Main.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f073f140",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Zemberek'i kullanabillmek için ilk önce java kütüphenelerini kullanmamızı sağlayan kütüphaneyi ekleyelim\n",
    "import jpype\n",
    "from jpype import JClass\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "111c743a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Zembrek'in jar dosyalarını okuyalım ve hangi modüller var bakalım\n",
    "import zipfile\n",
    "\n",
    "def read_jar(jar_file):\n",
    "    zf = zipfile.ZipFile(jar_file, 'r')\n",
    "    try:\n",
    "        lst = zf.infolist()\n",
    "        for zi in lst:\n",
    "            fn = zi.filename \n",
    "            # print(fn)\n",
    "    finally:\n",
    "        zf.close()\n",
    "        \n",
    "read_jar(\"zemberek-full.jar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "23297faf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ok\n"
     ]
    }
   ],
   "source": [
    "# Zembrek'i çalıştıralım\n",
    "jar = r\"C:/Users/Dilemre/Documents/GitHub/bitirme/zemberek-full.jar\" # Zemberek'in yolu\n",
    "jvmpath = r\"C:/Program Files/Java/jdk-17.0.2/bin/server/jvm.dll\" # JVM'nin yolu\n",
    "if not jpype.isJVMStarted():\n",
    "    jpype.startJVM(jvmpath=jvmpath, classpath=jar)\n",
    "    print(\"ok\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d1edb584",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Zemberekten gerekli kütüphaneleri import edelim\n",
    "try:\n",
    "    TurkishTokenizer = JClass('zemberek.tokenization.TurkishTokenizer')\n",
    "    Token = JClass('zemberek.tokenization.Token')\n",
    "    TurkishMorphology = JClass('zemberek.morphology.TurkishMorphology')\n",
    "    TurkishSpellChecker = JClass('zemberek.normalization.TurkishSpellChecker')\n",
    "    WordAnalysis =  JClass('zemberek.morphology.analysis.WordAnalysis')\n",
    "    TurkishSentenceExtractor = JClass('zemberek.tokenization.TurkishSentenceExtractor')\n",
    "except:\n",
    "    print(\"wrong path\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9c88ebc0",
   "metadata": {},
   "outputs": [],
   "source": [
    "morphology = TurkishMorphology.createWithDefaults()\n",
    "tokenizer = TurkishTokenizer.ALL\n",
    "spell_checker = TurkishSpellChecker(morphology)\n",
    "extractor = TurkishSentenceExtractor.DEFAULT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5a14b3d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Verilerimizi almak ve işlemek için gerekli kütüphaneler\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ea988c86",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>info</th>\n",
       "      <th>title</th>\n",
       "      <th>caption</th>\n",
       "      <th>link</th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19 Mart 2021, Cuma</td>\n",
       "      <td>MEHMET BARLAS</td>\n",
       "      <td>Türk siyasetinde Devlet Bahçeli bir tutarlılık...</td>\n",
       "      <td>https://www.sabah.com.tr/yazarlar/barlas/2021/...</td>\n",
       "      <td>\\n\\nDün MHP'nin Büyük Kurultay'ında Genel Başk...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18 Mart 2021, Perşembe</td>\n",
       "      <td>MEHMET BARLAS</td>\n",
       "      <td>Amerikan iç politikasında kimin kimi hedef ala...</td>\n",
       "      <td>https://www.sabah.com.tr/yazarlar/barlas/2021/...</td>\n",
       "      <td>\\n\\nAmerikan iç politikasındaki gelişmeler kes...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17 Mart 2021, Çarşamba</td>\n",
       "      <td>MEHMET BARLAS</td>\n",
       "      <td>Amerikalılar, yalancı Amerikan başkanlarını ar...</td>\n",
       "      <td>https://www.sabah.com.tr/yazarlar/barlas/2021/...</td>\n",
       "      <td>\\n\\nBugüne kadar her konuda Yunanistan'ın ve K...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16 Mart 2021, Salı</td>\n",
       "      <td>MEHMET BARLAS</td>\n",
       "      <td>Darbeler geçmişte Türkiye’nin dış politikasını...</td>\n",
       "      <td>https://www.sabah.com.tr/yazarlar/barlas/2021/...</td>\n",
       "      <td>\\n\\nMısır'la diplomatik ilişkilerin başlaması,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15 Mart 2021, Pazartesi</td>\n",
       "      <td>MEHMET BARLAS</td>\n",
       "      <td>Müflis Yunanistan, Türkiye’ye gövde gösterisi ...</td>\n",
       "      <td>https://www.sabah.com.tr/yazarlar/barlas/2021/...</td>\n",
       "      <td>\\n\\nTabii ki hiçbir ülkeyi küçük görmemek gere...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      info          title  \\\n",
       "0       19 Mart 2021, Cuma  MEHMET BARLAS   \n",
       "1   18 Mart 2021, Perşembe  MEHMET BARLAS   \n",
       "2   17 Mart 2021, Çarşamba  MEHMET BARLAS   \n",
       "3       16 Mart 2021, Salı  MEHMET BARLAS   \n",
       "4  15 Mart 2021, Pazartesi  MEHMET BARLAS   \n",
       "\n",
       "                                             caption  \\\n",
       "0  Türk siyasetinde Devlet Bahçeli bir tutarlılık...   \n",
       "1  Amerikan iç politikasında kimin kimi hedef ala...   \n",
       "2  Amerikalılar, yalancı Amerikan başkanlarını ar...   \n",
       "3  Darbeler geçmişte Türkiye’nin dış politikasını...   \n",
       "4  Müflis Yunanistan, Türkiye’ye gövde gösterisi ...   \n",
       "\n",
       "                                                link  \\\n",
       "0  https://www.sabah.com.tr/yazarlar/barlas/2021/...   \n",
       "1  https://www.sabah.com.tr/yazarlar/barlas/2021/...   \n",
       "2  https://www.sabah.com.tr/yazarlar/barlas/2021/...   \n",
       "3  https://www.sabah.com.tr/yazarlar/barlas/2021/...   \n",
       "4  https://www.sabah.com.tr/yazarlar/barlas/2021/...   \n",
       "\n",
       "                                                text  \n",
       "0  \\n\\nDün MHP'nin Büyük Kurultay'ında Genel Başk...  \n",
       "1  \\n\\nAmerikan iç politikasındaki gelişmeler kes...  \n",
       "2  \\n\\nBugüne kadar her konuda Yunanistan'ın ve K...  \n",
       "3  \\n\\nMısır'la diplomatik ilişkilerin başlaması,...  \n",
       "4  \\n\\nTabii ki hiçbir ülkeyi küçük görmemek gere...  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# zemberekten aldığımız stopword dosyasını kullanarak bu kelimeleri metinlerden çıkartalım \n",
    "stopwords = [x.strip() for x in open('stop-words.txt','r', encoding=\"UTF8\").read().split('\\n')]\n",
    "df = pd.read_csv(\"data.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e6be3a7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "95"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#15'ten az köşe yazısına sahip yazar sayısı\n",
    "df[df.groupby('title').title.transform('count') < 15].title.unique().size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a29e103a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "328"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#15'ten az köşe yazısına sahip yazarların toplam köşe yazısı sayısı\n",
    "df[df.groupby('title').title.transform('count') < 15].text.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "bd7f6c16",
   "metadata": {
    "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>info</th>\n",
       "      <th>title</th>\n",
       "      <th>caption</th>\n",
       "      <th>link</th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19 Mart 2021, Cuma</td>\n",
       "      <td>MEHMET BARLAS</td>\n",
       "      <td>Türk siyasetinde Devlet Bahçeli bir tutarlılık...</td>\n",
       "      <td>https://www.sabah.com.tr/yazarlar/barlas/2021/...</td>\n",
       "      <td>\\n\\nDün MHP'nin Büyük Kurultay'ında Genel Başk...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18 Mart 2021, Perşembe</td>\n",
       "      <td>MEHMET BARLAS</td>\n",
       "      <td>Amerikan iç politikasında kimin kimi hedef ala...</td>\n",
       "      <td>https://www.sabah.com.tr/yazarlar/barlas/2021/...</td>\n",
       "      <td>\\n\\nAmerikan iç politikasındaki gelişmeler kes...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17 Mart 2021, Çarşamba</td>\n",
       "      <td>MEHMET BARLAS</td>\n",
       "      <td>Amerikalılar, yalancı Amerikan başkanlarını ar...</td>\n",
       "      <td>https://www.sabah.com.tr/yazarlar/barlas/2021/...</td>\n",
       "      <td>\\n\\nBugüne kadar her konuda Yunanistan'ın ve K...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16 Mart 2021, Salı</td>\n",
       "      <td>MEHMET BARLAS</td>\n",
       "      <td>Darbeler geçmişte Türkiye’nin dış politikasını...</td>\n",
       "      <td>https://www.sabah.com.tr/yazarlar/barlas/2021/...</td>\n",
       "      <td>\\n\\nMısır'la diplomatik ilişkilerin başlaması,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15 Mart 2021, Pazartesi</td>\n",
       "      <td>MEHMET BARLAS</td>\n",
       "      <td>Müflis Yunanistan, Türkiye’ye gövde gösterisi ...</td>\n",
       "      <td>https://www.sabah.com.tr/yazarlar/barlas/2021/...</td>\n",
       "      <td>\\n\\nTabii ki hiçbir ülkeyi küçük görmemek gere...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      info          title  \\\n",
       "0       19 Mart 2021, Cuma  MEHMET BARLAS   \n",
       "1   18 Mart 2021, Perşembe  MEHMET BARLAS   \n",
       "2   17 Mart 2021, Çarşamba  MEHMET BARLAS   \n",
       "3       16 Mart 2021, Salı  MEHMET BARLAS   \n",
       "4  15 Mart 2021, Pazartesi  MEHMET BARLAS   \n",
       "\n",
       "                                             caption  \\\n",
       "0  Türk siyasetinde Devlet Bahçeli bir tutarlılık...   \n",
       "1  Amerikan iç politikasında kimin kimi hedef ala...   \n",
       "2  Amerikalılar, yalancı Amerikan başkanlarını ar...   \n",
       "3  Darbeler geçmişte Türkiye’nin dış politikasını...   \n",
       "4  Müflis Yunanistan, Türkiye’ye gövde gösterisi ...   \n",
       "\n",
       "                                                link  \\\n",
       "0  https://www.sabah.com.tr/yazarlar/barlas/2021/...   \n",
       "1  https://www.sabah.com.tr/yazarlar/barlas/2021/...   \n",
       "2  https://www.sabah.com.tr/yazarlar/barlas/2021/...   \n",
       "3  https://www.sabah.com.tr/yazarlar/barlas/2021/...   \n",
       "4  https://www.sabah.com.tr/yazarlar/barlas/2021/...   \n",
       "\n",
       "                                                text  \n",
       "0  \\n\\nDün MHP'nin Büyük Kurultay'ında Genel Başk...  \n",
       "1  \\n\\nAmerikan iç politikasındaki gelişmeler kes...  \n",
       "2  \\n\\nBugüne kadar her konuda Yunanistan'ın ve K...  \n",
       "3  \\n\\nMısır'la diplomatik ilişkilerin başlaması,...  \n",
       "4  \\n\\nTabii ki hiçbir ülkeyi küçük görmemek gere...  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#15'ten fazla köşe yazısına sahip yazarları data DataFrame'ine alalım ve işlemlerimize buradan devam edelim\n",
    "data = df[df.groupby('title').title.transform('count') >= 15].copy()\n",
    "data = data.reset_index(drop = True)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e874d6cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20, 5)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['title'].str.contains('FERHAT ÜNLÜ')].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0809abc3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(17, 5)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['title'].str.contains('MEHMET BARLAS')].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6e5bd466",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "109"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#toplam yazar sayımız 109\n",
    "data.title.unique().size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "78d69e6a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2087"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# toplam köşe yazısı sayımız\n",
    "data.text.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b29d1ee4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Buradaki tüm işlemler köşe yazısı bazlı yapılır\n",
    "\n",
    "def dataCleaning(text):\n",
    "    \"\"\"Satır boşluklarını kaldır, metini küçük harfe çevir, noktalama işaretlerini kaldır\"\"\"\n",
    "    text = text.replace(\"\\n\", \" \")\n",
    "    text = text.lower()\n",
    "    text = \"\".join([i for i in text if (i.isalnum() or i == \" \")])\n",
    "    return \" \".join(text.split())\n",
    "\n",
    "\n",
    "def removeStopwords(text):\n",
    "    \"\"\"Zemberek'ten aldığımız stopword kelimelerini kaldırır\"\"\"\n",
    "    for word in text:\n",
    "        if word in stopwords or word in string.whitespace:\n",
    "            text.remove(word)\n",
    "    return text\n",
    "\n",
    "\n",
    "def wordTokenize(text):\n",
    "    \"\"\"Önişlenmiş metini kelimelere ayırır ve stopword'leri kaldırır\"\"\"\n",
    "    text = text.split(\" \")\n",
    "    text = removeStopwords(text)\n",
    "    return text\n",
    "\n",
    "\n",
    "def sentTokenize(text):\n",
    "    \"\"\"Önişlenmemiş metini cümlelerine ayırır, bunişlem sırasında önişleme yapar ve stopword'leri kaldırır \"\"\"\n",
    "    sent_list = []\n",
    "    text = text.replace(\"\\\"\", \"\")\n",
    "    results: TurkishSentenceExtractor = extractor.fromDocument(jpype.JString(text))\n",
    "    for result in results:\n",
    "        result = dataCleaning(str(result))\n",
    "        result = removeStopwords(result.split(\" \"))\n",
    "        if len(result) == 0:\n",
    "            continue\n",
    "        else:\n",
    "            sent_list.append(\" \".join(result))         \n",
    "    return sent_list\n",
    "\n",
    "\n",
    "# unk kelime sayısı da nitelik olarak eklenmeli -yabancı kelime sayısı özniteliği-\n",
    "def lemmas(word_list):\n",
    "    \"\"\"Kelime token'larından kök tokenları oluşturur\"\"\"\n",
    "    lemma = []\n",
    "    for word in word_list:\n",
    "        result = str(morphology.analyzeAndDisambiguate(word).bestAnalysis()[0].getLemmas()[0])\n",
    "        if result == \"UNK\":\n",
    "            lemma.append(result)\n",
    "        else:\n",
    "            lemma.append(result)\n",
    "    return lemma\n",
    "\n",
    "\n",
    "def wtDist(wt):\n",
    "    \"\"\"Kelimelerin dağılımları\"\"\"\n",
    "    wt_dist = dict()\n",
    "    wt_dist.fromkeys(set(wt))\n",
    "    for i in set(wt):\n",
    "        wt_dist[i] = wt.count(i)\n",
    "    return wt_dist\n",
    "\n",
    "\n",
    "def wtLenDist(wt):\n",
    "    \"\"\"Kelimelerin harf olarak uzunluk dağılımlarını çıkarır\"\"\"\n",
    "    wt_len = [len(str(word)) for word in wt]\n",
    "    wt_len_dist = dict()\n",
    "    wt_len_dist.fromkeys(range(1, 29))\n",
    "    for i in range(0, 29):\n",
    "        wt_len_dist[i] = wt_len.count(i)\n",
    "    return wt_len_dist\n",
    "\n",
    "\n",
    "def stLenDist(st):\n",
    "    \"Cümlelerin kelime olarak uzunluk dağılımlarını çıkarır\"\n",
    "    st_len = [len(wordTokenize(sent)) for sent in st]\n",
    "    st_len_dist = dict()\n",
    "    st_len_dist.fromkeys(range(1, 29))\n",
    "    for i in range(0, 29):\n",
    "        st_len_dist[i] = st_len.count(i)\n",
    "    return st_len_dist\n",
    "\n",
    "\n",
    "def typeTokenRatio(wt):\n",
    "    \"\"\"Kelimenin toplam kelime sayısına oranı\"\"\"\n",
    "    return len(set(wt))/len((wt))\n",
    "\n",
    "\n",
    "def avgWtLen(wt):\n",
    "    \"\"\"Ortalama kelime uzunluğu\"\"\"\n",
    "    return sum(len(word) for word in wt)/len(wt)\n",
    "\n",
    "\n",
    "def avgStLen(st):\n",
    "    \"\"\"Kelime olarak ortalama cümle uzunluğu\"\"\"\n",
    "    return sum(len(wordTokenize(sent)) for sent in st)/len(st)\n",
    "\n",
    "\n",
    "def puncNum(txt):\n",
    "    \"\"\"Noktalama işareti sayısı\"\"\"\n",
    "    return len([x for x in txt if x in string.punctuation])\n",
    "\n",
    "\n",
    "def numStopwords(clean_text):\n",
    "    \"\"\"Stopword kullanma sayısı\"\"\"\n",
    "    return len([w for w in clean_text.split() if w in stopwords])\n",
    "\n",
    "\n",
    "def numUpper(raw_text):\n",
    "    \"\"\"Tamamı büyük harf olan kelime sayısı\"\"\"\n",
    "    p = string.punctuation + \"’\" + \"“\" + \"”\"\n",
    "    raw_text = ''.join(' ' if c in p else c for c in raw_text)\n",
    "    return len([w for w in raw_text.split() if str(w).isupper()])\n",
    "\n",
    "\n",
    "# Noktalama işareti dağılımı eklenebilir.\n",
    "# Eklerin dağılımı -veya bazı eklerin sayısı (özellikle iyelik ekleri)- eklenebilir."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "8fe3a172",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['clean_text'] = data['text'].apply(lambda x : dataCleaning(x))\n",
    "data['word_token'] = data['clean_text'].apply(lambda x : wordTokenize(x))\n",
    "data['sent_token'] = data['text'].apply(lambda x : sentTokenize(x))\n",
    "data['lemma_token'] = data['word_token'].apply(lambda x : lemmas(x))\n",
    "data['ltDist'] = data['lemma_token'].apply(lambda x : wtDist(x))\n",
    "data['wtLenDist'] = data['word_token'].apply(lambda x : wtLenDist(x))\n",
    "data['stLenDist'] = data['sent_token'].apply(lambda x : stLenDist(x))\n",
    "data['ttr'] = data['word_token'].apply(lambda x : typeTokenRatio(x))\n",
    "data['lttr'] = data['lemma_token'].apply(lambda x : typeTokenRatio(x))\n",
    "data['avgWtLen'] = data['word_token'].apply(lambda x : avgWtLen(x))\n",
    "data['avgStLen'] = data['sent_token'].apply(lambda x : avgStLen(x))\n",
    "data['puncNum'] = data['text'].apply(lambda x : puncNum(x))\n",
    "data['numStopwords'] = data['clean_text'].apply(lambda x : numStopwords(x))\n",
    "data['numUpper'] = data['text'].apply(lambda x : numUpper(x))\n",
    "\n",
    "# yazarları tam sayı olarak kodlar ve tam sayı-yazar sözlüğü oluşturur\n",
    "data['target'], mapping = pd.factorize(data.title)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "ec23d319",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'FERHAT ÜNLÜ'"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mapping[11]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "6fad63a7",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>info</th>\n",
       "      <th>title</th>\n",
       "      <th>caption</th>\n",
       "      <th>link</th>\n",
       "      <th>text</th>\n",
       "      <th>clean_text</th>\n",
       "      <th>word_token</th>\n",
       "      <th>sent_token</th>\n",
       "      <th>lemma_token</th>\n",
       "      <th>ltDist</th>\n",
       "      <th>wtLenDist</th>\n",
       "      <th>stLenDist</th>\n",
       "      <th>ttr</th>\n",
       "      <th>lttr</th>\n",
       "      <th>avgWtLen</th>\n",
       "      <th>avgStLen</th>\n",
       "      <th>puncNum</th>\n",
       "      <th>numStopwords</th>\n",
       "      <th>numUpper</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19 Mart 2021, Cuma</td>\n",
       "      <td>MEHMET BARLAS</td>\n",
       "      <td>Türk siyasetinde Devlet Bahçeli bir tutarlılık...</td>\n",
       "      <td>https://www.sabah.com.tr/yazarlar/barlas/2021/...</td>\n",
       "      <td>\\n\\nDün MHP'nin Büyük Kurultay'ında Genel Başk...</td>\n",
       "      <td>dün mhpnin büyük kurultayında genel başkan dev...</td>\n",
       "      <td>[mhpnin, büyük, kurultayında, genel, başkan, d...</td>\n",
       "      <td>[mhpnin büyük kurultayında genel başkan devlet...</td>\n",
       "      <td>[mhp, büyük, kurultay, genel, başkan, devlet, ...</td>\n",
       "      <td>{'aç': 1, 'mümkün': 1, 'isim': 1, 'kurultay': ...</td>\n",
       "      <td>{0: 0, 1: 0, 2: 3, 3: 15, 4: 10, 5: 21, 6: 33,...</td>\n",
       "      <td>{0: 0, 1: 1, 2: 3, 3: 2, 4: 2, 5: 1, 6: 2, 7: ...</td>\n",
       "      <td>0.873684</td>\n",
       "      <td>0.710526</td>\n",
       "      <td>7.184211</td>\n",
       "      <td>7.869565</td>\n",
       "      <td>61</td>\n",
       "      <td>57</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18 Mart 2021, Perşembe</td>\n",
       "      <td>MEHMET BARLAS</td>\n",
       "      <td>Amerikan iç politikasında kimin kimi hedef ala...</td>\n",
       "      <td>https://www.sabah.com.tr/yazarlar/barlas/2021/...</td>\n",
       "      <td>\\n\\nAmerikan iç politikasındaki gelişmeler kes...</td>\n",
       "      <td>amerikan iç politikasındaki gelişmeler kesinli...</td>\n",
       "      <td>[amerikan, iç, politikasındaki, gelişmeler, bi...</td>\n",
       "      <td>[amerikan iç politikasındaki gelişmeler bizimk...</td>\n",
       "      <td>[amerikan, iç, politika, geliş, biz, ilgi, çek...</td>\n",
       "      <td>{'saygı': 1, 'seçim': 7, 'alan': 1, 'isim': 1,...</td>\n",
       "      <td>{0: 0, 1: 2, 2: 6, 3: 12, 4: 30, 5: 48, 6: 36,...</td>\n",
       "      <td>{0: 0, 1: 1, 2: 2, 3: 2, 4: 1, 5: 0, 6: 2, 7: ...</td>\n",
       "      <td>0.826531</td>\n",
       "      <td>0.595238</td>\n",
       "      <td>7.102041</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>76</td>\n",
       "      <td>79</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17 Mart 2021, Çarşamba</td>\n",
       "      <td>MEHMET BARLAS</td>\n",
       "      <td>Amerikalılar, yalancı Amerikan başkanlarını ar...</td>\n",
       "      <td>https://www.sabah.com.tr/yazarlar/barlas/2021/...</td>\n",
       "      <td>\\n\\nBugüne kadar her konuda Yunanistan'ın ve K...</td>\n",
       "      <td>bugüne kadar her konuda yunanistanın ve kıbrıs...</td>\n",
       "      <td>[bugüne, konuda, yunanistanın, kıbrıs, rumları...</td>\n",
       "      <td>[bugüne her konuda yunanistanın kıbrıs rumları...</td>\n",
       "      <td>[bugün, konu, yunanistan, kıbrıs, rum, ol, ame...</td>\n",
       "      <td>{'sal': 2, 'kamuoyu': 1, 'tarih': 1, 'birleşik...</td>\n",
       "      <td>{0: 0, 1: 0, 2: 6, 3: 8, 4: 13, 5: 26, 6: 25, ...</td>\n",
       "      <td>{0: 0, 1: 0, 2: 2, 3: 1, 4: 5, 5: 4, 6: 1, 7: ...</td>\n",
       "      <td>0.906593</td>\n",
       "      <td>0.681319</td>\n",
       "      <td>7.340659</td>\n",
       "      <td>7.217391</td>\n",
       "      <td>59</td>\n",
       "      <td>61</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16 Mart 2021, Salı</td>\n",
       "      <td>MEHMET BARLAS</td>\n",
       "      <td>Darbeler geçmişte Türkiye’nin dış politikasını...</td>\n",
       "      <td>https://www.sabah.com.tr/yazarlar/barlas/2021/...</td>\n",
       "      <td>\\n\\nMısır'la diplomatik ilişkilerin başlaması,...</td>\n",
       "      <td>mısırla diplomatik ilişkilerin başlaması ülkel...</td>\n",
       "      <td>[mısırla, diplomatik, ilişkilerin, başlaması, ...</td>\n",
       "      <td>[mısırla diplomatik ilişkilerin başlaması ülke...</td>\n",
       "      <td>[mısır, diplomatik, ilişki, başla, ülke, dış, ...</td>\n",
       "      <td>{'meclis': 1, 'birleşik': 1, 'müttefik': 1, 'd...</td>\n",
       "      <td>{0: 0, 1: 0, 2: 5, 3: 4, 4: 12, 5: 20, 6: 22, ...</td>\n",
       "      <td>{0: 0, 1: 0, 2: 3, 3: 0, 4: 1, 5: 0, 6: 0, 7: ...</td>\n",
       "      <td>0.810000</td>\n",
       "      <td>0.595000</td>\n",
       "      <td>7.765000</td>\n",
       "      <td>10.263158</td>\n",
       "      <td>56</td>\n",
       "      <td>47</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15 Mart 2021, Pazartesi</td>\n",
       "      <td>MEHMET BARLAS</td>\n",
       "      <td>Müflis Yunanistan, Türkiye’ye gövde gösterisi ...</td>\n",
       "      <td>https://www.sabah.com.tr/yazarlar/barlas/2021/...</td>\n",
       "      <td>\\n\\nTabii ki hiçbir ülkeyi küçük görmemek gere...</td>\n",
       "      <td>tabii ki hiçbir ülkeyi küçük görmemek gerekiyo...</td>\n",
       "      <td>[tabii, hiçbir, ülkeyi, küçük, görmemek, gerek...</td>\n",
       "      <td>[tabii hiçbir ülkeyi küçük görmemek gerekiyor,...</td>\n",
       "      <td>[tabii, hiçbir, ülke, küçük, gör, gerek, bazı,...</td>\n",
       "      <td>{'düzenle': 1, 'twitter': 1, 'uçak': 7, 'bae':...</td>\n",
       "      <td>{0: 0, 1: 7, 2: 6, 3: 11, 4: 26, 5: 47, 6: 26,...</td>\n",
       "      <td>{0: 0, 1: 0, 2: 2, 3: 1, 4: 3, 5: 1, 6: 1, 7: ...</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.608000</td>\n",
       "      <td>6.936000</td>\n",
       "      <td>11.523810</td>\n",
       "      <td>67</td>\n",
       "      <td>56</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      info          title  \\\n",
       "0       19 Mart 2021, Cuma  MEHMET BARLAS   \n",
       "1   18 Mart 2021, Perşembe  MEHMET BARLAS   \n",
       "2   17 Mart 2021, Çarşamba  MEHMET BARLAS   \n",
       "3       16 Mart 2021, Salı  MEHMET BARLAS   \n",
       "4  15 Mart 2021, Pazartesi  MEHMET BARLAS   \n",
       "\n",
       "                                             caption  \\\n",
       "0  Türk siyasetinde Devlet Bahçeli bir tutarlılık...   \n",
       "1  Amerikan iç politikasında kimin kimi hedef ala...   \n",
       "2  Amerikalılar, yalancı Amerikan başkanlarını ar...   \n",
       "3  Darbeler geçmişte Türkiye’nin dış politikasını...   \n",
       "4  Müflis Yunanistan, Türkiye’ye gövde gösterisi ...   \n",
       "\n",
       "                                                link  \\\n",
       "0  https://www.sabah.com.tr/yazarlar/barlas/2021/...   \n",
       "1  https://www.sabah.com.tr/yazarlar/barlas/2021/...   \n",
       "2  https://www.sabah.com.tr/yazarlar/barlas/2021/...   \n",
       "3  https://www.sabah.com.tr/yazarlar/barlas/2021/...   \n",
       "4  https://www.sabah.com.tr/yazarlar/barlas/2021/...   \n",
       "\n",
       "                                                text  \\\n",
       "0  \\n\\nDün MHP'nin Büyük Kurultay'ında Genel Başk...   \n",
       "1  \\n\\nAmerikan iç politikasındaki gelişmeler kes...   \n",
       "2  \\n\\nBugüne kadar her konuda Yunanistan'ın ve K...   \n",
       "3  \\n\\nMısır'la diplomatik ilişkilerin başlaması,...   \n",
       "4  \\n\\nTabii ki hiçbir ülkeyi küçük görmemek gere...   \n",
       "\n",
       "                                          clean_text  \\\n",
       "0  dün mhpnin büyük kurultayında genel başkan dev...   \n",
       "1  amerikan iç politikasındaki gelişmeler kesinli...   \n",
       "2  bugüne kadar her konuda yunanistanın ve kıbrıs...   \n",
       "3  mısırla diplomatik ilişkilerin başlaması ülkel...   \n",
       "4  tabii ki hiçbir ülkeyi küçük görmemek gerekiyo...   \n",
       "\n",
       "                                          word_token  \\\n",
       "0  [mhpnin, büyük, kurultayında, genel, başkan, d...   \n",
       "1  [amerikan, iç, politikasındaki, gelişmeler, bi...   \n",
       "2  [bugüne, konuda, yunanistanın, kıbrıs, rumları...   \n",
       "3  [mısırla, diplomatik, ilişkilerin, başlaması, ...   \n",
       "4  [tabii, hiçbir, ülkeyi, küçük, görmemek, gerek...   \n",
       "\n",
       "                                          sent_token  \\\n",
       "0  [mhpnin büyük kurultayında genel başkan devlet...   \n",
       "1  [amerikan iç politikasındaki gelişmeler bizimk...   \n",
       "2  [bugüne her konuda yunanistanın kıbrıs rumları...   \n",
       "3  [mısırla diplomatik ilişkilerin başlaması ülke...   \n",
       "4  [tabii hiçbir ülkeyi küçük görmemek gerekiyor,...   \n",
       "\n",
       "                                         lemma_token  \\\n",
       "0  [mhp, büyük, kurultay, genel, başkan, devlet, ...   \n",
       "1  [amerikan, iç, politika, geliş, biz, ilgi, çek...   \n",
       "2  [bugün, konu, yunanistan, kıbrıs, rum, ol, ame...   \n",
       "3  [mısır, diplomatik, ilişki, başla, ülke, dış, ...   \n",
       "4  [tabii, hiçbir, ülke, küçük, gör, gerek, bazı,...   \n",
       "\n",
       "                                              ltDist  \\\n",
       "0  {'aç': 1, 'mümkün': 1, 'isim': 1, 'kurultay': ...   \n",
       "1  {'saygı': 1, 'seçim': 7, 'alan': 1, 'isim': 1,...   \n",
       "2  {'sal': 2, 'kamuoyu': 1, 'tarih': 1, 'birleşik...   \n",
       "3  {'meclis': 1, 'birleşik': 1, 'müttefik': 1, 'd...   \n",
       "4  {'düzenle': 1, 'twitter': 1, 'uçak': 7, 'bae':...   \n",
       "\n",
       "                                           wtLenDist  \\\n",
       "0  {0: 0, 1: 0, 2: 3, 3: 15, 4: 10, 5: 21, 6: 33,...   \n",
       "1  {0: 0, 1: 2, 2: 6, 3: 12, 4: 30, 5: 48, 6: 36,...   \n",
       "2  {0: 0, 1: 0, 2: 6, 3: 8, 4: 13, 5: 26, 6: 25, ...   \n",
       "3  {0: 0, 1: 0, 2: 5, 3: 4, 4: 12, 5: 20, 6: 22, ...   \n",
       "4  {0: 0, 1: 7, 2: 6, 3: 11, 4: 26, 5: 47, 6: 26,...   \n",
       "\n",
       "                                           stLenDist       ttr      lttr  \\\n",
       "0  {0: 0, 1: 1, 2: 3, 3: 2, 4: 2, 5: 1, 6: 2, 7: ...  0.873684  0.710526   \n",
       "1  {0: 0, 1: 1, 2: 2, 3: 2, 4: 1, 5: 0, 6: 2, 7: ...  0.826531  0.595238   \n",
       "2  {0: 0, 1: 0, 2: 2, 3: 1, 4: 5, 5: 4, 6: 1, 7: ...  0.906593  0.681319   \n",
       "3  {0: 0, 1: 0, 2: 3, 3: 0, 4: 1, 5: 0, 6: 0, 7: ...  0.810000  0.595000   \n",
       "4  {0: 0, 1: 0, 2: 2, 3: 1, 4: 3, 5: 1, 6: 1, 7: ...  0.800000  0.608000   \n",
       "\n",
       "   avgWtLen   avgStLen  puncNum  numStopwords  numUpper  target  \n",
       "0  7.184211   7.869565       61            57        12       0  \n",
       "1  7.102041  10.000000       76            79         3       0  \n",
       "2  7.340659   7.217391       59            61         3       0  \n",
       "3  7.765000  10.263158       56            47         0       0  \n",
       "4  6.936000  11.523810       67            56        12       0  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "ac954e7b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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Eu7r4ElH63gLylFeGhVx20wRi1smqoeHjI/GRCIRcvb37iknXzJrs/+P92EUboQrMrMn1P349jTsbGX4+WAb6jk++L09ptIRdXNnT3LKnhcd/+fEFURHhpnYiLWsY/MZ/oSdrSKzbPvvdrFNmxjyTSDyzSqH7BFOHnkG6Nkoogm+ZaLHEwou7ApxCFtv3SG2+geypl1FUjZrr9tH76f+FohuE65uX/O0//F2RVFohFhfEYgqRqCASEdTXK+zea6DrK5CwgFSDwcbdSeI1GqWsy7mX8hSm5yaLto1RttycQgioljxO78+RHZ9/rRUVOrfF6dgcQ9Fg/KKJEVYWkEGqQWftdQlqmgw8VzJ6sUr/qRJ2dWXTVw+pqHowqaq6gqILXNPD9yRCDc5TKBCKqigzf1eygbNs2/3NxOtCnH92AtcOoiikLwNpAwhFNbTQ3IRtxDRe/HQfOx5sYfsDzZz41uiyFr5hCG6/ZXVaN4BfWd4pIiV8+oslPv3F4DkVCBSh4UuXkVGP3/6j7PIDCDBSYXzHwy2v7Ih8teBMTVAc+NY8Vi+dOkbp1LHZv/OHXpxHOJWe8wC4+dz37Tjtap7+I1+b99nwqUcByI2cXvDZJZSm+2f/7VTzVPNj13wMOgaGiOBJh7iSxsVF4qMTwpJVDBHGkhU8XKpydVFcr5h0xw6P4cw8MNKTTByfwCk5NO9qniXdq8XIgZFFw9C8ahnfNmm85XV4lol07KVnF9+jPHCB9LbdNN3xBpDg5KeZPvb8kuMm1m4l3NyJka6j7obbKfWdpTRwAYmkOtqPOTVC7c5bSW7YQbH3NObkKA377g90MaHMLGMW4vOfnhPwFQWMEITDgs1bdf7+/9SsSLrRhMb9H2hBDykomqBtY5SeIwU+/1f9FGeIV9UE8bRO64YIa7bFmRoyF5DudXfV8IafaEdKmB612HZrmmSdjnqZE6O+PcTrP9hG87oohWkbI6Ry+zs09n9lghe+Orki8datidG+I0UlZ7P9dS2YBYfMYIXCpEXbthSxOoNIQmfd3joKkwGpea7kwv5J7vmpjQwczZIZrIAMpAffC8LGStMWu97afrnPjtKkxflnJsgOVbjl/WspTlj0vjS99LHVqtywY3nJ4lqhCJW40YAA8tbqXnIjGaL1jrXkzk+RO3ftvo5rwkpBs69VHwRmQtKUNAJBQqmlIgt40sEQEQwZIqakyHhjCMT3j3St/HxrwMpZeI5HuHb1FsWVWCru1ynmGHvm6yi6gWdb5M8dxTOrZE+9hHQdXKcMYZXK+ABWeRrXLJDtPoKWSuMXy0jpgfSpjg/hOhWkCDzX0naRUmIVp7BLOcrjvXi5Ap5ZQXouE899E8+s4NsW00eeQygKnllh9Mkvo4Yi+I5N7swhnGJuxXPzfTCrYFYl01P+qgLGFRWmhk2e/fw4nifZsCvJj/zJBgZOl3nsv0cBGDxXZqyvypZ9KTq3LvQupxsNbn9bE44t+eQf9lDOuzR0hHn4FzoxwoH1qIcUbnygjtaNUb7294MMX6igGwr73lzPPe9roe94if7Ty8cqT/aWWHNjLTe/vwur7PHUv1zALLmcfnSM5k0J3vfR3ZSmbCo5m0p+ZlKQMNVXxiq7lDM2xclgOV/OWJx9cpyb39/Fze/r4uLBaab6548vgf4jWVLNEXa/vYP8WJXpgcqix3bP7RHise9dwE5UTyFQVk26iqEGUttrBPf/MASWrGDKMllvHA8PkCgEuva0NwoIIsrSER1X4hWTrha60uGkIBSBa157ysyS8ZtS4hQWLtm8SkDSnnDQIhFid+xCiUWxugcQmoIIh1Droti9AyAlamMatSGNHlGJXr8Fe3AUr1AitL4TdzqLX6pgZcZn93/5mF51bkJwS3nc0ny9KCSi1GhNTDj9+Lw6YXHVksfRJ7JkxgKSOvpEhskhk837Ujz+iVGkDFQVu+pjVRaPfW7oDNO8LsK3PzbM+MUqvg+FaYeLJ0qs2RY4uqIple23p+k/VWaku4Jj+jimz+CZCne/W6VxTYSBs+UlHWx9hzKz2qqiCaQPrhUcT3a4wld//8Ts574fCMeX9GchIDNUYfRcAc8JPnMtnyNfHeLEt0ZBBM4+RQHXCYLr+w9n8GwfKeH4N0c4+d3R2TjecAjuui3CDTsMdu0Mcf12g5amOQfQD70nwbvfFl+U71xX8sd/neMf/31hNMCu6wz+7s/q2LLRwPMk9751lDMXHAQKnu8Q0VMIxKxrC+D1D0T42Ecb0DTBCy+b/NrvTXO+x0UL6wue9Z/5sSS//xs1jIy5/PrvZ/juk1UUBRobVN711hhveCDKxnU6hiGYmPTo7nV4/JkqX/9OhdHxVx733liv8Du/XMN73h5HCJjOePzEL03x3IGFkTAN9Qp/8ju1PPyGxQlHSrj3rSOcOreydNK1RuPZr7cSCgmyOZ93/fg4x06uLumlq1PjhW+3oaowNe3xwV+a5PmXlpaGohHBddsM3vamGLfcFKK1WcPQYXzC5/Axiy9/s8z+AyblisSWVWxpslCxnX9Olr/6ENNXTLq1m2tRdAVv5uVKr0ujR3Uy5zIr/PLVh58rQk0aZ2QCadt4hRJC19CbG/BKFZyxYAnn5YrozQ1oDbW4U1m8fBG1JoVftXDHptA7W1c9pkAhosTR0Cn7eRxpIaRAoBIWUWxZJTTzf0OE0YVByc/js/oXxHMlxczcA+h7kty4TSSuYkTUJYn2ckRiKuGYSmbUYvY9l1DKOjgzEQi6oVDTaLD2ugQ3PlC7YB+KGjiy5BKiTkCyM4y8yPvi2v6Cz8MJjaYNCTpvrEH6kuGTuflj+joRtwbLK2EoYRynSliogAAXdEXiSgeNEFVrbgLs6tT56ieW1tkNXWAsIes4jkS/zPd3OYmqKsSiCsmEguvKuRRX6QZRBNJHCBUp54wOXRPE4wqGLohGBYoSjOuUbDKnx9Gic5KHYUAiLmhsUEmnFAwD7r8zwl/+YR3ru+Y7JBvqVLZvMXjoviiT0z5f+Wb5FRnNjQ0qv/PhND/xQ0lA0jfg8nO/Ob0o4UJAqq4r8f0gzlbXAkexooiZSAyJsnSgwzyMj3s884LJO94cI5lQeNPropw+Zy+IfFgMP/SuOOlUcE2fes5mYGhpg2/LBp1f+dkU73hzjGh0/qqnqQF2bjf44ffGefI5k9/78wxHT9o4zmou6uov/KtCuuvfsJ7MhQx6RGf9G9Zj5S2GX5zTc/WYjh7V0WM6akhFj+rEW+I4FQen5Lwiq/hy+JUq5qnzCz63+0fmb1csUXn5+OI70VTkau70DEIiSkKpQRUaEjkvVjKl1pPzxqnRmsh640RFkpASoSrL+HL1pCsUgXGZAwkROKIc2w/SdlcBzw2sSiOszAss0gxllgSkD47lc+TxaQ58fXJBFMTYxWpgob6KiKQNtr+uhUrO5sCn+rGvmEAEAk0xcHydlNGIj4/llojrdQihMGX2oyGI6zVUvTnSzRd8/uPTxXn7un6Hwa7rgmSNc902h4/ZWPbC8/E8yamzDiAIKzE0oVP2cktONrO/820c3yKm11K0J1Y8dzWkYqTCeNWFz38sqlCTVnnr62P89R/WUlerMjruMjbuUa5INA1qa1TamlUuDrr0DTiviHDbWoJkjh97fwKJ5PhJm//551meem5pC65UknzmS2WOn7JJJhRSSYU1HRr33hGhtmaVbDsDy5Z89islHrovQiyq8K63xvg//1lgcnr55zuVFLz59YGlXalKXjhoMjax+Lu1Z1eIv/qDWm7eE8b3JcOjLn0DLrmCD1KSTqmsX6vRWK9yz+1hWj/awG/9UbDauDJt+pXgFZGu7/qc+ewZWva0sO6hdehRHbtoc/ifDlOdCm6WoilseOMGWva2oIU1Em0JovVR9v3qPjzL4+KjF7n43VeW730pYP9VgevhjK7eqSHxmckNQuJjiAhRJUVEyeJKm7TaSFRJknHH8IVHRCRQ0XBYZeI8EIoorNkRZ6wvsDhqmgya1oQ58ngGdxHSWAz5SZvCtMPmfSlOPpfDcyThuEpjZxh9htCrJY+BM2USdQZjF6tkRufMUkW9hjhwRUFvqsUZnVpyk+xghW9+5Mro/Dm40qHsZvGlT9GZwpUOAnBtB1+6VNw8mmJQcefLPKPjHj/1K/PH/e0Pp2dJ99kXTH73T7Nk80u/1AKFuFqDLkJUvAJymdWJIjQUoSIQ2N4qlpoCQnUxpCcXTTAydMFD90ZYu0bDMAT/8G8Fnj1gMjzqUi4HpFtfp7JhrY7rymWtu5XQ1anx2x9O8963x5HA4WM2v/+RLM+8UF3W52BakmdeMHnmhTlL+PodBps36FdNur4Px07aHDpqceetETau17nvzgif+fLyPoS7b4vQ2aYhhKBvwOGFl61FiwGt7dT449+qYe+NIUzL57GnTT75+RJnzttkcx4SqEmr7Nhi8BM/lOCOm8NsXKfz4Z9OMTjscvLMqxddsjzpCqjdXE8oHWb0xaF5X+X6cuz/k/0UB4v0P9lPpDYCSuBIK42VZrUq3/MZfnGY6XMzXmUhZqr2BHfTkhGEoQcB6N1Znvmfz1CZWtwRcjnqmjXSdRpDPRY33hFnoMdiYsjBWSUJvVqwZZWsN46KiiUrCBRGnR5saVKlhC5CFL0stqyiSZ2CP4kjr65AixFSufXhRmqaQ5TzLnserAMBL349mBwUBdJNBtGkRtvGKHpYoXV9lFLOpVJ0yY7ajPdXOfdynpvf3IDvSYbOVVizPc66nfFZa7lacnn5W1O841e6eO//WMfJZ3KY5cDhFo6rPPWZMaYGl5gsFIHe2oCaiOKMZQh1NFI9N4CxpikIz1MDAvYyRYShIQwNe2Acv7x81pzEx/QCHd32F38uHN/E8ef2I/QQSH9+/PU1QlMM4moNE3bfstv50sOXPrZXwfKKy24LwerFKZqYpodiLHTuCQH33BHBsnx+4bem+dZjFQrFhbricy+YaDrYq3ykfG9+vHBHm8rv/HKad74lhqbB8VM2v/EH07x0eHHy+l5ibNzjO09WuWVvGF0TfOiHk3z2K0tLJqoCb34oSiwqZlYnNsdOLbwQQsAH3h1n7+5gwn35sMVv/WGGnj5n3qQyMelzvtvh1Fmbr3+qmfZWlb27Qjx4b5QLvQUs69XhlhUtXSNhEK6NgDKXAioAt+KS680hPYk/5VOZDF4I6UmUeAw1EkZaNr5lURw3KU05SMdFq6shtKGLytFT+KZJaE37TFqijh+Ok88I3MzKVqDvwYYdYcyKT0uXwfiwgxEWK5KuEEE5vfUbNO64O8yNu3XaOlVSqeDBz+d8erpdXn7R5tmnLEZHvGWXFpJAbL/syKjIOYnBk3MvviMtPN/FY/VPs+dKzh7I8cQnx3jwx1ppWhthasji337tPL3Hgpc7WW/w03+3hbrWEIomMMIKb/3FTjxXkp+0+cO3HcOxJN/61yFcy2fvGxvY/bo6jj+V5anPjHPnu5pmnXFnD+T51B/1cvf7mnnog21oOkyN2Bx5dJpqcf6FSKzbTvNtb0CLxhn4ziegyaBy5BzS9VGuW4dQBFp9DdL1sIcmEKpKeHsX3lQes2d4yUpdqZ03UThzdE7muRQnNvv2iZnkq8X/jq/bRHzjdka/8ZlVX+fFISm4k6irWBAKoRIz6ghrCaarK1ebkp7EM13WPrydzKlxykMLnXYhA/7uX4p87dsVqtWF+xOCQNp2g4l3341hXjhoLrvqq5pylmiaGlV++8M1vPvhOLoO57sdfuTnJjnXPUdGQhHU7Wpn6sjQXEnDy6/7qxh5YdmSFw9adPc6bN1kcP0Og1tuCi3pFNu6SWfnNgNVhVze5ztPVhe9ThvX6dxze4RIWFAqS/7tE0Uu9C4+IUsJZy84fOoLRX79F9KEQgq33xzms18uMTSykAgEgobwWibM1SeqrPg0SUA1VDrvXUsoGcLMVFHDGkPP9LP1fdfR8/Xz7P6lfUydmsR3fYae6kPZshN3OovWWI8zNIpvWWjpFPbwKGgawtAD1vR9jHVrcKczKNEoxtpOtHSK0gsv42Vyyx5XdtLl/LEqm3ZGePpreabHVyayaEywa7fOD/94jH23hjAWCdlsalbYtEXj9W8KUyhIHvlqlU/9V4W+XvcVz/yWrFx1ydJv/esw3/rXQB8/8cziwfa5CZs/edcSGvVlqOQ9vvq/B/nq/55fIeuJT47O/tv3oOdokZ6jq7DWVA01HEWNxILz8jyiN2zCGhxHTcXQmmpxxqbxskXC69qQnoesWPgVMyDcRa6FUduAZ1Zn40jVaJxwSwfStakM9qFoGpG2LtRIFGt6HGtynGjHWhTDwBwfwS3kKJ47QWLTjhWPfyVoIkRIxHBXsTKR0idTHSBpNLDam+xZLhMvDy35/fiEx9e+XV6USAB2XRfiphtCs0bB7fsivHhoedItV4IiM431Cr/3azX8yHvjeB4cOmbzQz8zQW/fFQ+5IkhuakCN6JhTJSLNSZyihWc5GMkwmROjuMWljSSBACUo4RgUlJCBiXpZaVAu8xMcP2Vx8KjFpvU6oZDgh96d4MBBC+8KmUMIuHVfmI62gMLGJjy+8/jiK6HtW3XWdATblco+33liZfnnmRcsfv0XgnF2bDGoSasMjXjEtToc3ySm1yLxEQjqw2teXdJVFEHLvjYmjo7R8/XzNN+00LOvxw16vnp2NtsssVUEEQGJQCNS4jHUmhQMj+IXS0jLDjKNtED3UVNJ8CXuxBR+oYjQ5w7LiBvUbaklUhMOJlZPkunOUh4pIgSMDzvE0+qKpNvUrPDeD8R41/si1NatTm9KpQTv+6EYt90R4h//rsjj37UolV6LqVwM0vGwzo+gJmO4o9Pkv74fALsvIHSrZ3WJMl61Qnz9Zir93UjPJ9zYQqStk+pQH0IIjLpGomvW4zs2brmIEAI9VYOWTKMna8ge2v+qnZMvPSQ+jrRmtPuloQgFXQ1TWIUD7RKizQkUTcHKLE4CZ7sdMtnFxxUCVEWw/yWTYinYxnVXNjwrFZ9USuF3PpzmR9+XwHYkz7xQ5Tf/ILOQcCFIUqnY2IUqsfY0nu2RWFtL/twEiqEtmdI/C00hsr4FaTsITcW3LrMwJQhdxZ7Iz2b/FUuSx5+p8uC9URrqVG7bF2bDOp1z3fMt05q0wt4bw6RTKr6UPPLdClOZhddKCFjTrlFXG7zzvg/33RlZ/pgJNOBLqKtRCIcEAkFES2LICA3hLipuHiECR+/VYEXSFYrAtTwUQ0ULqfiuj6IraGEN1QhOxLO9eem9UoIwdJyxCZzxCbSGBlwh8MsVpOfhjE8idBXpSJyRMfB8vGIJSuXgxpTnZqyWm5ppvr6R1r2tDB8YJr02zYVv9GA4VTZeH6FzQ5ixAZv+c0vPts0tCr/8Gwle/+bIvOwvKYOycuNjHqViEP6TSAiaW1VUdW67zi6V3/yfSRqby3zmvysUF2hrK0MVBg3hNZheibw9tqIn/JogINmVJt6WZPzgCJ33r2Pq+DjFgVWUJ3sV4JUqV13ce8E+qmX8WYFSYk2No4TCGLUNVIf6guw/wMlOYWenMOoaAHBLhSVrX1wrfDwkEkOEuaJSwwJIJHG9Dl/zqDh5VmPteqaL1qRjq4tfs6lpf0krV0o4dNycp0keO20tO6yUQSr0L/1kkh99XwIhAhL+7JfKnL2wxHLblxS6p/CqDqVqFqTEHC/ilC2MdAS5QmEloSpE1jaCItCSUdxiFXs0ixLW8QpVwl2N+KaNfVnK9ZPPmvT0OTTUKTQ1qLzhgSjne/LzJpSN63T23GAEae8VySe/sHhCla5DXY1KyAjuX1uLxif/pXHZY74S4bBA14N7PGleRFfClJzMrI+hFFo6C3IxrPiU+q7PxKFRKpMl1r91M4NP9dNxdxfhdIRQTZggQX7+b5yRMaze/tklol0emPe91T0XrWCeOrfs+InWOCMHx4g2RDnyr8do2dNCKBWiXPA4/kIFs+zjLWPkxhOCn//lhYRbKvl89YtVnnvaYnLCx7aDgPtQSNDWoXL/68Lc80CIaFRBCEG6RuGHfzxGoSD50ucqsy1WVgspPRShEtXSFOyJZT3h1wwhSHSkaLqpjamTE3Tetw4rU/2+kS6+nF9A5CohVI1I+xpCjS0kt+2iePYYWiKFlkwTqm8GRUW6LuHmNgDMiVHUSIxox1qcQg63XEQJR4iv3Yxe20Bsw1YqFy8ErVSuAQoKCiq6CLEi6UofiVy2AteVsAsWxf7skslAti2XDdFrqFNpalDpaNOIRhRu3h3m2ClryeQVKeH974zT1anNxiHH4wrvfEuMZ14wF4+AkJLcqYUZdoqhIn2JU1jBEWp7FF66gNBVhKYG2Z++j3R9pONhTxVwc/MjFMYnPb753Qo3XhfU4r395jCf+XKJ0bHg2dL1QFpZtyY4iWeer3K+Z/FJw9AF0VWUVV0OijIXWw2gCR1FaNh+BVXoxPU6MtbSMtGVWJ50JUwcG0ecmMB3PKZPTeKUHY7+/Usz7VoETsnmxT9+Zt7PrJ6Lq2qIpOoKobhGJbs0g/luoP1IXxJtiCJ9STgVolLyMSs20+PLe6jf+o4I9z8YRps5UyklvT0ef/FHBQ6+ZFOpLKz+dPK4w/PPWLx8IMLP/GKchsaAeOvqFX7oR2OcPe1w7PDcuHGtjrhex6R5EUWodMVvYLRynq7EjYxUzpGxBvHxsLwyurLy0mZRiJnEhCtfwkvPwoxENv7yMBNHRoP78gdPBTHQipjVzYQiFvCHlMzT1RaOPdcPam6SvQZL/ZLn59IBX7Ef6blUhwcY+dqnglKCtoU/NU6osZX88ZfwbZPkthuYfOY7KEYIo7aBSn839vQEvuciXRfpOpR6zlAe6MG3rWsm3ODIfMpeFkeaK06SilBxfYuonl6QkRYyxKKUregKtTuaqIyXKA3lF1xSyfJXuVTy6WjVCIcULvQ4bNu0/DJXCLhuuwESDh6xaW9VaW5Sue/OCP/zV9P8/G9OUzVXd19926M6unL9Xin9BaQ6bz/m4u/+J75Q4hd/MkVdrcLObQa7rjMYHQtkmJqUwv13hTEMgZSS//h0cUlnt++D6106FsmREza/8ydXn7h14vTccapCpz6yBs0yaI/tYLSyvOF4JVa2dO25s7kkISyst3DFbHel6r0ENt7RSCiuce7JsSWbFI4eHEWognNfvcD9f3kP1WmTw/92NDg2H+xlHpINGzXe8OYIydRMmJqUjAx7/PWfF3j2aWvJecH3IZ+XfPaTFXRD8Iu/EicWD0hv3QaVt/9AhJ4LLqUZmaHi5mgIr0FXwiT1evL2BCU3Q84e5ZrI6QoIRbDmwQ103reOwx99gdKMpzuUDnP9z+6lPFrk7KdOsO937sQzPQ78ydNAcF/2/Y+7UMMaz/+Px4k2xdj3u3eTaE8G+1UV9JhO/3e6OfzRF2azCufGVQk3tFJ7/a3E2zegRmK4lRLF3lNkThyYcWSvcK8VhVCqntTG60ls2IGRCnpY2blpin1nyJ05hJWdnKsO5zp4V4R6Sceeca5J7OlJ4uu3gu9RvHAK3zJnJYdL8G2LVTcQWxaChFaPikrZW0IyuBRYgY8QCopQqY12Ml3pm90knVQWNZR92ydcF0MNaZQH81TGV9lrcAblquTYaYtT52yqpsRylq/lIQRMTfn81d/n+Lf/LvLA3RH+4S/qqa9TeN8PxOkfcvmL/5XDWu0q7nvo3hgd8/jiN8p86IcStLeq3HpTmKf3B6m5ba0ad98WGC+nzzs8//LS99q2JaWyPxsLrSjw1HPLOxuXg6FEsH2TspNjc/p2evIvUXSWjkNfDK+uCHYZjFCCaKyBUnEM11ncq1icDJZCywX45y7mgwsm4es/9k0QYlXlIVUVdu812LpNm63Batvw2HdMnn9macK9HFLC5z5Z4dbbDe6+L4jxE0Lw0BsjfP7TFU4eDyYKH4+KVyCsxkkZzfQWD66886uA9CXZ89N0PbSB9PpaSsMFkBBvSxBvS9L/3R48a8aivcKvIRQRWLdAdarCgT98KnB+KILazfWsf9tWJo6O4V+Z2aYopDbdQMvdD6OGo3hWFbdcBCGo2XYT8TWbKA10L3/gQiG5bgdNNz9IqK4JzyzP1K4Q6Ik0DXvuJb3lRsae/Qb5C8dZbF0sHZvC6SOzf1f6L1Dpv3Atl/GqIZGoQiOiJBCOEtROlfOdVXNtZgSe7+L6NplK/7z9bFyvoyxm6s4oEdXx0lUT7iXU1ais69TJF3327Q5z9sLymWmf+VKJ//h0kaoZOKz+6h9y/MYvpKlJq/zyT6cZGvH41BdLr1pM6ivBxz9Z5Ad/IE4sqnDXbRH+67Mlevoc3vRAlHhcwfclX/5GeVnntusF8b+lsiQeU2ht0mhuVK+pToVAYW1iNxA8GxW3QH24i6TRyMXioVXv53tGuqnadazb9HrOn/wC2enFX87hE4uHQF2OxusbsfImud48dmlxKUFR5wo/X0K6RmHPPoPoZVWlJic8vvOIiXkVvh7Lkvz7v5S54+7QrESRSiu8/k0RTp8szpJ3xhqiI7Yd0yvhSYeImiKu1aErEVzfwvTKJI1GdBHG9Ark7PGlB10EpeEC2XPTNN/czvjBYTzLo3ZrA1a2SqE/tyqrQ3qSykSw1Is2xWna28bE4RFG9g8scIjE2tbRfPubUENhqmP9ZE8fxMpOAArh2gaSm4JWRcs5r2Jta2m5882okRj5C8coXjyDU8yCEBjJOlIbdxLr2EDLXQ/jmRVKAwtTuF9NXF4w3DDEbN2E5eBIi5hIoc7UybUdZpfgQgTps8dOBpEOVSeH7VXmSQuxqGD39aFFx/KqLoXeDPYyIVfLoSal0NqssW2zwfXbQzz2zMpJRZerSOWK5FNfKNHeqvFj708QjQh+85dS5As+X/t2+fuWHLFURumZC0Exn7c8FOO6bQbbtuiMjru87U1B2u/YhMdT+6uYK0wQZ7sdhkddtmw0iEYFb7g/ysc+uXJI5JWQ+FwovHDl0RPT0le1n+9rY8prQeP2euLN89u9KCpEYgqxpEKqTmXrruiC39XWKey8fq5AiO9LhoY1uofSV30Mx4/Z9HbPfwLvfyg8254FgqyoiJpk0uwDgqSI8Wo3WWsYx7fwpUvWGmbSvIjtV7natZlnuowfHqFmYx3RpjhaVA8cZqcmMJfL4FvEwlIMhU3v2QG+pPfr53BK81cOQtWove4WtFgCt1Jk7LlvkjnxAuXBbsqD55k+/jwTL34Xp5RH0ZfQEYVC480PoidqKF48w9iz3yB35iDloR7Kg91kTx1g9NmvY2Un0WJx6nffvfS+VglNh3BEzKu5ezkuT/ltbVZnO9IuBYWgToUrbWq0JgAKRZ/pzEyLdgEP3nPp2ZOUnQyFK8o6vu6eCJvW6wu7hQhBalN9UMR8lXLclYjHA+/+VNbjpSPmrHZ5NZic9vm7f8nz3SeClN/2Vo1f/bkUN+8OreA6fGUIhQVdG3QSKYVd+0Js3KYTi88f0bYl//3ZIpYtCYcE998ZYc8NITatD97rFw5a9FxceWY4fsrm+Ckb15VEI4L3vTPO1k1X38AUAj0XBIYSwVAihJQojeF1V7WP/+dJtzxRRr1Ud3QGa7eEueONSW59MPhv256FpFvfoNDeMceKngdnzoIM1xOqbSTWtg6hakRb1xJubCe+ZjPhhlZSG68n0tRBqK4FI1WHGo7i2HDo5flWdlu7SvtMLF9UTbEheTNFZxrrspTVrD1Czh6l6hVwpU3OHgsaCF5RJ2C1mDo+jpmt0n5XF/G2JNHGOFPHxi7rmrHwN0YytOCzjW/fRs3GOs588jjVyYWEHaptJFzXDEJQGuqhPNw73xSRkurYAJWh7iUbk0Zbu4g2d+JWSxR6TgYW7hWwpscoD/WALwnVNBKua1ndhVgCHesMfuxX6qhtWDyC4MRpe/Z49+0Os2vn4hboJfgEWqAnPTQRTAgTkx7dFx18P4h2efNDUR66d6FzVAjYtzvEh38qRW2NsvA6SUn+/BTDT/WSv3B1IUeXMDrmcvi4RWOdRqkkMc1rI++hEY9f/4MMJ87YKAJ2bjP4jV9I09nxPVsIA9DUotLRpbHt+hDhsKCmfv59kxKOnXI4eMRCSrjjlghvfiiKqkKl6vP8AZPxyZVnmnzB5z8/U2QqE3RRvmlXiD/73VpuuM5Y8v4rCmzfYvBzH0yy54YZaRGFpsgGaowWNqVuY018F2sSN5AKNV3Vea94VbVECiNdjzU1hhqJImZad1rTc8tjXY/Ruf4e6hq2IfHJTp6nXFoYZpKqWUtLx17iiVY0PYLrVJgYO8lQ39NBc7hFUM2Y7Prg9ax7oAszZwGSqaNjPPXVQTxPohuCVO3809A0WLtWm9cRwfeg+7xLqL4Fz7HQogkUPYSRqqM6MYRvmUSaOsCXhOtbcUo5hFCojA8iJZw7YwNz5K4osHWbxsUel4qXp6dwYIbzvndamFtxGH6mn43v3IYeMygN5cmcm3thzUyVZFcaPW7gWS6hdJiaTfVMnZi5VwJab1vDhrdv44Xfe4LyaDFooSPn1zA20g2o4aAFdXmoZ1Gt1XcsrOwU0nUQ2sLHKN6+IWj6Z5m4lRJqZPHmlG6liJQSRTfQ03Uw1r/odq2dOqWiR7no09CsUSr6xJMKazYY9J6zmRxx6TtvU8r788J7LseZcw6Hj9vcuNMgHhN8/H838LFPFDlwyCST9YlGFRrrA+vx0adMuk/HSWi1VLwiGSeoVFc1JU88W+WNr4vS1aHR0qTyL39bz5e+Ueap/SbFoqShXuGe28O84f4oNWmV518yuXXv/JXRpfv5SuB6UF+roqpQrHjcsCPEMy+aqyxFOB+Dwy4//DOTfO2TzXS2q9x3V4Tf+eU0v/Z7GfKFxZMOYjNlKpWgOTGKEkgel793tWmVulovqKE8o4f7vsS0JL0XHG7YG+LIAZMzJ+xF/SzjEy7f+G6Fm/eE2LBWIxYJpIXuXoeXDq/ONwPwxLMmf/bRHH/0W7Uk4gqvuyfCLTcFadMHDllksh6qKqirUdiwVmfPDSE62jQmpz0+/DvBOybxGSqfIqql6CsepuhMIxB0Jq6/qmu9ckaaqhNubsczK4Tqm3GK+XlhOEKorN/6ZlI1a5kYPYJlFkjVdNFWf8eCfaVrN6CqBuMjh3Fdk3iyjY6u2xFAf89ji45vZk3OfHF+SEbuYgHXlbStDZGoUZkamf/wKgo0tsx/wn0JQwM21dH+wENumUSbO0BKjGQtnlnBmh5HC8cwM+PosSRKJDpbtHxoYP7dFTDPkl4q2UGNGRjJSJBkUrWxMyvrbsth+Ll+Nr/nOlpv7+Tkxw4jL3OADT/TR+OuW7jh5/dRHimQ3lRPZWLOQZPoTLHzp/aQ686QXl9Hen0dANXpCmMvDc3qulo4itB0ghZHS4fXeGYF37FRFyFdo6YhqKRV28i6d/70iuclXQVVX2iVX0IsobD37ihPf7PEHQ/GOfpilRtvi3DuuMW7P1jDf/2vaQo5f1knUqns82cfzfHXf1hLR5tGIq7wSz+VAlLztrNsSd/AJMdO5IAg2+zyjLTHnqryic+V+NAPJ2dIWuOnfzTFT//o/P2Uyj7ffLTCb/1Rhhe+00Yq+eov2Csz7ZPWdRoYunhFTUkv9Dr8wm9N8b//vJ6ONo0PvCvBwJDL3/xjnsoVSRqN9Sof+b1a2ts0knFBIq6QSCjEY4LQTAEfIeAbn26mVJYUSz7Fkk+p7DOd8fn3TxbYf9Ci+4wTaLpLVLCzbDhwyORCr8OWjQbtbRquKzlx2ubkmdUHy0sJ/+c/izgO/OJPJlnTrpFKKjx0b5SH7l24UoZgcqiaEnNehJSk4uYu+wsGSydWfRywCtL1rCrWxCi+6+CVi9iZSdTI3EHGk63UNWxhqH8//d2PAZKx4YNs3/UBorGGefsa7Hsa33dnLSchVGLxZmobNi1JutmeHNme3HxtUkJNg8bWG6MMXDC58fY43/7s3PJVKEEK7+WQUlKYLlMeXsXSXlERmoZdmCOcYvHK5G+oWaJdup6OEF9Ti297uFWbWEcNsc4ait1TTB3oW3n8ZWBlTbIXpkmtr2HspeEgTEkzEEJh8tg4J//PUeqva0YN6Vx85DyKCBNpCDKq9HCMkWeGUKMKNZsaUFQd6fvk+ycRipglXaHORXz47tIPtvS9JUPGAn1W4NkW1vTYivGynm0F0RFLoOeMxX1vjtO10aBc9AlHBSP9DmeOmqzbbNDYolPILe+Q8nx4/Okqv/77Gd7ztjjbtwbdJCKRoJtF1ZTkCj5DIy5jE0E2mq6ESGuNlNzsbKEi24G/+ac8w6Mub3pdjC2bdBrrVaIRgeNK8gWf7l6Xp/YH5Nw/5NLT53DjzqUnlWvF2ITH/peqbNtkcPSkuWy49UrwfXjmeZO//Pscv/3hNC1NGh/+6dQsSV4eShaNCO65PUxT49IUIkSQyVWTFtSk596VStXn+YMmOQ927QsSkD76xxmsJRzcp885vHTIYuM6HVUV5As+jz5VXXVM8SVICf/5mSInTls8/IYYu68PsXaNRm1NoO8Lgmcgm/cZHXPp7nV54aDJiRXI/fKiVqvByqRbLVPuD7zKTjaIR/Mqcy9HsmYNUvrkpi9waaryPZvs1PkFpCulT139JmKJFnQjhqIahMJJPM9mNvD+CsSaYmx4/TpSnUlQgs7CF5/oJ3N8jGLOpaFFZ3TwCkcQgXd6/thBX7JVwfewc1PziOLKmR5Y1BEjVIXEunqi7ekgm+/ZbqojecKNCXKnRhZsf7UwEgaJ9iSDj1/EzptEU60k67rwXJNyboTJQ1mK5xQKUxcJRVJE02kyxyeCdjfZGOc/3YtZmiKebicUq6U41Ydtzp+IAjK9lEyxzCMiFJbK1AquncQt5Rl/8TuLtlmat730Z9suLQbfhwNPV3jTe1P8619M4TiSDVtD3POmBKlalakJl007QqzdZLDvnihPfK1IpbzwnlVNyde/U+Hloxbr1+g01KuEQ8FKyLIkhZJkcsqj56KDRJJ3J6h6pQWdPqpVyX98usRjT1dZ16UH+flhgetCseTTPxgUyL5EDL/7pxlamjUmJj1Gl6kT8t0nq0xlgvest89ZMeW8pUkjlVDxPLj71iiRiEL/oEPf4NwYX36kPFtV6/RMTO/lEAhCSgzTL1E1JZ//apnBYZf6OjVIkJryFqwgKvkEv/I/M7PvQESJY/vm7MQUUqL40sWVC5vHep7k5FmH6YKH50k6uvRlZYJc3mdwxMVxQVEkQyMujz97benmngcvH7E5ecaho02jvTXo0BEygqQhywomzckpn/EpDw9JdoVC6leLV6yUG3ocicSx52ed2Nb8F0hRdTZtfweJVDuF7ACV8jiua5JIti8bdtR2cyue4xFtjHLm82dp3dsCMrhxfedMrKqkXFgopl95EwVBScfV4krLTF/EN7NYFoz0fTzLIdKSZOK5XryKQ2xNDW7Jwi1dZe7wJQiI1EWJd6TouLsLLaLR+7WzAKh6CNUIY5anCMVqKOdGUVQdIRQcu4wRTgSdSovjCCGC7xAoqoFjFnGshUTnmZWgBq4RQk+klzws1QihaBqLEa9TyM5UlhL4tjWv59y1YrjPIZ/xGB8O7s1T3ywRjggOPivJZzwcW/LvfzuNVZVYtsCoqQvksCuSLXwfRkY9RkZXdsJ40qUql868GhrxFi35dyUef2Z1JHH6nMPpVfQUu4R4VPCWB2M8/UIFIYJWQ5Hw/BXY0RM2R08s/uwJFMJKDEUEL4eCilWK8t3HK/h4RJUkjnSYLxMLQnYb3/jGRRxp4kufpB6l6BTx8TBEhBojTsnNUvEqhNU4tl9dYBEqCmSnfc4ct5dNq2+oU4K+cHrwzn3zsSqTU68sjb5qSs73OAvSh5MpQV2DykCfSyKhUFunkJ32SSQE0ZnY4Mlxn9o6BU2DyYnlJa3F8IpJ1/cdBAJVm9/9V7miOVJD805qG7Yw2PskQ/37QfpIoKFpB4Y6Xwu7HEbcINudpX5rHYPPD2OXHWrWpZk4MsrmG6I0teucernCuaPV2Vq6UkKlsjBdNhpR4BprHkRjC4mleuUYMwNpYZ1QfYxYR5rSxWkab1/PwBePXv2gQgHpo6gqjXta2fFjN1IeK/HSnz2LmQ1eYul7eK6F61TRQwnC8XoSdWuwKtnAYgWMSBJVCxOONxBNt2JX8kjfxfPsoEPyFbALGTy7ihaLE2tbS+7MwmQPoajoyRoUY6b+xhUoD/VQt+tOtGicaFMHlZGLC7a5Gmy7Mcw9b4zzuX/Lzj7k2am50C0JlEs+5RkZSGg6kYZmJBKvUka6DqHGVtxSAbdUQE/VAODksyi6gZaswS3mZrPb1EiUhgfejPQ8pp78Fl5l+Q4GryaUSJT6ux8kse16hKohPYeev/6DRbc922Pzvz6WI5vzuNDr0H3xatv2BPnjdaE2zGqJptBaAEy1HNSdUEJE1Dj9lRN4M33fBKArBnVGO5ZfJWMPk9abMb0yAkjrzSS0WqpeiebQuqDdkrQYNy/O08Z9P3iHFn+P5rBti8HuG0IoiqBq+nzic1cXY2sY8Es/k+QTnykzMjb3vL/poQgDQy4nTgXXrLFJYe+tIbIZn8E+l2hMsG2Hztiox/2vj1Au+XSt13j82yabt2msXa/xpc9WGR2+Ok55xaRbLo0FveETrRTzQ1wqnBlPts3bLhyuQQDZ6W6kH9w8XY8RidXjLRG5AGAXLYQqsPIWHbe1E6kLwnM8V3LheBVNE6zbFiaeUjnxYplyMUiFnLqiT5IiBOnaa3dk1NQuDGcZH1t4sZWQSqQtRc/HD1CzsxUtotP7Xy9d3WBCQTVCRFvWYk6NoIYiDD45zNBTI0g/IGE9nsJ3bCqlCSqFsXnaa2FyLhnFLE/PFqEZ65kre1jMLE0i5tQodnaKUE0D8c7NhBvaMCdHuJxcQ3XNQdjdTHr1lSgNnMecGiHa3Ely407Ko/1UxwcXRkIIgWqEQQg8c2kn4+nDJqcPL7QWkzUqiZRKtRJ4YjKX3Xc1HCG6ZgOVgV7wXLREiviGbRTPHUdP12HU1FE8dxK9pg49mabcZ82lFAsROBOXkVC+V/DNKpnnn6J46hi1t99HtLNryW1tO4g8ADi3RNEXEBhqBNtbeH0lEtMrBQ01AVVo5J1JNGEQ1ZKoQsX2Ta6sHuFJn5KbRVdCKELFlx66EgpkIulScrNIfCJaEik9ql5ppibF1SEaEdx1a3i21OIj363Qs0gJytYWFcOAclkynfFpbAhkI8eFfN7HMIJb2dig4DhBq6HT5xymLpNOEimFzJRHT7eL789US1QCGTEUgke/ZWEYgvoGhdY2lUpZXtOT8YpJN5fpxaxkaGrfjWUVcOwSkWgtqZqueduVS2NIoLZhC1L6qKpBQ/NOhFg+VHj4pVEUVVCeKLPjfduwCzbnv95NKKxQ06Cx/9sFqmWfVO38mNzBAW+20D0ECRVdazWeefLql/hCwPqNCy9V38VFZA3LI3t8hMS6eordk9j51bdmvgQtmiBc14KRrEVRVVyzQqKmMahk5booRohi32ni7ZsCS9csU5kcWrTI0LVU/ZKuQ/b0y0Rb1qDHUzTf/kYyx5/HntFl9USa9OZdhGoa8V0bsYhuI32PiRe+Tet97yTa0kXzHW8if/YwVm4ykC6UYGLRYynCDW3Y+UmmDj+zYD8roWtTiNpGjf4LFqGwmEe6QlW55BoPNbZi1DagzJTXkq6LWykjVA23mCfS3I4WS+Lmr9SeVyo78z2AlLj5bPBfcc+SsdCrRURPkQw1Ml4KfDMChZAWw5MujmeiCA0J+NIn70wQVhOU3SwVK09Sr8fxLdzLpAGJpOxmCSkxqn4BgcCTDoYSpuhkMJQohhLB9W0mrItE1RRVr3BVHbBhplD53hDvflscRRFkcx7/9PGFUk9DvcKP/1CcyclgZfeNb1X58M8l6e1zGJ/w2P9CUG5gXZfG9dcZHHjZYnzC4+d/MsE3vl3liaeDWgwjgx4dnSq33xXmka9UCYcFUkI8LrjY6+LYkoF+l7GZbjKxuGD6GmSOV0y6rlOl5/w36Vh7F+s2vR7XrWCZBUYGD9C57u7Z7bLT3YwPH6SxeSc1tevxfZdyaZyJ0ePUNmxecv/lsTmL7PmPHACYLcxy8qW5mTufmTt534fhIY9c1qemNiB1VYUt264tC0UI2LFz/m9tG84t1qxOSsp9Gcp9196CXtF0hKoGk1M4hlsto0bimJNDqJE4qhEm0tiBFo3jlPKo4RhCUWf7zr0aKF48zeShp2jcez/xzk2E61twSoHDTYsl8R2L7KmXSK7fHoSHLbaPgQuMPfdNGvbcTax1LdHmTpzSjMaqKKhGGC0SBwGTB5+6puMc7LGpbVAp5jxKl5kd0vMoXzwPCKTrYmcmcUsFhKJiZzMgJb5t4eSmUaMxKgM9uMXcwgEkhFs7CLW0o0aiONlpSudPzyNnoemEWtqItHaiJQOpzMlOU+45P+t8vgQ1Fie6bhOh+iaEbuBVK9iTY1T7e/CqVxlOqCiEGluItK9BS9UgVBW3mKfSewFrfM5pG9FTs9XtBILW5HZc38bxqhTNceJaDSU3g49PwZ2i4M4ds7mI5g8wac+Ppx4x51K4s84I2Zm4ZnwouSun+ysKbNmoU5pxftamFW7dG+L9P5BgfZeO50v++3MljiyiTe+63mBdl8bgkEtHu0o6rXDugoOqQibrUypLkkmFn/1Qgn/5WJGXDwf7OH7Sxr6s7ku1Kjl/xiWR9LBtycUel4s9Lioa2f4YCjrnjvgYxDk+XiEsItjSCU7yKnBVpBuJCLo6NM5ecGb62gef56Z7MCvT6EYQAO/YJVzdxS35uG0qkaZOACaU84yfOIyqhEBKKpVJEIKJ0SMsbk2ImSVJMNCVVbCWQzbjc+6sw823BmE6igJbtuo0NCpMTlzdRUomBXv2zk9RPXvKIbtIpfpXA04ph+9YmJMKvucFXtXsBL7rIBQVMRONbmUnMJK1+K7zikoYLgbpuUwfew4rM07N9r1EW9YQrm/GrVYo9Z8je+ol3EqRaMuaJUkX36Nw4RjW9CiJtdtIdG0lVNeImqhB+i5OuUDh4inKAxco9C7dEXg55DIuLz9TpnxlAL/0cXKXTXxXcIc1OZe84xbzuMXFQwnDbZ0Y9Q24hTxCUYlv3kG4rZOpJ76FW8gBoNfUUnfHAyhGCCefRajBdpHOtUw98a3Z41BjceruuJ/ImvXYk+NI3yPc3Eqkswsnl7lq0lXDEWpvuxe9phYnm0H6PrGN24it38Lk449gjQY1Xit2hoZooNWG9RSGGmOy3IsvPTzpUHKzM5bs/70iN9GI4J/+qp7ITDREKCRobFCDCm0ElcE+9oniFTGzAcplSSbrs/9FKyhwM+bRN+CyYZ3GLXtDlEoS25Z87ZtV7rkrzP4D1pKNPKcmPaYm5y8aNXRCREiQxsMlwwQeLmEZp0Txqq/aqkk3lVL4H7+SprFe4U/+Js+m9Rrf+M6lpbPErGYxq8GMFt25ls4PvRGhayQuSwj3LYe+X/5nonqaztSNDIkSJXsKxy4RN+rRlBA5c66tS8yoJW7UMVXuw5NLhZUtHmo2Nelx6IDN3psNFCUo7djYorDvFoNvfPXqwk1e94YwycvifqWUfPsRc9mGla8E0nNxK6tzFviuG0gIq1iCChFY/KoyU5uXoDuscxlfq0pQOct2JNKxKV48TXnwAkLV0HWBokjMsoPvOiiKYPTb/47rKfi2iWEE2rmU4LhBA0Tpe3j5MfLHJ8kcey7QeQjqI+iaxHM9HMtB+n5QnX9GS9O04Bh9Pzi+K09v9jwUgWdJDD1oV/NK4lQXgxZPMPbUd6j0Bgk6sc3baXzgLVR6L1A4cTgg92yG8Ue+ENTvdV2EEMQ2bKHxwbdiNDTNkq6WSBFbv4XMC09RPHkEKSVCUWYz964Wnlll8rFvIB0bf6aJZ6ihmbb3/CjRNetnSdefdZaKGW1W4voWUgaxyI58NcpgvjIoCqzv0qmrnZMbpQTHgQOHq/zun2WWLFT+8mGLO28L8TM/EadvwOORb1f5sQ/EiYQFmZzP2LhHuSJ5dr9JLqfzMz+e4MgJmwfvj3Br0aeuVuEr3wi4bDEyjhBDR0dBwUVSQwMl8kSJEyNFkatb1a6adNet0XjuRZObdgXV3A09SP9bbEVb++ZbyHz1eXKPHV508qw4OcrO3IGqQiesJak6AWkLoVITbiWq18xU41eI6A1EtARlO0PFzZEINaIKHU0xmKr0ceVApgkvH7B5Y7/Hmi4NIaCuTuFNb41w8CWbsdHVWaktrSo/9qH4vCIqYyM+j3/32mtyvprw7dVrxts26/zEB1LcsidEQ52K48LBIxZ/+Q9Zjp2yEcC7Ho7zh79Zy4/8/AT7D5jBMtyx0Xybf/hIA3ffGuG6OwepuHDjToN//qs6fvG3p1jTEeEnPpCks02jXJH8/G9N8cwLVVqbVP72j+sZn/T43T/PkJupxby+S+MfPtLAiTM2/+NPp0kmgu0Ghhwmpjze87Y4zU0aF3oc/vafczz2dAV75p1rbFD5kXcneOi+KB2tGqoq6L7o8I8fz/Otx8o4jjqjU87psVJKYqFaStY0hhZDSg93Rs/0pXsZMc2HOTqENTo4S4rmYD/W+AjRtespnTuBb1lBhmO5hBKOoIYjoAS1FqT0UUJzCRGXMiFDLW1UBy/iFvLXRLaz8H3cUgE1HEWLxUFVEaqCb5mo0SCBSVeitCV3ENaS1Ee7yFQH8KTLloZ7KVqTDOWPLdv/TU/VIX1vcenlCqjRoAWQWy6ixRIAyya8XA7bgc9/rcS2zQbplILrQP+Qy3efqPCVb1XI5Zc+RtuGP/vr+Vrvb//B/OP9878Jvh8c9vgmwTV/+rmFk43QdPR4KpD3AN+skCtPkSOQXDR04qSoUKSPs6s6tyuxatLt7nXYe2NAuNdvNzjf4ywZ0Cw0leq5oVWvVnzpoSkGiVATZSdLKtRMbbSTsp0lrMaIGjW0J6+naI1TF+2iO7OflsQ2StYkZTvDUgOdPO6w/xmLllaVUCiwdnfvM3jPD8b4z4+VV5QHWtsUfv7DcVrb1dkMLcuSfOGzFSYnvzfSwvcSHW0a4RB85stlRsdcmptU3v/OBH/4m3W890NjlCuS0+dsJqc8bt8X5uARC2tG89q4Xue6rSG+9Eh5XnC9EPDj70+STAg+/7US+YLP5g0GfYMzoUsi2Gaxyl/iiiYWQsC73xbnxYMmH/tk8JK85cEYf/gbtUxnPF6aKXySSig0Nqg8tb9K90WHkCF4y0MxfvuXahgcdum+0EDMqMHz7YBU8SmZkzQmt2BnDhPW4ri+TdRIoylhStYkprN4LK5XKc1akQC+VcWrlNAS6RmrHdREiuR1NxJpX4Oi6UghUCMR1Gicy8/QyWXJvfwc6T230fKOD1Dt66Hccw5zZBBvleR0OZRQmMSOXcTWbkQxQkghUHQ9OLaZcR2/wsXs/OiZwdwRNtTdxljp7IoNN9e8/YPYhQwDX/zX5Q9GCBpuvg9F0xl99Is03PIAIBh9/EurWoWZpuTDv3PtfpBXC6HaBur33YeerCXc0ELuzCFGv/P52e9dXIrkXtEYqyZdzw+yZQ4cssjnffoX66c0g9LBC9S8cR+FF07jFcpwqVarlFgDC7ulSnwsr0RIDYpZRPQU+eoopltCCatEtCSWW6Ti5KjOvByeb5OpDmC6Sz+spZLkc5+qsGOnznXX6yiKIJFQeM8PRqmpVfjKFyqcOuksaDIQjQp27TF4x7sj3HVPaDa7zfclh162+dbXq6vPbrsMykz4STgiCIcF7R3qgiIo6RqFjjUqZjXI+TZNedX92JbCE89UefyZKpc4RFGC2OWf+2CKUEhQrkh6+hwOHbd48+ti/Ot/F2ZJ9+Y9YepqFb7w9dKCd2jndoO3//AYA8OvXFfO5X3+8u9zHD1pIwScOe/wDx9p4L47oxw7ZQeFUvocfudPprGdufe5t9/h3/+ukbYWlf7eIIzJ8h2iRhzHq2I5RVwvCOTX1BCaGkIVGgVzYknCBRbM51LKwGE5M4sITSd1w00kd+6mcPQlKn2BQ8yob6LpDW+f/1vXoXDiMNbYCJHOdUS71tP44FsoXzhL5sWnF4mcWB7xrddRe9u9lE4fo3T+FG65hGKEaHn7D67426nyxZW7flwNpKTYcwqhKEjpU+g+Ofv5/5dgTY0z9uRX0aIJOt76I4tsIWez7q4VqybdkCG4944wtbUqT+83GRp1l7R0jc4GYrs3ElrXgjTnyulJ22X4jz9NItRAOtwWtL7wguZuDdH1aIpB0ZqkYI6xJr0byy3jSotsdYhEqJGonsbyyri+vWSBmStx/qzL336kyJ//bZqm5rkmk+94V4Q9e3UG+jy6L7hksz6KgHStwuatOmu6VFpbVbSZZpZSwsVej4/9S4n+vpXF3FRK8NCbImzeqhGLCWJxZbarqKYJNB1iMUEkOmcJKQrcfV+IzVs1HCcouu26YFuSSkVSKUvKZZ+vf7k627XiauD5cNveMPfcHmFNu048rrBpvU46qcx2Py6VJS+8bPLG+6Ps3Gbw1H6TdEph766gK8GldNLL8dT+KkOjr44j7+wFh7GZsC8poW8gCPjftcMgZAhMK+hpt75L58F7o2zZYJBKKTQ3qtTXqRi6wNCimG4RRdGYKJ7Hn+noMFm8gO87lMzJYP9IXG/55b0ajSH0ucgVxQgFbYuKefB9FMMI9NOxUfLHDuGVAgLXEimUUHjhDn0fa3wEa3yU0tkTxDZupe6O+6kOXqR4NaQrBLENW/HKJfJHXsKeDowZLZmeVxtlMUh8subqGymuFuXLOnqU+763Bem/V5C+h1sq4Fkm/hLOaVWbiSt3rj4cFK6CdHN5ny98rUxdrcqP/2CCD/90kn/5jyLPvWguqDA/9aknmP7sUwt3IoMbXrKnODv1BEiJ41sITC5mDwAiEPiRXMg8F8T/+S6edLiYfQlVaHh+4GXtzx2a+ffykBJeetHm134xx19+NEVTSyAVaLpg3Qadtes1br0zhOcFgc6KKoJAajHfcTY66vORPyrw4n57VQ60RFLhgYfC7LvVQBFBkLUQSxfYhuD7+gaV+svqwQbzlZwN1vZ9OHPS4dQJ96qMCEOHX/7pND/5wykOHTd57kWTySmPu26L0NWhzVvmP/tiUKf03Q8neGq/ydpOjd3Xh/j4pwsUSwtn2pHRhbn514pq1Z/X4aFSlRSKPu2tGooaTEz33xnhr/6gnqmMx+PPVhl8yaWlSWV9lw5CMFm4gKHFMJ0inj+3TKjYAald2YNtOYRa2wk1NAWRChJCjS2Em9uYevo7+I6NooSRjnNZWySBEgqR3H7DAtIVqo5i6EG/N4K28db4CCjKPGJfFIu0V5C2HagIM+OiCFI33LQi6b5S1O27j/SWXUy++CiFc8eId22m6a43M/bU12brtADE1myi+e63MP701yn1BY7IxMYdNNz8AANf/DcS67eS3rEXNRzFnB5n7Imv4JbmokgirWup23MnobpmpG1S6D1N5uAz+M78pWli/TbSO2/GSNUFk1p2ksL5YxTOHZu3Xbixnbqb7iLc2BY0MO07R+bQs6tyWofj9UTTrUjPnc32DNevRVF1KsVxqvmFpWyXwqpJNxwS3HNHhDtvCXPwqMU//XuBt78pxrkLzoJ+Q5HN7aiJuRsfEJjENx0Kz53Elx6+NzdLSCSOP9/icLz5s4jrW7jMXezLX6aV4Ptw8IDNh34ky6/+dpIb9+hEZ2qBChFkmyyWdeT7gXV5+qTDH/yPAj0XVm/NCRF0Mri87fu1ICBpMU+GUNSr3+fO7SHe/qY4jz5d4Rd/e2q2xUlLk7aAMAeHXV48aPKON8epSSls3WgQjQgOHrVwFuGr5dqEX+opduVkEzJEUGTkCoTD4rK+Y8H1C4UEpXKQaZhOKvzIe5NMTnv87G9Mcq7bAQm33BSeLa3oeCbOChbsauEWCtTf/XoS23chXYfo2o2Yo4NULl4A38e3LErnT1F/z0M03P9mnOw04ZZ21HgCZ3py/rm1ddD88HuxxkdwchmEbhDtXIc9OY45Ohe1o8YTRNdsQAmFCTU2I1Sd9E234Vsm9vQk1tgw0nUpnDxCy8PvoeGBt2CNDmHUN2HUN2CNDV95Gq8MM7dXaDp1u26ndtftTL30xCypCU1Hi6dQtPkTh6LpaIkU4rKOIIpmYKTrqLvpLiLNnZQHukEIwk3teOZcTH5y0/W03P82KqMD5M8cQosnqdl5M9GWNQx+9T+RM1msqW27ab73YcoD3eROH0QNRYi2rCFU3wLnjs8efHztFlrufwdWdpL8mSOokSjJTTuJtq1l4Mv/jm8tb7WGYnUoiobruRiRFFJ6RJJNjPe+QE3Ltu8N6cbjgtExj1///QyeF1gc33myOlvgWIlHiGxsw+ofJ7KlE6MxPXOVBUo4hN5ci3lhiMJzJ1d9cK8mpAykhl//xSwPvTHCvQ+E6ehUqalViEaD5T4zISqlsiSb8Rjs93j8UYtvfq26aMWqy2Ek6zBSdXhWherEEGZVcvyIg3WZ00kNRwnXtSAUhfJw72y2mFAUtEgcLZZEDUVmCsX7eLaFU8zilOdrjmOjPvH2jbi2RXVyCC2SwEjWoOghpBfEvzrF7LzY3UQ8qHV68KgVhINJSCYUNm3QZ2MjL8eXv1nmXQ/HedfDca7fbnDwqEX/4NVLCLYTWKqNDeosyQoBazp0Wpo1YL7VsmGtQX2dOisxtDardHVqPPFsFduWJOIKDXUqvf0OkzMpnJoGG9fpQVUsgjAyCSjhEIiZNt++RGgq0lndOUjPwxofoXj2OH61SmLb9aixBIWTRygcP4iTnSke73sUTx9DSkl03UaMuqAtfOncaeJbduAW5+6dnZkif+gFQo0txDdtx6uUKBx/meLp4ziZuYQEPV1L8vo9AHjVKpW+bmIbtgJgDvXhZKbwXJdqfw/j3/wS8c3bMeobsafHmXz8EaJd65DX2AJowXUApPRQjBDpHXtJbdvN1MtPkjn87DXvU2g6Rl0TQ498Are0UE/X4inq995LsfcMI9/53KyFb02O0XTnG0ms30bhfED4kZY14HmMfvfz89LIhapyiXDVSJzaXbdTHR9i+JFPIGeWquboAM33vY3kpp3kThxY/pgJ6o34rg1CoOkhzNIU0vewzZVb0c87v9VuODnlMzkVvCD1dQp1tSrnLsyZPUrYILy+FTdTZPK/Hp1/wCGd+E2biV23dsn9h40UDenN+NJjdPo4/iLSgSI0Gmo2EzZSTGTPUrXmezsjoVqSsVZCeiKosuWWKZRHKZuTs06DXFbymU9U+NY3quzbs4Ht2zrw1W6kkiceaUajmXxW52KvxclTUwwMX8T1Vl47Rxraabv7HTiVPD1f+kcmJ6v8zUfmL1uS69pou/tN2LlJLn79KL4TxB4n1mwmvXk3kYY29HgaRTNmYnULVMYHmDr+HJXRvssuqGDbj38AKzfJ5OEnSW24gXjberRoHN+2qE6Pkj37MvnzR2drMoyMuYyOezx0b5TzPYFEcvu+MLt2hBZtQPjSYYsLPQ7veVscwxD89+eCdidXi2JRcvq8zU/+cIr3vzMetJepV3nT62LzaqxeQme7xgd/MMmjT1WwHcnDr48Rjyk89nTQgFDTfE6dtbnlpjAP3B2lbyAobv2BdyWozrSr0Vsb8B0PoSooiRhCEQHxSondt7rymr5tUew9DoAzOhVYtktta5kUjr5E4ej8KIHMc/Mrq3mlApn9TyB0ndZ3fAAnnyN74DnkFZ5Sc6if4U/9K4oWSBaevTiBSs+ldPYEpbNzRbQVVZA/PLno9teGYKmS2rKL1NZdZI+/SPb4lc0Zr3KPnkup5/SihAsQae5AiydR8tPU77tv9nM9UYNQFCItnbOkWxnpI7l5Jw23PUi57xzVsUHccnGWWAFC9U0Y6Tqs7BR1e++dJXE9lkQoCtHWrhVJ17HLOGYRFIVydr4eXphYoSv2FVgV6V63zSAWFdx8U4hwSFBbo3C+x51HutKysfrG8EoLzXRpOVRO9lH/A3cuOYai6DTWbCcRbSJfGqJUXVgKUNeibGh7AM+3yBb6uDSSEAr1qY10NO4jHm1EVyMgFDzPpGJlGZ44yMjU0XnhMfmcpPt0G9bUHrqHcwiaaGu4kWioFlU18KVHXWyMCSNDqbryUrU4eA4rO0G4oZXkmq3kzh+ef356UMBGi8QZf/Fb+LNMF3SuCKXqKQ/3Up0awbeqaLEk6U03ktpwA1o0Qf+3/nNBQZhQTSNNe1+HmRln7MC3kI5DuLGNmk27ad73ejyzSqH3JCC52O/wz/+R5+d+PM1f/F495arP6XMOn/taiZ/6keSC8zEtyWe+UuIvfq+OU2dtXj6y+tYol8OyJV9+pExnm87735HgB9+ZYHLa46n9JrU1C+MkDxwyURT49Z+rIZUSFAqSv/nHHC8fDeKiyxXJf3++SGuzyq/+bBrTkgyPunztO+XZyVFrqkNJJrAHRjHaG1HiUez+UdR0ArtvhHDnWtK7b0YY87MMKxfOUjhxBOnYGJ3NJO7dA75P5fBZzLP9JF93M16hhHQ93Mkcak2CyqEzGJ0thLd24ZcqqPEovmUjPR9ZsVDrkiiREKVnjuBOz+iVvk/h+CHSN99JYttOCsfmqrhtur+N848F8kBtV4INd7Zy6NMXcKoBiTRtTVOaNClPLXwmY/Vh2nfVce7R+fLC5fsE0GsbqLv3IYQ6f9Jzshmmvvv1eZ8JBKH6ZiKNbfieF3ReeYVZQdL3cWay+RaDFo2jaBqKESJUN7//WOHCCaypuaV8qecUY0hqduwl3rUFOzdFsecUuVMHZyczNRwNHKCGQai2cd7+ir1nMCdWlmPK2SEUzVi2qP9qsSrS7bnosG2LHtT6PGvT2aFRm54f66REw0S2rcHNFtEb0yjRuaBwoanEb9yIM5lbcoyqlaVQHiKd6KCpZtuipFubXEsklGJ48jBla643WDLWxsb212HocYYnDzKZv4Dvu9Ql19PeuIeNHa/DcopM5RdaK4qi01x3PboaJlcaoHfkaTzfIRauR1UNHHd1qZm+bZI9d4jW5k5SG28g33N83vJeiyVJtG/Cyk1QHuubV20re/Yw+d5T+I4V3FTfR6gq5bE+1r7pg4Rrm4nUt1Eamn/8aihK8eJpxl78Fk45D1Ki9J/GrZZouul1NOy6m2L/GaQXtA7/6rfL7H/JJBpV8L2gWLNpSb7wtRLT2YUv0ic+X+SJZ6uYpmR0wiXc3E7tjXegJdOMPPIpTp4p8PYfGaNQWL6maN+gy+//RYZ0SkHXgwiETNbj459W8Dw5m/QAMDnl8dF/LiClj6YJqlWfqYw/q0H7Phw9afGzvzFFMqEgBJQrPrm8z+e/WqJY8qm6PQhNQ9oO7vg0CJCeT3hTFwB6MkVs8/YFDievVEScPo50wJ3OYQ+O4xfLWD3DSCmJXLee6f/+Jl6hjN7SgN5SHyw103HC69spHzqD3tlMef8xYnu3407msAZGkY5H4p6byH4h6I4iVA1rbCTQPJPp2fEbNqWwynMXIzdYJlIXQlGDkqSRtEE4qVMYDZ5JRRXUrksSimlMns+jR1TSnQm0sEo4aVDJmDRsTGEW5hOFGo0S37pjngYrpcQaXYR8RGDUZI6/SHztVur23svItz+Lb69siAhtGefgMoWYpB88T9njL1IZ7Fnkp3PvlW9bFM4eozLQQ6i+mZqdN9Nw8wPoiTQTz36LwFIPGozmTh+m2H1yoUNylSn0rwbhwipJt1KVHDoaDChl0LY5HJqvAzrjWSY/8ThIn9YPvxOjrW72O+l42CPTjP/bt5ccQ0qPTOEiTbXbaam/gYujz+LLy3qxodBWfyOOWyFX6sfzAitJVQw6GvcSCaW5MPQYQ5MHZ6WJQnkYX7qsa72LDe33M53vWRAMrio6sXA93UOPMTp9fLa+bKbQe+nIVnOJAMidP0Tj3gcI1zUTbVlL+RJJCkGkvpVwQwtTx56dLRxzCb5j4jvzH2LpeVTH+nFKWfRELVo0sWA8z6xQGu7BKeXm9mVblAbOk95wA9GWLvR4CjsfTFC2zUw90fkP/MWBxR+6UlnOS710xoYZ/e4XWPPen0EoKqYlV6XzSgm5gk/uivoI5crCF08I0GUng6PTOL6JqgQvrqF5aIqON5MnnM/7FPIgUPGkjS89pqZ0XM9BShvJzPN6GaNXj51bMN6Sx2y7+BUTr1TFr5igKvimgzM8s3Rv9GYcxDMdGQX4hQrScnCmckHx9qqFly3iTuVI3r93dt/pfXeQ3LELc2SQ/KG5pbpreex8eC39L0zM/u1fFsnhe5KW6+owCw7VnE2yLcZ1D3fR/8I4WjgwgoyIxsZ7WqlkLEoTVVzLY88HNjHw0jVKDhKszARTBx7Hmh6n+e4303DrA0w8881Zn0RAwD5qNHbZDwWhhhaupSymnZvCt00iTe2LkOSVaf9BFTm3XMAtF6gM99L6wA+Qvm4fE899O4iQKuTwKiUiTW3kTx28psp7ryZWrekmEwrhMIxP+FiWxLIWIaOZ9efI330pCAq9SuTLQ5TNaWoTa6lPb2Qie2b2u3i0gVSig0JpmFxxYO4E1DD1qY2UqpNki/1XaMGSTL6H9oY9REO1xKNNFCujC8YtlIeZyp2/oqD31cdA+Y5N5uTzNO55gETnJiojgbNMqBrpTbtwq2XKw72LWgmKHkKLxFCM8EyPsqCozaUuvWKRWDPfNnHLCwu1OKU87kxDzXBtyyzpLhxUQU+kA+edbiBdBzs3je9Y6MmaoCOw72MXMvhmFZDB0vJ7HPCuq2FqYh0YWgyEwHGraIqOrkWp2nkMLYrtVVCEhudZOH7QJdpQY+SrwxTNhQk4wFUdt/R98Hy0+jRqOj4jm11GgOUqSjyC0d6I3lw3/3GZ+bcSj6C31KPVp7EvzlmRmWcfI/Pswp6A2f4SnrP0e2MVHcpTc+nn1ZxFecoklNCD50RA3foknuNx4YlAu57uLeKvwiexIqSkeOEkRqqG+n33YWenyJ18OVhF5abxTJPkpp1UR/rxPRcjVUti3ZZlQySXQnVsgMpwL+kde6mO9mNOjwerON1AT9ZQHuyZlQ6iHevwHRvfrCJ9DyUURglH5rV+sqbHKA92k962h/JgD+b4cJCirenoyRoqI32z6dhC02ekjQhCUVA0HTUSC6xvx35VCHvVpNvaorLruhCf/uLCjKQrkbxtO9UzAziTq2gCeRlcz2Q6f4F0vIOWuuuZzJ2bdYC11N8Y9GIrDVC1c7O/iYRS6FoE2ynTWLOFmkTnvH1qagRVNQJr00gvQroSyy5iu4sU9V4kNnIl5M4fpW7HbcRa1mKkG7AyYxiJWuIdmyiPXqQ6eUVQulCINLaTWreDaNMatFhyJr4qGNeI1ywZpC2lH1Qhu/Jzz53V3RTdWPD9JYRqm0htvQHPtoi2diFUlbEnvoIWayHSvm6m6aWGWy2TPfrCAofP9wpFc5xc1UcVOqqiE52pmWCV+5CAphgIBL70cH0LTQljaFGqTg57lXLQinA97MFxwpvXoDXU4JWqVI7MWcrORAarbxSjowl3Oo9XKONO56ie7sWvVDEvDKKmYmgNafyqReGZI8sOpxoKdWuTRGvDNG5Okx0oEm+KkmiM0Ly9huFj04RTBunOONKX5IfKKKrC5PkcbTfWkx0sYeZt8sNlsgMl2nbVMXoyS01nnFhtiPqNKTIXC/Ms56uHJHPkebRIgvp99+FWKxQvnMApZMkcfpaa62+m9cF341lVEIJiz2lqd91+9aO4LhP7v0uD59Jw64N41TJSyiDFWvpUR/rxZp7F1OYbiLR04tsmvuME6dC+x+Tz352V8KTnMX3o2SAOft99wf58PzBuVIWhr38C3zIRmkZqyy4iLZ0oRhgtmiDasoamu96MdB3y544uKndcLVZNuqWy5PZbQrQ2qziO5OBRi/0HFq9OlH7dHqy+a+uJNZE9Q2fTLSRjrcQjgWUa0hPUpzbgeTYT2fklAFUl0I6jkTrWhhe2fb8E33dRFmmyKKXEX6Sbp1qTQq1L4/QPrzrMCMAp58n3HKNm616ijR1Y2QnSm3aBEFRG+hZIC5H6VlpuexORhnby3cfInn0Zz6zie0HlrdY7H0aPpxcdK5iJF55T8DAFy01/GaI00nX4vkf26AvYuWniXZtw8llqrr8Fo64RJzuFEooQamih1H0aO7uKJapgXmfhxbcRKIaKdLxZS75Y8vnn/8jjeTA2PVfzQVUMLLeM7ZZx/cWfN0OLUbImVpUsczVwRiZxRubOufT0Zc5RX1Lef2zhb8aCVUX16Dmie7Zhnu/HGVrC8r4MUoLneBz9fA+eE4TC+Y7P6W/2YxZspC+RrmTgwASu5c2kI0si6RClcZPcQAnfk5z4ah+liSrRmhDSl3i2x9Ev9OIvY0Evh/FnH0EKidZYizuRQXouUy8/gTk5MpPIENyn7PEXsbLjhLvW4IxN4eQzWNPjWNPjmGODs/urjPQx+tiXMKeWj2t18tOMP/0I4cY2tGhQc9mzLZxCNiD1GUwfegajpgE1FGSJ+baFnZvCmp5/zd1ijon93yXcdAotlgwq2NkWTjE3lxzhy2BVOGNolfvmy1HLNU69GqyadMfGXf767wPCkJJlq/640wWEsVwX2aUtSNPOM13opqV2Jw3pzRQro9Qm12NocQrlIQpXWKquZyKlJFvso2/02cUtVgJyNe3VWd5aUz1GZyuoCs7QGHprI9LxcEeCiUSJx9Dbm/Gyedzx+UWqpeuQ7zlBetONxDs2Uhq6QHrTjbjlIsWBc/McaCgK0ZYuYs1ryZ0/xNiL38K9IiZ3ufWZoofRogsjD/RoAi0c6GtWbukX3pwaI75hO80PvA2hqGSPPB8QuRGieO4E1dGgULXvOkvWm70S0dYU0bYU2eMjeObik5WeDNH2hu3kz4yRPRosu01L8uyLC2UXz7ep2MsXQlnqnn8voCUjKCENezJ4UYWqYDQksKeKSHfu3nqlKuWXTiGt1a0OfMdnurfIdO9cmGF+uEx+eO7cytMm5em5a6SogmrOxq64mMUgSWTqQnCfzHww7lR3ganuq4sjvRzF7pMoiRixfddhD43hjmcwulopnDyBsbaN0KY1eNkCensjTq6ILI5gZwMHZODkOjpvf04+g5NfXWGbkJZALTiURk7juRa6EScWb0KEa3DsEkLVsbNT+KUSQij4vkcs3oRyeVGOy+Db5rKWqvQ9KoM9VHjl1uxyWDXpaqpA1wW6JhAKy/acn/7Sc9S+9VZyjx/BGcvMar1SBg4Ko6MBa3ACLRHFK1cRmoaXm5tFhicP0Vq/i3S8E0NPUJfagKroDE8dXlCko2plsZ0SqmpguxWKldVnhiwGvaWB+D234kxMoVwKKfIlyQfvJPPxz4OmEr/7ZtyxCYQQC0gXwMpOUhq6QLxjI6nJ69FiCYr9Z6lODM7bToigewKKgl3Kz5vBURQSHZvQYwtJ9RLUcJRY61qKfadnNVyhqERb1xKqacScHJ5tsbMYhKIEAfoXTuDkM/iOjXQdrMwEobpGij2nQPqo4eiMNq0iND0gZt0Iyt9dIW8ohkqoPk7dnk4K3ZPY0xWMmgh2wSRUG8PJV3HyJpWBDGooePzCDXF818N3PNSwDhLUqE60OUlpIEtYWnRdF2e8r0q8RieWUHFsSbxGJztmcf7gtZPKUlDCOqGmFPZ0Ea/qEFvXiJuvoEQMIu21VCMG5kgWJWygxcPYk0W0ZAQ1EQbXwxov4JdeJaljBkJTgphje8aBJaH/wDizFSwVAb5EjehEm+JUJ0q4FWfGl3X1Utnl8LIFhBYsx9VUAqFrKJEQVs8QajIGCMKb1oCq4jsO5sTSxKpEDKTrIZ2l9dGg72ILqqqTruliqP852tfcTiE/QCiSxvdd0rVryWV6aWrbzeTYcVQJ4XCKlvabuHj+O9j26iu3qUoQJrpYo9ZXG6vPSIuJ2dKO7W0aZ845/PdnFze30w/uJtTZSPOH3jDvc99yGPzDT2C01SNdD602iZqMUj3VN8+fXiyPkS8NEQnV0FK3k1i4DsspMJVbWETD9W3GMidob9hDU812XNfEcmca7QkFVWhoM52Kq9bKBUWMtZ1Uj53BKxQxOloAcIYuI3LXw52YQm2oxc1cXHQfbqVIsf88ic6t1G7bB1KSO3uQK51z0nOxcpP4lkl6842Y06NY2UkUTSfWtp70xhvwLRMlFFl0HOm5xDs30ywl+e7jeLZJtKWL+p2BjjZ55KnZJqALIARqOIKeqqHx9odAgFsuMfH0I+SOHyB93V5aHnoXQiiUek9jH32R1LbdRDs3ID2X+lsfoNx7jsK5owskjFhnDZnDQ2z4kX30fuIg6W1NZE+M0nBLF5kjQ5R6p+fJDzU7W3GKJnbOJNKSBCQN+7oYf6aHtoe2kHvyFOFoUHyooSPE0NkK225Nc/ixaTbemOTCocIsnyjRCL5pBWF3hoHQNfzyVZKfEMS3tBJqSlI45qCGDbR0lLq7tzL56Am0RJja2zYx9eRp/KpNdEMT5miO9J61uCWT2Ppmxr56EK+yOis36LgiceylLXahCOquaybakmDk6YsIRZDoTGMXTMypCtHGGHrcIHduinBthHBDDLtgomgKkaY40vMp9F5dFbNZ+D4ibCBm/q9EQ6h1KfxSFWnZqKkgltbLl/HL1SBeORHDKyzOD9s+/gtMfe1lxj719JJDKqoBCDQ9HsTH+h7VyhTRaD3ZTA+2VQAEkWgDhpHAquaIxBpR1BCaHg2yOlcJIVQaGrZRqWYolUZnnfGqGhhdlxrnRsO1KKpGpZrB9x0URUcIgeddna9j1aSbL/o8+WwVVRWs69KorVGXVAmmPvc0QlvkpCVIx8UensKvWMiohZ0vBdbwvM18RiYPs3nNG2iu3UHYSDE08dKimp3vOwxPHiIWrqe98SaSsRby5SFcz0ZTDQw9QSLaTKkyzum+r654nu5kBmNdB0ouOvvSao11qPEoWlM9XjaPMzyGtB3id99Mpndgkb1IKmN9mNMjQUff6VGKg4tnNJVHLzJ9cj+p9Ttpv/fdwa89F7uQIXPqRYSq0XjT/Yv+1i5myXcfI9q8ho7XvR81FMH3HOz8NNMnnqfQd3pJ60bRQ0Q71lO8cILC2WMI3aD5vrdh1DVS6b9A5uAzZA7ObxSZO/ESuRMrdzYu92fInR4juamBUO1cLKyiL/4iyJniDJeKxkhfMn14kPy5ccJNCTwURnsraIbCWK+JHlLoPV6kUnAZ76/Om8pCm9bjF0s445NEdmzBK5YwT60+VGzmgLCnisQ3NWPUxRGGTqS9FiUchK+Zozns6TJCUxGGjhrW0ZJB8XKvYuOWzcWf/0UghEJT+424rsXYwNJZUXLmuLyqi+94dL5uI0JTkL5k6ugoDTe0kj07gRpSqbu+BadkE+9I41ku9de3MvzUtS+Z/XKV8v6js39fqVGbZxY3Pl4JEsk2orEGioWhQDJQVIqFYSKRWlrab+LC6WEq5UkaW24gO92Nomgk051I38W2ClddtlJRdBLxFkJGglz+Iq5rk06tRVUNSqXRwFHfeB2e7wLdlMpjRCN1JJOdjIxeXbfvVZNuLKqw+4YQkXBQKetct7PkasXLBQ9keEMraiKKX7EwL47iVwJHiHkuWGY7o0uEMgHZYj+WXSAebcJ1q4xfFj52JcrmNBcGH6Wpdju1qQ10NO4Lssp8B9spU6pOMJlfXak5q28QETGQjoczMhG0X1EE5QNHgwweIRC6TmjzWsrPLn2xnVIuSFgAMqdfQi4RWO2WC0wceoLiwHmMRBqh6fi2hZmdwMqMoUUTgRY1vhi5Q2noArlzhwjXtaKGI4E8kJukOjW65JgQELtTyBJtX4dR24hQVNxyAWtyYUjd1SLcEKfhli70VITqaIFoW4qGfWuIr6ll+uAg4eYEqW3NQRhS9xTmRInG29Zi502cooVbMufVDijnXXIDiwfjd2fnLyHtoREi27cQ2bkN6+IAVm//or9bCV7VpnBiEHuyiNBVvJJJuWccZ7qEVwzidr2KjaKrVHon55bKAsrd47jFayv7tyR8iVOyibYmUQ0Vp+IQb09RGsghXZ/KeIliXy5QEVwfPapjZasgodiXoTr+6jiBvl8oFUdRVQPXtchOBQaLomi4TpWx4UMAVCvTKEKhVBjC810KuX5CoSTTk+fw3KsveOQ4FQwjTjTSQKk0imHEiceaEUIwOXUG17OwrAKmNaObWzk6U3d+70i3WPJ59oUgRdN1oLBIib9Lpq+ajNH4ow+iN6TwSlWUSAjftJn4j+8uS7SXw3TynB/7EuFohMJUlbK5UDudg6RkTlAdyzE6fRxNCyNEUExZSYWxzSLV3MJxRzLHmC70YDuXPZCOi3l8vmVk9w1j983FWbrT2cDTeXHpmqRGqg49UYtXLZO/cHTZc/Vtk/JwN4stLp1ilszpxS0gMaPTWdkJrOzKHvLLEeS/n8GaGpspsCNxyoVX7KGtjBQYefQcQlPInx7DnCoxdaAfLREic3SY6lgRBIw+Flxjt2xRuDCJV7HxXR+nZAUOKQFu2Wb82R7c0srLN725kfgd+4DA0Rnq6gwK3khJ9dipqz4PZ7qEkynNKkJLdRHzACcb3LnqwBTVgWl8c/EoCk2P0tSxh2RNJ1JKxgdfJjfVjZSSZLqDeLIVVQ8zcnE/xdzCSbY0lMcuWni2x/iLA2STYdyqg1uxZz4PpKTxlwbRIjp2LnAyl0cW1rhYFALC9VGQYE6/unr01cJ1KkxPzje0ivlBLiVH6Eac+qbt5LK9OHYFpE+lNE6ldG1RU1J6WFYQjaEoKun0Wnzf5f/H3F/HSZal953w91wMjshIpmKGrqZqpoEeRg1oJI+8op2VDPK+5gV7TWv5Nci2wLJsr0bSSEOa0TD0NE13V3MXM2VVMgbT5fP+cbMyMyoioapbevf3mf5MVsS9Ny6d5zzngd+vUp0iCAKCwMXzGrhuHc9bpOb0rObE+AaxYaM7NKCxY6vGzKzPP/g7Gb72rSo//MnybG5u6aP3Vz9I7psvET+0Da9QYe6LP0Z6IfFI+j130/W5J5j+j99c2qdnU4TB3XEmL9Xo6DMxIgoLEzZDe+JMXqyi6iWEKNHXp3LX9m7yUzanX1g9LhVmunOYPd0EdQu3WMXs6sKr1pDSR5g6RncaNx96R8kP7KX85iXs8QJaNoliqDhzZbRsAjVq4lfqeMVWUxhU65S+8zSyHVMMYclWctNuol0DLJw60kRZ1wJlcUAEQbhc9G6vtKfpkGE+hRtES0LcYIta/k9VYbjXQ9cXGLnu3k4vCyAwewfQkxmk51G7fpHA9mhMNye27HwdO988iGtjzc+xMtJ+MnbyGxv8Xi5P+ZkVzFdKOCFJ+22ILt5i3ql2ZXZNZcwgcCnMXWBh+hSGkWTHwU9yYuF3Q55lRWXiyvMYkRRb93yQc0f/BO8mkmzf8mhY4aQYOH6YJLtx7BUcs04xjI8v77fxa1BUhZ57Blg4OUN1svk5ah0Jsk8eIn3/LrRMHL9qUT09yvy3X8ddKKNEDfo+/ziZh/eCplI7N87ov/nmumqhRl+GTf/gk9jjC0z9j6fxq2udcHgs16kxO3UMGfgbCyUs3uMw8Stv+vci90v3ARAqExMvY5oZenvuwPMsCsWwO7VWm2d48CFU1aBYuk531z5isW4G+g8zO3dqqUt2PWzY6HZ3KiSTCvfda/L//GmFdDLkeL2RvHamcsz8/vfwChW6Pvs407/zbfzy8oApPXeCzb/5y833QRHEMxrRhMaWAwmqRY/u4QjHn8lRLXh0DZskO3QiMRUZQCSx/ulGtvYS3z1EfWQGN1/FHOhE0TQaNYv43mH0ziTVU9cJGg5qOkbgeBh9HcT2b8Ls66Dw3Ek6P3QvvuXiTMxTOXENv3TTwJeyqb0UQE9kwg4uoZDctJuuQ4/ilHIUzr2O2pEAVQknICFAUwlqDbSOFGomDoHEK1YwhntonLtOUGncdqa5t1tl7y6DN09Y/PynElgN6OpSsa2A+VzAhcsOV665/PLPJ7Esye6dBt97qsYrb966urEaixPpHcQrF/Ctd4a/9nYhPQ+/WApnGCUss9MyabT+PuwrG4s5hgonb6N5YB3joukxugfvJBLtAARmNLNUElgpTmDV8zhWGc9tEI13USmOr3m8vwxocZ3EcJrKaLHJ6Bo9aQa+8D6iW3spvnwee2IBPZsMnYbFdzWwHOa+8SrFF87S/8vvQY23Uc5YhESCIohu6WHw1z6Au1DegMFtPkLgb7wuO3vXw3Q98B4mf/hVatcvkTlwH10PPcnUD79Cffwqk1OhiMKNYzesPKXyaFM1Q6U6yfmL31w8e8nM7HFmZtduemmHDRvdyyMeD9wbYWzc49p1l83DWhPrlJqMEr97J7XjV3Cm88QObMWvWQSWg2LqxA9uxZ1p9m7shs/01TqaIbh2qoJdD/B9ycHHs5x/tYgRUVF1hVrJw7EC3HatxzfBnSvBvk1ENvfQuDIdEpJ0JBDj87gLJaLb+9A6EjTmJ8MH7PmIqImWihE0wqWtV67jzpXwSnWEsrGlWWrbAXoOP4kWjRO4DnZhjvljz+GUcqQ/eD9BY3EWXKQZtC6MYmzuDcMtQRCyYUUjRHdvpvbW7amMAhi6IJtR0DXB8IBOLu/z7It1ejpVHn0wyuS0h6pCNqvy3IsNro17betjNwIZ+KhmFK0viW/Vm4rg14SioEZjqNE4SiwWlp9pNxo6xGILrrfY3mnh12v4N9H1tRwyHkeJRdE6O1BiUWQg0TIppOdv2OiG8hytHwvDQEum0RJJFNNEqPpiy39AYFsEjTpuuUTQqK8xWQqS6SEMI8nl098iGu8k07V96VvdiKEoGqpmoqg6vreO16Sq6Kk0aiKFEomE5DUivHfSsfHrdbxKCb9eayV4WWNCd8o2hfPz+CvLuRRB4tAWolt7mfyDpyi/daX9BCPBK1TD/8oN1Fh7oyuREEhiOwfo+dmHscbmmf6j527B4N46tHgKoZvoqQwoYvH9i6HGkizzOdx8n9p0e64j5Lmhc9nohsVSwH/9ozKuC5oa8uuufHaB7eJMLRA0bAo/eoOOjzxAdO8wQSM0ukJTyX3nleZjzjoU55yWF92IKCyM36bEuRA0Lk+FagWKwF0oI7QwARbYLvULE3iLyZfa2VGUmIkzV6J6YgTF1PGKVWqnRvHrNoHtLBvLdVCbuMKs76EYEQLHojE/gbUwjQx8gpqFPTkfkqJUGygRA+l6uFMLuHOFxUy3QPo+0vGW3oGufp3HP5bm/NE6595a4W1LyewbPwmrHEo5BrYaPPi+NMePVKjMuCSTCu99PEY8KliQUKkEdHao5As+hw6YdGbDlUOtHrBru8menfqa6s6rIXAc6mNXEZq+xOS/1nPRM9lQ/qZvED2TRUulQy2xRaMhdI0bRle6DoFt4ddqeOUiTn4Be2qC+vUr+NU29ZeLWkZ6Xy9+pYp0rbCT8Fb4FvzmpaoSjRHbup3o5h2YPX1omQ7UWDxsrRYC6Xv4jTp+uYy9MIs1MUb98nncYrsaVYnrVFFUnYEtD4VSVIuG1fdd4ql+BrY8iG4mqJYmsert61yFbhDdtIXY1p2YfQPoHZ2o8URYUy4UpO8RWBZeuYSTn8eaHKN++SLOwnKsU3ruqg69ooUy7itDF4qpE9nUjT2RwxqdW9ej3wiM3jT9f/0J3EKNma+89JdqcAFKF07gVovUrl2EIKA6ch7fbtCYvMbbWt3cBtY2ukJBS2dCDyPwcRsN1HgU37YJAgXFVJCeizBMFM3EujCDX6vg5cosfO2nmINdqKkYQdXCnlrAmWyTDGtzvWPn1ud3gNAo/cLf6+W3/9HE0tLYrzSoX5pcOm7j0nICzJ13cRfKS9/Vzy17Zta15ZeyfnFjon2KCnc/liTbo/H012ew8u0bM2rHLoaqyDfBLy1/trI55AY6ejQ+9Nc68T3ZbHSB3KkjS3/33Z3g/Z/LMjfl8OoFi5++3CAWFZy9YFOrS+YXfErlgItXHDoyKo4jOXPBZm7ep1YPKR5vZ4ITioqR7cLI9oYVFzOt901oGtFNW0keuAuzdwAtlUaNJ5balNsfVwFNQ43G0DNZGBxGBgF+rYozP0vp+BtUz50KjcciglqdoFan9uZxgnpjsbbURIm2r3Fuh/A9X+S86O2n89H3ENm8DS2RarviEYqxSMKSwRwcJrF7P86heyi+9QqVU8e4eRarlqaYHHkRRTNx7QrF3FVkEJCbPUspP4KqGgihUq/NE7Spr9Y7Osk8+DjxHbvD+6IoLURIN85JS6aIDA6T2LUP+8BdlE++Rfn4m+FkZq1SWSHBytWYO+bi1pwVx1RQIgZ+wyFYo6Fho1A0lfiBTQSWi4gYaKkoXu7WJehvBfb8FPb8MoG9vTDTxMv7V4k1ja5QFYxMFq9SRk0k0BJp7IVZzN4EZu8A1vQERrYTPZOlPnI5pFCrVch84F5Kz53AnV0MJwQSYWhk3nM3wtCoHruyZhWD52zMAhx8IM7BBxKIkG50GWvt/g5OaoapcO8TSRw7WJMbp53B3QiunbP4+5++ilVb2wU9/XqNf/TZq9QqPp5PW7rFGx2EIbXjMtqp+97Als/cyaZPHUIIQXW0wIXfe5Ha6HKISI1E8Rt1ArsReog33YTYlh10PPYeIv1DoUhjGyOxUQhFQUumUBNJjN5+jK4eCq/8tMWAaB0ZXMdFOg5CURDGOoKPKyD90DOObtlO3yc+h5bKrDk5NJ2fCJeskaHN9HR2Y/YMkPvpU0hneaUUBC7VcqtyhWOV15V8MXr66P3oZzAHhsPOwA3eR8WMEBnchNHZjZHtJv/iM/iNOqsNhMANcEqtNKNeuY451IkSWZ1AaaOQfkD5jSvkf3Kc/l96D4P/ywe4/q++vjFvVygkd+wnfeAwZrZ7KayydGwpKRx9ifzxl5v5rJNpMgfuI7Ftb6jmXK9Qu36R4onX2gpTClWj//2fASGYe+EHKIZJ9u5HiA1tRagaTmGB/PGXQ9XjW6xgWNPoSj/Ab9RQ4wm0ZBoCiZZMo6cyIVlEfh5FUXELebxaFcUIyWdSj95BdPsgSjxC7fgVykdO0/mzT2AOdOJXGsQObGH+T59t8XwVBaIJBTOioKiCIJC4jqRRDfBc2bxNVOHwu5JoOnQPGEvfW/WASnHZsKgapDs1qiUf35PEUyqGqYRcDLWAWiVo+v1ILDy2qoUpf8cJqJV9VhJ96YYgmlDI9ujsPxzn4ok63QP6kmJvteRj1ZsfhKYL4ikF3Qh/225IauVWFd14UiGWbB7omiFoV08WjSsk0svbKsrNXKNvD5M/uUDh7Ay9j26n6/AmlJsK/r1aGem5SN9HS6RaZh01niC2dceGuoNujjOuZlSEEKixOB0Pv4vAcSi+9uKSxysMHXPPDgLXJShX0Pt60Hq68GY3xiUrPY/I5q30f+rzoTd+8zlI2czk2uYchRAo0RiZww8BkvyLz6zuWW4QRncfA5/7ZfRs55rG9kYiUITM483nFImSue9hAHIvPh1OBjcz0AnCEI+6qCW3uHwMLJf6pUmy772D1D3bKNYsAscL69YVgV+zl6hcb4TybjS6KIaG9IKw7vrGM5aEtfuj88x86acM/52PMvS3Psz4f/4eQWON8kCh0P3w++i6/9045QL27CSoGtH+TWjxJG4xR+XKWerTo2FeILx4YkPb6HvPJ9AznQRWg8B10FMddN73btJ772bie1/CnrtpMhQiVLAwoyR3HaTjjvvD+G/gh8yAg1tQzx3ldsbb2uEFGWDP3lws3zywvXIrEYowNLxSDfvMNaK7h4nmSkR3DDL9u9/BL1bJfOg+Ilv7moyupgv2H47xvp/NMrDVJBpX8FxJbsblz39/nrNv1pABpLIa7/tclrseTrB1XwQzovBP/8eWpTM68oMiX/uduaVww/AOk3/8e5v58n+aI/Al7/5Uhr5NJroueOkHRf7k3y2HFbbsifChX+hk274o0XhoePOzLk99Jc8L3y0urRa37Yvy/p/Lsn1/hMHtJtlejf2H40jAsQK+/ntzvPLjZc8lnlJ44uMdPPqRNJkuDSSMXrb57hfnOf9Wfem4igKPfjTD+342i6qBYSj0Dhu8/MMS/+HvNiepVA3ufSLJp36tm2RGJduj83v/xyTPfOM2Wz3bwC1ZlMqzxIczdB3e1LqBEASuTWPqOnoy0/J17epF7OkpIoPDTZ9LKZGei1+vETQa+FYD6bpL7cSKrqOYkXCyT2VQbpInF0IgNI3sI+/CmrhO4/pV0FTMrZsxt25CjcUIbBuhazjX2jeVtEBKjM5ukgfuajK4gefiV6t41TKBbSEdBxkEKIaBGo2hpTOoiVSTMRRCIAyD9N334xbylI+/vmp54XrQMh30fuyzbQ2ulBLpurilAn61Ekq7Bz6KEQnPb9FZEoYR7quqZA4/hF+rErgeLVOhomDu2o5QNdzJabzccly5dnac3FMn6PzQvSTv2oabr4a5Cc9n7huvYE/k0DuTpB7cjRo1MAezKIZO9ycewK9Z1C9OUr+0wrDdCP9dnWH6j55l4Avvp/dzjzD39ZdDI94Gke5+snc9glPMMfHdLy2FC2JD2xj+1K/iVUvkj76Iu4JvRE910HX/u9GTGXKvP0/++CsEVg01lqTj0P1k732cgfd/lvG/+MNWsinA6Oii49AD1K5fonj6DZxiDjUSMu9Zs1O3VWW04UTaMtb/kaBuk//Oy/iVBigKWiaBMLQwMWU7+IVqk5wPQO+wwWf+Rg+NWsB3/nCBesUnndXo22TQqC3HHBtVnzefLXP29Sq/+I/76d9s8Pv/dBJ/kSc0P++1rTl98P0pXCfgtZ9UKOULdHRr5Gabl9bxlEqjFvCTr+Yp5T1iSZX3frqDX/rf+rlypsH4lfBlmJt0+MnX8qQ7Nf7m/z3I6Ver/OjLeWQgCQKYHm2erT/0+U4+8POdvPjdIldO14nEVN71yQz/8z8Z4Pf+j0kunwo9oSCAl39Q4swbNQxDkOnS+Lv/odlg3YDvwbGXKoxdsbj70SSf/vVuhIBIVCypNQe+pH9YY3bKx3Ukmi5wHfmOcJCr0Rhm98ASmUpscAsLrzQLkgaOTen465i9/aF8ju/jzM9iz05hz07j5ObxinncUjEkkV7hnaiJFEZXN7EtO0jsuwOjq6clrqpGY3Q8/G4aY9fA87GuXEOYBt7cAoFlIz2XoLZxLzOx/xCKHvbTyyDALeapnj9NY3QEe3Yav1peMp5CVdFSGSKDm0js2U9sx54W+R81GqPjgcewpyewJm69M04YJtmH301kcLitwXXmZ6mcPkb92hWc+dllj3qxOkTv6iEyMERi7x1Eh7eE4RZNI/vYe9vHqBc9Yj9fIHCa32G/ajH/rdeon5+g4z13EN3WR/HIOayxBdzFeKwwNMyOFAKF0ssXUYWO0FX0dAI9Ft4bQ0SY/9Zr2BdmiYs0lqxROTbCzB8/T7pvECVqrm50+8PQSmNmvCk+W5+8jj0/hRpPome6lo2uEET7NhEb3k7p3DHyx44sSa379Qq5N1/A7O4nuX0/qb13kX+rlQtCjcQoXzzJ/KvPECxqFHpVd1VRzY3gNozu+rCvz9D5M4/iTOeI7d+CEouEOlKb+6jXR1FTsaYkEkCqQ6WzT+e7X1zgpe8Xl5bzmh6GGZY6gyzJtfNh7Kda8vFcybm3anjrJM933xnjX/zqda5fWEGNd9N7d/5ojcunGk2hARlIfvX/HGDPXbElo1uY9yjMe2S6NDwnIDfrcu7NWtvsf++wwQd/PsupV6t8/ffmaCzGZ8evWvzLP9nKwx9Ic+V0Y8kQVko+lZK/dE9uhE3aoVYOqJVteocMfE+iG4IH3xUjEhWYEUFuziedVZiZ8CjmffqHdU4ftSgWJNlDg2TvHCTaF5Km1yeLTP3kQktjw2qQQYBfry4RrFtqm1cpCGhcv0pjdITAc6meP4M9M4lXKoRxxdWsv5T4lRKNSglrfJTG6FW6nvwoZv9gi/GJbd2O0d2LMzsNvo914fJS1YISi6J1d+LNrdXNuIhFAqDw5yX27DQLP/ke1sQogdNqBKTv4xZyuIUc9dGrpBfm6LjvEdR4omk7o7OLjoceZ/obf9qSWFsP8e27iO/e39ZAWhOjzD/1Xeyp8dZSusWko1+rYo2OUB+5TPqeB8nc+yBCVZfZ826+JimRjoO5fQuB4xBUmpO7QcOhcnyEzBMHcHNlCs+dbmoecqYLFP/0LRQUHGnRq24iF0xiihgGJjoR0qKLua+9QlrpokPpJRdMExFRnNemsZQ6SuChYeDRPsxwg4OiPUTzikPVlwx1fexqi2KL9FyqIxdI7jhAfPOOtkbXtxvUJ0aWDG7LLyoiPJ1b8GQ22B94a8h962UCxyW6dxP1s9fJ/cVLTP/ud+j8zKNs+hf/E9F9m2mMNIct8nMuM2MOn/iVbj7817uWYpWeK9fSsNswRi9ZjF5svuk3jwHPpSUWO3XdwXMD4qmNsxatxL57Y0TjKqderS0ZXIDxyzbVkk/fZoNY4p15DALQNDAjAqsekEwrpDIKvg8H74mwbbeBrgsUXWXLz95FZn8/brmB33AY+vB+9v7G42gJc93fgVB23Cku4FVKuIUF6pPX0FIdLds5uQWmv/VlZr/zNcon38SenmhbO7oapOdSv3aFhed/HEqy3Bz71XTi23eDriGiEUQ0gtbdidbThbF5CGPT0IZ+ZyX8aoW5736NxujlFoPbLqTqV8oUXn2B8tkTBJ7XfI5CEN+9n8jQ5ls6ByUaI7HnAFoy1fyjUuIUcsx9/5tY49c3pMzrzE5TOPIs5dPH1qzRFUKgxKL4xTJrdcrMffMVFNMg+74723wrUYWGh4NE4kgLT9r4eHjYaMIAJLas05AVQKJhkFX7iIo4KaUTn/YeVGNqDAKf6MBmjBUqwdG+ISK9g/j1Ck5xeYIVioKeSBG4Dp7V/p27we2rRhNtVVYCx0b6DoqhokY0hCrQ4gZGOoJiqBiZCF13DaBGdRRzYz7sxrZSFdSIAZqKUNu8dYFsmvH8YpWFr/00fFkCuXSxk9dmMAY6cabzLRn92XGXL/+nWf7a/6eXz/6Nbn7mC128+N0iP/laganr9ts2vLPjzrrjPJZQeOB9ae57T5KeQZ1YUiWeUElk1NvSegLoGdQxooL/5Z/18yv/R1/Td7oRqtnqpgKVW/OC2sFxJM98v4ZguZBANwQ79xscebpOfsFfGksn/8VTBI4XPh8BhVNT7P61h0nt6CJ/Yn1JasUwifQOEtu0I+ThdV28RhVb1ZoVJgIfv7KO9yyga0ea7j0dTB1foDRxU/mclNQvnaNx/SrxPQdado8MbUa7fBq1I0N0/26UaITAcVHjMdypWysLkkHAwnM/hsoMW+5MM3WxSiSu4jkSRYXBfSkmz5XRDAUjqqGZgslzFaRtk3/hGWJbdmB0N8uGC1Ujc98jzIytaNIQYbJLiUQI6q1elNnbT2QxJNB0fr5P4cVnsOdujZjIK5covfUqkb5BjN7+tgk5GQQ4k1NoHdnFHvJWKLFFRQrbWer8W7okBBGRICKiVCkBAlNECQjQhEFEJIiKOHGRwpUOvvCIiDiq0JAywMbGkRamiGPJ1hJKe36K/NGX6LzvXWz+2V/Dmh4PhQAGNuPVa+SPv9xMkL7i9NYev2J1+UwpSW/PEusaQotoBL5ET4R12na+TmUkj5mO0HPfMF7dYf7N9ctN1zW6wtBIP7yPro/fj96VRk1ECCwHoSohV6ntYI3OM/IPv9i8Y9Da4eFX6jQurt5Lf/lUg3/1hVEOPRTnsY918MCTaQ7en+C//8spzh+tv61YpL+ONlQ8pfA//5MB7n4syWtPl3n9mTLFBY+BLSY//7/2vI3fBd+VPP+tImOXWkti5qZcGrV3hjhZyrB6ZeWV2pbkzNH2MbLYQBotFjauCFVBaCp6emN1rTfoGO3cbChuGY3RmB5FtJEP2giskkN5soZmru71l06+1dbo6p3dePM5vPkc+D7O5DTSslE7Mmhd2Vs6Dze3QPXcSUwDOjfFiKZ0BnYnWRiro6iCZJdB95YYsYzBtbcK+E1qEWVKb71K9wc+3lw9AMS37URLZfDKRYRhoHd3IzQNY6CfyquvNZ+EqmL2D6J3dHIzGuPXqV+/esuhCgBrcpz66AhGd0+Yib0JQlEwhocQioI33z4kk7hjM9kn78Qam6fwkxNN30kk+WB5Mpjwl5n9Gn5oREe800uf2cGtV3Xkjx0h0rcJs7MHxYwgPZfi2bconTvW0hEp/QC3WkbRDRQzxs1FAEDYoQb4dn1VaSvPcjGSJtXxEsktHWH1heNhpMzQA45qCDegPr2xBo91R4g5kCX7gbuxp/IUnj1Jz+ceI//UsZC8edcARl8Hc199cb3DbBieKzn6QpUTL1e594kUv/D3e7nvvSmunm1gN5pv2A2VUyFab+atYsfBKPvujfH6M2V+/59OLpXeRaLKmocO/MXxtcpUOXnNxvdg4qrND/9sYzIlf9mIdCfY9KlDxIcy+A0XKSV6wgyvYYMevXTDbjQ904k1NRYqCydSNBZuj+Up2mGS3ZZi9ly+3dgAwjgmi8b+BgRhvXCYOQywr15f+i6oN/AWNsZqdwPVi2dCr10GzFyuomqCiXNlGmWXZLfJ1IUKnhNg13zKczbp3uZwTOXMCToff7I5trtIBxrbtpPyiTcRhoGSSIDvEzRaB6oaiRId2tzWy61fv4pXLt7SNS0h8KlfvUhy/x1hiV8bKIYRhixWeQ/Kr12i/NrGaFL/MpA5eJhI3yAzz3yLyqVTa4appO9iTY8jPY/Y8DaqI+eWVH8hXIHEt+wGGVAfG1n1OJVrBWrXLxEfTDF/tEakM46dr9OYryJ9SXWseEvXsK7R1TIJhKqQ+85r1C9P0fmheym/eoHG5Sn07jQDX3g/0W29VI+9PV2hSExB0wXVxSSS74UGq1zwicSUtjWoxQUPVRX0DOlMjrw9pVpVDesK83PuksGNJxXufVcSM9be+wp8Sbngk+zQiCXUpXNfiTNv1MjNuDzwZIo3ny8zO74cr4rEBJqhUC2+M57uRjH4oX30PbGTS//tZUoXZvHrLqndPRzc17f+zisgNB2zsxc904l0HYqnVifh3ggUTSEzlKA0UcMutz5Pv15Dui7CXGHobizTzQhozc9Jy2ZQ0ykahY3pu8kgoDE6EibJfMnEmeWwSKLTYOZKFbva/Kyq+Zuy/LUKjbFrJPYebPpcqBrR4c2UT7xJUK9jXb68pG7Rch/MCGbfYOv116o4s9O3XX4GYE2OEjQaEE+2rrmlxCuW0LIdrVnmVdARGSKQPmV7BvkO1oivhsTWvaGn6dotzTgtkDKUc5+4SmrnQZzCPKXTb+JbddRojPS+e0hs2Y1TylG6sD5xTW2RAOjtchOvvxZUFQLLxa/b4cVaDloqLP9w50tUTozQ8Z47mf9GyKsgEGiKiRvcWi/1nrtifO43epi8ZjM77qLp4Wf9mwx+8KUcdqN1OfXqT8o89rEMv/4vBjn5ShVFEYyca/DWTyu3THM5eskiN+Py5Gc6CAKw6wH7Dscwo8qSR30zrEbA8SMV3vezWXxXMnndQdMEb/20zMjZ8PorBZ8v/psZ/tZvDvL3fmuYc0frWLWAbK9G/2aDp75a4MgPbk2qHsKqjt13xoinFPYfjmOYCrvvilIt+dSrPqOXLEq59sY8sSWLV7VZeH0Ub7HdM3uodZCvhzDbvyiNrSjrD4LVIKFRtHHrLqqpouqruFlS4tsWinlTsk+AMTxA/OHD+Csy7jdiuhtdxPq1yqIAZ+s1VHMbn9Tr1662GF0UBb2zB6HpKIk4ajKBMzGJdFqPq8ZibUMLXrWMW7g1z/1m+NUqbjGP3tXT6swKgRIxCKpVNsr1WXUWFjm31n/uppogpmcoWJPc7sq0NnqJ2PA2+p/8dJhYJcwZ+VbIo1A8+xbuCu5st1xg4fXn6Xv3J+h+4EkyB+4jcCwU3URPZghci5mnv7Vhscx3AusaXWk7YWdJNMw6OvNlYnuGqJ4cWVzpCdTo8mytCp2ImsBdlMtWUBYfSYCyWI4dMvUIBAJJgEQyP+0wfsVmz90x7nk8rJcdu2Txu//7JKderbYNYR17scJ/+SeTfPQXe/jgL3RTK3nkZ91wdbroGAcBNGoB7hqlVwD5hYD//ptzfPZvdPOen8ngunDspSrf/6N5/t5/3EQgWhMLjiX5wZ/kEALuf2+KOx9JMDPmcOrV5rbC4y9V+FdfuM5H/6cu7n9vCt1QKMy7nHi5ypXTt0cWncqq/P3/PIyihF66lPDg+9Pc/94UUsIf/LNpXvlxe2OeOzpO9q4hdn3hISojC2T29mH2JJvUGhRDJbG1EyMdJb27Fy1pkr1rCD0Vwc7XqI0W8OpVGtPjIaNXPHF7BncR1dkGF36wqD68lnz7Kr8R1OrUXnkTe+T60mdaVydad9eGz8EtFQkcG6GE3X036q4R4b9Xli7eaLJrl+C1pydawyCLSTMtkcSz6sTvvgstncavVrGvr6jhFQK9o6tt+7Ffr21YlXkt2HMzxLbvbvF0ZRDgTEyhZjItSbJ2EKgEMiC4iY1LESoCBRBIfALpI1DIxoaJamkqzjyB9Amkt7i9tpTKCqS3qgFX40kU3SSwGqiR2FKJX9hKB7HBLUT7NzHzzLeWqxikpD4xwvi3/pDMoQdJ7TyAkenCq1cpnH6dwvFXVjG4MmS5c+zbip+vhXWNrleo4ddt9I4kDQTVo1fo/szDIAReuU7nB++lfvlGtluQNvqIKHGqbp64lqE3so2qV6DozNAb3Y6umJScWaJqEk2Y1P0y8/Z1FiYi/Nd/Mo8fbNyjCHx44fsVTk9lUHSf+mSJ6kghNBiDGarXcoxdsvm7n7hKckf3msfqfnAr6h2D/OkPi4x/+xSxwTSRvhTF2YD//Rcn2PMbjwM/adkvP+fxJ/9utqmzreU8A7h6xuI//YMJlEwSWWuEtaTrQFHClyloY2jysx6/9NAyBaQiNKT0Q1FYFAJarYGmmASBx+SPz6OaGl0PbCE+3EHx7DSX/tur7Pjl+3Er4WQZ6Umy+395GC0eTqhuscHAk7tR3refyvV5Tv3rn4CUCE3Hb9Rw8remXHEDQtMQi8rCQlURioIaWr0wVr/4nxACoWqrciF4CzmCyamwMSCTRhg60nWxLm087BXU6yj47LkvRSKjYTcCLh+rkO036R40yc/YjF+sk+nWGdoVStyffaXYYnjdUgEZBC3nqugGaiKJWylRP30mVFN2byqPEgI92+rlykVvzr+50uE20hleqdB28hJCoCaTeAv5MASxBhSh0ZfYzebMPUxVzjJWOoGUPoYaY1P6btKRXkCwULvGWOk43fFtbErfjaaYpMw+qs4CV/JHSBhdDKYOEtMzBNJjpnKBudrVFgpFLZFm8CN/DTPbw9RTf05t/MpSfFYoKkamk66H3k9q10HMrr6m0jGkxC0XmH/ph8y/9MMN3SPpeUx85483tO2tYl2j6yyUyH3/jcWuE0nplfNEdw6QeiCcKe2pHAvfvEHZKKm4C8QiKQQCRajU/TKz1lVMJU7FXSCqJknp3dS8IjP2FbYl76Usc/R07KVYHadUXZ2TVegKqR09CEWAEmZbG7NlauMFrJkKia1ZzK44mQP9dNwxxOQPz2LnaiS2dTLwvr2Mf/sU9ckiakQj0pdCURXKl+fR4gbZu4aojOQonJgIu2gyUdyS1aRaq8YMov0prJny0rJ8TSgCc/vmpriduX0ztVePhZn2dTCwxUDVBIW5tQ20JgzSZh9VN0cgPXQlSsMrLz2DAB8pJR3mADW3QN0rMvbNU0x+6wJ+4CBQUITGxf9wBE86qMLAnqzz5t/9FiDQFIMg8AgISBld1NxieHlGhPim7Xi1Kolte1h45RnWswBCVdEyWfR0R0jtmMqgpdOosQRqJIpimAhdX+TX1RZl35f/Xi/WaG7bjLltE8IwkJaFdWkEZ2z9EjgA37ZIpgX3f6SL48/k2XIgwdyYxeOf7mF21OL+D3fyzf80zr4H09RKHqV5t20YS/oefr0W1tiuvHZNQ4lEl0rFlGiEwLIgt+JdEAI1nmw9aBCEpWWyeTUSHeygMVVET0fxGy6KruLk1yZY8quVVVcMQlHQ+3vCrr7y6sxfgfSYqpxFVyNLHiuArkRQFZ3J8llK1hRe4CAJmKtdwdQS6EqUa4XXkEgECsPpQ/iBz0zlAjGjg57ELorWNLbfHDeNDW3FyHZTvXaByuXTTd9J38MpLNCYukZqzyGEHpLgmAmNvv1ZRl+79eSuogqG7u1m9lwBu7JG55WAvv1Z0oNxKrN15s4X8ey1czQbCC94VE8s1xf6lQYzf/ws5nA3QgF7qoC3KH+jCJWk1klC6yCud4Sz8+IDSehZssbAUplIID186cMif2rDLmJocVTFWNXbVSM63Q9uJfADvJpNckc3C69cQ43qzOav0P3wNuZfHsHIxBCqQGgKQhHoqchiSZSCFjfoODSIb3uopkZ0MEP+6BgoAkVVwv2A2GAGPRmhPlkM762m0nnPMIquYs1ujIZOaBrmji24M/PLSxRNWTUzfPfjCbr6DRoVn1gyrBkOAslrT69e52qqCeJ6ho7IIA2vjCJUoloSy6uQjQ4RyABV0VmoXyeQPopQEChkzH40xaDhlYlqKUw1gRPUqToLKEIjZfQyW79MTE9jqgkqzjyO3yCud9LwqkhdIdo/jKKbKKaLbzdYy+AqsTixLduJbQ3rWPVMJ2oyiaJq6xVR3hKMLZuonziLN7+AsXkYfaBvw0ZXug71sotnB6iawvnXSsTTGpWCy8z1BguTVtgh6UvGL9SZW0UsEylDL+xmo6uoKIaJmkhg9Pai9/fhl0o4Y8uOhkC0tBMDS4TpN6BnYkQHMiR29eAU62jJCNH+DPZCFaE2UKM6ejoWKqDkmg2Yb1lIWl9DGUic8XEUXUNKiWJoCE0hcD1kIFF1jcDz15SUanhl5mpXSBrdDKXvpGLPMVNtT8qvKhqGGqfqL6AIlYZbpmRN48tWIycDHyQY2W70TGdT3FZoOrGhbSR33YFXKeJViiAliZ4od39+120ZXdVQuOtzOznye6fXNLqRtMEDX9jPyAuT1PNqyHi4DtZPpInQq2SFx+dXGtTPtRKJSBlQ8XJY9SqOXyfAx1qcsUrOLJZfCY2wBE+6BPiM1U4RCImUAQ073xIfuhle3cEpNbDmqyS2rFiGLb5BbtWmcnUBRVcpXwhvdvV6ntT2boqnp4j2p0nt6UW6AYHr41YsGtNlGpMlShdmlqgLa6N5ktu7l46d3NqJV93MtS+9gVfdGLG5dD2qR94kqCx7Hu7kbFOyZyUGNpu8/+eyROPhk5sdd/iD/2uKy6dWj/vGtDR+4GF71TB2LiWKCPkD4lqWucZVOswBNGU5+SSEIGl0UbCmMNU4MT2NgkbDK5HQOxFCJRsZYK5xFT9wiZsdOH4d26+hCA1VqLiejT0/jZMPydlX7YxSVeLbd5O572HM3gHURHLDahy3A2nbRPfvxp3rRO/vxc8XN76v7+M0fHo3h4oH518rc+lomT33pdi0N06j4nHu1TKb9gY89IkunHrAM1+awbFvMkJSErTrSxcCoaph9cLICIHrhqu2mzdrV+ss5RJVoZ6JMfCJO2mM5YkNZdGTEdL7BnDyNVIHBpj9yTm6Ht2JV7WJb+ti9I9fwW8sn0/IytYmvKAIUju7ELqKoquoEZ3A9fGqFoHrIwNJ9cr8mkZXIKg5OSr2HJnIANuzDy4Z3UD6GGqEGzERP3Cxvcqi13wOIcIkvBe0jq/6xDUa02Mktu1h+OO/iFspENgWQtPR4km0RBrFMMkffQnrFhtH3g7SA3E0U+H0t0Y2nLxf1+jGD26h+9MPUXjuFJU3LxE03FUDyxKJE9RxgmUj4RO+KJ508NpIgjf8MrFIN5nEMK5XbyuR0fwjEgKJ9MMEnO94GJ0xjHQULRYu46UfLLXsSV8ivQAloiFUBd92sWYr5I+N05guEzjtf0+ooZd8owOvNponf2yc7L2bmH76wsYEJKVsMrgQ1o5Kr/1vPvW1PD/9TjGcLSV4nsSuB2vG8W2/RjY6TFLvJmdPENVSdJj9WF4ZBHRHty4mNiChd5I0upisnsPyq3RFN1OyZ3ADB01IvMDG1OOLBjr0+HU1iqJo6EoEQ42T0LNAwHTtIl5tbY9f6AYdDz1Ox4OPo5iRVY2tXKmYibzxP1b8Ecr3OM66Rrv2+lEiu3eg9/XgjE811e2uCxlw+ANZfvzFaebGLHYfTqGbCk//yQxGVMG1A1w74NgzedLdfZx7pYjjrPJw2j20xdi0dBy8YhG/Umn18gWrxK3lEl1hcncv9dE8pVMTmF1JYpu7cIp1CifGMLqTxLd3hxLyZycxOmKYXQnq48vMW6u3DofjKr65k8Dz8W0Pr2ZjdidRowZ2ropqFggsF0ONsbPzUTKRAQA6o1u5kj+CRLKz8xGiWgo/8BgrLZdi5eqjdMe2cf/Qz5NvjHMp9wLXi2+xpeMw9w//PBLJdPkc46UTLck0v15l6sdfJbP/XpK77iDStymsmvE93GqJ6sh5yhdO0Jgea+LRTfZEefc/vou+/Vkm3prnyO+eRtEVdj05zN4PbUIzVc5+5zpnvnuNWDbCvb+wi4FDncxdKC4dI5Y1uecXdjFwqAur7HLszy4xdXKB+35lL1sf7iMzlODTf/AEF58a4+x3ruO7a9uGdY1u0LBRoyb9v/Re+n/hXZRevUD5lQvY03n8agP5DjDJO26FfOUagVxMFd88CwslrNuXArds49VcAtvHydWpXM7Rdf8WjHQsNLBuQG2swMAH9jH88TuY+ellGlMlhCLY+vl7mXnuEqWzM/Q8tgNFU1h4/Tr5YxO4ZWvJAEd6k/Q+sRMjHUVKyfyr16hPlci9NUbvY9uJb+qguop67c1Q4lGk56PEwyVjZP9OrHOX8XPFlm1dW+KuEw+6GXWvSH1FmVPdLZC3wuVq2uxjqnp+Kak2WlkeAHP1qyzd6xWORcmZZeUzKNkzlO25pcTGxcLGGmGEYdD9ng+Tvv+R8N9tMuWBbRFYDdxSEXtmEje3ENIU1qoh76ljh8QrrhNyAagq2//Rv0K9uWRsBbSuTsxtm9E6MiiGgZ8vbCh+Hp6kwtiFOnc+kmB4d4z5CZvinEPgh5SdS+cuoZJz1k5iteMQluFKRE0m0Xt7MYeHEZpK9dhx/FIprL+VrOLULHPk+nUHszuJljAReljSqaUi6MkIakTHrzvIhEng+GFFykbWvID0JcWTExRPtmllFRAbziIXlTUcv87ZuafaHufE9Hfafm55ZU7MfOemzypcmH9uQ+cXWA3yR18if/Sl9Te+cdqq4PwPRjnyO6f59B88TnZbCjOhM3hnJ0d+9zTSl7zv/zrM3IUCPXs78N2Av/jbR+jcmuTJf3ovQhEc+swOavMWT//Lt+jclubgz2yjMFrh1d8/y+irM9z/q/v41t/a+Dmta3Qbl6e49s++THzfMMm7todJtPt2YU8XqB67Qv3KNM5krq1U+dKFmzpKPIZfWBGbXBHIF0IJvStVLsZ0l62AakQxoikURSfwHXJH5sNliBln6psjiMDgwn/+actvnv+t5gd54beXGYTq40UKp5rjfFNPnV/625qtcOm/NN/Ey//tZQAmf3hu1etsB2P7ZvADIvt2EtTq6AO92FduneZvbbQf+bnGGLoSxQ5WK+YO9zOUKE5goQkdTzotx7tlMT5FIXPfw6Tve6gtJaFfq9EYvULt8gUa169uvPZ0A1n66MF91E+dw52awdy+hciubVQ3aHSFqjJ9zWbybB5FFShq6JkqWmiQxIrmxNd/mFt9OSkEyhohAumHk7AzN4fQVJR4jKBeX2p6aNv8sFi9AVC+OEN8ew+pvf3Y81Uql2ZIHxgkc9dm7PkKtWsLizXNLvXxQlhjf9N13jIk1Mf+impZhUDRdALPDXMGuo5v1RGKSuB5IVOaFnIsh15t2BzjN0JSG7minKQ622D2fIHAk1RmGsSyEWJZk7792cXnJ5k8No9qKMQ7IxTGqjhVl4WrZeo5Cz2qkR6MYSQyZLemAMn8xWLTfblVbKhRPqhZVN68TOWtyxgDWaLb+4ntHCB53y4y77oDe2KB+sVJaqevY40vNBVWK6kE8fvuwNg6SP5PvoPW1YGSTmCfWy7lCQKPcn2aIHBbwguaESXeMQhCQVFUnEaFwLOJdQzguzZWeQ6r2mZQqWpYNtSmzfKvEtapC6jZDM71idDoDvXjl/5y9aBuoOGX6Y/uYsEeRRUGmtAJpI+6WBfpyJD1Ka33sGCPktCz5OyN6cOthcjgJjL3PtTiYUkp8Yp5ci8+Q+3CmZBtDFDNKGbXAI2ZcDISioqe6sAu3BBB3PibLV0Ho78XJRZF7+1GicWI7NmJfW0Uaa9dcaIYRsg5K2BwTwLfk0RTGkJAveiiGgoygHhGY+pSjcrCKsdb5KVtOTffD733eh372nX8Wg3FNJsIbyRhaVjLIRWx1BQiHZ+pbx9vakZZePlKWFu76ImWTodORf711vZWYYTho/+3QigqZmcfMghQzSh6IkV9ehQ1EkUoWiivIwSKFj4vxYygxZLUJ6+BELjl5ckhCCTBDd6VRQpGq+Qwe67AK79/hnreJpo1cSouvfuzxDsjaKZKvCuCkdDxnYBG0WHqVI4z37qGBIy4tnZFwzq4NXYSCc5kHnehQmC7mIOdmDsHMHrSxPdvouM9hyi/eoHcD95c0jsytw4hTB2tpzOsvTQ0ogd3NRldP3Bo2O1nUadeojw3EtaEqhqe00AGHo3yHEIoxDoGmi+oswN9oBclFsOvN7BOn2973L9KrEzmuNOz73ix9VrQhEmXuZm6VyapdyKlj6oY5O1xuowhqn4RIQQBAYbSmjW/ZQhBcv+doWLBzV6u5zL/4+9QvXSu6R6o0Th6qiOsNElksAtzGJkuVDNGfWqF0RCgrOOluTNzaJ1Z1FQS/AC/VEZNpxCKuq7pViLR0JsUEE3pdG+OEklo5CYadA6FRtSIqtQKLpG4RTVPW29XLIpq3owbSr3CNInu3YOW6cArFam+tqJ9Wkr8WpuViaK2HnNptXijE0iydswjhBqLv5MFI+84FN0g0jWAU1wg0j0QlrElOzBSHUgZYOVmiHT2oegm2lJ5nVhSL1lpdG+GDGD2fIHuXWke/7uHEIqgtmBx5HdPM/HWHIc+s4Mn/+m9lKZqVGYa+K7PuR+Msv+jW3j/Pz8MQjDx1hxnvnu9qZz0VrBxoytAiRikH9tPxxN3YA514haqzH39CMWXzqKYOpl33UHHk3ciNIXZr74EfoCImni5IrIeGmHpei21lmZM4YnPD3Dfh5vZvHJTNj/6r2NcO1lsOZ1gMSnnOs1hDa9YQhImK1aKEpoxhf/tG3djRJt/e/Jijd/7tbNrXvo/+PIhOvpb44iVvMt//ZtnKcys7UHpQ30osShKKkl07w5KP3yeoFRZWkZ+4AvDPPLZ/qYw4MiJCt/69yPkJtevlEj3GHzhP+1tOcfxczV+9L9LKvUKUTWBJMDyq8RECiewEEIlqibQhIGpxEloWUwljh0s39NIXOXJXxniwU820xUi4dzLBb73O6OU5pavX0ulMXv7W6kBpaR8/E1qVy60TDq+YxHYDSLdgyAD3GoJPdWx6NmoS8tFdVHcci2403NED+5B7+/DuTZG5cVXCar11iaENlBjiVDhIoCRo0XGTpcBie/KsMpgsTPNdwN8T64aXtAz2bZL+MB18KoV1EQCv1hCicbCVeHK9mkp20q4C8JJQYlEkZaNIUx8fHQMdGHiSAsfn4zSTcGfXZUEHEBPpdmop2tgcpd4nHF5mSmWS0cVVDaJXURJMCLPYNOgmwF6xTAxUigo1ChzXV6kwvL1DLKNLjHAefkWm8ROOukHJNNylDFCIh3fblC6eByhhM0y9ZlQ86wxM7ok9dSYHV9KTCa27EGoOnZ+tokprDBa4cf/5/KE9vS/egu34eE7ASe+dgU9piOUUIzTdwJyI2WO/N5pNEPFX0yQOnWXwJe8+gdn0czwmboNb8ngzp4r8OP/840N3csbWJ/aUVeJbO4h/eh+0o/sR2gK9tg8U3/wYyrHrhKsiBfN/slzBDWLjnffwdw3XkH6AUG5hjbQHapH9HYSP3wQb6q5e0lRBJkek4HFLp+lkzNVzPjans3KTCWqCr6PYugQMxGKip8Ls7ZCCDK9BpH48iVLKankVgzGVbgD0j0GHX1mk+cmJSQ6dB76dB8//L2xNTtg1c4satQETcW+Pk7qyUcILJvSt8MOt2hSo6PPDMUwFzE/ZqFoGxsYiiJI95hk+yNNnxdnHSYap/DcgJAXOvSCbtAIjNfPsNIzulxpDtMoquDu93fx/l8dRjOWjZ2UkqnLdd78/lyTwQXQ0x1hhUGbxFnl7Im28Uq/XqUycrbpXMqXTrZspybT69b0xu49RO21YzgT00R2biWyZye1V99ac58b0NKZpSW85wR4q1UmrINIfytxulys3fWrZaTvI207XPkpN71zUuLm5sOOtpUTzKIgp5ZMEXMkhhKl7M8TVRJ40iGmJMlqA3iBjSQg708TrBKL17u6N1wb7eLSoEqX6GNWji9VIxmYdDPAvJzCIXSoesVmBJIZOYpA0Cc2c0DczzH5U+xFBgwVnTSdbBP7UVGZlCOYRPFWEpdLGRLaANWxZUazlUPsxriXQPnyqbbnHniSRnH5/bRKy3+7DR+30RzKlAHYZRe7DYm6XXHR7DSB9HHcwmJtTzgBN4obKyG9gXWNbmzvMEO/8VH8SoPKm5coHTlH/fx4U5/+8llL7MlcyM8qwhtiXxlF7coQNCw6PvN+7Euj1N443bJr2O0pWj67FcT27MYaHUXr7UIYeriU3wiEwIxn0cw4vlPHqqwv73KDfPzg4528+b155kZXb5t0J6YxHz2MfWkEhEL57OU1mfnfWchV/l7rsxC7H0jzqX+4rcXglhdcnvmjCS6+0coDoCx2ld0Mr1zEW4/MfJ1lcXTT1nX2BzwPVBU1GQ9jyquU57WDFoujZ7LY05M3JXpDRQ7DCF9I25b4/qpNXUS3bG/9MAhw5ufCSUdVURIJgkaDoNaagPZqVbxyET3TzAWsJZLoHZ3UF65hyggKGgIVRehowqDul7BkbbEBqf3gEZqG2d234cElCZiRY2wV+0iSoUg4NpJk0NApkVsq7zorX29KupZknjvEQyTpWDK6EHrJDg1G5HluPHOBIKlkqQbFW0jcCuIiRV1Wbj3Ze4sw1BgRPUkQePiBQ0RPoSkmxcYkQZtmjrWwrtF1cxXmv/kKtTOj2JO5pUD9anCm8ix89/WlWlTpuNRefIv6G6dBUZC2Hbbp/SUgum8v7kIOv1hG7UivxQffBEU1iGUHMeNZhKIwfe751UfUCggh6N0S5Y53Z3nuS1PLAfub4OcKVJ4+AoSZael6f6Vx3dvB1kNJPv2PthFLN78irh3w7B9N8Mb35trayBv8CTcjFJ58GxONohDfuXfdzeyR0VCiZ7A/bHbYqBowgBDEtu6gdulck0ee7VC4916D3IKP64Guw+iYz/x86zNUE0miw1taPpe+hzV+PbyUSARz82a8+Xkiu3bhzjdLxAe2hT0z1WJ01UQKo6ef2pWLzPrhddX9WxNINHsHUOOtK5G1UCaPj0eKTkrkAUmf2EyNMhWW6391dFJ0YhJFRcPARCDQaaavDPCZk1OsfIEUNLbrhzhrv4LLxjxHAcSUJA2/9pdudDsTW6la86Si/aiKjqoYmFqChlug4d4aCdH63AuTOfJTuQ0nkK3ROazRVvITaYU3Uu1IEdmzndqr6/NXNkEIsh//KO7MLH61SuzQHS2bRLZuoXzkZbTOjlDVwmwvwNdyboGHWy8TSXZjVxY2ZHCXfjOhsv/RLKeey6/q7SqpBMn3PIw3PYdXLONOza3Z1/7/b/RsifLhv7mZ/u3xlpDKC1+e5oWvTOOvwtomfa+pZGcJSsg6dbuIDm9pkXJvBy9XIHpgL9LzqB9rv+xcC/Fd+8m/+EyTV75vr87huw2+/6MG27dpDPSrlEpWW6Ob3H8ojNWugJSSwHGoX7uCkkgQ2bYNJWKidYU8xDcjsBpYE6OhKOWK+69oGrHN26icOYZXLLTstxHEtu9Cjd1awtTBJi9nyYguZuUYAoU0nVyXF5bCAlES7BJ3oqBQpoAnXaRYvYlqLcOaUXqIK2mmvKto6HSq/ahCJyaSlIIcc/4oOiY92jBJJUvBn1syuWmlm061HwUFTRjMeNcpBqE9imztw+jLUHnz0lJzU/K+3aQe2INfsyg+fxJrpL28k+M1iBlZVKERSA/Pc0L2tA3WQK/ExhJpt1OL1tfddjbVujvQh25D/kZKrOvX8csVIlu3IG0bZ7K51lbvCmn8vLn5kJl/o+2mUmLX8tgjeXzn1iREhBBsuzPF1juSLIw32jqwxvAA3kIeKQRqOolfKP2/1ujG0xrv+euD7H4gg6KuNLiSYz9e4JkvTmDXV/dYA8dpm7TSkunbXuEI0yR9+GHUWGxdDy1+zyHs0XGie3cjVBV9qH/jzRGEcd3UnYfJv/Ts0mfHTzicO+9SqQZcv+6hKIJKtb2Xm777gbZL9/rI5VDxQVGwrlxZ2qZdd5j0PKzpSbxyCT2dafouumUb0cHNVErFW3IOAPRsJ7GtOxFtBBjXQoBPiRzdDBIhRoZOJAELLMugD7KVFB2ckq9QpYwkICUzDIkdG/4diSSpdNCp9jPjj4Z0sEKlUx2g4M8y4V/CW+Ry8XDI+zP0qJtQVhRQV4MClqwRETF2GHctil+GyDy2n9i+zZRfvwhA7OBmen/h3Yu0taB3xJn+4tN4udaxWaiPoSohva0fhO+3QKxLW9AO6ytHZJNkHttP7fR1GiMzICGytZe+X3wvkc09lF+7wPQXn0HazQOt9x/+cnMzxCKEoWOPrM4kthbqJ0+DlBi9vdQnp2hcbJYN0fsHwmywYWBdvLrhl1ICkVQ3ye6tWJV5SlMX8V2bjc42RlTh8Ed6OP9akfJ8q+fizeWI3XuQoGEjbRv76i02RwiBEo2FS3QpMXr7cKan1t/vFqEZCvd9rIf7P9GLtoJIXAaSkZMVfvBfRim1ub6V8KrlZXn1lV5aJEpkeAvO/OwtGQuh63Tc/yjxXXs31lmlKnhzC4g79iNM45YbAYQQdDzybhrj12mMhqWK1Vr4H4Tin+3eC6FpZB95N3pXd6s/H/gU3ziy+HdAJNGFW68QuDaqZiJj4WAOPBfphrXq9swk1sQoWqq59E7oBtkn3oc1PYGb34C0/I39DIPUwbuJDG++pdDCDVQpYVEjK3roYZicnMViub5YRUcisaghCflzu7k1JWZTxOjXtjHnj1EJliseHNmgIvM0VohV3lAavpnC1McDKRg2dnPJPootl52o6I4BrOuzEEiEptLxxB0gA0b+3n9H78vS/6vvJ7pzENuYpevTj+LlK+R+8AZ+sUYgPQL/5iTwjcSvQBHKhg3wukZX706RfuxAGDKQoCYi9HzuMfRsgtq5UVIP7sEamyf/w+YMceP0ZfJf/IvWH+ztIn5fa2hgQ1gcrNWjx8I+9JsGb+PsWfxalcjgNqTv45crBOUNSGtIiV3JoepRjGiSjuEDlGeu4NSLbTd3FwlONGOR51UI9jyUYcvBBKeez7eMSb9YovLMy2jdWZyJGfyFW1waCoHR24saTyB9LzS6M9O37Oms8xPsfSjDB76wiUhs2VBJKZkfa/DUfxtj6sr6hOteoYBbyBMZah7cQgg67n8Ua3QEZ2Fj3LtaKkPq0D1kHngsLBfbANypWWJ334GaTBDdt5vGuVvX81LMCL0f+yzzP/o2jbERAnvtGKMSjZO++z6Sd9yz1Cm1BCmpXjiLNbkcWxaqRnJoZ0hsIyVCKPiOjfRdajPXcGtl/GqF2sWzRDdvCwniF++lEAKzp4/ej36GuR99K7yX6+QH1ESS5IG7yDz4eFuZ8Y3ApkFJ5ugVm4gS5wJHm76fZ5IsPewR91ClTJQYPv5StcNGEODjSIuUkqXk53AWk28BcpEiYH2oaAzru5j3JqjI5tI7JR7BXQjjr5EtPUS29JL/0VG8SgNhVPCKNTrefUeontFw0NMxOt5zJwvffLnldzRhYKhRJKArJoYSpejMtCXradl33YuImkjLWWrzjWzrI7q1l9k/fZ7ikXMM/toHST+0t8Xolp860vZ4QbWGdWF1EbiNQDpOmKFOpZqWrO5CjsCy8StVlGgEoWk4GzC6QlFQ9Qi13BgluwaEbcaroTTvMH21zoFHO5bClJqu8O6/PsiFV4s4N0kLKckEWl83QbUWlgrdChSVyKaQHzayeQtKLEb15Il31OACbNqf4JN/byvp7uVBKaWkVvR49o8nOXukuCHHX/oe9WuXie/a21LMb/T00f3BT1J49QWsiTECq9WIC1VDS2eIDG4isecg8d37UPTllk+vWm1Zcq+EdeEy7swc1vlLeLcQxpFBgAz8pdpQPdtFz0c+TeXMcerXruAszOJXl2urURS0RBKzt5/47gMk9x9CjbW+M04+R+GVnzYZRrdaRPouKOqiwW2EMlexZBM7WfXSOWI7dpPcf2eLxx7duoO+T/4c5eNv0Bi7jltYWJ4chEAxTLR0BqOrh8Teg8R371+auLxqJayBvkXl5gWmUaQKQlCm2aDlmeWSPEFadCJQyMlZ5pmiKkrUWF7xVigwyUhbY+xJh3HvIl3qIP3aVsa9i6ueiyEidCi9RESMLnWISpCnEuTpUgdJK1040qJHHaYaFKkvhhj8Ug0tm0LoGrF9m0AIqqeuhcUBAoSm4OVrOPNlFr55hMi2fhJ3tK+Yiesd6EqEiBbSd0p8on6SyjthdFHEIodmqBDa8e47cOZKVE+GJ2uNLRA/1HpiK2txlWQMrSuLMzpF0LCxr95CRrkN1FSK5IMPYA4PtcTPct/8C4J6A72/d2M8qovKBHa9gKJopHp3UJq+iNtYPSOpqPD8lybZfleKWGr5Fu64J83eBzOcfK75hQxqdfxCCTWbJvnkowSlCrW3ThKUNuCF32gpFWCNjYa1nbfTO78GOgcjfOofbKN/x00JoAB++meTvPlcBZFOQrGK0FSk74cDdvFlxQ+alDCqF86SOnQP0S07Wrzd2PZdGNkurOmJJUMWuC7CMFAjMfRMFr0ji9HT3xTDlVJSeus1vEqZrvd+qOUa1Ewa1dDQsln0vhUqIUEQso1dubamoKNfq1A5d4r4jj3o2S6EEOiZDjoeeIzEvkO4hYVQGNNxwus3DLR4Er2zK2yGaJM/8Os1iq+/iD3T/B46lTxOZcU7cmOVelPoOWjUyR95DrOnH6O3v+Vemv1DdHZ24y7MhURB9ToEPkI3UUwDLZlG7+hEjSeW9g1sm/yLz5C+537M3uZuzvVQpUSV0qqTb44ZcrI5ETUhm1UgCsxRkK0rnQCPMfc8jmww410nqWQBiStt5rwxbNk8QUsZfjfqnsfFWfKE67LMtDdCQICUQVOtcuXYFTo/9iCKrhLdOUD5zctL8VvF1FHjEQrPnKDy1mWk7WIF03iF9mO07pVIm1GKdhjm86W3IS8XNsIyZjkopo6WiiGGu0ncuY3c997AK4U3QYmuvVzRurOkPvAoxpZBZv/9H2IM96H1dlF7aWMF6+0Q2bUTrTNL6acvNPWtA2g9nagdaZRYDLVQWjeJoigq0VQPkVQ3imYS6xikNL36DAtgxjTmRhu8+YM5Hvvc8mBQNcGTvzzMmRcL+DeVj2k9nRiDfbjTs7izOZLvemipOWJN+B71K5dCUqCODpRoFGe2fYb1dhBNaXzsf93C9nuaCbellLzx/Tme/aNJ3EgSc9cwykye6J7NuAtF/EIFJWbiTi2g9WSxzo0sDcagUWPh6e8z8PO/2jTgITQWerYTrSOL9NxFZi252CiwqBJxs/R4EFA69jq5F59GjcXJPv7kkvd7A4Ftg22h7diGfW2MoLYYy1MEsTv3487Nr8mt6+ZzlI++TmN0hN6PfTakohQCoWkY2U6MbCfIxYrUxXj1WrHRwHUon3yL8qljaxr7dFajs19nYcrBtiSaDq4j0XSFesXHmZ1m7gffpP+z/1NL04kQAtWMoA5uIjK4aYn6cbVzCzyP4htHKJ98C6O3/5aN7kpo6IBYs/MNYEDZxoKcajGaBlF61CEcaZELpvHxyAfhex3gUAiW3/GbwwQALvbS9itRCQpNZWwrUXjuJEZ/lvj+zdTPj1N89jiBFbLFGb0dCF3Fy5WX8lNesYpXbG903cCiYE22JVxfD+uXjM0WcefLDP/9TyI0Da9QpfDMcrdQdGsP7uzqXqGxdRBnfAqtNyQcD+oWxnAfawuKrHPSqSTW1RGskWut8awZBWPrJoxhY/klXAOB79EozWJVc0jfpzp/fd19IouS7M9/aZLDH+5p8naH9sQ59J5Ojj21nORQsxn8UoXS8bNLNbpK4hbKdnyfyLatRHbsIKjVSBy8g9wPvve2QwyaIXj3Lwxw6N3ZJUYtCBNnl98q8Rf/boRG1Uczw98xh3tQYib4Ae7kPNGD29G6MhhDPdgXRpsy8dbkODPf/ip9n/g51Hi8xQgIIcIs+hoxRikl0nUoH3+D3IvPhDIzgD07RXRoc/O2DZvAtvAKRdzJaYLa8iAPtm1e815JKXELOZzcHPZcKHHe+7HPhhwFN3WFicX/X/OcHTs85+efalJ7uBmaLjj83hSxuEIqm+bM6zX23xfn6pk6c5MuI2fDiaMxfp3pr/8xvR/7LHpn97J23E1Yi69YOjalY69TeOWnBFYDe2oc7nmg/XlhkFQy2NKiLssoqMRFChWdmgxbo4fUnXg4LPhTNKiSElkMEaESFLBpECVOTKSIiDiKVNAxSSoZGrJGQ1YJ8HCwUNEwRZQAH1/6SCQqKjYWt1U2tQaCms3MHz4NqhKuzm40zkionLhK7ewogb1xI3o7Bhc2IkyZqzD/jSN4xTsRmkruB2/ilRbZoeIRUFVKr6xFKiOQjre4FBUosciGRBnXgpvLYwz0o3d34VerTQMqaFioqQTW+Sso0Y0lX6SUKIq2mIhcP7Or6gp6RCE/YfHad2Z5118bCAnPhcCIqTz0M72ce7mAVQ0fqjc9hzfdvKSyTm6ciEdoGsI0qJ0+hZtbIPPYu0JRwzU8qHWvQRPc9b4uHvlMf3NrdCCZvFTjG/92hPLC4oyfK+G9clMXoRDY16cJ6hbW+ettf6N+5QJTX/1Dut79Acz+oZBQZoOZ88CxcfM5Ssdep3Ts9aV61sBxsCbGWozujQHaOHGm5ViNcxcJqqsnAQPbxp6dWrqftYtnmfzT/0bnuz5AZGgzajS2rtrFDcPmFgsUXnuR8vE31p0UpZTUSj6uFTB60SKaUMnPuuRmPTJd2soNaYxdY+prf0znY+8lunUH6mJybb37GTgObiFH6eirlI6+vqgaAfbUBFLKlv1VNLqVIUDSrQ4x7l1ECIUt2j5m/NEw8Req6uFJFx8PgSAqkmFNrdbPde8cfdpWykGepNKBHhhklT48HHrUYa5554grKeIihSFMqn6RlOjCERa+dDBFjIVgqq246tuF9Pz2XYpesMTnAiBQkARLDVbLpOoCVahLMmS3gw1F0q3rc0z/j9alsF+zGPs331jz5fLmckT2bUfNpkk8fBd6XzeN02sv39dD0GgQ27c37OpZWGjysErPPo+0bCJ7duCMrk9TKIRA1Qzi2UGMWCZkMSqt3z4ciavYVsDxnyxw53u7yC6SzSiKYGhPgn0PdzR5u28H0vPwy2XMoWH0rm78SnkN9v/1IQRsOZTkfb8yTEffcsuulJL8tM2P/mCcyQvrrEWkxJtdh19VSqzx68x86yvEd+0jtnUnekcWLZlGMU2Epi/yXQThNVoW/mILrDU5RvX86bDEbOUhXZfqxTMoK8Q+/UYdVRcYURO77KBoCjII+W8DXxIUCyhCLA1ht5CnfOroUiY/sBrUR640/Y49PRme9869xLfvQu/qQUumwwTUjcTeIhG7X63gFnJYE6NUzpzYMD+w78GrT7WuEqMJhUSqNW7vzM8w+/1vENu+i/jOvRhdvWipFEo0FoZbhBI2p9h22EpcLGBNjlI5e7LlPjq5OUpHX2uaTNxiARUNRQgW/GkkkqiSpCGrVIMi88GN8SSwZI2aLOFgYRJDQVlshhCYIoYlqxSDOTqUHjRhIIVP3p9FRSMmkvjSQ0pJlTK2bBAX6UXztn4TjYhEiB3Yh1BVrMtX8PK31yjS9mdkWJmQMfopObOoyuKzlgGq0MJ7oqaoePN4gXtbnXC3lr5sh3Vmc2d8BvTwZ0TEpHHqIta5K2vusx6CapXykdYyDggHpdbVAUGAUNev6xSKipnsQtEMNDNOo7SxeGkkroKEqcs1zr6Y55HP9C3pXSU7DQ4+0cmlN0pUC7fPu7kEKbGnp/AbDRTTpL4w/7ZCCx39Jh/721sY2pNo+rxR8fnpn01x5oVcS0wa4O57daYmfWamb+1F88olSm+9SuXsSfRMFi2VRo1EQqOrKKHSresSWA28agWvmF/i2m2BDGiMXKYxcrnp43h3lK69Xbg1F4RAi6j4jo9dcUJ+1O4YMyfnqM01sCbHmkq4VkPQqFM5dZTaxTPoHZ1oqUzo9S6qzUrfD8+5UsYt5EJKRilR0RgwdlDzS+T9W9fralQDGm2aLyBsEa6eO0Xt0jn0bBdaugM1GgsnoRvntEis4+bzeNVy23clsG3mvvfnLZ+raLjSpVsdxBQxiv4CIlTfW7GVxMWhU+nHC1x0DFJKlkpQIBA+tqzTQQ/dyjCmiOJiY8gIPeoQUZEk581gCJOU0kkpWEAgsGSdLnUAT7o4cu0GJXNokO6f+yxC05j7wz/GKxQ3PB6Sh3cR2zuEEjHbGt3SC6dxLy6Q1DqpeQUiShyJxFTjOEEDJ6gT1zIEeJSc+dYDbADrN0d0JIjv27TmNoHrUXmjuR7S2DK43BHmB9hXxsKXwvVQMym8+dtnoXempnHnFzA3b0JNJKmdPBl6TIoSEp5IGcZtNrCUlYGPXc3h1ksbiufewI1a1lrJ4+xLefY/1rHE8qVqgt33pxneF+f8y8XbucRmqCpGTy/S94lu2058z14Kzz97W4bXiCh8+h9vY+fhdNPt8X3JG9+b5cifT2PX2w/4sVGfWu32jX3QqGM36tjTb58ovR3MpEEsG0E1VFzLxS67pIeTeI6PHtWJdUapzd1axyEshh9mprBnNtaQogiVHm0TC0yuaXQjSpzNxn6u2afp0TbhyAbz3vhNBk6QVftajiM9D2dulmReIaoEzDgj7DTvYdK9QjXYeAfezbiR0DJEBJBYsoaCYNq/1rRdPpilLio40saiju9fJcCn4M1i02A2GENBoeLlaMgaFnV0YVJgDhebTqWPCf8yBiYJkaEo5/H8UK7dlc7qoQVFENm+9ZbL3QCS9+6k/39+f9hkowiEIvCrDfSuNEIJy8ek6+NKK4wtC42IlkAVBkhJyZvFlx6B9FDR0YSG+5fRkRbZ0svA32gu0QkHqwiZ6gEvX2kxuumPv2eJz1bLpgmcMFOtJuJUnjpC5bnXbvlkb0BNJcl+5MOYW7aAolA7fQq9q5vEffdSfukI7nwerSuLV1yfDETKsKPETHbSKM1tuA3YiKlLpT6X3igxdrZKR6+55O129JkcfDzL9VMVGpW3F5sSqoqWzaLG4ri5HObgEELT2/btrwVFhY/+xmYOPt7Z0uJ76Y0i3/oP15dafB982OBDH43SaEh+/IMGrgu/+utx/vi/1zh31uVDH41y6C6dRFLhh99r8NzTdssc8H//uzRj1z2khMuXPAaHVL7+5Tr7Dujs2KlRLgfc/5CBpgq+8xcNtmzVOPaWw8S4z7/4zTT/8d9V+MLfTJBKKfzo+w1e+qm9KjlbbaHB1aevh/dLUQj8ACQomliSZQlWkE53ZXfT3bmPi1e/RxC8vRzD7UJBJaGkUVCIiDjtEkcJJc12807y9VbjLQBdRIiIOAKFmJpG894+mZSHsyjbFCJANrGE3dimumKbqiw2fW/JmwRZ8XGlvXjeYTy4Qwk5mqf9a/h4LcdoC6EQ3bXrFq5mGZl3H8Ir1Rj7N39O5vE7UJNRct97HaEqdH/mUYSm4MyH4Z7x+hkC6VH3StwY6DcmgunGpVCE4jZJdtY1utbINBO/9e3mDxWBnk2SvGc7WibBxG9/t2W/+f/8JwBE9u1Azaaov3Ea6XqYe7ah93Xd1sneQHTvHrxKhdx/+m0G//7/BwC/VERLp0Lpjo6wdVLLpPEX1veoA8/BiKYwE1k8u05p6sK6+xjmcsytXvY4+qN5tt+dJtGhhZl5RXD3B7p57dtzjJ3bQD3uGgiZyVyM/n7Kr74SckzcImOXqgke+GQvD36yD81oNrgTF2r88f92acngJhKCJz8Q4bf+bZlCftkQnDzmLokTGAa8csTh5HGHX/vbCZ5/1ubmSX94WOW3/r8VcgsBe/ZpbN+pLdIkCsyIoD+p8fKLDmdPueRyAYmEwpatGt09Km++4VAsBvz3/1Jl8xaNO+7UefM1Z3VPW7JEPM2KwbCaPVUUFVX9y5WtESiYIoYqdKT0F4nGF/loRQQpA47WnwbginNsyYA2ZBUFFUNE6dE2YYgIcSUDSHzpYcsGkgBTRMl708zIsNnoaP0pTBHDFFFs2UDHRBEKvvTQlQgCgS89HNngZrXdv0pIAuaCceaCW6cDUGMxzC1rr7xXg9GfpfL6RbxcJUy4J6MElkNQt1n4zqv0/dJ7ie4YoHrsylJlgmzjca/GU7xRrF+9UKpTOdo+Blt88QzDf/+TdLz3Tmb+6Nm22yipOMIwQv7dRZltrbvjbZ20Eo3hzs0T2PbyqyOUUNIkkyRoWBiD/bgzG2s3lTKgmhtHM6IbXrJrpmgSRjnzYp6HP1Nl9/2ZpW0yPSb3f7yHyUu1tjHSDSMIaFy+ROPqFfB9KrXqLSXShIDNB5M8/nMDJDqWPSEpYfZag2/8mxFKs8uF3ZGooFGXtLSar7iEekNSLAZYFhh6exLNhiXJLQQ3LgFVFQgBsXi49fe/0+A97zX5hV+K861vNJid9dm0WeXe/Tp/8fUGB+/QefxdJrYDsZhYNVpk6HHi8T401cAPXKrVGRw3nOh0LUYi3oemRQgCj3pjnobVnHgRQiEe7QahUKvPIaVPIt5LxOxASp9qfQ7bDj2eaKQDRVFRFJ2ImcHzLYql0RZtPxBk1G4SSpqIkkAVKjPudSaci/h4bDEO0KH18nrtB0t7bDb20K1t5tXad4gpSYaM3XSqA0SUOHsi9wFQ8XOMOudwpMXeyAM0ZI0L1rI6wp7IYXzpc8Y6wqCxg6zWT8UvkFA70NAJpMc158xtxZr/34DIzu2tqiQbhLRdhB7u6zccoukYiqER1G0Cy0E6HlrqHZCsWgdvK5EWNBzq58bJPH5wVaPrzS4Qf/geMp96EvwAJZWgcertVS94uRyRXTuJLCwgVBWjr4/I1q0hWYjvo2cz2NdGN5hIU9AicTQzjh6JY0TT1Ivrv5DqTaoOjYrPC1+eZsc96SaymPs+2sORr88wfXV93oI1IeUS8fnNDSHrIdVl8MRfG2DwJmWOWtHl+T+b4uqJctNcUywGzM76fPAjEUolyaWLLpom2LlHQwtr4qE978uqWJgPSKcF7/9QlP4BlXo9YOs2jUZDoijQ0Sk4c8pjz14dz4NyRbIvo6BqgtKcj6Gv/iwjkQ6ymW0EgRf+nd7Oles/QVV0ervvIBbrxHFqYVmgoMnoCgSJxCCDfYfJFS5Tr8+TTAwyNPAADbuApph0Zfdwbex5PK9BZ8cuMuktVKqTKCLUXSuVxlpuhUpI5D/uXMAKamS0bjYbB6gFJRa89WPataDMVfsEvuHSo23mdONFAAIZ4OFu2D9PKV1U/SJXrGOAYJt5kGFjD4XGbMjihbpYfyxQhIqhxmi4pSXKwvVqUUUkgtHfh97bjZZMhnXXgU9gWfilcph/Wci9M6T9ikJs3/qcyqvBGp0junMQFIG3UMbozZA8vIvqqWtENvWgd6UJVilnNaIqXVvjxNJ6S67IafiMHd94BcXbrl7QsknkGmQUzvgM8qdvoA/2IFQV9+QF3LG3N8tal6+gxGKkHn8MNRaj8zOfxsvlqLz2OvbIKELXUBLxJametSCEgqqZuFYFz6qS6Noces3rEGysjInewNkX81w7VWHnPemlzxIdOk98foCv/PO3V7Fxu9B0hQc+0cOhd3e2TBSaEUrF3Cyw57nwvW9bfOwTURwnoFiQKIrkq39ax3Ek+VzA5LiP40hqVckX/0et7Zj6z/9+mfegkA/45tcaaDqcOeXSsCQCqFYCrlz2GLnqYdsQTwhOHHexLcnRNxzmZn1sGzxP0mi0t/K12hwNq4DnWaSSQ+zd+TGujj6LqpkkEr2UyuPMzp9aMpI3IGVALNrF5qFHmVs4y0L+AoH02DT0MIXidabnjqOqBvt3f5pMZisLC+dRlDCWP587T8MqoCqh93gzJJKiP0fOnwYktlunT9tGWuligfWNbphQssPSKgIceXOTxcbDItedM0v7F/15BvTtCARCqHREhohqKdzApmhNEtc7iGgJTDWJIlTyjXHqXus4EppGdP8+Evfejd7bjRpPhBUBihK+U54X8qCUy9hj41ReeQ1ncuqWkr9KMoHR04Peu/hfVxfmti1N26Tf914SDz7Aeh5A6bkXKL16gez77kIxdaxrMzgzBbo/8ygd77sbNR7BzZWxx1pXx8luk3f92na23N2BHlGbjK70JdePF95Zo6tl4sR2t1K0icX+5cyj+yi9skYM1PNxJ2dx53LhexLIt62aEFgWtWPHqZ89t1SvGVgWgWVBEGBdHllsHlh/dg18D7dRIdW7g2i6J5Tq2QCjkaK0vvSOFfD0/xhnx92ppqLzez7QzU//dOrte7u3iLAeN8X7v7AJM9a6JDNjKo98up+LrxWZGWlOlBTyAfVGQDQqyOfC+7HQhrQb4PLF9t7BuTPLn0sJV6+snrBKJAU/+/NR4nHByBWPIIByWXL29PpJLsOIMzzwEIl4L6pqEDGzCCFw3RrF0nWGBx+mI72F8alXqVSXJ3xFUdmz42PkileYWziDlCElYSa9lUx6K5uHHwFA1+KUyuMscB4QNBo5GlYBKX28VTw4SbCYjJKL/w6pCHXRKmW0jHcivtx8DFdauCuTYtJHIfRiNcUkaXQjCbD9GroSIW50ImWAL12kDNBVk5u5aYRp0PkznyB26GBYb92mcUSoKoppoqaSGP39RHfvovijn1A9cWJdCSU1kaDzZz+NuWkYoWuhGommgaq2dASaQ4Mbuiu1o8epnTiJdWUybP2VMPunz9Px7jyRHf1UT16j9NKZUB3nJux+rIc9j/bw8p9dp15wOPyZTbz2lVE23dnB4L4Uz//+rTlU61cvbO1l8G9/pPULGRaHN0Zmmfvqi6vurw/2kv7Ee4js24G0HVAVqj99g9K3nrmlE12J6P596J1Z6ucv4FeqIXPXyhn0BkHPBhF4DqXpixSnzqGoby/7e/nNEpfeKLL7/jBuLYQgmlR5/OcH+Pq/vvK2FGtueBEb9RaiCY3P/4udRBPtY2BCCIb2xnnX5wf5i383gn0TO9o3vtpANRQ6NifRIioLl4soWjjAhCKQgSTwAhRNQVEVEGEyK/BufVK1LMlXvlQnCMI5WVFA1wUoEsciZIGi3Xwt2LvrZygURjh57s+ImBkO7f8FIPRkZ+ZOUiyN0tdziAN7fpaxiZeZnHkTgHism4X8RTrSW5mNnqJWnwMBMvA4d/nblMrLtbyhQV4kjQn8NVd34VkJVKE3/VsXBtYSJ6xEcJMqtoi2SEzJRb7Wdlhkcm36DVPEqMvlqp21kj6OX+d6qZkDpVpcu9xMTSbp+vzPEd29c7lt3PeRzg0u4LBBQmgqwjDCbkpNRevM0vnZn0GYBpVXX1/T8RK6jt6ZRU0lm7+QEikEYgVX80Za/cMNw/P0q8sziFeoMv/NlxePJVd1lru2xLh2NM8bXx+nYzDK/qLDtTfznPrRNJ/6lwe552eGeOZ3LrffuQ3Wr164Nsvk73y/9Ro8H7dYxR6bR7qrWxJjuA/7wsii+u2zRA7sRDpvr2EgqFbRD+ync/dunKlp7PFx3Nk5vEIhpH28RRixNLHsEE6tQKJrmLkrb2zI220Hu+Fz5M9n2HJHCjMaGjtVU9j7cIbB3QnGb7OSQU0miWzeij0xjmKaGyK96dvenBSoFlxUTRBJqEsDRgjBQ5/q48KrRY4/3dxBJ4Ghw72YSR0zoVPLWfTuyyL9AN8LCDxJabxK9+4M8e4oqq4wfTLH3IVb6xBSFNi1Rw/jrQ2JIgSqBrVKwKatGteueERjgnhC4eoll0Z9hbaWohI1M4yUriEDn2SiH3Vx4hRCQVMjOG6N0YkjeL5DV+eeJaNbrc1xfewFBvruYXjwIa6NPY9tlyiWx+lIb6FSmcQPPHQtguvd2ipFCIWk0klSyWLLBkklQ1RJMuVeBaAeVOgXMVJKFks2iIgYSTXbYnQdaaMJnZiSwpU2Ukp8XCTQCCoklSxxJYUnXeJKhpiSoh7cmm7aRqHEonR89ENEd25HCBGeS6WCdWWExvmLOBMTBI0GKKGRjezYRuzAPoz+foSiIEyT7Mc+jJvLY11YPa8TOA718xfQplvf8dj+vRCJLN0l+9p1vGJx3fyCm88TP7AFr1TDnS81qZiv58QIIbBr3qKTEZYemjGVioRrbxU4/Jl32Oh6xdqSvMXtQCJCd36xesEZmSD+0J3UXz1x28e0R8ewx8bRslnMTcMYA/2Yw8NIz8WZnsG6OtJWZXVVCIGq6RixNHa1cNsGF8JKrpHjFa4eK7Pv4eUqjY5ek7vf38X0lRqec4uVDJqGOThMdOcuvGKB+L4D5J99mtbygvaQUrIwYfHCl6eIJTWe/NVhNH25GkA3FT76G5uZvFxj7vpymEEIQbwzQnWuQSwboWNTkvRQgupcndm35kn2x8gMJ0gPJQi8AN8NEG1i3Ru6RF0wtEklnlCIxRVsO+C1F20GhzW6e1VkAJ3dClY94Mql5esOAp9c4Qr9fXeTSW9BVY2lQaRpUfq678DQ4wTSJxrpYG7hXNPvBtJnevY4m4ceZaD3bsYnX2V04iU2Dz/Klk2PE/geQlEYm3wF31udvOZmuDKUQt9k7EWgEFUSzHsT5PywwWLem6RLG2JX5DCNoAoIGkGVuJJuOk7Bm6aqbWa3eThUUPDzTHvXcKXNjHedhNnBLvPwYsxWUPJvr0tqXagq8bvuJLZ/bxi6kxIvX6D0zHNUj51AWs33xsvlsC5dpn76LJkPvo/Yvr1hKaVpkv3oh5idmcUvFtv+VFCrUfjeD9t+N/iP/h56f9/Sv8svvETt1JkNrf4G/vUvohga9Yvj1C9O0rg0iTNXXFdstzJvsf2BLhRV4NmhgzmwL0153iY7FL3loNC6RlfoGoqp4dfCJbyimxgdXbiVIoHrhK2ciyoOUi6SASvKEruSN59Dicewr4yR/fzHkL6Pdf7tkZgDYe9/Lof0PZREnPjBg2HzRTxO/I6DlJ57HmdqYwk7p1Yk3zi9KKj49usXi7M2J5/NsfVQkmgivMV6RGXvgyEfwy17u3KxVECA0dsXEvlsVIookExdrvO937nO+VcKqJrC8L4EBx7PsnLZ2rctxgd/bRNf+WeXcaxgad+xN2ZRVIXieBXpB9TzFk7dw646+OMBZjL0gH03bEawq+FKY9PuKB/5lR6e/eoCF48tT4Dv/VwXqiZ4+svzPPLxLIoiOPLdPCOXXXLzPooKe/YbXLviks/5vPmqDTKUyRECyqWbJ0TJtbHnScTDgVhvLFAojoQND1JSLF3HNFOAIF+4QqUWek+l8jiWXSYIPILAZXz6NUwjSSB9qrVpro0+RzSSRQgF12vgeTZSBsznzi9m9tfgG5EuV62TeNJGVyKYIoonXapBYalBwJY1LlpvEFfTCBTsoI4nHSJKc2t2XVa4aL9BTE0R3TaIJUz8C+GkU/YXuGi9SUQJq1IaQZi4VNEQps4805StfFMR/4I3QUOrk/7IfRS+/+oG3qAQemcn8TsPocTD35K2TeXlV6m+ebStJt4NOBOTlJ59Hr0zi9HXF1J79vaQuOcuSs8+v+HfvyUI0XZ8zH7pWeL7N5O4axvJw7tx82WsK9OU37pM/dxYSMzVBhNnSmw9nEWPqNRLLvMjVZ74wnbu+tgAvTuTvPUXt9ZhuYGOtB56P/8Ec197ifq5cRLb9qIaJlo8udTNpccSIe1jvYqR6cSv1yhfOU3g2DjXp5ZKt9zJWVAV3In1CWXWhKoS2byZ+L13Y27ZjD1yjdKzz+FMzyB9n/iB/aQef4yFr3xtgweUyHewK8n3JBdfLzJ5scb2xaSaEDC4J86u+9JMXa41qekGvly6l+0P6GONjSEMEzUapfTqyxtqjpBSMnKizJ/9X1eYuVZflIgP+PZvXWfrnSkSmRUxRwUOPNbBPR/s5tVvLT+fyvTqy2qn6uJU2w+4VKfGPe9Oc/yFMqwg8tyyL4puKAgFNu2KomqCl78H1Yqkuti5Nz9rYVsSKaFcWv+52E4Z21leUjessCEmkB6V2jSVWuvkazsVbGe5usK2S4u1uCHqjQXqjVbCooa1foutJKAcLO7rr65c0ZBVGl7zBFxvs30tKFETFeLJGEJTlzqjJJJqUKAa3FR3rKnE9m3FnS9Rm2i+9oasYuHQu/O+da9j+YACY3gQc/PwUljBy+WpvPbGmgY3PEmJMzFF48Il9N7eJY7i6L69VF5749ZWpOuep4LR3UNkeDPVMydaZJbq58dpXJ2m8NwJzMEuUg/uJfnAHlIP78OdK1J6+RylI2fx8s3PZPxUkW//87PUSy7Sl7z1FxOoukLf7tDgvvKl67d0muvL9SSjS5pBNy4MTUdLZnDLBYxMF265gBaNoac6CGwLt1oKPWDHRuhayLnQsHCuTSAiJkLXliTZbwfJBx8gef991E6dpvjU0/iVSlNgvnHxEtGDB277+O8EZkbqnH+lwPC+xFJsV9MFd7+vi1PP5ZgfW16O+b5c13GVnkvt3Jmw3nGDXu7c9Qb/5dfPUrvJcE2P1PnBfxnj0/9wK+qNxJgQJDp0HvlMH6NnKkxd/suptPjiPx8PE24efPnfh4oKN88f1iqlYX+VEJqGlsqgLErc2LNTbZM/QlXRu3pwC7nlfIKiLIkfxnfuxZoaxysVb+9EFEHX559EGGEWv356hNS77sLc1o+QUPjRaxgDXcTv3onQVIpPvYmaSZD+wH0E1QbVN85TP3Od7CceRk3EaJy7Tu1kGFfWezuIH95D/cQVnInVwxJKNEp09y7EDWY3KamdPLVhgxkqd08RNBpLaiBaOone24M9cm39A2wUQuBVSjSuXyVYhfZUOh5evoqXr1I7O8rsl54lee8uMo8foOdnHwul2J892bSP70oq88v2qjDR4Kn/eGmxslTecjRy/TpdPwi7NfzwyOULx5a/U1T8Rg1rdpLVllvRO/egREyqL70FfoA+1Evsrr0U//ypWzvTFaidPEn1raMtcaSlU67VKHzvBy2f344K6m1DwtEfz3PPB7rp3xFbErDceijFzsMZcpMzS8Ym8ALkGnEloetEd+zEKxRuSQW4VvKwG23aGD3J8afm2ftAhgNPZJfK34QQbD6Y5P6P9/Kj/zq2xAe8EXT263T06KE8edFDXSW2m8hoZPsMjIjAdST5GYfigrf0+sSSKv1bTSYuW6SyGpkuHaFAveIzO27j2s33KRpX6B4yiSYUAh/KeY+FKQffC8MRnf0GQoTNLKmsRnHepVzw6N1kommCqWtWK6OXEEQ3byO2YzduLvRYnYXZ9plyVcXoCOV9vHKJwHWJ9A8SWFZIiO66rWKVtwBzSx/S88l99TmSjxzE6OlASUSovHgSLZsifs9u/GIV6/IE1pVJnOk8ykyBakcS++oU9vUZ4nfvRHo+pWeOEj+8G/XaNEosQuqJO6mfvLqmwQVQIibG4MDy+JGSxqVbK5PyS2WCWh01FiZ3lWgMvaf7JqMrUDQdVTNRdAO3Xl5U4N3YJCwUsShRlMWrVNbs2hS6ipZJoHelMAayKBEDr1hrTrAt4kZN/kruDj2iEElqNMoebpsxthbWV46YL+FXGphDXdgTC81B58DHml07niEUJVyCLBptaTuh97sCkvbOW0gctmgQNBUlohM0HGSjEZLtiHAjvTOFUJVlwxVIvIWbXiTR2kUGi4Q3QiGa7sWMdwCS4uTGCcbXwszVBiefzdG7Nbr024oqePzn+jn13ALVQjgbe+7as2U42AWxXXvQs2EdZf3c2bd1bqV5hxe/Nk3/zhjdw9GlzzVd4b6P9HD1aJnTL+Q2NItvOxDlU3+rn95hk1rZp1b2mL5mc3P55pZ9UT78iz0M74ri+2En2tQ1mx/90RxXTtWXtvm139zMj/9kjn33JUl2aBiR8Nn+9C9y/OTPlpf82V6d9/18N/sfSITPVxXUyj4vfjvPke/lUVXBE5/uZHhHBKsesPPOGLPjDmdeqXDokSR9WyI889V5vvvf27SLS7CnJrGmJxaL/VcfWFo8jppIhHSLiWRIA5nOYM+tn1Mwowp7DieJpzVOvlikVmr+HaHroXyMlEjHQ+gaaiqOubWfwHJonB/FnckT27+F5CMHqR29hDO5sDQ2AJSoid7dgTHUhX1tGr9mocYXqQ0VsWoM9AaUWAy9e5kvRQYB7sythQgD224KRQhdQ403d0gKVSXWMUC0ox8jlqY8fRmrsoBbX12ZZiVkEKDGE6ipNKsZai0dJ7K1l8iOfuJ7N2EOdeHMl2hcmaJ2boz6uVbKz57tCTbdmeHYdybx7IBUb4R7PjnEwL4ks5eqvPwn12iUNx6eXNfougtlyq9fIv3IPvSuFPbYfFiNsPJiPZ/GlfYvmF+pEdmzDX24D9mwid6xBy93002UtOUmUDSBZiwuf3WN2P5NSNsDVUHvSFB+6zJB3SaypTfkdOiIg1Dwy3XKbzTHxoyIgqq3Gl3flai6iZnIEs8OIhSN4uQFNjK7CqEQVZJ40sEJ2rOTvfT1aR74eA8d/csqFsN7Exx6TycvfyN8cT2nmQGrBUEQlogFAdL3bovS8WbIAC6/VeLojxd41+cHlkIgEDKkPfkrQ4yeqVCaX7sELxpX+Nn/dYDuIYM/+7dTFOddejeZfPRXepoaSNKdGp/9O/109hv8+W9PU5xzSWY1PvqrvXz+Hw/yH//2NUq55Rf3w7/cy/f/n1munKyh6Qof+sVuPvlrfRz/aZn5SQdNF7z/893c/a4Uf/7bMyxMOxim4IlPdfKZ3+hn4nKD8cvhSmjLvhhf/a0pjr9Q4td/czOuHfCDL87xyMeyvPdz3Xz/D+eaQxxS4tWqmP0DRNRN4b9LBeQq9ceB54EQKGYEs6cfLZlGqBqKYYTCkLE4bqnQVukj06Pzqb8zSO+mCP/+CzaXjzfHE52JOZIP7SP1nrvROpK4Uzn8Sj2sfzVCQVBzSx9aNoUSiyB0jcDxwA9I3LcHoatYlycwhnsQERNZtSCQuAslys+fJHF4F361gdOmE+sG1HQKxVxu6hCKQvZnPn5L76Eaj6OmlyszbjRPrIQMAnzXolGaxa4s4NRLoWryRiEWRVt9v+3w7fzIfcQPbcMcyBK4HvUzoxSeO4k1Ooc7k1/1+W49nOXA+/s4+cNppIS97+5hzxPdjJ0ocugjA5RmLd78842T96xrdGM7B+j9ucfQulIk79oWepp+88l5lTpX/+7/03Z/58oYem8XHZ/7MELXsK+MUflJMwG5lBLPab1gVRPo5qJrbzuoMRMlm1r8t7uoUCzQu1IErh960L4f6hya2nIcGpp0zJrOzwplOmq5cWTgYyY62XgFg8RQImS1QepekZI71yItnZ+yeeVbs3zo1zctLc8UVfDeXxzizR/M4zQCPCcgWKdsRXou1ui1sA7yHYJd8zny9Wn23J9h88FmAcltd6Z47y8N8c1/u3alybaDMbbsj/GDL85x9PkSSLh2ts7wrgh9W5YH1b77E+w4FOdLvznJW8+WblT2I4C/9e+3cPjJDM98ddmLHT3f4JmvLOAultdle3T2359keFeU+UmHgW0R7ngkxZHvFXjzmeKSRy6E4M7HUhx+MsP45RkEkJ9xuHi0imMFlHIec+M2p16p0DNsctcTKWIJlepNHqa7MEepUmxqAGgH6bpUL5zhBoG4V62gaBpSLvJMnz+15v6uLSktuJhRlXqbcE5Qsyh895VlsveGA4pAiZqAJKhaeIUq7nwRcVzg5crg+VTfvIgaj+BXGwR1m+JP3gyVnB0Pv9pg4U+fwZsvUX7xVGikV4MQLdLyQlVJ3nfv6vtsBIrSsuJFBljlcIWqaMaGaVZX7u9bDYzu3nCFfdPX6ScO4hWqzH39JRqXJ8NwQsNed7ineiPkR+sEXkAsrbP7kW7OPzvLa18dI/Ale9/V884aXWe+TO6HR4HwhZaL/39DERUp8a3VvaHAsqi+8AbVl4+FI8zzWzTSfE9Sb8M5a8ZU4ukwHmYOduEV61RPL9ZZSrkUsig8fXzxo8W7J2lJenRvinIzpIRK3l36R3VhlMrs1bVuR8v+N7LNUt7Mrr+MF74yzSOf7SfdtSwx07Upyv0f7+Wlr07jucG6y3gtnSFx6C68UpHa2TN4uXdGCmhh3OKp/zHOX//Xu4jEl5smVE3w+M/3c/qFHJdeX315178lgmEILh+rLr28QQAjZ+pN1zS0PUI0rnDm1crySy7h4rEaEth1V7zJ6F46Xl0yuADFnEvgS1LZ0CPv3WSQ7tT45K/38ZFf7lnaTlEFhqmQ7dVv/ATVkofvSYIg/LtWDu+3tdiBp5mtKyCzf4iu935w0TuVTH/zywSNNslFKZvEJ6XrNPWA3RDTXA35WYff/o0rCEXgWu1fAi/X2uwQVJsNUlBZPjc1Fkc14zhzywojfmGFlqAQePOlsCoh2R2Wcc5ONo3pldio1uAtoU1+RdUjCEVFNSIk+7aTu3rslqqKhKriVcrUr14i8Fo95NF/+ZXQaXQ3noyGMMRp1UJe6OxQlGS3ybWjBayKR26sxo6Hbo2qdv3wwlyR6otht08k1Y1nVYlkegk8B6Go+I5FeepS+51VFa27A3PHphtnj1AUnPFpnKvLM4PvSap5d1HXavlhROIqmV4TRRXY4/PY4+0D/htp+d10INnymQygOBN2+ZjJTlK9O3DqRUozl/EalXVbPUESSA9fumvqJdWKLi99dZoP/fqmpaC8pgse/lQfJ36ygGsHa4cXpMSenMAvFTE3b6XrIx/n/9fef8dJdp73nej35FM5dM49Mz0ZMxhkDEAABAhSJEFKIimJEhVtZckrr6+ctF6tJd+P7spareQrm7JEWpKpHChGkQCRcxxMjj2hc64cTz77x6np7pqq7ukBQNpXd378kJyuOnXy+7zP+4Tfb/FP//g9CTMAHHsyw/4HUtz/qd6mqjVFE/nkv9zOH/yzMxSW2k+sihZwB1vXJLiubSlWNDGIk16TdKhXXXxvTWH5Kkq5awbbqr0ITlBRAwayZ/4mw8y40XIrlhrVIVdbh1ftvL8uIbLR7RME5FiU0vG3sIsF9L7B9+xet8CnkRx87/Yv6WFCfUNIeghjeQFBFNG6+nDKBQCUeBLP8zBXFvFsa1XqR9JDqOkunGoFe3078DU8I75tY6+8u0nf932c0roJSRBQI0nUaApZi6BGUm0N82YQNZ3wyHakeALnSBHPbZ6Y3OI7q8gpLRtsv7uD7h1R7vjkICsTFRYvBBNhNK3htVmlb4YtsYx5tkGsdwyrVkSJJBAEES3eRXHmDJGOofaBeEkitG87brmG3NHgIdAUlP7uwPCuM7q+B+WshVFzV5sJIBhgnUM60ZS8qkz7TiAIsOO2eMvnvu+vktA4RpVqbg5JUYmmB6kVFjArmxOgC4JITOogpfbh4zNXO99W38m1fY4/leGuj3XRMxpevbauYZ1DH+ykWrBxNzO6goDS2YU+Mgq+T/aJb7znRuAbvz/F6MEYg7vXivMFQaB/LMzDPzLAN35/Cqve+nJVSy6e69PRp3L51NpLHU1ITQa8lHNwPejoVamV1zzDdI+KKEJ++Zrne53Lq5ZdLMNjccrk+S9l224vt4nhbwm+j5VZRgpHkSIxRH1zTy8UlejfrpPqVdHDQbeWWfMoZW0WpwzKOafpcUXiEvvvizdxYjiWz4kXC1QK7R0IPSJy64NJ8ksWV05XSXYpDO4MEUkE46Wcd5gZr5FfbNxHUUTvG8Kt1xBVDa27DyXVAZ6L1t2P2TDG67302J5bQRRRLAOnUlpVDvZq16hGlMss/Jc/2PLtbA+/ucbX96kXFqmXVsBzkfVokL+4Abi1GlZmGbFS3rB+WN/RhzbQgagqbcviq2ensa4hvbn8epbdD3XxyV+/Bd+Hpz97EbPqIggweEuClYkba3baktF1bRNJ0TCKyyihGKafRRAk7HqJamZ6g7ZZH8+0sCbnsCbnVj/VxkbQ9mxr2bqStyksWU1GF2B4f5RUn/6ujG73aIjh/dGWzz3XZ/JUOaCH9Vwq2SlcsxYscZTrL6l838fy6jheoO20mWT0yqzBsaeyfPCfrFUyhGIyB96f5vQLuRZ6xZZzNQzM6SnsXO6GZXq2gsKixTd/f5of/vWdRNY1TSi6xO0f7uTS20VOvZBrMW6TZ+uYdY87Hklw9Lkiju0jKwK3HI41JdLGj1WpFBwe/ESav/6deVwnkA+698NJBBGOv3hjfAGz43VW5izu/3iK1x8vUM6vDVAtJDaFJt4J7EIBK7MSJMHyGTyrfV354K4QH/zhHsYORegc0AhFJDzPx6h6FDMW3/zjRV75Wrap9TuWknnk090MjIUIRSW0sEi94vIbP1qnUmjvjcXTCp/5N0NcPFbh2PMF7vlwmu0HI0STwXgp5WwmTtf4ymfnmZunIUgJiBJaTz9yNI5TzOOYBm6tgpXLIIXDSJqOHEtiZTN4toUST+KUCmvlcb6PV20+J1EPBbkFz0NARBY1XN9GFKRAJQNvdZWYCPWRq00DArocxfZMPM9py9MrSDKCKIEko8c6qZi1d+Rc+K7bNtQX2j1I3099F3I6hihLIAp4dQs5EQZRoH55AXM+12J0ly6Weer3LtK9PUJ+rs5Ug8ZREAXGX1phYXzzENK12JLRFUSJwuw5HLOGYzRbdddqXysrSBL63jHM8am1D0UBMRFFjEVati+uWKxM1+m7hqSla0hn+6E4c+crOPY7G0h3fay7KV55Fbl5k+XJOoIoI0gy8fQOtEiSxfMv4ZhbKfz2qbklHHMioPnbhPDZrLqceznPoQ900Lu9UasoCmy7NUY5Y1+X7VJOJIjfdQ+l119FGxqm9MZr76m36/tw7tU8b359mff/cP9qmEcQoKNf5/7v62VuvEpuvtn4zF02ePOpAu/77jSuM8T0eJ2R3SH6dzRnpq+cqfHc32X5rh/pIpqQmbpQp3dY4+4PJXn98QJn3rixFze3ZPOtP1/hJ351kF/6nVGOvVDEtnw6+1TGbg3z2X89RSl7Y57Seqid3UjhCFZ2mfD2ndjZVsPbOaDys7+5jeE9YbLzFi9/OcP8lTqiJNAzrDO6P0yl4DR1HwJkFiz++69PoYVFugY0PvVLA3T0q2wFu++MsfP2KMWMzVf/a1At0jus8+CnOrn9kSR6WOR3/9kk9fkgxu67Dp5RQ5DkIM7puRjz08G15CUEUURUFvEsk8rFM4h6OPB+15VzOIUCnmWt0qiKqoKcTuFksgE/h5omrCRxPTtQzHWqKFIIxzPQpAgJvY+I0oGPR8VcIa73MVM81nRdgiiRHr11NS8T6RiilpsLtO62CiHIfWj9g1hLCy3hhfSHbkfUFGZ/+++J3bULKaKTf+Y4oqaQ/sideHULs03Nsu/DzIkCMycKTZ97rs9bX5zZPDTYBlvzdK36DWcSfcum9OTLRA4fIvn9H1793MkV2jZGFJYs5i9W2f9AuqmeVpJFHvmxfk6/kGNl+sYz98P7otz5ka6WGl3f93n7iRVcx0dSJPR4J2ooju/7CKK8pQC+KEhEpTSmVyMipym7WWxv4067iZMlLh0t0j0SWo3tRlMKtzyUQo9t/CgEWUGKRLBWlvF9DykWe0fClNdDvezy+teW2H5bnOH90aZqi1seSnPhjSIv/fV80+TnOj5/9dvzVEsu9z2W4sB9MU6+UuKP/v0Mv/JHY6svpGP5fO3zS6zMW3zoM53c9lCcYtbh8S8s88zfZrCMYDvfA9v0WsItnudjWf7q574Pbz1dILto8fGf7ObDP9aNLEFu2eHsm2VqZRe/cX6u7QcGqFElc3UfnucHsehrx4wo4hTzhIZHiO7ZT/HoG3ht2Ot+4F8MMrovwuWTFf7k16aYv2I0Wq2DeyYrApbhreWvRBHf83Asn8VJY/We16tbL65P9ShcPFrh9//lZXKLNp7nI0kCMxdr/NxvbWdkb5jt+zXOvr62cnA2aNW+iqtH912nKdyw+n2tjr24jDbc4NUWBLTRUZxMFklQiGhpfN/H9gxcz0aTo4SUOPn6LKZbI671IAgSvu8R13tRpNZVpO+5FOcu4NomAlDLzt24YKjnYy4uIOqtSXMIaGrLb45TOzeDvi0QxTRnMnh1E7dq0P2Zh9CHu6memmz5baAGJrRtsBIFWibWzfCulSM2g183qb19BvPidKM8JAg5+GbrC+y5PpfeLnHXYybpfr0pht45qPOpf7ONv/z3F28ozHC13rRrWG+5WfWyy5tfC2oTXdugXlimmp1B1iJbzphKgkynFiQJXd+h7GweA7bqHse+lWHf/SlSvdpql1q8a3Mv5yoLv9rdgyDQIGh/d/SYG2H6TIXXv7pE13CIUExefQ6SLPLhnxnk0tvFFsIeo+bxt/9pgb/9T8212j//vtNNf9uWz4tfzvHilze+T+feqvDLH2ltTjn3VpVfeKB5f54Ll07U+N1fmmzZXo2kkBSfL/1+Mz3gr//IBLIeQ5QUXvl6nle+3sxbgCSTuud+BFkJ6kgFkeieW7CyK020of07dPbfl6BecXnivy8xfdFGSXQi2BaiFsKr17DwESISsu8hRWLIkSj1mcm2hm2r8Dx48s+Xyc5bq6sjx/M5+VKRatEl0anQM6xx9p2LbbfANwzMmRnUoYFgHIki0dsPUT12HNs1mCueav1Rwz+q2QW2mnJzjAqSFkbRo4GT12Yl19IV2NSB4yOqCgKgdnZhzDY3Oviut1q65xk2UlQPNNPqAZuiZzrIHa0Jd0UXOfjRfu781CCJbh3xGgcuO13j8z/+RsvvNsK31egCyL1dhO/YH1xcYwRbE3PU3jzZsu3lt4tMniqT7NWa2kgFQeDWRzrwXZ8n/2iWufFq26TOVYiSwNC+KA9+uo9Dj3as8gtchef6vPblJbILDa9UCAi4tWgaSQ1TL1yfqxbA9iymaqfw8NpKtrTD+FtFpk6VSXZrCNLa9W0GUdOwV5YoHz2CFI5gzhx/zxNpmhzFdCr4Hhz55go770py6wc6Vp+DIECiS+O7//kof/KvzlO7gQ6c/xHYft8P4jomF5/746bPQ8l+hu74GLPHH6ey3Kbv33MpnTzakjm/NjEzvCeMognUKy5nXi+hprsIDW8LjLWqYS4v4NZrCLKCmkwjRaLYxTxqVw/G7BTvFMWMzfKM0RKO8lwo522SXQqqfn1twI2gdHSidHXhFPJYy8tBbbBhUL8wTuTWg0jRCIIgoA0PEd67h9rpd9cZuR6CKBPtHCaUHkDAZ/HsSy3NEdfqA0rRSGB4G8bUtx3q05Nt1SysuSz6tt6gZC5XRu1NEdo1QH18DrUniZyItKV53Hl/Fw/91HaWLpaZeCu3upq5imr+xlac336jm06A71M7cmbVU/Cq7cME9YrLi3+1wI7b4yS61CZjJAgCt36gk55tYc68lGfyZInFK3WqRRur7iGrAuG4TNdIiJH9MW55KM3Q3mjb1t+ZcxVe+/Iijrn25vqei+c6JLu3BRLsWzRqoiATEkPoYpScPX9dIT/b8HjhrxfYe38KLdwaZ26BLBPZtx9R1xFkGc80iR66ncKLz91YR5CoEFKSOJ6F59voSgLDLqNIGr7vkQgPsFK+hCiIlLM1nv7jGUb2R0n3a03nuO99KR74dC9P/rfZb1sV1bcTVq3AyvjrWBtVpvg+bqWMnEiCIKIk08ixGJVzpwIvqXHR8Q4FSRIwax7lnIPaE3hh5vwMTqWM7zj4toXWN0h9LoihCpLUaG5pKBW8A9TK7oZJwtUw7DulGBEERF0nvHcfbrmM/eLzwZj1fcypGYwrE4QP7EcQBMRImMQH3o9bLmNOz9xY3auu43teq+CA72HXK+iujWubbfdpZ7PoO8dW/9a2b0N46+3VZ2PnNvarS2+Ok/rgoUAjbWoZt1yn54cfxpxZCZJriow138oiN3BLnMxkla/82pkbNrDtsKnRVXWRvYcTzF2skZ0z+cjPDPLsny9wz8c6iXUoXDxS4tLRMh/+yQGKmeBkTr9caKrp9Ko11OF+1MGe1aYIY3yCynNvtj3mxbeLfOtzM3z/r+xY9QSvQpQE+ndG6B4NUcl1Uc7Z2KaHY3tIkoCiiURSColOdbV9+FrkF02e/bO5Jr0yQRDRImm0WAdGKbPl8SAIAmEpTkzuQBZUSs7KdY0uBJI+F94ocvDh9PUP4rqY83Nog8O42Sx2PkfsjruaZvetQFfiSJJCRO9EFGRylUkEQUBXEuhKDElU6I7vYqkYLO0nTpZ5+k9m+YF/t6NpP6IID/5gPxMny5s2TfzPCrteIjt5dPONBAGtdyBoVVU1lFQHqfvSOOUSpWOB6sRVg9AQzcXO5/DqNdxatand12gY3HerC3gVnutvSo70rtCoB1cud6J0dDR95RaLlF9/A3WgH6UjHdT+Dg+R/uT3UH7pFarHT7Ztc74KQVVRB/oI7dqJNjxE/omnsGaaeVt838eq5inMnMVzLJRwvGVyNC5dIXbvPat/h/buIXLLfipHrvNMgcrRS5gzK3i2g2dYrPzdy6QePYQ20o21lKf40hmMqdZ2aFESKC4amLX3ZnW3qdF1LA/b8BjZF0UNSTiWx8j+CKou8tY3Mtz10S6Wpgz23pfkz37tEqWMjVlrNgRSMo5x5iK1Y2dXX1Tf3NgweY7Py19cJNah8uGfGQpW/td4g7IikuzRSPZsJvLXDN/3qRUdnvnCLEefyDQFvn3PpZqbpZafuyEic9/3qLnFVcVWx9/aLOjaPt/63DQHHkq3TCxtDoK1sozS2RnwkUpSIHV9g4PY9310JY4giHieTSo6hO3UVw2uj0/NzBLROinVF/A9ePVLi9zy/jT77k81yfukejUe/fFB5i/WqOQ2mWQEgTW3y2/2XAShcZv91t+sbtfoE77W42n329VjtUmMrW5zdSL2N/fMfPAsk8Ttd1M6cRQxFKJ68VxQztRAMePguj5aSCTZpVBYMXHalJV5xnvXtv1eQunuIfXBDyFHYxiTExReehGvtknFju9TPz9O+eXXSH740VX9M214CPX7PkH8kYcxL13CWloK6npFEVHXkZNJlN5u1J4exEgEQVXAdRG11jyGKCvE+3aiRJJB9ZDrUlfD1HJrxrl+9jxOoYiSSgIghUKkP/W9aNtGqZ48hZPLg+8jahpiNIqUiCNHo9ROn8VeXm5qsDIml1j682cDqlnHxTPttuGFpfEKd3xqED0qU7Gsd93HsqnR9bwghhRNK+y9J8HJ53PsvjvJLQ+m6BzUqZYcHMvDMlyWJjagWSyU0Q/uIpaK4zU4dO2ZReonNlYQtg2PJz43jVF1+cCPDxBLK20lz7cK1/YoLFt87fcmefPryy1lxZIaIpzqwyit3FCVhiCIqwbX8108f+ue5/TZCieezXLo0c7rN964LrXxC2hDw0jRGNnHb7w5omblqFl5rr4xAsI60cP2+zJrAeH5wK4Iye71hCew864E93y8m+f/Yr6FrEjWInTtvIfU8EEULYLnOtSLS8yfeopabg41nGD0nu+jkp1m4czzq3G7RP9uhu/8HiZe/Rsq2RlG7vpetEiKK6/+FY4ZrEyUcIJd7/+nZCfeZvFcIIgaTg0wcOuHCKcHcIwKyxffaLmk9Oghhm57LOjpt00m3/gipYUNOinxqU9expibxrdtzIVZ3Fq16Z5PnK5Sr7iEYxK3P5Lk+S+uvDvR0e8kRJHOT3yS/FNP4hSLpN7/MJHduykfP7b5e+W6FJ97HiSRxCMPIYZCgfZZKITar6P297afIFf/2eBR2YQ/xK6XkUMxwql+ls+/jJ7ogXXOrlevU/zWU6Q/+T1rJWyhELH7RqEd4wAAUEFJREFUDxO77962+/RNE2txEXu51Yv1DBuMzVenl9/Isu8D3fzY79/B21+eo7BQx10X4rHqLtPH85vsoRnXjelm5gx6t4WIJGXKOYepM2UiSZnTL+WpV1zqLZwJATP8VTYst1CkfvzC1a8CT6t6/eytVfd49k9nWbhU5X3f38vQvijJbm3Lxtf3fWzTIzdvcuV4iWe+MMfchfYzuedYSLJGrHsbrmVQXNiaJlxESqIJEVQxhCJoLJiXNmQbuxaO7fPiXy+w594koU3KxVavx7IwLt8Yh2mbvaz7l9/yWTssXqnx7J/O89gvDq8ykQUKxzL3fE8PV46XmDhZbtpN96776Bq7m8VzL2JV88h6jHCqrzkpIggtIowgNKRwrjITig3vdN3AXf280U6tx9h+/w/hOiYLp5/Bd12SwwfQkz1Us2vZ68LsWWq5OWLd2+g/8MHr3ilR01G7exvH9KnX67BuUl2eMTnyZJ4P/kgPH/yRHopZh8kzVWoVF1EMGjRiKYVaySGzYL0b2b33HHIshtbbR/yue7j64DzXvS7FIwC+T/HpZ3EyGWLvuw+1vy8wvleN6yYehO/5eNUq1uIibrW1CcRzLIzSCo5Vo+jYRDqHKc5d45z5PpVjJ5ASCWKH70aKx9eSZhsc27/KJxIPI4Y17KXC6nUqPUnU3hS+7WLOZnBLred123cP0LMzhigJvO/HR1u+z83W+ZOffmvD674W1x3tZi1IUs1fqmFUXWbHa0RTCr3bQhSWLQpLJm99cy14LYXC6OkerFIu4Ld0VZzxOUSl4Sl5Lp5joSY6EGQF16zjVNrHBh3L59TzOa4cLzF2R4Jtt8bo3REm3acT61AIx2RkVWyEN32suke97FBYtsjOGsyNV7l8tMTU6VIL+fV6+J5LZWUSUVZpZ4Se/4t5wvFmIurLR0tYno0gSIhIeIKLd92Rtc6r9GHqVJmv/O4Eia7WMElmtk5tC1I1AEbF4fm/mGs5x/yiecOF29fCNjyOfGMZ1/aaOtWgMYg8v2WsSoqO51hUMlPUsrNb4LB450gN7kMJxZl57a8pzgZkSMXFi+x/7F80bXd1QMtaZENiolUIAvrgMNG9B3CrFaRIFHN5qSVU8PXPL5DsVrnjA0l+4t+PMHG6Sn7ZRpIDsvbeEZ1Xv57liT9dWq220UIiY4eidPSpKJpIqkch3aOiqCLv+54Odt0WxTI9qkWXyycqre3R7wE808QpFim88BzW4gKCpgX8JVuWM/epHj+JOTVDaO9utNERlJ5u5GQCMRRICl3lIPaMOm65glMo4iyvYM3PY1yZwMm2T2Ta9RJ2PagxNsvtk2K+YVB87gWs+QXC+/ei9vUip1MIuh6UU7ouvmXhGQZusYSdyeIWiiQeuIXw7gHmfu+r+I6P2pem58ceIbx3CM+wKT5/iszXX8erNoeJzj6zxNyZjfMXtvEekpjLqsDBB1N0DOgceSKzuow8/VKhabvXvroWJ9FSXThGjXDfNmqLU6jxNJ5tIkeT+I4FCBjZRUI9Q9iVAk7t+p1I1YLDyWdynHmxQKxTJppSCEUl1JCEJAsIYpC5dSwPq+5SKzmUszaVgr1FD8PHsWpgtW/BfPpP5tp+DuAJLq7v4OGgiCqOu/bABEFEElVcz0ESFRQ5jO87eJ6DIEr4hsQrf5NHVxOYdhnXNREECdezEEQBKaQiqkHiRJBEBFnEMxwQQFQl3FowIG1knvvrZaSQilOzAr9QANd02ICDZ0voFPpJiB1MLZznmS9sfA+uRXbyGHqii9G7P0UtP0dh7jylxYs3TtW3BYTTg/iuHbSjN2BV81iVrS/3NoIxO41n24SGQ6s6f+tRWLb5y9+c5vxbJW5/JMWOW4O2XMfyKeUdZsdrTJypNeUPYimZR3+4m333xJGUoIEi4IgQ+NCP9uDYQTNHZt7ir/7j9LfH6BoGhRefJ/HAgw2OZpfiSy9iF/IkH3gIfXQUMRRGDIcpvvEaTqaN8fN9nFyO8quvUz1+EikRR4qEEVQtuFe+H9TF2hZe3cCt1vDK5U2TbVfREx7D8SyyxgwbrcR806R28hTG5cvIiQRiNIqgKKsNKDgOnm3j1Wp41RputUr01geDc3I8EASSDx8ktKOPhc89gZKOkXhgP5WTEy1E5pnJKpnJ907LbVOj69o+F94qMX60TL3qI6WTuMXy5llzH7RkF4IoEe4ZBt/Dc2wkPUx9KYOW7CLSP4ogKxgrFdx2dHlt0KEN4fkuuYU58gvvXF/tvYSASELpISYHXWk+PqoQougEsaNkdBhdTQShDqeGZVfxkVD1jiD04tkIokQ03EPU76JqZEhEh5ldfpPItk6kkIIc1XCqFlJIRZAEfMfFzFZJ3TbM7N8fJTycRo7p1GZyJPb3IcdD+K6H1hll6elzGIs3xmmwHkU/S7cwiIgEbH3w13JzTLz2t0Q7R+jcdjvDt3+MWmGBqTe/jFUrbHw/BWFLzFLrE6uiJAdMnteQo3g3Qn59LXyf+vREUGPb1UN96kqLyOFVZBcsnvubFd58Isd3/XgvD36yk9/+6XEqRQer7lEru00x7/yyzZ//xvR1a2ldN+DZXX+c3/jR8/i+32SI0/0ad3y0i1PPZvnsL19GUQXK1zK0NSA0QjWyrONenKE0PYusRZAkDb9qoMhhjNPnqJ89h+ta4HrXpaYMuBmqeNXqDbwhmyMip7C8+rq8w8bwqjWsNqGKdlA645TfDEKHam+SyL5hiq+co/zmBZTOBNHbx1DSrc0RAOGkwugdKRJ9IVoarYo2x762dadkU6PrSxJu3zakWBSlXEEMh3AKxUanTlBvq/R24+QL4Lq4pQqmWaC2OM36GSo6OIaZXUDv6CN/9k3Wsswb31BRkInJQdlK2QnaDXUpuvq37RnElS50KUrJzuD5DiEpjuvbaFKUvDWPLKgklC5Mr0bJzrDRrLkeUjiCoCh4dmAQEYK6QlHRAnFB38etrXVkub6F69vElS5m62eJSKlVoyuJKrIcwnEMFDm0Sv2oq0kKlemgfEtUEAUZxzOJhftWz9H3fGpzBeJ7+4hu76I6nUWWVaSYjtoZa7p3aiqMUzHQuuPUF4r4jotTMnBKG8fOVTQGpTF0IUrRz7LoTjImHUQWVIpehgVvEgericSnU+hjVN7HWfstapToEPvoEgcQEZl2x6n4hcaWPo5RoTB7huL8eaJd2xi563vo3H4H82eeC7iHPRdBUlY5miFIaF4lGgoMqR0katZVDYiyihxaGxhWNR+U/IUT1IsNOkdRQg0ngtXLO4TvusixOHgu9emJTT00xw6ERbfdEmH+cp3Cik0xY7d9vV3HJ7tw47WeruOzPNNev0vVJURJIDu/+X71cAfhWA+KEsbHR5Z1TKOAY9bp7j2IHkpj1HJ4nk05N40kyNQFC0mL4XkWoqggiSqWXUEUZXzfW43By5KOKEqrysuSoAIeohB0oq5RnwrIgtpQFfYafCV+gzhHxcdHENYIyAVEJFFBQMDzXVzfRkBo7D/Y9urn10NQNRVUxIR2DiB3xCn+8ZP4jofvuPieh6C16tl1bY/wyf9wgM7RgDNGDUlYdbehRCNw5uml987oCqKEFI/hWxZSMo55eRJ9327cbB7zyiT6rh0IkoSUSuKVKyh9YczJGa5d01dmLyGHY9QXp4NldUPzK1gKtPeaO9RBwnIi4BoQFARBJCqncTyLHn07C/VxQlIcWdTYEb2DJeMKuhSjUxsmZ84iqiJ1t4QsagxHDnA8/60tPZjYLbfhuTa+ZaOk0tQmguSVqCj4TgRBlleNro9Hxcnj+R5Za46E0kXGWrv5dTNPoTyFswEfQ80Ilm35cmtnVHUi+C776hqputoRRQorGPOFVVay2nQOQRIxFkvM/v31axWvIizEcX2Xs27QviggMONeRBV0EmIHChomzUYr4y+Q9npW/5ZRKHk5in6Gmh94RIIgBhlnfFzbDOK5votrGwGLFEF81azmiXQMEk71Y1XzKKE4yYG9awbW96kXl0j07ybWvY3KyhSirNC5425Eae21LcxfoGvnYTrH7mZ5/FV8zyfeuwNJbSZOEiWlwR6nBWEfRUNS9EZTTOt7ERoYJrJ7P3Y+S+zgHWSfeXzT8i89LDF3sc5d35XiX//RLn7vly6v8isAjNwSpbhirVaBrMzUqRYcBBGS3Rq+56NFJKIpBaPisnC5iudColsl1ashigLFFWs1Tq9HJfq2hxFlgXMv58gvrr1jsirQORQiHJexDZflqTpmzcOxA9Yu264hCCJGLYdtlJCUEPVqlkppYY1vQ5RRpBDJ+CiKpFOqzgW802oM3/fIl6YQRZlouDuo77Urq4lREYn96Yepu2VCUgxJVFmsjrNYGyelDdAb3oUi6TieyVz1LAVzkf7IXrrD27HcGroUZal+BUEQ6QmN0aEPIYkKplvlSvEtdDnKntSDFK0lInIS2zM4k3vuul2h9csLRO8YozY+S/zwXuoX57AzwUpQUGWksNZ2ct39YDd6TOaL/+4ktuHy0E/t4JnPXqRnZ4xbHu3hhc9vXfgArpdIEwiq4QUBZzmDGA5hTUzjWRaeZWHNzCMl4kFtnAByRwqv3GY54vs41RJSKEKoow/PDiQyPMfCzK+0GGkQCMtxsuYMsqihiSFERArWImU7Q0LtJSwlkAQZ17MavxCoOFlCUoySs0JETuH5TqOUy2uTKW8Pz7YCPSzAKRexlhcDPtt0ZxCgvya0YvvmqmdbdQtN31WNzVVWbxRWtgKtDTOrBvpGICI2ebFRkvRKI9T9CqrQ4Kq4zsIg6y2QEnvoFUfJeHMU/SyIEp077iTSMRQYXddGUnRso0p++hS+7+HaJvmZ04ST/Qwe+iiOFZRj+b6/LvzgU5wfJ9m/h95978euFxuJO5vaujbtanaa5fFXSQ0fIJzqxzFr+K5DZWVtIhMVje6dh1EjSbRIClkN07XjbmLdO3DMCvOnnl67KEEIhCVTaczFOczFeaRI9Lphj+UZk7/4zWnKeZvHfqqP3XdGm4zuT/3OXk4+mwUBkj0amZk6X//PU4ii0NDQ0yiu2MRSMtWiwxOfq5PuV3nkRwdQ9IAA3nV9Xv/KEpMny0QSCvsfSBPvUhjeF+NLv32F8TeLSLLA3vtS3PpIB7bloYclFidrPP0nc/i+R6U4h2WWrpvcFEUFQh3YpTqe5+K6RjAmXBPPtagbOcJ6mlo9gyQq1Mw8mrJGnyqLOnVzjouF1+jUh+kMjZI359iRvJe8MUvJXiap9dGpj1B3SozGb+PYyj9gulX2pB5EFERUMcS2xB3MV8/jehb9kb2k9H5MpwIITJdPYrpV7u75FGE5QcVuMzjWIffUUfq399L/c49hZ0ss/9ULuOVgIlVSsVVF4GsR79KYPl7gyhtZUgNhrLpLYcFg+niBjqEw7/uJbXzl1063/G4jbB5ecFysuflAXTebb5E8d7K5piykm9+8Q0lP96LGUoiygu95VOcutzG4AD5lO0OXPgr45K0FdClGXO1ClcLYnoEoyCSUbkpOplE10NCyahTHS0hElTQC4vWz1etQuzyOUyq0fG7Ob10D6d1Ck2P0pw8S1jtavlvMnyFXnthQpeIqQmqSoc67kCWdfGWShXwzKUmdKkmhi2FpFzW/guNbqA3v1vd9JCS6xSEiQoIucYCcv0SIKFEhSbc0QNaTkFHQhRCKoDTivuC7DisXX6e8dKURKvBxrDpOrRTwMasRbKtKZXmC6SNfQYt1IkoyVq2IVc2jx7sxGllrq5pn+u2vE0r2Iikajlmjlp9Hj3fiGI3B4fssnnuB8vIEaiiG59pUc3PIWgRZbbBNeR5mOYtjVKhlZ8lPr92La2PBCAJa38Aq4Y0uy/iOvSmhth4R2bY/4NPddXsM124NBQhC0Ob+zBdmSfVp/MRv7uHtJzIsXq6haCJ6VOaZL8yRmzfQwjK25XH7hzoRRPjGZwMC+cOf7OG2D3WyNFEjO2fwD5+domNQ4+P/bHTtucck7vhIF8eezHDu1TzpPo2f+y/7OftSgZlzFRx7a8lMz7MpV1s5SAyzsPrvmtFs5Cyr2eHKG3OAj+2Z+PgoDQeq5hRwfZeV+iSGU0IUZERBpuYE+zbdKp7voYgakqBgOCV8YLJ8jLKVQRV1TLdK3SkBPpZrIAvXp8Y0p1aY/9zjqJ0J7GwpUDdvxICslQLLf/MCxkTrNTu2x9X6Itfx8D2fUFymtARLl8oc/uHR6x57PTb3dF0Xe26BIIB742lwXYiwQ7uNrDPHkjOJkZnHyMwH3rMPrrnxC1Cwlqg39MdMt0ZFyFO0FgEhIEL2HaY8A893yTKD4wdE4nWnhONbVN1CEA8SZJaNibZctwIiu7W7sH2TCesUHm5bg3ujiGidjPU9TMVYYWrldRzXQJFCDHfdQ1Tv5uLCM9TMjWdlWdLojI+RjAw3n68gUDFWyFUmr+uFKnKEnuQ+NCWK5zktRtfwq2S9BeJCmppfxvBrWO5ZPAICaBsL27eo+AUc38bGwsHmgnsU13dxsBAQiNNBxpun6F/1tn2M0sqqwCAES/uu7gPEQv1IksLczKv4nkstP08tP9984mKZ1GgEUQlapI2iSWGmmVTlaknRVXiORXmpuYbZqq5VL3iuTX6mDRNWO3getcvjrYQ3bagdryISl3n/93ex644YlYLN3/zOLJeOX8M77fhMnCgFVI6VGpWcxeCeKIuXayAKzJ6vkpk1cG0f27TRQiK928NceKNAaSVgFJu7UGVwd4RIUmlTHx9AC0scfLiDkVtiPPYLwyBArEOhd3uImXM3pnDwbnGtY2B75uqYXqpdRBYDU+Z5Lq5nk1B7qNoFQnICyzMw3RqWW8V0q+TNeTQpiu3VUcVgMm8eBGvPS06kcMol2nWrmJPLmJPBylSOJ3H9CmIogpLoo/xG+4at4oLB6O0pRFnErrsIwNi9HVQyJtvuSmPdYHvw9avyvWsvbusQENEEvRFM39zIthwWd/UBBafhNpVjAdTcVs/abcR13C2yfmlCaGNPWGg0ejTiPKKs4F2Hw1YQRDriO+hO7CYVHWYxfxrHNdCUOIMdt6HKEfKVKWbM/Ibeas3KcXr6a2hKDFWOENE72N7zAJLQGuR/p/DxcbAbIRoXD5cazZ6KSR1znfyQh4vtrz0DEQnHtxEQ8TbxvD3XplSYwnFNXGfjypOu3Snu/cUDqGEZxwwGzNzbyxz541aqxxuFFFIY+8WHufDbT1532xtp3ZUiGuptO/nCf7wM3hw7fu4hzn75fCu/qt/8b99rZiW0Dbe5plq4Wgq5ro2lURO9WaRDlARWpuv81X+4RGYmWJkG8kFbryVNPHSQnp/5CPULs8z+1t/htxGevZpw8q2gCQpRRNTkoBzL8fF8e3Vc+firob5zuecYS97LSOwQjm9zpXiErDHFhcJL7E49iO3VsT2z8f8GFwuvsS1+J3ukCKZb43T2KXz8pvHt+mv6hIKqovUN4Bo1tM5+pEgUp1RETqYDIiLLRO3uxVpaQE6lMWam8IwaUqg9By/A5NEcA7ckkDWRasHmyls5PvALYzzy8ztBgG/+Xzf2fn5bWcbqfpmj9aevv+H/IPh4nDCe3/B7Nd1J4s7DVC+ew8qsEN62k9KJ63Se+MHSLJgkrHUvnofj2ci+24hDb9Ks4XvUrTx1K/DWQvUko933IfHeGV2Aql+i6r7zkjIPlzlva11ygijRmd5LrbpCsTDZdpvEUJTpVxc49XeX3nVTB4KA1hlBUCScsonveYiajCCLKHEdp2ohh1VEXcF3PexiHTmqISoSgixiZqqAj5oMpFysbBUlEQI/kGlBAHOljKTL5I9OY+ZNBEXE9kQ8H9R0GNewV2upRUVgYFeE8TeLRFMKsU6VxSsbG3ez5pKZNRjYHeHMy3lsw6NzMLRBF+gajKpLccUi1aMye76C7wdJvhsh2UYSkUIaoqbSLrQv6Cqjv/lP8QyLxT/8JubEIvHDe+n9uccovXSapT96guOZb65uX7KWKFlLAFhenZOZVhGDrDFN1phu+TxnzpJbaSbGsawaxexTq3+fWHcs33EQFBlRUQMhXFVD1EM4pSLm4jyhkW3B9cXiCLKMqKp4+AiyGuhHtfGOly5W+NKvrq2Ujn1tDqNi07MjxuypIqef2hoV7FXcsNGNiHEiYjLQQvI9bN+k6hUx/LUAtCaE6ZD7V5NXBXeFqldo2k9C6kQXoiw70y0eX6c8gIjEshMUR8sohMUEuhhBFmQ836PuVSh72RYPSxciRKUUSiPGY/sWNa9MzVvzihVBIy31rsaB6l6ZnLtEO0NYuzyOHI0jJ1JIoXDL99fCxyNbusyU8jo1K49pB96jYZWYXHqFkJYkW758Q3HmfwyQJDWobNBTFJlsu41RNNETKnJIxrqO2sH1oPfE6P3wLThlA892yb5+JSCM39tLeKSD8vlF0neN4tQsREXGXC4T2dGJW7OQIxq16RxWsUZsrAff9bAKNaI7urBLBnJYRY6oTPzJqyRuGSB5aIjJL7yGawRkKNGxbuJ7eskfnaY2HeQ8RFFg5ECMB3+wj+7RELPnK0ydKW+a3j32ZIZHf2KQ93+mH8fy6NkW5ui3VqjkN743tZLD209kOPBwBx0DOq7rI6siz/3ZXJNO27uBqCnIiQiuICCFgxCB3BFHUGSkeBhBkfBvRGbnPYQgy/iWhaiHcKtrcf9AMy5oinCLBTwzoNq8SlTvGXVETd2UF+IqjLLDsa/OX3e7jXBDRjcpdbNDO4SEjO2biIKMKmjMWheZsddcbEmQiYkpQmKUtNzPJfMYVavIeqOWFLsZ0fZTq5Uoe2vJOEXQ2a3dTcXLN4wudMgDDKt7G8QyHoqoIvgC0/Y55u21co2omGS7dpCQEMPyDURBQhE0ss4cl8zjq8ZdRCIiJgiLcbqUQbLOIvn6coshdOs1fM/DvnIRfWAIOdq+cPpa1O0ilxafa96XZzKXO7a1G/2PEKZZQqppyHKrVMv9//xWZF1Cj6vE+iP0396FWQrqN5fP5Tn3teaSOikWI3rbbcjpDirHj2FOTrbsM763D707RjFbIdSXQNJltJ4Y3Y/sZe4rx1ESIZyaxcI3TxMZ7aDvI7dgLJVYefEigiQy8sP3UDozT+7NCWqzefb+u49Sm8pRncwgh1UiI0GSs3B8hvDwGkWnkggx+L2HmP3ysVWDC0G35OkXcnieT+W4zeW3S9iGhygJnHouG8j6XGOn5i/WeOpPZhm5JYYkC7zyxQWmr1HtEBCCuufGq3tVebqwZNI1pON5PvMXay2kRO8GbrnG4ue+ie/5GJOBB1t+7Sxu1cCaywQkMv+D4FsW1QtnV/++ll+3fuVi299VChurmfTsjBLv0rn8ZraFwPyd4IaM7qCym5AQ5Xj9ORzfQkREEXRsrom1emUum8eJSEmiYqrtvjLuHAP+LvqUHZTNtQvukPrQxBDj5hGuGumKl+eSeQzLr+P6LoqgsFO/gxF1Pwv2Wia/Sx4mJfVxvPYcpl9FQEQR1EZyaO2NNv0aU9ZZZEElKiU3vF63VsWzrYb0xxSC8t4u7///CaoaRVWiqwXy6yfg6dcWEeX2HVrVTKvnIYbDRA4eRB/dhrUw39boujULK1ejcHyG/JEpnKqJUzYonV0gtruH+mweUQ3kiERFxrNd3LodeGi+H+SOXQ9BkRBVGc9yG3wCQRvpRpy2rmmTeX2CxMFBKpdXghgnQVw1M2tw8a3mPITn+kyc2Ljra+FSjYVLzfXSggidAzrVokP/zjBaWMKorMU4zZrLhdcLXHgPJXuaT9qn/FpzHNNeKVJ85h+nU7Hn/d30jEWZPJbHc949ldwNhhd8JCEocDf9WqM8q90LEyRprlYUtEPVK1Jwl+lTtnHZPI5L0C3SrQxjeyYZZ65p2xolaAQsLF+g5OYZUXsbrYLrz09EEkRMrx5UM2wwMbk4+L63KUmNqKqk7n2I0NAoC1/+S6J7D1B8u/2bHNE6OTDyCTQlxlLxHOdnH99wv/uGPk5XfCeGXeTU1Fc2rWTYEO9cfGAVoiAz0nUvg513IAoiK8Vxzs89idem0kMUZNKxUXqS+4mH+1BEHdutU6zNMZ89QbE2t2kZm+MYGMZVasnmE595c6mt2oGsSUS7Nk5wbIbimXni+/sZ+tTtVKeyZF6+hF2sk339Ct0P7w7ko/DZ8XPvx6mZrLwwTmRbZ6NeWMAu1alOZuh5dC+irjD/1eMkbx/G9wIuDN/1kKMafR89QHxPEIJYefEibtUi9+YEne8bo+PwdjIvBTFvx3zvyMdlVeQH/48xukdDFJYsXvrrhSZS/i1DDBqUgsxc4/zcq5PEBttLQSvxVUGCFghCE+FN8+fi2jEaPQCB6nTj+K7ftkpKkAOpL9912/LdNp9fIICJ0+ZdbLpegmP6BOezSXdsJKVSLznv3fO7kY0nrVPEpDS3hR8l7yyyYF+m5OWw/HcmtLdgX6ZHHqZPGWXWvkhMTBEVk8zbl5sGsC5E6FO2k5Z7UQQdCRlNDCEhsX60LthXSErdHAy9n5K7wrx9hYK7vDZB3CDkWAJzeQEpFEbUdARJDlLO7V4MQUSRQ6hyeLUUZsP9ShqqHA6IbYTNe/DbQRAFOseS5KfKOIYbCOUJAp699TiaJCoMdNzOaPdhRFFhpTTOhfn2BjesdbCj50G6k3sRBRHXCzLTiqwT1bvoTx9iNnuEK4svYjntB78gSESivQiCQKnYXPMsKSKu7QUkP8ra/Yj1Rdj9kRFe//0tlnutg1MxmfjjV5o+O/8fgwTO/NdOAFA80ZygKZ1dE9a8/PsvAFA+t5YkKZ5qOAKSxMqLF8H3ufL5l65eIRIy478dZNeXnzy3rpVV4Dc+cQyf9TzGW4MgCYiigCAENaIQhCp+/xfOIogBVzSCEPQwiQKiLCDJAtZ1qhWU7iTJD95O7N49KJ1JvLpB9eQEuX94A0EUWpqAAOIPHKDnJz6IGNYZ/8z/2TZuG71jJ4P/2w9SPzfD7G/9LW4xiKtG79xJ709/lPr4LAt/8A9EDmwj+cE70Hf0BU0JuTKVt8cpPHUskEFv7FtORen9+Y8RvXMXi3/4TQpPH139ruk+qTId332Yzs88TOnl0yz+4TdXKWQFXUXf1kvsrl2ED4yi9nUgqDJe3cKay1B69Syll0/jtmmMACgtGfTujr8rTu/1uCGjW/GKvF17ij5lOx1SH3tDhym7eS6bx5visltFwV2m4hXoVbaxYE+QlLqRBZUlZ024TxPC7NHvISommbUvUHJz2L7JoLqLAWVn0/4Mv8rJ+ov0KqN0ygPs0G7F9A0mzVNk3Hlu1DW0SwX0gWEERSE0uiOo4X2PZFfeDWRVJNYbpjBdQQlJpLcn8F2f5Qv5LV2iKMj0pw+xved9CILEYv404/NP4XqtBldX4uzse4TuxB5Mp0KhOkOpNo/jmqhyhGRkkFRkhMH07QhIXFp4FtttDQlIkoppFFDUSMt32x4a4NLTM6R3JBi5t3fVuISSGqHk1tVBvhOQEgn0sR0YVyaQImGs2TkkFGJSCl0IU/WKAX8AUtDyTNANGazRBBwsbN9sKr3bCKGkQrRTR1ZFJFXEKNpYdZdopxY0AKnBZGXXXWRNpLhYp3Mkih5XmD2Vp5ZrX96oDnbS94vfTWjXAE6+gjGxAJ6Hvr2Pgf/1k9Qvz7c3bFdtznVszxq3but32nA36cfuIf3YPTj5MtbMMsgSak+a1EfuRh/rZ+nzT2BcDhJVTqlG5eglIge3k3jwAKWXT7fl45YiOpE7d+FbDtW3L+HV1+5vaKyf3p99DKUzjp0pYU4v47seYkhFG+mmZ9cg4b1DLP3REzi51lrmCy+u0Lcnzth9ncycKGDVnCbH2Pd8bGPrduGGqxcsv86UdYYF4TIpqYed2h2MabdxrP7Mje4KH58Z6wK79bvolAdISJ2U3CyGt3bhcSlNXEozbZ1n0moukm/X2utiM2dfZLFhxLdptzCm306+uoTLjRUx+55H5cIZzKUFfM/Dqb7z8qr3Eq7jEesNI4ckPMcjMRBB1iVWxvMbrJLWPhQEkaHOu9jWcz+iIDGfP8HE4sttDSUI9KdvpSuxC8upMrn0CnO546ut1xAY5R29D9GfPkR3YjeF6nRLIwaA65p4fohatbU12rMDryo1HCUxHGXpTDCBy7qE5l6n0+g7qI4pyDLqwAD6yAj24hLhAwexFpdQXY2E1Inru8SkjtUVmIuDELTo4GIjImP6NWpeeUtGN5xQ6d+XJDNRJtETQo8q1IsWO+7vYeLNFeLdOtEOjYVzRWRNwqw49OxJYNUcEr0havlWaRlRV+j6gYcI7RzAnF4h+6WXMS4v4DsuSneSxEMHib9vP4Ly7akmVXpTpD96N4Vnj1F+4zxOtoygyoR2DtD5Aw8SGhsgevcuzNmVgKDG9TDG5zBnVtB3DqCP9VM7caVlv9poD/r2XszJpaCrbF0owJxZofD00YCofHoZJ1fGd1ykqE74llG6fuhhwvtHiRwao/js8ZZ9J3p1tKjMx/7tXmZPFqmXbPx17105Y/LMZ7cuMHBDd1YRtNWXxfINlp1pBtXdRMTEjeymCRlnju3+QQaUXSiixox1vq1xtNfpj0XEJDGxA4HmpbmMEsRq8XFxyLrzdLj9DY/4xpcGkqoT3jZG9dJ5tP5BtL4ByiffvuH9vNcIp3QSg1G6d6dYGS+AAB3bE6uxuSb4a40igiAx2nUvI92HkUSZudwJJpZexnTaJ3JUOcRQ552IgkSmfImF/Okmgwtg2CWuLL1MT3I/qhwhHd1GpnSpxYhbZhlVjZDu2IUoyk21uldeCLya3JUShZkKmfECANGeEGOPNnflXQvf8xAUhfC+fYR2jCHF43i2jTUzHWhmFVp5dZXOLuIPPIDS0UHx+eepX2qT0RZFIvv3E7vnXuzlZXJPPL6qOCvICmp/f1BQ73mYfp1Fe63CQkBEAFzcdUbXWc0/rC/sVyIJ0rvvpjh1Bs+26L71/Sy9/SR2rURhoY5jZagVLcorJlbVAXzqRZtKxqA4X8dzPSRFomt7FKNsc+mlJRzTw6o7bVc9+s5BQnuH8R2X/DffpPTKmVUDZS8XcItVtKEuQrsHN73v7xSCLFE9P0Pm715q8lithRxKZ5yuzzyCvq0PKaLjNLQUzdkM9Quz6Nt6SH3XHa1GVxRIPHAAQZKonZvGWm5+5m6xSv6JI2uNHA3Yy4FBjt61m/DeYbShrrbnHO/WcS2P2VPFgKEs0ZxQd6wbW/3egNEVuC30ASzfoOzm8XCISWkSUidT1rmm7WJiElXQiUgJZEElLqbpkgexfZOaW8RaV+3gYLNoT7JdO0jZzVN2c00xr7Kbp+5V2aYdQBYUREQ65AEkQW4ibAHYqd9BRExQdDPYvklYjNEpD7LizOCv2zYkxAiJEVRBRxV1BE+gWx7C8k3qXgnDD+KSrlnHLhXoevQxrGKB4tuv3tDN/XahljN54/NncS0X1/KoZU3Gn57ZINDv47oWAiLDnXcx0n0vsqgxnz/BpYXncNyN4/HJyBCqHMVxTYrVOWy3fby2bhWomhkS4X7CegeaEm0xupqeQJJ0FuffRpKD0jHHaT52fqrcNDdWVwzOfOn6DE5dn/5BQrt2BSTWUiOJs28f0UO3kf3mNzAuNXshYkhHHxlBHRigcnQDZjZBQE6nCe/ZgxEKBQkay8KcnAzqUcNhii+8AF5QKb6+c+9GIMoqoY4+qotXcAWBaO8Iy3IwqF3Lo7gQ7Neure3fKAdG22p8JogC1ayBY3mBd7sJQmP9SBEN37QpvXa2JTFlZ4rUzk1924wuQPG5E60hAtejfjGImUvxUJOn7Vs21ZNXiN2zm8ihHaj9aaz5tXCmFAkRvWc3Tr5M/fwsfpuStVUxXEFoesd818deyMH+EURdbStZdPKJBc4+u7Th9dxogm3LRleQBBa9CbqEIXqVUcCn7lc4Z7zBot2YeUQBWZDZpd1JuOH9+vik5V5Sci++73HZPM6Cs36m8ll2phjVbqHsZal7zTEVw69yznid7eoBBpWd2L7JvH0Zw6sypt/G+uk848yhKiG65aGAus6rMmWdZc66uNpEISIyqO6iV9kWXBcCmhhil34XALPWBSas02g9fUT23AL4CJqOEk8QGdtL+fR7WxYjSBJSMh7cCdNC0IIH71t2IOTZJqHhuR5mae3z6kqdWt5s69lcbcHsTd3CaPdhFCnMQu4E52Yfx7+OkGYiPACA41n4voeubLCiEcBxAgOgSiGklkSigGHkMY0ikWgPpWJr5xGAGpEDmspCMCn7no9d3zwklHzkEaRIlPr4OPVLl/AdG31oiMith1CHhkh96LvIVKrYiwub7mer8Op1aieD8ImcTG66rSDJSGojAbtupHuOhWvWkBQd1zaZeuYvVilOz3/xd5FDESQt1NQ2L4gykqYjiMGQ9X0PzzLwHAvf83E2kaNaD6U7iaAqWAt5vErrhOuZNvbKtzeMdjVeey3cWvDcBUlq6XWunZrAnM8S2TdC8gO3sfxna+HM+Pv2I+oaxqV56hfaEFOJAlI0RGj3ENE7xtCGu5ETEYSQhqjKiLraINBvbC4JTR2RjunhmO9dLmdLRldUJOJjnazkZphePoekK3iWsyojI0UUnJqFlgpkTd5eeRpBFhFFAd/zrptVlwUFx7couMs4tM7UFS/PSePFls8z1Wbi4BVnhhVnczYwD4+L5ttcNIMwgYDYIEJuHtxWLoN77A1EQVotKxO/DTk0MRImfGAskK12XcRoFHthKSAbWslel7kNGh7iBhAQiIf6iKV70JQYvu9TrC+wlYybKgdJL12JsX/441u7HkFGvEZXXpY1YvEBJElHD6U2NLpD9/YSTmmc+uLlLXsPSrqDzFe/Qvn111c9lMqRI1TPX6D7059GHxkhvHcPpczKlqRiNoIgSWjDw02xvOgdt5P76tfaxpVFRSM9djvx0X1IagglHEONpbEqBXIX3mLl1Et0H3qYxMg+Ln3jczi1Bq+rKLHjwz9FdXmK2Ze/BICkR0jvvIPktgOIWgjB9/Fch8zZ18hfPIKkRxAlBatS2IC1b911qAqIAm5tgxWO5+NZ9juPlW8hiuduQZj2Wnh1i/Jr5wjvHiJ65y5y33gDJ1dBUGXiD9yCbzvUzs3g5K4ZC6JAaPcQXZ9+iNC+EZxcCSdXxlrM4Rk2nmER2j2INtC5+pOx+zoprxgsnA/2lejVcR2PSvbdy6/DFoyuHFGJjaZJ3dJL9ugsclQj1BnFLNQwMlUSu7qxSwaFc0toqXDQk56p0nnbAL7r4Vou+TOLG9bXCYj0KWPUvQoF573ln90IqhzBcU00NY6AgCwFNaeyqGC7Br4fsOo79TqxcDcVYwnXcwlpKZBtdDWJ59tU6q2SzltBUwLQ97BmF8B1EMNhrLlFvLqBGNI3roW8kWMJEl2JnTiuTc3Mo6txdvQ+iO3UWCycZbO3SGgYT9ezMawijnd9xQPLrrTEfR3HoFJewPc9wlb3hr+1aw5OWAlKlrZodM35ecpvvdViJIzLl6ieOkn88H2Edu6i8vbbuKV37sEJuk7k0CGs+TUvTYpt3KEY6hygY9+95MaPUJo+j5boZODej1Oeu8jKqRdxrU0MzzWGq/vgQ6R330X27GtUl6bwPQc1mqKeX0TSo8T6dyAqOvlLRwOu6s3gBXFpUZE23ORaOZotQ2BrCbh3WO9afv08HR+/FzkdI3rnbgpPvk1o5wDaYBdupU7lzVYVb6UzQccn7id8cBvVty+Sf+IIxsQiTrG6WqHR9798T5PRvf9HRpg9U1o1urd/zwDVvMWRL81+ZzrStFQIJaFj5uv4QPfhUTzDRlBFPNtDTegUzi2ter1yKJhJEzu7WH5zinBfAkmRApHEdUhInYSEGDEpRZc8wKR1hnrbRov3HqoSQVPj6GoCARHTLtIR306pOk8yOoQshfA8m4qxgipHVr0bVQ7j+y6JSD+e71Cpr/BOpj5Z0rg6stxyFXNlBSEcJGV8Ixg0777vZQ01s8DE8svYTp1d/Y8S1bvZ3vMAtlsnW27NBF+F3QgZ2E6NqZU3KNZmN9z2KjzPxbBbvXNNS6CH043kZ/vOjnreZOeHhon1hallA6NUmKkw8/rGhCLGxETbEIxv29QvXSZ+72HUvj5ETXtX99Q3TYovvohbKKx+Zq+stPcIBRE90YXn2JRnxzGLK9jVItWVGSRtC80e63apRJOkd95BafocS8efa5axByQtjFUtosY2NqLr4RSr+I6LnI4HzQrXNBEIioQUD7elMls/EQqKjO9eMwlLEvIGGmPvBdxChdIrZ+j41PuIHNxG6ZXTRO/Zg6ApGGemMKZa465KVyJQ+60aZL74EvULre+wHG/mVFEjMraxZq+inVpQY/3elOly3cp8p2ajJnSSu7sRJZHKZA45ouJULTzbxSoaWCUDUZWIDCRIH+xHSwcXYWSqeJYDYuvZdkj97NLvpFseYca6wJx18TtGAlMzcnQld1GuLSKKEroaxCotu4IkqqssYZZdaTQBhNCVGJFQF6ocwfPdlrG2nol/s4YHWdJQpNaB59fqqwb3vYSPR7E6y1L+LNnyFS7MP4njmUT0Tka77yOit8/YApSN4CUWBRnHNSnXl67736qZaVvv67oWthVo6G00UTmmS2muEkjXxFS0mIqib25M3EK+veHzfdxyGd91kSIRkN9dCZTvOLilElIyidigATRnNg5l+Z6HIMpr8kOCgCireI7dSpx+LdYNFy3RhaioVBautBhcCOLGSjjepCO3GayZgBtBjGjoYwMt34thHW24/WrEt4I2aUEQULpb4/uiphD+NibgAApPHcW3HLShLiK37iA01o8gCRRfOLFh04QY1nGrRmvoARAjOvqO/uZjLNTZ94EeBm5JEEmryKqIokmE4gqhROt/9eiNvVvX3drM1Zh7cpy5p8aDDpQLsPL61Go7ZHW2ENAZWi7zz18Kkn+uz+W/Oorv+Sy/MdV2jE1Yp5i0TjeaQt8ZZ6+kisi61CBHWYMoi6gRGbNir2qJNV20pFGtr2DZZRZzp9f5XT5zmSBRdlU4bzF7arU7rlJfuuYs1wXb3YYeGAKaHGnEgtt09YT6UeRWRdFvJzx/jXsiV57g/Ozj7Bv6GOnoNka7DnNx4Vksp7UoPFu+guc5KHKYRGSAldJ4S+hgq6jXs9Tr15FTuVLkrYlGCGC1U3Pz98LbJATje4G8kihJCO/S6F49FzmRIHLoVpxiifrZszjFAv61MVDfo56dA8Gnc//9FCZOEUr3oae6WXz7yTYqFELTP9cb0NX3ZAMtwUCoM7qpssV6VE5eIZ0tow130fGJ+1n8r/+AW6kHDR2yTGjvENHbx5pi11dhLRVwK3XkVJTUY/ew/N+fxG/UWAuyRPT2MaJ37W772/cKdq5M6dWzxA/vI/HgAeSOOHamTOVIeyIb37RxyzXkVBSlJ4WTrwQajYKAGNbo/tFHEaPNJEyv/vkUn/6tW/mnn7sLBFB0Cd/3OfyZ4bavY3a6yu9/+rUtX8OW3sRr2/289UuS9SfhNRMut3y/fp+N/7wb7PrgELd9eoy/+cnngnbIBgbv7OLhf3WIr/+r18hdaY3juZ7DSuHCqlFsZ0ivvjjr25E3O1/TqWDaZUJqipCaJB3dRrZ8pen3mhKlL3UATf72LcG2gsXCGcJampGuw/SlD1C3CkytvNbioZp2mbnccYY676AnsZdSbYFM6WKbRgoBRdKRJR3HNTcsLbseJEUkMRgllNIak55PdaVOYXpjxQNR3bh5QpDkoNTL8244Pi6IIsK1+/Z9rPl53EoFfccOUo99FHNqitq589gLzdURRnGFwsRpUttvRUt0YlXyzL/+D5Rm1uKOnm0GYpmqxtXuaVmLoESSq9tYpSy+6xDuHqZw5WSLkKvvWDi1MqJ8fbkaADdfIfe1V+n+8Q8SvX2MgX/1fVSOXcI3HbThbsK3bsdazKP2t0pFmdPL1E5PovZ1kHj/rcgdcepnA6dK395L5NbtGJNLaEOdbY78HsH1KD53gvh9+wjfMoqgyGT//qVmnod1sFaK1M5OEbt7D32/8DHy33obJ1tCTkWJ3r0HpStJ7cQVonfuWv3NzIkCn/+JN9hxdweRtMq+R3swKw4TR3JteZ7rxRtjVfu2kph/u5GfLHH+W9M3PLO6G6jzvjv4LORPkwgPoqsJtvU+gK4mVonINTlKR2wHqegwplNGV+Ib7kkSVSRRXtWOCmmp1eSbJkeJaB04noXnOXi+E3AhXKf8q+lMfY/pzFuNSeAgo92HsZwqs9mjNE9BPtMrbxDRO0lHR9jV/yjJyFCjDTgQKpREBVUOE9E6CakJ5vInWMxvXaRvPTp3pdj38VH6DnWxciFPYiDK5efnOPZn7WVUAOTOzra1lQgicjKBIMs4hQI4awMjeF+C7VsM69WfyzJy/JoltCgid3WhDQzgGXWyX/x7BFlG37Gjxegq4TjR3lEy514jd/7NtqrXtZVpum55H6mx2yhNn0OUFZLbb2X9MzBLOQqTp0huP4hZzFDLzILnIekRPNvEyC9h18vIegQ92Y2RX8JzNl+NFF88haDKJB48iDrUSdeuQXzbwVzIUXjybay5DF0//EjrD12P7JdfBd8ncusOIvtGiB7aEZSZLeXJPf4W9fOz9PyTD256/HcLc3aF6pkpYnfsxCnVKF3DeLYeTqZE7htvAgLh3YN0/+ijjdBTHWNqiZU/fxprMU/k9rGm3xUXDI5+NaiOCidV6iWbV/504sbI4DfA/08b3cUzeRbPtHYc/Y/CYuEM8XAfA+lDJCODRPUu7IYLo0ghEATmcydwPZvRrnvb7kOVw2zveYiQmkAQJERBQpa0QJ0V6EnuJREeaKgcO3iey0L+FCul8RsyvLZTY3L5dTQ5Rkd8jO2NioalYvMLXLPyXJx/htHuw3QldjHYcTte+uCqdJIoyshiUFts2RX83Dt/KWM9ITIXi/g+vPnfztCxI0HX7vbUoFcR2rEDQVFaNMwERSa0M/BerPl5PHNtovVte9XzVXt6aAcxFEIbvqYbzvfxKuUglut5qyTZtfOtk4LvOri2RXrnHUR6RoOBbtWpLk5QmrmAZ5tUl6bJjR8hMbKP+OBuXNugtjxDdWly/Z5YOvoMrmmQ2nkH6V134vsevuuQv3QMs7CMKCl4ttmQkt/8/v/oD0RZWHR59tlj1M7NoPamEEMavuNiLxcwp5YQdY3lLzyF73pBTmYdfvZ74eSl1zny6jnkVDQgLLccnEwJY3IJQZVZ/sLT+L6PV1u758aVRZb+5EnEkLphSMhezDP/e1/BLddwixuvbnzTxppZgTt2Uj1+GSezSVWK71M/O8XyShF1sBMpFqh/BNy/WezlPIIssfCfv4q1kGsbzpo/V8S1/etV420ZN2R0JU1i/8dHGLm3l0inhlG0OP/EDBe+NY2sSdzyPdtAEOg/2IFrexz9i3Fu+8xOfM/ntT84Q3mpjiAJDBzq5OCntpPoj1CYq/D2n42zfL6wepxHfuU25o9lsOouBz6xDT2mcuXFBd760/Pgw+j9vdz5Y7vR4yr1gsnf/3xrDe96aFGFR37lNspLdV7/3Fk8x2P0/j52fWCQ5FAEx/SYfHWB01+dxChahFIad/7oLrJXynTtTNB3sINq1uDs1ye58tL8hjffcQ0uLjxLoTJDb+oW4qFedCWxSoG4kD9NpnSRRGQAv/Pu9vdYVOmM7yCkthob3/fRlURLk0KpHiz7b9Tc1cwclxdfJKQmiehdbO99aJXUZt1RKdUXOD/3BEvFc3TFdwWdakok0EjzDIq1eQrVGbLlyxRrcxse73rwvSAs5fsQSmjU8yaxvs3VOpTOThIPPkjhmWeaBkxo9x4it9yC77rULo7j1tZCHm6phFMuowORAwcovfpKUIlwFbJM4n3vQ+3tXVtFCQLa4CBIEvrYGL5pEj54kOLTT+NVriEWFyViQ7tRwnGM/FKgXCwIqJEkvXd8FwgihSsn8GyTxaPPkL3wFoIk47sOdrUYVDisb1etFlk69gzluXE69txDceI09dw8Tq3cSNApyKEo1aUpPGfzpe69d+qcH7d57pU61sxKYLyugWvXqLw13vb3D78vRKVS46UX2tda+7ZD5e118VVRQOtN4Vk21ZOXCY/1IUf1RsdfFKdYQ5RFRF3FmFqh+NyJTc8fQAxphMb68R2X8hvn8dpouDVtL4mMvK+P2ecnqeZaOwd9y6H4/MkNf3/hxZUgb7WBhJQWS5McOUh58TK1zPVVw2/I6Pquz/Bd3SyczFJarDFybw8P/+tD5Kcq5KdLJIai9OxNcfYfprjnp/aSHI4y+eoiI/f2sO2Bfk5/5Qp9Bzr4wK/czvQbS1x+fp6Rw7189Dfu4Zv/7o1Vw5sajpEejWMULC4+PYukiE2KmzNvLZObKHHo0zvZ8+GhTc85nNZ45N/ejqJLPPd/HccxXARRYPC2TkoLVa68vEBqJMqt3z+GWbY587VJJFmg70AHuz40xMVnZjn6F+MM39vDfb+wn3rRZP74xgkh26kxnz/JYuFMIwnS4Ar1/UYM2SdTuszzZ34H8HG95lm/bhV47cIfciNcEUE1xZqXK6fSGIrLq+N/GJSh+S5KRyfxw/ejD48w/9/+oOEZ+pTq87w+/keNc/XxvHZeiI/lVFkqnGOlNE54xy7MqbmGgGNwbb4A2vAw+s4DVI4eAQJvsfNj30vmH76yJRmU7KUC4U6d4kyZD/+fh/Fcn7e/sPHS0fd9zNkZku9/GH3HGPULF/BtC21oiMjBWxFkmfrFcernzzeVlXn1OvXxcUI7diAlEvT9zM9SPXkSO5dFjESI7NuPnExiLS6i9vWtHc/zkONxaqdO4eRyJB55uEm49CokPUz3wYdYOfUSuQtrmnpyKEb/vY8R6uinNHUWz7HwbAOz0Fyz267W1rODjkMt3omoaFilHL7nokRTCLKyqbEN6QKyHNyC9YVEsgyaKqyylRqGv0p1q2kCjuujKgKSBLYTfA9BNCcaCegmLcvHXGfzdE1AURr7M308UUTpjCOnooRGu/AMG7U7CQLY2TJyNGioukp6dF0IoI/1E9ozTH18FnNyKag7lsVVvl7PdoO/ReEqHz2hznBg3BUR36elVG4zXI8m0zHr1POBUvlWcENG13M8/uHfrJF4T726yMBtnQwc6iA/XUIAZo4sc+6bU/QdTBPtCvH6586ix1US/RFkTeLW79/B0vk8L/+X09h1h0vPz/HpP3qYvY+NrBpdQYBIWuMb//Z16vnWF9C1PErzNarLmwxkH6KdOnf+6C48x+Ob//uR1SoH3/N58T+tzWyyKhLrDtOzL835J4IZXFJFJl5e5PXPn8Oq2Mwdz/Dx//s+OrYnmoyuLIUQhLWa1qsH9/z2hCNXv9+sCqBdyVULBAE5kURQVUTfxynk8W0bUdMJ796DoKjUL17AKRXxahZeNkPhuWfo+r5PN+9G04Mif1EMlt2FfBAPjQcEOleX7k4pqL0Vkwki99yN88pL+PksTrkMnosYiuBWK9jZ9Z6T0FDbEBDDYURVxSk0yzatR2GmQmEm8Br/7p8+g6SIbZ//6n0qlVj+y78k+f6HCe/diz46usq94NXrGBMT5L/1ZLMX20DlyBGUdIrIoduQIhHiDzwANErDKhVKb7yBNTtD5yc/tXotciqFV6+j79iO0xkki9pVDQiiiKSoiLKKpIUa5WMSeqoHLdZBbXmmbYz3eqguTpA9/yYde++hNHUWx6jgGBVqKzOE0v1tf9PXI/F//3oHI0MKFy7bbBuWOXPeRpYEPvj+ED/5I3HSCRHThs/+UZGvPl4llRT5D/82zYkzJofvCrFrh8LTL9T4zd8rAHDX7Trf+9EIXZ0SL71W5z/9YZHFZZftIzL/8heT7NmpYhg+X/x6hT/7YhU7V0EMqZSPTyBIIm7NREkH3ZGirmDO51F6Nm4xFzQlcApEEaUrQdcPPYzvOJTfuoC9XECURXb94C2EOsJU5srMvzLF8Ad3oEQ1PNNh5rmAjEhN6Aw9up3SRIGF19Y8UkWL4XkOrr2xPdH0BLIaplpq004uCEhqED4UFe26DSo3HNNNb4uRHIyiRhUkWUTWJNTIGuuOWbYDboCyjdiYVl3bQ5AFREmka3eSwkyF3R8KPFRBCnqe430RRCVouPB9yFwubTrgrgctqnDo02MkBiI8/qtvtpSVxXpDpEfj6AkVSRWJ9YUxCtZqBbRre+Sny6sCiZ7rY5ZtlNDaLRMQiYd7EUWFfHmi4c06DUMsYDt1REFCEERkScN2GqU5gojn2UiiiuvZgZabHMbzndUYcDNamwkEOYhZyokkcjxB9fRJahfHkZNJ9OHRwBgrCvXLlzCmJtrsM4DS2Ul4524EWUaKRCi+/AJu3aDjse/GWl5CEAQEVaX05uu4pSKhHWOoPX2Ed+/BWlqgduE8Xq2G2tNL9PY7cHI5Cs81K0BLsRjhXbvxLIvykbc2LH+K9QXeSHGmglWxUcIy3ftSrJwPYnZXDZVXr1M7ezaIzVUqZL7yZUJnTqOPjCBGoviOjbWwgHnxMk6xfRu179jkHn+c2vg4+sgoUjxIbLrlMsbEFYwrV1A6Oym9+Sa+ZeHbFrXTpxEUBc+2EUMhSi++1Hb+cM06+csnSIzeQqijH99zEWUFJZLEKCxTmjl/w0ZXkBVi/WOE0r0YuQU8z0EQJbR4x9q9aROP/F9+KoHrwff/5CI7RhR++z90IssCnudz8qzF//b/zjK/6PLJj0X4lV9K8tXHgzh1PCZy+C6d3/rPeRaXXMJhYdXTHdum8LO/vExYF/jVX07zwL0hvvJ4hX/2U0nmFlz+j99cYtuIzOd/t5sjx01Ons1gzmZazm09NvpeDGkkHjiA0psK6oAPbEPtTVF5+yKVN87jOy7hviixwThv/ebLAKR2dxIdjDP91BXiI0mSY2kkTWb3p29h7pXpJoMLkOocC+rQ8zM4Vi1YGaoRBFHGtirBikKLMrTzEc4d+bOWc5RkNWCVM2tbap++IaM7criH2z+zi8JMmWrGwLW9JqZ/IDhoIzncLgYiiAKhhEbXrrWZbeFElsyl5sGxPpzwTiDrEkbRQosp7Hp0kNxkeZUDonNXgrt+bHdQjL9Qw7U8FF3CWLc68Bwf12oeGMLq/zRDkXXS8R2UKrOIkkI6th3Xs6jUl5FEhUR0CMMq4vsuNTOPKoUpVmfpSIxRKE8Ri/QjSyq2U6fsZFFiSeqZ+cDgyTKCICKpIaxKoTG4AnkRt1pBkCTkWBx9ZBu1ixewlhapXbyAIMmUjx1p2621Hr5pru5H7epG6enDnZxA1ENUT53AzmWJ33UPancP1eUlykfeInrgVoqvvoxbWntmxuQVxFAItae3af9iKET0wK04xSKVk8c2NLgAXbtTKGGFYsPbDXfo7P7oKJWZeUCgVg4Um91SicKzzzb9tn7+fBBGuPqsBJFIvB8pBEatlWBfCyWxrRrGpUstLGRXYa+skH98Td4bUUSKx3HLJeR0itC+vVTfbmUp82yLpWPPEO4aQokk0cMdGGYZo/Q29dzCKs/C+nMNR7qxzDK23apesL6mu7YyE8RuLbNBqBPCs622dd+CAO+/P8Rvf7bA/KLLStZl/LK1KmTpeXD4Lp2BPpmebomhARmxMZwlSeAbT9UYv2zjeVBYd8pPPldjctpBEOD0eYv9exSefkHkk49FeOuYwa/+cpCP6ExL3HOHzsmz76y2G4KEqL57kNi9exAVGSdfofjcCXJPHAkSX7AaRlh/3ZIiI0oCpck8tcUK6b1dVOZLRHqiLZ14gigRiw0hyyFqlWWqxXli6WEkScN1LHJLZ6kU53DbNKcgiOjJXpRIArOau27lCNyg0T3wie2IssCbf3wes2wTSqnc+v07tvx7zwvUXW3D5fX/dq6pttZzrk+McyOoF0xOfukKekLlgX9+kGrW4MTfBjSBI/f0kN4W5/FffZPSQhVZlegcSzTLcVydPK4DUVToiO9kMXcSy63Tn9pN3QjoKcN6B6IQaDbVjAzJ6DCe76JIIYSaSFhLU6zMoilRQlqKpfwZpFAEJZxAGYpjljLoyW7KcxdR4x3oqR7quQWsUhalq5vI3v0UX305GHyRKBu1124EQVGJ3noIe2UFc2UZpbNrtTDfM+o45VJAgGLbwbL9HUBUVJTuHtxyeUPVDUEUGLyrm/7bu5DVhny3AKnROL7royhhoqlhUl07KWQu4vuQ7BqjUpihVl4m3bsPfCjnp9AjHejhNIXMJRQtgqRoqFqMWjVDR+8+PNehXJihs/8AnmNRyFymVt64zbjpPGUZtacHQdfB91B7eqjKMrQh0nHNOuXZcTQtjpzcRqk4hW1VUNQorigjiSqOU0OUFPBBVWOB8rRdRRAkVC2GbVUQBJGunoMU8hOUppvj275rY+SXCHX0I6lai0MgCBCJiBRLwX23bagbgcGNhEV+7V+nmJ1zeeqFGoMDMt/z4UhTq2s257ZNGlerazpqhukTjYgoioAiCzzxbI0rk8H9+Mo3K1xq/FsQBbSEhl23cY3NHYFQRwirYuGaLm6lTuZvXiD/+FsIooBnOTjZEm5pbUVYW65SW6pw6y/cRXWhwsIbsxSv5Og80INVsTDydZy6zdSTl+m8tZdtH93Jla+t1Uv7vke9soJtVtD0OLXyIrKsE44FzIi5pbPtTvPqj3FtA8+xtyy9dUNGt5qp07U7Qc/+NL7rs+uDgyj61nfh2R4n//4yD/+r27jrJ3YzdzSDHJLp2B5n8VSWKy9tgX5PADUsoyc0wp06oiyS3hbDKFkYRWuVkMJzfayaw9LZPHrsHA/8r7dSXqxx5aUFjJKFHlfo3Z8inNYYvqubvoMdzJ/YfAnUHj6uZ6LK4Qa7ltCSHDPtMo5rBok0PwgvCIKELAcUiIu504S1FKN9D3Jh6WmI+eiJLiQ9jGvWUaNJQqleEASMQkCyI0Wi+K6HnVkhevBQgz6wcUaOE+i6qSqeYQSjQxBAkgLvWZLxBRtBkRFDYex8DqdYRO3qxriqrNuyTFon5mdaSJEIbmXNkAqShCCJgfDfOh05p1yi8MKzxO+8B21oGGOiHdeDj+f4RDpChNNaUHYL1DIGJ//2EgJdmLUcpfw0vSN3M3flJWrlJaLJQWyzSiTWx9SFbxGO9aCHO7DMEn2j91LMThJLDZNdPEuycweiKOP7HrHUELZZoZyfpl5pxHsFEVkL4bkukqziWgairOCYa56n77ogSYR276b0/PMo3T0I/sYtM+FIN+FIF7HEENXqIvHEMKoapVpZRNOTFPITJJLDZFfOB92MgoAgSHR270dVItRqWaqVBSLRHoqFqbbHkBQtmDwmTuPZzV6W58HCosuObQrPvlwnEhJIxgP2v1hU4PYDGr/3+WVOnbV4LNlaJbKR3zE6HHjEsgydHSK5vEet7jE1a1MoebzwWh3fD16DqwutSG8U13bp2NOJWTBJbEtSz9bQkjp2JaCn1OIaxaki0d4oufEsrumC62Ev5bGXNi4N9WyPy185z8CDI+TPZ6gulBn/29OBiKYfrFjP/ukJXNOhtlhBuEZ52vd9bLOC65qokkKyayeirJGZP0myqyGAIDQklwSR5pnIX+XWaJ+EbsUNGd3X/vAsgiRy70/vwyxbnPz7K5Tmq9RyBr4H1axBvRjQn9WyxippRC1nBLOU5zN3LMvT/5+j3PoDOzj8c/ux6w6Lp3PkZ9bKbsoLNaor7YPaoaTG3T+5h22Hg2WsUTT57t++D8f2eOn/e5Kp15aw6w7F2Squ5eJ7PuefmCHSFeLAJ7eTuVRi/MkZQkmNg5/cgSDC5efnOfKF84SSWpD9dH1KizWM8tpywnO94LPi+hfbx3bqLOZOoytxInoXK4XzDPfci+vZZArjyJLeoI50cRyDqpEhGRthqPsu8AVEQaQzsYtEdICakcWuFihWC1wbiaxnmzlIzbkZIvtuoeuTP4CdWQkSWo0hYk5Pod//IJ2f+H4qR49QO38WfXiU2J13I4bCpD/yMeoXL1A9dQJj4gqJw+8LSqvGL+CZBngeTrHQMJxBUmq1Dtb3KR87QvqDH8aYmaZ85A3ccpn0ox9G6epCUBRSDz9K8dWXwfdwCgXsXJbi66+QOHw/5sI8vtGcrfc9mHt7Gc/xkHWJmTeaiUsS6S5ss4pjVVHUCF19BxuDIBg8tlVp1KgGz8T3PZZnj6NH0kiyhiQHdc6+72EZJeqVFeLpUcQGN4LveqjhBJHOIdRwEiUcp5abo7oy3WR0cV1qZ85QO3cOHAenUmkr4HgVshzCMstUK0uAQDjchdtoaslnL9LdexDXtXFdC1FSkCUtCDVc3c63se0qtlXFMtvXovqeS6hzgGjfNjJnX29J4vz3vy7x8/8kwey8Q1+PzM7tKi+/YVAzfHIFj8N36KQSIj/0yRiKvLXs+0P3hfj4d4WJRUX2jqn85u/lqdZ8/uuflPixH4hjmj7Fssf+3Sp/9rdlqjUfNabiez7xoThzc3MIYtCuH+kO4yV1bMNm6egSRq5ObCCGdAPOHIBruY1wnBjE+k2X9bRRbiPh6TmtSsGuYwRyUq6N65jYVo1U1y5UNYplVpAVnXTPXmRFo3vwNjLzJ1ffN0GUiHSPIsoKViWHU78+aZewWTeXIAjfGQaaf5TYfKm/XhVWFBVGe+9neun1TZUc/rFDUkQQguqU9QhFuxGAejVD99AdWEYRWY3iuTbl3CTR1BC5xbOIokKyawxRUtc8WEDRItQrGZJdO7HNEpXiArKsEUkMUCnOUq+sIMoq4Y5BRFHCsUwkRQ0aGLLXr7vcCIoSIZYYJBRKk1k+g6rF0fUkpdIsplGgp+92yqU5DCNPKj2GKIjkshcJhTsIhTqolOeo13N0dR/AtIqUCq21sXIoSnRgJ7IWJjd+pMXoyhL89I/FueNWjXPjNqbpc+6ixYuv1XnocIhPfiyCZcPffqXMpz8R41/87xnCIYF/8kNxnnu5zunzzd7z/+vnE7x51ORjH4qQSoo88UyNJ56tUTd8dE3gE49FeOhwCFGC8cs2//nzBUwLwt0RYgMx1ISGVTIRBIF6rk6oI0Q9W0OJqBQu58GHntt7qWVq5C/mbiRa9p5CEMQmEquNNxSRZDVIbAriakzX9/0NZ7CbRvd/AoiiQjzcS6m60EKmfhPvBJtNeOu/23g7QZKR1TB2/X8OMdKNzlUORdHTfSjhOMXJ09fn072J7whuGt2buIl/rGhQRkqKhl2v8J71qt7Eu8JNo3sTN3ETN/EdxDs2ujdxEzdxEzfx3mJrhWU3cRM3cRM38Z7gptG9iZu4iZv4DuKm0b2Jm7iJm/gO4qbRvYmbuImb+A7iptG9iZu4iZv4DuKm0b2Jm7iJm/gO4v8BbYswl6KcZUUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Bazı eğlenceli görselleştirmeler\n",
    "from wordcloud import WordCloud\n",
    "import matplotlib.pyplot as plt\n",
    "word_cloud = WordCloud(\n",
    "    max_font_size = 80\n",
    ").generate(\n",
    "    \" \".join(\n",
    "        \" \".join(lemma) for lemma in data[data.title == \"BERCAN TUTAR\"][\"lemma_token\"])\n",
    ")\n",
    "\n",
    "plt.imshow(word_cloud)\n",
    "plt.axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "c127ec55",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Modelimiz yazarları tahin edecek, kodlanmış kısımı y'ye atıyoruz.\n",
    "y = np.array(data.target)\n",
    "#Öznitelikler x'e atılır.\n",
    "x = data.loc[:, 'text' : 'numUpper']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "cb68c334",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Verilerimizi train ve test olarak böleriz\n",
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.33,random_state = 42)\n",
    "x_train.reset_index(inplace=True)\n",
    "x_test.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "bf96c858",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer, TfidfTransformer, CountVectorizer\n",
    "from sklearn.feature_extraction import DictVectorizer\n",
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "f2bf00ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "#train verilerimizi vektörleştirelim\n",
    "scaler = MinMaxScaler()\n",
    "normalizedLabels = scaler.fit_transform(x_train.loc[:, 'ttr':'numUpper'])\n",
    "\n",
    "#kökleri tek bir string metin (corpus) olarak birleştiririz\n",
    "train_corpus = np.array(x_train.lemma_token)\n",
    "for i in range(0, len(x_train.lemma_token)):\n",
    "    train_corpus[i] = \" \".join(x_train.lemma_token[i])\n",
    "    \n",
    "len(train_corpus)\n",
    "\n",
    "#bag of word vektörü\n",
    "count_vectorize = CountVectorizer()\n",
    "sparce_matrix = count_vectorize.fit_transform(train_corpus).toarray()\n",
    "sparce_matrix = (sparce_matrix - sparce_matrix.min())/(sparce_matrix.max() - sparce_matrix.min())\n",
    "\n",
    "# tf-idf vektörü\n",
    "tfidf_vector = TfidfVectorizer()\n",
    "tfidf_matrix = tfidf_vector.fit_transform(train_corpus).toarray()\n",
    "\n",
    "#kelime-cümle dağılım vektörleri\n",
    "wtDict_vector = DictVectorizer()\n",
    "stDict_vector = DictVectorizer()\n",
    "wtLenDist_matrix = wtDict_vector.fit_transform(x_train.wtLenDist).toarray()\n",
    "stLenDist_matrix = stDict_vector.fit_transform(x_train.stLenDist).toarray()\n",
    "wtLenDist_matrix = (wtLenDist_matrix - wtLenDist_matrix.min())/(wtLenDist_matrix.max() - wtLenDist_matrix.min())\n",
    "stLenDist_matrix = (stLenDist_matrix - stLenDist_matrix.min())/(stLenDist_matrix.max() - stLenDist_matrix.min())\n",
    "\n",
    "#bütün vektörlerin birleştirelim\n",
    "attribution = (tfidf_matrix, sparce_matrix, wtLenDist_matrix, stLenDist_matrix, normalizedLabels) \n",
    "attribution = np.concatenate(attribution, axis = 1)\n",
    "X_train = attribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "f87daa7d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#vektör modellerini kaydedelim\n",
    "import pickle\n",
    "with open('./model/mapping.pkl', 'wb') as f:\n",
    "    pickle.dump(mapping, f)\n",
    "\n",
    "with open('./model/scaler.pkl', 'wb') as f:\n",
    "    pickle.dump(scaler, f)\n",
    "    \n",
    "with open('./model/count_vectorize.pkl', 'wb') as f:\n",
    "    pickle.dump(count_vectorize, f)\n",
    "    \n",
    "with open('./model/tfidf_vector.pkl', 'wb') as f:\n",
    "    pickle.dump(tfidf_vector, f)\n",
    "    \n",
    "with open('./model/wtDict_vector.pkl', 'wb') as f:\n",
    "    pickle.dump(wtDict_vector, f)\n",
    "    \n",
    "with open('./model/stDict_vector.pkl', 'wb') as f:\n",
    "    pickle.dump(stDict_vector, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "d09a4700",
   "metadata": {},
   "outputs": [],
   "source": [
    "#oluştruduğumuz vektör modelleri ile test veri setini vektörleştirelim\n",
    "test_normalizedLabels = scaler.transform(x_test.loc[:, 'ttr':'numUpper'])\n",
    "\n",
    "test_corpus = np.array(x_test.lemma_token)\n",
    "for i in range(0, len(x_test.lemma_token)):\n",
    "    test_corpus[i] = \" \".join(x_test.lemma_token[i])\n",
    "    \n",
    "test_sparce_matrix = count_vectorize.transform(test_corpus).toarray()\n",
    "test_sparce_matrix = (test_sparce_matrix - test_sparce_matrix.min())/(test_sparce_matrix.max() - test_sparce_matrix.min())\n",
    "\n",
    "test_tfidf_matrix = tfidf_vector.transform(test_corpus).toarray()\n",
    "\n",
    "test_wtLenDist_matrix = wtDict_vector.transform(x_test.wtLenDist).toarray()\n",
    "test_stLenDist_matrix = stDict_vector.transform(x_test.stLenDist).toarray()\n",
    "test_wtLenDist_matrix = (test_wtLenDist_matrix - test_wtLenDist_matrix.min())/(test_wtLenDist_matrix.max() - test_wtLenDist_matrix.min())\n",
    "test_stLenDist_matrix = (test_stLenDist_matrix - test_stLenDist_matrix.min())/(test_stLenDist_matrix.max() - test_stLenDist_matrix.min())\n",
    "\n",
    "test_attribution = (test_tfidf_matrix, test_sparce_matrix, test_wtLenDist_matrix, test_stLenDist_matrix, test_normalizedLabels) \n",
    "test_attribution = np.concatenate(test_attribution, axis = 1)\n",
    "X_test = test_attribution"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6b4453b",
   "metadata": {},
   "source": [
    "# SVM default parameters:\n",
    "\n",
    "    C = 1.0\n",
    "    kernel = rbf\n",
    "    degree = 3\n",
    "    gamma = scale\n",
    "    coef0 = 0.0\n",
    "    shrinking = True\n",
    "    probability = False\n",
    "    tol = 1e-3\n",
    "    cache_size = 200\n",
    "    class_weight = None\n",
    "    verbose = False\n",
    "    max_iter = 1\n",
    "    decision_function_shape = ovr\n",
    "    break_tiles = False\n",
    "    random_state = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "f0200086",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Support Vector Machine Classifier has fitted, this process took 49.11 seconds\n",
      "Accuracy = %55.00725689404935\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "from sklearn.svm import SVC\n",
    "from sklearn import metrics\n",
    "\n",
    "#sss = StratifiedShuffleSplit(n_splits=5, random_state=42)\n",
    "svm_classifier = SVC()\n",
    "\n",
    "start = time.time() \n",
    "svm_classifier.fit(X_train,y_train)\n",
    "#result = cross_val_score(svm_classifier, x, y, cv = sss)\n",
    "end = time.time()\n",
    "\n",
    "process = round(end-start,2)\n",
    "print(\"Support Vector Machine Classifier has fitted, this process took {} seconds\".format(process))\n",
    "#print(\"Avg accuracy: {}\".format(result.mean()))\n",
    "\n",
    "\n",
    "pred_svm = svm_classifier.predict(X_test)\n",
    "ACC_svm = metrics.accuracy_score(y_test,pred_svm)    \n",
    "print(\"Accuracy = %\"+ str(ACC_svm*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "793a22dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import randint\n",
    "from sklearn.experimental import enable_halving_search_cv\n",
    "from sklearn.model_selection import StratifiedShuffleSplit\n",
    "from sklearn.utils.fixes import loguniform\n",
    "from sklearn.model_selection import RandomizedSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "52091bd0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Hiper parametre ayarlaması\n",
    "\n",
    "rng = np.random.RandomState(0)\n",
    "tuned_parameters = [{'kernel': ['rbf'],\n",
    "                     'C': loguniform(1e0, 1e3),\n",
    "                     'gamma': loguniform(1e-4, 1e-2),\n",
    "                     'class_weight':['balanced', None]},\n",
    "                    {'kernel': ['linear'], \n",
    "                     'C': loguniform(1e0, 1e3)},\n",
    "                    {'kernel': ['poly'],\n",
    "                     'gamma': loguniform(1e-4, 1e-2),\n",
    "                     'degree' : [2, 3, 4],\n",
    "                     'C': loguniform(1e0, 1e3)},\n",
    "                    {'kernel': ['sigmoid'],\n",
    "                     'gamma': loguniform(1e-4, 1e-2),\n",
    "                     'C': loguniform(1e0, 1e3)}]\n",
    "\n",
    "rsc = RandomizedSearchCV(\n",
    "        estimator=svm_classifier,\n",
    "        param_distributions=tuned_parameters,\n",
    "        n_iter = 30,\n",
    "        cv=10,\n",
    "        random_state=rng).fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "2c57b208",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The best parameters are {'C': 216.00820741402038, 'kernel': 'linear'} with a score of 0.80\n"
     ]
    }
   ],
   "source": [
    "print(\n",
    "    \"The best parameters are %s with a score of %0.2f\"\n",
    "    % (rsc.best_params_, rsc.best_score_)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "02d25aa7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Support Vector Machine Classifier has fitted, this process took 44.53 seconds\n",
      "Accuracy = %80.11611030478954\n"
     ]
    }
   ],
   "source": [
    "# hiper parametre ayarlaması yapılmış svm\n",
    "\n",
    "import time\n",
    "from sklearn.svm import SVC\n",
    "from sklearn import metrics\n",
    "\n",
    "tuned_svm_classifier = SVC(C = 216.00820741402038, kernel = 'linear')\n",
    "\n",
    "start = time.time() \n",
    "tuned_svm_classifier.fit(X_train,y_train)\n",
    "end = time.time()\n",
    "\n",
    "process = round(end-start,2)\n",
    "print(\"Support Vector Machine Classifier has fitted, this process took {} seconds\".format(process))\n",
    "\n",
    "t_pred_svm = tuned_svm_classifier.predict(X_test)\n",
    "t_ACC_svm = metrics.accuracy_score(y_test,t_pred_svm)    \n",
    "print(\"Accuracy = %\"+ str(t_ACC_svm*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "5c92a864",
   "metadata": {},
   "outputs": [],
   "source": [
    "#svm modelini kaydedelim\n",
    "with open('./model/SVM.pkl', 'wb') as f:\n",
    "    pickle.dump(tuned_svm_classifier, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "04ce4844",
   "metadata": {},
   "outputs": [],
   "source": [
    "#tekrar modeli yükleyerek çalıştırmayı deneyelim\n",
    "with open('./model/SVM.pkl', 'rb') as f:\n",
    "    reloaded_model = pickle.load(f)\n",
    "    \n",
    "r_pred_svm = reloaded_model.predict(X_test)\n",
    "r_ACC_svm = metrics.accuracy_score(y_test,r_pred_svm)    \n",
    "print(\"Accuracy = %\"+ str(r_ACC_svm*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3a92abce",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_pred = reloaded_model.predict(X_train)\n",
    "new_acc = metrics.accuracy_score(y_train,new_pred)    \n",
    "\n",
    "print(\"Accuracy = %\"+ str(new_acc*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "7955f646",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
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\n",
      "text/plain": [
       "<Figure size 3600x3600 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# confusion matrix\n",
    "\n",
    "from sklearn.metrics import confusion_matrix\n",
    "import seaborn as sns;\n",
    "plt.figure(figsize = (50,50))\n",
    "cm = pd.crosstab(mapping[y_test], mapping[r_pred_svm])\n",
    "ax = sns.heatmap(cm, vmin = 0, vmax = 10, center = 5, \n",
    "                 cmap = sns.diverging_palette(20, 220, n = 200),  \n",
    "                 square = True, annot = True)\n",
    "\n",
    "ax.set_ylim([cm.shape[0],0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "889a10a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig.savefig(\"cm.png\") "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d9e6f83",
   "metadata": {},
   "source": [
    "* \n",
    "* \n",
    "* \n",
    "* \n",
    "* \n",
    "* \n",
    "* "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17d4c43b",
   "metadata": {},
   "source": [
    "# MLP default parameters:\n",
    "\n",
    "    hidden_layer_sizes=(100,)\n",
    "    activation='relu'\n",
    "    solver='adam'\n",
    "    alpha=0.0001\n",
    "    batch_size='auto'\n",
    "    learning_rate='constant'\n",
    "    learning_rate_init=0.001\n",
    "    power_t=0.5\n",
    "    max_iter=200\n",
    "    shuffle=True\n",
    "    random_state=None\n",
    "    tol=0.0001\n",
    "    verbose=False\n",
    "    warm_start=False\n",
    "    momentum=0.9\n",
    "    nesterovs_momentum=True\n",
    "    early_stopping=False\n",
    "    validation_fraction=0.1\n",
    "    beta_1=0.9\n",
    "    beta_2=0.999\n",
    "    epsilon=1e-08\n",
    "    n_iter_no_change=10\n",
    "    max_fun=15000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "cd5ced95",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy = %76.48766328011611\n"
     ]
    }
   ],
   "source": [
    "#MLP ile deneyelim\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn import metrics\n",
    "\n",
    "\n",
    "mlp_classifier = MLPClassifier(random_state=42)\n",
    "mlp_classifier.fit(X_train,y_train)\n",
    "\n",
    "pred_mlp = mlp_classifier.predict(X_test)\n",
    "ACC_mlp = metrics.accuracy_score(y_test, pred_mlp)\n",
    "print(\"Accuracy = %\"+ str(ACC_mlp*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "c385cccd",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "GRID = [\n",
    "    {'solver': ['sgd', 'adam', 'lbfgs'],\n",
    "     'learning_rate_init': [0.0001],\n",
    "     'learning_rate': ['constant','adaptive'],\n",
    "     'max_iter': [300],\n",
    "     'hidden_layer_sizes': [(500, 400, 300, 200, 100), (400, 400, 400, 400, 400), \n",
    "                            (300, 300, 300, 300, 300), (200, 200, 200, 200, 200)],\n",
    "     'activation': ['logistic', 'tanh', 'relu'],\n",
    "     'alpha': stats.uniform(0.0001, 0.9),\n",
    "     'early_stopping': [True, False]\n",
    "}]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d4b4ebbf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 72 candidates, totalling 720 fits\n"
     ]
    }
   ],
   "source": [
    "grid_search = GridSearchCV(estimator=MLPClassifier(), param_grid=GRID,  \n",
    "                            n_jobs=-1, cv=10, refit=True, verbose=1)\n",
    "\n",
    "gs = grid_search.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f3b3c0d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\n",
    "    \"The best parameters are %s with a score of %0.2f\"\n",
    "    % (gs.best_params_, gs.best_score_)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba9f48c0",
   "metadata": {},
   "source": [
    "* \n",
    "* \n",
    "* \n",
    "* \n",
    "* "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0e3ce98",
   "metadata": {},
   "source": [
    "# LSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "138824ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "x_train, x_test = X_train.reshape(-1, 1, 37414), X_test.reshape(-1, 1, 37414)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "36496aba",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer=Adam(),\n",
    "              loss='sparse_categorical_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "#Adding an early stopping\n",
    "es = EarlyStopping(monitor='val_accuracy', \n",
    "                   mode='max', \n",
    "                   patience=10, #Stop the model training if validation accuracy doesnt increase in 10 consecutive Epochs\n",
    "                   restore_best_weights=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "afd88d8e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#LSTM ile deneyelim\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, LSTM, Dropout, Embedding, SpatialDropout1D\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "from keras.callbacks import EarlyStopping\n",
    "\n",
    "\n",
    "model=Sequential()\n",
    "\n",
    "model.add(SpatialDropout1D(0.4))\n",
    "model.add(LSTM(256, activation='relu', dropout=0.2, recurrent_dropout=0.2, return_sequences=True))\n",
    "model.add(LSTM(128, activation='relu', dropout=0.2, recurrent_dropout=0.2, return_sequences=True))\n",
    "model.add(LSTM(128, activation='relu', dropout=0.2, recurrent_dropout=0.2, return_sequences=True))\n",
    "model.add(LSTM(74, activation='relu', dropout=0.2, recurrent_dropout=0.2))\n",
    "model.add(Dense(len(data.title.unique()), activation='softmax'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "5e96f94f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "44/44 [==============================] - 44s 467ms/step - loss: 4.6920 - accuracy: 0.0057 - val_loss: 4.6926 - val_accuracy: 0.0044\n",
      "Epoch 2/100\n",
      "44/44 [==============================] - 22s 492ms/step - loss: 4.6909 - accuracy: 0.0072 - val_loss: 4.6939 - val_accuracy: 0.0058\n",
      "Epoch 3/100\n",
      "44/44 [==============================] - 16s 372ms/step - loss: 4.6902 - accuracy: 0.0072 - val_loss: 4.6952 - val_accuracy: 0.0044\n",
      "Epoch 4/100\n",
      "44/44 [==============================] - 17s 376ms/step - loss: 4.6887 - accuracy: 0.0136 - val_loss: 4.6927 - val_accuracy: 0.0087\n",
      "Epoch 5/100\n",
      "44/44 [==============================] - 15s 350ms/step - loss: 4.5223 - accuracy: 0.0122 - val_loss: 4.3444 - val_accuracy: 0.0232\n",
      "Epoch 6/100\n",
      "44/44 [==============================] - 15s 334ms/step - loss: 4.2249 - accuracy: 0.0179 - val_loss: 4.1843 - val_accuracy: 0.0319\n",
      "Epoch 7/100\n",
      "44/44 [==============================] - 15s 343ms/step - loss: 4.0316 - accuracy: 0.0300 - val_loss: 4.1435 - val_accuracy: 0.0261\n",
      "Epoch 8/100\n",
      "44/44 [==============================] - 17s 386ms/step - loss: 3.8260 - accuracy: 0.0443 - val_loss: 4.0263 - val_accuracy: 0.0435\n",
      "Epoch 9/100\n",
      "44/44 [==============================] - 16s 360ms/step - loss: 3.6249 - accuracy: 0.0579 - val_loss: 3.9363 - val_accuracy: 0.0464\n",
      "Epoch 10/100\n",
      "44/44 [==============================] - 16s 354ms/step - loss: 3.4887 - accuracy: 0.0794 - val_loss: 3.8494 - val_accuracy: 0.0537\n",
      "Epoch 11/100\n",
      "44/44 [==============================] - 16s 366ms/step - loss: 3.3513 - accuracy: 0.0844 - val_loss: 3.7190 - val_accuracy: 0.0885\n",
      "Epoch 12/100\n",
      "44/44 [==============================] - 15s 351ms/step - loss: 3.1455 - accuracy: 0.1173 - val_loss: 3.6624 - val_accuracy: 0.0943\n",
      "Epoch 13/100\n",
      "44/44 [==============================] - 17s 394ms/step - loss: 2.9762 - accuracy: 0.1330 - val_loss: 3.7283 - val_accuracy: 0.0726\n",
      "Epoch 14/100\n",
      "44/44 [==============================] - 17s 385ms/step - loss: 2.8362 - accuracy: 0.1488 - val_loss: 3.6460 - val_accuracy: 0.0900\n",
      "Epoch 15/100\n",
      "44/44 [==============================] - 15s 351ms/step - loss: 2.7110 - accuracy: 0.1717 - val_loss: 3.5332 - val_accuracy: 0.1147\n",
      "Epoch 16/100\n",
      "44/44 [==============================] - 15s 349ms/step - loss: 2.5693 - accuracy: 0.1946 - val_loss: 3.6741 - val_accuracy: 0.1016\n",
      "Epoch 17/100\n",
      "44/44 [==============================] - 16s 363ms/step - loss: 2.4881 - accuracy: 0.1996 - val_loss: 3.5782 - val_accuracy: 0.1263\n",
      "Epoch 18/100\n",
      "44/44 [==============================] - 16s 369ms/step - loss: 2.3635 - accuracy: 0.2346 - val_loss: 3.7267 - val_accuracy: 0.1132\n",
      "Epoch 19/100\n",
      "44/44 [==============================] - 16s 376ms/step - loss: 2.3180 - accuracy: 0.2303 - val_loss: 3.6200 - val_accuracy: 0.1335\n",
      "Epoch 20/100\n",
      "44/44 [==============================] - 16s 353ms/step - loss: 2.1970 - accuracy: 0.2439 - val_loss: 3.7313 - val_accuracy: 0.1350\n",
      "Epoch 21/100\n",
      "44/44 [==============================] - 18s 404ms/step - loss: 2.1366 - accuracy: 0.2790 - val_loss: 3.7654 - val_accuracy: 0.1451\n",
      "Epoch 22/100\n",
      "44/44 [==============================] - 18s 420ms/step - loss: 2.0804 - accuracy: 0.3011 - val_loss: 3.5505 - val_accuracy: 0.1509\n",
      "Epoch 23/100\n",
      "44/44 [==============================] - 18s 418ms/step - loss: 2.0268 - accuracy: 0.2861 - val_loss: 3.8932 - val_accuracy: 0.1669\n",
      "Epoch 24/100\n",
      "44/44 [==============================] - 16s 357ms/step - loss: 1.9543 - accuracy: 0.3355 - val_loss: 3.8476 - val_accuracy: 0.1597\n",
      "Epoch 25/100\n",
      "44/44 [==============================] - 20s 446ms/step - loss: 1.8524 - accuracy: 0.3598 - val_loss: 3.8737 - val_accuracy: 0.1524\n",
      "Epoch 26/100\n",
      "44/44 [==============================] - 18s 419ms/step - loss: 1.7690 - accuracy: 0.3677 - val_loss: 4.2127 - val_accuracy: 0.1829\n",
      "Epoch 27/100\n",
      "44/44 [==============================] - 16s 368ms/step - loss: 1.7327 - accuracy: 0.4070 - val_loss: 4.2750 - val_accuracy: 0.1597\n",
      "Epoch 28/100\n",
      "44/44 [==============================] - 17s 389ms/step - loss: 1.7500 - accuracy: 0.3920 - val_loss: 4.1456 - val_accuracy: 0.1916\n",
      "Epoch 29/100\n",
      "44/44 [==============================] - 17s 382ms/step - loss: 1.6248 - accuracy: 0.4213 - val_loss: 3.9137 - val_accuracy: 0.1901\n",
      "Epoch 30/100\n",
      "44/44 [==============================] - 16s 356ms/step - loss: 1.5660 - accuracy: 0.4521 - val_loss: 4.2686 - val_accuracy: 0.1858\n",
      "Epoch 31/100\n",
      "44/44 [==============================] - 16s 362ms/step - loss: 1.5288 - accuracy: 0.4735 - val_loss: 3.8416 - val_accuracy: 0.2119\n",
      "Epoch 32/100\n",
      "44/44 [==============================] - 18s 414ms/step - loss: 1.4907 - accuracy: 0.4964 - val_loss: 4.3024 - val_accuracy: 0.2134\n",
      "Epoch 33/100\n",
      "44/44 [==============================] - 17s 388ms/step - loss: 1.3765 - accuracy: 0.5100 - val_loss: 4.1309 - val_accuracy: 0.2511\n",
      "Epoch 34/100\n",
      "44/44 [==============================] - 17s 385ms/step - loss: 1.3346 - accuracy: 0.5415 - val_loss: 4.1874 - val_accuracy: 0.2598\n",
      "Epoch 35/100\n",
      "44/44 [==============================] - 16s 357ms/step - loss: 1.2016 - accuracy: 0.5701 - val_loss: 4.4970 - val_accuracy: 0.2729\n",
      "Epoch 36/100\n",
      "44/44 [==============================] - 17s 387ms/step - loss: 1.1294 - accuracy: 0.5894 - val_loss: 4.4741 - val_accuracy: 0.2612\n",
      "Epoch 37/100\n",
      "44/44 [==============================] - 17s 383ms/step - loss: 1.0864 - accuracy: 0.6195 - val_loss: 4.6641 - val_accuracy: 0.2729\n",
      "Epoch 38/100\n",
      "44/44 [==============================] - 16s 354ms/step - loss: 1.1308 - accuracy: 0.6037 - val_loss: 4.5638 - val_accuracy: 0.2743\n",
      "Epoch 39/100\n",
      "44/44 [==============================] - 16s 365ms/step - loss: 1.0666 - accuracy: 0.6488 - val_loss: 4.3003 - val_accuracy: 0.2845\n",
      "Epoch 40/100\n",
      "44/44 [==============================] - 15s 346ms/step - loss: 0.9549 - accuracy: 0.6516 - val_loss: 4.4330 - val_accuracy: 0.2874\n",
      "Epoch 41/100\n",
      "44/44 [==============================] - 15s 350ms/step - loss: 0.9657 - accuracy: 0.6602 - val_loss: 4.4557 - val_accuracy: 0.2874\n",
      "Epoch 42/100\n",
      "44/44 [==============================] - 15s 343ms/step - loss: 0.9048 - accuracy: 0.6910 - val_loss: 4.6062 - val_accuracy: 0.3091\n",
      "Epoch 43/100\n",
      "44/44 [==============================] - 15s 339ms/step - loss: 0.8152 - accuracy: 0.7167 - val_loss: 4.8965 - val_accuracy: 0.3004\n",
      "Epoch 44/100\n",
      "44/44 [==============================] - 15s 342ms/step - loss: 0.8237 - accuracy: 0.7282 - val_loss: 4.4045 - val_accuracy: 0.3149\n",
      "Epoch 45/100\n",
      "44/44 [==============================] - 15s 347ms/step - loss: 0.8018 - accuracy: 0.7318 - val_loss: 4.4346 - val_accuracy: 0.3048\n",
      "Epoch 46/100\n",
      "44/44 [==============================] - 16s 356ms/step - loss: 0.7044 - accuracy: 0.7682 - val_loss: 4.6860 - val_accuracy: 0.3179\n",
      "Epoch 47/100\n",
      "44/44 [==============================] - 15s 353ms/step - loss: 0.7464 - accuracy: 0.7382 - val_loss: 4.9444 - val_accuracy: 0.2946\n",
      "Epoch 48/100\n",
      "44/44 [==============================] - 15s 348ms/step - loss: 0.6854 - accuracy: 0.7589 - val_loss: 4.8438 - val_accuracy: 0.2975\n",
      "Epoch 49/100\n",
      "44/44 [==============================] - 15s 341ms/step - loss: 0.6696 - accuracy: 0.7854 - val_loss: 4.9818 - val_accuracy: 0.3019\n",
      "Epoch 50/100\n",
      "44/44 [==============================] - 15s 344ms/step - loss: 0.6315 - accuracy: 0.7833 - val_loss: 4.9467 - val_accuracy: 0.3266\n",
      "Epoch 51/100\n",
      "44/44 [==============================] - 15s 345ms/step - loss: 0.6047 - accuracy: 0.8076 - val_loss: 5.0052 - val_accuracy: 0.3179\n",
      "Epoch 52/100\n",
      "44/44 [==============================] - 15s 343ms/step - loss: 0.5257 - accuracy: 0.8190 - val_loss: 5.0926 - val_accuracy: 0.3193\n",
      "Epoch 53/100\n",
      "44/44 [==============================] - 15s 341ms/step - loss: 0.4915 - accuracy: 0.8448 - val_loss: 5.1055 - val_accuracy: 0.3454\n",
      "Epoch 54/100\n",
      "44/44 [==============================] - 15s 348ms/step - loss: 0.5280 - accuracy: 0.8305 - val_loss: 5.5478 - val_accuracy: 0.3309\n",
      "Epoch 55/100\n",
      "44/44 [==============================] - 15s 344ms/step - loss: 0.4676 - accuracy: 0.8455 - val_loss: 5.5355 - val_accuracy: 0.3280\n",
      "Epoch 56/100\n",
      "44/44 [==============================] - 15s 347ms/step - loss: 0.4936 - accuracy: 0.8333 - val_loss: 4.9738 - val_accuracy: 0.3440\n",
      "Epoch 57/100\n",
      "44/44 [==============================] - 16s 356ms/step - loss: 0.3980 - accuracy: 0.8720 - val_loss: 5.4073 - val_accuracy: 0.3483\n",
      "Epoch 58/100\n",
      "44/44 [==============================] - 15s 346ms/step - loss: 0.4530 - accuracy: 0.8598 - val_loss: 5.8894 - val_accuracy: 0.3091\n",
      "Epoch 59/100\n",
      "44/44 [==============================] - 17s 388ms/step - loss: 0.4896 - accuracy: 0.8398 - val_loss: 5.1127 - val_accuracy: 0.3628\n",
      "Epoch 60/100\n",
      "44/44 [==============================] - 16s 365ms/step - loss: 0.4990 - accuracy: 0.8391 - val_loss: 5.4802 - val_accuracy: 0.3324\n",
      "Epoch 61/100\n",
      "44/44 [==============================] - 16s 363ms/step - loss: 0.4095 - accuracy: 0.8691 - val_loss: 5.0640 - val_accuracy: 0.3512\n",
      "Epoch 62/100\n",
      "44/44 [==============================] - 16s 364ms/step - loss: 0.3607 - accuracy: 0.8999 - val_loss: 5.1966 - val_accuracy: 0.3396\n",
      "Epoch 63/100\n",
      "44/44 [==============================] - 20s 450ms/step - loss: 0.3719 - accuracy: 0.8884 - val_loss: 5.1215 - val_accuracy: 0.3599\n",
      "Epoch 64/100\n",
      "44/44 [==============================] - 16s 365ms/step - loss: 0.3475 - accuracy: 0.8963 - val_loss: 5.3602 - val_accuracy: 0.3541\n",
      "Epoch 65/100\n",
      "44/44 [==============================] - 17s 391ms/step - loss: 0.3314 - accuracy: 0.8999 - val_loss: 5.4690 - val_accuracy: 0.3541\n",
      "Epoch 66/100\n",
      "44/44 [==============================] - 17s 378ms/step - loss: 0.3517 - accuracy: 0.8834 - val_loss: 5.5752 - val_accuracy: 0.3483\n",
      "Epoch 67/100\n",
      "44/44 [==============================] - 17s 385ms/step - loss: 0.3152 - accuracy: 0.9013 - val_loss: 5.3441 - val_accuracy: 0.3512\n",
      "Epoch 68/100\n",
      "44/44 [==============================] - 16s 354ms/step - loss: 0.3194 - accuracy: 0.9020 - val_loss: 5.2332 - val_accuracy: 0.3614\n",
      "Epoch 69/100\n",
      "44/44 [==============================] - 16s 360ms/step - loss: 0.3003 - accuracy: 0.9006 - val_loss: 5.6493 - val_accuracy: 0.3541\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(x_train, y_train, batch_size=32, epochs=100, callbacks=[es], validation_data=(x_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "ccda8b25",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " spatial_dropout1d (SpatialD  (None, 1, 32102)         0         \n",
      " ropout1D)                                                       \n",
      "                                                                 \n",
      " lstm (LSTM)                 (None, 1, 256)            33135616  \n",
      "                                                                 \n",
      " lstm_1 (LSTM)               (None, 1, 128)            197120    \n",
      "                                                                 \n",
      " lstm_2 (LSTM)               (None, 1, 128)            131584    \n",
      "                                                                 \n",
      " lstm_3 (LSTM)               (None, 74)                60088     \n",
      "                                                                 \n",
      " dense (Dense)               (None, 109)               8175      \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 33,532,583\n",
      "Trainable params: 33,532,583\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "2f8a1949",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "22/22 [==============================] - 2s 79ms/step - loss: 5.1127 - accuracy: 0.3628\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[5.112668991088867, 0.36284470558166504]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "dfafd39a",
   "metadata": {},
   "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": [
    "for i in ['accuracy']:\n",
    "    acc = history.history[i]\n",
    "    val_acc = history.history['val_{}'.format(i)]\n",
    "    epochs = range(1, len(acc) + 1)\n",
    "\n",
    "    plt.figure()\n",
    "    plt.plot(epochs, acc, label='Training Accuracy')\n",
    "    plt.plot(epochs, val_acc, label='Validation Accuracy')\n",
    "    plt.title('Results for {}'.format(i))\n",
    "    plt.legend()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8793948a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}