{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cjgz1WTcPWee"
   },
   "source": [
    "# Lab 2: EDA on Corpora\n",
    "\n",
    "![](../figs/deep_nlp/lab/eda.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prepare the environment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```python\n",
    "%pip install --pre ekorpkit[dataset]\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "BzxPwsOVPWef"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:ekorpkit.utils.notebook:Google Colab not detected.\n",
      "INFO:ekorpkit.base:Setting EKORPKIT_WORKSPACE_ROOT to /content/drive/MyDrive/workspace/\n",
      "INFO:ekorpkit.base:Setting EKORPKIT_PROJECT to ekorpkit-book\n",
      "INFO:ekorpkit.base:Loaded .env from /workspace/projects/ekorpkit-book/config/.env\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "version: 0.1.40.post0.dev6\n",
      "is colab? False\n",
      "project_dir: /content/drive/MyDrive/workspace/projects/ekorpkit-book\n",
      "time: 1.64 s (started: 2022-11-04 07:05:43 +00:00)\n"
     ]
    }
   ],
   "source": [
    "%config InlineBackend.figure_format='retina'\n",
    "%load_ext autotime\n",
    "\n",
    "from ekorpkit import eKonf\n",
    "\n",
    "eKonf.setLogger(\"INFO\")\n",
    "print(\"version:\", eKonf.__version__)\n",
    "\n",
    "is_colab = eKonf.is_colab()\n",
    "print(\"is colab?\", is_colab)\n",
    "if is_colab:\n",
    "    eKonf.mount_google_drive()\n",
    "project_dir = eKonf.set_workspace(workspace=\"/content/drive/MyDrive/workspace/\", project=\"ekorpkit-book\")\n",
    "print(\"project_dir:\", project_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "it1Y0MVPXRsJ"
   },
   "source": [
    "## Load the saved corpora"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:ekorpkit.io.file:Processing [1] files from ['wiki_corpus.parquet']\n",
      "INFO:ekorpkit.io.file:Loading 1 dataframes from ['/content/drive/MyDrive/workspace/projects/ekorpkit-book/data/wiki_corpus.parquet']\n",
      "INFO:ekorpkit.io.file:Loading data from /content/drive/MyDrive/workspace/projects/ekorpkit-book/data/wiki_corpus.parquet\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 15 s (started: 2022-11-04 07:05:46 +00:00)\n"
     ]
    }
   ],
   "source": [
    "data = eKonf.load_data(\"wiki_corpus.parquet\", project_dir + \"/data\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "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>id</th>\n",
       "      <th>text</th>\n",
       "      <th>split</th>\n",
       "      <th>filename</th>\n",
       "      <th>corpus</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4915400</td>\n",
       "      <td></td>\n",
       "      <td>train</td>\n",
       "      <td>wiki_92</td>\n",
       "      <td>enwiki_sampled</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7644961</td>\n",
       "      <td>Anaissini is a tribe of click beetles in the f...</td>\n",
       "      <td>train</td>\n",
       "      <td>wiki_49</td>\n",
       "      <td>enwiki_sampled</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6658552</td>\n",
       "      <td>The Vicky Metcalf Award for Literature for You...</td>\n",
       "      <td>train</td>\n",
       "      <td>wiki_24</td>\n",
       "      <td>enwiki_sampled</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16385169</td>\n",
       "      <td>Shri Shivabalayogi Maharaj (24 January 1935 – ...</td>\n",
       "      <td>train</td>\n",
       "      <td>wiki_36</td>\n",
       "      <td>enwiki_sampled</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11081255</td>\n",
       "      <td>Eylex Films Pvt is a chain of multiplex and si...</td>\n",
       "      <td>train</td>\n",
       "      <td>wiki_94</td>\n",
       "      <td>enwiki_sampled</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         id                                               text  split  \\\n",
       "0   4915400                                                     train   \n",
       "1   7644961  Anaissini is a tribe of click beetles in the f...  train   \n",
       "2   6658552  The Vicky Metcalf Award for Literature for You...  train   \n",
       "3  16385169  Shri Shivabalayogi Maharaj (24 January 1935 – ...  train   \n",
       "4  11081255  Eylex Films Pvt is a chain of multiplex and si...  train   \n",
       "\n",
       "  filename          corpus  \n",
       "0  wiki_92  enwiki_sampled  \n",
       "1  wiki_49  enwiki_sampled  \n",
       "2  wiki_24  enwiki_sampled  \n",
       "3  wiki_36  enwiki_sampled  \n",
       "4  wiki_94  enwiki_sampled  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 11.7 ms (started: 2022-11-04 07:06:02 +00:00)\n"
     ]
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2522593 entries, 0 to 2522592\n",
      "Data columns (total 5 columns):\n",
      " #   Column    Dtype \n",
      "---  ------    ----- \n",
      " 0   id        int64 \n",
      " 1   text      object\n",
      " 2   split     object\n",
      " 3   filename  object\n",
      " 4   corpus    object\n",
      "dtypes: int64(1), object(4)\n",
      "memory usage: 96.2+ MB\n",
      "time: 5.84 ms (started: 2022-11-04 07:06:03 +00:00)\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Filter and use only selected corpora"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "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>id</th>\n",
       "      <th>text</th>\n",
       "      <th>split</th>\n",
       "      <th>filename</th>\n",
       "      <th>corpus</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2174042</th>\n",
       "      <td>1339043</td>\n",
       "      <td>《그랜드 테프트 오토 2》()는 1999년 8월 22일 전 세계적으로 발매된 록스타...</td>\n",
       "      <td>train</td>\n",
       "      <td>wiki_34</td>\n",
       "      <td>kowiki</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2174043</th>\n",
       "      <td>1339044</td>\n",
       "      <td>알아크사 모스크( المسجد الأقصى; מסגד אל-אקצא; Al-Aqsa...</td>\n",
       "      <td>train</td>\n",
       "      <td>wiki_34</td>\n",
       "      <td>kowiki</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2174044</th>\n",
       "      <td>1339045</td>\n",
       "      <td>PlanetBoom(함경민)은 펜타비전에서 사운드 프로듀서를 맡고 있는 대한민국의 ...</td>\n",
       "      <td>train</td>\n",
       "      <td>wiki_34</td>\n",
       "      <td>kowiki</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2174045</th>\n",
       "      <td>1339046</td>\n",
       "      <td></td>\n",
       "      <td>train</td>\n",
       "      <td>wiki_34</td>\n",
       "      <td>kowiki</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2174046</th>\n",
       "      <td>1339047</td>\n",
       "      <td>《그랜드 테프트 오토 3》(, GTA III) 은 DMA디자인(현재의 록스타 노스)...</td>\n",
       "      <td>train</td>\n",
       "      <td>wiki_34</td>\n",
       "      <td>kowiki</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              id                                               text  split  \\\n",
       "2174042  1339043  《그랜드 테프트 오토 2》()는 1999년 8월 22일 전 세계적으로 발매된 록스타...  train   \n",
       "2174043  1339044  알아크사 모스크( المسجد الأقصى; מסגד אל-אקצא; Al-Aqsa...  train   \n",
       "2174044  1339045  PlanetBoom(함경민)은 펜타비전에서 사운드 프로듀서를 맡고 있는 대한민국의 ...  train   \n",
       "2174045  1339046                                                     train   \n",
       "2174046  1339047  《그랜드 테프트 오토 3》(, GTA III) 은 DMA디자인(현재의 록스타 노스)...  train   \n",
       "\n",
       "        filename  corpus  \n",
       "2174042  wiki_34  kowiki  \n",
       "2174043  wiki_34  kowiki  \n",
       "2174044  wiki_34  kowiki  \n",
       "2174045  wiki_34  kowiki  \n",
       "2174046  wiki_34  kowiki  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 218 ms (started: 2022-11-04 07:06:09 +00:00)\n"
     ]
    }
   ],
   "source": [
    "enko = data[data.corpus.isin([\"enwiki_sampled\", \"kowiki\"])]\n",
    "enko.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basic statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 14.3 s (started: 2022-11-04 07:06:13 +00:00)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import nltk\n",
    "from nltk.tokenize import sent_tokenize\n",
    "import seaborn as sns\n",
    "\n",
    "nltk.download(\"punkt\")\n",
    "\n",
    "# Character counts\n",
    "enko[\"num_chars\"] = enko[\"text\"].map(lambda x: len(x))\n",
    "# Word counts\n",
    "enko[\"num_words\"] = enko[\"text\"].map(lambda x: len(x.split()))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "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>num_chars</th>\n",
       "      <th>num_words</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2174047</td>\n",
       "      <td>2174047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>513</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2274</td>\n",
       "      <td>391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>184</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>264937</td>\n",
       "      <td>45338</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       num_chars  num_words\n",
       "count    2174047    2174047\n",
       "mean         513         92\n",
       "std         2274        391\n",
       "min            0          0\n",
       "25%            0          0\n",
       "50%            0          0\n",
       "75%          184         35\n",
       "max       264937      45338"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 130 ms (started: 2022-11-04 07:06:35 +00:00)\n"
     ]
    }
   ],
   "source": [
    "# format pandas display options\n",
    "import pandas as pd\n",
    "\n",
    "pd.options.display.float_format = \"{:.0f}\".format\n",
    "\n",
    "# min, max, mean, median, iqr, std\n",
    "enko[[\"num_chars\", \"num_words\"]].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 64.4 ms (started: 2022-11-04 07:06:36 +00:00)\n"
     ]
    }
   ],
   "source": [
    "# filter out outliers\n",
    "\n",
    "enko_filtered = enko[enko.num_words.between(10, 250)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:ekorpkit.io.file:Saving dataframe to /content/drive/MyDrive/workspace/projects/ekorpkit-book/data/enko_filtered.parquet\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 30.9 s (started: 2022-11-04 07:06:38 +00:00)\n"
     ]
    }
   ],
   "source": [
    "# save filtered data\n",
    "\n",
    "eKonf.save_data(enko_filtered, \"enko_filtered.parquet\", project_dir + \"/data\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "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>num_chars</th>\n",
       "      <th>num_words</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>603719</td>\n",
       "      <td>603719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>379</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>343</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>25</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>114</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>248</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>549</td>\n",
       "      <td>106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3432</td>\n",
       "      <td>250</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       num_chars  num_words\n",
       "count     603719     603719\n",
       "mean         379         72\n",
       "std          343         62\n",
       "min           25         10\n",
       "25%          114         22\n",
       "50%          248         47\n",
       "75%          549        106\n",
       "max         3432        250"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 39.8 ms (started: 2022-11-04 07:07:18 +00:00)\n"
     ]
    }
   ],
   "source": [
    "enko_filtered[[\"num_chars\", \"num_words\"]].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='num_chars', ylabel='Count'>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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47Gc/yw9+8IOea7e2tvLFL36RQCDAo48+yqGHHrrddccStaiIiIiIZNiNN95IV1cX11xzzXZdtBYtWsQHP/hB7r33XlpaWvoc++lPf9oTpAAUFRXxiU98As/zeO655/qcA/q2nCxdupTJkydzwQUXEIlEeOyxxwDYunUrL730EkcffTS5ublD/gxr165l/vz5PPPMM9x+++1JDVISDdQ6U1FR0RM8ANTU1GwXpAAccMABLFy4kIcffrhPV7Fus2bN4pJLLumT9pnPfAaAcDjMz3/+8z7XOfvsswmFQrz44osD1vVTn/pUnyAF4t3CysrKuOOOO/q0Ag3k2muvBeCmm27qE6RAvCvegQceyB//+MeetHvuuYeGhgbOPvvsPkFK4nXHErWoiIiIiGTYk08+CcT/ov/ss89ud7yuro5YLMYbb7zBIYcc0pPe/8sowIwZM4B4K0O3XXfdlTlz5rBs2TI8z+sZP3HCCSdw3HHHEQqFelpuHn74YZxzLFy4cMj1f/311znqqKNoa2tjyZIlI+oytjNnnHEG11xzDR/+8Ic57bTTOOGEE5g/fz677bbbgPn/7//+j9/85jc899xz1NfXE41G+xyvr6/fbnKAAw88kGAw2Cdt2rRpAOy5557bBT/BYJDJkyezfv36Aetw3HHHbZdWVlbGgQceyCOPPMKqVas48MADB/3MTz75JDk5OfzlL3/hL3/5y3bHI5EIW7ZsYevWrUyaNIkVK1YM6bpjhQIVERERkQzbunUrAD//+c93mK+1tbXPfv+/sgOEQvGvd7FYrE/6okWLuOmmm1ixYgU5OTls2bKFRYsWUVJSwmGHHdbT2jKS8SlvvPEGDQ0NHHjggRx88MFDLjcchx9+OI8++ig/+clPuPvuu7n99tsB2GuvvfjBD37AWWed1ZP3mmuu4cILL6SiooITTzyRmTNnUlhYiJnx97//nZdeemnA1oyBWhy67+dgrRGhUGjA1hmAyZMnD5je3SWvqalpB584/lxEo1Euu+yyHeZrbW1l0qRJPefb2XXHCgUqIiIiIhnW/SW4qamJ0tLSlFxj4cKF3HTTTTz44IM9Xbq6g5GFCxdyxRVX0NDQwNKlSykrKxtWwPGBD3yAvfbai0svvZRFixbx73//m0mTJiX9Mxx11FHcd999hMNhnn/+ee6//36uu+46zj77bKqrqznhhBOIRqMsXryYKVOmsGLFiu1aTbpbr9Jh8+bNA6Z3z/q1s65YZWVleJ5HQ0PDkK7Xfb6dXXes0BgVERERkQw78sgjAXj00UdTdo2FCxdiZixdupSHHnqIOXPmMGvWLCAesHiex+9//3tWr17NggULtusCtTPf/va3ufrqq3nhhRdYsGDBoF+WkyEvL4958+bxwx/+sGccxz333APEu3Q1NjYyb9687YKU1tbWnu5R6TBQN6umpiZefPFF8vPzmTt37g7LH3nkkWzbto1XX311SNfrDi53dN2xRIGKiIiISIZ95StfIScnh4suuog33nhju+ORSGTUQUxNTQ377rsvjz/+OMuXL+/TtWvevHnk5+dzxRVXAAxrfEqiCy+8kBtvvJFXX32V4447brv1X0bjiSee6FmPJVF3QNQ961dNTQ2FhYU8//zzfbrKdXV18bWvfY36+vqk1Wlnbr/9dl544YU+aYsXL6apqYmzzjqLvLy8HZa/6KKLAPj85z8/4L1sa2vjqaee6tn/0Ic+REVFBXfccUefyRQSrzuWqOuXiIiISIbtvffe3HLLLZx77rnsu+++nHzyyey55550dXWxbt06Hn30Uaqrq0e9aN+iRYt6Fi9MDFTy8vKYP3/+qNZP6XbeeeeRn5/P5z73OY499lgeeughZs6cOap6A/zsZz/joYce4phjjmH27NkUFxfz6quvsmTJEioqKvjCF74AQCAQ4IILLuDKK69kv/3240Mf+hCRSISHH36YhoYGjj/++AEXaUyFU045hfnz53P66aczdepUHnvsMR577DFmzZrFlVdeudPyixYt4sorr+Tb3/42e+yxB+973/uYPXs2ra2trF27lkceeYSjjz6a+++/H4Di4mL++7//mzPOOINjjjmGM844o+e6r7zyCsceeyzLly9P9cdOGgUqIiIikvW+ftJema5Cyn3yk5/kgAMO4Be/+AUPP/wwDzzwAEVFRUybNo3TTjuNM844Y9TXWLRoEddccw1mxvHHH7/dse4pi/fdd99RXeecc84hLy+PT3/60z3Bypw5c0Z1zi996UtUVFTw9NNP89hjjxGNRtlll1340pe+xMUXX8yuu+7ak/dHP/oR1dXV3Hzzzfz2t7+lrKyME088kR//+Mf84Ac/GFU9huOiiy7iIx/5CL/61a+46667KC4u5pxzzuHyyy+npqZmSOf41re+xfz587n22mt57LHHuOeeeygrK2P69Ol84Qtf4Oyzz+6T/7TTTuP+++/nsssu489//jN5eXkce+yxPPnkk1x55ZVjKlAx51ym6yApYmbPH3zwwQc///zzma4KEB/A9cofLmV2TQlFRUUw/4IxN/uEiIgk16pVqwAG7atvZumszojp+5QkWrx4MZdddhkPP/wwCxYsyHR1Rmxnv5/dDjnkEFasWLHCOXfIDjMOk1pUREREJGspABCZuDSYXkREREREso5aVEREREQkIxYvXjykfB/+8Id3uIK7jE8KVEREREQkI3a24nq3WbNmjalAZfHixUMOwmRwClREREREJCM0Bkl2RIGKZITnOerr6oD4wkyBgIZLiYiIiEgvfTuUjKhvaqN92XXU3Xc5dX7AIiIiIiLSTS0qkjHV5YXx9VRERERERPpRi4qIiIiIiGQdBSoiIiIiIpJ1FKiIiIiIiEjWUaAiIiIiIiJZZ0wFKmY2ycz+y8z+ZmZvmlmHmTWZ2WNm9jkzG/DzmNk8M/unmTX4ZV42swvNLLiDa51qZsv887ea2dNm9pmd1O8zZvaMn7/JL3/qDvIHzewivz4dfv3+aWbzhn5XRERERETGnzEVqAAfB24CjgCeBn4F/BV4D3Az8Gczs8QCZvYhYDlwLPA34HogF7gauHOgi5jZV4B7/fP+wb/mNOA2M7tqkDJXAbcBU/38fwD2A+71z9c/v/nX/6Vfn+v9+h0LLPfrLSIiIiIyIY21QOUN4IPALs65Tzjnvu2cOxfYG3gX+Bjw0e7MZlZKPGiIAQucc59zzn0TOBB4EjjNzM5MvICZzQKuAhqAQ51zX3bOXQTsD7wFXGxmR/UrMw+42D++v3PuIufcl4FD/PNc5Z830ZnAacATwIHOuW865z4HHO/X9yYzKxn5rRIRERn7zGxMvJJhzZo1mBnnnHNOUs6XTOeccw5mxpo1a3rShlvfxYsXY2YsW7YsJXWU8WdMBSrOuYecc/c657x+6bXAb/zdBQmHTgOqgTudc88l5O8Evuvvnt/vMucCecD1zrk1CWW2AZf7u+f1K9O9/xM/X3eZNcCv/fN9tl+Z7ut+169Pd5lngbv8ep+GiIiIiMgENJ4WfOzyt9GEtIX+9v4B8i8H2oF5ZpbnnAsPocySfnmGcp0lwPf8PD8AMLN8YJ5//UcHKfMpv8ytAxwXERGZUNxDl+88UwbYwkszXYW0uOKKK7jkkkuYPn36iM/xla98hTPPPJOZM2cmsWYyno2LQMXMQsCn/d3EYGEvf/tG/zLOuaiZvQPsC8wBVg2hzCYzawN2MbNC51y7mRUB04FW59ymAaq32t/umZC2GxAE3nbORbcvMmCZQZnZ84Mc2nso5UVERER2ZOrUqUydOnVU56iqqqKqqipJNZKJYEx1/dqBK4kPfP+nc+5fCell/rZpkHLd6eUjKFPWb5uKa5QPclxEREQmCM/z+NrXvoaZ8dGPfpSOjg48z+M3v/kNhx12GMXFxRQVFXHYYYdx44034nl9esgzbdq0AVtCdt11V8yMH/3oR33SlyxZgpnx/e9/vydtoDEqw6kvaIyKDN+YD1TM7ALiA9lfI95dasJxzh0y0Iv4PREREZExqrOzk49//ONce+21fPnLX+buu++moKCAT33qU5x//vls3ryZ//qv/+ILX/gCW7Zs4Utf+hKf+lTfr0MLFy5k48aNvPZa79eCN998k3Xr1gGwdOnSPvm79xctWpS0+oqMxJgOVPxpf68BVgLHO+ca+mXp3/rRX3d64wjKNPXbpuIajYMcFxERkXGuoaGBE044gb/97W9ceeWVXH/99QQCAf70pz9xxx13cNBBB7Fy5Up+9atfcfXVV/Pqq69yyCGHcMcdd3DHHXf0nGfhwvhQ2sSApPv9iSeeyJNPPkl7e3ufYwUFBRx1VJ9JTkdcX5GRGrNPj5ldCFwHvEI8SKkdINvr/na7sR7+uJbZxAffvz3EMlOBImC9c64dwDnXBmwAiv3j/e3hbxPHvLxFfAriOX49hlJGREREJoi1a9cyf/58nnnmGW6//Xa+9a1v9Ry75ZZbALjyyispLi7uSS8qKuKnP/0pADfffHNPenfLSP9AZfLkyVxwwQVEIhEee+wxALZu3cpLL73E0UcfTW5ublLqKzJSYzJQMbNvEV+w8UXiQUrdIFkf8rcnD3DsWKAQeCJhxq+dlTmlX54RlfGnI37Cv/4xw7iOiIiIjHOvv/46Rx11FBs3bmTJkiV84hOf6HN8xYoVBAIBFixYsF3Z4447jmAwyAsvvNCTtuuuuzJnzhyWLVuG53k451i2bBkLFy7kuOOOIxQK9QQxDz/8MM65nlaYZNRXZKTGXKBiZt8jPnj+eWCRc65+B9nvBuqBM83s0IRz5AM/9ndv7FfmViAMfCVxkUYzqwC65yD8Tb8y3fvf8fN1l5kFfNk/X/9phruv+2O/Pt1lDgPOALYAf93BZxMREZFx6I033mDTpk3MmTOHgw8+eLvjTU1NVFZWDtjiEQqFqKqqoqmp73w9ixYtYtu2baxYsYKXX36ZLVu2sGjRIkpKSjjssMN6ApWRjE/ZWX1FRmpMBSpm9hngh8S7TT0KXGBmi/u9zunO75xrBj5PfCrgZWZ2s5n9jHhLzFHEA5m7Eq/hnHsH+CZQCTxnZr82s6uBl4lPK/wL59yT/co8AfzSP/6ymV1tZr8GnvPP843ExSN9d/rXnwe8YGY/M7P/AR726/t5v/4iIiIygXzgAx/g8ssv58UXX2TRokVs3bq1z/GysjIaGhro6urarmw0GqW+vp7S0tI+6d0tJA8++OB2wcjChQt54YUXaGhoYOnSpZSVlQ0r4NhZfUVGakwFKsTHlED8i/yFxBdQ7P86J7GAc+7vwHHEF3j8GPBV4otDfh040znn+l/EOXcd8EHgVeLrs3wBqAXOcc59Y6CKOecuJr76fK2f/9N++Q84564fIL8DzvLrEfXr9VG/nsc65+7Z6d0QERGRcenb3/42V199NS+88AILFixg8+bNPccOOuggPM9j+fLl25Vbvnw5sVhsu0Bj4cKFmBlLly7loYceYs6cOcyaNQuIByye5/H73/+e1atXs2DBAoLBYNLqKzJSY2rBR+fcYmDxCMo9DrxvmGXuBe4dZpnbgNuGkT9KfKzN1cO5joiIiIx/F154Ifn5+XzpS1/iuOOO46GHHmLatGmce+65LF26lG9/+9ssW7aMwsJCANrb27nkkksA+NznPtfnXDU1Ney77748/vjjhEIhzjzzzJ5j8+bNIz8/nyuuuAJgWONThlJfkZEaay0qIiIiIhPGeeedxy233MLq1as59thjWbduHWeffTann346zz77LPvuuy8XXXQRX//613nPe97Ds88+yxlnnDHggPZFixbR0dFBS0tLnzEoeXl5zJ8/n7q6up58yayvyEiNqRYVERERmZhs4aU7zzROnXPOOeTl5fHpT3+aY489loceeog//elPHHfccdxyyy389re/BWDu3LlcfPHFnH/++QOeZ9GiRVxzzTWYGccff/x2x7qnLN53332TXt85c+aM6pwyMdkAQzRknDCz5w8++OCDn3/++UxXBYDa2lpe+cOlzK4poS3iKMo1ioqKYP4FTJkyJdPVExGRDFi1ahUQ/5I9EDNLZ3VGTN+nZDza2e9nt0MOOYQVK1ascM4dkszrq0VFREREspYCAJGJS2NUREREREQk6yhQERERERGRrKNARUREREREso4CFRERERERyToKVEREREREJOsoUBERERERkT6yYcY9BSoiIiKSMd3rpHiel+GaiEii7kAlk2sZKVARERGRjMnLywOgra0twzURkUTdv5Pdv6OZoEBFMqIgUk9J8xtYLJLpqoiISAaVlJQAUFtbS0tLC57nZUWXE5GJyDmH53m0tLRQW1sL9P6OZoJWppe0C8Y6mF23hAAe7V4TzZmukIiIZExlZSVtbW20t7ezfv36TFdHRBIUFhZSWVmZsesrUJG0yw03ECDeFzmvZW2GayMiIpkUCASYMWMGDQ0NtLS0EA6H1aIikkFmRl5eHiUlJVRWVhIIZK4DlgIVSbuA19XzPtjVinVuA6ZkrkIiIpJRgUCAqqoqqqqqMl0VEckiGqMiaRfw+o5Lyal/LUM1EREREZFspUBF0q5/oNK59llqa2s1NaWIiIiI9FCgImmX2PULIOf1+6i773Lq6uoyVCMRERERyTYaoyJp179FpdQ1EasozlBtRERERCQbqUVF0q5/oJLb1QReNEO1EREREZFspEBF0q5/1y/DEQo3ZKg2IiIiIpKNFKhI2gW97Vejz+moz0BNRERERCRbKVCRtOvf9QsgpEBFRERERBIoUJG069/1CyCnU4GKiIiIiPRSoCJpN2CLSmc9OK2jIiIiIiJxClQkvZzrE6hELReIt7IEm9/NVK1EREREJMsoUJG0ChDDcAB4FqAjd1LPsVD9qkxVS0RERESyjAIVSauQ6x2f4gVy6cip7NnP2apARURERETiFKhIWoXoG6h05vYGKmpREREREZFuClQkrUKudwV6L5BDZ6i895jGqIiIiIiIT4GKpFX/rl/RYEHPvnU2ZqBGIiIiIpKNFKhIWvXp+mW5xAK5PfuBcBM4l4lqiYiIiEiWUaAiadW3RSUHZ0E8CwFgLgbhlkxVTURERESyiAIVSav+Xb8AYsG83gwdDemukoiIiIhkIQUqklb9W1Ti28RAZVu6qyQiIiIiWUiBiqRVnzEqQb9FJTFQaVeLioiIiIgoUJE0G6jrlxdUi4qIiIiI9KVARdJqwDEqCTN/KVAREREREVCgImkWou+CjwBeML83gwIVEREREUGBiqSZWlREREREZCgUqEha7XSMigbTi4iIiAgKVCTNBpqeOKbpiUVERESkHwUqkj7O9ZmeOKZZv0RERERkEApUJH2inQRwAHgEwIJA/xYVdf0SEREREQUqkkaBSEvP+8QB9FqZXkRERET6U6AiaWMJgYqXEKjEgv1m/fK8dFZLRERERLKQAhVJm0Ckted9zHJ6D1iwZ2A9zoNwc5prJiIiIiLZRoGKpI1FegOQPmunAK7Poo8apyIiIiIy0SlQkbQJhAceowKa+UtERERE+lKgImljCV2/vMSuX4AXSmxRUaAiIiIiMtEpUJG0sUFm/QLwErt+tStQEREREZnoFKhI2vSdnrhvi0rfMSoKVEREREQmOgUqkjZ9u37taIyKBtOLiIiITHQKVCRtdtSi4qlFRUREREQSKFCRtNnhGBUNphcRERGRBApUJG36rExv/ddRSej61a6uXyIiIiITnQIVSZs+K9Or65eIiIiI7IACFUmbPl2/thtMr5XpRURERKSXAhVJmx1OTxzSyvQiIiIi0kuBiqSHc32nJ97Rgo8djeDF0lQxEREREclGClQk5TzPo3b9GsyLxvctiLNg30wWwMst9nccdDalt5IiIiIiklUUqEjK1dXV0fDPn/Tse/26ffWk55X17qj7l4iIiMiEpkBF0qK6ONTzvn+3r24uv7x3R4GKiIiIyISmQEXSIuBFet4PFqh4eeW9OwpURERERCY0BSqSFhZLDFQG6fqV2KKiRR9FREREJjQFKpIW3QPpAZyFBsyjMSoiIiIi0k2BiqSFud7phreb8QvwPEdrNKGlRYGKiIiIyIQ28J+2RZKtT6CyfXxc39RGacNLvQlanV5ERERkQlOLiqTFzlpUAAqKint31KIiIiIiMqEpUJG0MG/ngYoXyOvd0WB6ERERkQlNgYqkx066fgHEggmBilpURERERCY0BSqSFkPp+tWnRUVjVEREREQmNAUqkhZ9ApVBHru+LSqNKa6RiIiIiGSzMReomNlpZnadmT1qZs1m5szsD4PkneUfH+x15w6u8xkze8bMWs2sycyWmdmpO8gfNLOLzOxlM+swswYz+6eZzdtBmQIzu8zMXjezTjOrM7M/m9nc4d2VMcB5vW8HbVFJWLG+swk8b8B8IiIiIjL+jcXpib8LHAC0AuuBvYdQ5iXg7wOkvzJQZjO7CrjYP/9NQC5wJnCvmX3VOXd9v/wG3AmcBrwOXA9UAmcAy83sY865e/qVyQP+DcwHngOuAWYAHwfeb2YLnXNPD+GzjQl9B9MPEh9bAC+QQ8DrAhyEm6CgIj0VFBEREZGsMhYDlYuIBxBvAscBDw+hzIvOucVDObnfAnIx8BZwmHNum5/+c+B54Cozu885tyah2JnEg5QngEXOuU6/zG+Ax4CbzOwh51xLQpmvEw9S7gbOcC7e5GBmdxEPqm4xs/2608e8IYxRAXDBPPC64jsdjQpURERERCaoMdf1yzn3sHNutXPOpegS5/nbn3QHKf511wC/BvKAz/Yrc76//W53kOKXeRa4C6gmHsgAPS0w3df5f4nBiN/y8iiwD/FAbFwYymB6AC+Y37ujmb9EREREJqwxF6iM0DQz+6KZXepv999B3oX+9v4Bji3plwczywfmAe3EA4ydlgF2A2YCbzjn3hlimUGZ2fMDvRhat7i0sCFMTwzgJQ6o72xMYY1EREREJJuNxa5fI3Gi/+phZsuAzzjn1iWkFQHTgVbn3KYBzrPa3+6ZkLYbEATeds5Fh1hmL3/7xiD1HajM2Dbkrl9qURERERGR8R+otAM/Ij7m420/bX9gMXA8sNTMDnTOtfnHyvxt0yDn604vT0hLV5lBOecOGSjdb1U5eCjnSLUhDaanX4uKpigWERERmbDGddcv51ydc+77zrkVzrlG/7UcOAl4Gtgd+K/M1nJisMQ5AXY4RkWr04uIiIjIOA9UBuN30brZ3z024VB3S0YZA+tOb8xAmbFtOLN+ddMYFREREZEJa0IGKr4t/raoO8HvArYBKDazqQOU2cPfJo4teQuIAXPMbKCudAOVed3fDjYGZaAyY9qIBtOr65eIiIjIhDWRA5Uj/e3b/dIf8rcnD1DmlH558KcjfgIoBI4ZShniwc06YE8zmz3EMmNan0CFHXT9CmkwvYiIiIiM80DFzA422/7P92a2iPjCkQB/6Hf4N/72O2ZWkVBmFvBlIAzc2q/Mjf72x/50xd1lDiO+Ov0W4K/d6f4aMN3X+VliHc3sQ8QDnpXAIzv/lGPEEAfT9+36NdhcAyIiIiIy3o25Wb/M7MPAh/3dKf72KDO7zX9f75z7hv/+l8AeZvYE8dXsIT7rV/f6JN9zzj2ReH7n3BNm9kviK8e/bGZ3A7nEA45K4Kv9VqUHuBP4KPFFHV8ws3uBSX6ZIPB551xzvzK/BE71yzxtZkuJr63yceKzlZ07blalZ+gLPsasN1BxHQ1YSmslIiIiItlqzAUqwIHAZ/qlzfFfAGuB7kDlduAjwGHEu1PlAJuBPwPXO+cGWqAR59zFZvYf4i0oXwA8YAXwc+fcfQPkd2Z2FvEuYOcCXwU6geXAj/sHQ36ZsJmdCFwCnEW8haeZ+FTKP3DOrdzZjRhLthuj4gbOV98eo9p/77U17KCTmIiIiIiMZ2MuUHHOLSa+DspQ8v4P8D8jvM5twG3DyB8FrvZfQy3TDnzff41v/VtUBglUYoHeFhVT1y8RERGRCWtcj1GR7GFeby+2HXX98iwH53f4CkTb8brCKa+biIiIiGQfBSqSHkOcnhgzvEBuz279+jdTWSsRERERyVIKVCQthjqYHiCWEKiYFn0UERERmZAUqEjqeTHMH5QS/++O5/JKXPQxEO4/WZqIiIiITAQKVCT1YpGetx4BsJ0EKgktKoGwBtSLiIiITEQKVCTlzOsXqOxEn5m/FKiIiIiITEgKVCTlrH+Lyk707fqlQEVERERkIlKgIqk33EAlcTC9xqiIiIiITEgKVCTlLNbV8z42zK5falERERERmZgUqEjKJY5RcUPq+qXB9CIiIiITnQIVSb1hdv2KqeuXiIiIyISnQEVSLnEw/VC6fnmJXb+04KOIiIjIhJTUQMXMZppZ6U7ylJjZzGReV7LbqGb9iqhFRURERGQiSnaLyjvA13aS5wI/n0wU6volIiIiIsOU7EDF/JdIj2G3qPTv+uVcKqolIiIiIlksE2NUpgBtGbiuZMhwAxVnwZ585nVBV0fK6iYiIiIi2Sk02hOY2af7JR04QBpAEJgJfBL4z2ivK2OI17uOimdDiI3NiAXyCHh+gNKxDXILU1Q5EREREclGow5UgNuA7r45DviQ/+qvu0tYO3BZEq4rY8RwW1QgPk4lpztQ6WyEsukpqJmIiIiIZKtkBCqf9bcG3AL8HbhngHwxYCvwpHOuMQnXlbFiRIFK7zgVOrYlu0YiIiIikuVGHag4537X/d7MPgP83Tn3+9GeV8YPiyV0/RpGi0qPjsYk10hEREREsl0yWlR6OOeOT+b5ZHwwr2+LylCmhYsmtqho0UcRERGRCUcr00vqjXCMSg91/RIRERGZcJIeqJjZcWZ2n5nVmVmXmcUGeEWTfV3JXiMbTJ84RqUxyTUSERERkWyX1K5fZvZ+4oPpg8A64HVAQckElxioxAiQM4QyalERERERmdiSGqgAi4Eu4P3OuQeSfG4ZqxLWUXEjaVHRGBURERGRCSfZXb/eA9ylIEUSjajrl6lFRURERGQiS3ag0go0JPmcMsb17/o1FBqjIiIiIjKxJTtQWQocleRzylg32lm/1PVLREREZMJJdqDyLWA3M/uumQ1luQyZAPp0/bKhPXJRrUwvIiIiMqElezD9D4BXgcuAc83sRaBxgHzOOfe5JF9bstTIpifutzK9F4NAMMk1ExEREZFslexA5ZyE97P810AcoEBlouizMv0Qgw0L0GU55LguwMWDlaJJKameiIiIiGSfZAcqs5N8PhkH+raoDL1HYNTy/EAFaN+qQEVERERkAklqoOKcW5vM88k4EetdR2WoXb8AuiyPAlrjO+1bk10rEREREcliyR5ML7Id84Y/RgUgmjhORYGKiIiIyISS1BYVM5s51LzOuXXJvLZkr5EMpod4i0oPBSoiIiIiE0qyx6isIT5QfmdcCq4t2So2gsH0xMeo9FCgIiIiIjKhJDtY+D0DByrlwIHArsAyQGNZJpARt6io65eIiIjIhJXswfTnDHbMzALA94DzgM8k87qS3WyEg+n7tqg0JLNKIiIiIpLl0jaY3jnnOecuI9497Mp0XVeyQEKLSkxjVERERERkCDIx69cTwEkZuK5kSOKsX24466io65eIiIjIhJWJQKUSKMrAdSUTPA/zor27alERERERkSFIa6BiZicAZwCvpPO6kkH9B9Lb8Fam76ExKiIiIiITSrLXUXloB9eZAXSvs/LDZF5Xslgs3PPW2dCnJgaIWg7OApjzINwUX+E+mJPsGoqIiIhIFkr29MQLBkl3wDbgX8BVzrnBAhoZb6IJ41NsmA14Znh55QQ7/daU9gYomZzEyomIiIhItkr29MSZGPMi2SyhRcUbZouK5zm6ckoSApWtClREREREJggFFpJa0YSuX8N83La1dhDpaOtN0IB6ERERkQkjpYGKmZWY2QwzK03ldSSLxRK7fg2vRQUgkFvYu6NARURERGTCSHqgYmYhM7vEzN4EGokv8LjNzN7005M9LkayWWwUY1QAL5jfu6NARURERGTCSPasX7nA/cBxxAfQvwtsAqYCs4CfACeb2UnOuchg55FxJHEwPcNvUYkFNUWxiIiIyESU7BaVrxOf+ev/gLnOuVnOuaOcc7OAvYB7gWP8fDIR9BlMP/zHLRZQi4qIiIjIRJTsQOVs4os5ftg5tzrxgHPuLeCjwKvAJ5J8XclW0ZGvowL9W1QUqIiIiIhMFMkOVHYHljjnvIEO+ulLgN2SfF3JVqMcTK8xKiIiIiITU7IDlQhQvJM8RUBXkq8r2apPi8pIun6pRUVERERkIkp2oPIycJqZVQ900MyqgNOAl5J8XclWiS0qI3jcYn1aVDSYXkRERGSiSHagcj1QDTxjZp8zszlmVmBms83ss8DT/vHrk3xdyVbRka9MDxqjIiIiIjJRJXV6Yufcn83sQOAS4L8HyGLAz5xzf07mdSWLjXKMirMcnAUw50FXG3R1QE5BMmsoIiIiIlko6YsvOucuNbN/AJ8DDgLKgCbgBeAW59yTyb6mZLFRLviIGV4wn2C0Pb7f3gBl05NUORERERHJVilZJd459xTwVCrOLWNM4mD6ESz4COCFChICla0KVEREREQmgFGPUTGzXDN7xsyWmlnOTvItNbOndpRPxpmEFpWRLPgImqJYREREZCJKxmD6TwKHAL9wzg067bBzLgL8HDgcLfg4cYxywUcAL6RARURERGSiSUag8lHgbefcP3eW0Tl3P7Aa+HgSritjQWz0gYoLJgye1xTFIiIiIhNCMgKVg4Blw8i/HDgwCdeVsSA6unVUQC0qIiIiIhNRMgKVKmDzMPJvBiYl4boyFsQS11HRGBURERERGZpkBCodQPEw8hcDnUm4rowFsd5hSyMfo5LY9UuBioiIiMhEkIxA5V3g0GHkPxRYl4TryliQjMH0alERERERmXCSEagsA44ys50GK2Z2CDAPeDgJ15WxIHEw/YjHqGgwvYiIiMhEk4xA5XrAAX8xs7mDZTKzvYG/ADHghiRcV8aCaOI6KmpREREREZGhGfXK9M65183sh8Bi4AUzuxt4CFjvZ5kOLAI+BuQB33fOvT7a68oY0Wd64mTM+lUPzoHZaGsmIiIiIlls1IEKgHPuh2YWBX4AnA2c1S+LAV3Ad5xzVyTjmjJG9JmeeGQtKgRy8HIKCXS1x1e679gGhZVJqqCIiIiIZKOkBCoAzrnLzeyPwLnAfGCqf2gT8Bhwq3NubbKuJ2NEElpUALziqQS2vRXfad6gQEVERERknEtaoALgByI/SOY5ZYxLbFGxIPHhTMMXK5pCqDtQadoAU/ZLQuVEREREJFslYzC9yOBiiYPpR/64xYqn9O40bxhNjURERERkDFCgIqkVG/06KgBekQIVERERkYlkzAUqZnaamV1nZo+aWbOZOTP7w07KzDOzf5pZg5l1mNnLZnah2eDfnM3sVDNbZmZNZtZqZk+b2Wd2cp3PmNkzfv4mv/ypO8gfNLOL/Pp0+PX7p5nN2/mdGCOSMZgeiBVP7d1p3jiaGomIiIjIGDDmAhXgu8BXgAOBnf5p3cw+BCwHjgX+Rnzdl1zgauDOQcp8BbgXeA/wB+AmYBpwm5ldNUiZq4DbiE8icJNfbj/gXv98/fObf/1f+vW53q/fscByv95jX0KLyqi6fhVN7t1pWj94RhEREREZF8ZioHIRsCdQCpy/o4xmVko8aIgBC5xzn3POfZN4kPMkcJqZndmvzCzgKqABONQ592Xn3EXA/sBbwMVmdlS/MvOAi/3j+zvnLnLOfRk4xD/PVf55E50JnAY8ARzonPumc+5zwPF+fW8ys5Ih35Vstd1g+pHx1KIiIiIiMqGMuUDFOfewc261c24o00edBlQDdzrnnks4RyfxlhnYPtg5l/jClNc759YklNkGXO7vntevTPf+T/x83WXWAL/2z/fZfmW6r/tdvz7dZZ4F7vLrfdpOP2G2S9L0xLH+Y1SG9OMXERERkbFqzAUqw7TQ394/wLHlQDswz8zyhlhmSb88IypjZvnAPP/6jw7jOmOLc31m/RrNGBWXVwK5fgNTtDO+6KOIiIiIjFtJXUclC+3lb9/of8A5FzWzd4B9gTnAqiGU2WRmbcAuZlbonGs3syJgOtDqnNs0QB1W+9s9E9J2A4LA28656BDLDMrMnh/k0N5DKZ8yfYKUAJiN7nyl06D+9fj7pvVa9FFERERkHBvvLSpl/rZpkOPd6eUjKFPWb5uKa5QPcnxsSAxUAkl41Mqm977XOBURERGRcW28t6hMCM65QwZK91taDk5zdXolDKRnFAPpe5RO633frJm/RERERMaz8d6i0r/1o7/u9MYRlGnqt03FNRoHOT42JGmxxx6lu/S+V4uKiIiIyLg23gMVf0DD9mM9zCwEzAaiwNtDLDMVKALWO+faAZxzbcTXcyn2j/e3h79NHPPyFvEpiOf49RhKmbEnmuxAJaFFpUmr04uIiIiMZ+M9UHnI3548wLFjgULgCedcOCF9R2VO6ZdnRGX86Yif8K9/zDCuM7bEktz1q88YFQUqIiIiIuPZeA9U7gbqgTPN7NDuRH964B/7uzf2K3MrEAa+krhIo5lVAJf6u7/pV6Z7/zt+vu4ys4Av++e7tV+Z7uv+2K9Pd5nDgDOALcBfd/oJs1lii0ogGS0qClREREREJooxN5jezD4MfNjf7V4F8Cgzu81/X++c+waAc67ZzD5PPGBZZmZ3El8p/oPEpyG+m/jiij2cc++Y2TeBa4HnzOwuIEJ88cVdgF84557sV+YJM/sl8HXgZTO7G8glHnBUAl9NXDzSdyfwUf+8L5jZvcAkv0wQ+LxzrnnYNyibJLtFpbTfrF/OjX7KYxERERHJSmMuUAEOBD7TL22O/wJYC3yj+4Bz7u9mdhzwHeBjQD7wJvGg4tqBVrh3zl1nZmv883yaeMvTSuKryP9uoEo55y42s/8Qb0H5AuABK4CfO+fuGyC/M7OziHcBOxf4KtBJfCHKHzvnntjpnch2idMTJyNQyS+NL/oYaYkv+tjeAEWTRn9eEREREck6Yy5Qcc4tBhYPs8zjwPuGWeZe4N5hlrkNuG0Y+aPA1f5r/OkzmH7kvQw9z1FfVwfA5LLp2JbX4geaNyhQERERERmnxlygImNIkrp+1Te10b7sOuoKC6jIqyKv+0DzBpi6/6iqKCIiIiLZabwPppdMSuJg+uryQmoqiokVT+lJa1q/Cs/zRnVeEREREclOClQkdZI9RgXwinoDlfCL/0ud3yVMRERERMYXBSqSOgktKkmZ9Qv6tKiUBCM7yCkiIiIiY5kCFUmdaGfP26Sso0LfQCXQ1ZqUc4qIiIhI9lGgIqmTgq5fsaKpPe+DClRERERExi0FKpI6iS0qlpwJ5ryEFpVgV2t80UcRERERGXcUqEjqRJO7Mr3nOTY3thPLKY6f0sUItG8Z9XlFREREJPsoUJHUSfIYle71VFpjuT1poW1vjvq8IiIiIpJ9FKhI6vRZmT45Y1SqywuJ5Vf27CtQERERERmfFKhI6sSSPz0xQGdOec/7UIMCFREREZHxSIGKpE6frl/JGUwP/QKVbauTdl4RERERyR4KVCR1osmfnhggHCrveR9qeFMzf4mIiIiMQwpUJHVSsOAjQFewkJjlABCINEPr5qSdW0RERESygwIVSZ1Ycqcn7j2X0ZVb3rtftyp55xYRERGRrKBARVKnz4KPSQxUgEhioLLltaSeW0REREQyT4GKpE7i9MRJHEwPEEkYUK8WFREREZHxR4GKpE40cXri5D5qalERERERGd8UqEjq9FnwMcktKv0DFc38JSIiIjKuKFCR1Iklf2X6nlMHC/ECufGdziZoqU3q+UVEREQksxSoSOokdv1K4vTEAJgRza/s3d+icSoiIiIi44kCFUmdFM76BRDNSwhU6jRORURERGQ8UaAiqZO4Mn2yW1SAaH5F744G1IuIiIiMKwpUJHX6tKgkdzA99GtRUaAiIiIiMq4oUJHUcK7PYPqkrkzv6zNGpU4zf4mIiIiMJwpUJDViid2+csAs6ZfwQkV4uSXxnXATNG9I+jVEREREJDMUqEhqJK6hEsxJzTXM6Kqa27u/4fnUXEdERERE0k6BiqRG4tTEwbyUXaar5oDenfXPpew6IiIiIpJeClQkNRIH0qcoUPE8R33erN4EtaiIiIiIjBsKVCQ1EseoBHNTcon6pja2vv5Ub8LGFyAWTcm1RERERCS9FKhIaqShRQWgvHISsZzi+E5XO9StTNm1RERERCR9FKhIavQZo5KaFpVuXQWTe3c2aJyKiIiIyHigQEVSo8+sX6kNVCKFNb076zVORURERGQ8UKAiqdGn61eKW1QKp/TuqEVFREREZFxQoCKpkTCYPpXTEwNE8qpw/sr3bsvr0Nmc0uuJiIiISOopUJHUSGxRCaU2UNnSEqE9EF+h3nCwcUVKryciIiIiqadARVIjmvrpiftcrs84FXX/EhERERnrFKhIaqRpeuJu4fzq3h0FKiIiIiJjngIVSY1Y4vTEOSm/XGdeQqCy4TlwLuXXFBEREZHUUaAiqdFneuLUt6h05ZThBfwuZm1b2LL6OTzPS/l1RURERCQ1FKhIaqS56xdmfaYp7lzyferq6lJ/XRERERFJCQUqkhrRxOmJUz+YHiBSPL3nfZWrT8s1RURERCQ1FKhIaqS7RQUIF+3S8z63db3GqYiIiIiMYQpUJDVi6Z2eGCCSN4muUBEAwWg7wca303JdEREREUk+BSqSGn1aVNITqNQ3d7Cps6BnP2/DU2m5roiIiIgknwIVSY1o4vTE6QlUAJpyJve8z93wZNquKyIiIiLJpUBFUiPN0xN3q7dJPe9zNz4DXixt1xYRERGR5FGgIqmR2PUrlL5ApZViosF4969AuAlqX07btUVEREQkeRSoSGrE0j89MQBmdBRM7d1/Z3n6ri0iIiIiSaNARVIjA9MTd+vITwhU3n4krdcWERERkeRQoCKpEU3/9MTdOgp6V6hn3ZN9F58UERERkTFBgYqkRgamJ+65dE4J0dzS+E5XO7yraYpFRERExhoFKpIafaYnTm/XL4Bw8a69O6vuS/v1RURERGR0FKhIasQSpydOb4sKQLhsTu/OqnvB89JeBxEREREZOQUqkhoZWkelW6RoGl5eeXynZSNsfCHtdRARERGRkVOgIqmRoZXpe1iAzlkLe/dX/SP9dRARERGREVOgIqmR4RYVz3Nsrjy8tw6r7qV20yZqa2vx1A1MREREJOspUJHUyOCsXwD1TW3UvfYsMQsBYA1v0fT3/0fdfZdTV1eX9vqIiIiIyPAoUJHk8zzwunr3M9H1C6iqKCFSOqtnf5qrpaaiOCN1EREREZHhUaAiyRfrNzWxWcaqEi7tnf0rv/ntjNVDRERERIZHgYokX+JA+lD6x6ckCpfsigvkAJDTWU8w0pTR+oiIiIjI0ChQkeTLokDFBXMJzzi6Zz9/2+sZrI2IiIiIDJUCFUm+/l2/Mqxjzw/1vC/c9ho4zfolIiIiku0UqEjyZVGLiuc51hfuRzS3DIBgVwu565/MaJ1EREREZOcUqEjyJUxNTCg/c/UgPk1x26O/ZZOr7kkreO3uDNZIRERERIZCgYokXzTS+z6UmamJE1WXF9JRuU/Pfv47/4b2hgzWSERERER2RoGKJF8Wtah068ypoDOvCgDzuuA/f8lwjURERERkRxSoSPL1GUyf+RaVbi0lu/furLgdnMtcZURERERkhxSoSPL1GUyfHS0qAK3Fc3AWiu9s/g9sfCGzFRIRERGRQSlQkeTr0/Ure1pUvEAunWW79ey7p39DbW0ttbW1eJ6mLBYRERHJJqFMV0DGoT6D6bOnRQWgbdJ+FDT6iz6+8r80bPGIBgvh1EuZMmVKZisnIiIiIj3UoiLJ16dFJfMLPiaK5NfQNmk/ID6ofmb0HWoqijNcKxERERHpT4GKJF8soUUlC1amT1Tf1MaGcG9gUtjwCnjRDNZIRERERAYyIQIVM1tjZm6QV+0gZeaZ2T/NrMHMOszsZTO70MyCO7jOqWa2zMyazKzVzJ42s8/spG6fMbNn/PxNfvlTR/uZMyoLpydOFKzeg1hOCQCBWCcFjW9kuEYiIiIi0t9EGqPSBPxqgPTW/glm9iHgr0AncBfQAHwAuBqYD3x8gDJfAa4DtgJ/ACLAacBtZrafc+4bA5S5CrgYWA/cBOQCZwL3mtlXnXPXD/tTZoM+s35lz2D6HhagbdJ+lNY+AUBh/Ut0aKpiERERkawykQKVRufc4p1lMrNS4kFDDFjgnHvOT/8e8BBwmpmd6Zy7M6HMLOAq4gHNoc65NX76D4FngYvN7K/OuScTyswjHqS8BRzmnNvmp/8ceB64yszu6z7XmJKl0xMn6qjch6L6FwhGO8gJN5C7/nGYelqmqyUiIiIivgnR9WuYTgOqgTu7gxQA51wn8F1/9/x+Zc4F8oDrEwMLP/i43N89r1+Z7v2fdAcpfpk1wK/98312NB8kY7J4MH23mOWysWZhz37xit9msDYiIiIi0t9EClTyzOyTZnapmX3NzI4fZLxJ97fX+wc4thxoB+aZWeI38B2VWdIvz2jKjA1ZPJi+W31TG8+8tQ2HAZC76RlY+0SGayUiIiIi3SZS168pwO390t4xs8865x5JSNvL3243wto5FzWzd4B9gTnAqiGU2WRmbcAuZlbonGs3syJgOtDqnNs0QF1X+9s9h/LBzOz5QQ7tPZTySTcGWlQAcosraCncndIW/3Yvvwo+9b+ZrZSIiIiIABOnReVWYBHxYKUI2A/4LTALWGJmByTkLfO3TYOcqzu9fARlyvpth3ONsaPPgo/ZG6gANJbv19OqwltLYcNgMZ+IiIiIpNOECFScc5c55x5yzm12zrU7515xzp0H/BIoABZntoaj45w7ZKAX8FpGKpTl0xMn6soppbNs996ER3+ZucqIiIiISI8JEajswG/87bEJaf1bP/rrTm8cQZmmftvhXGPsSJz1K5iF0xP301ZzSO/Oa/dB7SuZq4yIiIiIAApUtvjbooS01/3tduNDzCwEzAaiwNtDLDPVP/9651w7gHOuDdgAFPvH+9vD347NlQhj2T89caJo/iQ6Z5/Ym/Dw5YNnFhEREZG0mOiBypH+NjHoeMjfnjxA/mOBQuAJ51zCt/EdljmlX57RlBkb+qyjkt1jVAA8z7Fu9lm9Ca//H7z7TOYqJCIiIiLjP1Axs7n+LFv902cB3Su//yHh0N1APXCmmR2akD8f+LG/e2O/090KhIGv+OftLlMBXOrv/qZfme797/j5Euv1Zf98t+7402WpMRao1De10fDi/WwrmN2b+OBloNXqRURERDJmIkxPfAbxleGXA2uBFmA34P1APvBP4qvKA+CcazazzxMPWJaZ2Z3EV5z/IPFpiO8G7kq8gHPuHTP7JnAt8JyZ3QVEiC8euQvwi8RV6f0yT5jZL4GvAy+b2d1Arl/fSuCrY3JVehgz0xMnqi4vJFo1D/fmu5gXhbWP4a1+kLrS/QCoqakhEBj3cb2IiIhI1pgIgcrDxAOMg4D5xMeLNAKPEV9X5Xbn+v7p3Dn3dzM7DvgO8DHiAc2bxIOKa/vn98tcZ2ZrgG8AnybeWrUS+K5z7ncDVcw5d7GZ/Yd4C8oXAA9YAfzcOXff6D52BvVf8HGMNEzE8srp2Ps0ClfeGd9/4PvU5R0KZnDqpUyZMiXDNRQRERGZOMZ9oOIv5vjITjNuX+5x4H3DLHMvcO8wy9wG3DacMlmvf4tKV+aqMhye51iz6xnMfePvWLSTnPqVzJwxnc7yIa27KSIiIiJJpL4sknzRsTXrV7f6pjaan/4Tdfm79aQV1z4JXjSDtRIRERGZmBSoSPIlaTC95zm2NLZR39SG56Wn/1h1eSFbSveny+L1DnW1UrTlhbRcW0RERER6KVCR5EtSoNLU1smtaybxh3er2NrcnoSKDY0XyKVx0sE9+0VbVhBo3Zy264uIiIiIAhVJhcQFH4Ojm/WrqLiEwuLiUVZo+JpL9qAjJz5rdMBFKXn6qp2UEBEREZFkUqAiyRWLJozpMAjmZLQ6I2YBNpYd1rNbsPof8O6zGayQiIiIyMSiQEWSK9av25dZ5uoySm35U2ktnNmbcO/XIBoZvICIiIiIJI0CFUmuMbYq/c5snXQYnvmzeNe9ivfoL6mtraW2thbP8zJbOREREZFxTIGKJFdCoOJC+dTW1lJXV8cAa2SOCdGcElomH9Gzb49eRdPfvkndfZdTV1eXwZqJiIiIjG8KVCS5Erp+eYSou+9y6v99De3t6Zu1K9nWBufQQDkA5nUxu/kpasoLM1spERERkXFOgYokV58WlVxqKoqpKivKYIWSwAKsLT0CF4hPDJDbUUfRlhUZrpSIiIjI+KZARZIrMVAJ5mawIsnVESyl9ZDze/aLNz9N3pqlGayRiIiIyPimQEWSq0+gMrpV6RtbO3Fkz9iWtgO/QGTq4QAYULb0G7D51cxWSkRERGScUqAiyRXt7H0/ikCloaWde2vL6Qh3JaFSo+d5jrqt21h98PeI5pQCEOhqhz+dCW31Ga6diIiIyPijQEWSKza8rl+e59jS2EZ9Uxue17f1JL+gIH4e57G1uX3APOmyrbWD9mXXsXn579g0+Xg8f7wKjevgdx+Als0ZqZeIiIjIeKVARZJrmGNUGlrauXXNJP7wbhVbmweeGay9rY27aqfsME86VJcXUlVWRCS3gqYZJ+LwF7OsWwm3ngKN7+J5ntZYEREREUkCBSqSXIkLPg5xMH1RcQmFxcU7zFNYVLzTPOkULp1N08KfggXjCQ1vwS0ns/WNp3nlD9/RGisiIiIio6RARZJrlIPpu7uCNTS3k81rRHqeY135UWw76ZrelqPm9Uz6+1nMKBi7a8aIiIiIZItQpisg40zCYPqBun51ByKtYY/CXIsHJLiecSgdW9t4oGUmGzoms2bSrrxBDRU5DcxmC1VE0vlJdqi+qY32ZdexNj+fbUdewaynvkMg1kmgcxu7hx+g6d1HYcrHM11NERERkTFLgYokV6w3mOjfouJ5jrWbG/m/ppk0t7SSm5dHJJxPeWUX4XAndzVNYWusgA2l+9GYW9JTbnNwMpuZTHm0jYPCbRxWmB0tFtXlhbRFHHWrnqYh7ygO6HicHBch6KJULDkP8hzsf3qmqykiIiIyJqnrlyRXxWzY9yOw1/uITtq7z6HuKYcDOXkUFBVRWFRMQVHvqvWuaBKrSo+g0Ur6nxWARoq4fOt8HmmZktKPMFzV5YXkVs5k0/T3EQnGP495Ufjfz8MT1/UMsNcgexEREZGhU4uKJNeeJ8VfQEdtLTx+bZ/D3VMO9+eAlczG8wenm/MoaV3L7sURNnplbA7U4CxAjAC/3Lw/UVvFouJ3U/pRhqsrt5w3a05h180PUOQ1xxMf+C4dm9+mrj4KZnDqpUyZkl2BloiIiEg2UouKZIW6QDVbrSy+4xwHR15kSv1z1NDI3K7XOZaXKKYjfhjjmtp9+N2m6RlbV2Uw0WARLxcvoHXSAT1pRS/dwh5tz1BTXpjBmomIiIiMLQpUJOOiBHkzZ7ee/ZlsptS19MlTQISjAq+xa6B3Ffi/tr2H32+clrZ6DlV9W4xXvd1oyp/Rk1bQ+Bpl7z4IXjSDNRMREREZOxSoSMatZhciFh94n+vC7MH6AfNF25uZ3vwKpV29wcrtzfuxpGmXtNRzOKoqSmmb834adn1/T1pB02rKH/w6xLoyWDMRERGRsUGBimRUh+Wxlsk9+7t3vUUOsUHzlxYVcGBsFRWuuSftxi378q/GLGxZae7gjeZC1ie0FuW//S/482f6LowpIiIiIttRoCIZtTkwOT7IHKhwzdR4W3ZaJojHIbxBOW09aT/fuC/3Nc5MWT1HqrqiiIaa+TSW7tOb+Pr/wV2fhK7OwQuKiIiITHAKVCRjHLA5WNOzP4M6bIhlQ8Q4PPgmu4Ya/XMZ/10/l3vD+1O3rY36prbsGWhvxtZJh9Ey6aDetNUPwJ/OhEh2rAkjIiIikm0UqEjGtFBIeyC+7kjAxahh27DK51qM71Y9zl55jT1pD0T34+L1R/G7d2vY2pxFQYAZ7+Tvx/r8ub1pbz9M5Jb34zXXZq5eIiIiIllKgYpkzCYm9byv8uoJMfzFEEsCEX40/TmOKO7tMrYxOIXn8w9hbVdpUuqZNGaEpx1Bc/XhPUm5tStw/70ANr6YsWqJiIiIZCMFKpIRjr6ByuRY3YjPlR+I8cMZL7KoZENPWisFfGfLAv6waRpdMfA8x5bG7OgStiZvLmsKD6C7FsHWTXDLyfDin8BlSXc1ERERkQxToCIZ0WRldPpTEue4Liq94XX76i9kjgtqXuFD3nKC/qxhUYL8uW0/vvDWETzdkM+taybxh3ersqJLWGzKQayZtIio5cQToh3w9/PgT2dB86bMVk5EREQkCyhQkYxIHEQ/hQYCDL8lwTmPrc3tPa0kZnAIb7Aw53VKvN4FI9dFy7iicQGr8vclWFSejOonRUvBLrxcsoho+ZzexDeW4H59ON5zt1G7aSO1tbV43vC7xImIiIiMdQpUJO08jLpgdc/+NOp3kHtw7W1t3FU7hdvXTeKN9fU9AUuJhTk48gJ7unU9rSsA67xJLIvtwwNts4llSQ+rdivmtWNvpH7OR3vSLNxM4L6vUXrL0bT+9SLq6nq7xXmep+BFREREJgQFKpJ2DZT0dHnKd52U0zricxUWFYPRE7Csr2/G4QjgmMMmDu98lqpo7xf9LkLc0nQg31h/JG9HKzM+bmVbawetj/0PL28JsGHqyURzeycAKOzayu5b/knZQ/8PGtcBUFdXxyt/+E6f4EVERERkPFKgImlXT1nP+6pY/ZDXTtmR7oDl3w3VdIS7etLzCXNo8B32j7xMoetdYPGtcBlXh0/h4neP5JZ3p2R03Ep1eSFVZUV0FkxhVdUHWJ8/l1jCr2bBG/fAdYfA/ZdiHQ1UlRVmrK4iIiIi6RLKdAVk4tmaEKhUeNsgmLxz5xUUDJg+ydvGFF5mjU3nHTeZLv/R3xCcSl2wmr3b/sOpBX1bKeIzhbXT3uXS1uLiAiHC045gdftcZjQ/S1H7u/EDsQg89WuqV/wOL393rCvzEwKIiIiIpJJaVCStugJ5tFh8kUdzHuVeY9quHcAxveNNDmp7morI5t46EeLmpoP4f+uP4PWO3q5XDS3tPTOFNbV1DnTKlImESqidsoi3qt9LS7Cy9zNEWpna/CJVfzoJnr8NNFZFRERExikFKpJWrfm9s32V0zqiRR5Hq7IwxIHea0yrfYyChO5gb4TL+co7R3Br/Z6EvfivRlFxCYXFxWmvY7e2vCnUzfwA22aeTLRsVk96sH0L3Ps1uOW9sHllxuonIiIikioKVCStEgOVKpoyWBMo7tjE0bzMHraRkD87mIfxt8bZfPHto3irqyKj9ethRkfJHFYefyvrD/omkUBC97b1z8Bvj4EHL8MLt1FbW6tZwURERGRc0BgVSRvnoDV/cs/+pAwHKgBBHLt0rKY8toY38+eyLRgPTt6NFHF5ZAG7B+rY3TZmuJZQ39RG+/Ibqe/yeJnj2D9/LdM6XyeAB14UHvsl3st/oT13b1rzp8GplzJlypRMV1tERERkxBSoSNpsjJUQDcZbA0KuizLaiGS4Tt0mFQapDr5FWU6M/w0fTJgcHMZqbzIbKWMX1mW6ilSXF9IWcaxt74gPuG/bk6lbH6c0Gl+HJtT8LnN4l46yPWhp2QgKVERERGQMU9cvSZtXI73dviq8xqRMS5xMHe1tbNjayj7bHqU8urUnvY18brYP8NvNvWNXskE4p5wtM95P49QFRHN6x9EUNK2m+s73wgPfw2trUHcwERERGZOy51uXjHuvdvV2+6r0tmWwJoMrLCqmvCDIEcE32avrDYIuPnbFWYA/b53Fhe8excr2sp2cJY3MWBfclZeKFlIbmtGbHIvAE9fCNfsT/MNH2Pb3S7VIpIiIiIwpClQkLSKe8Xqkqme/IksDlW4GTIvFB9tXxHrruqGrmAvWHM4NG2bTEc2eNqHyykrqpixkw9STiRT0tlwFIi1Ut65k79r/pfxfX4W3l/WZ0tjzPLW4iIiISFbSGBVJi5Ud5UT8x63QdfrTAudntlJDUECEA7peZnVrBVvL59Lp4mNX7u/Yk5ffnsqFU17lkNzmTFezR2fBFOorPkZX8VSmrLyJvNb4gpGGI/+dB+CdB4iWzaJ9nzMonv8F6lqj1N13ebywBuCLiIhIFlGLiqTFC229ixZmw2xfw2FAZfs6Li34P/awTT3pG6MlXLL+CK7ZNJfmWE7mKthPfXM7m958hcc5nKcDh9JeMLXP8VDTGkqf/Cl29VzKln6T6bkt1JQXZai2IiIiIgNTi4qkRd9AJXtaIIYq3NnBg3WVVIZfYJ/KTl63mcQI4mH8Y9sMljZN4Zzqt1hQuJZtLR14nstofXtmCOuYzKap76HEOgnQRcW6fxHsagXi41gKVv+DAqArv5q2ml2h6ly8QKhnPEtNTQ2BgP6eISIiIumnQEVSrt0L8lpH7wD0SprJ7Nf4kSksKiYYClFBHZMDLdRaFati0wBo83L49ea9uTMwk7ymN5jVtJbyouzp2lYbzqO93aMz7wT2KdtCedtqcjt6B9fndG6h/KFvwTO/pG3uGWxdX0ssWKDuYCIiIpIxClQk5VZ2VBDzexnmRxrJzYkSznCdRq19G1XRLeyX20BdwSw2x+LTA2/1CqHiQL62eS9OKd/ABydtoJDODFc2Lt7KUkBLbjm1+btjzRuY1PEWU6LrCPizm9G6mZJnr2UfC9JRvicdm1+GyZPBDM/z1NIiIiIiaaNARVLupY5JPe+Lw3WQPcM5RqWoqJjSUCefy7+Xx92+3N+5Fy1eHgDbvALuaNidOxt24+DCek4or2W/vC0UEe0p73kuo93ECqt2oT4ynXAwTFXnGgpb3ibYHg9EzMUo3LaKwr99HB7bGw76JPVTjqfuof+OF1ZLi4iIiKSYAhVJuZfbe8enFHVugeLx89i1t7Xxv001RMLr2T9vK+ttMhvy5xANxrt9eRjPtVfzXHs1ATz2K2zkPflbObikkcK2jfxlQyV77tJOYWFhxj6DF8ynpepgntvjYua8ei0VTa9Qmjh99JbX4IHvUh0IUVo8g46KuYRjkYzVV0RERCaG8fONUbJSY1eIdyKlAATwKArXA+PrL/HdY1fy8vKZE95CzrpVhKbuyeacXdgWrOjJ5xHgpfZKXmqv5I8NEMQjf0oztzdtYX+vlQNKmv1pm9OvvqmNd5+9kZnv2Y01hbOpdPVUtr9NXstagrEOAMyLkt/8DvnN7+Dd9gjs+V7Y+/2w+yK8vDJ1CxMREZGkUqAiKbWipXcQ/ezQNoIuuoPc40MAj1l57UwNv0xrZ4haJtFcOJ0mK+mTL0aAtpxy/q+tnP9rA2qhNBBh94IWZoaa2cXqmWyNHFaTuEBj6rqLVZQUxN+YEc6r4c1ANZ02lyluM5OCrRRtfbn3M3a1wav/G39ZgGjN/li7oyV/OnzkKqZMm570+omIiMjEokBFUurZ5t5AZW7uFtZmsC6ZMKkwRHF4M3k00dzpsZVStlFCa04l7cHi7fI3e7msaJvECiYBswHIbYwyK7eVPQtbKO/ayhP1BUxrbKGmuO9gH89zbGlsp73LUTApOeuiTKooozFSysb2DlzRVPbO2UhJ21pCXQlTTDuP3M0vMhmY3PIS3m2Pwe7Hw94fwNvzZOoa29TKIiIiIsOmQEVS6vmW8p73c3PqJlygkiiPCLPzWpkWricvsInWzihvNzqClbvQHCwnHCykk9ztykVciDfC5bwRLgdmQA38Vy1UBjuZnd/K9FArexS2kd9Rx1N1JTgvxhdytlJYWOgHL22jDl66ZwxryJ3Ku0UHsebN1zioso3S8EZKvW1YQt5ApBlW3gMr78EFC/CopvG936PykI9CUP/kiIiIyNDoW4OkTF1LhHWd8e5EuRZj95yGDNcou+QQpbijjjk5OYTDa4l2RGnpChDOn0SzKyBSUEUThUQsb8DyDbF8GtryeZ4qaALYF3KhgDBbtjYys60T2kqobS4mL9bBV0IbKS5KwqB9M6y4msjUfVgbcZQEwwRaNlIR2UhB+wZyvI6erMFYB9NYB//8PDxyKbznY7DHSTD9YI1rERERkR1SoCIp88y63u5B7ylsJMe8HeSWoqJiQuFO8vI6CIe3kUcD4XAn5BXT0JVDR045TV4uTV4BsZwiugb59e0gj/+EJ/OfMBDYFQJADjxXG2V6Tju75rczOdjGnMI2qq2NPYLtwMjHDnnBfFoKZxMtn0Nb2KPCmshpXkdlx9vkdjX1ZmzbAk//Jv4CvNJdKYzE6AoW0jb3vZRM2xNKpuEVT6YuWkzN9F0VvIiIiExgClQkZZa/1djz/rCiesbkcvRZII8olV4reXQQ7upkw6Y6JldX0twVpCu/ko5QCRUFATbEyqhzZbg+HbF6RVx8BrbuWdhImIG4PNBBNU3svamLamulim1UuBYOrBlmZc2I5FawrbSctkn7E2vbSlHj69QEGsjp3Nona6h5LaXdO8+90ZMeAGowYlVzCcyeBzOPgt1PgILyYVZGRERExjIFKpISHZEYTye0qMwr2UKseQcFZFj6t77EGqPMDoc5oLKKxgh05ZbTFA1R32kEiqtos3y6drDSZqNXQCMFrN7WNz2wzaM62Mbs/A5qQm3U0EgFLRTH8nE7CzzN6MydxOuxPakuKiDPNjPV20ip10h+tInADmaAC+AI1K+E+pXw7M24QIjI1EMJ77qQ4kM+TmDSnGHcLRERERmLFKhISjz2Zj3haPyb7IycVnbJa5/QA+lTrXstlwCOItdJHo7yWCeh+jrmFM8k3NlJZxTCOaXsU9rJhmgJr3ZNocXLozNYhLOBu1h5BNgcK2FzW8l2x/JfizI1p52Z+e1UBdqoooly18KBVY5Cwn3yVlcU0xYpoi13N+oijo62VgLhbcwoccTCbRTSAZE2ApFWcmLtFLjWPu1C5kXJ2/AUeRuegicuh5p94q0sM47Am34Ide3x3BrrIiIiMn4oUJGU+PfK2p73hxfVZbAm0q2sKJ9wuJl1W6NEwts4uLKNcLSTnGA+TWF4d1uE0skzaY7l0Bksoo08wpY/6Pk6+3cl69YUnzyhOqeT8kCYQEcDe24KURroZFp+hAIXZlphMfn55bTmx2jPcXTmGm0RR5G/3bBxEwfUQLBtM0Xt71Ic6zcRQ93K+It4V7HKYBHtudW0HnQWpXMXwpT98YK5GqwvIiIyhilQkaSLeY6lq3qDkyMUqGSV7taXbgGg0HVS3FHHLHIJRzvJC+YTDncSzCukMWK0uTxavFy8gkrarYB2L0QkMHgQE3FBNkSK2EARWCX/2TZwvhAxygJhqnK7KAuEqc6NUEQn7a3FbKoopDg3zPSyCEVd25gWW0d+67sUd24kQN+JGXJjbeR2tMETV8RfgRyiVXMJtUXozKlg24KvMWnv+ZBfOnBFREREJOsoUJGke/HdbWxtiwBQGYqwZ34TDDLAW7JbCI8S10lVXoxwuIE8Ggl3dvLW22vZfe6+NEYCtLlcWr1cvIIKWl0uHVZA1AYfD5MoSpCtXiFbO/2Edn8b2J37NvTNW2QRigNhKnKilLhWSr0mKt02Kt02yq2NYjoosQ6K6aAo1kFJbTPFdFBsGyn626dwdGHlM6BmLq5qL5pzaoiV7Ur5bgcTKJ0OanERERHJKgpUJOkeWLm55/3R5Q0EFKOMO/kFBeQSpcx1UpMXb33Jo4FwpJO8vHxaO7voiIVojxnbwkZ+aSWdXohoTiFhFyIWKiRMiJgN/Z+gNpdLWyyXzTGACmDGsOqcQ5TizR0U13VQTDsV1koFz1JhD1MR6KSiKJfykiKCAaOoqIzd99qXybvMJm/STOoa2wB1IRMREUknBSqSdP9OCFSOrWjQtMQTUA4xigtzCIc7iW2tY06ViwczfpeyvFBv17LWiIfLLaatC7ycQtpiARrbY+SWVBCxXDCjxeXjMboAoYsQ2yhhm/MnBkh8LmNAo//qthJgDcWspMqaqbA2qiZNYtrU6VRPnkZ1aT7VJXlMKsrFOluoKAwxfeoUAoEAnudpfIyIiMgoKVCRpHprSytvb4n/9Tk/FODQ0iaam3ZSSCasEB4FrpM8oNDrJI98wtFONmytY05JfLayWDRKOBymsHIybRGPcMzojDrySiroiDq2bGulvHoykRi4YC4Rz+giQNQFccFcogSIEhp0ZrOdaaWQVlfIGgdsAba0AasHzFuYE6A4P4f8ELS0dRIKwN7TyikrLiQ3FCAvFCQvFCAnaHSFO6goLSE/J0huKEBOwOhsbyU3GKB6Ujn5OSFyQwHyc4KUFYQozc+hJD+H/JwAZvFmSgVEIiIynilQkaRKbE05YtdS8gMeWj5FRqN78H8eUXCdVBZ0dzXzCMc6sbY65lRHeicB6Ip3P+vTgpOXT0dnmHc3b2OXmbvSHolBbiHtXQ6XU0g0FiNIDIfhQnmEyaHN5dNkxTtcf6a/9i6P9q7uqZnjwcSWt5uAwaL12kHSB5cTNErzcygtyKEg6Nja1EZO0HHYnBqmVZVRXpBLeWEO5YW5VPjb8sIcygtyCAUVyIiIyNihQEWSqj0Soyg3SFskxjFzyvp2pRHJoACOkBehkDBB10keUcIJrTj9g5sNm+o4ZNZM2sMRKvKh3NtGeaiLPIvQQhFbXBlbKI9vXTkNlI66e9pQdMUcW9siPRNWxAMiY/1/6oH6HZYtyQtRXphDUY5Rlh+iuryIyqJcygpyCMU6Kc0PMWtqFVXF+VQW5zKpKJf8nGCqP5KIiMiAFKhIUn39xD358vG78eRbW5maF4HnM10jkZEz4t3TIi6fdyNl1Fk+XeF2DijYwkdsBYfkvEOBdQEQc0Y7+bSRzxp24cm8+ayw9xArrCIajdIZjuBFo4SKSolFo7S0NFNQWkk0GsUFQsRiUTyCxLyYv/XoisaIeY6IB14gh66YwxvFDHot4Sgt4WhvwrqB2jvX9Nkryg1SWZRLSa5RXhCivCDEnCkVzKwspDQQZteKfPaaNV3dzkREJOkUqEjS5YWCLNirhtraeLcWz3NsaWynobkd5woyXDuR0fEIsNLblRfCk/lL3iIOdK9yfP7rzApspoQOSuhgCts4MvIfIi7Ic8znqZITqa3YG6+rk0BOEK+ri/qOZmqqqvG6unrSAjkhvK6ov+0kkFOA19VJ/aYN1MzcDa+rExfMIxwOE/W3sUAu4UgXXRaiMxKloaGBvLJqwlGPzqhHJAZdLkCXg5FME94WidEW6eibuKrvApwV+a+w1+RiDp5dzWGzKzl4ZgVlBUPvMiciIjIQBSqScg0t7dy1oYqmljwisbZMV0ckacLksjwyl6ftIGqi6zm0awUnVGyg0OLdsnItxrzwcuaFl7Mmd08eL1zEK6Fj+i1XOTzBgJEfgkCO4RkEcgJ4ua43AOpsp2Zqd6CT729ziUU6e4KbLsulMxJha8M2AvklRLpikFdMOBol7IJ+kOOIeIYbQnCzrdPjqbXNPLW2GZa9hQG7VxVw5O7VHDZ7EkfMrmRy6eALhIqIiAxEgYqkRVFxCRHPQbMCFRmf3o1V8dTmA3mw4BQOdK9yQv5KZga29ByfFXmDWZE3aG3+IysKjuIB9ibCbmmrnxnkBY2cHD+4CUFOUzs1U6f2aenpbc3JJxbpJBbMo6M7uAlHqG9oJFhaTWs4Sms0SHPEI+b6BjMOWF3fwer6ddz+1DoA5lQXMW+3SczbrYqj5kyioig3bZ9dRETGJgUqIiJJFCGHRyN784wdwIzoWhbmreKwnDcJWbwdpdhr5ti2f3Es/2L9pn/wbMHRvFSygFay74u7GeQGjVBCcJPb3EZN1ZSeoCYW6aSNPLa1RdjcFqUhHKAlFtpu+aS3t7Tx9pY2/vDUOgyYO7WEebtVMX/3Kg6bXUlxnv53JCIifen/DCIiKWG8GZvCu7FZ3NnRwMLCNzkm+DKVgd5WxV263mGXrnf4QPMdrMw/iBeKF7AytA+OsdNNygxKc4xic+xaUYTX1UksGGJLS5h361tpCRTR2BXsMwmAA1ZuamHlphZufuwdggZzJxexYO4U5u1excEzKzTbmIiIKFDJNDPbBfghcDIwCdgE/B24zDm3LYNVE5EkaXaFLIkezj1t7+GAgi0cYf/h4NA75AbirSwhYuzf+Rz7dz5HO4X8p+hI/pN7MG+FDh7VeJZMyQkYUwohlNNEzcwqusKdNMRy2dzSxZZwkIZOr8/Yl5iDV2rbeKX2La5/+C1yQwH2m17GQTPKOWhmBQfNLGdqWX7PQpciIjIxKFDJIDPbDXgCqAHuAV4DDge+BpxsZvOdc1szWEURSSJHgFXerrwYnkzj5vWcNruDIwOvsFuoridPIe0c0fYQR7Q9REdjAa/n7c9/mEJ9JMBGJmew9iMXDMDkvADVOY5ATohwuJOtXblsbu1iSzhAY7hv/kjU4/m123h+7TbgHQCqi3I4aNdK5k4tZc/JJew5uZhZVUXkaBFLEZFxS4FKZt1APEi5wDl3XXeimf0SuAj4CXBehuomIinU5uXyaGx3Hmzfg5n57RzCq8zLfZOqQO/aJgWugwM7n+ZAgNp7CJNLfc406oJT2Jo7nSZKaMmtockV0mY1NI2R6b9zAjCtOMCUPEcgJ4eOzk7e2tRAa6icrWGjzdv+f01b2rp4YOVmHli5uSctFDBmVxWx5+QSZlQWMr08n2nlBUwrL2B6RQGl+ZoiWURkLFOgkiF+a8pJxFdX+3W/wz8AvgB8yswuds5pqiyRcWyzq+Rv4cP4pzuaGdG1HJ6/jgMCb1ITbOmTL48I07vWML1rDXQOfK5WCglvLKGTfDqDRXSSR2ewmE5yiQYLibggXcFCIi7ANtrIb9tAJGbEokWEY0bUKyYSc8QooSsaxSyfWKzL30YI00phrJmYF8G8mL91xFwE84yY68JcCFwMw8Nc/IVz8dcA8oJQE+zgPTOm4XV1ErEQ9W1hNrfGaAxDUyyX6ABFo55jdV0rq+taBzxvcV6IquJcKopyqSjsfuVQUZRLSX6IgpwghbkhCnODFOQGKcwNkhcKEgwYOUEjFAyQE4hvQ0EjFDBCgQDBgGHEx+eoO5qISOooUMmc4/3tA865Pt3QnXMtZvY48UDmSGBpuis3Gp7nUVdXR21tLdbQEl/ocbs5gERke/4A/Ogs7ggfxpz8Vnbz1jA9upYDShopt53/zaKYdoqj7fGd6I7zAjCSzqUbhpH33cEP9R1gb7AOuheldN1pIYgFjTfddF5xs1nt7cJqN403velspGqHl24NR2kNR1mztX0YFR4Z8z9N98oz8a3zj7kBjxmAbZ9/uFceWbnu0iMrO9LwbDR1HalMXFMk03524iSOX/jeTFdj1MwN8hcuSS0z+znwDeAbzrlfDHD8euDLwJecczfu5FzPD3LogIKCguDcuXNHXd/h6OrqonXLeiLRGG0uFy/mEQwF8TxHNBolNzcHz3MEArbD7XDyjqTMWMubTXXRZ8xMXczFyA04coiSYx4BPELmCOIRMo+gjcWh9yPnESDscugkhy5CPa+Ii2+HsliliMh4NLMESkvL0na9VatW0dHR0eCcm5TM86pFJXO6n56mQY53p5eP4hqxjo6OphUrVqwZxTmGYm9/+9pQMg+nH9tI+ryl6vwZztvnHo+heg87b6rPv4O82z3HY6TeY82w/r3YMY94s1HH6E81viTxHssgdI/TQ/d5hN7cvPM8vmTd41lA884yDZcClXHAOXdIJq/f3aKT6XqMZ7rHqad7nB66z6mne5x6usfpofucetl+jzWvY+Z0t5gM1i7Xnd6Y+qqIiIiIiGQXBSqZ87q/3XOQ43v42zfSUBcRERERkayiQCVzHva3J5lZn5+DmZUA84F24Kl0V0xEREREJNMUqGSIc+4t4AHig4++3O/wZUARcLvWUBERERGRiUiD6TPrS8ATwLVmtghYBRxBfI2VN4DvZLBuIiIiIiIZo3VUMszMZgA/BE4GJgGbgL8BlznntmWybiIiIiIimaJARUREREREso7GqIiIiIiISNZRoCIiIiIiIllHgYqIiIiIiGQdBSoiIiIiIpJ1FKiIiIiIiEjWUaAiIiIiIiJZR4GKjJiZ7WJmt5jZRjMLm9kaM/uVmVVkum7Zxr83bpBX7SBl5pnZP82swcw6zOxlM7vQzII7uM6pZrbMzJrMrNXMnjazz6Tuk6WfmZ1mZteZ2aNm1uzfwz/spExa7qWZfcbMnvHzN/nlTx3pZ82U4dxjM5u1g2fbmdmdO7jOsO6XmQXN7CL/59fh/zz/aWbzkvG508nMJpnZf5nZ38zsTf/zNJnZY2b2OTMb8P/PepaHbrj3WM/yyJnZT81sqZm9m/B5XjCzH5jZpEHK6FkehuHc4/H0LGsdFRkRM9sNeAKoAe4BXgMOB44HXgfmO+e2Zq6G2cXM1gDlwK8GONzqnLuqX/4PAX8FOoG7gAbgA8BewN3OuY8PcI2vANcBW/0yEeA0YBfgF865byTn02SWmb0IHAC0AuuBvYE/Ouc+OUj+tNxLM7sKuNiv091ALnAmUAl81Tl3/Yg/dJoN5x6b2SzgHeAl4O8DnO4V59zdA5Qb1v0yMwP+TPzn8Dpwr5/3DCAf+Jhz7p5hf9gMMbPzgBuJL/L7MLAOmAx8FCgj/sx+3CX8T1rP8vAM9x7rWR45M4sAK4CVQB1QBBwJHApsBI50zr2bkF/P8jAN5x6Pq2fZOaeXXsN+Af8CnP/gJqb/0k//TabrmE0vYA2wZoh5S4n/IxQGDk1IzyceHDrgzH5lZhH/B38rMCshvQJ40y9zVKbvQ5Lu5fHAHoABC/zP9odM3ktgnp/+JlDR71xb/fPNGs3nzuJ7PMs/ftswzj/s+wWc5Zd5HMhPSD/M//nWASWZvnfDuAcLiX8xC/RLn0L8C7Uj/j95Pcvpu8d6lkd+r/MHSf+J/1lv0LOc1ns8bp5ldf2SYfNbU04i/uX71/0O/wBoAz5lZkVprtp4cRpQDdzpnHuuO9E51wl81989v1+Zc4E84Hrn3JqEMtuAy/3d81JV4XRyzj3snFvt/H8NdyJd97J7/yd+vu4ya4j/juQBnx1CfbPCMO/xSIzkfnX/nL7r//y6yzxL/K+r1cR/3mOCc+4h59y9zjmvX3ot8Bt/d0HCIT3LwzSCezwSE/5Zhp7ncCB/9rd7JKTpWR6BYd7jkcjKZ1mBiozE8f72gQH+B9BCPLIuJN4kKb3yzOyTZnapmX3NzI4fpC/uQn97/wDHlgPtwDwzyxtimSX98kwk6bqXuv8wzcy+6D/fXzSz/XeQd1j3y8zyif+1rx14dChlxrgufxtNSNOznFwD3eNuepaT5wP+9uWEND3LyTXQPe425p/l0GgKy4S1l799Y5Djq4m3uOwJLE1LjcaGKcDt/dLeMbPPOuceSUgb9P4656Jm9g6wLzAHWDWEMpvMrA3YxcwKnXPto/kQY0zK76Xfcjid+FijTQPUYbW/3XMUn2MsONF/9TCzZcBnnHPrEtJGcr92A4LA2865gb5Yjpt7bGYh4NP+buIXBj3LSbKDe9xNz/IImdk3gGLiY4AOBY4m/gX6yoRsepZHYYj3uNuYf5bVoiIjUeZvmwY53p1envqqjBm3AouIBytFwH7Ab4n3/VxiZgck5B3J/R1qmbJBjo9X6biXE/33oR34EXAI8f7iFcBxxAcvLwCW9usGmsqfSfkgx8eSK4H3AP90zv0rIV3PcvIMdo/1LI/eN4h3Ab+Q+Bfo+4GTnHNbEvLoWR6dodzjcfMsK1ARSQPn3GV+f+nNzrl259wrzrnziE8+UAAszmwNRUbGOVfnnPu+c26Fc67Rfy0n3qr6NLA78F+ZreXYYGYXEJ9x5zXgUxmuzri0o3usZ3n0nHNTnHNG/I9yHyXeKvKCmR2c2ZqNH0O5x+PpWVagIiOxs7/Od6c3pr4qY173gM5jE9JGcn+HWmawv3yMV+m4l/p9GIDfFeBmfzddz3fjIMeznj/16jXEpx493jnX0C+LnuVRGsI9HpCe5eHz/yj3N+JfjCcBv084rGc5CXZyjwcrM+aeZQUqMhKv+9vB+h12zzwx2BgW6dXdVJvYBDvo/fX7Vs8mPgD07SGWmeqff/0EG58CabiXzrk2YANQ7B/vbyL/Pmz3fI/wfr0FxIA5/s9tKGXGDDO7kPj6EK8Q/wI90CKwepZHYYj3eEf0LI+Ac24t8cBwXzOr8pP1LCfRIPd4R8bUs6xARUbiYX97km2/sm8JMJ94/8in0l2xMah7ZrTEf5Af8rcnD5D/WOIzqj3hnAsPscwp/fJMJOm6l7r/Axvo+YZh3i9/2ssniP+8jhlKmbHCzL4FXA28SPwLdN0gWfUsj9Aw7vGO6FkeuWn+NuZv9SwnX/97vCNj61l2WbCIjV5j74UWfBzOvZoLFA2QPov4rBgOuDQhvZT4XzyGsxjWbCbIgo/9PvcCdr7gY8rvJeNsYbFh3uOD6begnp++yP/cDpg32vvF0BYWK830/Rrmvf2e/5meAyp3klfPcurvsZ7lkd3jPYGyAdID9C5G+Lie5bTe43HzLJt/QpFh8Rd9fAKoAe4hPoXgEcTXWHmD+C/A1szVMHuY2WLigzeXA2uBFuLT+r2f+D/M/wQ+4pyLJJT5MHA38X8Y7gQagA8Sn6LxbuB01++X18y+ClxL/B+Uu4AI8YWWdgF+4Zz7Rqo+Yzr59+bD/u4U4L3E/zLUPY97feJnTde9NLNfAF8H1vvnzQXOIN53+KvOuetH9cHTaDj32J/qcg/i/x6s94/vT+/c+d9zzv14gGsM636ZmRFf2Ow04gOh7/XznkH89+hjzrl7Rv6p08vMPgPcRvwvoNcx8PixNc652xLKfBg9y0M23HusZ3lk/G51VwCPAe8Qf9YmE59lag5QCyxyzq1MKPNh9CwP2XDv8bh6ljMdJeo1dl/ADOLT7m4i/g/GWuBXJETiejmI/0PyJ/+XuJH4QmNbgH8Tn8vfBik3n3gQsw3oAP4DXAQEd3CtDwCPEA+G2oBnic+XnvH7kMT7uZj4X3AGe63J1L0EzvHztfnlHgFOzfQ9S+U9Bj4H3AesAVqJ/xVtHfEvEsck834RX/vrIv/n1+H/PP9Jv78MjoXXEO6xA5bpWU7fPdazPOL7/B7geuJd6+qJjy9p8u/HYgZpydKznLp7PJ6eZbWoiIiIiIhI1tFgehERERERyToKVEREREREJOsoUBERERERkayjQEVERERERLKOAhUREREREck6ClRERERERCTrKFAREREREZGso0BFRERERESyjgIVERERERHJOgpUREREREQk6yhQERERERGRrKNARUREZJTMbI2Zrcl0PURExhMFKiIiIiIiknUUqIiIiIiISNZRoCIiIiIiIllHgYqIiIyYmc0yM2dmt/nv7zSzejPrNLPnzOzUfvkX+/kX7Ohc/dJv89Nnm9lXzGylf/41ZnapmZmf7+Nm9oyZtZlZnZldb2YFo/x8u5jZtWa22sw6zKzBv8b3BslfZGY/N7N1ZhY2szfN7FvddeyX9xwz+6uZve2fu9nMHjezTw5y7mX+fcg1s++b2ev+NW7zj+ea2QVmtsLMtplZu3+P7jGzE0ZzH0REMiGU6QqIiMi4sCvwDPA2cDtQCZwB3GNmJzjnHk7CNa4CFgD3Ag8AHwR+AuSaWQNwJfB34FHgRODLQBA4fyQXM7NDgX8R/yzLgf8FCoF9gMXAj/oVyfHzTwOWAFHgw3698oHL+uW/EXjVP/cmYBLwPuB2M9vLOTdgMAT8FTjMv8bfgTo//TbgLOAV4PdAh1+Xo4GTgQeH+tlFRLKBAhUREUmGBcBi51zPl3EzuwO4H/gmkIxA5RBgf+fcBv/8i4E3/fO3A4c451b5x/KAF4BzzewHzrm6gU85MDPLBf5CPEj5hHPujn7Hdxmg2DTgJeBE51yHn+8y4A3gIjO73DnXlZD/Pc65twa47hLgEjP7Tfdn7WdXv2x9Qrky4EzgeeAI51ys33knDeVzi4hkE3X9EhGRZFgL/DgxwTn3L2AdcHiSrvGjxC/uzrlG4B/EWzlu7A5S/GNh4C4gF5g7gmt9AJgF/KN/kOKff/0g5S7oDlL8fHXAPUAZsFe/c7zVryzOuQjwa+J/SFw0yDW+lxikdBcFDAgD3gDn3TrIuUREspYCFRERSYYX+/8V3/cuUJGkazw3QNpGf/v8AMe6g5qBWj925kh/u2QYZZqcc28OkP6uv+1zH8xsppn92sxe88eTODNzxLt2AUwf5DrP9E9wzjUT7xI3D3jRH8NyvJkVDqP+IiJZRV2/REQkGRoHSY+SvD+KNQ1y/p0dyxnBtcr97UBdrwbTOEh6dz2C3QlmNod4wFFBfEzNA8Q/Q4x4S85ngLxBzlc7SPoZwLeAs+kdD9NpZncD33DObR7KhxARyRYKVEREJJ26uyUN9P+f8jTWY2ca/e1grRqj9XXig+c/65y7LfGAmZ1FPFAZkHPODZLeQXyQ/2IzmwEcC5wDfJJ48HPM6KstIpI+6volIiLptM3fzhjg2KHprMhOPOVvT0nR+Xf3t38d4Nhxoz25c+5d59wfgfcSn3DgaA2oF5GxRoGKiIikU/f4is+aWU+rit8C8P3MVGlA9wJrgA/6LRx9DDLr13Cs8bcL+p33vcB/DfdkZlZtZvsNcKgIKCbe/Swy3POKiGSSun6JiEjaOOeeNrPlxLslPWNmDwGTic+y9S8GbmlJO+dcxMw+TnzsyB1m9kXirSz5xGcRW8To/h96A/BZ4C/+GJKNwHuIr3fyZ+LjTYZjOvCCmf0HeJn4AP5S4FRgCnCtc65lFPUVEUk7BSoiIpJuHwJ+7m+/CqwG/h/xoOD0DNarD+fcc2Z2IHAJ8S5g84AW4l2pRtX645x72cyOJz6l8/uJ///4JeCjxMfHDDdQWQP8gHgLzfFAFdAAvO7X/87R1FdEJBNskDF5IiIiIiIiGaMxKiIiIiIiknUUqIiIiIiISNbRGBUREZkQzGzxELP+3Tn3YgqrIiIiQ6AxKiIiMiGY2VD/h7fdIowiIpJ+ClRERERERCTraIyKiIiIiIhkHQUqIiIiIiKSdRSoiIiIiIhI1lGgIiIiIiIiWUeBioiIiIiIZB0FKiIiIiIiknUUqIiIiIiISNZRoCIiIiIiIllHgYqIiIiIiGQdBSoiIiIiIpJ1FKiIiIiIiEjWUaAiIiIiIiJZR4GKiIiIiIhknf8P43CewHgifjoAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 262,
       "width": 405
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 5.72 s (started: 2022-11-04 07:07:22 +00:00)\n"
     ]
    }
   ],
   "source": [
    "# Histogram\n",
    "sns.histplot(data=enko_filtered, x=\"num_chars\", hue=\"corpus\", kde=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='num_words', ylabel='Count'>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 262,
       "width": 401
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 4.3 s (started: 2022-11-04 07:07:30 +00:00)\n"
     ]
    }
   ],
   "source": [
    "# Histogram\n",
    "sns.histplot(data=enko_filtered, x=\"num_words\", hue=\"corpus\", kde=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 1min 29s (started: 2022-11-04 07:07:44 +00:00)\n"
     ]
    }
   ],
   "source": [
    "# Sentence counts\n",
    "enko_filtered[\"num_sents\"] = enko_filtered[\"text\"].map(lambda x: len(sent_tokenize(x)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='num_sents', ylabel='Count'>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 261,
       "width": 401
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 7.09 s (started: 2022-11-04 07:09:42 +00:00)\n"
     ]
    }
   ],
   "source": [
    "# Histogram\n",
    "sns.histplot(data=enko_filtered, x=\"num_sents\", hue=\"corpus\", kde=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 191 ms (started: 2022-11-04 07:09:50 +00:00)\n"
     ]
    }
   ],
   "source": [
    "# Average number of characters per word\n",
    "enko_filtered[\"avg_num_chars\"] = enko_filtered[\"num_chars\"] / enko[\"num_words\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='avg_num_chars', ylabel='Count'>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 262,
       "width": 401
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 16 s (started: 2022-11-04 07:09:51 +00:00)\n"
     ]
    }
   ],
   "source": [
    "# Histogram\n",
    "sns.histplot(data = enko_filtered, x = \"avg_num_chars\", hue = \"corpus\", kde = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 3.03 ms (started: 2022-11-04 07:11:59 +00:00)\n"
     ]
    }
   ],
   "source": [
    "# Average number of words per sentence\n",
    "enko_filtered[\"avg_num_words\"] = enko_filtered[\"num_words\"] / enko_filtered[\"num_sents\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='avg_num_words', ylabel='Count'>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 262,
       "width": 401
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 9.31 s (started: 2022-11-04 07:12:01 +00:00)\n"
     ]
    }
   ],
   "source": [
    "# Histogram\n",
    "sns.histplot(data=enko_filtered, x=\"avg_num_words\", hue=\"corpus\", kde=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:ekorpkit.io.file:Saving dataframe to /content/drive/MyDrive/workspace/projects/ekorpkit-book/data/enko_filtered.parquet\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 30.7 s (started: 2022-11-04 07:12:11 +00:00)\n"
     ]
    }
   ],
   "source": [
    "# save filtered data\n",
    "\n",
    "eKonf.save_data(enko_filtered, \"enko_filtered.parquet\", project_dir + \"/data\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Term Frequency Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 1.19 s (started: 2022-11-04 07:12:44 +00:00)\n"
     ]
    }
   ],
   "source": [
    "# lower case\n",
    "enko_filtered[\"text\"] = enko_filtered[\"text\"].map(lambda x: x.lower())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "enko_filtered.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "enko_filtered = enko_filtered.sample(frac=0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "enko_filtered.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get all words of english and korean corpus\n",
    "\n",
    "words_en = sum(\n",
    "    enko_filtered[enko_filtered.corpus == \"enwiki_sampled\"][\"text\"]\n",
    "    .str.split()\n",
    "    .tolist(),\n",
    "    [],\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "words_ko = sum(\n",
    "    enko_filtered[enko_filtered.corpus == \"kowiki\"][\"text\"].str.split().tolist(), []\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import Counter\n",
    "\n",
    "counter_en = Counter(words_en)\n",
    "counter_ko = Counter(words_ko)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# top 10 words in english corpus\n",
    "\n",
    "counter_en.most_common(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# top 10 words in korean corpus\n",
    "\n",
    "counter_ko.most_common(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plot 20 most common words after removing top 20 common words\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "most_common_en = counter_en.most_common()[20:40]\n",
    "most_common_ko = counter_ko.most_common()[20:40]\n",
    "\n",
    "\n",
    "# Change the font to Korean font\n",
    "# You may need to install the font first\n",
    "plt.rcParams[\"font.family\"] = \"NanumGothic\"\n",
    "\n",
    "plt.figure(figsize=(20, 10))\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.bar(range(20), [x[1] for x in most_common_en])\n",
    "plt.xticks(range(20), [x[0] for x in most_common_en], rotation=90)\n",
    "plt.title(\"English\")\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.bar(range(20), [x[1] for x in most_common_ko])\n",
    "plt.xticks(range(20), [x[0] for x in most_common_ko], rotation=90)\n",
    "plt.title(\"Korean\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Most Frequent N-Grams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "\n",
    "# Initialize CountVectorizer\n",
    "vec_en = CountVectorizer(ngram_range=(2, 2))\n",
    "vec_ko = CountVectorizer(ngram_range=(2, 2))\n",
    "\n",
    "# Fit and transform\n",
    "ngram_counts_en = vec_en.fit_transform(\n",
    "    enko_filtered[enko_filtered.corpus == \"enwiki_sampled\"][\"text\"]\n",
    ")\n",
    "ngram_counts_ko = vec_ko.fit_transform(\n",
    "    enko_filtered[enko_filtered.corpus == \"kowiki\"][\"text\"]\n",
    ")\n",
    "\n",
    "# Get the n-gram counts\n",
    "ngram_counts_en = ngram_counts_en.sum(axis=0).A1\n",
    "ngram_counts_ko = ngram_counts_ko.sum(axis=0).A1\n",
    "\n",
    "# Create a DataFrame\n",
    "ngram_counts_df_en = pd.DataFrame(\n",
    "    sorted(zip(vec_en.get_feature_names(), ngram_counts_en), key=lambda x: x[1], reverse=True),\n",
    "    columns=[\"ngram\", \"count\"],\n",
    ")\n",
    "ngram_counts_df_ko = pd.DataFrame(\n",
    "    sorted(zip(vec_ko.get_feature_names(), ngram_counts_ko), key=lambda x: x[1], reverse=True),\n",
    "    columns=[\"ngram\", \"count\"],\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plot 20 most common n-grams\n",
    "\n",
    "plt.figure(figsize=(20, 10))\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.bar(range(20), ngram_counts_df_en[\"count\"][:20])\n",
    "plt.xticks(range(20), ngram_counts_df_en[\"ngram\"][:20], rotation=90)\n",
    "plt.title(\"English\")\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.bar(range(20), ngram_counts_df_ko[\"count\"][:20])\n",
    "plt.xticks(range(20), ngram_counts_df_ko[\"ngram\"][:20], rotation=90)\n",
    "plt.title(\"Korean\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Word Cloud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function for generating word clouds\n",
    "from wordcloud import WordCloud\n",
    "\n",
    "\n",
    "def generate_wordcloud(\n",
    "    data,\n",
    "    title,\n",
    "    width=400,\n",
    "    height=330,\n",
    "    max_words=150,\n",
    "    figsize=(10, 8),\n",
    "    colormap=\"Dark2\",\n",
    "    background_color=\"white\",\n",
    "):\n",
    "    wc = WordCloud(\n",
    "        width=width,\n",
    "        height=height,\n",
    "        max_words=max_words,\n",
    "        colormap=colormap,\n",
    "        background_color=background_color,\n",
    "    ).generate_from_frequencies(data)\n",
    "    plt.figure(figsize=figsize)\n",
    "    plt.imshow(wc, interpolation=\"bilinear\")\n",
    "    plt.axis(\"off\")\n",
    "    plt.title(title, fontsize=13)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate word clouds\n",
    "\n",
    "# English\n",
    "generate_wordcloud(\n",
    "    dict(counter_en.most_common(20)),\n",
    "    \"English\",\n",
    ")\n",
    "\n",
    "# Korean\n",
    "generate_wordcloud(\n",
    "    dict(counter_ko.most_common(20)),\n",
    "    \"Korean\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate word clouds for n-grams\n",
    "\n",
    "# English\n",
    "generate_wordcloud(\n",
    "    dict(ngram_counts_df_en.set_index(\"ngram\")[\"count\"]),\n",
    "    \"English\",\n",
    ")\n",
    "\n",
    "# Korean\n",
    "generate_wordcloud(\n",
    "    dict(ngram_counts_df_ko.set_index(\"ngram\")[\"count\"]),\n",
    "    \"Korean\",\n",
    ")"
   ]
  }
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