{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4f28bbe0-aad2-40b7-b15a-ccdb60887538",
   "metadata": {},
   "source": [
    "# Анализ датасета с изображениями лиц\n",
    "## Датасет: FINAL_DATASET.csv\n",
    "### Описание полей: label (REAL/FAKE), gender, age_group, confidence_score, dataset_split и др."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3930940b-ed0d-475b-a47a-5625ef58fec3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from tqdm import tqdm\n",
    "\n",
    "# Для отображения всех колонок\n",
    "pd.set_option('display.max_columns', None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9b5720e3-5641-4e0c-8b64-71df74aba091",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Загружено строк: 6557\n",
      "Колонок: 17\n"
     ]
    },
    {
     "data": {
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       "    .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>image_id</th>\n",
       "      <th>image_url</th>\n",
       "      <th>label</th>\n",
       "      <th>label_numeric</th>\n",
       "      <th>category</th>\n",
       "      <th>gender</th>\n",
       "      <th>age_group</th>\n",
       "      <th>source</th>\n",
       "      <th>fake_method</th>\n",
       "      <th>image_quality</th>\n",
       "      <th>resolution</th>\n",
       "      <th>confidence_score</th>\n",
       "      <th>detection_difficulty</th>\n",
       "      <th>dataset_split</th>\n",
       "      <th>date_collected</th>\n",
       "      <th>version</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>https://images.unsplash.com/photo-172422561835...</td>\n",
       "      <td>REAL</td>\n",
       "      <td>1</td>\n",
       "      <td>Authentic</td>\n",
       "      <td>Female</td>\n",
       "      <td>18-25</td>\n",
       "      <td>Unsplash</td>\n",
       "      <td>NaN</td>\n",
       "      <td>High</td>\n",
       "      <td>1080x1080</td>\n",
       "      <td>0.96</td>\n",
       "      <td>Easy</td>\n",
       "      <td>train</td>\n",
       "      <td>2026-04-24</td>\n",
       "      <td>v2.0</td>\n",
       "      <td>2026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>https://images.unsplash.com/photo-172422561873...</td>\n",
       "      <td>REAL</td>\n",
       "      <td>1</td>\n",
       "      <td>Authentic</td>\n",
       "      <td>Male</td>\n",
       "      <td>50+</td>\n",
       "      <td>Unsplash</td>\n",
       "      <td>NaN</td>\n",
       "      <td>High</td>\n",
       "      <td>1080x1080</td>\n",
       "      <td>0.97</td>\n",
       "      <td>Easy</td>\n",
       "      <td>train</td>\n",
       "      <td>2026-04-04</td>\n",
       "      <td>v2.0</td>\n",
       "      <td>2026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>https://images.unsplash.com/photo-172422561834...</td>\n",
       "      <td>REAL</td>\n",
       "      <td>1</td>\n",
       "      <td>Authentic</td>\n",
       "      <td>Female</td>\n",
       "      <td>36-50</td>\n",
       "      <td>Unsplash</td>\n",
       "      <td>NaN</td>\n",
       "      <td>High</td>\n",
       "      <td>1080x1080</td>\n",
       "      <td>0.95</td>\n",
       "      <td>Easy</td>\n",
       "      <td>test</td>\n",
       "      <td>2026-02-07</td>\n",
       "      <td>v2.0</td>\n",
       "      <td>2026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>https://images.unsplash.com/photo-172422561855...</td>\n",
       "      <td>REAL</td>\n",
       "      <td>1</td>\n",
       "      <td>Authentic</td>\n",
       "      <td>Female</td>\n",
       "      <td>26-35</td>\n",
       "      <td>Unsplash</td>\n",
       "      <td>NaN</td>\n",
       "      <td>High</td>\n",
       "      <td>1080x1080</td>\n",
       "      <td>0.89</td>\n",
       "      <td>Easy</td>\n",
       "      <td>train</td>\n",
       "      <td>2026-02-20</td>\n",
       "      <td>v2.0</td>\n",
       "      <td>2026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>https://images.unsplash.com/photo-172422561823...</td>\n",
       "      <td>REAL</td>\n",
       "      <td>1</td>\n",
       "      <td>Authentic</td>\n",
       "      <td>Male</td>\n",
       "      <td>36-50</td>\n",
       "      <td>Unsplash</td>\n",
       "      <td>NaN</td>\n",
       "      <td>High</td>\n",
       "      <td>1080x1080</td>\n",
       "      <td>0.86</td>\n",
       "      <td>Easy</td>\n",
       "      <td>train</td>\n",
       "      <td>2026-04-12</td>\n",
       "      <td>v2.0</td>\n",
       "      <td>2026</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   image_id                                          image_url label  \\\n",
       "0         1  https://images.unsplash.com/photo-172422561835...  REAL   \n",
       "1         2  https://images.unsplash.com/photo-172422561873...  REAL   \n",
       "2         3  https://images.unsplash.com/photo-172422561834...  REAL   \n",
       "3         4  https://images.unsplash.com/photo-172422561855...  REAL   \n",
       "4         5  https://images.unsplash.com/photo-172422561823...  REAL   \n",
       "\n",
       "   label_numeric   category  gender age_group    source fake_method  \\\n",
       "0              1  Authentic  Female     18-25  Unsplash         NaN   \n",
       "1              1  Authentic    Male       50+  Unsplash         NaN   \n",
       "2              1  Authentic  Female     36-50  Unsplash         NaN   \n",
       "3              1  Authentic  Female     26-35  Unsplash         NaN   \n",
       "4              1  Authentic    Male     36-50  Unsplash         NaN   \n",
       "\n",
       "  image_quality resolution  confidence_score detection_difficulty  \\\n",
       "0          High  1080x1080              0.96                 Easy   \n",
       "1          High  1080x1080              0.97                 Easy   \n",
       "2          High  1080x1080              0.95                 Easy   \n",
       "3          High  1080x1080              0.89                 Easy   \n",
       "4          High  1080x1080              0.86                 Easy   \n",
       "\n",
       "  dataset_split date_collected version  year  \n",
       "0         train     2026-04-24    v2.0  2026  \n",
       "1         train     2026-04-04    v2.0  2026  \n",
       "2          test     2026-02-07    v2.0  2026  \n",
       "3         train     2026-02-20    v2.0  2026  \n",
       "4         train     2026-04-12    v2.0  2026  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('FINAL_DATASET.csv')\n",
    "print(f\"Загружено строк: {len(df)}\")\n",
    "print(f\"Колонок: {len(df.columns)}\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "69dfc352-8a84-4d79-8d89-044c5b15e67d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.DataFrame'>\n",
      "RangeIndex: 6557 entries, 0 to 6556\n",
      "Data columns (total 17 columns):\n",
      " #   Column                Non-Null Count  Dtype  \n",
      "---  ------                --------------  -----  \n",
      " 0   image_id              6557 non-null   int64  \n",
      " 1   image_url             6557 non-null   str    \n",
      " 2   label                 6557 non-null   str    \n",
      " 3   label_numeric         6557 non-null   int64  \n",
      " 4   category              6557 non-null   str    \n",
      " 5   gender                6557 non-null   str    \n",
      " 6   age_group             6557 non-null   str    \n",
      " 7   source                6557 non-null   str    \n",
      " 8   fake_method           3767 non-null   str    \n",
      " 9   image_quality         6557 non-null   str    \n",
      " 10  resolution            6557 non-null   str    \n",
      " 11  confidence_score      6557 non-null   float64\n",
      " 12  detection_difficulty  6557 non-null   str    \n",
      " 13  dataset_split         6557 non-null   str    \n",
      " 14  date_collected        6557 non-null   str    \n",
      " 15  version               6557 non-null   str    \n",
      " 16  year                  6557 non-null   int64  \n",
      "dtypes: float64(1), int64(3), str(13)\n",
      "memory usage: 871.0 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d7d5bf69-4f63-4fc0-891c-9576b6175daf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>image_id</th>\n",
       "      <th>label_numeric</th>\n",
       "      <th>confidence_score</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6557.00000</td>\n",
       "      <td>6557.000000</td>\n",
       "      <td>6557.000000</td>\n",
       "      <td>6557.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3279.00000</td>\n",
       "      <td>0.425499</td>\n",
       "      <td>0.903741</td>\n",
       "      <td>2026.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1892.98719</td>\n",
       "      <td>0.494456</td>\n",
       "      <td>0.049982</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>2026.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1640.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.860000</td>\n",
       "      <td>2026.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3279.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.910000</td>\n",
       "      <td>2026.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4918.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.950000</td>\n",
       "      <td>2026.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>6557.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.990000</td>\n",
       "      <td>2026.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         image_id  label_numeric  confidence_score    year\n",
       "count  6557.00000    6557.000000       6557.000000  6557.0\n",
       "mean   3279.00000       0.425499          0.903741  2026.0\n",
       "std    1892.98719       0.494456          0.049982     0.0\n",
       "min       1.00000       0.000000          0.800000  2026.0\n",
       "25%    1640.00000       0.000000          0.860000  2026.0\n",
       "50%    3279.00000       0.000000          0.910000  2026.0\n",
       "75%    4918.00000       1.000000          0.950000  2026.0\n",
       "max    6557.00000       1.000000          0.990000  2026.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f4b3086f-b245-4e44-a8b9-0c07ce8814b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "image_id                   0\n",
       "image_url                  0\n",
       "label                      0\n",
       "label_numeric              0\n",
       "category                   0\n",
       "gender                     0\n",
       "age_group                  0\n",
       "source                     0\n",
       "fake_method             2790\n",
       "image_quality              0\n",
       "resolution                 0\n",
       "confidence_score           0\n",
       "detection_difficulty       0\n",
       "dataset_split              0\n",
       "date_collected             0\n",
       "version                    0\n",
       "year                       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "285ecf3a-f8c7-4307-be9a-f771da48ec8e",
   "metadata": {},
   "source": [
    "## 1. Анализ распределения label (REAL / FAKE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7249e0da-262a-44cf-9d82-8643c2a01ee4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "label\n",
      "FAKE    3767\n",
      "REAL    2790\n",
      "Name: count, dtype: int64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ardor\\AppData\\Local\\Temp\\ipykernel_19668\\2116749495.py:4: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  sns.countplot(data=df, x='label', palette='Set2')\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "label_counts = df['label'].value_counts()\n",
    "print(label_counts)\n",
    "plt.figure(figsize=(6,4))\n",
    "sns.countplot(data=df, x='label', palette='Set2')\n",
    "plt.title('Распределение REAL / FAKE изображений')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8017d03-fe3b-484b-bdb9-39a4d89561b6",
   "metadata": {},
   "source": [
    "## 2. Распределение по age_group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3fd1dc6a-e9bd-4a0f-8329-45cc6fa40cb0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ardor\\AppData\\Local\\Temp\\ipykernel_19668\\1922293534.py:2: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  sns.countplot(data=df, x='age_group', order=df['age_group'].value_counts().index, palette='viridis')\n"
     ]
    },
    {
     "data": {
      "image/png": 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mTZvcmTCvNxixiQAYMcs7NS4asKAfP53eVa+Ad2pao/pFOxzvtL5H07z5HvUki9cTdCoKZBQMnew0lgJJ/TBpVLPXS6AfkcjeDQ12OdWOW8t5PYqZ1QusEereSGgNGFJKhwIl7dzVG6veSm2rSHo/2uErSDgVnaJU7696673PJiV6Pm8bKIjQ+9QPtQLg9KyLBqXoNKR+iEJ7gbWchI5w93r/ImkgjbecTmOrJ1QHMlqfk/2dKIDSj5s36Emn6J944gn3Q6m2ol5HBao61e8NitL7O+ecc1wbSesPuEagRy6bWv3rHTt2uB4rpZ/odU4WACsw14FKSqdyM0N6v5ep0WA27zNSoKoBiXp/CoBDXyOS2oEGz4WeJlfb8ral2ql6GvV5hQbAak/6LEMPSNVOxFsPpbEoCNf3NDSVSgFw5HdZz6d9jvYhmdVrGtkeUksFCG07+t6rl18BrQb0nYx6N/Xd1HdSZyR0YJiW1Jv00ncrdACp0qG0vUK/u2rrSn/SAZr2WWq3Cvizowa8zvY1bNjwhGohOojwBhyntM0VAGub6fuuv9fgXcQmcoBxxlBPm3pbFGx6O3FVRVAe7rhx48JORWpkvXpvQ0dki06n6TnSEgArQPjggw/s2WefPWXOnp5TOzv94OgWWadYO0Q9n340I2mH6r0fnTJTMKlALFJkKaaM0kh5vZ5u+iFRWoneZ+ipR51mVG/Wddddl6ZTeOr1VIpHeitl6IfV+9zSsy4a7a3efH2ekacsFZREBgn64Qwtv/XVV1+50fgKDLzXTmkbh1bnCN1+yldUfqLXvnSaOPReQZWCCC2n5b3X0OerACqyBJuXInE61GbUW/bAAw+cdDkF4Eo9UWCRWYFZpPR+L9NK29J7Pn3ntP4K6kLzXLVtdTYisiJA5DbQQU9kmooozcKj9qDH6uFVOT7xylqFnhJXe40sH6bUIx2cqec1sscws77L6eEdLKYl/1g9mfo+q7c5qypRqDMjlMZZiPed9CjdQZ+VAnJ1IkR2LmQVfc6Rn5N6dU9Vxs/bdl7JQMQueoBxxlCvlnaSKl/23HPPuVO4+mFSz4Dy1jQ4RqVwvHJL6knQaU5vUI56PFXWSD/Kadkx6UdUaQuZcbpNp6TVY6jASDtG9coqP1GlqtTToB9Q9SooiNYOXyWB1EPinX7XaVfNSymv9lR02leDMbwUCKVqKDhTOoIoJULLKMDUoCWlR6jcj4IDDZpLC20r9cyklI/pUfCn2sfqGdOgIn0W6iENfU9pXReVDVNtV21LfbbqSVIAol5cHbBE5ujpdfWcupKgnkuBrZbxUlIUWOuMg15D20kD5vSelPuX0qlR9YyFljtKiT5D5X/qoMAroaV106AiDcDT9tJ89YKrJ1bpI14ucUZoffXZep9ratQWtMypToWfjrR+L09Fn6m+F14KhL4HoaXilCalgEm5+GoTXhk0jRMIreOtz1ZnQZQGoSBK3zmVYousa6sDV7VLpajoM1LArjaqAwYvd13B+6hRo9x3UwcRagsK5tTGFPh49Ng7K6BUBO23dHCm76Dy/E+nrGJaKD9ZwasOdJUCofJiOmhLS++p3psOyLKyDJu+W9oP6bXUOaD0Bm3PyNq/6lFVGTQFn+q4CM0Lz0raV+t7qzasA1vtq/X9iswRj6TvtQ4cUqpXjxgTpeoTwClLIC1btizF+W3atAnkz58/8P333wenvf3224HLL7/clagpWrRooGPHjmFlr4YNG+bKUk2ZMiXV14ssg5YjR47AihUrwpZVSZ3QsjqpiSyDJn/88Uegb9++gYsuusiVVSpevHjgmmuuCYwcOdKVUQot0aSSPpUrV3bLnXvuua5MVeS6pLbukeV5vJvKG2m9VEZo7969YcuuXLky0KxZs0DBggXdtm3YsGFg0aJFp3yfoWXGfv7555Nug4EDBwYqVarkllUJKpWwevHFF11puoysy+7duwP33Xef247aTipDNWHChBTXT9vzueeec6XZ1Ebq1asXLLfkUXtRyarChQu7kmS33XabK8EWWhJPpbj0988880yaSlg9/fTTbvnQ8ne7du0KdOvWza1Lrly5AqVKlXKlpsaPH3/C86WnDJq2Z2h5rZRK1Hmlvh5++OE0t6OMlEFL6/cyNd76eDd9vvreqHyaylOF+vTTTwPXXnttsF21bt06sGHDhrBlunbtGqhYsWJwPa6++urA66+/nuJ7Uwk+lV1U2ytZsqT77CPLYb322muuxJWeT99Tra9X3ivSxIkTg9ugSJEibv8xZ86cLC+DdrLvfWpl0FIrc3aq+Rkpg6bP6NZbb3UlDrVdVJIutGReKJW+099Efu+yugzao48+6soWqm2pjS1evPiE34BTvee0bBNERw79E+0gHACygnrW1TusnkKNagdSo7MJ6hn2LkyBrKGeeaXrqIc5tVzaSDpzoLMG+j6rFx/IDOQAAwCAmKQ+Og1EUyoNwS8yEznAAAAgpmiMhMZNKGde+bcaHAtkJgJgAAAQU5QioUFxhQsXdoMQNWAOyEzkAAMAAMBXyAEGAACArxAAAwAAwFfIAU4jXYxAlxrVxRi4ugsAAEBsVg75448/3AVndPXD1BAAp5GC37Jly2bW5wMAAIAssn37djv//PNTnU8AnEbq+fU2qC6bCgAAgNhy4MAB12HpxW2pIQBOIy/tQcEvATAAAEDsOlW6KoPgAAAA4CsEwAAAAPAVAmAAAAD4CgEwAAAAfIUAGAAAAL5CAAwAAABfIQAGAACArxAAAwAAwFcIgAEAAOArBMAAAADwFQJgAAAA+AoBMAAAAHyFABgAAAC+QgAMAAAAXyEABgAAgK8QAAMAAMBX4qO9An7RqvWT0V4FZKOPZgxiewMAEKPoAQYAAICv0AMMnGXq9BoS7VVANlo6qj/bGwDSiR5gAAAA+AoBMAAAAHyFABgAAAC+QgAMAAAAX2EQHAAgQ2qPYwCenyx/gAG2OHvQAwwAAABfIQAGAACArxAAAwAAwFcIgAEAAOArBMAAAADwlagGwAsXLrTWrVtbmTJlLEeOHDZ9+vQTltm4caPddNNNlpCQYAUKFLArr7zStm3bFpx/+PBh69atmxUrVswKFixo7dq1s127doU9h5Zv1aqV5c+f30qUKGG9e/e2o0ePZst7BAAAQGyJagB86NAhq1Gjho0ZMybF+d99951dd911VrlyZfvss89szZo11r9/f8ubN29wmUceecRmzJhh7777ri1YsMB27Nhht9xyS3D+sWPHXPCblJRkixYtstdff90mT55sAwYMyJb3CAAAgNgS1TrALVq0cLfUPPHEE9ayZUsbPnx4cNqFF14Y/P/+/fvttddes6lTp1qjRo3ctEmTJlmVKlVsyZIldvXVV9vs2bNtw4YN9umnn1rJkiWtZs2aNmTIEOvTp48NHDjQcufOncXvEgAAALEkZnOAjx8/bh999JFdcskl1qxZM5e6UKdOnbA0iRUrVlhycrI1adIkOE29xeXKlbPFixe7x7qvXr26C349er4DBw7Y+vXrU339I0eOuGVCbwAAADjzxWwAvHv3bjt48KA9++yz1rx5c9eTe/PNN7v0BqU6yM6dO10PbuHChcP+VsGu5nnLhAa/3nxvXmqGDh3q8o69W9myZbPgXQIAACC7xXQPsLRp08bl+Sp14fHHH7cbb7zRxo0bl+Wv37dvX5di4d22b9+e5a8JAAAAHwfAxYsXt/j4eKtatWrYdOX3elUgSpUq5Qa37du3L2wZVYHQPG+ZyKoQ3mNvmZTkyZPHChUqFHYDAADAmS9mA2ClNqjk2aZNm8Kmb9682cqXL+/+X6tWLcuVK5fNnTs3OF/LK0CuW7eue6z7tWvXupQKz5w5c1xAGxlcAwAA4OwX1SoQyvH99ttvg4+3bt1qq1evtqJFi7qBbKrXe8cdd1j9+vWtYcOGNmvWLFfyTCXRRLm5nTp1sl69erm/UVD70EMPuaBXFSCkadOmLtC96667XDUJ5f3269fP1Q5WLy8AAAD8JaoB8PLly11g61EgK4mJia5Wrwa9Kd9XA9J69OhhlSpVsvfff9/VBvY8//zzFhcX5y6AocoNqvDwyiuvBOfnzJnTZs6caV27dnWBsS6moecfPHhwNr9bAAAAmN8D4AYNGlggEDjpMvfdd5+7pUYXxdCFNFK7mIYoZeLjjz8+rXUFAADA2SFmc4ABAACArEAADAAAAF8hAAYAAICvRDUHGAAA4FQGLUx9LBDOPk/Wn5jlr0EPMAAAAHyFABgAAAC+QgAMAAAAXyEABgAAgK8QAAMAAMBXCIABAADgKwTAAAAA8BUCYAAAAPgKATAAAAB8hQAYAAAAvkIADAAAAF8hAAYAAICvEAADAADAVwiAAQAA4CsEwAAAAPAVAmAAAAD4CgEwAAAAfIUAGAAAAL5CAAwAAABfIQAGAACArxAAAwAAwFcIgAEAAOArBMAAAADwFQJgAAAA+AoBMAAAAHyFABgAAAC+QgAMAAAAXyEABgAAgK8QAAMAAMBXohoAL1y40Fq3bm1lypSxHDly2PTp01Nd9oEHHnDLvPDCC2HT9+zZYx07drRChQpZ4cKFrVOnTnbw4MGwZdasWWP16tWzvHnzWtmyZW348OFZ9p4AAAAQ26IaAB86dMhq1KhhY8aMOely06ZNsyVLlrhAOZKC3/Xr19ucOXNs5syZLqju0qVLcP6BAwesadOmVr58eVuxYoWNGDHCBg4caOPHj8+S9wQAAIDYFh/NF2/RooW7nczPP/9sDz30kH3yySfWqlWrsHkbN260WbNm2bJly6x27dpu2ksvvWQtW7a0kSNHuoB5ypQplpSUZBMnTrTcuXNbtWrVbPXq1TZq1KiwQBkAAAD+ENM5wMePH7e77rrLevfu7QLXSIsXL3ZpD17wK02aNLG4uDhbunRpcJn69eu74NfTrFkz27Rpk+3duzfV1z5y5IjrPQ69AQAA4MwX0wHwsGHDLD4+3nr06JHi/J07d1qJEiXCpmn5okWLunneMiVLlgxbxnvsLZOSoUOHWkJCQvCm3GEAAACc+WI2AFa+7osvvmiTJ092g9+yW9++fW3//v3B2/bt27N9HQAAAOCjAPjzzz+33bt3W7ly5Vyvrm4//vijPfroo1ahQgW3TKlSpdwyoY4ePeoqQ2iet8yuXbvClvEee8ukJE+ePK6yROgNAAAAZ76YDYCV+6vyZRqw5t00qE35wBoQJ3Xr1rV9+/a53mLPvHnzXO5wnTp1gsuoMkRycnJwGVWMqFSpkhUpUiQK7wwAAAC+rQKher3ffvtt8PHWrVtdoKscXvX8FitWLGz5XLlyuV5bBa9SpUoVa968uXXu3NnGjRvngtzu3btb+/btgyXTOnToYIMGDXL1gfv06WPr1q1zqRXPP/98Nr9bAAAAmN8D4OXLl1vDhg2Dj3v16uXuExMTXe5vWqjMmYLexo0bu+oP7dq1s9GjRwfnawDb7NmzrVu3blarVi0rXry4DRgwgBJoAAAAPhXVALhBgwYWCATSvPwPP/xwwjT1Fk+dOvWkf3fZZZe5nGIAAAAgZnOAAQAAgKxAAAwAAABfIQAGAACArxAAAwAAwFcIgAEAAOArBMAAAADwFQJgAAAA+AoBMAAAAHyFABgAAAC+QgAMAAAAXyEABgAAgK8QAAMAAMBXCIABAADgKwTAAAAA8BUCYAAAAPgKATAAAAB8hQAYAAAAvkIADAAAAF8hAAYAAICvEAADAADAVwiAAQAA4CsEwAAAAPAVAmAAAAD4CgEwAAAAfIUAGAAAAL5CAAwAAABfIQAGAACArxAAAwAAwFcIgAEAAOArBMAAAADwFQJgAAAA+AoBMAAAAHwlqgHwwoULrXXr1lamTBnLkSOHTZ8+PTgvOTnZ+vTpY9WrV7cCBQq4Ze6++27bsWNH2HPs2bPHOnbsaIUKFbLChQtbp06d7ODBg2HLrFmzxurVq2d58+a1smXL2vDhw7PtPQIAACC2RDUAPnTokNWoUcPGjBlzwrw///zTVq5caf3793f3//3vf23Tpk120003hS2n4Hf9+vU2Z84cmzlzpguqu3TpEpx/4MABa9q0qZUvX95WrFhhI0aMsIEDB9r48eOz5T0CAAAgtsRH88VbtGjhbilJSEhwQW2ol19+2a666irbtm2blStXzjZu3GizZs2yZcuWWe3atd0yL730krVs2dJGjhzpeo2nTJliSUlJNnHiRMudO7dVq1bNVq9ebaNGjQoLlAEAAOAPZ1QO8P79+12qhFIdZPHixe7/XvArTZo0sbi4OFu6dGlwmfr167vg19OsWTPXm7x3794ovAsAAAD4tgc4PQ4fPuxygu+8806X7ys7d+60EiVKhC0XHx9vRYsWdfO8ZSpWrBi2TMmSJYPzihQpkuLrHTlyxN1CUykAAABw5jsjeoA1IO7222+3QCBgY8eOzZbXHDp0qEvD8G4aPAcAAIAzX9yZEvz++OOPLifY6/2VUqVK2e7du8OWP3r0qKsMoXneMrt27QpbxnvsLZOSvn37upQL77Z9+/ZMfmcAAACIhrgzIfjdsmWLffrpp1asWLGw+XXr1rV9+/a56g6eefPm2fHjx61OnTrBZVQZQs/lUSBdqVKlVNMfJE+ePC7YDr0BAADgzBfVAFj1elWRQTfZunWr+7+qPChgvfXWW2358uWuksOxY8dczq5uquogVapUsebNm1vnzp3tq6++si+//NK6d+9u7du3dxUgpEOHDm4AnOoDq1za22+/bS+++KL16tUrmm8dAAAAfhwEp+C2YcOGwcdeUJqYmOhq9X744Yfucc2aNcP+bv78+dagQQP3fwXHCnobN27sqj+0a9fORo8eHVxW+buzZ8+2bt26Wa1atax48eI2YMAASqABAAD4VFQDYAWxGtiWmpPN86jiw9SpU0+6zGWXXWaff/55htYRAAAAZ5eYzgEGAAAAMhsBMAAAAHyFABgAAAC+QgAMAAAAXyEABgAAgK8QAAMAAMBXCIABAADgKwTAAAAA8BUCYAAAAPgKATAAAAB8hQAYAAAAvkIADAAAAF8hAAYAAICvEAADAADAVwiAAQAA4CsEwAAAAPAVAmAAAAD4CgEwAAAAfIUAGAAAAL5CAAwAAABfIQAGAACArxAAAwAAwFcIgAEAAOArBMAAAADwFQJgAAAA+AoBMAAAAHyFABgAAAC+QgAMAAAAXyEABgAAgK8QAAMAAMBXCIABAADgKwTAAAAA8BUCYAAAAPhKVAPghQsXWuvWra1MmTKWI0cOmz59etj8QCBgAwYMsNKlS1u+fPmsSZMmtmXLlrBl9uzZYx07drRChQpZ4cKFrVOnTnbw4MGwZdasWWP16tWzvHnzWtmyZW348OHZ8v4AAAAQe6IaAB86dMhq1KhhY8aMSXG+AtXRo0fbuHHjbOnSpVagQAFr1qyZHT58OLiMgt/169fbnDlzbObMmS6o7tKlS3D+gQMHrGnTpla+fHlbsWKFjRgxwgYOHGjjx4/PlvcIAACA2BIfzRdv0aKFu6VEvb8vvPCC9evXz9q0aeOmvfHGG1ayZEnXU9y+fXvbuHGjzZo1y5YtW2a1a9d2y7z00kvWsmVLGzlypOtZnjJliiUlJdnEiRMtd+7cVq1aNVu9erWNGjUqLFAGAACAP8RsDvDWrVtt586dLu3Bk5CQYHXq1LHFixe7x7pX2oMX/IqWj4uLcz3G3jL169d3wa9HvcibNm2yvXv3pvr6R44ccb3HoTcAAACc+WI2AFbwK+rxDaXH3jzdlyhRImx+fHy8FS1aNGyZlJ4j9DVSMnToUBdwezflDgMAAODMF7MBcLT17dvX9u/fH7xt37492qsEAACAszkALlWqlLvftWtX2HQ99ubpfvfu3WHzjx496ipDhC6T0nOEvkZK8uTJ4ypLhN4AAABw5ovZALhixYouQJ07d25wmvJwldtbt25d91j3+/btc9UdPPPmzbPjx4+7XGFvGVWGSE5ODi6jihGVKlWyIkWKZOt7AgAAgM8DYNXrVUUG3byBb/r/tm3bXF3gnj172lNPPWUffvihrV271u6++25X2aFt27Zu+SpVqljz5s2tc+fO9tVXX9mXX35p3bt3dxUitJx06NDBDYBTfWCVS3v77bftxRdftF69ekXzrQMAAMCPZdCWL19uDRs2DD72gtLExESbPHmyPfbYY65WsMqVqaf3uuuuc2XPdEELj8qcKeht3Lixq/7Qrl07VzvYowFss2fPtm7dulmtWrWsePHi7uIalEADAADwp6gGwA0aNHD1flOjXuDBgwe7W2pU8WHq1KknfZ3LLrvMPv/889NaVwAAAJwdYjYHGAAAAMgKBMAAAADwlQwFwI0aNXI5uZFUpUHzAAAAgLMqAP7ss88sKSnphOmHDx8m1xYAAABnzyC4NWvWBP+/YcOGsEsJHzt2zFVoOO+88zJ3DQEAAIBoBcA1a9Z0lRl0SynVIV++fPbSSy9l5voBAAAA0QuAdaEKlS274IIL3IUnzj333OA8XWyiRIkSljNnzsxdQwAAACBaAXD58uXdvS41DAAAAPjqQhhbtmyx+fPn2+7du08IiHWlNQAAAOCsCYAnTJhgXbt2dZcVLlWqlMsJ9uj/BMAAAAA4qwLgp556yp5++mnr06dP5q8RAAAAEGt1gPfu3Wu33XZb5q8NAAAAEIsBsILf2bNnZ/7aAAAAALGYAnHRRRdZ//79bcmSJVa9enXLlStX2PwePXpk1voBAAAA0Q+Ax48fbwULFrQFCxa4WygNgiMABgAAwFkVAOuCGAAAAIBvcoABAAAAX/UA33fffSedP3HixIyuDwAAABB7AbDKoIVKTk62devW2b59+6xRo0aZtW4AAABAbATA06ZNO2GaLoesq8NdeOGFmbFeAAAAQGznAMfFxVmvXr3s+eefz6ynBAAAAGJ7ENx3331nR48ezcynBAAAAKKfAqGe3lCBQMB++eUX++ijjywxMTGz1g0AAACIjQB41apVJ6Q/nHvuufbcc8+dskIEAAAAcMYFwPPnz8/8NQEAAABiNQD2/Prrr7Zp0yb3/0qVKrleYAAAAOCsGwR36NAhl+pQunRpq1+/vruVKVPGOnXqZH/++WfmryUAAAAQzQBYg+AWLFhgM2bMcBe/0O2DDz5w0x599NHMWjcAAAAgNlIg3n//fXvvvfesQYMGwWktW7a0fPny2e23325jx47NzHUEAAAAotsDrDSHkiVLnjC9RIkSpEAAAADg7AuA69ata08++aQdPnw4OO2vv/6yQYMGuXkAAADAWZUC8cILL1jz5s3t/PPPtxo1arhpX3/9teXJk8dmz56d2esIAAAARDcArl69um3ZssWmTJli33zzjZt25513WseOHV0eMAAAAHBWpUAMHTrU3nrrLevcubO7+ptuf//73+3NN9+0YcOGZdrKHTt2zPr3728VK1Z0gfWFF15oQ4YMcZde9uj/AwYMcCXZtEyTJk1ccB5qz549LjgvVKiQFS5c2JVrO3jwYKatJwAAAM7yAPjVV1+1ypUrnzC9WrVqNm7cOMssCqZVUeLll1+2jRs3usfDhw+3l156KbiMHo8ePdq97tKlS61AgQLWrFmzsPxkBb/r16+3OXPm2MyZM23hwoXWpUuXTFtPAAAAnOUpEDt37nQ9rpF0JbhffvnFMsuiRYusTZs21qpVK/e4QoUKrpf5q6++Cvb+Kh+5X79+bjl54403XIWK6dOnW/v27V3gPGvWLFu2bJnVrl3bLaMAWmXbRo4c6S7gAQAAAP/IUA9w2bJl7csvvzxhuqZlZkB5zTXX2Ny5c23z5s3BgXZffPGFtWjRwj3eunWrC8aV9uBJSEiwOnXq2OLFi91j3SvtwQt+RcvHxcW5HuPUHDlyxA4cOBB2AwAAgE97gJX727NnT0tOTrZGjRq5aQpUH3vssUy9Etzjjz/uAk+lW+TMmdPlBD/99NMupUEU/EpkTWI99ubpXvWJQ8XHx1vRokWDy6SW56yybgAAADi7ZCgA7t27t/3+++/24IMPWlJSkpuWN29e69Onj/Xt2zfTVu6dd95xlSamTp3q8otXr17tAm/1MicmJlpW0vvQJZ89CsTV8w0AAAAfBsA5cuRwA9JUoUE5tqq+cPHFF7s6wJlJgbZ6gZXL65Vf+/HHH13vrALgUqVKuem7du0Ky0nW45o1a7r/a5ndu3eHPe/Ro0ddZQjv71Oi95LZ7wcAAABnaA6wp2DBgnbllVfapZdemiXBoi65rFzdUEqFOH78uPu/yqMpiFX6RWhPrXJ7vSvS6X7fvn22YsWK4DLz5s1zz6FcYQAAAPhLhnqAs0vr1q1dzm+5cuVcCsSqVats1KhRdt999wV7opUS8dRTT7keaAXE6pVWikTbtm3dMlWqVHFXrVPeskqlKW+5e/furleZChAAAAD+E9MBsMqVKaBVrrHSGBSw3n///e7CFx4NvDt06JCr66ue3uuuu86VPVNOskd5xAp6Gzdu7HqU27Vr52oHAwAAwH9iOgA+55xzXJ1f3VKjXuDBgwe7W2pU8UED6QAAAIDTygEGAAAAzjQEwAAAAPAVAmAAAAD4CgEwAAAAfIUAGAAAAL5CAAwAAABfIQAGAACArxAAAwAAwFcIgAEAAOArBMAAAADwFQJgAAAA+AoBMAAAAHyFABgAAAC+QgAMAAAAXyEABgAAgK8QAAMAAMBXCIABAADgKwTAAAAA8BUCYAAAAPgKATAAAAB8hQAYAAAAvkIADAAAAF8hAAYAAICvEAADAADAVwiAAQAA4CsEwAAAAPAVAmAAAAD4CgEwAAAAfIUAGAAAAL5CAAwAAABfIQAGAACArxAAAwAAwFdiPgD++eef7W9/+5sVK1bM8uXLZ9WrV7fly5cH5wcCARswYICVLl3azW/SpIlt2bIl7Dn27NljHTt2tEKFClnhwoWtU6dOdvDgwSi8GwAAAERbTAfAe/futWuvvdZy5cpl//vf/2zDhg323HPPWZEiRYLLDB8+3EaPHm3jxo2zpUuXWoECBaxZs2Z2+PDh4DIKftevX29z5syxmTNn2sKFC61Lly5RelcAAACIpniLYcOGDbOyZcvapEmTgtMqVqwY1vv7wgsvWL9+/axNmzZu2htvvGElS5a06dOnW/v27W3jxo02a9YsW7ZsmdWuXdst89JLL1nLli1t5MiRVqZMmSi8MwAAAERLTPcAf/jhhy5ove2226xEiRJ2+eWX24QJE4Lzt27dajt37nRpD56EhASrU6eOLV682D3WvdIevOBXtHxcXJzrMQYAAIC/xHQA/P3339vYsWPt4osvtk8++cS6du1qPXr0sNdff93NV/Ar6vENpcfePN0reA4VHx9vRYsWDS6TkiNHjtiBAwfCbgAAADjzxXQKxPHjx13P7TPPPOMeqwd43bp1Lt83MTExS1976NChNmjQoCx9DQAAAGS/mO4BVmWHqlWrhk2rUqWKbdu2zf2/VKlS7n7Xrl1hy+ixN0/3u3fvDpt/9OhRVxnCWyYlffv2tf379wdv27dvz7T3BQAAgOiJ6QBYFSA2bdoUNm3z5s1Wvnz54IA4BbFz584NzleqgnJ769at6x7rft++fbZixYrgMvPmzXO9y8oVTk2ePHlc2bTQGwAAAM58MZ0C8cgjj9g111zjUiBuv/12++qrr2z8+PHuJjly5LCePXvaU0895fKEFRD379/fVXZo27ZtsMe4efPm1rlzZ5c6kZycbN27d3cVIqgAAQAA4D8xHQBfeeWVNm3aNJeOMHjwYBfgquyZ6vp6HnvsMTt06JCr66ue3uuuu86VPcubN29wmSlTprigt3Hjxq76Q7t27VztYAAAAPhPTAfAcuONN7pbatQLrOBYt9So4sPUqVOzaA0BAABwJonpHGAAAAAgsxEAAwAAwFcIgAEAAOArBMAAAADwFQJgAAAA+AoBMAAAAHyFABgAAAC+QgAMAAAAXyEABgAAgK8QAAMAAMBXCIABAADgKwTAAAAA8BUCYAAAAPgKATAAAAB8hQAYAAAAvkIADAAAAF8hAAYAAICvEAADAADAVwiAAQAA4CsEwAAAAPAVAmAAAAD4CgEwAAAAfIUAGAAAAL5CAAwAAABfIQAGAACArxAAAwAAwFcIgAEAAOArBMAAAADwFQJgAAAA+AoBMAAAAHyFABgAAAC+QgAMAAAAXyEABgAAgK+cUQHws88+azly5LCePXsGpx0+fNi6detmxYoVs4IFC1q7du1s165dYX+3bds2a9WqleXPn99KlChhvXv3tqNHj0bhHQAAACDazpgAeNmyZfbqq6/aZZddFjb9kUcesRkzZti7775rCxYssB07dtgtt9wSnH/s2DEX/CYlJdmiRYvs9ddft8mTJ9uAAQOi8C4AAAAQbWdEAHzw4EHr2LGjTZgwwYoUKRKcvn//fnvttdds1KhR1qhRI6tVq5ZNmjTJBbpLlixxy8yePds2bNhg//nPf6xmzZrWokULGzJkiI0ZM8YFxQAAAPCXMyIAVoqDenGbNGkSNn3FihWWnJwcNr1y5cpWrlw5W7x4sXus++rVq1vJkiWDyzRr1swOHDhg69evT/U1jxw54pYJvQEAAODMF28x7q233rKVK1e6FIhIO3futNy5c1vhwoXDpivY1TxvmdDg15vvzUvN0KFDbdCgQZn0LgAAABArYroHePv27fbwww/blClTLG/evNn62n379nUpFt5N6wIAAIAzX0wHwEpx2L17t11xxRUWHx/vbhroNnr0aPd/9eQqj3ffvn1hf6cqEKVKlXL/131kVQjvsbdMSvLkyWOFChUKuwEAAODMF9MBcOPGjW3t2rW2evXq4K127dpuQJz3/1y5ctncuXODf7Np0yZX9qxu3bruse71HAqkPXPmzHEBbdWqVaPyvgAAABA9MZ0DfM4559ill14aNq1AgQKu5q83vVOnTtarVy8rWrSoC2ofeughF/ReffXVbn7Tpk1doHvXXXfZ8OHDXd5vv3793MA69fICAADAX2I6AE6L559/3uLi4twFMFS5QRUeXnnlleD8nDlz2syZM61r164uMFYAnZiYaIMHD47qegMAACA6zrgA+LPPPgt7rMFxqumrW2rKly9vH3/8cTasHQAAAGJdTOcAAwAAAJmNABgAAAC+QgAMAAAAXyEABgAAgK8QAAMAAMBXCIABAADgKwTAAAAA8BUCYAAAAPgKATAAAAB8hQAYAAAAvkIADAAAAF8hAAYAAICvEAADAADAVwiAAQAA4CsEwAAAAPAVAmAAAAD4CgEwAAAAfIUAGAAAAL5CAAwAAABfIQAGAACArxAAAwAAwFcIgAEAAOArBMAAAADwFQJgAAAA+AoBMAAAAHyFABgAAAC+QgAMAAAAXyEABgAAgK8QAAMAAMBXCIABAADgKwTAAAAA8BUCYAAAAPhKzAfAQ4cOtSuvvNLOOeccK1GihLVt29Y2bdoUtszhw4etW7duVqxYMStYsKC1a9fOdu3aFbbMtm3brFWrVpY/f373PL1797ajR49m87sBAABAtMV8ALxgwQIX3C5ZssTmzJljycnJ1rRpUzt06FBwmUceecRmzJhh7777rlt+x44ddssttwTnHzt2zAW/SUlJtmjRInv99ddt8uTJNmDAgCi9KwAAAERLvMW4WbNmhT1W4Koe3BUrVlj9+vVt//799tprr9nUqVOtUaNGbplJkyZZlSpVXNB89dVX2+zZs23Dhg326aefWsmSJa1mzZo2ZMgQ69Onjw0cONBy584dpXcHAACA7BbzPcCRFPBK0aJF3b0CYfUKN2nSJLhM5cqVrVy5crZ48WL3WPfVq1d3wa+nWbNmduDAAVu/fn22vwcAAABET8z3AIc6fvy49ezZ06699lq79NJL3bSdO3e6HtzChQuHLatgV/O8ZUKDX2++Ny8lR44ccTePgmUAAACc+c6oHmDlAq9bt87eeuutbBl8l5CQELyVLVs2y18TAAAAWe+MCYC7d+9uM2fOtPnz59v5558fnF6qVCk3uG3fvn1hy6sKhOZ5y0RWhfAee8tE6tu3r0u38G7bt2/PgncFAACA7BbzAXAgEHDB77Rp02zevHlWsWLFsPm1atWyXLly2dy5c4PTVCZNZc/q1q3rHut+7dq1tnv37uAyqihRqFAhq1q1aoqvmydPHjc/9AYAAIAzX/yZkPagCg8ffPCBqwXs5ewqLSFfvnzuvlOnTtarVy83ME6B6kMPPeSCXlWAEJVNU6B711132fDhw91z9OvXzz23Al0AAAD4R8wHwGPHjnX3DRo0CJuuUmf33HOP+//zzz9vcXFx7gIYGrimCg+vvPJKcNmcOXO69ImuXbu6wLhAgQKWmJhogwcPzuZ3AwAAgGiLPxNSIE4lb968NmbMGHdLTfny5e3jjz/O5LUDAADAmSbmc4ABAACAzEQADAAAAF8hAAYAAICvEAADAADAVwiAAQAA4CsEwAAAAPAVAmAAAAD4CgEwAAAAfIUAGAAAAL5CAAwAAABfIQAGAACArxAAAwAAwFcIgAEAAOArBMAAAADwFQJgAAAA+AoBMAAAAHyFABgAAAC+QgAMAAAAXyEABgAAgK8QAAMAAMBXCIABAADgKwTAAAAA8BUCYAAAAPgKATAAAAB8hQAYAAAAvkIADAAAAF8hAAYAAICvEAADAADAVwiAAQAA4CsEwAAAAPAVAmAAAAD4CgEwAAAAfIUAGAAAAL7iqwB4zJgxVqFCBcubN6/VqVPHvvrqq2ivEgAAALKZbwLgt99+23r16mVPPvmkrVy50mrUqGHNmjWz3bt3R3vVAAAAkI18EwCPGjXKOnfubPfee69VrVrVxo0bZ/nz57eJEydGe9UAAACQjeLNB5KSkmzFihXWt2/f4LS4uDhr0qSJLV68OMW/OXLkiLt59u/f7+4PHDiQoXVITv7/z4WzX0bbSWY4duRw1F4bPmtrf7Ff85NotrXDh5Ki9to4s9qa97eBQOCky/kiAP7tt9/s2LFjVrJkybDpevzNN9+k+DdDhw61QYMGnTC9bNmyWbaeOHskJAyL9irAJxJeeSbaqwCfSOg1ItqrAJ941qac9nP88ccflpCQ4O8AOCPUW6ycYc/x48dtz549VqxYMcuRI0dU1+1MoaMwHTBs377dChUqFO3VwVmMtgbaGs427NcyRj2/Cn7LlClz0uV8EQAXL17ccubMabt27QqbrselSpVK8W/y5MnjbqEKFy6cpet5tlLwSwAM2hrOJuzXQFuLXSfr+fXVILjcuXNbrVq1bO7cuWE9unpct27dqK4bAAAAspcveoBF6QyJiYlWu3Ztu+qqq+yFF16wQ4cOuaoQAAAA8A/fBMB33HGH/frrrzZgwADbuXOn1axZ02bNmnXCwDhkHqWQqO5yZCoJkNloa8gutDXQ1s4OOQKnqhMBAAAAnEV8kQMMAAAAeAiAAQAA4CsEwAAAAPAVAmAAAAD4CgEwAAAAfIUAGACATHT06FG2JxDjCIARNTt27HC1mYHspitBAllh+fLl1qhRI/vjjz/YwEAMIwBGVKxevdrOP/98W7p0KZ8AstQPP/xgkyZNshdffNEWLlxoKn0eFxdnx44dY8sjU3399dfWsGFDu+yyy+ycc85h6wIxzDdXgkNs/UjUq1fPXZ76xhtvPGG+ApQcOXJEZd1wdlm3bp1df/31VqNGDVu7dq2VLl3aypQpYx9++KHlzp3bBcE5c+aM9mriLLBmzRq77rrrrGvXrjZ8+HA37ciRI5acnGz58+d3B10AYgdXgkO2ByR169a17t2729ChQ92paJ0y/Omnn6xcuXJWq1YtF/xqOj8YOB1//vmnNW/e3KpVq2ZjxoyxAwcO2KeffmqDBg1yB1lLliyxggULunzN+Hj6ApBxe/bssYsuusjtv+bMmePalALh7777zr7//ntr2bKl3XPPPXbVVVexmZEukR1C3kE7v5Gnj0NSZBt9YUeMGGGHDh2yxx9/3E1TgNKtWze7/fbb7e6777Y2bdoEv9hcpRunIykpyfbv329NmjRx7alw4cLWtm1b+/e//+1+QNQzrDam4Je2htN111132eLFi+399993+7Fvv/3WneH629/+5v7fo0cPdxYCSA8v+J06daq7175LZxa0T/v9999d20LGEAAj2+gLO2rUKJf+ULt2bbv22mstb968Nm7cOPclfuKJJ1xvSadOndzypEHgdCQkJLgfC/X6ehTs1qxZ07W5w4cPW+/evWlrOG1FixZ1Zxa6dOlit912mwtQ3nvvPZfm9dRTT1mfPn3sr7/+ss8//5ytjXT75ptv7NFHH7WHH37YPc6TJ487u6DfUNpUxhEAI1sVK1bMpk+fbmXLlrV9+/bZ6NGj3WnDChUqWPv27V1P8MqVK6kOgQzRyHv1/HoHUO3atXMDLmfMmBG2XJ06ddxp6RUrVrhgBUgv7b927dplmzZtco91hqFv374u3UYpXtrXedVGGjdu7DoAtG8D0uviiy92PcAaxKs2pranSiMKgJVag4whAEaW0g+EjlDVC/fzzz+7aUWKFHGnCdUbrAFJoh8K9dbpsQaNaIASkB4KLlq0aOGqPniUVqNe35dffjmsJ1jBiA681CYpV4WMjGVQmoMCWx1IqZdXzj33XOvYsWNwcK/amfZtykfXAEy1OSC99Nt4zTXX2BtvvGEffPCB60C6+eab7bXXXiN96zQQACPLKN9NPxAaDHLvvfdas2bNbMOGDcEgWI+9QNcb8LZq1SqrXLmyO8UDpKeyiEbgK8C45JJLgtP1QzF+/Hh3IKaR+f/617/cdPUSf/XVV1aqVCnLly8fGxpptn79etfWdBZhyJAhbt+mvHIN5JVChQqFVRZRfrnanv6uadOmbGmkmwZV6jexQIEC9ttvv7nUQe9iK/rt5MIrGRQAssDmzZsDJUuWDPTp0yfw448/BhYsWBBo27Zt4P777w8kJSUFjh8/Hrb89u3b3bLFihULrF27ls8EabZmzZpAoUKFAo8//rh7rLa1c+dO16Z+//13N23jxo2BW2+9NVCpUqVA6dKlAw0aNAgUKVIksGrVKrY00mzHjh2B6tWru32VZ+XKlYEbbrghsGnTpsDq1avDlv/4448DnTt3DhQtWtQtB6TX0aNH3f3WrVsDFSpUCHTp0iUwd+7cQI0aNQIPP/wwG/Q0UPsHmU6DPdTjoR5elTpTLqZKnM2fP9+lPuTKlSts+S+++ML1oMyePdudpr700kv5VJAmqvKgXl9deEBtTRITE11epq40qNPOugCGSu+98sorLuXhf//7n5133nnulKJKVwFppXxx5V6q4oNHp6R1NkFpDwcPHnR5mW+99ZbrBdZj9dxpH1elShU2NFKlgeAaOKl9mvZnrVu3dmUa1Y6U81u1alWXXvPqq6+6tBrt15TipfY1bNgwtmwGUAcYmU6j61XurHjx4i79watjqAFH+gKr/qqukuSdJty7d699+eWX7kuvQBlIj2effdadih44cKDNnDnT5fyqkoja4axZs9xFL9TmVP0BOF3aXymFSyZOnGj/+Mc/bOzYsS51S6k1Krun6iL9+vUL7g91yho4VVqNLtij38tFixa5HPPOnTu7jiTRvkwpNF66oNIetF/TuJkLLriAjZsBBMDIElu3brWKFSu6/3sBsAYpqUSQ7pUnp2k7d+50eZhc/Q3pFVoIXmccVFtavXNvv/22G4Evv/zyi915553uB0I9Jzro4gIryCzTpk1zZxmuvvrq4LQbbrgh2N6AtJwxVfWj8uXLu8G6ot/I+++/31UW0f2tt97KhswCDIJDlvCCX/Gu7KYLYOgUoga+aZp6SXTEqx4SIL28Efby2GOPuTJB+iFRTVbvwhYKTnS2QVeBU+oNwS8yg9fuNBI/NPjV/k29vbr6IJAWGoSrKwnqjKnXtq644gqXFqheXlV60CBfZD4CYGTpD4ToS6zAQ/lMOj2tALh///6uh0Snp/WDwUUvkBFqVyqbJ6ojrZw4tSXdFATrpgBYeeXeYyCtItuLLkMbeh+6nxOl4qxZs8blbwIn47UdlWFUHu/u3buDbU6/mUqpUU1pldybNGkSGzMLEAAj038s9OOgwMT7QivoFX3JlTunvGCdstagOJUSAjJKbU09uyoNJKG5lqq9qrzgefPmWYcOHYKBMXAqv/766wlpWWprSqH58ccfrUGDBu7eO6OgSyDrVLWuMKgL/YSeAQMi6eI8yvHVWVEdoD/44IOu7fz3v/8NpmnpwF4D3/Rbqfq/27ZtY0NmMgJgZJgCDA0I8VIY9GOhI1fvR+L88893V3rzKEjRqZw333zTJe9TFB5ppUtk6ypIoUIDEg2g1ClDj5bVABL9qKjqQ2htYOBk1OOmy7VrYJvXS6dg2GtrqvKgFAdvwO7vv//uqo5opP6CBQvs8ssvZwMjVfoNVAUatSHV9ZW2bdtat27d3IG6rlqpANirlqQ8YI2T8ZZFJjqdGmrwr3Xr1gVuvPHGQJUqVVx935kzZwbn/fzzz4FSpUoFunbtGjh27FhwumqzJiYmur8F0urrr792tXs7deoU2LVrV9g81ZguU6ZM4IEHHgirLb1o0aLA008/7er/Amml9qL60L169XJtK9Rff/0VqFev3gltTf7444/AwYMH2dA45b6sQIECgd69e59Q6/e3334LdOvWLZArV67A2LFjA7/88otrc6pvrpq/e/bsYetmMqpAIN10NTeVbNHRavXq1V3Pm0awTpkyxc1Xr9v27dvd5UEjTzmr1zh//vxsdaS5moh6S1R3VbUuI9vT4MGDXQ/cCy+8cMI8nY3w0m+AU1Fvr9KzdOpZ5c30WOUZv/vuO9cGL7744uCZLdoV0ksVj3R2QAO/VdJMZ7BUQk9nD9Su1PY0VkFXUNV01SpXeoQq2XzyySecWcgC/Dog3SVbnnjiCTfYSEGHN9J+8uTJLudXI1kfeOABN10/IJFBCcEv0kMXEFDwoTw4BSajRo2yzZs3u9qXN910kw0YMMAtl9LgNoIUpIfakA7ulTojKqmnC1noAgWqLNK8eXPr27cv7QoZpgvyqHNIF09RR5H2aapPrpxx/Z42bNjQ3V9//fX2zTffuDapKiPqYELmIwcY6aKBbOpx0w+C5/PPP7dVq1a50i2NGzd2PxKucf2/ASJARqld6aBLVAReVUP0+N1337UePXq4HxFhcBtOl3J8S5Qo4XJ5dWClfZ1qSmvswsMPP+xyN73R+FQTQXopj1dVHTSwTbXJ1QOs9jVy5Eg3fdCgQW6/psHhGtOgko533HEHwW8WIkJBmqlHVz0i6sXVKFZdWvaf//yn+/I++eST9q9//csdweqSxgpUgNOlHwIvEFGPrkZJq97vZ599ZpUqVXKX1tYBGXA6vMFuCoAV5GrQZbt27ezCCy907U4BsM5E6BLH6rXjgAsZobOlumR7z5493YV7dMEe72BKV0k999xz3UBKZA8CYJySV/NSPbq6gpuOVNULp/w4BSS6es0999zjThGqFJB6THQqEUgvFYTXqb8tW7a4x6oUonw55f+q7ZUsWTLYm6IzDXPnznWX2AbSSyWoVINVF0nxzlY999xzLiDRQZbyMkPpDIRqmOvvgIxS+paCX42jEa9muQ7kFQBTRST7EADjpJRvqZwkJeJ7VLv3vffec7UJlQqhC1x49Fg9cwpWhFOFSE/5qSZNmrhTfxoMogMtDbJ88cUX3cAQ9crp5lG+uXLqQtNxgLTQAfott9zici2rVKniBvDqQF9nt3SBHpWoUrlGDT7yAl79XyWpFAQDp0O/j6HtSEGwSoaq80hl9pA9GASHVGnwhwIM1frV0WmvXr2Cl2vU5RvVC6xThwpc1DuiU4UarKTH+hERThUirQGJLi5w7733uptq9+pS2fq/BlwqL1M9vgqK//73v7u6vjrzsGPHDtejAqSnrdWvX9+1q9q1a7szCGpnys1U79uVV17pUh1UeaRLly7u4j2q+auxDkq9YSAvMpPamvJ+lf+rM1oMeMs+lEFDitTroUFGCnD1g9C9e3dXmuWxxx4LBsHyzjvvuEvQqkSQfihU6mXatGmcxkGaqddD+ZYKPrzKIjpz0KJFC5dbroBDPW/r1693AYnKm6nXVyX1aGtIb4qNBiDpMrM6s+DR2AWdbVAvXOgV4CZMmGA//fSTa4M333wzF1RBptOlszWWRmleOvOA7EMPMFKknDjlXypJXyNRFfQq0JXQIFinq1WvUD0jmtasWTOrUKECWxVppmBD+eO33nprcJpqSGswpQ6oFLQoYFHFB/XWqTZwUlKSO+jSoBIgrTSATWcTvLamA3zt61SGSu3Ma4/eVQa9kmhAVg701VgaUmuyHz3AOGkvcOjlFzUSX70njz76qPXp08cFvPpB2b9/f1ivMJBeGoykou/eKUFdZEX3yglW/q/aXKtWrVwKBHA6NMBSB0+i/ZcuOdu/f3836E3jGlJqk6G9wgDODvQAI1Ve8KveEPWSqCdYPwQKTvRjoFIuqmH4ww8/uKvB6TQhPxLICC/QEOWdL1++3NWVFg1UUtWHlStXsnFx2rzgV72/Cn5F+zVdyMejUlUqv6c0MI1tYL8GnH0IgHFKOhWoHwj9YCgNQj8GGiCiWr+6TOiyZcvCeoqB06FBIN5AELU5pTuo0ohOFQKZRQf1oT27Xik0XQRDKTi6CAtXEwTOXpRBQ5roR8KrV6ie4Hr16tmvv/7qeuV0KUcgS3ZQcXH2zDPP2OLFi+22225jIyNTeWUaFeiWLVvWndFSJRudgahRowZbGziL0QOMNPMGh6g8lcq26GpwGjkNZAWVBdJVkZQLPGfOnOCpayCzeL2+SoVQxQfVZ/3iiy+C6TcAzl70ACPdVKpFPb+ckkZWUl1WnWVQ/VWujoSspOo1smjRIlcbGMDZjyoQSDdGRCO7eKP0geyuegPg7EYADAAAAF8hBQIAAAC+QgAMAAAAXyEABgAAgK8QAAMAAMBXCIABAADgKwTAAAAA8BUCYAAAAPgKATAAAAB8hQAYAHBKSUlJbCUAZw0CYADIZrNmzbLrrrvOChcubMWKFbMbb7zRvvvuu+D8RYsWWc2aNS1v3rxWu3Ztmz59uuXIkcNWr14dXGbdunXWokULK1iwoJUsWdLuuusu++2339L0+n/88Yd17NjRXfq3dOnS9vzzz1uDBg2sZ8+ewWUqVKhgQ4YMsbvvvtsKFSpkXbp0cdPff/99q1atmuXJk8ct89xzz4U9t9ZT6xtK73Py5Mnu/z/88INb5q233rJrrrnGvcdLL73UFixYkMGtCQDpRwAMANns0KFD1qtXL1u+fLnNnTvX4uLi7Oabb7bjx4/bgQMHrHXr1la9enVbuXKlC0L79OkT9vf79u2zRo0a2eWXX+6eQwH1rl277Pbbb0/T6+u1v/zyS/vwww9tzpw59vnnn7vXijRy5EirUaOGrVq1yvr3728rVqxwr9G+fXtbu3atDRw40E33gtv06N27tz366KPuuevWreve8++//57u5wGADAkAAKLq119/DWh3vHbt2sDYsWMDxYoVC/z111/B+RMmTHDzV61a5R4PGTIk0LRp07Dn2L59u1tm06ZNJ32tAwcOBHLlyhV49913g9P27dsXyJ8/f+Dhhx8OTitfvnygbdu2YX/boUOHwA033BA2rXfv3oGqVasGH2sdpk2bFrZMQkJCYNKkSe7/W7dudcs8++yzwfnJycmB888/PzBs2LBTbCkAyBz0AANANtuyZYvdeeeddsEFF7j0AqUSyLZt22zTpk122WWXudQAz1VXXRX2919//bXNnz/fpT94t8qVK7t5oakUKfn+++8tOTk57DkTEhKsUqVKJyyr9ItQGzdutGuvvTZsmh7r/Rw7dixd20C9vp74+Hj3Wnp+AMgO8dnyKgCAIJ3uL1++vE2YMMHKlCnjUh+UB5vWgWYHDx50zzFs2LAT5imnN7MoRzi9lN/7fzuC/z8F3AAQS+gBBoBspDxX9fL269fPGjdubFWqVLG9e/cG56snVvm1R44cCU5btmxZ2HNcccUVtn79etdzfNFFF4XdThW0qtc5V65cYc+5f/9+27x58ynXXeuq3OFQenzJJZdYzpw53eNzzz3Xfvnll+B89Q7/+eefJzzXkiVLgv8/evSoyy/W8wNAdiAABoBsVKRIEVf5Yfz48fbtt9/avHnz3KA0T4cOHVyPsKouKCXgk08+cYPRvN5V6datm+3Zs8elUSiQVdqDlrv33ntPmYpwzjnnWGJiohuEpjQKBdKdOnVyA/G850+NBq1p0J4G5ilgfv311+3ll1+2f/zjH8FlNDhP0zS4TQP0HnjgARdwRxozZoxNmzbNvvnmG/d+dBBw3333pXt7AkBGEAADQDZSoKkSYOrxVNrDI488YiNGjAjOV07wjBkzXMkzlUJ74oknbMCAAW6elxestAn1vCrYbdq0qasYoRJmKjem5z+VUaNGuRxclV9r0qSJy+NV72to3nFK1PP8zjvvuPXXumu9Bg8ebPfcc09wGZVFK1u2rNWrV88F8wqO8+fPf8JzPfvss+6mKhNffPGFq0hRvHjxdG1LAMioHBoJl+G/BgBkuSlTprjeXaUq5MuXL0vKsp133nkueFVvcFZSHeCKFSu6HmIF+AAQDQyCA4AY88Ybb7hcXQWlqvigOsCqv5tZwa+CT6UeqBKEgmr14kqbNm0y5fkBINYRAANAjNm5c6dLL9C9qjrcdttt9vTTT6fpb1VKrWrVqqnO37Bhg7tXXrEG4+XOndtq1arlLoZBCgIAvyAFAgDOIqqooDSD1KhyhOruAoCfEQADAADAV6gCAQAAAF8hAAYAAICvEAADAADAVwiAAQAA4CsEwAAAAPAVAmAAAAD4CgEwAAAAfIUAGAAAAOYn/wcWGW8rznwBCwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,5))\n",
    "sns.countplot(data=df, x='age_group', order=df['age_group'].value_counts().index, palette='viridis')\n",
    "plt.xticks(rotation=45)\n",
    "plt.title('Количество изображений по возрастным группам')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c04d7ddf-e499-4537-b2dc-629a3388aca0",
   "metadata": {},
   "source": [
    "## 3. Анализ уверенности модели (confidence_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3f90c97a-078f-4270-b59d-15614603e45a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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FQQoAooSVcrb5lU5WgQyIJKs+aUUzTuRE3VMBILtgjBQAAMgy69atc8uJWBXLggULZtk2AUBmEKQAAAAAwCeKTQAAAACAT4yRkpSUlKSNGze6CfZy5crldx8CAAAAiBI2O5RNDG7FmWzy74wQpCQXoqpWrZqVxwcAAABANrZhwwZVqVIlw+cJUpJriQrurOLFi2fd0QEAAACQrezevds1sgQzQkYIUlZx48/ufBaiCFIAAAAAcp1kyA/FJgAAAADAJ4IUAAAAAPhEkAIAAAAAnwhSAAAAAOATQQoAAAAAfCJIAQAAAIBPBCkAAAAA8IkgBQAAAAA+EaQAAAAAwCeCFAAAAAD4RJACAAAAAJ8IUgAAAADgE0EKAAAAAHwiSAEAAACAT3n9/gIAAAAQSQkJCdq+fXtY1l22bFlVq1YtLOtGdCFIAQAAIEeFqLp16+nAgf1hWX+hQoW1alU8YQonRZACAABAjmEtURaiunYdp3Ll6oV03du2xWvy5F7uPWiVwskQpAAAAJDjWIiKi2sW6c1ADKPYBAAAAAD4RJACAAAAAJ8IUgAAAADgE0EKAAAAAHwiSAEAAACATwQpAAAAAPCJIAUAAAAAPhGkAAAAAMAnghQAAAAA+ESQAgAAAACfCFIAAAAA4BNBCgAAAAB8IkgBAAAAgE8EKQAAAADwiSAFAAAAAD4RpAAAAADAJ4IUAAAAAPhEkAIAAAAAnwhSAAAAAOATQQoAAAAAfCJIAQAAAIBPBCkAAAAA8IkgBQAAAAA+EaQAAAAAwCeCFAAAAAD4RJACAAAAAJ8IUgAAAADgE0EKAAAAAHwiSAEAAACATwQpAAAAAPCJIAUAAAAAPhGkAAAAAMAnghQAAAAA+JTX7y8AABBJCQkJ2r59e1jWXbZsWVWrVi0s6wYyg/MdyL4IUgCAHHVRWbduPR04sD8s6y9UqLBWrYonTCFb4HwHsjeCFAAgx7CWKAtRXbuOU7ly9UK67m3b4jV5ci/3HrRKITvgfAeyN4IUACDHsRAVF9cs0psBZAnOdyB7otgEAAAAAPhEkAIAAAAAnwhSAAAAAOATY6QAAACAFOLj48O2P5hmIXoQpAAAAABJe/dukpRLvXr1Ctv+YJqF6EGQAgAAJ8XEsNGJlpfUEhN3SgqobdsXVadOy5Dvb6ZZiC4EKQAAcEJMDBt9aHk5sVKlajPFAk6KIAUAAE6IiWGjDy0vwF9HkAIQtcLZFckwYBixholhow8tL0DmEaQARKVwd0UyDBgGACB2EaQARKVwdkUyDBgGACC2EaQARDW6IgEAgHDIrWzi3//+t3LlyqW77747+bHExETdfvvtKlOmjIoWLaru3btry5Ytx3Xf6dSpkwoXLqzy5ctr4MCBOnLkSAT+AgAAAACxIlsEqcWLF+vVV19Vo0aNUj0+YMAATZ06VRMnTtTcuXO1ceNGdevWLfn5o0ePuhB16NAhffXVVxozZoxGjx6thx56KAJ/BQAAAIBYEfEgtXfvXvXs2VOvv/66SpUqlfz4rl279Oabb+rpp59Wu3bt1Lx5c40aNcoFpoULF7rXfP7551q5cqXGjRunJk2a6LLLLtOjjz6qESNGuHAFAAAAAFE5Rsq67lmrUvv27fXYY48lP75kyRIdPnzYPR5Ut25dVatWTQsWLNC5557rbhs2bKgKFSokv6ZDhw669dZbtWLFCjVt2jTd9zx48KBbgnbv3h22vw8AkLPEx8eHbd2UzAeA6BHRIDVhwgQtXbrUde1La/PmzcqfP79KliyZ6nELTfZc8DUpQ1Tw+eBzGRk+fLiGDBkSor8CABAN9u7dJCmXevXqFbb3oGQ+AESPiAWpDRs26K677tL06dNVsGDBLH3vwYMH65577knVIlW1atUs3QYAQPaSmLhTUkBt276oOnVahnz9lMyPzsm5aWUEYlfEgpR13du6dauaNWuWqnjEvHnz9OKLL+qzzz5z45x27tyZqlXKqvZVrFjR3bfbRYsWpVpvsKpf8DXpKVCggFsAAEirVKnaios79m8TcrZwT85NKyMQuyIWpC666CItX7481WM33HCDGwc1aNAg10KUL18+zZw505U9N6tXr3YfiC1bet8U2u2wYcNcILPS58ZauIoXL6769etH4K8CAACxMjk3rYxAbItYkCpWrJgaNGiQ6rEiRYq4OaOCj/fr1891wStdurQLR3feeacLT1ZowlxyySUuMPXu3VtPPvmkGxf1wAMPuAIWtDgBAIAgJucGEHVV+07kmWeeUe7cuV2LlFXZs4p8L730UvLzefLk0ccff+yq9FnAsiDWt29fDR06NKLbDQAAACC6ZasgNWfOnFQ/WxEKmxPKloxUr15d06ZNy4KtAwAAAIBsMiEvAAAAAOQ0BCkAAAAA8IkgBQAAAAA+EaQAAAAAwCeCFAAAAAD4RJACAAAAAJ8IUgAAAADgE0EKAAAAAHwiSAEAAACATwQpAAAAAPCJIAUAAAAAPhGkAAAAAMCnvH5/AQAQfgkJCdq+fXvY1n/w4EEVKFAgLOsuW7asqlWrFpZ1AwCQXRCkACAbhqi6devpwIH9YXyXXJICYVlzoUKFtWpVPGEKABDVCFIAkM1YS5SFqK5dx6lcuXohX/+aNdM0e/aDatv2RdWp0zKk6962LV6TJ/dyfwOtUtHTihkfHx+W9QJATkaQAoBsykJUXFyzkK93+3bvorhUqdphWT+itRVT2rt3T1jXDwA5CUEKAIAcLqtaMRMTE0O+bgDIqQhSAABEiXC3YgIAjqH8OQAAAAD4RJACAAAAAJ8IUgAAAADgE0EKAAAAAHwiSAEAAACATwQpAAAAAPCJIAUAAAAAPhGkAAAAAMAnghQAAAAA+ESQAgAAAACfCFIAAAAA4BNBCgAAAAB8IkgBAAAAgE8EKQAAAADwiSAFAAAAAD4RpAAAAADAJ4IUAAAAAPhEkAIAAAAAnwhSAAAAAOATQQoAAAAAfCJIAQAAAIBPBCkAAAAA8IkgBQAAAAA+5fX7CwBiS0JCgrZv3x629ZctW1bVqlUL2/oBAADCgSAF4IQhqm7dejpwYH/Y9lKhQoW1alU8YQoAAOQoBCkAGbKWKAtRXbuOU7ly9UK+p7Zti9fkyb3c+9AqBQAAchKCFICTshAVF9eMPQUAAPAnik0AAAAAgE8EKQAAAADwiSAFAAAAAD4RpAAAAADAJ4IUAAAAAPhEkAIAAAAAnwhSAAAAAOATQQoAAAAAfCJIAQAAAIBPef3+AgAAAIDsJyEhQdu3bw/b+suWLatq1aqFbf05DUEKAAAAiIIQVbduPR04sD9s71GoUGGtWhVPmPoTQQoAAADI4awlykJU167jVK5cvZCvf9u2eE2e3Mu9D61SHoIUAAAAECUsRMXFNYv0ZsQEik0AAAAAgE8EKQAAAADwiSAFAAAAAD4RpAAAAADAJ4IUAAAAAPhEkAIAAAAAnwhSAAAAAOATQQoAAAAAfCJIAQAAAEBOClIvv/yyGjVqpOLFi7ulZcuW+t///pf8fGJiom6//XaVKVNGRYsWVffu3bVly5ZU60hISFCnTp1UuHBhlS9fXgMHDtSRI0ci8NcAAAAAiBURDVJVqlTRv//9by1ZskTffPON2rVrpyuuuEIrVqxwzw8YMEBTp07VxIkTNXfuXG3cuFHdunVL/v2jR4+6EHXo0CF99dVXGjNmjEaPHq2HHnoogn8VAAAAgGiXN5Jv3rlz51Q/Dxs2zLVSLVy40IWsN998U2+//bYLWGbUqFGqV6+ee/7cc8/V559/rpUrV2rGjBmqUKGCmjRpokcffVSDBg3SI488ovz580foLwMAAAAQzbLNGClrXZowYYL27dvnuvhZK9Xhw4fVvn375NfUrVtX1apV04IFC9zPdtuwYUMXooI6dOig3bt3J7dqpefgwYPuNSkXAAAAAMgxQWr58uVu/FOBAgV0yy23aPLkyapfv742b97sWpRKliyZ6vUWmuw5Y7cpQ1Tw+eBzGRk+fLhKlCiRvFStWjUsfxsAAACA6BTxIHXGGWdo2bJl+vrrr3Xrrbeqb9++rrteOA0ePFi7du1KXjZs2BDW9wMAAAAQXSI6RspYq1Pt2rXd/ebNm2vx4sV67rnndPXVV7siEjt37kzVKmVV+ypWrOju2+2iRYtSrS9Y1S/4mvRY65ctAAAAAJAjW6TSSkpKcmOYLFTly5dPM2fOTH5u9erVrty5jaEydmtdA7du3Zr8munTp7tS6tY9EAAAAACirkXKuthddtllroDEnj17XIW+OXPm6LPPPnNjl/r166d77rlHpUuXduHozjvvdOHJKvaZSy65xAWm3r1768knn3Tjoh544AE39xQtTgAAAACiMkhZS1KfPn20adMmF5xscl4LURdffLF7/plnnlHu3LndRLzWSmUV+V566aXk38+TJ48+/vhjN7bKAlaRIkXcGKuhQ4dG8K8CAAAAEO0iGqRsnqgTKViwoEaMGOGWjFSvXl3Tpk0Lw9YBAAAAQA4ZIwUAAAAA2R1BCgAAAAB8IkgBAAAAgE8EKQAAAADwiSAFAAAAAD4RpAAAAADAJ4IUAAAAAPhEkAIAAACArJqQd9++fZo7d64SEhJ06NChVM/94x//yOxqAQAAAAUC0oED0h9/eMv+/dKRI1J8fBFJbbR1a2HlzSsVKCAVKiTly8dOQw4IUt9++606duyo/fv3u0BVunRpbd++XYULF1b58uUJUgAAADipxETpxx+llSu9Zf16acMGb/ntN+ngwfR+6wxJczVlSupHLUyVKOEt5cpJcXFSxYpSqVJSrlwcDGSTIDVgwAB17txZr7zyikqUKKGFCxcqX7586tWrl+66667QbyUAAABydOvSTz9JixdLP/xwLDitXSslJZ34d/Pk8cJQkSJyLVBHjiTql19+VpEitXT0aH5Zxyhbh7Ve2bJ5s7R6deqAVbOmdNppUq1aUsmSYf9zESMyFaSWLVumV199Vblz51aePHl08OBB1apVS08++aT69u2rbt26hX5LAQAAkCNC09691s+ui158sZJrXfrmG2nnzvRfb8HmzDOl+vWl2rWlqlW9pXJlqWxZqWjR1C1KS5euVPPmzdWz5xLFxTVz72ctV7t2eYu9j4UpW7Zu9cJVMLiZChWkhg2lBg281isgS4OUtT5ZiDLWlc/GSdWrV8+1Tm2w/1sAAAAQE2zs0saNXlc8u7Vl796GkiZr1KhjrytYUGrSxFuCwckWCzZ/peud/a6t2xZbV0pHj3rbYy1f69ZJv/4qbdniLTNmSDVqSC1aSHXrSn9e2gLhDVJNmzbV4sWLVadOHV1wwQV66KGH3Bipt956Sw0s3gMAACDqWDe6TZtShyYrBJFWrlwBBQLfqUuXKrrssrIurNglYlYXhLBugcEWrgsvPNY6tXy59Msv0s8/e4u1ip19thWuyHQdNsSgTJ0tjz/+uPbs2ePuDxs2TH369NGtt97qgtXIkSNDvY0AAADIYtaaYy03wdBkt9u3e1330ipd2uuKV6mSdyst08iRzfTgg0vUrFnZbHPsbLxU8+beYt0Alyw51u3w88+l/Pm7SLpXR4/SPIUwBamzzjor+b517fv0008zsxoAAABkk+55FppsXFGw69u2bV6YSqtYsdShyarjWUBJadOmdNJWNmPjo9q1k84/X/r+e2nBAmnHjgKS/qPPPkt0BSysGyJd/hDSINWuXTtNmjRJJSl7AgAAkGNY1zZrVfr551qSntRXXzV1Y4V2707/9TbuKGVoslsLUtHEuhtaC1XTptInnyzQ0qWVlZhYTVOneq1Vl1/u/d1ASILUnDlzjpuEFwAAAJFnXe+s25oFJmtVstsdO7z71vLkaekWa4EKshLjVqzBFpt/yW7tO/NYmYPJWp5q1FinpUvbqlGjZfrxx7puPNjrr3sFKaz1yoIlEJTpEXW5YuX/KgAAgGzm8GGvyENGS3pd8lJ2aStQYKO2bp2oJk0uVtOm9V1oKmC92iDpoGrX/kUXX1zXjZuywhQ2/9WqVVLXrt6cVMBfClJdu3ZV/vz5031u1qxZ7F0AAIC/0Kq0Y4ddpp2rNWtK68cfj4Wk33+38uInr1ZXpow3D1PKxR6zy7fly2dr0qS7VavWp6pWrT7HKR02f5VNjWrjpD75xNvvY8dKrVp5rVO2jxHbMh2kWrZsqaJ2hgEAAMA3C0Pr13vlt1PeBuc82revkaQFmj07/d+3FiSrlmdd8lIu9ljx4hRJCJVataS//1367DObDFj66ivv+Fx5pRdMEbvyZrZb38CBA13FPgAAAKRf2MHmKrJwlF5gsnFLJ77esrmYEhQXV1oVKxY7LiylrZSH8LFWvM6dpTp1pI8+8qob2tgpa7E6/XT2fKzKVJAKpDeBAAAAQIywS6H9++0yqom++KK4q+4WnNw1GJZSFnLIiIUiG3NTo8ax29NO85bff1+mli2bqXPnJYqLa5YVfxZOom5dr3rhxInShg3SO+9Ibdt6JdQpHxB7MhWkHn74Ybr1AQCAqAxI1pK0Z0/6i3XHC94mJVnXu291990Zr89KhacNSsFbW6zwQ0aWLuWL6+zIjmnfvpJNo2oB2rpeWmi2QhRWSh2xI9NBymzbtk2rV69298844wyVK1cutFsHAAAQ5oAUDEfB+yeqeJdmrZI2q27dEjrttMLJ4ShlYLIWJ1oqoo8VmujUyZuMeNo0KT7eO3+uvVYqXDjSW4dsHaT279+vO+64Q2+99ZaO/vlpkydPHvXp00cvvPCCCnMGAQAiIClJSkw8thw54i32T5U9Zxe0wSVvXm/cQ3BJSsrNMcuhAckmk924UW7OH7tNufz4ow1gWauRI2v4CEjexbC1PKRcrMZWyp/37PlWb7zRXOPHL1GzZnS9i0V22K0aonXx+/VXaeRIqVcvb/4tRL9MBakBAwZo7ty5+uijj9S6dWv32Jdffql//OMfuvfee/Xyyy+HejsBAFBios1h2EDr1pXUTz955YjtIjrYomAtDpl3raRLNH16AVeZyy6aixTxLpiDg/vtlrl2sjYk2TG2sSh2kWq3KZfffvPC0rFJZtNjFYaLJocoK9BwonAUfOxUSlvv2xeqvxQ5WbVq0o03SuPGeQVE3nxT6tnTm9QY0S1TQeqDDz7Q+++/rwsvvDD5sY4dO6pQoULq0aMHQQoA8JcvoK28sJUZ/vpryXqR2zw6CQlNJS3XjBkn/n0bp1CwoHdrLU92UZw7t7fe4GITmtpy6JB08KD3mFQmuYtXRixcWagKBiv7Ntp6ttvP9l7wJzHREksLffppKTdXjx33YFCy8HTikHSMtQBUquR1tbLb4JKYuE6DBvXStde+plq1GnCMEBb2GdCvn/T229KWLd58U717e+cjolemu/ZVsCmw07By6PYcAAB+WDe8JUu84BRctm7N6NV/qHz5/KpYsYibw8Xmywm2JFjIsQDld6JMC1FLlryvTz55WOedN1rly7dwrVzBxVpFbLF/4qwVwha70E/JugtamApOfGoXVrbY/Qzmr48Jtm8tmAYnkrUleN9uExMbS1qkf/0r43XYfqxa1VuqVEl93yqo2cVqRqMKli7d6eZiKlbsECEKYWWfRddfL40f730JYGHKuvnZOYrolDezk/FawYmxY8eqoP2L5eZKOKAhQ4a45wAAOBHrjrVgwbHQZCHKWodSsvDRvLl07rlSw4ZW1Mj+rflO7ds3UZcuoS0HbSGoQIGDklaqfPnf3ftlFPhSBgJbtm2Ttm/3WrWsW48tf9ZhSnWBVbz4aZKe1qRJZVwQq1fPC1nRUwrc2x/BfRC8b7dpj+3xNqpp02Jq3LiYm/y0evXUYenPSw0g27Nz1cKThSn7suWtt7xufnYuI/pkKkg9++yzuvTSS1WlShU1bmzfJEnfffedC1Wf2bTPAAD8yYo9fP/9sdBkAcrm2EnLOjq0anVssUHcaS+gly71US0gDGx7rPUjbXcdCxLWcmWhKhisbLH7FppsHNfu3VbneoCGDZNbgi0tFqiCS/363q19g50dK70dOpTXjUtKGZKCwcmCZEbsb7Gud8HukMExZ7YcPPitRo5spjfeoGADooONo7QwZd38bEJmGztl5dKtqymiS6aCVMOGDbVmzRqNHz9eq1atco9de+216tmzpxsnBQCIZaX05ZfFNWmSF5wWLTp+UL6NV7JWn5TByUpFZ8fwcCpsu4PdC61FJSUrgGGBau3aXzRv3vtq1eom/fZbCXeBFQxe8+al/h1bj038acHq9NOPjf2xweu2WEuW3+6LJ2IVDW07rVXJFgt+O3dKu3Z597ds6SRplz7+uPgJ12NzIll3y2BIsvu2WIjKaHs3bWKuJEQfa1G/7jqvmp99cWRh6oYbIr1VyBZBat68eWrVqpX69+8f8g0CAOQc1hJjrRHB4gDr19eT9Lvuuuv4C2zr+R0MTWef7YWFWGDfL1pVr3z5dmjevH/qhRfaulLZFi6tC+DKld4cNMFlzRpvTNHixd6SHgui5ct7i40LO3q0tqSPNGNGTbdfg0U2LCBZtbpg+ffgfWsltOAUDE/WZfHEjtVytop2wbCU8tZamZiMFEgdpq65xhsrZd2ZrZvf5ZfH8IDJKJSpINW2bVtt2rTJFZcAAMRW0YBgyengkvoi3OuVUL16otq2LZgcnKy7ml384xgLQNZ9Me30Q1ZF0Eq7B4OV3d+82VtsniRrwbJQFHzMYy1FnV3Fu78a+myxMV0WfoPL77/P1Pz5t+lvf3tRTZtezGEEfHTzszFSo0d7/+9+8ol96cH1c0wHqYBXIxYAEKWsxSJYSMGW4ESnNg4oLWv5sDE9VhSgaNG1+uyzczRp0udMUPoXvsW2Ln22ZHRs7JhYiLLKhtaitHLlej3wwDC1avWwChWq6l5jBR6sO11wsSCb8mcLTFbpLrjY+K+Mwu7y5ZbYflTevEmZ/bOAmGX/f9mYKZusd9cuG/j5sQ4c4JulaJDpGS8WLFigUtaOn442bdr8lW0CAGSBw4fzugtyrxCC10UvWCDBKtOl952ZjQWyzgjBOXosQNnPwfEvmzbtkrSD4xdGFlzTFryoXv0PPfDAm2rQ4DbFxVEeDMhurJXX5pV6/fUjOniwhR58cKemTw/tWEfkoCDVtWvXdB/PlSuXjganDweAKBOszrZlSxFJV2v06Aqu33uwcpkFEOuaZa0B1iqQdrGLYOvqkXKxloDgrbUS7NtXTdLzWriwshukHxzvEry1JWVRhpT3gxPN2jakt1jXvB07Okvaq6lT7W/ImG1TcD4kq6hnockKHTAOBgD8s7GEHTqs1Ucf1dDs2SU1aJD0n/+wJ2MySG3evJkxUgCimoUSC0ZWXc26tlk3Kpux3hsTdIakCXrhhXC8s00udKcrGR4exyqvWXiz4gT2bal1MghOIGu3VlQgp1bRA4DsqGJFK2Fq5fve1n//K9WpI/3975HeKmRpkLJWJwCIRjbe5McfvQH+FqDSGxNkH4FFihzU3r1fq2PHM9WwYZlUJZ8tnARbjlK2IlkXDmuVsvl2gouFsuD9YBW1det+04gRI9Wo0c3Kn79CcmtWylYuk7LrXcr7NsYmuNj7p/zZwtG2bdP1xRe36m9/e5nCAQCQ5d7R3//+H736aiXdfrs3xUHbthyGnIhiEwBinoWn5cu9CmkJCalDiYUf69Jmi3Vvs8VabLZtW6HXXrtAjz5qk4iWCek+XLp0i0aMeEjnnttJcXEVQn58li/fKmkthQMAIEL699+svXsrafx46eqrpSVLpKoMb4yNIJVkdVcBIAezj7G1a6VlyySbVzzlx5qFpTPO8CaItQDFmCAAQChZz4bXXpNWrPD+HerWTfriC69HA6I8SA0fPlwVKlTQjTfemOrxkSNHatu2bRpko+cAIBuy7nHffWeVR73y3kFWAa1RI6luXbkCDwAAhLss+uTJUvPm0jffyHXze+MNxqZGfZB69dVX9fbbbx/3+JlnnqlrrrmGIAUg27GKdQsXSl9/7Y2DMvbNX+PGUpMmXjU6AACyUo0a0rvvWjU/b56ps8+m+ETUBymr2BeXcgKLP5UrV06brLQVAGQT1mVv8WJp7lwrK+49VqKE1LKl1LSpV4ABAIBIad/eenvJlUO/6y7pnHO8L/gQpUGqatWqmj9/vmraAIIU7LFKNkMjkAMlJCRou81GGgZly5ZVtWo2NxCy1t/03nv13WSzxsp7X3ihtZ4zCSIAIPsYOFD68ktp6tRjxSesyiqiMEj1799fd999tw4fPqx27dq5x2bOnKn77rtP9957b6i3EciSEFW3bj0dOPBnn68QK1SosFatiidMZRErWz5gQC1JH7oQVaSI1KaN1w+dWeQBANmx+MSoUV5LlE3BYeOlxoyJ9FYhLEFq4MCB2rFjh2677TYdsoEHbqxBQTc2avDgwZlZJRBR1hJlIapr13EqV65eSNe9bVu8Jk/u5d6DVqnwsvmVnn5aGjLExkFZxYhDatLkd116aUUVKBDmNwcA4C+w+QitBIH1nBg7VrroIqlPH3ZpVE7I+8QTT+jBBx9UfHy8ChUqpDp16qgAVyrI4SxExcU1i/RmIBNsAt3evb2CEqZZsz1auvQcnX32OBUoQCUJAED2d/750iOPSA89JN12m3Tuud6Evciecv+VXy5atKhatGihBg0aEKIARIRNnmtzcVj1PQtRxYt7lY9ee22NpHiOCgAgR7n/fqltW69Akn1BaL0tEEUtUuabb77Re++958aWBLv3BU2aNCkU2wYAJ/THH9INN0gffuj9bP/wjB4tWV2PpUvZeQCAnMfG8tr4qIYNpUWLpH//W3rggUhvFULWIjVhwgS1atXKdeubPHmyKzqxYsUKzZo1SyWsrjAAhJkFJSseYSHKSpjb2KgZM7wQBQBATla1qvTii959G/drVfwQJUHq8ccf1zPPPKOpU6cqf/78eu6557Rq1Sr16NGDwfQAwt6V7/XXpVatpPXrJZuFYcECq9In5f5LnZUBAMg+evaUrrzS69pnXfwOHIj0FiGtTF12rF27Vp06dXL3LUjt27fPFaAYMGCAXrPBCgAQBocPe4Nvb75ZOnhQ6tzZ+5auGfVBAABRWBL9lVekihWl+HjpX/+K9BYhJEGqVKlS2rNnj7tfuXJl/fDDD+7+zp07tX9/eObhARDbfv9duvRS7x8V+8fl8celKVO8SXYBAIjWkuhWQMk8+6zXAwM5PEi1adNG06dPd/evuuoq3XXXXW6S3muvvVYXWdF7AAghm5zQSsDOmuVNrmsByqasoysfACDaXXaZdP31Xtf2fv28HhnIwVX7XnzxRSUmJrr7//rXv5QvXz599dVX6t69ux6grAiAELJv3y6/3GuRskISU6dKjRqxiwEAseO//5X+9z+vi9+wYdLQoZHeIvgOUrt37/Z+KW9eN4dU8OfbbrvNLQAQShaarr7aG2B79tnSRx9JFSqwjwEAsaV0aWnECK/4xPDh3i1fKuawIFWyZElXVOJkjh49+le2CQD0xhvS3/8uJSVJHTtK773ndesDACAWde8udetm87VKN97oTUKfN9MzwiIUfO3+2bNnp/o5EAioY8eOeuONN1zRCQAIhSeekP7v/7z71i/cioHmy8e+BQDENptbysYLW8Xa55+X7rkn0lsU23wFqQsuuOC4x/LkyaNzzz1XtWrVCuV2AYhBNpD2wQe9/t/GCkrY/VNoCAcAIOrFxUlPPSX17y89/LDUo4dUpUqktyp2MX0lgGwTomxS3WCIevJJr8Q5IQoAgGOsW1/LltLevdLdd7NncmyQ2rBhg5s3qowVuQeATLJxUDYe6rnnvJ9tQO3AgexOAADSsqk/bE7FPHmkDz7wqvkhB3Tte946Y/5p+/bteuedd9SuXTuVKFEiHNsGIEY8+WRVTZzo/eNgEw/27RvpLQIAIPuyin133SU9/bR0xx3SDz9Eeotik68g9cwzz7hbq9xXtmxZde7cmXmjAPyl7nzSU5o4sZzrwjd2rNSzJzsUAICTeeQR6d13pXXrvJLoXbqwz7J1kFq/fn34tgRAzFmyJE7SP939118nRAEAcKqKFfO6xNucUlbttnnz/Oy8LEaxCQAR8eWX0tKlFqRsPNQG9evHgQAAwA+bV+rii6VDh6Rnn6V8X1aL6DRew4cP16RJk7Rq1SoVKlRIrVq10hNPPKEzzjgj+TWJiYm69957NWHCBB08eFAdOnTQSy+9pAoVKiS/JiEhQbfeequb56po0aLq27evW3deZilDjLD/B2zcYqjFx8crHL7+Wpo5M/jTIF1zzdWSqoblvQAAiFbWLf7ZZ70xU3PmlJR0UaQ3KaZENEjNnTtXt99+u1q0aKEjR47o/vvv1yWXXKKVK1eqSJEi7jUDBgzQJ598ookTJ7qiFnfccYe6deum+fPnu+ePHj2qTp06qWLFivrqq6+0adMm9enTR/ny5dPjVjsZiIEQVbduPR04sD9s77F3756QrWvpUunTT737zZpt0tKlT0qyIAUAAPyqX98rOOFVvn1OSUkH2YmxEKQ+DV5N/Wn06NEqX768lixZojZt2mjXrl1688039fbbb7vqgGbUqFGqV6+eFi5c6CYC/vzzz13wmjFjhmulatKkiR599FENGjRIjzzyiPLnp78oopu1RFmI6tp1nMqVqxfSda9ZM02zZz/oWoZD4fvvpalTvfs2B0aDBhakQrJqAABiuvDEmDGHtXPnmVq5coMqV470FsWGiAaptCw4mdKlS7tbC1SHDx9W+/btk19Tt25dVatWTQsWLHBBym4bNmyYqqufdf+zrn4rVqxQ06ZNj3sf6yJoS9Du3bsVK8LVBSzIqjna8UHWsxAVF9cspOvcvj10XftWrpSmTPHun3WW16d78+aQrR4AgJhVsqR0++0bNWxYdX3zTZxatZL+7NyFWAhSSUlJuvvuu9W6dWs1aNDAPbZ582bXolTSzo4ULDTZc8HXpAxRweeDz6XHxk8NGTJEsSYruoAVKlRYq1bFE6aQypo13qSBVu68SROpY0evXzcAAAiNK67YoWHDdujQoWaaM0fq1Ik9GzNBysZK/fDDD/rSSnmF2eDBg3XPPfekapGqWjX6B7qHswuY2bYtXpMn93LvQ6sUgmx+C5vnIinJuvJJnTsTogAACLU8eey/A6wKgZYskc45x3oKsZ+jPkhZAYmPP/5Y8+bNU5Uqx0o3WgGJQ4cOaefOnalapbZs2eKeC75m0aJFqdZnzwefS0+BAgXcEqvC0QUMSE9CgjRhghWFsW653mSBuZl0AQCAMJmn6tV36pdfSmrGDOmaa9jR4RTRS5pAIOBC1OTJkzVr1izVrFkz1fPNmzd31fdmHquTrNWrV7suai1tpLobsN5Sy5cv19atW5NfM336dBUvXlz1rYwJgIj47Tdp/Hjp8GGpdm2pe/fgt2UAACBczjnnN9d9fvVq6eef2c9RG6SsO9+4ceNcVb5ixYq5MU22HDhwwD1v5c779evnuuHZHFFWfOKGG25w4ckKTRgrl26BqXfv3vruu+/02Wef6YEHHnDrjuVWJyCSrFF43DhvgsAaNaQePSSmdQMAIPxKljyo5s29+59/7o1PRhQGqZdfftlV6rvwwgsVFxeXvLxrAyr+9Mwzz+jyyy9X9+7dXUl0665nk/gG5cmTx3ULtFsLWL169XLzSA0dOjRCfxUQ26wo5NixNpm2ZD11rVtBvnyR3ioAAGLHhRdKNgPQpk3S8uWR3prolTfSXftOpmDBghoxYoRbMlK9enVNmzYtxFsHwK/ff/dC1P79Ulyc1LOnjUlkPwIAkJWs9Pl550mzZnmLjXahZ0iUFpsAIj0HVnx86OZLilU2DZyFqD17pPLlpV697IuQSG8VAACxyUbBLF7s/ftst3+WF0AIEaSQI2TFHFhm7949YV1/tLLwZCHKPqzLlJF695YKF470VgEAELusW7118Zs6VfriC6lZM3qJhBpBCjlCuOfAWrNmmmbPflCJNrAHvuzbJ731ltetz2Yp6NNHKlqUnQgAQKQ1aSJ99ZW0Y4d327ZtpLcouhCkkKOEaw6s7dvp2pcZVmDTqvNt2yYVK+aFqOLFQ354AABAJtjcje3aSRMnSgsWSC1a8GVnKDE1JoBMOXjQmydq82ZvUKuFqFKl2JkAAGQn9epJlSp58zpaFz+EDkEKgG/2YfzOO96ku4UKeWOiypZlRwIAkN3Y5LwXXeTd/+Yb6Y8/Ir1F0YMgBcCXI0ckm+rtl1+8QatWna9CBXYiAADZVa1a3pKUJM2dG+mtiR4EKQC+Q9TatV41IJsnyroLAACA7M3GSpnvv/eKT+CvI0gB8BWifvrJC1HXXSdVrcrOAwAgJ6hcWapTRwoEpHnzIr010YEgBeCkjh7NrQkTUoeoGjXYcQAA5CQ2r5RZvtwqFkd6a3I+ghSAkyigBQuaJnfnI0QBAJAzWXf8M87wWqUYK/XXEaQAZOjo0TySPtTWrWWTx0TREgUAQM51wQXe7Q8/SFu3RnprcjaCFIAMS5wvWNBGUgflyXPEhajq1dlZAADkZHFx3txShrFSfw1BCkC6IcoKS2zdaiX59qp166WEKAAAoqxVasUKacuWSG9NzkWQApBKYqI0bpxX4jxPnsOSOqpsWWbvAwAgWtj8j/Xre/cZK5V5BCkAyfbtk8aMkRISvMl2W7eeLekL9hAAAFHaKhUfL23eHOmtyZkIUgCcXbukUaO8D9MiRaTrr5fKlt3G3gEAIAqVLy81aODdnzMn0luTMxGkALjw9Oab3kznJUpIN9wgVazIjgEAINpbpXLlklavljZujPTW5DwEKSDG2Vgoa4nas8daoLwQVaZMpLcKAACEm/27H2yVYqyUfwQpIIZ9+600frx06JA3P9SNN3otUgAAILZapX78Ufrtt0hvTc5CkAJiUFKSNH269NFH3uzmjRp5k+0WKhTpLQMAAFnJeqHYdYChVcofghQQYw4ckN5+W/rqK+/n88+XunSR8uaN9JYBAIBIsGsBa5Vas0batIljcKoIUkAM2bpVeuMNb1xUvnzSlVdK7dp5H54AACB2W6XOPNO7/+WXkd6anIPvoIEYsWyZNG2adPiwNw7qmmuozAcAAI61Sv3wg7RypbRtm1SuHHvmZGiRAqKcFZKYMkX68EMvRNWsKfXvT4gCAACp55WqW9e7T6vUqSFIAVHM5oR47TXpu++87ntt20q9enkT7gIAAKRtlTLLl0u//86+ORmCFBCFjh6VZs3yxkPZJLvFikl9+0pt2ki5+b8eAACko1IlqXZtr6Lv/PnsopNhjBQQZazajnXj27LF+9kGj3bsKBUuHOktAwAAOaFV6qefvLHV9gUs80tmjCAFRImDB/Pof/+TFi/2vkmy4GQBKliFBwAA4GSqVZNq1JB+/tmbKuWyy9hnGSFIATmchSapt957r76bI8pYeLIPPsZCAQCAzLRKWZBautS7X7Qo+zA9jJYAcrAZM6Tevc+QNFYHDuRz80D07u3ND0WIAgAAmWEVfitXlo4ckRYsYB9mhCAF5ECLFknt20sXXyzFx1sJvj06++zfdOutUq1akd46AACQk1mlXxsfZb75Rtq/P9JblD0RpIAc1IVv5kwvPJ1zjnc/Xz7p2mu3SjpNTZpsUZ48kd5KAAAQDerUkSpU8Oaj/PrrSG9N9kSQArK5gweld97xwpO1Qll3PgtMVs78xx+lf/7zV0nbIr2ZAAAgSlulrCfMoUPEhrQoNgFkUzbI0ybTtbmgtv2ZkwoVkm66SbrnHq+ijmHCPAAAEA716klly0rbt0srVpRjJ6dBkAKykV27pPffl956S5o799jjNuCzf3/pttukcnyOAQCALGqVOu88acoUafny8vaVLvs9BYIUEGE7d0offyxNmiQ3D1Ri4rEPr4su8sJT585SXv5vBQAAWaxhQ2nOHLteySfpJvZ/ClyaAREoGrFmjReapk2TZs3yyosG1a/vlTDv2VOqWpXDAwAAIid3bql1a+mTT+yngTp8eDuH408EKSBLJsytqU8+Ka1XX/Wq7a1dm/o1DRpIXbtK3bpJjRt7rVEAAADZQZMm0uzZh7R/f1V98kmSK4AFghQQcklJ0pYtUkKCt/z8cwNJ6/TQQ8deY2XLrRLOZZd53fZOP50DAQAAsicbXtCo0VYtXFhFo0dX0MMPexWEYx0tUsBfZPMr/PqrF5o2bPDu22PH5LdXqVGjQ+rQoagbtNm2rVSsGLseAADkDPXqbdfChYW0YUMZTZwoXXNNpLco8ghSgE979x5rbbLgtGlTsPveMQUKeOObqlWTihT5UVOnNtaoUfPVrFkz9jcAAMhx8uVLkvScpKF6/HHp6qsZikCQAk7AAtKOHceCky1//HH860qU8EJTMDyVL3/sw2XTpr2S/izFBwAAkGO9oMKFH9by5XlcxWEbnhDLCFJACklJuVwrU7C1yW4PHDh+F1WocCw02WJBCgAAILrt1FVXbdOYMRU1bJh0+eWx3SpFkEJMszmbLDCtWNFY0hx99NF5rlhE2gGWNiFuMDRVqSIVLBipLQYAAIic667bqgkTKurrr735pWzcd6wiSCGm7NvntTL98ou3bN4cfKZBcsW9woVTd9OLi6MyDQAAgClb9ohuukkaMUKuVYogBUSp3buPhSYLUNu2Hf+aUqWsgt5aJSQ8rosvvl4tW54f083UAAAAJzJwoJLnxrSWqVidV4oWKUSVPXuk9eu9xcJTeoUhrBCEtTRVr+4tVoZ8+fKFSkgYqWLFehCiAAAATsCun3r1kkaPlqvg9+GHsbm7CFLI0Wy+JgtMa9d64Wnr1tTPW8tSxYrHQpMFKOu6BwAAgMz7v/+TxoyRPvrIvpCWGjaMvb1JkEKOYmOYbMLbYHCyQhFpi0PYmKaaNaUaNbxxThSGAAAACK0zzpCuvFJuct7hw6W33469PUyQQra3ZYs0ZUoZSZM0dmwj1wqVUsmSUq1a3mIBihYnAACA8Bs82AtS774rDR0q1a4dW3udIIVsOQnuDz9IU6d6zcWLFtlj1a1HrgtR1sJkgSkYnkqXjvQWAwAAxJ6mTaWOHaVp06Qnn5Ree00xhSCFbBOeFizwvtGw8PTzz6mfr19/n1aufEJdulynhg3rKnfuSG0pAAAAgu6/3wtSVnjioYe8+TZjBZejiCgbnGjNwtay1Lq19PzzXoiyViebLdtKa/72m/TWW6slPary5fcTogAAALKJ1q2lCy6QDh+W/vtfxRRapJDlLCi98443KNG68AUVLSp17eoNXLzoIqlIkWPPHZs4FwAAANmtVWruXO8LcLtfrpxiAkEKWcLGNk2Z4v0PNmvWscfz55c6dZKuvdZrgSpUiAMCAACQk1x8sdS8ubRkifTcc9Jjjykm0LUPYWWlyq3rnvWXvfpqL0TZ3E7W4vTmm15FvkmTpKuuIkQBAADkRLlySf/6l3f/xRelXbsUE2iRQlh88430zDPSe+9JR454j1WqJPXrJ910kzcxLgAAAKLDFVdYcTBp5UrppZe8L9KjHS1SCLE2uuWW2mrRwhsDZSHKBiBaq9Mvv3hzDBCiAAAAokvu3MfCk32Zvn+/oh5BCiGxbp2VLa8jaa4WLy6uvHml3r29vrJz5nhFJOwxAAAARKdrrvHm+ty2TXrjDUU9ghT+ko0brTS5t2zeXEzSQV111TatXSuNHSs1a8YOBgAAiAV580qDBnn3n3rKKzYWzQhSyJSdO6UPPpBef91rjcqTR2rQYKukWvq//9tA9z0AAIAY1LevFBfnFRyzL9qjGUEKvthka9ZVb8SIY3NANWok3XGH1KrVr9ZGxR4FAACIUQULSv/8p3f/3/+Wjh5V1CJI4ZT9+KP08svehGtWRKJGDenvf/fGP5UsyY4EAACAdPPNUunS0k8/SRMnRu8eIUjhpPbtk95/X3rnHemPP6RixaQrr5T69JEqVmQHAgAA4JiiRaW77vLuDxsmJSUpKhGkkKFAQPr+e68b34oV3mRrrVp53fjOPNP7GQAAAEjrzjul4sW9oSA2DU40IkghXVb73ybTnTxZOnBAqlBB6t9fuvhiKX9+dhoAAAAyVqqUdPfd3v0hQ6KzVSqiQWrevHnq3LmzKlWqpFy5cmnKlCmpng8EAnrooYcUFxenQoUKqX379lqzZk2q1/z+++/q2bOnihcvrpIlS6pfv37au3dvFv8l0cVKl9tYqFWrvMnV2rb1QpRVYAEAAABOxYABUokSXquUDROJNhENUvv27VPjxo01wvqOpePJJ5/U888/r1deeUVff/21ihQpog4dOigxMTH5NRaiVqxYoenTp+vjjz924exmG+EG36yqyuefS+PGSZZFy5b1AlSbNl55cwAAAOBUlSzphalgq1S0VfDLG8k3v+yyy9ySHmuNevbZZ/XAAw/oiiuucI+NHTtWFSpUcC1X11xzjeLj4/Xpp59q8eLFOuuss9xrXnjhBXXs2FH/+c9/XEsXTs3u3V5VFav5b2x3XnKJlC8fexAAAACZc/fd0rPPSitXetea11wTPXsy246RWr9+vTZv3uy68wWVKFFC55xzjhYsWOB+tlvrzhcMUcZenzt3bteClZGDBw9q9+7dqZZYZhPqvvqqF6Ks9v/VV0udOhGiAAAA8NeUKCHdc090tkpFtEXqRCxEGWuBSsl+Dj5nt+XLl0/1fN68eVW6dOnk16Rn+PDhGmJHMptKSEjQ9u3bQ75ea8FLW5Xvyy+lWbO8n62UeY8e3uBAAAAAIBTuukt65hlv/P2770rXXRcd+zXbBqlwGjx4sO4JRmPXrW23qlatquwSourWracDB/aH7T327t2jw4eljz7yBv+Zpk2tqyWtUAAAAAit4sWle++VHnhAGjrU6/0UDePvs22QqvjnTK9btmxxVfuC7OcmTZokv2br1q2pfu/IkSOukl/w99NToEABt2RH1hJlIapr13EqV65eSNe9Zs00zZ79oP74I0lz50q//eZV5bMAlaJ3JAAAABDyeaWeflpavVqaMMEKxuX8HZxtg1TNmjVdGJo5c2ZycLKWIxv7dOutt7qfW7ZsqZ07d2rJkiVq3ry5e2zWrFlKSkpyY6lyMgtRcXHNQrrO7duta19jzZ7dUlb4sFAh6aqrbF+H9G0AAACA41ql/vlP6f77j7VK5c22SSQHFJuw+Z6WLVvmlmCBCbtv3dtsXqm7775bjz32mD766CMtX75cffr0cZX4unTp4l5fr149XXrpperfv78WLVqk+fPn64477nAV/ajYd7wtW6yV7gslJhZ0pc1vuokQBQAAgKxxxx1SmTLSjz9K77yT8/d6RIPUN998o6ZNm7rF2Lglu2+T8Jr77rtPd955p5sXqkWLFi54WbnzglZa7k/jx49X3bp1ddFFF7my5+edd55ee+21iP1N2ZVl1a++aiupmMqV26F+/aTSpSO9VQAAAIgVxYp5rVLGWqWOHFGOFtEGtQsvvNDNF5URa5UaOnSoWzJiFfrefvvtMG1hzme794svpNmzg7l5vFq3LqeCBS+J9KYBAAAgBlul/vtf6aefrEFE6ttXOVa2nUcKoQlRn30WDFHS6aevkNRbuXNnHF4BAACAcClaVBo40LtvbSVWSTqnIkhFqaQkr7x5cF7iDh2kBg1sLBohCgAAAJFz++02N6y0bp305ps590gQpKKQ9TedONEbF5Url2S1Oc49N9JbBQAAAEhFinhzSpkhQ6T94Zs+NawIUlEYomzGaJs52iY669FDatw40lsFAAAAHHPzzVKNGtLmzdLzzytHyuHV25E2RNkEZ2vXenX5r71WqlWLfQQAAIDQiI+3eUlDo1+/0nrwwRp6/PEj6tRpkxo2rKqchCAVJWygnrVEWYjKl0+67jov5QMAAAB/1d69m6ymtnr16hXiznHfas+eRmrW7D2tXXuVqlWrppyCIBUlIcpaomzAnoWonj2l6tUjvVUAAACIFomJO13RsrZtX1SdOi1Dtt5ffinsqkwfOXKbVq78STkoRxGkoiFE2czQ69cTogAAABBepUrVVlxcs5Ctr2JFK5C2V1u2FNUbb1TUpZcqx6DYRA526JBkcxFbiMqfX7KWVlqiAAAAkFPkyiWdffZvkiaoV6+tykkIUjk4RFlL1M8/HwtROakpFAAAADBxcfskXatq1Q4qJyFI5eDqfClDVNWcVeQEAAAAyNEIUjnM0aPeZLspu/MRogAAAICsRZDKQZKSpClTpB9/PDZPFCEKAAAAyHoEqRwiEJA+/lj64Qcpd26pRw/miQIAAAAihSCVQ0LU559L337rVTbp1k2qUyfSWwUAAADELoJUDjBnjrRwoXf/b3+Tzjwz0lsEAAAAxDaCVDb31VfSvHne/csuk5o0ifQWAQAAACBIZWPffCNNn+7db9fOJiuL9BYBAAAAMHnZDdnTmjWlNXu2d791a+n88yO9RQAAAACCaJHKlrpozpzq7l6LFtJFF0V6ewAAAACkRJDKZhYsKCZpggKBXG48lI2Lskp9AAAAALIPglQ2Mn++dO+9p0kqoJo1/1DnzoQoAAAAIDsiSGUjlSpJZcseljRN7dr97CbeBQAAAJD9cKmejdSsKb355mpJ3ZUnTyDSmwMAAAAgAwSpbKZcuSOSEiO9GQAAAABOgCAFAAAAAD4RpAAAAADAJ4IUAAAAAPhEkAIAAAAAnwhSAAAAAOATQQoAAAAAfCJIAQAAAIBPBCkAAAAA8IkgBQAAAAA+EaQAAAAAwCeCFAAAAAD4RJACAAAAAJ8IUgAAAADgE0EKAAAAAHwiSAEAAACATwQpAAAAAPCJIAUAAAAAPhGkAAAAAMAnghQAAAAA+ESQAgAAAACfCFIAAAAA4BNBCgAAAAB8IkgBAAAAgE8EKQAAAAAgSAEAAABAeNEiBQAAAAA+EaQAAAAAwCeCFAAAAAD4RJACAAAAAJ8IUgAAAADgE0EKAAAAAHwiSAEAAACATwQpAAAAAPCJIAUAAAAAPhGkAAAAAMAnghQAAAAA+ESQAgAAAACfCFIAAAAA4BNBCgAAAAB8IkgBAAAAgE8EKQAAAADwiSAFAAAAALEapEaMGKEaNWqoYMGCOuecc7Ro0aJIbxIAAACAKBUVQerdd9/VPffco4cfflhLly5V48aN1aFDB23dujXSmwYAAAAgCkVFkHr66afVv39/3XDDDapfv75eeeUVFS5cWCNHjoz0pgEAAACIQnmVwx06dEhLlizR4MGDkx/LnTu32rdvrwULFqT7OwcPHnRL0K5du9zt7t27FWl79+51txs3LtGhQ979UNm2Lf7P2+X65ZdCCrXt21e7Wzsewb8jVFavXh22/RLufRPO/RLufcM5kzHO96zfL4bzPev3i+EzMn2c79H3Gcm/e5HZN9v/PF/sXMkO1+PBbQgEAid8Xa7AyV6RzW3cuFGVK1fWV199pZYtWyY/ft9992nu3Ln6+uuvj/udRx55REOGDMniLQUAAACQU2zYsEFVqlSJ3hapzLDWKxtTFZSUlKTff/9dZcqUUa5cuSKegKtWreoOXPHixSO6LbGCfc7+jnac4+zzaMc5zj6PBZznWcfamfbs2aNKlSqd8HU5PkiVLVtWefLk0ZYtW1I9bj9XrFgx3d8pUKCAW1IqWbKkshMLUQQp9nk04xxnn8cCznP2d7TjHGefR6sSJUpEf7GJ/Pnzq3nz5po5c2aqFib7OWVXPwAAAAAIlRzfImWsm17fvn111lln6eyzz9azzz6rffv2uSp+AAAAABBqURGkrr76am3btk0PPfSQNm/erCZNmujTTz9VhQoVlNNYl0ObDytt10Owz6MF5zj7PBZwnrO/ox3nOPscUVC1DwAAAACyWo4fIwUAAAAAWY0gBQAAAAA+EaQAAAAAwCeCFAAAAAD4RJAKsREjRqhGjRoqWLCgzjnnHC1atOiEr7dS7WeccYYKFSqkqlWrasCAAUpMTPS1Tnv97bffrjJlyqho0aLq3r37cRMUR7NQ7/Phw4erRYsWKlasmMqXL68uXbpo9erVqdZx4YUXKleuXKmWW265RbEg1Pv7kUceOW5f1q1bN9U6OMdDu89tXWn3uS32ORIUy+e43/P88OHDGjp0qE477TT3+saNG7vKsX7XyXke2n3OZ3nWnuN8lmf9PuezPBuwqn0IjQkTJgTy588fGDlyZGDFihWB/v37B0qWLBnYsmVLuq8fP358oECBAu52/fr1gc8++ywQFxcXGDBggK913nLLLYGqVasGZs6cGfjmm28C5557bqBVq1YxcVjDsc87dOgQGDVqVOCHH34ILFu2LNCxY8dAtWrVAnv37k1+zQUXXODea9OmTcnLrl27AtEuHPv74YcfDpx55pmp9uW2bdtSrYdzPLT7fOvWran29/Tp0616a2D27NmBWD/HM3Oe33fffYFKlSoFPvnkk8DatWsDL730UqBgwYKBpUuX+lon53lo9zmf5Vl7jvNZnvX7nM/yyCNIhdDZZ58duP3225N/Pnr0qPufYPjw4em+3l7brl27VI/dc889gdatW5/yOnfu3BnIly9fYOLEicmviY+PdxdFCxYsCES7cOzztOyDyvbn3LlzU11k3nXXXYFYE479bf/4Nm7cOMP35BwP/zlu5/Jpp50WSEpKCsT6OZ6Z89yC6osvvpjqsW7dugV69ux5yuvkPA/9Pk+Lz/LwnuN8lmf9Pk+Lz/KsR9e+EDl06JCWLFmi9u3bJz+WO3du9/OCBQvS/Z1WrVq53wk27a5bt07Tpk1Tx44dT3md9rw1/6Z8jXWLqlatWobvGy3Csc/Ts2vXLndbunTpVI+PHz9eZcuWVYMGDTR48GDt379f0Syc+3vNmjWqVKmSatWqpZ49eyohISH5Oc7x8J7jdlzHjRunG2+80XXfi+VzPLPn+cGDB13Xm5SsW+WXX355yuvkPA/tPk8Pn+XhO8eD+CzP+n2e8j34LM96eSPwnlFp+/btOnr0qCpUqJDqcft51apV6f7Odddd537vvPPOs5ZBHTlyxI1BuP/++095nZs3b1b+/PlVsmTJ415jz0WzcOzztJKSknT33XerdevW7mIy5XqqV6/uLv6///57DRo0yI2jmjRpkqJVuPa39RMfPXq0G9OzadMmDRkyROeff75++OEHN06Nczy85/iUKVO0c+dOXX/99cetJ9bO8cye5x06dNDTTz+tNm3auPEMM2fOdPvJ1nOq6+Q8D+0+T4vP8vCe44bP8qzf5ynxWR4ZBKkImjNnjh5//HG99NJL7gPop59+0l133aVHH31UDz74YCQ3LWr53ec2+N4u6NN+A3TzzTcn32/YsKHi4uJ00UUXae3ate4DD6e+vy+77LLk3dWoUSP3OruAf++999SvXz92ZZjP8TfffNMdAwtMnOOZ89xzz6l///6uN4C16tlnwA033KCRI0dy/maTfc5nefj3N5/lkT3H+SyPDLr2hYh1f8mTJ89x1fLs54oVK6b7O3ZR07t3b910003uYrxr167uAsgqDdm3Z6eyTru15lz7RvlU3zdahGOfp3THHXfo448/1uzZs1WlSpUTbotdsBq7aI1W4d7fQda6evrppyfvS87x8O3zX375RTNmzHCvPZlYOMcze56XK1fOfRu8b98+t0/tG2aroGpdVU91nZznod3nKfFZHv5zPD18lmfdPuezPHIIUiFi3euaN2/uml6D7KLFfm7ZsmW6v2PjDayPbEr2P5qxLjmnsk57Pl++fKleY91vbIxJRu8bLcKxz4O39g/v5MmTNWvWLNWsWfOk27Js2TJ3ay1T0Spc+zutvXv3upa94L7kHA/fPh81apQr8d+pUyedTCyc45k9z4NsPEPlypVdd8oPPvhAV1xxxSmvk/M8tPvc8Fmeded4evgsz7p9zmd5BEWgwEVUl7a0ssOjR48OrFy5MnDzzTe70pabN292z/fu3Tvwf//3f6kq3BQrVizwzjvvBNatWxf4/PPPXeWsHj16nPI6gyVzrTz3rFmzXPnzli1buiUWhGOf33rrrYESJUoE5syZk6r08/79+93zP/30U2Do0KFuX1t56Q8//DBQq1atQJs2bQLRLhz7+95773X72vbl/PnzA+3btw+ULVvWVdgK4hwP7T4PVoyyz41BgwYdd5xj+RzPzHm+cOHCwAcffOBKFM+bN89VTaxZs2bgjz/+OOV1Gs7z0O5zPsuz9hznszzrP1cMn+WRRZAKsRdeeMFdnNhcAVbq0v5HSFlOuG/fvsk/Hz58OPDII4+4ixybG8DmgrrtttuO+5/kROs0Bw4ccL9XqlSpQOHChQNdu3Z1F/6xItT73L5fSG+xuaVMQkKCu6AsXbq0+1CsXbt2YODAgTEzx06o9/fVV1/tyrza+ipXrux+tgv5lDjHQ/+5YvNL2Xm9evXq445xrJ/jfs9z+yKgXr16bl+VKVPGXRD99ttvvtZpOM9Du8/5LM/ac5zP8sh8rvBZHlm57D+RbBEDAAAAgJyGMVIAAAAA4BNBCgAAAAB8IkgBAAAAgE8EKQAAAADwiSAFAAAAAD4RpAAAAADAJ4IUAAAAAPhEkAIAAAAAnwhSAAAAAOATQQoAEHKPPvqoSpUqpX379qV6fMSIESpQoIC2bNnCXgcA5GgEKQBAyN18883av3+/xo4dm/xYIBDQCy+8oKuvvloVKlRgrwMAcjSCFAAg5CwoWWCy4BT02WefafXq1frHP/6hOXPmKFeuXG7JnTu3ypcvr379+ikxMTH59Rs2bFCPHj1UsmRJlS5dWldccYV+/vnn5Oevv/56denSRUOGDFG5cuVUvHhx3XLLLTp06FDya5KSkjR8+HDVrFlThQoVUuPGjfX+++8nPx/cjp07d6bafntsypQp7r69p/28bNmyVK+pUaOGnn322eSfExIS3DYWLVrUbYtte9qWt6lTp6pFixYqWLCgypYtq65du7rHL7zwwuT9kXZ55JFH0n0/AEBkEaQAAGFhgSk+Pl6ff/65+/m5555Ty5YtddZZZyW/xoLVb7/9pnHjxundd9/VqFGj3OOHDx9Whw4dVKxYMX3xxReaP3++CyiXXnppqqA0c+ZM9x4WiN555x1NmjTJBasgC1HWKvbKK69oxYoVGjBggHr16qW5c+eG9G+1wGYh6vfff3frnj59utatW+fCZNAnn3ziglPHjh317bffum0/++yz3XO23Zs2bXKL7aN77703+ed//vOfId1WAEBo5A3RegAASMUCk4WC559/3rWmWIuUhZ2UrCXKWpxsLFX+/PlVokQJ97iFKgsnb7zxhmuVMRay7LUWmi655BL3mP3OyJEjVbhwYZ155pkaOnSoBg4c6MZoWRh7/PHHNWPGDLcdplatWvryyy/16quv6oILLgjZEbNQtHz5cq1fv15Vq1Z1j1mAs21avHixa4UaNmyYrrnmmlRBz1rIjLW4BdnfZKGxYsWKnFEAkI0RpAAAYW2Vuu6669z4qEqVKql79+6pnq9SpYp7zsZT9ezZU9dee617/LvvvtNPP/3kWqRSsq5/a9euTRVELEQFWWDau3ev6xZot7beiy++ONU6rEWradOmx23HybRq1cp1QwyydQdZq5gFqGCIMvXr13fBz56zIGVdA/v376+/YtCgQXrggQfc32zrt3DWunXrv7ROAEDmEKQAAGFz5ZVXum5q06ZN02OPPaa8eVP/s2Pd9iwsWUuOFah4+umn3estBDVv3lzjx48/bp02HupU2DqCXeoqV66c6jmrHJjedgTVqVPnuPVZK1m9evWSf7ZxTX7YGK2/ylrbbGyYteA99dRT6ty5szZv3uxasQAAWYsgBQAI3z8yefOqW7duev31111QSsuKQFirTe3atV1r1eTJk12QatasmQsu1vXPCjdkxFquDhw4kBxSFi5c6LrFWcuQdZezwGRFIE7WjS+4HSdi67TtTPm3BVnAslYwW4KtUitXrnRFLKzlyDRq1Mh1AbzhhhuUWVagIrgNgwcPdkHT/r6U2wUAyBoUmwAAhIVd4FtwsHBk3fbSa0naunWra1H5+uuvXUW7unXrusft9RYarICDtRZZi5WNjbKugr/++muqbnpW7c9Ci7V6Pfzww7rjjjtcFzxrYbJCDVZgYsyYMa5L4NKlS10lQfs5lNq3b6+GDRu67bb3WLRokfr06eMCXLC4hm2bjRGzW+vuZ2OqnnjiCV/vc+TIEde9cceOHW5smI0pS9mdEACQdQhSAICwsMBw+eWX67TTTnNFINJzxhlnKC4uzr3OAseTTz7pHrcxQPPmzVO1atVci5a1+ATLo6dsobroootcN7w2bdq4Cnl/+9vfksuFGys68eCDD7rqfbYOq/pnXf2sBSqUrCDGhx9+6CYhtm2xYGWFLaxVLWVXwIkTJ+qjjz5SkyZN1K5dOxe4/Hbts9Y3C08WMC2kpu2mCADIGrkCNsoXAIAcxsYKWde54HxPAABkJVqkAAAAAMAnghQAAAAA+ETXPgAAAADwiRYpAAAAAPCJIAUAAAAAPhGkAAAAAMAnghQAAAAA+ESQAgAAAACfCFIAAAAA4BNBCgAAAAB8IkgBAAAAgPz5f+u6oCEW5MZ1AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "sns.histplot(df['confidence_score'], bins=30, kde=True, color='blue')\n",
    "plt.title('Распределение confidence_score')\n",
    "plt.xlabel('Уверенность')\n",
    "plt.ylabel('Частота')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb60ca85-7c6c-4ccf-be2c-ce480283297b",
   "metadata": {},
   "source": [
    "## 4. Сравнение confidence_score для REAL и FAKE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "347b5cd5-84b3-4172-9b4c-57668a8a6e7a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ardor\\AppData\\Local\\Temp\\ipykernel_19668\\2819408524.py:2: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  sns.boxplot(data=df, x='label', y='confidence_score', palette='Set1')\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "sns.boxplot(data=df, x='label', y='confidence_score', palette='Set1')\n",
    "plt.title('Уверенность модели для REAL и FAKE изображений')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29746ebe-43ce-4826-9b12-d40b286782c6",
   "metadata": {},
   "source": [
    "## 5. Аналитика по полу (gender)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d71cb5f0-7cce-461f-9163-ab9f3cd2e2c7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gender\n",
      "Unknown    4507\n",
      "Female     1033\n",
      "Male       1017\n",
      "Name: count, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gender_counts = df['gender'].value_counts()\n",
    "print(gender_counts)\n",
    "plt.figure(figsize=(6,6))\n",
    "plt.pie(gender_counts, labels=gender_counts.index, autopct='%1.1f%%', startangle=90)\n",
    "plt.title('Распределение по полу')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ddaae9a-4fe6-49b7-ae76-b34796a4ffb1",
   "metadata": {},
   "source": [
    "## 6. Распределение по dataset_split (train/val/test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "97cb6ca0-439a-4841-abdb-560f0279a23c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dataset_split\n",
      "train    4593\n",
      "test     1323\n",
      "val       641\n",
      "Name: count, dtype: int64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ardor\\AppData\\Local\\Temp\\ipykernel_19668\\1771478815.py:4: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  sns.barplot(x=split_counts.index, y=split_counts.values, palette='pastel')\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "split_counts = df['dataset_split'].value_counts()\n",
    "print(split_counts)\n",
    "plt.figure(figsize=(6,4))\n",
    "sns.barplot(x=split_counts.index, y=split_counts.values, palette='pastel')\n",
    "plt.title('Разбиение датасета')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "975704eb-8f65-4473-ad13-f72a6bfb8f1f",
   "metadata": {},
   "source": [
    "## 7. Группировка: средняя уверенность по возрастной группе и полу"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a716ef5e-48f0-4e47-83ea-d42e219af9b2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gender       Female      Male   Unknown\n",
      "age_group                              \n",
      "18-25      0.920709  0.922254  0.900649\n",
      "26-35      0.909707  0.916178  0.896528\n",
      "36-50      0.914904  0.918857  0.896203\n",
      "50+        0.922430  0.913606  0.896737\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x600 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "grouped = df.groupby(['age_group', 'gender'])['confidence_score'].mean().unstack()\n",
    "print(grouped)\n",
    "plt.figure(figsize=(10,6))\n",
    "grouped.plot(kind='bar', edgecolor='black')\n",
    "plt.title('Средняя уверенность по возрастной группе и полу')\n",
    "plt.ylabel('Средний confidence_score')\n",
    "plt.xticks(rotation=45)\n",
    "plt.legend(title='Gender')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b22e2713-ef07-4670-be9f-82fcd9f24775",
   "metadata": {},
   "source": [
    "## 8. Связь между качеством изображения и уверенностью"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "139c66f6-2fa8-465d-90e8-6ec985df0381",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ardor\\AppData\\Local\\Temp\\ipykernel_19668\\1243641881.py:2: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  sns.boxplot(data=df, x='image_quality', y='confidence_score', palette='coolwarm')\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,5))\n",
    "sns.boxplot(data=df, x='image_quality', y='confidence_score', palette='coolwarm')\n",
    "plt.title('Зависимость уверенности от качества изображения')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e87c755c-345e-4c08-96e1-5d21f3c349b6",
   "metadata": {},
   "source": [
    "## 9. Топ-5 самых распространённых категорий (category)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "719c55d2-5314-4393-829f-6c4f992ccc17",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "category\n",
      "AI Generated    3767\n",
      "Authentic       2790\n",
      "Name: count, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "top_categories = df['category'].value_counts().head(5)\n",
    "print(top_categories)\n",
    "plt.figure(figsize=(8,5))\n",
    "top_categories.plot(kind='bar', color='teal')\n",
    "plt.title('Топ-5 категорий')\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04ce32c5-7c4b-485b-9ad5-b339ca6fa45e",
   "metadata": {},
   "source": [
    "## 10. TQDM – пример обработки с прогресс-баром"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "0ce60f36-a4c6-4583-9b7d-ebc9260e3868",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Обработка строк: 100%|████████████████████████████████████████████████████████████| 100/100 [00:00<00:00, 32336.01it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TQDM работает\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# Пример: итерация по строкам с tqdm\n",
    "for idx, row in tqdm(df.head(100).iterrows(), total=100, desc=\"Обработка строк\"):\n",
    "    # Просто демонстрация\n",
    "    pass\n",
    "print(\"TQDM работает\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4a8dc57-510d-4c55-9dad-36cb829cbfad",
   "metadata": {},
   "source": [
    "## Выводы по датасету\n",
    "- Датасет содержит REAL и FAKE изображения в соотношении ...\n",
    "- Модель более уверена в REAL изображениях (средний confidence_score выше).\n",
    "- Разбиение train/val/test: большая часть в train, что нормально.\n",
    "- Качество изображения High даёт более высокую уверенность, чем Medium."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.14.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}