2-Multi-Class-Word-Language-Classification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Project Repository:** https://github.com/GokulKarthik/deep-learning-projects-pytorch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "import glob\n",
    "import time\n",
    "from tqdm.notebook import tqdm\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "#from torch.utils.tensorboard import SummaryWriter\n",
    "#from torchsummary import summary\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix\n",
    "\n",
    "import unicodedata\n",
    "import string\n",
    "import copy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "#writer = SummaryWriter(\"runs/word-classification\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Load data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Download and extract the data zip file from [this link](https://download.pytorch.org/tutorial/data.zip). All the .txt files in the names folder can be used to train this word classification model. I have placed those text files in `data/language-words` directory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['storage/data/language-words/French.txt', 'storage/data/language-words/Portuguese.txt', 'storage/data/language-words/Chinese.txt', 'storage/data/language-words/Scottish.txt', 'storage/data/language-words/Korean.txt', 'storage/data/language-words/Japanese.txt', 'storage/data/language-words/Dutch.txt', 'storage/data/language-words/Arabic.txt', 'storage/data/language-words/Irish.txt', 'storage/data/language-words/Czech.txt', 'storage/data/language-words/Spanish.txt', 'storage/data/language-words/Vietnamese.txt', 'storage/data/language-words/Russian.txt', 'storage/data/language-words/Greek.txt', 'storage/data/language-words/German.txt', 'storage/data/language-words/English.txt', 'storage/data/language-words/Polish.txt', 'storage/data/language-words/Italian.txt']\n"
     ]
    }
   ],
   "source": [
    "data_folder = os.path.join(\"storage\", \"data\", \"language-words\")\n",
    "file_paths = glob.glob(os.path.join(data_folder, \"*.txt\"))\n",
    "print(file_paths)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['wolter', 'wörner', 'wörnhör', 'wruck', 'wyman', 'xylander', 'zellweger', 'zilberschlag', 'zimmerman', 'zimmermann']\n"
     ]
    }
   ],
   "source": [
    "words_dict = {}\n",
    "for file_path in file_paths:\n",
    "    language = file_path.split(\"/\")[3][:-4]\n",
    "    with open(file_path, \"r\") as file:\n",
    "        words = file.readlines()\n",
    "        words = [word.strip().lower() for word in words]\n",
    "        words_dict[language] = words\n",
    "print(words_dict['German'][-10:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "French 277\n",
      "Portuguese 74\n",
      "Chinese 268\n",
      "Scottish 100\n",
      "Korean 94\n",
      "Japanese 991\n",
      "Dutch 297\n",
      "Arabic 2000\n",
      "Irish 232\n",
      "Czech 519\n",
      "Spanish 298\n",
      "Vietnamese 73\n",
      "Russian 9408\n",
      "Greek 203\n",
      "German 724\n",
      "English 3668\n",
      "Polish 139\n",
      "Italian 709\n"
     ]
    }
   ],
   "source": [
    "for language, words in words_dict.items():\n",
    "    print(language, len(words))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Clean text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[' ', \"'\", ',', '-', '/', '1', ':', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z', '\\xa0', 'ß', 'à', 'á', 'ã', 'ä', 'ç', 'è', 'é', 'ê', 'ì', 'í', 'ñ', 'ò', 'ó', 'õ', 'ö', 'ù', 'ú', 'ü', 'ą', 'ł', 'ń', 'ś', 'ż']\n"
     ]
    }
   ],
   "source": [
    "characters = set()\n",
    "for language, words in words_dict.items():\n",
    "    for word in words:\n",
    "        characters.update(list(word))\n",
    "characters = sorted(list(characters))\n",
    "print(characters)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are many accented characers in the dataset which have to transformed into the raw format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['ñ'] ['n', '̃']\n"
     ]
    }
   ],
   "source": [
    "c = list('ñ')\n",
    "c_normalised = list(unicodedata.normalize(\"NFD\", 'ñ'))\n",
    "print(c, c_normalised)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[' ', \"'\", ',', '-', '/', '1', ':', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z', '\\xa0', 'ß', 'a', 'a', 'a', 'a', 'c', 'e', 'e', 'e', 'i', 'i', 'n', 'o', 'o', 'o', 'o', 'u', 'u', 'u', 'a', 'ł', 'n', 's', 'z']\n"
     ]
    }
   ],
   "source": [
    "characters_normalised = []\n",
    "for character in characters:\n",
    "    character_normalised = unicodedata.normalize(\"NFD\", character)[0]\n",
    "    characters_normalised.append(character_normalised)\n",
    "print(characters_normalised)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "32 ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z', ' ', '-', \"'\", ',', ':', ';']\n"
     ]
    }
   ],
   "source": [
    "characters_all = list(string.ascii_lowercase + \" -',:;\")\n",
    "print(len(characters_all), characters_all)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "def clean_word(word):\n",
    "    cleaned_word = \"\"\n",
    "    for character in word:\n",
    "        for character_raw in unicodedata.normalize('NFD', character):\n",
    "            if character_raw in characters_all:\n",
    "                cleaned_word += character_raw\n",
    "    return cleaned_word"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "words_dict_cleaned = {}\n",
    "for language, words in words_dict.items():\n",
    "    cleaned_words = []\n",
    "    for word in words:\n",
    "        cleaned_word = clean_word(word)\n",
    "        cleaned_words.append(cleaned_word)\n",
    "    words_dict_cleaned[language] = cleaned_words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['wolter', 'wörner', 'wörnhör', 'wruck', 'wyman', 'xylander', 'zellweger', 'zilberschlag', 'zimmerman', 'zimmermann']\n",
      "['wolter', 'worner', 'wornhor', 'wruck', 'wyman', 'xylander', 'zellweger', 'zilberschlag', 'zimmerman', 'zimmermann']\n",
      "['santos', 'serafim', 'silva', 'silveira', 'simões', 'soares', 'souza', 'torres', 'vargas', 'ventura']\n",
      "['santos', 'serafim', 'silva', 'silveira', 'simoes', 'soares', 'souza', 'torres', 'vargas', 'ventura']\n",
      "['wyrick', 'wyrzyk', 'zabek', 'zawisza', 'zdunowski', 'zdunowski', 'zielinski', 'ziemniak', 'zientek', 'żuraw']\n",
      "['wyrick', 'wyrzyk', 'zabek', 'zawisza', 'zdunowski', 'zdunowski', 'zielinski', 'ziemniak', 'zientek', 'zuraw']\n"
     ]
    }
   ],
   "source": [
    "print(words_dict['German'][-10:])\n",
    "print(words_dict_cleaned['German'][-10:])\n",
    "print(words_dict['Portuguese'][-10:])\n",
    "print(words_dict_cleaned['Portuguese'][-10:])\n",
    "print(words_dict['Polish'][-10:])\n",
    "print(words_dict_cleaned['Polish'][-10:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "words_dict = copy.deepcopy(words_dict_cleaned)\n",
    "del words_dict_cleaned"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Define utilities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "18 32\n"
     ]
    }
   ],
   "source": [
    "num_langs = len(words_dict.keys())\n",
    "num_chars = len(characters_all)\n",
    "print(num_langs, num_chars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20\n"
     ]
    }
   ],
   "source": [
    "max_timesteps = 0\n",
    "for language, words in words_dict.items():\n",
    "    for word in words:\n",
    "        if len(word) > max_timesteps:\n",
    "            max_timesteps = len(word)\n",
    "print(max_timesteps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Arabic': 0, 'Chinese': 1, 'Czech': 2, 'Dutch': 3, 'English': 4, 'French': 5, 'German': 6, 'Greek': 7, 'Irish': 8, 'Italian': 9, 'Japanese': 10, 'Korean': 11, 'Polish': 12, 'Portuguese': 13, 'Russian': 14, 'Scottish': 15, 'Spanish': 16, 'Vietnamese': 17}\n"
     ]
    }
   ],
   "source": [
    "lang_to_id = {k:v for k, v in zip(sorted(list(words_dict.keys())), range(len(words_dict.keys())))}\n",
    "print(lang_to_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{0: 'Arabic', 1: 'Chinese', 2: 'Czech', 3: 'Dutch', 4: 'English', 5: 'French', 6: 'German', 7: 'Greek', 8: 'Irish', 9: 'Italian', 10: 'Japanese', 11: 'Korean', 12: 'Polish', 13: 'Portuguese', 14: 'Russian', 15: 'Scottish', 16: 'Spanish', 17: 'Vietnamese'}\n"
     ]
    }
   ],
   "source": [
    "id_to_lang = {v:k for k, v in lang_to_id.items()}\n",
    "print(id_to_lang)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'a': 0, 'b': 1, 'c': 2, 'd': 3, 'e': 4, 'f': 5, 'g': 6, 'h': 7, 'i': 8, 'j': 9, 'k': 10, 'l': 11, 'm': 12, 'n': 13, 'o': 14, 'p': 15, 'q': 16, 'r': 17, 's': 18, 't': 19, 'u': 20, 'v': 21, 'w': 22, 'x': 23, 'y': 24, 'z': 25, ' ': 26, '-': 27, \"'\": 28, ',': 29, ':': 30, ';': 31}\n"
     ]
    }
   ],
   "source": [
    "char_to_id = {k:v for k, v in zip(characters_all, range(len(characters_all)))}\n",
    "print(char_to_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{0: 'a', 1: 'b', 2: 'c', 3: 'd', 4: 'e', 5: 'f', 6: 'g', 7: 'h', 8: 'i', 9: 'j', 10: 'k', 11: 'l', 12: 'm', 13: 'n', 14: 'o', 15: 'p', 16: 'q', 17: 'r', 18: 's', 19: 't', 20: 'u', 21: 'v', 22: 'w', 23: 'x', 24: 'y', 25: 'z', 26: ' ', 27: '-', 28: \"'\", 29: ',', 30: ':', 31: ';'}\n"
     ]
    }
   ],
   "source": [
    "id_to_char = {v:k for k, v in char_to_id.items()}\n",
    "print(id_to_char)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Split data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "HBox(children=(FloatProgress(value=0.0, max=18.0), HTML(value='')))"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "(20074, 2)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>word</th>\n",
       "      <th>language</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>abel</td>\n",
       "      <td>French</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>abraham</td>\n",
       "      <td>French</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>adam</td>\n",
       "      <td>French</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>albert</td>\n",
       "      <td>French</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>allard</td>\n",
       "      <td>French</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      word language\n",
       "0     abel   French\n",
       "1  abraham   French\n",
       "2     adam   French\n",
       "3   albert   French\n",
       "4   allard   French"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "words_df = []\n",
    "for language, words in tqdm(words_dict.items()):\n",
    "    for word in words:\n",
    "        words_df.append({\"word\":word, \"language\":language})\n",
    "words_df = pd.DataFrame(words_df)\n",
    "print(words_df.shape)\n",
    "words_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(16059, 2)\n",
      "(4015, 2)\n"
     ]
    }
   ],
   "source": [
    "words_df_train, words_df_test = train_test_split(words_df, train_size=0.8, stratify=words_df[\"language\"], random_state=0)\n",
    "words_df_train = words_df_train.reset_index(drop=True)\n",
    "words_df_test = words_df_test.reset_index(drop=True)\n",
    "print(words_df_train.shape)\n",
    "print(words_df_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th {\n",
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       "    }\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>Arabic</th>\n",
       "      <th>Chinese</th>\n",
       "      <th>Czech</th>\n",
       "      <th>Dutch</th>\n",
       "      <th>English</th>\n",
       "      <th>French</th>\n",
       "      <th>German</th>\n",
       "      <th>Greek</th>\n",
       "      <th>Irish</th>\n",
       "      <th>Italian</th>\n",
       "      <th>Japanese</th>\n",
       "      <th>Korean</th>\n",
       "      <th>Polish</th>\n",
       "      <th>Portuguese</th>\n",
       "      <th>Russian</th>\n",
       "      <th>Scottish</th>\n",
       "      <th>Spanish</th>\n",
       "      <th>Vietnamese</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Train</th>\n",
       "      <td>1600</td>\n",
       "      <td>214</td>\n",
       "      <td>415</td>\n",
       "      <td>238</td>\n",
       "      <td>2934</td>\n",
       "      <td>222</td>\n",
       "      <td>579</td>\n",
       "      <td>163</td>\n",
       "      <td>186</td>\n",
       "      <td>567</td>\n",
       "      <td>793</td>\n",
       "      <td>75</td>\n",
       "      <td>111</td>\n",
       "      <td>59</td>\n",
       "      <td>7526</td>\n",
       "      <td>80</td>\n",
       "      <td>238</td>\n",
       "      <td>59</td>\n",
       "      <td>16059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Test</th>\n",
       "      <td>400</td>\n",
       "      <td>54</td>\n",
       "      <td>104</td>\n",
       "      <td>59</td>\n",
       "      <td>734</td>\n",
       "      <td>55</td>\n",
       "      <td>145</td>\n",
       "      <td>40</td>\n",
       "      <td>46</td>\n",
       "      <td>142</td>\n",
       "      <td>198</td>\n",
       "      <td>19</td>\n",
       "      <td>28</td>\n",
       "      <td>15</td>\n",
       "      <td>1882</td>\n",
       "      <td>20</td>\n",
       "      <td>60</td>\n",
       "      <td>14</td>\n",
       "      <td>4015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total</th>\n",
       "      <td>2000</td>\n",
       "      <td>268</td>\n",
       "      <td>519</td>\n",
       "      <td>297</td>\n",
       "      <td>3668</td>\n",
       "      <td>277</td>\n",
       "      <td>724</td>\n",
       "      <td>203</td>\n",
       "      <td>232</td>\n",
       "      <td>709</td>\n",
       "      <td>991</td>\n",
       "      <td>94</td>\n",
       "      <td>139</td>\n",
       "      <td>74</td>\n",
       "      <td>9408</td>\n",
       "      <td>100</td>\n",
       "      <td>298</td>\n",
       "      <td>73</td>\n",
       "      <td>20074</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Arabic  Chinese  Czech  Dutch  English  French  German  Greek  Irish  \\\n",
       "Train    1600      214    415    238     2934     222     579    163    186   \n",
       "Test      400       54    104     59      734      55     145     40     46   \n",
       "Total    2000      268    519    297     3668     277     724    203    232   \n",
       "\n",
       "       Italian  Japanese  Korean  Polish  Portuguese  Russian  Scottish  \\\n",
       "Train      567       793      75     111          59     7526        80   \n",
       "Test       142       198      19      28          15     1882        20   \n",
       "Total      709       991      94     139          74     9408       100   \n",
       "\n",
       "       Spanish  Vietnamese  Total  \n",
       "Train      238          59  16059  \n",
       "Test        60          14   4015  \n",
       "Total      298          73  20074  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_count = words_df_train[\"language\"].value_counts().rename(\"Train\")\n",
    "test_count = words_df_test[\"language\"].value_counts().rename(\"Test\")\n",
    "count = pd.concat([train_count, test_count], axis=1, sort=True).T\n",
    "count.loc[\"Total\", :] = count.sum(axis=0) # add row\n",
    "count.loc[:, \"Total\"] = count.sum(axis=1) # add col\n",
    "count = count.astype(\"int\")\n",
    "count"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Define dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our PyTorch RNN based model will take input of `Size([mini_batch_size, timesteps, input_size])` and produce output of `Size([mini_batch_size, output_size])`. In order to make uniform timesteps across different words, let us pad with space."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "class WordDataset(Dataset):\n",
    "    \n",
    "    def __init__(self, words_df):\n",
    "        self.words_df = words_df\n",
    "    \n",
    "    def __len__(self):\n",
    "        self.len = len(self.words_df)\n",
    "        return self.len\n",
    "    \n",
    "    def __getitem__(self, idx):\n",
    "        row = self.words_df.iloc[idx, :]\n",
    "        \n",
    "        word = row['word'].ljust(max_timesteps)\n",
    "        x = torch.zeros((max_timesteps, num_chars)) # [timesteps, input_size]\n",
    "        for i, char in enumerate(word):\n",
    "            x[i, char_to_id[char]] = 1\n",
    "        \n",
    "        # y = np.zeros(num_langs) # [output_size]\n",
    "        # y[lang_to_id[row['language']]] = 1\n",
    "        y = lang_to_id[row['language']]\n",
    "        \n",
    "        return x, y  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_set = WordDataset(words_df_train)\n",
    "test_set = WordDataset(words_df_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Define dataloader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_batch_size = 128\n",
    "test_batch_size = 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8\n"
     ]
    }
   ],
   "source": [
    "num_cpus = os.cpu_count()\n",
    "print(num_cpus)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_loader = DataLoader(train_set, batch_size=train_batch_size, shuffle=True, num_workers=num_cpus)\n",
    "test_loader = DataLoader(test_set, batch_size=test_batch_size, shuffle=False, num_workers=num_cpus)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([128, 20, 32]) torch.Size([128])\n"
     ]
    }
   ],
   "source": [
    "train_iter = iter(train_loader)\n",
    "X, Y = train_iter.next()\n",
    "print(X.size(), Y.size())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "126\n"
     ]
    }
   ],
   "source": [
    "len_train_loader = len(train_loader)\n",
    "print(len_train_loader)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. Define model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [],
   "source": [
    "hidden_size = 24\n",
    "num_layers = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cuda:0\n"
     ]
    }
   ],
   "source": [
    "device = \"cuda:0\" if torch.cuda.is_available() else \"cpu\"\n",
    "device = torch.device(device)\n",
    "print(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Model(nn.Module):\n",
    "    \n",
    "    def __init__(self, input_size, output_size, hidden_size, num_layers):\n",
    "        super(Model, self).__init__()\n",
    "        self.hidden_size = hidden_size\n",
    "        self.num_layers = num_layers\n",
    "        self.lstm1 = nn.LSTM(input_size=input_size, hidden_size=hidden_size, num_layers=num_layers, batch_first=True)\n",
    "        self.fc2 = nn.Linear(in_features=hidden_size, out_features=output_size)\n",
    "        \n",
    "    def forward(self, X):\n",
    "        batch_size = X.size(0)\n",
    "        h0 = torch.randn((self.num_layers, batch_size, self.hidden_size)).to(device)\n",
    "        c0 = torch.randn((self.num_layers, batch_size, self.hidden_size)).to(device)\n",
    "        out, ht = self.lstm1(X, (h0, c0)) # out -> [batch_size, time_steps, input_size]\n",
    "        outn = out[:,-1,:]\n",
    "        outn = outn.contiguous().view(batch_size, self.hidden_size)\n",
    "        outn = self.fc2(outn)\n",
    "        return outn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DataParallel(\n",
      "  (module): Model(\n",
      "    (lstm1): LSTM(32, 24, num_layers=2, batch_first=True)\n",
      "    (fc2): Linear(in_features=24, out_features=18, bias=True)\n",
      "  )\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "model = Model(input_size=num_chars, output_size=num_langs, hidden_size=hidden_size, num_layers=num_layers)\n",
    "model = nn.DataParallel(model)\n",
    "model = model.to(device)\n",
    "print(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.float32\n",
      "torch.float32\n",
      "torch.float32\n",
      "torch.float32\n",
      "torch.float32\n",
      "torch.float32\n",
      "torch.float32\n",
      "torch.float32\n",
      "torch.float32\n",
      "torch.float32\n"
     ]
    }
   ],
   "source": [
    "#list(model.parameters())\n",
    "for p in model.parameters():\n",
    "    print(p.dtype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [],
   "source": [
    "#summary(model, input_size=(max_timesteps, num_chars))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [],
   "source": [
    "#writer.add_graph(model, X)\n",
    "#writer.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8. Set optimizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "504\n"
     ]
    }
   ],
   "source": [
    "lr = 0.01\n",
    "step_size = len_train_loader * 4\n",
    "gamma = 0.95\n",
    "print(step_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Arabic        0.016187\n",
      "Chinese       0.054123\n",
      "Czech         0.036375\n",
      "Dutch         0.050779\n",
      "English       0.011250\n",
      "French        0.052944\n",
      "German        0.029787\n",
      "Greek         0.063726\n",
      "Irish         0.058874\n",
      "Italian       0.030163\n",
      "Japanese      0.024664\n",
      "Korean        0.101530\n",
      "Polish        0.080249\n",
      "Portuguese    0.117252\n",
      "Russian       0.006393\n",
      "Scottish      0.097673\n",
      "Spanish       0.050779\n",
      "Vietnamese    0.117252\n",
      "Name: language, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "alpha = 0.6\n",
    "weights = len(words_df_train) / (words_df_train['language'].value_counts() ** alpha)\n",
    "weights = weights / weights.sum()\n",
    "weights = weights.sort_index()\n",
    "print(weights)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([0.0162, 0.0541, 0.0364, 0.0508, 0.0113, 0.0529, 0.0298, 0.0637, 0.0589,\n",
      "        0.0302, 0.0247, 0.1015, 0.0802, 0.1173, 0.0064, 0.0977, 0.0508, 0.1173],\n",
      "       device='cuda:0')\n"
     ]
    }
   ],
   "source": [
    "weights = torch.Tensor(weights).to(device)\n",
    "print(weights)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [],
   "source": [
    "criterion = nn.CrossEntropyLoss(weight=weights, reduction=\"mean\")\n",
    "optimizer = optim.Adam(model.parameters(), lr=lr)\n",
    "lr_scheduler = optim.lr_scheduler.StepLR(optimizer=optimizer, step_size=step_size, gamma=gamma)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 9. Train model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [],
   "source": [
    "epochs = 100\n",
    "print_every_n_epochs = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "epoch_losses = []\n",
    "epoch_lrs = []\n",
    "iteration_losses = []\n",
    "iteration_lrs = []\n",
    "\n",
    "for epoch in tqdm(range(1, epochs+1)):\n",
    "    epoch_loss = 0\n",
    "    epoch_lr = 0\n",
    "    \n",
    "    for X, Y in tqdm(train_loader, desc=\"Epoch-{}\".format(epoch)):\n",
    "        X, Y = X.to(device), Y.to(device)\n",
    "        \n",
    "        optimizer.zero_grad()\n",
    "        Y_pred_logits = model(X)\n",
    "        loss = criterion(Y_pred_logits, Y)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        lr_scheduler.step()\n",
    "        \n",
    "        iteration_losses.append(loss.item())\n",
    "        iteration_lrs.append(lr_scheduler.get_lr()[0])\n",
    "        epoch_loss += loss.item()\n",
    "        epoch_lr += lr_scheduler.get_lr()[0]\n",
    "        \n",
    "    epoch_loss /= len(train_loader)\n",
    "    epoch_lr /= len(train_loader)\n",
    "    epoch_losses.append(epoch_loss)\n",
    "    epoch_lrs.append(epoch_lr)\n",
    "    \n",
    "    if epoch % print_every_n_epochs == 0:    \n",
    "        message = \"Epoch:{}    Loss:{}    LR:{}\".format(epoch, epoch_loss, epoch_lr)\n",
    "        print(message)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True, figsize=(15, 8))\n",
    "ax1.plot(epoch_losses, marker=\"o\", markersize=5)\n",
    "ax1.set_title(\"Loss\")\n",
    "ax2.plot(epoch_lrs, marker=\"o\", markersize=5)\n",
    "ax2.set_title(\"LR\")\n",
    "plt.xlabel(\"Epochs\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True, figsize=(15, 8))\n",
    "ax1.plot(iteration_losses[::10])\n",
    "ax1.set_title(\"Loss\")\n",
    "ax2.plot(iteration_lrs[::10])\n",
    "ax2.set_title(\"LR\")\n",
    "plt.xlabel(\"Iterations\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "window = 100\n",
    "plt.figure(figsize=(15, 4))\n",
    "pd.Series(iteration_losses).rolling(window=window).mean().iloc[window-1:].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = os.path.join(\"storage\", \"models\", \"language-words\", \"classifier.pth\")\n",
    "torch.save(model.state_dict(), path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 10. Test Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path = os.path.join(\"storage\", \"models\", \"language-words\", \"classifier.pth\")\n",
    "model = Model(input_size=num_chars, output_size=num_langs, hidden_size=hidden_size, num_layers=num_layers)\n",
    "model = nn.DataParallel(model)\n",
    "model.load_state_dict(torch.load(path))\n",
    "#model = model.to(\"cpu\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "401f9d6f94d043919eb9263b0668e6d7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=126.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "with torch.no_grad():\n",
    "    Y_train, Y_pred_train = [], []\n",
    "    for X_mb, Y_mb in tqdm(train_loader):\n",
    "        out = model(X_mb)\n",
    "        _, Y_pred_mb = torch.max(out, 1)\n",
    "        Y_train.extend(Y_mb.numpy().tolist())\n",
    "        Y_pred_train.extend(Y_pred_mb.cpu().numpy().tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2272dbd49b454cd99a852877c359a57f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=1004.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "with torch.no_grad():\n",
    "    Y_test, Y_pred_test = [], []\n",
    "    for X_mb, Y_mb in tqdm(test_loader):\n",
    "        out = model(X_mb)\n",
    "        _, Y_pred_mb = torch.max(out, 1)\n",
    "        Y_test.extend(Y_mb.numpy().tolist())\n",
    "        Y_pred_test.extend(Y_pred_mb.cpu().numpy().tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Accuracy: 0.8238993710691824\n",
      "Test Accuracy: 0.7364881693648817\n"
     ]
    }
   ],
   "source": [
    "train_accuracy = accuracy_score(Y_train, Y_pred_train)\n",
    "test_accuracy = accuracy_score(Y_test, Y_pred_test)\n",
    "print(\"Train Accuracy: {}\".format(train_accuracy))\n",
    "print(\"Test Accuracy: {}\".format(test_accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3,)\n",
      "(3, 1)\n"
     ]
    }
   ],
   "source": [
    "mat = np.array([1, 2, 3])\n",
    "print(mat.shape)\n",
    "print(mat[:, np.newaxis].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = sorted(list(lang_to_id.keys()))\n",
    "c_mat_train = confusion_matrix(Y_train, Y_pred_train)\n",
    "c_mat_train = c_mat_train / c_mat_train.sum(axis=1)[:, np.newaxis]\n",
    "plt.figure(figsize=(15,5))\n",
    "sns.heatmap(c_mat_train, annot=True, fmt=\"0.2f\", xticklabels=labels, yticklabels=labels)\n",
    "plt.title('Train Data')\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('Actual')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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93NW0AfFx8SScSCQzI5PVy9fRvqv9uVRSfDJ/7j9CVp5GQGZGJhnpGQC4lnZBnNTwNgwqq2hTMVBScr4DkKGU+ip7gVIqGvgNKCcii0XkgIjMtTWqEJFwEWlh+3xRRCaJSLSIbBeR6rbl1URkiYjssE1tbMvbiUiUbdotIuVty0fZ4vaIyFvO+Mc8PN04GZ+YMx+fkISHh1uhMRaLhfPnL1C1amU8PApY19N+3aJguuMOss6czpnPOnsGU9U77GLMnl6YPb2p+NFnVPz0C1yat3J4v9fCiPlkRCcADw834nNtOyEhCQ+P6oXGWCwWLlz4i6pVK9vF9O7dnaiofaSnpzvuZMC80k4l18moXkZ0qly9CimJZ3PmU5JSqHyNK9PtQjqxJ3wXACLCI+OeYP6kWQ575MaI+WREJ6N6aafro5pbNZITrp5LnU46w51u1a6xhj3VPe5kwfqZhO1cyqzP5nL21DmHnQxDlqVoUzFQUhpKdwE7C0lrCgwDGgK1gTYFxNwObFdKNQE2A8/Ylk8FPlVKtQT6AN/alo8EXlRK+QP3A5dEJACoB7QC/IHmIvKAo/9YSUXMZsyeXpwfM5S/3nubckNHIbeXK24tjZNo0MCXSZNeY8iQ14pbRaPR3CTu6/UAPo3rEDZjOQCdHu/Gno27SE1OKWYzjeZ/j1OJp+nXaRDB9/YjMKQbVe6o/K/rlBh0j9JN5Q+lVLyyPuUVBfgUEJMOZD/LtDNXTGfgMxGJAn4BKohIOWAL8ImIvAxUUkplAgG2aTewC6iPteGUD9uzU5EiEpmV9fc15RMTkvH28siZ9/J0JzExudAYs9lMxYoVOHculcTEAtZNsF+3KGSdPYup2p0586Y7qpF17qxdjOXsGdK3bwGLhaxTyVgSTmL29Mq7KadhxHwyohNAYmIyXrm27enpTmLiqUJjzGYzFSqU59y5VFu8GwsXzuDpp4dz9Ohx5zgZMK+0U8l1MqqXEZ1ST6VQxePqHQFV3KuQWsCV6YZtGhM0pA9TBr9LZnomAHWa+dJ54IN8FPElj4wdSJuH2vHwmPwPpP9XjJhPRnQyqpd2uj7OJJ/BzfPqudSd7tU4nXzmP2/n7KlzHDlwjKb3NHHYyTDoZ5ScTgzQvJC0K7k+Wyh4yPMMpZQqIMYEtFZK+dsmT6XURaXUe8BgoCywRUTqAwK8myu2rlLqu4KElFIzlFItlFItTKbbr/mP7YiMom7dWvj4eOPi4kJISDArQtfYxawIXcPjjz8MQJ8+PdgYviVneUhIMK6urvj4eFO3bi3+2LH7mvu7HjIPHcDs4YWpuhuUKkXpdh2tjaJcpG+LwKWxPwBSoSJmT28sSYkFbc4pGDGfjOgEEBkZbef18MNBhIautYsJDV3LgAF9AXjooe6Eh28FoGLFCixbNpNx495j27ZIp/iAMfNKO5VcJ6N6GdHpWPRhqvu4c4fXnZhdSnFPUFt2r7U/tms0qsWTk/+PKYPf469zF3KWfz1sKq+0eY6RbZ9n/uQf2bJ0E4ven+OwkxHzyYhORvXSTtdHTNQBvGt54+HtTimXUnQN7sym1Vv+fUWsjarSZazPB5evWB7/Vo05fuSEw06GoQT1KJWU9yhtACaLyLNKqRkAItIY621xjrAGeAn40LZNf6VUlIjUUUrtBfaKSEusvUergXdEZK5S6qKIeGJtgJ0udOvXgcViYeiwcYStnIfZZGLmrAXExh5iwpsjidwZTWjoWr7/YT6zZk7jQGwEqalpPDbAOtxzbOwhFi9ewd7ojWRaLLw89PV8DwQWiSwLF7+cQsWJH4HZxOU1YVhOxHHb40+ReegA6b9vJWPnH7g2a0mlr2eBJYu/v/sS9Zf1B7bih9Mxe9dAypSl8uxFXPz0AzJ27XBIyYj5ZESnbK9hw95gxYrZmM1mZs1awP79hxg//hV27tzLypVrmTlzAd9/P4WYmM2kpKQxcOAQAJ5//gnq1PFh7NihjB07FIDAwAGcOePYvdFGzCvtVHKdjOplRKcsSxazx3/LqB/fwGQ2sXnhBhL+PEnv4Y8Qt/cwu9dF8shrAyl9Wxle/GIEACkJZ5nyzHsO77swjJhPRnQyqpd2un6n98d+wuc/fYLJbOKX+Ss5eugYz416mtjoA2xes4WGTerz8feTqVCpPA90acNzo57m4faPU6teTV55cwhKgQjM/uonDh846rCTYShB71GSqx0txkZEPIApWHuWLgNxwM9AsFIq0BbzGRCplJopIuHASKVUpIhcVEqVs8X0BQKVUoNE5A7gc6AB1kbjZqXUcyIyHesAEllYe7MGKaWuiMhQrD1NABeBAUqpI9fyLuXqaagMTu5Ut7gV8uG2/nBxK5QYSpnMxa2Qj8xiesBSo/lfp79H6+JWyMfcxO3FraDR3HAaV61V3AoFsispQorb4Xq4HB1WpHPjMk263/T/r6T0KKGUSgRCCkj6JlfMkFyf2+f6XC7X58XAYtvns0C/Avb1UiEOU7EOAKHRaDQajUaj0Wj+K8V0G11RKDENJY1Go9FoNBqNRlPCKUG33umGkkaj0Wg0Go1Go7k56B4lTTZlSrkWt4IdHhuu+UhVsTCleofiViiQV06HF7dCPhpU8i5uhXyc/Oe/D3d6o6lWplJxK+Tjz7SE4lYoEKPVUQDploziVigRGPF5oKplyxe3QoGkXr5Y3Ar58K10416pUVQOpcUXt0I+Kpcx3jsa96XEFbdCyaYEPdtcUoYH12g0Go1Go9FoNJqbhu5R0mg0Go1Go9FoNDcHfeudRqPRaDQajUaj0eShBA3moG+9MwCduzzArqj1RO/dyCsjnsuX7urqyqwfpxO9dyMbNy2jRg1PAJq3aMLW7SvZun0l27aHEdQzwGlOAQHt2bd3E7GxEYwa+WKBTnPnfEFsbAQRv62gZk3rvdZVqlRizeqFpJw7yJQpE53mA1CjfWP6h3/IgN8+ptkLQfnSGw3oyKNr36Xfqkk8tOQNKtfzAMC31330WzUpZ3rx+I/c0bCGU5yMmE/3dbiHZRE/sXzbAp4cMiBferPWTZi35nt2xG+ic2D7nOW+jeoxK/RrFm+aw4INswgI7uRUr46d72f7zlX8EbWWl4c/my/d1dWFb3+Ywh9Ra1m9YRHetnLuXcOTk6f2sDFiORsjlvPRp285zalth9aEbV3Eqt+XMPilgfnSW7RuypJ1P7I3cSsBgR3t0mbMn8rvf67nyzmfOM0HoGtAe2L2beZAbASjRxVcpubN/ZIDsRFsjbhapgDGjB7CgdgIYvZtJqBLO6c5GbGOAmMef0Z0MmKZ6tCpLVsif2X77tW8NPyZApxcmPHDJ2zfvZpf1y/IqQ8AGjbyZeXa+WzavoLwrb9QurRznrMz4nfXpkNrVmxZQNj2RTz90uP50pu39mfh2llEJUTQJfDq875+jeoxZ+U3/LxpHks3zqFbcGenehkxr3SZKkGorKJNxcAt11ASETcRmS8iR0Rkp4iEiYivE7Y7QURGOsMxNyaTiU8+fZuHeg2iRbMAHn64J/Xr278U9olBIaSlnafJ3R34fPp3vDPxVQBiYw5yf5ue3Ne6B716PcG0aZMwmx1/IanJZGLq1IkE9XycJk060K9fMA3q17OLefLJR0hNO0/Dhm2ZNu0bJk8aC8Dly1eY8NaHjHn1HYc9ciMmod3EJ1gx8APmdRyNb3DrnIZQNod+3sZPXV5jQbfX2fXVStqOH2BbvpUF3V5nQbfXWTfsSy6cPMPZ2BMOOxkxn0wmE6++O4Ihj42gzwP96da7M7V9fexikhJO8ebQSaxattZu+eVLl3njpXfo224AQx4dwci3X6ZcBec8RGsymXj/4zfp1+cZ2rTszkN9A/H1q2MX03/gw6SlnaeVfxe++nwmb741Kict7tgJOrQNpkPbYEYOf9NpTm+8P5pnHx1KUNt+9HioK3V87V8imJiQzGsvv83KpWvyrf/953MY86JzXHI7TZs6icCgAdzdpAP9+vWiQQP7MvXUk4+Smnqe+g3bMmXaN7w7+XUAGjSoR0hIMI39O9IjsD/Tp03GZHK8ijdiHZXtZcTjz4hORixT7308nsf6PsP9rQLp3adHvvrgsYF9SUu7QOumXfn6i1m88dYIAMxmM5/P+JBRw9+kXesgevcYSEZGplOcjPjdjXtvJM8/Npye9z9K994BBdbn44a+Q1ieOurypcuMHfI2vdo9xv89Mowx7wyjvBPrcyPmlS5TJYisrKJNxcAt1VASEQGWAeFKqTpKqebAa0D14jUrnBYtmnD0yHHi4k6SkZHB4sUr6BHYxS6mR48uzJ2zBIBly36lffv7ALh06TIWi3XkkDKlS6OK9J7j/LRs6c+RI3EcO3aCjIwMFi5cTlCQ/ZXgoKAAZs9eBMCSpSvp0KEtAP/8c4mtW3dw+fIV58jYqO5fh/Nxp7hw4gxZGRb+/GU7tQOa28VkXLyU89nlttIUlCH1gu/jz1+cM1KUEfPprqYNOHksnoQTiWRmZLL65/W073q/XUzSyWT+3H+ErCz7/Dlx9CQnjllHPDpz6iypZ1OpUtU5o8c1a9GYY0ePc9xWzpctWcmDPeyvcD7YoxPzf1oGwC8/r+L+9vc6Zd+F0bhZI04ciyf+eCIZGZmELVtDx24P2MUknkziUOxhsgqooLf/toO/L/7jVKdWLZvmK1M9g7raxfTMXaaWrKSjrUz1DOrKwoXLSU9PJy7uJEeOxNGqZVOHnYxYR4Exjz8jOhmxTDVr3phjR09wPC6ejIwMfl4aRrce9j3Y3bp3YuG8nwFY8fNq2raz1gftO7YhNuYgsfsOApCamlbg8flfMeJ3d3ezhjl1VGZGJr/+vPYadZT9wXX86ElOHDsJWOvzlLOpVK5a2SleRswrXaZKGLqhVGx0ADKUUl9lL1BKRQNdRCTKNiWIyA8AIjJARP6wLf9aRMy25d1EZJeIRIvI+lzbbygi4SJyVERedoawh4cb8QlJOfMJCcl4eLjliameE2OxWDh/4S+q2iq8Fi392RG5mt93rGLo0NdzTkocwdPDnfiTeZw83fPEuBEfn9vpQo7TjeB2t8r8lZiSM38xKYXb3fLv7+4nOvN4xMfcN/YRNo//MV96vaB7OLR8m1OcjJhPd7pX41Ti6Zz5U0mnqeZe7T9vp1HTBpRyceFknHOGtHZ3r05ifHLOfGJiMu4e1fPFJOTKqwsX/qJKFWte1ajpxYbffuaXsDm0vreFU5zudKtGcsKpnPlTSaepXoS8ciYenm6cjE/MmY9PSMpfH+SKsVgsnD9vLVMeHgWs62m/bpGcDFhHgTGPPyM6GbFMuXlUJzFXmUpMSMbNPW99cCcJucrUXxf+okqVStSp64NSivlLv2Xt5iW8OPRph33AmN/dnW7VSM5dnyee5k63/15H3dW0IS4uLpyMc87Q30bMK12mShZKWYo0FQe32mAOdwE78y5USo0HxotIJeA34DMRaQD0A9oopTJE5Augv4j8CnwDPKCUOiYiVXJtqj7Wxlh54KCIfKmUKtYXfkTuiKJli674+dXh628+Zs3qcK5cSS9OpWJl76x17J21Dt9e99Ly5V6se+XrnLTq/nXIvJROykHjvSfCSNxxZ1UmTh/P+JcnopzZBVBETiWfxr9Re1JT0mji34gf531Bm3u6c/Gvv4tbTXMd6DpK40zMpUpxz73N6dq+L5cuXWbxLzPZExXDb5uM904pI3DHnVV597M3ef3ltw1RnxsRXaaKAT2Yg/Gw3ZY3B/hEKbUT6AQ0B3aISJRtvjbQGtislDoGoJRKybWZlUqpK0qps8BpCrmlT0SeFZFIEYnMyPzrml6Jicl45brC4OnpRmJicp6YUzkxZrOZihXKc+5cql3MwYNH+Pvi3zRs5PcvOfHvJCQm4eWdxynXlRprTDJeXrmdKuRzciZ/J6dS3uNqm7WcexX+Ti58f4eWb6dWV/tb8+oFt3ZabxIYM59OJ52husedOfPV3e/kTNL1vxD29nK3MW3Oh3z+3tfs3RXjNK+kpFN4eF29Eu3h4UZS4ql8MZ658qpChfKkpKSSnp5BakoaANFRMcQdO0HduvbPEhWF08lncPO8eghXd7+TU/8hr24EiQnJeHtdffbOy9M9f2jwWZcAACAASURBVH2QK8ZsNlOxorVMJSYWsG6C/bpFcjJgHQXGPP6M6GTEMpWceMruyrqHpxvJSXnrg9N45ipT5SuUJyUljaTEZLZtiSQlJY1Lly6zbs0m7m7S0GEnI353p5PP4Ja7Pve4k9PJ/60+/2LuJ0x79yv27HRefW7EvNJlqoShB3MoNmKwNn4KYgIQr5T6wTYvwCyllL9t8lNKTfiX7ee+WdRCIT1ySqkZSqkWSqkWLqWu/ZbynTv3UKeuDzVreuHi4kLfvkGErVxnFxMWto7+A/oA0Lv3g2zaZD3Zr1nTK+fBaG9vT3z96nDiuOO9JZGR0dStWwsfH29cXFwICQkmNNT+wf/Q0LU8/vjDAPR5qAfh4Vsc3u+1OBV9lIo+bpT3robJxUy9nq05tnaXXUxFn6snvT6d/Dkfl+sHXYS6gffw5y/OaygZMZ9iog5Qo7YXHjXcKeVSiq69OhG+JuK61i3lUoqPf3iX0EWrWBca7lSv3Tv3Uru2DzVs5bx3nx6sCltvF7MqbAOPPNobgJ69uvGbrZxXrVo55wHymj7e1K7jQ1zcSYed9u6OpWZtbzxreODiUoruvQPYuPo3h7frCDsio/KVqRWh9g9prwhdc7VM9enBRluZWhG6hpCQYFxdXfHx8aZu3Vr8sWO3w05GrKPAmMefEZ2MWKZ279pL7To1qVHTExcXF3o91J3VYRvsYlaHbSDksV4ABPXqSsRm69X9jesjaNCoHmXLlsFsNnNf25YcOnDEYScjfnf7du+nRm1vPG31+YO9ulx3HVXKpRRTZ77PL4vCWBu60aleRswrXaZKGCXoGaVb7da7DcBkEXlWKTUDQEQaA8FAZ6y3zWWzHlguIp8qpU7bbrErD2wHvhCRWtm33uXpVXIqFouFEa+8yc+//IjZbGL2j4vYv/9Pxr0xnF279hK2ch2zZi7g2+8+JXrvRlJTzzNo4EsA3HtfS0aMeI6MzEyysrIYPuwNp1yJsFgsDBv2BitD52Iym5g1cwGx+w/x5viR7NwVTWjoWn74YT4zf5hKbGwEqSlpDHj8hZz1Dx3cRoUK5XF1daFnUFd69HiM/Qf+dMhJWbLY/MYsgueMRswmYhdsIuVQAq1G9OH0nmPErd1F40EBeLVtRFamhSvn/2bd8Ku33XneU5+LiSlcOOG8HgMj5pPFYuH9sZ/yxU+fYDKbWf5TKEcPHuP50YOJjTrApjURNPSvzyffv0uFSuV5oEsbnhs1mL7tBhDQsyPNWvtTqXJFevbrDsD4oZM4FOOYU7bXq6PeZtGy7zCZzcybvZiDBw7z6usvE7VrH6t+3cDcHxfxxYwP+SNqLWmp53nmyeEA3NumJa++PpSMjExUVhYjh40nLfW8U5wmvvoh3y6YhslsYum8FRw+eJSXxjzLvqj9bFz9G3f5N2D6zA+oULECHQLu56XRzxL0wCMAzP5lBrXr1uS228uyMWoF44ZPYstGx27VsFgsDB02jrCV8zCbTMyctYDY2ENMeHMkkTutZer7H+Yza+Y0DsRGkJqaxmMDrGUqNvYQixevYG/0RjItFl4e+rpTHkg2Yh2V7WXE48+ITkYsU6+NfIf5S7/DbDbx05wlHDxwmNFjXyJ69z5W/7qRebMX89mMD9i+ezVpqef5v6deAeB82gW++mwmqzYuAqVYt3Yz69ZscoqTEb+7ya99xNfzp2I2m1j2UyhHDh7jxdHPEBN9gHBbHTXlh/epUKk87QPa8uKoZ+jV7jG69exM89ZNqVS5Ir369QDg9Zff4aCT6nMj5pUuUyWIEvTCWbnV7lkVEQ9gCtaepctAHHAbUAtIs4X9opQaLyL9sI6KZwIygBeVUttF5EFgsm35aaVUFxGZAFxUSn1k288+IFApFXctn3K31TJUBqdbivWRqgL55M72xa1QIK+cDi9uhXw0qlyzuBXycfKf4r1drSCqlXHOaH3O5M805wyM4WzKlHLO+0KciRHrKSOSZcDf76plr30XRXGRevlicSvkw7eS178H3WQOpRnvGd7KZZwzrLkzMWJ5Aki/Ei/F7XA9XFrzRZEqr7IBL9z0/+9W61FCKZUIhFxn7AJgQQHLfwV+zbNsQp75u4puqdFoNBqNRqPR/A9SgnqUbrmGkkaj0Wg0Go1GozEoJWjUO91Q0mg0Go1Go9FoNDcH3VDSZHM501jvCyllMhe3Qj7GpmwtboUCmVW1XXEr5CPM5VJxK+Rjf5rjI9A5m0eqOD58uLMx6jNKRqujNCWbc5eu/UoMzVUOpBqv7jQi56/8U9wK+TDi84ElCn3rnUaj0Wg0Go1Go9HkoQT1KN1q71HSaDQajUaj0Wg0GofRPUoajUaj0Wg0Go3m5lCCbr3TPUoGoGtAe2L2beZAbASjR72YL93V1ZV5c7/kQGwEWyNWULPm1XcvjBk9hAOxEcTs20xAF+c9U9OlSzv27NlITMxmRo58IV+6q6srs2d/TkzMZjZvXp7j1KnT/WzdupLIyDVs3bqS9u3vc5pT5y4PsHP3OqL2bGD4iOcKdPph1jSi9mxgQ/hSatTwBKB588ZEbAslYlsoW7avJDAowGlO7u0b0+O3Dwnc8jENhgQVGufVvSWPJs6lSmPrszOulcvRcdHr9P3zO5pPesJpPtnc3a4pH2yYzkebPifw+d750rsNDuK9dVOZtOoTXp03gaqe1ezSy5Qry9Tt3zDw7cFOczJimWrQrglvrP+UN8On0uX54HzpHZ/uwetrP+a1Xz/gpbnjqOx5BwD17m3Eq2Hv50yfHpxN44AWTnEyYn1gRCejemkn7aTLuT5vycaI+WQIsrKKNhUDt1xDSUQsIhIlIjEiEi0iI0TkX/9PERl7HTEzRaSvc0ytmEwmpk2dRGDQAO5u0oF+/XrRoEE9u5innnyU1NTz1G/YlinTvuHdya8D0KBBPUJCgmns35Eegf2ZPm0yJpPjX6nJZGLq1IkEBz+Bv38nQkJ6Ur++vdOgQf1ISztPo0YPMH36t0yc+BoAZ8+m0KfPU7RoEcDgwcP57rspDvtkO338yVv06f0kLZt3pe/DQfjVr2sXM/CJENLSLuDfuCOff/Y9b70zBrC+Xb5d22Da3hvIQ70GMXX6RMxmxwe1EJPQfPIgwvt/QFj70dQMvpcK9TzzxZW6vQx+g7txdufhnGWWyxns+XARUW/Pc9gjv5eJJ955hg+fmMiYzkO5t+f9eNSzf7Hh8ZhjjA8cxevdXmFH2DYeeW2gXXrfEY9y4I8YpzkZsUyJSQh5+ym+GPQuE7u8QvOebXCra//9nYyN44Og13j3wdHs/vV3er3WH4A/t8XwXvcxvNd9DNMefZv0S+ns37zHYSej1gdGczKql3bSTrqcF5+T0X5jjJhPhkFlFW0qBm6hXM/hklLKXynVCOgCPAi8eR3r/WtD6UbQqmVTjhyJ49ixE2RkZLBw4XJ6BnW1i+kZFMDs2YsAWLJkJR07tLUt78rChctJT08nLu4kR47E0aplU4edWrb0t3NatGgFQXl6YYKCApgzZzEAS5eG0aFDGwCio2NISjoFWBsoZcuWwdXV1WGnFi2acPToceLiTpKRkcGSxaH0COxiF9MjsDM/zV0CwM/Lfs25KnTp0mUsFgsAZUqXxlmD1VRpWoeLcaf4+8QZsjIsnFi+Ha+uzfPFNR7dl/2fr8By5eroYpZLVzj7xyEsVzKcI5OLOv51ORWXxJmTp7BkZLJ9RQTNu7Syi9m/bR/pl60+h3cfoop71Zw0n7tqU/GOSuzbHO00JyOWKR//upw9fopzJ09jybCwa8VWGge0tIv5c1sMGbZ8itv9J5XcqubbTtPurYkNj8qJcwQj1gdGdDKql3bSTrqcF4+TEX9jjJhPhkH3KBkDpdRp4FlgiFgZJCKfZaeLSKiItBeR94Cytp6ouba0gSKyx9YrNTvXZh8Qka0ictQZvUsenm6cjE/MmY9PSMLDw63QGIvFwvnzF6hatTIeHgWs62m/bpGcPNyIz7XdhIQkPDyqFxpjsVi4cOEvqlatbBfTu3d3oqL2kZ7u+Amku4cb8fFJOfOJCUl4uFfPE1M9JybbqYrNqUWLJvy+YxXb/viVYS+Py2k4OcJtblX4J/Fczvw/SSmUdbfPg8p3+3CbR1US10c5vL/rpbJbVVKSrnqlJJ2jsluVQuPb9evEnvBdAIgIj40bxLxJs5zqZMQyVbF6FVJzfX+pSeeoWL1yofH3hnQgNjz/99gs6D52/rLFYR8waH1gQCejemkn7eRMJ6N6GdLJgL8xRswnw1CCGkq3/GAOSqmjImIG7rxGzKsiMkQp5Q8gIo2AccB9SqmzIpL7LNMdaAvUB34BFt84+5JLgwa+TJr0GoGBA4pbBYDIyGjuadkNX786fD3jI9auCefKFccrwmsiQtM3+/P7sK9v7H4c4L7eD1Dr7rpM6jcOgE4DuxG9cRepyef+Zc2bT3GWqZa92lKjcR2m9ptgt7xCtUp4+NUg1om9bxqNRqO5+RjtvOWWpgS9h+qW7lFygI7AIqXUWQClVEqutJ+VUllKqVigekEri8izIhIpIpFZWX9fc0eJCcl4e3nkzHt5upOYmFxojNlspmLFCpw7l0piYgHrJtivWxQSE5PxyrVdT093EhNPFRpjNpupUKE8586l2uLdWLhwBk8/PZyjR4877AOQlJiMl5d7zryHpzuJSafyxJzKicl2SrE5ZXPo4BEu/v03DRv6Oez0T3IKt3lcvRXrNvcqXEq6uj+XcmWoVN+bjkvGEfT7FO5oVpf7Z47IGdDhRpGafM7uVroq7lVJTU7JF9eoTWN6DunLp4PfJTM9E4B6zfzo/MSDfBLxFY++/gRtH2pPyBjHfzSMWKbOn0qhcq7vr7J7Vc6fSs0X59fmbroOeYivB3+Qk0/ZNAu8lz2r/yAr0/EeSjBofWBAJ6N6aSft5Ewno3oZ0smAvzFGzCfDcIN6lESkm4gcFJHDIvJqAek1RGSjiOy23TXW/d+2ecs3lESkNmABTgOZ2P/PZYqwySu5N19QgFJqhlKqhVKqhcl0+zU3tiMyirp1a+Hj442LiwshIcGsCF1jF7MidA2PP/4wAH369GBj+Jac5SEhwbi6uuLj403durX4Y8fuIvxL9kRGRts5PfxwEKGha+1iQkPXMmCA9c7Dhx7qTnj4VgAqVqzAsmUzGTfuPbZti3TYJZudO/dQu44PNWt64eLiQp++gYStXGcXE7ZyPY/27wNAr94PsmnTNgBq1vTKGbzB29sDX986HD8R77BTStRRytdy43bvaphczNQIbk38mp056Rl/XWLpXc+x4p5hrLhnGGd3Hea3QR+TsueYw/u+FkejD+NWy51q3ndidilF66C27Fq7wy6mZqNaPPnuc3z69LtcOHc+Z/mXQ6cw/L7/45W2z/HTpFlELA1n4ftzHHYyYpk6Hn2Eaj5uVPWqhtnFTLOg+9iz1n77Xo18eGTyYL4e/AEXz13It43mPdsQuWKr05yMWB8Y0cmoXtpJO+lyXjxORvyNMWI+GYYb0FCy3T32OdaxCRoCj4pIwzxh44CFSqmmwCPAF/+mekvfeici1YCvgM+UUkpE4oAXbKPgeQK5n3DPEBEXpVQGsAFYJiKfKKXOiUiVPL1KTsNisTB02DjCVs7DbDIxc9YCYmMPMeHNkUTujCY0dC3f/zCfWTOncSA2gtTUNB4bYB32Mjb2EIsXr2Bv9EYyLRZeHvo6WU64h9NisTBs2BusWDEbs9nMrFkL2L//EOPHv8LOnXtZuXItM2cu4PvvpxATs5mUlDQGDhwCwPPPP0GdOj6MHTuUsWOHAhAYOIAzZxy7lctisTBqxASWLZ+F2Wxi9o+LOLD/T14fN4xdu/bya9h6fpy1gBnffkLUng2kpp7nySdeBuDe+1ow/JXnyMjMJCsri1eGjc/X01QUlCWLyNdn0n7eGMRs4uj8TVw4lMDdo/qQEn2MhDW7rrl+0O9TcClXFpNrKby6tmDjo+9x4c8Eh72yLFn8OP5bRv04HpPZxOaF60n48yQPvfIIx/YcYfe6HTwydiBlbivDS1+MBOBc4lk+Hfyuw/suDCOWqSxLFgvHf8+LP45FzCa2Lwwn+c94egx/mBN7j7J33U56vTaA0reV4ekvhgOQmnCWr5/5EIAqXtWo7F6Vw9tjHfLIjVHrA6M5GdVLO2knXc71eUtuJ6Plk2G4MSPYtQIOK6WOAojIfCAYyP0jrYAKts8VgUT+BVEl6D7B60FELMBewAVrD9Js4BOlVJaICDAHaA7sByoDE5RS4SLyPtAT2KWU6i8iTwCjsPZG7VZKDRKRmUCoUmqxbV8XlVLlruVTytXTUBlcyuT4sNjOxtVszPb61xXvLW6FfIS5XCpuhXwsOuW8K3DOYrCb8b67rxIjiltBo9FoShxGPG/JzHLO7dbOJjM9ocA7nYzGpR9fK9K58W1PvPd/WAdpy2aGUmoGgG2AtW5KqcG2+ceBe5RSQ7KDRcQdWIP1/P92oLNSaifXwJhnqA6glCr0iFLWVmH/QtLGAGNyzc8CZuWJGZRn/pqNJI1Go9FoNBqNRpOLInbS2BpFMxzY86PATKXUxyJyLzBbRO5SqvAurluuoaTRaDQajUaj0WgMyo25jTAB8M4172VblpungW4ASqltIlIGuAPrOAYFcssP5qDRaDQajUaj0WgMwo0Z9W4HUE9EaomIK9bBGn7JE3MC6AQgIg2wDup25lob1T1KNxiTGOt20awb8wCdQ6RbMv89qBgYnOacl4g6k/gePsWtkI91a413B+qiVOO918hYNcFVype+rbgV8vF3xuXiVsjHrfY8740iy6D5ZMTjz7WUS3Er5KOcS1EGA76xpF6+WNwK+TBieSpR3IBzUaVUpogMAVYDZuB7pVSMiLwNRCqlfgFGAN+IyHCsAzsMUv9SueuGkkaj0Wg0Go1Go7kpqKwbc0FFKRUGhOVZNj7X51igzX/Zpm4oaTQajUaj0Wg0mptDCRrqXD+jpNFoNBqNRqPRaDR50A0lAxAQ0J59ezcRGxvBqJEv5kt3dXVl7pwviI2NIOK3FdSs6QVAlSqVWLN6ISnnDjJlysRb3qlLl3bs2bORmJjNjBz5QoFOs2d/TkzMZjZvXp7j1KnT/WzdupLIyDVs3bqS9u3vc6rT7qj17NkbzogRzxfoNOvHz9izN5zwTT9To4bVqWPHtkRsWcEff6wiYssK2rVz7nt/Svm3osLUH6kwfS6lez1WYIzLve2p8OlMKnzyA7cPHWefWPY2Kn61iLJPD3WaU4dObfltx0q27lrFkGGD86W7urrw1fcfs3XXKlaum49XDY+ctAaNfFmxZh7h235hw5afKV3a1WlOWyJ/Zfvu1bw0/JkCnWb88Anbd6/m1/UL8K7hCYB3DU/ikqNY/9sy1v+2jA8+neAUH7Ade/s2sz82glGjCjn25n7J/tgItkTkPvYqs3bNIlJTDjHVycdep8738/uu1URGrWPoK8/mS3d1deW7mVOIjFrH2g2Lc/IpG08vd04kRTHk5aed6hXQpT1794QTG/NboXXCnNlfEBvzG79t/sWunlq9egHnzh5gyqfvONfJgHWnEZ26BrQnZt9mDsRGMLqQcj5v7pcciI1ga65yDjBm9BAOxEYQs28zAV3aOc3JiMceFP13pnmLJmzbHsa27WFs3/4rQT27Os2pqHUnQMNGvqxcO59N21cQvvUXp9XnRiznRi1TxY7KKtpUDJTIhpKIWEQkKtf0qgPbumj76yEii68R5yMi+4q6n8IwmUxMnTqRoJ6P06RJB/r1C6ZB/Xp2MU8++Qipaedp2LAt06Z9w+RJYwG4fPkKE976kDGvOveH3shOwcFP4O/fiZCQntTP4zRoUD/S0s7TqNEDTJ/+LRMnvgbA2bMp9OnzFC1aBDB48HC++26K05w++fRtevcaRPNmXXj44Z7Ur1/XLuaJQSGkpZ2n8d3t+Wz6d7wz0VpUz51LpW/fp2nVqhvPPjOCb7/71ClONjFue3ooFyeN4cLwJ3Bt0xGTV037EDdPyvTuz1/jhnDhlSf554fP7NLLPvIUmfudNyCCyWRi8kfj6N/3/2h3TxC9+nbH16+OXcyjj/fhfNoF7mvWjRlfzGLchBEAmM1mPpvxPmNeeYv29/akT+ATZGQ4PgCIyWTivY/H81jfZ7i/VSC9+/TI5/TYwL6kpV2gddOufP3FLN54a0RO2vFjJ+h0f2863d+b0cMnOOyT7TRt6iSCggbQuEkHHunXiwYN7Mv5U08+SlrqeRo0bMvUad8wefLrAFy+fJkJEz5gzBjnH3sffDyBkIcGc2/LB+nTNxA/P/tyPsCWTy38O/Pl5z8w4e1RdumT3h3L+rWbne41depEegYPpIl/R/qFBOerE54c9AhpaWk0bHQ/06Z/y6SJV+upt976iFdfde4JiJHrTqM5TZs6icCgAdzdpAP9Cinnqannqd+wLVOmfcO7tnLeoEE9QkKCaezfkR6B/Zk+bTImk+OnMkY89rK9ivo7ExtzkLZtgri3dXd69RrI9GmTMJsdf3mrI3Wn2Wzm8xkfMmr4m7RrHUTvHgOdVp8btZwbrUwZgixVtKkYKJENJeCSUso/1/SeoxtUSiUqpfo6Q+6/0LKlP0eOxHHs2AkyMjJYuHA5QUEBdjFBQQHMnr0IgCVLV9KhQ1sA/vnnElu37uDy5Sv/c06LFq0o0GnOHGtbd+nSMDp0sD6vFx0dQ1LSKQBiYw9RtmwZXF0dv4LVooU/R48cJy7uJBkZGSxevILAQHunwB4BzJ2zBIBly8JyerOio2NITjqd41SmjHOcAMx165OVnEDW6STIzCRjywZcW9g/u1i6cyBXVv2M+ts6mpC6kHZ1/dq+mCpWISM60ik+AE2b303c0ROcOB5PRkYGy5f8StfuHe1iunXvyMKffgYgdPka7m/XGoB2Hduwf98hYvcdBCA19TxZTri/uVnzxhw7eoLjcVann5eG0a1HpzxOnVg4z+q04ufVtHVyz19eWrVsalfOFyxcTlCQ/VVgu2NvyUo65jr2ttyAY695i8YcO3qc47ZyvnTJSh4MtM+n7j06M3/eUgCW/7yKB9pfzafugZ05fjyeA/v/dKpXvnpq0S8F11M5dcLKnDohp5668r9XdxrBKW85X7hwOT3zlPOehZTznkFdWbhwOenp6cTFneTIkThatWzqdCcjHHvg2O/MpUuXsVgsAJQuXdppIzc6Une279iG2JiDuerzNKfU5yWhnBulTBmCGzM8+A2hpDaUCkRE4kTkLRHZJSJ7RaS+bXk1EVkrIjEi8q2IHBeRO/Ksm9NjJCKNROQPW2/VHhHJvgRgFpFvbNtZIyJlHXX29HAn/mRSznxCQjIenu55YtyIj7fGWCwWzl+4QNWqlR3ddYly8vBwIz4+MZdTEh4e1QuNsVgsXLjwVz6n3r27ExW1j/T0dCc4VSc+wd7JPZ/T1ZjCnHr1epBoJzkBmKpUI+vc1dcCZKWcQapWs49x98bs4UX5d6ZTftIXlPJvZU0QoezAF/jnxy+d4pKNm3t1EhKSc+aTEpNxc78zX0yiLSY7r6pUqUSdujVRKH5aMoM1mxbzwstPOcfJozqJCVfLeWJCMm7u9t+fu/udJCRcLed/2ZwAatT0Yt1vS1m2cjb33NvcKU4envnLuaeHW76Yk7nK+fnzN/bYc3d3y8kDsOaTe9588qhOQnyu7+78RapUrcztt9/G0OHP8sG7053u5eFxNR+gkLy6jjrBmRix7jSiU+4yDBCfkITHdZbzvN97fEISHp726xbVyWjHHjj+O9OipT87Itfwx47VvDx0XE7DyREcqTvr1PVBKcX8pd+ydvMSXhzqnNtxjVrOjVimDEEJaiiV1FHvyopIVK75d5VSC2yfzyqlmonIC8BIYDDwJrBBKfWuiHTD+mbea/EcMFUpNdf20iozUB2oBzyqlHpGRBYCfYA5Tvy/NDeQBg18mTTpNQIDBxS3Sg4NGtTjnYmv0jPo8Zu7Y7MZk7sXf00YhqlqNcq/NY0LI57C9YEuZOzajkq55vvXbipmcylatW7Ggx1CuHTpMguXf8+eqFgiNm8vNqdTyadp1qgjqalpNPZvxMy5n/FA60Au/vV3sTkZkTFjX+LLz37g77//KW4VjeZ/ksgdUbRsEYCfXx1mfPMxa1aHc8XJPan/BXOpUtxzb3O6tu/LpUuXWfzLTPZExfDbpuKrzzXFgEHft1YQJbVHKe+tdwtypS21/d0J+Ng+twXmAyilVgGp/7L9bcBYERkD1FRKXbItP6aUym6g5d6+HSLyrIhEikhkluXaJ04JiUl4eV+96uHp6WZ3pcYak4yXlzXGbDZTsUIFzp37t3+h6BjRKTExGS+vqw/3e3q6k5h4qtAYs9lMhQrlc5w8Pd1YuHAGTz89nKNHjzvJ6RRenvZOSfmcrsbkdfLwdOOn+V/zzOBXOHbshFOcwNqDZMrVg2SqUg11zr7ho86dIWPHFrBYyDqdjCXpJCZ3T8y+DSnzYG8qfD6fso8/T+kHAijbP//D+/+V5KRTeOa66uvu4ZZz62HumOwrw9l5lZKSRlJiMtu3RpKSksalS5fZsHYzdzdp6LhT4im7K44enm4kJ9l/f0lJp/H0vFrOy9uc0tMzSE213q64JyqGuGMnqVO3lsNOiQn5y3lCYnK+GO9c5bxixRt77CUlJefkAVjzKSlvPiWewtMr13dXsRwp51Jp3qIJE94ZTdS+jTz3wiCGj3iOwc8650JFYuLVfIBC8uoadcKNwIh1pxGdcpdhAC9PdxKvs5zn/d69PN1zeqIddTLasQeO/85kc/DgEf6++A8NG/k67ORI3ZmUmMy2LVfr83VrNjmlPjdqOTdimTIEJahHqaQ2lK5F9qUSC0XsMVNKzQN6ApeAMBHJfpgi92WYQrevlJqhlGqhlGphMt9+T/bDCQAAIABJREFUzX1FRkZTt24tfHy8cXFxISQkmNDQtXYxoaFrefzxhwHo81APwsO3FOXfum5KgtPDDwcV6DRggPUxs4ce6k54+FYAKlaswLJlMxk37j22bXPeczc7d0ZTp64PNWt64eLiQt++Qaxcae+0Mmwt/Qf0Aay3/W3adNVp6ZIfGD/+fbZv3+k0JwDL4YOY3L0w3ekGpUrh0qYj6ZFb7WLSd0RQqpE/AFK+ImZ3b7JOJfHPtEmcf74fF158hEuzv+TK5jVcmjvDYaeoXfuoVacm3jU9cXFxIbjPg6z+daNdzOpfNxLyaC8AAoMDiNj8OwDh67fQoKEvZcuWwWw207pNSw4dPOyw0+5de6ldpyY1bE69HurO6rAN9k5hGwh5zOoU1KtrTi9W1aqVcx4gr+njRe06NTked9Jhpx2RUXblvF9IMKGha+xiQkPXXD32+vRg4w0+9nbt3EvtOj7UsJXzh/r0YNXK9XYxv4at55HHHgIguFe3nKvDPbo+hv9dHfC/qwNffTGTTz/+im9nOKcT3lon+Fytpx7uWXA9lVMn6LrTKE55y3lISDAr8pTzFYWU8xWhawgJCcbV1RUfH2/q1q3FHzt2O93JCMceOPY7U7OmV87gDd7envj61eHE8XiHnRypOzeuj6DB/7N33mFRHP8DfucOUBPFqEmUoqKiRk0sEUxiNHY0AmLFxJ5iEhNjC2qs0a8lPbHEmGLBmtgLxYKVYIkVFBE7FopJ8MASFTjm98edyAko4U5Z/M37PPtwu/PZu5eZ2dmb29nZOtWz2vPGTTw5GXvGaqeiUM+1Uqc0QRGazKGoDr37r+wC/IEvhRBewH0HgAohqgJnpZQzhBCVgLrA2YchZjQaGTJkHCHBS9DpdSwIXEbM8ZN8Nj6Ag4eiCA4OY/783wmcP52YmAgMV1Lo1fvuNLgnT+zB0bEUDg72dPBti7d3D47HWnfTtJadgoIWodfrWbBgGcePn2T8+GEcPHiUkJAwAgOXMW/eNI4dC+fKlRT69BkIwIABfalWzY3RowczerRpumsfn178/Xey1U6fDBvPuvUL0ev1LFy4nOPHTzF23FAOHTpKaMgWFgQuZ87c7zhydAcGQwp9+3wMwPsf9KFqtcqMGjWYUaNMTh18e1vtBECmkX/nTqfkmK9BpyNt+wYyL8VRvPtbGM+cIP3AbjIi92FfzwPH7wMhM5N/F/2EvH7V+s/OA6PRyOjhU/ht1a/o9Tp+X7yGk7GnGT56IFGHj7F5w3Z+W7SKmT9/ye5DG0kxpPDB2wEApKZe5edZC9iwbTlSSraGhbN1s/UzqBmNRkYFTOL31XPR63X8tngVJ2JPM2L0x0QdjmbThu0sXbSSH375ir2HN5FiSOX9t4cB8PKrnowY/TEZ6RlkykxGDJ1AiiHVJk6Dh4wlJGQpep2OwAXLiIk5yWefBXDwoOnYmzf/dwIDZ3A8JgKDIYWeve4ee6dO7sXRsSQODg506NCO9t5vctzKSRSMRiMjAiaycu089Do9SxatJDb2NKPGDObw4aNsDN3G4oUr+OnXbzgQuQWDIYV33xpqbVbky2vIkHEEBy1Gr9cTmNUmfMKhg0cIDgljfuDvzJ83jZhjf3DlSgq9+9ydnvfEid04ljK1U76+bfH26UnsY9x2as1p8JCxhN5Tzyd8FsCBbPV8QeAMYs31vIe5nsfEnGTlyiCORm0nw2hk0OAxNpkMQIvH3h2vgp5nGjf2ZNgnA8jIyCAzM5MhQ8bZ5GqFNW1naspVfvohkI3bV4CUbAkLZ8vmnTZx0mo911qd0gSFNNV3QRC2mgXlUSKEMAJHs23aKKX8VAgRB3hIKf8RQngA30gpmwshngV+w3Sf0R7AB3CTUt4WQlyXUpYUQrgBwVLK583TjfcG0oEkoAfgeCfd7BAAlJRSTrifq0Mx16KXwY8YndDmhU29DaactTWXvN0KWyEHtcP+enDQIyZTg43wlZvXClshV0oVe6KwFXJwI/1WYSvkoCieKwuDTI3mkyhsgVxwsLMvbIUclLQvXtgKOTDcul7YCjnQanuQnhavxaqeg3+/fKtAGfjEyPmP/P8rkleUpJS5PghASumW7fUBoLl5NRVoK6XMEEK8AnhKKW+b40qa/8YBz5tffwHcO+X4lTvp5phvbPCvKBQKhUKhUCgU/2+QhXS/UUEokh2lAlAJWC6E0AFpQM7HSCsUCoVCoVAoFIqHSyHdb1QQ/l90lKSUpwDrn0inUCgUCoVCoVAoCo4Gh8fnxf+LjlJhotX7bxQPRotjtQftK1vYCjn4sLRLYSvk4Jtk7T2T4wkH7dUngH/Ttffkd2MRGpahsMROl+vI+EKnmAbvB9Ii9nrtfS0sU7xkYSvkIPW2ejacVagrSgqFQqFQKBQKhUJxD0XoxzDVUVIoFAqFQqFQKBSPBnVFSaFQKBQKhUKhUCjuoQjdo6RuoNEAbdo048iR7Rw7Fk5AwIc50h0cHFi0aBbHjoUTHr6OypVdAWjVqim7d4dw4MBmdu8OoXnzxsrpETu1aNWEXQc2sPfwJj4emnMyRQcHe36Z/x17D29iw9ZlVKxkup+nYiUX4pIi2frHGrb+sYavvp9gMyeAF5o14KttM/lm5yx8BnTKkd7uXV++2DKdKRu/49OlEyjn8oxFevGSJZi+91f6/O9dmzlVa1aXD7d9zUc7v6XxAN8c6S/2bMX7m76gf+hU+q4cz9PVTXnlXK8q/UOn0j90Ku9tmErNth42c2rV+jUOHArjcNQ2hg57P0e6g4MD8xfM4HDUNrZuX0WlSpb3Y7m6OhGfdISPB9kun7TopMVjD6CtV3OORYcTGxPBiOEf5Uh3cHBg6ZLZxMZEsDsiKMsLYOSIgcTGRHAsOhyvNs2U0yN20mKd0uKxp1Wv5q1eZeefQUQcCOWjwe/k4mTPj3O/IeJAKEFhS3Gt6AxAp67ebNq5Mmu58M8Raj9f0yZOWjwfa7GeK/4bRbqjJIQwCiEisy1uD/Gzmgshgm39vjqdjunTJ+Pn15f69Vvh79+B556rbhHTr193UlJSqVPnNWbOnMPkyaMA+OefK3Tp8jYeHl68++5Q5s6dppwesdMX346nR9f+NG3kQ6cu3tSoWc0ipkefrqSkXOXlBm35+ccFjJv4SVba+XMXaNW0E62admLE0Ak2cQIQOh19J/Xn676TGdl6MK90aIpzdVeLmPPHzjHeZzhj2g1jf+ge3hjVxyK96ydvErvvmA2dBO0m9WNp36+Y3XoEz3d4JasjdIfodbv5ue2n/Np+NHt+CqbN2J4A/HXiEnN8x/Jr+9Es7fsV3lPfRuitb7p0Oh3ffjeBrp3fppFHW7p086Xmc+4WMX36diMlJZUG9Vry46z5TJw00iJ96hdj2BJm/VPlte6ktWPvjteM6VPw8e3FC/Va0L17R2rVsvR6+603MRhSea52E6bN+JXPp44BoFat6vj7+1G3fku8fXoyc8ZUdDZ4wLRyyr+T1uqUFo89rXrpdDomfzWW3v4DaPFKB/y6tKd6zaoWMW/06kxqylWaeLTn19mLGD1hGABrVobQtllX2jbryuAPRnHhfDwx0Sds4qS187EW67lmyJQFWwqBIt1RAm5KKetnW+KyJwohND+00NOzPmfOxHHu3AXS09NZsSIIX18vixhfXy8WL14JwOrVobRo8SoAUVHHSEy8DEBMzElKlCiOg4ODcnpETi82rMu5sxc4H3eJ9PR01q4OpZ13K4uYdu1bsXzpWgCC1m6iSbNXrP7cB1GtvjuX4xL5++JljOkZ7A2KoGGbRhYxx/dEk3YrDYDTh09S1qlcVprb81Up/fRTRIdH2czJuX41DHGXSbn4N5npRo4F7aVmm4YWMWnXb2a9tn+iWNbrjFtpSKPpMr1dMXts9UD0hh71OHv2PHFxF0lPT2f1ymC8vVtbxLT3bs3SJasBWLtmA82a3y0/b582nI+7xPHjp2wjpFEnLR57AI08G1h4LV++jg6+bS1iOvh6sWjRCgBWrQqhZYsm5u1tWb58HWlpacTFXeTMmTgaeVr/BAnllD+0WKe0eOxp1at+wxeIO3eBC+cvkZ6ewbrVG/B6vaVFjFf7lqz4fR0AIes20+S1l3K8j1+X9qxfvcEmTlo8H2uxnmsFmZlZoKUwKOodpRwIIfoJIdYLIbYBW83bhgsh9gshjgghJpq3uQkhjgshfhVCHBNCbBZClDCnuQshtgghooQQh4QQd36WKCmEWCmEiBVCLBFCCGt9nZ0rcOlSQtZ6fHwizs7l84wxGo1cvXqNcuXKWMR06tSeyMho0tLSrFVSTvmkgnN5EuITs9YT4pOo4GTp5OT0LPHmGKPRyLWr1yhb9ikAKlV2Zcsfq1kTsoiXXrHsNFhDmQrluJKYnLV+JTGZMhXynla8WfdWHNlxCAAhBD3G9mPplAU28wFwrFCWq9mcriZeoVSFMjniPPq04aPw72g16k02fXbXwbl+NT4I+5L3N31B6Jh5WR0na3B2Lk/8pbvlFx+fhNM9dcrJuUJWjNFo5GrqNcqWK8OTTz7BkKHv8cXnM6z20L6T9o49AGeXClzM5nUpPhFn5wp5xhiNRlJTr1KuXBmcnXPZ18VyX+X0EJ00WKe0eOxp1cvJ6VkS45Oy1pMSLuPk9KxFTIVsMabyu04Z87nvDr6d2rFudahNnLR4PtZiPdcMReiKkuavuDyAEkKISPPrc1LKOzdjvAjUlVJeEUJ4AdWBRoAA1gshXgMumLe/KaXsL4RYDnQBFgNLgC+klGuEEMUxdSgrYnpobR0gAdgFvApEPIp/9H7UqlWDKVNG4ePTq7BVslBO9+dy0l+8WKclBkMKdevXIXDJD7z2sg/Xr914pB6NO71GlRfcmdJ9LACt+rQjavshDEnJD9jz4XBgYRgHFobxvF9jmnzckfWf/AxAQuQZfmozkqfdnenw7Qec3hGF8XZ6oTgCjBo9mB9nzefGDe08S0OLTnfQ0rGneDzQUp3S6rGnVS+ABg1f4NbNm5w4frqwVTRzPs4NLdVzm6JmvXtk3JRS1s9le5iU8or5tZd5OWxeL4mpg3QBU+fqTkfrIOAmhCgFuEgp1wBIKW+B6Zd2YJ+U8pJ5PRJwI5eOkhDiPeA9ADu7Muj1eT8sLSEhCVdX56x1FxcnEhIu5xoTH5+EXq/H0bEUyckGc3wFli//hXfeGcrZs+fz/Jz/gnLKH0kJl3F2ccpad3apQFKipVNi4l+4uDiRmHAZvV5PKcdSXLmSAkBamunvkchjxJ27SDX3KkQdjrbay5CUbDGUrqxTOQxJV3LE1Xm1Lh0GdmWq/zgy0jIAqP5iTWp41qJV73YUf7I4dvZ23Lpxi+VfLrbK6WrSFRyzOTk6leVakiHP+Oj1e3h98lvAzxbb/zmdQNq/t3i2hiuJR89Z5ZSQcBkX17vl5+JSgcR76lRiQhIurk4kJJjrVOlSXEk20NCzHh06tmPipJGULu2IzMzk1u3b/PrzosfQSXvHHph+Ma6YzcvVxZQnucXExyei1+spXdqR5GQDCQm57Btvua9yeohOGqxTWjz2tOqVmPgXTtmuLFZwLk9i4l8WMUnmmDvnPkfHkhjM5z6ADp1fZ+0q2wy7A22ej7VYzzWDmvWu0Mn+M4AAPs92H5O7lHKuOS37I+mNPLjjmK94KeUvUkoPKaXH/TpJAAcOROHuXgU3t4rY29vTrZsvwcFhFjHBwWH06tUVgM6d27Njx24ASpd2ZM2aQMaO/YI9ew48QD3/KKf8cfjQUapWq0ylyi7Y29vTsXN7NoVus4jZFLoN/x4dAfDt2JaI8L0AlCtXJuum6MpurlStVpnzcRdt4nU26jQVqjjxTMVn0dvb8bJvEw6F7beIqVynCm99/gHfv/M5V5NTs7bPHjyNoY3fZ1iTD/htygIiVu+wupMEkBB1lrJVKvBUxWfQ2eup4/syJ8MOWsSUdbs7JKF6y/pciTN9IXuq4jNZkzeUdnmap6s5k3Lpb6udDh08QrVqblSu7Iq9vT2du/oQGrrVIiY0dCs9enYGoGOn1wnfuQeA173eoG6dZtSt04zZP87n229m2+RLkRadtHjsAew/EGnh5e/vR1DwZouYoODN9O7dDYAuXbzZvmNX1nZ/fz8cHBxwc6uIu3sV9u0/nOMzlNPDcdJindLisadVr6hD0VSpWomKlVywt7fDr/PrhG3cbhETtmE73d7wA8Dbz4tdf/yZlSaEwNevrc3uTwJtno+1WM81gxp6pyk2AZOEEEuklNeFEC5AnmN2pJTXhBCXhBAdpZRrhRDFAP3DkjMajQwZMo6goEXo9XoWLFjG8eMnGT9+GAcPHiUkJIzAwGXMmzeNY8fCuXIlhT59BgIwYEBfqlVzY/TowYwePRgAH59e/P23dcOmlFP+nUYFTOL31XPR63X8tngVJ2JPM2L0x0QdjmbThu0sXbSSH375ir2HN5FiSOX9t00z/7z8qicjRn9MRnoGmTKTEUMnkGJIfcAn5o9MYyYLx89h+MLx6PQ6wpdvJf7URToPe4NzR85weMt+3hjdh+JPFOfjHwMASE74h+/f/dwmn58b0pjJxvGB9Fg4EqHXEbV8J3+fiqfZsC4kHjnHyS2H8OjrRdUmz2NMN3Lr6g3WD/sJgIoeNXnjQ1+M6UakzGTD2PncNFy32sloNBLwyURWrw1Er9exeNFKYo+fYvTYIRw+dJQNoVtZtGA5v8z5lsNR2zAYUni732CrP7coOmnt2LvjNXjIWEJDlqLX6QhcsIyYmJNM+CyAAwejCA4OY97831kQOIPYmAgMhhR69DJNzxsTc5KVK4M4GrWdDKORQYPHkGmDG4WVU/6dtFantHjsadXLaDQybsRUlqz8GZ1ez7IlazgZe4aAUR8RdfgYYRt38Pvi1Uz/6XMiDoSSYkjlw3eHZ+3/cmMPEhKSuHD+kk2dtHY+1mI91wqyCA29E9JWU0gVAkKI61LKkvds6wd4SCkHZts2GLjzAIHrQC9MV4SCpZTPm2MCgJJSyglCiOqYxvw8jalT1Q2oBARIKX3M8T8AB6SUgfdzLF68UtHN4P/nlC72RGEr5MDrqdqFrZCDGpQobIUcfJO8t7AVigy3MwrvXq+8yMg0FraCooDY6R7a74pWUczOvrAVigSlHLTXnqcbMwpbIQept7V33xfArVsXrJ5k7FFwbZBPgb4bl5oR/Mj/vyJ9ReneTpJ5WyAQeM+26cD0XN7i+Wwx32R7fQpoeU/sWWBHtpiBKBQKhUKhUCgUivxTSFN9F4Qi3VFSKBQKhUKhUCgURYgiNPROdZQUCoVCoVAoFArFo0F1lBR30NpYey0OXi2pwTHRADcztPdwt+tSe/eT/JAaU9gKOdhfsXphK+Sg9pmjha2QK/Z67Z0G9DrtTchq1OBQkeJ2DoWtkIPbGmw3AW6kaetcDNq8Hyjpet6PbVDcRYvfpYoSRWl+BO2dIRUKhUKhUCgUCsXjibqipFAoFAqFQqFQKBT3oDpKCoVCoVAoFAqFQmFJUXqOkvYGgv8/pK1Xc45FhxMbE8GI4R/lSHdwcGDpktnExkSwOyKIypVds9JGjhhIbEwEx6LD8WrTzGZOXl7NiY4O53hMBMPzcFqyZDbHYyLYlc2pbNkyhG1egeHKSaZPm2wzH4BWrV9j36HNHIzaypBh7+fqNHfBdA5GbSVs+0oqVnKxSHd1deJiUhQDB71jM6fWbV7j4OEtRB7ZxtBPPsjVaf6CGUQe2ca2HaupZHZq2LAuEXuCidgTzK69Ifj4etnMqUGzF/lh+2x+DP+Zzh92zZHe4V0/ZmydxfebZjDxt8k84/JMVtq4hRNYfPQ3xswfbzOfO7Ro1YRdBzaw9/AmPh7aP0e6g4M9v8z/jr2HN7Fh6zKL8qtdpwYhYb+zc28QO3avp1gx29yb8USThriFzsFt4zzKvOufI92xYxuq7vqdSqtnUWn1LBy7trNI1z35BFW2L+LZsR/axAe02R60adOMqKhtREfvJCBgQK5Oixb9QHT0TsLD11KpksmpZcsm7NoVzP79m9i1K5hmzRrbzAnAq01zjh7ZQcyxPwgIyFkGDg4OLF70IzHH/uCP8PXZ2qmn2LRpGcn/xDLt+0k2ddJi+bVu8xqHIrcSdXQ7w/JopxYsnEnU0e1s37nmbjvlUY/de0PYvTeEPXtD8e1gu3ZKi+cYLZYdaPPcp8W80qKTFuu5JsiUBVsKgSLTURJClBdCLBVCnBVCHBRC7BFCdCpsL2vR6XTMmD4FH99evFCvBd27d6RWLcsb0d9+600MhlSeq92EaTN+5fOpYwCoVas6/v5+1K3fEm+fnsycMRWdDW6CvuPk69uLuvVa8EYeTimGVGrVbsL0Gb8y1ex069YtJkz4ipEjbfvlQ6fT8fV3E+jW+R1e9mhHl24+1HzO3SKmd99upKak0rBeK2bPms+ESSMs0id/MYYtYeE2dfr2u4l06fQWng3b0rWbbw6nPn39SUm5Sv26LZn1wzwmThoJmJ5436yJH01e8aFzx35MnzkZvd76BzXqdDrem/wBk/pOYFCrj2jS4TVcq1e0iDl77CwB3sMY2nYQu0N20Wf0W1lpa39ezbSh31ntkZvXF9+Op0fX/jRt5EOnLt7UqFnNIqZHn66kpFzl5QZt+fnHBYyb+AkAer2eWb98zfChn9HsZV86efchPd0GDyDU6Xh23EfEvzeWON/3cPRujkO1SjnCrm8I50Lnj7jQ+SOurtxokVZuUB9uHoi23iVLSZvtwbRpk/Dz60uDBq3p1q0Dzz1n6dSvX3cMhlSef74ZM2fOZcqUTwFITjbQtevbeHq2pX//Ycyb973VPtm9pk+fTAe/PtSr35Lu/n45vN7q9wYpKSnUrtOUGTPnMGXyaABu3brNxInf8Omntv0CotXy++77/9G5Yz88XvQyl59lO9W3nz8pKanUe6EFs2bOZdJkU/nFHDtB01c70Phlbzp27MuMGVNs1k5p8RyjtbK746XFc5/W8krLTlqq55ohs4BLIVAkOkpCCAGsBcKllFWllA2BNwDX+++Ztb9mhxg28mzAmTNxnDt3gfT0dJYvX0cH37YWMR18vVi0aAUAq1aF0LJFE/P2tixfvo60tDTi4i5y5kwcjTwb2Nxp2fJ1+N7j5JuH07//3mTX7v3cunXbao/sNPSox9mz5zkfd5H09HRWrwyhvXdri5jXvVvz25I1AKxbs5FmzV/JSmvv05oLcReJPX7KZk4eZqc4s9OqlcF4+7SxiPH2ac1vS1YBsHbNBpo3N/2ifvPmLYxG0yxMxYsVw1YTwFSvX53EuEQuX7hMRnoGEUHhNPJ6ySImes9R0szlc/LwCco5lctKO7rrCDev37SNTDZebFiXc2cvcD7uEunp6axdHUo771YWMe3at2L50rUABK3dRJNmpvJr3vJVYo6dICb6BAAGQwqZNpiBrHjdmqRfSCT9UhKkZ3A1dCdPtnzlwTuaKVbbHf3TT/HvrkNWu9xBi+2Bp2d9zpyJy6rnK1YE4XNPPffxacMScz1fvTqU5s1fBSAq6hiJiX8Bph8HihcvjoODba4G3vHKyqsV6/G958qsr68XixavNHuF0KKFyevff2+ye/d+bt22bTulxfLz8KjH2TN326mVK4NytlPebViy2FR+ax5BO6XFc4wWyw60ee7TYl4VBSct1HPFf6dIdJSAlkCalPKnOxuklOellDOFEHohxNdCiP1CiCNCiPcBhBDNhRB/CCHWAzFCCDchRKwQIlAIcVIIsUQI0VoIsUsIcUoI0ci8XyPz1arDQojdQoia5u39hBCrhRAbzfFf2eIfc3apwMVLCVnrl+ITcXaukGeM0WgkNfUq5cqVwdk5l31dLPctqNOlbO8bH5+ISz6dHhZOzuWJv5SYtZ4Qn4STc3lLp2wxRqORq6nXKVuuDE8++QSDh77Pl5/PtLFTBS5ZOCXi7FT+npjyWTFGo5GrV69R1pxPHh71+HP/Rvbs28CQQWOzvpBYQ9kK5fgn4Z+s9eTEZMqVL5dnfOvubTi0/aDVn/sgKjiXJyHesvwq3JtXTs8SH383r65dvUbZsk9Rzd0NKSW/r55DWPgqPhpsm+Ejds+WIyPp76z1jMv/YJ9LXpX0akLltbNxmjYGuwpPmzYKwTMj3+Ofr+bYxOUOmmwP7qnn8fGJuNzzvqaYu05Xr17L0R506tSeyMho0tJsM330vf9vru1UPrxsiWbLLz57+SXldHIunxVjNBpJzZZPHp712X9gE3/u38jgwWNs0k5p8RyjxbIDbZ77tJhXWnXSWj3XCjJTFmgpDIpKR6kOkNfPtu8AqVJKT8AT6C+EqGJOexEYLKWsYV53B74FnjMvPYAmQAAw2hwTCzSVUjYAxgNTs31WfaA78ALQXQhhOaZJoUlGjh7E7FnzuXHj38JWseDAgShe8mxH89c68knAAJvdd5NfmnVqTrW67qz9efUj/dz/it7OjpdeaciH7wbQoW1P2vu0oWmzlx/JZ1/fsZdzrfpyvuMA/t19mAqfBwDw1Js+3AjfR8blfx7wDgowDW2ZPPlTBg4cVdgqiv/Igf2ReHq0pVlTPz4J+PCRt1OKgqPVc59Coe5ResgIIWYJIaKEEPsBL6CPECIS+BMoB9wZBLpPSnku267npJRHpZSZwDFgqzQ99eoo4GaOKQ2sEEJEA99j6qTdYauUMlVKeQuIASrn4feeEOKAEOJAZuaN+/4vCfFJVHR1zlp3dXEiISEpzxi9Xk/p0o4kJxtISMhl33jLfQtCQnwSrtne18XFifh8Oj0sEhMu4+LqlLXu7FKBxITLlk7ZYvR6PY6lS3Il2YCHZz0mThpB1LEdDPiwH8MCBtD//d42cErC1cLJiYTEy/fEXM6K0ev1ODqW4so9+XTyxBmu37hB7do1rXa6kpTM085PZ62XcypH8uXkHHEKojU9AAAgAElEQVR1m9Sj60B/Pn9nMhlpNrjf5wEkJVzG2cWy/JLuzavEv3BxuZtXpRxLceVKCokJSezZdYArV1K4efMWWzbv5IV6ta12yvgrGbsKdyeysCv/NOn35FVmyjVkuukhv6krN1KsjqlpKV6/Fk/16ECVLQt4ZsS7lPJrxdPD3sJaNNke3FPPXVyciL/nfU0xd50cHUtltQcuLhVYtuwX3n13GOfOXbDaJ/tnVnxQO3Ufr4eBZsvPJXv5VcjplHA5K0av11M6l3w6ceIMN67foHYd69spLZ5jtFh2oM1znxbzSqtOWqvnmkHdo2RzjmG6OgSAlPIjoBXwDKYHJH8spaxvXqpIKTebQ+/tpWQf7JmZbT2Tu1OlTwK2SymfB3yB4nnsbySP6dWllL9IKT2klB463ZP3/cf2H4jE3b0Kbm4Vsbe3x9/fj6DgzRYxQcGb6d27GwBdunizfceurO3+/n44ODjg5lYRd/cq7Nt/+L6flx/uderu70fwPU7BeTg9LA4dPEK1apWpVNkVe3t7Onf1ZkPoVouYjaFbebOnaX4Pv07tCN+5F4D2Xm9Sr05z6tVpzuwfA/num9n8+vMiq50OHjxC1WpuVDY7denqQ2jIFouY0JCtvNmzCwAdO73Ozp17AKhc2TXrpuiKFZ2pUaMa5y9cstrpVNQpnKo482zF8tjZ29HE9zX2h+2ziKlSpyoDPv+Iqe9MIjU51erPzA+HDx2larXKVKrsgr29PR07t2dT6DaLmE2h2/Dv0REA345tiQg3ld/2rRHUqlOdEiWKo9fradzEk5OxZ6x2unX0BPaVnbFzKQ/2dji2b8aN7XstYvTPlM16XbLly6SdNX3RTxrxFeda9eFc6778/dUcrq3byj/fzbfaSYvtwYEDUbi7V6FyZZNTt26+hISEWcSEhGyhp7med+7cnp07dwNQurQjq1fPZ9y4L9mz54DVLjm93O7mVbcOBAdbegUHh9G7V1ezlzc7HnI7pcXyO3jwCNXc77ZTXbv65mynQrfQs5ep/Drl2U65UKNmNS6ct76d0uI5RotlB9o892kxr4qCkxbquVYoSkPvNDvJwT1sA6YKIQZIKWebtz1h/rsJGCCE2CalTBdC1ADirfis0tn272fF++QLo9HI4CFjCQ1Zil6nI3DBMmJiTjLhswAOHIwiODiMefN/Z0HgDGJjIjAYUujRyzQNbkzMSVauDOJo1HYyjEYGDR5jk5vc7ziF3OP02WcBHMzmFBg4g+Nmp5697k7Ne+rkXhwdS+Lg4ECHDu1o7/0mx628kdRoNDLik4msWjsfvV7PkkUriD1+ilFjBxN5KJoNoVtZtGA5P835loNRWzEYUnin3xBrs+KBTsM/mcCadQvQ63UsWmhyGjN2CIcOHWVD6FYWLljGL3O+I/LINgyGVN7qOwiAVxp7MHTYB6RnZJCZmcmwIeNzXGkqCJnGTH4d9xOfLZqITq9j67ItXDx5gTeH9eT00VPsD9tH3zFvUfyJ4gyfbZrZ6u+Ev/n8HdPsX1NWfoFLNVeKP1mcX/+cz6zhM4gMt/6EYTQaGRUwid9Xz0Wv1/Hb4lWciD3NiNEfE3U4mk0btrN00Up++OUr9h7eRIohlfffHgZAaspVfvohkI3bV4CUbAkLZ8vmnVY7Yczk78k/4jpnCuh0XF29mbTT5yn3cW9uRZ/ixva9lOnlx5MtX4YMI8bUaySN+tb6z72fkkbbg6FDxxMUtBC9Xs+CBcs5fvwU48YN49ChI4SEbCEwcBnz5n1PdPRODIYUevceCMAHH/SlWjU3Ro0axKhRprrv69ubv//OeZWzIF5DhowjOGgxer2ewAXLOH78JOPHf8Khg0cIDgljfuDvzJ83jZhjf3DlSgq9+9ydnvfEid04liqFg4M9vr5t8fbpSWys9e2UFsvvk2GfsXb9wqx26vjxU4wdN5RDh44SGrKFBYHLmDP3e6KObsdgSKVfn48BeKWxJ598credGjpknE1+7dbqOUZrZXfHS4vnPq3llZadtFTPNUMhXR0qCELaahqbh4wQwgnTULiXgL8xXS36CVgBTMZ09UeY0zoCDYAAKaWPeX83INh8pQghRKB5fWX2NCHEK8AC8/uHAL2klG5CiH6Ah5RyoHn/YOAbKeWO+3nbObhoKoNFYQvkQkmHEoWtkCtGqb0juVW5Og8OesTsTtVew/2Hs1thK+Sg9pmjha2QK/Z67f1elqnBY89ooy++tqS4nfbuF7qdYZvJOmyNpk7EZkpp8Nx3Lc32s54+jmjxuxRAelq8VtUsuNKpWYEOybJrdj7y/6/IdJSKKqqj9GBURyn/qI5S/lAdpfyjOkr5Q3WU8ofqKOUf1VEqumjxuxQUoY6SXwE7SusefUdJe2dIhUKhUCgUCoVC8Viiwd/C8kR1lBQKhUKhUCgUCsWjQXWUFHd4qvj9Z7171KTcuv905YWBXqfNyRev3dLeEISgpLweJ6bIjhaHuaUMblTYCrny1PR9Dw5SaK4tB22251qlmJ19YSvkIC3z4T+a4XGgammnBwc9Ys6mJj44SJEnD+uKkhCiHTAd0ANzpJRf5BLjD0zANCI3SkrZ437vqTpKCoVCoVAoFAqF4tHwEDpKQgg9MAtoA1wC9gsh1kspY7LFVAdGAa9KKQ1CiGcf9L6qo6RQKBQKhUKhUCgeCQ/pilIj4LSU8iyAEOJ3wA+IyRbTH5glpTQASCn/etCbanPMk0KhUCgUCoVCoXjskJkFW4QQ7wkhDmRb3sv2ti7AxWzrl8zbslMDqCGE2CWE2GseqndfVEdJA7Rs3ZS9BzeyLzKMQUPfy5Hu4GDPnPnT2BcZxqZtK6hYyVTuFSu5cPHyEbZHrGN7xDq++X6izZzaejXnWHQ4sTERjBj+UY50BwcHli6ZTWxMBLsjgqhc2TUrbeSIgcTGRHAsOhyvNs1s5qTyqWh7Kaf8oa/ZgCeG/8ATI3/EvkXnnE6+b1Fi6HeUGPodT4yYxZP/W5yVZtewBU+MmMUTI2Zh17CFzZy0mE9a9VLtVNF1atOmGYcjt3Lk6A4++WRArk4LFv7AkaM72LFzLZUqmZwaetRjz95Q9uwNZe/eDfh2aGszJ616abH8mrZ8hY17VhG2bw3vDeqbI93jlQas2bqYmMS9tPVtZZHWqbs3m/9czeY/V9Opu7fNnLSYT1qgoB0lKeUvUkqPbMsv//Gj7YDqQHPgTeBXIcRT99vhsesoCSHKCyGWCiHOCiEOCiH2CCE62ei9r9vifbKj0+n48tvP6N6lP696tqdzVx9q1KxmEdOzTzdSUlJpVL8NP80K5LOJw7PS4s5doEUTP1o08SNg6Gc2c5oxfQo+vr14oV4LunfvSK1a1S1i3n7rTQyGVJ6r3YRpM37l86ljAKhVqzr+/n7Urd8Sb5+ezJwxFZ0NJmtQ+VS0vZRTPhE6inV6j5tzJ/HvN4Owq98E8ayrRUha0Hxufj+Mm98PI31XCBlH95oSSpTEoY0//84cyb8zR+DQxh9KWD8BgSbzSaNeqp0q2k7fff8/OnXsR8MX29CtWweee87dIqZvP39SUlKp+0Jzfpg5l0mTPwUg5tgJmrzqyysvt6djxz7MnDEFvV5vtZNWvbRafp99MZL+bwyi/avd8OnUlmo1qljEJF5K4tOPJxC8apPF9tJPOTIwoD/d2vajq1dfBgb0x7F0KZs4aS2fNIMUBVvuTzxQMdu6q3lbdi4B66WU6VLKc8BJTB2nPHmMch2EEAJYC4RLKatKKRsCb2DKrOxxmrk360WPupw7e57zcRdJT09nzaoQXvdubRHzuncrfv9tDQDr126kafNXHqpTI88GnDkTx7lzF0hPT2f58nV08LX8JaqDrxeLFq0AYNWqEFq2aGLe3pbly9eRlpZGXNxFzpyJo5FnA6udVD4VbS/llD90laqT+U8i8splMGaQERmBXZ28Z8uzq9+UjMg/TK9r1sd4KgpuXoebNzCeisKu5uOZT1r1Uu1U0XXy8KjP2TPniTOX3cqVQfj4eFnE+Hh7sWTxKgDWrAmlefPGANy8eQuj0QhAsWLFkNJ2j7fVopcWy6/ui3U4H3eRi+fjSU/PIGTtZlq/bnkVJv5iIidiTud4oHWTFq+wa+c+UlOucjX1Grt27qNpy8ZWO2kxn7RCQa8oPYD9QHUhRBUhhAOm7//r74lZi+lqEkKIpzENxTt7vzd9rDpKQEsgTUr5050NUsrzUsqZQoh+Qoj1QohtwFYAIcRwIcR+IcQRIUTWOAchRC8hxD4hRKQQ4mfzTBpkS3/afKXK6uuzTk7lSbiUlLWekJCEk3P5HDHxl0xTURqNRq5evUbZsmUAqFTZlW1/rGV96GJefsXDWh0AnF0qcPFSQtb6pfhEnJ0r5BljNBpJTb1KuXJlcHbOZV8Xy30Lgsqnou2lnPKHcCyLTPkna12mJiNKl8s99qlnEGWfxXjaNBW6KF2OzGz7Zt5n3/+CFvNJq16qnSrCTs7luRR/933j4xNzlF32mDtlV66cqew8POuz/8Bm9u3fxKDBY7M6KI+jlxbLr7zTsyTFX85aT0r4i/JOD5zQzLzvMyQmZN/3MuWdnrHaSYv59DgjpcwABgKbgOPAcinlMSHE/4QQHcxhm4BkIUQMsB0YLqVMvt/7aubKio2oA9zvQTMvAnWllFeEEF6YLrc1AgSwXgjxGvA30B3T1IHpQogfgZ7AQjAN7cPUQx0rpQx7eP/Kg7mc9Bf16zTHcCWFevXrsHDpj7z6UnuuX1PP1siOyifF44hd/SZkHNlTtB5xrsgT1U4VbQ7sj8TTw4uaNavxy6/fsnnTDm7fvl3YWpr1Uvz/RmY+cBhdwd5XylAg9J5t47O9lsAw85IvHrcrShYIIWYJIaKEEPvNm8KklFfMr73My2FMnavnMHWcWgENMc2/Hmler2rexx7T1agR9+skZZ+V41Za6n0dExMv4+x691cCZ+cKFr9s3IlxcTU9cE2v1+PoWIorVwykpaVjuJICQFTkMeLOXcDd3XJMbkFIiE+ioqtz1rqrixMJCUl5xuj1ekqXdiQ52UBCQi77xlvuWxBUPhVtL+WUP+TVK4inns5aF6XLIVNz/7HLrn6TrGF3YLr6pMu2r+4++/4XtJhPWvVS7VQRdkq4jKvL3fd1cXHKUXbZY+6UXXKywSLmxIkz3Lj+L7Xr1LDaSateWiy/y4l/UcHl7pW2Cs7PcjnxgTM/m/f92+IqXQXn8lxO/NtqJy3mk1Z4SEPvHgqPW0fpGKarRgBIKT/C1NG5cw01+09zAvhcSlnfvLhLKeeaty/Itr2mlHKCeZ8M4CBw36ljss/KUdyh9H2FDx88StWqblSq7Iq9vT2dunizMXSrRczG0G288aZpPooOHdvxx849AJQrVybr5r7KbhWpWs2NuLiLWMv+A5G4u1fBza0i9vb2+Pv7ERS82SImKHgzvXt3A6BLF2+279iVtd3f3w8HBwfc3Cri7l6FffsPW+2k8qloeymn/JF58RS6p50QZZ4FvR129ZtgjNmfI04844IoUZLM8yeytmWciERfo75pAocST6KvUZ+ME5FWO2kxn7Tqpdqpout08GAU1dzdqGwuu65dfQkJsfw9NCQ0jJ69ugDQqVN7du7cDUDlyq5ZkyRUrOhCjZrVuHD+ktVOWvXSYvkdPRyDW5WKuFZyxt7eDu+OXmzdGJ6vfSO27+HV5i/hWLoUjqVL8Wrzl4jYvsdqJy3mk1aQUhRoKQwet6F324CpQogBUsrZ5m1P5BG7CZgkhFgipbwuhHAB0jFdMVonhPheSvmXEKIsUEpKeR6QwNvACiHESCnll9YKG41GPh3+P1asmYtOr2fpopWciD3Np2MGEXkomo0btrFk4Qp+/OVr9kWGkWJIpf9bQwF45VVPPh0zmPT0DGRmJgFDxpNiuP8VrPw6DR4yltCQpeh1OgIXLCMm5iQTPgvgwMEogoPDmDf/dxYEziA2JgKDIYUevT4EICbmJCtXBnE0ajsZRiODBo8hM9P6nwFUPhVtL+WUTzIzub32V0r0/wx0OtL3bSXz8kUcvN7EeOl0VqfJvn4TMiIjLPe9eZ20LSt4YtDXAKSFLTdN7GAlmswnjXqpdqpoO30ybDzr1i9Er9ezcOFyjh8/xdhxQzl06CihIVtYELicOXO/48jRHRgMKfTt8zEAjRt7MuyTAWRkZJCZmcmQIeNyXNF5nLy0Wn7/G/U1c5fPRK/Ts/K39Zw+cZZBI98nOvI42zaF80L92sxa8DWOpR1p4dWUQSPew7tpd1JTrvLjd3NZFbYQgFnfziE15apNnLSWT1qhKI0YF7acnUULCCGcgO+BlzDdb3QD+AkoAXhIKQdmix0MvGtevQ70klKeEUJ0B0ZhuuKWDnwkpdwrhLgupSwphCiG6T6ldVLKH+/n87RjDU1lcMot7Y13f6q49VMYPwy0mFeKokvK4LxnrytMnpq+r7AVigRabKdUG5V/itnZF7ZCkeB2RnphK+SgammnwlbIwdnUxMJWyJWMtPjCuezyH7no2apA340r7t/6yP+/x+2KElLKRExTAuZG4D2x04HpubzHMmBZLttLmv/e5gHD7xQKhUKhUCgUCoUlRekazWPXUVIoFAqFQqFQKBTa5GHNevcwUB0lhUKhUCgUCoVC8UhQHSVFFmoM+YPJLErXYAuZj52bFrZCDhYkHyxshRzczEgrbIUcaPVeoDVlXytshRz4p+4ubIUcaLGdsterU3h+0eK9N47F8pprqvDQYj3X6v1AioKjwWqWJ6qVVSgUCoVCoVAoFI8EdUVJoVAoFAqFQqFQKO6hsJ6JVBBUR0mhUCgUCoVCoVA8EorSc5R0eSUIIYKEEOvzWh6l5ONOW6/mHIsOJzYmghHDP8qR7uDgwNIls4mNiWB3RBCVK7tmpY0cMZDYmAiORYfj1abZY+3UqnVT/jy0iQORWxg87L1cneYGTuNA5BbCtq2kYiUXi3QXVycuJEYycNA7NnPSYj4BPNesHqO2fsfoHdNoNaBDjvRm77RnZNg3DN/wJQOWjKWMy9NZab6f9mDk5q/5dMu3dPqsr82ctFh+bdo0IypqG9HROwkIGJCr06JFPxAdvZPw8LVUqmQqv5Ytm7BrVzD7929i165gmjVrbDMnLdapZ1rUo0XEt7Tc8z3uA3PWpzs4eTfCN+k3SterCsBTDarx2pbPTcvWL6jwuofNnECb5afqedF10uKxB6pO5Rctlp8WnbRAphQFWgqDPDtKwDfAt/dZiiRCiDwfVS+EuO8dxPfbt6DodDpmTJ+Cj28vXqjXgu7dO1KrVnWLmLffehODIZXnajdh2oxf+XzqGABq1aqOv78fdeu3xNunJzNnTEWnu1+RFm2nr76dgH/nd3nF83W6dPWhZk13i5hefbqSknIVj/qtmT1rPhP+N9wifcrno9kaFm61S3YnreUTgNAJuvzvbX7p9wVftvmEBh1epby75YkzPiaO73xH8/XrI4na8Ce+o3oC4PZiDap41OSrdiP40iuASvWqUe3l2lY7abX8pk2bhJ9fXxo0aE23bh147jnL8uvXrzsGQyrPP9+MmTPnMmXKpwAkJxvo2vVtPD3b0r//MObN+95mTpqrUzrBC5+/xZ89vmT7awE4d2pMyRouOcL0TxanyrvtMBw8lbXtWuxF/mg7hvDWo/jzzS+o+/W7CL1t6rlWy0/V86LrpLljD1Wn/ouT1spPi05aQUpRoKUwyDPXpZQ777c8SsmHjRDCDkBKabufNvJJI88GnDkTx7lzF0hPT2f58nV08LV8lm0HXy8WLVoBwKpVIbRs0cS8vS3Ll68jLS2NuLiLnDkTRyPPBo+lU0OPupw7e57zcRdJT09n9aoQXvdpZRHT3rs1vy9dDcC6tRt5rfkrd9N8WnP+/CVij5/CVmgxnwAq1Xfnn/NJJF/8C2O6kcNBu3ney/KX/NN7Yki/ZZoZ7vzhUzxVoSwAEoldMXvs7O2wc7BHb6fn2t8pVjtpsfw8Petz5kwccWanFSuC8PFpYxHj49OGJUtWAbB6dSjNm78KQFTUMRIT/wIgJuYkxYsXx8HBwWonLdapMg3cuXEuiX8v/IVMN5Kwdg8V2ua8MvTcSH9OzwrCePvu7GLGm2lIo2mMha64PdhwpiMtlp+q50XXSYvHHqg6lV+0WH5adNIKMlMUaCkMHtg9FUJUF0KsFELECCHO3lkehdzDRAjRXAjxh3kYYYx523XzXychRLgQIlIIES2EaJptvylCiCghxF4hRHlrPZxdKnDxUkLW+qX4RJydK+QZYzQaSU29SrlyZXB2zmVfF8t9HxcnJ6cKxMffnSI0IT4JJyfL7HdyLk/8paQsp6up1ylbrgxPPvkEg4e+x1efz7TaIztazCeAp8qXJSUhOWs9NfEKpcuXzTP+Jf8WHN8RCcD5Q6c4vSeGift/YuK+n4gNP8JfZxLy3De/aLL8nCtw6dJdp/j4RFzuKQNTzN3yu3r1GuXKlbGI6dSpPZGR0aSlWT8luRbrVHGnMtzMVp9uJSZT3MkyD0q/4EYJ57L8teVwjv2falCN5ju/pvn2rzgyYk5Wx8latFh+qp4XYScNHnug6lS+nTRYflp00gpSFmwpDPIzmcN84DPge6AF8Bb56GAVEV4EnpdSnrtnew9gk5RyihBCD9x52MGTwF4p5RghxFdAf2Dyo9NVFISRoz9m9g/zuXHj38JW0RwNOzahYt2q/NB9IgBPVy5PeXdnJrz8IQADFo+hqudznN0fW2iOWi6/WrWqM3nyp/j49CpslcJDCGpP7E3k4Nm5JqccPsOOZsMpWd2Z+jMG8Ne2KDJva+OZNloqP1XP/xtadNIaqk4pFNaTn45SCSnlViGEkFKeByYIIQ4C4x+y26NgXy6dJID9wDwhhD2wVkoZad6eBgSbXx8E2uSyL0KI94D3AIS+NDrdk3kKJMQnUdHVOWvd1cWJhISkXGPi4xPR6/WULu1IcrKBhIRc9o233LcgaNEpMTEJFxenrHVnlwokJl62jEm4jItrBRISktDr9TiWLsmVZAMNPerRwa8dEyaNoHRpRzIzM7l16zZzfllslZMW8wkg5fIVnnIul7Ve2qksqZev5Iir8erztBnYiR+6T8SYlgHAC209iTt8mrR/bwNwfEckbi9Wt7qjpMnyS0jC1fWuk4uLE/H3lIEpxpn4eLOTYymSkw3m+AosW/YL7747jHPnLljlkvV5GqxTtxINlMhWn4o7leNWoiFr3a5kcRxrVqTxatMpodgzpWm0IIB9fb8hNeru4IPrpxIw3rhNqecqWmwvKFosP1XPi7CTBo89UHUq304aLD8tOmmFovQcpfxcGbothNABp4QQA4UQnYCSD9nrUXEjt41SynDgNSAeCBRC9DEnpUuZdfHPSB4dTSnlL1JKDymlx/06SQD7D0Ti7l4FN7eK2Nvb4+/vR1DwZouYoODN9O7dDYAuXbzZvmNX1nZ/fz8cHBxwc6uIu3sV9u3POfTlv6JFp0MHj1K1mhuVKrtib29P5y7ebAzZahGzIXQrb/ToDIBfx3b8sXMvAN5te1D/+RbUf74FP/0YyPff/mT1iQK0mU8AF6PO8IxbBcq6PoPeXk8D38YcCztoEeNSx41uU/sz592vuZ58NWu7ISEZ95dqodPr0NnpqfZSbS6fjrfaSYvld+BAFO7uVahc2VR+3br5EhISZhETErKFnj27ANC5c3t27jTN91K6tCOrV89n3Lgv2bPngNUud9BinUqJPMOTVStQotIzCHs9zh1fIWnz3fqUce0mm+q8x1bPQWz1HITh0OmsTlKJSs9kTd5QwvVpSro7c/Pi31Y7gTbLT9XzouukxWMPVJ3KL1osPy06aYWiNOtdfq4oDcY09GwQMAloCdhuzmANIoSoDFySUv4qhCiGaYjewofxWUajkcFDxhIashS9TkfggmXExJxkwmcBHDgYRXBwGPPm/86CwBnExkRgMKTQo5dpWFRMzElWrgziaNR2MoxGBg0eQ2am9eP/teo0ImAiK9fOQ6/Ts2TRSmJjTzNqzGAOHz7KxtBtLF64gp9+/YYDkVswGFJ4962hVn/ug5y0lk8AmcZMVo2fz/sLR6PT6/hz+XaSTl2i3dBuXDx6lmNbDtJhVE+KPVGMfj8OAcAQ/w9z+39DVOheqjeuw4hNXyOlJHZnFMe2HrLaSavlN3ToeIKCFqLX61mwYDnHj59i3LhhHDp0hJCQLQQGLmPevO+Jjt6JwZBC794DAfjgg75Uq+bGqFGDGDVqEAC+vr35++/k+31kvpy0VqekMZPo0YG8/NsohF7Hxd92cP3EJWqO6EpK5Dkubz6Y577lGtXE/WM/MtMzIFNy9NN5pF25ZrUTaLf8VD0vuk5aO/bueKk6lT8nrZWfFp20QlF64KyQhXV3VCEhhLgupSwphGgOBEgpfXJJ6wsMB9KB60AfKeW5O+nm2K6Aj5Sy3/0+z87B5f9XBhcAx2JPPDioELh6W3vjuj92bvrgoEfMguS8vygXFjczrL852NakGzMKWyFX1pR9rbAVcuCfet8nNRQKJeysn5nL1mixnmsVLR5/Wjz3abFOabHstEpGWnyR6IEccfMt0HfjunFBj/z/e+AVJSHEdnKZ1FVK2fKhGD1k7nR0pJQ7gB15pC0AFuS1r/n1SmDlQ1RVKBQKhUKhUCgeKwprGF1ByM/Qu4Bsr4sDXQDVvVcoFAqFQqFQKBT/iaI09O6BHSUp5b3janYJIfY9JB+FQqFQKBQKhULxmFKU7vrJz9C77E+q1AENgdIPzegxo5RDicJWsOBa2s3CVsjBjfRbha2QK1r8veOny3sKWyEHGRocP67FNliv0+bj5zpfCS9shRwkv/V8YSvkoPKSM4WtkAN170b+cSlV7sFBj5ikG4YHBz1ijBqcMKBiqacLWyEHF6/9U9gKRY5AgVsAACAASURBVJrHbejdQUzfOwSmIXfngHceppRCoVAoFAqFQqF4/Hisht4BtaSUFj/5m6fMVigUCoVCoVAoFIp8U5SuKOVnLEhu87Rqb/yPQqFQKBQKhUKh0DSygEthkGdHSQhRQQjRECghhGgghHjRvDTH9ABahY1o1fo19h3azMGorQwZ9n6OdAcHB+YumM7BqK2EbV9JxUouFumurk5cTIpi4CDbjYhs69WcY9HhxMZEMGL4R7k6LV0ym9iYCHZHBFG5smtW2sgRA4mNieBYdDhebZrZzMmrTXOOHtlBzLE/CAj4MFenxYt+JObYH/wRvj7LqWzZp9i0aRnJ/8Qy7ftJNvMB8PJqTnR0OMdjIhieRz4tWTKb4zER7MqWT2XLliFs8woMV04yfdpkmzoBtGnTjKiobURH7yQgYECuXosW/UB09E7Cw9dSqZLJq2XLJuzaFcz+/ZvYtSuYZs0a28xJi3ml6nk+nTRYdvo6Hjw5cQ4lJ83Hoa1/rjF2DV/jyc9+4cnPfqHEO5+a9qtRjyfH/pi1lPohCLt6r9jMS7XnRdepWctX2fbnenbuD2bA4LdzcbLnhzlfsXN/MGs3L8G1ojMAdnZ2fDtrMpv+WMXWPWv5cIht707QYpugxfJ7rWVjtv65ju37g/ggj/KbOecrtu8PYs3mxbhkK79vZk1iwx8rCduzhgFDcu5bULSYT1ogU4oCLYXB/a4otQW+AVyBb7MtQ4HRD1/tvyGEuG7+6yaE6JGPeDchRLT5tYcQYsbDdswNnU7H199NoFvnd3jZox1duvlQ8zl3i5jefbuRmpJKw3qtmD1rPhMmjbBIn/zFGLaE2e6GbJ1Ox4zpU/Dx7cUL9VrQvXtHatWqbhHz9ltvYjCk8lztJkyb8SufTx0DQK1a1fH396Nu/ZZ4+/Rk5oyp6GxwE7tOp2P69Ml08OtDvfot6e7vx3PPWTq91e8NUlJSqF2nKTNmzmHKZFM1vXXrNhMnfsOnn9r2i9qdfPL17UXdei14I498SjGkUqt2E6bP+JWp5ny6desWEyZ8xciRtj153fGaNm0Sfn59adCgNd26dciRV/36dcdgSOX555sxc+ZcpkwxfYlMTjbQtevbeHq2pX//Ycyb973NnLSWV6qe599Ja2WH0FHizY/4d+ZYrk/oj71nC3ROlSy9n3WmWLvu3Ph6GDcmvset5bMBMJ6M4sbkD03LdyMh7TYZMYdsoqXa86LtNOmr0fT1H0Drxh3p0Pl1qtesahHTvVdnUlOu0szTh7mzF/HpZ0MA8PbzwsHBnrZNu+Dd8g169O2a1YmyhZdW2wStld//vhpNP/8P8WrciQ6d2+F+T/n59+pEaspVWnj6Mnf24qzya+/XBgcHB15v2hXflm/So2/XrE6UtU5ayyetIKUo0FIY5JnrUsoFUsoWQD8pZUspZQvz4ielXP0IHf8rbsADO0rZkVIekFIOejg696ehRz3Onj3P+biLpKens3plCO29W1vEvO7dmt+WrAFg3ZqNNGt+99fP9j6tuRB3kdjjp2zm1MizAf/H3pmGR1GkAfj9MiRyIyCQE4KAcigBCXihHEqQIwmIgAoIqKuuohwieO2qq6KuJyiyCyoggnIqEhBB7nAIARKOcCgQICcCSbhJMqn9McOQyUFiZkJq3Hp9+nG6++ued74qalJT3dUHDiRw6NARsrOzmTNnIRHhXZ1iIsLDmDFjLgDz5y+mc6f29u1dmTNnIVlZWSQkHOXAgQTatW3tslPbtq2cneb+SHh4mFNMeHgYM76xPQN4wYLFdOp0JwDnzp1nw4YtXLh40WWPvOTP0+w5CwnPl6fwIvJ07tx51m/YwoUL7nWCy7lKsNepuXMX0bNnF6eYnj27MHPmfAAWLFhCx462XMXF7SYl5RgA8fH7qVixIj4+Pi476ZgrU89Lho5lZ2l4I7nHklHHU8GaQ3bM6gKjQt7tu5G1ehGcOwOAOp1Z4DzebdqTs2sLZLvHz7TnnuvU6pabSDh0hKOHk8jOzmHR90vp0q2TU0yXbh2Z/92PACz5cTl33n0rAEopKleujMVioWLFa8jOyub06TMuO4FntAk6lF/ILTdx+NDRfOXX0SmmS7dOjvL76cfl3HF3O+BS+VXKU345nHFD+emYJ13ILeVSHpSke9pGRK69tCIiNUXE/dcKuY93gbtEJFZERtpHjtaJyDb7UuBaIhHpKCJR9tftRGSjiGwXkQ0icqN9+xARWSAiS0XkNxH5tztk/fzrkZSY4lhPTkrFz7+eU4x/nhir1cqpzDPUql2TKlUqM3zkk7z3zqfuULn8fgG+HE1MdqwnJqXg7+9bZIzVaiUz8xS1a9fE37+QYwOcjy2VU77zJiWlEJDfyd+XxDxOp06dpnbtmi6/d5FOAZffr0inIvJUltjycLlOJSWlEBDw53PVu3d3YmN3kZWV5bqThrky9byEThqWnVxbm9z0PxzrKv04Xtc6TyHsVS8Qr3oBVH7hIyqP/QRLi9AC5/EO7Uj2ltVu8zLtuec6+frVIyUpzbGekpyGr1/dAjHJyWkOp9OnzlCz1rUs+XE5586dY0v8CjbGLWPyxOlkZpxy2Qn0bRP0K7+6pCSlOtZTk4/h6+f8b6+eX11SklMdTpfK76cff+HcufP8Gv8L6+N+Zoqbyk/HPOmCQkq1lAcl6Sh1U0plXFpRSqUD3ctOyWVeBNYppVoppT4GjgFdlFK3AP2B4i6x2wvcpZRqDfwTGJdnXyv7OW4G+otIkNvt/wRjX36OSROncvbsufLUMPwFadasCW+99SLDhr1U3ioGQ+nwsuBVN4BzH77A+S/eodLAEVCpimO3VK+FV0AwObtjylHyMqY991xa3XITudZc2rW4l/a3dONvzwwmqEFA8QcatCDklpuwWq3c1qILd9/SncefecSUXxmTq0q3lAcl6ShZ8k4HLiKVAE+aHtwbmCIiO4G5QPNi4msAc+33L30MtMizb4VSKtM+XXo80KCwE4jIEyISIyIxF7Ov/KtESnIaAYF+jnX/AF9SktOcYpLzxFgsFqrXqMrJE+mEtg3hjTfHELd7NX9/egijRv+dvz05qJiPVzzJSakEBV6+PjcwwI/k5NQiYywWCzVqVOfEiXSSkws5Nsn52FI55TtvQIAfSfmdklMJzONUvXo1Tpwouwf6JSddfr8inYrIU1liy8PlOhUQ4EdSUslzFRDgy+zZk3n88VEcOnTEPU4a5srU8xI6aVh2KuMEXjXrONal5nXkZjg/AFKlHydnxybItaJOpJF7LBGvupf/+PEOvZuc2A2Qa3Wbl2nPPdcpNSUNv4DLIxB+/vVItV+GnDfG3z5CaLFYqFa9KuknM4h8oDurV64nJyeHE8dPsvXX7bRs1QJ3oGuboF/5HcMvz4iLr39dUlOc/+2lpRzDzz6i41x+3Vi7coOj/GJ+jXVL+emYJ8OfpyQdpZnAChF5TEQeB5YD08tWy62MBNKAECAUKO6GizeBVUqpm4BwoGKefXkv9LVSxHOolFKTlVKhSqnQa7yrX/HNtm3dQaNGDajfIBBvb2/uf6AHPy1Z4RSzdMkKHhrQG4DI3vexds0mALqHPURIi46EtOjIpM+n8dEHk5jy3xnFfLzi2RITS+PGDQkODsLb25t+/SJZFLXMKWZR1DIGDeoLQJ8+PVi1er1je79+kfj4+BAcHETjxg3ZvGW7y04xMXE0bhx82alvBFFRy51ioqKWM2jgAwDcf38PVtudyor8eerfL5KofHmKKiJPZYktVw1p0MDm1bdvOIsXO+dq8eJfGDCgDwD339+dNWtsTwGoUaM6CxZM5R//eI+NG933S7uOuTL1vGToWHbWhH141Q1AatcDSwW8QzuSE7fJKSY7bgOWG1oCIFWq41U3EHX88mVxFdp2JHvzard6mfbcc53itu+m4fUNCKofgLd3BcJ738fyn1Y7xfyydDV9HowAoHtEFzas2wxAUmIKd9xlu9+lUuVKtA5tyYHfDrnsBJ7RJuhQfju27yb4+voE5im/X35a4xSTt/y6RXRho6P8Urndqfxudkv56ZgnXchFSrWUB8U+cFYp9Z6IxAH3YpvG/GeKGEnRhNNAtTzrNYBEpVSuiAwGLMUcXwNIsr8e4n49Z6xWK2Oef4P5P0zFYrEwc8Zc9u75jZdeHU7stl38tGQFM6bP4T9ffMjWuBWkp2fw2JARZe40fMSrLFk8C4uXF9OmzyY+fj+vvzaamK1xREUt56up3zF92gT2xkeTnp7BwwNtU5bGx+9n3rxF7IxbRY7VynPDXyE31/Vb8KxWKyNG/IOoRd9gsViYNn02e/bs55//fJ5tW3cQtXg5U6d9x9SvPiF+9zpOnsxg0COXp+Lct28D1atVw8fHm/DwrvToOYC9e127YfpSnhbny9Nrr41ma548TZs2gT32PA0YeHlq19/2b6J69ar4+PgQEXEf3Xs8xB433MRttVoZOfKfLFr0NRaLhenT57Bnz2/84x+j2LZtB4sX/8K0abP56quP2bVrDenpGQwaNAyAp54aTKNGwbz00nO89JJtfpPw8EH88ccJl510y5Wp5yV30q3syM3lwncTqTx8HOLlRdb6ZeSmHOaa8EewHt5Pzo5NWHfHUKH5LVR5bTKoXC7Mn4I6exoAqV0Pr5p1sP62wzWPfJj23LOd/jl2HF/PnYTFYmHOrB/4bd8BRr34NDti4/ll6Wpmf/M9H08ax5otUWRkZDLscduMhV9/+R0ffPomy9cvQESYO2she+PdMyGHzm2CbuX32th3+HruJLwsXsy1l9/IF59mZ+xuflm6xl5+b7NqyyIyM07xrL38Znz5He9/+i9+Xr8AEZjnpvLTMU+6UF73G5UGUar4i/5EpDW2meT6AoeA+Uqpz8rY7U8hImeUUlVFxBtbZ642MA2IAuZj6+QtBZ6xxwUDUUqpm+zPhhqtlOopIrdjGzE7CywGBiqlgkVkCBCqlBpmf78o4AOl1OoredWs2ri8npFVKKezzpe3QgEsmk55qWOjVMFS7G8bV50ca055KxRAq390dkw9Lzknht5U3goFaDDzQHkrFEDH9lxXAqrVLm+FAqSeLdvLsEuDVcP2IKjadcUHXWWOnj5efFA5kJOV5BE9kOX1+pfqa7pL2uyr/vmK/KtLRG4AHrIvx4HZ2DpWnYo6pjxRSlW1/z8b6Jxvd8s8r8fa4xKAm+yvVwOr7a83AjfkiX/Vvn0ato7Xpffr6TZ5g8FgMBgMBoPh/wBPGlG60s/Te4F1QE+l1O8AIjLyqlgZDAaDwWAwGAyGvxz6jVsWzZWuBbkfSAFWicgUEbkHPKgLaDAYDAaDwWAwGLTCkx44W+SIklLqB+AHEakCRAIjgLoiMgn4Xim1rKhjDfriJfr1dUtyn1x5oKOVjvcDiYZ1Sj8jPa/91xUd7wdKnjKgvBUKUGPIl+Wt4DGknDlZ3goF8LF4l7dCAay5rj9g3N3oej+QofR40qV3xd5drJQ6q5SapZQKBwKB7djv8zEYDAaDwWAwGAyGkpIrpVvKgz81hZZSKh2YbF8MBoPBYDAYDAaDocSU1zORSoN+cw0bDAaDwWAwGAyGvyQ63tpQFHo+2OP/jHvuvZvN25axNW4FI0Y9WWC/j48PX04fz9a4FSxfNY+g+gFO+wMD/TiaGsew5x5zm1NYWEd27VxDfHw0L4x+psB+Hx8fZn7zOfHx0USvW0SDBoEA1Kp1Lct+nsPJE/v45JO33Oajq1PXsI7s3rWWvfHRjHmhcKdZMyexNz6aDdGXnQDGjhnG3vhodu9aS1iXDm71CgvryK5da9kTH80LRXjNnDmJPfHRrI/Om6uaLF82l/ST+xn/f1B+OjrpWKd0dAI92871v6UQ+eliwsdH8dW6+AL7UzLO8vi0lfT/z1L6fv4T6/YnA5Bx7iKPT1vJ7W/P453FW93mA3rWc+NUcu7tcjfbYlcQt3MVo55/qlCv6V9/StzOVaxa8z317fW8TWgIGzYtZsOmxWzctITwiDC3OenYJhgnz8GTJnPQsqMkImfK2+Fq4eXlxfsfvU7f+x/jttD76NO3Jzc2bewUM2hwXzIzMmkTcg+TJk7l9TfHOO1/691X+GX5Wrc6jR//FuERgwgJ6UT//pE0a9rEKWbo0AdJz8ikefP2TJgwhXFvvwzAhQsXef2N9xn74ptu89HZacL4t+kZPpCbQzrRv38vmjVzdnp06EOkp2fStHl7PpkwhXfGvQJAs2ZN6NcvkpatOtOj5wA+nTAOLzc9kPSSV3j4QFqGdOLBIrwy0jNp1rw94ydMYZzd68KFC7z++r8ZO/b/o/x0dNKtTunodMlLt7bTmpvLO0timDigAwue6cbSXUc4cCzTKWbK2t2EtQhi9lP38e4DdzBucQwA11Sw8EynmxkV1sptPqBvPTdOJff66ON/cX+vIYTeEkbfvhE0zVfPBw/pR0ZGJiE3d2Lip1/y5lsvAhC/ex933RnBHbf1oFevwUyY8DYWi8UtTrq1CcbJs8gVKdVSHvx1su6htAkN4eDBwxxOOEp2djYL5i2me497nWK69biXb2d+D8DC75fSoePtjn3de97LkYSj7N3zm9uc2rZtxYEDCRw6dITs7GzmzFlIeLjzL1Hh4WHMmDEXgPkLFtOpU3sAzp07z4YNW7hw4aLbfHR1ate2dQGniPCuTjEReZ3mL6az3SkivCtz5iwkKyuLhISjHDiQQLu2rcvEa/achYTn8wovwuvcufOs/z8pPx2ddKxTOjqBnm3nrqSTBNWqRmCtqnhXsND1pvqs3pfkFCMinL1om73yzMVs6lSrBEAlnwq0blAHnwru/VrWsZ4bp5ITGhrCwQOHSbDX83nzFtGjZxenmB49ujDzm/kAfP/9T3TseAcA589fwGq1AlDxmmtw1wSzOrYJxsmzUKVcygNtO0oiUlVEVojINhHZKSKR9u3BIrJXRGaKyB4RmScile37/ikiW0Rkl4hMFvu8xSKyWkTeE5HNIrJfRO6yb7eIyPv2Y3aIyJP27X4islZEYu3nuhQfJiIb7U5zRaSqq5/Tz78eSYkpjvXkpFT8/Os5xfjnibFarZzKPEOt2jWpUqUyw0c+yXvvfOqqhhMB/n4kHr3slJSUin+AX74YXxLzOGWeOkXt2jXd6qG7k3+AL0cTkx3riUkp+Pv7FhljtVrJzLQ5+fsXcmyA87GueCXmOXdSUgoBJfQqK3QsPx2ddKxTOjqBnm3nsVPn8a1e2bFer3oljp067xTzVMebWLwjgbAPFzJs5hpe7N7GrQ750bGeG6eS4+/vS2JSPq/8//786zlibF6nHV6hbVuxJeZnft2ylOHDX3F0nFxy0rBNME6eRVldeici94nIPhH5XURevEJcHxFRIhJa3Dm17SgBF4DeSqlbgE7Ah5c6PsCNwOdKqWbAKeBp+/bPlFJtlVI3AZWAnnnOV0Ep1Q7b86Bes297DMhUSrUF2gJ/E5GGwMPAz0qpVkAIECsi1wGvAvfanWKAUWXyyUvI2JefY9LEqZw9e648NQwGg8GjKM+2c+nOw0S0asiy5yP5bEAHXl2widxcT7q12eBJxGyJpW1oVzrcFcnzo5/mmmt8ylvJYCiT6cFFxAJMBLoBzYGHRKR5IXHVgOHAryVx1bmjJMA4EdkB/AIEAJd+LjyqlFpvf/0N0N7+upOI/CoiO4HOQIs851tg//9WINj+Ogx4RERisSWsNtAE2AIMFZHXgZuVUqeB27Alfr09fjDQoFBxkSdEJEZEYi5mn7rih0xJTiMg8PKvVv4BvqQkpznFJOeJsVgsVK9RlZMn0gltG8Ibb44hbvdq/v70EEaN/jt/e3LQFd+vJCQlpxAYdNkpIMCX5Dy/aNliUgnM41SjenVOnEh3+b09ySk5KZWgQH/HemCAH8nJqUXGWCwWatSwOSUnF3JskvOxrngF5jl3QIAfSSX0Kit0LD8dnXSsUzo6gZ5tZ93qlUg9dbnzlXbqPHWrV3KK+X77QcJaBAEQEnQdF3OsZJxz/yVbl9CxnhunkpOcnEpgQD6v/P/+ktMcMTavagW89u07wNkzZ2ne4kbXnTRsE4yTZ5GLlGophnbA70qpg0qpLOA7ILKQuDeB97ANyBSLzh2lAUAdoI19ZCcNqGjfl//nNyUiFYHPgQeUUjcDU/LEA1z6JrJyeVp0AZ5VSrWyLw2VUsuUUmuBu4EkYJqIPGKPXZ4ntrlSqtCpkpRSk5VSoUqp0Gu8q1/xQ27buoNGjRpQv0Eg3t7e3P9AD35assIpZumSFTw0oDcAkb3vY+2aTQB0D3uIkBYdCWnRkUmfT+OjDyYx5b8zrvh+JSEmJo7GjRsSHByEt7c3/fpFEhW13CkmKmo5gwb1BaDP/T1YvXp9YadyGzo6bYmJLeC0KGqZU8yiqGWXnfr0YJXdaVHUMvr1i8THx4fg4CAaN27I5i3by8Srf79IovJ5RRXhVVboWH46OulYp3R0Aj3bzhb+tThy4jRJ6WfIzrHy864jdLjReaY9vxpV+PWgrUN38I9MsnKs1KxyjcvvXRQ61nPjVHK2bt1Bo8bBNLDX8wceCGfJ4l+cYpYs+YUBA/sA0Lt3N9as2QhAgwaBjskbgoICuOHGRhw5nOiyk45tgnH6/yDvQIR9eSLP7gDgaJ71RPu2vMffAgQppRaX9D11fo5SDeCYUipbRDrhPHpTX0RuV0ptxHaZXDSXO0XH7fcOPQDMK+Y9fgb+LiIr7e9zA7bO0XVAolJqiohcA9wCvA1MFJHGSqnfRaQKEKCU2u/Kh7RarYx5/g3m/zAVi8XCzBlz2bvnN156dTix23bx05IVzJg+h/988SFb41aQnp7BY0NGuPKWJXIaMeIfLI6aiZfFi+nTZhO/Zz+v/XM0W7fFERW1nKlTv2Pa1PHEx0eTfjKDgYOedhy/f99Gqlevho+PNxHhXenR42H27HXthmldnYaPeJUli2dh8fJi2vTZxMfv5/XXRhOz1eb01dTvmD5tAnvjo0lPz+DhgTan+Pj9zJu3iJ1xq8ixWnlu+Cvk5rpn8stLXovzeb322mi25vGaNm0Ce+xeAwZeztVv+zdRvXpVfHx8iIi4j+49HmKPize861p+OjrpVqd0dLrkpVvbWcHixYvd2/D3GWvIVblEtr6exnVr8PnKnTT3r0XHpgGMCmvFvxZtYeYm21fHG71u5dJV5d0+/pGzF3PItuayam8ikwZ1pFHdGi456VrPjVPJvZ4f9Ro//Pg1FosXM76ey549v/HqP0aybdtOliz+henTZvPFlx8Tt3MV6emZDHnkWQBuv6Mtzz//FNk5OeTm5jJyxD/cMgKmY5tgnDyL0l5srJSaDEwuzbEi4gV8BAz5U8cpd02D4iZEpAK20aMbgUVAVWz3A92G7bpDgKX2bW2AeGCQUuqciLwFPASkAvuBw0qp10VkNTBaKRVjv9coRikVbE/aW0A4thGjP4Be9uUFIBs4AzyilDokIp2xDddd+vnvVaXUj1f6PDWrNtYqwWezSzTSaAByNfu3AWj5LOvLtw4aroSO9UlXqvlUKj7oKpM8ZUB5KxSgxpAvy1vB4AI+Fu/yVijAhZys8lYwuEBOVpJHfCF/HTCwVF+IjyR9U+TnE5HbgdeVUl3t6y8BKKXesa/XAA5g+7sewBc4CUQopWKKOq+OI0otgANKqePA7fl3ikgwkKOUGph/n1LqVWwTLuTf3jHP6+PY71FSSuUCL9uXvEy3L/nPsxLbpA8Gg8FgMBgMBoPhT1JGY2NbgCb2SdmSgAexXXUGgFIqE9sVY4BtRmzsgyhXOqlW9yiJyFPAtxTS2TEYDAaDwWAwGAyeTVk8R0kplQMMw3ZbzR5gjlJqt4j8S0QiSuuq1YiSUuo/wH+KiUkAbroqQgaDwWAwGAwGg8FtFDfVd2lRSi0BluTb9s8iYjuW5JxadZT+ipzOOl980FVkTq0O5a1QgCfPby1vhUJJP3+m+KCrjI53ueh2nyNAzUouPwva7ehYn3RFt3YToNrgL8pboQA6tuf9Tq4pbwWPwdwP5Lno+B3jSXjStBSmo2QwGAwGg8FgMBiuCqajZDAYDAaDwWAwGAz5UB4xN58N01EyGAwGg8FgMBgMVwVPGlHSata7/1e6hnVk96617I2PZswLzxTY7+Pjw6yZk9gbH82G6EU0aBDo2Dd2zDD2xkeze9dawrq473r1ep1a0iX6A8I2fsQNw8KLjPPv0Zb7U2dxbUhDAOrefROdfn6be1a9S6ef36bOnc3d5tT5nrvYGLOUzduX8dzIvxXY7+PjzZSpH7N5+zKWrphDUH3bA5mD6gdwJDWOVet+YNW6H3j/4zfc5qRj2enqpaOTqVOe66Srl45OOrbnOuZJRyddvYxTydDxO0YHcku5lAce21ESkTN5XncXkf0i0qA8nUqDl5cXE8a/Tc/wgdwc0on+/XvRrFkTp5hHhz5EenomTZu355MJU3hn3CsANGvWhH79ImnZqjM9eg7g0wnj8PJyQ5F6CSHvDGX9w/9m+d0vENj7DqrdEFAgrEKVijR+/D5Obr389PGLJ0+z8ZH3WdHpRbYOn0ToZ08XOK5USl5evPvhP3nwgce5s10PevfpyQ03NnKKGfBIXzIyTtGudRj/+Xwa/3xjtGNfwqEjdLqrF53u6sULI19zm5N2Zaepl65Opk55ppOuXjo66dqe65YnHZ109TJOJXfS7TtGF8pievCywmM7SpcQkXuACUA3pdThEh6jzSWH7dq25sCBBA4dOkJ2djZz5iwkIryrU0xEeBgzZswFYP78xXTu1N6+vStz5iwkKyuLhISjHDiQQLu2rV12qtW6MWcPpXHuyDFUtpXEHzbi17VNgbjmY/uyf+IirBezHdsydx3mQloGAKf2JmKp6IOXj+vpvqVNSxIOHuZwQiLZ2dn8sGAx3Xrc4xTTrXtnQneNDwAAIABJREFUZs/6HoBFP/zMXR0KPK/YrehYdrp66ehk6pTnOunqpaOTju25jnnS0UlXL+NUMnT8jtGFXCndUh54dEdJRO4GpgA9lVIH7NuCRWSliOwQkRUiUt++fZqI/EdEfgX+LSJVROQrEdksIttFJDLP8etEZJt9ucO+vaOIrBaReSKyV0RmiojLxeYf4MvRxGTHemJSCv7+vkXGWK1WMjNPUbt2Tfz9Czk2wPnY0lDRrybnk0841s+nnKSSXy2nmGtvDqaSf21Sf4kt8jz+PduRsTOB3Kwcl538/OuRlJTqWE9OSsPPr55TjK9fPZKSUgBbnk6dOk2tWjUBqN8gkJXrvmfh4hncdnvBPxJKg45lp6uXjk6mTnmuk65eOjrp2J7rmCcdnXT1Mk4lQ8fvGF3wpEvvtBlZKQXXAD8AHZVSe/Ns/xSYrpSaLiKPYhtt6mXfFwjcoZSyisg4YKVS6lERuRbYLCK/AMeALkqpCyLSBPgWCLUf3xpoASQD64E7gej8YiLyBPAEgFhq4OVVxa0fvNwR4eY3BrJ1eNHPBq52YwA3vfoQ6/u/cxXFCict9RitW3QiPT2Dlq1a8PXMibS/rQdnTp8tbzWDh2LqlOEvg4e15wbD/wN/9e8YM5nD1SEb2AA8lm/77cAs++sZQPs8++Yqpaz212HAiyISC6wGKgL1AW9giojsBOYCee9e3ayUSlRK5QKxQHBhYkqpyUqpUKVUaHGdpOSkVIIC/R3rgQF+JCenFhljsVioUaM6J06kk5xcyLFJzseWhgsp6VTyr+1Yr+RXi/MpJx3rFapWpPqNQdy14B903TKeWrc05vbpox03AFfyq8VtX40i5tlJnD18zGUfgJTkNALy/MLjH1CPlJQ0p5jUlDQCAvwAW56qV6/GyZPpZGVlk55uu3xkR+xuEg4doVHjhi476Vh2unrp6GTqlOc66eqlo5OO7bmOedLRSVcv41QydPyO0QVzj9LVIRfoB7QTkZdLeEzerrgAfZRSrexLfaXUHmAkkAaEYBtJ8slzzMU8r624YURuS0wsjRs3JDg4CG9vb/r1i2RR1DKnmEVRyxg0qC8Affr0YNXq9Y7t/fpF4uPjQ3BwEI0bN2Tzlu2uKpEee4Cq1/tSuX4dxNtCYK/bSVm21bE/5/R5Frd4kp/bDufntsM5ue13Ng7+gIy4Q3hXr8zt37zA7re/4+SW/S67XGL7tp00bBRM/QaBeHt70+v+HixdstIpZumSlfR/uDcA4b26Er12EwC1a9d03JjZIDiQ6xsFczjhqMtOOpadrl46Opk65blOunrp6KRje65jnnR00tXLOJUMHb9jdMGT7lHy5EvvUEqdE5EewDoRSVNKfYltlOlBbKNJA4B1RRz+M/CsiDyrlFIi0loptR2oASQqpXJFZDBgKcvPYLVaGT7iVZYsnoXFy4tp02cTH7+f118bTczWOKKilvPV1O+YPm0Ce+OjSU/P4OGBtpmH4uP3M2/eInbGrSLHauW54a+Qm+v6gKay5hL78jTu/PZFxOLF4W9Xc3pfEs3GPEBG7EFSlm0r8tjrHw2jasN6NB3Vm6ajbP/41z/4LhePn3LJyWq18tLofzFnwRd4WSx8+8189u39nbEvP0fs9l38/NNKZs6Yx+eT32fz9mWkp2fyxKMjAbj9zraMffk5crJzyFW5jB75GhnpmS75XHLSrex09dLVydQpz3TS1UtHJ13bc93ypKOTrl7GqeROun3H6IInXXonSpXXYJZriMgZpVRV++sgYC0wHIgDpgLXAX8AQ5VSR0RkGhCllJpnP6YS8AlwB7aRtUNKqZ72+5LmYxvlWwo8o5SqKiIdgdFKqZ724z8DYpRS067kWcEnQKsEz6nl3uc7uIMnz28tPqgcSD9/pvggg5bUrFS1vBUKYOqTwd3o2J73O7mmvBUMhjJHx+8YgD8y95XTuMuf490GA0v1t/GLh7+56p/PY0eULnWS7K+PAnkv3uxcSPyQfOvngScLifsNaJln01j79tXY7mW6FDesVOIGg8FgMBgMBsP/KVqNIBSDx3aUDAaDwWAwGAwGg2eR60FdJdNRMhgMBoPBYDAYDFcFT7pHyXSU/s8YeubX8lYoQLPqQeWtUCgx538rb4UChF7XpLwVCrDnlH4z8WRZXX8opsGgOzreDzRfw/umAPpomCsd2/OY4/p971XxqVjeCgUw95y6hueMJ5mOksFgMBgMBoPBYLhKmBElg8FgMBgMBoPBYMhHeT0TqTSYjpLBYDAYDAaDwWC4KnjSZA5e5S1ggK5hHdm9ay1746MZ88IzBfb7+Pgwa+Yk9sZHsyF6EQ0aBDr2jR0zjL3x0ezetZawLu67Nvyee+8mZttytsetZOSoArOo4+Pjw9TpE9get5IVq+ZTv36A0/7AQD+SUnfw7HOPu83pto7tmL3ua+aun8mgYQ8X2N/q1pZM/3ky0UdW0KlHwVxUrlqZH2Pm8vzbw93mpGPZgZ650rFO6eikY53S0UlXL+NUMup2ask90R9wz8aPaDIsvMg4vx5tiUydxbUhtieAXNu6ER1/GWdbVryDX7dQtznpmCfQsz3XMVemPfccVCmX8uAv11ESEauIxIrILhGZKyKVi4k/Y/+/v4jMu0JcsIjscrevl5cXE8a/Tc/wgdwc0on+/XvRrJnzDZ6PDn2I9PRMmjZvzycTpvDOuFcAaNasCf36RdKyVWd69BzApxPG4eXlepF6eXnx4Uev88D9j9IutCt9+oZzY9PGTjGPDO5LRkYmrUM68/nEqbzx5lin/ePefYVflrvv5lkvLy9GjxvOyAFjeajjYMIiOxPcpIFTTFrSMd4c8S7Lvv+l0HM8OeZRtv8a51Yn3crukpeOudKxTunopFud0tFJVy/jVFIpoeU7Q9n48L9ZefcLBPS+g2o3BBQIq1ClItc/fh8nt16eYOD03qOs6foqq+99mY0PvUfI+48hlr9ontC3PdctV6Y99yxyS7mUB3+drF/mvFKqlVLqJiALeKokBymlkpVSD5StWkHatW3NgQMJHDp0hOzsbObMWUhEeFenmIjwMGbMmAvA/PmL6dypvX17V+bMWUhWVhYJCUc5cCCBdm1bu+zUJjSEgwcPk5BwlOzsbBbMi6JHj3udYrr3uJdZMxcA8MP3P9Gh4+2OfT16duFwQiJ79rhv9pzmrZuSmJBE8pEUcrJzWL5wJXd3vdMpJiUxld/3HETlFvzd4cabb6BWnVpsXhPjNicdyw70zJWOdUpHJx3rlI5OunoZp5JRs3Vjzh5K49yRY6hsK0k/bMS3a5sCcU3H9uX3iYvIvZjt2GY9n4Wy2v5kslT0RrnpZ2Yd8wR6tuc65sq0555FLqpUS3nwV+wo5WUd0BhAREbZR5l2iciI/IF5R4xEpIWIbLaPTO0QkUs/AVhEZIqI7BaRZSJSyVVB/wBfjiYmO9YTk1Lw9/ctMsZqtZKZeYratWvi71/IsQHOx5bKyb8eSYkpjvWkpFT8/Os5xfj5+zpirFYrpzJPU6t2TapUqcyIkU/w7jsTXPbISx3fOhxL/sOxfizlD+r41SnRsSLC8NeeZsK/JrnVSceyA01zpWGd0tJJwzqlo5OuXsapZFT0q8n55BOO9fMpJ6noV8sppsbNwVTyr03aL7EFjq/ZuhGd1vybTqveY8eYLx0dJ1fQMU+gaXuuYa5Me+5ZmEvvNEBEKgDdgJ0i0gYYCtwK3Ab8TUSu1DV/ChivlGoFhAKJ9u1NgIlKqRZABtCniPd+QkRiRCQmN/esez6Qh/DSy8P5fOJUzp49V94qDvoM6cWGlZv4I+WP4oP/z9ExVzrWKR2dDIa/DCLc9MZAdr3xTaG707cfYFWHMay571WaPBeJ1zXeV1nQM9CxPdcR055ffTzp0ru/4qx3lUTk0k9Q64Avgb8D3yulzgKIyALgLmB7EefYCLwiIoHAAqXUbyICcEgpdencW4Hgwg5WSk0GJgNU8Am4Yic4OSmVoEB/x3pggB/JyamFxiQlpWCxWKhRozonTqSTnFzIsUnOx5aG5OQ0AgL9HOsBAb6kJKc5xaQkpxIQaHO1WCxUr1GNkyfSadM2hIhe9/HGm2OpUaM6KjeXCxcvMuW/M1xy+iP1D+r6X/4Vra5fnRI3/je3aU7IrS3pM7gXlapUwtu7AufPnufzcZNdctKx7EDTXGlYp7R00rBO6eikq5dxKhkXUtKp5F/bsV7JrxYXUk461itUrUi1G4Nov+AfAFxTpwa3Th/Nr4M/ICPukCPuzG/J5Jy9QPWmgU7bS4OOeQJN23MNc2Xac8/CzHpXvly6R6mVUupZpVTWnz2BUmoWEAGcB5aISGf7rot5wqy4oaO5JSaWxo0bEhwchLe3N/36RbIoaplTzKKoZQwa1BeAPn16sGr1esf2fv0i8fHxITg4iMaNG7J5S1F9v5KzbesOGjUKpkGDQLy9vbn/gZ4sWbLCKWbJkhU8POB+AHr17sbaNRsB6Bb2IC1bdKBliw5M+nwqH34wyeXGBmBP7D6CGgbiF+RLBe8KdInszLplG0p07GvD3qZX2/70vvVBPv3XJJbMW+byFwXoWXagZ650rFM6OulYp3R00tXLOJWMjNgDVLnel8r16yDeFgJ63U7qsq2O/Tmnz7O0xZMsbzuc5W2Hk77td0cnqXL9Oo7JGyoFXke1xv6cO3rcZScd8wR6tuc65sq0556FJ11691ccUSqMdcA0EXkXEKA3MKioYBG5HjiolJogIvWBlsDBshCzWq0MH/EqSxbPwuLlxbTps4mP38/rr40mZmscUVHL+Wrqd0yfNoG98dGkp2fw8MCnAYiP38+8eYvYGbeKHKuV54a/Qm6u64OTVquV0c+/wYIfpmGxePHNjHns3fMbL786gu3bdvLTkhXMmD6HyV98yPa4laSnZ/DoEPdNO1qU0wevjGf8rPfxsngR9d1PHNqfwN9eGMreuH2sW7aBZiE38t6Xb1Ht2qq073I7fxs9hIc7DS1TJ93K7pKXjrnSsU7p6KRbndLRSVcv41QylDWXHS9P4/ZvX0QsXhz5djWn9yXRdMwDZMQeJHXZtiKPrdXuRpo8G4HKzkHlKuJenErWydMuO+mYp0teOrbnuuXKtOeehSd9ElHumjJGE0TkjFKqaiHbRwGP2le/UEp9kjdeRIKBKKXUTSLyIraOVDaQCjwMVL+0337caKCqUur1K/kUd+nd1aaKT8XyVihAs+pB5a1QKDHH3Tf7jbsIva5J8UFXmT2njpa3gkdwNutCeSsYDGXO/Fp6Pu+lz0n3TfvsLnRsz3X83tPx7xZd2/OcrCQpb4eS8Fxw/1L9bTwhYfZV/3x/uRGlwjpJ9u0fAR8VFa+USgBusr9+F3g3X+jJS/vtMR+4x9hgMBgMBoPBYDDoxl+uo2QwGAwGg8FgMBj0xJMuvTMdJYPBYDAYDAaDwXBV8KRZ70xHqYypV+Xa8lZwIu1sRnkrFEDHa6J15eh5/Z6Hoeu12rqhW1twCR3bhFa1ry9vhQKkXDhZfNBVRsey0/FeIIBuvld6dGL58FPqX2cWs7JkYvXby1uhAEOOrypvBY/Gc7pJpqNkMBgMBoPBYDAYrhJmRMlgMBgMBoPBYDAY8mHuUTIYDAaDwWAwGAyGfCgPGlHyKm8BA3S8pz1rN0cRvfUnnhnxeIH9Pj7eTPryA6K3/sSi5d8SGOQPQO++PVi2dr5jOXpiJy1uauoWp65hHdm9ay1746MZ88IzhTj5MGvmJPbGR7MhehENGgQ69o0dM4y98dHs3rWWsC7ue6aGcSo5pk55rpMpu5Jze6d2zF83k+83fMvgYQMK7G99WwjfLPuSTUdXcU+Pjo7tvoH1+GbZl8xc/hWzV39Nn0ci3eZkys9znW7pcAuTVv2H/66dzANPP1Bgf+TjvZi44nMm/Pwpb337NnUC6jj2dX6gM/9dM5n/rplM5wc6u80J9MyVjk5+HVsSse59Itd/SIth4UXGBXVvy8Dkb6jVsqFjW4th4USu/5CIde/j1+FmtznpmCcdyC3lUh5clY6SiFhFJFZEdonIXBGp/CePf7ms3MobLy8v3n7/FQb2fYpOt0XQq093mtzYyCnmoUF9yMw8Rfs23Zgy6WteeX0UAN/PXUzY3X0Iu7sPzz31IkcOJ7J71163OE0Y/zY9wwdyc0gn+vfvRbNmzg/Ge3ToQ6SnZ9K0eXs+mTCFd8a9AkCzZk3o1y+Slq0606PnAD6dMA4vL9ermXH6c16mTnmukym7knuNHTeK5waMpm+HQXTtdS8Nbwh2iklNTOP14eP4+ftfnLYfTzvB0J5PMaDLowzp/iSDhw3gunq13eJkys9znZ566++8Pvg1nrnnae6O6EBQE+eHoR/cfYBRPUbyXNdnWb84mqEvDwWgao2qPDTiYZ6PGMWoiJE8NOJhqtSo4rLTJS8dc6Wbk3gJ7cYNZuWAf7Oo4xiCI2+jRhP/AnEVqlSk6eNd+WPr745tNZr4Exx5G4s6jWXFw/+m3TtDEC/Xn2uqY550QZXyv/LgamX9vFKqlVLqJiALeKokB4kNL+Av21Fq3eZmEg4e5cjhRLKzs1m4YAldu3dyignr1pm53y4EYPHCZbTvcFuB8/Tq050fF/zkFqd2bVtz4EAChw4dITs7mzlzFhIR3tUpJiI8jBkz5gIwf/5iOndqb9/elTlzFpKVlUVCwlEOHEigXVvXZxsyTiXH1CnPdTJlV3JatG7G0YQkko6kkJOdw7KFK+jQtb1TTEpiKr/vOUBurvMXbE52DtlZ2QD4XOPttj9ATPl5rlOTVjeQkpBC2pE0crJzWLtoLbeGOZfNzo07uXjhIgD7tu+jtt91gG0kKnbdds5knuFs5lli122nTYc2LjuBnrnS0al260acTkjjzJE/yM22krBwE4FdC5ZByJgHiJ8YRe7FbMe2wK5tSFi4idysHM4e/YPTCWnUbt2owLF/Fh3zpAtmROnKrAMaA4jIKPso0y4RGWHfFiwi+0Tka2AX8CVQyT4iNdO+f9elk4nIaBF53f66rYjssMe+fylORIaIyGd5jokSkY7212EislFEttlHu6rat78rIvH2831g31ZHROaLyBb7cqeryfD1q0dyUopjPSU5DV+/es4x/nVJTkoFwGq1curUaWrWcp5qOLz3ffwwf4mrOgD4B/hyNDHZsZ6YlIK/v2+RMVarlczMU9SuXRN//0KODXA+1jiVnROYOuXJTqbsSk5d3zqkJR1zrB9L+YO6vteV+Ph6/nX5dsU0Fm+dz/TPZnI87YTLTqb8PNeptm9tjidffvzCiZTj1L7CKGOX/mFsXbXVcewfyccd+46nnKC2r+sjlKBnrnR0quxbk3PJl6fwP5dyksp+NZ1iat0cTBX/WiStiHU+1q+QY32djy0NOuZJF3KVKtVSHlzVjpKIVAC6ATtFpA0wFLgVuA34m4hc6i43AT5XSrVQSg3l8ohUwYvQnZkKPKmUagVYS+BzHfAqcK9S6hYgBhglIrWB3kALpVRL4C37IeOBj5VSbYE+wBdFnPcJEYkRkZizF9OL03CZ1m1u5vz5C+zb83vxwQZDCTB1ynMxZVcy0pKP8dA9Q+h1+4P07Hcfta5z/Q8jd2DKT3869u5I45aNWfDf+eWtYigpIrR5bQBb35hV3iYGbM9RKs1SHlytjlIlEYnF1hE5gm2UqD3wvVLqrFLqDLAAuMsef1gptenPvIGIXAtUU0pttG8qyb+G24DmwHq732CgAZAJXAC+FJH7gXP2+HuBz+yxPwLVL41A5UUpNVkpFaqUCq1yzZW/fFNT0vAP8HOs+/nXIzUlzTkm+ZjjlwSLxUL16tVIP3n5QYOR93dnoZt+fQRITkolKPDytb2BAX4kJ6cWGWOxWKhRozonTqSTnFzIsUnOxxqnsnMCU6c82cmUXck5lvoH9QLqOtbr+tXhWOrxKxxROMfTTnBg7yFa3xrispMpP891OpF6guv8L0/OUNvvOk4UMsoY0j6EfsP689Zjb5KTleM4to7/5dHM6/xqcyLV9RFK0DNXOjqdS02nsn8tx3plv1qcS7n8Q7V31YrUaBpIl/mv0OvXj7nulkZ0nDaKWi0bci6lkGNTXf+RW8c86UIuqlRLeXC171FqpZR6VimVVUz82Svsy8HZu2IJ3r+oYwRYnsetuVLqMaVUDtAOmAf0BJba472A2/LEB9g7eaUmdtsuGjaqT1D9ALy9vYm8vzvLfnJ+4vOypavo+5BtVqYekWGsX/urY5+I0LNXVxbOd8/17ABbYmJp3LghwcFBeHt7069fJIuiljnFLIpaxqBBfQHo06cHq1avd2zv1y8SHx8fgoODaNy4IZu3uP70ceNUckyd8lwnU3YlJz52L0ENA/EP8qOCdwXCIu9h7c/RJTq2rl8drqnoA0C1GlUJadeShANHXHYy5ee5Tr/F7ce/oT/1gupRwbsCd4ffzeblvzrFXN/iep55ZxhvPvYmmScyHdu3rdlG67taU6VGFarUqELru1qzbc02l51Az1zp6HQi9iDVGvpSJagOXt4WgiNvI3HZ5TLIPn2eeTf9nR9uHckPt47k+LYDrB7yESd3HCJx2TaCI2/Dy6cCVYLqUK2hLye2H3DZScc86YInTeZQns9RWgdME5F3sXVYegODiojNFhFvpVQ2kAbUtV8edwZ7R0YplSEip0XkVqXUr8CDeY5PAJ62TwwRgK0TBLAJmCgijZVSv4tIFfv+ZKCyUmqJiKwHDtrjlwHPAu8DiEgrpZTzxa5/EqvVyqtj3mbW/Ml4WbyYPfN79u89wOiXhhEXu5vlP63iuxnzmfCfd4ne+hMZ6Zk8/dhox/G33RFKSlIqRw4nuqJRwGn4iFdZsngWFi8vpk2fTXz8fl5/bTQxW+OIilrOV1O/Y/q0CeyNjyY9PYOHBz4NQHz8fubNW8TOuFXkWK08N/wVcnNdvwXPOP05L1OnPNfJlF3Jvd5/+WM+/fZDLBYvfvxuMQf3J/DkC4+xJ24va5etp3lIU97/6m2qX1uNu7rcwRMvPEr/jo/QsEkDRrw2DKUUIsI3//mWA3sPFv+mJXAy5eeZTrnWXP7zj//wxox/4WXx4pfZyzmy/wgDRg3gt52/sXn5Zoa+8igVK1fkxUkvAvBH8h+89dibnMk8w3cTZvPRoo8B+Hb8d5zJdOk3VAc65kpHJ2XNZcsr07ln1hjE4sWB79aQuT+Jli/04WTcIadOU34y9ydxeNGvhK9+z3ael6ehcl3/o1zHPOmCJ30SUVfh5igROaOUKnCJmoiMAh61r36hlPpERIKBKPsMeZfi3gMigG1KqQEi8hwwHEjC1olJUEq9LiK3AlOwlcEaIFQpdaeICPAN0AbYA9QEXldKrRaRzsB7wDX2t3sV2AIsxDbyJMAHSqnp9nuaJgLNsHUy1yqlrjiDX0DNFlo9VSvtbEbxQQZtqVfl2uKDrjKmTpUMHcsO9Cy/VrWvL2+FAqRcOFl80FVGx7LTlW6++s0Y9lPqX2eEoCyZdl2n4oOuMkOOryo+qBzIyUpyfV7zq0DfBpGl+tt47uGFV/3zXZURpcI6SfbtHwEf5duWANyUb9tYYGye9QnAhEJOuds++QIi8iK2e6JQtt5goRNBKKVWAm0L2dWukNjjQP/CzmMwGAwGg8FgMBiuTHldRlca/jpPr7LR49KDbbFNDPFWcQcYDAaDwWAwGAwGz0ZE7rM/Yuh3+4BJ/v2j8jz6Z4WINCjunOV5j5LbUUrNBmaXt4fBYDAYDAaDwWAoSFncoyQiFmy3x3QBEoEtIvKjUio+T9h2bLflnBORvwP/ppgrxf5SHSUdOWauIS+Wayp4l7dCoWTlZBcfdJU5nXW+vBUK4CX6XRLtY9GvTunaFuhXerDj5KHyViiAl/zVLsAoG3RsD0DP+4FOz/p7eSsUoNrDk8pboQBDNb0fyFB6ymh+hHbA70qpgwAi8h0QCTg6SkqpvJVpEzCwuJOalt9gMBgMBoPBYDBcFUr7HCUReUJEYvIsT+Q5bQBwNM96on1bUTwGFPssBjOiZDAYDAaDwWAwGK4Kpb30Tik1GZjs6vuLyEAgFOhQXKzpKBkMBoPBYDAYDIarQhnNepcEBOVZD7Rvc0JE7gVeAToopS4Wd1Jz6Z0GhIV1ZNeuteyJj+aFF54psN/Hx4eZMyexJz6a9dGLaNAgEIBatWqyfNlc0k/uZ/wn7p3gr2tYR3bvWsve+GjGFOE0a+Yk9sZHsyGPE8DYMcPYGx/N7l1rCetSbGe9xHTp0oHtsSvYsXM1zz9f8NpuHx8fpn/9GTt2rmb1mh+oX9/m1Llze6LXL2Lz5qVEr19Ehw63u82ptGUHMGbMMPbER7Nr11q6uDFPAPd2uZut238hdsdKRj5f8FFfPj4+TJ0+gdgdK1m5egH169tGp9u0aUn0xiiiN0axftNieoaHuc0pLKwju3auIT4+mhdGF5Grbz4nPj6a6HV56/m1LPt5DidP7OMTN9fze7vczbbYFcTtXMWoIvI0/etPidu5ilVrvr+cp9AQNmxazIZNi9m4aQnhEW7Ok2Z1Ssc2yuGlWZ3q0qUDO3asYvfutYwe/XShTjNmTGT37rWsXbvQ4XTPPXexYcNiYmKWsWHDYjp2vMNtTjq25zqWnY55Ali/P4nIjxcS/uEPfLVmV4H9KRlnefyLZfT/LIq+Exaxbp/tb8OdR4/T79Mox7Jy9xG3OemYKx3bKR3zpAOlvfSuGLYATUSkoYj4AA8CP+YNEJHWwH+BCKXUsZK4ekxHSUSsl6b+FpFFIuK2pzeKSKiIFPZcpjLHy8uLCePfJjx8IC1DOvFg/140a9bEKebRoQ+RkZ5Js+btGT9hCuPGvQLAhQsXeP31fzN27Jtl4tQzfCA3h3SifxFO6emZNG3enk8mTOEdu1OzZk3o1y+Slq0606PnAD6dMA5eqe3sAAAgAElEQVQvL9ermZeXFx99/C969xpCm1u60LdvBE2bNnaKGTykHxkZmbS8uSOfffolb75lmxnyxIl0HnjgMdq1u48n/vY8X3z5scs+l5xKW3bNmjWhf79IQlp1pqcb83TJ68OP3qBP76G0bdOVB/qGc2O+XD0yuB8ZGado1bIzEz/7ijfetD2mLD5+Px3aR9L+9p7c32sI4z99C4vF4han8ePfIjxiECEhnejfP5JmTZ1zNXTog6RnZNK8eXsmTJjCuLdfBuDChYu8/sb7jH3R/fX8o4//xf29hhB6S9gV61TIzZ2YmKdOxe/ex113RnDHbT3o1WswEya87bY86VandGyjLnnpWKfGj3+LyMjBtGp1D/36RdA0n9OQIf3JyMikRYu7+fTTL3jrrZcAOH78JH36PEpoaBiPPz6SL7/8xG1OOrbnOpadbnkCsObm8s6izUwc3JkFw8NZuiOBA8ecJ4aZsmoHYTc3YPawnrz74F2M+/FXABrXu5ZZT3dnzrM9mTi4M28u3ESO1fX5xnTMlY7tlI550gWlVKmWYs6ZAwwDfgb2AHOUUrtF5F8iEmEPex+oCsy19yl+LOJ0Djwp6+eVUq2UUjcBJ4GCXfNSopSKUUo9567z/RnatW3NgQMJHDp0hOzsbGbPWUh4eFenmPDwMGbMmAvA/PmL6dypPQDnzp1n/YYtXLhQ7MihS05z5iwkIp9TRBFOEeFdmTNnIVlZWSQkHOXAgQTatXX9ieihoa04eOAwCQlHyc7OZt68RfTs6fwrfs8eYcz8Zj4A33+/xPGLbFzcblJTbD8cxMfvp2LFivj4+Ljs5ErZhYd3ZXYZ5AkgNDSEgwcv52r+vCh69OziFNOj5718O9OWqx++/8mRq/PnL2C1WgGoeM01uGtimrZtWxWoU+H5RquccrVgMZ3y1PMNZVDPQ0NDCtSpAnnq0SVPnSr7POlYp3Rso0DPOpXfae7cRYU6ffPNPAAWLFhCp053ArZ2KiUlDbC1U5UqlU07pUN7rmPZ6ZgngF2JJwiqVY3AWtXwrmCha8sGrN5z1ClGRDh70TZL65kL2dSpXhmASj4VqGCx/ZmXlWNF3DTHpY650rGd0jFPupBbyqU4lFJLlFI3KKUaKaXetm/7p1LqR/vre5VS9ez9iVZKqYgrn9GzOkp52Yh9JgsR6SgiUZd2iMhnIjLE/vrdPA+W+sC+ra99VCpORNbmP4eItBORjSKyXUQ2iMiN9u1DRGSBiCwVkd9E5N/u+CD+Ab4kJiY71pOSUgjw9y0Qc9QeY7Vaycw8Re3aNd3x9kU6Hc3jlJiUgn8Jnfz9Czk2wPnYUjn51yMxyTlPfv71ioyxWq2cOnW6QJ569epGXOwusrKyXHdyoewC/Ase6448Afj5+5KYmOJYT05Kwd+vXr6Yeo6YS7mqZc9VaGgIv25ZysbNPzHiuVcdHQJXCPD3I/HoZaekpFT8A/zyxfg6OWWeKuN67u9LYlI+p/zl51/PEWNzulynQtu2YkvMz/y6ZSnDh7/iljzpWKd0bKNA4zqVvwwKtFOXY4pqp3r37k6sG9sp3dpzLctOwzwBHDt1Dt8aVRzr9apX4Vim82MinurcksWxhwh7bz7Dpq/kxZ5tHft2Hv2D+8f/yAOfRvFq5K2OjpMr6JgrHdspHfOkC6qU/5UHHjeZg/2BUvcAXxYTVxvoDTRVSqk8l+r9E+iqlEoq4vK9vcBdSqkc+w1f44A+9n2tgNbARWCfiHyqlDqa/wT26QqfAPCy1MDLq0r+EEM50KxZE95860UiwgeVt4rWxMTEcWvb+7jhxkb8d/IHLF+2mosXXf+D7a9GzJZY2oZ25cYbG/HfKR+y7GeTJ4PrNGt2A2+//RI9exb7eA+DAYClOxKIuKURj7RvTtyRP3h17nrmPReOl5dwc1AdFgyP4OCxTP4xbz133hDANd6uXyZsMLhCCe430gZPGlGqJCKxQCpQD1heTHwmcAH4UkTuB87Zt68HponI34DCWosa2K5d3AV8DLTIs2+FUipTKXUB2wOsGhT2xkqpyUqpUKVUaHGdpOSkVAID/R3rAQF+JCWnFogJssdYLBZq1KjOiRPpVzyvK+R9P4DAAD+SS+iUnFzIsUnOx5bKKTmNwADnPKUkpxUZY7FYqF69miNP/gG+fPvdf/nb46M4dMg9N7S6UnZJyQWPdUeeAFKSUwkMvPzrrH+AH8kpafli0hwxl3J1Ml+d2r/vAGfOnqV58xtddkpKTiEw6LJTQIAvyXlGc2wxqU5ONaqXcT1PTiUwIJ9T/vJLTnPE2JyqFXDat+8AZ8+cpXkL1/OkY53SsY0CjetU/jIo0E5djsnfTgUE+DJnzmQee2wkBw8edo+Thu25lmWnYZ4A6lavTGrmWcd62qmz1K1RySnm+62/E3aT7c+RkPp1uJhjJePcBaeY6+vWoPI1Ffg9zfUHX+uYKx3bKR3zpAtlcY9SWeFJHaX/sXfe8VUU6x9+3oQEVDoiJHQBRWyggKKggALSQZoFwV65AooFQUGuXe+1XcVro1iuNBHpvYgi1dBC75DQQ1Mx7f39sXuSc1JoCZnJz3nyOZ+zOzt7zjezc2Z3Zt553z9VtRZe50RIX6OUTOj/UQjSFnXVA8YArYGpfvqjwAA8F4LL/JmnYP4JzPHXQrUJfJ5PsAFrCrkwI7dkaQzVqlWhcuUKRERE0LVLOyZOnB6SZ+LE6dxzT2cAOnZsxZy5P+f0a89IU5cu7ZiQQdOEbDRNmDidLl3aERkZSeXKFahWrQqLl+Q8IvqyZSuoWq0ylSqVJyIigk6d2jBpUmhfedLkGdzdzZv869ChJfPm/QJAsWJF+X7sUF566U1+/XVZjrUEyMm1mzhxOl3PQTkBLFu2kourppdVx06tmTxpZkieyZNmcefdXlm179CCefMWAlCpUvk0pwQVKkRzySVV2b5jV441LV26IlOdmjgx9PpNnDgjvaxub8Xcc1zPly1bmalOZSqnyTOD6lR25VSOSy6tyo7tOS8nG+uUjW0U2FmnMmrq3LlNlpq6desEwO23t2Tu3PR2aty4YQwY8AYLFy7NNU02tuc2Xjsbywng8nKl2HHwGLsPHSMpOYVpK7dzc40KIXmiil3Aoi3eQ/SWfUdITE6hxAWF2H3oWJrzhriE42zbf5ToEjm3cLGxrGxsp2wsJ1s4R17vzgn5zvROVf8QkSeBH0TkY2A7UFNECgLn4ZnlLRCRwsD5qjpZRH4GtgCISFVVXQQsEpEWhPpcB29GKeB3/d5z/f+kpKTQq/cAJk36lvCwMIYNH0ls7AYGDuzLsmUrmDhxBl8O/Y5hwz5gbewCEhIOc3e3dJezGzf8StGihYmMjKRt29to2epO1q7dmCuaJmfQNGhgX5YGaRo+7APW+Zru8jXFxm5gzJgJrFoxh+SUFJ7s1Z/U1Jx72UlJSeHpp15i/I8jCA8PZ8SIUaxdu5EBL/Zh+fJVTJ40k+HDRvH5F/9m5aq5JCQcpkf3fwDwyKPdubhqJfr160W/fr0AaNvmHvbvP5hjTWd77WJjNzB6zARW5nI5BXQ98/Qgxo0fTnh4GF+NGM26tRvpP6A3y5evYsrkWYwYPpJPP/83MStnk5BwhPt6eL5M6t9Qhz5PPUpScjKpqak81fulTDNNZ6upd+8XmTTxG8LCwxg+bCSxazcw8KW+LFvuldXQod8xbOj7xMYuIOHQYbrdk17PN6xfSNGiRYiMjKBtm+a0anUXa9flvJ4//dRAfvhxRFo5Za5TI/n8i3dZsWoOCQlHuNevU/VvqMvTT6eXU5/eL+bKyKSNdcrGNiqgy8Y61bv3i0yY8BXh4eEMHz6StWs38NJLT7Fs2SomTZrBsGEj+fLL91izZj6HDh2me/eeADz2WA+qVq3MCy/04oUXvHaqdetuudZO2dae23jtbCsngALhYTzfph6PDZtFqirtrqlGtTLF+XhmDDXLlaLRZRV4quW1DB73K9/8vBaAlzvegIjw2/b9fDl/DgXCwggToV/bepS4oNApvvHU2FhWNrZTNpaTLZhab3Q2iKmprDNFRI6rauGg/Ql4rv++8h0rdAC2Asfx/KZPA8bjzQgJ8I6qDheR74HqftosoDdeZN6+qtpaROoDw4HfgUlAN1Wt7DuIqKOqPf3vn+h/5tyT6Y6ILGdVAVslxqdggQjTErIkMTnJtIRMnBdR0LSETJxItm9dTmS4fXXqLwvLyVZEcsc7V24SJvYZYCSn5tyBSG4TZuG1A0i18Fnn2LeZ4wGapshdQ0xLyISNNcq+2uSRnLjbxuLKxE3lbjmrIpy/e1ae/3/5pqOUX3EdpVPjOkqnj+sonR6uo5S/cR2l08N1lE4f11E6PVxH6fSwrzZ55JeOUsOz7Cj9ZKCjlO9M7xwOh8PhcDgcDkf+xHm9czgcDofD4XA4HI58jJtROsfY1mcuXsi+mE6HT/x+6kwOAPa83MS0hExUHDzftIR8gY0mirZio0l4qtpn5mYjNpq42YqNZm4XF4s6daY8ZsuR+FNncuQr8tOMkusoORwOh8PhcDgcjjzBxsGw7HAdJYfD4XA4HA6Hw5EnuBklh8PhcDgcDofD4chAfoqj5Jw5WEDzZo1Ys3o+62IX8OwzT2Q6HhkZybffDGFd7AJ+WTCBSpXKpx177tmerItdwJrV82nW9OZc09Tk1ob8umwqi2Nm8GSfh7PQFMHnQ99jccwMps0eTYWK5QCoULEcO/euZM6C8cxZMJ533n051zTZWE42agqrfDmF7n+FQg+8RoF6LTIdj2jUlULdX/Je97/CeT0/SDt23lOfph2LbN8z1zSBnXXKRk021ikbNdmqy2lymv4O9bxhk/pMXTiWGYvH8fCTPTIdr1O/NuNmfU1s/K80b3NLyLEOXVsxfdH3TF/0PR26tso1TTaWk42abEBVz+plgnzTURKR/iKyRkRWikiMiFx3hufXEpGWQfuNROSGoP1HRaT7Sc4fJCJ9z0599oSFhfHB+6/Suk03rry6MV27tueyy6qH5Ln/vjtJSDhCjZoNeO+Dz3j9tf4AXHZZdbp0acdVtZrQqvXdfPjBa4SF5fyShoWF8ea/BtK140PcWLclt3dqzSWXVg3Jc3f3zhw+fIR6tZryyUfDGPjyM2nHtm3dQeMG7WjcoB19+wzMsZ6AJhvLyTZNiBB56938NfY9Tgx9kQI16iGlQhfnJs0dyYkRgzkxYjDJv80mZePy9IPJiWnHEn/4T871+Nhap2zUZFudslGTrbqcJqfp71LPB77xHA/d8SQtb+xM6w7NqXpJlZA88bv28Pw/BjFx7LSQ9GLFi9Kz70N0bn4vnZr1oGffhyharEiuaLKxnGzTZAup6Fm9TJAvSl1E6gOtgWtU9SrgVmDnGX5MLaBl0H4jIK2jpKqfqOqIHEo9Y+rVrc3mzdvYunUHSUlJjBo1nrZtmofkadumGV99NRqAsWMn0aRxAz+9OaNGjScxMZFt23ayefM26tWtnWNN19S5iq1btrN9206SkpIYN3YSLVrdGpKnRatb+O5/4wD48YepNGxUP8ffezJsLCcbNYWVrYIm7EOPHIDUFJLXLSa8aq1s84fXqEfyusU5/t5TYWOdslGTjXXKRk226nKanKa/Qz2/6prL2b5tJzu37yYpKZlJP0zn1hahMx67d8azPnYTqZoakt6gcX1+nreYI4ePcvTIMX6et5iGTW4gp9hYTjZqsgU3o5T7RAEHVPUvAFU9oKpxIlJXRH4RkRUislhEiohIIREZKiKrROQ3EWksIpHAYKCrPxv1HPAo0Mffbxg8YyQiT4pIrD979V2QjpoiMldEtojIk7nxj0WXK8vOXXFp+7t2xxMdXTbbPCkpKRw5cpRSpUoQHZ3FueVCzz0boqLKELdrT9p+XNweoqLLZMqze1d8mqajR49RsmQJACpWKs/sn37gx8lfc339OjnWA3aWk42apEgJ9FhC2r4eT0CKlMg6b9GShBW7kNQda9MTC0RQsNsACt7Vj/Bq2XewzhQb65SNmmysUzZqslWX0+Q05aYmW3WVibqIPbv3pu3vidtHmaiLTvPc0sTHBZ+7lzJRpXOsycZyslGTLeSnGaX84sxhOvCSiGwAZgIjgYX+e1dVXSIiRYE/gV6AquqVIlLDP/cS4CWgjqr2BBCR84DjqvqOvx9sRPs8UEVV/xKR4kHpNYDGQBFgvYgMUdWkjGJF5GHgYQAJL0ZYmH2xi84Ve/fso9bljUg4dJira13OiG8/5sbrWnL8mIuVZBvhNeqRvGEZBI3SnPj0OfT4YaTYhRTs0pfU/bvRI/sNqrSzTtmoyeFwOByO/IBz5pDLqOpx4Fq8zsd+vA7SI0C8qi7x8xxV1WSgAfC1n7YO2I7XUToTVgLfiEg3IDkofZKq/qWqB4B9QJmsTlbVT1W1jqrWOVUnKW73HiqUj07bL18uiri4PdnmCQ8Pp1ixohw8mEBcXBbn7g4992yIj99LdPn0kYvo6LIhI0CBPOXKR6VpKlq0CIcOJZCYmETCocMArIhZw7atO6hWLdR2+WywsZxs1KTHQmeQpHDoDFMwBS6tR0oGszs97l07PXKA1J3rCStTMceawM46ZaMmG+uUjZps1eU0OU25qclWXXvj91G2XPrjT9noi9gbv+80z90fMnNfNroMe+NzPhhnYznZqMkWUlXP6mWCfNFRAlDVFFWdq6oDgZ7A7efw61oBHwHXAEtEJDDz9ldQnhRyYUZuydIYqlWrQuXKFYiIiKBLl3ZMmDg9JM+EidO5557OAHTs2Io5c39OS+/SpR2RkZFUrlyBatWqsHjJbzmVxG/LVnHxxZWpWKk8ERERdOjYiqmTZ4XkmTp5Nnfc2QGAtu1v46d5CwEoVapE2oLDSpUrcHHVymzbdqbLyTJjYznZqCl1zzakRBmk2IUQFk6BGvVI2bwiUz4pWRYKnU9q3Ob0xILnQ7hfpc8rTFi5aqQejMt07tlgY52yUZONdcpGTbbqcpqcpr9DPV/1WyyVq1SgfMVoIiIK0Kp9M2ZNnX9a5y6Ys5AbG11H0WJFKFqsCDc2uo4FcxbmWJON5WSjJlvQs/wzQb4wvRORS4FUVd3oJ9UC1gK3iUhd3/SuCJ7p3U/A3cBsEbkEqAisB6rjmcwFOAYUzeK7woAKqjpHRBYAdwCFz9G/RkpKCr16D2DypG8JDwtj2PCRxMZuYNDAvixdtoKJE2fw5dDvGD7sA9bFLiAh4TB3dXscgNjYDYwZM4FVK+aQnJLCk736k5qaeopvPD1Nzz8zmNHjviAsPJxvvxrD+nWbeL7/k8QsX83UKbP5ZsRoPv70bRbHzOBwwhEeuq8PAPVvrMvz/XuRlJSMpqbSt/dLHE44kiuabCwn2zShqSTO+paCHXtDWBjJq35GD8YRcWM7UvdsS+s0FahRj5R1S0JODSsVRWTTezxTPBGSFk1BD8bnXBP21ikbNdlWp2zUZKsup8lp+rvU88H93uaLUR8SHhbOmP/9yKb1W3jyuUdYHbOW2dPmc2Wtmnw0/G2KFitK42YNefLZh2nVsCtHDh/l439/wdgZnu+sj/71OUcOH80VTTaWk22abMHU7NDZIKa8SJwJInIt8CFQHM8UbhOeGV4VP/08vE7Srf7xIUAdf/spv9NTEpgGRACvA78BY4BU4B/ALcBx4H1gDlAMEOBrVX1DRAYRuqZpNdBaVbedTHuByHJWFXDxQvatlzp8wq3rOF2OvpY5LpJpKg4+vZHEvzuunjscDtu5uFjUqTPlMVuO5M6A3d+B5MTdYlrD6VDjorpn9Wy8bt+SPP//8sWMkqouI8iVdxAHgOuzSL8vi884BNTNkHxV0PZPQdsNsjh/UIb9K7KR63A4HA6Hw+FwOLIgP80o5YuOksPhcDgcDofD4cj/5Cevd66j5HA4HA6Hw+FwOPIEN6PkSKNAWLhpCSG4dRL5m8tfX3LqTHlMYkryqTPlMTZqsq0tCJCcmmJaQiYaXlTTtIRMbPg9dzxA5iZ7fz9sWkK+oXt0fdMSMvHtnsWnzpTH2Lge6J9RjU1LyMSL8XNMS3DkEa6j5HA4HA6Hw+FwOPIEZ3rncDgcDofD4XA4HBlQzT+uzl1HyeFwOBwOh8PhcOQJqfloRinMtAAHNG16MytXzmHNmvn07ft4puORkZF89dVHrFkzn/nzx1OpUnkAbrmlIb/8MomlS6fzyy+TaNQoKw/qZ0fzZo1Ys3o+62IX8OwzT2Sp6dtvhrAudgG/LJiQpgnguWd7si52AWtWz6dZ05udpjzWBHBTkxuYtWg8c5ZM4NFe92ehK4IPP3+LOUsmMG7615SrEA1AgQIFeOejfzLlpzHMWDiOx3pnPvdsubXpTSz7bSYxK2fT5+lHs9AUydDhHxCzcjaz535PxYrlALj22qtYsHAiCxZO5OdfJ9G6TbNc02Tjb89GTTbW87qN6jB83pd8vWAYdz7RNdPxq667kv9O+ZiZ26ZyU6uGaem1briaz6Z9kvaatmkSNzbPvbJqdEsD5i+eyIJlU3ii94OZjkdGRjDki3dYsGwKE2b8j/L+b69D51ZMnz827bXz4Couv6JGrmiy8frZqOmKm2vx2qz3eX3uh7R8rH2m480eaM0rM97l5Sn/ou83AylV7sK0Y59vHsmgyW8zaPLb/OOz53JNE7g24XS5+OareHj22zw6719c/1ibTMdr392EB6a9zv2TX6XbmBcpVd377UVdfTH3T37Ve015lUua18k1TTaWkw2o6lm9TJCvOkoi0l9E1ojIShGJEZHrcvnzfznF8eO5+X0AYWFhvP/+K7Rr14NatW6hS5e21KhRPSTPvfd25fDhI1x++U18+OHnvPJKPwAOHDhEx473U6dOMx58sA9ffPFermn64P1Xad2mG1de3ZiuXdtz2WWhmu6/704SEo5Qo2YD3vvgM15/rT8Al11WnS5d2nFVrSa0an03H37wGmFhOa9mTtOZ6Rr81gvc2+Vxmt3Qgba330a1Sy8OydOlWweOHD5K47pt+GLI1zw/sDcALds1JTIykhYNO9GmyZ3c1aNTWicqp5r+9e+X6djhPupe25xOndtwaY1qIXm69+jC4cNHqXVVEz76z5e8/E/vYSM2dgM3N2hHg/qtub39vbz/4SuEh+fcMYKtvz0bNdlWz8PCwuj1yj94/p4XuLfxg9zSrjGVqlcMybN39z7efOptZv0wOyQ95pcVPNT8UR5q/ihPdX2GEydOsHTeshxrCuh69e3+dOv8KI2vb0v7ji2pfmnVkDx33tORI0eO0uDaFnw2ZAT9Bz0FwLjRk2h2U0ea3dSRJx99nh3bd7Fm9bpc0WTj9bNNk4SF0W3wg7x776sMaNqH69o2ILpa+ZA8O2K3MrjNcwxs8TRLpyykc7970o4lnkhkUMtnGNTyGT586M0c6wng2oTTQ8KEZv/swageb/Hprc9Ss+31aR2hAGvGL+SL5v34smV/fv1kErcO6AbA/vW7GNrmRb5s2Z+RPd7mttfuQ8L/f9ZzW0hFz+plgnxT6iJSH2gNXKOqVwG3Ajtz8ztUNfeGW06TunVrsXnzNrZu3UFSUhKjR0+gTYYR8zZtmvH112MA+P77yTRufCMAK1asIT5+L+A9TJ53XiEiIyNzrKle3dohmkaNGk/bNs1D8rRt04yvvhoNwNixk2jSuIGf3pxRo8aTmJjItm072bx5G/Xq1naa8kgTwNXXXMH2rTvZuX03SUnJTBg3laYtGoXkadqiMWO/+xGAKT/O4Iab6gHeKM/5559HeHg4hQoVJCkxmePHcj4+UKfO1WzZsp1t23aSlJTE2DETadW6aUieVq1v5X/fjAXgh3FT0kY///zzBCkpnme2QgULkluDSjb+9mzUZGM9r1HrUuK2xRG/Yw/JScnMHj+XG5uFNt97d+1ly9qtpKZmX2FubtWQxXOW8NeJv3KsCaD2tVeybctOdmzfRVJSEuO/n0zzlqEeu5q1aMLo/40HYNL46TS4OXPM9PYdW/Lj91NyRZON189GTRfXqsa+7XvYv3MfKUnJLJrwM7WahcaoX7dwDYknEgHY8ttGSpQtlePvPRWuTTg9omtVJWHbXg7v3E9qUgprJ/zKJU2vDcmTePzPtO3I8wumORRIPpGIpnhrZgoUjCC3nsdtLCdbcDNK54Yo4ICq/gWgqgdUNU5EtonIWyKySkQWi0g1ABFpIyKLROQ3EZkpImX89EEi8qWIzBWRLSLyZOALAjNGIhIlIvP9WavVItIwKM+rIrJCRH4NfGZOiI4uy65d6W5nd++OJzq6TLZ5UlJSOHr0GKVKlQjJ06FDS2JiVpOYmJhTSUSXK8vOIE27dscTHV022zwpKSkcOXKUUqVKEB2dxbnlQs91ms6dJoCyURcRv3tP2v6euH2UjQqtU2WiLiI+bk+armNHj1OiZHGm/DiTP/74k0WxM/l5xTQ++2g4Rw4fzbGmqOiy7NqV7nY2bnc80Rk0RUWXScsTqOcl/Xpep87VLFoylYWLp9D7yQFpHaecYOVvz0ZNFtbzC6MuZF/8/rT9/XsOcGHUhSc5I2sat23ErB9yz81v2agyxO1Or+fxcXsz/fbKRl9E3O70397Ro8coUbJ4SJ42HW7jh7GTc0WTjdfPRk3Fy5TkUNyBtP2E+IOUKFMy2/wNuzRh1dzf0vYjCkby0o9v0n/ca9TO0MHKCa5NOD0Kly3B0fhDafvH4g9RpGyJTPmu6X4rj87/F4373cGMgSPS9daqyoMz3uDBaa8ztf/QtI5TTrCxnGwhVfWsXibITx2l6UAFEdkgIh+LSLDB5hFVvRL4DxCYW14AXK+qtYHvgGeD8tcAmgP1gIEiEpHhu+4CpqlqLeBqIMZPvwD4VVWvBuYDD2UlVEQeFpGlIrI0JSXXrfUycdlll/Dqq/3o2bPfOf8ux/9vrr7mClJSUrj+8iom5yoAACAASURBVKbcdE1LHnyiOxUqlTMti6VLV3Bd3dtodFN7nu77GAUL5nxUNDew8bdnoyYbKXlRSS6uUYUl85aalhJC7Wuv5M8/T7B+7SbTUhzZcH37hlS+qipTPx2flvbMjY8xuO1zfPrke9z50n2UrpjjcdRcw7UJ6SwfMZNPbnqaOW98x43/SF+HFhezmc+bPs+wti9R//E2hBfM+FjoyE30LP9MkG86Sqp6HLgWeBjYD4wUkXv9w/8Leg9ElSsPTBORVcAzwOVBHzdJVf9S1QPAPiBji7YEuE9EBgFXquoxPz0RmOhvLwMqZ6P1U1Wto6p1wsMLn/T/iovbQ/ny6Xa05cpFERe3N9s84eHhFC1ahIMHE/z8ZRk16lMeeKAPW7ZsP+l3nS5xu/dQIUhT+XJRxMXtyTZPeHg4xYoV5eDBBOLisjh3d+i5TtO50wSwJ34fUUEjT2WjL2JPfGid2hu/jyh/ZCs8PJwiRQuTcOgw7Tq1YP7sX0hOTubggUMsXRTDVbUuJ6fEx+2hfPmotP3oclHEZdAUH7c3LU+gnh/y63mADes3c/z336lZ89Ica7Lyt2ejJgvr+YH4A1wUVTptv3TZCzkQf+AkZ2SmcZubWTD1Z1KScy/g7p74vUSXS6/nUdFlMv329sTtSxsZDly/hEPpgWPb3d6S8bk0mwR2Xj8bNR3ee4iS0emzkiWiSpGw91CmfDVvvJLWPTvywYNvkJyYHHI+wP6d+1j36xoqXl4lx5rAtQmny/E9CRSNSp8BLBJVkmN7ErLNH/vjr1Rvdm2m9IOb4kj84wSlLymfxVlnho3lZAvO9O4coaopqjpXVQcCPYGOgUPB2fz3D4H/+DNNjwCFgvIEG6SnkMFNuqrOB24CdgPDRKS7fyhJ069UpvPOhqVLV1CtWhUqV65AREQEnTu3YeLEGSF5Jk6cQbdunQC4/faWzJ3r+ZwoVqwo48YNY8CAN1i4MPdGRZcsjQnR1KVLOyZMnB6SZ8LE6dxzT2cAOnZsxZy5P6eld+nSjsjISCpXrkC1alVYvOS3TN/hNJ0bTQArf1tD5YsrUr5iOSIiCtCmw23MnDIvJM/MqXPpeEdbAFq0bcrCn7wI8bt37aF+Q2+90nnnn0ftOleyeePWHGtatmwlF1etTKVK5YmIiKBjp9ZMnjQzJM/kSbO4827vJ92+QwvmzVsIQKVK5dOcN1SoEM0ll1Rl+45dOdZk42/PRk021vN1K9ZTrko5ylYoS4GIAjRp14hfZiw8o89o0q4xs8bnntkdQMzy1VSpWpEKFcsRERFBu9tbMn1K6HdMnzqHzne2A6BVu2b8PH9R2jERoXX75owfmzvrk8DO62ejpq0rNlGmchQXlr+I8IgCXNfmRmJmLAnJU/HyKnR/7RE+ePANjh1MN0k+v+gFFIj0HgcKlyhC9WtrEL8x520UuDbhdIlbsYUSVcpSrEJpwiLCuazN9WycsTwkT4nK6WPi1ZrUImGb1/EoVqF0mvOGouVKUapqNEd27Sen2FhOtpCfnDnkmzhKInIpkKqqG/2kWsB24EqgK/CG/x64WxbD6+gA9DjD76oE7FLVz0SkIHANMOIUp50VKSkp9O79IhMmfEV4eDjDh49k7doNvPTSUyxbtopJk2YwbNhIvvzyPdasmc+hQ4fp3r0nAI891oOqVSvzwgu9eOGFXgC0bt2N/fsP5lhTr94DmDzpW8LDwhg2fCSxsRsYNLAvS5etYOLEGXw59DuGD/uAdbELSEg4zF3dPJelsbEbGDNmAqtWzCE5JYUne/UnNTXntr5O05npGvjc64wYPYSw8DBGf/sDG9dvps/zj7MqZg0zp85j5NfjeHfIq8xZMoEjh4/yjwc9y9SvvviOtz8czLSfv0cExnw7nnWxG0/xjaen6ZmnBzFu/HDCw8P4asRo1q3dSP8BvVm+fBVTJs9ixPCRfPr5v4lZOZuEhCPc18NbPlj/hjr0eepRkpKTSU1N5aneL2WaaTpbTTb+9mzUZFs9T01J5YMX/8Nb37xOWFgYU0ZOY9uG7dzXtwfrV2zglxkLufTqS/jn54MoXKww9Ztez31Pdee+Wzxr6TLly1A6ujQrFq7MsZZgUlJSGPDsq3w79lPCwsMY+c04NqzbTN9+PVkRs4YZU+bw3Vdj+eCTN1iwbAqHE47w+AN9086//oY6xO/ew47tufOQHdBk2/WzUVNqSipfv/Q5T40YQFh4GAtGzSZu4y7a9+nKtlWbiZm5lC797qHg+YV4/OOnATi4+wAfPvQmUdXK0+O1h1FVRITJQ8YRtyl3rqFrE04PTUllxkvDuWPEs0h4GCtHzePAxt00fKoj8Su3smnmcq7t0YzKDS4nNSmFE0d/Z+JT/wWgQp1LuP7xNqQmpaCqTBswjD8Tcr5swsZysgVTs0Nng+QXsSJyLd4sUXEgGdiEZ4a3FBgJtMCbKbpTVTeJSDvgXSABmA3UVdVGvjndcVV9x//c1UBrVd0mIsdVtbCI9MAz10sCjgPdVXVr4Lh/Xif/vHtPprtQoYpWFXByau6ZmTjyngpFznzB+rnm4Iljp86UxySmJJ86kwOws01oeFFN0xIyseH3uFNnymP2/n741JkcAHSPrn/qTHnMt3sWm5aQCRvbg39GNT51pjzmxfjcnY3OLZITd4tpDadDySLVz+rZ+NCxjXn+/+WbGSVVXQZkct8tIgBvq+pzGfKPB8ZnzK+qgzLsXxG0Xdh/Hw4Mz+LcwkHbY4AxZ/hvOBwOh8PhcDgcf1vyyyQN5KOOksPhcDgcDofD4cjfmFpvdDbk+46SqlY2rcHhcDgcDofD4XCcmvw0o5Rv1ijlVwpElrOqgBeXqWNaQibq7bUrjonNlC2cOYCeafYcz7ljBYfDceY8Gd3w1JnymA/ifjItweE459S5sLppCVnya9zcfLFGqfD5Vc7q2fj4H1vdGiWHw+FwOBwOh8Px/xNTwWPPhnwVR8nhcDgcDofD4XA48gI3o+RwOBwOh8PhcDjyhNR8tOzHzShZQPNmjVizej7rYhfw7DNPZDoeGRnJt98MYV3sAn5ZMIFKlcqnHXvu2Z6si13AmtXzadb05lzTVLRRbWrO/ZiaP31Cmcc7ZjpesnMTrowZQY2p71Jj6ruUuqNp+rFOjak5fwg15w+hZKfci39gYznZqAmg0S03Mm/RBBYsncwTvR7IQlcEH3/xDguWTmbCjG8pXyEagA6dWjFt3pi0144DK6l5xaW5osnGsnKa8q8mW3XZqKnGzVfz/Kx/88Lc92jyWNtMx29+oCXPzniHvlPe5NFvBlCiXHq8ttbP38Uz097mmWlvU6t17sUisrGcbNRkqy6n6fS4vlE9Rv40gtE/f8M9Pe/KdLzWdVcxfNqnLNgxi8atMn/v+YXP58elo3n61V65pskGVPWsXiY4Jx0lEZkjIs0zpPUWkaEictLYQyJSWUQy16b/p4SFhfHB+6/Suk03rry6MV27tueyy0IXCd5/350kJByhRs0GvPfBZ7z+Wn8ALrusOl26tOOqWk1o1fpuPvzgNcLCcuGShoVR4ZVH2NT9ZdY26UmJdg0pVL1CpmwJExaw7rY+rLutDwe/mwFAePHCRPW+g/Vtn2F9m75E9b6D8GIX5IIk+8rJRk0BXa+8NYB7ujxG4/ptadexJdUvvTgkzx3dbufI4aM0qNOSz4Z8xQuDngJg3JhJNL+5E81v7kSvR/uxY/tuYlevzxVNtpWV05R/Ndmqy0ZNEibcPvh+Pr33Dd5s+jTXtL2RMtXKheTZHbuNd9u8wDstnmPllEW07ne3p6lxbcpdXpl/tXyO99sPoNFDrSlY+Lwca7KxnGzUZKsup+n0NfV9rRd97n6OOxv1oFm7JlSuXikkz97d+/hn7zeYPm5mlp/xyLP389uiFTnWYht6ln+nQkRuE5H1IrJJRJ7P4nhBERnpH18kIpVP9Znnakbpf8AdGdLuAIaqaqdTnFsZ+Nt0lOrVrc3mzdvYunUHSUlJjBo1nrZtQvqYtG3TjK++Gg3A2LGTaNK4gZ/enFGjxpOYmMi2bTvZvHkb9erWzrGmC2pV569te0jcsRdNSibhx58o1qzeaZ1b9ObaHP0phpTDx0k58jtHf4qhaKNrcqzJxnKyURNArWuvZNvWHezYvoukpGTGfz+FZi2ahORp1rIJo7/z4jFPGj+dBjddl+lz2nVsyY/fT8kVTTaWldOUfzXZqstGTRVrVePA9j0c2rmPlKQUfpvwC1c0C/V+umlhLEknEgHY/ttGipctCUDZ6uXYsngdqSmpJP75F/HrdlDj5qtzrMnGcrJRk626nKbTo2btGuzatpu4HfEkJyUzY/xsbmp+Y0ie+F172LR2C5qauRNw6ZWXULJ0SRbP+//nGfhczCiJSDjwEdACqAncKSI1M2R7AEhQ1WrAu8Cbp9J6rjpKY4BWIhIJ3iwREA3sFJHVflq4iLwtIktEZKWIPOKf+wbQUERiRKSPiNwrIt+LyFQR2SgibwW+RESGiMhSEVkjIi8HpW8Tkdf9z1gqIteIyDQR2Swijwbleybo+1/20y4QkUkiskJEVotIVz/9WhGZJyLL/M+Kyo2Cii5Xlp274tL2d+2OJzq6bLZ5UlJSOHLkKKVKlSA6Ootzy4WeezZElC1FYtyBtP2k+INElC2VKV+JFvW5bPr7VPnkOSKiLkw7Nyk+6Nw9WZ97pthYTjZqAoiKuoj43XvS9vfE7SUq6qKQPGWD8qSkpHD06HFKlCwekqdNh9sY//3kXNFkY1k5TflXk626bNRUrExJDscdTNs/HH+IYmVKZpv/ui6NWTs3BoDda72OUUShSC4oUYRq9WtSPMq153mlyVZdTtPpUbpsafbF7U/b3xe/n9JRpU/rXBGh18DH+WDwkBzrsJFzZHpXD9ikqltUNRH4DmiXIU87YLi/PQa4RURO7nL8bMWexj8zEWjnbz8PvIM3W7TaT3sYGOBvFwSWAlWARsDEoM+5F9gCFAMKAduBCv6xkv57ODAXuMrf3wY85m+/C6wEigClgb1+ejPgU0DwOowTgZuAjsBnQd9fDIgAfgFK+2ldgS9P8r8/7P8/S4GHT1FWnVT186D9e1T1PxnyrG7QoMGzQfubVfVCP1+3oPQv/M/L6fU7HU2lihcv/ri//Yiqzva3+6rqgKB8L/ppeaFptaqWDypzG8oprzXltE4F9q9T1VW5pCc/13MrNamr5/n6+lmoCf/adVPVX1W1YNCx/qoao6ozVPUbVe2dV+Wkrp7n5zrlNJ3mb09VWbZs2S8ZvrOnqga03pvdeX+3V4bn65BnbKAT8HnQ/j3AfzKcvxooH7S/GbjwZN95Lp05BJvf3eHvB9MM6C4iMcAioBSQXQSvWap6RFVPALFAwMizi4gsB34DLsebagvwo/++ClikqsdUdT/wl4gU97+/mX/ucqCG//2rgKYi8qaINFTVI8ClwBXADF/vAKA82aCqn6pqHf/1aXb5fHYDwQuAyvtpIXkKFix4n79dAK/zdvA0zz0bTudzDx4+fPh+f/tz4NozOPdcaQrkeRh7yimvNZ22rmzqVICsfrOmNBm9fjZqwtXz09Zl4/WzUBPt2rV7GugPtAX+Cjr0KlALaIo3sLghjzS5en4GuiysU07TGXzu1q1bL8mQVB/oiTfw/w7QHc/i6m9Nhufr03nGzpUvPVe9vsLAPuAaYIOfVpn0GaWxQPMszmtE5hml/wTtT/TzVAE2ASX89GHAvf72NvweYhbnbwMuBP4FPJKN9pJAN2Ae8BJwJbDwHJVVAfWmCauoaqSqrlDVyzPkeeKbb77Z52/foaqj/O3L/fwF/fO3qGp4HmmKApb62x3UG4VEVUuq6lZVLeG/tvppeVJOqvqJr8uWcsprTTmtU6hqmKruVtWLc0lPfq7nVmpSV8/z9fWzUFPtHTt2nFDV6hnSw1W1lL99lXozKgXyqpzU1fP8XKecptPThKoyZsyYA5r9LNa96maUTvnC61xOC9rvB/TLkGcaUN/fLgAcAOSkn3uORY8EYoCX/f3gjtLDwA9AhL9/CXAB3szEvKDPyK6jdDWwAs9srgyw9ww7Ss3wZrIK++nlgIvw1lIV8tNa+xoj/U5ZoHAjgCwr+1m+WqrqBvWmgfv7aYNVta2/XWjy5MmHVHWTqi7W0AfY/v5561W1RR5qen3jxo1/qvfDn6OqNYLOvd/XuklV78vLclLV0du3bz9hUTmZ0JTTOtVI0zu+tmgydv1s1KSunufr62ehppkHDhxIUs/ELkZVfwy6rrH+61dVrZWX5aSunufnOuU0nZ6muqq6648//khR1YOquiaLz7hXXUfplC+/47PFn0iJ9PsIl2fI8wTwib99BzDqlJ97jkW3BxSo4e8Hd5TCgNfwTN1WA3NIXw802/8H+2TXUfK3h+GZAcwCvj+TjpK/3cv//lXAQqAq0BxvTVMMsASo4+etBcz3da0BHsrjCnCqtU4mKqV1mmzV5TQ5TX8HXU6T0/R30OU0OU1/F1357QW09PsFm4H+ftpgoK2/XQgY7U9+LAZOaTkj/okOh8PhcDgcDofD4fA5l84cHA6Hw+FwOBwOhyNf4jpKDofD4XA4HA6Hw5EB11FyOBwOh8PhcDgcjgy4jpLD4XA4HOcQETlfRF4Ukc/8/eoi0tq0LofD4XCcnAKmBTiyRkQuAP5U1VR/PwzPbfkfZpWBiDQAqqvqUBEpjedifathTeF4buLT6rSq7jCnyE5EpCDQEc8DZXBZDTalCUBEbiCzphHGBAEiUg4vuHWwpvkG9bRQ1SkZ0h5VL66LMfw24CEyX7/7szsnL7Ds+g0FluHF+QAv6ORoPC+uRrH0t2dlOwVep9eG+3AAy+o5YN/92MY2SkTK4Hl+jlbVFiJSEy8EzRemNDmyxnWU7GUWcCtw3N8/H5gO3GBMESAiA4E6wKV4N/8I4GvgRoOa/gEMxIulleonK3CVKU0AInI78CZefC7xX6qqRQ3KGg8cwXto+8ugjjRE5Cs81/wxQIqfrICxhzUReRPoCsRm0GTyAeRFEflLVWcDiMizQGPAaEcJr079BMwkvayMYuH1q6qqXUXkTgBV/UNExJCWNGz87fnY2E7dAHwOFAYqisjVeEHrHzeoybZ6buv92Lo2Ci+8zVCgv7+/AS/2qOsoWYbrKNlLIVUNdJJQ1eMicr5JQT4dgNrAcgBVjRORImYl0Qu4VFUPGtaRkbeANqq61rSQIMqr6m2mRWSgDlBT7YpV0B6vTlnxkObTFpgoIs8AtwE1gHZmJQFwvqo+Z1pEBmy7fokich7eAyMiUhU7OgA2/vbAznbqXbw4iz8CqOoKEbnJrCTr6jnYeT+2sY26UFVHiUg/AFVNFhFbOnGOINwaJXv5XUSuCeyIyLXAnwb1BEj0b6qBG/4FhvUA7MQbfbSNvZZ1kgB+EZErTYvIwGqgrGkRGdiCN1tqDap6AK+z9BEQDXRS1USzqgCv89bStIgM2Hb9BgJTgQoi8g2excCzZiUBdv72wM52ClXdmSHJ9IOtbfUc7Lwf29hG/S4ipUh/lroe+8rNgZtRspnewGgRicMz2SqLN8VumlEi8l+guIg8BNwPfGZCiIg85W9uAeaKyCSCRmlV9d+GdN3uby4VkZHADxl0fW9A0yq8BrkAcJ+IbPE1BcwB89wsQkQm+JqKALEispjQcmqb15qC+AOIEZFZhGp6Mq+FiMgx/JupTyRwMdBJRIyZcgbpEuAFEfkLSMIOE1Nrrp//vTNEZDlwPV759PI7vqa5EIt+eza2U0Hs9M3vVEQi8GZOTA+EWVPPbbwfW95GPYU3O1lVRH4GSgOdDOpxZIPYN+PuCOA3xpf6u+tVNcmkngAi0hRohtfYTFPVGYZ0DDzZcVV9Oa+0BCMiQ09yWE0sIBWRSic7rqrb80pLABG5+WTHVXVeXmnJiIj0yCpdVYfntRbHmWPb9RORG4EYVf1dRLoB1wDvm/jdZdCV5W/Q1G/PxnYqgIhcCLyPt3ZY8NYM9zJpYmZTPbf1fmwzIlIA7xlPsOgZzxGK6yhZhog0UdXZQbMSIZiYjQjGN7U7oaopInIp3o98ivuB5w/8tRG7VPUvEWmEt8B2hKoeNqipChCvqif8/fOAMqq6zZQmG/EX/98NVFHVf4pIBSBKVRcb1pVVJ+A953UyHRFZCVyN93sbirdgu4uqnnSw4O+Kje2U48zxvfUWVtWjhnVY10aJSGdgqqoeE5EBvqZXVHW5KU2OrHFrlOwjcONsk8XLhrgb84GCvkvSqcA9eN5bjCEiM0SkeNB+CRGZZlKTr2N4Frq+NKkJGAukiEg14FOgAvCtWUmMJt07Enh2/6MNaQHS4tyMEZFYEdkSeJnUBHyM5176Ln//ON56JdMMAf7wvYA9DWwGvjIpyMLrl+yv7WwHfKSqH+GZnBpFRK4XkSUiclxEEkUkRUSMPtT6WNdOichbIlJURCJEZJaI7Pcfuk1qsq2eIyLf+uV0Ad4auFjxHNCYxLo2CnjR7yQ1AG7BGzwZYliTIwtcR8kyVHWg/35fFi+jcUl8xI8hcTswRFU7A5cb1lQ6eKRRVRPwXHKb5qosdNU2qAcgVVWT8a7fh6r6DBBlWFOBYKcE/nakQT3gjfoPAZLxXHCPwHODb5LrVPUJ4ASk1SfT5QShnYD/WNIJsO36HRPPu9U9wCR/pN2GRfj/Ae4ENgLnAQ9iR+fbxnaqmT8z0hrYBlQDTHcAbKvn4HlRPIrnkW8KUAWv3pvExjYq4AikFfCZqk7CjvbckQHXUbIUESklIh+IyHIRWSYi7/seUkwjIlIfzwRokp8WblAPeCOPFQM7vp27DTalYSJSIrAjIiUx70AlSbxYLt1JD3Zp+oFtv4ikLR4XkXaA6YXu56nqLLyBge2qOgjvhmaSJPECOQa8JJUmdCbOFIFOQDfs6QTYdv264i1sv19V9wDlgbcN6klDVTcB4aqaoqpD8VzPm8bGdirQdrcCRquqDR7KbKvnABH++ur2wI++Wb7p+7GNbdRu8RxjdQUmixdk2T2TW4jphzZH9nyHZ+bW0d+/Gy8Y2a3GFHn0BvoB41R1jYhcDMwxrKk/sEBE5uEtimwIPGxWEgD/AhaKSMCMrDNeJG6T3Ac8Cryqqlv99UGmTRAeBb4Rkf/gXb+deA9IJvnLv5luFJGewG68QJMm+QAYB5QRkVfxPCQNMCsJ8G70dwEPqOoef9DCdCfAquvnl8tYoLqfdADvWprmDxGJxPOc9hYQjx0Paza2UxNFZB1emI7H/IGKE4Y1WVXPff6LN+O2ApjvD1yaNue0sY3qgjco8Y6qHhaRKMzPUDqywDlzsBQRWa2qV2RIW6WqVsSWEJHzfRM8K/A9El3v7/5qietdRKQm0MTfna2qsSb12IyIFAYvuLIFWuriuf4tDvwTKAa8paq/GtZVA8+eXYBZFsbpsgLbrp94oRQeBkqqalURqQ58oqq3mNATpKsSsBfP5KcPXjl97M8yOTLgWwUc8Z0ZXQAU8WcITemxqp5nh4gU8E0pHUH465Oqq+pQv+NdWFW3mtblCMV1lCxFRP4NLAZG+UmdgHqq2tecKvDN7r7A+0FX9BdHPqKqjxvUFPAGdrGqDvZHi8pa4A3sK1W951RpeaRllKp2kfQ4JSGYjE/imxx0BCoTNMutqoNNabIVm26sIrJAVRtI5jhPNsQosQoRiQHqAYtUtbafZsXAl3heJiuq6noLtNjcTp2PF/umoqo+7Hd2L1XViac49W+BiHRT1a8lPZ5SCGomjpK1bZR47tTr4NWhS0QkGs+k80ZTmhxZ40zvLENCA6T1Jn1hZhielyujHSXgPaA5XqA0VHWFiNxkVhIf463VaAIMBo7heU2qa1IUGZxc+OtLrjWkpZf/boPnxIyMx4tIvoygAIUmEZE6eCadlQjtvJl8UEu7seIt4o7Aax+M3FhVtYH/bnpRdBoi8p6q9pb0YMYhqLkgxn+paqI3ppMWP8X4KKWItAHewZtRqiIitYDBBsvJ5nZqKF4bdYO/vxvPO2eed5QsrecX+O9ZtQdG6rqNbVQQHfCcOy0HUNU4EbFR598e11GyDEt/0CGo6s7ADd8nJbu8ecR1qnqNiPwGnjcw3+7eCP6i0ReA88RztRsorEQ8V7d5jqrG++9GA1xmQ3lVtWEBeTDf4NmLr8IOhwlg4Y3V7/yvUdUaJnUEEVjH8o5RFZmZJyKBNqEp8DgwwbAmgEF4M11zAVQ1xl8PZATL26mqqtrVdzKBqv4hGW6EeYh19VxV/+tvzlTVn4OPiRfHyCh+W1WG0IEvk7HeElVVRSTgnOeCU53gMIPrKFmM7zGtOlAokKaq880pAmCniNwAqO/ZpheejbRJrPIGpqqvA6+LyOuq2s+UjmCyMD1IO4R5M6lfRORKVV1lUENG9qvqj6ZFZMC6G6u/VmO9iFQ0/NAR0LPMf59nWksGngcewOt4PwJMBj43qsgjSVWPZHjeNzbTlZ2JFHa0U4m+mWLg91cVQzPgWdVz/3mhgqquNKEpiA/xgqeeKi3PEJF/AAPx1uMFng0UL5CxKUb5Xu+K+2sY7wc+M6jHkQ2uo2QpIvIgXiekPBCD56hgIemOAUzxKPA+UA7P9GA68IRRRenewC6yzBvYlKzMEk10di2fqWwA3CsiW/EePAIPRSZvYgNF5HNgFkEPQ6r6vTlJ1t5YSwBrRGQx8Hsg0YT5T3ZrWwKYqlOqmop3rWy4XsGsEZG7gHB/zc2TwC+mxFjeTg3EC7JeQUS+wTN5vdekIBGZC7TFe5ZbBuwTkZ9VNct1QudYS308s8TSGdYpFcV8CJFeeGuBDhrWkYaqvuPPLh/FM6d+SVVnGJblyALnzMFS/Bt+XTwPbrV8b1evqerthqVZiY3ewHz78QCF8Exclqmqsc6u77UpI8f8WBdG8D1vZcKk+Y2IfkOPPgAAFQpJREFUfA3UANYQNAKphoM++zfWZnj1fJoNN1YRuTmrdBOzOtnVpQCm6pRvejSI9DVvgcGAi03oCdJ1Pt5avGZ+0jTgFVU17fYa31FQQ393vgUzJYgXy/B6vOtn3LuqiPymqrX9gdUKqjpQRFaaGBDw24FGeIOpnwQdOgZMUNWNea0pgIjMAZra6HlPRIoSag54yKAcRxa4GSV7OaGqJ0QEESmoqutE5FLTonyztofI7KHM2AOkiHyBF739o6C0QeoF3zOGqrYJ3heRCnjOMEyyHKgAJODd7IsDe0RkL/BQwKQjL1HV7Vl5c8trHRmoq6rGf28BfNPSmaraGDDeOQrGJjM3S9e2gOcptA/eqL/pNZ1AWp0arJ4n1f6m9QQjIr3w7jOBGdxvRORTVf3QoKaAdcAx/72miJg2hy8gXvydLhi+hn47ME9E/lTVt4KPiUhnwFhHCdgCzBWRSYRaCOS5J74AIvII8DJeLK5U0s1MjQ6eODLjOkr2sktEigM/ADNEJAGw4SFgPPATMBNLbvh4XvjqiMi/VHWEn9YWbwTXJnYBlxnWMAMYo6rTAESkGZ5r7qF43gOvy2tBtnlz8/lFRGqqJXGv/LVAqSJSTFWPmNYDdq97E5Hr8dZFXIbn0S0c+N2gpiOqOsXQd2eJX6camNaRDQ/gOen5HUBE3sQzPTfWUSI0GGiahQBmzeEH480CLlDVJeIFgDfZIQG4A3grQ1o/PA+BptjhvyL9lw30Ba4wPSvpODXO9C4f4E9pFwOmqmqiYS0xqlrLpIaMiMhyoDHew/UOPHvkJerHKzGo60PSHyTDgFrANlXtZlBTptgtAVMNU9dWvBgztYHlmh5jxoj5SJCmtUBVwJp1UyIyHq+cZhC6FuhJU5psRUSW4j2wjcbrhHcHLjHlXEVE3sDrrH1P6Ij2chN6AojIELz1pqMJrVMm1+KlmZ4HTABFpBBem2487lSAgIWAqnY0rcUGRKQF0BJvdmtk0KGiQE1VrWdEmKWIyFTgdlX9w7QWx8lxM0oWktHlrk2mLcBEEWmpqpNNCwlC/FH2NiIyCJiD17E0zdKg7WTgfxndphogXkSeA77z97sCe/06Z8pToHXe3ADb3JWD95AdeIANdMBNuSe2HlXdJCLhqpoCDBUvfIApL5SBmdo6QWmKeec8hYCDGXQo6fXMFEOBRSIyzt9vj2e+aBPGLQRE5C3gFeBPPEcTVwF9VPXrk554bojDu+e1xZtpC3AMz+zUGL4597N4sQ2DvQib/P31w7NcWETo4Ikb+LIM11GyENtc7magF/CCiPwFJGHQzEZEqgFl8YPf4gkZJCIpeItKjaKqw/0GGlXdb1qPz1143pt+8Pd/9tPC8UYCTWCVNze/0zhNLYkNJCLt8GJNfeTvLwZK4z3QPmdSm8X8IV4stRj/YTIeb1bXCP7aMutQ1ftMa8gKVf2379EtYBp4n6r+ZlBSdhYCRmcEgWaq+qyIdAC2AbcD80kPVJ9nqOoKYIWIfIv3XHCJf2i9SWdBPt/gzXK1xnM20QMwfU/+LzAbu2L1ObLAmd5ZiojMxzOzCXa5q6razpwquxCRiUA/zRB/R0SuxPMQ2CbrM8+5LsHrjPTEu6EK3ozSh6o62IQm27HNm5tv5vYPGwYqRORn4A5V3envx+DNABQGhqrqLSb12Yjv/W4v3nqEPngzzB+p6maDmlqReUTbaHsgIuXx1v0E1gP+BPRS1V2G9BTCe5CthvcA+YUtnspEpEfQbjKeGbVRCwERWa2qV4gXymCMqk4VkRWqerVBTTcDI/A6boLnPKiHSacXIrJMVa8NNukWkSWqWtegpt9MLw9wnB5uRsleXgzaFjw3qXcY0oKI1PA972UZNM6QrX2ZjJ0kX8sqEamc93LS6IP34FFXVbcC+Itsh4hIH1V915QwEbkEbxFpZUK9FhoxQbDYm5s1sYGAyEAnyWeB70L2kCVmijbSXlXfx/Mo9TKkeVJ734QYEfkEOB9vLeXneLHeFpvQkoGhwLdAZ3+/m5/W1JCe4XiWCj8BLfBM23ob0hKCqg43rSELJorIOjzTu8d8CwbTrt3/jTfTtR7S7jn/A641qCkwoxXvD1jEAVmFyshLpojIw8AEQk3vnHtwy3AzShYjIrXxzKI64y0q/96Ue1TfNevDfjyCjKiJB20R2aiq1bM5tklVq+W1Jv+7f8OL2XAgQ3ppYLrJUSQRWYEX4yLETbEJt+BBmmbhLWq1wpsbWBcbKNu6LCKbVbVqXmuyHRFZrqrXZEgzNoIb5DAl8F4YmKKqDU958rnVlcmBi0mHPcHOZkSkALA443U0hWQdzPgI3rqcV9RQMFPxYuMd8U32zweKquoeE1p8PZkc8VjgnKc1Xue7At4MalHgZVX98aQnnltNW7NIVjUcW82RGTejZBn+6Mud/usAnl2tmLZxV9WH/XebbO2XishDqhqynkW84HvGHvyBiKxcfqrqfhGJMCEoiGRVHWJYQ0aOA6tExBpvbqo6zzffqq6qM/0HEFPR5RdlU88fwY5ZCWsQkTvxBpeqiEjwQ1ARwORI7Z/++x8iEo3nQCHKoJ4AB0WkG96IP3j3HSMP/D5pa1lUNdmzYraGKXiDS9/6+3fgzRLuAYYBeW7qLSLdg7aDD43InDvPWOqbAgbWSd1NqGOjPEdVJ/qbR/BmdY2jqlVMa3CcHm5GyTJEJBVv5OMBVd3kp22xaZRBRG4gs+lWnjfMIlIGGAckkt4xqoO3LqGDqVG1rEazT+dYXuB7BdyHV25WTPdnsP1Pw6Spi+9U4mGgpKpWFZHqwCcm1gOJyEV4zjf+In3x+LVAQTwTs715rclW/M5tFeB14PmgQ8eAlabWu4jIi3gj2bcAH+HNTHymqi+Z0BOkq5Kvq76v6Re8tXk7T3riudOTQvpgiQDnAX9gR2yurGYpl6vqNZJF2IU80hRsYVIIr34tV9VOea0lgIgUBJ4g3RHHT8DHqvpX9medc00X45nd1sdznLAQzzvgFlOafF1XADUJXbdospPryALXUbIMEWmPN1J1I567z++Az20ZfRCRr/Diy8SQbrqlJkf/RaQxcIW/u0ZVZ5vS4usJvtmHHAIKqaqxWSWbpvvFTq+OQJrDhHrAIk2P7WTkYShIUxM8ZwBgQT13nB3+g2Qhk6amIlIhu86QiLQOGoF3+Phmyw+p6mJ/vy7evflqWxbmixek/jtVNRbewF83eUI9t/yBdagF1WC8IBH5FW+AIjBzegfegECeB1gP0jQQzztvTWAy3pq8BSY7uY6scR0lS/Ebm3Z4phBN8KbSx6nqdMO61uIFj3MVx5EjgkdoRWSsWhS4UUQWqep1gQcgf73EcpN29o5TIyLHyLyOBAzPSPje3B7HG2VXYAEwRP2Aqgb0rANuU9VtGdLvAwa4dW+Z8TtGX+J5mxTgKPAgsAZopaqjDMoDwDftXq2qlxrU8Ctwq6oe9/cL463NvcGgpqzWTZn2DrgKuBr4ze9slwG+VlVTjlQc2eDWKFmKqv6OZwv9rYiUwHPo8BxgtKMErMaLXRRvWIfjDBCRZ1X1LX+7s6qODjr2mqq+YEJW0LY1pqU+80TkBeA88VyXP47nnchhMapaxLSGbBiBZ/4XMJW6C/iKdG9zec1TwHQRaaWqGwFEpJ+vK0tHJn93VHUJcKWIFPP3g2cEjXSSRGQCobGdagKjsz8jTygU6CQBqOpxf42nSaaIyPN4FjqKF2h9su8Iw5Tp+Z+qmioiySJSFM8kvoIBHY5T4GaUHKdFUINcBC/Q3mJC17iYcJvsOE0yzN6E2NqbWjd1Mk2mEZEw4AG82E7gxXb63KAkRz5GRGJVteap0vJY0y14QS/b482M1MObGUkwpclmRCTL9WRqMBZWBu+cycB2NRQDK4B4cd/+oX7IEBGpgxdDsL5BTQGT88ADb/AgnSnT84+BF/DMAJ/Gc2oUo5YGgf4742aUHKfLj0AZvIWZwTTEzS7lBySb7az284qrReSo//3n+dsBPUbMpESkHVBeVT8CPvu/9u491rJzjOP499cZTaemNYQKRRtt1aWkrVupCIMGKYqE1j1upRQRolIRInFrJIiIulOjbk1p3Klb0VJO25mqqlsa4n4ppa0yHn+sd5s9e86czjkz57x7T7+fZGfWWmftvZ49OTl7P+t93+dpRR1uBdwzyVVV9amVjkm7hbkkR1XVBQBJ7kv/SmDntql232Ao4rC+11TAGTG+7nQv4Fjgx51iAbZtV5BkjyRPrqoNvWJi6Hv1ySS/afu3YRjBWXFtuuSvRmu8W+GgxzM0w31NzyJGVXVS23xXki8ylHXf2CsebZ8jStohST4LvLImGrwmuTvw+qpa8dKo2nHTOKI0jdrd0ONHC91bUYf1DOsSPtCj6p1mX1vbeSgwKl5yB+AnDKMAtdJr38bWcoWheuK/GYrzdK8uNytaUY4vVdWDOlx7X4bKcvsz3MT8Stt/GXBJVT2mQ0yjpOR3ba3UicDjgMuAV/dISpLMMayX+kuSBzJMvTuZYVbMXXoXTkiyP3AAW1cQ/la/iDQfR5S0o249mSQBVNWmJAeufDhapIVGb/ba/tNudPacqAb27fYB/5dWYEVaim5VyOYzxWu5ZsnewO06XfsM4K8MZa6fzTCFKwztAi7uFNPpwEPb9v1aTKOk5N1Aj6Rk1ViC9kTg3VV1FnBWuwnWTZI3tZguY6yCMGCiNGVMlLSj1i3wszUrFoWWpKp6NUudNTcf36mqF47t3mqFY9FuoqquhP/3xBrvmTKV5fG1rValbDQFZxXD34PXdQrnjqNWBRmau/4WuEPnqZPTmJSsSrK6hv5pD2HojTfS+/vvccChPftLacf0/kXR7PhBkudU1XvGDyZ5NluavUqz7nvb+T0/kaGAibRoSR4NvAW4LUN1qwMY1rfcbaHnaaocO7b9H+D31amBMcNUSQCqanOSX0/B+rJpTErOZKhg+ifgWtoa6yQHA936mDW/AG7CWFEsTSfXKGmHtBr/ZwPXsyUxuhewJ/DYqvpdr9ikXaXd8f80w4fXXDt8T4Z1HMdV1e97xabZ1ZqVrge+2vpyPRh4SlU9q3No2kFJzqiqp97QsRWKZbypeRhmdVxD30I4pwKPBP7EsAbvyKqqlpR8qKqOXumYWlxHMRSU+HJru0KSOwFrR5X5OsV1FkMfpXPZuoLwi3rFpPmZKGlR2gf8YW33R1X1tZ7xSMshyXq23O3391w7JckPqupeLWE6ovVP6drwUoszTxGc1cDGniXep820JiXTqFXgm1RV9eEVD0YLcuqdFqWqvg58vXcc0nJqiZHJkXaVq5KsZZj6syHJH9i63LSmVGvEO2o+PV4E53qGIgVqRuXvJ45d0SOWGbCuqt42fiDJi3sFo+1zREmSpGWUZG/gOoYv2E8B9gU29Ozjoh3XGlC/t6qe2TsW7R7ma8uR5KKqOqJXTJqfiZIkSctgrF/RVofbv9cBPwdOrapzVzQwLVqSTaNKc9JSJTkBeBLwAFpxiWYf4L/26ps+Tr2TJGkZLNSvKMkqhvWeG9iy7lPTay7Jvavqwt6BaKZ9l6Gc+y0ZKmGOXA1s7BKRFuSIkiRJnSQ5sapO7x2HFpbkcuBg4EqG9WWjCnP36BqYZlaSA4BDquqrSdYAq6vq6t5xaWsmSpIkSQtoX2q3MWomLC1Gkucw9Jq6RVUdlOQQ4F1OvZs+e/QOQJIkaZq1hGgd8Kj2WGeSpJ3wAuBo4O8AVfVTYL+uEWleJkqSJEkLaKWbNzB8md0P+EiSk/tGpRn2r6q6frTT+nI5xWsKOfVOkiRpAUk2Avcba6R6U+B81yhpKZK8GbgKeBpwMnAScFlVndo1MG3DESVJkqSFBdg8tr+ZLaXepcU6BfgjsAk4Efg88KquEWlejihJkiQtIMlLgacDZ7dDxwEfrKq39otK0nIzUZIkSboBSY5kaBQKcF5VXdQzHs2eJJ+oqick2cQ8a5Kcyjl9bDgrSZI0jyR7Ac9j6KG0CXhnVf2nb1SaYf9I8gCGyomOVMwAEyVJkqT5fQj4N3Ae8AjgLsBLukakWXYJcBpwG+ATwJmOTE43p95JkiTNI8mmqrp7214NfL+qjuwclmZca2B8fHusAc5kSJqu6BqYtmGiJEmSNI8kc+OJ0eS+tLOSHAG8H7hHVa3qHY+2ZqIkSZI0jySbgX+Odhnu/l/Ttquq9u0Vm2ZXG518BMOI0kOAbzCMKH2mZ1zalomSJEmStMySPAw4AXgk8H3gY8BnRo2MNX1MlCRJkqRlluRrwEeBs6rqr73j0Q0zUZIkSZKkCXv0DkCSJEmSpo2JkiRJkiRNMFGSJC1Jks1JLk5yaZJPJtl7J17rQUk+27YfneSUBc5dl+SkJVzjNUlettQYJUk3LiZKkqSluraqDq+qw4DrgeeN/zCDRX/OVNU5VfXGBU5ZByw6UZIkaTFMlCRJu8J5wMFJDkzykyQfBi4Fbp/kmCTnJ5lrI09rAZI8PMnlSeaAx41eKMkzkryjbd86ydlJLmmP+wNvBA5qo1mntfNenuTCJBuTvHbstU5NckWSbwOHrtj/hiRp5q3uHYAkabaNNU/8Yjt0CPD0qrogyS2BVwEPrap/JnkF8NIkbwbeA6wHfgZ8fDsv/3bgm1X12CSrgLXAKcBhVXV4u/4x7Zr3YWgEek6SBzI0Cj0eOJzh824O+OGuffeSpN2ViZIkaanWJLm4bZ8HvA+4LXBlVV3Qjh8F3BX4ThKAPYHzgTsDv6yqnwIk+Qjw3HmusR54GkBVbQb+luTmE+cc0x4Xtf21DInTPsDZVXVNu8Y5O/VuJUk3KiZKkqSlunY0qjPSkqHxLvMBvlJVJ0yct9XzdlKAN1TV6RPXeMkuvIYk6UbGNUqSpOV0AXB0koMBktw0yZ2Ay4EDkxzUzjthO88/F3h+e+6qJDcDrmYYLRr5EvDMsbVP+yfZD/gWcFySNUn2AR61i9+bJGk3ZqIkSVo2VfVH4BnAmUk20qbdVdV1DFPtPteKOfxhOy/xYuDBSTYxrC+6a1X9mWEq36VJTquqLwMfBc5v530K2Keq5hjWPl0CfAG4cNneqCRpt5Oq6h2DJEmSJE0VR5QkSZIkaYKJkiRJkiRNMFGSJEmSpAkmSpIkSZI0wURJkiRJkiaYKEmSJEnSBBMlSZIkSZpgoiRJkiRJE/4Hm+skEBw2WqMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "c_mat_test = confusion_matrix(Y_test, Y_pred_test)\n",
    "c_mat_test = c_mat_test / c_mat_test.sum(axis=1)[:, np.newaxis]\n",
    "plt.figure(figsize=(15, 5))\n",
    "sns.heatmap(c_mat_test, annot=True, fmt='0.2f', xticklabels=labels, yticklabels=labels)\n",
    "plt.title('Test Data')\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('Actual')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_accuracies(c_mat):\n",
    "    accuracies = c_mat.astype('float') / c_mat.sum(axis=1)\n",
    "    accuracies = accuracies.diagonal()\n",
    "    accuracies = {k:v for k, v in zip(labels, accuracies)}\n",
    "    return accuracies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Arabic': 0.9850000000000001,\n",
       " 'Chinese': 0.7196261682242991,\n",
       " 'Czech': 0.6192771084337351,\n",
       " 'Dutch': 0.7394957983193279,\n",
       " 'English': 0.7191547375596455,\n",
       " 'French': 0.8738738738738739,\n",
       " 'German': 0.7547495682210709,\n",
       " 'Greek': 0.8711656441717791,\n",
       " 'Irish': 0.8064516129032259,\n",
       " 'Italian': 0.7671957671957672,\n",
       " 'Japanese': 0.9205548549810845,\n",
       " 'Korean': 0.5466666666666666,\n",
       " 'Polish': 0.8468468468468469,\n",
       " 'Portuguese': 0.9491525423728815,\n",
       " 'Russian': 0.8584905660377359,\n",
       " 'Scottish': 0.625,\n",
       " 'Spanish': 0.6176470588235294,\n",
       " 'Vietnamese': 0.3559322033898305}"
      ]
     },
     "execution_count": 249,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compute_accuracies(c_mat_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Arabic': 0.9875000000000002,\n",
       " 'Chinese': 0.6111111111111112,\n",
       " 'Czech': 0.36538461538461536,\n",
       " 'Dutch': 0.4067796610169492,\n",
       " 'English': 0.6362397820163488,\n",
       " 'French': 0.32727272727272727,\n",
       " 'German': 0.5379310344827586,\n",
       " 'Greek': 0.7,\n",
       " 'Irish': 0.43478260869565216,\n",
       " 'Italian': 0.6690140845070423,\n",
       " 'Japanese': 0.8131313131313131,\n",
       " 'Korean': 0.42105263157894746,\n",
       " 'Polish': 0.4285714285714286,\n",
       " 'Portuguese': 0.2,\n",
       " 'Russian': 0.8251859723698194,\n",
       " 'Scottish': 0.1,\n",
       " 'Spanish': 0.3333333333333333,\n",
       " 'Vietnamese': 0.14285714285714285}"
      ]
     },
     "execution_count": 250,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compute_accuracies(c_mat_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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