ToxicComment.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Package**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import re\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from nltk.stem import PorterStemmer\n",
    "from nltk.tokenize import word_tokenize\n",
    "from nltk.stem import WordNetLemmatizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package punkt to\n",
      "[nltk_data]     C:\\Users\\Administrator\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package punkt is already up-to-date!\n",
      "[nltk_data] Downloading package stopwords to\n",
      "[nltk_data]     C:\\Users\\Administrator\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import nltk\n",
    "nltk.download('punkt')  \n",
    "nltk.download('stopwords')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('dataset/train.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **1.Understanding The Data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(159572, 8)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 159572 entries, 0 to 159571\n",
      "Data columns (total 8 columns):\n",
      " #   Column         Non-Null Count   Dtype \n",
      "---  ------         --------------   ----- \n",
      " 0   id             159572 non-null  object\n",
      " 1   comment_text   159572 non-null  object\n",
      " 2   toxic          159572 non-null  int64 \n",
      " 3   severe_toxic   159572 non-null  int64 \n",
      " 4   obscene        159572 non-null  int64 \n",
      " 5   threat         159572 non-null  int64 \n",
      " 6   insult         159572 non-null  int64 \n",
      " 7   identity_hate  159572 non-null  int64 \n",
      "dtypes: int64(6), object(2)\n",
      "memory usage: 9.7+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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       "                 id                                       comment_text  toxic  \\\n",
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       "4  0001d958c54c6e35  You, sir, are my hero. Any chance you remember...      0   \n",
       "5  00025465d4725e87  \"\\n\\nCongratulations from me as well, use the ...      0   \n",
       "6  0002bcb3da6cb337       COCKSUCKER BEFORE YOU PISS AROUND ON MY WORK      1   \n",
       "7  00031b1e95af7921  Your vandalism to the Matt Shirvington article...      0   \n",
       "8  00037261f536c51d  Sorry if the word 'nonsense' was offensive to ...      0   \n",
       "9  00040093b2687caa  alignment on this subject and which are contra...      0   \n",
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   "source": [
    "df.head(10)"
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  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
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       "                      id                                       comment_text  \\\n",
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       "159567  ffea4adeee384e90  You should be ashamed of yourself \\n\\nThat is ...   \n",
       "159568  ffee36eab5c267c9  Spitzer \\n\\nUmm, theres no actual article for ...   \n",
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       "159570  fff46fc426af1f9a  \"\\nAnd ... I really don't think you understand...   \n",
       "159571  fffasdsddasdadss                                          my mother   \n",
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       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159567</th>\n",
       "      <td>ffea4adeee384e90</td>\n",
       "      <td>You should be ashamed of yourself \\n\\nThat is ...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159568</th>\n",
       "      <td>ffee36eab5c267c9</td>\n",
       "      <td>Spitzer \\n\\nUmm, theres no actual article for ...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159569</th>\n",
       "      <td>fff125370e4aaaf3</td>\n",
       "      <td>And it looks like it was actually you who put ...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159570</th>\n",
       "      <td>fff46fc426af1f9a</td>\n",
       "      <td>\"\\nAnd ... I really don't think you understand...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159571</th>\n",
       "      <td>fffasdsddasdadss</td>\n",
       "      <td>my mother</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      id                                       comment_text  \\\n",
       "159472  fe6585aaa6182d28                                You ignored him too   \n",
       "159473  fe77e59403ac395e     , as User:Dr.K wasn't saying it in the article   \n",
       "159474  fe7b1f2bd3756b0b  LaserActive \\n\\nHi SchuminWeb, I am confused b...   \n",
       "159475  fe93b5e609f5cfdd                            Lists of Islamic Jihads   \n",
       "159476  fea196598389c77c  \"\\n\\n URL Update (update to my previous, and o...   \n",
       "...                  ...                                                ...   \n",
       "159567  ffea4adeee384e90  You should be ashamed of yourself \\n\\nThat is ...   \n",
       "159568  ffee36eab5c267c9  Spitzer \\n\\nUmm, theres no actual article for ...   \n",
       "159569  fff125370e4aaaf3  And it looks like it was actually you who put ...   \n",
       "159570  fff46fc426af1f9a  \"\\nAnd ... I really don't think you understand...   \n",
       "159571  fffasdsddasdadss                                          my mother   \n",
       "\n",
       "        toxic  severe_toxic  obscene  threat  insult  identity_hate  \n",
       "159472      0             0        0       0       0              0  \n",
       "159473      0             0        0       0       0              0  \n",
       "159474      0             0        0       0       0              0  \n",
       "159475      0             0        0       0       0              0  \n",
       "159476      0             0        0       0       0              0  \n",
       "...       ...           ...      ...     ...     ...            ...  \n",
       "159567      0             0        0       0       0              0  \n",
       "159568      0             0        0       0       0              0  \n",
       "159569      0             0        0       0       0              0  \n",
       "159570      0             0        0       0       0              0  \n",
       "159571      0             0        0       0       0              0  \n",
       "\n",
       "[100 rows x 8 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>toxic</th>\n",
       "      <th>severe_toxic</th>\n",
       "      <th>obscene</th>\n",
       "      <th>threat</th>\n",
       "      <th>insult</th>\n",
       "      <th>identity_hate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>159572.000000</td>\n",
       "      <td>159572.000000</td>\n",
       "      <td>159572.000000</td>\n",
       "      <td>159572.000000</td>\n",
       "      <td>159572.000000</td>\n",
       "      <td>159572.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.095844</td>\n",
       "      <td>0.009995</td>\n",
       "      <td>0.052948</td>\n",
       "      <td>0.002996</td>\n",
       "      <td>0.049363</td>\n",
       "      <td>0.008805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.294378</td>\n",
       "      <td>0.099477</td>\n",
       "      <td>0.223930</td>\n",
       "      <td>0.054649</td>\n",
       "      <td>0.216626</td>\n",
       "      <td>0.093420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               toxic   severe_toxic        obscene         threat  \\\n",
       "count  159572.000000  159572.000000  159572.000000  159572.000000   \n",
       "mean        0.095844       0.009995       0.052948       0.002996   \n",
       "std         0.294378       0.099477       0.223930       0.054649   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         0.000000       0.000000       0.000000       0.000000   \n",
       "50%         0.000000       0.000000       0.000000       0.000000   \n",
       "75%         0.000000       0.000000       0.000000       0.000000   \n",
       "max         1.000000       1.000000       1.000000       1.000000   \n",
       "\n",
       "              insult  identity_hate  \n",
       "count  159572.000000  159572.000000  \n",
       "mean        0.049363       0.008805  \n",
       "std         0.216626       0.093420  \n",
       "min         0.000000       0.000000  \n",
       "25%         0.000000       0.000000  \n",
       "50%         0.000000       0.000000  \n",
       "75%         0.000000       0.000000  \n",
       "max         1.000000       1.000000  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **2.Preprocessing The Data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Xóa các kí tự đặt biệt và thay thế \\n bằng khoảng trống"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_special_characters(text):\n",
    "    text = text.replace('\\n', ' ')\n",
    "    text = re.sub(r\"[^A-Za-z0-9\\s\\.'']\", '', text)\n",
    "    return text\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['comment_text'] = df['comment_text'].apply(remove_special_characters)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Xóa các stopword "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import ENGLISH_STOP_WORDS\n",
    "def remove_stopwords_and_numbers(text):\n",
    "    words = text.split()\n",
    "    filtered_words = [word for word in words if word.lower() not in ENGLISH_STOP_WORDS and not re.match(r'.*\\d.*', word)]\n",
    "    new_text = ' '.join(filtered_words)\n",
    "    return new_text\n",
    "df['comment_text'] = df['comment_text'].apply(remove_stopwords_and_numbers)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Xóa cột không cần thiết"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(columns=['id'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Chuyển tất cả dữ liệu về chữ thường"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['comment_text'] = df['comment_text'].apply(lambda x: x.lower() if isinstance(x, str) else x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Stemming "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "stemmer = PorterStemmer()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Lemmatization  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "lemmatizer = WordNetLemmatizer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def lemmatize_sentence(sentence):\n",
    "    tokens = word_tokenize(sentence)  \n",
    "    lemmatized_tokens = [lemmatizer.lemmatize(token) for token in tokens] \n",
    "    return ' '.join(lemmatized_tokens)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "stemmer = PorterStemmer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['comment_text'] = df['comment_text'].apply(lemmatize_sentence)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "file_path_clean = 'dataset/train_clean.csv'\n",
    "df.to_csv(file_path_clean, index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **3.Chart**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Biểu đồ thống kê tỉ lệ % giữa các nhãn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = ['toxic', 'severe_toxic', 'obscene', 'threat', 'insult']\n",
    "sizes = [sum(df[label]) for label in labels] \n",
    "plt.figure(figsize=(8, 8))\n",
    "plt.pie(sizes, labels=labels, autopct='%1.1f%%', startangle=140)\n",
    "plt.axis('equal')\n",
    "plt.title('Phân phối các nhãn')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Biểu đồ tần số xuất hiện theo độ dài comment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df['comment_text'].str.len(), bins=300, log=True)\n",
    "plt.title('Histogram of Text Length')\n",
    "plt.xlabel('Text Length')\n",
    "plt.ylabel('Frequency (log scale)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Biểu đồ thống kê xem có sự mất cân bằng giữa các nhãn hay không"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<Axes: title={'center': 'toxic'}>,\n",
       "        <Axes: title={'center': 'severe_toxic'}>],\n",
       "       [<Axes: title={'center': 'obscene'}>,\n",
       "        <Axes: title={'center': 'threat'}>],\n",
       "       [<Axes: title={'center': 'insult'}>,\n",
       "        <Axes: title={'center': 'identity_hate'}>]], dtype=object)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1300x1000 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist(figsize=(13,10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Biểu đồ xem tần số xuất hiện mỗi loại "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'Labels Frequency'}>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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lfUo+h4iIiN5tRro8mFqtBgDY2tpqtdva2or71Go1bGxstIswMoK1tbVWHycnp1LHKNlnZWUFtVr9ws8pS15eHvLy8sTXGo2mPKdHREREeuSdursqODgYFhYW4ubg4CB1SURERPSW6DTk2NnZAQDS0tK02tPS0sR9dnZ2SE9P19pfWFiIjIwMrT5lHePZz3hen5L9ZQkMDERWVpa4paamlvcUiYiISE/oNOQ4OTnBzs4OkZGRYptGo0FsbCzc3d0BAO7u7sjMzERcXJzYJyoqCsXFxWjTpo3YJzo6GgUFBWKfiIgINGzYEFZWVmKfZz+npE/J55RFqVRCpVJpbURERCRP5Q452dnZSEhIQEJCAoCnk40TEhKQkpIChUKBiRMnYt68efjll1+QmJiIYcOGwd7eXrwDq1GjRujZsyfGjBmDs2fP4tSpU/D398egQYNgb28PABgyZAhMTEzg6+uLK1euYOfOnVi1ahUCAgLEOr788kuEh4dj2bJluHbtGmbNmoXz58/D39//za8KERER6b1yTzw+f/48unbtKr4uCR7Dhw9HaGgopk6dipycHIwdOxaZmZno0KEDwsPDYWpqKr5n+/bt8Pf3R/fu3WFgYID+/ftj9erV4n4LCwscPXoUfn5+aNWqFapVq4agoCCttXTatWuHHTt2YPr06fj3v/+N+vXrIywsDE2aNHmtC0FERETy8kbr5Og7rpNDRESkfyRZJ4eIiIioomDIISIiIlliyCEiIiJZYsghIiIiWWLIISIiIlliyCEiIiJZYsghIiIiWWLIISIiIlliyCEiIiJZYsghIiIiWWLIISIiIlliyCEiIiJZYsghIiIiWWLIISIiIlliyCEiIiJZYsghIiIiWWLIISIiIlliyCEiIiJZYsghIiIiWWLIISIiIlliyCEiIiJZYsghIiIiWWLIISIiIlliyCEiIiJZYsghIiIiWWLIISIiIlliyCEiIiJZYsghIiIiWWLIISIiIlkykroAInr7POcelLqElzoyw0vqEohIZjiSQ0RERLLEkENERESyxJBDREREssSQQ0RERLLEkENERESypPOQU1RUhBkzZsDJyQlmZmaoW7cu5s6dC0EQxD6CICAoKAg1atSAmZkZPDw8cP36da3jZGRkwMfHByqVCpaWlvD19UV2drZWn0uXLqFjx44wNTWFg4MDFi9erOvTISIiIj2l85CzaNEirFu3DmvWrMHVq1exaNEiLF68GN9++63YZ/HixVi9ejVCQkIQGxsLc3NzeHp64smTJ2IfHx8fXLlyBREREThw4ACio6MxduxYcb9Go0GPHj3g6OiIuLg4LFmyBLNmzcL69et1fUpERESkh3S+Ts7p06fRt29feHk9XfOidu3a+OGHH3D27FkAT0dxVq5cienTp6Nv374AgO+++w62trYICwvDoEGDcPXqVYSHh+PcuXNwc3MDAHz77bfo3bs3li5dCnt7e2zfvh35+fnYvHkzTExM0LhxYyQkJGD58uVaYYiIiIjeTTofyWnXrh0iIyPx+++/AwAuXryIkydPolevXgCA5ORkqNVqeHh4iO+xsLBAmzZtEBMTAwCIiYmBpaWlGHAAwMPDAwYGBoiNjRX7dOrUCSYmJmIfT09PJCUl4dGjR7o+LSIiItIzOh/J+frrr6HRaODs7AxDQ0MUFRVh/vz58PHxAQCo1WoAgK2trdb7bG1txX1qtRo2NjbahRoZwdraWquPk5NTqWOU7LOysipVW15eHvLy8sTXGo3mTU6ViIiIKjCdj+Ts2rUL27dvx44dO3DhwgVs3boVS5cuxdatW3X9UeUWHBwMCwsLcXNwcJC6JCIiInpLdB5ypkyZgq+//hqDBg2Cq6srhg4dikmTJiE4OBgAYGdnBwBIS0vTel9aWpq4z87ODunp6Vr7CwsLkZGRodWnrGM8+xl/FxgYiKysLHFLTU19w7MlIiKiikrnX1fl5ubCwEA7OxkaGqK4uBgA4OTkBDs7O0RGRqJ58+YAnn5tFBsbi88//xwA4O7ujszMTMTFxaFVq1YAgKioKBQXF6NNmzZin2+++QYFBQUwNjYGAERERKBhw4ZlflUFAEqlEkqlUtenTERE5cSHxtI/QecjOR9++CHmz5+PgwcP4vbt29i3bx+WL1+Ojz76CACgUCgwceJEzJs3D7/88gsSExMxbNgw2Nvbw9vbGwDQqFEj9OzZE2PGjMHZs2dx6tQp+Pv7Y9CgQbC3twcADBkyBCYmJvD19cWVK1ewc+dOrFq1CgEBAbo+JSIiItJDOh/J+fbbbzFjxgx88cUXSE9Ph729PT777DMEBQWJfaZOnYqcnByMHTsWmZmZ6NChA8LDw2Fqair22b59O/z9/dG9e3cYGBigf//+WL16tbjfwsICR48ehZ+fH1q1aoVq1aohKCiIt48TERERAEAhPLsU8TtGo9HAwsICWVlZUKlUOjmmPgzBAhyGfdfow99L/p18t/DvJL2JV/35zWdXERERkSwx5BAREZEsMeQQERGRLDHkEBERkSwx5BAREZEsMeQQERGRLDHkEBERkSwx5BAREZEsMeQQERGRLDHkEBERkSwx5BAREZEsMeQQERGRLDHkEBERkSwx5BAREZEsMeQQERGRLDHkEBERkSwx5BAREZEsMeQQERGRLDHkEBERkSwx5BAREZEsMeQQERGRLDHkEBERkSwx5BAREZEsMeQQERGRLDHkEBERkSwx5BAREZEsMeQQERGRLDHkEBERkSwx5BAREZEsMeQQERGRLDHkEBERkSwx5BAREZEsMeQQERGRLDHkEBERkSwx5BAREZEsvZWQ88cff+DTTz9F1apVYWZmBldXV5w/f17cLwgCgoKCUKNGDZiZmcHDwwPXr1/XOkZGRgZ8fHygUqlgaWkJX19fZGdna/W5dOkSOnbsCFNTUzg4OGDx4sVv43SIiIhID+k85Dx69Ajt27eHsbExDh8+jN9++w3Lli2DlZWV2Gfx4sVYvXo1QkJCEBsbC3Nzc3h6euLJkydiHx8fH1y5cgURERE4cOAAoqOjMXbsWHG/RqNBjx494OjoiLi4OCxZsgSzZs3C+vXrdX1KREREpIeMdH3ARYsWwcHBAVu2bBHbnJycxD8LgoCVK1di+vTp6Nu3LwDgu+++g62tLcLCwjBo0CBcvXoV4eHhOHfuHNzc3AAA3377LXr37o2lS5fC3t4e27dvR35+PjZv3gwTExM0btwYCQkJWL58uVYYIiIioneTzkdyfvnlF7i5ueGTTz6BjY0NWrRogQ0bNoj7k5OToVar4eHhIbZZWFigTZs2iImJAQDExMTA0tJSDDgA4OHhAQMDA8TGxop9OnXqBBMTE7GPp6cnkpKS8OjRI12fFhEREekZnYecW7duYd26dahfvz6OHDmCzz//HBMmTMDWrVsBAGq1GgBga2ur9T5bW1txn1qtho2NjdZ+IyMjWFtba/Up6xjPfsbf5eXlQaPRaG1EREQkTzr/uqq4uBhubm5YsGABAKBFixa4fPkyQkJCMHz4cF1/XLkEBwdj9uzZktZARERE/wydj+TUqFEDLi4uWm2NGjVCSkoKAMDOzg4AkJaWptUnLS1N3GdnZ4f09HSt/YWFhcjIyNDqU9Yxnv2MvwsMDERWVpa4paamvs4pEhERkR7Qechp3749kpKStNp+//13ODo6Ang6CdnOzg6RkZHifo1Gg9jYWLi7uwMA3N3dkZmZibi4OLFPVFQUiouL0aZNG7FPdHQ0CgoKxD4RERFo2LCh1p1cz1IqlVCpVFobERERyZPOQ86kSZNw5swZLFiwADdu3MCOHTuwfv16+Pn5AQAUCgUmTpyIefPm4ZdffkFiYiKGDRsGe3t7eHt7A3g68tOzZ0+MGTMGZ8+exalTp+Dv749BgwbB3t4eADBkyBCYmJjA19cXV65cwc6dO7Fq1SoEBATo+pSIiIhID+l8Ts7777+Pffv2ITAwEHPmzIGTkxNWrlwJHx8fsc/UqVORk5ODsWPHIjMzEx06dEB4eDhMTU3FPtu3b4e/vz+6d+8OAwMD9O/fH6tXrxb3W1hY4OjRo/Dz80OrVq1QrVo1BAUF8fZxIiIiAvAWQg4AfPDBB/jggw+eu1+hUGDOnDmYM2fOc/tYW1tjx44dL/ycpk2b4tdff33tOomIiEi++OwqIiIikiWGHCIiIpIlhhwiIiKSJYYcIiIikiWGHCIiIpIlhhwiIiKSJYYcIiIikiWGHCIiIpIlhhwiIiKSJYYcIiIikiWGHCIiIpIlhhwiIiKSJYYcIiIikiWGHCIiIpIlhhwiIiKSJYYcIiIikiWGHCIiIpIlhhwiIiKSJYYcIiIikiWGHCIiIpIlhhwiIiKSJYYcIiIikiWGHCIiIpIlhhwiIiKSJYYcIiIikiWGHCIiIpIlhhwiIiKSJYYcIiIikiWGHCIiIpIlhhwiIiKSJYYcIiIikiWGHCIiIpIlhhwiIiKSJYYcIiIikiWGHCIiIpKltx5yFi5cCIVCgYkTJ4ptT548gZ+fH6pWrYrKlSujf//+SEtL03pfSkoKvLy8UKlSJdjY2GDKlCkoLCzU6nP8+HG0bNkSSqUS9erVQ2ho6Ns+HSIiItITbzXknDt3Dv/973/RtGlTrfZJkyZh//792L17N06cOIF79+6hX79+4v6ioiJ4eXkhPz8fp0+fxtatWxEaGoqgoCCxT3JyMry8vNC1a1ckJCRg4sSJGD16NI4cOfI2T4mIiIj0xFsLOdnZ2fDx8cGGDRtgZWUltmdlZWHTpk1Yvnw5unXrhlatWmHLli04ffo0zpw5AwA4evQofvvtN2zbtg3NmzdHr169MHfuXKxduxb5+fkAgJCQEDg5OWHZsmVo1KgR/P398fHHH2PFihVv65SIiIhIj7y1kOPn5wcvLy94eHhotcfFxaGgoECr3dnZGbVq1UJMTAwAICYmBq6urrC1tRX7eHp6QqPR4MqVK2Kfvx/b09NTPAYRERG924zexkF//PFHXLhwAefOnSu1T61Ww8TEBJaWllrttra2UKvVYp9nA07J/pJ9L+qj0Wjw+PFjmJmZlfrsvLw85OXlia81Gk35T46IiIj0gs5HclJTU/Hll19i+/btMDU11fXh30hwcDAsLCzEzcHBQeqSiIiI6C3ReciJi4tDeno6WrZsCSMjIxgZGeHEiRNYvXo1jIyMYGtri/z8fGRmZmq9Ly0tDXZ2dgAAOzu7Undblbx+WR+VSlXmKA4ABAYGIisrS9xSU1N1ccpERERUAek85HTv3h2JiYlISEgQNzc3N/j4+Ih/NjY2RmRkpPiepKQkpKSkwN3dHQDg7u6OxMREpKeni30iIiKgUqng4uIi9nn2GCV9So5RFqVSCZVKpbURERGRPOl8Tk6VKlXQpEkTrTZzc3NUrVpVbPf19UVAQACsra2hUqkwfvx4uLu7o23btgCAHj16wMXFBUOHDsXixYuhVqsxffp0+Pn5QalUAgDGjRuHNWvWYOrUqRg1ahSioqKwa9cuHDx4UNenRERERHrorUw8fpkVK1bAwMAA/fv3R15eHjw9PfGf//xH3G9oaIgDBw7g888/h7u7O8zNzTF8+HDMmTNH7OPk5ISDBw9i0qRJWLVqFd577z1s3LgRnp6eUpwSERERVTD/SMg5fvy41mtTU1OsXbsWa9eufe57HB0dcejQoRcet0uXLoiPj9dFiURERCQzfHYVERERyRJDDhEREckSQw4RERHJEkMOERERyRJDDhEREckSQw4RERHJEkMOERERyRJDDhEREckSQw4RERHJEkMOERERyRJDDhEREckSQw4RERHJEkMOERERyRJDDhEREckSQw4RERHJEkMOERERyRJDDhEREckSQw4RERHJEkMOERERyRJDDhEREckSQw4RERHJEkMOERERyRJDDhEREckSQw4RERHJEkMOERERyRJDDhEREckSQw4RERHJEkMOERERyRJDDhEREckSQw4RERHJEkMOERERyRJDDhEREckSQw4RERHJEkMOERERyRJDDhEREcmSzkNOcHAw3n//fVSpUgU2Njbw9vZGUlKSVp8nT57Az88PVatWReXKldG/f3+kpaVp9UlJSYGXlxcqVaoEGxsbTJkyBYWFhVp9jh8/jpYtW0KpVKJevXoIDQ3V9ekQERGRntJ5yDlx4gT8/Pxw5swZREREoKCgAD169EBOTo7YZ9KkSdi/fz92796NEydO4N69e+jXr5+4v6ioCF5eXsjPz8fp06exdetWhIaGIigoSOyTnJwMLy8vdO3aFQkJCZg4cSJGjx6NI0eO6PqUiIiISA8Z6fqA4eHhWq9DQ0NhY2ODuLg4dOrUCVlZWdi0aRN27NiBbt26AQC2bNmCRo0a4cyZM2jbti2OHj2K3377Df/73/9ga2uL5s2bY+7cuZg2bRpmzZoFExMThISEwMnJCcuWLQMANGrUCCdPnsSKFSvg6emp69MiIiIiPfPW5+RkZWUBAKytrQEAcXFxKCgogIeHh9jH2dkZtWrVQkxMDAAgJiYGrq6usLW1Fft4enpCo9HgypUrYp9nj1HSp+QYRERE9G7T+UjOs4qLizFx4kS0b98eTZo0AQCo1WqYmJjA0tJSq6+trS3UarXY59mAU7K/ZN+L+mg0Gjx+/BhmZmal6snLy0NeXp74WqPRvNkJEhERUYX1Vkdy/Pz8cPnyZfz4449v82NeWXBwMCwsLMTNwcFB6pKIiIjoLXlrIcff3x8HDhzAsWPH8N5774ntdnZ2yM/PR2Zmplb/tLQ02NnZiX3+frdVyeuX9VGpVGWO4gBAYGAgsrKyxC01NfWNzpGIiIgqLp2HHEEQ4O/vj3379iEqKgpOTk5a+1u1agVjY2NERkaKbUlJSUhJSYG7uzsAwN3dHYmJiUhPTxf7REREQKVSwcXFRezz7DFK+pQcoyxKpRIqlUprIyIiInnS+ZwcPz8/7NixAz///DOqVKkizqGxsLCAmZkZLCws4Ovri4CAAFhbW0OlUmH8+PFwd3dH27ZtAQA9evSAi4sLhg4disWLF0OtVmP69Onw8/ODUqkEAIwbNw5r1qzB1KlTMWrUKERFRWHXrl04ePCgrk+JiIiI9JDOR3LWrVuHrKwsdOnSBTVq1BC3nTt3in1WrFiBDz74AP3790enTp1gZ2eHvXv3ivsNDQ1x4MABGBoawt3dHZ9++imGDRuGOXPmiH2cnJxw8OBBREREoFmzZli2bBk2btzI28eJiIgIwFsYyREE4aV9TE1NsXbtWqxdu/a5fRwdHXHo0KEXHqdLly6Ij48vd41EREQkf3x2FREREckSQw4RERHJEkMOERERydJbXfGY6E14zq34d8odmeEldQlERPQcHMkhIiIiWWLIISIiIlliyCEiIiJZ4pwcIiIiPcb5i8/HkRwiIiKSJYYcIiIikiWGHCIiIpIlhhwiIiKSJYYcIiIikiWGHCIiIpIlhhwiIiKSJYYcIiIikiWGHCIiIpIlhhwiIiKSJYYcIiIikiWGHCIiIpIlhhwiIiKSJYYcIiIikiWGHCIiIpIlhhwiIiKSJYYcIiIikiWGHCIiIpIlhhwiIiKSJYYcIiIikiWGHCIiIpIlI6kLICLSF55zD0pdwis5MsNL6hKIKgSO5BAREZEsMeQQERGRLDHkEBERkSwx5BAREZEsMeQQERGRLOl9yFm7di1q164NU1NTtGnTBmfPnpW6JCIiIqoA9Drk7Ny5EwEBAZg5cyYuXLiAZs2awdPTE+np6VKXRkRERBLT65CzfPlyjBkzBiNHjoSLiwtCQkJQqVIlbN68WerSiIiISGJ6G3Ly8/MRFxcHDw8Psc3AwAAeHh6IiYmRsDIiIiKqCPR2xeM///wTRUVFsLW11Wq3tbXFtWvXynxPXl4e8vLyxNdZWVkAAI1Go7O6Cp/k6uxYb5Muz/lt0YdrqQ/XEeC11BV9uI4Ar6Wu6MN1BN7Na1lyPEEQXthPb0PO6wgODsbs2bNLtTs4OEhQjbQsFkhdgTzwOuoOr6Xu8FrqBq+j7ryta/nXX3/BwsLiufv1NuRUq1YNhoaGSEtL02pPS0uDnZ1dme8JDAxEQECA+Lq4uBgZGRmoWrUqFArFW633dWk0Gjg4OCA1NRUqlUrqcvQar6Vu8DrqDq+l7vBa6oa+XEdBEPDXX3/B3t7+hf30NuSYmJigVatWiIyMhLe3N4CnoSUyMhL+/v5lvkepVEKpVGq1WVpavuVKdUOlUlXov3D6hNdSN3gddYfXUnd4LXVDH67ji0ZwSuhtyAGAgIAADB8+HG5ubmjdujVWrlyJnJwcjBw5UurSiIiISGJ6HXIGDhyIBw8eICgoCGq1Gs2bN0d4eHipychERET07tHrkAMA/v7+z/16Sg6USiVmzpxZ6ms2Kj9eS93gddQdXkvd4bXUDbldR4XwsvuviIiIiPSQ3i4GSERERPQiDDlEREQkSww5REREJEsMOURERCRLDDlEREQkSww5FcyhQ4dw5MiRUu1HjhzB4cOHJahIf02YMAGrV68u1b5mzRpMnDjxny9IBvLz85GUlITCwkKpS9FLhoaGSE9PL9X+8OFDGBoaSlARkbwx5FQwX3/9NYqKikq1C4KAr7/+WoKK9NdPP/2E9u3bl2pv164d9uzZI0FF+is3Nxe+vr6oVKkSGjdujJSUFADA+PHjsXDhQomr0x/PW7EjLy8PJiYm/3A1+m/OnDnIzS39BO7Hjx9jzpw5ElSk33799Vd8+umncHd3xx9//AEA+P7773Hy5EmJK3t9DDkVzPXr1+Hi4lKq3dnZGTdu3JCgIv318OHDMp9tolKp8Oeff0pQkf4KDAzExYsXcfz4cZiamortHh4e2Llzp4SV6YfVq1dj9erVUCgU2Lhxo/h69erVWLFiBfz8/ODs7Cx1mXpn9uzZyM7OLtWem5uL2bNnS1CR/vrpp5/g6ekJMzMzxMfHIy8vDwCQlZWFBQv093Hser/isdxYWFjg1q1bqF27tlb7jRs3YG5uLk1ReqpevXoIDw8vtSL24cOHUadOHYmq0k9hYWHYuXMn2rZtC4VCIbY3btwYN2/elLAy/bBixQoAT0dyQkJCtL6aMjExQe3atRESEiJVeXpLEAStv48lLl68CGtrawkq0l/z5s1DSEgIhg0bhh9//FFsb9++PebNmydhZW+GIaeC6du3LyZOnIh9+/ahbt26AJ4GnK+++gp9+vSRuDr9EhAQAH9/fzx48ADdunUDAERGRmLZsmVYuXKltMXpmQcPHsDGxqZUe05OTpk/ZEhbcnIyAKBr167Yu3cvrKysJK5Iv1lZWUGhUEChUKBBgwZafweLioqQnZ2NcePGSVih/klKSkKnTp1KtVtYWCAzM/OfL0hHGHIqmMWLF6Nnz55wdnbGe++9BwC4e/cuOnbsiKVLl0pcnX4ZNWoU8vLyMH/+fMydOxcAULt2baxbtw7Dhg2TuDr94ubmhoMHD2L8+PEAIP5Q2bhxI9zd3aUsTa8cO3ZM6hJkYeXKlRAEAaNGjcLs2bO1vpYuGRnj38vysbOzw40bN0p9i3Dy5Em9Hvnms6sqIEEQEBERgYsXL8LMzAxNmzYtM2HTq3vw4AHMzMxQuXJlqUvRSydPnkSvXr3w6aefIjQ0FJ999hl+++03nD59GidOnECrVq2kLlFv3L17F7/88gtSUlKQn5+vtW/58uUSVaWfTpw4gXbt2sHY2FjqUvRecHAwtm3bhs2bN+Nf//oXDh06hDt37mDSpEmYMWOG+AuOvmHIIaJXcvPmTSxcuBAXL15EdnY2WrZsiWnTpsHV1VXq0vRGZGQk+vTpgzp16uDatWto0qQJbt++DUEQ0LJlS0RFRUldYoWn0Wheua9KpXqLlciLIAhYsGABgoODxTvWlEolJk+eLI6E6yOGnApg9erVGDt2LExNTctc1+VZEyZM+Ieq0k8tW7ZEZGQkrKys0KJFixfOF7lw4cI/WBkR0Lp1a/Tq1QuzZ89GlSpVcPHiRdjY2MDHxwc9e/bE559/LnWJFZ6BgcFL54GVTEguazkOerH8/HzcuHED2dnZcHFx0fvRb87JqQBWrFgBHx8fmJqaindhlEWhUDDkvETfvn2hVCoBAN7e3tIWIzPFxcW4ceMG0tPTUVxcrLWPX6e+mqtXr+KHH34AABgZGeHx48eoXLky5syZg759+zLkvALOa3o7Ro0ahVWrVqFKlSpay5jk5ORg/Pjx2Lx5s4TVvT6O5BDRS505cwZDhgzBnTt3Si1ox9+YX52dnR2OHTuGRo0awcXFBQsXLkSfPn1w8eJFtG/fvsw1X4j+CYaGhrh//36puyj//PNP2NnZ6e0q5xzJqWCePHmitdjas+7fv48aNWr8wxXpr2PHjqFr165l7vvvf/+Lzz777B+uSH+NGzdOvMOqRo0avG38NbVt2xYnT55Eo0aN0Lt3b3z11VdITEzE3r170bZtW6nL0zvR0dEv3M8RxpfTaDQQBAGCIOCvv/7S+vlTVFSEQ4cOlbl8hL7gSE4F4+Ligh07dqB58+Za7T/99BPGjRuHBw8eSFOYHlIqlZgwYQIWLFgg3n3x559/YuTIkTh58iQePXokcYX6w9zcHBcvXkS9evWkLkWv3bp1C9nZ2WjatClycnLw1Vdf4fTp06hfvz6WL18OR0dHqUvUKwYGpRft//uaOfRiL5vjpFAoMHv2bHzzzTf/YFW6w5GcCqZLly5o27YtZs+ejWnTpiEnJwd+fn7YtWsX5s+fL3V5euXYsWMYNmwYIiIisGPHDiQnJ8PX1xcNGzZEQkKC1OXplTZt2uDGjRsMOW/o2fVGzM3NucrxG/r7LyoFBQWIj4/HjBkz+P/LV3Ts2DEIgoBu3brhp59+0lop2sTEBI6OjrC3t5ewwjfDkZwK6ODBgxg9ejTq1auH+/fvo3Llyti2bRuaNGkidWl6p2Tl0z179qC4uBhz587F1KlT+XVLOe3btw/Tp0/HlClT4OrqWmpdkqZNm0pUmf7JzMzEnj17cPPmTUyZMgXW1ta4cOECbG1tUbNmTanLk4UTJ04gICAAcXFxUpeiN+7cuQMHB4cyR8f0GUNOBVRcXIzx48dj3bp1MDIywv79++Hp6Sl1WXrpwoULGDJkCAoLC3Hv3j0MGjQI3377LZ8DVk7P+1qAt+qWz6VLl+Dh4QELCwvcvn0bSUlJqFOnDqZPn46UlBR89913UpcoC9euXYObmxsncr+G3NzcMheq1NdfZPh1VQVz8+ZNDBkyBGq1GkeOHMGJEyfQp08ffPnll5g/fz5X9iyHhQsXYubMmRg7diyWLFmCGzduYOjQoWjatCm2bdvGZd/LoeTZS/RmAgICMGLECCxevBhVqlQR23v37o0hQ4ZIWJl+unTpktZrQRBw//59LFy4sNS8RnqxBw8eYOTIkTh8+HCZ+/X2FxmBKpTKlSsLAwcOFB49eiS2nTp1Sqhbt67QvHlz6QrTQ3Z2dsKhQ4e02vLz84XJkycLJiYmElVF7zKVSiXcuHFDEISn/9Zv3rwpCIIg3L59W1AqlVKWppcUCoVgYGAgKBQKrc3d3V24evWq1OXplSFDhgjt27cXzp07J5ibmwtHjx4Vvv/+e6Fhw4bCgQMHpC7vtXEkp4L5z3/+g6FDh2q1tWvXDvHx8Zg4caI0RempxMREVKtWTavN2NgYS5YswQcffCBRVfrr+++/R0hICJKTkxETEwNHR0esXLkSTk5O6Nu3r9Tl6QWlUlnmYwl+//13VK9eXYKK9NvfRxgNDAxQvXr15y7DQc8XFRWFn3/+GW5ubjAwMICjoyP+9a9/QaVSITg4GF5eXlKX+FrkNcNIBp4NOHfv3sXdu3cBAFWqVMGmTZukKksvVatWDUVFRfjpp58wb948zJs3D3v37kVRURE6d+4sdXl6Zd26dQgICEDv3r2RmZkpDl1bWlpi5cqV0hanR/r06YM5c+agoKAAwNN5TSkpKZg2bRr69+8vcXX6x9HRUWtzcHDAkydPpC5LL+Xk5Ijr4VhZWYnLlbi6uur1I3AYciqY4uJizJkzBxYWFuI/XEtLS8ydO7fUUvr0Yjdu3ECjRo0wbNgw7N27F3v37sXQoUPRuHFj3Lx5U+ry9Mq3336LDRs24JtvvoGhoaHY7ubmhsTERAkr0y/Lli1DdnY2bGxs8PjxY3Tu3Bn16tVDlSpVeMvza1i0aBF27twpvh4wYACsra1Rs2ZNXLx4UcLK9E/Dhg2RlJQEAGjWrBn++9//4o8//kBISIheL0LLu6sqmMDAQGzatAmzZ89G+/btAQAnT57ErFmzMGbMGP6PsBx69+4NQRCwfft2ce2Hhw8f4tNPP4WBgQEOHjwocYX6w8zMDNeuXYOjo6P4YMk6derg+vXraNq0KR4/fix1iXrl1KlTWk9z9/DwkLokveTk5ITt27ejXbt2iIiIwIABA7Bz507s2rULKSkpOHr0qNQl6o1t27ahsLAQI0aMQFxcHHr27ImMjAyYmJggNDQUAwcOlLrE1yPxnCD6mxo1agg///xzqfawsDDB3t5egor0V6VKlYRLly6Vak9ISBDMzc0lqEh/NWrUSAgLCxMEQXvC7OrVq4UWLVpIWZreyM/PFwwNDYXExESpS5ENU1NTISUlRRAEQZgwYYIwduxYQRAEISkpSbC0tJSyNL2Xk5MjxMXFCQ8ePJC6lDfCiccVTEZGBpydnUu1Ozs7IyMjQ4KK9JdSqcRff/1Vqj07OxsmJiYSVKS/AgIC4OfnhydPnkAQBJw9exY//PADgoODsXHjRqnL0wvGxsaoVauW/t6KWwFZWVkhNTUVDg4OCA8Px7x58wA8vZWc1/nNVKpUCS1btpS6jDfGkFPBNGvWDGvWrMHq1au12tesWYNmzZpJVJV++uCDDzB27Fhs2rQJrVu3BgDExsZi3Lhx6NOnj8TV6ZfRo0fDzMwM06dPR25uLoYMGQJ7e3usWrUKgwYNkro8vfHNN9/g3//+N77//nut5fPp9fTr1w9DhgxB/fr18fDhQ/Tq1QsAEB8fz0eQlFNRURFCQ0MRGRmJ9PT0UnNAo6KiJKrszXBOTgVz4sQJeHl5oVatWuJidTExMUhNTcWhQ4fQsWNHiSvUH5mZmRg+fDj2798vLqJYWFiIPn36YMuWLbC0tJS2QD2Vm5srTp6l8mnRogVu3LiBgoICODo6llp5W5/vYpFCQUEBVq1ahdTUVIwYMQItWrQAAKxYsQJVqlTB6NGjJa5Qf/j7+yM0NBReXl6oUaNGqUffrFixQqLK3gxDTgWTkpICIyMjrF27FteuXQMANGrUCF988QUKCwtRq1YtiSvUPzdu3MDVq1cBPL2W/A2v/JKTk1FYWIj69etrtV+/fh3GxsaoXbu2NIXpmdmzZ79w/8yZM/+hSoi0VatWDd999x169+4tdSk6xZBTwRgaGuL+/fulfkt++PAhbGxs+D1zOcyZMweTJ09GpUqVtNofP36MJUuWICgoSKLK9E/nzp0xatQoDB8+XKt927Zt2LhxI44fPy5NYfTOu379Oo4dO1bmVyz8N/7q7O3tcfz4cTRo0EDqUnSKIaeCMTAwgFqtLhVy7ty5AxcXF+Tk5EhUmf5hYNQdlUqFCxculBoFu3HjBtzc3JCZmSlNYXoqPz+/zB/KHKktnw0bNuDzzz9HtWrVYGdnp/UVi0Kh4Nd/5bBs2TLcunULa9asKfVVlT7jxOMKIiAgAMDTf5hBQUFaow9FRUWIjY3lA+fKSfi/J2T/3cWLFznps5wUCkWZd6plZWUxLJbD77//Dl9fX5w+fVqrXeDT3F/LvHnzMH/+fEybNk3qUvRSv379tF5HRUXh8OHDaNy4camHQe/du/efLE1nGHIqiPj4eABP/2eXmJiodYuziYkJmjVrhsmTJ0tVnl6xsrKCQqGAQqFAgwYNtIJOUVERsrOzMW7cOAkr1D+dOnVCcHAwfvjhB3HF46KiIgQHB6NDhw4SV6c/Ro4cCSMjIxw4cKDMyZ1UPo8ePcInn3widRl6y8LCQuv1Rx99JFElbw+/rqpgRo4ciVWrVkGlUkldit7aunUrBEHAqFGjsHLlSq1/yCYmJqhdu7Z45xq9mt9++w2dOnWCpaWleIffr7/+Co1Gg6ioKDRp0kTiCvWDubk54uLiylwLi8rP19cX77//Pn9p+QedOnUKbm5uUCqVUpfyShhySLZOnDiB9u3bw8joxQOWCxcuxLhx43hL+Uvcu3cPa9aswcWLF2FmZoamTZvC39+fX/2Vw/vvv48VK1Zw9EtHgoODsXz5cnh5ecHV1bXUVywTJkyQqDL5UqlUSEhIQJ06daQu5ZUw5NA7T9/+0ZJ+0Wg04p/Pnz+P6dOnY8GCBWX+UOYIbvk4OTk9d59CocCtW7f+wWreDc8+u04fcE4OvfOY818uPDwclStXFkcg1q5diw0bNsDFxQVr166FlZWVxBVWXJaWllpzbwRBQPfu3bX6cOLx60lOTpa6BKrgGHKI6KWmTJmCRYsWAQASExMREBCAr776CseOHUNAQAC2bNkicYUV17Fjx8Q/3759Gw4ODuLk7RLFxcVISUn5p0vTSwEBAZg7dy7Mzc3Fu1LLolAosGzZsn+wMqqIGHKI6KWSk5Ph4uICAPjpp5/w4YcfYsGCBbhw4YLsVkjVtc6dO4t/7tat23PXbvLw8Ci12CKVFh8fj4KCAvHPz8M71whgyCGiV2BiYoLc3FwAwP/+9z8MGzYMAGBtba0154Re7HlrN2VnZ8PU1FSCivTPsyNjz/6Z/hn6Fh4ZcojopTp06ICAgAC0b98eZ8+exc6dOwE8Xdzuvffek7i6iu/ZxT5nzJjBxT5Jb+nbHEaGHHrndezYEWZmZlKXUaGtWbMGX3zxBfbs2YN169ahZs2aAIDDhw+jZ8+eEldX8XGxT6roZs6ciVGjRsHR0fGF/cpa+bwi4y3kJGs3b97Eli1bcPPmTaxatQo2NjY4fPgwatWqhcaNG0tdHr1juNgnVVTNmzfH5cuX0blzZ/j6+qJ///56s+DfizDkkGydOHECvXr1Qvv27REdHY2rV6+iTp06WLhwIc6fP489e/ZIXaJeKSoqwr59+3D16lUAQKNGjeDt7f3SxRaJSD/Ex8djy5Yt+OGHH1BYWIhBgwZh1KhReP/996Uu7bUx5JBsubu745NPPkFAQIDWAlZnz55Fv379cPfuXalL1BtXrlzBhx9+iLS0NDRs2BDA0/k41atXx/79+/lYByIZKSgowP79+7FlyxYcOXIEzs7O8PX1xYgRI0o976qiM5C6AKK3JTExscwHztnY2ODPP/+UoCL9NXr0aDRp0gR3797FhQsXcOHCBaSmpqJp06YYO3as1OURkQ4JgoCCggLk5+dDEARYWVlhzZo1cHBwEG860BcMOSRblpaWuH//fqn2+Ph4ceIsvZqEhAQEBwdrrWxsZWWF+fPnv3CtEiLSH3FxcfD390eNGjUwadIktGjRAlevXsWJEydw/fp1zJ8/X++eB8aQQ7I1aNAgTJs2DWq1GgqFAsXFxTh16hQmT54srvNCr6ZBgwZIS0sr1Z6eno569epJUBER6ZKrqyvatm2L5ORkbNq0CampqVi4cKHWv+/BgwfjwYMHElZZfpyTQ7KVn58PPz8/hIaGoqioCEZGRigqKsKQIUMQGhpaaml90vbsIn8nT57E1KlTMWvWLLRt2xYAcObMGcyZMwcLFy7kqsdEem7u3LkYNWqU7Ea5GXJIlgRBQGpqKqpXr44///wTiYmJyM7ORosWLVC/fn2py9MLBgYGpR4sCfz/FU+ffc0HSxLptzlz5mDy5MlaC1UCwOPHj7FkyRIEBQVJVNmbYcghWSouLoapqSmuXLnCUPOaTpw48cp9n30+ExHpH0NDw+c+V83GxkZvf5HhAhckSwYGBqhfvz4ePnzIkPOa/h5cMjMzsWnTJnGdHBcXF/j6+urdLaVEVNrznqt28eJFWFtbS1CRbnAkh2Rr//79WLx4MdatW8d1XN7Q+fPn0bNnT5iamqJ169YAgHPnzuHx48c4evQoWrZsKXGFRPQ6rKysoFAokJWVBZVKpRV0ioqKkJ2djXHjxmHt2rUSVvn6GHJItqysrJCbm4vCwkKYmJiUej5VRkaGRJXpn44dO6JevXrYsGGDuMJxYWEhRo8ejVu3biE6OlriConodWzduhWCIGDUqFFYuXKl1sisiYkJateuDXd3dwkrfDMMOSRbW7dufeH+4cOH/0OV6D8zMzPEx8fD2dlZq/23336Dm5sbcnNzJaqMiHThxIkTaNeuHYyNjaUuRac4J4dkiyFGd1QqFVJSUkqFnNTUVFSpUkWiqojoTWg0GvFhsS1atMDjx4/x+PHjMvvq60NlGXJI1vgUct0YOHAgfH19sXTpUrRr1w4AcOrUKUyZMgWDBw+WuDoieh1WVlbiHVWWlpZlTjwumZDMu6uIKpi/P4V8/vz5sLGxwcWLF7Fp0yY+hbwcli5dCoVCgWHDhqGwsBAAYGxsjM8//xwLFy6UuDoieh1RUVHinVPHjh2TuJq3g3NySLb4FHLdy83Nxc2bNwEAdevWLbVwGBHpp5SUFDg4OJQazSlZWLVWrVoSVfZmGHJItipXrozExEQ4OTlphZzbt2/D2dkZT548kbpEIqIKQa6LAfIBnSRbfAo5EdGred5igNnZ2TA1NZWgIt3gnBySrZKnkO/evZtPISciKkNAQACAp8+gmzFjhtZX0EVFRYiNjUXz5s0lqu7NMeSQbC1YsAB+fn5wcHBAUVERXFxcxKeQT58+XeryiIgkFx8fD+DpSE5iYiJMTEzEfSYmJmjWrBkmT54sVXlvjHNySPZSUlJw+fJlPoWciOg5Ro4ciVWrVuntejjPw5BDsnXy5El06NBB6jKIiEgiDDkkWyYmJqhZsyYGDx6MTz/9FC4uLlKXRERUIeXk5GDhwoWIjIxEeno6iouLtfbfunVLosreDOfkkGzdu3cPP/74I3744QcsXLgQTZs2hY+PDwYPHoz33ntP6vKIiCqM0aNH48SJExg6dChq1KhR5p1W+ogjOfROSE5Oxo4dO/DDDz/g2rVr6NSpE6KioqQui4ioQrC0tMTBgwfRvn17qUvRKYYcemcUFRXh8OHDmDFjBi5duqS3i1sREemak5MTDh06hEaNGkldik5xMUCSvVOnTuGLL75AjRo1MGTIEDRp0gQHDx6Uuiwiogpj7ty5CAoKQm5urtSl6BRHcki2AgMD8eOPP+LevXv417/+BR8fH/Tt25fPWyIi+psWLVrg5s2bEAQBtWvXhrGxsdb+CxcuSFTZm+HEY5Kt6OhoTJkyBQMGDEC1atWkLoeIqMLy9vaWuoS3giM5REREJEuck0Oy9v3336N9+/awt7fHnTt3AAArV67Ezz//LHFlREQVS2ZmJjZu3IjAwEBkZGQAePo11R9//CFxZa+PIYdka926dQgICEDv3r2RmZkp3k1laWmJlStXSlscEVEFcunSJTRo0ACLFi3C0qVLkZmZCQDYu3cvAgMDpS3uDTDkkGx9++232LBhA7755hsYGhqK7W5ubkhMTJSwMiKiiiUgIAAjRozA9evXYWpqKrb37t0b0dHRElb2ZhhySLaSk5PRokWLUu1KpRI5OTkSVEREVDGdO3cOn332Wan2mjVrQq1WS1CRbjDkkGw5OTkhISGhVHt4eLjsFrwiInoTSqUSGo2mVPvvv/+O6tWrS1CRbvAWcpKtgIAA+Pn54cmTJxAEAWfPnsUPP/yA4OBgbNy4UeryiIgqjD59+mDOnDnYtWsXAEChUCAlJQXTpk1D//79Ja7u9fEWcpK17du3Y9asWbh58yaAp0Ovs2bNgq+vr8SVERFVHFlZWfj4449x/vx5/PXXX7C3t4darYa7uzsOHToEc3NzqUt8LQw5JFuPHz+GIAioVKkScnNzcfnyZZw6dQouLi7w9PSUujwiogrn5MmTuHTpErKzs9GyZUt4eHhIXdIbYcgh2erRowf69euHcePGITMzE87OzjA2Nsaff/6J5cuX4/PPP5e6RCIieos4J4dk68KFC1ixYgUAYM+ePbC1tUV8fDx++uknBAUFMeQQ0Ttt9erVr9x3woQJb7GSt4cjOSRblSpVwrVr11CrVi0MGDAAjRs3xsyZM5GamoqGDRvK7mm7RETl4eTkpPX6wYMHyM3NhaWlJYCnKyBXqlQJNjY2uHXrlgQVvjneQk6yVa9ePYSFhSE1NRVHjhxBjx49AADp6elQqVQSV0dEJK3k5GRxmz9/Ppo3b46rV68iIyMDGRkZuHr1Klq2bIm5c+dKXepr40gOydaePXswZMgQFBUVoXv37jh69CgAIDg4GNHR0Th8+LDEFRIRVQx169bFnj17Si2gGhcXh48//hjJyckSVfZmOCeHZOvjjz9Ghw4dcP/+fTRr1kxs7969Oz766CMJKyMiqlju37+PwsLCUu1FRUVIS0uToCLd4EgOERHRO+7DDz/EH3/8gY0bN6Jly5YAno7ijB07FjVr1sQvv/wicYWvh3NyiIiI3nGbN2+GnZ0d3NzcoFQqoVQq8f7778PW1hYbNmyQurzXxpEcIiIiAgBcv34dV69eBQA4OzujQYMGElf0ZhhyiIiI3kEBAQGYO3cuzM3NERAQ8MK+y5cv/4eq0i1OPCYiInoHxcfHo6CgQPzz8ygUin+qJJ3jSA4RERHJEiceExERkSwx5BAREZEsMeQQERGRLDHkEBERkSwx5BAREZEsMeQQERGRLDHkEBERkSwx5BAREZEs/T+CsdmHIgSyXQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_labels = df[['toxic', 'severe_toxic',\n",
    "                      'obscene', 'threat', 'insult', 'identity_hate']]\n",
    "label_count = train_labels.sum()\n",
    "label_count.plot(kind='bar', title='Labels Frequency', color='steelblue')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Ma trận tương quan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ln6LFbT4mHIerq6sqBFRS5I5t5ra0tDRF7timSlWC0h1TqWqQIv/YZtG26/etqlQ1/f6SdDH6gq5eviRvnyL2CRxOwdXVVf4BFbV3l+Xn2p5dvyvAypaxwMPGpqR/7969atu27R3tRYsWVUxMTDojYC95Pd0UFFBKQQG3dkbxK1VYQQGl5FvcK4cjg6N4sm17bVzzgzat+0lnTx3X59PGKykpUU3/Nw1n1scjLHbpuZmSopNRh3Uy6rBu3kxRXOxFnYw6rPN/nTb3eaJNe0Ud/FM/LPpc5/86rd82rNGG1csV8gzbKD4s2r7yutasWKZ1q3/UqRPHNO3j95WUmKiW/9vN5+MxQ/T5rMnm/s+91EE7tv2mpd98qdMnj+vrz2bqyMF9av3CK5KkxOvXNXf6RB34c48unDurXX9s0+jBb6lkKV/VqtfQfJzo8+cUdeSgoi+cU1pqqqKOHFTUkYNKvH79vl4/clbrF1/Tzyu/14b/W6EzJ49r9qQIJd1IVItWt3bzmTJuuMUuPSkpKTp+9JCOHz2kmzdTFBsTreNHD+nc2dufa4mJ1819JCn6/F86fvSQLl44d38vDhlmMply5OUobJrTX6hQIZ07d05ly1r+On/Xrl0qVYpt+rJTrcpltHbuW+avPxz4giTpqx+36s0RX+dUWHAgDZq21JXL8Vr69WxdjotVGf8AvTtmsnl6T0z0BZn+sRtBfNxFDenzmvnrVUu/1qqlX6titVoa+uEsSbe29ew/7EMt/mKGli/8TEWKl9Rr3cPUqMUT9/fikGOaPvaELl+K19dzZyguLkb+5QM1ZsIM8+Le6AvnZXK5fV9VrlZDg0ZE6Ms50/TF7Kkq9UhpDYuYJL9yFSRJLrlcdDzqsH5e/aOuJVyVt09R1aobrNBuvS1+w/zVZzMsHuDVp3M7SdL4KXMVVOv2LlVwbo2aP67Ll+O16ItZuhQfq7L+ARo6bqp5D/6Y6PMWyVl87EUN7H77YVw/fvuVfvz2K1WpXlujJ86WJEUd2q8Rb3c39/li5kRJUrPHn1HfQZbrEgFHYDIMw8jsoIEDB2rbtm367rvvFBAQoJ07d+rChQsKDQ1VaGhohub1/5tnzT6ZHgPYYtPSD3I6BDwkCud3z+kQ8JC4nnQzp0PAQ6LqI/lyOoS7yvvi5zly3mtL7nwWxIPIpuk9Y8eOVcWKFeXr66uEhARVrlxZjz76qBo2bKihQ4faO0YAAAAAWZDp6T2GYej8+fOaMmWKhg8frr179yohIUE1a9ZUhQoVsiNGAAAAwDrHmV6fI2xK+suXL699+/apQoUK8vX1zY64AAAAANhJpqf3uLi4qEKFCoqNjb13ZwAAAAA5zqY5/ePGjdM777yjP//8097xAAAAAJnGlp3W2bRlZ2hoqK5fv67q1avLzc1Nnp6eFu/HxcXdZSQAAACA+82mpH/SpEl2DgMAAACwnSNV3XOCTUl/x44d7R0HAAAAgGxi05x+SYqKitLQoUP16quvKjo6WpK0evVq7du3z27BAQAAAMg6m5L+X375RdWqVdO2bdu0bNkyJSQkSJJ2795t09N4AQAAgKxgIa91NiX9gwcP1vvvv69169bJzc3N3N6iRQtt3brVbsEBAAAAyDqb5vTv3btXCxcuvKO9aNGiiomJyXJQAAAAQGY4UtU9J9hU6S9UqJDOnTt3R/uuXbtUqlSpLAcFAAAAwH5sSvpfeeUVDRo0SOfPn5fJZFJaWpo2b96sgQMHKjQ01N4xAgAAANaZcujlIGxK+seOHauKFSvK19dXCQkJqly5sh599FE1bNhQQ4cOtXeMAAAAALLApjn9bm5umjNnjoYNG6Y///xTCQkJqlmzpipUqGDv+AAAAABkkU1J/3//+181btxYpUuXVunSpe0dEwAAAJApLOS1zqbpPS1atFDZsmX13nvvaf/+/faOCQAAAIAd2ZT0//XXX3r77bf1yy+/qGrVqqpRo4Y++ugjnTlzxt7xAQAAAPfEw7mssynp9/HxUZ8+fbR582ZFRUXppZde0pdffik/Pz+1aNHC3jECAAAAyAKbkv5/Klu2rAYPHqxx48apWrVq+uWXX+wRFwAAAAA7yVLSv3nzZvXq1UslSpRQ+/btVbVqVa1cudJesQEAAAAZwvQe62zavSc8PFyLFi3SX3/9pZYtW2ry5Ml67rnnlCdPHnvHBwAAACCLbEr6N23apHfeeUcvv/yyfHx87B0TAAAAkCmOVHXPCTYl/Zs3b7Z3HAAAAACyic1z+r/66is1atRIJUuW1MmTJyVJkyZN0g8//GC34AAAAIAMMeXQy0HYlPTPnDlTYWFheuqpp3Tp0iWlpqZKkgoVKqRJkybZMz4AAADAqUyfPl1+fn7y8PBQ/fr1tX37dqv9J02apMDAQHl6esrX11cDBgzQjRs3MnVOm5L+qVOnas6cORoyZIhy5cplbq9Tp4727t1ryyEBAAAAp7d48WKFhYVpxIgR2rlzp6pXr65WrVopOjo63f4LFy7U4MGDNWLECB04cECfffaZFi9erPfeey9T57Up6T9+/Lhq1qx5R7u7u7uuXbtmyyEBAAAAmznKlp0TJ05Ut27d1LlzZ1WuXFmzZs1Snjx5NG/evHT7//bbb2rUqJHat28vPz8/Pf7443r11Vfv+duBf7Mp6S9btqwiIyPvaF+zZo0qVapkyyEBAAAAh5OUlKQrV65YvJKSktLtm5ycrB07digkJMTc5uLiopCQEG3ZsiXdMQ0bNtSOHTvMSf6xY8e0atUqPfXUU5mK06bde8LCwtS7d2/duHFDhmFo+/bt+uabbxQREaG5c+fackgAAADAZjm1ZWdERIRGjRpl0TZixAiNHDnyjr4xMTFKTU1VsWLFLNqLFSumgwcPpnv89u3bKyYmRo0bN5ZhGLp586Z69OiR6ek9NiX9Xbt2laenp4YOHarr16+rffv2KlWqlCZPnqxXXnnFlkMCAAAADic8PFxhYWEWbe7u7nY7/saNGzV27FjNmDFD9evX19GjR/XWW29pzJgxGjZsWIaPY1PSn5iYqLZt26pDhw66fv26/vzzT23evFmPPPKILYcDAAAAHJK7u3uGk3wfHx/lypVLFy5csGi/cOGCihcvnu6YYcOG6fXXX1fXrl0lSdWqVdO1a9f05ptvasiQIXJxydhsfZvm9D/33HOaP3++pFtzk5599llNnDhRbdq00cyZM205JAAAAGAzR1jI6+bmptq1a2v9+vXmtrS0NK1fv17BwcHpjrl+/fodif3fu2cahpHhc9uU9O/cuVNNmjSRJC1ZskTFihXTyZMnNX/+fE2ZMsWWQwIAAABOLywsTHPmzNGXX36pAwcOqGfPnrp27Zo6d+4sSQoNDVV4eLi5f+vWrTVz5kwtWrRIx48f17p16zRs2DC1bt3aYuv8e7Fpes/169eVP39+SdLatWv1/PPPy8XFRQ0aNDA/nRcAAAC4bxzk6bjt2rXTxYsXNXz4cJ0/f141atTQmjVrzIt7T506ZVHZHzp0qEwmk4YOHaqzZ8+qSJEiat26tT744INMnddkZOb3Av8TFBSkrl27qm3btqpatarWrFmj4OBg7dixQ08//bTOnz+f2UPKs2afTI8BbLFpaeb+kgC2Kpzffgu5AGuuJ93M6RDwkKj6SL6cDuGuinb5NkfOG/3Zyzly3syyaXrP8OHDNXDgQPn5+al+/frmOUhr165N96FdAAAAQHZyhDn9Ocmm6T0vvviiGjdurHPnzql69erm9scee0xt27a1W3AAAAAAss6mpF+SihcvfsfWQvXq1ctyQAAAAADsy+akHwAAAHhQONJUm5xg05x+AAAAAI6DSj8AAAAcHpV+66j0AwAAAE6OpB8AAABwckzvAQAAgMNjeo91VPoBAAAAJ0elHwAAAI6PQr9VVPoBAAAAJ0elHwAAAA6POf3WUekHAAAAnBxJPwAAAODkmN4DAAAAh8f0Huuo9AMAAABOjko/AAAAHB6Vfuuo9AMAAABOjqQfAAAAcHJM7wEAAIDjY3aPVVT6AQAAACdHpR8AAAAOj4W81lHpBwAAAJwcST8AAADg5JjeAwAAAIfH9B7rqPQDAAAATo5KPwAAABwelX7rqPQDAAAATo5KPwAAABwelX7rqPQDAAAATo6kHwAAAHByTO8BAACA42N2j1VU+gEAAAAn98BU+jct/SCnQ8BD4tEXhuR0CHhIxP8+LadDwEMizTByOgQgx7GQ1zoq/QAAAICTI+kHAAAAnNwDM70HAAAAsBXTe6yj0g8AAAA4OSr9AAAAcHgU+q2j0g8AAAA4OSr9AAAAcHjM6beOSj8AAADg5Ej6AQAAACfH9B4AAAA4PGb3WEelHwAAAHByVPoBAADg8FjIax2VfgAAAMDJkfQDAAAATo7pPQAAAHB4zO6xjko/AAAA4OSo9AMAAMDhubhQ6reGSj8AAADg5Kj0AwAAwOExp986Kv0AAACAkyPpBwAAAJwc03sAAADg8Hgir3VU+gEAAAAnR6UfAAAADo9Cv3VU+gEAAAAnR9IPAAAAODmm9wAAAMDhsZDXOir9AAAAgJOj0g8AAACHR6XfOir9AAAAgJOj0g8AAACHR6HfOir9AAAAgJMj6QcAAACcHNN7AAAA4PBYyGsdlX4AAADAyVHpBwAAgMOj0G8dlX4AAADAyZH0AwAAAE6O6T0AAABweCzktY5KPwAAAODkqPQDAADA4VHot45KPwAAAODkqPQDAADA4TGn3zoq/QAAAICTI+kHAAAAnBzTewAAAODwmN1jHZV+AAAAwMlR6QcAAIDDYyGvdVT6AQAAACeXpaQ/OTlZhw4d0s2bN+0VDwAAAAA7synpv379urp06aI8efKoSpUqOnXqlCSpb9++GjdunF0DBAAAAO7FZMqZl6OwKekPDw/X7t27tXHjRnl4eJjbQ0JCtHjxYrsFBwAAACDrbFrIu3z5ci1evFgNGjSwWDRRpUoVRUVF2S04AAAAICNYyGudTZX+ixcvqmjRone0X7t2jW84AAAA8ICxKemvU6eOVq5caf7670R/7ty5Cg4Otk9kAAAAQAYxp986m6b3jB07Vk8++aT279+vmzdvavLkydq/f79+++03/fLLL/aOEQAAAEAW2FTpb9y4sSIjI3Xz5k1Vq1ZNa9euVdGiRbVlyxbVrl3b3jECAAAAyAKbn8jr7++vOXPm2DMWAAAAwCasK7XO5qQ/LS1NR48eVXR0tNLS0izee/TRR7McGAAAAAD7sCnp37p1q9q3b6+TJ0/KMAyL90wmk1JTU+0SHAAAAJARFPqtsynp79Gjh3kHnxIlSvDrFAAAAOABZlPSf+TIES1ZskTly5e3dzwAAAAA7Mym3Xvq16+vo0eP2jsWAAAAwCYmkylHXo7Cpkp/37599fbbb+v8+fOqVq2aXF1dLd4PCgqyS3AAAAAAss6mpP+FF16QJL3xxhvmNpPJJMMwWMgLAACA+86Rqu45waak//jx4/aOAwAAAEA2sWlOf5kyZay+AAAAAKRv+vTp8vPzk4eHh+rXr6/t27db7X/p0iX17t1bJUqUkLu7uwICArRq1apMndOmpF+SvvrqKzVq1EglS5bUyZMnJUmTJk3SDz/8YOshAQAAAJuYTDnzyqzFixcrLCxMI0aM0M6dO1W9enW1atVK0dHR6fZPTk5Wy5YtdeLECS1ZskSHDh3SnDlzVKpUqUyd16akf+bMmQoLC9NTTz2lS5cumefwFypUSJMmTbLlkAAAAIDTmzhxorp166bOnTurcuXKmjVrlvLkyaN58+al23/evHmKi4vT8uXL1ahRI/n5+alp06aqXr16ps5rU9I/depUzZkzR0OGDFGuXLnM7XXq1NHevXttOSQAAABgM0fYsjM5OVk7duxQSEiIuc3FxUUhISHasmVLumN+/PFHBQcHq3fv3ipWrJiqVq2qsWPHZnrjHJsX8tasWfOOdnd3d127ds2WQwIAAAAOJykpSUlJSRZt7u7ucnd3v6NvTEyMUlNTVaxYMYv2YsWK6eDBg+ke/9ixY/rPf/6jDh06aNWqVTp69Kh69eqllJQUjRgxIsNx2lTpL1u2rCIjI+9oX7NmjSpVqmTLISFp3Yrv1L/jc+r8bGON6N9ZUYf23bXvmZNRmvz+IPXv+Jxee7Ke1nz/Tbr94mKiNePD4erxcog6P9dEg3u+qmOH92fXJcCJNKrlryWTuuvY2g+UuGuaWjfj+Ru4N8MwNH3qZD3WtLHq1QrSm1066eTJE/cct2jhAj3ZsoXq1qymDq+8pL179li8f/rUKfXv11vNGjdQw3q19E7YW4qNibHo0693D7V6rJnq1qymx5o21nuD31F09AV7Xh4eYIZhaMa0KWrZrIka1K6u7l07Z+jeW/zNAj31eAvVrxWk1199WX/utbz3YmIuaujgdxXStLGC69bUqy89r5/X/V82XQWyIqfm9EdERKhgwYIWr4iICLtdV1pamooWLarZs2erdu3aateunYYMGaJZs2Zl6jg2Jf1hYWHq3bu3Fi9eLMMwtH37dn3wwQcKDw/Xu+++a8shH3pbf1mnBbMnqW2Hrnp/6nyVLltB44f20+VLcen2T7qRpCLFS6ld594q6FU43T7Xrl7R6Le7KVfu3HpnzGSN/3SROnR9S3nzFcjOS4GTyOvprr2Hz6p/xOKcDgUO5PPP5uibBV9p6IiR+vqbb+Xp6ameb3a5owr2T2tWr9LHH0aoe6/eWvTd9woMrKie3bsoNjZWknT9+nX1ePMNmUwmzZn3pb78+hulpKSob+8eSktLMx+nbr0G+mjiJP2wco0mTJqiM6dPa+CAt7L9mvFg+GLeXH2z4Cu9N3yk5i+8de/17t7V6r33f6tXacKH49S9Z28t/G6ZAgID1at7V8X9796TpGHhg3TixHFNmjZD3y37US1CWmrQ2wN08AAFNNwSHh6uy5cvW7zCw8PT7evj46NcuXLpwgXLgsSFCxdUvHjxdMeUKFFCAQEBFlPqK1WqpPPnzys5OTnDcdqU9Hft2lXjx4/X0KFDdf36dbVv314zZ87U5MmT9corr9hyyIfe6u8XqvmTbdT08dYqVaacOvcdLHd3D/2ydkW6/f0DK6t9134Kbva4XF3d0u2z4rv58i5SVN3Dhss/sIqKFi+larUbqFjJR7LzUuAk1m7er1EzftKPG/bcuzOgW5XWBV/NV7fuPdW8RYgCAivq/YgPdTE6Wv9Z//Ndx3315ed6/sWX1abtC/IvX15DR4ySh4eHli9bKkmK3LVTf509qzEfjFOFgEBVCAjUmLHjtX/fn9q+bav5OK937KSg6jVUsmQp1ahZS2906aY9uyOVkpKS7deOnGUYhhZ+NV/d3uyh5i0eU0DgrXvkYnS0Nli5976e/4Wef/ElPdf2Bfn7l9eQ4f+7975fau6zOzJSr7R/TVWrBekRX191695T+fPn1/59d/9tPB4u7u7uKlCggMUrvak9kuTm5qbatWtr/fr15ra0tDStX79ewcHB6Y5p1KiRjh49alHkOHz4sEqUKCE3t/RzwPTYvGVnhw4ddOTIESUkJOj8+fM6c+aMunTpYuvhHmo3U1J0/MhBValR19zm4uKiKjXq6ugB2xdG79z6q8pVqKQpHwxWr1daaUjv17Rh9XI7RAwAdzp75oxiYi6qfoOG5rb8+fOrWlB17dm9K90xKcnJOrB/nxoE3x7j4uKiBg0amsckJyfLZDJZ/HBzd3eXi4uLdu3cke5xL1+6pJUrV6h6jZpydXW1x+XhAWa+94It772qQUHaszsy3TEpKbfuvX/ery4uLqrfINhiTPUaNbR2zSpdvnxJaWlpWrNqpZKSk1WnXr3suhzYyBEW8kq3ZszMmTNHX375pQ4cOKCePXvq2rVr6ty5syQpNDTU4jcFPXv2VFxcnN566y0dPnxYK1eu1NixY9W7d+9Mndfmhbw3b95UhQoVlCdPHuXJk0eSdOTIEbm6usrPz8/q+PQWPCQnJcntLv8qcnZXr1xSWlqqCnp5W7QX9PLWuTMnbT7uxfNntX7lMj3xfHs9266zjh3er/mzJihX7tx6tOUzWQ0bACzExFyUJBX2sZxyWLhwYcX8a/793+IvxSs1NVWFC9855vjxY5KkoOo15OnpqUkTPlLf/mEyDEOTP5mg1NRUXbx40WLcJxM+0qJvFuhGYqKCqtfQ1BmZm/MKx/T3ved9x33kc8faj7/Fx9+699Ibc+L4cfPXH06YpEEDB6hZowbKnTu3PDw8NHHSVJUuzcNIYZt27drp4sWLGj58uM6fP68aNWpozZo15sW9p06dkovL7bq8r6+v/u///k8DBgxQUFCQSpUqpbfeekuDBg3K1HltqvR36tRJv/322x3t27ZtU6dOne45Pr0FD1/MmmhLKLAizUiTX/lAtevUS37lA9XiqbZq/sRz+s+qZTkdGgAnsPKnH9WgTk3z6+bNm9lyHm9vb300cbJ++WWDguvWVOMGdXT16hVVqlxFLi6WVbZOb3TR4iXfa9aceXJxcdHQ8EEyDCNb4kLOWfXTCjWsW8v8yq57T5KmT5usq1evatbcz/X1oiV6LbST3h04QEcOH8q2c8I2jvJwLknq06ePTp48qaSkJG3btk3169c3v7dx40Z98cUXFv2Dg4O1detW3bhxQ1FRUXrvvfcs5vhnhE2V/l27dqlRo0Z3tDdo0EB9+vS55/jw8HCFhYVZtO09e8OWUJxC/gKF5OKSS5fjLRftXo6Pu+si3Ywo5O2jkqXLWrSV9PXT75s32HxMAPhbs+YtVK3a7YfDJKfcWlAWGxOrIkWKmttjY2MVWLFiusfwKuSlXLlymRft/nOMj4+P+euGjRpr5ZqfFR8fp1y5cqtAgQJq8WgjPfLkU5bH8/KWl5e3/PzKqlw5fz3+WFPt2R2p6jXu3GYajqtp8+aqGnR7R7GU/y1mjIv9970Xo8DA9HcV9PK6de/F3XHvxajw/+6906dOafHCBVqyfIX8y1eQJAVWrKidO3do8TcLNXTEKLteF5CdbKr0m0wmXb169Y72y5cvZ+hBAekteHhYp/ZIUm5XV5WtUFH7In83t6WlpWlf5B8qX6mazccNqBx0x/Sg82dPyado+qvDASAz8ubNp9Jlyphf/v7l5eNTRNu23X7ATEJCgvbu2a2g6ukn3a5ubqpUuYq2bb09Ji0tTdu2bUl3jJeXtwoUKKBtW7coLi5WzZq3uGt8fy96y8zuFnAMefPmU+nSZcyvcn/fe1st770/9+xRUPUa6R7D1fV/9942y3tv+7at5jE3biRKkkwmy3Qpl4uLDCNNgCOxKel/9NFHFRERYZHgp6amKiIiQo0bN7ZbcA+TJ9u218Y1P2jTup909tRxfT5tvJKSEtX0f3PvZ308Qos/n27ufzMlRSejDutk1GHdvJmiuNiLOhl1WOf/Om3u80Sb9oo6+Kd+WPS5zv91Wr9tWKMNq5cr5JmX7vv1wfHk9XRTUEApBQWUkiT5lSqsoIBS8i3ulcOR4UFlMpnU4fVQzfl0pjb+Z72OHD6koeHvqkjRomrx2O2nT3Z7o6O+WfC1+evXO3bWsiXf6sfl3+tYVJTeHz1SiYmJatP2eXOf5d8v1Z7dkTp96pR+WvGD3gnrr9dCO8mvbDlJ0p49u/XNgq918MAB/fXXWW3bukWD3wmTr29pqvwPAZPJpPavh2ru7FnauOE/OnL4kIa9N0hFihZV83/ce927dNKihbfvvddCO+n7Jd/pxx9u3Xtjx9y6955rc+ve8ytbTr6ly+j90SP05949On3qlOZ/MU9bt/ymZi1C7ogDOcvFZMqRl6OwaXrP+PHj9eijjyowMFBNmjSRJP3666+6cuWK/vOf/9g1wIdFg6YtdeVyvJZ+PVuX42JVxj9A746ZbJ7eExN9waLSEB93UUP6vGb+etXSr7Vq6deqWK2Whn54a+Gaf2Bl9R/2oRZ/MUPLF36mIsVL6rXuYWrU4on7e3FwSLUql9Haubf3OP9w4AuSpK9+3Ko3R3x9t2F4yHXu0k2JiYkaPXK4rl69opq1amvGp3Mttq87c/q0Ll2KN3/9xJNPKT4uTjOmTVFMzEUFVqykGZ/ONU+xkKQTx49ryicTdfnyZZUsVUpd3+yh1zt2Mr/v6eGh9T+v1czpU5WYeF0+RYqoUeMm+rB7r0xtaQfH1emNrkpMTNT7/7v3atSqremz5ljce6dPn9Kl+Nv3Xqsnn1J8fJxmTpuq2P/de9NnzTHfe66urpo681NN+WSC3urdU9cTr8vXt7RGfzBOTR5tet+vEcgKk2HjCqe//vpL06ZN0+7du+Xp6amgoCD16dNH3t7e9x6cjt+PXbZpHJBZj74wJKdDwEMi/vdpOR0CHhJpLFbGfZLH9cGtbD8+feu9O2WDtb0b5Mh5M8umSr8klSxZUmPHjrVnLAAAAACygU1z+tesWaP//ve/5q+nT5+uGjVqqH379or/x6/NAAAAgPvBUR7OlVNsSvrfeecdXblyRZK0d+9ehYWF6amnntLx48fv2IoTAAAAQM6y+Ym8lStXliQtXbpUrVu31tixY7Vz50499dRT9xgNAAAA4H6yKel3c3PT9evXJUk///yzQkNDJd16auLfvwEAAAAA7hcXx5lpkyNsSvobN26ssLAwNWrUSNu3b9fixYslSYcPH9Yjjzxi1wABAAAAZI1Nc/qnTZum3Llza8mSJZo5c6ZKlbr18J7Vq1friSfYAx4AAAD3Fwt5rbOp0l+6dGn99NNPd7R/8sknWQ4IAAAAgH3ZvE9/amqqvv/+ex04cECSVKlSJbVp00a5c9t8SAAAAADZwKYMfd++fWrdurUuXLigwMBASdL48eNVpEgRrVixQlWrVrVrkAAAAIA1DjTTJkfYNKe/a9euqlq1qs6cOaOdO3dq586dOn36tIKCgvTmm2/aO0YAAAAAWWBTpT8yMlJ//PGHvLy8zG1eXl764IMPVLduXbsFBwAAAGSESZT6rbGp0h8QEKALFy7c0R4dHa3y5ctnOSgAAAAA9pPhSv8/H7oVERGhfv36aeTIkWrQoIEkaevWrRo9erTGjx9v/ygBAAAAK3g4l3UZTvoLFSpksRepYRh6+eWXzW2GYUiSWrdurdTUVDuHCQAAAMBWGU76N2zYkJ1xAAAAAMgmGU76mzZtavH1pUuX9Nlnn5n36a9cubK6dOmiggUL2jdCAAAA4B4c6em4OcGmhbx//PGHypcvr08++URxcXGKi4vTJ598In9/f+3cudPeMQIAAADIApu27BwwYIBat26tOXPmmJ/Ae/PmTXXt2lX9+/fXpk2b7BokAAAAYA2FfutsSvr/+OMPi4RfknLnzq13331XderUsVtwAAAAALLOpuk9BQoU0KlTp+5oP336tPLnz5/loAAAAADYj02V/nbt2qlLly76+OOP1bBhQ0nS5s2b9c477+jVV1+1a4AAAADAvbgwv8cqm5L+jz/+WCaTSaGhobp586YkydXVVT179tS4cePsGiAAAACArLEp6Xdzc9PkyZMVERGhqKgoSZK/v7/y5Mlj1+AAAACAjKDQb51NSf/f8uTJo2rVqtkrFgAAAADZIEtJPwAAAPAg4OFc1tm0ew8AAAAAx0HSDwAAADg5pvcAAADA4TG7xzoq/QAAAICTo9IPAAAAh8fDuayj0g8AAAA4OZJ+AAAAwMkxvQcAAAAOj8k91lHpBwAAAJwclX4AAAA4PJ7Iax2VfgAAAMDJUekHAACAw3Oh0G8VlX4AAADAyZH0AwAAAE6O6T0AAABweCzktY5KPwAAAODkqPQDAADA4VHot45KPwAAAODkSPoBAAAAJ8f0HgAAADg8FvJaR6UfAAAAcHJU+gEAAODweCKvdVT6AQAAACdHpR8AAAAOjzn91lHpBwAAAJwcST8AAADg5JjeAwAAAIfH5B7rqPQDAAAATo5KPwAAAByeCwt5raLSDwAAADg5kn4AAADAyTG9BwAAAA6P2T3WUekHAAAAnByVfgAAADg8nshrHZV+AAAAwMmR9AMAAABOjuk9AAAAcHjM7rGOSj8AAADg5Kj0AwAAwOHxRF7rqPQDAAAATo5KPwAAABwehX7rqPQDAAAATo6kHwAAAHByTO8BAACAw+OJvNZR6QcAAACc3ANT6S+c3z2nQ8BDIv73aTkdAh4SXnX75HQIeEic/21KToeAh4Xrg1tNp5JtHd8fAAAAwMmR9AMAAABO7oGZ3gMAAADYioW81lHpBwAAAJwclX4AAAA4PBcK/VZR6QcAAACcHJV+AAAAODwq/dZR6QcAAACcHEk/AAAA4OSY3gMAAACHx5ad1lHpBwAAAJwclX4AAAA4PBbyWkelHwAAAHByJP0AAACAk2N6DwAAABwe63ito9IPAAAAODkq/QAAAHB4LpT6raLSDwAAADg5Kv0AAABweFSyreP7AwAAADg5kn4AAADAyTG9BwAAAA6PdbzWUekHAAAAnByVfgAAADg8tuy0jko/AAAA4ORI+gEAAID7aPr06fLz85OHh4fq16+v7du3Z2jcokWLZDKZ1KZNm0yfk6QfAAAADs9kyplXZi1evFhhYWEaMWKEdu7cqerVq6tVq1aKjo62Ou7EiRMaOHCgmjRpYtP3h6QfAAAAuE8mTpyobt26qXPnzqpcubJmzZqlPHnyaN68eXcdk5qaqg4dOmjUqFEqV66cTecl6QcAAIDDczHlzCspKUlXrlyxeCUlJaUbY3Jysnbs2KGQkJDbcbu4KCQkRFu2bLnrtY0ePVpFixZVly5dbP/+2DwSAAAAeMhFRESoYMGCFq+IiIh0+8bExCg1NVXFihWzaC9WrJjOnz+f7pj//ve/+uyzzzRnzpwsxcmWnQAAAHB4ObVl56DwcIWFhVm0ubu72+XYV69e1euvv645c+bIx8cnS8ci6QcAAABs5O7unuEk38fHR7ly5dKFCxcs2i9cuKDixYvf0T8qKkonTpxQ69atzW1paWmSpNy5c+vQoUPy9/fP0LmZ3gMAAADcB25ubqpdu7bWr19vbktLS9P69esVHBx8R/+KFStq7969ioyMNL+effZZNW/eXJGRkfL19c3wuan0AwAAwOE5ygN5w8LC1LFjR9WpU0f16tXTpEmTdO3aNXXu3FmSFBoaqlKlSikiIkIeHh6qWrWqxfhChQpJ0h3t90LSDwAAANwn7dq108WLFzV8+HCdP39eNWrU0Jo1a8yLe0+dOiUXF/tPxjEZhmHY/ag2OHbxRk6HgIdESS+PnA4BDwmvun1yOgQ8JM7/NiWnQ8BDoqDngzsz/IP1R3PkvEMeK58j582sB/dPDgAAAIBdkPQDAAAATo45/QAAAHB4JjnISt4cQqUfAAAAcHJU+gEAAODwXCj0W0WlHwAAAHByVPoBAADg8Kj0W0elHwAAAHByJP0AAACAk2N6DwAAAByeycT8Hmuo9AMAAABOjko/AAAAHB4Lea2j0g8AAAA4OZJ+AAAAwMkxvQcAAAAOj3W81lHpBwAAAJwclX4AAAA4PBdK/VZR6QcAAACcHJV+AAAAODy27LSOSj8AAADg5Ej6AQAAACfH9B4AAAA4PNbxWmdTpT9XrlyKjo6+oz02Nla5cuXKclAAAAAA7MemSr9hGOm2JyUlyc3NLUsBAQAAAJnlIkr91mQq6Z8yZYokyWQyae7cucqXL5/5vdTUVG3atEkVK1a0b4QAAAAAsiRTSf8nn3wi6Valf9asWRZTedzc3OTn56dZs2bZN0IAAAAAWZKppP/48eOSpObNm2vZsmXy8vLKlqAAAACAzGAhr3U2zenfsGGDveMAAAAAkE1s3rLzzJkz+vHHH3Xq1CklJydbvDdx4sQsBwYAAABkFE/ktc6mpH/9+vV69tlnVa5cOR08eFBVq1bViRMnZBiGatWqZe8YAQAAAGSBTfv0h4eHa+DAgdq7d688PDy0dOlSnT59Wk2bNtVLL71k7xgBAAAAZIFNSf+BAwcUGhoqScqdO7cSExOVL18+jR49WuPHj7drgAAAAMC9uJhMOfJyFDYl/Xnz5jXP4y9RooSioqLM78XExNgnMgAAAAB2YdOc/gYNGui///2vKlWqpKeeekpvv/229u7dq2XLlqlBgwb2jhEAAACwyoGK7jnCpqR/4sSJSkhIkCSNGjVKCQkJWrx4sSpUqMDOPQAAAMADxqakv1y5cub/z5s3L0/htcGKpYu05JsvFR8Xo3L+Aeo5YLACK1e7a/9f/7NW8+dO14Xzf6nUI6XVuWd/1QtuYn5/wgfD9PPqHy3G1K7XUO9PnGn++psv5+j3Lb/q2JFDyu3qqiVr/mv/C8MDyTAMzZg2RcuWfKerV6+oRs1aGjJ8pMqU8bM6btHCBfry888UE3NRAYEVNfi9YaoWFGR+//SpU5rw8XhF7tyh5ORkNWrcRIPfG6bCPj7mPv1699ChgwcVFxerAgUKqn5wsPqHDVTRosWy63LhYBrV8teA0BDVqlxaJYoU1MsDZmvFxj05HRYecIZhaPbMqVq+7DslXL2qoBo1Nei9ESp9j8+17xYt0NdfzlNsbIwqBFTUwEFDVKXa7c+1Hl1CtXPH7xZj2r7YTuFDR0qSDh86qPmfz1Hkrp26fCleJUqW0vMvttMrHULtfYnIJEeaX58TbJrTL0mXLl3S3LlzFR4erri4OEnSzp07dfbsWbsF56x+Wb9Gs6d9rA6du2vqZ4tUtnyghob11KX42HT7798bqXGjBqvVM201bd5iBTdprjHh/XXi2BGLfnXqN9KCH9abX4NGWi6qvnkzRU2at9TTbdhh6WHz+Wdz9M2CrzR0xEh9/c238vT0VM83uygpKemuY9asXqWPP4xQ9169tei77xUYWFE9u3dRbOyt+/T69evq8eYbMplMmjPvS3359TdKSUlR3949lJaWZj5O3XoN9NHESfph5RpNmDRFZ06f1sABb2X7NcNx5PV0197DZ9U/YnFOhwIHMv+LuVq88GsNHjJS875aLE/PPOrXq5vVz7V1/7dKkyaMV9fuvTX/m6WqEBCofr26KS7O8udvm+df0qqfN5lfffsPNL938MA+eXkV1ugPxmvR0hXq3LW7pk/9RN8uWpBt1wrYg01J/549exQQEKDx48fr448/1qVLlyRJy5YtU3h4uD3jc0rfL/pKT7Z+Xo8/3UZlyvqr7ztD5e7hobU/LU+3/w/fLVCd+g31YvtOKu1XTqHd+sg/oJJWLF1k0c/VzU3ehX3Mr/wFCli8/3qXXmrb7nX5+VfIrkvDA8gwDC34ar66de+p5i1CFBBYUe9HfKiL0dH6z/qf7zruqy8/1/Mvvqw2bV+Qf/nyGjpilDw8PLR82VJJUuSunfrr7FmN+WCcKgQEqkJAoMaMHa/9+/7U9m1bzcd5vWMnBVWvoZIlS6lGzVp6o0s37dkdqZSUlGy/djiGtZv3a9SMn/TjBqr7yBjDMLRowXy90a2HmjZ/TBUCAjVyzDjFXIzWLxvu/rm28Ksv1eb5l9S6zfMq519eg4eOlIeHh1YsX2bRz8PDQz4+RcyvfPnymd97ts0LenvQe6pVp55KPeKrJ59+Vq2fbasN69dl2/UC9mBT0h8WFqZOnTrpyJEj8vDwMLc/9dRT2rRpk92Cc0YpKSk6cviAatS5veDZxcVFNeo00IF96f/AO/DnHov+klS7fkMd+NOy/55df+iVZ5qp66vPaurH7+vK5Ut2jx+O5+yZM4qJuaj6DRqa2/Lnz69qQdW1Z/eudMekJCfrwP59ahB8e4yLi4saNGhoHpOcnCyTySQ3NzdzH3d3d7m4uGjXzh3pHvfypUtauXKFqteoKVdXV3tcHoCH0F9nzyg2Jkb16geb2/Llz68q1YK0d/fudMekpCTr4IF9qvuPMS4uLqpbP1h790Ra9F2z+ie1bBasV15orelTJupGYqLVeBISElSwYEHbLwh2YTLlzMtR2DSn//fff9enn356R3upUqV0/vz5e45PSkq649dvSUmG3N3dbQnHoVy5HK+01FR5eRe2aPfyLqwzJ4+nOyY+LkZeXv/q71VY8XG3t0etXb+hGjV9TMVKlNK5s6f1xeypGjawlybO+kq5cuWy/4XAYcTEXJQkFfaxvIcKFy581y124y/FKzU1VYUL3znm+PFjkqSg6jXk6empSRM+Ut/+YTIMQ5M/maDU1FRdvHjRYtwnEz7Som8W6EZiooKq19DUGawDAmC72P99dnn/6zPK29tHsbEX0xuiS/GXlJqaeueYwoV18sTtn7+tnnxGxUuWVJEiRXX08CFNmzxBJ08c14cTp6Z73D2Ru7Ru7Wp9MoXPNTzYbKr0u7u768qVK3e0Hz58WEWKFLnn+IiICBUsWNDiNWvyR7aEgv9pFvKkGjRuprL+FdTw0RYaNX6qDh/Ypz27/sjp0HCfrfzpRzWoU9P8unnzZracx9vbWx9NnKxfftmg4Lo11bhBHV29ekWVKleRi4tl6aPTG120eMn3mjVnnlxcXDQ0fJAMw8iWuAA4nzUrV6hpcG3z6+bN7Jse2PbFlxXcsLHKVwjQE0+31oj3x2njf37WmdOn7ugbdfSwBg7ora7de6lBw0bZFhMyxiWHXo7Cpkr/s88+q9GjR+vbb7+VJJlMJp06dUqDBg3SCy+8cM/x4eHhCgsLs2g7e+XhSAAKFPSSS65civ/XoqH4uFh5FfZJd4yXt4/i/7XINz4+Vl7e6feXpBKlHlGBQl46d+aUatapn/XA4TCaNW+hatWqm79OTrn1IL3YmFgVKVLU3B4bG6vAihXTPYZXIS/lypXLvGj3n2N8/rEzT8NGjbVyzc+Kj49Trly5VaBAAbV4tJEeefIpy+N5ecvLy1t+fmVVrpy/Hn+sqfbsjlT1GjWzfL0AnF+TZi0sdtj5+wGhcbGx8vnH51pcXIwCAiqle4xCXoWUK1cuxf3rcy0uNtZix7F/q/q/854+fUqP+JY2tx+LOqreb76hNs+/rC7demb+ooD7zKZ/oEyYMEEJCQkqWrSoEhMT1bRpU5UvX1758+fXBx98cM/x7u7uKlCggMXrYZjaI0murq6qEFBJkTu2mdvS0tIUuWObKlUJSndMpapBivxjm0Xbrt+3qlLV9PtL0sXoC7p6+ZK8fe79mxc4l7x586l0mTLml79/efn4FNG2bVvMfRISErR3z24FVU8/6XZ1c1OlylW0bevtMWlpadq2bUu6Y7y8vFWgQAFt27pFcXGxata8xV3j+3tnn79/aAPAveTNm1e+pcuYX+X8y6uwj49+335704CEhATt27tH1apXT/cYrq5uqlipisWYtLQ0/bF9q6oF1bjruQ8fPChJ8vnHz9Ooo0fUq1snPdX6OfXq2z9rFwfcJzZV+gsWLKh169Zp8+bN2r17txISElSrVi2FhITYOz6n1PaV1zXhg2GqULGKAitV1fJvv1ZSYqJaPt1GkvTxmCEqXKSoOve4ta3hcy910Lt9umjpN1+qXsNH9cvPa3Tk4D71e3eYJCnx+nUt+HyWGjUNkXfhwvrr7BnNm/GJSpbyVa16txdiRp8/p6tXLyv6wjmlpaYq6sitD7KSpUrLM0+e+/tNwH1jMpnU4fVQzfl0psqULqNSjzyi6VMnq0jRomrx2O2/s93e6KgWj7XUqx1ekyS93rGzhr03SFWqVFXVakH6+qsvlZiYqDZtnzePWf79UpUr5y8vL2/t3r1LH0aM1WuhneRX9tazPPbs2a19e/eqZq3aKlCwgE6fOqUZUyfL17c0VX6Y5fV0k7/v7YTKr1RhBQWUUvyV6zp9Pj4HI8ODymQy6ZUOoZo3Z5Z8S5dRyVKPaNb0KfIpUlRNm9/+XOv1Zmc1axGil1/pIElq/3pHjRoWrkqVq6pK1WpatGC+EhMT9cxzbSVJZ06f0v+t/kkNGzdVwYKFdPTIIX3y8TjVrF1HFQICJd2a0tOrW2c1aNhI7V/vZF43lcsll7y8ve/zdwL/ZHKkVbU5INNJf0pKijw9PRUZGalGjRqpUSPmsGVW08ee0OVL8fp67gzFxcXIv3ygxkyYYV7cG33hvEwut38JU7laDQ0aEaEv50zTF7OnqtQjpTUsYpL8yt3aetMll4uORx3Wz6t/1LWEq/L2KapadYMV2q23xc4qX302w+IBXn06t5MkjZ8yV0G16t6PS0cO6dylmxITEzV65HBdvXpFNWvV1oxP51r8hu3M6dO6dOl2gvXEk08pPi5OM6ZNUUzMRQVWrKQZn861+DX4iePHNeWTibp8+bJKliqlrm/20OsdO5nf9/Tw0Pqf12rm9KlKTLwunyJF1KhxE33YvZfFvYmHW63KZbR27u1nN3w48NY00a9+3Ko3R3ydU2HhARfaqatuJCZq7JgRSrh6RdVr1tLkGbMtPtfOnj6lS/G3P9datnpK8fHxmj1zimJjYhQQWEmTZ8xW4f9Nr3V1ddX2bVv0zYL5upGYqGLFiqv5Yy31xj+m76xft1bx8XFavXKFVq9cYW4vUaKkfli9/j5cOWAbk2HDarpy5crp+++/V/W7/ArNFscu3rDbsQBrSnp53LsTYAdedfvkdAh4SJz/bUpOh4CHREHPB3fp6vw/TufIeUPr+ObIeTPLpj+5IUOG6L333jM/iRcAAADAg8umOf3Tpk3T0aNHVbJkSZUpU0Z58+a1eH/nzp12CQ4AAADICBfm9FtlU9Lfpk0bO4cBAAAAILvYlPSPGDHC3nEAAAAAyCY2Jf1/S05OVnR0tHnf7b+VLl36LiMAAAAA+2Nyj3U2Jf2HDx9Wly5d9Ntvv1m0G4Yhk8mk1NRUuwQHAAAAIOtsSvo7d+6s3Llz66efflKJEiV4GAIAAAByFOmodTYl/ZGRkdqxY4cqVqxo73gAAAAA2JlN+/RXrlxZMTEx9o4FAAAAQDbIcKX/ypUr5v8fP3683n33XY0dO1bVqlWTq6urRd8CBQrYL0IAAADgHphubl2Gk/5ChQpZfDMNw9Bjjz1m0YeFvAAAAMCDJ8NJ/4YNG8z/f+LECfn6+ipXrlwWfdLS0nTq1Cn7RQcAAABkgE1z1h8iGU76mzZtav7/Fi1a6Ny5cypatKhFn9jYWIWEhKhjx472ixAAAABAlti0e8/f03j+LSEhQR4eHlkOCgAAAMgM5vRbl6mkPywsTNKtb+qwYcOUJ08e83upqanatm2batSoYdcAAQAAAGRNppL+Xbt2SbpV6d+7d6/c3NzM77m5ual69eoaOHCgfSMEAAAAkCWZSvr/XszbuXNnTZ48ma05AQAA8EBgco91Ns3p//zzz+0dBwAAAIBsYlPSDwAAADxIWMhrHVuaAgAAAE6OpB8AAABwckzvAQAAgMOjkm0d3x8AAADAyVHpBwAAgMNjIa91VPoBAAAAJ0elHwAAAA6POr91VPoBAAAAJ0fSDwAAADg5pvcAAADA4bGO1zoq/QAAAICTo9IPAAAAh+fCUl6rqPQDAAAATo6kHwAAAHByTO8BAACAw2Mhr3VU+gEAAAAnR6UfAAAADs/EQl6rqPQDAAAATo5KPwAAABwec/qto9IPAAAAODmSfgAAAMDJMb0HAAAADo8n8lpHpR8AAABwclT6AQAA4PBYyGsdlX4AAADAyZH0AwAAAE6O6T0AAABweEzvsY5KPwAAAODkqPQDAADA4ZnYstMqKv0AAACAk6PSDwAAAIfnQqHfKir9AAAAgJMj6QcAAACcHNN7AAAA4PBYyGsdlX4AAADAyVHpBwAAgMPj4VzWUekHAAAAnBxJPwAAAHAfTZ8+XX5+fvLw8FD9+vW1ffv2u/adM2eOmjRpIi8vL3l5eSkkJMRq/7sh6QcAAIDDM+XQf5m1ePFihYWFacSIEdq5c6eqV6+uVq1aKTo6Ot3+Gzdu1KuvvqoNGzZoy5Yt8vX11eOPP66zZ89m7vtjGIaR6WizwbGLN3I6BDwkSnp55HQIeEh41e2T0yHgIXH+tyk5HQIeEgU9H9x68cZDcTly3maB3pnqX79+fdWtW1fTpk2TJKWlpcnX11d9+/bV4MGD7zk+NTVVXl5emjZtmkJDQzN8XhbyAgAAwOHl1BN5k5KSlJSUZNHm7u4ud3f3O/omJydrx44dCg8PN7e5uLgoJCREW7ZsydD5rl+/rpSUFHl7Z+4fGw/uP9cAAACAB1xERIQKFixo8YqIiEi3b0xMjFJTU1WsWDGL9mLFiun8+fMZOt+gQYNUsmRJhYSEZCpOKv0AAACAjcLDwxUWFmbRll6V3x7GjRunRYsWaePGjfLwyNx0ZZJ+AAAAOLyceiLv3abypMfHx0e5cuXShQsXLNovXLig4sWLWx378ccfa9y4cfr5558VFBSU6TiZ3gMAAADcB25ubqpdu7bWr19vbktLS9P69esVHBx813EffvihxowZozVr1qhOnTo2nZtKPwAAAByeozyRNywsTB07dlSdOnVUr149TZo0SdeuXVPnzp0lSaGhoSpVqpR5XcD48eM1fPhwLVy4UH5+fua5//ny5VO+fPkyfF6SfgAAAOA+adeunS5evKjhw4fr/PnzqlGjhtasWWNe3Hvq1Cm5uNyejDNz5kwlJyfrxRdftDjOiBEjNHLkyAyfl3368dBhn37cL+zTj/uFffpxvzzI+/RvPhKfI+dtVMErR86bWQ/unxwAAAAAuyDpBwAAAJwcc/oBAADg8FwcZSVvDqHSDwAAADi5B6bSfz3pZk6HgIdE2oOxdh0PARZX4n4p3rBfToeAh0Tirmk5HcJdUee3jko/AAAA4ORI+gEAAAAn98BM7wEAAABsxvweq6j0AwAAAE6OSj8AAAAcnolSv1VU+gEAAAAnR6UfAAAADo9nc1lHpR8AAABwciT9AAAAgJNjeg8AAAAcHrN7rKPSDwAAADg5Kv0AAABwfJT6raLSDwAAADg5kn4AAADAyTG9BwAAAA6PJ/JaR6UfAAAAcHJU+gEAAODweCKvdVT6AQAAACdHpR8AAAAOj0K/dVT6AQAAACdH0g8AAAA4Oab3AAAAwPExv8cqKv0AAACAk6PSDwAAAIfHw7mso9IPAAAAODmSfgAAAMDJMb0HAAAADo8n8lpHpR8AAABwclT6AQAA4PAo9FtHpR8AAABwclT6AQAA4Pgo9VtFpR8AAABwciT9AAAAgJNjeg8AAAAcHk/ktY5KPwAAAODkqPQDAADA4fFwLuuo9AMAAABOjqQfAAAAcHJM7wEAAIDDY3aPdVT6AQAAACdHpR8AAACOj1K/VVT6AQAAACdHpR8AAAAOj4dzWUelHwAAAHByJP0AAACAk2N6DwAAABweT+S1jko/AAAA4OSo9AMAAMDhUei3jko/AAAA4ORI+gEAAAAnx/QeAAAAOD7m91hFpR8AAABwclT6AQAA4PB4Iq91VPoBAAAAJ0elHwAAAA6Ph3NZR6UfAAAAcHIk/QAAAICTY3oPAAAAHB6ze6yj0g8AAAA4OSr9AAAAcHyU+q2yqdI/evRoXb9+/Y72xMREjR49OstBAQAAALAfm5L+UaNGKSEh4Y7269eva9SoUVkOCgAAAID92DS9xzAMmdLZDHX37t3y9vbOclAAAABAZvBEXusylfR7eXnJZDLJZDIpICDAIvFPTU1VQkKCevToYfcgAQAAANguU0n/pEmTZBiG3njjDY0aNUoFCxY0v+fm5iY/Pz8FBwfbPUgAAADAGp7Ia12mkv6OHTtKksqWLauGDRvK1dU1W4ICAAAAYD8ZTvqvXLli/v+aNWsqMTFRiYmJ6fYtUKBA1iMDAAAAYBcZTvoLFSqU7uLdf/p7gW9qamqWAwMAAAAyitk91mU46d+wYUN2xgEAAAAgm2Q46W/atGl2xgEAAADYjlK/VTbt079p0yar7z/66KM2BQMAAADA/mxK+ps1a3ZH27/37EfmrV7+rX74dr4uxcXKz7+CuvR9VxUqVk2376kTUVr0xSwdO3xAFy+cU+deb+uZF9pb9Nm3Z6d+WDxfx44cUHxsjN4d9bHqN25+Py4FDzjDMDRz+lR9v+Q7Xb16RdVr1tJ7w0aoTBk/q+MWf7NAX37+mWJjYhQQWFGD3huqqtWCzO/HxFzUpI8/0tYtv+na9Wvy8yurLm92V0jLVtl8RXhQGIah2TOnavmy75Rw9aqCatTUoPdGqPQ97q3vFi3Q11/OU2xsjCoEVNTAQUNU5R/3Vo8uodq543eLMW1fbKfwoSMlSYcPHdT8z+coctdOXb4UrxIlS+n5F9vplQ6h9r5EOLBGtfw1IDREtSqXVokiBfXygNlasXFPTocFO+HhXNa52DIoPj7e4hUdHa01a9aobt26Wrt2rb1jfChs3rBWX8yaqJdD39RHsxaojH+Axgzqo8vxcen2T75xQ8VKlNJrXfuqkHfhdPskJSbKzz9A3foNys7Q4YC+mDdX3yz4Su8NH6n5C7+Vp6enenfvqqSkpLuO+b/VqzThw3Hq3rO3Fn63TAGBgerVvaviYmPNfYaFD9KJE8c1adoMfbfsR7UIaalBbw/QwQP778dl4QEw/4u5Wrzwaw0eMlLzvlosT8886term9V7a93/rdKkCePVtXtvzf9mqSoEBKpfr26Ki4u16Nfm+Ze06udN5lff/gPN7x08sE9eXoU1+oPxWrR0hTp37a7pUz/Rt4sWZNu1wvHk9XTX3sNn1T9icU6HAtx3NiX9BQsWtHj5+PioZcuWGj9+vN599117x/hQWLHka4U81VYtnnhWvn7l1L3/e3J399D6NT+k2798xSrq2L2/GrdoJVdXt3T71KrfSO3f6KX6jVtkZ+hwMIZhaOFX89XtzR5q3uIxBQQGaszY8boYHa0N63++67iv53+h5198Sc+1fUH+/uU1ZPgoeXh4aPn3S819dkdG6pX2r6lqtSA94uurbt17Kn/+/Nq/b9/9uDTkMMMwtGjBfL3RrYeaNn9MFQICNXLMOMVcjNYvG+5+by386ku1ef4ltW7zvMr5l9fgoSPl4eGhFcuXWfTz8PCQj08R8ytfvnzm955t84LeHvSeatWpp1KP+OrJp59V62fbasP6ddl2vXA8azfv16gZP+nHDVT38fCxKem/m2LFiunQoUP2PORDISUlRVGHDyqoVj1zm4uLi4Jq1dPh/XtzMDI4o7Nnzigm5qLqBzc0t+XPn19Vg4K0Z3dkumNSUpJ1YP8+1W9we4yLi4vqNwi2GFO9Rg2tXbNKly9fUlpamtasWqmk5GTVqVcvnaPC2fx19oxiY2JUr/7tJ7Pny59fVaoFae/u3emOSUlJ1sED+1T3H2NcXFxUt36w9u6JtOi7ZvVPatksWK+80FrTp0zUjbs8K+ZvCQkJFk+OB+DcTKaceTkKm+b079lj+S9kwzB07tw5jRs3TjVq1Ljn+KSkpDt+1ZuclCI3d3dbwnF4Vy9fUlpaqgp5WU7TKehVWGdPn8iZoOC0YmIuSpK8C1veb4UL+yg2JibdMfHx8UpNTU13zInjx81ffzhhkgYNHKBmjRood+7c8vDw0MRJU1W6dBk7XwUeRH/fP/++T7y9fRQbezHdMZfiL6V7b3kXLqyTJ27fW62efEbFS5ZUkSJFdfTwIU2bPEEnTxzXhxOnpnvcPZG7tG7tan0yZVZWLgkAnIZNSX+NGjVkMplkGIZFe4MGDTRv3rx7jo+IiNCoUaMs2noOCFevsPdsCQeAFat+WqH3R40wfz1lRvYlQdOnTdbVq1c1a+7nKlTISxv/87PeHThA8778WhUCArPtvMgZa1auUMT7I81ffzJ1Zradq+2LL5v/v3yFABUuUkS93+ysM6dP6RHf0hZ9o44e1sABvdW1ey81aNgo22IC8GBxoKJ7jrAp6T/+j8qedOtXsUWKFJGHh0eGxoeHhyssLMyi7ejFFFtCcQr5CxaSi0suXYq3XLR2OT5Whbx9cigqOIumzZuratDtXVBSkpMlSXGxsSpSpKi5PTY2RoGBldI9hpeXl3LlymWxaPfvMYV9bt2jp0+d0uKFC7Rk+Qr5l68gSQqsWFE7d+7Q4m8WauiIUXccF46tSbMWFjvsJP/j3vL5x70VFxejgID0761CXoXSvbfiYmPN91Z6/t416vS/kv5jUUfV+8031Ob5l9WlW8/MXxQAOCmb5vSXKVPG4uXr66sbN25keLy7u7sKFChg8XpYp/ZIkqurq/wDKmrvrtvb0aWlpWnPrt8VULlaDkYGZ5A3bz6VLl3G/CrnX14+PkW0besWc5+EhAT9uWePgqrXSPcYrq5uqlS5irZtuz0mLS1N27dtNY+5cePW/GqTyfJjJZeLiwwjzb4XhQdC3rx55Vu6jPlVzr+8Cvv46PftW819EhIStG/vHlWrXj3dY7i6uqlipSoWY9LS0vTH9q2qFlTjruc+fPCgJMnHp4i5LeroEfXq1klPtX5Ovfr2z9rFAYCTsSnpHz9+vBYvvr3d1csvvyxvb2+VKlVKu++yWAvWtX7xNf288ntt+L8VOnPyuGZPilDSjUS1aPWsJGnKuOH6eu7tuaspKSk6fvSQjh89pJs3UxQbE63jRw/p3NnT5j6JidfNfSQp+vxfOn70kC5eOHd/Lw4PFJPJpPavh2ru7FnauOE/OnL4kIa9N0hFihZV88dCzP26d+mkRQu/Nn/9Wmgnfb/kO/34w/c6FhWlsWNGKjExUc+1eV6S5Fe2nHxLl9H7o0foz717dPrUKc3/Yp62bvlNzVqE3BEHnI/JZNIrHUI1b84sbdr4Hx09clgjhw6WT5Giatr89j3Q683OFltptn+9o35Y9p1++nG5jh+L0vgPRikxMVHPPNdWknTm9Cl9NnuGDuzfp7/OntWmjf/RyGGDVbN2HfO0saijh9WrWyfVD26o9q93UkzMRcXEXFR8XPrbHuPhlNfTTUEBpRQUUEqS5FeqsIICSsm3uFcORwa7MOXQy0HYNL1n1qxZWrDg1gf2unXrtG7dOq1Zs0bffvut3nnnHfbqt0Gj5o/r8uV4Lfpili7Fx6qsf4CGjptq3oM/Jvq8xQPQ4mMvamD32w/j+vHbr/Tjt1+pSvXaGj1xtiQp6tB+jXi7u7nPFzMnSpKaPf6M+g5iqsXDrNMbXZWYmKj3Rw7X1atXVKNWbU2fNUfu//iN2+nTp3QpPt78dasnn1J8fJxmTpuq2JiLCqxYSdNnzTFPwXB1ddXUmZ9qyicT9FbvnrqeeF2+vqU1+oNxavJo0/t+jcgZoZ266kZiosaOGaGE/z34bfKM2Rb31tl/3VstWz2l+Ph4zZ455X8PfqukyTNmq3Dh2/fW9m1b9M2C+bqRmKhixYqr+WMt9cY/pu+sX7dW8fFxWr1yhVavXGFuL1GipH5Yvf4+XDkcQa3KZbR27lvmrz8c+IIk6asft+rNEV/fbRjgFEzGv1fjZoCnp6cOHz4sX19fvfXWW7px44Y+/fRTHT58WPXr11f8Pz7MM+rPMwmZHgPYolyxvDkdAh4SKTcz/fEK2KR4w345HQIeEom7puV0CHd1MvbuDwHMTmUKO8YUdZum93h5een06VvTSNasWaOQkFu/tjUMQ6mpqfaLDgAAAECW2TS95/nnn1f79u1VoUIFxcbG6sknn5Qk7dq1S+XLl7drgAAAAMC9ONKDsnKCTUn/J598Ij8/P50+fVoffvih+VHo586dU69evewaIAAAAICssWlOf3ZgTj/uF+b0435hTj/uF+b04355kOf0n4rLmTn9pb0dY06/TZV+STpy5Ig2bNig6OhopaVZ7sE9fPjwLAcGAAAAZBSze6yzKemfM2eOevbsKR8fHxUvXtxiK0mTyUTSDwAAADxAbEr633//fX3wwQcaNGiQveMBAAAAMo2FvNbZtGVnfHy8XnrpJXvHAgAAACAb2JT0v/TSSzx1FwAAAHAQNk3vKV++vIYNG6atW7eqWrVqcnV1tXi/Xz92EQAAAMD9xPwea2zasrNs2bJ3P6DJpGPHjmU6ELbsxP3Clp24X9iyE/cLW3bifnmQt+w8E5+cI+d9xMstR86bWTZV+o8fP27vOAAAAACbsZDXugwn/WFhYRozZozy5s2rsLCwu/YzmUyaMGGCXYIDAAAAkHUZTvp37dqllJQU8//fjYl/ZgEAAOA+IwO1LsNJ/4YNG9L9fwAAAAAPNpu27AQAAABgm+nTp8vPz08eHh6qX7++tm/fbrX/d999p4oVK8rDw0PVqlXTqlWrMn1Okn4AAAA4PJMpZ16ZtXjxYoWFhWnEiBHauXOnqlevrlatWik6Ojrd/r/99pteffVVdenSRbt27VKbNm3Upk0b/fnnn5n7/tiyZWd2YMtO3C9s2Yn7hS07cb+wZSfulwd5y85zl3Nmy84SBTO3ZWf9+vVVt25dTZt263uZlpYmX19f9e3bV4MHD76jf7t27XTt2jX99NNP5rYGDRqoRo0amjVrVobPS6UfAAAADs+UQ/8lJSXpypUrFq+kpKR0Y0xOTtaOHTsUEhJibnNxcVFISIi2bNmS7pgtW7ZY9JekVq1a3bX/3ZD0AwAAADaKiIhQwYIFLV4RERHp9o2JiVFqaqqKFStm0V6sWDGdP38+3THnz5/PVP+7senhXAAAAACk8PDwO55h5e7unkPR3B1JPwAAABxfDm3U7+7unuEk38fHR7ly5dKFCxcs2i9cuKDixYunO6Z48eKZ6n83TO8BAAAA7gM3NzfVrl1b69evN7elpaVp/fr1Cg4OTndMcHCwRX9JWrdu3V373w2VfgAAADg8R3kib1hYmDp27Kg6deqoXr16mjRpkq5du6bOnTtLkkJDQ1WqVCnzuoC33npLTZs21YQJE/T0009r0aJF+uOPPzR79uxMnZekHwAAALhP2rVrp4sXL2r48OE6f/68atSooTVr1pgX6546dUouLrcn4zRs2FALFy7U0KFD9d5776lChQpavny5qlatmqnzsk8/Hjrs04/7hX36cb+wTz/ulwd5n/7oqyk5ct6i+V1z5LyZxZx+AAAAwMmR9AMAAABOjjn9AAAAcHgmh1nKmzOo9AMAAABOjko/AAAAHB+Ffquo9AMAAABOjqQfAAAAcHJM7wEAAIDDY3aPdVT6AQAAACdHpR8AAAAOz0Sp3yoq/QAAAICTo9IPAAAAh8fDuayj0g8AAAA4OZJ+AAAAwMkxvQcAAAAOj4W81lHpBwAAAJwcST8AAADg5Ej6AQAAACdH0g8AAAA4ORbyAgAAwOGxkNc6Kv0AAACAk6PSDwAAAIfHE3mto9IPAAAAODkq/QAAAHB4zOm3jko/AAAA4ORI+gEAAAAnx/QeAAAAODxm91hHpR8AAABwclT6AQAA4Pgo9VtFpR8AAABwciT9AAAAgJNjeg8AAAAcHk/ktY5KPwAAAODkqPQDAADA4fFEXuuo9AMAAABOjqQfAAAAcHJM7wEAAIDDY3aPdVT6AQAAACdHpR8AAACOj1K/VVT6AQAAACdHpR8AAAAOj4dzWUelHwAAAHByJP0AAACAk2N6DwAAABweT+S1jko/AAAA4ORMhmEYOR0EMi8pKUkREREKDw+Xu7t7TocDJ8a9hvuFew33C/caHkYk/Q7qypUrKliwoC5fvqwCBQrkdDhwYtxruF+413C/cK/hYcT0HgAAAMDJkfQDAAAATo6kHwAAAHByJP0Oyt3dXSNGjGABErId9xruF+413C/ca3gYsZAXAAAAcHJU+gEAAAAnR9IPAAAAODmSfgAAAMDJkfQjS0aOHKkaNWrkdBj4l40bN8pkMunSpUs5HQoeQtx/yG7NmjVT//7978u5uJ/hLEj6HxJffPGFChUqZPfjDhw4UOvXr7f7cQE4jvuZgGWEn5+fJk2alNNhIBstW7ZMY8aMyZFzZ9fPUyC75c7pAHBvqampMplMcnF58P6Nli9fPuXLly+nwwDg4JKTk+Xm5pbTYcBBeHt753QIgMN58LLIB8SSJUtUrVo1eXp6qnDhwgoJCdG1a9ckSXPnzlWlSpXk4eGhihUrasaMGeZxDRs21KBBgyyOdfHiRbm6umrTpk2SpKSkJA0cOFClSpVS3rx5Vb9+fW3cuNHc/+8qwo8//qjKlSvL3d1dp06duue4u9m4caM6d+6sy5cvy2QyyWQyaeTIkZKk+Ph4hYaGysvLS3ny5NGTTz6pI0eOmOMuXry4xo4daz7Wb7/9Jjc3N3N1P73pPfPmzVOVKlXk7u6uEiVKqE+fPhn6niNzkpKS1K9fPxUtWlQeHh5q3Lixfv/9d4s+mzdvVlBQkDw8PNSgQQP9+eef5vdOnjyp1q1by8vLS3nz5lWVKlW0atUq8/v79u3TM888owIFCih//vxq0qSJoqKizO9b+3tw4sQJmUwmLVu2TM2bN1eePHlUvXp1bdmyxSK+//73v2rSpIk8PT3l6+urfv36mf+ewTF06tRJv/zyiyZPnmz+fDlx4oQkaceOHapTp47y5Mmjhg0b6tChQ+Zxf392zJ07V2XLlpWHh4ck6dKlS+ratauKFCmiAgUKqEWLFtq9e7d5XFRUlJ577jkVK1ZM+fLlU926dfXzzz+b32/WrJlOnjypAQMGmOOB8/nnb5f8/Pw0duxYvfHGG8qfP79Kly6t2bNnm/smJyerT58+KlGihDw8PFSmTBlFRERIuv1ZFRkZae5/6dIlmUymdH++Wvt5CjzwDNzhr7/+MnLnzm1MnDjROH78uLFnzx5j+vTpxtWrV42vv/7aKFGihLF06VLj2LFjxtKlSw1vb2/jiy++MAzDMKZNm2aULl3aSEtLMx9v6tSpFm1du3Y1GjZsaGzatMk4evSo8dFHHxnu7u7G4cOHDcMwjM8//9xwdXU1GjZsaGzevNk4ePCgce3atXuOu5ukpCRj0qRJRoECBYxz584Z586dM65evWoYhmE8++yzRqVKlYxNmzYZkZGRRqtWrYzy5csbycnJhmEYxsqVKw1XV1fj999/N65cuWKUK1fOGDBggPnYI0aMMKpXr27+esaMGYaHh4cxadIk49ChQ8b27duNTz75JMt/JrhTv379jJIlSxqrVq0y9u3bZ3Ts2NHw8vIyYmNjjQ0bNhiSjEqVKhlr16419uzZYzzzzDOGn5+f+c/26aefNlq2bGns2bPHiIqKMlasWGH88ssvhmEYxpkzZwxvb2/j+eefN37//Xfj0KFDxrx584yDBw8ahmHc8+/B8ePHDUlGxYoVjZ9++sk4dOiQ8eKLLxplypQxUlJSDMMwjKNHjxp58+Y1PvnkE+Pw4cPG5s2bjZo1axqdOnXKge8mbHXp0iUjODjY6Natm/nz5eeffzYkGfXr1zc2btxo7Nu3z2jSpInRsGFD87gRI0YYefPmNZ544glj586dxu7duw3DMIyQkBCjdevWxu+//24cPnzYePvtt43ChQsbsbGxhmEYRmRkpDFr1ixj7969xuHDh42hQ4caHh4exsmTJw3DMIzY2FjjkUceMUaPHm2OB86nadOmxltvvWUYhmGUKVPG8Pb2NqZPn24cOXLEiIiIMFxcXMyfVx999JHh6+trbNq0yThx4oTx66+/GgsXLjQM4/Zn1a5du8zHjo+PNyQZGzZsMAzDMH+exsfHW/15CjzoSPrTsWPHDkOSceLEiTve8/f3N39Y/G3MmDFGcHCwYRiGER0dbeTOndvYtGmT+f3g4GBj0KBBhmEYxsmTJ41cuXIZZ8+etTjGY489ZoSHhxuGcSvpl2RERkaa38/IOGs+//xzo2DBghZthw8fNiQZmzdvNrfFxMQYnp6exrfffmtu69WrlxEQEGC0b9/eqFatmnHjxg3ze/9O+kuWLGkMGTLknvEgaxISEgxXV1djwYIF5rbk5GSjZMmSxocffmj+IbVo0SLz+7GxsYanp6exePFiwzAMo1q1asbIkSPTPX54eLhRtmxZ8z8Q/u1efw/+/kE6d+5c8/v79u0zJBkHDhwwDMMwunTpYrz55psWx/j1118NFxcXIzExMaPfCjwA/pmAGcbtJOnnn382t61cudKQZP6zHTFihOHq6mpER0eb+/z6669GgQIFLD5jDOPW/fbpp5/e9fxVqlQxpk6dav66TJkyFBuc3L+T/tdee838XlpamlG0aFFj5syZhmEYRt++fY0WLVpYFOP+ltmk3zDS/3kKOALm9KejevXqeuyxx1StWjW1atVKjz/+uF588UW5ubkpKipKXbp0Ubdu3cz9b968qYIFC0qSihQposcff1wLFixQkyZNdPz4cW3ZskWffvqpJGnv3r1KTU1VQECAxTmTkpJUuHBh89dubm4KCgoyf53RcZlx4MAB5c6dW/Xr1ze3FS5cWIGBgTpw4IC57eOPP1bVqlX13XffaceOHXd9bHl0dLT++usvPfbYYzbFg4yLiopSSkqKGjVqZG5zdXVVvXr1dODAAdWtW1eSFBwcbH7f29vb4s+2X79+6tmzp9auXauQkBC98MIL5nsuMjJSTZo0kaur6x3nvnbt2j3/Hvztn/dwiRIlJN26TypWrKjdu3drz549WrBggbmPYRhKS0vT8ePHValSJZu/P3gw3O3Pv3Tp0pKkMmXKqEiRIuY+u3fvVkJCwh2faYmJieapZQkJCRo5cqRWrlypc+fO6ebNm0pMTNSpU6ey+3LwAPvnvWYymVS8eHFFR0dLujUFrWXLlgoMDNQTTzyhZ555Ro8//nhOhQrkGJL+dOTKlUvr1q3Tb7/9prVr12rq1KkaMmSIVqxYIUmaM2eORaL895i/dejQQf369dPUqVO1cOFCVatWTdWqVZN06wdWrly5tGPHDosxkiwWxHp6elrMRc3ouOwQFRWlv/76S2lpaTpx4oT5Wv7N09MzW+OAfXXt2lWtWrXSypUrtXbtWkVERGjChAnq27ev1T/LhIQESff+eyDJ4h8Nf9/PaWlp5uN0795d/fr1u+McfyeFcGzW/vwlKW/evBb9ExISVKJEiXTnUv+9W8rAgQO1bt06ffzxxypfvrw8PT314osvKjk52f4XAIfx7wKFyWQy32u1atXS8ePHtXr1av388896+eWXFRISoiVLlpg3yDAMwzw2JSXl/gUO3Eck/XdhMpnUqFEjNWrUSMOHD1eZMmW0efNmlSxZUseOHVOHDh3uOva5557Tm2++qTVr1mjhwoUKDQ01v1ezZk2lpqYqOjpaTZo0yXA8to77m5ubm1JTUy3aKlWqpJs3b2rbtm1q2LChJCk2NlaHDh1S5cqVJd1aAPXaa6+pXbt2CgwMVNeuXbV3714VLVr0jnPkz59ffn5+Wr9+vZo3b57pGJFx/v7+cnNz0+bNm1WmTBlJt35Q/f777xZbJ27dutWcQMfHx+vw4cMWFXRfX1/16NFDPXr0UHh4uObMmaO+ffsqKChIX375pVJSUu74YVqsWLEM/T24l1q1amn//v0qX768zcfAgyG9zxdb1KpVS+fPn1fu3Lnl5+eXbp/NmzerU6dOatu2raRb/1D4e+GwveOB8yhQoIDatWundu3a6cUXX9QTTzyhuLg482+azp07p5o1a0qSxaLe9HB/wVGR9Kdj27ZtWr9+vR5//HEVLVpU27Zt08WLF1WpUiWNGjVK/fr1U8GCBfXEE08oKSlJf/zxh+Lj4xUWFibpVvWqTZs2GjZsmA4cOKBXX33VfOyAgAB16NBBoaGhmjBhgmrWrKmLFy9q/fr1CgoK0tNPP51uTLaO+5ufn58SEhK0fv16Va9eXXny5FGFChX03HPPqVu3bvr000+VP39+DR48WKVKldJzzz0nSRoyZIguX76sKVOmKF++fFq1apXeeOMN/fTTT+meZ+TIkerRo4eKFi2qJ598UlevXtXmzZvVt29fW/4ocBd58+ZVz5499c4778jb21ulS5fWhx9+qOvXr6tLly7m3U5Gjx6twoULq1ixYhoyZIh8fHzUpk0bSVL//v315JNPKiAgQPHx8dqwYYP5HwR9+vTR1KlT9corryg8PFwFCxbU1q1bVa9ePQUGBmbo78G9DBo0SA0aNFCfPn3UtWtX5c2bV/v379e6des0bdq0bPm+IXv4+flp27ZtOnHihPLly2dRzc+MkJAQBQcHq02bNvrwww8VEBCgv/76SytXrlTbtm1Vp04dVahQQcuWLVPr1q1lMpk0bNiwO87n5+enTZs26ZVXXpG7u7t8fHzscZlwUBMnTlSJEiVUs2ZNubi46LvvvlPx4sVVqFAhubi4qEGDBho3bpzKli2r6OhoDR061Orx0vt5midPnvt0NUAW5PSiggfR/v37jVatWhlFihQx3N3djYCAAItFYgsWLDBq1KhhuLm5GV5eXsajjz5qLFu2zOIYq1atMiQZjz766B3HT05ONoYPH274+fkZrq6uRokSJYy2bdsae/bsMQzj7ouE7jXuXnr06GEULlzYkGSMGDHCMAzDiIuLM15//XWjYMGChqenp9GqVSvzbkAbNmwwcufObfz666/mYxw/ftwoUKCAMWPGDMMw7lzIaxiGMWvWLCMwMNAcY9++fTMUHzInMTHR6Nu3r+Hj42O4u7sbjRo1MrZv324Yxu2FZytWrDCqVKliuLm5GfXq1TPvkGIYhtGnTx/D39/fcHd3N4oUKWK8/vrrRkxMjPn93bt3G48//riRJ08eI3/+/EaTJk2MqKgo8/vW/h5kZHGcYRjG9u3bjZYtWxr58uUz8ubNawQFBRkffPBBNn3HkF0OHTpkNGjQwPD09DQkmTcj+Hvho2EYxq5duwxJxvHjxw3DSP+zwzAM48qVK0bfvn2NkiVLGq6uroavr6/RoUMH49SpU4Zh3Lq3mjdvbnh6ehq+vr7GtGnT7lhIvGXLFiMoKMhwd3c3+DHnnP69kPffC7erV69u/jk3e/Zso0aNGkbevHmNAgUKGI899pixc+dOc9/9+/cbwcHBhqenp1GjRg1j7dq1VhfyGkb6P0+BB53JMP4xkQ0AAACA0+HhXAAAAICTI+l3Ek8++aTy5cuX7uufT9QFAADAw4fpPU7i7NmzSkxMTPc9b29veXt73+eIAAAA8KAg6QcAAACcHNN7AAAAACdH0g8AAAA4OZJ+AAAAwMmR9AMAAABOjqQfAAAAcHIk/QAAAICTI+kHAAAAnBxJPwAAAODk/h96dYz2KMyzBQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rowsums = df.iloc[:, 2:].sum(axis=1)\n",
    "temp = df.iloc[:, 2:-1]\n",
    "train_corr = temp[rowsums > 0]\n",
    "corr = train_corr.corr()\n",
    "plt.figure(figsize=(10, 8))\n",
    "sns.heatmap(corr,\n",
    "            xticklabels=corr.columns.values,\n",
    "            yticklabels=corr.columns.values, annot=True, cmap=\"Blues\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **4.Creating A TF-IDF Model**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **4.1 Split data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('dataset/train_clean.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.datasets import make_classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df['comment_text']\n",
    "y = df.drop(columns=['comment_text'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0         explanation edits username hardcore metallica ...\n",
      "1         d'aww match background colour seemingly stuck ...\n",
      "2         hey man edit war . guy constantly removing rel...\n",
      "3         real suggestion improvement wondered statistic...\n",
      "4                                sir hero . chance remember\n",
      "                                ...                        \n",
      "159567                   ashamed horrible thing talk page .\n",
      "159568    spitzer umm actual article prostitution ring ....\n",
      "159569                          speedy version deleted it .\n",
      "159570    ... understand . idea bad away . kind communit...\n",
      "159571                                               mother\n",
      "Name: comment_text, Length: 159090, dtype: object\n",
      "        toxic  severe_toxic  obscene  threat  insult  identity_hate\n",
      "0           0             0        0       0       0              0\n",
      "1           0             0        0       0       0              0\n",
      "2           0             0        0       0       0              0\n",
      "3           0             0        0       0       0              0\n",
      "4           0             0        0       0       0              0\n",
      "...       ...           ...      ...     ...     ...            ...\n",
      "159567      0             0        0       0       0              0\n",
      "159568      0             0        0       0       0              0\n",
      "159569      0             0        0       0       0              0\n",
      "159570      0             0        0       0       0              0\n",
      "159571      0             0        0       0       0              0\n",
      "\n",
      "[159090 rows x 6 columns]\n"
     ]
    }
   ],
   "source": [
    "print(x)\n",
    "print(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split( x, y, test_size = 0.25, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **4.2 Transforming Text to Vectors**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "tfidf_vectorizer = TfidfVectorizer(max_features=3000) \n",
    "X_train_tfidf = tfidf_vectorizer.fit_transform(X_train)\n",
    "X_test_tfidf = tfidf_vectorizer.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('train/tfidf_vectorizer.pkl', 'wb') as f:\n",
    "    pickle.dump(tfidf_vectorizer, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['ability' 'abortion' 'about' ... 'youth' 'youtube' 'ytmndin']\n"
     ]
    }
   ],
   "source": [
    "print(tfidf_vectorizer.get_feature_names_out())\n",
    "with open(\"tfidf_features.txt\", \"w\") as f:\n",
    "    f.write(\"Features:\\n\")\n",
    "    for feature in tfidf_vectorizer.get_feature_names_out():\n",
    "        f.write(feature + \"\\n\")\n",
    "    f.write(\"\\nTF-IDF Array:\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **5.Applying Logistic Regression Model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score, classification_report\n",
    "from sklearn.multiclass import OneVsRestClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Huấn luyện mô hình"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>OneVsRestClassifier(estimator=LogisticRegression(C=0.1, penalty=&#x27;l1&#x27;,\n",
       "                                                 solver=&#x27;liblinear&#x27;))</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;OneVsRestClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.multiclass.OneVsRestClassifier.html\">?<span>Documentation for OneVsRestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>OneVsRestClassifier(estimator=LogisticRegression(C=0.1, penalty=&#x27;l1&#x27;,\n",
       "                                                 solver=&#x27;liblinear&#x27;))</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">estimator: LogisticRegression</label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(C=0.1, penalty=&#x27;l1&#x27;, solver=&#x27;liblinear&#x27;)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(C=0.1, penalty=&#x27;l1&#x27;, solver=&#x27;liblinear&#x27;)</pre></div> </div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "OneVsRestClassifier(estimator=LogisticRegression(C=0.1, penalty='l1',\n",
       "                                                 solver='liblinear'))"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = OneVsRestClassifier(LogisticRegression(solver='liblinear', C=0.1,penalty='l1',max_iter=100))\n",
    "clf.fit(X_train_tfidf, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Đánh giá mô hình"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Administrator\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1509: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in samples with no predicted labels. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
      "c:\\Users\\Administrator\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1509: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 in samples with no true labels. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
      "c:\\Users\\Administrator\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1509: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in samples with no true nor predicted labels. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"
     ]
    }
   ],
   "source": [
    "y_pred = clf.predict(X_test_tfidf)\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "report = classification_report(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9159731476127021\n",
      "Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.92      0.55      0.69      3783\n",
      "           1       0.53      0.20      0.29       410\n",
      "           2       0.92      0.63      0.75      2123\n",
      "           3       0.45      0.07      0.13       123\n",
      "           4       0.80      0.48      0.60      1955\n",
      "           5       0.69      0.20      0.31       364\n",
      "\n",
      "   micro avg       0.87      0.52      0.65      8758\n",
      "   macro avg       0.72      0.35      0.46      8758\n",
      "weighted avg       0.86      0.52      0.64      8758\n",
      " samples avg       0.05      0.04      0.04      8758\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy:\", accuracy)\n",
    "print(\"Classification Report:\\n\", report) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('train/logistic_regression_model.pkl', 'wb') as f:\n",
    "    pickle.dump(clf, f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **5. CHART**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Administrator\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1509: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in samples with no predicted labels. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
      "c:\\Users\\Administrator\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1509: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 in samples with no true labels. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
      "c:\\Users\\Administrator\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1509: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in samples with no true nor predicted labels. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"
     ]
    },
    {
     "data": {
      "image/png": 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cwbp16+Dv7w8zMzOkp6fD1dUVQ4YMQdOmTWFqaoqTJ0/iypUrOtmh/+KLL75Ax44d0aJFC0yaNAmenp6IiorCkSNHEBwcrL2fb7/9FhYWFvDx8cGFCxdw8uTJUnNSmjVrBplMhmXLliEjIwMKhQLdu3eHvb19qetOmjQJmzZtwpgxYxAUFIRatWph//79+OOPP7B69er/tNhHec2ZMwd79+7F6tWrsXTpUixduhS//vor2rZti4kTJ8LHxwepqan4888/cfLkSaSmpuocb21tjY4dO2Ls2LFISEjA6tWr4eXlhYkTJwIoXjxj2bJlGDt2LLp06YLhw4drlz+vVasWZs6cWa44W7ZsiT179mDWrFlo3bo1TE1N0a9fv2f2PJT3vvv27Ysff/wRAwcOhL+/PyIjI7Fx40b4+PggOztbez6lUgkfHx/s2bMH9erVg7W1NRo1avSvc/HKo7z/t0r82++IiKjGqIKVAomIKuzu3bvCxIkThVq1agmGhoaCmZmZ0KFDB2Ht2rVCfn6+tl5Zy5/Pnj1bcHJyEpRKpdChQwfhwoULpZYA37Rpk9C5c2fBxsZGUCgUQp06dYQ5c+YIGRkZgiAIgkqlEubMmSM0bdpUMDMzE0xMTISmTZsK69ev14nzvyx/LgiCcPPmTWHgwIGCpaWlYGRkJHh7ewsffvihdn9aWpowduxYwdbWVjA1NRX8/PyE0NDQUvctCILw1VdfCbVr1xZkMpnOUuh/v3dBEISEhATteQ0NDYXGjRuXiq0k5hUrVgh/h6cssf2kkmWwn1zu+0ldu3YVzM3NhfT0dG1MU6dOFdzc3AS5XC44OjoKPXr0EDZv3lzqnN9//70QEBAg2NvbC0qlUvD39y+1jLwgCMKePXuE5s2bCwqFQrC2thZGjBghPHr0SKfO6NGjBRMTkzJjzM7OFl5//XXB0tJSAPCvS6GX9bv/t+ejPPet0WiETz/9VPDw8BAUCoXQvHlz4fDhw2W2v/PnzwstW7YUDA0NdX5PT7vPLl26lPlVAH+/l/L+3xL7OyIiqu4kglDOWcFERETV1JkzZ9CtWzfs27cPQ4YMqepwqAz8HRHRi4ZzpIiIiIiIiERiR4qIiIiIiEgkdqSIiIiIiIhE4hwpIiIiIiIikZiRIiIiIiIiEokdKSIiIiIiIpHYkSIiIiIiIhLJoKoDoGejY7+zVR0CERFRjbTqs6ZVHQLpidbellUdwlMdkXtX2rn9C8Iq7dxViRkpIiIiIiIikZiRIiIiIiLScxK5pKpDqHGYkSIiIiIiIhKJGSkiIiIiIj0nNWBGSixmpIiIiIiIiERiRoqIiIiISM9J5MyviMWOFBERERGRnuPQPvHY9SQiIiIiIhKJGSkiIiIiIj3H5c/FY0aKiIiIiIhIJGakiIiIiIj0HOdIiceMFBERERERkUjMSBERERER6TnOkRKPGSkiIiIiIiKRmJEiIiIiItJznCMlHjNSREREREREIjEjRURERESk5yQyZqTEYkeKiIiIiEjPSdmREo1D+4iIiIiIiERiRqqa+PLLL7FixQrEx8ejadOmWLt2Ldq0aVPVYdUog/o4Y/ggN1hbGSIiMhurNoXjzr2sMuvKZBKMHOqO3t0dYGujwMOYXGzYdh+X/kzT1nljiBu6tLeFh4sxVGoNQkIzsWHbfTyMyXtet0TVlJi21ruHA+bPqK9TplJr0GPw79rH78/wRp8ejjp1LgWlYvbCkGcfPNUoYtoaAJiayDBppCc6+9rC3EyOhMR8rPkqAheDUgEASqUME0fUQmdfW1hZyHH3fjbWfBWB0H84J9UMJ3/+AaeO/oikxFgAgKt7bQwcNh5NW7Z/6jHH/vc9Th77ESlJCTAzt0Cb9t3x6qi3YGioqLQ4L507hf07NyE5MQ4Ozm4YNnoqmrXqoN3/w66vcPH3E0hNToDMQA5Pr/oY+sZkeHk3qrSYqJhEyoyUWOxIVQN79uzBrFmzsHHjRrRt2xarV6+Gn58fwsLCYG9vX9Xh1QjdO9ph2oQ6+OzLu7h9NwuvvuKClYsbY/jkK0jPKChVf9IbtdCrmwOWrb2L6Ee5aNPCCp++3xCT5wbj3v1sAEDzRpb48UgsQu9lQSaVYNIoT6xa3ARvvHUF+SrN875FqibEtjUAyM4pxOuTL2sfC2XUuRiUik9Xh2ofFxSUVYv0idi2ZmAgwaqPmyAtvQAfLr2NpBQVHO2NkJ1dqK3z3tv1UNvDBB+vDEVyqgp+XR2w+uPi17XkVPXzvD16xqxt7fHa6Lfg6OwGQQB+P30EK5fMwZLV38LVvXap+ufPHseeHesx8Z0PULd+Y8THRmPTmo8BiQRvjJ9RoRhuhwRh85qPsfrrg2Xuv3vnBr787EO8OmoKmrfuiPNnj2PVp3PxyaodcPOoAwBwcnHH6Dffhb2jC9RqFY7+73ss++gdfL7pB5hbWFUoLqLKwqF91cDKlSsxceJEjB07Fj4+Pti4cSOMjY3xzTffVHVoNcawAa44dDwOP59KQNTDXKxYfw/5Kg36vuRYZn2/bg74dm80LgalIjYhHwePxuFCUCqGDXDV1pm9MARHTyUgMjoX4VE5+HR1GBztjeDtZfa8bouqIbFtDQAEAUhNL9Buaeml3wSrCzQ6dbJyCss4E+kTsW3Nv6cjzE3lCFhyCyF3MhGfqELwzQyER+UAAAwNpejS3g7rt97H9VsZiInLxzffP0BMXB4G9nF+nrdGlaBFm05o1qoDHJ3d4eTijldHToGRkTHCQ2+WWf/enRuo26AJ2nfxg52DMxo3bwffTr1w/+4tbR2NRoOf9m3DzAkDMHZIZ7z/zghc/uNUhWM8fmgPmrRoh76DRsLFzRND35iMWrW9ceLIPm2d9l380KhZG9g7usDVvTZGjJ+OvNwcREeFV/i6VD4SmbTSthfVi3tnNYRarUZQUBB69uypLZNKpejZsycuXLhQhZHVHAYGEtTzMsPV64+H5QkCcDU4DQ29zcs8Ri6XQlWgm1VSqTRo4mPx1OuYmMgAAJlZZWcd6MVXkbYGFA+n2r+lLX74pi0C5zeEp7txqTrNG1ni0Le+2LWhNWZPqQtzMw4Y0GcVaWsd29rgZmgmZk/2wk87fLFjXSuMHOoO6V9/6WUyCQxkEqjVf3vtU//zax/VPJqiIlz47Reo8vNQt37ZQ+LqNmiCqIhQRPzVcUqMj8H1oPNo2vLxMLtD+7fj3K9HMfat97Bs3fd4uf9wbFi5EHdu/lmhuMJDQ9CoaWudsiYt2iE8tOxhzIUFBfj1+EEYm5jCw7Nuha5JVJn4l7qKJScno6ioCA4ODjrlDg4OCA0NfcpR9CQLczkMZBKkpul2cFLTC+DhWvoNKwBcvlacfbp+MwMx8Xlo2dQKXdrbQvqU8cESCfDORC/cuJ2ByOjcZ34PVDNUpK1FP8rD0jVhCI/KhqmJAYYPdMOG5c0xcuoVJKUUD6W6FJSKs+eTEZeQDxcnI0wa6YnPFjbG5DnXoOEoUr1Ukbbm7KhEiyZGOHEmAXMWhcDFSYnZU+rCQCbB1t0PkJdXhJA7GRgzzANRj3KRlq5Gz872aOhtjpg4zv18ETyMCsfCuRNQoFbDSKnEjPeXwaWMYX1AceYnKzMdi9+bBAgCioqK0OPlQej/6hgAQEGBGj/t24b3Pl6HuvUbAwDsHV0Qdvs6Th87gAaNWoiOLz09BeaW1jpl5pbWSE9L0Sm7duUc1q34AGpVPiytbDFv8VqYmVuKvh6Jw1X7xGNHqgZSqVRQqVQ6ZZoiNaQywyqKqOZZszkCc9+uh50bWkMAEBuXh59PxsO/Z9lDZmZNrova7iZ4a9615xso1Xi3wjJxKyxT+zjkTiZ2rm+N/i874+udUQCAU78naffff5CDiMgc7P26LZo3skTQjfTnHDHVVFIJkJ6hxvIv70KjAcIismFno8DwQa7YuvsBAODjlaEImO6N/233RWGRgLsRWTj5WyK8vUyrOHp6FpxcPLBk9bfIy83G5T9OY9Pqxfjg0w1ldqZuhwThp33bMGbyXHjVa4j4uEf47quVOLB7CwYOG4+EuEdQqfKxdMHbOscVFhagVm1v7ePxr3bV/qzRaFBYoNYp69D1ZYx76z1R99GgcUssWf0tsjPT8esv/8O6Ze9j4WffwOJvnTCiqsaOVBWztbWFTCZDQkKCTnlCQgIcHct+Ux8YGIhFixbplLnVHQ1377GVFmd1lpFZgMIiAdZWcp1ya0s5UtLKnjydnlmA95fcgqFcAnMzOZJT1Zgy2hOxCfml6s580wvtW1tjWsB1bQaB9FNF2trfFRUJuHc/G65OyqfWiU3IR1qGGq7OSnak9FRF2lpymhpFhYJOFvPBo1zYWitgYCBBYaGA2Ph8vB1wHUYKKUyMDZCSpsaiuQ0QG1/6tY9qHgO5HI7ObgAAT68GuB9+B8cO7cH4qQGl6u7fuQkduvVGt179AQButbygys/DN18Gov+rY5GfVzz64t0FK2FlbadzrFz++IPbJau/1f4ccfcWdm9fh/lLNmjLlMYm2p8tLW2QmZ6qc67M9FRYWtnolBkZKYvvw9kNXvUbY/abg3H2xE94ZegYMU8HicRV+8TjHKkqZmhoiJYtW+LUqceTNzUaDU6dOgVfX98yjwkICEBGRobO5uo14nmFXO0UFgq4G56Flk0er+YjkQAtm1rpZALKoi4QkJyqhkwmQZf2dvj9ou7wgplveqGzry2mz7+BuDI6WaRf/ktbKyGVArVrmSD5HzpedjaGsPirg0/6qSJtLeR2JlyclJA88V7IzVmJ5BQVCgt1V4HMV2mQkqaGmYkB2jS3xrlLKaAXj6DRoLCg7Hm9alU+pBLdt4HSkgl1ggAXN0/I5YZISYqHo7ObzmZj93g6wpPlVjZ2kMkMdMqezCJ51W+MWzeu6lzzZvBleP01dPCp9yEIKHjKfdCzI5VJKm17UTEjVQ3MmjULo0ePRqtWrdCmTRusXr0aOTk5GDu27AyTQqGAQqH7HQ/6Pqxv98FHmD+zPkLDs3DnbhZe7e8CpZEUR07GAwA+mOmNpBQ1Nu2IBAD41DODrY0C4fezYWujwLjXPSCVArt+jNaec/YUL/Ts7ICAJTeRm1cIa8viT4azc4tKTdYm/SG2rY0Z5oFbYZmIic2DqakBXh/oBkc7BQ7/EgcAUBpJMXZ4LZw9n4SUNDVcHJV4a2xtxMTl4fKfqU+Ng158YtvawaOxGNzXGdMneuGHwzFwdVZi5FB37D8coz1nm+ZWkEiA6Jg8uDgpMXVsbUQ/ytWek2quPdu/RNOW7WFj54D8vFycP3scd27+ibkL1wAANq5aCCtrO7w2eioAoHnrTjj6v13wqF0Pdeo1QkLcQ+zfuRnN23SCVCaD0tgEfQaOwHdfr4ZGI8Dbpylyc7Nx784NGClN0LmHv+gY/fq9hiXvT8bPB3aiWesOuPDbCdwPv4Nxf2XM8vPz8L+9W9GyTSdYWtsiKzMdJ47sR1pKEtp27PHsniyiZ4QdqWrgtddeQ1JSEhYsWID4+Hg0a9YMx44dK7UABT3d6XNJsLSQY8KIWrC2MkT4/WzM/ihEu8y0g50RNE98IGtoKMXEN2rB2VGJvPwiXLyago9XhiI7p0hbZ2AfFwDAusBmOtdasjoUR0/pDsUk/SG2rZmZGmDetHqwtjJEVnYhwsKzMHluMKIeFg+bKdIAdWqZoHd3B5iaGCA5VY0r11Lx1c4oFBTyu6T0mdi2lpiswqwFIXhnQh1sW9sKySkq7DsUg50/PP6AyNTEAG+O8oSdrQKZWQU4ez4Zm7+NRFER21pNl5mRho2rFyE9NRnGJqZwq+WFuQvXoHHztgCA5KQESJ7IQA14bSwkEgn2fbcJaalJMDe3RPM2HTH0jSnaOkNGvAkzc0sc2r8dWxJiYGJiBo/a3uhfwSF29Ro0wVuzP8a+nRux99sNcHR2w8z3l2u/Q0oqlSLu0QOsOf0zsjLTYWpugdpeDfDB0k1lfhcWPVuSFzhzVFkkgiDw1fMF0LHf2aoOgYiIqEZa9VnTqg6B9ERrb8uqDuGprnYpe0rJs9Dq7Iv5lT7MSBERERER6TmJlEsniMVnjIiIiIiISCRmpIiIiIiI9ByXPxePGSkiIiIiIiKRmJEiIiIiItJzL/L3PVUWZqSIiIiIiPScRCqptE2MoqIifPjhh/D09IRSqUSdOnXw8ccf48mFxgVBwIIFC+Dk5ASlUomePXvi3r17OudJTU3FiBEjYG5uDktLS4wfPx7Z2dk6dW7cuIFOnTrByMgIbm5uWL58uahY2ZEiIiIiIqJqYdmyZdiwYQPWrVuHO3fuYNmyZVi+fDnWrl2rrbN8+XJ88cUX2LhxIy5dugQTExP4+fkhPz9fW2fEiBG4desWTpw4gcOHD+O3337DpEmTtPszMzPRq1cveHh4ICgoCCtWrMDChQuxefPmcsfKoX1ERERERHquuix/fv78efTv3x/+/v4AgFq1auH777/H5cuXARRno1avXo0PPvgA/fv3BwDs2LEDDg4OOHjwIIYNG4Y7d+7g2LFjuHLlClq1agUAWLt2Lfr06YPPPvsMzs7O2LlzJ9RqNb755hsYGhqiYcOGCA4OxsqVK3U6XP+kejxjRERERET0QlKpVMjMzNTZVCpVmXXbt2+PU6dO4e7duwCA69ev49y5c+jduzcAIDIyEvHx8ejZs6f2GAsLC7Rt2xYXLhR/8e+FCxdgaWmp7UQBQM+ePSGVSnHp0iVtnc6dO8PQ0FBbx8/PD2FhYUhLSyvXfbEjRURERESk5ypzjlRgYCAsLCx0tsDAwDLjeO+99zBs2DDUr18fcrkczZs3x4wZMzBixAgAQHx8PADAwcFB5zgHBwftvvj4eNjb2+vsNzAwgLW1tU6dss7x5DX+DYf2ERERERFRpQkICMCsWbN0yhQKRZl19+7di507d2LXrl3a4XYzZsyAs7MzRo8e/TzCLTd2pIiIiIiI9FxlLn+uUCie2nH6uzlz5mizUgDQuHFjPHjwAIGBgRg9ejQcHR0BAAkJCXByctIel5CQgGbNmgEAHB0dkZiYqHPewsJCpKamao93dHREQkKCTp2SxyV1/g2H9hERERERUbWQm5sL6d8WvpDJZNBoNAAAT09PODo64tSpU9r9mZmZuHTpEnx9fQEAvr6+SE9PR1BQkLbO6dOnodFo0LZtW22d3377DQUFBdo6J06cgLe3N6ysrMoVKztSRERERER6rrp8j1S/fv2wZMkSHDlyBFFRUThw4ABWrlyJgQMHFscpkWDGjBn45JNP8NNPPyEkJASjRo2Cs7MzBgwYAABo0KABXn75ZUycOBGXL1/GH3/8gWnTpmHYsGFwdnYGALz++uswNDTE+PHjcevWLezZswdr1qwpNQTxn3BoHxERERGRnqsuy5+vXbsWH374Id566y0kJibC2dkZb775JhYsWKCtM3fuXOTk5GDSpElIT09Hx44dcezYMRgZGWnr7Ny5E9OmTUOPHj0glUoxePBgfPHFF9r9FhYW+OWXXzB16lS0bNkStra2WLBgQbmXPgcAifDk1wRTjdWx39mqDoGIiKhGWvVZ06oOgfREa2/Lqg7hqe4MfqnSzt3ghxOVdu6qxIwUEREREZGeEzsEjzhHioiIiIiISDRmpIiIiIiI9BwzUuIxI0VERERERCQSM1JERERERHqOGSnx2JEiIiIivbYgMLqqQyA9cXSbZVWHQM8QO1JERERERHquunyPVE3CjhQRERERkZ6Tyji0Tyx2PYmIiIiIiERiRoqIiIiISM9xsQnxmJEiIiIiIiISiRkpIiIiIiI9x8UmxOMzRkREREREJBIzUkREREREeo5zpMRjRoqIiIiIiEgkZqSIiIiIiPQcM1LisSNFRERERKTnuNiEeHzGiIiIiIiIRGJGioiIiIhIz3Fon3jMSBEREREREYnEjBQRERERkZ7jHCnx+IwRERERERGJxIwUEREREZG+k3COlFjMSBEREREREYnEjFQ18Ntvv2HFihUICgpCXFwcDhw4gAEDBlR1WDXOoD7OGD7IDdZWhoiIzMaqTeG4cy+rzLq9ezhg/oz6OmUqtQY9Bv+ufTxuuAd6dLaHva0ChYUahIVnY/O3kbh9t+xzkn4T0/769XLEy90dUdvDGAAQFp6NTTsin1qf9NezbldWlnJMGVMbbZpZwdTUANdvZmDVpnA8ist7LvdDz8dQfzuMG+qEg78kYdOuuDLruDsrMHKQI+rWUsLB1hCbdsXi4C/JlR5bx9YWGDXIAQ62hoiJV2HrvnhcuVHcRmUyYPQgR7RqYgYnewVycotw7XY2tu6LQ2p6YaXHpu+4ap94zEhVAzk5OWjatCm+/PLLqg6lxure0Q7TJtTB1u+jMH5GEMIjs7FycWNYWsifekx2TiFeGXleuw0Zf1Fn/8PYPKzaeA+jp13FW/OCEZeYj5WLm8DS/OnnJP0ktv01b2yJk78l4u33r+PNOdeQkKzCysVNYGtt+Jwjp+qsMtpV4PxGcHYwwntLbmHs9CDEJ+Vj9SdNYKTg24EXRT1PJfp0tcH96H/uHBsppIhPUv/VSSl4JtduXN8E2z6r/9T9DbyM8d5kdxz/LQ3TFtzDhWuZ+PAdD3i4KAAACkMp6ngo8f1PiZj20T18su4BXB0V+Gh6rWcSH/0ziVRaaduL6sW9sxqkd+/e+OSTTzBw4MCqDqXGGjbAFYeOx+HnUwmIepiLFevvIV+lQd+XHJ96jCAAqekF2i3tb39ITpxNxNXr6YhNyEdkdC7Wfh0BUxMD1KllUtm3QzWM2Pa3+PNQHPg5FuGROYh+lIdla8MglQKtmlo958ipOnvW7crNWYlG9c3x+YZ7CL2XhYcxefhs/T0oDKXo2cX+ed4aVRIjhRRz3nTHmq2PkJ1b9I9170bmYcueOJy9lIGCQqHMOhIJ8Kq/HbauqI+Dmxvhy8V10bGVRYXj6/+SLa6GZOGHo0l4GKfCtz8mIOJBHvr1tAUA5OZpMP+zSPx+JQMx8SqERuRiw3cxqOdpDDtrfohJ1Q87UlTjGRhIUM/LDFevp2nLBAG4GpyGht7mTz1OqZRh/5a2+OGbtgic3xCe7sb/eI3+LzshK7sQ4VHZzzR+qtkq2v6epFDIYCCTIDP72XwqTDVfZbQrubz4T75KrdE5p7pAgyY+FX9zTNXH1JHOuHI9E8G3n83fqdf62qNHByus3f4Ik+ffxYFfkjHnTTc09q7YB4oNvIxLxRYUko0GdZ7+99dYKYNGIyDnXzqG9N9JpJJK215U7EhRjWdhLoeBTILUNN03oanpBbCxKnuoVPSjPCxdE4b3PrmJj1eGQiqVYMPy5rCz0a3fvrU1ftnbEad/6IRX+7ti5oIbyMjkOG16rCLt7+/eGuOJ5FQ1rgan/Xtl0guV0a4ePMpFfGI+Jo/2hJmJAQwMJBgx2A0OdkblPidVX13aWqCOhxJb98c/k/PJDSR4ra89Vm15hD9vZiM+SY2T59Jw+nw6ene1rtA5rSwMkJah+zc0LbMQVhZlT9mXyyUY96ojzl5KR26+psw6RFWJi03UQCqVCiqVSqdMU6SGVMY/hOV1KywTt8IytY9D7mRi5/rW6P+yM77eGaUt//NGOsZOvwpLczn69XLC4nkNMGn2NaRnMHNAz8YbQ9zQo5M93n7/OtQFZQ+vIRKrrHZVVCRg/qe38N473ji6uwMKiwQEBafhwtUUSLjscY1may3Hm6874/0VkSh4Rq8jTvaGMFJI8ekcT51yAwMJIh7kax//uLGh9mepVAK5gUSn7PSFdKzbHiP6+jIZ8P5bHpBAUqHjSbwXeS5TZWFHqgYKDAzEokWLdMrc6o6Gu/fYKoqoamVkFqCwSIC1le74aWtLOVLS1OU6R1GRgHv3s+HqpNQpz1dpEBOXj5i4fNwKy8L3m1qj70uO+G7/w2cWP9Vs/6X9DR/oihGD3THjw+uIiMqpzDCphqmsdhUWkY2x04NgYiyD3ECK9MwCbP6sOULDuWJkTVa3lhJWFnKsW1RXWyaTSdCongn69bDFKxNCoBHZv1IaFb+p/mhVFJL/lhl9ck7V1AX3tD/Xr2OMcUMdMXfpfW1Zbt7jIXlpGaWzT1bmpbNUJZ0oexs53lt2n9koqrbYkaqBAgICMGvWLJ2yl4ddqqJoql5hoYC74Vlo2cQKv19MAVA8QbZlUyv8eKR8n2JJpUDtWia4cDX1n+tJJDCU8xMbeqyi7e/1QW4Y9ao7Zn90A2HhnHdHuiq7XRXPNymCq5MS3l5m+OqJTDzVPMG3szF5fphO2azxbngYr8K+I4miO1EAEB2rgrpAAzsbOULCnv5BT1zi4469rbUcRRrdsifdCc9FMx9TnWXWmzc0xZ2IXO3jkk6Us4MC7y2LQFYO50Y9Ly/yXKbKwo5UNZCdnY3w8HDt48jISAQHB8Pa2hru7u6l6isUCigUCp0yfR/Wt/vgI8yfWR+h4Vm4czcLr/Z3gdJIiiMni8eKfzDTG0kpamzaEQkAGDPMA7fCMhETmwdTUwO8PtANjnYKHP6l+Ps2jBRSjHrVA39cTkZyqhqW5nIM8neGrY0Cv/6RVGX3SdWT2PY3YrAbxo+ohUWf3UFcQj6sLYuzDnn5RcjjJ6/0l8poV9062CI9owAJSSrUrmWC6RO98PulZFy5xvl5NVlevgYPYnSH/OerNcjKLtSWz57ohpS0Amz7aw6VgUwC97+WHTeQSWBjJUdtdyPk5WsQl6hGXr4GPxxNwqThzpBKJLh1NwfGxjI0rGuM3DwNTv4hvs3870Qylr9XB4NetsXl61no0tYSdT2V+GLbIwDFnaj5Uz3g5aHER6ujIJVKtBmsrOwiFBZx+DNVL+xIVQNXr15Ft27dtI9Lsk2jR4/Gtm3bqiiqmuX0uSRYWsgxYUQtWFsZIvx+NmZ/FKJd0tzBzkjnEzkzUwPMm1YP1laGyMouRFh4FibPDUbUw+JPxTQaAR6uSvTu0RAW5nJkZhbgzr0sTH0vGJHRuWWFQHpMbPsb0NsZhnIplgQ01DnPN7ui8M33D55n6FSNVUa7srFWYNr4OrC2NERKmhrHTidg2x62OX1gbyOHIDxuMNZWBvhycT3t4yG97TCktx1uhGZj3l9D83b8mICMrEK82tcOjnYuyMnVIPxBHvYcSqxQDHfCc7FsUzRGD3LEmMGOiElQ4+MvHmg7ezZWcvi2KF5Bcv3H9XSOnbs0AiGhHAJdmZiREk8iPPm/imqsjv3OVnUIRERENZKZDb/DjZ6Po9uaVHUIT5U4f0ylndt+ybZKO3dV4mQPIiIiIiIikTi0j4iIiIhIz/FrEMRjRoqIiIiIiEgkZqSIiIiIiPQcv5BXPD5jREREREREIjEjRURERESk57j8uXjMSBEREREREYnEjhQRERERkb6TSitvE6FWrVqQSCSltqlTpwIA8vPzMXXqVNjY2MDU1BSDBw9GQkKCzjmio6Ph7+8PY2Nj2NvbY86cOSgsLNSpc+bMGbRo0QIKhQJeXl7Ytm2b+KdM9BFERERERESV4MqVK4iLi9NuJ06cAAAMHToUADBz5kwcOnQI+/btw9mzZxEbG4tBgwZpjy8qKoK/vz/UajXOnz+P7du3Y9u2bViwYIG2TmRkJPz9/dGtWzcEBwdjxowZmDBhAo4fPy4qVokgCMIzuGeqYh37na3qEIiIiGokMxurqg6B9MTRbU2qOoSnSv3kzUo7t/UHmyp87IwZM3D48GHcu3cPmZmZsLOzw65duzBkyBAAQGhoKBo0aIALFy6gXbt2OHr0KPr27YvY2Fg4ODgAADZu3Ih58+YhKSkJhoaGmDdvHo4cOYKbN29qrzNs2DCkp6fj2LFj5Y6NGSkiIiIiIj0nkUgrbVOpVMjMzNTZVCrVv8akVqvx3XffYdy4cZBIJAgKCkJBQQF69uyprVO/fn24u7vjwoULAIALFy6gcePG2k4UAPj5+SEzMxO3bt3S1nnyHCV1Ss5RXuxIERERERFRpQkMDISFhYXOFhgY+K/HHTx4EOnp6RgzZgwAID4+HoaGhrC0tNSp5+DggPj4eG2dJztRJftL9v1TnczMTOTl5ZX7vrj8ORERERGRvqvE5c8DAgIwa9YsnTKFQvGvx23ZsgW9e/eGs7NzZYX2n7AjRURERERElUahUJSr4/SkBw8e4OTJk/jxxx+1ZY6OjlCr1UhPT9fJSiUkJMDR0VFb5/LlyzrnKlnV78k6f1/pLyEhAebm5lAqleWOkUP7iIiIiIj0nEQqrbStIrZu3Qp7e3v4+/try1q2bAm5XI5Tp05py8LCwhAdHQ1fX18AgK+vL0JCQpCYmKitc+LECZibm8PHx0db58lzlNQpOUd5sSNFRERERETVhkajwdatWzF69GgYGDweQGdhYYHx48dj1qxZ+PXXXxEUFISxY8fC19cX7dq1AwD06tULPj4+GDlyJK5fv47jx4/jgw8+wNSpU7VZscmTJ+P+/fuYO3cuQkNDsX79euzduxczZ84UFSeH9hERERER6TlJJc6REuvkyZOIjo7GuHHjSu1btWoVpFIpBg8eDJVKBT8/P6xfv167XyaT4fDhw5gyZQp8fX1hYmKC0aNHY/Hixdo6np6eOHLkCGbOnIk1a9bA1dUVX3/9Nfz8/ETFye+RekHwe6SIiIgqht8jRc9Ldf4eqYwVb1fauS3mrK20c1clZqSIiIiIiPSdhDN+xGJHioiIiIhIz1WnoX01BbueREREREREIjEjRURERHqtxyuNqjoEoqpXwWXK9RmfMSIiIiIiIpGYkSIiIiIi0nMSCedIicWMFBERERERkUjMSBERERER6TvOkRKNzxgREREREZFIzEgREREREek5fo+UeOxIERERERHpOwkHqonFZ4yIiIiIiEgkZqSIiIiIiPQdh/aJxowUERERERGRSMxIERERERHpOQnnSInGZ4yIiIiIiEgkZqSIiIiIiPQd50iJxowUERERERGRSMxIERERERHpOYmU+RWx2JEiIiIiItJ3Eg7tE4tdTyIiIiIiIpGYkSIiIiIi0ncc2icanzEiIiIiIiKRmJGqBgIDA/Hjjz8iNDQUSqUS7du3x7Jly+Dt7V3VodUog/o4Y/ggN1hbGSIiMhurNoXjzr2sMuvKZBKMHOqO3t0dYGujwMOYXGzYdh+X/kzT1nljiBu6tLeFh4sxVGoNQkIzsWHbfTyMyXtet0TVlJi2BgCmJjJMGumJzr62MDeTIyExH2u+isDFoFQAwIDeThjQ2xlODkYAgMjoXGzb/UC7n/SXmLa29tOmaN7YslT5+SspmLv4JgDg/Rne6NPDUWf/paBUzF4Y8sxjp+cr+MxmRN48gYyk+5DJjeDg0RxtXp4NSzvPpx6TmnAPQSfWIjnmFrLTY9HO/z007ji60mO9H3IMV098gey0GJjbeKDNy7PhXr+Ldn/QyXWIuPEzctLjIZXJYevig9a9ZsDevWmlx6bXOEdKNHakqoGzZ89i6tSpaN26NQoLC/H++++jV69euH37NkxMTKo6vBqhe0c7TJtQB599eRe372bh1VdcsHJxYwyffAXpGQWl6k96oxZ6dXPAsrV3Ef0oF21aWOHT9xti8txg3LufDQBo3sgSPx6JRei9LMikEkwa5YlVi5vgjbeuIF+led63SNWE2LZmYCDBqo+bIC29AB8uvY2kFBUc7Y2QnV2orZOUrMbG7ZF4FJsHiQTo3cMBgfMbYtyMIERG5z7P26NqRGxbe//TW5AbPH4jZGEux9YvWuHXP5J06l0MSsWnq0O1jwsKhMq7CXpu4u5fQUPf12Hr2giCpghXjq/C0W/GY8jMw5AbGpd5TJE6H+bWbqjd2A8Xjix9JnHE3r+Ms/sCMHzeqTL3Jzy4htO730Vrv5lwr98VEcGHceK7tzFw2n5YO9YDAFjY1kKHVz6AmbUbCgvycfPcdvz8zQS89u5xKE2tn0mcRM8CO1LVwLFjx3Qeb9u2Dfb29ggKCkLnzp2rKKqaZdgAVxw6HoefTyUAAFasvwff1jbo+5Ijvtv/sFR9v24O2LE3WvuJ/8GjcWjVzArDBrji45XFbzD+/gntp6vDcHhne3h7meH6rYxKviOqrsS2Nf+ejjA3lWPynGAUFRW/YY1PVOnU+eNKis7jzd9GYUBvZ/h4m7MjpcfEtrWsJzrnANCjsz1UqiL8ek63I6Uu0CA1vXRHjGq23uO+0nncZUggvlvSAckxt+Dk2brMY+zcGsPOrTEA4PKxlWXWETQaXP/ta9y5vBd5WcmwsK2F5t2noHZjvwrFefOPHXCt2xFNO48HALTqNR0x4edx68IudBq4EADg1ayvzjHt/N9D2NUfkBofBhcv3wpdl/4dlz8Xjx2paigjo/hNurU1P3UpDwMDCep5meHb/dHaMkEArganoaG3eZnHyOVSqAp0s0oqlQZNfCyeeh0TExkAIDOLb0D0VUXaWse2NrgZmonZk73Qsa0t0jMLcOJsInb+EA1NGYlNqRTo1sEORkYy3ArNrKxboWquIm3t7/q+5IhTvyWWyqA3b2SJQ9/6Iiu7EEE30vHVd5HIzCp8ylmoplLnFw8BVSif/netPILPbkb4tUPoOGAhLGw9EB95FWf2zoXSxApOtduIPl9C9PVSwwdd63ZE1O2yM1hFhWqEXt4LQyMz2DjVr9A9EFUWdqSqGY1GgxkzZqBDhw5o1KhRVYdTI1iYy2EgkyA1TbeDk5peAA/XsoczXL6WimEDXHH9ZgZi4vPQsqkVurS3hVRa9vhgiQR4Z6IXbtzOYIZAj1WkrTk7KtGiiRFOnEnAnEUhcHFSYvaUujCQSbB19wNtvdoeJti4ojkMDaXIyyvC+0tuIeoh25q+qkhbe1KDumaoU8sUS7+4q1N+KSgVZ88nIy4hHy5ORpg00hOfLWyMyXOuldmxp5pJ0Ghw4XAgHDxaaIfLVURRoRrBv25Gn/Fb4ODRHABgbu2G+Kgg3Lm8t0IdqbzsZChNbXXKlKY2yMtO1il7cOdXnN79LgoL8mBsZoc+47bAyMSqwvdC5SBhRkosdqSqmalTp+LmzZs4d+7cU+uoVCqoVLpDgzRFakhlhpUd3gtjzeYIzH27HnZuaA0BQGxcHn4+GQ//no5l1p81uS5qu5vgrXnXnm+gVONJJUB6hhrLv7wLjQYIi8iGnY0Cwwe56nSkomNyMXb6VZgaG6BrBzvMn+mNtwOuszNFFdK3lyPCI7NLLUxx6vfHw/zuP8hBRGQO9n7dFs0bWSLoRvpzjpIqyx8/LUZawj30m7zzP50nI+UBCgvy8PM3E3TKNUUFsHFqoH289aOW2p8FTRGKitQ6ZV7N+mmH7ZWXc522GPT2j8jPTUPolX04+f1MDHhrD5SmNhW7Gfp3T/kwmZ6OHalqZNq0aTh8+DB+++03uLq6PrVeYGAgFi1apFPmVnc03L3HVnaI1VJGZgEKiwRYW8l1yq0t5UhJU5d5THpmAd5fcguGcgnMzeRITlVjymhPxCbkl6o7800vtG9tjWkB15GUUvb5SD9UpK0lp6lRVCjofNr/4FEubK0VMDCQoLCweN5UYaGAmLji9hcWkY0Gdc0w9BUXrPjyXuXcDFVrFWlrJYwUUvToZI8tO6P+9TqxCflIy1DD1VnJjtQL4o//fYzo0LPoO+lbmFqU/eFgeRWqij/IeXn0BhibO+jskxk8/vB20Ns/an9OengDl499Dv+J27Vlhkam2p+Vpralsk952SmlslRyQ2NY2HrAAh5wcG+GPZ/5IezqD2jWddJ/uieiZ4k5vGpAEARMmzYNBw4cwOnTp+Hp+fSlSgEgICAAGRkZOpur14jnFG31U1go4G54Flo2eZzyl0iAlk2tcCvsn+eYqAsEJKeqIZNJ0KW9HX6/qDvpf+abXujsa4vp828groxOFumXirS1kNuZcHFS6qwq6+asRHKKStuJKotEUjyXj/TTf3ld69bRDnK5FMfPJPzrdexsDGHx14dJVLMJgoA//vcxom6fhP+ErTC3fvoHsuVl6eAFmYEhstPjijs1T2ymlk7aek+WG1s4QCKV6ZQ9mUVycG+K2IiLOtd5FH4e9u7N/vX+igrZTiuTRCKttO1FxYxUNTB16lTs2rUL//vf/2BmZob4+HgAgIWFBZRKZan6CoUCCoVCp0zfh/XtPvgI82fWR2h4Fu7czcKr/V2gNJLiyMni5/KDmd5ISlFj045IAIBPPTPY2igQfj8btjYKjHvdA1IpsOvHxxO7Z0/xQs/ODghYchO5eYWwtiz+ZDg7twhqNScT6Cuxbe3g0VgM7uuM6RO98MPhGLg6KzFyqDv2H47RnvPNUZ64GJSKhKR8GCsN8FIXezRvbIlZH/G7ffSZ2LZWou9LTvj9YnKpBSSURlKMHV4LZ88nISVNDRdHJd4aWxsxcXm4/Ce/s6ym++N/ixFx/Qh6jVwHucIEuVnFwzgNjcxgIC/+jrpf986DibkD2rw8C0DxHKj0xAgAxcP1cjMTkRJ7BwZ/ZYMMFSZo3GksLhxZCkEQ4FirBdT5WYh/cA2GClPUazlAdJyNOozCoc2jcOP3rXD37oKIGz8jOeYWOg0sHmlToM5F8K+b4N6gG4zN7JCfm47bF3YhNzMBnhVcKZCosrAjVQ1s2LABANC1a1ed8q1bt2LMmDHPP6Aa6PS5JFhayDFhRC1YWxki/H42Zn8UgrS/lvh1sDOC5okP/w0NpZj4Ri04OyqRl1+Ei1dT8PHKUGTnFGnrDOzjAgBYF9hM51pLVofi6Kl//6SXXkxi21pisgqzFoTgnQl1sG1tKySnqLDvUAx2/vC4025lIccHM+vDxtoQOTmFiIjKwayPQnA1OO3vlyc9IratAYCbixJNG1pgxoc3Sp2vSAPUqWWC3t0dYGpigORUNa5cS8VXO6NQ8A/ZUaoZ7lzaDQA4/JXuinhdhnyKei0HAgBy0uN0sgO5WUn4ce0g7eMbv3+DG79/AyfP1ug7aQcAoNVL06E0sUbw2c3IOvAIhkZmsHX2QbNuFRti5+DRHN2HrcDVX9bgyvFVsLD1wEtvrNUuiiGRyJCedB93/zyI/Jw0GBlbws61MfpN+g7WDnUrdE0qJ86REk0iCAJfPV8AHfudreoQiIiIaqQBYztVdQikJ94dVH2HueXvWV5p5zZ6bW6lnbsqMSNFRERERKTvXuC5TJWFzxgREREREZFIzEgREREREek7CedIicWOFBERERGRvpNyoJpYfMaIiIiIiIhEYkaKiIiIiEjfcbEJ0fiMERERERERicSOFBERERGRvpNKKm8TKSYmBm+88QZsbGygVCrRuHFjXL16VbtfEAQsWLAATk5OUCqV6NmzJ+7du6dzjtTUVIwYMQLm5uawtLTE+PHjkZ2drVPnxo0b6NSpE4yMjODm5obly8V9lxY7UkREREREVC2kpaWhQ4cOkMvlOHr0KG7fvo3PP/8cVlZW2jrLly/HF198gY0bN+LSpUswMTGBn58f8vPztXVGjBiBW7du4cSJEzh8+DB+++03TJo0Sbs/MzMTvXr1goeHB4KCgrBixQosXLgQmzdvLnesnCNFRERERKTvqskcqWXLlsHNzQ1bt27Vlnl6emp/FgQBq1evxgcffID+/fsDAHbs2AEHBwccPHgQw4YNw507d3Ds2DFcuXIFrVq1AgCsXbsWffr0wWeffQZnZ2fs3LkTarUa33zzDQwNDdGwYUMEBwdj5cqVOh2uf1I9njEiIiIiItJ7P/30E1q1aoWhQ4fC3t4ezZs3x1dffaXdHxkZifj4ePTs2VNbZmFhgbZt2+LChQsAgAsXLsDS0lLbiQKAnj17QiqV4tKlS9o6nTt3hqGhobaOn58fwsLCkJaWVq5Y2ZEiIiIiItJ3EkmlbSqVCpmZmTqbSqUqM4z79+9jw4YNqFu3Lo4fP44pU6bgnXfewfbt2wEA8fHxAAAHBwed4xwcHLT74uPjYW9vr7PfwMAA1tbWOnXKOseT1/g37EgREREREek7qbTStsDAQFhYWOhsgYGBZYah0WjQokULfPrpp2jevDkmTZqEiRMnYuPGjc/5Cfl37EgREREREVGlCQgIQEZGhs4WEBBQZl0nJyf4+PjolDVo0ADR0dEAAEdHRwBAQkKCTp2EhATtPkdHRyQmJursLywsRGpqqk6dss7x5DX+DTtSRERERET6rhKH9ikUCpibm+tsCoWizDA6dOiAsLAwnbK7d+/Cw8MDQPHCE46Ojjh16pR2f2ZmJi5dugRfX18AgK+vL9LT0xEUFKStc/r0aWg0GrRt21Zb57fffkNBQYG2zokTJ+Dt7a2zQuA/YUeKiIiIiIiqhZkzZ+LixYv49NNPER4ejl27dmHz5s2YOnUqAEAikWDGjBn45JNP8NNPPyEkJASjRo2Cs7MzBgwYAKA4g/Xyyy9j4sSJuHz5Mv744w9MmzYNw4YNg7OzMwDg9ddfh6GhIcaPH49bt25hz549WLNmDWbNmlXuWLn8ORERERGRvqsmy5+3bt0aBw4cQEBAABYvXgxPT0+sXr0aI0aM0NaZO3cucnJyMGnSJKSnp6Njx444duwYjIyMtHV27tyJadOmoUePHpBKpRg8eDC++OIL7X4LCwv88ssvmDp1Klq2bAlbW1ssWLCg3EufA4BEEATh2dw2VaWO/c5WdQhEREQ10oCxnao6BNIT7w6qHp2VsuT/XP4vohXLqE/5Oyc1CTNSRERERET6Tlp9O3nVFZ8xIiIiIiIikZiRIiIiIr126LuLVR0C6Yl3B7Wv6hCeTiKp6ghqHHakiIiIiIj0XTVZbKIm4TNGREREREQkEjNSRERERET6jkP7RGNGioiIiIiISCRmpIiIiIiI9B2XPxeNzxgREREREZFIzEgREREREek5gXOkRGNGioiIiIiISCRmpIiIiIiI9B2/R0o0PmNEREREREQiMSNFRERERKTvmJESjR0pIiIiIiI9x8UmxGPXk4iIiIiISCRmpIiIiIiI9B2H9onGZ4yIiIiIiEgkZqSIiIiIiPQd50iJxowUERERERGRSMxIERERERHpOynzK2LxGSMiIiIiIhKJGalqYMOGDdiwYQOioqIAAA0bNsSCBQvQu3fvqg2shhnUxxnDB7nB2soQEZHZWLUpHHfuZf3rcT062WHRXB/8djEZ7y+5pS1/f4Y3+vRw1Kl7KSgVsxeGPPPYqXoT27a6dbDFhDc84WhvhEexudiwLRIXg1K1+88d6lLmcV9+E4HvDzwCACz9oCHq1jaFpYUhsrILcPV6OjZsu4+UVPWzvTmq1sS0PU93Y4wfUQvedczg5GCENV+FY99PMTp1BvR2woDeznByMAIAREbnYtvuBzrtk2qmJj7mGN7fGfXqmMLW2hDzl4bi3OWn/147tbXGgJcd4VXLBHK5BFEP87B1z0NcCU6v1Di7+tpg3HA3ONobISYuDxu/fYBLfxZfUyaTYMLr7mjXwhJODkbIyS1C0I10bPr2AVLSCio1LuL3SFUEM1LVgKurK5YuXYqgoCBcvXoV3bt3R//+/XHr1q1/P5gAAN072mHahDrY+n0Uxs8IQnhkNlYubgxLC/k/Hudor8DUcXUQfDO9zP0Xg1Lxysjz2m3hijuVED1VZ2LbVqP65vhojg8O/xKHcdOD8PvFFATObwhPd2NtnSfb1Csjz+PT1aHQaAScPZ+srfNnSDoWLLuN1ydfxgeBt+HiaIRP3vOp9Pul6kNs21MoZIiNz8fG7feRnKoqs05Sshobt0di/Iw/MWHmn/jzRlqp9kk1k1IhRXhUDlZ/db9c9Zs2NMfV6+mYt+QOJs65gWs3MxAYUB91PU0qHEOzhubYvbHFU/c39DbDh7Pq4edTiZg4+zp+v5yKJfPqa9ufkUKKerVNsGPfI0x89zo+XB4KN2clPg1oUOGYSASJtPK2F9SLe2c1SL9+/dCnTx/UrVsX9erVw5IlS2BqaoqLFy9WdWg1xrABrjh0PA4/n0pA1MNcrFh/D/kqDfq+5PjUY6RSYMHsBtiyKwqxCfll1lEXaJCaXqDdsnIKK+sWqJoS27aGvuKCS3+m4vsDj/DgUS6+3hmFuxHZGNzXRVvnyTaVml6Aju1s8WdIuk473Pu/GNwKy0JCkgo3QzPx3f6HaOhtDpmMnxjqC7FtL/ReFtZvvY9TvyehoEAos84fV1JwMSgVj+Ly8DA2D5u/jUJefhF8vM0r81boObh0LR1bvn+I3y+VL7u47psofH8wFqHh2YiJy8dXO6PxKC4f7VtZaetIJMCIQS7YvaEFfvm+LbasbIouvjYVjnFIXydcvpaG3f+LxYOYPHzz/UPcjczBwN7FbTontwizF93Gr+dT8DA2H7fvZmPN15Go72UKe1vDCl+XqLKwI1XNFBUVYffu3cjJyYGvr29Vh1MjGBhIUM/LDFevp2nLBAG4GpyGhv/w5mDMMA+kZxTgyIn4p9Zp3sgSh771xa4NrTF7Sl2Ym3E0rD6pSNtqVN8cV4PTdMouXUtFo/pl17eylKN9K+t/bIdmpgbo1dUeN0MzUVRU9htkerFU9HVNDKm0eGizkZEMt0Izn8k5qeaSSABjpQyZ2Y8/MBwxyAV+Xe3w+ab7GD0jGPsOxWL+9Lpo6lOxNtiwnhmCbmTolF25lo6G3mZPPcbEWAaNRkB2TlGFrknlJ0iklba9qPiusJoICQmBr68v8vPzYWpqigMHDsDHh8N4ysPCXA4DmQSpfxs/nZpeAA/XsoerNPExR9+XnDB2+tWnnvdSUCrOnk9GXEI+XJyMMGmkJz5b2BiT51yDRvNMb4GqqYq0LWtLQ6Sl685jSksvgLVl2Z+m9u7uiNy8Ipw9n1Rq35TRnhjU1wVKIxluhmZi7mLOz9MXFWl75VXbwwQbVzSHoaEUeXlFeH/JLUQ9zP1P56Sab1h/ZyiNpPj1fAoAQG4gwRuDXTF74S3cupsNAIhLSELjBuZ4pZcDrt8W3/m2tpQjLV23TadlFMDasuzhqoZyCd4c6YFT55KRm8eOFFU/7EhVE97e3ggODkZGRgb279+P0aNH4+zZs2V2plQqFVQq3fHvmiI1pDKmvctDqZThg1n1sXzdXWRkPn2o3qnfH7+xvf8gBxGROdj7dVs0b2SJoBvpzyFS0gf+LznilzOJUJcxFGvXgYc4fCIeDvZGGDfcAx/MrI+5i29WQZT0IomOycXY6VdhamyArh3sMH+mN94OuM7OlB7r2ckWo191w/yloUjPKO7ouDgZQWkkw2cfNdSpKzeQ4F5kjvbx0Z1ttT/LpIBcLtUpO/FbElZuKt+8rSfJZBIsfNcbEgkqdDxVABebEI0dqWrC0NAQXl5eAICWLVviypUrWLNmDTZt2lSqbmBgIBYtWqRT5lZ3NNy9xz6XWKubjMwCFBYJsLbS/UTL2lKOlLTSK5y5OBrB2UGJpR820pZJ/3rtOHOwM16ffBmx8aXnTMUm5CMtQw1XZyU7UnpCbNsCgNR0Naz+ln2yspQjNb10/SY+FvBwNcZHy24/5fqFyMgsxMPYPDx4mIMD23zR0Nsct8I4DOtFV5G2V16FhQJi4opf48IistGgrhmGvuKCFV/e+0/npZqpewcbzHmrDj767K7OsDulkQwA8N6SO0j+22qh6oLHwzImzL6u/blBPVO8OdIDMz58vFhWzhOZpNT0Alj9LftkZSFH6t+yVDKZBIverQcHOwVmLrjFbBRVW+xIVVMajaZU1qlEQEAAZs2apVP28rBLzyOsaqmwUMDd8Cy0bGKF3y8WD0mQSICWTa3w45GYUvWjH+Vi5NQrOmUTR3rCWCnDms3hSEwu+3m3szGEhZm81B8UenGJbVsAcDM0E62aWuksO926mRVuljEHpW8vR4Tey0J4VE6pfX8n/au3byjnJ4b6oCJtr6IkkuIsAumfHh1tMW9qHSxaeRcXg3TndkY9zIVKrYGDneIfh/HFPPHBo52NIYqKBJ2yJ926m4WWjS2w/3CctqxVUwvcCnu8pH9JJ8rFSYkZC27qzNmiyvUiz2WqLOxIVQMBAQHo3bs33N3dkZWVhV27duHMmTM4fvx4mfUVCgUUCoVOmb4P69t98BHmz6yP0PAs3LmbhVf7u0BpJMWRk8UT+D+Y6Y2kFDU27YiEukBAZLTuEJbsv1bjKylXGkkxdngtnD2fhJQ0NVwclXhrbG3ExOXh8p/8vhV9IqZtAcC+n2KwLrAphg1wxfmrKejZyR71vcywfN1dnfMaK2Xo1sEO67ZElLqmTz0z1K9rhhu3M5CVXQgXJyUmjKiFR7F5ZXbI6MUktu0ZGEhQy614/pTcQAI7GwW8PE2Ql1+kzUC9OcoTF4NSkZCUD2OlAV7qYo/mjS0x6yPOv6vplEZSuDgaaR872SvgVcsYmdmFSExWY+IId9jZGOLTL8IBFA/nC3jbC2u/icKde9naeUoqtQY5uUXIy9dgz/9iMHVsLUgkQMidTJiYGKBxfTPk5Bbh+JnS8zr/zf7Dcfji44Z49RVnXAxKQ/eOtvCuY4rPNhYP3ZPJJFg8xxv1apvgvU/vQCaVaOPKzC5EYSEX26HqhR2paiAxMRGjRo1CXFwcLCws0KRJExw/fhwvvfRSVYdWY5w+lwRLCzkmjKgFaytDhN/PxuyPQrSTWh3sjKAR8fpbpAHq1DJB7+4OMDUxQHKqGleupeKrnVEo4Au5XhHbtm6GZmLRZ3cw8Q1PTBrliUexeQhYcqtU571nZ3tIJMDJ3xJLXTNfpUEXX1uMf70WjIxkSElT4VJQGhbsuc32p0fEtj1ba0Ns+6KV9vHrg9zw+iA3XAtJx9vvFw+/srKQ44OZ9WFjbYicnEJEROVg1kchpVaapJrHu44p1nz8eMj6tHGeAICjpxOxdF04bKwMYW/7+EPYvi85wMBAipmTamPmpNra8pL6ALDl+4dIzyzEiEEucHaog+zcIty9n43vfqhYVvRWWBY+XnUP4193x8QR7ngUl4/5y0K1r4921obo2MYaAPDNymY6x07/8CaCb/GDpErFOVKiSQRB4F/lF0DHfmerOgQiIqIaSSb/5y9vJ3pWzv7YvqpDeKqsq8cq7dxmrV6utHNXJQ6GJCIiIiIiEolD+4iIiIiI9JzAoX2iMSNFREREREQkEjNSRERERET6jsufi8ZnjIiIiIiISCRmpIiIiIiI9JwAzpESixkpIiIiIiIikZiRIiIiIiLScwLnSInGjhQRERERkb5jR0o0PmNERERERFQtLFy4EBKJRGerX7++dn9+fj6mTp0KGxsbmJqaYvDgwUhISNA5R3R0NPz9/WFsbAx7e3vMmTMHhYWFOnXOnDmDFi1aQKFQwMvLC9u2bRMdKztSRERERER6TpBIKm0Tq2HDhoiLi9Nu586d0+6bOXMmDh06hH379uHs2bOIjY3FoEGDtPuLiorg7+8PtVqN8+fPY/v27di2bRsWLFigrRMZGQl/f39069YNwcHBmDFjBiZMmIDjx4+LipND+4iIiIiIqNowMDCAo6NjqfKMjAxs2bIFu3btQvfu3QEAW7duRYMGDXDx4kW0a9cOv/zyC27fvo2TJ0/CwcEBzZo1w8cff4x58+Zh4cKFMDQ0xMaNG+Hp6YnPP/8cANCgQQOcO3cOq1atgp+fX7njZEaKiIiIiEjPCRJppW1i3bt3D87OzqhduzZGjBiB6OhoAEBQUBAKCgrQs2dPbd369evD3d0dFy5cAABcuHABjRs3hoODg7aOn58fMjMzcevWLW2dJ89RUqfkHOXFjBQREREREVUalUoFlUqlU6ZQKKBQKErVbdu2LbZt2wZvb2/ExcVh0aJF6NSpE27evIn4+HgYGhrC0tJS5xgHBwfEx8cDAOLj43U6USX7S/b9U53MzEzk5eVBqVSW676YkSIiIiIi0ncSSaVtgYGBsLCw0NkCAwPLDKN3794YOnQomjRpAj8/P/z8889IT0/H3r17n/MT8u/YkSIiIiIiokoTEBCAjIwMnS0gIKBcx1paWqJevXoIDw+Ho6Mj1Go10tPTdeokJCRo51Q5OjqWWsWv5PG/1TE3Ny93NgpgR4qIiIiISO9V5hwphUIBc3Nzna2sYX1lyc7ORkREBJycnNCyZUvI5XKcOnVKuz8sLAzR0dHw9fUFAPj6+iIkJASJiYnaOidOnIC5uTl8fHy0dZ48R0mdknOUFztSRERERER6ToCk0jYx3n33XZw9exZRUVE4f/48Bg4cCJlMhuHDh8PCwgLjx4/HrFmz8OuvvyIoKAhjx46Fr68v2rVrBwDo1asXfHx8MHLkSFy/fh3Hjx/HBx98gKlTp2o7b5MnT8b9+/cxd+5chIaGYv369di7dy9mzpwpKlYuNkFERERERNXCo0ePMHz4cKSkpMDOzg4dO3bExYsXYWdnBwBYtWoVpFIpBg8eDJVKBT8/P6xfv157vEwmw+HDhzFlyhT4+vrCxMQEo0ePxuLFi7V1PD09ceTIEcycORNr1qyBq6srvv76a1FLnwOARBAE4dncNlWljv3OVnUIRERENZJMLq/qEEhPnP2xfVWH8FRJty5V2rntGrattHNXJQ7tIyIiIiIiEolD+4iIiEiv9Xr1xfy0nEgUibi5TMSMFBERERERkWjMSBERERER6TmB+RXR+IwRERERERGJxIwUEREREZGeEzhHSjR2pIiIiIiI9Jwg4UA1sfiMERERERERicSMFBERERGRnhPAoX1iMSNFREREREQkEjNSRERERER6jnOkxOMzRkREREREJBIzUkREREREeo7Ln4vHjBQREREREZFIzEgREREREek5rtonHjtSRERERER6jotNiMdnjIiIiIiISCRmpIiIiIiI9ByH9onHjBQREREREZFIzEgREREREek5zpESjx2pambp0qUICAjA9OnTsXr16qoOp0Yb1McZwwe5wdrKEBGR2Vi1KRx37mU9tf7QV1wwsLczHOwUSM8swJnzydi0/T7UBcJzjJpqArFtq0SPTnZYNNcHv11MxvtLbmnLlUZSTB5dG53a2cLCzACxCfnYfygG/zsWV5m3QTWQ2LZnaiLDpJGe6OxrC3MzORIS87HmqwhcDEp9jlFTVWrfQAJvVwlszIHCIuBRMnD6ugap//6S9Z90biRB8zoSKOTF1zx6VYO07Mf7h3aSwsESMDEC8tVAZIKA08ECsvMrNy6iZ4kdqWrkypUr2LRpE5o0aVLVodR43TvaYdqEOvjsy7u4fTcLr77igpWLG2P45CtIzygoVf+lLvaYPLo2ln4RhpA7GXBzMcb86d4QBGDdlogquAOqrsS2rRKO9gpMHVcHwTfTS+17e3wdtGhihY8/v4O4xHy0aW6NWVPqIjlVjT8up1Ti3VBNIrbtGRhIsOrjJkhLL8CHS28jKUUFR3sjZGcXVkH0VFXc7SUIChcQmyJAKgW6NZHi9a5SbPpZg4Kiip2zUyMJLEyAw5fK/qDRt74EretJcOiSBunZQJfGUgz/65pFmuI6DxIE/HFbQHYeYKYEejaXYnBHCbaf1FTwTum/4hwp8ZjDqyays7MxYsQIfPXVV7CysqrqcGq8YQNcceh4HH4+lYCoh7lYsf4e8lUa9H3Jscz6jeqbI+ROBk6cTUR8ogpXrqXh5G+J8Kln9pwjp+pObNsCAKkUWDC7AbbsikJsQumPWxs1sMDR0/G4djMD8Ykq/HQ8DhGR2Wx/pENs2/Pv6QhzUzkCltxCyJ1MxCeqEHwzA+FROc85cqpKu89qcCNSQHImkJgOHLqkgYWJBI7Wj+so5IB/awlmDJDi3cFSjOgmhb1lxa/ZxluCc7cE3I0BEjOAny5pYKYEvF0fv1G/fFdAbAqQmQvEpADnb2vgYgNI+V6eahB2pKqJqVOnwt/fHz179qzqUGo8AwMJ6nmZ4er1NG2ZIABXg9PQ0Nu8zGNuhmbCu44ZGtQtfuPq7GCEdq2sceEqh7/QYxVpWwAwZpgH0jMKcOREfJn7b97JQMe2NrC1NgQANG9sCTdnJS5fSyuzPumfirS9jm1tcDM0E7Mne+GnHb7Ysa4VRg51h5R/+fWaQl78b776cdmgDlIYG0mw56wGW45rEJ8mYEQ3KYwMxZ/f0gQwVUoQlfA4W6UqKO4sudiUfYyRIdColgSPkgENR9NXGUEirbTtRcWhfdXA7t278eeff+LKlStVHcoLwcJcDgOZBKlpukNdUtML4OFqXOYxJ84mwsJcjvXLmkEiAQwMpDjwcyy+3Rf9PEKmGqIibauJjzn6vuSEsdOvPvW8qzaFY+60eji43ReFhRpoBGD52ru4fivjmcZPNVdF2p6zoxItmhjhxJkEzFkUAhcnJWZPqQsDmQRbdz94HmFTNfRScykeJglI+uvlxdUWcLYGVh98POzuVLCAei4SNHCT4FqEuJ6NiVHxvzl/S77n5AswVeqWdWsqQau6EhgaSPAoWcDe3zisrypxaJ947EhVsYcPH2L69Ok4ceIEjIyMynWMSqWCSqXSKdMUqSGVVeCjIwIANG9kgZFD3fH5xnu4HZYFVycjTJ/kheRUd2zfw84UVYxSKcMHs+pj+bq7yMh8+ryUIf1c0NDbHPMW30R8Uj6aNrTArMleSE5V4er19OcXML1QpBIgPUON5V/ehUYDhEVkw85GgeGDXNmR0lMvt5TAzhLY8cQ8JAdLCQwNgFkDdbMGBjLA0rT4Zzc7YFjnx/tlUgASoMETQ/V+virg1gNxna6LdwRcvy/Awhjo1EiKV9pJsYedKapB2JGqYkFBQUhMTESLFi20ZUVFRfjtt9+wbt06qFQqyGQynWMCAwOxaNEinTK3uqPh7j32ucRc3WVkFqCwSIC1lVyn3NpSjpQ0dZnHTHjDE8d/TcDhX4qHXt1/kAMjIxnmTquHHXujIXCoAUF823JxNIKzgxJLP2ykLSsZ/3/mYGe8PvkyklPVmDTSE+9/eks7lDQiKgd1a5ti+EA3dqQIQMVe15LT1CgqFKB54n3pg0e5sLVWwMBAgsJCvrDpE78WEtR1kWDHKQ2y8h6XGxoA2fnAd6dLd2BKhv/FpQJfH3+8v3U9CcyUEpy+/risJANV8q+JEXRW4DMxkiAhTbfN5amLt9QsIDlTg3f6y+BiUzwMkJ4/QcKMlFjsSFWxHj16ICQkRKds7NixqF+/PubNm1eqEwUAAQEBmDVrlk7Zy8MuVWqcNUlhoYC74Vlo2cQKv18sfjWWSICWTa3w45GYMo8xUkgh/G1gtuavxxIJ2JEiAOLbVvSjXIycqjtkd+JITxgrZVizORyJySoYyqWQy6Wl2phGI+AFHlZOIlXkdS3kdiZe6mKv8xrm5qxEcoqKnSg949eieAn0b09rkPG3tUbi0wSYGkmgEVBqX4nCIugsXZ6nBgzlgk5ZifQcIDtPQC0HCRLSi9uZoUHx/Kg/w58eY8l7+DLe9hBVW+xIVTEzMzM0atRIp8zExAQ2NjalyksoFAooFAqdMg7r07X74CPMn1kfoeFZuHM3C6/2d4HSSIojJ4szTh/M9EZSihqbdkQCAP64nILXBrji7v1s3L6bBRcnJSaM8MQfl1N0Ps0lEtO21AUCIqNzdY7Pzike4ldSXlhYhGsh6XhrbG2oVEWIT1KhWSMLvNzNAWu59D49Qezr2sGjsRjc1xnTJ3rhh8MxcHVWYuRQd+w/XHbHi15ML7eUoKGHBPt+10Bd+HgOk6qguIMUmQA8SgGGdpTi1F/fL2VqBNR1liDskYC4Cqx5czlMQIeGEqRmCUjPKV7+PCsPCHtU3LFytgacbSR4mCQgTw1YmRbXSc0SEJP8DG+eRBEEZqTEYkeKXkinzyXB0kKOCSNqwdrKEOH3szH7oxCkpRdP1HawM9JZGWj7ngcQBGDiG56wszFEemYB/ricgs3fRlbRHVB1JbZtlcdHy2/jzdG1seDdBjA3NUB8kgqbv43CwaP8Ql56TGzbS0xWYdaCELwzoQ62rW2F5BQV9h2Kwc4fOO9Tn7SsW5zaHtlDN9Vz6FLxsugAsOesBl2bSNCvjRTGiuIhedFJArJVpU5XLhdCBcgNgD6ti1f+e5hUvAx7yWIWBUXFS6F3alQ8Pys7D4iIK/5eqSJ+eEk1iEQQOGjpRdCx39mqDoGIiKhG6j2iY1WHQHpi/rDqO3bxXkTlLUJTt45HpZ27KnEEPhERERERkUgc2kdEREREpOf4PVLisSNFRERERKTn2JESj0P7iIiIiIiIRGJGioiIiIhIzzEjJR4zUkRERERERCIxI0VEREREpOeYkRKPGSkiIiIiIiKRmJEiIiIiItJzgsCMlFjMSBEREREREYnEjBQRERERkZ7jHCnxmJEiIiIiIqJqZ+nSpZBIJJgxY4a2LD8/H1OnToWNjQ1MTU0xePBgJCQk6BwXHR0Nf39/GBsbw97eHnPmzEFhYaFOnTNnzqBFixZQKBTw8vLCtm3bRMfHjhQRERERkZ4TIKm0rSKuXLmCTZs2oUmTJjrlM2fOxKFDh7Bv3z6cPXsWsbGxGDRokHZ/UVER/P39oVarcf78eWzfvh3btm3DggULtHUiIyPh7++Pbt26ITg4GDNmzMCECRNw/PhxUTGyI0VEREREpOeqU0cqOzsbI0aMwFdffQUrKytteUZGBrZs2YKVK1eie/fuaNmyJbZu3Yrz58/j4sWLAIBffvkFt2/fxnfffYdmzZqhd+/e+Pjjj/Hll19CrVYDADZu3AhPT098/vnnaNCgAaZNm4YhQ4Zg1apVouJkR4qIiIiIiCqNSqVCZmamzqZSqZ5af+rUqfD390fPnj11yoOCglBQUKBTXr9+fbi7u+PChQsAgAsXLqBx48ZwcHDQ1vHz80NmZiZu3bqlrfP3c/v5+WnPUV7sSBERERER6TlBkFTaFhgYCAsLC50tMDCwzDh2796NP//8s8z98fHxMDQ0hKWlpU65g4MD4uPjtXWe7ESV7C/Z9091MjMzkZeXV+7njKv2ERERERFRpQkICMCsWbN0yhQKRal6Dx8+xPTp03HixAkYGRk9r/AqjBkpIiIiIiI9p4Gk0jaFQgFzc3OdrayOVFBQEBITE9GiRQsYGBjAwMAAZ8+exRdffAEDAwM4ODhArVYjPT1d57iEhAQ4OjoCABwdHUut4lfy+N/qmJubQ6lUlvs5Y0eKiIiIiIiqXI8ePRASEoLg4GDt1qpVK4wYMUL7s1wux6lTp7THhIWFITo6Gr6+vgAAX19fhISEIDExUVvnxIkTMDc3h4+Pj7bOk+coqVNyjvLi0D4iIiIiIj1XHb6Q18zMDI0aNdIpMzExgY2NjbZ8/PjxmDVrFqytrWFubo63334bvr6+aNeuHQCgV69e8PHxwciRI7F8+XLEx8fjgw8+wNSpU7VZsMmTJ2PdunWYO3cuxo0bh9OnT2Pv3r04cuSIqHjZkSIiIiIiohph1apVkEqlGDx4MFQqFfz8/LB+/XrtfplMhsOHD2PKlCnw9fWFiYkJRo8ejcWLF2vreHp64siRI5g5cybWrFkDV1dXfP311/Dz8xMVi0QQBOGZ3RlVmY79zlZ1CERERDWSibVFVYdAeuL49mZVHcJT/Xk3pdLO3aKeTaWduyoxI0VEREREpOeqw9C+moaLTRAREREREYnEjBQRERERkZ4TBGakxGJGioiIiIiISCRmpIiIiIiI9BznSInHjBQREREREZFIzEgREREREek5zpESjxkpIiIiIiIikZiRIiIiIiLSc5qqDqAGYkeKiIiIiEjPcWifeBzaR0REREREJBIzUkREREREeo7Ln4vHjBQREREREZFIzEgREREREek5zpESjxkpIiIiIiIikZiRIiIiIiLSc5wjJR4zUkRERERERCIxI0VEREREpOc0QlVHUPOwI0VEREREpOc4tE88dqSqgYULF2LRokU6Zd7e3ggNDa2iiGqmQX2cMXyQG6ytDBERmY1Vm8Jx517WU+ubmsgwaaQnOvvawtxMjoTEfKz5KgIXg1IBAEqlDBNH1EJnX1tYWchx93421nwVgdB/OCfpLzHtr18vR7zc3RG1PYwBAGHh2di0I/If2yvpJzHtqrOvLUYNdYeLkxIGBhI8is3D7oMPcfzXRJ06A3o7wbuOGSzM5RjzzlWER+Y8r9uhStS3uw38u9vCwdYQAPAgJh87/xePqzee/rpiYizDmMGO6NDKEmYmMiSmqLFxZwyu/MMx/1Wn1hYYPcgJDraGiElQYcveWO31ZDJgzGAntG5iDid7Q+TkanDtdha27I1FanphpcVEVFHsSFUTDRs2xMmTJ7WPDQz4qxGje0c7TJtQB599eRe372bh1VdcsHJxYwyffAXpGQWl6hsYSLDq4yZISy/Ah0tvIylFBUd7I2RnP36hfu/teqjtYYKPV4YiOVUFv64OWP1xE7zx1hUkp6qf5+1RNSe2/TVvbImTvyUi5E4G1AUajBjsjpWLm2DkVLYtekxsu8rKKsCOvQ/w4FEeCgo16NDaBgHT6yMtvQCXr6UBAJRGUty4nYnT55Lw3tvez/uWqBIlpRbgm72xiElQQQIJXupohYXTPTF1wV08iMkvVd9AJkHgnDpIzyzAJ+uikJJWAHsbOXJyiyocQ5P6ppg9wR2j371d5n4fL2METKmFb/bF4lJwJrr5WuGjJ2JUGErh5WGMXT8l4H50HkxNZJgywgWLZtTG2wvvVjguKh8ufy4e361XEwYGBnB0dKzqMGqsYQNcceh4HH4+lQAAWLH+Hnxb26DvS474bv/DUvX9ezrC3FSOyXOCUVRUPCg4PlGl3W9oKEWX9nYI+OQmrt/KAAB88/0DdGhjg4F9nPHVd1GVf1NUY4htf4s/1802L1sbhq7tbdGqqRWO/ZrwXGKm6k9su7p2M0Pn8b5DMXi5hwOa+FhoO1Il2SlHe0UlR0/P26XgTJ3H236IR9/utqhfx7jMjpRfZ2uYmcow85O7KPqr75SQrPtBjkQCvOpvjz5dbWBlIUdMvAo7/xePc1czSp2vPAb0ssPVkEzsP5oEANjxYzxaNDRD/562+GL7I+TmaRCwIkLnmC+/fYS1C71hZy1HUmrpDxCIqhJX7asm7t27B2dnZ9SuXRsjRoxAdHR0VYdUYxgYSFDPywxXr6dpywQBuBqchobe5mUe07GtDW6GZmL2ZC/8tMMXO9a1wsih7pD+9T9CJpPAQCaBWq3ROU6l1qCJj0Wl3QvVPBVpf3+nUMhgIJMgM5tvEqjYs2hXLZtYwt3FGMG3Kvaml2ouqQTo0tYSCoUUd8LLHrrZrrkF7oTnYNooV+z+oiE2LfHGsL72kD6RlBjW1wE9O1jji22PMOn9UPx4PAnz3vRAY2+TCsXVwMsE125l65QF3cxCA6+nn89EKYNGI/ynTBmVjyBU3vaiYkaqGmjbti22bdsGb29vxMXFYdGiRejUqRNu3rwJMzOzqg6v2rMwl8NAJkFqmu6b0NT0Ani4Gpd5jLOjEi2aGOHEmQTMWRQCFyclZk+pCwOZBFt3P0BeXhFC7mRgzDAPRD3KRVq6Gj0726Ohtzli4vKex21RDVGR9vd3b43xRHKqGleD0/69MumFirYrE2MZDmzzhaFcgiINsHLDPbYrPVLL1QirP6wLQ7kUefkaLP4iEtGxqjLrOtkZolkDU5y+kIYPVt6Hi70C00a7QmYgwc6DCZAbSDCsnz3eWxaBOxG5AID4pFQ0rGcC/262CAkTP7fOysIAaZm6bTotowBWFmW/HZXLJRj/mjPOXExDbr6mzDpEVYkdqWqgd+/e2p+bNGmCtm3bwsPDA3v37sX48eNL1VepVFCpdF8YNUVqSGWGlR7ri0IqAdIz1Fj+5V1oNEBYRDbsbBQYPsgVW3c/AAB8vDIUAdO98b/tvigsEnA3Igsnf0uEt5dpFUdPL5I3hrihRyd7vP3+dagLXuCP7ei5yM0rwtjpV6E0kqFVUytMG18HsfF5pYb90YvpUZwKb30YBmNjGTq1tsS7Ez0wJ/BemZ0piRRIzyrEmq0PoRGA8Kg82FjJMaSPPXYeTICzgwJGChkC59bROc7AQIKIB48/UDy4qbH2Z6lUArmBRKfs9Pk0fLH9keh7kcmA+VNrAQDWVuB4Ek/DVftEY0eqGrK0tES9evUQHh5e5v7AwMBSq/y51R0Nd++xzyO8aicjswCFRQKsreQ65daWcqSklT1xPzlNjaJCAZonPuB68CgXttYKGBhIUFgoIDY+H28HXIeRQgoTYwOkpKmxaG4DxMaXHmtO+qsi7a/E8IGuGDHYHTM+vI6IKK6cRo9VtF0JAhATV/waFR6ZAw83Y7wx1B3XboZUarxUPRQWCYhNLG4f4VF58PY0xoBedvhiW+mOSGp6IYqKBJ3vDoqOy4eNZXE21EhRPNb9w5X3kfK3zGhB4eOD3vowTPuzdx0TjH/VCXMDH79/ycl7/Ic2LaMQVua6bdrKQo60DN0V+Uo6UQ42hpi7NJzZKKq2OEeqGsrOzkZERAScnJzK3B8QEICMjAydzdVrxHOOsvooLBRwNzwLLZtYacskEqBlUyvcCsss85iQ25lwcVJC8sSHL27OSiSnqFBYqJsVyFdpkJKmhpmJAdo0t8a5SymVch9UM1Wk/QHA64PcMPo1D7y78AbCwrOfWo/0U0Xb1d9JJYChnH/q9ZVEAsgNyv79376XAyd7hc7fQVcHBVLSijvx0bH5UKs1sLcxRGyiWmd7ctGHJ8tT0gpQVKRblpH1uJN0JzwHzXx0R3W0aGimM4+rpBPl4qDAe8vDkZXDuVHPiyBIKm17UfHVtRp49913cfbsWURFReH8+fMYOHAgZDIZhg8fXmZ9hUIBc3NznU3fh/XtPvgI/fyc8HJ3B3i4GuPdt+pCaSTFkZPxAIAPZnrjzVGe2voHj8bC3MwA0yd6wc1ZCd9W1hg51B0//hyrrdOmuRXatrCCk4MRWjWzwhefNkX0o1ztOYlKiG1/Iwa7YcIbtRD4RRjiEvJhbSmHtaUcSiO+JNNjYtvVG0Pc0KqZFZwdjODhaoxhA1zh180Bx888XgnSzNQAXp4mqOVWPLnf3cUYXp4msLbUzRJQzTN2qBMaeZvAwdYQtVyNMHaoE5rUN8WvF4q/G3HOJHeMHfr4A9rDp5NhZlq8vLiLgwJtmppjWD8HHDqVDADIy9dg/7FEvPm6C3p2sIKTvSG8PJR4pactenawKjOGf3PwlyS0amyOwS/bwc1JgTcGOKKupxL/O1l8TZkM+HCaJ+rVMsayjQ8glUpgZWEAKwsDGMhe3Dfj1QUXmxCPQ/uqgUePHmH48OFISUmBnZ0dOnbsiIsXL8LOzq6qQ6sxTp9LgqWFHBNG1IK1lSHC72dj9kchSEsv/tTMwc5IZ/hCYrIKsxaE4J0JdbBtbSskp6iw71AMdv7weLVEUxMDvDnKE3a2CmRmFeDs+WRs/jZSu1w6UQmx7W9Ab2cYyqVYEtBQ5zzf7IrCN98/eJ6hUzUmtl0pjWSYPcUL9jYKqNQaPHiUi8Wfh+L0uSRtnY5tbTB/Rn3t48XzfACw7b0ILM0MMGeiB6wtDZCbV4TIh/mY/1kE/vxrlTw7a0Od4exJqQWYvyICb77ugo2feCM5vQAHf0nC3iOPv8B5+w/xyMgsxLC+DnC0N0RObhHCo/Lw/eGKfU3D7fBcLN0YhdGDnTBmiBNiE1RYtCZSuzy7rZUhfFsUr4y74ZP6OsfOCQzHjVBm76l6kQjCi9xP1B8d+52t6hCIiIhqJBNrfq0FPR/Htzer6hCe6pfrlfeF8L2avpgjpziOhIiIiIiISCQO7SMiIiIi0nMajlETjRkpIiIiIiIikZiRIiIiIiLScy/yMuWVhRkpIiIiIiIikZiRIiIiIiLSc1zHWzx2pIiIiIiI9JwGHNonFof2ERERERERicSMFBERERGRnuPQPvGYkSIiIiIiIhKJGSkiIiIiIj3H5c/FY0aKiIiIiIhIJHakiIiIiIj0nEaovE2MDRs2oEmTJjA3N4e5uTl8fX1x9OhR7f78/HxMnToVNjY2MDU1xeDBg5GQkKBzjujoaPj7+8PY2Bj29vaYM2cOCgsLdeqcOXMGLVq0gEKhgJeXF7Zt2yb6OWNHioiIiIiIqgVXV1csXboUQUFBuHr1Krp3747+/fvj1q1bAICZM2fi0KFD2LdvH86ePYvY2FgMGjRIe3xRURH8/f2hVqtx/vx5bN++Hdu2bcOCBQu0dSIjI+Hv749u3bohODgYM2bMwIQJE3D8+HFRsUoEgWt0vAg69jtb1SEQERHVSCbWFlUdAumJ49ubVXUIT3XgclGlnXtgG9l/Ot7a2horVqzAkCFDYGdnh127dmHIkCEAgNDQUDRo0AAXLlxAu3btcPToUfTt2xexsbFwcHAAAGzcuBHz5s1DUlISDA0NMW/ePBw5cgQ3b97UXmPYsGFIT0/HsWPHyh0XM1JERERERHpOgKTSNpVKhczMTJ1NpVL9a0xFRUXYvXs3cnJy4Ovri6CgIBQUFKBnz57aOvXr14e7uzsuXLgAALhw4QIaN26s7UQBgJ+fHzIzM7VZrQsXLuico6ROyTnKix0pIiIiIiKqNIGBgbCwsNDZAgMDn1o/JCQEpqamUCgUmDx5Mg4cOAAfHx/Ex8fD0NAQlpaWOvUdHBwQHx8PAIiPj9fpRJXsL9n3T3UyMzORl5dX7vvi8udERERERHpO7KIQYgQEBGDWrFk6ZQqF4qn1vb29ERwcjIyMDOzfvx+jR4/G2bPVbxoLO1JERERERFRpFArFP3ac/s7Q0BBeXl4AgJYtW+LKlStYs2YNXnvtNajVaqSnp+tkpRISEuDo6AgAcHR0xOXLl3XOV7Kq35N1/r7SX0JCAszNzaFUKssdJ4f2ERERERHpOUGovO2/0mg0UKlUaNmyJeRyOU6dOqXdFxYWhujoaPj6+gIAfH19ERISgsTERG2dEydOwNzcHD4+Pto6T56jpE7JOcqLGSkiIiLSa+/seq2qQyB9sT2sqiOo9gICAtC7d2+4u7sjKysLu3btwpkzZ3D8+HFYWFhg/PjxmDVrFqytrWFubo63334bvr6+aNeuHQCgV69e8PHxwciRI7F8+XLEx8fjgw8+wNSpU7VZscmTJ2PdunWYO3cuxo0bh9OnT2Pv3r04cuSIqFjZkSIiIiIi0nPV5QuREhMTMWrUKMTFxcHCwgJNmjTB8ePH8dJLLwEAVq1aBalUisGDB0OlUsHPzw/r16/XHi+TyXD48GFMmTIFvr6+MDExwejRo7F48WJtHU9PTxw5cgQzZ87EmjVr4Orqiq+//hp+fn6iYuX3SL0g+D1SREREFRNwbFJVh0B6wr+g+mak9l7QVNq5X/V9MWcTMSNFRERERKTnNIKkqkOocdiRIiIiIiLScxyjJt6LmWcjIiIiIiKqRMxIERERERHpOWakxGNGioiIiIiISCRmpIiIiIiI9JyGGSnRmJEiIiIiIiISiRkpIiIiIiI9J3D5c9GYkSIiIiIiIhKJGSkiIiIiIj3HVfvEY0aKiIiIiIhIJGakiIiIiIj0HFftE48dKSIiIiIiPcehfeJxaB8REREREZFIzEgREREREek5ZqTEY0aKiIiIiIhIpBeiI3XmzBlIJBKkp6dXdShERERERDWORqi87UX1Qgzta9++PeLi4mBhYVHVoVAVGtTHGcMHucHayhARkdlYtSkcd+5lPbX+0FdcMLC3MxzsFEjPLMCZ88nYtP0+1AXF/+P3fd0WTg5GpY778UgMVm4Mr7T7oOpPTFvr3cMB82fU1ylTqTXoMfh3AIBMJsGkN2qhXStrODsqkZNTiKvX07BheyRSUtWVfi9UvYl9XTM1kWHSSE909rWFuZkcCYn5WPNVBC4GpQIAxg33wLjXa+kc8+BRLkZMuVKZt0HPg1SKegvehsvrr0DhaIv82EQ82nEA4Z+u/8fDnIf3Q513J8DEywMFGVlIOv477sxbjoLU9EoL1bpzG/h89h5Mfeoi/2EcwgM34NGOA9r97m8Oh8ebw6H0cAEAZN++h3ufrEfS8d8qLSaiinghOlKGhoZwdHSs8PFqtRqGhobPMCJ63rp3tMO0CXXw2Zd3cftuFl59xQUrFzfG8MlXkJ5RUKr+S13sMXl0bSz9IgwhdzLg5mKM+dO9IQjAui0RAICJs/6E9ImcbW0PE6z+pCl+PZf0vG6LqiGxbQ0AsnMK8frky9rHT344Z6SQol4dM2zfE417kdkwNzXA9IleWPZBI0yY9Wcl3w1VZ2LbmoGBBKs+boK09AJ8uPQ2klJUcLQ3QnZ2oU69+w9yMOOD69rHRS/yx8V6pM6cifB4cziuj5uHrNvhsGjZCE2/DkRhZhai1n1b5jFW7Vug2dZluP1uIBIO/wojFwc0XrcQTTZ+jKBX365QHEoPF3QPP40jcu+y99dyReufNiF6824Ej3oXNt190XjTJ8iPS0LyiXMAgPxH8Qh9/zPkhD+ARCKB68gBaPXjl/i99UBk3+YHmZWFc6TEq3ZD+7p27Yq3334bM2bMgJWVFRwcHPDVV18hJycHY8eOhZmZGby8vHD06FHtMWUN7fvjjz/QtWtXGBsbw8rKCn5+fkhLS9NeY9q0aZgxYwZsbW3h5+cHADh79izatGkDhUIBJycnvPfeeygs1P0D9KSUlBQMHz4cLi4uMDY2RuPGjfH9999r92/evBnOzs7QaDQ6x/Xv3x/jxo3TPv7kk09gb28PMzMzTJgwAe+99x6aNWv2X55GvTNsgCsOHY/Dz6cSEPUwFyvW30O+SoO+L5XdwW5U3xwhdzJw4mwi4hNVuHItDSd/S4RPPTNtnfTMAqSmP97at7bBo9g8XLuZ8bxui6ohsW0NKP7j9GRbSkt//CY4J7cIMxfcwOlzSXgYk4dbYVlYuSkc9euawcFO8TxuiaopsW3Nv6cjzE3lCFhyCyF3MhGfqELwzQyER+Xo1CsqEnTaY0bm0//OUc1h5dscCYdOIfHoWeQ9iEH8j8eRdOIcLFs3efoxbZshNyoGUeu+RV7UI6T9EYTor/fA4m/HuI0bgi43fsbLWTfQJeQoPCa/XuE4PSYNQ17kI9yZuwzZoffxYP1OxP9wHJ7Tx2jrJB75FUnHfkNu+APk3ItC2ILVKMzOhVXbZhW+LlFlqHYdKQDYvn07bG1tcfnyZbz99tuYMmUKhg4divbt2+PPP/9Er169MHLkSOTm5pZ5fHBwMHr06AEfHx9cuHAB586dQ79+/VBUVKRzDUNDQ/zxxx/YuHEjYmJi0KdPH7Ru3RrXr1/Hhg0bsGXLFnzyySdPjTM/Px8tW7bEkSNHcPPmTUyaNAkjR47E5cvFnzwPHToUKSkp+PXXX7XHpKam4tixYxgxYgQAYOfOnViyZAmWLVuGoKAguLu7Y8OGDc/iadQbBgYS1PMyw9XradoyQQCuBqehobd5mcfcDM2Edx0zNKhb3HFydjBCu1bWuHA19anX6NXNAUdOxj/7G6AaoyJtDQCUShn2b2mLH75pi8D5DeHpbvyP1zE1lkGjEZCVzTe4+qoiba1jWxvcDM3E7Mle+GmHL3asa4WRQ911MusA4OqsxMFt7bD3qzZYMLs+O+wviLQL12DTrR1M6tYCAJg18YZ1h5ZIPPb04XBpl4KhdHOE3cudAQCG9jZwHOSHpGNntXWch/dDvY+mI2zBKpxt3AdhH65EvYXvwGXkgArFadmuGZJPX9ApSzpxDlbtmpV9gFQKp1f7QGZijLSL1yp0TSofjabythdVtRza17RpU3zwwQcAgICAACxduhS2traYOHEiAGDBggXYsGEDbty4gXbt2pU6fvny5WjVqhXWr388Lrhhw4Y6derWrYvly5drH8+fPx9ubm5Yt24dJBIJ6tevj9jYWMybNw8LFiyA9O9/iQC4uLjg3Xff1T5+++23cfz4cezduxdt2rSBlZUVevfujV27dqFHjx4AgP3798PW1hbdunUDAKxduxbjx4/H2LFjtff2yy+/IDs7u0LPnT6yMJfDQCZBapruUJfU9AJ4uJb9hvXE2URYmMuxflkzSCSAgYEUB36Oxbf7osus37mdLUxNDPDzKXak9FlF2lr0ozwsXROG8KhsmJoYYPhAN2xY3hwjp15BUkrpOVCGcgmmjKmNk78lIjevqIwzkj6oSFtzdlSiRRMjnDiTgDmLQuDipMTsKXVhIJNg6+4HAIDbd7Pw6epQRMfkwcbKEGOHe+DLpc0wctpV5LG91WgRyzfDwNwUXW4ehVBUBIlMhrAPVyH2+0NPPSbt/J+4NmoOWuxaDamRIaRyORIOncbNtxdr69Rb8DbuzF2K+IMnAAB5UY9g2sALHhNfQ8y3B0XHqXCwhSohWadMlZAMuYUZpEYKaPJVAACzRvXQ/vfdkBopUJSdi6AhU5F9J0L09aj8OLRPvGrZkWrS5HFKWSaTwcbGBo0bN9aWOTg4AAASExPLPD44OBhDhw79x2u0bNlS5/GdO3fg6+sLiUSiLevQoQOys7Px6NEjuLu7lzpHUVERPv30U+zduxcxMTFQq9VQqVQwNn78R27EiBGYOHEi1q9fD4VCgZ07d2LYsGHajllYWBjeeustnfO2adMGp0+ffmrsKpUKKpVKp0xTpIZUxnle5dW8kQVGDnXH5xvv4XZYFlydjDB9kheSU92xfU/pzpT/S464FJTKyf8k2q2wTNwKy9Q+DrmTiZ3rW6P/y874emeUTl2ZTILF83wACfDZ+nvPOVKq6aQSID1DjeVf3oVGA4RFZMPORoHhg1y1HamSRScAICIqB7fvZmL/lnbo3tEOR07wg6KazGlob7gM74drI2cj+3Y4zJs2gM/nAciPS3xqh8e0QR00XDkf9z75EkknzkHhaIcGy+ai8fpFuDFpPmTGSph4eaDJ5iVovPFj7XESAwMUZjxe9KRz8GEoPZz/2ln8Psov7fEcz9RzQbjSb6Ko+8kOi8TvrQbAwMIMToP80PSbZbjY4w12pqhaqZYdKblcrvNYIpHolJV0dv4+96iEUqn812uYmJj8hwiLrVixAmvWrMHq1avRuHFjmJiYYMaMGVCrH7/Z7tevHwRBwJEjR9C6dWv8/vvvWLVq1X+6bmBgIBYtWqRT5lZ3NNy9x/6n89ZUGZkFKCwSYG2l226sLeVISSu74zPhDU8c/zUBh38pfuNw/0EOjIxkmDutHnbsjdb5VMbBToFWTa0wP/BWpd0D1QwVaWt/V1Qk4N79bLg66b5OyWQSfDzPB472Rnhn/nVmo/RcRdpacpoaRYWCzjCaB49yYWutgIGBBIWFpT9uzs4pwsPY3FLtkWqeBkvnImLFZsTt/RkAkHXzLpTuzvCa++ZTO1J15r2JtPN/4v7KLcXHhIThZm4e2p/ZhbAFqyH81ZhuTP4Q6Zev6xwrFD1uaFdemQSJvPgtpZGzA3xPf4ffWw3Q7i/Ky9f+rPp/e/cdX9P9/wH8dbNubqbsvciQGDGihNo0aLVoqUitIlYQM41NQ9SoVaMUwY+irVG0fGPEiCBVWUQGIUYkQvaScX5/pI5eElwaCXk9H4/7ePSc8/6c8zm3Hzf3c9+fz+ekpkNqYih3LqmJIYqzcsRsFAAIxcXIv17+w2b231dQx60RbMcNQsyYOYq8LaQAZqQUVyPnSL2pxo0b4/jx4wqVcXZ2RlhYGIR/taLQ0FBoa2vD0tKywjKhoaH47LPP8NVXX8HV1RV169ZFfHy8XIy6ujr69OmDHTt24Oeff4aTkxOaNWsmHndyckJ4uPyys89uP8vf3x9ZWVlyL0t7L4Xu931SUiIgPjEHzRvrifskEqC5q55cJuDf1KVKEJ5Zqarsn+1/JSUBlE/gzsh6jLDwh/9txemd8zpt7VlKSkBdW02k/+vL8JNOlKW5DL4zo5Cdw7lRtd3rtLXoq9mwMJPJfYZZmcuQ/rCowk4UAMjUlWBhKnvlHwKo5lLWUH/u75pQWlqeqqysjExd7CzJlQEAiQSP0x6i8G4qNOyskH89We5VcPOOWKYg+d7T/cn3AEAutuje0xFEmecjYNBJflqGYefWyDgf8eIbVFKCkpQjb6hmeS87Uv7+/ggPD8eYMWMQFRWFa9euYd26dUhPT6+0zJgxY3D79m2MGzcO165dw4EDBzBnzhxMmjSpwvlRQPk8q+DgYJw7dw6xsbEYOXIkUlNTn4vz8vLC4cOHsXnzZnGRiSfGjRuHTZs2YevWrUhISEBAQACioqLkhhg+SyqVQkdHR+5V24f17dp/Bz09zNCtkwlsLDUwZYwDZOpK4uIQMyc6YeQgOzE+9OJD9Ophjs5tjWBmog63JnoY7mWH0IsP5X7NlUiAHl1MceREKkrf48mS9OoUbWtD+tugRVM9mJuow7GeFmZPcoapkRSH/pcCoLwTFfCNC5zstTB/aSyUlMqzDvp1VKGiUvnnAL3/FG1r+/+8Bx3t8uXzrcxlcHfTx8C+1tj7xz0xZuzXddGkoS5MjaVoWF8HC6c3RGmZgGOnKh4qT++O1MMnYf/NKBh3bw+ZjQVMPusCO9+hSD1wTIxxCpgE1y3fidtph0/CtHdXWI/0hMzOEnqtm6HB8pnIuBiJopTyNhE/fxXs/bxh6zMQmg620G7oCMvBfWDnO+S16nlrwy5o2FmhfuBUaDrVhc2oATDr2x1JK4Pk6qn/oRtkNhbQbugIp4BJMGj/Ae7trHy+F705PpBXcTVyaN+bcnR0xP/+9z9Mnz4dH3zwAWQyGVq2bAlPT89Ky1hYWOCPP/7A1KlT4erqCn19fQwbNkxc9KIiM2fOxI0bN+Dh4QENDQ14e3ujV69eyMqSXx67U6dO0NfXR1xcHAYMkF8y1MvLCzdu3MCUKVNQWFiIfv36YciQIeLKf/RqTpx9gDq6qhjuZQt9PTUk3sjF5DnR4jLTJkbqcv+Qt+6+BUEARnxlByMDNWRmFyP04kNs2J4kd163JnowNVbn3AESKdrWtLVU4OfjCH09NeTkliAuMQejpkXg5u3yVUeNDNTQtlX5MJeg1W5y1xrnH8Hl9msxRdtaWnoRJs2Oxvjh9RC02g3pD4vwy8G72PHb03mfRgZSzJ3iDB0dVWRmFSPqahZGTrmMzOyKn4FG744rEwLgNG8CGqyeA6mxAQrvpSF5424kBKwRY6RmRpBZmYnbd7btg4q2JmxHe8FlsR+KM3PwMOQ8Yv2XiDG3N/+K0vxC1J08DPUXTUNpXj5yYuKRtGrra9Wz4OYdhH86Ei7L/GE7bhAK79xH9MiZ4jOkAEBqbADXLd9BamaMkqwc5ETH4WKPYUg/fu61rklUVSSCwBGRNU3Xrl1hamqK7dsrfoBeRT7seerlQURERPQc/yPe1V0FqiU+Lo6r7ipU6oc/qq5L4NPj/Rxh8V5mpN4l+fn5WL9+PTw8PKCsrIyff/4Zx44dQ3BwcHVXjYiIiIiIKsGOVDWTSCT4448/sGDBAhQWFsLJyQm//fYbunTpUt1VIyIiIqJagmPUFMeOVDWTyWQ4duzYywOJiIiIiKpIJU8Vohd4L1ftIyIiIiIiqkrMSBERERER1XIc2qc4ZqSIiIiIiIgUxIwUEREREVEt9z4/OLeqMCNFRERERESkIGakiIiIiIhqOc6RUhwzUkRERERERApiRoqIiIiIqJYTqnSSlKQKz1192JEiIiIiIqrluNiE4ji0j4iIiIiISEHsSBERERER1XKCUHUvRQQGBqJFixbQ1taGsbExevXqhbi4OLmYwsJCjB07FgYGBtDS0sLnn3+O1NRUuZjk5GR8/PHH0NDQgLGxMaZOnYqSkhK5mJCQEDRr1gxSqRT29vYICgpSqK7sSBERERERUY1w6tQpjB07FufPn0dwcDCKi4vx0UcfIS8vT4yZOHEiDh48iF9++QWnTp3CvXv30KdPH/F4aWkpPv74Yzx+/Bjnzp3D1q1bERQUhNmzZ4sxSUlJ+Pjjj9GxY0dERETA19cXw4cPx9GjR1+5rhJB4GKH74MPe56q7ioQERG9k/yPeFd3FaiW+Lg47uVB1SRwT2mVndu/n/Jrl33w4AGMjY1x6tQptGvXDllZWTAyMsLOnTvxxRdfAACuXbsGZ2dnhIWFoVWrVvjzzz/xySef4N69ezAxMQEArF+/Hn5+fnjw4AHU1NTg5+eHw4cPIyYmRrxW//79kZmZiSNHjrxS3ZiRIiIiIiKiGikrKwsAoK+vDwC4dOkSiouL0aVLFzGmfv36sLa2RlhYGAAgLCwMjRo1EjtRAODh4YHs7GxcuXJFjPn3OZ7EPDnHq+CqfUREREREtVxVjlErKipCUVGR3D6pVAqpVPrCcmVlZfD19UWbNm3QsGFDAMD9+/ehpqaGOnXqyMWamJjg/v37Ysy/O1FPjj859qKY7OxsFBQUQCaTvfS+mJEiIiIiIqIqExgYCF1dXblXYGDgS8uNHTsWMTEx2LVr11uopeKYkSIiIiIiquWqMiPl7++PSZMmye17WTbKx8cHhw4dwunTp2FpaSnuNzU1xePHj5GZmSmXlUpNTYWpqakYc/HiRbnzPVnV798xz670l5qaCh0dnVfKRgHMSBERERER1XplglBlL6lUCh0dHblXZR0pQRDg4+ODffv24cSJE7Czs5M73rx5c6iqquL48ePivri4OCQnJ8Pd3R0A4O7ujujoaKSlpYkxwcHB0NHRgYuLixjz73M8iXlyjlfBjBQREREREdUIY8eOxc6dO3HgwAFoa2uLc5p0dXUhk8mgq6uLYcOGYdKkSdDX14eOjg7GjRsHd3d3tGrVCgDw0UcfwcXFBQMHDsTixYtx//59zJw5E2PHjhU7cKNGjcIPP/yAadOm4euvv8aJEyewZ88eHD58+JXryo4UEREREVEtJ5RVdw3KrVu3DgDQoUMHuf1btmzBkCFDAADLly+HkpISPv/8cxQVFcHDwwNr164VY5WVlXHo0CGMHj0a7u7u0NTUxODBgzF//nwxxs7ODocPH8bEiROxcuVKWFpa4qeffoKHh8cr15XPkXpP8DlSREREr4fPkaK3pSY/R2r+jpIqO/dsr/czd/N+3hUREREREb0y5lYUx8UmiIiIiIiIFMSMFBERERFRLVdWQ+ZIvUuYkSIiIiIiIlIQM1JERERERLUc50gpjh0pIiIiIqJaroz9KIVxaB8REREREZGCmJEiIiIiIqrlBKakFMaMFBERERERkYKYkSIiIiIiquW41oTimJEiIiIiIiJSEDNSRERERES1XBnnSCmMGSkiIiIiIiIFMSNFRERERFTL8YG8imNHioiIiIiolhPKqrsG7x4O7SMiIiIiIlIQM1L03ujTwxyefaygr6eG60m5WP5jImITciqMXb3QFU0b1Xlu/7nwh5g2PwbKyhJ4f2WLVm76MDeVIS+vBH9FZmDd1iQ8fPS4iu+EajpF2lo7d0MM6msNCzMZVFQkuHOvALv238bRk2lizHRfJ/TobCpX7sKlR5g8N7pK74NqPkXa2r91bmuEedNccPp8OqYvuCJ3zMZSA6OH2KFJwzpQVpbg5u08zAy8itQHRVV1G/Q2KCnBcfY4WAz4FFJTQxTeS8OdbfuQuHDtC4uZe/ZEvSnDoWlvg+KsHDw4egaxfotR/Cizyqqq3+4DuCz9BlouDii8nYLEwHW4s22feNx6pCdsRnpCZmMBAMi9moCEgLV4cPR0ldWJgDIO7VMYO1IAiouLoaqqWt3VoDfQ6UMj+Ayvh6Vr4nE1Pgf9PrXA9/MbwXNUODKzip+Ln77wClRVJOK2ro4qtqxyw8nQBwAAdakSHOtpY+vuZCQk5UJHSwUTRtjju5kNMXzS32/tvqjmUbSt5eQUY9ueW7h1pwDFJWVo08IA/hPqIyOzGBcvZ4hx5y89wsIV18Tt4mL+QavtFG1rT5gaSzH263qIiMl87pi5qTrWftcEh4LvY9POW8jLL4GdtSaKHnNMz7uu3tQRsBnpiciv/ZBzNRG6zRvC9adAlGTn4OYP2ysso9e6GZps+Q5XpwQi9dBJqFuYoNEPc9F4/be41G/ca9VDZmOBTokncFjVqeLjtpZo8fuPSN6wCxGDpsCgkzsa/RiAwpQHSA8+CwAovHMf16YvRV7iLUgkElgO7AW3vWtwpkVv5F5NfK16EVWFah3a16FDB4wbNw6+vr7Q09ODiYkJNm7ciLy8PAwdOhTa2tqwt7fHn3/+KZYpLS3FsGHDYGdnB5lMBicnJ6xcufK5c2/evBkNGjSAVCqFmZkZfHx8xGMSiQTr1q3Dp59+Ck1NTSxYsAAAsG7dOtSrVw9qampwcnLC9u0Vf/A8ER4ejq5du8LQ0BC6urpo3749/v776ZfsAQMG4Msvv5QrU1xcDENDQ2zbtg0AkJOTAy8vL2hqasLMzAzLly9Hhw4d4Ovrq/D7WZv172WJg0dT8MfxVNy8nY8laxNQWFSGT7qaVhifk1uCR5nF4sutiR6Kikpx8mx5RyovvxQTZ0fhxNkHuH23AFficvD9j4mo76ANEyPp27w1qmEUbWuXY7Jw+vxD3LqTj3v3C/HLwbu4fjMXjV105eIeF5fJtcmcvJK3cTtUgyna1gBASQmYPdkZm3bexL3UwueOew+0Q9ilR1gXdAMJN3Jx734hQi8+fGHHjN4Neu5NkXrwONL+PIWCW3dxf+9RPAg+izotGldepmUT5N+8i5s/bEfBzTvICL2E5J92Q/eZMlZff4H2UX+gW04U2kf/CZtRA167njbe/VGQdAex075D7rUbuLV2B+7/dhR2E4aIMWmHT+LBkdPIT7yFvISbiJu9AiW5+dBr2eS1r0svJwhClb3eV9U+R2rr1q0wNDTExYsXMW7cOIwePRp9+/ZF69at8ffff+Ojjz7CwIEDkZ+fDwAoKyuDpaUlfvnlF1y9ehWzZ8/G9OnTsWfPHvGc69atw9ixY+Ht7Y3o6Gj8/vvvsLe3l7vu3Llz0bt3b0RHR+Prr7/Gvn37MGHCBEyePBkxMTEYOXIkhg4dipMnT1Za95ycHAwePBhnz57F+fPn4eDggB49eiAnp3zYhZeXFw4ePIjc3FyxzNGjR5Gfn4/evXsDACZNmoTQ0FD8/vvvCA4OxpkzZ+Q6Y/RyKioSONpr46/Ip7/uCwLwV0QGGjjpvNI5PulqiuOn01BYVPmvsloayigrE5CTyy+4tdV/0daaN64DawsNRFzJktvftGEdHNzujp3rWmDyaAfoaHPAQG32um1tSH8bZGYV43Dw/eeOSSRAazd93L6bj2XzGuHgdndsWNoUbVsZVMk90NuVEXYZBh1bQdPBFgCg3dgJ+m2aI+1I5cPhMi5EQGZlCqNu7QAAasYGMO3jgQdHTokx5p494ThnAuJmL8epRj0QN+t7OM4dD4uBvV6rnnVaNUH6iTC5fQ+Cz0KvVZOKCygpwaxfDyhraiDj/OXXuiZRVan2v9Surq6YOXMmAMDf3x+LFi2CoaEhRowYAQCYPXs21q1bh6ioKLRq1QqqqqqYN2+eWN7Ozg5hYWHYs2cP+vXrBwAICAjA5MmTMWHCBDGuRYsWctcdMGAAhg4dKm57enpiyJAhGDNmDIDyDs758+exdOlSdOzYscK6d+rUSW57w4YNqFOnDk6dOoVPPvkEHh4e0NTUxL59+zBw4EAAwM6dO/Hpp59CW1sbOTk52Lp1K3bu3InOnTsDALZs2QJzc3PF38haTFdHFSrKEjzKkP9F9VFmMWwsNV5a3tlBG/VstbBoVXylMWqqEoweUhfHTqchv6D0jetM76bXbWuaGsrYF+QONVUJSsuA79cl4K+Ip1+QL1x6hFPn0pGSWggLM3V4D7TD0rmNMGrqZZRxxFWt9DptrbGLDj7paoahE/6q8Lierio0NFTw1RfW2Ph/SVgXdAOtmutjgX8DjJ8RiYiYrArL0bvh+uINUNHRQvuYPyGUlkKirIy4Wctx7+eDlZbJOPc3Lg+aimY7V0BJXQ1KqqpIPXgCMePmizGOs8chdtoi3N8fDAAouHkHWs72sBnxJe5u369wPaUmhihKTZfbV5SaDlVdbSipS1FWWD5XT7uhI1qf2QUldSlKc/Nx6YuxyI29rvD16NXxgbyKq/aOVOPGT9PHysrKMDAwQKNGjcR9JiYmAIC0tKcTs9esWYPNmzcjOTkZBQUFePz4MZo0aSLG3bt3T+yYVMbNzU1uOzY2Ft7e3nL72rRpU+GwwSdSU1Mxc+ZMhISEIC0tDaWlpcjPz0dycjIAQEVFBf369cOOHTswcOBA5OXl4cCBA9i1axcA4MaNGyguLsYHH3wgnlNXVxdOThWPK36iqKgIRUXyk4LLSh9DSVntheWoYp98ZIrEpNxKJ3ArK0sw388FkABL1ya85drR+yC/oBRDJ/wFmboy3Fz14DOsHu7dL8Dlf764Hj/zQIy9cSsP15PysOenlmjasA4uRWVWU63pXSKTKWPmpPpY/EM8srIrzppLlMrnhZ69kI49B+4CABKT8tCwvg56dTNnR+odZ9a3Oyw8e+LywMnIvZoIHVdnuCzzR2FKWqUdHi3nemjw/QwkBKzBg+CzkJoawfm7aWi0dh6ivGdAWUMGTXsbNN6wAI3WfyuWk6iooCTr6d/MdhGHILP550dgSXk788h4Orrm0dlLCO85QqH7yY1Lwhm3XlDR1YZZHw+4bv4O5zt/xc4U1SjV3pF6dpEHiUQit0/yzz/Isn9+lt21axemTJmCZcuWwd3dHdra2liyZAkuXLgAAJDJZK90XU1NzTeu++DBg/Hw4UOsXLkSNjY2kEqlcHd3x+PHT1d18/LyQvv27ZGWlobg4GDIZDJ069btja4bGBgol5UDACuHwbB2GlpJifdbVnYxSkoF6OvJtyX9Oqp4mPHiFfbUpUro3NYYm3bcrPC4srIE3/q5wNRYHeNnRDIbVcu9blsTBOBuSvl8lcSkPNhYaeCrvta4HFPxqnz3UguRkfUYluYydqRqKUXbmoWpOsxNZFg0q6G4759+E0L2t8OAUReRll6EkpIy3EzOlyt763Y+Gj0zZ4/ePc6LpuH6kg1I2fMHACAnJh4ya3PYTxtZaUeqnt9IZJz7Gze+31ReJjoOMfkFaB2yE3GzV0D457tX1KhZyLwYKVdWKH2aLg//1BsS1fKvlOrmJnA/8X8449ZLPF5a8HS+XlFqOqQmhnLnkpoYojgrR8xGAYBQXIz86+U/TGf/fQV13BrBdtwgxIyZo8jbQgp4j6cyVZlq70gpKjQ0FK1btxaH4AHA9etPf53Q1taGra0tjh8/XumQvIo4OzsjNDQUgwcPlruWi4vLC+uydu1a9OjRAwBw+/ZtpKfLp6tbt24NKysr7N69G3/++Sf69u0rdhTr1q0LVVVVhIeHw9raGgCQlZWF+Ph4tGvXrtLr+vv7Y9KkSXL7uvW/8Mr3+r4pKREQn5iD5o31cOb8QwDlP4g1d9XD3sN3X1i244dGUFVVwtGQ1OeOPelEWZrLMH56JLJzODeqtnuTtvZvShJATbXyKapGBmrQ1VZFOpfar7UUbWvJd/IxcGy43L4RA+2gIVPGyg2J/3SiBMQm5MDqmaGBVhYaSH3w/MIU9G5R1lCH8MzQLKG09GmPuqIyMnUIJfI/EAql/2xLJHic9hCFd1OhYWf1wiGCBcn3npb/53xPOkHPyjwfAaPu8t9xDDu3Rsb5iErPDwBQUoKSlCNvqtKz7Yde7p3rSDk4OGDbtm04evQo7OzssH37doSHh8POzk6MmTt3LkaNGgVjY2N0794dOTk5CA0NxbhxlS/lOXXqVPTr1w9NmzZFly5dcPDgQezduxfHjh17YV22b98ONzc3ZGdnY+rUqRVmxAYMGID169cjPj5ebvEKbW1tDB48GFOnToW+vj6MjY0xZ84cKCkpiZm4ikilUkil8ivH1fZhfbv238GMifVxLTEHsfE56PeZBWTqSjh8rHzC9cyJTnjw8DF+3JYkV+6TrmY4cz79uU6SsrIEAd+4wLGeFvzmx0BJqfyXYADIzi1BSQk/bGorRdvaV19Y4VpiLu6lFEBVVQnubvrw6GiCpevKh4nK1JUw1NMWp849wMOMx7AwlWHM0Lq4m1KAi38/qrb7pOqnSFt7XCwg6ZlMU+4/Kz/+e//Pe29j3jQXRMZk4u/oTLRspo/WHxhg/PSIt3ZfVDVSD5+E/TejUJh8DzlXE6HTxBl2vkNxJ+g3McYpYBLULUwQOdQPQPnqeI3WfwvrkZ548L8zUDczhsuy6ci4GImilPIpFfHzV6HB8pkoyS5/xpSSVA26zRtCVU8HSSuCFK7nrQ27YDPGC/UDp+J20G8w7NgKZn27I/zTkXL1fHDkNApup0BFWxPm/T+BQfsPcLHHsDd7k4j+Y+9cR2rkyJG4fPkyvvzyS0gkEnh6emLMmDFyS6QPHjwYhYWFWL58OaZMmQJDQ0N88cUXLzxvr169sHLlSixduhQTJkyAnZ0dtmzZgg4dOlRaZtOmTfD29kazZs1gZWWFhQsXYsqUKc/FeXl5YcGCBbCxsUGbNm3kjn3//fcYNWoUPvnkE+jo6GDatGm4ffs21NXVFXtjarkTZx+gjq4qhnvZQl9PDYk3cjF5TjQyMssnapsYqePZH1qsLGRwbaAL31lRz53PyEANbVuVDz0IWi0/n26cf4Q4t4VqH0XbmkxdGZNH28PYQIqix2W4dScf85ddw4l/ltovLQPq2WqieycTaGmqIP3RY4RffoSNO26imB32Wu11Ptde5vT5h1i6NgFf9bWCr7c9ku8WYGbgFURdza6CO6C36cqEADjNm4AGq+dAamyAwntpSN64GwkBa8QYqZkRZFZm4vadbfugoq0J29FecFnsh+LMHDwMOY9Y/yVizO3Nv6I0vxB1Jw9D/UXTUJqXj5yYeCSt2vpa9Sy4eQfhn46EyzJ/2I4bhMI79xE9cqb4DCkAkBobwHXLd5CaGaMkKwc50XG42GMY0o+fe61r0qvhA3kVJxHe58Xd30F5eXmwsLDAsmXLMGzYq//y8mHPUy8PIiIiouf4H/F+eRDRf+Dj4rjqrkKlxq2ouh9UVvu+2iNC3jXvXEbqfXP58mVcu3YNH3zwAbKysjB/fvmSo5999lk114yIiIiIagvOkVIcO1I1wNKlSxEXFwc1NTU0b94cZ86cgaGh4csLEhERERFRtWBHqpo1bdoUly5dqu5qEBEREVEtxoyU4ipff5eIiIiIiIgqxIwUEREREVEtx4SU4piRIiIiIiIiUhAzUkREREREtRznSCmOHSkiIiIiolqOj5ZVHIf2ERERERERKYgZKSIiIiKiWq6MQ/sUxowUERERERGRgpiRIiIiIiKq5ThHSnHMSBERERERESmIGSkiIiIiolqOy58rjhkpIiIiIiIiBbEjRURERERUywllQpW9FHH69Gn07NkT5ubmkEgk2L9/v3w9BQGzZ8+GmZkZZDIZunTpgoSEBLmYR48ewcvLCzo6OqhTpw6GDRuG3NxcuZioqCi0bdsW6urqsLKywuLFixV+z9iRIiIiIiKq5coEocpeisjLy4OrqyvWrFlT4fHFixdj1apVWL9+PS5cuABNTU14eHigsLBQjPHy8sKVK1cQHByMQ4cO4fTp0/D29haPZ2dn46OPPoKNjQ0uXbqEJUuWYO7cudiwYYNCdeUcKSIiIiIiqhG6d++O7t27V3hMEASsWLECM2fOxGeffQYA2LZtG0xMTLB//370798fsbGxOHLkCMLDw+Hm5gYAWL16NXr06IGlS5fC3NwcO3bswOPHj7F582aoqamhQYMGiIiIwPfffy/X4XoZZqSIiIiIiGq5mjK070WSkpJw//59dOnSRdynq6uLli1bIiwsDAAQFhaGOnXqiJ0oAOjSpQuUlJRw4cIFMaZdu3ZQU1MTYzw8PBAXF4eMjIxXrg8zUkREREREVGWKiopQVFQkt08qlUIqlSp0nvv37wMATExM5PabmJiIx+7fvw9jY2O54yoqKtDX15eLsbOze+4cT47p6em9Un2YkSIiIiIiquUEQaiyV2BgIHR1deVegYGB1X3Lb4wZKSIiIiIiqjL+/v6YNGmS3D5Fs1EAYGpqCgBITU2FmZmZuD81NRVNmjQRY9LS0uTKlZSU4NGjR2J5U1NTpKamysU82X4S8yqYkSIiIiIiquXKyoQqe0mlUujo6Mi9XqcjZWdnB1NTUxw/flzcl52djQsXLsDd3R0A4O7ujszMTFy6dEmMOXHiBMrKytCyZUsx5vTp0yguLhZjgoOD4eTk9MrD+gB2pIiIiIiIqIbIzc1FREQEIiIiAJQvMBEREYHk5GRIJBL4+voiICAAv//+O6KjozFo0CCYm5ujV69eAABnZ2d069YNI0aMwMWLFxEaGgofHx/0798f5ubmAIABAwZATU0Nw4YNw5UrV7B7926sXLnyuazZy3BoHxERERFRLfdfrq73Jv766y907NhR3H7SuRk8eDCCgoIwbdo05OXlwdvbG5mZmfjwww9x5MgRqKuri2V27NgBHx8fdO7cGUpKSvj888+xatUq8biuri7+97//YezYsWjevDkMDQ0xe/ZshZY+BwCJICj4lCyqkT7seaq6q0BERPRO8j+i2Jcnotf1cXFcdVehUgO+uVNl5965yLLKzl2dOLSPiIiIiIhIQRzaR0RERERUywllZdVdhXcOM1JEREREREQKYkaKiIiIiKiWK6shi028S5iRIiIiIiIiUhAzUkREREREtRwX8lYcM1JEREREREQKYkaKiIiIiKiWqykP5H2XsCNFRERERFTLsSOlOA7tIyIiIiIiUhAzUkREREREtVyZwAfyKooZKSIiIiIiIgUxI0VEREREVMtxjpTimJEiIiIiIiJSULV2pGxtbbFixYpXjr958yYkEgkiIiKqrE7/FhQUhDp16ryVaxERERERVRehTKiy1/uqWof2hYeHQ1NT8z89Z1BQEHx9fZGZmfmfnpdqvj49zOHZxwr6emq4npSL5T8mIjYhp9L4vp9aoHd3c5gYSZGZXYyQc+n4cesNPC5++g/eUF8No4fURavm+lCXKuFOSgEWroxDXGLu27glqqEUaWvdO5tghm99uX1Fj8vQ+fMzAABlZQm8v7JFKzd9mJvKkJdXgr8iM7BuaxIePnpc5fdCNZuin2tamsrwHmiHdu6G0NFWRWpaIVZuvI7zlx49F/vVF1YYNbgu9hy4g1U/Xa/K26C3QUkJjrPHwWLAp5CaGqLwXhrubNuHxIVrX1jM3LMn6k0ZDk17GxRn5eDB0TOI9VuM4keZVVZV/XYfwGXpN9BycUDh7RQkBq7DnW37xOPWIz1hM9ITMhsLAEDu1QQkBKzFg6Onq6xORK+jWjtSRkZG1Xl5eo90+tAIPsPrYemaeFyNz0G/Ty3w/fxG8BwVjsys4ufiu7Y3xqjBdbFoVRyiY7NgZaGBGROcIAjAD5vKv1Boa6pg3eKm+Ds6E1PmRiMzuxiW5jLk5Ja87dujGkTRtgYAuXklGDDqorj979/m1KVKcKynja27k5GQlAsdLRVMGGGP72Y2xPBJf1fx3VBNpmhbU1GRYPm3jZGRWYxZi67iwcMimBqrI7eCz6z6Dtr4tJsZEpP4o9D7ot7UEbAZ6YnIr/2QczURus0bwvWnQJRk5+DmD9srLKPXuhmabPkOV6cEIvXQSahbmKDRD3PReP23uNRv3GvVQ2ZjgU6JJ3BY1ani47aWaPH7j0jesAsRg6bAoJM7Gv0YgMKUB0gPPgsAKLxzH9emL0Ve4i1IJBJYDuwFt71rcKZFb+ReTXytetHLCcL7mzmqKq88tO/QoUOoU6cOSktLAQARERGQSCT45ptvxJjhw4fjq6++ErfPnj2Ltm3bQiaTwcrKCuPHj0deXp54/NmhfdeuXcOHH34IdXV1uLi44NixY5BIJNi/f79cXW7cuIGOHTtCQ0MDrq6uCAsLAwCEhIRg6NChyMrKgkQigUQiwdy5cwEARUVFmDJlCiwsLKCpqYmWLVsiJCRE7rxBQUGwtraGhoYGevfujYcPH770ffHz84OjoyM0NDRQt25dzJo1C8XF5X/g4uPjIZFIcO3aNbkyy5cvR7169cTt33//HQ4ODlBXV0fHjh2xdetWSCQSZtUU0L+XJQ4eTcEfx1Nx83Y+lqxNQGFRGT7palphfMP6OoiOzULwqTTcTytC+OUMHDudBhdHbTHG6wsrpKUXIXBlHGITcpCSWojwyxm4d7/wbd0W1UCKtjUAEATgUWax+MrIfPolOC+/FBNnR+HE2Qe4fbcAV+Jy8P2PiajvoA0TI+nbuCWqoRRtax93MYWOlir8F1xBdGw27qcVISImC4k38+TiZOpKmDO5PhavjucPQ+8RPfemSD14HGl/nkLBrbu4v/coHgSfRZ0WjSsv07IJ8m/exc0ftqPg5h1khF5C8k+7oftMGauvv0D7qD/QLScK7aP/hM2oAa9dTxvv/ihIuoPYad8h99oN3Fq7A/d/Owq7CUPEmLTDJ/HgyGnkJ95CXsJNxM1egZLcfOi1bPLa16WXKysrq7LX++qVO1Jt27ZFTk4OLl++DAA4deoUDA0N5Tojp06dQocOHQAA169fR7du3fD5558jKioKu3fvxtmzZ+Hj41Ph+UtLS9GrVy9oaGjgwoUL2LBhA2bMmFFh7IwZMzBlyhRERETA0dERnp6eKCkpQevWrbFixQro6OggJSUFKSkpmDJlCgDAx8cHYWFh2LVrF6KiotC3b19069YNCQkJAIALFy5g2LBh8PHxQUREBDp27IiAgICXvi/a2toICgrC1atXsXLlSmzcuBHLly8HADg6OsLNzQ07duyQK7Njxw4MGFD+IZSUlIQvvvgCvXr1QmRkJEaOHFnpfVPFVFQkcLTXxl+RGeI+QQD+ishAAyedCsvEXMuGUz1tODuUd5zMTdTRyk0fYX89Hf7S5gMDXEvMwbd+Lji43R2bVzRDz48q/7JM77/XaWsAIJMp49dNLfHb5pYInNEAdtYaL7yOloYyysoEfsmtxV6nrX3Y0gAx17IxeZQ9ft/mjm0/uGFgX2soPfOXftIoB5z76xH+isyswjugty0j7DIMOraCpoMtAEC7sRP02zRH2pHKh8NlXIiAzMoURt3aAQDUjA1g2scDD46cEmPMPXvCcc4ExM1ejlONeiBu1vdwnDseFgN7vVY967RqgvQTYXL7HgSfhV6rJhUXUFKCWb8eUNbUQMb5y691TaKq8spD+3R1ddGkSROEhITAzc0NISEhmDhxIubNm4fc3FxkZWUhMTER7du3BwAEBgbCy8sLvr6+AAAHBwesWrUK7du3x7p166Curi53/uDgYFy/fh0hISEwNS3/srpgwQJ07dr1ubpMmTIFH3/8MQBg3rx5aNCgARITE1G/fn3o6upCIpGI5wCA5ORkbNmyBcnJyTA3NxfPceTIEWzZsgULFy7EypUr0a1bN0ybNg1AeSfo3LlzOHLkyAvfl5kzZ4r/bWtriylTpmDXrl3ieby8vPDDDz/g22+/BVCepbp06RL+7//+DwDw448/wsnJCUuWLAEAODk5ISYmBgsWLHjZ/xL6h66OKlSUJXiUIT/U5VFmMWwsK/7CGnwqDbo6qlj7XRNIJICKihL2/XEP239JFmPMTWXo1V2G3fvvYNsvyXB20Iavtz2KSwQcOZFapfdENdPrtLXkOwVYtDIOiTdzoaWpAs/eVli3uCkGjg3Hg4fPz4FSU5Vg9JC6OHY6DfkFpVVyH1TzvU5bMzeVoVljdQSHpGLqvGhYmMkwebQDVJQl2LLrFgCgc1sjONbTwggOG33vXF+8ASo6Wmgf8yeE0lJIlJURN2s57v18sNIyGef+xuVBU9Fs5wooqatBSVUVqQdPIGbcfDHGcfY4xE5bhPv7gwEABTfvQMvZHjYjvsTd7fsVrqfUxBBFqely+4pS06Gqqw0ldSnKCosAANoNHdH6zC4oqUtRmpuPS1+MRW4s5/JVpfd5UYiqotAcqfbt2yMkJASTJ0/GmTNnEBgYiD179uDs2bN49OgRzM3N4eDgAACIjIxEVFSUXDZGEASUlZUhKSkJzs7OcueOi4uDlZWVXAfogw8+qLAejRs/TTmbmZkBANLS0lC/fv0K46Ojo1FaWgpHR0e5/UVFRTAwMAAAxMbGonfv3nLH3d3dX9qR2r17N1atWoXr168jNzcXJSUl0NF5+mth//79MWXKFJw/fx6tWrXCjh070KxZM7GucXFxaNGihdw5K7vvf9e7qKhIbl9Z6WMoKau9sBw91bShLgb2tcay9Qm4GpcDSzN1TPC2R/oja2zdXd6ZUpIA1xJzsGF7EgAg4UYu7Gw00Ku7OTtS9MquxGXjSly2uB0dm40da1vgs27m+GnHTblYZWUJ5vu5ABJg6dqEt1xTetcpSYDMrMdYvCYeZWVA3PVcGBlI4dnHElt23YKxoRQTRthj4uwouUV16P1g1rc7LDx74vLAyci9mggdV2e4LPNHYUpapR0eLed6aPD9DCQErMGD4LOQmhrB+btpaLR2HqK8Z0BZQwZNexs03rAAjdZ/K5aTqKigJOvpoiftIg5BZmP+z0EJAMAj42ln/dHZSwjvOUKh+8mNS8IZt15Q0dWGWR8PuG7+Duc7f8XOFNUoCnWkOnTogM2bNyMyMhKqqqqoX78+OnTogJCQEGRkZIjZKADIzc3FyJEjMX78+OfOY21t/UaVVlVVFf9b8s8/2BeNv8zNzYWysjIuXboEZWVluWNaWlqvXY+wsDB4eXlh3rx58PDwgK6uLnbt2oVly5aJMaampujUqRN27tyJVq1aYefOnRg9evRrXxMoz/bNmzdPbp+Vw2BYOw19o/O+q7Kyi1FSKkBfT1Vuv34dVTzMqHjVs+Ff2eHoyVQc+t99AMCNW3lQV1fGNB9HbNuTDEEAHmY8xs3b+XLlbt3OR4fWXCSltnqdtvas0lIBCTdyYWkmk9uvrCzBt34uMDVWx/gZkcxG1XKv09bSMx6jtETAv/8c3rqTD0N9KVRUJHCy14K+nho2rWguHldRlsC1gS76fGKBTn1O4z2eyvDec140DdeXbEDKnj8AADkx8ZBZm8N+2shKO1L1/EYi49zfuPH9pvIy0XGIyS9A65CdiJu9AsI/DSJq1CxkXoyUKyuUPm0s4Z96Q6Ja/pVS3dwE7if+D2fceonHSwuezi0uSk2H1MRQ7lxSE0MUZ+WI2SgAEIqLkX+9/IfN7L+voI5bI9iOG4SYMXMUeVtIAYLADwBFKdSRejJPavny5WKnqUOHDli0aBEyMjIwefJkMbZZs2a4evUq7O3tX+ncTk5OuH37NlJTU2FiYgKgfHl0RampqYkLYjzRtGlTlJaWIi0tDW3btq2wnLOzMy5cuCC37/z58y+81rlz52BjYyM3p+nWrVvPxXl5eWHatGnw9PTEjRs30L9/f/GYk5MT/vjjD7n4l923v78/Jk2aJLevW/8LlUS//0pKBMQn5qB5Yz2cOV++QIhEAjR31cPew3crLKMuVXouhV32z7ZEUj4XITo2C9YW8kNorCw0cD+Ni03UVq/T1p6lpATUtdWUm4/3pBNlaS7D+OmRyM7h3Kja7nXaWvTVbHRtbyx+hgGAlbkM6Q+LUFIi4K/ITAwcK//3ZbqvE27dKcCOX5PZiXrHKWuoP/d3TSgtLU9VVlZGpg6hRP47k/DkO5REgsdpD1F4NxUadlYvHCJYkHzvafl/zvekE/SszPMRMOreTm6fYefWyDgfUen5AQBKSlCScuQN1SwKPZBXT08PjRs3xo4dO8RFJdq1a4e///4b8fHxchkpPz8/nDt3Tly8ISEhAQcOHKh0sYmuXbuiXr16GDx4MKKiohAaGirOP3qSdXoVtra2yM3NxfHjx5Geno78/Hw4OjrCy8sLgwYNwt69e5GUlISLFy8iMDAQhw8fBgCMHz8eR44cwdKlS5GQkIAffvjhpcP6HBwckJycjF27duH69etYtWoV9u3b91xcnz59kJOTg9GjR6Njx47iPC0AGDlyJK5duwY/Pz/Ex8djz549CAoKeuF9S6VS6OjoyL1q+7C+XfvvoKeHGbp1MoGNpQamjHGATF0Jh4+VZ5xmTnTCyEF2YnzoxYfo1cMcndsawcxEHW5N9DDcyw6hFx+KXyZ2H7iLBk7aGNjXGhZm6uja3hifephh7+F7FVWBaglF29qQ/jZo0VQP5ibqcKynhdmTnGFqJMWh/6UAKO9EBXzjAid7LcxfGgslpfKsg34dVaiovPpnH71/FG1r+/+8Bx3t8uXzrcxlcHfTx8C+1tj7R/lnVkFBKZKS8+VehYVlyM4uRlJyfoV1oHdH6uGTsP9mFIy7t4fMxgImn3WBne9QpB44JsY4BUyC65bvxO20wydh2rsrrEd6QmZnCb3WzdBg+UxkXIxEUUoaACB+/irY+3nD1mcgNB1sod3QEZaD+8DOd8hr1fPWhl3QsLNC/cCp0HSqC5tRA2DWtzuSVgbJ1VP/QzfIbCyg3dARTgGTYND+A9zbWXlnjt4cH8irOIWfI9W+fXtERESIHSl9fX24uLggNTUVTk5PnxnQuHFjnDp1CjNmzEDbtm0hCALq1auHL7/8ssLzKisrY//+/Rg+fDhatGiBunXrYsmSJejZs+dzC1O8SOvWrTFq1Ch8+eWXePjwIebMmYO5c+diy5YtCAgIwOTJk3H37l0YGhqiVatW+OSTTwAArVq1wsaNGzFnzhzMnj0bXbp0wcyZM8VFIiry6aefYuLEifDx8UFRURE+/vhjzJo1S1xy/QltbW307NkTe/bswebNm+WO2dnZ4ddff8XkyZOxcuVKuLu7Y8aMGRg9ejSkUi59/KpOnH2AOrqqGO5lC309NSTeyMXkOdHiMtMmRur497/jrbtvQRCAEV/ZwchADZnZxQi9+FCcDwUA1xJyMH3hFYwcZIch/W2QklqAVRsTEXwq7W3fHtUgirY1bS0V+Pk4Ql9PDTm5JYhLzMGoaRHisFEjAzW0bVU+zCVotZvctcb5R+ByTNbbuTGqcRRta2npRZg0Oxrjh9dD0Go3pD8swi8H72LHbxVnBuj9cmVCAJzmTUCD1XMgNTZA4b00JG/cjYSANWKM1MwIMiszcfvOtn1Q0daE7WgvuCz2Q3FmDh6GnEes/xIx5vbmX1GaX4i6k4eh/qJpKM3LR05MPJJWbX2tehbcvIPwT0fCZZk/bMcNQuGd+4geOVN8hhQASI0N4LrlO0jNjFGSlYOc6Dhc7DEM6cfPvdY1iaqKRKjBT98KDQ3Fhx9+iMTERLnnLr3vFixYgPXr1+P27duvXObDnqdeHkRERETP8T/iXd1VoFri4+K46q5CpboPiaqyc/8ZVPnzzN5lCmekqtK+ffugpaUFBwcHJCYmYsKECWjTps1734lau3YtWrRoAQMDA4SGhmLJkiWVDoEkIiIiIvqvlXGxCYXVqI5UTk4O/Pz8kJycDENDQ3Tp0kVuBbz3VUJCAgICAvDo0SNYW1tj8uTJ8Pf3r+5qERERERFRJWr00D56dRzaR0RE9Ho4tI/elpo8tO+jgZer7Nz/2960ys5dnRRatY+IiIiIiIhq2NA+IiIiIiJ6+wQ+TE5hzEgREREREREpiBkpIiIiIqJa7n1+cG5VYUaKiIiIiIhIQcxIERERERHVcgKfI6UwdqSIiIiIiGq5Mg7tUxiH9hERERERESmIGSkiIiIiolqOy58rjhkpIiIiIiIiBTEjRURERERUy3H5c8UxI0VERERERKQgZqSIiIiIiGo5Ln+uOGakiIiIiIiIFMSMFBERERFRLcc5UopjR4qIiIiIqJbj8ueK49A+IiIiIiIiBUkEQWAej2qloqIiBAYGwt/fH1KptLqrQ+8xtjV6W9jW6G1hWyNiR4pqsezsbOjq6iIrKws6OjrVXR16j7Gt0dvCtkZvC9saEYf2ERERERERKYwdKSIiIiIiIgWxI0VERERERKQgdqSo1pJKpZgzZw4nyVKVY1ujt4Vtjd4WtjUiLjZBRERERESkMGakiIiIiIiIFMSOFBERERERkYLYkSIiIiIiIlIQO1JUa4WEhEAikSAzM/M/jSV6U3PnzkWTJk3E7SFDhqBXr17VVh96c4IgwNvbG/r6+pBIJIiIiKjuKhER0RtiR4pqrdatWyMlJQW6urr/aSwR0bOOHDmCoKAgHDp0CCkpKcjOzkbPnj1hbm4OiUSC/fv3V3cViWoMW1tbrFixorqrQfRS7EjRO+nx48dvfA41NTWYmppCIpH8p7H0fvsv2h7VPtevX4eZmRlat24NU1NT5OXlwdXVFWvWrKnuqlWKbZ3eNrY5etewI0U1QocOHeDj4wMfHx/o6urC0NAQs2bNwpPV+W1tbfHtt99i0KBB0NHRgbe3NwDg7NmzaNu2LWQyGaysrDB+/Hjk5eWJ5y0qKoKfnx+srKwglUphb2+PTZs2AXh+uN6tW7fQs2dP6OnpQVNTEw0aNMAff/xRYSwA/Pbbb2jQoAGkUilsbW2xbNkyuXuytbXFwoUL8fXXX0NbWxvW1tbYsGFDVb2FVEWetE1fX18YGhrCw8MDMTEx6N69O7S0tGBiYoKBAwciPT1dLFNWVobFixfD3t4eUqkU1tbWWLBggXjcz88Pjo6O0NDQQN26dTFr1iwUFxdXx+3RWzBkyBCMGzcOycnJkEgksLW1Rffu3REQEIDevXu/8nkEQcDcuXNhbW0NqVQKc3NzjB8/Xjz+os87ADh16hQ++OADSKVSmJmZ4ZtvvkFJSYl4vKK2DuCl7Z1qpl9//RWNGjWCTCaDgYEBunTpgry8PHTo0AG+vr5ysb169cKQIUPE7Sd/cz09PaGpqQkLC4vnOv0SiQTr1q1D9+7dIZPJULduXfz6669yMdHR0ejUqZNYB29vb+Tm5orHnwxbXrBgAczNzeHk5IQOHTrg1q1bmDhxIiQSCX/ApBqNHSmqMbZu3QoVFRVcvHgRK1euxPfff4+ffvpJPL506VK4urri8uXLmDVrFq5fv45u3brh888/R1RUFHbv3o2zZ8/Cx8dHLDNo0CD8/PPPWLVqFWJjY/Hjjz9CS0urwuuPHTsWRUVFOH36NKKjo/Hdd99VGnvp0iX069cP/fv3R3R0NObOnYtZs2YhKChILm7ZsmVwc3PD5cuXMWbMGIwePRpxcXFv/mbRW7V161aoqakhNDQUixYtQqdOndC0aVP89ddfOHLkCFJTU9GvXz8x3t/fH4sWLcKsWbNw9epV7Ny5EyYmJuJxbW1tBAUF4erVq1i5ciU2btyI5cuXV8et0VuwcuVKzJ8/H5aWlkhJSUF4ePhrnee3337D8uXL8eOPPyIhIQH79+9Ho0aNxOMv+ry7e/cuevTogRYtWiAyMhLr1q3Dpk2bEBAQIHeNf7f19evXIzMz86XtnWqelJQUeHp64uuvv0ZsbCxCQkLQp08fKPLo0CVLloh/c7/55htMmDABwcHBcjGzZs3C559/jsjISHh5eaF///6IjY0FAOTl5cHDwwN6enoIDw/HL7/8gmPHjsn9jQaA48ePIy4uDsHBwTh06BD27t0LS0tLzJ8/HykpKUhJSXnzN4SoqghENUD79u0FZ2dnoaysTNzn5+cnODs7C4IgCDY2NkKvXr3kygwbNkzw9vaW23fmzBlBSUlJKCgoEOLi4gQAQnBwcIXXPHnypABAyMjIEARBEBo1aiTMnTv3lWIHDBggdO3aVS5m6tSpgouLi7htY2MjfPXVV+J2WVmZYGxsLKxbt+4F7wTVNO3btxeaNm0qbn/77bfCRx99JBdz+/ZtAYAQFxcnZGdnC1KpVNi4ceMrX2PJkiVC8+bNxe05c+YIrq6u4vbgwYOFzz777LXvgarf8uXLBRsbmwqPARD27dv30nMsW7ZMcHR0FB4/fvzcsZd93k2fPl1wcnKS+4xds2aNoKWlJZSWlgqC8HxbF4SXt3eqmS5duiQAEG7evPncsfbt2wsTJkyQ2/fZZ58JgwcPFrdtbGyEbt26ycV8+eWXQvfu3cVtAMKoUaPkYlq2bCmMHj1aEARB2LBhg6Cnpyfk5uaKxw8fPiwoKSkJ9+/fFwSh/LPNxMREKCoqkjuPjY2NsHz58le+X6LqwowU1RitWrWSS+G7u7sjISEBpaWlAAA3Nze5+MjISAQFBUFLS0t8eXh4oKysDElJSYiIiICysjLat2//StcfP348AgIC0KZNG8yZMwdRUVGVxsbGxqJNmzZy+9q0aSNXXwBo3Lix+N8SiQSmpqZIS0t7pfpQzdG8eXPxvyMjI3Hy5Em5dle/fn0A5fNgYmNjUVRUhM6dO1d6vt27d6NNmzYwNTWFlpYWZs6cieTk5Cq/D3p3LFy4UK6NJScno2/fvigoKEDdunUxYsQI7Nu3Txya97LPu9jYWLi7u8t9xrZp0wa5ubm4c+eOuO/fbR14eXunmsnV1RWdO3dGo0aN0LdvX2zcuBEZGRkKncPd3f257SfZpleJiY2NhaurKzQ1NcXjbdq0QVlZmdzIjEaNGkFNTU2huhHVFOxI0Tvj3x/GAJCbm4uRI0ciIiJCfEVGRiIhIQH16tWDTCZT6PzDhw/HjRs3MHDgQERHR8PNzQ2rV69+ozqrqqrKbUskEpSVlb3ROent+3fby83NRc+ePeXaXUREBBISEtCuXbuXtruwsDB4eXmhR48eOHToEC5fvowZM2ZwkjXJGTVqlFz7Mjc3h5WVFeLi4rB27VrIZDKMGTMG7dq1Q3FxscKfd5Wp6HP2Re2daiZlZWUEBwfjzz//hIuLC1avXg0nJyckJSVBSUnpuSF+1TlH89k2R/QuYUeKaowLFy7IbZ8/fx4ODg5QVlauML5Zs2a4evUq7O3tn3upqamhUaNGKCsrw6lTp165DlZWVhg1ahT27t2LyZMnY+PGjRXGOTs7IzQ0VG5faGgoHB0dK60vvR+aNWuGK1euwNbW9rl2p6mpCQcHB8hkMhw/frzC8ufOnYONjQ1mzJgBNzc3ODg44NatW2/5Lqim09fXl2tbKioqAACZTIaePXti1apVCAkJQVhYGKKjo1/6eefs7IywsDC5L9ChoaHQ1taGpaVlpfV4WXunmksikaBNmzaYN28eLl++DDU1Nezbtw9GRkZy845KS0sRExPzXPnz588/t+3s7PzKMc7OzoiMjJRbACo0NBRKSkpwcnJ6Yd3V1NTkRncQ1VTsSFGNkZycjEmTJiEuLg4///wzVq9ejQkTJlQa7+fnh3PnzsHHx0f8hfTAgQPiRFZbW1sMHjwYX3/9Nfbv34+kpCSEhIRgz549FZ7P19cXR48eRVJSEv7++2+cPHnyuT8aT0yePBnHjx/Ht99+i/j4eGzduhU//PADpkyZ8uZvBNVoY8eOxaNHj+Dp6Ynw8HBcv34dR48exdChQ1FaWgp1dXX4+flh2rRp2LZtG65fv47z58+Lq6c5ODggOTkZu3btwvXr17Fq1Srs27evmu+K3rbc3FwxuwNAHI78oiGeQUFB2LRpE2JiYnDjxg383//9H2QyGWxsbF76eTdmzBjcvn0b48aNw7Vr13DgwAHMmTMHkyZNgpJS5V8FXtbeqWa6cOECFi5ciL/++gvJycnYu3cvHjx4AGdnZ3Tq1AmHDx/G4cOHce3aNYwePbrCh82HhoZi8eLFiI+Px5o1a/DLL7889zf5l19+webNmxEfH485c+bg4sWL4t9gLy8vqKurY/DgwYiJicHJkycxbtw4DBw4UG7xnYrY2tri9OnTuHv3LleIpJqtuidpEQlC+eTXMWPGCKNGjRJ0dHQEPT09Yfr06eLE6Momnl68eFHo2rWroKWlJWhqagqNGzcWFixYIB4vKCgQJk6cKJiZmQlqamqCvb29sHnzZkEQnl9AwsfHR6hXr54glUoFIyMjYeDAgUJ6enqFsYIgCL/++qvg4uIiqKqqCtbW1sKSJUvk6lZRnV1dXYU5c+a82ZtFb1VFE7Pj4+OF3r17C3Xq1BFkMplQv359wdfXV2yvpaWlQkBAgGBjYyO2j4ULF4rlp06dKhgYGAhaWlrCl19+KSxfvlzQ1dUVj3OxiffPs4tNPPlMefb17wn/z9q3b5/QsmVLQUdHR9DU1BRatWolHDt2TDz+os87QRCEkJAQoUWLFoKamppgamoq+Pn5CcXFxeLxitq6ILy8vVPNc/XqVcHDw0MwMjISpFKp4OjoKKxevVoQBEF4/PixMHr0aEFfX18wNjYWAgMDK1xsYt68eULfvn0FDQ0NwdTUVFi5cqXcNQAIa9asEbp27SpIpVLB1tZW2L17t1xMVFSU0LFjR0FdXV3Q19cXRowYIeTk5IjHK/tsCwsLExo3bixIpVKBX1WpJpMIggJrYRJVkQ4dOqBJkyZ8kjkREVE1s7W1ha+v73PPm/o3iUSCffv2oVevXm+tXkQ1DYf2ERERERERKYgdKSIiIiIiIgVxaB8REREREZGCmJEiIiIiIiJSEDtSRERERERECmJHioiIiIiISEHsSBERERERESmIHSkiIiIiIiIFsSNFRERERESkIHakiIiIiIiIFMSOFBERERERkYLYkSIiIiIiIlLQ/wNbSIHv/gAa6gAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "report = classification_report(y_test, y_pred, output_dict=True)\n",
    "report_df = pd.DataFrame(report).transpose()\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.heatmap(report_df.iloc[:-1, :].astype(float), annot=True, cmap='coolwarm')\n",
    "plt.title('Classification Report Heatmap')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Administrator\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1509: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in samples with no predicted labels. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
      "c:\\Users\\Administrator\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1509: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 in samples with no true labels. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
      "c:\\Users\\Administrator\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1509: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in samples with no true nor predicted labels. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import classification_report, accuracy_score\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.multiclass import OneVsRestClassifier\n",
    "\n",
    "# Đoạn mã để tạo và huấn luyện mô hình đã được cung cấp\n",
    "\n",
    "# Vẽ biểu đồ\n",
    "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(12, 6))\n",
    "\n",
    "# Biểu đồ dự đoán\n",
    "axes[0].bar(['Accuracy'], [accuracy])\n",
    "axes[0].set_title('Accuracy')\n",
    "axes[0].set_ylim(0, 1)\n",
    "\n",
    "# Biểu đồ báo cáo đánh giá\n",
    "report_dict = classification_report(y_test, y_pred, output_dict=True)\n",
    "precision = [report_dict['macro avg']['precision']]\n",
    "recall = [report_dict['macro avg']['recall']]\n",
    "f1 = [report_dict['macro avg']['f1-score']]\n",
    "axes[1].bar(['Precision', 'Recall', 'F1-score'], [precision[0], recall[0], f1[0]])\n",
    "axes[1].set_title('Classification Report')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}