Codesoft Task1 MOVIE GENRE CLASSIFICATION.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ea540c49",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score, classification_report"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e518a768",
   "metadata": {},
   "source": [
    "## Importing the data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "631126e7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\chatt\\AppData\\Local\\Temp\\ipykernel_8416\\3211111246.py:1: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support regex separators (separators > 1 char and different from '\\s+' are interpreted as regex); you can avoid this warning by specifying engine='python'.\n",
      "  train_data = pd.read_csv(\"train_data.txt\",sep=':::', names=['ID', 'TITLE', 'GENRE', 'DESCRIPTION'])\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>ID</th>\n",
       "      <th>TITLE</th>\n",
       "      <th>GENRE</th>\n",
       "      <th>DESCRIPTION</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Oscar et la dame rose (2009)</td>\n",
       "      <td>drama</td>\n",
       "      <td>Listening in to a conversation between his do...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Cupid (1997)</td>\n",
       "      <td>thriller</td>\n",
       "      <td>A brother and sister with a past incestuous r...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Young, Wild and Wonderful (1980)</td>\n",
       "      <td>adult</td>\n",
       "      <td>As the bus empties the students for their fie...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>The Secret Sin (1915)</td>\n",
       "      <td>drama</td>\n",
       "      <td>To help their unemployed father make ends mee...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>The Unrecovered (2007)</td>\n",
       "      <td>drama</td>\n",
       "      <td>The film's title refers not only to the un-re...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID                               TITLE       GENRE  \\\n",
       "0   1       Oscar et la dame rose (2009)       drama    \n",
       "1   2                       Cupid (1997)    thriller    \n",
       "2   3   Young, Wild and Wonderful (1980)       adult    \n",
       "3   4              The Secret Sin (1915)       drama    \n",
       "4   5             The Unrecovered (2007)       drama    \n",
       "\n",
       "                                         DESCRIPTION  \n",
       "0   Listening in to a conversation between his do...  \n",
       "1   A brother and sister with a past incestuous r...  \n",
       "2   As the bus empties the students for their fie...  \n",
       "3   To help their unemployed father make ends mee...  \n",
       "4   The film's title refers not only to the un-re...  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(54214, 4)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\chatt\\AppData\\Local\\Temp\\ipykernel_8416\\3211111246.py:6: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support regex separators (separators > 1 char and different from '\\s+' are interpreted as regex); you can avoid this warning by specifying engine='python'.\n",
      "  test_data = pd.read_csv(\"test_data.txt\",sep=':::', names=['ID', 'TITLE', 'GENRE', 'DESCRIPTION'])\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>ID</th>\n",
       "      <th>TITLE</th>\n",
       "      <th>GENRE</th>\n",
       "      <th>DESCRIPTION</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Edgar's Lunch (1998)</td>\n",
       "      <td>L.R. Brane loves his life - his car, his apar...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>La guerra de papá (1977)</td>\n",
       "      <td>Spain, March 1964: Quico is a very naughty ch...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Off the Beaten Track (2010)</td>\n",
       "      <td>One year in the life of Albin and his family ...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Meu Amigo Hindu (2015)</td>\n",
       "      <td>His father has died, he hasn't spoken with hi...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Er nu zhai (1955)</td>\n",
       "      <td>Before he was known internationally as a mart...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID                          TITLE  \\\n",
       "0   1          Edgar's Lunch (1998)    \n",
       "1   2      La guerra de papá (1977)    \n",
       "2   3   Off the Beaten Track (2010)    \n",
       "3   4        Meu Amigo Hindu (2015)    \n",
       "4   5             Er nu zhai (1955)    \n",
       "\n",
       "                                               GENRE  DESCRIPTION  \n",
       "0   L.R. Brane loves his life - his car, his apar...          NaN  \n",
       "1   Spain, March 1964: Quico is a very naughty ch...          NaN  \n",
       "2   One year in the life of Albin and his family ...          NaN  \n",
       "3   His father has died, he hasn't spoken with hi...          NaN  \n",
       "4   Before he was known internationally as a mart...          NaN  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None\n",
      "(54200, 4)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\chatt\\AppData\\Local\\Temp\\ipykernel_8416\\3211111246.py:10: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support regex separators (separators > 1 char and different from '\\s+' are interpreted as regex); you can avoid this warning by specifying engine='python'.\n",
      "  test_solution_data = pd.read_csv(\"test_data_solution.txt\",sep=':::', names=['ID', 'TITLE', 'GENRE', 'DESCRIPTION'])\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>ID</th>\n",
       "      <th>TITLE</th>\n",
       "      <th>GENRE</th>\n",
       "      <th>DESCRIPTION</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Edgar's Lunch (1998)</td>\n",
       "      <td>thriller</td>\n",
       "      <td>L.R. Brane loves his life - his car, his apar...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>La guerra de papá (1977)</td>\n",
       "      <td>comedy</td>\n",
       "      <td>Spain, March 1964: Quico is a very naughty ch...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Off the Beaten Track (2010)</td>\n",
       "      <td>documentary</td>\n",
       "      <td>One year in the life of Albin and his family ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Meu Amigo Hindu (2015)</td>\n",
       "      <td>drama</td>\n",
       "      <td>His father has died, he hasn't spoken with hi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Er nu zhai (1955)</td>\n",
       "      <td>drama</td>\n",
       "      <td>Before he was known internationally as a mart...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID                          TITLE          GENRE  \\\n",
       "0   1          Edgar's Lunch (1998)       thriller    \n",
       "1   2      La guerra de papá (1977)         comedy    \n",
       "2   3   Off the Beaten Track (2010)    documentary    \n",
       "3   4        Meu Amigo Hindu (2015)          drama    \n",
       "4   5             Er nu zhai (1955)          drama    \n",
       "\n",
       "                                         DESCRIPTION  \n",
       "0   L.R. Brane loves his life - his car, his apar...  \n",
       "1   Spain, March 1964: Quico is a very naughty ch...  \n",
       "2   One year in the life of Albin and his family ...  \n",
       "3   His father has died, he hasn't spoken with hi...  \n",
       "4   Before he was known internationally as a mart...  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None\n",
      "(54200, 4)\n"
     ]
    }
   ],
   "source": [
    "train_data = pd.read_csv(\"train_data.txt\",sep=':::', names=['ID', 'TITLE', 'GENRE', 'DESCRIPTION'])\n",
    "display(train_data.head())\n",
    "print(train_data.shape)\n",
    "\n",
    "\n",
    "test_data = pd.read_csv(\"test_data.txt\",sep=':::', names=['ID', 'TITLE', 'GENRE', 'DESCRIPTION'])\n",
    "print(display(test_data.head()))\n",
    "print(test_data.shape)\n",
    "\n",
    "test_solution_data = pd.read_csv(\"test_data_solution.txt\",sep=':::', names=['ID', 'TITLE', 'GENRE', 'DESCRIPTION'])\n",
    "print(display(test_solution_data.head()))\n",
    "print(test_solution_data.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6141a120",
   "metadata": {},
   "source": [
    "## Performing some data visualization method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f6a9f6b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABqQAAAK7CAYAAACUH+5lAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8pXeV/AAAACXBIWXMAAA9hAAAPYQGoP6dpAAC7OklEQVR4nOzdeVxV1f7/8fdR4MABGZxARSUHDA2cyHIKNA3TbqI24RSiOZSaGg7cLIcyEiU1y7IsMEtN08zUHDIx51BTU8nMkXvFi5mCIxCc3x/9OF9POAByPA6v5+OxH7HXXnutz97wT74fa22D2Ww2CwAAAAAAAAAAALCRUvYuAAAAAAAAAAAAAHc3AikAAAAAAAAAAADYFIEUAAAAAAAAAAAAbIpACgAAAAAAAAAAADZFIAUAAAAAAAAAAACbIpACAAAAAAAAAACATRFIAQAAAAAAAAAAwKYIpAAAAAAAAAAAAGBTBFIAAAAAAAAAAACwKQIpAAAAADaVmJgog8EgZ2dnHTt2rMD10NBQPfDAA3aoTEpKSpLBYNBXX31ll/mL6ujRo+rQoYPKli0rg8GgIUOGXLOvn5+fDAaDQkNDr3r9s88+k8FgkMFgUFJSkk3qlaSxY8fKYDDYbPzb3caNGxUREaFq1arJaDTK1dVV9erV0yuvvKJff/3V3uUBAAAAtwyBFAAAAIBbIisrS6NHj7Z3GXe0oUOHatu2bfr000+1ZcsWDR069Lr9y5Qpox9//FGHDh0qcO3TTz+Vu7u7rUq16NOnj7Zs2WLzeW5Ho0ePVsuWLXXs2DGNHj1aK1eu1JIlSxQVFaU1a9YoICBAubm59i4TAAAAuCUIpAAAAADcEu3atdPcuXO1e/due5dyy126dElms/mmx9m7d6+aNGmi8PBwPfzww6pevfp1+7do0UJVqlTRp59+atV+6NAh/fjjj3r22WdvuqYb8fX11cMPP2zzeezh4sWL17w2b948TZgwQf3799emTZvUt29ftWrVSm3bttUrr7yi3bt367333ruF1f7NbDbr0qVLt3xeAAAAgEAKAAAAwC0xYsQIlStXTiNHjrxuv6NHj8pgMCgxMbHANYPBoLFjx1rO87eD27Nnj55++ml5eHiobNmyGjZsmP766y8dOHBA7dq1U5kyZeTn56e4uLirznn58mUNGzZMPj4+cnFxUUhIiH7++ecC/bZv364nn3xSZcuWlbOzsxo2bKgFCxZY9cnfonD16tWKiopShQoVZDKZlJWVdc1nPn78uLp3766KFSvKaDQqICBA8fHxysvLk/R/Wwv+/vvv+u677yxb7R09evS677JUqVLq2bOnZs+ebRlL+nt1VNWqVdWmTZur3rd06VI1bdpUJpNJZcqUUdu2ba1WOS1ZskQGg0Fr164tcO8HH3xg+Z1I196y78svv1TTpk3l6uoqNzc3hYWFFXjnhw8f1nPPPafKlSvLaDTK29tbjz76qHbt2nXd546MjJSbm5v27dunRx99VK6urqpQoYIGDhxYIEQym82aMWOGGjRoIBcXF3l5eempp57S4cOHrfrlby35448/qlmzZjKZTIqKirpmDW+++abKly+vKVOmXPX5DQaDXnrpJZUuXdqq/fvvv9ejjz4qd3d3mUwmNW/evMB7zn+n+/btU0REhDw8POTt7a2oqChlZGQUmGfgwIH68MMPFRAQIKPRqNmzZ0uSDh48qK5du1r93b3//vvXfbcAAABAcRFIAQAAALglypQpo9GjR2vVqlX64YcfSnTsZ555RvXr19eiRYv0wgsvaMqUKRo6dKjCw8PVoUMHff3112rdurVGjhypxYsXF7j/3//+tw4fPqxZs2Zp1qxZOnHihEJDQ61CiXXr1ql58+Y6e/asPvzwQ33zzTdq0KCBnn322auGZ1FRUXJ0dNScOXP01VdfydHR8aq1nzp1Ss2aNdPq1av1xhtvaOnSpWrTpo2io6M1cOBASVKjRo20ZcsW+fj4qHnz5tqyZYu2bNmiSpUq3fDdREVF6cSJE1q1apUkKTc3V7Nnz1ZkZKRKlSr4v4Rz585Vx44d5e7urnnz5umTTz7RmTNnFBoaqo0bN0qSnnjiCVWsWFEJCQkF7k9MTFSjRo0UFBR0zZreeustRUREqG7dulqwYIHmzJmjc+fOqWXLltq/f7+lX/v27bVjxw7FxcVpzZo1+uCDD9SwYUOdPXv2hs+dk5Oj9u3b69FHH9WSJUs0cOBAzZw5s8CqsH79+mnIkCFq06aNlixZohkzZmjfvn1q1qyZ/ve//1n1TUtLU/fu3dW1a1etWLFCL7744lXnPnHihPbv36+2bdvK2dn5hrXm+/zzz/XYY4/J3d1ds2fP1oIFC1S2bFmFhYVdNfzr0qWL/P39tWjRIo0aNUpz58696jaOS5Ys0QcffKDXX39dq1atsrznBx98UHv37lV8fLyWLVumDh06aPDgwRo3blyhawYAAAAKzQwAAAAANpSQkGCWZE5OTjZnZWWZa9SoYQ4ODjbn5eWZzWazOSQkxFyvXj1L/yNHjpglmRMSEgqMJck8ZswYy/mYMWPMkszx8fFW/Ro0aGCWZF68eLGlLScnx1yhQgVz586dLW3r1q0zSzI3atTIUo/ZbDYfPXrU7OjoaO7Tp4+l7f777zc3bNjQnJOTYzXXE088Ya5UqZI5NzfX6nl79uxZqPczatQosyTztm3brNoHDBhgNhgM5gMHDljaqlevbu7QoUOhxr2yb0hIiPmpp54ym81m8/Lly80Gg8F85MgR88KFC82SzOvWrTObzWZzbm6uuXLlyubAwEDL85jNZvO5c+fMFStWNDdr1szSNmzYMLOLi4v57Nmzlrb9+/ebJZmnT59uacv/HeU7fvy42cHBwTxo0CCres+dO2f28fExP/PMM2az2Wz+448/zJLMU6dOLdTzXun55583SzJPmzbNqn3ChAlmSeaNGzeazWazecuWLVf9+0lNTTW7uLiYR4wYYWkLCQkxSzKvXbv2hvNv3brVLMk8atSoAtf++usvc05OjuXI/7u7cOGCuWzZsuZ//etfVv1zc3PN9evXNzdp0sTSlv9O4+LirPq++OKLZmdnZ6u/ZUlmDw8P859//mnVNywszOzr62vOyMiwah84cKDZ2dm5QH8AAADgZrFCCgAAAMAt4+TkpDfffFPbt28vsNXdzXjiiSeszgMCAmQwGPT4449b2hwcHFSrVi0dO3aswP1du3a12latevXqatasmdatWydJ+v333/Xrr7+qW7dukqS//vrLcrRv315paWk6cOCA1ZhdunQpVO0//PCD6tatqyZNmli1R0ZGymw2l8hqsqioKC1dulSnT5/WJ598olatWsnPz69AvwMHDujEiRPq0aOH1eopNzc3denSRVu3brVseRcVFaVLly7pyy+/tPRLSEiQ0WhU165dr1nLqlWr9Ndff6lnz55W79HZ2VkhISFKSkqSJJUtW1Y1a9bUpEmT9M477+jnn3+22nawMPJ/X/ny68r/vS5btkwGg0Hdu3e3qsXHx0f169e31JLPy8tLrVu3LlIN/1SuXDk5OjpajkWLFkmSNm/erD///FPPP/+8VS15eXlq166dkpOTdeHCBauxnnzySavzoKAgXb58Wenp6VbtrVu3lpeXl+X88uXLWrt2rTp16iSTyVTg7/ny5cvaunXrTT0nAAAA8E8EUgAAAABuqeeee06NGjXSq6++qpycnBIZs2zZslbnTk5OMplMBbZLc3Jy0uXLlwvc7+Pjc9W206dPS5Jl67bo6GirMMHR0dGybdsff/xhdX9httOTpNOnT1+1b+XKlS3Xb9ZTTz0lZ2dnTZkyRd9++6169+59zVqkq9deuXJl5eXl6cyZM5KkevXq6cEHH7Rs25ebm6vPP/9cHTt2LPD7uFL+u3zwwQcLvMsvv/zS8h7zv1EVFhamuLg4NWrUSBUqVNDgwYN17ty5Gz6zg4ODypUrZ9WW/3u+8vdqNpvl7e1doJatW7cW+3datWpVSbpq+JmUlKTk5GR9+OGHVu357+Wpp54qUMvEiRNlNpv1559/Wt3zz+czGo2SpEuXLl237tOnT+uvv/7S9OnTC8zVvn17SQX/ngEAAICb5WDvAgAAAADcWwwGgyZOnKi2bdvqo48+KnA9P0TKysqyai+JYOZaTp48edW2/H/wL1++vCQpJiZGnTt3vuoYderUsTq/csXV9ZQrV05paWkF2k+cOGE1980wmUx67rnnFBsbK3d392s+Q/7zXqueUqVKWa206dWrl1588UWlpKTo8OHDSktLU69eva5bS/7zfPXVV6pevfp1+1avXl2ffPKJJOm3337TggULNHbsWGVnZxcIdP7pr7/+0unTp61Cm/zf85W/V4PBoA0bNljCnCv9s62wv9PKlSurXr16WrNmjS5fvmwVjDZo0ECSdP78eat78t/L9OnT9fDDD191XG9v70LN/0//rNvLy0ulS5dWjx499NJLL131nvvuu69YcwEAAADXQiAFAAAA4JZr06aN2rZtq/Hjx1tWk+Tz9vaWs7Oz9uzZY9X+zTff2KyeefPmadiwYZZ/uD927Jg2b96snj17Svo7bKpdu7Z2796tt956q0TnfvTRRxUbG6udO3eqUaNGlvbPPvtMBoNBrVq1KpF5BgwYoP/9738KCQkpsHIsX506dVSlShXNnTtX0dHRlvdx4cIFLVq0SE2bNpXJZLL0j4iI0LBhw5SYmKjDhw+rSpUqeuyxx65bR1hYmBwcHHTo0KFCb2soSf7+/ho9erQWLVqknTt3FuqeL774QoMHD7acz507V5IUGhoq6e+tHt9++23997//1TPPPFPoWgrj1VdfVdeuXTVs2DC9//77NwyzmjdvLk9PT+3fv18DBw4s0Vr+yWQyqVWrVvr5558VFBQkJycnm84HAAAASARSAAAAAOxk4sSJaty4sdLT01WvXj1Le/43fT799FPVrFlT9evX108//WQJE2whPT1dnTp10gsvvKCMjAyNGTNGzs7OiomJsfSZOXOmHn/8cYWFhSkyMlJVqlTRn3/+qZSUFO3cuVMLFy4s1txDhw7VZ599pg4dOmj8+PGqXr26li9frhkzZmjAgAHy9/cvkWds0KCBlixZct0+pUqVUlxcnLp166YnnnhC/fr1U1ZWliZNmqSzZ8/q7bffturv6empTp06KTExUWfPnlV0dLTVt6euxs/PT+PHj9err76qw4cPq127dvLy8tL//vc//fTTT3J1ddW4ceO0Z88eDRw4UE8//bRq164tJycn/fDDD9qzZ49GjRp1w+d1cnJSfHy8zp8/rwcffFCbN2/Wm2++qccff1wtWrSQ9HcI1LdvX/Xq1Uvbt2/XI488IldXV6WlpWnjxo0KDAzUgAEDbjjX1URERGjfvn2aMGGCdu/ercjISNWuXVt5eXlKTU3VnDlzJEllypSR9Pd3uqZPn67nn39ef/75p5566ilVrFhRp06d0u7du3Xq1Cl98MEHxarlaqZNm6YWLVqoZcuWGjBggPz8/HTu3Dn9/vvv+vbbb0vk22UAAADAlQikAAAAANhFw4YNFRERcdWgKT4+XpIUFxen8+fPq3Xr1lq2bJn8/PxsUstbb72l5ORk9erVS5mZmWrSpInmz5+vmjVrWvq0atVKP/30kyZMmKAhQ4bozJkzKleunOrWrXtTq2sqVKigzZs3KyYmRjExMcrMzFSNGjUUFxenYcOGlcTjFUnXrl3l6uqq2NhYPfvssypdurQefvhhrVu3Ts2aNSvQv1evXpo3b54kKTIyslBzxMTEqG7dupo2bZrmzZunrKws+fj46MEHH1T//v0l/f29p5o1a2rGjBlKTU2VwWBQjRo1FB8fr0GDBt1wDkdHRy1btkyDBw/Wm2++KRcXF73wwguaNGmSVb+ZM2fq4Ycf1syZMzVjxgzl5eWpcuXKat68uZo0aVKo57mWN998U2FhYXr//fc1fvx4/e9//5Ojo6P8/PwUEhJiCWXzde/eXdWqVVNcXJz69eunc+fOqWLFimrQoEGh321h1a1bVzt37tQbb7yh0aNHKz09XZ6enqpdu7blO1IAAABASTKYzWazvYsAAAAAAKCkREZG6quvvirwnSYAAAAA9nP9vRQAAAAAAAAAAACAm0QgBQAAAAAAAAAAAJtiyz4AAAAAAAAAAADYFCukAAAAAAAAAAAAYFMEUgAAAAAAAAAAALApAikAAAAAAAAAAADYlIO9C8CdJS8vTydOnFCZMmVkMBjsXQ4AAAAAAAAAALAjs9msc+fOqXLlyipV6trroAikUCQnTpxQ1apV7V0GAAAAAAAAAAC4jaSmpsrX1/ea1wmkUCRlypSR9Pcflru7u52rAQAAAAAAAAAA9pSZmamqVata8oNrIZBCkeRv05e94DtlubjYuRoAAAAAAAAAAGynwoDu9i7hjnGjz/xcezM/AAAAAAAAAAAAoAQQSAEAAAAAAAAAAMCmCKQAAAAAAAAAAABgUwRSNpSYmChPT097lwEAAAAAAAAAAGBXBFIAAAAAAAAAAACwKQIpO8rJybF3CQAAAAAAAAAAADZHIFWCEhMTVa1aNZlMJnXq1EmnT5+2uj527Fg1aNBAn376qWrUqCGj0Siz2ayVK1eqRYsW8vT0VLly5fTEE0/o0KFDlvuOHj0qg8GgBQsWqGXLlnJxcdGDDz6o3377TcnJyQoODpabm5vatWunU6dOWe5LTk5W27ZtVb58eXl4eCgkJEQ7d+68Ze8DAAAAAAAAAABAIpAqMdu2bVNUVJRefPFF7dq1S61atdKbb75ZoN/vv/+uBQsWaNGiRdq1a5ck6cKFCxo2bJiSk5O1du1alSpVSp06dVJeXp7VvWPGjNHo0aO1c+dOOTg4KCIiQiNGjNC0adO0YcMGHTp0SK+//rql/7lz5/T8889rw4YN2rp1q2rXrq327dvr3LlzhX6urKwsZWZmWh0AAAAAAAAAAABF4WDvAu4W06ZNU1hYmEaNGiVJ8vf31+bNm7Vy5UqrftnZ2ZozZ44qVKhgaevSpYtVn08++UQVK1bU/v379cADD1jao6OjFRYWJkl6+eWXFRERobVr16p58+aSpN69eysxMdHSv3Xr1lbjzpw5U15eXlq/fr2eeOKJQj1XbGysxo0bV6i+AAAAAAAAAAAAV8MKqRKSkpKipk2bWrX981ySqlevbhVGSdKhQ4fUtWtX1ahRQ+7u7rrvvvskScePH7fqFxQUZPnZ29tbkhQYGGjVlp6ebjlPT09X//795e/vLw8PD3l4eOj8+fMFxr2emJgYZWRkWI7U1NRC3wsAAAAAAAAAACCxQqrEmM3mQvVzdXUt0Pavf/1LVatW1ccff6zKlSsrLy9PDzzwgLKzs636OTo6Wn42GAxXbbtym7/IyEidOnVKU6dOVfXq1WU0GtW0adMC416P0WiU0WgsdH8AAAAAAAAAAIB/IpAqIXXr1tXWrVut2v55fjWnT59WSkqKZs6cqZYtW0qSNm7cWCI1bdiwQTNmzFD79u0lSampqfrjjz9KZGwAAAAAAAAAAIDCIpAqIYMHD1azZs0UFxen8PBwrV69usD3o67Gy8tL5cqV00cffaRKlSrp+PHjlu9Q3axatWppzpw5Cg4OVmZmpoYPHy4XF5cSGRsAAAAAAAAAAKCw+IZUCXn44Yc1a9YsTZ8+XQ0aNNDq1as1evToG95XqlQpzZ8/Xzt27NADDzygoUOHatKkSSVS06effqozZ86oYcOG6tGjhwYPHqyKFSuWyNgAAAAAAAAAAACFZTAX9uNHgKTMzEx5eHjoUPxHKsNqKwAAAAAAAADAXazCgO72LuG2l58bZGRkyN3d/Zr9WCEFAAAAAAAAAAAAmyKQAgAAAAAAAAAAgE052LsA3JnK93n2ukvvAAAAAAAAAAAA8rFCCgAAAAAAAAAAADZFIAUAAAAAAAAAAACbIpACAAAAAAAAAACATRFIAQAAAAAAAAAAwKYc7F0A7kynPnlfl12c7V0GAAAAAAAAAMCOKvYfau8ScIdghRQAAAAAAAAAAABsikAKAAAAAAAAAAAANkUgBQAAAAAAAAAAAJu67QOpxMREeXp62rsMAAAAAAAAAAAAFNNtH0jda/z8/DR16lR7lwEAAAAAAAAAAFBiCKTuUtnZ2fYuAQAAAAAAAAAAQNJtGEglJiaqWrVqMplM6tSpk06fPl2gzwcffKCaNWvKyclJderU0Zw5c6yunz17Vn379pW3t7ecnZ31wAMPaNmyZZKksWPHqkGDBlb9p06dKj8/P8t5ZGSkwsPD9dZbb8nb21uenp4aN26c/vrrLw0fPlxly5aVr6+vPv30U6tx/vvf/+rZZ5+Vl5eXypUrp44dO+ro0aMFxp08ebIqVaqkcuXK6aWXXlJOTo4kKTQ0VMeOHdPQoUNlMBhkMBgkSadPn1ZERIR8fX1lMpkUGBioefPmWc0dGhqqgQMHatiwYSpfvrzatm2rqKgoPfHEE1b9/vrrL/n4+BSoHQAAAAAAAAAAwFZuq0Bq27ZtioqK0osvvqhdu3apVatWevPNN636fP3113r55Zf1yiuvaO/everXr5969eqldevWSZLy8vL0+OOPa/Pmzfr888+1f/9+vf322ypdunSRavnhhx904sQJ/fjjj3rnnXc0duxYPfHEE/Ly8tK2bdvUv39/9e/fX6mpqZKkixcvqlWrVnJzc9OPP/6ojRs3ys3NTe3atbNarbRu3TodOnRI69at0+zZs5WYmKjExERJ0uLFi+Xr66vx48crLS1NaWlpkqTLly+rcePGWrZsmfbu3au+ffuqR48e2rZtm1XNs2fPloODgzZt2qSZM2eqT58+WrlypWUcSVqxYoXOnz+vZ555plDvISsrS5mZmVYHAAAAAAAAAABAURjMZrPZ3kXk69q1q86cOaPvvvvO0vbcc89p5cqVOnv2rCSpefPmqlevnj766CNLn2eeeUYXLlzQ8uXLtXr1aj3++ONKSUmRv79/gTnGjh2rJUuWaNeuXZa2qVOnaurUqZbVTJGRkUpKStLhw4dVqtTfmd3999+vihUr6scff5Qk5ebmysPDQ7NmzdJzzz2nTz/9VHFxcUpJSbGsbMrOzpanp6eWLFmixx57zDLuoUOHLAHZM888o1KlSmn+/PmS/v6G1JAhQzRkyJDrvqsOHTooICBAkydPlvT3CqmMjAz9/PPPVv3q1aun559/XiNGjJAkderUSZ6enkpISLju+Fe+r3HjxhVo//2dt1TGxblQYwAAAAAAAAAA7k4V+w+1dwmws8zMTHl4eCgjI0Pu7u7X7HdbrZBKSUlR06ZNrdr+eZ6SkqLmzZtbtTVv3lwpKSmSpF27dsnX1/eqYVRR1KtXzxJGSZK3t7cCAwMt56VLl1a5cuWUnp4uSdqxY4d+//13lSlTRm5ubnJzc1PZsmV1+fJlHTp0yGrcK1drVapUyTLGteTm5mrChAkKCgpSuXLl5ObmptWrV+v48eNW/YKDgwvc26dPH0v4lJ6eruXLlysqKqrQ7yEmJkYZGRmWI39FGAAAAAAAAAAAQGE52LuAKxV2sVb+CqQr78tvc3Fxue69pUqVKjBP/jecruTo6Fhgzqu15eXlSfp7q8DGjRvriy++KDBWhQoVrjtu/hjXEh8frylTpmjq1KkKDAyUq6urhgwZYrUVoCS5uroWuLdnz54aNWqUtmzZoi1btsjPz08tW7a87nxXMhqNMhqNhe4PAAAAAAAAAADwT7dVIFW3bl1t3brVqu2f5wEBAdq4caN69uxpadu8ebMCAgIkSUFBQfrPf/6j33777aqrpCpUqKCTJ09ahVhXbt9XXI0aNdKXX36pihUrXndJ2o04OTkpNzfXqm3Dhg3q2LGjunfvLunv8OvgwYOWZ76ecuXKKTw8XAkJCdqyZYt69epV7NoAAAAAAAAAAACK47basm/w4MFauXKl4uLi9Ntvv+m9997TypUrrfoMHz5ciYmJ+vDDD3Xw4EG98847Wrx4saKjoyVJISEheuSRR9SlSxetWbNGR44c0XfffWcZJzQ0VKdOnVJcXJwOHTqk999/3+qbVcXVrVs3lS9fXh07dtSGDRt05MgRrV+/Xi+//LL+85//FHocPz8//fjjj/rvf/+rP/74Q5JUq1YtrVmzRps3b1ZKSor69eunkydPFnrMPn36aPbs2UpJSdHzzz9f5GcDAAAAAAAAAAC4GbdVIPXwww9r1qxZmj59uho0aKDVq1dr9OjRVn3Cw8M1bdo0TZo0SfXq1dPMmTOVkJCg0NBQS59FixbpwQcfVEREhOrWrasRI0ZYVh0FBARoxowZev/991W/fn399NNPljDrZphMJv3444+qVq2aOnfurICAAEVFRenSpUtFWjE1fvx4HT16VDVr1rRs9ffaa6+pUaNGCgsLU2hoqHx8fBQeHl7oMdu0aaNKlSopLCxMlStXLuqjAQAAAAAAAAAA3BSDubAfbsId6+LFi6pcubI+/fRTde7c+abGyszMlIeHh35/5y2VcXEuoQoBAAAAAAAAAHeiiv2H2rsE2Fl+bpCRkXHdBTq31TekULLy8vJ08uRJxcfHy8PDQ08++aS9SwIAAAAAAAAAAPcgAqm72PHjx3XffffJ19dXiYmJcnDg1w0AAAAAAAAAAG49tuxDkRR26R0AAAAAAAAAALj7FTY3KHULawIAAAAAAAAAAMA9iEAKAAAAAAAAAAAANkUgBQAAAAAAAAAAAJsikAIAAAAAAAAAAIBNOdi7ANyZ/jvzZWW6ONm7DAAAAAD3MN+BM+1dAgAAAIBCYoUUAAAAAAAAAAAAbIpACgAAAAAAAAAAADZFIAUAAAAAAAAAAACbIpC6iyQlJclgMOjs2bP2LgUAAAAAAAAAAMCCQAoAAAAAAAAAAAA2RSAFAAAAAAAAAAAAmyKQKoRNmzYpJCREJpNJXl5eCgsL05kzZyRJWVlZGjx4sCpWrChnZ2e1aNFCycnJlnvzt9FbtWqVGjZsKBcXF7Vu3Vrp6en67rvvFBAQIHd3d0VEROjixYuW+8xms+Li4lSjRg25uLiofv36+uqrr6zqWrFihfz9/eXi4qJWrVrp6NGjlmsXLlyQu7t7gXu+/fZbubq66ty5czZ4UwAAAAAAAAAAAAURSN3Arl279Oijj6pevXrasmWLNm7cqH/961/Kzc2VJI0YMUKLFi3S7NmztXPnTtWqVUthYWH6888/rcYZO3as3nvvPW3evFmpqal65plnNHXqVM2dO1fLly/XmjVrNH36dEv/0aNHKyEhQR988IH27dunoUOHqnv37lq/fr0kKTU1VZ07d1b79u21a9cu9enTR6NGjbLc7+rqqueee04JCQlWdSQkJOipp55SmTJlCvX8WVlZyszMtDoAAAAAAAAAAACKwmA2m832LuJ21rVrVx0/flwbN24scO3ChQvy8vJSYmKiunbtKknKycmRn5+fhgwZouHDhyspKUmtWrXS999/r0cffVSS9PbbbysmJkaHDh1SjRo1JEn9+/fX0aNHtXLlSl24cEHly5fXDz/8oKZNm1rm69Onjy5evKi5c+fq3//+t5YsWaJ9+/bJYDBIkkaNGqWJEyfqzJkz8vT01E8//aRmzZrp+PHjqly5sv744w9VrlxZa9asUUhISKGef+zYsRo3blyB9v1xkSrj4lS0lwkAAAAAJch34Ex7lwAAAADc8zIzM+Xh4aGMjAy5u7tfsx8rpG4gf4XU1Rw6dEg5OTlq3ry5pc3R0VFNmjRRSkqKVd+goCDLz97e3jKZTJYwKr8tPT1dkrR//35dvnxZbdu2lZubm+X47LPPdOjQIUlSSkqKHn74YUsYJckqvJKkJk2aqF69evrss88kSXPmzFG1atX0yCOPFPr5Y2JilJGRYTlSU1MLfS8AAAAAAAAAAIAkOdi7gNudi4vLNa/lLy67MhTKb/9nm6Ojo+Vng8FgdZ7flpeXJ0mW/y5fvlxVqlSx6mc0Gq3mvpE+ffrovffe06hRo5SQkKBevXoVqO16jEajZU4AAAAAAAAAAIDiYIXUDQQFBWnt2rVXvVarVi05OTlZbeeXk5Oj7du3KyAgoNhz1q1bV0ajUcePH1etWrWsjqpVq1r6bN261eq+f55LUvfu3XX8+HG9++672rdvn55//vli1wUAAAAAAAAAAFAcrJC6gZiYGAUGBurFF19U//795eTkpHXr1unpp59W+fLlNWDAAA0fPlxly5ZVtWrVFBcXp4sXL6p3797FnrNMmTKKjo7W0KFDlZeXpxYtWigzM1ObN2+Wm5ubnn/+efXv31/x8fEaNmyY+vXrpx07digxMbHAWF5eXurcubOGDx+uxx57TL6+vjfxNgAAAAAAAAAAAIqOFVI34O/vr9WrV2v37t1q0qSJmjZtqm+++UYODn9neW+//ba6dOmiHj16qFGjRvr999+1atUqeXl53dS8b7zxhl5//XXFxsYqICBAYWFh+vbbb3XfffdJkqpVq6ZFixbp22+/Vf369fXhhx/qrbfeuupYvXv3VnZ2tqKiom6qJgAAAAAAAAAAgOIwmAv7MSLcsb744gu9/PLLOnHihJycnG5qrMzMTHl4eGh/XKTKuNzcWAAAAABwM3wHzrR3CQAAAMA9Lz83yMjIkLu7+zX7sWXfXezixYs6cuSIYmNj1a9fv5sOowAAAAAAAAAAAIqDLfvuYnFxcWrQoIG8vb0VExNj73IAAAAAAAAAAMA9ii37UCSFXXoHAAAAAAAAAADufoXNDVghBQAAAAAAAAAAAJsikAIAAAAAAAAAAIBNEUgBAAAAAAAAAADAphzsXQDuTHtnPSs3F0d7lwEAAO5xQQOW2rsEAAAAAABQCKyQAgAAAAAAAAAAgE0RSAEAAAAAAAAAAMCmCKQAAAAAAAAAAABgUwRSAAAAAAAAAAAAsCkCqdtIUlKSDAaDzp49a+9SAAAAAAAAAAAASgyB1D2CsAsAAAAAAAAAANgLgdQ9ICcnx94lAAAAAAAAAACAexiB1C127Ngx/etf/5KXl5dcXV1Vr149rVixwqrPjh07FBwcLJPJpGbNmunAgQNW1z/44APVrFlTTk5OqlOnjubMmWN13WAw6MMPP1THjh3l6uqqPn36qFWrVpIkLy8vGQwGRUZG2vQ5AQAAAAAAAAAA8jnYu4B7zUsvvaTs7Gz9+OOPcnV11f79++Xm5mbV59VXX1V8fLwqVKig/v37KyoqSps2bZIkff3113r55Zc1depUtWnTRsuWLVOvXr3k6+trCZ0kacyYMYqNjdWUKVNUunRpdezYUV26dNGBAwfk7u4uFxeXQtWblZWlrKwsy3lmZmYJvAUAAAAAAAAAAHAvIZC6xY4fP64uXbooMDBQklSjRo0CfSZMmKCQkBBJ0qhRo9ShQwddvnxZzs7Omjx5siIjI/Xiiy9KkoYNG6atW7dq8uTJVoFU165dFRUVZTk/cuSIJKlixYry9PQsdL2xsbEaN25ckZ8TAAAAAAAAAAAgH1v23WKDBw/Wm2++qebNm2vMmDHas2dPgT5BQUGWnytVqiRJSk9PlySlpKSoefPmVv2bN2+ulJQUq7bg4OASqTcmJkYZGRmWIzU1tUTGBQAAAAAAAAAA9w4CqVusT58+Onz4sHr06KFffvlFwcHBmj59ulUfR0dHy88Gg0GSlJeXV6Atn9lsLtDm6upaIvUajUa5u7tbHQAAAAAAAAAAAEVBIGUHVatWVf/+/bV48WK98sor+vjjjwt9b0BAgDZu3GjVtnnzZgUEBFz3PicnJ0lSbm5u0QsGAAAAAAAAAAC4CXxD6hYbMmSIHn/8cfn7++vMmTP64YcfbhgmXWn48OF65pln1KhRIz366KP69ttvtXjxYn3//ffXva969eoyGAxatmyZ2rdvLxcXF7m5ud3s4wAAAAAAAAAAANwQK6RusdzcXL300ksKCAhQu3btVKdOHc2YMaPQ94eHh2vatGmaNGmS6tWrp5kzZyohIUGhoaHXva9KlSoaN26cRo0aJW9vbw0cOPAmnwQAAAAAAAAAAKBwDGaz2WzvInDnyMzMlIeHhzbFt5Obi+ONbwAAALChoAFL7V0CAAAAAAD3tPzcICMjQ+7u7tfsxwopAAAAAAAAAAAA2BSBFAAAAAAAAAAAAGzKwd4F4M70QJ8vr7v0DgAAAAAAAAAAIB8rpAAAAAAAAAAAAGBTBFIAAAAAAAAAAACwKQIpAAAAAAAAAAAA2BTfkEKxbEx8Sq4ujvYuA0ARhLyw3N4lAAAAAAAAALhHsUIKAAAAAAAAAAAANkUgBQAAAAAAAAAAAJsikAIAAAAAAAAAAIBNEUjZQGJiojw9Pe1dBgAAAAAAAAAAwG2BQAoAAAAAAAAAAAA2RSB1B8nJySnQlp2dXayxinsfAAAAAAAAAABAURFI2dCqVasUEBAgNzc3tWvXTmlpaZZreXl5Gj9+vHx9fWU0GtWgQQOtXLnScv3o0aMyGAxasGCBQkND5ezsrM8//1yRkZEKDw9XbGysKleuLH9/f0nSL7/8otatW8vFxUXlypVT3759df78ect417oPAAAAAAAAAADA1gikbOTixYuaPHmy5syZox9//FHHjx9XdHS05fq0adMUHx+vyZMna8+ePQoLC9OTTz6pgwcPWo0zcuRIDR48WCkpKQoLC5MkrV27VikpKVqzZo2WLVumixcvql27dvLy8lJycrIWLlyo77//XgMHDrQa65/3FUZWVpYyMzOtDgAAAAAAAAAAgKJwsHcBd6ucnBx9+OGHqlmzpiRp4MCBGj9+vOX65MmTNXLkSD333HOSpIkTJ2rdunWaOnWq3n//fUu/IUOGqHPnzlZju7q6atasWXJycpIkffzxx7p06ZI+++wzubq6SpLee+89/etf/9LEiRPl7e191fsKIzY2VuPGjSvGGwAAAAAAAAAAAPgbK6RsxGQyWcIoSapUqZLS09MlSZmZmTpx4oSaN29udU/z5s2VkpJi1RYcHFxg7MDAQKtQKSUlRfXr17eEUflj5eXl6cCBA9e8rzBiYmKUkZFhOVJTU4t0PwAAAAAAAAAAACukbMTR0dHq3GAwyGw2F2i7ktlsLtB2Zch0rbar3Xe1Oa421o0YjUYZjcYi3wcAAAAAAAAAAJCPFVJ24O7ursqVK2vjxo1W7Zs3b1ZAQECRx6tbt6527dqlCxcuWNo2bdqkUqVKyd/f/6brBQAAAAAAAAAAuBkEUnYyfPhwTZw4UV9++aUOHDigUaNGadeuXXr55ZeLPFa3bt3k7Oys559/Xnv37tW6des0aNAg9ejRw/L9KAAAAAAAAAAAAHthyz47GTx4sDIzM/XKK68oPT1ddevW1dKlS1W7du0ij2UymbRq1Sq9/PLLevDBB2UymdSlSxe98847NqgcAAAAAAAAAACgaAzmf37YCLiOzMxMeXh4aPm0tnJ1cbzxDQBuGyEvLLd3CQAAAAAAAADuMvm5QUZGhtzd3a/Zjy37AAAAAAAAAAAAYFMEUgAAAAAAAAAAALApAikAAAAAAAAAAADYlIO9C8CdqUXkV9fdCxIAAAAAAAAAACAfK6QAAAAAAAAAAABgUwRSAAAAAAAAAAAAsCkCKQAAAAAAAAAAANgU35BCsayY00UmF/58cPt7Muo7e5cAAAAAAAAAAPc8VkgBAAAAAAAAAADApgikAAAAAAAAAAAAYFMEUgAAAAAAAAAAALApAikb8vPz09SpU4t8X2Jiojw9PS3nY8eOVYMGDSznkZGRCg8Pv+n6AAAAAAAAAAAAbgUCqRLwzwDpZj377LP67bffSmw8AAAAAAAAAAAAe3KwdwGwlpOTIxcXF7m4uNhsDrPZrNzcXDk48OsHAAAAAAAAAAC2xwqpm5SUlKRevXopIyNDBoNBBoNBY8eOtVy/ePGioqKiVKZMGVWrVk0fffSR5drRo0dlMBi0YMEChYaGytnZWZ9//nmRV1yZzWbFxcWpRo0acnFxUf369fXVV19Z1WgwGLRq1SoFBwfLaDRqw4YNJfH4AAAAAAAAAAAAN0QgdZOaNWumqVOnyt3dXWlpaUpLS1N0dLTlenx8vIKDg/Xzzz/rxRdf1IABA/Trr79ajTFy5EgNHjxYKSkpCgsLK3INo0ePVkJCgj744APt27dPQ4cOVffu3bV+/XqrfiNGjFBsbKxSUlIUFBRUqLGzsrKUmZlpdQAAAAAAAAAAABQFe7bdJCcnJ3l4eMhgMMjHx6fA9fbt2+vFF1+U9HfwNGXKFCUlJen++++39BkyZIg6d+5crPkvXLigd955Rz/88IOaNm0qSapRo4Y2btyomTNnKiQkxNJ3/Pjxatu2bZHGj42N1bhx44pVGwAAAAAAAAAAgEQgZXNXrkTKD63S09Ot+gQHBxd7/P379+vy5csFgqbs7Gw1bNjwpueJiYnRsGHDLOeZmZmqWrVq8YoFAAAAAAAAAAD3JAIpG3N0dLQ6NxgMysvLs2pzdXUt9vj5Yy1fvlxVqlSxumY0Gm96HqPRWGAcAAAAAAAAAACAoiCQKgFOTk7Kzc21y9x169aV0WjU8ePHrbbnAwAAAAAAAAAAuF0QSJUAPz8/nT9/XmvXrlX9+vVlMplkMpluydxlypRRdHS0hg4dqry8PLVo0UKZmZnavHmz3Nzc9Pzzz9+SOgAAAAAAAAAAAK6llL0LuBs0a9ZM/fv317PPPqsKFSooLi7uls7/xhtv6PXXX1dsbKwCAgIUFhamb7/9Vvfdd98trQMAAAAAAAAAAOBqDGaz2WzvInDnyMzMlIeHh+a910YmFxbY4fb3ZNR39i4BAAAAAAAAAO5a+blBRkaG3N3dr9mPFVIAAAAAAAAAAACwKQIpAAAAAAAAAAAA2BR7rqFY2vdYdN2ldwAAAAAAAAAAAPlYIQUAAAAAAAAAAACbIpACAAAAAAAAAACATRFIAQAAAAAAAAAAwKYIpAAAAAAAAAAAAGBTDvYuAHem+V90kosLfz4ovB6Rq+xdAgAAAAAAAADATlghBQAAAAAAAAAAAJsikAIAAAAAAAAAAIBNEUgBAAAAAAAAAADApgik7hCRkZEKDw+3dxkAAAAAAAAAAABF5mDvAmDt6NGjuu+++/Tzzz+rQYMGlvZp06bJbDbbrzAAAAAAAAAAAIBiIpC6Q3h4eNi7BAAAAAAAAAAAgGJhy74Sdvr0aUVERMjX11cmk0mBgYGaN2+eVZ+8vDxNnDhRtWrVktFoVLVq1TRhwgRJ0n333SdJatiwoQwGg0JDQyUV3LIvKytLgwcPVsWKFeXs7KwWLVooOTnZcj0pKUkGg0Fr165VcHCwTCaTmjVrpgMHDtj2BQAAAAAAAAAAAPwDgVQJu3z5sho3bqxly5Zp79696tu3r3r06KFt27ZZ+sTExGjixIl67bXXtH//fs2dO1fe3t6SpJ9++kmS9P333ystLU2LFy++6jwjRozQokWLNHv2bO3cuVO1atVSWFiY/vzzT6t+r776quLj47V9+3Y5ODgoKiqqSM+TlZWlzMxMqwMAAAAAAAAAAKAoDGY+TGRzHTp0UEBAgCZPnqxz586pQoUKeu+999SnT58Cfa/1DanIyEidPXtWS5Ys0YULF+Tl5aXExER17dpVkpSTkyM/Pz8NGTJEw4cPV1JSklq1aqXvv/9ejz76qCRpxYoV6tChgy5duiRnZ+dC1T527FiNGzeuQPvMGa3l4sKOjyi8HpGr7F0CAAAAAAAAAKCEZWZmysPDQxkZGXJ3d79mP1ZIlbDc3FxNmDBBQUFBKleunNzc3LR69WodP35ckpSSkqKsrCxLSFQchw4dUk5Ojpo3b25pc3R0VJMmTZSSkmLVNygoyPJzpUqVJEnp6emFnismJkYZGRmWIzU1tdh1AwAAAAAAAACAexNLXEpYfHy8pkyZoqlTpyowMFCurq4aMmSIsrOzJUkuLi43PUf+ojaDwVCg/Z9tjo6Olp/zr+Xl5RV6LqPRKKPRWNxSAQAAAAAAAAAAWCFV0jZs2KCOHTuqe/fuql+/vmrUqKGDBw9arteuXVsuLi5au3btVe93cnKS9PdKq2upVauWnJyctHHjRktbTk6Otm/froCAgBJ6EgAAAAAAAAAAgJLBCqkSVqtWLS1atEibN2+Wl5eX3nnnHZ08edISFDk7O2vkyJEaMWKEnJyc1Lx5c506dUr79u1T7969VbFiRbm4uGjlypXy9fWVs7OzPDw8rOZwdXXVgAEDNHz4cJUtW1bVqlVTXFycLl68qN69e9vjsQEAAAAAAAAAAK6JQKqEvfbaazpy5IjCwsJkMpnUt29fhYeHKyMjw6qPg4ODXn/9dZ04cUKVKlVS//79JUkODg569913NX78eL3++utq2bKlkpKSCszz9ttvKy8vTz169NC5c+cUHBysVatWycvL61Y9KgAAAAAAAAAAQKEYzPkfJAIKITMzUx4eHpo5o7VcXMgzUXg9IlfZuwQAAAAAAAAAQAnLzw0yMjLk7u5+zX58QwoAAAAAAAAAAAA2RSAFAAAAAAAAAAAAm2LPNRTLc92+vu7SOwAAAAAAAAAAgHyskAIAAAAAAAAAAIBNEUgBAAAAAAAAAADApgikAAAAAAAAAAAAYFMEUgAAAAAAAAAAALApB3sXgDvTR192kosLfz53ope6r7J3CQAAAAAAAACAewwrpAAAAAAAAAAAAGBTBFIAAAAAAAAAAACwKQIpAAAAAAAAAAAA2BSBFAAAAAAAAAAAAGyKQOo2kpiYKE9PT3uXAQAAAAAAAAAAUKIIpO5Cubm5ysvLs3cZAAAAAAAAAAAAkgikrstsNqtChQpatGiRpa1BgwaqWLGi5XzLli1ydHTU+fPnJUkZGRnq27evKlasKHd3d7Vu3Vq7d++29N+9e7datWqlMmXKyN3dXY0bN9b27duVlJSkXr16KSMjQwaDQQaDQWPHjpUkZWdna8SIEapSpYpcXV310EMPKSkpyTJm/sqqZcuWqW7dujIajTp27Jj8/Pz01ltvKSoqSmXKlFG1atX00Ucf2falAQAAAAAAAAAA/AOB1HUYDAY98sgjlvDnzJkz2r9/v3JycrR//35JUlJSkho3biw3NzeZzWZ16NBBJ0+e1IoVK7Rjxw41atRIjz76qP78809JUrdu3eTr66vk5GTt2LFDo0aNkqOjo5o1a6apU6fK3d1daWlpSktLU3R0tCSpV69e2rRpk+bPn689e/bo6aefVrt27XTw4EFLrRcvXlRsbKxmzZqlffv2WUKz+Ph4BQcH6+eff9aLL76oAQMG6Ndffy30O8jKylJmZqbVAQAAAAAAAAAAUBQEUjcQGhpqCaR+/PFH1a9fX61bt7a0JSUlKTQ0VJK0bt06/fLLL1q4cKGCg4NVu3ZtTZ48WZ6envrqq68kScePH1ebNm10//33q3bt2nr66adVv359OTk5ycPDQwaDQT4+PvLx8ZGbm5sOHTqkefPmaeHChWrZsqVq1qyp6OhotWjRQgkJCZY6c3JyNGPGDDVr1kx16tSRq6urJKl9+/Z68cUXVatWLY0cOVLly5e3Wl11I7GxsfLw8LAcVatWvel3CgAAAAAAAAAA7i0EUjcQGhqqffv26Y8//tD69esVGhqq0NBQrV+/Xn/99Zc2b96skJAQSdKOHTt0/vx5lStXTm5ubpbjyJEjOnTokCRp2LBh6tOnj9q0aaO3337b0n4tO3fulNlslr+/v9WY69evt7rXyclJQUFBBe6/si0/7EpPTy/088fExCgjI8NypKamFvpeAAAAAAAAAAAASXKwdwG3uwceeEDlypXT+vXrtX79eo0fP15Vq1bVhAkTlJycrEuXLqlFixaSpLy8PFWqVOmqK5A8PT0lSWPHjlXXrl21fPlyfffddxozZozmz5+vTp06XXX+vLw8lS5dWjt27FDp0qWtrrm5uVl+dnFxkcFgKHC/o6Oj1bnBYFBeXl6hn99oNMpoNBa6PwAAAAAAAAAAwD8RSN1A/nekvvnmG+3du1ctW7ZUmTJllJOTow8//FCNGjVSmTJlJEmNGjXSyZMn5eDgID8/v2uO6e/vL39/fw0dOlQRERFKSEhQp06d5OTkpNzcXKu+DRs2VG5urtLT09WyZUtbPioAAAAAAAAAAIBNsGVfIYSGhmru3LkKCgqSu7u7JaT64osvLN+PkqQ2bdqoadOmCg8P16pVq3T06FFt3rxZo0eP1vbt23Xp0iUNHDhQSUlJOnbsmDZt2qTk5GQFBARIkvz8/HT+/HmtXbtWf/zxhy5evCh/f39169ZNPXv21OLFi3XkyBElJydr4sSJWrFihZ3eCAAAAAAAAAAAQOERSBVCq1atlJubaxU+hYSEKDc31/L9KOnv1VQrVqzQI488oqioKPn7++u5557T0aNH5e3trdKlS+v06dPq2bOn/P399cwzz+jxxx/XuHHjJEnNmjVT//799eyzz6pChQqKi4uTJCUkJKhnz5565ZVXVKdOHT355JPatm2bqlatekvfAwAAAAAAAAAAQHEYzGaz2d5F4M6RmZkpDw8PTfqotVxc2PHxTvRS91X2LgEAAAAAAAAAcJfIzw0yMjLk7u5+zX6skAIAAAAAAAAAAIBNEUgBAAAAAAAAAADApthzDcXS99mvr7v0DgAAAAAAAAAAIB8rpAAAAAAAAAAAAGBTBFIAAAAAAAAAAACwKQIpAAAAAAAAAAAA2BSBFAAAAAAAAAAAAGzKwd4F4M40cVEnOZv487GH155dZe8SAAAAAAAAAAAoElZIAQAAAAAAAAAAwKYIpAAAAAAAAAAAAGBTBFIAAAAAAAAAAACwKQIpGwsNDdWQIUMs535+fpo6dard6gEAAAAAAAAAALjVCKRuseTkZPXt29dybjAYtGTJkpseNzIyUuHh4Tc9DgAAAAAAAAAAQElzsHcBt6Pc3FwZDAaVKlXyeV2FChVKfEwAAAAAAAAAAIDbGSukJCUmJsrT01PLli1T3bp1ZTQadezYMWVnZ2vEiBGqUqWKXF1d9dBDDykpKcly3+nTpxURESFfX1+ZTCYFBgZq3rx5153ryi37/Pz8JEmdOnWSwWCQn5+fjh49qlKlSmn79u1W902fPl3Vq1eX2WwuMObYsWM1e/ZsffPNNzIYDDIYDEpKSlLTpk01atQoq76nTp2So6Oj1q1bV/QXBQAAAAAAAAAAUAwEUv/fxYsXFRsbq1mzZmnfvn2qWLGievXqpU2bNmn+/Pnas2ePnn76abVr104HDx6UJF2+fFmNGzfWsmXLtHfvXvXt21c9evTQtm3bCjVncnKyJCkhIUFpaWlKTk6Wn5+f2rRpo4SEBKu+CQkJioyMlMFgKDBOdHS0nnnmGbVr105paWlKS0tTs2bN1K1bN82bN88qxPryyy/l7e2tkJCQQtWYlZWlzMxMqwMAAAAAAAAAAKAoCKT+v5ycHM2YMUPNmjVTnTp1dPLkSc2bN08LFy5Uy5YtVbNmTUVHR6tFixaWsKhKlSqKjo5WgwYNVKNGDQ0aNEhhYWFauHBhoebM377P09NTPj4+lvM+ffpo3rx5ysrKkiTt3r1bu3btUq9eva46jpubm1xcXGQ0GuXj4yMfHx85OTnp2Wef1YkTJ7Rx40ZL37lz56pr166F3o4wNjZWHh4elqNq1aqFug8AAAAAAAAAACAfgdT/5+TkpKCgIMv5zp07ZTab5e/vLzc3N8uxfv16HTp0SNLf35qaMGGCgoKCVK5cObm5uWn16tU6fvz4TdUSHh4uBwcHff3115KkTz/9VK1atZKfn5+OHz9uVc9bb711zXEqVKigtm3b6osvvpAkHTlyRFu2bFG3bt0KXUtMTIwyMjIsR2pq6k09GwAAAAAAAAAAuPc42LuA24WLi4vVdnh5eXkqXbq0duzYodKlS1v1dXNzkyTFx8drypQpmjp1qgIDA+Xq6qohQ4YoOzv7pmpxcnJSjx49lJCQoM6dO2vu3LmW705VrlxZu3btsvQtW7bsdcfq1q2bXn75ZU2fPl1z585VvXr1VL9+/ULXYjQaZTQai/MYAAAAAAAAAAAAkgikrqlhw4bKzc1Venq6WrZsedU+GzZsUMeOHdW9e3dJf4dYBw8eVEBAQKHncXR0VG5uboH2Pn366IEHHtCMGTOUk5Ojzp07S5IcHBxUq1atAv2dnJyuOk54eLj69eunlStXau7cuerRo0ehawMAAAAAAAAAACgJbNl3Df7+/urWrZt69uypxYsX68iRI0pOTtbEiRO1YsUKSVKtWrW0Zs0abd68WSkpKerXr59OnjxZpHn8/Py0du1anTx5UmfOnLG0BwQE6OGHH9bIkSMVEREhFxeXG46zZ88eHThwQH/88YdycnIkSa6ururYsaNee+01paSkqGvXrkV8EwAAAAAAAAAAADeHQOo6EhIS1LNnT73yyiuqU6eOnnzySW3btk1Vq1aVJL322mtq1KiRwsLCFBoaKh8fH4WHhxdpjvj4eK1Zs0ZVq1ZVw4YNra717t1b2dnZioqKuuE4L7zwgurUqaPg4GBVqFBBmzZtslzr1q2bdu/erZYtW6patWpFqg8AAAAAAAAAAOBmGcxms9neReDqJkyYoPnz5+uXX36xdykWmZmZ8vDw0L8/bS1nEzs+2sNrz66ydwkAAAAAAAAAAEj6v9wgIyND7u7u1+zHCqnb0Pnz55WcnKzp06dr8ODB9i4HAAAAAAAAAADgphBI3YYGDhyoFi1aKCQkpFDb9QEAAAAAAAAAANzO2LIPRVLYpXcAAAAAAAAAAODux5Z9AAAAAAAAAAAAuC0QSAEAAAAAAAAAAMCmCKQAAAAAAAAAAABgUw72LgB3pgHfdpaTiT+fkpLQaaW9SwAAAAAAAAAAwGZYIQUAAAAAAAAAAACbIpACAAAAAAAAAACATRFIAQAAAAAAAAAAwKYIpAAAAAAAAAAAAGBTBFI2sGTJEtWqVUulS5fWkCFDbDZPUlKSDAaDzp49K0lKTEyUp6enzeYDAAAAAAAAAAAoDgd7F3A36tevn3r16qXBgwerTJkyNpunWbNmSktLk4eHh83mAAAAAAAAAAAAuFkEUiXs/PnzSk9PV1hYmCpXrmzTuZycnOTj42PTOQAAAAAAAAAAAG4WW/aVoKSkJMuKqNatW8tgMCgpKUmnT59WRESEfH19ZTKZFBgYqHnz5lndGxoaqkGDBmnIkCHy8vKSt7e3PvroI124cEG9evVSmTJlVLNmTX333XdW8125Zd+Vjh49qlKlSmn79u1W7dOnT1f16tVlNpsL9UxZWVnKzMy0OgAAAAAAAAAAAIqCQKoENWvWTAcOHJAkLVq0SGlpaWrWrJkuX76sxo0ba9myZdq7d6/69u2rHj16aNu2bVb3z549W+XLl9dPP/2kQYMGacCAAXr66afVrFkz7dy5U2FhYerRo4cuXrx4w1r8/PzUpk0bJSQkWLUnJCQoMjJSBoOhUM8UGxsrDw8Py1G1atVCvg0AAAAAAAAAAIC/EUiVICcnJ1WsWFGSVLZsWfn4+MjJyUlVqlRRdHS0GjRooBo1amjQoEEKCwvTwoULre6vX7++Ro8erdq1aysmJkYuLi4qX768XnjhBdWuXVuvv/66Tp8+rT179hSqnj59+mjevHnKysqSJO3evVu7du1Sr169Cv1MMTExysjIsBypqamFvhcAAAAAAAAAAEAikLolcnNzNWHCBAUFBalcuXJyc3PT6tWrdfz4cat+QUFBlp9Lly6tcuXKKTAw0NLm7e0tSUpPTy/UvOHh4XJwcNDXX38tSfr000/VqlUr+fn5Fbp2o9Eod3d3qwMAAAAAAAAAAKAoCKRugfj4eE2ZMkUjRozQDz/8oF27diksLEzZ2dlW/RwdHa3ODQaDVVv+Nnt5eXmFmtfJyUk9evRQQkKCsrOzNXfuXEVFRd3k0wAAAAAAAAAAABSNg70LuBds2LBBHTt2VPfu3SX9HSgdPHhQAQEBNp+7T58+euCBBzRjxgzl5OSoc+fONp8TAAAAAAAAAADgSqyQugVq1aqlNWvWaPPmzUpJSVG/fv108uTJWzJ3QECAHn74YY0cOVIRERFycXG5JfMCAAAAAAAAAADkI5C6BV577TU1atRIYWFhCg0NlY+Pj8LDw2/Z/L1791Z2djbb9QEAAAAAAAAAALswmM1ms72LgG1NmDBB8+fP1y+//HLTY2VmZsrDw0NdP39UTiZ2fCwpCZ1W2rsEAAAAAAAAAACKLD83yMjIkLu7+zX7sULqLnb+/HklJydr+vTpGjx4sL3LAQAAAAAAAAAA9ygCqbvYwIED1aJFC4WEhLBdHwAAAAAAAAAAsBu27EORFHbpHQAAAAAAAAAAuPuxZR8AAAAAAAAAAABuCwRSAAAAAAAAAAAAsCkCKQAAAAAAAAAAANiUg70LwJ2py7IRcjAZ7V3GXeG78Gn2LgEAAAAAAAAAAJtihRQAAAAAAAAAAABsikAKAAAAAAAAAAAANkUgBQAAAAAAAAAAAJsikLqG0NBQDRkyxN5lAAAAAAAAAAAA3PEIpG4jiYmJ8vT0tHcZAAAAAAAAAAAAJYpA6i6Vk5Nj7xIAAAAAAAAAAAAk3QOB1OnTpxURESFfX1+ZTCYFBgZq3rx5Vn0uXLignj17ys3NTZUqVVJ8fLzV9ZiYGD388MMFxg4KCtKYMWMs5wkJCQoICJCzs7Puv/9+zZgxw3Lt6NGjMhgMWrx4sVq1aiWTyaT69etry5YtkqSkpCT16tVLGRkZMhgMMhgMGjt2rCTJYDBoyZIlVnN7enoqMTHRauwFCxYoNDRUzs7O+vzzz29YEwAAAAAAAAAAwK1w1wdSly9fVuPGjbVs2TLt3btXffv2VY8ePbRt2zZLn+HDh2vdunX6+uuvtXr1aiUlJWnHjh2W6926ddO2bdt06NAhS9u+ffv0yy+/qFu3bpKkjz/+WK+++qomTJiglJQUvfXWW3rttdc0e/Zsq3peffVVRUdHa9euXfL391dERIT++usvNWvWTFOnTpW7u7vS0tKUlpam6OjoIj3ryJEjNXjwYKWkpCgsLKzQNV1PVlaWMjMzrQ4AAAAAAAAAAICicLB3AbZWpUoVq2Bn0KBBWrlypRYuXKiHHnpI58+f1yeffKLPPvtMbdu2lSTNnj1bvr6+lnseeOABBQUFae7cuXrttdckSV988YUefPBB+fv7S5LeeOMNxcfHq3PnzpKk++67T/v379fMmTP1/PPPW8aKjo5Whw4dJEnjxo1TvXr19Pvvv+v++++Xh4eHDAaDfHx8ivWsQ4YMscxflJquJzY2VuPGjStWPQAAAAAAAAAAANI9sEIqNzdXEyZMUFBQkMqVKyc3NzetXr1ax48flyQdOnRI2dnZatq0qeWesmXLqk6dOlbjdOvWTV988YUkyWw2a968eZbVUadOnVJqaqp69+4tNzc3y/Hmm29araqS/t7mL1+lSpUkSenp6SXyrMHBwZafi1LT9cTExCgjI8NypKamlkitAAAAAAAAAADg3nHXr5CKj4/XlClTNHXqVAUGBsrV1VVDhgxRdna2pL/DpcLo2rWrRo0apZ07d+rSpUtKTU3Vc889J0nKy8uT9Pe2fQ899JDVfaVLl7Y6d3R0tPxsMBis7r8Wg8FQoM6cnJwC/VxdXS0/F6Wm6zEajTIajYXuDwAAAAAAAAAA8E93fSC1YcMGdezYUd27d5f0d1Bz8OBBBQQESJJq1aolR0dHbd26VdWqVZMknTlzRr/99ptCQkIs4/j6+uqRRx7RF198oUuXLqlNmzby9vaWJHl7e6tKlSo6fPiwZdVUcTg5OSk3N7dAe4UKFZSWlmY5P3jwoC5evHjdsUqqJgAAAAAAAAAAgJt11wdStWrV0qJFi7R582Z5eXnpnXfe0cmTJy2BlJubm3r37q3hw4erXLly8vb21quvvqpSpQruZtitWzeNHTtW2dnZmjJlitW1sWPHavDgwXJ3d9fjjz+urKwsbd++XWfOnNGwYcMKVaufn5/Onz+vtWvXqn79+jKZTDKZTGrdurXee+89Pfzww8rLy9PIkSOtVlpdS0nUBAAAAAAAAAAAcLPu+m9Ivfbaa2rUqJHCwsIUGhoqHx8fhYeHW/WZNGmSHnnkET355JNq06aNWrRoocaNGxcY6+mnn9bp06d18eLFAmP06dNHs2bNUmJiogIDAxUSEqLExETdd999ha61WbNm6t+/v5599llVqFBBcXFxkv7edrBq1ap65JFH1LVrV0VHR8tkMt1wvJKoCQAAAAAAAAAA4GYZzIX9iBIgKTMzUx4eHmrzRT85mPi2VEn4LnyavUsAAAAAAAAAAKBY8nODjIwMubu7X7PfXb9CCgAAAAAAAAAAAPZFIAUAAAAAAAAAAACbcrB3AbgzLXoi7rpL7wAAAAAAAAAAAPKxQgoAAAAAAAAAAAA2RSAFAAAAAAAAAAAAmyKQAgAAAAAAAAAAgE0RSAEAAAAAAAAAAMCmHOxdAO5MXb6Nl6PJ2d5l3HFWdIqxdwkAAAAAAAAAANxyrJACAAAAAAAAAACATRFIAQAAAAAAAAAAwKYIpAAAAAAAAAAAAGBTBFJ3GYPBoCVLlti7DAAAAAAAAAAAAAsHexeAkpWWliYvLy97lwEAAAAAAAAAAGBBIHWX8fHxsXcJAAAAAAAAAAAAVtiyz0YSExPl6empZcuWqU6dOjKZTHrqqad04cIFzZ49W35+fvLy8tKgQYOUm5true9qW+55enoqMTFRkpSdna2BAweqUqVKcnZ2lp+fn2JjY695/3/+8x8999xzKlu2rFxdXRUcHKxt27bZ8tEBAAAAAAAAAACssELKhi5evKh3331X8+fP17lz59S5c2d17txZnp6eWrFihQ4fPqwuXbqoRYsWevbZZws15rvvvqulS5dqwYIFqlatmlJTU5WamnrVvufPn1dISIiqVKmipUuXysfHRzt37lReXl6hnyErK0tZWVmW88zMzELfCwAAAAAAAAAAIBFI2VROTo4++OAD1axZU5L01FNPac6cOfrf//4nNzc31a1bV61atdK6desKHUgdP35ctWvXVosWLWQwGFS9evVr9p07d65OnTql5ORklS1bVpJUq1atIj1DbGysxo0bV6R7AAAAAAAAAAAArsSWfTZkMpksYZQkeXt7y8/PT25ublZt6enphR4zMjJSu3btUp06dTR48GCtXr36mn137dqlhg0bWsKo4oiJiVFGRobluNZqLAAAAAAAAAAAgGshkLIhR0dHq3ODwXDVtiu30DMYDDKbzVZ9cnJyLD83atRIR44c0RtvvKFLly7pmWee0VNPPXXV+V1cXG72EWQ0GuXu7m51AAAAAAAAAAAAFAWB1G2mQoUKSktLs5wfPHhQFy9etOrj7u6uZ599Vh9//LG+/PJLLVq0SH/++WeBsYKCgrRr166rXgMAAAAAAAAAALhVCKRuM61bt9Z7772nnTt3avv27erfv7/VqqopU6Zo/vz5+vXXX/Xbb79p4cKF8vHxkaenZ4GxIiIi5OPjo/DwcG3atEmHDx/WokWLtGXLllv4RAAAAAAAAAAA4F5HIHWbiY+PV9WqVfXII4+oa9euio6Olslkslx3c3PTxIkTFRwcrAcffFBHjx7VihUrVKpUwV+lk5OTVq9erYoVK6p9+/YKDAzU22+/rdKlS9/KRwIAAAAAAAAAAPc4g/mfHywCriMzM1MeHh5q8/nrcjQ527ucO86KTjH2LgEAAAAAAAAAgBKTnxtkZGTI3d39mv1YIQUAAAAAAAAAAACbIpACAAAAAAAAAACATTnYuwDcmRb965XrLr0DAAAAAAAAAADIxwopAAAAAAAAAAAA2BSBFAAAAAAAAAAAAGyKQAoAAAAAAAAAAAA2RSAFAAAAAAAAAAAAm3KwdwG4Mz219GM5mlzsXcZta3nnF+1dAgAAAAAAAAAAtw1WSAEAAAAAAAAAAMCmCKQAAAAAAAAAAABgUwRSAAAAAAAAAAAAsCkCKQAAAAAAAAAAANgUgRQAAAAAAAAAAABsikDqGnJzc5WXl2fvMgAAAAAAAAAAAO54BFL/X2Jiojw9PbVs2TLVrVtXRqNRx44d05kzZ9SzZ095eXnJZDLp8ccf18GDB696X506dWQymfTUU0/pwoULmj17tvz8/OTl5aVBgwYpNzfXct/nn3+u4OBglSlTRj4+PuratavS09Mt15OSkmQwGLR27VoFBwfLZDKpWbNmOnDggFXdS5cuVXBwsJydnVW+fHl17tzZci07O1sjRoxQlSpV5OrqqoceekhJSUm2e4kAAAAAAAAAAABXQSB1hYsXLyo2NlazZs3Svn37VLFiRUVGRmr79u1aunSptmzZIrPZrPbt2ysnJ8fqvnfffVfz58/XypUrlZSUpM6dO2vFihVasWKF5syZo48++khfffWV5Z7s7Gy98cYb2r17t5YsWaIjR44oMjKyQE2vvvqq4uPjtX37djk4OCgqKspybfny5ercubM6dOign3/+2RJe5evVq5c2bdqk+fPna8+ePXr66afVrl07q0DtRrKyspSZmWl1AAAAAAAAAAAAFIXBbDab7V3E7SAxMVG9evXSrl27VL9+fUnSwYMH5e/vr02bNqlZs2aSpNOnT6tq1aqaPXu2nn76act9v//+u2rWrClJ6t+/v+bMmaP//e9/cnNzkyS1a9dOfn5++vDDD686f3Jyspo0aaJz587Jzc1NSUlJatWqlb7//ns9+uijkqQVK1aoQ4cOunTpkpydndWsWTPVqFFDn3/+eYHxDh06pNq1a+s///mPKleubGlv06aNmjRporfeeqtQ72Xs2LEaN25cgfa2cybL0eRSqDHuRcs7v2jvEgAAAAAAAAAAsLnMzEx5eHgoIyND7u7u1+zHCqkrODk5KSgoyHKekpIiBwcHPfTQQ5a2cuXKqU6dOkpJSbG0mUwmSxglSd7e3vLz87OEUfltV27J9/PPP6tjx46qXr26ypQpo9DQUEnS8ePHrWq6sp5KlSpJkmWcXbt2WcKqf9q5c6fMZrP8/f3l5uZmOdavX69Dhw4V+p3ExMQoIyPDcqSmphb6XgAAAAAAAAAAAElysHcBtxMXFxcZDAbL+bUWj5nNZqt+jo6OVtcNBsNV2/Ly8iRJFy5c0GOPPabHHntMn3/+uSpUqKDjx48rLCxM2dnZVvddOU7+nPnjuLhce4VSXl6eSpcurR07dqh06dJW164Mym7EaDTKaDQWuj8AAAAAAAAAAMA/sULqOurWrau//vpL27Zts7SdPn1av/32mwICAoo97q+//qo//vhDb7/9tlq2bKn777/favVUYQUFBWnt2rVXvdawYUPl5uYqPT1dtWrVsjp8fHyKXTsAAAAAAAAAAEBR3VQg9fvvv2vVqlW6dOmSpGuvKLpT1a5dWx07dtQLL7ygjRs3avfu3erevbuqVKmijh07FnvcatWqycnJSdOnT9fhw4e1dOlSvfHGG0UeZ8yYMZo3b57GjBmjlJQU/fLLL4qLi5Mk+fv7q1u3burZs6cWL16sI0eOKDk5WRMnTtSKFSuKXTsAAAAAAAAAAEBRFSuQOn36tNq0aSN/f3+1b99eaWlpkqQ+ffrolVdeKdEC7S0hIUGNGzfWE088oaZNm8psNmvFihUFtuQrigoVKigxMVELFy5U3bp19fbbb2vy5MlFHic0NFQLFy7U0qVL1aBBA7Vu3dpqNVdCQoJ69uypV155RXXq1NGTTz6pbdu2qWrVqsWuHQAAAAAAAAAAoKgM5mIsa+rZs6fS09M1a9YsBQQEaPfu3apRo4ZWr16toUOHat++fbaoFbeBzMxMeXh4qO2cyXI0XfsbVve65Z1ftHcJAAAAAAAAAADYXH5ukJGRIXd392v2cyjO4KtXr9aqVavk6+tr1V67dm0dO3asOEMCAAAAAAAAAADgLlWsLfsuXLggk8lUoP2PP/6Q0Wi86aIAAAAAAAAAAABw9yjWCqlHHnlEn332md544w1JksFgUF5eniZNmqRWrVqVaIG4PX315AvXXXoHAAAAAAAAAACQr1iB1KRJkxQaGqrt27crOztbI0aM0L59+/Tnn39q06ZNJV0jAAAAAAAAAAAA7mDF2rKvbt262rNnj5o0aaK2bdvqwoUL6ty5s37++WfVrFmzpGsEAAAAAAAAAADAHazIK6RycnL02GOPaebMmRo3bpwtagIAAAAAAAAAAMBdpMgrpBwdHbV3714ZDAZb1AMAAAAAAAAAAIC7TLG+IdWzZ0998sknevvtt0u6Htwhnv7mCzmaXOxdxm1lWZdIe5cAAAAAAAAAAMBtqViBVHZ2tmbNmqU1a9YoODhYrq6uVtffeeedEikOAAAAAAAAAAAAd75iBVJ79+5Vo0aNJEm//fab1TW28gMAAAAAAAAAAMCVihVIrVu3rqTrAAAAAAAAAAAAwF2qlL0LQPFERkYqPDz8un0uXryoLl26yN3dXQaDQWfPnpWfn5+mTp16S2oEAAAAAAAAAACQirlC6sKFC3r77be1du1apaenKy8vz+r64cOHS6Q4XNu0adNkNpuv22f27NnasGGDNm/erPLly8vDw0PJyckFvvkFAAAAAAAAAABgS8UKpPr06aP169erR48eqlSpEt+NsgMPD48b9jl06JACAgL0wAMPWNoqVKhgy7IAAAAAAAAAAAAKKFYg9d1332n58uVq3rx5SddzTzl27JgGDhyojRs3Kjs7W35+fpo0aZLat28vSdq3b59GjBihDRs2yGw2q0GDBkpMTFTNmjUVGRmps2fPasmSJVcdOzQ0VOvXr5ckGQwGhYSEKCkpSX5+fhoyZIiGDBlyi54SAAAAAAAAAADc64oVSHl5eals2bIlXcs956WXXlJ2drZ+/PFHubq6av/+/XJzc5Mk/fe//9Ujjzyi0NBQ/fDDD3J3d9emTZv0119/FWrsxYsXa9SoUdq7d68WL14sJyenYtWYlZWlrKwsy3lmZmaxxgEAAAAAAAAAAPeuYgVSb7zxhl5//XXNnj1bJpOppGu6Zxw/flxdunRRYGCgJKlGjRqWa++//748PDw0f/58OTo6SpL8/f0LPXbZsmVlMpnk5OQkHx+fYtcYGxurcePGFft+AAAAAAAAAACAUsW5KT4+XqtWrZK3t7cCAwPVqFEjqwOFM3jwYL355ptq3ry5xowZoz179liu7dq1Sy1btrSEUdfzxRdfyM3NzXJs2LChxGqMiYlRRkaG5UhNTS2xsQEAAAAAAAAAwL2hWCukwsPDS7iMe1OfPn0UFham5cuXa/Xq1YqNjVV8fLwGDRokFxeXQo/z5JNP6qGHHrKcV6lSpcRqNBqNMhqNJTYeAAAAAAAAAAC49xQrkBozZkxJ13HPqlq1qvr376/+/fsrJiZGH3/8sQYNGqSgoCDNnj1bOTk5N1wlVaZMGZUpU+YWVQwAAAAAAAAAAFA0xdqyT5LOnj2rWbNmKSYmRn/++ackaefOnfrvf/9bYsXd7YYMGaJVq1bpyJEj2rlzp3744QcFBARIkgYOHKjMzEw999xz2r59uw4ePKg5c+bowIEDdq4aAAAAAAAAAACgaIq1QmrPnj1q06aNPDw8dPToUb3wwgsqW7asvv76ax07dkyfffZZSdd5V8rNzdVLL72k//znP3J3d1e7du00ZcoUSVK5cuX0ww8/aPjw4QoJCVHp0qXVoEEDNW/e3M5VAwAAAAAAAAAAFI3BbDabi3pTmzZt1KhRI8XFxalMmTLavXu3atSooc2bN6tr1646evSoDUrF7SAzM1MeHh567LMZcjQV/jtX94JlXSLtXQIAAAAAAAAAALdUfm6QkZEhd3f3a/Yr1pZ9ycnJ6tevX4H2KlWq6OTJk8UZEgAAAAAAAAAAAHepYgVSzs7OyszMLNB+4MABVahQ4aaLAgAAAAAAAAAAwN2jWN+Q6tixo8aPH68FCxZIkgwGg44fP65Ro0apS5cuJVogbk8LO3a77tI7AAAAAAAAAACAfMVaITV58mSdOnVKFStW1KVLlxQSEqJatWrJzc1NEyZMKOkaAQAAAAAAAAAAcAcr1gopd3d3bdy4UevWrdOOHTuUl5enRo0aqU2bNiVdHwAAAAAAAAAAAO5wRVohdenSJS1btsxyvnr1ap04cUInT57UihUrNGLECF2+fLnEiwQAAAAAAAAAAMCdq0grpD777DMtW7ZMTzzxhCTpvffeU7169eTi4iJJ+vXXX1WpUiUNHTq05CvFbeXpJYvlaDLZuwy7WPbUM/YuAQAAAAAAAACAO0qRVkh98cUXioqKsmqbO3eu1q1bp3Xr1mnSpElasGBBiRYIAAAAAAAAAACAO1uRAqnffvtN/v7+lnNnZ2eVKvV/QzRp0kT79+8vueoAAAAAAAAAAABwxyvSln0ZGRlycPi/W06dOmV1PS8vT1lZWSVTGQAAAAAAAAAAAO4KRVoh5evrq717917z+p49e+Tr63vTRQEAAAAAAAAAAODuUaRAqn379nr99dd1+fLlAtcuXbqkcePGqUOHDiVWHP5PaGiohgwZUuj+R48elcFg0K5du2xWEwAAAAAAAAAAQGEUKZD697//rT///FN16tTRpEmT9M0332jp0qWKi4tTnTp1dObMGf373/+2Va24CUlJSTIYDDp79qy9SwEAAAAAAAAAAPeYIn1DytvbW5s3b9aAAQM0atQomc1mSZLBYFDbtm01Y8YMeXt726RQAAAAAAAAAAAA3JmKtEJKku677z6tXLlSp06d0tatW7V161adOnVKK1euVI0aNWxR413l9OnTioiIkK+vr0wmkwIDAzVv3jyrPhcuXFDPnj3l5uamSpUqKT4+vsA4BoNBS5YssWrz9PRUYmJigb5Hjx5Vq1atJEleXl4yGAyKjIwsqUcCAAAAAAAAAAC4riKtkLpS2bJl1aRJk5Ks5Z5w+fJlNW7cWCNHjpS7u7uWL1+uHj16qEaNGnrooYckScOHD9e6dev09ddfy8fHR//+97+1Y8cONWjQoFhzVq1aVYsWLVKXLl104MABubu7y8XFpVD3ZmVlKSsry3KemZlZrBoAAAAAAAAAAMC9q9iBFIqnSpUqio6OtpwPGjRIK1eu1MKFC/XQQw/p/Pnz+uSTT/TZZ5+pbdu2kqTZs2fL19e32HOWLl1aZcuWlSRVrFhRnp6ehb43NjZW48aNK/bcAAAAAAAAAAAARd6yDzcnNzdXEyZMUFBQkMqVKyc3NzetXr1ax48flyQdOnRI2dnZatq0qeWesmXLqk6dOnapNyYmRhkZGZYjNTXVLnUAAAAAAAAAAIA7FyukbrH4+HhNmTJFU6dOVWBgoFxdXTVkyBBlZ2dLksxmc6HGMRgMBfrm5OSUeL1Go1FGo7HExwUAAAAAAAAAAPcOVkjdYhs2bFDHjh3VvXt31a9fXzVq1NDBgwct12vVqiVHR0dt3brV0nbmzBn99ttvVuNUqFBBaWlplvODBw/q4sWL15zXyclJ0t8rtAAAAAAAAAAAAG4lAqlbrFatWlqzZo02b96slJQU9evXTydPnrRcd3NzU+/evTV8+HCtXbtWe/fuVWRkpEqVsv5VtW7dWu+995527typ7du3q3///nJ0dLzmvNWrV5fBYNCyZct06tQpnT9/3mbPCAAAAAAAAAAAcCUCqVvstddeU6NGjRQWFqbQ0FD5+PgoPDzcqs+kSZP0yCOP6Mknn1SbNm3UokULNW7c2KpPfHy8qlatqkceeURdu3ZVdHS0TCbTNeetUqWKxo0bp1GjRsnb21sDBw60xeMBAAAAAAAAAAAUYDAX9qNFgKTMzEx5eHjosdkJcrxOAHY3W/bUM/YuAQAAAAAAAACA20J+bpCRkSF3d/dr9mOFFAAAAAAAAAAAAGyKQAoAAAAAAAAAAAA25WDvAnBnWhje+bpL7wAAAAAAAAAAAPKxQgoAAAAAAAAAAAA2RSAFAAAAAAAAAAAAmyKQAgAAAAAAAAAAgE3xDSkUy7NLVsrRZLJ3GSVq6VNP2LsEAAAAAAAAAADuSqyQAgAAAAAAAAAAgE0RSAEAAAAAAAAAAMCmCKQAAAAAAAAAAABgUwRStymDwaAlS5bYuwwAAAAAAAAAAICb5mDvAnB1aWlp8vLysncZAAAAAAAAAAAAN41A6jaTnZ0tJycn+fj42LsUAAAAAAAAAACAEsGWfbfApk2bFBISIpPJJC8vL4WFhenMmTOSpNDQUA0cOFDDhg1T+fLl1bZtW0nWW/YdPXpUBoNBCxYsUMuWLeXi4qIHH3xQv/32m5KTkxUcHCw3Nze1a9dOp06dspo7ISFBAQEBcnZ21v33368ZM2bc0mcHAAAAAAAAAABghZSN7dq1S48++qiioqL07rvvysHBQevWrVNubq6lz+zZszVgwABt2rRJZrP5mmONGTNGU6dOVbVq1RQVFaWIiAi5u7tr2rRpMplMeuaZZ/T666/rgw8+kCR9/PHHGjNmjN577z01bNhQP//8s1544QW5urrq+eefL1T9WVlZysrKspxnZmYW800AAAAAAAAAAIB7FYGUjcXFxSk4ONhqZVK9evWs+tSqVUtxcXE3HCs6OlphYWGSpJdfflkRERFau3atmjdvLknq3bu3EhMTLf3feOMNxcfHq3PnzpKk++67T/v379fMmTMLHUjFxsZq3LhxheoLAAAAAAAAAABwNWzZZ2P5K6SuJzg4uFBjBQUFWX729vaWJAUGBlq1paenS5JOnTql1NRU9e7dW25ubpbjzTff1KFDhwpdf0xMjDIyMixHampqoe8FAAAAAAAAAACQWCFlcy4uLjfs4+rqWqixHB0dLT8bDIartuXl5UmS5b8ff/yxHnroIatxSpcuXaj5JMloNMpoNBa6PwAAAAAAAAAAwD+xQsrGgoKCtHbt2ls+r7e3t6pUqaLDhw+rVq1aVsd99913y+sBAAAAAAAAAAD3LlZI2VhMTIwCAwP14osvqn///nJyctK6dev09NNPq3z58jade+zYsRo8eLDc3d31+OOPKysrS9u3b9eZM2c0bNgwm84NAAAAAAAAAACQjxVSNubv76/Vq1dr9+7datKkiZo2bapvvvlGDg62zwL79OmjWbNmKTExUYGBgQoJCVFiYiIrpAAAAAAAAAAAwC1lMJvNZnsXgTtHZmamPDw81G72l3I0mexdTola+tQT9i4BAAAAAAAAAIA7Sn5ukJGRIXd392v2Y4UUAAAAAAAAAAAAbIpACgAAAAAAAAAAADZl+w8Z4a70ZXi76y69AwAAAAAAAAAAyMcKKQAAAAAAAAAAANgUgRQAAAAAAAAAAABsikAKAAAAAAAAAAAANkUgBQAAAAAAAAAAAJtysHcBuDN1/WajHE2u9i6jRH3dJcTeJQAAAAAAAAAAcFdihRQAAAAAAAAAAABsikAKAAAAAAAAAAAANkUgBQAAAAAAAAAAAJsikCoCg8GgJUuW2GXusWPHqkGDBnaZGwAAAAAAAAAA4GYQSBVBWlqaHn/8cZvPc7XgKzo6WmvXrrX53AAAAAAAAAAAACXNwd4F3El8fHzsNrebm5vc3NzsNj8AAAAAAAAAAEBx3RMrpE6fPq2IiAj5+vrKZDIpMDBQ8+bNs+oTGhqqwYMHa8SIESpbtqx8fHw0duxYqz5Xrlw6evSoDAaDFixYoJYtW8rFxUUPPvigfvvtNyUnJys4OFhubm5q166dTp06ZRkjOTlZbdu2Vfny5eXh4aGQkBDt3LnTct3Pz0+S1KlTJxkMBsv5P7fsy8vL0/jx4+Xr6yuj0agGDRpo5cqVluv59S1evFitWrWSyWRS/fr1tWXLlpt/oQAAAAAAAAAAAEVwTwRSly9fVuPGjbVs2TLt3btXffv2VY8ePbRt2zarfrNnz5arq6u2bdumuLg4jR8/XmvWrLnu2GPGjNHo0aO1c+dOOTg4KCIiQiNGjNC0adO0YcMGHTp0SK+//rql/7lz5/T8889rw4YN2rp1q2rXrq327dvr3Llzkv4OrCQpISFBaWlplvN/mjZtmuLj4zV58mTt2bNHYWFhevLJJ3Xw4EGrfq+++qqio6O1a9cu+fv7KyIiQn/99Veh311WVpYyMzOtDgAAAAAAAAAAgKK4J7bsq1KliqKjoy3ngwYN0sqVK7Vw4UI99NBDlvagoCCNGTNGklS7dm299957Wrt2rdq2bXvNsaOjoxUWFiZJevnllxUREaG1a9eqefPmkqTevXsrMTHR0r9169ZW98+cOVNeXl5av369nnjiCVWoUEGS5Onped0tAidPnqyRI0fqueeekyRNnDhR69at09SpU/X+++9b1dehQwdJ0rhx41SvXj39/vvvuv/++6/9wq4QGxurcePGFaovAAAAAAAAAADA1dwTK6Ryc3M1YcIEBQUFqVy5cnJzc9Pq1at1/Phxq35BQUFW55UqVVJ6evp1x77yHm9vb0lSYGCgVduVY6Snp6t///7y9/eXh4eHPDw8dP78+QK1XE9mZqZOnDhhCb3yNW/eXCkpKdesr1KlSpYaCismJkYZGRmWIzU1tdD3AgAAAAAAAAAASPfICqn4+HhNmTJFU6dOVWBgoFxdXTVkyBBlZ2db9XN0dLQ6NxgMysvLu+7YV95jMBiu2nblGJGRkTp16pSmTp2q6tWry2g0qmnTpgVqKYz8+fKZzeYCbVer70bPdCWj0Sij0Vjk2gAAAAAAAAAAAPLdEyukNmzYoI4dO6p79+6qX7++atSoUeBbS7eylsGDB6t9+/aqV6+ejEaj/vjjD6s+jo6Oys3NveYY7u7uqly5sjZu3GjVvnnzZgUEBNikbgAAAAAAAAAAgOK6JwKpWrVqac2aNdq8ebNSUlLUr18/nTx50m61zJkzRykpKdq2bZu6desmFxcXqz5+fn5au3atTp48qTNnzlx1nOHDh2vixIn68ssvdeDAAY0aNUq7du3Syy+/fCseAwAAAAAAAAAAoNDuiUDqtddeU6NGjRQWFqbQ0FD5+PgoPDzcLrV8+umnOnPmjBo2bKgePXpo8ODBqlixolWf+Ph4rVmzRlWrVlXDhg2vOs7gwYP1yiuv6JVXXlFgYKBWrlyppUuXqnbt2rfiMQAAAAAAAAAAAArNYDabzfYuAneOzMxMeXh4qMNny+VocrV3OSXq6y4h9i4BAAAAAAAAAIA7Sn5ukJGRIXd392v2uydWSAEAAAAAAAAAAMB+CKQAAAAAAAAAAABgUw72LgB3prkdW1x36R0AAAAAAAAAAEA+VkgBAAAAAAAAAADApgikAAAAAAAAAAAAYFMEUgAAAAAAAAAAALApAikAAAAAAAAAAADYlIO9C8Cdqcc3v8jR5GbvMorkqy717V0CAAAAAAAAAAD3JFZIAQAAAAAAAAAAwKYIpAAAAAAAAAAAAGBTBFIAAAAAAAAAAACwKQIpAAAAAAAAAAAA2BSB1D3g6NGjMhj+X3t3Hp7Dvf9//HUTWW9ZBAm1pBoiiBC0JUgojdLYqVIajqK1trY6PW0tbYNaWrTVOqeRqtLTWo46apdYa4+l1tqrcVKVJgQRyfz+8DPf3g2akDtBno/rmutrZt7zmffc334uV/s6nxmLEhISCroVAAAAAAAAAABQCBFIPeSuXbtW0C0AAAAAAAAAAIBCjkAqH506dUqRkZHy8vKSm5ubqlevrmXLlkmS4uLiZLFY9N///lfBwcFydnbWE088oX379tmMsWDBAlWvXl1OTk7y8/PT5MmTbc77+fnpnXfeUVRUlDw8PPTSSy/p0UcflSTVrl1bFotF4eHh+fK8AAAAAAAAAAAAEoFUvurfv7/S09O1fv167du3TxMmTJDVarWpGT58uCZNmqTt27erdOnSat26tTIyMiRJO3fuVOfOndWlSxft27dPo0eP1ptvvqnZs2fbjPH++++rRo0a2rlzp958801t27ZNkrR69WolJiZq4cKFOe45PT1dqampNhsAAAAAAAAAAEBuOBR0A4XJ6dOn1aFDBwUFBUmSKlWqlK3m7bffVvPmzSVJsbGxKleunBYtWqTOnTtrypQpeuqpp/Tmm29KkqpUqaIDBw7o/fffV1RUlDlG06ZNNWzYMHP/5MmTkiRvb2/5+vrmqufo6GiNGTMmV9cAAAAAAAAAAAD8ESuk8tGgQYP0zjvvKDQ0VG+//bb27t2braZ+/frmn0uUKKGAgAAdPHhQknTw4EGFhoba1IeGhuro0aPKzMw0j9WtWzfPeh41apRSUlLM7cyZM3k2NgAAAAAAAAAAKBwIpPJR7969dfz4cXXv3l379u1T3bp1NX369L+8zmKxSJIMwzD/fJNhGNnq3dzc8qZhSU5OTnJ3d7fZAAAAAAAAAAAAcoNAKp+VL19e/fr108KFCzV06FDNmjXL5vwPP/xg/jk5OVlHjhxR1apVJUnVqlXTxo0bbeo3b96sKlWqqGjRore9p6OjoyTZrKICAAAAAAAAAADIL3xDKh8NGTJEzzzzjKpUqaLk5GStXbtWgYGBNjVjx46Vt7e3fHx89MYbb6hkyZJq27atJGno0KGqV6+exo0bp+eee05btmzRjBkz9PHHH9/xvqVLl5aLi4uWL1+ucuXKydnZWR4eHvZ6TAAAAAAAAAAAABuskMpHmZmZ6t+/vwIDA9WiRQsFBARkC5PGjx+vwYMHq06dOkpMTNSSJUvMFU4hISH697//rfnz56tGjRp66623NHbsWEVFRd3xvg4ODpo2bZo+/fRTlS1bVm3atLHXIwIAAAAAAAAAAGRjMW71ESLku7i4ODVp0kTJycny9PQs6HZuKzU1VR4eHmr9xUYVc7UWdDu58m2H4IJuAQAAAAAAAACAh8rN3CAlJUXu7u63rWOFFAAAAAAAAAAAAOyKQAoAAAAAAAAAAAB2xSv7kCs5XXoHAAAAAAAAAAAefryyDwAAAAAAAAAAAPcFAikAAAAAAAAAAADYFYEUAAAAAAAAAAAA7IpACgAAAAAAAAAAAHblUNAN4ME04ruf5ehavKDbuKNp7coXdAsAAAAAAAAAAECskAIAAAAAAAAAAICdEUgBAAAAAAAAAADArgikAAAAAAAAAAAAYFcEUjkUFxcni8Wi33//XZI0e/ZseXp62m18AAAAAAAAAACAh0WhC6TyOkgCAAAAAAAAAADAnRW6QAoAAAAAAAAAAAD5q1AFUnFxcerZs6dSUlJksVhksVg0evRoSdKXX36punXrqnjx4vL19VXXrl2VlJSU47F/++03Pf7442rdurWuXr16y5pTp04pMjJSXl5ecnNzU/Xq1bVs2TKbmp07d6pu3bpydXVVgwYNdPjwYZvzn3zyiR577DE5OjoqICBAc+bMMc8NHTpUkZGR5v4HH3wgi8Wi//73v+axgIAAffrppzl+LgAAAAAAAAAAgHtVqAKpBg0a6IMPPpC7u7sSExOVmJioYcOGSZKuXbumcePGac+ePVq8eLFOnDihqKioHI37888/q1GjRqpataoWLlwoZ2fnW9b1799f6enpWr9+vfbt26cJEybIarXa1LzxxhuaPHmyduzYIQcHB/Xq1cs8t2jRIg0ePFhDhw7V/v371bdvX/Xs2VPr1q2TJIWHh2vDhg3KysqSJMXHx6tkyZKKj4+XJJ07d05HjhxRWFhYjn+z9PR0paam2mwAAAAAAAAAAAC54VDQDeQnR0dHeXh4yGKxyNfX1+bcH4OfSpUqadq0aXr88cd16dKlbKHRHx05ckTNmzdXmzZt9OGHH8pisdy29vTp0+rQoYOCgoLM+/zZu+++awZGr7/+ulq1aqWrV6/K2dlZkyZNUlRUlF555RVJ0muvvaYffvhBkyZNUpMmTdS4cWNdvHhRu3fvVkhIiDZs2KBhw4Zp4cKFkqR169bJx8dHVatWzeEvJkVHR2vMmDE5rgcAAAAAAAAAAPizQrVC6k52796tNm3aqGLFiipevLjCw8Ml3QiRbufKlStq2LCh2rZtq2nTptmEUdWrV5fVapXVatUzzzwjSRo0aJDeeecdhYaG6u2339bevXuzjVmzZk3zz2XKlJEk89WBBw8eVGhoqE19aGioDh48KEny8PBQrVq1FBcXp3379qlIkSLq27ev9uzZo4sXLyouLi5Xq6MkadSoUUpJSTG3M2fO5Op6AAAAAAAAAAAAAilJaWlpevrpp2W1WvXll19q+/btWrRokaQbr/K7HScnJzVr1kz//e9/9fPPP9ucW7ZsmRISEpSQkKB//vOfkqTevXvr+PHj6t69u/bt26e6detq+vTpNtcVK1bM/PPNgOvmK/j+eOwmwzBsjoWHhysuLk7x8fEKCwuTl5eXqlevrk2bNikuLs4M2nLKyclJ7u7uNhsAAAAAAAAAAEBuFLpAytHRUZmZmTbHDh06pPPnz2v8+PHmt6Burkq6kyJFimjOnDmqU6eOmjZtql9++cU8V7FiRfn7+8vf31+PPPKIebx8+fLq16+fFi5cqKFDh2rWrFk57j0wMFAbN260ObZ582YFBgaa+ze/I7V27VozfAoLC9P8+fNz/f0oAAAAAAAAAACAvFDoAik/Pz9dunRJa9as0fnz53X58mVVqFBBjo6Omj59uo4fP64lS5Zo3LhxORqvaNGimjt3roKDg9W0aVOdO3futrVDhgzRihUrdOLECe3atUtr1661CZP+yvDhwzV79mzNnDlTR48e1ZQpU7Rw4UINGzbMrLn5HanvvvvODKTCw8P15ZdfqlSpUqpWrVqO7wcAAAAAAAAAAJAXCl0g1aBBA/Xr10/PPfecSpUqpYkTJ6pUqVKaPXu2vvnmG1WrVk3jx4/XpEmTcjymg4OD5s2bp+rVq6tp06a3XV2VmZmp/v37KzAwUC1atFBAQIA+/vjjHN+nbdu2+vDDD/X++++revXq+vTTTxUTE2PzGj4PDw/Vrl1bJUqUMMOnRo0aKSsri9VRAAAAAAAAAACgQFgMwzAKugk8OFJTU+Xh4aG+X/4oR9fiBd3OHU1rV76gWwAAAAAAAAAA4KF2MzdISUmRu7v7besK3QopAAAAAAAAAAAA5C8CKQAAAAAAAAAAANiVQ0E3gAfTxMhyd1x6BwAAAAAAAAAAcBMrpAAAAAAAAAAAAGBXBFIAAAAAAAAAAACwKwIpAAAAAAAAAAAA2BXfkMJd+deSJLm4XinoNtSvvU9BtwAAAAAAAAAAAP4CK6QAAAAAAAAAAABgVwRSAAAAAAAAAAAAsCsCKQAAAAAAAAAAANgVgRQAAAAAAAAAAADsikAqlxYvXix/f38VLVpUQ4YMKeh2AAAAAAAAAAAA7nsEUrnUt29fdezYUWfOnNG4cePybFyLxaLFixfn2XgAAAAAAAAAAAD3C4eCbuBBcunSJSUlJSkiIkJly5Yt6HYAAAAAAAAAAAAeCKyQyqG4uDgVL15cktS0aVNZLBbFxcXpt99+0/PPP69y5crJ1dVVQUFBmjdvns214eHhGjRokEaMGKESJUrI19dXo0ePNs/7+flJktq1ayeLxWLuHzt2TG3atJGPj4+sVqvq1aun1atX24z98ccfq3LlynJ2dpaPj486duwoSfriiy/k7e2t9PR0m/oOHTqoR48eefjLAAAAAAAAAAAA3BmBVA41aNBAhw8fliQtWLBAiYmJatCgga5evao6depo6dKl2r9/v/r06aPu3btr69atNtfHxsbKzc1NW7du1cSJEzV27FitWrVKkrR9+3ZJUkxMjBITE839S5cuqWXLllq9erV2796tiIgIRUZG6vTp05KkHTt2aNCgQRo7dqwOHz6s5cuXq3HjxpKkTp06KTMzU0uWLDF7OH/+vJYuXaqePXvm+LnT09OVmppqswEAAAAAAAAAAOSGxTAMo6CbeFD8/vvv8vLy0rp16xQeHn7bulatWikwMFCTJk2SdGOFVGZmpjZs2GDWPP7442ratKnGjx8v6cY3pBYtWqS2bdvesYfq1avr5Zdf1oABA7Rw4UL17NlTP//8s7l6649eeeUVnTx5UsuWLZMkffjhh5o2bZp++uknWSyWHD3z6NGjNWbMmGzHp8w5KhfX7PfMb/3a+xR0CwAAAAAAAAAAFFqpqany8PBQSkqK3N3db1vHCql7lJmZqXfffVc1a9aUt7e3rFarVq5caa5iuqlmzZo2+2XKlFFSUtIdx05LS9OIESNUrVo1eXp6ymq16tChQ+bYzZs3V8WKFVWpUiV1795dc+fO1eXLl83rX3rpJa1cuVJnz56VdGMFVlRUVI7DKEkaNWqUUlJSzO3MmTM5vhYAAAAAAAAAAEAikLpnkydP1tSpUzVixAitXbtWCQkJioiI0LVr12zqihUrZrNvsViUlZV1x7GHDx+uBQsW6N1339WGDRuUkJCgoKAgc+zixYtr165dmjdvnsqUKaO33npLwcHB+v333yVJtWvXVnBwsL744gvt2rVL+/btU1RUVK6ez8nJSe7u7jYbAAAAAAAAAABAbjgUdAMPug0bNqhNmzZ64YUXJElZWVk6evSoAgMDczVOsWLFlJmZmW3sqKgotWvXTtKNb0qdPHnSpsbBwUHNmjVTs2bN9Pbbb8vT01Nr165V+/btJUm9e/fW1KlTdfbsWTVr1kzly5e/yycFAAAAAAAAAAC4O6yQukf+/v5atWqVNm/erIMHD6pv3746d+5crsfx8/PTmjVrdO7cOSUnJ5tjL1y4UAkJCdqzZ4+6du1qs6pq6dKlmjZtmhISEnTq1Cl98cUXysrKUkBAgFnTrVs3nT17VrNmzVKvXr3u/YEBAAAAAAAAAAByiUDqHr355psKCQlRRESEwsPD5evrq7Zt2+Z6nMmTJ2vVqlUqX768ateuLUmaOnWqvLy81KBBA0VGRioiIkIhISHmNZ6enlq4cKGaNm2qwMBAzZw5U/PmzVP16tXNGnd3d3Xo0EFWq/Wu+gIAAAAAAAAAALhXFsMwjIJuAvbVvHlzBQYGatq0afc8Vmpqqjw8PDRlzlG5uBbPg+7uTb/2PgXdAgAAAAAAAAAAhdbN3CAlJUXu7u63reMbUg+xCxcuaOXKlVq7dq1mzJhR0O0AAAAAAAAAAIBCikDqIRYSEqLk5GRNmDDB5rtSAAAAAAAAAAAA+YlA6iF28uRJu439t9al77j0DgAAAAAAAAAA4KYiBd0AAAAAAAAAAAAAHm4EUgAAAAAAAAAAALArAikAAAAAAAAAAADYFd+Qwl1Ztug3ubpeK9AeWncqWaD3BwAAAAAAAAAAOcMKKQAAAAAAAAAAANgVgRQAAAAAAAAAAADsikAKAAAAAAAAAAAAdkUgBQAAAAAAAAAAALsikAIAAAAAAAAAAIBdEUjdR0aPHq1atWoVdBsAAAAAAAAAAAB5ikDqIZSRkVHQLQAAAAAAAAAAAJgIpG5j9uzZ8vT01NKlSxUQECBXV1d17NhRaWlpio2NlZ+fn7y8vDRw4EBlZmZKksaOHaugoKBsY9WpU0dvvfWWJCkuLk6PP/643Nzc5OnpqdDQUJ06dUqzZ8/WmDFjtGfPHlksFlksFs2ePVuSlJKSoj59+qh06dJyd3dX06ZNtWfPHnP8myurPv/8c1WqVElOTk6KjY2Vt7e30tPTbXrp0KGDevTokePfIT09XampqTYbAAAAAAAAAABAbhBI3cHly5c1bdo0zZ8/X8uXL1dcXJzat2+vZcuWadmyZZozZ44+++wzffvtt5KkXr166cCBA9q+fbs5xt69e7V7925FRUXp+vXratu2rcLCwrR3715t2bJFffr0kcVi0XPPPaehQ4eqevXqSkxMVGJiop577jkZhqFWrVrp3LlzWrZsmXbu3KmQkBA99dRTunDhgnmfn376Sf/+97+1YMECJSQkqHPnzsrMzNSSJUvMmvPnz2vp0qXq2bNnjn+D6OhoeXh4mFv58uXz4JcFAAAAAAAAAACFiUNBN3A/y8jI0CeffKLHHntMktSxY0fNmTNH//vf/2S1WlWtWjU1adJE69at03PPPady5copIiJCMTExqlevniQpJiZGYWFhqlSpki5cuKCUlBQ9++yz5piBgYHm/axWqxwcHOTr62seW7t2rfbt26ekpCQ5OTlJkiZNmqTFixfr22+/VZ8+fSRJ165d05w5c1SqVCnz2q5duyomJkadOnWSJM2dO1flypVTeHh4jn+DUaNG6bXXXjP3U1NTCaUAAAAAAAAAAECusELqDlxdXc3gSJJ8fHzk5+cnq9VqcywpKcncf+mllzRv3jxdvXpVGRkZmjt3rnr16iVJKlGihKKiohQREaHIyEh9+OGHSkxMvGMPO3fu1KVLl+Tt7S2r1WpuJ06c0LFjx8y6ihUr2oRRN3tZuXKlzp49K+lGOBYVFSWLxZLj38DJyUnu7u42GwAAAAAAAAAAQG6wQuoOihUrZrNvsVhueSwrK8vcj4yMlJOTkxYtWiQnJyelp6erQ4cO5vmYmBgNGjRIy5cv19dff61//OMfWrVqlZ588slb9pCVlaUyZcooLi4u2zlPT0/zz25ubtnO165dW8HBwfriiy8UERGhffv26bvvvsvJowMAAAAAAAAAAOQZAqk85uDgoBdffFExMTFycnJSly5d5OrqalNTu3Zt1a5dW6NGjVL9+vX11Vdf6cknn5Sjo6MyMzNtakNCQnTu3Dk5ODjIz88v1/307t1bU6dO1dmzZ9WsWTNetwcAAAAAAAAAAPIdr+yzg969e2vt2rX6/vvvzdf1SdKJEyc0atQobdmyRadOndLKlSt15MgR8ztSfn5+OnHihBISEnT+/Hmlp6erWbNmql+/vtq2basVK1bo5MmT2rx5s/7xj39ox44df9lLt27ddPbsWc2aNcumFwAAAAAAAAAAgPxCIGUHlStXVoMGDRQQEKAnnnjCPO7q6qpDhw6pQ4cOqlKlivr06aMBAwaob9++kqQOHTqoRYsWatKkiUqVKqV58+bJYrFo2bJlaty4sXr16qUqVaqoS5cuOnnypHx8fP6yF3d3d3Xo0EFWq1Vt27a11yMDAAAAAAAAAADclsUwDKOgm3jYGIahqlWrqm/fvnrttdcKuh01b95cgYGBmjZt2j2PlZqaKg8PD82bfVyursXzoLu717pTyQK9PwAAAAAAAAAAhd3N3CAlJUXu7u63reMbUnksKSlJc+bM0dmzZ9WzZ88C7eXChQtauXKl1q5dqxkzZhRoLwAAAAAAAAAAoPAikMpjPj4+KlmypD777DN5eXkVaC8hISFKTk7WhAkTFBAQUKC9AAAAAAAAAACAwotAKo/dT29APHnypN3GbtnO+45L7wAAAAAAAAAAAG4qUtANAAAAAAAAAAAA4OFGIAUAAAAAAAAAAAC7IpACAAAAAAAAAACAXfENKdyVH+adl5tLer7eM7RHqXy9HwAAAAAAAAAAyBuskAIAAAAAAAAAAIBdEUgBAAAAAAAAAADArgikAAAAAAAAAAAAYFcEUvc5i8WixYsX59l4fn5++uCDD/JsPAAAAAAAAAAAgL/iUNAN4M4SExPl5eVV0G0AAAAAAAAAAADcNQKp+5yvr29BtwAAAAAAAAAAAHBPeGVfDs2ePVuenp5aunSpAgIC5Orqqo4dOyotLU2xsbHy8/OTl5eXBg4cqMzMTPO6W71yz9PTU7Nnz5YkXbt2TQMGDFCZMmXk7OwsPz8/RUdH3/b6n3/+WV26dFGJEiXk5uamunXrauvWrZKkY8eOqU2bNvLx8ZHValW9evW0evVqu/0mAAAAAAAAAAAAOcEKqVy4fPmypk2bpvnz5+vixYtq37692rdvL09PTy1btkzHjx9Xhw4d1LBhQz333HM5GnPatGlasmSJ/v3vf6tChQo6c+aMzpw5c8vaS5cuKSwsTI888oiWLFkiX19f7dq1S1lZWeb5li1b6p133pGzs7NiY2MVGRmpw4cPq0KFCnf1zOnp6UpPTzf3U1NT72ocAAAAAAAAAABQeBFI5UJGRoY++eQTPfbYY5Kkjh07as6cOfrf//4nq9WqatWqqUmTJlq3bl2OA6nTp0+rcuXKatiwoSwWiypWrHjb2q+++kq//vqrtm/frhIlSkiS/P39zfPBwcEKDg4299955x0tWrRIS5Ys0YABA+7mkRUdHa0xY8bc1bUAAAAAAAAAAAASr+zLFVdXVzOMkiQfHx/5+fnJarXaHEtKSsrxmFFRUUpISFBAQIAGDRqklStX3rY2ISFBtWvXNsOoP0tLS9OIESNUrVo1eXp6ymq16tChQzp9+nSO+/mzUaNGKSUlxdxut3oLAAAAAAAAAADgdlghlQvFihWz2bdYLLc8dvMVejf3DcOwqcnIyDD/HBISohMnTuj777/X6tWr1blzZzVr1kzffvtttvu7uLjcsb/hw4drxYoVmjRpkvz9/eXi4qKOHTvq2rVrOX7GP3NycpKTk9NdXw8AAAAAAAAAAMAKKTsrVaqUEhMTzf2jR4/q8uXLNjXu7u567rnnNGvWLH399ddasGCBLly4kG2smjVrKiEh4ZbnJGnDhg2KiopSu3btFBQUJF9fX508eTJPnwcAAAAAAAAAACC3CKTsrGnTppoxY4Z27dqlHTt2qF+/fjarqqZOnar58+fr0KFDOnLkiL755hv5+vrK09Mz21jPP/+8fH191bZtW23atEnHjx/XggULtGXLFkk3vie1cOFCJSQkaM+ePeratavNai0AAAAAAAAAAICCQCBlZ5MnT1b58uXVuHFjde3aVcOGDZOrq6t53mq1asKECapbt67q1aunkydPatmyZSpSJPv/axwdHbVy5UqVLl1aLVu2VFBQkMaPH6+iRYtKuhFueXl5qUGDBoqMjFRERIRCQkLy7VkBAAAAAAAAAABuxWL8+QNHwB2kpqbKw8NDK2Yek5tL8Xy9d2iPUvl6PwAAAAAAAAAAcGc3c4OUlBS5u7vfto4VUgAAAAAAAAAAALArAikAAAAAAAAAAADYlUNBN4AH05PPl7zj0jsAAAAAAAAAAICbWCEFAAAAAAAAAAAAuyKQAgAAAAAAAAAAgF0RSAEAAAAAAAAAAMCuCKQAAAAAAAAAAABgVw4F3QAeTIf+mSSry5V8uVe1l33y5T4AAAAAAAAAAMA+WCEFAAAAAAAAAAAAuyKQAgAAAAAAAAAAgF0RSAEAAAAAAAAAAMCuCkUgNXv2bHl6et6xZvTo0apVq1a+9JNXLBaLFi9eXNBtAAAAAAAAAAAA3FGhCKRyYtiwYVqzZk1BtwEAAAAAAAAAAPDQcSjoBu4XVqtVVqvVrvcwDEOZmZlycOBnBwAAAAAAAAAAhUehWiG1ePFiValSRc7OzmrevLnOnDljnvvzK/uysrI0duxYlStXTk5OTqpVq5aWL19uM97mzZtVq1YtOTs7q27dulq8eLEsFosSEhIkSXFxcbJYLFqxYoXq1q0rJycnbdiwQceOHVObNm3k4+Mjq9WqevXqafXq1TZj+/n5ady4ceratausVqvKli2r6dOnZ3um8+fPq127dnJ1dVXlypW1ZMkSSTfCL39/f02aNMmmfv/+/SpSpIiOHTt2Lz8lAAAAAAAAAABAjhWaQOry5ct69913FRsbq02bNik1NVVdunS5bf2HH36oyZMna9KkSdq7d68iIiLUunVrHT16VJJ08eJFRUZGKigoSLt27dK4ceM0cuTIW441YsQIRUdH6+DBg6pZs6YuXbqkli1bavXq1dq9e7ciIiIUGRmp06dP21z3/vvvq2bNmtq1a5dGjRqlV199VatWrbKpGTNmjDp37qy9e/eqZcuW6tatmy5cuCCLxaJevXopJibGpv7zzz9Xo0aN9Nhjj+Xod0tPT1dqaqrNBgAAAAAAAAAAkBuFJpDKyMjQjBkzVL9+fdWpU0exsbHavHmztm3bdsv6SZMmaeTIkerSpYsCAgI0YcIE1apVSx988IEkae7cubJYLJo1a5aqVaumZ555RsOHD7/lWGPHjlXz5s312GOPydvbW8HBwerbt6+CgoJUuXJlvfPOO6pUqZK5uumm0NBQvf7666pSpYoGDhyojh07aurUqTY1UVFRev755+Xv76/33ntPaWlp5jP17NlThw8fNvczMjL05ZdfqlevXjn+3aKjo+Xh4WFu5cuXz/G1AAAAAAAAAAAAUiEKpBwcHFS3bl1zv2rVqvL09NTBgwez1aampuqXX35RaGiozfHQ0FCz/vDhw6pZs6acnZ3N848//vgt7/3H+0pSWlqaRowYoWrVqsnT01NWq1WHDh3KtkKqfv362fb/3G/NmjXNP7u5ual48eJKSkqSJJUpU0atWrXS559/LklaunSprl69qk6dOt2yz1sZNWqUUlJSzO2PrzkEAAAAAAAAAADIiUITSEmSxWLJ0bHbnTMMwzz2xz//8fytuLm52ewPHz5cCxYs0LvvvqsNGzYoISFBQUFBunbtWq6foVixYtnOZ2Vlmfu9e/fW/PnzdeXKFcXExOi5556Tq6vrX97nJicnJ7m7u9tsAAAAAAAAAAAAuVFoAqnr169rx44d5v7hw4f1+++/q2rVqtlq3d3dVbZsWW3cuNHm+ObNmxUYGCjpxgqrvXv3Kj093Tz/x/HvZMOGDYqKilK7du0UFBQkX19fnTx5MlvdDz/8kG3/Vv3eScuWLeXm5qZPPvlE33//fa5e1wcAAAAAAAAAAJAXCk0gVaxYMQ0cOFBbt27Vrl271LNnTz355JO3fc3e8OHDNWHCBH399dc6fPiwXn/9dSUkJGjw4MGSpK5duyorK0t9+vTRwYMHtWLFCk2aNEnSnVddSZK/v78WLlyohIQE7dmzxxzrzzZt2qSJEyfqyJEj+uijj/TNN9+Y98+pokWLKioqSqNGjZK/v3+21wACAAAAAAAAAADYW6EJpFxdXTVy5Eh17dpV9evXl4uLi+bPn3/b+kGDBmno0KEaOnSogoKCtHz5ci1ZskSVK1eWdGMV1XfffaeEhATVqlVLb7zxht566y1Jsvmu1K1MnTpVXl5eatCggSIjIxUREaGQkJBsdUOHDtXOnTtVu3ZtjRs3TpMnT1ZERESun/1vf/ubrl27xuooAAAAAAAAAABQICzG7T58hFybO3euevbsqZSUFLm4uNzTWH5+fhoyZIiGDBlyz31t2rRJ4eHh+vnnn+Xj43NPY6WmpsrDw0NbJx+V1aX4PfeWE9VevreeAQAAAAAAAACAfdzMDVJSUuTu7n7bOod87Omh88UXX6hSpUp65JFHtGfPHo0cOVKdO3e+5zAqr6Snp+vMmTN688031blz53sOowAAAAAAAAAAAO5GoXllnz2cO3dOL7zwggIDA/Xqq6+qU6dO+uyzzwq6LdO8efMUEBCglJQUTZw4saDbAQAAAAAAAAAAhRSv7EOu5HTpHQAAAAAAAAAAePjlNDdghRQAAAAAAAAAAADsikAKAAAAAAAAAAAAdkUgBQAAAAAAAAAAALsikAIAAAAAAAAAAIBdORR0A3gwnfvgrNKcU+16jzIjytl1fAAAAAAAAAAAkD9YIQUAAAAAAAAAAAC7IpACAAAAAAAAAACAXRFIAQAAAAAAAAAAwK4IpO7B7Nmz5enpeceaqKgotW3bNl/6AQAAAAAAAAAAuB85FHQDD7sPP/xQhmHkqDYqKkq///67Fi9ebN+mAAAAAAAAAAAA8hGBlJ15eHjk+z2vXbsmR0fHfL8vAAAAAAAAAADArfDKvjywYsUKBQYGymq1qkWLFkpMTDTP/fmVfd9++62CgoLk4uIib29vNWvWTGlpaRo9erRiY2P1n//8RxaLRRaLRXFxcZKkffv2qWnTpuY1ffr00aVLl7LdIzo6WmXLllWVKlU0duxYBQUFZeu1Tp06euutt+z2WwAAAAAAAAAAAPwZK6Tu0eXLlzVp0iTNmTNHRYoU0QsvvKBhw4Zp7ty52WoTExP1/PPPa+LEiWrXrp0uXryoDRs2yDAMDRs2TAcPHlRqaqpiYmIkSSVKlNDly5fVokULPfnkk9q+fbuSkpLUu3dvDRgwQLNnzzbHXrNmjdzd3bVq1SoZhiFPT0+NGTNG27dvV7169SRJe/fu1e7du/XNN9/k+PnS09OVnp5u7qempt7lLwUAAAAAAAAAAAorAql7lJGRoZkzZ+qxxx6TJA0YMEBjx469ZW1iYqKuX7+u9u3bq2LFipJks4rJxcVF6enp8vX1NY/FxsbqypUr+uKLL+Tm5iZJmjFjhiIjIzVhwgT5+PhIktzc3PTPf/7T5lV9ERERiomJMQOpmJgYhYWFqVKlSjl+vujoaI0ZMybH9QAAAAAAAAAAAH/GK/vukaurqxlGSVKZMmWUlJR0y9rg4GA99dRTCgoKUqdOnTRr1iwlJyffcfyDBw8qODjYDKMkKTQ0VFlZWTp8+LB5LCgoKNt3o1566SXNmzdPV69eVUZGhubOnatevXrl6vlGjRqllJQUcztz5kyurgcAAAAAAAAAACCQukfFihWz2bdYLDIM45a1RYsW1apVq/T999+rWrVqmj59ugICAnTixInbjm8YhiwWyy3P/fH4HwOrmyIjI+Xk5KRFixbpu+++U3p6ujp06JCTxzI5OTnJ3d3dZgMAAAAAAAAAAMgNAql8ZrFYFBoaqjFjxmj37t1ydHTUokWLJEmOjo7KzMy0qa9WrZoSEhKUlpZmHtu0aZOKFCmiKlWq3PFeDg4OevHFFxUTE6OYmBh16dJFrq6uef9QAAAAAAAAAAAAd8A3pPLR1q1btWbNGj399NMqXbq0tm7dql9//VWBgYGSJD8/P61YsUKHDx+Wt7e3PDw81K1bN7399tt68cUXNXr0aP36668aOHCgunfvbn4/6k569+5tjr9p0ya7Ph8AAAAAAAAAAMCtsEIqH7m7u2v9+vVq2bKlqlSpon/84x+aPHmynnnmGUk3vvkUEBCgunXrqlSpUtq0aZNcXV21YsUKXbhwQfXq1VPHjh311FNPacaMGTm6Z+XKldWgQQMFBAToiSeesOfjAQAAAAAAAAAA3JLFuN0Hj/BQMAxDVatWVd++ffXaa6/d83ipqany8PDQ4TEHVNy5eB50eHtlRpSz6/gAAAAAAAAAAODe3MwNUlJS5O7ufts6Xtn3EEtKStKcOXN09uxZ9ezZs6DbAQAAAAAAAAAAhRSB1EPMx8dHJUuW1GeffSYvL6+CbgcAAAAAAAAAABRSBFIPMXu+jdF3yCN3XHoHAAAAAAAAAABwU5GCbgAAAAAAAAAAAAAPNwIpAAAAAAAAAAAA2BWBFAAAAAAAAAAAAOyKQAoAAAAAAAAAAAB25VDQDeDBlPTRfl1xtubZeD6v1syzsQAAAAAAAAAAwP2FFVIAAAAAAAAAAACwKwIpAAAAAAAAAAAA2BWBFAAAAAAAAAAAAOyKQOo+4ufnpw8++KCg2wAAAAAAAAAAAMhTBFIAAAAAAAAAAACwKwIpAAAAAAAAAAAA2NUDH0jNmjVL5cuXl6urq9q1a6cpU6bI09PTPH/s2DG1adNGPj4+slqtqlevnlavXm0zhp+fn9555x316NFDVqtVFStW1H/+8x/9+uuvatOmjaxWq4KCgrRjxw6b6zZv3qzGjRvLxcVF5cuX16BBg5SWlnbHfkePHq0KFSrIyclJZcuW1aBBg2zOX758Wb169VLx4sVVoUIFffbZZzbn9+3bp6ZNm8rFxUXe3t7q06ePLl26ZJ4rUqSIzp8/L0lKTk5WkSJF1KlTJ/P66Oho1a9fP2c/LgAAAAAAAAAAQB54oAOpTZs2qV+/fho8eLASEhLUvHlzvfvuuzY1ly5dUsuWLbV69Wrt3r1bERERioyM1OnTp23qpk6dqtDQUO3evVutWrVS9+7d1aNHD73wwgvatWuX/P391aNHDxmGIelG+BMREaH27dtr7969+vrrr7Vx40YNGDDgtv1+++23mjp1qj799FMdPXpUixcvVlBQkE3N5MmTVbduXe3evVuvvPKKXn75ZR06dEjSjbCqRYsW8vLy0vbt2/XNN99o9erV5j1r1Kghb29vxcfHS5LWr18vb29vrV+/3hw/Li5OYWFhOf6N09PTlZqaarMBAAAAAAAAAADkhsW4mbA8gLp06aJLly5p6dKl5rEXXnhBS5cu1e+//37b66pXr66XX37ZDHL8/PzUqFEjzZkzR5J07tw5lSlTRm+++abGjh0rSfrhhx9Uv359JSYmytfXVz169JCLi4s+/fRTc9yNGzcqLCxMaWlpcnZ2znbfKVOm6NNPP9X+/ftVrFixbOf/3IdhGPL19dWYMWPUr18/zZo1SyNHjtSZM2fk5uYmSVq2bJkiIyP1yy+/yMfHRx06dFDZsmU1ffp0vfrqqypatKhiY2MVHx+vKlWqyMvLS998841atGiRo9949OjRGjNmTLbjR9/bpOLO1hyNkRM+r9bMs7EAAAAAAAAAAED+SE1NlYeHh1JSUuTu7n7bugd6hdThw4f1+OOP2xz7835aWppGjBihatWqydPTU1arVYcOHcq2Qqpmzf8LRHx8fCTJZvXSzWNJSUmSpJ07d2r27NmyWq3mFhERoaysLJ04cULvvfeezbnTp0+rU6dOunLliipVqqSXXnpJixYt0vXr12/bh8Vika+vr3nPgwcPKjg42AyjJCk0NFRZWVk6fPiwJCk8PFxxcXGSpPj4eDVp0kSNGzdWfHy8tm/fritXrig0NDSHv7A0atQopaSkmNuZM2dyfC0AAAAAAAAAAIAkORR0A/fCMAxZLJZsx/5o+PDhWrFihSZNmiR/f3+5uLioY8eOunbtmk3dH1cs3RzzVseysrLM/9u3b99s34CSpAoVKqhfv37q3Lmzeaxs2bJycHDQ4cOHtWrVKq1evVqvvPKK3n//fcXHx5v3+vPKKYvFYt7zVs/75/7Cw8M1ePBg/fTTT9q/f78aNWqkY8eOKT4+Xr///rvq1Kmj4sWL33KMW3FycpKTk1OO6wEAAAAAAAAAAP7sgQ6kqlatqm3bttkc27Fjh83+hg0bFBUVpXbt2km68U2pkydP3vO9Q0JC9OOPP8rf3/+W50uUKKESJUpkO+7i4qLWrVurdevW6t+/v6pWrap9+/YpJCTkL+9ZrVo1xcbGKi0tzVwltWnTJhUpUkRVqlSR9H/fkXrnnXcUHBwsd3d3hYWFKTo6WsnJybn6fhQAAAAAAAAAAEBeeKBf2Tdw4EAtW7ZMU6ZM0dGjR/Xpp5/q+++/t1lF5O/vr4ULFyohIUF79uxR165dzRVH92LkyJHasmWL+vfvr4SEBB09elRLlizRwIEDb3vN7Nmz9a9//Uv79+/X8ePHNWfOHLm4uKhixYo5ume3bt3k7OysF198Ufv379e6des0cOBAde/e3XyloMViUePGjfXll18qPDxc0o3XAF67dk1r1qwxjwEAAAAAAAAAAOSXBzqQCg0N1cyZMzVlyhQFBwdr+fLlevXVV+Xs7GzWTJ06VV5eXmrQoIEiIyMVERGRo9VIf6VmzZqKj4/X0aNH1ahRI9WuXVtvvvmmypQpc9trPD09NWvWLIWGhqpmzZpas2aNvvvuO3l7e+fonq6urlqxYoUuXLigevXqqWPHjnrqqac0Y8YMm7omTZooMzPTDJ8sFosaNWokSWrYsOHdPTAAAAAAAAAAAMBdshh//ujSA+6ll17SoUOHtGHDhoJu5aGUmpoqDw8PHX1vk4o7W/NsXJ9Xa+bZWAAAAAAAAAAAIH/czA1SUlLk7u5+27oH+htSkjRp0iQ1b95cbm5u+v777xUbG6uPP/64oNsCAAAAAAAAAADA//fAB1Lbtm3TxIkTdfHiRVWqVEnTpk1T7969C7otAAAAAAAAAAAA/H8P3Sv7YF85XXoHAAAAAAAAAAAefjnNDYrkY08AAAAAAAAAAAAohAikAAAAAAAAAAAAYFcEUgAAAAAAAAAAALArh4JuAA+mpJmbdcXZ7Z7H8RnUKA+6AQAAAAAAAAAA9zNWSAEAAAAAAAAAAMCuCKQAAAAAAAAAAABgVwRSAAAAAAAAAAAAsCsCKQAAAAAAAAAAANgVgRQAAAAAAAAAAADsikAKAAAAAAAAAAAAdkUgBQAAAAAAAAAAALsikMpHs2fPlqenp1asWKHAwEBZrVa1aNFCiYmJNnUxMTEKDAyUs7Ozqlatqo8//tg816FDBw0cONDcHzJkiCwWi3788UdJ0vXr11W8eHGtWLFCkvTtt98qKChILi4u8vb2VrNmzZSWlpYPTwsAAAAAAAAAAHADgVQ+u3z5siZNmqQ5c+Zo/fr1On36tIYNG2aenzVrlt544w29++67OnjwoN577z29+eabio2NlSSFh4crLi7OrI+Pj1fJkiUVHx8vSdq+fbuuXr2q0NBQJSYm6vnnn1evXr108OBBxcXFqX379jIMI8f9pqenKzU11WYDAAAAAAAAAADIDQKpfJaRkaGZM2eqbt26CgkJ0YABA7RmzRrz/Lhx4zR58mS1b99ejz76qNq3b69XX31Vn376qaQbgdSPP/6o8+fPKzk5WT/++KOGDBlihlRxcXGqU6eOrFarEhMTdf36dbVv315+fn4KCgrSK6+8IqvVmuN+o6Oj5eHhYW7ly5fP098DAAAAAAAAAAA8/Aik8pmrq6see+wxc79MmTJKSkqSJP366686c+aM/va3v8lqtZrbO++8o2PHjkmSatSoIW9vb8XHx2vDhg0KDg5W69atzRVScXFxCgsLkyQFBwfrqaeeUlBQkDp16qRZs2YpOTk5V/2OGjVKKSkp5nbmzJm8+BkAAAAAAAAAAEAh4lDQDRQ2xYoVs9m3WCzmK/SysrIk3Xht3xNPPGFTV7RoUbO+cePGiouLk6Ojo8LDw1WjRg1lZmZq37592rx5s4YMGWJes2rVKm3evFkrV67U9OnT9cYbb2jr1q169NFHc9Svk5OTnJyc7uWRAQAAAAAAAABAIccKqfuIj4+PHnnkER0/flz+/v422x8DpJvfkYqLi1N4eLgsFosaNWqkSZMm6cqVKwoNDTVrLRaLQkNDNWbMGO3evVuOjo5atGhRQTweAAAAAAAAAAAopFghdZ8ZPXq0Bg0aJHd3dz3zzDNKT0/Xjh07lJycrNdee03SjUBq8ODBcnBwUKNGjcxjQ4cOVUhIiNzd3SVJW7du1Zo1a/T000+rdOnS2rp1q3799VcFBgYW2PMBAAAAAAAAAIDCh0DqPtO7d2+5urrq/fff14gRI+Tm5qagoCDzNXzSje9IlSxZUhUrVjTDp7CwMGVmZprfj5Ikd3d3rV+/Xh988IFSU1NVsWJFTZ48Wc8880x+PxYAAAAAAAAAACjELMbNDxgBOZCamioPDw8dnfC9iju73fN4PoMa5UFXAAAAAAAAAACgINzMDVJSUsxFNLfCN6QAAAAAAAAAAABgVwRSAAAAAAAAAAAAsCu+IYW7UrpfgzsuvQMAAAAAAAAAALiJFVIAAAAAAAAAAACwKwIpAAAAAAAAAAAA2BWBFAAAAAAAAAAAAOyKb0jhrvz66QpddXHN1TWlB7SyUzcAAAAAAAAAAOB+xgopAAAAAAAAAAAA2BWBFAAAAAAAAAAAAOyKQAoAAAAAAAAAAAB2RSAFAAAAAAAAAAAAuyKQAgAAAAAAAAAAgF0RSBVCmZmZysrKKug2AAAAAAAAAABAIUEgdZ8xDEOlSpXSggULzGO1atVS6dKlzf0tW7aoWLFiunTpkiRpypQpCgoKkpubm8qXL69XXnnFPCdJs2fPlqenp5YuXapq1arJyclJp06dyr+HAgAAAAAAAAAAhRqB1H3GYrGocePGiouLkyQlJyfrwIEDysjI0IEDByRJcXFxqlOnjqxWqySpSJEimjZtmvbv36/Y2FitXbtWI0aMsBn38uXLio6O1j//+U/9+OOPNgHXnaSnpys1NdVmAwAAAAAAAAAAyA0CqftQeHi4GUitX79ewcHBatq0qXksLi5O4eHhZv2QIUPUpEkTPfroo2ratKnGjRunf//73zZjZmRk6OOPP1aDBg0UEBAgNze3HPUSHR0tDw8PcytfvnxePCIAAAAAAAAAAChECKTuQ+Hh4frxxx91/vx5xcfHKzw8XOHh4YqPj9f169e1efNmhYWFmfXr1q1T8+bN9cgjj6h48eLq0aOHfvvtN6WlpZk1jo6OqlmzZq57GTVqlFJSUsztzJkzefKMAAAAAAAAAACg8CCQug/VqFFD3t7eio+PNwOpsLAwxcfHa/v27bpy5YoaNmwoSTp16pRatmypGjVqaMGCBdq5c6c++ugjSTdWRd3k4uIii8WS616cnJzk7u5uswEAAAAAAAAAAOSGQ0E3gOxufkfqP//5j/bv369GjRqpePHiysjI0MyZMxUSEqLixYtLknbs2KHr169r8uTJKlLkRr7459f1AQAAAAAAAAAAFCRWSN2nwsPD9dVXX6lmzZpyd3c3Q6q5c+fafD/qscce0/Xr1zV9+nQdP35cc+bM0cyZMwuucQAAAAAAAAAAgD8hkLpPNWnSRJmZmTbhU1hYmDIzM22+H1WrVi1NmTJFEyZMUI0aNTR37lxFR0cXQMcAAAAAAAAAAAC3ZjEMwyjoJvDgSE1NlYeHh36a+G8Vd3HN1bWlB7SyU1cAAAAAAAAAAKAg3MwNUlJS5O7ufts6VkgBAAAAAAAAAADArgikAAAAAAAAAAAAYFcOBd0AHkyl+kbccekdAAAAAAAAAADATayQAgAAAAAAAAAAgF2xQgq5YhiGpBsfKQMAAAAAAAAAAIXbzbzgZn5wOwRSyJXffvtNklS+fPkC7gQAAAAAAAAAANwvLl68KA8Pj9ueJ5BCrpQoUUKSdPr06Tv+gwXg7qSmpqp8+fI6c+YM32kD8hjzC7Av5hhgX8wxwL6YY4B9MccA+yroOWYYhi5evKiyZcvesY5ACrlSpMiNz455eHjwlwdgR+7u7swxwE6YX4B9MccA+2KOAfbFHAPsizkG2FdBzrGcLGApkg99AAAAAAAAAAAAoBAjkAIAAAAAAAAAAIBdEUghV5ycnPT222/LycmpoFsBHkrMMcB+mF+AfTHHAPtijgH2xRwD7Is5BtjXgzLHLIZhGAXdBAAAAAAAAAAAAB5erJACAAAAAAAAAACAXRFIAQAAAAAAAAAAwK4IpAAAAAAAAAAAAGBXBFIAAAAAAAAAAACwKwIp5NjHH3+sRx99VM7OzqpTp442bNhQ0C0B953o6GjVq1dPxYsXV+nSpdW2bVsdPnzYpsYwDI0ePVply5aVi4uLwsPD9eOPP9rUpKena+DAgSpZsqTc3NzUunVr/fzzzzY1ycnJ6t69uzw8POTh4aHu3bvr999/t/cjAveN6OhoWSwWDRkyxDzG/ALu3dmzZ/XCCy/I29tbrq6uqlWrlnbu3GmeZ54Bd+f69ev6xz/+oUcffVQuLi6qVKmSxo4dq6ysLLOG+QXk3Pr16xUZGamyZcvKYrFo8eLFNufzcz6dPn1akZGRcnNzU8mSJTVo0CBdu3bNHo8N5Js7zbGMjAyNHDlSQUFBcnNzU9myZdWjRw/98ssvNmMwx4Db+6u/x/6ob9++slgs+uCDD2yOP4hzjEAKOfL1119ryJAheuONN7R79241atRIzzzzjE6fPl3QrQH3lfj4ePXv318//PCDVq1apevXr+vpp59WWlqaWTNx4kRNmTJFM2bM0Pbt2+Xr66vmzZvr4sWLZs2QIUO0aNEizZ8/Xxs3btSlS5f07LPPKjMz06zp2rWrEhIStHz5ci1fvlwJCQnq3r17vj4vUFC2b9+uzz77TDVr1rQ5zvwC7k1ycrJCQ0NVrFgxff/99zpw4IAmT54sT09Ps4Z5BtydCRMmaObMmZoxY4YOHjyoiRMn6v3339f06dPNGuYXkHNpaWkKDg7WjBkzbnk+v+ZTZmamWrVqpbS0NG3cuFHz58/XggULNHToUPs9PJAP7jTHLl++rF27dunNN9/Url27tHDhQh05ckStW7e2qWOOAbf3V3+P3bR48WJt3bpVZcuWzXbugZxjBpADjz/+uNGvXz+bY1WrVjVef/31AuoIeDAkJSUZkoz4+HjDMAwjKyvL8PX1NcaPH2/WXL161fDw8DBmzpxpGIZh/P7770axYsWM+fPnmzVnz541ihQpYixfvtwwDMM4cOCAIcn44YcfzJotW7YYkoxDhw7lx6MBBebixYtG5cqVjVWrVhlhYWHG4MGDDcNgfgF5YeTIkUbDhg1ve555Bty9Vq1aGb169bI51r59e+OFF14wDIP5BdwLScaiRYvM/fycT8uWLTOKFClinD171qyZN2+e4eTkZKSkpNjleYH89uc5divbtm0zJBmnTp0yDIM5BuTG7ebYzz//bDzyyCPG/v37jYoVKxpTp041zz2oc4wVUvhL165d086dO/X000/bHH/66ae1efPmAuoKeDCkpKRIkkqUKCFJOnHihM6dO2czn5ycnBQWFmbOp507dyojI8OmpmzZsqpRo4ZZs2XLFnl4eOiJJ54wa5588kl5eHgwL/HQ69+/v1q1aqVmzZrZHGd+AfduyZIlqlu3rjp16qTSpUurdu3amjVrlnmeeQbcvYYNG2rNmjU6cuSIJGnPnj3auHGjWrZsKYn5BeSl/JxPW7ZsUY0aNWz+l+sRERFKT0+3eeUt8LBLSUmRxWIxV9Yzx4B7k5WVpe7du2v48OGqXr16tvMP6hxzyPMR8dA5f/68MjMz5ePjY3Pcx8dH586dK6CugPufYRh67bXX1LBhQ9WoUUOSzDlzq/l06tQps8bR0VFeXl7Zam5ef+7cOZUuXTrbPUuXLs28xENt/vz52rVrl7Zv357tHPMLuHfHjx/XJ598otdee01///vftW3bNg0aNEhOTk7q0aMH8wy4ByNHjlRKSoqqVq2qokWLKjMzU++++66ef/55Sfw9BuSl/JxP586dy3YfLy8vOTo6MudQaFy9elWvv/66unbtKnd3d0nMMeBeTZgwQQ4ODho0aNAtzz+oc4xACjlmsVhs9g3DyHYMwP8ZMGCA9u7dq40bN2Y7dzfz6c81t6pnXuJhdubMGQ0ePFgrV66Us7PzbeuYX8Ddy8rKUt26dfXee+9JkmrXrq0ff/xRn3zyiXr06GHWMc+A3Pv666/15Zdf6quvvlL16tWVkJCgIUOGqGzZsnrxxRfNOuYXkHfyaz4x51CYZWRkqEuXLsrKytLHH3/8l/XMMeCv7dy5Ux9++KF27dqV63/O7/c5xiv78JdKliypokWLZktEk5KSsqWnAG4YOHCglixZonXr1qlcuXLmcV9fX0m643zy9fXVtWvXlJycfMea//3vf9nu++uvvzIv8dDauXOnkpKSVKdOHTk4OMjBwUHx8fGaNm2aHBwczH/2mV/A3StTpoyqVatmcywwMFCnT5+WxN9jwL0YPny4Xn/9dXXp0kVBQUHq3r27Xn31VUVHR0tifgF5KT/nk6+vb7b7JCcnKyMjgzmHh15GRoY6d+6sEydOaNWqVebqKIk5BtyLDRs2KCkpSRUqVDD/+8epU6c0dOhQ+fn5SXpw5xiBFP6So6Oj6tSpo1WrVtkcX7VqlRo0aFBAXQH3J8MwNGDAAC1cuFBr167Vo48+anP+0Ucfla+vr818unbtmuLj4835VKdOHRUrVsymJjExUfv37zdr6tevr5SUFG3bts2s2bp1q1JSUpiXeGg99dRT2rdvnxISEsytbt266tatmxISElSpUiXmF3CPQkNDdfjwYZtjR44cUcWKFSXx9xhwLy5fvqwiRWz/Fbxo0aLKysqSxPwC8lJ+zqf69etr//79SkxMNGtWrlwpJycn1alTx67PCRSkm2HU0aNHtXr1anl7e9ucZ44Bd6979+7au3evzX//KFu2rIYPH64VK1ZIeoDnmAHkwPz5841ixYoZ//rXv4wDBw4YQ4YMMdzc3IyTJ08WdGvAfeXll182PDw8jLi4OCMxMdHcLl++bNaMHz/e8PDwMBYuXGjs27fPeP75540yZcoYqampZk2/fv2McuXKGatXrzZ27dplNG3a1AgODjauX79u1rRo0cKoWbOmsWXLFmPLli1GUFCQ8eyzz+br8wIFLSwszBg8eLC5z/wC7s22bdsMBwcH49133zWOHj1qzJ0713B1dTW+/PJLs4Z5BtydF1980XjkkUeMpUuXGidOnDAWLlxolCxZ0hgxYoRZw/wCcu7ixYvG7t27jd27dxuSjClTphi7d+82Tp06ZRhG/s2n69evGzVq1DCeeuopY9euXcbq1auNcuXKGQMGDMi/HwOwgzvNsYyMDKN169ZGuXLljISEBJv//pGenm6OwRwDbu+v/h77s4oVKxpTp061OfYgzjECKeTYRx99ZFSsWNFwdHQ0QkJCjPj4+IJuCbjvSLrlFhMTY9ZkZWUZb7/9tuHr62s4OTkZjRs3Nvbt22czzpUrV4wBAwYYJUqUMFxcXIxnn33WOH36tE3Nb7/9ZnTr1s0oXry4Ubx4caNbt25GcnJyPjwlcP/4cyDF/ALu3XfffWfUqFHDcHJyMqpWrWp89tlnNueZZ8DdSU1NNQYPHmxUqFDBcHZ2NipVqmS88cYbNv/hjvkF5Ny6detu+e9eL774omEY+TufTp06ZbRq1cpwcXExSpQoYQwYMMC4evWqPR8fsLs7zbETJ07c9r9/rFu3zhyDOQbc3l/9PfZntwqkHsQ5ZjEMw8j7dVcAAAAAAAAAAADADXxDCgAAAAAAAAAAAHZFIAUAAAAAAAAAAAC7IpACAAAAAAAAAACAXRFIAQAAAAAAAAAAwK4IpAAAAAAAAAAAAGBXBFIAAAAAAAAAAACwKwIpAAAAAAAAAAAA2BWBFAAAAAAAAAAAAOyKQAoAAAAAHnAnT56UxWJRQkJCQbdiOnTokJ588kk5OzurVq1aBd2OyWKxaPHixQXdBgAAAFDoEEgBAAAAwD2KioqSxWLR+PHjbY4vXrxYFoulgLoqWG+//bbc3Nx0+PBhrVmz5pY1N3+3fv36ZTv3yiuvyGKxKCoqKk/7SkxM1DPPPJOnYwIAAAD4awRSAAAAAJAHnJ2dNWHCBCUnJxd0K3nm2rVrd33tsWPH1LBhQ1WsWFHe3t63rStfvrzmz5+vK1eumMeuXr2qefPmqUKFCnd9/9vx9fWVk5NTno8LAAAA4M4IpAAAAAAgDzRr1ky+vr6Kjo6+bc3o0aOzvb7ugw8+kJ+fn7kfFRWltm3b6r333pOPj488PT01ZswYXb9+XcOHD1eJEiVUrlw5ff7559nGP3TokBo0aCBnZ2dVr15dcXFxNucPHDigli1bymq1ysfHR927d9f58+fN8+Hh4RowYIBee+01lSxZUs2bN7/lc2RlZWns2LEqV66cnJycVKtWLS1fvtw8b7FYtHPnTo0dO1YWi0WjR4++7W8SEhKiChUqaOHCheaxhQsXqnz58qpdu7ZNbXp6ugYNGqTSpUvL2dlZDRs21Pbt282eypUrp5kzZ9pcs2vXLlksFh0/ftzs7Y+v7Dt79qyee+45eXl5ydvbW23atNHJkyfN83FxcXr88cfl5uYmT09PhYaG6tSpU7d9HgAAAAC3RiAFAAAAAHmgaNGieu+99zR9+nT9/PPP9zTW2rVr9csvv2j9+vWaMmWKRo8erWeffVZeXl7aunWr+vXrp379+unMmTM21w0fPlxDhw7V7t271aBBA7Vu3Vq//fabpBuvqgsLC1OtWrW0Y8cOLV++XP/73//UuXNnmzFiY2Pl4OCgTZs26dNPP71lfx9++KEmT56sSZMmae/evYqIiFDr1q119OhR817Vq1fX0KFDlZiYqGHDht3xeXv27KmYmBhz//PPP1evXr2y1Y0YMUILFixQbGysdu3aJX9/f0VEROjChQsqUqSIunTporlz59pc89VXX6l+/fqqVKlStvEuX76sJk2ayGq1av369dq4caOsVqtatGiha9eu6fr162rbtq3CwsK0d+9ebdmyRX369Cm0r2EEAAAA7gWBFAAAAADkkXbt2qlWrVp6++2372mcEiVKaNq0aQoICFCvXr0UEBCgy5cv6+9//7sqV66sUaNGydHRUZs2bbK5bsCAAerQoYMCAwP1ySefyMPDQ//6178kSZ988olCQkL03nvvqWrVqqpdu7Y+//xzrVu3TkeOHDHH8Pf318SJExUQEKCqVavesr9JkyZp5MiR6tKliwICAjRhwgTVqlVLH3zwgaQbr8VzcHCQ1WqVr6+vrFbrHZ+3e/fu2rhxo06ePKlTp05p06ZNeuGFF2xq0tLS9Mknn+j999/XM888o2rVqmnWrFlycXExn7Fbt27atGmTuYIpKytL8+fPzzbWTfPnz1eRIkX0z3/+U0FBQQoMDFRMTIxOnz6tuLg4paamKiUlRc8++6wee+wxBQYG6sUXX7TLqwQBAACAhx2BFAAAAADkoQkTJig2NlYHDhy46zGqV6+uIkX+71/XfHx8FBQUZO4XLVpU3t7eSkpKsrmufv365p8dHBxUt25dHTx4UJK0c+dOrVu3Tlar1dxuBk7Hjh0zr6tbt+4de0tNTdUvv/yi0NBQm+OhoaHmvXKrZMmSatWqlWJjYxUTE6NWrVqpZMmSNjXHjh1TRkaGzX2LFSumxx9/3Lxv7dq1VbVqVc2bN0+SFB8fr6SkpGyrwG7auXOnfvrpJxUvXtz8TUqUKKGrV6/q2LFjKlGihKKiohQREaHIyEh9+OGHSkxMvKtnBAAAAAo7AikAAAAAyEONGzdWRESE/v73v2c7V6RIERmGYXMsIyMjW12xYsVs9i0Wyy2PZWVl/WU/N18vl5WVpcjISCUkJNhsR48eVePGjc16Nze3vxzzj+PeZBjGPb3KrlevXpo9e7ZiY2Nv+bq+m7/bX923W7du+uqrryTdeF1fREREtnDrpqysLNWpUyfbb3LkyBF17dpVkhQTE6MtW7aoQYMG+vrrr1WlShX98MMPd/2cAAAAQGFFIAUAAAAAeWz8+PH67rvvtHnzZpvjpUqV0rlz52xCqYSEhDy77x+DkuvXr2vnzp3mKqiQkBD9+OOP8vPzk7+/v82W0xBKktzd3VW2bFlt3LjR5vjmzZsVGBh4173f/G7TtWvXFBERke28v7+/HB0dbe6bkZGhHTt22Ny3a9eu2rdvn3bu3Klvv/1W3bp1u+09Q0JCdPToUZUuXTrbb+Lh4WHW1a5dW6NGjdLmzZtVo0YNM/ACAAAAkHMEUgAAAACQx4KCgtStWzdNnz7d5nh4eLh+/fVXTZw4UceOHdNHH32k77//Ps/u+9FHH2nRokU6dOiQ+vfvr+TkZHO1Uf/+/XXhwgU9//zz2rZtm44fP66VK1eqV69eyszMzNV9hg8frgkTJujrr7/W4cOH9frrryshIUGDBw++696LFi2qgwcP6uDBgypatGi2825ubnr55Zc1fPhwLV++XAcOHNBLL72ky5cv629/+5tZ9+ijj6pBgwb629/+puvXr6tNmza3vWe3bt1UsmRJtWnTRhs2bNCJEycUHx+vwYMH6+eff9aJEyc0atQobdmyRadOndLKlSt15MiRewreAAAAgMKKQAoAAAAA7GDcuHHZXs8XGBiojz/+WB999JGCg4O1bds2DRs2LM/uOX78eE2YMEHBwcHasGGD/vOf/5ivqytbtqw2bdqkzMxMRUREqEaNGho8eLA8PDxsvleVE4MGDdLQoUM1dOhQBQUFafny5VqyZIkqV658T/27u7vL3d39js/XoUMHde/eXSEhIfrpp5+0YsUKeXl52dR169ZNe/bsUfv27eXi4nLb8VxdXbV+/XpVqFBB7du3V2BgoHr16qUrV67I3d1drq6uOnTokDp06KAqVaqoT58+GjBggPr27XtPzwkAAAAURhbjz/+GBAAAAAAAAAAAAOQhVkgBAAAAAAAAAADArgikAAAAAAAAAAAAYFcEUgAAAAAAAAAAALArAikAAAAAAAAAAADYFYEUAAAAAAAAAAAA7IpACgAAAAAAAAAAAHZFIAUAAAAAAAAAAAC7IpACAAAAAAAAAACAXRFIAQAAAAAAAAAAwK4IpAAAAAAAAAAAAGBXBFIAAAAAAAAAAACwq/8H7BxC1eG4NHQAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 2000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,8))\n",
    "sns.countplot(y=train_data['GENRE'], order = train_data['GENRE'].value_counts().index)\n",
    "plt.title('Number of Movies per Genre')\n",
    "plt.xlabel('Number of Movies')\n",
    "plt.ylabel('Genre')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9bc056f",
   "metadata": {},
   "source": [
    "## Bar Representation of Genre vs Description length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5a5ff3c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_data['DESCRIPTION_length'] = train_data['DESCRIPTION'].apply(len)\n",
    "plt.figure(figsize=(15, 10))\n",
    "sns.barplot(x='GENRE', y='DESCRIPTION_length', data=train_data)\n",
    "plt.title('Description Length by Genre')\n",
    "plt.xticks(rotation=45)\n",
    "plt.xlabel('Genre')\n",
    "plt.ylabel('Description Length')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5832ca19",
   "metadata": {},
   "source": [
    "## Now to get to known about top genre which mostly people watched"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "229b5ba6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "top_genres = train_data['GENRE'].value_counts().head(10)\n",
    "\n",
    "plt.figure(figsize=(20, 10))\n",
    "top_genres.plot(kind='barh', color='cyan')\n",
    "plt.title('Top 10 Most Frequent Genres')\n",
    "plt.xlabel('Number of Movies')\n",
    "plt.ylabel('Genre')\n",
    "plt.gca().invert_yaxis()  # Invert y-axis to have the genre with the most movies at the top\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5f0b8ae",
   "metadata": {},
   "source": [
    "## now training and testing of the data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1a01bda7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Handle any potential missing values\n",
    "train_data['DESCRIPTION'].fillna(\"\", inplace=True)\n",
    "test_data['DESCRIPTION'].fillna(\"\", inplace=True)\n",
    "\n",
    "t_v = TfidfVectorizer(stop_words='english', max_features=100000)\n",
    "X_train = t_v.fit_transform(train_data['DESCRIPTION'])\n",
    "X_test = t_v.transform(test_data['DESCRIPTION'])\n",
    "\n",
    "label_encoder = LabelEncoder()\n",
    "y_train = label_encoder.fit_transform(train_data['GENRE'])\n",
    "y_test = label_encoder.transform(test_solution_data['GENRE'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e4e6fe3a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\chatt\\anaconda3\\Lib\\site-packages\\sklearn\\svm\\_classes.py:32: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Accuracy: 0.5836945494789265\n",
      "Validation Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.44      0.32      0.37       263\n",
      "           1       0.74      0.44      0.55       112\n",
      "           2       0.45      0.21      0.28       139\n",
      "           3       0.47      0.15      0.23       104\n",
      "           4       0.00      0.00      0.00        61\n",
      "           5       0.53      0.59      0.56      1443\n",
      "           6       0.39      0.07      0.11       107\n",
      "           7       0.69      0.81      0.75      2659\n",
      "           8       0.56      0.72      0.63      2697\n",
      "           9       0.36      0.17      0.23       150\n",
      "          10       0.13      0.03      0.04        74\n",
      "          11       0.82      0.68      0.74        40\n",
      "          12       0.00      0.00      0.00        45\n",
      "          13       0.65      0.66      0.66       431\n",
      "          14       0.61      0.53      0.57       144\n",
      "          15       0.25      0.04      0.07        50\n",
      "          16       0.43      0.05      0.10        56\n",
      "          17       0.20      0.06      0.09        34\n",
      "          18       0.49      0.25      0.33       192\n",
      "          19       0.36      0.06      0.10       151\n",
      "          20       0.50      0.28      0.36       143\n",
      "          21       0.44      0.36      0.40      1045\n",
      "          22       0.60      0.41      0.49        93\n",
      "          23       0.62      0.25      0.35        81\n",
      "          24       0.30      0.16      0.21       309\n",
      "          25       0.50      0.05      0.09        20\n",
      "          26       0.85      0.83      0.84       200\n",
      "\n",
      "    accuracy                           0.58     10843\n",
      "   macro avg       0.46      0.30      0.34     10843\n",
      "weighted avg       0.56      0.58      0.56     10843\n",
      "\n"
     ]
    }
   ],
   "source": [
    "X_train_sub, X_val, y_train_sub, y_val = train_test_split(X_train, y_train, test_size=0.2, random_state=42)\n",
    "\n",
    "clf = LinearSVC()\n",
    "clf.fit(X_train_sub, y_train_sub)\n",
    "\n",
    "y_val_pred = clf.predict(X_val)\n",
    "print(\"Validation Accuracy:\", accuracy_score(y_val, y_val_pred))\n",
    "print(\"Validation Classification Report:\\n\", classification_report(y_val, y_val_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c8f84354",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy: 0.09357933579335793\n",
      "Test Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.00      0.00      0.00      1314\n",
      "           1       0.00      0.00      0.00       590\n",
      "           2       0.00      0.00      0.00       775\n",
      "           3       0.00      0.00      0.00       498\n",
      "           4       0.00      0.00      0.00       264\n",
      "           5       0.00      0.00      0.00      7446\n",
      "           6       0.00      0.00      0.00       505\n",
      "           7       0.00      0.00      0.00     13096\n",
      "           8       0.00      0.00      0.00     13612\n",
      "           9       0.00      0.00      0.00       783\n",
      "          10       0.00      0.00      0.00       322\n",
      "          11       0.00      0.00      0.00       193\n",
      "          12       0.00      0.00      0.00       243\n",
      "          13       0.00      0.00      0.00      2204\n",
      "          14       0.00      0.00      0.00       731\n",
      "          15       0.00      0.00      0.00       276\n",
      "          16       0.00      0.00      0.00       318\n",
      "          17       0.00      0.00      0.00       181\n",
      "          18       0.00      0.00      0.00       883\n",
      "          19       0.00      0.00      0.00       672\n",
      "          20       0.00      0.00      0.00       646\n",
      "          21       0.09      1.00      0.17      5072\n",
      "          22       0.00      0.00      0.00       431\n",
      "          23       0.00      0.00      0.00       391\n",
      "          24       0.00      0.00      0.00      1590\n",
      "          25       0.00      0.00      0.00       132\n",
      "          26       0.00      0.00      0.00      1032\n",
      "\n",
      "    accuracy                           0.09     54200\n",
      "   macro avg       0.00      0.04      0.01     54200\n",
      "weighted avg       0.01      0.09      0.02     54200\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\chatt\\anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1469: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\Users\\chatt\\anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1469: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\Users\\chatt\\anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1469: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    }
   ],
   "source": [
    "y_pred = clf.predict(X_test)\n",
    "print(\"Test Accuracy:\", accuracy_score(y_test, y_pred))\n",
    "print(\"Test Classification Report:\\n\", classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0a8b03b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MultinomialNB()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MultinomialNB</label><div class=\"sk-toggleable__content\"><pre>MultinomialNB()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "MultinomialNB()"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.naive_bayes import MultinomialNB\n",
    "Mnb_classifier = MultinomialNB()\n",
    "Mnb_classifier.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a9d8e778",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([8, 8, 8, ..., 8, 8, 8])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Mnb_classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c2e82317",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(max_iter=500)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(max_iter=500)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression(max_iter=500)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "lr_classifier = LogisticRegression(max_iter=500)\n",
    "lr_classifier.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "833e8e86",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([8, 8, 8, ..., 8, 8, 8])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr_classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01b97508",
   "metadata": {},
   "source": [
    "## Now designing a function show that we can predict the genre of the movie"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "9864508c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " action \n",
      " drama \n"
     ]
    }
   ],
   "source": [
    "def predict_movie(description):\n",
    "    t_v1 = t_v.transform([description])\n",
    "    pred_label = clf.predict(t_v1)\n",
    "    return label_encoder.inverse_transform(pred_label)[0]\n",
    "\n",
    "sample_descr_for_movie = \"A movie where police cashes the criminal and shoot him\"\n",
    "print(predict_movie(sample_descr_for_movie))\n",
    "\n",
    "sample_descr_for_movie1 = \"A movie where person cashes a girl too get marry with him but girl refuses him.\"\n",
    "print(predict_movie(sample_descr_for_movie1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a079199d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}