COMP551 (Fall 2019) Applied Machine Learning Mini Project 2 (Subreddit Classifier).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# COMP551 - Mini-Project 2 - Team 97\n",
    "\n",
    "McGill University<br>\n",
    "Authors: Shantanil Bagchi, Manoosh Samiei, Nikhil Podila\n",
    "\n",
    "This is the implementation code for Mini-project 2\n",
    "\n",
    "###### Note: Some sections of the code have been compiled from various sources in the internet and in relevent papers, which have been referenced in our final report."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing Necessary Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 17
    },
    "colab_type": "code",
    "id": "kYtqm2wN5x6T",
    "outputId": "e8ac4054-a1cd-4c6e-de77-ded4528682e2"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>.container { width:95% !important; }</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import time\n",
    "import re \n",
    "\n",
    "'''Features'''\n",
    "from sklearn import preprocessing\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer\n",
    "from sklearn.decomposition import TruncatedSVD\n",
    "from sklearn.preprocessing import label_binarize\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.feature_selection import chi2, SelectKBest\n",
    "\n",
    "'''Classifiers'''\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.svm import LinearSVC\n",
    "\n",
    "'''Metrics/Evaluation'''\n",
    "from sklearn.model_selection import train_test_split, cross_val_score, GridSearchCV\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_curve, auc, roc_auc_score\n",
    "from scipy import interp\n",
    "from itertools import cycle\n",
    "\n",
    "'''Plotting'''\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set_style('darkgrid')\n",
    "\n",
    "'''Display'''\n",
    "from IPython.core.display import display, HTML\n",
    "display(HTML(\"<style>.container { width:95% !important; }</style>\"))\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "pd.options.display.float_format = '{:,.2f}'.format\n",
    "\n",
    "'''Classifiers'''\n",
    "from sklearn.feature_extraction.text import CountVectorizer  \n",
    "from sklearn.feature_extraction.text import TfidfTransformer  \n",
    "from sklearn.model_selection import train_test_split \n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "'''Lemmatizer'''\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "lemmatizer = WordNetLemmatizer()\n",
    "\n",
    "'''Stemmer'''\n",
    "from nltk.stem.snowball import SnowballStemmer\n",
    "#from nltk.stem import PorterStemmer\n",
    "\n",
    "'''Progress  Bar'''\n",
    "from tqdm import tqdm\n",
    "tqdm.pandas(desc=\"progress-bar\")\n",
    "\n",
    "'''Stopwords Corpus'''\n",
    "import nltk\n",
    "from nltk.corpus import stopwords, wordnet\n",
    "stop_words = set(stopwords.words('english'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 238
    },
    "colab_type": "code",
    "id": "eD2MY9yA9HCF",
    "outputId": "fe3ea8c0-6a09-41fa-f929-2963a28ca05f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading of the data files on process...\n",
      "Task Completed\n"
     ]
    },
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>comments</th>\n",
       "      <th>subreddits</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Honestly, Buffalo is the correct answer. I rem...</td>\n",
       "      <td>hockey</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Ah yes way could have been :( remember when he...</td>\n",
       "      <td>nba</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>https://youtu.be/6xxbBR8iSZ0?t=40m49s\\n\\nIf yo...</td>\n",
       "      <td>leagueoflegends</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>He wouldn't have been a bad signing if we woul...</td>\n",
       "      <td>soccer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>Easy. You use the piss and dry technique. Let ...</td>\n",
       "      <td>funny</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id                                           comments       subreddits\n",
       "0   0  Honestly, Buffalo is the correct answer. I rem...           hockey\n",
       "1   1  Ah yes way could have been :( remember when he...              nba\n",
       "2   2  https://youtu.be/6xxbBR8iSZ0?t=40m49s\\n\\nIf yo...  leagueoflegends\n",
       "3   3  He wouldn't have been a bad signing if we woul...           soccer\n",
       "4   4  Easy. You use the piss and dry technique. Let ...            funny"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Reading of the data files on process...\")\n",
    "\n",
    "df = pd.read_csv(\"reddit_train.csv\")\n",
    "test= pd.read_csv(\"reddit_test.csv\")\n",
    "print(\"Task Completed\")\n",
    "  \n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Checking for duplicate posts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "0j3Gy7Yogoxy",
    "outputId": "5dd2f4a6-eb1c-4c9f-8b1a-e4ada305b391",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>comments</th>\n",
       "      <th>subreddits</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>280</td>\n",
       "      <td>280</td>\n",
       "      <td>\\nVery short discussion posts are usually a si...</td>\n",
       "      <td>Music</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>313</td>\n",
       "      <td>313</td>\n",
       "      <td>Your submission has been removed because of th...</td>\n",
       "      <td>gameofthrones</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>369</td>\n",
       "      <td>369</td>\n",
       "      <td>**Spoiler Warning:** [EVERYTHING] means spoile...</td>\n",
       "      <td>gameofthrones</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>556</td>\n",
       "      <td>556</td>\n",
       "      <td>Your submission has been removed because of th...</td>\n",
       "      <td>gameofthrones</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>616</td>\n",
       "      <td>616</td>\n",
       "      <td>Your submission was removed because it looks l...</td>\n",
       "      <td>Music</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>69602</td>\n",
       "      <td>69602</td>\n",
       "      <td>In accordance with our [Low-effort Guidelines]...</td>\n",
       "      <td>Overwatch</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>69684</td>\n",
       "      <td>69684</td>\n",
       "      <td>[NO SPOILERS] means any comments with spoilers...</td>\n",
       "      <td>gameofthrones</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>69762</td>\n",
       "      <td>69762</td>\n",
       "      <td>Your submission has been automatically removed...</td>\n",
       "      <td>gameofthrones</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>69773</td>\n",
       "      <td>69773</td>\n",
       "      <td>Your account is too new, so your submission ha...</td>\n",
       "      <td>gameofthrones</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>69994</td>\n",
       "      <td>69994</td>\n",
       "      <td>\\nVery short discussion posts are usually a si...</td>\n",
       "      <td>Music</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>842 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          id                                           comments     subreddits\n",
       "280      280  \\nVery short discussion posts are usually a si...          Music\n",
       "313      313  Your submission has been removed because of th...  gameofthrones\n",
       "369      369  **Spoiler Warning:** [EVERYTHING] means spoile...  gameofthrones\n",
       "556      556  Your submission has been removed because of th...  gameofthrones\n",
       "616      616  Your submission was removed because it looks l...          Music\n",
       "...      ...                                                ...            ...\n",
       "69602  69602  In accordance with our [Low-effort Guidelines]...      Overwatch\n",
       "69684  69684  [NO SPOILERS] means any comments with spoilers...  gameofthrones\n",
       "69762  69762  Your submission has been automatically removed...  gameofthrones\n",
       "69773  69773  Your account is too new, so your submission ha...  gameofthrones\n",
       "69994  69994  \\nVery short discussion posts are usually a si...          Music\n",
       "\n",
       "[842 rows x 3 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To check for duplicated data\n",
    "df[df[\"comments\"].duplicated()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dropping Duplicate Post (not much useful)\n",
    "\n",
    "*_Trials after dropping duplicate posts didn't lead to improved accuracy_*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "Femo9Tztgox0",
    "outputId": "f54f5964-d8ca-49be-b3d4-f4b7ecdd4417",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# #To drop the duplicated data\n",
    "# df=df.drop_duplicates(keep='first',subset='comments')\n",
    "# df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "f5CGeOQIgox2"
   },
   "outputs": [],
   "source": [
    "X_datatrain = df['comments']\n",
    "y_datatrain = df['subreddits']\n",
    "X_datatest = test['comments']  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocessing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "4z_KWvP29rcx"
   },
   "outputs": [],
   "source": [
    "#Progress bar\n",
    "def process_data(data):\n",
    "    data = data.progress_map(preprocess_data)  \n",
    "    return data\n",
    "\n",
    "#Preprocessing the data\n",
    "def preprocess_data(review):\n",
    "    document = cleaning_data(review)\n",
    "    document = stopwords_data(document)\n",
    "    #document = lemmatize_data(document)\n",
    "    #document = stem_data(document)\n",
    "    \n",
    "    return document "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### _HTML removal, contraction removal, Lemmatizing and Stemming also didn't lead to improved accuracy - Thus dropped_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "V7py8Voj-AEi"
   },
   "outputs": [],
   "source": [
    "# Function to remove noise from text and deal with negation\n",
    "def cleaning_data(comment):\n",
    "    \n",
    "    #Remove Non-ASCII characters\n",
    "    comment = re.sub(r\"\\xa0\",r\" \",comment)\n",
    "    comment = comment.encode(\"ascii\", errors=\"ignore\").decode()\n",
    "    \n",
    "    #Lower case the data\n",
    "    comment = comment.lower()\n",
    "    \n",
    "    #Remove HTML Links\n",
    "    #comment = re.sub(r'\\w+:\\/{2}[\\d\\w-]+(\\.[\\d\\w-]+)*(?:(?:\\/[^\\s/]*))*', ' ', comment)   \n",
    "    #comment = re.sub(r'\\n','',comment)\n",
    "    \n",
    "    #Cleaning the data by removing special characters\n",
    "    comment = re.sub(r\"[^A-Za-z0-9!?\\'\\`]\", \" \", comment)\n",
    "    \n",
    "     #Handling negations\n",
    "    comment = re.sub(r\"youtu.be\", \" youtube\", comment)\n",
    "    comment = re.sub(r\"https\", \" http\", comment)\n",
    "    comment = re.sub(r\"i'm\", \" i am\", comment)\n",
    "    #comment = re.sub(r\"it's\", \" it is\", comment)\n",
    "    #comment = re.sub(r\"ain't\", \"is not\",comment)\n",
    "    #comment = re.sub(r\"aren't\", \"are not\",comment)\n",
    "    #comment = re.sub(r\"couldn't\", \"could not\",comment)\n",
    "    #comment = re.sub(r\"didnt\", \"did not\",comment)\n",
    "    #comment = re.sub(r\"dont\", \"do not\",comment)\n",
    "    #comment = re.sub(r\"doesn't\", \"does not\",comment)\n",
    "    #comment = re.sub(r\"hadn't\", \"had not\",comment)\n",
    "    #comment = re.sub(r\"hasn't\", \"has not\",comment)\n",
    "    #comment = re.sub(r\"haven't\", \"have not\",comment)\n",
    "    #comment = re.sub(r\"isn't\", \"is not\",comment)\n",
    "    #comment = re.sub(r\"shouldn't\", \"should not\",comment)\n",
    "    #comment = re.sub(r\"shan't\", \"shall not\",comment)\n",
    "    #comment = re.sub(r\"wasn't\", \"was not\",comment)\n",
    "    #comment = re.sub(r\"weren't\", \"were not\",comment)\n",
    "    #comment = re.sub(r\"oughtn't\", \"ought not\",comment)\n",
    "    #comment = re.sub(r\"that's\", \" that is\", comment)\n",
    "    comment = re.sub(r\"\\'s\", \" \", comment)\n",
    "    #comment = re.sub(r\"\\'ve\", \" have\", comment)\n",
    "    #comment = re.sub(r\"won't\", \" will not\", comment)\n",
    "    #comment = re.sub(r\"wouldn't\", \" would not\", comment)\n",
    "    #comment = re.sub(r\"don't\", \" do not\", comment)\n",
    "    #comment = re.sub(r\"can't\", \" can not\", comment)\n",
    "    #comment = re.sub(r\"cannot\", \" can not\", comment)\n",
    "    #comment = re.sub(r\"n\\'t\", \" n\\'t\", comment)\n",
    "    #comment = re.sub(r\"\\'re\", \" are\", comment)\n",
    "    #comment = re.sub(r\"\\'d\", \" would\", comment)\n",
    "    #comment = re.sub(r\"\\'ll\", \" will\", comment)    \n",
    "    comment = re.sub(r\"!\", \" ! \", comment)\n",
    "    comment = re.sub(r\"\\?\", \" ? \", comment)\n",
    "    comment = re.sub(r\"\\s{2,}\", \" \", comment)\n",
    "    \n",
    "    # Removing all the numbers\n",
    "    comment = re.sub(r'[0-9]+', ' ', comment)\n",
    "    \n",
    "    #Removing all punctuations\n",
    "    comment = re.sub(r'[^\\w\\s]','',comment)\n",
    "    \n",
    "    # Substituting multiple spaces with single space\n",
    "    comment = re.sub(r'\\s+', ' ', comment, flags=re.I)\n",
    "    \n",
    "    # Remove single letters\n",
    "    comment= re.sub(r\"\\b[a-zA-Z]\\b\",\"\", comment)\n",
    "        \n",
    "    comment = [word for word in comment.split() if len(word) >= 2]\n",
    "    comment = ' '.join(comment)\n",
    "    \n",
    "    return comment\n",
    "  \n",
    "#Removing the stop words\n",
    "def stopwords_data(comment):\n",
    "    more_stop_words = [\"wa\", \"gt\",\"amp\", \"u\", \"ha\", \"le\", \"doe\", \"don\",'ve']\n",
    "    stop_words_ = stop_words.union(more_stop_words)\n",
    "    comment = [word for word in comment.split() if not word in stop_words_]\n",
    "    comment = ' '.join(comment)           \n",
    "    return comment\n",
    "  \n",
    "#Tag words by their parts of speech \n",
    "def get_wordnet_pos(word):\n",
    "    \"\"\"Map POS tag to first character lemmatize() accepts\"\"\"\n",
    "    tag = nltk.pos_tag([word])[0][1][0].upper()\n",
    "    tag_dict = {\"J\": wordnet.ADJ,\n",
    "                \"N\": wordnet.NOUN,\n",
    "                \"V\": wordnet.VERB,\n",
    "                \"R\": wordnet.ADV}\n",
    "\n",
    "    return tag_dict.get(tag, wordnet.NOUN)\n",
    "\n",
    "#Normalising the data by lemmatizing\n",
    "def lemmatize_data(comment):\n",
    "    comment = [lemmatizer.lemmatize(word,get_wordnet_pos(word)) for word in comment.split()]\n",
    "    comment = ' '.join(comment)\n",
    "    comment = [word for word in comment.split() if len(word) >= 3]\n",
    "    comment = ' '.join(comment)\n",
    "    return comment\n",
    "\n",
    "#Normalise data by user-defined stemming\n",
    "def stem(word):\n",
    "    for suffix in ['ing', 'ly', 'ed', 'ious', 'ies', 'ive', 'es', 's', 'ment']:\n",
    "        if word.endswith(suffix):\n",
    "            return word[:-len(suffix)]\n",
    "    return word\n",
    "\n",
    "def stemmer(phrase):\n",
    "    r=[]\n",
    "    for word in phrase.split():\n",
    "        r.append(stem(word))\n",
    "    return ' '.join(r)\n",
    "  \n",
    "#Normalising the data by renowned stemming methods\n",
    "\n",
    "stemmer=SnowballStemmer('english')\n",
    "def stem_data(comment):\n",
    "    comment = [stemmer.stem(word) for word in comment.split()]\n",
    "    comment = ' '.join(comment)\n",
    "    comment = [word for word in comment.split() if len(word) >= 3]\n",
    "    comment = ' '.join(comment)\n",
    "    return comment\n",
    "    \n",
    "#list of cleaned words\n",
    "def cleandata(X_datatrain, X_datatest):\n",
    "    cleandata_Train = []\n",
    "    for sen in range(0, len(X_datatrain)): \n",
    "        cleandata_Train.append(preprocess_data(str(X_datatrain[sen])))\n",
    "        \n",
    "    cleandata_Test = []\n",
    "    for sen in range(0, len(X_datatest)): \n",
    "        cleandata_Test.append(preprocess_data(str(X_datatest[sen])))\n",
    "    return cleandata_Train, cleandata_Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 123
    },
    "colab_type": "code",
    "id": "-koxues0_1dS",
    "outputId": "74348fe4-70c5-4205-c2dd-021d887b6a13",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cleaning of Training data on process...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "progress-bar: 100%|██████████| 70000/70000 [00:04<00:00, 14716.63it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Task completed\n",
      "Cleaning of Testing data on process...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "progress-bar: 100%|██████████| 30000/30000 [00:01<00:00, 15213.32it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Task completed\n"
     ]
    }
   ],
   "source": [
    "print(\"Cleaning of Training data on process...\")\n",
    "cleandata_Train = process_data(X_datatrain)\n",
    "print(\"Task completed\")\n",
    "print(\"Cleaning of Testing data on process...\")\n",
    "cleandata_Test = process_data(X_datatest)\n",
    "print(\"Task completed\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LDA Implementation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Gensim\n",
    "import gensim\n",
    "import gensim.corpora as corpora\n",
    "from gensim.utils import simple_preprocess\n",
    "from gensim.models import CoherenceModel\n",
    "\n",
    "# Plotting tools\n",
    "import pyLDAvis\n",
    "import pyLDAvis.gensim  # don't skip this\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# Enable logging for gensim - optional\n",
    "import logging\n",
    "logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.ERROR)\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\",category=DeprecationWarning)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['honest', 'buffalo', 'correct', 'answer', 'rememb', 'peopl', 'somewhat', 'joke', 'buffalo', 'mantra', 'start', 'goali', 'win', 'game', 'get', 'trade', 'think', 'edmonton', 'front', 'offic', 'travesti', 'better', 'part', 'year', 'buffalo', 'systemat', 'destruct', 'term', 'competit', 'much', 'respons', 'chang', 'draft', 'lotteri']]\n"
     ]
    }
   ],
   "source": [
    "def sent_to_words(sentences):\n",
    "    for sentence in sentences:\n",
    "        yield(gensim.utils.simple_preprocess(str(sentence), deacc=True))  # deacc=True removes punctuations\n",
    "\n",
    "data_words = list(sent_to_words(cleandata_Train))\n",
    "\n",
    "print(data_words[:1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['honest', 'buffalo', 'correct', 'answer', 'rememb', 'peopl', 'somewhat', 'joke', 'buffalo', 'mantra', 'start', 'goali', 'win', 'game', 'get', 'trade', 'think', 'edmonton', 'front_offic', 'travesti', 'better', 'part', 'year', 'buffalo', 'systemat', 'destruct', 'term', 'competit', 'much', 'respons', 'chang', 'draft_lotteri']\n"
     ]
    }
   ],
   "source": [
    "# Build the bigram and trigram models\n",
    "bigram = gensim.models.Phrases(data_words, min_count=5, threshold=100) # higher threshold fewer phrases.\n",
    "trigram = gensim.models.Phrases(bigram[data_words], threshold=100)  \n",
    "\n",
    "# Faster way to get a sentence clubbed as a trigram/bigram\n",
    "bigram_mod = gensim.models.phrases.Phraser(bigram)\n",
    "trigram_mod = gensim.models.phrases.Phraser(trigram)\n",
    "\n",
    "# See trigram example\n",
    "print(trigram_mod[bigram_mod[data_words[0]]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_bigrams(texts):\n",
    "    return [bigram_mod[doc] for doc in texts]\n",
    "\n",
    "def make_trigrams(texts):\n",
    "    return [trigram_mod[bigram_mod[doc]] for doc in texts]\n",
    "\n",
    "def lemmatization(texts, allowed_postags=['NOUN', 'ADJ', 'VERB', 'ADV']):\n",
    "    \"\"\"https://spacy.io/api/annotation\"\"\"\n",
    "    texts_out = []\n",
    "    for sent in texts:\n",
    "        doc = nlp(\" \".join(sent)) \n",
    "        texts_out.append([token.lemma_ for token in doc if token.pos_ in allowed_postags])\n",
    "    return texts_out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['correct', 'somewhat', 'start', 'game', 'get', 'trade', 'think', 'well', 'term', 'competit', 'much', 'respon']]\n"
     ]
    }
   ],
   "source": [
    "# Form Bigrams\n",
    "data_words_bigrams = make_bigrams(data_words)\n",
    "\n",
    "# Initialize spacy 'en' model, keeping only tagger component (for efficiency)\n",
    "#python3 -m spacy download en\n",
    "#import spacy\n",
    "nlp = spacy.load('en', disable=['parser', 'ner'])\n",
    "\n",
    "# Do lemmatization keeping only noun, adj, vb, adv\n",
    "data_lemmatized = lemmatization(data_words_bigrams, allowed_postags=['NOUN', 'ADJ', 'VERB', 'ADV'])\n",
    "\n",
    "print(data_lemmatized[:1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[(0, 1), (1, 1), (2, 1), (3, 1), (4, 1), (5, 1), (6, 1), (7, 1), (8, 1), (9, 1), (10, 1), (11, 1)]]\n"
     ]
    }
   ],
   "source": [
    "# Create Dictionary\n",
    "id2word = corpora.Dictionary(data_lemmatized)\n",
    "\n",
    "# Create Corpus\n",
    "texts = data_lemmatized\n",
    "\n",
    "# Term Document Frequency\n",
    "corpus = [id2word.doc2bow(text) for text in texts]\n",
    "\n",
    "# View\n",
    "print(corpus[:1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[('competit', 1),\n",
       "  ('correct', 1),\n",
       "  ('game', 1),\n",
       "  ('get', 1),\n",
       "  ('much', 1),\n",
       "  ('respon', 1),\n",
       "  ('somewhat', 1),\n",
       "  ('start', 1),\n",
       "  ('term', 1),\n",
       "  ('think', 1),\n",
       "  ('trade', 1),\n",
       "  ('well', 1)]]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Human readable format of corpus (term-frequency)\n",
    "[[(id2word[id], freq) for id, freq in cp] for cp in corpus[:1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Build LDA model\n",
    "lda_model = gensim.models.ldamodel.LdaModel(corpus=corpus,\n",
    "                                           id2word=id2word,\n",
    "                                           num_topics=20, \n",
    "                                           random_state=100,\n",
    "                                           update_every=1,\n",
    "                                           chunksize=100,\n",
    "                                           passes=10,\n",
    "                                           alpha='auto',\n",
    "                                           per_word_topics=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(0,\n",
      "  '0.156*\"want\" + 0.148*\"team\" + 0.148*\"still\" + 0.127*\"come\" + 0.082*\"seem\" + '\n",
      "  '0.052*\"comment\" + 0.037*\"close\" + 0.028*\"allow\" + 0.020*\"exact\" + '\n",
      "  '0.018*\"face\"'),\n",
      " (1,\n",
      "  '0.115*\"work\" + 0.104*\"bad\" + 0.083*\"great\" + 0.061*\"place\" + 0.060*\"pay\" + '\n",
      "  '0.048*\"post\" + 0.034*\"system\" + 0.031*\"plea\" + 0.030*\"complet\" + '\n",
      "  '0.024*\"amount\"'),\n",
      " (2,\n",
      "  '0.279*\"much\" + 0.108*\"read\" + 0.106*\"fuck\" + 0.103*\"new\" + 0.067*\"interest\" '\n",
      "  '+ 0.065*\"stop\" + 0.052*\"serious\" + 0.030*\"coupl\" + 0.030*\"lead\" + '\n",
      "  '0.022*\"forc\"'),\n",
      " (3,\n",
      "  '0.258*\"thing\" + 0.087*\"man\" + 0.073*\"hit\" + 0.068*\"problem\" + 0.064*\"kill\" '\n",
      "  '+ 0.056*\"wait\" + 0.055*\"fight\" + 0.054*\"rule\" + 0.048*\"tank\" + '\n",
      "  '0.044*\"support\"'),\n",
      " (4,\n",
      "  '0.240*\"play\" + 0.216*\"also\" + 0.116*\"mean\" + 0.107*\"differ\" + 0.079*\"tell\" '\n",
      "  '+ 0.079*\"big\" + 0.047*\"current\" + 0.031*\"huge\" + 0.016*\"sub\" + '\n",
      "  '0.009*\"speed\"'),\n",
      " (5,\n",
      "  '0.096*\"music\" + 0.073*\"everi\" + 0.070*\"fact\" + 0.061*\"pretti\" + '\n",
      "  '0.048*\"record\" + 0.048*\"expect\" + 0.045*\"learn\" + 0.038*\"small\" + '\n",
      "  '0.038*\"other\" + 0.036*\"group\"'),\n",
      " (6,\n",
      "  '0.128*\"right\" + 0.125*\"give\" + 0.089*\"someth\" + 0.050*\"hate\" + 0.049*\"pick\" '\n",
      "  '+ 0.041*\"low\" + 0.039*\"win\" + 0.036*\"stuff\" + 0.035*\"number\" + '\n",
      "  '0.029*\"power\"'),\n",
      " (7,\n",
      "  '0.138*\"need\" + 0.137*\"season\" + 0.118*\"back\" + 0.071*\"deal\" + '\n",
      "  '0.061*\"instead\" + 0.050*\"share\" + 0.043*\"main\" + 0.043*\"free\" + '\n",
      "  '0.033*\"episod\" + 0.033*\"issu\"'),\n",
      " (8,\n",
      "  '0.263*\"get\" + 0.229*\"think\" + 0.093*\"feel\" + 0.074*\"first\" + 0.058*\"start\" '\n",
      "  '+ 0.053*\"ever\" + 0.024*\"month\" + 0.021*\"spend\" + 0.020*\"trade\" + '\n",
      "  '0.018*\"term\"'),\n",
      " (9,\n",
      "  '0.266*\"know\" + 0.082*\"put\" + 0.042*\"hand\" + 0.038*\"woman\" + 0.033*\"hold\" + '\n",
      "  '0.031*\"sell\" + 0.029*\"contract\" + 0.028*\"anoth\" + 0.027*\"stand\" + '\n",
      "  '0.026*\"report\"'),\n",
      " (10,\n",
      "  '0.109*\"point\" + 0.066*\"live\" + 0.061*\"enough\" + 0.054*\"question\" + '\n",
      "  '0.042*\"next\" + 0.042*\"case\" + 0.040*\"littl\" + 0.036*\"agre\" + 0.033*\"bring\" '\n",
      "  '+ 0.032*\"major\"'),\n",
      " (11,\n",
      "  '0.212*\"time\" + 0.170*\"year\" + 0.133*\"way\" + 0.112*\"actual\" + 0.108*\"game\" + '\n",
      "  '0.100*\"last\" + 0.064*\"hard\" + 0.047*\"watch\" + 0.012*\"decent\" + '\n",
      "  '0.004*\"tyrion\"'),\n",
      " (12,\n",
      "  '0.157*\"never\" + 0.135*\"sure\" + 0.093*\"day\" + 0.075*\"far\" + 0.073*\"life\" + '\n",
      "  '0.056*\"guy\" + 0.052*\"away\" + 0.046*\"full\" + 0.041*\"buy\" + 0.036*\"possibl\"'),\n",
      " (13,\n",
      "  '0.139*\"find\" + 0.122*\"show\" + 0.101*\"love\" + 0.090*\"least\" + 0.083*\"help\" + '\n",
      "  '0.059*\"whole\" + 0.058*\"week\" + 0.042*\"sign\" + 0.041*\"ago\" + 0.036*\"final\"'),\n",
      " (14,\n",
      "  '0.480*\"make\" + 0.103*\"call\" + 0.095*\"money\" + 0.069*\"hear\" + 0.047*\"film\" + '\n",
      "  '0.034*\"video\" + 0.029*\"direct\" + 0.029*\"night\" + 0.022*\"public\" + '\n",
      "  '0.014*\"special\"'),\n",
      " (15,\n",
      "  '0.123*\"peopl\" + 0.088*\"happen\" + 0.088*\"person\" + 0.077*\"keep\" + '\n",
      "  '0.057*\"old\" + 0.051*\"reason\" + 0.047*\"friend\" + 0.044*\"shit\" + 0.043*\"bit\" '\n",
      "  '+ 0.038*\"care\"'),\n",
      " (16,\n",
      "  '0.155*\"good\" + 0.149*\"see\" + 0.131*\"even\" + 0.103*\"take\" + 0.079*\"well\" + '\n",
      "  '0.071*\"player\" + 0.046*\"go\" + 0.031*\"run\" + 0.030*\"level\" + 0.023*\"real\"'),\n",
      " (17,\n",
      "  '0.229*\"high\" + 0.145*\"better\" + 0.109*\"certain\" + 0.091*\"enjoy\" + '\n",
      "  '0.078*\"set\" + 0.068*\"explain\" + 0.060*\"later\" + 0.046*\"perfect\" + '\n",
      "  '0.023*\"author\" + 0.023*\"present\"'),\n",
      " (18,\n",
      "  '0.390*\"say\" + 0.203*\"look\" + 0.087*\"part\" + 0.068*\"talk\" + 0.039*\"honest\" + '\n",
      "  '0.039*\"push\" + 0.029*\"develop\" + 0.015*\"score\" + 0.012*\"complain\" + '\n",
      "  '0.012*\"simpl\"'),\n",
      " (19,\n",
      "  '0.260*\"use\" + 0.180*\"lot\" + 0.139*\"end\" + 0.122*\"let\" + 0.091*\"side\" + '\n",
      "  '0.046*\"rate\" + 0.032*\"drop\" + 0.018*\"process\" + 0.010*\"rich\" + '\n",
      "  '0.009*\"zone\"')]\n"
     ]
    }
   ],
   "source": [
    "from pprint import pprint\n",
    "\n",
    "# Print the Keyword in the 10 topics\n",
    "pprint(lda_model.print_topics())\n",
    "doc_lda = lda_model[corpus]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Perplexity:  -16.129136787558355\n",
      "\n",
      "Coherence Score:  0.4570132405320681\n"
     ]
    }
   ],
   "source": [
    "# Compute Perplexity\n",
    "print('\\nPerplexity: ', lda_model.log_perplexity(corpus))  # a measure of how good the model is. lower the better.\n",
    "\n",
    "# Compute Coherence Score\n",
    "coherence_model_lda = CoherenceModel(model=lda_model, texts=data_lemmatized, dictionary=id2word, coherence='c_v')\n",
    "coherence_lda = coherence_model_lda.get_coherence()\n",
    "print('\\nCoherence Score: ', coherence_lda)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<link rel=\"stylesheet\" type=\"text/css\" href=\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.css\">\n",
       "\n",
       "\n",
       "<div id=\"ldavis_el807222853215266002272363393\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "\n",
       "var ldavis_el807222853215266002272363393_data = {\"mdsDat\": {\"x\": [0.3877494908044993, 0.16097759191557157, 0.05393422460603421, 0.07053873945795465, 0.03588208589186418, 0.010467840234314935, -0.0021221683605296693, 0.007370538338568437, -0.02012217884943732, -0.04927412374594509, -0.04353983889999769, -0.0508161947160043, -0.04890472193565632, -0.05288627930526811, -0.05213363981531567, -0.06501866276663867, -0.07231365743021263, -0.06922091235529501, -0.09213058922906986, -0.10843754383943648], \"y\": [-0.23200597258708863, 0.40220919420809553, 0.03616948213313328, 0.054326232137040895, 0.02065093574156088, 0.0038245273384009946, -0.0030553804869347364, 0.002040760260573422, -0.01080391128542525, -0.021516434510136483, -0.019443732352664018, -0.021941353334817205, -0.021194959060598123, -0.022434931257201772, -0.022103800743079473, -0.02574696889339775, -0.02760043416986888, -0.026782812108800715, -0.031540327164860014, -0.03305011386393133], \"topics\": [1, 2, 3, 4, 5, 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      "text/plain": [
       "PreparedData(topic_coordinates=          x     y  topics  cluster  Freq\n",
       "topic                                   \n",
       "16     0.39 -0.23       1        1 10.21\n",
       "8      0.16  0.40       2        1  7.96\n",
       "11     0.05  0.04       3        1  6.41\n",
       "0      0.07  0.05       4        1  6.39\n",
       "15     0.04  0.02       5        1  5.73\n",
       "6      0.01  0.00       6        1  5.67\n",
       "10    -0.00 -0.00       7        1  5.62\n",
       "1      0.01  0.00       8        1  5.45\n",
       "4     -0.02 -0.01       9        1  4.92\n",
       "9     -0.05 -0.02      10        1  4.44\n",
       "18    -0.04 -0.02      11        1  4.40\n",
       "5     -0.05 -0.02      12        1  4.30\n",
       "12    -0.05 -0.02      13        1  4.29\n",
       "13    -0.05 -0.02      14        1  4.25\n",
       "14    -0.05 -0.02      15        1  4.06\n",
       "3     -0.07 -0.03      16        1  3.71\n",
       "7     -0.07 -0.03      17        1  3.67\n",
       "2     -0.07 -0.03      18        1  3.60\n",
       "19    -0.09 -0.03      19        1  3.00\n",
       "17    -0.11 -0.03      20        1  1.91, topic_info=     Category      Freq   Term     Total  loglift  logprob\n",
       "170   Default 15,399.00   make 15,399.00    30.00    30.00\n",
       "131   Default 13,558.00    say 13,558.00    29.00    29.00\n",
       "3     Default 16,515.00    get 16,515.00    28.00    28.00\n",
       "9     Default 14,368.00  think 14,368.00    27.00    27.00\n",
       "300   Default 10,741.00   time 10,741.00    26.00    26.00\n",
       "...       ...       ...    ...       ...      ...      ...\n",
       "672   Topic20      0.08   lose      1.57     0.92   -12.21\n",
       "852   Topic20      0.08    ask      1.57     0.92   -12.21\n",
       "739   Topic20      0.08   idea      1.57     0.92   -12.21\n",
       "1640  Topic20      0.08  alway      1.57     0.92   -12.21\n",
       "1123  Topic20      0.08  leave      1.57     0.92   -12.21\n",
       "\n",
       "[801 rows x 6 columns], token_table=      Topic  Freq    Term\n",
       "term                     \n",
       "21        1  1.00  accept\n",
       "537       7  1.00  action\n",
       "268       7  1.00   activ\n",
       "473       3  1.00  actual\n",
       "615       5  1.00   admit\n",
       "...     ...   ...     ...\n",
       "625      13  1.00     wor\n",
       "583       8  1.00    work\n",
       "201       3  1.00    year\n",
       "494       1  1.00   young\n",
       "367      19  0.99    zone\n",
       "\n",
       "[445 rows x 3 columns], R=30, lambda_step=0.01, plot_opts={'xlab': 'PC1', 'ylab': 'PC2'}, topic_order=[17, 9, 12, 1, 16, 7, 11, 2, 5, 10, 19, 6, 13, 14, 15, 4, 8, 3, 20, 18])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visualize the topics\n",
    "pyLDAvis.enable_notebook()\n",
    "vis = pyLDAvis.gensim.prepare(lda_model, corpus, id2word)\n",
    "vis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualise our data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 356
    },
    "colab_type": "code",
    "id": "Gk3fLlkegoyB",
    "outputId": "1996baad-7e7a-4154-acf8-c80a40f9efa6"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby(\"subreddits\").comments.count().plot.bar(ylim = 0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Wordcloud of the most frequent words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 429
    },
    "colab_type": "code",
    "id": "J9LsjXD0goyD",
    "outputId": "70ce347a-e7ad-4db6-c8ec-6bbce175a0df",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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t+A/JWKenvZ6q8FMf/BISi8Hkj+iJfY3a4H8h7LnpXJt3kQnIdtL0xr7JcPoZyr33UO57AEOtxpEZMuZpvPp8xPhjaMsUvfF/JZbdTm3w1wl7bkMIjURuHz2xr9Md/Sfmlv31RKyC6YxhOqPMLfvfjKZfoS/+bSQmzeE/J2t10jn2FRK5/e/KWjEb5LMmr/7wLRasmcMffu3zRLpH+NE/P89AxxCP/tUTCCH4wl/8IuW1YZ759mvsff0ob7+wn9s/cT3hysIzKaUkOhTnP/76KbY8tZtwRZCPfuZGll43H3+Jj/hokgObj7Hlyd3seH4/Ukp+958+S2nV5MxciqZQ01KJN+Bh0fq5LFw7l/rWKvwlPnKZPMd3n+GVH2xjqGeUp775Ckuum8fS6+ZPe15W3uKH/8+zDPeNsXhDKzfcv4aaOZXYtk3b/k5efHQzg10jPPmNl1l6/XyW3/hOF49FZouhaJKcaeHSNUzLJpUzyZvWOZccl47XpaNegaYpkzP53qv7+MGr+8jkzYu2sx1J30icx17eSySa4ncevIHykG82TmuCsrCfxfNqsB2H0WiKweEElu2QTOfJZk0Cfjfz5lRiOw6Dw3ECvsJnKSV9g1Hau4fRdZWg30XA5yIU9LCotRpNU4nG0rPWTykljz23iy37zxRcJH71DuY3Vs7a/mdCXUUJFSX+CVcfgJa6MipKZuee6JrKgqZKFjSd+07Kwrmf6hli55HOqyb4a6pCa305rfXlk4+NpDcSZc/xnhkL/pmsSSKRQTc0QgEP+bxFe9cwbWcixBMZPG6DOU3lzGupxH8Z10YpJbm8RU/vGKfaI4yOpUBAeamfeXOrqKsuwTAuLpZJKYknsmSzU98zr9cg4Hdf8lzypkU0lkFVBCUhL44j6e2PcuLUACOjSYQQlIZ9tLZU0lAXxtCn9kVKiW07ZHMW6XSOnXvbcRxJ3rQYHEpMcefTdZXwVfb9l9IhacWx5dTrogodvza9BfxKqZ9bxca7lqONX5eWpfWEK4Nousb6O5cRKg9w+mAXmXSO+rlVuDwFpXAg5OW2j2+g90yEBWuaJ/qy5tbFBEp9tO3vIpPMUdtSgdvrQlEV1ty2ZGL+n20uK/gXhGBl/P8CVRHTTgoBw2BTy1zKxzX1SysrcWsaI+l0YdHQ30/AcHFDYxMBV8Fcur6ujmp/gB09Pdwxt3XKPqfDkRJHSgxVRRECVQgMVcVyJmfmcWkqd8xtpdLnB2BReQUhl5uhdAoomAJVzgn5ipj+vAAkNqripz70exMac4/efEEbk0jqJyjCoC70m3j0gmbIUKupCXyeaGYLw6mnKfXcDqik8yeJZ3dS5r2HCt+DKEJHSklt4FeJZjaTMo+d27d0GEo/jUBQF/wiPmMpQggMtQbLGaEtd2BKn4VQUIVv/BwvHpMhhII2fk5nFzGaEkRXyy+6zZWQyh9nNPMyIddGGkr+CF0552bgMxZNapvOH2Ms8zqlntuo8n8CVSn0x6XWYtpD9MS+RjS7jUrfwxPbBIxVePWF2O4U/Ynv4jMW43ctR1dLUYQL045Q8Bt97wR/KSVW3uIXvriJxRtbySSznD7YxSs/2E73iX4+8eX7uOcLt6BqCtl0jiM7TtHfPkR8NDnx4ju2w9af7WHH8/vxh7x89s8f4paHN0wMLFJK1m5aRkVdKd/7u5+x97UjbH1qN/f/xm2TBjxFUVi7aRkN82qobCzDF/RMmBOllKy+dQmVDaV887/+gOhwgr2vHb2o4C+lpPfUINfft4ov/s0nqagvRYy79a26ZQmVDWV87Q8fJT6aZPsze1l2w/yiv/97TCSWZDSe4VhnhO1HOznZHWE4lsa0bHxug7qKECvm1nD9kmYaK8OX9cmXUrKnrYenth2eVuhXFYHjyEme2znT4sVdJ6gI+fi1ezbgvoSgc6WoikAoAoEgb9p09Ixww7q5nOkcAiAWz9DWHqG7b4yG2jBHT/bT1h6hs3eUpvoy0pk8TXWlVJT6KQl52XWgA6EoCDFb3ucFkukcbx3u4FTPMIauks1d+7iq+soQX7h/PU+9eYjReIa5dWV85t51BHyXFh7fDUIU5l5NVa/5WCBEwaJROPbMt9t7oJNvPbqFluYKPvNLG3nlzaO88uYxRsdSmJaNqioEAx7Wr2rmUw9voHF8HLwQKSV9AzGeem4fW3a0MTqWIpcvWOJcLo3K8gC3f2Qh99+1gtLw9NYw07T5weM7eWPbiUnfK4rg3juW8+mPb7jkuXR0jfD3X3sRr8fgD764id37O3n6hf0MDiXI5U1A4DI0ysv83H37Uh66dxW+89zbpJS0dw7z0htH6e0bo3cgSm9fIXviqTMR/vi//WjKMRfOq+a//cl9qFfR9z/v5Hi27zv0ZE5N+a3O08LDDb+HLt69m17DvGoa5lVPfG5d3jjp96UbW1m6caosKxTBmtuWsOa2JZO+1wyNxevmsnjd3CnbrL9j2bvu78WYtRFXV9UJSwCANj5YOlJiS4dYLkvA5cJ1Xl59XVEpcbuJ5bIXzcAjmTzglnq83NI8h2dOniBtmliOw46ebu6dt4Cg69yNVYUysQgBUJXC4uWdpOwSqITc16OrF69rYNljBd97aTGUegpFnDOlW04ciU3O6sGWaVQRIGOewpZpgu71E22FEKhKEJ+xaJLgb8skGfM0hlqNR587MSAIIfDqC9GUmdVbeC9I5Y9gOylKPLegK9MPiOfaHsVxMgRca1DEuXsnhErAtRbQSOYOUe59YPx7HV0NFxY5ih8hNHS1EoGOEIU/R+YppDl9b2vVVTWVU9NSiRACt8/NnKUNIMDtc7H8hgVo45ksKhvK8PjdZNN5ktHUxPaxkSRvPr6TXDbPujuX85EH104I/VB4Fjx+F7c8vIHXfrSDrhN9vP3CAW775evwhyb76/pDXvwXDFhn96G7NNbevpTK+lK6Tw7Qe3oAKeVF71uw1M9Dv/1RKhvKJu1H01XWbVpGzZxK2o/00HG0B8u00WdRyPsgUQimlijq7LlSvBMGRxN87cmtHGofYCSemuKSc7w7wuaDZ3h2xzE+edsqNq2Zh9d1ccVBOmfy0u6TjMTPacNdusbS5mqWtlQT9LpIZfIc745w8MwAyUwOKAj/z+w4yoZFjayZXz8r10TXVFYuacClq9TXlFBVEaS2KsTAYJxVSxsoL/XjdusMjSSorgiyqLWaHXvbGRpJUFUeYPnCWprrSjlwrIdEMsuqpY2sWtqI26VRUxmiJPju/d5hfME8FKM3cvE829cCTVO5+/rFXLdsDnnTwu8pWDmKi/PJZLIm/ZEY8UQG07Q4cKSHupoSVi8vZD/q7B6h7cwgL79xlGQ6xx98aRMVZZOt5FJK+gdjfOPf3uCtXacxXBoL59VQVxtGSkl37yin2iN8//GdDAzF+eLnbiI8jeVFKILqyiCN9aWk03niyQy9/VEcRxJPZC57LnnTYjASJ5ez+P7jb/PWrjP4vAYb184hFPKSSGQ5cqKP3v4x/uPHO/B4dB66d/WkLFndfWNseavt/F4V/ntWeXrB43MtAo6FEPi0IG7FS87JkHeypK0EDjYBLYyc1WX7B58rnIXlJWM3lItoVVWhUOJ2k8znyJ0n4JuOTSybpSEUQlfVia3PTwuazOfI2+e0IS5V5fqGRnb0dJPI5yj1ePjtdRtZV1c3SWMvBKizNoAp6GoZl9IaWzKJ7aSwnThj6Zen/O5Sa3BrjQV7o5CYTmGVXNjvZDRlsq+c42RwnAyqEkRVJk8+iuJGUWZnQroamM4wAhVDLZ9SXGxq29GCBUIJTpl8NCWIKtxYzgiSgmZRoJwXuFuwTBWEfnHed5d+4Y8MRQi7PdQGrm5hs5KKwISgrigCX9CDqip4Am7C57njuDwGmq5i5kzymXMa1N5TA3Sf7Ec3NJZdPx/PNCZdIQShigB1c6voOtFH7+lBYsOJKYL/5fCX+PD4C4v4XDpfyNJzEW1N/fxqWpbWT/ubL+SlpKJgsUgns+Sz5s+t4D/UH2PbS4e46e7llFW9dwv1eDrH6/und/s7i2nZnOge4qs/3UIik+MXP7LsojEBQ9Ekh86cqw+iKgp3r1/Ib9y3gcoSP6qi4DgO0VSW1/ed5t+ef5v+0cT4tile2HWCZXNqcM3Cc2HoGmuXF/xKmuqnjqvZnElVeZCbN87H73ORy1tUlgW4aeO8CS23x21wZ8XiiW3KwgXhq74m/K77dz5H2wdIpHOzus93gqYqlM+Sa8+HnZGxFHsPdvHQvau4/64VlAQ9IATRWJqnntvPT57ew669Hby6+Tgff2DNpPisXM7iyef289au0wQDbn7ll67jlhvm4/e7QUI8keHVzcf43k928tqW49TXhPnlj62bEvehqQp3b1rGbTctxLIcBiIx/tc/PMvAFS4iszmT17ecYPmSen7tV26gubEcQ9cwTZsTpwf452+8Qkf3CC+8eoRbb1xIafjcM7JuVTMLWquAQuamr/y/L7D/UDdz51Tw539875S0orquXfX0urpwsanqk+RlDltaxPMjPNX7r0Ry3Vf1uB9UZjTaCsCj6cSyOcYyGfyGgSMlLk2b4g8/HYoQrKqu4ZmTJ9nR080dLXMRAvb099ObiPPQosWoQhByFwbfw0MRmsNhbMdhe3cXI+lzK1lbOuzu68Wj6zy8eAl+w1XQ5DvOJTWTF8Oj62RMi6FUigqvb8KN6EK3H3EZjbFARQgFr7GIeWX/MG0mGSH0cX95gRjX8k+fOvMC64dQQSiAg5TO5PWHlExXuOv9gkCnsGA0L3t/FGEgkdNeE4mNxC4I9pPuxZUPKGczTKXyeXb397KkvJKw200in8OrG7g1jZSZJ2/bGIpKwOXCtG1iuRweTcNvGFf8nLm8LrTzBnFFUQraf69rwl8QCsL72VNyxhfAUkr6O4ZIRlMIITi26zSZVHba41imTaRnBIBULE02Nb1wcTb95uhglEjXCPHRJJlUDjNnkknlGBuMTRz7Uqv9qnELxXQIUTBlQsFVybHfv8/p1ebwrjM894MdLF/f8p4K/lfCWCLDYy/tob48xEeWt0w71ndHoowlz43PpQEPD964lOpwcMKdQlEUSgNe7r9uEbbj8C9PbCWVLSSJ2HOyh/7ROM3VVz8w0NA17rt9KV5PYezVdZX7Ny3D57m26akdKdlzrCiQfBBZ0FrNQ/euoqzUP/FdVUWQj923iuNtA+w50MmWt9rYdPMiysfbSClp7xrmja0nkMAdtyzmvjuX4Tov2UGFK8ADd69kcCjBE8/u5cXXj3DjxlaaGye73AohcLk0XOMpH83xDFXvhJKQhy986noWLziXBELXVZYvrueu25fyzUfepKdvjIFIbELwF0Lg9Rh4x98Z23YmFu2GrlFVEZw0z10rhBB4ND8eCtfcEC4M5cOTgWu2mZngLwSramp4tu0Ef7N1Mw2hECGXm8+vXDWj3Pdnt39w4UIePbCf7d1dqEJwcmSEm5vmcFNTIdhhXmkZq2pq+Ld9e9jT1wsI4rnsJJcdR0p8hsHJkWH++MUX0BQFVRE0l4T5tVVrWFg+c990IQTzy8qo9Pn4h+3bmFdWhq6ofGHVKmoDVxZUoSthdKUcyxlDCAWXVnPRtlJKXGotoJAx2y8QiCU5q3dSe1X40ZUweXsAyxlFEedeVMuJYTmJK+rrxRHjPXhni6jpcGsNSBzS1mnCWOMLgYu1bQYJWasbKW2EUMf7I8mZ3Tgyg1trRFwiZmEmZC2Ln508Rso0aRsdYU4ozLOnThDL5TBUlfW1dfz0+FGq/QGylsV98xZwKDJAJFVwvbmndf5E7MhMUTVlcvGN8X+qqnJRbchZ86R0JInRFLZVEJzf+MnbMzqmbdlYFwTPSSlJRtO8/cIBtj+zlzOHu0nF0lh5ayISQgK5zOUD34QQ+EsunZml6DVQuA/H93eRnyYg771C1xSqw0Hm1JRSFvSiKgqJTI7OgVG6IlHSuUJfI9EkP37zIEvnVFMWnKoZHo6nyObPPWO1ZUFqy4LT3ndD19i0eh5bD7Wz9XD7xP6Pd0euieCvKILgedkyFDH587ViLJ6ecVGrIu8fVFVh+ZL6aV1wwiU+1q1uZt+hLrp7R+ntG5sQ/DwUXhoAACAASURBVB0pOXikh6GRBKGgh5uunz9J6D+Lx63zkevm8ermYwxEYuw/1E1Tw9WrubFofg0LWqunjN+KImhtqcTQVXL5QjBwkQ8XM7avrqmp5b/ffCu7+/rI2RYNwRDGuL/+vLIyPrl0+YTGHsCnG/zSkqUsqSxkKvDqBp9dsYpllVUcGBzEkQ6bWuayrq6OgKuwXYnbzZevv5EtXR30JRKUe71sqGvg+PAQ1f4AjpTs6Olhc0c7X1q7rpCTH8FYNsN/HDzA9w8d5C9uvoWGUJBPL1tBuffcC+pSVT62aDGtpZMnmDklYf7y1tvZ3t1FMp+jwufDo115qjtV8VPq3URP7OtEUk9QE/gMqiho9yV5LCeOItxo4xlyfMYCXGo1Y5lXKPfdj6FWAZK0eZJEfnKwriJcBN0b6Yv/K2OZN6n0PwxSR8ocY5nXsWdJ8NeU4HhtgW7kuJB+NmXrO10E+F0rcGsNjKSeJeTagN+1HIEBONiyMKCooiA8+l3L8ejNjGZeJey9DY/WAggsZ5Th9HMowkPQvWEiC9A7ZTSTYSid4lNLVvCjY4foTyYYzWb49NLlPHeqjSNDESRwb+sCXjzTxqHIAG92drC4opKO6Bjt0bErFvzFuwgullKSG89AoekqKz6ycJJ70MUw3DrB8zRTUkriI0m+95Wf8coPtpHL5KmsL2PlzYuobq4gWOqfKLj1468+z2Dn5YUTdRaqbL4ThgdjvPH0fhaubGTJeJYEM2+x+bkDDA/E2HDbYprmVSGEIJXI8OazBygp87PhtsUoiiCXyTPYO0bHyUFGI3Fs2yEQ8tA0r5rm+dW4PJMLmB3Z08Hpo71ct2kJhkvnxMEu+jtHcBxJRW0JC1c0UloZnFjESSkZ7Bmj4+QAPWci7NvWRiqR5aWf7KK08lxgXtO8KtbetGCS1Wc2kdNYa8IBDw/esJS71i2gqjSAW9cK18+yiSYzbDvSwfdf3UfnYMEd8XB7P4fO9HPzirlTxoF01sQ6z32zLOS7ZLBuid/DTStaePt4F6Zlk8tbHO+KcOfaBTOyHs8mZ4uNZfMWpjWexllKhFJIGqFrKoauYeizF4gqpaSjf5SBkZkVliry/sEwVOpqSqZV1KiqQmNdKR63QTZn0tM3xoqlDQDk8zZnOoZwHEllRZCqi2RqEULQUBsmXOIj1pXh5OlBHMe5KhV5FUXQ0lwxbQYhIQoBvpqmYuet8aDfIh8mZjzb6KrKurp61tVN9eddXFHJ4orJqcgCLhefX7l60ndeXeeGxiZuaGxiOoQQ1AQC/NKSydHM88oK/po5y2JzZwdeo7CoOJu6M2tZ7OnvYyiVwpYOzSVhvrh23aR9uDSNz6xYOeWYqqKwpLJyYoHyzlGo8D1EMneQ/vgjpPPH8eoLEUKQtyOk8yepDf4XSr23Fl4srZFK/8P0xb/DmdG/mEjnGctuR1dKJuXRF0JQ4fsFxtKv0hP7OjmrB0OtJmu1k8wfRlcm+5860iJjnsS0R3Fkhrw9iO1kiGa3krW6UIUHr74A7YJgZZ+xGJdWx0Di+9hOCl0txXYyhD234jVmlnXpQtxaAzWBL9Ad+yqnR/+UkPsGDLUKR2bImh0EXKupCnwagY5LraUm+AW6o/9E++j/IOS+EUUYJHJ7SeT2UOF7mIBr1buehOW4bltRBJqiABJFFDSAynhAeonLjc/Qcakqpu0QdLlYVlnFdfUN1PiuYjzANKcmhMDlLlg5dJfOPV+4hTWbls5gV2KiCiAU/DG3/mwPLzy2Bdu0uOH+NXz8D+6mbm4Vbt+5ir+peIYXHt08I8H/vSKdyPLEv29m/S2LWLSqCVUVjA4l+OH/eY2e9iFs26FxbiVCFUT6ojz21Ze4/RdWs/7WRZh5h8e/s5nXntpHOnnOZcq2HNxeg9seXM3Hf+NmfOdpgw/uOMVP/20LUsKxfR0c29uJZdqYpo10JPOXN/CrX76HlkU1hcxGjuSVJ/aw7cVDpBJZoiMJbMth83MHJ5nCr79zCSuvn8c70DXMCNuRZHLnrDduQ+PTt6/mE7etnBK0q2sqXrfBQzcuo8Tv4e9++Doj8TSJTI59p/q4cVnLlKqctuNM8gTzunQ09eKLQUURLGqspMTvYSiaxBkPdM3mzUsGEc8WZ4X9vqEYh04PcLIrQu9QjNFYmvS4+5Ghq3gMnZKgh6rSIPWVIVrry2mqKSUc8Eyb4nC64zjjqRszOZOxeJq+4Ti9QzF2HumcZCWxbcnz24+y72TPjM7hplVzaa65dKKEs1iWzWt72mZcwbaxOsxHVs69qhWWP6hoqnLJdJmBgBvDUMklTWLxc1pyy7IZGStYi0uCHtyXqGficeuEAoVjjMXS5PLWhFvNbKIIMa3lYjquVk2unJ0ha6dRFQ2fGkTiMJYfojdzirg5ikAQ0suo9c6lRC9HEddGyWRLi7SVYDQ/wGh+gLSVROJgKG6CehmVrgZCehmKmKoQMJ0cGTuJlBK36sNQ3JdN75qxU5hOtuAqrgbQlKtf7+YDFWmnKgoVXh87e3vY3NlJSzhMzrI5FBlgT38fDy5chKHO7ikpwoOhVaGIS6c4K6TXrGZO6V8QST5OLLuNSP4nICW6WorPWIRbP5dJRaBRE/gcQhiMpl9iIPEYulpGqfejuLVGOsf+ZpJbjFtrYk7p/6Q/8Qij6ZdAqHj1+TSW/AkDif8oFLsax5EpeuPfIp0/icTCcdIowmAg/uhEtps54T8nqE5eHHn0FppK/oTB5A8ZSj1JIag5TMC16h1fPyE0Kvy/gK6GGU49Qzz7NraTRAgDl1aHodVM+OwLoVLmvQtV+IikfspQ6gmQNoZWQ13otyj33j+R7UcIA12tmLgvQmgYagXq2d/HA4qnq0FQ6vFQ4nbzs5PHGU6nWVZRDfEojx87ioPD+toG0mYvCgKPplPhK8R+HIoMEnZ7qPJembb/iphmkBWKoLQ6hO7SsEyb0cEoLs+Vxxnks3neenYv+UyempZKPvnl+5izpGHadlb+2qcXvBLCFQHKq0roOTOEmbdQPQaDvWOkU1nC5X7aDvdgWQ6GqhDpHSOTzDFnYW3BtUpI3F6DxWuaWLSyidrmcjRNof3EAE9+dytPP7aNhSsa2HDb4knXOJvJ8/i336SmsZRP/c4m6poriEdTvPSTXezZcpKnHt3Kb/3Fg7i9BkIR3Pupjdz6wCoSsTTf+MunGOqP8nv/62M0zK2a2KfX53rHhc2klBMueRd7FlLZPN3nBf4taKjg7g0LLylka6rCdYub2LCoiefePoaU0N4/SiqbJ3RBqscLH1dNVS8byFcdDlAW8E5UjR2Jp8lkLy/4J5NZdF2d1k1iJjiOpHtwjGe2HuHNvafpH4mTm8Fzro5XoK0uDbB2cSO3rG5lWWvNJd+/yFiSp7ccoWtgjJ7BMYajKVLZPOlsflKBSCgsnh5//eCMz6O2PEhzzcxco0zb4ak3D7PraNeM2t+6dh7XLWsuCv7TIi6pfT/77MvxnPZnOZu7HwqZlC71fghFTGjhTdPGvlpxUQKM98hae5Yj8R28MfgTKt0NPFT/mxyL72LH8POM5PsxnUJsmqG4KXPVsLHsHpaX3IAmrnzeuxJGcwMciG6hLbGfkXw/OTuNJU0koAoVQ3FTolewInwTa8K34VH9k/ozkO3imb5vk7ESrArfykcqHkQTFx+vck6GFwce40zyEGVGNQ/Uf5FSo+qi7WeLS0rJhUqqFr2xOHnLprW8DElBk+HWtGue9ksVgvsXLGAkk+bRA/swbQdVUfAbBg8tXMTHFi6edXNxiedmfMZSjBnktRdCYGg11Id+k6rAJ3GcQoo7RbjQlBDKecEmhdRXIWqDv0Gl72MTlX51tRQpLXyVCzC0qvPaKwRcq/Aa87CcwkSuKSFU4cejzaEwBRdeZFX4aSr58ngqy+kx1KkWDiE0Sr0fJejegO0kx/vunsjz/05RhIuwZxNB93XYThwpTRAqqvCNZ/BRL2h7O0H3eiwnDlKiKj40JTSpnd9YzoKKr01YO9xaIwsqvj6R2lRXK2gt/8fxhcDkAc6j6Ty0YBGxXA6XqhEwDJZVVTGWyRAwXHh1nbpAEJemcUvzHHRFZWVVNSPpQuETn3FtgwGFEDTMryFcEWSod5Sjb5/ijk/fiNt7ZcFL+YzJSH+hWE91YzmVDVOfaSklQz2jxEaSs9L3q4Uv4KG2qYyTh7qJjSRx15dy6nAPgaCXuUtqOX2sn/hYivLqEB0nB3B7DWobC5ZDRVW495PXgQCX+5xLz+LVzeQyeb77jy9y4mA3a29aOJFmFQrXRtUUvvAn97BgeQNCKdQsqKgpoePEAMf2dRIdSVLtLWhjw+UBwuUBoiNJXC4NVVWoqCmhrnl2amQkkzl27jnD+rUtF9VEZvMmkei5e7m8pZbyaXz1L8TnNlgxt4aX95zEtGwi0STZvDlF8L+Q82LTL4rL0KgM+zneHQEgns6SvSAWJZs1SV0QmH70cA/NcypoaJyasedy2I7D/hO9fOPxbRw50z9F+L70tpJoIkM0kaGtewiXrrKs9eIxXADdg1G+89QObOfnN6D9w4aUEtOcPu04FDT7jlNwFdPPswop464z57e51DHOVtnWdfWquPm8X8jaqYKQL/NsjjzJ3rHXEEKhxt2MS/WSsuIM53rpy5zh+f5HsKXFmtLbUbl6C5aRfD/bh58ha6dwqz5KXdUEtDCKUEmYY4zk++nPtjMy2I8tLW4svx/tvJjDUqMKt+KlJ9/GkdhbrAjfdElBfiTXR1tiHzFzmDm+Jfi1a5P44ZKCfyKX47s79/FGWzsBt4u/e+CjdEdjHOmP8PFVS/HoV98kcT5CCGr8Af7ouhsYy2TI2TaqEHh1nRK3+4qqTc70eJrwoykz1/AKBAitsFC4zPMphECgTRLwC99ruJWp7lCF/gQm4gTOYmiVF7RTcWm1M+7z5G0VdLUUXZ3dYLsruZaFtkE05eIB1qriQVXOaasVYeDWzv+s4x6v8Dvd/v2GC79xTnB2UYhLOUvJuLtC4Lw2dcFr8LxfRGqqbalk6fULeO3Hb3Fgy3H2v3mM9XcuR5lmYijkjC8IHOr5mYRUMSHImnkL27KnbJfPmGx7eg/Rofe3D7KiCFoW1bJ7ywlGhxJU1JRw8mA3ZVVBVt0wn71b24j0jVFaGaCzbZBQqY/y2nPVG91eo3C+WZN8zsKyCi47oVI/iipIRjPT+scvWTOHOQuqJwK1hRBU1JRQUVPCQM8oyfi1C4RLJDPs3N3O6pXTu04CWLZD6mx8iKpQFQ7MSJsrhKCyxI/7bIXfbA7Lmh0hVlUUSvzn3KjSOXPKvvfv6+TQgS685y1uBwdi1NVf+bgkpeRMzwj//MM3OdEZmfSbS9fweQwCXhced+H9zuRMUpk8mZxJNmdOWiT4PC42LG2+rNJLVQQhv3tawT+Xtya5+gD4PcaMhbwLUzxesh+qwvoljfjcBslMjlQmTypbOLe8aZFI5y4piBY5h2U7l8yVn0hmyecLBb1CwfNqGmkq5WWFeS8Wz5DNmRdfqGfNiWOEZuhW9kEnYY6xY+R5Gn0LuKXyYSpd9ahCw3RynEzs5/XIfxIzR9gy9CSN3vlUuZuumtK51jOXZSU34lG9zPEtpcxVU3DXQZBzMhyL7eTNoZ+StKLsG3udxcH1VLrPyR1eNcDC4Do6UscYzvXTkTpKWK+YNpW5lA6nk4dIWlHcipcFwdWzUmRsJlzyqdrR0cOxwSE+tXYFzx09gSMlXt3grY5uPrpo3jUX/KEwIbk1jZqrnHe9SJH3hIvMwR6/m7s+dxNHd55ioHOIR/7ycZLRFCs+sghPwI0QBR/1bDpH35kIp/Z3suS6eSw6ryKg4TZoWlRH2/5Ouk/2c2DzcdbduQzDpWNbDvGxJJsf38mLj21FURRs5+LarfcDLYtqyGdNBnvHqG4opad9iOUb5tI0rwopoefMEA0tlQz2jFJVX0pw3KdVSsloJM7erW0c299JpGeMdDqHZdokomnMvHXRQn91zeWTrABQEK5UXUE6ciIF67XA7TYIhTyk0nlKQt5pJ8OzvuZQ0DxeSaCqfp5bgmVLnAsezkKq1yvvt6IIPOcFFZqWjS0nX7eKigD33LeS8Hn5w48f78PtufI5J2/a/PSNg5zsOif0a6rC8tZabl8/n8VzqgkHPBMLIst2SKZz9AzFONU9xPGOQdq6hxmNp1nSUs38xorLHnNufTl/9Vv3Tro+Z//52q6T/OS1cwkcdE3hS794I3PrJlsyLlZvfE7dzC0euqrwiTtXY5o2pmVj2g6WZZMzLXoiUf7p+2/Q8x4XEvugkM9b48WynClFqWzHoadvjEw2T8DvnlT3wTBU5jZXoCqCyFCCyFBiSoEvKLxPPf1RxqJpdF2lZU7FB0Ljf3Y8ke+wTJbEwacFuavms9S6W84bnwKsKb2V/Lg7zGh+gEOx7VS6GxBXSevvVQPcWf1pdMWFekFMgZcA68ruZDjfx86Rl4jlRxjK9U4S/IUQtAZWUDpSSSTXw/H4bhYF1+NRp1pZ03aS08mD2NKizjOXeu+1q2x/ScG/Y3SMpTVVbGiq5+UThVLIAbdBzrIuWmm3SJEis48QgkXr5/Ir//UBHvvrJ+k81sf/+fL3qGmupKK+FFVXSScyjPZHiQ4nyGdNfv+rn5ss+Lt0bv7F9ex59TBjkTjf+rP/ZO9rhymrDZOOZ2g70Mnpg120LKknUOrnrWf3vYdnfHkq68IESrx0nRqkrqmc4YEYrUvrKK8OUV4d4uShHhaubGRkMM6y9XPRtMIk2t81wr/+1dMc29tBfUsFcxfXUVoRxOMz6D49xIs/2XnRY16Y7ee9xO3S8XoMHvv+NhbMqxnP7S3weQ02rG/B4zZQFQXXuIbYdhzS2fyMU/WmsnmsccuRrinTulG+U1eW8wUnR04tDDmnpbLgNnTeMee0VE7kL78SBkcT7DzSOXEMIeC6Zc388advpaZ8arHAs8xvquSW1a0kMzn6hmLsPd5DU00YzwyCkIM+N2sWTo2fkVJOWoAU+iNY2FTJ8nnvzEp7KYQQuHQN1zSaY6/buGSgaZHJOI7k4NEeRsZSUwT3aCzD7v0dOI6kviZMXe0591hFKaQBrawIMjScYOuONlpbKqcUrcvmLLbvPE0snqG6MsiqpQ3v/3TIgong40zWJJs10f1XLpQ3+xZTPY0mXxEqi0Mb2Dn6IsO5Pk4lDnBD+f14tauj+BVC4FYvXvBSVwzm+Jawd/R1TJkjbSWmjKdhvZK5/uUM5froSp0gku2m0btgyrkNZDvpz3agoDA/sPqaufnAZQT/oNvFmZExUnkTZKGi7qmhEby6jlubXROUlBJL5sjaSRLmEAlrhKydwHRySBxUoeNW/QT1Skr0atxqEFVc+ziDs311sDGdDFk7RdIaJmWNkbWTWE4OBweBgip0DNWDRw3i10rxaWEMxYMmrl1ZdCklppNhJN/NSK6LrJ1CUwwCWjnlrib8ehkKF9cCOtImZY0xlOsgbg5iOXl0xU1Qr6TM1YhPC19y+3fff4e8kyVrx4mbQyStUXJOEsvJAwJNGLhVP369lKBeiVsNoItLR9JfK4QQ+EIeSqtL8JV4Jw3iLo9BaXUJofLAJHcdTVcJV4Rwe6cGfeqGxs0fW09JZYhnvv0ax3edputkH2cOd0/4n+uGjjfoYfmNLdS1XuBCpgiW37iQX/m/H+TJb7zMYPcILzy6BYlE01R8IS/r71zOL//RvfS3Rzh1oJNA2DclGb+qKoQrgziOnJT5ZpoLQCDsK5xnWWByHYNZIFweoLy6hO7TERpaKlFVhYY5lQTDPmqby+g8OcBg7xiJWIY5C6tRVAUp5Xgw7glufWAVn/7dOyivChXqLAjB5ucO8MoTuy91Su8bTNMqCPYunc7uEdTx6xsKeVmzuhkoaO1DfjcMFnzVuwaj5EzropV4z+I4Dj3j2XagYC0wL3ANs2yHeHpyITk5jRB/IVIyaV+qUKZcVzNvkUpP9fFvaq7A23hl5vCBkTij8fTEZ11Tue/GJdSUhy57PxVFEPS5CfrcLGiqLBRefx89A0WuLSfaBnjy2f08eM9KQkEPQggSySzPvHiAg0d7MQyNGza0UnpBxpzmhjJuv2kR//nELl56/Sh1NWFu3NiKb9yVLZXO8eb2k7z0+hE0VeGWGxbQ2HCBBWg8I5VlFYoh2o4klsgUFt+yUI03GkujKAJlvL6RYWhX1WogEMxpKoct0NcfZc/+TjaubUHXVRwpC8HJElyuS8tqDd55KBfR4gf1MsqNWoZzfcTMYeLm6FUT/GeCVwuiCAUk2HJqqlNN0VkUXM/B6DaS1hhtiX3Ue1pRzyvqakubU4n9ZKwEAT1Ma2DFNctaBJcR/Dc0NfBWezff2r6LzrEoj7y9l1PDo9y5sJWSGRTumgmOtElao/SkD9OVOsBAto2UFcV0slgyj5Q2EolAQRMGhuohqFdS51mCX78yf08VjZbAOsLGlWtWCguTPClrjEj2NAPZNiLZM4zl+8Yjv7OF6O/x/hZq/SooQkNTDHThxq+VUuluoc67mHrvEgJ6xaSH4Urpz5ykJ314wsCmCZ35gRsnrouUDkO5DvaMPkVHch8ZO4YlTRQUDMVDyKhmcehWFoduw6uGpryYOTvFifhWDsVeZiTXRd5O4+CMR7d7KNFrWRi6iUXBW/Bp4VkVtm1pEs0P0J0+RFfqIEO5djJWHNPJYksTZ7xasYKCqhgYwo1XK6HKPZdG33LqvcsI6OXv6vq+W3SXzsO/exd3f+5mvEHPpFztazcto3V5I5qhUVJxLpahdm4Vf/rIl3AcSVn11KBq3aWz5rYlzF/VTMfRHs4c6mYsEsdxHDx+N+W1YZoW1lHTXIE/PNW86PIYfPQzN7JkYytH3z5FpHsE23YIlQVoWdbAvFXN+ENeauZU8Lc/+zIujzElC0VlQxl/9O1fJZVLEy4NFSYkaWE5Fl7NU8hi4eQByRf++8N87I/i2JqN9DgTfvNZJ4cqVHJ2DonEp3mnmFYvh8/vpq6pjN7OYU4c7Ka0MkhlXQmqqjBvSR0vHNnFsX2duL0G1Q1lCCGwTJv2EwMIIbh+0xKq6s49t47j0H06MlEobbYQQiCUgivQbO47GPTwiV/aMMXdRggmJnuPS6emNMiB0/0A7DvVS1ckyry68ku+r6OJNLtOdE/4tw+OJXl+53E+e+dafOOpZZOZPKd7RyZtlzPtcSvAxe+l7UxeMLgMbUrcwf59nRzY34XXd067PjgQo7buyn388+NuLmdRFIVw0HvFAnwhe9IVH77IO8CREsE7rx9zNfC4deY0lfPkc/s4dKyHeS1VaJrCmY5hDh/rJZ+3WLuymU03L5oibBuGxgN3r6C7b5Rtb5/im4+8wZYdbTSNC/cdXcMcOd5HNmeyYfUcHrx35RSLgGU5vPTaEfYd6iaTyZPJmsSTWSLDCRwp2by9jVPtQ3jcOh63jt/n4p47lrFs8dQU7LOFELBxbQtPv3iAyFCCr3/ndXbt66D0/2fvvYPsOs8zz9/JN4e+nXNEN9DIGURgFCVRpERRlGTJCpbtsS177LE9W1s1oWandtazZZdrd707TpqxxpItS1SgSIkSMwkSBIkMNIAGuhsd0Ogcb44n7R/3ooFGXzQaaICkLT0sFrr7hO+ce849532/93mfp8SNrpvEExmqKwN88TM7l92PX7m54IEkSATUMkAgY6ZImXfLsLQ48rGeTtpMENXnSBhhsmYa3cph2gYz2VFMe3k1sCpnM7WuVnrjJ+mLn2J7ySOFc8gjoUcYTHZjYVHv6qBMq12V18/tYtmoqKEkwO/u38WLF/owLZtUTuepTZ081Na8arkv27bJmHEuxd/jXORVprMD6Fbm5utjodsZdCND0ggzke696bo3gyxo+NSy2wr8LdsibcaYTPcxmDjBaOocUX2anJW6xZb5qoBlmxhmlgxx4sYME5leuqNvUKLW0OG/n3X+B/HKy7+Eb4aR1DkOTv0dV9/8IhJ+pRK3nJfpHEtf5PXJv2U607+IfWdhkrESZDL9zGWvMJMZYn/5r+GRr2lDp40YR2af4WzkJbI3nKtpG6TNOGmzl5nsEJOZfg6UfRWfUr7qB7VlW0T1CS5G36IndohwbhxzGXUiCxPLSqOTJmmGmckO0RM7RKnWwDr/g7T79uGRQ3f2+YajDM2H2dVQi3YHFS5RFAiU+/CX+5a8xNw+J27f0uRZ1RQqG8uW8HvDqTQnRsbY01iHR9PwlXjYuK+Djfs6gPyLsmdqhqxh0l67vOKIrMhoNX4ce+v5Ysv+BSO+6+FwadS0LFUjsG2bmB3nuHqSjJTFztjsS+7GxuZk+AyfqX0CAYHXp9+mxllFQ2ktp8wuMmYGa9xiX+lumt0NHJx+B8u2SZhJbNvmY5UPEVBvr9QpiALN62ro6Rqh+8QQdS3lCxWI1s5a0slDdJ+8TKDETVlVft+iKODyaFiWzfR4BD1nICsyes6g7+wIh18+d9cl9FSHjDfgJBnPMHBxnLrWchRFxjQtBEFAUe+sYiYIAqZpMTg0w/R0jI72KgJ+F9mcjtfrRCAf+K+pK+O1U5cwTIsr02G+89opfueJPVSUeJfQd2zbJpHO8fzhC3QNjC/8PasbfP9gV/5a7ezA49R4q2uAc5cnF20/F0uSyek4l6GQZHIG49cZWPlc2hIqSqjMy2NP3MDxv3hnHH+npqAp8oJaimGYnB+YYENr1V0XhPglVg/Ltjk1MU6dz0+F5x5KJ98mNE3hs5/czulzV3jnaD/dPeMYhpVXenNrHNjaxpc/v4fysqWz0YIgUF7q5etfu5/ykJe33rvEqa5hjp4cBPIKPkG/m0cf7OTpT26lomypsIVlWfQPzXD67GJpVt91jcITk5GFnxVFYvuWxkXrKrJEMODGoSnLyuIqo8yAIwAAIABJREFUskRJ0E0uZyxJQG48r+aGMn7zy/v5px8eZWIqys9ePZevQBfUjfbtvrUHkCourxbmkNwIgGHrhWr/vYFpGYym++mOvsdQspu4Hka3spi2yVUHIBvrloG/U3LT4dvBYOI8M9kxhpM9+JV8nGfbNqPpS8xmxlBEjXbfNjTx/XUQXzaaEQWB9vJSWkpLyOpGvnQkywiwYp5oMdi2TSQ3ztG5H9ATO7SCIPqDQdZM0hs/TE/0IJPpS2SsuyNxaNhZprODzM1c4Uqyi/3lX6XS0Vq08/t2YGEyl71Cs2c7UX2Kt6a+yVTm0i2OJceF6Js4ZT/7yr6EIjgwrCwn55/ndPhnGHb2ltv3Rt/GIbo5UP41tGX4cbeCYeUYTJzg+NyPmEj3YXFnWvKGnWUy08dsdpjLyVPsLv0cVc6O2579H5ib58ULfWyqrryjwB8gkc1xYXKaLXXVRQPsYkjldM5OTLKtthq1MO5sMsWPznSzrrIcj1ac6pDW9SWyiDfDcDjCc+cusLOhdsXHBflH35G5E1jYHCi7j4vxPt6YPsRn6z7JTHaOmewcTsnBYGKYHcHNHJk7iW7rHCi7j75EP69MHeS3mr9MRI9h2RafqPoIoiDiusP7prmjiuh8gtnJCI88tX2BNlVRG0RzqVw6N8rmPa14fPn9i5LI7ofXceqdPp795iGmxsIEQh6mx8NcODVMRW0J0xN3t9nR4VTZtq+dM+/1872/ep3erhF8QReZVI629bU88MRmlGVerjeDrpu8/Op5jhwbIBZP8/mnd1JXU8LBQz187qmduN0aoiCwra2WMr+bifk4pmXz8vFe5uMpPr6zg476cvzuPDUumzMYngrz+ulLvHqij3Q2X8aWJRFFlogmM3zrlZO8dLwXl6YwMR8nfgMd58p0hPG5GEFv8etp2zbDU2Emrgv8ywKehSrCVTQ355XKcjmDZDKLbdvU1AQJlqzMdOh6VIZ8hPzuhWM1TIsfHzxLXUWAnZ0NONQPhjL6SyyFblpMp5P8qKebj7a0IQjgUVWcBYe7tGGQ0nPIoohX1ZBEkZxpkjUNRATSho5DVnArd78Xx7IsAn4nv/O1+3lwfwc9fRNE42lcTpW25nLWtVcv0H+KQRAEKsv9/OaX9/Pw/Wu52DfBzGw8rwxW6qWjrZLG+tIFdakboSgyX/7cbp56fGvR5UvHY4lJV2NdKX/yH57Etu1lDbyaG0v50//tM9i2TahI5fh6yLLIIwfW0tFayfmLY0xMRTEMC6dToSzkZU3rrXXpbT542VvTNjgTeZuD0z8knJsqeAhUU6ZV45EDaKITRdQI56Y5FX4DowjN5yoEQaDFs4GQVslkZpje+AnafdtwSC5MW6c/foaslaLS0UiDe+2Seyanm+RyOp5byCffKZZ92+RMk3Pjk3RPTpO9Tr/Wo6l8orMdn+P2pYds2yacG+fg9N8xGD+GRfEmYQEBSVDz7miAhYVp6Tdd/14gYyY5Nfc809nBe7J/0za4nDxFZiLOx6r/kDKtadUPq+nsEIad49T8TxlP96xoGwuT85HXaPJso8G1iaHkac6sIOi/fvuLsbdo9myn2bPzjs4hZ6Y4G3mZY3M/ImHM3XqDFcCwswwmThDVp9hf9hVavDuRljHTgHwfSyKTxYaFxkYomLAYBsmcjiQKeDQNWRTRTZOMYSAJImldRxZFPJqKKAikdYMzYxO80tNPpc+LR1PxOx0Ft+ClsG2bjG5wdmKSn5zrocrnxaNpBBzXvvxZw2AumUISRbxavnnTsmwimQz1wcAipS3DskjndGRJJFU4bq+mLZnltG2beDaXl8ZVl39hWrbFSHqMjJnl0Ox76JaOT/HilJw0uuu5EO0lqAYoUQOUqEFGUmMkzVRhXYOA4sMqKLg0expve5b/RlTUBqmsKyERS9O6rhpByJ9PsNRLU3sVPaev0L6pDj2nL8ys73poHclYhoMvnOHwy+cQRIHyqgCPfHob2w+087d/8hPcnsXPNodLI1jqRSvioimIAl6/C3/Is8iVd2G5ILD/sY2kU1kOv3Ke04f7sAGv37kqTf9wJMm57hG+/IU9vHt0AMuy8XgcTE3HSWd03O78ObTUhNi3oYlnD53DtGxyhsnh85c5fWmMgMeJx6nl75FsjkgiQzSZWaBkCcCujnpaa0r5/ltdpLM6ozNLEyOHKmOYFrPRJC8f76WxsmRJMA95qcw3Tl8iXJAsFAWBpsqSJeZdoiiQzRq8/up5LvVNIhX6M7745b23beBVFnSze30Dw5PzC/0HI1MR/vRbr/PgjjY+srOd1rpS3I57aw70S9waw7EIP7nUw5HRESKZDCGni4+1tLG3rp4rsSjf7z7HVCqJLAjc39DEw00tdM9M81zPBbyaxkQijlfV+MrGzTQF7i79FPLVCLdLY+vGerZurL/1BjdAEAQcDoW1a6pYu2b5yuyNEEWBUImH0CqUtjVNprY6uIL1lBWtdxWSJNJQF1qgLt0ussuwPQCyZgobUArU6XuBifRlDk7/gPncFCVqBQfKn6LNsxmX7EO5zjisL36arsjbywb+AAG1jFbPJqYzIwV5zzFqnK1E9FkuJy8C0OrdhFsMEImlFvVGTc/FmZ1PsHd7y032vjosG/ifGZ3g/3rzMCG3E6/DsUA9CDgdGHeo6pM2YxyZ/R4D8aNFszxVdFLmaKba2UGJWoNT8iEIIjkrTSw3zVRmgPF0D0lj7qbiUQICoqAgCzJSofnTLQfxKeX4lJW7onmUIE2e7Uxnh1hOt04SFGRBQxYVFMGBJCjYWOSsNLqVQbcyyyYsk5lLHJr+No/V/Fuc0uqaVuazo4yne+iJvYVdaDJ2SB4ckhfLNkkZEXR76ZcsbUa5EMlTkE7NP0/KzL/cBUQ0yY1D9AACaTNaoP4s/jwyZpyLsbepd29CuYXL8Y3IWRlOh3/G0dnvL1NVEVAEDUXUUEQnsqAWPuMMhpUhZ2ewipbfbOayVzg49XcICLR4d920iUY3TV7rHeD1vgFcal7n/apqyUQszg/PdDMWjSEJAnua6nm0o5WhuTDfO3UWv9PBZCyBaVl8dvN6ttRV897lKzzb1c3gXBjLtgk4Hfzarq2UeYrPoJiWzbEro/yoq5ve6Vm+8e4J/A6Nr+7MOycnczmeOXWOaCZDzjD51Ia1HGhpJG3o/Lirm5Oj42yvq+HXd28DYCqW4G/fPUaZ2814LE5a13lifQcPtTVf9+nYDM6F+c6JLj7S0cLuhqVKJIuugiBS4ShDRGRv6a58Y7AgoQgy631reXHyNTRRY0fJFsTCuqZtcqBsDwCiIC5w+W+X018M5dVB/uN/+zKmaVNWle9TCc/EMHSTr/+nT5FJ5XC6VI6/eZF9H9+IrMg4nCof+9xO9nykk1QiiyDkHXS9wXw5+d/8yWdRFGmRbOfDT25lx/0d+EPuJU3KLrfGb/37J9BzBqWVxRMZj8/Jp76ylwee2EK6YEylajIev3OJPOhKYZgWkiRRXu5bcPvM5oxCNfbaek5V4ekDG+m5Ms35ocmFb24qq5PKLv/yqinz86uPbGVNbRnJTI4XjlxYokHv1BQ+uWcd3ZenOH95kp++dwGf28Endq2lxOdClvLJaTyd5aVjPfz8aM+CxKjbobK5tbqom2kup5NKZtmyrRGfz8nUZJRsZvnjLQZFlvjU/Rs40zdGz3U6/rPRJD98/QxvnrjEljU17Nvcwqa2akoDbhT53gkW/HPD5XCYkViUPXV1yOLSe9WwLI6OjlLv91PnX3z/3y5Xv87n56mOdVyan+OL6zeyrqwcl6yQM02e6T6HLIr8mx17GI1H+fbZM9R4faQNndNTE/zBjt18bt16/vFcFz/uvci/3r7rjiu1v8T7i0hu5qbLLNskos8ANprkwindG/pXX/wUkdwsIhK7Q4+xNfhgUZZAzkov9BguB0mQ6fDtoCtyiJg+T3/iLFXOJoaTPYT1adyyjzbvFubnMzz74umFRm+AWDxNYxGDzbuFZb8VZ8cn6awq5/cP7MapKNeaDwRQ7oAbadkmffF36IsfLhr0h9Q6toWepMWzC7ccWBKg2bZNzkozlenn1PzzDCSOL+mqFhCocXXS7tuHTynHK5fmlV5ER/5/YeXZoohMu28fF6JvEjdmrvu7hEPyEFRrKHM0EtIaCKrVeOQQiuhARMQm3wwc06eYSPcylDjFVKa/aBc4wOXkKfrjR1jvf2RVL5yoPsl7M98jYYTRRA/r/A/S4duPVynFsk1GU90cm/sR87mRJduOpM7TFX6J8VS+UqCKLtq9e1nrfwC/WgnATOYyx+eeZTx9YUniNZ66SEyfIaQtHzxeD9M26Ike5NjcD4sG/SISAbWaevdGqp1rCWm1OCQvIhI2NqadI6bPMpXpZzh5hol0b1HqWESf4J2Zf8Qtl1DlXCqtBTAWjfHcuQt8bssGWktD/MPx04TTaXTT5PlzF0npOr+3bxczySTfOnaaSp8XVZI4NTrBV3du4bOb1/NCdy8/u9DLhuq8DG4klcapjPD1fTtxq+qyVTJJFNhWV0M0k0FA4Ot7d+LRVLwOjXg2RzSTpS7g5yttW3jj0gDPn7vItroaXKrCZ7esJ2uazKeuGczolknX2CSf3riOf72pk0ODl3nu7AV21uebvQQEhucjfO/UObbWVrGtruaW956IwJ7QDl6deotXpt5ERKDD10YwEKDKWYFTchDXEzS56xEQuK90By9PvskrkwcRBIE13ha2BjbikBwoRR6qpmFx4eQQyUQGVZOpqguRSedobK/iwokhmtZWMzY0w9ToPHWtFdQ2lxOdSzA9HiGdzFBeHeSlZ46Szehs2t3Khl0tXDx1GT1nQIET339ulJnxMPVrKvEGXAxdHCedzFJaFaBlXQ2x+QSTV+Yorw3S2lmLKIn4gm58QTe2bTM+PMvlnglcHgdrtzUyOjDN5Mgctc3lpBJZuo8PkU5mCZZ5adtQx8x4mMGL4zjdGuu2NxEPJxkdnKaitgR/iZtLZ0fIZHRyGZ3G9ipG+qdIp7L4S9y0bahn/PIMo4PTlJT7WLOxfiFR8Puc+HxOfvKzM4yN570ILvZMUFMdwONe7BLeUh3i957cy3/78WEuXpm6pXOtIEBdWYDfenw329bUIksiv/3EHsqDHl4+3stMJIENlHhdfHRHO5/Zv4HnDp/n4pVposkM33zxGG+fHaSzoYKQz01GN+i5Ms3ZwXES6Wsc3TV1ZXQ2VhY9BkWRqW8oJRB0cfTIANg2qtq07HEXPxeBpuoSfv/zB/ibZw9zYWhqoY/DtmE2kuTVY3280zVEbXmAnZ317F7fSEdjOV6Xo2hS8ouEi7MzHBwaYnt1TdHA37ZtopkMWffiCQ3Ltjk5PkZTMEipa2UULU2S8DscqJKET3MQcuYpY9PJJIORML++eSt1fj/lbjc/7+/j/MwUjYEgVR4vWyqrCblc7K6p47neC6R0/ZeB/z8TjKb6sTCRioSkMT3MbDYvTuBTQvhuU9RlJcgzUaawMHFJXqqdzUWDftu2mMqMYForo9RWORupc63hQuwo/fEzbArsZzBxDt3K0uxeT5WjkYwhsn9XG8311wL9ufkE03N3h1peDMt+KxyKTMjlKkoPuBPE9VnOR14jZy11vytRa3mo8rdpcG++6YysIAhokos61waCahXytIOL0YOLkggbG8s2afHsIqAWf6GsFIIgUKrV0+zZwdnIi6iimzJHIw3uTdS7NhFUa3DKvmW546VaPY3ubWwIPMr5yGucnH+etLnUFdW0dbojr7HGu3dVPPm0GeNKqgtJUNkZeprtoU8tkg8NqNU4ZR8vj//Fwqz+VcT0aU7MP4duZ5AEle0lT7Ij9BSqeM0cKKBU4ZFLeGHsz4joE4u2TxjzzGVHKFFrV5S82LbNePoiR+d+WPQzcUo+Ov0PsT7wKCVqLZIoF+18D2n1NLq3sCHwEQbixzgx/2Nms1e4sSoxk73M8flneaTyd3HLSxVzhucjOBWFLbXVlLicbK+v4bXeAZK5HH3Ts3x6Uyf1JQEqfV7qAn7Ojk2wvb6Wco+bPY11VPt9dFaWMzA7R860CDgdeDQNTZYJOp24b6H/LQhCPtDXNDRZIuhyLOLzh1xO9rU0UBPwsa6ygmPDo2QNA4+m4lZVnIpCKrc4sQw4HexvaaQm4KOzsoK3+ofIGPmHVjid5q/fOcaG6goeX9+xIq6/IAhUaGU8VfMJ4nocURDxK/lGNEWQebLmMQzLxCXlua6laoinah4npscQrlv34fL9KOJSysbkyBwn3+5l6/41/Pyf3uPA45sJz8RpbK/i6OvdiKLI0Tcu0LahlpefOcrjX97LOy+dZfN9baiqjOZU8Pic+EMeqhtLEUUBf8jDu6+c4/4ntjA9HuHUO710bm/i0M+76NjcwNsvnOahT2/n8Etn8ficHH75LGu3NC6RUwVIxtIcfP4Ua7c24itxMzsR4fjBi6zb1sh7r55nzcZ63n7hNA98aivH3riA06Px3ivnadtQh7/EQ2w+yTsvdlHXUsHbL5zh4ae2885LZ2ldX0ttUxmJaIqDPznF/U9s4dTbvbg8Do6+foHa5rIlNCOXU+XJx7fw8mvnSad1xsYjrGmr4GMf2bBQAbgKURTZvqaW//ilR/jh22c5fH6IuViK3A0SnZIo4ndrbF1Ty+fu38Tm1mrkwn0R8rn46qPb+ej2dsZmo1i2TVXIR3XIh6bI3L+phVdO9DEwPkcmZ3B+aJLzQ4ubf6+Hz6XxyT3rKPEVf96pqkTHumpcLg1/wEUuaxAI3tmzURRFtnXU8Z9+46P8+K1zvHG8j+lwYpFrbTqrc2lkhv6RGX72zgU6Wyp5ZMcadq1voMTn/oVOAGzyFceUri/IeQuCgG6aRLNZdtbWLHI5z5km04kEP+ju5tNr12Lbea7+1e3u6AhsG0m46pidn4S4msBev0tRFD4EjPFf4nYwnLrIZHqYamfzovvDsi364qcI56YAaPasx1HEDOtu4GocZ9lmQaFxcR+rbdvMZsfpjZ1cMeVcE110+LbTn+hiOjvKQOIcY+kBZEEpcP7dOP0CAZ9r0fNFlsR7xu+HWwT+G6sr+fax07zc009baQmSlOfbS6JIlc+DcjtNgbbNaOo8M5nLS5YpgoOtJZ9cNui/HoIg4JFL2RX6LDOZIWayQ4uWT2X66Ym9zc7Q03m91VVAElQ2BB5BFEQaPduodrYXpC9Xvl9REPEpZewMPY0meXh7+u/RiyQ/U5lBwrlRKp1rVnXMALWuTjYHP77EM0AURJrc22jybKc7+gbXB8c21sJx1bs2sKXkCVTJtSjYFgSBSmcbTZ7tnAn/bFHSZdhZ5nOjBYrRra9j2oxxYu45wrnxJcvccgl7y36Vdf4HCx3/y78sBEHALQdZH3iEgFrFG1PfYDozcMNaNkOJEwzEj7A+8OiSe8OybUTh2tlKYl7b3S58SoteOsK1l45DkRdmwq6ppNyJh+Hy0GR54TuXH0dY4GIvt426sA2LtollsjSVBBmaCzOfTFHpWxnNLP9Zu3DLriV/vxrYX/83l+zEJS9WLfApxcean45RXhNg/c5m3n353MK1sO28gc7sZISp0XlKyn2UlPvQHApt6+voPz+KrMjUtlQscPHLC1KdwTIvaqFhbmYsTFl1fv89Z4aJzMZpWFNJ544mzh8bwLZt2tbXMdQzgaxI1N3QmBYLp5Bkic4dzWgOhfPHBgmWelm/s4Whngnmp6PUtZSzfnsz/edGmR4NA7B+RxMOl8ZQzwRjQzO4fU58hUZVzaHQsaWB8uogY0Mz1DSV0bmjiSuXptCzBm0b6ujvHkVWJOqvOx5BEKiuCvArn91FPJ6n77ldatE+A8gHv221pfzR0wd4ct96zg6MMzQZJpJMY5oWLk2ltszP5tZqOurL8TqX+o0oskRdeYC68qWJc3NVCb/y0Bb+8sfvEEkuz9l1qDJP7FnHg1taixqDAaTTOu8dvsRDD3dSVubjyLuXkGWJ6pqVc48Xn79AfWWQrz+1l4e2t/HKkR7eO3eZybn4IrlPG4gk0hzuGuJkzyidzZU89cBG7tvYiNt5+31t/xIwlUjwNyeOM59KU+5x89XNW6j0eJhJJvnO2S66p6f5zW3b2dfQAMBQOMyPurs5NjpKKqcTdDp4vL2dnTW3lpaURRFZFJlKJohmMzgkGa+mUe8PcGpinMZAkMlEgplUksfa2jFti/F4nN65WdoppWtyghqPd1G/072AbdtMZvoYS11c+FuJVkuDe/P7KiNt2zbzuVGGk2dodG+hRLt38p33Cgk9zKuT3+Hhil+h1FGDhIRh61xOdvPOzPMYto5fKWWjfx8iN/aoWeSsLKZtYGNh2RYxfQ69wK4w7ByR3AxOyYMoiHmhdUFCEx0LcZwgCFQ6GpEFhYyV5kL0COWOOtySr7APnZnsKG9P/5jpzAgCwop8igVBoNmzgVKtmsn0Zc5HDhPT5wiq5TS51yMKImbBKfz6SYiZuTgzHxTHf2guzPmJac6OT+LVtIWMpNTt5j9//CEqvCvnWhl2lpHU+aL88lJHI63e3bdlYCAIAiVaLWt8+5idubzoIpi2zkD8KOsDj+CRV1cWyge6ayh3tKyyqURAFlU2BD7CeOoiF2MHl6yRtRJMZQZWHfgLiKz1HcAheYvOrsiiSpt3D72xQxhFpDIlQSlo+/uKzrCLgkSdewPnIq8saQCO5CawbOOW19K2bQYTxxlOnuHGIFkRHOwMfYb1/oeRxdt70YqCRK1rPbtDn+e1yb8iZUYWLc9Zabqjb9Dk2Y73Bu3g2oCPRDbHwOw8TaEgFyanyegGblWlKRTk9OgEHRVlzKfSjEaifHpjJ0CBw1r8eJyqQjKXYz6VxrJtXKpyy+qZU1FI6TpzyTS2Da5bmC2ZlkWm4KZtWBYZXV9RUl4X8PO7+3fxzKlz/P2x0/zuvl131LB/N+ENuJifjnPl0hTxSArNpRKdTzDcN0F4No4/5KGmqYxtB/J0LbfXSfO6Gjx+J6cLM/lOt8bMZIToXAJvwEUimiabyhGPpAiUeujvHuXKpSlyGR1vwEUynl64yy3TomFNJS6PgzPv9rFuexPSdTPtLq8DPWcwMjCFx+/CG3ARiyS5cmmSdCJLVX2IRCxduCnA6dawTIsr/dP4gm4cLpXa5nI27W5Fc6r4Qx5EUVik+y1d5ZcLeSWR6oYQmlPhxMGLrNvWtKAAZFkW3RfHee/YAKlU3jcBIOB38Zknt+P1LJ0xEgQBp6awrqGCtfXlC82+tm0jSyLqKrjtsiTx8R3t5HSDf3r9NJPzsSWUIlEQCPlcPL5nHV98eCueZQJp27aJRFLEYmlUTWZuLkFdw+p4r4IgoKkyG1qq6Ggo58n7N/Lu2SHePTdE/8gs8VR2UTKdyeqcvDhC3/A0H9uzlq8+voOygPcXTs9/LpXmt7c3U+X18tfHj/Fy/yW+tGkzFR4PX92ylf/j4EGS+rV3SUMgwKfX5bn6X9q0ibZQCPcKA3GfprGnto7ney/y9vBlHm9rZ1dNLU+v7eQ757v408OHMCyT++rq2VhewempCRyyzM/6e3m25wKWbfG1zdvQbmNicjnU1QR58uObkSSR8kUymzZRfZqhxEnSZpSZ7GXavPdR51r/vvvHTKb7ODj1P/hY9R/9swv8VdFBk7uToeR5vnvlz6l0NOCSvcT1COPpAVJmHE10cl/pJ6h0LnX3TZtJXpv6LtOZEXJWhpyVxbByxIx5AKYzI/zD5f+KLCgoooYqOijVqnmk8gt4rqv8t3m3cDp8kNH0JU6FDzKbm6DK0YQoiIRz04ym+slaKTYH72coeZ7Z7NIJy2LwKSHaPJuZTA8zlOzGtA2aPRsIFnT9Z+bi/OjF04smFaKJNE0fFMd/b1M97eVLB1ckiaDr9nRHs1aK6cxSdRwBgRrn2jsK0EUk6lydOCTvEqrIXG6EcG581YE/5IPJu+WqpggONgQ+Qn/8SNEkaDozVGSr24NT8lHhbFt2kjyk1eOS/MSMpU01XrmMamfHslWNoFqNIjowzMWBf8KYXVBtWQ5pM8qF6BtF+fiNni10+h+67aD/KkRBpMmzjSbPtiVVDYDpzABjqQu0+/YveojUBwM80NrEt4+fJuB0oEgSZR4XmizzqfVr+c7JLv78jUOYls3G6kq21VVzORzBo12bGVUkKf974cNvLy/F73Dw/779HlU+L1/duYWQe3m6QmtpCeUeN//f2+9R6fPw1Z1b84o8Dm2h6iBLIl5HXj1ocC7Mixf6OD8xRdYw+O/vneT+1kaCTidex7WE/fptFEnE59DwaCq/un0Tf3noCK/2XOLJjes+UG3zmuYyGtsruXRuFEWTqW4oJTafov/8GNsOdNDUUY1pWpx59xIVNUH8JW4GLoySjKXZ85H1SJJI64Y65qZi9J0bYd22Jvq6ruALurlw8jKb97bRvLaa7hNDbN3fQWmlH6dbQxAF2jfV43Cp9J0dJRZOsPPBdUsoM/6gm10PraOv6wr+Eg9b9q1hzcZ6Lp68zIZdzVTUhXC6NERRpLWzlurGUpzu/D59ARdbD7SzZd8aek4PEyj1sOm+Nto25scFcPsctHTWIEoizWur8QbcXOmfYm4qyua9a3BdF8xHoml++NwJamtKWNdRtXAPupwqyk1m/a+HIAjIkrBqT5br4XKofObARtY1VHCwa4DekRki8XzS63c7aK0JcWBjMxtbqnHeIqF1OBTqG0L87IUziKJAMOimrIhG+p1AEARURaa1rpTmmhCP71vHxcvTvNM1yPELV5iYjaJfZ7YWT2X58cGzpLM5/vALD+C7h2X4DyOaggE2VVbiVlV219ZxbHQU3TRxKgo+TVsy0eCQZQIFrr7f4SDkWjlFSxElPtPRyf76RgzLotSZp5q2lYT44117mc+kUSWJMpd7oZpZ6fa1srV+AAAgAElEQVTwG5u3IQgCPlUj6Ly5rObtor21kvbWYrRhgVbPbhrcmwlnx/jJ2P95V8b7RYNtW+wKfYxaVysn599gIHG2oJgjIAsyIbWSHaGPsqPk0aLuvqZtMJUZYSY7uujv11OCslaGLBkomH8ZtoFhLabFlqjlPFr1Jd6c+j7j6UGGEt0MJs4hICALCn61lPtKn2ZT8H5em/wuaTOJsoIYRRIk2n3bOR05SEyfxyl5WOPdilzoN1VVmf07W2muv2bwNTufYGb+3hmVLRv4l3rc+BwOxmMxJmMJHIpMfTBAwOm4aXn2ZkgbUVJmeMnfJUGlVGu4o8BaEAQCShVOybck8NetDLOZy9S51t/2fu8lBEGgzNGMX61kNnt5yfKYPrUqjwQAtxzMm4ItE/m7JD9upaRo4B/Uqm/piqyJLhySh/QNfQJpI7YC/pvNRLqXifRSjwFNdNPpfwSntDqZR1V00ubdQ3/8CFkruWhZ1kpxJXWWFu8uFOHaF1eTZZ7e3MmB1kZsG/xOjaxh4lYVvFqAPziwh7lUCkWUKC0kBC2hEv7g/j0EnPlAYH1VBQ0lATwFPn+5x80fP7iX+VQaRRLxr2BGPeR28YcP3MdcMo0sigScDvwOjT9+YO9C0tBWWsLvH9iDr5CgPNa5hsfW5StFgpDfh0tV+bcP7l1QEWooCfJHD+wl4HLiUlV+/4APp6zgUhT+4P49pHXjA1cyURSZfR/fBMDMeBiHS+WBT25ZtM6WvWvYsvdaVez+xxcv95e4efRz15wi739i8fJtBzoW/V5eoI5sf2Bt4feb3/uCKLBmUz1rNl2T8tt8Xxub72tb+L2sKj+LtGVf/hhDFX5a119reF+7tZG1WxsXft9+/7XjCYS8bL4vH9xuLBjf1DRdeyFcj3Q6h0NTePpT2xYZXX3QUGWJTS3VrG+sJJ7OFlSAbDRFxuNaGiTeDLIssWt3Kx2FZM8fcC1JxO4GRFGgxO/mvo2N7FhXx8h0hLdODvDq0V4uT8wtVC0M0+LVY32sb6nm0w9s+MC/K+8nrqdFioKAaa+E6HDn0GSZOt/id4AgCPgdDvyOIkmXIBB0OAk63z8jJEHIB4QyChnZVzQo/SVuDcM2kEWVA2VP0e7dznDqYkHlR6BEraDRvY4yR81Nqygu2ctTtb93U+GUYpAEGa+ymDIoCCLN7vWU1lUxnOphOjNCxkqhCBolagX17nZK1WpEUeLhis9zX+knFlUMlkNIq6RErSKmz1PpaKDG2brw/Aj6XQR8TkzTZmwqgsuhUlHqvaV/wmqw7FM0ldN5tqubl3ouYVo2lm1R5fPym3u201l5ey6tKTNW1HFNEmQ8yp1pvwKokgtHEQlMyzaJ6TeXiPog4ZS8BJTigX/WSq2YI38zuOXgLRuEVdGJQyw+e1aq1iPeolQpCSpqEbc53c7dRFbzGkzbZDBxgtwNATlAqdZQqDas7qUqCALljha8SinZ7NJxJtK9ZMz4koxdlWVqA8WTDq9Dw3tD4O5QZKqu46u7VGURNUcQBPxOB37nymcIBUHA53Dgu+EFV+W/No5DUagqlM6LHddVVPuvlaYdskxVgccvq+Ki4ww4nQTeX/PAW+LjX9yD13/nje7/UpHNGei6iSjmNcEHL8/QJouIhUqNWNAK/6CbUSVJJODJ31S2FQd0uE2pX0kSKQm9P+6tV6sALTWlNFaW8MC2Fv7h5yd4+UjPgqdHNmfw0nsXeWTnml+oWf8r0QhD4TBVXi9dk5M0B4MoopinFhoGpmXljbQMA0WSEAUBWRQRBYGZZJIanw/Hdf1GdxOVbg8H6ho+9Ao+tp1X+pvLjjCXHcawc7jlIBWOVjxyyZIKu21bJM0I05kB4voskqAQ0uoo1RoK763lv9+2bRMzZhhOnMIheWnybEO5hUPuBwG70Lgtiwo1rhZqXLfHa5cEmZC2OiGXqxAEAb9aykZ137LreZXgksRhOYRzM0Ry04hItPu24ZKvvcsFQUAQBM50X+HwiQFaGspobSwjnsiwbWPDHZ/Lclj2m3J6dJzDQ8P8qz3baQ6VkDEMXrzQxzOnzvG/Prz/psFGMRhWFsteOhMsCGLRAHKlEBCK2j3b2B9aR2AB4aYz6pZtYtnmqqhFXqUUgeXL96Igo0rFP3e/WrmkgeZGSIK0UKq6HrZt3tLOOmVEmUz3FW2OqXV1rtrL4CrykqvVzGaHlyxLGHPE9JklPP/3E7ZtFyRJDUzbwLItbCxsrlLE801IeT8KadXOzh808udrYRTO17Yt7MJ/Qt59A0mQkAQZSZAJhDz/bGZVLdsqch2vnVfevyB/XgLCqs7r0OE+jh4fxMZmZiZO94UxqqsDaIVELhh08aXP78Hn+/Bkcmb2NYz0S0iOh5DUnQhSFbC0efjDAkkSaaoO8Xuf3cdsNMnR89eeIZcn5pmaj//CBP6qJNEUCPKD7vMkcjlEQeTLmzYhiyKHhoc5OjrKSCzKqwMDTMTjfGJNOzU+HwGHg521tTxz/jyvDgzw5Nq1bKuuvuvH1xwsoTl49yUe7yZs2yZjxTk1/1MuRg9i2DlEJCwMgmote0o/T61rw0JlxbYtxtM9vDv7XeYywwhCXr5aEiTavfvYHnoKl3zzqrht28T0ad6a/iYT6V72ln3plpN5v8TKkE3nOH7wIgKw86FOFG35z9WwdLqj7xHT5wumXpuLetiMT0fpbK8mncmRSueIJZYXSFgNlj3ii1MzrK+qYH9z48LskbZxLf/1lbeIZ7O3Ffgvh9UWDW++/YfzpQLc1OTqasCwGrikACtRwSkWuAsIBanLW20vFk1O8nKqy3P8o/okEX2pzJ8kKJQ6Gu/aA0oRHXjk4tWknJUhpk9Rw9q7MtZKcVWBIGaEmcmMMZMdJ6LPENXnSJsJclYO0zaQBBlV1HDLPgJKKSVqBRWOOkq1atySNx9Avg9Bk2VbZMxUUfpW3s/Ctaxylm3b6HaWmB5mKnOF6ewY4dw0EX2WrJlGt3JYWEiCjCY6cMs+/EqIkFpFmaOacq0G1/t4vitF/rxyJIwIM9lxZjJjzOemieqzpMw4upXDsHUkQUYRNFyyF79SQolaQZlWQ7lWg0cJLHKEXCnaWivwem7+7NU0Bc1xbxVNbh8Kln4GM/cOglSNpO5D0u5HUjaA6Ef4EAYlgpCnAD2yY82iwD+ZzhK/hWrRcriRJmvbNpZtr5riea+ws6aWLVVVmJZNPJsl6HQScDgQBIGOsjIqvR4+tTZPV5MEcYFuo0gSX9iwkYebmjEsi3LP+1O5+TDCwuB85DVOz/+Udt9+1gceQRPdzGSHODr7Aw5OfZPHav6YUq0h39SuT/LW9P8kbcTYX/5rVDrbMKwsF2NvczbyCpKgsLv080g3SCILFJzYjRkOzXxrIehf639ggSpj2zbZnEH6DszwboQoCnhc2iJxgn/pSCUyPPuNNxEEgU33tS0b+Fu2yeXkBc5H3sXGZq1vB6Va8eS3qa6Ud471MxdJMjuX4MDutqLr3Q0s+7TVZJlwKk3ONNEKQV48k0UUhduWyVRER/FA0bbImkupGCuFZZtFtxcQVqWHf89xD2dvHZJnRSlPsRlkSVBQxJU0RhVkS26AvSB+eXPMZUfQi1h0a6Ir35twl15+IhKqWPweMK0cSSPyvrxsbdvGwiSam2MoeYG+RBejqQESRoRsEVnXYhAQcUkeAmopLZ4NrPFsosbVguM6j4V7gZQZ50ejf81cdmmiVqJW8FTtbxc1VLFtm5SZYDjZQ3fsGMPJHmJGmNwtrNmvQkDEKbkJqmW0ejbQ4dtOjbMJRfhgZ4kt2yJhRBlO9dIXP8Nwsoe4ESZjplYm74aAJrrwKUEa3R2s8W6mwdWBW/at+JnaUBeioS6EaVpkswZOp7LwmVhW/m8rae59PyFpD6EFazGzb2FmD2Gkn8XIPIcodyBrDyFq9yFKjSDc2/v5diEAHteNSdbqjs95Q1KmGxaxVSQS9xre67xEym4w6Sp3uyl335yL7JBl6gMr40H/S0Zcn6M78johrYE9pb+yQG8OqtXYts2rk39JT/Rt9pb9at5NPXGC6cwg95f/Omv9Dyw8G3xqBeHcKN3RN2j3H6BUq180jijIJIw5Dk1/m7HUxSVBP+Rlq196+wI//PnpVZ9X0O/i3339UaorfnmNIa8wNJEeRBTkQtPxMCfmXyeiz1DpaGRr8EFkofikTHN9iNKgm2Q6h9et4fPeu4rtsoH/1tpq/p+3DvONd4/TWhYilcvxzuAwnZXlBF23V+Z0y0EU0Qk3NIMatk50FQ2tKTNC2lza/SwKEj6l/Lb3txpcpTLYtlWwdC4QGWz72s/Y2LaFYWVvtbs7xmqoU6Ig3/TGvBvIO+SNFaUD5bPjU8xlr9y18W70eFgYC3NJ0++9gGVbhHMzdMeO0hU5zFRmpKiE6q1gY5E0YyTTMcbSg5wMv0mrewNbSx6g0bUWVbw3AbFlm8xlJ5m+QTEBIG5EiOuRRYF/nseqczl5kffmXmIw0U3mDih3NhYpM04qHWcsPcip8Nus8+1gV+hRKh11CIjva4Bo2zZJM0Zv7BSnwm8xmh5YcdK2aD/YZKwkmWyS6ewoXZHD1Lva2Bp8gHbvFpzSyulNM7NxXj94kac+uRVnQXI0FsvwwktdPPHYZvwfIqqPILqQ1M2IygZk1xew9C7M7JuYuaPkEn+BkPoWkroTSXsIUd2KIJYioNxcJ3eFsO28XKkiXeuBuB0YpsWFG0zIPE4V7ypoPmWBvITr9brdZy+Ns29T04cq6fkl7h7mc6PEjVkaPdtwX6c0KAgiVc52nJKPiXQfWSuFJMhMpHvQRBe1rs5FEwKa6KbetYmhxClmMoOLAn8BAcPK8e7MP3El2cX+8q8uCfoBsCESSzM0Orfq84onM4s8MH7REc5N8ePRvyZnpbFsi6yVxrRNgmo5D5Q/Tbmjruh33LZt3js1xNbOOspLvYxORLg8OsaOTR8Ax7+9opSv7drG8+cucnpsAlWS2F5fzVMbO2+7kcYl+fGrlURvoHiYdo6p9CWMQG6RwspKYNs2M9nLpIzIkmWK6FiSDd8tXA3wdStL1koS12dJGvOkzChpI0bWSpKz0hhWFsPWMe182d+0dAw7h2nr97TxOO83cGcvEFEQ7ykX0MIkacwvMv66ioyV4MjsM/ds7Bth2vqqG6lvBtu2yVkZeuOnOTz7c0bT/bfsfbgdJI0YXdHDDCTPszmwn92hRwmple9rH0DWSjOXm6TG1Qxcm+U/OvcK7829SLzI9/JOETfCHJ1/laHkBR6qeJr1vl1IvD/0H9M2GEld4tDMC1xKdK24arESZK00lxJnGUn10+Hbxr7Sx6l2Ni7b42PbNpZlk07nmJ2Lk80ZC6ZdqXSWkdF5dP3m99rVYDieyjIdSRCOp0hndUzLXjXNEKCxooSO+uKTLoIgIUjliNIjSNoBbHMUM/seZvZ1jMzrGJmXEOUWJO3BfBKgdCAIt25kvBl0w+K5g+eQJZEt7TVUhnw4VPmWScDVz+jY+WFeOdK7aFl9VZCKkjvrQxIEgepSP0Gvi7notYmHN05c4iO72mmrL7ttxbxf4sOPjBnHsk3csn/JM0uVXGiSm4wZQ7eyCKJA0gijSe4lk3h5Q8Q8tz9R0Km/CguT7ujrjKUvEFCqqHK2f6iVhhRBLXgFsfC8m5uMcvT1bjq2NNC0tnrRZ3Xp7AiDF8bY/sBaQpXX+hsMw2RieJbh3kkS0RSSLBIo9VLfWklplR/phupnNp3jyqUpRgemyOUMQhV+Wjpr8Ie8SwQRDN1kdGCKyz0TGIZJVUMpJeW+m753FEHFKwcI6zlAIKhWUOVoYlvJQzS719+E9WJzeWSOE13DRKIpPG4Hs/MJmus/IB1/y7LYUV/DltoqktkcsiTh1dQ70vlWJRd1rk5GkmeXBH2jqW5ms8NUOtpu60WetVL0x4rr4ZeotQTVu9dIdDXYz5gJZrKXC3KUvcxnR8lYcXJmGt3Oci8cW28X4vKXdVkIiMvKgK4WhpUjYybu2f5vB/lm87t/vfIcyzCHZ1/k+PxrpIpUpO4WEkaUd2d/zkjqEo9UfJ5mz7r3zTzGti0mM1fYyH3Ytk3CiPLq1Pc4FX6roMN810dkOjvKT8e+ScpIsLPk4Xse/GfNDGcih3hr5jnmC7bx9wIZK0VX5B0mM1d4qPwzrPVtR7mJYaBt25zuGubIsUF6eif43g+OoSoSNjA3H8fhUHA6ijXe26RzOt1DU7x9dpDT/WPMRpOkszq6aS6agV4NvvDQlpsG/teO5epPAiBCgSIoCD5sK4ae/DuM9LPIzqeQXV9BlELcSfBv2RZnL41x8FQ/5UEva5sq2NhaTVN1iMpSH363A0kSEYWCO7Rtk0znGJkKc7R7mFeO9DA1f+15pSoSH9nZgXcJ/WflqCr1sa65gkOnr/najE5H+LN/eJ0vfWw761uqcGpKgbCdd6zOGQaJVJZ4KktjVcmKKg6GaWFaFraV7yGwrvs3Z5hMzMbQ9cWztal0jvGZGD63hizn1XlEQSjQewUEsaDWI968Qd22bUzLwjQL49n2wjGYlkVON5mci2Fai+OAeDLL+GwMj1NDkcWF8a4fP3+tbjV2fhzLsheS5Px558eemI1hGIvHTqazjM9G8bsd18776tiFn6VbnPdyuCqWYRYROMG2sWwTSbhK2RMQBbkgEnDDd9KmwCZgSVBv2iZpM8aGwKP0Rg9xZPYZHqr4LZzyYjf1Dws6/Xuod+d7Q4IFdsbo0DR/+R9+wG/8+0/S2FG9qOB3/M0L/OBvXuc/1/+rhcDfNEzefPYEP/4fB0nGM0iyiJ4zwbapX1PJH/7ZFyirvkZDioWTPPvfD3L4513oOSNvkmhaNKyp4lf/6GOs2VS/EPwbusmbz53kR3/7BrFwEodTRdEUNuxuIZfRF9zgr0eJVsnn6v8Y3cpgYyMLKm7Zhyo6lr1vfF4nVRV+QkE3bpdGXXWQxtp717C+bITwWt8gQ3Pz7G1qoKW0BLem3vFshCTINLi3cjb8MnFjdtGyqD7F6fmf8kDFb+KUbp5NXQ/T1umPH2EwcbzoWM2eHTilu8M7y/N65xiMH6M3/g7TmcECveiDD/KLYfWzvvcukLJso5AgfThwt69g3j59ilennuF89Mg9CoAXw8JiONXLc2Pf4JGKz7HBvwdZvPfNnXYhEL/aAPz69A84Mf/mCnwcVoekGeO1qWdwSm42Bfbes0Q1bSY5PPszDs/+jPQq+pBWChubycwwL4z/PRkzxZbg/qIGMYIgUFdbwuhYmMHLM3g8GqqaDwKqq/xs3liPy7U48Ldtm6lwgu++cZoXj/UwF03es6eXYd28ud+2LbDjWPpFjOxrmNm3sM1pRKkGxf0VJO0RBNGLmTuJmf4JevJ/gq2jeP+wMPN/Z9ANi7GZKGMzUd44fgmPS8Pr0vC6HfjcGpoiY1gWqbROJJ4ikkgTS2auS1BAEgX2b27m0V3tq0o2VUXi0w9s5EzvGPHUtWfhuf4J/svfvUxlyEdpwI0sS+iGSSqdI5nJkcnpCAj877/zGBtaqm45zqtHe7gwNEUqkyOV0UllcqSz+X9zuklWN5gJL56E6bo0xv/yF8+hyBKqIuPSFFwOFacj/69LU9jYVs0DW1sXqkw3IlOQPB0YnV0Y9+oxpLM5coZJJmsQTy5+Dxw81U/XpXEUWUJTJVzadeM6FFyayu4NjezsrF8wMrwROd3k9eN9XLw8RTKTyyukXD2GrI6um2RyBnPRxfTD491XGBidvXbehfGuH3/LmloObGlBkm7/2nuUUhRRI6ZPLYg3wFUKYZiMGafC0YYquhARCShVTKUvkTLCBNRrUpUWJpHcJKIg4lcXS1hKgszm4CdY678fl+Tn5Pzz+JUKdpV+bslzRFPznhqWZeWl2q9LllaLq87Xt/qOuGTvIlnLO8HMeITv/9XrhKr8/O5/eZpgmZdc1mC4Lz/77yu51n9i6CYvfPsdXv/hMR770l52PdKJosr0dV3hn/7iZb71Zz/j3/7fX6S0Mh83Dpwf5bt/8TK+Ejdf+3dPUN1QyuSVOX70jTe50j9F6/qlDsmSIBNQb2+mXhAEQkE3T350Ey6H+r40Si8b+Ff5vBweHObP33iHKr+Xfc0NbKurptzjQZFun2Nb7miixbuLrvCLi2b9bSx6Y4dQRCfbQ5/Gr5TftNRt2zZZK8VA/AiHZ/6RjLV09jikNtDh23/bDcjFxjLsLIOJE5ycf56JdO9tmUR8ULiXM/arhY1VVNb1XwKuBv0vTX6H7ujRhZmZW0FERBYVRCREQURAxMK6TiJSXxEFYy43yYuT/4iNzabA3vdl5j+SmyFhRDkVfosT/z97bx5d532fd35+73Lfu2/Yd4AgAIL7KoqbRFm7ZEdeoliObTVx0iRN09O0J+3MtJ2ezpzOmU46Z7qknUmb2HHTyI4XyZa1y6IkSqS4k+JOitgBYgfuxd3vfbf54wIgQQCXWCnZ7qOjQ/Le933vu/7e5/ddnmf83YKkf8oBURJKvqQMaVIFysS0TYzJ0quFIGXGOTT0Q8qcNVQ4Z9u4LxcZM8WHI69wZPTVBZf25KVXb7+OYvo6WrYxeXx3v44xY5y3h/4WSUhsDR6YNYkTQlBWGuDggXV4PBr79zbNGeGfgm3bjE4k+c8/Pcrbp6+jmws7xysJ29axzSHM3AnMzFtY+jls20B2bEL2/DayYz9CrpxW9xHyGmRtP7mJf4mRfRvF/ZsIpeYuvzIbgnzU+HZYtk0smck31I5MzLPmTDgdCge2ruEPf30/wWU23QkhuG99Lc8+spXvv3WWdPbWOyWRztHWN0pb3+ic67o0FXOB1+/d020cPtu2qH3L5Az6hgufk4lkhv1b1hQg/jpvHb/G2Wuz+4IKIZnOkUwX7n9SFZmdrTXMx41yhsF7Z25w+Gz7on47ldVJ3eW401mDfVsalkTMwo5qSp2N9KUuM5LppNTZmK/Jt7O0x0+StVLUebZMlzvXebZyPfYB1+NHJ3X7XYDNRG6QjsTJye01zPgNgUCVnKiSk23hzzOhD/Nx5HUCjjI2BB6e5lSSJHh0/zpa1pQRT2aIJzLEk9npP5PpLKl0bvJ/naGxGOPRxfVpfXilk7Kgl5riEC7HIrOyi1g0lcgQiyTZsq+Jxo3VON15lbSG1srpCcgUhvrGefel02zd38wXf+dBXJ78ua5qKOFm5wg//fZhrp3tYt+kgeSxty8SiyR5/p88za6H1iNJgurGUkzTou1i78J3coFYTt/QYlGQGWytKqe1rITeSJSjnT28fa2NF89fZnNlOQ801rOurASftnA5OlU42RJ6kv7UVYazHTO+0+0s5yNvMJj+hBb/fipcLfjV0nyNmxBYtkHSiDKW7aYtfpLOxKk5Sb9LDrCz6EvLLvPJTzCSnBl/mbPjP5vlDHwn8hrkKrJQ8+ZYsgdVcqEIB4rkQBYqyuT3slDpS11mLLdyTay/KLAB7LnJjywUfErJPatTd8mBFZskTZW6HBr+0YJIv4SEVwlQ6VpDpauBUmc1AbUITXIhCQnTNkgZCSL6MIPpHvrTHQxleklbKQrlKWL6OG8Nfg+H5GS9f9eyJ793Q1Qf4/T4uxwbe3Oe7IbAKbsp12qocq2h3FVHUC3BrXiRhTKpb50imhtlMNNFT+oGA+muBTUEj+YGODLyCl+s/j0cy4gG3wnD0jk59nOOjr52V9IvC4WAWkSVaw0VznpKtEp8aghtUhnLtE1SRpyoPsJgpoebqXYGs71kzMLXMWFEOTT0I7xKgGbftjmvo9/v4oF9zTPcbG3bxjQt5NsCM7ph8pMjl3jn7Cdzkn4hQJEkhLQyT4MyBzGycsfIxf8Uy+hGSAFk7XPIzieQHVtBBGe9Q4QQIJUja/swE2fBXponiyxL7N3UQP9IjK6BcdIZfVaJyXwQApwOlbryEE/uW8+Te1qXTfqn4FAVnn9qF16XxkvvXWBwLDZtElYIymSpy//Ap4+cmSaqD5Kz0kRzA+h2lqQxTm/yIprsxSl7CTrKkYWKS/axI/wMhwb/nEOD/4V1/gM4ZS/DmQ6uxT6kzr2FJt/e6eeg1rOZFv8Brk68j25lqHStQ7cy3IgfI6GPc6D0ebzK3JFlgcAp+dhb/BwJfZRjI3+LTymmzrNt2iyqOOSlODRbXnVq/MjpJrphks0ZvPjGOf7m5dmVFYXQNRzhpeOXKPZ7uK+phs31FRR53ShLCBgXQlF5gOattRx94wKKKrP38U00tFbh8Tunj3UK3dcHGBmI0pg1eO8np2d8NzoQJZfV6e8cxTIt9JxB1/UBfCEPjRurpst/hBDUtZQTKv1slk8tFAWJvxACp6qwpjhMkddNidfDTy9e5efX2jjV00dDUYhv7NzK+vLSBQ1GQghKtHruL36O94f+gpgxs8HVwmAgc53BzA1csg9N9uZlQJEw7Bw5K03KmMCYp1TEKXnZGf4iLf79yySPNrqd4dTYTzg19uK8vych41YClGgNlDrXUKTVEnRU4JL9KMIxSfIVJCEjhIzElPa94O2B//grSfzz52Du286vlvJ01T9dMQOvu8Epee9qdLZQ6HaWE2NvczF67C6kXxBQw2zw72ZLcB9lzmq0u0hyWrZF0pigN9XG2ehh2uIXChLjCX2Md4Z+SEAtotrVuKo18AljgveGX5qzfEuT3DT5NrMjdJBq19rCkpUesO19JM04nYkrHBt7k67ktbuWDV2Nn2FH6gZrPBtW5Dht2+J6/Bwfjr5SULVHIFGqVbEluJ/1/p2EtbK7So3mr2OM7tR1zoy/R3vyIrkC6l4RfYR3h1+kyFFOsVY5a9uSJNC0mdmAnG5y/EQ727bWTsvBdQ1FeOPkNbJ31HP73RrNNSWsrSymNOTFvYxSztvRVD2bkFhmHyBQPb+D7HwUSWlE3NXFV+o3vLsAACAASURBVIBwIaRimMNzZCFQZIkn9qxj1/paPukZ4VL7AN2D44xGk8SSmemyF9Oy8s69iozXrVEU8FBbFmRLcxVbmqooC3uXpApUCG6ng+ce287ujXV8eK6dS+0DDIzFiSUz5HQDadJJ2OvWCPlclIZ9NFUXU1U6v3HT7di9oZagb+WjiJsaKwtGvTVVYf+WNdTcJvGYMw2EEKjS8ppN168pL+hIrcoyuzfUrdgE7XZsba6acQ+MZDuntfbNScf68Wwfh4b+HFmohByVPFL+h3lDTSFR69nCw+V/j48jr3Fm/GUs28Cp+GgNPMDW0OfxKLecYDXJw96S38SjhGmLH6MjfhIhZMKOKh4q+13W+u6fMZbKQsUp+5AnFfmEEPjVMvaXPs/PB/4zp8ZeIuSonFUedCeEECiKPJ3NsSwb7xIi0V9/YBuPbmniQvcAp2/08ta5T2gsL2JPSy3NlSW4NXX+sXK+eMgcSuH+kIff/ee/xk+//QEfvXmBI6+dZ82GSh78wnZ2fW49vuCtd+vY0ASWaXHh2A1unJ/NvUorQ6iagg3kMjqpeAbNqeK9Qx3Noal4VuC50q0UCb0Hj1KJKnnvqTBHQeKvmyYDsTjHu/o40tFFKqezo6aSf3RwL4ok8cqla3z7+Bn+5eMPEXIv7EGThEyT734sdI6OfI9Irp87r2Zeym+ClLmwNCwIfEoxO4u+yJbgk3PWxS4Glm3THj/B2fGX5yH9Ar9aSqv/AZr9+wg5qtAkz4KJh30Xg6tfZkhCnlQdmgsCjxwk4Ci7p/u0XNi2RVv8IifG30YvINUpIVHnWcdDpV+mwbN+3ubNWesJCZ8aotW/kwZPKxcmjvHByMuM5WZr609hKNPDu8M/5ivVfw9vAYfH5cOek/SHHWUcLP0SmwN7cS7QT0OIfBZkY+B+atxNHBr6EWejhwuqIaXNBBeiH1HvWYe8jKb2KYzlBnl3+MWCikSqcLAhsJsHSp6h3FmzYJft/HUMssF/H/WedZwaP8TR0ddIGPOPc32pNo6PvcVj5b+JJudfNrlcnkTJskQmq89IaSeTWS5c6qV1XQV+nwvLtjl5rYf+sVsZSyEEG+rKeP7xnexoqiLgcRUkUysBWXsYWXsQIZUjFuFKLmuPIKvbEPLSM7iSJFES8lIS8rJ3cz053czXzGd1dMPEMK38ORQCWRLIQuBzO/H7nDOELGzbxjAsMhkdt9uBJIkZ/15KCYgiSzTVlLC2uphkJkc8mSWT0zFNa/oaa6qCy6nicTrmjJjmG2gtZDHzu19/eOuC98OwzMlG1uWTD7fTwTee3Dnjs9f6jxNUfewr2bDs7QOYlpm/Xnfsr1NTF3Xcy0GJVs9j5X80b6BHEeoMd11ZKNR7tlHpaiFpRLFsA4fswiOHZhtxCYFHCbG7+Fk2BR8layWRkHEpfpySb9Y9UO/dzrPav57hRC+EoNy5lmeq/xk5K41TvjdRaiEEkgRlIR/7XBohj4s3zl7n8OUObvSPUBLw8o0Ht1FTPDvTN4W5uH8uNzujLISgrrmCP/hXX6L7kz2cPHSZk+9e4c//1UtcOd3Jb/1Pn8cXzL9/pMnn80u/c5B9T26eUyrYF3Tnn+PJxnLbBnuOvod5ihYWhYwxwunh/w2XUk6l5wAlzu24lXKke+BVU/BN+f6NTv7m9HmCLo2HmtZwf30NpV7P9GD43PbN/Ou33yOZyy2Y+APIkso6/4P41TJOj/2UruQZckvQxGbSDKfavYHt4V+j1rN5esa7VNjYJI1xzoy/PKfOu0BQ6WrlgdLfotLdes8UVH5ZoEgaznki+oaVI2slgF8s4h/Vxzg69npBsigh0ezbxlMV36REq1rSgy2EwKV42RX+HCFHCa8P/DWDmbmzRjY2bfELnI0cZl/x03NahK8WSrUqvlD5OzR6Ny6JSAghCDqKeaLi62StNBcnjhWsjW9PXGRCHyfsWJ5vh27lODb2FgPprnmXUYSD3UWPcrD0y3gWKERwJ4QQeJUA+4s/j1Py8POhv51X+cnC4sLEMdb5d7LWuwkhBEeO3cDrddJQV8xffvcDMln9VlmPbhKL3RpLM1mdq93DM7S2GyuL+JPfeJCNayruWdmIJC/+mRZCIORikFdO1k4IgeZQ0Bxzj9uWZfPxuW7kckEoMHvCOjgY5dWfnePJp7ZQV1/M0OAEP/vZWZ58cgsNa0qWtV9el4bXtfig1XAmQmdykN1F65ZUumjbNifGrtLoq6TcuTpKIpFcAmWFxiDbtjkf7SDk8NLgvXuT82rBIbsplhensy6EQJM9aPL8pme3QxYKPrUYH/M/A0IIJNOJUy9DdWh3fCcRcJRNT1qT2Swup2NVJ/q2bTMUTXC6rY8TN3rI5Aw21ZXzzYPb8budvHn2Oj84eoE/eeaBWevKcr7k0MgZ5On/pDGhaTFyMzqrbn8KmstB85Za1m6q5tFnd/PdP32V918+y4Gnt7JlX14tsrymCNWhEIsmKa0Oo84zBgA43Q4CYS8D3aNExxKEbpPwzCSzxKMpQiXLq0xQJDc+RwMj6TOMpE/hUsoode2i3L2PsNaKQw4ikFdlElCQtQbdTn5v7042VZbh02bPQgIujafXt+B3Lj7tIQmZStc6toe/wIQ+yFBmZhOSQJ5s9LOnPxGTpTKKUPGqRVQ4m2n07abWvRmnPHsWvCTY0Ju6xFBm7uagsKOah8v/gDLn2iX/ng0rqun+iwQJGa8SQiDNauTUrTQJI8K9tV1bHkzbnHamLYQadxNPVHx9yaT/dkhCZq13M4+Xf52Xb/4FUX3uRkDdznFq/B2afVspd66Op8Wd8Ckhnqx4fpqkLgcexc/DZc/Sl24vKKUZ0UcYyvQSUkuW8Uza9KZucD56dN7yIoFgc3APD5V+BfcKjDeq5GBH+CARfZijo6/NOybEjQhnI+9T416LU3bT0lSOqshkMvlo/+MPb0SbtI1Pp3UOH7l1L6ayOjdHb2UUVEXm6d2trK8v/8zVittWBEu/gKRuQ0h3j07atk0kkqStbQivx8napjLisTRtbUNoTpWmpnK6ukbIZgxUVaZlXQXJZJa2G4O43RpNzeXkcgafXB9A101aW6vIZHWGhiaorglj2zbDwzE6O0bweDTWtVZQVRWisipENpvPulRWhaiqCpHN6uRyBh3twzQ1l5NMZBkbi9OwpvBoljFzXI/3EcnFcMtONgUaUCWFtkQ/g5lxgqqXdf4aLNvmRrwPVVIYyUapcBXR6K1kID3Gu0Pn6EoOkTIy1HnKWOOtZCgzTmdyEN0yqPOUUesuRSAYyUZpS/SjWwbVrhKq3MVcmejm1f7jbA42UusuYVNwDX519qRnLBujPz1GwkhTqgUZzkZp9FZQpAXoTg7RlxrBozhp8dfgkZ3EjBRXJ3oQAuJGihItgGXb9KdH6UwOokkqLb4a/Kqbm+lRsqZOwkgTN9Ks89cQUD20J/oZzkRxSArr/LV4FCfXY728PnCCCmcRzelRWv21hB0++lKjdKeG8tv11+BX3AxmIiSMFBlTJ6onaPZVU6IF6E+P0ZUcwsam3lNGlesX2z/h7LluXnntY/74HzxGyTyE9OPzPbz+1gX+6A8epqhodn3/SsEGvvfBORKZLJ/b1MiW+kpCXtf0eLmvtZ4fHDl/G62/BbfPidvnpP3KTTKpHC6Phm1DT9sQ1893z0oFZNI5TMPE6XIgTZr0FVcEadpcw7G3L5LN3MrA16+roK6lnBPvXGb/U1tYu7EGWZGwbTB0g2xax+XNy9iqDoWWbXWcPnyVj49+QnVjKapDwTQtLp/uJDISWzbxd8rF7Cj5Z8RyHQymjjGUPklP4i16E2/jd6yhwr2fUtd9+Bx1KMK1oqVABYn/tqoKLJt5FXy8msbnN65b0g8blk5H4hQfjbwww11VEQ5q3JsodzWTtZLTDrdTNWxepYiwVkVALcerhAuUjSwNpm3Qk/x4TvUeCYUtoScpcy6/bjq7BDfTXwYIIQg7apCFMsvBNmuliOYGsG3rnta7LQcT+ijno0cKynb6lBAHS79EmTa3a99SIAmJJu9m9hY/yduDfzvv749lhzgX+YDHyp9b9eyULBT2FD1Bs28ruayBnjNwaAqqQyGX1TFyJs5JmUldNzF1E4dTRVFlchkdfbKExeXRptOypVoV24IHODT8IvMVf5q2QV+qjRbftiU3axuWzpnIeyQLlN2UOWt5sOSLK0L6p+CQNHaHH6UjcYm+9PxKJO3JSwxmeqhzt1BVma8FHhtPsG9PE7t21KOq+WubSuXo6hmdrkXOGSYTyVsNykGPkx3N1XM24H7asIw2crF/gxb89wsi/rmcyXvvXsXnc3Lt6gC5nEH/zQgANXVFZLM6r796nvv3rKW3Jz85brsxiMerce3aANmszuhoglzOoL6+GAQ4nSrDwzEi4wlKJqUBAU4cb8Prc1JXVzTv/gghOHO6k2DIQ1fnCIZhFiT+tm3zev9JOpIDrPPVENNTtPhruBzt5ueDZ9gQqONitIPe1DA7w818u+MNtoXWEnR4OTR0jt9rfBrLtkmbOWzsGaU+3ckhhjMRspbB0dHL/EHjF7Cw+KvOt6h0FRNQ3ThljQpXGN02yFr6dKnPfHd2T2qYV24eI+TwMp6LU+YM0ZUcZEOgnjcHTrHWW8nVWA834jf5QtUeXrn5EdFckip3MTfiN6l3l9OXGuGlvg+pcZcSycW5EuvmudqHuDjRyamx62wKNuCQFDJmDqfkoC3ej41NZ3KQ3tQIT1fej2GbZMxcPtI96TvTlRzipb4PqXOXETNSXJno5jdqD3It1sP7w+fZElyDJqukjSwxKcUPet6nylWMJqu4ZI0q19KzNZ8FJBKZvHHfvC66At0wuXT5JtGJ1KoSfwF87YGthLxunOrsd05l2M+3Htk1531WXl3E+h0NnD18jRf+3Zs0b60lEU3z0VsXprMBt+PyyQ5e/5ujtGyto6QqhKxIDPWO8/MfnaS2qZyatWXTz0S4xM+X/+5DfPv/+Bn/8X/+IXsf30RJZYhsJsfNzhFi40l+958/Q1F53mRtz+ObOPrGeX767Q9IJ7JUN5Yy2DvOqXcvTysCLes8CYEqvBQ5NxPWNtLo/3Ui2SsMpI4ykjnL1ch3aI/9iLBzM5XuAxQ7t+BUSpAo0B+xQBRkAkc7exhOJHlmUysOeeVKBUzboC1+jPeH/3KGg60medke/jzbw1/ALYfuiSvnnTDsLCPZrjm/86ph6r07lk1KDTtH8g7XvV8lFGt1OCQ3hjmT+NtYDKZvoAdzOO7a+PfpI1/bf4HBdPe8y0hIbAzspnEFIuB3QpFUtgYf4HrsHO3JS3MuY2FyaeIEO0IHKXXO1h1eSVQ6G9gZ/hy5pM5bPziObdk0rK+icUMVh358Cj1nUNlQQklliKOvf4w34MYbcHHg89t496VTJGN5abbf/EdP4JsssxBCYn3gPo6PvUWygBHaYKZ7MoO0tGdzMNPNJ/Hz85YUyUJhd/gRSuZosl0uQo5SNgX2MJjpmXcCF9cj3Ih/TI177XQvQzjk4cDe5hmlqk6nytNPbMHnzb+YLMsidxsZ8HuclAQXVmZwr2HbKWy7sNrR7cjldK5f66euvnj6HKxpLOX06Q6cLpWqqhCBoItNm2tQVZmurhGuXu2nri5fNqHrJgMDUR5/YhPlk9rdlmkRntT+tiybnp5RRkcTRKMpkonCCk+KItG8roJrV24yMhLnoYfXF1w+aWQ4E7nO7655inpP+fR9dXj4PA+WbmZP0Xp6UsN8p+NN1gfqcCsaT1bcR5kzREdigOFslO2hJpp91WiyyoHSzchCwrZt1ngrUSSFmJ7kXOQGUT3OzdQYLlnjN2oeRJNvlcRuCqzhsPMCO0JNrPVVFdznGncJ28NNHB25zAMlm/lg5ALHRq+wPlDHUxX3MZyJ8pcdb9CVHKQjMchvNTxOtbuYoUx+QvZxtI24np7OKJwav0ZMT2HbNmXOEE9V7J7eN90yaA3UMpKJEtNT9KZGAJtWfx3VrhI2BxvYGW6ZPGcXqHQV8+WaA0RzCf6i/XWGMvl3bJHm58nK+3DJ+Wcirqdxy05sbNb5a2nwlH+GBbBXCjbYoOvGLPOy1UDbwBhNFVAemj2B11SF0sDcEw+3z8k3/+QpfvBnP+foGxf44JVzeINudj+ygcd+Yzff+TevICu3xvhA2IOhm7z1g+Nk0npeicvtoHF9Nb/22wcor701UReSYM9jm3C5Nd78wXHe/ckZUvE0iqpQVB5g675mNNetQHJ5TRG/+y+e4aX/+h5v/+gE2FBeW8QX/s4Bzn5wnbHh2Iq9C4SQcCpFVCgHKHPfT8oYYjh9mpuJQwwkP2QgeRivWku5ew9VnocIOlqQxMIVNe9EQeJ/bWiElK7Pa5axFNi2zXCmg6OjL8wg/bJQ2Rp6it1Fv4FDXvmO/IUia6bIzmPW41dK8CrLq4G0bZuEPkpCH1vWdn6REXCUEdaqSKVm18T3p68S10cJa1WfaT8CyGu9X4ufK9jQ61fDbA0ewLHMhvP54FUC7Ag/RE/qk3n3I6qPcD1+bkXKjOaDjMyO8EF8SpCJWIKxwQm2P7iOhnWV3LjQi8uj8ehX7+cHf/Y2tmUjKzKf/zsH+Os/fZWBrlHikRT3PbKBj498MktFodhRQbFWSTJ1fd7fj+qj6FYWWV58VsO2ba7GzhSO9ms1rPPvXHAj72IgCYlm31aOj71FRB+Zcxkbm47kFfaYyelmbSEEhmHQdzPC6FiChvpi/D4XDoc83WgqCTEjuu9yqEtyXl8M8iZdKViskZs1DizcJ0VRZGrritm+o4FwyIPP7yKVyrJvXzOHDl2mqjpMPJah/2aE4eEYJSU+auuK2ba9nqIiL26Pxs2+CL09YyiyjGdyspTNGqTTOtmMzsULvTz86EaGhyamv8tmddLpHLpuYhgmuaxBOp3DMCyam8r53gsfUV0TJhwuHFU1J0UeNHlm1lq3TLTJZk9FyJNeFxYuWcOj5GUKFSFjTRklIWbMlWJGir/uepsmbxVhLU+8LNsma+m45NnKTYsZEVRJQREymqRO9+/oloFDKHmfDklGADnLwMbGIeU/d0gKCMga+cyCaVv4FBdPV96PR8kHeYIOL8ptqj8Xoh28P3ye7aEmVElhho/tzEPGsA1UISMhUISELATGpF9MQPWg3pbt9CpOnqs7yIVoB28PnGaNt4InK+5bsR6EewXTtMhmdSwbMlkj78ydzpG4wxgN2yaZynLqTCcOVcGprW7m17bhtdNX+ebBHXMS/7uhcX0Vf/xvn2N8KIauG7g9GuGyAKZhUddSQdltjraNG6v5J//hm8QiCbIZHex8vX+oxDet6X87FFVmx8F1rN/VQHQkTjajIysSHp+LQJF3xqRCkgSt2+v543/7NSLDMSzLJhD24A972bBrDbmMjnMFIv+3w7JNclaceK6biewnpMyhvBysku+Rap94kd7Ez2nwPUNj4FkcUmBJ7/SCd0CJ10PXeBTDslbsZWHYOS5H32E8O9MAoVirZ0voyU+V9EPeEXi+WltVcqIsUVbudgxkPiHxKxzxd8kBqlzruZm6OqvOf0Ifoit5hpCjYlHqH58GRrL99KUKG+TUe1pXtb5eEhJrPBsocVbTn+6YcxnTNrgeP8fO0EO4lNVJ8YYcpTR7t+Zl5IIeHvvq/Zz94Bp9bcOUVoVwuh04NAVJFhiGSUll/jNJlvGHPURH45z78Dpb9jXP2rZDclLqrKa7APHPmmkyZhrnApvmbkfSjNGWuFBQhrXZtw2/Gpr3++Ui6Cih3Fk3L/EHGM32M5YdnCb+hmHyzvtXee/wVWKxDF//6v1UVYU4euwGX3lmB263hqrI+G9rGM0axoq4cxaEHSU78b9gm4szcLKtGLa9MLM0yGc39h9o5vy5blSHwv33r6W3d4zOjhHWrCmlpNiHokhcu9qPx6OxeUstVdVhzp3torNTYu/eZvbtb+bEiTYGBqLcv6eJgf7odB9AuMjL9p0NXLrYR3VNEYGgm+vXBkincty4MUhxsZexsQSpVJa2G0OUlPqpqAji9mg0NpbdtYHSq7qochXz7tA5toeaMGyTNd4KdoSbODZ6BU1SuRLrptZdilueyoDO3mbA4WZwbJzrsV6KtQCWbTGRS1LrKSVl5MhZ+ffZWl8lR0YvcnLsGkGHF01SWeOtQJFkNEnherwXgaDKXYxTvst77rbd2BRs4ONIOyXOIH2pEYIOL7XuUoocfo6OXmaNt4L2xADlzjDrA3V0p4YocvhwyRqyJE9Pcu48stFsDKfsoNwZpi89Mt3YKQsJl6zRHu8nqHqpcIVp8ddwaPAsZyI3GMvGUCWFUi1EX2p01naTZobOxCAlWpB6Txk306PotonCZ/t9cycGhyb49nc/ZHQ0zngkychInP/r/359hq8HTLoDp7KMDMfZu2ctJautQS+gIuQno+d7kJZCTN1eJ27vzKy/6oD6lpnN3EIIvAEX3sDCeaMQYs7tz7es1++aHYyqCM6zxuJh2zaWnSNp3GQodZL+1PtEszcQQiKsbaQl8A1KXDsAwVjmAp3xl/lk4gVkSaMp8DUEixe0KUj8t9dUcravn59euMq6suJp8q/KMg3hIA5l8TPHpBGhOzk7pV7mbJwhRfVpYco5dS5YmHfVFS8E27bJWSmuRN9dsEPpLyMkIdHou5/LE4dmTYBMW+dy9BD1nh2EHasXoV4ubNumL91OsoCxm0PSWOvdjENa3bIlrxJgrXfTvMQf8qUsY7khqleJ+Ne4m6atyhMTaTqv3kRzqug5kzUbqnjnRyd598VThIr9FJUFGNLz111zqsQiKWRVRpIlLh5ro6axDPdtOslCCIodFeSpwdykVbdzC3bYvROj2X5Gsjfn/d4puVnjXY+0isTAKbkpdVZzLX523rEhZSYZzvZR625GCEEkmuL0mU6+/tX7OXm6E9Oy8Ho0bvZHSad13G4Nl6ZSUeTncne+OTqWzBBNpCmdw7xnpWDbBpZ+A+wYSIsZ0xc3tgohqK8vob7+Vn12uMjLtm31AKTTOYqKfTz2xCa8ky95j0ejtnZmnf6vPbNj+u+lpX62bL01Ua+tLYL7bi1bU1PE1m23lFyqa4rYsjX/71zW4NTJDjxux4IUfmQh8Vzd5zg8fJ7jY1cJqB7qPGU8ULIJgeDU+HWCDi9fqTmAhMTWYOO0Dn5roI4iR57Atfhq6EmOcHL8GttCa1nvr+Oxip1ciHZS7gzxSNkO/KqHIs3PV6oPcC7SjoVFq7+WBipQhMwTFfdxYuwaH5vthDX/nMQ/7PCxxltBUPXS5K0ioLpp8dWwK9yCImTOR9rxKE6+XH2AsMPHM9V7+WD4IjfiN9lbvIE6Txktvhp0y+DiRCe2Dev8eTfmKncJWTM3g6TfV9RC0khzNnKDGncp1a4SFCEjC4mDpVs4MnKJs5EbPKhsZoO/npxpcHmiC6es8uXq/fhVN+WucD7rcNt7xLJtulNDRHIJvIqTx8t34ZSWpwb4aSAU9PDggRbOX+jh/IX5nWSFEBSFvOzeuYZnvrAdt2tl+yJn/R6wqa6cHx45T+/oRD7wMHn6tzVUEfJ+usHdzwps2yJnxYhkr9KfPMxQ+gQZYxSXUkqt93GqvJ8jpK1DEbek4t1KBSHnek4N/yv6k4ep830ep7z4KpSCzP1C/yCfDI9ytrcfn/OWqk+xx83/+vhDlPkW//JIGONk5qjVFUJ8Jko7VMk1r7562oiRNVN5N+ElwMbik/hRelNz12P/KqHU2UCdZxtXJt6dNQkcynRwdvxl9pc+nzfZ+gyS/5yVpS/dVlCdySMHqHavroEW5OvP69wtOCTnvOQ3bSbpTd2g2t244r8vIU9Kd+ZJicur0bihGsMwJ6P9Go8/dz/xaIrSyTRt3WTk5qnn93H9XDfrttezafdaXvlvH5JKZGYQfwCvEixA+/PqSktRyrJtm+7kdbLm/JMGnxpa1TIpmHw5O8pRhDJvyZZp64xmBzBtM7+cbqIoMjVVYc5fzEfXzWlN+vw6bs1Ba10p759vxzAtJpIZrvQM0VRdvLr3pVBRXL+J4v7qglcxsx+gx/9sxXZB0xSefGoLrlUmOlOQZEF1dZj166sW/Jthh48vVe+f9fkj5dtnffZM9b7pvz9Wfmuy4lacPFO9d8ayB0o2caBk06xtbAquYVNwzazPW/w1tEyS8PlQ5ymjzpMvOahy5yd0Fa78JGpfyUb2lWycsXyNu5Sv1z88aztbQ2vZGlo747Mtc+xTyOHjyzUH7rovU9hV1MKuopYZn7X6Z2db/ap7znP+iwa328GDB1o4sK+J9w5f42++d4x//A8fp/SOiL6AfDmLW1uS18RScHM8X/9+9Gon6m39ofUlof9B/CeRMcc4O/pvGE1/jI1J0NFMo//XKXfvw6tWIc0hTZ/3d6gkpK1jMHUcq4CoSCEUJP4H1tSzqWK2/rIiSYQXodt/O2zbmrOBrj91jeFMO6XOxlWpo10oHJITr1o8Z4PvhD7EWLYbrxJe9EvTsi16U5c4NvL9eZ2Af5XgkFxsDj5OT/IC8TscnG1MLk28g0v2syP8zIpItdq2jYVJ2owhEDNcEpeCrJViKDN/lAWgWKvAt8zfWQjyjthVeJUA47m5CaxpG/Sm29htmyv+fGmyk3LnrSio6lCoukPNJFwWIFx2y8xmShWhqCxA6456jr99iY/ePM+m3Y2ESmanoh1y4VpK27awlmCMZ2HSl24vmMkLO0rx3AM3ab8aQi5A/AEiuWEMO4eCgt/nxO128NY7lxgYjGKaFh2dI5SW+PC48+dLkgT3ravlpQ8vcXN0gkzO4N1zbezf2ECRv7Bj9HIghIqQq5Hkws2it8OSKmGZPiy3Q5Kkguoltm1jWTaGaWFaFraVfzMJIZCEQJYFiiwvWPNcUWSqa1ZHB38+InwlvQAAIABJREFU3H4MlmVh2TZT8768kVLeBGyqz+OzGESB/HGYlo05dS3mOA5FlvLKLp/RY7gTM+6vyQn57ffX1PEsV1NfkiSqq0IUF3sJBFyEQ59+8/5z+7dg2Ztnfa6uoEjMYmHZk/eXaWFZ9m3XI98LJUn550SSxD25xwwrRdoYpty9j2rv5yhybkaTFiZqIwsNpxxGWqJpZcG1Qm4XQZdzejCRV+CEuJUADslNypzZ2Dma7eKtgf/ExuAjVDib8ChhHJILIeS75AEEQkhIyMhCXrbijixUKl0tdCZOz/oua6X4OPIGZa4mXLKXhbRF2baNaet0Js9yeOg7RPX53VZ/1VDhWsem4GOcHPvRLGlP3cpwcuxF4sYI28PPUOSoRRbKou6/PNk3yJopxnN99CQv0JU8y8bAI2wOPb6sfY8b0YKGXZAn/s571LPiU4ME1KLCeve5ITJmCreysiTWp4TwKUureRRCEC4N8NQ39hVc7m5lNvbkf4tF2kgQyc1fVw9QrFXek2CES/be1fAsbkSnMxsej8YXP7+NV944z+DQBKOjcdauLeOZp7dN6/oDrK0q5tEdTbxw6Cy6YXH6ei8vfXiRrz+yHbe2fGm4OyGEC8X5DJK6OJdWIbkRwstClJlsO0+ostnZWR5FkdAcc48VU+tFJ1J09I7R1j1C70CE4bE4qXQO3TDRHAo+j5OKUj91VUWsrSumriqcj5iucmP0QmDZNtmsztBonJ7+cbr6xukfnmBkPEE6nSObM5BlCaemEgq4KS/xUVcVprYyTE1FCK9bu2fkpuBxWBbpjM7gSIzu/nF6bo4zMBJjNJIklc6R0w2UyeMIBz1UlPqpryqivjpMZWkQtyt/767Gcdi2jW6Y5HKzAwKqKuNQ5zZWmjLKGosmae8Zpb1nhN7+CKORBKl03pFZ0xT8XieVpQHqq4torCumpiKEx6UteRJQXRXmuWd3E/AvzCV9tWFYFpqizDierG5MjtH35r6zJ4l+PJmldyBC181xevvHGRyNMxFLk8nqmJaFQ1XwuB2UhH1UleWvSV1VmJKwd95xZCXgUkq5r/R/x6NUIEnagiteBBJr/V+lwfclHHLg7ivMgYLE37JtusYjfNTZg23bfHHTetKGQSqXozYYXNJN6lWKqHA1E9X7Z3xuYzOUucHIYAcu2Y8me5CFWpDIS0gokoZL9uFXSwk7aihx1lPkqEFbwEt0Lggkatybcco/m6MkyaYjcZKPRl5gV9GX8SpFcxKCqSYkw84RzfVzeeJdLkV/Tsq8pRoiC+VX1sRrCoqUV3IazXZxI34c+46oq2FnuRR9h77UFZp9e6n3bqfIUYND9uT1m6ev79Ts3Z7sik8R10eJ6oMMZ9q4mbpGJNdHyowhIdHiX36aN5IbIVegPEQWCkG15J45OytCochRRmfyyrzLRPUxUmZ8xYm/R/HjUlY3yrRaHCVpxonphRvt3bKPnJUt6NWwErBsk7u9FFNGfHK5Sbv62mJ+/1sHiScyCMDrc6IqM8ckTVX49Qe30DEwztFLnWRyBi8cOksmp/OVBzZTFvKtSFBnCkLyoXr/7qLXk5S1qP5/ipDLF7T8sbMd/NWPj8/6fPO6Kv7wGwfQHLeyB1MkoLs/wuETN/jwVBs3h6IkU/NnVyAfCQz4XTTWFvPQnmb2bl9Dcci7qs6n88GyLCITKc5e7uXI6XautA0yFkmSzd39PSJLgoDPRX11EXu2N7B3+xqqyoMo9ziCPhXZHxqNceZiDx+d6eBG9wjj0SQ5/e49HooiEQ64WVtXyr6da9i1uY6yYv+K3r9TeOuDK7z01vlZn+/f2cg3v3Qfjts06qcmCh09o7x3/AbHznbQPzxBOlN4zJAlQTDgpqWhjIf2NLN7Sz2hgHvR95fHo7Fje/2i1lkt2MB33jnFr+/dTFnwVsbtB0fOs6+1nsby+b0wVuT3bZucbtLZO8axcx0cP9fFzcEIE4nMgoQNnJpKSdjLppZKDuxay5Z1Vfi8zhV/5hXJhd/RsOj1hJBwqwsbI+f97UJfdo9H+beHjjCRzmBYJo+2rKVrPMobVz7hHz+0j4Br8U2LDsnF5tDj9KevMTFH9NvCJGlGSJqRRW9bIHDKfkq0eloDB2n03YdnkX4AQgjKnGup82zleuzDWd8bdo5z468ykL5Os28/Za5GPEoIReRnbLqdJW1MENUH6U1epDd1kZg+NCMa6ZFDbAk9ybnIq6QLNIf+KsCjhjhQ+jwZK0Fv8uKsxkYbm0juJifGfsT56Jv4lGICjnJ8ShEOyY0kJEzbRLfSpM04SWOclDFBxoyTtuJYqzC5srFJGBMFSzJkoRBQ713aXxIyQUfhhsK0kSA9j1TtcuCWfahideRKVxspM37Xc3Jy/Odcnjix6vui2zkyd9kXwzYw7ZkESVXlu6b3K8I+/uGXD+BQZD640EE8leVv3jnLqWu9fG57E1vXVlJdHMDjdCBJ0rImWrIkLckgTEghFO3BBS8/Ec/wSefwnN/F4hlKivLE37ZtJuJp3v7wGj95+2N6B6LTwZm7wbJtIhMpTl/s4eOrfbx5+Apf/fwO9mxrQHOoqzYhvR1TqixHTrfz8jsXudY+uCCSfDtMy2Z8IsX4RIqPr/bxs3cu8vgDrTz54AZKi3z3ZCJj2TZjkSTvfnSdNz+4Qkfv6KI15Q3DYngswfBYgpMXulhTU8zTBzfw0J5mwkHPipL/sWhqzvvL73WSSuemib9t24xGkrz+/iVePXSJgZEJFnh7YVr5c/JRpIMzl3rYvK6Krz69ne0ba3GoyoLvr6myok8zkzNVOmOYFjfHY2RyOoZpTX/XPRJhY93yCOvdYFoW3X3jvPLuRQ6fuMHwWHzB12IKmaxO70CE3oEI75+4wZZ1VXz58a1s21CDU1v5DED+vJl57jPfzgqxIsZdUyhI/I939VDu8/JHB3bzZx/kIyvlPi83J2Ikc7klEX8hBNXujTxQ+lt8NPICY7k+FmrYcjfY2KTNCXpS57mZvsonsaPsKXmOSlfroqL/DsnFjvAzDKY/YUKfXTphYdKfvkZ/+jqa5J7OTkC+llq30mTMxJzqHC7Zz/7S52ny7aE9cepXnvgL8k6+D5f9Ae8Pf5vuxMdYzE3WM2acjBmf4fT8qcDOE8ZCGRtZKLiVVZZNuw0SMt67pP1M2yRRQKt+qfAoK+dke68R16N3Vdia0MeY+Iz4bthYi84U5gyTeCpDRte5r7WWT/pG6BmOYpgWl7uHuNIzTNDjpMjvpjjgIeRz43Qos/TeF4rdrbU8smO2LOv0Mdg2+THfIp/hWNmo88h4gkgsTUmRD9u26R+e4Ds/Osa7H11fNGG+HYZhcfF6P103x/nSo1t47gs78Hudq3rvW5ZNV98Y//2nJ/ngZBuZ7PKzTpZl0zsQ4a9+fJxTF7r57a/sYeuG6lWN/uuGyfmrN3nhpyc5d6WvgMPswmEYFp90DtPVN8aJ891844u72NBUgaKsblne4EiMeDJL0O/Gsmw6+8b49g8/4qMz7ejLMMfK5gxOXeimo2eU33h6O198dAueObTo58LoWIL3D19jy+YamptWl1wXQt9olEMX22kfHOPHxy4ScOc5Yjqn0zMSpdi3epnhdCbHe8dv8L2fnaKrb2zRhH8upNI5jp3r5GrbIE89tJFnn9pGSXjlBEdMO0ckc4XhzGkyxsi8vWZOOUxz4Bs45JXhFAWJfzybo8zvzRP8yePMD9lLP6N5zVKTEq2etb49xCOvkrNSS97efDDtHJ3J08SMYR4u+33qPFsXXP8vhKDC1cLekq9zeOjbM0p0ZsImayXJWguLonqVIvaVfIONwUcQSJQ6GxjK3FjgEf3yQghBsVbHY+V/nxNjP+bqxGGyVuLT3q15YdkmaaPw/klIuOR7W2+pyW4E0rxE1sYiaazsRDOfZfts1JUuBQlj4hdKWtfGLug3MBdeP36V7793jmgiTSyZJavPnDjYtk0kkSaSSNPWv/wJjlNT5yT+tm2DHcPSr2IZ7dhWBISCJJcjKesQSgMCx7LrulLpHDeHojTVl3BzaIL/8Ffvcfxc57Th1XIRT2T4/iunicRS/P7X9hMKrM79b1oWZy/18p//+2FudBXuQ1nS9k2L81dv8n/++Vt869m9PLp/3by168tBJqvz8yPX+O6LxxkcWflAV043+ehsB70D43zr2b08uLsJzbF6JZaxRIah0RjV5UHae0b4D3/1Hh9fvbngLNLdMBZN8t0XjxONp3n+S7vxL0BvvrNrhBe+fwyXS/1UiX/Q46KmKIBDkaezEAjwuTT+/lN7qSpa+WCYbdvE4hm+/+ppXnrzY5LpwuV7S0E0nuaHr52hd2CcP/jNA9RVLV7g5U7Yts1A8gjnx/4fMsYoklAwbR2wkYQDGxPL1nHKRZS6dq3oe6rg09EQDvHm1Ru0lY5jmBajyRRHO7op83rxaYtL7eebXHMMZm5wZeJ9upLniOvDq17nPpbt4fDwd3m66k8o1mpYaGOJLBRa/QeRkPho9HtEcwNLnvBIyJQ617Cv5Bs0eHcgifxDUeVez8Xo20va5i8bhBAEHOUcLP0WVa5WzkVeYzjTPqvpd8nbR6BKLkqdayhyLM9Qy8K6a723JCTUVXLrnQtCCFRJRRYyxjzqNjY2OWvlFaXkFVRiudfIWukViQx9ltE3OsGNvtFPdR9s28Y2rpNL/CfM7BGwE9w+Fgu5HMX1FVTPbyHE8sxxDNOiq3eM8XVV/NfvH+H4x/OTfknKK8ZMNdZZto1lWXe9J3TD5I33L6M5FH7/a/tXXBvdsmyOn+vk333nXQaGF0aWZVnKZ2kmdW+nVEzuhqHROP/fCx9gmCZPHdwwo3Z9uchkdV5+5wL/7cXjTMQX5rMhT14TbrsmCzmO3oEof/bXh0llcjz54IZVI//ZnEFPf4TK0gB//sKHBUn/XPfXQo4lndH5yVvncTpUvv7MLlzOwmNsKpnDtCzq6j49LyQhBH63k4c3ryWaTPPA+jWUBlfPLwTylTGxRIb/8rdHeOP9ywvK6EmTKlFTxN0mP0G5W/2/YVocOd1BJmvwx7/90LLJv24l6I6/hmllWBf6bUpdu+iI/YSMOUJL8HkyZoTu+Ksokov1od/DIS2tkXcuFHwydtfXcHlwiD/74BgDsTj/4rV3CLtd/OH+3Xi1hQ90tm2TNMb5OPI6F6JvkTBmRpUEAkU4cCkBXLIfVWgFo/NTTZyGnSNrJsmYcQw7O+8kYjjTzseR1zhY+rsoizDqUCSV1sBBwloN58ZfoTN5hrQRW6CJl5is8y6jybeXzaEnCKrlM26UEq0BTfIsOGPwqwCH7GZ94HPUeDbTHj/JJ/GjDGc6yJqJRZqnCSRkVEkj6Kig3NlMo28XFa4W3PLyiIWNjXHXCatAvseytBLyvOZzedgrNpG6HXMdZ0yPkbUylGilc6yRx5T85lKa8FcKhpVjpUoN/wfmh22NkIv/KWbuBLJjF5JjJ0IqAXRsoxszexQ9+RcgVFTP7yOW0RRvmhbXOobIvn6WD07emPVC1xwKFSV+6qrDVJUFKQ55cTpVbMsmlshwcyhKR88oXTfHSRWIHhqmxavvXqSqLMhXnti6Yhrptm1z7kov//6v3itI+iUh8Ho0aivD1FaGqCgNEPA5cagKumESjaUZGJ6g++Y4Pf3jJFLZeSc00Viab//wIwI+Fw/sWrsix6IbJm8evnJX0i8EuF0OaspD1FaFqSoLEPS7cagyumEyEc/kj6N/nN7+CIlkdt6J3Hg0ybd/+BGqIvPYgdZZje4rgVzO4GrbIB29o5y62DOL9Ls0lcqyAHVVYSrLghQF3WiaimVZTMQy9A1GaO8ZpXcgUrD5N5PVefHNc1SWBXjigVakAopSXq+GpqmYyyg1Wkk8vaMVWZZmlXRNT05XCOlMju++eJzX37+MXoD0q4pMaZGXuqoiqiuClIZ9+cm6yG9jdDxB70CUrr4xhsbi827Ltm1OX+zmv37/CH/8rc8tq+xHt2LE9S4qPQ/QEvwmiuRmMHUcw0oR1jahSl5C2jrODP9rBlIfsjbwVcQS5TvvRMGt+J0av7/vPh5uXktvdAK3qtJSWkyZf+EHa9s2cWOED4a/y7XYh3c0Wwq8ShFrvDtp9N1H2FGNJrkn1Xzm374N2JOGPYadJW3EGcq20xE/SU/yAvodtu82Fu3xE2wJPkmJs35B+z0FSciUO5t4tOKPGMl20pU4y0D6OjF9hLQZQ7ey2FhIQkYWKk7Jg1sJEnJUUuVeT7V7IwG1bJb6jxCCoKOcTcHHSN7mXht0VC6YCBU5amj1PwjYZE0zrw0sSfjV2WTLsPL6yA75Viq30rUO08qnlqbgkFy47qgj000T27ZRb1tXFgp1nq1479Cp9yjhZUe6hRD41RK2hp6iNfAAY9k+bqYuM5RpJ6YPkzZjZK0kpqVjYeYpvlBRJSea7MEtB/CrJRRr9ZQ51xJyVOBWgjOugW4ZM8h7PiOgLJCs29h30YzPR3jubd17XtqOeXmsbbMkrfulYCDTz1BmEH84gCIpSEjodt7CXZEUBIL2RBuSkKhx16IKddKEy5xe/p5oKdvGLz3t97ocqx55ux0+1+zn39IvYOrnUN1fQ/X+A4TkZ+r5sG0LxdVGNvbPMTNvoLi+jJArlrUPpy92c+pC94yaa4cqs6mliqcObmBzaxXFIc+c0W3LsojG0lz6ZICX37nA2Us980YSM1mDv331NBuaK1i/tnwF0v/QNxjl//3vH9A/NH8/TlHQwwP3reXhvS3U1xQR8LrmbNC1LIuJeIa27hHePHyZo2c6iCfnzvqNR1P85Q8+oqosSFN9ybKOxbZtzlzs4a9/cqIg6fd5ndy/tZ7HDrTS3FBK0O+eszHctCxi8QwdvaMc+ug6R063MxaZO2A2Hk3x3RePUxL2snNT3Yo3LtvAe8c/mdaEn4JLU9m2oYYnH1zPhuYKwkHPnBMP07QYjyanm6wvXu+ft+chNllW1txQSmPt/KZ7DQ0lNNQXc+p0J62tlYtqDF4NJLM5fn7+BkPR+IxJ2rN7N1NTvLzA2xQM0+K19y7xyjsX5yXqiiLR0lDG4w+0snNTHWXFvjklOqeUgIZGY5w8383r71+mrXtkzuyMbcPRMx1UlQf51rN775qNmQ+WrWNYKbxqLbLIl3MpkhPTTmNjIoTAp9YR0loZTH1Ene/zaEuU77wTBYl/X3QCgWBdWTEbK0qXNBDkrDQnRn/MtYkPZkRsBYIK1zoOlDxPlXvDoiLxdyLkgApXC63+Bzg9/jInR2frwieMcfrTVxdN/GGyjEJoVLrWUeFsyU82zDg5K41p6dhYCCEho6BKGg7Zg1Py3LWnwCX7+Vz57y16f6bQ6LuPRl/eT/4vT56mJhjgsaa1c16nCwODnOvv5+/s2I4y+f3W0FNsDT1119851NbOYCLB17dumTbgcEgu9pZ8bcn7vhAIIXDKPqrcrVS5WzFtg6yZJGel0K0sFia2bSEQkxMvB6qkoUluVMlZ8Py/P3KU94ePTJM+t+zk2ZpnaPGtnXed6f1aQDTfXsDkYKXx/7P33lGSned55++7sXLonOPkPJiETORAAowgKYqSSFEyZcmWvfLx0VrWymfX67T2WXt1JNtykMQggkkEA0iQiEQGBjMYTM6hc+7q6q5cN3z7R3X3TE9XVVcnkJT5nIOD6bq36t6qm97wvM/jSrcsz1QI8b7Ji0okV5KXSdgJInqUfdF9vDv1LnFriiZPEx3+Lt6YfB1NqMTyMXaFd/NO7G2SdpIas4Z90f2IJbT71wKVJNmG4lmRjr+UNuAi0NdMj9Sj+FDKdHXcWY6ouEEB4sO3b+fuXYudUdcLYf9iTrJ0RgoJuucxxE0tayEU0LpRzQewU38JbpLVHvrsTfr+oYCHJx7dy8ce2k00XN64TFEUqiJ+7jrQza4tTfztj9/jW88cKyn9OTqR4BtPH+Wf/94jKw4Cru+3xZe+8zYXrhX34xACdmxq4rc+dTt7tragaWrZU0tRFKJhHwd2tbNzcxMHD1/iL7/9FoMjxT1Iegcm+cbTR/mD37qPYJHjWCmGx2b48lOHGZ24WRL7Ojpbq/mNjx3izv3deD3li33q7PfYF25jx6Ym7j6wgS9/521OXxouStEYGp3mS985TFN9hOb68JoXEW6u1FdH/Hzm8f188N7tSw58q6pCbXWQB+7Ywu6tLXz96aM8/eKpktX/noFJvvfcCX7/c/eUpC+FQ14+86lDPPnNw3zzW4c5eKCLYNCzqLouBFRVBTDWcQYC4OuvHefaaIx93c1oyvWL2dDWZrtSSs5cHOJrPzhCpsTAe8Bn8pEHd/GJR/ZQV11egEIIgWlo814Xt+7t5G++e5jnXj9fVC7Xdlye+ekZdm1p5o593StKLoVQUYSB5aYKMSQKhhIm58Sx3BSmGkGgoqsBstkJ3DXs1pc9Cm9e6+P7p85xS2sTd3d3srmuhoBhIJTKrAaklPSmjnNu5uVFNI2o0cy99b9Nk3frmlyUc0HinugH6UsdZyB9ZsFyR9qMZq8gpVzV9gpJgAddWXhTnJP0OtM7xo6NEbyB6w/n2HSabM6iqS686D22466JmsJQIoG/DP1qQ3U19YEA6gq2E8tkGEkkfuaV0YJSThgfq896p/JxrqZ65//2qz4ydqai9851GMpBIt93nwZXOksOfmrvIx8/alRxb+39PD38PXaGd1Fn1qMrOqdnTrMncgubg1uI6hE2B7fSk+7hauoq20LbODl9gk3BLYT1teM0loImDMo0SdCFwSMNn6Xdv3nZnx1L/5S0dZWm0K+jiJVxwKV0kTKPMnu/0YROWC+tgx1Lv0jeGaMh+JlCwgFUBX1UBX+2A9hC8QMGlEw8RSHREz5Y4W9VCj6PwWc/eoBPPnrLsnjfQggiIR+/9tGDaJrKV546XFIz//DxHo6f6+fWPV0rzvGklLzx7hVeOXypJCVn/852/slv3Udr4/JkqqEwdP3gnVsJBjz8v//zxaKDthJ4/egV7j60kbsPbFhRQGNZDk+/dIqzl4dLrrOps45//Pl72bWlednbMA2NQ3s6qK8J8mdffoUjJ3uLUn/OXBzi6RdP8ptP3IbHXL/7Xjjo5Qufuo0P3bMDXa88YxVCUFcd5AufvA1VUfjbn7xXtHLtupLXjlzmwTu3sGtLS9Hz69z5Yb71t0fo75/kveO9fP/p9/B6FydThqHxz//3x+juKk3BXC2khIHJaX77wYNsa61fl20kUzm+9v0jjE8WF9nw+wy+8Mnb+MiDu5Z97IUQtDRE+L1fuxtVU/nhS6eKys7GExmeevY42zcWujvLhaYE8OmNTOcvY7tpDDVEwGjDchOMZQ7jUR/FdjMk8r0ItIoNviradrmFj2zdSHM4xEuXrvKnL79JxOfh7u4ODrW30hIJLelimHczXJh5bZERliI0tobvodG7ec0zca8aosW3Y1HgD5KUPYUjLbQKHixzLR5XFrRxpVtIGCzbQVEEuqZi2y6aphTsnwFdUzh1fohoyEfQXz/fPrrcM8b4VJLaaGD+BmXoakHt4I0LPHD7ZkxTWzNXyHgmw5nRMXY21BMwTY4PDTOWSlHn99MYCiIo0HfOjI4R9XrpjcdxpWR7XR01/kKAMJ5KcW5sHI+mkcyvPS/8FxmKUDDV8tUwKR1ybmWJxFpAysLgritL8xwFAvN9chIWCKrNGkzVREGlP9PHleRlNgQ2zndCBODOhtyWm8ereokaVdxf9yC+90kpyFBMylGyXFwiejXN3uVXzDX7PZKYNHk6UJWVVU9T+Yskcqep8320ouTBcibJ2SPwc6ZUpGg7EGodbv4wirYRuC5TKKVEuuM41ruoxoFZ7v8abVcI7j60gY89tGfFw54eU+eJR/bSOxjjudfOFV0nlcnzzE/PsG9Wf30lKCiHHCtZ+e1sreb3P/eBFQX9c1AUwaHdHXzm8f3816+9uqgzAoXv8sMXT3HL9taKFGVuhJSSy33jPP/6uZIa/fU1QX7nV+9cUdA/ByEEHS3V/P3P3kV8Js2FInr7tuPy3GvnuW1vF7u3Nq8LdVBTFR65exuP3L1tWUH/jQj6PfzK4/voG4rxxrtXi64Tm07zwhsX2LaxsSh9yHVdNE1l44YGNpZpWuuGuq6KR1DoKnTVV3F1NEZHXRXqDcdYV9VVU6+klLxx7CpHT/UVXa6pCh9/eM+Kgv4bEQp6+Y2PHaR3MMZ7Z/qLrnPy/BBHT/XxwB1blv29DCVEtbmT4fSrZJxxDDVEWO/CrzdzNvbfGc+8S95NMJE5TlvgYTRl7aRQl+D4e7ijq51DHa0MTyd4p3eA5y9c5pmzF/l3jz9EQ6i8A2jKjjGSvbjoda8SpNO/b0Xt86WgoBbluEPBfKtcYHQjLvaMkc1ZXOmbYOfmJq71TxLwm1zuHcfnMbjn0EZeO3qFh+/cQu/QFDPJLId2t1NTdZ1Le/zcIMfPDZBM5ehsqebJp4+i6yqZbJ6H79rKxWtjPPf6OZLpHPfftpn6mtU5qgogns3yF4ffwafr7G4q8GRT+TyvXeshmc+zs6EeRVXJWDb/+a238eg6bZEw/fFpnjUu8cf33UPOtvn3r7yGoapEvF5ODg+ztW79KgRLwXJTZOwJgnoL4n0emC0GgYJPDZaVznSkS8ZZe5naUpBIMm6qrPKUEAoBbf2r6DDrqi3UwuC+Uvi/Ix1i+evzLNVGDReS5zEVkwZPI6Z6lql8DK/qfd8Go/1aqBAQlPjZHOmQc7MVdQqllFjuJKn8OQQ6tnu94OFKm4x1law9gKYE8RtbUEUAyxkn74yDUMjZQ+hKFX5jK4rQSFtXGEv9gKzVh6oE0JQQYXP/fPV/KThummT+NB6tFUWYZO0hFKGTtQfRlBABYxuq4kNKh4zdR9bqQRFe/MYWFGGSss7j17dgu9NkrB5myqIeAAAgAElEQVSC5m5sdxrbncanbyxJpZMyj2tfgPlZKwHSRTVuw0r9DdKZRDH2IUQAcJDuGHb2OaTdhx7+lyBWTjG5GTVVAT7xyB78q1Td8fsMPv3YPt4721+yynj87AC9gzE2diz/Ximl5K1j10pSfExD49c+coDO1tI870qhqgoP3LGFV9+5xLuniwc0py8OceHqKPt3ti1re7bj8vxr5xgrQfHRNZUPP7CLfdvbVh0ACiHY0F7Lpx/fz3/6yxeLzi6MxxI899o5NnfV4fWsbScJoLUxyuP371h1R6E64ueJR/dy9vIIU9OLnxuuKzlyspfhsWnamhYbQ+7c0cr2bc0VbWuthtDLwdBU/sdzh3nxxCVCvuvX8+fv209n/eqMLROpHD966XTJ7tu2jQ184pGVJ/o3oq46yMcf3s3Fa6NFqX7ZnMXLhy9x+y1dBPzLm21UhEpH8DHqvPvxaYVYzVSjbAr/Oqdif85g6mVAUGVupyv88fk5gLVA2V+mMEQryVk2sXSa8WSKnGMT9pgVVaenrVEyzuIbgF+LEijTrl4tSrVEFCrXKDZ0lcu948RnMvQPTzEyPkO6P88nH93LucujHDnVy/hkAtuRpDJ5ZpILq7uO43L4eA+P37+DkxeGyOVtRidm+MQjexkcjXPuyiiHdndw5tIwn3ls35pUIzKWzV+8/Q4B0+AL+/fh0wutvrs6O8jaNs9cWJiEpS2bhzZu5BM7ttMbj/N/vvAio4kk12IxUpbFn9x/L37D4N/89OVl7cfNTnQFTVoXV1ooQgMEUjqF0oCUKEKbVWoqvjxtjzOYep0NoY+gKV4E6oLPK/ztFIJe6aIIvWLPhpVACIFfC6EJHUsWH5RzpE3SLs6jXQ+40iFhlXe71oSG/30yFWvxtdLgaUQg+EDtvQS0IFE9iiIUtoW2AdDp78Kv+dGETlALcm/t/UzlYwS0wJq2NcshpFWhoJTRi5Ik7DgSueQ+2W6c3qn/iMRBV6pJ5s/g0dsBSSz9EuOpp/ForeSdCUytkZbw3yOZP0f/9H/BZ2xEFQGS+TM0Bn+FKu995OwhslYvlhsna/Whq9VIszJFMcfNMJb6Lhmrj9bwF0lbl+mZ+o/49C5UJUwqf45a/6PUB55gOvsOw4knMbUmHDdBLPMi9YFPMTzzdVojf5+Z7LsMJ55kc81/IJE/Sd4Zx6dvLL15d5r89J/g2j037NLssLs7g5X6L5AyQZiABJkGbBBhrOSfYYT+FUJrqeB7LvErCNi/s40N7avvIAgh6G6r4e4DG/jOT44XXWdqJsPRU31saF/+YGw2Z/OTV86WrJLv3NzEHfu710wRJRz0cP8dWzh5YagovSSRyvH28Wvs2dqyrEr2yPgMbx/vwSkhjdjdXsNDd25ZcXX8ZiiK4La9nby6s42X317siSMlHD7Rw4cHd7K5q35Nq/6KIrjtlk5aG6NLr7wEhBBs39TEgZ1tPPf6+aLrjE0mOHl+qGjHpyAb+rMvis1he1sDTVWLnzVh3+qD1zMXhzh3eaToMkNX+ciDu6mOrI3JlhCCW7a3sqmzjvfODJTYn2F6h2IrGu4PGu0EjfYbtqfQ4LuVoNFOIn8NReiEjG486uoT/htRNvAfnUny5rVeXrncw3gyxYbaKn7z0D52NtYTqcC1N+skZlVjFqLgdLs+7SYXh+QNKjk3wlA8FWuOV0f8DI9N09laQ9/QFNURP5mshc9r4vcZjMUSCFFosWVzFjff5ySF4N/vNYkEvYxNJgj4TKrCPqYTGRLJ7DxXb24gc7UH9rlLl0jm8/z7Rx+ZD/rLwW/odFRFUBSBz9AxNA3LdRicmaE5FMSr62iKQkc0yuB05cYrrrS4kngagYJAoTVwDyPpIyTtIbxqDV6tmons6dlASqHFfyczVh9JawCPWo1Xq2UyexooVNfDZhcz+R4uTv8tHrWKtsB9jGaOkbB6MdUoLf67GEi9NjvYCG2BBzDXyOGuFMJ6FYZiYjmlAn+LaWty1TMllcLFYSpf3uTHr4bwKO8PhcareucHNKuMQpLf6G1asI4mNJq816tUIT1ESH//3I6hUPE3FA+WU5rONpUfw5XOkoPAidwp8k6MjTX/Cl2J0DP1n7DlDLY7zUT6R9QGPky1937yzhhXYv83ydwpoNCtaQn9Nh6tjaHEV5nJHafKez9VvnvJ2oPk7GGaw7+JIiqrKEksRpPfJmeP0Br+HQytnqw9gMShMfQb+PXNjKWeYiZ7jKj3XsbTzxD13kl94JPY7jRXp/4tydxJDK2OjNVD3hnDq3eSti6Td8bxaC3lE2thohq3omgrGCgWflijbo9p6Nyxr3vNdOk1VeGeWzfxk1fOFjUJklLyzokePv7w8quNV/rGuXC1eLVfUQQP3bWVgG/tfEGEEOzZ2kJtNMDQWHH1oBNnB0mmcxUblBVkSAdKmnQpiuCeQ5uoq1nbazzoN7n/ts0cOdlbtCo7EUty+EQvGzvqUNW1uxcH/R4O7elcM6dgn0fnroMbePXIlaIOzbm8zbHTfTx011aMNUqc1guN0WDRIN+zyiq847i8cri0g3VHczX7d6y+m3QjwkEvt2xr5eT5oaIqP7HpFOcvj7C1e3mJpePmSNsjmGoUVfGgoFJwMVcJ6C0E9NUXP0qh7FF45fI1Xrp4hbu6O7ijq52WSGhe1aUSlKIduNJZM5e7m2G5WYYzFxa9LijIXFZKL/J6DCzbpbE2xNW+CdqaoiiK4PnXzzGTzHJwdwcnzw/y6pHL9A9PsaW7gZ6BGAPDUyiKoCYaoK25ihffusBELElrUxRNUxCicANUVQXT0DANjVfeucy+Ha2EAqvjX+9taiTs8fCld4/xhx+4iyqvt/wkOwL15ge4hIBpksjlC7MLiiSRyy1rsFfikrQG6Qx+kJDeSsoeYTTzLnXePYxljuHTGvBpdaSsYQJ6M2PZ40znrlDr3c1Y5jg+rR6vVk3KHiOg1ZO0hjDVEN2hx7ma+BFjmfcYSR+mxruTiewpgnorKWuYRt9tVJmbUN6HAdaoXodX9ZNyij/kXFxi+TFsaaGv8bBiMVhunli+eBVkDmG9Gp+2OjrZ3zX4tSBhvbrkcQQYyw5gSwuN8udVzhnC1OpRhR8hNLx6G8n8BRw3ie2m8GptCKGgq9VoSpCcM4Su1ODRmtDVqsIyJUJGXmM1HP2Z7LtIbFrD/wBDrZvvVJhqPYZaqEZrShiJgyNTWE4Mr96FEAqaEsRU68g5Y5hqA2nrErY7TdDcSdq6jOXGiXruLLt9oYQwQv9sxfu/VqitCrChY+3mBeaq/q1NUc5fKR6k9w1NMRFL0txQuWShlAUaRzJdvIhQVxVk15a156jXVQdobYqWDPxHxmcYHIlXHPjnLYf3zvSXpGBUR/zs39lWVK5zNZirlrc3VXG2SCXYdlyOne7jIw/sJBJau8JHQat/9dX+OQgh2NLdQENtiJ6B4g7a1wYmic+kqatefB+Xs+ZgjuPOzx0u2gZgmlpZT4DVQgJPvXWayyMF00DXlUwm0nh0jX/xKw/StQqqz8RUilMXB0vGI/t2thFZYyftufPLNLSivh6uKzl7eYQP3rsDr6fy3zXtjPLO2J8AgpDRSdjYRMjowKc14lFr0BTPrLKdWPNrv2zg/9CWDTy6bRMB00BKuew2o654C4H2TUcpacfIugkCrI7rdTOkdBlMny0a+GtCp95bpj19E3Rd5YlH9hIKeKirDhL0m2ztbmBgJI7Pa9BQE6KlPsLw+DQHdrUT8JmksxYP3bkVoRSkoR68Y8vs+joBn0k2ZxHwm2xsr6WjuQpdU/nEI3uZnEqhr4HMVWskwke2buFP33iLLx09xu/eehBNURhKJBicmWEmm+PKZIy6QGDBwM3N2N3YwA/OnuPZS5cImSZHB4forlreDU4THrxqNapi4kgLVeh41Rq6Qo+TtIZQhIahhlCFSc6Jowgdj1pDV+gxUlbh5m0oQTTFQ86dRlN8aIoXdVb+SggVr1pDZ/CD+LVGxsUJfFoN6vvklutR/VSZDUzkSytXTOSGyDgpdGX9A/+4NUFiCWpRlVGPqbw/w72/KPCpIaJGHUPZayXXiVljzFhTeJYYONaUAI6bKtDOpMR2U4CLEAaKMLDdRMHGXmZxZR5VFOaB5m7uxSBmR6CRsmJbCK/eScizj/H0j/DpXXj17tkl6iK6koKGKrzY7vTsvlk4bhpDb8TUGomlX0BTowSMHYwmnwIkurrQHVRKSTZvk5/VIjd1FY/xs3dzbmkomCetJQI+k20bGksG/pNTKYbGppcV+Fu2w/GzxWkEAO0tVaue/yoGj6nTXB+ZY1Quwkwyy9DYNNs3NVYUeMSmU1zpLd117GypXtbvshxUhX1s29jIucsjRYPC3sEYw+Mzaxr4dzRXr7pYdzOiIR8bO2pLBv4j4zOMTyYXBf5SSgaH4jzzk5OcOzdYckDc49H5337/ITrW0eFXAJ+7b9+8N4ErJcNTCb71xgkCq5yz6BuKlewo6ZrCri3NZWOblaKhNkTQb5Y09OsZnCSbs5Yl52soIRp8dxDPXSCeu8Rw+k2ktNGVIB6tmpDRRcTYRFBvx68349Ma12wutmy0GfF6GIjP8Nz5S9iuy2PbN5OzHTKWRVM4tGQiENSqMRTvImfalB2jJ/keVUbLmn0RKV0mcn28PfFNMkWqdyG9jnrP0hrtc1CEmL/Z3ngwb+SLBvwmG/3XB7nCQS+NtQvbmBtvqDiFg4WbhOa7/p2rI36qVyAFdTO21tbSGArg1XW+eOgAX3n3Pfri04S9Hp46fZZYOk3E6+E7p89wqK2VW9tauaW5kYin0I7zaBq3NDURNE0aggF+59BBXrh8mZDp4WPbthbUjSpO/ESBez+7vl+rx6vVknbGMGRwlm4lZmcuVHx6AwAZe2w2GdALpmgU+PsKKml7lL7ki1hummb/XeTcOOnZ9YP63Hn0/jnAmqqXFm8XlxLHS3a2YvlRpvJjhPS1qwoVg5SS0Ww/Sbu04Y8mdFp9G36mLrk/j9AUjRZfN+dmjpZ0hk5YU4xke6k1m8oGQIXg+LtMpH6ModYxnT2Modahq1UEzT2Mp36ElBZp6woSh4C5k3R+MS/5RhhaA1PZN4hn30FXq/Abm5ZU9zHUOmr9jyOlxeDMX9MW+UeUml5WlSBhzyEm0y+gCA95Z5TcvBwopK1rVOvdePUObHcaXalCUxYGHXnb4WsvHuOFdwvf5UO3buXXH9y3aFuFLq9d+K+kyIIA4Vn1EP+cJN9ayzgqimDrhgZ4tvhy23G42j/BgV3txVcogvHJgmtoKWzpqkdbRqe9UgghaKwtqPPZRSgMjusyMBLHcSVaBRSZkbEZxiZL6/Zv6W5Ytc9BKei6yqbOOkxTK6pUNJPMcrlnnC1rxPNXVYWWxsiaU24MXWVjRx3Pl+D55/I2fUMxtm9aaHCXSGb5b//zpxx9t4eW5ij5vMNkLElbazX5vM3wSLwg5/rAdkLBtRsSLQYhBBH/woSoLhzgB++cZXByhrrwys0ET18cJp8vfu+IhHzr4tkABWfpaNhX0pciPp0hNp2quDsGYKoRtld9EVfa5Jxpcs4UaXuY6fwVEvlrJPK9BWUfZ4aw0c2h+n+LlCHi2SyudFGFQq3fj7aC7k3ZwL9/apr/58VXGU2kEMA9Gzq5NjnFc+cv8wf33k7IU/4ECul1RI0mEvbEgtdtmefE1I+p92yg2bdtVcGIlBJHWgxmzvLG+N8wmFkst6ag0hU8QLiE2s/fBXxy1475f1f7fPzBXXfM//1Pbvj3jfidQwfn/x32ePjdW6//fXt7G7e3t61oX1Sh0xV6DF0pXOCGGqQ79GFS9gi68GGoQUDgSnt2OFdBeuXscu+i5a60aJF3Y7kpmnwRPGo1XcHHSNnDqMKDpvhpDz68Zq52lX1HlRbfBjyqj4xT3EEy46ToTV+gzbdpXXn+tsxzLXW2rG+AV/XT4qs88f1fCZ3+bZiql4xTXK3FlhYXEu+xNbQPvQzP3qO10RL+IpOp58gq/dT6Pzg7gG7QGPwME6kfM5F+Fk0J0xb+B5hqA7Y6RcDcOW/FbmqN+GV2PvANew6Rs4eJZV7Eo7Xi0zvL6tx79FYUxY8iTOoCH2c08W1S+fOYWiNBcxdi9r2GWkfA2IEiDGr9H0IRHibTL6IqPlrDfw+f3oXjpoh4biVo7kQVQcKeg+hKFEUsfKg7jkvv6BQXBwrV3gPx1kX7JaVEOlew09/EsY7PKv7cnIwIEGHM8L9GaJUHzsWgqoLGdQoCmurCeD160aqqlAW6z3JmeyamkkxNF7+HAHS0VK+bC2s4VJ4OOh5L4sx6zSyF3sFYSZqPrqm0N1etOc3nRrQ3V+H1GEUD/1zepn94quIkZikYukp9TXlTqJVAUQRN9WFMQytuHGU7DI7GF51fPT0TnD03xEc/fAuf+Og+3j3Ww9PPHOeP/vAxTEPj1OkBvv3UEbo6a4msQaGxHKSUXBmZJJm9Xh2fTmXpHZ/Ct4pE3HUlV/vGi3o2QKGwWh1dn+9mGmpZZbBMzmJ6prRDdWkIBBqmGkVXAhhKAF0JFMy7shp5dxpX5rHcJDO5DN89e5VkPoeqKERMD09s307QXD7LobyBV08fNX4/v3vnIf781bcBaAgF6Y9Pk8zllwz8PWqAruBBBjJncW8KSibzfTw/8uccrP4kXYH9eNRAYRi0Ask8KAwz5t0M49lrXJh5nYuJ10nZxVVNqswWdkYeWhf50F9iMYRQ8GkLkyxTDS8ZmC9nINdQg7MJQgE+be24vJWiydNJldHAYOZK0eWOtLmUOMG+6D3rpqYjpWQqP87V5Nmy6zV42okaf3cT39Wg1myi3myhJ128ygZwJXma8dwQjZ6OkvcoIRQinkNEPIcWLVOEQUPwU4teD5jbCZjb5/8Oew4S9lxPwDUlQFPo1yr+LlHv3TdsU6c5/Jvzf/uNTfP/Dpq7CJq75v+uCzxOXeDxBZ+lqSHao/94/u/m0OeLbtORsqR75jzcSfIz/wYn9zpCbQCZQ8oZhNoMbhrpjiPUWlTPQYRY/cNbVRRqq1ZeWSwFIQThoIeAzyxJpxgemy4wsyqMCfuGpsgXUdaBwkBxKOgpuXy1UFWl7H7GZ9JFnXFvhpSS/pGpkqpEPq9ObdXaKK2UQnXET8BnFpXDhIKbbzZnrcmQtKapa9KpvxlCCKrCPrwevWjg77iS0YnEovMrNpUCCfd+YAs1NUECAQ9IME2dmuoAd9+1mYnJJN9/+j3239JB1TpcG3OQwDPvnufKyHW6kpRw384Nq5LyTKVzZZ2gg4GC2mSp5HM1cBxZVgY1b9kk08sL/F1pkcj3krD6iOcvEs+dJ2UPkXemUYWBT2ukxrOXqvCvEjG3kMh68Rs6v7FnD+ZsB3ClKl9lA//pTI6mcLCg4DP7+QU+YDm18OtQhMqm4O1cnHmDoSKV+IlcL88P/xl1ng10Bm6h3rOBgFaFqfoLjppCKfBOcbDdPHk3Q9ZJkLDGGc1dYSRzkVhugKxbvFIH4FMj3FrzaaqMxVWotUDht5DkXYupfJxpa4aMk8WRTsEGWjEJ60GqjCi6sjSPXxXa4oHbCvbBxSXrZJnMT5GwkuTcPK500RWdgOanyogS1PyoonJJ0+VuP+fkiOXjzNgJsk5udvsafs1PlREhqAXQbqAAreX2826eWD7OlDVN1skikRiKQVgPUmNU4VE9iBuGZNZiD4J6hM3BPQxlrpXU8x/IXKE3dZ6toQPr8tBzcbmQOMZUfrGBzRxUobE5eMv7pujziwafGmRLaB996Usl6T5xa4L3pl6ltqH5fRnW/kWC60rSSwT+rn0G1zqB5nsC3f/b2NlncbIvYIb/FaBjZ3+Mk3sRzXwYlNVLPStCEAmuzzxLwO/BV6b6l0rnyOWtinXjh0sM10Ih0PvzL7+C17s+FJmZRHaei10M2ZxV1htkDo7rMhVPl6zGekydSHh954tMQ6Mq4qN/uHgBcDKeIp+3YS0Cf1VZc37/HEIBL15TJ05xA8hEKodl2Zg3VM/nOgBzHRWPRyebs8nMctJVVaG7q46nvv8usan0ugb+AvjtBw9iOW6BQiYlhq4R8BiregYm07lFsuk34nLPOH/wr7+zLt0x15UMlKHjOY677OQ8bY9weOyPSVnD+PVGgnoHbYFHiJpb8GvNeLRqNOGf/81cJ83lyRj/4+hRol4vQdPgoe4N+I3lP4/KRqLtVRFeuHCZ3liB5xfPZHnzWh81AX/F7YWwXs+tNZ/ihZH/yoy1ODixZI7BzBkGM2dQhY5HDaALD6qiI1AAiSsdHGlhuXnybhq7hHb6zfCpEW6v/Qzd3ttwLBehr9109FzAP56b5L2pkxyLn2I0O0bSTpFz84XAH4GhGPg1HyEtiLakzq7go02Psq9qd8X7YEmb3lQ/R6eOc3bmArH8FCk7g+UWbtiaUPGqXsJ6iI3BLg5W7WVTsBuP4ln1byGlxJY2/ekhjsVPcnr6HBO5SVJ2mvzs9lWh4lU9hPUgXf4ODlTtZUtoI37VtybbTzopTsXP8fbkEXrTAyTsJDknj0TOJj0+6j117I/u4UB0D9VmFYpQ0BQdgajooVYKhYB6L+9OvcK0NVF0nYyT5OjUy7T7t6x51b9Q7R/l2NQrJQNWgIhey6bg7vdFVvQXEUIItob2807sBWL54kObIDkef41NwT1sCOxcV5+IXzS4rksmV97d23WGAIHm/TiK1oVQqguynUo1ilqHrv4m0hnESv81pr4VxCrNDBVRNjhfDTymVlauM5OzyObsigP/8anShSspJddKDHq+H8jnnZLmdjcil7OLGmjNwdC1NZUjLQZdVwn6S7MQphOZ+QH01UJVxLrNK/i8elmfg3QmT85yFgT+oZAXIWB0fIaurjqqon4sy+HCxRGamwoD1TOJDLmcheuuv6u3oii8deYaL5++Qiqbp7uxmo8c3E5H3cpdpzNZi2QRudY5JFK5kvr+6w3HldgrOLfmmDCq8OLXm/BpjehKEF0JzM5yydnujiBgGHx821b6p6cRCNoikWWpbN6IsoH/bR2tnBwa4T+89DqjMwn+2dPPEjRN/uHdtxI0K7upCaHQGdjP3XW/yetjXyFulVZBcaRVkq6zHAgUas0Obqv9FTYEb+XEq5d5+9mTHHxgBxt3txGM+BHKypOAQoXZ4nDsXX449BwDmaGiAaREknNz5PI5YvnKvteMXbqVdfM+jObGeWb4BQ5PvlvyfZa0sewEM3aC/swgb00eYXd4O483PUyHv21BFXw5kFIykY/x3MhPeWPyHabyxbNhW9ok7CQJO8lAZpjDsWNsD2/mscaH2BjoRhVL07uKwZUufekBnhr8ESfiZ8i7i28IhS5AoRNwYeYyb068w0ebP8juyA68qgdVqNhlePGVoNHbwdbgPg7HniuZRFxOnuRE/A0OVT+4Zv4VhXMwx9uTzzGSLe6+CYX5lu3hg1SbjSXX+SWgxmxiR/hWXh9/GrdE9yZhx3lh9FtE9Bpqlhj0/V8JjlsB1UdaFGTpCsmvEB6QGZgr4gg/irEXK/nfkO4kQlld4K+pyro5lBq6Vnao07KcslX0mxGfLl3F/FnDlW5FpRHLdktqqwMYhromTqrloKlK2WQvm7MKFf81gK6pa+oJcCNMQy/rDZC3bGxn4fnV0hwlEvFx/EQ/hw50UV8forW1iq8++Sbx6TSGrvLjZ08RCfsIh9Zf2e2FE5d46eRlHtqziYDX4EzfKH/+zBv8H598gOgKOyXZvFWSXvczh5Qlu12l4NXqubX+3xDPXyKWPU0se5r+5AtIaWOqUYJGO1FzOxFjI369BSGrOTs2zng6hZSQsiw2Vq+sO1r2Sgx7PfzDu27loc0b6J2K4zN0ttXX0Rhe3lCLKjS2hO4iqNVwePJb9KVOVly1Xw4UVIJ6LZtDd7Ar8ihRo/Bw7trewmj/JC986zA//urrbD3Qxd67ttCyoR7D1BHLkH+aC/qfHXmJ7w0+Q8ZdyOtShYouNDRFw3ZtLGnjlFSwKEAgUIRAQUGtYA5BSsn5xCW+2vttelK9y6pZZ5wsb8fe5Vq6j19p/Rj7o3tQl+FoPLf9K6kenuz9DhcSl6js0VBAzs1xbOokfakBPt7yGHfUHEJnefQfKSUXE1f4Su83uZbqq+g9Li5XUj385bWv8anWj2AqxpoE/row2Ru9mwvJ90rSbfJultfGn6bGbGBDYPeqlXUKA+02J+KvczT2UkmaEUDUqOOW6AcqOq/+V4YqVPZH7+P8zLuM5UpLK/alL/Kj4S/zoabPU2NUJnO4Usx1FXNOGkWomOrPpxSr7bhklwiohFII+KU7CmxCKNVIN4V0BpDqnFGNBJkCWb57UAkURayZy22xzy6XVMxpqVcCKeW6cJLfb7iuWzbZ0bW1p5jeDEUIdK30cXFdWdSleEXbUsS6uYsrikAto9Riz+r034iqaIAnPr6fqmgAEHi9Bk98bD//3589x1/8958CEA57+Y3P3kHNOkjD3ggJvH2hl8/ft59dHYWC022b2/mX33yBvvGpFQf+tl35dfWLAFUYRMzNRMzNtAc+hC3TZO1xElY/8dx54vlL9CSeJmuPEzY20OL9I2zX5Yv7D+BKyTdPnWI6m8UTWD5ta8kU3Gfo7GlpZE/L6iqGilBp8W2nxvynXE0e4ez0y4xkL5F1ksgyNIVyKFx6Kl4tSI3ZTqd/H93Bg0SN5gWDvJGaII989g7u/fhB+i4N8/ZPTvJnf/h1Gtqruevxfey+cxP+JdQN5iCRHJ06zveGFgb9XtXDzvA29kV30+RtQBcalrQZzY5xbOoUx+OnSTsLh44aPHV0+tuoNWuoNWuoM6vp8JdX0pkLev/71a8wkl0YaGpCo9asptasIaQHUFBI2mli+Rgj2TGy7vVkazQ7zpd6voErXQ5V70OlssBQShhhL0IAACAASURBVMm1VC9/de1ri4JuVahUG1XUe2oIaUFUoZJ2MsTyU4xkx8g4mfkUYSIf48m+7+BIlw/U3o5eYSVcSslQdoQn+76zaPsKCtVmlAZPPWE9BEiSdorx3CTjuQnyrkXcmuZvB37A9tCWNbltCyFo9naxP3ofPx37DrYsXpGYssb40dBX+FDT5+gO7EQRyooeHAV6VZ4T8Td4fvSbZN3ig2wAmjA4WPUAdWZzyXV+ieuoMRu5veZRnhn+Knm3+KCWRHIh8R7ZgQwP1n+adt9m1DWcW5kL9m1pMZEb5kryJJeTp7iz5jE2BiujAL7fyOatonKQN0KonSBMnPx7KMYdCLUdITxY6b9BV4IgHZzs86AEQfx8Jjg3olxg5rqVV/9cVy6q3v4iwpUSpwyFZKkh4rWAEKKsMZWUcsnz9OcF5boJTpHAX9dVHn5w54LXdu1s4f/6Fx/jzNkBbNtl44Z6NnTXr1sn7EZoqko6Z+G4LgJB3nKwHGdVqk6WvX7Grz8LFL6LROJgu1lyToyMM0HOiWHLDFI6SOngSgtbZgiaOlnb5nvnzhXuL4IV8fuhgsB/LSGEwKuF2Ba+j43B24jlBxnKnGMoc4Hp/AgZZ4acm8Z2C8OxEneWiqKgCh1NGBiKF1MN4FPDVJst1Hm6qTU7COn1GErx4F1KiWM7xEanuXpqgMGrYzR21NC5rZl3nj/FmXcu82v/9DF8S+jbFjjV0zwz/DwZ53pgEND8fLr1o9xVcxumuvBAdPs7OFB1C+/EjvHVnm8toOQ0eur5e12/jncZlbzx3CRf6f3WgqBfAK2+Fh5uuJcdoa1EjQja7BCvI10SVpKedB/Pj7zMqemzWLNV7mlrhm/0f5cqI8rm4IaKgpcpK87X+767KOhu8jTwYMMH2BPeQZUZRRc6Qghc6ZK0U/SlB3hp7DWOTZ0iN5uAJO0UTw38kCojwp7Ijooq4Tk3xzPDL3Al2bPg9ZAW5N66O7mz5lbqPDWFREKA4zrErRnOzlzg2ZGf0pPqI5aP88bkO7hybR4CmqKzP3ovvanzXEweL7neaK6fpwb+G3fXfpjdkTvwqZV3zuYCwhlrksOx53l78rmS8pNQSIq7A9vZV3XPL9WsKoQiFPZE7qIvdZH34q+V7KRIJD2pc3yj70/ZF72HPdG7qDEa52lcy+1eFbyuXbJOmsn8CAPpy1xMnGAoc5WEHcdUvByqemjBe5KZPH1jU6SzFuGAh7a6CKZePAGxbGddA57pVHbJz1e0DlTjNsABLIRah+p5FCv1X3Dz7wAS6SbQ/J9FqKtXn3JdWdSUaq1QjictltltWKoAUKgu/2ygKEpF2xaifJV6vY8HFCrN5QLDQmKwNr9kKWfctUI5JaVKu1mKotDeVk172+qH5ZcDAdy/awN//dIRTvQM4fcYXBqawGvoq1L1WXK7YuUqN6tFIbFd3rYtN0FP4mmmcudIWv3knCny7sxsMbsWv9ZEi/8+IuZmgkYHAb2Bj22r4tLkBEjYXFuLX1/ZnMn7GvjPQQiBofpo8G6kwbuRvdItKPa4KSw3g+3mcaWDRCIozAkoQkMTBq5UGUyn2RpuWxRkl8JwzwQ//NIr9F8aoamzjg997i427GzDGzBJxtP8xZ98m7GBSTq2Ll0ZPZ+4RG/6OhVAQfCB2ju4p/YOtCKqPUIIDKFzW/V+xrMTfHvgB/Nc8DMzFzg/c5m90Z2L3lcMtmvzzPDzXEv1Lnh9R3grn+v4FZo8DYtOPlUoRIwQe4wdbAx08f3BH/Ps6Evk3UJlejw3yXcHf8TvbfgCIa18IOpIhxdHX+Vc4uKC1zcHNvAbHZ+mw9+6KHhXhEJID7IjvJUufwfPel/i6eFn5xOnKSvO94d+TIu3iVqzuuz2CxSnyxyNHV/Aww5ofj7d9jHuqjmEriy8EDRFo8as4q6aW+kOdPDlnm9yevrcmgX9cwjpVTxQ/ymmrUlGc6U593FrnGeGv8rZmXfYF72XDv8WgloURSx2VYXZeoC0mcqPcTl5knenXmE4c60kD30OtWYTD9R/at1kRP+uwqP6uL/+k0xZ4/SkzpUd/k7YU7w8/j2Ox1+jy7+djcE91HtaCGlVmKp39lq48ZjK2Y6NRdZJk3YSJKw4E/khBtNXGcn1Ec+Pky6T0AGMTiX406de5/C5XjI5m6DP5EOHtvKFRw8Q9C0uXnzvjdP84M3ycq+rQSaXJ5Yo3XkCQAQxQn88a85VGPLU/b8GQuDkXgEp0XyH0Hyfm1++Giyn6r5cLFU5VhWlbOX5RiiKQCtDTwkFPHzqg7esGy9bSonjuAWqaZFqbHXUX3bQdA6qIspWc23bWffA33VlSTlRKDyL9TLc+eXAceW6VZ8LXaDS30NTlZIJjJRyntKUzVoIReDx6OhaITB9P+aShBDcvrmdoNfkjXM9DE3OcEtXM/fs6MK7CkdvYwm62IFd7dyxr3tFyZ0rJY50AYEmxHwSKQFNUXBnOfyKEPOiIIoQ1+8xAjZ11eG4hWupkt8568S4OvMUQqj4tUZqvfuJmpsJ6G141BpMNYJyAxNiKpPh4sQE7ZEI3z13loHEDI9v3oJHW34Yv+6Bf0Hq8fpNuMBln5Unml0GoCs+TNU/y1+WCFFYLmBe3tJFcnF6jB/0XaR9axOKUNEqGA6dGpumvrWaD33ubhraaxa0ujx+k123b8JbgfSbIx3OzJxfwNkP6kHuqDlQNOi/EQoKh6pv4cWxV5mcHfTNu3mOxN5jT2RHRSdKT6qPNyePLAhGmjwN/Hr7p4sG/TfDr/n4WMsHiVlx3px4Z/5zzs5c4GjsOPfW3Vm2+jSQHub1icMLvn+tWcOvtn+CTn/bktv3aV4ebXyAuDXNi2Ovzl5ocCXZw1uTR/hQ40NoZarTedfi7cmjJG7omigofKD2du6oPrAo6L8RQgiaPA080fI4o9kxxnLFVXhWCiEELb4NPNjwaZ4e+mumrdJKHLbMczl5imupc0T1Wuo9rTR6O4gadXhVPwoqjrRJOwkmcyMMZXsYy/YzbU1WpEIU0qp4qOEztHi7142H+ncZVWY9jzV9nqcG/oLBzDXKy5pI4tYEx+Kv8F78NfxaEL8awqcF8alBDMWc7bw5WG6erJMi52bIORmybpqMkyprvFYMr5y4yk+PX56Xj8tN2zz12il2dTVy797FJm2jU0nO9Pxs1C7mIIQCIrLwNSWK7v89dN/nZ1/wrZlakuO65K314c7nlxje1TSlbDB/I4QQZd2FTUPjvts30968PpXS08f7OH7kKjv2tLPrlvaiwX8l0DR1gcrMzSj8Zus7y+C4btkhc11TMPS1CXlWIt9YKfKWjVMmgSkMFi88TnPJ6IULw7z86nkuXBwhnc4jBIRCPnbtaOGeu7fQ1la9Zl2PUpBSYjkOO9sb2NPZBBRiuUzOWpa/xc3QNBVNU0pee+3N1Xz0wV1lB6OLIWvbPHP1Av2JONO5LJ/bvpc3h/ooFG0ED3Ru5JX+awwlpumOVLOlqoZzk+M83LmRH129QJ3Xz7GxIX4ycYX6zCiPdVUWjPu0Bm6r//cYaqhg2CW0ss/rRD5P3/Q0o8kknZEoE+n0+nH8VwMpJePZJD/oP8XF6YJM3t0NG3i0eTvjuSTfunaM3mQMTVG4r3Ez9zVuYig9zfd6TxA2vJybHsFUND7duY/N4TpeH73K3/a+x8XpUWasLGHdy29vup1GX3ljqE17O9i4ux3dXNwK1w2Nh3/19oq+jy1tBtMLVYlqzWpqjKVbaUIIasxqas3q+cAfoD8zRM7N4VGXphm9PnmYhH29Gqig8GD9PbR4Kx8y9KpePtjwAOdmLhCbVeKxpcNr429xoGovIb344I+Ukndix5jIx65/JwQfqL2NLn97xdv3qCYP1t/D6enzDGULwYgjHd6ePMrtNQepNUv/lhO5Sc7PXFoQhtWYVdxZcwhTXbpKKISg09/GvuhufjLy0qqkPItBEQpbQ/uxXIufjPxN2eAfCgZfE/lhJvLDnJl55/p+rkJmNKhFeaTxs7O+Ab+UnFwJBIImTycfbf4iTw/9Ff3pSxUdD4lL0p4maU/D2msXzGM0nlw0pJjM5hicKK0H//OE65XSuWqZf80rkVJKkun1OQjZnEUuXzro85o6nmUo2FSFS/tr5C173YZ/+3sm+OmPT9LaWUNDc5RkMkv/tQl0Q6Wju45sxmJmJkM8lqKhKUJ1bemOsGloBP2l78F5yyaVzlO7fkwPbNspe8yDAQ+GsVYVf5d0ZvVD6MWQyebLyo76vMYiVSnHlbzw4ln+6kuvkkzlqK0N4veZSCkZGYlz5uwAL792nt//3Qe4ZW/lz+uV4muvvse+7pb5wN9xXP7Hc4d54vadtNRElnh3cXhMHZ/XKKnsk0xlV9RVimXTDKZmeLRzEy/1XSFierkan+KJTdvpDEe5Eo8xkUnz69v28uT5EwCMppNICaOpJLqikMjn+K2d+/nG+ZMMJgsJwlLQFA9hs3vJ9Ww3g+WmCJleZnJZ8o7DJ7Zt5+3+frQKO4uLtr2id1WIrGPz5ctvk3YsPrfhVoQAQ9FQhEATCrdUt/Jw81Yuz4zz5NUj7Ig0knHyvDR8kQ+2bOcLG2/j2cFzPHn1CH+8+xF2VzUTy6dASn5n050EdJMaz9LZztkjV7j4Xi+f+N0HEKuQ4Mo6eTLOQuk1v+rDu0TQPgdVaIT0hdSLjJMh4ywd+E9bM5yeXugsWmNWcUt017Iv4jZfM1uDm3lj8vD8a73pQXpS/ewMby36eQk7yZmZ8wsoMlEjwr7oniW7HTej0VvPrsg2hkdG5wOqoewolxJXqTGqSs5pXE31ErdmFry+ObiRBk/lnGBd0dkZ3sar42+RcpagJ6wAqtDYFbkNTWg8O/J1JvJDy/6MlQb9VUY9Dzd8hh3h236p4rNKCCFo8XbziZbf5SfDX+Ni8viyK/PrhYZoEEPXyN1Q0fYaOrWR9TPlWQtIKUFmcZ2rSPsq0o0BGkJtRNE2INQmBOrKS4I3wHUl8Zn1kclMpfNksqWDPp/XKFvFvxl1ZVRW0hmLZDo3b9C0ltB0FVVTCQS9GKZGOpkjkchw5cII2YyFZTscffMyew90LqlRrqkK0bBv1uBz8fJszmY6sb6ypbm8Q7yEay9AJORbM6qP7bhlzaRWg5lkwQCuFPw+c1HnYmhwim986zA11QH+4B89RHd3PV6vjpSQTGY5eaqfv3nyLb729bfYsKF+XSU9pYRLQxPcubVz/jVdU0lm84zPpFYc+Pu8BkGfyeRUqujy8VhqRfS+qMdLxrZ4rucyBxqaMVQVn65T7w/g0XQyto1fNwgaBl5NJ2sXjo0jXXKOjQSChknQMDFVjewad7bGMke4NvN99tb+EZ/esRNNUfBoGvd3d/98cvyn8mnOTo/whzseZGukYf51KSVRw0dHoJrRzAyaopB3bDJO4QetMn080rKN9kAV8XyGb1w9StaxqDL9VJt+fJpBoy9EQK8s4E5MpbHy9rJkO4vBxV0kXakIZVnyjDe78jrSwV1C7hNgODvKWHYhPaXL3061Ga1423PQFI3dkW28HTs6T9vJulkuJq+wPby5qMLPRG6SocxCukCrr5l6T+2yt68Kle2hLbw89ibZWfWUvJvnUvIK+6v2YIjFJ7NE0pvuX6DXrwqVjYFOTGV5nOBmbyMRI0wqs/aBf2G/NLaHDxHSozw3+g16UueWlHRdDRRU2v2bebjhM7T5Nq9aLvSXKEAIQb2nlSdaf483J37M4djzJO3S7o3ruR83doA/sLuLE1eGePNMD+lcnoDX5L69GziwZWl3ck1VaKuL4KvQXKoSZHIWvaNTZXnJUkqk04OV/M/YuZfAjXOdQqUi1FZ032fQfL+KEKtPYFxXMhErPyuxEkgpmUlmy1aWa6oCy1JOaW2IlgyYLdthZHwaaFm8cJVobI5S1xima1MD0So/F04PMjIwxcTYDFOxJP6Ah+5NDey/feOS9BAhBC0NEXRNLUqByWTyTMZT65LAzGEmmSmbXDTUBJeVkJWDbTvE4mv//JBSEp9Jl6xqK4qgJupfdH719U8yPZ3m73/xg9x6sHvBbxwOeWlsCJPJWHz1yTeZmEisr5a/KAT6iUxuvrtnOy6ZfL7sAPhSCPpNIiEfDMaKLo/FUyTTuWX7RaQtC8txUIXCqYlR2kIRVHFdrrUjHOHwcD/fv3yejGVxe1MbP7x6nh9fu0h/YpoNkWouTk3w9JXzpG2LxsDaztVlnRgpe5DR5AwXx6e4v7sbVVEIVWiiWwzrGvhbbkF+yactvNhcJC8Mn+dH/afZHK5HV1TyrsPcg8CnGRizLrfqogG55aNlQz2XTvQSG50mckN1RZQYaCoFQ9ExbuKRZ50ceTe/ZMUeQEqXhLUwWzUUoyw3fQ596UGsm6QiuwOdKKzsQmrxNeNVPSTt6/tzNdmDI52i1eKBzDBZZ6HEYaevtWIZzpvR6G0goPvJ5q5/Zm9qgLybX/QbA+TcPOO5hRx3r+qh3lO37AeJV/VQY1YzmCltJrdaKEKhzbeZT7f+Iw5PPseR2EvM2FNUZINZIQSCoB7lQPQ+DlY/REhbuSviL1Eafi3EffWfYENgJ69P/JDLyZPkSsh9riUUVKqMOraG9tPk6Zh/vT4a5I9+9T4GJ2bI5PL4vSYtNWG8FQQ1Yb+Hf/aZ+9jcunrlnDlcGhznj/7nM4zHi1fiAHBj5BP/Dif3Coq2A9V7oKDeIy1c5xpO7i3yyT8FBJr/C4hVdqwc12VobHpdAs2xyURJmocQ0Na4vGJMS2MUn8cgVeIzL14b55G71y9gnsM7b17i4O0bgetKQ7qhVdyA6WiuxtC1ooF/Nm8zMBzHdeW6GF9JKRkYjpcMmFVF0NwQKWu8thzkLYeRiZk1P7+khNGJRMnvoakKTfWLK+aulJimRkNduOj+CCFobAyjKgJZRjFoLSCAWze38Vf/P3vvHR7XeZ55/06b3geD3gtBgCTYqyhSjerVkiLLTY5TnI0dO4433fvt7rf58jnd8cYb9xIXNcuxJVGWRFGiKJKS2DtIEAABEr0OMH3mlP1jAJAgZkBUSU58XxcvAoMz7+nnPO/z3M997z7EUCiK02bmxMVuQrEkpYG5ZfsBrBaFkgIvJxo7Mr5Fh0ej9A2G8Lltszon54f7qfUFuL2smh83nmAkEefR2hU4x6Qy/RYbj9auoCcS4qaSCrwWKx+ubWAkEWdtXiGjiQTVXj81Xj/bSyrwmjPHguMKbrOFqkcxDB1JEAglkwTjcZwmU7pZXZybAeqiBv4uxYJLsXB6uJtCmwcB0Ma6p9/pb2Otv5SPVm3g/EgPu7rOcXWAn63JYXySkNA1rIaOOAPnWTWp0nK6g//5xNfJLfZNNH/Ub6ji/t+6acb7YxZN5JoDk6QsB5JDDCdHKLBen6MfTI0wkJzM+/aaPNjl7BzPcXTHpjbnFVjzZrjlU+GU7bgV56TAfzA5REyLYxKnZgN7430TzbjjyLUE5kwpsUtWvIqbgcSV4zGcChJRozhk+5TlE1qCkWtoPmbRjNc0fX9HJphEEx5l8dVuBEHApfi4Je8R6lzrODS0m8bQEUKp4LSmW9eDiIhL8VPnWsc6783kW8t+Te1ZZEiCTIWjnkJrBRcjZzk6/CatkbNE1NF5ncup65FwyT4KrZXUu9ZR4ajHowSmVHEcVjO1JbOvtplkiYDHgdM2f+Wccfidtutax2upE2jJQ8jWRzA5v4ggehh/3huGjq6eIznyF6jxF5Cs9yFI+dOOdz0YBnT3jRCNpydGCwXdMLjQ1peVSywKAuUls5NPDPgdBPxOIh2Ze4Iam7uJxVPYF/CcjSO/0IPNlg4ilq8u4+jBVqxWE76AA5NZmbZh91oU5LrI8dmzVkMutPWRSKrTuuvOFYZh0HKpP2tzr81qorxoetW42UDXDbp6gySS6oJVESBd4Wm9nF14QpElSjJMLIsKvdjtZjq7hikvz5m0n+PKTW1tg+TlufH5pr5fFxKCIHBbQw2qprPzSCOJlEpJjofP37cVdwbVsdmMu7Qqj5f2nEbLMHkZGY3RemmA2sq8WaWKazw5XBge5MfnTlDkdFHh8mK9ikIjCAK5Nju5tivHrdjpptiZjj06w6PUenNYnpNOYmdDf/wII4kLs9iyNPpiBzFQkSWRluEhTvX2YJZlvBYrH13ZgCvLRGM6LGrg7zZZeLh8NU+1HmF/XysCAiu8BTxUtorlngJe6WykOzZCXFNxjtF2xl1sx8+cwGRt1kpnDqIg8OWTr1Joc/PRyvXkWqd3onP5HdzzxI1TPs8pmN3sUxIklrvrODR0dILyM5Qc5mjwJHdZbp2WYmFgcDJ4lv6r6DoCAivcdTMK2oZSkykGJlHBIc+9Kc4smrFJkyccMS1BVI2NmV9NRjA1OcBRBBmH7Jjz+se3/2oktCRhNUIeUwOalKESVSeXVk2iMmUfZgJZlLC9hy6okiBTbKsm31LOluRdNIWOcz50nP5EJ2F1ZEK6NjPSU2BZUHApPgLmIpa61lDlWI7PlL/oAb8sKJTbl+JRcjJvnSDgN80vSJsJ7JKbJc5VWSX0zJINyxyuhdnCLFlZ6lpLtaOB/kQXrZEzXAidYCDRzag6REpPkr7bp8vsjKc1BEyiGYfsxq3kUGgtp9y+lAJLOW7FjzyDSuBsYTEpmBdI2WQcZpNyXWMeQ+tCQEC2fghBnBy4CIKIKNciWe5EjXwX9BDMM/AH6OgJMjQcXdDAP5nUONOUvVLodlozBmbTweuyUVuZS1uWwL+9c5iLHYMsq1l4p+itt9RP/LxmQyVrNlTOeSy/10F1WS5tHZmpGE1tfQwMhym1LnyHbyiSoLG5J6v+fY7PQcUsJ2TXQ1vHEKPh+IIG/pFogvOtvVn/7vfaKcid+n4uKfZxx47lPPuzQ2i6QWVFAItFwdANQmMc/12vn+H+e1ahGwaDg1docCaTjPM6HkazhVmRuX99Pfeuq0PXDeQ56NxnQn1NAU6HJWP/jm4YHDzRxm03LJ0V3Sdgs/PEsjVz3qYih4uiGdB7OsK7aRl9hnG1oJnDwKEU4zZb+PS69QzHYvisViRRxPZB5PiLgsiNedUsdefRExtFFEQKrW5MosS9JSto8BURU1PkW10kdJUcswMDg8/X34zPlH6JL3Hn8pm6bRMTgzyLk79ouIOu6AiKKOE2XT+Ayy3yESj0jjWXMWeuvyiIrHDXUWjNp2OMJqIZGr/s3k2BJY8Gdz2SIE2ZbevonBu9wPNdL6NdFTznWnJY672+G6dhGJMMwyDt0ivPkWYDaZ7/tRQjVVcnceivXn9ci08KZSRBzkjJmSkkQZqyfs3QJsy9roVuaBPeA+NQRHlOga+ImLGqsdiQRZk8Sym55hI2+u9gJDXIUKKH/mQ3Q4leotooST2OZugoogmzaMEpe/GZ8vCb8/GZcnHK3kUJCLPBJjv5UPHvvWfry4Yyey2/WfGX7/dmTEAWFfItpeRbStno20FYHWEkNchgsodgsp+R1CBxLUrKSKIaKiIisqhgEW3YZAcu2YvHFMCt+HHIbhyyG1kwLTqdw241zcs9MxNkScRyPX1uwQqYQMgWYAgIwtjfM/T4zAVDwQjN7f0UF3gW5LgahsHl7uGsATpAYZ6bXP/0iahrIYoCm1dX8Nq+cxkzmaFInH2HWqiryl8UmsxCwWySWV1fzN6DFzLSffoGQpw630VxvndBJSUNw6CtY5ALbf1Zl6mrzk/zwxcQPf2jtHcOEfDNPQF2NdJViwG6erOrc5UW+vBmUIE639TDgXdaOHe+h//vyy/g9dgwjwX+kWiSkZEoJpPML144xs+fPzrpXb52dRmf+8yOeW//tRAEAUkQWMjHTVGem8rSHI6ezuyVc6Kxk0tdQ1SXBT6QtFeHUkKV6xGkWbiT98beZjTZSlxNsfN8I439/Xx63XrODw6wqbgEt+UDlvGHdLY+z+oizzp5RmSWZGpcmXmm1a4rGV+nYpkI+mFMFtPimJGazzh0XaftbBd7XziKrul86NO3EgpGMFkV8opnWZY1+7kj/xZ+2P7MRCA6mBzi6y3fZ5N/LWu8K/GZPCiCgmpojKRGOB48zf6BgwRTV25os2jinoId5JlnVq6/1nBK4IofwlwgZvi+Mda+PKP1C/Nbf/qhcI0WMUZWY63xCdTVEAVpbgIg76PDH4ybupkJmAsJmAupneH3xg1FNEO/QhUU0tfC+N7M3jU23XMzoawojI0lzN4BYKHH+6CjO96LW3Fhl214TDl4TDnYZD81DgtO5YOpsOO0mhc88JdEAdt1sp6SsgxV9KEnjyLKtcCVDGA6ITOCljyGqKxGEGdPYcqERFLlwNFWtq6vWhA1F8MwOHSyneEsyjGCAA1Li7DPgcqysq6YonwPl7qGM/5994Hz3HlTPWWFmVXPPihYs7yE/IAr434kUxpvvnuBreuqcM/AN2emSKk6bx1qYXg083mxWU1saCjHYl7YcCccTXDoZDurl5UgL8CELKVqHDzZzmg4c/+QLIusqC3M2MuTTKpIosCyusIpf0vfTdnjHIdjYbP9iwmb1cyN66s5cbYTLYN7dv9QmFffaqSsyL9g/RwLCYdSSrnzAUzSzJMDupEklLrEYDSGLErUBXIxgP5IhJiqMnuy8/vk3Pteo6u1nx/8zfPkl+bQ2dJHPJag5fRlutsGePwLd81qLFEQuSFnIz3xPl7teYPUmMRfSA2zq/dNXu/bh12yoYgyqqERVaMTy4zDLJq5p2AH2wNbZvYQF5iS2TYwpigMzQY6xqTqw/i+iZky6JnWb+hTvj8bGBNOeVevRsiawU9PNCYHLPrVAfCsVj6H73BFKcTtss6olHg1LWU2L+uh4QiiKOAZy+wYhkEwEed4fzeH+zpoGRliNJFIm7OYLJQ43Szz5VLny6XM5cEkTu9waBgGf4qTFAAAIABJREFUKV3nQnCAfV3tnBroYTAeRRJEip0uNuSVsKmghALb9E7OV4+nGjotwSH2dbVxcqCH/lgEURAosrtYl1fMlsJSCu2u93XCtdDY07+Pdd5VLHFeMc16d/AwAXOAjf617+OWZYdjETL+oihODkYMFV1tAeNK9dBAR7LcjBr9PoYxgqSsA9EBho6h96HGd2JorZhcfwXCwmRmDQOOnL7ExcuD1JTPLwNoGAaDwxF27WvM+viwmBU2r66Y03oCPgc3rq/mJ784lHH8rr4Rntl5lM89cRNm01Q/mg8KCnLdbFhZTkdPMCPt5vjZDg6dbOeWzbULkvU3DIPm9n7ePHghK82nvNhPw9KiBT9mum7wzrGL3HvLckoK5iesYBgGnT0j7DvUnHUZl8PC2uUlGdezelUZq1aWzmndH9BLKSMEAbasqeS5Xx6joyezwtrLb55l0+oK1izLfKzeLyiiDZucP8mNdyaQRRsCEm6LmaQW5nhPN32RMDk220QD8mzxnyLwbzvXRVltAY9+9g6+/qVnEAQBt9/J+aNtcxrPIpl5uPg+vCY3L3e/PsnUSjM0Rq9ylr0aIiKF1nzuLdjBlpwNM1LzgXRAbL+Gk57SU6T01JxVBVK6OoU6Iwsy5gxldgEBm2ydZCylGhpJPTnn9auGRuIaWpEsSpizUHDEDNSglJ5CY/YymQYGKX12WrvjDVIXLvZRv6QAkyKlj8REZnt8uSu/B0djnGvuYcPq8okpi2EwlqW/Mu7V2XDDgONnLmOxKGxZV4Wm6xzq7eCrx9/mcF8nCS3zdosI5FjtPFqznM+v3oJZynxrG4ZBfyzCt04f4mctZxjIIGn6TNMpar0BPtOwiTvKaqZt3DQMg+FEnO+cOcwzTSfpi01Vdnn2wmmqPD5+v2ET91YsxXSdRtAPOlRdJaJFSegJImpkopKn6RqDyWEC5sz9EO8XinPcrKtNS33Wl+cvmJb5ONIZ/yv3raEHSQT/GF1ru2opMf380IfRQ39LCssY/UcHIwKoIHhJhf8Vk/t/IEhTM5dzQf9QmJ/vOsHnP3nzrGX+roamG7y8t5GWS9kbL2sr8lhSMTfBBUEQuPumZew+cJ6e/tGMy7yy9yxLynO595blSAvEmc6E8YTFXMZXZIk7ttXz1qFmegemvgcjsSRP7zxCbWUexfnzo2CNS6s+u/MI3X2Zj5lJkbhtSy05i9TQ2t41xMtvnuWTj2yalytwIqmy843TWYNZgBW1RZQVZc7cpydRH5wgdzFRmOvi9hvr+MFz72Skxg2NRPnmk/v50mfvnPc1dj3M5l6pdn8YAwNJmF3PkVXOw22qxm12cF9tDkVOJ6IosLaw6IPJ8f+gwGSWScRSqGMOiJqq09cxiH0ax8TpICBgEc3cHLiRYHKUX/bsRjM0BATMoomUoaIbOpIgYhbNOBUH+ZZcVnsaWONdgT+LSdV08JsnN0SlDHWSi+9skdDiU5plLZIFWxaFIa/JgygIaOO6vIZKKDX39Sf1JBF1qrRpJkUfSDcTX9uQm9JTRNUYPtPsmuk0Q5vwD5gpDANOn+/iZGMHtVV5jIbivPjaybSMmknh9u11NF/s5/T5LgJ+B5vWVPLqm2c5dvoylzqH2LGtjkudw5w424HHbWX7piW0tvVz6lwnbpeVW29cytmmbs42dRMcibJxTQWGYXB6sJe/PPAqzSNDSIJImdNDkcOFQzGR1DSGEjF6IiEG4lGGEzFcJvOEFO7UfTDoi0X4n+/s5uX2JjTDwGO2UOny4bNYSWganeERLoVGODvUx1++/SoRNckj1csz6i+PVyL+33d383xrI5ph4DKZqXL78FtsJHWNrnCIttFhLgQH+dLbuwglE3xs6ap56Tm/3xhMDrGz+1VOjZylNXwR60RTsYFTdlJhL3tftw8gHk1yqbUPq93MvZvquG9LuolTQJiS4YtFk7Rf6MWb4yCvaPa+IKIoYLNcrYKhICr1COLsmzgFwQZZ6H5zga4b7N5/nmU1Bdy5rX5W+vrjMAyDY2cu8+xLR7NmlWVJZMfWpTimca+9HkoLfdx3ywq+++yBjAFNPKHyjSf3IUkit99Yh0mZvrI3WxiGgarpdHQP47BbCPjmRlerKQtw65Zant55FC2Dt8O55l6+++zbfPbj2/F5Zie9ePW2xhMqz718jDcPNmdt+l9Skcu2DdWL9rxRVZ0XXz/Nkso8tq2vQpzDejRN58DRVl7acybr9WWzmrhl85JFUUT6VYMoitx90zL2HmymuT1zX8eZC13843d28/lP3kxZUfo5tFD3yvi1Fook6O4bobI0Z0bJFKs8NwpjrnUdOZYGEqrEzqazdIyOYhjQF4nw0NI6LHMI/v9TBP41q8rYt/M4//tPn6SrtY8ffPl54tEET/zZ/XMazzAMIlqUZy7/gj19+9AMDbNoYltgMzfmbE4z5Q1jLPBPB7NuxTVrh9urUWydmgHrivXA7N/TQJqaNJqanJHxm7xYpMwvrkJLWkHmanpOT7wfzdDm1GQcUaMMJydnN7yKJ+vEwyyZpqgNJfQkwdQIxcwuO5jSU4ykMldlskEUBeqX5NN4oXuica1vIMRj96/jnaMXaWnrp6W9n7IiHyvqi/C6rWxYXY4kiTx67xpGQ3H2vtNEXU0BTa29NF7opqzYh2YYvH24hbZLg5xq7OTuW5fz5tvpsnVC0/j3ljM0jwxhEiU+UbeaT9avpcDuQBYlDMMgnErSGw1zaqCHo/1d3F5Wk/UBl9BUvnX6EC+3N2EA24sq+NyqzdT78rAqCrqh0xsJ81zLGb5+8l2CiThfOXaAWm+AlTn5U8ZVdZ3vnj3M862N6IbBpvwS/mjNVpb787ApponqwvOtjfzzsQOEUgn++fgBlvlzWZu78KX39wq55gCPlz6MpdNMpb2cUlvaXEkURFyya8ZO3osFwzA4+nYzXe2DrN5SnVZGQ8DgqsqUbkz8LgjQ1txLy7lu7nlsw6zXJ0sSKyoKCEXTk+nq4krM7rk9WxcD4WiCbzy5D1EUuHVLLYo8s4DZMAwMA06e6+Qr33uDwWl8CsYDzPlAFAUevL2BI2cuZW1eHA3H+efvv8HFjgEevWsNeQFXWiNkjsEzgKYZ9A+FONvcw96DzRxv7OCzH9/Ojq1L57QfiiLx4O0rOd7YydkLUxWQdMNg94HzGIbBbz92A0V5aZbyTPfBMAxGQnGe2XmEZ395jEQycxXU6bDw6N1ryMtZXOnmwWCE//PDvQjAlrWVM1awMQwDTTd499hFvv7jt6Y1H1u5tIgNDe9/QuGDgvyAi48/tIG//eZrRDLIxxoGHDzRzn//yk5+85FNbFxVMdHjMZ97JRpLcqlrmIMn29h/uBUE+Ls/e2hBe1auhSjIiIJMT2wEEPijzVvQDYOnTp9iJJHALM+e+veeBP7ZZuOLjfGD4clx8qkvPcjxt87T0dKLx+9g9fY6CivmNgNTDY2Xunfxet9bE4ZXd+bfyoeK71k0tZhSWxFWyUpMu/JwuBBunVPgbRgG7dEO4tco6FTas+vBF1rzcch2hq4K1i9G2knqqVlPaAzDoDPWTeSaikOZvTjr8TOLZgJm/yS6UVSL0RPvY5lr6awu/JiWoD+eXZljpvB77XjcNqxWEyBw69alvHO0lRd3neKhu1alFxq79lOqRjKlYbOa2LKuEp/Hzq43G6ksDyCKQnoyIQi4nFZ8XjsCAglN5cxgHwD5dicfWbqKEueVVh5BECaswqs9fh6sqr92EydgGAYnBnr4WfMZNMOgISef/7X5NspcV2aOkiBS6HDxO8vWMRyP8p0zR+iOjPKT8ydY7s9FvuraMAyDc8P9PHX+JJphUOPJ4a9vuIMq95UsryAI5NkcfLJ+DcPxGF87+Q6D8Sg/aDzGykAByq+o94AgCFglK1v8G3ErLrymuZvSLAbOnbjMnpdOYrYoVC8rZHgwzP5dZ4nFkqy7oQZvjoP9u84QjSRYe0MN1fWFFJX66bw8t3vCJEs8sr2BR7Y3zGj5bO+DxZwIDg5H+Mp33+B8Sy8P3L6S0oIryjLXrnd8+0bDcV5/u4kf/fxgVvoNgNWs8Ojda/B55k8n8bhs/N7jN/L/fOXFrOuMxVM8s/Mo7xxr4/Yb69iyppKSAu+Mm1eTSZXh0Rh9gyGaLvZx9MxlLrT10TsQmsjQz/edXZjr5hMPbeDvvvlaxgmTpum8tv88l7qGeOyetWxcVYF7TFIy03Uwvj3xRIrTTd08/eIRDp1qR1UzV4dkSeTOsWOzkApC2dDZG+Rvv7mL+29r4O6bllGY6572+jKA4WCUl/ee5ZmdRxgYzj6p9LisPHTHKlyLGFxmO98zuQ4M472/pwVBYNv6ahpbevnpL49mvQ6a2/v5q6+9zLoVpdy5rZ766gL8XvuMrgndMAhHEgwFI3T0BDl5rpOT57po7xoiNNaAXVX23tE6rYpCe3CY7x07RkrXaBsOIgoC64uKWJY7O4rhogX+6YvbYDgRozs2QndslN5YiNFkjISuoi/iZMAsyfxG+ZoJff+B7iDhkSg33LMKeazTe64X5HjQuqfvAJqRzvwWWvK5Pe+mRZWIzLPkUmTNpzl8ceKztshl+hOD5M/SvVY1NE4Ez0xsP6QD6xpnVVYn4Byzn1Jb8aTAvzPWRVe8hyp7+azWrxkap0fOTeL4m0SFakdFVidgURDHJgbKxPc0Q6M5fJGtOZuyViquhWEY9CX6GUpmVs/IBlXTab88RHffCM0X+ygtmkzXSqkal7uGcTos9PaHSKU0HHYzI6EYR05doro8QHmJn+BoFEGwIYkiiaRKPJ5iNBzHZlWwW03s2X+e5vZ+Nqwqn7T+pKYSSU3fUzHdOUjpOrsuNTMYj6KIIg9XL6PUmTlgNUsyd5cv5bnmMwQTcQ71dNAdCU+adOiGwauXmukba+J9uLqeSlfm8pMsiNxTUctTTScZjEc53NtBZ3iU8izL/6qgzFZCQk/SGetCERRyzP4xit/CUTDmwreuW1VKZ/sg/lwnKzdU8urPjhCLJnB6bOzbdRp/rotIOIHbZ2ffrjOUVC6Mis71kN6XOHrqDFryEIbWBYKCKFcjKRtArkBYAKd2AJ8nXTkcCl5JLoSjCZ79ZZoasnpZMRtWllNa6MVpt0wogCSSKkPBKKebuth7sJnzF3uzBhWQrpbcsqWW7fPM9l+N+pp8/uCJm/i7b+wimCULbBjQ3jnEt57azzM7j1JS4KGyNEBpoZccrx2rxYQoCqiqTiyRYjQUYzAYoadvlJ6BUQaDEQaHI1mz5fOFIKQlSj/ywDq+88zbGZ2ODcPgfGsfX/76q1SW5rBxZTkraosI+B3YLCZkWUTTdeJxleHRKOdbenn3xEXONvcQiWZ2OU6vGzasLOMjD6xfNGpMrt9BIqkyErpCGQ2Oxvjhvx/ktX3nWLuilHUrSinO9+CwmVHGesLiiRSDQxGON3aw73ALLZcGMtKhxiFLaVrL+oa5Ne6OY5zGlUioJFMqyZQ28X9q7Od4QiUSSxKJJghHE0SiScLROKen8a0IRRJ876dvk+t3YreZsVtN2G1mHDYTNqsJkyJjUiSUsf/T/2QURcJikufVq2IyyTzxoY0MDofZfeB8VppULJ7irUMtvH30IvkBF2VFPqpKA+TnunA7LJhMMrpukEpphKMJgqEYfQMhuvtG6B8KMxiMMBKKZR1/Phh3803qI6T0SFYzSBEZm+LnwaV1JPV03HZDafqa8Fnn4GU05y3OAsMwiGopjg9e5uXOsxwf6qA7OkpYzazPvhhwKhZuLaidCPw7W3p5+p9fweWzs+mOBpZvqsaf70EQr+/6mwmXoh0MX2Wo5TV5cCmz026eLWyylbXelbSE2yYy3kPJYQ4OHeW+wjuyOh1fC8MwaItc4lxosoNcia2Qcnv2LniLZGaNp4EzI+cmVIpGUqMcHDpKma0YZYba2+mJUw+nRs5OMjnKM+dS46ia9nxU2stxKy76Elca7BpHm+iN91FqK57RudTROT3SOMmxeKYwmWRuu7EORZZwu6zcvr0es0lm4xilJxyOo8gitbfmj2X/DO66ZTmRaBKrxcRdNy/jctcwkiSS43dw/+0NBEdj1C8pwOexU5jvoasnSEN9MV63DZMss8yfx8HeDvpiEf7hyFt8fvUWlvvzZ23VHUklOdiTpg+4TBbW5WU/XoIgUOxwkWO1E0zE6Y6G6I6MTgr8Y5rK293tANhlhU35pdOOV2h3kWuzMxiP0heN0BEe+ZUP/AeTQ/y04xdcjnZSbi/j8dKH2dP3Fis9yykZo//MB4ahE9MGMUseJOaubR8JxzFbTRSW+KlbWcrRAxcwWxUKin0sbSiZSIYsJgzDwND7SYW/ihr7BRijTHJqlwpR7L+DbHscIavW/8yxsq6Ybeur+afvvj5FHrFvMMQrext5ZW8jVouCw2aeFPiPhuMZdegzYUVtEZ96dPOsHG6vB0EQuHFdFcmkyv/+tz2TJi+ZMBKKMRKKTRugvR+QZYkHd6wkEk3y5AuHicUzu+omUxrnWno519KLJArYbWZsY+pTmm4QT6QIRxKk1OufE0GA1fUl/P7Ht8+5R2Em2LqumvJiH9/4yT4iV01qDMOgq2+Ert2nePH1U9gs6UBYkSUMDBKJ9PU1s30R2LiqnMfuWTuvxmFIV1ieeuEwuw80kUqpJMcq0KmJSYA27QQkGxJJldf2n8/6d0WWUMYCfkW+EvSbFIn7bl3BQ7df38doOrgcFj77ie0YhsEb71yYdh9UTaejJ0hHT5D9R1rntd6FgGEYJPRhLgR/Qnd0Hyk9hJGlv8muFLEp78usyF8Ys8wFC/wN0vKKZ4a7+XbTfvb1tRJVs8/K30usvKGW0iUFnD/WxjuvnGT3Tw9SVJnLLY9soHZ1+azGMjCIa5MnMb2JfjpiXZTZSiY+W+gSl4DAJv869vTtpzfRP7Etr/XuZYW7ngp79sBrYtsNg5gW48XuVxlJXSkji6QlSp1y9gelgMBqbwNv9O/jYuTS2PphX/87NLjrZ0S3MQyDuJ7glZ7X6U9coRWIiGz0r8F3HcpEwOyn1lkzKfAfSAzxZv/bPFbyACZxegOk8WrNwaFjWf0KskGWRMqzOD+Ol/jdTitFk1w7BYqv+t1skllafeXGzc91k597JZi2WpRJXEHDMHiwqp43Oy/SOjLEns6LnBzsYWthOfdX1rEmUIjPkl7+ese+Nxqmf0xxxyLJaUnQZPbJeDSVRB3TSVZ1bYpaz0AsQm803dxtkRXaRoeJZVEcAkhp2sR4mqFPfPe9RlILo4hzd7y+GgcGD5Jj9rPBt5ZjwVMogkxCT9IV75134K8bGl3RgzSPvsjGwBexynNzOxUEgZplRZw51k48lsBt2KhdUcyZo+nfXbqVUDBKe0sffd1Bui4Nkl/snVOT4rQwYqTCX0ON/RzJtAXJcjuiVIhBCj11Di32AsnwVxFED5LlwXmfn1g8ycZV5fzmI5v59tP7JwVnk5dLZQ1Ir4fqsgBf+NTN5AcWnkMuSSK33VCL3WriX374ZlZ9/w86LGaFj9y/HlmWePKFwxMUiWzQ9LRSTzYt++kgigIbVpbx2Y/fRHnR4vodxOJJbt2ylOBojJ88f5h4Yuo1ZBhpFaNs1950SHtCFPL7H9tGzgJMYAwDuvtGudDWN++xZoOUqpFSNaIZCld9g7Prs8uGHK+DL3zqVlwOKy/tOU08sThVrIWGgUbLyLM0jfwYs+jGphQSTfWgGQkcSgkpPUxE7cJnrqfS9RCKuHAT2QUL/DVdY1fXOf7xzOtcjnywHlKCKODNdbHpjgZWb1vKiX1NvPj9vfzyR/tmHfgLCOSYfciCjDqW+e6N9/HVC99ivW8V5fYynLIdUZj64kw7xirYZTtek3vW1KA8c4Ad+Tfx5KWfTdB0BpKD/KDtST5V8dHrZr0jWpTnOl7k6PDJSZ/XOCvZ6Ftz3aqB3+RlR95N/Fvb0xP9AcOpEX586ad8svxxahyVGfd7HDEtzs7uVzkweHBS4F1qL2aLf8N1XXhNoonN/nUcD56aUDTS0dnbf4BCax7bA1uyVh4Mw2AwOczPOnbSGftgZcayQRAEVvjz+Ostt/NPx/ZzpK+ToXiM51sbeaW9iSq3nx2l1dxTUUuV2488TbA2kowTVdMvp87IKJ/b88KstiWhTn6YhpIJIqn0eP2xCH+4d+esxouri/dw1nWdlt4hDMOguiBnknfA2eCTFNm3kGOun3dgEEqFWOauw6u4J907ujF7idmroelJWkIvc2LoO8iCjbkYT6y9oRp5LEtYv6oMr9/B6EgMT46Dsuo8vDlORoNRvDlOEAQqavMpq85sqLgQ0NVzaInXkK2PYHL+VxCu8OEl0zZkyw4SwS+ixv4dyXwzCPPrm4hE07S4B3Y0IEsi33n2AMHR7M2Ts4EgwLKaAv7ot2+lpnzxjpkoimxZW0lBrptvPrWfd45dnFGmeCGwkDGzxSzzkfvWkZ/j5Hs/fZvL3dklK+cKm0Xhjm31fOJDGxfMSXc6hKMJZEnk8fvWpSc1zx8mFJn9ZCUTRFFgfUMZn3vipglFml9jeridFj7z8W3UlAf44c8PTut+vJAQZsy1mIqEFqQ39i4upYI1gT/HY67lzNC/Ek51sDbwF+iGRlvoeXqi+3GaKmYtAzodFiTw1w2DvT3NfPnULnpj2Zug3i9oqkZfxxCn3r7Aod1niEeT1K4u48b752CyI0CVo5wlzkrOjjZNfNwd7+X5rlem/erVgX/A7GeVZzmb/GvJNc/MXEYQBG4ObOVCqJV3h45MfN4UbuWfmr7O7fk3s9qzAr/ZhyKkO711Q2c0FaIl3MYrva9zdrRpErffrbh4uPhePIr7utsgCAKb/etoDreyp+/ARPDeFrnMv1z4NrfmbWOddxUBs38i+64bOiE1TFvkMrt693AyeGaSoZlTdvBA4V3kWa5/DARBoM5VwzrvKt7s3z9hYBbRojx16d/pjveyPWcL+ZZcFFFBEAQ0Q2MkNcr5UAuv9LzOhVArBgZm0URST02iG80GqqbzbuslTlzuIc/lYEt1GS+dOo+m6VgUmQ2VJbT2D7FjWTWnOnpIaTqarnO0vYtCjzO9/MnzaLqOIkncv6qO9sEg77Zexmu3cteKJbisFiRRZFN+Cf96ywPsutTMTy+c5vRgDzFV5exQH41DfTzVdJIHKut4on4NRXZXxuOY0rUJp0MB0pKfk5YbMxiYAgMlgyFYStdQx7iGV8abOFNTxrj6c4HFd04OxRO8cqKJL967DdNVUmvDiVYuR/azJffP8ZmXzCtAKLIWcGz4JEucVcS0GI2hJjpiXazxzr18ndRCnAk+RWPwWVJ6GJcyN26vN+cK9VCSRYqvETIoLp/clOb1L67TsK42g5FEtt4LQoaKi1SGZLkNNfoTDH0IQZxf4B+Lp0gkNdxOift3NFCY7+Y7Tx/gXGvvvLi6VovCbTcs5ZMPbyIvZ2YGd/OBIAhUlPj5b39wF2++e4FnXzp6XV74XCFLIsUFXratr2Jl3fypauMQBAFFkdixtY7q8lx+/ItD7DvcPC1Pf6aQJJElFbk8ft86blhb+Z6Zm0WiSVKqhsdl5fH71lJS4OEHz71Dy6XBeTVGuxwW7r5pGY/fvw6/Z2Eqk/8ZIAgCFrPCvbesoGFpEc++dIw97zYt2GT/WrgcFuprCrj3luVz7iNRjSgxtZ8yx114zXVj6j1mdENFQMIieahyPcxQ/DQXR3+By1+JvAA0SFigwL89PMT/Obf3Axn0Axx/6zzP/MurFFYEuP3xLdSuLsPpnZsCg4CAS3bysbJH+be2pzkfaplx8KijE9cTxJMJBpNDnAtd4I2+fXyo+F62+NfPSB3HKln4aNkjhNQwZ0evcOt6E/38qP1Zdna/So7Jj0txIgrihGxmf2JgioOwXbLxWMmD1LtqZ9xPZxbT5mWjqTBHhk9M7PtAcohnLv+CV3reIMfsw624kASJmBZjOBmkLzFI8hrDLqto4YGiu1jrbZjxA84smrmn8HYux7poDl/h6UW0KC91v8b+gYPkmXNwKekAOKJGGUoOM5AYmqjQFFnz2eRfz8vdu4lo0/Nns0EUBUr9aXOQN89fpDzHS9fwKE/csIZ9F9rpD0XoGw3TMTTC+Z4BqgI+jrV38ei65ey70M6Rtk46hkb4xA1rOHixgyPtnRxp62J5cR7nuvs42t7FTUsrgfRDzWe28ljNCu4uX8KJ/h52XjzH3q42usKj9EbDfPv0YY73d/PXN9xOjWeq0oAkiBPVmGqPn79YfxMe88xUIgSY0gh89XglTg//feMt+CwzbzK6ul/geoinVH727mlkSUQUBO5es5Q3TrdwaTDI0sIAG6tLeO1UMx2DIywryePGpRWUB7zYTJmrP0OJJt7p/1u25P45HtP0fSXTYYNvHUPJILt69xDVoox2j3JT7lZKrHMLmiKpXo4PfZvW0C5044NBk1wwjO2PIGTLxooIghPDSMI8KyaQpmKkUiqCICCJsHFlOdVlAV5+8ywvv3mWS11DGbXys8FmNbF8SSGP3LWadStKF1xDfzoIgoDdauKu7fVsWlXOu8fbeOWtRpou9k0rATkTWMwKBQEX9TUFbF1XSX1Nut9oMfZMFAUqS/z8ye/exl3b63lh9ymOnb183R6GTLBZTdSUBbhjWz1b11VNNHO/V+ckEksH/oIgoMgSN29aQl1VPi++fprdB87T2ZvZuTgbnHYLq5cV8/Cdq2lYWjhjydlfYzJEUaCsyMcffupm7r9tBa/ua+TdY2109ATnVS0TBAGv20pZoY/1K8vZuLKM8mL/vEwBMXQMQ0UWrQhjoiqKaCOlh9FJP7sU0YXLVElf7BCqHkEWPyCBf0rXeOHyKRpHerMuIyDgM9vItTjxmm04ZPO0tIT5wiopuEwWVE3nQtcAtkI3X/h0m2qNAAAgAElEQVTHjxEo9iHOsaF3HIZhMKqG6Ir1Is/QeXc69Cb6+d7FJ1F1lZtyb5iWKgPpCzDH5OPTlU/ww/ZnOBY8OaGtb2AwlAxOUt7JBr/Jx2MlD7DZvx5xzFVzJhAEAa/i4ZPlj2OVLLwzeHhiQmFgEEyNTDiZTgeP4uahorvZHrgBWZBntf5CSx4fK3uE7198krboZL3rkdTopP6Fa5FnDvCR0kfIswTY2//2nAP/kVicnSfOU5OX5v3rhkGO047PYcNuVpBEkYqAj4MXOxAAn8OGLIl47VbcNgsDoQg5Tjt+hw2H2UQkkSSeSmFVZLbWlFMRmFziHb9mXSYLWwvL2FxQyqVQkBcunuPJ8yfojoQ43NvJ108e5K+27MAqT742XSYzVllmJJnme9Z4cmYVfF8Lh8mMTVEYTsTQDYNqj3/RmnV1XedcVx+/e+tG/E4b7QNBjrd1saW2jF0nL1BbGKC2MIDLZmFv40U2L7m+3vVAvJF3+v6ezXl/hlspm9MzwSpZeLDoHm7N3U5cj+OQ7dik2SssGIbBcLKZIwNfozt6OKuyw68yBHHsPlGbEOXaDFySOLp6Lr2cODdjxauRSKqkxtR4xs+t32PnIw+s545t9Zxo7ODgiXYutPUxPBIlHE2QTGnouo4siZhNCi6nhVyfk4alhWxaXcGSilwsZuV9C8gEQcDnsXPn9npu2byEy93DnG7q5lRTF20dg4yG4oSjCRJJFVXV0HQDURSQRBGzScZiVnDYTLhdVkoKvNRW5lFTHqAoz4PHZZs4JYu5f+OZ2XUrSllVV8zl7mGON3Zw7MxlLnUNEwzFiEQTpFLpCqUkiiiyhM1qwuWwUJDrYvmSQlbVF1NVFsA+lm19r89JPJGaqLqMrzs/4OK3fmML9966guNnL3PwRDutlwYYHo2OVQhUdN1AliQsZgW300JewMWqumI2riqnqjRn0SoWkiTy2L1ruXVL7YKPPVcsRm8MMDEZW1KRS015Lh+5fz3Nbf2cONfBuZZe+gZChCJxorEUKVVD1dITAnHsWjObZGxWBeeYiV1laQ51VfmUFfnID7gmgv35nidRMKNITmJqH7qRQhLMWKQcEtowMbUPi+QjHVVpY4o/C0fzm3fg3x0d5bWuc5OMncYhCSL1nnweKlvJ+pwy8q0u7LL5PTOWDseS/PVTu9laX86dVWU8+Y8voak6D/zOzURGYpitCjmFMw9WUrrK0eBJnu/8Je3RjgnKjCRIuJW0cU9a6/wa3V4MdEMfc6uNEtWik2oEcT3Oc50vUu2smDADmg6CIBAw+/m9qk+yu28vr/XupT+R3Ur+alhEMys89TxQeCcV9rIxJ8/ZnRFBEPCZ0sF/laOCV3veoDveO6PKh0k0Ue9awn2Fd1DrrE5POuaw/iWOKv5L9W/yXMeLHA+enlJNuBaSIFHrrOaR4vuodVYTUsPkmHwzPm7XQtN1YqkUCVUlFEv3OwjX/KvNz2HPuRbuXFFLgduJWZb55ckmukdCrKso4mBrx8SVYjebqcnzMxJLc0fNUvZ+B0EQkAWBCpeXzzZsoiEnnz9+65f0xyK803OZ7kiISvfkiUOezUGO1U5PNExfLExPJDSvwD9gtZNnddAZHmUgtvgqPU6LmVy3A5MsEYknkEQBsyzx8MblBCMx3jjTworSAjRdn3GpvS9+knf7/p7NuX+KU5mZKtTVEIQ0w9NtcuEm/RLrjHVjGAbFtpmZyumGRk/sCIf7/4XhZAtz4fPPFaqmMRKJE4omSKTUWWXAp4PfaSPPN1nlTFSWI4h5pCLfQhB9iMqqMZ6/jqEPosVfQou/gmR9AEGcv7yopqWfuVcjfb4gx2vn1i213LK5lkgsQXAkRiiaIDV2DGRJxGKWcTmseN3WCUWVD0oGVhAEzGaFqrIAVWUBHtjRQGxMFjgcSRBPplBVHV3XEUQBWRIxKfKEgpHDbp7kNJptv/rDEf7+zX1gwJ/cfCN+++wnZOFEgm8fPEIsleLTmzbgs10RI1AUiYoSPxUlfh7Y0UA4kmAkFCMSTZIcOxeSmF7ObjXhclhxOswTLrzv5/lQNX2KJLkgpF2x83Oc3LmtnjturCMUSRAcTU9mkqqGrhsosjgW+FvxOK0o85QYnwnGM+H/mXoGxs+H32PHt9LGhpVlaLpBKBwnFIkzGklwtq+XwWg6+ZcO/NPnJtflYH1pMU6bedJ5yXSONF2nJTjEUHxq9c2uKCz1BVAyvM9NkguXUs5Q4ixJfQSrmIvbVI1uJGkZeZZq94dJakH6ooewSD7EGSonzgTzCvwN4NRwJ53RqRlmiyTzSPkafnvJFvIs6ZfA+3GjGoZB18V+vvf0cfz5HnraB4hHEzSdaKf38iAf/vydMxpH1VV29+3l2cu/IDpmomUSFVZ7VrAtsJkCSz422ZrW8L52G0hrzif1FBE1Qnu0g129e2gJt00sM5QcZv/AQUpKi2aU/RYEAZts5d6C29ngXc3h4RMcC56iL95PWI2Q1JMYgCzI2GQrXsVDjbOSDb411DgqrquAM5P1W2ULO/JuYrVnBceCJzk6fJLueB9hNUxCS2JgIAsyVsmCx+SmylHOBt9qljiqsUqWea+/xFrEpyuf4MzoOQ4MHKIteomRVIiElsDAwCQqOGUHRdYCNvnXsdrbgEtOUw2skoUVnrqJyYpVsmCXZ07/8tlsPLx2OcFYnN+9KQ+f3UqBx4lFkdlcXYYiiaiaTpHXTVXAh82k8OCaetoGgqwpLyLXZSff5cRiUthQWYwsisiiSGv/EJIoYslCU7n2GEiCwPrcIup9Ad7sjDB6VRPv1XCZLGzIK+b0YC+RVJJdl5pZnVs458qbXVbYXFDK0f4uEprKy21NbMovXdRK3jhKc7zYzCYSqoYoCFhNCqqmE4oliKdUEqpKU/cAvSNhmrr6WVIYmMTzvxo9sWO82/8PbMr9Yxxy4byfUa3hNiRBmlHgr+lJLoZ3cXzw20TU7BXThYSuG3QOjLD/9EXePttO58AIo2OBv67PteNlMh67aSV/8NDWSZ8JUiGK43dJjv4v4sO/jyiXI4guDEPD0PsxtMuIyloU28dhHtKl4zAwss6hxs+xIKQpFk77++u0PFdcfa3arGnddGY4Z0pqGiJMBNGZkFBVTnT1pGUH59iM3x+J8vPTjaQ0jbuXLpkI/Mcxvg+SIOB2WhfVAXVBYczk+voV26f/wBg/J7Ik4HXb8LpthJMJ/ub8PvZ1tE9ZfmVuPj+sKp2RullcU/n7g/vY1dY85W91/lx+dO8jGbX2ZcFGvu0GOiN7SOkRrIBDKSHftpmLo7+gO/oWmpFEN5Is8/0XFHHhJOPnFfhrukbjSA+Ra2Q7RUHgnuLl/EHddlzK/AK8+UJA4GJjJ9ur83j0D+7gG//tWQRBwJPjpOlY24zGMAyDc6Fm/r3jxYmgXxFk7iu8g3sL7sA8iyA6x+SjzFZCtaOCvz//NXriV+S1zo1eIKklMc/QjGo8W59nyeWegh3cnn8Tw8kgITVCQksCBrIoY5dseExubJI1PakQmEcv+rXrT8ts3p53M7fk3shwcoSQGroS+IsyNsmKR3Fjl20Ltn41pRENxXB4bKzzrmK1Z8UEzSeuJ9A1HT0BfruHgN0/ofYzfp4UQeHBwrt5oPCuSfszU4xz/K9uvXRZ0wFEwGmnbzTMy6eaWF6Uh89hS19zNiurSq+8BIp96Yx7juPKhKOhpGDi55SmoWNglqa/TSNqiuFEWlHCrpiwZFheEUXuKl/C862NDMSj/Lz1LFsKS9lWVDFto62qa6i6gUWePKYoCNxVvoSfNp+mNxpm58XzbCuq4LbSqmnpaqquo+oaFnnmwZ1ZkfmNzWl1FoAcp43HtjTQMTiKz2GlyOee2IdlJXnIooTNrPDA+voZODQadEUPcbD/K2wKfBGbnDftvTyYGOLlntdI6pklIDtjXWwL3HDdfUpqYRqDz3A2+DRJfWFk7a6HSCzBC2+f5ek3TnC5P7hoJorJDFxaQRCRLHdhEj2okR+iq2fR1UsgiAhiDortk8i2jyJI2b1Efo2FQUrT+MreA1T6vTy0vB5pEY93jt3GffW1pDSdUu8Hy+H61/jPDUWUuKWsCq/FynA8xkgiTvPwEFE1xWwejYoosr2kAqusTIzTEhwinEpOy4IQBIESx+0U229FFtMxgCRYWOr9FJJgoT9+BAGFIvt2KpwPILBwfivzCvxjWor28NCUz0tsXj5WtWHGQX8knuSt0628dfoi0USKpcUB7lpfR0kgrTRz8mI3u49d4OaV1ew93UpL9yA5Ljt3r1/KqqpCJFHEMAyCkRg7DzZytLkTj93K9hWVCAIoikwqqaGPcfJ03WCgaxjbDGfiKSPFG337GFWvaI9XOyq5M//WGTvGjmP8eBRY8ljuWjop8B9VQ4TV6IwD/2vHNAkm8iy5zM68ef64OpjOteSQS2Yb69F4nOb+Ifx2GyVe97yUXfq7hvnZN17n0c/choCA3W3FZ/fiN/sIDUd47huvc2J/E56Akwd/6yZWbKpGlK4EpBNZmUUingWcdj62eTWCMPdKV1NwgG+ePsTa3ELW5BZRYHPiNluQx673hKZxORzkh43HOTeU9nZY7s8jzzZVoUUQBBpyCvhwbQNfP3WQ3miYv9j/Kp+sX8stJZUErHZMkoSq64RTSQbjURqH+nirs407y5dwd3ntlPGW+gJ8om41Xzm2n6FEjL888CrNwbXcVlpNrs2BeWy8SCrJUDzGueF+3uho5ZaSKh6qqp/xcZBEkbLAFRqRIAjkuZ3kua9kQJYWTZZVbCgtYOYw6Iy8zUG+ysbcL2CVcrKes1E1RG+8n+XuOsQM8rMxLXbdayqq9nN88Du0hl5BM7J7KUiCiULbRuQF4LyHYwm+tfNdnn3zBPFFcmudHgKCoCCZbkQybcDQ+jCMUPplJvoRRB/Mgfb3a8we/eEIe1ouYlWURSeWOUwmvrAtPRH+9Zn91UdK13j23Gl0w+DRpcuvm5T6IMMkSTyxfDVPLF+NYRj0RSN8ZtfzHO2dndy3Ikp8pL6Bx+sb0rLhsSh/+PpLHOi8dN3vyuLUCphVyqXB/zlSehRBkMaUfObXmzplvfP5clJT6Y9PNeLZECijwumf0YbGkym+v+sw+89cZNuKSuwWEwfOtnG4uYP/9vgOSnM9DIdjvHKkiROtXayoKGDDkhIOnG3jr5/azf//qbtZUhQglkzx9Z3vcPD8Je5atxSTIvH03hN0DY5y26pywkd6+NqfPUVHcy8//NsXiIzE+Pif3jej/QwmR2mNtE38LiKywl2HfQ6NfONI+wFMNoRSdXVCeeY/Ik539/G5517k/uVL+ZPbtk3JIs8Gakrj+L4mLpy8RCgYpagiwMf/+B6qV5RwcPcZ3n75JFvvXU3PpQF+9A8v8cV/+hj5ZZknJIuBcX7hfBBTVfZ3tfN8SyMus5l8mxOfxYpDMWEAwUScjvAIvZEwOgYlDjefql+LQ8ksL2aWJH5r2XqG43GevXCKzsgoXz68h2+fOUTAascsSaTGA/9YlFAyzaXfVFCScTxZEHmibg2DsSg/Pn+cvliEvzv6Ft89e4S8qwL/8YnEaDKBYRisDswmKF9YKGJmOpeBzuXIXsR+mQ2Bz2ORMhsAmUUTa7wr2ZqzKaPvhEUyI2WpeBiGQTB5kSMDX6MrenDaZi2T6GSZ93GWuh+et4Sbpum89O45ntt78n0K+q8gfUwtCPLcZEp/jfnBAFqHhukceW8U+Mb7Kn6N/xjoCYf55vFDeK1WHqip+5UO/Cddm4KAWZKuK66SbRwYm9gKAiZJyvoOmPl4EiZp4ag912JeZ001dMKpyRkrsyhT5ynAPANpSoDzHf3sOdnC5x7Yyg315QgC3FBfzh9/+0X2nGzmE7etAyCpqmxdXsETt61DlkQ21JbwJ9/ZyamL3SwpCtDcNcjbZ9v4rTs3cN/GekCgriSPL/3by3gDLh790iaO7mmkqDIXd46TNdvrKKmZmf3xqDpKRL2i/iIKwox056+Ha+kCsiiPNQf/x4RhGOiGgW6wID2MqWSKsiUVrNy6hKNvnmPnv+3jd//Hh+ho6aVmZSkP/94tRENxvvqnT9F04tJ7GvgvBFwmM6VOD8FEjGAiTjCR2SDGIsmsyS3kMys3sTE/O1VCEAS8Zgt/tn4b1R4fPzp3nIujw/RGwxmddM2STI3HP0XK8+rxHIqJ/7r2Rqo8Pn5w9hgtI4P0xyITLsFXwyRKVHi8i9oEfD0s836Y0VQ7/fEzU/5moNMefgNJUFiX81nMkmfKscy35JFrDqR7eTIc5xXu+ozNxYah0RM7zuGBf2Eo0cR0N4BdzmWV73cod96GJMyvFwegc3CEn711iug17qKSKFDgc1Ga5yXf58RuMc2AGnV9rK4umvJZWqYzAoIbYR4vxfcShmEwEo/zVms7By930BsKp6tNDjvLC/LYUFJMmXfyNWIYBpFkkv1tl9jfdoneUBi7ycSaogJuraki3+WcCDY0Xedv3niLXIedW6or+cHhY0SSKR5eUc/a4kJeaWrm1fPN5DrsfGztKsqvWVdK0zjR1cMbza1cHBpGFASW5OZwW00VtYGcSfz9wWiUV85doLGvn6Od3cRSqf/L3nvH2XWV5/7ftdvpfXrv0qgXS5ZkuUhuuBvH2GB+1CSUOLmXBBIS+JncXJIAoaQACSHYgAmYUIwB924VS7J6r9N7nzn9nN3uH2eKRjMqVrOd8Hw+9sees/fafa1nvet9n4cnDhxiR2fX5PkEnE7+19WrqY3MVBRLGwbPHz3BhpZWBuIJAk4HqyrLWVdXQ9A5fWU/pet8d+sOdnd1T/6twOvl09deRaFv5mrkSDLFNzdvJeJ28cDSxezp7uG1plZ6YjF8Do0V5WVcX19DxO2e8S0kslm2tHawsaWV7mgM3Zw+mXYqCh+8Yilrqn430bxQ2LZN0+gw3YkYQefv6hbeybgg4m/ZNro1/UNzyArFrtlNhE6Fbduc6B6ke2iMR55/g8de3Q3kOrS+0Tidg2OTeaguTWV5fdmkGkFewINLU4mnc/UFXYNjGJbF/MqiyYKM2uIIEZ8bRM659/r7rsS27ak883McUA3LnKYQYY9f+4VAt/VpqwgAXsWDR7nwZf3/KcgrDnLfn9xESVUeNfPL+P7f/5p0MoOhmzhcGrIs4Q97yC8JMdg7VYBuZndhY6JoKyb/ZhktWGYfiuNKJhalLXMIM7MZxXUz4iK65p0r6oIR/nXdnRwa7mf/YB9tsRGG0inSho4sJIIOJ7XBCMsLSlmcV0TQcfbUOiEEPtXBhxqXcWNFPa8eO8Yb3R1EHSZpw8AhKwRlB/mmxrqFc5kTzCcyS2HSydBEF/fXFXNNkWB7XzN7h520jJ4glunC62gg31NEbSDMwkgB9X6DsGOMTGYHkhREUSoR4/UXlpXCME6gKLVY1jCm1YdAQ1GqmNB/N4wOLGsMVZ2LEFPdl2kOY5htqMocpDOkxgS1WlYV/AVb+v+BwVnJv0lz7AUkobE87xM4ZD8nJylIJ/kXzIaAOlOizrR1WmMvsWfoP4gbZ15GDmm1LM97kGL3FQguXMvbtm22H+mgrW+6m3pJxM8D1y/l2kW1FIS8yNKEFtWFY7ZTtvT9ZGNfR3Hdg+r+vYt2rEuJgUSC//Pcy2xqaSPsceNzODAsi73dvfxi30E+eMVSPrvu6sntbdumP57gSy+/xkvHmwk4HQRdrhxpPnaCX+4/xEM3XMeyslzhtw3s7+klrRvs7OymdWSErrEYe7t7uH/JQn66Zz+SELx0vImeaIyv3XHLpDdFWtf53hs7eXTHbiQhEfG4MC2bTa3t/GLvAT51zRrunN84WWzfF4vz/LETZAyT1LjjtmFZpHVj8nk5DGPWcS1rmnxny3Y2NLcScOakuHfGEzx1+Bi3NXbyueuvIeiaIoKSEOR73YTdLvrjCfb39JHncZPSZ19typome7t70E2LwUSS544ex6NpaIrMQDzJ00eOs6mljYduXEfBSfVQ8UyWb27awi/2HaTI76MqFCSZzXKof4BEJsu8wgJWVZbPKCj+Hc4PFjY7ervImpfHPfp3uHS4IOIvhJih4iEJgVs5dyeztG7gcWpcu7CGoGfqA71tRSNVhaHJ4UGSBB7nVLuTizTj/VR2XOFDOSmPW1PkGdJf5zOQOmUnmqSSGH/fLduiNdnOGnvleeWp27bN/rHDHIs1Tft7tacCh3T5CeY7EYoioTpUzHETFdMwScTSjA3FSSezWFbO11eWBJIksIypiZtlDYFtYFsxbCuKkIJYZg9C5AYV29axjBZsawDLbM0ZCr0FPEUSgkKPj0K3l+vKamZVKplYrjzX99q2bY7vacMX8lBWmUfjqJtyo5yrb5tysY4Ox9n/+nGuKj4XfXuTWOw72HYMpzXI6kAL64vfSza7j6x+GJdDIhT8AEIoJJM/IZ74IaNpAAvbTuNy3kbA/2cI4cQ0OxgaeRCX8xYy2W3YdhLLGkNT5xEKfglJKiSb3c1Y7MvkhX+AotSPn59NIvEoqfQL5EV+AJye+AshCGl1rD4j+Tdoij6FLFSWRP5gnPyfH3QrwZHRX3Jw5DEy1un9LQQSRa5lLM97kLCj/qJFxTO6wf6WXjInka7isI/Pvm89a+ZVXrCvybnCMpqx9EMI9/su+bEuBizbZkNzK682tXDXgkY+vmoFQZcLwzIZiCc51NfPvMKCafcua5p8c9MWnj96grsXzOPDK5aS7/GQ1HVebWrhHze8zt+88DL/ds+dFPunlvGPDAxyS2MDf3Pz9fxox26+u20HP961ly/cuI45BXl89qnn2dvTS+fYGA35edi2zZOHj/K9bTtYXVnBJ1avpDzox7Rs9nT38OWXN/CNDa9THQ6zpKQIIQSNBfk8fN+7AfjPXXv58ssb+L1F8/n4qhXTxvDZxrOusSiyEHzu+mtYWVGGKssc6Rvg71/ewHNHj3N9fQ03NdRN1ZrJMu9dsoj7Fy+kNxbnjx7/DfHM2Q3pjg4MEs9m+dTVa1hbU4lTUTgxOMw/vLKRV5tauK62mnsWzps8zhvtnTx+4BDLy0p46MZ1lPh9ZAyDx/bs5182bmFhcSH/a+0qXOrFk0G8HLBtm9FMmmPDg+zt7+X4yBAj6RRCQNjpZk44jytLymkIRWaViZytvbie5cBgPzt6OmkaHWYsk0aRJEJOF9WBEAvzi5gTziPPNX1VxbZtkobOUCpJbyLOax0tACT0LLv6unHPItLQEM4j4HBOa2MgmaAtOjr5u19zzNrv2LbNsZEhopk0TkVhTjgfbZZrTOhZmkaG2dvfw+HhAQaSSWzbxu9wUBuMsLK4lAV5hTiVy+PkfCEYSadoGR3BtC1KfX6KPWd2BLdsm/boKAPJBIokUxcK49PeHG+8IOIvC2lWkm9a525AE/G5cagKy+vKWFB15tSbMz0+v9uBadlEk1OpR7FUTtP4QhHWgoS0ECPjxlQ2NtuHd7MqspxaT/WberEMy2D/2GH+s+0XkwpBkNPXXxledsle0r1dPTy2cx8fXbWczc1t7O3u5aqaSm6d18DOjm5+s/8wPqeDB5Yvpi5vKr85kc1yuHeAN9o7Od4/SFLXCTidLCotYn19DcX+mS+pbprs7uzh+SMn6IlGyfd6uGFOLZZln/YZpnSdHe1dbGhqpWssiltVWVZWwvqGGgp9M90+/WEvoTwfD3/xCRqXV3NwexOpeJpvf+5n9HcOU1FfzHDfGLIs0d85TMOSk5d6BbYdQ0/9CkmpQ5YCOYURDGRtMWZ2B2Z2Owgn9hnI2uVAYizJ5id3MzYUZ/6VtaTiafLLwhSW57HlmT00rqhh36ZjjA5Emb+qjtLaQjY/uQvbhkDYi9vvIpTvo6yuiM1P7aawIsJvv/cqnoCLZdfOQ1FlDrx+nO7mAWoXlrN47Ry2Pr2XTCqLEILje9vYs+EoetagZn4pi65qYMOvd9LXPoQkS7z749cCBobZTjj4NWLxR0glf0Io+FUsa4hY7NvY1gCSXIJDW4miVKPIVSAkUqmnicb+GbfrLjRtPgCWNUo6s4FQ4G9QlEqy+j6GR/6CVOpZPJ4P4XCsQsQ9pNIv4vPWAQLTGiWVeRGncx2SdHad6knyn//nOfKfOTRjGwuDY9FfIwmVxeGPoMkzUxTOhqQxyL7hH9AUfRrDnj1NC0BCocp3PUsif4hXKb6ofUAirdM5MLXaJUuC21Y1cmVjBbJ8GVNu7CxCSAi5mHdCtN+2bUZTaSzbpjIUpDwYmAwiRdxu5hbkzdj+6MAgzx09QX1+hD+9Zg15nnE1L9vJ/YsX0jk2xsPbdvLU4WP8wZVTE22fw8E1NVXke9xcW1vN93fsojwYYE1VBZoss6i4kL3dvYylc+/QaDrNz/YewO9w8Kmr19CQP1VPd319LccGhnITkGMnWFhUiCKLSelfmC5sIEvSGSU9c9vDA8sWc2vjHGSRa2tNVQW3zm3gW5u3cqR/kPV1NZMkdDJ3Wog3HRi7e0Ejdy+chyrlCr1XlLu4c/5c/uHVjRzpHyBrmjgUBdu2OdDXR0rXuaamirJALsvArWmsr6vhx7v2cqR/kISu49bOPRD5VsOwLJ5pPsb39+/k6PAQSV2foQwjCUGey839cxfysSUrzkj6TMtiZ183/7p7G9t7Osfbmw5BLiXqwWWreHDpldPO5du7trGho4X+ZILBVILUuKzr8ZEhPvr04zOOJwnBI7fcw9XlVdP+/mJbEw9tfBGAR2+7lzWlp0+9+vstr7Kps43aYIQf3X4vhZ7pfe+27g6+tWsb+wd6iWYzM/0UAJ/m4Obqej6zci0Fbs/bmvz3xGP86ctP0xkb4676eXzx6uvxnKZOD2Ask+b/bn6F1zpaaAjn8Z2b7rq8xN8hKeQ7pz8U3TIZ08/NRlwIwcLqYvIDHh57dYwWCl8AACAASURBVDcfvXklIa+bjG4wMBanPD9I2HduqS91JXmEvC6e3HaIwqAXRZZ4ZV8TA9GZucZvFh7FzfLQIloSbZMfYX9mkO82/Yg7Sm5mQaCRgOqbNQUgl9duMWZE6Uz2sGVoOztG9hA3pp/XFeGlNPhqL/hcT4ehZIqXjjVjWBYtQyMMJpJsbGqlZyzGqydaMC2LtuFResZifPXud+Fz5F6kLa0dfOGp3Aeb580VgDYNDvP0oWM8d/g4f3f7jZSHgpNDuWFZ/HLvQf7ltS2YlkVJwE/XWJSNTW0sLSue1SQols7wb5u38cs9B/E6HITcLtK6zivHm3n60FE+f9N1zC2cXlPh9jm595M38PN/e5HXn9tHZUMxH/7LOxjsGSOVSHPiQCdfefAHWKaN5lRpvKJm6oC2hZF+CcVxFbK2AiFUJKUWy8wZKFlGM7K2EkkuJpuc2bmdC7JmjDG9Hdu28KrFuJU3X19g2zYHt52gt22Q8voiNv5mF8vXzePozhYkSWK4b4y2w910NfdT1VjCa7/awS0fXEvbkR7e8yc34w266Grq5+C2JpweByP9Ua66bSkLVtVTs6CMhmVV7HjpIKECPzfcdyVPfn8Di9bUs+Saubz0s60AjA3GScXT3Pqhq/n1d1/GF/ISH0uy+tbF7Nt0DE/ATXYUFKUGRWlA05aQ1fegqUswzXZsbGw7Vx+jKPXIdiGm2YdtJZGkENgGlj1yUl68jcd9D5q2AiEETulaVKUK3TgO2EhSPi7nLaTST+Nx348sR9Cz+zDNAVzOG4BzI7O5eod6VhX8OVv7vzo7+bd1jo79ElmoLAx/4LSFwbM9tzG9jV2D/0ZXYgsWpy+oVYSLucHfY37ofTikmTUFF4qsbjASm+qPAx4Xq+blfCYuJ4RcADiwrbO7ir8dIAnBkpJiQm4XP9yxm2g6wy1z66nLi5w2gnygp4+xdJo758+dJP0w7rchwXW11Ty6Yw+bW9v48Iqlk7/7HBp+p2OcuKo4ZIXSgB9VzqV6eTUHpmWhj6vSdY1FaRocpiTgYziZZFfX9Do7eXwV53BfP6ZtoZzjN3E6hF1ulpeWTJL+iWsqC/hRJIloOn1RpGEDTidXlJVOkv6J4xT7fWiyTCyTmTZ+6KYJNjOi3ookIQmRMxW9fH54FwWCHLE7MNCPQ1FYkF9AXTBCsdeLadkcGR5gZ283/ckED+/bScTl5oMLls46wbJsm1c7WvjrTS/RGYsigGKvj+pAiAK3B9O26YnH6IpHSeo6ywqn+5jY2PQlYqQMHZ+m4VFVWqOjpA0Dl6JQ6Q/OOK4QYlbSattgjr8jZ3sklm1j2vasprAAumWxp78HwzKpC0WoC4Yp9weQhUTL2AhvdHcwlE7x+LGDeDWNv7zyGhwXICRyqVEVCHFFUSnt0VG2dXfQPDrMwvzTB8GPjwyxf6APy7ZZXVJOkefNB6Uu6G44FYVyz/RCvYxp0JHIDeTnMogVh/z88Z1r+Y9ntvGXjzyNKstYtoXP5eAv3rPunIl/SdjPh29cwfee28b//s6v8To1yvKDzCm9cBdICYm1eavYPXqAE/Hmyb93pLr4j+YfUeQsoMRVRKEzH4/iRhUKpm2RMtOM6VEGM0MMZocZyAzN6jA711fPvWV3TGrNXyok9SzDyRTfuvcOmoaG+fSvnuYnO/fyVzdey7V11XzxuVfY3t5J52iUxsLcfVtYXMhn1q9lTmE+pePus/2xBP/46mZeOHKCDU2tvG/Zosmo0fGBIf7j9R24VZXP33wdV5SXYtk2W1s7+OrLG0nq01dgLMvmV/sO8bNdB3hXYz0fXb2cYp+PhK7z1MGjfGvDVv5t8xt88dYbCLimlg+FEFTPK+HPvvF+MuksDqeGosrUzM85Hy+9ei6VDUVEhxMsvXouJdMKe21kdSG2FcUy25CVUyZcwoFtRbGFCzi/FaPBzFE29f4fdCvFsrxP0hi897zaiY8mkRUJX9jDNXcvp7AswsuHuziyo4XqxlKiIwlkWeANurn2nhVoDpVAno9wUQBZliitKWDPxiMc2HKc2oXlyKqMJAskWUKWJWRZUFpTiD/iRZIFpmGNy57mvl1JFpTWFoz/nquZGOgcYf/m4yxc0zBxw5CElwnJRiEcCKGRI+G5VBwwSaV+SzzxExACgYZNFstOcqposjLteaggNOxxtSshBC7XLSSTPyeb3YHTeQPJ9NOoaiOKMudNEWchBGFHwxnJv2lnOTT6X0hCZX7ofahnkda0bYv+9D52DHyLwcxhzjTMOeUwi8Ifos5/G4pwXZKolDmexz2BgMdJceTcarAuJiR1MZLagJl5GVlbjTjHSdRbBSEEi0uK+Jub1udy6Xfu4Rf7DrCgqJBbG+ewrq6aiHvqXbCB7mjOj6E0MPP+CiEIuVx4HRrDyRRJXcczHolWZRlVGo+Wk4uwnzy5ECJHwiaCTkOJJEld52j/IB/52czAhG3nyFM8m70ohNyjqQTdM+uHJCkX2b9YfhAuVZm1gDe3UiHGjzN1rPq8PFRZZkdnFzc11BF0uzAtix0dXQwnU1xRVjJZE/FOgSQE11fWkjYMFhcUUR/Kw6dp4+8ApA2D35w4wpe2vEY0m+GppqPcUTd3Rh2Wbdu0jI7w1W0b6YxFcSsq9zcu5IF5i6nwBSbTu7KWSXc8Rnc8yqJTyKYiJD63+jqM8QyOpJHlD555gkND/dSFInznprtmJfmeS3zPlxYW82dXrKEmGGZeXgEhpwuRK9vEsGxe72rn8xteoCse5YXWEzwwbzH1ochZ232r4FIUbqyu44XWE/QmYmzsaKMxUjCrGaZpWWzqbGM4nSTodHFdRc2sqVBnwwURf1XIzAsW45JVUuZ4wZBtcWg0Z+rlVc++/CBJgqW1JXzpI7fQ3j9KPJ3BqSkUh/0UBHMzmYVVRfz1+2+kJBKY3M/j1PiL91xHXsA72c71S+torCigeyiKy6FSVRiibzSO4zSunecKIQT5jggfqHwPP2h9jJbElD6rbut0pLroSHXNqt99JgMHVSgsDy3m/op3U+A4vXb4RYMNKypKKfB5UGSJ4oCfjGGwqqqcgNPBvMJ8Nja1MpaaSkso8Hp496Kc5vrE+VWEAtyzaD6vHG+hZWgE07Imif/Gplb6YnE+ftUK1tZUTkZjrp9Ty67Obh59Y/e0UxpMJHnq0FHyvG5+f/UVVIVzUU+XpnLXwkY2NbWyo72LYwODrKgom7avEAJVU1C1ma+xP+zhhnuvxMYel9acurdCCiNryxFSGDO7H6w0RmYjtj2Ckd6Ioi1HTz2FZTQhyeVwXvnWNhYmFib2aSIX54LKuSX0tQ+BDYoiE8jz4gt6aDrQwbJ1jUSH4nQ19YENsiyhqDnn6ImrdbgdlFTls3/Lca68eRGSEAQiPo7saMbldYzfj5MUORJp2o50M9g9QvuxHkzDmp7HnNbJZnSSsRQthzqpnjfhGnHyuzvzPTaME4xG/w6P+wG8nvcjSSF04zgDg/fP2Pbkot3ZvglVqUPTriCZ+jWK2kA2sw2f70HEecheTpH/z7Cl/6sMZQ7P2Ma0Mxwc+QmyUGkM3ocizX4c09Zpj7/G7qHvEtM7z3hcv1rOssgnKPOuReLS5aHmos1TbXuc2jnlBV/085DCqJ5PkI19nWzsSyjO2xFSiBnvipARctlbUkx/KhRJ4oaGOpaXlbK9o4tnjx5je0cX29o7eeJAMX+5/hoWFM10TTnbo7Qn/zW+/Sz7nKmJCaLdWFjA/7ds8WlTdUIu10V51pIQKJdBiUmapWbwdBBCsLa6khsb6njx+An6Ygka8iOMJFO80dFJacDPfUsW4n6H5fcLISjyePnoouWz1m65FZXba+fwSlszz7eeoC06ylAqOYP4G7bFb04c5ujwELIQ3Dd3AX++ci0uRZ3WpkuSqAmEqAnMVFrLrTZNEXtFlyb7EllI+B2ON51icjHgVlQ+tHDZrPdHk+HqskpurWng4f076U8m6IyOva2JvxCCZYUlNEby2dbTycvtzfzenPkzUpwABlNJXu9qw7Rt5ucVMD+v4LzGjgsu7l0cKqXME+R4dGDy73uGOzkRHWBJpOwMe09vJ+h1EfTOXn0f9rlnRP5VRWZJ7ZRs3EQOY3l+kPL8KflBv/vi2LELIaj31vDHdX/Ak93Ps2NkDzFjugTiuRreOyUn1Z5y1hWsZXloMS750kT7ZkOBN5fvpskyTkVGlXMfsBACp6pg2fbkDB9y123bNrFMluFkkpRuoJsm/fE4kLNyn7hqw7I41j+IIkvML5o+Y1UkiQXFhWinTMJ6olFah0epiYToGosykpxKS7BsG0WWiaYzdI5GWXFKWmAimmLPpqM0Hewim9Y5eTT1h73c9oG1ePwz3ylZm8ivFUhKLk/coU4YVEmAhOb9xOQ255o+crEhhKB6fhkOl8ZI/xj+sBdZkVl500LmLK8mlO8nmO9HdagM944SiHgJ5vu59p4VCCn33LBtPAE3FXNK8IdzHcmSa+fSeqgLIQR1iysxDRPVoXLDfatwuDTcPhdX37UcPWNQu7Acy7JRFJnr71/FiX3trLp5EXOWVfGb771KbCTJufBt0xzEsmI4ndciy4XYto2hn8CyZtcTP9PnIISKx30PI2N/TSr5BCDj0Fafxx2eaE8QdsyZjPzPRv4NO8X+kR8hCY05gXejnFKEr1spjo79ioMjPyZtjszY/6Sjke+cz/K8B8l3LpjVBOxiQpGlaaIIhmnOKjd6qWFmXkSP/zu22Ymh78RI/WK8mP6UBy0FcYa+g1DqLvs5noqJXPWw28XNc+pYV1dN89AIP961l18dOMS3Nm3lH++6FZeqIoCS8YLd7rHYjBXvCWnQRCZLMM95QZHosNuFQ5FxyDK3zK2fXDn4n4aQy8n7ly3icF8/fbE4pmXhcajct3gh75pbT0049LbO7T4dzuR9IITApagsyC/k+dYTJA2dlDFzVTqayfBKews2NqW+AA/MWzyD9J/c5jsJZ/OGkCWJRQVFKEIia5rE9Mw5Z6C8VchzuVlXWcOuvh6ODg+yu6+bm6vrZ5zzgcE+jo8Mo0oS6yqqCTrPj99ecOJTiTvAuuI5NMeGJnOy+lMxfta6i1p/Hj714hDvtwOEEBQ7C/lw9XtZX3g1e0cPcCh6jKHMMAkzScbMYNhmLsqMQBYymqTilB24ZTf5jgj13hrm+OuodJfjvoyEfwKaopz00Yhp+ZSzxZl6ozF+secgm5pbGYgnJ68tY5rTUgggl3OZyGZxKApex8yq/YDLOcPYYiSZQjdMdnV087GfPjHj+KadKwg+9VjZtM7P//VFXvjZNsIFfjSnOo0s5hUH0U8jHzddMWWCeJ1K7t8eOYGyLFFeXzTNcyJcGCBcOLX6VVZXSFndVOSxtCbnYmtZFoe2N3FiXzvX3H3F5PNwe53MWzmznqRs/BgLVs1OusrrcwohW57ZS/uxHqrnlxLI8xKdaQEwy3UUI0th4vHvY7oGMM02UukXEeL85Gs1bTmyVEA8+WNcztuQ5aIL+paEEEQcc1hV8Bm29n9tVvKvWwn2DT+CJBQa/HciSznClTKG2T/yQ46PPYlhn76+SSBT7lnLsryP41cruBx69i6HSlHIx9GOXGBmLJkhnsqSP7s1wyWEgpBC41H+00MIL/D2GjMm3iuHojC3II9PrF7B1vYOWkdGGUulJ9NyFhYXEXA52dnZzUgqRfikVCDLttnU0kbGNFlZUYoiSZM5z28WpYEA1eEwLcMj7O/pY1Xl6b07ZoMqSQhyqZ9vxSTwYiGRzfKTXftQZImv3fEuasb9B6TxouK3M9G7EAiYTLGxbWatm+uMjdETz6WeNUbyqQpc/PqhtzO84+lRMPv9ebtBEoLryqt57NA+2qKjvNzezDXl1dNWrLKmycaONqKZNBX+IFeVVp6X4RhcBHajSjJ3lS9kU98JDo32Ajm91+e7DlPnz+f9NSve0e5up0IIgSY0aj1V1Hgqub34Jsb0GDEjRsrMYFg6FjaSkFCEjCZpuGUXXsWLR3EjCwlxHvbL2YxO28EOhCRRNb8MRT2/e/pmDjuaSvN3z7/KpuY21tfX8qGVyyj0eXGqCscHhvjC0y9O2z7X4UqTRl2nwrZnronIkgQCVlaW8cDyxTNWBCDX0TUUTK/VGBmMsXvjEe752Dpueu8qnO7pEVghBLLyzjAKulQQQtC4opbGK2rG8/YvvL2yukLu+eQNgI0kSwhhj+vs5zooSYqgKnMBGSHcaOp8hHAiy6UEg39PIvlT4onvoyr1BP3/P/HEfyJJvvH2nWjqEiQxXT5TVRqQ5eJTzsWHy3kD0dg/43K9i6kJ3IVdX8Qxd5z8f5WhzJEZ22StOHuHHkYWKrX+W4jrvewe+g4d8U1nLOKVhYM6/+0sCn8Ql3wZ0vrG4XFqNJTns3F/C5ZtE02kOdY5QFXR5Y2Gyo71yI5rz3Hrt368MCyL544cx+fUqA6HCTidyJIgmdXZ2t7JSDLFvMICvOMiCEIIGvIjvGtOPY/vP8S3Nm/jQ1csJeJ2kzZ0NrW08V97DlAVCnL7vLkXdG4Rt4v7Fi/gSy+/xj+8upE/vmoVjYUFaLJM1jAYSiY52NfPqopyqsIzJ1plAT+aIrO1rZNjA0NUhgLYdo5Y+J2OtyQV7HyQ1A1aRkaIpjPs6+kjlslOEn6PplLs8+F1XLgB3lsB07IYSCVoHh3hxMgQPYkYY5k0Kd0gbeo0j55pVRGGUyliegZJCKr8ockakv8usG07J4M5NsLxkSG6YlFGM2kSepaMadATj6G/CXXJtwOqAkFWl5bTHh1la3cHbdERGiMFk7/3JmJs6+nABlaVlFPhD55x5eNMuOAeVghBlS/CxxrW8rd7n2Uwkwv/xY0M3z26ibSh896a5YS0mUU772TklpsEDtlBgeyggEvrCjvaN8aXPvBNNKfGl5/5HMGCwNl3ukAc6Rtgc3M7y8tL+fzN1xEaL64VQjAQn6mWpMryuFGLzkA8MWN5rS8Wn5ZGBFDo8+J1aLmXuaqcoOvcon2GbiDLMgtX1eELXrpCQdu2yFhR0sYwhp3GxkYWGprkxSmHz8lZdeJX086SNAbJmGPY2CjCiVuJoEnnbniXtWIkjUEMO4VAwiH7ccv5k9HnaccVAlm+uN+cEAJFnRpEbNvGN5kWBU7HtTgdVwMKilJDOPRPTHQzTsd1OB1ryaVkSYBMKPj3TJB2WS4nL/Iwp3ZLwcAXYFrlAoCNYfWgagvR1AUXrW+ZTv6/Niv5z1hj7B76LnG9l97UTgbTh7A5/SCjSX7mhx5gTuDdaNJMadpLCUkIVs4t51ebDjA4liCRzvLa3ibWzK/C5758+bm51Y13zkTctm1ea27lmSPHKPR5CbtdKJJELJOlY3SUsNvNR1Ysm1bIqMkyD665krF0ml/sPcBrTS3ke3M6/m3DoxT6vPzV+mumHHjPM9ouhOCu+Y0MJZP8aOcePv3bZyjy+XAoMmk9R/w1WWbOPXlUMZP4LywuZE1VJa+caObBX/2WIp8X27bxaBoP3bCO6siFOWtvaGplc2sbsUyGwWSSjtGcueYXX3yFfI8Hr0NjSUkRNzXUzxroOVf4HBqrKst5ZNtOHnrupdzbJXLvvFdzsKC4gE+uXsnS0pLz8tt5K2DbNv3JBI8d3sezzcdpi46QNU1sOxe00+ScN9Gp4+ipSJu5lFwJMZnK+98FsWyGJ44f5vFjBzk+PETKmJIo1SQZRZKwmD34+HaGQ1a4sbKOZ5qP0x2PsamzjYZQzoHbtm129nbTHh3Fpzm4rqIa1wUoFV2U0IosJNYVNzCmp/jmodcmyf9oNsW/H93EzqF27q9ezhV5Ff/tJgCXC7ZtY2RNJNk4x0qCC0cym0W3LEJuF251Kj8wpeu8fLyZjDHdwU8SgmXlJTyx/zCvnWjh2rpq/M4cuUhkctbqWWN6VLQk4GdZWQmbm9vY2NTKrfMaUE6KOFmWTSKbxXPS0h2Ax+8iXOinp32IukXlk27NFwu2bRM3ejgRfYruxDbiRi+GlRwn7A4cchCfWkKxewU1vptxKbPrx+dWd2SG0kc5MvoL+tJ7SBkj2Fiokhu/Wka17yZqfDedVivetm3S5ghNsWdpj79GTO9CtxIIZJxKiDxHI3MCd1PgWoQ0Xhhr2zZ96T0cGvkpbqWAxeGP4lKmBvSY3s3eoUfIWGMEtCoWhT+MdpLSykimmX3D30cWDpZG/hCPOrOIESbSIE7uRmSmou+z1Uecmtt86v/PNoGZ2sayU2BnyeoHSKdfxO/730wYr10s5Mh/4xnJf9oc4cDIj85I+AE8ShFLI39IpXf9OU0SLzaEEDRWFHL1wmp+vfkglm2z+UArz+84yp1XzX/HRHcvN2RJ4iMrllEVCnJkYIDhZArTsikL+HnXnDrW19VO08+H3L0u9Hn5vzffwGt1LWxobqUvHqfY5+OWOfXc2FBHTWTKI0UA9fl5xDMZlPGIrEtVWVRcRFlwKrBT6POypKR4UmIZck72H7vyClZXlvPKiZZJ86tCr5f1dTWsrCyjIX/2YFTA6eShG65jaUkxOzq7iGUyeB0O5hUV4HNOHUNTZBoL87FtZiXoIZeLxSVFlAcD0+5D++go+3r6sOyciWJdJFdYGctkiGcygMDn0MZTg3N1ZnPy84imMzhnWckOuJwsKi6kMjQlH5nIZvnhjt28eKyJ62qrmVuQj6bI2ORM644ODPJ6azv/bGzh63fcQr737a0iNYHeRJwvbHqJV9qbsW2b2lCE1SXlzIsUkO/24FQUZCF4qukojx7cc5bWBDb2eaeUvRWwTpMtMIGknuWr2zbysyMHyJgGpb4At5Q0sKigiGKPF5eioskyO3u7+fr2Te+oqL8QgsWFxSzIK2BzVzsvtzVzd/088t0eUobOxs5WErrO8sISlhZemN/LRVtT1SSZd1csJqS5+faRDRwb68t9hJbB5v5mdg91UufPZ1mknKWRMso9IfyqE5esoUpybonuIp2LEAKnrJx3/tPvkENFKEi+183W1g5+te8Q84sKiGbSPHf4BDs6umbVxl1TXcGy8hJePNaEW1NZX1+LZdu8cryZI30DM1QbPJrKB1Ys4djAEF99eSO7u3pYXFKEpigMJRIc7h3Aqar86bo10wY+j9fF4jUN/ObhVxnsGaVmXimaQ50MCmsOlaq5xbMq/pwLxrJtbBv4On2pXOfqlAO4lXxsLHQrQUzvIqq3E9O7KXFfeUbiP5pt5nj0t4xkTqBJPlxKGMs2SJnD9Kf3MZw5TsoYZGH4QzOKRm3bJqZ3smPwm3QltmFh4JB8uJX8XBvGIK36S/Sn97Io/GHq/LcjTxBl26YvtRdJyDQE7pwk/rZtM5ptoTX+EqadYTTTTL3/DjTNc9LvzbTFXyHkqJ+cTLwdkEr9lkTiMUxrEId2FS7nu7gUhlAT5P/Kgk+ztf9rDGeOztjmbKQ/rNWzLO+PKHYvRyC/ZQEPl0PlfeuXcqJrkP0tvcRSGb771FYM0+LWKxvxui7fhMS2dWyzC8tsB2sEUBByIUKuREiRy1L3cC6QhGBuQR4N+REs254Mzgtx5hxyIQQBp4M75s/l1saGyUjtbPtIQvDQDdcBOfEDw2glKD3Kl68fxenMIlgCCO6cN5fb5k4PiAhy9VrLSktYUlKcI3c2kxHv3EKfPWtRoxCCIp+Xj65cxodWLD1lv6lt8z0evnLbzUCuLuBUrK4sZ2V5GZI0fb/3Ll3EfYsXnPX+TqgRhVwu/ubm67FPc5ylpSV89967x/fJHWdrWwc/3LGbd82p59PXrp0hITmaSvO5Z17gQG8fPdHYO4L465bJE8cPT5L+u+sb+dQVayj2+if5kRACw7LY2dt9xrY8qoZTkUnqOoOp5Nu+uHUCWcskY5qn/X1DRyu/PHaQtGmwuqSch9asoy4UQRmvW5m4xsFU8h1xvaci7HSxvrKGHb1dHBoaYN9AL+srauiIRdnZ240sBNdVVJPnurD3+YJHdNu2SZk6o9kUw5kEumVS7glxIjowzYAhaWbZN9LF/pFufty8HY+iEdBceBQHDkmZZg5yoXArGp9bdDOV3rO7eP4Op0dVOMQfrV3Fw1t38JWXNiALCZeqMK+ogM9efw3f3rR1xj55Hjd/vn4t//LaFp49fJwn9h3GraksLiniT65ZzTde2TxteyEES0qL+dvbbuCRrTt58WgTj+89hGVbOBSFkNuVWwU4ZUAYGYiy+Zm99LQN8ot/fRFJnp4KUlQR4fP//tFpRbDnCtPKciL6JH2p3TjkAPOC76PEvRKH7MPGImNGieod9CZ34lIi+NSS07dlZ2mOPT+uA/9+KrzX4JRDWHaWgfRB9g8/SlTv4ET0KUo8V1LoWjxt/6wVZe/wI3QmXkeR3Mz130Oldz0uJYJl64xkmjky9gv6UnvZO/QILjmPcs/anBSbWoxTDpIw+onr3YQd9UCOsI5kTmDaWRThIm2OENe7CGgV47+bjGVbsbHwqcU45EufVnaucGirkKQIkvCgqvMR4tKlzgghyHPMY1X+p9k68PVZyf+s+yFR7L6CZZFPEnbUv+VkVghBTXGEP3vPtfzTLzeyv7mHgdEE3/zVJrYcauPWK+cyv6qIPL8HTb00ExTbtrGtHvTEw5jp57HNPsAEBAgNSalDcT+A4roLIWZXd7vcmFCKe7NrIhOqI9JZVlOEENNWXBLxRxBCJuh/IGduN96fncld93TnqGcPYpqdOJw3MtvE+FyubUL97XTIndfMvyuSBG9iBfbU+3AqJCFmrDi0DI+Q1g0a8vPwzZLHPzFeiJMmGG93pHSDzZ1tGJZFidfHRxctp8wXmHFtlm0zmjmzSWqB20PA4SSh6zSPDJPQs3jfAunNCZwsKXymNKVoJsNo+vTX9lpHK0ldxykr/P6i5TRG8mftr0bTqXdk4bokBNeU/rDZQQAAIABJREFUV/PjQ3tpHh1hQ0cra0or2NbdQW8iToHby9VlVdMm2ueDCyb+L3Qf4cfN2xlIxRjMJEgZWUzbxjpNQoqNjW6ZjGZTjGbPzeH3zcKnOkkaM42yZj0f2yY6FKenuY+iqvzJ3Hk9a9B+uBM9Y1A+twSPP6fQkE5k6DjahTfkpbh6qvDCNEyGe0cZHYhimxaeoIeC8giqY6Zsm23bDHYNM9I3RmVjKZpLIzYcZ6BzCCNr4PQ4iZSE8ATOLS3K0A3aj3STTqQprMwnXDSzgn9hcSHfvPd25hXlztmtqXxm/VoETLp4XltXRVnQP2nepcgSdy9qZGVFKe2jYximRdDlpDoSwqNpBF1OHIo8jZQLIWgszOcrd95M89AwY+kMPodGbV4Yh6KQ53GPF5BN7SNLEktLi/nKHTfTPjrGYDyBYVl4NI0iv5dCn3fGABTI8/EHD92NYcweHdAcKr7Q+c2KdSvBUOYINhYl7iuZG7xnmsGSTy0h4phLpXcdtm0hi9PL6dlYWLbBwtAHaQzed1K6h41PLQcbtg18g5Q5TG9yFwXORZPHsW2LrsQ22uMbEEKmMXgvC0MfQBZTZjp+tYKQo4ZNfV9kMH2IQyOPkeech1uJ4JADeNViYno3o9lWKsgVV1q2zmD6MJrkodh9Be3xjQxljlLqWT1+XJORTBMCiaBWy5unPuePXHTVPm1EVVEqUJTT271fbAghyHPOP2fyLwmVau8NLIn8Ph7lwpZjT4c3jrRzoKX3nLeXhMDpUHE7VK5eWM3QWILOwTGSGZ0N+5rZdriNwpCPkoiforCPkM+NS1ORpPM79wVVRaxsPOUZ2WPosa9hpJ9GkquR3WsRUgGgYxmtWNldZKNfBkwU13sR5yBzumJRJV/+i7tm/U1VZIry/bP+9naDZY1iGO3o+j6c4ytYQuTGIdtOYxgd2HYcSfiQlQqEmFB0MTHNLixrGCE0ZLkcIVyYZiep1BPYdhpJCiMkL4rScE739J2CQq8XSQi2tXdwVVUFJeMuwhnDYDCR5Okjx9jZ2c2SkiKKx2VW3yzWr26gtmL2dCmnQyV8kWvLTNtiKJ1zOc93echzeWbtP+J6lv0DfWdsq9TnpzYYpjse49BQP/sG+lhTemH9pizEpCqfaVtvSi3Hf9JqfXd8dglngObRYbrH1YhOhW3bDCQT2IDP4aDEO3ttnG6Z7O7vOWsdxNsVFb4AV5VW0jKaK/JtGR1ha3cHGdNgeVEJtaHwBY8rF0z8m2ODbBtovdBm3lIc39nMP3zk29z3mTu5989uB6CnqY+/vf+fGOkf40+/+3GuvXcVAE17W/nb9/4Tt3/iRt7/uXuwbZvhnlF+/e1n2fLkTgY7h7FMi0C+j4VXz+M9n76dynll0x+UDS88+hpPfOtZ/u7Jv2Swa5iff/23tB3qJJvW8QTcrH33Sj75jQ8jK2d+wIZu8PyjG/jBQ/9F+Zxi/uRbv0+4aKZWX77Xw/qGKQlHVZa5oqJ02jZlwcC03FLIRU4qwkEqwjPbXFJWPONvML7c7XKytGxmJHxl5ezeDkIIPA4tN+koPLvbssOpUrew/KzbnQ9smDTdMu0Mtm1OM7nKDcwgo84WTJuBoFZDte+GU9J4BJKQKXAtxqVEiOmdxPUuLIxcu+S049vir2DYKYJaDbW+W1Ck6dFQIQR+tZx6/x0Mp48xlDlKX2o31b4bkIRKSKulO7mNsWwblm0iCZmMOUZUb8ejFFHkWkZH4nWGMyewbANJKOhWkpjejSSU8Yj15VsyPdzax0gsyVULqy/bMc+GCfJ/Zf6fsW3gGwxnjnE6R95S9yqW5X0Slxy5ZPft9QOt/PilXW9yL3GSvN30ATGjm7T3j9LePzq1XC4m9nrzeOD6ZTOIv6nvw8xsQHHeiur79Djpl8jdRwNL30d27K8xko8jO25CyGfuA4QQFOX73zHk/kwwjU4y6RcwzX6y+m4sawin6w4kKUIms4lU8lcI4cIw23G57sLtfh9gkUw+Rib9EpKUj20ncXs+iKouIJvZSja7HYQKaQeKXDruhv3fh/ivqipnfV0NL51o4tjAECV+H6osk9J1emNx+mJx5hbk84nVK89ZMOJkCCGoKAlTUXL5sgYkISYNscYyaWLZzAwTJ900ebb5GPsGzjzxd6sqt9fOZVtPJyPpFN/atZUij5fqwOxqXqZlYdjWGRUYFUkiNK4bP5BM0J+Mn7OOfJk3gFfViOtZXmxt4q66xml1g5Ar2v3PQ3tJzuJLMIGJCURS1xkZj+qf3IZl22zpaufV9pbLVgt5saHJMjdW1fJk01G6YlFebm/m4GA/bkVlXUUN3lnckt8s3j7Ju28RhBDklYXRnBqthzqwLBshoLupl7GhGKZhcmx7E9f83pUA9LYOEB2OUzWvPGfKMhDl25/6Aduf3c3clXVc/8BaVIfKsR1NvP7r7bQebOczD3+S6gWnDISGRXQozqbH32DD49sorS3ktj+8AYDm/e0UVuaPp6+cHoZu8PwPX+ORzz9G+dxSHvznj1DZWHZZidrbEbHRBJue3svVty/FO4uB19mgSm5Cjjr60nvpTr7BvuEfUhe4Hb9adl757hFHAy5l9siRJnsmi2p1O4VlG5M5+iljmJFsEwBhRz0etWjWNhCCPOc8nEqYpDFAf2ovVd71SCgEHTUIJBJGH7qVwCH7iOldpIwhClyLiTjm4pQCxPRO0uYobiWPpDlA2hxBk3z41JkTNduGeCpNNJmhJOKnbziGadmU5OX+2+3U8Lkd9I/EGYom8bkdlET8SJJgJJbCtCyyuslYIkXE76Eg5MW0bHoGx3hm62E0RSbgceFxaVQXX3h042JACEG+cwGrCz7LjsFvjtd+zBxaxrJtpIwhXPKlc4q0bPs8tKnPbfvcpNc+181nxaxSvkYnYKG434uQSk55pgqSugzZeStG8j/BHgPOPvm/VOhOjnFktJf1JXMm/xbNptk+2Mb6kjkXvaJEUefhVeeQze7B7X4PDse1TJB0TVuFpq1ECCfp9EskEz/B7b4P02gjlXwcf+ALqOp8bNsg53it4nLfi2E0I0lBPN7fZ3rB/cXHRErF5fxO89xu/ur6a1lbU8nWtg66xqIkszpuTWVlRQnLS8u4sqKMIr/vghR9LNsanwxfBs8NRWFpYTHbezrpikd59OAePrpwGRGXG9OyGUgleK75GI8e3IMkpDNel4TghqpaXmlv5rmW42zt7uB/v/QU986Zz9KCEjyqho1NPJulaXSYnX3drCwu5e76eadtUxYSK4vLebW9hf5Egm/t2srHl6wkz+XGxs55+xgG5T4/7pPIqRCC6mCIeXkFvNHTyeauNr62fRP3zV1AyOnCsCy64zF+cmgvr7Q141FVEvpM8i+EYGVxGb89cZSEnuUHB3YRdrko9foBwWgmxebOdr67dzspw0AREoY9e9TftCx6E3ESehZ9vK6gLxFnLJMGIJrNsKW7gzyXG02W0WQZl6JS5PFOS0szLYv+ZIJYNjPZzkAywcj4yk08m2VrTyeFbi8OWUaVZVyKQpHHd9o0OiEEC/KLWJRfyIaOVp5qOspAMkFVIMSK4tKL8p39jyf+AJGSMOHiIH2tA6RiKZweB0372sgrCeMJuGk92E42lUVWFdoOduAJuCmpLcS2bF7+6Wa2PrmDq++5kk/+44fwhX0IIJvO8vT3Xubhzz/GT7/yaz79Hx9Hc06fqZmGybPff4UP/vV7WP++q9Bcud/1rHFaqTcxngJhZA2e+8GrfP+hn1KzqJI//uZHKZ9z6oD6PxPxsRSvP72HFevmnRfxl4VGrf9WBtL7Gcoc49Dof9GeeI1i9woqPNeS52xEk3zndK8FMm61AOm0n5qYTKXJuRxMPfekMUDWjCOQ8aolp21DIHAreTjkAEmjn5jejWGnUSQXPrUETfKRNPrJmFE0yctothXdShJQK/Bp5biUCHG9h6QxgEuOkND7yVhjhLV6XPLsEa/mriGe2nKIzzywnu89uY1MVuehj9zMo8/u4OaVc0hmdB5/bR9+t5OReJLrltZx+5p5bNjbxMs7j1MQ9KKbFv0jcT7zvnUUhX1sP9zB/qYefO6c/FxJnp+q4vAlKN09P0yYfK0u+Cy7Bv+VjsRmbKanmkX1Dg6P/pyV+Z86rUrT/0gIDdAQ4nTfjYyQfLko9SUmqSPZJCOZJE5ZxbQtCl1+upNjpE2dck+IuJ6hLTGMbpn0paIUOH10JkcmtdBjeprRbIqYnqbYFSCouehJjTGUSaAKmWpf3pvyrpmUOhWC3LUrk+mAptFLOv0CljWKafVg28nc381uhHChqvMQQptM/8lBASHl/kG55Ck+3bEYw6kUCwoKLtv4I4Qg4nZx1/xGbm+ck0sRBOJ6H33pQzQE6lDE7E615wrbtmiLb8WrFpDvvPRO0qokc1ddIxs7Wjk0NMBPDu1lY0cr+W7PJFEdSCaYn1fAPXPm8y87XidpnM6kUhB2uvjslddg2javtDezf6CPw0MDeFQVt6JhkVPMy5g5pcBy35lXzwRwa00DzzQfZd9AH082HWVrdwcRlztX62kY6JbJd266i0UF04NUXlXjk0tW0jI6zEAqyQ8P7OappqOEnS70cfKcMnRuqqqjwO3hhwd2z3oO11fW8nTzMTZ2tPJSWy4SniP+uYLe7niUQo+Xv1p1DT84sJtjw4OztjOSSfOFTS+xf6CXtGGQNU0sO5eGDtA6NsIfv/DbXH2JLOOQFaqDIb627l1U+KeyH6LZDF98/RV29HaRMQ0ypollTbXTGRvjUy8+hTRex+KUZcr9Qb563c3Uhk4fHAo6nFxfWcu27k6ODA0ghOCa8kqKPOeXtnYqfkf8AYdLo7KxjAObjxAdiiGrMse2N1FSW0j1ogpe+9kWRvrHCBUGaTnYQUFZhFBRgFQizabH30Bzatz2sRsJ5E3lnDncDta/7yqe++Gr7H5pP10nemdE/QEarqjlhg9cg8M11XHLZ9A2VjUFIQme/f4r/OAL/0XDFbU8+M8foaS28L896Y+PJsmks4QK/FimzXD/GJY5c4LU2z5EKpE57+PkCF4Dawo/x+HRn9OZ2ERM7yI21k1L7EUijgaqfTdR4bkah3xmR0QhJFRxfg7NupXEQkcI6az67xIq6nhRpG4lMe0sKm48ShFOJUjSGCRlDuFR8hnKHEYSMiFHHapwEdCqGMk2MZptJuxoIKp3YNl6btIgz+xohIC8oIdUxqB3KEpWN/h/7L13lFzndeX7+26snDsndAIagYgECBLMFCmKYlag7LEsSxrPLDnI9nOaeW+CvawlP1see3k09lgjzciWLFmUrUBKNJMokgIjwICcO+fclatufH9UodDN7gYaQDdF+Xn/A/QN371VdcP5ztlnb8t2GZ9Jk8oV0DWFv3vyIA/dspXrNjVzvHeMv/n+K+zd3IJtu2RyRf7rJ9+P36PxhX94njdODfDR27dz/01bODc8RUttlIdv2bqqSl+rhfPUqj3V/xfadIje9DPY7vx+IpeB7IvU+3bTGnzfmgRdqiLjuUK1qncDSzVqSupGhBTFNo8jlA2LM6huGsc8iqRuQUhr54liuw5PD59kPJ8maxWJaF7ubthEX2aG6WKWt6YH2Z1owXIcfjxyGsOxubOhi6lChtcn+7i5toPjs6P8YPAo1yZaeH70DPc3beWHQ0eJan56M1P85qbbV+VcHSdFKvV5NP16vL4HMM3T5KxvlFYKtaSQ5JoIsVTT5sp8AgzL4vneXvrm5tiQSHBdYyPPnDuH5Tg4rsvdnZ28OTLC2elp2qJROmIxXujrw7BtagIB9jQ08M3DhxnLZLi+qYl7u7rwXIW++EpgOQZpaxzTyaNJXgJKNZqkkrWmGc6/zkThNBG9Gk3yE9OakYRK0cmQtSaxXQuvHCGgJBBCwnQK5O05NMlPxiwFWGG1DheYNQY4m36eWs8mbNfEr8QIKGs3uRFCsCGW4HM33cmXDh/grbERhtJJ+pKzpf44r4+H1m/i01t3Ue3z8/b4CKOZ9AJ313eOty4c4fM338kT3af5Qfcp+pNzpIpFUsWSuZdHUan2BeiIxthRs7xIxfnxmkJhPn/zXfzPt1/njbERZot5JnNZVEnGq5Yy4ktlsoUQ3NS0ji/cdjdfOfImp6YnmS2U9vWUM+B3t3byS9fspHtuhiOT49QHgotUnhJeH3+47w7+5tAB9g/1MZXPMZpJo0oyEY+HW5pa+bfbrmVbdS2DqSQ+RSXqWZz8kxBEdA/1gZUH0jGPt9LjUPlcCMKXOU7c671kw7kkBDc2ttAcCnNmdpq4x8stTa1Lql5dCa76Dr2ltpOE/t6SylIlmTrfyrmfiirTtrWZ/d97ndnxJJIiM9Izzr4Hd9O+bR2P//UzTAxMo3k0xvsmad7YQDAaYLR3gsmhaWJ1UWrXLewuF0IQiAZo3tBA//FBRrrHFwf+Arr2dKB5lr5xl4KsyLz6+Bt89T8/Sl1bNb/+xU9R2/ruZVqWw0x+P2OZ7+K6y/PzLgVZCtAU+hR+bf2S65959DVOvNHDb3zh58im8vzZZ79OcjqzaDvTsC7rO10KQkhEtQ72VP0WHaF76E8/z3DudTLmCGP5t5gsHGcg8wLb4v+WhN51kVLwUlr2l4PzL++V/L7z+0hK++lyiIDSQNocJWUMEtZamC12o0p+IlorklCI6xvoTT/DdOE0rYE7mTN6EJQmBstJ4sZCfmRJcHpggrC/1Gx8emACpdy0PTQ5x5OvnWT/4W4KRYtM3iCdK03G2hsSxMrN8rWxYGW5IpfK15IkUGTpp35NLwchBD65il2JX0WXQ5ye+x6We0GowHSynJh7lIRnI0G1adU/x/03bGZ319r0t7wTB7uHiPq9dNSunLpUF1v87JWUdhTvQyUqDxayug2EF7Bx7Wms4pPY5jG0wK/gOjO4zsz8vUuSnxdpor8cyEJia6yenvQUUc3HrJEjaeaZNXKM51Ncm2jmZHKMlJnnM10345FV1odreHtmECjdkRsjtdzTuJn/fuJ55sw8Rdsm7vEjBHiVq3v2nIfrmjhOClluBDRM8yjnnweK0oYQGvncd9D1fThuHkmKIcv1ZSWbagzzCJbVjRC+8vLF97IkSTSFwyiSxIu9vVxTU8PhsTEe3LiR5nCYqWyWl/r72d3QwAu9vViOQ+/sLJ/auZPvnDjB9tpadjY0kCoUuGf9+ouqAK0GLMfg2NxjjBdOowkvDg7XRO4nprcyXjjFUO4tstY0vZlX8MoRgmoNCi6nks8wWTgDQNFOsyv+89T5tpAyR3lj6u8JqjUYTomesTP2CAjBQOYgU4VuHNchY01S79tKQKm+2OldNWRJYkdNHX922wfoS84xmSspJXoVlbpAkKZgqFJN+uNb7sJ1XbSLVJeEECS8Pj6+eTsPdG5kMJ1kOp+jaNnIkiCgalT5/NQFgiu6biUh2Jyo5k9vu5v+5BwTuSxF20KTZYKaTo0vQN28INh1XZKFAv2zcyiSxJ7aJnbW1NOXnGMqn8N0bCzLIaJ72N3QiCpJJLw+vnHfR5BYrCp1fjLzhzfeTn8qyWgmXTl+tS9ASyiCX1VBCH5r9z4+6zpLuhZHPR4+f/Ody4vQuAbZ4uv4tB1IUqlyK5Y4n7Cu8wc33n5ZZmFLjbMUIrqHan+AM7PTbK2qpWsZBaMrwVUH/l3hGjaElzb3+Wnicr4eIQka19cjyxJDZ0cxiyapqTQd21upba3GG/DQfbgPb0AnPZuh7ZoWJFnCyBuYRZNAxL+keo8Q4I/4SpzoucVOtwKxYuWe8xjpGedrf/iP5DN55iZSTI/OUtu6tg+jlSBvDjKVfRaXlakpLQVVilLjf5DlppFbb+ikvrUK3asxN5WhkDf44CduIhjxLdhueizJy/98KXOTS0MIgSq8VHu2kfBspsv8CKO5g/Skn2aqcILh3OuYTp4ba/8TQbXh0gNeJhTJi4SK5RYxncXXz3w4WFjlF5cieZDKQZIkVKJ6OyO510iZg2TNCfL2NAGlDp+SAARhbR2y0Eka/RhOipQxiCQUonony91JqiITD/s51T9BY3UERZY4dHaYSMBL0OfB79G5dUcHdfHSS0CWJOoTIQ6dG0FVSpKRS2pLlxgOC2A6WeaKfYtXrCWEQEJBEgqSUJGEilz5v4KEii4F2Br7JLoU5vjsNyg6F9QqZopnOTH3KG3Bu65KFUmWdCLaOqR5JmYtNVFaaq7OXXWlGMtkUBWZWMRPfTSEV1MYmUlhWDYN8TB5wySTL1KwLBpjYTRFZmwuw7mxKRpiYbxlfXW7+BJW4Skc6xRG8j8jpBAIH2DjOilwcyACGJm/BNdm/m8tpBB65C8RSvvSJ3mZkMrqJKokI4Bjs6MoksTeqnX8cPAYANWeICHVy+nkONtjC/tcBOBT9JIENYKo5mWmmKU/M8MN1a1XrJmuKG1I4sKkSZKi+P0fp1B4CiH2l+Rr0QCBJFUTDP0e+dw/YRgHEULH5/8FZLmUtfV478ayB8ikv4iqbi5z/RcH/mPpNM/39NARj1foDn5NozkcJuL1MpRKUSzTSW5va8OvaTSFw9QGg3gVBcO20SQJtcyDXuvJuuUWmCicpcW/m7bAjRUndVmotAZuoGAlmTZ62R3/RVRJL997Ll3hO9kUuQdwOTj1NUbyR6j1bsbFJWkOsz50B03+XZXxBLApcg/TxV66wnfR6N+JQFxeYAEkjV5Gc6/RHroPVQrgujbDuVfAdWkM3LxgW9PJ0pd+mjnjHF45wfrYw1xTVVOmp52lP/0Njs0a1Pmup8G/b8V0svPSrVGPd8ns90pQErtwEaL0G/tVjU2JapbvCLiA2VyB58708JPuXr5w/910ViUWUIHeGBjm2Ng41zc1V87Ve5HMtihXKjbEEmyILV8dLAXXy/Pol/IhOg/DGidX+CfCni40ZflnrRDismh9l4OTM1Ocnp5Cl2Vub2kjrF9+k/pyuOozPq9ZvFI4rkNftpvT6ePckLiVsLpYLcZ2bU6mjjJeGGNz+Bqq9Frydg6/vDaa3UIIqlsSBCJ+Bk4OYxkWsirTuKGOWE2ESHWYc2/3UtNSRS6Vp317Syko9KgomoJZNLHMpbl2+UypWcQbWPpHu9zPY+QN7v7kbUSrw3z9j/6JL/3O1/m9v/1VGtevjXTgewntWxpp39xYefhGqoLccv9OIlULy2wjfZO8vX+x2+qVQgiBjEpQbSAYrqcxsI/js9/k9Nz3mC6eYiR7gA2Rh1bteOfhV6rQ5CBFJ0nGHK2o7iyFgjWD4aQBgV+pRpFK15uEQlRrAwQZc4S0OUTRTlLr3VmhKQXVBrxKjKw1xpzRT8GerUiBLgdJErTURvnei0f57EduwnFdHn/pOA/ctIVExE9XSzW9I9N0NCZwHJd0roi6AgpAVSTAmcFJBsbn0DWZmmiQ2WI3zw7/1iUNs1YbklDKQYV+4V9Jq/xfERqy5EFCRpX8CwJ/F5szycc5l/rnqzqHkNrMnQ1/saxB3FqjYFqcHpkkmS2gqwotiQhHBkbx6RqnRyfJGybDMyniAR8nvRN01CZ4o2cQn6ZxZGCM+3dtLBlPuSYIFUndtugYl2JDCRFgtVippUDdR0j1kNAD+BSNam+IQzNDHJ4ZpsEXxiOrbI7UcV3VOn40coqY7ucnY2cZz6d5aug4VZ4gMc0HCGp9IfoyMzT5o7T4Yzw7cppab4jYFVTBQ6H/uOBzCiHj8d6Lx/v+8hKVkvdBqQdAVbeihjdS8UOY53otSbXl8WzO9w0shbxpUrBKHO/zAf58I6TaQIAqvx9NlvFrGl5VXUTBi3i9HBkf58TEBBuqqhZ5riwHy7GYNmaw3BIf2iN7iKrhixpvapKfjuDNnEk9x2j+BG2BfTT4tpfjEBkhJAQSspArz0rbtZku9jKaO4bpFpgonKHeu7UypkcOk/C0LzJPlMpBriRk5Cs0MSzac0wVjrEueHdZq00QUluW3HYyf4Th7H7Whz+CLkeQy9RN2y1wZu5R/Go9Dd6b8SnvbuO767pMF45juXlqfXsue/91sQif2L2Dt4dHWEqTYEdjHdsaat8TtE7HNUjlniaZf5pM4TVG5v4ISfgI6Nfh9+xlLvt9EsFP4Dh5xlN/RcR3L359NzPZb6Er7fj0HWQKL5PO/xjHLeDXryXkuxtFujwvnJxp8oNzJ5ku5FgfTbCvoeWqmtTfiXedKCoQ1HsbOZs5iWEXQYWCnWfamMIn+wgqYYbzgxxJvsXG4DX45AB92W6Opw6zI7KbGk89mrQ6Jd/5iNdFidZGGDo7SiFboHZdFfG6KB6fzrrNjQydGaH7UB++oJfadaUMe7Q6TLwuyuDpEaZHZqlqXFgOz6XzDJ8dxR+6sM/Voro5wUOf/QChWIBcpsCjf/oYX/mP3+A3/+cvE6lebPbxLwnzZQbjNWF+7rPvxx9ezJ/3eHWa19ehXKRX4oqPT4nm0Rm6j4HMCyVzLGt0TZwRPUqcqNZO2hxi1jhHzppcMhh3XYfp4hny9gyy0KjybKm8PEtGXvXocpCcNcV08TSu6xDTL/CsvUqMgFLHVPEks8VuDCdDUG1ctrEXSj9DR0OC+qow9YkwjutSFw+xsaUGTZH55Af38Nj+Y3z1iQMossTWjnq2ttcRDXqpi1/IalZF/Avy+Hfu3sDXn3qD//3D19i0rpZH7tiOi4PlFnhXM/6UpFxNLl5pKWGJMgWl4N92l3ehXOk5vNufez40Rea6jiY21Ffx2BsnSOcL7GxtoCEW4nsHjuPVVK5prmVjQzXfO3Cc2Wye7rFpaiIBXEoTh4AsI3vuRPbceoVnsTCovRpIQnBLbQdCCK6J1nNe5nR3oqWyXhYSDb4wspD4cOtOZAQfad1Z9peQUISES0nX/KPrdvLqZC8BVSeo6QjcK3aMX4qrX+oRmf8cm+feK5b/Xi62bj5etvEXAAAgAElEQVRaolHuaG/Hdhw+tWsXIV3n3g0bKrzxuM/Hw5s20Tc3h09VaQiFCGoasiRxZ0cHNX4/qixjOQ7mZeqmD+SG+HLv35GxSvfYNeHNfLzlo3jl5bPSkpBZF7iBGm8XI7ljHJ17DNs1aQvuO//JgYV3zFThHG9PP0pX+P1EtCbMcmX0wpgK4qJ0zMu7/2zXIFtO1JhOvnJOtmuSNvqxXQPvvOC9tP0I08VjCKGgySE0KYgkFArWDClzgJTZT5V3O4rQ0aTzHg9OSbTBSaJLIXxKNULIOK5JzppAlyPkrAkc1yag1qNKvvI+kxSdOTQpiE+pQSBRtGdxcbDdIoaTwSvH8chxXCwy5ih9mafRpCCq5EeVAgTV5pWJW4gLJnTvTBHbjsO3Dx3j5PgE7Yk4H792OwIYnE3y/WMn+NR1u/BrGpbj8LcH3uK65kY219VwfGyCp0+dJWcYXNvUwO2d7XjU1UoMSOhqB35nlrxxnLD3bhQ5jirXIFDIG8cwrGEse5Js8VVUuQaP2km2eABNaSGZe4qZ7DcJ++5FFiGS+ScxrEGqQ7+KJK2s2lK0LH7YfYpnes8hC8G9HRtovETj9eXi3Q/8yzNoqZzmMR2Dt+cO4LqQtpJsCe8gb2cxHYPzN1zKnCNpzpKx0lRd5Yt0OWhejZZNjXQf6mN6ZJb2bS34wz5woXNXG28/d5RTB89R1ZQgWnaD9Qa97HtgN1/9L4/y7NdepGVTYyWzb1s2rz/xNv0nh9mybwONnctnTy8HQhLIiozm0fjQb9zD1NA0z31jP3//ue/w6c//PL7gT8f1UpY8qHIEy0njuAawNr/TeXgDOpt2ty25LloV5BO/fy8e35UFC45r4rg2stCXfbhZTh7HtRHlbO9aQBVeWgK3MpI7QNLopy/zYzZFPrqA9uG6Ljl7iu70k9iuQVTroNa3k/k1ab9ag0eOkrenmSqcRJY04vr6yoNYFhoxzwbGC4eZLBzDdLKE1EY0aXlVGiEEG1qq+S+/dBeaKuMCf/Dp96OVaTxVkQCf/OAeTMtBCFBkGUkS3LytfQEf8p7rFxaL6+JBfuuRm7EdF3kpW9D3JH5WFaMvDVkSZTOv0otbVxUyBYNs0axUe1O5AtmigSQJwj4PO1rruXlTGxLg00v3YElq8qffkCyEQDlfYph3a7/zWjv/ftLK/8rLBIaqJLM70cLJuTGKjsXDLTsIq6tXkl9raLLM9rqF76b1iQv0CSEEjeEwjeELGcuot/SOaY9dSAzsqLu895uLy7lMD6P5ccxyX1jGzFzSbdVw8kwVzuGRg0S1ZnQpQN6eq6z3ymEy1iSzxX48coiAWoXhZHFxCao1WG6RpDFCtWfDRY5SgiQUVMnHZKGboFqLLgXwKosZCvNhuwbdyccYzR/AK8cxnFSl+b/k1n6Yoex+qj072Bz7BFCi+IzmXme6cJKcNcFg5nkiWjs+pZqk2cdI9mXy1jQT+bfImCO0Bu9GkbwMZ1+iP/M0ivBhOmlagu+nyX8rRTvFoem/Jqg2YdgpbLfIhsjHiGjtjOReoSf1BKrkx3DStATuoClwOwPZHzOaex2PHMN2Cxh2hp2J30CXI4zlXmeqcBRdjmK7JiGtmaB69SaKkhDc0r6OnGFwdGSs1JcmBEGPzuGRMc5MTLG9oY6RZIqXewe4c30HA7Nz/I/9r3L3xvVEvV7+4a3DpUnoho5VyYgLoeDVNuK6BWQpgF/fjaaU6HOOW0SWwhj2MEWzm4BnH4Y1iGmP4zg5FCnGdPrrhDx3EPd/DJDRlHpGZj9PxHcfHm3pa24il+WJ7lPIQsKwbY5PTfCToT5mC3luaGjmwc6NK66irRQ/9Sdxzs5xMnWMar2WnJ0lbSZp9rXSmztHR7CLoBKiztvIZHGCruCWNctoK4pM29YW9n/ndYSAD3zqNiRJwnVdWroaKOQMzr7Zy873XUOg7NgnSYLb/82NHH7xOD/6+/1kkjn23L0d1aNy5o0efvT3PyEcD/LI796PN7j6LwNfyMsn/uCjTI/M8szfvUhVY5wP/eYHl+w3WGvEvLfiVVqx3Sy2k8N20pjOHJaTxLTnMJ0kljOHac+Qt3rLk4Mrx0UVbmQJ3zLUqpUgaQxwYu5bxPT1JDyb8coxFMmLQGC7xcr6gj2LR45R5dm8RhQ0iXr/dTQFbqI3/SNOzH4L13VoDtyCLodxXYu0OczJuW8znnsbVfKzIfIwfmWhlNp5Pf7R3EGK9hw+uWqBJ4BAJqZ3gusyln8Lx3WI6h0XaVguQZYkZK1cWQA82oXrTgiBIsslmsc8KPO8KYQQqMri5i1tlbI3/4qrR8Tnxe/RUGWJ6nCAbS11vHSqj1MjE+xub6R7fJqTw5OMzWXY1d5AczzCj46e5bGDx9nSVMvu9qUN+94J13UpmjamZZdlbS8OgcDnUS+pjrHWEELgUzR2Jd49N+l/CTAdk55sXyXoXylKHPlDzBj9SEhEtCbWBfZW1tf6NjNV7Obw7HcIqXVsj32YKs966n3XcHTucfxyjEbfdrxl3rYidCLL+LOowsOG0Ps4lXyaN6e/SVvgRlqDN1z0/LLmGMO5l9kS+xRRrZPu1OOM5F4tH8tHW/A+inYShwvUYF2K0Bl+GEXyMFk4xtb4v0cgIZCp8mwjrLUya5ylM/yhUqUWmbw9TU/6h2wIf4SEZytjuQN0px+n2rMDcMmao9R697A5WppcSEKlaCc5l3qMztDDVHt3MJF/m9PJR6ny7sBxDSwnx9aq/4AsNN6c+nMm8m/THrqfttB9zBndxPQuWkMfvKJeh6UghKA+HKI+HOLkxGRledijs6uxnv09/Wyrr+PgwDDNkTD14SDfO3oCF2iJRlAkicZImJd6+rm9sw1pjRvLBRqq0oBh9mFYQ/j1a0kXXqBgnkaS/AihY7spVKUBhIxAoMgl4RXTmcLD0oH/dD7H37x9kJlCDpdSJUQIwc6aen57z43LOhRfDX7qb1hZyMS1KnZF9+JT/HhlL5azkC8vIWFjY7kWSkXjeHUhJEFjZx22aaNopUkAlC7OmtZqgjE/Y32TtG8tNfaeXxevi/Krf/lJHv3Txzn49CFe+8GbuK6L7tNp3dLEI7/3ANfctGnNehNidRH+3Z/+An/6yb/i23/2A6qbEtzyyA1rni3NmgZps0iNt9R3ockxVDnGXDHHrJGlxhugWtUoZUOdSnNQwRrk+MSvk7d6r+r4xYLBWP80desSaO+Y6GSSObKpPIn66BV9D5abZzj7Gj2pp1ElP14lXpbTlDCdbFnnPoUq+Vgfvp8qz+ar+iwXgy6F2Rb7JQw7xUjuIIdmvsLZ1A/wyjFs1yRrjVGw51AlH13hD9EWvLOSrTwPudzgO5jdDy4lt+D5NB5BKcMvhyjYMyjCQ1Rfe93qf8V7H/s2rEOIUnbu3p1dKLJEQyyESylbPDg9x74NLexub6xUez68dyu241QUnpaD67qk80XePD3E6yf7GZlOUzTMJXnA74RPV/mdR26lqfriGdh3E7blkJxKkZ7JYBRNZFkiFA8SrY38DFWv3h2kzDQDuaHL3k+T/OyMfwynXPkvNdpfeN55pBA74z+H41olh5SyyMH22EfLyySkMlULIKzWsbfq0xXTxPkQQqLOu5lqz3pc3BWZN+atCRThIaA0oEhewlobE4VD5fFEOaCXFvh/lJarpR4FJCSUStJFICOhIsp+L1LF3HGCjDlEb/qfGcy8gOFkMOwUpptFER4UyUvcs2mB03veniRjDtGXforh7H5MJ1vax8kCEhGtHY8cw8XBp1RjOukSQ4MSFUoIecnvabUhhODm9lb+8sVXmMhkeLm3n/u2dKFIEjO5PBPpLI8fO4lAYDkOG6oTaxBfifI1coG+JoTAo3aRyj+L65r4tO3kim+TLvwEXVmHIoWRhA/LnqlULxwnjevayNLyVJ2I7uH2ljZ6kjOYtkPc62V3bSN3tXbQHIqsKrf/PN71wN90TE6kjjBZHONI8i22R65lfXAjR5Jv4pX97IjsRpVUwmoUuXxDh9QwHsnDq9Mvsiu6F7+y+sY4Qgg6d7Xxe3/3q8iyRPPGCyotiYYYv/bfP0U2maNrT8ei/eraavjMn3+C+87excTAJLblEKkO0dzVSDAWQJIWK5fc9PB1NG2op3Nn64rOL5wI8St/8QkkWcI/z5RKCEHzxgZ+5yufof/EEPH66Ir0m+ejYJjYjotPX7nhSX9mhm+ee5vmQJSgqvO+hk5USea/HXmRwcwcLcEYv3nNTcR0X+UzA8jCtyra5lMjc3zpv36H3/jCz1HTtLC34uzhAZ7+1qt85nMfIRy7/GvFr9SyLng7o7k3yNszpM0h7HJmShYqmhSk1ruT9tAHaPbfgiwWVxdUyUdM68RyixdtzJSQCWvrcHEIqg2LuKYl3fgW9lb/LmeSjzOQeZGsNU7aHEYgoclBarzb6QzdS3PgFhSxmOpVyhxtIaFvBKDWuwt5Hp9YUOoDqPVuJ22O4JGja6JS9K/42cP84P18dUafV5HZ2dqArioLqj2acmmnWNd1GZtJ89ePvcKLh7rJFY3LemwFvTq54tVVDVcLju3Qc6SfZ7/2E46/epqp4RmKuSKKqhBviFHfVrOkN0vbtmY+9vsPVp6506OzfONz3yU9kyGUCPLz//dDxOuWVxSxTJtHv/AYfUdLMqMf/u17Wb+rDSEEjuPww795lqMvnQIX9j20h5s/dF0laXUx9J8Y4h//2w8o5gxidREe+b0HiNWu7gRrtDDOVHH6svc7L7SwXABaWq8sasZdall5D5SLyMQKIaEs6ZOw3PYqLnZFiMBx7ct+H6/0OJoUpMF3U8UVXhIqXjmB6WSQkBdNVCShokoBGvw34ivLkkpCKfUGAJLQymprcCHwLf0fsXQf01qhPR7Dr6s8e+YcOdPkmrpaEILWWJSGSJBfu+l6grqG7ThASQloNSFJAXAt0oWX8WlbkKUIqlyHrrRiWH3oSjuKXIWq1JHJvkLQcwuyFCXkvZ1k7ik0pR5ZijCTeRSP1oWurFv2WDX+AH+w73Zs1y1PMAWqJJdUw9aK4bImo17sgEJhc2grG4NbAIEqqUS1OO2B9aW/yzf0nug+5HKAqEsebkrcjoNTWb8WCMUC7Htg96LlqqZw7V2L1SjOQwiBx6/TsX0dHdvXXfQYrusynsyQj+g03LyeRCLCRDLDXDZPQyyMadsosoxfV5lK5wh6dbKFItPpHJvv2IJPV5lO5zCyOWzHJRH0k8oXMONetn5wO4okMTiTwqMqGJZNfSxEKldgKp2lJhJEU2TmsnnSeYN40IdPV/nx0XNkCga7OxpZVxVbPFFZAo7r8sbkEBHNy0g2yUQ+w+0NHRRti8/t/gD/5/QBTs6Os692ZROby4XjuBgFE3eJ9KCkSEyNJrGMpZWWLjm2WySgJNgYeZiQ2orp5rGdsta8pOOV4wTU+koVYCnE9S5uq/8ToDRZMOwM4/mD1PluWKAeoUoB9lb/Di4OArmSoZoP08nilWNsi3+KzvC9pI1hDCeDJGQ8coyQ1oQuhZY9F8vNUePdxl2NXyz97eQYzb1GvX8v52dkuhRmX81/Kp+HVDkPyykwkHkOF4v20APYTpH+zHM4rklH+IEr+n4vFxIKHjkK77Kqz3sBuhzm6nwg1hYNsctTqziPgmHxzR+9xTMHT2PZDl5dpSrsx6urK8pw+TwanmWMi95N2LbDy987wP/+f/6BsZ5xHMfFF/LiC3qxDIveowN0H+pbct9cOg+/f+HvfDrPqz94g6nhGaqa4jz06x+Ai1DnXcfh8AvHefu5kgTprR+7gfW7Sn1PQgi8QS+vPHYQo2AyPTbLzju2EIpf3GjIdV1e+v4BfvSN/dimzS0fuR6vf+WB70rgui7dmR4KzpWbLL5XEVBLDeOjudeI65sZyx+oeHzYrlHOsOdwsSnYs6iSf8ln/iWPo9QRVJvJWqNE9HYc18J2i8hCZTnylE+pIay1kjFHiOqdOK6N5eYvmcUXgEeOMlc8R9oYRJb0ysThUnBdl5d7Bzg9OcVoKs0/nzhN38wsN7Wto2hZvNDdy8GBIXqmZ/n2oWNcU1fDptpqdEXmxrZ1fPX1N3l/VydRrwcB7G1p4sDAEH/xwstUBXykiwb3bFzPzsaLm49dLjSliXjwE6Tyz5DKP0vY+wGi/gdQ5So0ZR0+fReS8ODTtpMtvoGutiOERMT3AAKF2ex3cNwiXm0TMf9HkcTyCUjpEtKia4GfSnOvgorh2hiORd4qLHida5KLX9FQJGXhPu9CiWkBXBeXAljduFZ/SWd6SUgI/VaEvDKDm8lUlscPniDi99A3McuHr7+Gpw+doS4a4vWzg3TWJZjJ5LhrWyf/9OoR7tzayU9O9FIdDrD/ZC8P772GLz97gO2tdYR9XpK5Av/4yhGubW9kaCZJXTTE6EyKVL5IyKdzw4Z1HOkbIRb0s/9kL9vX1fPYwePsbGtkeCbFh/duYWg6ieU4zGULuFUuKyPwCW6sXcdnNt1A0ijw5VOvkTUNPLJKwuOnyR9hrpi/9DCXiWLBIDmdZXosiWFYTI0lEfMmKrblcPS1bmRFQlZkCvYslpPDdk0CSh1CKOSscRzXwK/U4eJiOGlMJ1sK5JEYzDyP45rUeHYQ0JrIWxM4rlneHgwnRdGewXLySELBdLLIQkUICY8cJ29NYbl5/EodklDJWqMU7FlmjXNlObQLL9LzCgwuFn6lDtNJAxKK5KFgzyIh05d5Gp9SQ1zfSECpQ0KqjG+5hVIDl1PAxcUrx8hZE9iugV+tw3UtetNP4ZGjxD2b8SnVpO3ByiTBdV0MJ0nOmkSTgniVOFlrHNsp4ldrUYSPuGcT/elngVJWKOHZRF/66cq+klBRhI+CPY0mhZBXWXUrrLdye93/uyLu988KXBdms3nCPg/yRSbaiqSjy1dX4Xzj9BCHu0cA8OoKt+/ooHYJk60rxdhMmhP9Y9i2gyxLbGqpueT4Q5NzvHikB8t22NBUxSfu3s2W1lqCPn1l2Tshfuruxa7r0n2ojy//h28w1juBx69z58dvZt+De4hUhSgWDM6+1cv3v/gkQ2dGAWje2MDeD+6kpqWK1q1LyzquBoQQbL91M43r6+k50k/3oT7OvNnDrju3XjSLmJ7J8NaPjmKbNqpH5fr7dqGvcuBfcAr0ZgeuWvHqvQivHGND5BF6U08yVThGSG1B1jUkZGaLZ+lLP0XWGgUXTsz+HU3+O6jyXgOUJvml/qyFv48QEkG1qSLRDKWE0aboL9KbfoJjs19FQqXWdy0xvQtJKAS1pkUTCkV42Rz9BD2pH5b3Uaj27iSmd6HLscpxSz4VVfOqxxItgTs5NfcPHJv9PyQ819AZfnjF34lHVWgMh/jNW25AliR8mooQ5Wq2rnNT2zpubG1BkaRKVVEIwe0dbQQ0ja31tZVkQMij8xs3X8+J8UnShSIhj05n1eo7fUtCJ+b/MFHf/bi4lMwDZWQpSmP0c2WxAgm/vhuftrWiyCVLfqL+DxHx3VfO3quwRvT0q8G78uS0HJvJQobezDQn58boy0wzXcySt0xMx17gnra3qpV/v+HGNTNFWClcdxYn80XcwpPgpFm2zCU0ZKUdVhj4F02LomlRHQrgujAwNUdrdYz3be3gr59+jbDPw5s9Q/RPzuFRFZK5AscGx9kMjMykSOeLCElwy6Y2fLrGscExWqtj3LOri//x5CukcgU2N9VwemSShliY44NjHBkYY1NjNSOzadZV5WlORLlnZxdfeuY1HBc66hLIkmBHa/2KL9CQpjNTzPPG5CAzxRxnk1MYts2ckSdvm+QsA2kNGu9Geid59IvPMHBmjJG+Kf7it7+Bol4oo9uWg1k0uf9Tt+APeelJfxfTySALnYDagFeJM104gRAyuhwhqDTSl3mauL6RoNpc0s23kxUqzHThGFOFYyjCg0eJEVSb6Uk9QdyzCa+cYKZ4gqKTwnUddDlMTN/InHEWWXiQxQmCajNj+QOokg/DTi76PJabI2l0l+XXohWN+LhnE72pJ2jw30zaHEIgY6gZDCPNSPYVNDmE4DC2W6Rgz2K7BpoUoilwGymjj7w9hVw8QZ1vLxlzCNe1MZw0XhJkzVFmi2ep811H0ZnlbPK7+JU6/EoNqhRgrniOgj3DZOEIneEHS4Y25euipMqlVRSB5ordFOwZGvw30Z36AZ3hh5FXSXrxPDTJT5V3y6qO+dNG0bQ4OtpL0/oW/J7Vlyeej4OnB/jqkwcBiIa8dDVfOjC/HPSOTvMn33qeVLaIqsh85v7r+dht2y/6LOkbm2UmmSXs9/Dv7ruem7e2rajS+F6CYzs8/dXnGeudQAjBXb94C5/+45/HG/Bw3qBuw+4O6ttq+OOPf5H0TAbNo/HQZ+8hVlfy0FjLgCBeH2XPB3bQd3yQXCrPy48dZNstmy4q/tBzdIDeowMANLTXsnnfhlU/xzkjxVBueFXHfK9ACJlqzw7i+mYoB36uayMJjZi+gbC2bv7WC7Ltdb7rqfUu1slXhJft8V9bsG3Jf6WJLbFPV/oZSkGmhCaF2R7/9UWBvxACv1LP5tincFyzso9Apjlw27zEikRb6D4qEwEhCKrN7Er8Fg420mWEjUIIrm1amjbqVVXuWL+8KV/U5+X9XZ2Lxgt5POxtWXvnciGUcoD/zuXz6bQyQvjesV5+xzbvPaxpdJ23TI7NjvD0yAkOTg0wnJ2lYFvY7nJGyVDjCeK4ly7pm45N3lpY1BICfIqGfIU6yufhujZu/p9xc98GpQXheQAhRVkyEy5kkFcuZRYL+JjN5OmdmGXv+mZS+QJTqRwF08K2HapCfqJ+Hy8c72ZrSx26qtBZF+eBPZtKZWRNRZMldFWpvCgD3pKuslQ+P0WWKo12qiLTXhPnvmtLsoljc2kmU1lkSSCVX06KJFEwSzx/WVqZqVidL8Rt9R18q/sQXkXlE+uvZbaYYyAzxx+99Sw50+CuxktLpl0umjpq+fjv3svRV8/yvf/1PHc+spdw/EJGVJIFiboInVubUdRSZ33Ccw0+pYbB7PPMGd0UrGk0OYxpl/SKvXKcxsCtlaaqiN5ZUsLRWhhPfpdq7058chW96SfQ5SgeJUqT/1YcLOaMc8T0DRTtOVxcxvNvUrCnSzrNrovl5InpXUS0Ns6lvr/o89iuWWqyclIU7SQRrR1XlJxLbdfEq8QJq+uo8m4norXTm36SmGcjMb2LU3PfRBIqMX0jGWsEVfgo2sly01Ya0xmhNXgPIa2VmN5FTC/9HhG9g6TRA0DaGESTgrQG3w8IDCeF5eQwnUypEnGRJLsQgrDexmTyEEmjB10Oo12kieliMEyLZCpPJORDVZfmhzuOSyZbRNNkdO29l0VZKRzH4c2eYfrKzwDbcThwdpBzY9M0xsNcv76ZQ32jnB2dojEepquhirOjU1y/voUD5wZpjIeZSmU5NTxJXTTIDV3rLqpjbTsuVllj3badS0omXi5C/lIm8nxSo3d0Bst2Fik2zUcqV6Bo2rTWx9nUUv0zF/RDKTt+7OWSUaA/4uN9v3DzAjllIQSyLNh26ya69nRw8KlD9B0fpO/YIPH66Jpfv5Isse/B3Tz9t88zO57krWePMN4/SeP6pWkRju3w+hNvkU2WqtvX3rWNeN3qn+dgfog5M3XpDX9GUeoLmNf3VQ4eRdkJfDlIQqlsu3A8sXC8ectltCWpQkttf2GfxT0SAvUd2yyeNMhCvwoP8n/FewlrEvjbrsOZ5ATf7DnI86NnmSlmWT7UvzKcS03yV6d+QsYsVJbpssovr7+BaxNXW0I1cY2XQa5BDn0e1E2UeLbLPQBXNtFwXZeeiRnqYiEa4iF+fOwc91+7iZ6xGR59+TBbW+qIBX1sW1fH04dOs76+Ck2ROTU8yQ8OnqA+FuamjeuoiQQrD2OvphL1exFAdThAwKvh9+jEg37CPp3mRJhuZYYn3jxJbTREW02MaMCHQFAVCqDKEh21cZ546xQ/PnqO26/pWCC5uBxUSeaepi7uaOgo6XvLCi4uGdPg6Mwocd1HW2hlVZDLgaLKNLRWoXtUDr18hn33bKO2efFxzn8/Djamk8VycghkdMlH0NtEjXcnklDImCPIkneBkkJlDASypGE5WSwpAEJCQkYRHiSh4JZ1/CUUJKFiuyaq5COg1lPvuwGBxFj+ALZTwHLzFSWK83Bdl8n8YUAQ1zdXKhGWk6dgz2G52dJZCAnbLeLioAgd08lVqD2y0EoOs6iAYLpwDFXyUeXZWpGRE0jYbgHHtRcp/khCxXLzlebl6cIpDCdDlWc7A5kfXfL30KUQXjnBSO5VGnz7rjhI6Omf4s+//By//yt30d6ytDNlvmjyV197gc7Wah6+eweXOlTGLDCcn0YSgkZvHK+ykLKQsQqkzTw1nqtTTshaRYZz0wigzhcjoFxcSlYIwcaGat7qGca0HYRhcahvlC3NNXTWJjBth8N9I2xsrKazLkHRtOgZn2FPZzP9k3MUTZtDfcNsbqrlYPcQtZEgm5pqrvj8rxYhnwdtHkd1bDZNvmheNPA/b8bn1dSLbvdeRnIqTWo6A0CkKky8YekmflVTad7YwMGnDmEZFv0nh9h119Ylt11trNvSxKYbNvDy9w8w3j/J2z8+Tn177ZJNvjNjcxx+8QSO7RCMBtj9ge0oq0inKlEDTU6nz1H8GeT3u7jYjk3OzpG3CxiOie3aZfUgCU3S8SlefLL3ik3cVuU8XRfLtcjZeQp2EdMxyslWt3yuMqqkoJfPVxUrF/VYTZiOyZyZJG+XKMG65CGsBtGl5f1zzsNxXbJWhpSVwXRMZCHjlT2E1RCK+NlNCr1bWPXAv2CbPDN8ii+feYnu9NQCs57VRMITYLaY5c3pwcoyCUGtN8TWWAOadBUfzXVwnRmE0g7qpjK/a3Uwl80T8XmIB32cGVQQ/YUAACAASURBVCmpU3zo+i2YtoNHVZCEoKuhiraaGB61dAE/sHsThmVXMvkfvWFrhRfcVhNjXVVJ+eHhvVvKVAzB5qaaigxfV2M1hlnaX5El2qpjCAEPXrcJRZYRwMdv2YmAi/KNF3xFrstoLsVbU8OkzWKlTBjVfdzZ0Im2xlStcCzAw798G5FE8BI3ucNU4ShJqYda3x48cpSh7E/oSz9NlXcrqvDjkSLMn9RpUghV8gGCau8uhjIvMlM8RbVnB7ocRpfPK21IeOQIapmD7WAR0DczWThMX/opYp6NJDyb6c88R9Yaw6skFqn2BNQGRnOvYDhJvEqCsNbGQOY5ctY4XqUKSShEtE7GcgexHYOEdxsDmefoMweo9mzHcguoUgBdjpZUHZQ4M8VTWG4Rn1KFQCKqr2c09yqOaxLW2hjOvkTWGmUo+xOqPNvQi2FOJ79NSG0hqDYxXTzGTPEkPqWGgj3NUHY/WXOcocyLRPUuhrP7yVrjDGZeoN5/AxG9k7l0D6EFZezLQ75g0t0/SaGwvK63qkgMj86RyRZ56P3bWX4iXsKcmeXFieMcmD7Lb3Xdx+bwQq31/uwEh2f7+EjzvgUToqxVJGsVqNIX6ie7rstYYY6o5scjX3gmpM08L02e5PXpM3y6/X3siS8sT78TQgg0Ra6Ysnh1ldu3tPP6uQHG59J8cNdGbt3SzoGzg4wnM2xrqasc37JtcoZBOl/EdV2ubW8gEVobA7mVwqMpC/j2M6kcBcPiYqeVCPnx6SrJbJ580SR68Z7T9yRMw6oICCiqjKIsH+zNlxw2LnKNrzZ0r8bNH9rLG08dopg3eOl7r3PbIzcQiC7+cU6/0c3QmVIvSPv2Fjq2r7viAMpxHQp2gYyVZc5MMloYZzg/ylhhnHOZniX7dXpz/Xyl9+sXDNYuA6qkcmvVjXQGl6eOXAlc16XoGIwVxjmVPktPppeJ4hQpM03BKVSkx1VJxSd7Cashaj01rA920Blsp0qLL+hXXAu4rovpmswYswzlRujN9jNSGGXWSJK2MhTsAqZj4uAiCwlFKHhkD37ZR0QLUeeppSPQxjp/MzEtWhFVuRIkzRSPjzxJ2kxXll0T3syNib2Va8l2bc6ku3lhcj+92X5S5W39ip8Gby17YrvYGd2OV/Isuv5c12XGmOXlqdc4nDzGRHGKol1EkRRCSpAmXwPXxa5lS3jjiiYQ/3/Fql6RecvgW71v8ZUzLzNdXInV/ZUjqnm5saadt6aHKg8RB5c3pvoZzaVoCSwvoXhJCAkhxcAtsNoOtLvaGjg+OE7BsLh/9ybCPk85ELiwjSxJeLWFMnrzs2Lz5fRkSeJ88kZfptwvA+o8c4vz28/P0nm1y2ueninm+JPDzyMJQa03WOF8G7a9ZpO9+VB1hfXbL13ZkYVOvX8fcX1jmbcv6Aw9jINdLndKBLSFfMFa37VAKUALKA10hj8EuOX9Xfxl5QZZaDQEbi5/9tJVWJLnbC1xIYWKhMKG8COAixAlTebzEELM432K8nqZrsjPLdg+4dlMRO+oVBY6Qw+Vz1+npHQjytkcAEGNdxfnKwVA5RglLWaF9tD9pfGRkYVOW+iD2K5RGT+g/pvK/gJ5wfaSUMv8TxeBhGEnSRo91PiuXSARuhaQJAlJlkhnCpfeGKj3xvhI8w2cTo0seU1uCDbQEahDeUd27tBsLyP5GR5uvG7BS7BgG/zTwCs82HgdTf4LDWU1njAfarqensw49gpoipZtMzSdZCqdZWBylnXVMfKmSUsiyrHBMTKFIgXDpDkR4djgGLIkkS0avH5mgN6JWdpr4zTEwng1laBXJ+hZ2+/9UpBlqeLQC5DOFzGsiytqdTTEaayKMDAxx6HuEWrjoTXRq15L+IIePH4dJiGbzJFL54ktIb/pui6TQxekK1dbGvNiEEKw9ZaNNG9s4OxbvZx5s4dzh3rZdutC00HLtHj9h29SyBaRZInr77u25Fx/Gcjbec6mexjMDzOSH2WyOM2MMUPSTGE6FrZrXbTyP2PM8vrMG1f0OT2SzubwRjpZvcC/YBc5nT7L/qlXOZ0+S8pMY7nLXNc2zJlJRgpjnEqf5eXp14hrMbZFtnBj4nqafY1XFVAveUjXZqo4zcnUGY4kj9OfG2DWmMN0LJxLKaCZ5X6zLEgcQZVUqvQEW8NbuKnqehq99VdUtSjaRd6YeZsp48L1nrML7EtcV9bdt3hh8iW+P/wEc2ZywQQwbWUYK4xzPHmKY8mTfLTpIaJqpHKduq7LuUwv3xr8DucyPQt/C7s06RjMD3N47hg3JK7jwfoPElZX3/zqXwJWLfA3HJvv9R/mS6f3M2esvprLOyELiV3xZiKal1njguLOSC7J0dnhqwv8URH6zTjZ/wXGEdD2cElewQoghMCna+zuWPvGlLXGZD6LJAT/acf7CGmeSu61xEdc+zKnUTAZG5ymrjmBqi+8jLOpPNlUnnhdhBrvbjQ5uMDIZDFX8Z30l3cqSnnesfW8SRSLJ0zvHH85vmVpfGmR9v7i7WXUeQ1EC8df/DJZzCMVC44hvbMZCbViDFM6/sLzeef25/92XZeCO4NfqSPuWRuTuvNwXRgZn2NqOk3zMpSKd0ISAkXIiwJKx3V4ZvQQJ1PDNHhjPNy0F0XIFGyDn0yc4ImRNzEci5H8DDWeCA81XsdIfoZnxg7x0uRJUmaekOrljtqtbAw1lq4RSVp0HNd16ctO8uLEMVJmng2hBm6q2ojsyuQMk+s6mymaNq7rlnp0JME9O7sIeT2MSxkkIbhnRxeNiTB3bu1kOp3jwT2baamK0FYTo2d8BkmInzo/XiBQ52W7i6aFfQkXrupokIdvvoYvfvclvvb0m0QDPnatb/iZcm2O1UZp6mpgrG+S2fESTaa+o27B7+G6LmN9k5w6cA6AYCxA+7a1U/NZ+jwjXHfPTroP95OZzfLq42+y+YYNC5p8x/unOPbyaVzHpbolwfbbN69I838+BnLDfKnnq6StzM+0Yo/ruowWxnhq7DkOzLxFxspclqJYSSnOZLQwzvjYJIfmjnJ79c3cUrWPwCp4EBmOQX92kNdm3uBo8jgThanLdkCeD4dSVWMoP8JIfoy35g7zgdr3cVPienT56pMKM8YMOTuPX/bx2swbfHvw+2Tt5RPDBafIy1OvIxD84rqP4ZVL76P+3CB/2/cN+nMDF/01snaO5yf2A/BI08N45YtTL68EtuMgrXFz/nwYjsWskSGhhyo9rJZjM2tkiGoBFOnyJpWr8pR1XJfXJ/v4ytlXLhr0q5JMRPPiVzQ0SSFp5BkvpJfd/mIQQtAWTNDojywI/Au2xRtTA3ygYfOK7dxdJw3O+MLxlU6E3IGd/hyS98OI/4+9946u6zzPPX/fLqc39N4IgCRAUuwUm0RR3aq2JVtusZ3EiZ2bm0xmZW7WSjJ3HGfNzE1ynUzmJhM7TrEtO5ZtyZYtq1GVEimSothJsQMkegdOb7t888cBQYIASAAHkOzcPJTWArDP3vs7++yzv/d73+d9Hn0NjNM/rhsJqFWIGwR3/x7h1R34NGfOwVNRP/BV9VDvGN/66k/4vb/81BQDr/PHO3n1Rwf4yp8/RqCwdoYj/AfyhRACn16Fb55mX/1DUX7x6glMy2ZgOIpp2vz05WO8PR4kXQvDsDh7sZ++wSiP3Ls6r/tNIFhd0EDYSHIy3MGj1ZvQUFGFSpO/gnJXCE2o3FG6Aq/mQlNUQg4vrYEajoy2c2txM2WuEBXumc2VAIYyUb7d/jorg7Us8ZXzat8xTNvk/sp1rG2Y3GAZ8k5ebK2sLZ/0+9LKyX0PIa+bioKFU+bJB1JKsqY16fcrtKSOgTBZY/osaWNlEfdsWMoL+8/w5999hU0ttaxpqqSqOIjH5bgh7VBVBLVlBZOMw240vlygcG24IPKuMDjdDu79wg5O7jlDOpHhR3/1HN6Ah1W3teD2uzANi4GOIX74Fz+j52I/Qgi2PLye2paqD/R5qSgKWx7ZwEv/+gYjvWO898oxHv3P91HZePUeO7X3LAMdQwCsuq2FyiXlcx6jLS2ydvZXOui3pMWZ6Hme6f45bfFLN8+c3wQ2Nv3pQX7S/Qu6k718vPphip1FExXy+aA/PcA/X3qS3lR/3uO7HrnxDvCjrp8SNWI8VHkfjjzlmSNGlKgRJWJEebbn+RsG/deO48DoIVoCS7mteCsJK8FPun9OR7JrVkswU5rsHd5Pi38ZGwvXLXg18ekLJ8naNtsqaqnxh9DHKdOLhaF0hJ/3vMsXGu7EO95DFjWS/LBzD5+p20GRc25cyQUJ/IfSMb574QC9yalyhQqCam+IraVL2FhcR423gCKnF4/m4OnLR/ib99+Y93l9upPWUAUnx3on/iaRXIgOETFSFDpnx32V2QPY0T+77q8CMMAexY79JQgXCCdTGnmFAzX0d6AvjtygLbMY1ihZa4S02U3a7MWwRjBlFFtmyU1gGqrwoCkhHGopbq0Kh1aOQy1CFb5FmWTcmk44m+JPDr5IS6h0gtNf7PLy8YZViy7HatuSdDI7vYGXKhjqHcOYp4HXtZBSYssUWWuYjDVA2ugiY/Vj2GFsO4WNQa59VkdVvOhKEIdaglOrwKGWoqsF41beH04mU0ob046StQZJGh1kzG4y1iCWTGDLLIpwoAovTrUMl16LR6vDoZWhCu+iBydSSnoHI5xvH2BoJI5l27zxzrkZM9gul86OLc3cvX15XgU4IQQV7gKqPUWcjlztEdIVlSW+MkpdQXRFY0WwdiKTUuDw0eSvwK+5afZXUuedvgH5Wrwf6SRmpGj2V+DRnNR4i3lvtI2dZavwaB8uPWchEU2mCcevJnycuoaqKkQSGf7s27voGBibso8QOZqiEALDtBgMx3nhwGleee8cmqrc9N7ze5z87X/+KM3VM2t4SykZzER4qe8QXYnhSUFSa6CWT9Run8e7vfZNwKaPrOWhL9/Dc/+wi772Af6fr3yL6uYKgiUBsmmD3rZ+RnpGc7r6d67ks3/6GI4Fkm+VUk77/JsOda3VrNq+nN1P76evbYDjb52mvKEURVHIJDO8++IRshkDp8eR0+53L67E7C8jLGlzPHyKpzqfoTfdf8PXCgSa0FCFikRi2gbWDYLwjJ3hnZF3SdtpPlP7SUqc8xe+KHQUUuosoTvVe/MXAwoKuqKhCAVb2rOiAiWtFC/1v0aRs5Dbi7fmNRfEzDij2TGOjB2nP301waoLHUUoZO3stBWVrJ3lzcE9rAmt4t3Rw5yMnJn0Ol1o4/sb0+6fstK8PfwOq4KteLSFldc8MtTLz9pOU+kNsKm8mrtrmlhTUkGx27ugi4xcQ7xJ3EyTMNPEjfREImMkG2M0E5uVCub1yDsasaXN7v4LHB7pnLLNpzl5uGYVTzSso95fhFOZ3G2db3DoUDSWBUtREJO4gz3JMKOZ5KwDf6H4EdrS+Q1CaLlFwQJBSokl46SMDiKZw0TTx0iZl8mYvVgyjZTmuB24zdUMliD3KFJyGrLo6Gohbr2eoHMtBe7b8OrNKGJqs8z8xwlri6tImkbOWnp8vZuvlOrNkE0bRMcSjA5GMLImIwMRlGtoBpZp8/7B9nE++Pw5lbbMkjI6GEvvI5I+SMJoI2sNIWU2J/c68RlcwfWfgYamBHBqFbj1egKOW/A7V+PWa1DFzRqS80NusZIhYZxjNPU2kfR7pIzLGHZk/P7JyYXm/r8ybhUhdBxqIR69mQL3Ngrdt+HWahELzE29grLiAH/0lXsZDSd4+90L/NNTe/ni41uonoYnrSiCUNBDY20xPu+vRtNWOJtkJBvj5b5jaELBlBZLfGW/clz2G8GybY5c6GE4cjWLF/K6ceoaUkqSmSyJdHZWx5ISsqY1qXowExQhbjrhGdLima69XIj2sq6wEYdytTpQ6c6HCpqDEAK3z8Vn/uTjOFw6z/zN8ySjKdqOX86ZmWkqbq+L2tZqtj26kQe+dBclNQundGZkTIzM7CgeDpfObY9v5sALR0gnMux99iC3P74Zb8BDb9tAjookoWZZJS2bmyeZIs4WZa5SPlXz+Mw8eHKZ2HeGD9CR7JqyrcZdxbbizfPKMGtCpcEzfwqVlJLzsQv8sOunMwb9LsVFlbuCZn8jVe5KApoPXdGwpSRlpRnKDHMx0U57/DIRIzolGLWkxZGx43hUD5+ufRyfNr+mfI/qZmvxrbwfPTtFHUkVCgEtQLGziBpPFeWuMkJ6ELfqQhUqlrRIWin60wOcj7VxOdFBwprenDRpJXmp71Va/Msodc3fJMuSFvtGDnIychqAYkcRW4o2ssTXgC40etP97B7cS2+6b8q+l5NdHB07we7BvRN0phJnMbcWbqDRV48mNAbSg7w9tI/OVPeU/dvjl+lN99HobVjQOUMRChnL4lJ0jMvRMV68dI7mgmJuq6xnZ80SmkPFeLX81ZIkkndHzvFa/3HOx3oZyyYmqNQpK8syfxU+fe6LmrwD/7Fsil09Z0hZkx9Afs3Jby3bxqeXbMCnLc5ELYAqTwiP5iBuXv0CxIw0fakoTYGbZ+UA0DeghFblMZD8Av/cCs4kbXYxmtrDWGoP8expDCuKZDaTZi6Ik9hIaQIZLDNO2uwknNpHb+yHFLi3Uen/NH7HimlNKeaKYpeHLy7dMOlvtoSkmUWfI99sLui5NMSP/79X6brQT0/7IP/vf3lqkuScZVikU1ke+vx2fMG5fyGktEgYF+iP/5SR5JtkzL75fwZWgozVRzRzlEHxPLoSwudYToFrKwXu23Dr9ZO49QsBWxrEM6foiz/DaOptDGsEyY0qH9eO2yBtJkmb3Yyl3qFPf4oSzwOU+T6KS6te8O+wogjcLp3KsiC3rm3g6ReOsH5VLSuWLaz9+lyhCZWsPTWgurK4Ne3ZURkq3QWUuwr4fEOuFGvJ3LV2Kh+wC/kiwbAsjl/s5ce7j5O+prpWVRzE53aAhMd3rJ5UDVgoOHSNopuoGZm2xeX4IL/WcCerQ5Mn/nyoFtdjqCvHj7csm433r2Hnp7YBoDs0AkV+qprLKawoyPmK3OA7dK2hl7TlTf0WEtEUsdHZiWgIIVi5bTl1K2o4d/Ai595r4/KpLlq3LOXoG6cYG4iAgI33r6WgNDjLdz4ZBXqInaU3rqJk7CwX4+3TBv4lzmLuLL1tgtM9V+TzmQ5khvhpz/P0TJNFV4XKcn8zd5beznL/UryaZ4pkpJQSG5uMlaU71cPuoXc4OHqIlDVZiMCUFgdG3qPGU8U9ZTvn1fCrCIWWwFKWeOs4EzuPgkKhI0STbwkrgi3Ue2opdZXgUPTcOBFTxmpJm7Sda8Z+uf81TkfPTVsF6E33cWjsKB8pvzuv5//e4QNY0qLSVc5vNHyOZl8jqsh9H26RK2j2LeEf279Nf3pw0n5ZO8uzvS8wms1VDes9tXy+/lMs8dZPfAa2tGn2N/KPbd+ZsniImXEuJ7pY4q1f0O/8J5pX4lBU9vd10hkLEzOyHBns5cRQPz88f4LVxRXcVdPI5ooaqryBSeIqc4FAsKGwGaeio/Yd4c6y1bhVHYTArTqo85Tgmsd8kncEeCEyyNnIZH68KgQP1Kzg00s24NcXj/suhKDE5cOnOycF/qZt05sMz+E40xtnfBCQ0iKWPcVA/OeMpfaQnnWgOcvjY5K1BhiI/4xI+iA1wd+izPtRVCW/0pcYb5y8FrFshp9cOskTjavx6YtDZahuLOUzf3AfJ/Zd4Of/8hZ3Pb6JQOHVAEBRFUoqQixbVz/J0Xc2sOwkg4kX6I5+h5TRPp4ZXwhIpMyStQYZTQ0yljqAP/kKLSVfx6VVL8wZpMS0x+iL/Zi++I9Jm72QB/9TkiVptNEZ+Saj6bepCXyJIs9OlAWUtr0CIQRFIS/LlpThWEDd8GNjl7gY66MvNcaewdOMZuKsL2pESsnBkQscG7tEd3KEl/qOsjxQyVJ/TsmiJVjNU5f38J1Lb1LpKuCeitXoikZQ91DqCvKjzneo9hSxvWQ5S3zlvB/p4ly0h+7kCAeGz5G2sqwraGRFsIbDo208eWk3Za4QSSvDxsImNhY1LygfNJ01ON81NGOmvH/0qlmSYdmc7xrKy8TLtGxGY0lOXepn76lL9AxdpXg6NJWVS8onVMI+ccdqZnQ9zxM36+FShKDI6R/3u7g5fWg+SMZSPPm1pzn59hma1tbz+3//m5TVl8zrXJpDmxAqyCQzpBM3Vq8a6BhiuHd01scvKA2y+cF1XDxyichwlKOvn6SutZojr5/EzJoEi/1svH816hyfm1cgrqn8zgTlBq8Q4/8+aA38jJXhjcG3OBe7MGWbQ3Gwo2QbD1XcR6GjYMax5UyvVDyam2ZfriJQ5a7g5z0vTuG0p+0Mrw28xXL/Uuq98+tBC2h+bi/ZigTWFdzCykArZa4SHIrjptfvytztU3ysCa2i2lPJDzqf4b3RI9NUKWyOhk+ws/S2vJpkLWmhC52PVj3EMn/zpDGqQqXJt4S7S3fyg86npyxAhjLDQE7u8/HqR2n2NU7aXxEKS7z13F6ylR93PTtpf4nkcqIDS25b0PtqXUklK4vKGEjGOTrYy+7uSxwa7KE3EWUgGeeVzgu83XOJGn+QW8truLu2iVVFZRS6PHOq+Aoh8GhOVgRrEUJhdagep5p/4iivWdaWklPhPiLZyaWiCneQx+vX4fsAeKwhhye3AroGlrQZTscX/dwLASlNuiL/xHDyNfIJ1G4Om7TZzaWxv8ayU1QFPosyD/nFkXQCt6ajCoWBVGySTOJYJsX5yNCiynnqDo2apnLcXhcnD1zk9kfW3dDAa7Yw7Rg90e/SHX0S0579onE+kGRxahVoyo2bQ2d9PClJm51cDv8dw8lXsOXs5C5ndWwMYpnjXBj5Gimzg0r/Z9GUhdeN9/tc/OFv303At3CJAoEgqHv5TP1tqEKdRENThcLKUC0twRp0oXJt0/6aggacis5QJkKxMzAxYbhUnS8s2cm5aA+WtPGP80YF4NfcPF67BVUoE1k8r+bi1xru4Fy0h7CRwKu5aPZXLHgT2GA4zle/+8okus21uHZBEE9m+Luf7Z218MF0kEhsW5I1pkr31pSG2NJSN/H9m40Z4GJBFyorg/U83fkOQ+koxc7ARG9IocPPEl/5jQ8wCwx0DHPmwAWklNS1VlNaWzzvBYY36JnQ149HkrSf6KRp7fQUBdMw2fvTd0nNUtoWQCi55uIX//l1hrpGOPzaSW7ZsYL24x0ALN3QSMPK2l8JGt1CQUrJhXg7+0fem9KUrKKwtWgTH696CL82e3qmEAKP6uau0tuJm3Fe7HsF87pjD6QH2Tu8nyp3Jfo8dP4Fgk2F61kTugWv5pm3VKgQgmJHEY9VPUJ3sndauk1vqo/R7ChV7vwqsTWeKtaEVk679FOEwprQKl7uf22SFOi1WBlooTWwbNoAXhEKKwLL8WoeYubk2K8/PZhbeCyger0QOdPSWn+Ial+Qe2qb6YqHeW+ghze72jk50s9QKsGF8AhtkVF+0X6G5YUl3F7VwI6qBhqDhbjnQAXyaC7WFDSMz1U52NLGlvNLauR1JdKWQVt0aLyEfRVri2po9M//ATgXeFQdt3Z94C+JZPMLfqSUIMNI432wumEmuSyhIJz3ItRZ0oqu3104KHLvZCT5xjhvfJb7oSFEzq01x9ueXZXAtKN0Rb6FW6+lyH3nFLfam+G5jtOsKiwnoLv46uFduFSNK0FT1jY/EA1/gEChl0d/8w5CRfk3L9syS3/sJ3RHv4Npz85KXqCPU6YEYI83Ws/u81OFlyL3nahibjrZ00FKScq8TNvof2MstWeWVQplnGKkcHXsN/7cDHuYzvA3sOw4NcEvL3jwryiC4sL8pe6uxeqC+hm33VE2czO+Q9Gm3VcIQZkrRJlrsg57a7CG1uD0Er1+3c2GoqZZjXe+kFKSTM+OSy9hEi1nIeFzO3n89luoLVuYBW2+MKTF2WgXg5kwP+3el5sgx7etK2xakMBf09SJHqMTb5/huW+8QvO6hpy+/ziEEGgODW/ATbAkgDaDZKk34KFxdT3nD7UjbcmL//w6K7Yto6ppssJOJpXlraf388ZTe2fd3HsFNcsqueX2Ft74wTv0tvWz5ycHCA9HUTWVrY9swONf2CbIucKSkt0DZzg62kGpy8/Hazbi1hav0ThjZ9g38i5j2anJnjpvDQ9U3DOnoP8KhBA4FSd3lNzG+5GztCUuTdpuY3MsfJKdpbdT5a6Y87iFELhUF64FkKrMiR2Usa34Vp7u/tmU7XEzwVBmJO/AvyWwFI/qmfFaFjuLqHCXTRv4KyisL1hzw/6PkCNIgSM0JfCPmTHSVhrXAkiTTgdFCDy6zrKCEppCRTyypIW28AgH+jvZ3X2Jc2PDhDMp3u3v5shgL/929hhrSyu5q6aRTWXVlHv9N6VHx80Ur/UfZ0fpygkFn8F0hP3D57ivYu2cxSLyCvwzlklfanKgpCsqraHyKVn4xYIiFNzq5JtBIklbebgjSgl2P3bsvyEze0FmmDYwEg5QylH1dTDPwB+gwH07XsdS4tnT025XhQ+HWoJbr8ejL8GlVaErIRThAiGQMothjZEw2ohmjpI02rDlzLxawx6lK/Iv+B2rcGplcxrro3UrcGka7dERVhSU89mmdRMOpOFsin+7cGROx5svHE6dlvUNwFX5wGs5srOFlJJo5jg9se/dIOgXONRiPHozPkcLbq0aTQmhKDmVJylNbJkcV17qImm0kzZ7yFoj2HJq45TH0UTQtSH/xh8pyVr9XBr7OqOpt5l54SHQ1SJ8+nL8zlW4tBo0xY8QGlIaGHaElHGZWOYkCeP8jBUPSyboiT6JEDq1wd+eV8Xo5u8JDNMknTFnDGpUTcHrdvxPlZm8EQIeF3etb+bo+R76x2JEk2k+oPX3BAp8bp7YuYYHN7egzUL/PZHK8uQrh/B7GDuc1gAAIABJREFUnDx2+y24nbOfLyzbpm8kyrmuIUajSXxuB42VxdSVF0wyMXQqOl9u+si08pLzybJOh7K6YrY8vIGX/vkNBjuH+dYffR+Ha3ImTygCp8dBYXkByzYs4d4v3EHz+iWo110noQju/PQ29v70XWJjCc68e4H//uv/wB1PbKWuNddjM9Q1zLE33+fdl44ibUllYxm9bQPXD2tG6E6d2x/bzL7nDhEdjrH32YOYGZOKJWXcsqN1Xk29CwlFCJYHKuhPRTgyeomHq9exmEuRnlQfp65Ti4Gc4sy24s1UuOYua3oFQgiKnYVsLFzLpUTHFArLcGaUM9FzVOZxjoWCgkJrYBle1TuFmmRLm+HM9Fn4uaDR23DD7ZpQqXJXTjQBX4uAnnPlvRE8qgf/ND4JGTtLykoRYn69K3OBKhQCDidrSipYVVzOE0tv4ezoEPv7u9jf18nl6BgDyTgvXjrHG13t1PlDbK2o5e7aJlYWlRFwTN8PGzfTnI50ckfp1YSVIhROR7vYXtL6wQb+hm0Rvo7m41Q0qjyhD+xGFoKJwPNazMZBcyZIbGR6FzL9OjjWoDh3Is1OZOY1FM9nc9szb4FMofj/GLQleYxf4FCLKPU+Qjx7livBm6YE8ehNFLi24Heuxqs3oqkhFOFAkMs0T27YsZEYGFaEcPoAvbHvE82cYKZgMJY5wVhqL2W+j80p61/oymWpKzwBPlq/gjp/aKL0FjLctIRKF13Z5wqMrMmpd9s4vPsMtm3zsd/aSVFZkO72QRxOnbKawpveh7ZMMRh/jrQ5VREAcp9DqfchSr0P4dGbUBX3eLUlp4ZzBTnOtI0tTSQZstYwyWwb0ewJIulDJI02TDuCQKXQdRtOtTTv92/JBF3RbzOS3M1Mn7OuFFDi/cj4+JtRFc814x/3G5Y5So9lJ4hlTzMY/znDydew5FS6nCWT9ES/h1urodT7yJwrRjdCMpXlzX3neO94B+FoCtue/j0tqSvh9754B+oi0EhyjW8Z0laYtBUmZQ6TtsKYMoUtjby48fOBUw1Q778b/QY9OSGfm9//2HYiiTQdA2Oc7RzkRHsfF3uGGY4kSM5S+WWucGgqBX43rXVlPLxlBZtaanHPskcjY5i8ceQCLqfOvRuW4XJo2LYEwQ2NcUzL4o0jF/n2S+/RNRTGsmwURVDgc3PPhqV87p71FAVz1aic87aL4UyU96OdhLMJylxBWgK1CyKnKqXEyJos39jEvucOMdIzipExplXaiY3CcPcoF460c/jVE3zlr7/A5ofWTXmfrVuW8bHff4Aff/050okMp/ef58KRSxPcf8uwyKYNPAE3n/7jj2JkTL73589gW7Ob74QQtG5ZypJVdby/7xzDPbkegTU7Wymru9qbkEpnGRqNo6kqpUU+JDAwHCOTNfB7XXg9TjIZg1gyQ8jvxutxMjgSQ0pJaZEfRQgGR3JU0NIi/yTn+RuODyh3h6j3FXN8rGPK9Y4YKUazcZyKRqkrgK5oJM0sCTONKhTC2SRuzUGJM4CmKEgpCRtJRjPxCWZCgcNLsdOHjeT96BnCxlQZ8isutvnywhUUlvqb8es+IsbkxJIhDc7HLnJ7yVYci9A7NRfkFilFBHX/lMBfIqdk0ecKp+Kg6CYSpldoR2Lcif5aBPUAhY4bx5UORZ+2D8G0TbL2wvVNzga5PgpBocvDlopa1pRWcm9tE693tfGL9rO0RUZImQZnx4Y4Hx7mZ+2nWV9axSeaV7Ktsh6v7phEiLry87XsmhzV51p1x9kjb47/9Zl1TVEWtaH3ekg5fZA/VyezyTCQ2fdArUb1fxW0RsjsRhrvIVyPINQKcD2IHf0qZN8Fx+Y8zgUgKPbcQ3/8GSQ2Re4dFLpvx+doQVX8kwK1GY8gFAROnFoppd6HCDhvoX3s6zP2DkgMhpKvUOL9yLwoJ4VOD4XOyfv5NAdPNK5ZdA1/ANuy2fv8UX70969i2xIza3LPJ2+lqCzIOy8eZ2woym/8ySO4PDee4NNmL+H0u0z35dGUAHXB36Xc//hNde1z29RxrqUTTQng1hoo9OzAtKMkshcIp/cTz56lyHNn3hKZUlqMpt5iIP4skukDO4/eRH3o9yh070AR7mnHLxDk/nOgqA4KXFsJOFcRct1KR+TvSZs9U/Yx7TAdkW/idSzH51ie1/u4Asuy+eFzh/jhc4eAHOd/JgOnUCB/itSU89sZIkYXfclDDKVPEc12kDRHsGQWiTW+QLoigfrBIeCoo9q79YaBvxACl0PH5dApDflYv7Sax7ImI5EEF3qGeeqNoxy5kPsc3U6dj2xaTmXR/M2/FEXgdugU+N3UlISoLgnhdTvmJVM6HElwumOAjoNjXOrLZRVrSkNsXVFPc3XJpOqBlJKznYP8w8/20TUURtdUvC4HWcOkbzTGD988hmHZ/O5Ht+F26kgpaYv388/tu0iaGTyak5iRotxVwG833T+FsjUXSCnpPNPDk197mqOvn0RKuGVHK7XLK3F5XRO9BLYtyaYNRvvGaD/RSf/lQXrbBnjyaz9m6foGiiony4o6XDqP/a8PUlRVwK5v76bnQh+JaIpULI2iKfiCHpZuaOSB37qL7R/dyPnD7TSursM0LLyB2eXGg8UBtjy8nrPvXcQyLNw+F5sfWo/DdbXqMhJOcrZ9kN6BMJtW1xFPZujuD9PZO8bKpRUUBr0cPNFBQ3URSxtKae8apq1jGMu2qasqwulQJ36vrSzk1tX1eTlMSyk5G+3j6Y53x51xLVaFani0Zh3no3382+V9FDq8ZGyTsWyCx2s3sb1kKe3xIZ66vA+36uBibACJ5DeadlDs9JGyUpyLXZy2ItToq6fIkT9lTQhBmbOEkB6cEvgD9Kb6iRsJCp0fvm+CR3XPSB2aTuVsbsf24FWnn4OuhV+fnu4Z0oO4lBvHlVfoVdfDkhbGh2AqZ0vJaDrJyZEB3uxq49BAD13xCEkzdy2DDheFLjdDqQSj6RSvdV7k0EAPDy9ZzpdXbqLKF5i4XgHdg1t18lzPu2wvaUUieXvwfYK6Z8LQay7IL0IT00tofZBVK0vaJM3JqzkFgScfqpG0kfYwQqsDrQEhVKTQQVqAkaNIqPUI533Yye+hup+APNRZhBC4tCqai/4Mh1qCS6tEMH8qgxAKLq2OhoL/jYzZRyx7ctrXJbLnSJu9eB3z4yD3JWMcH+klZlxVVAo5XdxR0YQjDw392SA6luDNZw+x/cE1rNm+lH/68xw3UVEVyuuKOL7vPKlE5qaBf9K4SNYannZbgWs7Zb6PoSnz457nVC4cONRidFcRQdd6LDuJugD8+LTZR0/0SUx7arYKwK030FT4p4Rcm+e0yMhlKvyU+h5BUwJcHP0/yVhTG75SxmW6I/9Kc9GfoSr5B+KDwzFe3v0+JYU+fuOJrdTXFM2YJXQ6tLyCiGthS4vRzHkuRl+gJ7GfpDWMPVM/z4eAuY5FCIEqBF6XA6/LQXVpiIs9wxy72IstJS6Hxn0bl7GuaX5uy7mTXLm3595Efz1GIkn+6qk3GI0lMczxaqeq8Pz+M/z6/Rv5yK3LJ+4D07J5+3g73UNhCgMevnDfBlpqywgnUrx44Ax7Tlzi5YNnuW1VA7e21mFIi5f6DrHMX8UjVZtxqjoxI8kPOnbz5sBxPlW3Y97jjgxF+dYffZ/3dh0jUOTnS//3Z9jyyAZcXueke1OSk+a0TIu+9kH++re+yYXD7Vw62cX5w5fYXFEw5Rp6/G7u+8IdbH5wHV3n+hjtGyOTyqI7NIqrCqltqSZY7EcogtYty/iLXf87SIl7lvx8oQiWrK5D0zUsw6KutZplG5smjcM0LQzDZCySpG8wimXbeN0OKkqDVJaGSCQzlBX7uXPLUlRF8OreM2QNC11XkT0jjIUTmJbE6VDp6Bll/coaHHnQq9KWwS+6j9DkL+Oh6rUMpqN868KbNPnLAElvcoxP1G6iJVjJs12H2Td0gQ1FDbw7fBGf7uJLjXdwcKSdN/rfp9mf6+0Yy4bpSU19tmlCpdZTnbdj7RU4FAdFjsJpJUxHjTFiZpxC54ffF6MIFW0GdUM7z8DZpTpn1Y8wXeAOUHCTbD+Mm6pNc49JJDIPBshckTINLkfH2N19id3d7ZwZHSJmZLClRFMUqn1Bbqus4+7aJhoCBZwZHeLVzovs6b3MYDLOU+eOE86k+a+bdlLqycUeHtXJx2o282zXAf7hwotIoN5bysdrtkyhus8GeQX+CgLXdQG2LSUp84ObOCNGirgx1cSiyJVHo6AAUAGLK9lyITxAFsZ54AKB1KrAHgE5BuQpyygEQdfGiYVUJpXl+L4LbLyzdV6TqxACt1ZHVeDznB/5r9MqvWStYdJmFx69cc7nGMuk+Kvjb2JJm1LX1QbbMrcPu9wmd/0WD4lYmlg4yfo7WvCHJjcMeX0usiljVuXvtNk77bURwkHQtRFNmX9mdPLxxERWPV9IaTKSfI1Y5tS02zUlSG3wd+Yc9F+FQBE6hZ47qLGGaB/7i2mukc1I6g1K0w9R6Ll9HueYjJFwgqGROP/pCzvYuXXZggX2N4JhJ7kYfYEz4WeIGz1zaq7/VYEiBDWlBbgcGsmMgRj/2/X88g8Llm0zGE4Q8rnwe1wgJeFEmq6BMf7p+QOUF/nZtDwneZg1LM52DuYkDJureHzHLbgcucz+spoSwvE0J9p6eet4OxuW12DaFj3JEb645O6Jhjiv6mR9QRMHRs7Ne8xSSk7uPcvRN04ipWTLwxu463O3od+E5rTkllpufWAtFw63Y5kWvW0zu8OqmkpheQGF5TcOCDVdJTDHpnjbsjm++zRGxkAIwa0PriNQ5J/0/t5+7yIrmitIpHKuqmXFAV7Zc4alDaWUl/hp68jgczsnKjJej5OWykKW1pfgdGi8+NZpqstDLGsoxenQZk31mQlxM01/OsL9lbcQ0N24VJ0ip4/2+CBLfCWUu4M0+cvw626qPAVcjg9h2Ta6opGxTJJWhqSZwaXq6OP68QPpQRLmVCUsh+KkzFW6YHRlVSgzZrKzdpawEaGO6cUBPkgsxEJ+JuiKY5KB3kyYiSbs13yz0uFXmLq/HP+3mLCkzVAywZHBXl7rusjB/m4GknGy454vAYeTlUVl3FPbzPbKOmr8QVxqjslR6w9xW1U9x4f7+caJA7zT28GrHRfYVFbNZ5atnnA4b/CW8btLHyAxLl3v1Zw4lfmZhOUV+OuKSsg5OcuQsUwGUrF8DjtrSCnpSYQJZyc3smqKQqUnn0YOFaHVII1TYI+BWg5KMeBAZt8FfRUgwOqDG5ojzR7X3tS2LRnqHcMcV9+IR5KomorL4yAyGsfjdZFKZoiOJigsC+DxTe/IK4Qg5NqCW28gkT0zZbskO85tv+LgOnsMpuPYUvKna+8m5Li6khdC4FhEA68r0DQFTVdJJzL4Q1czzrZt0981gsfvmlE941pYMjGtEo6Cjq4EP/Smq+mQtYYZTLwwA8VHoci9k2LPPXnTiQQapd4HCKfeYTj16pTtph2lL/40QdfGvH0hrnDnK8uCH0jFMGslODn2JOciz2LYvxrSv/NFTWkQt1NfNK7/fGBLiZSgayr3bFjKo9tWUFkcRNqSy/2jPPXGUQ6e6eLld8+yurESp65hSZuxWApFEVSXhCYaeYUQVBQFuHfDUk609XKhe4hYMoPLreLTXHQmhmj2V6IgyNomXclhChzzTwxJW9J+ogMjY6KoCss3Nd406L8C/Zom5rkq8iwUhntGObTrOLZlU1gRYsO9q1G1yQFTTUUBZ9r6MUyb6vIQnb2jLKktxrJt3j12mYrSIL5x5SIhBFvWNHDg+CX6BsOsW1HL5jX1HDh2mf6hCGtbawjmqRakCIEqFMzxzG3OndyeCBQdijZBNbt2Lt1UvIQDwxf5xwtv4lZ1Hqhag0dzIqVkMDNE2spMe660laEzOX3f11xh2iZZa3qOuS1tYubixUu5bLfElCambWJJC0va2NgTHPErP2dtg8wM48wXutDQZmVWOf3DP1/1osX4pkkpSZoGF8MjvNHdztvdlzgfHiZhZJGAJhTq/CG2j2f3bykup8DpntLDJITAqzvYUl5DyOHiD/e8wOnRIfb0XubRxhYC4/FVysoylIli2Lm4cCwbR1dUKt1FczZNzSvwd6kaFe7JAXbWNrkYG8SwrUV1cIXcKuvk2GSqCYBHc1Dvy8eSXUfo65Hp15HmBYRaDmoZQl+Onfw+ikyDcGKnfja+KMjf/n0SpKS/c4STB9rYcv8tXD7by+hAlK0PrGbXD/Zz672rOLz7DP6Qh3g4yYNf2I7TNX0m2aEW43O0ksieZbrbP2MNkKtqzC0D6NF0fLoDdVzP9oMOkINFPupbKtn1w/3ces8qjIzBQNconRcGeP2Zg2y9fzXewM0fFmKGyoQtDQx7dEIt6JcFORWiYySNqWYzkGvmLfc9tiBSoUIINCVIuf8TjKX3T9vsm2tcvojfmYfzNVAY8lJU4OVy1whb18+/WX42sKTB2cgznA3/BHMa1aV/bygOeinwexiJ/vK8197hCLFUhuW1pXzlkS1UFl3ls1YWBwh4XbT3jXK2c5CxWIryQj/InFuwIgQe5+RMlyIEzdXF+D1OhiIJYskMAW+A20tX8qPOPbwf7SCkexlIhxnMRPhy4/3zHrsEbPNqdciYpTxqJpXlzIHc91YogvKG/Bv85wrbtjnw/GG6zuV6PtbsXDmhGHQFQgi2b2gklTbQNQUp4Se7jtHaVE44miKZzrJsSdmkHpz6miLKSgJYlo3bpaOqCuXFAczx32c9PiQxI0XcSJO1TSLZJLqi4tfcNPnL2Dd0gWKnj95kmOFMnEeDlSTNqcH7FWQsA11R2VaylGKnnzJXYCIDHDYi07rVJswk37n8bwtq+DRTQG0jFyzYllKStbNEzRgjmTEGMoMMZYaJGFESZpKMnSFrGxi2gSUtTNvMLQikhSVNDNua0ti7UMj5m8z/euq/ZG7nA8k47/R28HpXG4cHexhKJTDHxSgCDicthaXcW9vE9sp66gKhiez+jSCEoLmgiA1l1ZweHaIrFiFjWuCAtJXlx517OTx6kbiZRlc0srZBa6CW32n+yA17wKZDXoG/U9VpChSjCmWiwVYCx0a6GUzFqPLOv3lqNhjJJHir/+KUMk6tt5Ayl3+GvW4OgQDHFhTvbyLUKxQeF8LzWaRxBjvxzfEX+lB8vwNK/nrQ10JRFZauqePY3vMA1DSVcWzveRouVyEUQTyc5PzRDpatraO7bYB4JDVj4C+EgldvJreSnhr4m3YciZx1vv/1ngtcjI5g2had8TBfPbSLloKycU6/oNjp4aG61kVv8HW6HTzy6zt46m9f5ntff4HR/gj/8KdPo+kqa29bxl2Pb0KdRXlZVwpQhANbTp48JFnC6fco9T6Cri7ufTwX2DLDWGov1gwBq995Cz7nigVbrAghCDjX4HO2EkkfnLLdsMcYSb6Jz7Eyr3OWFfu5d0cLr7x9mqVLylixtAK3a35lzBtBIulLvsfZ8NP/UwT9AF6Xg6riIBd7pu9l+TDQMRAmmkhzx5pGygv9UwLPuvICaktDXO4fJZnJBUbXPr2up4IJISjwe/C6HMRTGdJZA0UobC5ejkt18N7IeQbSYUqcQT5WvYVm//w1yRUhqGouR1EEtmVz4BeHue3jt1JYPj0PWUpJbDTOS//yBsfezNHzKhpKaVxTN+8xzAe2bXPuvTae+8YrZNMGgWI/935+xyTPgSvQNRXdp06Mf+fmpVzuGcHvdbJuZQ0uhzZl4eV1T56DPO650xoj2RTP95ygMzFMzEjz/UvvsLawnjvLW/lo9Xqe6z7Cv1x8C01RebhqLc3+Ms5H+ylzBScCdZ/mpHQ8wO9IjJC2DPYOnUMgSFtZPlm3meWBMmLG9JU+iSRpzSyHvaCQctrm4tnvLjGkQX96gNPRc5yPtdGT6mUsG8YYz/JPt7j5oKEIJa+FVG7R8MuTgPvX9w/x5JmjpExjIrtf6w+yrSKX3V9dUkGB0406R4lxTSiUuL2oQmDYFvb4Uy+cTXAx3sdXmj/CkdE2ar0laELlbLR7Xguq/Dj+QrAiVEHI4WYkc3Wl2BYbZnf/BT7VsD4vh8gbwbQtXus9x5nI5OYcVQjWFFURcuaR8RQC1CqE90vj0pnj3Dd9PWrw6zm6DwborQj91rwpFTdDoNCHv8DLwddO0bSqBodTo7qxlO0PrgEgVHTjsrVDLUGgTMthlnLmbMl0yNrWRA/H+uJqLGljShvLzN2gaW1xzIGuhxCC6sZSvvznj9F2qpvB7pwsXUVdMY0rq/EGbq4gAODW61GFd0rgDxBO72Mg8XMqfJ/Mm8qyUDDssXGZ1qkQ6BS4NqOKhTXX0hQ/ha7t0wb+YBPJvIclk2h5nDeVNigrDpBMZfm//seLtC6tpKzEj0PXplB/ykuCPHLPLfPqA8haMc6EnyZljc7q9apwoiteNOFECG1WPNOFhF+vzPv54nE6qC0NoakKqqr+UlSwDNPEsmWuUXua8ahC4NQ1DNPOyXzCeKfszMd06iqapmIk01jj+zgUjY2FzawpWIIlbTShos3D6XISBKzc3kJtazWXT3VxfPf7/M1v/yN3PLGVhpW1E4ICZtZkdCBM+4kOjrx2kpN7z5JOZHC4HTz8O/dSVluyqJ/F5fe7GO0P4/Q4yKYM2o5d5tXvv03n2R4UVWHnp7bRumXprDKR1eUhqssXPwES0N18qm7zRMADuc9QFyqlrgCfX7KdtGWgCgW35kAVCksDFdT7SiaaHNcU1rEyVE3SzPJG//v8euNtNPnLMW2Lf217i9ORHpr9JaTtuc19i4X50lAM26Atfom9wwc4FT3DWDaMKT+Y+XeuECh5PTs/6OfuzTCWTpE0Dfy6k5bCEu6ubeK2qnrq/QW4tTwYEEJg2blUtnecUQE5dotL0al0F3JB70VKyfJgNfuHz5Iw0x+sjj9AU6CU5cEy3hlsn/hbyjL40aXDrCyo4JaCqgV/uNlScnS0m++3HSRtTb7Rgw43O8qa89aSz+mTX2ewIjSkfgtCXwnIHE1kER7c8WiK/btOMDYUZd9LJ9hwZyvL19Xz1s8Pc9fjm3C6HZw/3sk7Lx2npDLElvLVNzzejYJWOUcd2HuqlnJXZfOM24XIrVoXG5Zpk0ll8Ye8rLt9+QRH/Mq9ZtuSbCabc9ZUZ57oPfoS3Ho9RmZqIGjaMTrD38CwRin3PYZLq1r0Rd7NkDIuk7GmbwpUFR8B55pFCCYU/M7VqMKLJaeWgpPGJTJmL5pj5vviZrjcPcI//WAvmayJZdkcONKOUMSEesy1WLmskofvHu+zmSMGUkcZTE2vcnUFqnAQcjRQ7tlAoXMpXq0Mp+pHFc5pm8cg9w1KmiliZgJDmigouFQnfs2LrmhTRmojiRpxEmYq95DXXAQ036TnlgTCRoK4aaMKk7FsFBubQkcQXWhEzThxM4lP8xLQpsrNSilJWCmiRoI1qwspKNlAiTtEXdmHX8Hyup3omkL/SIxU1sBznZxhPJ1lcCynCx9NpHN82kyWrJnLjtrT8ONtmXvPiqIgRO7nkWwMTagEdQ9Zy+BI+CIezcnKYN28TbyEEJTXl/DFr32Sb/3R9+ltH+DgS0c58dZpXF4nzvHAP5s2yKayZNNZjExuniqsKOCR/3QvD/zmXYtulvXOz97jJ3/7AkiJLSXZVM5jQFEE6+9exWP/ywPTZvs/TKhC4NdnTto5VR3ndYIiuqJOohU7FA2HkusJCehuXu8/zfloPxEjxVA6xs7yViQ2pv3LGSTfDFJKxowIrw3sZu/wfkazYwvSvKqgLFp14Jcg17CgKPP6+WTzKu6ubWJtSQWFLs+cs/vTQUpJocvNhtIqNpRV4dZy97pXcxHQPcSMFBXuQnb1HuFirI+klZnXcyzvwD/kcHNvVQtHRrpIXaPpfzE6xF+fep0/XHkXKwsqF8zUybQtDo908denXuNyfLKbnAA2FNWxomDuFtizxRW99sWE2+tk6/2r2XzPKlRdRXdoNK2sprqxdKKR994nNpMel3hTbmJkJObI378Rrpil9SQinBzt4+6q5gnPhNF0kgODHdxV1bzoVJ+RgTC7frCfB79wG6qq4PI4JnSok7E0r//kIO8fbCdU7OfuT2xiyYrqaTPEulpEsedu4tlT2HIq1/KKy/FY6h1KvQ9S6NmJS6tGmUH2bDEhpSRtdmHa0zeDObVyHNrC0s7gitxsNbpajDWNCoZhjZEyO/HmEfhXVxTwB1+6a1avDQXc8wqabGnQEX8L8wau1gG9luWhx6nxbsetFaJwc7pRwkxxYOQYB4aP05MaJGXlzIRCjgCrQ8v5RPW9uK/RWg5no7w5eJCDoycZTI8ikZQ4C9hYuJI7yzZToOf47oZt8lLXXmJGgpbAEnb17yVrm9xXvo1l/gae7nqZy8le6j2VfK7+Eeo9lRNjzVhZDo6eZPfge3Sn+snaBsGAj5XBZpJaEYXS86Fm/mtKQgS9bo5d7GH30TZ2rm3CNd4gG09lefngOToHw2RNi++8fAhbStp6RxiOJLBtSTiewpZyUrUgEk+RTBt4nHquWiAtnu7cy7JANbeVtPJs9372DZ9FEYLP1d/BluKWeY9fURU2P7SegrIQu767m5NvnyE8GCEeThIZyi1YVE1BdznwF/goLA+x6vYWtn9sE8s3NaM7F78vStUULNMincggbYnu0imtLWbTR9bw2B88SHnDwinXLDZyLuXD2DKFU6uc1fPXp7n49cYdnIn0kDAzFDi83FneSq2nmLSdmjFYFggciuMDyTLrioY2h2SSlJL+9CA/6vopx8InMG6S4XcpTryaB4/qIaAH8Gs+PJobp+LEOa6041Ac6IqORPJ87y6Gs/m79P57x2+0rsepqri1haWjCuCTS1fx0cZWdEWZCPwDupvfLymbAAAgAElEQVTHa7cR0r0UOf30pUbpTg7zYOVG/Prc2Qh5Ry+qUNhR1szLhafZP3Rp4u82kkPDnfwfR57ns42b2FnRTJHTNy+TF8h1vw+m4uzqOc1Tlw7TER+Z8rUtcvr4RMNafAvgygg5kySsLmT2ENLqAGmieD+LUKuRMpuT8hQehLKwVtCqqkxSqgFAUfFeo4zgcOmTDFc+aIxlkhwd7mFnZdPETRQ3s+zubeO28oZFD/wzKYP9r5yk80I/0bEE1Y2lfPRLO6luLOXo3nM8/909tKxvoOfSIN/7+ov83l88QXHF1EynIjSKPfcxknyLSObdac8lyRLLniBhnKM//lMKPTsoct+J19GMKnwf2OQpMUgZHTCNChGAUy1DXyD50evhUItwqEWkzY4p2yQWyewF8MwucJ8OBUEPd29fGDOwmZA0RxjJTN/kDlDsbGVjye9R7FqBYHaUmJSV5pmuXbwysA+f5qE10EiRM5RTBUn0AXIStzVmJHiq80X2Dh+h2VfHfeXbUITC2Vg7P+99k97UEJ+vf5SQwz9x/OPhc5jSZFPhLRwcPcHPel6n3ltJmbOYImcBe4YOsXfoMNW1ZehCw7RNXh88wDNdr1DsDHF7yQY8qovLiV72DB2mOznAlxs/SYW7ZCEu67xQWxZi3dIqdh08x98+8zZvn2inpjSElJJLfaMcPt+NZUtuWVLBqUt9/JdvPo9pWaQyBg5N49TlfgZGY1SMG5GZlsWxi71EEila6srwe1yYtkVHcojbS1cwmI5wLNzOl5vupy3ex7sj5/MK/CEnudmyuZklq+sY6R2jr32A8GCETCqLbUscDg1v0ENhRQFldcX4C33ozoXvW5kJOz65heqlFcTGEtiWjSfgprq5kuqlFbi8zl+ZoD8HSdrqYTCxi7rgl3CoN3aBhfHKjDtIuXvq/KzYYkbN+kJHAR+regi/lock+CyhCIVq9+z6TXIuxBF+1PVTDo8dmzE771E91HmqaQkspdZTQ4mzmJAeRFd0VKEgUHLKMuQy1FeqmEkrxe7Bvf8R+M8CBa7Fof7mZNj1iYB/4u8IKlxXpX0fqtyIJXNytfOJqRckOit1+/l80620xYYZTF/NRtpIzkUH+cuTr/B810m2lzWyrqiWSk8Qn+ac6IKeDqZtk7YMYkaanmSYg8Md7Olv42ykf1Jl4QocisqjtavYUFS3IA80KQ1k5jXs+DfAugQyAzjBdS+o1WBHsKNfA60BxfeHiEXOAOfky9JYMoFlJ7FkEtOOYtkJLJnClmlsmcWWWaTMTPxsywwp49K0kpXzgS0l74/1c2iom854mN29beiKggTOh4dy7RGL1NdxPRKRJEbGZPW2pZw7epkXv7+XL/zRw7S/30398kq++McPExtN8I3/+gwXTnROG/gDuLQqakNf5uLIICnz0rSvgVxjbcI4RzJykYH4zwk4V1Po3kHQtRGXVoEiFrdsLqU5I80HcgpOilisB5KOU52pkiZJmZ2Lct6FRNzoI2EMTLvNq5Wyrvh3KHGtGqf5zQ7Hxs7y6sA+Kt2l/GbDx6n3VqEJFRtJ2sqiCDGhXy2l5NDYKfYMH2ZDwQp+rf5RCh0BQBAzEvyw80XeHjrEUn8d95RvmzhH1s6ypWgNW4rW4Nc9PHn5ORyKg0/XPkDGztIW76Qj2UvGzqIrGp3Jfp7vfYsyVxFfbvwk1e5yFCFIWWme7X6d5/t28/bQIR6rvjdPh/P5w+ty8Kk719LRP8aZzgFePXR+oiJn2xJVUdi6oo7f/dg23jx6kWf3nCJu2bTWlbO6sYLn9p3mf/xkDw9sbqHQ7+F0xwA/evMYti1Z2VBOyOsiIw3EuFTxichlihwBWgI1xIwUZ6ILI9UohMDlcVLVVE5V08JX2/JBRUMZFQ1lH/Yw5oWsNcJo6h1smcGpllLg3oJXb0JX9iOljS1NopmjJIw2XFoVBa5bSRkdhDNHUYWbIvftpMxO4tkzONUyQq5bUa9xflWEgnMGTxVNqKwILKfU9eEtjKeDKU3eHNzD0fCJaYN+XWi0BpZzZ+ntNPsb8aoeVPHL0dPzH8gPYSPB0dF2tpW04FR1NKGSNg32DZ5lU1HznN17FyRaVYRgS2kDn2+6lW+e3UP8OnmthJnl4HAHR0e7cx33bj9lLj/D6ald9eciA3z91OtkLJPRbIK+ZJT+VJSYkZ7Q8J1yfgRbS5fwucZNU1ZK84GUEoxT2LH/DgiE+wmwo8j0/8/ee0fJdV1nvr9z863YOSc0ciYJAiBIgjlBFCVKNpWsYEv2c3zz7LE9M56wZrw8a+z3lu3xWLbssUb2k/2ssURZmRQpUiTBDIIBRM5Ad6MbnburK9183h9VCI0O6ASKMvlhNZvVN52qe+veffb+9vc9ftlBUyB0pPsCxH4e1KW9wUopCWUONzhPzjtG3j+G4/fghgP44UhZjccvc/SjctkyKnPdSz+lZt6rdMTNf2RMeA5Hxwc5mx3lyXPHytkDSOgmH++8boqp27VCVX2az/6bB2lf3cjJAz187c+epJh38ByfRMrGjpnYcZP61moGemZu5hRCocLazvKqf8fZ8S+S8w7DLFxHSYgXDjBc+BGjxRewtCbS1laq7TtImpvQlap5BY9zRem4M2VjFHSl8poctwSBodbMODIvHEbKcFE9EFf2aVxAEITkCh6moWItImOa8c4STuuEK1ie3EWdPb+gP4gCXhs9gBcF7GrYyapkx6Ts/pWGNV7k88boYRQEt9dupeYyN8oKI8ld9dvZM7qfPaMHuLV2C2bZOTSm2rTY9WiKSoNViy402mNNJPQ4SqCQ1hPkgyKhDEvmUpnjjLjj3NdwM22xxotjimsxtlZt4KmBlzk8cYpdYZHUAl2pFwshBGvb6/j3n76bb79wkDdPnCNbKD03KhI2t27s4OFbN9JSm6altoLt69oZyxZZ1lCJrmkc7R7k6TdP8NLBs2iqgusHOF5AW30lu7atQVUV9EilI17P/3f2WQqBy8+23YKuqIx6OeLqu4vb/j4mI4iyZNx9NCV+huHibiy9FVO9JH9acmlNYaoNjBZfwtKaGSo8Q4V1I6bWQCgLDBWeJmGsYsx5FV2tInWZ5LAmNBIzZPTdyKMYTjV1vBKFoIAilBk15oMoYMwfxVRMUnp6yuv5QEpJb/E8Lwy/Mm0Dry507qrbyUNNu6jQ353+M+9j4cgFDvvHz7KtetXFHhc39NkzcowN6bafTOAPYKk6j3RcT9Z3+MdTe6cE/wB+FDLmFRjzChzLTJ95O50b4cw0NJ6ZIBBsqWnlN9ffSYO9VDSHgMh5AqSHkv5vCGM70n1mcuCPhtBWId1XIRpdksBfSokkxAm6GC2+xHjxZXLeUfxovOyc+u6Q5dpR30GNleDZvpN8rHMzuloK9jShYKoLKz3NFyVev4kZM1A1FdM2cAouuUwRz/EvBpGqqqAb6lW1thWhU2Xfhqk10Dvx9wwXniaIMlcdRySLFPxTFPwzDOV/SMJYQ7V9F1Wx27G0VpQ5mZbMDVKGBGXn6CshUNCUhUvYXg0XHrQzIYzyRNJDXWDFIZKSJ549REXKZtv1yy46go6M5Xj0B29y9FQ/FakYH7xnIzdsaEVZQFUpF/Qz3XfIVqtoTdyGwvzOVSF0GHBGSGpxlsWbrypXlw+LDLojxLUYjfZURZdqo4IqI01/cZh8UMA0SoG/pqhY5UBVEyqKUIhrdkknQwgUoRLKCCkhkCG9xUF8GfDW2BEGnMkTxXxQxAk9Rr0MXvSTNfRSFYU1bXX81iO3MZotMDpRQAI1qTjV6RiGVspW2qbO5uWX6BBRJPk/HtrBX333JY73DDHhepi6xpq2Oj6/axur20oBoiZUHmrezrMD+6k2k9xYtZJISixV55badXMY4YWnUOk8SSmRMosQBkIszlBoroiiAmHUjyIqUJTKJapmO0TSQRHv7gDRVGuJ6Z3o7lsEYWZS4O9HE4wWX8VQKwmjAkGYRRKWqgJqmqLfjRcOEcl2EsYadGWyA7IqVCr09LQNrX7kM+aPX9VN90j2MHE1zprU9JSxUAYcnTiKF7ncXX8voQw5lj2GExa5p/6+eX0WEREHM0cYdqdK8goEmyrW8+HmB0nrC4+BLph5vY+5o+D7nMtl6MqOM+4W8aO5MytShsXdrctnTVZHUnI2P8DB8S7OO6PsGTmOrepIoLdYkqpdSNV2SfkpKd3i8yt3ENcM/v7kHobdhZlBzDXo14TCjrpl/Nb6u1iVql/Cm1gAwRGEtgJhbEUIcxqlewGiAqQD0zSFzhdSRhSDbgZy32K48COcoHdaicl3AxQhaE9W8uGO9VSUnejeaSQqYiTSNt/80tOs27qcA6+eIDOa46v/9w/oPnGexvZaJsbyqKrCyMAEnetbrrpPIVTi+hqWV/0eVfZtnM99kwnnzWmVbKYiIogyjDt7mHD30Z/7Z6pj91Ab30VM71yiCUBU6i2ZfvTXnGqkKDPvXxIgF+FiPZEt8uhjb1JXneTGTe2gKvhByNe+s5fvPPk2FSmbE2eGOHl2iD/43YfoaKme9/fdCadXv0gZbaT01nnvL5AhXlQyCJqJNnA5QhnhRQGqUKa1r7/w9wmZw7tMcUQgpkwqlBkkKUMZ4oYukYzoKfQz7I5NWafGrKDGrFxSg6KF4kJg32ymaa6ZWxZUUQQ3rGrmD76wi8NnBxjPFUnFLda01dFSk0YtTxqFKPFiP9G2szxBUkqa9HUb50RHlDLA8/Zimju44IPieftRtWZ0bdki3vXcUXR+SBSNYZo3o1wRvC4UfnAGz9tHPPYxxgayZEfzCEVQ01KFHZ/7hGZ8MMPEaA4hBNVNlcQW6c57JQp+N+POXvxoDF2txA368cIxnLAPTUniR6PE9U4kEZoSRxEm4+7rWGoDulqFrbVjqnVoSgpDnWyyKYSg3qzFVE2KV2j2u5FHvzOAlLN7k7iRy7lCDwNuP8vindSadRyZOEwhyNOZWEGj1UhrrJVTuZMAmKpJm93G8dyxeX8WXuRzNHt8kszpBcTUGHfX3U5KW1zix41c/J9wMuCnBVJKToyP8HeH3+CF3rOMu0UCKS8mHOeCznQV2xtar8pSyQcOJ7Pn6S2M8vzQwYvN4LZqsqtxCylt/tL1Sxr4CyFI6RafWb6d9kQV/++JPRwY652RorMYVBg2D7Vu5DPLt9Man944ZeGQSBkglAQzK/hIkHkQBiyC31/K8nsM55+iZ+J/kfeOLyqAuhwCFYFa9ilc2i+0pWrENYMneo5yZGwAQ9XYWtvK9TXN17yxFyCZjvHBz+3km3/1NN/+m2eoa6niC//hYQZ6Rli1uY2Bc6N86T8+ChLGh7Os2tw2p/0KIdBEiprY/aTNGxlzXmEw/wMm3LfmVAGAC70AxylkTjNceJK6+EPUxz+MqTUuioojiab1YiiP/BpLjYqLnhbTQcqwTDtbGIZHc/QPTXDb9hXoeul9nOke5qkXjrB5XQu/9tnbOd01xH//yjO8uPcU7c3V85aIC6Ii06UVKowO1FkmNTNBFxqmajDqZSiGV5+k60LDVk0KQXHa9X0Z4oQuhqJfpPlcwFzfqipUbNXCUg0+0baLrVXTOyorQiG5gAfGuwWqotBcM7fJghcFOJE/6cwbkYJJF673BopIYVl3I2UWx9mNJMCy7sDz9pEvfAM7OI1t3YeUBTz/TWJaC1JKguA0rvcKipLCMu/EcZ5F4iGlh209gHpZsCmlpFj8funeLn1sexeRzOE4zyOEjm3dTRD2EQY9hNEwlnkrQdhDofh9DH0DApMw7MZxX0QIG9u6mzDsJwjOEkYjGPpGgrCLKJpACAtNbUHVWnHdFwGBZd6OlA6Ou7u8jg5IDr1ygpe/+zpnDnbzq3/yWTbdNveG56N7T/HCt17jzIFuPv9fP8GN922a+wmcAwy1ilAWqLbvwFDryHoHSJpr8cNxbK2FavtWgihPQ/whTK2R+vgust4RvGgMW++gLr6LvH8copK55JVotOtJaLEpgX8gA87me/AiD3MWSlgkQwzFoNFq4mDmAI1WE33FXiqNKl4bfZVdDQ8u2WdRDIoMukPTLmu06+mIty06Bsr6OfLBe8PUcLHoy2f5r689y4t9ZwnnEexDKWFdZcXoTFeiXyUBoQjBhnQ7VUaSajPFrqYtFzX7VaFgKtqCEjhLHqFd6Eq+p2kNa9INPH7uED88d4izuRG8eZRBZkJSM7muuoVHOm7g5rpO4ppxDcqVGkJrR/r7IOwDreOK5RJkDuntBbUBlKsrDEyHCw27vRP/wLmJr+BHU7NzV0KgoiopNCWBrlSgqzXoSgWqkkBVbFRhoQgLRZjlH4OMu4/z2X9iKalCTuDzd8f20p0bZ1mqCj8K+erx1xl28+xqWXPNG3yFIth40wo61jSRmyiQSMdIVsSQUam7YfDcKM9+63UmxvJ88HM7aVs1P4lXIRQMrZa6+AeptG8l6+5juPA0486ruEH/nCZnkoCCf4ruzF8xVnyZlvQvUGXvRBHzd7QsQZlVmnUxgffVIZlJTQiARZoi5YsehYJHZ1upj0BKye5XT1B0fH72wRtY3l5DfW2Sbzz2JkdP9pczK/M73kyTJlutXpDkra1aNFq1HM+e5WS266p0n7hm02I30JXvo6dwnhZ7cpWy3xlixBtnTbKTuLaw7KkmVFpjjUgJg84oCS22YL36KxEGIY7jEysrwoRBhON4F1+/2yCl5Fi2l3/ueYkxL0coo9IVIwS31Kzhw42taGorjvs8mtaB572JotZj6mtRRAW6vhZNbcO27kdRKpAkQRiE0Siq2kSh+B0s6w6C4BSO+wKO+xwx+2HCsBfP24tt33/5aHDc3cRiP1OaMLiv4AdHUZR6wvAcRecZhNAJgrPE7IdQlGoMpQpdW4Fl3YWqNpHLfxnD2EYU9uM4u0FoBMEJbPtDAHjFAyhKDCFSuOEAeHtRlBRRlKXoPIWMJtDKLu5RmTK47YHraF/bzJ/+ypevSoe8Elvu2Ujr6ib+x69/Bd+bT2Jp+mvl8mqcInRieic1sbsu3i8rre2T1r9S2UdT4lha06TXMX3mhE+lUUmr3cKQO7Vv6lTuDCPeKE32zM8NTWi0xFqotxo4lj3KsDeEEzkoQqEj1rGk34l8WMANp6/2VhmVxNXFmTZKKTnv9JObRq75fUxGJCWPnz3Gq/09hFJSYVpsrW9hdWUNCd3kR90neHOwj7tbO9lU04gbBvTlJtg/3E93NkOlZfO7W3Zyd9tyUsbVK2xCCOqsNA803UCVkVgSQYZrlppVhUJbvJIvrLqZ+5vX8urQGV4aOM2xzAAjbn5aZZ7pIIB4uSF4c1UzdzSsYkt1K1Vm/BpSTDSEeTfSfYYo9+cosc9BlKFUCchAcIqo+G2k/xYi9klQFtb9Lwk4n3uU7sxfE8rp7cOh5Mhqac2kzOtImpuJ6cswtUY0JY0itHImVkWIC+54JXvrCzceSUR/9utL2uLbV5igKzfGb2+6nXo7iUTy5nAv3z5zgDsbVxBXFhrczh2KqpCuTpC+3Lm4HHc1ttfw8d+4lyiS6Ia2YLMcIRQMtYoq+04qrO0U/LOMOy8zUthN3j9W5tzP/slG0iXjvoYz0kNL+vM0Jh5BVeafbRUo5UzddJBELJ5yNhum8zm4AAVt1orA1SDK/zH00j4y2SKvvHmazrYaNqxqRAhBzDKI2wYT2as33k0HdQYq1OVqH/OBpqjsqN7Ma6MHeOz881SZFaxOdmAqBqGMKIYOhbBIo1WHpqhoQmVHzWZeHzvIj/pLSkDNdj0CGHLHeOL8i4QyYkfNZmzVJFzARE4IwXUVq3kmVs8Lw2/QEW9iY8UqLMUsKw25jHjjWNIk7sVxHR87ZpBM2xRyLr4f4jo+1bVJwjBibCRHLG4ST1icPNLHkf09bLttNdW1SbpODnJoXzfbb19NZVWCMAxJpGycok8URcQTM3+uUkocLyCaZ7bs4vsELEObtdfDiwK+37sHXdFYlmhg1M2yoaKdV4eP0h6rwnFfKNE4pYOU+VKm3bobTWstjzGBEDaKUlO6l0oVpexOLaWLlDl0vZQh97w3UZTK0muhEYY905ybOLq2HIGK5x/GD85g6hWoSgOa1k4Y9KLra1DVS8p0QsRQy2IBUTSGoa8lUJI4zgtoWgu6tgpN7SCKRlCUNKpajyKS+MFxgqALXV+FqlSjae0UnWextZUoSiWe9wZQkoWOJe2L9Kgr4eQdxgYniMKIdG1qkiO6burYSWvGbac9b2Iqbe3S+fIvUllMtYH6+C7EPPtu5oOYarM2tYr9mYMEcnJSY8gd5u3xg9Rb9bN4EAmUyxgB9WYDUkoarAZs1SaSIWPeGBPBBOP+OKZiMuqPkg0mGPfGSempOWdrQxnOyL83FGPRPkm+9NmfOfQ+x38OyPseL/d14YYBNXaMf7vldu5rX1F22lUYKOR4a7CPHY3t/Py6G0CCF4V0Z8f56uE3+d6Zozx29hhb61uoMueW4NGEiqFovD56kgm/cHGSHFNNbqxeedG1eq64ppwMIQSGUOlM1tCeqOKDrRvpL0xwKjtEd36Mc/kxhp08Gb+IE/oEUVRuENWpMGxqrQStiUqWJapZmaql1kpiq9deB1kIBYwdCPsTyMI/Enp7QJiAT5T90xLFJxpCGDtQYp9aEMVCSsmE8xY9mf85S9AviOnLaUj8LNX27ZhaI4qwyu//J5th86MQTSikdPOiqVelYRPIaFoe4jsNIQSasXSXtxACVcRJmutIGKupT3yErHuIseLzjDkvU/R7kFcJvN3wPF3jXwKgKfnJeWf+hVDQZlBhkUSE0cyTx8VDzmgcBpSvy4U/pOMxk0TM5HT3MFuv62Dv2110947y+Y/fTKIcQAZhRBCEGAs8r7oS5wJX+3JEi7C535BeyYeb7uR7fc/xFye+Rke8iZSewI98htwxqo0K/q9Vn0FTSgHT+tQKPtR0J9/re5Y/O/73LIu3oAiFrnwvg+4o99TfxNaqjShCWVDgD9Bg1/Lx1l38Y/cP+PLpb9IWa6LCSBJEIWN+hnEvyz3WLQw95tK6rJZ81uGuBzfz8rNHQEoqqhNsuL6dt187TbHo4xQ8tt++mrMnBzlzfIDGliriCYuuU4OcOd5PY2sVURjx1p7T3PPQdezbc4pEymbTjTPz4POOx19/7xUGxxZ2zVqmxq88tIOmWag+oYwYdDL8Quc9jHo5TuXO86Hm7ZiKzoHx06yoOYtl3kbpelBR1WZc9xWiaBRVa0MIE4jw/QNo2gqkzBGGQyhKH5q2DEWpw3VfJgx60LTlhOEgV7q9X45IZnC91wmDnvKEQaIolahaC5raRhj0MjO1VL84vjAaQtc6S/TNy549paSPuPhK19ejKAk0rRNNbUZTj+F6rxNFY3OqDg50D/Ot//E4588MgoR0bYqHf/1+Oje2LTiRogp12v4WgGyQu8gxF0JFFdeWiqYIhQ3pdfx4cDf9zuCkZb70eWlkDxvT62m2G6eNOTriHehCx1Is1qc30mg1YqkWo94IDVYTxdAhIqTerGfMGyWtVxDKkHqzgTF/lKQ+d06+KtQZXcP9yCMiQl2gsaiUku7COQ5mjixo+/caxl2HnlyJ9ntv6woeXLaauH7pWW6qJVECLwxRECWBEVVlTWUtv71lJxnP5enuk/zD0bf43S0756REWQhdvnrmGfqLY9SYqUtqcHqczZXL3l2B/+VQhUJKt0ilLVamaomQeGFIIEOCqBQsSinLjWwCVVHQhYqhqkvm+jsviBhK/JeQWifSeRwZnASlrpT5V2sR9sMo9kcRytzMN65EKAucm/g7vHB63h4oVNt30FH5m8T1FTBHQ6F3CvV2ElVR+Ntje9lU3YgXhjzff5r1lfVY7wDH/ycHgRAahlpDlX0bFdZ2GoNzjBVfYrjwJFnvMJGcmScZRGOcy/wdMb2TSuvWeZ1TgYY+o6RmhB+Ol75D1+A6kUj8cGZJVF2tXFTGv6EuzcpldXz3R28zMDzB3re7qK1OsnPbyouhTGaiyFimwLpVjQua98a1egTKFE8LLyxV8xYCUzV4sOl22uJNvDZygO5CH6NeBl3RqDOr2Vq1flKgY6oGuxp30mjX8srwProKfeUsYS0faLydrVUbiKulLJAAKvU0DVYNajnAs1STJruORJmfryCoMSowL8v6qUJhS9U6qsw0rwzv43j2LF35PlShUGNWckPlOlYGHeRiZ9l573peeOoQ57qGcR2PTTd2snJtE6PDWd549RQdK+oZHsgwOpxl5bpGXMdn++2rEUKwYm0jhbzLTbevxvdDVFVw9sQAwwMTrNowezO954fsOdzF6fMzX1OzIWmb/Nw9N8y6jgAMVceNfGKqyaAzjhf62KrBkFckZn+IIDyHbe9C09rR9JV43usEYR+q2oJQUtj2BwjCXlStlTAcRNNaQIbIKE889hFc7000bQWGcSOKSCCUGJrWgToN/VMRSZA+mr4Cw9iGbmzCc/cShv1oaju6sWFKhtuy7kApZ/xj9sN43utoaiuGsZUoujTREEoc07wFIWIIoaOq9ahqK57/Vnn/zdj2B3C919G1ShS1ntkmKYEf8v2/fop8psgv/dGnMG2TJ/72WR790x/wG3/28yQqF0YtUVBIaUkEYkqjfcbPMOQOU2Gk3xHXXIAGq44bKjbzRP+Pp2S7uwvn+GH/U3y89aOkpgnSG6xLNKDOeCcAq5KrJ62zxdg66XWVMbnJeK6IqTbGDFX0cS9DPihMO8arQUpJPszzRP+PGfPHFzS29xqc0CfjuqhCcF1tI7ErAndT1RAIcr5LKOXF6ZgQghorxiMrN/BSXxdPd5/kYys3sqbq6oyRjF9g1M3yyyseoN661NMqmCobPRf8RCI0IUptp7amwDUs5S0GQggQCbAeQph3QjQGMgfooFSCUrHgDKeUkpx3iHFneqdYgNW0FpsAACAASURBVLS5heVV/wFLa1l0ICfl0ph3XY60YfGF1dv4XtchvnPmILqicl1NEw+2rUV7F6iFvBMoVQEs4sYKYvoyauP3M1Z8kf7ct5lw356xAuCG5+mb+BpJYyO6Or2p2PTH0zBnlI2V+NEIkXRRr4HUoJThLOZhAlNrWNR1GrN0Pv2RbfzFV5/j6RePkoxbfO6Rm2huuCQ5eKZnmOGxPGtXNC6I5pcyWhBCnfJ9yPp9RDJEWWCTvqWa3Fi5no3pVXiRRygjFAS6omGqJsoVQYylmmyv2sTmitW4ZcqjoWhYqjmp9K8KlY+23EMgAxJaKdBakWjj36/9pYvynpZq8emOh4iknNQXoAqV5fFW2mNNOKFLIEPEhTEpBqMDWfb6JwmDiDCMUBUFVVVJJC1UTUEogqrqBDfctJxY3KSiOs75c2OEYUgYRKiagqIohEHpta6rLFvZwL49p6hrqqCyenZ/AFURNNWkCaOZJ1wSSRRJXC8gW3RxvAClrP9/07p2atKzB5+6orEx3c6AM86WyhUMuxP88dFvM+xOcHPtWgxjEwaTG1Jt695Jr03j0uRCNSoxjA2TlsfsD1xa19wGgKLa0ybuVbUR07oDVbnwnbex7UuyjipTJwuTjq/WYNsPXHytKJcqKgIbw7j8vbSXjqBOdtOO2bumDmwa5MZyHH/jNA/98j20rCxlvG/9yFb+7Ne+wkD30IIDfyEEDVYduqLjRZPvjxN+lrczB1kWb0dfQDCzEOhC59aaHezPHOJcsW/SslCGvDKyF1u1eLDx/iXRx5dS4kYeQ+4wCS1OpTG3+7+t2lQblfQ556csO+8McK7Yyzp9fs7nUkqcyOGJ/h/zxti+eW37XkYYSQIZoggFe5oe04RhogrBmFMsV20vq8oJwcqKaupjCc7lMpzKjMwp8DcUjZQew1A0bHXxfa3/klOzSwIhFBCpkmHXkkEyVnxxRoqPKhK0VfzykgT9UKouLDX5RghBZ6qaz626kZzvIQTEtFLWcf5tlz/9EELF1BqoT3yECnsH/dlv0Zf9Gn40neGWJOO+SdbbT5V929yPgYatd1DK1E0t1bvhIEGUXTBnfTYE0QReOFVDujQuhZi+YlH7F0Jw/cZW/uB3PkRv/zi1VQnaWqomXf+V6Ri/9tnbuXlL54KOkdbbsNQ0hWBylW3C78YNM2hK3Qxbzm38lmpgzbHkKoTAVi3sGcx/LqxzZZOvrmhUGJfuRYoQF7P/022vC23G5t7MWIFnHnsbVVNo7ayhv3cMrayolK6IsXpjC2/vPU0yHeOm21dTVZMkDCW7nzzAjjvXUlmTQErY/eQBbrpjDc3t1bzw1EG27lx1Vd53Mmbxn3/+PsJwFqM8KfGCkPGcQ1f/KM/vP80rh7qIWQb33LiKquTsVBBVKDzYVMq4xjWTT3fcyYtDh1mVbOK+hutn3XbpIUjEP4sQi2vCfKcQBhFREGJYl65n3dJBgOcsTnWuLd5KXI1NCfwjIl4a3sPKxHI2ptddrHJdSwghaLabuLf+Tr7e8y0KU6Q9XX48uJthd4T7Gu6iM74MU5lf4BVJiRM6jPljnMyd4WDmCD2Fc3ys9SNzDvwNRWdFspMDE4enLMsGOZ4e2E2L3URqjjr+UkpGvFGe7P8xzwy+MOVcvI+ZoSklv6KxyCHve1Oq7FWmjaoIurMZ3DCYQuUxVY2kYeKEAaNO8crdTwtLKWn3f+nE46xNtV408UpoFrfXbbio9DPn9zCvtd8jkNJHui8gtDZQW5bcrEUSMOG+PePyhLmOtHnjklE2vHCQpTb/8qOQx7uP8GL/GYLo0r6b42l+ff0tS+Kg/NMIIVRMtYnW9BfQ1QrOjv/5tDKgJc3/18p0n7lVSIRQsLVWNCU57T7doA8/GsZkaa3mpZS44fkZA39F2MSWQNdcVRRamyppbZper3xVZz2rOhdulBfXG6gwOqcE/jm/n1H3ODFtqqnWtYIfBRycOEwhKLAhvZa4Gudk7gzL4m2zSggey56gxqim2lwYZeByNLZWcdv9G8ou1wY779twsX9CNzS237aafM5FUQXxhIVQBB/42RsJ/BDT0rFjBrvKrxUh6O0aoaG5kqa2q6ucKYq4auB+AS21sL6jnps3dPAPP3qDrz+7j68+sZd/96m7SMZmnzgldbucZfVZlWxmTarUU/FO00eFEAhx7Qz2lhrxihg1LdWc2t/FdXeuRzc0zhzowbB0qmf4fs4V9WYdy+LtjI1PpZYMucP8Y/ej7Gq4h83pDaT1FKqYTHMtSWBL/CggkD5e5BPKkLSeWlCloNSkv5Xe4nmeGXx+ijOuF/m8PraPU/mzrEutYWN6He2xVhJavNxYqyKgJLYsQwIZ4EU+WT/HgDtEb7GPnkIvPYVzjPsZ3MjDUqx59fCoQmVdag1PD+wmG0zutZJI3hp/m1iPza6Ge6i36tCmmexLKQllRMbPcCR7nOcGX+RE7tTF9ysoJSOunPy8j8mI6wZVls35fJaz2TH8KMJQL01SW5NpTEXjzMQoPdkMacOadP16UUgh8MqqjnNLyYZIGu1KLFVnIigggtL+/CggWkAv2PuB/3SQPlH+yxCNIIxtCPMOhL4JlOol0UoPouyMQRRA2rwBZYEOqFcikgFF/ywL5TDPhL78BN85e4hdratpiqcvZvjjmnFVbdp/6ShRgGLUJx4m5x2hP/fPTP38I4r+GUJZQBOz0yIuh60vw1QbZphMZMm5R0gYc9finhskee/ojK7BltaMqS2s1+WdhCZsWuK30F94k+gyX4tAFjmb+zENsRvQr3FDIUAQBXQVutk/fojrKzahC4OJIEsuyCGRBFFAPiyUdMQVk6SWoBgWyYcFTmRPo6X0RQf+iaTNlh0rqKpNomllQ5jY5GqFbmhUVE1+RFyp1HPhdTZTIDdR5MZbVxGLL72RnBCCioTNR2/byIsHzvDKoS4OnO7n5g0dM24jpaQQuvx44G3eHjvDpooOPti8jbfHzlBvVdAcW5gM80JRetB7eFGGUHoowsBW37nJ5pUoTBR5/an99J44z9C5EV7+3usMdg9z3R3raFhWxwe+cBeP/vcf8Lf/8Z8wLJ3TB7q599O3UdWQpphzeOPpA5w73sdA9zB7HnuL8YEMG3eupXnl7LS/mGqzvfpGjmaPTxtk9hXP849dj7I79hLtsVaqjApMxUQCgfRxQresmFUgFxTI+llUofL5ZZ+mNda8oM8ipsb4YOP95IMCe0ZfnxL8SySj3hgvDr/C66NvEtfiVBmVJLUEpmogEGWDPo9CWCxp4od53NDDKzffLgZCCDpibWxKr+elkVenLPcin+eHX+Zk7jTr02toj7VRbVSWPzdJISww4o1xrtDLqfxZzhX6cKJL6mgCwfrUGjak1/Loue8sWFjgvYC0adGerODQyCBvD50n73sY6qV4rTmRojGe5Pj4MN8+dZi2ZAUpoyyBHEW8NdhHb24CU9VIGXO7V6Y0m4+13Uo+cJjwCwgEKT1GTDPRFxCTvh/4Tweho1gPEDk/QDo/RDo/BG0linkrGDtB60SIBPN2ECojiopEcuZZtaXNzWxqLvDCIQr+mSXb3wUUQ596O8GDbesmdbS/j0tQRYLq2F0MF56cVhHHD8eIpAPMPfDXlUqS5iby/lT3x0i6jDuvUpd4cEldfCPpMlp8iZmqRglj7RRN7WsF1/Xxg4h4bP48RyEELfEdHM98h3Hv9KRlvflXOF/YS2t856JM1uYCXwb0Fs+TC/I4kYMQMOyOcCx7ktXJlUyEWX7Y/xRNdiOFoMBN1Vt5a3w/Ciq9xT7WplYtegyxhMnqjVd3s54rkukY225bffUVFwEhBNWpOCuaazjbP8b+0+dnDfwjJE/172P34AFs1eBEtg+QHJ3o4VCmi88su+uajvdySBky5h7lTPZ7jLmH8cIMleY6bqr/QwBGnUOMuodpid+FqVa9I5MBKSW+5xNL2Tz0K/eiaiqBFxAGpe/5+ltWk6pOcPS1kwR+yPYP3MCqLctQNRWv6OO7PnbC4sFfvBtFUwj8kDC4ej9ZSXJ2A/srNvHKyN5pg2IncjiZO83J3GkUlIu9LxJJJKMpjcGVZcWchUIIQZVRycdaH8ZUdF4aeQ03mt6Uz4lcHM9lxFtYY/pCYasW9zfcxZl817Rc/1CG9BR7OVfsw1AMdEUr+5OUPjNfBviRNyUFJRCsSi7nU22PIITgyf5n3m/0nQW2qrGlrpmnu09xYnyEY2NDbG+45PxeZ8fZ2tDCsfFhvnH8ADnfZWdTB3Hd4MT4CI+eOMCE59IcT7E8PfcEztncAN/tfY3zxdJ112xX83DLdjoTDfN+D4sO/N2w1HClL4GpwLsFAg1in0K1HkD6B5Dei0j3VaL8V6DwTwj9eoR5O8LYCmojYp6yjJLZXU5n0htfCLLu2zjBuSXb3wXUWHFUofD6UA+rKmovNltqQqXStK+hx8JPD4QQWFozqogTMDXwj/DnbbqlCJNK+xYG849Nox4kybhvUPDPkjCWJgiTUlLwTzHhvjnjeKpid1xj1+BL2L3nJMdOD/Brn70NdQHXWEJrZFX6Yd4Y/ktCeenB7kYT7B/9KnGtgSpz1TUNvGzVYm1yFVk/x+b0BkzVpDXWwqGJo0hK5Xhbtbm5ehvPDb3E2UIPbuhxT/3tFML3trOmogiSMZMwihjOzC4F6kUBr4+e4GNtO8kGRfaPnUFBocZM8/roiXdoxKXv0JCzjzeH/pBiMIChpnHDUZxwBModUV6U4eDoX2IoSVoT919tl0uCeDrG3Z+8dcblqiroWN9Kx/rWKctiKZs7P37zwo+txvlQ0wfI+FkOTxydNSMeES2IzjBfCCGoNqp4pPUj1Ft1PDXwHCPe6JRJxtIca/59cEIIOuJtPNLyYb7W802G3OlZAxKJG7kzTlwuhypU1qfW8InWn6Et1kIxLFJjVr8f+M8CIQS3NLXTkkgRSMm450zqa9QVlYeXr+P5c2c4mx3n0RMH+cGZY6hC4AQBXhSiCsGdrZ10VswtYZYNiny/dy+diXp+tnUHEbB35AQ/6HudX1x+L3FtfnT0RQf+z5w/xuHxfm6tX86qVB1p419A0CdESVZNrQelDmHeCrHzSO91pPcC0nsb6b0MagPC2FGaBOjrQVTMKVsohI6YRUFkNr30+SCIsgzkvlPOKi8tvDBgyMnxJ/t3U2cnLk78WhMV/ObGncS096sAUHLTnenBoWDOO2AWQiFlXk9cX0HW2z9luRucZyj/ODG9E2URuvoXIPEYzP9gRtlZW19G2pxdVnEpcbprqOzcu7DthVBYlryHweJ+unLPTHLzHXGPsXf4z9lS/atUW2tR3qHJzHSIa3H0Mn/4wjUUlbnN72UEYcToRAEBV3UHv8BpTuo2haAUBElKmtjGErkZzwVBlOfo2N8RSofNNf+aGus69g7+/qR1EnoruhJn2HmL1sR9/EuXRyg11Tby6faP8d2+x3lrbP8k6slPclwpPcl9DXfTmVjGs4PPczBzhIlgaZ7JuijR9Dal19Mam3/FTRUqN1RuRlM0vtv3GGfyXQum5SS1BDuqt7Gr4R5qzZJJnamaLIu3cSJ3akH7fK+gM13F7229g/pYglWVNZO+rUIINtU08BvX7eCL+16hOztO3r/UPG2pGne0dPL5dTcSn2MvZM53cCOfO+s2UmWWeoWSmsWXT/6IQuC+84F/V26Uvz3xMt/u2sf6ykZurVvO9tpltMYrsN4Bs61rjdL4LdCWgdqOsB6A4AiR80Ok8wSy8PfI4ncR+jqEdS+Y9yKUullpQKqIz+rcWghOz7hsrpAyZCj/5KySoYtBhRnjX23YOaU5xdI0zHfwofpuhpSSYtA1o7GWrlaiLKBx3FBrqYs/SM47UjLxufyYBAzmv0+lvZO0uWVR3z8pJRnnLQbzP2C6HhGBRm3sAQx14Wo480W+6LHgqL8MU0lzXfUXcMJRBor7Lgv+JQPFfbw8+IesrfgYrfGdWGrlNbmHqUIlrsUQQuCGHocyRxh2R3g7c5AWu4m4Giu5lqs2tWYNuSDPS8Ov4obenHWbCwWPI4d76esbY/PmdmpqEhSKHlVVCZQrDJiKjsfoWGHBTrrTIWbpVFbEpxxroYiiiMNnBzh0th9VVaivnJ0iZygaa1ItPNH3BtVmilzgsGfkGC8MHeKDTVtn3fZySCkJw4iJrIPj+khKrsGppI2uX31ymAt6GXMPszL9SZYlPwQoqMKYNIkzlBS6kiIf9M28o1nguj5DI7kZp4WqIqiuSmAuoanhYiGEoMVu4rPtn2BDai2vjLxGV6GHfFCYMydeExq2alNv1c6oc78QGIrO2uQq2mMtnMyd4Y2xfRzLnmDEG8MJnTlPwFWhYqsWaT1FR6yN1cmVrE6uoNasWfB4NUXjuoqNNFj1vDT8Kq+PvcWQO4w7B2UeTWik9SQrEsu5pWY761JrJimLKSisSHTy1MBz7/kkw2wwVY1720pKdtM9H3RF5cOd6+hIVvLjnlOczIzghSF1sTg31jVzR0sn9bHEnJ8thqoRSUl3Yfiios/Z/FDJ72oB8daS3AVCKRl28+zuP8mrg2dpsFNsqWljZ/1yrqtqodZKoP2UU4GkDCAaKmX93d1I/00gBP0GhFpXogRl/wTh/Bgl+dsIbf2Mwb+qxDHVRvIcnXb5hPMmQZRBUxdmZCJlRMbZS3fmfxLK/Ly3nwtims6Gqvlzy95NCKMiocyjKxWzVmAWiiCaYKTwzAznQMXWO1AX0MQtUKmO3cNQ4YdMuFP1l52gl+7xv2Rl9X/B0toWFLhKKSn6Z+ga/4uyKtRUxPRO6uIfnDcnXkrJ8TODHDrWx3XrW+hsq2UsU2DfoZ6rbnumZ3jRj6NSVq+NbbW/xRvDX+J8YS8RF5r5JOPeGfYOfZGu3LO0Je6gwb6emFaDJmJLNgmoMNLsqN6KLnSkkKxOrmR5YllJG1q1aLTq0YTG1qob0IRGs92EG7looqTDfzV4XsDjj+1j//5ucjmXRNJCInnyif188lM7SCYnX3cHDvfyxa88Sz5/dXrAXHHTjZ38n1+4E9ueOt4wihgYzeLPgRMeScgVXQ6dHeD7Lx9icCxHRcJmY2fjrNupQmFX4xa+3v0Czw8dpBi4DLrj3Fyzlm3Vc+uTkFJyfiDDD398kLcOdDM8mkNKqKqIsWFtMw/eu4n2ltk5+X6Uw4/yVJpryz4S013BJR77Qj1XTp4Z5Pf/+DGCGT7PZMLi935zF2tWvLvu2Rcy7LfV3sz1lZvoKZzjTL6bvuJ5RrwxckH+Ii9dEyqGYpDQ4qT0JNVGFdVmFfVmHfVWHZXGzC7O80UURYyNF1BVhY3p9axJrmLcH6en0EtvsY9+Z4gxf5xckMOL/JIPCAqGYmBrNiktSZVRSa1ZQ4NVR4NVR0pPYirmktxDFKHQaNXzcPOD7KzdwcncGbry3fQ7g2T8CYqhgyQq3S9Uk7SeosaootluoiPeRqPVgK1a045ldXIVP9f2CEH5WhQIlic65jSuuBbnQ027pjRt18xRjKDZbuATrT8zZdKxKrliTpHQtqotNFh1k7Y2FJ1ac+l70K52Hg1V5cb6ZjbXNuKEPpGUGIqKpenzZsVU6HG2Va/k690vkCxLPBcClweabiCpzz+GWPJox40CuvKjdOdHear3CMuSNeyoW8atdctZma4jpVs/NVQgKSXIAgSnkN5uInc3BGdAxBHGVoR1D0LfAsKG8FypClD8J6Lcn6Ok/xghptfUFSikzOsYLT477fK8f5zhwlM0JD46yZJ9Loikz1jxJc6M/QlO0DXv9/xeQsE/Tdf4F0mYG6iybsHWO9GU5KKbO0sUgxz9uUcZKUx/jjUlUc7Iz/9Ypd6BJpqSn6Hgn55GbUcy7uzh9Nj/Q0fFbxHTO+d1HCkj8v5xzoz9KRn39WnXUYRNU/LnsLT5l6ulhG98/w2effkY9962lt/79Qfo7h3lj7705FXFpxwvYP2q2QO+uUAIQYXRyU11v8OR8W9wPPM9gssa7gNZoK/wGgPFt4lrdVSZq6gwOkkZrcS1Okw1jSoMBMqirpdiWKIQqKL0AxCUubnFMItARVUq0MoGX3NFJlPgxPF+PvvZW3n55ZPISJJImIyPF3GK/pTA3/UCRkZz5JYw8J/IFmcIciFbcPn9r/6IroGxOe0rCCMKjo/rB2iqwm2bO1nbPru0qxCCWjPNL3bex4A7Ts53qDDi1JlpDPXqVRMpJcOjOb74v57h1TfOTPIcOD+Q4ciJfo6dHODf/qsHaG6YWY9dFQaqMHCjmd+rF2VwwwnSxsqrjms6+EHIyGhuxomU5wczTgreDVCEQlpPkU6vY11qDV7k45dlOi9k/wWiLMWqogkVXdFRUK5JRW5oJMef/OWPqK1J8qu/cAeJuEm9WkedVcsNcjNe5BPIgCAKkOV/ovxPFSqaoqIJHe0KKdKlRMmnQ6fBqqferGN71Y14kUcgg4sN0Bc+M01oGIqOJrRZxyOEoMasYlfjvTOuMxviWoy7629f6Fui3qrjg00L73HZVLGeTRXrF7z9UkMIgaGqk+Q+FwJNUbmzfiOdiXrO5odQEHQm6mmJ1SxImvia1f0kkA1c9o/1cmj8PN/q2sf6ikZ21q9gW207LbFKLHX2i/AnBSllKbvvv4F0foz0XgOZBbUVEfs0inkHaCtLZiwXxi/WoCQ6iGQBWfwOhL2zmH4JKqyb0JTUtBKJkfTozvwVhlpNpX0rgqtTpqQMcYI+BvPf53z267jh1K7/nzwucJRni+7kFEOMa4VIeky4bzNafJ7z6tdJGutJW1tImteX9fLTKGLuGZqSXF+BvH+Kgdy3GMw/Riin54YmjY0kzY0LHrsQKtWxO8i6++jL/m8kV8rPBYwUnsENh2hOfppK+xZ0pXLWIFXKCD8aZbT4Ar0T/0DOO8z0Sj4KNbF7qI1/YIETF7h12wo0TeHmLcsBLjq03nPrGqoqZjY5euWNxdHgpIwIpIMfFXDCMbJ+L4rQ0RSTYBppwVC6TPg9TPg9CJ5DU0xUYaAIA13YKMIo9wJcm+s1oTdwU92/wVLnl82MIolQBMnUJTpKoeAjpUS5irnWO4EokoxOFBgan3tFUlUE1akYOzd18vld24hbs1c+SnrvYKkGHfH6UhUr9Mp3IHnVaqqUkhdfPTkl6L/8PRw4fI4nnznIL3zylhnvEzGtgbjeTHf2SersrdhXUOOCqEhP7inccJQ6eyv/0vn9V4MiFCzVnNdEd8nHoAhMQ8O2jEmJSoEom/WZwE9ufFfiamZ9FyClRMoiMsqAdAEBwkIoFXMWKSlN5j1klAXpACGglPeTXITvkURKBxmNg/TKY7MRSgpYvFvtTzOCKGTEzdIWr2NFsiSdXQhchtwJ6qz0vIP/d4TwF8qIISfHc/0neGXwDI2xNDeWqUCbK1uoseLvMiqQS5T9I6T7HAgdoW9GmPcijJtKDb3TNU0KAdJC6JuRhf9d/lJNDyEECWM1aWsbI4Wnp13HCc5xfOQ/U5/4EDX2vVh6C6qIlSkpotzw5xJGeYpBD+POHkYLz5HzjiG5nOunUGHdhBN0L7m6TyhdwiiLlAFSBkQERLJIGBUIZWHS79Lf83jhMF44nZttyWH4fPafGHdeRVViKMJGFTFUJYYq7PLv0t8VYSCEhkBDEQaaklpw5lUS4IUDjBQHGC2+gKaksLRm4sZKbL0DW2vDVBvQ1IrSRACF0sNZIgkIowJu2E/BP03WPcCE+xZucB7J9Bk2XamiIfkIurI4IxxVJGhJ/QJOcI6R4m6uDNIlIVl3Hyf8U6SMTVRYO0ia6zG1JhRhIVCQRESyiBP0kXMPMua8QtY9MKOrNAhS5mbaK34dXV24m/Xt21dw0/XLMI1L3/uYZfDIg1tY3l4z43bZnENX78Jl9E5ln6CvsIes30shGMKPCgSRM+O5uhySED8q4PPOKev4Ub5EM5wn0mmb+vo03/j6HoaHs5w/P87bb/fQ1lZNMrn0zs7zhWlo3Ld1NUOZqwf+ghKnvrYiwZq2Ota01RG3rh4IREheGzlOSo+xLtXK0YlzfOfcK6T0GB9r20mtNftkKggiXntr+qD/AsJI8taBHj7xEZ/YNJQmAEutpjP1UQ6Ofom9g/+FhtjNZUUfwZns9xh19tObf44a6zrq7Zuu9nG8j3cA1ZUJfuc37kfTFGz7nTWjjMIh/OI3kdE4irYa3X5o+pijDCkjQm8PgfscYKDbH0TVV1+xTom9EHgvEzg/IvSPIKNRQEFRalGNzWjWg6jGdTMeS8oQGZ4n8PYSenuJgpPIaAgpHQQGQq1B0VaiWfegmbcgZqGxht5+fOdxFLUBPfZJQCF0X8V3vkfoHyoF/6goah2qthYj8csIbaq61HsF436er3U9z8913E69Vaoujnk5vtnzMp9bdhcVxvwcwRcd+K9O13NdVQuns8Nkfeeq/Fs3CjibG6ErN8KTvYdZnqxlR90ybqlbzspULcl3BRVIAhHCfhhh3oXQN4CYY2ApLNBWgDJ745kibJqTn2HCeQs/mj4Q9sIBzmX+loHcd7G01rI0ZCn4j6SLH43hBn24QT9BNDEl61uqLGxnZfV/ojvzZZxcL0tp5JVxXqMn82WCKEMYFYmkiyQo3SAIy78juPj/4azHj6TLUOGHlIu6gIoQaplOoSJQL/5NEXp5YmCTMNfRWfE7aIsIRC9A4uNHI/jeCFlvf3liYaEIE0UYqEq8NOlALQX90ilPbBzCqHjFpGsqFGHRkPgoVfZti6YUCSEwtWY6Kv81oXTKjdxTg5QwyjLmvMS48xqqEkcVcTQlgRA6UvoEUZZQ5gmj/DTX0KQjkjDW0Vn5b7G1ZSw0M1lyMRXY1qX3r6qCqoo4ibh50VBqOiTi0/NS54qe3At053cvePufFpimzkMfup7ndx+lkHcJgpDVqxu57fY13HP8sAAAIABJREFUc2pIvdaImTqfu/9GojncjgSlDKymzo/W4UcBT/fv4676TeRDl2/2vERKtxlyJ3hmcD8fb9s56/auFzA0MrtkKMB4pkA+784Y+IOgPfkgQVTg9MS3ODT6N4TSRQD7hv8YRajUWNexoerX3zEN/3cDokgylilg6Cq6rpKZKGKZOsmEhR+EZDIFNE0lnbJRr6hS+X5INufguj6qppJKWJjmJQZBJCWZTAHXDaiqimPo2pRjj2cKeH5AdVUCvXzPyeYcJrKXKn+WpWNbxoxaHVKWKpXZrIMfhBiGRippTTnevCB0QncPgftMKZA2rkfM5owuC3j5rxI4jyPUdnT7Q1PGKKNRvPzf4Be+UQ749RJFmYgw7CX038J3nsZM/Ap67GPTZv9lNIqT+U8E3qsg85Sy/DEEGhHjEJ4l9F4ncJ7EiP8iRuKXZqwihMExvNyXyhOF+wicZ3CzX0RG/eWxqSA9wrCLKOhFj//8Qj/NnzjCKKIQlDj+lqZhKPOnf7lhybjOvEzYwVR1ckERL5p/YmjRgf+t9ctZm27g/2fvvaPkuq4z39+5sXJV5xzQQDdyjiRAkCApZuUsWXrPltNYtqWZZ4/t8YzHXs8zs944ztiWJcuy5LGyJcoSLYqSQJEEAZLIOQOdc6jqrlw3nfdHNRpodDfQjUDQtr61uMCuurfuqVv33rP3Pt/+vlPj/ewbbufgSBd92XFyrn3D/SSQsgsci/dyMtHPtzuPsqqklh1VS9hS3kR9IIZ5z6hAJkr4d0CJIoTJQgIcYWxHLVkDyo2bWYQQRH0bqY/8X3RNfGZOyU2Ji+UOY7nDJAuHF/AdFGK+zSwu/R38WgtRcxPD6e/dJLBbGCxnmGThKN4NVjduDXIySXCvCrjcJEDwuPH1dusjcXBl+moF/DZosorwUxV6F/WRn0VTFpahzwUhBEG9lcWlv0NH4s9I5F6d8zeW2DjeOA7jFBb8PYoyoi0lv0HEXHfH78vGulJ+/ed2UVZy4/NSVR5maDT5b5wMMTdyOauYEJoasViAd75rI4VC8d4wTX1GAHUF1ZURHtq+dIrnn84WyOdtbMfFcTyca/+9QQV8vhBC3F5wNA94UpJyclT4onSkB0k5OX5pyROcmuhi/9iFm+/vSTzv5t/Vk/KGakhCCDQCtMY+QlVgK6O5Y2ScPlxpYShRYmYbFb4Nb1rQn0jnEED0miQ6lS1QsB1KIwEUIbBsh0Q6h+t5hP0mIf+daUq9FgXL5otf2UtJLIiuqbz82nmqKyJ85P1bOXm6j92vnCUYNPnYB7axcW0TiiKQUjI0nOQHL57i8LEuJlI5TENjWVsNTz26mqWtVaiKgvQkP9h9ihf3nOWTn9jF+jXTRQ6SqRx//rndFAoOv/lrj1NeWizU7TtwiW9/7wiW5ZDL22xa38QnP7GLYGAmrcfzJJc6hnn+xyc5c76fbM4mHDJZt7qRZ962mtqa2C2dMyHCaL7HcQr78JwenMI+dLV59s+SEs/pwLWOAgLN2IoywwA0j5X5Ilb6i4CHau5E9z2KUOtB2njOeezcd/GcyxRSf4ZQytB8T8w4nlDCCLUMoURR9Z2o+joUrQEhgkiZwinsx859D+mNYWU+j2psRjO33PC7Si+Jnf0eVuYLCCWM7v8EitYCwof04nj26aKhqnZrjsx3CyO5DIeG+lhRWkFtMIKmqjPmJE9KulPjPN9xnpNjQ1iuQ00wzIN1i7i/tomANn/VS0PRcDyPwXyCyGQz72BuHE/KW/LQuu0nr66oVAciVPnD7KhazGA2yZF4D/uGLnMs3sdwPoXt3TjKcKXHcD7NTwYusG+4nVp/lM3lTTxQtZg1pXWUmW8uFUgIpajhv+D9BIgAMLdU57Tt0amNfBTHS9Gf+gruDEOmW4PAoCywi+bYpwjoixFCEDZXoinROVcX/i3iCl/zzYChVlEb/iA14Q+j3yQpXCiEUAjqS1lS+l/oTX6R4cxzON6dM2BRRZDywKM0RH+ZgL7otlcqZkMsEmDLuuabbvf4gyt48L62OyYR+a8Ne1+9QDTqp21pNUePdHH/9lYCswQt16OlqYJf/cQuXNfDcTxc16VgOWSyFpls4Zp/i/+fGM+wd/8lBodn9ii9VaAIQVA16UgP0Z4eZGm4jnIzgkDM2XR8LUxDmzXgux7BgInfd2M6iBACFYMSczkxY1mxUCHl1Ermm1ng2nOindOdg3z6vTsJmDqeJ/naS0eQEn7uiS0k8xbffvUEp7uGcFyPkpCfDzy4luVNVXd0NV5KmEjlOH66l/WrG9myvpkXfnKaz33xFSrKw+zYtoTdr5zhn394nBVtNQSDJqPxNJ/9+z2cOdfP1o2LaGooY3wiy2sHLnOpfYhf+4VHWLG0BkURLGmp5JvfPcTBY52sXlE/baXrcucIp8708fDOZUSuob5tXNNERWmYgaEJvvCVvSSTszeoSym53DHMX/zNixQsh22bWohG/PT2J3hxz1l6euN86pceoaI8vODzIoSKZt6Hoi3Cc87h5HcXq/iziIVIXBzrDaQ3BCKE5ntsslh5dZxu4SB29iuAje5/F2bktxBK5eRzXCLlo6jGZnLjv4l0e7Ayf4NqbEKoFdcdzcQMfRIj8DEUbRGI0NRcIKVEMx9FUSsopP4c6cVxCi/OI/CPY2U+i6qvw4z8RxRtCaADHo47hOZ/F0gLxx1FUSIoIoKUOVwvjhB+1Ov61qT08GQKz0sjcRDoqEoUcZ0imycLuO4wqlKOEDqul0DKHKChKjGE8N/wntzb38kf7n+JulCE/7r1ETZUTU9MpJSci4/whwde4uBQL9Y1MfDznRf4+PL1fGLlJsLG/PpEYkaQZZF6/r7jJzQHK5ESOjNDbC1rI6TdQ1WfYsOLTnO4jKZQKY/VLqcrHefAaCf7hts5Oz5Iwrq5TnTBdehIj9GZHuOFvjMsDpdzf2UL26sWsyRcTli/vaX+txKKk0GIxti/w9Sq6Ev+Azmnh9kbKuf1iZhqDdXh91IT+iCGWjF1rky1Fr/eiF341xj43xp9ydRqqQy+g9Hs7klO/p1eNRBoSpSYbws1oQ8Q9W1GVRZ+k87rSJMuwYtKPk3EXMdA6uukrJO3Zd6mCJOgvoya8PspDzyGpkTv+b0XDvlY+FT6bwejoykcxyWVynPqVC+bNrdgzKLdfv3vqKrKDagq0yGlZHwiR1dv/I4F/p6UZHIWoxMZhhIpktkCiij2fVTGQpRFgkSC5k1Nu66FrmjcV76c7/S+Tlj38ytLnkICw/lx6gI3l/fTdJWlS6o5fnru3ighYOniKkLB+Td6XkkC7tWy1ZpF1Xzv9dNc7h9lVXM1o8kMhy/08dFHN6CqCj84eI5L/WP80tPbCPlNvrPvFP/nx4f5zx99hHDgzveH2LbLu55aR3lZiM6eOOcuDPDxD93PulUNjCUynDrTSzZn4fcbvLLvAkeOd/Hh92zmnU+tx+/TcV2Ptasa+NPP/IjvfP8oTQ1lhIImSxZVsqixnKMnekg8laGyohg4O47LkRPdeFKyaV3zFM0HoKI8TEV5mJHRFF979sCcY84XHJ770QnGkzl+45OPsWp5HYoiKBQcyktDfO3Zg+w7cJl3Prn21qr+ag2abxdW+jyufRzXPoNmzuz/kDKFk/8J4KFqbajG+utHip37DtIbQ6j1GKGfR1GvlXQVCKGhGpvR/U9jpT+La53GtQ6j+J+YPiYh5qQcCSGQ+NF8z2Blvor0BnHtC/MQ6rBBBDAjv4miLZ/a1vXSDCV+m6BvF7bTTSb/EqaxirLIrzGR+UcyuRfRtVrKo/8JU1+BEALXS5LM/CPZwl4spwspLRThx9RXEAt9DJ+xYSpJsOwLDMQ/RVnkU7juKKncD3DdYYQw8BkbKQn/LIa2dNaxO57L/sFexvJZTE3Dr+szbuUJq8BnT+7n9cHidRYxTAKaTsoqMJbP8sUzh2mOlPD2lmXzaszVFY2n6zZR4y/hUroo3PJM3WY2lCy+NxX/2SCEIKSbrCypYVmsinc2ruFCcpjXhzt4Y7iD9vQY6Zv0A0ggaec5OkkF+lbnUVaX1rKjsqgKVBuIYipvTVWghaA4CQSpCX+YsLmO4fR3iedepeAOzjNoE6gigKlVE/PdR2XwKcLm6mKzzTXnRlMiRH2bJxtrryYWulrKrc5AqhLEp9XfBarPwmBqVZM9AQuDoVbQHPsUVaF3Mp7fz0T+MBnrAraXmOS7LzwRKMovhjDVKiK+DZT6HyJqrkdTbm3Zd0HHFgJNhKkMPkXUt5F47lXGsi+Stk5ju+Pz+j4CA12NETKWU+p/iDL/g5haDQt1GP4p7jwc2yWTzFHIWyiKQklFBFWbft3X1MR48cXTnD3bz6WLQ3zly/umBf7hsI8nn1pLKHTrAZwQAlUVd6T6K6VkIpNn38kOXj52mfM9I6RzhSk6kaYq+E2d5uoS7lvZzK71S6gpi8zr2IoQPFi5iiXhGgKqSeVkM++G0iVTy+U3gqoIHtrexiuvnWdoZHZ1rvqaEp56dDXKPFyEJQ4Zu58J6xI5dwQpHXQlREhvImK0YCiRN2U+a6iMsbS+gr2nOljWUMmJ9n50TWFlUzUFy2H/2W4cz+Ol45dRBIyMp+kYjBNP5e5K4F8SC1BaEiTgNygrDRIMmjTUlaBpCrGIH8u+svpU4MiJbiJhHzu2tU4lqpqmsmpZLSuX1XHiTC8DQxO0tlQSjfjZuK6Jf/zuYc5dGqSiPIwQgsRElqMnumlqKKN1cdUtnfN4Is3RE93U15QQDvsYGrmaAFdXRVEUwbkLAzz1tlW3SGkz0MyHsbPfKlbP8z8uVuGv8ZuRUuLZ5/Gcs4CG5nsIoUxPaKU7hGsdAUDVV09W1GeDhmpsohgSFnCtQ+jXBf43gxACRa1AKKVFrr5MU4w1bjx3aOYOlOuDbOnhuCNMZL5OwNxGwHc/ycy3cL0EmlJOKPA4E+mvksx8g4rY7wEaUhbI5l8FoRPxvx1FCWM5XaSyz2E7XdSU/RX6JGVIShvb6SGe/GuE0AiY21HVMgrWGVLZ7+J6CapL/yeqmCnTO2EVuDw+hgSWlVTQEJouEiClZF9/Fy/3duBJydKScj65ZhtLYmUcHxngMyf205Oe4NlLp9lZ10ypb34MkaDmY0fFCrZXLC+e79tgLNx1VR9VKJSaQbaWN7OxrJEPLdrEqUQ/rw23c3B0fv0AjvQYyqcY6j/P3qHL1AZibClv4oGqJawqqZ2kAt17ibpbhRACgU7YWE2wpI2a8EdIFY6Tts6Sd7qx3FEcWVT3EEJDESa6EsVQK/BpDQSNVkLGSky1ek75SSEEjdFfpDb8kWmvq8KPuMXLoNS/k7C5ljvZMHwrKMoxLryxVwhRbA42lhPUl1Idei+2Gydnd5Fzuik4A1juKLaXwPGSuHKygVkWm5QF6mTDbwhdiWFqVfi0BgL6YgL6Ygy1vKie8yYnp0Ko+LRaakLvpyLwOFm7nbR1hox9kYLTj+WO4slc0XRG6KgigKGWY2q1BPUlBI3lBPRFt6WU9FZFc/hRSsy5Jr+3HnxqDFX46e8Y4aXvHOTk65eID01QVh3lP/7l/01JZYSBzhFO7b/Mso3NbNnagq6rnDvXj2GoBIPmtMDfH3jryOJJKekcTPB3z+9nz4l20rnZG+MnMnkG4ymOXOxjz/F2PvHUFjYta7hp9V8gMFWdltB006rWSTm8m0EIwdIl1fzix3fytWcP0jeQoFAo9s/4/TpLFlXy0fdtpW3JjWmhUkosL0l78lt0pZ4n547gyatUH00JUGquYlns45T51t71e05TVR7d0Mrnvv8GvaMT7DnZwZZljcSCPrIFm7xlUxYNEg0Wg/xljT42ttVP/X2n4fcZqEqxcVtVFAxDw9CLRb0rlD5PSnJ5m+GRJOWlISLX+VD4fDo1VVH2H2pneCRJa0slqqqwcW0T33vhOIePdbF1QwuGoXLh0hD9g+O89+0biEVubQV2ZCxNKp3n+Oke/vN/+6dp7zmOSz5vky/YOLaHcQuiQEIIVH05qrERJ/8CTmEvhtuPmMbfd3AKryK9BEKpQjMfnnHteG4vnjcCKAilBOkOzj1bywIIE2QGz+25YbX+iqQnMoeUNuCA9JBTwT5wU+nuIlR9LXMnBy6lkV8FPHKFA9j2JSrL/wu63ky+cIy8fRIp7eKqhVJGZcn/QFGCKKLoiutNVv3H01/Csi+ga7VcLXRKHHeImtI/JeDbAWh4cgJPZskVDmDZl/GbG2eMKG0VGMkV1chWlVXh06bHTynb4rn2syStPGHd4JdXb+XpRcvQFIW2knKGcxn+8vjrnE+M0JUcn3fgD1dixdt/fr9p/t1FnVmVmkCEan+YB6oXM5Cd4PBYD68Nt3Ms3stIPn3TfoC869CeGqUjNcrzvadpjVQUqUCVLbREKghrd74J6c1CMRD1ETQWF11ReWZSLceabNiUXFG8EUJHESaq8ME8TUw0JXJLAfJcUJWi1Oa/BgihoIkwmhLGrzdNVuhcpLTwpI3Evkal6MrD7MpvoSIwUBVzxkrLvYQQCroaI6puIGKux5MFPJnHkxbgTmqZXzt+31tq/HcDzaGH35TjdGT6GMyPsbl0BdptukL3Xh7ib37v25w+2I4/aJDPWLiOOyUzmcsU+OZf/JAH3rGRj3z6CbbvaKNtaTV+v8E737WR4AJoKG8WpJQMjCX5i2dfZe/JDoQQNFTGaKuvoK48SshfpMLkCjaD8RQXe0fpGR7n8IVextM5fvsjD7O+te6uX6u6pvLwjmUsb63h7MUBRidVfmqqoqxYWkt5aeim/SYSl4sTX+XC+JcJaFU0BB8joFUhhIrtpRgvXGQkf5jsaD9bq/47Eb3lrn+vZQ2VlIT8/ODAOfrHJvjow+tRFIHf1GmojGFoGs9sXUHIb2A7LrbrETDvjqyloohpi85CMOsitPQkjuuhauqsKjuapiCR00zMGupKWd5aw4nTvYyMpagsD3P4eBeGrrFhbdOcze43g2O7eJ6kbXEVD25vmzUYq6mO3p6ClggVm3zzL+O5nTjW6+hqw9S1UeTR7wFANTaiaIun7V5U85kompDiYeeem5T8nAPSmtwWpMwCDkW+/dQHInHwnE5c60BREtTtQ8oJpJcFLKQsIN3BhX1NZW7anabWoioxJN4kn99E1xqKsY9aimVfnlTS8xfnO60aT1q43jhSFgAXTa1CSgtXJpGSadeO31yH39zCFQlTVcTwGavJ5vfgztEPWXBd0raFIgR1oQjadcnW+fgIh4f7kMCGyjp21i2aKkzrisr9NU186cwRJqwCPekJ1lfOrxBxJ/GmBf7X4ko/wKJwOc2hMh6vK/YD7B/p5LXhds5NDN20H+AKFejwWA/H4338Y+dR1pTUsaNqcVEVKBh7i3kDLAzFzM5AUefHu/0p7hykdHFlBs/LTiZcAoGGqviv8VL4l4UrSaXKvddwv5d4c4zhJK8MH6EnO8i62FJ07dYDJqtg88JXXuPSyR7e+YmH2Pb4ap7/h72ceuPS1DalVVHKqmNcPNZFLlMgbBQVfR59dCW+mzSd3itYjst3951m36lOgj6Dd+1YxVPbllNTFsFn6KjXVHoLlsNYMsvLxy7z9Z8cpX1gjC//+AiL68rvWhX6WqiqQn1tCfW1t+a7kbH76Up9n5i5lA3lv0VIb0IVJkKAJ11sL0VX6nlOx/+a7tQLrCr9d9ztBgC/qfPQ2sV87vtvsHpRDY2VJUWqoCp41/2r+Jvn3+B//9OrlEWCZPMWdeVR3r39Vmkrdwa6oRIJ+0ilctj29AKh60qSqTyqqk5bDQgGDDatb+L4qR7OnO9HVeo4cbqXpa3VNNbdushCKOTD0FXKy8I8+ehqfHchKRJCQTO2oOiL8ezTxSZf39MgQsWmXfsknnMZhG+yqXfm6kWxYfVKBd4B78YCIkJEJ/8Ncm21vugDkMbKfh07+3U8pwdwEEq4KOkpAiACKCJSDJjlAmiyN5hPFSXMdHlv/2SQLiblvSVIb3KMFtnCATK5H2E57UUzVengeqNFmquc2Tupa4u4XnJUET6KTc+zq+N5UuJ6HppQ8F+nzON4Hi/1thPP5zBVlSeaWinxTX9GlfsDBHWdpJUnad16D97t4J5HMEIIwrqPVSW1LI9V8+6mtVyYGOb1kQ4OjHTSkR4jaeXxbrBk5EiPwVySwVySPUOXWBKu4E+2vIem0J1VT/kp3lrI252MZZ9HyjxR305Cvo2zVl4kkmRuL6nCQRQRpCz4DOYs8mBSehScLhK5F0nlD1Fwe3C9TPERo4Qw1Br8ehth32bC5ibUOyTJeTM4XoGLyRexvAxLo48t2MX1bkFKj/b0XhJWF8siTxDSr1eB+LeJjJPlUroHRYhJp+pbRzKe4cRrF1j3wFLe88uPEI4F2PPd6bK+voBBaVWUjrN9WFZxstJ1jcqq6deJ63p0d49RUxO75wnB4FiSF49cBOBdD6zm55/eOqsTryoEAZ9BwGfwwV3rCAdM/vSbr3D0Uh9nO4fYtrLpzR76gpF3R8k7o6wo+QWiRtu0QEERGqZaQnP47XSlvs+4df5NGZMQgmUNlWiKws7Vi/BdQwdb3ljFf3jvgxy91EcinaMyFmJtSy2aem8LaeGgSdviKl7cc47LnSOUlgSnzuVYIs2FS0NUVUaoq7nKyxZCsHZlPdGon6MnewCIJzK848m1C2rIvh5VFRHq60q5cGmQ7t44rS2V16nGSJDctvqYUKvQzEew7DO41lFc5xyasQko4ORfBplG0VaiGVuYbRnkKo1XRfe/G9335PyOq5QwPTx0sbJfoZD6c5C5SWOxd6IaaxFq7WTSoSJlhlz8F/GcO3Mdi+soQHP18kkpSeV+wOj4/0BVywn5H0PXmlFFmKx1kHjyr2bdT7mBydhc0BQFU9WYsPIUXHcaJWowm+LV/k4c6bEoXMa2msYZzbuqEGiKiicl9jzkgu8G7nngfy2m+gEqmtlY3ki8ZTPnJ4Y4NNrFobFuOlJjTNi5G64E5F2brkycrHNjI6Ur9u0JK4sAwrqfsO6bc5vi+ARlZnhGF7WUkriVIe/aqEKh3AxNW22QUuJKjwk7R382QXd2jLFCenJ7QVj3U+uP0RKqpMIXmbNL+9rjAIQ0k4h+Y9mpO7HvWxV5p4uB5OdwvSSKCBLyzeTjXcFE/jUGkp9FV8oJmxtnBP5SeqQKh+gd/2PSheNInEl+vg7SpeD0keEUidyL+HO7WVL+vwgYS+c42p2FKy3OJV8g44zSFLrvrRP449GR3ktHei+Nwc23Ffi70qMvO8zZZDtxK4l7XXUmoPl4pGoLUb2otz1upXhx6AAroi20hhrozg5xLtVJyk4T0Py0hhppCzeiTJN6k0zYac6nOunNDuNKlwqzlOWRZip9pdO2haIB1GhhnPZMH4P5USzXJqD5aAhU0xpqJKRdvX886dGTHaIj0097upf2dC9Bzc83e36MPlnRMlWDByrWU+27uaLMFRRyFhNjabY/tY7gHI67QhGomoLreHCDZ2OhYLPnlXM8+dTaex74tw/EGUqkqIgFeXLLslmD/uth6Co717Tw3GunOdUxyKmOgRmBfyFvo6gKuq7S1zmKY7s0tc5PmjkxmmKwN8HyddfroN8ehFARkwH+XM9bVfEV/5vD8OhOw/Mk53tHKI8FWdNSOz0ZUQRNVSU0Vd2es/idhqapPLR9KQePdvKN7xxE0xTqakpIZwo8/+OT9PTH+ch7t1J6nRdIZUWENSvrOXayh3Q6TyjkY83Khhm/hed5FCwX1y2ag3lekTaUSheAooGcYWooQhAJ+3jykVV89kuv8Hdf2ce7n15HfW0JnicZGUvR3Ztg07qmW14lugodzfcwdvYbSG8UJ/8iqr5+smn3dUBFM3cg1OoZewoBQokVjbpkAaHE0MydsyYIN4N0+7AzXweZRVEX4Yv9f6j6mhmiD557667qtwMpM0ykv47EoTL2B/iMdVNjs93BO8KLv4KgblDmDzCQTdE+MYbteRhqMZB/pbeDS+NjCODBukXUBmdSqx3Po+A6KEKg36Pe1LdU4H8FV/oBqvxhKn0h7qtcRKKQ5WJyhMNjXRwe6+FycpRxK4szy/LNfHE03sUfnfk+HpL3NW7h4y07pr0vkfyw/yR/e/llAIKayR+seQ/Lo9ODRstz+KMz3+fkeA8NgTL+37XvpcI3KR3muVxIDbJn6ByH4h10Z8bIOAUsz8GVHoLij+9TDap9UR6pXsm7GzZRZoZmnSS+1X2A53qPIIEdFUv5jRVPTgUWN0LayfPfT32Pc8l+FAS/2PowT9etvaM3xL9k2N4Y/ROfIVU4hK5WUBp4ipCxBlWJIGUByx0ia58nUzhBwFiBodXc6yH/q4EnPd4YPcFXun6ALR1KjQgpO0tfbhhXeiwK1rE00jQtGZiw0zzb+xMyTo7e7BD/1PcyGSeHK11sz2Fz6UoWt314KpiXUnI53cvXul/gYqp76nVHulSZpXyo6XE2lCyfVp05k2zn79q/S8JKTjVVOV6xN2Jr2Sp+pvlpSoxw8XXpsmfkCMfHL5BysmTcHJZ0ODB2akqBJqgFWBtrXVDgr2oKps8gk8zhuh7KLJzkQtYiMZwkVhbicvsIE1mLqqoo+/ZeIJ+/uuSez9tcvjSEnI9d7l1GPJUlX3BorQtTFpl/n1DAp9NUVcKxS/0MjV911ZVSUsjbvPGTs0RLg7QsraGvc5TxeIZg2Eco6kc3NFLjWRzHJRILIGUxsbIKDqGIn/GxDGePd9O2up5MKk8o4rupWs98ENRqCeuNjOVPUh24H+W657VEkrZ7yDtjNIQe527TfA6e7+Z4+wCHzvfw9NYVCzr/9xJCCJa1VvNzH9nO1549wB/9xQ/x+wws28HzJM88tobHH145TZ4Tin4Mm9c1s2ffBQ4f72bXjqVUV85VjydkAAAgAElEQVQMyPoGxvnWc0eIx9Mk03nG4mmyuQJ//Jc/JBg0qSyP8NH3baEkFkRVFR64r5V0Js9zL5zgj//yR1P9AlJKohE/y9tmBuO38p2LMp2bcfL/jFPYhxH8OK51DM/pLgbzvkfnUF0TCLUeoZQj3R48+yxS5oq0nIVASjy3B8/tBQSquR1Vn60JXYJMIr3ZFbDuJjyZxfXGUJUYhtYydT48WSBvHb2jxqUlpp/WWBmnx4bY3X2ZXfUttMbK6UjG+fqFE2Qdm+pAiCea2zBnWSUbyWfJOTa6ohLU7g2V+y0Z+F+LK0lApT9MhS/E1oomJuw8nakxjsV7OR7v42Sij8FcakHL6kIIIoafjFNgzEpzdqIfx3OnVeodz+NIvJOBXNEMSRMq55IDMwL/cSvLheQAA7lxGgJlhK+Risu6Fp+5sJv9o5dwJ6txhqIR1Ew0oeBJSda1SNo5knaOjvQIvdk4/8+Kpwhr0xVhhBCsL2niyx37yDgF9gyf44NNW1kUqrhp5f5iaojD8Q6Sdo5KM0JbpJpEJs93j51hQ1MdLeWlnOwb5OLQKJbrUh0Js6GpltrYTOk8KSWJbI6TfUO0j8SxXZfqaJh1DTXUxSJF50QpeeVCBz3xCZ5es4zSoB8p4UBnD4e7+lhRU8mOJc1oqkK6UOD5k+cpCQR4aOki9HuwpJyzL5GxTiHQqAx9mJrIL6EqPq5MwlJKPJnHdkcBiSp+qiZ/JyClZKwwwVe6foArPT7d9hHq/JXk3ALf7PkRuwcPsKtqE09U349Pnbk0fyB+Gr9q8lDlRpaGm9EVjYHcKCVGeFoQH7cm+LuO79KXHebd9Q+zNtaKKlTOpzr5ZveP+VLHc1SYJTQFaqbupRI9wuroEpqDtTQFa/CrJgP5Mb7V82NeHj7EimgLD1dunnxGabyz7iGerNlOf26E/3nuS9T7q/hk6wfxT45bQRDWFzbphmNBmpbVcGzveXa+YyOLV9VPe9+2HI7vu0D7mT4eed8WXClJpwv4fFl+8pMzNDSUok0GQpblTEsE7iWuPFHmauScF6573E/EMxw/0E5ZZQRdV3FdjzNHu8ik8/j8BtvftpJjb1wmMZqirDJCtDTIgVfOU1ETRdc1lq5pQHqSU4c6GR0cZ+eTa1CMhQX+RQWfiaJqz9QwJU3hZ2hPfhtTjVHp34KhFo3EXGmRsjq5nPwWYb2R+uCuWzwZ80eRy6/woV3r2bq8cUHeCHcSuqaya8cyHMfFMIoqPvdtbmHxooopQ7T1qxuJRvxEJ3n7mqayY1sri5oqOH2un7FEmoDPoHVxJUtaKmfl2gshWLOyno998D6yWYtN65swZ/G20HWV6srIlNLPulUN094PBc1pzcABv8E7nljH+jWNnLs4SDyeQSiC8tIQSxZV0Fg//wT/hhABdP/jOIUX8ZzLuNYxHGsfUEDVt6Pqy+fcVdFqUY0NOLkeXPskrnUY1dyx8JV+acOkHLS4zjRrahMpcaxDSG9kYZ99B6CIEJpaS946TLawD7+5GSkt0rkfky3s5U4qD5qqymNNrbzU287F8TF+89Uf0BQpoTs1TldqHEUIHm9qY2Vp5YzzLKXkYmKUvOPg13TK/fOnC+ecAudSfQzk4qyJNVNqhMm5FiVGaMHSym/5wP9aFDXvFcK6SWOoFA+J7bmk7DyjhcxNFYGuR7UvRqkZZMxKM5gfJ+XkKTGu/hBpJ8+l9BBQDPpd6XJ2oo931W+82lmPZLiQJG5lEEBLqAJTuXpag5rJhpJmjsW7qAuUsDrWQFukhlp/jKDmw/JsOtIj/GjgJCcSPdjS5YcDJ9lSvpinatfOGPOKaB3LI7UcincwnE/y2uhFmkMVN5w7PSnZO3yetF1sJFlX2khTsIzu0SR/t+8wHWMJDFVl36UuHM/Ddl3ytkNrVTm/9cROVtVe1TqWUnJhaJTPvLyf470DUxec43rUxML80s4t7GxdhKoIzg2O8PWDJ1hRW0lpsA7Hc/nesbP807Ez7GxtZmNTHWHVZCSV4fOvHuTJVUt5sG12g5C7DddLIqWFEBqGVjvZ4DM96VKFH1VpmPtD7jomz7WXp+ClkdJDU3wYSmgGTQWuaIe7WF4GxysgkahCx1CCqGJu9Z5ikmNT8DK40obJ/XQlgDaHXOz18KRHzk3gSQefGkG/gXFZd3aQ3twQ76x7iBWRoqJJjDBvq9rGqyNH6Uj34VONWR9ug/lRfmXJ+9lZsXEqaV8Wbp6x3f6xU5xNdvD+hkd5pvYB9Ml7tD5QxbiV5qtdP2D/2CkaAlWok7zS+kAlH1/0DLq46hdS568k7xb4s/NfpiPdh1uxEUUUVbUievHZkXPzKCjoikapESF4C86KVxAI+3j4vVv43O99i8/8p2+w7Yk1dF8cJJ+1eOOHJxjtT/Dqc0eJlYXY8cx6lqwp0lRGRlKsXdvIM29fP0XryWYLfOPrb9wz46hrEQv58Rkaw+NpEqkcZZH5TYDZgk338DiqolARC029LoSgvDrKkhW1tK6so21VPW+8dJaWZTU8+NQavvP3eynkLHwBA1VTOXeih/X3LaGiOsoj71jPP35hD02tVfS0j5BO5njXx7ajzxIc3hweJ8f+N4nCVY6zmMxucu4IJ+N/hal8GV0JIYSG42WxvSSWl6bCt47B7Os0h99xC8edPza1NbCp7V4+x4rQdZWHH1g27bX7Nk9Xplm/ppH1a6ZTr1RVobG+lMb6+ffwxaIB3veOuamgANWVUT78nhs7zV4PXVdpaaqgpenu9TcJoaAam1G0Njz7FE7+h7j2GcBE8z0CNyxC+TECH8AtvIb0Rimk/hxT8aPqqxCYk5m3nGzezSG9MTy3H9VYhxC+KwMAJVo8jkwWVw68NEKZvP8m5xnXPoGV/jzccSPMm0MIP7HQzzAy3sHw+O+jqbUw6dwbDX6YROrzd/BYggdqm3lXywq+cfEEnalxOlPF4rAiBJsq6/iZZevwzyLqUHBdjo0MkHcd6kIRGsLzo+1ansM/9x/iUPwSKTuLXzVoDlbxo4GjfLT5QULz8CW5Fv8iAv9iFcVltJDm7PjgpJJPL93pOEk7P80OeSEI6SYNgTIupoYYzieZsLJTgb9E0p8bZzifJKT5aItUcyTeSUd6hIxTIHSlH0DCQG6ctJ1HEQrLotfxJRE8WbeWmkCM1dEGyn3hGcZjm8sWc19FK39w4jscS3RheQ67B07xeM1qtOuW8IKayWM1qzma6MKVHj8ZPM076jfc0JAmYWXYP3oZD4mhaOyqWoGpXL0of3T6IovKS/jkrm0sq6kkZ1k8e/QM3zl6mq+8cYzff8cj+PTi9qPpLH+6ex/nBob5mW3ruH9xE4aqcrJviL/de5A/272P6kiI5TWVtFWVU7AdBiZSRX51Lk/XWILG0ij9EynGsznCPpP+8RQ5y6GlvBT9FuXVbheGWoWqBLHcIRLZFwgaq/DrS1AmFQTeChAojFvdnEx8h8HcKRyZJ6zXsCT8EC3hnRjXSKtK6TFWaOdy6hWG8+dIOyN40sWnRqj0LWVp9AkqzCUzdZ+lw2j+EpdSLzGUP0fOTQASnxKj3LeY9aUfJqzfmDPtSZfe7BEOjH6BoFbOtopfosSYO9AoUnQ8Yvr0CSyg+TAUnaw7t/JBla+MDSXLp63UXZ+YuNLl+PgFVKFQ768iYU13m630lSAEXEwVjYvUyRUnRSjoCGzPIe8VcDx36h4SQiHrFib1rO8ehBBs3LWcj/z7J/mnz7/EN/7XD7EtB9f1+MIfFvXDG1ur+dCnn2Dx6oapimRZWYinnl5LMGhONRgGAibb7mu9LfOuO4Xm6lLKo0EG4il+dPA8deVR/DdRRrEdl30nO7jQO0LQb7C6ZSbdTlGUKalFRRHESoMYZvF5e/5kLyMDEzS1VjLYG0cIQbQ0hGHqKELguR6KIlBVhYlEhkhJ4JZ6oDzpTqMWXLlEgjPogcXEXVN8+KnE8pKM5o/RHH47b5Vnzk/x1oBQKtB9j1KwT+IUXkHKPIpWj2puv+E1KoRANTZjBD9BIf1XuPYhcol/j2Y+gKq1ghIEaeF5Y0inE9c+g1Bi+Ev/+mrgDyjaIlR9Fa71Go71GoXUnxR9A5QI0hvHtY9h576L9FIIdRHS7bi97yt8xEIfQVXKAAWQRILvoVi5Lz6fQ/4n8JmbEJNeOUHfQ2hlFeQKB3G9FJpajt/cgqZUoogQpn41ydTUasrCv4rf3DT5+VfhMzZQGvkkhj6330vYMPnVdfdRHQyzu/sSQ7k0Id1gc1U9H25by5JY2ay/y2A2xYXx0altK+ZZ8U9Yac5M9PCzix5h7+gZPCkJaT5GrRQ51/rXE/hLKXGkx1g+w6nxfvaPdHJ4rJuezPhNXX/nC12otEWq+cnQGSasLCP5FM2hisnjQ2dmhAkry+JwFfeVt3I03kV/bpzRQmoq8PeQXE4N4yEJqQaLQtMzfyEE1b4oT9SsmfMGVYSgMVDGexo3cyLRjYekJ1tMakrN4IzPu69iCbX+GD3ZOOeTg5we72Vb+ZI5P//MRC+dmeLyW1OwjHUlTTMUCH7hgc3sWrp4KkiI+n0c7OjlVP8QiWyemmhxUn71YieHOnv5mW3r+Ni2Dfgm5d1aKkpJ5vP8+e7X2H3uMq1V5TSWxvAZOt3xcVxPMpzKEM/meGhpCy+evUx3fIKG0hgdo3FMTaWhNHrPmo19+mKivp2MZp5lPLeHgtNHif9RYv5d+I1WVBGedXnzzUTBS3F47MsYSpDawFocWaAve4zXRj6L5WVZFXvHFH9YIhnMn+Zi6kVKjEYaAptQhMZYoZ2zEz9gtHCZR2t+l7BeOfX5Unp0Zw7x+shnyThjlJuLqfGvBiBpDxIvdHKzJVNPuvRkDrFv5DPowse6kg8S02cqKF2LMjOKTzHoyPRjSwd98rHUmx0m6+Sp81fO2YsS1gKEblJRz7sWCStFwbX4fPuzM3pibOlgeQ4pJzPp01BE2slxLHGeE+MXGMiPkncLONIj6+QouNZNz8WdgukzeOT9W1m6oZmTr1+k9/Iw+UwBX9Cksa2atdvbqGmumEZD0HWVaHQ6rUjTFJYtq5mi/txL1JZH2LF6EV9/6Rjf3nMCCTy5dRlVJWF8hjZJF5zk7tsO8VSWPcfb+eruI2TzFrvWLWFl8/QEVFEEdU1lnDrUCYBh6iCLz8xg2Ec4GqDr0hC9HaMEwz4MU5s0NINgxI9h6rStrmfF+iaOvn6Jipoo/sBClV8U1pR/ahrVZ0F7C4OfBv0/xUxoaOZDWNmvId1+QEE17kNRb75yIzAxgh8HYWJnvoTn9mJnv4yNDkKdlLksruwWHYPvn6GeI0QMM/Qr5JNDeE47VuZL2NlnQeggbaRMI9QazMhvF1cWkv/ttr6toviJhT427bVo8APT/g4Hnr5ujBo+Yy0+YyZToiT8s9P+1rVayqKfnvXYfnPjrMZd16PCH+TnV23ifa2ryDs2mqISM334VG3OOCaoG3xyzTZsz2V5aSWBeUo9e9JDEYJSMzQ1f+U9GynlLTmov+UCf1d6TFg5zk8M8/pwO/snJT1TCwj2fao2Q0JpNqhCYVGoEl2o2J5LR2aEzeUtU+M4O9GPh6TWH2N1rJ6QZhIvZOjNJqYSBFd6XEoV6UAVvggV5symofkGs0tClQQ1k5STJ+da5FwLmJkRVvtj3F/Rxje63iDnWuwePM2mshb0WRp8XM/j5aFzU2o+2yvaKDND07apK4mwrqF2mvRYaTBAWShA/3iSrFVUSHI8jwOdRUm0lvJS4pnpmsA10TCqIjjdP4zluJQE/NRGw3SMJrBdl0tDo6hCsKmpnj0XOukcS7B1UQMdowlCPpO62J0zF1soVBGiNvrLgCSR203OvkDOvsxI5llC5jpK/I8Q8d2PoVbN0Uh191FwU0QCddxX8QsEtDI86TKYO8XLQ3/C6fHnaAxuJjZZWRcoLArtoMq3nLBeja4EEAiyTpx9w5+hK/MGw/lz0wL/tDPCobH/Q9ZJsKX8Z1kcfghzchXB9vLYXpagNveS9rVBv6kE2VH5a1T6lt4wYRJC0BysZUvZKg4nzvDVrhdYEmogaad5YfA1qnyl7KzYMOf+qlCZT6AkkZiqwebSlTNWFq6g0lc6tcKWcwt8s/tH7B7aT42vglXRxVT4SgioJsP5BN/o+dFNj3knoekqzctqaVpaU6xoTzb6aro67+eLZbm8tu8C6zc0E4vd24ZOU9d49wOrOdczzNGLffzDjw7zkyMXWdZYSW15lKCvSEXL5S2GEmnO9wzTNZSgYDm01lfwscc2Er4uKBdCsHx9E7VNZRimjmFqSFlMAB5/7yZ8foP6ReUoioJuqOiGhud56LrGE+/bhOnTaWipwB80iZWFpjkezxdFj5qfyki/lSClxHU9CpaDZReN7zxPTgVNiirQNRVD1zAM7bYlOO8GhBAo2mJUfSOO2w8ijO5725Tx1E12xnZ9JJx3o5srCSp7ce3jk+ZbBYQwEKIERWtE0VejmTtm0IeEEKjm/fhjf4KVexbPOon04hQbiMtQ9VVo/negGuuRbi/Sy6CotVxfTb8CVVuOEfp1ABT13tB77wR0RZ131R6KycITzW0LPk5UD1JuRvhm9z4G8wniVooT4500BstvWviaDW+JwF9KSdqx6ErHOTDSwb4rJl6F7A31+6+FJhTKfSHWltaxs6qV2sDNuVNCCOoCJUSNAGOFNB3p4SlN1rxrc26iqIDTHKqgIVhGmRmiJzvG2WQfOyqLP17GKdCXLUpY1fhjlBq3pu0uhMBUNQxVA6eYULhydgqTKhTeVrOK5/uOkXLyHBi9TH8uQWNg5vLSYH6Co/FOJBDV/TxQuXRGUlQS8BM0p3eXF+3TxSTfu/hawXYYTWXJ2w5/8uO9mNdZVVuOQ862SebyeFIS8Zs0lEZpH0mQtWzODIwQDfhprSqjNhbm/OAIiWyO3vEJGkujhHx3yWF0HpeQEAJTa6ax5HeJ+Xcxln2OVOEwtjtCIvsCE7k9BI1VVIQ+REngUTTlzW/u1RQfrZFdhCaDdUWoVPtX0hjcwrmJFxjOn78a+AtBUCsjqE1vMAtqZdQHN9CR3kvGudqEJaVkKHeGscJlWkIPsCL6NJpiTju2nxizoUiIUejJHGbfyGfwqRF2VP4qFWbbvILSgOrjQ42P050Z4MeDb3DIOIMmFBoCVTxefT+LQrfn0GoqBjE9jILCQ5WbWB2dewn3Crozg7w4dIBqXxmfavswdYEKlEmH7GOJC/MqLNwNCCFuyD3P5SwKhdkVLLLZAqdP97FixY1XYN4MCCFori7l0+/byRe+v5/9Z7vpHEzQOZiYfL9o0XOtdLOhqWxoq+cXntnGykXVs14Tuq5SXjXz2R+OBpBSoppaMQAseFAoFkOSmQIA2UkPhHS+WOjIJeanBBKLBmZtFr2CnDNCyu4mZixBV+79yuHdgOO4jE8qT90IQkAk5Mc0566K3gm4nkc6XWBgaIL2rhG6escYHk0xFs+QyRYoWA6eK9F1BdPUiUb8lJeGqK2OsaihjObGcspLQ3d9nAtH8fyq+nIUfc2897o4OMbvf2c3m1vq+c2nPo2USfBygAsoIEyumHDN9X2F0FD09fj0FZOOwJOS6cJEKJEpapDQmvFF/sMNx6Maq1CNVfMe/791+FWDd9dvY/fgcYby40zYWZZF6nhb9ToMZeFh/D0L/KWUFDyHwWySI2M97Bu+zLF4L8P59LybdAUQ0X0siVSyvbKF+ytbWBwpJ6TNrwERoMoXpcwMMVpI0ZdNkPds/KrBcH6CgVyxiaw1XE2JEaA2UEJnZpQLyYEpBaCR/JXGXsHiUOUNNfgd6ZGyc4wV0gwXkiTtHHnXxvZcLM9hOJ8kdxP/gStYFqlhVaye10cvMZAbZ//oZRoay2bUPo8luujLFSfTFdE6WsMzJ0xdVee1uFxsFpWYmsb2xU1UhmdPcqqjYQxVRVNUFleUcbxnkJ7EOB2jcZZUllEeCrKovITT/cMMJdMMjKd4ZPkSzLtGQfCQ81h6L6pdRCkJPEHEdz9Z+yzjuVeYyO0h51wmVThAzr6A5fRSHfkEqvLmVk114SOsT+cJq8Kg1GjGkzbjVu+09zzpkncnGLd6SNlDWF4GV1oM589Pvn8NDxmXuNWJlB61gTXTgv6bQRUaw/nznEh8CyndBQX9V3Bi/CIFz+ZXlryfxaF6VKES1Pz41fnfy3OOT1FYG2vlUOIMB+OnaQs3YipXm5slEsdzUYSYXEGAcTtJ1s1TF6ik2lc29brjuZxJXsb25g4KFRQ0RSPr5HHmSN5nQ/H+8pieqRZdKws5i7HBCRLDSWx79mObPoMLPXGOHOlE02feS47tMjGRm/d47jYURbCiqYrf+egjvHzsMq+eaOdy/xjZvIXjeUhZ/O38ZlHCc/uqZh7Z2EpN2UylsfnAsl3+9st7OXSs8459B1VV+K1ff4I1K+rn3CZld/LG0O9Sai6nOrCdSv9mAlrNpHvvWymovDXYjssr+87z9e8cJD2ZRM0GoQhaW6r4xEe2L6gxd76QEmzHoacvwaFjnRw61kV71yjJdG7OZPh6KIog4DcoLwuxalkdWzcsYs2KOqKRwD1dCZBS4jpnca1DgIHme2TSZGt+KDgO/YkkiXQOITSEKJ2rGH9DFK9XH0JdWJ/QRDbPkc4+ti5uIGDeGwnLf8kQQlDtL+FDTQ9MMkEgoBqoc8SbN8ObGvhP8fYLGU4l+nltuJ0Do130ZcbJufPnRPpUnbpAlC0VzTxQtZhVsVrKzOAtyZIFNZOmYBnnkwMM5SdIWjl8Pp3uzBhxK0NANWkOlqMJlaXhGl4buUhPJk7CylBuhhmabApWhWBZpHbGg1zKovLQuWQ/e4bPcTTexUBunJxrYXkurvTwpMTDu6Ex2fXwawaP1azm4FgHjnTZPXCKJ2vXTJMStVyHV4bOYnsuqlDYVb2CgHbrVXWfrlMaDKCpgqdXL2X7khu7ZgoBbZVlWI7DxaExBpNpdi1bjE/XWFpdwb5LXVwYGiVdsGgpL0Fb8O93nRagnP7n1ZcdXJme+cac4y4mABF1GyFzA5WhDxDP/pDh9FcpOD0Mpb9CyLeRqO/+BY739qAKHfU6froQoCvFh7DjXW2CdT2LjvQ+To1/j4wzMqn+E0QTBtlZTFYkkoKbnqQqLMwgzPKyHI1/jYKbQhEaKXuICrOVhXCVDyXO4EqXoOafqohm3ByWZxPU/Ghi/pSW6yEQ3Fe+hv1jp9g9tB+ADSXLCGtBCp7NSCFOd2aQnZUbaA7WAlBmxAhpfi6mejiVvExDoIq8a3EkcZa9I8du6H8R0gNU+0ppT/fx8vBhNpQsQ0FQ8GyqfWUEtNknzbwb50Lyezje1eDcp8YoSe/kub99g4MvniYzkcObw+2xurGc1retZvXqBpYum9n4mstZvPTS2XmftzcDQggqYiHeu3MNj21qY2Qiw2A8RSZn4XoeAZ9BVUmIqpIw0ZDv9qQnpSQ+nqF/aOKOjV9VlZsGlX6tinLfWuL5U4zkjuDTyqn0b6Lafz+lvlVFYy9u/fq+l7Adl5f3nefz//Aqg8PJObcTApa31vDeZ9bTUDe3kdmtwnU9unrH2L3nHK++foH+wQlsZ+GiH54nSWcKpDMFOrvHeOW18yxvq+XJR1axZX0zoeA9StZkmv+fvfeOsus8z3t/3+779DNzps9gGgaV6EQlQLCIoChKVKGKZcqSJdlXduJ05+bGWelO7r2JsxIncRLbkhzL6rZVckVJJMUOEARAECDRgQEG03s7/ZxdvvvHGQwwnIIZEANCip61Zq055+yzz+7f873v8z6vk/1rpD+Eoq1EMx/+uckcSSk53TPAf372Nf7ocx/+JfG/BTi+S0d6kLpAOZZqcHL8Ct3ZEe4tW0lDILHka/KOEH9P+iSLedpTwxwe6uDw0BUup0aWpNvXhEK5GWRjWR17q1rZkWikJhCd5ZCzVGhCpS1czbP9pxkppJhwMpSbIS6mBij6Ls2hChJWyRN8VaQaVSgM5icZyicpN0NczYzgSI+IbtMUSsxYt5SSjFfgrzqP8VddRxjITeIjMRWNkGZRbocI6zYB1cBUdQqewxtjHaWMx00OjECwI9FKQ6CMjsww55J9nJ/s597y5unj0Z0d5dRESZNfY8fYXtbyrsrGVEVhR1M9z59r5+VLHWxuqCFo3hA5laWIqJjqcghQXxZFU1XODZR0/y2JOIoQtFUmcH2f4529mJpGQ1lsyedRCI1r5NKXBUpp0LnqHDIU3d5b2mdFGFh6M9WRL6AqEbrG/y2ON0K6cIKIufuODgKedPDkTJIhZUl/DyU5Tuk9yVDhIoeH/xhVMbm3/NeostejKzYKCpdSL/Da0P+YsZ5SIzm7lInzMkvervrANuoCWzg68lWOjHyFoFZOlbVuUcdHIlkfaeXtiUv8+/Nfw1JLA4MqFGJ6mM3x1TxWcx8xPXzLx7vMiPLF1o/wna5neWnoDV4aemOqgE3iI4npIXYnrqfOGwJVHKjezTMDh/mPF74xraO0VJNHa3bz3MCReX8rrAV4tHoP3+j8Md/u/Ck/6HmxtC9GmL+58pM0h+aW2mTcYc6Nf5eCf52Y2rKWkW/nefYvTlFZX8bGPW1YAXPOOVUsEaZ1SyOtbdXUzRFRzeWKdHSMzNkE7L2GogiiIZtoyGZlXeLmX/g5Qkhr4N6Kf0ba6WQwd4TB7BF60i/Qk/4ZYaOZansPVYGdRIxmNBH8uZkALJ70C+5ZU8tvf/4B1q2qua2RcylLRP2Vwxf53tMnuNI5clO50VKQShc4+mYH5y/1c9+OlXziiW20NCZuS2O3+SClDzJzw+skTvaHOLkfAQa6/WEUrWnZfn85cOxKD5lCcUm9ln6J65h0svyg53V+rflBxosZvtd9mLBu050d4TdaHyE4TzBpPiwb8XXiU8IAACAASURBVJdSknWLdGfGOTrSyaGhK5yd6GeskJluZHUzCCCsW6yMVLCnsoX7KltoDVcQ1m/frFsVgpZQJYaiknWL9OcmaQwmOD/ZB0BDoJy4UXoYNwTKieoBkk6OK+lhVkVquDxV2FtlRUiYM3XfnvT5Qfdx/rT9RXJeEV1R2VnWzANVa1kbraPcDKErKppQUYTganqEs2/8BZN+dlHB0iorwt7KVVztKFmMvjB4hi1ljWhCRUrJkdHLjBRKke7t5S3UBJZOrm+EEJQcec5f5um3L6AKhX1tTcQDNgXXZWAyRfvwGA+vaWVdbUmHHg/YVEWCHO3oIWjqNJSVdOKV4SBR2+LNrj7ClkFNdOmaeU2JoggdDyi4Xfgyjypmyo+klOSdy2SdS7e831CaAASNtagigC8z+P6dl0w4Mk/S6afMbJqOOHuyyHixE0Vo0+45Ep+h3Dky7ihbyz/Nqsgj09EhX7oUvNnZD4FK3GwEIRjIn2a1/z5UZXGRGVXorIm+n0prDSA4OPRfODz8xzxY/X8SM+pY6GL2pc8bY+c4OXGBtlADtXZl6fpF4kiXnuwQf9n9M1zf4zNNH5je74ge4om6/cSN8KJkH0IIGgM1/I2Vn6A93U1Hpo+Mm8NQNCrMMlpD9VRbcRw/j8RHFSofrruPtlAN7ekucl6aKquGDdHVxAwdhSJxowJP5sl5WaT0sdQwICh4KTbF6ogbH+FKeoSUm8VQdGqsBAlz/vS86+dmuAoBJEfTvPnyeZrW1vLb//oTNKyqnuHeM2MfFYGqqfMSK9PU+cDjmwgEfhlxu5MQQmCoYcrU9cTNdTSHP8J44TyDucMM5d7g4uQ3uJL6HuXmBqoD91Fhb8HWqkq1M3fpJMBxPV48WCL9g8MLk/5N6+v57c/vZ3Vr9W0n/YPDSb79/WM8+9LZBWVG7xbJVJ5nXjzD1e5RPvfJ3Wzf2jSrQ/Btg0yST/47pJwE6eN7PfjuJZAFNPNB9MAnp4Jes+FLyXAyzcnOfgYmUwRNgw0N1fi+nPNaumazfbZnaNqEoyoSYuOKaqqj4RkZtr7xJM+cusTD61sJmgYnO/voHU8iBDQl4mxaUUPEvh58Gs/kONM7SPfYJM+fuUwqX+Dbh98mYl9XHTy6cdV76ub384KC5+BKH13RODJ6gc3xZvZVruerl58j6xbeW+J/zW9/MJfkxFgPhwYvc2Ksh8Fcckle+5aqURuIsSPRyN6qVjbE6yg3g7cgBbk5hBDU2DGieoDxYoauzAjronX0ZMdQhcKaaM20br/ajlJhhRkvZjif7OPh6nV0Z0YBqLHjxG4o7JVS0p+b4K86j5LziggEj9Vu4m+ueh8Jc+7IpaVqLMUiUBEKD1ffw//qOcGkk+XwcDuDzZPU2nFyXpFXhy7gSR9bNXiwat2crj9LRSIU4HcP7ONPXjnKT05f4MenL6AKgQR83ycWsGdIgCKWRUM8xpudfTyyro3IVAFvLGDTUBbjp6cvcmB9G6FbSP/pSgJTrcfxhknlj5IuvkXE3DntuiOlT9HrYzD1Fzje/N0EpZTknEuAxNTqUERgRhq1VOCcI5V/A0+mUISFqdXf8YeV6+dpT75AhdlGQCsrEfz8BboyRwnr1VTa13yKxdT2Sxy/gCddVHR8Sh79HemDs9Zdsp1dR9xYQWf6dS7az9Mc2oMmSpFuTxYp+GlsNTYtLbrxu4pQUYRKS2gvWXeUY6N/zpGRr7Cv8m8R0ObX8w4XJvizjh9SZkT4B2s+WyrCFdeyOD4D+VH+zdkvc2LiAh/33jctk4kbYT614sCSjp8QgrAeZEt8DVvia2Z9PlHsYbzQSdYbI6LXkHVHqTRtdJHBUqNU283kvRE60xeot6E+0MSF5E/wpYuPR629EWWq3mGy2EuVvYYn6h6ctli9GVyZnUX8C3mHydEU+z69j9YNDXNq9xcLRRFEIrfeTGw5IaXE8308X7KYuJAQpdqku9GBZX6IkpROK6dGu4+qwA6y7iCD2dfpTj9Lb+Yl+rMHCep1VNu7qQ89TMxcc9dNABzH48VDNyf9iiLYunEFv/3r+2ltqrztpL+zZ5Qvf/0gh49duSVZz1Lh+5JzF/v5z19+gS8+dR8P7FmN/i7ux/kgpYvnnMR3znLNt16IMKr1EGb47yCUufuoSCm50D/Mf/zpQc72DmFoKrqqEgtYbGuumxUgkVJyeWiM//7865zo7EMgEAI8X1ITC/MbD2xn/5rmafLfPTbJf33uNdL5Ah0j45ztGcSbstv1peThda38vcf2EQuUntFneof46svHyRQK9E8kcXyfl85dmdGrZ2tzHQ1lS5OWAqQyebKFpVvmCiAatjH15RW7ZHJFMrnCkvMbAggHrVk9TUxVRwKHRy5wNT3EZ5sfRBPKkuThN+Jd770EPN9jvJjlzPgArw1d4cjIVbrTY2SXoNtXp6Q8G+K1JSlPRRN1t0HKsxhU2VESZpiRQoquzCgDuQnGihkMRWVtpHZ6uZBu0RKq5EKyn470MENThb0KgpXhqlmFve2pwenC2jIzyK807qLCmtuyUkpJ1itO224uFm2RKjbGG3h16AK9uXGOj16ltj7OlfQwFyb7AVgZrmJ9dLYzSlnQ5jO7NlMZDs3Szhqqwgc2rGYimyceuE70hBC0VpTxTx5/kLN9Q5wfGCaZz2OoGjWxMGurK2hKXI9qalPrSYQDbFtRN+37b2oqT2xaS10swrYVtbd0I+pqOTH7QTLFsxS8HrrG/g2J0EewtFZAUnC7Gc89T6Z4CktbQd7tnmdNkrHsTxnNfJ+gsZGAsQ5Tq0NVwoDE8cZIF04wnn0GXxYImZsJWzuXvL3vFiGtgqTTz0uDf0CFuQpXFujOvEHBS7Gt/NeITBX+CgSV1lpCeiXtqRcAn5BWScYdYSB3BmsO8g4Q1qvZWvYUrw//CYeH/weXUy8T1UvXf8YdIeOO8mD171Jmzm+/pgiNtbEPkHGHOTPx/3FCS7A98fkZzcVuxHhxkoH8CFvja6g0Z2t/TbXka64K5ZYKOpcCXbHJeqMU/Sxpd5iil0ZVTAwlyOrIAQSCE6lv0xrejyPz9OXeJu9NsiK4E1XoDOXPoys2Qa0cRaiEtepFk34Ax8+V0vw3QNUULNtAVRUWs/uu4/HWaxdJ1MRoXDVb5w8wPpzkwskuNt83JRuagpQl0i3E4i2I3w1KHv0eV/pHOdMxQP9YkmzeWdRgZukaT71vK1Vli88UKorC+jW1FB2PTLZAJlsglyviOD6u5+Pd8Od6/rI1ZysFEopk3H6GckcZyB4m7XRjqjFiRhsFf5KO1A/pybzAyugnaY18HBXrriD/juPx4sHz/OnXD96U9O/Y0sRvfW4/zY1L1yAvBCkl3b3j/I8/f4XX37iC7y/uPBmGhmloaJqCoigISmTedb0pq093UZPOvoEJ/vQvXkVTVe7f3TZvBu5WIZQQZuhv4LtXpuw2Qyj6GlRjE0LMXx+RzBX4kxeP8lZXP5/etYlHN65CVRSOXenmG6+dZDI7sxHiWCbHHz5ziBOdffzq7s08sLYFU1c53zfMn71ynD94+hXKgjabG69zIF9KvnPkbbY21fHPPvowtbEIA5Mp/uTFo/yvE+fY3lLPB7esBWBbUy1tVY/i+j7/4JtPM5LK8K8//gjVN2T348FbC0R89/mT/PjQ2UWdrxthGRr/8NceYsvq+Yvxbwd++vo5vvPsCdwlys4MXeVLH93Dg/e2zXg/pgfZVtbKsdFL7ChfRUOwgp7sCDV2fFoauxS8a+LfkRrhmd6zHBq6QntymMlibtGzHEGJTLeGE9NSnpWRCiL6nX3IBVSDxmCCc8k+hvIpOjMjJJ0cNXaM+sD1aKUqFNZFa/lJ31sM5ifpSA+TcnIoQmF1ZOYgK4GRQgpvaiBPmGGq7YVntu3JQQoLuIXMBVstFfm+PtKO43u8OHiWA7UbODpymaSTQwD7K9cQMWbfYIlQkN/aPzeBNTSNT2zbMOdnQgiitsXu1hXsbl0x5zI34r6VjbMKgYUQ7F/VzP5Vt+7hK4RGIvQkOecS47mfkXXO0j1+CWWKZPp+FiEMEsEnCBob6Zr4vxdYl0LB7SXvXmU0+yMUYU917S3VD/gyj0AnaGyiLvp3sbSb7/ftg8BWYyTMlayKPMK5yR/TkT6EKwuE9So2xD9KW/jhaZIphKDCamN3xW9xbvJpOtKvIVAI6RW0RR6myl7HoaH/hq7MvCYUodIS3ktAi3Ex+Twj+UuMFzpBgK3GqLTWYKozJ66WEiaolqNw/bd1bDaX/QpZb5zOzBEqrTWsDD805z0d1oPE9AhvTVzi5MQFGgLVKELB8V0G8iM8P3iU0cIED1Vux1AW1+zkVmGpEXLeJGG9mowzjKXGUIWOrcZQhYHEQyIRQkFIBaREVQwsNTJVKeAT1qu5nHqZMrOJqFE3bQ+8GJSkPjOfnpF4iJWbVnDxZCfjwynKqxdOi7uux+FnTrH+3pZ5ib/vSYq54qxBMzWRpeNcH+u3N6Mtc0RMSslkJs93XzzJjw6fY2g8hbOEQTJsmzy+e+2SiL+mKTz5wa188JGNuK6H4/o4rks2WySdLZCZKuq89pfJFujuG+fYmx14iySXC0FKn6KfYqJwnv7sqwxkD5P3RrDUCupDD1Mb3E/UaMOTeUZyJ7mc/GvOjX+FgFZFffCRd/377xaO4/HCwfN8+euvMjicmnc5VRHsureVL33ufhrry277WD46luar3zzEkeMdNyX9AdtgRV0Za9qqaW5MUFMVJRaxMc1SFqVYdJlM5hgYmuRK5wjnLvbT2TNKJruwu97AUJI/+/Yh4rEAm9bf3uyvEBa6/cElfUdKycWBYY5e7mZrYx1f2H/vtOymMRFjKJnhKy8dm7H88Y5eDrd38cHNa/jC/nung3LNFWVYusbv/eUz/NWx09xTX4U21c0cCSHT4O8c2ENrVfn0+nOOw4nOPo5f7eWDW9YihCBgGgRMA9fzMTQVVVGoiASpjr17K+yJVI6eoYklE3/b1MkXl8axbgWpTIGeoYlbIv6Z/OxrT1NUHq7axJ7EGgKqiaao1NnlfKpx35JlPnAbiP9L/Rf5r+demSa4i4GpatTaUe5NNHJ/VSsbyupImKFlkfIsBppS6uD70/63GSumOTvZhyd96gNlM5pdKQhaQ1XYqs5oIc2V9BApN09YN1kRnOmXLmCGv6ov/XlrG6SUjBezPNP/9i1t//byFpqCCS6lBjmX7KM9NcjR0cv4SBJmmD0VbSi/oN0gDbWGFfHfI2RuZjJ/kILbjefnUBQLy2wmZj9IWeD9SFnE8UdASgyt+h1rEZQHPoSCSbr4NkW3F9efmCoYVjDUakytgZC5jXjgfVha0x1t4mUoAfZW/i1UoWOpUcrMJvJeEik9dCWAqYZR3rE9qtBpCe2lLrCZ4lSxrq7YmGrpofu+mt+bMwqvCp1aezNV1lryXgpvygZVUwxMJTxD9y9Q2Vj2KdZEP0jMqKPoF1CEiiY0bDXG3srfIe8l5432A1Rb5Xy84X18v+cF/vDitwioFqpQKPouRd9BCMFjNffx/po9Czrp3A4oQsNQAgTVMhwvS0CLowkTMfVcEqjUBDZwNf06Ep8a+x6G8xdRhIqUCpowSTtDBLQyXL9AX+5tWkJ7EYt8zDpzSH2soMkjn9rNt/6fV/j6HzzNQ0/uoKYxMafkR1EVdLP0W57nMzmaxvP8qa60pfezqTxSSjbuacO0ShMpKSW5TIHTr7fz5isXSNREsYMW0bIg6jLpmB3X5wcHT/O1Z4+TKzjoqkIsZKGp6qIyGyHbvE5GFgkhBJapY5k3n0CWovKS4yc7OXGqC2+RdpBzwZcuGaePodwx+rIvM144i5QeMXM1rZEnqQrsIajXzpD0NIQeJWI0c2jgd+nNvEhd8GHEHMYFdwrXSP+f/sWrDI0sQPpVhX27VvKbv3Y/9TXvrqZsLmRzRb739AkOHW1fsIjXMnU2rq/jwP51bFzfQFk8gK7N75wkpcT1fMbGM7x1upufPH+aU+d6KTrzS4g6u0f5+l+9Tk3Vo1RV3Lnmk76UFIoulnFdDSGBjuFxMoUimxtrCN/QE8dQVTY31mLccC97vs9bXf14ns/9a5qnST+AIgSbVtRQHQ1zpmeQ8UyOisgUDxKwuqaCporrWX0hBFWRMLahM/GOrMIvcfsgKTlEjnrX7z9VKFPBojvs6nPNkvJmUIVCmRngnlhJyrOzoom6QGzB9sZ3CgqC5lAFpqIxWkhxaqJ7Wr5j35BGEUJQG4hTZoYYzE3y1ngXju9RaUVnFfZCyUnHVHQKvsNAbpILyX52J1bO2N8S6c/wtSsHeWv8BinKEmayCTPMvso1tKeGGMmneHXoPFfSJU37pvgKmkK3N9V6N0EIgaFVUxX+HIngx/BkGik9hFBRRRj1hoY5ddHfAUoPvdF0Ftfz0TWFqG1h6Y1UR76IL7N4fhZJASk9QJApSFQlSNQuuyXC7/lFXJnHUEIIoUzZ2paKg/V5SLEnHVw/N/2dkH69Y64u7FnR+rmPjYKlRrDU2YNSWJ9bJ1r6nkATFqE55EDvXG6sOEHKmaTcbKIjfYaYUU6FWYNEMlwYpt5uWfDaU1B4tHoXayNNXEh1MlKYwJM+pqKTMGO0hOqptysxlOXXOQsEqyMHUIROhbV6ajIlpsm4EIJaexMxvQGBwNbixIwV07IpUw1xbvInNAS2U/CTJIt9JenOIjfbnUPqkxxL0/P8GXKpPD/77uscefYU4XgQ0559PCpq4/zmv3gS6UtOHLzA+RNXSY1naF5XxxO/fj920OTs8Q4O/ugkTtHlS//iY0TLQ7iOx4lXzvPcd48w2DOG5/nEEiE+/MUHiJXP7PJ9u9A/luTHr58jV3CoKY/woT3r2LaqnrJwAHURWnBFEVTFl6+JnhACVQg0bXE9ThZCsniZI0P/lKwzgKFGqAnspTb4AOXmRkw1NqctoxCCsNFMUKsm742xpAHhNsNxPJ5/tRTpX4j0a5rC/j2r+M3P7KOm6vYXbPq+5PXjV/jRc6coLBC1LS8L8bEPbOGxh++hvGxxLklClLr3VlVEeN/+dWxcX8/3nj7Bj559e96iYSnh5Olunn7uFJ/5xE6MKfLsej6pTGmCHQlZ+L4klS1gGRqGrpHLl4IpEomhqaVMlyy9tqcyEalMAUNXsS2dXN7BcT0URRCyTQZHU7x28gr7t7cRC9vomoovJROZPAJBeWhmEy4hBGHTmCGn9SUMpzKoqkIiNHsMClsmIdNgMJkmU3C4NvoIIBGebZ1+reHecsnj3omQbVIWCeK4Xqk2aEqe5031/3ivEbB0yiIBCkUX179BQjjVMXqpKPouT/ce49Xhs6TdPKpQcH2PVZE6vrTyUSL60noKLWs+tyTlMWkJJdhd2czeqlbaIpWEdWvZ9bpLwbUC39hUB9/xYhZNUVk3hy4+YYaoteP0Zsd5c+wqAHV2nKhuz1pnS7iStnAVpyd7SLl5/vvFn5F286yN1GKoGmmnQHtqgGf7T3No+CIJM0Tec5hwFufqcw2KEDxUvY4fdL/BWDHDc/2nGSuk0YXKw9XrMZdZInE3QAgVTY2hzdNd9kZkCg5/efQUb3X1E7ZM/vGHHiAeLPnHqyKEqlwnO67v84Pjx5EM84V95YuKRr4To4VLdKRfYHPZ5zCnZCHd6dcQQqU5/OCc3xkvXKE9+RM2l39+QV/9xUwi3i2klCTdCTozlzAUi+bgKjJeiq5MOxPOGEEtzIQzymC+lzKjEl/6tKfP8NrIz9gY20Fb6B7C+tz7UJpkaLSE6mkJLa/ucjG4lhGZD4pQZ0zCrBuWt9U4K4I7GC1cQREaK0I7p+Vii4Ezh6tPPlOg/VQXumnRsPJ6psotzo5EOkUXkBQLLtKXfPgL+0mNZ/jmHz7D5vtWsXpLI5v2tGGYOj/48ovTEVNNU9mybw3jI2kuvdXFp//Oo9hBg2B4+QqBO/rHGBhLEbB0vvDYDj60Z93yuaS8x3D8DKowWRV7itrg/YT1JlSxCDmrBEONoislt6j3AiXSf44vf/3ggqRf11Qe2reGLz51H1UVkWWZpA8MTfL9p08wMZmdd5nKRJgvPrWXh/auxlxEZmcuKIqgujLKZz+5C8vU+c4Pjk2T9XeiWPR47uWz7L63hTVt1fhS8ubZbi5cHSQattm5oYm3L/YyPJZG0xRa6hOcPN+D43rYlk7QNplM5Si6HgFLZ1VjJVJC9+A4UsKOexp55rVzlEUD5PIOD+5o43zHIMfOdGFbBvdtaSEWthGAqpZMNtw5+nz4zCyaF5Q6YUsp55TYub6P6/uoijJrIq7dBXbAH394E/u2tJDKFkhnC6SyBVKZAulc6S+VyXPsbBejC1wry4kDO9ewcWVtabuyBVLZ/PR2prNFUtk8Jy/20j8yf53MjZgopnljrJ2PN+zhbLKbejuBJ32GChPod0vnXlPRqAlEuDfRyL6qVjbF60lYQbRb7DJ2J3Ataj+YT4L0KTdDtIQqZi1nKBprIjUcG71C1iuiIGgLV825b2VGiKea9/Dvz/6YsWKaM5O9/OtTPyCmB6blDEknR84r0hAo5++ufT/P9J26JclPa6iSLWVNPD9whquZEQCag+Vsjjf+wkb7bxUh0+DTuzaRCAV47kw73jwNkQCQMJ4p1a3caiDBlXly7tj1yDEKdcEdLDSYe7JA1h2dFQGeDZ+u9EFMNUp9cHkKjn083hw/RFALM5DvwZUOg/keqqx6Cn4eWwax1SA5P0fKnaTMqCCqx7HVIA2BFkz17nSSud24VltRYbXdfOF3QEo51bhr5vlO1Mb5h//11zHFzbt0qpqKHTLRDJV19zZT31pJejJHLBEmOZ4pZcdMnUBoph2yUASBsEUgaGJYOpF4YEbR73JgPJUlX3RoqS1n17oVv7CkHyBmrmJP1b/H0ipmSfIWgiI0Nid+FwVtqufEnYXjePzslXN8+euvMjw6fwNEQ1c58MA6Pv/p+0iUh5ZlvHHdUtbh3MX+eZcJBU1+9cmdPLxvDYbx7qlNKGjxsce30D84yXMvn523nqB/cJJnXzpLS1MFjutxur2f9+1aTXUiwngyQ2f/GJ94ZAtHTnXy1oVeQgETX0oqy8K8fbGXmkSUAKXXHb2jdA+M01KfYGA0ycWrQ+QLDvdvW8npS330DE6ysa2OvqFJPrDvep8URQiqo2GEgK6RCXxfTrsoSSkZSWXIOdcnL4oiaK0sw/Ml7YMjbG263nxUSslQMs14JkdlJEQ0sHQN+WwIfLk4x67FoDwapDwanPW+lLLUhC1X4B//0Y/eM+IfC9vE5gicXJMQZvNF/t3XXlg08Xd8D1s1WRttYDA/QVCzWBet5887XiDj5mcoUxaD20b8VaEQNwKsj9ewt7KVXZVN1Afid4WUZzGwVYP10foS8QdWR2qoMGdLJFShsCHWQKUZwUeiKyrrY3NHKhUheLB6Hb6UfOvqYToyw+Q9h153fKphkkpQM9mRaOUzzfexKb6CtJPn+FgH5UYIdQmd+UxV50DNPRwcujBdILynYhUJc3lS9csJ1/NJ5wuEp/x+J7N5AoaOqWtkCkVURcHSdVzPI1Uo4PuSgGlg69evtWyhiKQUnUhPFcuELQND00rNggIWkYA1J/X2pSRTKFJwXPQl6ohhSjPtjdKfPYHEw5NFrk0bHD9LT+Z18t4E5eZqKu31N3xnjIHsCTyc6XRgyZP/DD4eeXecvDdB3GyhwlqL4+foz75Je/KnhPQa0k4/EaOBGnvrbb3nPOnRl+uk1m7EUEx0oZFxUzQHV6EKlZQzgamYBLVS9FsIQUiLYqtBEkb1z8X9/15D4uHK2fpYVVNI1MSwF7BEvRH5XBFVLTkBCVGy51OURabg7+Bp8qXElxAJzLau+0WDroTQlaU/h4VQCMyqR7pVLO3kLpb0m4bG+x++h1//1G7K4svXfKxvcJIXD16YV3OvKIL7d6/ikf1rbwvpv4ZoxOajj2/m1Lle+gYm5lzG9yVHT3TwoUc3UlERASSaqkzdd1N2x2opci6lxDI0EAJzajstszRumYaG70tMQ6etsYKtaxuwTZ3uwXFCARPT0HFdDyFKJNrzJarC1H0uWFWdoCoS4vX2Li4NjrCyqhyBYCSd4YUzl2fURChCsLO1gcpIkKdPXmBn6woayqMoQpDKF3j65AVG01k+vv2eGfUCtwJFEYQtg4v5Ar3jSWpiJctmb2pycjvVH0IIVFVg6Notd/mWUpLKlXhFJPju1CnZQhFDU6frka5JCA1dW5IblK0aWKpO2smTsKIcHbnIaCFJ2s3fUv3mu75DbE1nQ7yW3RXN7K1ayapoBRHdvqukPFDyxpV+EomLEEEUZeZs0VBU/o+2B/l0024ALFUn+g4nnJLdXZI9iRX88a4vcK2mosqKTn8OeaR0UZTw1Ho1DtRuYHNZI2cmeriaGSHjFtCEQpkZoi1cTVu4isiUVOih6nVsiDWgKgqV1tL8bVdFaogbQQbyk4Q1i/1Va5Y0ebhbMJzK8McvHOFLD+3E8Tx+/4cv8MTWtTy6YRXfOHySlZXlbGuu44fHz3Kiqx/X86gvi/LkvfeUHnZC8JO3LzKYTGPrGm929iGAz+7dyr3NC8tJfCk51T3Ad468TTKXp74sSt94kobym0uIrsHxs5wa+yauLBA1VjCUOz1F/ksForoS5Gr6ZYp+epr4uzLHmfHvUPBSxM1mhvNncfwsUnr0ZF5nIHeSusB2fByujr7M9sRvE9KrkPh4sogqdDTFQl2CtAQg5+UYyg/iT0WaVaFSbdVg3FDEq6BSbdVTZzcR1eOEtAid2cv057oYzPcSUIPk/Rx5L0vWS+P4RVShIfEZKQ4S08vR/zeQm70b+NLF8xcujPNcn2wqR2oySzHnYAYMQtEAgbC1qEFESolTcCnkHDzPp5Ar4hRdNzQa6QAAIABJREFUNL1U9GgHTTLJHBMjaUJRj0DIWrYuv7GgjWVoZPLFBQsof3FQinZ6soDEY778oUBdnAxoiVAVsWiisVjSb5k6Hzywgc98YhfxaGDZSL/vSw4fu0xXz+i8y1RVRPjQgY2Egrc3UyWEoLWxkj3bW/jrH705b8R6YCjJGyc7+djjW2htqODFY5dIxINsWlVHTSLCT149i+t6rG+tYTJdkmYaukrQNrEMHaGUXpdFA9RWRLnUOUwkZLG+tYagbaAIgaGrICEcNNFVlReOXGT3pibCwVJEfkUixkfvXc+fvXKcf/n959m7qhFNVTnbO8RIKjPt8nNtv1ZWJfjs3q386UvH+Cd/9Qy7VzZi6xrn+4c53N7FnrZGPrxt/bs+rwLYt7qJ19u7+E8/Pcj+tS2YWimI98TWtaxYwth6J+BLydtX+3mzvZffeHQHgVvoMXQNz524xIamGlqqFxe4mQ9RPcCH63cSNQJs1Bu5mOzl7Ymr3Fexbsn6frgNxP/RunUcqF1LhRW6a6U8UnoU8j8jn/shQgQwrYew7MdnLCOEoMwMUbZghNwhk/pvGOZOGoMPzblEsXAUz71KIPS56fdUoVBjx6ixb36BBzSTxtCtPbw60sOl+gBgbbSOVeH5o62en0YR9rzFqr4s4vmTqEoERZhT75WKnBRR8lZfLtiGRs5x6B2fJJUvUHBdLvSPsKOlgc6RCTavqOXpk+c51tHDr+/bRjxg86OT5/nKy2/wjz64n3jQJpnL89zpS3xyx0b+1iN7KLgu1ZGbFwNOZvP8+cHj1MYjfPH+e+ken+QPnzm0JOKfdHqYdLrZWfG3ieh1mGqEztQrAGiKSW1gGyP5czO+k3L6GS9cYUfl7xDVG+lIv0B78qdAKeofNRq4p+xXphxC/jPjxSuUW23UB3fRk3mdansLTeH9SzjKUxZwqQt8s+vreLKUJQprEb7U+ttUW9etIFWhsiW+h/b0WZLOGKvDm9gU28mVzHmiepwKs4bBfKkBzHhxhAlnjHKjkrWRzVxJn2d9dOsvif9N4EsXV85TREjJe//Q0yc5/Mzb9F8dwXVcdEOjrqWS3Y9tYs9jm4jEgwggGLYw7NLxFkJMufroZFN5nv/rY1w528vEcIoffvVlVm9pZM/7N2FaOis3NHD8pXN8/T/8mKqGcj78xf1E4rPT6bcDLbVlVMVD9I8mudQ7QmV8eSQidwOklDh+mr7sy4zm36LopWbVclxDxGhmffxL3O7nq6ap08WnC8FxSpr1L3/9ICNj85N+29L58GObeerJHUTC9rKeu8lkjiPHO+adIAoBO7c109pcsSzbYRgqO7c28+xLZ0mm5p6cO47H22d6ePyRDeze1MzYZAYpJeWxIA/tWMXYZJaAbWCbOo7rldpyKYK2FZVTbkOlyLjvSwxdZXSiJM2LRwM8fv89BCydTatLndltU+fDD20kmyvOcKgyVJVf2b0JS9d47nQ73z9+lqBpsLOlns/fv40/fObQdBYdQFcVntx+D+WhAE+fPM+PT57H8X0qwkF+ZedGPrb9HiojwRnLJ8LBOZttaopCIhyYMbm4EY9vXsN4JsdL5zr4yyOn0FSFykiQAxuWLou83RhLZTnfM0S24FBXHmF1XQUbGmt4u6Mff8rt6WzXIAPjKVZUxojYFrmiQ0t1GZf6RogGLJK5Ap1D49SWRVhZm+BS3zCD42muDIyyun62ZHypUBWVlaEair5LwXf4VOM+pJTYqnFLwd13Tfyr7TtnY3WrkDJPPvd9DPMhTGs/Qtx6EaTvD+P7mXk/142t6Po9t7z+W0XBc/lp39vkPQdVKDxcvY6ANv8EYjzzXaL2B9DnSSV73hijmT8nZN5PyCplQfLOOQQqtrG8+xcyTcpCAfrGkwwlM2xrqmdwMsXARIpssUgsYHGso5c9bY1sbSp1JPzQlrX88+89R8fwGPFg6QFZF4/w/o2rltQkZGAyRd94it/Yv53WqnJq4xHuqZ/fAWcu5L1JdGFPO/IE1ASasnDUoOAlURUTUykVxgXUcrSpCZdAJaTVlLzkpURXAtMZhHcDT3pcSl9ktDgy/Z5EznLpEkJQZlSwo+z6xCKsR6my6mYs1xSc+RBfFZ67D8StQkqJJ4t4Mo8vfRShoE31W/h5J40+Hu48Ef/xoSTf/YNDHPrxSeyASaImhmkb5DIF2k91c+6NK3Sc7eWpv/8BwvEgH/mNBzCt0vVmB00+8Tfehx000Q2NHQ+vZ+v9q6fXbQXM6e6jiZoYn/tHHyI1kUE3NILh26HtnRu1iSiPbl/D1555g2/+7E2q4iGaa8rvisLB2w2JR/vkt7kw8TUAVMXC8VIIoaEpNp6fx5V5okYLMWN5iJCuKzeVwBQdl5+9fO6mpD9gG3zsg1v4lY/sIPyOepHlwOWrQ7RfHZr381DQYtfWlmnpzO2GEILmxgqqK6PzEn+A9qtDjIymaWwopzpxnRfpmkpt5fXs/Y3bGbDmHhdqKm5YfmrCduOyc2nIr/XXeWrPFj6weQ3ZQhFdVSkL2Riqxu9//MAM6aoQAtvQef/GVexZ1UgyW8CXkoChEQ/as+xy19ZW8idf/Nic0p8ViRj/5deeKMmY3gEhBBHb4jcf3MGT2zeQd0o2zbau33IDr9uJ4ckML7zVzoGtq3n+rXYqo6F37HspzaMqgmfevMijW1Zx7FI35ZEAr5/vYlNLDYfPddJSXc6zb14k77gcvdDF5pY6UrnCbalrkFLSkRnkp/1v0psd42MNu6izyzk72c3Wshb0JTSKhGV29XmvIaWP67bjFI/gOhdR1SYKeYlp7sNHUiwcxjD3oCgRfG+EYvE4hrkXIQL4/ghO8Q18fwwhghjmfShK9IZ1SzyvE9c5jWHuRwiLQv5FfG8AVWvGVPcBpSZSxcIhVLUS17kAQsEw96CqtVPbdxHXOYX0s0hcNK0Rw3xwSbaRUkrOTPZwbPQKAA2BMnZXtC0YM3K9QdKFg6hOlKC5C4lHtnAcgSBgbkNTq7D01cipKKTrjZIvnsc2NgICz58kWzgOgG1spOh2YulrkbgUnCsY2gpyxbeQ+ASMLajK/B0H3wlNVWgoi9I5OsFENsd9bU2MpjOc6hkgalvYhk7RcQmZxvQ+2kZJ05e5oY13LGDP8C5eDPKOi0QSNM3pbQmZS8vAGEoQVxZwZcnSreAnpyPqC33Hk0UcP4sl4xT86x76At5h+Sdm/C+Egs/SfcYLfoHL6ctL/t57AU86DOZO0J1+lYliB46fQ1eClJkraQo9RLm1ZlFdckfyZ8m680sG3isU/RRZb2TW+77n8/xfHuHIs6d48KPbeeRTuyivjqFqCq7jMdg9yk++fpBXfnic5rW1HPj0HuI3eIorqkJZ5fXX1SvKZ/3GNQghCMcChGO3xx3Kl5KxVI5McW5HlAc2t9I5OMaLJy7zL//nszxy7yrWNVVTGQthmdqCclEhxLTv/92OnDtMZ/rH2FoV68p+k6jewvGRf0tYb2Zl9JOkip1cTn6XkL6C1ugnWI5sqqapmHP0friGxZL+UNDkE09s4xNPbCMYWH7S7/uSU+d6SaXnzoYB1FZHWdmyPNH+awgFTZpXlHPx8uC8y0wmc3T1jtHYMP89diegqQoV4SCEZ2brauNzB2mvTRii80Trr8E2dJoSc5sMmJrGisTCWXFdVamK3p01h/WJGJtbanm7o4+JTJ7EDZmOdK7I+Z4hFEUwlsyQiAbRVIW3O/oJ2QaqotA1PEEkYFEWDpDK5gmYBltaaznbPXhb7ua0m+evuw8T0wOYqsZEMUOVFePFwbdZFaklbiztuP5CE/8SfEABBAgDIQwQCr4/Ti7zDTR9LYoSwfMGyGW/hW5sBpknnfoPIEHTV+L7I0i5CbhO/D2vg0zqv6EbWxHCBARCCeHkz+A4ZzCtEvGXMkUm/UeoahO6sR63eJZi4QiR2O/jeyOkk/8Ow9yH9EfJ535EOPovWMqDX0rJUD7Jn19+lfFiBgXBY7WbqLYWbp7iySwCFcftI8NruN4oEh/fz+D5k0QDT8xYXhEBpCzgeD1Yci3J3LP4MjvdGMv3kwhh4vsZil4nBfcSvp8pFS16I8SDT7KUy21lVTlvdPSgKQprays53TPA4fYuNq+opSxoUxEJ0jk6QdH10DWV3vEknpRU3VDpfyvjQDxgoykK3WMTNJRHyRSKDEym5n1ozoWo0YCtlnFh4ofEzRb6sm9M9QSAnDvGRLGTlNMLKAzmThHR6wnrtYS0Ks5P/pBys43+7An8m0wWoGQvGdHr6M0cmerOW0OFteam35NIRorDDBfmH8juFkgp6Uy9wBsjf0TuHeR4IHecnswhdlT8PWoDO286+J+b+Eu6MweXc3NvDVLOWdybHM9w/GenWbW5kV/9e48Rr5x5HSZqYiRqYvRfHeHoz06z70NbCUWXx9Z1qUhm8vzzP3uG8Uxuzs81VcHzJZ7vc65riIs9I0QCJrapo6nKgucyaBn8qy+8n+aad6edvRPIecPk3GE2lP1NGoIHADCUGLoSIGasJmasJqjXcGTwnzKce5O64NwWv+8GuqbOG/EvOi7PvXSWL3/jIKNj82ezwyGLT390Ox99fAsB27yl5+tSkckWuNA+uGCzrpXNlURCyxs5Ng2N2qqFiW2h4NLZPcrenSt/7jOQ/7uhZ3SS9r4R8o5L0DJIZvPkig7JTJ50vshoKsueNY2c7RpEUxVW11Xy/FuXeGTLKhKRAE2Vcba11WNNGYyc6xnmcv8ow5Pp2zKPTzk58l6Rj7U8zI/63gDAUg086eP4S6+R+oUm/kIo6PoaVLWOfO5pTOthDGMzAK57dZ5vSRznLTy3h1j8P6Go5Tc4YpSImO8PkEm9iG5sxw48WZpMAIaxC8/twimemLlGWcQOfBjDfADXPUdq8p/j+xN4Xg9S5ggEP4Pvj1EsHkPVVszZ0OUaujKjjBczWKqO63v0ZMf5ce9JDo9cAmBdrI7H6zaj3uTBo4oQtrEJX6bJFI5ScC6jqZUoIoAyhwOFothoagXXruJ88SyKEkJVwqhKBENtIO9cwPdTBMx7mcj+EEUYqEoUVQmzVDPM2liEkVSG1spyogGL+rIoz5y6xOOb1hA0DR7ftIb/efA4X3nlDaK2ybGOHvasXEFD2c21+P0TKS4OjPBWZx/DqQyvXOigLh5l04pqqqIh7m2u51uvv0XnyATpQoH+iSR1SyD+hhJmY9lTdGUOkXVHaQ4/RMFLoQqTpNPDcP4MtpZAIBjKnUZXbOJGKxviv0pX5iBZd4Sm8H4K3iS6EqDCXj8dzVaESnVgE7ZaiiopaLSGH6Uz8wqTxU6MxbqHSOjN9pB0Fmcn9l6i4E9wduI7s0h/CZKk082p8b+g3FqDpS58/l0/j7OAVO9uQzadZ7h3nN0P7CFSNve5La+O0byujjNHL1PIO4SW5gmwbPB8Sd/IJEPJxR1vz/cZT+cYT889UbgRYduk6N56N907CV8W8GWRkLECIUrOLpoSoOiV7j0hBDFzLQGtiv7sq9QFH+B2R/11fW7iP036v36Q0fH5z1M0bPPUx3fyxPs3EbBvvdhxqRifzHK1e/4MnaIIVtSXYZrLS2UURRCPBdBUBXeeSYjr+QyOpPB9iar+kvj/PEHXFM52DbJrdSNB0+D1q51EAzZnugbZ1FxLS1UZvWNJdq1egalpVMVDWIZOU2WccMBk15pGznQOUBYOsL2tgS0ttVzqG2FjUw1l4XcfiDFUDYGgJztK0XPI+w7tqX40RcVSl14/9wtN/JeG607tnjeEopYhlHJKUopr/ralQuF87seATyD0JQQ3P+iKEkJV66ZstwKACtJFUUtFH4X880iZQogIiliYuDw/cIZvXT2MKpSS7aSbJ+uV9N41dozfanuIGvvmrdJ9maHgXsbzJ9HUSkDB0OoxtZWoSgwp83h+ChQF388BEt9PgdCRMoept6IqZVj6GlQlCihkCocQwsTQGjG1FlQljKWvQ1UiLPVSKwvavH/jalaUx7A0ja2NdQxvzbC2rhIhBNua6wiYOofbuxhNZ3ls42r2rGzE0kvnY11dJZXREPocmuGxTJZzfUOoisLO1ga6xyZJF4qsqa0gYpl8du9WXjp3hZ7xSdqqyvnSQzsRN1wHN4MQgohRzz3Gp2Z9Vm61UT6Pz3vYqGW98clZ79/oz68IjcbQ/TN+K6hXsC725KK27Rpc6dKebp9287mbkXGGSDrdCy4zVrhIstiNtYgC+p8nCARCEXiuN9WafTZKskMfRbkVY7flg6Gp7FrVOEN+d7tgmdqMQsW7GYowUIRO0btuB2mrCcYKp/FkAU1YKGioikXOXZ4MnGXq077u1+B6Pi8evFCK9C9A+mPRAJ/95C4ef2QjtnVni/QHhiZJpefX1VumTvUyNQy7EUIIAgETTZuf+ANMTmYpFN07Ojn6Jd4dNFWhrbaCJ3asm64xemTLqhnLPLrtel3UwHiKw+c62dJaSzhgoioKG5tr2Nh83RBj5+oVt3Ub43qQHeVtfKvzFYYLSQITJiHN4oN12wlpS6/Fek+I/7UmBp708d8xmClCQRcLp3lvD9SSrZosTjV9GENOdUBVlGjJ+lOmESI8FfGf2k6hYFkHQChk039KOPJ/oaiJm/zWlNTonVug1qJqjRQLB1G1FYQifxehLNyox5M+SSeH45eIgCoUgprJqnA1v966jx3lrYs6diFr75QUxyds7kXiks6/SqZwhJC1F8frRcocrlek6HUipYcnUyCh4F4lbB8gkz9EpnCEoLUHQ23ANjahijCKCBCxD0yvL2juQlWWpnsMmAa/unvz9OumijhfevA6AVYVhXvqq7mnfu7i5J2t89946+uqWF83f8FueSjAk9vvfIH2nUTWy3I12/Feb8ai4Mkinj+/xhfA9XM/V5H8xSIYsahekeDM0SsM945TvaJ8xv0tfUnv5SHa3+qioa0KK3D3EI6QbfI7H92LtQxkUcCchYR3Iyw1gaWWM5J/ixWhxwBBzFzN1dSPGModpcLeStYdIuP0EDFal2UbAlN9Ha7B932OHL/CV24i7ymLB/n8p/fw6IPrZzjI3Cn09I1TWGDiqGkqiqIwtsDE5XahUHRuOramMnkmUzly76hr0TWVkG3OmnzNBSklhaJLJj/bwEFRBJHA4ux7ARzXI5UtzOjjoSoK4aC5ZJ/7a82xMvkio5MZBkZTjCez5AoOnu+jqSoh26AsGqQ2ESEWtrHNu994oT4RJRENzupQPB8iAZM9a5tIRAK33CtgqVAVlQerNtIaquZKZhCBoDVUTUMggfJeuPosBr6UZN0iI4U0Xelx+nOTjBYypIp5ir43w0VkfbyWDzdswFCXO3UXRVFi5LLfRdPXUyy8hpQuIND1TeTF/yKT/mN0fS1SZtCN7aWoPQJFrce0HiCT+kMy6a8QDP8O4OE6F3Hddnx/gGLhKKq2goVStlLm8b0+dH0zQgRxnSuoSjVCnZ8k76tcjalojBczOL5HSLdoDVWyIdZAhRVZdP+EiH1g1nslHf41NGAbM11ZbGP9jNex4Effsc5Hpv/X1HJiwY8salt+iTsLKSWD+QFGC3dfketc0BSrJI3w55claYqFrtwd2vbbiXAsyO73b+B7/+kIX/39H/DQkzuoa63EtHRymQJdF/t59tuHGR9O8qm//ShWcPmceJYKIUpF9/M5l/zvgoBWSZl1DxmnB9fPoilBEtYmTDXGiZH/l7i5jozTR8YdYGX00yxHcW+J+Jf+96Xk9Lk+/uRrrzIwNP89lSgP8RtP7eXh+9cum2POQpBSMjScwnHn1zBnMnn+6Ksv3pHty+aK5AsLy8sKBZdnXj/PT14/N8PNZU1TFX//Vx+Ys5vrO+H7kqcPneU7z52Y9Vk8bPN7XzhAY/XNO3lLKXnrUi//8ZsvzziGLXXl/KPPPUx8kRIUKSVFx6Ojb5SjZ7v+f/bePEyO+j73/dTW+96z76N9l5AAWUICxI4N2MY7xj6JsZ1jJ06eJI6z33NzcpJ7buLz3JOcOInjxNhxbGNDbAzYrAIkkJAQAu0zo5Fm35ee3teq+t0/ejRSa7pnRqORNNi8z4OYqa6uruqp5f19f+/3fTnc0kv30ATxZIZ0Noeum5hCoMgSmqpgt2p4XXZWNJZz49pGbljdQIXfhSzPfdb8asKqqVPOSXOBw2q5LG//+UKTFZa6q1nqzs8sGMJkJB0maPVgkReRq49uGgymohwc7WL/SAct4SFCmQRxPTvNMvAc7s2muK9ubdHXLoQhTAxz+jZUWZlGfiVJw2a7G0Uuu2CZC6f7t8mkX8AwerDZ78EwRpAkO5LsxuX5Gpn0y+RyLciyBwkrIGOx7kBVm5AkFw7XF0infoZphgBBNnsACQVVXUY2ewALoGqrsdruRpLzEgRZck/+7iCbeR1ZrkCS3fnG2ewedL0Nt+erJY97paealZ7zU0qmKUiEE+gxg0is8Cauaiou38IGqySiKbLpLLIi4/I6UC7RNec9XHsIBL3JHpLGu6NC7lSrCFiXMZR6u+Q6Xkszbq225OvvVkiyxC0fup5wn+DlJ97kyGttOD32KVefRDSFzWHlg5/fxZZda+ZUUXwPVxcSKqt9n8NER5Hz4VwOtZo1/i/SMvGvjKQOoUhWmlwfoN51xxXZB4fdgkS+v6Cza5R/+u4eOnuK9czk4fc5+I3P3MyunSvn5P9/JaDrJpFoakY7RMMUMw5erjZ0w8Ru0Rgej5G8YKYiZxhEE+k5Ef9MTudway9dg6Fprw2HNDr7x+dM/M/2jdHeO1qwvKHKj3OOg3HDNOkenODp107wyuEzDI1HMc3ifxDDFBhZnXRWZyKWomswxKuHz7C0rowP3byeXTcsx+tc+HC6X1VkjBzPD77DB2qup/wSw16vyBWdd5qJ80L/KX7Wc5wzsRHSxsI2YvXEQ/ys51jBdq2KygP161nqKQxMkCRbQaBWflm+8VfTijugqGozquuRacvtjgenflaUSpyuL0797nL/VtFtOV2/PvWzrARxun493yuQfhGrdSdW2z2AQTqlTWsMng3pRJq//53v0HWqb9prK7Ys4Xe/8TnUBbxx/+h/Pc0bz7xNsNrPV/7u16hdemke9+/h2kMXOdrj7SU144sNFtnFOv/DpPRxorm+yeTTPCQUXFo16/0PY1NmfxgWh4QmO5C4loNYk5yZKji2c/AGXHz699/Pqs3NHHr5JEPdY2TTOewuK/XLqth2zwbWvW8Z1vd0xYsSkiThtS67aJlMnesOArY1JPUhNNmFS2tAla5MAu65iv/gcIR//u5eTrUNzrh+wOdkxbJKtGtY2MnmdKIz6PsXI0xTUFvhxe20FRD/aCLNyESMhjkQ9lgyQ+fAdNIPkM0ZnO4Z4ZbNs0t6dcPkbH/hrK4sSzTXBmf9uwohyOoGrx/p4NGnD3KmdwxzHob02ZxBS+cwnf3jvN3WxyMffB+NVXO39v5VR0d8iN5E8QF6ysjSER+eJpefCxac+OumybGJfr7Vto/9ox1kFpjwn4MmK7w61E5r5HwzlCLJCAG/s+bWq6a9mj9kbLZ7SaeeIpc9ikAHkcHufPiStiJJEppVwzQFqViabDpLIprCNEx85Z4FCY+4EOODYXpaB0gnM+QusWlPCIGJQdrIENfjhHMTxHMxUmYa3cwhAFVSsat2vKqXoLUMp+LEIlsW3Y1CCIEudDJmhkguQiQXJqbHyBoZdGEgSzIWScOhOvFqXnyaH7tiv6bHIhAgYCI7QV+q55rsw3wgSRLVjhu4ufov6I2/TiTXjW5mUCUrHksD9c6b8FuXzeiGNROssoct5b+FV6tf4D2fOzJGjHfG/4WJbHvR111eBzd/cDPvu3s9yXgaQzdRNQWnx46qKYvu+pgN5qSDj2kKvE47lhk85i+EEIJ4KotpmjjtlneFj38pyJKCS6vHdRXOO4fDQiic5Fv/8RpvHe0u0HwXQ2fPGP/xxEF+89dvxb/As8Zzha6bpIro3Bc7yn0uynxOhkOxqWXZrEHP0ATXr5696bN/JMJ4pPhsrGHmyXw2Z8wqb8rqBl0XDSBURWZF/cyZB0IIMjmdn+05waNPHyQUTc74OdLkPzOdUumszgsHWxkOxfjqw7tYPss+vIc89oyc4ES4h4B1uqNbzjQYz85vtmtBiX/ONNg7dIb/feoVzkRHrmg9scLmZlt5cwHxN4TJG6MdPJS5ftEnCkuShNV2J5plI8KMg6Qgy36kWVx9LobVYeULf/1J0okMuazOSM8Y//C7/85gZ+mkw6sNIQRJI0lvsofT8TbOxs8wmhklZSTJmll0oWMKE4FARkaTNayyFY/mpdnZTI29DuUSAs3OQZUUVrlXU2FbuFkJU5jE9BjdyS5Ox1rpTHQynhkjZaTJmhkMYWBiIiGhSMrksdjwah7q7Q2scK9kqWsZQUsZinTlCFt+kGWSM3PkzCzRXJSx7BhjmTE6E2dL6vszRpY3QwfwavN3x5GAOkcDy10Ll0IqSwoB6wr8luUYIouJnndCkS5/IKXJDipsa/FamhZmZ+eBjBHBrvqZmIHnSJKE1W75pajsx5IZ/vt3X2QiluQPP3Uba5uLN+lfjFQ2x//5yWt0DoX48ge3c93yuiu8p5ePfOJ0hniul6Q+kM9rKMGSrIqfCvuNC35f0A2T/3j8AHvfaJ/RE/8cTFPw6r42KoJuHv7Y1mnNwVcDpmmi64vfdexi2KwaTdUBTnYMTS3L6jo9Q2FMU8woxzOFoHsoRCxReqajbzhMOJaiMuiecT8moknGwoVhbE6bZVaZkG6YPPPaSb715H6iiemmCqoiE/A4aKwOUBlw551tFJlUJkcokqB7aILBsSipiwqDQsCR0/38r++/yp8/che15d73yP8scKl2PtG4g3XexmmvJfQ0P+jeO6/tLhjxN4TJvuGz/M3xF+lOFJ/X0hFpAAAgAElEQVSmWkhossLW8iae6HqHmH7+5OyMjdMaHlr0xB9AkhQUpZrLURjIsoT/gjhwl9eBzbE4bO6EEKTNNK3RFvaPv86Z+BniemxGiYmJScbMkDEzRPUofamZbRxngk228V+aPrcgxF8IQSQX4XjkKAdDB+hJdpM0SldCBPkZAd3QSRkpwrkJupPdHAwdoNJWxWbfZm4IbKXCVjmvQU2x/cuYGZJGglA2xHhmnNHMCEPpQYYzw0RzEZJGkrSRnvH7T5spfj749GXvz52Vdy8o8T8HSZJQJSuwcOe4IllQpYUJABJCEBmLMTEcoXZ5FYZuYLNbkWbR3suSiiKVbsw1DJPQUITutgEmRmNY7Ro33rEOm8NKLquTy+Sw2LQFlfVdSZzz+B+NJEjnLm1WeCKe4njHEC3dI+8K4m+IFC0T36Y3/jy6SM1YbQ/aNlBhv4GFbvB99fU2hkej5HJzD/vJ5QyefPYI5WUu7rtzQ8kAsCsFUwj0GRp7FyusmkJzTRDpgiq4EDAwFiGdzc3Y7J7LGZzuGcWY1NFLEnicNuLJzNSykYkYIxOxGYm/EILhUJzwRVKpyoCbgNdZ4l35Ad+bJ7v59tMHp5F+SZJoqPRx97ZV7Ny4hMqgJ59cO+kwJASkszkmYimOnu7n6ddOcrS9f5r96ZHTffzbzw7w1c/chuNd4PpzLXFrxTocqhWHOv15p8oKWwJLsSuXXghakCtZCEFbZJi/b9kzI+mXJQmrrKLKCookkTV0ksb8PJ4lSWKFt5Jqh5dY9Hx1O2VkOTTWzc1Vy+fscPMeFh5CCEYzI7w4/AKHQgeJG6Vj4Bc7cmaO9vhpXhx+nrZYK1lz/tPPOZGjL9XLQKqfI5Ej3FZxO1v812OT7Zd1A8yaWX7S/zgt0RYSeoKUkSQnFt4//VojL7HKTM0QabIVIQwUyUJOZJBRMNExzByqbEWdw4yAIltR5YUh/tFQnBd/sI/h7jE+/nvv5/jrbWy/bzN218xuOxIqqlx8nWxG582X3uFn//oqve2DpBJZqhvLWHPDUmwOK50t/fzkn3dz64evZ+ud63+pH6SyJON12jAMk+GJ2OxvWASIZrs4G30cr2UFtc5dWBUvEsVlaVYlwJVw9enpn18xLpHM8B+PHyTod7Fj67I520guGGb5KmRZwuWYfWB9teB2WVFVhfoqH3arheQFUqWhsSixZGZG4p/K5GjvOc9nNFVhx8YlvHG8a0pyk8rkONM3xvplNSW3I8gPNBKpQvJeXe7FW+JelB8sRPn2UwcZCxdKjWRZYuvaRv7rgzexoqG86HkgSeCwWXDYLNSUebh+dT2PPvMmT+09UeAqJATsPnSaG9c2cs+22VPmf5VxYdNu/tlnkDXzhRKrrHFT2ep53e8XhPhHc2n+/cxBWsND016TALdmY4W3gvX+WpZ7yqmxe3FbbDzbd5J/Pb1/3p/rs9hZ6a3k9AXE3xCC1sgw8VwGj2XxWNv9KkEIQV+qlyf6fkxL9NS7IiSqGPIzFin2je3jheHnCGUXzv7SxKQn2c2Pex+jP9XP3ZX34NVmD14rBUMYdCY6GEwPLNg+LkboIsuh8Z/iUv2ospWgpZ5Qtp9Vnp0cm3iOclsTnfG3caheVEljg+8eVGnmiogmO1CkhfEoj4zF8AScCNMkl9GZGIlizKFqKUvq5EzGRRDw9p4WvvM/diPLEpt2rqLvzDDZC6bR3T4HXS0DHHAcZ9OOlYtmxu9KQJLyZMgUgkT63TGwFZgIIVjq+Qj17ruRFknMmiRB5WQf2PBoaa3wWCjOv/3gdQJ+J2tX1lw15yhZlmdtQq2q8PIHv3kXPu/isPK1WlR8Hjt1FT48TmsB8R8NJ4jEUlQGSlfqh0Mxhi7oDbBaVLZvaKa1e3iK+Od0g/ae0RllQ4Zh0tk/XqAok2WJpTM09hqGyTOvn+Jk53Qet2lFLV99eBd1FX7m8oiSJIlyv4v/+uB2wrEUuw+dLng9ndV5fPcRtq5rxO++vKLXrwKEEPQmx3hl+Dg9yVEkJJqcFeyqXE+NPXDJ399lE38hBAdHu3hlqH1ad7FFVtgcbOCTzVu4vqwBv9WBckED3ltj3Zf12VZFZbWvimd6jxd8ck9iglA2UZT4CyEwdIPIWIy+9iHGBydIJzPIsozDYydY5aOqqRy334VmVYt+oUIIjJzB+FCYntZ+JoYjGLqJy+egbkU11c0VWEtoIoUQZNM50okMSEw25xX/MwghSEZT6DkDWZVxeuzIV7hpOZvOMdAxTE/rAIlIEotdo7q5gsZVtTg8s1+gQghGMsP8uPcxWmMtJWUlEhJW2YpTdeHRPFjkPDnLGHmJT0KPkzWz83KeUSQFRVKwK3ZUaX6n+Lm+hBeGn+OVkd0zynouB0kjySsju0noCT5U+yB+7T3Hg5kgMInpY6z33Yld8TKa6SBt5h+UST1M1kgiSzIbfffyxthjpI04Ljkw4zYtshtKVGAvFYFKL7mMTsfxXpKxNI2rarHOgYhLyKhFpD7JeJq3fnwAu9PKb/zlR1m9pZl//3+f4eCLx6fWcfucVDeW0ds+RDqZ/aUm/oZhEo6lkKBk4I4QgvFkksN9A6RzOivKg6yurLi6O3oBPJYmGtx3M5Dci0OtwqnVIJcYjOYHgM4rfg+w2y1s3dzER+/fQjSW5u+/tXtGW8yunnG+9b3X+OqX76K+9urcoxRZQpul6VuSoK7GT2X54pL2lnmdlPtcDI2fJ/GpTI7+0QgrGoufi2JS3x+OpaaWBT1OmmsC1Ff6Ods3PrkeeR/9VAZPidwO3TDpHCgsVKmKzPL68qLrCyHoG43w3Bst0+w6g14HX/jgNuoqfHMi/ecgSRIep43PfuAGjp7uZ+yihuW27mHebu3l9htWlNjCeziHmJ7ih917sSkWtpetQiA4MtHFj3te54vL7sZ5iem9l038Y7kMv+g7SSSbKlhulVXub1jPl1bupMZxZZo4ZCQanH5sikbqAsnQRCbJSCpGk6swCEuYgtH+EK8+/gavPfkWw92jk8Ran3LHcbhtlNUG+OCX7uKOh26a9plCCEJDEV76/mu8+sQBhnvGScXTCFNgsWv4K7xcf8d67v/iHTSsrilK1Pc99RaPff1pZFnmd//xEVZuWVLyGP/5a9+n7e0OapZU8nv/9Hk8gend3QsBIQQTwxGe+uZL7PnPg4wPTJBN51A0GU/Axcab1/CR374Hi21mTV7aTPHs0M9pi7WWJO1+zc8673rWeTdQY6vFrthQJgm6LvKa+P5UHycixzgRPUEkFy66HQBN0vBoXjyaB4/qxat58Vv8BCxByqxl1Nnn55iRNtO8NPwCu4dfJG2WbrRSJRWn6iRoKcOr+SaPRcEQRl7bnw0znh0jYSQwRPHKry503gwdQJVUHqz7CC515qatUrDIVqzy7KTPFOaMMiCLbLmsqqSENO8B11xgkWzYFQ+qrCEhYQoDXeTIiTQCcKlBNDl/TplF7DEvhlVZuPuTxaaxdEMDbr8TWVWoX16FPAd5hCRJqLIDCRlxwQxZbCJBV+sAt957K+u2LkOzqNMevpoln9fR3zmKfgka7qsJczL181wCum6eLxMZholuzLzfQuTJzNGzAxztGERRZKpKVE5zhsn3Dh+hLxxlTWUFle4rc8+cK1TJQZ3zDo6M/S37h/8Ai+xBkSwUG2z6ravYUv6nXAm5D+Srvk31QT70/uu4bcdK3C4bhikIR5N887t7iURTJd977FQfjz62j9/83C6C/is/OFFVBad95vtZ3vln8c382KwaTTUBjp89b5uazel0D00ghCj63emGydneMbIXXMPVZR7KfC6W1ZXx6uEzU8v7RyNMRJMliX8skWZwrFAK53ZYqa8sbtoghOCNY530j0QKlksS7LxuKRuW18zr7y1JEsvry7lxXSO/2Heq4LWcbvLyW+3cunkZ6nt5QDMirqfJmjqPLL2TMmt+kLvW28i3zr5AUs9cfeLfFR/nSKjQQ14Ctlcs4bdW33JFm2wlSaLC5satWQuIf8406EuGufGCdYUQdLf2829/9iPefvkEes7AarfgCbpweh2YpiA2EScRS5M6PYilSLVfCMFYf4h//bMf8frP3spbZpa5qV9RjaIpTAxHGOsP8fNvv8Lpdzr50t8+zOoblxVuR0AsFKf7VD+SLOUr/zNgqHuU7lP9IMC8gg4H8XCS7/zFE7z82H70nIE74KJpTR2qRWF8MMy+p95ioGMYb9BdctQvhOB45BhvTxwuKu+RkFjmWs591Q+w3L0CTSo+iPDjp8ZewwbvRtpirTwz+BSdiY6iA4lGZxMfrfs4ZZYyrIoNq2xFkqTLIq66qXModJCXR14qSfotsoUmRzObfNexwr0Sn+bHptjQJsmoQJAzc6SMFKHsOKeiJ3kn/Db9qb6iAwBd6BwMvUGZtYw7K+9Gky9NemKRLdxX/QBxffZeioFUP88PP1u0V8GhOLiv+gH8lpmr5DNBQqJyAZ2ULt62Jts4R4ycaoCkHqE1ugfdzKJIGqpsRQJUyTqn88Aqe0pqri8VyVia9ne6yKZzREJxzh7t5pO/fx+Kc/YBmSbbpxF/wzDJpnU8fieKWnwfxWQTpKopizbAq28kzMtvt5OZJDWpySbATE7nFwdaeKe9f8b3m0IwGo5zqK2X4VCMoMfBxqWFGmchBFnDYDyZ4ujAEJ/ZsonNdTU4LZZJZx1BKpfDME00RcGuaUhAxjBA5A0qcoaJTVWxqgvnuJXUhzk2/vck9WE8lmasin8Gjf98syhmh9tl45btK/jIfZtpqg9O6bRVReLOW9YwNh7nhz95sySRNk3B3v3tlAfdfPbj23A6rqzTj9Wi4vXM3HuTzepEokmgdNr9tUCxBl/dMOkbDmMYZlGim8nqtF2g75ckqC334nFaaa7JS3TOaeXD0SR9IxEaq4vfp4cn4oTjhbPUlQFPycbeZDrH3iMd07z6HVYLd9648rLyHBRZYsfGZp7b3zJt+61dI4xFElQFF9eMTc4wyBoGDm1xNB87FCtezcFQagKbYgEh6E+O41HtCCCpZ9BkBW2OCb6XRfyFEByfGGA8UziFE7Q6+fTSG6i0za9yeSnwWx3Y1cJpU0OYjKbPj3bPVbO/8xdPcOjFY8iyzLrtK7jj0ztYdf0S7C47QghiEwnOHO1mqGuEdTdNbzrJJLP85B+eZ+9P3kRVFXZ9ajsfeOQ2KuqDyIpMPJzk4LPv8JP/8xynD3fyrT95jD969EtUNpRN29b8cGUMUoUp2PufB9nzxEH0nM667Sv55NceoGlNHYoiEw3FefWJAzzzrd10HOspmdyXNBLsG9tXUhbT6Gzik/UP0eBonNPFZFWsrPduwKW6+F73d+kt4j3fl+xlMDXAEucS5AVyx+lMdPD80LMkSiTbllnK2FVxOzcG3odvBl2+oijYFBt+i59m5xJuCNzIyyO7eWNsHylzemUtY2Z4dfQVmp1LWOW+tKYdVVZZ7Vkzp3Vboy28NPIiMJ34a7LGWu96au2LMwVXlSxs8n9gaobIpQbZ5H8/usjS7NyCJtsxhQ5IbPDfhU2Z/R5kUTwsVIXVG3Rz76/fCkKQjKXZ/dj+OWn8gXzFX5ILLnOr3YKvzEXf2RHSiSwOd2FlRwhBeCxG/9kRqhqCWGwL06uw0BgKxXh8zzFGw4lpietPv3GqxLuKw2ZRufuGlaxuLBxcCiF4u3+Q59vaOTse4j+Pn2RfVw8f37iO5WVB9nV282xbO1ndwKaqfHrzBlZVlPNc62nOjofI6gYj8QTVHjef37qFgGNhdOMpfZhYrpMVvs+y1PMgquyg1PmWHxAsPNGwWlU+99BN3Hv7OuxFZm2tFpWP3LeZsVCcX7x0vKSNZk43eOq5o5QHXTxwz6ZZveQvB7IsEfA5kWWp5DMnnc0xHlp8CeSyLFNf6cNhs5BInb/PDo5HiaeyRRN8Q9EkvcPnZ7c1JT94kGWZmsmm3HNNt1k9H+S1fUNT0QLl8HiU2EVFxZpyT9EZgrzMJzxNGgRQW+Flae3lD6qaqgO4nVYiF7kMjYbjDI5FqQy4FwXBPoe28TH29/Tw2U3XYVOvvVOaBERzKf6u7Wmq7QFMYdKfGsetOfiXM88BErdWrmNH+dw4wGUdUcbUaY+OkDMLH2wbArWs989vauhS4VSt2JXCh50pBOHMeWJlmoI3nnmbtyZ1sZtvW8uXv/4ZqpdUFOxjdTMsv64J0zCnTc8LIWg73MFLP3gdYQpuvHcTX/zrh/AEz08j+yu8VDeXY3fZ+OYf/oDWQ2d5/rt7ePhPPjyn6f5rhfBYlBe//zrpZIaKhiCP/I9PsOqG88mA/kovn/zq/WQSGZ78pxdKbqcn2UNXoqPoa3bFwd2V91LvaLik80KSJJqczdxZeRc/7P0+KaOQMKfNNPvH97HWu57AZVSpzyGux9k98iIjmeI5CFW2aj5S9zHWezagznF0DXk3kkpbFR+qeRCn4uSF4efImNNne0LZEK+MvkyDoxGnWtp27VcVkiTj0gIX/C7h1ooPrJ3q3Kqn+Yr/wtyroqE4rz15iFw6Ry6nY7FZUOdIjrRJqc+F8AScrN+2nEM/b+XVnx5i270b0XMGQkAmlWWkN8TTj+5lqGecuz65Dccs7kHXCmubqvjzz97J0TMDtPQMc7Z/nKFQDFMIHFZtTm4xFlWhpszLrRuX8sCOtdithfd9SZLYUF1JjcfNmbFxPrFxPWurKnBb87OAdT4vn92yCafFwhNHT/DEsZP82R23MpZIsr+rlz+74xYcFgv/69XXOdDdx/tXL4z22KFW4bOsxCr70GQ3sqTNMMN0ZZ6ZVovKymWVOGbIgHC7bHz249sYDyV4462zpQs8qSw/+M83KQu42Pm+FaglZqIuF5IkUVPtw2pRS85CpNM6A8Oz++NfC9RW+PA4bAXEf3g8SjyVmUb8hRD0Dk8wcUFYlkVTaa7J3+uqAm4CHscU8dcNkzO9Y+iGOa0ab5iC7sGJAhtNRZZYWleGWuI66+wPES4i82qs8hcdpFwKJEnC47LjcdqmEf9MVqdvJMymFYur0NTs81PmcGBZJAGBFlnlrupNZGcIxG10FO/fKIbLIv5pQ6c/WagJUySZ9f5a3NrVeQCpkjTNx9REFNiEJqOp/MM4k8Nf6eVjv/uBaaT/HCRJQikyrWXoBvufPkx0PI7T6+ADn7+tgPRP7Y+msvPDN/LSD/Zx6kA7+54+zPsfuY2ymoWYwr0yN7auU310t+TlWjfcuYGlG6ZX5K12C7d+fBsv//gNJoYj07YhhKA11jKNmJ/DEucSVntWI88jXVWWZNZ619Mw3khbrHXa673JHjriZ/H7L6/pzBQmJyLHOBk9UVRW5NW8PFDzITZ4N87Le19CwqE6uL3yDsayoxwYf6PI5wjaoi20xVq5zrd5UVVB3u0QQqCnNTS8BUTTrp63UNRzBrmcgc0+vylei01j9Y1L0SwasiLjr/TOuQqvSfZphFCzqNz9qe30tyT5zv98mpcef5NoKE5kPMa//eVPGekLMdg9xg23r2X7vRsXbYHBabfwvjWNbF3dQCKd5ezAOP/3o88zFknwlY/sZGWJpsNzkCUJu1XD67QR8DiKJrNLkjQp6wGLouCz2yhz5gfPQgh8dhunR8boDUfImSahZApB/rpcX13JpppqDFOwvDzIcHzh7IdV2Umt63Y6Y08ykTmFS6uf7OeYfg+xq+XUOm9bsM++VJQH3XzhMzsIRxKcOj1YMo11fCLBt3+wj4DfyfrVdVeMdDfUBrDbLSWJvxCCzp5x0pncjIOaa4Gg10FFwMXg+Pmm6XA8zehEnLqKQq29KQSdA6GCQYLPbZ/y6nfYLTRWBzjdMzr1et9IPsir3F/IQwzD5Gz/WMEyVVVYUeIaM0zB2b6xaTIcWZJoqPIviKGI3aJOG6ifw4UN0FcLaV3n+PAQg/E4aV1HlWW2VNdQ7/Xy9uAAPZEIZQ4HQbsDWVGIpNO8PTjIjbW1U9LBw4MDeK02lgUCTKTTHB0aIpbNsMTnZ2VZGdoCDhrsqpUbgwvXBH1ZxD9n6tNkPjZFpcHpv2oe+pIkYZGnf8H6BbMQI71j9LXnm2yWbmxk+XXTp8dmQyqeoeXNfHNNdXM5TWtKB8c4vQ427FjFqQPtDHaM0N8+uEDE/8qg41gPmVQWSZZYu30lmrX4aVFRH6S6uaIo8c+YGfqSvSW1/Ws8a3Eo8586d6tulrtW0B47Pe0z0maas4kzbPRtQrsMW8aYHuNA6EDRwYsiKWwNbGOTb9NlB245FRe3lt9GW6ytqEVowkjw9sRbrPWsw6r88jq0XG2kklmO/HgJt913D0uWn0+KdWqVU/eDs6eHOPZ2Fx/6xFa0ecgYJkYijPSG2HrPpZPwYlIfgPrllXz5rz7OM9/Zyzt7WwmPxRCm4Nj+dgKVXt7/mR3c9+s3469YXDrZYpAkCZfdytKaII1VfibiKZbWBKfp9Rca4VSa/713Pw6LhZXlQUwhpgbdkgQuy6ReXRIokjxjyNalIprr5EzkMXQzQVIfnHHdoG0Dtc5dXKkiz2yQJGhuKOOLn72Zr//jC/T2T5Rct7svxLe+9zq//+U7aaoPXpEiRVnQRVWFh9BEaTnPmY5hwpHkoiP+dotGU02Qo+3nLZYzOZ3e4QmuW1nIH7I5g7bukYJLv7rsvDRHUxWW1ZXx4sG2qdeHxqOMRRLTiH8qkyuQDEE+BKz2gqDPC2Gagp7h6X9nAfSOhPnZnuPT33SJyOaMadKjc4jEzw3Arx5e6jjL7o6zbK6u4cWOM/hsdq6vyd+DJCRax0bpjUTYUl2DpijkTIMfnzyO06JxQ00t4XSa7x55h4+tXUeF08k/HTpI2jAI2Oz84nQbn1y/gZsbmxZtltRlEX9DCFJ6oU5YlRV8loUJw5kL8j4R02/SF9qGjg+GiYeTSLJE/YrqWcN0iiEyFiU86XUcqPLhCZSWYciyRMPq/EmUy+r0nRli4y1z015dCwx2jmAaArvLSll1ac2602PHW1ZcM500koSyxUNirLKNGnttntTME7IkU2evwyJbijbc9qf6yJqZS26KPQchBGfjZ+hMnC36erm1gm3Bm7DMwTVnNkiSRJ29njWetbw+Vjxyuz3ezmhmhDrH/FyJ3sN0mIZJYtSK3azHby3+vdY3lREsdxed9ZsLNItGeDTKcO84dqcVWZVxeZ1zqoiea+69GJIk0biymi/8twcZHZxgbCBMKp7G7rJRUeunrMY/r0HKtYRFVWisDHDkzNXJnRhNJOgKhfmT22+h2uOmNxy5Ui1T0+DRmrix4i/mtK4mu7hWpP8cJEliw9o6Pvepm/iHf3uF8RlI94mWfh794T6+8shtlAVdC07+vW47q5ZV0TLD7MPwaIyW9kGqK6+Me+B8cU6qI0nS1EAylzPoGQpPc/aJJzN09J8vAknkG3tdk9a8iizRVB3AqqlkJpOuE+ksHf1jrG4q7HUZDceZiBX22VUF3fg9xQtvhmkyHpn+NxZC8MKBNl440FbkXQuHTFbnajP//b09bG9o4KOr1+KyWDgyNEiF04UsSWypqSGRy/L4yRNT6wfsDtaUV3Cov5/rqqo5PT6OKQRryys4NjxEZzjMH+3YScDu4LETx3jh7BlurK3DoS3OnqvLe1qI6aRbJk/+rxZMIQocfSA/YrNdoPtPxlJk0zlkRcZX7pnXzSGdyJCdnG50uEp775+D2+cEKX/xRMYWd8JkIpqPkdes2oye46pFxVqiqpIx0iRKpPPaFRse9fJ11H5LIK+rL9J3NpGdIGvmmK8qPidynIweL9qYfG7GospWVeSd84NFtrDKvZo3QweKuuvE9CidiQ5qHXWLJvDnlwWjwxFefzmGpqms3lCH22NHCDh1tIfhwTD+oAt/8DwBy2ZytLcMMjoSzVvcqjKr19dTXTddWqZZVcb6Qzz33b2omoK/wsOdn95R8ropeK/sxKJ4Cu6plguIoMWmUdtcQW3ztfOkXyhoqsKy2iBlXifaAsuTJAmcFkuBHKjK7WJlRRmPHjqM12ZDN03KXE4kwKqq2C64n9u0vKvPQsGieCi3b1mw7V0NKLLMzdtWMBaK853H3iCRLF6tNYXg9YNnKA+6+bVPbsfltC4o+dY0hfVr6njulZMkk8UT05OpLAfe6uB9W5bgXEQZFrIsUV/pw2nTiE9KeEwh6B+NkM7qBdKX/tFIgc+9qio01QSmzmFJkvINvm4bI6H8c1bXTU53j/L+7ecHEfn03RjRi7T0eXeg4gXPTDZHOnPtLFGNEr0kVxJL/H5OjYzwti/AqdFR6jxe1BmKM7IksbOxkX85/BbjqST7errZXF2D12ajLxqlNxLhH948iCJJxDIZqtzuadKpxYTLIv6yJGG5qMHRFIKMcfVOokQuQyxXeFNSJAm/9fzoVpgCIQSyJM07ctwwTMTkCSrJUkkv3nO40FJyvjdCIcRFU85X5kQyJ5uAJEmaNfq8VCVUFzoZo/jDQZVVrMrl93zYFTtyiaa4nJklW6RZdq6I5CKcibcXfc2m2FnlXn1JzbyzQZIkau21uFR3UblP1szSlexiu9hx2dKi93AembTOay+donl5JX3d45w40sPDX7gFi0VFAKdPDTA+GmPtxoape8WBvad5Y08rq9bXcWBvGy63ndXri88YuHwOHvzKPaiTwUN6zigpnbsYbq2OW6v/GnGB3asiWVBMJ0M9Ywz3hkjG01PX68WwOays27p0ToOMaw1Jkrh10zLWL6mm0r+w7m82VeVru3ZS5jz/DHBbrfzOzm0Mx+JoikLAbieZyyFLEveuWo4+eW9XJImPbVhXMhzsVwmapnD/3RsZG4/z01+8U+AvfyF03eSZF49RHnTx4fdfh7WElnu+WLOimvoaP21nhkuu89bRblrbh9i84dLMI640ast9eJy2KeIPeYlOPPfCzlEAACAASURBVJmZIv5CCLoHQ0QT58m61aJOC9uqDLgp97mmiL8pBF2DIZLp7FTegSkEfcNh0tnzTaCKIrOsrqxoXwzk/fT1EveUq4FrQY931Dfy9Tde5+XOszT5fNy9bHmBSqQYlgWCODWN17q76QxP8IUt16PIMn67nSafj/96/Q14rNapHqPFWu2HyyT+qizjvUjWkzUNxjJXx15LCMFgKkI0V6jJVmWZ6gvyAyx2C6qmoOcM4pHkrKS9GGwOy9QDPJVIYxoCRS29jWQsPTXYcPnmV4fWc8ZVCeSxu2xIkoSe08nNEIZimoJcicqAKcyi+v48JOQFqVpLlAoREHBZI+yBVD8T2eJ6Vq/moc6+8JV3r+bDU4L4A4ykh0kZKVzqtQ0g+mWCaZrsvH0NN9+5lr7ucb7xN79gsH+CpqUVrL+ukVQiw+5fHCt4z5FDnWy8oZm7H7gOl8dOy9FeghXT7eeEEAx1jTE2EGLjzasxdJODzx7h+jvX45iDM4YqWwlYlxUs03M6ux9/k6f+7VVCo1FMwywpeahqCPLfvvMb7wriD/lAIfcVqNAqsky9r1DPLEkSbqsVt/X857lt+Z8vtO2UJIly13tuWufgsFv41EduZHwiwSv72jBKEMRUKscPf3qIYMDFrptWLmggU3mZm/dtWcKZztGSnx+aSPD080dZ2lyOr4Sk5Vog4HFQEXAzMHa+wXdkIk40kZ7S5mdzBu29hcdW5nNSc1Easd2qsayujJMdQ1PL+kbChKKpKeJvGGaBZAhAU2SWzdA8bwpR8p4iTf1z5XAtxthDiTgSEo0+P3ZVpS8awWu15gdT4TAdExNMpNOcHB2h3uOl0uXCqWlsqa7h6dNtVLvcLPEHkIDrqqp58ewZ9nR1sSIYJJrJ0OjzEVwgO+Argcsi/lZZpdJeWK3JGDqdsXEMYc46grpcmAhaI8NEsoXTWjZFo9F13vLPV+bG4bYTHo0y2DGCntXRLrEq4Q16cAdcjPaFCI9ESUQSeILFK1XCFPSfzVcnFE2hurnworuQMIgZprmS0RTJAoutK3OFlNX6kWSJVCJDZDxWcmCUSWaIR4p79MuSjFzEpQLAFMaMSbFzRdbMIETxG78syXMOr7gYpjDpTfaQMoqHdZVbK3BcAWtNVVLxaMUbrgDCuTDpdxnxN4XgrTN9jETj7FjVhM956f0+E/EUh872sqauktrA/KR5pWC1aQQr8tv0+h1oFoXYDGmlALUNATpOD9NyvI+O00NU1viKzhym4mmO7DnFYNco0fE4uYzO6ECIy7luJ0aiPPXtV8lmde799E2U1/rzpKrId+Jw2XDOEnj0Ht7DpSLgc/K5h24iFE7wzvHeko3PE+Ek3/nhfsr8Ljauq18wpx9Vkdn5vuXsfq2VvoHixRkh4I3DHax55RQfvPfK5gtcCmxWlSW1QY6cPh9Ql0xnGRyPsrQub0Ocyeq09xa68DRU+vC7C4mjpiqsaKgoCAULRZIMjUenEnmzukH3YGGvnc9tp6a89HNGkeWifytZkth53RKqAlfWNGDt0qqSoaBXAvFsluODozT6fEykUgwbBk+2tfDbN76PWo+XV7s6GUsmafb52NvdxcbKKsqdDlRZYWdjE53hCXY0NE5V9CucTn576zZe6jjLgb4+PFYrq8vKF7VA97KuDpuq0ewKIiNxLoDdRHAs1M9EJkmZ7coSllguzWvDZzEuIoPVdi/VjvMnelVTOWW1AcKjUc4e62awc5SGVZfmIuH02lm+qYmOYz0MdY0y0DlSkvhnUllOvnEagGC1n5qlFzTfSGA711ws8r7fpTDaN874ULjk6wuF5nX1qBaFbCrHmSNdbL33uimpwoUIj0YZ6S1enbbIVhyqo6jOP2Nm5pQoOxsiuQi6KO5j61AcaPL8Kp05M8dwZrggMfVCBCxBrAvQ1HsxZEnGrpQmakk9UdTrfzHDNAWP7TvK/tPdfPtLH50X8d9zqoO/fGI3D+3YxO/dv3PW9bO6gSnEnNJWc1mDRDw/G5dO5TB0E9ssdpubty7l0W/s5o09bVRW+9h5+5qixF/VVIJVPpKxNA63DdmvsO6mFdjnkNpbCoYhyGV0brp3Iw/93r2z9ha9h/dwJVBb7eOLn9nJ337jBc52jZZcr3dggm/9x2t89ct30dxYtmCD9uaGMm7bsYof/ORN9BKBeKlUjh89eQi/z8Et21dguQrXihACw8hnCBQjzxZVpakmgCxJUzPSuZxB71AYNubXCUWTjITO9wHKskRzTbBoTkVjtR+n3Up8suciq+u0945yw5oGACLxdEGvAEBV0IN/hhlHi6YUTeaVZYn7d67j5uuWzuGbePdgJBHneGiYv9h1G+UOJ4lclrbxMUKpNBurqvmN628o+d5aj4c/3nlLwTJJkmj0+Xhk87unj+eyrgxFklntq8KlWYnmzldLWyJDvDXWw121q6+YnZEpTA6OdvPOeG/BcglY568maD1fofUE3Vx/5/o86e8a5RePvsJn/vTDONz2ojemc9r6C/1rVYvK9vu38NpPDxEdj7H7h/toXlM3rRnWNE2Ovd5K66EzSJLEplvWUFFfmHxXVuNHtajoWZ3j+1q56YEtBdp5IQSGbvDak4eIz+CosFBYsr6ByoZyetsGOPDsEe58eCfVzYU5B6ZpcnRPS0ni71Ac+DU/o0WCr1JGipH0CCtcKy+r3yHv3FO8wavMWo5lnsQ/Y2YYy5R+mIWyIQ6GDiy41EcXOcZLyHwAcjP0Tfwyw223EnQ5KPPMPssihOCZwy2MROJ84Y4bUWY5v4QQ7Hu5BWEK2lsG8QWcVNcFyGZ0BvpC9PWME40kOdM2SGW1j2CZm/HRGJIsUdcYxGbTGBmK4PbYpw2OLTaNLXesZ+Mtq+ck7ZkLAhUedj14A23vdHH09dM0rqrB5rAUzyCRJWwO66ILMnoP735IksSq5dV8/uEd/O9v7mZ4NFpy3ZNtg3z7B/v4yhduo6JsYRJZNU3h7l1reOtoF6faSluijo7H+eZ395JMZbl952qcJa6Vy4EQIm8qksrS2TPOydYBbtm+gurK6VV1WZaor/DjtFuITZL1nG7QP5oPHZOkfGPvhcFWVk1lZWPltP2WpHyTbsDtmCL+OT0f5GUYJooiMzYRn2abWVfhw+0o3WNn1dSSib7h2Myzoe9GlDucLJeCfOPNg7itVmLZLPUeL+srK2d/8y8JLntIvMZbTZMryLGJ81NZ0VyaH3a+xRpfFfXOywtVKgYhBF3xEN89c6BgwAHgUq3srFyGdoGzkKop7PrYNg4+e4TOE7089509mIaZJ7hN5aiamg/3yRmEhsL0tPZTVhNg9dbzeltJkli/YyXb7tvMyz/az+4f7qe8Nsjtn9qOy+dEkiRymRwtb57hu3/xBPFwkqrGcj7wyK6CKp0kSdQuq6KqsZy+9kFef/IQG3eu5rrb1qFZ8vuRjKXY97O3eO7RPciKXLKZT5iCbCaX1/2aAsMwCY9G0fV8VTyX0QmPRLC7bMiyjCRLyIqM5aLI9oq6IDfdv4XHO4bpOtnHD//2aT751fspq/FPHdfJN07z0288j54tXnF3qk5q7XW0x09Pc3oyhEFr7BQ3BG7ANkOFeyYkjQRn4u0YYnq1R0KiwdEwb+KfNbNE9dIPsuORoxyPHJ3Xti8HArEgEql3G3asaqL5Nx6k0jf7jGHOMNl9/AwOi1ZSp3oOqqZw8x1r8QWctJ7oR1VlHnrkZpwuKxOhBAf2thGeSFBV6+fga6dZs6Ee2yaNluO9VNX4CIfi5LIGu39xjIc+fzOr1k3P8rDYtDkHds0FmlVl2z0bOXmog6//9r/jCTix2ixFMwLKanz81v/8JL4Slrvv4T1cDmRZYuvmZj7zsa38y/deIxorLo0UQrD/0FnKgi4+99AO3K6FcfqprfbziQ/dwN998yVC4eKSU4CRsRjf/O5e2jtGeODujTTVB9G02WcDZ4IQAt0wSadzDAyFOdk2yJtvd9J6ZgiE4Lr19UWJP0BtRd5R5xzxF+R1/ulsDptFo28kTCJ9nqy7HdapxN6L4XM7qK3wFvju942EiSUzeF02RsMJ4qnz21InG3tnKgbIskRVEfWCEDAwOj2z590Ot9XK72zYxlA8TtYwsGsqlU4XNvVXZzb1so80aHNyW/UKWsKD5C6Q3Bwe6+EfW/fy22t2UW1fOJ2uKQQ9iRB/d/IVjoT6pr2+xlfNlmBDwTJJkmhYVcNn//xBvvXHP2Swc4Snv/kS+546TFVTOd6gC0PPk+aJkQixUIJP//GHCog/gMNt51Nfe4DxgQmOvd7K9/7qJ7z20zdpWluHxaYx1DVK+ztdRMZi+Co8fOoPH2DZpqZp+1hWG+COT+/g+//Pk4SGIvzdVx5l1Y3LqGosI5PK0nWqn+6WPmqWVLJiSzOHXjjXbFjIbGITcb7/P39G/5lhUvEUqUSGTDLDcE9eL9hxvIc//dDX0awaNqcVh8tG3fJqHvqjD+L2n6+mKqrCvZ+7lZMH2jmxv43dP3id04c7WL6pGatdY6h7jDNHunB47Nx4z0YOPT+dBMuSzBrvWg6E9hcNwGqNttCR6GC1e80lnwtCCNpibXSU8Nh3qx6WuZbP2/0mbaQWaWVdYJSQNl1tnHvwZQ0DYeZ7QBRFxqLIMyY7GqZJJmdgCjPvAqaqRWPjTVOQyuampsPLPU7UEtsVQmCYAt0w6BkL0zEcYmVNOfF0psD+zm5RC5wsbHYL738wPx27/dZVBdsMBF188teny4r6usdoOznAl792L8FyN6lEln/8+rNEJkoTj4VEIpLisb9/jhMHzlBe68dX7inZOOmYbNK/kjin784HYM228rnGwXNBWe+emQgx2fBomuasx2kKMVW9fTcd43ygqgp37VrLaCjOj376VkkbSN0w+cXuE5SXufnIfZuxLYDTjyxLbLt+CR1dG/nRk6U/GyCeyPD088c4fKSbm25cytbrl7CkoQyX04qqKsiyVLQ5H/L3IsM0MQyTVDrH2Hic/sEwZzpHONU2QHdfiIlwYsqG0ueduYnT77ZTGXDTfwGJHpuIk0hnkWWJjv7xgqJFXYWPQIltWrS8zv+N411Ty4bGY4RjKdxOK0Pj0QKHHk1VWFZfNuP+KbLEsrrp65hCcLZ/DN0wUBcwhXYxwGmxsDRQfHD1q4DLJv6arHBnzWqe72+hJXK+21wXJj/vPUE4m+KRFdtZ56vBpqiXJfVIGTkOj/fwr6f389ZY9zQXF7dq5cNNGwlYp180siKz9Z5NuLxOfvqN5zmxv43wSITxgYn8BS+BLMuT3ttegkWSdiVJom55FV/5+1/j8f/v5xz4+Tt0HO/h9DudIJiqpq/ZuowPf+Uetn1gc9HKnCxL3P+F20jFUrz4/deJjsc49PzRSXmRhMVmYcWWZn7tv32Uoa5RTh1sx+60TbPazKRznNh/moGOQpszzapNNS+P9hc2+kTGYmTTWbjI8b6yoYwv/e2n+fe//AnHXmulp3WArpN9SLKEZlFpXlvPQ3/0QWRZ5vRbHThc9mmEb6lzGQ2ORtpirdOOOaJHeHbw5wS0AJW2qjmfB+ckPi8MP1eyT2CFewV19vkHXaWMVNGZhMWAxeAEbJgmLX0jvHisnVN9I8RSGayaSm3Aw6amGu7euALvRVPFsiQRS2f46cET7D3VyVgsidtu5fqlddx//WoqvYVhP6PROH/1ny8zGk3kiZQQfHz7Bj76vvXT9kc3TJ4+3MKhM320D44xOBElmkzzxX/+yVSTmN9p548f3EVj+eUlZvsCLhqXlPPYt1/D6bKRiKcpr/SwYs2VTZo9h1g4wel3utl613oe/v334yt3lxxonZP6zAV5bbKJMWl1LMz8d26YgmxWJ5PRyWRzZKZ+Pr8snc4xEUkyOEv/Ue/ABE8+ewSv247NpmG1qFgtGlarisWiTv6uTv2uKjKSJCFLUp5EyxLaHPo2ZoI5OUDMH9+kTMMU6LqRP6YLj/XccWZ1MpkcyVSOjq5RDL201aFpCvbsa6N/cAK7zYLVqhYc57ljtJw7Tk1BlmQkedI+eXIArRQhoosVNqvGx+7fwvh4nOdeOYle4vtJp3P86Mm3KAu4uH3nqgVx+rFZNT56/2YmIkme3X2C3Ayud0II+ofC/Pipwzz78gmqK3w0NwZpqAtSFnDisFtR1fy1ZJiCXE4nmcwSiaUYG48zPBpjeDRCOJoiGk2VtDOddZ8tGs21Qd5uO1+oHIskSKayWFSFzoHC4K7Gaj+eEtIcVZFZVl+GqshTBD8STzE4HqWm3EPfSOE16ffYqQrO3JwrSRLL6stxWDWSFw2mOgdCjE4kqC5b/Kng72HuWJC5jUZXgE8tuZ6/Of4icf185TQnTPYMtdMSGWJX1Qp2Va9gpacSr8WGKiszTs2bQmAIE900CGdTtESGeGmgjT1D7YwXsQuVJYmbq5azq2pFyRuooiqs37GSJRsa6DzRS9tbZxnqGiWdyKBqKp4yN3XLqli2qbGwIfcCSJJE7dJKvvQ3D3PXwzs5vq+Noa5RDN3AV+FlxXVNrHnfcgJVpRNwJUnC6XXw8J98mO33b+H4vlaGOnrIpFN4Ai5WbFnChp2b8FcGaVpbR+PqWiw2Dae3kKz7yj187V9/o6TFZjFYbBreItN6kiSxZH0Dv//Pn+f4vtO0HGwnPBbD7rKxdEMD1926hvL6MrKpLH/1sz9AVmQqGwurBG7VzU3BnfQku4tW/dtirfyw9/t8oPp+mpzNaJJW8jsSQpATWTriHTw9+DPOxs8UXc+r+bipbCcOZf7WWbrQSzb2/qpDCMGJnmH+/LEXSGSyrKotp6HMRzyd5UTPMKd6h9m6vH4a8RdC8L0973B2eJyllQGq/W46Ryb4p+ffoH1wjP/rY3fgsp2XZjltFnatW8ZQOMbJ3mH2nOpgLFq8qm4KQSyVwW230lTu5+zwOD6nnY1N1VPnk8tmwW65/Cqjy23j4S/ewuhwlFzWwGbXCJa7sS6gnGfGz/c5WL6xgUClB3+FB7vTiiRLxftNLoE3ZjI6z79yktYzQyRT2an/0ukcuZyBrht5f2/dQNdNcvr5ZaVcXS5GZ88Y//Lvr+V3TZLQVBlVVVAn/3/+9/zPVquGw27BYbPgcGj4fU4+dv8WgoH5m0T0D07w1PNHCUdSk8eYIZnKks0aBcek5wx0o/CY5wIhBM+8eHzqd0WRC45LVWW0c8esKFg0Bbvdgt1uwWHPH+916+q5/ebV8z7GawGP285nP7Gd8YkEB9/uxCzhTheO5J1+gn4nmzc0zDg7OFd4PQ7+yye2oesGL+1tJVtCfnohYvEMsfgwpyeLZJIEqqKgKPmLxjTPD4QXGpqm0FwTQJalqe8pmc4yEUshgNGJeMG6yxvKS+YNSZJEXYUPn9vOWDjPg3K6QUf/OBuX10yT5lQFPfhncfqSJInmmgC1FT7aewt73QbHohxt76cquDC9Gu9hcWBBiL8qy9xTu5rWyBCPd71Dzjw/MhbAcCrGY52Heab3BNUOL0tcQRpcAc7GpjdUDqWiPN/fQtbUGUrF6IyNcSY2ykAyQkIv3tgJsMpbyRdWbJ+WK3AxJEnC5XWw/qaVrL9p5byOV5IkbE4ra7etYO22FfPehsWmseqGpay8vh4z+t8RehtggDCQXb8F3IHT4ygqFwLQLCqNq2vn9fml9skTdHPTA1u46YHiHeo2p5Ul6xuKviZJEtf5/3/2zjs8jus8978zdfui917YwC6SIik2UVSXrWbJNbbjEvfYiUuee+PESe4TO3Hs5ObaieMWx70XNUoUKVOFFCmSYgc7CBIgiQ4stpeZOfePBUCCAEiwirL1Pg8fYmdmZ87szp7zne+83/vO41DkAFv7tozR9XdwaA7v50ziDLNz5tAUmEmRWYxf92MoOiDIOBkiVpiuZBfN4f3sCe0mlBlfwk0XOssLVjD1CoqGIVuDMNlg5o8NtiN54UALp/sH+btH17BmdiOGppLK2PRFYvTHEpSNI/cWS6Y50dPPFx5Zw5yaUjRVob13kL/52Tqebz7Oo0u7WVB/liPvc5k8eHMTAM/uPsKLB45P2CZT13jPquzz2dzWyaZDrUyvKOKvHlg1Lo3oSuH2mFTVTqyDfS1hugyW3j2HJ7//El/73E+pnVGOL+hB1ZQxwb/H72LR7TMnlfVPZyw2vXKMV3a2Xqumj4KUknTGvqSsadDv5vaVM64o8O/qCfPEs3sndH292rDtLEWE1OQpepblvO4Cf4DiQj8f/JPlhAbjHDraOeHq5OnOEN/+0SY++7E7qK8pvCoBZGG+nz/7kxV43CZPP7eP2CV+v1JmA+bMdWBSKkPButdtjBTeZiyHrv4IqbQ1wv2HrFb/+cZd56M0P0BB0DsS+NuOw8nOfuLJzMg2yE5uKoty8Lou3h/k+D0snlk9JvDPWDZPbmpm6axaAr4rN+F8AzcGrlo1Q8Bw88EptxBKJVh/5hCZcagTUSvF0XA3R8NjlV+Gsbv/FHv6T3NxZuVZVHvz+Ium1UwJvl6rslWE6y6EPR2Z3oVM/g6cay/jeS3gVj3cXXIfvanecQt9AQYy/bzQs5EtfZvxaX7cqgtNZDOolrRI2gkiVmRCBR/IauAvylvM6qLbMNQrk9pUUCYcjBQUqr01FJrXP/AzFJOgNrH+8vWCIhSklAxEk9iOgxAaHlPHY+ZQWZAz/psEvHnBDBY1Vo4oe9UW5bJqRh37TnZyomdgVOD/BsZHX9cgv/nmc5xp7eXY3ja2bdg/4bGl1QXMWFQ/abrPG3gDVwIhBPU1hfzZu1fwlf98ltMdE49Zh4528N0fb+aTf7aa4sKrU/OXl5v1FygvzeGXj++go2vwogX+rxXKC3MIet0jgb9l23T1RUimMsTOKcYtzvNTnHfh4nyPW6euPJ9DJ7NxlJTZzHxH7+Cowl5VyRp3TUblS1UEty5oZO3LB+g7z6tnz5EzrH35AG+5be41Say8geuPq1rGXOIO8JlZazA1jWdOHSBhX7oiiYRxg8XxIIB6fyF/OXM1SwvrJiUdKqXEwR4K9kY/xI60kUhUcX2ru4VQEeYypLwFlMJs4P86RomrhLdVvYOft/+Uo5GjOIyf5Us76Qlday8El+JiSf5S7i19E37tyrmHmqJNKNWpCpVlBctZUbDqiq/zeoSqCG6dWc/G5ha+teEVdhw/xeqZ9cyvLac01z/Cyz4fmqIwt6ZszG8y1+dBAvHU9cnAvt6RW+jnA194mMwk6AymWyeQ94br7Bu4fhBCMHdmJX/6tqX85/deoD80vvy0lLD11eMU/MrHB961jID/6hSi+7wm9989l4baIn795E627zpBLH79hBpcpkZNZR7ei0y2s1x7/wgH37IdOvrChGPJUfSiqpJcci4iBaxrKlOqi1j78sGRbT0DUdo6B4glzvarhq6NW7Q7HoQQNFYWsnrBFH71+92jJlCpjMUP1m6nIMfHyvn142r+XwqGhSJSaQuv++rLrb6Bi+OqRrhCCErdAT4383aqvXn8vPVVOhPha1KgaCgqCwuq+ei0FczLr0CZpEuwg82J6A5K3FPwaqOrujsSh8g4SWp8N74RQ5aakgGZAQQIDRjNmZcyAzINwgDGFlaf3W8iOMcNVEok1tC55TnnntxnLISg0l3Fu6v/lHWdT7MztIOYFZv0hG4iqEKl2Czm1qLbuDlvCR7t6lhim4qJMoEikIMzovjzx9hBCSGYXl7El95xF7/d1symgyf4p6PtFOf4uHVmPQ8uaqKqYGw9i66peF1j5VWHj7tRM3M3Glwek1mLz6qLDavNMOyu/UegJPMGbmyoqsKqZVPp7Y/yg19unZBWZdsO6zbupzDfx6P3L7iocd5koakKs2eUU1ddwNZXj7Nu4wEOHukgGktdEwqnrqvkBj1MayzhlkUNzJ9dRWH+helopp518N1xMOs7JCV09UdG1UZoqkJ9eQHmRYzHFCGoKc3D4zKIJ7OfdX84zsnOgVHFuflBz0VXD86Foas8vHoOOw+fouXUaCfh3lCMf/3xRnoGoty5ZBo5Pvek/UKGv4OM7RCNpzja1sOWfa2Yhs6HHlp6Qzvc/qHiqqe2hRDkmh7eN2Up8/Mr+cnxHWztaWUwnbziwA9AEwrl3hzeXDmbt9TMpcg1+aKTjJOiK3mEpBNFoGBLi95kK0knQoFZR8qO0ptuA6DArMar5d+Qg6qUGWRmJzK5FmkdAwRCm47iug+pzxwK0AVkdmNH/gXF/SjC/RDnVv9JKZGJX+EkHkP1fwb0m4a2O2Adw0k+hczsAZlEaFUI804wlwKTy9QIISh2FfNo5duYFpjOb079it70xCZZE54HgaEYFJnFzMmZw6K8xZS4Sic90ZsMPJoXcwIPAFvaRK0oEnnVDbxeL1AUwZSyQj79puW87ZY5vHK0nfV7jvKTl3azq/UMX3z7nZTnj6YkCfHH+mldG0hHEgnFaD1wmo6TvcSjKbx+F2W1hdRML8MX9FxiXyUwDA33ZQRfViZb/GqY2jU1C3O59Cs+v6IouF0G8hoUbV4t6OO4pF8qFEXB7dbRMuP3i26XcVUKayeCoWvcf/dc+gZiPP3c/mydwwR4cv0+KivyWLG4ccIi1kuFEAK/z8WaFdNZfFMdzYfOsHnbMZoPn+FM5yCptHXBNl3ovJqqYJoaRQV+aqsKmNNUwczp5ZSX5uIyJ6dUaOgqtWX5qIoYyfCf6gqNytBnjbuKLno+IQTlhUFyA+6RwD+WSLPr8KlR91iSHyDnIoW955+3pjSP97/5Zr78g98Tio4W6OgdjPEfv3qJl3a3cOfiacysL6U434+uqqN+p8PJCdtxiMRT9AxEOdUdormlkz3HznCqa4BoIs2di6ed34QrxlkZXjluvCk5K9c6vBp9PWO8Ue2bYFI63L7hMfRatO+acVqGM/IzckrZ3X+KZ08fPLhwlQAAIABJREFUZEdfGx3xQdKOhX0JM3FVKHg0nVpfPiuKG7mjfDp1/oIJdb4ngoKCqfjoS22n2NXAYKaTtthu8oxKDgxuIKAXEc50EdSL2T+4noX5b0Hl+qh3TBZSOsjkUziRr4CSi9Cbshn61EvYqY0ogb8GY2X2YVHLwQkjk48jXHeBOCcrIaPI5FMgB0EtBSGyD2JmD3b4H0AmEPosEAbSOoJM/TWK90MIzzuByRllSSkJZQY4EG4makVG7VOFSlAPIiWknRS2tLPSdkLFVEy8mpegnkOZq4wG3xSqvdXk6LlXNeAfhqEY+PUA3eO4DgOEMiEyTgbzCmsJXu/QVJXqwlyqCnK4fXYjX3/mZX69dT9bjrbxlvyxspvXBUIAYkJVkUuFlBKJgyMtbJki7UTJOFFsJz1EWbu+AaQqTIJaHYe2t/HL/1jPwR2tJGIphMhmDT1+F02L6nn0E3cwdW71uPLB48HrMfjIe1cSjS++pPZIKVn3xG42Pruf931gBVObrp64wPlQFYWyCUyRxmuXY0sUdbQs5tSGYr78tw9dE7WWq4XgVXB5bqgt4qv/8OiEwYSqKFSUTlCPc5Xg9Zj86duXctfqpot+3jkB9zUJaIYnAIsX1LFwXg19A1FOtvdz9HgXLSd66O2PMhhOEI2lSA+pVzlOVkZb01RcpobbZeD3mQQDbooLA9RUFlBTlU9xYYDcHA/GRTLyE7UrW+BrEo5ljc/aOgdGfV85ATeVJZP7jvJzvJQVBDndnVXxyVg2+1rOOhoLkaUNjbfqeiEoimDFvAZ6QzG+9bstIw7Bw0hnbHYcbGfXkdMU5ngpzvNTWhAg6HNjaCq2I0mmM4RjSXpDMUKRBIOxJOFo4op/g7bjEImlSA7LDGesMf8nUhbxZJpwNDnKN2EYlmWzYdthTnb0ZxXEzCGZYV0b+d8Yea3iMY1J05EcRxJNpEgkM+O2LZm2SKYzxBNpIvEUx85bVYHsqtjmPceJxJNZdTPTOCt7PNSmc9voMXW87kt3a7+mZHYhBD7dZFlxPTcX1tCZCHNksJs9A6c4HumjJxkhnE4StVJkHBtbOigoGKqKVzPJMVwUutxMCRRzU0EtjYFC8k3fpLj840FVdPLMCrxqTrZYMX2aYlcj5Z4mtvX9kpQdo8Q1lVrfQrp7j5Gy43hugOLKUbBP4kS/hlArUIJfArU6u906gj34VziRf0fNnQZKCSjFCPNmZOJpsI4h9TlnH2DrCDJzCOG+H5Si7DYZwYl9A7BRc74C2nQECtLpxBn8PE7sf1CNhaA1naUFTQApJa2xVn556mccix4dNfv2qB6W5t/C0oLl6EIjIzM40kEgUISCruiYiguP6sFQrj0H0FRMCo1CWhhfMrQn1U3SSf5RBv6OlHSFIgQ9LgxNQx3qYLwunerCHEBOWvrwWsDUVVyGRtdghEgiRc6QrKgj5ZAe/GSXox0yToxQupXe5CH6UgcJp9tJOxEsmcSRVnbSfZ0D/4BeQXnnR/nW559hsDfC3OVTqZ5aisfnIjoYp/XAaQ5uP85//c0v+eRX3knt9LJJ3bOqKlSM41VyMTiO5NUcL5oUVJbmMq2h5HJu66ojEk7w/Ppmlt06nbxzaBdej0lj3etV9GHy8LgNpp4nQZ1xbHb0ttE/JH99pPdsYiPX9LCooOaSk2cXg8/rorHuxlB/UVWFooIARQUBFs6rwbads7K1qQyWlVVgkvKsIaGuqxh6diXM49bRrtBH4lyUFQYI+lwjgf/5PkSVRbnk+idHXzV1jSlVhWw/0DaybTRtSKWxsuCy2m7oKg+smoWiCP778VfoH0dW2bYdOvsidPZF2HP0zCVf43LQ2Rfhi99bT/dAZER2N2PZZOyzcsMXm1xYtsNTmw+MvBYwIrWrD0sMn/P33CkVfOrtK3FPwoCuLxzjX374e06c6R9SjLKxhtqWGZINtp0Lj5W2I9n46jE2vno2FtFUZUzbNE1BV7O1Hp95560XrQs5H9etilVXVCq9uVR6c1ldOoWMYxOxUsStNEk7g+U4WDKFioKumrhUHbeqMJhci0tLUO6ru6rtEULgVoNErB4imSwFRVMMQukOwpluQEE7j/6RzSo5pM+RahMia5h1tZYsLwaZ3gL2aYTvE6DWnP1h61NR3A/iRP4Zmd4JrnuGioZvRyYeQ6Y3ZTP4qNn7SL0AOAjzVkYeA+sYMr0D4XkXqFUgU9kwRwQR5hJkegsysx+0GRekvUgpOZM8zc/afzzGbden+XhT6QPcUrAMl3pjDBCGYmRNxRDjBna9qR5C6QGC+g02CbwOyFg2//bkJhLpDLOqSigO+pBAS1cf63YfoSI/h5vqrizrK6XkZG+IwViCZMbi4OkeJNDWO8DWo224dA2vy6SmMAfjPFv14qCfaeVF7Gg5xVefeJEF9RXZ4jHH4fbZjeR4L9whSilJOSFOx7ZyPLKOvuRhUk6YG8M6DSKxAZ7+8SZi4QQf/eJbWXBbE8YQvUBKSTKeZtOTO/mfLz7Oxt9sp+Kz92JcBZfU1xOklLS2dPPYr7Yza17VqMD/jxlJO8M3Dr3E9t6TY1Ty5uVV8L1l7xozxv0hQ1UV/D4X/tdIljLoc1NaEKS9a6z6kRCCmrI8fO7JJZc0VaGxsnAUdehcmLpKXfnkCnvHg8vQeXDVbEryA3z3sa0caeu5aNB6qbjUmCmdsWg90zdKsvRKIclKq2Ysh8Q4fkiFub5JryZblkNb5wAnOvovfvAlwLIdLNshMU7Nutulj3Jqniyur3zNEIQQGKpGvqqRb2ZVKKS06Yg9i0srIs+V5Zs7Mk1/om+kuPJKMLykll3Kz/K1y9wzOBZ9mePRbdT7bkYgSDtxWqPbqfMtQlfGdhC7Nh3hB//6zMhrTVf5yN89SOOsy3eOvaT7yBwE4UZodefN5gXoMwCBtA4huAtQs1QgrREn9Tyq550gckEOINObQWtA6E1nCy6t4yATyOTj2OlNjHIEcgYAG5xewAEm/tEmnSRrO56iNTZai10TGquL1rC8cAXGDTTgKEKhylONW3UTt8dmN6JWlOOxFqo81TdkzcfVx9mOTlEE9cX5PLfvKPvbOkmkLRQhCHhMZlQU8/Zlc6kvyR/1bkNTcRv6GKdpyA5YHlNHP6fTtx3Jfz6zhT0nOsjYNqmMjdvQeaG5la1H2tE1hbLcAP/4jrsoP88zwO82+dhdS/jms1t5+fBJNuw9luXKlhWwfFrt+QbVo+BIm57kPvb1/5DOxE5sef3UQCaLcH+Uw7tOsODWhSxYPWOUcZgQArfX5JZ75rJtQzMHtx8nHklev8BfZDN/Vibrg6EOZaLG+42c65grHZl15dXVCXnnw8c6Q4OaGKJiKOc43A67D2cyNkcOdhAJJ0glLRJDvGkBQ3UIf5wShKaicX/VLKYGixnMJBhIxdnV304kc+M9538McBnZAt9tzSfH7DN1lalVk5PehCHqUHEOAZ+LgfBYs8yCHB9FuVc2AdY1leVz66gvz+fxF/ezftsROvvClxVoDkNTFfKDXmY1lHLfshlv1IG9RrjswN92EkQyLaSsHhTFxK83YqoFZJxB4tYpVOEiYZ1GU7wEjOloihcpbeLWaWKZEyjCIGBMRVdysGWMUGofHbG1+IxG0nYIj1aOz6gbulac/uSrZJwIXr0ar1YNCByZIpI5RsruwaUW4jMaUYU51LZjuNQiopnjSGkTNJuI2wk6EofJOElcagBT9dAUXDPqvordjRe873AoztF97SOvdUMlEb2OHamMgdAZj2cvcIFQs8cMbxM5CHMFMvY9ZOYAGEuz/1stKN4PgzgnkJJRQCLUWtBqGNcKVJ85/vZzcDRyhH2De8dkz0tdpSzNv+WGCvqHUeGuIN8oIJ5oG7MvIzMcCDezKG8xXu0PQy5xom9wRDVmCLqq8v7bFvLgzU0MxpOkLRshwGsaFAa8eMzR36WqCD561xLenZxPTeFYKsny6TV89yNvoSTHP+o9H75j8QUlPnVVpWCCZfCZlSV86Z130zUYJZWx0FWVoMekIDDxd+VIixPR37Or95tErY4Jj3utkU5ZRAcTFFXkoZvjd9emxyS/JEjbkQ5sa/IGWVcCISA8mODxX21n5/ZWkok0dQ3FrLl7NvVTikcF21JK+nqjbH7hELu2txIOJ8jL87JgcQNLV0zBfw7fW0pJMpHh1W0tbN10lI4zA0hHkpvnY+qMMpavnk7pEEUpNBBn3ZO7OXzgNIcPnCESTvJv//TkiFqM6dL52F/eRfVrZL72WsNQNR6umQdkP9fuZJSPbvkZ+0M37vP+h4jhSaymKdRX5ON1G2MKjXMDHhouYtx1PsoKApTmB0gkx2aqq0pyLpn+MR6EEJQX5fDBB5dy99LpvLT7ONua2zh+po9wNInlOOMWqipCIBSBKgQuU6M4z091aR7zp1Ywd2oFlUU5GPql0aiEELgMHZdx/fLVhq5djNU8AiHANLTr2j7zEtp3Li67hXGrnTPRJ1CEQcLqRFO8TMv9S+JWOwf7/wWvVo2h5hJOH6bIvYKqwFsZTO2jNfwj3GoJlozTGdtAY86HkUhimZMkrW50JZe4aENTsoO8xKE3sYWME8GWKZJWB9PzP4dHK+dU9Hf0J3fg0kqIZ05R5FlOhe8B0k6IowPfwKtXowoXjszg1sow1QLyzEoqPbPxqNe20OmaQckFmcpm5ocl/YYgZRSkhRDnUFKEQDFXYcd/iEy9iDAWIlO/B+FBmLcgzs3cKwFAIMzbEJ53TFq+81w40mF/eC+JcTLnNd5aAjcoXSagB5nqn8apRPu4dJ9j0aO0RI8xKzj7dZ/1V4U6IVUrIzPYcrRmvKYqFAV9FAUvnkESQlA1gamXEII8n4c8n2fM9rrivHHfMxkIkc38+ye5TC6lQ3t0Ezt6vk7CHltgdSNBN1S8fhd9nYNkUhame+ykOZVIM9AdHnL0vXKFmMkgnbJ4/Nc7cGyHiqp8DEPjxd8fZN+eNj75uXuYMlRrIKWkuyvMt7+2gUMHTtM4tYTKqny6OkL89zee4/DBM7zvw7fiH1IfsW2HZ9fu4cf//RIVVXlUVOXjOJLuzkHWr91L47TSkcDfcRxUVaGyuoCe7giRSJLpMyvIyc1O+HRdxeP946vLGQ9CCDRlYqPCN3Dt0HG6n6d+u5NH3rWEVfMbmFJZNIbfb+gqVSWXVnOTG/DwhQ/eRXycwD/gdY3hpUspSSUzmOb4q7EXgqYq1JTlU12ax0O3zqarP0J7V4i2zgG6B6JE49li6eHg1+8xyQt6KSsIUF4YJD/oJTfgmdD3ZTIozQ/wTx+/j8x1rCnzubPFtZNBXsDL377/TpKT8Fy5WnCbOkHvpVPXLjvw9+p1TMn9cwQqscxJDvZ/mYyTraKW0qIq8FaCRhOdsXV0J14kbffTEVtHnjmf6sDbsJwYhwb+lZ7EJir8D1Dmu5dQai8l3jUUeVYAWaoP0sGn1zE195M4ZDjQ9yXCqUMIFLriz9OQ8yECxlR6E1toj/yaIvdKANLOAJWuhyn23DrUYjHE679yw6fXEkKfi4z/BJnZj9DnMJy7ldKBzE5AAb2JUVQctR6hzUKmXwbrAWT6VYQ+E9T6UUW6QpsCSgCZeQUh7wd8o4t4hzurC/xwU06K7mT3uMGzoZg3rCSmrujMyZnLtv6thK3wmP0RK8KLvc9T7a0hoF0d58nXCrpioCka4/mqWY417v3/oUAiGUgfZ1f/t274oB8gkOelflYlOzY2s3BNE7OXNqIbQxx/R5JOZdi+YT8Hth9n5QML8PivD385mcxg6Cqf+N9voqwiFytj88JzB/ivf3+WtY/torquCJdLx8rYrHtiNwebT/GhT9zO4uVT0HWVWDTJT7+/mWee2E3T7Apuu3MWQgiSiQybnj9ESWmQz/7N/ZSUZSeR8XiaUH+UgqKz/Xd+gZ9H3rmEdNoiHk/T1RHi/rcsoLb+D7+Y9w28fpBKWpxp78fK2BTm+Qj6rjwTD1mlptqy/IsfOIRYNMna3+7k3ocX4L3MCbEQAq/bpK7cvKIagsuBaWhMqSq6rte8FBi6Sv0kDdNea1xW4C+lJGGdpiP2LGl7AFvGSTshJNmZmKkW4FKLhrIMAaR0sGWClN1DkWcFQqhoihe3VkYsM5ZacS6E0PDqNaiKCxzQlQC2TJKye0hYZ2gd/H6W3iOzPDeHNCDQFB8+vfaystbXE9klMkmWNy+B4dmiBVhIKcgG8dmJizAWgz4TJ/FTVH0aUmsCJDKzGyfxO4QxB6HPHRWYCsWDcK1BRr6Ck3wa7FNZWU5xXpCg1SPMVcjkBmT8p+C+D5ScrJGX0w12F+jzEGJiCoUtbSw5/oy3LX6SiBUhV8+9IQPnak8N0wNNbOvfOu7E5UC4mZd6XuCOkjsxxOs3k+jVPJiKCxgrd2ZLmxOx48wMzLohv6Mrhe2kORj6OYPpsTzb8SFQhsztXotJq9frZ82ji/nO7nX8v8/+hPkrp1M/swK310V0MMGR3SfYvekwReV5rH54Ifp1WmbWDZWblzVSVpH9LeuGxoLF9dQ9VUzz3nYG+qOUluUSCsXZsukIdQ3FzJxbheNIUikLTdeYt6CWZ9fuYc/Ok6y8rQldVxGKwO026O4I0Xail5w8Ly5Tx+s1LzNYkdmkCMP/1KH/gRGpZgcYzpoaCKGcNUhEIdsXC4ZNDM/22emR8wiUofHPGjrHkDwyabLmiZe/EuNIieXYIwW6ihDoQ+e73hrkjpRYQ+pWUmZzQKpQ0MRrs5ogpcSWEls6OFKOtEcVymWr/8mh81nSGXWPFzunlJJoJMmJlh5i0SRen0n9lJKRfR2nQ7Qe68blMWiYUoLHa5JOW7Sf6KWnO4ymqdTUFZJfmKVBdpwewHEkmbRNd2eIgqIAtfVF2I5D+4k+eroGUVWF6rpCCoqyySjbdujqCHG6rR9HSkpKc6iozqe3O8yOrS28svko+UUBvF6TpjmV+PyubFFqaw893WFycjzUNhZjmjqZjE3LkU6KioO0n+wllcowZVoZOW+4g7/ucVmjhCOTnAz/DEXo1ATeTtoOEU23juwXqJzPIhZoqMJL2h4c6kAsLCeKoeaNHAFiZPIw6r3jdJqq8OJSi6gJvAO3VjpyXVMtJGX3DjnR3thB/zBkZgcy9XJWW986CoCTeAxhHQXhQ+izEGa2FkGoBaj+z2GH/w926NMIrTo7wNgnQClA8X0GlLFZAGEszmbzE78GJQdhLBwzKRLCjeL9GI4TxYl9CxK/AcUL0gIZAaUANedrXKhiUhf6hGo9x2PHeeLM71hTdAeFriJUoaKcszLxWgeabtXNsoLlHIkcYiAzMGZ/2kmzoXs9HtXDLQXLr7rUqJQSS2aI2wl8mg/1CoKFC8Gn+gjoAbpTXWP2OTg0h5tZWbiagP76Xh0bD/2pw7THNnNh1R6BofjJMWrJNesJGtW41QJ0xT2hw/O1giZcBMsaEV/w8sv/WM8Lv3uV537xSpbmpwhMt0HTonre/qm7qJmklOfVgGFolJSMdmx2uw3KKvNobelmcCBOaVkuvd1hBvqi9PaE+bvP/XzUamEqmSEeSxEZTJBJW+i6itutc8/98/j21zfwb196kplzqrhl1VRmzakir8B3WYW6VnoLVmoL0ulB1RpxnEGQCUz/xxC4SCefwEq9Agh01wp0111IJ0Iq+k2EWoqdaQYsDM8jaMYtgEUmuQErtQEpLTRjEYb7IRy7jVTsR7gDnwPhB5Ikw19Cdz+KZsycVFtPRvs5PNhF0HCzoKCKWCbN5u4WXuw6Rnss2ydV+/JZUdzAkqJa/Jp5zb9zWzqE0gkOD3axp/80R8Ld9Caj2NLBp5tUe/O4qaCKhfnV5JljjeS6EmH2D3RgSYfGQCG1vgsbY9rS4UCokzPxQVyqxty8CoLG6Cy5lJK0Y3NosJNNXcdpDnUwkI5hKDq1vjyWFNWysKCGXGNivwBbOhwe7KI9FqLKm8vUYDGWdDgU6mRj5xGaQx1E0kncmkGNL5+bC6tZUdyIWxu/eH6gP8ZPvvsi0WiKgiI/tuVQWJztQ/t7o6z97avkF/o5cbyHOTfV8PA7FtPTFebZJ3ejKAoD/dnavI/8xR34Am5e2XSU/bvbKCnPQSAoLg1SU19EX0+UdU/sRlEEg6E46bTFxz5zN8EcD3t3nuQ3P91KaXkuqqpQXJpDeWUe/b1R2lp7CQ8mONPej8dr0jitFMcreWF9My+/cIiSslw6zwwwc24V9z+6iGQizY++8yLllXkYpkYqmSG/wP9G4H8JkFKSiKUI98coKAkiyQrCvNaxzhWmhwQpu5f+5KsjGfcJL6S4KXAvpjvxIroSIO0MEMu0Uea9GwAFDVPNoz+5A10JYKqFuLWJl2y9eiU+o57exBYK3EtxZAohFFwXeM8NC7sThgJ+IXxg3jm0vQvoymbdRyAQ+gLUnK8hUxuyWvxCIFxrEOZqUCvHf6jUShT3o0MUoVmgVozbFKFVoQT/MSvdmd4OTh8IF0KtG5o8XJiLbSgG5e5y9ob24Jw3ibOlxabelzgYPkCNt5ZydyW5eg6Gal40yFWFgi4MXKoLvxbAr/sxFAOFq5dpEkJQ72tgacEy1nU+Pe7KRdSK8Lszv2Ug08+qwtXkGnkIJq8Xfy4kEmQ2yx62wpyMnWDP4C6klDxS+TZ82rWRJdQVg2pPDceiR8fdfzJ2klf6tnBr0W1ZStAfCKR0OBHdSMoeu9IxDF3xUuldTkPgXvLMRgxl8s7g1xKL75jNlLk1HNvbxskjHSRiKfxBD3VN5dTPrMSXc6nOvVcGRRFjVheEIjANLRuUDfFck8kMtu2Qm++lcVrpGJrgzLlV1NYVoWrK0HkVFi5poKgkyHPP7GP7lmO8+uUWKqryedNDC1h522hlo8lAOr3YmUMYngdIRf8L0/dRMsmnsTMHkc4AVvI5TN/HgQyp6DcQwo+qTyOT3oRu3orL9yGs9BZS0e+g5c7BzhwkHf8JLt8nQBgkI/+KUPLRzVtwnDNYmb1oxlLszDFsqxVTLZt0W1/sPMY/7n2GxkARX17wAN89uoX1Zw6SdmwUBI6UbO9t48n2fdxVPoO/aFpN8SW4118OdvW1838PbORAqJOYlUaQVUITgC0lkmP8vHUni4tq+UzTbTQGCke1pz8V54t713EqHuJNlbP4+7n34NUnXr3pT8X5p33PsqO3jVm5ZXzt5kdGBf5SSkLpBD86vo1ftO6iJxkZ8oDJyjFv7Wnlt217WVZUx8enr2RqsHjcTH3Gsfl5605+1voqD1fP5XMz1/DUqWa+c2QzHYkwDK3xOUg2dx/nSLibm/Krxg38pZTs3tHK4GB8JAh3nGwN3omWbhBw38MLaJpTydaXjvD7Z/YRj6UoKcvhvR++FVVV6e0J82//+CT9fTF8ATe2lVWsett7luHzu5BSoigKRcUB3vPhVWiqykB/lH/5+8fo6wnjdus8/dhOlqyYyl1vmotQsisAmqYyfVYFjiPp6gjxwFsXjdTU9PdGeO6ZvTz6rqVMn13J4ebT/PDbL7B89XRcboPwYJyVt8/gtrtmAxeT9RgLKbMutB19EU73DJJIZdBUhVy/m7KCIDk+N0KcTfodONFJ90CUuQ3lY4qTO/sjHGztpKY0j5rSvJH3xBJpdh89TUVRkMqiXELRBCc6+okkUrgNnYqiICV5gTHuwmnL5lR3iM7+SLaPCnioKcnDN0nDrskgHkmy7mdbON3SzaMfv53Du0+yYNWM60bJnAiXNaorwkWl/2HORJ+iM7aBgDmNCt/9qMIDCuSYs1CHqBCmmk/QnIkiTIo9qxFCpTvxAqowqQ2+B78xZeicJhW+hzgdfZyO2NMUupfh1krx6fWoQ5QUIRT8xhTcWhmq8FIf/AAdsXV0xJ5BFS4K3EsAgSpc5LrmoonJmWG8lhBCINxvBvebL+VNCK0aob3/Et6iIXwfmdyxSg7CdTe47p58m0auI5gTnMeWvi30p/vG7JdIetO99KZ72TGwfdLnVVBGXH3dmodcPZc6Xz3T/TOo9tbgUT1XxdXXUAxWFt5KW7yN/eMoEwHE7RjPdq3jcOQwS/OXMSMwg1wjF1VoE04ChmVkJRJHOiTtJP3pPjqSZzgSOUxLrIWeVDdpJ810/4wJHTivBlShMtU/jZd6XyDtjFXSycg0T3euxVRdLMq7GVOZXFZxRDJ3SC53dOF5VlvfdrLL8bqqIhgit9nOiOGWfk7xlyMlGdsGmQ00h4sTs4PJWVtzfZIFY0k7RHdiDxNl+91qAfPyP0CN/3Y04bohAv5hCEWQXxIkv2QWN9/xGrkknwPbliOymcNwbId4PI2iKriGipDdbgNNU2iYUsqHPnkHxiSoSKqqUNdQTPVHCnnzwwvYtaOVJ3/7Kt/++gZMl8bK25omfvO4X61A1apQ9bkoaiWqPhM7sxvp9GOltqKZK9GM2Uhpoxm3kEk+h6pPQwgvhvteVD07RmWSG5BOGCu9JetoLkNIx0EoAez0VnTXnejmbWQST6MZN2OlXkQz5qMol26U1pkI83/2PMPBUAdzciuYn19JjuGmMxHmpa4WWiK9PN62D0NR+V+z78St6tfseTUUjdOxQXRF5ab8ShoDRVR4c9CESntsgM3dxzkZ7efFzqP4NIO/m3cvAf1sYFPrz2dRYTWnT4bY0XuS49E+ZuaUTthPHgx1cjTcg4JgRXEDBa7RCZC4neHbRzbzo5btONKhKaeUhQXVlLgDRKwku/pOsauvnec6DhO1UvzDvPuo8l6YXtqdjPC7tr1868hmDEVldekUyjw5KELQER+kJdLL3Lxycozx+flSZgvQi0tyCA5NwlX17PVy87wUlQSzpqZDQZ9tOfT1RHhhQzN9PRHSaYuB/iiOc1bCtqIqbyRIH+77+vujvLC+mZ6uMFbGZqAvim1LkskMA31RpkwvHXHw1i4sUevRAAAgAElEQVRS7B8aiNPdGeaxX2xn3RO7SaezhV/plIXLbeAydWrriy/ZGXb4M0mkMvxswy6e2nKA/nCcjGWjCIHb1CkvDPLX77mdunPqFH7x3G7W7zjC1/7iIeb7Rycndx89zRe+8zQfeNNi3nffzahD32d3KMo//mA9993SxMJplXz3yVdoOd07Msm4ZXYdn3/P7SPFzlJKekJRfrTuVV7Y3UIomsBxJB5TZ1Z9Ke+/bzFTq4ou657PR6g3gtfvpry+CMdx6O8aJHMdi38nwmUF/kII/EYDU/M+Oe7+upz3jfwdMKcSMKeOvC713kmp985xz+kzasecs8x3NvhUhEGl/6GR1y6tkNrgu8acy1Bzacz58ORv6A1cVVR5qrm1cDVPdTxB0klelXM6ODjSIWNniNpRelLdHIke5vnu31PnrWdF4UqagrNwKVcesOXquTxQ9hBRKzLGi2AYtrQ5HmvhZPwE+UYB1Z5qqr01FJpFeFQPmsgWYDrSwZIWSTtJxArTl+qjK9VJT6qbUDpE1IqOWRm51hBCUOetp8JdOcZgbRhha5BftP+UA+H9LMxbRKmrDJ+WXWURiJFajrSTIuWkSNpJEnaCqBUhlA4xKzibck+243akpPlMF4/vOUgokURXFd6zeD5TigvYc6qD3+46QCydxqPrvPPmuUwtLsB2JOsPHuXlljaSlkVp0M97l8wnz+uhNxrnF6/u40TfAD7T4OH5M2kqLbro9x6zOolkTo+7T1c8zMv/IPWBu1HEH84qx7VCOpXh1Mk+HMdBUbK891g0RduJXvLzfeTkZJMuhcUBCouDtLZ009MVHqkJGMbwZPFcOc/h18NUhTvvm0txSQ5f+sJv2bPzJCtWN41aOBCAqgocR46RSRw5QuhD+VttiIqqAA5SJhHCfc5xbuTQ6rUQLhjZlw2kstz2ONIZxErvze5Ra9D0WdkJrbmcTHItduYgVvpVXP6/vKjL+XgIZ5Ls6T/FnzYu5oNTbiGgu0YCv7fUzOMLu55iR18bT51q5rayaawsbrjka0wWU4PFfGbmbRS7AzQGCkfaAlkVt+ZQJ5/f+QSHBrt4peckLeEe5uWf9bVxqTq3lU5j/ZnDdCUjbO4+zvRgMdo4q7yWdHipq4VIOkmBy8uy4np05exxjpQ833GEX5/cjS0d3lw1i49PX0mZOzjy+YQzSX7Yso3vHHmZ7b1t/KJ1J38+YyWmOvFK0f6BMxwZ7GZOXjkfmbqcqcFiTDXbD2Qcm+5kBF2oaMr4gbQQw7Upg1gZG31o5WsYiqKc8xhk/8hYNuse38XAQIyH376YdNriREsP5x6lqqOvZ9sO65/cS8fpfh599y04tsPxY1nKpqap6LpGeDAxJnEkhBi5/rm7TJdOTq6H+x5eQFll7sixBUV+kokMiiquKAB+ef8JvvPkVhZNr+KjDy0jz+9mMJrk4MkuOvsiBL1XL8Gy6/Apth9so6o4l3uWTMdl6LR29FGSF0A/ZwIUS6T5j19v5sU9LaxZMIUVc+swNI2dR07x2xf38ZWfbuTv338X5YXBK25bIM9HMp7myO42IgNxispzcd0ASmNvjHBv4KpDUzRuLboNIQQbup4llBnrVHi1kHSSHIg0czx2nMX5S7i75B7yjAtzSC8GIQRVnioerXgbP2//KSfjJ8bN/EN2AtCd6qI71cX2gW0IBIZijFCXJNnCPEtmJjzHa4GAHmB5wQpOxdtJy/H185NOkh0D29kd2kVAD+AentAgcHCwpU3GyZBx0qSdNCknhSUtFBQKzULKPVkX3a5wlH/dsJl7Zk7hpupyosk0hf5sFs9vmrx5zjTyPB6e3HeI727ewZcfuoueaIyf7djHB5YtoDToZyCewNQ0LMfhO5t34NI0/mz5Ina3n+HrG7fwxQfuIM974RW+wXQbljM+JbHKu5Ia/203ZNBvZbJZOPUcc6xh196+zhCarpFfEryu3FHLctj84mHm3FRNfWMJ6YzF8xuaaT/Zy533zR3hAQdzPKxa08SP/vtFfvnjLTz0tpspLA5kA7RQnM4zISqqCygYKmiMRVMcO9JJcUmQ3HwfiiLIpC26OrO1YTk5Y/nFiqpQUBggncqwf0875ZX5qJqC40h0/UIZTw1Vn46V2Yvu3AlY2Jl9aPrEKyoCBVWfgWO1Y/renxU6kCmyQ6lAqGWo2jTS8R8hFB+qfmFfmAuhMVDEexsWEzDcIxQLIQQNgUI+NHUZh7b9mqiV4qn2/SwtrMVQr82za6oa91aOX6OgCIWmnFLurWiiJdJLKB2nPRZibl7FqGdxbl4504LFbO89yfMdR3ioag5Fbv+Y83UlImzvPYGDZE5eBQ3+0dr2g+kEv2vbSyidYEawhA9NWUa55ywVVghB0HDzaM18tvWe5JWeEzzXcZgHq+fQEJhYJ38gnWBObjmfm3k7Nb68UW3XFXXUNcaDEIKmOZVs39LC+qf2UFVXSCKepr7xwtRjKbOTgnA4weHm08SiF0+UDVN+IuEExw51Egln3+Ny69y0uJ61v9uJlBJdz04+muZUomkqgaCHVDLD7u2tFJYEqK4tpLAowPSZFezY2sJScwq25ZBOWxSVXB3Z7SNt3ViWw0MrZ7N8zlnT0ZXz6klnbIwL/j4vDc0nOvngmxbz9tvn4zLOZvcloylKrxw4yfO7jnH34ml84pEVeF3Z1cmbpmUTVT9ct4ONO4/xjtvnj1q1uRz4gm5uffAmpi+oRUpJRV0RxgR+LNcTV60Fcijb4gwtvyuqMsplcdSxUmJbztCSlkBRs9mdKxm0ssZDWXdIeZ6hhBBZM4nhWfeNtIR/tSClxLEljuOcc+/Z2bqiZO//et23I7NBYbWnhgZfI7tCO7HltTUWSjoJXux5nogV5pGKt5F/FYL/Bl8j76p+D78+9QuORA9P6h4kkpRz4ztjKkJhfu4CDkcO8coEKkbDsKRFf7ofuDwr8mPdfShCcP+cGRjnZF6klJTnBkjbNp2RKD6XSU8khgT8pkFp0M/Gw8dZM62eWeUleAyd3micPe0d3D1zCu0DITRFoTsS43QofNHAP5I5Pa54gKH4qAvchSaujsze1YTjODz7sy30doS4//2rCOb7kFLS2xHix19dy55Nh9EMjZX338QDH7wVX/D60BvzCnyUV+Tyf//pKfLyfaSSGdrb+miYWsI9988bofRomsqau2fT2xNmw9P72LWjlWCOBykl8VgK25Z85q/fNBL4D4ZifPP/rSeVzJCX78PtNohGE3ScDlFdV8iq25vGJNAVRTB/YS0bntnLj7/3EpteOITL1EHARz51B4UFCsOBedb8UAAqCB3DfS/JyL8TH/w8IBHCje6+l6zW7fCxDGXtDRACzVyFnd5PIvR5hOIHmcL0fQBVycoo6+67iA18Erf/04grUP+6ubCGPNM7Lq96dl45tf589g2cYX+og/50nBL3a1OMrwjBlGAxhqISs9LErLH9X57pZVVJI3v6T3E03M3u/lPcXjZtzOrPnv5TtMUGcKkaq0oa8Z1XC3A80kvzkAHZ0uI6yr3jB+T5Li9z8yrY1nOC7mSEQ4NdFwz8dUXlzvLpVPsuX3GutqGYd75vOZufP8TRQx3kFviprS/C4zWZ1lQ+4qgdCLqZMqMMj8fgtntms/6pPTz/bDN1DUXcff98vL4stbK0IpdAzug+SVUVVt81i3VP7Gbjuv3UNhRz74M34fO70DSVu+6fhz/gYvPz2dq/GbPO1vyVlOdw74M3sXP7cTwek8KiAPmFfh79k6U8v76Zjc82o+sqs+dXI0T2tztjduUVeWE0VBSiaQo/Wb8TXVNpqi3B6zZQhJi0Pv5kUZLn586bp40E/TAU+51zjONIXt5/Aikli2fWoCkKqcxZ6s2chjJ+sl5hT8sZ3nLrHNzqldGHQ71R2o52UFSex3O/2sapY10sf9O8i7qr9/ZHSSTSlJbkoE3QhkQyQ2gwRm7QO2JaOFlM+pO3bYcTh87QfSabvS0uz6VmailCESTjaXZvPsKW9fvpOtWPy2sybU41S+6YSWVDMepQw6WUpBIZ9m1rYev6/Zw63pM1YGkoYsGq6cxcWIfLM/nCiuEJRKgvyunWbtqOdtF+rIuejhCxcCJb2GJo+AJuCkqCVDUUUzOtjKqGYrwB91XhcF0qpJRk0haHdp8kFh49u8/J9zFldtVIodtkzuXYkr6uQVqaT3FkbzunW3sY7I9hWzam2yC/OEDNtFKmzK6iZmopXp8rO/5dg0mAlJLBzCB7B3fzSv9W2uNtxMcx8rpWcHDYNbATvxbg4YpHcKtXFsgJIaj2VPPemvfzbNczbO3bQsyOXqXWXvi612OO5lE93F/+IFEryoFw8zWjHCUtC0NTx3Rgadvm6xu3MhBPMLW4gP5ofKTg2ecy+as7VvDisVZ+tbOZJ/ce4tO3Lydj26Qsi5befgbi2ez9mmn1Fw36ARJW37gTHL9eQZ7ReEMmBGLhJFvX7WWwL8o9f7IMyK4APPX9l3jhdzsory9GOpLHvvs8ReV5rHnrzZelfDNZCAEVVfncee9c7nvwJg41n2bHKy0kk2mWrJjKitUzKC4dnS0MBN28+wMrWbC4gZ3bjtPZEUJRBCWlOcycUzUieQhQUBTgfR+5lb07T9LdOUg6Y1NZXcBtd81m0ZKGEZWU0W0S1DYU89m/uZ8XnzvAmVP9aJpKRXU+Xp8LzVyKps9DqAW4/H+OUAswPe8ExYsQQdyBv8ax25AIVLUKofiQMo078L9R1KxinKKW4w78L4SSj0DHFfg0jtWGlAmEEkBRy89pTxBVrUYzF1/RZz01WDRhMWVQd1HtzWPfwBn6UzG6E5HrEvhLKXGQI5nU4SSTJpQRuVtbOmOyrIoQrCxu4Oetr9IWG+D5zqMsK67Ho501pEvZFi91tRCz0jQGCllYUD2mKPdwuJtoJoWpaFR783CkJGWPz5nONz3oikrCynAmHhpjeHkufJrBjJzSK6oTU1WF6bMqmD5rrHDG2967bOTv6rpCquuykxCvz8V7P3zrmOMBlqyYOmabEIKSshze86FV477H6zW547653HHf3DH7NE1l2erpLFs9fdT2QI6HNz+ycMzxbo/Bu/9s/OtMBkIIls2u5U/uXMDjm5r53998irqyfFbOa2D57FqqinMnTA5fDnL8HvImcHcfRjKdoas/Qipj85+/2cz3146uM4yn0qTSFgPhOLZz5eNhdDBOd3s/HSd6qZ9ZQcfJXhLR1EUD/yfW76X50Bk+/6l7yJkgmXOivZf//J8XeMeDi1iyoO6S2jXpwD+Ttnj8+5t49pfbAFh29xw+9c9vxXEcfvGN51j74y3Ez1mm2v77A2x8fCfv/ew93HxbE4oiiA4m+NW3NrL2xy8TDZ9dct+1+QjP/WYHtz+yiLd//HYCuRPLRWUz+9kP9PDuNl55rpn924/TfXqAROzimVZfwE3t9DLueGQRS+6Yicd3/Yr4hoP+p3+6lR98dS3x6Nn2FpQE+dDfPjDpgE86kq7T/Wz49XZeemoPZ072jlACxoM34KZxVgX3vH0JN62chtt7dWXgLMfiYOQAazue5Hjs+Bj3V8hqJKhCRVcMTMVAuYCDLDBSCJvlkqexpTU08Ez8g3Rw2Na/lan+aSzIXXjF9yiEIN/M5+GKR2gKNPFc9wZaoseuWu3CMBQUPJqHWm8dywpW4FKuffZZCEGBUci7qt/Dkx2PsaN/+1W/L4DyYIDBRJLW3n6q8nJGinL7YwlebTvN3967mobCfH67u5m9pzsBSFsWqiK4Z+ZUltXX8KlfPElrXz8zSoupzA1yc00la6bXIxDE0xkCrotnpTIyxnjVnzlGLbpyYwoBxCMJOtv6mLWkAf8Qb/5MazebntrFjIV1fPyf346VtvjKn/+Abc/t55Z75+INXLtnRwjBqjVNrFqTLbAtLA6w/LxAYjy43AbzF9Yyf2HtBY8zTZ2FixtYuPjSOOuKImiYUkLDOZOIs/CO+BmqWvb6QjsbqCP8Q9n6czYJYxRNRwgXqn62TQLXSNHvMKQTxraOkkmsRTOXIJTx2jJ55JkTj4NCCIrcfgQQt9JEx8myXy0Ma+V3JyMcDXdzNNzD6XiIUCpB3E6TtC36UzHi9viUwWFU+fJYXFhLeyzEKz0nOBntZ1qweKSPPhUPsbOvHQHcUlQ/ZiIjpaQrESbtWAgh+O7Rl/nVyV0TXq8vGSPjZD0HolZ6RHRgPOiKRq55Y/YBr2d4XAYffPMSVt/UyHOvHuXlfa1867GX+fXGPbx1zVweWjkbU9cmNU6PX79zFpMRebAdiWU7qIog6HONcXvP9bspLwhSXZKHehUSKME8LwO9EQAW3tZEdDAxqcRuPJ4mFI6PcXg+F6ah0dE9yOnOsdLjF8Nlr7V0tvcRCcXZ8uw+nvzhZpLx0T96KaH9WBc/+OrTFJfnUtlQzNqfvMxj33uR1DgW07FIkqd/soXcAj8PfXDVBavRj+xt43/+ZS2Hd58cc92LIRpOsO+VFo7sbaN5+3He/em7ySm49pJ9Fwr6i8pz+cjfPcii1TMmla2zLYedLx3mB19dS8uBM5NSgImFE+zefJRDu05y6/3zefsn7qCg5MqLVwAyTpqXel/6/+y9d5gkZ33t/6nUOU5PT845bk7aoFVYrVY5I5GNweRwcQD7cjEYCxvzwwana2zAJAESkoUklCObV9qcd2Yn59g909O5q6vuHz1hZyfszAatxI/zPPtIU1VdXdXd9b7n/YZzeLbnGQLqTKlERVDIs+RTZa+ixFqG2+DGIBonJeHmgj4eWVL1JHEtTiAxSm+0l9ZQM62hVkYTI3Oo7oTZN7yXGkctVvnyaA4bRAP1zqWU2sppHGvgLd8+WkLNjCRG0MYNbRaDiUWQTbaRZcqm0l5FraOOHHMuRuntczgWBAGPwcND+e+n1lHPjqHttIVaiCajF3VPoiBilWwYpQklLoGyDA83VJbynVd2kWG3ElOTPLRqCVVZXqqzvPx490HSbGYi8QRuixkE6AsE+bc39mGQJdRkkgyHjXy3C4tB4cPXrOBn+w6zu7kdQRDw2ix88to1mMT5oyiaPnPcAbAqmbxTjf4ScZXwWARvbhqKUUbXdA5tP0PAF+Khz28jK9+DqiYprcuj4XA7sUj8ihL/P2BuaNoQ8fBvEKVsDNb3X/JvSrmAxLFZSjUsJzSNhHZlSiknpDP/p/0IT3ccoz3oI64lkQQBSRAxSjKKKKHp+gXnIaMksyWnkpe6T9MbCbBnoIVyRwbyeFPuW0Pt9EUCuAwWrs0sxXBeI62OTlhNTGYaOoJ+BGF+0pNK6guTAcO5hlVBAOUKZsreDkSTCU6MdCAJIrWufAzvEClmWRKpLMigIt/LgzcsY//pTn78/Jv8x5N7KMh0s6F+KhggCEIqNqMzI0MzEoxccpecUZGxW4yYjAqfvHs9KypmlzW/XLC7rdz6gQ2pfiODzOobarDYLp+Up6ZpxC5CJeiifxm+wQAn3mrmhV/tJRZJYLYaMZoUQsEoidjUhXQ29fPabw6y+vpqnvvFHmLRBLIiYbWbSSaThMeiaFrq64xFE7z+m4Ns2LaE3OK56/EEoLt1cAbpF4RUo5esSJitRiRJJB5TiYbjqInktIEpFknw6pMHMFoMfOQvbsNoNnClMB/pz8pP49PfuI+V11YtqPQomdTY+fwR/uvhp/EPjk3bJ0opbW2TxYgoCsSiCWKReGqlPH7r0XCcFx97E/9QkE9//R7Ss12XRP41XePN4X083f0koWRoxn6vMYOtmdtY6V6JXXZc8kJjOZDQEnSGO3i29xlOjB6fNQPQFm6lN9pDme3im+vOhyAIWGUry90rqHPWMxQboi3cQkuwmd5oL4FEgHAyRGw8O6HpGqKQkiE1iEZMogmrbMWpOMkyZVNoKSLbnIPHkIbxMqgRXcp9mSQTq9yrqXXU0Rnp4HTgFG2hVvwJPyE1REyLomoqOjqSIE1Kq5okExbJiktxkWHKpMBSQJYpmwzjVFObQZb4o2uWs6W6lNFIFLOikJ/mxChL/OmWjXT4RpBEkRynHX84igBkO+18avNaAtEosiiS53ZORvVXFORQ6HHRNxpEEAQyHVaM8oWHMlGYfWGgiFYWr1D99kAQBSRZQtdSJVBjgTCHt58hPdtF3brS8f0iJouRWDQ+OZb+AW8/JLkEi+vhy3Y+9QI9RQktiQ6TJPxKIKTG+d6pN3iy/QiqplFs97Axs5Ql7lyyzQ5MsoIiSBz1dfF3x14inJx9cT2Bencuta4s9gy28kbfWe4qWEK6yUZQjbF7oJmYprLKVUD1HHKfE5vMksLdBUsotc/NE85FrXv28/0+YSQe4tunnsEqG/nHFR8izXhlfGAWionF1kRfpSAIeJxWbl5bRVLT+NqPXuR0W/804m8zG0gkk/jHppcIxxMqx5t7L3l8U2SJ+tJsdh9r5cjZbpaU5iBL54omwARZuhy/l0gwyuv/s5+zxzuRZQmnx8aDn70J8yzkX9O0yfvTxl2jk0kNVZ05DqhJjWOnu4nFVayWxfdgXDTxD46kynaG+ke54Z6VbH1gDXaXheaT3Tz2f1+lq2Vw/GZ03nztJB1n+xjqG6WoMou7/3gz5fX5xKNxXn5iP68+sX9S27Svy8epg63kFKXP8eALFFZks2pzFS8+ug9BELDYTeSXZlBen0/F0nxyCtOx2EyIokgioTLcN8rhXY3seeU4A11TEQI1keSNpw6x5voalm+suGJ174m4youPziT9OUXpfPZv72fZ+nKEBZB+Xdc5uucsP/jmM9NIv8EoU7mskHVbaqlYkj/evyASiybobh1k/xunOLDjDGP+1MOkj38nDpeFj3/1rosud9J1nc5IJ8/3PTcr6c8wZvCBgg9R5ai5LBr7E1BEhRJbKffnvQd/3E9npGPGMUE1SFe4k1Jr2RX5XhVRIducTbY5m3Vp64lrccLJEJFklIQWJ6knJ1PLqfImBYNoxCyZMUmmK+bKeykQBAGLbKHSXkWFrRJVVwmpQSLJyHipVeqeREFERMIgKuPkP3VP833HsiRR5JmpZ243GanNmVokOMypAVGRJIrTZ9c/FwSBdJuVdNvisjmKYIFJ94ApXEnPhEuF2WokPdtFy8kuRobGOPlWM41H29n60DV4MlONjWoiSSQUxWCQFzSO/AHvDozG5zbF1HUdfzwM6BglBdM8UpWXgr2DrTzXeYK4lmR9RjFfWbKNEnv6jNr73sjogsZZt8HMddkVHBzupGG0n+P+Hq7LKqd1zMdxXw+KIHJdVjlOw0xiJCDgVMxIQsqsa623iG25Nb/3hP7djJf3N+BxWMjPdOGwpHjGcCDEseYeJFEkO316P1BNcRaiIPDUzhOU5HjI8jgIRWK8cbiJQ41dl3w9ggA3rCjnlbcaeeKNozgsJtbWFmIzG0moSXxjYVp7fNQUZ1KUNb9Z6ULgGwggGyQqlxVStaKIM4fa5ixZ2rHvLNv3NpLUdJpaBxgJRPjH/3wFg3IeTddhLBjlbNsAdquJytLFm9ZeNPGPxxJ0nO1j461L+ZP/fSdOT2p1WVieBQL83689SWSc5Pa0DdHbMYw328Un/voelq4rm5ygvDlueloHObq3KXXeaJymE13ccM+qOaWUjGaF6+5cQWdzP1XLCll3Ux2F5VnYnLPbc5fW5LJycxWb71jOf/7tU5w53D65b2wkzM7nj7L0mjKkC5hdLBbzRfrzyzL43MMPULemZMED11DvKD/5/57DNxCY3OZIs/Lez97EjfeuwuaYef8VS/LZeMsSDu5o4Ed//1u6WgZS16bp/O63h6lbU8KN9666qMEzqSfZNbidwdjAjH2KYODmrFuodFRfVtJ/LrJM2SxxLaE70jWrS/BgfJCknkS+whKNgiBglIwYJSOLterRdR1NTy0Qrkaz+VwQBAFFUHAZ3LgWfVfvTJhlDwLCjBKmhDZ77f87AY40G8uvreKpH7zB1z70fXz9o9icFjbevhxlXBYuPBalp22ItEznDDfdywld14kmVM4ODBNNJCj1ekizzj7mvl0IxuIc7+ojpqpIosjS/Cwcpqvrinm50BocntEkO4GYptIZGkEHXAYz6fP0A1wsdF3nrcE2QmockyTznqKVlNpnBuR0XccfC5NcwAJaEAQ2ZZbyq5YDtAaH2dnfxDpvMXsHW/DFwmRbnKzzFs+awRAEgWK7B5OkEFETtAV9aOhI79Bs3R8Ar+5v5MCZDjxOK1ZTSrglEIoyHAhxw8ryadF+gGtqi1hfX8y+E2184Z9/g9tuIRpPEEskue2aGh5/48glX1Ou18kXH9rMvz2xi399YiePvHQQk1EhmUwSCMcQBYG//ZNbLgvxN1uNuL0OdE1n/+un0ObpU3C7rCiKTFvrAIPDQeIJlcPHOmaOr0IqkJaV4eC+21ZQVpSx6Ou6pFnCYjdz/V0rcaRNDTqiJLJsfQX5pZk0Hp0eiV1zQw01K4unRaVc6XaWri/n6L6m8bouGOj2Ew3Hsc5haywIAjWrivg///ERHG7LguriJUmkclkBH/6zW/mH//UII+MNF7qu03Kqm1FfiLSMy6eKMB/pL6rM4nPffIDqFUULnjQ1TefFR/fRdHLKgMhoVvjQn97CtgfXzrtoUQwya2+sAeC7X3qUgD8VnY9HEzz3yB5WXluF27v4PgdffJhTgVOz1oLnWfJY6lx+RSPboiCSby7EIBpmbUoNJsZSxP8dbFcRV5P8es8xirxuNtXM3/h4OTHhfgs6srT472hC1SOpadMcdSeIysTfcPEpU23c7l0SRURBmPH3YmFX8hAQZ0h6htQ+dF17R1b7yLLEtvdvwD84xvG9jWTkpXHXR6+jpHaqNrWvY5jBbh/X3b0K8xU0h9F0nWePneF7r+4mnEhwW30lX962GfsCGquvFLr9o3zlqZfpHR3DajTw4z+6j6OX0swAACAASURBVPrcS2uqfafg4FAHETWBRZ7pytse9NE6NgRAgTVtVk38y4HRRAQNHYtkIMNkm/VZTugaR3xdxOdQ1zkfeVYX6zOKaQ/6eGuoneaxIQ4MdZDQk6z1FpE/h0QnQK0rm2yzk6axQXYPtHB3wVKyzJe/Ry/V0DwuTY6Aznj5BeNisMLcjaQT4+BCj594zYRaEjD5vvO9x7nHX2xwLfX65GXxmBHGZXLPveZP3bOePcdzaOkdJhCKIiBQW5zFioo81tYWYDNPFxlx2c185UNbeP3QWY419RKNJ8jzurhhZRkepw3/WJj8TPe0HjiLUWF1dQHpLuuCgmeCILC8PJdvfeo29p1s51hTD/5gBJMik5/pZnlFLrXFc48hF/rMzv0c3F4HdWtLGR0OkpblxJlmwzIHr11ak0d9VQ7+0TA/+tVujp/u5vMfvQH7eWVBAik+63ZZcDsXds/n45IYUVqGg+KqmbVzNqeZ4qrsacTfbDGyZF3ZDPMCSRLJLvBgNCqTTb+jvlCqSW2ODwhAUWRcnsXVsAmCQOWyAupWF7PrhWOT24f6RhkbCV8k8RcQz+vSnq+8p6Qml89/834qlhYsarAa7PHzxjMHU7W+41i1uZob71m1oEyFIAisvLaSVZureOPpg5PufS2nezh1sI0N2+Y2rZkL3ZFu/InZtd3LbRXYLlNj7XywK3akOSL6CV2dlgkYiAQ5OzKETTFQk5Y5zRHyakHTdbqHAzjNbz952tfYjiiIrK8qvKjXNw0N8+tjJ3jvsiWUelLRkTc7Osmy2ylOS2UJugNj5DoubmLu8I/wyOGj3FdfS3WGl86RUR45dJR76qqpyVx8lMNpKEQRLcS0wLTto/F2EnoYiSvX53MpSM928Ylv3MfoUBCDWcF+3gSXU5TOX33/o3hz3Fc04h9Tkxzq6ME3LqN6oK2bkUj0qhL/txMTJOvcxe2VxHF/L2/0NXJLXg2iPrWYjiZVnmg7zEA0iCQIbMmpvGCpz2Rz68TfE//mkbgUBAG3wYKIQDgZpz86hqbrk4vuiWzlnoEWXu9tRFsgeTSIMjdmV/J81yl6wqO80dtAw2g/dtnI5qzyee8lx+Jka2417Q0+jvq6+GXLfj5asR6nMiUmkCrT1tHRCalxwmqcdJNtUX0QY2qEf2t4iUyTk9tyV7B3sJE3h5sIJMJ4TQ42eqtY763EIk2XH9d0nf7oCNv7T3HY38ZYIorX5GB9egXXeCuwy9PLanVdJ66pHBvpYMfAKdqCg0iCSIUjhxsyayl3ZM+47qSu0Rjo4eXeYzSN9WGSDKzxlLLEXbhoUQhND9Pl/xrRxOwO9YuBUSkkz/11ZME5ufjJynCw9dpKDJKMy2CaFhCC2Z8hj9PKA9cv44HrZ0qSfv2j22Zsy0yz8zezbJ8PgiCQ4bZz58Y67tw4uzndXNCJ0zv6zwSj+2bdb1LKyHN/FUmwEw3HefXXb9HZ1MeDn9vK2WMdOD22ORt8RVHE47ZRVZpFR5eP0iIvabOYFl4qLmmWyMx1z3oDBqOMN2f6qt1iM5JfNnstks1pwWCaIv6RUGxeacpLgcGkULm0cBrxj4RiRMIXJ4cmyeKkWQ3ME+kXoKI+n8998wFKa3MXPWkc2XOW/s4pkm0wytz8nrUYzQuv7VQMMhu2LWHn80cneyoScZXDuxpYu6UG5fxasgugN9ozp6lVlil7TkJ+OaFPTmEzIZ8nF5rUNBpGBjk01M3frtl61eTbNE2ndyRA93AAi1GZ1AvWdZ2YmqS130cgHCXTZSc/3Ykkiql0eihCa3/qN1CS6cFlTT17cTVJ24AffyiC2aBQ6HXhtJjo9gXQdZ1gNM5oOEpumoNcj5O4muRszyDPHjhDltuOrut4HBYqc7wL/l0KgkCpJ40it4twPIGu6wyHI/jDUYrT3Gi6zom+fn5x+Ch31lRRl5VaaDUMDmKUZcrTPYxGowRjcQaCIcq9HnRdp3nYh9VgoCLdQ1Gam/J0D6F4qom/0O2iwushGI+jahrt/hGK3C5iapLBUIgC1/wqVTYlG4ehgMHoiWnbg4leRuPtGE2XR+XqSsBgVPDmzl5y5Uiz4Ui78o18sijisVomsy8emwXTApqqf18QU5O8erqJmKqypboMp/nKlRRJgoiOzj8cf4X+SIC13iKsspHRRITnOk/weNthdHTq3bkpI6zzXp/QkrQHfQQSUWJJlVhSZSA6Nt4XAP5YmJe7z+AymDFKMkZJxiYbKbKnTVODWZVewBNtRwiqMX7W9CYug5kyuxdREPDFQuweaOGR5v3EtSRGUSamLSzqX+vOod6dw87+Jp7vPsVIPEKtK4ul7vnnRkWUeKBoOcf93ezub+GnTW9yNjDAttwa8m1uFEEiriUZjoU4PdLHgeEOqp1ZfLH2+mmeARdCQtM4O9bH6dFuzgR6aBzrIcuUev7eGmpie/9pPlRyLR8o3oRhfJ7TdZ0zgW6+e+Y5WoMD5FvSscgGGgLd7Bg4xY2ZdXym4mbc5zTdxrQEj7Xv5VdtuzFJCjlmNzFN5ZmuA7zSe4zPVW7jusypHjlN1zkw3My3Tz2DLx6ixJaBqms81r6XXYMNhJOxRd0nJAknThOJH1/Ea2aHpgfRxyW3h6IhHjt7jNc7mxmNx7g+t4SvrE75FXSHAuzr62BzbgnpJss5jbUzFwR94TFG4xEqnAufm644dI1YoolQ/MDsu0mgj8uZ+wYDqR7Ussxx48XRlPjNBYbrsuIM2rqG5zTvulRc0qjtSrdjmMUxTBRFrLaUQdZEl7LTY5szgm80KZMmX5AioxfSbL1YiKKA2zs9Lapr+jQlosXAYFSQDVMP/lykv3p5IZ/75gMUV+Us+j2SSY0Dvzs9raM9Iy+NkpqcRT8MuSVe7C7LtD6B5lPdxCKJRRP/MXUMTZ/5PcmCjFl6e2p/xxKBORcfJsk8rdQo2+rg2pxiTo/0TztOG49cQUohYyIqkUqlpqJ7k1Gu8eMZ37bYyJ+u6zT0DPIfL+7FY08Neg09g9QXZBJLqPxy5xFOdfbjcVgZHA1y64oqbqgvo9s3yvdf2ocsiaiahgB87taNeOwWnj1wml1n2sh22wlF49y0tJx1FYW8dLiBvY0dFHrdJFSVvpEgf3nvdbitFk51DdA+6EdNJjEbZEoyPVTmLEwhYy6IgsDpwUGyHXYybCmH2UQyiUmWERB4oaExNfiFwoxGo/gjUU72D7AmP5dEUiMYjxOIxtjR0oZQXUmVN33O9xKAFxvOcmdNFT2BMXoDY+S7nPPGu4yikyzzSoaip6aV+8S0UbpCu0k31SBxZZokfx+gSCL3r6xD1TTGojHuWlZNmvX/H9Khuq7T6R/lOy/vRACW5+dcUeJvlhQ+WLqG33Qc5TsnXsNhMGGRDATVGIFEqqyx0JrGF2tuIMM0M6Pmj4f52uHnOD3aR1xLzpD7bA/5+PLBp4AUmTaIEiX2dP5l7f3kWKaCduu8xdyUU8VvO49z2NfFF958Aq/JhojASCKCLxamwOrmr5Zs5bHWgxwc7lzQ/TkVE9dnV/DWUBstY0NIgsDmrHLSFhCMyTY7+Mv6rfyj+Bq7+pt5o+8sv+trwiIryIJIXEsSTSYmMzMF1ovvUTo71otBlPjGkgepdKTm7oO+Fr596mme7znMlqx6CqypcWokEeKHTa/RHfbxxarbuDajGrNkYDAW4MfNv+Pl3qMU2TJ4X9EGREFE03X2DZ3lF607WeIu5NMVW8mzeEhqGof9rXzn9G/5YfPrlNuzyJ94j3iIn7RsZzQe5vOV27gpawkGSaYjNMS/NLxAf3SUdOOVKftaKPyxCF/Z+zI7ulvJttrpDwcZiEwZXwbiUf7+wO9QNY0Hy5dMbvfFwowlYhTapr6vPf1tHBjo4hurb0Z+pxD/RcDhthIORmk43J6qKslyYrJeeGFWWZZJSWH6ZXc3nsAlndVqNyErs5dLyAYJURTRxgccu9MyLTJ+LiRZnFb3r2naFVPaEAQBWZGmLUrOdSBcLAxGGUWRUnrziSQvPvbmNNIvCFC7uoTPPnx/qvH5IhDwBelomk5Ws/LSFl3qBGCxmWYQ/4AvRMAXxLZI/e+5CPdEFH6+NPLlgKZrdEU6iWszvRwEBNyK+4KNvfFkkjd6mtjb144kiGzNr2BVRh6dYyM81XoSXyxMmtHC+yqW4zKYeaWrkYODXaiaxsbsYm7MK5u1ucw3MEr7mR6SqoYjzUZJTS6yQUbVNLafbKEky8Nnb1mPPxjmrx99BYCmvmEOt/bw53ddS0G6i72NHTy+5xj1hdm8frwZh8XE52/bQCyh8u2ntrP7TBu3rayidcBPdW4GD21citmoTF5NUtNxWkz82Z3XAvCtJ9/geHsfd62p5e41tZzpGmB1eT63rqi6tC+C1HPlMpvIdaTK5URBoMDtIsNmY0VuDkldZ1drOzkOO6qm4wtH0HSdFTnZbC4pRtd1Wn1+OkZG6QsGJ8tJ5oIkiizNzuJEXz+DoRDXFl+4X0YQBApt19MceJ5wcnDavtaxVymy3UCasfKdE1l6h0EQBIrS3fzlLZuv9qVcFTQNDDE4FiLDfuVLGGOayrqMIjZmlvLjpr0c9/UwFAsiIJBtdrDck88fl19D7Ryyl4ogUebwYpYXvpDNNNlRztN+tysm/rzuRrItTl7pOU1vOEBH0I8iSniMFm7Lq+VDpWuodmUxEo9gkQ3kWOZfgEPqt7Q+o5g8i5umsUG8JjvrM0uQF1B+KQgCpQ4v31xxBy93n+aF7lO0BocZjUeIayqKKJNpdpBldrA8LY878usxSTPnAVEQKXN42ZRZittgwSLNJGRGSeHegrUscU2V5i53F7HMXcTuwQZ6IyMUWNPRdZ0TI50c9XewJbt+kpADZJvd3JW/mt2DDewebOC23BW4DVaiyTgv9x5DEATeX7SJEtt4RYQIqzylXJdRy+MdeznoayV//D3OjvXSGOhhmbuIm7OXYpFTZXaltkzuzV/LEX/7jHt4e6HzUnsj+/o6+PLKzWwtKOfz23877Yg8q5Nsi52DA928p3wJjAfZXu9uIqwmeF/5ckRdmNbHpek6qqYhCqm5/dyeMk3XU1Lu4786bVxN79xes2klauecc+J15x6nc3FBvdngcFvZcv8aqlcWI0kiGXlpCyrHlEQRyXjlfCUuifgbzYZpkfpzIQrCtGY5o9mwIMcygHmqN2Y/fKL2UtfRNJ1oOE44GCUWiZOIqahqkqSqTf639UwPl2tdoYxH/LWkxvZnD/Pzf3zhHNIvsPSaMj7z8P3kzeNLcCH4h4IzNPsBDu1sXLR8XygQmSzzmUA0Ep/Wh7BQGEXjrCopST3JaGJ0XqfES4Wu6/jiPk4FTs2q42+STGSaMi/44J7w9fK77hbeV74MXyzCL88epsjuZndfO5Fkgj+qWoWm6zgUI6PxCC93NnJPcR0FdhcmSWEuC7IT+5r43hcfIRKKsfzaKr78/T/GmWYjoSYZCoSoLchElkSsJiN5nhRZ7hsZw24ykm63pkiW100wEsMXDNM64ONM1wBff/SVVB3paJDKHC8GWeLO1dU8sv0wX3vsFdZVFLBteSVWowFJFCjJTMNiVIirSVxWE5H4/DrbC4U+btijjTfBTWRNzv0njUe1gvE4JlmmwO1ia3kZuU47Rlnm1bPN0/T3X248y/uXLyUYjzOxfNTGm5AnsiyaNnX+uqwM/uvNA6RbreQ6HQv6pbmNJRTbt3Bq5LFpUf+Q2scx309Zl/EXmCT3H8j/HzANSU3nQFv3vE6alxOqlkTTdVZ5C6hzZ9MTHiUQj4KQUvHJMTtRRGnO36nDYOKh4lXkWBw4DfMHdFRNo3F0gByLE5dx5rHpJhufrb6W9xavZDAaJKapKKKEy2Am02SfJLgfLlvLh8vWLvgerbIR1/i1rfTkU2KbO8N3PgRSzsYPFq/kjoJ6BiJjBBIxkrqGIkrYZCPpJis2eW6HeoMo8cHSNXywdM2c72NXzFQ7p5cfyaKES7Gi6RrRcbdiDZ2msT6iyTgiAgd902vmRxJhBEGgLzJCMBHFbbASSERoGuvDIhnpjfiJDk4FsHR0YlqChJakLTQ4vg26I34iyTjljuxJ0g8prlFg8eBSFlu+KiAgkrK2nvhtX/xvXNdhV28bNWkZvLdiGUZJQj5PfMVmMOIwGOkNpThNSI3zePMxnmw9hixKHPf1UmxP45M11wDgi4X41xO7aA/6KbS5+UjlatxGM/54hMebj3LGP4DDYOI9pUupdmfys4YDZFrsbMtPlcCd8vfzbPspPlu3kbOjQxwc6sQqGzk01EWa0cKnatdjV1J/P9N2kkAixjJPDvcU1+NQ5v79XAiapqFrOha7ifIl+QDse+UES64pn3Rhn/tznF0YY4rrTnlaXMz1XTTxFwRQDHMPPDPeSJEWpL6zGOi6jpbUGOobpfV0Dw1HO2g53YNvIEA4GCUeTZBIJEmOE/5kUpv8/8sFg1FGkkUO7mjgx//wHMHAVKSyrC6Pz//dA2QXLnxAmw3B0TChsemqNQe2n+HA9jOXdN4JJFVtxmJgIXAb3OMpy5mf59lgIxvSN2GWrkwpgKqr7B7aSWd4poY/gFNxkWvOv+B5GkaGaBwZ5Fdnj6DqGhE1QUiNc01WAa1nh/lZw0E2ZZeQb3PhNJjYnFPCCx1nKLKncVvh4iPloiBiVGRC0cQ4cdaIjn/2FmPKvCSeTGLRdcKxOLIkYlQkHGYjq8vzuW9dHRMraqclVWpQnp3O/77/Bs72DPHLnYcJRmJ8+PpVANNtx897VlNRjkXfApCaGva0d9LmH2EkEsVmMDAQDNE87GMgGMRuNFDqSaMuK5NfHj7K3bU13FlTxStnmxAFgdurK/HarNgMhslruaawgBcazmI1KLjNZt7s6KR5yEdvYAyXyYQvEuHs8DBdgQAuk4lKbzo2g4HydM+MyWXuz1+m0nkvfZEjDMdOT9vXGdqFcdjJcs/Hf2/I/8REcT5hFS5TROtSMBGxm1bbe841Xcq1Xc771nWdYCzGsa6+i76eS4FJUiixL24OUTWNnX3NXJ9dfkHiH0nG+ffTu/hQ2SrWZhTN2K/rOi2BYbItDmrd2Yu6jrmg6zpHfV00BgYwSwrXZ1dgXVRtegopU0UjxfYr02BulhRMs2QCBGEiPpn6fSU1jZF4mISe5MnOt/hN51uzns9rcpAcny9DapRoMkFfdIRvHH9i1uMFQSA2boqmoxNMRMf9DGYSR7NsmFyELRSiYCLH+SViageq5iOp+VE1P2rSj6aH0PQwSS2CpofR9AhJLQDM3385Go+RZrRgnEMtLhUs0jGMB41NksItBZU0jA6QbXFwb3E9RkmejNA3jAxyd1EdN+dX8t1jO3ilu5H7iuv50em3iGkqf1KzjqPDPXz3+A6+teY2Sp3pPNZ0hI1ZxdgVIy93NeIwmDBKMoFElCdbjvOhylV8tGoNCU3DIiu0jfn4j5N7uL9kKblWJz88vQ9FlHiodBnSRY5Drzz2Jp1N/YjnBMd9/aPUri654GtjcZXObh9ZGU5sVuNkRmIkEGHfwRb6BgKUFXtZuaQQs2mm6teFcPERf0FYFJEXzssAXAomCH/rmV5eeWI/B7afob/LR3IWh7MrDcUg03yiix/+/W+nlc8ARMIxwsHYJZe8jI2E0bQr0/MAU9HbxSLXnIciKqizyLg1BM5wdqyROmf9ZdXx13WdhJ5g19AO3hh8DVWffcFSba/BbXBPe13qv0zKxuq6jtNgpCYtk49Vr0kNNgi4TWZEQeDPlm6mcWSQnzcewq4YWZ2Rx51FtWzKLuaFjgZ+3niILy7dtKhmKqMiUVuQyUuHG6nMTWc4EOZs3xCry/KozElHlkReOtxAbX4Wrx0/S1l2OjluB2srCvj17mN0Do2SZrcwPBamKtdLIqmxv6kTi9GAIomk2SyEFxDVF0WBdIeVY229lGSmYTcbyfM4L/i6ydcLAteWFHFtSdHktrJ0D+uLCqYdd2fN1OIoEyj1TEXXchzTVbS2lJeypbx02rZrCgvO+zu1mEskkxzo6gYB6rMyFvV82ZQcVqZ/mj39f0dQ7Z3crpOkKfAckaSPpWkfIc1YjsDCgxtXCzFV5UzvIMFYnDSrmfKMdCRRIKnrtA352dvSwbGuPoaCIURBwGuzUpnlZXl+NtU5GXO6HvvDERr7h1Dn6LeaeK/FNqDpuk48maRpYJj9bV2c7h3EFwqjAzlOO8sLcvHarTOmC0GAIo+bHNfs6msCqfilpun0jAbY19LJ4Y4e+gJj6HrqequyvKwtyacy04sizS6xOLEgUZMa/nCEoWCIo529dPhGgFST7+GOHnpHZ2ZhAZxmIzU5mRclObsQTFzf+F+pe0afln2URZGPVq6bkZGc6F2aPME5u3WYJKXnvm4sEeORpgN8oGwVJXbP5H1d0pyWiPHbzuOMJaIsTctldXrhO/I5ExeYsxaEiV4JmYcK17PeWznrcYookWVOjbOSICEKAiW2DD5ZfhOOOaL159bsK6KIDjN6NiBFqBc7jwuCgsO8acb21G8sjqZF0PQomh5B00N0+f+GYOzNeU4IuVYHjSNDhBJxrMr0uVHXdTrG/HQHR9lWlPqMZFEky+LArqQyQHnW6SILNe5MbsgtxyBJrPbm0R0aZTga5uBQF9vyK/FFw9gUI4OREO1BP0s9OTzadJhT/n7Knek0jAzw6doNkwTebbRwS34VjnNM4g4NdaPqGgZJIhCPkmG2sX+wk3uK67CIF6f2ZndZuPWDG6YJ4Lz5yolpC4G50NI+xMPfe447b17K/bevRJYEQuEYP3hkJy++cRI1qWEyyrz37tW87961i+4FuKRSn6vxoOq6TjgY5aXH3uQ3/72Dod6RWY8TBAFREjCaFBSjgqJIyIqMrEhEwzGG+kYvy/X4BwP86FvP0tM2NGNfV/MA//nwU/z5P74Pb7broj+vCbWjK4aL/BqzTFlkmbJpDc2UAgsmgzzV/T9YZAsl1tLJuryLhT4uzzYQG+D1gVfZO7ybSHL2OnCX4mZV2moUYaq+VdN1jg33sruvjdYxP8+2n2J9VhFrMgo4MtQ7Tu4NpBkt3F1cx87eVo77ejGKqciD02hiMBrikcZDyILIcDRMns2JvMhFjSAIbK4pIRSN88xbpyjwurlzVQ1ep400m4VPbF3HM/tPcrLzKCWZHu5YXY3JoLCmPB90eONkM7G4Sk6ak+q8lKTlwGiQA81doEOh18Xda+sQBMj1OCdJmyik9nlsqclFFkXuXF3DY7uP8sudh1lTlr8o4n+1IQgCdqOR++pqcS7SsEkQBLLMy1nj/VP2D32PscSUN4ZOkq7QLnyxRkrsWymybcFpKEQSpjIT7zSMhKM8/NzrnOgZYENpAd954FYMssT/HDrJT/ccomckMDN5f/Q0tTmZ/Nt77yDLOXsz4Mmefv7i8RcYicz0yAC4oaqUv79n66Tb8kKg6zr9gSD/vfsAzx1vwBea+Qw/fvDELK9MuTn/2U0b+fD6FbPul8fVr547fob/3PEWrUP+GRH/5443kGYxc+eyGv5k02rclpmu5TE1yU/2HOTNlk76x4IMB8OMRWOTn6E/HOH/PP3KnPe4piiPH374XsSL8MdYKH7YsBdd12kL+tmUVcLOvhbWZxZxa34NHUE/P258i8FokM/WbKLWneotS2oab/Q2saOvmZFYmEAiympvIQ8ULwVdZ29/G7/tOElETXBbQQ3XZZXRMDrAr1sO87veJgYiY9gUE+8pWcaq9AtnUycwfaECETXOr9sOsbO/GaMkc3fBUrzGK69KdSUhCxI5ljREBBRRYpm78IIBL5fBgsdoZzg2RoElnWLb/AEMEYF0owNREOiJ+Ejq2qTUp67rjMRDhNUYDuXSs+ypzJgRUZrKpOi6iiTO3zgsADcXlPNyx1n++9QBHiivH1+QpEp6Wkf9/OuxPQTVOFsLKhZEPZwG0+RiUxYlNDWeauBWE5zw9dEbTgVcN2UX4zFZcBhMrMss5Hc9zYTUODbFSJnDM/nZOg2mGVLeoUSMkViEvX1tqfp6UeSazMJFz+/nYu1NdYiSSFLVCPiCiJLINduWYLVf+Ptpah0gFI6R5XUgialo/4Gj7ezYd5bVy4rYsqmKl7ef4sU3TrJ+VSmVZYvrH31XabHpeqp+//Hvv85v/nsH8XMJsZBqIC6qzKa8Pp+iqmwyctxYbEYUQ4rwy4qELEvsfvE43//Gby7LNQ33T0X5RVHA6jAzNhKe3HbirRZ+8u3n+PQ37lt08+wEZqvjr1lZTFld7kWd73yYLIaL8jBwyE6WuZbTEW6ftdG3I9LBj1r/ixsybmKFayVuw5TxxoUI1JRmtk4kGaE32sPRkSMc9B9gMDYwp3mGJEis81xDsXW6I7IgCLiMZtZk5LMmIz+VIlYMpJutfH7JBtrH/CQ0jSyLDauisDYznxyrAx2dHIsDr9lGUte4s6iG0XgUq2yg0O5edGoVwGoy8MD6JTywfsmMfUUZbj5/28YZ2xVJYkN1ERuqi2bsu2dtHfesnalFvG35VNRJliTuWzfl1SAIAnke52Tj7zsZ+kSa5hxIAlR6PRNHXETGSiDPeg0Gyc7h4f9kIHJ0Ws1/WB3ghP8RmgLP4TFWk2NZRZqxEovsxSg5UETLeG3s24GFL5qHQxHGonGeO36GH+7cTyieQJFEbEYDsigRSSQIxxNouk6ZNw3HPP4RVoOBonQ3A4EgkYRKNJEgpiYvus5d13V8oQjfeXknL5xoRNN1nGYjtTmZFKallGTafCOc6hlgdHyxIQA2kxG3xYTHZiXLOTdBlCWR18408+j+Y4xGohhkCa/NisWgEE2oDIyFiKkqvnCER/YdJpFM8udbN2E6T80srqq8erqJ072Dc7xTKsI7Vyz47VgcHhrqYo23EKts4Kn249xdWM8z7Se4LruMPKuLx6/4awAAIABJREFUj1Wu4+uHXmQsMbVo6w6P8kjTfj5Tk4rw/vOJ7azPKMIkKYTUOF3hET5cvoaWwBA/aXyLKmcmBTY3txfW0Tw2zAfKV1NgdZM2Sx/AfOiNBPh16yFkUSSWVDk50suh4U6iSZWtOVVsza2eXpL4LoQgCCx1FZBpdrJj4DSbM2qocGRP/kZ0ID5es28fJ+ZW2cQGbyU/af4dL/Qc5iOl12M+p6xIQyeoRrFIxsl+jlJbJl6jgyP+NtpDgxRbU8EfVU9ywNeCPxEiyzy3AdqVh8D67CLeX7mMH57az29aTjIQCdE8Osz7XnyU7lCAqJrg00uuYXn69MZ0oyQTVGPjTvbnxiNnPk8ug5lcq5PNOaXckl+FKEB4wuwO2Jxdyt8ffo3u0Cg35VVgOrfJfZbHs8yZTqbZzseq15FhtqFqSZK6fkleP5IsER6L8uKv9uDrH0XXdAqrcrju7pUX7HcdGB7DYFDIz01DEATCkTjb953FbFb4owevoaosG5vNxLf//SWa2gZ//4n/vldP8Nuf7Z5G+o1mhQ3blnD7BzZQXJWNyTL3ZKbr+pzOaZcCZ5qVbQ+tY/nGCn7w8DM0n0pFEXVNZ+fzR/HmuHn/F7ZiMC5eLtBsnplqWrGpgvd9buuim3svJwRBYF3aNRwdOUJLqHnWYwZjgzze+SjbB1+nzFZBua2CDGMGFtmKQTQgCSlNnFTdX5KEniCWjBJUgwzE+ukMd9AZ6WQwNjBnhH/yehCoc9RzY8YWDOel50RBoNiRRrFjpg23TTFSmzb9wfGYrHhM09U7REGizHlp/Rp/wOIRSHRywv/IZVH6EgUJRbSM/7OiiFbyrOsZS3QRVmcSvWjST3d4D93hPUiCEZPkwiA6MEhWDKIdWTAhCjKcN1VdLlhkD3XuD2CQFhYRHY1EefroKX755lFMisIdS6u5rrKELIcNWZIIx+M0D/rY19LBxrIizMrc41Fdbib/8tAdRBIJogmVaELlN4dP8uj+Y3O+Zj5ous7Lp87y6ukmNF0n3+3kS9uuZWNZESZFRtdTZUu7m9r5hxe30+kfxWxQ+Oz167ixugynyYjFOHfafSQc5ef7DhNXk2wqL+J9a5ZS6vVgNsjEEioN/UP8cNcBjnT0oGoazx47w5bqUtaVTC8nsxgMfO2OGwnFppotmwaG+adXdxOJJ3BbzPzFzZvIniNT4jCbFtxzcrEwSBJL0rLxmCwookS9O5sXOk8TTarYFRNekxXjeUGJUCKVtahwelOCBeOGShNlKrfm11DvzibTbOeFrjP442FyrU6yzHbMkkKW2U6+bfGk0hcL86vWA4zEp8ZvRRC5NrOUL9ReT5rh6vipXG4UWr28t3AD3z/7Cl899ms2Z1RTYE1H1ZL0RPw0BHrYnFnD/QXrgPHPPGc5x0Y6eKLjTVqCA6zylGKTTQQSYc6O9TGmRvnz6tvJNqfKVvOsHm7OWcojrTv51smnuT13BXbFzPGRDvYONmKVrr6ZnlGW+dzS9dR5sniy+QQGUSKaVIkkVVZ4c7i/rJ7r8kpQzsuIrc8s4l9P7KIvPEaZI50PVKQye+evowUELLLCH1Wu4kdn3mJXb8u42ZyZz9ZtxKoYyLM6ybe52D/QyYr0vKnyNGZfsK9Iz+PgYBffOPgybqOFiJrgrqJaNmWXXNKo7hsMYDIb+NCXbkdNJHn+57uJBGMoBnneAEFCTSJLAtZx7tfd6+fE6W6W1xdQlO9BECA7w4EgCIwEwnOeZy68q4h/wB/ixUf3EQ5ORTEUg8zdH7mWBz+9ZcF29ZdTKlQQoG5tKR/4ws3UripGlEQ+/tW7+M6f/YrBHj8AaiLJMz/dSWaem5sfXDenEtJcsLutKIpEIj4VVfcPjl1R1ZyFIs3g4a6ce/hp+4/xxYdnPUZDoy/aR1+0j11DO1AEBbNkRjmX+JMi/qqWIKbFiGvxRdmIi4jUOGp5IP9BXMrF6zZfDZyrSjUBQZge5T23D2Nin67rJJMa/v5RhvtGiUXiSLKE1WnGm+PGOp5hWkgEcuLcaiLJUO8II4MBYtEEkizicNvIyHVjss6ucDDlCppSzhaE2dUIpo6ZeX9z3SdANOmjKfAc50f9324k9RghtZ8Q/Rc++DLBoRRQ7XqACzq+jGNwLMSPdx8k02HjSzdfy4aywhkTbH1uFncsqRqXs5v7t6FIEt7zpCvfal2YTvtsCMbivHa6mZiaRJEk7ltZx/WVJZPRXkEAkyKzuaKYtmE/3311F5F4gqNdfTywsh6zYf6giappaAmdW+sr+atbNpNmnU4oc1wO8txO/tdjz9I65CcQjfHa6WbWFudPV22RROpzpwcCjLKMPB5kMcoSS/OyKfHODCJcKrIsDq7JKAF0nPOUbIiCiCSIiIKI4Vx1n3kekVyriwyTjX868TtMoky6yUqRLXUPBknGKqdcaMXxf5drnvSarNxTuJSOoI+EpuE12VjrLZqU0Xwnls9NLOPndDVGGK//n672c3vuSlwGK4937OOZroME1SiCAA7ZTIE1nWzT9IVTpsnJX9bcxWPte9g5cIYDvhbimopRVPAYbazxlE3LAhhEmfcWbkDTNV7qPcq3Tz2NLMiU2bP4eNkWnus5hC8W5GpCIBW9v7mgnC35ZYzEIsSSKgZJxm00T3rlnI+1mQVkW26dlNAWEbg2u4TlntzJ+vzbCqtJahqiILDKm0+RPY3u0Oh4+aZ9Ur5WFAQyzHbWZxWRaZkaO5ekZZNtcWA4b0w0ywqfrFlPR9DPaDyKTTGQb3NdMrsyW420nenh6R/+jlgkQV/HMNufOciSa8opmsfTyW41kdR0xkIxvEmNfYdaicQSrFtZjGk8eDw5n17EY/quIv7tjX20NUxXVqhYks8dH9q4KNIfjczUfb9YSLLEQ5/ewpJ1ZZPb6taU8Mdfvo1/++r/EBpX+YmG4/z8uy+Snu1i9XXVixrsHG4rdpd1WvNwd9sgSVVDNFzdFKkgCFQ7anhP3kM83vUYw/GZvQ7nI6EnSKiXr2/BKBpZk7aO27PvIM3geUdOJHNB13VCgQhP/eANju1uQNdT5mwPfmEbBef4PrSc7OLn336WWDjO1vetZ/PdK+lo7OPZH2/n8M4z+PoCxKJxJFnEajeTU+zluntXc8O9a7A65zdT03WdeEzl2O4GXvrFHpqOdzAyFCQ+TvztLit5ZZnc+MAaNty2HKtj+vnURJIffeNJmk904cly8fG/uY+0zOn9AiODY/zX155gqHcEQRC44b41bPvAhhnXsuPpgzz70x2IosjHvnYvZfULryX+A1Lk1yTIfOLaNWwqL5qzhEISRa5cBfrs8IcidPhTPVlWg8KaorxZr0+WRJbmZeE0m/CFIpzuHWAoGCY/7cI9KLluBx/buGoG6YfUWFWc7mZLdRk/2LkfXddpGhwmHE9gnSeT8HbippwqbspZoFrYHI/0uWpJEyIGACZJxmU0k291syQth2J7Gg7FRFCNzXe6yTryaFKdU2ZwPmSOG269m2BXzHy59i6SuobHMD2zIwsS9xWs5brMWgqt0zPABknm+sxa1qaX0RsZIaTGEBDGszCOGRH5FGF18dnKbTxUuJ6heJCEpmIUZdwGG+lG+wxvA6fBwsfLtnBn3ip8sRCKKJFtduFQzJTYM4gmE5PlRFcTgiAgCwLp5oX5XkiCmMrKM7WgTjdZST8n855tmV6S7DXb8JqnB0V6QqO0B0fYP9DJZ+rWT6vTdxrNOOcoVTNIlz+jb3OY2fLAWtTzhGcc7vk/k7IiL/G4yvOvHqeiNJMXXj9Bcb6HJdV5k8/ekC+IpulYLIsfu941xF/Xdfo6fQTPSWuIkkjdmtJF1acnVY3Bbv9luy5BAFme/mCKosiGbUsY6hvlZ//0wqQr8MhQkB88/AxpGQ5Ka+a3Jj8XnkwH3hzXNOLf1zHMcN8omflpV53oioLICvdKHIqDp7ufpCnYRPICkl+XA5IgUWApZEvGVpa6lmGSrpyT5pXABOl/7J9f4ukfvUEippKZn8Y9n7iB3JKMaceGAhFOH2gl4A+SVZiOK93O97/6OB0NvdOOU+NJRoeDjA4HOXu0g7NHO/iTr9+Lwz17xFjXdcZGwjz2zy/ywiO7iJzn56AmkvgHA/gHA5w+0ML+107y0b++l6yCqQWWKArEYyon9jVhdZi571M3TiP+uq7T1zHEmy8fJxJKnd/qNLP1fddMUwbTdZ3DO85wYm9TyhXcOH869A+YHXW5mWwsm5v0Xy0EY3Hi4xOgQZbmdb61m4yTakOBaIyoemG5YQFYVZg7byReFkWqsryIgoCm6wQisXcU8V8ohPP+C1NkfFd/C0eGu2kZG+bp9uO0BX3cnFeJLEj0RcZomtQyN3B/8TLKHOlzdSsAKRWUYoeHfz25g1yrk7sK61mStngH+ncTFFGadOo9H6IgkGfxkGfxzLo/JTFqosy+8LprSRDJNLvIXGB9vixKs15DofXS3NcvB4KJGOhgVQxXZfze0dvCwaFu7i2po9addVXnEKPZQMWywkW/rrYyh1VLC3nqxSMkNR2Xw8xH37uBNFdqwaDpOmdbB9CB7MzFi3K8q4h/cDSClpzKa8iKRGbe4vS2Y5E4jccvPl29UCgGmds/sJ6Bbj/P/WIP2ri6SlfLAN//m6f40vfev2ClH7PVSM3KYhqOTGnWD/SMcOpQG5n5lz/dfDEQBZEKeyWfKP0Mb/n2sXtoFz3R7jndfS8FNtlGoaWINWnrqHfWY5cd7zqCOBvpzypM55N/+wCrb6ydW/JLh2N7z3LizSa6m/vJLkxn5fU1lNblYzAp9LYN8ubLx2k+2YWaSLL9qQMUVGRx36duQjyvH0TXdSKhGL/4znM899MdJFUNk8VA7doylm6sxJPlJDwW4czBNg5tP41/IMCe548QCcb44vc+iCcrJbsmiCKldXkAREIx+tqHKF86fbBrO9MzSfoBelsHCfhCuNKnommJuErXuEN1WpYTT9bVbFJ792JJXva8pPpqQZHEyVpbNakRTcxO5nVdJ5JQSSRTY0eqzObCixhFkqjLyZxR2nQuBEHAalBQJJGYmiSuTr3PuwmfqdlEltlOgdXNMk8uXpONz9dswmk0k2NxYhRl1nhTz6BBlDCKCk+0HqHMns62vGpkUeTQUBc/O/sWD6+6jS/UbibTnHoWnQYzX6y7jtzx6KpJkvlczSZax3yoepIC27urlPIPeDuh88iZI+zqaeNbG7aRZ3v7leIeKlvOQ2XL3/b3vZyw20x8+sPXsaw2n0FfkKU1edRX5U7O4fG4SjKpsWltORXFmYs+/7uG+IMwg7igp5x6Fwpd12k83knr6Z7LfG2zw2g28P4vbGWod4S9r0xJ1J060MJ/f+tZPvfw/ZN12PNBEATWb63nxUf3TZInLanxwq/2smpzFXbXO6dO0qE4uDHjJlanreXsWCNHR4/QHmrDn/BdsDl3NggIWGUrTsVFlimLSnsVZbZyskxZKKLhqvc4XAx0XSc8FuXX//ISz/zodyRiKjnFXj758HtYeV31BXV+e1oGEESBlTfU8rG/vpf88qzJZ0PXdba+dz3/9bUn2PXsYRIxlZ3PHOKG+9bMINK6rrP3xaO89Ms9JFUNR5qNj/zvu7j+vtUYz2kov+WDmzhzsJX/+MqvaT7eyZFdZ3ji31/ho399z3iTEuSVZWE0K8SjKm0NvWw6733OHGwFoKg6h+7mAYZ6Rxjq9U8j/iNDYwz3p2R2c4u9V6QJ//cdiiSS7bAtWlv/7UC6zUqmw0bv6BjhRILj3X1UZ3tn6QeBxv6hSWWfXJcD1wIWMpIokue+MNFISRVOqa1c3c6RxUMQBMocqZIEmzJVOlLmTEV7Sx3plDpmliwMRIOIgjDu8aDREx7FbUw1B5c7pyLFBlGiwjk9cmxTjNSnXR7zrj/g9xe6DgcGuhiJR3AYrn6j8bsZaW4rt980U/kPwGRU+OD96y763O8a4i8IYHNakGRx0nlXVZP0d/rQNX1B6jaj/4+98w6z4yrv/+fMzO393u29aHellVa9uNtyxxj32IBxDAQcSgKkk5AE8ksCCSRAQg3BxIANptjYBlywLNuSZTWr97K93929d29vU35/3NVKq+2SbGzw93n0aO/MmTNn7p05877ved/vdyTOUw+9MoFu8/WEEAK3z8GH/u4WQkPR8Yi9YcArz+ynqNzH+z51I2bL7D9DQ0sFyy5ewLYNh8a3HdndwVPf38zdH73mnNiCXi8IIfCYPKzyrWalbxVxNUYoG2I4M0wwEyScDRFXY6S1NKqhYmAgCxlZKFhlCw7ZgVNx4Tf78ZsDeExePCYPDsUxZzrQNxXOGmoyluanX/sNTz74EtlMjvL6Ij76r3ez4oqFcxbFK68r4gOfuZWqxolLmUIICsq83PbA1RzcfpLRoRh97UMMdI1MMvwjw3F+/dDmsaJgiZvffwXX3nMRimlixFSWJZrX1PFH/3A7X/jjB4mFE7z0xE6uun01C1fVIoSgsMyLt8DNYPcIXcf6JzyTqUSG9iN9CCFYd10LL8Z2MtQbovvEIAtaTjOqhINRRofy6WwLlla9tX7jNwlkSZq1CPa3BbfNwvqmOg72DpLOqfzktf00FhewtKJkPC1J03UO9A7w4x37yGl5QZ0rG2vHVapngiTAaXnb2JgO99av4tG2PXzn6FYkIahx+nlg4SWYz4Oy8G28jbOh6jpuk2WCU/r7ilxGJRlP4/I5Jgeuf4t4Cxn+gpJKP06PnchIvmpd1wz2bTvJYF+YkorpU14MwyAyEufhrz7Hay8feaOGDOTHXVod4COfvZ0vfvJh+rvyzDeaqvHU91+hqNzHTe+5eNYor9lq4s4H1nNkdweRUGKsD52ff+clDANuff/lc478G7pBeDjGkT0dqFmNy96xFFm58JP/qcia2+TBpbipttecY0fTc2a/FWAyK+OGTSKa4qdf/w1P/O9GsukclQ0lfOzzd7P00sY5G/2SLLHuuhaqm8qm/L2FEFQ1llBSVcDoUIxcVmWwa4TFa0+r4hqGwfF9nbQd6gGguDLA+jvXTDL6z+yzeU0dK65YyKYndxENJdj01G6aVtYghMBf7CFQ6mWwe4Rgb4hELIXTky+wHOoNM9QbwmxVWHZZE/tfPUGwJ8Sx3R2sv2PN+Dn62odIJ7NIsqBuccXp709yUGxbjmHo0zIBzXX7dMWJ57L9fMcyl+0OpXiMLnRuEPC6KcaeLxRJ4ualC9ne3sPW1k6O9A/xqZ/8mrU1FdQV+gBB23CInR09DEbz0ekrG2u5dfmiuV2TEJjehCsdbxaU2t382ZIrJ21/28F+GxcMApYEitnY00o0m8Y7T82H3zUk42me+/GrmCwmmpZXU7OoDNs07HhvJN4yhj9A1YJiqhYUc2DkNF1V66EeHv7ys9z7qesprghM8KpO5TAf2dXB4w++xL5XT6JpOiazgqbp43n3rzeEEDQtreLDn7mFr376p0TDecM9k8ry8FeepajMx5r1MzP9CCFYtLKGu/74an745WfIjhUMZ1JZfvKNDezdcpxr71zDopU1+ApdWO0WhMgXZ+ayKvFoimgoQefxAfZvb+XkgW76u0a49s41XHpjy7TnvZDfwe8rLDYzsiKRjKX42Td+wxPfyRv91QvL+Njn72bJRQ3zigZYrCZaLmmYkRbWZFLG02gM3SARm5hmpak6R3d3kBljuKptLqe4cupitVMwW0ysXt/Mpid3YegGR3e1kYjmDXyTWaFmYSmHd7QSDkaJDMdweuwYhkFfe5DISJziygAl1QVUN5Vy5LU22g71kM3kxlerTjkhnoCLojMceZ95ATeUf23O389vG9F0hu/v2M0NixpoLJyYcvFKWydtwyHuWdmCRVHIaho/2X2A5pJCVlVOFOTTDD1PG3ge6pFvJgghKPW4+MxNV/H1F7ey8WgrwVicXx04Oqmtz27jppYmPnTZagJTMPRMf5ILOODfMfw+z8Fv442BQHB7/WI29bXzVNsR3tO4bMaam991uP0O7vjINfS0Btn+mwO88NhOll7SwOqrFs2Y5h2JpQgORSkt9uCwX3hH4S1l+Lu8dq69cw3H93ePGyyaqrPxyV0c3dvJkjV1VDWWYHOYSSez9HcOc+JAD53HB8Zz4z0BJ3d86Eqe/9lOetqCb9zgBay9upn3feoGHvzCL8mMCZBFQgm+8y9P4i9ys2BJxYxdyLLEu+67lEgozpMPbR5nC9I0ncO7OjiyuxO334GvwIXVbkZIIm/4Z1Ti0STRcHKi2vEcMBt927nQu/2+wWIzo6o6v35oE09850Wy6Ry1zeV87Av30Lymft5LgFaHZVYjHcF49N4wDLTcxAJGTdXoOpavdZFkiYr64mmj/Wf2WVJdgM1pIRXPjPH95w18SZKoX5Kn3owMxwkFo5TXF2MYBsf2dGLoBoFiD75CNw3Lqnj2kS0Ee0KEB6MUVwXQNZ2Osdobf7EHf7Fn/J7K///Wub803aB1KEy8Loc4y2gfSaToCkUwDIEQEiZJcEV9LR6bdUJbTdf55YFjLCsvpa7gzVHAfyEghKDc52ZVdTmbT3QgCYnFZUWYZBlJEgQcdhaWFLK2poKG4gAmWX57bvk9gG5kyWlBsmoX6dxJMmonOW0QXU+ik0ESFiThxCyXYjHVYTU1YFGqUCT/pGfsbfw2YaAImXsalvKdgzvYNtDFqqJynKbJrFmFdifry+vekOfbMDRy2hAZtY1U7gjpXBuqNoxmJBFIyJITk1yC1bQAm6kJi1KLLE1HviJgjqrt6WSWHS8covv4AAVlXlavb6anLcjBHa2su3bJtMdtfa2N7z6ymQ+/73Kuv7L53C56BrylDH8hCS65oYXj+7t59tGt47n+hm7Q2z5Eb/v0EusAvkIX7/+rd3LpjS20Hup9Qw1/IQSSLHH93esY6gvz+IMvj4+/t32Ib33uF/zNf72PovKZGRPMVhPv++QNePxOfvY/G4mOpf3A6ZSmyMiFEfDQDYOX9rdyyaIarObJt4puGGw60Ma6pipsb6IagzcbFEXm5V/s5LFvPU8mlcXmsHDnx65l8Zr6c1JetjksmK3n933rmk5oMJ9PL0ligqE9HYQQONw2bA4rqXiGeCR5WkxPQMWCYiw2M7lMjp6Tg7Rc3ICa0zi+pxOA8vpiLDYTVU2lmK0mQoMRgr0hiqsCxEYTDPWF8u3qirC7TkdDDKBtaIRitxOTLHNiaIQavxeTLHNscBiH2URdgZ/+aIxUNkdtgZ+BaIzBaJz6wgAWRaZ1KESFz4NFkekJR6gaO34qRFJpYpkMVkWhZzSK3WyiNuDDJMtkVJWOkTDJXI5yj4dCZz69TtcNeiMRhhMprIoyoWA0nsnSMRJGliQyqjruwyQyWY4Fh9ENA5f1dD5sLJ3h8ECQZ4+cIKvpDCeSVPk8FLucb3kjWNN1njlwnP/e+CqqrvPA5Wu5/5IVUyoIv9Wv9WwYhkE8s43+yH9Nub/Y/QBu6/p5X7dhaPRHvkw8s2vaNgHHH+B33IYQ84u+GoZBMPZdoqmNkwQVzUoZ5d6/xSSfG4WkYejktEFi6S2Mpp4jlT1MThvEYHadHUk4MCsVOC1r8dlvwm5ehizNTeRuvoimNhGMfRfdmDwuWbJT7P4oTsuaKY48P6RzHfRHvkROmyyMKYSEz34LBc53X/Dzng8MA/5pxwts6e8go2l0xkZ5uvPYlG3XFVdyVfn5KePOBt3IkszsJZR8glh6K1mtB8PIzHiMJBxYlFo8tmvw2d+J1bQAcUa6pRAKQsyN/ldTNQpKPKy8vAmn146a0ygo9U4QoZ0KfQOjqJpOUcHU6uDni7eW4S8EDpeV+z51Aza7mWd/so14ZHamGEmWaGip4N5P3MDKyxsRQrBwRTWbfrX3DRj1aQghMFsU7v7oNQT7Rtn0qz2cEkc8srud737hKT7x+btxzrAEJITAYjNz+wevoGl5FY995yX2bzs5gSpxLrC7rCxYUsG6a5rH6wu6hsJs2HsSWQiuX9nI8d4hHt20l8Pdg9y4sglZlnj5QBu6YfCOVU2c7B/hx5v2cqBzgBtWNuK0mnlh30k0XefqZQuoLJgbXem5IpvO0X64B0WRqV5UhmJ6Y29nXTcY7BwmFIxQsaAYT2Dqh/Twa23s2HBw/F5NJ7Ns/Nl2lqxbMHvkfgooJuW8Odo1TSeXHaNTFAKLbW6OhMmsjOtWqFkNdWwlIV9U7MNb6GKwe4SOsdWEcDDKYPcwQggal1eNFQL78Bd7GOgcpvVgDy0XNxAORgmP6VQ0LK3i7Nvmhzv3ct3CBRQ47Pz5Y0/z9zdeRanbxWeffoFKn4cv3XYj/7dtN+0jIb72B+/ihzv2srm1g7+4+jIqfR7+8oln+LP1l1JX4Od/tuzkczddM63hf6BvkB/v2kepx42qaVgUhY9fsY6cpvOdLTtoHwljM5mIpNN8/IqLWFxSxKvtnfzftt2UelxkVY2+0fy1xDMZ/uulV+mPxPE7bHSHI1SOMc+kVZX9vQM8ffg471+3gpsWNwEQjCfY3tFNV2iUfT399I1GkRfUUOx6fQybNxKhRIpHd+4nmsqwvLKUO1YuxmYyvemN/Pya02kmoHOFJKwks3vRjckEE470UtzW9fPuU9VDRFIvkModnraNIvvxOW5GzFO2TTeSxNKvEMtsmbTPySVIYv4FnIZhkNMGCSV/QSjxGOlcK8xT90U3EqRzx0jnjhFKPI7TchGFrvtwWS9BEheWDSynDRHLbMMwJhtrknARcNxzQc93CrqRIJ55jZzWP8VeGbt5+ety3vOBEPDRlot4d+OyWdv6LLbXzeg3DIO0eoJg9DuMpp5D0yNzPlY3EqRyB0nlDjGS+CkBx90UOO/FJBePzVMysjQ3UTKH20bzmtOrGrteOsKSdfWUVM383jcwMCkyTof1dZkb52wpSZKgvLYmg27gAAAgAElEQVSQJWvrgPyLvrB8ep5tX5GbxWvq0MYEW6oaiqctYLU7rSxcUT3OthMo9kwb0RRC4PY7uO/Pb2T1lQt54YldHN3TQTgYI5XIoOk6sixhtZlx+RyU1xRw8fUtXHTtYvxFeb53wzBYtKKGlVc0kU3nc4wdc6QO9Aac498B5KO5DvfcJ5pTUdMPfvpmhIDhgdM3ZDScYPfmY1zxztkfaFmRWbKmjoYlFRzf3822DYc4vKudkcEIiUiKTEbF0HVkRcZkVrA7rbh8dkorAyxZV0fzylqqG0uwnqH6drJvBLMic1VLHQG3g9V2K/va+7n/mtU4rWYiiTTL6krZcayboz1B1jVVsa+tj/uuXoXbbuEHG3dhVvKiSxv3neTeq1Zieh2Khk8hNBjhSx95ELffyT8+/PEJ1JBvBDRV41ffe4nnHt7Mn3/9A1zyzqm5g7uO9SPJErXN5SRjaQa7R9j3ynEe+c+n+fDn7sDlndskcgoXIvNFkqTTKUaGMb76NBt0TUfX822FNJFi11/kJlDiZbBrhL62IJqqEewJMTIQwWIzjRfs+orcFFX4Gegc5tieDgzDYLh/lNhoEkmWJhT2MnapjUUFdIyEiaTSLC4t4lhwGMOA5pIiktksvaNRUrkcdrOZ3tEoo6k0F9VU0hUeJatpNBUX0jo8gtWkUOh0zFIEatA7GuVTV11CfWEAXdeRJYltHd0cD47wuZuuwW+38T9bdvDrg8eo9Hp46sBR3rG4kduWNnN8cJjPPfMCAAf7g3SFI/zTTdfgd9j5ysYtxDN5J91vt/HuVUs5OTQxolcX8HH3yhaOD43wntVLWVI6f57mNytCiSTBWH5F0qzIKJL0pjf6AcyKMu4oprI5Urn5K48LITDJJZjkIjJqx6T9qdwx8gbw/AIYWa2frNY7Y5tM7iSaHkWaZ3Re00fJalPTX9tMCxHzNPx1I0s09TKD0W+QyO5jvgb/1H0miKZfIJHZid9xO0XuD2OWK98S99XvHgSri8pnb/Y6wjByjKZ+Q9/of5BR2zh3V90gp/UzEP068cx2yrx/g8O8AiEkZMkNyMx0/z7/0+10He+fYPeODERYOEaIMROqyv0YwEg4TkNt0YxtzwVznmFMZoW7/vhq7nrgdERipsFfdO1i1l19Rm6SEJOieKdQ3VjC33/r/ad/HzFz3/nIuYmlFy9gydo6RkfihIIxUok0mjZm+NstePwOfIUuTGNpKmfmDFe3lLHis2u5oXwRDsU855SLFZc1ojQ7ieUyXFxYA8CR6CCvBtu4qLCWHUOdlNrdVDv9GIbBUDpOoXXiEr0QgsJSL3/1lXsn35PzmKyEEFjtFlrW1bNkbT3pZIZQMEo8kiKTzp02/C0KDpcNj9+Bw2UdvxHP/o4vWljF1qNdPLblAO9a10yJz4UQp1lCNuw9gcWsIEuCnJYvPETkafQAIok0FQUeSnxeir1O5NedvsrIy9LPQ8vhgo9g7Pynah2mgtNj46Y/vJx33n8F7Yd7+fqnH2W4L8zLT7xGYZmPez5xw3mn7swXkixwevNFk4ZhEI/OTWMhk86NF5Zb7eYJ4zZZTNQ05Qt8Q8EosdEk3ScGyKZzVCwoprAsn8ZmMinUL6lk/5bj9JwcJBlP031iAEM38BW7KayYnO7WUFTA80dOEIwnuKSuikP9QQzDYF1NBTs6ezg8EMSiKFT5rBweCKIbOpfVV7OlrZNIKs266gqOBYdRpGEWFPpnXTEp87gp93qQhEAaM/hah0N0hML82/MvIwnBUDxBidtJNJ0hns1S4/chC0GRy0GhM+/M9YxGKHG78FitmGWZ+gI/hwfyKYZ51ismOXFCiNNzFeJ3yoBxWi04x1RyD/QM8KXnNnFVUx1+h20Cc48iS9jNZopcjrH6h9/ud+Bz2HDbLIwkkiQyWXZ29LCotGjeDEomuQCTXDal4Z/V+tD0KIo8v5qOdPYomj5zemc+f75n3mk5OX2YnDY4xR4Jm7kJwdznLU2PMRT7AcHY/6LqoXmNY079G1GG4j8kmTtMufdvx4y0qQNP3fGX6Iq/iAB0NHQjiyTM6EYOh1LMYv8HsMpvC5WdC36bz6puZBmJ/4S+yH+g6eEL1KtGPLONzpG/oML3OdzWy1EkHwIJYwbD32o3c/27L8buPO0cb99waE41fSuWVFFbVcDLW0/Q3FCK23Vh2ZHmbPiLccN9bj+qEAIhz6PtOdwsQghkRSZQ7CFwlmyxYRgk1CzHYkOY0zLVDj8jmQTFNjfxXJq4mqU9EeJoLEiBxUGVw8doJklayzGSSVLnCiALQVtsBLtipsrpI6XmaI+P0BofRhYSSX3sc3QYIWA0mySWS1MrB9ANg6ORQR5t28UtVS00eYpwmU6vDOR0jf5UlKyuIYCAxYFdMdMaHcYiydQ4A+QMjZPRYQSMC7K0Rodh7HNGU+mMh7ArZmqcAYRVJuLTSLkFxdYARTYXbbERZEmi1OlHmYWvuWc4gkWRsZpNRJMZqgq9mBWFrUc7WVFfTjydwWJSGE2kqTQMTIqExaSw9VgXK+vLaakppXckQk7TUd4uyhtH/ZJKbn/gajwFLvzFHt77Z+/gwX/+BYloil9+7yUKy31cd89Frwul6nSQZZmymkL2vHwUXTMY6BiallryFAzDIDwUJTHmJHj8znHKTsg7iPUtpwp8Y0RG4pwcU8kurS7AW5RXAkVA04pqEDAyMEpoIEL3mGJv4KzC3lMocTmIpNIkszluam5kb88Ax4MjrKuppHc0yqH+IGUeF4VOJ3t6+nCYzdT4fTx35ATpnMo1TfUcGRziWHCIu1e0zDqLmWRpkg9uN+VrCT5w0UosSn7qdJjN2EwKkhBj+fsCTTfIjTGGWRUT6ZyKPuYYZlR1RifxTAh+O+JSeeXcHLF0hqyq5RVuNY2sqtEzlsIEMJpMcaB3AK/dhkmWMSsyZlnGZjLhtlmmdK4KnQ5uWbaIb728nWQ2x5P7jvDU/iOT6HoVScJhMVPmdXP1wjruWLGYYrdrPnGRC4qAw05LeQntw2E0w+ChV3fjtlpZW1uB3WxCNwwyOZVkNodZUagOTLcarmA3LyKeeXXSHlUbJqcNzcvwNwyDRHYPMPOKnaqPklE7sJuXz2tezuTa0KdIcZElNxalmrnaA6oeoT/yVYbjP8Iw5i/kOHfoJDI76Rz5Kyp8n8VtvXxK4z9gXYxdKUIzMnTGN+C3LMRjriWW62Y004r01sqCfhvka11CiSfoi3xxXqk9c0VGbaMn/I9U+b+ALHlByGBMv/J38Q0tyMpEO+ii61sm1K9NB4fdzN3vWsX3fryFf//Gc6y/tImigGtKyu/iQheF06QZT4ffqbs7ncrS1zmCySxTVOHj2b4jqLpGqd2D12znsY593N+wlt2hHiLZFMF0jLbYMFsG27ihfCGHRvvpT0ZZ7C2l2OZiS7BtPGq/pqCaI5EBJCFoiw6zyFvCE137859jIzR7S9ANg32hXoptLgqtTlJqllgug6prnP2uD2WT/LB1J4Zh4LfY8ZptuE1W0ppKKJtkhb+CUCZBMB2n3O6h3OFl+1AHA6koFXYv5Q4PoUyKwXSMQ+F+bihfxMnoMKqhsTfUyzWljRwc7SeaTRNTMwx6iri0aOYKep/TTjyV5aqWempL8lHROy5ZQs9wBFkIblm3mK6hMIuri/E6bMiSxG0XLaFneBRZElzWXEPbQIhkJjsnwZ0Lhje5fyHJEkKWxhxVifV3rmG4P8zPvvE8sdEkP/7KMxSU+Vh15aJzKvY9F8gmmcblNTzz8BZ0Tafz+ACxcAK3f/o8cl0zOL6nczx9r7S2EF+h+3SDMwp8Y+EEocEIXcf6QUDDsqoJ9KPl9cU43XaiI3GCvSH6O/KF+eULirE7Jt87BQ4H8WwWm8lETcCHw2KiIxSm2OWksaiAb2zexqeuupQCp52Hd+7hxkWNFLocpHMqQghqAz5cFjMngsOUuF3n5JSurCzjuaMn6AiN0lRUQCSVRhKCYreTBQV+XjjWis9mY3/fAAPRGADNJYU8vu8gr7R1UuxysrWje1yFNpHNEkqkSGZzhFMphhNJPFYLJlnGqiiYFZn9fQPYTAoBhx2f/Y3hxDaAx3cf5kc79o4Z/uoZDoA63m53Vx8ffeRJzIqCZczotygK9UUBPnvz1RS5J95LhgE5TaO2wE+V38vRgaHx7WcXjmY1jWwyRTiZ4uhAkL3d/fzDO9dT6Z8+vfT1hEmWuHt1C9vaugjGEgxG43zuly9Q4nZit5jQdYP0mOF/2YJq/u3OG6fty26eWo1T1UfJaQNYjcY535+6ESeVO87sLqJOMnsQn/1mmEeUPpU7jjGFcaNIPsxKxZzGqekJBiJfZzj+yJR58mf1jEkuwqyUYZbL85FVYUI30uS0IXJaL1m1b2zFYPprzqit9IQ/R6X/n3FZLp3E/GNXCrErhSTVILKwUOG4AovswW2qZjRzkqwewyy/samjvwswMPjvfVtojcy+orPAE+DjSy++YLojscxW+iP/MSejX5Z8WJSqMWaoAoRQ0I0kWW2AnNpHVu1FM6KTjsuoHfSO/jsu62UIxIxP3VQ1hyf2dbJ4bf2EgNlUeOq5/Tz53F7CkSQHj/WxeftJZFlM+V198D2Xcu8d62a95gljm1frNzE0TWfjL/fy0Feew+Gy8ol/voOSGhc7h7uocPgwS/L4y8Uw8qkZhVYn15Y2sS/cy4noEIYBqwqquLiwhqSW45WBVsrsHtKaSm9ylP5khA81XsJuazdDmQR9yQgfbryEPaG8RLXf4qDC4cMgH/2sdRVQZvewrnDqnK4iq5OAxUGB1cmR0QF2j/RQYnOR1TX6UxFqnQEOhPsotbuxySZKbR72h/ootXmwyiaiuRB9iQj9qSjDmQRpLYdZlimxualweHnw+FacJiuGYeA129AxkGewkgs9Dgo9jrO2OSn0nH6B+132GY9pqjg3hofzgSxLjAajbH7yNQ5tPYGa02hYXs3lt66mpKZwwtKarhuEB0c5srONg1tPMNg9DAYUlvtZcdUill2+cEqPPBqKs/3ZfezddIR0Mkvt4gouvXnlvKk4TxVn3/JH6xnqDfPCz3cQ7Anx0OefxFfoom7x3F6m5wshYOGqGoor/PR3DtN5tI+juztYOwPF2OhQlB0bDgD5GpM1Vy9GMZ+Opp1Z4DvUE6L1YDfhYBRZlmhccfoZOKX0W1DqpeNoH0dfax9nompYWjWlI2c1KSwuLcZlMWOSZZaWlaDrBn6HnfoCPwsKAtT4vTitZuoLAiwtL8FuMtFcWoxJkjArCkvKiknlVAqdM0+6bquVugL/pEm2yu/l41dcxK8OHOXlE+24LBbuWrEYWQjeu3oZP9q1j//bvovmkiKubarHaTZT7fdy/9qVPHPkOA6ziUtqq1A1HUkSvHKyk02tHcQzWXZ09tATjnLXiiXUF/hxWS3cvaKFpw4c5WDfALcva2ZtdeWcftvzhWEYjCQStA/PvlSe03RyWpYzuQUUWR5f8Tizz/bhMN94cRsvn2hH1TQWlhRSX+jHY7cijxlmhmGQ03UiqTTdoVHahkKkciqvtnbyyPZ9/MX1l/1WeMGFECyvLOUzN63nvze+SttwmJym0R2ebGScfe1nw6LUIQknunF2eo5GKnccl/WKOY8rpw2SVbvn1DaZPYhuZJHF3Ax/3ciSybUzVR6zeYxKczYYRo6RxE9mNfolYcdpWYffcSt283LMcukYc8qZz6CBZiTIqt3E0lsJJ58klT2EgTplnxm1jb7Rf6Pa/x9YTQunnFcVYUXVU5yM/AKvpYFIto2sHsMkzUM74m2chgGHQ0H2DvWftdkgp2lEsxlUQ6fC6cFjvnDBwaw6QH/kK+S0gRnbmeRS/I7b8dpuxGqqRxJ2Tt9jBmCg6VEyageR1EbCyV+OpeWdNvGT2X2kcsdmdWI3/GwHXccHkM7IfBkZiNC0ombW6wn4HSxuKpu1HUBNxTkQhMz7iDcp1JzG8QM9xCIp0qksnccHuGblKirsXn7euRev2YYBxHIZ+pIRbIqZhJolkksRyaZwmaxkNRWrnC9OlRFUOLy8q3IJRba8538yOkQ0lyacTSIAWUhEcmnCmeSUk4osBJqhk9FUzLIyyZCQhDT2L7+9zO7hHRXNlNs9mCUFzdC5b8FaftT6GiVWN3WuAPctWMOP23ZRbHPxTM8R7qpZTkrLgWFQZvfwXO8RlvsrCFgclNjcXF5cT60rgEmSkd6koXHDMIiMJgkGo6g5DafTSnGpB8scKUJjo0n+5zOPMtg1gsvnIJXI8NqGA7z66z18/Ev30rC8evz3yaaz/OSrz/L8j7bgCThx+50YGBzZ0crzP97CLR++mvf85c1Y7afz8sLBKA9+9udsfuo1fIVuPAUuuo/3s/25/bj98yvMhdMF6u/9i5sYGYyw++UjtB3q4aHPP8mffvG9E8SrXi8IISitKeSyd63k5998nkQsxWPf3EBNUylFUzANZVJZfvX9TbQfzhcRVjeVsPa6lkn3vb/ITUGJl2B3Xpl3dCSGJ+CirGaiQ+jyOiirK6LjaB+HdrQSG00iK/kC6OnG+5FL146pOMNNi5t4R3MjkhBU+7184Zbrx5+jf7n5WqSx9ME/unhV/njg+oUNXNe0YNYIU0tZMUtKiyZdmyQELaXFLCkpGn8NnOqr0OngE1decjpdysg7V0IIrlhQw2X11ePXcWo81zTVc3Vj3aRznPp/bXUFq6vKJ2yfCl67lc+882ri6QySJLGg6PzuH0kIblvezKqqcyvSs5tNBM5yrobjSb743CY2nWjHYTbzsasu4s6VS/DarVOqchsYxNNZHtt9kK+9uJV0TmVraz7aXu49vcpU4fPw73feSCanIksSNYHZ87Jbykv4xr23oOtGfqxzFAiThODa5gU0lxWxra2Lvd39DETjZFUNm9lEkctBXYGftbXT67FMLPCdnJefyh0lb2TMba7O85FPLA5XpACy5Cajtk/YntV6yWlBZKl2Tn3nC3unLhrOF/bORmloEM/sYDD6nSmcnNOwKg0Ue/4Er+1aJJEPMk0X/FCEG9nUjM20CL/jXYzEf0Yw9n+o+tRU3snsIfojX6XS/6+Y5IJJ+02Si0W+e+mOb6Q3sRmHUsIi772YJc8Uvb2N2SAE/MOaq0mpE50xA4OsptETj/DIsT0oksyfLrvkglgkhqExHH+EZGbPDK0kXJaLKfP+NXZzCzB9KrIi+5ElH3bzMnyO2xiMfotw8qkzaECNOaWrWawmrr17LXbnaQdnx4aDcwoWXn3ZQq6+tGnWdnBuNRW/M4a/osjULSzF5rDg9tqpaixm53AXR0cHKbV5KLd7aPGV8kTXfmQhUesKMJrxsrH/BAK4sqSBfaFenEre4DPLCteVL+T5vmNYZIWbKhZzWXE9T3YdwCIrtPhKqXMFeKprPxbZxGJvCVuD7RyLDDKQimJXzNQ6AzS4C/lB6w5uq1o67kAAmCWZYpsLj8mK22Sl1O5hkaeYDX3HkIXEzZWL6U6Osnu4G7fZRpnDw6HRfnYNd+MyWSm3e1kZqOA3vUdQhIxdsbA31MMlRbVEcmk29B3jHRXNbOg7zvahDm4ob6baefqlqOsxUsnHyWRfBSOHybwMh+PDSBcw0pFIPIym9eFy/ilCmjpNQdN0tm4+zk8eeZWuzhHUnIbLbWXNRQu47wOXU1jsnvXG7jnRT3VTKX/8+XdTUl1AMpri6e9v4udfe47Hv/kb/vQ/7xtXybPYzFxx+2qWXtZI3eJKfEVuDMPg6GttfOtvfsxLj+3gitvXUN9SNT6+lx/fwStP7WLNtS384d/dSlFlgGgowdMPvcwT394wo4LudBBCUFwZ4P1/eyujQzHaDvWwZ9NRfvTlp/nQZ++YdSnwQkAxydx032Uc3HaSI6+1cXDbCb78Zz/kzo9eR93icmwOK2pOJdgT4jePbuX5Me0Mu8vKnR+9jsIpNCdMFhPVC8s4tKOVA9tOkIynqWosJVA6MUVDSIKmFdW8+vReju3pQM1qeAtcM+pYnDlh5suNTn+Wp/n7TIP57GOmw0w1RzPuO7P/M5oIISaMaa7jme64s2FRFJZXls7abq4QQlAd8FHh9+Slas5TIMkwDLa1dbG9vRvDgIvrq3jP2mU4LeYZnm2B22bhhiWN/HzXQdpHwgRjcaKp9ATD32Exs6ZmZuHDs+Fz2LhkzBGbD04VY5d73dy5cgm3r1h8Jh/FhDYzwSQHMCvlY4wjE5HJtWEYGYSYW1pXIrtnUoGhRanCYVlDMPYgZ0brVW2YrNqNRZmdVSTfPkx2GipJm6lx1sLenDbCYPQ709BR5uEwr6LC9zns5iUgpCmdwLNxStBPkYoocv8xFlM9vaP/SlbtmqK1TiT9Eo7EExS53j+Bjz3fF1glLyX2dWh6BoRANdJjKyPzpyp9G4Jy5/ROU7O/iGZ/EX/0wmM823mM9zVNzYQ3H6RzJwklHp925QfAZb2cKv+/zpnt6RRtp1Wpp9L3WUxSgGD8/2bVADgTF93Qkqe+PuN0l75z+YyKvacg5aNGcz7XfPE7Y/hLsuDa21ayoLkMq91MVX0Rkiy4orgeIQQSgvWljVxZ0pCPxCG4uKh2PLgiIbiyZMH4byQJwRJvKYs8JUDemCixuVgZqBw/XgAr/Kc/GxisK6weOz4fX7+9ehm6YUx6gfvMdt5ZsXj8c4uvFIGg3lWAMXa+UruH1YHK8fGvKahm1RmfrytbyDWlTQiRLxbeEmzFabIQzqawmUzUuQr4cGNgvL8zkUz+lET829jsdyFLhQjhmDQpni/U3ElU9SQG6rTTeWf7EA9+eyM93adzAkeG4zz/zD6cTgvvf+CqWSP/Tq+Dmz5wJdULy8ZTad7xh1ew9+WjHHz1BL2tgzSOLa8JIVi8bsH4sacmgZZLm1hxVTMbf7qNYHdo3PBPjCbZ/tx+bE4Ltz5wDZWNpePnuOn+K9i98RC9rVOxXswOIQT1Syp4/9/dwtf++scM9YZ56RevUVTh566PX4f5dRZFO+V8fPhzd/D1Tz9K28Ee9r1ynGO7Oymu9GN3WcllVYb6RomOxDEMA7vTyt1/egOX3bxiyglUEoIFLacVfAFqFpZhc0x+ida3VCErEslYfsk0UOKdk5DY23j9oRs6m4OHKbP5aXTPbcl5OuQ0nWODw6RzKook0VxaNIvRn4cQAquioIw51rphoP0WGbxO4dS45+KUTQ0Zm6mZWHrzpD05fYicPoxFmj2tSzcypLKHOLuw12pqwGFZhYg9NMEp0I0k6dxxXNZLYQ58/hm1C12PTdquSB7MymStjTNhGAaR1HPEM9uYLhffamqkwveP2M0tk3Lw54K8k2XCa7sODJXu8OemjPwbRoqR+I9wWy/FZl40YV9OT3F49IdEs13j6T1m2U2z9z5k6W3D/0JDCEGF00ON28eW/k7ubVoxpW2QyuTYsq+N9asbZmRgMwydUPLJGelsLUo95d5PnxPFa97WclHs/hhZrZ9w8pfMVkh/CiazQiqR4aVfvMaJ/d0ISeArdHHnR67B9gYSeUyF3xnDXwiB3WGhecXESM6Zqyr56NWZP7yYGJmbok/lrBtl5s9iUieTz3m6bzGp5cSXSf5YeZbPY6kBkuCe2pV0xsO0+MpY4C7I75/ioTEMnWxmGybzClyuTwCWM3p8Y7F3VweDA5PzZDXN4LUdbdx+91qKimdedvUEnJTXF48/1EIIPAVOaprLObm/k7624Ljhf2q/pmpERuLEwgmy6RxqTiWVSKPrOrns6WK22GiC/vYghRV+yuqKJpzD5XdStbDsnA1/yBf+rrhiEe/985t48P/9gngkyZPffZGCMh/X3LX2dWf6EZKgaWUtf/W1+3n0v55l5wuHSMbSdB6bGKWTFYmaheXc8ZFruezmFdPTj55R4JtJZRGSYOGqqSOMJVV+vIVuRvpHgfxx1ikchLcBGS3HYHqUMpufmJpiNJug0l5ATE2R0zW8ZgeD6VEi2SRW2USZzY99bPUyo+XoT4VJqGnsipUSmxeLpDCYjmCWFCK5BBlNpdTmw2Oyk9aytMUH2TCwj6XeGlJaliKrhxLruQnyGYaRZzwaw1yL+QzDoDscYTSZdwzdttNUoG91OMwtTMXbdIrZxyzPXuuT04bIqJ2TtttMi7AqtSiybxIVZyJ7gALUOQl5pXMnpinsDWBRKpjpfaHqQUYSj6NPkxIhCSfF7o9iNy89J6P/TAih4LFfRyp3lMHotzGYPOa02k4o+SSlpnqkM1KUcnocVU+xuvDPsZzBpiQ4vzG9jemhGfm0H83IG9ADI1E8ThvDowkCHjvpTI6RaBKLSUEgSKazhKJJook0RT4nAY9j/NnIaQNEUs8znTEuMFPk+tBYatq52TdCCGTJQ7H7ARLZvWSneOamw8hgBE3TqW4qpWlFNSf3d6PNUgM0V+i6QSSWGhP6mt9783fG8P99hxCCYpubYpt72jaGkUVTu9D0MJrWjywXks3uRSAjK5VIUgmGkSaXO44slyPLfk5N7mquFQBZqR2fqA1DR9eDaNogoCGEE1kuG1s9mCJv19DRtHYMPYViakAIC4MDEVR1ai7ceCzNaDg5q+FvdVjHtRpOQVZknB4bmqqRjJ1++WiqxtHX2nj2h5s5ua+LZCyVH6uAWDgxqe9UPE0uo+L02lHOOoeiSNOm5CxZt4DPPPhhdFXHHXBOyPM7G6eYfiobSkhGUyCYyJYD1Cwq59Pf/iBqVsXmtMwqVmaymLjnkzdw/bsvRkiCigUl07aVJEH1wjI++Z/3cnxvJ3s3H6PjSB/xSBKL1URJTSGL19WzZN0CAiXeaXMUdT3NaPx72Ev38MlvFOE0fwhEXrH3bAghKCoP8Dff/ADpscrQkprJObhzQSz5NLoRx22/67yNiDcrRnMJvt/+Ih9ZcCMvBg/w4sAB/l0jnUoAACAASURBVGnpe9g8dBiBoMldxi+6tyMJwXAmRqOrjPvr1iMLice6t3I40oPLZCWna9xWsY4GVyk/7dpCXyqEz+QkkktikRT+pOkmsprKztBJ2hNBZCExko2xwldLifXcGHUUWaLQ6UAAqq6zv2eAaDqDxzb9M2EYBsFYgke272U4kX8um0oKKXDNv6bmzQiLqXaswHdiRF03kmTVLhzm2VMgsmrPpGJGgQWLqR6TXIRJLptk+Kdzx9H0OJI8s6FgGBrpsdXaSWNXqvN0hjMglt5KKju9krDbdiUe2zVTUm2eCwQWAo67iaZfJpndP0ULjdHkc/gdd2IzNYxvlYUVk+QkqQ6hSPYxg18gTyosfhtzRTSbRtWnNm4zmsa2gS72Dfdza11+9eWlXSdpqCpk0+5WLlteRyqTw2W3sO1gJxcvraWjP8Sjv9nNJS21vPjaST7wrrXYrXnnLZHZTSbXMe1Y7OYleO3Xw3k6ckIIbKaFeG3XE4x9j7kKz1msJrwFLnRd5+D2VtLJzAXTHUqmMvzvw5tprC/mthvnp+I8L8PfMAwSsTRH93VzcFcHAz0hNFWntNLPktW1OM9SsFVMMtULirFMER3MZVW6WoPs295G+7EB4rEUNruF2qYSll9UT01D8SRj7uyxhIfjDPRMpo2SJEFFbSHOWXKpMukcXa15hdHK+iJsdguDvWF2bznB8YM9xEaTWO1mKmoLWX5RPQ2Ly2cdUzajcvJwL/t3tNPdPkQmlSNQ6GLZujp8hZONteqGYhwzGIUXEro+Qiz+NTS1G1VtRdO60SKfH1stuR+b7TY0bYDR8J/gcv8lNtu7xo+Nxb4CSHh9XwSsGEaaROJHpJI/HatuF4CO0/kxbPa7OHsp2TA0MplNxKJfwma7BVmpQwhm5zSfw9yrqdqU/aiqlvfWz8jBP7T9JP/1yR+QzeS47r2XsnjdgrxRb1L45f9uZNMTOyf0IckSQhLoqs7ZnKwGU48/Fknh9DpYdVXz5H3RFBt+vQ+v38H6G1roaA0SGo6xcl09i9fWAzAcjNLRGhwXjgJw+xysuGLh7F/GGGRZYkFLFbTMrb0QApvDytJLGmm5qAFdNziVByeEQJJn19oQwozLfju6kaT54gjFvukZggBMFoWWixtmbDMX5LQuNG2UubDeD6ePkFKHJ2yThJkC6yIs8vRO8+sN3VBJqEH0syKsJsmBTfbjUKzYZQt9qRChTByfxUlXcphQJs5CdzkNzjI+tfBdWCUTJ2L9fOvEs8RyKayymZOxAS4qaOTq4paxVUwJzTBIqVmKLB4+2nAjMTXFV4/+ktbYAGsDDdxasZa2+CA3l69mpb9+TrnX00ESgpVVZRS6HARjCba0dvKV51/hrlUtVPjcOMbSfjRNJ5nNEUokOdA7wON7DrOnqw/DyKsc37a8GYd5/ulvhmGQzqgMhWIkkhlA4HXbKAq45lWfYxgGiWSGoVCcZCqbX/VzWCgqcGOZ4b1wNk4V+JrlYtLq5FSaVO4oPm5htskvmd2PflbOsSL7MculyJIHi1JNMjux4DEv5NWLSZ6ZCUTTo2TVnin3Wc0zF/bqeopI6gV0Iznlflm48NtvQxYXroBWCIFZqcBrv4lU9igG2UltsmoPsfQrWJX6MwIEBgm1nz3D/41VKUAgYZE9LPZ9AJvy+pMsXAicPDHAa9vbuP0P1syZEOP1gmEYfH7nixwMTb0KnlFVehJRAhYbt9UtRgAFXgddA2EKfQ7aeoZpaSijvNCLzdI+1ic0VBVy3bom2n7xKvFUBrvVjGFoRNMvT/lb5yHw2m9GkfwXJH1UCAWv7QZCicfmLEDnL3Kz8sqFGIZB26FePH4nDteFsfdyqk53f5iSovm/t+Y8WxmGQefJIA9/fQO7t5wgncohpPykrukGjz/0yqR5qqDYw+e/+0HKqgMT+hkdifPED17lhad2Ex6OjxkZebz8tMAbcHLNLSu4/f5L8c0Q2dy28Qjf/NenJm23Oyz83Vfey/KL6me8puHBCF/8658QCsb45D/fgaxI/PBrG+hpG5qwHCMkwZMPv8oNd67mngeumtJQP3Vdj3xzIy8/s5/EWN6yLEtoms7TP90x6fuRZYnPf/eDLF5VM+M4Z4Oqahw+2EtsTFjJYlFobqnAbp8Y1ZGkYjyeLwA5wqGPIMuluD3/CJjGJnKJfMW6CsZZdHxoZ7DWGqRST5OIfxu74z6s1usRwoKuDyPLZUz0rvMOQSbzErHof2Kz3Yrdcd948VpRsQdFkcnlJnvQbo8Nn2/2CF8ikiQ+msRXdPpFkk5mGekbxWw14RtbMVBzGtue2cdwX5j7//52bvnw+nGBjVxGxWSZ/Dg4vXYcbhuhwQipeBrPGfdjLqMSHjydpmQYBuGROM88sZuK6gKaFpfjCzjo7wmjaTplFX6cLisLl1RwfIwdJxZJMRSMkk7niIQSeP0OggORcWc5OpokFk2RTGYpKfNid1gY7BslMprEajNRUR3ANAVf8LnilPDedFpveSrcFDmtC11PIEteTEo1QigIIaHIxchywQQuZcMw0PQQhpFFCJmc2ossuTEpNYCEYaTJqu0Y5DDLVUhSPqVE11PktG50PYYkuTErNYgxKkJNj5JTOxDClr9fAd1IoKp9mJWGsUiiQTbXhiR5kccm/4Phh+mKb5pwTVbZx/qyL1AoL+a3hYwWYUfwK4SyxydsL7GtZF3hX2CX7fgtLk7G+0nrORpcZbTFBojmklTYA4SzcTYPHWYwHSGaSxJTU+iGjlOxcF3pMn7V+xp7wu1cWbSYtYG8s2WSFBa4SrDJZnTDwGWykdZy4zVEkK9Vks87FUOwpLyEe1Yv5buv7CSVU/nZroNsOHKSUq8bl8WCJAQ5XSOezjIcTxJKJMejhn6HjQcuX8vlDXMrSp3wvWZVtu9p58nn99PeNUwsMVZP4nNSVuyZqC4uBKuWVPHuW1ZP6MMwDGKJDBs2H2HDlqP09IeJJ7MIAV6XjYbaIm69fhmrl1ZjmmNqniL5MSsVpNWTk/alc8fIpy7MlNuskczu5ewUB5NciEkpQQgZm3nRWE7yGQW+emhMyGsyI9eZUPUwWa1v0naBgs3UgJjBdMhqvWOiYtPk9pubcFimrhE6Hwgh47ZexbD8CFltMsWpQYZYejN+++0ocn7FwiQ5WOL7IKqRRjMyyMKCLKwXhMP/VFBouuvMqRrdg6NIQlBR7M2ntg2OIgRUFs9dNTg6muLk8YELlkJyvpAlCdM0LxCnzcLFpdXcVtfM0oKS/Mqvz8WRjiCrFlaw41An61dPDgbZzObx9OhTsTZVD5HMHmS6+0yRCnFZ13G+0f7TEFhNC7CaGsdqV2ZHeDhGx5E+VlzeRNPyal576TAFpV7sF8D41zSddHp6AbGZMGeLITQU43tffpbXNh/H5bZx3R2rWL6uHrNFobt9iA1P7KbtaD8IQWVtIXVNJdQ0luDyToy6x0aTPPTV37Dxl3vRNZ2y6gALl1XhK3ASCSc5fqCb7tYhfvGDLYSGY3zoL9+BNzC1qFBFXQFX3NhCbDRFPJZiaCDCyGAUVdXntpxigKbqpNNZnv35Tvo6R4iEEzSvrKauqXT82g7t6iAaTvLUw1vx+p3c/oeXThJaymZUfvStjTz7s53IisSV71jKJdc243BZGRmMsvGXe9m/ow1dNygs9dC4pILSygBFZecvSDMyFOOr//5r+nrzvNuFRW4+/5/vxl490fAXQkII+5ihJJM3+B3jxtRcYRgZ0qmnMZlacDjuR4i8IJJhTKQszP9tIZPZRDz2P9jtd2O33wPCOt5m2cpqAgUuBsbyvE9BViQuvrwJj3d2dptwMMq+zUcpqytCVuT8BHqsj6O72igs91PVlGc80XWdVDyNkATeAte40W8YBn3tQQ5vb53Ut8vroGF5NVt+tYd9rxyjqDKAJEv5Y9qCnNg3Md8vm9UIjyQoKHLn6waSWfp6QgwNROhsC7L+hqUTeH0R+ZWnTc8fwu2xsWx1LcGBCIP9o7SsqGb3jjYO7+umZkERB3Z3sHRVDbu3tyHLEul0lj+479JpvxdVV9kzup8GZz1ec9750Q2do9HjFFkLCZj9dCV72BbayfrCyym0FIx/vqrwcoqtU2kyGMTTG4innkUSNnJqFx7He3A77pjxN0pmthBPPYMiFaEbaWTJS4HnL9GNJCOR/0DVgwhMGEaGQu/fo8gVJDObiCafRBJ2smoHbvvteBzvQTeiDI3+P3Q9iSR5yKpt2Mwr0fQwQ6P/QqH3c1hM9Wh6lKHI5/G5HsBmXpsfvaFPYkHRUSet5rzRkIQJA52kOrE4cTh9hIwexSQ7KLZ6OBrpxWmy0ugqZcvQUTTDwCqb+VHnZsySwvWlyxjNJulKDo/1K7Eu0MgidwUHRjt5qncnWV3lssJFSCJPSXz6eT276kjMWWV4NlgUmT+8eAVeu5Uf79xPx3CYkUSKkcTUOeCCvMG/vLKUd69ZxrraCszK/BzcnKrxqxcO8OCjW4gnM5QXe2mqq0ZIgu6+ELv2d44XC9usJlwOK3WVE9PNDMNgJJzgaw+9xKYdJ8jlNFwOC4V+Z14TJJLklZ2t7D/Sy723reUPbl6JeQ6OuBASNvNioumXJu3Lqn1oegxFnt74U/Uw6Vz7pO15jYD8O9duakYIE4Zx5v2ukcodxsc7mEnIK6cNoGqTo5qy5B0r7J3eaE9mD6Fqw9Pud1ouQpbmbtjOBxalEru5hWxqam2DVPYoOa1/3PAXyOSMFK2RJ8noo5glF/XudyExP6aos2EYBgdO9lMScFHkn9qJONo5yPaDnaxtrkLXPRzvCrL1QAdrF1dNCIbOB7qu0987isksUzjGWDc0GCUcTuD22Cku8fD/2XvvMDnO68z391Xq3NOT82AiMBgMciAIgAgMoMRMKlCJkmw5yGHtq/Xae23vru1r39W99tW11nqcvZKVI0VRJBhAkADBAIIgcs6Ywcxgcujp3FX17R/VM5iZ7kkIlOT1+zx8OOgKXVX9VdX5znnP+6qZ91cinqarcwjbtimvyMefEXPo6RnB73fT2xPG4zEorww56jRzgBCCP1l377hT+aRlmeVKRq1sbAwVhXwUBD3UlhdwsaMfVVV45d2zDIxEeWnfaeoqCinM84KA0sLg+OQ6ZXaRsnJXpQDceu2cFazmClUJ4TEWE0m+y1yafOORJP3XhsenJt3tA6TXTqbPJRJpdu49Rf9ghA9sW0JFaYjDJ65y5sLMngTRaJK+geyK4VwwpyeplJLDb1/g+IHL6LrKE5/dxGOf3oiRyZCuvLOR5mXV/NX/+UO6rw6yYn0Dn/0/tmO49Ul8YNO0eO35I7z+wjGklGx7eAUf//w2SivyEYpA2pLBvjA//vpbvPD9/bzx0nEqawr5yK9sQdOzB17r6jpaVi5ASomZsnjmG2/xza/smvdFsC3JkX0XCBX6+fwfPsSm7a24PQYIh5L07p4z/N3//Rwjg1F2P3+ErQ8uo2ACB1tKyeVz3ex96Ti2bXP3Ayv5/B8+eF3JRMKKOxv54n/8LqePtFNVV8zv/tnjeAPueRtA5cLV9gG6u4fHZ/yWZd/SOMZ5+V/foW1HsOxeXK6NCOGZ1PA6FaZ5mfDI/4Oi5OFyb5sU9APUNZTw6c9t5gff2UdX5xCWaZEX8rH13hYeeXw1eo7ffSpcHoPn/mU34cEoi1bXMdwX5sWv72W4N8wn/uBhiiqcl4yua9QtqeK1H77Dzu+8hS/PQ15hgI4L3bz2g3eIhGOoU8yB3D4XWz98B0f2nuF7X9rB6FCU+qXV9HcNsedH+0mnzPF4SQhBYXGAiup8WpZVU7WgiJ5rwwwPRhnsjzAaTjA1OyFtybGDbTQ1l7N1eyuGS2NBfTEDfc4NrSiClmVVrLyjgZ9+fz/R0QSWZVNYFEAzghgzUc+Q9Cb7qPZWMZwaQRUqNjYxK4GWUXCq8JQT1ALELScTWukpJ6gFiVvT6RQL/O678bvvQQidcOxZIvGXCfgen5kOIi1S6UsUF/0RmlqWCb41Yok3SVtXKSv4a4Rw0Tv0J4Rjz1IQ+C28rk14XRsRwkUkvouR6LfJ8z1JLPEOpjVAeeGXERh0D/0+INHVCgytgVhiL4ZWTzJ12hkfN9HY9X5BE25cajb1IWmNMJruJKBXUO0t4qcdB7i/fAW1vhKevvoONd4iXIpG1ExQ6i/FUDTOjXaRsJzyd8xMcjrcQZ7updQdIqh7CKdzUzAmQlc0vJrB2dFOit1BApqXAtf0rs6zQQiBz2Xw5NplbF5Yx/GObk509dA+OMJoIkHastFVlTyPi/K8IE0lhSwqK6auKB+voc/795NScqm9n+8+e4DRaJKt65v49U/eRWnmuT04FOXrT7/D87uO4/e5+M1Pb2HJwnIKQ5MrjMmUyTeefofd+86iayqP3LeMD2xdQlGBH9tySu0/fvEw+49c4RtPv0Nhvo/7t7TM4XhFRk88u8E3bfWTtnpnDPzT1rWcQY/XWDzeuGtoVWhKIekpiieOkVd6RiMvp7E3m0KhqYUY6vT+DlLaxFJHsacxN1KEz6k23Kb2wrH9D8d3Qo7+BCdLfHJc3Sdtj3J59AVKPKvIM+oYTbdxefQl/HolLnXuSbmRSJzX3jtPNJ5idXM1qir43iuHKA75uXNZLW5Dx9BVFpTl8+bRy9SU5fPc3pPEEikW1pRwtWeI5944QSSeYmFNMaoqMKdXp8wJaUsOv3eFl54/woc/vp6iogD73jzHKy8dJxB0MzIcY8vdLWy7r5XwSIxvfe0NRsPxcSrnL//63Ugp+dv//2WKShwaXG93mK33tnDfB3O7TeeCMU+DvfKiIB+7zzHD/Pj9q1EVwaObW3lkcysKjkBJc20JAnh0c+t4zJRIX8C2s/vyxuA1lqGIW0ujFkLBoy9GCGMOLtQO1Sc8FOWZf3oNM20TCHmzBCwGR6L84Nn3uNYXprQ4SEVpiH0HL/L9Z9+bdf83OkGc092XTlucO9FBIpaipCLE+rsXY7i06wGfKqhdWEbr6lq62gY4+u4lwiMxSn2TH1x910bY9cwhkok0zcur+eRv3kNZVf71h6QKxeUhPvorW+i80s97b5xj108Pc+e9S6htKp20L0dPG5RMSUlRlJuSP1RUhQ9+dB33PLJy0iRDcevcsW0xB988z85nDtLVPkBP5/CUwB8unuoiPBTDcGlse3A5Hp/r+nkJKCwJsuHeFk4faefc8Q66O4doWnJjBjlTcexwO2YOqsyNYeyFJZFyTErWBJmCTCZJCBWBlhn4Mw88KWP4/L9EPPYckcg/Egz+Z8QEbqemqdy9vZWlK2q41jWMmbYoLPJTWV2AYWizvkAFcPeT66moLeGFr7/Oj77yEpZpEyoO8MRvbecDT901rowjFMFdj67m6rlrvPXcIb74y/+I7tIJhHxsenQ1Wz90B9/+y8nUMSEEyzct4pf+2xM8/bc7+fZfPoe0bfKKg2x5fC0rtrTwzN/tnLC+8z3JRBrLsjlxuA2vz8WSFTVcOJutZy2B6lqHCtd+pY+GhZObcMekQ8eUUEIFfnqvjeB266xe33DdMGoG9CR6aY91sL5wLQqC85ELFLsKCRl5KIhxnXanlHr937khSZltRBOvYtthUmb7nLWNDa0WLaNW4mT3Jcn0SVLmFfqG/wyAlHkFR1ZWkrY6icR3YtsjpK2ujEKIJGVewNAbUUQQIVRc+uLMC0Ah4H2QwdF/IOh9nEhiF173ZhRx82X72w1F6Dl7DNJ2jGi6Bykl5Z58av0l1PlLCRk+6v2lNPrLCRk+Hqpcw0vXDnO17S0a/WVsLGpGVzQkkpMj7VwY7XbcxH2lbClZgiIE5Z4C8g0nmFeFoMpTQFB37nGPavBAxWp2dB7kG5HX2VbayqbixVnHNx+M+RJUhoJUhoJsX9KELeWkJIUQjJuvjWUIbwS2lBw+eZWevjChPC9PPryGyrLrqkQlRQE+8uAq3j1yhb7BCP1DEepyZPuPne7k5b2nQMKDd7fy+U9txuO+PhGpLAvRWFvCX/zNCxw83s63f/IuK5ZUU1Y8O+/WpdWiiiCWnKxqZtpDpK0e3HLhtOcfT53BsiebYgl03HoTY9QGTS3C0CqyAv+U2Y5p9aEqub0MpJTE0+dzquO4tXpUZfpzs2Us43SaOxuqKSHctzgLOxFCKLj1JlThxZLhHMcXJ2FeREoLIVRMGUcAZd41uNQ8vFoxfYnjpO3YvAL/nsEIPQOj3LNuIeVFQQxNpbW+nNWLq2msKmL3wfO4DZ2Kojzau4fYsKyWTcvriCXTbFhWhyJg0/J6IvEkG5fXz1n5avy8gaOH29nz6kmeePIOFjaXMzQY5aUdR3nio+toXV7DscNtPP39/axYXcvBA5eJRZP8zu8/AFLy93/zCm/tPcOdmxYRDsd59MNrWbOunmNH2vjhd95h3YZG8vLmrhwjpSScSnJisIfTg72EUwkChovF+SW0FpaSZ1xPADp9eM7fWub/2ZS5jOrhhGp5wryInCHr7tYXMRfZ2vnCrdWjYGAxe+DvDbh58KlNXGvrR1EViitCWX2ihfl+fvkTGxkYjLJmmXNPShvKS/L40IOr8Hpy99OEIwl++NzBGzqHOQX+ZsoiPORkiQJ5HnwBd9aNqxsaBZnm1fBwbJzjPgYpJWePXaWzrR9NU7ljazOllflZ+xFCkF/o5677Wzm6/xJ910Y4su8CCxqznTRvJfKLAmy6b0lWZUEIgW5oLFpWzc5nDpJOmgz2Ty6vSCkZzGRo3R6DvMJsVRshoCRjYhSPJhkenN7JcD5IJNKcOtFxCzP8OkK4MK0unIe3imV1YZpX0HXnxS9EAE1rIpU8gGV1o2b0ca9XBq6X8TStHq/3SXR9CSPDf0I0+nX8/l9DTJiJq6pCWXmIsvL50Z5CxUF++0tPUVpTSHFVAWu3L6X7Sh+2bVNUUUBFXTHalMlDqDjI5/70w9z/qU0M9YZRNYXC8nzKa4tJJdKUVBdQ3TTZDEkzNLZ+6A6WblhI1+VeZ3JSFqKyoZTR4SiNy2qobakYP5em5gr2v3WeeDxFTV0x7751HrfHoLgkSNfVQd7bd4HBvggH3j5PMM/LoiVV1NQWcfxwG5qmsv/N8/RcG+advWfxeA3cbh1FVSgsCdJ+uY8FDcUUFAZ4a88ZHvrQGgIzNLEnrCRv9L/N9tK7KTIKsLEpchUi59AImwuW3Uv/yBfxex7A7/kA8eQBIvGds2+IQAhX1n2hCC8ubSEh/2euf6bkZ+g8f4HPvQ2/7z6SqdMMR7+V2caNOaFx0Jl4OOfj0lsRwkU0sZu0eYk835O8n+ocltmBmT6F4b6b+fhiCCFwqSEE6mTtddJEzR4kNoVGgN9rftQpkyP4fOP9mdK5wvJQLUvyagCJIpxS/hg3/5O1W7CkPW7GNcbf/3D1neO/h1s1+ETt5vGAQwhBa14NzcGq8X3eKkzUwb9dYrWWZdPeOYgEigv8lBVPNgIUQlBaFKSowE93X5hzl3qxLHtSs69p2bz61hmisRQlhQEe3b486yUshKAo38eHH1jJ8TOdtHcO8s6hSzx2/+wqG47yTgmWOVXO2CRhXiDAXTm3k1JmOPSTgx5NyUdXKyZc3wAurY5o8j0mJmjSVj9J6+q0lB1HWaiNXEkdj9GMMkOlwGkKzu4NuH6MBWhqLgrhrYNLq0FRvFhWduAPkmT6CraMoYoAmvA61NDoHvKNhYyknEmBrsyvulVbns/q5ir2HLzA0sZy1mSy/pqqZMaU0yFn2zaWZTt0F1VBU5RxnwpVVVAn/Hs+6Ooc4jtff5P7PrCURYudMTDQP8q1ziF2PHuY1145STyWJBJJEBlNcPlCD1cu9/EPf/MKUko6OwYpKHDO2ed3U1GVj6arVFYXYlo2oyPxOQf+UkpODPTw/x3ey4GeDlK2hSoULGmjKyqriiv4T6vuYkVRxQ3HdFLKzBjNHfgrwoOhVnA7nv2aWoCi+KYZX5OPEcAX9Ix72+zfdYIla+snqQEausrWDYvG3d7HUF6ax/YtLQSn6QfoH4yw+60zN3YOc1lJUQS64TyikwkzZ3bZsmxiGVk+TVOyKAiWaXPlfA+JeBp/0ENTa9W0NBehOBWEYL6XgZ4wF093YaatGRV1bhZlVfkUTSMbKQTjikUSSMYnl0AF4HI7x5ZOW6QSuWt0kcxkSFEU3O5bo0fd2zNCx9WB2VecI1S1CMNYSSz2XQQKihIimXwLKSOM3URCqHh9H2dk+A8YHvo93O67EcKDabVhGCtxux/k+kxbAAqGsZ5A4D8wOvplVLUcj+fxeQVGueD2ulgxQemmvLaY8tqZXypCCNw+F43Ls7NdhltnxebsrKYQAlUTlFQXUlI9WQ2jsCxEYVlo0roty6pZtKQCRVFQhKC2sdSpBGSqVB99aiMS575SFIGUzt+l5Y55VWV1wfjysW2EENz30HLe2n0Gr9eF4VJRFDGrKokqFBb6G7kYucwCbzWacnPX3JYpbBlHU8tBWiRSRxgbF1KmsewhbHvEabS1elFEYNIkbyq87k1EE3uw7CE0tQrL6kdVCpAyjS0jaFo5oBBPHRrfxm2sZjS+g3hyP0K4iCf3487IHwrhwee+m+HotzG0RvRZ+MizQdoxbLsfKcMIpQhFBJEyglCKsO1eQEHaYaf0SwppxzDN0yjpElS1EqEUIe0hLLsLVSlFKAXYdj/IBFImMvK4zrPApQRQhIolJz9fY2YftkyjKW70CfKH2kRPDyEmLZv4vlMRORt0p26viRxJj1sktwhOFv7AwAX29Jziseq1LJqnMZgtbXb3nOKVa8f4UPUdrC2aXrxBSkilnOewqio53zUT75+0aTlGixOWj0YSnMl4dNRWFVJVPj31ZmFdKSVFATquDfPe8XYeuW/5rDROTcnH0KtJmOezlsVTZxh3l5wCW0aIp88yVWmTYgAAIABJREFUNTDX1GIM9XrFUAgFr9HCYFRlIu3FlhES6QsEXBty7t9p7M2uTgoM3FoTM4UNloxi2tO/j3StDOU2O+KqShBNyc+SOh1D2up2+owIoCt+GvMe41J4B92xd/Fp5TTmPYahzK9KODwax5KSwjwvfUNRFEUh4HVz/MI1XIZGKOD8PTQaIxKfu/vrXOF262y+u4WD716idXkNDU2l6LpGIOhh231LKMokZTVNpbg0iNtjUN9YykOPrQKcUZCXEdIw09Z4jJdOZ+6hefjJXI2M8Mf7XmYoGeczi1ezvKgMj6YTN9Mc7e/m+cun+eN9O/n7rY9RE7hRb5Bkzh6UMSjCj6be2L5ngyK8mfE1vSM1wJE3z5FOpjl98Mp4T2h/1xCLVkyOPcY9nSYcqtdrUF6Sh66r056Dpqm4b5DlMqcowHDr1C4sQ9NVBvvCnDl6lZKK0CRzqIEe53OA6voSglOaMtNpk/6MUZPh0igqnbkUml8UwON1XogDfaPEY6nbGvgH8ry4fdMH4xObeadm14UiqG+uwOXWScZTHHr7PE1LKlG169cnEUtx8E1HsaO4PI/ieWa3p8OlC70MD83O2Z0KTW9EVYrJfvAb+ANfQIl+k2RyL0L4cHvuRzdWIu1wZn2Bri8jFPpr4vFnSSbfBGwUtQJVKR/fp6pVZf5UEULF7fkgtj1EMvEGhrEaTau/mVP/uYUT0F8fq1MVDhQjd7A+9nBVpgnmNU1l7YZGrlzsxbYk9z24YvweyQWBoNhVzMJAA23Rq1yMXsa0TTpiXUTMKLqi0ZccoDN+jZgVR1d0+pL9dMa7iFpR3KqLMvdkip2ulpPn+xijsZ+gKEHcxkosuxeAlHmZ4cjXMK1epEwxEP5r/J4H8Lo2oSr5GRWfCccnBC59CQXB32Y09jxSOs26+f7PoakVhPyfJRJ7EUXx4daXoSh+QOA2WsnzfYrh6HfQlEL8nvsRwsVYpcnrWs/Q6L/g99yD4OYm2JbVRizyDxiuTVjmJXTXFqz0Gdy+T5KM/xgAaY9i2yMoSgGqVoNlnsdUS0klX8PteYJkYheKkk/KehW35wkSsW+jqFXO/aJWQybwNxR/TmOlhDWMLedJ+P05RNo22dt7hp92HKTaWzjvwF9KuBrt583eM2wsXjTjuqoiKCly3jFDIzHCowkKpvD3R0bjDI/EEMKpCkzNtA4OxxgZjSMEVJTmYUzTbySEID/PS36el45rw3T3jhCNJwn4ZuYXC6Hi1VsJx1/LWubQUVI5J81pq4eUmd286tIXZO6R6/Doi1GEkTV+YqnjSNIIsoNwx0QsW45RU0MY2szup5Ydxran6w9yzL9uF79/DEK40GZoHjbtwXEZVImNW81nWcGvMSYZk7ZjSOw5mZyNwePScRsaDZVF1FcWIgRsXF7Hpc4BpITWhnI8Lh1bwrKGclyGTnNt6SSOdvOCEswbVObJL/Rz931L8Hh0vvett/m137qHkrI8FtQVceVSL5VVBZiWRTyewuXSWbG6lh98ex+xaJLCogDDQ1HyM0moWCzJG3vOoCiC/W9fID/fR8E0AivZkOy4cobu2Chf3vww60qrJ5mS3lvdxOaKWn5373PsuHKW31h6xw2dry3jWHJ6fr+ieFDEjfckzQQhDBRldrXBhStq6L/m0JebljteNgdeOzWnvs6H71uGZdm4cygNjsHQVRprSyjMn/95zjnjv27LIvbsOMqFU1185+9fIx5L0bysCk1X6ese4YUfvMul09cI5HnY9tDyLGMj25LEY87NphvarBKEuqGOr5NMpLGmMXm6VdB1Nauxc64QQrBoWRUr1jewf88ZfvrtfWiayqqNTbg8OpGROLufP8J7b5xH01W2PbyC4vKb1zCWUnL00JUbkPFSCQb/COdJl53lU9UqAsE/wJGBEzjDxMbJMGmZ9RQ0vYWAvojrcnFKZn/OwPb5PpvZTs9sY+D1PYXX93FmUpT4d+SGEAKf382S5dmGWLmgCpX1hWtQUCg0CsbpPYuDi8aXl7pKaQk2OyNBqJS4ilkcXDT+72xoBL1PEPA+kmnmVRkrtxpaA8WhP5l8zJnx4nVvwuvekGN/Cl7XXXhd65HIjIGOs03A85AT1E/6HicDEvQ+TsD70IRl17OjptWPrlXiNlZx8wkfiaY14vI8Qizy90h7iLHxLqXjgKrqLQizHUWtRNp9qNpC3J5HiUe+Sjp1iHTqXTS9GdsawLYHARXDtRlFnex+qikehFCyGBZJa8RRHpoA07xMIvEqtj2EplZjWZ0Yxlp0YxWJ+A5M8xyg4fE8jKKWEI8/h9f7OIpSQCp1FDN9Bo/3scyE6f2BrqisLqhjMDnKsvzc/PJbBVVVWLmkimdedtM/GOHFPSd46kPr8XtdGV3/NK+8cZqunhG8boP1q+qyAtpEMp2pGggC/mx669Tv83lcme1MkkmTuXiNeY1WnOfm5Gd42uolbffjUrLVZRLpy5hWdlbdo7dkBauGWo6mFpEy26fs46yjipXDyCthXsS2s/nLmuL0DMwEyx7KUs2aCFUJwi2sIuWCEFrWBGgibBnHzvRHpKwwF8LP0Jj3BG4lRNIOcyH8LA3Bh/FoM3sdTETA52bFwsm/VZ7fw8pF1z9bUj+ZQlqYN3mAFOTdmDmdz++isqoA3dDYfHcLo+E4+98+zwceWsnHP72JnS8c5XvffAtNV1m5upaWJZW0tFbx0GOr2L3rJKmUSSjfx2MfWouiKgSCHoSAH37nHQyXzic+sxGXS2MuplVSwqHeThYXlLCutBpNmTyZ1oRgbWk1LQWlHO7rmqamNTtsmZzWFRpAEa5xdatbDSH0Oe3bF/Dg8bqorCtGy8SyGx9YjncWfymAooLZg3mvx+DXP7153v0gMA85z4oFRXzmd+/jX7/8CpfPXuPv/uKn+IMeNE0hOpogmUhTWBLksU9vZNN9rTlnNWMPTtu2Z5WJk9JZD5xMp6LcOo5pTghuKkjwBz185ne3k0ykOf7eZb7xN6/w9NfewOXRiUWSxKNJvH439z62isee2jAv45jpEBlNcP7szJJPueD8DjNkisVYsD9xeGQfb+71Ji7P/tzRV7+9D/5/hwMhBGrmWk9U3JkY0CuZisx0/861zwx5ZMKnmb/F1M+Zsk72suvBlJHjBSCmZCQnUlOUrGxl2rzGSPTbJNOnCHgfQ1WuO0/fDITwQsbRE6EgSSDtYWx7wKHvoILQM8tA2iNIexRJEqEE0fSFuL0fQ6CDcIPQQLiY6jKsCiOnMpIpE8gpvhq2HSadOobhWksisQu3+z4SyT1o+hI0vQndaCWVOkQ8/lN8/s9jWz2kUodxuTaTTL6OqpTwfk++BYItpS1sKmnOohXd8u8SgiULK/jg1iX8+KXDPP3iEc5f7mVRg6Mdfqmtj0MnryIE3L+1hTVLp2l0JVPjnMswyqwj5dw7aAxtAaqSh2UPTfrctAYwrT4MrTJrTERTh3M46mp49GamPqc1tQhDrcoK/NNWDymrK4dykCSRPpfTsdetN6DOkkW1J/Tb5IIiPDOrf90CCNQZ1VyktMcz/pZMkbIjmYQDCBTSdgRrDootPy9oWlRGfWMpWoZh8NhH1mHbElUVlJQG+cSnNznJQeFMUIUi0BTBHRuaWHNHPdLO0N40hb7eUTRVYeu9Sygrd6gymqZkevjmdjxp28JQtUmZ/olQhMBQVZLWjVcxJea4f0suCDSUGUzmbgYC1XmWzwED3SP0XRuiZU09tmVz6WQni1YuQPPf/PNPCDEv48CJmPNWqqqwZHUd6+9eTMeVPvIL/RSWBEEI8vK9NLZUsnpTE3UZSlDW9ppCIEP/SSfN8X6A6RCPJEklnR/W63eN9xj8vEIIQU1DMfc+torzJzsxDI3K2iJsW+ILuqltKmX1xiYWLavO6WR8I+i+NkxX59wc5P4d/45/61AUPx7XWvye+3Dpi7k1Qb8HRS0BBIpahqrWYpudJOI/RFHyUNQyhBJCkSkUJR9HDStKPPZ9VLUK3bgDZIJk7GkUtRTDvR1FKSOXd4YzEcjF6U6TK5hS1Qp0fRmmeQldX0LaPIWUcUyzzXHlNjudyYfQMFzrSSX3o6kLMM3LeAIPjk88LGkzkBxlKBXFtC10RSNPd+Q7cxnxWNJmOBVlMBUlZZnoikrI8FLoCuTsJRhNx+mIDWJmehcUFCq9+YSM6TOcUkoiZoKexAhp2yKoeyhyBXJen+ngcet8+ok7QEp+/PIRDhxr59CJq84L06VRWRrivs2LeejuVjw5nslul4ZLV4ngGHjNBMu2iWV6v1wufc4vZEMtRVdLswJ/W0ZImlfxGismnbItU8RzmBbpahEubUHWBEURPjx6E5HkvknbmNYgSbPNqRJM2MiWKZLpy+TK7joyhjO/uxwJ0OkjRCcZdLub7ZVZ6ET2uNurprix7CQDiZPku5oYSV0hbUfQblO2+HZAURQm5kVVVTCRvKBqYhLteAyOKmLu66QoYkap6GkhoClUzCtXz3NldIi6YLb78ZXwEOeG+rl/wcIbHwnSIpdc63Uot7GypGQSmDMjmUhz9kgbHRd6AEE6mebMwcs0tk6uDEkpsW053tP3fmDOv6xt2bz58nGe+84+ahpK+M3/8gh1TaUgnAZFJdNANd2BGy6NippCFEWQTKbpauufUc6yt2uI6GgCIQRlVQW4Z+Ay/zxASsnpo1f5xt+8gttj8Dt/9hjL1tVnmjMduSpFVW7ZD5tOm5w4dpVI5NY3Cv07HH3c3p4Rjh5u49zpa4yG47g9OtU1hbQuq6ausRT3DBM407S42j7AsUNtXLrYSzSaxOPRqagsYHFrJY1NZfj82So3twpSSmLRJL09Ya62D9B5dZC+3jCxaJJ02kLXVXwBN8UlQWpqCqmqKaQk0/T1iwpVCeBzb7ml+1TUBRhqNaDi9nwIUFC1eq47q4rMf3LC/yVjtCRQMdwfBNKZ9XXc3o+Qq/ph2cmc8nTTStaNTxTU8ZR0MrkHM30Gr+8zmOZZkhmDKENfQTLxGvH4s6hKKWpGi30wGeFH7fvZ03NyPMh2KRqFrgCrCur41aZ7yJ8QoF+LD/ODtn0cHLhEd2KYhJXGUDRK3EHuLmvlIzXryTMm0zwvjHbzpdPP05+MkLBS6ELlP7U8zP0Vy3Oeli1tjg618fVLezk90knKTlPoCnJP2ZJ5Z4tHIgkutvfj87r40AdW0lRXghAONaOiNI+CkA9FUXJm9AtCPvKCXvqHolzrHRm/b3J+z2iC4YxzenGBH98c7yNVCeHSakiks9U5EulzTG3wTVu9JM22rHUNtQJDK2dqUC2EwGssc+SXJ8hzStLEU6cJebYzMQyw7OHcjr3ChVtv4N9Otda5ToYSoNq/hYvh57g06hhb1gU+mNNT438H5OV5+OQv3UVh4Y1JIAsED9Qu4qeXT/FHb7/Mp5tX0RgqRFdUUpbFhZF+vnHmEGnb4qHa5tl3OANmLkCMPZdvPTKi17OuJ22bWCTBcP8oV893o6gKa7a1ZFF9orEk3//pe1RXFLBt4yJ0TaVvYJRYPEVl2dzN0+aDOQf+kdEEb7x8gvBwjO1PrHGaV+dBVxFCsGhpFcF8HyODUY4fuMyd9ywZNwGbCDNtcfJwG6PhOB6vweIVNbef6nOTsC2bN146Tm/XMGvuWsjKDY2z9jHMBVJKYrEU4eEYg4NROtoHuHK5j472AS6e78npUDwyEuPvvvwyHu+N8XdLy/J46pfvwuefm/nFyEAEf8g7aTzE4yl+9N13uHSh94aOAWDT1kVsvWfJLaFFjSGZNNnx7EGOH7neHFdQ5OdTn72L/AInwEklTd7Yc5offW8/7Vf6SafNcU8DRVEI5nlYv7GJj3z8TqpqCiYF71JKhodiPP+Tg+x88Rj9veHrhmrCsTP3eA1al1Xz4Y+vp3VZ9S09P8uy6eoc4p23znPgnYu0XeljdCSObcsMxe76umPno6oKeSEv9Y0lrN/YxLo7GykuDma5U//vCCcrPvb7jN3P8/29ptKccj8XEtbIFJfVzLcJnbm+xBQRcPwVkm9hmpcZez0KJYRuLCMa+RqB4O8BLtK2xTNXD/DdK2+xrrCBj9Ssx60Z9CXCnBi+ikRmqfqk7DTnw9co9+azvWIZhYafnkSYl7qO8M1LbxDUPXy4Zv0k3umiYAX/bemHCafjPNdxkN09J7Fk7smMlJKLo7186fTzdMdHuL9iOYuDlQykRnm95zSDybnLIKdNi6dfOMSRUx08fM9SPvn4unmVxgN+N80NpVxs66OtY5Cu3mEWVGbzvsfMwnr7wwghWL64as7vK0d5p5WRHJK41wP/iefUlVOtxmO0oIjcLudufaEjP2hPdkePpY4jZXoSJdO0hkiZ2WolmpI/a2Ovcz4GM41Vh55xu12y7ZxUpesQ41QNIRRKPWsIuZpIWiO41BAuJZhFw/vZYKyv7v2Dy63Tuqz6pvaxtLCMP1i1hS8feZMvvPE8eYYbl6aRME3CqQQVviB/uGYbLQU3IdEulFmar+1MVeBnB7fXxV0PrmD11sXkFzkTKSFE1u0xGk2y5+1zbFzbwLaNTv/dsy8d5djpDv7s9x8hPy/3fX0zmPtTUEqHJybh8Nvnqa4vpnJB0TivTAiBpqsE8jzkFwVy6uHXN5ezdE0tb7x8gv2vn2X9PS2s2tA0qR9ASsnF0128vuMY0pbUN5ezZNXtbQS7FZA4kqUAl892s+N7+2lcXHFdiUg4vQpev5vCksCMZmOxaJKTxzu4cqmXtiv9dF8bpr9vlKGBCKmUhW3bMzq2pZImBw9k27nPFXUNJXzsqQ3Yls1AT5iC0iCWaTM6HCWY76P76gACQWl1AQM9I7zwzbdZvbWZ2uZygvmOh4Fp2pw4dpVDN3EcVTUF2NskN9hznRO2ZXP2VBdvvn49w5Zf4OPu+5aQX+AjnTZ56fkjfPNre7PUkqR0AuuhwSgv7zhKV+cQv/2F+1lQVzz+ABvoj/DVf9zN66+eGpcTvL4DZ/vIaIL9b5+nva2fX//te1m/semmJ7ZSSvr7Rnl5x1Fe3XmC7q4hTHPmpu+x87Esm77eMH29YQ69d5kdzx7mwUdXsvWeJfin0RCe7hgi5jBHh/cynO5FFRotwfUs8C3GljYdsXOcCr+DKdPU+5ayKLgGBZWEHePY8Ov0Jq7i0/JYmb+NkF7Cu4MvIlAYSvXQ4F/O+chhGv3LafSvfN9Kou8XpLQZSV3ByuGWqgtvViCiaQvweB5FVcvwej+Eqlbi9X4UVV2AqlUj7VEM10bAwnHXVlDVWlStGl1fihCCuJni5MhV/LqbX226h3p/KYIMEcI2MW0LnzY5eVDpKeBPln0Yv+7GUDQUBJa0qfMX8+fHf8yhwcs8WLlq0nZezcXCYDmmbXF4cObngSktdnUf40qkj6fqNvOZhs24FB1bSlbm1/Jfj/5gztfUNG3OX+5FSqeMnkqZ6Lo652Y4TVW4Z2Mze945R29/mBd2n+RzT27AmJDQkdKhAT278wiJpElpcZANa+rn1S/m1XM3+CbNDiwZRRPXFfDi6bPj/PTrEPiMFUw3IdXVMgy1gviUwD9ptmPagxjK9cp7yurEltmTK8dzoDzr86lwmh6nP3lbxjNN/LcPEivHNboOIdRxSVEpbQaTZ7k0+jxerYT6wIN0x9+j2L0cTXn/Gt9zwUkCvL+B/62AKgSP1rewuKCEvZ2XODXYSySdImC4aC0oZUtlPQ15BTfUlDoGkVEKnA4Sa5bJ341DzjqxvA6hCA69fpqLJzoQQhAqCvDIL2/BM8G910xbxGIp/D4XaiYOSKZMYvHUrL2wN4o5B/6+oIctDyzj0pkuLp6+xlf+9CcOdSWzXAgwXDr5xQGWrqnloY+tp6ahZFLW0Bdw88gn7+Ti6Wt0tQ/wz//vCzz+6Y20rq3F43GRTKY5d7yDZ77+Fh1X+ggV+nn0qQ3js6UxSCmJhOMMD0Yx0xbplEkilqLzSh/gBODnT3ViuHUMl4auq+iGRqjQj28egcx8oKoKWx5YxsG3z9N9dZB/+asXnUaaCYNbNzSC+V4aWyp45BN3snhl7krGpYu9/F//5UekU9YNKPbcOkgpeWPHYe64t5VYJMHlU12Eivz0dg6RSpnUNJQSKvLTd22I8FCUdPIXU3IwHkvR2TFES2sVhw5c5nvfentWiVTblhw/0s53vvEWv/2F+wnmeYlFk/zwO/vYs+sk6VmclKWEro4hvv4veymryKe+oeSGj9+ybI4daedbX93LqROdmDehgJVOWVw4180/fGUXRw+18enPbaaqpnBOgbZEcnjoNcLmIK15G4lbETRhIKWkN3mV3b0/YFX+PRiKi30Dz2MoLur9y3mr7yck7TiteRtoj51hV/e3eaTy85wNH6TO30rMGuXA4Ms0BVbx3uAuan1L0N9HNZr3A3FrgN7E8Zy0HreWjzKlmUxRQihKKPN3Xub/SwAwjFWT1pXSxLJ6SKXexjDuRFGcrLWhaJS683hv4BIvdR3l0arVlHvy0RQVj2rkZHVoikqxe7IUsyZUmgLlBHQ34XSclG3iyyETORfEzBSHBq8Q0D1sLW3BrTqUGVUImgLlLAvVsLvn5Jz2pWkKjXUlnDh3jZdeP8XFtj7ygt7xgENRBUG/m6baEtYsW0B5ad6kYEQIwdLmSj6wpYVnXj7Ksy8fRVUE2+9qIZTnQUpJd98oP37pMPsOXkbXVZ64fwXVM+j954KhVaMp+Vn696btNPhqGadcKSWxHPx+VQni1huZLuDW1QJcWi3x9Kkp++8jaXZgaNcD/0T6Us6myTE33NmgKSGngjBNvGLZYScTexsjfylNbHt02uWKcI/LMabsUa6MvoRfqyBqdmPJFD3xAwT1BWhK6bT7eD8gZWrGBtafVzieIILF+cUsChVhSnu8Yq4JZdyZ++a+wzVLA3cK+zY1aEtM7BwJmlwY6B4hHk1RUllA0/IaLp/qxJ4S0ymK00AdHk045m45+qpuNeYU+Esp6bs2TG/XMKqqoqgCw6Vd1xvPKPBEIwnCwzGuXuzl8rkefudPH6NmQkAjhKB5eQ2f/cJ2/vWvd9J+qZe//+/PUVAcwOt3k4ilGOgLk4ynCRX6efJXt7B286IshSDbsnntuSP8+GtvkkqmSSVNTMsel/xMJdN88yu7HGc8XcUwNLwBN5/4zbu5+6HZHRXnCykl4eEYbRd7Mh3wYBga6oSqh7QlyUSKrrY4Xe0DnDveye/99w/TuqY2a3+2ZZNKmjNm9d8PqJpKXXMFl052EosmqF9cwe5nDzmWXKpj0ta8upbymkJWblqYJeH6i4JkMk3X1UH6+0Z55ocH6Oud2ZFvDLYteXffBd69s5Ft9y5h/74LvPLS8VmD/olou9zHzh1H+dznt92QT0U6bfHGntP86z/t4VrX8OwbzBHJRJq9u0/T2xPmt75wP02LymYv8yPwaSHaY2dJWFFqvIvwqk7QcilyFF0xCBmOwVqeXsy50UOUumu5FD3GuoIPoituStw1nBzZx3C6D0Wo1Ppa8GlB+hId1PuWcn70EGk7hf4zzsbdStjSoj2yl6HkhZzLA1o5qnLjggC2PUQ0+g0EKh7vA+OZMpei8Xj1OrpiQ/yo/R1e7znFqoI6tpS20BqqJqBlS1hKKRlMRTgz0sX50W76k2FiZopwOs5gMkKZO5RtdDIPRM0kg8kIBYaf0JReAU1RqfBmNwtOB0UINq1t4MDRK3RcG+bo6c6sdYQATVWpqczn1z9xF+tX108K/t0ujU8+fgeDwzH2vnuBb//kADv3nqYw349t2/T0jzI8EkPXVR68u5VHti+bN3VPzzT4Tg38LXsI0xpAak6vmC3jJM2LZDf2lmJoVTPcnxoeo4Xh+MtMbNq17DBJ8zJ+uRaRcXtOmpdyZjM9ejNiDiopqhJEEV4sproROzDtgduWiR2DlClMe/pnoaqExjP+lkw6dB/vGtpGX0GgIKU1oyTp+wVLxjLN0r9IkFwYHqDI4yXPcKMqCuq8qZGzQxFuFDG9OIAtE9j29Dr/NwMp09j23LyTdJdGfnEAaUsunewgkqHdToTP66K8NMTr+85RXBigtrqQnv4wsXiKY6c6CMxCua4oC1FeMr+elFkjDSkll892849ffJ6zxztoXl7Np3/nPkorQ5Pc3CzTIjwc48Dec7z+4jHOHG1n74vH+Phv3D3pQajpKnfe00JhSR47vvcOx9+7zGDfKD2dQ2i6SjDkY+X6Bj740XWsWN8wDSXGmXjkFc5d91bX1azGLN3QqF1Yhj/PQ3n1zJq9jttwJZqmTjInk1IyNBDhH7/4PPt3n6G8poDP/9HDVNcXTwrk7Iyz8anDbbzw/Xfp7hjk2W+9TfPy6pwqSD8vqF9SyfNffxOX16CiroRQoZ+mpdXUNlegZ6opEjITFXu8gqGpCvWNpURGEyQzk7NUyiKVMkmlTNKpn/3EZgxSQsfVAd7cc4ZTxzsAZ+JWXBIkmOchlTLp6R4Zd16eiGgkyRu7T9PYVMaOnxxiNNPgpyiCgkI/+YWOMdDgQIS+3nDWOVuWzbvvXOChx1dRNcsYnArTtHhzz2n++W9fpb9v+gyXrquE8n34A27cbh1FVUinTOKxFMPDMSKj8Zyxmm1LTp/s4Ctfeonf+8OHqK2f2RUZYGneRrxagDPhAxwe2s36wgeo9bUQNcOMpAc4OrzHuT5CocRdQ1omSVhxLkSO0BE/h5SSam8zuuJGFSqK0FBQ0ZUx+cuxBtp/G7ClSUf0bU4Ofxczhy61InSCxoKsjP98oCiFBAK/jZPCv94vIISgKVDGf132Ifb3n+fV7hPs7T3Nru7jLM+v5am6u1ieX4OSoRnZUnJ48DL/fOFVrkT7KXEFKXHnEdQ9eDVjfL2bgSktLGwMRc1SCBI4k5W5IBpP8ezOozz3yjFi8RTrV9ZRVhJEz7yzHC3vRxGHAAAgAElEQVR/k57+MOcu9XKxrZ9//u5b1NUUTXqJCiEoLvDzhV+5h4X1pex68wzXekfo6R9FEc5Le3FTGQ9sa+Wejc34bkCEQlWCuLQFWRl5WyZIWV2MveVMuy+no65ba0RVpjeEdBp8WxFCn9JDIomnTiN9JgIDW8ZJmV1Mvb8U4cGt1zOX3hZF+NHVomldTVNmN7ZM3dYWYcsOY05RSZoIXS0Z96/QFS+K0OmM7iVm9tAWeQVQMJSZDUZvN6SUjhnaL5CsKDjv0i++t5uomWJbVQNbKuuoCxZgKNM70N4IFOF2PCGmgW1HMe3ck8+bhS0TWHL69+1E5BcHqVtcQXgoymBfmPqWyiyhmmDAzfYtLfzTt/byd1/fg6oomJaNbdv8+ZdfmLU49ksf28Ann5ifEdqsT9FkIs2O7+/nxMErNCyu4Df++GGq60ty6vRLKWleXkNXWz8nD7Vx6cw14tEk/ildzJqmsnhFNfXNZXR3DNJ3bYRkIo3u0igqCVJeXYDH65q2uVBRBfc9tpptD84jey/ICrCLyvL4z3/1JNKW07qljmHZ2nr+8l9/FQSTJhDSlry+4yhvvXKSYMjHb/zRw7Suqc1J4ZFSsvLORvqujbBnx1HaLvQwMhR1ZFEnoLQ8xGd+ZQv2LJmzA/sucupER9bnXp+L7Q8sIy90Yxn4/Hwfbo8TaATzfbi9BiVVBXh8Buu3t/LurpNcPNXJ2m2LqW4spW5xBbt+9C533r+U6ganPOr26PzSr20lnbZIxNMk4ikSiRTxzN/xeIpoJElH+wDP/eTQrPKutxvHjrRz/mw38XiKiqp8PvLx9axeV08o5COVMjl5/Crf+tobOX0Tzpzq4tmnD3D2jKOG4Q+4+cBDK7hneytl5SEUVdDbPcKzT7/HyzuOZlUE+nrDnD3VNa/A37Ylx49e5Wv/tGfaoL+g0M/a9Q2su7OR6ppC8gt8uNw6QoBlSaKRBL3dI5w62cne105z4Xz3eJ/KGKSEs6e7+J//8Bq/94cPEcqfebIthGBRYA0N/mUcHNzFwaFXqPUtochVyWCqm3tKP4kuDCxpIoSCLU2CegFL8+6iwb8UEKRlCpdyeyh5Pw+QUmLKGKPpLtpGd3NhdAcxsy/num41nwJX0029NJ0JU255QiEERa4AH6xYydbSFi6N9rLz2jFe7DrC/0iO8ufLn6Ta54zL4VSUr17czcXRHn6t6V62lS0Zrwp0xYY4NpStNjNfuBQdQ1GJWWnSU5rzJJK4NXsG1LJsXnvrDF/7wdv4vS7+46/ey7rltU7QP+EySulMnne9eYb/8dXXaOsc4OzFnqzs2Zgz78cfXcv9m1u4em2I0UgCRRGEgl6qK/IJBtzj/Nz5wmnwXcpw/MWsZcnxBm1ByuwmbWWPE69rxaxuuC5tAbpSRMqa/L5IpM86WWVhYNnhnPvXlAIMdfbGXmfdIIZaRYzjOZdb9iCm1Yuuzi/JMR+kzKszZmRd2oJx2pImfDQEH6F9dBdCqNgyTWPwUfQsZ9b3u6fIJm31TlJi+oWAgDvKanjm4gm+cvRt/vXUQVaVVLK9pom1JVWU+vw5JX/n/z0KLq2a6RJBloxkHOTlLe8HcyYV008sJ2JkIMIbzx8hvziA1+fGzkh3ToSiKNxzVzNlJUGOn+5kaCTKkZMdDAxG2LC2AZcxc9KnsXb+NOFZA/9IOM7pI+3YtqR5efW4JGcuCCHw+lyEMvbOyUR6Wo66EAK3x6C2qYzaprJ5HfRYI/HNZsoVRcxJU18IkdHCzc7mWJbNkX0XsUybqroiFi6dXtFBCIFuaBSVOS+WdCbzPRUlpUGe/FQul9PJCA/Hcgb+Pp+LBx9ZSfWColn3MR0URWCZFm3nurEsm0UrahBCUN1QSllVIbaU6IaGEIKNH1iWJXUnhKMDPJiIc2awjw31CzAmVIjGmlbarvSza+eJn3ngPzQYZYgohUUBfu237uWODY3jMloer8H6jU3ousaXvvgcA/2RKdtGxgN6t1vno5+4k8c+sha3Wx9/6CyoK+aTn91E97UR3tt/cdL2yUSac2euse2+JXNq8pUSertH+OZX9+ak92iawup1DTz5qTtZuKgcw6XlfPj5fC6KS4Isbq1i2z1LePH5wzzzwwNZlQ0pJQffvcSOZw/xsac2TktlkEgODO5kONWLobjpS3ZQ52sFoCmwivboaXZ2fwOfGiRhR1mVfy/l7jrWFXyAg0O7aIudREpwqx42Fj0263UAuBp5k4HkmTnXAEZSV7I+M+0E58I/pSP2zhz3cqOQjmGQFSZidhNOtRMz+2ekFRS5mgnq2e6ttxqKEPg0N62hauoCJcStFK91n+BypHc88O9LhrkS7afWX8K2siUZXf1MY3lylNgcgvLZ4NNcVHoKOD58lc7YIOWe63z5pGVyKTK7SlgyZbL77XPEE2k2rmlg4+qGaWU4dU2hdVE5eQEPg8NRBkdy0wOEEGiqoKQoQEnRjUkdzgSPsQSnIjN5LCTSlxgLbBLpM1nUDyFceOfgWaGpxRhaVVbgn7KuYVoDqIo/kynPDvwdKtLc3tFCeHDp9RDPPhcA0x4maV7BrTfflgZ9KSVx8wK2nOZ3xIVLq2Vi6OPTylmc/ylsmca04+iqf9KxOWaB09UZ5W2hBUnSmUnfLxYEgl9qWc3DdYs52n+N1zou8G5PB693XqLKn8eG8gVsr2licX4JQePG5awFApdWh1OFynX9bRLmhcyym1dXnIi01T1nqo+iKsRG46iqwHDrqJqSs2HXZWisbK1meUsVti35n999k4PH2vncxzfNquozXTw+E2a9IrYlx420HL3jmb8kPBzl2lXHVCqvwDejes2/BUgc6UpgTgYMyUSatvNO1tgX8ODNwd8a0/2fDTN915hM481gjMJzx72tFJWFxr/PmDJZUjV1Eu1rIvacu8zO0+dZVVM5KfAf25cibreP49yhKILN25pZs64+SztXURRal1ez7s5GXnzuyKRlUjKexV+2cgEfeGgFnik63kIICgoD3LW1mWOH2yYp/kgJXZ1DTmf/HCRU02mTF58/knPSp2kK99y/lM/+6lYKi/yzjsexsVZUEuDJT24gmOfla/+4m1hscoCRTlu8+NwR1m9somGaibpAsCiwmr5kJ7a0WBhYTbmnDiEEPjXIfWVPcS1xmZQVx6P5KTQclZDm4FqKXBUMproRKBS6ylGFxoaiRyjQS/GqAco9dfi0IBuKHsZQrmevO2P7ODfyHHOl/+RqnrVkgovhF3h/snoSOa71PzN0xcsC/905so+3BlEzwbGhdiq9BRS6ArhVHVvaDCYjDKWi6IqGX78+Ht2KjkvRGE5F6U+EKTD8GbWmAZ5u3084nU1Vmi88msHG4kW8N3CJp9vfpdyTT6k7j4SVZm/vaY4Pt8+6D9O0GBqJOXr9fve48tx0GA7HiSdSCCHI8/9sjJsMrSrT4Ns/6fOU1elkfaVCLHWKqco/ulKCodXMep+rIoBbbySS3M9kI68BUlYXLn0Bpt2HlaMp1pEDnVsFTgiBz1iFIjw51YFsGSWWOkHQc++c3U/nA1tGiadOTNtHoCl5eI3rpmVpO0p79FWqfJsZTJzmSuRlilytNAQfRVWcZ7hTTZluDNm3hUtu24mMnOsvHnRFpcwXoMzrZ1tVPV2RMAf7Otl19QI728/xk0snaQ4Vs62qgc2VddQG82+ICuTWF6EI97STvGjqWJZc7a1APH0WOYNq1ESkkw6TpaqhFN3QcPtc08ZlY+9iVYWCfB+6pqJpyrRJi5vBrFfE5dEpqQjReaWfEwevcOV8D/XN5dkNt7ZNT+cQP/rqG1y92IvLrbNkde04ZeTfKhRFUF1XzPEDl2m/2MuJ9y6z4s7GrB9XSsnIYJSXn36P4weuIAQsXVuXRYP6eYKmqTSvvHEp1ZRlcaTjGrFUbufRnzcEAm7u2NiU01sCwO3WWbG6lj2vniIey85uut06d21tJpSfe4auKILGhaUE8zxZ9Jz+vlHicwj8pZRcvtjLqy8fz6LlCAErVtfymV/ZMqegf/K2Tt/M9geWcbVtgOeeeS+L99/XG+blHcf49f9QkvPhJYSg0FVBoasi5zKvFqDBvyx7GSol7hpK3DWTPl/gWwyAB3/WZ2OQUt6SZsFpTbJ+ZhCUe9dR4Vt72zTFI+kEf3tuJzEzSbknH7/mwpQ2XbFBehJhPlixnIWB6xKOJe487ipp5sdX3+XPj/+YhkAppm3RHhug3BOatO4YYmaSV7tP0Pu/2Hvv8DjP88z39351+gwGvRCNBMEqdkoiRUok1SXLlmy523HseB2XzV6bbE7OyebsbrKnbJKz155cSTZOHMfHceLEdlxky5atXimJFHsvIED0NijTZ77ynj8GBAFiQIAgKFGKbl0UOTPffPPV932+57mf+86Ok7SzHBrpIO/aPNV7mAvJQfyqQb2/jO0VK/CoOgqCu6pWc3i0g5cGT9GZGqbGV0LKzpGwMmyMNvH60Lmr7peuq0RLfMgOON8+SP9QnKry0Iz7wXUl3f2j/PAXh0ikclRXhFjaOHcfy41AQS6zakbg7zhjOG4cVQSLZoANbQm6Orf6TIHnv3aGkZcjE+TtbqR0ydldRVVkvMaKawrSvcYKDK2WrHWm6OeJ3JuUu2Mo6uIf67zTQzp/dNbPTb0JQ7s8ztgyTTzfQd6zgf7MPpb4dzOUPUzOHcU3oeqjKL5ZlYqktCboUdON1q4XObuzqFHbuwUCQAhMVaMxVEJDqIT76pfzRn8nP2w7zgvdbewb7OZbpw5we1U9n1i+jvXlNWjXQJcztFp0tWqi4X0msvlz5J0+PErz4uwU4Moc6fzxeVOw/CEvZdUldJzuRTM0qurLcN2555rGujJWtsQne5IWG3MG/oGQlzvuXcOZo120n+njf/zBD9l6ZysNyyrx+k2svM1YLMnF84OcPHSRzrZBpCu5bXcrt+9eteBSzrsFqqqw84G1vP78SUaHk/z5Hz7B7btXsmxVLYGwd7LpubtjmFOHOjl/qpd81mL5mjoe+vjWBZVpFgPnh2J8+42DfGrLeizH4bkzbQzEk9SEQ+xZsZTWyrJJzqqUku+8eYiusXG+uH0LFcHLgdhYJsvf7X0Ln6Hzuds24dE1usfGeelsO6f6h3i1rQPbcfmDnz6DPiHIXxUK8OWdtxIwby5llvLKMPUNZbNes0IImporCAY9RQP/aGmAVWuupq4BpWXBooF/fDxNNjv3YGJZDi89d5LBgZnKQ5ESP49/4nbKyoMLuu+EEHi9Bg99cANv7j3HQP/05ijXlex/4zyPPr6F6trLFAwpJdKViLfRcvy9jojRxJrIpzCv0rR5vQgbfj7bvJP9sTZ60iMMZMfRFY3WcA2fbb6TbeXLp+nxe1SdzzbvpMob4Y3hc/SkRygxAnygdhM7K1fwbN8xutMj07juGcdi79BZ+rNjBWt6JC3BauJWmrdibYWekFwNW8qW4lEL1LhSI8BvrXiAleE69g2fZyyfosFfzn1L70IVCik7N8MdeCoMQ2PX7a0cOdnDyfP9/F9/+UvuvLWFhtoopqGRyzvERpOcbR/kwLFO2ruG8Zo6H7j7FuqqbtzxvhpUEcTUG8lYx6e9b7vj2M4oUrGwnJk0J7+xdlKhZi549RUoihfHnTrOSLJ2G+CQtztn0FYU4Z9CqZgfdLWSgLmVrHWOKysUUOgrSOeOEPLuWdTxQkqXRPa1og3QBagEzNvQlMtjl0ABKRnMHEQTPso8qxnOHsWd8gCkKmEExZu2JRZ5uxNXWijzUD2a3344pPIHsJzhuRe+ySGlZCyX5Wisj19ePMu+gW5Gs2nWlFZxZ20zvak4r/V2sLf/Iv9+/R18eOmaeffK6GoFXn35rIG/5fSSyh3E1BoXLXli2f1krPnJCUNBQEbVFPI5m1zWQlHEvJgYm26pZ93qOowbJPwyZ+Cvqgp3PnAL8dE0v/jem7Sf6efiuQFUrUD7kRPNCo7jIoDSihDb713DBz99OyVlgblW/66HEIJVGxr4/O/cz/f+5iV6Lw7z0398HVVTJ4N613Vx7ILpVqjEx+27V/LRL95J7XVw8K8XI6kMPzt2GldKzg3G0NWC6NZrFy7y1Mkz/P79d7GtuQFFCCRwsKuX470DfGLzOiqmUFyzlsUr5y8S8Xr4zNYNAAwlUpwZGCaRzeFMNLKIif8u/ftmRG1dCT7/1SfRcMRHSTRQNPCuq48SiV6dlmEYGiVFGmRzWZtsZm6O9NBgnH1vnJ/BExQCNm5pYtXaqz94zAUhBHX1pWzY1Mgvf35kxucD/eOcPN49LfB3HZdnfrCP9duXo6oKkfKCgV/X+QGe/t6bRCtC3Pvx225IdcuvV+LTKsjYsZtCgm8xENYb2FT2FUo9N4YHfQkeVefuqrXcVbEKR7oTFKQC3hg+w7lEL5tLl02+J4QgagT4aP3tfGjJloIxlhDoE0ZeH2vchitdzCnSoyWGn/+09sO4V6n4qUJM+44QgnIzxMcbtvFY/VaklKhCxVBUXCR/vOGTGFdR91GE4K7bWhiMJXji6SMcPtHFsdM9aOqlOaswJtuOiyIE9TVRPnD3Wh7es3YGxe/twiUH37H0k9PeL/Dux3BltkjjrYLPWMd8g3JdrUJXawpa+lOQtc7hSmtC0Wf6PaQpUcx5OPZO3xedsPduRtM/m+EWXNincUbST+D3bJ1mTna9sJx+RtM/n5WGoatlhLw7mWpQYahhwmYzsexJloYeQaBgKEE05fKDpa6UoSr+ov0PABnrNLYzgqFdW6/ibLDdUcbTv4IbLHt6oyClxHJdOuIjvNjTznNd5zk9OkRAN7i1agn3N7SysbyGiOnFkS7nx2L8ycGX+J/H3mBr5RIaQ/PzwRDoBD07GM88U7TqK7EZTT9J2HfvolxnUkoSuTfI2zMptrMh1j+OlbP5yJf3YFsOz/3LPtKJHKHo1UNvVb1+qvbVMC/yky9g8tjntrNx2zKO7r9A28leYoNxclkbVVPwBz1U1ERoXlFN69o6ljRXzNpQ+F6Epqvsemg9rWvrOPLmBc4e62awb4xMOj/Z8FxWGaJxeSWta5fQtKIar894x4+P60pePtfB7927kx3LGlEVwf6LPfzXXzzPN17dz8qqCkr9164MdEttFauqKoil0vz2D3+B40r+4MFdBCcy/IpgMvt/M6GsIoQ5C83nEgrSmMWPSVV1BN8ccn6qqhR1w3Vdt2gVYSqklJw63kNfz8zJ1OMx2Lajdc7tnw90XWXD5iaefuroDAUC23Y4dKCDXfesmfJgK3ntqSO8+vPDaIbG8luW8OgXd/Hz77xGuDRAT/sQrzx5iAc+OXfD+rViZeSj1Pm3MZa7wGD2GMPZkyTtfnLOOO8GetlUqMKgyruJW6K/RplnFcpVnCkXC4oQGOr0a8aVLg3+Ckx1Jr1DCIEqBN4iGVCjCJdWEQJvEVGEuTDb76gIfNrVH86FEPh9Jp/60Fa2rmvk4PFOzrUPEhtLYVkOhq4RDJjUVEZoba5kVUs11ZVhtBs40c4HPn01hSl5Sv8POWxnCImLK6c3FGpKFFNvmvf6NbUUU6sna52e9n7e7sZ2BrGKBLaGVo12jZQcgcBvbiRgbmU88wzF7sN49kXimZcomeIrcT1wpcVI+gnS+eJqQiAImtsm/Aguz7sKGk3BB2kI3IsqDCSS5ZGPok0xh1KVELpaQ87uKLrmrN1GOn8UXau87qSWlA7xzAuk8ofnXvimhOTVvg5+3HaCfQPdxPNZmkNRfnPtrZPSnh71cmyoobAqWsGvr9zMV178Cb2p+PwDfyEIem5DV6tmNK1fQjK3j0T2NSLe+64762+7MUbTT+AWkV2eDR6/QSqR4fibbeRzNoqioBnFr3fbdcm7zrSknqGoNyRWmleUUOD/6rSsqWXpqhpsy8F1Cm5siMLgrqgKml7IqLxdAW3WybJvZB/dmW7Cepg7y+8koF29ypC0k7wee52t0a2E9WszPZgNEsmh+EG0qMaDH9vKvY9twrl0fJjStDFRBXinA/6puKW2it2tS/FNSEbtWNrIjmWNPHXiLOcGh4k2LrnmdaqKgqoomJpWqBgIiampePTFbbJZTCiKIFLin/MpW1WVolUBVRVEy4Jzfl/MoiTlSjmt4bcY8nmb40e7ii5XVhFk2fK5Tbbmi5q6EoIhL+Nj04MNKaHrYox0KjftAUY3NHY/tpm6pZU8+e1X6L4wSHwkyUOf2U4mmeOZH7y5KNt1JXTFS9RsocRYRkNgF5abIm51MZI7x2D2KCO586TtQawbZOZy/RCYSogSs4Wm4N0s8W/Ho0av6TzmXZvB7DgZJ49PNSgzQ5iqju06DOfiJOwMhqJR4YngUXRG8klc6VJmFnjvlmvTnx2jwgwjEHSlh7GlQ3TKWJpx8sStNLrQGMkn8KoGlZ4ImqIWyvlWmuFcHEcWqB0R3U+lJ/y2jnVSSg4f7uRXvzqG47rU1JTw+Ee2sGJZFbbt4LpykomtKAJVUSYMF2+O8bjAWS7FcgamvZ93eic0yd0rlq/GUGvnvf0CHa++kvHMc0zN7NvuGFm7HdsZmfGdSw2U1wpVhCj1P04ytx+niPSh444zEP86Hr0Jr776us6BlC7J7GsMJ/4BOYvuvaaUUuJ/FEVMjw+EEAg0lImHVgEoVzgUK0oAn7GKZG5v0XU7bpyR9I8ImFvR1IVTxaSUZKzTDCa+OeMh790CKeFvju/j/FiMrVVLuL9hOZvKa4l6fKizxIZCCCKm95r4/ZdgaA0EPTuIpf6ZYg+YrkwyGP8GPmP1vCVpi0FKm9HUT0jlDl7T9yJlIbY/sI62491ousaOD2zAWyR+ODUyyPfPHyNpTa9WPdS4grtqF69H4RKuKRK73HX8zmZGLuF88jwHRg9wZ/mdBPUg+jzcLXWh0+BrwFxk58+uTBemYrI+sh7DvDmOz3ywrLx0mtqOpiqsqq7gx4dP0j0WZ+s7uG1vJzRNJRic6VR6JYQiinbZa5pKJOybl4pOUTqBZE5Ds3Qqx7kzxfmrtXXRohSihUCIwkNQIOCZEfgDxGIJxsfSk4G/EBApDVLXXEFtYzken0n3+QGymULFy+M3sK/BzXih26wKA1Ux8GgllHvWsCz0EFlnjPH8RWK5MwxkDjGQOYJzRXAgUAjo1ajz5Epfx1YiEChCx6OG8GvVRMwmomYLYaMRQwkiuLZANO/aPNG9jxPjnZiKjkTy2JLbWRGqpS3Zz4+63sCRknErxapQHZ9o3MHRsYscGGnjyy334dc8nEv08f3OvXy15QFMVWP/yHn2Dp3mrso1PLbkNgA6koN868LzlJoBHOkynEvwQPVG9lTdQld6mO92vIxPNelIDZJx8nyueRcVnvDbTuqrqgqzdWszr+09x/79F/jgIxsIBDzvaDbfdV1On+6nsbHsqhXBQoNv5czA3+4m78w01vLoK1CV+dNpJxt8rzDyctw4Oasd270y8BeFDPkC1HcK2djtRLz3Ekv9C8UkFzPWCXrG/ht1kT/Aoy9fUEZWSodkbh89Y39C3umaZSmViO9+AubmhfU+oeM3NqAI/ywKMpJ45mVixr9QHvzsgrj+Urpk7TZ6x/6YzBUVmXcThIBPLl9PXSDM0nAUr6bP65jX+IP8/uZdLItcm7+DInRK/Y8ynnl6hvP1JaTyh+gb/zNqI/8bmlJ6zdeAlDbjmecX9EA2MjBOKp5h5wc24rqSE/vb8Ac9eK8Q8XiltwOfpvOBxhXTehyqfYsvHQzzDPzHU1m+/fRbeA2NT+zeQMB7eYJMZfN874XDxNNZfu2+LZRMyKFJKUmkcxw818PRC72MJNJoqkplSYDVjVWsbaoi6Ju+8znL5tTFQfad7qR/JIHH0FhZX8GtqxooD/unSHBZjORHuJC6gCpUSowS/KofXejYrs2YNYZf8zOWH8PFpcwow1AMknaSWD6GT/Whiuma8nk3TywfQyKJGlG8qndyXR7Vw1h+DFVRKTPK0BW98HTuZBjJj0y+fjfCZ+gzJudLlJxULj9PA4x3575PhaoqeOfhuimEKKq1r6oK/sDcgaNgdhnWuQL/4aEEI7GZEnlCQGV1eFEVtLw+A9NTfHhIxLOkpvguKKpCy7olfPtPfo43YJJN54mPpsim8xzZew7HdgjMoUW82BBCQRMeAkoVfq2Sat9mWkIP8WLff2QwO131w1CC3Fr+25R6Vtz47UJBoCKEioI6kWlceBUw6+Q5Md7J1tIWdpSvwpUuHrVwHdf7y/nSsnvxaibnEn188/wzPFS7mZWhOp7tP0J7cpCV4ToOjbazxFdKieFHFQofrNvKcC6O7V4O1lwpGcqN8+Elt7E20sDT/YfZFzvHtvJWDo+2YygaX1x2DyfGO3miez8twZq3PegXQlBdHaGiIsRwLElf30xK3DuB0dE0f/+dV/naV+/G54vOupwigpha8wxVmox1sqjMpt9Yz7U03UJB1UZTSqY567oyTdo6hnOF06kqAhNUooWdSUX4qQh+nnT+2AxX4olfJpF9lc6R/5XK0FcJebajKN55/Z5E4rgJxtO/YiD+VxMNysXh1VdSHvgsilhYYkQIgc/cgEdfOqtikCuTDMT/CiEUSv0fuaqr7JX7Id0sidxe+sf/glT+EMUaot89ENzXsPyq81wxlHv9fLTllgVdaT5zA2HvvcRS36P4sXMYTT0B0qYq/FuYWtO8HzIdN85o+uf0x/+SvNNzTduVz1mcO9pJ1/kBhKJg5SxO7r9A44qaGXaK68qq+dGF4+wb7MavXZ7HtQqFCt/i98rOK/DP5i1eOXqBoNfksR1rpwX+edth3+lOBkaTfPSu9ZOB/3gqy1/+5DVePNJG0OfBa2hYjksineXJ10/yX37tPja01E6uJ53N870Xj/AvLx9FUwR+r4llOzz91lla95/h3314By21BcWVjA8cekMAACAASURBVJPhyPgR2pJtjORH2DeyjzpvHVEjStyO88PuH1LhqSBpJ7Fci/uq7qPGU8OoNcobsTdoT7Xz+abPU+kpyHUl7ARP9T/FeH4cicSv+Xmk5hEs1+K7nd8lrIeRUjJijbA2vJY9FXtI2Al+3PNjck4Or+qlP9fPmtCaxTw3bwuyljUjbM9YBSqJR5+7T8N2XPL2u7+pUlEF5nV4TiiKMi8zOCgE6gvB0GCcVHJm45qiKEhXcv7sQJFvLQzZbH7WLL2Vt0mnL2+HEIJdH9pETUMZifEMS1fX4vWbpOIZfvXPbzA6lOCxf7Nr0bbtWlEo56t41BKCeu2MwB8hMJQgHnV+3NKbCX7Vw86K1Tzff5Qz8R7uKF/FukgjUkrSdo43Y2cZyI4znk8RtzM40qXcDNEarOGtkTYqPWHOJ/r4YN1WVFGoNmhCQSkyBZeZIZoDlfg0kypPhCOyA1u6eFSdrGMRt9KMW2lMVUOfosstpctg9hSD2VNknXHqfJvJOnGW+LcQy11AFYX7pj9zDI8aZon/VuJWLwOZY5hqiAb/NuJWL/2Zo5hqkAb/dgx1Dgfpq3wmpSSbtRgaTmDlHYJBD6WlgWmVbMdxGRtLE49ncF1JIGBSdgWVT0pJKpUjFksWegcMjZISH4FAoXJoWQ6xWJJjx7q4eDFGR8fwpOdLbW0JXu/0Pq9CRn4No+knmJpMSRUJNhURwKu3zrGnM6Er5RjakmmBP0jGM8/O4C5rahRDXbhYgBACj76cqvBv0T36n2dUMgpwSeUP0jnye4S8d1HiexivvgpNLUUIbRpnviDdm8dyhkjnDzOa+imJ7F4cOVNo4fL+VlAV+ioe/frcrw21koj3fjL5U7NKOdruEL1j/51U7hCl/g/jNdagKSUzehgK+5HDdmKk8ycZy/yCeOaliYz15fN+6UFlNp36G4FCAtNBSgsXCyktpMwjsXBlFucqlElX5sjZHehKEiH0wh/0Kf/WQAiK9UGI6/DzUYRJRfBzpHJvkbWLS/1K8oykf0rGOkdp4HFCnjsxtBoEV5qISVyZx3KGSeePMJr6CfHsa7hy6oO3Oml4d7Wkp2O7jA8nGeoZ5dzRTlRFYd225fiL9PidGxsilklRH4iQcy5TeZ15SH8uBDeMdH2io58XDrexY20Tn39wKxG/h5zl0BuLMziaoLnmcknHdSUvH73Ad587yM5bmvn47g1URgKkcxYvHjnPN37+Jt/65X5+/5N7CPpMglqQXeW7MBWTtmQbj9Y+ioKCKgp808HcIKtCq7i/6n4AjAkjjjpvHbsrdvOdi9/BneCiutLl2PgxElaCj9d/HFe6fL/7+xwZO8LK0EpG8iNsKdnChpINnEmc4fnB57ktehsnxk+QttN8puEz2NLmWx3fulGH8oaiIzaG47iT/DpXStqGYuiqQlUoOHkz6qpaaD65IsiPpdKMpDPTJD4nIQo3tCvlDE34mw2KKE7hmS+Ewg0x2rgEKSXjY8UlPx3H5aknD/Psr44X+eaCf3BWeVEpJbkpnwkhML0Gyzc0kE3lJlRTJL6Ah4//23tR1Pk/FN1ICKEQNpoQqO8ZBSBVUbizfDWrQnUcHm3n+52vkXctNkeX8f3O18g5Frsq15K0M5yO90x+Z0tpC9+9+Ar7Rs5jKBrNgco5AyNdUVEmMmVTp+l1kUZeGTzF37Y9S0Dz8FDNJgLa5ZyW5WbpSx+l2reOrtSbqIpJLNNGrW8DSasfictIrp0Ss5FYrg1d8ZK0BjCUIBWeVQihMpA5gaEEqPCsmjRWWgiklHR3j/BP//QGFztjuK6Lrqns3LmChx9ej8ej4zguzzx7gl/96hi5nIXrFtSLdu9ZxQcf2YiuF+aZ9vYhvv33rzI0lCgETBLqG0r5ypf3EA776O0b5e///jUuXhwmFkvynX94DdMoBD9f+coeWpfPVIHxGqtmaO0X463ragXGNartQKFR1aM1k8rtZ2rQcmW2H8BQCz0H1wMhVMLePVjOAH3j/6Ooyg+A7Q4zkvoR4+mnMfVGTK0RQ61BVUtQ0HFlDtsdIW/3kLUvkLe7JmgXV1GLUsJUhL5E2LtnERqINSK+hxjLPEM6f2jWpVyZYDT9M+LZl/BoSzH15sJ+KCEEAldmsNwYlt1Lzr5Izu6aCOyn74cifFQEf4O83cVI+kfXue3T4bgpUrm3sJxhXJnEkSkcN4nrpnBlClemcWW28Mct/C0nXhdrAL+EnN1F+/CXUYQPRXhQhAcx8ffkH8WHKvwoSmDib3/hIdZoxaMt/OHMo7dSGfpNukf/8CoPgg4Z6zg9o+cY0r6NV1+OoTVMXOMaUuaw3djENdY+6zUW9NxO0HMH/eN/dtVGX6/fZPtD61m5uYmSihACJvo8Z1Yb/IbJurIadtUtxVDVydG11HNjKuU3LPC3HRfHdQsKC6ZBwGsS9AlKQz5omj7gJbM5frHvNGG/h0/fvYnGqhKEEIT8Hh7YupLXjndw4Gw3XYNjrGosTFC60FGFioKCLqbzyEzVpDnQjFedXlARCFShTpu0XOnSme5kIDfAk31PFjL7+RGG8wUNXb/mp8HfgEf1UGoUBsG8m6c/20+dr46AXnCvrPPW3ahDeUNxsKuXg929bGmoQxGCMwNDvHK+g/pohJaKy3y4hmiE5860caCzh+ayKLqqMJ7J8tOjp4hni8unmZpG0GNwdiBB99g4IY8JExKw11oKvNEQ89TXvcoait7QiwXXLQT+s1HKrLyDlX/7gllrSjVASsmZQxd54u9eIpezJu8vRRF85j88SP0sTr/vBCJmE4pQceR7I/DP2Hnakn0EdR/LQ7Xsi51nOJfAli4j+STN/krKzBDnE33kJvTbBYIGfzmlRoBf9B7g3qr1k4F6zrFI2BmyTp6Uk2M8n5pTRSfrFAz6tpWtoMwToswMcrmNFlRFR1e89KTfIqTX4FVLEBSmUxcbV9pknBEiNFDuWU7YqKfEaKQrtY+O5Cu0hh9gif9WutKXXj+Iqi5MmCGVyvHtb7+KBL76lT34/SZHjnbxgx/so6IiyM6dKxCKoKG+lMce3URdXRQEvPD8KX784wNs3dJMfX0priv5xVNHGRvL8O9+6178frNQIUhk8PkKx6umuoSvffVuDh7s4K++/jy/9W/vpaam0Pw5m1GfodagqWVXZORnwtSa0NTZaUOzoSAbupaR1A/nNCHyGCsQi9D3ogiTUv/HkNKiP/6XRZt9C3BxZJx0/ugEpaZAiytATpjszS8DqioRKkO/SVngEyiL0M8nhMDU6qkMfZGukf88q7RnAS6OO0Yqf4BU/gAFLapLc8N89kMj4nuQ8uCvMZp6YtEDf8sZpHv0j8g5XSAL2yIn7sbro+3aWE7/HMuIiT/KxDyhgBBUBL9ATfh3WSitTAiFEt/D5OwuBhJfn7XJGwpKWTn7Ajn7Atd6jXn05VSHfxtFeBiMf3NOhR/HdnjzmeNkUjkyqRyhqJ9Hv7hrRoOvgmDfYBeHhnsxp6j4PNa8hnvqW+ba/WvGDQv8VzZUsrl1Cc8fOkdfLM7OW5rZsmIJSyoimFeou4zE03T0jZDJW/z1k69PazZ1XMnFgVHSuTwDowlWNc7tUqgJbRqHfy7oik6Vp4oNkYIO/caSjUSNwqCqCnXaui4FNapQsd0pJZl3aSBRGQrw/zzzCq2V5RiayuGuXkbSGX5nzx1UTsni725dylMnzvLXr+zjSE8fQdPk4sgYWdtmSUlxPqPP0LljaSMHO3v5o58/z5qawrmLeL18YfsmfMbiGJ4sFq7HTE1M/u/GQLqS9Bxyn+8UHMfll999nYbWGtZvb0GZ8gBVXv3OGCLNhoBWha4EcIoomBSD5ToM5xKUmUF05eaToLWlw5uxc3SmhlGEoMYb5fay5XhVg3uq1vFM/xG6Ooap85WxOboUbWIs86oGq8P1nIx3s76kEWXiIfzEeBcvDR6nJzPCQG6cf+jIck/VejyqTrWnBHUi43+J7gPQmR4i7zrsi50DAUkrwyN1W9laWpiwpJQ40iLrxNGEZ0JhSdCbPsRQ9ixV3tWUGE1owsSrRjCVACl7kLBRR2/mMDkngeWmCeu19GYOk3dSeBYY+Hd0DHPiRA+f+tQ2dEPDshwaG8owTY033rzAjh2tCKC1tZolS6LE4xlyeZv6+lKyWYt4fGKiF+DzGqRSOZLJLDU1JdROeFtcSmjoukokUqD+KEIQCnnnbMC/5OA7V+DvN9cXqBMLgNdYiSK8OPJqgb+CV29d8G9cCVXxURb4NKoSpH/8L67SiDsV7oIctQ21lsrQV4j6H0NRfCzWwCyEStizh3yoj/74n89avZgJ5xoqjBoR7z1Uh38bTSnF0OpRhG+RVX7cySz+2w/JpUd+OeUtedVrcX5QFC8VoS/gygzDye/M85jN/xoztaXURf4Av7EeyxnG0Kqx81d7AIRQiZ+HP7cD6UqS42n2/vJoUQrtvfUtbKtuYDibQhUKZROZfp92Yyrl139XS4rSOMrDfn7n8Tt59uA5nj1wlr996k2++/whtq6o50N3rGZVQ+Vk93IykyNn2eRth87BsclJ6BJCPg8VkQDe6+BgSynJutkC719apJwUaTuNR/WwPLCcl4ZeQld0QlqIpJ3Eo3qw3OIXoxCCRn8jzw4+S2eqE1vadKQ6WBdZt6DtyjiD2G4Wv1aFushqQ3Nh1/JmasJBnj3dxlAyRUO0hC/vvI07WxqnnYfWyjL+y8N7eOLIKdqGYmiqypqaSj54y0qePHaaRC4347wpQvDBW1aiKgovnL3Asd4BvLrO5vram87ESyBuaOB+vZBSTsuy31SQBQO/W+9eTdPKmnd6a64KjxrBp5WRnWfgP55P883zL/Kllt2UexbPbGixENA8fLrxTizXAQGGoqGLAr9+c3Qpa8L1SCSGouFIiTHx8OIiidtp1keaqPJc7m1YE6mnJVg9+VoApmqgCMFvLLtn0mhrZaiOlmA1Wcfiuf5jfLzhDlaG63Ckyz93vMLJ8a7JwD9tD+NKi1tKPkpP+gBxq5elwd3E8700Be4kZFRT5VWI5c7j4qCKgryiI/M0BXYQ0CoYt7onXt9BQK9Y0LGSUhKLJRkZTfHjH7+FcWk+kRLbdjEmKDyuK3n9jfM89+zJQgVLEWQz1gTtpxAkKELw4EPriMcz/PXfvEA0GuDWW5eyY8dyykoXrsShCD8evfmqdBKBjs9Yw7U29l6CoVaja1U41uzceFUJYmoNLOagqCo+ov6PYGhLGIj/Fcnsm0gWL5khMAh4bqUy+CUCntsWzUl32m8oHsoCn0QInYH417Gc3kVbtyL8RP0fojL01UmZVkOrRVUiuM67U97z7YamhKkO/1s0tYTB+N9huzPdrq8dCn5zIzXh3yVgbkEIDU0JY6i1pCne7H0J2XSe42+ex8oXnHuzqRyiSIIxa1v8w5lD7B/opjFUwn/YsIMXey6wKlrBipKFjXdXw3UH/q50yeZnBshCCCpLAnx813ru29LKqY4BXjzaxivH2jjW3sfvf2oP65cWggRD11BVhRVVFfynz95DyDezDCoEMyoFftU/mZm/BFVRKTfL0a4wk7Glzd7hvXSmO5FIXh56mRpvDTvLdrIitIK0k+bloZdxpENAC3B35d3oQp+2Ll25/Lo12Ep/tp9f9v+SsB5mdXg1Ie3aAwNH5nhr6L8znm/njqr/mxJz2dxfWkRoisKDa1q5Z+UyHFeiKgoeXZsRxKuKwub6WtbWVGE5DkKAoRbMJRpKb0NKWVSnP+gx+ejGNTyydgWulIgJ8y7jJjTwupkhYVaaD/C2+kOo6nTJSUVRWLa2jteeOoKqKRgTfH4BlFSELgdYNwEMNYRfq2Akd3Zey7tIss7MBvibBUIITFUvaralCGUaTefSEsfGLvL68Bm60zE+0bBjmguuoWizuuJ61cuBlKaoaKi4UlJmBnmu/yhnE70krQyd6WE+VHfr5LKmGkRX/bQnX0YgqPGtJ6hXEzGme4T4tMviwVE1QNS8rF8dNZunvV4oFEUQCHj44r/ZRUP9dP66aRYoox0dQ/ztN15i585W7rtvLeGwj7a2Af6P//Onk8sKIaisCPGbv7mbzs4YBw528PTTxzhwoIPf+e37Fyyte0lycyT1I2ajXWhqGabWsOD7/bKR1+z3QCHbfH0u4MWgCJ2guQ1PdCmj6Z8ykvoRWetcUefV+UKg49GXE/U/SonvA+hq5XWbNc3+W2KievEJPPoyhhLfJJF947oacAUaHn0F5cFPE/E9hCpCk8fdUCvR1bJFfcB4r0NVQlQEv4BXX8Vg4m9J5vYtuLqhKeWU+B+hPPAZTK1x8roSwoOpN0JGpZhU7SREQfVOURUCIS+tj2zEV0T977nuNoYzKR5ftpa9fRdRhGAok+J4bOCdC/wVIdA1lUzewnaml0WGx9MMj6cwigR9Qgg0VVAe9lN2SxObW+torory9Z+9zt7jHaxrrkaIAu+/siRIbyxOMpOjtmx+Zdy14bWsCq2a9l5IC/F43eOTDb2TOyo0tpdt5zZ525T9UjAVE4Fga3Qr6yPrcaU7YQ9fcPGbuq5ys5wP130YQzEQCPZU7CHv5gu9BgscaNL2ALHsSSyZWpRy10KgCDEv2k1BpUGbEeB79asHdqqi4DdvLlrPuw1CCPRZDNB0XeUDj22i9W3KtgshWL7iclZYSsnYcIJ9z53k+JttmF6jMOApCp/7vYdoWF59lbW9vVDQCOn1CJQZJV5Xuhwf62F/7AKWa7M6UseyYCV51+G5/hPErQx1vii7K1fhInm+/wS9mTE8qs7dVasJ6V6e7T+BoWh0p0eo8Zawu2oVhqLyxnAb5+L9k7Shh+s2AJIXB04zmkuxLFTJ9vKWSSlOx3UZGE6QyhT6Z8pKAkSC3kUJxEK6l5ZgNbsr19I0j6beq8GrGnyq8U7OJHpI2VlqvVHuqV5PnfdyUG0oAVpD9+NIC1Vob4NfQnEIIaiqCuP16gwMjLNlc9NkX48zMa8JIRgaSpBIZLnjjuXU1UVxXcnwcHJSkQcK17xtu3g8Oi0tlTQ3l9PYWMaf/ukv6Osfmxb4K6qC7bhY1vyCW7+xHp+xBimLL+/RW9HVhd9TApOAeTt5u4/Z+Mw+Yx2aUrbg37jq7wsFQ6uiIvh5wt57iGdfZjzzLFnrDLYTm9dDgMBEU8vwGasIeXYT8tyBodUAcyvRLcIeoAhzwgl4Jcnc64ylf0kqdxjLHUDK4j1v06GgKSV49GVEvPcR8u7G1OoRVyQsFeEjYN5WJDZQ0dWFBYRCGHj0ZdfkAXGjoatzU7ivBYowCXl24jNWE8+8zFjm56Tzx7CcYa4aqFMI6g21mqDnDkp8D+Mz1qGI6WNv4QF9PT5j7YzzXZALLSQ2VVUhOZ6h/WQPQgh6LgzxwKe34fFNHwN7U3E2V9RxS1kV+wa6EAh0RSXn3Jgq/7wCf4+hU1Ma4q2z3Rw4282ejS3oqkosnuLJ108Qi6epLr2c7ZaywMvP2w5V0SB+T6FULLmcLdS1y4Fy2O/h7o0tfP1nr/OdZw7y+Qe2UB0NoSiCXN5mJJEmk7NoqSufZsSiKRoaV94oCh61WMVAFH1/8nNmfn7le1euWxMa2izZsflASslY/jxZJ4aqXLs74vv41wNFEXhn0elXFEHrihp23/POyMkqqsL9n9zG7se2TP9AQEXN3BKZUkq6UqOcjvdzT/XKGRN3f2acl/rP8XjjphmVqGtFwSWyuWjg35mK8Y/tr3Fv9VqiZoCAVkgKjFtpHOmyKdrEj7r2U+2NsDxURYUnRL2/jH3DbTzbf4L7qtfyXN8JtpY1synaxE+7D1DhCVFqBnhp4DSP1G3gmb7jhaqaqvOP7a8RNQJsLm3iF71H8Cg628oLyhaZrMXXv/8qbxxpB+CLH9nOR+5df137fgkN/goa/IuTRRJCEDUD3G62XnUZTZho3NiA37YdBgfjpNN5BofiZDIW7e1DxBNZSkr8hENe6utL2b1rFT/+8QFGR1LU15eSTufp7IqxZ/cqWlurKS0N4PHqPPvcCVKpHL19Y7z88plpql2O4/Ld776Oz29QU12CbTu88WYb0aifyBWeFWVlAQxD44mfHuLWW5ux8g5rb1lCdJaqgM+4hWUV32W2jH/BaXbhah9CCMqDn6E08NGr/IaOIq5UG19MCITQ8ejNmFoDUd+HyNkXyVpnydrnydvdE6ozGaS0UYSJovjR1Up0tZZ4uoqofyVlwaUowr8Iqj0L2AOhoKulRLwPEvLsmlCDOUvWaiNnd2I7gzhuAlfmEUJBEV5UJYKp1WFqzXj1Vkx9KZoSnhHwX/4ND9Xh36Yq/LUrP0FZ4P1kqDU0lf35gvonbhQK+zJzbE/bcZJ2bKL5eDpUoRPRq9BmMW4tnJ9yov5HCfvuIWddYDS7n8H06+Tsi9juKK7MIFBRFB8etZoK320EzbV49ZUYag1CzG7qGfbuIejZxpX30NT7M9Y/zlgsgT/sZenqOrrO9+PYM497bSDMsVg/YdNDxrY4MzbE6dFBHm5aOceRWxjmFbX6PQZ3b2zhSFsvf/7jV3nxcBseQ6c3No6U0FJXRjIznav36rF2/umFwzRURCgN+9EUhZFEmtOdgzRVRdmxtnnygKqKwgNbV9A9NMYv95/h1MUB6itL0FWFRCZHbyzOppZa/peP73pHHRgXH5KBzMGb6gZ8HzcnFEUQjhScga+k/LiuJD5+dXWBGwkhBJV10yl3UkraT/WSjGcKFYA54NcNarzFG4HH8xn2DXfwkcaNLAbnOKTXoyomrjs9s3gm3ke1L8KOilYMtTA0DmbjRHQfOytWsMQXZe/QWUbzKVwpGbcynE8MMpCNYygqjpQEdS93lLfSFChnf+wCI/kkXlVHFYIabwkVnhB+zSRl5zgy2klToJyRfJKRXJLezCgSiaCQJMlkLRITRmn5eWaL/zUjHs/wT//0Br19Y2SzFoah8p1/2IvXq3PvvWvZdddKDEPj8ce3UlUVZt/+Cxw+3Inp0WhqLCc8EbA3NZXx65/bwQsvnOK7//QGtbURPvrRrTz99HE8nsK1LISgpMTH/rfa2bv3HIqiUF0d4WtfvZuqqssVayEES+qi/MYX7uT5F07yg+/vIxzxsXRpxayBvxAamlhY8/J8cUle8WaAECqW4yeRbaYiuBaEPVHtcCbnxksKMAKVnA3/+Oo+1jcEuHftO993I4SCKvx4jeV49BbwFnTw5eT2F8brgrKPMuFPoM+LjlRwJPcBiyfrKISKKt754zYfnEu+wQsDf4eUM2OksFHJY3X/kYhxddW4gpljCM1cT0d2lFfiT2O5HqBiYi4t0GRDepRHSz9GxGyaV9VIEcacfSSarhKtCCFdyUBXjHQyN9knNBW7apvpiI/wjRP7iGXT/MXR19lR08jG8toia71+zI/qowjuXL8UTVN57uA5eobGMXSV1Y1V3L+llbfOdnPwXDf6lKB868p6huMpzvcM0943gpSSSMDLh+5Yw56NLSybouMvhKAk6OXLj2xj/bJa9p7o4OLAKJbtEAl42bV+KXfeshRdm99TvZQuWWeU0dwZRnNnyTgxJC5etYxSzyo8RWTQBAoBvQ5tluZaKV3S9hAjudOM5c+StUeQgEctocRspdSzEq9aNufNLKXElhnyToKMPchQ5tDk+uNW16xP/h61BI967XbTs6GptIQ/fHgPq6orrjuL+j5uPIQQREp8mB6NbGZ62ddxXIYG4/N0WX57ICUceuUMKzc1Ulo5exAjpSSWSxHLJSnz+Ke9P5xLMp7PkLByk+G+7Tr0ZeLkXZsKT5CAZk64yiZIWDl8mkGlJzTpS1EMAb2ShsCuyQZfXQlgqEF0JUnesafqTQAFPrtHLVAIFKEgpeS5/hN0poZ5oGY9pqrRnhyaWFchm19YVhS03f1l2NLlO+2vEdBM7qpciSYUApqHTdEman0l3Fm5knIz+I40vbuupL0nRt/QOMvqy6kqW5ygIJ3Nc/xcH5qmsHppdUHD/gYiEvHxpS/tKtoLYxgTfSdCEAiY3HffWu66ayWO4yIUgaGrkxl9TVPZvXsV27a14LoSXVcxDI1VK2sm16MoggcfXMfdd6/GcSQI0DUV05xJNVFVhV27VnL77csKngBXqd79a8XJnkGOdPbx6e3rMTQDrhpQWWTyNtYNokFcDwrnXpt1Hn8f1wZD8eLXIlhuDkdaWG6OvJtGIvE4gWtOmhqKl4BWQc5N40gL282Tc9NI6WJJl8KD2eKNwdHKMBt2tOI4LqcPdNCyrh5fcGYlrcT08uW1t9OdHGc8lyXq8VIXCE+KMSw25n11eg2d3euXsX11Y6G5E4Gpq+iaSlN1lA9tX4N/SjakpbaMrzyynbxt47qFgVhRBKauoakzD+4l3f57Ni3nznXNWLaDlIXvGLqGXuQ7xeDIPH2p1zkz/j1Gcmew3eyklJZARVPMKbqtl6ErAXZW/+mM5lopJZab4mLyadriPyOe78CRuWnrVIWHsNnEstCHaAjcjabMPLFSSkZzp2lP/JKk3UPaGiBtD5J34xPbnWX/0H8rum0AyyOPs7bki3Pu/3xRHvTz6PrVi7a+93HjUV4ZJhDwzAj8XVfS2zNKPme/rUZZPe1DtJ/s4fb7b+Hp773JUO9lfW4pJSf3X6B1Q8Oc6xnMxvlZ11Ek8Htr7gOgPTnMX595hSpviLF8hpxr40rJk93HODnWi6louFLyxeV3YEmX73ccQEroSY/y4YaNbC1rnHW88KqlbC3/91zmNwtUYbI6nOSlgdN8t30vJaafUiNAS6hqIhSfvi5dUUlaOc4l+jifGJiUuSxWkbgUiK6N1FHtjRDQPQQ0ky2lzRwd6yJpZ7Fcl61lze/Ig1sml+cff7afN46287VP3smDOxdnXDjXMcgf/+0zVJQG+KOvPUx59MZyihVFmVUffyqEOsOoCQAAIABJREFUEKiqwOcrHlxe+tx/hda2bwovVwiBpqlo80hGzba+9xKkhGeOn6MmEqS+LMIP3jzG1qVLaCiL8Ozx8+xc0cSpnkEOX+wj7zi0Vpexa9VSdFXhtbMXeeLASYYSKUZTGcqCPj68ZQ0Bj8loKsNLpy7QMTSKogi2L29kRU05IGkfGuVvX9xPMptj69IlbG1e8h5jBLyPlsCt1HpXkHcz5N0MI/lenh/4Jil7fqpsV6LJv5Gq+mXkJtY3lu/jxcFvMW4thvrPTGi6SjBSSGjdOkHFLTbGCyHwaTrLI5f7avpSCTK2RXP42v065tyua1lYUQReU8fL9ODCY+h4rhhDhShkUYxrdDNVFDGxvmsPYFzp0J18mUOxPyNtD6IKk6jZWnDrFCpJq5uR3NnJYFugYChBNMWPT6tAvSLLIKUk78Y5PvJN2uJPYssUoGCqIfSJxhjLTZFzxohljxPPXyRhdbG65NfQlZll3FjuFOfjP+FSwCEn/rsEx83POvG7szR6vY9/PSgvD1FaFmR4KDHjs+7OGKMjKapq3j7dfFVV0HQV13E58NIpliytnLQjlxK0WZqRr8SKcDVxK8tTPScm33u29zQrwlV8rGkzbwxd4F8uHmQom+BXPSf4WONmIoaXb5x7leNjvdxW3swHl6wjZed5eeAsB2IX2VzWgDpL9lwIBb0If7nCE+JLLbs5E+/Dli5V3ggh3csHl2wiqBX2a1flSiKGj7DuI2r4ybk2jy7ZjCNdIoaPR+o2EjYKZfmdFSsIaCYdqUI14Ey8jxPjPTjS5deX7uQDdRs4Nd5LLJ8kpHspMRamBHO9GI1nOHWhn3R2pnjDQuG6kjMXBxkYSRAKeIpydN/HewsD4wlGUml0TeX5k21oqoLX0DnU0csdrY2kchbLqgrmZ08cPEnAY3LH8kaWlEaoigQxNI1tLfUEPCaGppHJW3z7lQOMpbPcsbyQcDR1DQHkbYejnX18cNMqxlIa337lIFXhIM0Vix8kzRcZJ81YfgQXF4/ipcQoXbDox/soQFMMQkr55GuvGkK/DnEATTEIKmVcEtz1axH0IonaG4FrTeqcGBlgJJt+5wP/mx0pu49TY98hbQ+iCR+rSj5Nc+gRDKVwmh2ZpTe1l8Ox/0nGGcJQQqwv+yplnlvwqBF0Zbr+sistzo7/C+fjP8GROUy1hKXBD1Dj34ZXK1yMGXuI3vTrXIj/jKwzwtmx72MoQVZEPoFyRbmv0ruZ2yr/98nXlpPg2Mg3yThDqMJkTfQLBPTinK6QPnfm9H28t+H3m6xcXcuZUzOl3QYGxjl3tu9tDfwrl0SpqC3BdSVNK2t48FPbCU3wlqWUIOamHglRILeoV0yQA9k4t5c341V1qrxhDEUjaeUYziU5OtqDpig0B8opMXwcinXybN9p6gNRBrNJ/JrBFOPYeUMIQY2vhBrf9IbkLaWXZSRXRy47dG8rXz5jHZtLmyb/vTJcg5SSn/ccZk2kju3lyxnIjvP9i2+StnOU+PxsmrL8O4UL3cMMjy1cjrAYsnmLk239k2o57+O9DSFgaUUp+y50cdYYZk1dFYPxFB1Do5SH/IS8Hna0NjKazpDJW0R8XvrGEqiKYFllKUuiYUxNY2Nj7aRsd8dwnOPdA/zuQztZXnU5E5q1bIQQbF/ewP23LGc8neXwxT76xuLvaOB/PnGKH3R9m5ybZVVoHR+r/zwe9e0JKt/Huw+j2Qw/aT9J2iruZXF6dIiNFTdGqe89E/hL6TKUOcJYrg2ASu9GWsKPY6qX+aoaHuoDexi3Ojg5+m0sN0naHiKkz9REllIykjvN+fEf48gchhJkfelXaAzcN81kK6DVUmquIqQ3cHD4/yXvxjk7/n0qvBsp81wumQshCBn1hIz6yfcy9jCnx/6ZjDOEECrV3q1EPfPv4lZmKWs6jvv+hPsehG6orF1fzy9/fngG3SebyfPaS2fYcutSPPNopl0MCCEQqkAokg994U68fhNlglsvpeS2e9ZMljlnQ4FK55B1LOyJv01VpzlYxtHRHm6J1nF6vJ+cY1Ni+lniK2FLWQNNgTIyjkWlN8g3z75GjS/M3dUr+EHuAHn35uL+bi1byiuDZ+hKvY4jJdvKWyg3b47mOsdxOXNhgHQmP0215noxlshwruPGlM/fx82JmpIQ8XSOC4MjrKqt4Hj3AKd7B1lSGmEsleEfXjuE5bhEfB76xuKsc6/elJnJFca4qN83Y37WVYXSgB9VUVAVBV1Tcdx3tqpkS5u0kyTn5si52ferXO/jqhjJpXm55wKro5XoRXrSnCINzYuFGxL4O67LQDJJznGoC4XQ3wazJlfajObO4mKhoFHuXTeZ6Z8KRRhUeNZzVvwAW6YZzh7Dlhn0K+TRXCwuJp8m4wwBghr/dhoC98xw1i008RnUB/YwlD1CW/wJ0vYQ7fGfEzVXoNxAmbGC4cxM5+RcziKbsZCykIl5H+8NCCFYubqWxqYKTp/smfaZlHDwrXaOHeli861vM1dcwvhwEiT4ggX5MyEES5ZdfWIHsKXLz7qPcny0l5Fcim+d38uH6tdzT80q/r/ze/nm2dcoMX20hCqIGj4+0byVF/rO8PLAOcK6l8cbN7GzqoWfdR3le+1v4ddMSk3jpnFhFkKwvqSe1lA1tuugCgWPaqBe4/m5dD4zWYv2nmFOtvXTOzRO3nLweQxqKsKsbK6koTqKaVxdy1xKSTZvk0xl6Y8lOHymB1dKpJQMjSRp6xou+j1VVagpDxX1bJESXNcllcmTSGXZd+wigyNJAHJ5m4u9I5MKRVci6DcpLwnM2OZszqI/FsdxJJGgl9KIv9AMPp7m5Pk+zl4cZCyRQVdVairCrG2ppqmu7IY3EV+CKyV9g+Nk8zaaqlBd5NhIKRmIJUhNqN6ZhkZNeRjlCvdOx3Enz+dUhPweykr8M45NLm/TOzQ+2T9XHg0Q9JkzlkuksgyNJKeFoIoiqCoL4S1irDc8mmQ8WTA7MjSV6vLQjD4G13UZiCVIZy10TaGqLIyhq0R8Hhwp6R9P8MD6VjqGRjnTN8ympjqOdPbRMxrndx/aid80uDA0naOtqSqW8/+z995Rcp3nmefvpsq5uqpzQqORcyJBgjmLkkgrR9uyLXvsWcten52dOesz6zNrz+za69mxRw4jWyuHtS1ZEpVJMYiZIAEQOaPR6JyrK+e64ds/bqOBRgfEZpD0nEOiTvW93w11w/u97/M8rzknWPa7bTvdkXSWOr9n9pzbkC69294j9/q7CUtYVMw8FbOAKQxkScEhu3ArgRm7y6VPkkDYBiZmjqpZxBImsqTglD241QDKdYiVhRAILCpmkapVxBQ6QggUScUhu3ErfmTpnei38N6GKsvsaergkys34VTmn98Xhs+Trd1Y47Grbns5Bi0bBv993z56pqf58gc/SHNg+bNbApOKaYsLJUnBpdQtKqJwKAFU2YVhlqiZOQyrjCbPDfyrRprJ8mEAFMlJm/f+BUW7MNPNT3LS6r2PwfxzGKLCZPkwZWMar3ZrG1NcjnDEi31Dz438S6UaY2Np1qxv4udPxZ8uhCNe7r5/Lb094xhX+AFn0kW+9fV9tLVHqW985yg/pmXx9D/tJTNdYP2uTjbf0U28OYJ2DQGYKsk83LSO+xvWzH7n05yoksxvr7mPmmXiVGwhryxJ7Ii2sz7YiC5MNFnBrTiIOD10+WMIIXAqGgKB/B667mVJxqveOC9VkkBVZIYn0nzjmcPsPXKBTL6MblgIYbvEaIpCOOjh7h1dfPLR7TTGAvOef5YQHDs7wt4jfYxOZZmYzjGVzM8GejXd5J9/9Dbfeu7wgvsRCXn5L7/zITqa53a8LZSqPLf3DD0DU0xM55iYzjGdKVKaCXaHxlP8/p//cFH3sEf2rOVLn70XRZn794HRJP/HXz9LKlfiA3et4zc+sYeDp4b4l6cPcq5/kkrVwLQsuxu4qhAOeLh3VzeffHQ7DXX+ZQ8sLNPiK9/cy4ETA/i8Lv749z5MV2tszjK6YfL//MNLHD9nT9TbmiL88e89QTgw932Typb4g794hrGpDG6XA8uyqNYM7tq+kv/wxYfmTRTP9k3wH//iaWo1W/v1hY/s5uMPb5l3zC/t7+FvvrUXyxL4vU7yxSoOh8p//DePsn1d65zlDdPiq0+9xStvnwchqK8L8Ie//ThtjXPpM7lChT/9uxc51TtOZ0uUP/rSh4iGvPjdTpyqQrZUpiHoI+r3sK93iKawH7fD5uw/c/QshiWYzpdQLstydjdEefVMH1979RBNIT+PbFpFPODjrjUd/NMbRzjQOIxpCdY1x9nR2cLPIixhcj6/n77CIQJaHdsjH8Qp+8jpU5zKvkJ/8TBZfQrdqqBIDrxqiJizjW2RD9LkXrzPhikMJisXOJ19lbHyOfJGEsOqokoOfFqUJvcq1gbuptG9askJgBAWRSPDaPksg8VjTFeHyBvTs645muzEq4apc7az2n8H7d7NqJLjfTcByNYmOZT+EVWzhE8NsyPyYdzq1WPcnJ7gUOqHVMwiXjXEptAH+PjKjXi1hc/BllgTZWN5mrouT2pECIq1Gtlq9bIZ+vLj0skTWEt0wbWEMesLa88852flC8Y4ZcMW5bmUCAHH0lxcm8rTjkuto6CPUDTGKRrjyxr4NzWHURQJw5h7joUlOPBWL3vuWY1zgazOz/H+haLI7LlnNa+9fIazp+Zn/U8cHeQfvvoaX/iNe6mL3brgx7IEhUIFl0vDcUVArygyH/s3D9BzbJCTB/o48noP9S0RttzZzcpNrQTDPiR54f2QJAm/trATi1t1cOVUWwK82pVB9OJj/DRAkWWSmSJ//v+9wv4Tg1imhcftsEWzwg68qzWDiekc33nhGMlMid/9/L3Uhee66AhL8ObRfr7+9KFFn8vlqj5LsbgSmrYwnSKTL/PNZw8zNJ5eYC0wLbFoth/sKsbC61nkihWy+TK9Qwmef/MMf/fdfUwkcjgdKqGAGwkoVmqUKzqTyTxPvXCUiekcv/v5+26ZLelisDPnfrKFCoVyjbGp3LzAP5kpcmF4enZyNTCSJJEqzOvCnMwWGRpPUSzXWNURZ3Qyy1TBrr5Uqjpe99xr/nTfJMl0cfZ3PNkzxkce3MzljAEhBD0DU6RzJbxuJyta6njjSB9mvszIRIZta1vnVIRzhTK9Q1Nk83ZPkJphMjqZpbUhPGdfs4UK/aNJsoUKAZ97tnIgSzIf2raWiq7jcTi4a3UHTaEA9UE/jaEAv3H/LsbSeer8HvasasdzWTf3DS0NfPG+XYykskR8bhRZwqEqfGzXRtY3xxlL51EVmc5YGKem8PjW1cT89vXt1lSe2L6O1sjy9j54tyGwGCuf42jmx4S1RtYG7iZrTfHS1NcYLp3EvCLmyRsJUrVR1gfvX3RM3apwPPMCB5LfJatPzbPHzBkJxss9nM/vY2fkSbaEH0NbxPI8q0/x7PiXGS2fQ7fK86lOpr3M2Mx4OyJPcFv0Izcl1n03oMpOxsvnGSodxyl7aXR30+XbddUq60DxKIdSP0IXFVb778SlOOc0hDUsi0y1TNU0casq9W7fnMnxLT2GZRn1XYAsafi1VsAO7LO1/tly1eUQwqKgj6BbdhnarcbmZfuFEFTNNLplC95cSmhB2tCVcCohHHJgZh90isbETR/XUmhoChGOeElMzXd5Obj/Aoff7ue2O7rnlZV/jvcvJEkiXh/kyY/t5C+HkuTzc0uBhmHxyounKJWqfPJzd9C9uuGaLAcXghCCYrHK+GiaIwcHOH1yhC/8+r20d84NbiRJIhzzs+uB9Wy+cxV9p0d5/hv7+Mp/+i6da5vYetdq7vnwNjzXYLX4c8yHJQTPvnGadK5MNOTlwdtXs3VtC+GAB8sSDE+keW7vGQ6fHsYwLd44fIHNq5v4yINbUC7TAUmyxJ5tXcQjl55lpUqNH7x8grGpLKoic/9tq9jQvbCgzO3SqFug6VTI7+aXn7yNQumSSO1c/yTPv3kG3bCoj/p54v5N+DwLv+A7W6KLTgwv4mzfJP2jSUplnYfuWMOe7V00RO3u7pPJPK++fZ7XDvZSqRm8eaSPhroAv/nJu5aV9iNJEt3tcSTJpr/0jyS5a3vX7N+FEIwnckynC7PP4EKpysBYiu72uffQ2FSGUsU+f7s3d/L64QuMJbKkcyVS2dKcwN+yLM5cmMASAk1V0A2TwbEUhVJ1TiWhWjMYmcwgBEQCHu7YuoL9JwYwTYuBsSSmZSFf5hOeSBcYT+SQZ6h65YrOwGiSXZva51QcplJ5cjMTma7WulltiCTBxtZL9L72ujDtdZeE8ls7mtnasfC5dKgKWzua2Nox99rzODS2d7aw/Yq82+VZf4eqcltX68ID/5SiYhVIVAc4kn6G4dJJQlo99a6VBDT7usrpCRLVfhyyh6hz4QqJKXSOZZ7n9al/omIVcCtB2j2baHSvxCF7qJh5RspnGSodJ6tP8UbiX5Alla3hxxakMLsUH6YwMKwqAS1OzNlBzNWBX40gIZPTE/QXDzFZ6ads5jiQ/A71rhWsvErQ/F6DRwmy0r+L0fIZqlaR3sIB2r1blpzA6KJCf+EwuqigSg66fDtwXhZ3pqtl/vX8MY4mxmer3Lsb2vjIivX4HLd+YnTTT0UhBBXDYLpUomaa+J1OnMrCRnq2kM8iXS5TrNWwhMCtaUTcblzqJc5X1TAYzeVwaxr1Pt+8EnFZ1xnP5/HM/N12BlGIu7bgVMJUzTRjpTdpq9xHnWvjbFMtIQQFfZT+/I+xMFAlN42e25GZnxXXrdKsV78iuZClq2fOZUm7jA4kqJnZaz+RN4D6+iBd3Q0LBv65bJmv/vVLWJZg285OXC7tmm4uyxKYpoWq3tpGFj/HrYOiyOy+s5vec+N879sHMYy5vGBdN3nrjR76Lkxx1z1ruOOuVTS3RvD73SjqwhkEIQSGblIq1cjny4yNpDl3ZoxzZ8bo650kky7hdGmz1IIr161VdEb7Exx65Qy9J0fwBtx87vceIxT18frTR6lVdJ74lXvmrWsYJpNjGXR9viA3EHQTjs7nfv+swbIEU6kCzfEgX/rcvdy2qQOHpsyel/XdjWxa1cSf/v1L7D8+QLVm8NqhCzy0ew2hywJBWZLYtKqJTasuBVepbIm9R/oYm8qiKDLb17fxoXs3XNf+ed0OHt2zbs53L7x1jhf3n0M3LMIBD4/fvX5JH/+r/cb5UpWqbvDpD+zgM4/vwO+9xGdf19XAtrUtRENevvXcEXTD4idvnePend1sWbO8tJDm+iB+j4tcsULfyPS8Jno9A1PUdJOO5gjCEgyOpzl9YZyHds+lXpwfnEYIcGgKqzvrGZ3KcOTMCOlciWSmSEt9aHbcbKHC0LjNkd+5sZ39xwdIZApMTufmBP6ZfHlWZ9EQC7C6M0444GE8kWNwNIVpWnOaYvYNJymUqtTX+fF5nJwfTNAzOIVumCiOS8+NgdEUNd1EUxU6miI/985/F1C1yrw5/U2y+gQ7Ih9ic+gR/FodCipINn2nbOQomlm8anje+kIIxkrn2Df9bSpWgbDWyH31v0qHdwua7JrpHm5RNYuczL7Eawl7crA/+RQtnnXUu1bMG9Mpe7k9+jHS/nHavJsIajFU2Yk805vIwmCz/gg/mfgbegv7qVr22Ct82+39fp9AkiRWeLdzRHuGdG2MgeIxMrUJYq7FnRfTtXFGy2cBCDkaafVumNPs9SfDvfRmknx29RbCTjdT5SLfuXCSRq+fh1q7b/kx3NTZFkLQn07z94cPs29khJpp0uDz8dDKlRgLtCW+kErxL8ePc2xiguliESEEPqeTHU1N/NK2bawI2yXFTKXCf3r5ZZyKwv/58MNEPZ4523xreJj//MorfGrTJn51+3Yk7B8j4lpHu+9Bzme/Q14f4kDij2n3PUjIsRJJUinoIwwXXyFRPoqETKNnN83ePQu+dC4veV0UK14LLu+8eb1d5a4XmkPhvgfXc/jt/gUDsqGBaf7sT55h285OtmzroK0jitfnQpYlLEtQq+qUinagl82UyKRLpJIFWtujfOjJ7ag36PIhhD15MAwL0zAxDAvDMDENC8O0P+s1g3KpxvBQcsF9BxgfTXPs8AA+vwunS0NTFRRVRlUV20P+4mdVRlUUZOXaf6f3O9weBx/91G2kUkVeffH0PBcnyxKMj6Z56l/388Kzx2lrr6OlLUIsHiQQdKNpCkKArhsUi1Vy2TLpVIHkdIGpiSzZbIlKWZ8z7mK0Mcu0eOpvXqb3+DArNjTzxBfupn1NIx6vCyRwuh08+419C66by5b5kz/4LuMj82kij390B7/4G/fe+En6KYJDU/iFBzeze3PnPPcdWZJorg/x6J61nOgZpVTRGR5PM50pzgn8YX6AvdDtcr330IJaqsv+f/Hjzd6b3e1xnnxgE4ErKkeSJBH0u/now1s4fHqYczP0llfePs/GVU3LVi6XJIl4xE8k5CFXrDCWyFKu6nhmmtpYluBk7zgAazrrsWYC//ODCXTDnBUCW5bgwrBNK40EvdRH/azqqEeS7Kz96GSGzasv2TwnUgUmpnN4XBq7N3dw5sI42UKF4ckMqzvrZ89zKlsimbED/67WOuIRP/VRP+OJHJOpPPlSFdfMPS2E4Gz/JIZh0dlSR33Uz4XhafpHkpTKtdm+OpYQDIwlMUyTSMBLUzw453fVLZ1ULYGERNgRRULiQuEcPfnTWFh0eLtY5V+PS3HbwWd5iNO5YxSMPHWOOGsCG6lz1s/zvy8aBfJ6FoHAo3gJaME5gdOVKJslsnoaIQQuxU1QC1/VU98SFjk9w3hlhPHyKDk9jS50VEnFo3qJOOqodzYTcUbxKL6rjCfNnI8ak5UxBoq9TNcSGJaOS3ETc9bT5llBzFk/I769PljCIFHtZ3v4Q9xZ9xmcytz7XJE0HA43QRamGhuixtHMs+SNaVTJwW11H6Pbf/ucY5JQcCl+NoceYazcw+ncK+T0KU5nXyHu7Fzwfu70baMTFqwIKGiEHA3siv4Cw6UTVK0SyeoQFbOAV33nNGm3AmFHI+2ezWRq42RrUwyWjhF1tix43EIIBovHKcw0Hev0biGgzq34TZUK3NnYwZ5Gu/GkEIK+XIrpcmlZ9v+mAv90ucyfv/UWbw0P81h3Nxvq65kqFnm2p4eRXA6PY66tYL5apT+dZmtjI12RCKosc2R8nB/19FA1Tf73++7D63AQdrvZ3NDA148f58TkJPd2XqrzVU2T1wcGKOk625qa5lQDVMnN2tDnMEWNwfwLZGsXOJkanHHikbBEDVPU0GQfzZ49bIj8Kk554QtOldxcFM6aonZNDbSEMDHFJS7rQk28biUkSWLn7V1s2d7Bgbd6F1wmky7y0gsn2fvqWRxOFVVVkGUZ07SwLGs2w2+aFqZh/3v3/Wt5/MPbbmif9r95nrf3XaBcrlGtGFSrOtWKTqWiz3y2v9N1E8u0JwKV8sI+tm++3sOhA33IsoysyDidqv2fS8Pp1OZ+dmn4/C4eeHgDK1dd3U3m/Q5JkojW+fnlL96DBLz60ul5Yl+w3ULSqSLpVJFjRwZRVNv+TpJtO6iLbiymKWY7zN7Ivqzb3sl9T26nrjGEelk2GqCpI8ZDH9u14LoOTWH1+mbcHifFfIVcrsTUuO1Wsth18U5ACEG5VEMIgcc73y3lnUZDXYA7t65YlLYlSRKr2uP4PC5KFZ1KzSCVLQKxBZd/v0GWJbasaSG+SNXgYhC+e0sn5wamsCzBqd5xsvkykeDyPYcDPheNsSADoymSmSKZXHk28M8XKwyOJQFY1REH4Pk3zzKeyJLMFGmM2Zz0fKnCxLTdVDIe8REJeelsjuJyaJSrOn0jyTkObX0j0xTKNRqiftZ01hOP+EnnyvT0T3H/rlWzIumRiTSVqo4kwcq2GAGfi+b6EMfOjpLNl5lOFYjN6EBK5RoDo0ksIehqidLSEOZpRWY6U2RiOj97DsvlGuNTOYSAUMBN/RU6ilQtwVf7/gwhBJ9q+xUS1Sl+PPEUmVoaZoL2u2IP8kD9B+kvnuf7o19nomI3tdMklbbMCj7a8nla3HM7bx/PvM0z49/BEDo7I3v4YNMncEiL2xb35E/z3ZF/ompVWB/YwkdbfxH3FcHxRQghyBtZDqX3cSS9j8nKGDWriilslyEJCVmSUSQVl+JmpW8tH2n+LEHH/Ez6RSiSSk7Psi/5KkfS+8jpGQxhzBgPyKiyRkiLsDOyhz2xB/CpV6cSX4mgVs/m8CPzgv5rQaY2zlDxBAARRwsrfTsXnMhIkoQmu1jlv50zudcQWAyVTlK1irgU37xlJZZOFkpIRB3N+NQI1VpptoOul/dX4K/IGqsCuzmbf4OKmed8fj/rAvfgUefrTKpWkYHiEUyh41b8rPDtQJE0DMskWSljCUHc4+VsJkFvNolXc5CulhnJZ/lAx+Ki7JvBDQf+QgjeHh1l79AQj69axf+yZw8+hwPDsuiKRPj9F17gystxQ309f/roo/gcDhwzFp/3rVhBslTi4OgoyVIJr8OBJsvc09nJd06f5sULF9jd2opTtXd1slBg3/AwG+rr6Y7OdZew2x7Hafc9zFT5CAV9FK/WhCLZtmAOJUDQ0Um9ewdx91Yc8nzni4vj2A29POhWkZqZQ7eKOJWlxUO6VaBm2hkWCQW3Er/Bs3vt8PldfO4LdzExnmFoYGEbPgRUqwbV6vJ3/z15fIQffu8Qlnnzom5dNxekgCwGt1tj1erGn4nAH+zrtKExxK/91gNE6nw8+6Nj5HPlJdcxDQvzFleiJFliw21dTE9kGO1PXDKZkqChNUog4mX9rvmlYQCv38Wv/NsH7OqQaTFwYYo/+vffJJdd+jiWG+VSjX/8m1cQpsWv/E8P4nS9uyL5Fa111IV9S9rzul0anpn9NE2L8k3c74ZhUi3reHzUahuvAAAgAElEQVTv/qQH7Alid3tstk/EQlAVmTWd9TgdKtWawXgiRzpbWtbAX1VkuttivHW0n3S2RDJbpCkeRAjBRDLPZNIWpXa1xpBmlk9mikxM52ios98/dmbe1pOtaKnDqanEIj6iIS8jkxn6Ry7x8YWwJzSWZVEX9tHRHKUpHuTcwBTnBiZtWs4M9ebC8DSmKfB57IBfliRWtsZQFJlcscJUOs8aYVcIktkSI5MZZEmiozlKR1MEt8tBNl9mZDLD2hX2crlihYmkPUlpawzjvaJniCUsikaBklngRPYwZ/MnEEIQcUTJ6mmKZoG90y8TccbYn3ydZDVBWKujYpUpGDn6Cj28Mf0SH2n+LM7LhI81S6dg5DGETtWscKWT3ZUwhL181apQNsuLJjWEECSqEzw9/m1OZo9Qs+zEnSqpeFU/qqRiCYuqVaFqValZVSxh4lCW5l3XrArPjn+Ho5m3EQh8qh+n7MRCUDKKlMwCU9VxXpj8ATWrwqONv4BjEdHsYqh3dRHSGq9rnUvHPDibgY46W/CqSzQ+kyCgxXHIbqpWkYKepGRk5wX+1wpVdqLOHKslzGtKqr7XICHR6OqmwbWSgeIRJiu9TFR66fRum/e8nK4OMVGxE7Nx5wrqXV1IkkSiVOK/HnmdXK2CISwmSwWOJcbwag6ytSpCCO5rWfi9ebO44cDfFIITk5NYQnB3Rwc+h21JpCkK2xobaQuFyFXnOjloikLE7aao66TKZWqmSUXXCTidFGo1KoZ9AUiSxKpolG2NjewfGWEkl6MrEsESgiPj40wVi3x282b8V1QUhBBka30cT32Foj5Oq+8+NkZ+Fadiz8wlFFTJiXwNFlIerQGXEkW3ipTNJCVjAp+2eBc1IQRFY4KKaWd4nEoYr3a1APSyfRBXe5QtMoIksXptI7/1uw/z1b96kQvnp244c3trcIMHcmu2zLu28XcJdubfx+e+cBcruxv4/lMH6Tk3jnEdE6ZrgaYpNLdG8Cwg0BQC3nr+BE//4xvUqjp6zUSWJbwBN1/6vz5JQ1t0gREv7f/lDceCYc9VhZ7vBBKTOfa+dIZozI+1AG3xnUZTLDjb0XQxSJI029RPCHHD+20YJj3Hhzl3bJhd960l3hSiUq5h1Ewq5RqBsAfTFPiDbiqlGpYl8PqXV7itKjL1kaWzopIkURf24XU7qNYMSuUaqVyJriXXujlcLvCt6gYjExk2zoijB0aTFIpVomGbviNJEuGAh0S6QM9gYlZ/cLmlqk3VgXDAQzzqZ2QyY9NyilUiQQ+Vqs6FYVsP0NkSxe3UWNkW49W3exlP5EhlSzTFgxgzgl9LCCJBz+z2u9rqUFWFSlVnZCKDJQSKJDE6mSGdLeHzOmmMBYmGfcQjPs7ny1wYTnDfrm40VZkzSelqrcOxSAXKFCYHUq/T7VvH/W0fwCW7eSXxHPuSr5A3sjw3/j1A4smWz7DKv56J8ijfHf1nEtUJLhTOkjOyxJTlNwPIG1meHv82R9NvY2Hikt2sDmxgXWAzMWc9TsWNaRnkjAzj5REGSxfYHNqJaxFr74voK/RgCpOII8ru6H10+9fi14JYwmSyMs6+5KucyB6iZlU5kHqDtYHNdPuvvXmnhExIa7guf/2LEFikaqNYMxrG6eoQP5n4H0uuUzKzs65BNauMLhb3lxdCULEKFPQkmdoEeSNJ1SphWFUMoVOzSuT1RZKU7yO4lQDdvl0Ml05SNvNcKLxNm2cj6mWVKEtY9BeOUDZyyCh0+XfiVuznWMTl5tc37FqQFn8RcffyJC1uOPCvmSZTxSJORaHRP9c20KWqxL3eOYG/EIJ0pcLz58/z+uAgiWIR07IwhGAin59p+nAJXoeDB7q6eGt4mDeHhlgRDlPWdV4bGCDidrO7rW2+TzUGg4UXmK6cxKWEWRP6NAFtYS7a1eBWYtS51pPXhzCsEmPFt6hzbV60IZfAYqJ0gJplZ0PCzm686tJWnpKkzFqJCkwENzbzlWWZrds7+He//2G+9+232bf3PJl0cV5jr2uFosg/t/9/H0GSJDweJ/c8sI61G5rZ+9o5Xn/5LAP9CUpL2CheDZqmEI766F7VwI7buti6vYOGpvklWcu02P/CSR777B0oqsxY/zTb7lnNj//5LTTn+0e0dTn6eibIpItEY9dfgl8O+DzOeT73C+FW3LamYTHUO8ng+QmaOuoIhD288sOjmKZFuM5Pa1eMUwcHeODJbRzZe55g1MeGHUvbHd8sZFnGcw0dqT0ux6yTjyXErPvMcuKiwDdfrNA30/xMCDjVO44A6kI+IkEPiiwTi/hIpAuc7p2AR+z1+0amMU0Lp0Olo9nOvDo0hRUtUQ6fHiaTK5HKFokEPUyni4wnbNOI7rYYkmxPPBRFJpm1/9YYC5ArVphM2qYPjbEAIb8dqNZHA4QDbsYTtmOPaVrIkszZ/klqhkks4qM+6ifkd9MYC3J+MMH5gQT6jJh3aDxFtWagqQrtTZE5rlFXQkLirthDtHu6QII76+7nTO4Yqdo0yVqCe2OPsjN8Jw7FSViLcjJ7mER1gqKRJ12bJuZcPitssCcnR9L7OZk9goWJV/XzSP0T7IzuwavMNRUQQrAhuI2qWUGTr26UUbHKRB1xPtLyedYENswJ0KOOOPWuJkpGgZ7CafJ6lrO5E6z0rbkOLaGMU5nf0fhaILAoGJc0VYnqAInqwDWvb2FhLdBV1k5+pjmb38uF/AES1UGqVglLGFjCmtE8ip+ajsaSJNHp20Yo/TTJ2gj9hSNsC0/NcVEqmVkGS8ewMAlq9bR7N89qU5yKyqpQHWA/q/pzKV4d7SdRLtLk9XN38woiruuncV0LborqYzdPkVCvKL8u9F1J1/kfBw7wgzNnuL2tjU9t3Ejc50OTZb5+4gRvDg7OWV6WJHY2N9MaDPJyXx8fWr2ayUKBo+Pj3N7SQssCTcGEMMjrIzBzgdXMPKaoonD95WpFctDue5jR4hvUrDxDhZ/Q5N1DzLVp3lhCCNLVc/Tnn5lZ10WH/2EUaemMhSq5cMh2ucwUNTLVPqLODTd0M8uyTGdXnN/80sM88MhGDu3v48TxIaYmspSKNapVHWOm4Y+dGZTQVAWHU8Xp1PAHXDQ2helYEWPLto4lH+hLIRT20tZRd0uoPtcLp0vF4126XCrJEtGYn7b2ujnf+wMuXK6rBxaSBNE637z1fX4nrmugg0iSRCQ6f323x4H7GgKbpaAoMg2NIX7hY7u45/519Jwd5+SxIXrOjjM1maNUqlKt2NeBZVkIYXOnFVVG01RcLhW3x0k8HqCjK87K7nq6uhtoaAzi9jiXsIUVmIZFW3cDpUKFwZ4JOtc04XRpTI9niNa/c/7aQgiKhQr5XAVdN1FkCa/PhT/ovuZr2jQtTh4dQq/d2qrJzcBxhW5iOeF0aXSta0avmey6dw2mYVGr6Kzb0cGqja2YusmZw4MM9EyQnMqxZuvibha3CrIkoVxDJUhV5Vk6kBBiXifcWw1JkmZpObliZdYms1Yz6BmwBbvtTWG8HicSNpXn9IUJBsZsBx2fx0nvoL1cNOglHr2URFvdUY8sSWQLdvfdrtY6RqYypLIlXE6NjuYoEvbEI+hzMZ0p0j+aZOvaVjK5Mom0TTtd2Rab1YaEA24a6gKMJ3IMjadnzo/E+cEpTNOiKRYkFHDj0BQ6m6PsPXyB0akM6XwJt0tjYDSFbpiEA555wt4rEXJEaXA1zS4T0iJEHDFStWmcsosu32o02X7mKZJCnbMeRVLQLZ2CUViGX2su8nqOw+n91KwqiqRwW+Qu7qi7bw7F6CIkSUJBwaNeWwZWkRR2RHaz2r9hXlZekiQijjo2hXbQWziLhcVkdYyaVcN5FQrRnHG4QdG6sOONi/Aq4QWdfxaDJrvmefkLIUjWhnhp8msMFo9hiBqq5MSvRQlq9fjUCC7FiyY5EcCJzAsUzcyN7f97CCGtgXbvZlK1MTL6BEOlE4QdTbN6icnKBaardmzb6tlARGuaYwBzET2Zaf76xD5ibi8Rl5vebJKj0+N8adMdtAeu/be5Vtxw4K/KMgGnE900yVSu8BK3LPJX0HwGMxmeO3+e9fX1/G93303Ma7chL+s6Pzx7dsFtxLxe7u3s5OvHj3M6kaA/naZQrXLfihWzGoHLIUsqQUcnEjIVM82h6f9KxLkOp+KHy24SRXLiUsKEnd2EnatwyPMfYJIkEXNvod33ML2571Ewxjk8/WdsCP8yda6Ns8JdXZRIVk5zKv33M5MOiSbPHTR57rzqi1qRHESca0lUjgGC3tx3CDm7CDpWoEgOBBaWMOzJi+REldxLjilJEm6Pg01b2li3oYVisUIyUSCRyJHPlqlUbJcWRZFRNQWP24HP7yIU9hAKe3G5HDhdKopy41aeH/jQVu5/aAPvCuVGkvAu4hV+EU6nymd/aQ8f//TtV6xqB4hXg8Oh8vHP7OaJj+64ofU1h8JHP3kbH3xyrnhakiS8V5m0XAskSUJRJWLxAHUxPztv76JcrpFJFUlO58lmSpTLOrpugABFVXDNTJiCIQ+RiO+Si9I1BpuSLNPUGSM1laVlRZz+02N8/2uvMXxhEpfn5iYz1wohBOlkgTdeOsPBt3oZHU5RmulSGm8MsXl7B/c9soHGlsiCE5hkIs/YcIqRoSTDA9Mc2Hve5mmPZfjb//7CPFGt2+3g0Se20tiyBDf2fQxZkWeduIQQyIqM129PnmRZomNVA0ff7KWhNUIoemNc3+uBJcQ1NYM0TYGwLolMrqVKcrMI+tw0xoP0jyZJpAsUS1Wy+QqTyRwSsK6rEXnGqWPdygZ+9OpJplJ5EukCmqowPGkHQPGoj2jwUoavozmCy6lSqui2Hz92fwTdsGiO+2e7M9eFfNTXBZjOFDnbP4lpWUwm8+SLlUu8/plr3ut20lIf4uhZ2yo0my/j0FRGJ+0qwurOehyqfd+v6oijqgrpbImxqSyxsI+RyQyWJYgEPNRHl26QFtTCs4E9gCZreBT7velS3IQckdnni62rcyOjYAoT3Vp+Yf94ZZjJytjsvm4P775unv1i8Che1gW2oMoLh1iyJBN1xnHITipWmbJZwhA1nLwDzawk2/XH/iizLngvu+s+fh2rSziVuROgqlXk1al/5ELhICCIOzvZEfkwrZ4NuNUAiqQiY19XNi3mwE9F4K/IGqv8uzmTe52ymeN8fj9rAntwK35MYdBfOEzFLOKQ3az07USTF44R9o4PsKmugU+v2oJTViibBn9zcj+HE2PvrcDfoSisjEbRTZNjExNsb7Jt04QQTBQKDGWzc2zUctUqZV2nwecj7HbPWhYlSyXOJBIL75wsc3dHB0+dOsULvb1MFAq0h8NsamhYxEJOpc33IJPlw0yVD5HXh8nrwwuMLCGjosle6twbWRf6/By//9ntS27Whj9H2UwyWnydVPU0+6b+MyFHFx6tHgmJkjFFptpL1coAMjHXJjZFvogmX8vLUKLd/xBDhRcpmwlS1XPsnfiPRF1r0WQfljAwrDI1K0dX4AnafQ9dw5gzSnxNIRTyEgp56epe3pLp5XB7HLjfoWDvelDKl3nun/dSzJX4wC/dQ+QGs9AXA/QbDdIlScLjdV61MnErIEkSDoeKw6ESDHrmNd66VZBlicc+sxtFVQiEvdz3C9s5+kYPd39wK41XVDaWA0LAUP80X/uLn3Dk7X6qVR2Px4nTqVIsVDh5ZJDTx4Y4+FYvX/itB9i4rW2eSPTrX3udV184SbVqUKsaszqZdLLA008dmrfNYMjDzjtW/tQG/tF4AFM3ee2ZY+y8Zw3+kBvNcbFRk0RzZ4w3njvJrvvqb7g6eD2wLLFoh9/LUanp6DN9LWQJfO7lv88U2Rb4vnmkj3S2TCZfYSqZJ5Ut4XCos44+YPPiXQ6VXL7C6GQWj8tBKmtb9q1orUO7TMdRF/YRDfkoTaRtvr5pcX4wgRCCpnhwVrTs8zhpawxzqnec/pEklarO4HjKFvZ6nTRd9qyTZYmu1hiKLJMrVklmizg1lalUHlWVWdkWm/092xrD+D1OktkiwxMZOlvqSMz0BWhtDM+6Fy0Gj+KZk5WWkFFmqK2qpM5z2VEkZcZITyy7FbYQgsnKGBXLPveNrhaizvgtq6oFtTBhx+LaJrAnQhfPh2EZC9JnlgMSMr4ZMa9AULNKeJSlqzdLwbZl7aGvcBgQ+NQoDzf8Ji2edYtarr5Tx/pOoMG1kkZXN33FQ4yXe5iq9NPm2UhBTzFUOoHAIupoocmzOJWrahrE3D5cioosSXgkDa/moGYtj/D5hgN/SZK4vbWV9nCYp06dosnvZ308TqZS4evHj5OtVIhc5r9f7/MR8Xg4MTnJwdFR2kMhkqUS/3ryJMO53IKG0pIksTIaZVtTE68NDFA2DD6xYQN1nvm8JyEs8voIg/nnqZgpW8gre1AkxzxvfUvo6FaZqpVhtPgGZSPB7vgfEHTOVVBLkoRXbWRb3e/gUWMMFn5C1UwzVTkMV1BHHXKAJu+drA//4jXrCiRJIuJcw6bor3My9TWKxgRFY4xiYWzess3ePVcd7+dYHJVSjde+f5DpsQx3fXjHDQf+1wvLvJQxvdYHq2VatujuJiov7yQkSaKu0eb+CyHY/fBGtt+zFhBLurDcKmRSBb765Rc4+GYvLo+DDzy2nR27uwiFvZRKNc6eHOGFHx3l7MkRvvJnz/Hv/uBJOlbOfclv2dlJpM6erNdqBi8/e4KJsQyx+gAPfXDLPO98p0sj3vj+sqC7HgQjXj7w6dsxDBOP38WeRzfNBqWVco2R/gSNrRGa2pcObm4VTNNiOrM0/UMIQSZXojhjA+twqESCy8ORvRySBN3tMSQJsoUy6VyR/tFpdMOkpT5E44zlpSRJ1EcDRMM+Ricz9AxOEY/4yOTt4HNNZ/0cEsBFUe7wRJrhiTSZfJmxKTszv3ZFA+pMMz5ZlljTWc/ze88wnSmSSBcYnkhjCWuOsPciutrsbruFUpVkuohumpQqNUJ+D60NlxqFRYNeGmNBpjNF+oan2bSqafY3WNlWN++euBLqFSYalx+bLMmoCwhTbQPt5YeFRVZPYwp7khh1xnAuko29EXhV/5xqx8K4/Iy8c8x3CZmosxUZBQuTVG1kQXvO68FE5TzGjJV5s3sNzZ61iwb9NbNEzVoef/p3Ay7Fz0r/bQyVTlAyswwUj9DiXst4pYd0bRwJiRW+HfiWoFNtijbyr+ePI4Sg3uNjuJDlVGqSL65f2Ab7ZnFTyrv2UIjf2rWLv9y/nz985RV8M1acW5uaeKCri6Pj47PLNvv9fGbzZv7fgwf5988/T8jlwrQsVtXV8amNG/nu6dMLbsOjaTzY1cVLfX14HQ7u7uiY15BFCEG61svh6T9junwclxphXfgXqXNtmLHsvLi8wBImhlUipw/Rn3uadK2HdPU8/YXn2Oz4jXkX68Xgf3P0t2j23sV4aR/pag8V0xbHuJQwIcdKGjw7ibk2z2b6TatK1UzgUKKXdfOdDwmFTv9jBLROxkpvkKqcpWymsNBRJRcuJYJPayHm2nK9P8/7Cm+MDfLaSD+Pdaxia3xx96T3EyzL4sBPTjA9mubhz9yB4xo0BJYlOPzqaUZ6J3ns83fhvEne/zsNSZKQFAmnW+PNZ4/TvCJOx+rrt5y7Vggh+Mkzxziyvw9FkfnE5+/kiU/uwu25FHRs3dnJytUNfPmPn6GvZ4Lv/Ms+/u3/+tgcN6E77l3D7ntsz+RiocKpY0NMjGWoiwf42Od2z1kW7Ff2e8F96FZg7uRSzH7nuYy6plx2/LWqQblYZee9a3AvVrmaY1h2805fNcOkfySJ2CUWnQzbzaVSlKt24B8L++Y1MFsuNMdD+L0uCsUqE9P5WX5/UzxIJHSJFhEJemiKBRidzHB+cIrVHXFKZR2XU6O9ae4kSlMVOluiHDw1RCpbZGQyQyJdQJEl1qyon9PDprs9hkNTyeRsWs7EtO213xQLEvTNff/UR/2EAx7GprJMJvOkskUM0yIe8c3x5ff7XLQ0hDhxfsymMaUK5ItVHJpCW2PkqpoLWVqIzXwREu+mg4QlrBlbUBsuxT2bfb8V0GQN5UY5+MsMSYK4s4OAFiOjT9h2k+VeOnybubHfRFA1LwXyfq1uURMUgMlqP0UjewPbeW9CkiQ6vVsJORqYrg4xUDzKltCjDBSPoltlPGqITu9W5CXC7Z31LZQMnVdH+8jWKsTcXj7dvZk14eWp0t9U4K/KMg90dbEiEuHk5CQlXac5EGBzQwPD2Sy7W1sJueyXh6aqfHz9etbHYvQkk5hC0OT3s6m+nrJh0B4KEffOF87IkkRXJELQ6WRtPD7Pux/AEGV6Mv/KVPkwmuxlY+SLdPgfQVmiwUeDuA2PGmff5B9iiBKpyhkMUUaT5u+DJElokocG9y7irq0YojzrPStLKqrkmmMRKoRgqvwqPekv0xn4PG2BTyy6H3bTC4061wYirjWYlj223ThERpZUFMlxTRakyw3TskhWSvgdTtzqrfU1P5GY4B9OH6YrGPmpCfyrpRqvfe8gtbLO/R+/Dcc1JJRqFZ3Xv3+IdCLHQ5/a/Z4O/GsVnVy6uODfhBCcPTJIcJn535lUkVeeP4Wum6zd2MKjT26dE/QDqJrCjju62XP/Wn7wrbc58OZ5PtC7nTUbmmeXs3n/Fz/Lc6qEsiy9I3SWdwOyLOOcydyalkWpos8aACyGQMjDznvWLDmuU1NnA8NK1UA3b05ka5oWx3vGyBUqBP0LJ1IKpSoHjg/aza6wRa3vTMZ/RuAb9M407UoxNGEnhtZ01qNepkdTFZnVHfW8fXKI8URulroTDXqIR+Y6yUiSxOqOOLIkUSjVuDA8Ta5QIRTw0FIf4vIgrT4aoC7sZXgiw7n+KVIZOxC7nLpzEWG/h4ZogLGpLKNTGaaSeSxLsLItNtsH4uK+2sJgmel0gZ6BKXTDJOBzX1XYO3MEN/Xnm4G4KpVkeTPskt2qehm3cDOQCGr1dPl2cij9I8pmgbdT3yPibCagLR5oCiEwhY4008zs8vHcyqUJY8FIYQlzweC/oKc4kn4G6wYdDN+rCDridHi3kqyOkKqNMVg6znj5PAJBo2sVda72Je8XVZK5v6WLOxvb0S0Th6LiVtRli/lu2mtPlWW6o9F5AXnY7WZTwyUfewlwaxrbm5vZ3tzMlWgNLky9sITgTCJB2TC4t7MTn2N+IFQyppgqHwEEAa2dRs/tSwb9YLeUDmhtOBQ/hlHCEEVMq7Zkt11JklAkBwpXC8YEVTNB2RijYi6sX1hwbDQU5d1tFLQUJksF/mDfi/zahh3c1tD6bu/OjeMdeB4LIcgmC1w4MUzrymtrKCaEIJ8ucv7YEHUL2Ga+19BzbIh/+m8/npMZvgghYKx/itseXL+s+zA8mGRk0LZQ3LG7i2BwYYs7TVO4/a5VPPeDo2RSRU4dG2LVuqZ3RPz5XoZDU4jOZKQNw+aQlyv6NVlnLoVwwIPLqVEs10hliwyMJmmK3TiPGOD0hQleefs8j9+9fp7Y2jBM3jh0geM9owC4XQ72bFuBtojP/K1GwOeiaUbge7ZvglSmiCJLrF/ZOC/+W9tla9TSuRKneu2qeDzqX3CS0tEUxeVUKZSqnOgZwzQt4hH/DH3n0nKRkIfGWJDhiQynLoyTzpVsPn9bbF5m3uN20NIQ4sjZYXqHpimWq0iSLexVrzDNWNUew6mp5IoVTvTa279IH3ovo2bVluSRy5KC8zIhb8WsYApzQfrRTyMUSWNL+DGGSidIVAfoKxzm+Ym/Zkfkw8Sc7WiyCwkJS5jookrZyDFZ6WO0fIYdkSfmWFYC1LtWoEoODFFjpHSaoeIJWr22o5E9YTBI10Z5K/ktRkqnkJCvquMQwsLCxBImprD/LRvZ2f4DFhYlI4dT9iJLKrKkoKDMTDikBd0X7fGM2TFLRhZrhu5lCYuSmaNkZJElxR4TZaYSNH+8K8/nKv/tnM6+MiPy3UemNo4iaaz078K5RFwJ8OpYPy5FZXdDG9JVYtdbgff0VS6EYCyX4wdnz9IcCHDnAt79ADUziyHsTp8OJYAqLd1c4+LYNauAOdOpT5GcyLfsppeo99yPIrmJupaHo/VuoDeb5GhijLJxdZHdexWSZPuUnznYx6n9veTTRcLxABt2d9Oxpgl1gSZJlmUxPZbh9IFehs9PYOgmseYw63etpHVVw5x1ahWds4f6GTw7ytnD/UwOTaPXDP7uj76LOuMvrmkqdzy+hTXbbU2JXjU4d7ifgbOj9BwZYKx/ilK+zN//0fdQZ3zwVVVh92ObWbdr5Zx9E0KQnspx+sAFBs+OUa3UiNQHWbezi851zWjOuRNJ07A48MJxBs6M8tgv3o2mKZzcd57zx4aoVXXCsQDrb19J96b2q1JZysUqXetbuO/J7fPPmRA8/4191/CL3BwG+6aoVQ273NrdsOQ+1zeGCIY9VMo1es9NYM04XP0sw+XQWNNZz4v7etANk7eO9vF0Zz1371iJ1+1AwnZp03VzphmUF/UazllTPEhzfYhkpkihVOUbzxzC73HR3hRBVRUsy8IwLWq6icupzqOjXAlZlihXavz99/ZTqtTYs62LoM+NJEGxXGP/8QH+8QcHKJZrSBJsXdvCjg1LZ9luJRRZprs9xt4jfZzum6BWM2c48/N5vW2NYQJeJ9l8mVMX7MC/q7VuwUlKXcRLNORjdCrD4TPDWEKwsq0O9xW0QaemsqKljrdPDnHmwgSlSg2/10VzfH5CzRb41qHIMheG7YqDz+Okoykyb5LSUGeLiMens5zuncC0LNobI/O2v9y4Hja87Sefx1yiI6yMTFALz/Lck9UpqlYF9ZpMOd7/kCSJOmcr98Z/mRcn/5ZUbZTe/H5GS2eJOlvwqRFkSUG3qpSMDDkjQdnMIyGxMfTQvLEa3Ctp826ir3CQgjgCdEAAACAASURBVJHk2fEv0+XfSUCLYwmDTG2C4dIp0rUxWjzrUCSV/uKRJffxZPZlhkun0K0KuqiiWxUqZoHiTA+CopHm+Ym/wqX40WQnmuREk13EXR1sDT+OckV4eyb3GoPF4+hWeWa8KhWzQE6fAqBsZnlx8m9xK4GZsez/os42toUfR5OWNgqod3XR6F7FhcLb9BePYFhVIo4W2jwbZu09F8Op5CSt/nfO9vo9GfhnymW+c/o0mUqFE5OTnE0k+N077qB5Ae9+AEV2zwbtFSNJzcrhWEKoIoTAECVGiq9QtXKAREBrR5NvTVlYkiTcaiOt/o/ckvHeTZiWRUGvUdCrvDrST0nXmSjm6cumZpfxa06ibs8czqkQgoppkKlWqJr2A9itaoSdbjT5OoSuQjBdLlLQa4ScLsLOuZamlhAU9CrZahVTWLPbWMjuFWzKwMtP7efA8yeolKqYhkW5WCHaEOKTv/MY9zy5YzZAv7j8sdfP8o3/9gxDPRMzziYStUqNYJ2fJ3/9AR74xO2zlJx8psj3//ZFxvoTFDJFahWd9GSWQy+dQprRpjhdGt1b2lkzEy8XcyW+/9WXGO2dpJAtUavoZBI5Dr18aR3NqbJiQyvrLptHWpZFz5EB/vn//hHnjw3Z9puyRK2i4/a5+MAv3c0Hv3AP7ssy8pZlceLNHl769gFWb+/kzR8dYd9zx7FMC9O0MGoGD3/mTlZuar9qYaSpo45gxMvKjfOrP8ISDGwex7nMAUJ6uoAQ4HCq+AOuJa8rl9sx68aUmrbpDT/rkGWJ2zd38vybZzl9YYJ0rsxXvvkGz+09Q13Yi4REpaZTKFbx+1z8h197iPhVOugCBP1uHr5jDb2DU5QqOodPj/AHf/k07U1RPC4Nw7QolWsUylUe2r2Gz35w55LjxSN+VnXEeetoP1/55l5++PJJGmMBFEUmkSowNJ6iNOP6094U5XMf2knwGux1bxVsga8tGM/mbe54Z0uUWMR3xXI2LSgW8dM7lKCm2wmrNZ0LO6+FA5cEvhcdddZ1Nc6zpJUkm/evyBKZvD1mc71nTl+Ay9HVaotz8zMN/tqbIgvSd8JBm9YzPJEmnbPpQ11tC09SlhOqPEPfE1C1Kktm83VRY7IyjrVERtkOVptxK26KZoHxygjT1Um86s9G4A921aPTt41H5d9mf/IphksnKZkZSqWFbTZVyUnE2YxzgTjJJfu4O/Z5amaJsfI50vo4B1M/RJGU2Uy7Kjno8u3knvgvMVE5z0Dx6JL71184zKncy4v+3RQ6E5Xeed+3ezaxOfTovP4JQ8XjHM08y2KCI1MYTFYuzPu+2b2WzaFH0K5iteqUvXT7b5+ZXNjPgA7vZgJafMn1ADoCYbK1CjXLxKksf1j+ngz8q6bJwbExBtJpIm43v717N4+vXj1P1HsRXjWOX2ujZEyR0wc4l/0mq4Mfx63GkNFmrUMFJrpVoqiP0Z//MX35HwEWLiVKq+8+pCs4aZbQKdQukK4eo2JMIrBwKEF82goCjnU4lbo5D8qKkWA4/20Ma4b3LEnE3XcTdc9/qQlhMVZ8mpqZptX/UYr6EKnK29TMFKrsJejcRNi5GVma33xMCJtKlKmeoKD3YVgFZMmBW20i5NyIT1sxR6R8cflU5TBFvQ8hTNxaExHXDjxqy7zjvhzn0tP81fF99GfTDOTSFPUaf3LwdVzqpUvnwyvW8j9vu3P2gq2ZBi8P9/H8UC/n0glSlTIy/z977xknyVme/f6fyt3VeXpyDjuzOa82KmeEkBCykES2MRjMDwOO58X2sX/2G/zar7GPAdtwbAzCJBNlkARCYpE2aKVdafPsbJqd3cm5c6qq53zo2dmdndkgkAT28fVB2umu56nq6uqq+7nv67puQaXPZmNtI492raIxePWyvyclB8aG+N97n8PxPD6xbhsbaxpnA9KcU+In/ad5/FQ3x6fGKbgulT4/W+uaeahzJU0L7GN6LMmLPzzEHe/YwrKNixACDuzo4bufe4av/vUPqGurYvG61tlrpvdIP//8Z98mly7w8MfvZsm6NhRNofdIP9/5x2f4yl99n0hliM13r0IIQSQe5CN/+Q481+PwCyf49O98hcUb2vjgnz2EL2DOXBYCf/BCUBKMBfjN//UIrutxbF8vf/vxx2hf3siH/+KROdvZoQtZUSklw33j/NOffpvhvjHe+hu3snJLJ4alc+7EMN/7/LN84/95inBFgNsf3jIvE55JZPnWZ57GKTk8+jv3UN9Whet6DPWO0dQ1P7BYCHUtlZfPvQm44d7VqK9zgFAsOpTdg67Owz/frAyYsex8XQ/tPwwaaiK8/8EtfP6buzhxZpRMrjhLQbkYLfUx3GtcLGmqwu2buxidSPHv2w8zlcwyPJ5ieDw1ZztFCDauaLmm+R550zrqKkM8taOb3oEJegcm5m6jKSxurebXHtjMis66Nyzbfx51VeFyJn+mW3BbQxx7AcpU0G/RXBvl5NkyDdRn6jTXLWwLq2vlDr57j5wFwPYZtDZULLgob6mLYfvLlQSA+qowIXvhxU9VRZBYyM9AviyybKqNLqid8FsGLXUxXjrUhyclpqFdk7D3tUZQC6IJjRJFxgoj5NwMlrpwlWisMMKZzPyA8FLUWo3U+Oo5le5hujTF3smdVFt1l533Fw2BSntgA361bFjS4P/5aZSq0GjyryBuNnI2c5iz2UOMF86ScxN40kNXTGwtSsyop9pqp963mIgxn7oqhKDGaueumoc5kT5Mf+4MqdIYrnQwVT8RvZZmeyUdwY3YagiNFLdUvQdNCeBXF6a1Lg3fRLWv7VUbA4T0ynlBP0BXaBtRs55Xe+MP6BVo10C/EULQ5F9BWK9iongOSwnQFlh/TWMrLD/fPX2EnqkxavzB2STq5pomVle+9prHX8rAP+73899vu42S66KrKkHTnNcJ+GIYSpCO0P0kiqfJu5OcSHyLsdwrRM0ufGoFitDxZImClyRTGiJRPE3WGUPiYCghusIPUWmtmjOnJ0sMpB/ndOILuF4eRRiUXYGKeDg0Bx+iI/LhOWMkLnl3hFxpgKzTT9YZwFRiCwb+AKPZ7UwXjuBJh4H043iyCEgcLw1CoSX0DtrC70Nwga4hpcdEfg8npz9Hqni8LPpFReLiySI19u0sq/jkrH+ylJLpwkGOT/0d6dKp2W7CrsxhqlUsinyIKv8Nlw3+Q4bJtrpmNtU28d2TRzkwPsS9bYtZFL3gz94RjqFdtNBwpWR7fy9HJ0ZZXlFNfTBEyXV5YfgcXziyj6l8jj/adAsB/fI/CE9K9o8N8d/3/ITpYp7fXXc966rqZ38QJdflmycO85kDL1Dls7mtqQNb1zk2OcZXeg5wcnqCP950K/WB+VWirfeu4d5fvRljRsjWsqSe1FSG737uGV54cj8dK5vQDY1S0WH7t19iqHeM9/3RW7nrndtmaT1tyxuRUvIPn/wG27/9Iquu7yo3ONJUopXlfYaiAYQiME2dWHUI/2VEiaqqEJkZE44FUBSBbmnEqkPYl3ElcV2PXT/Yz4n9fTz00Tt54EO3o89UKlqXNWD5Tf7qI1/gmX/bw6a7VhG6JPNYKjpMjyb57U+/l5alDbOBvrz+2luqC+WCBNYpufQdH+LkoXMUCw7VjRUsWduCdZWGaj8vym47olytKJW7EV8u3ju/TXmc/survQNMXeXNNy5j9eKyHmrtkqtraoK2ySP3rGc6mcXQVdobr80RQlUUrlvRTE1FiJ37T3P4xCAj4ylyhRKaqhAKWFRXBFnaXkvoVfSfCNoW775vIyu76tlz8Aynzo2TSOVwXQ/L0omF/NRXR9i4quWqc+UKJcIBi/c/uJX1yxvZvu9lTp4dI5HKYBkhKmKSjctWcuP6pdRVhXDlNPlSGk2xMZQoQpR7zHgyT8GdBDw0JYCuhGfe8yh6UzheBkXoGEoEVfEhpUfJS+HMVIYNNYo2w9eV0qXoTuLILCCJxop84O3ryeUUJCW62k1yzgCGGkO7KEsqBLztjtUsaS8HUH7LoKk2SlkbNjlzDBqGEkMRJnduW0I06lLyUoT8EVrqyvShgjuBQMGVBQwlTKwiywffvo5sFgruFEtamygxjOdomGp8tiruyiKBYIpH72smnTZQhZ+ulmoMTSnTZr0koGCoYVRhc+fWJVRV+Mk54xi6R2frtXWvfS1RadVga0FybpbR/CAHp/exJX7zHMtMKSWJ0hQ/Hf0h48WRq84Z1EOsi26mP3uGglfgxcmdBPUwmypuJKjNTxp50iPnZsm7OcJ69LLNuV4vKEKhyV5Ok738NZ237FwYZXFoG4uC11HyCrM0qbKuUUcTBqq4KIkq85R57xffD1y8zGdYGXiYtbEHcWQJpEQIZZY6I4SC645RTP9vVoV+D9O4/GfpCG6ggytXAl8NWgNraA2sedXjXjnQR7KpSCx6df2lN6NFAKi0WqnxLbqmBIQEVsVrKXkeJc+dfa66r1N26pcy8FcVhajv2lfdQqjU29fjyBw9018jWTzLZOEYk4VjQNmhoywkuXASNeEnYi6hI3Q/jYFbUC/p2Jd3hulNPIYiDJbG/wBbbykH9s4IyeIxIuaKeV+opVaxJPa7eLLEYPoHHJ38i6see94Zpi/5FZpCv0KV/2ZU4SNROELP1Kc4k/gyVb4bCBpds/tKlU5yZOJ/UnQnaAy+jbhvC4YSwZEZ0sVT5Wz/RV9r3h3m2ORfkXdHWBT5EDFrPUKoTBcOcWLq7+mZ+hv8ej0BfeELtCEY5uGu8qLoyPgIRydHubmxjZsa2uZte+E8aPzGyutQhKDC8mOqGhI4OT3BJ577AbuG+hhMJ+mMzm3uJERZQFMO+gf58z3bSZcK/LcNN3JDQyu6cmFx0jM1zj8f2UdjIMyfbr6NjkgFqhAkink+s/8Fvnb8IE/09vC+ZevmLBr9QR9Lr+uYDfoBDEtn1bYunvryDk4ePEsunUePBUhPZzm8+wSm36CqoYKRs3MzjIGIjekzONM9QCaRxb5MYP96oJAt8spz3aiaQkNHDaPn5h6b5Tewgz76Tw4zPZacF/grqsKam5bQsrR+Tna/HMy/uojY8yQvPnOEJ768k+rGCnRT48DOE+z9yVHe83v3vK7npbI6hBDlhUxiuhyAXU69ncsWSc9kQysqQ9dU1fhFwdA1btywiLwzSKpwBF2Fc8knUYVFlX03mhKm4A4xmdtB0Z3CNtqJWpu554ZlFN1xJnI7KDh7OJfwEfVtwdY7AEHe6Wcyv5uSO4Wuhqnw3YCl1QFFQhXdbNlylPXrNQLaRky1iaI7TNo5gM9QccSLTDs9aM7NmNrVmwIKIbB9BltWt7J+WRO5QgnHdUGWqy+apmLqGsZV/OChTDn0ZDnjfd3KKoyq/dxU1EgXBgibiymKszRF4jSENzGZ38dA+vGZBaxHfeBeKqzNlLwkZ5JfIu+MIvGw1Epaw+9DV4JM5l+iP/04ChoeJWrtO6ny30jRm6I38UUK7jieLGCpNXREfwNNBJjIv8Rg+vtoSoDJ/MsEjQ7uuuHDGGqMs8mvkSyd4PiUxFQraIv8GqYamz0va5Y0suaixZyULsOZpxnJPosiDDxZoiX8DsLGcmrqB1gb+ikgys8gVSHE9Zya/jygkC31ETaXkSmdZfmaZppDD3Ns8q/R1cP0JtOU3CTV9q3U2nfiyQJ9ya+TLp2kfbnAUGO0hN6BpVaTKh2nL/k1kB4eRWLWRuoDb6azLUagageT+XKDpilxgIjzLvzawpq71wMxPU5XcBkThTEKXoGnR/6dZClBV2g5AS1IwcsznBvgwPRLnEh3E9IipJxkOfi8DFShsjpyHb2Zk+yb2k3WTfOj4cc5keqmM7iUSrMGU7VwPIeUk2CsMMJg7iyW6uPBhvcQVK7cufg/GoQQaMJEu2r3Ykk29xSqWotlbpzzuudNImTpij0BhAgQ8D+Ipl6b6cWrhet6TE5mEAKiUZtMpkCx5OC5klisvGidnMqgKArRiJ9MtoDPMsq6z6KDaepMTWcoFl1sv8HR7rIXf6nkEoval+1fIaXH6cw+ks4YCiodM9WZa8GmmkY2Vs9P7iiv0+/rlzLw/1mgKRatwTcRN5cznNvLZKGbdGmQopfCkyUUoaIpNj41TlBvJGYtJm4uL9OBFsh2l7w0JS9J0FhExFyJPvMF+vUmYtb6BY9BCAVN2EgpZ7NCV4PEIe7bQmvovbOLD79WR6J4hN7EF0gWjxM0yv7iUkr6U98mW+qjPfJB2sO/OocKFDXXcmldbCTzDInCERZFP0JD8K0oM626/VoDeWeY41OfYSS7nUC4HXhtaBlCiAXbTLdHYnRG4jw3cIapQm7e+6oQGIrK/rEh/nzPdnJOiU9edxNb61rmBO9SSnYMnmE4k+LdS9bQFYujzlQcYpafO1oW8a2TR9g9dJaHOlcQNi+Uuw1LJxKfy1EWQlBRG8GwdKbGUhQL5YdFJpljcnSa6bEUn/roF1EuoZK4jktyMo2mq5SKb6w9WT5bYHxwimw6z2f/4KvzKDWe55GcTGP5TQoLdDxVVYX6turXpMGW53rs/uFB7nnXNlZv60RRBMmpDP/4J99h6Mz4ghqA1wot7VX4bZN0Ks+JY4NsvXnxZZ16BvsnSSZyIKCjq+ay1CAx+59fPArOKOeSXyTq24hPa6b8+1YoedP0JT6PrkSwtAZGMk9SchPUBu6j5E7jeElMrZZM8Th9ic/TGfskQuj0p76MIkxsfRGOl8L1ckjpMZ57hvHsdqLWJlR1gmTpMVoDv0nRyzA48VVUNmLri5jM7aLojtMc/uDsveRqEEJgGhqm8XM8buTF/5RIZYxF1e9mLPc8qlCImPczln2OvHs9famvU2vfTsy6jvHcDs4m/42QsZS8M8x04TCLIh/Grzfgenk0xY/EYzy3G0utpjn0diRypsILmgjQEHgruhqm5E5zaPz/Jls6R9DopD/1HSr9N1Dlu57TiS8ghIZPq2U8t5vJwsssif0+qjA4Nvl/GMn8+Iq2zllnkL7UV2kP/zphcymuLKIrARyZpjfxZWrtO4j7NpMsHqM38RhBvZOcM0iNfQemGiNVPE5T6GF6E1+i5KXJOueoMtppC7+PZOEoZ5L/SsxaS7LYQ6J4mM7oR9GETW/iXxhMf5/W8HuZyped8doi70edyeQKFJLFbkay21kU+RCmWsXpxD8zmH6C9sj751SjX0/oisHW+K0M5s5xJnOSRGmKZ0a/z87xZ1CFhodH0StQ8ko0+lu4ofJ2nhr6DuPF0SvOG9BC3F37AAqC/dMvUfDyHEsd4kT6KLowUISCROJIB8crIZF0BBa/ge22fjZI6eB503gyDaioShQhzleqMkiZQwgfrjdZ7qisViKEiefl8LxJVLUaMVMhkrKI646iqpVI6eF6o6Sz38Nn3YCqVILQLwniPRx3FCmzKCKAosRmqceuO47nJTGMFYiLhNRSSlxvBEWEkDKDJzPzxkrp4nrjSJmdpesIxUZV5jZjPHykn1OnxyiVXJYvq2fvvjOYpkbJcVm5vIFCwaHv7ASe57FqZRNHugfYdF07pZJL97FBqipDnOuf5Fz/JMuW1pNJ53nlwFmOHB1gcVcta9csbBqQciY4ntyFK0tE9FpaA2vndK6+EgTQn0lyYHyIRCFH3GezKl5Ljf/1cc/6TxP4Q9lTP2y2ETJacGQeVxaQ0p3xxBczvvgGmmIi0PCkZMfJPgKmwdqmuRajPq2WsLmMyfxejk7+BbX2nYSNpRhq9Iqc+J/hqKny33hJxUHB1lsAKHpTs686Xpqp/H4MNUqtffs8/n/53xcLXx0m8i+iCB2fVkvOmdsR2FTLVIBE4QgeDuprFPhDuQV1fyrB8elxhjNpUqUCBcfl2NQYnpR4C5SwNEWhLzXNV3oO0Jea4n9svXNe0A9Q9FxOJSYpeR67BvsYyszlDU8VchRch+FsalZYfB5CLMwFV1W1XG1wXc7rxpySi+dKglGbbfeuxRdcmC/rD/oIvAF+4RfDdTxcx8Vnm2x+02qC0YUXmoapzVKP5kCUBcOvCURZfxCJB7D85YApGLEJRvxo15DN/XlQ31RBe1cNB/aeYd/uU9z74IaZKsDcG3Ox4LDrJ8coFhyisQBLVzZe1gFI1ZTZrE6x4FAquvje2K93DiQuVf434dcvVNmm8rvJOeeoDr8ZQ41R8iaZzO+k0n87fr0FXY3N0PkqOZP4B1yZRRNhQCKlS8DoxNIaUYWFK9NM5nYQ999Elf9NuDLDyam/ZCq/B9voQFV81AYewK+3owqb8ewzuDJ3zYH/6wFV8ePX6vFptajCwlIrkUhypX4ypT6m84fIFPsoeJMU3DFKXhKfVkvIWMzZ1NcJmyuo8l0/Ux2VxH2b6Ut9g97kl4lbm4hZa2eCO0neHWUi/yKuzFPyUrhypvGTECDd2WpymUYpmC4cpOhOM5R+ApCUvDSp0nzR4MXIls6ioFPhu27OeU0Xe3G8JFFrLYYaJWKuQvIFck7/rK5LoOB4aSy1GgUVKYuowkfEXIGhxAiZyyhXe0ZIFA4RMhbj1xpQhEaFbyPnUt9EyhJRay3ThQOcST5GzFpP3NoECJKFYxSccUayP0FBp+COUnSnZhJqc68BTWhUmJVYqkVACzFnBS0EIT1CpVlNzIjPe974VD9xoxoPD+uSppdCCOp9TTzY8G6eG/sRx9NHyThpsm5mdr+2FmRRZAk3VN5B3KiiJ3UYkRGE9chlXVWEEMSNKu6vf5S2QBevTO1hMH+OvJul4OXLi0AUVKU8f1SPsSy8Zo4V6HmYikXcrKboFQjp0atWTg3FoMKswu/aRIyKqzq/XCuklBSKe0mlv4gnUyA9VK2BSPDjqGoNucIOMtl/Q1NbcJzTeDKDZW4lFPgArjvEZOJPiIb+AMNYOjPXARKpv6Mi+hc4ziDp7NcpFPfieZMUCi+iqHEiwd9CmamA5Ao7yeZ+iCenAYVw8LcwjXUIIcgXdpHN/ZCic4x49FOYxsqZo/aYSvwZmtqM457D8yYBQTj4m5jGJsAjk3ucXO6HIHRKpW6E8BO034ntfxvnk5ZSSnbuPonfb+C6HidPjZAvlNi0sR3Hcek5MczUZIa771pJOp3n4KF+0pl8mQbqeORyJUbHUoTDfjxPUlsTpr9/krVrmtF1laPdg6xa2TjPTrjk5Tk4/TTD+ZMIFDpDm4kZ9ddcETsyMcpnD7+ArekEDZO9owM81Xecj67aQnv4te+O/p8q8D+PMqfMj86Vn9au6/FS3wBVQXte4K8rYbqiv8WZ5JcZy+5kLPs8AaONSt/11Ni34dcauVxL6lcDReiYanz+67M3xQvOBI4sVyF0JYKhVlz1onJljoI7gSNzHJ34n7Mr+POQsoSkRMlLMtv15jXAZD7Lvxx9mSd6e3ClJGJahAwTQ9VIFguXHVd0Xb7Wc4Ci55J3HI5NjrGtrhntktbnjueRLZVwpcf+8WF6psbnzVXps4n77HmlMqfkkknNrzakExmckoM/6JsNVn0BE8tvoOkqb/7Vm6hvvwy9QfCG20KaPgN/0EdyKsMdj25l0army257XtD6ekERgurGCr7x2R+zYmMHuqFx7tQIg33jHNh1gqN7e4nXRrju1iuL0aSUyIvEo9KTyPOZnctc63bA5M63rOH40UHOnB7lu1/bw9vfu41Q+IL7U7Hg8NyPj7D7+R4ANl3fSXNb5WXnNEyNisoyhWh8NMnJniFWb2j7hVGDdCWCrkbnHG/RnaDgjDCY+rcZDVMeS60FJFP5FxnLPoMqfHgUcL0sUkpU4ac++A7Gsj+kL/E5LK2e+uDbUYSF46Ux1XLmTMFCV8IU3XFsOtCVKJpS5jyrig+QcNUGSa8vBOqMmEOU/z37RrnXil9vwFBi2LRQ6duCqcZQhU1H5AMkCt2M53bQM/U3LIn9HpZWRcxaj09rYDK/l3Opb5F1BmgKPshIdjtDmSepC9yDJmxUxZrdf519N72JL5EsHkfi0GA/MHt0hhKZTd7Yeis+/SoCPSFmFxqXvl6GvOT/gjKNVSmfg0uSURJ5UROr8/OWx1zc3Kq8aCnfH4J6B13RTzBVeIXR7E9JFU/QEfl1EAJNCWLrLSjo2HoLphafrYpcjJhZyfvbPoaUEkMx5/ji60Ln7toHuK36zShCIaAFcVyPXQd7qasMs7R2Na12JyDxqzaeJxHiwm/fdSUnj7msjN3NDa13MFIYJOOk8aSHX7OptuqoseqxFB8Sydsa3kXJK6Er+oKB+uw5kGBrQTZV3MjK8DpGC8OMF0bJuCk86aIKDb9qEzUqiJvVBLTQgn7/HcEl/Eb77wHyqvsEaPC18IH2T8z8Nsv7eK2gqfUEA+9FUxtxvUkmp/+YfGEntv9tSJmjUHgJf/guQoFfo1B6henk/8HvuxNNa0FVq8jmn0LXFwMeufyP0NQ6VKUSxYgQ0T5GsXSEgP0ofus2EAqKCAEuIHHdfqKhP0RRAkwnP0Uq80VMo2xh5/Pdjq4vZmzyw8hLaFiuO4rrjhMN/xGqEiGR+jSp9JcwYxvwvAnSmccI2u/FMreSzn6dXP55fL474JKsumVqLOmqpb4+iu03efwH+7Ftk2y2UL6mlLImzHU9hCg3MSwWHRKJHI7jUlMd4qfP99DeVkVNdRhdV7FtEynLtFbHKzKVH50VEGfcKU6k9nBo+mkcWaTKbGVF+NZrEvWex87hM6ysqOGRzlWYqkbOKfGPh1/k5dGB//iBv5SSvOMwnc3jeB4BwyDst1CEoOS6JPMF/LrOdK6cUamwfRiahut5TGVz5EsOQcskZJV/UIl8AQGEfRaelCRyeXRFKTccyRUwNZVEPo+mqFTYfjS1LPDKFktM5XJoM4KvhSCEIKC3syT2uzQE7mc8t5Ox3A5OJf6J4ezTdEU/RoW18efmOAqUV1FBWODBcA3QhI+6wJswlXM33AAAIABJREFU1IUvIJ9WM29R8LNCSsl3Th7lnw7vZUttE7+2fAOt4SiGoiKR/PHuH7NjoG/BsY7nsTJew690ruCLR1/mse5XqLUD3N++bI49p6Yo+HUdv6bzO2u3sa2+ZcH5dEUhas7NHGVTOc72DLH0uo7ZQM7zPE4f7ieXLlDfXo1vRsAYjPhpXdrAvmePcHz/GRoX1cyj+1wJilq2LS3N8AuvbYxAKAKn6OJeYYzlN1i0qpkzRwc4uucknWtafmGe9JJysFxZF2VscGo2k962pI7RgZmK1QK/k0y6wEu7TpCYypLPFcnlioyNJMlliwAcfKWPf/70M1g+A8un4/MZNLdXsWxV4+zvTgjB5hu7OHrgLE99bz/f/9ZehgenWL+5g1hFgGy2yNGD59jxbDeJ6SydS+p46yMbMa3LZ6sVRWHdpnaef+YoieksX/jss9x2zwS1dRE8T5LJFHBKLtdt6yQaeyOEjuXg7mKYaiU+rZ6m8Psw1RokXrmqKQQjme8TMBZTY99HpnSCVOHwzCiJpdXRGHovBWeY3unPMp79KTWBt6ArEfLOYLmUL7OU3Cn8VuvMuF8S3tM1wKfW4tcaUYRB1Fo906yngCr8lLxpCu4Ett6EqtzGscm/ouQlMGWcdKm33HfFt5G8O0qyeBSQZJ2zmGqcmLmWjNOH611IGriygKXVUOW/AV0JYahRQBC11jBdOIStN89UY1Iz710ettaExGEsu5OwuQxPFtCUAJZaialWMp7bTaVvG4nCERR0/PqV6XOezDORfxG/3kCicBgQ+LQaYtZazia/Qbp4Ek0JMp7dSdRajRA6Wedc+fjN1bhejpHss3jSIWwsZVTZjqVWYestODKDKnxzF1wzUIVKWF/4s5ZFpHO53xIPy9TRNRVTNTHV8r3X8yQvdZ+luSZKTUU5k+y4HnsO97F2cQMrWxfTZF9eZyYQ2NrVaRKelOw/MUA8YtNUHSWghwjoIdoCnVcdeykMxcBYoMHo5aArOmHlytfFzwqhBPBKCbLFQ3gyh5Q5XO9CgkzTmvBZt6KqMRAqAh3PSyAwsX1vJpH6NAH7UUCSL7xIJPTbCKGVK2SKQKChKEHUi2IKKV1A4LNuxzAWIyVY5hZSma9QTmCqKMKHooQXvHYAfNbNGPpShFCwzM0k01+YMS7JI2UBTWtGUWJoWitS/hgx49o4+7mFYON17Rw60s/IaJI1q5sJBS00TUHXNUJBH+1tVex64STSk6xb28LEZJoX9/biOh6xigBDwwnqaiMUiw4v7+/D5zPQdbW8QLRNMu40Tw3/HYnSKAKBI4vk3TQeLkGtgs3xh4ibr07/4tcMLFUjZJgoQkFXVKKWb4574muJNzTwL7keTx05wZ7es3hSIiW8e9MaVtTXMJJM8/kdL9EUi9A/ncD1JO/bvI6GaIhne06z/fhpBAJDU3lg9TKW1Fby3f1HMVSVR69bRcl1+cqLB2iuiLCyvoZPb3+BeMBPIpcnmStw17JO7l7eyUQ6yz/v2st4JkvIMjk3laA6tLAQpSx2sYmYqwiby2kIPsBg+glOJT5Pb+KLRMzlaOKN8/3VRABdCZF3Ryi6EzNuFVfqJufDVCtIi9PU2LfPaAAug2u4RpUZRb/ryctKKPOuw4vD/ShC8Oji1WysaZg9xkQhT6KQv+L8NzW0cUtjO3GfzR/ueppP73+BSl+AGxpaZnn8hqKyKFKBKyVjuQxVfnuO8PdKKBUctn/7RdpXNtHUWYNA0NczyLPf3INuaKy9aQnmDF3FF/Bxw/3rObTrON/73LOEYgG61rSgWzqe65HPFBg6M04g4qdtWcO8fYViNpZt0H9yhL6eQdpXlOklbslDN7VZF56LEYwG8Nkmg72jnDnaT+eaFhRF4FwyxrB0rr9vHS/+6CBPfOl5KutjLN+yCNNnIF1JPldg9NwEiqqyeF3rvP28llAUwR1v33RFX/yFsuXTk2m++PfPMjaaxHMlrjs3i3yqZ5hTPcMIRaAqAkVVuOWuFSxd2TDnuvf7TR59/40IRWH7Dw+z8yfH2LvrJJqhlb+nXAlNV1i9rpV3/8ZNNLbEr3pTXrepnVvvXsmPnzhQriacGsUwyiJ11/GIVtgsWlL3BgX+8xEwluDX2xhMfRO/3oIrswT0xUSs9RhqBZniScbE02RKJzmfEXO8NMOZ78zMIPBkFkONowo/cf8tjGSeoORNU3KnkHhErY0U3CtzpH8RECjljDM6uhJCFRaKMDHVGLoaoTX8bvrTjzOZfxmBQtRaja03U3AnOJP4Eh4loGy17NPqkXhM5F5kunAAIVQUdBqC9wMKlb5tnEk+xonpv8dQo0SttajCxJMFsqVzFN1JBtM/wMNBUwIsinyIqLmGnD1Ib/KxmcWYTkvonbPi3oXg0+poDb+X4czTDGd/jEChKfQQYWM5reH30p/+9uznaQm/c2ZBUIEiTFTFj66Ey05AWhyBiip8FN1JTk79A47M0hT8FUw1jq6EyfoHOJP8MhKJT6ulzr4HgUqi0M1Y9qezlZSGwP2owiRoLKIhcN/s51SERn3gLVjqlQXeJcclmckTDfrJForkCyViIT/ZQgkpJaauMTadoS4eInqRe1mh6DA4nuDJXd3cuLadkuMSDvhmewc4jsfoVArXlUSCPixDQwiB47pMpXKUSi4h28L2GUggkc5haCq2z8TzJNPpHD5TR1UEw5MpntzdzdquBgQQsi1CdrkfiOu4ZNN57JD/stW+q1Ukr4ZLx0spyaULGJa2YDPJa5szSyL517juOJZ1A7qIoyg2FycNhbARM41Oy3Q3BZAIITCMNQgRIJ/fiRAGQvgwjFUL7mshqEoF5ehAgtDhEmOVaxsLzDgpgkRVKtG1xaTSX8Q015HPP4/PuuUSV6EyliwuZ/td1yMYsLjj9uVYpo4nJTXVIQxDo6W5AkUIgkGL1pY4nYtqZhsTPv6D/axe2ch0Isv0dJZbblqCfyYmqKgIUFSnKMkCGWcKbybhYig+4mYT62P30RnctKBu9Eqot0P86/H9jGTTREyLkWyafWMDbKpp4ivH9xPUTW5taMd/BSfEV4M3NPDXFIUNzfVsbG1AUxT+ZffLbD/ey/K6alzPo3t4jKV11fzmjZvwpCTsszg3leDfD3bzjutWs6gqzvbjp/ja3oP89u3byBSKlLTz3C5IFQpkiyUcz+Pk2AQbWxq4sbON50/28qPuE2zraGbHqT5GUhl++7by+D9/cuEGEZ4swYzIS4jyCten1VAfuJeB9OPk3VEcL/eGdvrTlCBRaw19ya8ylHmK9vCvo2DOuWlIXARlvroiVOK+LYzldjCSeYaQsaScqbmofOzKUln7cA2XQqXPpuS5HJ0cYVNtIz5Nx/HKwdr5plyKEOiKguN5TBdyuFKiAplSkafOHOfIxOgVbRSVmTlWxKv5xNqt/OkLz/Cpl3dQYflYHq9BmXH+uaG+lW+dOMJ3Tx1lcayS9dX1WGr5x51zSoxk04RNk/rA3G54dW1V6IbG3378sVm/+lOHzjF6boKbH9zIupuXzZ4fRRGsu2UZb/3QbTz++Wf5m499iaauWkLRAMVCiYmhaRKTaR7+2N0LBv7VjRWsvXEpP/7GC/zd7/4rHSuaUDUFp+Ry97uuZ/nmRfPGxOuirLtlGU99eSef/r2vsmh1E5pWFg/f+Y5trNpWFnoLIVi6oZ2HP/Emvv63T/KZ3/8KjZ21RCqDOEWXyZEEkyMJ3vSeG16zwF9KSTKdJ2ibc0TBQogFFzGXjpVSznlABsM+7n9kE7ns5elfl6KlrWrBh2xFPMj7PnwLqze0suMn3RzvHgLPwzR1ahuirFrfyqbrO6msDl+RspNM5/FbOoGgxbs+eBNV9RGO7D/L6OA0k1MZYlGbaCxA++IawtHXn/hvabXUBN6Cekk3ck0J0RR+P9P5vRTdMQy1Ep/ejCIs6oOPMJV/ESlLVPnvIGptQldDKMLAr7eTK5UrbnXBhwmbaxBCIebbiq5ESJd68Ost1AbehqnWIFCptu+dNSvway1U2XehiDeuOdal0BSbRZEPoykBau07AYEiNFq196EKi5CxlM5oM66XRaDM3KPLuqmu2MfxZBGBiqYGUYWJlJKG4P3U2LcDHqrwoSkBhBCEjMUsif0+nizOCIElqjBJFU8wXTjI8vgfoQobx0tzaPyPyTlDhIzF1AfeQpX/JqQsoQgTXbly9lkIhSrfDUTNNXgyjxAa+gxnOmQspjP6UVwviyKsmbkEHZEPoil+ArIFz1qLKvwsinwIkAhUau27CejNIFQMJYwQKqpQaQjcR7X/ZqT00JXgrFas2n8TFdZ6JC6KMNCVEEKo5WvAfwsxawOeLMzo5K7+3BubTvOvT+3jA/dv5pmXTvBS91l+/123sv3lk1imxrquRp7cdZRXjg/wK7eu5qa15a7kQxNJntjVzaFTgxQdl33H+rlxTTvL22vxPMnOg70cPDlIIp2jpTbGO+5aj6Gr/OiFY7zc0w9CEPAZvPXGldRXhvnGj/fT3hDntg2d5AolHntyL9cta6KxKsJTu7vZf3yARDrHkdPDbFzWxLZV5UpCcjLD8//+Mnc8svmytsS5TIHB3jHalzf8TMF/IVfk7PFhFq1qmtGYeRzde5rmrloq6362aoDrTZIvvEAk9Pv4rFtxvWGS6S9dstXlj1URQfy+u8jmnwAMfNbNM1Se8yMVECrSS12GMfHzVAgXHqsoPgxjOfnCC0hZwPbfh2VuZSFDEkVRiFykuTNmnk0qzC4eoxH7ou0hFj0vfJZs3dzBmb5xdF1j08YOgsELjSE1TUX3wtxY+W6mSyOUvDyq0AnrVVRb7QT1ilcd9MNMD4BghIlCluliHsdz6YpUkioWSBULxCw/zmtIr3xjqT5ICo7L4cFhMoUSQ4kUVcELX4Df0NnQXE88cOG1volpLF1jWV01IctkQ3MjTxw+zmRmPk/74msw4rdY11xPPOCnPR7jmWOnyBSLnBqbYGltJXXhIEXXpbN6Pr8eyoLX4czTRMwV+PUmVOGj5CUZy+0g745S5bt+zs283A04gyeLSOlQ9BKUhV0p8s7oTCbJQFPsn1kbIISgIfgA47nd9CW/SslLE/dtnrHzzJItnQUETcEHKV/mgmr/rYxmt9OffhwPh0rfFnQlgivz5JwBUsVTNATfSsiYH4Reiq31zXzzxGG+ePQVehNTVPj85JwSG6obuKd1MdqMK8+NDa08P3iGzx54gcMTI9i6wZnEFOfSCbqicU5MT1x1X6pQuL6+hQ+v3MRf7nueT72ykz/ZdNtsU66OSAUfWb2JT728k0/ufJplFVXEZxYmw5kUo9kMH1+7dTbwV1SFeG2UZRs7uP2RLTz/vX288tNuUlMZIpVBbn1oE7c+tGmeSNZnm7zl/bfQsqSe3U/up/fIABND/eiGRrw+xsY7V7Jiy8JlYV/A4qGP3UU4HuTgzh6O7TuNburUNMfRjIVvDpbf4MGP3EkwanPg+R569vWiGxpVjRXzhLK6qXH7w1uob69m5/df5uTBc5w5OoiqKVTURlh9/WKuu33FnDFCQCgWoLa5ck5H32uB43h8/0cHecudqwi+irFSSoZGE6iKQvVFQuNQ2M99D113hZGvDnbAYutNi4lUhzh8tJ9bti1GVRVMS8dvG9fkYPTDnxxh23Ud1FaHCUf8iJDJuz50M37LYOeek2zb2IEdMMnkimTyJWKXLGZea5haNVXanfNeF6JsxVhl3zHvPUurozZw/4LzVfi2gW/bvNcVYRC21hC25vpcl/d/++zfPr0R31VoJq8VdE2jNh7CMjRCAR/6jFZFCAVjpumPchFF0VAvZMN0EUC/JDgVaAtSbsqVXf8cr/0L713Y18VQhQ9PlhjN/hRNCZB1BjDUCiy1cjZRdKUM/0Io7ysMhOe9p4vgvMXD7HGJC+GPoUYouUlgRj+mze/lUNaVzad9qoo1q2GYf2zqgufhSvCbBoWSw+hUmsHxBPliiaGJJINjCVYtqqMibPMrt65mcDxJvniB711fGebN25ZyemCCB25aQVdzNaau4nqSouPgt3Tec88GplM5/vG7uzkzNIkAnj9wmnfdvYHaihBP7DrK488f5j33bCCVLZCbcWqTUpLM5CkUHWoqgtyzdRmnBiZ489alrO5swLhItOl5HoNnxnjuey8Tqw6zYnMH/adH6T3ST7w2SsfKRnY9eYADO4+z4ZZlrL9l6RyTB8/16O0epPfoAJHKICs3L2J6Is2RPScRimDFpg4O7jrOi08fYcNty1l/y1Kmx5KcOzFMy+I6PE/Sf3KY4/v7iFSGWLy2hUO7T1AsOCiKYO1NSxa0SlaUEJrWTib3HUrOCVx3iCtn3C99T+Azryed+RqeTBIJfeKSe5yKZawnnf0mjjuEokQJ+B9YMPt+MTwvTS7/Uxy3D9ebIJt/Esc5jWVuRb1K9UhKSanUg/RSuO4InjeJJwv4rVuAhbtT/ywQQtDaUklry+V7oOiKRXvwtesvAHB9XQtbai6v0xOizHR4rfCGBv7nphJ8evtuVjXU0l4Z4/T45JxLztQ0dHXuh7uQnC5ved5G67zl3nl3GCkl+dKFm4ehqrNuMLNuN/LiqcSFeRaAKwsMZ59hMPMDzvcCOL//mLmWtvCvolwk3vFkkRNTnyFZ7MH1shTcMUDSn/4247ldqIqFX2uiK/axV/1AuBhBvYNlFZ/kVOLzDKWfZCj9ZPlTiDKvscp/0xwajqnGWRL7XU4l/l9GMs8wlPnh7FwCBUutoiFw3zXte3W8lj/ceDPfPnmEQ+MjeEiipjWns5wQgrtbOil6Lk/09rC9vxdL1eiMxvnE2m3kHYfPH35pXlvqkGnSFIwQuIgjqSsq97YtYSKf5Xununn8dDfvX74en6ajKQp3NXdS4w/yxJkeDo4PcyY5haYo1NhB7mnrYnnFhZtJKGrz4b94BF3XsMM+3v6xu3nzr96EU3LQDQ075FuwtCqEwGebXHf7ClZt6yKXLuA6LkIRGJaOz7YuK54VQlDTFOfR37mH+z9wC05pZpypz+nIe+mYqoYYj3z8Ht7y/ptxihfGXBqoCyFmexAsWd9GLp0v70MIdFPDF7DmLRZUrSxUvvXtmwlG/HieR8/JEc4NTuH3GXhSsnpZA0d6hkgksyzprKWmKsRLr/SRyRYYGUtSLDnsO9DH2GSKjpYqGmqjvHzobNnGVlNZubSB/YfPMp3I0dleTTTs59s/eAVNVVizopG1K5p5+dBZJqYyLO6opqEuyt79F3QfG1a3cOBoP6PjSVoaKljSWTvnxu66HgeOnGNkLEVTfYx4RYCJyTRLOmvZu78PIWAimWXv4bO0NFbQ0VbFS/v7cF0PT0o2rGrm+OlRBoamaKqP0dFWxcEj/UwlsvQPTlEsOew90MfUdIb+oSmuW9vC8EQSzdKIVQYpFh2efr6byckM61Y1s2ld22W9nf8LPzta6yv4Hx9/C57noQiFaOiXp6OqX29iUfRDJAvHcLw0ttZIvX3vZXVUbyQUYVIfuBef9vr4pF8rAn6TcMDHiXNjFB2H9vo4J8+Nk84VqK0sV94sU5+lWJyHrqn4LQNNVfBZBsGZbHuuUELXVFZ11FFbEcK2TCIBi1S2wOhkispIgI6GOD5TZ/2SRj733d1kcsU5c18cb2iqin1+P+aF/VyMQq5EbUuc7r29KKrg+P4+Vm7t5PgrfVi2QW1znJFzk6zY3DG/KjDjnBausDmwo4d4bYQDO3qobopT31aJP2BR21xJVWOMlVsW4Q+YCBEmnyuSSWbx2SY7nzjAyi2L6D06gFty2fOjQ9x4/3r6jg9x6tA5Vi6QdFJEiGj4/yJf2ImUefy+u/H77p3l1Rv6UgL2w7O6PiH8s0Lg8t8CVa1E0xoBBV1ruXQPhAIfIJd/FtebRFUilENJlaD9LnS9a3ZLQ19C0H4nZSqRiyeTM9u9ByHKugIpC4AgYD+CoS+9MFbrJGi/E4FGsXQIT6YxjNUoSgApS6TTX0LKLAH/w/xH0iEtBF1R0d9Aid4bGvhPpLNkCkU2tjbg1w3ShSIh35VXiW3xGCXX45VzQyyuqWT36bPUhoPEgzZhy+Lw4AgD00lGk2mOj06wrO7yK0dNVVhUFecnx09zdnKaTLHI8ZFxmmLzMxlRcxVrqv6KdPEURW8KKR1U4cPWmwiZSzGU2By7LiEUgkbnFUVcuhK6yP5MUGvfTdhYtiBXMmQuoTP60Xm8fCEUYtZ6AkYbiUI32dLZGWs9a9aC9GLvWCEEtt7G0or/RqrYQ6p4CsdLz1jB1RA0OvFp19YSWldV7mhexObaJnJOCUmZ4hPQTdSLArOAYfL2zpXc3dJFwXVQhMDWDWxNp+i5rKysIWLODWLf0raEWxrbCBtzX/frOu9bto63dixDU5Q5q15dVVlfXc+KeA3pUoGS5yEAU9UI6MYcG1BFVebYWuqGNs/P/0oQQmD5zVfdiVaIctBuVF679eH5wD1iXlvzDyEEps/A9F1bW/FA2D+bmXIcj+4TQ3hScuL0CBXRAJlMgXQmz9KuOrbv7GHF0gZ6z46zalkD+w70cebcBPsO9tHZVs2TzxzmwXvX8twLJ7jvzlWEwz4UReD3meRyJX78XDfve3gL1ZUh4lGbzvYaTveNcbJ3lA2rW3jm+WPcf/dqtu/q4f67VhMO+cnmCryw7zRbN7TPKdmeRyZb4IV9vWxc10ok7GdiKkPP6VEWL6rllcPnWNxRTb5QorUpznMvnKAiFmDHnhPcsm0xlfEgYxNpdr10isUdNTz9XDclx+Vg9wCb1ray90AfI2NJDh8bZOPaVvYd6EMIQW11hD0v9+I4Lj5Lp6YqTDjgY2lX3S9MVP2fHbquUhl946iUrwaK0IiYK4iYK66+8RsMVTGpDcyvEr3xx6FQFQ1wemACn2nQVlfB0d5hHNej4jIdyedghiZ+6ZzGDKd/1vFHlt1/Lq74X0pBOf+353kUSgv1XFk4Ix6rCtG2rIGRc5P0nxpBKIJFK5uYHC5TKWtbKgmEfMSq53f6LeZLHNlzElVTyabyZJI50okc169qIlZdruoEIn7soI+KmvBMkgn8dvkZmMsUcEoOi1Y2kcsUGDozRrQqRPuKRvLZAqnp7MKnTQh0rRldWziDbOhdGBcF54riJ2i/fc65ctwhHOcc4eBHEJdYtpYXBnEC9vy+FAH7wTnbGXonht4583eYoP3IgscEEPA/MOdvXW9H19sByOaeQlFiREK/BRhAEdcdwHH64SJnqv/CteENDfzb4jG6aip5bM9+wpZJVdAm6i9ncTRVIR7wz/Nsr4uEeNuaZTzdfZKnjh7Hb+g8smEVYctkS3sT3cOj/M0zO4naPpbWVhIwDXRVIR6wZzMJhqbOzr2tvZlTYxN8evtuYraPzuo4QXN+wKQqFhFzORHz2tpjK0KnMfjWaz4XQghq7Nsu+17I6CJkdF3mfQVTjVPlv/6a96WLIDFr/WWbj10rFCEIm9acplgLQVMUYtb8DJ2palT75z/Mg4ZJ0Fg4qPZpOj5t4cBZCIGlaa+b+v3/D9B1jXgsgOt6BGyTgeFplnXVsqi1ihf2nWZgcIrqyiCd7dWEQj7GJ9JMJ3NkckXaWioBQShgsaitClVVGBpJcPT4IKGARSKZQ9NUgrZJJOwnEvJxuHuA+toIi9qq+Onu46TSeQK2yaL2anRNxXE91ixv5EjPIIWiQ231XOqD7TdZvbyR7uNDFIsO4ZAfZjQE3ozmpKE2Smd7NTtfOkU6W8DvM+hsr8b2mxztGWRiKk0qk6e1KU42WyQc9NHZXk08FiCVzhMKWHS1V/NCPIgAggELfaYipGkqoYCFrqnEIv7XlerzX/gv/EeFENBUE+W5V05x24ZO2hviPLm7m5baGKahUXRcMrkiRcclX3DI5ItYuoaqKuiqiqYqDIwlqKkI4reunNToaqpi96EzHOsboS4eZs+Rs7TWVRAJ+LAtg97BCYYnUvQOTdA/Oj07TlPLLoADYwla6zJYpo590b4mRxMcP9DHxPA0i9e20PNKH8f29TI6MMmKTYuwfAbZdJ7+U6PUNFXM0ToVciWGz06w7qYlnDhwFt3QCEb8HHv5DHWtlVTVxzAtg0KuyLmTI9Q0VpBKZElNZ5gcKXdbN30G3ft6GTw9Sk1znOmx1IxF9QKrotcAjjNAJvfvFIsH0fX2Szrz/uJg6Ev4/9q7l582riiO49+Z8cx4bPzE2DxiXuaZQAJtStqQR/NQqqqbqv0nsuqiXfRPafddddNVl+2myqJpyQMaIAmhBIPBkIBtbOPBzy7sUqEoidREkRKfz3akKy8szW/OvefcbP4HMtnvURUXleo2pXIcv/cLXtfFo83kjaYlv8vJ9QtT5Owiuqbi1HUqjRd1xOPhm6vn8FpHA6VDVZmO9TDe1c5BuYzbMGgx6w23fa0Bvv3kAvvFEpauoykKqqpgaBpfX5k+XKuvNchXlz7CZ9VHh16/cIasXR/36dC0t3yTSIhXU6+c0TibrNAR9rKWSOG2TByaRrQryOzCOvcfJcnlbMIhD5GQl+FYpLEdr6OqyuEYzyc7WUrlCqFWD7XaBijQ0uIksZmmqyNAR8THzVsrLC4lAfB6rMPGcIBSqUI4VD+3ee9+gqnJviMN4aVShbbWFhQF5h9scPncCKn0PotLmzzdzQGwvpli4eEm1UoVj9uJov63fsDvpiPsY6AvjO7QMHSNxaVNFpeS7KbzTIxFWV3bZXEpyU4qT6VaZTeVI5c/YCeVxzQcuF0Ga4ldktsZIm3e13IDshDvmmjYj9PUiUYChHxuvG4nfZ1BDIeDPxZWuTm/SnIny+/FVZ6mc1z7cITuSACPy+T8qX5+u7PMzGKca2dGGIu142+xcJn1IpCqKAQ8FqahEzsW4soHQ/x8Y4FqrYrPbfH5xXHclsHF92L8+MsdvvsLKpWaAAACpElEQVTpBkGvi+HuMFZjDcupc34ixq9/PuTe30k+nowx3WjutVqcnP10gnymwPBkL6On+/GHvazMJxg82U3vaCe1Wo3R032sP0oSavcdCf5ur8XpS8dJPckydfUE4WNBghEvizMrJJa3aI348Ld5GD87yNpSkkDIw1Z8h0Cbl8xOjnKpwvRnEyzdjRMdamfoVA+W28Rw6kQHI88dRf5KFBVFMXA6p7Gcl1Ff0pT+pljWNVTVT6lUvzPD4ejH7foS3TEghZf/QUmn08/99/h8zzYZCSHeHdVqjeXH21iWwV62gNGYdRxf3yG9V2A4FiHS5uX2XJyDYhnTdDA+2sWjlW22nuzR2e5noDfM/IMNJk92oyoK2ZzNzOxjTENH01SmJnvZy9rMzK7SGnQzNtzJ3EKCp7s5hvrDdHUEuHtvjfdP9aCqCgW7yK3ZOAW7yEBfmN7o0cvqCnaJ23Or7BeKxHrb6j0Gc3HsgxK6rhHtDLC+mSa/f0B3V5C+nhB/LSY4efwYukOr9zUsb7GWSBEKujkx0sXCgw3SewUMXePEcCcrazukM/sYusZgf5jlx0/Y3KqH/Imx+lnY23NxnKbO5PizNzkKIeqz9zO5Ah5XvYiQyRUwDQcup0E2b5O3i4d1a1VR8LdYmI3w/O840Erj4900HGTzNobuwDJ1qtUae/s2lqFjGg4qlSp7eZtSuYLbMnE56zPeq9Ua2X0bu1iu7xzUaui6htPQG7+xwl7OplSp4nGZL91dEOJtkMlknvtMgr8QQgghhBDviBcFf9mfFkIIIYQQogm88Iz/i74YhBBCCCGEEG8PqfgLIYQQQgjRBCT4CyGEEEII0QQk+AshhBBCCNEEJPgLIYQQQgjRBCT4CyGEEEII0QQk+AshhBBCCNEE/gFruQ8/iUrt6wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Word Cloud \n",
    "plt.style.use('fivethirtyeight')\n",
    "from wordcloud import WordCloud, STOPWORDS\n",
    "string = []\n",
    "for t in cleandata_Train:\n",
    "    string.append(t)\n",
    "string = pd.Series(string).str.cat(sep=' ')\n",
    "wordcloud = WordCloud(width=1500, height=750,max_font_size=190,background_color='white', stopwords=stop_words).generate(string)\n",
    "plt.figure(figsize=(12,10))\n",
    "plt.imshow(wordcloud, interpolation=\"bilinear\")\n",
    "plt.axis(\"off\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Find the most corelated unigrams and bigrams of each subreddit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# 'hockey':\n",
      "  . Most correlated unigrams:\n",
      ". crosby\n",
      ". goalie\n",
      ". leafs\n",
      ". nhl\n",
      ". hockey\n",
      "  . Most correlated bigrams:\n",
      ". \n",
      "# 'nba':\n",
      "  . Most correlated unigrams:\n",
      ". kd\n",
      ". cavs\n",
      ". nba\n",
      ". kyrie\n",
      ". lebron\n",
      "  . Most correlated bigrams:\n",
      ". \n",
      "# 'leagueoflegends':\n",
      "  . Most correlated unigrams:\n",
      ". adc\n",
      ". riot\n",
      ". na\n",
      ". lane\n",
      ". tsm\n",
      "  . Most correlated bigrams:\n",
      ". \n",
      "# 'soccer':\n",
      "  . Most correlated unigrams:\n",
      ". psg\n",
      ". club\n",
      ". chelsea\n",
      ". neymar\n",
      ". barca\n",
      "  . Most correlated bigrams:\n",
      ". \n",
      "# 'funny':\n",
      "  . Most correlated unigrams:\n",
      ". repost\n",
      ". fap\n",
      ". peta\n",
      ". dog\n",
      ". funny\n",
      "  . Most correlated bigrams:\n",
      ". \n",
      "# 'movies':\n",
      "  . Most correlated unigrams:\n",
      ". trailer\n",
      ". films\n",
      ". movies\n",
      ". film\n",
      ". movie\n",
      "  . Most correlated bigrams:\n",
      ". \n",
      "# 'anime':\n",
      "  . Most correlated unigrams:\n",
      ". myanimelist\n",
      ". fate\n",
      ". episode\n",
      ". manga\n",
      ". anime\n",
      "  . Most correlated bigrams:\n",
      ". \n",
      "# 'Overwatch':\n",
      "  . Most correlated unigrams:\n",
      ". genji\n",
      ". hanzo\n",
      ". doomfist\n",
      ". mercy\n",
      ". overwatch\n",
      "  . Most correlated bigrams:\n",
      ". \n",
      "# 'trees':\n",
      "  . Most correlated unigrams:\n",
      ". bong\n",
      ". smoking\n",
      ". trees\n",
      ". smoke\n",
      ". weed\n",
      "  . Most correlated bigrams:\n",
      ". \n",
      "# 'GlobalOffensive':\n",
      "  . Most correlated unigrams:\n",
      ". cs\n",
      ". major\n",
      ". valve\n",
      ". globaloffensive\n",
      ". sk\n",
      "  . Most correlated bigrams:\n",
      ". \n",
      "# 'nfl':\n",
      "  . Most correlated unigrams:\n",
      ". rodgers\n",
      ". yards\n",
      ". brady\n",
      ". nfl\n",
      ". qb\n",
      "  . Most correlated bigrams:\n",
      ". \n",
      "# 'AskReddit':\n",
      "  . Most correlated unigrams:\n",
      ". faskreddit\n",
      ". mom\n",
      ". car\n",
      ". textbox\n",
      ". askreddit\n",
      "  . Most correlated bigrams:\n",
      ". \n",
      "# 'gameofthrones':\n",
      "  . Most correlated unigrams:\n",
      ". spoilers\n",
      ". guide\n",
      ". jon\n",
      ". spoiler\n",
      ". gameofthrones\n",
      "  . Most correlated bigrams:\n",
      ". \n",
      "# 'conspiracy':\n",
      "  . Most correlated unigrams:\n",
      ". clinton\n",
      ". hillary\n",
      ". cnn\n",
      ". trump\n",
      ". conspiracy\n",
      "  . Most correlated bigrams:\n",
      ". \n",
      "# 'worldnews':\n",
      "  . Most correlated unigrams:\n",
      ". israel\n",
      ". china\n",
      ". korea\n",
      ". worldnews\n",
      ". trump\n",
      "  . Most correlated bigrams:\n",
      ". \n",
      "# 'wow':\n",
      "  . Most correlated unigrams:\n",
      ". pvp\n",
      ". mythic\n",
      ". legion\n",
      ". raid\n",
      ". guild\n",
      "  . Most correlated bigrams:\n",
      ". \n",
      "# 'europe':\n",
      "  . Most correlated unigrams:\n",
      ". uk\n",
      ". poland\n",
      ". germany\n",
      ". eu\n",
      ". europe\n",
      "  . Most correlated bigrams:\n",
      ". \n",
      "# 'canada':\n",
      "  . Most correlated unigrams:\n",
      ". trudeau\n",
      ". canadians\n",
      ". khadr\n",
      ". canadian\n",
      ". canada\n",
      "  . Most correlated bigrams:\n",
      ". \n",
      "# 'Music':\n",
      "  . Most correlated unigrams:\n",
      ". artist\n",
      ". songs\n",
      ". album\n",
      ". song\n",
      ". music\n",
      "  . Most correlated bigrams:\n",
      ". \n",
      "# 'baseball':\n",
      "  . Most correlated unigrams:\n",
      ". dodgers\n",
      ". pitcher\n",
      ". yankees\n",
      ". mlb\n",
      ". baseball\n",
      "  . Most correlated bigrams:\n",
      ". \n"
     ]
    }
   ],
   "source": [
    "#Find the most corelated unigrams and bigrams of each subreddit\n",
    "tfidf = TfidfVectorizer(sublinear_tf=True, min_df=2, norm='l2', \n",
    "                        encoding='latin-1', ngram_range=(1, 1), \n",
    "                        stop_words='english')\n",
    "\n",
    "features = tfidf.fit_transform(cleandata_Train)#df.comments)\n",
    "labels = df.subreddits\n",
    "subreddit_classes = y_datatrain.unique()\n",
    "N = 5\n",
    "for subreddit_class in subreddit_classes:\n",
    "    features_chi2 = chi2(features, labels == subreddit_class)\n",
    "    indices = np.argsort(features_chi2[0])\n",
    "    feature_names = np.array(tfidf.get_feature_names())[indices]\n",
    "    unigrams = [v for v in feature_names if len(v.split(' ')) == 1]\n",
    "    bigrams = [v for v in feature_names if len(v.split(' ')) == 2]\n",
    "    print(\"# '{}':\".format(subreddit_class))\n",
    "    print(\"  . Most correlated unigrams:\\n. {}\".format('\\n. '.join(unigrams[-N:])))\n",
    "    print(\"  . Most correlated bigrams:\\n. {}\".format('\\n. '.join(bigrams[-N:])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "P_lFxzj4goyF"
   },
   "outputs": [],
   "source": [
    "def confusion_matrix(ypred, y_test):\n",
    "    from sklearn.metrics import classification_report, confusion_matrix\n",
    "    fig, ax = plt.subplots(figsize=(12,12))\n",
    "    sns.heatmap(confusion_matrix(y_test, ypred, labels=np.unique(y_test)), annot=True, fmt=\"d\", xticklabels=np.unique(y_test), yticklabels=np.unique(y_test), ax=ax)\n",
    "    #display(pd.DataFrame(confusion_matrix(y_test , ypred), columns=['Pred: Reddit','CS:GO','Overwatch','Music','anime','baseball','canada','conspiracy','europe','funny','GOT','hockey','movies','LOL','nba','soccer','nfl','trees','news','wow'], index=['AskReddit','GlobalOffensive','Overwatch','Music','anime','baseball','canada','conspiracy','europe','funny','GOT','hockey','movies','league of legends','nba','soccer','nfl','trees','news','wow']))\n",
    "    print(classification_report(y_test,ypred))  \n",
    "    Accuracy = accuracy_score(y_test, ypred)\n",
    "    return Accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pipeline to get a preliminary idea of the best features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 100 candidates, totalling 500 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  34 tasks      | elapsed:   15.2s\n",
      "[Parallel(n_jobs=-1)]: Done 184 tasks      | elapsed:  1.4min\n",
      "[Parallel(n_jobs=-1)]: Done 434 tasks      | elapsed:  3.4min\n",
      "[Parallel(n_jobs=-1)]: Done 500 out of 500 | elapsed:  3.9min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best cross-validation score: 0.55\n",
      "Best score: 0.5476810179662365\n",
      "Best paramters: {'nb__alpha': 0.2, 'nb__fit_prior': True, 'tvec__max_features': 20000, 'tvec__min_df': 1, 'univ_select__k': 15000}\n",
      "Best estimator:  Pipeline(memory=None,\n",
      "         steps=[('tvec',\n",
      "                 TfidfVectorizer(analyzer='word', binary=False,\n",
      "                                 decode_error='strict',\n",
      "                                 dtype=<class 'numpy.float64'>,\n",
      "                                 encoding='utf-8', input='content',\n",
      "                                 lowercase=True, max_df=0.7, max_features=20000,\n",
      "                                 min_df=1, ngram_range=(1, 1), norm='l2',\n",
      "                                 preprocessor=None, smooth_idf=True,\n",
      "                                 stop_words={'a', 'about', 'above', 'after',\n",
      "                                             'again', 'against', 'ain', 'all'...\n",
      "                                             'being', 'below', 'between',\n",
      "                                             'both', 'but', 'by', 'can',\n",
      "                                             'couldn', \"couldn't\", ...},\n",
      "                                 strip_accents=None, sublinear_tf=True,\n",
      "                                 token_pattern='(?u)\\\\b\\\\w\\\\w+\\\\b',\n",
      "                                 tokenizer=None, use_idf=True,\n",
      "                                 vocabulary=None)),\n",
      "                ('univ_select',\n",
      "                 SelectKBest(k=15000,\n",
      "                             score_func=<function chi2 at 0x000001ECCC9E95E8>)),\n",
      "                ('nb',\n",
      "                 MultinomialNB(alpha=0.2, class_prior=None, fit_prior=True))],\n",
      "         verbose=False)\n",
      "Model score on x_train: 0.7199508368578968\n",
      "Accuracy score of prediction: 0.5511133603238867\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pred: Reddit</th>\n",
       "      <th>CS:GO</th>\n",
       "      <th>Overwatch</th>\n",
       "      <th>Music</th>\n",
       "      <th>anime</th>\n",
       "      <th>baseball</th>\n",
       "      <th>canada</th>\n",
       "      <th>conspiracy</th>\n",
       "      <th>europe</th>\n",
       "      <th>funny</th>\n",
       "      <th>GOT</th>\n",
       "      <th>hockey</th>\n",
       "      <th>movies</th>\n",
       "      <th>LOL</th>\n",
       "      <th>nba</th>\n",
       "      <th>soccer</th>\n",
       "      <th>nfl</th>\n",
       "      <th>trees</th>\n",
       "      <th>news</th>\n",
       "      <th>wow</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>AskReddit</td>\n",
       "      <td>214</td>\n",
       "      <td>12</td>\n",
       "      <td>35</td>\n",
       "      <td>15</td>\n",
       "      <td>29</td>\n",
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       "      <td>56</td>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
       "      <td>127</td>\n",
       "      <td>11</td>\n",
       "      <td>18</td>\n",
       "      <td>15</td>\n",
       "      <td>44</td>\n",
       "      <td>19</td>\n",
       "      <td>21</td>\n",
       "      <td>23</td>\n",
       "      <td>62</td>\n",
       "      <td>42</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>GlobalOffensive</td>\n",
       "      <td>11</td>\n",
       "      <td>426</td>\n",
       "      <td>2</td>\n",
       "      <td>46</td>\n",
       "      <td>15</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>15</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>39</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Overwatch</td>\n",
       "      <td>38</td>\n",
       "      <td>6</td>\n",
       "      <td>420</td>\n",
       "      <td>9</td>\n",
       "      <td>16</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>7</td>\n",
       "      <td>25</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>11</td>\n",
       "      <td>23</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>31</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Music</td>\n",
       "      <td>16</td>\n",
       "      <td>32</td>\n",
       "      <td>7</td>\n",
       "      <td>465</td>\n",
       "      <td>14</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>13</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>52</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>anime</td>\n",
       "      <td>25</td>\n",
       "      <td>16</td>\n",
       "      <td>15</td>\n",
       "      <td>14</td>\n",
       "      <td>422</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>14</td>\n",
       "      <td>9</td>\n",
       "      <td>32</td>\n",
       "      <td>29</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>41</td>\n",
       "      <td>11</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>17</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>baseball</td>\n",
       "      <td>13</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>422</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>14</td>\n",
       "      <td>9</td>\n",
       "      <td>32</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>23</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>canada</td>\n",
       "      <td>53</td>\n",
       "      <td>8</td>\n",
       "      <td>19</td>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "      <td>6</td>\n",
       "      <td>349</td>\n",
       "      <td>62</td>\n",
       "      <td>57</td>\n",
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       "      <td>10</td>\n",
       "      <td>17</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>7</td>\n",
       "      <td>14</td>\n",
       "      <td>11</td>\n",
       "      <td>23</td>\n",
       "      <td>100</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>conspiracy</td>\n",
       "      <td>29</td>\n",
       "      <td>14</td>\n",
       "      <td>19</td>\n",
       "      <td>20</td>\n",
       "      <td>9</td>\n",
       "      <td>13</td>\n",
       "      <td>41</td>\n",
       "      <td>322</td>\n",
       "      <td>36</td>\n",
       "      <td>48</td>\n",
       "      <td>17</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>19</td>\n",
       "      <td>15</td>\n",
       "      <td>20</td>\n",
       "      <td>17</td>\n",
       "      <td>21</td>\n",
       "      <td>129</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>europe</td>\n",
       "      <td>25</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>71</td>\n",
       "      <td>31</td>\n",
       "      <td>345</td>\n",
       "      <td>19</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>28</td>\n",
       "      <td>6</td>\n",
       "      <td>92</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>funny</td>\n",
       "      <td>85</td>\n",
       "      <td>24</td>\n",
       "      <td>17</td>\n",
       "      <td>21</td>\n",
       "      <td>29</td>\n",
       "      <td>10</td>\n",
       "      <td>27</td>\n",
       "      <td>38</td>\n",
       "      <td>29</td>\n",
       "      <td>148</td>\n",
       "      <td>13</td>\n",
       "      <td>17</td>\n",
       "      <td>15</td>\n",
       "      <td>36</td>\n",
       "      <td>14</td>\n",
       "      <td>20</td>\n",
       "      <td>23</td>\n",
       "      <td>56</td>\n",
       "      <td>28</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>GOT</td>\n",
       "      <td>22</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>16</td>\n",
       "      <td>428</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>21</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>hockey</td>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>43</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>430</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>39</td>\n",
       "      <td>30</td>\n",
       "      <td>27</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>movies</td>\n",
       "      <td>6</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>12</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>441</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>league of legends</td>\n",
       "      <td>38</td>\n",
       "      <td>7</td>\n",
       "      <td>23</td>\n",
       "      <td>11</td>\n",
       "      <td>54</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>22</td>\n",
       "      <td>12</td>\n",
       "      <td>44</td>\n",
       "      <td>29</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>403</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>15</td>\n",
       "      <td>24</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>nba</td>\n",
       "      <td>4</td>\n",
       "      <td>13</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>37</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>32</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>441</td>\n",
       "      <td>40</td>\n",
       "      <td>20</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>soccer</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>44</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>40</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>37</td>\n",
       "      <td>422</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>nfl</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>30</td>\n",
       "      <td>15</td>\n",
       "      <td>6</td>\n",
       "      <td>19</td>\n",
       "      <td>17</td>\n",
       "      <td>419</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>trees</td>\n",
       "      <td>56</td>\n",
       "      <td>20</td>\n",
       "      <td>23</td>\n",
       "      <td>8</td>\n",
       "      <td>15</td>\n",
       "      <td>14</td>\n",
       "      <td>22</td>\n",
       "      <td>29</td>\n",
       "      <td>18</td>\n",
       "      <td>62</td>\n",
       "      <td>18</td>\n",
       "      <td>10</td>\n",
       "      <td>17</td>\n",
       "      <td>16</td>\n",
       "      <td>18</td>\n",
       "      <td>15</td>\n",
       "      <td>10</td>\n",
       "      <td>386</td>\n",
       "      <td>20</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>news</td>\n",
       "      <td>31</td>\n",
       "      <td>6</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>55</td>\n",
       "      <td>94</td>\n",
       "      <td>103</td>\n",
       "      <td>22</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>11</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>210</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>wow</td>\n",
       "      <td>10</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>21</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>25</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>24</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>510</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Pred: Reddit  CS:GO  Overwatch  Music  anime  baseball  \\\n",
       "AskReddit                   214     12         35     15     29        15   \n",
       "GlobalOffensive              11    426          2     46     15        12   \n",
       "Overwatch                    38      6        420      9     16        12   \n",
       "Music                        16     32          7    465     14         9   \n",
       "anime                        25     16         15     14    422        10   \n",
       "baseball                     13      7          5      4      8       422   \n",
       "canada                       53      8         19      5     15         6   \n",
       "conspiracy                   29     14         19     20      9        13   \n",
       "europe                       25      9          9      7     13         5   \n",
       "funny                        85     24         17     21     29        10   \n",
       "GOT                          22      2          5      1     14         3   \n",
       "hockey                        9     11          7      3      6        43   \n",
       "movies                        6     32          1     18     12         7   \n",
       "league of legends            38      7         23     11     54         9   \n",
       "nba                           4     13          7      4      2        37   \n",
       "soccer                        7      9          8      7      3        44   \n",
       "nfl                           5     11          8      4      6        12   \n",
       "trees                        56     20         23      8     15        14   \n",
       "news                         31      6         13      5      6         8   \n",
       "wow                          10     16          3     21     11         8   \n",
       "\n",
       "                   canada  conspiracy  europe  funny  GOT  hockey  movies  \\\n",
       "AskReddit              56          26      26    127   11      18      15   \n",
       "GlobalOffensive         6           3       7     15   13      10      39   \n",
       "Overwatch               5          11       7     25    9       8      11   \n",
       "Music                   7           9       4     13    4       8      52   \n",
       "anime                   5          14       9     32   29       3       7   \n",
       "baseball                6           4       3     14    9      32       3   \n",
       "canada                349          62      57     42   10      17       5   \n",
       "conspiracy             41         322      36     48   17       7       8   \n",
       "europe                 71          31     345     19    6       9       5   \n",
       "funny                  27          38      29    148   13      17      15   \n",
       "GOT                     3           8       5     16  428       2       4   \n",
       "hockey                 16           2       3     10    4     430       6   \n",
       "movies                  7           3       8      5    5      10     441   \n",
       "league of legends       5          22      12     44   29       5       6   \n",
       "nba                     4           3       3     12    3      32       9   \n",
       "soccer                  6           5       3     12    6      40       7   \n",
       "nfl                     5           3      11      9   10      30      15   \n",
       "trees                  22          29      18     62   18      10      17   \n",
       "news                   55          94     103     22    8       7       8   \n",
       "wow                     4           8      10     25    8       5      24   \n",
       "\n",
       "                   LOL  nba  soccer  nfl  trees  news  wow  \n",
       "AskReddit           44   19      21   23     62    42   12  \n",
       "GlobalOffensive      9   12      11   10      5     8   18  \n",
       "Overwatch           23    4       4   10     31     7    7  \n",
       "Music                7    8       9    8      4     6   30  \n",
       "anime               41   11       7    5     17     4   12  \n",
       "baseball             9   23      30   23     11     1    4  \n",
       "canada              12    7      14   11     23   100    6  \n",
       "conspiracy          19   15      20   17     21   129   13  \n",
       "europe              10    4       2   28      6    92    4  \n",
       "funny               36   14      20   23     56    28   17  \n",
       "GOT                 21    2      10    5      4     7    4  \n",
       "hockey               4   39      30   27      5     4    4  \n",
       "movies               5    5      10    6      6     5   20  \n",
       "league of legends  403    9       6   15     24    10   11  \n",
       "nba                 10  441      40   20      9     6    3  \n",
       "soccer               6   37     422   20      1     5    3  \n",
       "nfl                  6   19      17  419      5     4    5  \n",
       "trees               16   18      15   10    386    20   12  \n",
       "news                11    7       5   12     12   210    4  \n",
       "wow                  8    6       6    7     10     6  510  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 precision    recall  f1-score   support\n",
      "\n",
      "      AskReddit       0.31      0.26      0.28       822\n",
      "GlobalOffensive       0.63      0.63      0.63       678\n",
      "          Music       0.65      0.63      0.64       663\n",
      "      Overwatch       0.68      0.65      0.66       712\n",
      "          anime       0.60      0.60      0.60       698\n",
      "       baseball       0.60      0.67      0.63       631\n",
      "         canada       0.50      0.43      0.46       821\n",
      "     conspiracy       0.46      0.39      0.43       817\n",
      "         europe       0.49      0.49      0.49       700\n",
      "          funny       0.21      0.22      0.22       667\n",
      "  gameofthrones       0.67      0.76      0.71       566\n",
      "         hockey       0.61      0.65      0.63       663\n",
      "leagueoflegends       0.63      0.72      0.67       612\n",
      "         movies       0.58      0.54      0.56       743\n",
      "            nba       0.63      0.67      0.65       662\n",
      "            nfl       0.60      0.65      0.63       651\n",
      "         soccer       0.60      0.69      0.64       604\n",
      "          trees       0.55      0.49      0.52       789\n",
      "      worldnews       0.30      0.33      0.32       627\n",
      "            wow       0.73      0.72      0.73       706\n",
      "\n",
      "       accuracy                           0.55     13832\n",
      "      macro avg       0.55      0.56      0.55     13832\n",
      "   weighted avg       0.55      0.55      0.55     13832\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Defining a pipeline that prints the score, parameters, recall, precision, conf matrix of the best gridsearch model\n",
    "pipe = Pipeline([\n",
    "    ('tvec', TfidfVectorizer(stop_words=stop_words,sublinear_tf=True,max_df=0.7,ngram_range=(1,1))),\n",
    "    (\"univ_select\", SelectKBest(chi2)),\n",
    "    ('nb', MultinomialNB())\n",
    "])\n",
    "\n",
    "grid_params = {\n",
    "    #'tvec__max_df': (0.7,0.9),\n",
    "    'tvec__min_df': (1,2),\n",
    "    #'tvec__max_features': [20000, 25000,30000, 35000, 40000],\n",
    "    #'tvec__ngram_range': ((1, 1), (1, 2)),\n",
    "    \"univ_select__k\": [10000,15000],\n",
    "    'nb__fit_prior': [True],\n",
    "    'nb__alpha': [0.07, 0.1, 0.15, 0.2, 0.3],\n",
    "\n",
    "}\n",
    "\n",
    "\n",
    "X_training, X_testing, y_training, y_testing = train_test_split(cleandata_Train, y_datatrain, test_size=0.2,stratify=y_datatrain)\n",
    "    \n",
    "    grid_search = GridSearchCV(pipe, grid_params, cv = 5, verbose = 1, n_jobs = -1)\n",
    "    grid_search.fit(X_training, y_training)\n",
    "\n",
    "    print(\"Best cross-validation score: {:.2f}\".format(grid_search.best_score_))\n",
    "    # Printing the best score/parameters from the grid search\n",
    "    print('Best score: {}'.format(grid_search.best_score_))\n",
    "    print('Best paramters: {}'.format(grid_search.best_params_))\n",
    "\n",
    "    # Best model from grid search\n",
    "    grid_model = grid_search.best_estimator_\n",
    "    print(\"Best estimator: \", grid_model)\n",
    "    print('Model score on x_train: {}'.format(grid_model.score(X_training, y_training)))\n",
    "\n",
    "    # Prediction and score\n",
    "    prediction = grid_model.predict(X_testing)\n",
    "    print('Accuracy score of prediction: {}'.format(accuracy_score(y_testing, prediction)))\n",
    "\n",
    "    # Confusion Matrix\n",
    "    conf_matrix_grid = confusion_matrix(y_testing, prediction)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Count Vectorization along with TFIDF Transformation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "GcCN4FWsIoVP",
    "outputId": "9100cf7a-fa74-4bd3-84ac-b47bbbb6b187"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(70000, 34044)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vectorizer = CountVectorizer(stop_words='english',ngram_range=(1, 1),min_df=2,max_df=0.7)\n",
    "X_train_vect = vectorizer.fit_transform(cleandata_Train) \n",
    "X_test_vect = vectorizer.transform(cleandata_Test)\n",
    "X_train_vect.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "_rCHKZIZStI9"
   },
   "outputs": [],
   "source": [
    "tfidfconverter = TfidfTransformer(sublinear_tf=True, use_idf =True, norm='l2')  \n",
    "X_train_tfidf = tfidfconverter.fit_transform(X_train_vect)\n",
    "X_test_tfidf = tfidfconverter.transform(X_test_vect)\n",
    "X_tfidf=X_train_tfidf\n",
    "y_train=y_datatrain"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CHI2 test to select K best features\n",
    "(Novel method implemented: Select best features using a Chi^2 test rather than by frequency)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "wD5Rxh4GUmdo"
   },
   "outputs": [],
   "source": [
    "chi2_selector = SelectKBest(chi2, 15000)\n",
    "X_tfidf_sel2 = chi2_selector.fit_transform(X_tfidf, y_train) \n",
    "X_test_tfidf_sel2 = chi2_selector.transform(X_test_tfidf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 647,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "QuHYQZ97Uqaj",
    "outputId": "8f992720-36b6-4565-a162-59a6cc951930"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5955571428571429\n"
     ]
    }
   ],
   "source": [
    "# Basic multinomial NB - Only accuracy score\n",
    "clf = MultinomialNB(alpha = 0.08, fit_prior = False)\n",
    "pred_acc = cross_val_score(clf, X_tfidf_sel2, y_train, cv = 5)\n",
    "print(pred_acc.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TFIDF values of the most frequent words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "mG9NMMsTUsrD"
   },
   "outputs": [],
   "source": [
    "dfmat = pd.DataFrame(data={\"count\":X_train_tfidf.sum(axis=0).tolist()[0], \"name\":vectorizer.get_feature_names()})\n",
    "dfmat = dfmat.sort_values(by=\"count\", ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 514
    },
    "colab_type": "code",
    "id": "MeuKi6yEVid3",
    "outputId": "ce5ed91d-8e64-4d7b-e349-0e2c36da06be"
   },
   "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",
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       "    .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>count</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>17242</td>\n",
       "      <td>1,241.35</td>\n",
       "      <td>like</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8632</td>\n",
       "      <td>998.77</td>\n",
       "      <td>dont</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30258</td>\n",
       "      <td>879.26</td>\n",
       "      <td>think</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21996</td>\n",
       "      <td>866.36</td>\n",
       "      <td>people</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12561</td>\n",
       "      <td>738.66</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24246</td>\n",
       "      <td>705.48</td>\n",
       "      <td>really</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30444</td>\n",
       "      <td>680.76</td>\n",
       "      <td>time</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16447</td>\n",
       "      <td>673.88</td>\n",
       "      <td>know</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17927</td>\n",
       "      <td>527.76</td>\n",
       "      <td>make</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33843</td>\n",
       "      <td>511.01</td>\n",
       "      <td>youre</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11978</td>\n",
       "      <td>507.65</td>\n",
       "      <td>game</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32920</td>\n",
       "      <td>504.49</td>\n",
       "      <td>way</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25402</td>\n",
       "      <td>499.49</td>\n",
       "      <td>right</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12529</td>\n",
       "      <td>491.49</td>\n",
       "      <td>going</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12610</td>\n",
       "      <td>485.81</td>\n",
       "      <td>got</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29899</td>\n",
       "      <td>479.32</td>\n",
       "      <td>team</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8016</td>\n",
       "      <td>472.52</td>\n",
       "      <td>didnt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2772</td>\n",
       "      <td>472.38</td>\n",
       "      <td>better</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32791</td>\n",
       "      <td>465.44</td>\n",
       "      <td>want</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14128</td>\n",
       "      <td>463.23</td>\n",
       "      <td>http</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         count    name\n",
       "17242 1,241.35    like\n",
       "8632    998.77    dont\n",
       "30258   879.26   think\n",
       "21996   866.36  people\n",
       "12561   738.66    good\n",
       "24246   705.48  really\n",
       "30444   680.76    time\n",
       "16447   673.88    know\n",
       "17927   527.76    make\n",
       "33843   511.01   youre\n",
       "11978   507.65    game\n",
       "32920   504.49     way\n",
       "25402   499.49   right\n",
       "12529   491.49   going\n",
       "12610   485.81     got\n",
       "29899   479.32    team\n",
       "8016    472.52   didnt\n",
       "2772    472.38  better\n",
       "32791   465.44    want\n",
       "14128   463.23    http"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfmat.head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model selection and Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### List of classifiers\n",
    "     We have used the following models  in our project.\n",
    "                  1. Linear SVC, \n",
    "                  2. Logistic Regression, \n",
    "                  3. Ridge Classifier, \n",
    "                  4. Stochastic Gradient Descent,\n",
    "                  5. Multinominal Naive Bayes, \n",
    "                  6. Complement Naive Bayes\n",
    "                  7. OneVsRest Classifier,\n",
    "                  8. Bernoulli Naive Bayes from scratch,\n",
    "                  9. Ensemble Methods\n",
    "                    \n",
    "\n",
    "#### Also we have used Ridge classifier, Multinomial Naive Bayes and Complement Naive Bayes with ensemble model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Spliting the dataset for training and validation:\n",
    "X_training, X_testing, y_training, y_testing = train_test_split(X_tfidf_sel2, y_datatrain, test_size=0.2,stratify=y_datatrain) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Linear SVC Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 650,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 8 candidates, totalling 40 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  40 out of  40 | elapsed:  1.3min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best cross-validation score: 0.57\n",
      "Best parameters:  {'C': 0.5}\n",
      "Best estimator:  LinearSVC(C=0.5, class_weight=None, dual=True, fit_intercept=True,\n",
      "          intercept_scaling=1, loss='squared_hinge', max_iter=1000,\n",
      "          multi_class='ovr', penalty='l2', random_state=None, tol=0.0001,\n",
      "          verbose=0)\n",
      "Time to train and predict in Linear SVC Model: 86.39978694915771\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pred: Reddit</th>\n",
       "      <th>CS:GO</th>\n",
       "      <th>Overwatch</th>\n",
       "      <th>Music</th>\n",
       "      <th>anime</th>\n",
       "      <th>baseball</th>\n",
       "      <th>canada</th>\n",
       "      <th>conspiracy</th>\n",
       "      <th>europe</th>\n",
       "      <th>funny</th>\n",
       "      <th>GOT</th>\n",
       "      <th>hockey</th>\n",
       "      <th>movies</th>\n",
       "      <th>LOL</th>\n",
       "      <th>nba</th>\n",
       "      <th>soccer</th>\n",
       "      <th>nfl</th>\n",
       "      <th>trees</th>\n",
       "      <th>news</th>\n",
       "      <th>wow</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>AskReddit</td>\n",
       "      <td>192</td>\n",
       "      <td>12</td>\n",
       "      <td>30</td>\n",
       "      <td>7</td>\n",
       "      <td>23</td>\n",
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       "      <td>14</td>\n",
       "      <td>6</td>\n",
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       "      <td>35</td>\n",
       "      <td>8</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "      <td>59</td>\n",
       "      <td>41</td>\n",
       "      <td>10</td>\n",
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       "    <tr>\n",
       "      <td>GlobalOffensive</td>\n",
       "      <td>11</td>\n",
       "      <td>469</td>\n",
       "      <td>13</td>\n",
       "      <td>31</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>12</td>\n",
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       "      <td>15</td>\n",
       "      <td>5</td>\n",
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       "      <td>25</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>23</td>\n",
       "      <td>16</td>\n",
       "      <td>5</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Overwatch</td>\n",
       "      <td>31</td>\n",
       "      <td>6</td>\n",
       "      <td>490</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>28</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>20</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>21</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Music</td>\n",
       "      <td>10</td>\n",
       "      <td>29</td>\n",
       "      <td>8</td>\n",
       "      <td>463</td>\n",
       "      <td>14</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>29</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>43</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>anime</td>\n",
       "      <td>37</td>\n",
       "      <td>9</td>\n",
       "      <td>16</td>\n",
       "      <td>11</td>\n",
       "      <td>451</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "      <td>32</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>39</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>16</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>baseball</td>\n",
       "      <td>15</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "      <td>465</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>19</td>\n",
       "      <td>6</td>\n",
       "      <td>17</td>\n",
       "      <td>7</td>\n",
       "      <td>14</td>\n",
       "      <td>24</td>\n",
       "      <td>21</td>\n",
       "      <td>24</td>\n",
       "      <td>17</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>canada</td>\n",
       "      <td>45</td>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>333</td>\n",
       "      <td>47</td>\n",
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       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>24</td>\n",
       "      <td>68</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>conspiracy</td>\n",
       "      <td>38</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>45</td>\n",
       "      <td>336</td>\n",
       "      <td>28</td>\n",
       "      <td>35</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>19</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>22</td>\n",
       "      <td>82</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>europe</td>\n",
       "      <td>23</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>41</td>\n",
       "      <td>33</td>\n",
       "      <td>399</td>\n",
       "      <td>27</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>15</td>\n",
       "      <td>18</td>\n",
       "      <td>73</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>funny</td>\n",
       "      <td>94</td>\n",
       "      <td>22</td>\n",
       "      <td>26</td>\n",
       "      <td>11</td>\n",
       "      <td>25</td>\n",
       "      <td>13</td>\n",
       "      <td>26</td>\n",
       "      <td>39</td>\n",
       "      <td>21</td>\n",
       "      <td>193</td>\n",
       "      <td>16</td>\n",
       "      <td>15</td>\n",
       "      <td>10</td>\n",
       "      <td>41</td>\n",
       "      <td>17</td>\n",
       "      <td>14</td>\n",
       "      <td>17</td>\n",
       "      <td>52</td>\n",
       "      <td>29</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>GOT</td>\n",
       "      <td>14</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>27</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>17</td>\n",
       "      <td>516</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>26</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>hockey</td>\n",
       "      <td>14</td>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>31</td>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "      <td>441</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>29</td>\n",
       "      <td>27</td>\n",
       "      <td>25</td>\n",
       "      <td>16</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>movies</td>\n",
       "      <td>7</td>\n",
       "      <td>41</td>\n",
       "      <td>7</td>\n",
       "      <td>34</td>\n",
       "      <td>14</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>24</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>491</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>league of legends</td>\n",
       "      <td>24</td>\n",
       "      <td>11</td>\n",
       "      <td>25</td>\n",
       "      <td>7</td>\n",
       "      <td>32</td>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "      <td>18</td>\n",
       "      <td>16</td>\n",
       "      <td>28</td>\n",
       "      <td>21</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>419</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>21</td>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>nba</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>20</td>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>24</td>\n",
       "      <td>6</td>\n",
       "      <td>37</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>450</td>\n",
       "      <td>30</td>\n",
       "      <td>23</td>\n",
       "      <td>13</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>soccer</td>\n",
       "      <td>19</td>\n",
       "      <td>12</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>34</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>17</td>\n",
       "      <td>7</td>\n",
       "      <td>18</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>25</td>\n",
       "      <td>463</td>\n",
       "      <td>22</td>\n",
       "      <td>16</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>nfl</td>\n",
       "      <td>14</td>\n",
       "      <td>7</td>\n",
       "      <td>14</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "      <td>18</td>\n",
       "      <td>16</td>\n",
       "      <td>27</td>\n",
       "      <td>28</td>\n",
       "      <td>9</td>\n",
       "      <td>22</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>18</td>\n",
       "      <td>15</td>\n",
       "      <td>430</td>\n",
       "      <td>14</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>trees</td>\n",
       "      <td>35</td>\n",
       "      <td>9</td>\n",
       "      <td>26</td>\n",
       "      <td>6</td>\n",
       "      <td>19</td>\n",
       "      <td>9</td>\n",
       "      <td>23</td>\n",
       "      <td>26</td>\n",
       "      <td>14</td>\n",
       "      <td>59</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>399</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>news</td>\n",
       "      <td>36</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>69</td>\n",
       "      <td>116</td>\n",
       "      <td>90</td>\n",
       "      <td>40</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>13</td>\n",
       "      <td>249</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>wow</td>\n",
       "      <td>11</td>\n",
       "      <td>17</td>\n",
       "      <td>9</td>\n",
       "      <td>17</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>22</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>4</td>\n",
       "      <td>528</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Pred: Reddit  CS:GO  Overwatch  Music  anime  baseball  \\\n",
       "AskReddit                   192     12         30      7     23        17   \n",
       "GlobalOffensive              11    469         13     31      7        10   \n",
       "Overwatch                    31      6        490     10      9         4   \n",
       "Music                        10     29          8    463     14         9   \n",
       "anime                        37      9         16     11    451         9   \n",
       "baseball                     15      9          7      9     11       465   \n",
       "canada                       45      9         11      5      7         6   \n",
       "conspiracy                   38     11         11      8     10        12   \n",
       "europe                       23      6          6      5      8         5   \n",
       "funny                        94     22         26     11     25        13   \n",
       "GOT                          14      9          5      3     27         8   \n",
       "hockey                       14      9         11      7      6        31   \n",
       "movies                        7     41          7     34     14        11   \n",
       "league of legends            24     11         25      7     32         8   \n",
       "nba                          11      8          9      6      8        20   \n",
       "soccer                       19     12          9      5      6        34   \n",
       "nfl                          14      7         14      9      9        11   \n",
       "trees                        35      9         26      6     19         9   \n",
       "news                         36      6          7      5      8         9   \n",
       "wow                          11     17          9     17     11         4   \n",
       "\n",
       "                   canada  conspiracy  europe  funny  GOT  hockey  movies  \\\n",
       "AskReddit              36          38      35     98   14       6      10   \n",
       "GlobalOffensive         7          12      10     15    5       2      25   \n",
       "Overwatch               7          13       5     28    5      10       4   \n",
       "Music                   5           6      11     29    6       5      43   \n",
       "anime                   8          15       7     32    7       2       8   \n",
       "baseball               10           5       5     19    6      17       7   \n",
       "canada                333          47      31     40   10       7       9   \n",
       "conspiracy             45         336      28     35    8       4       4   \n",
       "europe                 41          33     399     27    4       6       4   \n",
       "funny                  26          39      21    193   16      15      10   \n",
       "GOT                     9           9       6     17  516       5       9   \n",
       "hockey                 14          10       7     14    4     441       5   \n",
       "movies                  2           6       6     24    5       3     491   \n",
       "league of legends      12          18      16     28   21       6      10   \n",
       "nba                     9          11       2     24    6      37      11   \n",
       "soccer                 11           9       3     17    7      18       4   \n",
       "nfl                    18          16      27     28    9      22      11   \n",
       "trees                  23          26      14     59    4       7       5   \n",
       "news                   69         116      90     40    8       3       7   \n",
       "wow                     3           6       9     22    8       3      10   \n",
       "\n",
       "                   LOL  nba  soccer  nfl  trees  news  wow  \n",
       "AskReddit           35    8      14   15     59    41   10  \n",
       "GlobalOffensive      9    6      10   23     16     5   14  \n",
       "Overwatch           20    5      11    6     21    10    5  \n",
       "Music                6    4       3    4      9    10   26  \n",
       "anime               39    2       4   10     16    10    7  \n",
       "baseball            14   24      21   24     17     5   10  \n",
       "canada              16    3      11   10     24    68    8  \n",
       "conspiracy          19    6       8    7     22    82    6  \n",
       "europe              12    3       6   15     18    73    6  \n",
       "funny               41   17      14   17     52    29   19  \n",
       "GOT                 26    5       5    6     13     3    5  \n",
       "hockey              11   29      27   25     16    10    9  \n",
       "movies               6    5       4    5      6     7   16  \n",
       "league of legends  419    8       7    8     21    13    6  \n",
       "nba                  9  450      30   23     13     7    6  \n",
       "soccer               6   25     463   22     16    11    3  \n",
       "nfl                 12   18      15  430     14    11    5  \n",
       "trees               12    9       7   10    399    11   10  \n",
       "news                11    3       7    6     13   249    7  \n",
       "wow                 12    5       5    3     13     4  528  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 precision    recall  f1-score   support\n",
      "\n",
      "      AskReddit       0.28      0.27      0.28       700\n",
      "GlobalOffensive       0.66      0.67      0.66       700\n",
      "          Music       0.66      0.70      0.68       700\n",
      "      Overwatch       0.70      0.66      0.68       700\n",
      "          anime       0.64      0.64      0.64       700\n",
      "       baseball       0.67      0.66      0.67       700\n",
      "         canada       0.48      0.48      0.48       700\n",
      "     conspiracy       0.44      0.48      0.46       700\n",
      "         europe       0.55      0.57      0.56       700\n",
      "          funny       0.24      0.28      0.26       700\n",
      "  gameofthrones       0.77      0.74      0.75       700\n",
      "         hockey       0.71      0.63      0.67       700\n",
      "leagueoflegends       0.71      0.70      0.71       700\n",
      "         movies       0.57      0.60      0.58       700\n",
      "            nba       0.71      0.64      0.67       700\n",
      "            nfl       0.69      0.66      0.67       700\n",
      "         soccer       0.64      0.61      0.63       700\n",
      "          trees       0.51      0.57      0.54       700\n",
      "      worldnews       0.38      0.36      0.37       700\n",
      "            wow       0.75      0.75      0.75       700\n",
      "\n",
      "       accuracy                           0.58     14000\n",
      "      macro avg       0.59      0.58      0.59     14000\n",
      "   weighted avg       0.59      0.58      0.59     14000\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.5840714285714286"
      ]
     },
     "execution_count": 650,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Linear SVC Model:\n",
    "Cs = [0.01, 0.1, 0.5, 1, 1.5,2, 2.5, 3 ]\n",
    "svc_clf = GridSearchCV(LinearSVC(), param_grid=dict(C=Cs), cv=5,verbose=1,n_jobs=-1)\n",
    "start = time.time()\n",
    "svc_clf.fit(X_training, y_training)    \n",
    "print(\"Best cross-validation score: {:.2f}\".format(svc_clf.best_score_))\n",
    "print(\"Best parameters: \", svc_clf.best_params_)\n",
    "print(\"Best estimator: \", svc_clf.best_estimator_)\n",
    "svc_clf = svc_clf.best_estimator_\n",
    "ypred_val_svc = svc_clf.predict(X_testing)\n",
    "y_pred_test_svc = svc_clf.predict(X_tfidf_sel2)\n",
    "end =  time.time()\n",
    "print('Time to train and predict in Linear SVC Model: {}'.format(end-start))\n",
    "confusion_matrix(ypred_val_svc, y_testing)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Ridge Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import RidgeClassifier\n",
    "#Ridge Classifier\n",
    "param_grid = {'alpha': [0.5, 1, 2, 3],}\n",
    "              #'solver' : ['auto', 'svd', 'cholesky', 'lsqr', 'sparse_cg', 'sag', 'saga']}\n",
    "grid = GridSearchCV(RidgeClassifier(class_weight = 'balanced',solver='saga',fit_intercept=False,normalize=True), param_grid, cv=5,verbose=1,n_jobs=-1)\n",
    "start = time.time()\n",
    "grid.fit(X_training, y_training)\n",
    "print(\"Best cross-validation score: {:.2f}\".format(grid.best_score_))\n",
    "print(\"Best parameters: \", grid.best_params_)\n",
    "print(\"Best estimator: \", grid.best_estimator_)\n",
    "rc = grid.best_estimator_\n",
    "ypred_val_rc = rc.predict(X_testing)\n",
    "y_pred_test_rc=rc.predict(X_tfidf_sel2)\n",
    "end =  time.time()\n",
    "print('Time to train and predict in Ridge Classifier Model: {}'.format(end-start))\n",
    "confusion_matrix(ypred_val_rc, y_testing)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### SGD Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 652,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 6 candidates, totalling 30 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  30 out of  30 | elapsed:   25.8s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best cross-validation score: 0.56\n",
      "Best parameters:  {'alpha': 1e-05}\n",
      "Best estimator:  SGDClassifier(alpha=1e-05, average=False, class_weight=None,\n",
      "              early_stopping=False, epsilon=0.1, eta0=0.0, fit_intercept=True,\n",
      "              l1_ratio=0.15, learning_rate='optimal', loss='hinge',\n",
      "              max_iter=1000, n_iter_no_change=5, n_jobs=None, penalty='l2',\n",
      "              power_t=0.5, random_state=None, shuffle=True, tol=0.001,\n",
      "              validation_fraction=0.1, verbose=0, warm_start=False)\n",
      "Time to train and predict in Stochastic Gradient Descent Model: 29.46174120903015\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pred: Reddit</th>\n",
       "      <th>CS:GO</th>\n",
       "      <th>Overwatch</th>\n",
       "      <th>Music</th>\n",
       "      <th>anime</th>\n",
       "      <th>baseball</th>\n",
       "      <th>canada</th>\n",
       "      <th>conspiracy</th>\n",
       "      <th>europe</th>\n",
       "      <th>funny</th>\n",
       "      <th>GOT</th>\n",
       "      <th>hockey</th>\n",
       "      <th>movies</th>\n",
       "      <th>LOL</th>\n",
       "      <th>nba</th>\n",
       "      <th>soccer</th>\n",
       "      <th>nfl</th>\n",
       "      <th>trees</th>\n",
       "      <th>news</th>\n",
       "      <th>wow</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>AskReddit</td>\n",
       "      <td>147</td>\n",
       "      <td>22</td>\n",
       "      <td>46</td>\n",
       "      <td>12</td>\n",
       "      <td>29</td>\n",
       "      <td>24</td>\n",
       "      <td>30</td>\n",
       "      <td>36</td>\n",
       "      <td>40</td>\n",
       "      <td>71</td>\n",
       "      <td>18</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>38</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>64</td>\n",
       "      <td>42</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>GlobalOffensive</td>\n",
       "      <td>8</td>\n",
       "      <td>465</td>\n",
       "      <td>8</td>\n",
       "      <td>35</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>29</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>17</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Overwatch</td>\n",
       "      <td>24</td>\n",
       "      <td>8</td>\n",
       "      <td>503</td>\n",
       "      <td>11</td>\n",
       "      <td>13</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>18</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>19</td>\n",
       "      <td>12</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Music</td>\n",
       "      <td>11</td>\n",
       "      <td>31</td>\n",
       "      <td>9</td>\n",
       "      <td>467</td>\n",
       "      <td>19</td>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "      <td>18</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>37</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>anime</td>\n",
       "      <td>23</td>\n",
       "      <td>10</td>\n",
       "      <td>19</td>\n",
       "      <td>13</td>\n",
       "      <td>462</td>\n",
       "      <td>13</td>\n",
       "      <td>7</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>25</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>36</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>14</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>baseball</td>\n",
       "      <td>15</td>\n",
       "      <td>9</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>6</td>\n",
       "      <td>463</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>15</td>\n",
       "      <td>6</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>28</td>\n",
       "      <td>24</td>\n",
       "      <td>20</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>canada</td>\n",
       "      <td>40</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>318</td>\n",
       "      <td>40</td>\n",
       "      <td>37</td>\n",
       "      <td>28</td>\n",
       "      <td>15</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>12</td>\n",
       "      <td>29</td>\n",
       "      <td>66</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>conspiracy</td>\n",
       "      <td>27</td>\n",
       "      <td>14</td>\n",
       "      <td>13</td>\n",
       "      <td>9</td>\n",
       "      <td>13</td>\n",
       "      <td>17</td>\n",
       "      <td>40</td>\n",
       "      <td>327</td>\n",
       "      <td>35</td>\n",
       "      <td>21</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>19</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>28</td>\n",
       "      <td>69</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>europe</td>\n",
       "      <td>14</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>34</td>\n",
       "      <td>35</td>\n",
       "      <td>405</td>\n",
       "      <td>20</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>14</td>\n",
       "      <td>21</td>\n",
       "      <td>62</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>funny</td>\n",
       "      <td>77</td>\n",
       "      <td>17</td>\n",
       "      <td>34</td>\n",
       "      <td>13</td>\n",
       "      <td>35</td>\n",
       "      <td>24</td>\n",
       "      <td>25</td>\n",
       "      <td>35</td>\n",
       "      <td>23</td>\n",
       "      <td>160</td>\n",
       "      <td>23</td>\n",
       "      <td>18</td>\n",
       "      <td>20</td>\n",
       "      <td>39</td>\n",
       "      <td>19</td>\n",
       "      <td>17</td>\n",
       "      <td>22</td>\n",
       "      <td>48</td>\n",
       "      <td>26</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>GOT</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>28</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>13</td>\n",
       "      <td>518</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>22</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>hockey</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>33</td>\n",
       "      <td>12</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>441</td>\n",
       "      <td>8</td>\n",
       "      <td>11</td>\n",
       "      <td>30</td>\n",
       "      <td>25</td>\n",
       "      <td>30</td>\n",
       "      <td>17</td>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>movies</td>\n",
       "      <td>5</td>\n",
       "      <td>38</td>\n",
       "      <td>9</td>\n",
       "      <td>37</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>18</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>491</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>league of legends</td>\n",
       "      <td>17</td>\n",
       "      <td>9</td>\n",
       "      <td>27</td>\n",
       "      <td>12</td>\n",
       "      <td>38</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>19</td>\n",
       "      <td>15</td>\n",
       "      <td>17</td>\n",
       "      <td>26</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>409</td>\n",
       "      <td>13</td>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "      <td>19</td>\n",
       "      <td>12</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>nba</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>20</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>7</td>\n",
       "      <td>38</td>\n",
       "      <td>14</td>\n",
       "      <td>11</td>\n",
       "      <td>454</td>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
       "      <td>13</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>soccer</td>\n",
       "      <td>15</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>27</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "      <td>27</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>29</td>\n",
       "      <td>462</td>\n",
       "      <td>21</td>\n",
       "      <td>16</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>nfl</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>17</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>14</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>28</td>\n",
       "      <td>19</td>\n",
       "      <td>10</td>\n",
       "      <td>22</td>\n",
       "      <td>16</td>\n",
       "      <td>6</td>\n",
       "      <td>18</td>\n",
       "      <td>14</td>\n",
       "      <td>443</td>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>trees</td>\n",
       "      <td>24</td>\n",
       "      <td>13</td>\n",
       "      <td>28</td>\n",
       "      <td>7</td>\n",
       "      <td>18</td>\n",
       "      <td>15</td>\n",
       "      <td>16</td>\n",
       "      <td>17</td>\n",
       "      <td>16</td>\n",
       "      <td>43</td>\n",
       "      <td>7</td>\n",
       "      <td>15</td>\n",
       "      <td>10</td>\n",
       "      <td>15</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>14</td>\n",
       "      <td>399</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>news</td>\n",
       "      <td>29</td>\n",
       "      <td>16</td>\n",
       "      <td>13</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>11</td>\n",
       "      <td>62</td>\n",
       "      <td>112</td>\n",
       "      <td>84</td>\n",
       "      <td>31</td>\n",
       "      <td>14</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>13</td>\n",
       "      <td>17</td>\n",
       "      <td>226</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>wow</td>\n",
       "      <td>6</td>\n",
       "      <td>19</td>\n",
       "      <td>8</td>\n",
       "      <td>22</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>13</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>532</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Pred: Reddit  CS:GO  Overwatch  Music  anime  baseball  \\\n",
       "AskReddit                   147     22         46     12     29        24   \n",
       "GlobalOffensive               8    465          8     35      6        10   \n",
       "Overwatch                    24      8        503     11     13         7   \n",
       "Music                        11     31          9    467     19        14   \n",
       "anime                        23     10         19     13    462        13   \n",
       "baseball                     15      9         13     14      6       463   \n",
       "canada                       40     14         15      9      7         9   \n",
       "conspiracy                   27     14         13      9     13        17   \n",
       "europe                       14      9         12      8      9         8   \n",
       "funny                        77     17         34     13     35        24   \n",
       "GOT                           5      6          4      5     28         9   \n",
       "hockey                        8      7         11     10      7        33   \n",
       "movies                        5     38          9     37     13        10   \n",
       "league of legends            17      9         27     12     38        10   \n",
       "nba                           8      7          9     10      7        20   \n",
       "soccer                       15      9         10      7      7        27   \n",
       "nfl                           9      9         17     11      8        14   \n",
       "trees                        24     13         28      7     18        15   \n",
       "news                         29     16         13      9     12        11   \n",
       "wow                           6     19          8     22     12         4   \n",
       "\n",
       "                   canada  conspiracy  europe  funny  GOT  hockey  movies  \\\n",
       "AskReddit              30          36      40     71   18      13      14   \n",
       "GlobalOffensive         4          13      13     16    6       6      29   \n",
       "Overwatch               7          14       3     18    7      10       4   \n",
       "Music                   4           7      11     18    9       5      37   \n",
       "anime                   7          12      10     25   10       6       7   \n",
       "baseball               10          10       7     15    6      17       3   \n",
       "canada                318          40      37     28   15      12      13   \n",
       "conspiracy             40         327      35     21   12       6       6   \n",
       "europe                 34          35     405     20    8       8       6   \n",
       "funny                  25          35      23    160   23      18      20   \n",
       "GOT                     7          10       7     13  518       9       7   \n",
       "hockey                 12           7       7     11    5     441       8   \n",
       "movies                  2           7       9     18    4       5     491   \n",
       "league of legends       8          19      15     17   26      11       9   \n",
       "nba                     5          10       1     19    7      38      14   \n",
       "soccer                  8           7       4     14   10      27       4   \n",
       "nfl                    11          12      28     19   10      22      16   \n",
       "trees                  16          17      16     43    7      15      10   \n",
       "news                   62         112      84     31   14       6       9   \n",
       "wow                     5           7       8     13    9       6      14   \n",
       "\n",
       "                   LOL  nba  soccer  nfl  trees  news  wow  \n",
       "AskReddit           38   10      13   14     64    42   17  \n",
       "GlobalOffensive      8    8      10   17     11     8   19  \n",
       "Overwatch            9    7       9    6     19    12    9  \n",
       "Music                2    5       2    9      6     8   26  \n",
       "anime               36    5       3    9     14     6   10  \n",
       "baseball            11   28      24   20     16     3   10  \n",
       "canada              11    3      13   12     29    66    9  \n",
       "conspiracy          19   10      12    8     28    69   14  \n",
       "europe              11    5       4   14     21    62    7  \n",
       "funny               39   19      17   22     48    26   25  \n",
       "GOT                 22    9       5   11     13     5    7  \n",
       "hockey              11   30      25   30     17     8   12  \n",
       "movies               5    5       4    7      6     6   19  \n",
       "league of legends  409   13       9   11     19    12    9  \n",
       "nba                 11  454      26   26     13     8    7  \n",
       "soccer               7   29     462   21     16    11    5  \n",
       "nfl                  6   18      14  443     14    10    9  \n",
       "trees               15   12       8   14    399    11   12  \n",
       "news                10   10       7   13     17   226    9  \n",
       "wow                 10    6       2    5      9     3  532  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 precision    recall  f1-score   support\n",
      "\n",
      "      AskReddit       0.29      0.21      0.24       700\n",
      "GlobalOffensive       0.64      0.66      0.65       700\n",
      "          Music       0.62      0.72      0.67       700\n",
      "      Overwatch       0.65      0.67      0.66       700\n",
      "          anime       0.62      0.66      0.64       700\n",
      "       baseball       0.62      0.66      0.64       700\n",
      "         canada       0.52      0.45      0.48       700\n",
      "     conspiracy       0.44      0.47      0.46       700\n",
      "         europe       0.53      0.58      0.55       700\n",
      "          funny       0.27      0.23      0.25       700\n",
      "  gameofthrones       0.72      0.74      0.73       700\n",
      "         hockey       0.65      0.63      0.64       700\n",
      "leagueoflegends       0.68      0.70      0.69       700\n",
      "         movies       0.59      0.58      0.59       700\n",
      "            nba       0.66      0.65      0.66       700\n",
      "            nfl       0.69      0.66      0.67       700\n",
      "         soccer       0.62      0.63      0.63       700\n",
      "          trees       0.51      0.57      0.54       700\n",
      "      worldnews       0.38      0.32      0.35       700\n",
      "            wow       0.69      0.76      0.73       700\n",
      "\n",
      "       accuracy                           0.58     14000\n",
      "      macro avg       0.57      0.58      0.57     14000\n",
      "   weighted avg       0.57      0.58      0.57     14000\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.578"
      ]
     },
     "execution_count": 652,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import SGDClassifier\n",
    "#SGD Classifier\n",
    "param_grid = {'alpha': [1e-1, 1e-2, 1e-3, 1e-4, 1e-5, 1e-6]}\n",
    "grid = GridSearchCV(SGDClassifier(epsilon=0.1), param_grid, cv=5,verbose=1,n_jobs=-1)\n",
    "start = time.time()\n",
    "grid.fit(X_training, y_training)\n",
    "print(\"Best cross-validation score: {:.2f}\".format(grid.best_score_))\n",
    "print(\"Best parameters: \", grid.best_params_)\n",
    "print(\"Best estimator: \", grid.best_estimator_)\n",
    "sgd = grid.best_estimator_\n",
    "ypred_val_sgd   = sgd.predict(X_testing)\n",
    "y_pred_test_sgd = sgd.predict(X_tfidf_sel2)\n",
    "end =  time.time()\n",
    "print('Time to train and predict in Stochastic Gradient Descent Model: {}'.format(end-start))\n",
    "confusion_matrix(ypred_val_sgd, y_testing)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Bernoulli Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 5 candidates, totalling 25 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  25 out of  25 | elapsed:    1.3s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best cross-validation score: 0.57\n",
      "Best parameters:  {'alpha': 0.1}\n",
      "Best estimator:  BernoulliNB(alpha=0.1, binarize=0.12, class_prior=None, fit_prior=False)\n",
      "Time to train and predict in Ridge Classifier Model: 1.8151700496673584\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pred: Reddit</th>\n",
       "      <th>CS:GO</th>\n",
       "      <th>Overwatch</th>\n",
       "      <th>Music</th>\n",
       "      <th>anime</th>\n",
       "      <th>baseball</th>\n",
       "      <th>canada</th>\n",
       "      <th>conspiracy</th>\n",
       "      <th>europe</th>\n",
       "      <th>funny</th>\n",
       "      <th>GOT</th>\n",
       "      <th>hockey</th>\n",
       "      <th>movies</th>\n",
       "      <th>LOL</th>\n",
       "      <th>nba</th>\n",
       "      <th>soccer</th>\n",
       "      <th>nfl</th>\n",
       "      <th>trees</th>\n",
       "      <th>news</th>\n",
       "      <th>wow</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>AskReddit</td>\n",
       "      <td>194</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>20</td>\n",
       "      <td>11</td>\n",
       "      <td>36</td>\n",
       "      <td>29</td>\n",
       "      <td>21</td>\n",
       "      <td>163</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>42</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>13</td>\n",
       "      <td>67</td>\n",
       "      <td>32</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>GlobalOffensive</td>\n",
       "      <td>17</td>\n",
       "      <td>462</td>\n",
       "      <td>6</td>\n",
       "      <td>24</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>64</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
       "      <td>17</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Overwatch</td>\n",
       "      <td>41</td>\n",
       "      <td>4</td>\n",
       "      <td>429</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>86</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>31</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Music</td>\n",
       "      <td>14</td>\n",
       "      <td>31</td>\n",
       "      <td>6</td>\n",
       "      <td>453</td>\n",
       "      <td>21</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>52</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>25</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>anime</td>\n",
       "      <td>33</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>448</td>\n",
       "      <td>8</td>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>73</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>32</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>28</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>baseball</td>\n",
       "      <td>11</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>479</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>51</td>\n",
       "      <td>4</td>\n",
       "      <td>28</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>19</td>\n",
       "      <td>30</td>\n",
       "      <td>12</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>canada</td>\n",
       "      <td>37</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>314</td>\n",
       "      <td>42</td>\n",
       "      <td>43</td>\n",
       "      <td>87</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>41</td>\n",
       "      <td>71</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>conspiracy</td>\n",
       "      <td>38</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>42</td>\n",
       "      <td>319</td>\n",
       "      <td>37</td>\n",
       "      <td>92</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>29</td>\n",
       "      <td>71</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>europe</td>\n",
       "      <td>28</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>39</td>\n",
       "      <td>38</td>\n",
       "      <td>366</td>\n",
       "      <td>71</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
       "      <td>90</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>funny</td>\n",
       "      <td>98</td>\n",
       "      <td>11</td>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>19</td>\n",
       "      <td>27</td>\n",
       "      <td>30</td>\n",
       "      <td>18</td>\n",
       "      <td>308</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>35</td>\n",
       "      <td>8</td>\n",
       "      <td>11</td>\n",
       "      <td>14</td>\n",
       "      <td>47</td>\n",
       "      <td>22</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>GOT</td>\n",
       "      <td>24</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>36</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>53</td>\n",
       "      <td>453</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>30</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>hockey</td>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>28</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>443</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>40</td>\n",
       "      <td>22</td>\n",
       "      <td>24</td>\n",
       "      <td>27</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>movies</td>\n",
       "      <td>13</td>\n",
       "      <td>40</td>\n",
       "      <td>4</td>\n",
       "      <td>30</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>54</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>441</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>15</td>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>league of legends</td>\n",
       "      <td>37</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>42</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>72</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>434</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>23</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>nba</td>\n",
       "      <td>17</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>26</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>57</td>\n",
       "      <td>2</td>\n",
       "      <td>29</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>464</td>\n",
       "      <td>27</td>\n",
       "      <td>15</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>soccer</td>\n",
       "      <td>17</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>31</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>51</td>\n",
       "      <td>4</td>\n",
       "      <td>33</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>32</td>\n",
       "      <td>445</td>\n",
       "      <td>14</td>\n",
       "      <td>21</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>nfl</td>\n",
       "      <td>18</td>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>24</td>\n",
       "      <td>8</td>\n",
       "      <td>11</td>\n",
       "      <td>20</td>\n",
       "      <td>66</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>16</td>\n",
       "      <td>13</td>\n",
       "      <td>434</td>\n",
       "      <td>18</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>trees</td>\n",
       "      <td>46</td>\n",
       "      <td>3</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
       "      <td>19</td>\n",
       "      <td>8</td>\n",
       "      <td>103</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>18</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>404</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>news</td>\n",
       "      <td>40</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>69</td>\n",
       "      <td>100</td>\n",
       "      <td>72</td>\n",
       "      <td>92</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>7</td>\n",
       "      <td>24</td>\n",
       "      <td>234</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>wow</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>37</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>15</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>529</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Pred: Reddit  CS:GO  Overwatch  Music  anime  baseball  \\\n",
       "AskReddit                   194     13         13      5     20        11   \n",
       "GlobalOffensive              17    462          6     24     10         7   \n",
       "Overwatch                    41      4        429      2     15        12   \n",
       "Music                        14     31          6    453     21         8   \n",
       "anime                        33      6         10      2    448         8   \n",
       "baseball                     11      7          3      6      3       479   \n",
       "canada                       37      7          2      7      9         4   \n",
       "conspiracy                   38      6          5      2      7         7   \n",
       "europe                       28      3          2      5      4         6   \n",
       "funny                        98     11         13      3     12        19   \n",
       "GOT                          24      7          4      3     36         7   \n",
       "hockey                       13      3          3      3      8        28   \n",
       "movies                       13     40          4     30      6         8   \n",
       "league of legends            37      6          7      6     42        12   \n",
       "nba                          17      8          4      4      7        26   \n",
       "soccer                       17      5          6      3      5        31   \n",
       "nfl                          18     20          4      3      5        24   \n",
       "trees                        46      3         25      0     15         4   \n",
       "news                         40      4          2      4      9         8   \n",
       "wow                          14     15          3     16      8         8   \n",
       "\n",
       "                   canada  conspiracy  europe  funny  GOT  hockey  movies  \\\n",
       "AskReddit              36          29      21    163    6       8       5   \n",
       "GlobalOffensive         4          10       7     64    3       9      20   \n",
       "Overwatch               6          10       4     86    2       5       3   \n",
       "Music                   2          14       2     52    6       5      25   \n",
       "anime                  14          10       5     73    5       1       7   \n",
       "baseball                6          11       2     51    4      28       4   \n",
       "canada                314          42      43     87    2       6       1   \n",
       "conspiracy             42         319      37     92    3       5       3   \n",
       "europe                 39          38     366     71    4       4       2   \n",
       "funny                  27          30      18    308    6       4       6   \n",
       "GOT                     3           8       3     53  453       4       4   \n",
       "hockey                  9           5       2     47    1     443       1   \n",
       "movies                  5           4       4     54    3       8     441   \n",
       "league of legends       4          10       4     72    7       3       3   \n",
       "nba                     5           5       1     57    2      29       5   \n",
       "soccer                  7           7       2     51    4      33       5   \n",
       "nfl                     8          11      20     66    1      21       3   \n",
       "trees                  15          19       8    103    1       2       4   \n",
       "news                   69         100      72     92    2       2       7   \n",
       "wow                     1           1       2     37    7       6      15   \n",
       "\n",
       "                   LOL  nba  soccer  nfl  trees  news  wow  \n",
       "AskReddit           42    7       7   13     67    32    8  \n",
       "GlobalOffensive      3    6       4   15     17     4    8  \n",
       "Overwatch           22    7       4    4     31    10    3  \n",
       "Music                9    9       4    7     13     5   14  \n",
       "anime               32    7       0    4     28     2    5  \n",
       "baseball             6   19      30   12     15     3    0  \n",
       "canada               5    7       3   10     41    71    2  \n",
       "conspiracy          15    4       2    8     29    71    5  \n",
       "europe               7    5       1    8     16    90    1  \n",
       "funny               35    8      11   14     47    22    8  \n",
       "GOT                 39    1       7    5     30     4    5  \n",
       "hockey              10   40      22   24     27     6    5  \n",
       "movies              11    8       9   15     16     4   17  \n",
       "league of legends  434   11       5    5     23     7    2  \n",
       "nba                  5  464      27   15     12     3    4  \n",
       "soccer               5   32     445   14     21     6    1  \n",
       "nfl                  5   16      13  434     18    10    0  \n",
       "trees               18    4      11    2    404    11    5  \n",
       "news                 4    4      12    7     24   234    4  \n",
       "wow                 10    5       1    2     19     1  529  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 precision    recall  f1-score   support\n",
      "\n",
      "      AskReddit       0.26      0.28      0.27       700\n",
      "GlobalOffensive       0.70      0.66      0.68       700\n",
      "          Music       0.78      0.61      0.69       700\n",
      "      Overwatch       0.78      0.65      0.71       700\n",
      "          anime       0.65      0.64      0.64       700\n",
      "       baseball       0.67      0.68      0.68       700\n",
      "         canada       0.51      0.45      0.48       700\n",
      "     conspiracy       0.47      0.46      0.46       700\n",
      "         europe       0.59      0.52      0.55       700\n",
      "          funny       0.18      0.44      0.26       700\n",
      "  gameofthrones       0.87      0.65      0.74       700\n",
      "         hockey       0.71      0.63      0.67       700\n",
      "leagueoflegends       0.78      0.63      0.70       700\n",
      "         movies       0.61      0.62      0.61       700\n",
      "            nba       0.70      0.66      0.68       700\n",
      "            nfl       0.72      0.64      0.68       700\n",
      "         soccer       0.70      0.62      0.66       700\n",
      "          trees       0.45      0.58      0.51       700\n",
      "      worldnews       0.39      0.33      0.36       700\n",
      "            wow       0.85      0.76      0.80       700\n",
      "\n",
      "       accuracy                           0.58     14000\n",
      "      macro avg       0.62      0.58      0.59     14000\n",
      "   weighted avg       0.62      0.58      0.59     14000\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.5752142857142857"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.naive_bayes import BernoulliNB\n",
    "#Bernoulli Naive Bayes\n",
    "param_grid = {'alpha': [0.01, 0.1, 0.2, 0.5, 1]}\n",
    "grid = GridSearchCV(BernoulliNB(fit_prior = False, binarize = 0.12), param_grid, cv=5,verbose=1,n_jobs=-1)\n",
    "start = time.time()\n",
    "grid.fit(X_training, y_training)\n",
    "print(\"Best cross-validation score: {:.2f}\".format(grid.best_score_))\n",
    "print(\"Best parameters: \", grid.best_params_)\n",
    "print(\"Best estimator: \", grid.best_estimator_)\n",
    "bnb = grid.best_estimator_\n",
    "ypred_val_rc = bnb.predict(X_testing)\n",
    "y_pred_test_rc=bnb.predict(X_tfidf_sel2)\n",
    "end =  time.time()\n",
    "print('Time to train and predict in Bernoulli Naive Bayes Model: {}'.format(end-start))\n",
    "confusion_matrix(ypred_val_rc, y_testing)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Multinomial Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best cross-validation score: 0.59\n",
      "Best parameters:  {'alpha': 0.07, 'fit_prior': True}\n",
      "Best estimator:  MultinomialNB(alpha=0.07, class_prior=None, fit_prior=True)\n",
      "Time to train and predict in Multinominal Naive Bayes Model: 3.761303186416626\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pred: Reddit</th>\n",
       "      <th>CS:GO</th>\n",
       "      <th>Overwatch</th>\n",
       "      <th>Music</th>\n",
       "      <th>anime</th>\n",
       "      <th>baseball</th>\n",
       "      <th>canada</th>\n",
       "      <th>conspiracy</th>\n",
       "      <th>europe</th>\n",
       "      <th>funny</th>\n",
       "      <th>GOT</th>\n",
       "      <th>hockey</th>\n",
       "      <th>movies</th>\n",
       "      <th>LOL</th>\n",
       "      <th>nba</th>\n",
       "      <th>soccer</th>\n",
       "      <th>nfl</th>\n",
       "      <th>trees</th>\n",
       "      <th>news</th>\n",
       "      <th>wow</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>AskReddit</td>\n",
       "      <td>245</td>\n",
       "      <td>9</td>\n",
       "      <td>28</td>\n",
       "      <td>12</td>\n",
       "      <td>23</td>\n",
       "      <td>9</td>\n",
       "      <td>44</td>\n",
       "      <td>32</td>\n",
       "      <td>21</td>\n",
       "      <td>89</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>44</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>9</td>\n",
       "      <td>50</td>\n",
       "      <td>38</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>GlobalOffensive</td>\n",
       "      <td>17</td>\n",
       "      <td>465</td>\n",
       "      <td>8</td>\n",
       "      <td>25</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>19</td>\n",
       "      <td>8</td>\n",
       "      <td>25</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>26</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>17</td>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Overwatch</td>\n",
       "      <td>50</td>\n",
       "      <td>8</td>\n",
       "      <td>436</td>\n",
       "      <td>5</td>\n",
       "      <td>17</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>13</td>\n",
       "      <td>7</td>\n",
       "      <td>40</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>30</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>34</td>\n",
       "      <td>8</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Music</td>\n",
       "      <td>13</td>\n",
       "      <td>33</td>\n",
       "      <td>9</td>\n",
       "      <td>497</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>22</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "      <td>19</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>anime</td>\n",
       "      <td>32</td>\n",
       "      <td>5</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>485</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>26</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>38</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>20</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>baseball</td>\n",
       "      <td>21</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>446</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>31</td>\n",
       "      <td>2</td>\n",
       "      <td>27</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>24</td>\n",
       "      <td>35</td>\n",
       "      <td>11</td>\n",
       "      <td>14</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>canada</td>\n",
       "      <td>52</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>358</td>\n",
       "      <td>38</td>\n",
       "      <td>59</td>\n",
       "      <td>34</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>24</td>\n",
       "      <td>72</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>conspiracy</td>\n",
       "      <td>40</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>57</td>\n",
       "      <td>340</td>\n",
       "      <td>28</td>\n",
       "      <td>42</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>18</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>92</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>europe</td>\n",
       "      <td>30</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>46</td>\n",
       "      <td>39</td>\n",
       "      <td>391</td>\n",
       "      <td>32</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>82</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>funny</td>\n",
       "      <td>114</td>\n",
       "      <td>20</td>\n",
       "      <td>23</td>\n",
       "      <td>14</td>\n",
       "      <td>21</td>\n",
       "      <td>15</td>\n",
       "      <td>28</td>\n",
       "      <td>30</td>\n",
       "      <td>26</td>\n",
       "      <td>205</td>\n",
       "      <td>18</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>35</td>\n",
       "      <td>6</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>57</td>\n",
       "      <td>28</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>GOT</td>\n",
       "      <td>15</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>23</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>14</td>\n",
       "      <td>5</td>\n",
       "      <td>25</td>\n",
       "      <td>507</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>35</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>18</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>hockey</td>\n",
       "      <td>12</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>20</td>\n",
       "      <td>16</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>14</td>\n",
       "      <td>8</td>\n",
       "      <td>455</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>31</td>\n",
       "      <td>35</td>\n",
       "      <td>17</td>\n",
       "      <td>19</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>movies</td>\n",
       "      <td>22</td>\n",
       "      <td>43</td>\n",
       "      <td>13</td>\n",
       "      <td>50</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>446</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>league of legends</td>\n",
       "      <td>38</td>\n",
       "      <td>6</td>\n",
       "      <td>14</td>\n",
       "      <td>7</td>\n",
       "      <td>26</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>18</td>\n",
       "      <td>3</td>\n",
       "      <td>44</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>455</td>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>15</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>nba</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>16</td>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>26</td>\n",
       "      <td>2</td>\n",
       "      <td>17</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>470</td>\n",
       "      <td>41</td>\n",
       "      <td>18</td>\n",
       "      <td>15</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>soccer</td>\n",
       "      <td>31</td>\n",
       "      <td>7</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>21</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>18</td>\n",
       "      <td>6</td>\n",
       "      <td>20</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>42</td>\n",
       "      <td>464</td>\n",
       "      <td>9</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>nfl</td>\n",
       "      <td>22</td>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>19</td>\n",
       "      <td>14</td>\n",
       "      <td>11</td>\n",
       "      <td>17</td>\n",
       "      <td>27</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>21</td>\n",
       "      <td>452</td>\n",
       "      <td>15</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>trees</td>\n",
       "      <td>60</td>\n",
       "      <td>4</td>\n",
       "      <td>28</td>\n",
       "      <td>4</td>\n",
       "      <td>13</td>\n",
       "      <td>9</td>\n",
       "      <td>20</td>\n",
       "      <td>18</td>\n",
       "      <td>9</td>\n",
       "      <td>64</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>407</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>news</td>\n",
       "      <td>49</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>79</td>\n",
       "      <td>118</td>\n",
       "      <td>88</td>\n",
       "      <td>35</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>16</td>\n",
       "      <td>248</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>wow</td>\n",
       "      <td>17</td>\n",
       "      <td>14</td>\n",
       "      <td>9</td>\n",
       "      <td>30</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>27</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>530</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Pred: Reddit  CS:GO  Overwatch  Music  anime  baseball  \\\n",
       "AskReddit                   245      9         28     12     23         9   \n",
       "GlobalOffensive              17    465          8     25     12        10   \n",
       "Overwatch                    50      8        436      5     17         5   \n",
       "Music                        13     33          9    497      9         5   \n",
       "anime                        32      5         13      5    485         7   \n",
       "baseball                     21      6         11      7      9       446   \n",
       "canada                       52      5          7      6      5         2   \n",
       "conspiracy                   40      5          7      4      2         4   \n",
       "europe                       30      6          6      5      7         2   \n",
       "funny                       114     20         23     14     21        15   \n",
       "GOT                          15      6         10      6     23         2   \n",
       "hockey                       12      9          5     10      9        20   \n",
       "movies                       22     43         13     50      5         3   \n",
       "league of legends            38      6         14      7     26         9   \n",
       "nba                          17      3          4     12     10        16   \n",
       "soccer                       31      7         12      5      3        21   \n",
       "nfl                          22     13          6     10      5        19   \n",
       "trees                        60      4         28      4     13         9   \n",
       "news                         49      6          8      5      7         1   \n",
       "wow                          17     14          9     30      8         2   \n",
       "\n",
       "                   canada  conspiracy  europe  funny  GOT  hockey  movies  \\\n",
       "AskReddit              44          32      21     89   12       5       6   \n",
       "GlobalOffensive         8          19       8     25    3       3      26   \n",
       "Overwatch               7          13       7     40    4       6       3   \n",
       "Music                   6           9       2     22    5       2      16   \n",
       "anime                   7          10       4     26   13       1       3   \n",
       "baseball               10          12       5     31    2      27       2   \n",
       "canada                358          38      59     34    5       6       3   \n",
       "conspiracy             57         340      28     42    8       3       7   \n",
       "europe                 46          39     391     32    8       1       4   \n",
       "funny                  28          30      26    205   18       9       9   \n",
       "GOT                     4          14       5     25  507       3       5   \n",
       "hockey                 16          12       4     14    8     455       2   \n",
       "movies                  5           9       3     22   10       3     446   \n",
       "league of legends       7          18       3     44   15       4       3   \n",
       "nba                     8          12       4     26    2      17       4   \n",
       "soccer                 10          13       2     18    6      20       7   \n",
       "nfl                    14          11      17     27    2      15       7   \n",
       "trees                  20          18       9     64    2       1       5   \n",
       "news                   79         118      88     35    5       3       3   \n",
       "wow                     4           5       3     27    6       3      12   \n",
       "\n",
       "                   LOL  nba  soccer  nfl  trees  news  wow  \n",
       "AskReddit           44    5      12    9     50    38    7  \n",
       "GlobalOffensive      7    9       9   17     13     6   10  \n",
       "Overwatch           30    4       8    4     34     8   11  \n",
       "Music               19    7       6    6      9     9   16  \n",
       "anime               38    7       4    6     20     7    7  \n",
       "baseball            11   24      35   11     14    11    5  \n",
       "canada               7    2       6    5     24    72    4  \n",
       "conspiracy          18    3       4    1     30    92    5  \n",
       "europe               5    1       7   10     12    82    6  \n",
       "funny               35    6      16   16     57    28   10  \n",
       "GOT                 35    4       3    3     18     8    4  \n",
       "hockey              11   31      35   17     19     9    2  \n",
       "movies               6   11       8    8      9     5   19  \n",
       "league of legends  455    7      11    2     10    15    6  \n",
       "nba                 11  470      41   18     15     8    2  \n",
       "soccer               7   42     464    9     14     3    6  \n",
       "nfl                 11   10      21  452     15    13   10  \n",
       "trees               18    6      11    3    407    13    5  \n",
       "news                 6    8       6    5     16   248    4  \n",
       "wow                 10    2       2    2     12     2  530  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 precision    recall  f1-score   support\n",
      "\n",
      "      AskReddit       0.27      0.35      0.31       700\n",
      "GlobalOffensive       0.69      0.66      0.68       700\n",
      "          Music       0.66      0.62      0.64       700\n",
      "      Overwatch       0.69      0.71      0.70       700\n",
      "          anime       0.69      0.69      0.69       700\n",
      "       baseball       0.73      0.64      0.68       700\n",
      "         canada       0.49      0.51      0.50       700\n",
      "     conspiracy       0.44      0.49      0.46       700\n",
      "         europe       0.57      0.56      0.56       700\n",
      "          funny       0.24      0.29      0.26       700\n",
      "  gameofthrones       0.79      0.72      0.76       700\n",
      "         hockey       0.78      0.65      0.71       700\n",
      "leagueoflegends       0.78      0.64      0.70       700\n",
      "         movies       0.58      0.65      0.61       700\n",
      "            nba       0.71      0.67      0.69       700\n",
      "            nfl       0.65      0.66      0.66       700\n",
      "         soccer       0.75      0.65      0.69       700\n",
      "          trees       0.51      0.58      0.54       700\n",
      "      worldnews       0.37      0.35      0.36       700\n",
      "            wow       0.79      0.76      0.77       700\n",
      "\n",
      "       accuracy                           0.59     14000\n",
      "      macro avg       0.61      0.59      0.60     14000\n",
      "   weighted avg       0.61      0.59      0.60     14000\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.593"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Multinominal Naive Bayes Classifier:\n",
    "param_grid = {\n",
    "            'fit_prior': [True],\n",
    "            'alpha': [0.05, 0.07, 0.08, 0.1, 0.15]}\n",
    "grid = GridSearchCV(MultinomialNB(), param_grid, cv=5,n_jobs=-1)\n",
    "start = time.time()\n",
    "grid.fit(X_training, y_training)\n",
    "print(\"Best cross-validation score: {:.2f}\".format(grid.best_score_))\n",
    "print(\"Best parameters: \", grid.best_params_)\n",
    "print(\"Best estimator: \", grid.best_estimator_)\n",
    "mnb = grid.best_estimator_\n",
    "ypred_val_mnb   = mnb.predict(X_testing)\n",
    "y_pred_test_mnb = mnb.predict(X_tfidf_sel2)\n",
    "end =  time.time()\n",
    "print('Time to train and predict in Multinominal Naive Bayes Model: {}'.format(end-start))\n",
    "confusion_matrix(ypred_val_mnb, y_testing)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#####  Complement Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 664,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best cross-validation score: 0.59\n",
      "Best parameters:  {'alpha': 0.6, 'fit_prior': True}\n",
      "Best estimator:  ComplementNB(alpha=0.6, class_prior=None, fit_prior=True, norm=False)\n",
      "Time to train and predict in Multinominal Naive Bayes Model: 2.5628182888031006\n"
     ]
    },
    {
     "data": {
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       "<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",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pred: Reddit</th>\n",
       "      <th>CS:GO</th>\n",
       "      <th>Overwatch</th>\n",
       "      <th>Music</th>\n",
       "      <th>anime</th>\n",
       "      <th>baseball</th>\n",
       "      <th>canada</th>\n",
       "      <th>conspiracy</th>\n",
       "      <th>europe</th>\n",
       "      <th>funny</th>\n",
       "      <th>GOT</th>\n",
       "      <th>hockey</th>\n",
       "      <th>movies</th>\n",
       "      <th>LOL</th>\n",
       "      <th>nba</th>\n",
       "      <th>soccer</th>\n",
       "      <th>nfl</th>\n",
       "      <th>trees</th>\n",
       "      <th>news</th>\n",
       "      <th>wow</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>AskReddit</td>\n",
       "      <td>170</td>\n",
       "      <td>14</td>\n",
       "      <td>41</td>\n",
       "      <td>14</td>\n",
       "      <td>32</td>\n",
       "      <td>11</td>\n",
       "      <td>55</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "      <td>63</td>\n",
       "      <td>19</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>42</td>\n",
       "      <td>5</td>\n",
       "      <td>16</td>\n",
       "      <td>14</td>\n",
       "      <td>58</td>\n",
       "      <td>42</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>GlobalOffensive</td>\n",
       "      <td>10</td>\n",
       "      <td>467</td>\n",
       "      <td>18</td>\n",
       "      <td>37</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>15</td>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>18</td>\n",
       "      <td>14</td>\n",
       "      <td>8</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Overwatch</td>\n",
       "      <td>18</td>\n",
       "      <td>8</td>\n",
       "      <td>521</td>\n",
       "      <td>18</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>17</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>19</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Music</td>\n",
       "      <td>11</td>\n",
       "      <td>24</td>\n",
       "      <td>16</td>\n",
       "      <td>502</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>18</td>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "      <td>23</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>anime</td>\n",
       "      <td>19</td>\n",
       "      <td>8</td>\n",
       "      <td>26</td>\n",
       "      <td>19</td>\n",
       "      <td>482</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>16</td>\n",
       "      <td>6</td>\n",
       "      <td>14</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>35</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>baseball</td>\n",
       "      <td>14</td>\n",
       "      <td>6</td>\n",
       "      <td>19</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>486</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>16</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>26</td>\n",
       "      <td>23</td>\n",
       "      <td>13</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>canada</td>\n",
       "      <td>28</td>\n",
       "      <td>11</td>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>378</td>\n",
       "      <td>36</td>\n",
       "      <td>44</td>\n",
       "      <td>20</td>\n",
       "      <td>17</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>9</td>\n",
       "      <td>24</td>\n",
       "      <td>50</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>conspiracy</td>\n",
       "      <td>22</td>\n",
       "      <td>9</td>\n",
       "      <td>28</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>55</td>\n",
       "      <td>325</td>\n",
       "      <td>29</td>\n",
       "      <td>16</td>\n",
       "      <td>23</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>7</td>\n",
       "      <td>32</td>\n",
       "      <td>79</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>europe</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>17</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>49</td>\n",
       "      <td>38</td>\n",
       "      <td>429</td>\n",
       "      <td>13</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>49</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>funny</td>\n",
       "      <td>73</td>\n",
       "      <td>19</td>\n",
       "      <td>47</td>\n",
       "      <td>21</td>\n",
       "      <td>33</td>\n",
       "      <td>16</td>\n",
       "      <td>33</td>\n",
       "      <td>32</td>\n",
       "      <td>23</td>\n",
       "      <td>127</td>\n",
       "      <td>34</td>\n",
       "      <td>17</td>\n",
       "      <td>14</td>\n",
       "      <td>48</td>\n",
       "      <td>25</td>\n",
       "      <td>12</td>\n",
       "      <td>17</td>\n",
       "      <td>55</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>GOT</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>31</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>545</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>24</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>hockey</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>16</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>19</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>451</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "      <td>31</td>\n",
       "      <td>19</td>\n",
       "      <td>26</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>movies</td>\n",
       "      <td>6</td>\n",
       "      <td>34</td>\n",
       "      <td>15</td>\n",
       "      <td>47</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>490</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>league of legends</td>\n",
       "      <td>19</td>\n",
       "      <td>10</td>\n",
       "      <td>26</td>\n",
       "      <td>15</td>\n",
       "      <td>26</td>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "      <td>16</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>29</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>448</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>14</td>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>nba</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "      <td>11</td>\n",
       "      <td>30</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>472</td>\n",
       "      <td>30</td>\n",
       "      <td>20</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>soccer</td>\n",
       "      <td>15</td>\n",
       "      <td>11</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "      <td>16</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>31</td>\n",
       "      <td>479</td>\n",
       "      <td>19</td>\n",
       "      <td>17</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>nfl</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>18</td>\n",
       "      <td>14</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>19</td>\n",
       "      <td>13</td>\n",
       "      <td>25</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>16</td>\n",
       "      <td>10</td>\n",
       "      <td>16</td>\n",
       "      <td>23</td>\n",
       "      <td>15</td>\n",
       "      <td>440</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>trees</td>\n",
       "      <td>29</td>\n",
       "      <td>10</td>\n",
       "      <td>39</td>\n",
       "      <td>6</td>\n",
       "      <td>23</td>\n",
       "      <td>10</td>\n",
       "      <td>19</td>\n",
       "      <td>22</td>\n",
       "      <td>11</td>\n",
       "      <td>32</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>15</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>411</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>news</td>\n",
       "      <td>30</td>\n",
       "      <td>9</td>\n",
       "      <td>13</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>88</td>\n",
       "      <td>113</td>\n",
       "      <td>92</td>\n",
       "      <td>15</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
       "      <td>234</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>wow</td>\n",
       "      <td>3</td>\n",
       "      <td>17</td>\n",
       "      <td>15</td>\n",
       "      <td>17</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>546</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Pred: Reddit  CS:GO  Overwatch  Music  anime  baseball  \\\n",
       "AskReddit                   170     14         41     14     32        11   \n",
       "GlobalOffensive              10    467         18     37     12         8   \n",
       "Overwatch                    18      8        521     18     14         2   \n",
       "Music                        11     24         16    502      9         8   \n",
       "anime                        19      8         26     19    482         5   \n",
       "baseball                     14      6         19     13     10       486   \n",
       "canada                       28     11         20      6      8         3   \n",
       "conspiracy                   22      9         28      6      8         5   \n",
       "europe                       12      6         17      8     10         7   \n",
       "funny                        73     19         47     21     33        16   \n",
       "GOT                           8      4          7      4     31         4   \n",
       "hockey                        6      9         16      9      9        19   \n",
       "movies                        6     34         15     47     12         1   \n",
       "league of legends            19     10         26     15     26         7   \n",
       "nba                           8      5         18      7      8        11   \n",
       "soccer                       15     11         18      5      1        20   \n",
       "nfl                          12     12         18     14      8         7   \n",
       "trees                        29     10         39      6     23        10   \n",
       "news                         30      9         13     11      9         6   \n",
       "wow                           3     17         15     17      8         4   \n",
       "\n",
       "                   canada  conspiracy  europe  funny  GOT  hockey  movies  \\\n",
       "AskReddit              55          40      33     63   19       6       9   \n",
       "GlobalOffensive         5          12      15     13    6       2      20   \n",
       "Overwatch              12           9       6     10    5       6       6   \n",
       "Music                   4          10       6     18   13       6      23   \n",
       "anime                  10          16       6     14    9       1       5   \n",
       "baseball                7           8       5     11    9      16       5   \n",
       "canada                378          36      44     20   17       6       8   \n",
       "conspiracy             55         325      29     16   23       4       9   \n",
       "europe                 49          38     429     13   12       5       4   \n",
       "funny                  33          32      23    127   34      17      14   \n",
       "GOT                     5           7       7      9  545       3       4   \n",
       "hockey                 10          11      10      5    9     451       3   \n",
       "movies                  5           8       9      9    4       2     490   \n",
       "league of legends      11          16      10     11   29       7       8   \n",
       "nba                    10          11       4     16   11      30       7   \n",
       "soccer                  6          16       6     10    9      17       2   \n",
       "nfl                    19          13      25     11   10      16      10   \n",
       "trees                  19          22      11     32   11      11       9   \n",
       "news                   88         113      92     15   19       1       6   \n",
       "wow                     7           7       3      9   17       2      15   \n",
       "\n",
       "                   LOL  nba  soccer  nfl  trees  news  wow  \n",
       "AskReddit           42    5      16   14     58    42   16  \n",
       "GlobalOffensive      9    8       4   18     14     8   14  \n",
       "Overwatch           17    5       5    7     19     6    6  \n",
       "Music                9    2       4    3      6     2   24  \n",
       "anime               35    5       6    4     15     4   11  \n",
       "baseball             9   26      23   13      8     2   10  \n",
       "canada              10    3      12    9     24    50    7  \n",
       "conspiracy          18    5      11    7     32    79    9  \n",
       "europe              11    4       6    7     10    49    3  \n",
       "funny               48   25      12   17     55    27   27  \n",
       "GOT                 24    4       5    6     11     5    7  \n",
       "hockey               9   25      31   19     26    10   13  \n",
       "movies               6    3       7    3      9     8   22  \n",
       "league of legends  448    9       8    6     14     8   12  \n",
       "nba                  9  472      30   20     11     4    8  \n",
       "soccer               4   31     479   19     17     7    7  \n",
       "nfl                 16   23      15  440     11    12    8  \n",
       "trees               15   11       5    7    411    10    9  \n",
       "news                 8    3       9    8     16   234   10  \n",
       "wow                  7    5       3    2     12     1  546  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 precision    recall  f1-score   support\n",
      "\n",
      "      AskReddit       0.33      0.24      0.28       700\n",
      "GlobalOffensive       0.67      0.67      0.67       700\n",
      "          Music       0.56      0.74      0.64       700\n",
      "      Overwatch       0.64      0.72      0.68       700\n",
      "          anime       0.64      0.69      0.66       700\n",
      "       baseball       0.76      0.69      0.73       700\n",
      "         canada       0.48      0.54      0.51       700\n",
      "     conspiracy       0.43      0.46      0.45       700\n",
      "         europe       0.55      0.61      0.58       700\n",
      "          funny       0.29      0.18      0.22       700\n",
      "  gameofthrones       0.67      0.78      0.72       700\n",
      "         hockey       0.74      0.64      0.69       700\n",
      "leagueoflegends       0.75      0.70      0.72       700\n",
      "         movies       0.59      0.64      0.62       700\n",
      "            nba       0.70      0.67      0.69       700\n",
      "            nfl       0.69      0.68      0.69       700\n",
      "         soccer       0.70      0.63      0.66       700\n",
      "          trees       0.53      0.59      0.56       700\n",
      "      worldnews       0.41      0.33      0.37       700\n",
      "            wow       0.71      0.78      0.74       700\n",
      "\n",
      "       accuracy                           0.60     14000\n",
      "      macro avg       0.59      0.60      0.59     14000\n",
      "   weighted avg       0.59      0.60      0.59     14000\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.6002142857142857"
      ]
     },
     "execution_count": 664,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.naive_bayes import ComplementNB\n",
    "#Complement Naive Bayes Classifier:\n",
    "param_grid = {\n",
    "            'fit_prior': [True],\n",
    "            'alpha': [0.4, 0.5, 0.6]}\n",
    "grid = GridSearchCV(ComplementNB(), param_grid, cv=5,n_jobs=-1)\n",
    "start = time.time()\n",
    "grid.fit(X_training, y_training)\n",
    "print(\"Best cross-validation score: {:.2f}\".format(grid.best_score_))\n",
    "print(\"Best parameters: \", grid.best_params_)\n",
    "print(\"Best estimator: \", grid.best_estimator_)\n",
    "cnb = grid.best_estimator_\n",
    "ypred_val_cnb   = cnb.predict(X_testing)\n",
    "y_pred_test_cnb = cnb.predict(X_tfidf_sel2)\n",
    "end =  time.time()\n",
    "print('Time to train and predict in Multinominal Naive Bayes Model: {}'.format(end-start))\n",
    "confusion_matrix(ypred_val_cnb, y_testing)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### OneVsRest Classifier using Bernoulli Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time to train and predict in OneVsRest Multinomial Naive Bayes Model: 2.18916916847229\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pred: Reddit</th>\n",
       "      <th>CS:GO</th>\n",
       "      <th>Overwatch</th>\n",
       "      <th>Music</th>\n",
       "      <th>anime</th>\n",
       "      <th>baseball</th>\n",
       "      <th>canada</th>\n",
       "      <th>conspiracy</th>\n",
       "      <th>europe</th>\n",
       "      <th>funny</th>\n",
       "      <th>GOT</th>\n",
       "      <th>hockey</th>\n",
       "      <th>movies</th>\n",
       "      <th>LOL</th>\n",
       "      <th>nba</th>\n",
       "      <th>soccer</th>\n",
       "      <th>nfl</th>\n",
       "      <th>trees</th>\n",
       "      <th>news</th>\n",
       "      <th>wow</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>AskReddit</td>\n",
       "      <td>199</td>\n",
       "      <td>8</td>\n",
       "      <td>26</td>\n",
       "      <td>5</td>\n",
       "      <td>25</td>\n",
       "      <td>6</td>\n",
       "      <td>40</td>\n",
       "      <td>28</td>\n",
       "      <td>25</td>\n",
       "      <td>158</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>37</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>67</td>\n",
       "      <td>23</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>GlobalOffensive</td>\n",
       "      <td>13</td>\n",
       "      <td>472</td>\n",
       "      <td>7</td>\n",
       "      <td>30</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>31</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Overwatch</td>\n",
       "      <td>29</td>\n",
       "      <td>6</td>\n",
       "      <td>440</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "      <td>69</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>23</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>43</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Music</td>\n",
       "      <td>4</td>\n",
       "      <td>34</td>\n",
       "      <td>2</td>\n",
       "      <td>461</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>60</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>31</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>anime</td>\n",
       "      <td>21</td>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "      <td>7</td>\n",
       "      <td>486</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>56</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>33</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>baseball</td>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>458</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>49</td>\n",
       "      <td>3</td>\n",
       "      <td>29</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>34</td>\n",
       "      <td>26</td>\n",
       "      <td>16</td>\n",
       "      <td>17</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>canada</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>330</td>\n",
       "      <td>31</td>\n",
       "      <td>33</td>\n",
       "      <td>101</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>31</td>\n",
       "      <td>72</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>conspiracy</td>\n",
       "      <td>39</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>37</td>\n",
       "      <td>334</td>\n",
       "      <td>34</td>\n",
       "      <td>79</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "      <td>84</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>europe</td>\n",
       "      <td>22</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>34</td>\n",
       "      <td>37</td>\n",
       "      <td>385</td>\n",
       "      <td>60</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>19</td>\n",
       "      <td>84</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>funny</td>\n",
       "      <td>103</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>27</td>\n",
       "      <td>9</td>\n",
       "      <td>30</td>\n",
       "      <td>24</td>\n",
       "      <td>18</td>\n",
       "      <td>279</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>43</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "      <td>71</td>\n",
       "      <td>20</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>GOT</td>\n",
       "      <td>15</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>21</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>54</td>\n",
       "      <td>493</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>34</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>22</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>hockey</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>21</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>43</td>\n",
       "      <td>5</td>\n",
       "      <td>488</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>21</td>\n",
       "      <td>28</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>movies</td>\n",
       "      <td>11</td>\n",
       "      <td>41</td>\n",
       "      <td>5</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>38</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>487</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>league of legends</td>\n",
       "      <td>28</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>32</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>4</td>\n",
       "      <td>71</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>459</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>nba</td>\n",
       "      <td>13</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>53</td>\n",
       "      <td>3</td>\n",
       "      <td>23</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>485</td>\n",
       "      <td>28</td>\n",
       "      <td>12</td>\n",
       "      <td>19</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>soccer</td>\n",
       "      <td>16</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>56</td>\n",
       "      <td>3</td>\n",
       "      <td>31</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>37</td>\n",
       "      <td>452</td>\n",
       "      <td>17</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>nfl</td>\n",
       "      <td>18</td>\n",
       "      <td>19</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>19</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>20</td>\n",
       "      <td>45</td>\n",
       "      <td>2</td>\n",
       "      <td>22</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "      <td>465</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>trees</td>\n",
       "      <td>42</td>\n",
       "      <td>6</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>16</td>\n",
       "      <td>13</td>\n",
       "      <td>7</td>\n",
       "      <td>91</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>440</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>news</td>\n",
       "      <td>40</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>58</td>\n",
       "      <td>108</td>\n",
       "      <td>103</td>\n",
       "      <td>75</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>17</td>\n",
       "      <td>233</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>wow</td>\n",
       "      <td>9</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>20</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>44</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>526</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Pred: Reddit  CS:GO  Overwatch  Music  anime  baseball  \\\n",
       "AskReddit                   199      8         26      5     25         6   \n",
       "GlobalOffensive              13    472          7     30     10         8   \n",
       "Overwatch                    29      6        440      1     17         7   \n",
       "Music                         4     34          2    461     15         5   \n",
       "anime                        21      7         11      7    486         8   \n",
       "baseball                     20      4          6      6      2       458   \n",
       "canada                       41      1          4      3      1         5   \n",
       "conspiracy                   39      9          5      2      8         4   \n",
       "europe                       22      7          5      4      6         5   \n",
       "funny                       103     13         13      5     27         9   \n",
       "GOT                          15      9          6      4     21         2   \n",
       "hockey                        9      4          3      7      6        21   \n",
       "movies                       11     41          5     28      1         7   \n",
       "league of legends            28      5          9      4     32         9   \n",
       "nba                          13      7          5      4      9        12   \n",
       "soccer                       16      9          4      2      3        22   \n",
       "nfl                          18     19          7      5      7        19   \n",
       "trees                        42      6         14      2     11         5   \n",
       "news                         40      7          5      2      3        10   \n",
       "wow                           9     13          5     20     13         1   \n",
       "\n",
       "                   canada  conspiracy  europe  funny  GOT  hockey  movies  \\\n",
       "AskReddit              40          28      25    158    4       7       4   \n",
       "GlobalOffensive         4          10       9     40    1       8      31   \n",
       "Overwatch               7          15       7     69    2       9       3   \n",
       "Music                   8           8       3     60    3       7      31   \n",
       "anime                   9           7       1     56   11       2       6   \n",
       "baseball                4           5       0     49    3      29       4   \n",
       "canada                330          31      33    101    1      16       3   \n",
       "conspiracy             37         334      34     79    2       5       3   \n",
       "europe                 34          37     385     60    2       4       1   \n",
       "funny                  30          24      18    279    3       5       4   \n",
       "GOT                     3           6       1     54  493       2       4   \n",
       "hockey                 10           4       3     43    5     488       4   \n",
       "movies                  5           5       3     38    4       3     487   \n",
       "league of legends       1          17       4     71   11       4       4   \n",
       "nba                     5           4       6     53    3      23       4   \n",
       "soccer                  8           7       3     56    3      31       4   \n",
       "nfl                     5           6      20     45    2      22       5   \n",
       "trees                  16          13       7     91    2       4       2   \n",
       "news                   58         108     103     75    0       4       5   \n",
       "wow                     1           8       2     44    8       7       7   \n",
       "\n",
       "                   LOL  nba  soccer  nfl  trees  news  wow  \n",
       "AskReddit           37    8       4   12     67    23   14  \n",
       "GlobalOffensive     10    7       5   15     14     2    4  \n",
       "Overwatch           23    6       4    2     43     9    1  \n",
       "Music               11    4       1    9     15     3   16  \n",
       "anime               33    2       1    4     20     3    5  \n",
       "baseball             9   34      26   16     17     4    4  \n",
       "canada               9    4       3    9     31    72    2  \n",
       "conspiracy          16    2       3    4     25    84    5  \n",
       "europe               6    5       4    8     19    84    2  \n",
       "funny               43    7       7   11     71    20    8  \n",
       "GOT                 34    4       6    8     22     3    3  \n",
       "hockey               2   21      28   18     18     2    4  \n",
       "movies               1   13       5   10     10     4   19  \n",
       "league of legends  459    4       4    3     16     9    6  \n",
       "nba                  1  485      28   12     19     5    2  \n",
       "soccer               9   37     452   17     13     2    2  \n",
       "nfl                  6   20      10  465     11     6    2  \n",
       "trees               15    6       8    6    440     7    3  \n",
       "news                 6    5       4   11     17   233    4  \n",
       "wow                  3    3       3    4     22     1  526  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 precision    recall  f1-score   support\n",
      "\n",
      "      AskReddit       0.29      0.28      0.29       700\n",
      "GlobalOffensive       0.69      0.67      0.68       700\n",
      "          Music       0.76      0.63      0.69       700\n",
      "      Overwatch       0.77      0.66      0.71       700\n",
      "          anime       0.69      0.69      0.69       700\n",
      "       baseball       0.74      0.65      0.69       700\n",
      "         canada       0.54      0.47      0.50       700\n",
      "     conspiracy       0.49      0.48      0.49       700\n",
      "         europe       0.58      0.55      0.56       700\n",
      "          funny       0.18      0.40      0.25       700\n",
      "  gameofthrones       0.88      0.70      0.78       700\n",
      "         hockey       0.72      0.70      0.71       700\n",
      "leagueoflegends       0.79      0.70      0.74       700\n",
      "         movies       0.63      0.66      0.64       700\n",
      "            nba       0.72      0.69      0.70       700\n",
      "            nfl       0.75      0.65      0.69       700\n",
      "         soccer       0.72      0.66      0.69       700\n",
      "          trees       0.48      0.63      0.55       700\n",
      "      worldnews       0.40      0.33      0.37       700\n",
      "            wow       0.83      0.75      0.79       700\n",
      "\n",
      "       accuracy                           0.60     14000\n",
      "      macro avg       0.63      0.60      0.61     14000\n",
      "   weighted avg       0.63      0.60      0.61     14000\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.598"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.multiclass import OneVsRestClassifier\n",
    "#OneVsRest Model:\n",
    "ovr_mnb=OneVsRestClassifier(BernoulliNB(alpha=0.1, binarize=0.12, class_prior=None, fit_prior=False),n_jobs=-1)\n",
    "start = time.time()\n",
    "ovr_mnb.fit(X_training, y_training)    \n",
    "ypred_val_ovrmnb = ovr_mnb.predict(X_testing)\n",
    "y_pred_test_ovrmnb = ovr_mnb.predict(X_tfidf_sel2)\n",
    "end =  time.time()\n",
    "print('Time to train and predict in OneVsRest Multinomial Naive Bayes Model: {}'.format(end-start))\n",
    "confusion_matrix(ypred_val_ovrmnb, y_testing)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### OneVsRest Classifier using Multinomial Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time to train and predict in OneVsRest Multinomial Naive Bayes Model: 0.8994519710540771\n"
     ]
    },
    {
     "data": {
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pred: Reddit</th>\n",
       "      <th>CS:GO</th>\n",
       "      <th>Overwatch</th>\n",
       "      <th>Music</th>\n",
       "      <th>anime</th>\n",
       "      <th>baseball</th>\n",
       "      <th>canada</th>\n",
       "      <th>conspiracy</th>\n",
       "      <th>europe</th>\n",
       "      <th>funny</th>\n",
       "      <th>GOT</th>\n",
       "      <th>hockey</th>\n",
       "      <th>movies</th>\n",
       "      <th>LOL</th>\n",
       "      <th>nba</th>\n",
       "      <th>soccer</th>\n",
       "      <th>nfl</th>\n",
       "      <th>trees</th>\n",
       "      <th>news</th>\n",
       "      <th>wow</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>AskReddit</td>\n",
       "      <td>220</td>\n",
       "      <td>8</td>\n",
       "      <td>22</td>\n",
       "      <td>10</td>\n",
       "      <td>23</td>\n",
       "      <td>10</td>\n",
       "      <td>43</td>\n",
       "      <td>20</td>\n",
       "      <td>23</td>\n",
       "      <td>146</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>43</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>67</td>\n",
       "      <td>25</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>GlobalOffensive</td>\n",
       "      <td>12</td>\n",
       "      <td>450</td>\n",
       "      <td>6</td>\n",
       "      <td>25</td>\n",
       "      <td>11</td>\n",
       "      <td>13</td>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>68</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>17</td>\n",
       "      <td>23</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Overwatch</td>\n",
       "      <td>34</td>\n",
       "      <td>6</td>\n",
       "      <td>422</td>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>66</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>41</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>45</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Music</td>\n",
       "      <td>7</td>\n",
       "      <td>29</td>\n",
       "      <td>7</td>\n",
       "      <td>483</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>55</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>19</td>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>anime</td>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>484</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>56</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>40</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>23</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>baseball</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>457</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>55</td>\n",
       "      <td>4</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>28</td>\n",
       "      <td>26</td>\n",
       "      <td>11</td>\n",
       "      <td>21</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>canada</td>\n",
       "      <td>48</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>353</td>\n",
       "      <td>37</td>\n",
       "      <td>48</td>\n",
       "      <td>74</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>26</td>\n",
       "      <td>59</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>conspiracy</td>\n",
       "      <td>37</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>56</td>\n",
       "      <td>331</td>\n",
       "      <td>31</td>\n",
       "      <td>76</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>32</td>\n",
       "      <td>72</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>europe</td>\n",
       "      <td>19</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>44</td>\n",
       "      <td>33</td>\n",
       "      <td>399</td>\n",
       "      <td>60</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>21</td>\n",
       "      <td>65</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>funny</td>\n",
       "      <td>95</td>\n",
       "      <td>15</td>\n",
       "      <td>17</td>\n",
       "      <td>6</td>\n",
       "      <td>25</td>\n",
       "      <td>14</td>\n",
       "      <td>24</td>\n",
       "      <td>28</td>\n",
       "      <td>17</td>\n",
       "      <td>285</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>27</td>\n",
       "      <td>7</td>\n",
       "      <td>15</td>\n",
       "      <td>17</td>\n",
       "      <td>61</td>\n",
       "      <td>19</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>GOT</td>\n",
       "      <td>16</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>27</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>55</td>\n",
       "      <td>481</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>34</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>19</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>hockey</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>22</td>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>32</td>\n",
       "      <td>3</td>\n",
       "      <td>480</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>27</td>\n",
       "      <td>22</td>\n",
       "      <td>19</td>\n",
       "      <td>23</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>movies</td>\n",
       "      <td>21</td>\n",
       "      <td>34</td>\n",
       "      <td>5</td>\n",
       "      <td>42</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>47</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>451</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>league of legends</td>\n",
       "      <td>28</td>\n",
       "      <td>5</td>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "      <td>22</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>71</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>461</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>nba</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>53</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>482</td>\n",
       "      <td>31</td>\n",
       "      <td>16</td>\n",
       "      <td>19</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>soccer</td>\n",
       "      <td>26</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>22</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>32</td>\n",
       "      <td>6</td>\n",
       "      <td>24</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>43</td>\n",
       "      <td>454</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>nfl</td>\n",
       "      <td>16</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>22</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>14</td>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "      <td>467</td>\n",
       "      <td>17</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>trees</td>\n",
       "      <td>51</td>\n",
       "      <td>6</td>\n",
       "      <td>17</td>\n",
       "      <td>4</td>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "      <td>8</td>\n",
       "      <td>94</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>20</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>417</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>news</td>\n",
       "      <td>42</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>73</td>\n",
       "      <td>120</td>\n",
       "      <td>88</td>\n",
       "      <td>66</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>26</td>\n",
       "      <td>223</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>wow</td>\n",
       "      <td>8</td>\n",
       "      <td>18</td>\n",
       "      <td>6</td>\n",
       "      <td>26</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>44</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>524</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Pred: Reddit  CS:GO  Overwatch  Music  anime  baseball  \\\n",
       "AskReddit                   220      8         22     10     23        10   \n",
       "GlobalOffensive              12    450          6     25     11        13   \n",
       "Overwatch                    34      6        422      2     16         7   \n",
       "Music                         7     29          7    483     10         8   \n",
       "anime                        20      6         11      3    484         8   \n",
       "baseball                     15      5          5      4      8       457   \n",
       "canada                       48      5          7      5      3         3   \n",
       "conspiracy                   37      4          7      2      3         5   \n",
       "europe                       19      5          6      4      8         1   \n",
       "funny                        95     15         17      6     25        14   \n",
       "GOT                          16      5          7      4     27         4   \n",
       "hockey                        8      4          3      6      8        22   \n",
       "movies                       21     34          5     42      6         8   \n",
       "league of legends            28      5         13      6     22        11   \n",
       "nba                          12      4          2      5      5        13   \n",
       "soccer                       26      5          6      2      5        22   \n",
       "nfl                          16     13          1      6      5        22   \n",
       "trees                        51      6         17      4     13         6   \n",
       "news                         42      3          4      3      9         4   \n",
       "wow                           8     18          6     26      4         3   \n",
       "\n",
       "                   canada  conspiracy  europe  funny  GOT  hockey  movies  \\\n",
       "AskReddit              43          20      23    146    6       6       4   \n",
       "GlobalOffensive         7          11       5     68    0       3      22   \n",
       "Overwatch               7           6       6     66    2       8       1   \n",
       "Music                   3           6       5     55    4       2      19   \n",
       "anime                   3           5       4     56    9       4       1   \n",
       "baseball                5           8       3     55    4      28       1   \n",
       "canada                353          37      48     74    0       7       2   \n",
       "conspiracy             56         331      31     76    3       7       5   \n",
       "europe                 44          33     399     60    6       2       2   \n",
       "funny                  24          28      17    285    9       7       6   \n",
       "GOT                     5          10       4     55  481       3       2   \n",
       "hockey                 14          10       3     32    3     480       2   \n",
       "movies                  4           8       2     47    9       5     451   \n",
       "league of legends       4          11       0     71   13       5       2   \n",
       "nba                     5          10       5     53    0      20       2   \n",
       "soccer                  7          10       3     32    6      24       6   \n",
       "nfl                     9           8      14     60    1      18       2   \n",
       "trees                  13          16       8     94    1       1       3   \n",
       "news                   73         120      88     66    2       3       3   \n",
       "wow                     3           7       3     44    6       4      10   \n",
       "\n",
       "                   LOL  nba  soccer  nfl  trees  news  wow  \n",
       "AskReddit           43    3       7    9     67    25    5  \n",
       "GlobalOffensive      5    8       5   17     23     3    6  \n",
       "Overwatch           41    5       6    4     45    10    6  \n",
       "Music               15    7       3    6     12     4   15  \n",
       "anime               40    6       2    7     23     2    6  \n",
       "baseball             8   28      26   11     21     6    2  \n",
       "canada               5    2       4    9     26    59    3  \n",
       "conspiracy          17    3       3    2     32    72    4  \n",
       "europe               7    0       6    8     21    65    4  \n",
       "funny               27    7      15   17     61    19    6  \n",
       "GOT                 34    6       4    3     19     5    6  \n",
       "hockey               8   27      22   19     23     4    2  \n",
       "movies               4    9       8    7      8     3   19  \n",
       "league of legends  461    7       6    5     15    11    4  \n",
       "nba                  7  482      31   16     19     5    4  \n",
       "soccer               9   43     454   15     15     3    7  \n",
       "nfl                  2   14      15  467     17     7    3  \n",
       "trees               20    5       7    4    417    10    4  \n",
       "news                 7    8       4   10     26   223    2  \n",
       "wow                  9    2       3    2     17     1  524  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 precision    recall  f1-score   support\n",
      "\n",
      "      AskReddit       0.30      0.31      0.31       700\n",
      "GlobalOffensive       0.71      0.64      0.68       700\n",
      "          Music       0.74      0.60      0.66       700\n",
      "      Overwatch       0.75      0.69      0.72       700\n",
      "          anime       0.70      0.69      0.69       700\n",
      "       baseball       0.71      0.65      0.68       700\n",
      "         canada       0.52      0.50      0.51       700\n",
      "     conspiracy       0.48      0.47      0.47       700\n",
      "         europe       0.59      0.57      0.58       700\n",
      "          funny       0.19      0.41      0.26       700\n",
      "  gameofthrones       0.85      0.69      0.76       700\n",
      "         hockey       0.75      0.69      0.72       700\n",
      "leagueoflegends       0.83      0.64      0.72       700\n",
      "         movies       0.60      0.66      0.63       700\n",
      "            nba       0.72      0.69      0.70       700\n",
      "            nfl       0.72      0.65      0.68       700\n",
      "         soccer       0.73      0.67      0.70       700\n",
      "          trees       0.46      0.60      0.52       700\n",
      "      worldnews       0.42      0.32      0.36       700\n",
      "            wow       0.83      0.75      0.79       700\n",
      "\n",
      "       accuracy                           0.59     14000\n",
      "      macro avg       0.63      0.59      0.61     14000\n",
      "   weighted avg       0.63      0.59      0.61     14000\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.5945714285714285"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.multiclass import OneVsRestClassifier\n",
    "#OneVsRest Model:\n",
    "ovr_mnb=OneVsRestClassifier(MultinomialNB(alpha=0.07),n_jobs=-1)\n",
    "start = time.time()\n",
    "ovr_mnb.fit(X_training, y_training)    \n",
    "ypred_val_ovrmnb = ovr_mnb.predict(X_testing)\n",
    "y_pred_test_ovrmnb = ovr_mnb.predict(X_tfidf_sel2)\n",
    "end =  time.time()\n",
    "print('Time to train and predict in OneVsRest Multinomial Naive Bayes Model: {}'.format(end-start))\n",
    "confusion_matrix(ypred_val_ovrmnb, y_testing)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### OneVsRest Classifier using Complement Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 682,
   "metadata": {
    "scrolled": false
   },
   "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",
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       "    .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>Pred: Reddit</th>\n",
       "      <th>CS:GO</th>\n",
       "      <th>Overwatch</th>\n",
       "      <th>Music</th>\n",
       "      <th>anime</th>\n",
       "      <th>baseball</th>\n",
       "      <th>canada</th>\n",
       "      <th>conspiracy</th>\n",
       "      <th>europe</th>\n",
       "      <th>funny</th>\n",
       "      <th>GOT</th>\n",
       "      <th>hockey</th>\n",
       "      <th>movies</th>\n",
       "      <th>LOL</th>\n",
       "      <th>nba</th>\n",
       "      <th>soccer</th>\n",
       "      <th>nfl</th>\n",
       "      <th>trees</th>\n",
       "      <th>news</th>\n",
       "      <th>wow</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>AskReddit</td>\n",
       "      <td>226</td>\n",
       "      <td>12</td>\n",
       "      <td>37</td>\n",
       "      <td>9</td>\n",
       "      <td>21</td>\n",
       "      <td>7</td>\n",
       "      <td>51</td>\n",
       "      <td>38</td>\n",
       "      <td>29</td>\n",
       "      <td>66</td>\n",
       "      <td>14</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>49</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>63</td>\n",
       "      <td>33</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>GlobalOffensive</td>\n",
       "      <td>15</td>\n",
       "      <td>471</td>\n",
       "      <td>19</td>\n",
       "      <td>35</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "      <td>12</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>30</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>16</td>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Overwatch</td>\n",
       "      <td>38</td>\n",
       "      <td>5</td>\n",
       "      <td>501</td>\n",
       "      <td>11</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>19</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>26</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Music</td>\n",
       "      <td>13</td>\n",
       "      <td>26</td>\n",
       "      <td>14</td>\n",
       "      <td>496</td>\n",
       "      <td>16</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>anime</td>\n",
       "      <td>23</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>16</td>\n",
       "      <td>479</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>22</td>\n",
       "      <td>7</td>\n",
       "      <td>12</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>35</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>22</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>baseball</td>\n",
       "      <td>20</td>\n",
       "      <td>7</td>\n",
       "      <td>18</td>\n",
       "      <td>14</td>\n",
       "      <td>8</td>\n",
       "      <td>468</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>17</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>26</td>\n",
       "      <td>32</td>\n",
       "      <td>15</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>canada</td>\n",
       "      <td>39</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>379</td>\n",
       "      <td>44</td>\n",
       "      <td>45</td>\n",
       "      <td>27</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>22</td>\n",
       "      <td>58</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>conspiracy</td>\n",
       "      <td>33</td>\n",
       "      <td>7</td>\n",
       "      <td>23</td>\n",
       "      <td>6</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>62</td>\n",
       "      <td>352</td>\n",
       "      <td>24</td>\n",
       "      <td>17</td>\n",
       "      <td>13</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>77</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>europe</td>\n",
       "      <td>22</td>\n",
       "      <td>7</td>\n",
       "      <td>15</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>47</td>\n",
       "      <td>41</td>\n",
       "      <td>409</td>\n",
       "      <td>17</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>14</td>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>funny</td>\n",
       "      <td>110</td>\n",
       "      <td>21</td>\n",
       "      <td>29</td>\n",
       "      <td>18</td>\n",
       "      <td>31</td>\n",
       "      <td>10</td>\n",
       "      <td>34</td>\n",
       "      <td>37</td>\n",
       "      <td>16</td>\n",
       "      <td>156</td>\n",
       "      <td>21</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>47</td>\n",
       "      <td>15</td>\n",
       "      <td>10</td>\n",
       "      <td>14</td>\n",
       "      <td>67</td>\n",
       "      <td>33</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>GOT</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>32</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>532</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>32</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>hockey</td>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "      <td>19</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>20</td>\n",
       "      <td>8</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>447</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>35</td>\n",
       "      <td>30</td>\n",
       "      <td>18</td>\n",
       "      <td>25</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>movies</td>\n",
       "      <td>8</td>\n",
       "      <td>33</td>\n",
       "      <td>15</td>\n",
       "      <td>48</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>492</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>league of legends</td>\n",
       "      <td>30</td>\n",
       "      <td>11</td>\n",
       "      <td>21</td>\n",
       "      <td>7</td>\n",
       "      <td>29</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>20</td>\n",
       "      <td>9</td>\n",
       "      <td>14</td>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>462</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>19</td>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>nba</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>14</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>21</td>\n",
       "      <td>10</td>\n",
       "      <td>29</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>476</td>\n",
       "      <td>26</td>\n",
       "      <td>21</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>soccer</td>\n",
       "      <td>17</td>\n",
       "      <td>7</td>\n",
       "      <td>15</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>32</td>\n",
       "      <td>479</td>\n",
       "      <td>14</td>\n",
       "      <td>16</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>nfl</td>\n",
       "      <td>12</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>15</td>\n",
       "      <td>6</td>\n",
       "      <td>14</td>\n",
       "      <td>9</td>\n",
       "      <td>18</td>\n",
       "      <td>19</td>\n",
       "      <td>21</td>\n",
       "      <td>450</td>\n",
       "      <td>11</td>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>trees</td>\n",
       "      <td>46</td>\n",
       "      <td>8</td>\n",
       "      <td>31</td>\n",
       "      <td>6</td>\n",
       "      <td>22</td>\n",
       "      <td>9</td>\n",
       "      <td>29</td>\n",
       "      <td>21</td>\n",
       "      <td>9</td>\n",
       "      <td>40</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>407</td>\n",
       "      <td>12</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>news</td>\n",
       "      <td>38</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>87</td>\n",
       "      <td>119</td>\n",
       "      <td>82</td>\n",
       "      <td>21</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>19</td>\n",
       "      <td>252</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>wow</td>\n",
       "      <td>10</td>\n",
       "      <td>14</td>\n",
       "      <td>9</td>\n",
       "      <td>21</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>540</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Pred: Reddit  CS:GO  Overwatch  Music  anime  baseball  \\\n",
       "AskReddit                   226     12         37      9     21         7   \n",
       "GlobalOffensive              15    471         19     35      7         4   \n",
       "Overwatch                    38      5        501     11     14         2   \n",
       "Music                        13     26         14    496     16         7   \n",
       "anime                        23      3         22     16    479         5   \n",
       "baseball                     20      7         18     14      8       468   \n",
       "canada                       39      9         12      6      6         2   \n",
       "conspiracy                   33      7         23      6     15         3   \n",
       "europe                       22      7         15     10     12         5   \n",
       "funny                       110     21         29     18     31        10   \n",
       "GOT                          11      3          4      5     32         5   \n",
       "hockey                       14     10         19     10      6        20   \n",
       "movies                        8     33         15     48      9         2   \n",
       "league of legends            30     11         21      7     29         3   \n",
       "nba                           9      5         14      8     10         8   \n",
       "soccer                       17      7         15      8      4        16   \n",
       "nfl                          12     14         14     10      9         5   \n",
       "trees                        46      8         31      6     22         9   \n",
       "news                         38      4          9      7      6         5   \n",
       "wow                          10     14          9     21      8         1   \n",
       "\n",
       "                   canada  conspiracy  europe  funny  GOT  hockey  movies  \\\n",
       "AskReddit              51          38      29     66   14       5       9   \n",
       "GlobalOffensive         4          16      12      9    6       2      30   \n",
       "Overwatch               9           7       4     19    5       6       0   \n",
       "Music                   4          11       6     20    6       4      25   \n",
       "anime                  11          22       7     12    7       4       6   \n",
       "baseball                6           8       2     15    3      17       5   \n",
       "canada                379          44      45     27    9       7       5   \n",
       "conspiracy             62         352      24     17   13       4       6   \n",
       "europe                 47          41     409     17   10       5       2   \n",
       "funny                  34          37      16    156   21       6       9   \n",
       "GOT                     5           8       5     11  532       5       4   \n",
       "hockey                  8          11       8      9    4     447       2   \n",
       "movies                  5           9       5     13    3       1     492   \n",
       "league of legends       6          20       9     14   19       4       7   \n",
       "nba                    10          10       4     21   10      29       7   \n",
       "soccer                 16          15       3     13    7      16       1   \n",
       "nfl                    18          18      18     15    6      14       9   \n",
       "trees                  29          21       9     40    6       8       5   \n",
       "news                   87         119      82     21   10       3       5   \n",
       "wow                     8          11       5     15   12       3       8   \n",
       "\n",
       "                   LOL  nba  soccer  nfl  trees  news  wow  \n",
       "AskReddit           49    3      10    9     63    33    9  \n",
       "GlobalOffensive      9    6       5   16     14     4   16  \n",
       "Overwatch           19    9       7    4     26    11    3  \n",
       "Music                9    3       2    3      8     6   21  \n",
       "anime               35    3       5    4     22     4   10  \n",
       "baseball             7   26      32   15     12     5   12  \n",
       "canada              10    0      10    6     22    58    4  \n",
       "conspiracy          20    1       6    1     24    77    6  \n",
       "europe              10    2       4    7     14    60    1  \n",
       "funny               47   15      10   14     67    33   16  \n",
       "GOT                 32    4       5    6     13     5    5  \n",
       "hockey               8   35      30   18     25     9    7  \n",
       "movies               7    5       4    4     10     5   22  \n",
       "league of legends  462    7       9    4     19    13    6  \n",
       "nba                 10  476      26   21     11     5    6  \n",
       "soccer               6   32     479   14     16    10    5  \n",
       "nfl                 18   19      21  450     11    13    6  \n",
       "trees               12    9       5    6    407    12    9  \n",
       "news                 8    3       7    5     19   252   10  \n",
       "wow                 11    5       3    1     15     0  540  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 precision    recall  f1-score   support\n",
      "\n",
      "      AskReddit       0.31      0.32      0.32       700\n",
      "GlobalOffensive       0.70      0.67      0.68       700\n",
      "          Music       0.60      0.72      0.65       700\n",
      "      Overwatch       0.66      0.71      0.68       700\n",
      "          anime       0.64      0.68      0.66       700\n",
      "       baseball       0.80      0.67      0.73       700\n",
      "         canada       0.47      0.54      0.51       700\n",
      "     conspiracy       0.43      0.50      0.46       700\n",
      "         europe       0.58      0.58      0.58       700\n",
      "          funny       0.29      0.22      0.25       700\n",
      "  gameofthrones       0.76      0.76      0.76       700\n",
      "         hockey       0.76      0.64      0.69       700\n",
      "leagueoflegends       0.77      0.70      0.74       700\n",
      "         movies       0.59      0.66      0.62       700\n",
      "            nba       0.72      0.68      0.70       700\n",
      "            nfl       0.70      0.68      0.69       700\n",
      "         soccer       0.74      0.64      0.69       700\n",
      "          trees       0.50      0.58      0.54       700\n",
      "      worldnews       0.41      0.36      0.38       700\n",
      "            wow       0.76      0.77      0.76       700\n",
      "\n",
      "       accuracy                           0.61     14000\n",
      "      macro avg       0.61      0.61      0.61     14000\n",
      "   weighted avg       0.61      0.61      0.61     14000\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.6052857142857143"
      ]
     },
     "execution_count": 682,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.multiclass import OneVsRestClassifier\n",
    "#OneVsRest Model:\n",
    "ovr_cnb=OneVsRestClassifier(ComplementNB(alpha=0.6),n_jobs=-1)\n",
    "ovr_cnb.fit(X_training, y_training)    \n",
    "ypred_val_overcnb = ovr_cnb.predict(X_testing)\n",
    "y_pred_test_ovrcnb = ovr_cnb.predict(X_tfidf_sel2)\n",
    "end =  time.time()\n",
    "print('Time to train and predict in OneVsRest Complement Naive Bayes Model: {}'.format(end-start))\n",
    "confusion_matrix(ypred_val_ovrcnb, y_testing)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 542,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 8 candidates, totalling 40 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  40 out of  40 | elapsed:  4.0min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best cross-validation score: 0.55\n",
      "Best parameters:  {'C': 10, 'multi_class': 'ovr'}\n",
      "Best estimator:  LogisticRegression(C=10, class_weight=None, dual=False, fit_intercept=True,\n",
      "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
      "                   multi_class='ovr', n_jobs=None, penalty='l2',\n",
      "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
      "                   warm_start=False)\n",
      "Time to train and predict in Logistic Regression Model: 268.332083940506\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pred: Reddit</th>\n",
       "      <th>CS:GO</th>\n",
       "      <th>Overwatch</th>\n",
       "      <th>Music</th>\n",
       "      <th>anime</th>\n",
       "      <th>baseball</th>\n",
       "      <th>canada</th>\n",
       "      <th>conspiracy</th>\n",
       "      <th>europe</th>\n",
       "      <th>funny</th>\n",
       "      <th>GOT</th>\n",
       "      <th>hockey</th>\n",
       "      <th>movies</th>\n",
       "      <th>LOL</th>\n",
       "      <th>nba</th>\n",
       "      <th>soccer</th>\n",
       "      <th>nfl</th>\n",
       "      <th>trees</th>\n",
       "      <th>news</th>\n",
       "      <th>wow</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>AskReddit</td>\n",
       "      <td>219</td>\n",
       "      <td>12</td>\n",
       "      <td>28</td>\n",
       "      <td>8</td>\n",
       "      <td>19</td>\n",
       "      <td>17</td>\n",
       "      <td>32</td>\n",
       "      <td>36</td>\n",
       "      <td>27</td>\n",
       "      <td>95</td>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "      <td>13</td>\n",
       "      <td>39</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>14</td>\n",
       "      <td>46</td>\n",
       "      <td>40</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>GlobalOffensive</td>\n",
       "      <td>16</td>\n",
       "      <td>456</td>\n",
       "      <td>10</td>\n",
       "      <td>22</td>\n",
       "      <td>8</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>16</td>\n",
       "      <td>11</td>\n",
       "      <td>20</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>34</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>15</td>\n",
       "      <td>18</td>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Overwatch</td>\n",
       "      <td>36</td>\n",
       "      <td>7</td>\n",
       "      <td>461</td>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>15</td>\n",
       "      <td>9</td>\n",
       "      <td>29</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>23</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>31</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Music</td>\n",
       "      <td>17</td>\n",
       "      <td>35</td>\n",
       "      <td>7</td>\n",
       "      <td>470</td>\n",
       "      <td>21</td>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>28</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>24</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>15</td>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>anime</td>\n",
       "      <td>34</td>\n",
       "      <td>8</td>\n",
       "      <td>18</td>\n",
       "      <td>11</td>\n",
       "      <td>430</td>\n",
       "      <td>12</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>37</td>\n",
       "      <td>14</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>36</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>22</td>\n",
       "      <td>13</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>baseball</td>\n",
       "      <td>18</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>442</td>\n",
       "      <td>11</td>\n",
       "      <td>14</td>\n",
       "      <td>6</td>\n",
       "      <td>32</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>25</td>\n",
       "      <td>33</td>\n",
       "      <td>18</td>\n",
       "      <td>16</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>canada</td>\n",
       "      <td>52</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>337</td>\n",
       "      <td>39</td>\n",
       "      <td>31</td>\n",
       "      <td>56</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>14</td>\n",
       "      <td>12</td>\n",
       "      <td>20</td>\n",
       "      <td>68</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>conspiracy</td>\n",
       "      <td>32</td>\n",
       "      <td>12</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>49</td>\n",
       "      <td>307</td>\n",
       "      <td>39</td>\n",
       "      <td>53</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>14</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>13</td>\n",
       "      <td>20</td>\n",
       "      <td>93</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>europe</td>\n",
       "      <td>36</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>49</td>\n",
       "      <td>46</td>\n",
       "      <td>351</td>\n",
       "      <td>33</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>92</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>funny</td>\n",
       "      <td>104</td>\n",
       "      <td>11</td>\n",
       "      <td>27</td>\n",
       "      <td>13</td>\n",
       "      <td>19</td>\n",
       "      <td>8</td>\n",
       "      <td>22</td>\n",
       "      <td>35</td>\n",
       "      <td>37</td>\n",
       "      <td>213</td>\n",
       "      <td>19</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>27</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>19</td>\n",
       "      <td>59</td>\n",
       "      <td>36</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>GOT</td>\n",
       "      <td>23</td>\n",
       "      <td>12</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>21</td>\n",
       "      <td>503</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>28</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>16</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>hockey</td>\n",
       "      <td>15</td>\n",
       "      <td>13</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>28</td>\n",
       "      <td>17</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>16</td>\n",
       "      <td>14</td>\n",
       "      <td>409</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>39</td>\n",
       "      <td>29</td>\n",
       "      <td>32</td>\n",
       "      <td>17</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>movies</td>\n",
       "      <td>16</td>\n",
       "      <td>36</td>\n",
       "      <td>5</td>\n",
       "      <td>35</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>28</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>469</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>league of legends</td>\n",
       "      <td>31</td>\n",
       "      <td>4</td>\n",
       "      <td>22</td>\n",
       "      <td>7</td>\n",
       "      <td>36</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>27</td>\n",
       "      <td>8</td>\n",
       "      <td>41</td>\n",
       "      <td>21</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>406</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>13</td>\n",
       "      <td>24</td>\n",
       "      <td>12</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>nba</td>\n",
       "      <td>15</td>\n",
       "      <td>14</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>21</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>36</td>\n",
       "      <td>6</td>\n",
       "      <td>18</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>461</td>\n",
       "      <td>32</td>\n",
       "      <td>18</td>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>soccer</td>\n",
       "      <td>30</td>\n",
       "      <td>14</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>26</td>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>34</td>\n",
       "      <td>9</td>\n",
       "      <td>28</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>30</td>\n",
       "      <td>423</td>\n",
       "      <td>21</td>\n",
       "      <td>14</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>nfl</td>\n",
       "      <td>19</td>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>9</td>\n",
       "      <td>14</td>\n",
       "      <td>13</td>\n",
       "      <td>38</td>\n",
       "      <td>4</td>\n",
       "      <td>29</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>30</td>\n",
       "      <td>14</td>\n",
       "      <td>433</td>\n",
       "      <td>10</td>\n",
       "      <td>18</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>trees</td>\n",
       "      <td>39</td>\n",
       "      <td>6</td>\n",
       "      <td>26</td>\n",
       "      <td>4</td>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "      <td>17</td>\n",
       "      <td>27</td>\n",
       "      <td>11</td>\n",
       "      <td>63</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>408</td>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>news</td>\n",
       "      <td>47</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "      <td>55</td>\n",
       "      <td>113</td>\n",
       "      <td>91</td>\n",
       "      <td>42</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>29</td>\n",
       "      <td>228</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>wow</td>\n",
       "      <td>22</td>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "      <td>17</td>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>23</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>17</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>517</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Pred: Reddit  CS:GO  Overwatch  Music  anime  baseball  \\\n",
       "AskReddit                   219     12         28      8     19        17   \n",
       "GlobalOffensive              16    456         10     22      8        11   \n",
       "Overwatch                    36      7        461      5     18         6   \n",
       "Music                        17     35          7    470     21         6   \n",
       "anime                        34      8         18     11    430        12   \n",
       "baseball                     18     10          7      4      7       442   \n",
       "canada                       52      5          6      7     10         5   \n",
       "conspiracy                   32     12          9      6     10         8   \n",
       "europe                       36     10          6      1      7         2   \n",
       "funny                       104     11         27     13     19         8   \n",
       "GOT                          23     12          9      4     20         3   \n",
       "hockey                       15     13         11      8      5        28   \n",
       "movies                       16     36          5     35     11         6   \n",
       "league of legends            31      4         22      7     36        10   \n",
       "nba                          15     14          9      6      9        21   \n",
       "soccer                       30     14          8      5      3        26   \n",
       "nfl                          19     13          6      6     10        12   \n",
       "trees                        39      6         26      4     14        10   \n",
       "news                         47     11         12      5     15         7   \n",
       "wow                          22     14          4     17     13         3   \n",
       "\n",
       "                   canada  conspiracy  europe  funny  GOT  hockey  movies  \\\n",
       "AskReddit              32          36      27     95   14       4      13   \n",
       "GlobalOffensive         5          16      11     20    5      11      34   \n",
       "Overwatch               8          15       9     29    8       5       3   \n",
       "Music                  12           4       4     28    3       3      24   \n",
       "anime                   7           8      10     37   14       5       6   \n",
       "baseball               11          14       6     32    3      22      10   \n",
       "canada                337          39      31     56    4       7       2   \n",
       "conspiracy             49         307      39     53    3       5       5   \n",
       "europe                 49          46     351     33   10       6       4   \n",
       "funny                  22          35      37    213   19       8       7   \n",
       "GOT                     9           6       6     21  503       6       3   \n",
       "hockey                 17           8       5     16   14     409      10   \n",
       "movies                  5          11       6     28    4       7     469   \n",
       "league of legends       5          27       8     41   21       6       4   \n",
       "nba                     4           7       3     36    6      18       6   \n",
       "soccer                  6          12       5     34    9      28       8   \n",
       "nfl                     9          14      13     38    4      29      10   \n",
       "trees                  17          27      11     63    7       6       8   \n",
       "news                   55         113      91     42    4       0       8   \n",
       "wow                     7          10      11     23    2       5      17   \n",
       "\n",
       "                   LOL  nba  soccer  nfl  trees  news  wow  \n",
       "AskReddit           39   10      10   14     46    40   17  \n",
       "GlobalOffensive      5   11       6   15     18     9   11  \n",
       "Overwatch           23    5       6    7     31    10    8  \n",
       "Music               11    6       6    8     15     8   12  \n",
       "anime               36    9       7    5     22    13    8  \n",
       "baseball             5   25      33   18     16     9    8  \n",
       "canada              11    6      14   12     20    68    8  \n",
       "conspiracy          14    6       7   13     20    93    9  \n",
       "europe               8    6       6   11     11    92    5  \n",
       "funny               27   12      12   19     59    36   12  \n",
       "GOT                 28    7       7   10     16     7    0  \n",
       "hockey              12   39      29   32     17     8    4  \n",
       "movies               4    9       6   11      6     5   20  \n",
       "league of legends  406    3      11   13     24    12    9  \n",
       "nba                  7  461      32   18     16     4    8  \n",
       "soccer               6   30     423   21     14    13    5  \n",
       "nfl                  8   30      14  433     10    18    4  \n",
       "trees               15    5       6    4    408    14   10  \n",
       "news                 6    6       7    9     29   228    5  \n",
       "wow                  7    2       6    5     11     4  517  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 precision    recall  f1-score   support\n",
      "\n",
      "      AskReddit       0.27      0.31      0.29       700\n",
      "GlobalOffensive       0.65      0.65      0.65       700\n",
      "          Music       0.67      0.66      0.66       700\n",
      "      Overwatch       0.73      0.67      0.70       700\n",
      "          anime       0.63      0.61      0.62       700\n",
      "       baseball       0.69      0.63      0.66       700\n",
      "         canada       0.51      0.48      0.49       700\n",
      "     conspiracy       0.41      0.44      0.42       700\n",
      "         europe       0.51      0.50      0.51       700\n",
      "          funny       0.23      0.30      0.26       700\n",
      "  gameofthrones       0.77      0.72      0.74       700\n",
      "         hockey       0.69      0.58      0.63       700\n",
      "leagueoflegends       0.72      0.67      0.69       700\n",
      "         movies       0.60      0.58      0.59       700\n",
      "            nba       0.67      0.66      0.66       700\n",
      "            nfl       0.65      0.60      0.63       700\n",
      "         soccer       0.64      0.62      0.63       700\n",
      "          trees       0.50      0.58      0.54       700\n",
      "      worldnews       0.33      0.33      0.33       700\n",
      "            wow       0.76      0.74      0.75       700\n",
      "\n",
      "       accuracy                           0.57     14000\n",
      "      macro avg       0.58      0.57      0.57     14000\n",
      "   weighted avg       0.58      0.57      0.57     14000\n",
      "\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pred: Reddit</th>\n",
       "      <th>CS:GO</th>\n",
       "      <th>Overwatch</th>\n",
       "      <th>Music</th>\n",
       "      <th>anime</th>\n",
       "      <th>baseball</th>\n",
       "      <th>canada</th>\n",
       "      <th>conspiracy</th>\n",
       "      <th>europe</th>\n",
       "      <th>funny</th>\n",
       "      <th>GOT</th>\n",
       "      <th>hockey</th>\n",
       "      <th>movies</th>\n",
       "      <th>LOL</th>\n",
       "      <th>nba</th>\n",
       "      <th>soccer</th>\n",
       "      <th>nfl</th>\n",
       "      <th>trees</th>\n",
       "      <th>news</th>\n",
       "      <th>wow</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>AskReddit</td>\n",
       "      <td>34</td>\n",
       "      <td>34</td>\n",
       "      <td>29</td>\n",
       "      <td>35</td>\n",
       "      <td>40</td>\n",
       "      <td>23</td>\n",
       "      <td>43</td>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "      <td>48</td>\n",
       "      <td>30</td>\n",
       "      <td>40</td>\n",
       "      <td>32</td>\n",
       "      <td>43</td>\n",
       "      <td>44</td>\n",
       "      <td>22</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>29</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>GlobalOffensive</td>\n",
       "      <td>34</td>\n",
       "      <td>23</td>\n",
       "      <td>38</td>\n",
       "      <td>36</td>\n",
       "      <td>31</td>\n",
       "      <td>38</td>\n",
       "      <td>44</td>\n",
       "      <td>36</td>\n",
       "      <td>37</td>\n",
       "      <td>38</td>\n",
       "      <td>35</td>\n",
       "      <td>37</td>\n",
       "      <td>32</td>\n",
       "      <td>48</td>\n",
       "      <td>25</td>\n",
       "      <td>33</td>\n",
       "      <td>35</td>\n",
       "      <td>31</td>\n",
       "      <td>38</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Overwatch</td>\n",
       "      <td>42</td>\n",
       "      <td>27</td>\n",
       "      <td>42</td>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "      <td>25</td>\n",
       "      <td>42</td>\n",
       "      <td>41</td>\n",
       "      <td>38</td>\n",
       "      <td>36</td>\n",
       "      <td>34</td>\n",
       "      <td>36</td>\n",
       "      <td>27</td>\n",
       "      <td>37</td>\n",
       "      <td>31</td>\n",
       "      <td>24</td>\n",
       "      <td>39</td>\n",
       "      <td>42</td>\n",
       "      <td>34</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Music</td>\n",
       "      <td>32</td>\n",
       "      <td>38</td>\n",
       "      <td>38</td>\n",
       "      <td>38</td>\n",
       "      <td>36</td>\n",
       "      <td>38</td>\n",
       "      <td>38</td>\n",
       "      <td>54</td>\n",
       "      <td>27</td>\n",
       "      <td>39</td>\n",
       "      <td>28</td>\n",
       "      <td>25</td>\n",
       "      <td>32</td>\n",
       "      <td>36</td>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "      <td>28</td>\n",
       "      <td>44</td>\n",
       "      <td>30</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>anime</td>\n",
       "      <td>43</td>\n",
       "      <td>37</td>\n",
       "      <td>29</td>\n",
       "      <td>32</td>\n",
       "      <td>31</td>\n",
       "      <td>34</td>\n",
       "      <td>43</td>\n",
       "      <td>42</td>\n",
       "      <td>41</td>\n",
       "      <td>46</td>\n",
       "      <td>32</td>\n",
       "      <td>28</td>\n",
       "      <td>34</td>\n",
       "      <td>40</td>\n",
       "      <td>34</td>\n",
       "      <td>31</td>\n",
       "      <td>26</td>\n",
       "      <td>34</td>\n",
       "      <td>36</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>baseball</td>\n",
       "      <td>39</td>\n",
       "      <td>33</td>\n",
       "      <td>30</td>\n",
       "      <td>37</td>\n",
       "      <td>34</td>\n",
       "      <td>29</td>\n",
       "      <td>38</td>\n",
       "      <td>44</td>\n",
       "      <td>39</td>\n",
       "      <td>42</td>\n",
       "      <td>24</td>\n",
       "      <td>25</td>\n",
       "      <td>37</td>\n",
       "      <td>41</td>\n",
       "      <td>36</td>\n",
       "      <td>36</td>\n",
       "      <td>30</td>\n",
       "      <td>43</td>\n",
       "      <td>23</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>canada</td>\n",
       "      <td>55</td>\n",
       "      <td>33</td>\n",
       "      <td>27</td>\n",
       "      <td>36</td>\n",
       "      <td>31</td>\n",
       "      <td>32</td>\n",
       "      <td>34</td>\n",
       "      <td>44</td>\n",
       "      <td>36</td>\n",
       "      <td>46</td>\n",
       "      <td>28</td>\n",
       "      <td>27</td>\n",
       "      <td>29</td>\n",
       "      <td>39</td>\n",
       "      <td>35</td>\n",
       "      <td>37</td>\n",
       "      <td>34</td>\n",
       "      <td>39</td>\n",
       "      <td>34</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>conspiracy</td>\n",
       "      <td>36</td>\n",
       "      <td>23</td>\n",
       "      <td>34</td>\n",
       "      <td>32</td>\n",
       "      <td>36</td>\n",
       "      <td>27</td>\n",
       "      <td>37</td>\n",
       "      <td>35</td>\n",
       "      <td>44</td>\n",
       "      <td>39</td>\n",
       "      <td>35</td>\n",
       "      <td>32</td>\n",
       "      <td>37</td>\n",
       "      <td>34</td>\n",
       "      <td>28</td>\n",
       "      <td>40</td>\n",
       "      <td>36</td>\n",
       "      <td>37</td>\n",
       "      <td>39</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>europe</td>\n",
       "      <td>40</td>\n",
       "      <td>30</td>\n",
       "      <td>39</td>\n",
       "      <td>32</td>\n",
       "      <td>41</td>\n",
       "      <td>23</td>\n",
       "      <td>25</td>\n",
       "      <td>44</td>\n",
       "      <td>27</td>\n",
       "      <td>47</td>\n",
       "      <td>30</td>\n",
       "      <td>33</td>\n",
       "      <td>37</td>\n",
       "      <td>39</td>\n",
       "      <td>39</td>\n",
       "      <td>30</td>\n",
       "      <td>22</td>\n",
       "      <td>52</td>\n",
       "      <td>25</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>funny</td>\n",
       "      <td>46</td>\n",
       "      <td>41</td>\n",
       "      <td>36</td>\n",
       "      <td>43</td>\n",
       "      <td>46</td>\n",
       "      <td>36</td>\n",
       "      <td>39</td>\n",
       "      <td>29</td>\n",
       "      <td>28</td>\n",
       "      <td>41</td>\n",
       "      <td>34</td>\n",
       "      <td>30</td>\n",
       "      <td>34</td>\n",
       "      <td>34</td>\n",
       "      <td>30</td>\n",
       "      <td>28</td>\n",
       "      <td>19</td>\n",
       "      <td>37</td>\n",
       "      <td>35</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>GOT</td>\n",
       "      <td>41</td>\n",
       "      <td>39</td>\n",
       "      <td>35</td>\n",
       "      <td>40</td>\n",
       "      <td>27</td>\n",
       "      <td>24</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "      <td>31</td>\n",
       "      <td>43</td>\n",
       "      <td>35</td>\n",
       "      <td>30</td>\n",
       "      <td>17</td>\n",
       "      <td>45</td>\n",
       "      <td>34</td>\n",
       "      <td>39</td>\n",
       "      <td>28</td>\n",
       "      <td>47</td>\n",
       "      <td>37</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>hockey</td>\n",
       "      <td>43</td>\n",
       "      <td>39</td>\n",
       "      <td>37</td>\n",
       "      <td>34</td>\n",
       "      <td>35</td>\n",
       "      <td>33</td>\n",
       "      <td>35</td>\n",
       "      <td>43</td>\n",
       "      <td>29</td>\n",
       "      <td>42</td>\n",
       "      <td>27</td>\n",
       "      <td>44</td>\n",
       "      <td>26</td>\n",
       "      <td>41</td>\n",
       "      <td>33</td>\n",
       "      <td>29</td>\n",
       "      <td>30</td>\n",
       "      <td>33</td>\n",
       "      <td>30</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>movies</td>\n",
       "      <td>38</td>\n",
       "      <td>37</td>\n",
       "      <td>36</td>\n",
       "      <td>42</td>\n",
       "      <td>39</td>\n",
       "      <td>28</td>\n",
       "      <td>47</td>\n",
       "      <td>27</td>\n",
       "      <td>39</td>\n",
       "      <td>40</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>24</td>\n",
       "      <td>41</td>\n",
       "      <td>31</td>\n",
       "      <td>26</td>\n",
       "      <td>41</td>\n",
       "      <td>49</td>\n",
       "      <td>28</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>league of legends</td>\n",
       "      <td>42</td>\n",
       "      <td>38</td>\n",
       "      <td>30</td>\n",
       "      <td>27</td>\n",
       "      <td>37</td>\n",
       "      <td>31</td>\n",
       "      <td>45</td>\n",
       "      <td>53</td>\n",
       "      <td>27</td>\n",
       "      <td>39</td>\n",
       "      <td>34</td>\n",
       "      <td>24</td>\n",
       "      <td>26</td>\n",
       "      <td>34</td>\n",
       "      <td>29</td>\n",
       "      <td>38</td>\n",
       "      <td>28</td>\n",
       "      <td>48</td>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>nba</td>\n",
       "      <td>44</td>\n",
       "      <td>34</td>\n",
       "      <td>23</td>\n",
       "      <td>39</td>\n",
       "      <td>34</td>\n",
       "      <td>22</td>\n",
       "      <td>30</td>\n",
       "      <td>46</td>\n",
       "      <td>42</td>\n",
       "      <td>44</td>\n",
       "      <td>38</td>\n",
       "      <td>22</td>\n",
       "      <td>25</td>\n",
       "      <td>35</td>\n",
       "      <td>38</td>\n",
       "      <td>38</td>\n",
       "      <td>29</td>\n",
       "      <td>48</td>\n",
       "      <td>30</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>soccer</td>\n",
       "      <td>33</td>\n",
       "      <td>30</td>\n",
       "      <td>37</td>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "      <td>30</td>\n",
       "      <td>34</td>\n",
       "      <td>45</td>\n",
       "      <td>43</td>\n",
       "      <td>47</td>\n",
       "      <td>28</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>45</td>\n",
       "      <td>30</td>\n",
       "      <td>32</td>\n",
       "      <td>40</td>\n",
       "      <td>38</td>\n",
       "      <td>40</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>nfl</td>\n",
       "      <td>34</td>\n",
       "      <td>36</td>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "      <td>36</td>\n",
       "      <td>31</td>\n",
       "      <td>33</td>\n",
       "      <td>61</td>\n",
       "      <td>45</td>\n",
       "      <td>34</td>\n",
       "      <td>33</td>\n",
       "      <td>29</td>\n",
       "      <td>34</td>\n",
       "      <td>39</td>\n",
       "      <td>25</td>\n",
       "      <td>40</td>\n",
       "      <td>26</td>\n",
       "      <td>47</td>\n",
       "      <td>32</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>trees</td>\n",
       "      <td>48</td>\n",
       "      <td>27</td>\n",
       "      <td>41</td>\n",
       "      <td>38</td>\n",
       "      <td>38</td>\n",
       "      <td>27</td>\n",
       "      <td>44</td>\n",
       "      <td>33</td>\n",
       "      <td>40</td>\n",
       "      <td>22</td>\n",
       "      <td>35</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "      <td>37</td>\n",
       "      <td>38</td>\n",
       "      <td>31</td>\n",
       "      <td>43</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>news</td>\n",
       "      <td>37</td>\n",
       "      <td>33</td>\n",
       "      <td>38</td>\n",
       "      <td>43</td>\n",
       "      <td>38</td>\n",
       "      <td>25</td>\n",
       "      <td>24</td>\n",
       "      <td>45</td>\n",
       "      <td>28</td>\n",
       "      <td>43</td>\n",
       "      <td>30</td>\n",
       "      <td>33</td>\n",
       "      <td>29</td>\n",
       "      <td>30</td>\n",
       "      <td>29</td>\n",
       "      <td>35</td>\n",
       "      <td>40</td>\n",
       "      <td>53</td>\n",
       "      <td>29</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>wow</td>\n",
       "      <td>39</td>\n",
       "      <td>39</td>\n",
       "      <td>25</td>\n",
       "      <td>31</td>\n",
       "      <td>36</td>\n",
       "      <td>35</td>\n",
       "      <td>37</td>\n",
       "      <td>41</td>\n",
       "      <td>30</td>\n",
       "      <td>38</td>\n",
       "      <td>34</td>\n",
       "      <td>31</td>\n",
       "      <td>41</td>\n",
       "      <td>43</td>\n",
       "      <td>40</td>\n",
       "      <td>29</td>\n",
       "      <td>32</td>\n",
       "      <td>36</td>\n",
       "      <td>34</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Pred: Reddit  CS:GO  Overwatch  Music  anime  baseball  \\\n",
       "AskReddit                    34     34         29     35     40        23   \n",
       "GlobalOffensive              34     23         38     36     31        38   \n",
       "Overwatch                    42     27         42     32     32        25   \n",
       "Music                        32     38         38     38     36        38   \n",
       "anime                        43     37         29     32     31        34   \n",
       "baseball                     39     33         30     37     34        29   \n",
       "canada                       55     33         27     36     31        32   \n",
       "conspiracy                   36     23         34     32     36        27   \n",
       "europe                       40     30         39     32     41        23   \n",
       "funny                        46     41         36     43     46        36   \n",
       "GOT                          41     39         35     40     27        24   \n",
       "hockey                       43     39         37     34     35        33   \n",
       "movies                       38     37         36     42     39        28   \n",
       "league of legends            42     38         30     27     37        31   \n",
       "nba                          44     34         23     39     34        22   \n",
       "soccer                       33     30         37     32     32        30   \n",
       "nfl                          34     36         28     28     36        31   \n",
       "trees                        48     27         41     38     38        27   \n",
       "news                         37     33         38     43     38        25   \n",
       "wow                          39     39         25     31     36        35   \n",
       "\n",
       "                   canada  conspiracy  europe  funny  GOT  hockey  movies  \\\n",
       "AskReddit              43          45      45     48   30      40      32   \n",
       "GlobalOffensive        44          36      37     38   35      37      32   \n",
       "Overwatch              42          41      38     36   34      36      27   \n",
       "Music                  38          54      27     39   28      25      32   \n",
       "anime                  43          42      41     46   32      28      34   \n",
       "baseball               38          44      39     42   24      25      37   \n",
       "canada                 34          44      36     46   28      27      29   \n",
       "conspiracy             37          35      44     39   35      32      37   \n",
       "europe                 25          44      27     47   30      33      37   \n",
       "funny                  39          29      28     41   34      30      34   \n",
       "GOT                    40          33      31     43   35      30      17   \n",
       "hockey                 35          43      29     42   27      44      26   \n",
       "movies                 47          27      39     40   23      30      24   \n",
       "league of legends      45          53      27     39   34      24      26   \n",
       "nba                    30          46      42     44   38      22      25   \n",
       "soccer                 34          45      43     47   28      29      29   \n",
       "nfl                    33          61      45     34   33      29      34   \n",
       "trees                  44          33      40     22   35      23      30   \n",
       "news                   24          45      28     43   30      33      29   \n",
       "wow                    37          41      30     38   34      31      41   \n",
       "\n",
       "                   LOL  nba  soccer  nfl  trees  news  wow  \n",
       "AskReddit           43   44      22   23     30    29   31  \n",
       "GlobalOffensive     48   25      33   35     31    38   31  \n",
       "Overwatch           37   31      24   39     42    34   39  \n",
       "Music               36   32      32   28     44    30   35  \n",
       "anime               40   34      31   26     34    36   27  \n",
       "baseball            41   36      36   30     43    23   40  \n",
       "canada              39   35      37   34     39    34   24  \n",
       "conspiracy          34   28      40   36     37    39   39  \n",
       "europe              39   39      30   22     52    25   45  \n",
       "funny               34   30      28   19     37    35   34  \n",
       "GOT                 45   34      39   28     47    37   35  \n",
       "hockey              41   33      29   30     33    30   37  \n",
       "movies              41   31      26   41     49    28   34  \n",
       "league of legends   34   29      38   28     48    35   35  \n",
       "nba                 35   38      38   29     48    30   39  \n",
       "soccer              45   30      32   40     38    40   26  \n",
       "nfl                 39   25      40   26     47    32   29  \n",
       "trees               40   33      37   38     31    43   32  \n",
       "news                30   29      35   40     53    29   38  \n",
       "wow                 43   40      29   32     36    34   29  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 precision    recall  f1-score   support\n",
      "\n",
      "      AskReddit       0.04      0.05      0.05       700\n",
      "GlobalOffensive       0.03      0.03      0.03       700\n",
      "          Music       0.06      0.06      0.06       700\n",
      "      Overwatch       0.05      0.05      0.05       700\n",
      "          anime       0.04      0.04      0.04       700\n",
      "       baseball       0.05      0.04      0.04       700\n",
      "         canada       0.05      0.05      0.05       700\n",
      "     conspiracy       0.04      0.05      0.05       700\n",
      "         europe       0.04      0.04      0.04       700\n",
      "          funny       0.05      0.06      0.05       700\n",
      "  gameofthrones       0.06      0.05      0.05       700\n",
      "         hockey       0.07      0.06      0.07       700\n",
      "leagueoflegends       0.04      0.03      0.04       700\n",
      "         movies       0.04      0.05      0.05       700\n",
      "            nba       0.06      0.05      0.06       700\n",
      "            nfl       0.05      0.05      0.05       700\n",
      "         soccer       0.04      0.04      0.04       700\n",
      "          trees       0.04      0.04      0.04       700\n",
      "      worldnews       0.04      0.04      0.04       700\n",
      "            wow       0.04      0.04      0.04       700\n",
      "\n",
      "       accuracy                           0.05     14000\n",
      "      macro avg       0.05      0.05      0.05     14000\n",
      "   weighted avg       0.05      0.05      0.05     14000\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.046857142857142854"
      ]
     },
     "execution_count": 542,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Logistic Regression\n",
    "param_grid = {'C': [1, 10, 100, 1000 ],\n",
    "                 'multi_class':['multinomial','ovr']}\n",
    "grid = GridSearchCV(LogisticRegression(solver='lbfgs'), param_grid, cv=5,verbose=1,n_jobs=-1)\n",
    "start = time.time()\n",
    "grid.fit(X_training, y_training)\n",
    "print(\"Best cross-validation score: {:.2f}\".format(grid.best_score_))\n",
    "print(\"Best parameters: \", grid.best_params_)\n",
    "print(\"Best estimator: \", grid.best_estimator_)\n",
    "lr = grid.best_estimator_\n",
    "ypred_val_lr = lr.predict(X_testing)\n",
    "y_pred_test_lr=lr.predict(X_tfidf_sel2)\n",
    "end =  time.time()\n",
    "print('Time to train and predict in Logistic Regression Model: {}'.format(end-start))\n",
    "confusion_matrix(ypred_val_lr, y_testing)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Ensemble Methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 669,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time to train and predict in Ensemble model: 17.231451988220215\n"
     ]
    },
    {
     "data": {
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       "<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",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pred: Reddit</th>\n",
       "      <th>CS:GO</th>\n",
       "      <th>Overwatch</th>\n",
       "      <th>Music</th>\n",
       "      <th>anime</th>\n",
       "      <th>baseball</th>\n",
       "      <th>canada</th>\n",
       "      <th>conspiracy</th>\n",
       "      <th>europe</th>\n",
       "      <th>funny</th>\n",
       "      <th>GOT</th>\n",
       "      <th>hockey</th>\n",
       "      <th>movies</th>\n",
       "      <th>LOL</th>\n",
       "      <th>nba</th>\n",
       "      <th>soccer</th>\n",
       "      <th>nfl</th>\n",
       "      <th>trees</th>\n",
       "      <th>news</th>\n",
       "      <th>wow</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>AskReddit</td>\n",
       "      <td>227</td>\n",
       "      <td>11</td>\n",
       "      <td>34</td>\n",
       "      <td>10</td>\n",
       "      <td>25</td>\n",
       "      <td>11</td>\n",
       "      <td>48</td>\n",
       "      <td>38</td>\n",
       "      <td>28</td>\n",
       "      <td>73</td>\n",
       "      <td>16</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>39</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>54</td>\n",
       "      <td>38</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>GlobalOffensive</td>\n",
       "      <td>17</td>\n",
       "      <td>487</td>\n",
       "      <td>14</td>\n",
       "      <td>34</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>17</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>17</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Overwatch</td>\n",
       "      <td>34</td>\n",
       "      <td>4</td>\n",
       "      <td>514</td>\n",
       "      <td>14</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>18</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>19</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>19</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Music</td>\n",
       "      <td>16</td>\n",
       "      <td>28</td>\n",
       "      <td>11</td>\n",
       "      <td>490</td>\n",
       "      <td>13</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>25</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>28</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>anime</td>\n",
       "      <td>29</td>\n",
       "      <td>11</td>\n",
       "      <td>19</td>\n",
       "      <td>16</td>\n",
       "      <td>486</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>21</td>\n",
       "      <td>6</td>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>30</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>baseball</td>\n",
       "      <td>19</td>\n",
       "      <td>7</td>\n",
       "      <td>14</td>\n",
       "      <td>12</td>\n",
       "      <td>9</td>\n",
       "      <td>496</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>23</td>\n",
       "      <td>21</td>\n",
       "      <td>14</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>canada</td>\n",
       "      <td>41</td>\n",
       "      <td>11</td>\n",
       "      <td>14</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>383</td>\n",
       "      <td>34</td>\n",
       "      <td>41</td>\n",
       "      <td>29</td>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>21</td>\n",
       "      <td>54</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>conspiracy</td>\n",
       "      <td>36</td>\n",
       "      <td>13</td>\n",
       "      <td>20</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>57</td>\n",
       "      <td>349</td>\n",
       "      <td>24</td>\n",
       "      <td>18</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>26</td>\n",
       "      <td>73</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>europe</td>\n",
       "      <td>25</td>\n",
       "      <td>6</td>\n",
       "      <td>14</td>\n",
       "      <td>8</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>47</td>\n",
       "      <td>41</td>\n",
       "      <td>424</td>\n",
       "      <td>17</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "      <td>51</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>funny</td>\n",
       "      <td>107</td>\n",
       "      <td>27</td>\n",
       "      <td>39</td>\n",
       "      <td>17</td>\n",
       "      <td>34</td>\n",
       "      <td>11</td>\n",
       "      <td>26</td>\n",
       "      <td>32</td>\n",
       "      <td>18</td>\n",
       "      <td>174</td>\n",
       "      <td>21</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>42</td>\n",
       "      <td>18</td>\n",
       "      <td>8</td>\n",
       "      <td>14</td>\n",
       "      <td>53</td>\n",
       "      <td>25</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>GOT</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>29</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>536</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>27</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>hockey</td>\n",
       "      <td>13</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>24</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>448</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>30</td>\n",
       "      <td>28</td>\n",
       "      <td>16</td>\n",
       "      <td>22</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>movies</td>\n",
       "      <td>8</td>\n",
       "      <td>42</td>\n",
       "      <td>15</td>\n",
       "      <td>45</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>497</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>league of legends</td>\n",
       "      <td>32</td>\n",
       "      <td>10</td>\n",
       "      <td>28</td>\n",
       "      <td>9</td>\n",
       "      <td>36</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>19</td>\n",
       "      <td>10</td>\n",
       "      <td>21</td>\n",
       "      <td>19</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>445</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>13</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>nba</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>18</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>24</td>\n",
       "      <td>8</td>\n",
       "      <td>30</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>470</td>\n",
       "      <td>24</td>\n",
       "      <td>23</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>soccer</td>\n",
       "      <td>27</td>\n",
       "      <td>12</td>\n",
       "      <td>15</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>23</td>\n",
       "      <td>8</td>\n",
       "      <td>13</td>\n",
       "      <td>4</td>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>30</td>\n",
       "      <td>477</td>\n",
       "      <td>16</td>\n",
       "      <td>13</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>nfl</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>18</td>\n",
       "      <td>11</td>\n",
       "      <td>7</td>\n",
       "      <td>12</td>\n",
       "      <td>19</td>\n",
       "      <td>16</td>\n",
       "      <td>22</td>\n",
       "      <td>16</td>\n",
       "      <td>8</td>\n",
       "      <td>14</td>\n",
       "      <td>7</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "      <td>12</td>\n",
       "      <td>445</td>\n",
       "      <td>11</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>trees</td>\n",
       "      <td>47</td>\n",
       "      <td>10</td>\n",
       "      <td>31</td>\n",
       "      <td>7</td>\n",
       "      <td>18</td>\n",
       "      <td>9</td>\n",
       "      <td>21</td>\n",
       "      <td>26</td>\n",
       "      <td>12</td>\n",
       "      <td>48</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>403</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>news</td>\n",
       "      <td>36</td>\n",
       "      <td>8</td>\n",
       "      <td>13</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>86</td>\n",
       "      <td>115</td>\n",
       "      <td>85</td>\n",
       "      <td>26</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
       "      <td>240</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>wow</td>\n",
       "      <td>12</td>\n",
       "      <td>18</td>\n",
       "      <td>11</td>\n",
       "      <td>19</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>541</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Pred: Reddit  CS:GO  Overwatch  Music  anime  baseball  \\\n",
       "AskReddit                   227     11         34     10     25        11   \n",
       "GlobalOffensive              17    487         14     34      8         8   \n",
       "Overwatch                    34      4        514     14     13         2   \n",
       "Music                        16     28         11    490     13         7   \n",
       "anime                        29     11         19     16    486         8   \n",
       "baseball                     19      7         14     12      9       496   \n",
       "canada                       41     11         14      7      8         3   \n",
       "conspiracy                   36     13         20      7     10         7   \n",
       "europe                       25      6         14      8     13         5   \n",
       "funny                       107     27         39     17     34        11   \n",
       "GOT                          16      3          4      3     29         6   \n",
       "hockey                       13     12         13      8      8        24   \n",
       "movies                        8     42         15     45     11         2   \n",
       "league of legends            32     10         28      9     36         3   \n",
       "nba                          12      6         18      6      9        11   \n",
       "soccer                       27     12         15      6      3        23   \n",
       "nfl                          17     17         18     11      7        12   \n",
       "trees                        47     10         31      7     18         9   \n",
       "news                         36      8         13      9      9         6   \n",
       "wow                          12     18         11     19      7         2   \n",
       "\n",
       "                   canada  conspiracy  europe  funny  GOT  hockey  movies  \\\n",
       "AskReddit              48          38      28     73   16       7       8   \n",
       "GlobalOffensive         6          14      14     11    6       2      17   \n",
       "Overwatch              10           8       3     18    4       6       2   \n",
       "Music                   3           9       7     25    9       4      28   \n",
       "anime                  10          21       6     20    4       2       4   \n",
       "baseball                8           6       3     16    4      13       5   \n",
       "canada                383          34      41     29   13       6       6   \n",
       "conspiracy             57         349      24     18   12       5       7   \n",
       "europe                 47          41     424     17    4       5       2   \n",
       "funny                  26          32      18    174   21       9       9   \n",
       "GOT                     4           9       6     11  536       5       4   \n",
       "hockey                 11          10       9     11    5     448       3   \n",
       "movies                  3           8       8     17    3       1     497   \n",
       "league of legends      11          19      10     21   19       5       8   \n",
       "nba                    11          12       2     24    8      30       6   \n",
       "soccer                  8          13       4     13    6      17       1   \n",
       "nfl                    19          16      22     16    8      14       7   \n",
       "trees                  21          26      12     48    2       9       6   \n",
       "news                   86         115      85     26   12       3       4   \n",
       "wow                     7           9       2     14   11       2      11   \n",
       "\n",
       "                   LOL  nba  soccer  nfl  trees  news  wow  \n",
       "AskReddit           39    3      12   10     54    38    8  \n",
       "GlobalOffensive      7    5       4   17     15     4   10  \n",
       "Overwatch           19    6       5    4     19     9    6  \n",
       "Music                7    3       4    4      6     3   23  \n",
       "anime               30    2       3    5     14     3    7  \n",
       "baseball             7   23      21   14     12     3    8  \n",
       "canada               7    1      10    6     21    54    5  \n",
       "conspiracy          16    4       7    4     26    73    5  \n",
       "europe              11    3       3    8     12    51    1  \n",
       "funny               42   18       8   14     53    25   16  \n",
       "GOT                 27    4       5    5     13     5    5  \n",
       "hockey              10   30      28   16     22     9   10  \n",
       "movies               5    3       3    2      5     4   18  \n",
       "league of legends  445    5       6    5     13     9    6  \n",
       "nba                  8  470      24   23     10     4    6  \n",
       "soccer               2   30     477   16     13     8    6  \n",
       "nfl                 13   16      12  445     11    15    4  \n",
       "trees               12   11       6    8    403     8    6  \n",
       "news                 9    3       6    8     16   240    6  \n",
       "wow                  9    5       3    2     14     1  541  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 precision    recall  f1-score   support\n",
      "\n",
      "      AskReddit       0.29      0.32      0.31       700\n",
      "GlobalOffensive       0.66      0.70      0.67       700\n",
      "          Music       0.60      0.73      0.66       700\n",
      "      Overwatch       0.66      0.70      0.68       700\n",
      "          anime       0.64      0.69      0.67       700\n",
      "       baseball       0.76      0.71      0.73       700\n",
      "         canada       0.49      0.55      0.52       700\n",
      "     conspiracy       0.44      0.50      0.47       700\n",
      "         europe       0.58      0.61      0.59       700\n",
      "          funny       0.29      0.25      0.27       700\n",
      "  gameofthrones       0.76      0.77      0.76       700\n",
      "         hockey       0.76      0.64      0.69       700\n",
      "leagueoflegends       0.78      0.71      0.74       700\n",
      "         movies       0.61      0.64      0.62       700\n",
      "            nba       0.73      0.67      0.70       700\n",
      "            nfl       0.74      0.68      0.71       700\n",
      "         soccer       0.72      0.64      0.68       700\n",
      "          trees       0.54      0.58      0.56       700\n",
      "      worldnews       0.42      0.34      0.38       700\n",
      "            wow       0.78      0.77      0.77       700\n",
      "\n",
      "       accuracy                           0.61     14000\n",
      "      macro avg       0.61      0.61      0.61     14000\n",
      "   weighted avg       0.61      0.61      0.61     14000\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.6094285714285714"
      ]
     },
     "execution_count": 669,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import VotingClassifier\n",
    "estimators=[('Complement NB', cnb), ('Multinomial Naive Bayes', mnb),('Ridge',rc)]\n",
    "ensemble = VotingClassifier(estimators, voting='hard')\n",
    "start = time.time()\n",
    "#Fitting the model on training data\n",
    "ensemble.fit(X_training, y_training)\n",
    "ypred_val_en   = ensemble.predict(X_testing)\n",
    "y_pred_test_en = ensemble.predict(X_tfidf_sel2)\n",
    "end =  time.time()\n",
    "#test our model on the test data            \n",
    "ensemble.score(X_testing, y_testing)\n",
    "print('Time to train and predict in Ensemble model: {}'.format(end-start))\n",
    "confusion_matrix(ypred_val_en, y_testing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 723,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time to train and predict in Ensemble model: 8.203211069107056\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pred: Reddit</th>\n",
       "      <th>CS:GO</th>\n",
       "      <th>Overwatch</th>\n",
       "      <th>Music</th>\n",
       "      <th>anime</th>\n",
       "      <th>baseball</th>\n",
       "      <th>canada</th>\n",
       "      <th>conspiracy</th>\n",
       "      <th>europe</th>\n",
       "      <th>funny</th>\n",
       "      <th>GOT</th>\n",
       "      <th>hockey</th>\n",
       "      <th>movies</th>\n",
       "      <th>LOL</th>\n",
       "      <th>nba</th>\n",
       "      <th>soccer</th>\n",
       "      <th>nfl</th>\n",
       "      <th>trees</th>\n",
       "      <th>news</th>\n",
       "      <th>wow</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>AskReddit</td>\n",
       "      <td>241</td>\n",
       "      <td>13</td>\n",
       "      <td>33</td>\n",
       "      <td>7</td>\n",
       "      <td>19</td>\n",
       "      <td>8</td>\n",
       "      <td>46</td>\n",
       "      <td>37</td>\n",
       "      <td>31</td>\n",
       "      <td>73</td>\n",
       "      <td>14</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>59</td>\n",
       "      <td>37</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>GlobalOffensive</td>\n",
       "      <td>15</td>\n",
       "      <td>484</td>\n",
       "      <td>14</td>\n",
       "      <td>33</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>17</td>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>27</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>15</td>\n",
       "      <td>13</td>\n",
       "      <td>4</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Overwatch</td>\n",
       "      <td>40</td>\n",
       "      <td>5</td>\n",
       "      <td>503</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>21</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Music</td>\n",
       "      <td>14</td>\n",
       "      <td>28</td>\n",
       "      <td>10</td>\n",
       "      <td>491</td>\n",
       "      <td>15</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>25</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>28</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>anime</td>\n",
       "      <td>26</td>\n",
       "      <td>6</td>\n",
       "      <td>21</td>\n",
       "      <td>14</td>\n",
       "      <td>480</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>20</td>\n",
       "      <td>7</td>\n",
       "      <td>17</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>34</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>21</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>baseball</td>\n",
       "      <td>21</td>\n",
       "      <td>6</td>\n",
       "      <td>16</td>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "      <td>479</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>19</td>\n",
       "      <td>3</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>23</td>\n",
       "      <td>24</td>\n",
       "      <td>16</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>canada</td>\n",
       "      <td>45</td>\n",
       "      <td>8</td>\n",
       "      <td>11</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>380</td>\n",
       "      <td>43</td>\n",
       "      <td>45</td>\n",
       "      <td>28</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>18</td>\n",
       "      <td>56</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>conspiracy</td>\n",
       "      <td>40</td>\n",
       "      <td>8</td>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>58</td>\n",
       "      <td>356</td>\n",
       "      <td>25</td>\n",
       "      <td>20</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>75</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>europe</td>\n",
       "      <td>26</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>46</td>\n",
       "      <td>40</td>\n",
       "      <td>414</td>\n",
       "      <td>18</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>14</td>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>funny</td>\n",
       "      <td>110</td>\n",
       "      <td>24</td>\n",
       "      <td>30</td>\n",
       "      <td>18</td>\n",
       "      <td>29</td>\n",
       "      <td>10</td>\n",
       "      <td>31</td>\n",
       "      <td>35</td>\n",
       "      <td>16</td>\n",
       "      <td>179</td>\n",
       "      <td>17</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>43</td>\n",
       "      <td>16</td>\n",
       "      <td>9</td>\n",
       "      <td>14</td>\n",
       "      <td>59</td>\n",
       "      <td>31</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>GOT</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>29</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>533</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>32</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>14</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>hockey</td>\n",
       "      <td>16</td>\n",
       "      <td>12</td>\n",
       "      <td>14</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>23</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>451</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>33</td>\n",
       "      <td>28</td>\n",
       "      <td>16</td>\n",
       "      <td>24</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>movies</td>\n",
       "      <td>9</td>\n",
       "      <td>37</td>\n",
       "      <td>12</td>\n",
       "      <td>47</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>495</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>league of legends</td>\n",
       "      <td>30</td>\n",
       "      <td>10</td>\n",
       "      <td>22</td>\n",
       "      <td>8</td>\n",
       "      <td>33</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>22</td>\n",
       "      <td>10</td>\n",
       "      <td>20</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>456</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>15</td>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>nba</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>13</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>25</td>\n",
       "      <td>9</td>\n",
       "      <td>28</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>472</td>\n",
       "      <td>25</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>soccer</td>\n",
       "      <td>24</td>\n",
       "      <td>10</td>\n",
       "      <td>14</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>17</td>\n",
       "      <td>12</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>14</td>\n",
       "      <td>6</td>\n",
       "      <td>19</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>31</td>\n",
       "      <td>481</td>\n",
       "      <td>14</td>\n",
       "      <td>16</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>nfl</td>\n",
       "      <td>15</td>\n",
       "      <td>14</td>\n",
       "      <td>16</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>17</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>19</td>\n",
       "      <td>7</td>\n",
       "      <td>14</td>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
       "      <td>18</td>\n",
       "      <td>19</td>\n",
       "      <td>451</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>trees</td>\n",
       "      <td>50</td>\n",
       "      <td>8</td>\n",
       "      <td>28</td>\n",
       "      <td>5</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "      <td>27</td>\n",
       "      <td>24</td>\n",
       "      <td>9</td>\n",
       "      <td>44</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>406</td>\n",
       "      <td>12</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>news</td>\n",
       "      <td>40</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>85</td>\n",
       "      <td>122</td>\n",
       "      <td>79</td>\n",
       "      <td>23</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>19</td>\n",
       "      <td>252</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>wow</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "      <td>9</td>\n",
       "      <td>20</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>539</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Pred: Reddit  CS:GO  Overwatch  Music  anime  baseball  \\\n",
       "AskReddit                   241     13         33      7     19         8   \n",
       "GlobalOffensive              15    484         14     33      6         6   \n",
       "Overwatch                    40      5        503     12     12         2   \n",
       "Music                        14     28         10    491     15         6   \n",
       "anime                        26      6         21     14    480         7   \n",
       "baseball                     21      6         16     13      6       479   \n",
       "canada                       45      8         11      7      7         2   \n",
       "conspiracy                   40      8         20      6     12         5   \n",
       "europe                       26      7         10      9     10         6   \n",
       "funny                       110     24         30     18     29        10   \n",
       "GOT                          15      3          4      4     29         5   \n",
       "hockey                       16     12         14      8      7        23   \n",
       "movies                        9     37         12     47      9         3   \n",
       "league of legends            30     10         22      8     33         3   \n",
       "nba                          11      6         13      7     10        10   \n",
       "soccer                       24     10         14      5      4        17   \n",
       "nfl                          15     14         16      8      7         7   \n",
       "trees                        50      8         28      5     20        10   \n",
       "news                         40      6          9      8      6         6   \n",
       "wow                          13     16          9     20      8         1   \n",
       "\n",
       "                   canada  conspiracy  europe  funny  GOT  hockey  movies  \\\n",
       "AskReddit              46          37      31     73   14       6       6   \n",
       "GlobalOffensive         3          17      14     10    6       2      27   \n",
       "Overwatch               9           8       4     21    5       5       1   \n",
       "Music                   2          10       8     25    7       4      28   \n",
       "anime                   9          20       7     17    5       4       5   \n",
       "baseball                6           8       2     19    3      18       5   \n",
       "canada                380          43      45     28    9       6       5   \n",
       "conspiracy             58         356      25     20    9       5       6   \n",
       "europe                 46          40     414     18    7       5       2   \n",
       "funny                  31          35      16    179   17       6      10   \n",
       "GOT                     5           9       5     11  533       5       4   \n",
       "hockey                 10          10       7     10    3     451       2   \n",
       "movies                  4           8       5     17    3       1     495   \n",
       "league of legends       8          22      10     20   15       5       7   \n",
       "nba                    12          12       3     25    9      28       7   \n",
       "soccer                 12          11       3     14    6      19       2   \n",
       "nfl                    17          18      18     19    7      14       8   \n",
       "trees                  27          24       9     44    5       8       5   \n",
       "news                   85         122      79     23    7       3       4   \n",
       "wow                     7          11       3     16   11       3       8   \n",
       "\n",
       "                   LOL  nba  soccer  nfl  trees  news  wow  \n",
       "AskReddit           41    1      10   10     59    37    8  \n",
       "GlobalOffensive      9    3       6   15     13     4   13  \n",
       "Overwatch           18    7       6    4     25    10    3  \n",
       "Music                8    3       2    4      8     5   22  \n",
       "anime               34    3       4    4     21     5    8  \n",
       "baseball             8   23      24   16     12     4   11  \n",
       "canada               9    0      10    6     18    56    5  \n",
       "conspiracy          18    1       6    1     23    75    6  \n",
       "europe              11    2       3    9     14    60    1  \n",
       "funny               43   16       9   14     59    31   13  \n",
       "GOT                 32    4       4    5     14     5    4  \n",
       "hockey               9   33      28   16     24    11    6  \n",
       "movies               5    4       4    4      8     4   21  \n",
       "league of legends  456    6       8    3     15    13    6  \n",
       "nba                  9  472      25   20     10     4    7  \n",
       "soccer               5   31     481   14     16     8    4  \n",
       "nfl                 16   18      19  451     10    13    5  \n",
       "trees               11    8       6    7    406    12    7  \n",
       "news                 9    4       6    5     19   252    7  \n",
       "wow                 11    4       3    2     14     1  539  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 precision    recall  f1-score   support\n",
      "\n",
      "      AskReddit       0.30      0.34      0.32       700\n",
      "GlobalOffensive       0.68      0.69      0.69       700\n",
      "          Music       0.62      0.72      0.67       700\n",
      "      Overwatch       0.67      0.70      0.69       700\n",
      "          anime       0.66      0.69      0.67       700\n",
      "       baseball       0.78      0.68      0.73       700\n",
      "         canada       0.49      0.54      0.51       700\n",
      "     conspiracy       0.43      0.51      0.47       700\n",
      "         europe       0.58      0.59      0.59       700\n",
      "          funny       0.29      0.26      0.27       700\n",
      "  gameofthrones       0.78      0.76      0.77       700\n",
      "         hockey       0.75      0.64      0.69       700\n",
      "leagueoflegends       0.78      0.71      0.74       700\n",
      "         movies       0.60      0.65      0.62       700\n",
      "            nba       0.73      0.67      0.70       700\n",
      "            nfl       0.72      0.69      0.71       700\n",
      "         soccer       0.74      0.64      0.69       700\n",
      "          trees       0.52      0.58      0.55       700\n",
      "      worldnews       0.41      0.36      0.38       700\n",
      "            wow       0.77      0.77      0.77       700\n",
      "\n",
      "       accuracy                           0.61     14000\n",
      "      macro avg       0.62      0.61      0.61     14000\n",
      "   weighted avg       0.62      0.61      0.61     14000\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.6102142857142857"
      ]
     },
     "execution_count": 723,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import VotingClassifier\n",
    "estimators=[('Complement NB', ovr_cnb), ('Multinomial Naive Bayes', ovr_mnb),('Ridge',rc)]\n",
    "ensemble_ovr = VotingClassifier(estimators, voting='hard')\n",
    "start = time.time()\n",
    "#Fitting the model on traininf data\n",
    "ensemble_ovr.fit(X_training, y_training)\n",
    "ypred_val_en   = ensemble_ovr.predict(X_testing)\n",
    "y_pred_test_en = ensemble_ovr.predict(X_tfidf_sel2)\n",
    "end =  time.time()\n",
    "#test our model on the test data            \n",
    "ensemble_ovr.score(X_testing, y_testing)\n",
    "print('Time to train and predict in Ensemble model: {}'.format(end-start))\n",
    "confusion_matrix(ypred_val_en, y_testing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import VotingClassifier\n",
    "estimators=[('Complement NB', ovr_cnb), ('Multinomial Naive Bayes', ovr_mnb),('Bernoulli Naive Bayes', ovr_bnb), ('Ridge',rc)]\n",
    "ensemble_ovr = VotingClassifier(estimators, voting='hard')\n",
    "start = time.time()\n",
    "#Fitting the model on traininf data\n",
    "ensemble_ovr.fit(X_training, y_training)\n",
    "ypred_val_en   = ensemble_ovr.predict(X_testing)\n",
    "y_pred_test_en = ensemble_ovr.predict(X_tfidf_sel2)\n",
    "end =  time.time()\n",
    "#test our model on the test data            \n",
    "ensemble_ovr.score(X_testing, y_testing)\n",
    "print('Time to train and predict in Ensemble model: {}'.format(end-start))\n",
    "confusion_matrix(ypred_val_en, y_testing)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fitting Ensemble Method on the full training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'ensemble' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-39-05a4e92a9b68>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m#Ensemble fit over the full data\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mensemble\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_tfidf_sel2\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0my_datatrain\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[0my_pred_test_en\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mensemble\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_test_tfidf_sel2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'ensemble' is not defined"
     ]
    }
   ],
   "source": [
    "#Ensemble fit over the full data\n",
    "ensemble.fit(X_tfidf_sel2,y_datatrain)\n",
    "y_pred_test_en = ensemble.predict(X_test_tfidf_sel2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Saving File "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'y_pred_test_svc' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-22-1eae5bd27b6a>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mrawdata\u001b[0m\u001b[1;33m=\u001b[0m \u001b[1;33m{\u001b[0m \u001b[1;34m'Category'\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0my_pred_test_svc\u001b[0m \u001b[1;33m}\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0ma\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrawdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolumns\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m'Category'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0ma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'y_pred.csv'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mheader\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'y_pred_test_svc' is not defined"
     ]
    }
   ],
   "source": [
    "rawdata= { 'Category': y_pred_test_en }\n",
    "a = pd.DataFrame(rawdata, columns = ['Category'])\n",
    "a.to_csv('y_pred.csv',index=True, header=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bernoulli Naive Bayes from Scratch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average accuracy on test set:  0.5617142857142857\n",
      "\n",
      "Accuracy on test set for each class: \n",
      "AskReddit :  0.29\n",
      "GlobalOffensive :  0.6485714285714286\n",
      "Music :  0.52\n",
      "Overwatch :  0.6785714285714286\n",
      "anime :  0.6185714285714285\n",
      "baseball :  0.6228571428571429\n",
      "canada :  0.4642857142857143\n",
      "conspiracy :  0.3942857142857143\n",
      "europe :  0.5071428571428571\n",
      "funny :  0.4685714285714286\n",
      "gameofthrones :  0.6657142857142857\n",
      "hockey :  0.6142857142857143\n",
      "leagueoflegends :  0.5971428571428572\n",
      "movies :  0.6328571428571429\n",
      "nba :  0.6742857142857143\n",
      "nfl :  0.6128571428571429\n",
      "soccer :  0.6314285714285715\n",
      "trees :  0.6\n",
      "worldnews :  0.2957142857142857\n",
      "wow :  0.6971428571428572\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import scipy.sparse\n",
    "\n",
    "class NaiveBayes: \n",
    "    \n",
    "    def __init__(self):\n",
    "        self.theta_k=0.\n",
    "        self.theta_jk=0.\n",
    "        \n",
    "    def fit(self,X,y):\n",
    "        index=[]\n",
    "        self.theta_jk=np.zeros((20,X.shape[1]))\n",
    "        self.theta_k=np.zeros(20)\n",
    "        \n",
    "        self.classes = np.unique(y)\n",
    "        \n",
    "        #calculating theta  for each \n",
    "        for k, class_val in enumerate(self.classes):\n",
    "            #finding indexes where inputs are in class k\n",
    "            index=np.where(y==class_val)\n",
    "            #finding pobability of each class\n",
    "            self.theta_k[k] = (len(index[0]))/float(y.shape[0])\n",
    "            #computing conditional probability of each feature for each class \n",
    "            #Laplace smoothing is used to deal with words which are not observed \n",
    "            #in the training data but are available in the training set\n",
    "            self.theta_jk[k][:] = (scipy.sparse.csr_matrix.sum(X[index],axis=0)+1)/(float(len(index[0]))+2)\n",
    "                  \n",
    "        return\n",
    "    \n",
    "    def predict(self,X):\n",
    "        \n",
    "        prob_y=np.zeros((20,len(X)))\n",
    "        for k, class_val in enumerate(self.classes):\n",
    "            \n",
    "            w0 = np.log(self.theta_k[k])+((np.log(1-self.theta_jk[k][:])).sum())\n",
    "            w = np.log(self.theta_jk[k][:])-np.log(1-self.theta_jk[k][:])\n",
    "            #The probabilitis of being in each of k classes for each of input data(comments)\n",
    "            prob_y[k]=np.matmul(X,w.T) + w0  \n",
    "        #selecting the class with higher probability for each input data (comment)\n",
    "        y_predict=self.classes[np.argmax(prob_y.T, axis=1)]\n",
    "\n",
    "        return y_predict\n",
    "    \n",
    "    def score(self,y,y_pred):\n",
    "        #number of predictions       \n",
    "        num_pred = np.float(len(y))\n",
    "        #calculating average accuracy over all classes\n",
    "        average_acc = np.sum((y==y_pred)*1)/num_pred\n",
    "        #calculating accuracy for each class \n",
    "        acc=np.zeros(20)\n",
    "        \n",
    "        #classes = np.unique(y)\n",
    "        \n",
    "        for k, class_val in enumerate(self.classes): \n",
    "            index=np.where(y==class_val)\n",
    "            num_pred=np.float(len(index[0]))\n",
    "            #acc array contains accuracies for each of 20 classes in the order of\n",
    "            #the below dictionary:\n",
    "            #dictionary={'anime':0, 'Music':1, 'trees':2, 'conspiracy':3, 'canada':4, 'hockey':5, \n",
    "            #'worldnews':6, 'funny':7, 'GlobalOffensive':8, 'AskReddit':9, 'nba':10, \n",
    "            #'nfl':11, 'europe':12, 'soccer':13, 'wow':14 , 'Overwatch':15,\n",
    "            #'gameofthrones':16, 'movies':17, 'leagueoflegends':18,'baseball':19} \n",
    "            acc[k]= (np.sum(y_pred[index]==class_val).astype(np.int))/num_pred\n",
    "        return acc,average_acc\n",
    "    \n",
    "    def get_classes(self):\n",
    "        return self.classes\n",
    "    \n",
    "def test(X_training, y_training, X_testing, y_testing):\n",
    "    \n",
    "    \n",
    "    d = NaiveBayes()\n",
    "    \n",
    "    #X_training = X_train  \n",
    "    y_training = np.array(y_training)\n",
    "    X_testing = X_testing.toarray()\n",
    "    y_testing = np.array(y_testing)\n",
    "    d.fit(X_training, y_training)\n",
    "    y_prediction = d.predict(X_testing)\n",
    "    (acc, average_acc) = d.score(y_testing, y_prediction)\n",
    "    print(\"Average accuracy on test set: \", average_acc)\n",
    "    print(\"\\nAccuracy on test set for each class: \")\n",
    "    for a,b in zip(d.get_classes(),acc):\n",
    "        print(a, \": \", b)\n",
    "        \n",
    "test(X_training, y_training, X_testing, y_testing)     \n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Doc2Vec Implemetation using Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from gensim.models import Doc2Vec\n",
    "from sklearn import utils\n",
    "from gensim.models.doc2vec import TaggedDocument"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def label_sentences(corpus, label_type):\n",
    "    \"\"\"\n",
    "    Gensim's Doc2Vec implementation requires each document/paragraph to have a label associated with it.\n",
    "    We do this by using the TaggedDocument method. The format will be \"TRAIN_i\" or \"TEST_i\" where \"i\" is\n",
    "    a dummy index of the post.\n",
    "    \"\"\"\n",
    "    labeled = []\n",
    "    for i, v in enumerate(corpus):\n",
    "        label = label_type + '_' + str(i)\n",
    "        labeled.append(TaggedDocument(v.split(), [label]))\n",
    "    return labeled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(cleandata_Train, y_datatrain, random_state=1, test_size=0.2)\n",
    "X_train = label_sentences(X_train, 'Train')\n",
    "X_test = label_sentences(X_test, 'Test')\n",
    "all_data = X_train + X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_dbow = Doc2Vec(dm=0, vector_size=300, negative=5, min_count=1, alpha=0.065, min_alpha=0.065)\n",
    "model_dbow.build_vocab([x for x in tqdm(all_data)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for epoch in range(10):\n",
    "    model_dbow.train(utils.shuffle([x for x in tqdm(all_data)]), total_examples=len(all_data), epochs=1)\n",
    "    model_dbow.alpha -= 0.002\n",
    "    model_dbow.min_alpha = model_dbow.alpha"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_vectors(model, corpus_size, vectors_size, vectors_type):\n",
    "    \"\"\"\n",
    "    Get vectors from trained doc2vec model\n",
    "    :param doc2vec_model: Trained Doc2Vec model\n",
    "    :param corpus_size: Size of the data\n",
    "    :param vectors_size: Size of the embedding vectors\n",
    "    :param vectors_type: Training or Testing vectors\n",
    "    :return: list of vectors\n",
    "    \"\"\"\n",
    "    vectors = np.zeros((corpus_size, vectors_size))\n",
    "    for i in range(0, corpus_size):\n",
    "        prefix = vectors_type + '_' + str(i)\n",
    "        vectors[i] = model.docvecs[prefix]\n",
    "    return vectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_vectors_dbow = get_vectors(model_dbow, len(X_train), 300, 'Train')\n",
    "test_vectors_dbow = get_vectors(model_dbow, len(X_test), 300, 'Test')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "logreg = LogisticRegression(n_jobs=-1, C=1e5)\n",
    "logreg.fit(train_vectors_dbow, y_train)\n",
    "logreg = logreg.fit(train_vectors_dbow, y_train)\n",
    "y_pred = logreg.predict(test_vectors_dbow)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print('accuracy %s' % accuracy_score(y_pred, y_test))\n",
    "print(classification_report(y_test, y_pred))"
   ]
  }
 ],
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