{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "dd72d1539056a64",
   "metadata": {},
   "source": [
    "Import Cell\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "12579bf734bb1a92",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-23T13:53:57.753560Z",
     "start_time": "2025-11-23T13:53:56.325948Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded 13 test sentences\n"
     ]
    }
   ],
   "source": [
    "import spacy\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from collections import Counter\n",
    "import string\n",
    "from IPython.display import display, HTML\n",
    "\n",
    "nlp_lg = spacy.load(\"en_core_web_lg\")\n",
    "nlp_trf = spacy.load(\"en_core_web_trf\")\n",
    "\n",
    "nlp = nlp_lg\n",
    "\n",
    "# Test sentences covering different similarity types\n",
    "test_sentences = [\n",
    "    # Direct copies and near-copies\n",
    "    \"The cat sat on the mat.\",\n",
    "    \"The cat sat on the mat\",\n",
    "    \"The  cat  sat  on  the  mat.\",\n",
    "    \n",
    "    # Paraphrases\n",
    "    \"On the mat, the cat was sitting.\",\n",
    "    \"The feline rested on the rug.\",\n",
    "    \n",
    "    # Structural changes\n",
    "    \"The quick brown fox jumps over the lazy dog.\",\n",
    "    \"Over the lazy dog jumps the quick brown fox.\",\n",
    "    \n",
    "    # Different content\n",
    "    \"The dog ran in the park.\",\n",
    "    \"I love programming.\",\n",
    "    \"She enjoys reading books.\",\n",
    "    \n",
    "    # Edge cases\n",
    "    \"Short.\",\n",
    "    \"A B C D E F G\",\n",
    "    \"\"\n",
    "]\n",
    "\n",
    "print(f\"Loaded {len(test_sentences)} test sentences\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c26616777253f10",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e003ac06a58cfbb4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-23T13:54:12.922343Z",
     "start_time": "2025-11-23T13:54:12.896440Z"
    }
   },
   "outputs": [
    {
     "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>sentence_id</th>\n",
       "      <th>sentence</th>\n",
       "      <th>char_length</th>\n",
       "      <th>word_count</th>\n",
       "      <th>alpha_word_count</th>\n",
       "      <th>avg_word_length</th>\n",
       "      <th>has_punctuation</th>\n",
       "      <th>is_empty</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>The cat sat on the mat.</td>\n",
       "      <td>23</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>2.833333</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>The cat sat on the mat</td>\n",
       "      <td>22</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>2.833333</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>The  cat  sat  on  the  mat.</td>\n",
       "      <td>28</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>2.833333</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>On the mat, the cat was sitting.</td>\n",
       "      <td>32</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>3.428571</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>The feline rested on the rug.</td>\n",
       "      <td>29</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>3.833333</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>The quick brown fox jumps over the lazy dog.</td>\n",
       "      <td>44</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>3.888889</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>Over the lazy dog jumps the quick brown fox.</td>\n",
       "      <td>44</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>3.888889</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>The dog ran in the park.</td>\n",
       "      <td>24</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>I love programming.</td>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>5.333333</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>She enjoys reading books.</td>\n",
       "      <td>25</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>5.250000</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>Short.</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>A B C D E F G</td>\n",
       "      <td>13</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    sentence_id                                      sentence  char_length  \\\n",
       "0             0                       The cat sat on the mat.           23   \n",
       "1             1                        The cat sat on the mat           22   \n",
       "2             2                  The  cat  sat  on  the  mat.           28   \n",
       "3             3              On the mat, the cat was sitting.           32   \n",
       "4             4                 The feline rested on the rug.           29   \n",
       "5             5  The quick brown fox jumps over the lazy dog.           44   \n",
       "6             6  Over the lazy dog jumps the quick brown fox.           44   \n",
       "7             7                      The dog ran in the park.           24   \n",
       "8             8                           I love programming.           19   \n",
       "9             9                     She enjoys reading books.           25   \n",
       "10           10                                        Short.            6   \n",
       "11           11                                 A B C D E F G           13   \n",
       "12           12                                                          0   \n",
       "\n",
       "    word_count  alpha_word_count  avg_word_length  has_punctuation  is_empty  \n",
       "0            7                 6         2.833333             True     False  \n",
       "1            6                 6         2.833333            False     False  \n",
       "2            7                 6         2.833333             True     False  \n",
       "3            9                 7         3.428571             True     False  \n",
       "4            7                 6         3.833333             True     False  \n",
       "5           10                 9         3.888889             True     False  \n",
       "6           10                 9         3.888889             True     False  \n",
       "7            7                 6         3.000000             True     False  \n",
       "8            4                 3         5.333333             True     False  \n",
       "9            5                 4         5.250000             True     False  \n",
       "10           2                 1         5.000000             True     False  \n",
       "11           7                 7         1.000000            False     False  \n",
       "12           0                 0         0.000000            False      True  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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cubPCwsKszw8dOiSLxaIPPvjgno8tSYMHD5bFYrHLWEBuQVEKyGUmT54si8WimjVrOjqUHKVcuXKqXLlymvYFCxbIYrGoQYMGabZ99tlnslgs+uWXX+wR4h2FhYXp8ccfd3QYtzR79mxNmDDB0WEAAO5jqQWM1IeHh4dKly6tnj17KiYmxtHhaf369Ro8eLBiY2Pv+Vi7d+/W4MGD7VLMyajUH6pSH+7u7ipcuLAaNmyo9957T6dPn86WcS5duqTBgwdr5cqV2XK87JSTYwNyIopSQC4za9YshYWF6ffff9eBAwccHU6OUbduXe3atUtxcXE27evWrZOLi4s2b96sq1evptnm7OysWrVq2TPUXIuiFAAgpxg6dKi+/PJLffzxx6pdu7amTJmiWrVq6dKlSw6Na/369RoyZIjdilJDhgxJtyj1yy+/OPRHt9dee01ffvmlpk+frn79+snf31+DBg1S2bJl9dtvv9n0feGFF3T58mWFhoZm+PiXLl3SkCFDMl34+eSTT7R3795M7ZNZt4vtv//9ry5fvnxPxwdyG4pSQC4SHR2t9evXa9y4cQoMDNSsWbPsHkNKSoquXLli93HvpG7dukpJSdH69ett2tetW6d27drp8uXL2rp1q822tWvXqlKlSvL29r6rsRMSEu5qfwAAkDnNmzfX888/r+7du2vmzJnq3bu3oqOj9f333zs6tBzBzc1Nbm5uDhu/Xr16ev7559WpUye98cYbmj9/vrZs2SJnZ2e1bdtWJ06csPZ1dnaWh4fHPb2sLTVXc3V1lbu7+z0b505cXFzk4eHhsPGBnIiiFJCLzJo1S35+fmrRooWeeuopm6LU1atX5e/vry5duqTZLz4+Xh4eHnrjjTesbYmJiRo0aJBKliwpd3d3hYSE6M0331RiYqLNvhaLRT179tSsWbNUvnx5ubu7a8mSJZKkDz74QLVr11ZAQIA8PT0VGRmZ7rpCly9f1muvvaaCBQvK29tbrVq10rFjx2SxWDR48GCbvseOHVPXrl1VuHBhubu7q3z58vrss8/ueG7q1q0r6XoRKtWVK1e0bds2tWnTRiVKlLDZdvr0ae3bt8+6nyT98ccfat68uXx8fJQ/f349/PDD2rhxo804qZcNrFq1Sq+88ooKFSqkYsWKWbdPnz5dERER8vT0VI0aNbRmzZo7xp5ZX331lSIjI+Xp6Sl/f3+1b99eR48etenTsGFDVahQQbt371ajRo2UL18+FS1aVGPGjElzvMOHD6tVq1by8vJSoUKF1KdPHy1dulQWi8X6K1/Dhg31008/6fDhw9Yp+TeuySBdL1iOGDFCxYoVk4eHhx5++GFm8wEA7KJx48aSrv+AJ916TaXbrSmU+h3u7u6u6tWra/PmzWn237Nnj9q1a6fAwEB5enrqgQce0DvvvCPp+npB/fr1kySFh4dbvy8PHTpkHWfmzJlpjnlzPnT48GG98soreuCBB+Tp6amAgAA9/fTTNjOiZs6cqaefflqS1KhRI+tYN35v3/z6T506pW7duqlw4cLy8PBQ5cqV9fnnn9v0yez5yIzKlStrwoQJio2N1ccff2zzWm5eU2rLli1q1qyZChYsKE9PT4WHh6tr167WGAMDAyVJQ4YMsb721HPYuXNn5c+fXwcPHtRjjz0mb29vdejQwbrt5vwl1fjx4xUaGipPT081aNBAu3btstmekc/UnWJLb02pa9euadiwYdZzHRYWprfffjtNTp66zMPatWtVo0YNeXh4qESJEvriiy/SP+FALuHi6AAAZNysWbPUpk0bubm56dlnn9WUKVO0efNmVa9eXa6urnryySc1f/58TZs2zebXsYULFyoxMVHt27eXdL140KpVK61du1YvvviiypYtq507d2r8+PHat2+fFi5caDPub7/9pu+++049e/ZUwYIFrV+8H374oVq1aqUOHTooKSlJ33zzjZ5++mn9+OOPatGihXX/zp0767vvvtMLL7yghx56SKtWrbLZniomJkYPPfSQtRAWGBioxYsXq1u3boqPj1fv3r1veW5KlCihIkWKaO3atda2zZs3KykpSbVr11bt2rW1bt06vf7665JknVGVWpT666+/VK9ePfn4+OjNN9+Uq6urpk2bpoYNG2rVqlVp1vB65ZVXFBgYqHfffdf669unn36ql156SbVr11bv3r31zz//qFWrVvL391dISMjt3toMGzFihAYOHKh27dqpe/fuOn36tCZOnKj69evrjz/+UIECBax9z58/r0cffVRt2rRRu3btNHfuXL311luqWLGimjdvLun6L4eNGzfWiRMn1KtXLwUFBWn27NlasWKFzbjvvPOO4uLi9O+//2r8+PGSpPz589v0GTVqlJycnPTGG28oLi5OY8aMUYcOHbRp06Zsee0AANzKwYMHJUkBAQFZ2n/27Nm6cOGCXnrpJVksFo0ZM0Zt2rTRP//8I1dXV0nSn3/+qXr16snV1VUvvviiwsLCdPDgQf3www8aMWKE2rRpo3379unrr7/W+PHjVbBgQUlSYGBgptZS2rx5s9avX6/27durWLFiOnTokKZMmaKGDRtq9+7dypcvn+rXr6/XXntNH330kd5++22VLVtWkqz/vdnly5fVsGFDHThwQD179lR4eLjmzJmjzp07KzY2Vr169cr0+ciKp556St26ddMvv/yiESNGpNvn1KlTeuSRRxQYGKj+/furQIECOnTokObPny/p+vmcMmWKXn75ZT355JNq06aNJKlSpUrWY1y7dk3NmjVT3bp19cEHHyhfvny3jeuLL77QhQsXFBUVpStXrujDDz9U48aNtXPnThUuXDjDry8jsd2se/fu+vzzz/XUU0/p9ddf16ZNmzRy5Ej9/fffWrBggU3fAwcOWM9hp06d9Nlnn6lz586KjIxU+fLlMxwnkKMYALnCli1bjCSzbNkyY4wxKSkpplixYqZXr17WPkuXLjWSzA8//GCz72OPPWZKlChhff7ll18aJycns2bNGpt+U6dONZLMunXrrG2SjJOTk/nrr7/SxHTp0iWb50lJSaZChQqmcePG1ratW7caSaZ37942fTt37mwkmUGDBlnbunXrZoKDg82ZM2ds+rZv3974+vqmGe9mTz/9tPH09DRJSUnGGGNGjhxpwsPDjTHGTJ482RQqVMja94033jCSzLFjx4wxxrRu3dq4ubmZgwcPWvscP37ceHt7m/r161vbZsyYYSSZunXrmmvXrtm89kKFCpkqVaqYxMREa/v06dONJNOgQYPbxm6MMaGhoaZFixa33H7o0CHj7OxsRowYYdO+c+dO4+LiYtPeoEEDI8l88cUX1rbExEQTFBRk2rZta20bO3askWQWLlxobbt8+bIpU6aMkWRWrFhhbW/RooUJDQ1NE9eKFSuMJFO2bFmb1/7hhx8aSWbnzp13fO0AAGRE6vfwr7/+ak6fPm2OHj1qvvnmGxMQEGA8PT3Nv//+a4y5/j2Y3ndvp06dbL7LoqOjjSQTEBBgzp07Z23//vvv0+RU9evXN97e3ubw4cM2x0xJSbH++f333zeSTHR0tE2f1HFmzJiRJqab86H08p0NGzak+V6fM2dOmu/qVDe//gkTJhhJ5quvvrK2JSUlmVq1apn8+fOb+Pj4TJ+P9KTmBHPmzLlln8qVKxs/Pz/r89T3NPWcLViwwEgymzdvvuUxTp8+nea8perUqZORZPr375/utvTe/xs/O8YYs2nTJiPJ9OnTx9qW0c/U7WIbNGiQufGf4Nu3bzeSTPfu3W36peapv/32m7UtNDTUSDKrV6+2tp06dcq4u7ub119/Pc1YQG7B5XtALjFr1iwVLlxYjRo1knR9qvczzzyjb775RsnJyZKuT10vWLCgvv32W+t+58+f17Jly/TMM89Y2+bMmaOyZcuqTJkyOnPmjPWROvX95lkyDRo0ULly5dLE5OnpaTNOXFyc6tWrp23btlnbUy/1e+WVV2z2ffXVV22eG2M0b948tWzZUsYYm7iaNWumuLg4m+Omp27dujZrR61bt061a9eWJNWpU0enTp3S/v37rdvCw8NVpEgRJScn65dfflHr1q1VokQJ6/GCg4P13HPPae3atYqPj7cZq0ePHnJ2drY+37Jli06dOqX//Oc/NrPUOnfuLF9f39vGnVHz589XSkqK2rVrZ3N+goKCVKpUqTTvW/78+fX8889bn7u5ualGjRr6559/rG1LlixR0aJF1apVK2ubh4eHevToken4unTpYvPa69WrJ0k24wEAkB2aNGmiwMBAhYSEqH379sqfP78WLFigokWLZul4zzzzjPz8/KzPb/4OO336tFavXq2uXbuqePHiNvtm91pIN+ZXV69e1dmzZ1WyZEkVKFDgjrnQrfz8888KCgrSs88+a21zdXXVa6+9posXL2rVqlU2/e90Pu5G/vz5deHChVtuT531/eOPP6a5SU1mvPzyyxnu27p1a5vPTo0aNVSzZk39/PPPWR4/I1KP37dvX5v21Jn9P/30k017uXLlrO+FdH1m1gMPPECuhVyNohSQCyQnJ+ubb75Ro0aNFB0drQMHDujAgQOqWbOmYmJitHz5cknXF09s27atvv/+e+t16PPnz9fVq1dtilL79+/XX3/9pcDAQJtH6dKlJV2fNn2j8PDwdOP68ccf9dBDD8nDw0P+/v7WKcs33gHv8OHDcnJySnOMkiVL2jw/ffq0YmNjNX369DRxpa6TdXNcN7txXSljjNavX686depIkipUqCAfHx+tW7dOV65c0datW639T58+rUuXLumBBx5Ic8yyZcsqJSUlzZpNN7+ew4cPS5JKlSpl0+7q6mpT6Lob+/fvlzFGpUqVSnOO/v777zTnp1ixYmkSZT8/P50/f94m7oiIiDT9bn5/MuLmJD01mb1xPAAAssOkSZO0bNkyrVixQrt379Y///yjZs2aZfl4d/oOS/1Hf4UKFbI8RkZdvnxZ7777rkJCQuTu7q6CBQsqMDBQsbGxae4ynFGHDx9WqVKl5ORk+8+/1Mv9UvOYVPfyO/3ixYu3vclMgwYN1LZtWw0ZMkQFCxbUE088oRkzZqRZY+l2XFxcbNb8vJOb8zdJKl26dLp3NsxOqXnyzXlXUFCQChQocMf3RUqb2wG5DWtKAbnAb7/9phMnTuibb77RN998k2b7rFmz9Mgjj0iS2rdvr2nTpmnx4sVq3bq1vvvuO5UpU0aVK1e29k9JSVHFihU1bty4dMe7ef2jG3+xS7VmzRq1atVK9evX1+TJkxUcHCxXV1fNmDFDs2fPzvRrTElJkSTrnVrSc7vr8aXrC2h6e3tr7dq1euyxx3Tu3DnrTCknJyfVrFlTa9euVUREhJKSkmwWOc+s9M7JvZaSkiKLxaLFixfbzNJKdfMaT+n1ka7PSrsX7D0eAOD+VaNGDVWrVu2W2y0WS7rfP6mzy292r7/DbjWbKr14Xn31Vc2YMUO9e/dWrVq15OvrK4vFovbt21vzpXvtXp2Pq1evat++fbct7lksFs2dO1cbN27UDz/8oKVLl6pr164aO3asNm7cmCbfSY+7u3uaAtzdyuxnKrPHzghyLeRFFKWAXGDWrFkqVKiQJk2alGbb/PnztWDBAk2dOlWenp6qX7++goOD9e2336pu3br67bffrHeFSRUREaEdO3bo4YcfzvKU83nz5snDw0NLly61ubXujBkzbPqFhoYqJSVF0dHRNr9C3XxXtsDAQHl7eys5OVlNmjTJUkzOzs566KGHtG7dOq1du1Y+Pj6qWLGidXvt2rX17bffWn+NSi1KBQYGKl++fNq7d2+aY+7Zs0dOTk53XKg8NDRU0vXZTKmXQUrXk6/o6GibomBWRUREyBij8PBw66y2uxUaGqrdu3fLGGPzWUjvrnn38lbNAABkJz8/v3Qvabp55klGpc56vvmObDe71Xdl6kyj2NjYO8Yzd+5cderUSWPHjrW2XblyJc2+mfleDg0N1Z9//qmUlBSbYs2ePXus2+1h7ty5unz5coZmtT300EN66KGHNGLECM2ePVsdOnTQN998o+7du2d7TpK6vMON9u3bZ3Onvox+pjL7vqSkpGj//v02i9THxMQoNjbWbu8L4EhcvgfkcJcvX9b8+fP1+OOP66mnnkrz6Nmzpy5cuKBFixZJuj4j6KmnntIPP/ygL7/8UteuXbO5dE+S2rVrp2PHjumTTz5Jd7zUu8ndjrOzsywWi82vQ4cOHUpz577UpGPy5Mk27RMnTkxzvLZt22revHnpJnwZvWtN3bp1dfr0ac2YMUM1a9a0Sbxq166tvXv36vvvv1dAQID1y9/Z2VmPPPKIvv/+e5tp2jExMZo9e7bq1q0rHx+f245brVo1BQYGaurUqUpKSrK2z5w5M00SmVVt2rSRs7OzhgwZkuYXMWOMzp49m+ljNmvWTMeOHbN+fqTriW96nw0vL68sXzYAAIA9RUREaM+ePTb5w44dO7Ru3bosHS8wMFD169fXZ599piNHjthsu/E72cvLS1La4pOPj48KFiyo1atX27TfnB9J1/OSm7/nJ06cmGZGzq3GSs9jjz2mkydP2qw7eu3aNU2cOFH58+dXgwYN7niMu7Vjxw717t1bfn5+ioqKumW/8+fPp3n9VapUkSTrJXypd9PLrhxr4cKFOnbsmPX577//rk2bNlnvVixl/DOVmdgee+wxSdKECRNs2lOvZkjvbtVAXsNMKSCHW7RokS5cuGCzEPWNHnroIQUGBmrWrFnW4tMzzzyjiRMnatCgQapYsWKa2wO/8MIL+u677/Sf//xHK1asUJ06dZScnKw9e/bou+++09KlS287JV66/iU5btw4Pfroo3ruued06tQpTZo0SSVLltSff/5p7RcZGam2bdtqwoQJOnv2rB566CGtWrVK+/btk2T7a9KoUaO0YsUK1axZUz169FC5cuV07tw5bdu2Tb/++qvOnTt3x/OVOvtpw4YNGjx4cJpzZbFYtHHjRrVs2dJm7OHDh2vZsmWqW7euXnnlFbm4uGjatGlKTEzUmDFj7jiuq6urhg8frpdeekmNGzfWM888o+joaM2YMSNTa0odOHBAw4cPT9NetWpVtWjRQsOHD9eAAQN06NAhtW7dWt7e3oqOjtaCBQv04osv6o033sjwWJL00ksv6eOPP9azzz6rXr16KTg4WLNmzZKHh4ck2/cnMjJS3377rfr27avq1asrf/78atmyZabGAwDAHrp27apx48apWbNm6tatm06dOqWpU6eqfPnyaW5eklEfffSR6tatqwcffFAvvviiwsPDdejQIf3000/avn27pOvflZL0zjvvqH379nJ1dVXLli3l5eWl7t27a9SoUerevbuqVaum1atXW/OhGz3++OP68ssv5evrq3LlymnDhg369ddfFRAQYNOvSpUqcnZ21ujRoxUXFyd3d3c1btxYhQoVSnPMF198UdOmTVPnzp21detWhYWFae7cuVq3bp0mTJhw2zWesmLNmjW6cuWKkpOTdfbsWa1bt06LFi2Sr6+vFixYoKCgoFvu+/nnn2vy5Ml68sknFRERoQsXLuiTTz6Rj4+PtYjj6empcuXK6dtvv1Xp0qXl7++vChUqZHnNr5IlS6pu3bp6+eWXlZiYqAkTJiggIEBvvvmmtU9GP1OZia1y5crq1KmTpk+frtjYWDVo0EC///67Pv/8c7Vu3dp6gyMgT7P37f4AZE7Lli2Nh4eHSUhIuGWfzp07G1dXV3PmzBljzPVbE4eEhBhJZvjw4enuk5SUZEaPHm3Kly9v3N3djZ+fn4mMjDRDhgwxcXFx1n6STFRUVLrH+PTTT02pUqWMu7u7KVOmjJkxY0aaW90aY0xCQoKJiooy/v7+Jn/+/KZ169Zm7969RpIZNWqUTd+YmBgTFRVlQkJCjKurqwkKCjIPP/ywmT59eobOV0JCgnFxcTGSzC+//JJme6VKlYwkM3r06DTbtm3bZpo1a2by589v8uXLZxo1amTWr19v0yf1tsW3uk3x5MmTTXh4uHF3dzfVqlUzq1evvuUthG+Weqvf9B7dunWz9ps3b56pW7eu8fLyMl5eXqZMmTImKirK7N2719qnQYMGpnz58mnGuPm2xcYY888//5gWLVoYT09PExgYaF5//XUzb948I8ls3LjR2u/ixYvmueeeMwUKFDCSrMe51e2fb3f7awAAsuJO38M3+uqrr0yJEiWMm5ubqVKlilm6dGma78HU76r3338/zf6SzKBBg2zadu3aZZ588klToEAB4+HhYR544AEzcOBAmz7Dhg0zRYsWNU5OTkaSiY6ONsYYc+nSJdOtWzfj6+trvL29Tbt27cypU6fSjHP+/HnTpUsXU7BgQZM/f37TrFkzs2fPHhMaGmo6depkM9Ynn3xiSpQoYZydnY0ks2LFCmOMSTf3iImJsR7Xzc3NVKxYMc13dGbPx81Sc4LUh6urqwkMDDT169c3I0aMMKdOnUqzT+p7mnqetm3bZp599llTvHhx4+7ubgoVKmQef/xxs2XLFpv91q9fbyIjI42bm5tNbJ06dTJeXl7pxne793/s2LEmJCTEuLu7m3r16pkdO3ak2T8jn6nbxZZennz16lUzZMgQEx4eblxdXU1ISIgZMGCAuXLlik2/0NBQ06JFizQxZTTPBHIqizGsigbA/rZv366qVavqq6++UocOHRwdDm4yYcIE9enTR//++2+Wb68NAAAAALfDmlIA7rnLly+naZswYYKcnJxUv359B0SEG938/ly5ckXTpk1TqVKlKEgBAAAAuGdYUwrAPTdmzBht3bpVjRo1kouLixYvXqzFixfrxRdfvONd7XDvtWnTRsWLF1eVKlUUFxenr776Snv27NGsWbMcHRoAAACAPIzL9wDcc8uWLdOQIUO0e/duXbx4UcWLF9cLL7ygd955Ry4u1MYdbcKECfrf//6nQ4cOKTk5WeXKldObb76Z5q6NAAAAAJCdKEoBAAAAAADA7lhTCgAAAAAAAHZHUQoAAAAAAAB2R1EKAAAAAAAAdpfnVxhOSUnR8ePH5e3tLYvF4uhwAABALmOM0YULF1SkSBE5OfF7HgAAQHbJ80Wp48ePc8t5AABw144ePapixYo5OgwAAIA8I88Xpby9vSVdTyR9fHwcHA0AAMht4uPjFRISYs0pAAAAkD3yfFEq9ZI9Hx8filIAACDLWAYAAAAge7EwAgAAAAAAAOyOohQAAAAAAADsjqIUAAAAAAAA7I6iFAAAAAAAAOyOohQAAAAAAADsjqIUAAAAAAAA7I6iFAAAAAAAAOyOohQAAAAAAADsjqIUAAAAAAAA7I6iFAAAAAAAAOyOohQAAAAAAADsjqIUAAAAAAAA7M7F0QEAAOwrrP9PDhv70KgWDhsbAAAAQM7CTCkAAAAAAADYHUUpAAAAAAAA2B1FKQAAAAAAANgda0oBAHIER611xTpXAAAAgGMwUwoAAAAAAAB2R1EKAAAAAAAAdkdRCgAAAAAAAHZHUQoAAAAAAAB2R1EKAAAAAAAAdkdRCgAAAAAAAHZHUQoAAAAAAAB2R1EKAAAAAAAAdkdRCgAAAAAAAHaXo4tSgwcPlsVisXmUKVPG0WEBAAAAAADgLrk4OoA7KV++vH799VfrcxeXHB8yAAAAAAAA7iDHV3hcXFwUFBTk6DAAAAAAIE+5mHhNZy4k6vTFRJ25kKgzFxN1+mKSTv//P5+5mKgLV67pWnKKrqUYJaeY//tvcoqSU4ySjVGKkTxdneXl5qx87i7ycneRj4eLfD1dVSCfq/zyualAPjcF+3qouH8+hQbkk7eHq6NfPoAcIMcXpfbv368iRYrIw8NDtWrV0siRI1W8ePFb9k9MTFRiYqL1eXx8vD3CBAAAAIAcKSb+ivbFXNC+mIvaH3NB+2IuaP+pi7pw5Vq2jZF0LUVxl69muL+/l5u1QBXqn0/FA7wUGpBPEYH55e/llm1xAcjZcnRRqmbNmpo5c6YeeOABnThxQkOGDFG9evW0a9cueXt7p7vPyJEjNWTIEDtHCgDIq8L6/+SQcQ+NauGQcQEAude15BTtPhGvP47Eas/JC9YCVHw2Fp+yy7mEJJ1LSNL2o7FpthX3z6cqIQWuP4oXUPkiPnJ3cbZ/kADuuRxdlGrevLn1z5UqVVLNmjUVGhqq7777Tt26dUt3nwEDBqhv377W5/Hx8QoJCbnnsQIAAACAPV25mqyth89rU/Q5bTl0TtuPxupSUrKjw7prR85d0pFzl7Rox3FJkpuzk8oGe1uLVJHF/VU8IJ+DowSQHXJ0UepmBQoUUOnSpXXgwIFb9nF3d5e7u7sdowIAAACAey8lxWj7v7Faf+CM1h04q61HzivpWoqjw7rnkpJTtOPfOO34N06fbzgsSQov6KXGZQqpcZlCqhHuL1fnHH1jeQC3kKuKUhcvXtTBgwf1wgsvODoUAAAAALjnklOMNkWf1eKdJ7X0r5M6dSHxzjvdB6LPJOjTtdH6dG20vN1dVLdUQTUuU0iNyhRSwfxMUgByixxdlHrjjTfUsmVLhYaG6vjx4xo0aJCcnZ317LPPOjo0AAAAALgnriWnaMM/Z/XzzpNatvukzlxMcnRIOdqFxGtavOukFu86KYtFqlSsgJqWLaQnqhRViD+X+QE5WY4uSv3777969tlndfbsWQUGBqpu3brauHGjAgMDHR0aAAAAAGSblBSjNQfO6Kc/j2vZ7hidv5TxO9nh/xgj7Tgaqx1HYzV22T5VC/XTk1WLqUWlYPl6ujo6PAA3ydFFqW+++cbRIQAAAADAPXP2YqK+2XxUX/9+RP+ev+zocPIUY6TNh85r86HzGvzDX2pWPkjPVg9RrYgAWSwWR4cHQDm8KAUAAAAAedHWw+f0xYbDWrzzpJKS8/5i5Y6WdC1FP+w4rh92HFdYQD61qx6idtVCWH8KcDCKUgAAAABgB5eSrmnBH8f01cYj+vtEvKPDuW8dOntJY5bs1Ye/7lfbyGJ6qX4JhQZ4OTos4L5EUQoAAAAA7qHTFxI1bdVBfbv5qC4kXnN0OPj/Eq+laPamI/p281E1rxCk/zSIUIWivo4OC7ivUJQCAAAAgHvgzMXrxaivNh7R5avJjg4Ht5CcYvTjnyf0458nVK9UQb3cMEK1Iwo6OizgvkBRCgAAAACy0bmEJE1bfVBfbjisS0kUo3KTNfvPaM3+M6oSUkAvN4zQI+UKsyg6cA9RlAIAAACAbBB7KUnTVv+jL9YfUgLFqFxt+9FYvfTlVlUJKaCBj5dVZKi/o0MC8iSKUgAAAABwFy4mXtO0VQc1Y90hXWTNqDxl+9FYtZ2yQS0qBav/o2UU4p/P0SEBeQpFKQAAAADIAmOMFvxxTKMW79GpC4mODgf30E9/ntCy3THqWidcUY0i5O3h6uiQgDyBohQAAAAAZNKuY3EatOgvbT183tGhwE6SrqVo6qqDmrv1qPo0La321YvL2Yn1poC74eToAAAAAAAgt7hw5aoGfb9LrT5eS0HqPnXmYpLeWbBLj324RpsPnXN0OECuRlEKAAAAADLgpz9PqMm4Vfp8w2GlGEdHA0fbG3NBz0zboKE/7NaVqyxsD2QFl+8BAAAAwG3ExF9R/3l/asXe044OBTlMipE+WxetFXtP6f2nKqlaGHfpAzKDmVIAAAAAcAtLdp1UswmrKUjhtqLPJKjdtA0a9iOzpoDMoCgFAAAAADe5lHRN/ef9qf98tVWxl646OhzkAilG+nRttB77cI22HmatKSAjKEoBAAAAwA12/hunxz9aq282H3V0KMiF/jmToKenbtCIn3Yr8RqzpoDboSgFAAAAAJJSUoymrDyoNlPW6Z8zCY4OB7lYipE+WROtp6Zs0NFzlxwdDpBjUZQCAAAAcN87EXdZHf63SaOX7NHVZG6th+yx81icHp+4Vr/ujnF0KECORFEKAAAAwH1t86Fzevyjtdrwz1lHh4I8KO7yVfX4cotGLd6j5BQKnsCNKEoBAADgvrVy5UpZLBbFxsbetl9YWJgmTJhgl5hys0OHDslisWj79u2ODiXD5mw5qg6fbNLZhCRHh4I8zBhp6qqD6jzjd8WxcD5gRVEKAAAAud7UqVPl7e2ta9euWdsuXrwoV1dXNWzY0KZvaiHq4MGDql27tk6cOCFfX19J0syZM1WgQIFsialz586yWCyyWCxyc3NTyZIlNXToUJsY77XBgwerSpUq9+TYnTt3VuvWrW3aQkJCdOLECVWoUOGejJmdUlKM3vv5b/Wb+6eSklMcHQ7uE2v2n1GrSWu19+QFR4cC5AgUpYD/1959h0dVJW4cfye9BwKpEAi99yagFAVBUMFlBVyU4i4WQER0RdZV0FUBXV1EENSfCmtZu4goKEaaoPRQpAqhB0JJ75mZ3x+ss8bQApk5k8n38zx5ZO5M7nnnQoJ5OedcAABQ4fXs2VPZ2dnauHGj49jq1asVExOjdevWKT8/33F8+fLlqlWrlurVqyc/Pz/FxMTIYrE4JVffvn2VkpKiffv26eGHH9bUqVP1wgsvOGUsd+Dt7a2YmBj5+PiYjnJR2QXFuuedjXp91QHTUVAJHTqTq9teXaNvfz5hOgpgHKUUAAAAKrxGjRopNjZWK1ascBxbsWKFBgwYoDp16uinn34qcbxnz56OX/+6fG/FihUaNWqUMjIyHDOcpk6d6vi83Nxc3X333QoNDVWtWrX0+uuvXzKXv7+/YmJiVLt2bd1///3q1auXFi1aJEnq0aOHJkyYUOL1AwcO1MiRIx2PExIS9Nxzz1103KNHj+qOO+5QRESEgoOD1b59e61bt07z58/XU089pa1btzrez/z588+7xC49PV0Wi8Vx/axWq/785z+rTp06CgwMVKNGjfTyyy87Xj916lQtWLBAX3zxhePcK1asOO+5V65cqY4dO8rf31+xsbF67LHHSswW69Gjh8aPH69HH31UERERiomJKXHdy9vRtFz9ce5afbcr1WljAJeSW2jV/e9t1gfrD5uOAhhFKQUAAACP0LNnTy1fvtzxePny5erRo4e6d+/uOJ6Xl6d169Y5Sqnf6tKli2bOnKmwsDClpKQoJSVFjzzyiOP5F198Ue3bt9eWLVs0ZswY3X///dqzZ0+ZMgYGBqqwsGx7F11s3OzsbHXv3l3Hjh3TokWLtHXrVj366KOy2WwaMmSIHn74YTVr1szxfoYMGXJZY9psNtWsWVMff/yxdu7cqSeffFJ/+9vf9NFHH0mSHnnkEQ0ePNgxEywlJUVdunQpdZ5jx46pX79+6tChg7Zu3aq5c+fqzTff1DPPPFPidQsWLFBwcLDWrVun559/Xk8//bSWLVtWput0OTYePKsBs9doN0un4AasNrse+2y75iz/xXQUwBj3nlcLAAAAXKaePXtqwoQJKi4uVl5enrZs2aLu3burqKhI8+bNkyT9+OOPKigoOG8p5efnp/DwcFksFsXExJR6vl+/fhozZowkadKkSfrXv/6l5cuXq1GjRpfMZrfblZiYqG+++UYPPPBAmd7XxcZ9//33derUKW3YsEERERGSpPr16zs+NyQkRD4+Pud9Pxfj6+urp556yvG4Tp06+vHHH/XRRx9p8ODBCgkJUWBgoAoKCi567ldffVXx8fGaPXu2LBaLGjdurOPHj2vSpEl68skn5eV17t/IW7ZsqSlTpkiSGjRooNmzZysxMVG9e/cuU+6L+X73Sd337mYVFrN/FNzLC9/s0ensAj15c1OnLSUG3BWlFAAAADxCjx49lJOTow0bNigtLU0NGzZUZGSkunfvrlGjRik/P18rVqxQ3bp1VatWrTKfv2XLlo5f/1pcpaZefAnY4sWLFRISoqKiItlsNv3pT38q89K0i42blJSkNm3aOAqp8jRnzhy99dZbOnz4sPLy8lRYWFjmTdN37dqlzp07l/hBu2vXrsrOztbRo0cdvw+/fY+SFBsbe8lrWxZLd5zQ+P9sYUNzuK231xzU2ZxC/fP2VvL1ZkETKg9KKQAAAHiE+vXrq2bNmlq+fLnS0tLUvXt3SVJcXJzi4+O1du1aLV++XNdff/0Vnd/X17fEY4vFIpvt4iVHz549NXfuXPn5+SkuLq7EBuBeXl6y2+0lXl9UVPpW8RcbNzAwsEzv4ddxJZUY+/fjfvDBB3rkkUf04osvqnPnzgoNDdULL7ygdevWlXm8y3El1/Zyfbn1uB76MEnFNvulXwwY9EXScaXlFmnenW0V5MeP6qgcqGABAADgMXr27KkVK1ZoxYoV6tGjh+N4t27dtGTJEq1fv/68S/d+5efnJ6vVWm55goODVb9+fdWqVavUHekiIyOVkpLieGy1WrVjx44ynb9ly5ZKSkrS2bNnz/v8+d5PZGSkJJUY+7cbk0vSmjVr1KVLF40ZM0Zt2rRR/fr1tX///kue+/eaNGmiH3/8sUQBtmbNGoWGhqpmzZqXfH9X69NNRzWBQgoVyKq9p/SnN9YpPbdse88BFRWlFAAAADxGz5499cMPPygpKckxU0qSunfvrtdee02FhYUXLaUSEhKUnZ2txMREnT59Wrm5uU7Lev311+urr77SV199pd27d+v+++9Xenp6mc5xxx13KCYmRgMHDtSaNWt04MABffrpp/rxxx8lnXs/ycnJSkpK0unTp1VQUKDAwEBdc801mj59unbt2qWVK1fq73//e4nzNmjQQBs3btQ333yjvXv36oknntCGDRtKvCYhIUHbtm3Tnj17dPr06fPO8hozZoyOHDmiBx54QLt379YXX3yhKVOmaOLEiY4ZW87yn/WH9ddPtspKIYUKJulIuu56c72y8kt/TQGehlIKAAAAHqNnz57Ky8tT/fr1FR0d7TjevXt3ZWVlqVGjRoqNjb3g53fp0kX33XefhgwZosjISD3//PNOy3r33XdrxIgRGj58uLp37666detetDA7Hz8/P3377beKiopSv3791KJFC02fPl3e3t6SpEGDBqlv377q2bOnIiMj9Z///EeS9NZbb6m4uFjt2rXThAkTSt0N795779Uf/vAHDRkyRJ06ddKZM2ccm63/avTo0WrUqJHat2+vyMhIrVmzplS+GjVq6Ouvv9b69evVqlUr3Xffffrzn/9cqgQrbwvWHtTfPt8u+ihUVNuPZejP8zcqv6j8Zm4C7shi//1Cdg+TmZmp8PBwZWRkKCwszHQcADAu4bGvjI19cHr/Cz5nKtfFMknumwuuw/9LABXLmz8k6x+Ld5qOAZSLHo0i9cbw9mx+Do/Fn2wAAAAAHuHTTUf1zFcUUvAcK/ac0oQPkliGCo9FKQUAAACgwlu+O1WTPt0mz14Hgsroq+0pmvzZtlJ36wQ8AaUUAAAAgApt06E0jXlvM3fZg8f6aONR/WPxLtMxgHJHKQUAAACgwko+naO/LNigPDaEhod7a02yZn6313QMoFxRSgEAAACokNJyCjXq7fVKyy0yHQVwiZnf7dOXW4+bjgGUG0opAAAAABVOQbFVo/+9UQfP5JqOArjUo59s08/HM0zHAMoFpRQAAACACuevH2/TxkNppmMALpdXZNU9/96kszmFpqMAV41SCgAAAECF8vaaZC1iCRMqsWPpeRrz3iYVW22mowBXhVIKAAAAQIWx5XCanvuau5ABPx04q2e+4msBFRulFAAAAIAKIT23UOPe36Iiq910FMAtzF97UB9tPGI6BnDFKKUAAAAAuD273a6JH23VsfQ801EAt/L3hTu05TD7q6FiopQCAAAA4Pbmrtyv73enmo4BuJ3CYpvGvLdZGblFpqMAZUYpBQAAAMCtrTtwRi9+u9d0DMBtpWTk62+fbzcdAygzSikAAAAAbutUVoEe+M8WWW3sIwVczFfbU/TJpqOmYwBlUqFKqenTp8tisWjChAmmowAAAABwgUmfblNqVoHpGECFMHXRzzp8Jtd0DOCyVZhSasOGDXrttdfUsmVL01EAAAAAuMAXScfYRwoog+yCYj38cZJszCxEBVEhSqns7GwNGzZMb7zxhqpWrWo6DgAAAAAnO5tTqKe+3Gk6BlDhbDiYprfWJJuOAVyWClFKjR07Vv3791evXr1MRwEAAADgAk99+bPO5hSajgFUSP/8do8OnMo2HQO4JB/TAS7lgw8+0ObNm7Vhw4bLen1BQYEKCv635jwzM9NZ0QAAAAA4wfe7T+qLpOOmYwAVVn6RTY98vFWf3NdFXl4W03GAC3LrmVJHjhzRgw8+qPfee08BAQGX9TnTpk1TeHi44yM+Pt7JKQEAAACUl+yCYv398x2mYwAV3ubD6fpw4xHTMYCLcutSatOmTUpNTVXbtm3l4+MjHx8frVy5UrNmzZKPj4+sVmupz5k8ebIyMjIcH0eO8EUIAAAAVBTTl+zS8Yx80zEAj/DPb/YoI6/IdAzggtx6+d4NN9yg7du3lzg2atQoNW7cWJMmTZK3t3epz/H395e/v7+rIgIAAAAoJ+uTz+q9dYdNxwA8xpmcQs38bq+m3NLMdBTgvNy6lAoNDVXz5s1LHAsODla1atVKHQcAAABQcdlsdj35xQ7ZuZM9UK7e+fGQ/tSxlhpEh5qOApTi1sv3AAAAAFQOn205pt0nskzHADxOsc2up77caToGcF5uPVPqfFasWGE6AgAAAIBylF9k1Uvf7jEdA/BYP/xyWkt3nFDf5jGmowAlVLhSCoBZCY99ZWzsg9P7X/A5U7kulgkAAFye+WsPsrk54GTPfr1TPRpFKsC39N7MgCks3wMAAABgTHpuoV5d/ovpGIDHO3I2T2+sOmA6BlACpRQAAAAAY2Z//4sy84tNxwAqhddWHVBGbpHpGIADy/cAN8aSNAAA4MmOpuXq3z8dMh0DqDSyC4r11ppkPdS7oekogCRmSgEAAAAw5J/f7FFhsc10DKBSeXtNsrLymS0F98BMKQAAAAAut/dklr7Yetx0DIejc++WNTO11PGQNv1V7cb7JUkFx3YpbdU7KkzZI1m85BdVV1GDn5aXr/95z2kryFX66neVu+9H2XIz5BdVV1V73SP/2P/NUslY95ky138qSQrvNEhhHf/geK7g+B6d/fZVxQx/SRYvNqdG+cjML9a/fzyksT3rm44CUEoBAAAAcL15K/bLbjed4n9iR/xLsv1v1lbh6UNK/fDvCm7cVdK5QurkR1MU3vl2RfS6VxYvbxWmJstiufDikzNLX1HRqUOqfvPD8g6JUM7Py3Xyg78r7i+vyie0ugpTk5Xxw3uK/OOTkt2uU58+rYA6beUXmSC7zaoz38xRtb7jKKRQ7t78IVmjuiYoyI9KAGaxfA8AAACASx1Lz9MiN5olJUneQeHyDqnq+Mj7Zb18qsTKP76FJOls4v8prN0tCr/mdvlF1pZvtZoKbnKdLD6+5z2frahAuXvWqErPUQqIby7fqnGqcu0w+VaNVdaWJZKkojNH5RuZoMDarRSY0Fq+kQkqOnNUkpS57lMFxDcrMasKKC9ncwr13k+HTccAKKUAAAAAuNb/rT6gYpsbTZP6Hbu1SDk7VyikZW9ZLBZZc9JVmLJHXsFVdOKdR3TklTt14v3HlH/05wufxGaV7DZZvEuWVhYffxX89/P8IhNUnHZMxZmpKs5IVfHZY/KrXltFaSnK3v6dqlx3lzPfJiq511cfUH6R1XQMVHKUUgAAAABcJiO3SB9uOGI6xkXl7v1JtvxsBTe/QZJUnH5CkpTxw/sKadVH0YOfkl90PZ384HEVnT123nN4+QfJP66xMtZ+oOKsM7LbrMr+ebkKju+WNSdNkuRbPV5Vug3XyQ+f0MmPnlCV7iPkWz1eZ7+Zrao9RikvebOOvzlGx98er/wjO1zz5lFpnMoqcPuvRXg+FpACAAAAcJn31x9WbqF7z87I3vatAuu2k09oNUmS/b+bX4W07quQlr0lSRHR9ZR/aKuyty9T1e4jz3ueajc/rDNLXtaxV0ec2xg9pp6Cm3RTwYlfHK8JbdNPoW36/W/s7Ymy+AXKv0ZjHXvjPsUOf0nWrDM6veh51bj3zQsuFwSuxGsr9+vOa2rL28tiOgoqKUopAAAAAC5RbLXpnR8Pmo5xUcUZqco/tFWRt/3Nccw7pKokybd6rRKv9a0Wr+LMUxc8l2/VWMX8abpshfmyFebKJyRCp76YId8qMed9vTU3Qxlr3lf0n2ao4Phe+UbEyTeihnwjashuLVZR2jH5RSZc/ZsE/ut4Rr6W705Vr6bRpqOgkmL5HgAAAACXWPrzCR3PyDcd46Kyty+Td1C4Aut1cBzzCY+Wd0iEiv+7Cfmvis4ek09Y1CXP6eUXIJ+QCFnzs5WXvFmBDa457+vSvv8/hXYYKJ+w6pLdKrv1NzPKbNYSdwcEyssHG9jwHOYwUwoAAACAS7y95qDpCBdlt9uUvf07BTe/QRYvb8dxi8WisI6DlP7De/KNqiO/6LrK2Z6o4rNHFTJwsuN1Jz/4mwIbdFZYu1skSXkHNkmSfCJqqDgtRWkr3pJvRE2FtOhVauy85C0qOntM1fo/JEnyi2mo4rNHlbd/o4qzTkte3vKJqOHMt49KavmeUzqRka+Y8ADTUVAJUUoBAAAAcLp9J7O06VCa6RgXlX8wSdbMU459o34rrMMA2a2FSvv+/2TLz5JfZB1FDfmHfKvGOl5TlHZC/nmZjse2glylr1qg4qzT8g4IVVCjLqrSbbgs3iV/DLMVFejsd/MUeeskWSznFrP4hFVX1V736vSSmbJ4+6pa/4fk5evvpHeOysxqs+ujjUc0/oYGpqOgEqKUAgAAAOB0n285/13q3ElgnbaqPWnxBZ8Pv+Z2hV9z+wWfr3n/WyUeBze5TsFNrrvkuF6+/qox+rVSx0Nb9VFoqz6X/Hzgan244YjG9awvLzY8h4uxpxQAAAAAp7Lb7foi6bjpGAAu4Fh6nlbuu/Cm/YCzUEoBAAAAcKqfDpzVsfQ80zEAXMQH69nwHK5HKQUAAADAqRZWgKV7QGWXuCtVqVnufXdMeB5KKQAAAABOk19k1dc7UkzHAHAJxTa7vtjCMlu4FqUUAAAAAKf5btdJZeUXm44B4DJQIMPVKKUAAAAAOM3nm1m6B1QUSUfSdSKDJXxwHUopAAAAAE6RllOoVdzRC6gw7HZpKbOl4EKUUgAAAACcYsXeVBVZ7aZjACiDJTtOmI6ASoRSCgAAAIBTrNp72nQEAGW08VCaMnKLTMdAJeGSUiozM1MLFy7Url27XDEcAAAAAMPsdrtWs3QPqHCsNrtW7E01HQOVhI8zTjp48GB169ZN48aNU15entq3b6+DBw/Kbrfrgw8+0KBBg5wxLHDFEh77ysi4B6f3NzIuAACAs/18PFOnswtNxwBwBb7blaoBrWuYjoFKwCkzpVatWqXrrrtOkvT555/LbrcrPT1ds2bN0jPPPOOMIQEAAAC4kZV7mSUFVFSr9p5SsdVmOgYqAaeUUhkZGYqIiJAkLV26VIMGDVJQUJD69++vffv2OWNIAAAAAG6EUgqouDLyirT1aLrpGKgEnFJKxcfH68cff1ROTo6WLl2qG2+8UZKUlpamgIAAZwwJAAAAwE1kFxRry+E00zEAXIWNB/kahvM5pZSaMGGChg0bppo1ayouLk49evSQdG5ZX4sWLZwxJAAAAAA3sfaX0yqy2k3HAHAVNh6ilILzOWWj8zFjxqhTp046fPiwevfuLS+vc91X3bp12VMKAAAA8HCruOseUOEx2xGuUO4zpYqKilSvXj0FBQXptttuU0hIiOO5/v37q2vXruU9JAAAAAA3sulQuukIAK7S6exCJZ/OMR0DHq7cSylfX1/l5+eX92kBAAAAVAD5RVbtO5llOgaAcrDx4FnTEeDhnLKn1NixYzVjxgwVFxc74/QAAAAA3NTuE1kqtrGfFOAJNrGvFJzMKXtKbdiwQYmJifr222/VokULBQcHl3j+s88+c8awAAAAAAzbfizDdAQA5YTNzuFsTimlqlSpokGDBjnj1AAAAADc2I6jlFKAp9h/KlvpuYWqEuRnOgo8lFNKqbffftsZpwUAAADg5pgpBXgOu11KOpKuHo2iTEeBh3LKnlKSVFxcrO+++06vvfaasrLObXR4/PhxZWdnO2tIAAAAAAYVFFu1L5VNzgFPsv8Ud+CD8zhlptShQ4fUt29fHT58WAUFBerdu7dCQ0M1Y8YMFRQUaN68ec4YFgAAAIBBu1OyVGRlk3PAkxw4xcQSOI9TZko9+OCDat++vdLS0hQYGOg4fttttykxMdEZQwIAAAAwbMdxlu4BnuYAM6XgRE6ZKbV69WqtXbtWfn4lN0NLSEjQsWPHnDEkAAAAAMP2nmDpHuBpDpxmphScxykzpWw2m6xWa6njR48eVWhoqDOGBAAAAGDYobO5piMAKGcnMwuUXVBsOgY8lFNKqRtvvFEzZ850PLZYLMrOztaUKVPUr18/ZwwJAAAAwLDDlFKAR0pmCR+cxCml1Isvvqg1a9aoadOmys/P15/+9CfH0r0ZM2Y4Y0gAAAAABtlsdh1NyzMdA4ATsIQPzuKUUqpmzZraunWrHn/8cT300ENq06aNpk+fri1btigqKuqyzzN37ly1bNlSYWFhCgsLU+fOnbVkyRJnRAYAAABwFU5m5auw2GY6BgAn2M9MKTiJUzY6X7Vqlbp06aJhw4Zp2LBhjuPFxcVatWqVunXrdlnnqVmzpqZPn64GDRrIbrdrwYIFGjBggLZs2aJmzZo5IzoAAACAK3A8nVlSgKdKPk0pBedwykypnj176uzZs6WOZ2RkqGfPnpd9nltuuUX9+vVTgwYN1LBhQz377LMKCQnRTz/9VJ5xAQAAAFyllIx80xEAOMmpLL6+4RxOmSllt9tlsVhKHT9z5oyCg4Ov6JxWq1Uff/yxcnJy1Llz56uNCAAAAKAcnaCUAjxWWk6R6QjwUOVaSv3hD3+QdO5ueyNHjpS/v7/jOavVqm3btqlLly5lOuf27dvVuXNn5efnKyQkRJ9//rmaNm16wdcXFBSooKDA8TgzM7OM7wIAAABAWTFTCvBcabmFpiPAQ5VrKRUeHi7p3Eyp0NBQBQYGOp7z8/PTNddco9GjR5fpnI0aNVJSUpIyMjL0ySefaMSIEVq5cuUFi6lp06bpqaeeuvI3AQDlKOGxr4yMe3B6fyPjAgAqr1NZBZd+EYAKKT2XmVJwjnItpd5++21JUkJCgv76178qKCjoqs/p5+en+vXrS5LatWunDRs26OWXX9Zrr7123tdPnjxZEydOdDzOzMxUfHz8VecAAAAAcGHZBcWmIwBwkkKrTdkFxQrxd8oOQKjEnLLR+fDhw3Xs2LFSx/ft26eDBw9e1bltNluJ5Xm/5+/vr7CwsBIfAAAAAJwrh1IK8GhpOSzhQ/lzSik1cuRIrV27ttTxdevWaeTIkZd9nsmTJ2vVqlU6ePCgtm/frsmTJ2vFihUaNmxYOaYFAAAAcLXyiqymIwBwIvaVgjM4Ze7dli1b1LVr11LHr7nmGo0bN+6yz5Oamqrhw4crJSVF4eHhatmypb755hv17t27POMCAAAAuErMlAI8Wxr7SsEJnFJKWSwWZWVllTqekZEhq/Xy/wXlzTffLM9YAAAAAJwkt5CZUoAnS2emFJzAKcv3unXrpmnTppUooKxWq6ZNm6Zrr73WGUMCAAAAMIhSCvBsOQV8jaP8OWWm1IwZM9StWzc1atRI1113nSRp9erVyszM1Pfff++MIQEAAAAYlFvI8j3Ak1ltNtMR4IGcMlOqadOm2rZtmwYPHqzU1FRlZWVp+PDh2r17t5o3b+6MIQEAAAAYUlhsU5HVbjoGACey2vgaR/lzykwpSYqLi9Nzzz3nrNO7lYTHvjIy7sHp/S/6vDvmMpVJuvT1AgAAwJXJY+ke4PHoneEMTiulJCk3N1eHDx9WYWHJDdFatmzpzGEBAAAAuJLFdAC4Wo2AAl1X9azpGHChGj41TEeAB3JKKXXq1CmNGjVKS5YsOe/zZbkDHwAAAAD35u/jlF1B4MY6V0nX9LSHTceAK9meldTadAp4GKf87TFhwgSlp6dr3bp1CgwM1NKlS7VgwQI1aNBAixYtcsaQAAAAAAyhlKp8tmWFmo4AV/Ny6kIrVFJO+VP1/fff64svvlD79u3l5eWl2rVrq3fv3goLC9O0adPUvz97+wAAAACewmKxyM/bS4VW7s5VWezNCZI9JFCW4jzTUeAqXt6mE8ADOeWfNHJychQVFSVJqlq1qk6dOiVJatGihTZv3uyMIQEAAAAYxGypyqcoJM50BLgSM6XgBE75m6NRo0bas2ePJKlVq1Z67bXXdOzYMc2bN0+xsbHOGBIAAACAQf6+lFKVTXYAP9tVKpRScAKn/Kl68MEHlZKSIkmaMmWK+vbtq/fee09+fn6aP3++M4YEAAAAYJCfN6VUZXPWN1oRpkPAdXz8TSeAB3JKKXXnnXc6ft2uXTsdOnRIu3fvVq1atVS9enVnDAkAAADAIH9f9pupbFIUqfqmQ8B1gqqZTgAPVO7/nFFUVKR69epp165djmNBQUFq27YthRQAAADgodhTqvI5ZKWkqFRCokwngAcq9785fH19lZ+fX96nBQAAAODGQvzZb6ay2Ztf1XQEuFJItOkE8EBO+eeMsWPHasaMGSouLnbG6QEAAAC4meiwANMR4GI7csJMR4CrWLxYvgencMo/Z2zYsEGJiYn69ttv1aJFCwUHB5d4/rPPPnPGsAAAAAAMoZSqfLZnBcse4COLjckIHi+omuTFvnEof04ppapUqaJBgwY549QAAAAA3FBMOHfmqmyKbBZZg2Pkk3XUdBQ4G0v34CROKaXefvttZ5wWAAAAgJuKCQ80HQEG5AbFKYxSyvMFR5pOAA/FLTIAAAAAXLUYlu9VSmm+MaYjwBWYKQUncdotMj755BN99NFHOnz4sAoLC0s8t3nzZmcNCwAAAMAASqnK6YQlSrVNh4DzhTBTCs7hlJlSs2bN0qhRoxQdHa0tW7aoY8eOqlatmg4cOKCbbrrJGUMCAAAAMCiaPaUqpSNW7shWKTBTCk7ilFLq1Vdf1euvv65XXnlFfn5+evTRR7Vs2TKNHz9eGRkZzhgSAAAAgEH+Pt6KCPYzHQMutq+wqukIcAVKKTiJU0qpw4cPq0uXLpKkwMBAZWVlSZLuuusu/ec//3HGkAAAAAAMYwlf5fNzbrjpCHCF6g1MJ4CHckopFRMTo7Nnz0qSatWqpZ9++kmSlJycLLvd7owhAQAAABhWLyrEdAS42JbMENllMR0DzmTxliKbmE4BD+WUUur666/XokWLJEmjRo3SQw89pN69e2vIkCG67bbbnDEkAAAAAMMaRVNKVTY5xd6yBbMJtker3kDyZRYknMMpd997/fXXZbPZJEljx45VtWrVtHbtWt1666269957nTEkAAAAAMMaRIeajgAD8oPiFJyTajoGnCW6uekE8GBOKaW8vLzk5fW/SVhDhw7V0KFDnTEUAAAAADfRiFKqUsrwi1Gw6RBwnhhKKTiPU0opSUpPT9f69euVmprqmDX1q+HDhztrWAAAAACG1IoIUpCft3ILraajwIVSvaIUZzoEnCe6hekE8GBOKaW+/PJLDRs2TNnZ2QoLC5PF8r+N7ywWC6UUAAAA4IG8vCxqEhumTYfSTEeBCx21V1dr0yHgPMyUghM5ZaPzhx9+WHfffbeys7OVnp6utLQ0x8evd+UDAAAA4Hmax4WZjgAX218YYToCnCU4UgqNMZ0CHswppdSxY8c0fvx4BQUFOeP0AAAAANxUsxrhpiPAxXbl8XvusdjkHE7mlFKqT58+2rhxozNODQAAAMCNtaCUqnSSMtng3mOxdA9OVm57Si1atMjx6/79++uvf/2rdu7cqRYtWsjX17fEa2+99dbyGhYAAACAG2kUHarwQF9l5BWZjgIXOVHgJ1uVKvLKTzcdBeWNTc7hZOVWSg0cOLDUsaeffrrUMYvFIquVu3EAAAAAnsjLy6Jr6kbom59Pmo4CFyoIrqFASinPU7uL6QTwcOW2fM9ms13WB4UUAAAA4Nm61q9uOgJcLMufzbA9TvWGUpV40yng4cp1T6nvv/9eTZs2VWZmZqnnMjIy1KxZM61evbo8hwQAAADgZrrUo5SqbE57R5mOgPJWv5fpBKgEyrWUmjlzpkaPHq2wsNK3gQ0PD9e9996rl156qTyHBAAAAOBm6keFKCYswHQMuNAxe6TpCChv9W4wnQCVQLmWUlu3blXfvn0v+PyNN96oTZs2leeQAAAAANxQl/rVTEeACyUXR5iOgPLkEyAldDWdApVAuZZSJ0+eLHWnvd/y8fHRqVOnynNIAAAAAG6oK0v4KpXdeVVMR0B5qt1V8g00nQKVQLmWUjVq1NCOHTsu+Py2bdsUGxtbnkMCAAAAcENsdl65bM0KNR0B5ak+S/fgGuVaSvXr109PPPGE8vPzSz2Xl5enKVOm6Oabby7PIQEAAAC4oZjwANWNDDYdAy6yPzdQdt8g0zFQXtjkHC7iU54n+/vf/67PPvtMDRs21Lhx49SoUSNJ0u7duzVnzhxZrVY9/vjj5TkkAAAAADfVu0m0Xjt1wHQMuEhRcJz80n8xHQNXKzxeimxkOgUqiXItpaKjo7V27Vrdf//9mjx5sux2uyTJYrGoT58+mjNnjqKjo8tzSAAAAABu6pZWcXptFaVUZZEdGKsISqmKr971phOgEinXUkqSateura+//lppaWn65ZdfZLfb1aBBA1WtWrW8hwIAAADgxprXCFfd6sE6cDrHdBS4wBmfaHEPPg/A0j24ULnuKfVbVatWVYcOHdSxY0cKKQAAAKCSurklNzqqLFIUaToCrlZAuNTgRtMpUIk4rZQqD9OmTVOHDh0UGhqqqKgoDRw4UHv27DEdCwAAAMBlurV1nOkIcJGDxdVMR8DVav5HyTfAdApUIm5dSq1cuVJjx47VTz/9pGXLlqmoqEg33nijcnKY/gsAAABUBPWjQtU4JtR0DLjAnvwqpiPgarUZZjoBKply31OqPC1durTE4/nz5ysqKkqbNm1St27dDKUCAAAAUBa3tIrT7hOsePB023PCTEfA1YhsItVoZzoFKhm3LqV+LyMjQ5IUEXHh7fMKCgpUUFDgeJyZmen0XAAAAAAu7NZWcXrhG0opT/dzVpDsgb6y2IpcPva01QX6bHeRdp+2KdDHoi7x3prRy1+Nqns7XnPvl3n6LrlYx7PsCvH732sa/+Y1v/fZriLN21ioTSk2nc2za8u9wWodU/L1E7/J1/ykQgX7WTT9hgANa+nreO7jn4v0721F+vKOoPJ/0+WNWVIwwK2X7/2WzWbThAkT1LVrVzVv3vyCr5s2bZrCw8MdH/Hx8S5MCQAAAOD34iOC1KZWFdMx4GRWu5esIWY2tl95qFhjO/jppz8Ha9ldQSqySTe+m6ucQrvjNe3ivPX2gEDtGhuib+4Mkt0u3fhOrqw2+wXPm1No17W1fDSjl/95n/9yT5He316kb+8K1vO9AvSXL/N0OtcmScrIt+vx7ws0p18F2KPJy0dqOdR0ClRCFaaUGjt2rHbs2KEPPvjgoq+bPHmyMjIyHB9HjhxxUUIAAAAAF3JHh1qmI8AFcgLNbGy/9M5gjWztp2ZR3moV4635AwJ0OMOuTSlWx2vuaeenbrV9lFDFS21jvfXM9f46kmnXwfQLl1J3tfLTk9391avu+RcZ7TptU48Eb7WP89YdLXwV5m9Rctq58z26LF/3t/dVrfAK8GN3gz5SCHdPhOtVgK8Oady4cVq8eLGWL1+umjVrXvS1/v7+CgsLK/EBAAAAwKxbW8cpItjPdAw4WZpvtOkIkqSM/+7oEhFoOe/zOYV2vb2lSHWqWBQffv7XXI5W0d7aeNyqtDy7Nh23Kq/IrvoRXvrhcLE2n7BqfKcK8meepXswxK33lLLb7XrggQf0+eefa8WKFapTp47pSAAAAACuQICvt+7oGK85y/ebjgInOmmJUoLhDDa7XROW5qtrvLeaR5Xc/+nVDYV6dFm+coqkRtW8tOyuYPl5X3kp1ae+j+5s6asOb2Qr0NeiBQMDFewn3f9VvuYPCNTcjUV6ZX2hqgdZ9PrNAWoWdeH9q4wJjjw3UwowwK1nSo0dO1bvvvuu3n//fYWGhurEiRM6ceKE8vLyTEcDAAAAUEZ3XZMgH68rLwDg/g7ZLnxTKlcZ+1W+dqRa9cEfA0s9N6yFr7bcG6yVI4PUsJqXBn+Sq/ziCy/fuxxTewTol/Gh2n5/iG5r4qtpqwvVq46PfL2lZ1YV6IdRQfpLG18NX+imP8e2HCJ5u/V8FXgwty6l5s6dq4yMDPXo0UOxsbGOjw8//NB0NAAAAABlFBMeoJtamNkIG67xS0FVo+OP+zpPi/cVa/mIYNUMK/3jbniARQ2qeatbbR99MjhQu0/b9Pmu4nIbf/dpq97dXqR/XO+vFQeL1a22tyKDvTS4ma82p9iUVXB1BVi5s3hJ7UaaToFKzK3rULvdzb5gAQAAAFyVkV0S9OXW46ZjwEl+zgk3Mq7dbtcDS/L1+e5irRgRpDpVLz3/wm4/91FgLZ+fO+12u+5dnK+XbvRXiJ9FVptUdO5GfI7/ltNQ5afpAKl6A9MpUIm59UwpAAAAAJ6lXe2qalXTTHEB50vKDJVdrl+iOfbrfL27rUjv/yFQof4Wnci26US2TXlF51qgA2k2TVtdoE3HrTqcYdPaI8W6/eM8Bfpa1K/B/+ZqNJ6drc93FTken82zK+mEVTtPnbuL357TNiWdsOpEtq1Uhv/bXKTIIItuaeQrSepay0ffJxfrp6PF+tePBWoa6aUqAe60fNUidfur6RCo5Nx6phQAAAAAzzOqax1N+DDJdAw4QY7VS7Yq0fLOOeHSceduPFck9ViQW+L42wMCNLK1nwJ8pNWHrZq5rlBpeXZFh1jUrba31t4dpKjg/83V2HPGpozfLLFbtKdIo77Idzwe+um5faGmdPfT1B4BjuMns216dnWB1v452HGsYw1vPdzZX/3fz1NU8LlN0N1Ko35SdDPTKVDJUUoBAAAAcKn+LWM1fcluncjMv/SLUeHkBcUpxMWllH1K2EWfjwv10tfDgsp8npGt/TSytd8lPy86xEsHJ4SWOv5kd3892d3/kp9vRHdmScE8lu8BAAAAcClfby/d36Oe6RhwknT/GNMRcCn1e0lxbUynACilAAAAALje0I7xigsPuPQLUeGkWqJMR8CldHvUdAJAEqUUAAAAAAP8fbw1pmd90zHgBEft1UxHwMUkXCfV6mQ6BSCJUgoAAACAIUM6xKtmVTfb/BlXbX9hVdMRcDHccQ9uhFIKAAAAgBG+3l6a2Luh6RgoZztzq5iOgAuJ7yTV7W46BeBAKQUAAADAmIGta6hJ7MXvnIaKJSmz9F3o4CZ6/s10AqAESikAAAAAxnh5WTSpbyPTMVCOThX6yhbAEj6303SgVLeH6RRACZRSAAAAAIzq0ShKXeuzObYnKQiuYToCfssvROo7zXQKoBRKKQAAAADGTbmlmXy9LaZjoJxk+seYjoDf6v6oFBZnOgVQCqUUAAAAAOMaRodq9HV1TcdAOTntHWU6An4V2Vi6ZozpFMB5UUoBAAAAcAvjb2ig2tWCTMdAOThqr246An7V7wXJ29d0CuC8KKUAAAAAuIUAX289M7C56RgoBweK2CPMLTT/o1Snm+kUwAVRSgEAAABwG9c1iNSA1ux9U9Htzg83HQF+oVKfZ02nAC6KUgoAAACAW3ni5qYKD2S5UUW2NTPMdAT0nCyFsuE83BulFAAAAAC3Uj3EX4/d1Nh0DFyFg3kBsvsGm45ReUU1kzreazoFcEmUUgAAAADcztAO8eqQUNV0DFyFwpAapiNUTt5+0sA5kreP6STAJVFKAQAAAHA7FotFL97eWqH+/GBdUWUHxJqOUDn1mirFtTGdArgslFIAAAAA3FKtakF67g8tTMfAFTrjE2U6QuXTsK/UeazpFMBlo5QCAAAA4LZuaRWnoR3iTcfAFTiuSNMRKpfQOGngXNMpgDKhlAIAAADg1qbe2kwNokJMx0AZHSyuZjpC5WHxlgb9nxQUYToJUCaUUgAAAADcWoCvt+YMa6sAX358qUj25lcxHaHy6PZXKaGr6RRAmfFdHQAAAIDbaxgdqidvbmY6Bspge3aY6QiVQ+1rpe6Pmk4BXBFKKQAAAAAVwp861VL/ltzRraLYmR0ou5ev6RieLTBCGvSG5OVtOglwRSilAAAAAFQY0/7QQnWrB5uOgctgtXupOCTOdAzPNnCuFMY1RsVFKQUAAACgwggL8NVbIzuoahAzcCqCnCAKE6e57hGpUV/TKYCrQikFAAAAoEJJqB6s14e3l58PP864uzTfaNMRPFPzQdL1fzedArhqfBcHAAAAUOF0SIjQC39sKYvFdBJczAlFmY7geeI7SQNeFX/44QkopQAAAABUSANa19BDvRqajoGLOGStZjqCZ6laRxr6H8k3wHQSoFxQSgEAAACosMbf0ECD2tY0HQMXsK+giukIniMwQhr2sRRM0QfPQSkFAAAAoEKbPqiFOtflB3V3tCMn3HQEz+AbfK6Qqt7AdBKgXFFKAQAAAKjQfL29NO/OdmoQFWI6Cn5nW1aw7BZ+7LwqXr7SkH9LNdubTlKpJSQkaObMmaZjeBy+OwAAAACo8MKDfPXe6E6qWz3YdBT8Rp7VW7Zg7sB35SzSwLlS/V5l+qyRI0fKYrFo+vTpJY4vXLhQlqvcIH3+/PmyWCyyWCzy8vJSzZo1NWrUKKWmpl7VectixYoVslgsSk9PL/dzz58/X1WqVCl1fMOGDbrnnnvKfbzKjlIKAAAAgEeICg3Q+6OvUe1qQaaj4DfyguJMR6i4+k6XWt5+RZ8aEBCgGTNmKC0trZxDSWFhYUpJSdHRo0f1xhtvaMmSJbrrrrvKfRx3EhkZqaAgvreUN0opAAAAAB4jJvxcMVWjSqDpKPivdL8Y0xEqHouX1P8l6Zr7rvgUvXr1UkxMjKZNm3bR13366adq1qyZ/P39lZCQoBdffPHS8SwWxcTEKC4uTjfddJPGjx+v7777Tnl5eZo6dapat25d4vUzZ85UQkKC4/HIkSM1cOBA/fOf/1RsbKyqVaumsWPHqqioyPGagoICTZo0SfHx8fL391f9+vX15ptv6uDBg+rZs6ckqWrVqrJYLBo5cqSk8y+xa926taZOnep4/NJLL6lFixYKDg5WfHy8xowZo+zsbEnnZmCNGjVKGRkZjtlgv37u7899+PBhDRgwQCEhIQoLC9PgwYN18uRJx/O/Xod33nlHCQkJCg8P19ChQ5WVlXXJ61uZUEoBAAAA8Cg1qgTqg3uuUXwExZQ7SPWKNB2hYvHykW57Xerw56s6jbe3t5577jm98sorOnr06Hlfs2nTJg0ePFhDhw7V9u3bNXXqVD3xxBOaP39+mcYKDAyUzWZTcXHxZX/O8uXLtX//fi1fvlwLFizQ/PnzS4w7fPhw/ec//9GsWbO0a9cuvfbaawoJCVF8fLw+/fRTSdKePXuUkpKil19++bLH9fLy0qxZs/Tzzz9rwYIF+v777/Xoo49Kkrp06aKZM2c6ZoKlpKTokUceKXUOm82mAQMG6OzZs1q5cqWWLVumAwcOaMiQISVet3//fi1cuFCLFy/W4sWLtXLlylJLKis7H9MBAAAAAKC8xUcE6cN7OutPb/ykg2dyTcep1I5Yq6ut6RAVhU+AdPt8qdFN5XK62267Ta1bt9aUKVP05ptvlnr+pZde0g033KAnnnhCktSwYUPt3LlTL7zwgmP20aXs27dP8+bNU/v27RUaGnrZ2apWrarZs2fL29tbjRs3Vv/+/ZWYmKjRo0dr7969+uijj7Rs2TL16nVuP626des6PjciIkKSFBUVdd79ny5mwoQJjl8nJCTomWee0X333adXX31Vfn5+Cg8Pd8wEu5DExERt375dycnJio+PlyT9+9//VrNmzbRhwwZ16NBB0rnyav78+Y7rctdddykxMVHPPvtsmTJ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AAJejlAIAAAAAAIDLUUoBAAAAAADA5f4fXPOjT6YRgRQAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1200x800 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def analyze_text_statistics(sentences):\n",
    "    stats = []\n",
    "    for i, sent in enumerate(sentences):\n",
    "        doc = nlp(sent)\n",
    "        words = [token.text for token in doc if not token.is_space]\n",
    "        alpha_words = [token.text for token in doc if token.is_alpha]\n",
    "        \n",
    "        stats.append({\n",
    "            'sentence_id': i,\n",
    "            'sentence': sent,\n",
    "            'char_length': len(sent),\n",
    "            'word_count': len(words),\n",
    "            'alpha_word_count': len(alpha_words),\n",
    "            'avg_word_length': np.mean([len(word) for word in alpha_words]) if alpha_words else 0,\n",
    "            'has_punctuation': any(token.is_punct for token in doc),\n",
    "            'is_empty': len(sent.strip()) == 0\n",
    "        })\n",
    "    \n",
    "    return pd.DataFrame(stats)\n",
    "\n",
    "stats_df = analyze_text_statistics(test_sentences)\n",
    "display(stats_df)\n",
    "\n",
    "# Visualize basic statistics\n",
    "fig, axes = plt.subplots(2, 2, figsize=(12, 8))\n",
    "\n",
    "# Character length distribution\n",
    "axes[0,0].bar(range(len(stats_df)), stats_df['char_length'])\n",
    "axes[0,0].set_title('Character Length by Sentence')\n",
    "axes[0,0].set_xlabel('Sentence ID')\n",
    "axes[0,0].set_ylabel('Characters')\n",
    "\n",
    "# Word count distribution\n",
    "axes[0,1].bar(range(len(stats_df)), stats_df['word_count'])\n",
    "axes[0,1].set_title('Word Count by Sentence')\n",
    "axes[0,1].set_xlabel('Sentence ID')\n",
    "axes[0,1].set_ylabel('Words')\n",
    "\n",
    "# Average word length\n",
    "axes[1,0].bar(range(len(stats_df)), stats_df['avg_word_length'])\n",
    "axes[1,0].set_title('Average Word Length')\n",
    "axes[1,0].set_xlabel('Sentence ID')\n",
    "axes[1,0].set_ylabel('Characters')\n",
    "\n",
    "# Sentence type breakdown\n",
    "types = ['With Punctuation' if x else 'No Punctuation' for x in stats_df['has_punctuation']]\n",
    "type_counts = pd.Series(types).value_counts()\n",
    "axes[1,1].pie(type_counts.values, labels=type_counts.index, autopct='%1.1f%%')\n",
    "axes[1,1].set_title('Punctuation Distribution')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "5e488a878a5cfccb",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-23T13:55:23.734853Z",
     "start_time": "2025-11-23T13:55:22.744266Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linguistic Analysis\n",
      "============================================================\n",
      "\n",
      " 1. ORIGINAL:           'The cat sat on the mat.'\n",
      "    Direct:             'the cat sat on the mat.'\n",
      "    Semantic:           'cat sit mat'\n",
      "    Syntactic:          'the cat sit on the mat .'\n",
      "    Tokens:              7 | Alpha:  6 | Stopwords:  3 | Punctuation:  1\n",
      "    POS:                {'DET': 2, 'NOUN': 2, 'VERB': 1, 'ADP': 1, 'PUNCT': 1}\n",
      "\n",
      " 2. ORIGINAL:           'The cat sat on the mat'\n",
      "    Direct:             'the cat sat on the mat'\n",
      "    Semantic:           'cat sit mat'\n",
      "    Syntactic:          'the cat sit on the mat'\n",
      "    Tokens:              6 | Alpha:  6 | Stopwords:  3 | Punctuation:  0\n",
      "    POS:                {'DET': 2, 'NOUN': 2, 'VERB': 1, 'ADP': 1}\n",
      "\n",
      " 3. ORIGINAL:           'The  cat  sat  on  the  mat.'\n",
      "    Direct:             'the  cat  sat  on  the  mat.'\n",
      "    Semantic:           'cat sit mat'\n",
      "    Syntactic:          'the cat sit on the mat .'\n",
      "    Tokens:             12 | Alpha:  6 | Stopwords:  3 | Punctuation:  1\n",
      "    POS:                {'DET': 2, 'SPACE': 5, 'NOUN': 2, 'VERB': 1, 'ADP': 1, 'PUNCT': 1}\n",
      "\n",
      " 4. ORIGINAL:           'On the mat, the cat was sitting.'\n",
      "    Direct:             'on the mat, the cat was sitting.'\n",
      "    Semantic:           'mat cat sit'\n",
      "    Syntactic:          'on the mat , the cat be sit .'\n",
      "    Tokens:              9 | Alpha:  7 | Stopwords:  4 | Punctuation:  2\n",
      "    POS:                {'ADP': 1, 'DET': 2, 'NOUN': 2, 'PUNCT': 2, 'AUX': 1, 'VERB': 1}\n",
      "\n",
      " 5. ORIGINAL:           'The feline rested on the rug.'\n",
      "    Direct:             'the feline rested on the rug.'\n",
      "    Semantic:           'feline rest rug'\n",
      "    Syntactic:          'the feline rest on the rug .'\n",
      "    Tokens:              7 | Alpha:  6 | Stopwords:  3 | Punctuation:  1\n",
      "    POS:                {'DET': 2, 'NOUN': 2, 'VERB': 1, 'ADP': 1, 'PUNCT': 1}\n",
      "\n",
      " 6. ORIGINAL:           'The quick brown fox jumps over the lazy dog.'\n",
      "    Direct:             'the quick brown fox jumps over the lazy dog.'\n",
      "    Semantic:           'quick brown fox jump lazy dog'\n",
      "    Syntactic:          'the quick brown fox jump over the lazy dog .'\n",
      "    Tokens:             10 | Alpha:  9 | Stopwords:  3 | Punctuation:  1\n",
      "    POS:                {'DET': 2, 'ADJ': 3, 'PROPN': 1, 'VERB': 1, 'ADP': 1, 'NOUN': 1, 'PUNCT': 1}\n",
      "\n",
      " 7. ORIGINAL:           'Over the lazy dog jumps the quick brown fox.'\n",
      "    Direct:             'over the lazy dog jumps the quick brown fox.'\n",
      "    Semantic:           'lazy dog jump quick brown fox'\n",
      "    Syntactic:          'over the lazy dog jump the quick brown fox .'\n",
      "    Tokens:             10 | Alpha:  9 | Stopwords:  3 | Punctuation:  1\n",
      "    POS:                {'ADP': 1, 'DET': 2, 'ADJ': 3, 'NOUN': 1, 'VERB': 1, 'PROPN': 1, 'PUNCT': 1}\n",
      "\n",
      " 8. ORIGINAL:           'The dog ran in the park.'\n",
      "    Direct:             'the dog ran in the park.'\n",
      "    Semantic:           'dog run park'\n",
      "    Syntactic:          'the dog run in the park .'\n",
      "    Tokens:              7 | Alpha:  6 | Stopwords:  3 | Punctuation:  1\n",
      "    POS:                {'DET': 2, 'NOUN': 2, 'VERB': 1, 'ADP': 1, 'PUNCT': 1}\n",
      "\n",
      " 9. ORIGINAL:           'I love programming.'\n",
      "    Direct:             'i love programming.'\n",
      "    Semantic:           'love programming'\n",
      "    Syntactic:          'i love programming .'\n",
      "    Tokens:              4 | Alpha:  3 | Stopwords:  1 | Punctuation:  1\n",
      "    POS:                {'PRON': 1, 'VERB': 1, 'NOUN': 1, 'PUNCT': 1}\n",
      "\n",
      "10. ORIGINAL:           'She enjoys reading books.'\n",
      "    Direct:             'she enjoys reading books.'\n",
      "    Semantic:           'enjoy read book'\n",
      "    Syntactic:          'she enjoy read book .'\n",
      "    Tokens:              5 | Alpha:  4 | Stopwords:  1 | Punctuation:  1\n",
      "    POS:                {'PRON': 1, 'VERB': 2, 'NOUN': 1, 'PUNCT': 1}\n",
      "\n",
      "11. ORIGINAL:           'Short.'\n",
      "    Direct:             'short.'\n",
      "    Semantic:           'short'\n",
      "    Syntactic:          'short .'\n",
      "    Tokens:              2 | Alpha:  1 | Stopwords:  0 | Punctuation:  1\n",
      "    POS:                {'ADJ': 1, 'PUNCT': 1}\n",
      "\n",
      "12. ORIGINAL:           'A B C D E F G'\n",
      "    Direct:             'a b c d e f g'\n",
      "    Semantic:           'b c d e f g'\n",
      "    Syntactic:          'a b c d e f g'\n",
      "    Tokens:              7 | Alpha:  7 | Stopwords:  1 | Punctuation:  0\n",
      "    POS:                {'DET': 1, 'NOUN': 2, 'PROPN': 4}\n",
      "\n",
      "13. ORIGINAL:           ''\n",
      "    Direct:             ''\n",
      "    Semantic:           ''\n",
      "    Syntactic:          ''\n",
      "    Tokens:              0 | Alpha:  0 | Stopwords:  0 | Punctuation:  0\n",
      "    POS:                {}\n"
     ]
    }
   ],
   "source": [
    "\n",
    "class TextPreprocessor:\n",
    "    def __init__(self):\n",
    "        self.nlp = spacy.load(\"en_core_web_lg\")\n",
    "    \n",
    "    def extract_linguistic_features(self, text):\n",
    "        \"\"\" Extract linguistic features \"\"\"\n",
    "        doc = self.nlp(text)\n",
    "        \n",
    "        features = {\n",
    "            'tokens': [token.text for token in doc],\n",
    "            'lemmas': [token.lemma_ for token in doc if token.is_alpha],\n",
    "            'pos_tags': [token.pos_ for token in doc],\n",
    "            'dep_tags': [token.dep_ for token in doc],\n",
    "            'is_stop': [token.is_stop for token in doc],\n",
    "            'is_alpha': [token.is_alpha for token in doc],\n",
    "            'is_punct': [token.is_punct for token in doc]\n",
    "        }\n",
    "        \n",
    "        return features, doc\n",
    "    \n",
    "    def direct_preprocessing(self, text):\n",
    "        \"\"\" For direct copy detection \"\"\"\n",
    "        return text.lower().strip()\n",
    "    \n",
    "    def semantic_preprocessing(self, text):\n",
    "        \"\"\" For semantic similarity \"\"\"\n",
    "        # Remove noise like stop words\n",
    "        doc = self.nlp(text)\n",
    "        processed_tokens = []\n",
    "        for token in doc:\n",
    "            if not token.is_punct and not token.is_space and token.is_alpha and not token.is_stop:\n",
    "                processed_tokens.append(token.lemma_.lower())\n",
    "        return \" \".join(processed_tokens)\n",
    "    \n",
    "    def syntactic_preprocessing(self, text):\n",
    "        \"\"\" For syntactic similarity \"\"\"\n",
    "        # Normalize\n",
    "        doc = self.nlp(text)\n",
    "        processed_tokens = []\n",
    "        for token in doc:\n",
    "            if token.is_space:\n",
    "                continue\n",
    "            elif token.is_punct:\n",
    "                processed_tokens.append(token.text)\n",
    "            elif token.is_stop:\n",
    "                processed_tokens.append(token.lemma_.lower())\n",
    "            else:\n",
    "                processed_tokens.append(token.lemma_.lower())\n",
    "        return \" \".join(processed_tokens)\n",
    "\n",
    "\n",
    "# Initialize preprocessor\n",
    "preprocessor = TextPreprocessor()\n",
    "\n",
    "# Analyze all sentences\n",
    "print(\"Linguistic Analysis\")\n",
    "print(\"=\" * 60)\n",
    "\n",
    "for i, sentence in enumerate(test_sentences):\n",
    "    print(f\"\\n{i+1:2d}. {'ORIGINAL: ':<20}'{sentence}'\")\n",
    "    features, doc = preprocessor.extract_linguistic_features(sentence)\n",
    "    \n",
    "    print(f\"    {'Direct:     ':<20}'{preprocessor.direct_preprocessing(sentence)}'\")\n",
    "    print(f\"    {'Semantic:   ':<20}'{preprocessor.semantic_preprocessing(sentence)}'\")\n",
    "    print(f\"    {'Syntactic:  ':<20}'{preprocessor.syntactic_preprocessing(sentence)}'\")\n",
    "    \n",
    "    # Basic counts\n",
    "    print(f\"    {'Tokens: ':<20}{len(features['tokens']):2d} | \"\n",
    "          f\"Alpha: {sum(features['is_alpha']):2d} | \"\n",
    "          f\"Stopwords: {sum(features['is_stop']):2d} | \"\n",
    "          f\"Punctuation: {sum(features['is_punct']):2d}\")\n",
    "    \n",
    "    # POS distribution\n",
    "    pos_counts = Counter(features['pos_tags'])\n",
    "    print(f\"    {'POS:':<20}{dict(pos_counts)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "83fc18c9de2e354",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-23T13:55:33.587912Z",
     "start_time": "2025-11-23T13:55:33.565711Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Sentence 0: 'The cat sat on the mat.'\n",
      "Dependency Parse:\n",
      "--------------------------------------------------\n",
      "The          det          cat          []\n",
      "cat          nsubj        sat          ['The']\n",
      "sat          ROOT         sat          ['cat', 'on', '.']\n",
      "on           prep         sat          ['mat']\n",
      "the          det          mat          []\n",
      "mat          pobj         on           ['the']\n",
      ".            punct        sat          []\n",
      "\n",
      "Dependency Tree Visualization - Sentence 0:\n"
     ]
    },
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      "\n",
      "======================================================================\n",
      "\n",
      "\n",
      "Sentence 3: 'On the mat, the cat was sitting.'\n",
      "Dependency Parse:\n",
      "--------------------------------------------------\n",
      "On           prep         sitting      ['mat']\n",
      "the          det          mat          []\n",
      "mat          pobj         On           ['the']\n",
      ",            punct        sitting      []\n",
      "the          det          cat          []\n",
      "cat          nsubj        sitting      ['the']\n",
      "was          aux          sitting      []\n",
      "sitting      ROOT         sitting      ['On', ',', 'cat', 'was', '.']\n",
      ".            punct        sitting      []\n",
      "\n",
      "Dependency Tree Visualization - Sentence 3:\n"
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      "\n",
      "======================================================================\n",
      "\n",
      "\n",
      "Sentence 4: 'The feline rested on the rug.'\n",
      "Dependency Parse:\n",
      "--------------------------------------------------\n",
      "The          det          feline       []\n",
      "feline       nsubj        rested       ['The']\n",
      "rested       ROOT         rested       ['feline', 'on', '.']\n",
      "on           prep         rested       ['rug']\n",
      "the          det          rug          []\n",
      "rug          pobj         on           ['the']\n",
      ".            punct        rested       []\n",
      "\n",
      "Dependency Tree Visualization - Sentence 4:\n"
     ]
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      "\n",
      "======================================================================\n",
      "\n",
      "\n",
      "Sentence 5: 'The quick brown fox jumps over the lazy dog.'\n",
      "Dependency Parse:\n",
      "--------------------------------------------------\n",
      "The          det          fox          []\n",
      "quick        amod         fox          []\n",
      "brown        amod         fox          []\n",
      "fox          nsubj        jumps        ['The', 'quick', 'brown']\n",
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      "lazy         amod         dog          []\n",
      "dog          pobj         over         ['the', 'lazy']\n",
      ".            punct        jumps        []\n",
      "\n",
      "Dependency Tree Visualization - Sentence 5:\n"
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    "\n",
    "def analyze_dependency_structure(text, sentence_id):\n",
    "    \"\"\"Analyze and display dependency parse information\"\"\"\n",
    "    if not text.strip():\n",
    "        print(f\"Sentence {sentence_id}: [EMPTY]\")\n",
    "        return None\n",
    "    \n",
    "    doc = nlp(text)\n",
    "    \n",
    "    print(f\"\\nSentence {sentence_id}: '{text}'\")\n",
    "    print(\"Dependency Parse:\")\n",
    "    print(\"-\" * 50)\n",
    "    \n",
    "    # Extract dependency relationships\n",
    "    dependencies = []\n",
    "    for token in doc:\n",
    "        dependencies.append({\n",
    "            'token': token.text,\n",
    "            'lemma': token.lemma_,\n",
    "            'pos': token.pos_,\n",
    "            'dep': token.dep_,\n",
    "            'head': token.head.text,\n",
    "            'children': [child.text for child in token.children]\n",
    "        })\n",
    "        print(f\"{token.text:<12} {token.dep_:<12} {token.head.text:<12} {[child.text for child in token.children]}\")\n",
    "    \n",
    "    return dependencies\n",
    "\n",
    "def visualize_dependency_tree(text, sentence_id):\n",
    "    \"\"\"Create dependency tree visualization\"\"\"\n",
    "    if not text.strip():\n",
    "        return\n",
    "    \n",
    "    doc = nlp(text)\n",
    "    \n",
    "    # Use displacy for visualization\n",
    "    from spacy import displacy\n",
    "    \n",
    "    print(f\"\\nDependency Tree Visualization - Sentence {sentence_id}:\")\n",
    "    html = displacy.render(doc, style=\"dep\", jupyter=True, options={'distance': 120})\n",
    "    \n",
    "    return html\n",
    "\n",
    "# Analyze dependency structures for key sentence pairs\n",
    "key_pairs = [\n",
    "    (0, \"The cat sat on the mat.\"),\n",
    "    (3, \"On the mat, the cat was sitting.\"),\n",
    "    (4, \"The feline rested on the rug.\"),\n",
    "    (5, \"The quick brown fox jumps over the lazy dog.\")\n",
    "]\n",
    "\n",
    "for sentence_id, sentence in key_pairs:\n",
    "    deps = analyze_dependency_structure(sentence, sentence_id)\n",
    "    visualize_dependency_tree(sentence, sentence_id)\n",
    "    print(\"\\n\" + \"=\"*70 + \"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e413238c1af12f62",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-23T13:56:21.733459Z",
     "start_time": "2025-11-23T13:56:21.702279Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "VOCABULARY ANALYSIS\n",
      "==================================================\n",
      "Total unique words: 35\n",
      "Total unique lemmas: 34\n",
      "Total content words: 21\n"
     ]
    },
    {
     "data": {
      "image/png": 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qVd23b5+qZs8WBlu2bNHZs2frkSNHdPv27Vq3bl398ccfTWu3t7s9Ls2bN9ciRYqou7u79u/fX1esWKFJSUlapUoV/fDDD7O5SmQU+W1bjpjf58+fV1XV+Ph4PXz4sKqqXr9+XTt06KChoaE6ceJEqy0Yr1y5kq31mXHE9Yz0OFt+q5LhmeWM+a16e4+xl19+WX19fbVBgwbG9GvXrqmq6qxZs7R8+fL6999/O1TdMEfm2BaZk3XIHNtwhs/yNWvWWD0u58+f1/Lly+srr7xil3ruJTk5Wa9du6bPP/+8Dh8+XFVvP/fFihWzW80pz92NGzd069at+vXXX2vPnj01NDRUZ8yYYYxz1L17aKTfp3PnzmmpUqV0y5YteubMGS1WrJjVl/Avv/xSDx48aJfazp8/r0WLFlWLxaKDBg2ympfyZbx27drGLjD2kPJGOXfunF65ckUvXryoqrd3zfvqq680LCxMW7Roof/++6+qqk6cOFG//vprhwnGFB999JGGhYVp9+7djRUOVdWDBw9my26GKY/Hjz/+qDExMfrSSy/ptm3bjBW6lGZ6jRo1dP78+frGG29ojhw59MiRI1le2/04e/asFi5cWD/++GOr6ceOHdM6deqoxWLRCRMm2Km62+78sF61apVGRERoy5Yt9ZVXXtGgoCAdNmyYxsTE6IcffqgffvihDhkyRDdv3myniv+zdetWrVu3rjZp0kT3799vTN+wYYM+/vjjWrVqVWN6VoVScnKynj9/XkNCQrR48eJasmRJLVCggFosFo2MjNQPPvhAN23apH/88YeePHkyS2rIjNSPx5o1a3TVqlW6fPlyY9ry5cs1Li7O6jYNGza0WpGHYyK/H4wj53fqL/xnzpzRunXratOmTXXbtm1GzR06dNC6devqJ598oklJSfrWW29pixYtHKZZcCd7r2fcjTPktyoZ/iCcLb/Tewz27t2rr732mrq6uqb5oXvu3Llavnx5u36e4t7InOxF5jwYMufBOcNneer378aNG7VkyZJaqlQpnTx5su7YsUNVVRctWqSVKlVK833RkTz++OM6b948PX78eJrvQ8uWLdP/+7//y5Y6Uj6vV65cqT169DDWKf7880/t0qWLhoaG6syZM61qO3r0aLbUdr9opN+nW7duaYcOHXTUqFFaokQJffHFF4031OnTp7VTp046Z84cuzV+169fr8HBwVqrVi0j9FJqOXDggPr5+WmjRo3seqyr5cuXa/PmzTUwMFDbtm2r33//var+t2JUv359rVy5sr744otqsVisjj1nj3p/++03/eabb/T777/XXbt2GfM//vhjrVu3rnbr1k2PHj2qw4cP18DAQGNFL6stXrxYvby8NCoqSps0aaLe3t46duxYPXbsmKre3vqyVq1aGhwcrKVLl9bt27dnS113Su+9cOPGDe3WrZu2bdvW+JU7Re/evbVp06ZavHhxnTNnTnaVaeXOZupff/2lqrc/5J944gktUaKE5s2bV1944QWtU6eO1q1bV5s3b65PPPGEQ6wgL1q0SGvUqKFeXl5G7Sk2bNigLVq0UH9/fz1w4ECW13LhwgVVvf0+Wrt2rZYsWVJ9fHz06aef1uLFi6uHh4fWr1/f+CJkT6lfqwMGDNCCBQtq8eLFtWDBgtq4cWM9dOiQMf/SpUt64MABfeKJJ7RatWp2P04n7o38zjxHz+/UeX3u3DmdN2+eNm/eXFu1amXV2OjatatWqVJFAwMDtVChQnb/kuPo6xnOmN+qZLitOEN+p36u9+7dq0eOHDGew/379+ugQYO0RIkS+t577+m///6rhw4d0ubNm2vjxo0ddus2kDlZhczJGmTOg3OGz/LUy1m+fLnu2LFDz507pwMHDtTmzZtrgQIF9J133tEpU6Zo48aN9csvv1RVx9m7Q/X2/3Dt2jV96qmntGvXrlqmTBnt0aOH1Z4Jzz//vE6ePDnb6l64cKHmz59fBw4caNWv2rNnj3bt2lXr1Kmjb7/9tg4bNkwtFovDbBiagkb6HW7dumW8oK5fv27VJBk0aJBaLBaNioqyOmnE0KFDtWLFitn+K8lvv/2mq1at0mXLlunp06d148aNWrZsWW3evLkxJuV/OXTokN22uFO9/StS7ty59YMPPtAvv/xSu3btql5eXrpw4UJVvb1itHr1au3Vq5c++eSTdm+iL1q0SH19ffWxxx7TKlWqaOPGjY2VOFXVCRMmaK1atbRYsWJarFgx3bJlS7bUFxcXp35+fvr555+r6u3HLWfOnFqsWDF97733jBBMTEzUPXv22O2QAKkD59SpU0aTX/X2CS7Kly+vgwcP1j///FNVVS9evKhPPfWUzpgxQ5999lnt2LGjXr9+PVsbW6mXNXToUC1VqpR+9tlnevXqVVVVXb16tbZs2VKbNWumv//+e7r3Ya/A/OWXX3TFihWqevuHlsDAQK1fv36aLcZiY2O1TZs2Nv0sSE5ONh67I0eO6K5du/TQoUNpdrN99913tVmzZqp6e9fMP//806pBbS9mJ7X57bfftHz58lqjRg3jxDtff/211q5dW8PDw41dNR1pRelRRn5nDUfN79SHL/Px8dERI0ZoUlKSfvvtt9q4cWOrxkZCQoLOnz9fJ06cmOYLeXZz9PUMZ8xvVTI8M5w9v1VVo6Oj1dfXVytUqKCNGzfWy5cvq6rqvn37dODAgeri4qLe3t7atWtXjYqKMg4NQDPdcZE5tkXmZA0yx7Yc9bM89f1HR0erxWLRHj16GNP+/vtv/frrrzUsLEybNWumFotFy5UrZxx+yl5SXp9nz57VxMRE4/BSP/74o+bNm1erV69uNf6NN97Q0qVLZ8vGdqq3v3N7e3tbHcJFVY29Yv766y8dNGiQBgUFafXq1Y3PVkdCI/3/u/PYf99//71GRERoVFSUxsTEGNPbtm2rRYsW1YEDB+r777+v3bt31/z58+uvv/6arfUuWLBACxUqpEFBQWqxWLRevXo6fvx43bhxo5YpU0YjIiKMsfY+PMr+/fu1Ro0aOmXKFFW9vQWgv7+/VqpUSfPmzavz58+3Gm/vM1v/8MMP6u3tbZwZfunSpZovXz4tV66czps3zxi3Y8cOXbFiRbb+Ovb111/r66+/rqq3myulSpXSfv36aXR0tObMmVNHjRpl11/rUn8pU1UdNmyYVqtWTX19fbVatWr69ddfq+rt/6NKlSoaEhKirVq10pCQEOMD/bXXXtNatWrZbeXiww8/1CJFiujmzZuNrbJSLF++XCMjIzUyMlJXrVplTL/z/84uKcc7a9GihUZFRRnT5s6dqw0aNNBmzZoZDeAUKSt4DyplK5XUK+glS5bUMmXKqKurq3bt2lXXrVtnjB8zZoxWqFDBJsvOCjNnztSWLVtqx44drVaaLly4oMWLF9dOnTqp6u1DcSxZssR4fbJFuv2R31nH0fN7+fLl6uHhoZ9++qkeP37cmL5kyRJt2rSpVWPDkTjiesbDkN+qZPj9eFjye9WqVVqmTBn9/vvvdebMmVqzZk0tU6aM8ZgdPHhQX331Va1YsaK+/fbbxu3s/T0Dd0fmZA0yJ+uQOQ/OUT/LU38f7N+/vxYsWFC7du2qXbp0MaanPI9//fWX/vrrr9q5c2ctXry4cWx/e/5ou3TpUg0KCtKQkBDt1q2b7tmzR1VVZ8yYoRaLRZ966int2LGjduzYUb28vLL1KAbLli3T0NBQVb29Nfzs2bM1IiJC/fz8NDo62mj+JyQkOMTe6+mhka63Q8Nisegbb7yhqrd3s/bw8NBevXpp586d1c3NzeoNM3ToUG3ZsqWGhIRo9+7drXaNyg7bt2/XwoUL68yZM/XcuXN68uRJ7dy5szZq1EgnTpxoHLepbt262VpXaikfKomJifrvv//qK6+8ov/8848eP35cy5cvr7169dK9e/dq/fr1NW/evHbdLSu169eva+/evXXgwIGqenvLm1KlSulTTz2lTz/9tJYuXdrquMlZLeVx3LFjh544cUL/+usv3b17t167dk2bNWumL7zwgjG2WLFi6uXlpWPHjrXrCkVKze+//74WKlRIv/76a42NjdX27dtr5cqVjeOc/fTTTzp+/Hh97rnnNDo62gjDzp07a9euXbPlxEF3LuP69ev6+OOPpzkWW+pm6dq1a7VmzZrGa8QeUh7j1Ltquru768SJE43pc+fO1fr162tkZKT+/fffNl1+z549tXv37sbjsnHjRs2TJ49OnDhR9+zZo/Pnz9fw8HCNiooyvozv3LlTS5YsafXFw1Fk5KQ2qbEluv2R37bnLPl97do1bdu2rfHcX7lyRfft26cffvihrlmzRmNiYrRly5baqFGjbH+ezTjaekZqzpTfqmR4Zjhzft/ZkPjhhx90zJgxqnr7Mdu3b5+GhoZq6dKlrY63+tprr2mFChWMJiIcC5mTtcgc2yFzbMPZPstfeeUV9fT01AMHDui8efO0fPnyeu3atbv+GPLss89qkyZNsrXGFCk17dq1S/PmzaujRo3SV199VSMiIrRq1apGM33Dhg3asWNHbdOmjQ4dOtSYnh21qapu2rRJLRaLDhkyRGvUqKFPPvmk9u/fX0ePHq25cuVyiHMI3AuNdL39IThjxgx1d3fXESNG6HfffWe8mVN2H/P09NTnn3/euM3Nmzf1+vXrdmmkfPPNN1q5cmVNSEgwXpAnT57UDh06aHh4uF65ckV/+OEHrVixotXuUdklpabY2FgdMGCAHjp0yNj6ZcCAAfrMM8/opUuXVFW1V69e6u3trSVKlLD6f+xpz549+tNPP2lCQoKGhIQYu+9899136urqqgULFtTFixdneR2pdyEsWrSovv3228ZuOYcOHdKqVavqypUrVfX2r6DPP/+8Dh482OpkIdnlzTfftDrp4j///KN16tRJE3SDBw/WgIAA3bRpU5r7OH78uEZHR6uXl1e2rICGh4fr+++/bzXtzJkz6u3trbNmzVJV66C/du2anjhxQlVvH2LH3rsFb9y4Ub/55htjBeOjjz7SwMBA47FNTk7WBQsWaLVq1fTpp5+22WfVt99+q97e3lZb8b7//vvGbt8pNmzYoPXq1dOXXnpJVW/vRujr65tmSwd7yOxJbRyhdlgjv23LmfL76tWrWqNGDX3llVf033//1b59+2rDhg21aNGi6u/vr2PGjNHZs2drixYt7N4AvJOjrGeoOmd+q5LhmeHM+Z3682XcuHHar18/DQ4O1ldeecVq3r59+zQsLEzLlStnPN8HDx7UIUOGqLe3t06bNi3ba8fdkTnZg8x5cGSObTjbZ/nq1as1MDDQyM2FCxdqQEBAulvDpzzHP/30k1aqVMku6/GqqvHx8TpjxgwdOXKkMW3jxo0aFRWllStXNg41lF2Hx0l5Xv/55x+9dOmSnj9/XlVVP/vsMw0LC9OBAwdaHf6oZs2aumbNmiytyRYe2UZ6ei+YadOmqbu7u3HyxtRWr16t+fLl0+7du2dXiXf17bffapkyZYwPwpRfPQ8fPqwWi0V/+OEHVc2eXXHuZtGiRerh4aHvvPOO/vLLL6p6+6Qh4eHh2r9/f2Ncnz599NNPP7XbLhspb+w//vhDN27caHXMxzVr1miNGjWMY+du2bJFmzZtqkOGDMm249Wm3oUwJURUbx9Xys/PT7/44gs9cuSIjhgxQhs0aGCX5/z8+fMaHh6uDRo0sDp2e6VKlXTq1Kmqar3rVZ06dfTZZ59V1f8e/0uXLmnv3r2tgiqrbdq0yagr9cpCixYttGXLlsZrMmXe5s2b9fXXX7fabc9eK0Xnz5/XgIAAdXd31+bNm+uOHTv08OHD+swzz+iwYcOMLx3Jycm6ZMkSm+6i+eGHH2rFihVV9fYuY+PGjdMPPvhAQ0NDNTEx0Wol6IsvvlB3d3djy4Zz587ZrI7McoaT2sAc+Z21nCW/VW9/xnh4eKinp6c+9dRT+sUXX6iqar9+/Yzjzad8HtqLI69nOGt+q5LhmeGs+Z36eXr//fc1X758+tRTT2nVqlXV19c3zaE09u/fr2XLltU2bdoY0/bt26dvv/12th3/FfePzLEtMidrkDkPzhk/y69evWp17Pg9e/ZoQECA1Vb777//vh4+fNi4/uKLL2pAQIDRMM5KQ4YMMd5Lqrc3zmnSpInmyZNHX331VauxP/30k0ZFRWm1atV0x44dxvSs/FEy5b6///57rVu3rj722GNarlw5/fbbb1U17V4e0dHRGhAQkObkt47okW2kq6oeO3bMONbavHnztEOHDvrZZ59p/vz5rU4ikGLt2rVqsVi0T58+2V2qlQMHDqibm5u+9dZbVtOPHDmigYGBdj8z+N69ezUgIMA4vl1qgwcP1tKlS+uUKVP0lVde0aJFi9r9hEVLlizRvHnzatmyZdXNzU2nTZumt27d0hUrVqinp6euX79eVW+/sbt27Zrm+GdZJb1dCA8ePKijRo3SdevWadOmTbVQoUJatmxZ9fb2tssx+VI+HE+fPq1t2rTRxo0b68yZM1VVNSoqSuvXr2+MTfmgfPnll7Vjx45p7uuff/7J8l3J0hMTE6MdO3Y0GlcTJ07U4OBgffPNN409AC5fvqwtWrTQiIgIu60EpQ65q1ev6qhRo7Rx48b68ssvq5eXl3766afatWtXLV26tP7xxx9ZVsfWrVuNE8FYLBZdunSpzps3T3PmzKkbNmywGrt582a7/iJvxlFPaoP7Q35nDWfLb1XV3bt369q1a1X1v/dnnz59tGPHjtm2u/e9OOJ6xsOQ36pkeEY4e34fPnxYu3XrZuzyffbsWW3UqJEGBASkOZnf8ePH02xRyTlNHA+ZkzXInKxD5jw4R/0sv/O5unHjhvF3yuN54MABzZs3r3EEgMcff1x9fX2tanz55ZezZX3+woULOnLkSP3tt9+spn/zzTdat27dNA1/VdWff/5Z69evr7Vr1862z6sVK1aoh4eHjhkzRn///Xft06ePWiwW3bRpk/G4Llu2TDt37qze3t7Zeqz2B/HINtJv3Lih7dq107CwMB0wYIBaLBadNWuWJicn62effaa5cuVK80VXVXXdunXGWaPt6euvv1ZXV1cdOnSo7t+/X0+fPq1vvvmmFi9e3GrLZXuIjY3V8uXLW/2amfIm2b59u7788ssaEBCgISEhdn2jJCUl6b///qt169bV6dOn6/79+/WDDz5Qi8WiMTExGhcXp88884z6+/trnTp1NG/evGk+qLLS3XYh9PX11VKlSunEiRP1u+++02XLlln9CpqdUofG5s2btWHDhlqzZk1duHChbt++XUuWLGlsSZAyNiwsTPv162d1P9m5e+adIbl48WLNkSOHvvzyy8a0N998U0NCQrR8+fL65JNPanBwsFatWtUIVHsdgig+Pt7YiuTgwYNapUoVXbRokW7ZskU7deqkPXr0UIvFonXq1MnSLVp79+5tLCdFhw4dtFChQrpu3Tpjhfy1117TwMBAhztJiKOe1Ab3h/zOOs6S33ezZ88efeONNzR//vy6c+dOe5fj0OsZzpjfqmT4g3LW/P7qq6/UYrFo+fLlNT4+3ph+8eJFowGT3nuec5k4NjLHtsgc2yNzbMtRP8tTP8/Tp0/XXr16abt27fS7776zquH48ePq7++vv/32mz799NNaqVIl43lO3XjPaimvqZTvpmvWrNFJkyYZ8xcvXqx169bVxo0bpznU1JYtW7L8R/LUr/lOnTppdHS0qqoePXpUy5Urpz179jTm37p1S+fMmaMdOnTQ3bt3Z2ldtvTINtJVb+/mUrt2bbVYLFYfhteuXdOZM2dqzpw50/0y7giSk5P122+/1Xz58mmJEiW0fPny6u/v7xBnCl+yZIkWL17cWClKSkoy3kybNm3SuLg4vXz5crbs7pKelFquXbumV69e1TfeeMNql9Xx48eri4uLfvLJJ7pq1SqdNm2avvHGG3ZpwNxtF8K+fftqs2bNHGbr2EGDBmmrVq20Vq1ami9fPq1YsaJOmzZNFy9erKVKldIKFSpoZGSk1qlTRytVquQQWwXt2bPH2HJg5cqV6u7ubvWhvm7dOn377be1d+/eOmrUKKNme9V+5swZfeKJJzRfvny6cOFCVb19cpZixYrp77//rmfOnNGlS5dqsWLF1NPTM8uOZXr16lVt3Lix9ujRQytXrqzt27dX1dsh2KlTJ3Vzc9PAwEANDQ3VggULOsQXH2c7qQ3ujfzOGo6e32bi4+O1ffv2WqlSJatdVu3BmdYznDG/VcnwzHCm/E5v/bZ169ZqsVj0yy+/tGpYXLx4UZs2baoeHh7ZduhF2AaZYxtkTtYjczLHGT7LUzd9X3/9dS1WrJh2795d+/XrpxaLRadOnWo08pOTk7VSpUrq6uqq5cqVM+rPzud5ypQpWrlyZWPZV69e1ejoaLVYLFbHjk85eXjjxo3tcqiUJUuW6KRJk7RGjRq6du1avXTpkvr5+WmvXr2Mx3zq1KlGbSnvL2fxSDfSb9y4oY0bN9agoCBt1qyZfv3118a8q1ev6syZM9XDw8OuZ1m+lyNHjujq1at1xYoVDnNik0OHDqmHh4dxSJLUBgwYoG+99ZbdG8BLly7ViIgIrVy5slasWDHNr/Fjx45Vd3d3HT58uN1rvdsuhJ06dXKILWS/+OILLVCggG7btk3/+ecfPXHihDZt2lQbNGigs2fP1r/++kvfeust7devnw4bNszuKxaqt4/HmDdvXp03b57xC/yKFSuMlaK7Pef23rLpxIkTGh0drT4+PtqpUyddsGCBTpgwQV999VVjK7LLly8bWx9klZSg++yzz7RChQpWJ3JcsGCBfvLJJzp+/HiHOBaps53UBveH/M4azpDfd3P16lXduHGjwxyKwhnWM5wxv1XJ8AfhTPmtevtcQSm7/6uqRkREqK+vr8bGxlq9DhMSEvSVV16x+3OMjCFzbIfMyTpkzoNzxM/yKVOmWP0I9sUXX2jJkiV169atqnp7K2+LxaIuLi76wQcf6M2bNzUxMVEbNGigNWvWtNvrMy4uTkuWLKkNGzY0mulHjx7VYcOGab58+awOlTV//nxt2rSpPvbYY9m612t8fLxxEuPu3bvrc889p/7+/tq7d2+j5itXrmjLli31448/ttteGw/ikW6kq97eHeLkyZMaFRWljRo10q+++spq/tixY9XHx0fPnDljpwqdU8ru9YMHD9adO3fqH3/8oUOGDFEvLy/ds2ePXWv75Zdf1NPTU1966SXt2rWr5sqVS/v375/mxBoxMTHq5eWlZ8+etVOlaTnaLoSqqsOGDdO6detabUVy/PhxrVmzppYtW1YXLVqU5jb2XrFQVW3VqpWWLl1aFyxYYLVS5OHhob179zaOmW0vKY/liRMn9M8//7TaUmDJkiXas2dPDQgI0PLly2v9+vWNEzRlp0uXLunnn3+uFSpUMLZscyTOeFIb3D/yO2s4cn47C2dZz3DW/FYlwx+Uo+e36u09w3x8fLRLly5Wj0+TJk3Uz88vTQMmhaO8RnF/yJwHR+ZkPTIn8xzxs/zQoUPq7++vvXr10l27dqmq6oQJE4wm9Pfff6+enp766aef6rhx49TFxUUnTJigqrfzM6U2e/3Is23bNi1TpoyGhYUZNRw/flzffPPNNM30r776Slu2bJllJ5G90/79+3XYsGE6ZMgQVb39GV+2bFmtVauW1SGEoqOjtWzZsk67J9kj30hPcfDgQY2KitImTZrol19+qaq3P+y7dOniMMcHdCZJSUk6f/58LVCggPr7+2vZsmW1QoUKdj/Mw4EDB3TYsGEaExNjTJsyZYr6+/vr0KFD03zApN4tzt4caRdC1f8COyYmRmvUqGF8MKb8yvh///d/midPHq1cubIuXbrU6jb2qDM9zzzzjJYsWdJqpWjlypVqsVj0o48+yq4S00ipefHixVq1alX18fHRoKAgfeaZZ4wxf/31l65cuVIrVaqkFotFmzRpYpdaL1++rJ9//rkGBgZqy5Yt7VLDvTjqSW1gG+S3bTlqfjsLZ1jPcJb8vtdyyfAH40j5nZycnO5zPX/+fA0MDNTu3btbNWCaNm2qxYsX1+XLlztMow2ZQ+Y8GDIna2pND5lzf7U4y2f59u3btUaNGtqjRw89ePCgnjt3Tg8dOqRHjx7VwMBAHTt2rDHO3d1dLRaLfvbZZ8bt7b23zC+//GLaTE+9N/XFixezpaaEhAStUaOGent764ABA1T19g8ir776qlavXl0bN26sAwcO1DZt2miBAgX0119/zZa6sgKN9FQOHTqkTz31lAYGBmqNGjU0f/78umXLFnuX5dROnDihmzdv1ri4uCw79tb9SnljFy5cOM0uhJMmTdJixYrpm2++aXVGeEfazcTRdiFMsWvXLs2ZM6eOGDHCavqKFSv0ySef1DfeeMPuQaOqOnnyZF2/fn2aWp566in18fHRBQsWGLs8b9682e7N03Xr1qm7u7t+8sknGhsbq9OnT9eKFStqjRo1rMZduHBB3377bbtutXP58mWdMmWK1qpVy+4nS7yTo57UBrZFftueI+W3s3C29QxnyW9VMjyrOEJ+33loq4SEBKvrCxcu1EqVKmn37t2t9iQLCgrSJ598MltqRNYjczKOzMk6ZE7GOeNn+fbt2zU4OFhfeOEF3bt3r6qq/vzzz1qlShXj+p49e7R///66bNkyuz/Pd9q2bZuWKlUqTTN92LBhaRr/2WX79u1arlw5DQoKMp7nmzdv6uzZs7Vr1676+OOP68CBA51+jyMa6Xf466+/9LPPPtORI0fa5QQcyFopb+y6deumaZ5NnTpV3d3ddeTIkQ73IenoZs2apbly5dLXXntNt27dqgcOHNAnnnhChw4daozJ7hWjO1cUg4KC1N/fX3/++ec0tQQFBWn16tX1iy++sDruvD1fB6+//rp27NjRuJ6cnKzx8fFaoUIF7dy5s6r+95g6wg8+V65cMY63Z0/OcFIbZA3yG47A2dYzHDG/Vcnw7GTP/O7du7cOHjzYuD5hwgTt27evHj582Grc/Pnz1dvbWzt16mTVgHGUphtgL2SObZA5D8aZP8u3b9+ujz32mPbo0UP37Nmjv//+u/G9cefOnRoVFaWtWrUyxmf385x6K//du3frypUrdfXq1cZju337dg0ICLBqph85ckTfe+89u30f+u2337RatWrao0ePNOdreFjQSMcj57ffftOgoCCrY2KlmDlzpu7bt89OlTm3hQsXapEiRdTf31/9/f01ODjYaFpm95fE1Mv75ptvdM6cOaqq2rhxYy1VqpRu2rTJ6uzbzz33nHp5eVkdGzu7pQ7IW7duaffu3bVmzZppxo0ZM0Zr1arlUIcdckSOeFIbAI8GZ1vPcKT8vnOZZPjDbenSpcZrLTExUWfPnq2FChXS6OjoNIekGDFihHp5eWnbtm119+7dxnTyG486MufBkDkPztk/y1NvmX7gwAEdNWqUWiwWDQgIsNvr887DsSxatEiLFi2qYWFhWrFiRQ0LCzPODxUfH69lypTRBg0aOMxJelP/QHHn59LDgEY6Hkmp39ipP8DxYE6cOKFbt27V9evX2+0kHKl/0d65c6cGBwdr9erVdcWKFaqqGh4erqVKldKNGzcau+R169ZN//jjD7tv2fTdd99pQECAbtmyRefMmaOPPfaYUXeKxYsXa6lSpRzuECqOxBFPagPg0eJs6xmOkN+qZPij4s5mxOzZszUyMlIvXryo8+bNU19fX3399dettmYcN26cNmzYULt06WL35xpwNGRO5pA5D+Zh+ixPaab36tVLDx8+rAcPHtTNmzfb5fXZs2dP7d69u7Hs//3vf1qwYEGdPHmyqt4+Nn/OnDn13Xfftarfy8tLIyIisq3Oe9m+fbvWqlVL27Vr5/SHcrkTjXQ8sh7mN7ajsGdj8rXXXtOnnnpKa9asqV5eXhoQEGCcwKZZs2YaEBCgzZs319DQUK1UqZJRq70OQfP333/r008/bQTkkSNHtF69etq6dWv9/vvvjbGvvvqqhoaGOsRhVByBM53UBsCjxZnXM+z9+UiGP1qmTJmitWvX1g4dOujFixf122+/1aJFi+qQIUP0f//7n964cUOffvppXbBggfGYO1IDBnAEZE7mkTm24eyf5SknIH3mmWesjvmena/Pb7/9Vr29va1OuDxz5sz/1979h9RZ9nEc/9z+mj9GLkKpNHIRmzGc06QYNbd/RkpuC8vJBm2WoEvHkGTE8I9QCJvWxoZT0sofkXNzK3BjoVNqq0a6dG6FxGQ6KBXpEM1SJ6VXfzx543oepD2enVvPeb/+PNc58JX7nOtz+z3XuS6TmppqjDFmcHDQxMTEmD179tjjs7VeuXLF9Pf3e6zWf6Orq8ts3LjRDA8PO12KW9FIh0/z1g+2r6uvrzf333+/6e7uNi6XywwNDZnNmzebJ5980rS0tBhjjHn77bdNbm6uycvLs79hdirIL1y4YHbs2GE2bdp0xyqS77//3iQnJ5u1a9ea2NhYk5qaasLDw5f0CdfutBQPtQHgW7jPuHtkuG+qq6szzzzzjMnMzDRjY2OmubnZxMXFmQcffNCsWrXKrFmzxr7WTu8rDyxWZM7dI3Pca6nP5Z2dnSYrK8ux61tWVmZiY2ONMf/ZMufw4cOmurra5OTkmJGRERMVFWVyc3Pt+tra2kxZWZn57bffHKn335icnHS6BLezjDFGgA+7ffu2goODnS4DbvTmm2+qo6NDFy9elGVZsixLQ0NDSk9P1+joqN555x299NJLkqTp6Wn5+/vrzz//VEBAgCP1fvHFF9q+fbtcLpdOnDihjIwMe+zHH3/Ud999p7a2Nj3yyCNKS0vT6tWrHalzMcnPz1dYWJjKysokSUePHlV/f78KCwsVExNjP6+5uVn5+flKSUlRQUGBEhMTJUkzMzPy8/NzonQAPob7jLtDhvsWY4wsy5Ik1dXV6f3331d0dLSqqqrkcrn07bffamxsTNnZ2QoICLCvOYD/jcy5O2SOe3jTXD77tzjx/+Lly5f18ssvKyoqSp9//rlOnz4tSdq5c6fCwsK0c+dOHT161H5+bm6uJicnVVVVpbCwMI/W6tMcbeMDgBvNfqtdWlpqkpKSzMTEhDHG2AeEtLe3m5CQELNx40ZTXV1tv86Te57NzMzYPw9zuVz2Kur+/n7z2GOPmZSUFNPV1eWxepaqpX6oDQDgTmS475q7KrG2ttY8++yzJjMz0wwMDNwxTm4DcBcyx/28aS53crV8Xl6esSzLrF+/3n5s3759xs/Pz5w/f978+uuvxuVymTfeeMNERESYvr4+x2r1VSzHA+A1Zr8F37Jli3p7e+3VyoGBgZKkqakpPffcc3rggQf00Ucfqa6uTpI8sqLg3Llzunr1qizLkr+/vz755BM9//zzSkhI0NatW9Xf36/29nZdv35d5eXl6u7utl87MzNzz+tbKszfP6Latm2bAgMDVV9frxdeeEHp6emqrKxUbW2tqqqqdPPmTfs14eHhio+PV2hoqGJjY+3HF+sqCADwRWS477Isy873rKwsvfrqqxoeHtaBAwc0ODhovzfIbQDuQua4nzfN5bO1etrk5KR++OEHZWdn69atW9qxY4ckqbS0VBkZGUpLS1NSUpLS0tLU1NSk1tZWPfHEE47U6stopAPwOmvWrFFNTY3eeust7d+/X5cvX9aNGzdUWVmpuLg4VVRU6KGHHtKhQ4f08ccf3/N6RkdHtXfvXh05ckQDAwPq6+tTVlaWtmzZopycHEVFRWnr1q26ePGizp8/r+7ubr377rvq7OyUJLYgmeOfNzUTExP65ZdftGfPHqWmpurw4cNqaGhQVVWVurq69Mcff+jLL7/U3r17VVtbKz8/P5oaALCIkeG+aW4D5pVXXlF2drZGRkb03nvvaWpqyh4DAHcic9yLuXxhQkJCdObMGdXU1KiwsFBXrlzRrl27FBoaqqamJp08eVJFRUU6cOCAvvrqKyUkJDhdsk9ij3QAXuv06dPKy8tTUFCQJCkiIkKXLl1ScHCwfvrpJxUVFamkpESPPvroPa+lp6dHubm5evrpp7VixQpNTU2pvLxckjQ2NqaGhga9/vrr+uyzzxQZGank5GS9+OKLOnbsmJYtW3bP61vK6uvrVVNTo+joaNXU1Ki1tVUlJSX6+eefdd999ykwMFC9vb0KCAi4Y/8+AMDiRYb7prk5vX//fn3zzTfq6Oiw3wcAcC+QOe7FXL5wv//+u5qbm3Xw4EElJiaqsbHR6ZLwNxrpALza8PCwhoaGND4+rg0bNsjf398+hMfTh5z09PTotdde0+joqNLS0lRRUWGP3bp1SwUFBbp9+7aOHz+uS5cuKTIyUo8//rjH6ltqjBcdagMA+G9kuG+azffi4mI1NDSop6dH4eHhTpcFwMuROe7FXL5w4+PjOnnypA4dOqSVK1eqpaXF6ZIgGukAfIzTzdRr165p27ZtCg4O1vHjx7Vu3Tp7rKioSGfPnlVnZ6eCg4Mdq3Ep+Wcz/YMPPlBUVJRKS0u1cuVKe9zp6w4AWDin53Iy3HOMMTp16pRWrVql+Ph4p8sB4IPInIVjLl+48fFxNTQ0qK6uTp9++qkefvhhp0vyeWzaB8CnON1MXbt2rVpaWhQYGKgjR47o6tWr9pjL5VJkZKSmp6cdrHBp8aZDbQAA83N6LifDPceyLGVkZNB4AeAYMmfhmMsXLiwsTLt371ZbWxtN9EWCFekA4IDZg0MmJiaUnJysZcuW6dSpU2pvb79jtQH+nbkr0+vr6/Xhhx9q/fr1Ki4uVlBQEPuiAwDchgwHAHgKmQMsLqxIBwAHJCQkqLGxUX5+furo6FBMTIy6u7u5Gfo/zV2Zvnv3bj311FP6+uuvZVkWTXQAgFuR4QAATyFzgMWFRjoAOCQuLk5NTU2KjY1Vdna2R06B92Zzm+nLly/X8PCwJicnHa4KAOCNyHAAgKeQOcDiwdYuAOCw2dPg4R4cagMA8BQyHADgKWQO4Dwa6QAAAAAAAAAAzIOtXQAAAAAAAAAAmAeNdAAAAAAAAAAA5kEjHQAAAAAAAACAedBIBwAAAAAAAABgHjTSAQAAAAAAAACYB410AAAAAAAAAADmQSMdAAAAAAAAAIB50EgHAAAAAAAAAGAeNNIBAAAAAAAAAJgHjXQAAAAAAAAAAObxF774uTQQxNHJAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1500x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def analyze_vocabulary_distribution(sentences):\n",
    "    \"\"\"Analyze word frequencies and vocabulary across sentences\"\"\"\n",
    "    all_words = []\n",
    "    all_lemmas = []\n",
    "    content_words = []\n",
    "    \n",
    "    for sent in sentences:\n",
    "        doc = nlp(sent)\n",
    "        for token in doc:\n",
    "            if token.is_alpha and not token.is_space:\n",
    "                all_words.append(token.text.lower())\n",
    "                all_lemmas.append(token.lemma_.lower())\n",
    "                if not token.is_stop and token.pos_ in ['NOUN', 'VERB', 'ADJ', 'ADV']:\n",
    "                    content_words.append(token.lemma_.lower())\n",
    "    \n",
    "    # Calculate frequencies\n",
    "    word_freq = Counter(all_words)\n",
    "    lemma_freq = Counter(all_lemmas)\n",
    "    content_freq = Counter(content_words)\n",
    "    \n",
    "    return word_freq, lemma_freq, content_freq\n",
    "\n",
    "word_freq, lemma_freq, content_freq = analyze_vocabulary_distribution(test_sentences)\n",
    "\n",
    "print(\"VOCABULARY ANALYSIS\")\n",
    "print(\"=\" * 50)\n",
    "print(f\"Total unique words: {len(word_freq)}\")\n",
    "print(f\"Total unique lemmas: {len(lemma_freq)}\")\n",
    "print(f\"Total content words: {len(content_freq)}\")\n",
    "\n",
    "# Plot most common words\n",
    "fig, axes = plt.subplots(1, 3, figsize=(15, 5))\n",
    "\n",
    "# Original words\n",
    "common_words = word_freq.most_common(10)\n",
    "axes[0].bar([w[0] for w in common_words], [w[1] for w in common_words])\n",
    "axes[0].set_title('Top 10 Most Common Words')\n",
    "axes[0].tick_params(axis='x', rotation=45)\n",
    "\n",
    "# Lemmas\n",
    "common_lemmas = lemma_freq.most_common(10)\n",
    "axes[1].bar([w[0] for w in common_lemmas], [w[1] for w in common_lemmas])\n",
    "axes[1].set_title('Top 10 Most Common Lemmas')\n",
    "axes[1].tick_params(axis='x', rotation=45)\n",
    "\n",
    "# Content words\n",
    "common_content = content_freq.most_common(10)\n",
    "axes[2].bar([w[0] for w in common_content], [w[1] for w in common_content])\n",
    "axes[2].set_title('Top 10 Content Words')\n",
    "axes[2].tick_params(axis='x', rotation=45)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "6aff51eb71eb2238",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x600 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def create_similarity_matrix(sentences, method='direct'):\n",
    "    \"\"\"Create similarity matrix between all sentences\"\"\"\n",
    "    n = len(sentences)\n",
    "    similarity_matrix = np.zeros((n, n))\n",
    "    \n",
    "    preprocessor = TextPreprocessor()\n",
    "    \n",
    "    for i in range(n):\n",
    "        for j in range(n):\n",
    "            if i == j:\n",
    "                similarity_matrix[i][j] = 1.0\n",
    "            else:\n",
    "                if method == 'direct':\n",
    "                    sent1 = preprocessor.direct_preprocessing(sentences[i])\n",
    "                    sent2 = preprocessor.direct_preprocessing(sentences[j])\n",
    "                    # Simple character-based similarity\n",
    "                    if not sent1 or not sent2:\n",
    "                        similarity = 0.0\n",
    "                    else:\n",
    "                        min_len = min(len(sent1), len(sent2))\n",
    "                        max_len = max(len(sent1), len(sent2))\n",
    "                        similarity = min_len / max_len if max_len > 0 else 0.0\n",
    "                \n",
    "                elif method == 'semantic':\n",
    "                    sent1 = preprocessor.semantic_preprocessing(sentences[i])\n",
    "                    sent2 = preprocessor.semantic_preprocessing(sentences[j])\n",
    "                    # Jaccard similarity on semantic tokens\n",
    "                    words1 = set(sent1.split())\n",
    "                    words2 = set(sent2.split())\n",
    "                    if not words1 and not words2:\n",
    "                        similarity = 1.0\n",
    "                    elif not words1 or not words2:\n",
    "                        similarity = 0.0\n",
    "                    else:\n",
    "                        intersection = words1.intersection(words2)\n",
    "                        union = words1.union(words2)\n",
    "                        similarity = len(intersection) / len(union)\n",
    "                \n",
    "                similarity_matrix[i][j] = similarity\n",
    "    \n",
    "    return similarity_matrix\n",
    "\n",
    "# Create and visualize similarity matrices\n",
    "direct_matrix = create_similarity_matrix(test_sentences, 'direct')\n",
    "semantic_matrix = create_similarity_matrix(test_sentences, 'semantic')\n",
    "\n",
    "fig, axes = plt.subplots(1, 2, figsize=(15, 6))\n",
    "\n",
    "# Direct similarity heatmap\n",
    "im1 = axes[0].imshow(direct_matrix, cmap='viridis', aspect='auto', vmin=0, vmax=1)\n",
    "axes[0].set_title('Direct Preprocessing Similarity Matrix')\n",
    "axes[0].set_xlabel('Sentence ID')\n",
    "axes[0].set_ylabel('Sentence ID')\n",
    "plt.colorbar(im1, ax=axes[0])\n",
    "\n",
    "# Semantic similarity heatmap\n",
    "im2 = axes[1].imshow(semantic_matrix, cmap='viridis', aspect='auto', vmin=0, vmax=1)\n",
    "axes[1].set_title('Semantic Preprocessing Similarity Matrix')\n",
    "axes[1].set_xlabel('Sentence ID')\n",
    "axes[1].set_ylabel('Sentence ID')\n",
    "plt.colorbar(im2, ax=axes[1])\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0fc7b35",
   "metadata": {},
   "source": [
    "Key observations:\n",
    "- Direct similarity catches exact copies but misses paraphrases\n",
    "- Semantic similarity identifies meaning-based relationships\n",
    "- Notice how sentences 0-2 (direct copies) are highly similar in both matrices\n",
    "- Sentences 3-4 (paraphrases) show higher semantic similarity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "98f16f7d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DATA EXPLORATION SUMMARY\n",
      "==================================================\n",
      "\n",
      "1. TEXT CHARACTERISTICS:\n",
      "   - Sentence length varies from 0 to 44 characters\n",
      "   - Word count ranges from 0 to 10 words\n",
      "   - 10/13 sentences contain punctuation\n",
      "\n",
      "2. LINGUISTIC FEATURES:\n",
      "   - Direct preprocessing preserves exact character matching\n",
      "   - Semantic preprocessing extracts content words (nouns, verbs, adjectives)\n",
      "   - Syntactic preprocessing normalizes structure while preserving grammar\n",
      "\n",
      "3. DEPENDENCY PARSING INSIGHTS:\n",
      "   - Different sentence structures can express the same meaning\n",
      "   - Dependency trees reveal grammatical relationships\n",
      "   - Root verbs often indicate the main action of the sentence\n",
      "\n",
      "4. SIMILARITY PATTERNS:\n",
      "   - Direct copies show high similarity in both preprocessing methods\n",
      "   - Paraphrases show low direct similarity but higher semantic similarity\n",
      "   - Different sentences show low similarity in both methods\n",
      "\n",
      "5. IMPLICATIONS FOR PLAGIARISM DETECTION:\n",
      "   - Need multiple similarity measures for comprehensive detection\n",
      "   - Semantic analysis crucial for identifying paraphrased content\n",
      "   - Syntactic analysis may help with structural plagiarism\n"
     ]
    }
   ],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
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