main.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jt7wrpU0Xjsf"
      },
      "source": [
        "# Deep Learning Project\n",
        "## Istanbul Technical University\n",
        "### Computer Engineering Department\n",
        "### YZV\t303E\tDeep Learning\n",
        "### Fall 2023\n",
        "\n",
        "<b>Student Name: Muhammed Rüşen Birben</b><br>\n",
        "<b>Student ID: 150220755</b><br>\n",
        "<b>Student Email: birben20@itu.edu.tr</b><br>\n",
        "\n",
        "<b>Student Name: Ahmed Burak Ercan</b><br>\n",
        "<b>Student ID: 150220749</b><br>\n",
        "<b>Student Email: ercana20@itu.edu.tr</b><br>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "OssGDrFlXjsi"
      },
      "outputs": [],
      "source": [
        "from transformers import AutoTokenizer, AutoModelForSequenceClassification, Trainer, TrainingArguments\n",
        "from os import makedirs\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import torch.nn.functional as F\n",
        "import torch\n",
        "from sklearn.model_selection import train_test_split\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "from sklearn.metrics import f1_score, precision_score, recall_score, accuracy_score\n",
        "from importlib import reload\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import random\n",
        "import os\n",
        "from collections import Counter\n",
        "from transformers import AutoTokenizer, AutoModelForSequenceClassification, Trainer, TrainingArguments\n",
        "from datasets import Dataset\n",
        "# custom modules\n",
        "import Scripts.utils\n",
        "# Reloading custom modules, for production purposes\n",
        "reload(Scripts.utils)\n",
        "from Scripts.utils import get_model, model_predict, eval_model\n",
        "\n",
        "\n",
        "# sns\n",
        "sns.set_theme(style=\"whitegrid\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "eCqkK8Z9Xjsk"
      },
      "outputs": [],
      "source": [
        "def seed_everything(seed_value=42):\n",
        "    \"\"\"\n",
        "    Seed random number generators to ensure reproducibility.\n",
        "    Args:\n",
        "    seed_value (int): The seed value to use for all random number generators.\n",
        "    \"\"\"\n",
        "    os.environ['PYTHONHASHSEED'] = str(seed_value)\n",
        "    random.seed(seed_value)\n",
        "    np.random.seed(seed_value)\n",
        "    torch.manual_seed(seed_value)\n",
        "\n",
        "    if torch.cuda.is_available():\n",
        "        torch.cuda.manual_seed(seed_value)\n",
        "        torch.cuda.manual_seed_all(seed_value)\n",
        "\n",
        "    torch.backends.cudnn.deterministic = True\n",
        "    torch.backends.cudnn.benchmark = False\n",
        "\n",
        "# Usage:\n",
        "seed_everything()  # Call the function with default seed=42 or pass a specific seed value if needed."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FoMGPuFbXjsk"
      },
      "source": [
        "### Loading the Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "id": "UpD5npj2Xjsl",
        "outputId": "ebef7740-4de6-4fb4-b79e-b1351fc6f19a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Lenght of the dataset: 26270\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Source</th>\n",
              "      <th>Text</th>\n",
              "      <th>IsAI</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>LLM</td>\n",
              "      <td>Have you ever heard of the Crusades? A time in...</td>\n",
              "      <td>yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>LLM</td>\n",
              "      <td>The professors, who likely have nearly a decad...</td>\n",
              "      <td>yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>LLM</td>\n",
              "      <td>Kemba Walker does a good job of defending Foye...</td>\n",
              "      <td>yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>LLM</td>\n",
              "      <td>Ganias' lawyer, Stanley Twardy, urged the gove...</td>\n",
              "      <td>yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>PDF</td>\n",
              "      <td>The Circuit Court of Appeals of New Jersey had...</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Source                                               Text IsAI\n",
              "0    LLM  Have you ever heard of the Crusades? A time in...  yes\n",
              "1    LLM  The professors, who likely have nearly a decad...  yes\n",
              "2    LLM  Kemba Walker does a good job of defending Foye...  yes\n",
              "3    LLM  Ganias' lawyer, Stanley Twardy, urged the gove...  yes\n",
              "4    PDF  The Circuit Court of Appeals of New Jersey had...   no"
            ]
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Reading the data.\n",
        "df = pd.read_csv('Data/final_data.csv')\n",
        "print(f\"Lenght of the dataset: {len(df)}\")\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "A few data refactoring steps are left."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "_4W1w51EXjsl"
      },
      "outputs": [],
      "source": [
        "# map yes/no to 1/0\n",
        "df['IsAI'] = df['IsAI'].map({'yes': 1, 'no': 0})\n",
        "# rename column IsAI to labels\n",
        "df.rename(columns={'IsAI': 'labels'}, inplace=True)\n",
        "# rename columns to lowercase\n",
        "df.columns = df.columns.str.lower()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "2n3OnMgpXjsm"
      },
      "outputs": [],
      "source": [
        "df.dropna(inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "87yutB6TXjsm",
        "outputId": "f03f072a-f9a1-42b7-aeff-13714ded0ca2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Length of the train dataset: 16812\n",
            "Length of the validation dataset: 4204\n",
            "Length of the test dataset: 5254\n",
            "Cuda is available: True\n"
          ]
        }
      ],
      "source": [
        "# Splitting the DataFrame into train and remaining datasets\n",
        "# get a portion of the dataset\n",
        "df = df.sample(frac=1, random_state=42)\n",
        "\n",
        "# Splitting the DataFrame into train and validation and test datasets\n",
        "train_df_pandas, test_df_pandas = train_test_split(df, test_size=0.2, random_state=42)\n",
        "train_df_pandas, val_df_pandas = train_test_split(train_df_pandas, test_size=0.2, random_state=42)\n",
        "\n",
        "# length of the datasets\n",
        "print(f\"Length of the train dataset: {len(train_df_pandas)}\")\n",
        "print(f\"Length of the validation dataset: {len(val_df_pandas)}\")\n",
        "print(f\"Length of the test dataset: {len(test_df_pandas)}\")\n",
        "\n",
        "# see if conda is available on torch\n",
        "print(f\"Cuda is available: {(device:=torch.cuda.is_available())}\")\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pEsBNoklXjsn"
      },
      "source": [
        "### Model Development"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vOCx6RduXjsn",
        "outputId": "5ec19eac-c5ca-438b-d319-26df5f8997fb"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model roberta-base-openai-detector already exists, loading...\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model chatgpt-detector-roberta already exists, loading...\n",
            "Model chatgpt-detector-lli-hc3 already exists, loading...\n"
          ]
        }
      ],
      "source": [
        "# setting up three models\n",
        "model_names = [\"roberta-base-openai-detector\", \"chatgpt-detector-roberta\", \"chatgpt-detector-lli-hc3\"]\n",
        "models = dict()\n",
        "model_tokenizers = dict()\n",
        "\n",
        "for model_name in model_names:\n",
        "    models[model_name], model_tokenizers[model_name] = get_model(model_name)\n",
        "    if device: models[model_name].cuda()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZImi37NCXjso"
      },
      "source": [
        "testing the out-of-the-box models on the dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PgGYwJyPXjso",
        "outputId": "34979c4b-899f-4d3c-f8b3-3e6366d3dbbc"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "roberta-base-openai-detector\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 53/53 [00:40<00:00,  1.31it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy: 57.08%\n",
            "Recall: 57.05%\n",
            "Precision: 57.06%\n",
            "F1-score: 57.04%\n",
            "--------------------------------------------------\n",
            "chatgpt-detector-roberta\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 53/53 [00:40<00:00,  1.31it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy: 60.73%\n",
            "Recall: 60.97%\n",
            "Precision: 62.02%\n",
            "F1-score: 59.95%\n",
            "--------------------------------------------------\n",
            "chatgpt-detector-lli-hc3\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 53/53 [00:43<00:00,  1.22it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy: 44.33%\n",
            "Recall: 44.06%\n",
            "Precision: 43.30%\n",
            "F1-score: 42.53%\n",
            "--------------------------------------------------\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        }
      ],
      "source": [
        "# test all three models on the same data\n",
        "model_confusion_matrices = {} # for storing confusion matrices for each model on the test data before finetuning\n",
        "model_scores_before_finetuning = {} # for storing scores for each model on the test data before finetuning\n",
        "all_predictions = [] # for storing predictions for each model on the test data before finetuning\n",
        "for i, model_name in enumerate(model_names):\n",
        "    print(model_name)\n",
        "    predictions = model_predict(models[model_name], model_tokenizers[model_name], test_df_pandas['text'].tolist())\n",
        "    all_predictions.append(predictions)\n",
        "    model_confusion_matrices[model_name], scores = eval_model(predictions, test_df_pandas['labels'].tolist(), return_scores=True)\n",
        "    model_scores_before_finetuning[model_names[i]] = {k: v for k, v in zip([\"accuracy\", \"recall\", \"precision\", \"f1\"], scores)}\n",
        "    print(\"-\"*50)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {},
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype):\n",
            "c:\\Users\\rusen\\anaconda3\\envs\\deep_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
            "  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n"
          ]
        },
        {
          "data": {
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            "text/plain": [
              "<Figure size 1500x500 with 3 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# # Splitting the DataFrame into train and validation and test datasets\n",
        "# train_df, test_df = train_test_split(df, test_size=0.2, random_state=42)\n",
        "# train_df, val_df = train_test_split(train_df, test_size=0.2, random_state=42)\n",
        "\n",
        "test_df_pandas['roberta-base-openai-detector_pred'] = all_predictions[0]\n",
        "test_df_pandas['chatgpt-detector-roberta_pred'] = all_predictions[1]\n",
        "test_df_pandas['chatgpt-detector-lli-hc3_pred'] = all_predictions[2]\n",
        "\n",
        "\n",
        "# Define the sources and models\n",
        "sources = ['LLM', 'PDF', 'YouTube', 'gpt-3.5-turbo']\n",
        "models_name_temp = {\n",
        "    'Roberta-base-openai-detector': 'roberta-base-openai-detector_pred',\n",
        "    'Chatgpt-detector-roberta': 'chatgpt-detector-roberta_pred',\n",
        "    'Chatgpt-detector-lli-hc3': 'chatgpt-detector-lli-hc3_pred'\n",
        "}\n",
        "\n",
        "# Initialize a dictionary to store accuracy scores\n",
        "accuracy_scores = {model: [] for model in models_name_temp}\n",
        "\n",
        "# Calculate accuracy scores using nested loops\n",
        "for model, pred_column in models_name_temp.items():\n",
        "    for source in sources:\n",
        "        accuracy = accuracy_score(test_df_pandas[test_df_pandas['source'] == source]['labels'], \n",
        "                                  test_df_pandas[test_df_pandas['source'] == source][pred_column])\n",
        "        accuracy_scores[model].append(accuracy)\n",
        "\n",
        "\n",
        "# Bar plot of the accuracy scores\n",
        "fig, ax = plt.subplots(1, 3, figsize=(15, 5))\n",
        "fig.suptitle('Accuracy scores of the models', fontsize=18, fontweight='bold', color='black')\n",
        "\n",
        "\n",
        "# Create an array with the position of each bar along the x-axis\n",
        "x = np.arange(len(sources))\n",
        "\n",
        "# Plot each bar plot showing the accuracy scores\n",
        "for i, (model, scores) in enumerate(accuracy_scores.items()):\n",
        "    bars = ax[i].bar(x, scores, width=0.4, label=model)\n",
        "    \n",
        "    # Add numbers on top of the bars\n",
        "    for bar in bars:\n",
        "        yval = bar.get_height()\n",
        "        ax[i].text(bar.get_x() + bar.get_width()/2, yval * 1.05, round(yval, 2), \n",
        "                   ha='center', va='bottom', color='black', fontweight='bold', fontsize=8)\n",
        "    \n",
        "    ax[i].set_title(model, fontweight='bold', fontsize=14, color='black')\n",
        "    ax[i].set_xticks(x)\n",
        "    ax[i].set_xticklabels(sources, fontweight='bold', fontsize=12, color='black', rotation=90)\n",
        "    ax[i].set_ylim(0, 1)\n",
        "    ax[i].set_yticks(np.arange(0, 1.1, 0.25))\n",
        "    ax[i].set_ylabel('Accuracy', fontweight='bold', fontsize=12, color='black')\n",
        "    ax[i].tick_params(axis='x', colors='black')\n",
        "    ax[i].tick_params(axis='y', colors='black')\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0_2FeO8iXjso"
      },
      "source": [
        "### Fine-Tuning"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Iyi0mTTAXjso"
      },
      "source": [
        "tokenizing the data for each model"
      ]
    },
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        "id": "TzMTY_5GXjso",
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            "text/plain": [
              "Map:   0%|          | 0/16812 [00:00<?, ? examples/s]"
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      ],
      "source": [
        "tokenized_datasets = dict()\n",
        "\n",
        "# Convert DataFrame to Hugging Face Dataset objects for preparing the data for training\n",
        "train_df = Dataset.from_pandas(train_df_pandas[[\"text\", \"labels\"]])\n",
        "val_df = Dataset.from_pandas(val_df_pandas[[\"text\", \"labels\"]])\n",
        "test_df = Dataset.from_pandas(test_df_pandas[[\"text\", \"labels\"]])\n",
        "\n",
        "# Tokenization function\n",
        "def tokenize_function(data, tokenizer):\n",
        "    return tokenizer(data[\"text\"], padding=\"max_length\", truncation=True, max_length=64)\n",
        "\n",
        "\n",
        "for model_name in model_names:\n",
        "    tokenized_train = train_df.map(lambda examples: tokenize_function(examples, model_tokenizers[model_name]), batched=True)\n",
        "    tokenized_val = val_df.map(lambda examples: tokenize_function(examples, model_tokenizers[model_name]), batched=True)\n",
        "    tokenized_test = test_df.map(lambda examples: tokenize_function(examples, model_tokenizers[model_name]), batched=True)\n",
        "    tokenized_datasets[model_name] = {\"train\": tokenized_train, \"val\": tokenized_val, \"test\": tokenized_test}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "zJ3qGuhoc2aw"
      },
      "outputs": [],
      "source": [
        "def majority_voting(predictions):\n",
        "  majority_votes = []\n",
        "\n",
        "  for i in range(len(predictions[0])):\n",
        "      votes = [predictions[0][i], predictions[1][i], predictions[2][i]]\n",
        "      vote_count = Counter(votes)\n",
        "      majority_vote = max(vote_count, key=vote_count.get)\n",
        "      majority_votes.append(majority_vote)\n",
        "\n",
        "  return majority_votes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 822
        },
        "id": "y-SpMR5mXjsp",
        "outputId": "c36386c4-49d5-4f28-b716-5b5156ea5df0"
      },
      "outputs": [
        {
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              "version_major": 2,
              "version_minor": 0
            },
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{'eval_loss': 0.2459188848733902, 'eval_runtime': 10.9095, 'eval_samples_per_second': 385.352, 'eval_steps_per_second': 6.05, 'epoch': 1.0}\n",
            "{'loss': 0.4207, 'learning_rate': 3e-05, 'epoch': 1.14}\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Checkpoint destination directory ./results\\checkpoint-500 already exists and is non-empty.Saving will proceed but saved results may be invalid.\n"
          ]
        },
        {
          "data": {
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{'eval_loss': 0.1721942126750946, 'eval_runtime': 11.2247, 'eval_samples_per_second': 374.531, 'eval_steps_per_second': 5.88, 'epoch': 2.0}\n",
            "{'loss': 0.1574, 'learning_rate': 3.269896193771627e-05, 'epoch': 2.28}\n"
          ]
        },
        {
          "data": {
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              "version_major": 2,
              "version_minor": 0
            },
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{'eval_loss': 0.20272040367126465, 'eval_runtime': 11.0638, 'eval_samples_per_second': 379.979, 'eval_steps_per_second': 5.965, 'epoch': 3.0}\n",
            "{'train_runtime': 431.6616, 'train_samples_per_second': 116.842, 'train_steps_per_second': 1.828, 'train_loss': 0.23641968740407657, 'epoch': 3.0}\n",
            "Evaluation of the model: roberta-base-openai-detector\n"
          ]
        },
        {
          "data": {
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              "model_id": "d9c62b10c37c4896b343d0f73a962e66",
              "version_major": 2,
              "version_minor": 0
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy: 93.41%\n",
            "Recall: 93.37%\n",
            "Precision: 93.56%\n",
            "F1-score: 93.40%\n",
            "\n"
          ]
        },
        {
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          "metadata": {},
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        {
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{'eval_loss': 0.3657659590244293, 'eval_runtime': 10.8857, 'eval_samples_per_second': 386.196, 'eval_steps_per_second': 6.063, 'epoch': 1.0}\n",
            "{'loss': 0.6095, 'learning_rate': 3e-05, 'epoch': 1.14}\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Checkpoint destination directory ./results\\checkpoint-500 already exists and is non-empty.Saving will proceed but saved results may be invalid.\n"
          ]
        },
        {
          "data": {
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{'eval_loss': 0.21764086186885834, 'eval_runtime': 10.9084, 'eval_samples_per_second': 385.391, 'eval_steps_per_second': 6.05, 'epoch': 2.0}\n",
            "{'loss': 0.2495, 'learning_rate': 3.269896193771627e-05, 'epoch': 2.28}\n"
          ]
        },
        {
          "data": {
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              "model_id": "f8879dad68dc4841abbcda4e4a3d3364",
              "version_major": 2,
              "version_minor": 0
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{'eval_loss': 0.20537638664245605, 'eval_runtime': 10.9728, 'eval_samples_per_second': 383.128, 'eval_steps_per_second': 6.015, 'epoch': 3.0}\n",
            "{'train_runtime': 433.5931, 'train_samples_per_second': 116.321, 'train_steps_per_second': 1.82, 'train_loss': 0.36572970062758686, 'epoch': 3.0}\n",
            "Evaluation of the model: chatgpt-detector-roberta\n"
          ]
        },
        {
          "data": {
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              "model_id": "0106fd466475492bbede36ba159998ac",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy: 91.85%\n",
            "Recall: 91.80%\n",
            "Precision: 92.06%\n",
            "F1-score: 91.84%\n",
            "\n"
          ]
        },
        {
          "data": {
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              "model_id": "c8059d3b1db345e1bb6c82831c802475",
              "version_major": 2,
              "version_minor": 0
            },
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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              "model_id": "cc71bd68d14f4866b418b9df5a407519",
              "version_major": 2,
              "version_minor": 0
            },
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{'eval_loss': 0.22011025249958038, 'eval_runtime': 11.5149, 'eval_samples_per_second': 365.091, 'eval_steps_per_second': 5.732, 'epoch': 1.0}\n",
            "{'loss': 0.5436, 'learning_rate': 3e-05, 'epoch': 1.14}\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Checkpoint destination directory ./results\\checkpoint-500 already exists and is non-empty.Saving will proceed but saved results may be invalid.\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "7b9a65b8e3ec42d7bec55e3fc4f33ca3",
              "version_major": 2,
              "version_minor": 0
            },
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{'eval_loss': 0.1704646497964859, 'eval_runtime': 11.4703, 'eval_samples_per_second': 366.512, 'eval_steps_per_second': 5.754, 'epoch': 2.0}\n",
            "{'loss': 0.169, 'learning_rate': 3.269896193771627e-05, 'epoch': 2.28}\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "17697f8b86d24b4eb6a5182293bdceac",
              "version_major": 2,
              "version_minor": 0
            },
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{'eval_loss': 0.16633230447769165, 'eval_runtime': 11.5689, 'eval_samples_per_second': 363.387, 'eval_steps_per_second': 5.705, 'epoch': 3.0}\n",
            "{'train_runtime': 436.225, 'train_samples_per_second': 115.619, 'train_steps_per_second': 1.809, 'train_loss': 0.2906906662180699, 'epoch': 3.0}\n",
            "Evaluation of the model: chatgpt-detector-lli-hc3\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a867e4f6d6554e1fafaa785f8eb13467",
              "version_major": 2,
              "version_minor": 0
            },
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy: 93.03%\n",
            "Recall: 93.03%\n",
            "Precision: 93.04%\n",
            "F1-score: 93.03%\n",
            "\n"
          ]
        }
      ],
      "source": [
        "fine_tuned_model_confusion_matrices = {} # for storing confusion matrices for each model on the test data after finetuning\n",
        "model_scores = {} # for storing scores for each model on the test data after finetuning\n",
        "all_predictions = [] # for storing predictions for each model on the test data after finetuning\n",
        "history = {m : [] for m in model_names} # for storing training history for each model\n",
        "\n",
        "# Define training arguments\n",
        "for i in range(len(model_names)):\n",
        "    training_args = TrainingArguments(\n",
        "        output_dir=\"./results\",          # output directory\n",
        "        num_train_epochs=3,              # total number of training epochs\n",
        "        per_device_train_batch_size=64,   # batch size per device during training\n",
        "        per_device_eval_batch_size=64,    # batch size for evaluation\n",
        "        warmup_steps=500,                # number of warmup steps for learning rate scheduler\n",
        "        weight_decay=0.02,               # strength of weight decay\n",
        "        logging_dir='./logs',            # directory for storing logs\n",
        "        logging_steps=300,             # log saving step.\n",
        "        evaluation_strategy=\"epoch\"      # evaluate each `logging_steps`\n",
        "    )\n",
        "\n",
        "    # Initialize Trainer\n",
        "    trainer = Trainer(\n",
        "        model=models[model_names[i]],        # the instantiated Transformers model to be trained\n",
        "        args=training_args,                  # training arguments, defined above\n",
        "        train_dataset=tokenized_datasets[model_names[i]]['train'],         # training dataset\n",
        "        eval_dataset=tokenized_datasets[model_names[i]]['val'],             # evaluation dataset\n",
        "    )\n",
        "\n",
        "    # Train and evaluate\n",
        "    trainer.train()\n",
        "\n",
        "    # save the training history\n",
        "    history[model_names[i]] = trainer.state.log_history\n",
        "\n",
        "    # Evaluate on test set\n",
        "\n",
        "    print(f\"Evaluation of the model: {model_names[i]}\")\n",
        "\n",
        "    predictions = trainer.predict(tokenized_datasets[model_names[i]]['test'])\n",
        "    predictions = np.argmax(predictions.predictions, axis=-1)\n",
        "    all_predictions.append(predictions)\n",
        "\n",
        "    fine_tuned_model_confusion_matrices[model_names[i]], scores = eval_model(predictions, test_df['labels'], return_scores=True)\n",
        "    # scores -> (accuracy, recall, precision, f1)\n",
        "    model_scores[model_names[i]] = {k: v for k, v in zip([\"accuracy\", \"recall\", \"precision\", \"f1\"], scores)}\n",
        "    print()\n",
        "\n",
        "    # Save the model\n",
        "    trainer.save_model(f\"Models/fine_tuned/{model_names[i]}-model\")\n",
        "\n",
        "    # save the tokenizer\n",
        "    model_tokenizers[model_names[i]].save_pretrained(f\"Models/fine_tuned/{model_names[i]}-tokenizer\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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seMutwOf8/vvv2bNnDwBDhw5l+PDhJCYm4vf7Q27FFPDyyy9zzTXX0L9/f6Kiorjkkkt46aWXWL16Na1btwZg69at/PHHHyQlJQU3QOzatYu2bdsG52Hnzp2ZPHkyv/76K4mJicFD2Uv7XG+++SadO3cONl9ff/11yK2jAF566aXg/w8aNKhE3bt37+azzz4LPs7Pz+err74KPu7UqdMJ1Xu8jjfrw5k5c2aJ+bRhwwYSEhJo0aJF8DMHbmkHRYepl3YLu+Lf9czMzJDvek5ODt9++y3btm0L+a4f7rtcfFoOhyM4nWHDhvHll1+yZMkS8vPzg413ly5dgtMono/X62Xo0KHUqlWLOnXqkJube8T3Lev343DLrIhIJNIebBERCXH11Vfz7LPPsn//fiZNmsS+ffs455xz2Lx5M08++SR+vx+A++67r9RGaPHixZx55plceumlrF69mn/961/BYZdddhlQdD5zeno6O3fu5O233wZgyJAhrFq1iscffxy/309KSgqjR48+5rqL7yV76623WL16NaNGjQrZ6/jggw/i8/k4cOAAzz77LF6vF+Cw9w8+ki1bttC/f39uvPFGsrKyePTRR0t8zuLv/eGHH9KmTRvq1KnDm2++yYoVK4LDAu//888/8/rrrwNF57recMMNJCUlsXDhQtauXQsUHfIcOKrg2muvZcyYMfh8PgYNGsSdd95JSkoKTz/9NMuXLwfgoYceKrXpKa5GjRpcccUVvPjii7jdbvr06cNdd91Feno6H3/8MV988QVQdCjwNddcU+o0LrzwQsaMGUNGRgavvvpqsKHt1q0bJ510UrnWe6TPEVCeWRd31VVXcc899wBw/vnnc++995KcnMzLL79c6rn7vXr1om3btvzyyy/88MMPnHPOOYwePZpt27bx4IMPkp+fj9PpDB41AqHf5Zdeeom6dety6623cvXVVzNx4kQsy+Kmm27i999/p23btkyZMiV4j++BAwfy17/+FYDrrruO6dOnA0X3tV61ahUtWrTgo48+4vfffwegR48eJCUllXjfjz/+GMuy6NOnDy1btizz90NEpNqo8MuoiYiIcYpfnTlwC6Dipk6dasXHx4dcBbj4v2uvvfaw0xs9enSpr7niiitCXjNt2rTgLZUO/RcVFRVy1eNjudrzU089VWI6c+bMsdasWRO8Jdah/wLv73Q6S9w/uDTFryJe/Crgxf8NHjzY8nq9lmUVXf07cOXsw703YH399deWZVnW7t27Q64gXdq/F154IVhPYWGhddpppx123H79+ln79+8/6ueyLMvKz8+3Bg8efNhp1a5dO+Se1sWvEl2nTp1S7/OckpJirVix4oTrPdL3tLSrxh9v1idyFXG32x28Yveh/4rXVPw2XcuWLQvekqu0f//+979D3uOWW24pMU7gftVHus933bp1rTVr1oRM60j3wc7IyLC2bt0aHHfKlCklxgncWq8s34+OHTse07wVEYkEarBFRKqhozXYlmVZv//+u3XLLbdYLVq0sGJiYqzk5GRr8ODBpY5ffHqbN2+2XnrpJat169ZWdHS01bJlS+upp56yfD5fidetWLHCuuiii6x69epZ0dHRVr169awRI0ZYCxYsCBnvWBqhAwcOWFdddZWVnJxsxcbGWu3atbMWLlxoWZZlLViwwBo8eLCVkpJiJScnW126dLGef/55a/z48cHpfvjhh0edb8Wbm9mzZ1sfffSR1alTJ8vlclmNGjWy7rvvPqugoCDkNVu3brUuvfRSq169elZcXJzVpk0b64477gj5TNdff31w/NzcXGvChAlWly5drLS0NMvpdFq1a9e2zjnnnGAjXpzb7baeffZZq0uXLlZCQoIVHx9vtW/f3nriiSeOubkO8Pv91ttvv20NGjTISktLs6Kjo63mzZtbd9xxR4lbox3aQK1fv94aPny4lZCQYCUlJVkjR44s0ewdb73H22Bb1vFlfSINtmUVfdfGjh1rNWzY0HK5XFbnzp2t//znP9aPP/5YaoNtWZa1ceNG629/+5vVqFEjKzo62qpVq5Y1ZMgQa/r06SWmv3PnTusvf/lLcP506dLF2rBhQ3D4p59+ag0ZMsSqWbOm5XK5rKZNm1o33nijtXnz5lLr/eCDD6x+/fpZiYmJwe/gnXfeWSJTn89njRkzxqpVq5blcrmsFi1aWJ9//nlweFm+HyIi1YXNso7hcpoiIiIixWzYsIEmTZoA0LFjx+Ah3iIiItWZrjohIiIiIiIiUg7UYIuIiIiIiIiUAzXYIiIiIiIiIuVA52CLiIiIiIiIlAPtwRYREREREREpB85wF1CVLVu2DMuyiIqKCncpIiIiIiIiUgE8Hg82m41OnToddVztwS4Dq+g+4uEu46h8Pl+4S5BDKBMzKRfzKBMzKRczKRfzKBMzKRfzmJ7J8fR92oNdBoE91+3btw9zJYdXUFDA+vXradKkCTExMeEuR1AmplIu5lEmZlIuZlIu5lEmZlIu5qkKmaxYseKYx9UebBEREREREZFyoKuIl0FgS4bJe7BFRERERETkxB1P36c92CIiIiIiIiLlQA12hCssLGTDhg0UFhaGuxQ5SJmYSbmYR5mYSbmYSbmYR5mYSbmYJ9IyUYMd4SzLIj8/v0pc7by6UCZmUi7mUSZmUi5mUi7mUSZmUi7mibRM1GCLiIiIiIiIlAPdpktERERERKqU47kvcXF+vx/LsvD7/fj9/gqoTI5XODOx2WzYbLZynaYabBERERERMZ5lWfh8Ptxu9wkfTux2u7Hb7RQWFkbMIclVXbgzsdlsREdH43A4yqXZVoMd4aKioqhXrx5RUVHhLkUOUiZmUi7mUSZmUi5mUi7mUSblz+124/V6cTqdwWboeBui6Oho7HY7cXFxOByOCqpUjke4MgkcBeHz+SgsLMTpdOJyuco8XTXYEc7hcFCjRo1wlyHFKBMzKRfzKBMzKRczKRfzKJPyFWiEoqOjy7TRwm63KxfDhDsTp9OJ3W7H4/FgWVaZ92LrImcRzuv1smfPHrxeb7hLkYOUiZmUi3mUiZmUi5mUi3mUSfkK7G2028vWvni9Xvbu3atcDGJCJna7/YTP6y8xrXKoRwzm9XrJysrSSsQgysRMysU8ysRMysVMysU8yqRilHXvos/nY8+ePfh8vnKqSMrKhEzK80JnarBFREREREREyoEabBEREREREZFyoIuciYiIiIiIhNE999zD1KlTDzs8LS2NuXPnltv7LViwgEsvvZS3336b7t27lzrOJZdcAsA777xTbu9bHajBjnB2u534+PgyXxDCePnbILZuuKs4JtUmkypGuZhHmZhJuZhJuZhHmZjJbrcTExMTkovP52P27Nls27aNunXr0rdv37Dcwis9PZ3nn3++1GGRfLu30jKpytRgR7jo6GgaNmwY7jIq1vp3YeG10O01aHJRuKs5qmqRSRWkXMyjTMykXMykXMyjTMwUFRVF3bp/7pSZMmUKt956K1u2bAk+l5GRwbPPPsvIkSMrtbbo6GhOPvnkSn1PExyaSVUXGZsJ5LAC9wwsj0vOG8myYPMU8OXDjxfD4lvB7wl3VUcU8ZlUUcrFPMrETMrFTMrFPMrETMVzmTJlCqNGjQpprgG2bt3KqFGjmDJlSpiqPLyXX36Zdu3asXfv3pDn33zzTdq2bcvu3bsBWLRoEVdddRVdu3alXbt2DBw4kOeeew6/31/uNc2dO5cLL7yQU045he7du3PnnXeybdu24HC/38/TTz/NwIEDg7U8+eSTeDxFf7NblsWnn37KsGHD6NChAz169OCuu+4iKyur3GutDGqwI1xhYSFr166lsLAw3KVUDJsN+vwX2o4rerz23/DtwKJDxg0V8ZlUUcrFPMrETMrFTMrFPMrETG63m02bNpGfn8+tt95a6gaQwHO33XZbpd86yuv1lvovUNM555yD1+tlxowZIa/74osv6NOnD6mpqaxevZrLL7+c5ORknn76aV566SW6dOnC888/z1dffVWu9X7yySdceeWV1K1bl6eeeop7772XZcuWcf755web/ddee40PPviAG2+8kTfeeIO//vWvTJw4kZdeegkoOh/87rvvZtCgQbz22mvce++9zJ8/nzvvvLNca60sOkRcqj67Azo+DKld4cdLYOcc+Koz9P0Y0nuHuzoRERERqUD//e9/uf/++9m3b98xje/z+fB6vezateuw41iWxebNm6lTpw4ul+uYppuYmMjDDz/MqFGjjmn8Q23dupW2bduWOmzMmDFcddVV1K9fn65du/L5558zevRoADZt2sTPP//M008/DcDq1avp1asX48ePD57X3Lt3b7777jsWLFjAWWeddUL1Hcrv9zNhwgT69OnDk08+GXy+c+fOnHnmmUycOJExY8awcOFC2rVrx1/+8hcAunXrRmxsLImJiQAsXbqUmJgYrrzyyuBzycnJrFixAsuyyvUe1ZVBDbZEjoxhcPpimD0S9q6EbwfA2WshoXG4KxMRERGRCjJ+/HhWr15dIdM+UhN+uFpOtMFOT08P7tU9VPFzlIcNG8YDDzzAzp07SU9P54svviAhIYGBAwcCMHz4cIYPH05hYSHr169n48aN/Prrr/h8vuBh2eVh/fr17Ny5s8Se5oYNG9KpUycWLlwIQPfu3XnyySe58MILGThwIP379+fiiy8Ojt+lSxeee+45Ro4cydChQzn11FPp06cPp556arnVWpnUYEtkSWoBp8+HBVdDfBM11yIiIiIRbsyYMdx3333lugc7IC0t7bj2YP/9738/pnFLEx0dTfv27Y863tChQ3n44Yf56quvuPTSS/niiy84/fTTiYmJAaCgoICHH36Y//3vf3i9XjIyMujUqRNOp7NcrwmQk5MDFM2jQ6WlpbFq1SoArr76auLj45k8eTITJkxg/PjxtGjRgnHjxtGjRw86duzIQw89xJdffsmkSZN49dVXSUtL47rrrgveKqwqUYMtkccZD73eB4qtQPZvBn8hJDYPW1kiIiIiUv5GjRp1zHuNCwsLyczMpHbt2rRq1YqtW7eW2nTabDYyMjJYv359WG7ZdSSJiYkMHDiQr776ih49evDbb79x3333BYf/85//ZPr06TzzzDP06tWLuLg4AHr27FmudSQnJwOl7+XfuXMnKSkpQNFtuC666CIuuugidu/ezQ8//MDLL7/MzTffHLy3d5cuXRg2bBh+v5/58+fz9ttv88gjj9CxY0c6dOhQrnVXNF3kLMK5XC5atGhxzFveIobNBraDX29fAcz+C0zrAls/D29dVONMDKdczKNMzKRczKRczKNMzBQdHU2DBg2IjY3l2WefBShxjm/g8TPPPGNccx1w7rnnsnz5cj744APq1atHt27dgsOWLFlC9+7dGTx4cLC5XrlyJXv27CnXq4g3adKE9PR0Pv889O/rzZs3s3z5cjp37gzABRdcwCOPPAJAamoqI0eO5KKLLiI3N5e8vDyefvpp7rzzTqKiooiNjWXAgAHcfffdAGRmZpZbvZVFe7AjnM1mw+ms5jF79oHdCZ698MM50O4+aPdA0cXRwkCZmEm5mEeZmEm5mEm5mEeZmKl4LiNHjuTjjz8u9T7YzzzzTKXfB9vtdrN8+fLDDm/VqhWxsbEA9O3bl+TkZD788EOuvvrqkI0EHTp04KuvvuKDDz6gWbNmrF69mpdeegmbzUZ+fv5x1bR9+3befPPNEs+3bNmSXr16cccdd3Dvvfdy5513MmzYMLKzs3n++eepUaMGV1xxBQBdu3bljTfeIC0tjU6dOpGVlcWkSZPo1q0bNWvWpGfPnrz55pvce++9DBs2DI/Hw+uvv05ycjI9evQ4rnpNoKU+wrndbrKysqhduzbR0dHhLic8YtJh0ExYegf89gKsfBh2L4Je74GrZqWXo0zMpFzMo0zMpFzMpFzMo0zM5PF42L17N6mpqURFRTFy5EjOPfdcZs+ezbZt26hbty59+/YNy57rnTt3cv755x92+CeffEKbNm0AcDqdnHXWWbzzzjsMGzYsZLx77rkHj8fDM888g9vtJiMjg+uvv55169bx3XffHdetxzZt2sRjjz1W4vlRo0bRq1cvRo4cSXx8PK+88go33ngjCQkJ9O3blzvuuIP09HQAbr31VqKjo5k8eTIvvPBC8BD3wMXRevXqxbhx45g8eTI33XQTNpuNU045hbfffjt4GHpVYrPK80z3ambFihUAx3QxgnApKChg/fr1NGnSJHjhg2pt/Tuw8Nqiw8bjm0C/KZBycqWWoEzMpFzMo0zMpFzMpFzMo0zKl9/vJz8/n9jY2OCtp05E4BzsevXq6fB9Q5iQydG+X8fT9+kcbKlemlwCQ36EhKawfz0svB60jUlERERERMqBGmypflJOhqGLodFfode7RRdEExERERERKSM12FI9RadA7/chsdmfz/3xNhzYGr6aRERERESkSlODHeGcTie1atXSVSyPJnMazL8cpp0CO2ZV6FspEzMpF/MoEzMpFzMpF/MoEzM5HA5SUlKMvf1WdRRpmajBjnBOp5PU1FSt3I8msQUkt4eCLPh2IKx+psLOzVYmZlIu5lEmZlIuZlIu5lEmZnI6nSQnJysXg0RaJmqwI5zP5yM3N/e4LsdfLSU2K7r4WeOLwPLB0tth3oXg3V/ub6VMzKRczKNMzKRczKRczKNMzOTz+cjLy1MuBom0TNRgRziPx8PWrVvxeDzhLsV8zjjo+Q6c8m+wOWHjf2B6D8j9rVzfRpmYSbmYR5mYSbmYSbmYR5mYyev1snPnTrxeb7hLkYMiLRM12CLF2WzQ6mYY9D3E1IG9K2Hn7HBXJSIiIiIiVUBkHOguUt5q9YEzlsKGD6DZleGuRkREREREqgDtwRY5nNi60OaOPx8X7ob5VxX9V0RERERE5BBqsCOczWYjJiYGm80W7lKqvgXXwB9vFN3Ka8/SE56MMjGTcjGPMjGTcjGTcjGPMjGTzWYjOjpauRgkkEmkUIMd4VwuF02aNMHlcoW7lKqv/T8goRns3wgzesHvk05oMsrETMrFPMrETMrFTMrFPMrETNHR0dSvX9+4hu6SSy7hkksuCXcZR+TxeBg5ciTz5s0Dyrfmt956i+nTpx/z+FOmTKFVq1Zs2bLlsONkZ2fTv39/Nm/eXB4lHjM12CLHKqUDDF0M9c4GfyEsuBIWXge+wnBXJiIiIiJSoV5++WXq1KlDr169ynW6O3bs4K233ir3q4inpKRw+eWXM3bsWCzLKtdpH4mRDfaMGTPo2rUrcXFxNGnShAkTJhzzTPF6vXTr1o3+/fuXGLZ48WL69+9PQkIC9erVY+zYsbjd7nKu3iwFBQWsXr2agoKCcJcSGaKT4dT/QfsHARusewW+ORUOZB7zJJSJmZSLeZSJmZSLmZSLeZSJmQoLC1m/fj2FhdpBcjx27NjBq6++yg033FDu0w70YxVxm64LL7yQtWvX8vXXX5f7tA/HuAZ7/vz5nH322bRu3ZopU6Zw0UUXMWbMGJ544oljev3jjz/OokWLSjz/xx9/MHjwYGJjY/noo4+48847eeqpp7jlllvK+yMYpzK32FQLNju0vx/6fwFRyVCwAxwxxzUJZWIm5WIeZWIm5WIm5WIeZVKJvPsP/88XupHD5jtwhPHzj326h45bCebOncuFF17IKaecQvfu3bnzzjvZtm1bcLjf7+fpp59m4MCBtGvXjoEDB/Lkk0+G3I/9888/Z9iwYXTo0IEePXpw1113kZWVdcT3nTRpEvXq1aNdu3Ylhr3wwgv06tWLTp06ccMNN5Q4JPubb77hwgsvpFOnTrRr146hQ4fy3nvvAbBlyxbOOOMMAO677z4GDhwYfN0PP/zABRdcwMknn0yfPn24//77yc3NDZn2Tz/9xAUXXED79u3p378/r7/+esjw6OhoTj/9dF555ZUjfr7yZNxtuh544AE6derEO++8A8DQoUPxeDw8+uij3HrrrcTGxh72tT/99BOPPvooderUKTHsiSeeIDExkf/9739ER0dz5plnEhcXx0033cTYsWNp2LBhhX0miVD1zoAzlhStYF01i54L/JDqwhkiIiIileejhMMPq3dm0Y6Rgxou7ordf5jmuNapMHjmn4//1xgKd5U+bs0uMLTkjr2K8sknn3D33Xdz9tln87e//Y3s7Gz+/e9/c/755zN16lRSU1N57bXX+OCDD7j77rtp0KABP/30E08//TRRUVHccsstLFmyhDFjxnDDDTfQtWtXtm/fzvjx47nzzjt59913D/ven332GSNHjizx/JIlS9i9ezf3338/Pp+PJ598kksvvZTPPvuMhIQEZs6cyY033sill17KzTffTEFBAe+//z4PPfQQ7dq1o02bNjz99NPcfvvtXHvttcFm+/vvv+f6669n0KBBPPPMM+Tk5PCvf/2LrVu3MnHixOD7/+Mf/+CWW27h1ltv5aOPPmL8+PE0a9aMAQMGBMcZOnQoH374IevXr6dJkyblmEjpjGqwCwsLmTlzJg8++GDI86NGjeJf//oXc+bM4bTTTiv1tW63m0svvZRbbrmF+fPnlxg+ffp0zjrrrJALGowaNYobbriB6dOnc80115Tvh5HqIaFp6ON1r0LWt9B9IkQlhqcmEREREYkofr+fCRMm0KdPH5588sng8507d+bMM89k4sSJjBkzhoULF9KuXTv+8pe/ANCtWzdiY2NJTCz6u3TJkiXExMRw7bXXBvui5ORkVqxYgWVZpV5d/ffff2fnzp106NChxDCHw8Ebb7wR3MHZtGlThg8fzieffMLFF1/MunXrGDFiBP/3f/8XfE2nTp3o3r07CxYsoGPHjrRu3RqABg0acNJJJwHw3HPP0aZNG55//vlgTdHR0Tz77LPs2vXnBo877riDv/71rwCcfPLJfP3118yfPz+kwW7fvj0AP/74Y/VrsP/44w/cbjctW7YMeb558+YArFmz5rAN9kMPPYTH4+HBBx/k9NNPDxmWn5/Pxo0bS0w3PT2dpKQk1qxZU6a6Dz23xm63Ex0djd/vL/Uc75iYosOJCwsLSxw6FBUVhcPhwOv1ljgPITBdy7JKPW/E5XJhs9lwu934/f7ge/j9/uC0fD5fyCEiUHRp/MAVLks7Tyg6Ohq73Y7H48Hn84UMczgcREVFHfWzVtR0K2MeBjidTpxO5+HnIXmw7C7w5uHPXoGnx4dYiS1DPqvb7Q5mEnj/o063grMJxzys9GyOcR4WzwUqfh6amI1J64hAjV6vt8Rwk7/fZZmHJmZz6Dws/ri6rSMqez17PNMN/LYEaoy0eWhaNscyDyvi997EbCprPXvo9/vQeu0jsomKisKyrJL12hxEH2wevV4vWzsvID09PdhgOhyO4Dz0+vxQrC7bGWuD45Wo12Ynyu8P+Tvi0M8amIeHZhP4rECJv92hKNfAPPT5fPzxxx/s3LmTW265BY/HE5xu7dq16dixIwsWLKCwsJAuXbrwzDPPcOGFF3LqqafSt29fRo8eHZz2KaecQn5+PmeddRannXYaffr0oWvXrvTo0QOv11vqPPzjjz8AqF+/PkDws/r9fk4++WRSUlLwer04nU5atmxJRkYGCxYsYPTo0VxyySXYbDb279/P+vXr+f333/nll18AOHDgQHA5gaI+pbCwkIKCAlatWsUNN9wQrMnv9zNo0CAGDRoU/A4AdOnSJfjZ7HY7qamp5OTkUFhYSFRUFHa7nZiYGBITE9m4cWNw/h6aTeB7brPZgq8JTNeyrMNufCiNUQ323r17AUhKSgp5PrDF5dBj7gMWLVrEhAkTmDVrVqm3QjjcdAPTPtx0j4XP52P9+vUhzyUlJVG/fn28Xm+JYQBt2rQBYNu2beTnhx6eUq9ePWrUqEFubm6JcyHi4+Np2LAhfr+/1Om2aNECp9NJVlYWeXl5wJ/n/hQUFJCQkMD+/fvZunVryOtiYmKCW3M2bNhQYiXZtGlTXC4Xu3btIicnJ2RYamoqtWrVIj8/n02bNoUMczqdtGjRAoDNmzeXWME2bNiQ+Ph4srOz2b17d8iw5ORk6tati8fjKfFZbTZbcEtXZmZmiR+M+vXrk5SUxN69e9mxY0fIsISEBBo0aFBqbgAtW7bE4XCwfft29u/fHzKsdu3a1KxZk7y8PDIzQy9qFhsbS+PGjWHAdDwzRxC1bzXOb3uxrdFj7EseTLNmzYiOjmbnzp3B7+PWrVux2WykpaWRnp5Ofn5+iXNWoqKighuYNm3aVOLHpFGjRsTFxbFnzx727NkTMiwlJYU6dergdrtLfFa73U6rVq2CdRz6g5GRkUFiYiI5OTns3LkzZFhiYiIZGRmH/X63atUKm83Gtm3bOHDgQMiwunXrkpycTF5eXsj5QgBxcXE0atQIy7JKnW7z5s2Jiopix44d7Nu3L2RYeno6aWlpHDhwoMTtGlwuF02bFh1psHHjxhI/fk2aNCEmJia4zARyAahZsya1a9emoKCAjRs3hrzO4XAEN9pt3ry5xA9ngwYNSEhIIDs7O2RLK5i1jgioVasWqampRq0jGjRoQNOmTcnJySnx/a6y6wgodbrF1xGH/iaZto6wLIs6deoQHR3Nnj17qs06Yvfu3WRnZ4cMM2kdEVgu3W438fHx1WIdYfrfEYf+rlSXdQRUzN8RlmWRmppKQkIC+fn5JZabmJgY6tZNxvL7ydyx89DJkpERR1RUFPv27cNyxLEz+wA2W9Hyk5ycTEpKPAWF+9mxYzfw57IeFRVFRkYKANu27Czx/a5XLxaXy8XevXtLrHuSkpJITU2lsLCQ7du3l5iHjRo1AiArKys4DwPfjdq1axMXF8e+ffvIycnh999/B4q+v9nZ2dSqVQufz0dmZibx8fFs27aNzMxMhgwZQlJSEpMnT+bpp5/mqaeeolGjRtxwww107NiRli1b8uqrr/Laa6/x9ttvM3HiRFJSUrjgggs4//zzqVu3Ln6/P+Q7GvheBTY07N69m/z8fAoLC0lLSyMzM5OUlBSSk5PJz88nISGBnTt3kpmZyd69e3n++eeZO3cuNpst5Dzu3NxcMjMzg393ud1uMjMz2bVrF5ZlYbfb2bdvHzVr1iwxDwPr48Dpw1lZWfh8Pvx+f3AZq1OnDrGxsezbtw+Xy0VWVlbwcyUkJJCeno7X6yUzMzO4UcF/cIPJoeuIQKN/LIxqsA/9UTuU3V7ymmwFBQVcdtll3HbbbXTr1q3cpnusHA5HiUMNAtNzOp1HPAyhbt26pW7xg6IFMi4urtTp2u32UqfrcDiAogUyPT09ZJjTWRR1fHx8idcW3xoTWLmXVlNaWhopKSmlvmdsbOwRP2uDBg1KPBdYSFNSUkps/AhMNyoq6ojTrVev3mHnYY0aNYiPjw8ZFpiHpeVWfHidOnVK3UIMRQvkYedhei98py3AsfASHLvmkLH+Frwt78Juf7RocHo6qamppU63tHlYPJvSrhMQmIc1a9akRo0aIcMC8zA6OvqI87B+/fqHnYfJyckkJISe03S073eg5sAKurgjzcPAdG02W6nTDby2Vq1apKWllToscOeB0uoBgj9kxQXm4ZG+38X/eCxNgwYNDjsPU1JSghsJA7SOKOlw6wi73X7E73eVW0fAEb/fVWkdYbfbq9U6IjU1leTk5JBhWkcU0d8RJYdrHVH+f0cUPyIzNjaWevXqlTrdQBN3qMB8Sk1NPeLv/aGvLT4P69atW2K6xb8vh1uWXS5XqTUF1K5dO7iTMDBeoN7ExETi4uKCG4IsywrW73A4qFevHnl5eaSlpQVfe9FFF3HRRRexbds2Zs+ezWuvvcYjjzzCzJkziY2NpW/fvvTs2ZO8vDwWLlzIe++9x0svvUS/fv2oW7cudrs9pN5AToENy6mpqfj9flwuF263m3r16oUsy7m5uTRt2pR69erx4IMPsn79et588006deqEZVnk5+fz1VdfkZiYSL169YIbsuLi4qhXrx7JycnYbDYsywrOU5fLRWpqKgsXLqRDhw4lMqxdu3ZwvgWmE8gmMTGRvLw86tevH/xcxb+H9erVw+/3U1BQgMvlCukNA+uItWvXHja/QxnVYAcW7EO3/gS21B264AOMGzcOv9/PfffdF1zoih+65nA4givdQ6cbmHZp0z0egUMIDlX88ILSlLa3PSBwuE9pbDbbEadb/Dxzj8fDrl27gn9sOByO4AJQmiNNNyoq6rBbbo72WStqupUxDw911HmY3BgGfwfL7oY1T+NcOwFyf4b+XxIdHR2SSfHPHa5swjEPw5bNEaYLRVtDD80FKm4empiNSesIj8dDVlYWaWlpRq9nD1WWeWhiNofOQ4/Hw44dO4LLSnVZR4RjPXs80y3+2xI43DWS5qFp2RzLPKyI33sTs6ms9azf7w8esXG06R6pJij6+z85ObnEZz7aPDzSdI+0d9Nutx/xtYENyqW9R+CztmrVivT0dKZPn855550XnO6OHTv4+eefufTSS3G5XFxwwQW0a9eOcePGUbduXc477zzy8/N59NFHcbvdvPDCCyxcuJCPP/6Y5ORkhgwZQsOGDTn33HODje6h87D4nvbin9Vut7Ns2TLcbnewEV65ciWZmZnccMMNuFwuli1bxvnnn0/37t2D05s5c2bIfCm+gcblcuFyuWjTpg2zZs3i5ptvDo67YMECbrrpJj777LMS+QfqtdlsOByOkPoPHDhAQUEBDRs2LDF/AzUEDnmPiYkJabCLT/dYGdVgN2vWDIfDwbp160KeDzwOHBJV3Mcff8zGjRtLbB2DovAnTZrE5ZdfTv369UtMN3AYWWnTjRQ+n4+cnBxSUlKO+bAGKQf2KDjlKUjtBguugtqngr1oha1MzKRczKNMzKRczKRczKNMzBQ4hLi0U0fDbfv27bz55pslnm/ZsiW9evXijjvu4N577+XOO+9k2LBhZGdn8/zzz1OjRg2uuOIKALp27cobb7xBWloanTp1Iisri0mTJtGtWzdq1qxJjx49mDRpEvfccw/Dhg3D4/Hw+uuvk5ycTI8ePUqtK7A3esmSJSWuh+X3+7n22mu57rrryM7O5sknn6Rly5YMGzYMgA4dOvDZZ5/Rtm1b6tSpw9KlS3n11Vex2WzBDSaBI0Xmz59Py5Yt6dixI7fccgvXX389d9xxB8OHD2fXrl089dRTDB48mJYtW7Jy5cpjnq9LliwBoE+fPsf8mrIwqsGOiYmhX79+TJkyhbvuuiu4pWDy5MnUqFGj1EPAP/vssxLnffztb38D4JVXXgke0jBkyBA+//xznnrqqeCWiMmTJ+NwOELutyZSrhpfAGndIb7YYYeeEz/nX0REREQi06ZNm3jsscdKPD9q1Ch69erFyJEjiY+P55VXXuHGG28kISGBvn37cscddwRP67j11luJjo5m8uTJvPDCCyQmJjJw4EDuvPNOAE499VQmTJjAG2+8wU033YTNZuOUU07h7bffLnEaTHGnn346s2bN4p577gl5fvDgwdSrV4+///3veL1eBgwYwP/93/8F+63HH3+chx9+mIcffhgoOo3kwQcf5NNPP2Xx4sVA0WkTI0eO5KuvvmL27NnMnTuXAQMG8PLLL/P8889z4403UrNmTc4555zgHu3jMWvWLDp06BC8SFtFs1mHnjQRZt999x2DBw/mL3/5C1deeSXz5s3jn//8J48//jhjxowhNzeXVatW0axZsxLnBwX0798f+PPwA4DVq1fTqVMnevbsyR133MHatWsZO3YsV155JS+++OIJ1bpixQrgz0u/m6igoCB4z7ejHR4rlcC7H/+0HuyNak1sn9eIiU8Od0VykJYV8ygTMykXMykX8yiT8hU4RDw2NrZM108qLCwkMzOTevXqHfVQcvlTVlYWgwcP5o033qBr167lOu2KzOTAgQP07duXJ554gsGDBx92vKN9v46n7zvxb2cFGThwIJMnT2bNmjUMHz6c9957j/HjxzNmzBgAli5dSs+ePfniiy+OMqVQrVu3ZsaMGRw4cIBRo0bx1FNPcfvtt/Pss89WxMcQKd32b7Hl/kLK7o+J/mEQ7N909NeIiIiIiIRR7dq1ufzyy3nttdfCXcpx+c9//kOLFi2Ct/eqDMbtwa5KqsIebI/HQ3Z2ts7/MYh385fY51+M3ZMNrjTo/R+oU3kLvZROy4p5lImZlIuZlIt5lEn5Kq892B6Ph3379pGYmKhcjpPb7Wb06NH8/e9/L9fzmSsqkz179jB8+HDeeeedUu8SUVx57sFWg10GVaHBFkPlbYDZf4HspWCzQ8dHoc0YOI4rFIqIiIhUF+XVYIuUJqIPEZfy5ff72b9//1HvBS6Vx+/3s9+Wjn/QLGh6BVh+WH4PrHgg3KVVa1pWzKNMzKRczKRczKNMzBRopJSLOSItEzXYEc7tdrNp0ybcbne4S5GDgpn4HdB9InR9GeIyoPm14S6tWtOyYh5lYiblYiblYh5lUjHKevCtx+Nh+/bteDyecqpIysqETMrzoG412CLhZLNBi7/B2WuLmuyAnF/CV5OIiIiIYWw2GzabLWL2copZ/H5/8DtWVkbdB1uk2nLG/vn/m6cUnZ/d5u9F52bbtZiKiIhI9Waz2XA4HLjdbvx+Pw6H44QaIr/fj2VZ+P1+NeuGCFcmlmVhWRY+nw+v14vT6VSDLRKRsn8u+u+v42HPEuj9AcTUCm9NIiIiImEWHR0dbLK9Xu8JTcPtduN2uykoKFCDbYhwZ2Kz2XC5XDid5dMaq8GuBsrryyLl54iZdPgHJLeF+VdA1ncw7RToMxnSulVafdWVlhXzKBMzKRczKRfzKJPyZbPZcDqdOJ3O4N7HE5mG3W7H5XIRExNTAVXK8QpnJuV1WHjINHWbrhOn23RJhdq7CmaNgH1rwR4NXZ6DZtfoVl4iIiIiIpVIt+kSiQQ1ToKhiyBjBPjdsPBvsHtBuKsSEREREZHDUIMd4QoKCvjtt98oKCgIdyly0HFlEpUEfSdDx8eg9R2Q1qPiC6ymtKyYR5mYSbmYSbmYR5mYSbmYJ9Iy0Ykh1cCJXgRCKs5xZWKzQdt7Qp/L3w57V0KdweVbWDWnZcU8ysRMysVMysU8ysRMysU8kZSJ9mCLVDV+D8w5D74bAr88CpaugCkiIiIiYgI12CJVjeWHpFaABT/9H8weCe694a5KRERERKTaU4MtUtU4XND9Nej2atHVxbf8D6Z3g5xfwl2ZiIiIiEi1ptt0lUFVuE2X3+8nPz+f2NhY7HZtTzFBuWayayHMGQUHNoMzHrq/AY3OK59CqxktK+ZRJmZSLmZSLuZRJmZSLuapCpnoNl0SZLfbiY+PN/bLWh2VayZp3WDoEqg9ELz7YcU/wOcu+3SrIS0r5lEmZlIuZlIu5lEmZlIu5om0TCLjU8hheTweduzYgcfjCXcpclC5ZxKTDgOmQ9tx0HcKOKLLZ7rVjJYV8ygTMykXMykX8ygTMykX80RaJmqwI5zP52P37t34fL5wlyIHVUgmdid0fBhqtP7zuXWvwa755fceEU7LinmUiZmUi5mUi3mUiZmUi3kiLRM12CKRaOdcWHQ9fNMPfnsJdKkFEREREZEKpwZbJBIld4CM4UX3zF50A8y/Arz54a5KRERERCSiqcEWiURRidDnv3Dyv8Bmh/Vvwde9IW99uCsTEREREYlYarAjnMPhIDk5GYfDEe5S5KBKy8Rmg5P+DgO+BlcaZC+DaadA5rSKfd8qSsuKeZSJmZSLmZSLeZSJmZSLeSItE90Huwyqwn2wRQDYv7noftm7F8Ipz0KrW8JdkYiIiIhIlaD7YEuQ3++nsLAQv98f7lLkoLBkEt8ABs+C7hOh5c2V975ViJYV8ygTMykXMykX8ygTMykX80RaJmqwI5zb7eaPP/7A7XaHuxQ5KGyZOFzQ7MqiQ8cB3Hth5tmQs7Jy6zCUlhXzKBMzKRczKRfzKBMzKRfzRFomarBFqquf7oXML2B6d9jwn3BXIyIiIiJS5anBFqmu2j8EdU4D3wGY91dYcnvRbb1EREREROSEqMEWqa5i0qD/V9B2bNHjNc/At4Mgf3tYyxIRERERqarUYFcDtsA5t2IMYzKxO6DjP6HfJxCVBDtnw7TOsGdZuCsLC2NykSBlYiblYiblYh5lYiblYp5IykS36SoD3aZLIkruWpg9Ajy5MHQJxNQKd0UiIiIiImF3PH2fs6KLEZEqIqklDFkABzaHNtd+D9ijwleXiIiIiEgVoUPEI1xhYSHr16+nsLAw3KXIQUZnEpUANdr8+Xj9OzCtK+T9Eb6aKonRuVRTysRMysVMysU8ysRMysU8kZaJGuwIZ1kWBQUF6EwAc1SZTHxu+Pl+yPkJvjoFtn4Z7ooqVJXJpRpRJmZSLmZSLuZRJmZSLuaJtEzUYItI6RzRcNpsSO0Bnhz44WxY8SBY/nBXJiIiIiJiJDXYInJ4cRkweCa0uB6wYMU/4Idh4M4Oc2EiIiIiIuZRgy0iR+ZwQdcXoceb4IiBzC9gWjfw7g93ZSIiIiIiRlGDHeGioqKoX78+UVG6CrQpqmwmTS+D0+ZBfGNodD4448NdUbmqsrlEMGViJuViJuViHmViJuVinkjLRPfBLgPdB1uqJXc2OJPA7ih6XLADopKLztkWEREREYkwx9P3aQ92hPN6vezevRuv1xvuUuSgKp9JdMqfzbWvAGaeBd8OgAOZ4a2rjKp8LhFImZhJuZhJuZhHmZhJuZgn0jJRgx3hvF4vO3bsiJgvbCSIqEz2/gr7foNd82DaKbBjdrgrOmERlUuEUCZmUi5mUi7mUSZmUi7mibRM1GCLyImr2QlOXwQ12kHBdvh2IKx+FnTmiYiIiIhUQ2qwRaRsklrA6fOh0V/B8sLS22DexbrKuIiIiIhUO2qwRaTsnPHQ6z3o/AzYnLDxfVj4t3BXJSIiIiJSqdRgRzi73U5CQgJ2u6I2RcRmYrNB61th0HdQ4yTo8HC4KzouEZtLFaZMzKRczKRczKNMzKRczBNpmeg2XWWg23SJHIblB1uxlWTWTEjv++fVx0VEREREqgjdpkuCLMvC6/Wi7SjmqBaZFG+uM78quvjZD2dD4Z7w1XQU1SKXKkaZmEm5mEm5mEeZmEm5mCfSMlGDHeEKCwv57bffKCwsDHcpclC1y8STC44Y2DYNpnWBPcvCXVGpql0uVYAyMZNyMZNyMY8yMZNyMU+kZaIGW0QqVqPzYciPkNAU9q+Hr3vBH2+HuyoRERERkXKnBltEKl5KRxi6GOqdCb4CmH8ZLLoRfO5wVyYiIiIiUm7UYItI5YhOgVM/g/b/AGzw24uQ+UW4qxIRERERKTfOcBcgItWIzQ7tH4CaXWHH99BgRLgrEhEREREpN7pNVxlUhdt0WZaF3+/Hbrdjs9nCXY6gTEpVuBs2fQzNry26n3YYKBfzKBMzKRczKRfzKBMzKRfzVIVMdJsuCbLZbDgcDmO/rNWRMjmE3wdz/wqLriv6rycvLGUoF/MoEzMpFzMpF/MoEzMpF/NEWiZqsCOc2+1m06ZNuN26mJQplMkhbHaoPwxsTtj0IczoAblrK70M5WIeZWIm5WIm5WIeZWIm5WKeSMtEDXaE8/v97N+/H7/fH+5S5CBlcgibDVrdBINnQmxd2PsLTO8KW/5XqWUoF/MoEzMpFzMpF/MoEzMpF/NEWiZqsEXEDOm9YegSSO8DnlyYNRx++r+iQ8hFRERERKoAYxvsGTNm0LVrV+Li4mjSpAkTJkzgSNdjKygoYOzYsTRq1Ii4uDh69uzJ9OnTS4wTFRWFzWYL+ZeQkFDRH0dEjkVsXRj0HbS6tejxxv+ANzznZIuIiIiIHC8jb9M1f/58zj77bM4//3wefvhh5syZw5gxY/B6vdxzzz2lvubqq6/ms88+47HHHqNly5a89dZbnHXWWXz//ff07dsXgJUrV+L1enn33Xdp1qxZ8LUOh6NSPpeIHAN7FJzyDKR2gxptIbpGuCsSERERETkmRt6m6/TTTycnJ4cFCxYEn7v77rt56aWXyMrKIjY2NmT8DRs20KRJE55//nluvPFGoOhY/ubNm9O9e3c++OADAF5//XWuv/568vLycLlcZa6zKtymy+v1kpubS1JSEk6nkdtTqh1lcoLWvQY2BzS7skImr1zMo0zMpFzMpFzMo0zMpFzMUxUyqdK36SosLGTmzJmMGDEi5PlRo0axb98+5syZU+I1devWZdGiRVx88cXB5+x2O06nk4KCguBzy5cvp3Xr1uXSXFcVTqeTmjVrGvtlrY6UyQnI+QUW3wgLroKFfwNfYbm/hXIxjzIxk3Ixk3IxjzIxk3IxT6RlYtyn+OOPP3C73bRs2TLk+ebNmwOwZs0aTjvttJBhLpeLLl26AEV7rrdu3cqTTz7J77//znPPPRccb/ny5TidToYMGcLcuXNxuVyMHj2aCRMmkJiYeMI1F2/ioai5j46Oxu/3l3q5+ZiYGKBoY8KhBxBERUXhcDjwer14vd5Sp2tZFoWFJRsMl8uFzWbD7XYHr8Ln8/k4cOAASUlJuFwufD4fHo8n5HU2my240eHQzwIQHR2N3W7H4/Hg84VecMrhcBAVFXXUz1pR062MeRjgdDpxOp1lmodutxuPx8OBAweIi4vD4XCUy3Sr2jw87um6muBqex+2FQ/Aulfx716Ku/v7ENcgZLplmYcFBQXs27cvmEtlzEMTszFpHeFwODhw4ECpG0VN/n6XdR1hWjaHzkOfz4fb7SYpKQnLssxYRxzjdE2Zh1D+64jA732NGjWIjo6OuHloWjbHMg8r4vfexGyqxN8Rxaabn59PXl5eyO99VZ2HJmZzIvOwPH7vKzoby7KO+T7dxjXYe/fuBSApKSnk+UADnJube8TXP/HEE4wdOxaAa665hsGDBwNgWRY///wzlmVx9dVXM27cOBYtWsSDDz7IqlWr+OGHH7Dbj3+Hvs/nY/369SHPJSUlUb9+fbxeb4lhAG3atAFg27Zt5OfnhwyrV68eNWrUIDc3l6ysrJBh8fHxNGzYEL/fX+p0W7RogdPpJCsri7y8ogtDBb40devWpU6dOuzfv5+tW7eGvC4mJoYmTZoARYfbH7ogNm3aFJfLxa5du8jJyQkZlpqaSq1atcjPz2fTpk0hw5xOJy1atABg8+bNJRbihg0bEh8fT3Z2Nrt37w4ZlpycTN26dfF4PCU+q81mo3Xr1gBkZmaWWKDq169PUlISe/fuZceOHSHDEhISaNCgQam5AbRs2RKHw8H27dvZv39/yLDatWtTs2ZN8vLyyMzMDBkWGxtL48aNAUqdbrNmzYiOjmbnzp3k5OTgdruDC31aWhrp6enk5+ezefPmkNdFRUUFNy5t2rSpxMohcFG/PXv2sGfPnpBhKSkp1KlTB7fbXaImu91Oq1atANi6dWuJlWhGRgaJiYnk5OSwc+fOkGGJiYlkZGQc9vvdqlUrbDYb27Zt48CBAyHD6tatS3JyMnl5eWzbti1kWFxcHI0aNcKyrFKn27z1PUSldsU/5wLs2YtxftODrY0ncCCxB+np6aSlpXHgwAG2bNkS8jqXy0XTpk0B2LhxY4kfhCZNmhATE8POnTvZtWtXMBeAmjVrUrt2bQoKCti4cWPI6xwOR3BD4ObNm0v8mDRo0ICEhASys7PZtWtXyDCT1hEBtWrVIjU11ah1RJ06ddi+fTtJSUkl1v2RvI449LOato7w+/1YlkVsbCx5eXnmrCOaNycqKoodO3awb9++kGHlsY7YvXs32dnZIcNMWkcU/yMxPT29WqwjTP87Yt++fSG/99VlHQGG/h1xcB2RlZVFdnZ2yO99dVhHBJj4d0T9+vXJzMwkPj6+xG+rKesIr9dLVFRUiXlSGuPOwZ43bx69e/fm66+/DjbHQPBDPfbYY4e90BkUXchsz549zJkzh4ceeojRo0fzzjvv4Pf7mTVrFunp6bRt2zY4/nvvvcfFF1/Ml19+yRlnnHFctQaOxQ8EG2DSVqXCwkK2bNlCo0aNSEhI0JZnA/ZO5efns2XLFjIyMnC5XFV2q2nY9k5lr8E573zse3/Cwo63/T+xtbmrzPNw3759bNq0KZhLZcxDE7MxaR3h9/vZuHEjDRo0KHHYmMnf70jfO1VYWMjWrVtp2rRpcHkuLuzriCowD6H81xGB3/vGjRsTHx8fcfPQtGyOZR5WxO+9idlUtb8jSvu9r6rz0MRsTmQeBn7vMzIySjSxpqwj1q5di81mO6ZzsI3bg12jRtEVgw/d+hzYWhcYfjjt2rUDoF+/fni9Xh544AH++c9/0rBhQ/r3719i/LPOOguAn3766bgb7IBAAIey2+2HHQYc8VzwwJe0NDab7YjTjY6OLlFHYFoOh+OIV00/0nSjoqIOu+XmaJ+1oqZbWfOwuLLMw8DK0G6343K5QsYNVzbhmIdlmm5KKzj9R1h0Pbb1bxFlB8rp+11aLlBx89DEbExaRwR+LJ1Op/Hr2eLKuo6oiOmW9zoicJickeuIKjIPA8pzHWG320sc8lqaqjgPTcvmWOZhRfzem5hNVfs74ki/91VtHpqYzYnMw8DvfVRU1An/3ld0Nsd6eDgYeJGzZs2a4XA4WLduXcjzgceBQx6K27hxIxMnTiyx5aJz585A0a79zMxMXnvttRKHFgQOm0hPTy+3zyAiFcwZCz0mwamfw0l3//m8WQfkiIiIiEg1Y1yDHRMTQ79+/ZgyZUrIIQ2TJ0+mRo0adOvWrcRrNm7cyNVXX83UqVNDnp8xYwbR0dG0atUKr9fLtddeyyuvvBIyzocffojD4QjeKzvS2Gw2YmNjj2uri1QsZVJObDaof1bRfwG8++HrPrB5yglOTrmYRpmYSbmYSbmYR5mYSbmYJ9IyMe4QcYBx48YxePBgzjvvPK688krmzZvH+PHjefzxx4mLiyM3N5dVq1bRrFkz0tPT6dOnD4MHD+bmm28mNzeXZs2a8fnnn/PCCy/w4IMPkpKSQkpKCldccQXjx48nNjaWnj17MmfOHB599FFuuummElctjxQulyt40QwxgzKpIGuehV3zYPa8or3aHR4B+7Gv4pSLeZSJmZSLmZSLeZSJmZSLeSItE+MuchYwdepUHnjgAdasWUP9+vW58cYbufPOOwGYOXMmAwYMYNKkSVx++eVA0TnbDz74IJMnTyYzM5MWLVpw++23c9VVVwWnWVhYyPjx43nnnXeCJ9Jfc801/P3vfz+hK4gfzw3HRaSC+T2w/B5Y/VTR49qDoPcHEKPTP0RERETkxB1P32dsg10VVIUGu6CggPXr1wdvISDhp0wq2MYPYcFVRYeMxzWAvpMhtetRX6ZczKNMzKRczKRczKNMzKRczFMVMjmevs+4c7BFRMqk0fkwZAEktoADm4vOy97yabirEhEREZFqQA22iESe5LZw+iLIOBeik6HmKeGuSERERESqASMvciYiUmbRNaDvFNi/CeLq//m8e2/RMBERERGRcqY92CISuWx2SGj85+NNH8NnzWH7N2ErSUREREQily5yVgZV4SJnfr8fr9eL0+k8oSulS/lTJmFiWfDtANjxQ1Hj3eGfRbfzOnjPReViHmViJuViJuViHmViJuVinqqQiS5yJkF2u53o6Ghjv6zVkTIJE5sN+n8FTa8Ayw8/3Quz/wKeXEC5mEiZmEm5mEm5mEeZmEm5mCfSMomMTyGH5Xa72bp1K263O9ylyEHKJIycsdB9InR7BezRsGUqTO8Ge39VLgZSJmZSLmZSLuZRJmZSLuaJtEzUYEc4v99Pbm4ufr8/3KXIQcokzGw2aH4tDJ4NcRmQuwamd8O/f7NyMYyWFTMpFzMpF/MoEzMpF/NEWiZqsEWkekrrBkOXQO0B0OwaiK1/9NeIiIiIiByBbtMlItVXTC0YMAOwwO0req5gOxBXNExERERE5DhoD7aIVG92J9ijiv7f8hC94CKYdgrsWhjeukRERESkylGDHeGcTidpaWk4nTpYwRTKxExOp5NaiRY29x44sAW+6QvrXi26vZeEhZYVMykXMykX8ygTMykX80RaJroPdhlUhftgi8hx8uTC/Ctg85Six02vgC4vFF2BXERERESqHd0HW4J8Ph95eXn4fL5wlyIHKRMzBXOxx0Ofj+Hkx8Fmhz8mwdd9IG9DuEusdrSsmEm5mEm5mEeZmEm5mCfSMlGDHeE8Hg+bN2/G4/GEuxQ5SJmYKSQXmw1OuhsGTAdXKmQvhR8vDXeJ1Y6WFTMpFzMpF/MoEzMpF/NEWiZqsEVEDqfOYBi6FGoPhO6vhbsaERERETGcGmwRkSOJbwiDvoWkVn8+t+V/4N4bvppERERExEhqsEVEjsf272D2SJjeFXJWhrsaERERETGIGuwIZ7PZiIqKwmazhbsUOUiZmOmYc4lKgtgM2PcbTO8OGz+snAKrIS0rZlIuZlIu5lEmZlIu5om0THSbrjLQbbpEqqmCXTDvr7D9m6LHrW6HTk+APSq8dYmIiIhIudNtukREKlJMGvSfBifdU/R4zdPw3WDIzwpvXSIiIiISVmqwI1xBQQFr166loKAg3KXIQcrETMedi90BJz8GfaeAMxF2zIKt/6vYIqsZLStmUi5mUi7mUSZmUi7mibRMnOEuQCpepNy0PZIoEzOdUC4NRkBSG9jwLjS7pvyLqua0rJhJuZhJuZhHmZhJuZgnkjLRHmwRkbKq0Ro6PgKBi3O498Lye8CbH966RERERKRSqcEWESlvC66EVU/A170gb324qxERERGRSqIGW0SkvLW8CVzpkL0cpp0CmdPCXZGIiIiIVALdpqsMqsJtuvx+PwUFBcTExGC3a3uKCZSJmco9l/2bYc4o2L0QsEH7B6Hd/4FNmR8rLStmUi5mUi7mUSZmUi7mqQqZ6DZdEmS324mLizP2y1odKRMzlXsu8Q1g8Cxo/jfAghX3ww/ngjunfKZfDWhZMZNyMZNyMY8yMZNyMU+kZRIZn0IOy+PxkJWVhcfjCXcpcpAyMVOF5OJwQbeXofsbYHdBznLwu8tv+hFOy4qZlIuZlIt5lImZlIt5Ii0TNdgRzufzsWfPnoi69H1Vp0zMVKG5NLsChsyDvlMhplb5Tz9CaVkxk3Ixk3IxjzIxk3IxT6RlogZbRKQy1OwMqV3+fPzHW7D4FvBHxtZaEREREQFnuAsQEal28rNg0fXgy4fsZdDnI4itG+6qRERERKSMtAdbRKSyxdaG3h9AVBLsnANfdYadc8NdlYiIiIiUkRrsCOdwOEhJScHhcIS7FDlImZip0nPJOBdOXwQ12kLBdvimP6x5DnTnxCAtK2ZSLmZSLuZRJmZSLuaJtEx0H+wyqAr3wRYRw3nyYMHVsOnDoseNL4Geb4HNFt66RERERATQfbClmMCN2/1+f7hLkYOUiZnClktUQtHh4p2fBpsDEhqruT5Iy4qZlIuZlIt5lImZlIt5Ii0TNdgRzu12s379etxu3XvXFMrETGHNxWaD1rcVHTLe7oE/n/dV7++IlhUzKRczKRfzKBMzKRfzRFomarBFRExRsxPYD55/5CuAr/vAz/8AKzK26IqIiIhEOt2mS0TERJunwp5Ff/7r9S5Ep4S7KhERERE5Au3BFhExUeO/Qo+3wBEDmV/CtC6QvTzcVYmIiIjIEajBrgbsdsVsGmViJuNyaXopDPkR4ptA3h8woyesfyfcVVUq4zIRQLmYSrmYR5mYSbmYJ5Iy0W26ykC36RKRSlG4B+ZdBNumFT3uNAHa3BnemkRERESqCd2mS0QkkrhqwqmfQ7v7wJUKDUaGuyIRERERKYUa7AhXWFjIH3/8QWFhYbhLkYOUiZmMz8XugA4PwdlrIaHJn88f2BK+miqY8ZlUU8rFTMrFPMrETMrFPJGWiRrsCGdZFoWFhehMAHMoEzNVmVxcNf/8/61fwKdNYfUzYHrdJ6DKZFLNKBczKRfzKBMzKRfzRFomarBFRKqqzC/B74Gltxedo+3dH+6KRERERKo1NdgiIlVVl+fhlH+DzQkbP4DpPSD3t3BXJSIiIlJtqcEWEamqbDZodTMM+h5i6sDelTC9C2z5LNyViYiIiFRLarAjXFRUFBkZGURFRYW7FDlImZipSudSqw+csRTSe4MnF2YNgz3Lwl1VmVXpTCKYcjGTcjGPMjGTcjFPpGXiDHcBUrEcDgeJiYnhLkOKUSZmqvK5xNaFgd/BsrvA74aancJdUZlV+UwilHIxk3IxjzIxk3IxT6Rloj3YEc7r9bJr1y68Xm+4S5GDlImZIiIXRzR0+Td0ffHP5wp2Vdm92RGRSQRSLmZSLuZRJmZSLuaJtEzUYEc4r9fLzp07I+YLGwmUiZkiKhfbwVW73wfz/gpf94I/3gpvTScgojKJIMrFTMrFPMrETMrFPJGWiRpsEZFI5csHezT4CmD+5bDoBvC5w12ViIiISMRSgy0iEqmiEuDUz6D9PwAb/PYSfHMqHNgS7spEREREIpIabBGRSGazQ/sH4NTPISoZds+HaadA1sxwVyYiIiIScdRgRzi73U5iYiJ2u6I2hTIxU8TnUv9MGLoYkjtCwQ5YdB34zT7XKeIzqaKUi5mUi3mUiZmUi3kiLRMjP8WMGTPo2rUrcXFxNGnShAkTJmBZ1mHHLygoYOzYsTRq1Ii4uDh69uzJ9OnTyzzdSBAdHU1GRgbR0dHhLkUOUiZmqha5JDaDIfOg2TXQ+yOwm32nxmqRSRWkXMykXMyjTMykXMwTaZmUucG2LIspU6ZQUFAAQE5ODhdddBENGzZkwIABLFmy5LimN3/+fM4++2xat27NlClTuOiiixgzZgxPPPHEYV9z9dVX88ILL3D33Xfz6aef0rx5c8466yxmz55dpulGAsuy8Hg8Eb8hoSpRJmaqNrk446D7q5DS4c/nNvwHcteEr6bDqDaZVDHKxUzKxTzKxEzKxTyRlonNKsMnyc/P55xzzuH7779n8eLFdOrUiWHDhvHFF18ARTMrMTGRpUuX0qxZs2Oa5umnn05OTg4LFiwIPnf33Xfz0ksvkZWVRWxsbMj4GzZsoEmTJjz//PPceOONAPj9fpo3b0737t354IMPTmi6x2LFihUAtG/f/rhfW1kKCgpYv349TZo0ISYmJtzlCMrEVNU2l13z4Zt+YI+Bnm9BgxHhriio2mZiOOViJuViHmViJuVinqqQyfH0fWXag/3YY4/x3XffAfDrr7+yYcMGPv/8c4DgFoi8vLxj3ktcWFjIzJkzGTEi9A+8UaNGsW/fPubMmVPiNXXr1mXRokVcfPHFwefsdjtOpzO4V/1EpisiUi3EN4bUHuDdB7NHwvKxRffPFhEREZHjVqYT8KZOnQpAy5Yt6dGjB19++SUAtWrV4qeffuKaa67hs88+CzbhR/PHH3/gdrtp2bJlyPPNmzcHYM2aNZx22mkhw1wuF126dAGK9lxv3bqVJ598kt9//53nnnvuhKd7PAKNfIDdbic6Ohq/34/bXfKes4EtM4WFhSUOhYiKisLhcOD1ekvcbD0wXcuyKCwsLDFdl8uFzWbD7Xbj9/uD7+H3+4PT8vl8eDyekNfZbDZcLlepnwWKzouw2+14PB58vtA/vB0OB1FRUUf9rBU13cqYhwFOpxOn01mmeeh2u4OZBN6/PKZb1eahidl4PJ6QXKDi56ER2diSofcXRP8yDvtv/4ZVj+HbtRBP1zfBlVY0SpjWEYEavV5vieEmf7/Luo6oqO93ea0jij+ubuuIyl7PHs90A78tgRojbR6als2xzMOK+L03MZuq9ndEab/3VXUempjNiczDwLQOfR2Ys46wLAubzVZivNKUqcFev349NpuN22+/naZNmzJr1iwABg8eTK1atRg1ahSfffYZW7duPabp7d27F4CkpKSQ5xMTEwHIzc094uufeOIJxo4dC8A111zD4MGDy2W6R+Lz+Vi/fn3Ic0lJSdSvXx+v11tiGECbNm0A2LZtG/n5+SHD6tWrR40aNcjNzSUrKytkWHx8PA0bNsTv95c63RYtWuB0OsnKyiIvLw8g+KXJy8sjISGB/fv3l8gjJiaGJk2aAEWH3B+6IDZt2hSXy8WuXbvIyckJGZaamkqtWrXIz89n06ZNIcOcTictWrQAYPPmzSUW4oYNGxIfH092dja7d+8OGZacnEzdunXxeDwlPqvNZqN169YAZGZmllig6tevT1JSEnv37mXHjh0hwxISEmjQoEGpuUHRxiKHw8H27dvZv39/yLDatWtTs2ZN8vLyyMzMDBkWGxtL48aNAUqdbrNmzYiOjmbnzp3k5OTgdrvZsmULdrudtLQ00tPTyc/PZ/PmzSGvi4qKCm4I2rRpU4mVQ+DCfnv27GHPnj0hw1JSUqhTpw5ut7tETXa7nVatWgGwdevWEivRjIwMEhMTycnJYefOnSHDEhMTycjIOOz3u1WrVthsNrZt28aBAwdChtWtW5fk5GTy8vLYtm1byLC4uDgaNWqEZVmlTrd58+ZERUWxY8cO9u3bFzIsPT2dtLQ0Dhw4wJYtofd4drlcNG3aFICNGzeW+EEIHI50aC4ANWvWpHbt2hQUFLBx48aQ1zkcjuBGu82bN5f4UWjQoAEJCQlkZ2eza9eukGEmrSMCajW9n9T0nlgLrsKx41v8M7qxpem/KYhrG7Z1RJ06dYCidfih6+lIXkcc+llNW0f4/f7gd6A6rSN2795NdnZ2yDCT1hGB3/sDBw4QHx9f/uuIWrVITU3V3xHHsY7Yt29fyO9KdVlHgNl/R+zevbvE7311WEcEVNjfEWVYR9SvXx+A7OzsEr+tpqwjvF4vUVFRJeZJacp0DnZ0dDQ+n48PP/yQUaNGkZGRwbZt23juuee44YYbeO+997jkkktISkoqsUItzbx58+jduzdff/11sDkGgh/oscce45577jns61euXMmePXuYM2cODz30EKNHj+add94p83QPJ3AsfiDYAJO2KhUWFrJlyxYaNWpEQkKCtjwbsHcqPz+fLVu2kJGRgcvlqrJbTSNt79S+ffvYtGlTMBeoplued/+Ebe4o7Hnr8LR7BF/LO8O6B3vjxo00aNAApzN0e7DJ3+9I3ztVWFjI1q1badq0aXB5Li5S1xGm750K/N43btyY+Pj4iJuHpmVzLPOwIn7vTcymqv0dUdrvfVWdhyZmc6J7sDdu3EhGRkaJJtaUdcTatWux2WzHdA52mRrsevXqkZWVxT/+8Q/OOussunTpgs1mY+XKlbRp04aRI0fyySef0LZt22AzeiS//PIL7dq1Y8qUKSHnS+/Zs4fU1FRefPFFrr/++mOq7aGHHuKBBx5g48aN7Nu3r9ymW1xVuMhZ8UMajvWwBqlYysRMyqUY9174/TVofSeEcV4oEzMpFzMpF/MoEzMpF/NUhUwq7SJnPXr0wLIs/vnPfwbPYW7evDlt2rThwgsv5JNPPsFmszFkyJBjml6zZs1wOBysW7cu5PnA48DhDsVt3LiRiRMnlthq0blzZ6Bot/6JTDdS2Gw27Ha7sV/W6kiZmEm5FBNdA9rc9Wdz7cmDHy+D/ZuP/LpypkzMpFzMpFzMo0zMpFzME2mZlKnB/vvf/47T6cTtdgfPObj77ruLJnzwnIbk5GRuu+22Y5peTEwM/fr1Y8qUKSGHM0yePJkaNWrQrVu3Eq/ZuHEjV199dfCCawEzZswgOjqaVq1andB0I4Xb7Wbjxo2lHvog4aFMzKRcjmDpHbD+bZh2Cmw/totWlgdlYiblYiblYh5lYiblYp5Iy6RMFznr2bMn06ZNY8KECeTl5XHRRRdx5ZVXAkV7hU866STefvttGjRocMzTHDduHIMHD+a8887jyiuvZN68eYwfP57HH3+cuLg4cnNzWbVqFc2aNSM9PZ0+ffowePBgbr75ZnJzc2nWrBmff/45L7zwAg8++CApKSnHNN1I5ff7OXDgQInzJCR8lImZlMsRtL0X9iyG7GXw/WnQ8fHQPdwVRJmYSbmYSbmYR5mYSbmYJ9IyKdMebICBAwfy5ZdfMmvWLP72t78Fn7/llltYuXJl8FDt45ne5MmTWbNmDcOHD+e9995j/PjxjBkzBoClS5fSs2dPvvjii6IPYLczZcoULr/8ch5//HHOOussvvnmG1599VXGjRt3zNMVEZHDSGgCp82FppeD5YflY2DOaPDsO+pLRURERKqTMu3BPpKdO3ficDhOaO/wiBEjQi5GVlz//v1LXA0vMTGRCRMmMGHChBOeroiIHIEzFrq/AandYcktsHky7P0F+n0KSS2O/noRERGRaqDMe7AzMzO55ZZbgvfOW758OQ0bNqRFixbUqlWLV155pcxFioiIAWw2aHEdDJ4FsfXBnQ3O+HBXJSIiImKMMjXYGzdupEePHrzwwgusWrUKgEsvvZQtW7ZgWRYHDhzghhtu4Pvvvy+XYuX4OZ1O6tatW+IeshI+ysRMyuU4pPWAoUug/xcQV+/P50/8ro+lUiZmUi5mUi7mUSZmUi7mibRMytRgP/TQQ2zZsgWANWvWsGjRIlauXInNZqN169bY7XYsy+LZZ58tl2Ll+DmdTpKTkyPmCxsJlImZlMtxiq0NNU/58/Gm/8J3p0HBznJ7C2ViJuViJuViHmViJuVinkjLpEwN9nfffRe8z/Ull1zC119/DUCrVq1YtWoVt9xyCwALFy4se6VyQrxeLzk5OXi93nCXIgcpEzMplzLw5sPiWyDrW5jWGXaVzzpfmZhJuZhJuZhHmZhJuZgn0jIpU4O9bds2AC655BKio6OZM2cONpuNAQMGANC9e3cAdu/eXcYy5UR5vV62bdsWMV/YSKBMzKRcysAZCwO/gcSWcGALfNMX1r1W5skqEzMpFzMpF/MoEzMpF/NEWiZlvsgZFN0qy+fzMXfuXAB69eoFQG5uLgDJycnl8TYiImKq5LZw+kLIGA5+Nyy8FhZcDb6CcFcmIiIiUmnK1GBnZGQAMG3aND744AP27duHzWZj0KBB7Nq1ixdffBGAxo0bl7lQERExXHQN6DsZOj4KNjv8PhG+7qsmW0RERKqNMjXYgwYNwrIs3nnnHS677DJsNhu9evWiTp06jB49mp9++gmbzcYFF1xQXvWKiIjJbHZoey/0nwauVKh1Kjhiwl2ViIiISKUoU4N9//33U6dOHSzLwrIsnE4njz32GAAtWrQAoGPHjlx77bVlr1ROiN1uJy4uDru9XM4GkHKgTMykXMpZ3dPgjOVw8uN/PufJPa5beSkTMykXMykX8ygTMykX80RaJjbLKtuNS3fs2MGbb75JXl4eo0ePpn379gC88sor/PzzzzzyyCOkpKSUS7GmWbFiBUDwM4uIyGH43PDdQHClQ8+3ICop3BWJiIiIHJPj6fvK3GBXZ1WhwQ4cXWCz2bDZbOEuR1AmplIuFWznXPh2YNEF0BJbQr+pUOOkI75EmZhJuZhJuZhHmZhJuZinKmRyPH1fueyH37dvHxMmTODss8+mZ8+eDBs2jH//+98cOHCgPCYvZVBYWMiaNWsoLCwMdylykDIxk3KpYOm9YfBsiMuAfWthejfY9N8jvkSZmEm5mEm5mEeZmEm5mCfSMnGWdQKLFi3i3HPPJSsrK+T5L774gieffJLPPvuMDh06lPVtRESkqkvrBkOXwtwLIOs7mHMetL6z6Dxte5l/jkRERETCrkx7sLdv385ZZ51FVlZWcNd+8X+bN2/mjDPOYOfOneVVr4iIVGUx6TBgOrQZU/R49ZOw+Mbw1iQiIiJSTsrUYP/rX/9i165dAJx77rl8/fXXrFq1ihkzZjB8+HCgqAkfP358mQsVEZEIYXdCpyegz8cQW79oL7aIiIhIBChTg/3FF19gs9k444wzmDp1KoMGDaJ169YMHjyYKVOmcOaZZ2JZFv/73//Kq14REYkUDf8Cw36HpJZ/Ppf903HdyktERETEJGVqsDdt2gTAeeedV+rw0aNHh4wnlc/lctG8eXNcLle4S5GDlImZlEuYOIrN7+3fwrTOsOBK8OYrE0MpFzMpF/MoEzMpF/NEWiZlarATExOBonthlyZw7nVSku53Gi42m42oqChjL3lfHSkTMykXA+SuKfrvH2/C132w7d+oTAykZcVMysU8ysRMysU8kZZJmRrs9u3bY1kWEyZMYN26dSHDfv/9dyZMmIDNZqNjx45lKlJOnNvtZsuWLbjd7nCXIgcpEzMpFwO0vAEGfA2uNMheijXtFHb+/J4yMYyWFTMpF/MoEzMpF/NEWiZlarAvv/xyoGhPdfv27Rk2bBg33HADw4YNo127dsE925dddlmZC5UT4/f72bdvH36/P9ylyEHKxEzKxRB1Bhbdyiu1Gzb3HtJWXoJt1WNgKRdTaFkxk3IxjzIxk3IxT6RlUqYbj15yySX897//5fPPP8ftdvPFF18Eh1kHL1IzePBgLrroorJVKSIi1Ud8Axg8C++CG3FumEjUqn9AWmfIOCfclYmIiIgcUZn2YANMmTKFe+65h/j4+JB7YMfGxnLDDTfw6aeflkedIiJSnThceDs/T2bDh/E2vhLqnx3uikRERESOqkx7sAGcTiePPvooY8eOZcGCBezatYvatWvTuXNnLMsiKysLgIYNG5a5WBERqV72pv6Fmk2a4Axc+MSdA1nfQ4MRYa1LREREpDRlbrADEhISGDRoUMhzDz74IA899BA2mw2v11tebyXHwel0kp6ejtNZblFLGSkTMykX85TIxPLDvEsg83NodRt0+hfYo8JaY3WkZcVMysU8ysRMysU8kZZJmQ8RP5rAIeMSHk6nk7S0tIj5wkYCZWIm5WKeEplYFiS3L/r/Nc/Ad4Mhf3vY6quutKyYSbmYR5mYSbmYJ9IyqfAGW8LL5/Oxb98+fD5fuEuRg5SJmZSLeUpkYnfAyY9C3yngTIQds2DaKbBzXngLrWa0rJhJuZhHmZhJuZgn0jJRgx3hPB4PW7ZswePxhLsUOUiZmEm5mOewmTQYAUMXQY2TID8Tvu0Pa18o2sMtFU7LipmUi3mUiZmUi3kiLRM12CIiUvUktYIhC6DhaPB7YNUT4N0X7qpERESkmouMA91FRKT6iUqA3h9Cag+o1ReiksJdkYiIiFRzarBFRKTqstmgzR2hz214H6KSof6ZYSlJREREqq/jarCvvPLK45r48uXLj2t8KX82mw2Xy4UtcA9ZCTtlYiblYp4TyiRnJcy/EvxuaP8AtLsPbDobqjxpWTGTcjGPMjGTcjFPpGVis47jHlp2u/24P7hlWdhstoi5KlxxK1asAKB9+/ZhrkRERADwFcKS22Ddy0WP650Fvd6B6JSwliUiIiJV1/H0fce9WT9wX+tj/SciIlJpHC7o9hL0mAR2F2R+AdO6QvbP4a5MREREqoHjOkR80qRJFVWHVJCCggI2btxIo0aNiImJCXc5gjIxlXIxT5kyaXo5JHeA2SMh73eY0QO6vQZNLqqQWqsTLStmUi7mUSZmUi7mibRMjqvBvuyyyyqqDqlAfr8/3CXIIZSJmZSLecqUSc3OMHQJzL0Qts+Afb+VX2HVnJYVMykX8ygTMykX80RSJrqKuIiIRC5XKvT/Eja8C00uCXc1IiIiEuF0aVUREYlsdgc0vezPq4l782Hm2bBjdnjrEhERkYijBltERKqXVY8VXfzs24Gw5t+gC3KKiIhIOTmu23RJqKpwmy6/34/b7SY6Ohq7XdtTTKBMzKRczFNhmXj3w4KrYeN/ih43uhC6vwrO+PJ7jwimZcVMysU8ysRMysU8VSGTCr1Nl1QtdrudmJgYY7+s1ZEyMZNyMU+FZeKMh17vQ+enweaAje/DjJ6wb135vk+E0rJiJuViHmViJuVinkjLJDI+hRyWx+Nh+/bteDyecJciBykTMykX81RoJjYbtL4NBn0HMbUhZwVM6wJZM8v/vSKMlhUzKRfzKBMzKRfzRFomarAjnM/nIzs7G5/PF+5S5CBlYiblYp5KyaRWPxi6FNJ6Fu3Njm9cce8VIbSsmEm5mEeZmEm5mCfSMtFtukREpHqLqweDZsK+tZDQ+M/nfW5wRIepKBEREamKtAdbRETEEQ3J7f58vPVz+OIkyF4etpJERESk6lGDLSIiUpzlh58fgLzfiy5+tv6dcFckIiIiVYQa7AjncDioWbMmDocj3KXIQcrETMrFPGHLxGaHgV9D3TPAVwA/XgqLbio6ZFy0rBhKuZhHmZhJuZgn0jLRfbDLoCrcB1tERE6Q5YcVD8LKh4oep/WEPv+FuPrhrUtEREQqle6DLUF+v58DBw7g9/vDXYocpEzMpFzME/ZMbHbo8CCc+hlE1YBdP8K0U6BgR3jqMUTYc5FSKRfzKBMzKRfzRFomarAjnNvtZuPGjbjdOrTRFMrETMrFPMZkUv9sGLoYkttDg5EQUyu89YSZMblICOViHmViJuVinkjLRLfpEhEROZrE5jDkR7AV+9ks3AP2aIhKCF9dIiIiYhTtwRYRETkWznhwuIr+3++FOefBjB6Q+1t46xIRERFjqMEWERE5Xvs3wt5fiv5N7wJbPg13RSIiImIANdjVQKRc8j6SKBMzKRfzGJtJYjM4Yymk9wFPLsw6F34aB35fuCurFMbmUs0pF/MoEzMpF/NEUia6TVcZ6DZdIiLVnN8DS++Ctf8uelxnCPR+H1yp4a1LREREyo1u0yUiIlIZ7FHQ5Vno9R44YmH7DJh7QbirEhERkTBRgx3hCgsLWbduHYWFheEuRQ5SJmZSLuapUpk0vhCGzIfkDtD5qXBXU6GqVC7ViHIxjzIxk3IxT6RlYmSDPWPGDLp27UpcXBxNmjRhwoQJHOlI9sLCQh599FFat25NfHw8rVq14qGHHipxL7WMjAxsNluJf7t27arojxQ2lmXh8XiOOP+kcikTMykX81S5TFI6wBnLiu6XHZA1E3yR8QdDQJXLpZpQLuZRJmZSLuaJtEyMuw/2/PnzOfvsszn//PN5+OGHmTNnDmPGjMHr9XLPPfeU+ppbb72Vd955h/vuu4+uXbuyePFiHnzwQTZu3MjEiRMB2LVrF1u3bmX8+PH06dMn5PXJyckV/bFERKQ6sBXbbr1zHnx3GtQ8Bfp+DHEZ4atLREREKoVxDfYDDzxAp06deOeddwAYOnQoHo+HRx99lFtvvZXY2NiQ8Xfv3s2rr77KE088wd///ncABg0aBMA999zD448/Tnp6OsuXLwdgxIgRNGvWrPI+kIiIVE/e/eBMgN0L4KvO0OdDqD0g3FWJiIhIBTLqEPHCwkJmzpzJiBEjQp4fNWoU+/btY86cOSVek5uby3XXXcewYcNCnm/dujUAf/zxBwDLly8nMTGRpk2bVlD1IiIixdQ9Dc5YAskdoXBn0d7sXydAhBwCJyIiIiUZtQf7jz/+wO1207Jly5DnmzdvDsCaNWs47bTTQoY1adKEF198scS0PvnkE6KiooLTWr58OTVr1mTUqFF88803+Hw+zjrrLJ555hnq1q1bproLCgpCHtvtdqKjo/H7/SXOAweIiYkBijYoHHquQVRUFA6HA6/Xi9frLXW6lmWVehEAl8uFzWbD7Xbj9/sB8Pl81KpVC5vNFnzs8XhCXmez2XC5XKV+FoDo6GjsdjsejwefL/Qerw6Hg6ioqKN+1oqabmXMwwCn04nT6SzTPHS73cFMfD4fBQUF5TLdqjYPTcwGCMkFKn4empiNSesIh8NBgwYNSh1u8vc7ZB4660G/74hafjOOTe/Dsr/j2/Ejns4vQ1Ri8LXF1xGmZXPoPPT5fNSvX5+oqKhqtY4Ix3r2eKYb+G0JrM8ibR6als2xzMOK+L03MZuq9neEZVklfu+r6jw0MZsTmYfl8Xtf0dlYlhXsp47GqAZ77969ACQlJYU8n5hY9EdIbm7uMU1n6tSpvPXWW9x0002kpKQARQ321q1bufbaa7ntttv49ddfuf/++zn11FNZtmwZ8fHxJ1Szz+dj/fr1Ic8lJSVRv359vF5viWEAbdq0AWDbtm3k5+eHDKtXrx41atQgNzeXrKyskGHx8fE0bNgQv99f6nRbtGiB0+kkKyuLvLy8EsNdLhf79+9n69atIc/HxMTQpEkTADZs2FBiQWzatCkul4tdu3aRk5MTMiw1NZVatWqRn5/Ppk2bQoY5nU5atGgBwObNm0ssxA0bNiQ+Pp7s7Gx2794dMiw5OZm6devi8XhKfFabzRY8QiEzM7PEAlW/fn2SkpLYu3cvO3bsCBmWkJBAgwYNSs0NoGXLljgcDrZv387+/ftDhtWuXZuaNWuSl5dHZmZmyLDY2FgaN24MUOp0mzVrRnR0NDt37izxPU5LSyM9PZ38/Hw2b94cMiwqKiq4gWnTpk0lVg6NGjUiLi6OPXv2sGfPnpBhKSkp1KlTB7fbXaImu91Oq1atANi6dWuJlWhGRgaJiYnk5OSwc+fOkGGJiYlkZGQc9vvdqlUrbDYb27Zt48CBAyHD6tatS3JyMnl5eWzbti1kWFxcHI0aNcKyrFKn27x5c6KiotixYwf79u0LGZaenk5aWhoHDhxgy5YtIcNcLlfwyJWNGzeW+EFo0qQJMTEx5OTkkJ2dHTKsZs2a1K5dm4KCAjZu3BgyzOFwBDfgbd68ucSPSYMGDUhISCA7O7vEhRRNXEfUqlWL1NRU49YRCQkJ7Nixo+qvI+o/QuNavWHpbTi2TmGHvQM5aecFh1fFdYTD4WDXrl3VZh2xe/fuKrGOcDqdREdHV5t1hP6OMHcdYerfEbt3767W6whT/45ISEhg27Ztxq4jvF4vUVFRJeZJaWyWQZdrmzdvHr179+brr79m8ODBwecDH+ixxx477IXOAqZMmcKFF15It27dmDFjRnDrw48//ojT6aRr167BcefOnUufPn148cUXuf7664+73sANxwPBBpiyVcnn8zFr1izWr19P8+bNg+ema8tzePdgu91ucnNzSUpKCk6zKm41jbS9U/n5+WRnZwdzAW15DgjXOsJut7N3714SEhJKbDU2+ft9xHm480e8v7+Ft+PTUOwzVaW9U16vl/3795Oamhp8XFykriNM3zvl9XrJzc0lNTUVl8sVcfPQtGyOZR5WxO+9idlUtb8jSvu9r6rz0MRsTmQelsfvfUVns3btWmw2G+3bty8x7qGMarB/+eUX2rVrx5QpU0LOw96zZw+pqalHbYSffvpp7rrrLvr3788nn3wS3PN9JMnJyVxwwQW8/PLLx11voME+lhld2aZMmcKtt94ashUuIyODZ599lpEjR4axMikoKGD9+vXBLZ5iBuVinmqRiScP1r0CrW4DuyPc1RyTapFLFaRczKNMzKRczFMVMjmevs+oi5w1a9YMh8PBunXrQp4PPA4c7nAoy7K45ZZbuOOOOzj//PP56quvQprrvXv38sYbb7By5cqQ1wW2WKSnp5fzJwmvKVOmMGrUqBKHuGzdupVRo0YxZcqUMFUmIiJBlgULroJld8HMoVCw6+ivEREREaMZ1WDHxMTQr18/pkyZEnI4w+TJk6lRowbdunUr9XVjx47lueee44477uC9994jOjo6ZLjL5eKmm27iscceC3n+008/JT8/nwEDIue2KT6fj1tvvbXUG7UHnrvttttKHCYhIiKVzGaDjBHgiIPt38C0U2D34nBXJSIiImVg1EXOAMaNG8fgwYM577zzuPLKK5k3bx7jx4/n8ccfJy4ujtzcXFatWkWzZs2C97d+4okn6Nq1K6NHj2bBggUh0zvppJNISkrinnvu4YEHHqB27dqceeaZrFixgn/84x+ce+65DBw4MEyftvzNnj27xJ7r4izLYvPmzbz//vtcfPHFx3w1PBERqQCNL4DkdjB7JOz7Db7uA11fgGZXhbsyEREROQFG7cEGGDhwIJMnT2bNmjUMHz6c9957j/HjxzNmzBgAli5dSs+ePfniiy8Agnu7Fy1aRM+ePUv8W7p0KVDUuL/44ovMmDGDc845hyeffJLrrruODz74IGyftSIcekXFw7n00kvJyMjgoosu4rXXXuO3334rda+3lD+73U5SUlLwVipiBuVinmqTSXI7OH0R1B8G/kJYcDUsuBZ8JS8wY4Jqk0sVo1zMo0zMpFzME2mZGHWRs6rGxIuczZw584QPea9Xrx79+/cP/mvevLn2cIuIVBbLD6seh5/GQUxtOGMZxNYJd1UiIiLV3vH0fWqwy8DEBtvn89G4cWO2bt162D3SNWrUoHv37sydO7fEPRqLq1+/fkjD3axZMzXc5cDv9+P1enE6nRGzpS4SKBfzVNtMts0oOi+7Vp9wV1KqapuL4ZSLeZSJmZSLeapCJlX2KuJSdg6Hg2effRagRDNss9mw2Wy88cYbTJ8+nezsbH788Ucee+wxTj/9dOLj40PG37p1K++99x7XXHMNLVq0oEGDBlx88cW8/vrr/P777zqk/AS53W5+//33Uu+5J+GjXMxTbTOpOyS0ud74Eaz6V9FVxw1QbXMxnHIxjzIxk3IxT6RlogY7Ao0cOZKPP/6Y+vXrhzyfkZHBxx9/HLwPdlRUFD169OCee+5h2rRpZGdnM2/ePB599FGGDBlCXFxcyOuLN9zNmzenYcOGXHLJJUycOFENt4hIRTiwBRZcCcvvhjmjwJMb7opERETkCIy7iriUj5EjR3Luuefy7bff8vPPP9OhQwcGDRqEw+E47GuioqKCF4e799578Xg8LF68mJkzZzJz5kzmzJnDgQMHguNv2bKFd999l3fffRcoauADh5MPGDCAJk2a6JByEZGyiK0PnSbAkltg8xTYuwr6ToEabcJdmYiIiJRCDXYEczgc9OvXjwYNGtCkSZMjNtelObThdrvdIQ333Llzj9hwN2jQIOQcbjXcIiLHyWaDFtdByskwexTkrobp3aDHJGg4KtzViYiIyCF0iLgcs+joaHr16sXYsWOZMWMG2dnZzJ07l3/+858MHjyY2NjYkPE3b97MO++8w1VXXUWzZs1o1KgRl156KZMmTWL9+vU6pFxE5Fil9YChS6BWf/DmwZzRsGyMMedli4iISBFdRbwMTLyKeDi53W4WLVoUsoc7Pz//sOM3bNiwxB5uERE5Ar8Xlt8Dq5+ENndBp/HhrkhERCTi6TZdlUQN9pEFGu7vv/+emTNnMm/evCM23I0aNQppuBs3blx5xYqIVCXbvynam20/eKaX5QebDkoTERGpCGqwK0lVaLALCwvZtm0bdevWxeVyhb2WQ/dwFxQUHHb8SG24TcpE/qRczKNMjpHPDTOHQqMLoNk1RedtVyDlYiblYh5lYiblYp6qkMnx9H26yFmEsyyL/Px8I853drlc9OnThz59+jBu3Lhgw118D3fxhnvjxo289dZbvPXWWwA0btw4pOFu1KhRuD5KmZiUifxJuZhHmRyj9W9D1vdF/3YtgK4vgCOmwt5OuZhJuZhHmZhJuZgn0jJRgy1hU7zhvu+++ygsLGThwoXBPdyHNtwbNmzgzTff5M033wRCG+4BAwbQsGHDMH0SEZEwanYVuHfDT2Phjzcg5yfoOxniq+ZGSBERkapMJ2yJMVwuF3379uW+++7j22+/JTs7mx9++IEHH3yQAQMGlDhkJNBwX3755TRq1IimTZty5ZVX8vbbb7Np06YwfQoRkUpms8FJd8OA6eBKhT1LYNopsO3rcFcmIiJS7WgPthgrJiaGfv360a9fP+6//34KCgpK7OEuLCwMjr9+/XrWr1/PpEmTAGjSpElw73b//v1p0KBBuD6KiEjFqzMYhi6F2X+BPYvh+9Oh++vQ7MpwVyYiIlJt6CJnZVAVLnLm8/nIy8sjISEBh8MR7nLKVUFBAQsWLAg23D/++GNIw32opk2bhpzDHa6GO5IzqcqUi3mUyQnyFcDim2HTR3D6YkhqUb6TVy5GUi7mUSZmUi7mqQqZ6CrilaQqNNjVyYk23IE93BkZGZVYrYhIBdu/CeKLXZvCnQPRyeGqRkREpMpSg11JqkKD7fV6yc3NJSkpCaezep0RUFBQwPz580MabrfbfdjxmzVrFrKHu6Ia7uqcicmUi3mUSTna/k3RoePdXoNG55VpUsrFTMrFPMrETMrFPFUhE92mS4K8Xi9ZWVnExcUZ+4WtKDExMcFmGSA/P58FCxYEbws2f/78kIb7999/5/fff2fixIkANG/ePKThrl+/frnUVZ0zMZlyMY8yKUfrXgdPLsw9H3YvgJOfAPuJzVPlYiblYh5lYiblYp5Iy6TqfwKRYxQbG1ui4S6+h/vQhnvdunWsW7eO119/Hai4hltEpML1erfotl2//gtWP1V0pfHeH0Js7XBXJiIiElHUYEu1FRsby4ABAxgwYADwZ8NdfA+3x+MJjn9ow92iRYtgs33qqaeq4RYRc9md0OkJSO0G8y+HHT8U3cqr78eQ1iPc1YmIiEQMNdgiBx3acB84cKDEHu7iDfdvv/3Gb7/9xmuvvQaENtz9+/enXr16YfkcIiKH1fAvUKMtzB4Buavhm34wdBkktw13ZSIiIhFBDXaEs9vtxMfHY7fbw11KlRMXF8fAgQMZOHAgUNRw//jjj8GGe8GCBUdsuFu2bBmyhzvQcCsTMykX8yiTClKjNZy+EOZfAc4EqHHScb1cuZhJuZhHmZhJuZgn0jLRVcTLoCpcRVwqztEa7kMFGu4BAwZw6qmnUrdu3UqsVkTkEJYFfg84ooseu7OLbuWV0CSsZYmIiJhGt+mqJFWhwbYsC7/fj91ux2azhbuciLZ///6QhnvhwoVHbLhbtWoVsodbDXd4aVkxjzKpRJYffhgGu+ZBr/eh3tDDj6pcjKRczKNMzKRczFMVMlGDXUmqQoNdUFDA+vXradKkCTExMeEup1o5tOFesGABXq/3sOMHGu7AHu46depUYrWiZcU8yqQSubPh+6GweyFggw4PQduxYCt5uJ5yMZNyMY8yMZNyMU9VyET3wRYxQHx8PIMHD2bw4MFAUcM9b948vvnmG77++mtWrFgR0nCvWbOGNWvW8MorrwDQunXrkD3carhFpMJEp8DgWbDkVlj3Cvx8X1Gz3fNtiE4Od3UiIiJVhhpskUoSHx/PaaedRt++fbn88supVasWS5cuDd4WbNGiRSEN9+rVq1m9ejUvv/wyAG3atAlpuGvX1v1rRaQcOVzQ7WVI7Q6Lroetn8G0rtBvKiS3C3d1IiIiVYIabJEwCTTcp512GgB5eXnMmzcveEj5oQ33r7/+yq+//spLL70EqOEWkQrS7ApI6QCz/wJ562Du+XDmilIPFxcREZFQarBFDJGQkMCQIUMYMmQI8GfDXXwPt8/nC45/aMN90kknhTTctWrVCsvnEJEIUPMUOH0xLLgK2t+v5lpEROQY6SJnZVAVLnJmWRY+nw+Hw2HsVfmqmxPNJC8vj7lz54bs4S7ecB9KDffx0bJiHmVioK1fYKV0xhedrlwMo+XFPMrETMrFPFUhE11FvJJUhQZbIte+fftCGu7FixcfseFu27ZtSMOdnp5eidWKSJW3ezF83QdcqdDnv5DeK9wViYiIVAo12JWkKjTYbrebrKwsateuTXR0dLjLESoukxNtuAcMGEC/fv2qfcOtZcU8ysQwuWth9gjYuwrL5sTXcTzONreCoXsbqhstL+ZRJmZSLuapCpkcT9+nk6oinN/vJy8vD7/fH+5S5KCKyiQxMZGhQ4fy+OOPM3/+fPbs2cOXX37JmDFj6NatGw6HI2T8X375hRdeeIFRo0ZRq1Yt2rdvz80338zkyZPZuXNnudZWFWhZMY8yMUxSSxiyAF/9v2CzvDiX3w4/XgreA+GuTNDyYiJlYiblYp5Iy0QXOROJUElJSZxxxhmcccYZAOTm5pbYw118RbZy5UpWrlzJ888/D0C7du1C9nCnpaWF5XOIiEGiEvB0e4ddC5pSa9tT2Da8Czk/Q98pkNgs3NWJiIiEnRpskWqitIZ7zpw5wYZ7yZIlR2y427dvHzyHWw23SDVms7Gn9hWkNBtM9MKLixrsTR9C27HhrkxERCTs1GCLVFNJSUmceeaZnHnmmcCfDXfgtmBLly4NabhXrFjBihUreO655wDo0KFDSMOdmpoals8hIuHhT+8HZyyFtS/ASfeEuxwREREj6CJnZVAVLnLm9XrZu3cvNWrUwOnU9hQTVJVM9u7dG7KH+9CG+1BVveGuKrlUJ8rETEfMxZsPP/0ftL8PolPCU2A1peXFPMrETMrFPFUhE11FvJJUhQZbpLwEGu7AHu5ly5YdtuG22WwlGu6aNWtWcsUiUukW/g3WvQoJTaHvVEjpEO6KREREykwNdiWpCg22z+dj//79xMfHl7iKtIRHpGSSk5MTsoe7qjfckZJLJFEmZjpiLnuWweyRsH8DOGKh26vQ5OKw1FndaHkxjzIxk3IxT1XIRLfpkiCPx8PWrVvxeDzhLkUOipRMkpOTOfvss5kwYQKLFy9m9+7dfPbZZ9xxxx107twZW7F741qWxU8//cSzzz7LiBEjSEtLo1OnTtx+++3873//Izs7O4yfpEik5BJJlImZjphLzU4wdDHUPR18+fDjJbD4ZvC5K7/QakbLi3mUiZmUi3kiLRMzD3IXkSon0HCfffbZQNEe7tmzZ4fs4Q4cMGNZFsuXL2f58uU888wz2Gw2OnbsGLwtWN++fUlJ0fmbIlWSKxVO/QJWPggrH4a1z8OepdD3Y4itG+7qREREKpQabBGpEMnJyZxzzjmcc845AGRnZ4c03MuXLz9iw33yyScHDylXwy1Sxdgd0OEhqNm1aC/2vjXgj4w9EyIiIkeiBltEKkVKSgrDhg1j2LBhwNEb7mXLlrFs2TKefvrpkIY7sIc7OTk5jJ9GRI5Jxjlw+iIo2A7xDcNdjYiISIVTgx3hbDYbMTExIefDSngpkyKHNtx79uwJabh/+umnIzbcnTp1CtnDXdaGW7mYR5mY6bhzSWpR9C9gy6ew6SPo9go44yumyGpIy4t5lImZlIt5Ii0TXUW8DKrCVcRFqqpAwx24LdhPP/102HHtdnuJhrtGjRqVWK2IHBNPHnzaGAp3Q3J76DsFEpuHuyoREZEj0m26KokabJHKs3v37hJ7uA9HDbeIwXbMgjnnQUEWRNWAXu9C/bPDXZWIiMhhqcGuJFWhwS4oKGDDhg00btyYmJiYcJcjKJPyEmi4A3u4f/7558OOa7fb6dy5c7Dh7tOnT4mGW7mYR5mYqVxyOZAJc0bBrh+LHre7D9o9UHRxNDkhWl7Mo0zMpFzMUxUyOZ6+T+dgVwPahmIeZVJ2qampDB8+nOHDhwNFDfesWbOCe7iLN9x+v5/FixezePFiJkyYUKLh7tu3L9HR0crFQMrETGXOJa4eDJoJy+4suo3Xyodh92Lo9wk4osuhwupJy4t5lImZlIt5IikTNdgiEhFSU1MZMWIEI0aMAGDXrl0he7gDWx6h9Ia7U6dOdOzYkXPOOYeBAweSlJQUro8iUj04oqHLc5DaHRZeCwlN1FyLiEiVpwZbRCJSWlpaiYa7+B7uQxvuJUuWsGTJEt544w3sdjunnHJK8LZgvXv3VsMtUlGaXAw1O0NCsYud+QrB4QpfTSIiIidIDbaIVAtpaWmMHDmSkSNHArBz586QhnvlypXBcf1+P4sWLWLRokWMHz8eh8MRbLgD53AnJiaG66OIRJ4aJ/35/34vzDwDktpA56e1V1tERKoUXeSsDKrCRc78fj8ej4eoqCjsdnu4yxGUiamysrL4/vvvmTNnDj/88ENIw32oQMM9YMAA+vfvT+/evdVwVwAtK2aq8Fy2fwPfDQEsSOsJff4LcfXL/30ijJYX8ygTMykX81SFTHQV8UpSFRpsETkxO3bsCNnD/csvvxx2XIfDQZcuXYJ7uNVwi5TR1i9h3kXgyYGYWtD7I6h9arirEhGRakoNdiWpCg22x+Nh165dpKWlERUVFe5yBGViqqPlcrwNd9euXUMa7oSEhIosPyJpWTFTpeWy73eYPRJyfgabAzqNh1a3gc1Wce9ZhWl5MY8yMZNyMYvP5+P7779n7dq1tGzZkgEDBuBwmHfLxuPp+8zcBy/lxufzkZOTg8/nC3cpcpAyMdPRcqlVqxajRo3i+eefZ+XKlWRlZfHRRx9xww03cNJJJ5WY1vz583n88ccZOnQoycnJ9OzZk3vvvZfp06eTl5dXGR+pytOyYqZKyyWxGQz5ERpfDJYPlt4By++p2PeswrS8mEeZmEm5mGPKlCk0btyY0047jRtvvJHTTjuNxo0bM2XKlHCXVia6yJmIyAmoVasWo0ePZvTo0UDROdyzZs0K3hbs119/DY4baLgDTbfT6QzZw92rVy/t4RYpjTMOer5ddCuvFfdDk0vDXZGIiJSDKVOmMGrUqBL3v966dSujRo3i448/Dl6YtqpRgy0iUg5q165douH+4YcfgoeUF2+4vV4vP/74Iz/++COPPfZYiYa7d+/exMfHh+ujiJjFZoNWN0GTSyC6xp/P798I8Y3CV5eIiJwQn8/HrbfeWqK5BrAsC5vNxm233ca5555r5OHiR2PkIeIzZsyga9euxMXF0aRJEyZMmFBqAAGFhYU8+uijtG7dmvj4eFq1asVDDz2E2+0OGW/x4sX079+fhIQE6tWrx9ixY0uMIyJSHmrXrs15553Hiy++yKpVq9i2bRsffvgh1113Ha1btw4ZN9BwP/bYY5x++ukkJyfTu3dv/u///o+vv/6a/fv3h+lTyP+zd9/hTZX9G8Dv7HSXDjqhlDJEkCV7yLAqCiogouJ4EVFxICq+iAqi4KsgIKL+FFFZ7vcVcOJAZCMqCigKCFhoaUsX3SNpkuf3R82xadKdNk/T+3NdXLRn5cm5c87pN2c8JJHKxXXmHuCzzsDhJwAbL/MkIpKZzWZDRkYGDh48iM8//xxz5szB2bNnq51eCIGUlBTs3r27GVvpPtKdwd6/fz/GjRuHG264AYsWLcKePXswZ84cWCwWzJ3r+t6rWbNm4e2338b8+fPRv39/HDhwAE8//TTOnDmDt956CwDw119/ITExEYMHD8Z///tfHD16FE888QTOnz+PVatWNedbbFYajQahoaEt8tsfb8VM5NTUuURGRmLy5MmYPHkyAODcuXMOZ7iPHTumTGuxWLBv3z7s27cPzz77LLRaLQYMGICRI0di1KhRGDx4cKs4w81tRU5S5JKxHbCVA78/C+T8BAx5DzCGea49EpAiF3LATOTEXNyrsLAQqampSEtLc/l/amoq0tPTYbFY6r3s9PT0Jmhx05PuKeJXXHEF8vLy8MMPPyjDHn30Ubz22mvIyMiAj4+Pw/Q5OTkIDw/HkiVL8O9//1sZvmTJEsydOxeZmZkIDw/H3XffjS1btuDUqVPQ6/UAgNdeew33338/kpKS0L59+3q3tSU8RZyIWoZz584pxfaOHTtw/PjxaqfV6XRKwW2/h9vX17cZW0skgdPvAz9MB6wlFZeKD98IhFzs6VYREXmF8vJypKen11g8p6WlobCwsMnasH37dowcObLJll8fLbabLpPJhMDAQDz99NMOZ6t/+uknDBgwAN988w0uu+wyh3mSkpKwdOlSzJo1C127dlWGf/LJJxg/fjz279+PgQMHokOHDhgzZozD2eqsrCy0bdsWq1evxp133lnv9raEAttms6G0tBQ+Pj7Sdtze2jATOcmWS3p6usMZ7roU3KNGjcLIkSMxePBgryi4ZcuEKkiVS95vwK4JQNEpQG0A+r8KJEzzbJs8RKpcCAAzkVVrz0UIgZycHOXscnXFc2ZmplteLzw8HDExMYiJiUF0dDRiYmIQGRmJefPmIScnx+VtwCqVCrGxsUhKSpLmSoP61H1SXSL+119/wWw2o0uXLg7DO3XqBAA4fvy4U4EdHx+PV1991WlZH3/8MXQ6Hbp06YLS0lKcOXPGabnh4eEIDAys8Q/XuigrK3P4Xa1WQ6/Xw2azubzH22g0Aqj4QqHqh0qn00Gj0cBisThdSmFfrhACJpPJabkGgwEqlQpmsxk2m015jbNnzyIuLg7+/v6wWq0oLy93mE+lUsFgMLh8LwCg1+uhVqtRXl7u1KWBRqOBTqer9b021XKbYx3aabVaaLXaRq1Ds9mM0tJSnD17FrGxsTAYDG5ZbktbhzJmU1xcjOTkZCUXoOnXYU3LjYqKwoQJEzB+/HgAQFpaGnbv3o29e/di586d+PPPP5Xpy8vLsXfvXuzduxfPPPMMdDod+vfvj0suuQSXXHIJBg4cCF9f3yZfh+7eR9hsNiQnJ6Ndu3bQah0PVzJ/vhu7j5Atm6rr0GQyITU1FR07dlS258qadR9h7AyM3APdgTugObcF+OEOlOsiYG17qdTrEGj8PqLqcu3H+w4dOsDPz0/Kz3dr+zuiKY73MmbT0v6OcHW8b6nrsOpyi4uLkZ6ejuzsbKSmpiI5OVm5RDstLQ3p6elIT093yzOo/Pz8EB0djejoaERFRSE2Nhbt2rVDZGQk2rZti6ioKERFRUGv17tch0FBQZgyZQpUKpXDe1epVACA559/HuXl5bDZbFLsI+wPX6sLqQrs/Px8AEBgYKDD8ICAAABAQUFBnZazefNmrF+/Hvfffz/atGmDc+fOuVyufdl1Xa4rVqsVSUlJDsMCAwMRExMDi8XiNA4AunXrBqDiDFVpaanDuOjoaAQFBaGgoAAZGRkO4/z8/NC+fXvYbDaXy+3cuTO0Wi0yMjKUfnbtH5qioiL4+/ujuLgYqampDvMZjUbEx8cDAE6fPu20gXfs2BEGgwHZ2dnIy8tzGBcaGoq2bduitLQUycnJDuO0Wi06d+4MAEhJSXHaObRv3x5+fn7Izc1FTk6Ow7jg4GBERUWhvLzc6b2qVCrlIVFpaWlOG1RMTAwCAwORn5/v9O2bv78/2rVr5zI3AOjSpQs0Gg3OnTvn9GCpiIgIhISEoKioCGlpaQ7jfHx80KFDBwBwudyEhATo9XpkZWUhLy8PZrMZZ8+ehVqtRlhYGMLDw1FaWoqUlBSH+XQ6nfIFU3JystPOIS4uDr6+vjh//jzOnz/vMK5NmzaIjIyE2Wx2apNarVau+EhNTXU6EMXGxiIgIAB5eXnIyspyGBcQEIDY2NhqP99du3aFSqVCeno6SkpKHMZFRUUhODgYRUVFTvfV+Pr6Ii4uDkIIl8vt1KkTdDodMjMznS5HCg8PR1hYGEpKSpwemmEwGNCxY0cAwJkzZ5wO1vHx8TAajU65AEBISAgiIiJQVlaGM2fOOMyn0WiUL+1SUlKcDsjt2rWDv78/cnNzkZ2d7TCuofuIAQMGYPz48QgKCsLvv/+OL7/8Ej/++CN++uknnD59WpmuvLxcuYd78eLF0Ol06NmzJ8aMGYPRo0ejXbt2Tttj27ZtERoaKtU+IjIyEkDFsaHqftqb9xFV36ts+wibzaZ8BqTZR0Q+jy6h/aEpPIYMVU8UVhnvjn1ETk4OcnNzHcbJtI+wH+9LSkrg5+fntr8j7GTcR8j+d0RhYaHDcaW17CMAuf+OyMnJcTrey76POHfuHLKzs5GZmYnMzEzk5+ejpKQEKSkpOHXqFDIyMpCVleWWy7W1Wi2ioqLQpk0bREREIDw8HBEREYiIiECfPn2UfUjVYtO+jygoKEBqaipsNpuyr3C1j+jVqxdefPFFPPvssw77qoiICDz22GPo1asXkpKSpNlHWCwW6HS6Oq1DqS4R37dvH4YOHYqtW7ciMTFRGW5/Q88991y1Dzqz27RpE6ZMmaJcUm40GpGWloaYmBi88cYbmD59usP0sbGxGDduXIMedGa/VMAerB3PYDu/V9m+8eMZ7Jb7zXNTZVNYWCjVGez6Ljc1NRV79+7Fnj17sGPHDpw4ccJpuZXfc9Uz3AEBAdKdnbLZbDhz5gzPYEu2j5DqDHbV5QIw/33GA+WFUOX/ChE2VLp1CPAMtp03/x3BM9hy/h3h6njvqXVoMBiQm5uLpKQkpKWlKf/sZ5rt90BnZGTU2KNSXYWGhiqXakdFRSEyMtLpLHRMTAxUKlWz7SOsVit2796NI0eO4MILL8SIESMcLguXZR/x559/QqVStbxLxIOCKrrgqPrti/2bOvv46qxYsQKPPPIIRo4ciY8//lhZMfYz166+1SkoKKh1ubWxv05VarW62nEAlA+aK/YPqSsqlarG5dof4la5HfZlaTSaGu9lqGm5Op2u2m9uanuvTbXc5lqHlTVmHdr/YFKr1TAYDA7TeiobT6xDGbPR6XQucwGabh26c7kJCQlISEjAbbfdBqCi4N65cye2b9+OHTt24OTJk8q0ZrNZuaT8ueeeg16vx6BBg5SHpg0aNMjpgZJ2zbmPsB8stVqt9PvZyhq7j2iK5bp7H2E/cyHtPkII4KdbgbObgN5LgAtmA5BrHdq5cx+hVquV9yfj57u1/R3RFMd7GbNpaX9H1HS8d+c6tJ9xrfpE7cr3Oru6gqIhfHx8nO5zrvp/VFRUje2vqjk/hyNHjkRcXJxyNYArnt5H1PXycECyAjshIQEajcbhD0EAyu/2S6KqEkJg1qxZePnll3HTTTdh3bp1DsH7+/sjJibGabn2S02rW663qG4HRJ7DTOTkTbnExMRgypQpmDJlCgDg7NmzDg9Nq1pw79q1C7t27cLChQudCu7BgwfX66DsTt6UiTeRPhdbOaD1BYQNOPhvIPsHYNAaQBfg6ZY1KelzaYWYiZwak4vVakVGRobL7qgqD6t6uXhDqNVqREVFVVs02/8PCgqqVwEoI2/aVqS6RBwARo8ejdLSUuzbt0/5oDz66KN4/fXXkZaW5vLJuI899hgWL16Mhx9+GMuWLXP5AZs2bRq2bduGP//8U/km4rXXXsPMmTPx119/sZsuImpV7AW3/Qz3qVOnqp3WYDA4neH2VMFNVGdCACdXAT/Pqii4A7sBwzcBQRd4umVEJCEhBPLz82vt0/ncuXNOl0c3RJs2bWosmqOjoxERESHNU7RbuxbbTRcAfPfdd0hMTMR1112HadOmYd++ffjPf/6DxYsXY86cOSgoKMAff/yBhIQEhIeH49ChQ+jbty/69euHl156yWl5F154IQIDA3Hs2DH06dMHgwcPxsMPP4w///wTjz/+OKZNm+byKeR1wQKbiLxFSkqKwxnuuhbco0aNwsCBA1lwk7yyvgf2TAJK0wBtADB4HdBuoqdbRUTNqKysrE59Old9qFpDGAyGOl2u7Q3dabYmLbrABiqeAr5gwQIcP34cMTExuO+++zB7dsX9Uzt27MCoUaOwdu1aTJ06FU8++SQWLVpU7bIqd1C+e/du/Pvf/8ahQ4cQFhaGW2+9FQsXLqzzE+GqagkFdllZGVJSUtCuXTv+ASwJZiIn5uIoJSVFKbZ37NiBv/76q9ppDQYDBg8erJzhdlfBzUzk1CJzKc0A9t4AZO4EfKKBq/8EtH6ebpVbtchcvBwzaXo2mw2ZmZk1Fs2pqalOT5BuCJVKhYiICIdC2VXx3KZNmxZ/uXZzawnbSosvsFuKllJgJyUl1fjQAGpezEROzKVmycnJDme461Jwjxo1Sim4a3oITnWYiZxabC62cuDw40DseCB8qKdb43YtNhcvxkwax97dU03Fc3p6utPTxBsiKCjIqVCuWjxHRkZ61X3CMmkJ20p96j5+SoiIqFbt27fHrbfeiltvvRVARV+glQvuyn1ImkwmZThQ8WTPqme4G1JwEzWKWgf0Weo4LOVjwDcGCO3vkSYRtUZms7lOl2tX7Yu9IfR6vdINlb1Qbtu2LXQ6HXr16oX4+HhER0fDz8+7rmghz2KBTURE9RYXF4fbbrtN6RaspoK7rKwM27dvx/bt2wFUFNxDhgxRCu4BAwaw4Kbml/c7sO9mQFiAfq8Ane70dIuIWjSbzYacnByXT9Su/H9WVpZbXq9t27Y13uccExOD0NBQp8u1W8LZUmrZWGATEVGjVS24T58+7VBwnz59Wpm2rKwM3333Hb777jsALLjJQ3xjgajLgLOfAD/eBeT8UFFoa/gHN1FVRUVFtXZLlZaWhvLy8ka/lr173ZqK58jIyBr7YibyJN6D3Qgt4R5sm82G0tJS+Pj4QK1We7o5BGYiK+bStOwFt/1MdnJycrXT+vj4YMiQIRgxYgQGDRqEYcOGwcfHpxlbSzXxqm1F2IA/FgOH5wEQQEg/YPhGwK/+XXd6mlfl4iVaQibl5eU4d+5crcVzQUFBo19Lq9U6Xa7t6v+AgKbtr74l5NLatIRM+JCzZtISCmwiIhmdPn1aObtd14K78hlunrkgt0r/Bth7E2A+DxhCgaEfAJGJnm4VUYMJIXD+/Plq+3K2/5yZmQl3lAJhYWG19ukcHh4ubfFEVBsW2M2kJRTY5eXlyM3NRZs2bRrcHRm5FzORE3PxrNOnT2P79u1KwZ2SklLttD4+Phg6dKhScPfv358FdzPy2m2l6HRFf9nnfwa6zQH6LPF0i+rFa3NpwZoqk5KSkmrvb658ubbJZGr0a/n6+tbaLVVUVFSLuq2H24p8WkImfIo4KaxWK3JychAYGCjtB7a1YSZyYi6e1aFDB9x+++24/fbbIYTA6dOnsXXrVmzZsgU///wzzp49q0xbWlqKb7/9Ft9++y0Ax4J71KhR6NevHwvuJuS124p/B+CyPcCf/wd0neXp1tSb1+bSgtU3E4vFgoyMjFr7dM7Ly2t02zQaDSIjI2stngMDA72uT2duK/LxtkxYYBMRkVRUKhXi4+Nx2223Yfjw4ejQoQPOnTvncIa7poLb19fX4Qw3C26qM40R6Db7n9+tJuCHO4Huc4GgCz3XLmrRhBDIzc2ttVuqc+fOwWazNfr1QkJCau3TuW3bttBoNG54d0RUFQtsIiKSmr3gjo+Px7Rp0yCEQFJSksM93JUL7pKSEmzduhVbt24F8E/BPWrUKKXg9oZvyKkZHFkInH4bOLsZGLQWaD/J0y0iyZSVlVVbNJ89exZnzpxBdnY2SktLG/1aRqOx1m6poqKi+FBIIg9jgU1ERC2KSqVCx44d0bFjR6Xg/uuvvxwK7tTUVGX6qgW3n5+f0xluFtzkUtdZQPb3QMZ2YM/1QLd/A72eBdT888nbWa1WZGVl1dqn8/nz5xv9Wmq1GhEREbUWz8HBwV53uTaRN+JDzhqhpTzkLDs7G2FhYfwDUhLMRE7MRT4NzaS2grsqPz8/DBs2TCm4L774Yn4GatDqthWbBTj8OHB0acXvEaMqnjJubOvZdlXR6nJpICEECgoKqu2Oyv5/eno6rFZro18vKCgI0dHRiI2NrbZ4joiIgFbLL22aC7cV+bSETPgU8WbSEgpsIqLWTgiBU6dOORTcaWlp1U7PgptcSv4I2H87YCkCfGOBSz4BQvp6ulVUiclkQnp6eq3Fc3FxcaNfS6/X19otVXR0NPz8/NzwzojI01hgN5OWUGDbbDaUl5dDp9Ox70FJMBM5MRf5NFUmlQtu+4PTaiq4/f39HQruvn37tuqCu1VvK/lHgd0TgNJzwJgDQEAnT7dI4c252Gw2ZGdnV9uXs/3/7OzsRr+WSqVC27Zta3yydnR0NEJDQ2u9XNubM2nJmIt8WkImLLCbSUsosMvKypCUlIT4+HgYjUZPN4fATGTFXOTTXJkIIXDy5EmHM9zp6enVTl+14L744otb1eWdrX5bKS8A8v8Awgb9M0zYAJVn/yhsqbkUFhbW2qdzeno6ysvLG/1aAQEBtXZLFRkZ6bYv0FpqJt6OucinJWTCfrCJiIjqSKVSoXPnzujcuTPuvPNOpeC2n93esWOHQ8FdVFSEr776Cl999RWAioJ7+PDhDme4W1PB3eroAh2L6/StwOEngGH/rehLmwBU3FNZ+XLt6ornwsLCRr+WTqdDVFRUjcVzdHQ0AgIC3PDOiIhqxr8AiIiIKqlccN91110QQuDEiRMOZ7jPnTunTF9UVIQvv/wSX375JYCKs2T2M9yjRo1Cnz59WHB7K5sV+PkBoOAY8NXFwND3gajLPd2qJiWEQE5OTo1P1k5NTUVWVhbccZFkeHh4jU/Wjo6ORlhYmLSXlRJR68MjPhERUQ1UKhW6dOmCLl26OBTclc9wVy64CwsLnQruyme4WXB7EbUGGPU1sPs64PwBYPsYoOcioPtjHr9kvCGKi4urLZor/282mxv9Wn5+frV2SxUZGQmDweCGd0ZE1Hx4hG8F2GeifJiJnJiLfGTMpHLBfffdd0MIgT///NPhDHdGRoYyfWFhIbZs2YItW7YA+KfgHjVqFEaOHInevXu3uIJbxlw8xq89cNlu4MBM4NSbwK/zgPM/AYPWA/qgZm1KdblYLBacO3eu1uI5Pz+/0W3QaDROl2u7+j8wMLBVfI5aw3tsiZiLfLwpEz7krBFawkPOiIioeQkhcPz4caXg3rFjh0PBXVVgYKDTGW6NRtOMLSa3OfkmcOA+wGYGAjoDl+8HDCFN9nJCCOTm5tbaLVVGRgZsNlujXy80NLTGojkmJgbh4eH8/BKR1+FTxJsJC2wiIqpNQwruSy65RCm4e/fuzYKlJcn5qeKS8chLgYFrgAaelSktLUVaWlqtxXNZWVmjm2w0GpXLsmvq01nWp/sSETU1FtjNpCUU2CaTCWlpaYiOjuZ9TJJgJnJiLvLx1kyEEDh27JhDwZ2ZmVnt9LIV3N6ai1uVZQNaP0DrU/F7eSGgMQJqHaxWKzIzM2vt0zk3N7fRzVCr1YiMjKy1T+fg4GCvujxTFtxW5MRc5NMSMmE3XaQQQqCsrMwtT/Ik92AmcmIu8vHWTFQqFbp164Zu3brhnnvuqbXgLigowOeff47PP/8cABAUFORQcPfq1atZC25vzaWxhBDIz893PsucehZTO26CylqCO9b44cjJLFit1ka/XnBwsEOh3LZtWxgMBlx00UXo0KGDMqyl3d/vTbityIm5yMfbMuFel4iIyINcFdxHjx51KLizsrKU6fPz8/HZZ5/hs88+A+BYcI8aNQo9e/bkJeVuZj+7Ul1fzvb/S0pKnObtFgM8+zQQ6AN8cX8hrlsJ/HCy+tcyGAy1dksVHR0NX19fh/nKysqQlJSE+Ph4XspNRORBLLCJiIgkolKpcOGFF+LCCy/EvffeqxTclbsFy87OVqavWnAHBwc7nOFmwV09m82GrKysavtytv+ck5PT4Nc4mgoMmA9segi4MAbY/aQKbx7sgcMlgxETE+tUPIeEhPBybSKiFowFNhERkcQqF9z33XcfhBD4448/HM5wVy648/Ly8Omnn+LTTz8F4Fhw289wq9Utr4/m+iooKKi2Oyr7z+np6bBYLI1+rcDAwFq7pYoM9QN+vhu6lI9wT7/fgPi+QP9H/rlPm4iIvAIfctYILeEhZ1arFcXFxfDz8+MZDEkwEzkxF/kwk7qx2WwOZ7h37tzpUHBX1aZNG6cz3PUpuD2di9lsRnp6eq3Fc1FRUaNfS6fT1dotVXR0NPz9/eu2QCGAY8uBQ48CwgbEjgcu2dzodgKez4WcMRM5MRf5tIRM+BTxZtISCmwiImpdbDab0xnumi5xthfco0aNwsiRI3HRRRdVW3BbrVbs3r0b6enpiIqKwvDhw932x5AQAtnZ2dV2R2X/v6YnrtdHeHh4jU/WjomJQWhoaNOc7c/YDnw/taK4Dunr/uUTEZFbscBuJi2hwLZYLMjPz0dQUBCfJCoJZiIn5iIfZuIe9oK78hnu2gruESNGKGe47QX3pk2bMGvWLJw9e1aZNjY2FitXrsTEiRNrbENxcXGNfTmnpqYiPT0dZrO50e/Xz8+v1j6do6KioNfrG/1ajWI1A5pKbTh/EGjTC1A1rKDn9iIfZiIn5iKflpAJu+kihcViQWZmJvz8/KT9wLY2zEROzEU+zMQ91Go1evTogR49emDmzJmw2Wz4/fffHc5wnz9/Xpk+NzcXH3/8MT7++GMAQEhICDp16oQff/zRadmpqamYNGkSnn32WXTp0qXa4rmgoKDR70Or1SIqKqraJ2vb/w8MDGz0azWLysV1zk/A1uFA5GXAkLcBfXC9F8ftRT7MRE7MRT7elknLfwdERERUZ2q1GhdddBEuuugipeA+cuSIUmzv3LnToeA+f/68y+IagNJn6WOPPdaoNoWGhtbYLVVMTAzCw8O99+FsRUkV/6d9DnzVDxi+CWjT07NtIiKiBmGBTURE1Iqp1Wr07NkTPXv2xAMPPOBUcH/77bcoLCxs0LJ9fHyqvVS78uXarb7f5rjJQEACsPs6oOgU8M0gYOCbQIcpnm4ZERHVEwtsIiIiUlQtuN99913ccssttc43efJkXHnllQ5FdFBQEPt0rquQi4ExPwN7bwLObQX23Qxk/wD0XQaodZ5uHRER1RELbC+nVqvh7+/vvZfVtUDMRE7MRT7MRA4xMTF1mu6ee+7ByJEjm7Yx3s4QCoz8EvhtAfD7f4A/X6p48FnCtFpn5fYiH2YiJ+YiH2/LhE8Rb4SW8BRxIiKixrBarejQoQNSU1Ph6k8GlUqF2NhYJCUlSdt/aYt09hMg+X/A4A0NfrI4ERG5R33qPu6xvZwQAhaLxeUfReQZzEROzEU+zEQOGo0GK1euBACny73tv7/44ossrt0t9lpgyDv/FNeWEiDpHaCa7YHbi3yYiZyYi3y8LRMW2F7OZDLhxIkTMJlMnm4K/Y2ZyIm5yIeZyGPixIn46KOPnC4Xj42NxUcffVRrP9jUSEIAP84Avr8V+P62imK7Cm4v8mEmcmIu8vG2THgPNhEREdVq4sSJuPbaa7Ft2zb8+uuv6NmzJy699FKeuW4uIX2AM+8Bp98B8n6t6MorIMHTrSIioipYYBMREVGdaDQaXHLJJWjXrh3i4+NZXDcXlQq44CGgTV9g7+SKAvurfhWXkMeM9XTriIioEl4iTkRERNQSRIwAxvwChA4CyvOAneOAX58ChM3DDSMiIjsW2EREREQthW8MkLgT6Hxvxe8n/g8oy/Bsm4iISMFuuhqhJXTTJYSAzWaDWq12evoreQYzkRNzkQ8zkRNzkchfGyoK7shLmYuEmImcmIt8WkIm9an7eA+2l1OpVLxHTjLMRE7MRT7MRE7MRSIdb1N+VKlU0KRvqbh0PP5Wz7WJFNxW5MRc5ONtmfAScS9nNpuRnJwMs9ns6abQ35iJnJiLfJiJnJiLnMy5J2Dbe3NFN14/3Q9YmY+ncVuRE3ORj7dlwgLby9lsNhQXF8Nm4wNQZMFM5MRc5MNM5MRc5GQzxiAn/JaKX078H7BtFFCS5tlGtXLcVuTEXOTjbZmwwCYiIiJq6VRqZEfNhHnwRkAXBGTvA77qC2Tu8nTLiIhaFRbYRERERF7CFnUVMOYAEHxRxdPFt40Gjr3o6WYREbUaLLCJiIiIvElAJ+Dy74G4KYCwAkWnPN0iIqJWg08R93JarRYRERHQahm1LJiJnJiLfJiJnJiLnJxy0foBQ94BYsYB7a7zbONaKW4rcmIu8vG2TNgPdiO0hH6wiYiIiAAANguw53qg4zQg9mpPt4aIqMWoT93HS8S9nNVqRX5+PqxWq6ebQn9jJnJiLvJhJnJiLnKqUy4nVgFnPwZ2XQMcng/YmGFT4rYiJ+YiH2/LhAW2lysvL0daWhrKy8s93RT6GzORE3ORDzORE3ORU51y6XQX0GVmxc+//wfI+bF5GtdKcVuRE3ORj7dl4h0XuhMRERFRzTR6oN9LQOgAoDQVCB/s6RYREXkdFthERERErUn8LZ5uARGR1+Il4kRERERERERuwALby6lUKvj4+EClUnm6KfQ3ZiIn5iIfZiIn5iIn5iIfZiIn5iIfb8uE3XQ1ArvpIiIiIiIi8m4tvpuub775Bv3794evry/i4+OxbNky1PV7gIMHD0Kn0+H06dNO42JjY6FSqZz+ZWdnu/kdEBERERERUWsjXYG9f/9+jBs3DhdccAE2bdqEm2++GXPmzMGSJUtqnffIkSMYO3YsLBaL07js7GykpqZi6dKl+P777x3+BQcHN8E7kUNZWRmOHj2KsrIyTzeF/sZM5MRc5MNM5MRc5MRc5MNM5MRc5ONtmUj3FPEFCxagT58+ePvttwEAY8aMQXl5OZ599lnMmjULPj4+TvOYzWa8/PLLePLJJ2E0Gl0u99ChQwCACRMmICEhocnaT0RERERERK2TVGewTSYTduzYgQkTJjgMnzRpEgoLC7Fnzx6X823ZsgVPP/00Hn/88WrPdB86dAgBAQHo2LGj29tNREREREREJNUZ7L/++gtmsxldunRxGN6pUycAwPHjx3HZZZc5zde/f3+cPn0aISEhWLdunctlHzp0CCEhIZg0aRK+/fZbWK1WjB07Fi+++CKioqIa1e6qlzOo1Wro9XrYbDaYzWan6e1n2U0mk9O95TqdDhqNBhaLxelSd/tyhRAwmUxOyzUYDFCpVDCbzbDZbMpr2Gw2ZVlWqxXl5eUO86lUKhgMBpfvBQD0ej3UajXKy8thtVodxmk0Guh0ulrfa1MttznWoZ1Wq4VWq23UOjSbzUom9td3x3Jb2jqUMZvy8nKHXICmX4cyZiPTPsLeRovF4jRe5s93Y/cRsmVTdR1W/r217SOaez9bn+Xajy32NnrbOpQtm7qsw6Y43suYTUv7O8LV8b6lrkMZs2nIOrQvq+p8gDz7CCFEnZ9yLlWBnZ+fDwAIDAx0GB4QEAAAKCgocDlfTExMrcs+dOgQUlNTcdddd+HBBx/E0aNH8eSTT2LEiBE4ePAg/Pz8GtRmq9WKpKQkh2GBgYGIiYmBxWJxGgcA3bp1AwCkp6ejtLTUYVx0dDSCgoJQUFCAjIwMh3F+fn5o3749bDaby+V27twZWq0WGRkZKCoqAgDlQ1NUVAR/f38UFxcjNTXVYT6j0Yj4+HgAwOnTp502xI4dO8JgMCA7Oxt5eXkO40JDQ9G2bVuUlpYiOTnZYZxWq0Xnzp0BACkpKU4bcfv27eHn54fc3Fzk5OQ4jAsODkZUVBTKy8ud3qtKpcIFF1wAAEhLS3PaoGJiYhAYGIj8/HxkZmY6jPP390e7du1c5gYAXbp0gUajwblz51BcXOwwLiIiAiEhISgqKkJaWprDOB8fH3To0AEAXC43ISEBer0eWVlZyMvLg9lsxtmzZ6FWqxEWFobw8HCUlpYiJSXFYT6dTqd8wZScnOy0c4iLi4Ovry/Onz+P8+fPO4xr06YNIiMjYTabndqkVqvRtWtXAEBqaqrTTjQ2NhYBAQHIy8tDVlaWw7iAgADExsZW+/nu2rUrVCoV0tPTUVJS4jAuKioKwcHBKCoqQnp6usM4X19fxMXFQQjhcrmdOnWCTqdDZmYmCgsLHcaFh4cjLCwMJSUlOHv2rMM4g8GgXLly5swZpwNCfHw8jEajUy4AEBISgoiICJSVleHMmTMO82k0GuXLwJSUFKeDQrt27eDv74/c3FynBynKtI+wa9u2LUJDQ6XaR0RGRgKoODZU3f978z6i6nuVbR9hs9mUz0Br2kfk5OQgNzfXYZxM+wj78b6kpAR+fn6tYh8h+98RhYWFDseV1rKPAOT+OyInJ8fpeN8a9hF2Mv4dYa/lcnNznY6tsuwjLBYLdDqd0zpxRapuuvbt24ehQ4di69atSExMVIbb39Bzzz2HuXPn1riMdevW4fbbb0dSUpKyowKA77//HlqtFv3791eG7d27F8OGDcOrr76Ke+65p97ttT+u3R6snUzfKtnPXhuNRuj1en7zLMHZKXu7tFot1Gp1i/3W1NvOTplMJphMJiUXgN8823lqH6HVapVirup6kPnz7e1np2w2m/K6la+QsvPWfYTsZ6fsWfj4+ECn03ndOpQtm7qsw6Y43suYTUv7O6KsrAxms9nheN9S16GM2TRkHbrjeN/U2fz5559QqVR16qZLqgL7999/R48ePbBp0yaH+7DPnz+P0NDQOhXC1RXY1QkODsaNN96IVatW1bu97AebiIiIiIjIu7XYfrATEhKg0Whw8uRJh+H23+2XO9RXfn4+1qxZgyNHjjgMt39jER4e3rAGtwBmsxmpqakuv5khz2AmcmIu8mEmcmIucmIu8mEmcmIu8vG2TKQqsI1GIy655BJs2rTJ4fKAjRs3IigoCAMGDGjQcg0GA+6//34899xzDsM//fRTlJaWYtSoUY1qt8xsNhsKCgqcLuMgz2EmcmIu8mEmcmIucmIu8mEmcmIu8vG2TKR6yBkAzJs3D4mJiZg8eTKmTZuGffv2YenSpVi8eDF8fX1RUFCAP/74AwkJCXU+82w0GjF37lwsWLAAERERuOqqq/Dbb7/hqaeewrXXXovRo0c38bsiIiIiIiIibyfVGWwAGD16NDZu3Ijjx49j/PjxePfdd7F06VLMmTMHAPDLL79g8ODB+OKLL+q13Hnz5uHVV1/FN998g6uvvhrLly/HjBkz8P777zfF2yAiIiIiIqJWRqqHnLU0v/zyC4QQ0Ov1nm5KtYQQyhMs69p3GzUtZiIn5iIfZiIn5iIn5iIfZiIn5iKflpCJ2WyGSqVC3759a51WukvEWxJZPwCVqVSqOvfZRs2DmciJuciHmciJuciJuciHmciJucinJWSiUqnqXPvxDDYRERERERGRG0h3DzYRERERERFRS8QCm4iIiIiIiMgNWGATERERERERuQELbCIiIiIiIiI3YIFNRERERERE5AYssImIiIiIiIjcgAU2ERERERERkRuwwCYiIiIiIiJyAxbYRERERERERG7AApuIiIiIiIjIDVhgExEREREREbkBC2wiIiIiIiIiN2CB3QKdPXsWwcHB2LFjR63Tvv/+++jevTt8fHzQrVs3rF+/3mmaAwcOYOTIkfD390d0dDQef/xxmM3mJmi5d6trLiaTCc8++ywuuOAC+Pn5oWvXrli4cKHTOo+NjYVKpXL6l52d3YTvwvvUNZeTJ0+6XN89evRwmO6bb75B//794evri/j4eCxbtgxCiCZ8B96nLpmsW7fOZR72f5X3ZdxWGsZms2HVqlXo2bMn/P390bFjRzz00EMoKCiocT4eV5pWQ3LhcaXpNSQXHleaVn0z4XGledhsNixbtgydO3eGj48PevXqhXfffbfW+VauXIlOnTrBx8cHffv2xZYtW5ymaTHbiqAWJTk5WXTr1k0AENu3b69x2o8++kioVCrx4IMPiq+++krMmDFDABDvv/++Ms2pU6dEUFCQGDNmjPjiiy/EsmXLhMFgEHfffXcTvxPvUp9c7r77buHr6yuee+458e2334rFixcLHx8fMW3aNGWarKwsAUAsXbpUfP/99w7/ysvLm/jdeI/65PK///1PABDbtm1zWN+HDx9Wpvn++++FTqcTt9xyi/jyyy/FE088IVQqlXjuueea+J14j7pmkpmZ6fTZ//7770X37t1Fu3btRGZmphCC20pjPPfcc0Kj0Yi5c+eKrVu3iv/7v/8TISEhIjExUdhsNpfz8LjS9BqSC48rTa8hufC40rTqmwmPK83jiSeeEDqdTtkfPfzwwwKAeO+996qdZ/ny5UKj0YiFCxeKLVu2iOuuu05oNBqxe/duZZqWtK2wwG4hrFarWLt2rQgNDRUhISF1Khi6dOkiJk+e7DBs8uTJolOnTsrvd911l4iNjRUmk0kZ9uqrrwq1Wi3OnDnj1vfgjeqbS3Z2tlCpVOL55593GL548WIBQNm5b926VQAQJ0+ebMrme62GbC9PPPGEiI2NrXGayy+/XAwYMMBh2Jw5c0RAQIAoKSlpbLO9WkMyqWrlypVCrVaL/fv3K8O4rTSM1WoVwcHB4t5773UY/sEHHwgA4qeffnI5H48rTashufC40vQaur3wuNJ0GppJVTyuuFdxcbHw8/MTjzzyiMPwESNGiEGDBrmcp6SkRAQHB4s5c+Yow2w2mxg0aJBITExUhrWkbYWXiLcQv/76K2bMmIHbbrsNb7/9dq3Tnz59Gn/++ScmTJjgMHzSpEk4efIkTpw4AQD4+uuvMXbsWOj1eodpbDYbvv76a/e+CS9U31wKCgowY8YMXHPNNQ7DL7jgAgDAX3/9BQA4dOgQAgIC0LFjR/c3uhWoby5AxTrv3bt3teNNJhN27NjhcpsqLCzEnj17GtNkr9eQTCrLyMjAvHnzcM8992DgwIHKcG4rDVNQUIBbb70VU6ZMcRhu3xedOnXKaR4eV5peQ3LhcaXpNSQXgMeVptTQTCrjccX9DAYD9u3bh9mzZzsM1+v1KCsrcznPDz/8gLy8PIftQKVSYeLEidi+fTtKS0tb3LbCAruFaN++PU6ePIkXXngBvr6+tU5/9OhRAECXLl0chnfq1AkAcPz4cZSWluLMmTNO04SHhyMwMBDHjx93U+u9V31ziY+Px6uvvoquXbs6DP/444+h0+mULA4dOoSQkBBMmjQJQUFB8Pf3xw033ID09PQmeR/epr65ABXrvLCwEEOGDIHRaERkZCTmzp2L8vJyABV/pJrN5hq3KapeQzKpbMGCBVCr1XjmmWcchnNbaZjg4GC89NJLGDp0qMPwjz/+GADQvXt3p3l4XGl6DcmFx5Wm15BcAB5XmlJDM6mMxxX302g06NmzJyIjIyGEQEZGBhYvXoxvv/0W9957r8t5ajq2WK1WnDp1qsVtKyywW4iQkBDExsbWefr8/HwAQGBgoMPwgIAAABXf/FU3jX262h50Q/XPxZXNmzdj/fr1mDFjBtq0aQOgYueempqKiy++GJ9//jleeOEF7Ny5EyNGjEBxcbE7mu7V6ptLdnY2UlNTcezYMcyYMQNff/017rrrLqxYsQJTp04FULdtiqrXmG0lMzMT69evx/3334/g4GCHcdxW3OeHH37A4sWLcfXVVzs9hAngccVTasvFFR5Xml5tufC40vzqs63wuNL0PvjgA0RGRuKxxx7DVVddhVtuucXldI05tsi6rWg93QBqGjabrcbxarW6TtNQ09q0aROmTJmCYcOG4fnnn1eGv/HGG9Bqtejfvz8AYPjw4ejevTuGDRuGDRs24J577vFUk72Sn58fvvnmG3Tu3BkdOnQAAIwYMQIGgwHz5s3DvHnzuL140Jtvvgmr1YpZs2Y5jeO24h579+7FuHHjEB8fj7Vr17qchseV5leXXKricaXp1SUXHleaV323FR5Xmt6AAQOwc+dO/Prrr5g/fz7GjBmDHTt2QKVSOUznjccWuVpDbhMUFAQAKCwsdBhu/4YnKChI+Rao6jT26ezLoKaxYsUKXH/99Rg6dCi++OILGI1GZdzgwYOVHbvd0KFDERQUhMOHDzd3U72ej48PLrvsMuWPILuxY8cCAA4fPlynbYqaxkcffYTLL78c4eHhTuO4rTTehx9+iMTERLRv3x7btm1DaGioy+l4XGledc2lMh5Xml5dc+Fxpfk0ZFvhcaXpJSQk4JJLLsH999+PlStXYteuXdi9e7fTdHXZDlratsIC20vZ78U6efKkw3D77926dYO/vz9iYmKcpsnMzERhYSG6devWPI1tZYQQeOCBB/Dwww/jhhtuwJdffqlc4gJUXCqzZs0aHDlyxGE+m80Gs9ns8mBAjXPixAm8/vrryMvLcxheWloKoOL+0YSEBGg0mhq3KXK/1NRUHDx4EJMnT3Yax22l8ZYtW4abbroJgwcPxq5duxAVFVXttDyuNJ/65ALwuNJc6pMLjyvNo77bCsDjSlPKysrChg0bkJmZ6TC8b9++AIC0tDSneWo6tuj1enTs2LHlbSsefoo5NcD27dvr1MVNfHy8y+5UOnfurPx+++23i/bt24uysjJl2Kuvvio0Gg27U6mnuuYyd+5cAUA8/PDDLvtpLC0tFT4+PmLKlCkOwzdv3qz0p0l1V5dcdu7cKQCI1atXOwyfNWuWCAwMFLm5uUIIIUaNGiUGDRrkkNucOXNEUFCQKC4uborme6W6bitCCLFx48Zqu0vhttI4q1atEgDEDTfc4NClVk14XGl6DcmFx5WmV99ceFxpeg3ZVoTgcaUpnTlzRgAQzz77rMPw5cuXCwDi999/d5rH3rVX1W66Bg4cKC677DJlWEvaVlhgt0Cu/jjNz88X33//vdLfpRBCrF27VgAQ99xzj/jyyy/FjBkzBADxwQcfKNMcPXpUGI1GMWrUKPHZZ5+J5cuXC4PBIO65557mfEteoS65HDx4UKhUKtG/f3/x/fffO/3Lz88XQgjx9NNPCwDioYceElu3bhUvvPCCCAwMFNdee60H3lnLVpdcrFaruPTSS0VAQIBYuXKl2Lp1q3jwwQeFSqUSK1asUObbtm2bUKlUYtKkSWLLli1i3rx5QqVSiSVLljTzu2rZ6roPE0KIp556ShgMhmqXxW2lYdLT04WPj4/o0KGD2L17t9O+KDMzk8cVD2hILjyuNL2G5MLjStNq6D5MCB5Xmtq0adOE0WgUS5cuFd9++61YsGCBMBgM4o477hBCCJGZmemwbxJCiAULFgiVSiXmzZsntmzZIiZNmiS0Wq3Ys2ePMk1L2lZYYLdArv44tQ9bu3atw7SrVq0SnTp1EgaDQXTr1k1s2LDBaXm7du0SAwcOFAaDQcTExIi5c+cKs9ncxO/C+9Qll/nz5wsA1f6zz2u1WsWrr74qunfvLoxGo4iJiRFz5swRJSUlzf/GWri6bi/5+fni4YcfFh06dBAGg0FceOGF4o033nBa3qZNm8RFF10k9Hq9iI+PF8uWLWuGd+Fd6rMPu+eee0RERES1y+K20jBvvfVWjfuitWvX8rjiAQ3JhceVptfQ7YXHlabTmH0YjytNy2QyiWeeeUZ07txZ6PV6kZCQIJYsWSKsVqsQ4p8vaiv/DWC1WsWiRYtEu3bthNFoFH379hVbtmxxWnZL2VZUQghRjyvKiYiIiIiIiMgFPuSMiIiIiIiIyA1YYBMRERERERG5AQtsIiIiIiIiIjdggU1ERERERETkBiywiYiIiIiIiNyABTYRERERERGRG7DAJiIiIiIiInIDFthEREREREREbsACm4iIqIV66qmnoFKp6vTvwQcf9HRzq1X5fbz44ouebg4REVGDscAmIiIiIiIicgOtpxtAREREjXfDDTfgxhtvrHZ8QkJCM7aGiIiodWKBTURE5AUuuOACjB8/3tPNICIiatV4iTgREVErMnLkSKhUKmi1WphMJsyfPx9xcXEwGo3o3bs3NmzY4HK+8vJyvPLKKxg8eDBCQkLg6+uLHj164Mknn0ReXp7LeQ4cOIDrr78ekZGRMBgM6NChA2688UYcOnSoxjZ+8803GDJkCHx9fREVFYX77rsPubm5jXznRERETY9nsImIiFqpq6++Glu3blV+P3z4MP71r3/h5MmTWLhwoTI8Pz8fV1xxBX744QeH+X///Xf8/vvveOedd7B161aHy9Dfe+89/Otf/4LFYlGGnTlzBmfOnMHHH3+ML774ApdeeqlTmz766CPs27cPQggAQGlpKV599VUcOXIEO3fudNt7JyIiago8g01EROQFnn766RqfIl6V1WrF1q1bMXXqVGzYsAF33nmnMu6ZZ57BwYMHld/vuecepbiOj4/HypUrsXr1agwaNAgAkJSUhPHjx8NqtQIA0tLSMH36dFgsFqjVatx7771499138cADDwAATCYTpk2b5lB82+3duxfjxo3Du+++i/nz50Oj0QAAdu3ahV9//dVNa4uIiKhp8Aw2ERFRK3XnnXdi9erVAIBbb70VGo0Gq1atghACGzZsQJ8+fXDq1Cl88MEHAICQkBD8+OOPCAsLAwDcfvvtGDRoEH7++WccOXIEGzduxOTJk7FhwwaUlpYCAO6//36sXLkSADBlyhSUlpbi1KlT6NWrF7KyshAVFeXQpn79+uGTTz5RvhQ4dOgQPvvsMwDAiRMn0LNnz6ZfMURERA3EApuIiMgL1PYUcVeqTn/TTTdh1apVACouFweA7du3K5dr33TTTUpxDQBarRYzZsxQzn7v2LEDkydPxoEDB5RprrzySofXsBf01bn88ssdzri3a9dO+bmkpKTO742IiMgTWGATERF5gYY8RTwkJMTh94iICOXnwsJCAEB2drYyLDY21mkZMTExys85OTkAKu7ZtqtckDekTTqdTvnZfgk6ERGRrHgPNhERUSt19uxZh98zMjKUn4ODgwE4Ft1Vp686LDw8HAAQFBSkDLMX3Xbnzp3D6dOnlbPiVdnvuSYiImqJWGATERG1UmvXrnX4fePGjcrPffr0AQAMHTpUGfbBBx/g/Pnzyu9Wq9Xhkm/7U8H79eunDPvkk08cXuPRRx9FfHw8/P39sX37dje8CyIiInnwEnEiIiIvcOzYMXz88cfVjvf19cXll1/uMGzTpk2YPHkyrr76avz000945ZVXAABqtRr/+te/AABdunTBVVddhS1btiAnJwcDBgzArFmz4OPjg7Vr1yr3W/fp0wfXXnstAOC2227DwoULUVpaitdeew1msxmjRo3CwYMH8fbbbwMA/Pz8HIp3IiIib8ACm4iIyAt8+OGH+PDDD6sdHxcXh9OnTzsM69OnD/73v//hf//7n8PwhQsXonv37srv69atw+WXX45Dhw7h1KlTSndbdp07d8bmzZuhVldcGBcdHY0333xT6Qf7rbfewltvvaVMr9Pp8P7770Ov1zf07RIREUmJl4gTERG1UqtXr8bChQsRGxsLg8GA3r17491338UTTzzhMF14eDh+/PFHvPzyyxgyZAiCg4NhNBrRvXt3LFiwAAcOHEBcXJzDPFOmTMHevXsxceJEhIeHQ6/XIz4+HjfeeCMOHDigXE5ORETkTVSiuqeMEBERkdcZOXIkdu7cCQA4ePAgevfu7dkGEREReRGewSYiIiIiIiJyAxbYRERERERERG7AApuIiIiIiIjIDXgPNhEREREREZEb8Aw2ERERERERkRuwwCYiIiIiIiJyAxbYRERERERERG7AApuIiIiIiIjIDVhgExEREREREbkBC2wiIiIiIiIiN2CBTUREREREROQGLLCJiIiIiIiI3IAFNhEREREREZEbsMAmIiIiIiIicgMW2ERERERERERuwAKbiIiIiIiIyA1YYBMRERERERG5AQtsIiIiIiIiIjdggU1ERERERETkBiywiYiIiIiIiNyABTYRERERERGRG7DAJiIij+rQoQNUKhVUKhXy8vKa7HWOHDmCt956q8mWXxshBNavX49ff/3VLcsLDg5W1ltjeWrdZGdn49lnn232121qTz31lJLNiy++6NG2fPbZZ/juu+882gYiotaEBTYREXm18+fP4+6770bv3r2xe/duj7Rh37596NevH6ZOnYrz5897pA2ueGrd2Gw2LF26FJ06dcLq1aub7XVbk99//x2XXnoprrnmGiQnJ3u6OURErYbW0w0gIiJqSrt27fJ4Eff888/jl19+8WgbXPHUuikoKMCcOXMAVJyJJ/d74403eOaaiMgDeAabiIiIiIiIyA1YYBMRUZOw2WxYtWoVBg0ahMDAQAQEBKBHjx5YuHAh8vPzXc5jNpvx9NNPo0OHDjAajejZsyfWrl3rNF1xcTEee+wxdOvWDT4+PjAajYiJicHkyZNx6NAhZbqpU6diwoQJyu/r16+HSqXCU089pQwrKirC3LlzERcXB6PRiF69euG9997Djh07lPtoK08/cuRIqFQqaLVamEwmzJ8/X5m3d+/e2LBhg0NbVSoVPvnkE+X3UaNGQaVS4fTp03Vaj1999RWGDx8OPz8/hISEYOrUqcjIyKhxnvfffx9DhgyBv78//P39MXDgQLz++uuw2Wz1Wjd5eXmYM2cOOnbsCL1ej7Zt22LSpEn4+eefXb5uWloaZs6ciYSEBBiNRkRFRWH06NEO73/dunVo06aN8vuZM2egUqkwcuRIh2UlJSXhgQceQOfOneHj44OQkBBcdtll2Lhxo9PrTp06Vclq//79GD16NAwGAyIjI7F9+/Ya11Xl+6W/+OILXHfddfDx8UFoaCjeeecdZbrMzEw8/vjjuPDCC+Hn54egoCAMGzYMb775JqxWa42v8f7776NXr14wGo3o0KED5syZg6KiIpfrb8aMGYiNjYVer0dMTAz+9a9/4cSJEw7TnT59Wmnz9OnT8corryAiIgI+Pj649tpr0aFDB6xcuVKZ/vbbb4dKpcKOHTuUYRs3bsTIkSPRpk0b6HQ6hIaGYsiQIVi9ejWEEDW+HyIiqoEgIiJyM4vFIq6++moBwOW/iy++WOTn5wshhIiLi1OGDx8+3OX0b731lsOyR48eXe2yfX19xbFjx4QQQvzrX/9yOc2CBQuEEEKUlpaKAQMGuJxmwoQJTtMLIcSIESMEAKHRaMRll13mct758+cr01fXzqSkpFrX4+rVq4VKpXKaNyEhQRiNRuX3yh588MFqX/P6668XNputTusmMzNTdOnSxeU0Op1ObNq0yeF1jxw5Itq2bVvtaz/zzDNCCCHWrl3rcvyIESOUZX399dciMDCw2mX961//Ut5H1ffSrl075WeDwSDy8vJqXMcLFixwOS8AcfToUSGEEL/88ouIioqqtj2XX365KC0tdbnMHj16uJynX79+DvMcO3ZMREZGupw2ICBA7Nu3T5k2KSmp2jY/+uijDttU5X/bt28XQgixZs2aat8LALFw4cIa1xkREVWPBTYREbndihUrlD/Wo6OjxQsvvCDWr18vBg4cqAx//PHHhRCOBbbRaBQLFiwQ7733nkhMTFSG9+nTR1n2pk2blOEDBgwQGzZsEG+//bbD9E899ZQQQoiff/5ZzJ07Vxk+atQosXnzZqVweuqpp5RxwcHBYvHixeLNN98UgwcPdll0CvFPgW3/N3XqVLFhwwZx5513KsNUKpX45ZdfhBBCbN682aGIX7Rokdi8ebMoLi6ucR1mZWUJHx8fZb5x48aJdevWiXnz5gmDweDQBrsvvvhCGXbhhReKV199VWzYsEFcccUVyvA333yzTuvm+uuvV8bdcsst4p133hEvvPCCiI6OFgBEUFCQyM7OVl67d+/eyvRXXnmlWLdunViyZIlSKKtUKnHs2DFx5swZ8c477yjThoeHi82bN4vdu3cLIYRIS0sTISEhyviJEyeKdevWiUWLFomgoCBl+OLFi5XXrvplwdy5c8XatWvFokWLav2sVi6GAYjp06eLt99+WzzyyCNCCCFKSkpEQkKCw3p68803xfLly5V1AUDMmDGj2mVecsklYs2aNeKZZ54R/v7+Tl86CCFE//79BQChVqvFzJkzxXvvvSeeeeYZERwcLACIDh06iPLyciGEY4FtL+I3bNggHn/8cXHkyBHx9ddfi3HjxinjZ86cKTZv3iyysrJEWVmZCA8PFwCEv7+/WLp0qfjwww/F/PnzhVqtVl6LiIgahgU2ERG5XdeuXZU/7n/++Wdl+Pnz58WFF14oJk2aJF5++WUhhGOBbR8mhBA5OTnKcH9/f2X4uXPnxCeffCIWLVokUlNTleFHjhxRpr/rrruU4Zs3b3Y481lZx44dlXFbt25VhpvNZtG5c+daC+w777zTYXkzZsxQxj344IPK8GuvvdbpLGJt3njjDWWekSNHOox7/fXXXRbYY8eOVYYlJycrwy0Wi4iPjxcARN++fWtdNxkZGUqxNXr0aIfX3rZtmzLPSy+9JIQQ4vvvv1eG9erVy+Hs8htvvCEGDhwo7rzzTvH9998LIYTIzc1Vpo+Li3NY/pNPPlltXgcOHFDaFRQUJMxmsxDCscC++uqr67R+7SoXwz179nQaX/ls76hRo4TValXGpaSkCD8/PwFUXNGQlpbmtMyuXbsqhbEQQnz00UfKuO7duwshhPjpp5+UYdOmTXN4/bfeeksZ9+mnnwohnAvsytuY3axZs5Txa9euVYabTCaxa9cu8corryjLs+vXr58AIPR6fb3WIRER/YNPESciIrcqKirC8ePHAQARERHo27evMq5Nmzb4/fffq533iiuuUH4OCQmBr68vSkpKUFJSogyPiIjANddcg8svvxw//vgj3n33Xfz4448O99qazeZa21lYWIi//vpLea3ExERlnE6nw0033YSFCxfWuIwbb7zR4febbroJq1atAgAcPny41jZkZ2djz549TsP79u2LI0eOKL9ff/31DuNvuOEG3H333U7z/fjjj8rP7du3d/mahw8fRklJCXx9fatt14EDB5T7tb/77rtq+9ret28fZs6ciQMHDijDxowZ4zD99OnTMX369Gpfq6pt27YpP99///0O4y6++GIMHjwYe/fuRX5+Pg4ePIgBAwY4TFP1Xm4A+OWXX1x2VTV+/HiH30eMGFFje+69916o1f88viY2Nhbjx4/Hu+++C6vVit27d2Py5MkO81977bXQav/5c2vChAnQarWwWCw4evQobDabQ25r1qzBmjVrnNoBVKzvq6++2mFYYGCgwzZWG71ej+HDh2P48OE4evQo1q5diwMHDmDnzp3KtlmX7YeIiFxjgU1ERG5V+QFmYWFh9Zo3JCTE4XedTgcADg/nslqteOyxx7Bq1SoUFhYq81100UXYuXMnANTpIU32eQGgbdu2TuOjoqLq3d6IiAiXy6/OkSNHHB40Zrd27VoUFxcrv1ddj0FBQTAajSgrK3MYnpubW+trWq1WpKenIyEhodpp6rIcADh79iyAxmVeVXZ2tvJzbGys0/iYmBjl55ycHKfxrrJ86aWXsH79eqfhVT8nruZtbHtCQ0Mdfler1QgLC8O5c+dgs9lQWlpa7/VdWXh4eJ3mrezrr7/GrFmzlC/CdDodevfujbZt2yIzM7PeyyMion+wwCYiIrcKDAxUfnZVcBw9ehRRUVEu+z/WaDS1Lv8///kPli5dCgC444478Mgjj+CCCy7AqVOn0KlTpzq3Mzw8HBqNBlar1eVTudPS0mpdxtmzZ9G7d2/l98rLaWz/zkFBQcrPVYuegoICp+IaAAICApCbmwuNRoP//e9/1Z55rq0oCwgIUH6+9NJLnc4k29nfY+W2Vs28pKQEp0+fRufOnZUvTGoSERGhFH5nz55FZGSkw/jKRaar91HTmfnauJq38pcmrgrc2tpT9XNks9mUdaTT6eDn5+ewvm+66Sans+B20dHRdWpzTU6cOIFrr70WJpMJXbt2xSuvvIJhw4bBaDRi+PDhLLCJiBqJ3XQREZFbBQQEoGvXrgCAc+fOOVz+WlBQgIEDB6JNmzbo0aOHw5npuqrcTdO8efNwwQUXAKi4DNiVypf0Vj5jqdPplII8NzcXW7duVcaZTCa8++67tbalahdildvWp0+fWtswcuRIiIrnoTj8mzp1qsNlvx999JHD61TuPqqyiy++GEDFWerg4GCMHz8e48ePR2JiIjZu3Ihff/0VwD9fglTXrr59+yrFeXJyMq666iplWTqdDl999RWSk5OVs9X9+vVT5v38888duq368MMP0b17d/j6+uI///lPja8LAMOGDVN+fvXVVx3GHTx4EPv37wdQcfVA5S837Cov227dunUu13Nd5q3cntdee81hvtTUVHz66acAAK1Wi0suucRp/o0bNzrc4vDZZ5+hvLwcwD+fEXtuQEVBbl/X48ePR15eHrZt24b09HSXZ9hdtbnq8Mpt/vzzz2EymQBU3OKQmJgIo9GIkpIS5YsNIiJqOJ7BJiIit5sxYwYeeughAMDYsWMxZ84cREZGYvXq1cql04mJidUWBzWpfLZ02rRpmD59Oo4fP44VK1Yow+0FBOB4hm/v3r14++23ERUVhcTERId23nDDDXjsscfQpk0bvP7668r92TXZtGkTJk+ejKuvvho//fQTXnnlFQAVxc2//vUvl21Yv349jh07hkmTJtV4Jvnaa69FaGgocnJysHPnTlx33XWYNGkSfvvtNyxfvtzlPHfddRe+/fZbAMCkSZPwyCOPIC4uDm+++aZyj/q0adOUe49rWjdXXnkltmzZghMnTmD06NG44447UFxcjKeeeko5A7tr1y4AwKBBg9CrVy8cPnwYR44cQWJiIm677Tbk5ubimWeeAQBYLBZcddVVAAAfHx+oVCoIIZCeno7Vq1fDx8cHt956K6ZPn46VK1eiuLgYa9euRWFhIa6++mqkpKRg+fLlypcy8+fPd7i3uanccMMNePLJJ3Hu3Dl89913uOyyyzBlyhTk5+fjhRdeUPqzvu+++1wWwGfPnsXIkSNx3333ISMjA88++6wyzv4ZGTJkCLp3747ff/8dO3fuxNVXX43rr78e6enpePrpp1FaWgqtVluvArhyth999BGEEBg2bJjD9vPKK68gLCwMBoMBr732GrKyspRxJpMJBoOh7iuKiIgqNO8z1YiIqDWorR/sXr16iYKCAiGE41PEc3NzHZZTuVsmu8pdPFX+p1KphFarFQDE0KFDlekzMjKU4fZ/06dPF0JUdMFkf3Jy1WVVflr4008/rSyv8vA+ffq4bEvl7peEEOKFF15wmmbPnj21rsdPP/1U6HQ6p3nj4uIcusWqbOrUqdWu9wsvvFBkZGTUad2cOXNGtG/fvtplzZ492+F1jxw5onT/5Opf1b6Ve/bs6TC+U6dOyrjNmzcrT+d29a/yU+KFcHyK+ObNm2tdr5VVfuL3ihUrXE6zb98+ERYWVm17xo0bJ0wmk8tlTpo0yeU8iYmJwmKxKPMcPHhQ6ZLL1T/7E9uFcHyKeK9evVy2uXJ3dvZ/77zzjsjKyqq2v229Xq/8fOLEiXqtRyIiqsBLxImIyO00Gg02b96MV199Ff3794evry8CAwPRs2dPLFy4ELt27XK477Q+br75Zrzzzjvo06cPfH19ERkZicTERHz99dcYM2YMAGD//v1IT08HUPHgqrVr16JTp07Q6XSIiopCXFwcgIozqdu2bcOsWbMQHR0Ng8GAfv364bPPPsOkSZOU1/Tz83PZltWrV2PhwoWIjY2FwWBA79698e677+KJJ55wmG7GjBm44447EBwcDB8fH/To0QN6vb7W93r11Vdj+/btGD16NHx8fBAWFoY77rgDP/74o8O9wZWtWbMG69evx/DhwxEUFAQfHx907doVjz/+OHbv3u1wlrWmddO+fXv88ssveOSRR9ClSxcYjUaEhIRg2LBh+OCDD7Bs2TKH1+3evTsOHjyIe++9Fx06dIBer0dUVBQuvfRSfPrpp5g/f75TOwcNGgS9Xo82bdrgwgsvVMaNHz8ev/76Kx544AF07twZRqMRwcHBSExMxObNm/H666/Xuu7cafDgwfj999/x+OOPK5e7BwQEYOjQoVizZg0++eSTavOcNWsW/vvf/6JPnz4wGAyIi4vD/Pnz8fnnnzs8c6B37944fPgw7r77bsTFxUGv16Nt27a4/PLL8fXXX2PmzJn1avO1116LOXPmoG3btjAYDOjcuTOCg4MRFhaGHTt2YPz48Wjbti0CAgLQq1cvLFy4EOvWrVPm37RpU4PWFRFRa6cSog6PWiUiIvJCn3zyCcxmM6KiotClSxeH4vPxxx/Hc889B6Dinuebb74ZQMV90/anlR88eNDlfcBERETUOvEebCIiarU++OADfPDBBwCAhIQEPPbYYwgMDMSxY8fwwgsvAKi4n3r48OGebCYRERG1ECywiYio1XrooYfw8ccfo6ysDKdOncL06dOdprn//vvRvn17D7SOiIiIWhreg01ERK3WgAEDsH//ftx6663o2LEjjEYjNBoNQkJCMHLkSKxfvx4rV670dDOJiIioheA92ERERERERERuwDPYRERERERERG7Ae7Ab4eDBgxBCQKfTebopRERERERE1ATKy8uhUqnQp0+fWqflGexGEEKgJVxhb7VaPd0EqoKZyIm5yIeZyIm5yIm5yIeZyIm5yEf2TOpT9/EMdiPYz1xfdNFFHm5J9crKypCUlIT4+HgYjUZPN4fATGTFXOTDTOTEXOTEXOTDTOTEXOTTEjL57bff6jwtz2ATERERERERuQGfIt4I9m8yZD6DTURERERERA1Xn7qPZ7CJiIiIiIiI3IAFtpczmUw4ffo0TCaTp5tCf2MmcmIu8mEmcmIucmIu8mEmcmIu8vG2TFhgezkhBEpLS1vE085bC2YiJ+YiH2YiJ+YiJ+YiH2YiJ+YiH2/LhAU2ERERERERkRuwmy4iIiIiImpR6tMvcWU2mw1CCNhsNthstiZoGdWXJzNRqVRQqVRuXSYLbCIiIiIikp4QAlarFWazucGXE5vNZqjVaphMJq+5JLml83QmKpUKer0eGo3GLcU2C2wvp9PpEB0dDZ1O5+mm0N+YiZyYi3yYiZyYi5yYi3yYifuZzWZYLBZotVqlGKpvQaTX66FWq+Hr6wuNRtNELaX68FQm9qsgrFYrTCYTtFotDAZDo5fLAtvLaTQaBAUFeboZVAkzkRNzkQ8zkRNzkRNzkQ8zcS97IaTX6xv1pYVarWYukvF0JlqtFmq1GuXl5RBCNPosNh9y5uUsFgvOnz8Pi8Xi6abQ35iJnJiLfJiJnJiLnJiLfJiJe9nPNqrVjStfLBYL8vPzmYtEZMhErVY3+L5+p2W5oT0kMYvFgoyMDO5EJMJM5MRc5MNM5MRc5MRc5MNMmkZjzy5arVacP38eVqvVTS2ixpIhE3c+6IwFNhEREREREZEbsMAmIiIiIiIicgM+5IyIiIiIiMiD5s6di82bN1c7PiwsDHv37nXb6/3www+47bbbsGHDBgwcONDlNLfeeisA4O2333bb67YGLLC9nFqthp+fX6MfCCE1IYDi04B/vKdbUietIpMWiLnIh5nIibnIibnIh5nISa1Ww2g0OuRitVqxe/dupKenIyoqCsOHD/dIF17h4eF45ZVXXI7z5u7eXGXSkrHA9nJ6vR7t27f3dDOa1ul3gR+mAT2fAS6YDajl7tOwVWTSAjEX+TATOTEXOTEX+TATOel0OkRFRSm/b9q0CbNmzcLZs2eVYbGxsVi5ciUmTpzYrG3T6/Xo3bt3s76mDKpm0tJ5x9cEVC17n4HueOS8tM59C9jKgUOPAt+NBorPeLpFNWoVmbRAzEU+zEROzEVOzEU+zEROlXPZtGkTJk2a5FBcA0BqaiomTZqETZs2eaiV1Vu1ahV69OiB/Px8h+Hr1q1D9+7dkZOTAwD46aefcMcdd6B///7o0aMHRo8ejZdffhk2m83tbdq7dy+mTJmCiy++GAMHDsTs2bORnp6ujLfZbFixYgVGjx6ttGX58uUoLy8HUJHJp59+imuuuQY9e/bEoEGD8MgjjyAjI8PtbW0OLLC9nMlkwp9//gmTyeTppjSdQWuBgW8BWn8gcxewpSeQ9HbFpeMSahWZtEDMRT7MRE7MRU7MRT7MRE5msxnJyckoLS3FrFmzXH4BYh/24IMPNnvXURaLxeU/e5uuvvpqWCwWfPPNNw7zffHFFxg2bBhCQ0Nx7NgxTJ06FcHBwVixYgVee+019OvXD6+88gq+/PJLt7b3448/xrRp0xAVFYUXXngBjz32GA4ePIgbbrhBKfbfeOMNvP/++7jvvvuwZs0a3HTTTXjrrbfw2muvAai4H/zRRx/FpZdeijfeeAOPPfYY9u/fj9mzZ7u1rc2Fl4hTy6dSAQnTgIiRwL5bgex9wPe3AamfAf1XAYYQT7eQiIiIiJrI//73Pzz55JMoLCys0/RWqxUWiwXZ2dnVTiOEQEpKCiIjI2EwGOq03ICAACxatAiTJk2q0/RVpaamonv37i7HzZkzB3fccQdiYmLQv39/fP7557j++usBAMnJyfj111+xYsUKAMCxY8cwZMgQLF26VLmveejQofjuu+/www8/YOzYsQ1qX1U2mw3Lli3DsGHDsHz5cmV43759cdVVV+Gtt97CnDlz8OOPP6JHjx647rrrAAADBgyAj48PAgICAAC//PILjEYjpk2bpgwLDg7Gb7/9BiGEW/uobg5SFtjffPMNnnjiCfz++++IiIjAfffdh9mzZ9e4cr/44gs8/fTT+O233xAaGorrrrsOzz77LPz8/JRpDhw4gEceeQQHDhxAYGAgpk6diqeeegp6vb453hY1Nf+OQOJO4I8lwG9PAcn/AzrNACJHe7plRERERNREli5dimPHjjXJsmsqwqtrS0ML7PDwcOWsblWV71G+5pprsGDBAmRlZSE8PBxffPEF/P39MXp0xd+848ePx/jx42EymZCUlIQzZ87g6NGjsFqtymXZ7pCUlISsrCynM83t27dHnz598OOPPwIABg4ciOXLl2PKlCkYPXo0Ro4ciVtuuUWZvl+/fnj55ZcxceJEjBkzBiNGjMCwYcMwYsQIt7W1OUlXYO/fvx/jxo3DDTfcgEWLFmHPnj2YM2cOLBYL5s6d63Kezz77DOPHj8dtt92GxYsX448//sDjjz+OrKwsvPfeewCAv/76C4mJiRg8eDD++9//4ujRo3jiiSdw/vx5rFq1qjnfIjUltRbo8QQQdQWQudOxuBai4mw3EREREXmNOXPmYP78+W49g20XFhZWrzPY//73v+s0rSt6vR4XXXRRrdONGTMGixYtwpdffonbbrsNX3zxBa644goYjUYAQFlZGRYtWoRPPvkEFosFsbGx6NOnD7RarVufCZCXlwegYh1VFRYWhj/++AMAMH36dPj5+WHjxo1YtmwZli5dis6dO2PevHkYNGgQevXqhYULF2LLli1Yu3YtVq9ejbCwMMyYMUPpKqwlka7AXrBgAfr06aP0tzZmzBiUl5fj2WefxaxZs+Dj4+M0z0MPPYRJkyZh7dq1AIDRo0fDarXipZdeQklJCXx9fbFkyRIEBATgk08+gV6vx1VXXQVfX1/cf//9ePzxx/mUR28T2q/in13RX8DeKUD/14CQPp5rFxERERG51aRJk+p81thkMiEtLQ0RERHo2rUrUlNTXRadKpUKsbGxSEpK8kiXXTUJCAjA6NGj8eWXX2LQoEE4ceIE5s+fr4z/z3/+g6+//hovvvgihgwZAl9fXwDA4MGD3dqO4OBgAK7P8mdlZaFNmzYAKrrhuvnmm3HzzTcjJycHO3fuxKpVqzBz5kylb+9+/frhmmuugc1mw/79+7FhwwY888wz6NWrF3r27OnWdjc1qR5yZjKZsGPHDkyYMMFh+KRJk1BYWIg9e/Y4zXPw4EGcOnUKM2fOdBg+a9YsnDp1SvlAff311xg7dqzD5eCTJk2CzWbD119/3QTvRg4GgwGdO3eu8zdvXuuXR4CcH4BvBgK/LwZszfvAisqYiZyYi3yYiZyYi5yYi3yYiZz0ej3atWsHHx8frFy5EgCcbkO1//7iiy9KV1zbXXvttTh06BDef/99REdHY8CAAcq4n3/+GQMHDkRiYqJSCx05cgTnz59361PE4+PjER4ejs8//9xheEpKCg4dOoS+ffsCAG688UY888wzAIDQ0FBMnDgRN998MwoKClBUVIQVK1Zg9uzZ0Ol08PHxwahRo/Doo48CANLS0tzW3uYi1Rnsv/76C2azGV26dHEY3qlTJwDA8ePHcdlllzmMO3ToEADAaDRi3Lhx2LZtG3x8fHDbbbdhyZIlMBgMKC0txZkzZ5yWGx4ejsDAQBw/frxR7S4rK3P4Xa1WQ6/Xw2azwWw2O01vv3zDZDI5fWOm0+mg0WiUJwa6Wq4QwuUTKQ0GA1QqFcxms8uNR6vVurz3QqVSKTv/qu8FqNgRqdVqlJeXOz1JUaPRQKfT1fpem2q5dV6HvV6CzmaFJu1T4PBjEGlbYO77BoRfnMO8Na1DrVbb6HVYebn2trljuc2yDv/WmM+h/b021XIbsw4tFotyyZhdU69DGbORbR+h1Wpb3OfbXfsIdy63KdahSqVqVfsIT3wOG7JclUol7ee7Rf8d8beGrEN3Hu9lzKa59rM2m01po/2YXXW5Op0OQgiX7dXr9cp+y76ssWPH4v3338cjjzzi0FVXTEwMli1bhrFjx8JsNisn6Fy1V6fTKeuwajb2NtlsNpf3PtuzMZvNsFqtMJlMyn3LWq0WarUaVqtVea9dunSBj48P1Go1hg8fjuDgYHz44YeYOnWqw3vu2bMnvvzyS7zzzjuIj4/H8ePHsXr1aqhUKpSUlACAsszy8nLlfVVdhzabDRkZGXjzzTeVNtmzSEhIwKBBg/DAAw9g/vz5eOihhzB+/HicP38er7zyCgIDA3HzzTfDZDKhb9++WL9+PcLCwtCjRw9kZmZizZo16NevH/z8/DBo0CCsW7cOjz76KMaOHYvy8nKsXbsWQUFB6Nevn/KaNa3DxmZj/5yrVCqo1Wqnz3d9HrYmVYFt788tMDDQYbj9aXIFBQVO82RlZQEAJkyYgClTpmD27Nn46aefsGDBAmRmZuK9996rdrn2Zbtabl1ZrVYkJSU5DAsMDERMTAwsFovTOADo1q0bACA9PR2lpaUO46KjoxEUFISCggKnvt/8/PzQvn172Gw2l8vt3LkztFotMjIyUFRUBKDiCYjl5eWIjIxEREQEiouLkZqa6jCf0WhEfHw8AOD06dNOO8mOHTvCYDAgOztbudfCLjQ0FG3btkVpaSmSk5Mdxmm1WnTu3BlAxTdZVXew7du3h5+fH3Jzc5XH+NsFBwcjKioK5eXlTu9VpVLhggsuAFDxrVbVA0ZMTAwCAwORn5+PzMzMf0a0fQ4RfpcgJOkpqLJ2Q7v1YmTEPoH8kGuVe7O7dOkCjUaDc+fOobi42GG5ERERCAkJQVFRkdO3aT4+PujQoQMAuMwmISEBer0eWVlZyM/PR3l5ufIHalhYGMLDw1FaWoqUlBSH+XQ6nfIFU3JystPBJC4uDr6+vjh//jzOnz/vMK5NmzaIjIyE2Wx2apNarUbXrl0BVDyxsupOKTY2FgEBAcjLy1O2MbuAgADExsZW+/nu2rUrVCoV0tPTlZ24XVRUFIKDg1FUVOTQPyIA+Pr6Ii4uDkIIl8vt1KkTdDodMjMzne6xCg8PR1hYGEpKSpz6sjQYDOjYsSMA4MyZM0472Pj4eBiNRmRmZiI7O1vJBQBCQkIQERGBsrIynDnj2L+6RqNRvrRLSUlx2um3a9cO/v7+yM3Ndbp0SqZ9hF3btm0RGhoq1T4iOjoaBQUF0Gg0Tv19Nsk+AoC/vz/atWvnct8ONM8+ouoxSbZ9hBACBoMB7dq1Q0FBQavZR+Tk5CA3N9dhnEz7CPvxPiYmBmFhYa1iH9Hsf0egfvuIoqIih+N9a9lHAE3zd4QQAqGhofD390dpaanTdmM0GhEVFQUhhMuznrGxsdDpdMjOzkZhYSG0Wi1UKhUGDBiAgwcP4siRI0hKSoLRaET//v2h0WiQlpYGnU6H2NhYABXbXNXPd3R0NAwGA/Lz8532PYGBgQgNDYXJZMK5c+ec1mFcXMWJnoyMDJSUlCA7O7vGe47/7//+DwkJCfDz80Pbtm0xZswYvPfeexgwYIDDe547dy7Ky8uxcuVKlJeXIyIiAjfccAPOnDmDn376CVarVfncZ2dnK/Pa16HNZkNaWhpMJhNSUlKwdOlSp7ZcccUVaN++Pfr374958+bho48+wn333Qc/Pz/07dsXt99+O8xmM9LS0nDjjTfCx8cHGzduxCuvvKIU1dOmTUNaWhoGDBiAefPm4cMPP8TWrVuhUqnQvXt3LF68WHkKuqt1qNFolNt8MzIynD7fkZGR8PHxQWFhodPfEf7+/ggPD4fFYkFaWprDlwpqtdppH2GxWKDT6arNpjKVcOed7o20b98+DB06FFu3bkViYqIy3P6GnnvuOacHnT3zzDOYP38+Zs6ciZdeekkZvnjxYjz22GM4fvw4/P39ERMTgzfeeAPTp093mD82Nhbjxo1r0IPOfvvtNwBQdv52Mp2dMplMOHv2LOLi4uDv789vntVq6M1nIfbdBlV2xT0f5n5rYGt/E4DmOTtVWlqKs2fPIjY2FgaDodV88wzIfXaqsLAQycnJSi6AnGenZD776u59hM1mw5kzZ9CuXTvlW/P6LrelrUMZs6m6Dk0mE1JTU9GxY0dle67MW/cRsp/Bth/vO3ToAD8/P69bh7JlU5d12BTHexmzac4z2BaLBf7+/g5nsysvty5nsIuLi3Hu3Dm0bdtWOTOt0WiUdVi1vSqVqlnOYFddh1qtVlmH1b3XplpubevQ1XttzDq02WxKJlWP93V9r+44g11WVgaDweDyDPaff/4JlUpVp4fQSXUGOygoCACcvv2xf1NnH1+Z/ez2uHHjHIaPGTNG6ejc3tebqycLFhQUuFxufdgDqKpyOK7UdE+OfWfpikqlqnG5VbsdU6vVyrI0Gk2N95LUtFydTlftNze1vdemWm6D1qG+I1SJO4GjS4Czn0CfcDOgcVxnNXXd1ph1aP+DSa1Ww2AwOEzrqWya63PYHMtt7Dp0lQvQdOtQxmxk2kfY/xjUarXS72cra+w+oimW6+59hP0qj9a2j2ju/Wx9l6tWq5X3523rULZs6rIOm+J4L2M2zbWftdlsyhUbtS23tjbZi/yq09W2Dmtabk1nN+2fg+o0dH/YVMutbR3W9F4bsg7txbFWq632dWt7r43NxmazwWazwWg0KmfNKy+3Pn1xS/WQs4SEBGg0Gpw8edJhuP13+yVRldnPHlf91sL+DYePj49yBrvqcu2XkblaLnk5tQbo/jhw2d5/imtbOfDHUsBSWvO8RERERERELkhVYBuNRlxyySXYtGmTw+UMGzduRFBQkMPT8ewuueQS+Pn54f3333cY/umnn0Kr1SqPo7/88svx+eefOxTiGzduhEajUTplp1ZIXembuyP/AQ7NAb7qC5z/xXNtIiIiIiKiFkmqS8QBYN68eUhMTMTkyZMxbdo07Nu3D0uXLsXixYvh6+uLgoIC/PHHH0hISEB4eDj8/f2xcOFCzJ49G23atMHEiROxb98+LFmyBLNmzUJ4eDiAig7o33//fVx55ZV4+OGH8eeff+Lxxx/HXXfd5dV9YGu1Wpf3M5ALYYMBnyig4Bjw9UCg59NAt0crzna7ETORE3ORDzORE3ORE3ORDzORk0ajQZs2baTtfqs18rZMpHrImd3mzZuxYMECHD9+HDExMbjvvvswe/ZsAMCOHTswatQorF27FlOnTlXmWbt2LZYvX44TJ04gOjoad911Fx599FGHa+h3796Nf//73zh06BDCwsJw6623YuHChXV+IlxV9oec1eVmd2ohTDnAj3cDKRsrfg8fCgx+G/CP92y7iIiIiFox+z3Y9m6qiNypts9Xfeo+KQvslqIlFNhWqxXFxcXw8/Pzmm+FmpwQQNIG4MBMwFIIaP2BIe8BsVe7ZfHMRE7MRT7MRE7MRU7MRT7MxL3cVWBbrVZlOcxFDjJk4s4Cm1//eLny8nKkpqa6fKw9VUOlAjr+C7jqMBA+DBBWILCL2xbPTOTEXOTDTOTEXOTEXOTDTORksViQlZXl1JUUeY63ZcKbQoiq4x8PXLoDyDsMBHb9Z3jhSSCgk6daRUREREREkuIZbKKaqDVASN9/fs/cA3zeFfjpPsBS4rl2ERERERGRdFhgE9VH1m5A2IATr1Z055VzwNMtIiIiIiIiSbDA9nIqlQpGoxEqlcrTTfEO3R8DRn0D+EQDBceBbwYDR54BbHW/Z4SZyIm5yIeZyIm5yIm5yIeZyEmlUkGv1zMXidgz8RYssL2cwWBAfHw8DAaDp5viPaIuA676DWh/PSAswK/zgW8vAQpP1Wl2ZiIn5iIfZiIn5iIn5iIfZiInvV6PmJgY6Qq6W2+9Fbfeequnm1Gj8vJyTJw4Efv27QPg3javX78eX3/9dZ2n37RpE7p27YqzZ89WO01ubi5GjhyJlJQUdzSxzlhgEzWEIQQY+mFFH9m6QCD7eyBzh6dbRURERETUJFatWoXIyEgMGTLErcvNzMzE+vXr3f4U8TZt2mDq1Kl4/PHH0Zw9U7PA9nJlZWU4duwYysrKPN0U76NSAfG3AFf9CvRYAHSc9s+4GjZiZiIn5iIfZiIn5iIn5iIfZiInk8mEpKQkmEwmTzelRcnMzMTq1atx7733un3ZZrMZAJqkm64pU6bgzz//xNatW92+7OqwwG4FmvMbm1bJLw7o+VRFwQ0A5jzg64FA6hfVzsJM5MRc5MNM5MRc5MRc5MNMmpGluPp/VscvOVTWkhqmL637cqtO2wz27t2LKVOm4OKLL8bAgQMxe/ZspKenK+NtNhtWrFiB0aNHo0ePHhg9ejSWL1/u0B/7559/jmuuuQY9e/bEoEGD8MgjjyAjI6PG1127di2io6PRo0cPp3H/93//hyFDhqBPnz649957nS7J/vbbbzFlyhT06dMHPXr0wJgxY/Duu+8CAM6ePYsrr7wSADB//nyMHj1amW/nzp248cYb0bt3bwwbNgxPPvkkCgoKHJZ9+PBh3HjjjbjoooswcuRIvPnmmw7j9Xo9rrjiCrz++us1vj93Yj/YRO72xxLg/E/AznFApxlA32WA1s/TrSIiIiLyXv/1r35c9FXAyH9OfLQ/0B9qWzXFcdsRQOKOf37/pANgynY9bUg/YMxP9W5qQ3388cd49NFHMW7cONx9993Izc3FSy+9hBtuuAGbN29GaGgo3njjDbz//vt49NFH0a5dOxw+fBgrVqyATqfDAw88gJ9//hlz5szBvffei/79++PcuXNYunQpZs+ejXfeeafa1/7ss88wceJEp+E///wzcnJy8OSTT8JqtWL58uW47bbb8Nlnn8Hf3x87duzAfffdh9tuuw0zZ85EWVkZ3nvvPSxcuBA9evRAt27dsGLFCjz00EO46667lGJ7+/btuOeee3DppZfixRdfRF5eHp5//nmkpqbirbfeUl7/qaeewgMPPIBZs2bhv//9L5YuXYqEhASMGjVKmWbMmDH48MMPkZSUhPj4eDcm4hoLbCJ3u2gBYDUBx1cAJ1cBGduAwe8AYQM83TIiIiIiaoFsNhuWLVuGYcOGYfny5crwvn374qqrrsJbb72FOXPm4Mcff0SPHj1w3XXXAQAGDBgAHx8fBAQEAKgoiI1GI+666y7lQW/BwcH47bffIIRw+XT1U6dOISsrCz179nQap9FosGbNGkRGRgIAOnbsiPHjx+Pjjz/GLbfcgpMnT2LChAl44oknlHn69OmDgQMH4ocffkCvXr1wwQUXAADatWuHCy+8EADw8ssvo1u3bnjllVeUNun1eqxcuRLZ2f984fHwww/jpptuAgD07t0bW7duxf79+x0K7IsuuggA8P3337PAJmqRNEbg4heAmLHA9/8CCk8AW4cAPZ4Euj/u6dYREREReZ/JRdWPU2kcfk3u9xOioqKqecJ7lTtorz1dw4s23922SUlJyMrKwuzZsx2Gt2/fHn369MGPP/4IABg4cCCWL1+OKVOmYPTo0Rg5ciRuueUWZfr+/ftjxYoVGDduHK644gqMGDECw4YNw4gRI6p9bfsl37GxsU7j+vbtqxTXANCtWze0a9cOP/30E2655RZMnz4dAFBcXIykpCQkJyfjt99+A/DPvddVlZWV4Y8//sDMmTMdCv6rrroKV111lcO0/fr1U3728fFBWFiY02XkAQEBCAwMrPGJ4+7Ee7C9nF6vR8eOHaXriqBViLwUGPsbEHcjIKzAbwuA355mJpJiLvJhJnJiLnJiLvJhJs1M61f9P41RmUyn0yG6fWfofIKrmd6n7sutOm0TysvLAwCEhYU5jQsLC0NhYSEAYPr06XjyySdRVlaGZcuWYezYsRg3bhz2798PoOLs8erVq9GuXTusXbsWN998My655BK8/fbb1b62fdk+Ps7v11V7QkNDlSL3/PnzmDlzJvr164fJkyfj5ZdfRlFRxZch9mcU6HQ6ABVnwwEgPz8fQgiEhobWul6qtkmtVrt89oGPj4/yuk2NZ7C9nFqtZv+LnqRvAwx9H4i5Gvj9P8AFDzITSTEX+TATOTEXOTEX+TATOanV6hb5pUdwcDAAOFwebZeVlYU2bdoAqHh/N998M26++Wbk5ORg586dWLVqFWbOnIm9e/dCr9dj+PDhGD58OEpLS7F//35s2LABzzzzDHr16uXyMnD7squeGQYqimFX7enTpw8A4JFHHsFff/2FdevWoU+fPtDr9SgtLcV///tfZXr7WWr7//7+/lCpVDh//rzDck0mE/bv349evXrVur6qKigoUN5HU+MZbC9XXl6O9PR0hycHkgd0mAJc+StgCK3IJC0N1iPPA2WZnm4Z/Y3binyYiZyYi5yYi3yYiZzKy8uRlZXV4nKJj49HeHg4Pv/8c4fhKSkpOHToEPr27QsAuPHGG/HMM88AqDiTPHHiRNx8880oKChAUVERlixZguuuuw5CCPj4+GDUqFF49NFHAQBpaWkuXzs6OhoAcO7cOadxP//8s3KGG6h4qndqaioGDRqkjL/88ssxcOBA5YuNXbt2Aai4r7zy/1arFQDg5+eHbt26Yfv27Q6vtWvXLtx1113IzKzf38/5+fkoLS1V3kdT4xlsL2e1WpGXl4c2bdool1+Qh6grLnuxWq0Qf22AJvkx4M/lwMC3gJhxHm4ccVuRDzORE3ORE3ORDzORk81mQ1FREQIDAz3dFCfnzp3DunXrnIZ36dIFQ4YMwcMPP4zHHnsMs2fPxjXXXIPc3Fy88sorCAoKwu233w6g4h7rNWvWICwsDH369EFGRgbWrl2LAQMGICQkBIMGDcLatWsxd+5cXHPNNSgvL8ebb76J4OBgpSiuqmPHjoiOjsbPP/+Myy67zGGczWbDXXfdhRkzZiA3NxfLly9Hly5dcM011wAAevbsic8++wzdu3dHZGQkfvnlF6xevRoqlQqlpRVPcvfzq+htZ//+/ejSpQt69eqFBx54APfccw8efvhhjB8/HtnZ2XjhhReQmJiILl264MiRI3Verz///DMAYNiwYXWepzFYYBN5QJnvBbAFdoe64Hdg59VAp7uAvi+wOy8iIiKiVio5ORnPPfec0/BJkyZhyJAhmDhxIvz8/PD666/jvvvug7+/P4YPH46HH34Y4eHhAIBZs2ZBr9dj48aN+L//+z8EBARg9OjRysPRRowYgWXLlmHNmjW4//77oVKpcPHFF2PDhg3KZeiuXHHFFdi1axfmzp3rMDwxMRHR0dH497//DYvFglGjRuGJJ55Qbo9YvHgxFi1ahEWLFgEAOnTogKeffhqffvopDhw4AKDikvCJEyfiyy+/xO7du7F3716MGjUKq1atwiuvvIL77rsPISEhuPrqqzFz5sx6r9ddu3ahZ8+eiImJqfe8DaESru4CpzqxPwHP/uh3GZWVlSl9vhmNxtpnoCanZNI+Csbji4BjL1SM8O8EDHkHCBvo2Qa2UtxW5MNM5MRc5MRc5MNM3Mtms6G0tBQ+Pj5Qqxt+l6vJZEJaWhqio6N5j3w9ZGRkIDExEWvWrEH//v3duuymzKSkpATDhw/HkiVLkJiYWO10tX2+6lP38R5sIk/RGIG+y4HR2wDfWKDoJLB1KHD8ZU+3jIiIiIhIERERgalTp+KNN97wdFPq5YMPPkDnzp1x6aWXNttrssD2chqNBqGhocpj78nznDKJHA1c9SsQdxMgbEBwD882sJXitiIfZiIn5iIn5iIfZiIntVqNoKCgRp0Fb61mzpyJjIwM7Nmzx63LbapMzp8/j3Xr1mHJkiUO/Wk3NV4i3ggt4RJxamFyDwFtev/ze8FxIKAL0Iw7BSIiIiLZuOsScSJXeIk41ZnNZkNxcbHy+HvyvBozqVxcF54CvroY2HkNUJrRbO1rrbityIeZyIm5yIm5yIeZyMleSDEXeXhbJiywvZzZbEZycjLMZrOnm0J/q3Mm538GbOVA2ufAlouAs582TwNbKW4r8mEmcmIucmIu8mEmTaOxF9+Wl5fj3LlzLa4fbG8mQybuvKibBTaRrOImA2MOAMEXAaYsYNe1wA93AuVFnm4ZERERUbNSqVRQqVRec5aT5GKz2ZTPWGOxH2wimQVfBFzxE/DrPODocuDUm0DGdmDw20D4YE+3joiIiKhZqFQqaDQamM1m2Gw2aDSaBhVENpsNQgjYbDYW65LwVCZCCAghYLVaYbFYoNVqWWATtQoaA9BnKRA9Fvj+NqDoFJD6GQtsIiIialX0er1SZFsslgYtw2w2w2w2o6ysjAW2JDydiUqlgsFggFbrntKYBXYr4K4PC7lPgzKJGFnRndfR5UCP+f8MF4JPGXcTbivyYSZyYi5yYi7yYSbupVKpoNVqodVqlbOPDVmGWq2GwWCA0WhsglZSfXkyE3ddFu6wTHbT1XDspos8zlYOfHcZEHcj0OluFtpERERERG7GbrqIWoukt4HMncBP9wA7xwGl5zzdIiIiIiKiVosFtpcrKyvDiRMnUFZW5umm0N/cmknHqUDfFYDaAKRtqejOK+Xjxi+3FeK2Ih9mIifmIifmIh9mIifmIh9vy4QFdivQ0IdAUNNxWyYqNXDBg39359ULMGUDuycA++8Aygvd8xqtCLcV+TATOTEXOTEX+TATOTEX+XhTJiywibxBcA/gih+AbnMAqIC/1gA/TPd0q4iIiIiIWhVpC+xvvvkG/fv3h6+vL+Lj47Fs2bIanxR48uRJ5Slwlf/16NFDmaasrAw6nc5pGn9//+Z4S0RNS2MA+iwBLt0OBHYDei7ydIuIiIiIiFoVKfsO2L9/P8aNG4cbbrgBixYtwp49ezBnzhxYLBbMnTvX5TyHDh0CAGzbtg2+vr7K8Mo/HzlyBBaLBe+88w4SEhKU4RqNpmneCJEnaD9MngABAABJREFURIwAxh6puHzc7sRrQNtRQNAFnmsXEREREZGXk7KbriuuuAJ5eXn44YcflGGPPvooXnvtNWRkZMDHx8dpnnnz5mH9+vVISUmpdrlvvvkm7rnnHhQVFcFgMDS6nS2hmy6bzYbS0lL4+PhArZb2goVWpdkzydwFfDsS0BiBPkuBzveyOy8XuK3Ih5nIibnIibnIh5nIibnIpyVk0qK76TKZTNixYwcmTJjgMHzSpEkoLCzEnj17XM536NAh9O7du8ZlHzp0CBdccIFbiuuWQq1Ww8/PT9oPa2vU7Jn4dwIiLwOspcCB+4EdVwGl6c3z2i0ItxX5MBM5MRc5MRf5MBM5MRf5eFsm0l0i/tdff8FsNqNLly4Owzt16gQAOH78OC677DKn+Q4dOoROnTphyJAh+OWXXxAcHIypU6di0aJF0Ol0yjRarRaXX3459u7dC4PBgOuvvx7Lli1DQEBAg9tc9ZHyarUaer0eNpsNZrPZaXqj0Qig4suEqhcQ6HQ6aDQaWCwWp6fp2ZcrhIDJZHJarsFggEqlgtlshs1mAwCUl5ejoKAAISEh8PHxgdVqRXl5ucN8KpVK+dLB1ePx9Xo91Go1ysvLYbVaHcZpNBrodLpa32tTLbc51qGdVquFVqtt1Do0m80wmUwoKChAYGAgdDqdW5Zb4zo0RsI8eDM0f62C9rcnoEr/CuKLi6AauBpoN9Ej61DGbEpKSpCbm6vk4rAOm+hz6G2fb3fvI1QqFfLz8xEQEABVlasuvHkfIVs2VddheXk5iouLERoaCpVK1Wr2EZ44VtVnufbjfWhoKIxGo9etQ9myqcs6bIrjvYzZtLS/I1wd71vqOpQxm4asQ/vx3t/f36nIlmUfIYRw+lukOtIV2Pn5+QCAwMBAh+H2ArigoMBpnuzsbKSmpsJiseD5559HXFwctm3bhiVLliAlJQXvvvsuhBD49ddfIYTA9OnTMW/ePPz00094+umn8ccff2Dnzp0N+tbEarUiKSnJYVhgYCBiYmJgsVicxgFAt27dAADp6ekoLS11GBcdHY2goCAUFBQgIyPDYZyfnx/at28Pm83mcrmdO3eGVqtFRkYGioqKAED50Gg0Gvj4+KC4uBipqakO8xmNRsTHxwMATp8+7bQhduzYEQaDAdnZ2cjLy3MYFxoairZt26K0tBTJyckO47RaLTp37gwASElJcdqI27dvDz8/P+Tm5iInJ8dhXHBwMKKiolBeXu70XlUqFS64oOJe4rS0NKcNKiYmBoGBgcjPz0dmZqbDOH9/f7Rr185lbgDQpUsXaDQanDt3DsXFxQ7jIiIiEBISgqKiIqSlpTmM8/HxQYcOHQDA5XITEhKg1+uRlZWFvLw8mM1m5OXlQa1WIywsDOHh4SgtLXW6xUGn0ylfLiUnJzvtHOLi4uDr64vz58/j/PnzDuPatGmDyMhImM1mJJ0+A6ivhL5LZ8SceRTG0qPA7uuALjORGvKg0040NjYWAQEByMvLQ1ZWlsO4gIAAxMbGVvv57tq1K1QqFdLT01FSUuIwLioqCsHBwSgqKkJ6uuNZdF9fX8TFxUEI4XK5nTp1gk6nQ2ZmJgoLHbsgCw8PR1hYGEpKSnD27FmHcQaDAR07dgQAnDlzxumAEB8fD6PRiJycHOUzbt8XhISEICIiAmVlZThz5ozDfBqNRvkiMCUlxelg0q5dO/j7+yM3NxfZ2dkO42TaR9i1bdsWoaGhUu0jIiMjkZOToxQOlXnzPqLqe22WfUSVNqnVanTt2hUAkJqa6rCPsNlsEEIo23Jr2kfk5uY6jJNpH2E/3uv1ehiNxlaxj5D974jCwkKH431r2UcA8v8dkZub63C8bw37CDsZ/46IiYlBTk4OysrKnI6tsuwjLBaL8oVMbaS7B3vfvn0YOnQotm7disTERGW4/U0999xzTg86Ky0txZ49e9C5c2dl5wQA//nPfzBv3jz88ccf6Nq1K3bt2oXw8HB0795dmebdd9/FLbfcgi1btuDKK6+sV1vt1+Lbg7WT6Vslk8mEs2fPIi4uDv7+/vzmWYKzU6WlpTh79ixiY2NhMBia/1tTmxnaP56B9s9lwMC3YIqd0qK+eW6qbAoLC5GcnKzkUuM6/Bu/ea7QVPsIm82GM2fOoF27dtBqHb8P9uZ9hGzZVF2HJpMJqamp6Nixo7I9V+at+wjZz07Zj/cdOnSAn5+f161D2bKpyzpsiuO9jNm0tDPYro73LXUdyphNQ9ah/XgfGxvrVMTKso/4888/oVKp6nQPtnRnsIOCggDA6dtn+7d19vGV+fj4uLxsfOzYsZg3bx4OHz6Mbt26YeTIkS6nAYDDhw/Xu8C2swdQlVqtrnYcgBrvBbd/SF1RqVQ1Llev1zu1w74sjUZT41PTa1quTqer9pub2t5rUy23udZhZY1Zh/adoVqthsFgcJi2+bIxAv2eBzrdBgR1h8F+uUvBccAvHtD8896bah3KmI1Op3OZC9B0n0Nv+3y7ex9hP1hqtVrp97OVNXYf0RTLdfd+1n6ZXGvbRzT3saq+y1Wr1cr787Z1KFs2dVmHTXG8lzEbT+xnG7Pcmo73LW0dyphNQ9ah/Xiv0+kafLxv6mzqenk4IOFDzhISEqDRaHDy5EmH4fbf7Zc8VHbixAm8/vrrTpcd2S+JCA8PR1paGt544w2nSwsqT0PUqgT3+Odp4uZcYNulwDeDgfyjnm0XEREREVELJV2BbTQacckll2DTpk0OlzRs3LgRQUFBGDBggNM86enpmDFjBv73v/85DP/www8RGBiIiy++GBaLBXfddRdef/11p2k0Gg2GDx/eNG/IwzQaDYKDg9nXt0SkzKTgOGArA3J/Ab7qCxx/GRC22ufzIlLm0soxEzkxFzkxF/kwEzkxF/l4WybSXSIOVPRpnZiYiMmTJ2PatGnYt28fli5disWLF8PX1xcFBQX4448/kJCQgPDwcAwbNgyXXnopZs+ejdLSUlx44YX44osv8NJLL+GFF15AcHAwgoODcfvtt2Pp0qXw8fHB4MGDsWfPHjz77LO4//77nZ5a7i10Oh2ioqI83QyqRMpMwgYBV/0G7J8GpH8F/PwAkPo5MGgt4Bvt6dY1CylzaeWYiZyYi5yYi3yYiZyYi3y8LRPpHnJmt3nzZixYsADHjx9HTEwM7rvvPsyePRsAsGPHDowaNQpr167F1KlTAVTco/30009j06ZNSE9PR0JCAh566CFMnz5dWabJZMLSpUvx9ttvKzfS33nnnfj3v//doCeI16fDcU+x2WwoLy9X7jchz5M6EyGAE68CBx8BrGWAPgQY8DrQfpKnW9bkpM6llWImcmIucmIu8mEmcmIu8mkJmdSn7pO2wG4JWkKBXVZWhqSkJKULAfK8FpFJ/jHg+1uA8z8D7ScDwz70dIuaXIvIpZVhJnJiLnJiLvJhJnJiLvJpCZnUp+6T8hJxIvKwoAuAy/YBx5YBnWb8M1zYAJWc3ywSEREREXka/1ImItc0eqD744AhpOJ3IYC9NwKH5gJW5/4CiYiIiIhaO57BJqK6ydoLJP/9pP70r4HB7wDB3T3bJiIiIiIiifAMditQn47RqXm0yEzaDgOGbwQMoUDuIeCri4FjK72qO68WmYuXYyZyYi5yYi7yYSZyYi7y8aZM+JCzRmgJDzkjcrvS9H+68wKAyMv+7s4rxrPtIiIiIiJqAvWp+3gGm4jqxycKGLkF6Pd/gMYHOLcV2Dmu4h5tIiIiIqJWjAW2lzOZTEhKSoLJZPJ0U+hvXpGJSgV0uRcY8wsQOgDo+2LFsBbMK3LxMsxETsxFTsxFPsxETsxFPt6WCR9y5uWEECgrKwPvBJCHV2USdAFw+X7H4vr0BxVnuSNGeK5dDeBVuXgJZiIn5iIn5iIfZiIn5iIfb8uEZ7CJqHEqF9eFJ4Ef7gC2jQIOzgGs3vFNJBERERFRXbDAJiL3MUYAHW4CIICjS4GvBwJ5RzzdKiIiIiKiZsECm4jcRxcADHwTGL4ZMIQBeYcruvM6+oJXdedFREREROQKC2wvp9PpEBMTA51O5+mm0N9aRSbtxgNX/QZEXwXYzMDB2cD2MYDN6umWVatV5NLCMBM5MRc5MRf5MBM5MRf5eFsmLLC9nEajQWBgIDQajaebQn9rNZn4RAIjPgf6v1bRnVfoAEAt73tuNbm0IMxETsxFTsxFPsxETsxFPt6WCQtsL2exWJCTkwOLxeLpptDfWlUmKhXQeQZw1a/ARQv+GV50GjDneapVLrWqXFoIZiIn5iIn5iIfZiIn5iIfb8uEBbaXs1gsyMzM9JoPrDdolZkEdALUf1/2YzUDu68DtvQEMrZ7tl2VtMpcJMdM5MRc5MRc5MNM5MRc5ONtmbDAJqLmVZoKlOcDJSnAtkuBXx5hd15ERERE5BVYYBNR8/KPB648BCTcCUAAx5YDX/cH8n7zdMuIiIiIiBqFBTYRNT+dPzBwNXDJJ4AhvKK4/qofcHQ5u/MiIiIiohaLBbaXU6vV8Pf3h1rNqGXBTCqJvQYYewSIubqiO6/kjzxWYDMX+TATOTEXOTEX+TATOTEX+XhbJiohhPB0I1qq336ruKT1oosu8nBLiFo4IYBTbwERIyseiAZU9JktcbdeRERERNQ61Kfu846vCahaQghYLBbwexR5MBMXVCqg0/R/imsAODwX2HsTYM5tliYwF/kwEzkxFzkxF/kwEzkxF/l4WyYssL2cyWTCiRMnYDLxKc2yYCZ1UJwCHF8JnPmgojuvc9ua/CWZi3yYiZyYi5yYi3yYiZyYi3y8LRMW2EQkH792QOIeIKAzUHIW+C4R+PlhwFrm6ZYREREREVWLBTYRySlsAHDlQaDTjIrfj68AvuoP5B72bLuIiIiIiKrBApuI5KX1Awa8Boz4HDC2BfKPVJzNtpR4umVERERERE60nm4AEVGtYsYCVx0BfrwTiJ0AaH093SIiIiIiIifspqsRWkI3XUII2Gw2qNVqqFQqTzeHwEwaxb67sq+3c9uA0nSgw83/DGvwopmLbJiJnJiLnJiLfJiJnJiLfFpCJvWp+3gG28upVCpoNOxLWCbMpBEq73RN54HvbwNK04DUz4D+rwGGkEYsmrnIhpnIibnIibnIh5nIibnIx9sy4T3YXs5sNiM5ORlms9nTTaG/MRM30QUCne8BVBog+b9/d+f1bYMXx1zkw0zkxFzkxFzkw0zkxFzk422ZsMD2cjabDcXFxbDZbJ5uCv2NmbiJWgv0mAdc/j0Q0AUoTQW+uwz4+UHAUlrvxTEX+TATOTEXOTEX+TATOTEX+XhbJiywiahlC+0PXPlLxdlsADi+Evi6H2DO82iziIiIiKj1YYFNRC2f1g/o/yow4gvAGAEE9wL0wZ5uFRERERG1MlIW2N988w369+8PX19fxMfHY9myZajpYecnT56ESqVy+tejR49GLZeIWpiYq4Crfqsotu3KsoGi0x5rEhERERG1HtI9RXz//v0YN24cbrjhBixatAh79uzBnDlzYLFYMHfuXJfzHDp0CACwbds2+Pr+0z9u5Z8bslxvoNVqERERAa1WuqhbLWbSxIzh//wsREXf2ee2Af1eAeJvrbY7L+YiH2YiJ+YiJ+YiH2YiJ+YiH2/LRLp+sK+44grk5eXhhx9+UIY9+uijeO2115CRkQEfHx+neebNm4f169cjJSXFrcutTUvoB5uoVSsvALZfCWTvq/i93XXAgNcBQ6hn20VERERELUZ96j6pLhE3mUzYsWMHJkyY4DB80qRJKCwsxJ49e1zOd+jQIfTu3dvty/UGVqsV+fn5sFqtnm4K/Y2ZNCNdIJC4E+j5DKDSAikbgS0XAWlfO03KXOTDTOTEXOTEXOTDTOTEXOTjbZlIdR7+r7/+gtlsRpcuXRyGd+rUCQBw/PhxXHbZZU7zHTp0CJ06dcKQIUPwyy+/IDg4GFOnTsWiRYug0+kavNy6Kisrc/hdrVZDr9fDZrO57M/NaDQCqCj8q15AoNPpoNFoYLFYYLFYXC5XCAGTyeS0XIPBAJVKBbPZrDzm3mQy4ezZs4iLi4O/vz+sVivKy8sd5lOpVDAYDC7fCwDo9Xqo1WqUl5c7ffA1Gg10Ol2t77Wpltsc69BOq9VCq9U2ah2azWaUlpbi7NmziI2NhcFgcMtyW9o6bPZsejwBRF0B296boS76E9gxBpaOM2C56FnofYKgVqtRUlLikEtzrEMZs5FpH2Gz2ZCWloZ27do5XTYm8+e7sfsI2bKpug5NJhNSU1PRsWNHCCG8Yx8h6X62Psu1H+87dOgAPz8/r1uHsmVTl3XYFMd7GbNpaX9HuDret9R1KGM2DVmH9uN9bGwsdDqdwzhZ9hFCCKiquc2wKqkK7Pz8fABAYGCgw/CAgAAAQEFBgdM82dnZSE1NhcViwfPPP4+4uDhs27YNS5YsQUpKCt59990GLbeurFYrkpKSHIYFBgYiJiYGFovFaRwAdOvWDQCQnp6O0lLH/nqjo6MRFBSEgoICZGRkOIzz8/ND+/btYbPZXC63c+fO0Gq1yMjIQFFREQAoH5qioiL4+/ujuLgYqampDvMZjUbEx8cDAE6fPu20IXbs2BEGgwHZ2dnIy8tzGBcaGoq2bduitLQUycnJDuO0Wi06d+4MAEhJSXHaiNu3bw8/Pz/k5uYiJyfHYVxwcDCioqJQXl7u9F5VKhUuuOACAEBaWprTBhUTE4PAwEDk5+cjMzPTYZy/vz/atWvnMjcA6NKlCzQaDc6dO4fi4mKHcREREQgJCUFRURHS0tIcxvn4+KBDhw4A4HK5CQkJ0Ov1yMrKQl5eHsxmM86ePQu1Wo2wsDCEh4ejtLTU6TYHnU6nfBGUnJzstHOIi4uDr68vzp8/j/PnzzuMa9OmDSIjI2E2m53apFar0bVrVwBAamqq0040NjYWAQEByMvLQ1ZWlsO4gIAAxMbGVvv57tq1K1QqFdLT01FSUuIwLioqCsHBwSgqKkJ6errDOF9fX8TFxUEI4XK5nTp1gk6nQ2ZmJgoLCx3GhYeHIywsTDloVmYwGNCxY0cgtB9Odv4QYSlLEZL9HmxnP8MZ/2mI63QRjEajUy4AEBISgoiICJSVleHMmTMOy9VoNMqXdikpKU4Hk3bt2sHf3x+5ubnIzs52GCfTPsKubdu2CA0NlWofERkZCaDi2FB1P+3N+4iq71W2fYTNZlM+A161jwBw5swZpz8a4+PjYTQakZOTg9zcXIdxMu0j7Mf7kpIS+Pn5tYp9hOx/RxQWFjocV1rLPgKQ+++InJwcp+N9a9hH2Mn4d0RMTAwAIDc31+nYKss+wmKxOBX/1ZHqHux9+/Zh6NCh2Lp1KxITE5Xh9jf03HPPOT2QrLS0FHv27EHnzp2VHRMA/Oc//8G8efPwxx9/IDc3t97LrQv7tfj2YO1k+laJZ7Dl+GaOZ7AryHJ2Sn3uGwidP0TokIp1qFKhsDAfySmpPIMt0T7CZrPhzJkzPIMt2T6i8hls+/ZcmTfsIyprKWeneAZbvmMgz2DL+XdEYWEhkpOTeQZbon2E/Xgv8xnsP//8EyqVqk73YEt1BjsoKAgAnL55tn9TZx9fmY+Pj8vLu8eOHYt58+bh8OHDyoqoz3Lrwx5AVWq1utpxAJQPmiv2D6krKpWqxuXq9XqndtiXpdFooNFoqp23puXqdLpqv7mp7b021XKbax1W1ph1aN8ZqtVqGAwGh2k9lY0n1qHHs+lwjeOIE6vg99cGGCKehsGQ4PT6TbUOZcxGpn2E/WCp1Wql389W1th9RFMs1937CPtlcl67j3DBE/vZ+i5XrVYr78/b1qFs2dRlHTbF8V7GbFra3xE6nc5lLkDLW4cyZtOQdWg/3ut0ugYf75s6m7peHg5I9pCzhIQEaDQanDx50mG4/Xf75Q6VnThxAq+//rrTJUf2yyHCw8MbtFxvoVKp4OPjU68PBTUtZiIZSynw29NQ53yPjscmQHNmQ0X3XuRx3FbkxFzkxFzkw0zkxFzk422ZSFVgG41GXHLJJdi0aZPD5QwbN25EUFAQBgwY4DRPeno6ZsyYgf/9738Owz/88EMEBgbi4osvbtByvYXBYECHDh1q/AaLmhczkYzWB7h8HxA+DGpbMXQ/3w3svg4oy659XmpS3FbkxFzkxFzkw0zkxFzk422ZSHWJOFDRp3ViYiImT56MadOmYd++fVi6dCkWL14MX19fFBQU4I8//kBCQgLCw8MxbNgwXHrppZg9ezZKS0tx4YUX4osvvsBLL72EF154AcHBwXVaLhG1Yv7xwKU7gKNLgd+eBM5uBrK/BwatAaKv9HTriIiIiKiFkOoMNgCMHj0aGzduxPHjxzF+/Hi8++67WLp0KebMmQMA+OWXXzB48GB88cUXACqum9+0aRPuvPNOrFixAuPGjcM333yD1atX48EHH6zzcr1VWVkZjh496vLGf/IMZiKnMnM5jqqvhWnkLiCwG1B2Dth5DVB8pvaZqUlwW5ETc5ETc5EPM5ETc5GPt2Ui3RlsAJgwYQImTJjgctzIkSOdnoYXGBiI5cuXY/ny5Q1eLhERAIjg3sCYn4FDcwF9G8AvztNN+n/27js8qir/4/h7WnqjJCQkBELoigUBQRQBKQqogCPYC2tBZUVRERFFcZciWFgV2678FFl1DQFEkKYgIEUsgICCtJCEQAIJJCFl2v39EWZkmADpc2byfT0Pj85tOXM/c8+dc++dc4QQQgghhI9QsoEthBBeZQyGzrPcOzs7sRMyF0H7caCXqlMIIYQQQnhS7hFxIYRQhrM3S4cVNt4F256HVddC4X7vlksIIYQQQihJGthCCHEhOiO0GwumCDi2AZZeCvs+kuG8hBBCCCGEG5129g+aRYX99ttvAHTs2NHLJTk3h8OBzWbDaDSi18v1FBVIJmqqUC6n0mDjPZC9tux1whDo+gEERddZOesTOVbUJLmoSXJRj2SiJslFPb6QSWXafWq+A1Fj9Ho9AQEByn5Y6yPJRE0VyiW0OfT5Di6bDnoTZCyEpR0hf0+dlbM+kWNFTZKLmiQX9UgmapJc1ONvmfjHuxDnZLFYyMzMxGKxeLso4jTJRE0VzkVvgA7jYMCPEHkRhCVDWMu6KWQ9I8eKmiQXNUku6pFM1CS5qMffMpEGtp9zOBzk5+fjcDi8XRRxmmSipkrn0uAyuP4nuGb+X72K20sg99daK2N9I8eKmiQXNUku6pFM1CS5qMffMpEGthBCVJUhCIJj/3q97XlY3hV+ewUcNu+VSwghhBBCeIU0sIUQoiZoDijOAs0Gv70IK6+Bgn3eLpUQQgghhKhD0sAWQoiaoNPDVfOg+6dgioTjm+CbS2Hvv2U4LyGEEEKIekIa2H7OaDTSuHFjjEajt4siTpNM1FQjueh0kHQnDNwOMb3Adgp+fBDWDoGS7Joqar0hx4qaJBc1SS7qkUzUJLmox98ykXGwq8EXxsEWQniJ5oA/Xi/7XbYhqKzRHdrc26USQgghhBCVJONgCxe73U5hYSF2u93bRRGnSSZqqvFcdHpo/zQM2FL22PiZjWuHtWb+hp+TY0VNkouaJBf1SCZqklzU42+ZSAPbz1mtVtLT07Fa5Qu9KiQTNdVaLg0ugYQb/3p9eDksuRiOba7Zv+OH5FhRk+SiJslFPZKJmiQX9fhbJtLAFkKIuqJpZT2MF+yBlT1g+0synJcQQgghhB+RBrYQQtQVnQ56L4Pmt4Fmhx0vlzW08//0dsmEEEIIIUQNkAa2EELUpYAG0OOzsiG9TJFw/Ef45jL4830ZzksIIYQQwsdJA9vP6XQ6TCYTOp3O20URp0kmaqrzXFrc8ddwXvYi2DIKsr+vm7/tI+RYUZPkoibJRT2SiZokF/X4WyYyTFc1yDBdQohq0xzwxxuQ/ztc+W9vl0YIIYQQQpxFhukSQghfodND+6fcG9cl2fDzWLAWeq9cQgghhBCi0qSB7edKSkrYs2cPJSUl3i6KOE0yUZNSufz4MOx+o+y32TkbvV0ar1EqE+EiuahJclGPZKImyUU9/paJNLDrAX8ZtN2fSCZqUiaXto9DSDMo3AerrobtL4LDP8aGrCxlMhFuJBc1SS7qkUzUJLmox58ykQa2EEKopknvsg7QWtxZ9hvtHa/Aih6Qv8fbJRNCCCGEEOchDWwhhFBRQBRc9Sn0+BxMUZC7peyR8aOrvVwwIYQQQghxLtLAFkIIlTUfAYN+gybXQVAsNOzs7RIJIYQQQohzkGG6qsEXhulyOByUlJQQFBSEXi/XU1QgmahJ+Vw0BxQfhpCE0681yFkPMdd4t1y1SPlM6inJRU2Si3okEzVJLurxhUxkmC7hotfrCQkJUfbDWh9JJmpSPhed/q/GNcCfs2FVT9j8AFgLvFeuWqR8JvWU5KImyUU9komaJBf1+Fsm/vEuxDlZrVaOHj2K1Vo/eyBWkWSiJp/LpSQb0MG+/5wezmuDt0tU43wuk3pCclGT5KIeyURNkot6/C0TaWD7ObvdTm5url91fe/rJBM1+Vwul7wM162GkEQo3A+rroFtE/1qOC+fy6SekFzUJLmoRzJRk+SiHn/LRBrYQgjhq5pce3o4r7vLfqO985+wojvk7/Z2yYQQQggh6iVpYAshhC8LiISrPoGr/wcBDSBvK1hOertUQgghhBD1ktHbBRBCCFEDEm+FxldB9vfQuOtf0+0WMAR4r1xCCCGEEPWIknewV6xYQZcuXQgJCSEpKYmZM2dS0dHEbDYbXbt2pVevXh7zEhIS0Ol0Hv+OHTtWw+9AHQaDgQYNGmAwGLxdFHGaZKImv8glJB5a3PHX6xM7YHEypC/wXpmqwS8y8UOSi5okF/VIJmqSXNTjb5kodwd706ZNDB48mBEjRvDKK6+wfv16xo0bh81mY/z48Rdcf9q0aWzZsoVrr73WbfqxY8fIzMxkxowZXH311W7zoqKiavItKMVkMhEbG+vtYogzSCZq8stcdr0KRRmwbhi0vB+umAWmcG+XqsL8MhM/ILmoSXJRj2SiJslFPf6WiXIN7EmTJnH55Zczd+5cAK6//nqsVitTpkxhzJgxBAcHn3Pdbdu2MWXKlHID2rp1KwBDhw4lOTm5VsquIofDgcViISAgwG/GlvN1koma/DKXK/9ddld713TYPweOroGr5kJ0D2+XrEL8MhM/ILmoSXJRj2SiJslFPf6WiVLvoLS0lDVr1jB06FC36WazmYKCAtavX3/OdS0WC/fccw+PP/44bdu29Zi/detWwsPDadmyZY2XW2UWi4UDBw5gsVi8XRRxmmSiJr/MxRAAl02Fvt9DaHM4dQBW9YRtz5f9NltxfpmJH5Bc1CS5qEcyUZPkoh5/y0SpO9j79+/HYrHQpk0bt+mtWrUCYPfu3fTr16/cdSdPnozVauXll19mwIABHvO3bt1Kw4YNMZvNrFq1CrvdzqBBg3jzzTeJi4urVrlLSkrcXuv1egICAlxXY84WFBQElF1QOPu35SaTCYPBgM1mw2azlbtdTdMoLS312G5gYCA6nQ6LxYLD4XD9DYfD4dqW3W73GMRdp9MRGBhY7nsBXFeTrFarx/h0BoMBk8l0wfdaW9uti33oZDQaMRqN1dqHFovFlYnz79fEdn1tH6qYjdVqdcsFan8f1lk2EV2gz48E7hiH7sDHsHMK1oB47EkjXYt4+3NY3nadOdtsNo/5Kn++q1tH1Nbnu6bqiDNf17c6oq7r2cps13lucZbR3/ahatlUZB/WxvlexWx87XtEeed7X92HKmZTlX3o3NbZ64E6dYSmaeh0Oo/lyqNUA/vkybKhZSIiItymh4eX/WYwPz+/3PW2bNnCzJkzWbt2rSu8s23dupXMzEweeughnnjiCX7//XdefPFFrr32Wn799VdCQ0OrVGa73c6BAwfcpkVERBAfH4/NZvOYB9C+fXsAsrKyKC4udpvXtGlTIiMjyc/P5+jRo27zQkNDSUxMxOFwlLvd1q1bYzQaOXr0KIWFhcBfj1wUFhYSFhbGqVOnyMzMdFsvKCiIpKQkAA4ePOhxILZs2ZLAwECOHTvGiRMn3OY1atSImJgYiouLOXTokNs8o9FI69atAUhPT/c4iBMTEwkNDSUvL4/jx4+7zYuKiiIuLg6r1erxXnU6He3atQPg8OHDHgdUfHw8ERERnDx5kuzsbLd5YWFhNGvWrNzcANq0aYPBYODIkSOcOnXKbV6TJk1o2LAhhYWFHD582G1ecHAwLVq0ACh3u8nJyQQEBJCTk8OJEyewWCxkZGSg1+tp3Lgx0dHRFBcXk56e7raeyWRyXWA6dOiQR+XQvHlzQkJCyM3NJTc3121egwYNiI2NdV0VPJNer3c96ZGZmelRiSYkJBAeHs6JEyfIyclxmxceHk5CQsI5P99t27ZFp9ORlZVFUVGR27y4uDiioqIoLCwkKyvLbV5ISAjNmzdH07Ryt9uqVStMJhPZ2dkUFBS4zYuOjqZx48YUFRWRkZHhNi8wMND15EpaWprHCSEpKYmgoCCPXAAaNmxIkyZNKCkpIS0tzW09g8HguhiYnp7ucVJo1qwZYWFh5OXleXSk6LU6osu/McbfSNHvH5Cm9YQzlomJiaFRo0ZK1RHOn/ucPHnSo/735zri7PeqWh3hcDhcn4H6VEccP36cvLw8t3kq1RHO831RURGhoaE19j3CScU6QvXvEQUFBW7nlfpSR4Da3yOOHz/ucb6vD3WEU023NZyqU0fEx8cDkJeX53FuVaWOsNlsmEwmj31SHp1W0e6568CGDRvo0aMHK1eupG/fvq7pzjc0depUj47OSkpK6NSpEzfddBPTpk0DcPUgvmbNGtdyGzduxGg00qVLF9e0H374gauvvprZs2fzyCOPVLq8v/32G4ArWCeVriqVlpaSkZFB8+bNCQsLkyvPCtydKi4uJiMjg4SEBAIDA332qqm/3Z0qKCjg0KFDrlygnlx5tpdg/G08XDQBY3iiUnWEw+EgLS2NZs2aYTS6Xw9W+fPt73enSktLyczMpGXLlq7j+Uz+WkeofnfKeb5v0aIFoaGhfrcPVcumIvuwNs73Kmbja98jyjvf++o+VDGbqt7BTktLIyEhwaMRq0odsWfPHnQ6HR07dvRY9mxK3cGOjIwE8Ljy7LxS55x/pokTJ+JwOHjhhRdcH5IzH10zGAzodDq6d+/usW6PHj2IjIxk27Zt1Sq3M4Cz6fX6c84Dznm3Hf76kJZHp9Odd7sBAe5j3p65LYPBcN4u8M+3XZPJdM4rNxd6r7W13brah2eqzj50VoZGo5HAwEC3Zb2VjTf2oYrZmEymcnOB2tuHSmTzy0TY/z5kfAld38eQaFamjigpKUGv12M0GpWvZ89U3TqiNrZb03WEsxz1rY6o63q2sts1Go21no18jyhTkX1YG+d7FbPxte8R5zvf+9o+VDGbquxD5/neZDJV+Xxf29lU9PFwUKyBnZycjMFgYO/evW7Tna+djzucKSUlhbS0NMLCwjzmmUwm5syZw9ChQ5k/fz5du3bl4osvds13XrGIjo6u4XeijqCgoHI7fRPeI5moqd7mkvw3OLoa8n6B9bdC0r3Q+V9girjwurWs3maiOMlFTZKLeiQTNUku6vG3TJTqRTwoKIiePXuSmprq9jjD/PnziYyMpGvXrh7rLF68mC1btrj969SpE506dWLLli3ceOONBAYGMnr0aKZOneq27ldffUVxcTG9e/eu9fcmhBBKimwP/TfCRRNAp4cDH8PSSyB7nbdLJoQQQgjhc5S6gw1lj3z37duX4cOHM3LkSDZs2MCMGTOYNm0aISEh5Ofns2vXLpKTk4mOji73OXhnp2idO3d2TRs/fjyTJk2iSZMmDBw4kN9++42XXnqJm2++mT59+tTZ+6trzt/KxcfHn/cxEVF3JBM11etcDAFw6T+h6UDYcPfp4byuhSv/A8n3e61Y9ToThUkuapJc1COZqElyUY+/ZaLUHWyAPn36MH/+fHbv3s2QIUOYN28eM2bMYNy4cQD88ssvdO/enSVLllRquxMnTmT27NmsWLGCG2+8kddee41Ro0bx2Wef1cbbUIazkwKF+rKr9yQTNUkuQHQPGLgVWt5f9oh47HVeLY5koibJRU2Si3okEzVJLurxt0yUu4MNMHToUIYOHVruvF69el1w55/Ze7iTXq/nkUceqVJv4UIIUW+YIqDbR3DJPyCk6V/Tj66GmGvLHiMXQgghhBDlkm9KQgghPJ3ZuD78DXzbB1YPgKLMc68jhBBCCFHPSQNbCCHE+ZXkgCEYjqyCpR0h7X/eLpEQQgghhJKkge3nTCZTuYO2C++RTNQkuZxHy3vg+l+g4RVgyYMfRpR1hmY5Wat/VjJRk+SiJslFPZKJmiQX9fhbJjrNX35N7gW//fYbQLk9mQshhN9xWOG3ybBrCmgOCEmEq+ZCTE9vl0wIIYQQotZUpt0nd7D9nM1m49ixY9hsNm8XRZwmmahJcqkAvQkufQX6roOwllB0CEqya+3PSSZqklzUJLmoRzJRk+SiHn/LRBrYfs5ms5GTk+M3H1h/IJmoSXKphOir4IatcOVHkGj+a7q9pEb/jGSiJslFTZKLeiQTNUku6vG3TKSBLYQQovJM4ZB8/1+vi4/C4tbwxxtlj48LIYQQQtRD0sAWQghRffv+DUUZ8MtY+K5/2f8LIYQQQtQz0sAWQghRfRdNgC7vgSEEjn4LSzrCwc+9XSohhBBCiDolDWw/p9frCQ8PR6+XqFUhmahJcqkmnQ5aPww3/AoNu4D1BGy4HX64EywnqrRJyURNkouaJBf1SCZqklzU42+ZyDBd1SDDdAkhRDkcVtjxD9j5T9Ds0P4ZuPxVb5dKCCGEEKJK6nSYLk3TSE1NpaSkrPfYEydOcOedd5KYmEjv3r35+eefq/snRDVomobVakWuo6hDMlGT5FKD9Ca45GXotx7iBsDFL1ZpM5KJmiQXNUku6pFM1CS5qMffMqlWA7u4uJh+/fpx66238vvvvwNwzz338Pnnn5OZmcn3339Pnz592LdvX40UVlReaWkpe/fupbS01NtFEadJJmqSXGpB427QexmYwspeaw746XE48VuFVpdM1CS5qElyUY9koibJRT3+lkm1GthTp07lu+++A+D333/n4MGDfP311wCuKxCFhYVMnz69msUUQgjh8/a+D3vegmWd4ffXZTgvIYQQQvidajWwFyxYAECbNm3o1q0bS5cuBSAmJoYjR45w4403ommaqxEuhBCiHksYBk0Hg8MCvz4F3/WDU+neLpUQQgghRI2pVgP7wIED6HQ6nnzySVq2bMnatWsB6Nu3LzExMZjNZgAyMzOrX1IhhBC+LbgJXPsVdH3/9HBe38HSS+DgZ94umRBCCCFEjahWA9tisQDQsGFDANavX49Op6N79+5lGz/d1XpgYGB1/owQQgh/odNBq4fghq3Q6MrTw3ndAVuf83bJhBBCCCGqrVoN7MaNGwNlv7/+5ZdfOHz4MAC9e/cGYP78+QA0a9asOn9GVENgYCBt27aVixwKkUzUJLnUsYjWZb2Md3wJDEHQ7BaPRSQTNUkuapJc1COZqElyUY+/ZVKtBna3bt3QNI1//vOf9OvXD4BWrVrRvn177rjjDhYuXIhOp6N///41UlhReTqdDr1ej06n83ZRxGmSiZokFy/QG6HjJLjpIDTq/Nf0o2vAXiKZKEpyUZPkoh7JRE2Si3r8LZNqNbCfeeYZjEYjFouFvLw8AJ599tmyDZ9+PDwqKoonnniieqUUVWaxWEhLS3M9zi+8TzJRk+TiRcFN/vr/vO2wegAs74o1+2fJREFyrKhJclGPZKImyUU9/pZJtRrY3bt3Z9myZVx//fVcffXVvPvuu4wcORKA9u3b06FDB1auXCmPiHuRw+GgqKgIh0OGw1GFZKImyUURluMQEAUnfsP43VUEHXgbh93m7VKJM8ixoibJRT2SiZokF/X4WybG6m6gT58+9OnTx2P6448/zvPPP1/dzQshhKhPmvSGgb/B5gfRZX5Fk8Mzsa/7EXrMhdBEb5dOCCGEEOK8qnUH+3xycnIoKiqqrc0LIYTwV0Ex0HMh1stn49AHYzi2VobzEkIIIYRPqHYD+/Dhwzz++OPk5uYCsHXrVhITE2ndujUxMTG8//771S6kEEKIekanw550P/vbpeJo2BWsJ6HokLdLJYQQQghxXtVqYKelpdGtWzfeeecddu3aBcA999xDRkYGmqZRVFTEo48+yurVq2uksKLyjEYjcXFxGI3V/jWAqCGSiZokF/UYjUYat+iGo8/3cOV/oN3Tf820yRNS3iLHipokF/VIJmqSXNTjb5lUq4E9efJkMjIyANi9ezdbtmxhx44d6HQ62rVrh16vR9M0Zs2aVSOFFZVnNBqJiorymw+sP5BM1CS5qMeVSUAQJI8EvaFshq0YlneBn58o+39Rp+RYUZPkoh7JRE2Si3r8LZNqNbC/++471zjXd999NytXrgSgbdu27Nq1i8cffxyAH3/8sfolFVVis9k4ceIENpv0wqsKyURNkot6zpnJ4a/h5C7YPQuWd4a8rV4pX30lx4qaJBf1SCZqklzU42+ZVKuBnZWVBcDdd99NQEAA69evR6fT0bt3bwCuvPJKAI4fP17NYoqqstlsZGVl+c0H1h9IJmqSXNRzzkwSb4Vrl0BQk7KG9vKusGs6OOzeKWg9I8eKmiQX9UgmapJc1ONvmdRIL+J6vR673c4PP/wAwFVXXQVAfn4+AFFRUTXxZ4QQQogy8QPLhvNKGAIOK2wdD9/2hsKD3i6ZEEIIIeqxajWwExISAFi2bBmfffYZBQUF6HQ6rrvuOo4dO8bs2bMBaNGiRbULKoQQQrgJioZrUss6QDOGQc46+OVJb5dKCCGEEPVYtRrY1113HZqmMXfuXO699150Oh1XXXUVsbGx3HrrrWzbtg2dTsdtt91WU+UVQggh/qLTlXWANnAbNB0End/ydomEEEIIUY9Vq4H94osvEhsbi6ZpaJqG0Whk6tSpALRu3RqASy+9lIceeqj6JRVVotfrCQkJQa+vkV8DiBogmahJclFPpTIJawm9voaQhL+mbXsBslbUXgHrKTlW1CS5qEcyUZPkoh5/y6Ra7yI+Pp6tW7cybdo0Jk6cyE8//USPHj0AuOKKK3jkkUf49ttvCQ0NrdR2V6xYQZcuXQgJCSEpKYmZM2eiaVqF1rXZbHTt2pVevXp5zPvpp5/o1asXYWFhNG3alAkTJmCxWCpVNl8TEBBA8+bNCQgI8HZRxGmSiZokF/VUK5OsFbDzH7B6APz0uAznVYPkWFGT5KIeyURNkot6/C2Tag82FhMTw7hx4zymP/zww1Xa3qZNmxg8eDAjRozglVdeYf369YwbNw6bzcb48eMvuP60adPYsmUL1157rdv0/fv307dvX7p3787//vc/fv/9d55//nlyc3N57733qlRWX+B8ukCn06HT6bxdHIFkoirJRT3VyiT6amj9GPz5Dux5C46sgqs+hYadaqew9YgcK2qSXNQjmahJclGPv2Wi0yp6a/g8CgoKeP/991mzZg3Hjx8nOjqavn378sADDxASElKpbQ0YMIATJ06wefNm17Rnn32Wd999l6NHjxIcHHzOdbdt20b37t2JjIykbdu2rFmzxjXv4YcfZunSpezbt891deTdd99l9OjRHDhwgMTExMq9aeC3334DoGPHjpVet66UlJRw4MABkpKSCAoK8nZxBJKJqiQX9dRIJoeXwab7oeQI6E3Q8WVoPw70hpotbD0ix4qaJBf1SCZqklzU4wuZVKbdV+0H3bds2ULbtm159tln+eabb/jxxx9ZsmQJTz75JO3bt2f79u0V3lZpaSlr1qxh6NChbtPNZjMFBQWsX7/+nOtaLBbuueceHn/8cdq2besxf/ny5QwaNMjt0QOz2YzD4WD58uUVLqMQQggf0vT6suG8mg0rG85r2wRYb/Z2qYQQQgjhp6r1iPiRI0cYNGgQx48fL/c30unp6dxwww1s3bqV6OjoC25v//79WCwW2rRp4za9VatWAOzevZt+/fqVu+7kyZOxWq28/PLLDBgwwG1ecXExaWlpHtuNjo4mIiKC3bt3X7Bs51NSUuL2Wq/XExAQgMPhKPc33s4rM6WlpR77zWQyYTAYsNlsHoOtO7eraRqlpaUe2w0MDESn02GxWHA4HK6/4XA4XNuy2+1YrVa39XQ6HYGBgeW+Fyj7XYRer8dqtWK3293mGQwGTCbTBd9rbW23Lvahk9FoxGg0VmsfWiwWVybOv18T2/W1fahiNlar1S0XqP19qGI2KtURzjLabDaP+ZXKhjDo/CmGmE8xbnsKR/M7MZzermr7UMVszv4cnvm6vtURdV3PVma7znOLs4z+tg9Vy6Yi+7A2zvcqZuNr3yPKO9/76j5UMZuq7EPnts5eD9SpI5yPsFdEtRrYr776KseOHUOn03HzzTczevRo4uPjycjIYPbs2SxcuJAjR44wY8YMXn311Qtu7+TJkwBERES4TQ8PDwcgPz+/3PW2bNnCzJkzWbt2rSu8imzXue1zbbci7HY7Bw4ccJsWERFBfHw8NpvNYx5A+/btAcjKyqK42L3jnaZNmxIZGUl+fj5Hjx51mxcaGkpiYiIOh6Pc7bZu3Rqj0cjRo0cpLCwEcH1oCgsLCQsL49SpU2RmZrqtFxQURFJSEgAHDx70OBBbtmxJYGAgx44d48SJE27zGjVqRExMDMXFxRw6dMhtntFodPUmn56e7nEQJyYmEhoaSl5eHsePH3ebFxUVRVxcHFar1eO96nQ62rVrB8Dhw4c9Dqj4+HgiIiI4efIk2dnZbvPCwsJo1qxZubkBtGnTBoPBwJEjRzh16pTbvCZNmtCwYUMKCws5fPiw27zg4GDXeO/lbTc5OZmAgABycnI4ceIEFouFjIwM9Ho9jRs3Jjo6muLiYtLT093WM5lMrgtMhw4d8qgcmjdvTkhICLm5ueTm5rrNa9CgAbGxsVgsFo8y6fV615MemZmZHpVoQkIC4eHhnDhxgpycHLd54eHhJCQknPPz3bZtW3Q6HVlZWRQVFbnNi4uLIyoqisLCQrKystzmhYSE0Lx5czRNK3e7rVq1wmQykZ2dTUFBgdu86OhoGjduTFFRERkZGW7zAgMDadmyJQBpaWkeJwTn40hn5wLQsGFDmjRpQklJCWlpaW7rGQwG10W79PR0j5NCs2bNCAsLIy8vj2PHjrnNU6mOcIqJiaFRo0ZK1RGxsbFAWR1+dj1dtTriagztlxHb4GIiTm+34MAKLIHNsRsbAGrUEWe/V9XqCIfD4foM1Kc64vjx4+Tl5bnNU6mOcJ7vi4qKCA0NrRd1hOrfIwoKCtzOK/WljgC1v0ccP37c43xfH+oIJxW/R8THxwOQl5fncW5VpY6w2WyYTCaPfVKeav0Gu23btuzdu5cbbriBr7/+2mP+4MGDWbp0Ka1bt67QXeINGzbQo0cPVq5cSd++fV3TnW9o6tSpHh2dlZSU0KlTJ2666SamTZsG4OpB3Pkb7MOHDxMfH8+HH37IAw884LZ+QkICgwcPrlJHZ85n8Z3BOql0Vam0tJSMjAyaN29OWFiYXHlW4O5UcXExGRkZJCQkEBgY6LNXTf3t7lRBQQGHDh1y5QJy5dnJm3ew09LSaNasGUaj+/XgGsmmIB3D8stBZ8J6xfs4Yvt7fR+qmE15d7AzMzNp2bKl63g+k7/WEarfnXKe71u0aEFoaKjf7UPVsqnIPqyN872K2fja94jyzve+ug9VzKaqd7DT0tJISEjwaMSqUkfs2bMHnU5Xod9gV+sOtvMqwvDhw8udf+utt7J06VKPqw3nEhkZCeBx5dl5pc45/0wTJ07E4XDwwgsvuD4kZz66ZjAYXHeuz96uc9vlbbcyzvVjfL1ef94f6pd3t93J+SEtj06nO+92z/ydeWBgIG3atHFty2AwYDCcu3Of823XZDKd88rNhd5rbW23Lvbh2aqzDwMCAjCZTK5MznzUxFvZeGMfqphNWFhYublA7e1DFbNRqY7QNI1WrVqVm0lFt3vefegohKAYyP+dgA03l/U6fvmrQIBX64ja2G5N1hGBgYGEhIS4cqkvdYQ36tnKbPfs872/7UPVsqnIPqyN872K2fja94jzne99bR+qmE1V9mFNnO9rO5vK9G5erU7OnI9un/34jJPzkZDyHs0uT3JyMgaDgb1797pNd752Pu5wppSUFHbv3k1YWJhr561du5a1a9diMpn4+OOPCQsLIz4+3mO7zsfIytuuv9DpdJhMJr/o8t5fSCZqklzUU+uZRLaH63+GNn8ve/3nO7DsCsj9uXb+np+QY0VNkot6JBM1SS7q8bdMqtXA7tixI5qmMXPmTI/G6759+5g5cyY6nY5LL720QtsLCgqiZ8+epKamuj3OMH/+fCIjI+natavHOosXL2bLli1u/zp16kSnTp3YsmULN954IwD9+/fn66+/dnvcYf78+RgMBvr06VOVt+8TnL8xKe/RB+EdkomaJBf11EkmxmDo/C/otQyC4yD/D1jeDXZOgeqPYumX5FhRk+SiHslETZKLevwtk2o9In7fffexevVqcnJy6NixI/369SMhIYGMjAxWrlxJaWkpOp2Oe++9t8LbnDhxIn379mX48OGMHDmSDRs2MGPGDKZNm0ZISAj5+fns2rWL5ORkoqOjy30O3nlnvXPnzq5p48aN47PPPuOGG25g7Nix7NmzhwkTJvDQQw9VaQxsX+FwOCgoKKBx48beLoo4TTJRk+SinjrNpOmAsuG8fhwF6SlwKg385Ep6TZNjRU2Si3okEzVJLurxt0yqdQf77rvvZvDgwWiahsViYcmSJbz//vssWbLEdae4b9++3HnnnRXeZp8+fZg/fz67d+9myJAhzJs3jxkzZjBu3DgAfvnlF7p3786SJUsqVdZ27dqxYsUKioqKMJvNvP766zz55JPMmjWrUtsRQgjhpwIbwdX/gx5fwOWv/TXddkruZgshhBCiQqp1BxsgNTWVF198kbffftutu/bg4GDuv/9+XnvttfOsXb6hQ4cydOjQcuf16tWr3DG3z+TsPfxs11xzDZs2bap0eYQQQtQTOh00P6PjTs0Ba4eAMRy6fgBB/nF1XQghhBC1o9oNbKPRyJQpU5gwYQKbN2/m2LFjNGnShE6dOqFpmmt8NX9+DFsIIYSfyvsVsr8HhxWObYRuH0HTG7xdKiGEEEIoqlqPiJ8pLCyM6667jhEjRtCrVy8iIiJ48803SUpKcg3OLuqe0WgkOjr6nN3wi7onmahJclGPEpk0vAL6b4bIDlByBNYMhC2Pga3Ie2XyMiVyER4kF/VIJmqSXNTjb5nUWAP7XDRNu+Aj3aL2GI1GGjdu7DcfWH8gmahJclGPMpk0vBwG/ARtx5S9/nM2LOsEx3/ybrm8RJlchBvJRT2SiZokF/X4Wya13sAW3mW32ykoKMBut3u7KOI0yURNkot6lMrEGAxXvAm9V0BwU8jfDZtHlv1Gu55RKhfhIrmoRzJRk+SiHn/LRBrYfs5qtZKRkYHVavV2UcRpkomaJBf1KJlJXL+y4bya3wHd5oCu/p1GlcxFSC4KkkzUJLmox98y8Y/78EIIIURdCWwIPea5T9v9FhiCIflvMn62EEIIUY9JA1sIIYSojvzd8OtTZT2NH/4aun4IQdHeLpUQQgghvKD+PdsmhBBC1KSwVnDJP0FvgoxFsLQjZC7xdqmEEEII4QWVuoM9cuTISm1869atlVpe1DydTkdgYCA6eWRRGZKJmiQX9fhMJnoDdHgG4vrDhjvh5E74fjC0GgWdZoIx1NslrFE+k0s9I7moRzJRk+SiHn/LRKdVYgwtvV5f6TeuaRo6nc5veoU702+//QZAx44dvVwSIYQQSrCXwLbn4Y/Xy15HXgw3/Ap6+UWWEEII4asq0+6r9CPiznGtK/pPCCGEqDcMQdDpNejzLYQkQMt7pXEthBBC1COVOuvPmTOntsohaklJSQlpaWk0b96coKAgbxdHIJmoSnJRj09nEtunbDgvU8Rf0/K2l/U0HtHae+WqAT6dix+TXNQjmahJclGPv2VSqQb2vffeW1vlELXI4XB4uwjiLJKJmiQX9fh0JgFRf/2/rQh+GA6n0uGKNyD5QZ8ezsunc/Fjkot6JBM1SS7q8adMpBdxIYQQorbZCiG4KdiL4MeH4fuboPiot0slhBBCiBomDWwhhBCitgXFQJ9VcPlM0AeUjZe9tCNkLPZ2yYQQQghRg6SBLYQQQtQFnR7aPwUDtkBURyjNgbU3weaHwF7q7dIJIYQQogZUapgu4c4XhulyOBxYLBYCAgLQ6+V6igokEzVJLurx60zsJbBtYtlwXrF9ofeysga4D/DrXHyY5KIeyURNkot6fCGTyrT7ZOwQP6fX6/2iNz5/IpmoSXJRj19nYgiCTjMhfhCEt/mrcW0rAr2p7J+i/DoXHya5qEcyUZPkoh5/y0TNSwSiRtjtdlatWsW7777LqlWrsNvt3i6SAKxWK0eOHMFqtXq7KOIMkot66kUmTXpDSPxfr39+Alb0gPw9XivShdSLXHyQ5KIeyURNkot6/C0TaWD7qdTUVFq0aEG/fv149NFH6devHy1atCA1NdXbRav37HY7eXl5csFDMZKLeupdJsVHIT0FcrfAN5fDn++Bgr/iqne5+AjJRT2SiZokF/X4WybSwPZDqampmM1mMjIy3KZnZmZiNpulkS2EECoKbgIDt0OTPmXDeW15BL6/UYbzEkIIIXyINLD9jN1uZ8yYMZTXd51z2hNPPOE3V4iEEMKvhCRAn5XQ6XXQB8LhJbD0YshY5O2SCSGEEKICpIHtZ9atW+dx5/pMmqaRnp7OunXr6rBUQgghKkynh3ZPwvU/QdQlUHoMNj8I1nxvl0wIIYQQFyANbD+TlZVVoeVGjhzJtGnT+PPPP2u5ROJsBoOBhg0bYjAYvF0UcQbJRT31PpOoi2HAj9D+Gej6AZgivF0iQHJRleSiHslETZKLevwtExkHuxpUHAd7zZo19O7du1LrXHrppZjNZm655Rbat29fSyUTQghRI9JTIW8rXPyC0sN5CSGEEP6iMu0+uYPtZ6655hoSEhLQ6XTnXMZkcv9Ctm3bNl544QU6dOjARRddxKRJk9ixY0e5v+MW1edwOCgqKsLhcHi7KOIMkot6JJNyWE6UPS6+45XTw3ntrvMiSC5qklzUI5moSXJRj79lIg1sP2MwGJg1axaARyNbp9Oh0+n4/PPP2b9/PzNmzODKK690W2bXrl1MnjyZjh070q5dO55//nl+/fVXaWzXIIvFQlpaGhaLxdtFEWeQXNQjmZQjIAq6vAsBDc4YzuvdOh3OS3JRk+SiHslETZKLevwtE2lg+6Fhw4aRkpJCfHy82/SEhARSUlIYNmwYSUlJPP3002zatIm0tDTeeOMNevTo4bb8nj17mDJlCp06daJVq1Y8++yzbNmyRRrbQgjhTc2Hw8DfILYv2Ithy6Pw/WAoPuLtkgkhhBD1njSw/dSwYcM4ePAgy5cvZ8aMGSxfvpwDBw4wbNgwj2UTExN54oknWL9+PRkZGbz11ltce+21bnfA9+/fz6uvvkrXrl1p0aIFTz31FBs3bvSbRzmEEMKnhMRD7+XQ6c3Tw3kthaUdoeSYt0smhBBC1GvSwPZjBoOBnj17MmjQIHr27Fmhnvni4+MZPXo0a9as4fDhw7z77rtcd911buseOnSI119/nauuuorExETGjBnDunXrZGxtIYSoSzo9tBsD1/8MDS6DZmYIauztUgkhhBD1mjSw64GqdnkfGxvLqFGjWLVqFVlZWXz44YcMGDAAo9HoWiYzM5N//etf9OzZk4SEBB577DFWr16NzWarqeL7JX8ZhsDfSC7qkUwqIOoi6L8JOr3+17RT6ZCzodb+pOSiJslFPZKJmiQX9fhTJjJMVzWoOExXXcjNzeWrr75i/vz5rFixotwOCaKjoxk6dChms5levXp59FwuhBCilmgO+K4vZH8PHZ6DjpNkOC8hhBCiGvximK4VK1bQpUsXQkJCSEpKYubMmeftXKukpIQJEybQvHlzQkJC6N69O8uXL/dYxmQyuXrTdv4LCwur7bfjVxo2bMh9993H4sWLyc7O5tNPP2XIkCEEBga6lsnJyeGDDz6gf//+xMbG8re//Y1vvvnGb3oHFEIIZdlLIaRZWUN75z9hRXc4+bu3SyWEEELUC0o2sDdt2sTgwYNp164dqamp3HnnnYwbN47p06efc50HHniAd955h2effZavvvqKVq1aMWjQINatW+daZseOHdhsNj799FM2btzo+rd69eq6eFteUVpayt69eyktLa2V7UdGRnLnnXeyYMECcnJy+PzzzzGbzQQHB7uWyc3N5aOPPmLgwIHExMRw77338tVXX1FSUlIrZVJdbWciqkZyUY9kUkXGYOj+MVz9JQQ0hNyfYVkn2P12jQznJbmoSXJRj2SiJslFPf6WifHCi9S9SZMmcfnllzN37lwArr/+eqxWK1OmTGHMmDFujTeAgwcPMm/ePN5++20effRRAPr06cMPP/zA7NmzueaaawDYunUrRqMRs9nsdrfVn2mahtVqrZOhtcLDwxkxYgQjRozg1KlTLFu2jJSUFBYvXsypU6cAOHnyJJ988gmffPIJYWFh3HjjjZjNZq6//npCQkJqvYwqqMtMRMVJLuqRTKop0QyNr4JN98ORFfDz3+Hw19D902p1hia5qElyUY9koibJRT3+lolyd7BLS0tZs2YNQ4cOdZtuNpspKChg/fr1HuvExcWxZcsW7rrrLtc0vV6P0Wh0u0u6detW2rVrV28a194UGhrKLbfcwmeffUZOTg4LFy7krrvuIiIiwrVMYWEhn332GbfccgvR0dEMHz6c//3vfxQWFnqx5EII4UdCmkLvZXDFW2AIgqJ0MIZ6u1RCCCGE31LuDvb+/fuxWCy0adPGbXqrVq0A2L17N/369XObFxgYSOfOnQFwOBxkZmby2muvsW/fPt566y3Xcs472P379+eHH34gMDCQW2+9lZkzZxIeHl7lMp/9qLNerycgIACHw1Hub46DgoKAsosJZ1+pMZlMGAwGbDabR0/czu1qmlbuIxSBgYHodDosFotrfOrS0lIcDodrW3a7HavV6raeTqdzXXQo77HtgIAA9Ho9VqvVYygug8GAyWQ673sNDg5mwIABDBgwgLfffpvvvvuOBQsWsHjxYk6cOAFAUVERX375JV9++SVBQUEMGDCA4cOHM3DgQNf+8tY+dDIajRiNxmrtQ4vF4srE+fdrYrtVzQa8sw9VzMZqtbrlArW/D1XMxlufw/K26yyjzWbzmK/y57u6dUStZNPyYRwNrgbNhmbTga0Egx5MOgsOQ1il9uGZr+tbHVHX9Wxltus8tzjL6G/7ULVsKrIPa+N8r2I2vvY9orzzva/uQxWzqco+dG7r7PVAnTpC0zR0Op3HcuVRroF98uRJALc7nYCrAZyfn3/e9adPn86ECRMAePDBB+nbty9Q9ujB9u3b0TSNBx54gIkTJ7JlyxZefvlldu3axffff49eX/kb+na7nQMHDrhNi4iIID4+HpvN5jEPoH379gBkZWVRXFzsNq9p06ZERkaSn5/P0aNH3eaFhoaSmJiIw+Eod7utW7fGaDRy9OhR111g54emsLCQsLAwTp06RWZmptt6QUFBJCUlAWWP2599ILZs2ZLAwECOHTvmahA7NWrUiJiYGIqLizl06JDbPKPRSOvWrQFIT093HcTt2rXjueeeY9asWfz444/MnTuXJUuWuLZdUlLCokWLWLRoEQEBAfTo0YP+/fvTu3dvIiIi0Ol0tGvXDoDDhw97HFDx8fFERERw8uRJsrOz3eaFhYXRrFmzcnMDaNOmDQaDgSNHjrgea3dq0qQJDRs2pLCwkMOHD7vNCw4OpkWLFgDlbjc5OZmAgABycnI4ceIEFouFjIwM9Ho9jRs3Jjo6muLiYtLT093WM5lMrotLhw4d8qgcnJ365ebmkpub6zavQYMGxMbGYrFYPMqk1+tp27YtUDbU2tmVaEJCAuHh4Zw4cYKcnBy3eeHh4SQkJJzz8922bVt0Oh1ZWVkUFRW5zYuLiyMqKorCwkKysrLc5oWEhNC8eXM0TSt3u61atcJkMpGdnU1BQYHbvOjoaBo3bkxRUREZGRlu8wIDA2nZsiUAaWlpHieEpKQkgoKCPHKBsg79mjRpQklJCWlpaW7rGQwG14XA9PR0j5NCs2bNCAsLIy8vj2PHjrnNU6mOcIqJiaFRo0ZK1BFOsbGxQNl54ey6Pyoqiri4OKxWq8d79fU64uz3WnN1RCAQCLllfz/h5KeYjv4X6xUfcqAg3m2989URDofD9RmoT3XE8ePHycvLc5unUh3hPN8XFRURGhpaL+qIxMREQkNDycvL4/jx427zVKgjCgoK3M4r6tcR9eN7xPHjxz3O9/WhjnBS8XtEfHzZOSgvL8/j3KpKHWGz2So8KpJyw3Rt2LCBHj16sHLlSlfjGHC9qalTpzJ+/Phzrr9jxw5yc3NZv349kydP5tZbb2Xu3Lk4HA7Wrl1LdHQ0F110kWv5efPmcdddd7F06VJuuOGGSpXV2V27M1gnla4q2e12SkpKCAsLIzAwUNkrz6Wlpaxbt44FCxawaNEijxOac9/06dPHNfxXo0aNfObK3JlXnq1WKyUlJQQFBWEwGHz2qqm/3Z0qKSnh1KlTrlxArjw7eauOMBgMlJaWYjR6XgtW+fPtE3enHBYCv+2CrmAPGjrsbZ7C1uEF0Ae4FjnXPrTb7djtdkJDQ9E0rd7UEarfnXKe78PDwwkICPC7fahaNhXZh7VxvlcxG1/7HlFcXExRUZHb+d5X96GK2VRlH9bE+b62s9mzZw86na5Cw3Qp18DeuXMnF198MampqW6/w87NzaVRo0bMnj2bRx55pELbmjx5MpMmTSItLY3ExMRylzlx4gQNGjS4YMO9PPV1HOzaZrfbWb9+PSkpKcyfP9/jCiWUHRR9+vTBbDYzZMgQYmJivFBSIYTwYdZ8+HkM7P+/stcNLoerPoXIDl4tlhBCCKEanx4HOzk5GYPBwN69e92mO187H3k4U1paGv/5z388rlx06tQJKLu1f/jwYT788EOPRwucj01ER0fX2HtQic1mIycnx+MKlcoMBgPXXnstb731FhkZGaxfv54nn3ySZs2auZax2+2sXLmShx9+mLi4OPr06cPs2bPLbYyrxhczqQ8kF/VIJrXMFAHd5sA18yGwEeT9CsuugN3/KhtD+xwkFzVJLuqRTNQkuajH3zJRroEdFBREz549SU1NdXukYf78+URGRtK1a1ePddLS0njggQdYsGCB2/QVK1YQEBBA27ZtsdlsPPTQQ7z//vtuy3zxxRcYDAbXUF7+xmazcezYMZ/9wOr1enr06MHrr79OWloamzdv5plnnnH9TgnKfne2evVqHnvsMeLj4+nZsyf/+te/PH5Dowpfz8RfSS7qkUzqSLNhMPA3iLse7CXw6zNQuP+ci0suapJc1COZqElyUY+/ZaJcJ2cAEydOpG/fvgwfPpyRI0eyYcMGZsyYwbRp0wgJCSE/P59du3aRnJxMdHQ0V199NX379uXvf/87+fn5JCcn8/XXX/POO+/w8ssv06BBAxo0aMD999/PjBkzCA4Opnv37qxfv54pU6YwevRoj17LhXp0Oh1du3ala9euTJ8+nV9//ZWUlBS+/PJL1xMOmqaxbt061q1bx5gxY+jWrRtms5lbbrnFrVEuhBDiDMFx0Gsp/Plu2d3r8FbeLpEQQgjhk5S7gw3Qp08f5s+fz+7duxkyZAjz5s1jxowZjBs3DoBffvmF7t27s2TJEqDsLmdqair33Xcf06ZNY9CgQaxatYoPPviAiRMnurb77rvv8uKLLzJ37lwGDRrE3LlzmTx5Mq+//rpX3qeoOp1OR6dOnZgyZQp79uxh27ZtvPDCC64e/5w2bdrE008/TVJSEl26dGH69Ons27fPS6UWQgiF6XTQ5lFoO/qvacd+hE33g+Wk98olhBBC+BDlOjnzJb7QyVlJSQkHDhxwDSFQH+zatYuUlBRSUlJcGZ3tsssuw2w2YzabXcNM1JX6mIkvkFzUI5l4mcMOSztC/u8Q2hy6fwIxPSUXRUku6pFM1CS5qMcXMvHpTs5EzdLr9URERFRpjG9f1aFDB1588UW2b9/OH3/8wT//+U8uv/xyt2W2bt3KxIkTadeuHR07duTll19m586dHkMZ1Ib6mIkvkFzUI5l4md4AV/4bwlrCqTRY1Qt+fRa9ZpVcFCTHi3okEzVJLurxt0zkDnY1+MIdbPGXffv2MX/+fFJSUtiyZUu5y7Rr1851Z/uSSy5Bp9PVcSmFEEIx1gL4+QnY/1HZ66hL4ap5EHWRV4slhBBC1JXKtPukgV0NvtDAdjgc2Gw2jEaj31wVqglpaWmuxvbGjRvLXaZVq1auxnanTp1qrLEtmahJclGPZKKY9IXw44NQegxNH4jWbyP6RpdfcDVRN+R4UY9koibJRT2+kIk8Ii5cLBYL+/btw2KxeLsoSmnevDljx45lw4YNpKenM2vWLK655hq3RvTevXuZNm0anTt3pmXLljzzzDNs3ry52o+RSyZqklzUI5koptkQGPgb9iYDOBXaBUtIuwuuIuqOHC/qkUzUJLmox98ykQa2qPcSEhJ4/PHHWbt2LZmZmbzzzjv07t3b7QrawYMHmTlzJt26daN58+Y8+eSTrF+/HofD4cWSCyFEHQuOxXrVAjKS3ijrdRzKHiFPX+DdcgkhhBCKkAa2EGeIi4vj0Ucf5bvvviMrK4sPPviA/v37YzAYXMukp6fz5ptvcs0115CQkMDo0aNZs2YNdrvdiyUXQog6otOhGUL/ev3zE7BuGGy4CywnvFUqIYQQQgnSwBbiHGJiYnjwwQdZvnw5R48e5aOPPmLgwIGYTCbXMllZWa473k2bNmXUqFGsWrUKm83mxZILIUQd0TQIiQedHg7Og6WXwNE13i6VEEII4TXSwBaiAho1asT999/PkiVLyM7O5pNPPuGmm24iMDDQtUx2djbvv/8+/fr1IzY2lgceeIBly5b5ze9JhBDCg04Hl0yGvushLBmK0uHbPvDrM2Av9XbphBBCiDonvYhXgy/0Ii5qV35+PkuWLCElJYWlS5dSUlLisUxUVBQ333wzZrOZfv36uTXKhRDCb1gL4ZcnYd+/y15HXQJXp0BEa++WSwghhKgmGaarjkgDW5ypsLCQb775hpSUFL7++muKioo8lomIiODGG2/EbDYzYMAAgoODvVBSIYSoRRmLYPODZf8/8DcIbuLd8gghhBDVJA3sOuILDezS0lKysrKIi4uTO6d1qKioiOXLl5OSksLixYspKCjwWCY0NJTBgwdjNpu54YYbCA0NLWdLoq7IsaIeyURNFcql+CgU7oPoq85YMRcCG9ZNIeshOV7UI5moSXJRjy9kIuNgCxdN0yguLq722M2ickJCQhg6dCjz5s0jOzubr776invuuYfIyEjXMqdOneKLL77g1ltvJTo6GrPZzOeff15uY1zUPjlW1COZqKlCuQQ3cW9cH/oSvkqGg5/VfgHrKTle1COZqElyUY+/ZSINbCFqWVBQEDfeeCMff/wx2dnZLFy4kGHDhtGw4V93coqLi5k/fz6333470dHRDBkyhE8//ZSTJ096seRCCFFD9s0B6wnYcAf8cAdY8rxdIiGEEKJWSANbiDoUEBDAgAED+Mc//sHBgwdZuXIlDz/8MNHR0a5lSktLWbRoEXfffTfR0dEMHjyYOXPmkJub68WSCyFENVy7CC6eBDoDpH1WNpzXke+8XSohhBCixkkDWwgvMZlM9O3bl/fee4/Dhw+zevVqHnvsMWJjY13LWK1WlixZwsiRI2nSpAkDBgzgww8/JCcnx4slF0KIStKb4JKXoN8PENYKijLgu+vg57Fg9xx9QQghhPBV0slZNfhCJ2d2u53CwkLCwsIwGAzeLo7gwpnY7XY2btxISkoKKSkpZGZmeiyj1+vp1asXZrOZoUOHujXKRdXIsaIeyURN1c7FWgi/PgV7Pyh7fd130KR3zRayHpLjRT2SiZokF/X4QibSi3gd8YUGtvBtDoeDH3/80dXYTktL81hGp9NxzTXXYDabGTZsGPHx8V4oqRBCVFLm13D8R7hksrdLIoQQQpyXNLDriC80sG02G/n5+URERGA0Gr1dHEHVM9E0jZ9//tnV2N63b1+5y1111VWYzWZuueUWEhMTa6rYfk+OFfVIJmqqtVxOpcFPj0PntyBU6q7KkuNFPZKJmiQX9fhCJjJMl3Cx2WwcPXoUm83m7aKI06qaiU6no3PnzkybNo0///yTX3/9leeff542bdq4LbdhwwbGjh1L8+bNufLKK5kxYwb79++vybfgl+RYUY9koqZay2XLY5D5VVkHaAfmgVz/rxQ5XtQjmahJclGPv2UiDWwhfJBOp+Oyyy7jH//4B3/88Qe//fYbkyZN4qKLLnJb7scff2TcuHEkJydzxRVXMHXqVPbs2eOlUgshxHlc8SY06gbWk7DxLvjhdhnOSwghhM+RBrYQPk6n03HxxRfz0ksvsWPHDnbt2sUrr7zCpZde6rbcL7/8woQJE2jbti2XXnopr7zyCrt27fJSqYUQ4izhraDfOug4uWw4r0NfwJKOcORbb5dMCCGEqDBpYAvhZ9q3b8/EiRPZunUre/bsYerUqXTu3Nltme3bt/Piiy9y0UUX0aFDByZNmsRvv/2GdMkghPAqvRE6vgD9N0J4GyjOhO/6QsYib5dMCCGEqBBpYPs5vV5PaGgoer1ErYq6zKR169aMHz+eLVu2sH//fmbOnEm3bt3clvn999+ZPHkyl1xyCW3btmXChAn88ssv9a6xLceKeiQTNdVJLo26wA2/QOtHIPJiiBtQe3/LT8jxoh7JRE2Si3r8LRPpRbwafKEXcSHKk56eTmpqKikpKfzwww/lNqaTkpIwm82YzWa6dOmCTqfzQkmFEPWe7RQYQ8v+32GHg/OgxZ2gV3OsVCGEEP5HhumqI77QwNY0DYfDgV6vlwaSIlTL5PDhwyxYsICUlBTWrl2Lw+HwWCYxMZFbbrkFs9lMt27d/OYK45lUy0VIJqryai47p8C25yGmJ3T/BEKb1+3fV5gcL+qRTNQkuajHFzKRYbqES2lpKXv27KG0tNTbRRGnqZZJ06ZNeeyxx1i9ejWHDx/mvffeo2/fvhgMf90dOnToEG+88QY9evSgWbNmPP7446xduxa73e7Fktcs1XIRkomqvJpLcNOyu9nZa08P5zVXhvM6TY4X9UgmapJc1ONvmUgDWwjh0qRJEx5++GFWrlzJkSNH+Pe//83111+P0Wh0LXP48GHeeustrr32WuLj43n00Uf57rvv/GbsQiGEwlreBzdsg8bdwZoPG++BH0ZAaa63SyaEEEIA0sAWQpxD48aN+dvf/sY333xDdnY2//d//8fgwYMJCAhwLXP06FHeffddrrvuOuLi4njooYdYsWIFVqvViyUXQvi18GTouxYueQV0Rjj0JSztCNnrvV0yIYQQQhrYQogLa9CgAffeey+LFy8mOzubTz/9lCFDhhAUFORa5tixY3z44YcMGDCAJk2aMHLkSJYuXeo3j/sIIRSiN8LFE8uG84poC6XHIaCBt0slhBBCSANbCFE5kZGR3HnnnSxYsICcnBy++OILzGYzISEhrmXy8vKYM2cOgwYNokmTJtxzzz0sWrSIkpISL5ZcCOF3GnWG63+B3t9A1EV/TS/J8V6ZhBBC1GvSi3g1+Eov4na7HYPBoGyvfPWNv2ZSVFTEsmXLSElJYfHixRQWFnosExYWxuDBgzGbzdxwww1ujXJv89dcfJlkoiblczm2Gb7tDRe/CO2fqTfDeSmfSz0kmahJclGPL2Qiw3TVEV9oYAvhDSUlJaxYsYKUlBQWLVpEfn6+xzIhISEMHDgQs9nMoEGDCAsL80JJhRB+5+exsPuNsv+PvqZsOK+wFl4tkhBCCN/m88N0rVixgi5duhASEkJSUhIzZ87kfNcBSkpKmDBhAs2bNyckJITu3buzfPnyam/XH1gsFtLT07FYLN4uijitPmQSFBTETTfdxCeffEJ2djZLlizh/vvvp0GDv34jWVRUREpKCrfddhvR0dEMHTqUefPmcfLkSa+UuT7k4mskEzUpn0un16DbHDCGQc66suG89n/s98N5KZ9LPSSZqElyUY+/ZaJcA3vTpk0MHjyYdu3akZqayp133sm4ceOYPn36Odd54IEHeOedd3j22Wf56quvaNWqFYMGDWLdunXV2q4/cDgcFBYW4nA4vF0UcVp9yyQwMJCBAwfy0UcfcfToUZYvX86DDz5I48aNXcuUlJSwcOFC7rrrLmJiYrjxxhv5+OOPycvLq7Ny1rdcfIFkoiblc9HpyobzGrgNonuArQA23Qfrby3rDM1PKZ9LPSSZqElyUY+/ZaLcI+IDBgzgxIkTbN682TXt2Wef5d133+Xo0aMEBwe7LX/w4EGSkpJ4++23eeyxx4CykFq1asWVV17JZ599VqXtVoQvPCJeUlLCgQMHSEpKcuvxWXiPZFLGZrOxdu1aUlJSSE1N5ejRox7LGI1G+vbti9ls5uabb3ZrlNc0yUU9komafCoXhx1+nw7bJ4Fmg64fQqsHvF2qWuFTudQTkomaJBf1+EImPvuIeGlpKWvWrGHo0KFu081mMwUFBaxf7znGZVxcHFu2bOGuu+5yTdPr9RiNRlePxVXZrhCidhmNRvr06cPs2bPJzMzk+++/5+9//ztNmzZ1LWOz2Vi2bBkPPPAAsbGx9OvXj/fff7/cxrgQQnjQG+CiCTBgE7QZDcl/83aJhBBC+Dmjtwtwpv3792OxWGjTpo3b9FatWgGwe/du+vXr5zYvMDCQzp07A2V3rjMzM3nttdfYt28fb731VpW3WxlnDz2k1+sJCAjA4XCU+1sC55WZ0tJSj9+Am0wmDAYDNpsNm81W7nY1TSt3bOHAwEB0Oh0Wi8X1iEVpaSkOh8O1LbvdjtVqdVtPp9MRGBhY7nsBCAgIQK/XY7VasdvtbvMMBgMmk+mC77W2tlsX+9DJaDRiNBqrtQ8tFosrE+ffr4nt+to+LG+73bp1o2fPnrzxxhusXbuWBQsWsGDBAjIyMoCyz+6qVatYtWoVjz76KFdffTVDhw7lpptuIjExsUb24Zm51MU+VDEbleoIZxltNpvHfJU/39WtI1TL5ux9eObruqwjqrXdiEsxdn7rr31ozcf04z3YOkyChp2UrWcrs13nucVZRhU/3/Xte0RtnO9VzEaV7xEV3W5553tf3YcqZlOVfejc1tnrgTp1hKZpFe7hXKkGtrNzo4iICLfp4eHhAOX2RHym6dOnM2HCBAAefPBB+vbtWyPbPR+73c6BAwfcpkVERBAfH4/NZvOYB9C+fXsAsrKyKC4udpvXtGlTIiMjyc/P97hLFxoaSmJiIg6Ho9zttm7dGqPRyNGjR11DJGmahsPhoLi4mLCwME6dOkVmZqbbekFBQSQlJQFlj9yffSC2bNmSwMBAjh07xokTJ9zmNWrUiJiYGIqLizl06JDbPKPRSOvWrQFIT0/3OIgTExMJDQ0lLy+P48fdfxcXFRVFXFwcVqvV473qdDratWsHwOHDhz0OqPj4eCIiIjh58iTZ2dlu88LCwmjWrFm5uQG0adMGg8HAkSNHOHXqlNu8Jk2a0LBhQwoLCzl8+LDbvODgYFq0aAFQ7naTk5MJCAggJyeHkydPui4G6XQ6GjduTHR0NMXFxaSnp7utZzKZXBeCDh065FE5ODv2y83NJTc3121egwYNiI2NxWKxeJRJr9fTtm1bADIzMz0q0YSEBMLDwzlx4gQ5Oe7jyYaHh5OQkHDOz3fbtm3R6XRkZWVRVFTkNi8uLo6oqCgKCwvJyspymxcSEkLz5s3R6XTExsbyyCOPMGrUKH777TdWrFjB6tWrXX/P4XCwdu1a1q5dy9ixY+natSu33347/fv39yhPYGAgLVu2BCAtLc3jhOB8HKmgoMAtF4CGDRvSpEkTSkpKSEtLc1vPYDC4Ltqlp6d7nBSaNWtGWFgYeXl5HDt2zG2eSnWEU0xMDI0aNVKqjoiPjycmJobCwkKP3+P7cx1x9jlJtTpC0zQiIyMxGo1eqSM0TSt3u61atcJkMpGdnU1BQYHbvOjoaBo3bkxRUREZGRnEZEyjUc5y9Ee/Ja/ZGAJ7TAe94bx1xPHjxz0+hyrVEc7zfWlpKaGhofWijlD9e4TzN6XO80p9qSPAu98jLlRH5OXleZzvz64jzlTR7xGq1xFOKn6PSExMJCYmhvz8fI/Ptyp1hM1mw2QyeeyT8ij1G+wNGzbQo0cPVq5c6WocA643NHXqVMaPH3/O9Xfs2EFubi7r169n8uTJ3HrrrcydO7fa2z0X57P4zmCd/O2qUn298lxf706BmleeAwIC2LZtG1988QXz589n7969HssAdOnShaFDhzJ06FBatGih9D5UMRupI6SOcPK1OqJS2ZQex/TraAyHF5YtEN0Dus+lxBjnsV2pI8p4+3PoT59vqSN8oI44g8r7UMVs/LWO2LNnDzqdzvfGwd65cycXX3wxqampbr+Xzs3NpVGjRsyePZtHHnmkQtuaPHkykyZNIi0tjYKCghrb7pl8oZMzu93OqVOnCA0NxWAweLs4AsmkJmiaxo4dO0hJSSElJYVdu3aVu9wVV1yB2WzGbDa7ruCfi+SiHslETX6Ti6bBgU/gp7+X9TRuDIMr/lXWA3kFHwNUid/k4kckEzVJLurxhUx8tpOz5ORkDAaDx50p52vn4w5nSktL4z//+Y/HVYtOnToBZbf1q7Jdf2G1WsnMzCz3Nw3COyST6nNeQXz55ZfZuXMnO3fuZPLkyVxyySVuy/38888899xztG7dmssuu4x//OMf/PHHH+VuU3JRj2SiJr/JRaeDlveeHs7rarAVwuaR8PtMb5esSvwmFz8imahJclGPv2WiVAM7KCiInj17kpqa6vY4w/z584mMjKRr164e66SlpfHAAw+wYMECt+krVqwgICCAtm3bVmm7Qgjf0aFDB1544QW2bdvG7t27mTJliusim9O2bdt44YUXaN++PRdffDEvvfQSO3bs8Hh0SghRz4QlwXVr4NKpENKs7A62EEIIUUVKdXIGMHHiRPr27cvw4cMZOXIkGzZsYMaMGUybNo2QkBDy8/PZtWsXycnJREdHc/XVV9O3b1/+/ve/k5+fT3JyMl9//TXvvPMOL7/8Mg0aNKjQdoUQ/qFNmzY899xzPPfcc+zfv5/58+eTkpLCjz/+6FrGedf75Zdfpm3btpjNZm688UZXx4dCiHpGb4CLxkPbMWAM/mv6/v+DxOFglO8JQgghKkapO9gAffr0Yf78+ezevZshQ4Ywb948ZsyYwbhx4wD45Zdf6N69O0uWLAHKfoyfmprKfffdx7Rp0xg0aBCrVq3igw8+YOLEiRXerhDC/7Rs2ZJnnnmGzZs3c/DgQV5//XWuuuoqt2V2797NP//5T7p168b111/PxIkT+emnn+TOthD10ZmN64Ofw6b7YVknOP6T98okhBDCpyjVyZmv8YVOzkpLSzl8+DBNmzZ19d4nvEsy8b7MzExSU1NJSUlh3bp15Tammzdv7uogrWvXruj1yl2P9HtyrKip3uRy5FvYeA8UHwadETpOgg7jQa/cw39APcrFh0gmapJc1OMLmVSm3ScN7GrwhQa2EOL8jhw5woIFC0hJSWHNmjUew05A2Xiet9xyC2azmauuukoa20LUF6W5sOUROPS/steNu0P3uRCe7N1yCSGEqFPSwK4j0sAWwr/k5OSwcOFCUlJS+Pbbbz3GTASIi4tj2LBhmM1mrrnmGmWHkxBC1BBNg4Pz4KfHwJoPxlDo+iG0uN3bJRNCCFFHfHaYLlHzSkpK+OOPP8odfF14h2SippKSEo4fP87dd9/N8uXLOXr0KB999BGDBg3CZDK5lsvKyuKdd96hd+/exMXF8fDDD7Ny5Uq/GVpCJXKsqKne5aLTQdJdMHA7xFwLtlMQ2MjbpfJQ73LxAZKJmiQX9fhbJtLArgfkIQX1SCZqOjOXRo0acf/99/P111+TnZ3N3Llzufnmm91+G5STk8MHH3xA//79iY2N5W9/+xvffPMNFovFG8X3S3KsqKle5hLaHPp8C31WQVz/v6YXH/Femc5SL3NRnGSiJslFPf6UiTSwhRDiAqKiorjrrrtYuHAhOTk5fPbZZ9xyyy0EB//V43Bubi4fffQRAwcOJCYmhnvvvZfFixf7zdVYIQRlw3nFXvfX68KD8HVb+PGRsjvbQggh6j1pYAshRCWEh4dz2223kZKSQk5ODl9++SUjRowgNDTUtczJkyf55JNPuOmmm4iJieHOO+9kwYIFFBcXe7HkQogal7Ws7HfZe9+Dby6HYz96u0RCCCG8TBrYQghRRaGhoZjNZj7//HNycnJYsGABd955J+Hh4a5lCgoK+O9//8uwYcOIjo5mxIgRfPnll5w6JXe7hPB5rUdBn5UQHA8Ff8LKq+C3yeCwebtkQgghvER6Ea8GX+hF3OFwYLVaMZlMMrSQIiQTNdVkLqWlpaxcuZKUlBQWLVrEiRMnPJYJDg7mhhtuwGw2M2jQICIiIqr1N/2RHCtqklzKUZoLWx6FQ1+UvW7UDa6aC+Gt6qwIkot6JBM1SS7q8YVMZJiuOuILDWwhhHdZLBa+++47UlJSWLBgAbm5uR7LBAYGMmDAAMxmMzfeeCNRUVF1X1AhRPUd/G9ZQ9t6Ejo8C5dN83aJhBBC1ABpYNcRX2hgW61Wjh07RuPGjd2GGhLeI5moqS5ysVqtfP/996SkpJCamkpOTo7HMiaTiX79+mE2m7n55ptp2LBhrZTFF8ixoibJ5QJOHYKdU+CKWWAIvPDyNURyUY9koibJRT2+kImMgy1c7HY7J06cwG63e7so4jTJRE11kYvJZKJv37689957ZGVlsXr1ah577DFiY2Ndy1itVpYuXcrIkSNp0qQJAwYM4IMPPiA7O7vWyqUqOVbUJLlcQGgidH3vr8a1wwZrh0LG4lr9s5KLeiQTNUku6vG3TKSBLYQQXmAwGOjVqxdvv/02mZmZrFu3jieeeIKEhATXMjabjRUrVvDwww8TFxdHnz59mD17NllZWV4suRCiUvZ+ABkLYe1N8OPDYC30domEEELUImlgCyGEl+n1eq6++mreeOMN0tLS2LRpE08//TQtWrRwLeNwOFx3vOPj4+nZsyf/+te/yMjI8F7BhRAXljwS2o0t+/+9H5wezmuzd8skhBCi1kgDWwghFKLX67nyyiuZMWMG+/fv56effmL8+PEkJye7ltE0jXXr1jFmzBiaNWvGVVddxeuvv05aWpoXSy6EKJchCDq9Bn2+hZAEKNwLK3vA9pfAYfV26YQQQtQwaWD7OYPBQKNGjTAYDN4uijhNMlGTirnodDquuOIKpk6dyp9//snWrVuZOHEibdu2dVtu48aNPPXUU7Ro0YKuXbvy6quvsm/fPi+VuuaomImQXKostg8M3A7NbwfNDjtehi2P1NjmJRf1SCZqklzU42+ZSC/i1eALvYgLIfyPpmns2rWLlJQUUlJS2LFjR7nLXX755ZjNZsxmM23atKnjUgohzungZ/DrU2V3tSPbe7s0QgghLkCG6aojvtDAdjgcFBcXExwcrOzA7fWNZKImX87ljz/+YP78+aSkpLB169Zyl+nYsaOrsd2hQ4e6LWAV+XIm/kxyqSH2UvdhvA5+Bk36QHCTKm1OclGPZKImyUU9vpCJDNMlXCwWC4cOHcJisXi7KOI0yURNvpxLu3bteP755/n111/5888/mTZtGp07d3Zb5rfffmPSpElcdNFFdOjQgRdffJHt27ej8jVWX87En0kuNeTMxnXOBth4FyztCBlfVWlzkot6JBM1SS7q8bdMpIEthBB+pFWrVjz77LNs2bKFAwcOMHPmTLp16+a2zO+//84rr7zCpZdeStu2bZkwYQK//PKL0o1tIfyaKQIiL4LSHFh7M2x+UIbzEkIIHyUNbCGE8FMtWrTgqaeeYuPGjaSnpzNr1iyuueYadDqda5k///yTqVOncsUVV5CcnMwzzzzD5s2bpbEtRF2KuhgGbIH2TwM62Pdv+OYyyNno7ZIJIYSoJGlgCyFEPZCQkMDjjz/O2rVryczM5J133qF3795uv3U684538+bNefLJJ/nhhx9wOBxeLLkQ9YQhEC6fAdd9ByHNoHAfrLoadk71dsmEEEJUgjSw6wGj0ejtIoizSCZqqi+5xMXF8eijj/Ldd9+RlZXF+++/T79+/dyGx0hPT+fNN9/k6quvplmzZvz973/n+++/x26312lZ60smvkZyqUVNepUN59XiLtAcENCwwqtKLuqRTNQkuajHnzKRXsSrwRd6ERdCiIo6fvw4ixYtIiUlhVWrVmG1Wj2WiYmJYdiwYZjNZq699lq/OiEKoZyjayDmWnD+rKMoE4Kb/vVaCCFEnZBhuuqINLCFEP4qLy+PxYsXk5KSwvLly8vt2bNRo0YMHToUs9lMnz59MJlMXiipEPWE5SQsvaSsM7RuH0FwrLdLJIQQ9YYM0yVcSkpK+PPPPykpKfF2UcRpkomaJBd3DRo04J577uGrr74iJyeHefPmMXToUIKCglzLHD9+nH//+99cf/31NGnShPvvv58lS5ZQWlpaI2WQTNQkuXjJ8c1QchSyvoGlF0P6ArfZkot6JBM1SS7q8bdMpIFdD9hsNm8XQZxFMlGT5FK+iIgI7rjjDlJTU8nJyeGLL77g1ltvJSQkxLVMXl4e//d//8fgwYOJiYnh7rvvZtGiRRQXF1frb0smapJcvCCuP1z/MzS4DEqPw7phsGkkWAtci0gu6pFM1CS5qMefMpEGthBCiAoLCwtj+PDh/O9//yMnJ4f58+dz++23ExYW5lomPz+fTz/9lCFDhhATE8Ptt9/O/PnzOXXqlBdLLoQfiLoI+m+CDs8COtg/B5ZeCjk/eLtkQgghTpMGthBCiCoJCQlh2LBh/Pe//yUnJ4dFixZxzz33EBkZ6VqmsLCQzz//HLPZTHR0NGazmS+++IKCgoLzbFkIcU6GQLhsGvRdA6HN4dQB+ONNb5dKCCHEadLAFkIIUW1BQUHcdNNNfPzxx2RnZ7N06VJGjhxJw4Z/DTFUXFzM/Pnzue2224iJiWHIkCF8+umnnDx50oslF8JHxfSEG7ZBm79Dl3e9XRohhBCnSS/i1eALvYg7HA6Ki4sJDg5Gr5frKSqQTNQkudQOq9XKmjVrSElJITU1lWPHjnksExAQQL9+/TCbzdx0002uRrlkoibJRU3OXEK2/x1doyug9aMynJeXybGiJslFPb6QiQzTVUd8oYEthBCqsNlsrFu3jpSUFObPn8/Ro0c9ljEajVx33XWYzWaGDBlC48aNvVBSIXxU1kpY3b/s/+OuPz2cV5x3yySEEH5AhukSLlarlezsbKxWq7eLIk6TTNQkudQ+o9FI7969eeedd8jMzGTt2rU8/vjjxMfHu5ax2WwsX76cBx98kNjYWHr27Mk777xTbmNceIccK2qyWq1k6ztiv+wNMARB1jJY2hHSU71dtHpLjhU1SS7q8bdMlGxgr1ixgi5duhASEkJSUhIzZ87kfDfaS0tLmTJlCu3atSM0NJS2bdsyefJkLBaL23IJCQnodDqPf+U9sugv7HY7x48fx263e7so4jTJRE2SS90yGAxcc801zJo1i0OHDrFhwwbGjh1LYmKiaxm73c66desYPXo0cXFx9OrVi7fffpvDhw97seRCjhU12e12jufmYW056vRwXpefHs7rFth0P1jzvV3EekeOFTVJLurxt0yUa2Bv2rSJwYMH065dO1JTU7nzzjsZN24c06dPP+c6Y8aM4Z///Cf33XcfX331FSNHjmTatGk88sgjrmWOHTtGZmYmM2bMYOPGjW7/oqKi6uCdCSGEKI9er6d79+689tprHDx4kB9//JFx48aRlJTkWkbTNL7//nv+/ve/Ex8fz9VXX82bb77JoUOHvFhyIRQV2eH0cF7PUTac1//BmsEgvwoUQohaZ/R2Ac42adIkLr/8cubOnQvA9ddfj9VqZcqUKYwZM4bg4GC35Y8fP84HH3zA9OnTeeaZZwC47rrrABg/fjzTpk0jOjqarVu3AjB06FCSk5Pr7g0JIYSoMJ1OR5cuXejSpQsvvfQSS5cu5ccff2TBggX8+eefruV++OEHfvjhB5588kmuvPJKzGYzt9xyi1ujXIh6zRAAl02BpgNh073Q8UXp9EwIIeqAUnewS0tLWbNmDUOHDnWbbjabKSgoYP369R7r5OfnM2rUKG666Sa36e3atQNg//79AGzdupXw8HBatmxZS6UXQghRk3Q6HR06dODll19m9+7dbN++nRdffJEOHTq4Lbd582aeeeYZWrZsSefOnZk6dapbY1yIei3mahj0O8T2/WtaxmI4uct7ZRJCCD+m1B3s/fv3Y7FYaNOmjdv0Vq1aAbB792769evnNi8pKYnZs2d7bGvhwoWYTCbXtrZu3UrDhg0xm82sWrUKu93OoEGDePPNN4mLq14PmyUlJW6v9Xo9AQEBOBwOj9+BQ9l4sVB2QeHs35abTCYMBgM2mw2bzVbudjVNo7S01GO7gYGB6HQ6LBYLDocDKOs0IDQ01PV37Ha7RwcCOp2OwMDAct8LlA2ho9frsVqtHr+NMBgMmEymC77X2tpuXexDJ6PRiNForNY+tFgsrkyc26iJ7fraPlQxG4fD4ZYL1P4+VDEbleoInU5HVFSU6/20bt2a5557jueee449e/bw1VdfkZKSwrZt21zr/Pzzz/z8889MmDCBSy65hCFDhjB06FDXRVfV96GK2Zz9ObRarURERNS7OsIb9Wxltus8tzh5bNdaUrZdSybahjvBYcXSaw1a1KWuRVTeh6plU5HPYW2c71XMxte+R5R3vvfVfahiNlXZh87zvcPh8JivSh2haRq6Cj4FpFQD++TJkwBERES4TQ8PDwfK7lZXxIIFC/j4448ZPXo0DRo0AMoa2JmZmTz00EM88cQT/P7777z44otce+21/Prrr24npcqw2+0cOHDAbVpERATx8fHYbDaPeQDt27cHICsri+LiYrd5TZs2JTIykvz8fI9ec0NDQ0lMTMThcJS73datW2M0Gjl69CiFhYUe6wYHB3Pq1CkyMzPd5gUFBbkeqzx48KDHgdiyZUsCAwM5duwYJ06ccJvXqFEjYmJiKC4u9vgtpNFopHXr1gCkp6d7HMSJiYmEhoaSl5fH8ePH3eZFRUURFxeH1Wr1eK86nc71Zfnw4cMeB1R8fDwRERGcPHmS7Oxst3lhYWE0a9as3NwA2rRpg8Fg4MiRI5w6dcptXpMmTWjYsCGFhYUenSwFBwfTokULgHK3m5ycTEBAADk5Oa7PsXP7jRs3Jjo6muLiYtLT093WM5lMrgtMhw4d8qgcmjdvTkhICLm5ueTm5rrNa9CgAbGxsVgsFo8y6fV62rZtC0BmZqZHJZqQkEB4eDgnTpwgJyfHbV54eDgJCQnn/Hy3bdsWnU5HVlYWRUVFbvPi4uKIioqisLCQrKwst3khISE0b94cTdPK3W6rVq0wmUxkZ2dTUFDgNi86OprGjRtTVFRERkaG27zAwEDXkytpaWkeJ4SkpCSCgoLIz8/n1KlTbrk3bNiQJk2aUFJSQlpamtt6BoPBdQEvPT3d42TSrFkzwsLCyMvL8+hIUcU6IiYmhkaNGilXR8TFxZGdne1RR0RHRzNx4kSeeeYZvvvuO1auXMmKFSvYuXOna5nt27ezfft2Jk+eTHJyMv3792fAgAH06tWLyMhI5esIJ1XrCJPJxLFjx+pNHXH8+HHy8vLc5qlYR5SWlrrqtHLriOggaNydosJ8DuWGQd5f2/fFOsIXvkc4/1vf6ghVv0fk5uZ6nO/rUx2h6veIuLg4srKylK0jbDYbJpPJY5+UR6lxsDds2ECPHj1YuXIlffv+9SiT8w1NnTqV8ePHn3cbqamp3HHHHXTt2pUVK1a4rj5s3LgRo9FIly5dXMv+8MMPXH311cyePdutQ7SKco6H5gzWSaWrSg6HA5vNRlBQEAEBAXLlWYG7U85yGY1G9Hq9z1419be7U6WlpZSWlrpyAbny7OStOsJZHsBjP5xruwcPHmTBggUsWrSIzZs3e2wTyr4AO8fZvvjii92uSHt7H6qYzdmfQ2f5goKCXOeYM/lrHaH63SlnFsHBwZhMpvPvQ4eD0lPHwOR+Q0PlfahaNhX5HNbG+V7FbHzte0RJSQkWi8XtfO+r+1DFbKqyD6tyvj/7vdZ2Nnv27EGn01VoHGylGtg7d+7k4osvJjU11e132Lm5uTRq1OiCDeE33niDp59+ml69erFw4ULXne/ziYqK4rbbbuO9996rdHkrM+C4t5SUlHDgwAHX1TXhfZKJmiQX9VQ3k0OHDpGamkpKSgo//PBDucu0bNkSs9mM2Wymc+fOFX78qz6TY0VNkot6JBM1SS7q8YVMKtPuU6qTs+TkZAwGA3v37nWb7nztfNzhbJqm8fjjjzN27FhGjBjBN99849a4PnnyJB999BE7duxwW895xSI6OrqG34kQQghvS0xM5IknnmD9+vVkZGTw1ltvce2117o1ovfv38+rr75K165dadGiBU899RQbN270uDIvhBBCCFERSjWwg4KC6NmzJ6mpqW6PB8yfP5/IyEi6du1a7noTJkzgrbfeYuzYscybN4+AgAC3+YGBgYwePZqpU6e6Tf/qq68oLi6md+/eNf9mhBBCKCM+Pp7Ro0ezZs0aDh8+zLvvvst1113nejwQyu54v/7661x11VUkJiYyZswY1q1b5/FYmRBCCCHEuSjVyRnAxIkT6du3L8OHD2fkyJFs2LCBGTNmMG3aNEJCQsjPz2fXrl0kJye7xreePn06Xbp04dZbb/X4zV2HDh2IiIhg/PjxTJo0iSZNmjBw4EB+++03XnrpJW6++Wb69OnjpXcrhBCirsXGxjJq1ChGjRpFTk4OixYtIiUlhW+//db1e7TMzEz+9a9/8a9//YvY2FiGDRuG2WzmmmuuwWhU7tQphBBCCEUodQcboE+fPsyfP5/du3czZMgQ5s2bx4wZMxg3bhwAv/zyC927d2fJkiUArrvdW7ZsoXv37h7/fvnlF6Cs4T579mxWrFjBjTfeyGuvvcaoUaP47LPPvPZe64r8plA9komaJBf11HYm0dHRPPDAAyxbtoyjR48yZ84cBg0a5NZT6JEjR5g9ezZ9+vShadOmPPzww6xcudKjE5f6RI4VNUku6pFM1CS5qMefMlGqkzNf4wudnAkhhKi8kydPsnjxYlJSUli2bFm5vak2bNiQm2++GbPZTN++fT1+niSEEEII/1CZdp80sKtBGthCCOH/CgoKWLp0KSkpKSxZssRjTFGAyMhIbrrpJsxmM/3791e2F1QhhBBCVJ7P9iIual5paSkHDhwo9+6L8A7JRE2Si3pUySQ8PJwRI0bw5ZdfkpOTQ0pKCrfddhuhoaGuZU6ePMncuXO5+eabiY6O5o477iA1NZWioiIvlrx2qJKLcCe5qEcyUZPkoh5/y0Qa2H5O0zRKSko8Bm0X3iOZqElyUY+KmYSGhnLLLbfw2WefkZOTw8KFC7nrrruIiIhwLVNYWMhnn33GLbfcQnR0NMOHD+d///sfhYWFXix5zVExFyG5qEgyUZPkoh5/y0Qa2EIIIUQVBAcHc/PNNzN37lyys7P5+uuvue+++4iKinItU1RUxJdffsmIESOIjo5m2LBh/Pe//yU/P997BRdCCCFErZEGthBCCFFNgYGBDBo0iDlz5nD06FGWLVvGAw88QKNGjVzLlJSUsGDBAu68806io6O56aab+OSTT8jLy/NiyYUQQghRk6SBLYQQQtSggIAABgwYwIcffsiRI0dYtWoVo0aNIiYmxrWMxWJh8eLF3HvvvTRp0oSBAwfy0Ucfcfz4cS+WXAghhBDVJb2IV4Mv9CJut9s5deoUoaGhGAwGbxdHIJmoSnJRj79lYrfbWb9+PSkpKcyfP5+srCyPZQwGA3369MFsNjNkyBC3Rrkq/C0XfyG5qEcyUZPkoh5fyESG6aojvtDAFkIIoR6Hw8HGjRtJSUkhJSWFjIwMj2X0ej09e/bEbDYzbNgw4uLivFBSIYQQQkgDu474QgPbZrNx8uRJIiMjMRqN3i6OQDJRleSinvqSicPhYMuWLa7G9sGDBz2W0el09OjRw9XYbtasWd0X9LT6kouvkVzUI5moSXJRjy9kIuNgCxebzUZ2djY2m83bRRGnSSZqklzUU18y0ev1XHnllcyYMYP9+/fz888/89xzz9GqVSvXMpqmsX79ep544gkSExPp3r07r732WrmN8dpWX3LxNZKLeiQTNUku6vG3TKSBLYQQQihCp9PRqVMnpkyZwp49e9i2bRsvvPAC7dq1c1tu06ZNPP300yQlJdGlSxemT5/O3r17vVRqIYQQQjhJA1sIIYRQkE6n45JLLmHy5Mn8/vvv7Ny5k5dfftnj8bSffvqJ8ePH07p1ay6//HL++c9/snv3bi+VWgghhKjfpIEthBBC+IAOHTrw4osvsn37dv744w/++c9/cvnll7sts3XrViZOnEi7du3o2LEjL7/8Mjt37kS6WxFCCCHqhjSw/ZxerycsLAy9XqJWhWSiJslFPZLJubVt25YJEybwyy+/sHfvXqZPn06XLl3cltmxYwcvvfQSF198MR06dOCFF15g27Zt1W5sSy5qklzUI5moSXJRj79lIr2IV4Mv9CIuhBCi/khLS2P+/PmkpKSwcePGcpdp1aoVZrMZs9lMp06d0Ol0dVxKIYQQwrfIMF11xBca2JqmYbfbMRgM8iVKEZKJmiQX9Ugm1ZORkUFqaiopKSmsX7++3DvXLVq04JZbbsFsNtO1a9cK3T2QXNQkuahHMlGT5KIeX8hEhukSLqWlpfz555+UlpZ6uyjiNMlETZKLeiST6klISODxxx9n7dq1ZGZm8s4779C7d2+3RvTBgwd57bXX6N69O82bN+eJJ55g/fr1OByOcrdpt9tZuXIlb775JitXrsRut9fV2xEXIMeLeiQTNUku6vG3TKSBLYQQQvi5uLg4Hn30Ub777juysrL44IMP6N+/PwaDwbVMRkYGs2bN4pprriEhIYHRo0ezZs0aVyM6NTWVFi1aMGDAAJ555hkGDBhAixYtSE1N9dbbEkIIIZQjDWwhhBCiHomJieHBBx9k+fLlHD16lI8++oiBAwdiMplcy2RlZbnueDdt2pT+/ftzyy23kJGR4batzMxMzGazNLKFEEKI04zeLoAQQgghvKNRo0bcf//93H///Zw4cYLFixeTkpLC8uXLXY/qZWdns3LlynLXd/6u+5FHHqFp06aEhIQQGBhY7j+jUb5yCCGE8H9ythNCCCEEUVFR3H333dx9993k5+ezZMkSUlJSWLx4MVar9bzrZmdn07179/Muo9fry214BwQEnLNRXtV/Fd3mmY/ICyGEEDVBehGvBl/pRdzhcKDX65Xtla++kUzUJLmoRzJRw5w5cxg5cqS3i1ErDAZDjTbYa+IiQFXHgZXjRT2SiZokF/X4QiaVaffJHWw/p9Pp5Aq9YiQTNUku6pFM1JCUlFSh5YYMGUJ0dDSlpaUV+mexWNxe22y2Wn4nnux2O0VFRRQVFdX53z4Xo9FYq3ftq7JNVb/wqk7qMDVJLurxt0ykge3nLBYLR44cITY2loCAAG8XRyCZqEpyUY9kogZnr+KZmZnljqWt0+lISEggJSWlWl+Q7Ha7R6O7Kv9qYhvOf+carqw22Ww2bDYbp06dqvO/fS4mk6nOH98/3z+TyeQTjX6pw9QkuajH3zKRBrafczgcnDp1yitfEkT5JBM1SS7qkUzUYDAYmDVrFmazGZ1O59bIdjZy3nzzzWrffTAYDAQHBxMcHFyt7dQkm81W44326l4I8MYv+6xWK1arlcLCwjr/2+fijcf3z/fPaDR6NPqlDlOT5KIef8tEGthCCCGEOK9hw4aRkpLCmDFj3IbqSkhI4M0332TYsGFeLF3tMRqNGI1GQkNDvV0UoOx3is5G/9n/8vPzOXjwII0aNQKo04sB3uDNv10enU5XbkNdp9MRFhZGUFBQnV8EkJ77hfAOOfKEEEIIcUHDhg3j5ptv5ttvv2X79u1ccsklXHfddX71uznV6XQ6TCYTJpOJsLAwt3klJSWEh4eTlJREUFBQnZVJ0zSsVqtXH+U/e5sWi6XO3v+Z+6GkpISSkpI6/9vncq6e+2vz8f0LbU/qC1EfSANbCCGEEBViMBjo2bMnzZo1IykpSb4sC3Q6HQEBAQQEBBAeHu7t4gBljd2aasBXdzvFxcVYrVavdOLncDgoLi6muLi4zv/2uVS05/7avAigaRoFBQWUlJQQEBBQ5Z77Rc2w2+2sXbvWry7cSgPbzxmNRpo0aSKPCSlEMlGT5KIeyURNkouaJJe/nPm4tjfZbDby8/OJiIhAr9fXyd37yvyz2+11vk/8qef+2roI4PxpQX2Qmppa7k+PZs2a5dM/PZJxsKvBF8bBFkIIIYQQ4mx2u92rj/KX989fOrmqrrMb6XXx+P75/tVGz/2pqamYzWaPjhudfyclJUWpRraMgy1c7HY7hYWFhIWF+fzjFv5CMlGT5KIeyURNkouaJBf1qJ6JwWAgJCSEkJAQbxfF5Vyd+NXkhYCSkhKKioqw2WwVWs8bLBYLFouFgoICr/z98tRko91kMjF16tRyR0XQNA2dTscTTzzBzTffrOSxcyHSwPZzVquVw4cPy2/lFCKZqElyUY9koibJRU2Si3okk8qri577S0pKOHDgQIU6BDxfz/11+Tj/2et7Q11ecNA0jfT0dNatW0evXr3q5G/WJCUb2CtWrOD5559n586dNGnShMcee4ynnnrqnI8mlJaW8tprr/HJJ5+Qnp5OQkICd955J+PHj3cbrPynn37i6aef5qeffiIiIoL77ruPl156yS8GNBdCCCGEEELUnPP13O8t5XXi5+3f9Vut1lp5r1lZWbWy3dqmXAN706ZNDB48mBEjRvDKK6+wfv16xo0bh81mY/z48eWuM2bMGObOncsLL7xAly5d+Omnn3j55ZdJS0vjP//5DwD79++nb9++dO/enf/973/8/vvvPP/88+Tm5vLee+/V5VsUQgghhBBCiEpTpRO/Mzkcjgo1yJ3L/PLLL7z00ksX3G5cXFztF74WKNfAnjRpEpdffjlz584F4Prrr8dqtTJlyhTGjBlDcHCw2/LHjx/ngw8+YPr06TzzzDMAXHfddQCMHz+eadOmER0dzfTp0wkPD2fRokUEBAQwcOBAQkJCGD16NBMmTCAxMbFu36gQQgghhBBC+Di9Xk9QUNAFH7l3GjhwIP/+97/JzMws93fYOp2OhIQErrnmmpouap1QauC30tJS1qxZw9ChQ92mm81mCgoKWL9+vcc6+fn5jBo1iptuusltert27YCyO9cAy5cvZ9CgQW6Pg5vNZhwOB8uXL6/pt6IMnU5HcHBwvenu3xdIJmqSXNQjmahJclGT5KIeyURNkov3GQwGZs2aBeCRg/P1m2++6bN9Fyh1B3v//v1YLBbatGnjNr1Vq1YA7N69m379+rnNS0pKYvbs2R7bWrhwISaTiTZt2lBcXExaWprHdqOjo4mIiGD37t3VKndJSYnba71eT0BAgOtxibM5r+6UlpZ6XLUxmUwYDAZsNhs2m63c7WqaVm4nA4GBgeh0OiwWi9swB7Gxsa4PqN1u9/idhPNRk/LeC5QNFaDX67FarR5jJhoMBkwm0wXfa21tt672IfzV8UZ19qHFYkHTNGJjY9E0jZKSkhrZrq/tQxWz0ev1brlA7e9DFbNRrY5o0aIFVqvVY77Kn+/q1hGqZVPePmzWrFm9qyO8Uc9WdruxsbGucbD9bR+qlk1F9mFtnO9VzMbXvkfodDqP872v7kMVs6noPhw4cCD//e9/efrpp8nMzHQtGx8fz4wZMxg4cCAlJSXK1BHO3s0rQqkG9smTJwGIiIhwmx4eHg6U3a2uiAULFvDxxx8zevRoGjRowJEjR8rdrnPbFd1ueex2OwcOHHCbFhERQXx8PDabzWMeQPv27YGyH+4XFxe7zWvatCmRkZHk5+dz9OhRt3mhoaEkJibicDjK3W7r1q0xGo0cPXqUwsJCt3kxMTE0atSIU6dOuX2IoewDlJSUBMDBgwc9DsSWLVsSGBjIsWPHOHHihNu8Ro0aERMTQ3FxMYcOHXKbZzQaad26NQDp6ekeB3FiYiKhoaHk5eVx/Phxt3lRUVHExcVhtVo93qtOp3M9oXD48GGPAyo+Pp6IiAhOnjxJdna227ywsDCaNWtWbm4Abdq0wWAwcOTIEU6dOuU2r0mTJjRs2JDCwkIOHz7sNi84OJgWLVoAlLvd5ORkAgICyMnJ8fi8NW7cmOjoaIqLi0lPT3ebZzKZXBeYDh065FE5NG/enJCQEHJzc8nNzXWb16BBA2JjY7FYLB5l0uv1tG3bFoDMzEyPSjQhIYHw8HBOnDhBTk6O27zw8HASEhLO+flu27YtOp2OrKwsioqK3ObFxcURFRVFYWGhR8cVISEhNG/eHE3Tyt1uq1atMJlMZGdnewxbER0dTePGjSkqKiIjI8NtXmBgIC1btgQgLS3N44Tg7EX0+PHj5OXluc1r2LAhTZo0oaSkhLS0NLd5BoPBddEuPT3d42TSrFkzwsLCyMvL49ixY27zpI4oI3XEX6SOKCN1RBmpI8pIHfEXqSPKSB1Rxl/qiEsvvZRly5Zx+PBhjh8/TkBAAG3btsVgMLj+vip1hM1mw2QyeeyT8ui08h5895INGzbQo0cPVq5cSd++fV3TnW9o6tSp5+zozCk1NZU77riDrl27smLFCoKCgjh8+DDx8fF8+OGHPPDAA27LJyQkMHjw4Cp1dOYccNwZrJNKV5VKS0vJyMigefPmhIWFyZVnBe5OFRcXk5GRQUJCAoGBgT571dTf7k4VFBRw6NAhVy4gV56dvFVHOBwO0tLSaNasmeuuXGW362v7UMVszt6HpaWlZGZm0rJlS9fxfCZ/rSNUvzvlPN+3aNGC0NBQv9uHqmVTkX1YG+d7FbPxte8R5Z3vfXUfqphNVfah83yfkJDg0YhVpY7Ys2cPOp2Ojh07eix7NqXuYEdGRgJ4XFVyXqlzzj+XN954g6effppevXqxcOFC145x3rkub7D2/Pz8C273Qs71g37nD/7P5Xy9/zk/pOXR6XTn3e7Zw47p9XrXtgwGw3l/z3C+7TqHKSjPhd5rbW23rvbhmaqzD52VoV6vJzAw0G1Zb2XjjX2oYjYmk6ncXKD29qGK2ahUR5z56J7q9eyZqltH1MZ2a7qOcD4mV9/qiLquZyu7Xb1e73p//rYPVcumIvuwNs73Kmbja98jzne+97V9qGI2VdmHzvO9yWSq8vm+trOpzG/2lerkLDk5GYPBwN69e92mO187H3c4m6ZpPP7444wdO5YRI0bwzTffuB4rh7LHeeLj4z2263xE5FzbFUIIIYQQQgghKkqpBnZQUBA9e/YkNTXV7XGG+fPnExkZSdeuXctdb8KECbz11luMHTuWefPmlXtVpX///nz99ddujzvMnz8fg8FAnz59av7NCCGEEEIIIYSoV5R6RBxg4sSJ9O3bl+HDhzNy5Eg2bNjAjBkzmDZtGiEhIeTn57Nr1y6Sk5OJjo5m69atTJ8+nS5dunDrrbeyefNmt+116NCBiIgIxo0bx2effcYNN9zA2LFj2bNnDxMmTOChhx6SMbCFEEIIIYQQQlSbUp2cOS1YsIBJkyaxe/du4uPjeeyxx3jqqacAWLNmDb1792bOnDncd999vPjii7zyyivn3Nbq1avp1asXAOvWreOZZ55h69atNG7cmLvvvpvJkydXuEe4szk7OavIj929xeFwYLPZMBqN6PVKPbBQb0kmapJc1COZqElyUZPkoh7JRE2Si3p8IZPKtPuUbGD7Cl9oYAshhBBCCCGEqLrKtPvUvEQgaozFYiEzM7Pc7ueFd0gmapJc1COZqElyUZPkoh7JRE2Si3r8LRNpYPs5h8NBfn6+x1h1wnskEzVJLuqRTNQkuahJclGPZKImyUU9/paJNLCFEEIIIYQQQogaIA1sIYQQQgghhBCiBkgnZ9Xwyy+/oGlaueNuq0LTNFevfDqdztvFEUgmqpJc1COZqElyUZPkoh7JRE2Si3p8IROLxYJOp6NTp04XXFa5cbB9iaofgDPpdLoqD0MmaodkoibJRT2SiZokFzVJLuqRTNQkuajHFzLR6XQVbvvJHWwhhBBCCCGEEKIGyG+whRBCCCGEEEKIGiANbCGEEEIIIYQQogZIA1sIIYQQQgghhKgB0sAWQgghhBBCCCFqgDSwhRBCCCGEEEKIGiANbCGEEEIIIYQQogZIA1sIIYQQQgghhKgB0sAWQgghhBBCCCFqgDSwhRBCCCGEEEKIGiANbCGEEEIIIYQQogZIA1sIIYQQQgghhKgB0sAWQgghhBBCCCFqgDSwfVBGRgZRUVGsWbPmgst+9tlnXHTRRQQHB9O+fXs+/vhjj2V++uknevXqRVhYGE2bNmXChAlYLJZaKLl/q2gupaWlTJkyhXbt2hEaGkrbtm2ZPHmyxz5PSEhAp9N5/Dt27Fgtvgv/U9Fc9u7dW+7+vvjii92WW7FiBV26dCEkJISkpCRmzpyJpmm1+A78T0Uy+b//+79y83D+O7Muk2OlahwOB++99x6XXHIJYWFhtGzZkieffJL8/PzzrifnldpVlVzkvFL7qpKLnFdqV2UzkfNK3XA4HMycOZPWrVsTHBzMpZdeyrx58y643qxZs2jVqhXBwcF06tSJpUuXeizjM8eKJnzKoUOHtPbt22uAtnr16vMum5KSoul0Ou2JJ57Qli1bpo0aNUoDtM8++8y1zL59+7TIyEjt+uuv15YsWaLNnDlTCwwM1B5++OFafif+pTK5PPzww1pISIg2depUbdWqVdq0adO04OBgbeTIka5lcnJyNECbMWOGtnHjRrd/Vqu1lt+N/6hMLl9++aUGaN9++63b/t62bZtrmY0bN2omk0m76667tG+++UZ7/vnnNZ1Op02dOrWW34n/qGgm2dnZHp/9jRs3ahdddJHWrFkzLTs7W9M0OVaqY+rUqZrBYNDGjx+vrVy5UnvnnXe0hg0ban379tUcDke568h5pfZVJRc5r9S+quQi55XaVdlM5LxSN55//nnNZDK56qOxY8dqgPbf//73nOu89tprmsFg0CZPnqwtXbpUu+WWWzSDwaCtW7fOtYwvHSvSwPYRdrtdmzNnjtaoUSOtYcOGFWowtGnTRhs+fLjbtOHDh2utWrVyvX7ooYe0hIQErbS01DVt9uzZml6v19LS0mr0PfijyuZy7NgxTafTaa+++qrb9GnTpmmAq3JfuXKlBmh79+6tzeL7raocL88//7yWkJBw3mX69++vde3a1W3auHHjtPDwcK2oqKi6xfZrVcnkbLNmzdL0er22adMm1zQ5VqrGbrdrUVFR2qOPPuo2/fPPP9cAbcuWLeWuJ+eV2lWVXOS8UvuqerzIeaX2VDWTs8l5pWadOnVKCw0N1Z5++mm36ddee63WrVu3ctcpKirSoqKitHHjxrmmORwOrVu3blrfvn1d03zpWJFHxH3E9u3bGTVqFPfccw9z58694PIHDx5kz549DB061G262Wxm7969/PnnnwAsX76cQYMGERAQ4LaMw+Fg+fLlNfsm/FBlc8nPz2fUqFHcdNNNbtPbtWsHwP79+wHYunUr4eHhtGzZsuYLXQ9UNhco2+eXXXbZOeeXlpayZs2aco+pgoIC1q9fX50i+72qZHKmo0ePMnHiRB555BGuvPJK13Q5VqomPz+fu+++mzvuuMNturMu2rdvn8c6cl6pfVXJRc4rta8quYCcV2pTVTM5k5xXal5gYCAbNmzgqaeecpseEBBASUlJuets3ryZEydOuB0HOp2OYcOGsXr1aoqLi33uWJEGto9ITExk7969vP7664SEhFxw+d9//x2ANm3auE1v1aoVALt376a4uJi0tDSPZaKjo4mIiGD37t01VHr/VdlckpKSmD17Nm3btnWbvnDhQkwmkyuLrVu30rBhQ8xmM5GRkYSFhTFixAiysrJq5X34m8rmAmX7vKCggKuuuoqgoCBiY2MZP348VqsVKPuSarFYzntMiXOrSiZnmjRpEnq9nn/84x9u0+VYqZqoqCj+9a9/0aNHD7fpCxcuBOCiiy7yWEfOK7WvKrnIeaX2VSUXkPNKbapqJmeS80rNMxgMXHLJJcTGxqJpGkePHmXatGmsWrWKRx99tNx1zndusdvt7Nu3z+eOFWlg+4iGDRuSkJBQ4eVPnjwJQEREhNv08PBwoOzK37mWcS53oY5uROVzKc+CBQv4+OOPGTVqFA0aNADKKvfMzEyuuOIKvv76a15//XW+//57rr32Wk6dOlUTRfdrlc3l2LFjZGZm8scffzBq1CiWL1/OQw89xBtvvMF9990HVOyYEudWnWMlOzubjz/+mNGjRxMVFeU2T46VmrN582amTZvGjTfe6NEJE8h5xVsulEt55LxS+y6Ui5xX6l5ljhU5r9S+zz//nNjYWJ577jkGDhzIXXfdVe5y1Tm3qHqsGL1dAFE7HA7Heefr9foKLSNqV2pqKnfccQdXX301r776qmv6hx9+iNFopEuXLgBcc801XHTRRVx99dV88sknPPLII94qsl8KDQ1lxYoVtG7dmhYtWgBw7bXXEhgYyMSJE5k4caIcL17073//G7vdzpgxYzzmybFSM3744QcGDx5MUlISc+bMKXcZOa/UvYrkcjY5r9S+iuQi55W6VdljRc4rta9r1658//33bN++nRdeeIHrr7+eNWvWoNPp3Jbzx3OLWqURNSYyMhKAgoICt+nOKzyRkZGuq0BnL+NczrkNUTveeOMNbr31Vnr06MGSJUsICgpyzevevburYnfq0aMHkZGRbNu2ra6L6veCg4Pp16+f60uQ06BBgwDYtm1bhY4pUTtSUlLo378/0dHRHvPkWKm+L774gr59+5KYmMi3335Lo0aNyl1Ozit1q6K5nEnOK7WvornIeaXuVOVYkfNK7UtOTqZnz56MHj2aWbNmsXbtWtatW+exXEWOA187VqSB7aecv8Xau3ev23Tn6/bt2xMWFkZ8fLzHMtnZ2RQUFNC+ffu6KWw9o2kajz/+OGPHjmXEiBF88803rkdcoOxRmY8++ogdO3a4redwOLBYLOWeDET1/Pnnn7z//vucOHHCbXpxcTFQ9vvR5ORkDAbDeY8pUfMyMzP59ddfGT58uMc8OVaqb+bMmdx+++10796dtWvXEhcXd85l5bxSdyqTC8h5pa5UJhc5r9SNyh4rIOeV2pSTk8Mnn3xCdna22/ROnToBcPjwYY91znduCQgIoGXLlr53rHi5F3NRBatXr67QEDdJSUnlDqfSunVr1+v7779fS0xM1EpKSlzTZs+erRkMBhlOpZIqmsv48eM1QBs7dmy54zQWFxdrwcHB2h133OE2fcGCBa7xNEXFVSSX77//XgO0Dz74wG36mDFjtIiICC0vL0/TNE3r3bu31q1bN7fcxo0bp0VGRmqnTp2qjeL7pYoeK5qmafPnzz/ncClyrFTPe++9pwHaiBEj3IbUOh85r9S+quQi55XaV9lc5LxS+6pyrGianFdqU1pamgZoU6ZMcZv+2muvaYC2c+dOj3WcQ3udPUzXlVdeqfXr1881zZeOFWlg+6DyvpyePHlS27hxo2u8S03TtDlz5miA9sgjj2jffPONNmrUKA3QPv/8c9cyv//+uxYUFKT17t1bW7x4sfbaa69pgYGB2iOPPFKXb8kvVCSXX3/9VdPpdFqXLl20jRs3evw7efKkpmma9vLLL2uA9uSTT2orV67UXn/9dS0iIkK7+eabvfDOfFtFcrHb7dp1112nhYeHa7NmzdJWrlypPfHEE5pOp9PeeOMN13rffvutptPpNLPZrC1dulSbOHGiptPptOnTp9fxu/JtFa3DNE3TXnrpJS0wMPCc25JjpWqysrK04OBgrUWLFtq6des86qLs7Gw5r3hBVXKR80rtq0oucl6pXVWtwzRNziu1beTIkVpQUJA2Y8YMbdWqVdqkSZO0wMBA7W9/+5umaZqWnZ3tVjdpmqZNmjRJ0+l02sSJE7WlS5dqZrNZMxqN2vr1613L+NKxIg1sH1Tel1PntDlz5rgt+95772mtWrXSAgMDtfbt22uffPKJx/bWrl2rXXnllVpgYKAWHx+vjR8/XrNYLLX8LvxPRXJ54YUXNOCc/5zr2u12bfbs2dpFF12kBQUFafHx8dq4ceO0oqKiun9jPq6ix8vJkye1sWPHai1atNACAwO1Dh06aB9++KHH9lJTU7WOHTtqAQEBWlJSkjZz5sw6eBf+pTJ12COPPKI1adLknNuSY6Vq/vOf/5y3LpozZ46cV7ygKrnIeaX2VfV4kfNK7alOHSbnldpVWlqq/eMf/9Bat26tBQQEaMnJydr06dM1u92uadpfF2rP/A5gt9u1V155RWvWrJkWFBSkderUSVu6dKnHtn3lWNFpmqZV4olyIYQQQgghhBBClEM6ORNCCCGEEEIIIWqANLCFEEIIIYQQQogaIA1sIYQQQgghhBCiBkgDWwghhBBCCCGEqAHSwBZCCCGEEEIIIWqANLCFEEIIIYQQQogaIA1sIYQQQgghhBCiBkgDWwghhBBCCCGEqAHSwBZCCCF81EsvvYROp6vQvyeeeMLbxT2nM9/Hm2++6e3iCCGEEFUmDWwhhBBCCCGEEKIGGL1dACGEEEJU34gRI7jtttvOOT85ObkOSyOEEELUT9LAFkIIIfxAu3btGDJkiLeLIYQQQtRr8oi4EEIIUY/06tULnU6H0WiktLSUF154gebNmxMUFMRll13GJ598Uu56VquVt99+m+7du9OwYUNCQkK4+OKLefHFFzlx4kS56/z000/ceuutxMbGEhgYSIsWLbjtttvYunXrecu4YsUKrrrqKkJCQoiLi+Oxxx4jLy+vmu9cCCGEqH1yB1sIIYSop2688UZWrlzper1t2zbuvfde9u7dy+TJk13TT548yYABA9i8ebPb+jt37mTnzp18+umnrFy50u0x9P/+97/ce++92Gw217S0tDTS0tJYuHAhS5Ys4brrrvMoU0pKChs2bEDTNACKi4uZPXs2O3bs4Pvvv6+x9y6EEELUBrmDLYQQQviBl19++by9iJ/NbrezcuVK7rvvPj755BMefPBB17x//OMf/Prrr67XjzzyiKtxnZSUxKxZs/jggw/o1q0bAAcOHGDIkCHY7XYADh8+zAMPPIDNZkOv1/Poo48yb948Hn/8cQBKS0sZOXKkW+Pb6YcffmDw4MHMmzePF154AYPBAMDatWvZvn17De0tIYQQonbIHWwhhBCinnrwwQf54IMPALj77rsxGAy89957aJrGJ598wuWXX86+ffv4/PPPAWjYsCE//vgjjRs3BuD++++nW7du/Pzzz+zYsYP58+czfPhwPvnkE4qLiwEYPXo0s2bNAuCOO+6guLiYffv2cemll5KTk0NcXJxbmTp37syiRYtcFwW2bt3K4sWLAfjzzz+55JJLan/HCCGEEFUkDWwhhBDCD1yoF/HynL387bffznvvvQeUPS4OsHr1atfj2rfffrurcQ1gNBoZNWqU6+73mjVrGD58OD/99JNrmRtuuMHtbzgb9OfSv39/tzvuzZo1c/1/UVFRhd+bEEII4Q3SwBZCCCH8QFV6EW/YsKHb6yZNmrj+v6CgAIBjx465piUkJHhsIz4+3vX/x48fB8p+s+10ZoO8KmUymUyu/3c+gi6EEEKoSn6DLYQQQtRTGRkZbq+PHj3q+v+oqCjAvdF99vJnT4uOjgYgMjLSNc3Z6HY6cuQIBw8edN0VP5vzN9dCCCGEL5IGthBCCFFPzZkzx+31/PnzXf9/+eWXA9CjRw/XtM8//5zc3FzXa7vd7vbIt7NX8M6dO7umLVq0yO1vPPvssyQlJREWFsbq1atr4F0IIYQQ6pBHxIUQQgg/8Mcff7Bw4cJzzg8JCaF///5u01JTUxk+fDg33ngjW7Zs4e233wZAr9dz7733AtCmTRsGDhzI0qVLOX78OF27dmXMmDEEBwczZ84c1++tL7/8cm6++WYA7rnnHiZPnkxxcTHvvvsuFouF/2fvzuOjKA8/jn/2yn0REiAQjnCjiILch0IMimeV4lFPaqm1VtFqi2hRK94Cauut9URq/VnAWq0KngiIIoeCKIpiCAECIQlJyLHZ7Pz+iLuybAIJOfbJ5vt+vfKCndl59pn5zj67z8zsPBMmTGDdunXMnz8fgNjY2IDOu4iISDhQB1tERCQMvPLKK7zyyit1zu/evTs//vhjwLTBgwfz6quv8uqrrwZMnz17NkcffbT/8fPPP8/JJ5/M+vXr+f777/3Dbfn06dOHxYsXY7fXXBjXuXNn/vGPf/jHwX7mmWd45pln/M93uVy8/PLLREREHOnqioiIGEmXiIuIiLRRTz31FLNnzyY9PZ3IyEiOO+44FixYwF/+8peA56WmpvLZZ5/x8MMPM3r0aJKSkoiKiuLoo4/mtttu4/PPP6d79+4By1x44YWsWLGCyZMnk5qaSkREBBkZGVxwwQV8/vnn/svJRUREwonNqusuIyIiIhJ2xo8fz0cffQTAunXrOO6440JbIRERkTCiM9giIiIiIiIiTUAdbBEREREREZEmoA62iIiIiIiISBPQb7BFREREREREmoDOYIuIiIiIiIg0AXWwRURERERERJqAOtgiIiIiIiIiTUAdbBEREREREZEmoA62iIiIiIiISBNQB1tERERERESkCaiDLSIiIiIiItIE1MEWERERERERaQLqYIuIiIiIiIg0AXWwRURERERERJqAOtgiIiIiIiIiTUAdbBEREREREZEmoA62iIiIiIiISBNQB1tERERERESkCaiDLSIiIiIiItIE1MEWERERERERaQLqYIuIiIiIiIg0AXWwRUTESD169MBms2Gz2SgqKmq219m4cSPPPPNMs5V/OJZl8cILL/Dll182SXlJSUn+7dZYodo2+fn53H333S3+ugd6/vnn/dvxuuuu80//61//6p/+0EMP+adPnTrVP/21116r12u0xD6+a9cupk+fTkZGBpGRkXTq1IlLLrmE77//vlleT0SkrVMHW0RE2qSCggJ+97vfcdxxx/Hxxx+HpA4rV65k6NChTJ06lYKCgpDUoTah2jZer5c5c+bQu3dvnnrqqRZ73XCVk5PD8ccfz8MPP8yPP/6I2+0mLy+Pl156iaFDh/LFF1+EuooiImFHHWwREWmTli1bxlNPPUV1dXXI6nD//fezdu3akL1+XUK1bYqLi5kxYwb79u1r0ddtCtOnT2fx4sUsXryYESNGhLo6AMyaNYsdO3YA8Ktf/YoFCxZw7rnnAlBUVMSVV14ZyuqJiIQlZ6grICIiItLaDRkyhCFDhoS6GgG++uorHA4HxxxzDAsWLMBms3HeeeexZMkS9u3bx6pVq6isrCQyMjLUVRURCRs6gy0iIi3K6/XyxBNPMHLkSBISEoiPj2fgwIHMnj27zjOXbreb22+/nR49ehAVFcWgQYN47rnngp63f/9+brrpJgYMGEB0dDRRUVF06dKF8847j/Xr1/ufN3XqVM455xz/4xdeeAGbzcZf//pX/7TS0lJmzpxJ9+7diYqK4thjj+Wf//wnH374of93swc+f/z48dhsNpxOJ5WVldxyyy3+ZY877jhefPHFgLrabDb+85//+B9PmDABm83Gjz/+WK/t+PbbbzNu3DhiY2NJTk5m6tSp5OXlHXKZl19+mdGjRxMXF0dcXBwjRozgySefxOv1NmjbFBUVMWPGDHr27ElERAQdOnRgypQprFmzptbX3bFjB9dccw29evUiKiqKtLQ0MjMzA9b/+eefp127dv7H2dnZ2Gw2xo8fH1DW1q1bmT59On369CE6Oprk5GQmTpzIwoULg173wN9Fr1q1iszMTP/vkD/44INDbquGOpLfYB+ovvs4wOeff865555Lp06diIyMpEePHlxwwQUB+7jveWVlZbzzzjv+3+Tv27ePsrIyANq3b6/OtYhIU7NERERaiMfjsc4880wLqPXv+OOPt/bt22dZlmV1797dP33cuHG1Pv+ZZ54JKDszM7POsmNiYqxvvvnGsizLuuyyy2p9zm233WZZlmWVl5dbw4cPr/U555xzTtDzLcuyTjzxRAuwHA6HNXHixFqXveWWW/zPr6ueW7duPex2fOqppyybzRa0bK9evayoqCj/4wNdd911db7mueeea3m93nptm927d1t9+/at9Tkul8tatGhRwOtu3LjR6tChQ52vfeedd1qWZVnPPfdcrfNPPPFEf1nvvPOOlZCQUGdZl112mX89Dl6Xrl27+v8fGRlpFRUVHXIbH1ifa6+91j/9tttu809/8MEHa32txYsXHzZDy2r4Pm5ZlrVgwQLL6XTW+tzIyEjr3XffrfW1du3aZT366KPWMccc43/+Qw89VK96iohI/amDLSIiLebBBx/0f7nv3Lmz9cADD1gvvPCCNWLECP/0m2++2bKswM5HVFSUddttt1n//Oc/raysLP/0wYMH+8tetGiRf/rw4cOtF1980Zo/f37A8//6179almVZa9assWbOnOmfPmHCBGvx4sXW119/bVmWZf31r3/1z0tKSrLuvfde6x//+Ic1atSoWjudlvVzB9v3N3XqVOvFF1+0fvvb3/qn2Ww2a+3atZZlWdbixYsDOvF33HGHtXjxYmv//v2H3IZ79uyxoqOj/cudccYZ1vPPP2/NmjXLioyMDKiDz5tvvumfdtRRR1mPPfaY9eKLL1qnnHKKf/o//vGPem2bc8891z/v4osvtl566SXrgQcesDp37mwBVmJiopWfn+9/7eOOO87//FNPPdV6/vnnrfvuu8/fUbbZbNY333xjZWdnWy+99JL/uampqdbixYutjz/+2LIsy9qxY4eVnJzsnz958mTr+eeft+644w4rMTHRP/3ee+/1v/bBBwtmzpxpPffcc9Ydd9xx2H21pTvY9dnHc3Nz/dnb7XbrqquushYsWGBNnz7d//xu3bpZVVVVQa/1wgsv+J9jt9utRx99tF51FBGRhlEHW0REWky/fv38X/LXrFnjn15QUGAdddRR1pQpU6yHH37YsqzAzodvmmVZ1t69e/3T4+Li/NN37dpl/ec//7HuuOMOKzc31z9948aN/udfccUV/umLFy8OOPN5oJ49e/rnLV261D/d7XZbffr0OWwH+7e//W1AeVdeeaV/3nXXXeef/otf/MI//YMPPqjXNnz66af9y4wfPz5g3pNPPllrB/v000/3T9u2bZt/usfjsTIyMizAGjJkyGG3TV5enmW32y3AyszMDHjt9957z7/M3//+d8uyLOuTTz7xTzv22GMDzi4//fTT1ogRI6zf/va31ieffGJZlmUVFhb6n9+9e/eA8m+99dY68/r888/99UpMTLTcbrdlWYGd3jPPPLNe29enpTvY9dnH77nnHv/06dOnB5T129/+1srMzLT++Mc/Wjt27Ah6rdtvvz1g30hNTbVeffXV+m0MERGpN93kTEREWkRpaSmbN28GoGPHjgE3hGrXrh1fffVVncuecsop/v8nJycTExNDWVmZ/7ekvjLPOussTj75ZD777DMWLFjAZ599FvBbW7fbfdh6lpSU8MMPP/hfKysryz/P5XLxq1/9itmzZx+yjAsuuCDg8a9+9SueeOIJgHoNjZSfn8/y5cuDpg8ZMoSNGzf6H/vuCO1z/vnn87vf/S5ouc8++8z//27dutX6ml988QVlZWXExMTUWa/PP//c/3vt999/v86xtleuXMk111zD559/7p82adKkgOdPmzaNadOm1flaB3vvvff8/7/66qsD5h1//PGMGjWKFStWsG/fPtatW8fw4cMDnnPwb7kB1q5dy7Zt24Kmn3322fWu16GUlZWxZMmSoOn9+/enf//+AdPqs48fuD1PPfXUgOUPN6zZ7373O66//nq+/vprfvnLX5KTk8P555/PunXrGDRoUIPWS0RE6qYOtoiItIgDb2CWkpLSoGWTk5MDHrtcLoCAm3NVV1dz00038cQTT1BSUuJf7phjjuGjjz4CwLKsw76Wb1mADh06BM1PS0trcH07duxYa/l12bhxY8CNxnyee+459u/f73988HZMTEwkKiqKioqKgOmFhYWHfc3q6mp27txJr1696nxOfcoB2L59O9C4zA+Wn5/v/396enrQ/C5duvj/v3fv3qD5tWX597//nRdeeCFoen32k/rYvXt3rTnedtttATeNg/rt443Znr59cNiwYcyaNYvf/e53eL1enn/+eR544IEGlSUiInVTB1tERFpEQkKC//+1dYC+/vpr0tLSSEpKCprncDgOW/5dd93FnDlzAPjNb37Dn/70J/r378/3339P7969613P1NRUHA4H1dXVtd6V2zeu8KFs376d4447zv/4wHJqW7+GSExM9P9/9+7dAfOKi4uDOtcA8fHxFBYW4nA4ePXVV+s885yamnrI146Pj/f//6STTgo6k+zjW8cD63pw5mVlZfz444/06dPH35k8lI4dO/qvgNi+fTudOnUKmO/r1Ne1Hoc6M2+C+uzjh9qeu3btoqKigu7du2Oz2XC73XzzzTds27aN9u3bM2rUKP9zO3fu7P9/ffZnERGpPw3TJSIiLSI+Pp5+/foBNZ2BAy9bLi4uZsSIEbRr146BAwcGnLWrrwOHaZo1a5b/Ety1a9fW+ny7/eePwAPPWLpcLn+HvLCwkKVLl/rnVVZWsmDBgsPW5eDhlQ6s2+DBgw9bh/Hjx2PV3Ccl4G/q1KkBl9b/+9//Dnidl156qdb6HH/88UDNWeqkpCTOPvtszj77bLKysli4cCFffvkl8PNBkLrqNWTIEH/nfNu2bZx22mn+slwuF2+//Tbbtm3zn10dOnSof9k33niD6upq/+NXXnmFo48+mpiYGO66665Dvi7A2LFj/f9/7LHHAuatW7eOVatWATVngg88uOFzYNk+zz//fK3buan06NGj1vIPPntdXwduzwOHOAO48cYbycjIIC4ujg8++IDc3FyOPfZYzjzzTK666qqA99SBP5vo06fPEdVFRERqpzPYIiLSYq688kr++Mc/AnD66aczY8YMOnXqxFNPPeW/dDorK6vWztDhHHh27/LLL2fatGls3ryZBx980D+9srLS//8Dz2iuWLGC+fPnk5aWRlZWVkA9zz//fG666SbatWvHk08+6f999qEsWrSI8847jzPPPJPVq1fzyCOPADWdvMsuu6zWOrzwwgt88803TJky5ZBnkn/xi1/Qvn179u7dy0cffcQvf/lLpkyZwoYNG5g3b16ty1xxxRW8++67AEyZMoU//elPdO/enX/84x/+ztbll1/u/+3xobbNqaeeyv/+9z++++47MjMz+c1vfsP+/fv561//6j+rumzZMgBGjhzJscceyxdffMHGjRvJysri0ksvpbCwkDvvvBMAj8fDaaedBkB0dDQ2mw3Lsti5cydPPfUU0dHRXHLJJUybNo2//e1v7N+/n+eee46SkhLOPPNMcnJymDdvnr8Decstt+B0hufXm0svvZTZs2dTXl7O448/jtvtZsKECaxbt4758+cDEBsby5gxY4iIiGDMmDGsWLGC9evXc84553Duueeybt06/va3vwEQERHB1KlTQ7hGIiJhqCXvqCYiIm3b4cbBPvbYY63i4mLLsgLvsFxYWBhQzoHDMvkcOMTTgX82m80/bvCYMWP8z8/LywsaT3jatGmWZVlWWVmZNXTo0FrLOvBu4bfffru/vAOnDx48uNa6+MZ89nnggQeCnrN8+fLDbsfXX3/dcrlcQct27949YFisA02dOrXO7X7UUUdZeXl59do22dnZVrdu3eos64Ybbgh43Y0bN1qpqal1Pn/27NkBzx80aFDA/N69e/vnLV682IqNja2zrAPvEm9ZR3Znb5+Wvot4ffZxyzr0ONgulytgHOxvvvmmzjHInU6nNX/+/AZtExEROTxdIi4iIi3G4XCwePFiHnvsMYYNG0ZMTAwJCQkMGjSI2bNns2zZsoDf+TbERRddxEsvvcTgwYOJiYmhU6dOZGVl8c477zBp0iQAVq1axc6dO4Gam14999xz9O7dG5fLRVpaGt27dwdqzqS+9957XHvttXTu3JnIyEiGDh3Kf//7X6ZMmeJ/zdjY2Frr8tRTTzF79mzS09OJjIzkuOOOY8GCBfzlL38JeN6VV17Jb37zG5KSkoiOjmbgwIFEREQcdl3PPPNMPvjgAzIzM4mOjiYlJYXf/OY3fPbZZwE3VDvQs88+ywsvvMC4ceNITEwkOjqafv36cfPNN/Pxxx8H3ATsUNumW7durF27lj/96U/07duXqKgokpOTGTt2LP/617+YO3duwOseffTRrFu3jquuuooePXoQERFBWloaJ510Eq+//jq33HJLUD1HjhxJREQE7dq146ijjvLPO/vss/nyyy+ZPn06ffr0ISoqiqSkJLKysli8eDFPPvnkYbdda3fhhReyYsUKJk+eTGpqKhEREWRkZHDBBRfw+eefc9JJJ/mf269fP9avX89VV11F9+7dcblctG/fnnPOOYeVK1dy8cUXh3BNRETCk82ymvDHRiIiImHgP//5D263m7S0NPr27RvQ+bz55pu55557gJrfPF900UVAze+mfXcrX7duXa2/AxYREZHwFp4/UhIREWmEf/3rX/zrX/8CoFevXtx0000kJCTwzTff+Ic0stvtjBs3LpTVFBEREcOogy0iInKQP/7xj7z22mtUVFTw/fffM23atKDnXH311XTr1i0EtRMRERFT6TfYIiIiBxk+fDirVq3ikksuoWfPnkRFReFwOEhOTmb8+PG88MIL/jsxi4iIiPjoN9giIiIiIiIiTUBnsEVERERERESagH6D3Qjr1q3DsixcLleoqyIiIiIiIiLNoKqqCpvNxuDBgw/7XJ3BbgTLsmgNV9hXV1eHugpyEGViJuViHmViJuViJuViHmViJuViHtMzaUi/T2ewG8F35vqYY44JcU3qVlFRwdatW8nIyCAqKirU1RGUiamUi3mUiZmUi5mUi3mUiZmUi3laQyYbNmyo93N1BltERERERESkCegu4o3gO5Jh8hlsEREREREROXIN6ffpDLaIiIiIiIhIE1AHO8xVVlby448/UllZGeqqyE+UiZmUi3mUiZmUi5mUi3mUiZmUi3nCLRN1sMOcZVmUl5e3irudtxXKxEzKxTzKxEzKxUzKxTzKxEzKxTzhlomxHewlS5YwbNgwYmJiyMjIYO7cuYfc6Fu2bMFmswX9DRw40P+ciooKXC5X0HPi4uJaYpVEREREREQkjBk5TNeqVas444wzOP/887njjjtYvnw5M2bMwOPxMHPmzFqXWb9+PQDvvfceMTEx/ukH/n/jxo14PB5eeuklevXq5Z/ucDiaZ0VERERERKTJNWRc4gN5vV4sy8Lr9eL1epuhZtJQoczEd8K1KRnZwb7tttsYPHgw8+fPB2DSpElUVVVx9913c+211xIdHR20zPr160lPTyczM7POctevX4/T6WTKlClERkY2W/1FRERERKRpWZZFdXU1brf7iC8ndrvd2O12Kisrw+aS5NYu1JnYbDYiIiJwOBxN0tk2roNdWVnJhx9+yO233x4wfcqUKdx///0sX76ciRMnBi23fv16jjvuuEOWvX79evr379+mOtcul4vOnTvjcrlCXRX5iTIxk3IxjzIxk3Ixk3IxjzJpem63G4/Hg9Pp9HeGGtohioiIwG63ExMTo6tYDRGqTHxXQVRXV1NZWYnT6WySfqJxHewffvgBt9tN3759A6b37t0bgM2bN9fZwe7duzejR49m7dq1JCUlMXXqVO644w5/w+Y7g33yySezYsUKIiMjOffcc5k7dy7x8fFHXOeKioqAx3a7nYiICLxeL263O+j5UVFRALUepXG5XDgcDjweDx6Pp9ZyLcuq9S57kZGR2Gw23G53wOUVkZGR/teprq6mqqoqYDmbzebfmQ5eF/h5p6+qqqK6ujpgnsPhwOVyHXZdm6vcltqGAE6nE6fT2aht6Cs3MjKSqqoqqqqqmqTc1rYNTczm4Fyg+behidmY1kYkJiYGZNLQclvbNjQxm9q2YVxcXJtrI0LRzja03AO/GIbbNjQtm/puw6b+vDcxm5ZqZy3LoqqqiujoaGw2W1B97XY7LpcLy7JqrW9ERIR/uaioqIDLkR0Oh38bHlxf39lN37oezOVy+bfhwdn46uT1eoOy8a0rUOsZed9BBI/HU+e6Nle5h9uGta1rY7dhXZ/39V3XxmRTXV2NZVmUlZXh9Xqx2WxB+7dlWfU+mGNcB3vfvn0AJCQkBEz3dYCLi4uDlsnPzyc3NxePx8P9999P9+7dee+997jvvvvIyclhwYIFWJbFl19+iWVZTJs2jVmzZrF69Wpuv/12Nm3axEcffYTd3vB7vlVXV7N169aAaQkJCXTp0gWPxxM0D2DAgAEA7Ny5k/Ly8oB5nTt3JjExkeLiYvLy8gLmxcbG0q1bN7xeb63l9unTB6fTSV5eHqWlpcDPl9J06tSJ1NRU9u/fT25ubsByUVFRZGRkAPDjjz8GvRF79uxJZGQk+fn5FBUVBcxr3749HTp0oLy8nG3btgXMczqd9OnTB4CcnJygN1u3bt2IjY2lsLCQvXv3BsxLSkoiLS2NqqqqoHW12Wz0798fgB07dgR9YHTp0oWEhAT27dvH7t27A+bFxcXRtWvXWnMD6Nu3Lw6Hg127drF///6AeR07diQ5OZnS0lJ27NgRMC86OpoePXoA1Fpur169iIiIYM+ePezbt4/q6mr/kdeUlBRSU1MpLy8nJycnYDmXy+U/uLRt27aghrB79+7ExMRQUFBAQUFBwLx27drRqVMn3G53UJ3sdjv9+vUDIDc3N6hRSk9PJz4+nqKiIvbs2RMwLz4+nvT09Dr37379+mGz2di5cydlZWUB89LS0khKSqK0tJSdO3cGzIuJiaF79+5YllVrub1798blcrF7925KSkoC5qWmppKSkkJZWRnbt28PmBcZGUnPnj0ByM7ODmpgMzIyiIqKYs+ePezduzfg8qDk5GQ6duxIRUUF2dnZAcs5HA7/gcCcnJygRr9r167ExcVRWFhIfn5+wDyT2gifDh060L59e6PaCN82crvdFBYWBswL5zbi4M8509oIy7JITEykY8eObaqN2Lt3b9B+aFIb4fu879KlC8nJyW2ijTD9e0RpaWnA531baSOgeb5HWJZF+/btiY2NpaysLOh9ExUVRVpaGpZlBW1fX51cLhf5+fmUlpYGfN4nJSXRrl07KioqgjJ3uVykp6cDNe+5g/fvzp07ExkZyb59+4LanoSEBNq3b09lZSW7du0K2obdu3cHIC8vL2j/7tixIzExMZSUlAS9b2JjY+nQoQPV1dW1rqvv/Zifnx+UTUpKCvHx8ezfvz8oc9829Hq9tZbbtWtXnE4ne/fuDfp+0q5dO5KSkigvLw/KPCIigi5dugDUWq5vP6usrAz6bE1MTCQ5ObnWbehwOOjWrRtQsw0P3r87depEdHQ0JSUl/j6mT1xcHKmpqXg8Hnbs2OE/qOD1erHb7UFthMfjqffVKDbLsB8frFy5kjFjxrB06VKysrL8030rdc899wTd6Ky8vJzly5fTp08ff+MEcNdddzFr1iw2bdpEv379WLZsGampqRx99NH+5yxYsICLL76Y//3vf5x66qkNquuGDRsA/I2/j0lnpyorK9m+fTvdu3cnLi5OR54NODtVXl7O9u3bSU9PJzIyss0ceQazz06VlJSwbds2fy5g3tmp+pYbLmenvF4v2dnZ/g/0Iym3tW1DE7M5eBtWVlaSm5tLz549/e/nA4VrG2H6GWzf532PHj2IjY0Nu21oWjb12YbN8XlvYjYt1c56vV48Hg9xcXH+s44Hl1ufs6/79+9n165ddOjQwX9WVWewA8tt6TPYXq/Xn8nBn/ctcQbbN7+iooLIyEjsdnvQ/v3tt99is9k45phjgl7nYMadwU5MTAQIOgLkO1rnm3+g6OjoWi8bP/3005k1axZffPEFAwYMYPz48bU+B+CLL75ocAfbxxfAwQ4MpzaHusbf11jW5sDLFmrj24EPrIevLIfDccjfNhyqXJfLVeeRm8Ota3OV21Lb8ECN2Ya+L0x2u53IyMiA54Yqm1BsQxOz8TXCB+cCzbcNTczGpDbC92XQ6XQa384eqLFtRHOU29RthO+sT1trI1q6nW1ouXa73b9+4bYNTcumPtuwOT7vTcympdpZr9frP2t6uHIPVydfJ//g5x1uGx6q3EOd3fTtB3U50vawuco93DY81LoeyTb0dY4P9Rvow61rY7Px/WQgKioq4KpmX7kN+a2/ceNg9+rVC4fDwZYtWwKm+x77Los60HfffceTTz4ZdPmE702YmprKjh07ePrpp4MuPzrwOSIiIiIiIiJHyrgOdlRUFCeccAKLFi0KuKRh4cKFJCYmMnz48KBldu7cyZVXXsmrr74aMP2VV14hISGB448/Ho/HwxVXXMGTTz4Z9ByHw8G4ceOaZ4VEREREREQOYebMmfTr16/OvzFjxjTp63366af069ePTz/9tM7nXHLJJVxyySVN+rptgXGXiAPMmjWLrKwszjvvPC6//HJWrlzJnDlzuPfee4mJiaG4uJhNmzbRq1cvUlNTGTt2LCeddBI33HAD5eXlHHXUUbz55pv8/e9/54EHHiApKYmkpCR+/etfM2fOHKKjoxk1ahTLly/n7rvv5uqrrw66a3m4sNvtxMbGHtEN3FqVsh0Q0znUtaiXNpNJK6NczKNMzKRczKRczKNMzOS75P3AXKqrq/n444/ZuXMnaWlpjBs3LiRDeKWmpvLII4/UOi+ch3urLZPWzMgOdmZmJgsXLuS2227j7LPPpkuXLsyZM4cbbrgBgLVr1zJhwgSee+45pk6dit1uZ9GiRdx+++08+OCD7Ny5k169evHUU08xbdo0f7mPP/44PXv2ZP78+dx5552kp6cze/Zs/vznP4dqVZtdRESE/+56YWvrAvjstzD8Kci4ONS1Oaw2kUkrpFzMo0zMpFzMpFzMo0zM5HK5SEtL8z9etGgR1157bcAIA+np6fztb39j8uTJLVq3iIgIjjvuuBZ9TRMcnElrZ+xhgnPOOYcvv/ySyspKfvjhB3/nGmD8+PFYlsXUqVP90xISEpg3bx5bt26loqKCr776KqBzDTU/Up81axabN2+moqKCLVu2cOONN4bN0ZLa+IbtMOxm8U1r+2KoLodPLoHVV0N18F0PTdImMmmFlIt5lImZlIuZlIt5lImZDsxl0aJFTJkyJWj4vtzcXKZMmcKiRYtCVMu6PfHEEwwcODBo2Knnn3+eo48+2j9k3erVq/nNb37DsGHDGDhwIJmZmTz88MNBd9NuCitWrODCCy/k+OOPZ8SIEdxwww0BQy16vV4efPBBMjMz/XWZN2+e/67glmXx+uuvc9ZZZzFo0CBGjhzJn/70p6ChBluL8O1ZClBzV75vv/221lvXh40xr8DA22r+/92j8O6JULb90MuEUJvIpBVSLuZRJmZSLmZSLuZRJmZyu91s27aN8vJyrr322loPgPimXXfddUFDWTU339BmB//56nTmmWfi8XhYsmRJwHJvvvkmY8eOpX379nzzzTdMnTqVpKQkHnzwQR5//HGGDh3KI488wltvvdWk9X3ttde4/PLLSUtL44EHHuCmm25i3bp1nH/++f7O/tNPP83LL7/MH/7wB5599ll+9atf8cwzz/D4448DNb8Hv/HGGznppJN4+umnuemmm1i1alXACdbWxMhLxEUaxO6AQX+F9sNh5UWwdxW8NQTGvgIdJ4S6diIiIiLSjF599VVuvfXWoGF+6+Ibqzk/P7/O51iWRU5ODp06dTrkEFAHio+P54477mDKlCn1ev7BcnNzOfroo2udN2PGDH7zm9/QpUsXhg0bxhtvvMG5554LwLZt2/jyyy958MEHAfjmm28YPXo0c+bM8V+pO2bMGN5//30+/fRT/zDFjeX1epk7dy5jx45l3rx5/ulDhgzhtNNO45lnnmHGjBl89tlnDBw4kF/+8pcADB8+nOjoaOLj44Gan/9GRUVx+eWX+6clJSWxYcMGLMtq0BBZJlAHW8JHl9Pg1DXw8S+hcD28fzKcuRnieoa6ZiIiIiLSTObMmcM333zTLGUfqhNeV12OtIOdmprqP6t7sAN/o3zWWWdx2223sWfPHlJTU3nzzTeJi4sjMzMTgLPPPpuzzz6byspKtm7dSnZ2Nl9//TXV1dX+y7KbwtatW9mzZ0/QmeZu3boxePBgPvvsMwBGjBjBvHnzuPDCC8nMzGT8+PFcfPHP900aOnQoDz/8MJMnT2bSpEmceOKJjB07lhNPPLHJ6tqS1MGW8BLXEyauhNW/h+jO6lyLiIiIhLkZM2Zwyy23NOkZbJ+UlJQGncFuzM2TIyIiOOaYYw77vEmTJnHHHXfw1ltvcemll/Lmm29yyimnEBUVBUBFRQV33HEH//nPf/B4PKSnpzN48GCcTmeT3hOgqKgIqNlGB0tJSWHTpk0ATJs2jdjYWBYuXMjcuXOZM2cOffr0YdasWYwcOZJjjz2W2bNn87///Y/nnnuOp556ipSUFK688spWOUyYOtgSfpzRMPI54IAGZP82qCqGpIEhq5aIiIiINL0pU6bU+6xxZWUlO3bsoGPHjvTr14/c3NxaO502m4309HS2bt0akiG7DiU+Pp7MzEzeeustRo4cyXfffcctt9zin3/XXXfxzjvv8NBDDzF69GhiYmIAGDVqVJPWIykpCaj9LP+ePXto164dUDMM10UXXcRFF13E3r17+eijj3jiiSe45pprWLFiBVBzFvuss87C6/WyatUqXnzxRe68806OPfZYBg0a1KT1bm66yVmYi4yMpE+fPvU+8hY2bDaw/bR7V1fCx1PgnRHw48uhrRdtOBPDKRfzKBMzKRczKRfzKBMzRURE0LVrV6Kjo/nb3/4GEPQbX9/jhx56yLjOtc8vfvEL1q9fz8svv0znzp0ZPny4f96aNWsYMWIEWVlZ/s71xo0bKSgoaNK7iGdkZJCamsobb7wRMD0nJ4f169czZMgQAC644ALuvPNOANq3b8/kyZO56KKLKC4uprS0lAcffJAbbrgBl8tFdHQ0EyZM4MYbbwRgx44dTVbflqIz2GHOZrPhdLbxmKvLICKx5t+VF0L+Khg8BxwRIamOMjGTcjGPMjGTcjGTcjGPMjHTgblMnjyZf//737WOg/3QQw+1+DjYbreb9evX1zm/X79+REdHAzBu3DiSkpJ45ZVXmDZtWsBBgkGDBvHWW2/x8ssv06tXL7755hsef/xxbDYb5eXlDarTrl27eP7554Om9+3bl9GjR3P99ddz0003ccMNN3DWWWdRWFjII488QmJiIr/+9a8BGDZsGM8++ywpKSkMHjyYvLw8nnvuOYYPH05ycjKjRo3i+eef56abbuKss86iqqqKf/zjHyQlJTFy5MgG1dcEeteHObfbTV5eHh07diQiIjQdypCLaAfj34YNt8JXd8O3f4eCz2HsqxDTucWro0zMpFzMo0zMpFzMpFzMo0zMVFVVxd69e2nfvj0ul4vJkyfzi1/8go8//pidO3eSlpbGuHHjQnLmes+ePZx//vl1zn/ttdcYMGAAAE6nk9NPP5358+dz1llnBTxv5syZVFVV8dBDD+F2u0lPT+f3v/89W7Zs4f3332/Q0GPbtm3jnnvuCZo+ZcoURo8ezeTJk4mNjeXJJ5/kD3/4A3FxcYwbN47rr7+e1NRUAK699loiIiJYuHAhjz76qP8Sd9/N0UaPHs2sWbNYuHAhV199NTabjeOPP54XX3zRfxl6a2KzmvKX7m3Mhg0bAOp1M4JQqaioYOvWrWRkZPhvfNCmbX8dPrkUqvZBVMeaMbQ7tuwdCpWJmZSLeZSJmZSLmZSLeZRJ0/J6vZSXlxMdHe0feupI+H6D3blzZ12+bwgTMjnc/tWQfp9+gy1tS/pZMOlzSDoGKvJg3Z/AarrfooiIiIiISNulDra0PfG94eRV0PtKGPvKzzdDExERERERaQT1LKRtcsbA8McDx8n+7knY93Xo6iQiIiIiIq2aOthhzul00qFDB93F8nB2LoXVv4d3hsO2V5v1pZSJmZSLeZSJmZSLmZSLeZSJmRwOB+3atTN2+K22KNwyUQc7zDmdTtq3b6/G/XDaHQsdJ4CnFJafB2tvAG9Vs7yUMjGTcjGPMjGTcjGTcjGPMjGT0+kkKSlJuRgk3DJRBzvMVVdXU1xc3KDb8bdJUR1gwjtw1Myax988AO+dBOW7mvyllImZlIt5lImZlIuZlIt5lImZqqurKS0tVS4GCbdM1MEOc1VVVeTm5lJV1TxnY8OK3QnH3QPjFoEzHvZ8DG8Pgd3Lm/RllImZlIt5lImZlIuZlIt5lImZPB4Pe/bswePxhLoq8pNwy0QdbJGDdT2nZiivxKOhfCcUfxPqGomIiIiISCsQHhe6izS1hL5wyqeQ/S/o9ZtQ10ZERERERFoBncEWqYszNrBzXbkXPjwdijeHrk4iIiIiImIsdbDDnM1mIyoqCpvNFuqqtH5rr4cd/4O3h8G2hUdcjDIxk3IxjzIxk3Ixk3IxjzIxk81mIyIiQrkYxJdJuFAHO8xFRkaSkZFBZGRkqKvS+h13H3Q4ATwlsHwKrJsB3obfjEGZmEm5mEeZmEm5mEm5mEeZmCkiIoIuXboY16G75JJLuOSSS0JdjUOqqqpi8uTJrFy5EmjaOr/wwgu888479X7+okWL6NevH9u3b6/zOYWFhYwfP56cnJymqGK9qYMtUl/RnSDzXeh/Q83jr+fA+xOhPC+09RIRERERaWZPPPEEnTp1YvTo0U1a7u7du3nhhRea/C7i7dq1Y+rUqdx8881YltWkZR+KOthhrqKigm+++YaKiopQVyU82F0wZC6MfRWccbD7w5qhvAq/rHcRysRMysU8ysRMysVMysU8ysRMlZWVbN26lcrKylBXpVXZvXs3Tz31FFdddVWTl+12uwGaZZiuCy+8kG+//ZalS5c2edl1UQe7DWjJIzZtRrcpcMpnkDAA7JEQ27VBiysTMykX8ygTMykXMykX8yiTFuTZX/dfdeBBDlt12SGeX17/cg9+bgtYsWIFF154IccffzwjRozghhtuYOfOnf75Xq+XBx98kMzMTAYOHEhmZibz5s0LGI/9jTfe4KyzzmLQoEGMHDmSP/3pT+TlHfqKzOeee47OnTszcODAoHmPPvooo0ePZvDgwVx11VVBl2S/++67XHjhhQwePJiBAwcyadIkFixYAMD27ds59dRTAbjlllvIzMz0L/fRRx9xwQUXcNxxxzF27FhuvfVWiouLA8r+4osvuOCCCzjmmGMYP348//jHPwLmR0REcMopp/Dkk08ecv2akobpEjlSiQNqhvIq3wkR7WqmWRZ43eDQ761EREREWsz/xdU9r/NpMP5N/8Nunw/D7q2jc9zhRMj68OfH/+kBlfm1Pzd5KExa3eCqHqnXXnuNG2+8kTPOOIPf/e53FBYW8ve//53zzz+fxYsX0759e55++mlefvllbrzxRrp27coXX3zBgw8+iMvlYvr06axZs4YZM2Zw1VVXMWzYMHbt2sWcOXO44YYbeOmll+p87f/+979Mnjw5aPqaNWvYu3cvt956K9XV1cybN49LL72U//73v8TFxfHhhx/yhz/8gUsvvZRrrrmGiooK/vnPfzJ79mwGDhzIgAEDePDBB/njH//IFVdc4e9sf/DBB/z+97/npJNO4qGHHqKoqIj777+f3NxcnnnmGf/r//Wvf2X69Olce+21/N///R9z5syhV69eTJgwwf+cSZMm8corr7B161YyMjKaMJHaqYMt0hiu+Jo/n++fhs0Pw7hFkNAndPUSERERkbDh9XqZO3cuY8eOZd68ef7pQ4YM4bTTTuOZZ55hxowZfPbZZwwcOJBf/vKXAAwfPpzo6Gji42u+r65Zs4aoqCiuuOIK/43ekpKS2LBhA5Zl1Xp39e+//549e/YwaNCgoHkOh4Nnn32WTp06AdCzZ0/OPvtsXnvtNS6++GK2bNnCOeecw1/+8hf/MoMHD2bEiBF8+umnHHvssfTv3x+Arl27ctRRRwHw8MMPM2DAAB555BF/nSIiIvjb3/5Gfv7PBzyuv/56fvWrXwFw3HHHsXTpUlatWhXQwT7mmGMA+OSTT9TBFmlVqivgq7thfza8MxRGvQjpvwh1rURERETC33mldc+zOQIebhu6mrS0tDru8H7QL2h/8eMhXrTlfm27detW9uzZww033BAwvVu3bgwePJjPPvsMgBEjRjBv3jwuvPBCMjMzGT9+PBdffLH/+cOGDePBBx/kjDPO4JRTTuHEE09k7NixnHjiiXW+tu+S7/T09KB5Q4YM8XeuAQYMGEDXrl1ZvXo1F198MdOmTQNg//79bN26lW3btrFhwwbg599eH6yiooJNmzZxzTXXBHT4TzvtNE477bSA5w4dOtT//+joaFJSUoIuI4+PjychIeGQdxxvSvoNdpiLiIigZ8+exg1FEJYcUTBxJaSOgapiWHY2rL8ZvNUBT1MmZlIu5lEmZlIuZlIu5lEmLcwZW/efI8r/NJfLRedufXBFJ9Xx/Oj6l3vwc5tRUVERACkpKUHzUlJSKCkpAWDatGnceuutVFRUMHfuXE4//XTOOOMMVq1aBdScPX7qqafo2rUrzz33HBdddBEnnHAC8+fPr/O1fWVHRwevb231ad++vb+TW1BQwDXXXMPQoUM577zzePjhhyktrTkY4rtHgcvlAmrOhgPs27cPy7Jo3779YbfLwXWy2+213vsgOjra/7rNTR3sMGe324mMjMRuV9QtIqYznPQB9Lu25vGme+CDU6Bij/8pysRMysU8ysRMysVMysU8ysRMdrudiIiIVpdLUlISQMDl0T579uyhXbua+wHZ7XYuuugiFi1axIoVK7jnnntwu91cc801/jPG48aN45lnnmH16tU88cQT9O3blzvvvJMvv6x9VBxf2QefGYaaznBt9UlOTgbgT3/6Exs2bOD5559n/fr1vPXWW9x8880Bz/edpfb9GxcXh81mo6CgIOB5lZWVfPTRR/6DDQ1RXFzsX4/m1rr2LGmwqqoqdu7cGXDnQGlmdhcc/xCMfhkcMZD3HrwzDDxlgDIxlXIxjzIxk3Ixk3IxjzIxU1VVFXv27Gl1uWRkZJCamsobb7wRMD0nJ4f169czZMgQAC644ALuvPNOoOZM8uTJk7nooosoLi6mtLSU++67j1/+8pdYlkV0dDQTJkzgxhtvBGDHjh21vnbnzp0B2LVrV9C8NWvW+M9wQ81dvXNzcxk5cqR//sknn8yIESP8V3MsW7YMqPld+YH/VlfXXPUZGxvLgAED+OCDDwJea9myZVxxxRXs3r27XtvMZ9++fZSXl/vXo7npN9hhrrq6mqKiItq1a+e//EJaSI8LIOkY+Hgy9LwMnDGAMjGVcjGPMjGTcjGTcjGPMjGT1+ultLSUhISEUFclyK5du3j++eeDpvft25fRo0dz/fXXc9NNN3HDDTdw1llnUVhYyCOPPEJiYiK//vWvgZrfWD/77LOkpKQwePBg8vLyeO655xg+fDjJycmMHDmS5557jpkzZ3LWWWdRVVXFP/7xD5KSkvyd4oP17NmTzp07s2bNGiZOnBgwz+v1csUVV3DllVdSWFjIvHnz6Nu3L2eddRYAgwYN4r///S9HH300nTp1Yu3atTz11FPYbDbKy2vu5B4bGwvAqlWr6Nu3L8ceeyzTp0/n97//Pddffz1nn302+fn5PPDAA2RlZdG3b182btxY7+26Zs0aAMaOHVvvZRpDHWyR5pR0NExa4+9cA1C2HZu3ou5lRERERKTN2bZtG/fcc0/Q9ClTpjB69GgmT55MbGwsTz75JH/4wx+Ii4tj3LhxXH/99aSmpgJw7bXXEhERwcKFC3n00UeJj48nMzPTf3O0E088kblz5/Lss89y9dVXY7PZOP7443nxxRf9l6HX5pRTTmHZsmXMnDkzYHpWVhadO3fmz3/+Mx6PhwkTJvCXv/zFfwO5e++9lzvuuIM77rgDgB49enD77bfz+uuv8/nnnwM1l4RPnjyZt956i48//pgVK1YwYcIEnnjiCR555BH+8Ic/kJyczJlnnsk111zT4O26bNkyBg0aRJcuXRq87JGwWbX9ClzqxXcHPN+t301UUVHhH/MtKirq8AtI8/Lsx/v2SNxVHmwnLCKy/YBQ10h+oveKeZSJmZSLmZSLeZRJ0/J6vZSXlxMdHd2o309XVlayY8cOOnfuXMddxKU2eXl5ZGVl8eyzzzJs2LAmLbs5MykrK2PcuHHcd999ZGVl1fm8w+1fDen36TfYIi2p5HtslbuJKv+GiPdHQ+4bh19GRERERCSEOnbsyNSpU3n66adDXZUG+de//kWfPn046aSTWuw11cEOcw6Hg/bt2/tvey8h1m4QnomfUpU4FFtVEXx0Jnx5a9BQXtLy9F4xjzIxk3Ixk3IxjzIxk91uJzExsdXdRdwE11xzDXl5eSxfvrxJy22uTAoKCnj++ee57777AsbTbm66RLwRWsMl4mKoajesuwG+faTmcaeTYcw/IfLw4/2JiIiItDVNdYm4SG3C/hLxJUuWMGzYMGJiYsjIyGDu3Lm1Dhjus2XLFmw2W9DfwIEDG1VuOPB6vezfv99/+3sJPa/Xy/6KKrxD/gajXgJHNOxaAqt/H+qqtWl6r5hHmZhJuZhJuZhHmZjJ15FSLuYIt0yM62CvWrWKM844g/79+7No0SIuuugiZsyYwX333VfnMuvXrwfgvffe45NPPvH//fOf/2xUueHA7Xazbds2/8DyEnoBmWRcBKd8CqljYMgDoa5am6b3inmUiZmUi5mUi3mUSfNo7Mmxqqoqdu3a1erGwQ5nJmTSlCddjRum67bbbmPw4MHMnz8fgEmTJlFVVcXdd9/NtddeS3R0dNAy69evJz09nczMzCYtV6RFJB0DWR/Dgb8N2bYQOp8GTu2XIiIiIr4rVL1er37XLk3O6/X697HGMuoMdmVlJR9++CHnnHNOwPQpU6ZQUlJS5w/q169fz3HHHdfk5Yq0mAPfzDmLYPkUWDoGSreGrk4iIiIihrDZbDgcDtxuN5WVlXg8Hqqrq/F6vQ3+syzriJbTX/P9hSKT6upqPB4PlZWVuN1uHA5Hk3SwjTqD/cMPP+B2u+nbt2/A9N69ewOwefNmJk6cGLTc+vXr6d27N6NHj2bt2rUkJSUxdepU7rjjDlwu1xGXW18VFRUBj+12OxEREXi93lovC/KNhVhZWRl0OYLL5cLhcODxePB4PLWWa1kWlZWVQeVGRkZis9lwu914vV7/a3i9Xn9Z1dXVQZdf2Gw2/5hzB68LQEREBHa7naqqKqqrA+927XA4cLlch13X5iq3Jbahj9PpxOl0Nmob+j4UvF6v//UPLtduReOKSMFWuA7rreOxjVkAnU8Nq21oYjZVVVUBuUDzb0MTszGpjfDV0ePxBM03ef9ubBthWjYHb8MDH7e1NqKl29mGlOv7bPHVMdy2oWnZ1GcbHu7z/kDh1EZA87WzDoeDyMhIysvLay3X6azp2tRW34iICADKy8upqKigpKTEv00cDgcOhyPgO7OPzWbD5XLVWa7L5cJms+HxeILq66uTZVm1Xv7sq1NVVVXQNnQ6ndjtdqqrq4Oyae5y61pXX7m1rWtjtqFlWbjdbvbv319rv6o+69qYbDwej///vtc/eP+2LKvenW+jOtj79u0DICEhIWB6fHw8AMXFxUHL5Ofnk5ubi8fj4f7776d79+6899573HfffeTk5LBgwYIjKre+qqur2bo18CxjQkICXbp0wePxBM0DGDBgAAA7d+6kvLw8YF7nzp1JTEykuLiYvLy8gHmxsbF069YNr9dba7l9+vTB6XSSl5dHaWkpgL9RLC0tJS4ujv3795ObmxuwXFRUFBkZGQD8+OOPQW/Enj17EhkZSX5+PkVFRQHz2rdvT4cOHSgvL2fbtm0B85xOJ3369AEgJycn6M3WrVs3YmNjKSwsZO/evQHzkpKSSEtLo6qqKmhdbTYb/fv3B2DHjh1Bb8QuXbqQkJDAvn372L17d8C8uLg4unbtWmtuAH379sXhcLBr1y72798fMK9jx44kJydTWlrKjh07AuZFR0fTo0cPgFrL7dWrFxEREezZs4eioiLcbjfbt2/HbreTkpJCamoq5eXl5OTkAD1w9nmF9K3XEV22AT48HY65jW0R51PtDcyme/fuxMTEUFBQQEFBQcC8du3a0alTJ9xud1Cd7HY7/fr1AyA3NzfoAy49PZ34+HiKiorYs2dPwLz4+HjS09Pr3L/79euHzWZj586dlJWVBcxLS0sjKSmJ0tJSdu7cGTAvJiaG7t27Y1lWreX27t0bl8vF7t27KSkpCZiXmppKSkoKZWVlbN++PWBeZGQkPXv2BCA7Ozuogc3IyCAqKiooF4Dk5GQ6duxIRUUF2dnZAcs5HA7/QbucnJygRr9r167ExcVRWFhIfn5+wDyT2gifDh060L59e6PaiE6dOuF0Otm3b19QOx3ObcTB6xrcRvzM5XL5DxZv27Yt6MtSc7QRXq/X/x5pS23E3r17KSwsDJhnUhvh+7wvKysjNja2TbQRpn+PKCkpCfhcaSttBDT/94jq6upa2wjf/n3w+xFqtqGvjaioqGD37t3+tiwlJYWUlBRKSkqCyo2MjPTv3zk5OUFtRI8ePYiKimLXrl1B+3e7du3o2LEjZWVlQeU6HA7//r1jx46gNiI9PZ3o6Gj27NkTtH8nJCTQuXNn/+/8D+bbv3fv3h30PSItLY3ExEQKCgqC9u/Y2Fj//l3bNuzduzdOp5Pt27cHtRGpqam0b9+e4uLioHWNiory79/btm0LaiM6d+6M3W5n//79QZ+tycnJdOjQgf379weV63Q6/fu3rz94oK5duxITE8Pu3buD9u/ExETS0tJwu91B75va2giPx+M/SHA4Rg3TtXLlSsaMGcPSpUvJysryT/et0D333MPMmTMDlikvL2f58uX06dPHHxzAXXfdxaxZs9i0aROFhYUNLrc+fLdr9705fHR2Knhdw+GoaYsfea6uxPXln3FsfbrmYcdJVA17BiKSg8ptbdvQxGxMPzvVkHJb4zY0LZtW0UY0oNzWtg1NzEZthNoICP02NDEbtRFqIyD027C5s/n222+x2Wz1GqbLqA72V199xcCBA1m0aFHA76ULCgpo3749jz32GL//ff2GMlq/fj2DBw/m5Zdf5phjjmmycg+kcbClRfzwAqy+EqorYNxC6Do51DUSEREREWkzWu042L169cLhcLBly5aA6b7HvkuiDvTdd9/x5JNPBl2S4bscIjU19YjKDRcVFRV89913tR7VkdBocCY9L4OTP4Fj71HnuhnpvWIeZWIm5WIm5WIeZWIm5WKecMvEqA52VFQUJ5xwAosWLQq4nGHhwoUkJiYyfPjwoGV27tzJlVdeyauvvhow/ZVXXiEhIYHjjz/+iMoNJwdf/iGh1+BM2h0HRx/wM4byXbBuBlQHX74jR07vFfMoEzMpFzMpF/MoEzMpF/OEUyZG3eQMYNasWWRlZXHeeedx+eWXs3LlSubMmcO9995LTEwMxcXFbNq0iV69epGamsrYsWM56aSTuOGGGygvL+eoo47izTff5O9//zsPPPAASUlJ9SpXpNWwLFjxK9j9IeR9COP+DbHdQl0rEREREZE2z6gz2ACZmZksXLiQzZs3c/bZZ7NgwQLmzJnDjBkzAFi7di2jRo3izTffBGp+jL9o0SJ++9vf8uCDD3LGGWewZMkSnnrqKa677rp6lyvSathscNTMmpudFayGt4fAziWhrpWIiIiISJtn3BlsgHPOOSfgZmQHGj9+fNDd8BISEpg3bx7z5s074nJFWpXOp8CkNbB8ChSsgQ8mwaA74OibwGbccTMRERERkTbBqLuItzat4S7iXq+X8vJyoqOj/WP9SWg1aSbVFfD5dPi+ZigvupwJo16EiKRG17Ot0XvFPMrETMrFTMrFPMrETMrFPK0hk1Z7F3Fpena7ndjYWGN31raoSTNxRMGIp2DEM2CPhJLvwOZofLltkN4r5lEmZlIuZlIu5lEmZlIu5gm3TMJjLaROVVVV7N69O2jAdwmdZsmk1+Vw8koYtxhc8TXTdHFKg+i9Yh5lYiblYiblYh5lYiblYp5wy0Qd7DBXXV3N3r17qa6uDnVV5CfNlknyEEjs//Pjbx6A1VdpKK960nvFPMrETMrFTMrFPMrETMrFPOGWiZE3ORORRtqfA1/cBN6qmpugjf03xHYNda1ERERERMKazmCLhKPYrjDuNXAlwd7Paoby2vVeqGslIiIiIhLW1MEWCVddToNT10C746AyHz44Gb66V7/NFhERERFpJupghzmHw0FSUhIOh+4sbYoWzSSuJ0xcCT2nguWtuWx8xfnqZNdC7xXzKBMzKRczKRfzKBMzKRfzhFsmGge7EVrDONgiQE2H+vun4fNrYPAc6Dc91DUSEREREWkVGtLv003OwpzX66WqqgqXyxU2Y8u1diHJxGaD3ldAhwkQ3/vn6VXF4EpomToYTu8V8ygTMykXMykX8ygTMykX84RbJq1/DeSQ3G43P/zwA263O9RVkZ+ENJOEPjWdbQD3Pnh7KHw+Haq1f+i9Yh5lYiblYiblYh5lYiblYp5wy0RnsEXaqh1vQcl3NX8Fa2Ds/0FMl1DXSkRERESk1dIZbJG2qscFcMJ/wJUI+StrhvLK+zDUtRIRERERabXUwRZpy9LPgkmfQ9IgqNgN72fB13N1l3ERERERkSOgDnYbYPP95laMYVQm8b3h5E+gx8VgVcO6P8NXd4e6ViFhVC4CKBNTKRczKRfzKBMzKRfzhFMmGqarETRMl4QVy4LvHodv5tV0uKM6hLpGIiIiIiIh15B+n85gi0gNmw36XgWnbwrsXBesDV2dRERERERaEXWww1xlZSVbt26lsrIy1FWRnxifiSPy5/9vnQ9vHw9rrgdvVejq1AKMz6UNUiZmUi5mUi7mUSZmUi7mCbdM1MEOc5ZlUVFRgX4JYI5WlUnpDzX/bn4Q3jsJyneGtj7NqFXl0kYoEzMpFzMpF/MoEzMpF/OEWybqYItI3Y65DcYtBlcC7PkY3hoCu5eHulYiIiIiIkZSB1tEDq3r2XDKakgcCBW74L0J8M3fNJSXiIiIiMhB1MEWkcNL6AunrILuF4LlgbXXQcHnoa6ViIiIiIhRnKGugDQvl8tFly5dcLlcoa6K/KTVZuKMhdEvQcpIcBdA+2GhrlGTarW5hDFlYiblYiblYh5lYiblYp5wy0TjYDeCxsEWAfZnQ8Ea6Do51DUREREREWlyGgdb/DweD3v37sXj8YS6KvKTsMqkugI+ngIf/xLW/Rm8rXedwiqXMKFMzKRczKRczKNMzKRczBNumaiDHeY8Hg+7d+8Omx02HIRVJjYndBxf8/+v58L7E6E8L6RVOlJhlUuYUCZmUi5mUi7mUSZmUi7mCbdM1MEWkSNnd8LgOTD2VXDGwe4P4e0hsGdlqGsmIiIiItLi1MEWkcbrNqVmKK+EAVC+A949ETY/rKG8RERERKRNUQdbRJpGYn845TPodl7NUF4/PAted6hrJSIiIiLSYjRMV5iz2+3ExcVht+tYiinCOhNXHIz5F6SOhS6ngyMy1DWqt7DOpZVSJmZSLmZSLuZRJmZSLuYJt0w0TFcjaJgukXr6ei7E9YauZ4e6JiIiIiIiDaJhusTPsiw8Hg86jmKONpfJ7uWwbgZ8fA6sv8nYobzaXC6tgDIxk3Ixk3IxjzIxk3IxT7hlog52mKusrOS7776jsrIy1FWRn7S5TFJGQL/rav6/6V74YBJU7AlplWrT5nJpBZSJmZSLmZSLeZSJmZSLecItE3WwRaR52V1w/AMw5hVwxkLeezVDeeV/GuqaiYiIiIg0KXWwRaRldD+v5i7jCf2gbDu8Ow62PB3qWomIiIiINBkjO9hLlixh2LBhxMTEkJGRwdy5c+t9Tb7H42H48OGMHz8+aF56ejo2my3oLz8/v4nXQERqlXhUTSe76y/BWwU2DWQgIiIiIuHDuG+3q1at4owzzuD888/njjvuYPny5cyYMQOPx8PMmTMPu/y9997L6tWrOfHEEwOm5+fnk5uby5w5cxg7dmzAvKSkpKZcBRE5FFcCjH0Vdi6Bzqf8PN3ygs3IY34iIiIiIvVi3DBdp5xyCkVFRXz66c+/z7zxxht5/PHHycvLIzo6us5lv/jiC0aNGkViYiL9+vXjww8/9M979913mThxIlu2bKFXr15NUtfWMEyXZVl4vV7sdjs2my3U1RGUSa0q8uH9k2DQnZB+ZkiqoFzMo0zMpFzMpFzMo0zMpFzM0xoyabXDdFVWVvLhhx9yzjnnBEyfMmUKJSUlLF++vM5l3W43l156KdOnT6dfv35B89evX098fDw9e/Zs8nqbzGaz4XA4jN1Z2yJlUouv74OiL2HZWfDFLPBWt3gVlIt5lImZlIuZlIt5lImZlIt5wi0Toy4R/+GHH3C73fTt2zdgeu/evQHYvHkzEydOrHXZ2bNnU1VVxe23384pp5wSNH/9+vUkJyczZcoU3n33Xaqrqzn99NN56KGHSEtLa1S9KyoqAh7b7XYiIiLwer243e6g50dFRQE1BxQOvoDA5XLhcDjweDx4PIHjBfvKtSyr1tvYR0ZGYrPZcLvdeL1eoObAQ35+Pp06dSImJobq6mqqqqoClrPZbERGRta6LgARERHY7Xaqqqqorg7s+DgcDlwu12HXtbnKbYlt6ON0OnE6nY3ahm63m4qKCvLz80lJSSEiIqJJym1t2zCo3H634Koqw7HlMfjqLqr3rKJq2PMQmdKgchuzDffv309eXp4/l5bYhiZmY1IbAbB7925SUlKw2wOPB5u8fze2jTAtm4O3odvtpqCggM6dO2O321umjWiick3ZhtD0bYTv8z4tLY3o6Oiw24amZVOfbdgcn/cmZmPE94gGlFvb531r3YYmZnMk2xBqPu/bt2+Pw+EImG5KG2FZVr0PABjVwd63bx8ACQkJAdPj4+MBKC4urnW51atXM3fuXJYtW+YP72Dr168nNzeXK664guuuu46vv/6aW2+9lRNPPJF169YRGxt7RHWurq5m69atAdMSEhLo0qULHo8naB7AgAEDANi5cyfl5eUB8zp37kxiYiLFxcXk5eUFzIuNjaVbt254vd5ay+3Tpw9Op5O8vDxKS0sB/DtNTEwMMTEx7N+/n9zc3IDloqKiyMjIAODHH38MeiP27NmTyMhI8vPzKSoqCpjXvn17OnToQHl5Odu2bQuY53Q66dOnDwA5OTlBb+Ju3boRGxtLYWEhe/fuDZiXlJREWloaVVVVQetqs9no378/ADt27Ah6Q3Xp0oWEhAT27dvH7t27A+bFxcXRtWvXWnMD6Nu3Lw6Hg127drF///6AeR07diQ5OZnS0lJ27NgRMC86OpoePXoA1Fpur169iIiIYM+ePRQVFeF2uykvL8dut5OSkkJqairl5eXk5OQELOdyufwHmLZt2xbUOHTv3p2YmBgKCgooKCgImNeuXTs6deqE2+0OqpPdbvdf6ZGbmxvUiKanpxMfH09RURF79gSOWR0fH096enqd+3e/fv2w2Wzs3LmTsrKygHlpaWkkJSVRWlrKzp07A+bFdJxB99TRWJ9dgWP3e3iXDGd7xkNUxNZcitO7d29cLhe7d++mpKQkYNnU1FRSUlIoKytj+/btAfMiIyP9V65kZ2cHfSBkZGQQFRVFQUEB+/bt8+cCkJycTMeOHamoqCA7OztgOYfD4T8YmJOTE/Rh0rVrV+Li4igsLAy6kaJJbYRPhw4daN++vVFtRKdOndi/fz8OhyOo/Q/nNuLgdTWtjfB6vf7L+YqLi1uujYiJoXv37liWVWu5zd1G7N27l8LCwoB5JrURvs/7+Ph4oqOj20QbYfr3iJKSkoDP+7bSRkCIvkfUs43Ys2dP0Od9W2gjfEz8HtGlSxf/Z+rBn62mtBEejweXyxW0TWpj1G+wV65cyZgxY1i6dClZWVn+6b4Vuueee4JudFZRUcGQIUM466yzuPfeewH8dxA/8DfYn3zyCU6nk2HDhvmnrVixgrFjx/LYY4/x+9//vsH19V2L7wvWx6SjSpWVlWzfvp3u3bsTFxenI88GnJ0qLy9n+/btpKenExkZ2WqPmjbbkefCL7GWTca+/3ssewRVI1/F2+nkZs+mpKSEbdu2+XNpiW1oYjYmtRFer5fs7Gy6du2K0xl4PNjk/Tvcz05VVlaSm5tLz549/e/nA4X67FRr2IbQ9G2E7/O+R48exMbGht02NC2b+mzD5vi8NzEb475HHKbc2j7vW+s2NDGbI9mGvs/79PT0oE6sKW3Et99+i81mq9dvsI06g52YmAgQdOTZd6TON/9As2bNwuv1csstt/h3Et+O5PF4/Nfzjxo1KmjZMWPGkJiYyBdffNGoevsCOJjdbq9zHlDn2Xb4eSetjc1mO2S5vstdDqyHryyHwxF06cWBDlWuy+Wq88jN4da1ucptqW14oMZsQ19jaLfbiYyMDHhuqLIJxTY8ZLntBmE7dQ2sugxb0QYiOp8AET+/TnNl43K5as0Fmm8bmpiNSW2E78PS6XQa384eqLFtRHOU29RthO8yuZC0Ea1w/26pNsJut/vXL9y2oWnZ1GcbNsfnvYnZGPc94jDlHurzvrVtQxOzOZJt6Pu8d7lcR/x539zZNOT34UZ1sHv16oXD4WDLli0B032PfZc7HOjf//432dnZxMXFBc1zuVw899xznHPOOSxcuJDhw4czcOBA/3zfEYvU1NQmXhMRaZSIRBi3CCryICKpZpplQeUeiOoQ0qqJiIiIiNTFqLuIR0VFccIJJ7Bo0aKAyxkWLlxIYmIiw4cPD1rmv//9L6tXrw74GzJkCEOGDGH16tWceeaZREZGcvXVV3PPPfcELPv6669TXl7OhAkTmn3dQsXpdNKxY8c6j1BJy1Mm9WSzQ/QBNyDc8iS80R9y/9csL6dczKNMzKRczKRczKNMzKRczBNumRi3FrNmzSIrK4vzzjuPyy+/nJUrVzJnzhzuvfdeYmJiKC4uZtOmTfTq1YvU1NRar4P33RRt6NCh/mkzZ87ktttuo2PHjpx22mls2LCBv/71r/ziF78gMzOzxdavpTmdTpKTk0NdDTmAMjkClhd+/Ce4C+GjM2DgrXDMrTWd8CaiXMyjTMykXMykXMyjTMykXMwTbpkYdQYbIDMzk4ULF7J582bOPvtsFixYwJw5c5gxYwYAa9euZdSoUbz55psNKnfWrFk89thjLFmyhDPPPJN58+Zx5ZVX8vLLLzfHahijurqaffv2Bf2wX0JHmRwBmx0yl0Kf3wMWbLwdPjwDKgsOu2h9KRfzKBMzKRczKRfzKBMzKRfzhFsmRt1FvLXx3UW8PneTC5WKigq2bt3qH0JAQk+ZNNIPL8Lq30F1BcT2gHELIXlIo4tVLuZRJmZSLmZSLuZRJmZSLuZpDZk0pN9n3BlsEZFD6nkpnPwJxPWE/T/CuydAxe7DLiYiIiIi0tyM+w22iMhhtTsOJn0OKy+B1LG6s7iIiIiIGEEdbBFpnSLawYmvB04r+R7sTojtHpo6iYiIiEibpkvEw5zNZiM6OrpBg6NL81ImTchm//lO4p79sOxseGsI7FzS8KKUi3GUiZmUi5mUi3mUiZmUi3nCLRPd5KwRWsNNzkTajPKd8NFZUPA5YINBs+Hom5t0KC8RERERaXt0kzMRaXui02Dix9D7CsCCL2+Bj34B7qJQ10xERERE2gh1sMNcRUUFX3/9NRUVFaGuivxEmTQjRxQMfxJGPAv2SNjxBrx9PBR+cdhFlYt5lImZlIuZlIt5lImZlIt5wi0TdbBFJPz0+jWcvLJmnOzSH2DtDaGukYiIiIi0Aepgi0h4Sh4Ck9ZAj0tg1Auhro2IiIiItAHqYItI+IpMhtEvQkyXn6d99zjszwldnUREREQkbKmDLSJtR84iWH0VvD0Edr0X6tqIiIiISJjRMF2N0BqG6fJ6vXg8HpxOJ3a7jqeYQJmEUOlW+PiXULiuZviuQXfCUTeCza5cDKRMzKRczKRczKNMzKRczNMaMtEwXeJnt9uJiIgwdmdti5RJCMVlwMQV0PPXYHnhi5vh48ng3qdcDKRMzKRczKRczKNMzKRczBNumYTHWkid3G43ubm5uN3uUFdFfqJMQswZDSOegeFPgT0Ctv8H3h5K1Z61ysUweq+YSbmYSbmYR5mYSbmYJ9wyUQc7zHm9XoqLi/F6vaGuivxEmRjAZoPev4WJyyGmG5Ruwdr3lXIxjN4rZlIuZlIu5lEmZlIu5gm3TNTBFpG2q/2wmqG8hj+NN/3cUNdGRERERFo5dbBFpG2LSoHe035+XL4Tlk2GstzQ1UlEREREWiV1sEVEDuBadw1sX1wzlFfeh6GujoiIiIi0Iupghzmn00lKSgpOpzPUVZGfKBMz+XKxhsyDpGOhYje8nwWb5oBGMwwJvVfMpFzMpFzMo0zMpFzME26ZaBzsRmgN42CLyBHwlMHqq2DrCzWPu06Gkc+BKyG09RIRERGRFqdxsMWvurqa0tJSqqurQ10V+YkyMVNALs6Ymg71sCfA7oKcRfDOcCj9IdTVbFP0XjGTcjGTcjGPMjGTcjFPuGWiDnaYq6qqIicnh6qqqlBXRX6iTMwUlIvNBn1+B1kfQ0w6YIfIDiGtY1uj94qZlIuZlIt5lImZlIt5wi2T8LjQXUSkuaSMgElroaoYXHE10ywLLE/N2W0RERERkZ/oDLaIyOFEpUJ8r58ffz0X3susGdJLREREROQn6mCLiDSEuwg23Qt7lsNbg2H3slDXSEREREQMoQ52mLPZbLhcLmw2W6irIj9RJmaqdy4RSXDyKkgcCBV5NWeyv35AQ3k1A71XzKRczKRczKNMzKRczBNumWiYrkbQMF0ibZhnP3x6BWT/s+Zxt3NhxDPgig9tvURERESkSWmYLhGR5uaMhdEvwfEPg80J216FpePA6wl1zUREREQkRNTBDnMVFRV8++23VFRUhLoq8hNlYqYjysVmg35XQ9YyiO4MvaaBXYMzNBW9V8ykXMykXMyjTMykXMwTbpnom2AbEC6DtocTZWKmI84ldRSc/hW4En+etj8HotPU4W4kvVfMpFzMpFzMo0zMpFzME06Z6Ay2iEhTiEiqOaMN4N5Xc/Oz97OgPC+k1RIRERGRlqMOtohIUyvaABW7YPdH8PYQ2LMy1DUSERERkRagDraISFPrMBZOWQ0JA6B8B7x7Imz+u4byEhEREQlzGqarEVrDMF1er5eKigqioqKw23U8xQTKxEzNkktVKXw6Dba9UvO4+69gxNM1dyCXw9J7xUzKxUzKxTzKxEzKxTytIRMN0yV+drudmJgYY3fWtkiZmKlZcnHFwZiXYciDNUN5Zb8Ma//UdOWHOb1XzKRczKRczKNMzKRczBNumRi5FkuWLGHYsGHExMSQkZHB3Llzqe+Jdo/Hw/Dhwxk/fnzQvM8//5zx48cTFxdH586dufnmm3G73U1ce7NUVVWRl5dHVVVVqKsiP1EmZmq2XGw26H8dnPQ+JA+DQbObtvwwpveKmZSLmZSLeZSJmZSLecItE+M62KtWreKMM86gf//+LFq0iIsuuogZM2Zw33331Wv5e++9l9WrVwdN/+GHH8jKyiI6Opr/+7//44YbbuCBBx5g+vTpTb0KRqmurqagoCCsbn3f2ikTMzV7Lh3GwSmfQlTqz9NyXgOvp3leLwzovWIm5WIm5WIeZWIm5WKecMvEuAFab7vtNgYPHsz8+fMBmDRpElVVVdx9991ce+21REdH17nsF198wd13302nTp2C5t13333Ex8fzn//8h4iICE477TRiYmK4+uqrufnmm+nWrVuzrZOICPDzMF4AP7wAq6ZCx8yay8ijOoSsWiIiIiLSNIw6g11ZWcmHH37IOeecEzB9ypQplJSUsHz58jqXdbvdXHrppUyfPp1+/foFzX/nnXc4/fTTiYiICCjX6/XyzjvvNN1KiIjUhzO25i/vfXj7eMj/NNQ1EhEREZFGMuoM9g8//IDb7aZv374B03v37g3A5s2bmThxYq3Lzp49m6qqKm6//XZOOeWUgHnl5eVkZ2cHlZuamkpCQgKbN29uVL0rKioCHtvtdiIiIvB6vbX+xjsqKgqoOaBw8G/LXS4XDocDj8eDxxN46aivXMuyqKysDCo3MjISm82G2+3G6/X6X8Pr9frLqq6uDvp9g81mIzIystZ1AYiIiMBut1NVVRV06YbD4cDlch12XZur3JbYhj5OpxOn09mobeh2u/2Z+F6/KcptbdvQxGyqqqoCcoFm3obdpuCN7w/Lp2Av2Yy1dByeY+dSnfFbon66Uqe17d9N3Ub46ujxeILmm7x/N7aNMC2bg7fhgY/bWhvR0u1sQ8r1fbb46hhu29C0bOqzDZvj897EbFrb94jaPu9b6zY0MZsj2Ya+smr7DbYpbYRlWdgOvBLxEIzqYO/btw+AhISEgOnx8fEAFBcX17rc6tWrmTt3LsuWLfOHV59yfWXXVW59VFdXs3Xr1oBpCQkJdOnSBY/HEzQPYMCAAQDs3LmT8vLygHmdO3cmMTGR4uJi8vLyAubFxsbSrVs3vF5vreX26dMHp9NJXl4epaWlAFiWRXV1NWVlZcTFxbF//35yc3MDlouKiiIjIwOAH3/8MeiN2LNnTyIjI8nPz6eoqChgXvv27enQoQPl5eVs27YtYJ7T6aRPnz4A5OTkBL2Ju3XrRmxsLIWFhezduzdgXlJSEmlpaVRVVQWtq81mo3///iGJ47QAAHQRSURBVADs2LEj6A3VpUsXEhIS2LdvH7t37w6YFxcXR9euXWvNDaBv3744HA527drF/v37A+Z17NiR5ORkSktL2bFjR8C86OhoevToAVBrub169SIiIoI9e/awb98+qquryc3NxWazkZKSQmpqKuXl5eTk5AQs53K5/AeYtm3bFtQ4dO/enZiYGAoKCigoKAiY165dOzp16oTb7Q6qk91u91/pkZubG9SIpqenEx8fT1FREXv27AmYFx8fT3p6ep37d79+/bDZbOzcuZOysrKAeWlpaSQlJVFaWsrOnTsD5sXExNC9e3csy6q13N69e+Nyudi9ezclJSUB81JTU0lJSaGsrIzt27cHzIuMjKRnz54AZGdnB30gZGRkEBUVRXFxcUAuAMnJyXTs2JGKigqys7MDlnM4HP6Ddjk5OUEfCl27diUuLo7CwkLy8/MD5vnbiNi+bM14ibRtfyGhaAmu9deyf9v7RGX9E5wxLdJG+HTo0IH27dsb1UZ07tyZdu3aUVJSElRuOLcRB38mmdZGWJZFbGwsDoejTbURe/fupbCwMGBes7cRDfge4fu8r6ioIDY2tk20EaZ/jygtLQ34XGkrbQSY/T2isLAw6PO+LbQRPiZ+j+jatSvt2rVj3759Qe23KW2Ex+PB5XIFbZPaGDUO9sqVKxkzZgxLly4lKyvLP923Qvfccw8zZ84MWKaiooIhQ4Zw1llnce+99wL47yD+4YcfAjUbpkuXLjz99NNMmzYtYPn09HTOOOMMnnjiiQbX1zcemi9Yn3A7qtRWjzy31bNTYPaR53Dahv5yLQvHlr/h3DgLm1UNme9Cp5Na3f6tNkJtBKiNOJDOTtVQG1FDbUQNtRE/UxtRozW0Ed9++y02m61e42AbdQY7MTERIOjIhe9InW/+gWbNmoXX6+WWW27x7yQHXrrmcDj8Z64PLtdXdm3lNoQvgIPZ7fY65wG1nm338e2ktbHZbIcs98Dfmft2Gt+4cg6HA4fDUeeyhyrX5XLVeeTmcOvaXOW2xDY8WGO24YGNoe9N3xTltrZtaGI2DoeD6urqoFyg+bZhQLnHzIQOo6Dgc+h0UqPKDdU2bOo2wuv1UlFRQURERKvavxvbRjRHuU3ZRvjaMJvN1qbaiFC0sw0p98BcDldua9yGpmVTn23YHJ/3JmbT2r5HOJ3OWnOB1rcNTczmSLZhU3zeN3c29b08HAy7yVmvXr1wOBxs2bIlYLrvse9yhwP9+9//ZvPmzcTFxfk33rJly1i2bBkul4sXXniBuLg4unTpElSu7zKy2soNF77LesJ9vO/WRJmYyYhcOp4IA274+fH+bNhwB3jDY9iKhjIiEwmiXMykXMyjTMykXMwTbpkY1cGOiorihBNOYNGiRQGXMyxcuJDExESGDx8etMx///tfVq9eHfA3ZMgQhgwZwurVqznzzDMBOPnkk3njjTcCLndYuHAhDoeDzMzM5l85EZGG8Hrg41/Chlvhw1OhIv/wy4iIiIhISBl1iTjUXPKdlZXFeeedx+WXX87KlSuZM2cO9957LzExMRQXF7Np0yZ69epFampqrdfB+26KNnToUP+0GTNm8PLLL3Pqqady/fXX8+2333LzzTdzxRVXaAxsETGP3Qn9b4BPp8GupfD2EBj7b0gJPtAoIiIiImYw6gw2QGZmJgsXLmTz5s2cffbZLFiwgDlz5jBjxgwA1q5dy6hRo3jzzTcbVG7//v1ZsmQJZWVlTJkyhQceeIA//vGP/O1vf2uO1RARabwev4JTPoX4PlCWA++Og++eBHPuTSkiIiIiBzDuDDbAOeecwznnnFPrvPHjxwfdDe9gvruHH2zcuHGsWrWqsdVrdQ6+gYOEnjIxk5G5JA2EU1bDqqmw/TVYfSXsXQVDHwNndKhr1+yMzESUi6GUi3mUiZmUi3nCKROjhulqbXzDdNXndu0iIo1iWfD1/fDFzZB0HJy8Ahx13/VSRERERJpGQ/p9Rp7BFhGRg9hscNSNkDwM4jLUuRYRERExUPici5daVVZW8sMPP9Q6WLyEhjIxU6vJpVNmTQfbZ8Pt8OVtYTmUV6vJpI1RLmZSLuZRJmZSLuYJt0x0BjvMWZZFZWXlYX+3Li1HmZipVeZS9FVNBxsL9n4Go1+CyPahrlWTaZWZtAHKxUzKxTzKxEzKxTzhlonOYIuItFZJR8OoF8ARDTvfhrePh4I1oa6ViIiISJulDraISGuWcQmcvAriesH+bFgyBr5/JtS1EhEREWmT1MEWEWnt2g2CSZ9DlzPBWwmfToPPrwl1rURERETaHHWww5zL5SI9PR2XyxXqqshPlImZWn0uEUlwwmtw7F1gs0Py8aGuUaO1+kzClHIxk3IxjzIxk3IxT7hlonGwG0HjYIuIkfZtgsSjfn7s2Q/O2NDVR0RERKQVa0i/T2eww5zH4yE/Px+PxxPqqshPlImZwiqXAzvXFfnw5kDYcAdY3tDV6QiEVSZhRLmYSbmYR5mYSbmYJ9wyUQc7zHk8Hvbs2RM2O2w4UCZmCttctr0C+3+EDbfCR2eBuzDUNaq3sM2klVMuZlIu5lEmZlIu5gm3TNTBFhEJZ33/ACOfA0cU7HgT3joeCtaFulYiIiIiYUkdbBGRcNdzKkxcCbEZsH8rLB0NP7wQ6lqJiIiIhB11sEVE2oLkwTVDeXU+DaorYNVU+O7JUNdKREREJKyogx3m7HY78fHx2O2K2hTKxExtIpfIZDjxv3DM7RDfB7qfH+oaHVKbyKQVUi5mUi7mUSZmUi7mCbdMNExXI2iYLhFptTzl4Iyu+b9lwb6NkKS2TERERORgLTpMl2VZLFq0iIqKCgCKioq46KKL6NatGxMmTGDNmjWNfQlpBMuyqKqqQsdRzKFMzNTmcvF1rgG+exzeOg6+utuoobzaXCathHIxk3IxjzIxk3IxT7hl0qgOdnl5ORMnTuTcc8/l66+/BuDSSy/lX//6F7m5uXz00UdkZmby/fffN0llpeEqKyvZsmULlZWVoa6K/ESZmKlN51K0oaZj/cVfYNk54C4KdY2ANp6JwZSLmZSLeZSJmZSLecItk0Z1sO+55x7ef/99AL7++mt+/PFH3njjDQD/EYjS0lLuu+++RlZTRESazfDHYfjTYI+E3Nfh7WFQ+GWoayUiIiLS6jSqg7148WIA+vbty8iRI/nf//4HQIcOHdi1axdnnnkmlmX5O+EiImKo3tNg4nKI6QalW2DJSNj6UqhrJSIiItKqNKqDvXXrVmw2G3/84x/p2bMny5YtAyArK4sOHTowZcoUAHJzcxtfUxERaV7th8KkNdDpZKguh1WXQfG3oa6ViIiISKvhbMzCbrcbgOTkZACWL1+OzWZj1KhRAP5brUdGRjbmZUREpKVEpcD4/8HG28EZBwl9Q10jERERkVajUR3slJQU8vLy+Prrr1m7di07duzAZrMxYcIEABYuXAhA165dG19TOSKRkZH069cPm80W6qrIT5SJmZTLAewOGDQ7cFrxt1CeCx0ntFg1lImZlIuZlIt5lImZlIt5wi2TRl0iPnLkSCzL4q677mLixIkA9O7dmwEDBnDhhRfy2muvYbPZOPnkk5ukstJwNpsNu90eNjtsOFAmZlIuh1BVCh9PhvezYNP9NeNmtwBlYiblYiblYh5lYiblYp5wy6RRHew///nPOJ1O3G43hYWFANx44401Bf90eXhSUhLXXXdd42opR8ztdpOdne2/nF9CT5mYSbkcgs0ByUNrhvJafyMsnwJVxc3+ssrETMrFTMrFPMrETMrFPOGWSaM62KNGjeLtt99m0qRJjB07lscff5zLL78cgAEDBnDUUUexdOlSXSIeQl6vl7KyMrxeb6irIj9RJmZSLofgjIaRz8GwJ8AeATmLaobyKvqqWV9WmZhJuZhJuZhHmZhJuZgn3DJp1G+wATIzM8nMzAyaPn36dP7yl780tngRETGBzQZ9fgftBtecwS75Ft4ZDiOegR4XhLp2IiIiIkZo1BnsQ9mzZw9lZWXNVbyIiIRCyvCfhvLKguoy+O6RmkvHRURERKTxHewdO3Ywffp0CgoKAFi/fj3dunWjT58+dOjQgSeffLLRlRQREYNEpcL4t2HQnTDm/8DWbMdqRURERFqVRn0rys7OZuTIkTz66KNs2rQJgEsvvZTt27djWRZlZWVcddVVfPDBB01SWWk4p9NJWloaTmejfw0gTUSZmEm5NJDdAQP/AjGdf5628S7YvazJXkKZmEm5mEm5mEeZmEm5mCfcMmlUB3v27Nls374dgM2bN7N69Wo2btyIzWajf//+2O12LMvib3/7W5NUVhrO6XSSlJQUNjtsOFAmZlIujZT7Jnw5C97LhK/nNclQXsrETMrFTMrFPMrETMrFPOGWSaM62O+//75/nOtLLrmEpUuXAtCvXz82bdrE9OnTAfjss88aX1M5Ih6Ph6KiIjweT6irIj9RJmZSLo3UcTz0uAisalj3J1h+HlSVNKpIZWIm5WIm5WIeZWIm5WKecMukUR3snTt3AnDJJZcQERHB8uXLsdlsTJgwAYARI0YAsHfv3kZWU46Ux+Nh586dYbPDhgNlYibl0kjOWBg1H4Y+AnYX5Py75i7j+74+4iKViZmUi5mUi3mUiZmUi3nCLZMmuTON3W6nurqaFStWADB69GgAiouLAUhKSmqKlxEREZPZbND3D3DSRxDdGYq/qelkb1sY6pqJiIiItIhGXeienp7O1q1befvtt/F4PJSUlGC32znppJPIz8/nscceA6BHjx5NUVcREWkNUkfBpLWw4gLY/SGgYbxERESkbWjUGeyTTjoJy7KYP38+l112GTabjdGjR9OpUyfOPfdcvvjiC2w2GxdccEGDy16yZAnDhg0jJiaGjIwM5s6di3WIm+ZUVFRw88030717d2JiYhg1ahTvvPNO0HNcLhc2my3gLy4ursH1ExGRQ4juCJlL4cT/Qrdzf57eBDc/ExERETFVozrYt956K506dcKyLCzLwul0cs899wDQp08fAI499liuuOKKBpW7atUqzjjjDPr378+iRYu46KKLmDFjBvfdd1+dy0ybNo1HH32UG2+8kddff53evXtz+umn8/HHH/ufs3HjRjweDy+99BKffPKJ/y+chxGz2+3ExMRgt2ucWlMoEzMpl2Zgd0KXM35+XLYD3j4e9qyo3+LKxEjKxUzKxTzKxEzKxTzhlonNOtRp4XrYvXs3zz//PKWlpZx77rkcc8wxADz55JN8+eWX3HnnnbRr165BZZ5yyikUFRXx6aef+qfdeOONPP744+Tl5REdHR3w/B9//JGMjAweeeQR/vCHPwDg9Xrp3bs3I0aM4OWXXwbgH//4B7///e8pLS0lMjKyMasNwIYNGwD86ywiIoew6jfww7Ngc8LgudBves3vtkVEREQM1pB+X6MHG+vQoQMzZswImv673/3uiMqrrKzkww8/5Pbbbw+YPmXKFO6//36WL1/OxIkTA+alpaWxevVq/1lzqDkS4nQ6qaio8E9bv349/fv3b5LOdWvhu7rAdzm8hJ4yMZNyaQHH/w2qyyD7X7D2Oti7CoY/Da7af6ajTMykXMykXMyjTMykXMwTbpk0yWjeJSUlPPnkk3z44Yfs3buX1NRUsrKymDZtGjExMQ0q64cffsDtdtO3b9+A6b179wZg8+bNQR3syMhIhg4dCtScuc7NzWXevHl8//33PPzww/7nrV+/HqfTycknn8yKFSuIjIzk3HPPZe7cucTHxx/JqgMEdOKhpnMfERGB1+vF7XYHPT8qKgqoOZhw8AUELpcLh8OBx+MJulW9r1zLsqisrAwqNzIyEpvNhtvtxuv1+l9j+/btdO/enbi4OKqrq6mqqgpYzmaz+Q86HLwuABEREdjtdqqqqqiurg6Y53A4cLlch13X5iq3Jbahj9PpxOl0Nmobut1uysvL2b59O+np6URGRjZJua1tG5qYTWlpKdu2bfPnAs2/DU3Mpln3w2onDHkWR+JQnBtmYsv+FxRtgHGLqIjoFlSu1+slOzubrl274nQ66y43jLZhyLJpwDasrKwkNzeXnj17+t/PBwrXNiIU7WxDyvV93vfo0YPY2Niw24amZVOfbdgcn/cmZtPavkfU9nnfWrehidkcyTb0fd6np6fjcrkC5pnSRvgOANRHozvYq1ev5he/+AV5eXkB0998803mzZvHf//7XwYNGlTv8vbt2wdAQkJCwHRfB9g39Fdd7rvvPm6++WYAfvvb35KVlQXUHBn58ssvsSyLadOmMWvWLFavXs3tt9/Opk2b+Oijj47ouv/q6mq2bt0aMC0hIYEuXbrg8XiC5gEMGDAAqBlHvLy8PGBe586dSUxMpLi4OGibxsbG0q1bN7xeb63l9unTB6fTSV5eHqWlpQD+naa0tJS4uDj2799Pbm5uwHJRUVFkZGQANZfbH/xG7NmzJ5GRkeTn51NUVBQwr3379nTo0IHy8nK2bdsWMM/pdPqvKsjJyQl6E3fr1o3Y2FgKCwuDxkpPSkoiLS2NqqqqoHW12Wz0798fgB07dgS9obp06UJCQgL79u1j9+7dAfPi4uLo2rVrrbkB9O3bF4fDwa5du9i/f3/AvI4dO5KcnExpaSk7duwImBcdHe2/W35t5fbq1YuIiAj27NlDUVERbreb7du3Y7fbSUlJITU1lfLycnJycgKWc7lc/oNL27ZtC2ocfDf1KygooKCgIGBeu3bt6NSpE263O6hOdrudfv36AZCbmxvUiKanpxMfH09RURF79uwJmBcfH096enqd+3e/fv2w2Wzs3LmTsrKygHlpaWkkJSVRWlrKzp07A+bFxMTQvXt3LMuqtdzevXvjcrnYvXs3JSUlAfNSU1NJSUmhrKyM7du3B8yLjIykZ8+eAGRnZwd9IGRkZBAVFRWUC0BycjIdO3akoqKC7OzsgOUcDof/QGBOTk7Qh0nXrl2Ji4ujsLCQ/Pz8gHkmtRE+HTp0oH379i3TRjhOI7p3J9J/vB7nvq/g7aHs6fMMpVEDA5bt1KkTUPO5cHDbH85txMHralob4fV6/ftAW2oj9u7dS2FhYcA8k9oI3+d9WVkZsbGxrbuN+Elr/x5RUlIS8LnSVtoIMPt7xN69e4M+79tCG+Fj4veILl26AFBYWBj02WpKG+HxeII6/3Vp1G+wd+3axaBBg9i7d2+dd/hOS0tj/fr1pKam1qvMlStXMmbMGJYuXervHAP+lbrnnnuYOXNmnctv3LiRgoICli9fzuzZszn33HOZP38+Xq+XZcuWkZqaytFHH+1//oIFC7j44ov53//+x6mnnlrPNa/huxb/wEvTwayjSjqDbcaROZ3BrmHy2amSkhKdwW7p/bBiF1GfXwaVe6k48UNwBl4qrjPYTVeuzmCbtQ1BZ7B9wvl7hM5gm/k9orbP+9a6DU3MJlzPYH/77bfYbLbm/w32/fffT35+PjabjV/84hdcffXVdOnShe3bt/PYY4/x2muvsWvXLubMmcP9999frzITExMBgo4++47W+ebXZeDAmjMgJ5xwAh6Ph9tuu4277rqLbt26MX78+KDnn3766QB88cUXDe5g+/gCOJjdbq9zHnDI34L7dtLa2Gy2Q5YbERERVA9fWQ6HA4fDUeeyhyrX5XLVeeTmcOvaXOW21DY8UGO2oa8xtNvtREZGBjw3VNmEYhuamI3L5ao1F2i+bWhiNi26H0b1gMx3oTKfqOiUmmmWBe5CiEz2f1g6nU7j29kDNbaNaI5ym7qN8F0m19baiJZuZxtart1u969fuG1D07KpzzZsjs97E7Npbd8jDvV539q2oYnZHMk29H3eu1yuI/68b+5sGvLb8EbdC/3NN9/EZrNx6qmnsnjxYk466ST69+9PVlYWixYt4rTTTsOyLP7zn//Uu8xevXrhcDjYsmVLwHTfY98lDwfKzs7mmWeeCTpyMWTIEKDm1P6OHTt4+umngy4t8F02Ud8z7CIi0oTsLohO+/nx1/fD/wbBnk9CVycRERGRI9SoDravs3reeefVOv/cc88NeF59REVFccIJJ7Bo0aKASxoWLlxIYmIiw4cPD1omOzubadOmsXjx4oDpS5YsISIign79+uHxeLjiiit48sknA57zyiuv4HA4GDduXL3r2JpERkbSu3fvNnXndNMpEzMpFwNUu2HrfCjPhfdOJDL7aXr36qVMDKP3ipmUi3mUiZmUi3nCLZNGXSIeHx/P3r17g24A4eO7qcHBNyw7nFmzZpGVlcV5553H5ZdfzsqVK5kzZw733nsvMTExFBcXs2nTJnr16kVqaipjx44lKyuLa665huLiYnr16sUbb7zBo48+yu233067du1o164dv/71r5kzZw7R0dGMGjWK5cuXc/fdd3P11VcH3bU8XNhstnr/IF9ahjIxk3IxgCMCTv4EVl0OOf/GtmY6rr2fwvAnwRkb6trJT/ReMZNyMY8yMZNyMU+4ZdKoM9jHHHMMlmUxd+7coEu6v//+e+bOnYvNZuPYY49tULmZmZksXLiQzZs3c/bZZ7NgwQLmzJnjH2977dq1jBo1ijfffLNmJex2Fi1axNSpU7n33ns5/fTTeffdd3nqqaeYNWuWv9zHH3+cW2+9lfnz53P66aczf/58Zs+ezQMPPNCYzWA0310Sa/vxvoSGMjGTcjGEKx7G/h8Mnodlc8CPC/C+MxKKvwt1zeQneq+YSbmYR5mYSbmYJ9wyadRdxOfPn89ll12GzWYjIiKCiRMnkp6ezvbt21m6dCmVlZXYbDZefPFFLrrooqastxF8dxGvz93kQqWiooKtW7f6hxCQ0FMmZlIu5qnc/i6OlRfg9OyFyBQ464eaDriElN4rZlIu5lEmZlIu5mkNmTSk39eoS8QvueQSXn31Vd544w3cbrf/jDLg//10VlZWWHauRUSkeVkpY9nafyG9dt6EPeMida5FRETEeI26RBxg0aJFzJw5k9jYWCzL8v9FR0dz1VVX8frrrzdFPUVEpA3yuDrgHrcE+lz188TizVCxJ3SVEhEREalDo85gQ80YanfffTc333wzn376Kfn5+XTs2JEhQ4ZgWRZ5eXkAdOvWrdGVFRGRNsjuBN/4k+4i+PB08Lph7L8hJXhkCREREZFQaXQH2ycuLo6TTjopYNrtt9/O7NmzsdlseDyepnopaQCn00lqamqdA8lLy1MmZlIu5qk1k8p8sNmhLAfeHQfH/x16X/FzB1yand4rZlIu5lEmZlIu5gm3TBp9ifjh+C4Zl9BwOp2kpKSEzQ4bDpSJmZSLeWrNJL43nLIa0s+pOYu9+kpY9WvwlIeuom2M3itmUi7mUSZmUi7mCbdMmr2DLaFVXV1NSUkJ1dXVoa6K/ESZmEm5mKfOTCISYdxCOO6+mrPZW1+ApaOh9IfQVLSN0XvFTMrFPMrETMrFPOGWiTrYYa6qqort27dTVVUV6qrIT5SJmZSLeQ6Zic0GR82ACUshMhUK18O6G1u8jm2R3itmUi7mUSZmUi7mCbdM1MEWEZHWq1MmnLoWuk6BYY+HujYiIiLSxqmDLSIirVtMOox7FaJSfp727aNQuTd0dRIREZE2SR1sEREJL98/B59fDW8fD3s/D3VtREREpA1p0K3aLr/88gYVvn79+gY9X5qezWYjMjISm4awMYYyMZNyMc8RZ5J8PMT1gtLvYekYGPoo9J7WPJVsg/ReMZNyMY8yMZNyMU+4ZWKzGjCGlt1ub/CKW5aFzWYLm7vCHWjDhg0AHHPMMSGuiYiIBHAXwSeXQe7rNY97/QaGPgKOqJBWS0RERFqfhvT7GnyJuG9c6/r+iYiItLiIJDhhMRx7d81QXt8/A0vGQOmPoa6ZiIiIhLEGXSL+3HPPNVc9pJlUVFSQnZ1N9+7diYrSmRsTKBMzKRfzNDoTmx2OvgnaD4MVv4LCtbBvE8T1aPK6tiV6r5hJuZhHmZhJuZgn3DJpUAf7sssua656SDPyer2hroIcRJmYSbmYp0ky6ZQFk9bArqXQ5bTGlyd6rxhKuZhHmZhJuZgnnDLRXcRFRCT8xXar+R22T+lWWHERVBaErk4iIiISdtTBFhGRtsWy4JNLIPuf8PZQKFgX6hqJiIhImFAHW0RE2habreaO4rEZsH8rLB0NPzwf6lqJiIhIGGjQMF0SqDUM0+X1enG73URERGC363iKCZSJmZSLeZo9E3chrLwYdvyv5nHvK+D4v4MjsulfK4zovWIm5WIeZWIm5WKe1pBJsw7TJa2L3W4nKirK2J21LVImZlIu5mn2TCLawYn/hWNmAzbY8hQsHQflec3zemFC7xUzKRfzKBMzKRfzhFsm4bEWUqeqqip27dpFVVVVqKsiP1EmZlIu5mmRTGx2OOYWGP+/mg43XohIbL7XCwN6r5hJuZhHmZhJuZgn3DJRBzvMVVdXU1hYSHV1dairIj9RJmZSLuZp0Uw6T4JJa2HcQnD8NAan5a35kwB6r5hJuZhHmZhJuZgn3DJRB1tERAQgrgfEdv/58Ya/wrJzwF0UogqJiIhIa6MOtoiIyMHKdsDXcyH39ZqhvAq/DHWNREREpBVQB1tERORgMZ1h4sc1Z7RLv4clI2HrS6GulYiIiBhOHeww53A4SE5OxuFwhLoq8hNlYiblYp6QZ5J8PExaA2mToLocPrkEVv8Bqt2hqY8hQp6L1Eq5mEeZmEm5mCfcMtE42I3QGsbBFhGRRvJWw8Y7YOPtNY87ToDM98BmC229REREpEVoHGzx83q9lJWV4fXqTrimUCZmUi7mMSYTuwMG/RVOfLNmKK+MqW26c21MLhJAuZhHmZhJuZgn3DJRBzvMud1usrOzcbvb9iWNJlEmZlIu5jEuky6nwRnfQs9Lf55Wlgtt7EIw43IRQLmYSJmYSbmYJ9wyUQdbRESkvqJSfv5/xZ6am599/Etw7wtdnURERMQY6mCLiIgcib2fQsVu2L4Y3hkGRRtDXSMREREJMXWwRUREjkSXM2DicojpCiXfwTsj4MeXQ10rERERCSF1sNuAcLnlfThRJmZSLuYxPpP2w2DSWuiUBdVlsPJC+Hx62A/lZXwubZRyMY8yMZNyMU84ZaJhuhpBw3SJiAhQM5TXhlvhq7trHh/9Fzj2ztDWSURERJqEhukSERFpSXYHHHsXnPAfSB4KA/4c6hqJiIhICBjZwV6yZAnDhg0jJiaGjIwM5s6dy6FOtFdUVHDzzTfTvXt3YmJiGDVqFO+8806jyw0HlZWVbNmyhcrKylBXRX6iTMykXMzTKjNJPwtO+QwiEmseWxbkvhFWQ3m1ylzaAOViHmViJuVinnDLxLgO9qpVqzjjjDPo378/ixYt4qKLLmLGjBncd999dS4zbdo0Hn30UW688UZef/11evfuzemnn87HH3/cqHLDgWVZVFVVhf2BhNZEmZhJuZin1WZis/38/28fhY/OhOXnQVVJ6OrUhFptLmFOuZhHmZhJuZgn3DJxhroCB7vtttsYPHgw8+fPB2DSpElUVVVx9913c+211xIdHR3w/B9//JEFCxbwyCOPcNVVVwGQmZnJihUreOyxxxg3btwRlSsiItJodifYXZDzb9i3EcYtgsQBoa6ViIiINBOjzmBXVlby4Ycfcs455wRMnzJlCiUlJSxfvjxombS0NFavXs3FF1/sn2a323E6nVRUVBxxuSIiIo3W50rIWgbRXaD4m5rxsrP/L9S1EhERkWZi1BnsH374AbfbTd++fQOm9+7dG4DNmzczceLEgHmRkZEMHToUAK/XS25uLvPmzeP777/n4YcfPuJyG8LXkfex2+1ERETg9Xpxu4OHaomKigJqOv4HXwrhcrlwOBx4PB48Hk+t5VqWVetvFCIjI7HZbLjdbrxer/81vF6vv6zq6mqqqqoClrPZbERGRta6LgARERHY7Xaqqqqorq4OmOdwOHC5XIdd1+YqtyW2oY/T6cTpdDZqG7rdbn8mvtdvinJb2zY0MZuqqqqAXKD5t6GJ2ZjURvjq6PF4guabvH8HbcO447BlfkLk55dB3gew4nw8ecvxDLyr5uw2gW2EadkcvA0PfNzW2oiWbmcbUq7vs8VXx3DbhqZlU59t2Byf9yZm09q+R9T2ed9at6GJ2RzJNvSVdfByYE4bYVkWtgN/AnYIRnWw9+3bB0BCQkLA9Pj4eACKi4sPufx9993HzTffDMBvf/tbsrKymqTcQ6murmbr1q0B0xISEujSpQsejydoHsCAATWXB+7cuZPy8vKAeZ07dyYxMZHi4mLy8vIC5sXGxtKtWze8Xm+t5fbp0wen00leXh6lpaUA/h2ivLycuLg49u/fT25ubsByUVFRZGRkADWX3B/8RuzZsyeRkZHk5+dTVFQUMK99+/Z06NCB8vJytm3bFjDP6XTSp08fAHJycoLexN26dSM2NpbCwkL27t0bMC8pKYm0tDSqqqqC1tVms9G/f38AduzYEfSG6tKlCwkJCezbt4/du3cHzIuLi6Nr16615gbQt29fHA4Hu3btYv/+/QHzOnbsSHJyMqWlpezYsSNgXnR0ND169ACotdxevXoRERHBnj172LdvH5ZlkZubi81mIyUlhdTUVMrLy8nJyQlYzuVy+Q8Ebdu2Lahx8N3Yr6CggIKCgoB57dq1o1OnTrjd7qA62e12+vXrB0Bubm5QI5qenk58fDxFRUXs2bMnYF58fDzp6el17t/9+vXDZrOxc+dOysrKAualpaWRlJREaWkpO3fuDJgXExND9+7dsSyr1nJ79+6Ny+Vi9+7dlJQE/pY1NTWVlJQUysrK2L59e8C8yMhIevbsCUB2dnbQB0JGRgZRUVGUlJQE5AKQnJxMx44dqaioIDs7O2A5h8PhP2iXk5MT9KHQtWtX4uLiKCwsJD8/P2CeSW2ET4cOHWjfvr1RbUR6ejpdu3altLSUwsLCgHmtso2YsAS+nAWb7sOx5VFy7GOoiB0EBLYRB38mmdZGWJZFSkoKLpeLwsLCNtNG7N27N2g/NKmN8H3eu91uYmNj20QbYfr3iNLS0oDPlab6HmF6GwFmf48oKioK+rxvC22Ej4nfI7p3707Xrl3Zt29f0P5tShvh8XhwuVxB26Q2Ro2DvXLlSsaMGcPSpUv9nWPAv0L33HMPM2fOrHP5jRs3UlBQwPLly5k9ezbnnnsu8+fPb3S5dfGNh+YL1ifcjiq11SPPzXUG27RsWtuR53DahiZmozai+beh+/tXsFXkUt3rqqB1NTEbtRFqIyD029C0bPQ9Qm2Ej9qIGqHehs2dzbfffovNZqvXONhGdbC/+uorBg4cyKJFiwJ+L11QUED79u157LHH+P3vf1+vsmbPns1tt91GdnY2JSUlTVbugRoy4HioeDweCgsLadeuHU6nURcstFnKxEzKxTxtJpN938CuJdD3msA7kBuqzeTSyigX8ygTMykX87SGTBrS7zPqJme9evXC4XCwZcuWgOm+x77LHQ6UnZ3NM888E3TUYsiQIUDNaf0jKTdceDwe8vPzg45QSegoEzMpF/O0iUw85fDxZFhzLaz4FVSVHn6ZEGsTubRCysU8ysRMysU84ZaJUR3sqKgoTjjhBBYtWhRwOcPChQtJTExk+PDhQctkZ2czbdo0Fi9eHDB9yZIlRERE0K9fvyMqV0REpNk5omruNG5zwrZXYMkIKN4c6lqJiIjIETLuHPysWbPIysrivPPO4/LLL2flypXMmTOHe++9l5iYGIqLi9m0aRO9evUiNTWVsWPHkpWVxTXXXENxcTG9evXijTfe4NFHH+X222+nXbt29SpXRESkxdls0G86JB8Py8+FfZvg7WEw6nnoOjnUtRMREZEGMuoMNkBmZiYLFy5k8+bNnH322SxYsIA5c+YwY8YMANauXcuoUaN48803gZof4y9atIipU6dy7733cvrpp/Puu+/y1FNPMWvWrHqXKyIiEjKpY2DSWuhwInhK4ONfwroZ4A2Py+VERETaCqNuctbatIabnLndbvbs2UNqaioRERGhro6gTEylXMzTJjPxeuCLm+DruZA6Fk563z9WtinaZC6tgHIxjzIxk3IxT2vIpCH9PnWwG6E1dLBFRKQVylkMKSMhOi3UNREREWnzWu1dxKXp+cZ2O3isOgkdZWIm5WKeNp1J13MCO9frboTNj4ABx8TbdC4GUy7mUSZmUi7mCbdM1MEOc263m++//77WAdQlNJSJmZSLeZTJT3Yvh6/vhzXXwCeXgGd/SKujXMykXMyjTMykXMwTbpmogy0iImKy1DEweB7YHPDjAnhnJBR/F+paiYiISC3UwRYRETGZzQYDrq+54VlUR9i3Ed4ZCtv/E+qaiYiIyEHUwRYREWkNOpxQM5RX6hioKoZlZ8OGO0JdKxERETmAOtgiIiKtRUxnOOkD6HddzePEASGtjoiIiATSMF2NoGG6REQkZArXQ7vjfn5cXQGOqFDVRkREJGxpmC4REZFwd2DnuiwX/tsXvnvCiKG8RERE2ip1sMNcZWUlP/74I5WVlaGuivxEmZhJuZhHmTTAlqehLAdW/x5W/Ro8Zc32UsrFTMrFPMrETMrFPOGWiTrYYc6yLMrLy9EvAcyhTMykXMyjTBrgmNvguPvBZoetL8CS0VDyfbO8lHIxk3IxjzIxk3IxT7hlog62iIhIa2ezwVF/hsx3IaoDFH0Bbw+F3DdDXTMREZE2RR1sERGRcNFxQs1QXimjoKoIPjoDti0Mda1ERETaDHWwRUREwklMFzjpQ+h7DSQOhM6TQl0jERGRNsMZ6gpI83K5XHTu3BmXyxXqqshPlImZlIt5lEkjOCJg6N+hqhScsTXTLC+UfAcJ/RpVtHIxk3IxjzIxk3IxT7hlojPYYc7hcJCYmIjD4Qh1VeQnysRMysU8yqQJuOJ+/v+m++F/g2ruON6IG8koFzMpF/MoEzMpF/OEWybqYIc5j8dDQUEBHo8n1FWRnygTMykX8yiTJmRZULAavG747Ar4dBp4yo+oKOViJuViHmViJuVinnDLRB3sMOfxeMjLywubHTYcKBMzKRfzKJMmZLPB2Ffh2LtrhvL64VlYOgZKtza4KOViJuViHmViJuVinnDLRB1sERGRtsBmh6NvgglLIDIFCtfB28fDjrdCXTMREZGwoQ62iIhIW9LppJqhvNoPB3chLDsHyneGulYiIiJhQXcRFxERaWtiu0LWMlhzHbQbBNFpoa6RiIhIWFAHO8zZ7XZiY2Ox23WxgimUiZmUi3mUSTNzRMLwxwOnFX4JVjUkD65zMeViJuViHmViJuVinnDLxGZZjRiro43bsGEDAMccc0yIayIiItJI7kJ4eyiU74Chj0GvX4e6RiIiIkZoSL8vPA4TSJ0sy6K6uhodRzGHMjGTcjGPMmlpNkgYANUV8Onl8Nnvav5/EOViJuViHmViJuVinnDLRB3sMFdZWcm3335LZWVlqKsiP1EmZlIu5lEmLSwiCU58HQbdAdhgy1OwdBzszw54mnIxk3IxjzIxk3IxT7hlog62iIiI1LDZYeAsGP8WRCRDwefw1hDYuSTUNRMREWkV1MEWERGRQJ1PgUlrIPl4cBfANw9CmFy6JyIi0pzUwRYREZFgcT1g4nI46kYYNR9stlDXSERExHjqYIuIiEjtHFFw3L0QleKf5Nx0J5Fl34SwUiIiIubSMF2N0BqG6fLdlc/hcGDT2QcjKBMzKRfzKBMD/fhPWHkRliMKhj2Bredloa6R/ETvF/MoEzMpF/O0hkw0TJf42Ww2nE6nsTtrW6RMzKRczKNMDJQ2CdImYauuwLZqKqy+CqrD466vrZ3eL+ZRJmZSLuYJt0zUwQ5zbrebnJwc3G53qKsiP1EmZlIu5lEmBopMxj16Mfu6XYeFDb57HN49Ecq2h7pmbZ7eL+ZRJmZSLuYJt0zUwQ5zXq+X0tJSvF5vqKsiP1EmZlIu5lEmZvJasKP9FVSNXgQR7WDvpzVDeeV9EOqqtWl6v5hHmZhJuZgn3DJRB1tEREQazNtpUs1QXu0GQ2U+VFeEukoiIiIh5wx1BURERKSVisuAiStg11LofOrP0y1Lw3qJiEibZOQZ7CVLljBs2DBiYmLIyMhg7ty5HOpm55WVldx9993079+f2NhY+vXrx+zZs4Ou409PT8dmswX95efnN/cqiYiIhCdnNKSf9fPj0h9gySgo2hC6OomIiISIcWewV61axRlnnMH555/PHXfcwfLly5kxYwYej4eZM2fWusy1117L/PnzueWWWxg2bBiff/45t99+O9nZ2TzzzDMA5Ofnk5uby5w5cxg7dmzA8klJSc29WiHjdDrp0KEDTuf/t3fn8VFVB//4P7NlsickZCOTjRBWRbGCIKCIgNYdGkF5aqs8rYLyLYiKVFEU/QkIKtSWUq1FsRSsgPRpxbIJsooiBAQRZElIQshG9m228/sjzpibmck6kzkz+bxfL16Qe+8czr2fuffk3HMX6aLutpiJnJiLfJiJnFrN5ZsnG+/L3jocuOFdIHVq11awm+L+Ih9mIifmIh9/y0S692DfdtttKC8vx6FDh+zTnn32Wfz5z39GYWEhgoKCFMuXlpYiJiYGS5YswTPPPGOfvmTJEsybNw9FRUWIiYnBjh07MH78eJw9exbp6eluqasvvAebiIioS9WXAAemNl42DgB9/x8wZBmgCfBuvYiIiDrIZ9+D3dDQgN27d2PixImK6ZmZmaiqqsK+ffscPlNZWYnp06fjnnvuUUzv378/AOD8+fMAgKysLISFhaF3794eqr2cLBYLKisrYbFYvF0V+hEzkRNzkQ8zkVOruQT2BMZ8Bgx6vvHnM28DO8cAtfldVsfuiPuLfJiJnJiLfPwtE6nG4c+fPw+j0Yi+ffsqpvfp0wcAcPr0aYwfP14xLy0tDStXrnQoa/PmzdDpdPaysrKyEBUVhczMTOzYsQMWiwV33nknli9fjoSEhE7Vu75e+eRUtVqNgIAAWK1Wp+9zCwwMBNB4QqH5BQQ6nQ4ajQZmsxlms9lpuUIINDQ0OJSr1+uhUqlgNBrtj7lvaGhAXl4eUlJSEBoaCovFApPJpPicSqWCXq93ui4AEBAQALVaDZPJ5PDF12g00Ol0ra6rp8rtim1oo9VqodVqO7UNjUYj6urqkJeXB4PBAL1e75ZyfW0byphNbW0tcnNz7bkAnt+GMmYj0zHCarUiPz8fSUlJDpeNyfz97uwxQrZsmm/DhoYG5Ofno3fv3hBCuN6Gg1+BKXwIdIf/F6qSgxCfXQfj6M8QEDPEJ48R3jjOtqdcW3ufmpqKkJAQv9uGsmXTlm3oifZexmx87fcIZ+29r25DGbPpyDa0tfcGgwE6nU4xT5ZjhBACqjY+vFOqDnZFRQUAIDw8XDE9LCwMQONodVt88skn+OCDDzBz5kz06NEDQGMHOz8/H48++ihmz56NU6dO4cUXX8TNN9+Mo0ePIiQkpEN1tlgsuHDhgmJaeHg4EhMTYTabHeYBwIABAwAABQUFqKurU8zr1asXIiIiUFlZicLCQsW8kJAQJCcnw2q1Oi03IyMDWq0WhYWFqK6uBgD7l6a6uhqhoaGoqalBfr5yFCEwMBBpaWkAgOzsbIcdsXfv3tDr9SgpKUF5ebliXnR0NGJjY1FXV4eLFy8q5mm1WmRkZAAAcnNzHXbi5ORkhISEoKysDKWlpYp5kZGRSEhIgMlkclhXlUplv0Lh0qVLDjtUYmIiwsPDUVFRgaKiIsW80NBQJCUlOc0NAPr27QuNRoPLly+jpqZGMS8uLg5RUVGorq7GpUuXFPOCgoKQmpoKAE7LTU9PR0BAAIqLi1FeXg6j0Yi8vDyo1Wr07NkTMTExqKurQ25uruJzOp3OfoLp4sWLDgeHlJQUBAcH48qVK7hy5YpiXo8ePRAfHw+j0ehQJ7VajX79+gEA8vPzHQ6iBoMBYWFhKC8vR3FxsWJeWFgYDAaDy+93v379oFKpUFBQgNraWsW8hIQEREZGorq6GgUFBYp5wcHBSElJgRDCabl9+vSBTqdDUVERqqqqFPNiYmLQs2dP1NbWIi8vTzFPr9fbr1zJyclxaBDS0tIQGBjokAsAREVFIS4uDvX19cjJyVF8TqPR2E/g5ebmOjQmSUlJCA0NRVlZmcODFGU6RtjExsYiOjpaqmNEfHw8gMa2ofnx35+PEc3XVbZjhNVqtX8HWjtGnKsfCF3GehjOz4ZQ65BTrEbfnsInjxGlpaUoKytTzJPpGGFr72traxESEtItjhGy/x5RVVWlaFe6yzECkPv3iNLSUof2vjscI2xk/D0iMTERAFBWVubQtspyjDCbzQ6df1ekugf7wIEDGDlyJLZv345x48bZp9tWaNGiRS4fdGazadMmTJ06FcOGDcO2bdvsZx8OHjwIrVaLoUOH2pfdv38/Ro0ahZUrV2LGjBntrq/tWnxbsDYynVXiCLYcZ+Y4gt1I5tGpqqoqXLx4kSPYEh0jrFYrcnJyOIIt2TGi6Qi2bX9uyuk2NNcCpgogKKFxGworjHXlsGqUJ7dlPkbIPjrFEWz52kCOYMv5e4Sz9t5Xt6GM2XR0BDsnJ0fqEewzZ85ApVK16R5sqUawIyIiAMDhzLPtTJ1tvitvvfUWnn76aYwZMwabN2+2bxgAGDFihMPyI0eOREREBI4dO9apejf9f5pSq9Uu5wGwf9GcsX1JnVGpVC2WGxCgfJCMWq22l6XRaKDRaFx+tqVydTqdyzM3ra2rp8rtqm3YVGe2oe1gqFarodfrFct6KxtvbEMZs9HpdE5zATy3DWXMRqZjhK2x1Gq10h9nm+rsMcIT5br7GGG7TK7t2zAQQNRPM4+/hIDcj4HRm4CIgQ6flTEbbxxn21uuWq22r5+/bUPZsmnLNvREey9jNr72e0RL7b2vbUMZs+nINrS19zqdrsPtvaezaevl4YBkDzlLT0+HRqPB2bNnFdNtP9sud2hOCIHf/e53mDNnDqZMmYLPPvvMflk50Hh54d/+9jecOHFC8TnbGYuYmBg3r4k8bDtIe74U5FnMRE7MRT7MRE6dzsVUBVz4AKg8DWwdBuT8070V7Ka4v8iHmciJucjH3zKR6hJxABg7dizq6upw4MAB+0Z+9tln8Ze//AWXLl1CcHCww2d+//vfY/HixZgzZw6WLVvmEE59fT2ioqIwceJErF271j598+bNmDhxInbu3ImxY8e2u658TRcREVEH1BcD+x8ACj9v/LnfbGDI64C6bfe3ERERdaX29PukukQcAObPn49x48Zh8uTJmDZtGg4cOIClS5di8eLFCA4ORmVlJb777jukp6cjJiYGWVlZWLJkCYYOHYr7779f8f5sABg4cCDCw8Mxb948LFiwAHFxcbjjjjvw7bff4qWXXsK9997boc41ERERdVBgDHDLVuD4C8B3i4HTy4Erh4FR/wSCOvdmDyIiIm+S6hJxoHEEe+PGjTh9+jTuu+8+rF27FkuXLsXcuXMBAEeOHMGIESPw6aefAmh8qJkQAl9//TVGjBjh8OfIkSMAGjvuK1euxLZt23D33XfjjTfewPTp07Fu3TqvrWtXqK+vx/fff+/0xn/yDmYiJ+YiH2YiJ7flotYC1y4CRn8C6MKB4n3A9lGAxfFhM9Q67i/yYSZyYi7y8bdMpBvBBoCJEydi4sSJTueNGTNG8TS8hQsXYuHCha2WqVarMWPGjA49LdzXSXYXAIGZyIq5yIeZyMmtuSTdB0QcBvZOAgY+C2hcPzyHWsb9RT7MRE7MRT7+lImUHWwiIiLqRsIzgNu/UXauK74DgpMBXaj36kVERNRO0l0iTkRERN1Q0851fRHw+YTGp4xXfO+9OhEREbUTO9hEREQkl7pLAARQeQrYOhS4uNHbNSIiImoT6V7T5Ut84TVdVqsVJpMJOp0OajXPp8iAmciJuciHmcipy3KpKwT2TwGKvmj8ecDTwDWLGh+ORg64v8iHmciJucjHFzJpT79PzjUgt1Gr1dDr9dJ+WbsjZiIn5iIfZiKnLsslKA4Yu6OxYw0Ap5YBn48D6i579v/1Udxf5MNM5MRc5ONvmfjHWpBLJpMJBQUFMJlM3q4K/YiZyIm5yIeZyKlLc1FrgSFLgVEfA9rQxtHs4y96/v/1Qdxf5MNM5MRc5ONvmbCD7ecsFgvKy8thsVi8XRX6ETORE3ORDzORk1dySc4EbvsaMNzb2OEmB9xf5MNM5MRc5ONvmbCDTURERPKL6A/ctBkIiGj8WQjghz8D5hqvVouIiKgpdrCJiIjI95z5I/D148DW4UDlGW/XhoiICAA72EREROSLelwLBMYDFScaX+WVu9nbNSIiImIH299pNBpER0dDo9F4uyr0I2YiJ+YiH2YiJ2lyiR0N/PwIEDMKMFUCeycCWb8HrGbv1stLpMmF7JiJnJiLfPwtE74HuxN84T3YREREfs1qAo7OBU4vb/w5biwwch0QGOvVahERkf/ge7DJzmq1oqamBlar1dtVoR8xEzkxF/kwEzlJl4taB/zsLWDkekAb0vgqr8rvvV2rLiddLsRMJMVc5ONvmbCD7eeMRiMuXrwIo9Ho7arQj5iJnJiLfJiJnKTNJWUKMOEQcMNfgdibvF2bLidtLt0YM5ETc5GPv2XCDjYRERH5h8hBQO+Hf/q54hTw1WOAudZrVSIiou6FHWwiIiLyP1YLsG8ycPYdYNsIoOqst2tERETdADvYfsxisWDPnj349NNPsWfPHlgsFm9XiYiIqGuoNcD1bzc+7Kz8OPDf64G8f3u7VkRE5OfYwfZTmzZtQmpqKm677TY888wzuO2225CamopNmzZ5u2oEQKvVersK5ARzkQ8zkZPP5BI3Brj9CNBzBGCqAPbcAxyb3zi67Yd8JpduhJnIibnIx58y4Wu6OkHW13Rt2rQJmZmZaB6tSqUCAGzYsAGTJk3yRtWIiIi6nsUIHH0aOPN248/x44FRHwMBEd6tFxER+QS+pqsbs1gsmDVrlkPnGoB92uzZs3m5OBERdR+aAOD6PwA3rgU0wYClHtAGe7tWRETkh9jB9jN79+5FXl6ey/lCCOTm5uKxxx7D3//+d+zZswfZ2dkwmUxdWMvurb6+Hj/88APq6+u9XRVqgrnIh5nIyadzSZ0K3HYIGPVR4/uzAUBYAT+4mM+nc/FTzEROzEU+/paJ/1zsTgCAgoKCNi333nvv4b333rP/rFar0atXLyQnJzv9k5KSgoiICPtl5tQ5ZrPZ21UgJ5iLfJiJnHw6l8irlD8fmQOYKoHr/wRog7xTJzfx6Vz8FDORE3ORjz9lwg62n0lISOjQ56xWK/Ly8pCXl4cDBw44XSYsLMxl5zs5ORm9evWCTqfrTPWJiIi6TuXpxvuyhRUoywJGbwRC07xdKyIi8mHsYPuZ0aNHw2AwID8/3+l92CqVCrGxsfjDH/6A/Px8XLx4UfGnqKjIZdlVVVU4efIkTp486XS+s1FwW+fb9oej4EREJI3wfsAt24D9DwBlR4H//gwY8Xcg8Q5v14yIiHwUO9h+RqPRYMWKFcjMzIRKpVJ0sm0d25UrV7p8inhdXR1yc3MdOt5N/zQ0NDj9bHtHwZt3vjkKTkREXS7+1sZXee3LBEq/Ar64C7jqReDqFwEVH1VDRETtw9d0dYKsr+kCGl/VNWvWLMUDz5KSkrB8+fJOvaLLarWiuLjYacc7JycHFy9eRHFxcYfLt42CO+t82/5ERkZ2uHwZWK1W1NXVISgoCGo1f3mTBXORDzORk9/mYmkAjjwJ/PDnxp+TpwCj1nu3Tu3gt7n4MGYiJ+YiH1/IpD39PnawO0HmDjbQ+MquvXv3oqCgAAkJCRg9ejQ0Go3H/9/mo+C2jnfTP0ajscPlh4eHt/gwtl69evnVy+qJiKgLnV8DfD0duPHvQFLHT0gTEZH/YAe7i8jewQYAk8mEsrIy9OjRQ5pLr5uOgjvrfLtjFDwxMbHVe8G9RcZMiLnIiJnIqVvkUncZCIp3/bOEukUuPoaZyIm5yMcXMmlPv4/DfH7OYrGgtLQU4eHh0nxh1Wo14uLiEBcXh6FDhzpdpra2ttV7wV2NglutVuTm5iI3Nxf79+93ukzTUXBX94J7ahRcxkyIuciImcipW+TStDNdmwd8dh1guA+4/g+AJtBr1WpJt8jFxzATOTEX+fhbJuxgk5SCg4PRr18/9OvXz+l8q9WKoqIil53vnJwclJSUuCy/srISJ06cwIkTJ5zOt42Ct3QvuDdHwYmIqItc/hxoKAHOvQuUHWl8lVdIirdrRUREkmIHm3ySWq1GfHw84uPjMWzYMKfLuBoFt12Wnpub26ZRcFciIiJavBc8ISGB94ITEfm63r9qHNE+MBW48k3jaPbIdUDCBG/XjIiIJMTf/slvtXcU3Nn94C2NgldUVODbb7+135PRnEajcbgXPDk5GQkJCVCr1YiOjkZgoJyXGhIRURMJE4DbvwH2ZgJXDgO7bgcGLwQGPcdXeRERkQI72H5Oo9EgMjKyS54e7mvaMwru6mFsLY2CWywW+3KuNB8Fb35JOkfBuw73FfkwEzl121xCUoDxe4FvZgFn3wGOvwBoQ4D+T3q7ZgC6cS4SYyZyYi7y8bdM+BTxTvCFp4iTZ1mtVhQWFrb4MLaWRsFb03QU3NX94OHh4W5cIyIiatW51cDZVcCtnzd2somIyK/xNV1dxBc62FarFSaTCTqdTtoXt/u7mpoah/eCZ2dnIz8/Hzk5OcjNzYXJZOpw+ZGRka3eC+4vZwQ9ifuKfJiJnJjLj4T1p8vDhRW4vBNIGO+16jAX+TATOTEX+fhCJnxNF9kZjUZcuHABaWlpvN/XS0JCQtC/f3/0798fAFBfX6/IxNUoeNPL0ktLS12WX15ejvLychw/ftzpfI1GA4PB4LTzbft3WFiYR9bdl3BfkQ8zkRNz+VHTe6+/Wwwcex7oMx342XJAo+/y6jAX+TATOTEX+fhbJlJ2sLdt24bnn38eJ0+eRFxcHJ544gk89dRTUKlUTpdvaGjAG2+8gTVr1iA3NxcGgwH/8z//g3nz5iEgIMC+3OHDh/H000/j8OHDCA8Px8MPP4yXXnpJsQxRV1Or1UhISEBCQgJuuOEGp8s4GwVvfi+4q1Fwi8WCnJwc5OTkuKxD81FwZ/eCcxSciMgFqwWAqvGy8bIjwKgNQEiSt2tFREReIF0H+8svv8Rdd92FKVOm4JVXXsG+ffswd+5cmM1mzJs3z+lnZs2ahQ8//BAvvPAChg4disOHD+Pll19GTk4O3nvvPQDA+fPnMW7cOIwYMQL//Oc/cerUKTz//PO4cuUKVq1a1ZWrSNRuzUfBm2s6Cu7qgWydGQXXarWt3gvOUXAi6raufgGIvh448D9A6VfAf398lVf8OG/XjIiIuph0HewFCxZgyJAh+PDDDwEAt99+O0wmE1577TXMmjULQUFBiuVLS0vxzjvvYMmSJXjmmWcAALfeeisAYN68eVi8eDFiYmKwZMkShIWF4V//+hcCAgJwxx13IDg4GDNnzsRzzz2H5OTkrl1RIjdq6yh4Sw9ja2kU3Gw220fB9+7d63SZyMhIl51vjoITkd/r9fMfX+X1C6DsKLDrNmDwq8DAZ/kqLyKibkSqDnZDQwN2796Nl19+WTE9MzMTr7/+Ovbt24fx45UPEKmsrMT06dNxzz33KKbbRvrOnz+PmJgYbN26FXfeeaficvDMzEw8/vjj2Lp1K3772996aK28z9Wl9eQ93sgkJCQEAwYMwIABA5zOt1gsLp+IbhsVv3LlisvybaPgx44dczpfq9U6vRfcNiqelJTk9VFw7ivyYSZyYi4uhKYB4/cDh58Azq8Gvn0JMEwEIpxffeRuzEU+zEROzEU+/pSJVB3s8+fPw2g0om/fvorpffr0AQCcPn3aoYOdlpaGlStXOpS1efNm6HQ69O3bF3V1dcjJyXEoNyYmBuHh4Th9+nSn6l1fX6/4Wa1WIyAgAFar1ek7km037zc0NKD5Q9x1Oh00Gg3MZjPMZrPTcoUQaGhocChXr9dDpVLBaDTCarXap6emptrfpWyxWBxGKVUqFfR6vdN1AYCAgACo1WqYTCZYLBbFPI1GA51O1+q6eqrcrtqGQGMHUavVdmob2tYlNTXVvqw7ynXXNoyKikJUVBSGDh3qdBtWV1cjPz8fly9fRk5ODi5cuGAf/c7NzUVeXp7DNrcxm83Izs5Gdna20/kA0KNHDyQnJ8NgMMBgMCApKQnJyclISkpCnz59kJCQAIvF4pFsNBqNIpeObkObtnwP/e377YljRP/+/WEymRzm+/MxQrZsnG3DjIwMj25DGbNp+zZUAdf8CZqI66HTBQAR/bvsGJGamgqdTgcAPr4NHdfVF3+PANzf3suYjTeOs50pV61WO7T3vroNZcymo9uws+29p7MRQrT5JIBUHeyKigoAcHivr21Uq7Kysk3lfPLJJ/jggw8wc+ZM9OjRA5cvX3Zarq3stpbrjMViwYULFxTTwsPDkZiYCLPZ7DAPgH0EsaCgAHV1dYp5vXr1QkREBCorK1FYWKiYFxISguTkZFitVqflZmRkQKvVorCwENXV1Yp5sbGxiI6ORk1NDfLz8xXzAgMDkZaWBgDIzs522BF79+4NvV6PkpISlJeXK+ZFR0cjNjYWdXV1uHjxomKeVqtFRkYGACA3N9dhJ05OTkZISAjKysoc7g+OjIxEQkICTCaTw7qqVCr7FQqXLl1y2KESExMRHh6OiooKFBUVKeaFhoYiKSnJaW4A0LdvX2g0Gly+fBk1NTWKeXFxcYiKikJ1dTUuXbqkmBcUFGQ/WDsrNz09HQEBASguLnb4vvXs2RMxMTGoq6tDbm6uYp5Op7OfYLp48aLDwSElJQXBwcG4cuWKw+hyjx49EB8fb38yY1NqtRr9+vUDAOTn5zscRA0GA8LCwlBeXo7i4mLFvOjoaFxzzTUwmUw4e/asYp7FYkFkZCTy8vLwzTff4MKFCygoKLD/KSwsRFlZmcP2sSkrK0NZWVmLo+AJCQmIi4tDQkICevXqhYSEBPTv3x9XX301evTo4fAd1ev16N27NwAgJyfHoUGwPbGytLTUoW5RUVGIi4tDfX29w0PiNBqN/aSds8vrk5KSEBoairKyMod3ofMY0YjHiJ/40zEiLCwMBoPB5fe7X79+UKlUKCgoQG1trWJeQkICIiMjUV1djYKCAsW84OBgpKSkQAjhtNw+ffpAp9OhqKgIVVVVinkxMTHo2bMnamtrkZeXp5jnuWPEOPTt89MxQl1xDPr686iMugsAjxE2PEY04jHiJ93nGMHfI2x84RhhNpvtJzBbI9V7sA8cOICRI0di+/btGDfupweD2FZo0aJFLh90ZrNp0yZMnToVw4YNw7Zt2xAYGIhLly4hMTER7777Ln7zm98oljcYDLjrrrs69KAz2/vQbMHayHRWyWg0oqioCAkJCQgJCeGZZwlGp+rr61FUVITY2FgEBAT47FnTjm5D22vKLly4YB/5to1+2/7tahS8LXr06GEf+bZdkt67d28kJycjNjYW8fHxinvBbduwuroaly9ftucC8MyzjbeOEUIIeybN79+X9fvdHUanjEYjSkpKkJiYaN+OTXl7dErKbVhVCN3O4VDXZsPcezrMg5dApw9x6zHC1t736tULwcHBfrcNfbEN9ER7L2M2vjaC7ay999VtKGM2HdmGtvY+JibGftWtjSzHiDNnzkClUvnee7AjIiIAwOGsku1MnW2+K2+99RaefvppjBkzBps3b7ZvGNvIdfNybWW3Vm5rXL2vTa1Wt/guN9sXzRnbl9QZlUrVYrnNXztmMpnsv5xqNJoWHzTVUrk6nc7lmZvW1tVT5XbVNmyqM9vQdjA0mUwICAhQLOutbLp6G4aGhuLqq692eYCyWCy4fPmyy4ex5eTktGkU3NUT0XU6ndN7wePj46FSqRAZGelwtYuntqFs2QByHSPq6+tRX18PjUYj/XG2qc4eIzxRrruPEbZfODy1DWXMplPbMKQnkPZL4OSr0J5fBW3lMWDUx0BwoluPESaTCWp14wPV/G0b+mIb6In2XsZsvHGc7Uy5Wq3WaS6A721DGbPpyDa0tfdarbbD7b2ns2nPPeJSdbDT09Oh0WgcLju1/ezq4UxCCMyaNQtvv/02HnzwQbz//vuK4ENDQ5GYmOhQru0SEVflElHX02g0SExMRGJiIkaMGOF0maqqKsV7wZs/jK2le8FtlwI5u/TJJioqyuXD2GydcdsvsURErVJrgGteAaKHAQcfAkoO/vgqr/VA3C3erh0REbmRVB3swMBA3HTTTdi0aROefvpp+5mCjRs3IiIiAsOGDXP6ueeeew5vv/025syZg2XLljk9wzBhwgT85z//wZtvvmk/E7Fx40ZoNBqMHTvWcytFRG4XFhaGgQMHYuDAgU7nOxsFb/5+8JZGwW33omVlZTmd72wUvPkrykJCQtyxqkTkTwx3A7cfbnyVV/lx4PNxwDWLgQFPA370BF0iou5Mqg42AMyfPx/jxo3D5MmTMW3aNBw4cABLly7F4sWLERwcjMrKSnz33XdIT09HTEwMsrKysGTJEgwdOhT3338/Dh06pChv4MCBCA8Px9y5c7Fu3Tr8/Oc/x5w5c3DmzBk899xzePTRR/kObCI/095R8JycHJw/fx6nTp1CWVkZ8vLy3DYK7urd4BwFJ+qmwvoAEw4CX88ALqwBCncC/ecAKteXVBIRke+Q6iFnNp988gkWLFiA06dPIzExEU888QSeeuopAMDu3btxyy23YPXq1Xj44Yfx4osv4pVXXnFZ1q5duzBmzBgAwN69e/HMM88gKysLPXv2xEMPPYSFCxe2+YlwzdkectaWm929xWKxoKamBiEhIS3eD0Fdh5nIqXkutlHw5iPfbR0Fb41Op7M/jM3Vn+4+Cs59RU7MxU2EAC58ACTeDeijO10cc5EPM5ETc5GPL2TSnn6flB1sX+ELHWwi8pzKykqn94LbRsXz8vIcnlrZHtHR0S3eCx4XF8dRcCJ/cngW0HMEkPqAt2tCRERNsIPdRXyhg202m1FRUYGIiAiXTwqkrsVM5OSJXCwWCwoKClw+jO3ixYsO73tsD2ej4E0vSU9KSvLpUXDuK3JiLh6S929gzz2N/+43CxiyFFC3/Qo75iIfZiIn5iIfX8ikPf0+OdeA3MZsNqOoqAghISHSfmG7G2YiJ0/kotFoYDAYYDAYcOONNzpdpvkoePNL0lsaBTeZTDh//jzOnz/vsg5NR8Gd3Q8u8yg49xU5MRcP6XUHMPD3wHeLgNMrgCuHG1/lFZTQpo8zF/kwEzkxF/n4Wya+vwZERD4sPDwcgwYNwqBBg5zObzoK7up+8JZGwUtLS1FaWoqjR486nW8bBXf1MLbk5GQEBwe7Y1WJqCVqDXDta0DPG4CDvwKK9wOfDQFG/ROIvcnbtSMiojZiB5uISGLtGQV31QHv7Ch4z549XXa+U1JSEBsbK+0oOJHPMdwL3HYY2DsJqDgB7BwLXP9HIGO6t2tGRERtwA42EZGPa20U3Gw2O70XvOmoeEVFhcvyS0pKUFJSgiNHjjidHxAQ0Oq94BwFJ2qH8Azgti+BQ48COeuAkDRv14iIiNqIHWw/p1arERoaytEliTATOflzLlqtFklJSUhKSsLIkSOdLlNRUeHwRPSmI+L5+fkuR8GNRiPOnTuHc+fOuaxD01FwZ5ejOxsF9+dMfBlz6SLaEODGvwP9nwSir/9pusUIaAIcFmcu8mEmcmIu8vG3TPgU8U7whaeIExG5Q/NRcGeXo7c0Ct4a2yi4q3vBOQpOBKD6PLBjDHDdm0ByprdrQ0TUbfA1XV3EFzrYQghYLBZoNBqoVCpvV4fATGTFXDrPNgru6l7wlkbB2yImJqbFe8FjYmL85uy3zLiveNHXTwA/rGz8d/+ngGsXA+rGixGZi3yYiZyYi3x8IRN2sLuIL3Sw6+vrceHCBaSlpSEwMNDb1SEwE1kxF8+zjYK76oDn5OSgsrKyw+Xr9XqHe8GbXpKelJSEoKAgN65R98R9xYusZuDYc8CppY0/x94MjFwPBMUzFwkxEzkxF/n4QiZ8DzYREUmn6b3grlRUVNg73OfOncO3336Lqqoq5OXl2UfBrVar0882NDTg7NmzOHv2rMvym4+CN78kPTY2Vtqz50RQa4EhrwPRNwBfPgIUfQH897rG92WH/czbtSMiIrCDTUREEomIiMDVV1+Nq6++2ukZbbPZjEuXLrl8GFtOTg6qqqpcll9cXIzi4mJ88803Tuc3HQV3dj84R8FJCsm/ACKv+vFVXt8BO8ZAPfLfAFK8XTMiom6PHWwiIvIZWq3W3tl1pekouLPL0Ts7Ch4bG9vqveAcBSePC+8HTDgEHPoNUH0e1ugRQM0lb9eKiKjbYwebiIj8StNRcGdso+At3Qve0ih4UVERioqKcPjwYafz9Xq9y863bRRc1nvMyMfoQoGR6wBzFWD58dVdwgJUZwOhqd6sGRFRt8WHnHWCLzzkTAgBq9UKtVrNERVJMBM5MRf5eDOTiooKlx3w1kbB26L5KHjzy9FlHgXnviIney7fvgDVmT8CIz4AkiZ6u1rdGvcVOTEX+fhCJnyKeBfxhQ42ERG5n8lkcrgXvOkl6Tk5Oaiuru5w+YGBga3eC85RcHJgNQGfj298+BkADHwWGPyq/VVeRETUMexgdxFf6GAbjUZcvnwZ8fHxCAgI8HZ1CMxEVsxFPr6ciRBCcS+4s/vBL1261OlRcGedb0+OglssFnz++ef4/vvv0b9/f4wdOxYajcat/wd1jH1/iY1GwHcvAt+/2TgjbmzjZeSBsd6tYDfky8cwf8Zc5OMLmfA1XWRntVpRU1PTqV/iyL2YiZyYi3x8OROVSoXIyEhERkZi8ODBTpdpOgru7HL01kbBbfeCf/31107nBwYGtvgwNoPB0K5R8E2bNmHWrFnIy8uzTzMYDFixYgUmTZrU5nLIM+z7C2KB695ofJXXoWlA4efAZ9cBozcAPYd7u5rdii8fw/wZc5GPv2XCDjYREZEX6HQ6pKSkICUlBaNHj3aYbxsFb+le8JZGwevr63HmzBmcOXPGZR3i4uJavBe8Z8+eUKlU2LRpEzIzM9H8orf8/HxkZmZiw4YN7GTLJmUyEHl146u8Kr8Hdv0cuC8H0IV7u2ZERH6NHWwiIiIJNR0Fv+aaa5wuYzKZkJ+f77ID3tooeGFhIQoLC1scBU9KSsLFixcdOtcA7NOmT5+OyMhI6HQ6aDQaqNVqj/0t6wNwpBQxALjtK+DLaYDhXnauiYi6ADvYREREPkqn0yE1NRWpqalO5wshUF5e7rLzbRsFd/U4lvr6evzwww+t1qO4uBi33nprZ1alzVQqVYc76J7u/Hvzb7PZjJKSEpw+fRpBQUHKZVT/D5oCLdSFX0Kj0SDU+D2ELgLmwFSe+CAir7JYLNizZw+OHz+OwYMH49Zbb/X5Z3uwg+3ntFot4uLioNUyalkwEzkxF/kwk85TqVTo0aMHevTo0eZR8OaXpJ87dw4NDQ1dXHPXhBCwWCywWCzeropPig0Hjvx/QIgeuPU14Ei2e8rtzImPzv4twwkOZ38DP92q4a6rO3jio/PYtsjDX5/twW+Wn9NqtYiKivJ2NagJZiIn5iIfZtI1WhsF37VrF8aOHdtqOQ8++CASExNhtVphsVhc/t3SPG/+3V1eqqJWA9klgF4LnMx3X7k88SEnb5748NcTJ+74myc+4NfP9uBrujrBF17TZbFYUF1djdDQUJ+/3MJfMBM5MRf5MBM5WCwWpKamIj8/32knVKVSwWAw4MKFCz6dkxDC4x15T5ZtNptRV1cHrVbb6rrAakSgug7l9fouO4nBEx9ESt3tpELzKyueeuopXLlyxem2kbFd4Wu6yM72Gpi0tDRpvqDdHTORE3ORDzORg0ajwYoVK5CZmQmVSqXo8NhGYZYvX+7zGalUKvsvuL6ovr4eFy5cQFpaWrtevya7rjjx4am/GxoaUFJSgoiICKjVainq5K6/eeKj82wnnkwmk7erIh0hBHJzc7F3716MGTPG29VpN3awiYiIqEWTJk3Chg0bnN4rt3z5cp+9jI/k58snPvz1pAfw04kPX7wlxHbiIzIyEiqVSrqrNnji4ycFBQXerkKHsINNRERErZo0aRLuvfde7Ny506+e9kpE7df0xIdOp/N2ddqFJz68f0vIqVOnsGLFilbXJyEhoQu2mvuxg01ERERtotFocNNNNyEpKYmX7hMRScZXTnxYLBZs3Lix1Wd7jB492gu16zy1tytAnqVSqRAUFMSnFUqEmciJuciHmciJuciJuciHmciJuXif7dkeABxy8Idne/Ap4p3gC08RJyIiIiIiko2z92AnJSVJ+WwPPkWciIiIiIiIpGV7tsfevXtRUFCAhIQEjB492mdHrm14ibifq6+vx6lTp1BfX+/tqtCPmImcmIt8mImcmIucmIt8mImcmItcNBoNhg8fjmuvvRbDhw/3+c41wA42ERERERERkVuwg01ERERERETkBuxgExEREREREbkBO9hEREREREREbiBlB3vbtm0YOnQogoODkZaWhmXLljl9CbkzR48ehU6nQ3Z2tsM8g8EAlUrl8KekpMTNayCPgIAApKenIyAgwNtVoR8xEzkxF/kwEzkxFzkxF/kwEzkxF/n4WybSvabryy+/xF133YUpU6bglVdewb59+zB37lyYzWbMmzevxc+eOHECd955J8xms8O8kpIS5OfnY+nSpRg1apRiXmRkpDtXQSpqtdpvvqz+gpnIibnIh5nIibnIibnIh5nIibnIx98yka6DvWDBAgwZMgQffvghAOD222+HyWTCa6+9hlmzZiEoKMjhM0ajEW+//TZefPFFBAYGOi03KysLADBx4kSkp6d7rP6yMRqNKC4uRkxMjF99cX0ZM5ETc5EPM5ETc5ETc5EPM5ETc5GPv2Ui1SXiDQ0N2L17NyZOnKiYnpmZiaqqKuzbt8/p57Zs2YKXX34Zzz33HJYsWeJ0maysLISFhaF3795ur7fMrFYrKisrYbVavV0V+hEzkRNzkQ8zkRNzkRNzkQ8zkRNzkY+/ZSLVCPb58+dhNBrRt29fxfQ+ffoAAE6fPo3x48c7fG7o0KHIzs5GVFQU3n//fadlZ2VlISoqCpmZmdixYwcsFgvuvPNOLF++HAkJCZ2qd/MX1dsuc7BarTAajQ7L20bZGxoaHO4t1+l00Gg0MJvNDpe628oVQqChocGhXL1eD5VKBaPRaP+CNjQ0wGq12suyWCwwmUyKz6lUKuj1eqfrAjTeF6FWq2EymWCxWBTzNBoNdDpdq+vqqXK7YhvaaLVaaLXaTm1Do9Foz8T2/7ujXF/bhjJmYzKZFLkAnt+GMmYj0zHCVkez2ewwX+bvd2ePEbJl03wbNv25ux0juvo4255ybW2LrY7+tg1ly6Yt29AT7b2M2fja7xHO2ntf3YYyZtORbWgrq/nnAHmOEUIIqFQqh+WckaqDXVFRAQAIDw9XTA8LCwMAVFZWOv1cYmJiq2VnZWUhPz8fjz76KGbPno1Tp07hxRdfxM0334yjR48iJCSkQ3W2WCy4cOGCYlp4eDgSExNhNpsd5gHAgAEDAAAFBQWoq6tTzOvVqxciIiJQWVmJwsJCxbyQkBAkJyfDarU6LTcjIwNarRaFhYWorq4GAPuXprq6GqGhoaipqUF+fr7ic4GBgUhLSwMAZGdnO+yIvXv3hl6vR0lJCcrLyxXzoqOjERsbi7q6Oly8eFExT6vVIiMjAwCQm5vrsBMnJycjJCQEZWVlKC0tVcyLjIxEQkICTCaTw7qqVCr0798fAHDp0iWHHSoxMRHh4eGoqKhAUVGRYl5oaCiSkpKc5gYAffv2hUajweXLl1FTU6OYFxcXh6ioKFRXV+PSpUuKeUFBQUhNTQUAp+XaHtxQXFyM8vJyGI1G5OXlQa1Wo2fPnoiJiUFdXR1yc3MVn9PpdPYTTBcvXnQ4OKSkpCA4OBhXrlzBlStXFPN69OiB+Ph4GI1Ghzqp1Wr069cPAJCfn+9wEDUYDAgLC0N5eTmKi4sV88LCwmAwGFx+v/v16weVSoWCggLU1tYq5iUkJCAyMhLV1dUoKChQzAsODkZKSgqEEE7L7dOnD3Q6HYqKilBVVaWYFxMTg549e6K2thZ5eXmKeXq93n7lSk5OjkODkJaWhsDAQIdcACAqKgpxcXGor69HTk6O4nMajcZ+MjA3N9ehUUhKSkJoaCjKysocHqQo0zHCJjY2FtHR0VIdI+Lj4wE0tg3Nj//+fIxovq6yHSOsVqv9O9CdjhGlpaUoKytTzJPpGGFr72traxESEtItjhGy/x5RVVWlaFe6yzECkPv3iNLSUof2vjscI2xk/D3C1pcrKytzaFtlOUaYzWbodDqHbeKMSrT18dxd4MCBAxg5ciS2b9+OcePG2afbVmjRokWtPujs/fffxyOPPIILFy7YD1QAcPDgQWi1WgwdOtQ+bf/+/Rg1ahRWrlyJGTNmtLu+R44cgRDC6ca2neFwtnnbMq8zn206TwgBi8UCrVbrkTq5o75dWW5bP+vJcm2ZaDQa+5PsO1uus/n+vA09Va7ZbLbn4q5yO1snXyrX2fzOlAs0Hv+1Wsdzwcym85/taLnN2xXZ1tUXtqG7Puup9t4ftqEM2XiivfeHbNr6WX9o7/15G7qzXKDr2/v2lmsymaBSqXDdddc5rX9TUo1gR0REAIDDmWfbmTrb/I4YMWKEw7SRI0ciIiICx44d61CZznZKV8u0d567ylWpVPazc7LUqbuX6ywTb9eJ5TbOa+nMpL+tqyfK9USd2nK2WKb6ylquO+vU/Bgm27r6wjb0RLnubO+76zZ0d7meaO+ZjXvKZXsvX7myt/e2fbotpOpgp6enQ6PR4OzZs4rptp9tlzu0V0VFBTZu3Ihhw4bhqquusk+3XU4VExPToXKHDBnSoc8RERERERGR/5HqKeKBgYG46aabsGnTJsVQ/caNGxEREYFhw4Z1qFy9Xo+ZM2di0aJFiun/93//h7q6Otxyyy2dqjcRERERERGRVCPYADB//nyMGzcOkydPxrRp03DgwAEsXboUixcvRnBwMCorK/Hdd98hPT29zSPPgYGBmDdvHhYsWIC4uDjccccd+Pbbb/HSSy/h3nvvxdixYz28VkREREREROTvpBrBBoCxY8di48aNOH36NO677z6sXbsWS5cuxdy5cwE0PlhsxIgR+PTTT9tV7vz587Fy5Ups27YNd999N9544w1Mnz4d69at88RqEBERERERUTcj1VPEiYiIiIiIiHyVdCPYRERERERERL6IHWwiIiIiIiIiN2AHm4iIiIiIiMgN2MEmIiIiIiIicgN2sImIiIiIiIjcgB1sIiIiIiIiIjdgB5uIiIiIiIjIDdjBJiIiIiIiInIDdrB9UF5eHiIjI7F79+5Wl123bh0GDRqEoKAgDBgwAB988IHDMocPH8aYMWMQGhqKXr164bnnnoPRaPRAzf1bW3NpaGjAa6+9hv79+yMkJAT9+vXDwoULHba5wWCASqVy+FNSUuLBtfA/bc3l7NmzTrf3VVddpVhu27ZtGDp0KIKDg5GWloZly5ZBCOHBNfA/bcnk/fffd5qH7U/TYxn3lY6xWq1YtWoVBg8ejNDQUPTu3RtPPvkkKisrW/wc2xXP6kgubFc8ryO5sF3xrPZmwnala1itVixbtgwZGRkICgrCNddcg7Vr17b6uRUrVqBPnz4ICgrCddddhy1btjgs4zP7iiCfcvHiRTFgwAABQOzatavFZTds2CBUKpWYPXu2+O9//yumT58uAIh169bZlzl37pyIiIgQt99+u/j000/FsmXLhF6vF4899piH18S/tCeXxx57TAQHB4tFixaJHTt2iMWLF4ugoCAxbdo0+zLFxcUCgFi6dKk4ePCg4o/JZPLw2viP9uTy8ccfCwBi586diu197Ngx+zIHDx4UOp1O/PKXvxSfffaZeP7554VKpRKLFi3y8Jr4j7ZmUlRU5PDdP3jwoBg0aJBISkoSRUVFQgjuK52xaNEiodFoxLx588T27dvFn/70JxEVFSXGjRsnrFar08+wXfG8juTCdsXzOpIL2xXPam8mbFe6xvPPPy90Op39eDRnzhwBQPzjH/9w+Zk33nhDaDQasXDhQrFlyxbxi1/8Qmg0GrF37177Mr60r7CD7SMsFotYvXq1iI6OFlFRUW3qMPTt21dMnjxZMW3y5MmiT58+9p8fffRRYTAYRENDg33aypUrhVqtFjk5OW5dB3/U3lxKSkqESqUSr7/+umL64sWLBQD7wX379u0CgDh79qwnq++3OrK/PP/888JgMLS4zIQJE8SwYcMU0+bOnSvCwsJEbW1tZ6vt1zqSSXMrVqwQarVafPnll/Zp3Fc6xmKxiMjISPH4448rpq9fv14AEF9//bXTz7Fd8ayO5MJ2xfM6ur+wXfGcjmbSHNsV96qpqREhISHi6aefVky/+eabxfDhw51+pra2VkRGRoq5c+fap1mtVjF8+HAxbtw4+zRf2ld4ibiPOH78OKZPn45f/epX+PDDD1tdPjs7G2fOnMHEiRMV0zMzM3H27Fn88MMPAICtW7fizjvvREBAgGIZq9WKrVu3uncl/FB7c6msrMT06dNxzz33KKb3798fAHD+/HkAQFZWFsLCwtC7d2/3V7obaG8uQOM2v/baa13Ob2howO7du53uU1VVVdi3b19nquz3OpJJU4WFhZg/fz5mzJiBG264wT6d+0rHVFZW4qGHHsLUqVMV023HonPnzjl8hu2K53UkF7YrnteRXAC2K57U0UyaYrvifnq9HgcOHMBTTz2lmB4QEID6+nqnnzl06BDKy8sV+4FKpcKkSZOwa9cu1NXV+dy+wg62j0hOTsbZs2fx5ptvIjg4uNXlT506BQDo27evYnqfPn0AAKdPn0ZdXR1ycnIclomJiUF4eDhOnz7tptr7r/bmkpaWhpUrV6Jfv36K6Zs3b4ZOp7NnkZWVhaioKGRmZiIiIgKhoaGYMmUKCgoKPLIe/qa9uQCN27yqqgo33ngjAgMDER8fj3nz5sFkMgFo/CXVaDS2uE+Rax3JpKkFCxZArVbj1VdfVUznvtIxkZGR+MMf/oCRI0cqpm/evBkAMGjQIIfPsF3xvI7kwnbF8zqSC8B2xZM6mklTbFfcT6PRYPDgwYiPj4cQAoWFhVi8eDF27NiBxx9/3OlnWmpbLBYLzp0753P7CjvYPiIqKgoGg6HNy1dUVAAAwsPDFdPDwsIANJ75c7WMbbnWHnRD7c/FmU8++QQffPABpk+fjh49egBoPLjn5+fjZz/7Gf7zn//gzTffxBdffIGbb74ZNTU17qi6X2tvLiUlJcjPz8f333+P6dOnY+vWrXj00Ufx1ltv4eGHHwbQtn2KXOvMvlJUVIQPPvgAM2fORGRkpGIe9xX3OXToEBYvXoy7777b4SFMANsVb2ktF2fYrnhea7mwXel67dlX2K543vr16xEfH4/f//73uOOOO/DLX/7S6XKdaVtk3Ve03q4AeYbVam1xvlqtbtMy5FmbNm3C1KlTMWrUKLz++uv26e+++y60Wi2GDh0KABg9ejQGDRqEUaNGYc2aNZgxY4a3quyXQkJCsG3bNmRkZCA1NRUAcPPNN0Ov12P+/PmYP38+9xcv+utf/wqLxYJZs2Y5zOO+4h779+/HXXfdhbS0NKxevdrpMmxXul5bcmmO7YrntSUXtitdq737CtsVzxs2bBi++OILHD9+HC+88AJuv/127N69GyqVSrGcP7YtctWG3CYiIgIAUFVVpZhuO8MTERFhPwvUfBnbcrYyyDPeeust3H///Rg5ciQ+/fRTBAYG2ueNGDHCfmC3GTlyJCIiInDs2LGurqrfCwoKwvjx4+2/BNnceeedAIBjx461aZ8iz9iwYQMmTJiAmJgYh3ncVzrvo48+wrhx45CcnIydO3ciOjra6XJsV7pWW3Npiu2K57U1F7YrXacj+wrbFc9LT0/HTTfdhJkzZ2LFihXYs2cP9u7d67BcW/YDX9tX2MH2U7Z7sc6ePauYbvt5wIABCA0NRWJiosMyRUVFqKqqwoABA7qmst2MEAK/+93vMGfOHEyZMgWfffaZ/RIXoPFSmb/97W84ceKE4nNWqxVGo9FpY0Cd88MPP+Avf/kLysvLFdPr6uoANN4/mp6eDo1G0+I+Re6Xn5+Po0ePYvLkyQ7zuK903rJly/Dggw9ixIgR2LNnDxISElwuy3al67QnF4DtSldpTy5sV7pGe/cVgO2KJxUXF2PNmjUoKipSTL/uuusAAJcuXXL4TEttS0BAAHr37u17+4qXn2JOHbBr1642veImLS3N6etUMjIy7D8/8sgjIjk5WdTX19unrVy5Umg0Gr5OpZ3amsu8efMEADFnzhyn72msq6sTQUFBYurUqYrpn3zyif19mtR2bcnliy++EADEO++8o5g+a9YsER4eLsrKyoQQQtxyyy1i+PDhitzmzp0rIiIiRE1NjSeq75fauq8IIcTGjRtdvi6F+0rnrFq1SgAQU6ZMUbxSqyVsVzyvI7mwXfG89ubCdsXzOrKvCMF2xZNycnIEAPHaa68ppr/xxhsCgDh58qTDZ2yv9mr+mq4bbrhBjB8/3j7Nl/YVdrB9kLNfTisqKsTBgwft77sUQojVq1cLAGLGjBnis88+E9OnTxcAxPr16+3LnDp1SgQGBopbbrlF/Pvf/xZvvPGG0Ov1YsaMGV25Sn6hLbkcPXpUqFQqMXToUHHw4EGHPxUVFUIIIV5++WUBQDz55JNi+/bt4s033xTh4eHi3nvv9cKa+ba25GKxWMStt94qwsLCxIoVK8T27dvF7NmzhUqlEm+99Zb9czt37hQqlUpkZmaKLVu2iPnz5wuVSiWWLFnSxWvl29p6DBNCiJdeekno9XqXZXFf6ZiCggIRFBQkUlNTxd69ex2ORUVFRWxXvKAjubBd8byO5MJ2xbM6egwTgu2Kp02bNk0EBgaKpUuXih07dogFCxYIvV4v/vd//1cIIURRUZHi2CSEEAsWLBAqlUrMnz9fbNmyRWRmZgqtViv27dtnX8aX9hV2sH2Qs19ObdNWr16tWHbVqlWiT58+Qq/XiwEDBog1a9Y4lLdnzx5xww03CL1eLxITE8W8efOE0Wj08Fr4n7bk8sILLwgALv/YPmuxWMTKlSvFoEGDRGBgoEhMTBRz584VtbW1Xb9iPq6t+0tFRYWYM2eOSE1NFXq9XgwcOFC8++67DuVt2rRJXH311SIgIECkpaWJZcuWdcFa+Jf2HMNmzJgh4uLiXJbFfaVj3nvvvRaPRatXr2a74gUdyYXtiud1dH9hu+I5nTmGsV3xrIaGBvHqq6+KjIwMERAQINLT08WSJUuExWIRQvx0orbp7wAWi0W88sorIikpSQQGBorrrrtObNmyxaFsX9lXVEII0Y4ryomIiIiIiIjICT7kjIiIiIiIiMgN2MEmIiIiIiIicgN2sImIiIiIiIjcgB1sIiIiIiIiIjdgB5uIiIiIiIjIDdjBJiIiIiIiInIDdrCJiIiIiIiI3IAdbCIiIiIiIiI3YAebiIjIR7300ktQqVRt+jN79mxvV9elpuuxfPlyb1eHiIiow9jBJiIiIiIiInIDrbcrQERERJ03ZcoUPPDAAy7np6end2FtiIiIuid2sImIiPxA//79cd9993m7GkRERN0aLxEnIiLqRsaMGQOVSgWtVouGhga88MILSElJQWBgIK699lqsWbPG6edMJhP++Mc/YsSIEYiKikJwcDCuuuoqvPjiiygvL3f6mcOHD+P+++9HfHw89Ho9UlNT8cADDyArK6vFOm7btg033ngjgoODkZCQgCeeeAJlZWWdXHMiIiLP4wg2ERFRN3X33Xdj+/bt9p+PHTuGX//61zh79iwWLlxon15RUYHbbrsNhw4dUnz+5MmTOHnyJP7+979j+/btisvQ//GPf+DXv/41zGazfVpOTg5ycnKwefNmfPrpp7j11lsd6rRhwwYcOHAAQggAQF1dHVauXIkTJ07giy++cNu6ExEReQJHsImIiPzAyy+/3OJTxJuzWCzYvn07Hn74YaxZswa//e1v7fNeffVVHD161P7zjBkz7J3rtLQ0rFixAu+88w6GDx8OALhw4QLuu+8+WCwWAMClS5fwm9/8BmazGWq1Go8//jjWrl2L3/3udwCAhoYGTJs2TdH5ttm/fz/uuusurF27Fi+88AI0Gg0AYM+ePTh+/LibthYREZFncASbiIiom/rtb3+Ld955BwDw0EMPQaPRYNWqVRBCYM2aNRgyZAjOnTuH9evXAwCioqLw1VdfoWfPngCARx55BMOHD8c333yDEydOYOPGjZg8eTLWrFmDuro6AMDMmTOxYsUKAMDUqVNRV1eHc+fO4ZprrkFxcTESEhIUdbr++uvxr3/9y35SICsrC//+978BAD/88AMGDx7s+Q1DRETUQexgExER+YHWniLuTPPlH3zwQaxatQpA4+XiALBr1y775doPPvigvXMNAFqtFtOnT7ePfu/evRuTJ0/G4cOH7cv8/Oc/V/wftg69KxMmTFCMuCclJdn/XVtb2+Z1IyIi8gZ2sImIiPxAR54iHhUVpfg5Li7O/u+qqioAQElJiX2awWBwKCMxMdH+79LSUgCN92zbNO2Qd6ROOp3O/m/bJehERESy4j3YRERE3VReXp7i58LCQvu/IyMjASg73c2Xbz4tJiYGABAREWGfZut021y+fBnZ2dn2UfHmbPdcExER+SJ2sImIiLqp1atXK37euHGj/d9DhgwBAIwcOdI+bf369bhy5Yr9Z4vForjk2/ZU8Ouvv94+7V//+pfi/3j22WeRlpaG0NBQ7Nq1yw1rQUREJA9eIk5EROQHvv/+e2zevNnl/ODgYEyYMEExbdOmTZg8eTLuvvtufP311/jjH/8IAFCr1fj1r38NAOjbty/uuOMObNmyBaWlpRg2bBhmzZqFoKAgrF692n6/9ZAhQ3DvvfcCAH71q19h4cKFqKurw5///GcYjUbccsstOHr0KD788EMAQEhIiKLzTkRE5A/YwSYiIvIDH330ET766COX81NSUpCdna2YNmTIEHz88cf4+OOPFdMXLlyIQYMG2X9+//33MWHCBGRlZeHcuXP2123ZZGRk4JNPPoFa3XhhXK9evfDXv/7V/h7s9957D++99559eZ1Oh3Xr1iEgIKCjq0tERCQlXiJORETUTb3zzjtYuHAhDAYD9Ho9rr32WqxduxbPP/+8YrmYmBh89dVXePvtt3HjjTciMjISgYGBGDRoEBYsWIDDhw8jJSVF8ZmpU6di//79mDRpEmJiYhAQEIC0tDQ88MADOHz4sP1yciIiIn+iEq6eMkJERER+Z8yYMfjiiy8AAEePHsW1117r3QoRERH5EY5gExEREREREbkBO9hEREREREREbsAONhEREREREZEb8B5sIiIiIiIiIjfgCDYRERERERGRG7CDTUREREREROQG7GATERERERERuQE72ERERERERERuwA42ERERERERkRuwg01ERERERETkBuxgExEREREREbkBO9hEREREREREbvD/A5aoruGwUAE4AAAAAElFTkSuQmCC",
            "text/plain": [
              "<Figure size 1000x1500 with 3 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Plotting\n",
        "fig, axs = plt.subplots(len(history), 1, figsize=(10, 15))\n",
        "\n",
        "\n",
        "# For each model\n",
        "for i, (model_name, records) in enumerate(history.items()):\n",
        "    epochs_eval = [r['epoch'] for r in records if 'eval_loss' in r]\n",
        "    eval_loss = [r['eval_loss'] for r in records if 'eval_loss' in r]\n",
        "    axs[i].plot(epochs_eval, eval_loss, label='Eval Loss', marker='o', color='black', linewidth=2)\n",
        "    \n",
        "    # Check if 'loss' (batch loss) is available\n",
        "    if any('loss' in r for r in records):\n",
        "        epochs_loss = [r['epoch'] for r in records if 'loss' in r]\n",
        "        loss = [r['loss'] for r in records if 'loss' in r]\n",
        "        axs[i].plot(epochs_loss, loss, label='Loss (batch)', linestyle='--', color='orange')\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "    # grid\n",
        "    axs[i].grid(True, alpha=0.8, linestyle='--')\n",
        "    axs[i].set_title(model_name, fontsize=15, fontweight='bold', color='black')\n",
        "    axs[i].set_xlabel('Epoch', fontsize=15, fontweight='bold', color='black')\n",
        "    axs[i].set_ylabel('Loss', fontsize=15, fontweight='bold', color='black')\n",
        "    axs[i].tick_params(axis='y', labelcolor='black', labelsize=12)\n",
        "    axs[i].tick_params(axis='x', labelsize=12, labelcolor='black')\n",
        "    axs[i].legend(loc='upper right', fontsize=12, framealpha=0.2)\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j6ktbFynhaOY"
      },
      "source": [
        "### Ensemble Method"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JzvJA8sRf_GV",
        "outputId": "94a3fe02-4afa-435f-d776-e6c82055fc15"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy: 94.21%\n",
            "Recall: 94.18%\n",
            "Precision: 94.31%\n",
            "F1-score: 94.21%\n",
            "\n"
          ]
        }
      ],
      "source": [
        "voted_predictions = majority_voting(all_predictions)\n",
        "conf_matrix, scores = eval_model(voted_predictions, test_df['labels'], return_scores=True)\n",
        "# scores -> (accuracy, recall, precision, f1)\n",
        "_ = {k: v for k, v in zip([\"accuracy\", \"recall\", \"precision\", \"f1\"], scores)}\n",
        "print()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sfDCxE6XXjsp"
      },
      "source": [
        "confusion matrix for each model before and after fine-tuning"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "97MYnL1YXjsp",
        "outputId": "0dffd108-174f-4fd6-ca23-0e20685c9737"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1500x1000 with 12 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig, ax = plt.subplots(2, 3, figsize=(15, 10))\n",
        "fig.suptitle('Confusion matrices of the models', fontsize=18, fontweight='bold', color='black')\n",
        "\n",
        "for i in range(len(model_names)):\n",
        "    sns.heatmap(model_confusion_matrices[model_names[i]], fmt='d', annot=True, ax=ax[0][i], annot_kws={\"size\": 16})\n",
        "    ax[0][i].set_title(model_names[i], fontweight='bold', fontsize=14, color='black')\n",
        "    sns.heatmap(fine_tuned_model_confusion_matrices[model_names[i]], fmt='d', annot=True, ax=ax[1][i], annot_kws={\"size\": 16})\n",
        "    ax[1][i].set_title(model_names[i] + '\\n fine-tuned', fontweight='bold', fontsize=14, color='black')\n",
        "\n",
        "    # change x and y labels. map 0 to 'human' and 1 to 'AI'\n",
        "    ax[0][i].set_xticklabels(['human', 'AI'], fontweight='bold', fontsize=12, color='black')\n",
        "    ax[0][i].set_yticklabels(['human', 'AI'], fontweight='bold', fontsize=12, color='black')\n",
        "    ax[1][i].set_xticklabels(['human', 'AI'], fontweight='bold', fontsize=12, color='black')\n",
        "    ax[1][i].set_yticklabels(['human', 'AI'], fontweight='bold', fontsize=12, color='black')\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 512
        },
        "id": "aBjGOJXMXjsp",
        "outputId": "01ec8f9e-f517-418f-e53c-2b2214f70327"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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3t++Dg4MtLCzM5s2bZ3l5eda0aVP3Pv/fTkBAgG3cuNF9jG8ar2bX9+OXX37pvrYej8caNGjgnmPUqFHuuSvq/4ULF7plQ0ND3ccLCgqydevWXffcmzdvboGBgRYeHu7WGzZs2C3s9Zpl6NCh7useFBRkSUlJ9uKLL1rr1q1t+fLldvToUbdftm/fXt3NlduAclu5rdy+MeW2clu5LTWNclu5rdy+MeW2clu5ffP0DfMqUFJSgs/no1WrVmzfvp2cnBz27NkDwLlz5zhw4ADz588nOzubhg0b8vnnn5Ofn8+cOXOAsk0niouL2bx5M3/7298A+Otf/0p+fj6ZmZk4jsMnn3zinvNmxcXFceLECU6fPs2gQYP4/PPPue+++0hLSyM/P59z587x6KOPAvDRRx+57Z41axYAU6dOpbCwkKysLJKTk8nNzWXt2rV06NCBjh07AvDWW28B8OWXX7pX/EaNGvWt2lsTXN1nu3btoqCggOHDh3Pu3Dny8/OZPn06RUVF7hW8gwcP8sorrwCwePFiCgoKOHDgAJGRkRw+fJgtW7ZUapzUdUVFRezdu5fCwkKee+45ANauXVtubAcEBHDw4EHOnDnDuHHjWLBgASdOnKB79+6cOXOG/Px8/vCHP+Dz+dxvcFRmvFYkMzOTgoICUlJSOHfuHLm5uWzYsIEePXoQFxeHmVVYLycnh2nTpgHws5/9jIKCAvLz8xk0aBClpaWMHz++3BVz/3M/dOgQ+fn5fO973wNg06ZN/0Vv1i5r1qxhypQpALRo0YKsrCwmT57MgQMH3G82iFQV5bZyW7ldOcpt5TYot6X6KbeV28rtylFuK7dBuV1p1Txhf9v597//bX/5y19s9OjR7tWbbdu22X333WeATZgwwS1bVFRkWVlZ7u1p06YZYPfcc0+5cx49etSuXLni3vaft7JXvMeOHVthWwsLC23Lli02a9Ysa9WqlQHWo0cPMzPbtGmT+zhnzpxx62RlZblXb83MlixZYoDdcccdZma2aNEiA6xDhw430201RkV91qhRIwMsJibGkpKSLCkpyb0yGhkZaT6fz1566SX3SrfP53PrHj9+3EpKSq57nBuNE7O6fcX7ySefdI8VFxdbWFiYAbZkyRL3inevXr3K1X3ggQfcvvb3v/81ASw3N7fS4/Xafjxw4IAFBQUZYO3bt7fp06dbRkaGXbhwoVwbrq23dOlS9zW6dOmSW+7w4cNuO7Zs2VLuuU+cONEt96c//cn99svtZObMmQZY69atr7tPV7yluii3ldvK7espt5XbZsptqZmU28pt5fb1lNvKbTPl9s3SN8yryIoVK0hJSaF169ZMmDCh3DpaPp+Ps2fPAtCgQQP3uH/dLb+KykDZmkwBAV//Un7d2mWJiYnlbufm5jJkyBBiY2Pp3bs3b7zxBsHBwW5br25LQEAAMTExbt2kpCRCQkLc28OHD8fj8XDkyBF27NjBmjVrgNp9tRvK99mZM2cAyM/PJzs7m+zsbPLy8gA4f/48BQUFbn/FxcXhOI5bNzk5maCgr/be/aZxUtc1atTI/T04OJiGDRsCZVes/a4dr/7+P3/+vNv/p06dcu/Pysqq9Hi9Vps2bVi/fj0dO3Zkz549zJ07l4EDB5KQkMDzzz9/w3r+tdAaNWpEaGioe9y/ftrVZfyu/rsOCwsDuOEVdRG59ZTbym1Qbn8T5XYZ5bZI9VNuK7dBuf1NlNtllNtSGZowrwL79+9n5MiRfPHFF2RkZJCfn8+GDRvKlYmLiwO+2rUWwOv18sorr5CZmYnX662wDMCbb77J5s2b3Tcp/xuE1+t1yxQUFNywfVe/wUDZ5iFr166lZ8+e5OTksH//fgYNGlRhe30+X7k3y61bt7Ju3TqOHTsGQHR0ND/84Q8BWLBgAZmZmQQFBZGWlnbD9tQGV/eZP1CWLFmCmWFmXLp0idLSUszM3VADyt60r/4wlZGRwcaNGzl58mSlxkld5x83UPahMzc3F/hqvMH149Xf/1OnTnX73+v1UlxcjJnRoUOHSo/XigwYMIAPP/yQEydOsGLFCkaNGsXFixf51a9+xT//+c8K6/g/iJw6dYrLly+7x48ePeqG8rUfRK7+IHf1hzwRqXrKbeW2crtylNtllNsi1Uu5rdxWbleOcruMclsqQxPmVWDfvn3uH23Tpk1xHKfcbtw+n4+ePXsCZesKHTx4EICXX36Z8ePH07t3b7xer1vm0KFD7hplO3bsYOTIkaSmprJz504A94qef+2ybwqCa98s9u7dC0BUVBSxsbFkZWW5a075r7o+8MAD7hvpb3/7W3w+H3l5eYwdO5bBgwfz6quvuud74oknAFi1ahU+n49+/fq5VzJrq6v7rFu3bkDZ65WTk8OVK1d4/PHHiYiIYOTIkQD06NEDKNuV+8UXXwTgyJEjjBw5koEDB5KRkVGpcVLXrV692h3HCxcu5OLFiwB07drVLXPtePX3/4oVKzh8+DAAM2bMIDw8nIcffhgzu6nxerUFCxYQERFBu3btCAgIYPjw4Tz77LPuN0BycnIqrJeamkpgYCBer5epU6dSUlLCxYsX3TXDEhMT+e53v/ut+khEbj3ltnJbuV05ym0RqQmU28pt5XblKLdFbsItX/RF7MiRI+4u2IGBge6uzv6ft99+u9yu3UC5Xbt//etfm9n1u3bXr1/f3bW7e/fu7rpqjz76qFumRYsWVr9+fXdX44rWVJs9e3a59v7kJz8p9xj+naMBa9eunVvu6l2Qw8PD3efYqFEjy87Odsv5fD5r3rx5uedbW1XUZwcPHrSIiIjrXt/AwEB777333HJX79odGRnp9mvbtm3t/PnzlRonZnV7TTV/P169a/ePfvQjM/tq1+7x48eXq5uXl+euoXbtjvevvvqqW64y4/Xafjx+/Lg1bNjQfU3i4+MtICDA/dsqLCyssN61j3f1rt3BwcHldhOvaEytWrXKrXs70ZpqUlMot5XbZsrtr6PcVm6bKbel5lBuK7fNlNtfR7mt3DZTbt8sfcO8CjRv3pzVq1dz9913ExwcTFxcHOnp6e7OvFu2bCE+Pp7t27eTlpZGXFwcXq+Xdu3asXjxYp599lmg7Erf+vXrmT59OsnJyVy+fJmUlBSmTJnC+vXr3XXVlixZwpAhQwgPD+fChQuMGzeO+fPnV7q98+bNY+TIkcTGxuI4Dg8++CCvvfYaUPbvbv51vtLT03nttde45557KCkpISYmhkceeYRt27bRuHFj93yO49CnTx+g7Gr8wIED//tOrUFat27Nhx9+SP/+/YmIiODKlSt07tyZjRs3kpqa6pZ7+eWXmTt3Li1btsTr9ZKYmMiYMWPYsmULERERlRondd0vfvELfv7zn+M4DrGxsUycOJFly5Z9bZ24uDgyMzN57LHHiI2NpaioiPbt27Ny5UrGjh3rlqvseL1acnIymZmZjBgxgsTERAoLC0lOTuaJJ55g69atREZG3rBd6enpvPPOO3Tt2pXAwEBCQkLo06cP27Zto3///t+ug0SkSii3ldug3K4M5baI1ATKbeU2KLcrQ7ktUnmOmVa5l1vr5MmTtGvXjrNnz/LTn/6Ul156qbqbJDVMs2bNOH78OLNnz2bGjBnV3RwRkduaclu+iXJbRKTmUG7LN1Fui9w8fcNcbpn//Oc/JCUl0bx5c86ePUu9evWYNGlSdTdLREREKqDcFhERqT2U2yIit44mzOWWady4MSUlJQDce++9rF+/nlatWlVzq0RERKQiym0REZHaQ7ktInLraEkWERERERERERERERH0DXMREREREREREREREUAT5iIiIiIiIiIiIiIigCbMRUREREREREREREQATZiLiIiIiIiIiIiIiACaMBcRERERERERERERATRhLnJbyMjIoFevXsTGxhIcHExiYiJDhw5l9+7d1d00ERERuYZyW0REpPZQbovUPY6ZWXU3QkRuneXLlzNy5EgAgoODiYqK4uzZs5gZHo+Hv//973Tq1KmaWykiIiKg3BYREalNlNsidZO+YS5Sxz377LMAjB49moKCAvLy8jh8+DDJyckUFRUxe/bsam6hiIiI+Cm3RUREag/ltkjdpAlzkTouOzsbgPr16xMaGgpA8+bNWbJkCU8//TRdunRxy7777rt07tyZ0NBQ4uLiSE1NZceOHeXOd/z4cX784x+TmJiIx+Phrrvu4ne/+x1Xrlxxy/zmN7/BcRwGDx7Mk08+SVhYGG3btsXr9VJUVMS0adNo0qQJISEhtG3blkWLFpV7jJMnTzJ27FhSUlLweDw0btyYtLQ0jh8/fqu6SUREpEZQbouIiNQeym2ROspEpE7r16+fAQZYixYtbOLEiZaRkWEXLlwoV27lypVuudDQUPN4PAZYRESE7du3z8zMDh8+bLGxsQaY4zgWGRnp1hk6dKj5fD4zM5s5c6YBVq9ePXMcx+rXr28DBgwwM7P+/fsbYAEBARYXF2eO4xhgM2bMcNvyne98xwALDAy0+Ph4CwwMNMDuvPNOKy4urqKeExERqXrKbRERkdpDuS1SN2nCXKSOO3bsmLVs2dINWv9PRESETZ482S5evGhXrlyxpKQkA2zYsGFWXFxs58+ft06dOllAQIClp6ebmdkPfvADA6xly5Z29OhRMzN7/fXX3XO+8847ZvZVgAP2xhtvmJnZ6dOn7f333zfA4uPj3fq7d++24OBgCw4OttOnT1tubq5b9+DBg2ZmdvToUevatauNGTPGvvjii6rtQBERkSqk3BYREak9lNsidZMmzEVuA16v115//XX7/ve/b9HR0eWC/JFHHrH9+/e7t3fu3OnWO3XqlHtl3Ov1WlBQkAG2dOnScud/6KGHDLARI0aY2VcB7vF4rLS01C03Y8YM90p4UlKS++O/or1mzRorLS21lJQUA6xJkyY2fvx4W7lypZ06daoKekpERKT6KbdFRERqD+W2SN2jNcxF6rji4mJyc3N5/PHHWb9+PWfOnOHjjz+mb9++ALz99tucPXvWLd+gQQP394SEBMLDwwE4e/YspaWlQNmabFfz3z59+nS54/Hx8QQGBrq3z5w547YpOzvb/fGvx5aVlUVgYCDvvfce/fv359SpU/zxj39k+PDhNG7cmGHDhlFUVPQ/6RcREZGaSLktIiJSeyi3ReomTZiL1GFbt24lJCSEJk2acOjQIQACAgK4//77mTt3LgBmRmRkpFvHv2kJwM6dO1m9ejUHDx6kQYMGBAUFAXD06NFyj3PkyBEAEhMTyx33b3ri57+/U6dOWNl/uGBmnD9/HjNj0qRJALRp04ZVq1aRl5fHu+++y/Tp0wkLC+Ott966bsMSERGRukK5LSIiUnsot0XqLk2Yi9RhnTt3JioqCoBx48Zx8uRJAAoLC3nhhRcAiIyMpEWLFiQlJQEwd+5cvF4vFy9e5KmnnuKxxx5j1qxZBAUF0adPHwBmz57NsWPHAFi+fDn/+Mc/ABgyZEi5x3ccp9ztbt26AWUfDDIyMgDYsGEDUVFR3HHHHWRnZ7N9+3bi4+OJiYnhX//6FwMHDuSZZ56hZcuWAOTk5PxP+0hERKSmUG6LiIjUHsptkTqsGpaBEZEqtGrVKne9NMdxLC4uzl3DDLDf//73ZlZ+126Px2OhoaEGWHh4uO3Zs8fMzA4cOGBRUVEV7tqdlpbmPqZ/TbXWrVtf157evXu7dWJiYtxdu/3rsZWWllqXLl3cMg0aNLB69eq57dq1a1cV9JqIiEj1UG6LiIjUHsptkbpJ3zAXqeOGDRvGpk2b6NOnDzExMRQUFBAdHc3DDz/MunXreOqppwBIS0tjw4YN3H///QCEhYXRt29fPvjgA+69916g7F+3PvvsM0aNGkVCQgJer5c2bdowf/58li9fXqn2rF27lqeffprk5GQuXrxIs2bNeOaZZ1i6dCkAgYGBbNq0iRkzZnDnnXdy4cIFYmNj6devHx988AEdO3a8Bb0kIiJSMyi3RUREag/ltkjd5JiZVXcjRERERERERERERESqm75hLiIiIiIiIiIiIiKCJsxFRERERERERERERABNmIuIiIiIiIiIiIiIAJowFxEREREREREREREBNGEuIiIiIiIiIiIiIgJowlxEREREREREREREBNCEuYiIiIiIiIiIiIgIoAlzERERERERERERERFAE+YiIiIiIiIiIiIiIoAmzEVEREREREREREREAE2Yi4iIiIiIiIiIiIgAmjAXEREREREREREREQHg/wDH+5kzHkpfgQAAAABJRU5ErkJggg==",
            "text/plain": [
              "<Figure size 1500x500 with 3 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Plotting the bar plots side by side\n",
        "fig, ax = plt.subplots(1, 3, figsize=(15, 5))\n",
        "fig.suptitle('Scores of the models', fontsize=18, fontweight='bold', color='black')\n",
        "\n",
        "\n",
        "for i, model_name in enumerate(model_names):\n",
        "    scores_before = list(model_scores_before_finetuning[model_name].values())\n",
        "    scores_after = list(model_scores[model_name].values())\n",
        "    labels = list(model_scores_before_finetuning[model_name].keys())\n",
        "\n",
        "    # Create an array with the position of each bar along the x-axis\n",
        "    x = np.arange(len(labels))\n",
        "\n",
        "    # Plot each bar plot\n",
        "    ax[i].bar(x - 0.2, scores_before, width=0.4, label='Before Fine-tuning')\n",
        "    ax[i].bar(x + 0.2, scores_after, width=0.4, label='After Fine-tuning')\n",
        "\n",
        "    ax[i].set_title(model_name, fontweight='bold', fontsize=14, color='black')\n",
        "    ax[i].set_xticks(x)\n",
        "    ax[i].set_xticklabels(labels, fontweight='bold', fontsize=12, color='black')\n",
        "    ax[i].set_ylim(0, 1)\n",
        "    ax[i].set_xlabel('Scores', fontweight='bold', fontsize=12, color='black')\n",
        "    ax[i].set_ylabel('Score value', fontweight='bold', fontsize=12, color='black')\n",
        "    ax[i].tick_params(axis='x', colors='black')\n",
        "    ax[i].tick_params(axis='y', colors='black')\n",
        "    ax[i].legend()\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1500x500 with 4 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# filter warnings\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "\n",
        "test_df_pandas['ensemble_pred'] = voted_predictions\n",
        "test_df_pandas['roberta-base-openai-detector_pred'] = all_predictions[0]\n",
        "test_df_pandas['chatgpt-detector-roberta_pred'] = all_predictions[1]\n",
        "test_df_pandas['chatgpt-detector-lli-hc3_pred'] = all_predictions[2]\n",
        "\n",
        "\n",
        "# Define the sources and models\n",
        "sources = ['LLM', 'PDF', 'YouTube', 'gpt-3.5-turbo']\n",
        "models_name_temp = {\n",
        "    'Ensemble': 'ensemble_pred',\n",
        "    'Roberta-base-openai-detector': 'roberta-base-openai-detector_pred',\n",
        "    'Chatgpt-detector-roberta': 'chatgpt-detector-roberta_pred',\n",
        "    'Chatgpt-detector-lli-hc3': 'chatgpt-detector-lli-hc3_pred'\n",
        "}\n",
        "\n",
        "# Initialize a dictionary to store accuracy scores\n",
        "accuracy_scores = {model: [] for model in models_name_temp}\n",
        "\n",
        "# Calculate accuracy scores using nested loops\n",
        "for model, pred_column in models_name_temp.items():\n",
        "    for source in sources:\n",
        "        accuracy = accuracy_score(test_df_pandas[test_df_pandas['source'] == source]['labels'], \n",
        "                                  test_df_pandas[test_df_pandas['source'] == source][pred_column])\n",
        "        accuracy_scores[model].append(accuracy)\n",
        "\n",
        "# Bar plot of the accuracy scores\n",
        "fig, ax = plt.subplots(1, 4, figsize=(15, 5))\n",
        "fig.suptitle('Accuracy scores of the models', fontsize=18, fontweight='bold', color='black')\n",
        "\n",
        "\n",
        "# Create an array with the position of each bar along the x-axis\n",
        "x = np.arange(len(sources))\n",
        "\n",
        "# Plot each bar plot showing the accuracy scores\n",
        "for i, (model, scores) in enumerate(accuracy_scores.items()):\n",
        "    bars = ax[i].bar(x, scores, width=0.4, label=model)\n",
        "    \n",
        "    # Add numbers on top of the bars\n",
        "    for bar in bars:\n",
        "        yval = bar.get_height()\n",
        "        ax[i].text(bar.get_x() + bar.get_width()/2, yval/2 + 0.01, round(yval, 2), \n",
        "                   ha='center', va='bottom', color='#CCCCCC', fontweight='bold', fontsize=8)\n",
        "    \n",
        "    ax[i].set_title(model, fontweight='bold', fontsize=14, color='black')\n",
        "    ax[i].set_xticks(x)\n",
        "    ax[i].set_xticklabels(sources, fontweight='bold', fontsize=12, color='black', rotation=90)\n",
        "    ax[i].set_ylim(0, 1)\n",
        "    ax[i].set_yticks(np.arange(0, 1.1, 0.25))\n",
        "    ax[i].set_ylabel('Accuracy', fontweight='bold', fontsize=12, color='black')\n",
        "    ax[i].tick_params(axis='x', colors='black')\n",
        "    ax[i].tick_params(axis='y', colors='black')\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
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