yAQzx/kzHmR2PMfGPMd8aYJnmeuzf0cWnGmHMimzwO5Gz0tz3KxmRsnQffXwkT6sGKf8MJl8FfFkGnCVDlNBWWJToULQctH4ctP8DqN3ynkaORNhqy9kLju30nEZGAUIE5yHbvhkGDoH17uFAz00REYt4VV8DMmVC+vBufMWyYOnlEYoQxJhF4FjgXaAJckbeAHPKWtbaZtbYlMAwYEfrYJsDlQFOgG/Bc6PNJYSnfwt1Hw5gMa2HjF/BlV/i8NWz4BBrdCReugvavQLmmvhOK5F/da6FiW5jXHzJ2+k4jfyYjHZY9CzUvhjINfKcRkYBQgTnInn0WNmyAxx9X94GISLxo2hRmzYJLLoF77nH3O3b4TiUix64tsMJau8pauw94B+ie9x2stXmrKiWBnFeYugPvWGv3Wmt/AlaEPp8UlqLloUStYG/0l50Ja96Fz1Pgyy6w/UfX9XnRWmj1BJSo4TuhSMGZBEh5Bvb8Cose9p1G/syKFyFjOzTu7zuJiASICsxBtWMHPPYYdOsGHTv6TiMiIpFUujS89x6MGAETJkBKCixc6DuViByb6sC6PI/Xh47txxhzqzFmJa6D+fb8fKwco/ItYPt83ykOlvmH6xac0AC+vxyydkG7MdB9NTS5x40XEIkFFU+GE6+HpSNhR6rvNHIoWftg6Qio0hEqtfOdRkQCRAXmoHrySdi6FR591HcSERHxwRi4806YOhV27XLjksaO9Z1KRMLMWvustfZE4B7g/vx+vDHmBmPMbGPM7M2bNxd+wFhWviXsTIPM3b6TOHu2wI+D4aNabvOz5OPgjA/hvCVwYi9ILOY7oUjha/EoJJWEOX00JiyI1rwFuze4F7dERPJI8h1ADuHXX13XWs+ebqMnERGJX6efDnPnwuWXw9//Dj/8ACNHQjEVFuKetbB8OaSmQnIylCnjut/LlHG3UqW0QXCwbABq5nlcI3TscN4B/pXfj7XWvgC8AJCSkqLqTH6UawE2C3Yshoop/nL8/pPrEFz5EmTthuoXuEvRq5zmL5NIpBSvAs0fhjm3w/oP3ZxfCQabDUuGQbnmUK2b7zQiEjAqMAfRI4/A3r3wsGZPiYgIcNxxMGUK3HcfPPEEzJkD48ZBrVq+k0kk7dzp5nNPm+Zu06e7q53+TMmSuQXnAwvQ+XmcnKz9II7dLKC+MaYOrjh8OXBl3ncwxtS31i4PPTwPyHn7Y+AtY8wI4HigPjAzIqnjSfmW7n7bfD8F5q3zIHUYrH0PTCLUvhoa94OyB+4FKRLj6t8MK1+EuXdCtXMgqYTvRAKw4VPYmQod3tCaQEQOogJz0KxaBc8/D//3f1C/vu80IiISFElJMGyYG5Vx7bXQujW89RZ07eo7mYRDdjYsW+aKyDkF5UWLci8XbtwYLroIOnSA5s0hIwPS010ROud2uMcrV+Y+3rEDsrKOnCcx8dgK1HkfFykS1j+6oLLWZhpjbgMmAonAy9baxcaYh4DZ1tqPgduMMV2ADGAbcE3oYxcbY94DlgCZwK3W2qM4cZIvpepAUinYHsGN/qyFX79wXYEbJ0NSaWh0FzTsAyU0ZlviVEIStHkavujk/m00H+Q7kQCkDoWSJ8AJPX0nEZEAUoE5aB580P0S98ADvpOIiEgQXXIJnHQS9OjhNoIdPBgGDoQEbasQ1XbuhBkzcgvK06fDtm3uubJloV07d+47dHBvlytXOF/XWtiz588L0od7vHUrrF6d+/j334/uaxYvfvgC9FVXwbnnFs73FkDW2s+Azw449kCet/v8yccOAYaEL51gEtxGf9vmh/9rZWfC2nGuY3nbPCh+HLR8HOrdBEXLhv/riwRd1Y5wwuWuqFn3GvcCkPiz+Xt3azPavQAgInKAiPxkMMY0BN7Nc6gu8ADweuh4bWA1cJm1dlskMgXSjz/Cm29C//5w/PG+04iISFA1aOAKkDfe6F6QnDYN3ngDKlTwnUyORnY2pKXlFpKnTYPFi12x1xho0sS9gNC+vSsoN2oUvhcQjHHjL5KToWrVY/tc2dmuyFyQYvX69e6+U6dC+bZECqxcS/jpdTdr1ITh313mLlj5CiwdDrtWQ5mG0G6MG4ehTftE9tfqCVj/McztC2f8x3ea+LZkKBSrCCde7zuJiARURArM1to0oCWAMSYRN3fuP8AA4Atr7ePGmAGhx/G7HenAga575574/SMQEZGjVLIkjB0Lp54KffpAmzYwfrwbnSHBsmOH607OGXUxYwZs3+6eK1fOFZIvvdTdt2vnOpajUUJCbieySLQq3wKWp7vib6m6hfd592yBZc/A8mdg729QqQO0Gek28AtHIVskFpSoASfdDwvug18mQTWNBfNi+2LYMAGaDYKkkr7TiEhA+bi2oTOw0lq7xhjTHegUOv4a8BXxWmD+/nuYMAEefRTKl/edRkREooExcPPNrqj817/CKafAs89Cr16+k8Wv7GxITd1/dnJqam53ctOmrpjcoYMrKDdsqPEmIkHyv43+FhROgfn3VZA6Ala9DFm7ofqF0KQ/VD712D+3SDxo1BdWvgxzbodzF0JiUd+J4k/qE5BYAhrc5juJiASYjwLz5cDboberWmt/Cb29ETjktZnGmBuAGwBq1aoV9oARZy0MGADHHQe33+47jYiIRJt27WDuXDe/9v/+D374AZ55xo09kPDatm3/2ckzZriOZXAvGLdvD5df7grKbduqu1ck6Mqe5DqKt82HmhcX/PNsnes2J1v3PphEqP03aNwPyjYutKgicSGxGLQZBV+fB2mjoMndvhPFl13rYPWbUP8WNyJDROQwIlpgNsYUBS4E7j3wOWutNcbYQ32ctfYF4AWAlJSUQ75PVPvvf+G77+C559wlzyIiIvlVubL7/2TQIHjkEVdwHj8e6hbiJd7xLjsblizZf3Zyaqp7LiHBbb7Ys6crJnfo4GZlG+M3s4jkT1IylG4I2xfk/2OthY1T3MZ9G6dAkTLQqB80vB1KVC/8rCLxovpf4PjzYdFDUPsqKKH9iiJm6VOAhUZ3+k4iIgEX6Q7mc4G51tpfQ49/NcZUs9b+YoypBmyKcB7/srPhvvvgxBNd15mIiEhBJSbCww+7rtmrr3ZzmceOhfPP950sOm3dmjs7efp09/bOne65ChVcEfnKK3O7k0uX9ptXRApH+RawZdrRv392Jqx93xWWt82H5GrQcijUuxGKRulMdZGgaTMSPm0C8++BU8b6ThMf9m6FlS/ACZdDqdq+04hIwEW6wHwFueMxAD4GrgEeD91/FOE8/r37LixYAG+9BUWK+E4jIiKx4LzzXAdzjx5wwQVuE9nBg10BWg4tKyu3OznnlpbmnktIgGbNcovJ7dtD/frqThaJVeVbwpp3YN82KPone6Nk7nKzYZeOcJsClmkE7V5yHZaJxSKVViQ+lD4RGt8Ni4e4F2+qnOY7Uexb/i/3c65xf99JRCQKGGsjM3HCGFMSWAvUtdbuCB2rCLwH1ALWAJdZa7f+2edJSUmxs2fPDnfcyNi3Dxo3dh1Pc+dqkx8RESlcu3dD797w0kvQpYt7MbNyZd+pgmHHDrfBbk4xeeZMSE93z1Wq5IrIOaMuUlLUnRzjjDFzrLUpvnMUtphaN0fSzxPhq27Q+Suo2vHg5/dshmXPuNu+rW7Dvsb9ofr5bn6ziIRH5i74pLGbBXzObEjQC+dhk7kbPjoBKqTAmZ/5TiMiAXK4dXPEOpittbuAigcc+w3oHKkMgfPSS7BqFXz6qYrLIiJS+JKTYcwYVyS99VZo3Rref98VT+NNVhbMmgUTJ8KkSW7cRVaW6+pu3tyNFMkpKJ94orqTReJZ+Rbuftv8/QvMv6+C1OGw6mXI2gM1uruOysqneokpEneSSkLr4fDdZW50Q/2bfSeKXT+9Cns3Q5N7fCcRkSgR6REZkmPXLnjoITj9dDj3XN9pREQklvXqBa1awV//CmecAU89BbfcEvtF1LVrcwvKU6bA9u3ue05JgQEDoHNnOPlkKFXKd1IRCZLk46B41dyN/rbOgSVPwLr3wSRCnb9Do7ugbGO/OUXiUc2/QtUzYcFAqHkpFK/kO1Hsyc6E1CehYjuocobvNCISJVRg9mX0aNi4EcaNi/1f8EVExL/WrWHOHPjb3+C229xYiOefh5IlfScrPLt2wVdfuYLypEmwdKk7Xr06XHwxnHOOKypX0i+jInIE5VrAr1/CF13g1y+gSBlo1A8a9oESx/tOJxK/jIE2T8N/W8DC+6Htv30nij3rxrsrNlo9qVqFiBw1FZh92LYNhg1zGy+dqkvqREQkQsqXh48/hsceg3/+E+bPhw8+gAYNfCcrmOxst1HupEmuU/n7793+BsWLQ8eOcMMN0LUrNGmiX5BEJH8qtIaNkyA7A1oOg3o3QNGyvlOJCEC5ptCgN6SNcv82K7T2nSh2WAtLhkKZhm4MkIjIUVKB2YehQ93mQkOG+E4iIiLxJiEBBg6Etm3hiivcuIhXXoEePXwnOzobN8Lkya6gPHkybNrkjjdvDrff7grKp5/uiswiIgXV+G6odApU6wqJxXynEZEDNRsEa96C2bfB2d9pg83CsnEKbJsH7cboz1RE8kUF5kjbsAFGjYKrroJmzXynERGReHX22TB3Llx6qZvNfNdd8PjjkBSwpcGePa4zOWeW8oLQTNRKlVwxOedWrZrfnCISW4pVgBoX+E4hIodTtCy0HArTr4Of3oC6f/edKDYsGQrJx0Ptq30nEZEoE7DfIuPAww+7XesHD/adRERE4l2tWvDNN9C3LwwfDrNmwTvv+C3WWutmJ+cUlL/6CnbvhiJF3FipRx91s5RbtnTd2CIiIhKf6vwdlj8P8/tDzYvcrHQpuK1z3Mz5lsN05YaI5JsKzJG0fDmMGQM33wx16/pOIyIiAsWKwbPPQocObmZx69bw3ntuzESkbN0KU6bkbs63bp073qAB9OrlCsqdOkGpUpHLJCIiIsFmEiDlaZjYFn4cDK2H+04U3ZYMdUX6ejf4TiIiUUgF5kh64AE3E/L++30nERER2d/VV0OLFm4W85lnus1o77wzPJvjZWTAjBm5m/PNmuU6l8uWhc6d3f+TXbtC7dqF/7VFREQkdlRMgRP/D9JGu/uyjX0nik7pK2DdeDd/XhuaikgBqMAcKfPmucuOBw6EqlV9pxERETlYs2au2HvddW4m8w8/wMsvQ5lCuOR01arcgvKXX8LOnW7ERdu27gXYrl3d20GbAS0iIiLB1mIIrH0f5twOZ04Kz4vjsS51OJgkaNjHdxIRiVL6LS5S7rsPKlSAu+/2nUREROTwypaF8ePdTOYBA2DRIve4adP8fZ70dJg6NXeW8ooV7nitWtCzpysod+4M5csX/vcgIiIi8aN4ZWj+MMzpDes+gFo9fCeKLrt/hVWvQJ1rIFmbJotIwajAHAlffQWffw5PPOF+cRcREQkyY6BfPzj5ZFcMbtvW7SFwxRWH/5jsbJg7N7eg/MMPkJkJJUq4kRu9e7tZyg0aqLNIREREClf9m2DlizC3Lxx/LiSV8J0oeiwbDdn7oHE/30lEJIqpwBxu1sK990L16nDrrb7TiIiIHL2OHV3RuGdPuPJKVzQePhyKFnXPb9iQuzHf5Mnw22/ueKtWbsTGOefAKae4jQRFREREwiUhyW34N6Wj26yu+WDfiaJDRjosew5qXgJlGvhOIyJRTAXmcJswAaZPhxdfhORk32lERETy5/jj3czke+6Bp56C2bNd0XjiRFi82L1P1arwl7+4gnKXLtprQERERCKvyhlwwhWuwFz3GihV13ei4FvxAmRshyb3+E4iIlFOBeZwyspys5cbNIBrr/WdRkREpGCKFIERI6BDB7j+etfVfPrpcM01bpZy8+YaeyEiIiL+tXoCNnzsRmWc8aHvNMGWtQ+WPgVVz4SKJ/tOIyJRTgXmcHrzTdfd9d57kKQ/ahERiXKXXuo6lQFKlvSbRURERORAJarDSf+E+QPg58/h+G6+EwXX6jdh9wZo95LvJCISAxJ8B4hZe/fCAw9AmzbQQ7vYiohIjChZUsVlERERCa6Gd0Dp+jCnj+vSlYPZbEgdBuVbQrWuvtOISAxQgTlcnn8e1qyBxx6DBP0xi4iIiIiIiIRdYjFoMxrSl0HaSN9pgmnDBNi5FBr315gzESkUqnyGQ3o6PPIInHWW2+xIRERERERERCLj+G5Q/UJY9DD8scF3muBZMgxK1oZal/pOIiIxQgXmcBg5EjZvdt3LejVQREREREREJLLaPAXZGTCvv+8kwbLpO9jyAzS6CxK0V5SIFA4VmAvbli3wxBNw8cXQtq3vNCIiIiIiIiLxp1RdaNIf1rwFm771nSY4lgyFYpXgxOt9JxGRGKICc2F77DHYtcuNyBARERERERERP5oMgBK1YPZtkJ3pO41/2xfBz59Ag96QVMJ3GhGJISowF6a1a+HZZ+Gaa6BJE99pREREREREROJXUgloPRy2L4QVz/tO41/qE5BYAhrc6juJiMQYFZgL0+DBYC0MGuQ7iYiIiIiIiIjU7AFVO8PCf8KeLb7T+LNrLax+C+r9A4pV9J1GRGKMCsyFZelSePVVuPVWqFXLdxoRERERERERMQZSRkNGOiwc6DuNP0ufcveN+vrNISIxSQXmwnL//VCiBNx7r+8kIiIiIiIiIpKjbBM3d3jFi/DbbN9pIm/vVlj5IpxwBZRUQ5yIFD4VmAvDrFkwfjz06weVK/tOIyIiIiIiIiJ5NXsQileB2b3BZvtOE1nLnoXMXdCkv+8kIhKjVGAuDPfeC5UqQV9daiIiIiIiIiISOEXLQsuh8Nt0+Gms7zSRk7kblo2G48+Dcif5TiMiMUoF5mM1ZQp88YUbkVG6tO80IiIiIiIiInIodf4GFdvD/P6wb4fvNJGx6hXYuwWa3OM7iYjEMBWYj4W1rnu5Vi246SbfaURERERERETkcEwCnPwM7NkMPw72nSb8sjMh9Umo1AEqn+Y7jYjEMBWYj8UHH8Ds2fDQQ1CsmO80IiIiIiIiIvJnKrSBev9wYyO2L/adJrzWjoNdP0Hj/mCM7zQiEsNUYC6ozEwYOBCaNIGrr/adRkRERERERESORvMhUKQMzLndXZkci6yF1KFQphHUuNB3GhGJcSowF9Rrr0FaGgwZAomJvtOIiIiIiIiIyNEoXgmaPwK/fgnrxvtOEx4bJ8O2+dD4bjcaREQkjPRTpiD27IFBg6BdO+je3XcaEREREREREcmPejdCuRYwty/s2+47TeFbMhSSj4faV/lOIiJxIMl3gKj03HOwfj2MHas5RiIiIiIiIiLRJiERUp6BKafDuPJQoiaUaehGSpRumPt2ierR1wH822zXnd3qCUjUflEiEn4qMOfXjh3w6KNwzjnQqZPvNCIiIiIiIiJSEFVOgy5fw6ZvYWca7FwKP70OGTtz3yexBJRpkKfw3ChUfG4ASSX9Zf8zS4ZCkbJQ7wbfSUQkTqjAnF/Dh8Nvv7kis4iIiIiIiIhErypnuFsOa2HPr67YnFN03pkGv82ENe8CeTYFzOl63q/w3BBK1PDX9bxzuZsr3WSA28hQRCQCVGDOj19/hREj4LLLoHVr32lEREREREREpDAZA8nHuVvVTvs/l7UH0lfsX3jeuRRWjz1C13NOAToCXc9Lh0NCUWjYJ7xfR0QkDxWY82PIELfB38MP+04iIiIiIiIiIpGUWBzKneRueR3U9RwqPP82E9a+BzY7931L1DhE4bmQup53b4RVr0LdayG56rF9LhGRfFCB+Wj99BP8+9/Qqxc0aOA7jYiIiIiIiIgEQX67ntPTDt/1fOC4jdINoEipo8uRNhpsBjTuV2jfmojI0VCB+WgNGgSJifDAA76TiIiIiIiIiEg0OGLX8wHjNrbOgnXv57/rOWMnLH8OavaA0vUi9/2JiKAC89FZtAjGjoV+/aB6dd9pRERERERERCSa7df13HH/5/J2PaenwY6lh+l6Ts7dZDB7D2TsgMb9I/t9iIigAvPRef11KFMGBgzwnUREREREREREYll+up7T01zX867VUO1cqJjiJbKIxDcVmI/G0KHwj39AhQq+k4iIiIiIiIhIPPrTrue9YFTiERE/9NPnaBgD9ev7TiEiIiIiIiIicrDEYr4TiEgcS/AdQEREREQkHhhjuhlj0owxK4wxB81eM8b0NcYsMcYsNMZ8YYw5Ic9zw4wxi40xqcaY0cYYE9n0IiIiIiKHpgKziIiIiEiYGWMSgWeBc4EmwBXGmCYHvNs8IMVa2xwYBwwLfewpwKlAc+Ak4GTggGujRURERET8UIFZRERERCT82gIrrLWrrLX7gHeA7nnfwVo71Vr7R+jhdKBGzlNAcaAoUAwoAvwakdQiIiIiIkcQsQKzMaacMWacMWZp6NK+DsaYQcaYDcaY+aHbXyKVR0REREQkgqoD6/I8Xh86dji9gP8CWGunAVOBX0K3idba1DDlFBERERHJl0hu8jcK+Nxa+1djTFGgBHAO8JS19skI5hARERERCSxjzNVACqExGMaYekBjcjuaJxtjTrfWfnuIj70BuAGgVq1akQksIiIiInEtIh3MxpiywBnASwDW2n3W2u2R+NoiIiIiIgGwAaiZ53GN0LH9GGO6AAOBC621e0OHLwamW2t/t9b+juts7nCoL2KtfcFam2KtTalcuXKhfgMiIiIiIocSqREZdYDNwCvGmHnGmDHGmJKh524L7ZT9sjGmfITyiIiIiIhE0iygvjGmTuhqvsuBj/O+gzGmFfA8rri8Kc9Ta4GOxpgkY0wRXGezRmSIiIiISCBEqsCcBLQG/mWtbQXsAgYA/wJOBFri5skNP9QHG2NuMMbMNsbM3rx5c2QSi4iIiIgUEmttJnAbMBFXHH7PWrvYGPOQMebC0Ls9AZQC3g/tT5JTgB4HrAR+BBYAC6y1EyL7HYiIiIiIHFqkZjCvB9Zba2eEHo8DBlhr/7f7tTHmReCTQ32wtfYF4AWAlJQUG+asIiIiIiKFzlr7GfDZAcceyPN2l8N8XBZwY3jTiYiIiIgUTEQ6mK21G4F1xpiGoUOdgSXGmGp53u1iYFEk8oiIiIiIiIiIiIjIsYtUBzNAb+DN0My5VcB1wGhjTEvAAqtRZ4aIiIiIiIiIiIhI1IhYgdlaOx9IOeDw3yL19UVERERERERERESkcEVqkz8RERERERERERERiTHG2ujaM88YsxlY4+FLVwK2ePi6cng6J8Gi8xE8OifBo3MSLDofwePrnJxgra3s4euGldbNkofOSbDofASPzkmw6HwEj85JsPg8H4dcN0ddgdkXY8xsa+2BIz7EI52TYNH5CB6dk+DROQkWnY/g0TmJDTqPwaNzEiw6H8GjcxIsOh/Bo3MSLEE8HxqRISIiIiIiIiIiIiIFogKziIiIiIiIiIiIiBSICsxH7wXfAeQgOifBovMRPDonwaNzEiw6H8GjcxIbdB6DR+ckWHQ+gkfnJFh0PoJH5yRYAnc+NINZRERERERERERERApEHcwiIiIiIiIiIiIiUiAqMB8FY0w3Y0yaMWaFMWaA7zzxzBhT0xgz1RizxBiz2BjTx3cmcYwxicaYecaYT3xnETDGlDPGjDPGLDXGpBpjOvjOFM+MMXeGfmYtMsa8bYwp7jtTvDHGvGyM2WSMWZTnWAVjzGRjzPLQfXmfGePJYc7HE6GfWQuNMf8xxpTzGFEKQGvmYNG6OZi0Zg4WrZmDR+tm/7RuDpZoWTerwHwExphE4FngXKAJcIUxponfVHEtE7jLWtsEaA/cqvMRGH2AVN8h5H9GAZ9baxsBLdC58cYYUx24HUix1p4EJAKX+00Vl14Fuh1wbADwhbW2PvBF6LFExqscfD4mAydZa5sDy4B7Ix1KCk5r5kDSujmYtGYOFq2ZA0Tr5sB4Fa2bg+RVomDdrALzkbUFVlhrV1lr9wHvAN09Z4pb1tpfrLVzQ2+n4xYA1f2mEmNMDeA8YIzvLALGmLLAGcBLANbafdba7V5DSRKQbIxJAkoAP3vOE3estd8AWw843B14LfT2a8BFkcwUzw51Pqy1k6y1maGH04EaEQ8mx0Jr5oDRujl4tGYOFq2ZA0vrZs+0bg6WaFk3q8B8ZNWBdXker0cLs0AwxtQGWgEzPEcRGAn0B7I95xCnDrAZeCV0CeYYY0xJ36HilbV2A/AksBb4BdhhrZ3kN5WEVLXW/hJ6eyNQ1WcY2c/1wH99h5B80Zo5wLRuDoyRaM0cJFozB4zWzYGmdXNwBWLdrAKzRCVjTClgPHCHtXan7zzxzBhzPrDJWjvHdxb5nySgNfAva20rYBe6hMmb0Hyy7rhfYo4HShpjrvabSg5krbWA9Z1DwBgzEHdp/5u+s4jEAq2bg0Fr5kDSmjlgtG6ODlo3B0eQ1s0qMB/ZBqBmnsc1QsfEE2NMEdwi+U1r7Qe+8winAhcaY1bjLoc9yxjzht9IcW89sN5am9OlNA63eBY/ugA/WWs3W2szgA+AUzxnEudXY0w1gND9Js954p4x5lrgfOCq0C8vEj20Zg4grZsDRWvm4NGaOXi0bg4urZsDJmjrZhWYj2wWUN8YU8cYUxQ3YP5jz5niljHG4GZkpVprR/jOI2CtvddaW8NaWxv37+NLa61eZfbIWrsRWGeMaRg61BlY4jFSvFsLtDfGlAj9DOuMNpAJio+Ba0JvXwN85DFL3DPGdMNdOn6htfYP33kk37RmDhitm4NFa+bg0Zo5kLRuDi6tmwMkiOtmFZiPIDQ0+zZgIu4H23vW2sV+U8W1U4G/4V7xnx+6/cV3KJEA6g28aYxZCLQEHvUbJ36FumLGAXOBH3H/977gNVQcMsa8DUwDGhpj1htjegGPA2cbY5bjOmYe95kxnhzmfDwDlAYmh/5//7fXkJIvWjMHktbNIkemNXOAaN0cDFo3B0u0rJtNALqoRURERERERERERCQKqYNZRERERERERERERApEBWYRERERERERERERKRAVmEVERERERERERESkQFRgFhEREREREREREZECUYFZRERERERERERERApEBWYRkYAzxmQZY+bnuQ0oxM9d2xizqLA+n4iIiIiIL1o3i4j4keQ7gIiIHNFua21L3yFERERERAJO62YREQ/UwSwiEqWMMauNMcOMMT8aY2YaY+qFjtc2xnxpjFlojPnCGFMrdLyqMeY/xpgFodspoU+VaIx50Riz2BgzyRiT7O2bEhEREREpZFo3i4iElwrMIiLBl3zApX498zy3w1rbDHgGGBk69jTwmrW2OfAmMDp0fDTwtbW2BdAaWBw6Xh941lrbFNgO9AjrdyMiIiIiEh5aN4uIeGCstb4ziIjInzDG/G6tLXWI46uBs6y1q4wxRYCN1tqKxpgtQDVrbUbo+C/W2krGmM1ADWvt3jyfozYw2VpbP/T4HqCItfaRCHxrIiIiIiKFRutmERE/1MEsIhLd7GHezo+9ed7OQvP5RURERCT2aN0sIhImKjCLiES3nnnup4Xe/gG4PPT2VcC3obe/AG4GMMYkGmPKRiqkiIiIiIhnWjeLiISJXm0TEQm+ZGPM/DyPP7fWDgi9Xd4YsxDXTXFF6Fhv4BVjzN3AZuC60PE+wAvGmF64joubgV/CHV5EREREJEK0bhYR8UAzmEVEolRollyKtXaL7ywiIiIiIkGldbOISHhpRIaIiIiIiIiIiIiIFIg6mEVERERERERERESkQNTBLCIiIiIiIiIiIiIFogKziIiIiIiIiIiIiBSICswiIiIiIiIiIiIiUiAqMIuIiIiIiIiIiIhIgajALCIiIiIiIiIiIiIFogKziIiIiIiIiIiIiBTI/wMefAlx3idk4AAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1440x864 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Setting up a larger canvas to display all graphs\n",
    "fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(20, 12))\n",
    "fig.suptitle('Training & Validation Metrics', fontsize=16)\n",
    "\n",
    "# Plotting the Training Loss\n",
    "axes[0, 0].plot(train_losses, label=\"Train Loss\", color='blue')\n",
    "axes[0, 0].set_xlabel(\"Epoch\")\n",
    "axes[0, 0].set_ylabel(\"Loss\")\n",
    "axes[0, 0].set_title(\"Training Loss\")\n",
    "axes[0, 0].legend()\n",
    "\n",
    "# If evaluations during training is enabled, plot the other metrics\n",
    "if config.do_eval_during_train:\n",
    "    # Plotting the F1 Score\n",
    "    axes[0, 1].plot(val_f1, label=\"F1 Score\", color='green')\n",
    "    axes[0, 1].set_xlabel(\"Epoch\")\n",
    "    axes[0, 1].set_ylabel(\"F1 Score\")\n",
    "    axes[0, 1].set_title(\"F1 Score\")\n",
    "    axes[0, 1].legend()\n",
    "\n",
    "    # Plotting Accuracy & Top-2 Accuracy on the same plot\n",
    "    val_top2_acc_percentage = [v * 100 for v in val_top2_acc]\n",
    "    axes[1, 0].plot(val_acc, label=\"Accuracy\", color='red')\n",
    "    axes[1, 0].plot(val_top2_acc_percentage, label=\"Top-2 Accuracy\", color='purple')\n",
    "    axes[1, 0].set_xlabel(\"Epoch\")\n",
    "    axes[1, 0].set_ylabel(\"Accuracy\")\n",
    "    axes[1, 0].set_title(\"Accuracy & Top-2 Accuracy\")\n",
    "    axes[1, 0].legend()\n",
    "\n",
    "    # Plotting the MAE\n",
    "    axes[1, 1].plot(val_mae, label=\"MAE\", color='orange')\n",
    "    axes[1, 1].set_xlabel(\"Epoch\")\n",
    "    axes[1, 1].set_ylabel(\"MAE\")\n",
    "    axes[1, 1].set_title(\"MAE\")\n",
    "    axes[1, 1].legend()\n",
    "\n",
    "# Display the plots\n",
    "plt.tight_layout()\n",
    "plt.subplots_adjust(top=0.90)  # Adjust title spacing\n",
    "plt.show()\n"
   ]
  },
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   "source": [
    "## Inference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from transformers import AutoModel, AutoConfig, AutoTokenizer\n",
    "import torch\n",
    "import os\n",
    "\n",
    "MODEL_PATH = output_dir # best model path\n",
    "\n",
    "model = ClsModel()\n",
    "loaded_state_dict = torch.load(os.path.join(MODEL_PATH, \"model_weights.pth\"))\n",
    "model.load_state_dict(loaded_state_dict)\n",
    "model.eval() \n",
    "tokenizer = model.tokenizer\n",
    "\n",
    "def predict(text):\n",
    "    encoding = tokenizer(text, return_tensors=\"pt\", truncation=True, padding=True, max_length=512)\n",
    "    inputs = {\n",
    "        \"input_ids\": encoding[\"input_ids\"],\n",
    "        \"attention_mask\": encoding[\"attention_mask\"],\n",
    "        \"token_type_ids\": encoding[\"token_type_ids\"]\n",
    "    }\n",
    "    with torch.no_grad():\n",
    "        logit = model(inputs)\n",
    "\n",
    "    prob = torch.nn.functional.softmax(logit, dim=-1)\n",
    "    prediction = torch.argmax(prob, dim=-1)\n",
    "\n",
    "    return prediction"
   ]
  },
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   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Preference: like\n"
     ]
    }
   ],
   "source": [
    "text = \"동남아는 똥남아가 아니라 갓남아임. 국결은 동남아인이랑 해도 성공임\"\n",
    "\n",
    "class_labels = [\"hate\", \"dislike\", \"neutral\", \"like\", \"love\"]\n",
    "\n",
    "result = predict(text)\n",
    "print(f'Preference: {class_labels[result.item()]}')"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
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