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"cells": [
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "view-in-github"
},
"source": [
"<a href=\"https://colab.research.google.com/github/bwbayu/TalentMatch/blob/development/model/training/Training_Glove.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "N5EIcheeUwnD",
"outputId": "cb08f782-0144-4873-b75c-e736b6a88405"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Mounted at /content/drive\n"
]
}
],
"source": [
"from google.colab import drive\n",
"drive.mount('/content/drive')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "MnHJP8RWctYX",
"outputId": "77c33436-d7b6-4dc9-a2ad-2ef8a587a274"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Requirement already satisfied: gensim in /usr/local/lib/python3.10/dist-packages (4.3.3)\n",
"Requirement already satisfied: numpy<2.0,>=1.18.5 in /usr/local/lib/python3.10/dist-packages (from gensim) (1.26.4)\n",
"Requirement already satisfied: scipy<1.14.0,>=1.7.0 in /usr/local/lib/python3.10/dist-packages (from gensim) (1.13.1)\n",
"Requirement already satisfied: smart-open>=1.8.1 in /usr/local/lib/python3.10/dist-packages (from gensim) (7.0.4)\n",
"Requirement already satisfied: wrapt in /usr/local/lib/python3.10/dist-packages (from smart-open>=1.8.1->gensim) (1.16.0)\n"
]
}
],
"source": [
"!pip install gensim"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "HsrEWVUvp1a6",
"outputId": "33d593c3-6dae-4f99-9575-55c56f2fefcc"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Collecting wordsegment\n",
" Downloading wordsegment-1.3.1-py2.py3-none-any.whl.metadata (7.7 kB)\n",
"Downloading wordsegment-1.3.1-py2.py3-none-any.whl (4.8 MB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m4.8/4.8 MB\u001b[0m \u001b[31m29.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hInstalling collected packages: wordsegment\n",
"Successfully installed wordsegment-1.3.1\n",
"Collecting num2words\n",
" Downloading num2words-0.5.13-py3-none-any.whl.metadata (12 kB)\n",
"Collecting docopt>=0.6.2 (from num2words)\n",
" Downloading docopt-0.6.2.tar.gz (25 kB)\n",
" Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
"Downloading num2words-0.5.13-py3-none-any.whl (143 kB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m143.3/143.3 kB\u001b[0m \u001b[31m7.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hBuilding wheels for collected packages: docopt\n",
" Building wheel for docopt (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for docopt: filename=docopt-0.6.2-py2.py3-none-any.whl size=13704 sha256=67658f5ba71f4b5bebf6a44015b1a0248f143baa08d7f0e125a06c92ea3da0a6\n",
" Stored in directory: /root/.cache/pip/wheels/fc/ab/d4/5da2067ac95b36618c629a5f93f809425700506f72c9732fac\n",
"Successfully built docopt\n",
"Installing collected packages: docopt, num2words\n",
"Successfully installed docopt-0.6.2 num2words-0.5.13\n",
"Collecting deep-translator\n",
" Downloading deep_translator-1.11.4-py3-none-any.whl.metadata (30 kB)\n",
"Requirement already satisfied: beautifulsoup4<5.0.0,>=4.9.1 in /usr/local/lib/python3.10/dist-packages (from deep-translator) (4.12.3)\n",
"Requirement already satisfied: requests<3.0.0,>=2.23.0 in /usr/local/lib/python3.10/dist-packages (from deep-translator) (2.31.0)\n",
"Requirement already satisfied: soupsieve>1.2 in /usr/local/lib/python3.10/dist-packages (from beautifulsoup4<5.0.0,>=4.9.1->deep-translator) (2.5)\n",
"Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests<3.0.0,>=2.23.0->deep-translator) (3.3.2)\n",
"Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests<3.0.0,>=2.23.0->deep-translator) (3.7)\n",
"Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests<3.0.0,>=2.23.0->deep-translator) (2.0.7)\n",
"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests<3.0.0,>=2.23.0->deep-translator) (2024.7.4)\n",
"Downloading deep_translator-1.11.4-py3-none-any.whl (42 kB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m42.3/42.3 kB\u001b[0m \u001b[31m2.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hInstalling collected packages: deep-translator\n",
"Successfully installed deep-translator-1.11.4\n"
]
}
],
"source": [
"!pip install wordsegment\n",
"!pip install num2words\n",
"!pip install deep-translator"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Nl33g6huN7Nv"
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "TYL7R84nS6aU",
"outputId": "8042db13-a319-4db7-a031-71259badbfac"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"[nltk_data] Downloading package punkt to /root/nltk_data...\n",
"[nltk_data] Unzipping tokenizers/punkt.zip.\n"
]
},
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from gensim.models import KeyedVectors\n",
"from gensim.models import Word2Vec\n",
"from nltk.tokenize import word_tokenize\n",
"import nltk\n",
"nltk.download('punkt')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "m7_57tauSFNW"
},
"outputs": [],
"source": [
"df = pd.read_csv('/content/drive/MyDrive/compfest dataset/dataset/combined_all_nverbs.csv')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "H_T7UZTMjcgF",
"outputId": "e6320bcf-abb4-422b-e74d-062645c08e99"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 35413 entries, 0 to 35412\n",
"Data columns (total 2 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 clean_data 35413 non-null object\n",
" 1 text_length 35413 non-null int64 \n",
"dtypes: int64(1), object(1)\n",
"memory usage: 553.5+ KB\n"
]
}
],
"source": [
"df.info()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "dbkTJvb7nA4w"
},
"outputs": [],
"source": [
"def tokenize_text(text):\n",
" return word_tokenize(text.lower())\n",
"\n",
"df['tokenized_data'] = df['clean_data'].apply(tokenize_text)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 321
},
"id": "II8dE3g9czzm",
"outputId": "26a0be60-e904-4163-994c-22a133274126"
},
"outputs": [
{
"ename": "FileNotFoundError",
"evalue": "[Errno 2] No such file or directory: '/content/drive/MyDrive/model/glove.6B.50d.txt'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-104-1fe01c90bfb2>\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mglove_file\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'/content/drive/MyDrive/model/glove.6B.50d.txt'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mglove_model\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mKeyedVectors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_word2vec_format\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mglove_file\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbinary\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mno_header\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/gensim/models/keyedvectors.py\u001b[0m in \u001b[0;36mload_word2vec_format\u001b[0;34m(cls, fname, fvocab, binary, encoding, unicode_errors, limit, datatype, no_header)\u001b[0m\n\u001b[1;32m 1717\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1718\u001b[0m \"\"\"\n\u001b[0;32m-> 1719\u001b[0;31m return _load_word2vec_format(\n\u001b[0m\u001b[1;32m 1720\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfvocab\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfvocab\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbinary\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbinary\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencoding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mencoding\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0municode_errors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0municode_errors\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1721\u001b[0m \u001b[0mlimit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlimit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdatatype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdatatype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mno_header\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mno_header\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/gensim/models/keyedvectors.py\u001b[0m in \u001b[0;36m_load_word2vec_format\u001b[0;34m(cls, fname, fvocab, binary, encoding, unicode_errors, limit, datatype, no_header, binary_chunk_size)\u001b[0m\n\u001b[1;32m 2046\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2047\u001b[0m \u001b[0mlogger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"loading projection weights from %s\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2048\u001b[0;31m \u001b[0;32mwith\u001b[0m \u001b[0mutils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'rb'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mfin\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2049\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mno_header\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2050\u001b[0m \u001b[0;31m# deduce both vocab_size & vector_size from 1st pass over file\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/smart_open/smart_open_lib.py\u001b[0m in \u001b[0;36mopen\u001b[0;34m(uri, mode, buffering, encoding, errors, newline, closefd, opener, compression, transport_params)\u001b[0m\n\u001b[1;32m 175\u001b[0m \u001b[0mtransport_params\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 176\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 177\u001b[0;31m fobj = _shortcut_open(\n\u001b[0m\u001b[1;32m 178\u001b[0m \u001b[0muri\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 179\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/smart_open/smart_open_lib.py\u001b[0m in \u001b[0;36m_shortcut_open\u001b[0;34m(uri, mode, compression, buffering, encoding, errors, newline)\u001b[0m\n\u001b[1;32m 361\u001b[0m \u001b[0mopen_kwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'errors'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 362\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 363\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0m_builtin_open\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlocal_path\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbuffering\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbuffering\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mopen_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 364\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 365\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/content/drive/MyDrive/model/glove.6B.50d.txt'"
]
}
],
"source": [
"glove_file = '/content/drive/MyDrive/model/glove.6B.50d.txt'\n",
"glove_model = KeyedVectors.load_word2vec_format(glove_file, binary=False, no_header=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "o2G0PQ1GjF39"
},
"outputs": [],
"source": [
"# model 10\n",
"base_model = Word2Vec(vector_size=300, min_count=5)\n",
"base_model.build_vocab(df['tokenized_data'])\n",
"total_examples = base_model.corpus_count\n",
"\n",
"# model 50\n",
"base_model_50 = Word2Vec(vector_size=300, min_count=5)\n",
"base_model_50.build_vocab(df['tokenized_data'])\n",
"total_examples = base_model_50.corpus_count"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "TTsWvYPHkeRq"
},
"outputs": [],
"source": [
"base_model.build_vocab([list(glove_model.key_to_index.keys())], update=True)\n",
"\n",
"base_model_50.build_vocab([list(glove_model.key_to_index.keys())], update=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "soBnDVEJpaKL"
},
"outputs": [],
"source": [
"total_examples = len(df['tokenized_data'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "WCelGtEWk2BK",
"outputId": "7b25fc73-b86d-406b-e026-3071e97313d3"
},
"outputs": [
{
"data": {
"text/plain": [
"(8833154, 9980770)"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"base_model.train(df['tokenized_data'], total_examples=total_examples, epochs=10)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Oy3DpnpPxe9g",
"outputId": "8313407b-af6a-407b-abe1-556188c88de7"
},
"outputs": [
{
"data": {
"text/plain": [
"(44164606, 49903850)"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"base_model_50.train(df['tokenized_data'], total_examples=total_examples, epochs=50)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "zF4Qrs-ObWS8",
"outputId": "6a9a160e-91bd-4f3e-e806-14fa501d509a"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Requirement already satisfied: spacy in /usr/local/lib/python3.10/dist-packages (3.7.5)\n",
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"Collecting en-core-web-sm==3.7.1\n",
" Downloading https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.7.1/en_core_web_sm-3.7.1-py3-none-any.whl (12.8 MB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m12.8/12.8 MB\u001b[0m \u001b[31m53.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"Requirement already satisfied: wrapt in /usr/local/lib/python3.10/dist-packages (from smart-open<8.0.0,>=5.2.1->weasel<0.5.0,>=0.1.0->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (1.16.0)\n",
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"\u001b[38;5;2m✔ Download and installation successful\u001b[0m\n",
"You can now load the package via spacy.load('en_core_web_sm')\n",
"\u001b[38;5;3m⚠ Restart to reload dependencies\u001b[0m\n",
"If you are in a Jupyter or Colab notebook, you may need to restart Python in\n",
"order to load all the package's dependencies. You can do this by selecting the\n",
"'Restart kernel' or 'Restart runtime' option.\n"
]
}
],
"source": [
"!pip install spacy\n",
"!python -m spacy download en_core_web_sm"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "BzQ9y9QTpxNc",
"outputId": "0c319f15-6d16-4823-b133-950a608de388"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
"[nltk_data] Unzipping corpora/stopwords.zip.\n",
"[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
"[nltk_data] Downloading package averaged_perceptron_tagger to\n",
"[nltk_data] /root/nltk_data...\n",
"[nltk_data] Unzipping taggers/averaged_perceptron_tagger.zip.\n"
]
}
],
"source": [
"import re\n",
"from nltk.corpus import stopwords\n",
"from nltk.tokenize import word_tokenize\n",
"from nltk.stem import WordNetLemmatizer\n",
"from wordsegment import load, segment\n",
"from num2words import num2words\n",
"from deep_translator import GoogleTranslator\n",
"from nltk.tag import pos_tag\n",
"import spacy\n",
"from sklearn.metrics.pairwise import cosine_similarity\n",
"\n",
"nltk.download('stopwords')\n",
"nltk.download('wordnet')\n",
"nltk.download('averaged_perceptron_tagger')\n",
"\n",
"load()\n",
"translator = GoogleTranslator(source='id', target='en')\n",
"\n",
"nlp = spacy.load(\"en_core_web_sm\")\n",
"\n",
"def split_text(text, max_length):\n",
" \n",
" words = text.split()\n",
" parts = []\n",
" current_part = []\n",
"\n",
" for word in words:\n",
" if len(' '.join(current_part + [word])) <= max_length:\n",
" current_part.append(word)\n",
" else:\n",
" parts.append(' '.join(current_part))\n",
" current_part = [word]\n",
"\n",
" if current_part:\n",
" parts.append(' '.join(current_part))\n",
"\n",
" return parts\n",
"\n",
"def translate_batch(text, max_length=4000):\n",
" \n",
" parts = split_text(text, max_length)\n",
" translated_parts = [translator.translate(part) for part in parts]\n",
" return ' '.join(translated_parts)\n",
"\n",
"def remove_verbs(text):\n",
" doc = nlp(text)\n",
" for token in doc:\n",
" print(f\"{token.text}: {token.pos_}\")\n",
" non_verbs = [token.text for token in doc if token.pos_ not in [\"VERB\"]]\n",
" return ' '.join(non_verbs)\n",
"\n",
"def preprocessing_data(text):\n",
" \n",
" text = translate_batch(text)\n",
"\n",
" text = text.lower()\n",
" text = remove_verbs(text)\n",
"\n",
" text = re.sub(r'http\\S+|www\\S+|https\\S+', '', text, flags=re.MULTILINE)\n",
" text = re.sub(r'[^\\w\\s]', ' ', text)\n",
" text = text.replace('s1', 'bachelor')\n",
" text = text.replace('s2', 'master')\n",
" text = text.replace('s3', 'doctorate')\n",
" text = text.replace('d3', 'associate degree')\n",
" text = text.replace('d4', 'professional degree')\n",
"\n",
" \n",
" pattern = r'\\b\\d+\\b'\n",
"\n",
" def replace_with_words(match):\n",
" number = int(match.group())\n",
" return num2words(number)\n",
"\n",
" text = re.sub(pattern, replace_with_words, text)\n",
"\n",
" \n",
" segmented_text = segment(text)\n",
" text = ' '.join(segmented_text)\n",
"\n",
" text = re.sub(r'[^a-zA-Z0-9\\s]', '', text)\n",
" text = text.replace('\\n', ' ')\n",
" text = text.replace('etc', ' ')\n",
"\n",
" stop_words = set(stopwords.words('english'))\n",
" tokens = word_tokenize(text)\n",
" tokens = [word for word in tokens if word not in stop_words]\n",
"\n",
" lemmatizer = WordNetLemmatizer()\n",
" tokens = [lemmatizer.lemmatize(word) for word in tokens]\n",
"\n",
" preprocessed_text = ' '.join(tokens)\n",
"\n",
" # preprocessed_text = remove_verbs(preprocessed_text)\n",
"\n",
" return preprocessed_text"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "mVpsN_e7qluy"
},
"outputs": [],
"source": [
"job_description = \"responsibility collect process analyze large datasets extract meaningful insights trends generate monitoring report needed ensure seamless monitoring reporting cycle work cross functional teams understand data needs provide relevant insights perform data validation ensure data accuracy integrity collaborate it teams ensure availability reliability data creating database schemas represent support business process stay date industry trends best practices data analysis visualization requirement bachelors degree data science reputable university fresh graduate outstanding performance welcome proficiency data analysis tools software sql excel python r similar experience data visualization tools tableau power bi similar strong analytical problem solving skills excellent attention detail accuracy good communication interpersonal skills ability convey complex data insights non technical audiences\"\n",
"cv = \"I am an analytical and detail-oriented Data Scientist with a strong background in data analysis, visualization, and database management. I possess proficiency in tools such as SQL, Excel, Python, and R, with a keen ability to extract meaningful insights from large datasets. I am an excellent communicator capable of conveying complex data insights to non-technical audiences. As a fresh graduate with outstanding academic performance and hands-on experience in data projects, I am eager to contribute my skills and knowledge to a dynamic team. I hold a Bachelor of Science in Data Science from a reputable university, where I graduated in June 2024 with a GPA of 3.95/4.00. My relevant coursework includes Data Analysis, Data Visualization, SQL, Python Programming, R Programming, and Database Management.\"\n",
"jd2 = \"lead developer fullstack developer using java springboot react job description lead manage team developers providing technical guidance mentorship oversee design development implementation scalable web applications manage prioritize development pipeline monitor evaluate progress implement maintain ci cd pipelines using tools jenkins similar devops tools facilitate agile ceremonies sprint planning daily stand ups retrospectives identify address technical challenges bottlenecks within team resolve incidents ensure system availability within sla conduct code reviews provide constructive feedback team members foster collaborative innovative team environment stay updated emerging technologies industry trends drive continuous improvement education bachelor higher degree computer science related fields experience needed long duration needed experience length service five years professional experience software development least two years leadership role additional expertises need proficiency fullstack development expertise technologies spring boot react js strong understanding restful api design implementation experience databases oracle oracle pl sql familiarity version control systems git ci cd pipelines knowledge agile methodologies practices excellent leadership team management abilities strong problem solving skills attention detail effective communication interpersonal skills ability motivate inspire team members experience cloud services aws azure google cloud knowledge docker container orchestration familiarity project management tools jira confluence\""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "xl90gejPqrMB",
"outputId": "fb105875-8fb1-4a06-c29c-2e941d7866e2"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"responsibility: NOUN\n",
"collect: NOUN\n",
"process: NOUN\n",
"analyze: VERB\n",
"large: ADJ\n",
"datasets: NOUN\n",
"extract: VERB\n",
"meaningful: ADJ\n",
"insights: NOUN\n",
"trends: NOUN\n",
"generate: VERB\n",
"monitoring: NOUN\n",
"report: NOUN\n",
"needed: VERB\n",
"ensure: VERB\n",
"seamless: ADJ\n",
"monitoring: NOUN\n",
"reporting: VERB\n",
"cycle: NOUN\n",
"work: NOUN\n",
"cross: NOUN\n",
"functional: ADJ\n",
"teams: NOUN\n",
"understand: VERB\n",
"data: NOUN\n",
"needs: NOUN\n",
"provide: VERB\n",
"relevant: ADJ\n",
"insights: NOUN\n",
"perform: VERB\n",
"data: NOUN\n",
"validation: NOUN\n",
"ensure: VERB\n",
"data: NOUN\n",
"accuracy: NOUN\n",
"integrity: NOUN\n",
"collaborate: NOUN\n",
"it: PRON\n",
"teams: NOUN\n",
"ensure: VERB\n",
"availability: NOUN\n",
"reliability: NOUN\n",
"data: NOUN\n",
"creating: VERB\n",
"database: NOUN\n",
"schemas: NOUN\n",
"represent: VERB\n",
"support: NOUN\n",
"business: NOUN\n",
"process: NOUN\n",
"stay: VERB\n",
"date: NOUN\n",
"industry: NOUN\n",
"trends: NOUN\n",
"best: ADJ\n",
"practices: NOUN\n",
"data: NOUN\n",
"analysis: NOUN\n",
"visualization: NOUN\n",
"requirements: NOUN\n",
"bachelors: PROPN\n",
"degree: NOUN\n",
"data: NOUN\n",
"science: NOUN\n",
"reputable: ADJ\n",
"university: NOUN\n",
"fresh: ADJ\n",
"graduate: NOUN\n",
"outstanding: ADJ\n",
"performance: NOUN\n",
"welcome: ADJ\n",
"proficiency: NOUN\n",
"data: NOUN\n",
"analysis: NOUN\n",
"tools: NOUN\n",
"software: NOUN\n",
"sql: PROPN\n",
"excel: PROPN\n",
"python: PROPN\n",
"r: VERB\n",
"similar: ADJ\n",
"experience: NOUN\n",
"data: NOUN\n",
"visualization: NOUN\n",
"tools: NOUN\n",
"tableau: PROPN\n",
"power: PROPN\n",
"bi: PROPN\n",
"similar: ADJ\n",
"strong: ADJ\n",
"analytical: ADJ\n",
"problem: NOUN\n",
"solving: VERB\n",
"skills: NOUN\n",
"excellent: ADJ\n",
"attention: NOUN\n",
"detail: NOUN\n",
"accuracy: NOUN\n",
"good: ADJ\n",
"communication: NOUN\n",
"interpersonal: ADJ\n",
"skills: NOUN\n",
"ability: NOUN\n",
"convey: VERB\n",
"complex: ADJ\n",
"data: NOUN\n",
"insights: NOUN\n",
"non: ADJ\n",
"technical: ADJ\n",
"audiences: NOUN\n",
"i: PRON\n",
"am: AUX\n",
"an: DET\n",
"analytical: ADJ\n",
"and: CCONJ\n",
"detail: NOUN\n",
"-: PUNCT\n",
"oriented: VERB\n",
"data: NOUN\n",
"scientist: NOUN\n",
"with: ADP\n",
"a: DET\n",
"strong: ADJ\n",
"background: NOUN\n",
"in: ADP\n",
"data: NOUN\n",
"analysis: NOUN\n",
",: PUNCT\n",
"visualization: NOUN\n",
",: PUNCT\n",
"and: CCONJ\n",
"database: NOUN\n",
"management: NOUN\n",
".: PUNCT\n",
"i: PRON\n",
"possess: VERB\n",
"proficiency: NOUN\n",
"in: ADP\n",
"tools: NOUN\n",
"such: ADJ\n",
"as: ADP\n",
"sql: PROPN\n",
",: PUNCT\n",
"excel: PROPN\n",
",: PUNCT\n",
"python: PROPN\n",
",: PUNCT\n",
"and: CCONJ\n",
"r: NOUN\n",
",: PUNCT\n",
"with: ADP\n",
"a: DET\n",
"keen: ADJ\n",
"ability: NOUN\n",
"to: PART\n",
"extract: VERB\n",
"meaningful: ADJ\n",
"insights: NOUN\n",
"from: ADP\n",
"large: ADJ\n",
"datasets: NOUN\n",
".: PUNCT\n",
"i: PRON\n",
"am: AUX\n",
"an: DET\n",
"excellent: ADJ\n",
"communicator: NOUN\n",
"capable: ADJ\n",
"of: ADP\n",
"conveying: VERB\n",
"complex: ADJ\n",
"data: NOUN\n",
"insights: NOUN\n",
"to: ADP\n",
"non: ADJ\n",
"-: ADJ\n",
"technical: ADJ\n",
"audiences: NOUN\n",
".: PUNCT\n",
"as: ADP\n",
"a: DET\n",
"fresh: ADJ\n",
"graduate: NOUN\n",
"with: ADP\n",
"outstanding: ADJ\n",
"academic: ADJ\n",
"performance: NOUN\n",
"and: CCONJ\n",
"hands: NOUN\n",
"-: PUNCT\n",
"on: ADP\n",
"experience: NOUN\n",
"in: ADP\n",
"data: NOUN\n",
"projects: NOUN\n",
",: PUNCT\n",
"i: PRON\n",
"am: AUX\n",
"eager: ADJ\n",
"to: PART\n",
"contribute: VERB\n",
"my: PRON\n",
"skills: NOUN\n",
"and: CCONJ\n",
"knowledge: NOUN\n",
"to: ADP\n",
"a: DET\n",
"dynamic: ADJ\n",
"team: NOUN\n",
".: PUNCT\n",
"i: PRON\n",
"hold: VERB\n",
"a: DET\n",
"bachelor: NOUN\n",
"of: ADP\n",
"science: NOUN\n",
"in: ADP\n",
"data: NOUN\n",
"science: NOUN\n",
"from: ADP\n",
"a: DET\n",
"reputable: ADJ\n",
"university: NOUN\n",
",: PUNCT\n",
"where: SCONJ\n",
"i: PRON\n",
"graduated: VERB\n",
"in: ADP\n",
"june: PROPN\n",
"2024: NUM\n",
"with: ADP\n",
"a: DET\n",
"gpa: PROPN\n",
"of: ADP\n",
"3.95/4.00: PROPN\n",
".: PUNCT\n",
"my: PRON\n",
"relevant: ADJ\n",
"coursework: NOUN\n",
"includes: VERB\n",
"data: NOUN\n",
"analysis: NOUN\n",
",: PUNCT\n",
"data: NOUN\n",
"visualization: NOUN\n",
",: PUNCT\n",
"sql: PROPN\n",
",: PUNCT\n",
"python: PROPN\n",
"programming: NOUN\n",
",: PUNCT\n",
"r: NOUN\n",
"programming: NOUN\n",
",: PUNCT\n",
"and: CCONJ\n",
"database: NOUN\n",
"management: NOUN\n",
".: PUNCT\n",
"lead: NOUN\n",
"developer: NOUN\n",
"fullstack: NOUN\n",
"developer: NOUN\n",
"using: VERB\n",
"java: PROPN\n",
"springboot: PROPN\n",
"react: PROPN\n",
"job: NOUN\n",
"description: NOUN\n",
"lead: NOUN\n",
"manage: VERB\n",
"team: NOUN\n",
"developers: NOUN\n",
"providing: VERB\n",
"technical: ADJ\n",
"guidance: NOUN\n",
"mentorship: PROPN\n",
"oversee: PROPN\n",
"design: NOUN\n",
"development: NOUN\n",
"implementation: NOUN\n",
"scalable: ADJ\n",
"web: NOUN\n",
"applications: NOUN\n",
"manage: VERB\n",
"prioritize: VERB\n",
"development: NOUN\n",
"pipeline: NOUN\n",
"monitor: NOUN\n",
"evaluate: PROPN\n",
"progress: NOUN\n",
"implement: VERB\n",
"maintain: NOUN\n",
"ci: PROPN\n",
"cd: PROPN\n",
"pipelines: NOUN\n",
"using: VERB\n",
"tools: NOUN\n",
"jenkins: PROPN\n",
"similar: ADJ\n",
"devops: NOUN\n",
"tools: NOUN\n",
"facilitate: VERB\n",
"agile: ADJ\n",
"ceremonies: NOUN\n",
"sprint: NOUN\n",
"planning: NOUN\n",
"daily: ADJ\n",
"stand: NOUN\n",
"ups: NOUN\n",
"retrospectives: NOUN\n",
"identify: VERB\n",
"address: NOUN\n",
"technical: ADJ\n",
"challenges: NOUN\n",
"bottlenecks: NOUN\n",
"within: ADP\n",
"team: NOUN\n",
"resolve: NOUN\n",
"incidents: NOUN\n",
"ensure: VERB\n",
"system: NOUN\n",
"availability: NOUN\n",
"within: ADP\n",
"sla: NOUN\n",
"conduct: NOUN\n",
"code: NOUN\n",
"reviews: NOUN\n",
"provide: VERB\n",
"constructive: ADJ\n",
"feedback: NOUN\n",
"team: NOUN\n",
"members: NOUN\n",
"foster: VERB\n",
"collaborative: ADJ\n",
"innovative: ADJ\n",
"team: NOUN\n",
"environment: NOUN\n",
"stay: VERB\n",
"updated: VERB\n",
"emerging: VERB\n",
"technologies: NOUN\n",
"industry: NOUN\n",
"trends: NOUN\n",
"drive: VERB\n",
"continuous: ADJ\n",
"improvement: NOUN\n",
"education: NOUN\n",
"bachelor: NOUN\n",
"higher: ADJ\n",
"degree: NOUN\n",
"computer: NOUN\n",
"science: NOUN\n",
"related: VERB\n",
"fields: NOUN\n",
"experience: NOUN\n",
"needed: VERB\n",
"long: ADJ\n",
"duration: NOUN\n",
"required: VERB\n",
"experience: NOUN\n",
"length: NOUN\n",
"service: NOUN\n",
"five: NUM\n",
"years: NOUN\n",
"professional: ADJ\n",
"experience: NOUN\n",
"software: NOUN\n",
"development: NOUN\n",
"at: ADP\n",
"least: ADV\n",
"two: NUM\n",
"years: NOUN\n",
"leadership: NOUN\n",
"role: NOUN\n",
"additional: ADJ\n",
"expertises: NOUN\n",
"need: VERB\n",
"proficiency: NOUN\n",
"fullstack: NOUN\n",
"development: NOUN\n",
"expertise: NOUN\n",
"technologies: NOUN\n",
"spring: NOUN\n",
"boot: PROPN\n",
"react: VERB\n",
"js: PROPN\n",
"strong: ADJ\n",
"understanding: VERB\n",
"restful: ADJ\n",
"api: NOUN\n",
"design: NOUN\n",
"implementation: NOUN\n",
"experience: NOUN\n",
"databases: VERB\n",
"oracle: NOUN\n",
"oracle: NOUN\n",
"pl: PROPN\n",
"sql: PROPN\n",
"familiarity: PROPN\n",
"version: PROPN\n",
"control: PROPN\n",
"systems: PROPN\n",
"git: PROPN\n",
"ci: PROPN\n",
"cd: PROPN\n",
"pipelines: VERB\n",
"knowledge: NOUN\n",
"agile: ADJ\n",
"methodologies: NOUN\n",
"practices: VERB\n",
"excellent: ADJ\n",
"leadership: NOUN\n",
"team: NOUN\n",
"management: NOUN\n",
"abilities: VERB\n",
"strong: ADJ\n",
"problem: NOUN\n",
"solving: VERB\n",
"skills: NOUN\n",
"attention: NOUN\n",
"detail: NOUN\n",
"effective: ADJ\n",
"communication: NOUN\n",
"interpersonal: ADJ\n",
"skills: NOUN\n",
"ability: NOUN\n",
"motivate: VERB\n",
"inspire: NOUN\n",
"team: NOUN\n",
"members: PROPN\n",
"experience: VERB\n",
"cloud: PROPN\n",
"services: PROPN\n",
"aws: NOUN\n",
"azure: VERB\n",
"google: PROPN\n",
"cloud: PROPN\n",
"knowledge: PROPN\n",
"docker: PROPN\n",
"container: NOUN\n",
"orchestration: NOUN\n",
"familiarity: NOUN\n",
"project: NOUN\n",
"management: NOUN\n",
"tools: NOUN\n",
"jira: PROPN\n",
"confluence: NOUN\n",
"responsibility collect process large data set meaningful insight trend monitoring report seamless monitoring cycle work cross functional team data need relevant insight data validation data accuracy integrity collaborate team availability reliability data database schema support business process date industry trend best practice data analysis visualization requirement bachelor degree data science reputable university fresh graduate outstanding performance welcome proficiency data analysis tool software sql excel python similar experience data visualization tool tableau power bisimilar strong analytical problem skill excellent attention detail accuracy good communication interpersonal skill ability complex data insight nontechnical audience\n",
"analytical detail data scientist strong background data analysis visualization database management proficiency tool sql excel python r keen ability meaningful insight large data set excellent communicator capable complex data insight nontechnical audience fresh graduate outstanding academic performance hand experience data project eager skill knowledge dynamic team bachelor science data science reputable university june two thousand twenty four gpa three ninety five four zero relevant coursework data analysis data visualization sql python programming r programming database management\n",
"lead developer full stack developer java spring boot react job description lead team developer technical guidance mentorship oversee design development implementation scalable web application development pipeline monitor evaluate progress maintain ci cd pipeline tool jenkins similar dev ops tool agile ceremony sprint planning daily standups retrospective address technical challenge bottleneck within team resolve incident system availability within sla conduct code review constructive feedback team member collaborative innovative team environment technology industry trend continuous improvement education bachelor higher degree computer science field experience long duration experience length service five year professional experience software development least two year leadership role additional expertise proficiency full stack development expertise technology spring boot j strong restful api design implementation experience oracle oracle plsql familiarity version control system gitc icd knowledge agile methodology excellent leadership team management strong problem skill attention detail effective communication interpersonal skill ability inspire team member cloud service saw google cloud knowledge docker container orchestration familiarity project management tool jira confluence\n"
]
}
],
"source": [
"jd = preprocessing_data(job_description)\n",
"cv = preprocessing_data(cv)\n",
"jd2 = preprocessing_data(jd2)\n",
"\n",
"print(jd)\n",
"print(cv)\n",
"print(jd2)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "8X9CSPhNlKi6"
},
"outputs": [],
"source": [
"def average_vector(sentence, model):\n",
" words = sentence.split()\n",
" vector_list = []\n",
"\n",
" for word in words:\n",
" if word in model.wv:\n",
" vector_list.append(model.wv[word])\n",
"\n",
" if not vector_list:\n",
" return np.zeros(model.vector_size)\n",
"\n",
" return np.mean(vector_list, axis=0)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "0ZB4wzXi4sd-"
},
"outputs": [],
"source": [
"def word_contributions(sentence1, sentence2, model):\n",
" words1 = sentence1.split()\n",
" words2 = sentence2.split()\n",
"\n",
" common_words = set(words1).intersection(words2)\n",
"\n",
" contributions = {}\n",
" for word in common_words:\n",
" if word in model.wv:\n",
" vec_word = model.wv[word].reshape(1, -1)\n",
" vec_sentence1 = average_vector(sentence1, model).reshape(1, -1)\n",
" vec_sentence2 = average_vector(sentence2, model).reshape(1, -1)\n",
"\n",
" sim1 = cosine_similarity(vec_word, vec_sentence1)[0][0]\n",
" sim2 = cosine_similarity(vec_word, vec_sentence2)[0][0]\n",
"\n",
" contributions[word] = (sim1 + sim2) / 2\n",
"\n",
" return contributions"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "7zwJ4JH2l5hf"
},
"outputs": [],
"source": [
"\n",
"vector1 = average_vector(jd, model10)\n",
"vector2 = average_vector(jd2, model10)\n",
"vector3 = average_vector(cv, model10)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "aI9onNmNmLNI",
"outputId": "60b96e3e-6a77-4bc4-accd-462b911e9821"
},
"outputs": [],
"source": [
"similarity = cosine_similarity([vector1], [vector3])\n",
"similarity2 = cosine_similarity([vector2], [vector3])\n",
"\n",
"print(\"for 10 epoch:\")\n",
"print(f\"Similarities between the sentences jd and cv: {similarity[0][0]}\")\n",
"print(f\"Similarities between the sentences jd2 and cv: {similarity2[0][0]}\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "dQ8slGp04zv9",
"outputId": "139b4f47-652c-4eda-ffcf-1f02e5738453"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Kata-kata yang membuat kalimat jd dan cv serupa:\n",
"detail: 0.1361837536096573\n",
"experience: 0.45516836643218994\n",
"bachelor: 0.21932366490364075\n",
"database: 0.40236592292785645\n",
"visualization: 0.47202038764953613\n",
"excellent: 0.3385521173477173\n",
"sql: 0.4556054174900055\n",
"reputable: 0.09687164425849915\n",
"large: 0.3640759587287903\n",
"team: 0.1939774453639984\n",
"graduate: 0.11058397591114044\n",
"outstanding: 0.14738258719444275\n",
"fresh: 0.03831607103347778\n",
"meaningful: 0.1543671190738678\n",
"tool: 0.41531068086624146\n",
"analysis: 0.5109637379646301\n",
"data: 0.6104699969291687\n",
"ability: 0.40025365352630615\n",
"python: 0.3781082034111023\n",
"strong: 0.435779333114624\n",
"university: 0.051478054374456406\n",
"set: 0.2821767032146454\n",
"audience: 0.2184278964996338\n",
"analytical: 0.5181758403778076\n",
"nontechnical: 0.12228911370038986\n",
"insight: 0.36665111780166626\n",
"excel: 0.2563852071762085\n",
"science: 0.3380487561225891\n",
"skill: 0.45019614696502686\n",
"proficiency: 0.3761020302772522\n",
"relevant: 0.38866573572158813\n",
"performance: 0.17087388038635254\n",
"complex: 0.39957892894744873\n",
"\n",
"Kata-kata yang membuat kalimat jd2 dan cv serupa:\n",
"detail: 0.11331835389137268\n",
"experience: 0.5286638736724854\n",
"knowledge: 0.4604893922805786\n",
"bachelor: 0.2222256362438202\n",
"skill: 0.42056500911712646\n",
"strong: 0.4345294237136841\n",
"proficiency: 0.374183714389801\n",
"project: 0.3895094394683838\n",
"team: 0.24781064689159393\n",
"excellent: 0.33576133847236633\n",
"two: 0.2048722803592682\n",
"tool: 0.39538338780403137\n",
"science: 0.3574932813644409\n",
"management: 0.42703765630722046\n",
"ability: 0.36876180768013\n",
"five: 0.2041117548942566\n"
]
}
],
"source": [
"contributions1 = word_contributions(jd, cv, model10)\n",
"contributions2 = word_contributions(jd2, cv, model10)\n",
"\n",
"print(\"\\nWords that make the sentences jd and cv similar:\")\n",
"for word, contribution in contributions1.items():\n",
" print(f\"{word}: {contribution}\")\n",
"\n",
"print(\"\\nWords that make the sentences jd2 and cv similar:\")\n",
"for word, contribution in contributions2.items():\n",
" print(f\"{word}: {contribution}\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "3L4w9rUEzI9d"
},
"outputs": [],
"source": [
"\n",
"vector1_50 = average_vector(jd, model50)\n",
"vector2_50 = average_vector(jd2, model50)\n",
"vector3_50 = average_vector(cv, model50)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "dKzC8rL7zQ-U",
"outputId": "68c803cc-9e63-4c7b-93eb-96bf707eb968"
},
"outputs": [],
"source": [
"similarity_50 = cosine_similarity([vector1_50], [vector3_50])\n",
"similarity2_50 = cosine_similarity([vector2_50], [vector3_50])\n",
"\n",
"print(\"for 50 epoch:\")\n",
"print(f\"Similarities between the sentences jd and cv: {similarity_50[0][0]}\")\n",
"print(f\"Similarities between the sentences jd2 and cv: {similarity2_50[0][0]}\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "jpc_OwQSP4mp",
"outputId": "bdb5c912-cf21-4ad0-d761-e81d90063a81"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Kata-kata yang membuat kalimat jd dan cv serupa:\n",
"database: 0.3318568468093872\n",
"communication: 0.1957397758960724\n",
"ability: 0.29015493392944336\n",
"problem: 0.18861272931098938\n",
"skill: 0.3442988693714142\n",
"science: 0.2926821708679199\n",
"bachelor: 0.12361519038677216\n",
"experience: 0.38176393508911133\n",
"team: 0.15643739700317383\n",
"software: 0.45441168546676636\n",
"university: 0.044621068984270096\n",
"\n",
"Kata-kata yang membuat kalimat jd2 dan cv serupa:\n",
"stack: 0.35122960805892944\n",
"five: 0.1811729371547699\n",
"member: 0.12175917625427246\n",
"management: 0.3893547058105469\n",
"problem: 0.10825730860233307\n",
"lead: 0.15303266048431396\n",
"full: 0.08783716708421707\n",
"development: 0.4594123959541321\n",
"boot: 0.27243170142173767\n",
"role: 0.2007565200328827\n",
"ability: 0.2722695469856262\n",
"skill: 0.33186769485473633\n",
"computer: 0.23201048374176025\n",
"team: 0.24185842275619507\n",
"design: 0.3907355070114136\n",
"software: 0.5308446288108826\n",
"implementation: 0.315414160490036\n",
"two: 0.18774710595607758\n",
"communication: 0.22178637981414795\n",
"web: 0.30469974875450134\n",
"spring: 0.3152084946632385\n",
"science: 0.32246607542037964\n",
"pipeline: 0.24943339824676514\n",
"react: 0.34650981426239014\n",
"developer: 0.42738795280456543\n",
"cloud: 0.31983423233032227\n",
"methodology: 0.3160865306854248\n",
"application: 0.2910619378089905\n",
"year: 0.34758204221725464\n",
"j: 0.15048830211162567\n",
"experience: 0.49432259798049927\n",
"bachelor: 0.15085121989250183\n",
"leadership: 0.2859252095222473\n"
]
}
],
"source": [
"contributions1 = word_contributions(jd, cv, model50)\n",
"contributions2 = word_contributions(jd2, cv, model50)\n",
"\n",
"print(\"\\nWords that make the sentences jd and cv similar:\")\n",
"for word, contribution in contributions1.items():\n",
" print(f\"{word}: {contribution}\")\n",
"\n",
"print(\"\\nWords that make the sentences jd2 and cv similar:\")\n",
"for word, contribution in contributions2.items():\n",
" print(f\"{word}: {contribution}\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ZMVZNYzIu_gD"
},
"source": [
"# Pre trained"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "76dw10l9u-s0",
"outputId": "a661772f-4e65-4f49-9faf-e6bd1798f565"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Requirement already satisfied: torchtext in /usr/local/lib/python3.10/dist-packages (0.18.0)\n",
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"Collecting nvidia-cuda-nvrtc-cu12==12.1.105 (from torch>=2.3.0->torchtext)\n",
" Using cached nvidia_cuda_nvrtc_cu12-12.1.105-py3-none-manylinux1_x86_64.whl.metadata (1.5 kB)\n",
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"Collecting nvidia-cudnn-cu12==8.9.2.26 (from torch>=2.3.0->torchtext)\n",
" Using cached nvidia_cudnn_cu12-8.9.2.26-py3-none-manylinux1_x86_64.whl.metadata (1.6 kB)\n",
"Collecting nvidia-cublas-cu12==12.1.3.1 (from torch>=2.3.0->torchtext)\n",
" Using cached nvidia_cublas_cu12-12.1.3.1-py3-none-manylinux1_x86_64.whl.metadata (1.5 kB)\n",
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" Using cached nvidia_cusparse_cu12-12.1.0.106-py3-none-manylinux1_x86_64.whl.metadata (1.6 kB)\n",
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" Using cached nvidia_nccl_cu12-2.20.5-py3-none-manylinux2014_x86_64.whl.metadata (1.8 kB)\n",
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"Downloading nvidia_nvjitlink_cu12-12.6.20-py3-none-manylinux2014_x86_64.whl (19.7 MB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m19.7/19.7 MB\u001b[0m \u001b[31m11.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hInstalling collected packages: nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, nvidia-cusparse-cu12, nvidia-cudnn-cu12, nvidia-cusolver-cu12\n",
"Successfully installed nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.20.5 nvidia-nvjitlink-cu12-12.6.20 nvidia-nvtx-cu12-12.1.105\n"
]
}
],
"source": [
"!pip install torchtext"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "8gLDX6wluN1V",
"outputId": "14aaa32c-7c63-40b5-df3c-b60c3a39bc2f"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/local/lib/python3.10/dist-packages/torchtext/vocab/__init__.py:4: UserWarning: \n",
"/!\\ IMPORTANT WARNING ABOUT TORCHTEXT STATUS /!\\ \n",
"Torchtext is deprecated and the last released version will be 0.18 (this one). You can silence this warning by calling the following at the beginnign of your scripts: `import torchtext; torchtext.disable_torchtext_deprecation_warning()`\n",
" warnings.warn(torchtext._TORCHTEXT_DEPRECATION_MSG)\n",
"/usr/local/lib/python3.10/dist-packages/torchtext/utils.py:4: UserWarning: \n",
"/!\\ IMPORTANT WARNING ABOUT TORCHTEXT STATUS /!\\ \n",
"Torchtext is deprecated and the last released version will be 0.18 (this one). You can silence this warning by calling the following at the beginnign of your scripts: `import torchtext; torchtext.disable_torchtext_deprecation_warning()`\n",
" warnings.warn(torchtext._TORCHTEXT_DEPRECATION_MSG)\n",
".vector_cache/glove.6B.zip: 862MB [02:39, 5.41MB/s]\n",
"100%|█████████▉| 399999/400000 [00:17<00:00, 22483.33it/s]\n"
]
}
],
"source": [
"import torch\n",
"from torchtext.vocab import GloVe\n",
"\n",
"\n",
"glove = GloVe(name='6B', dim=50)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "-LPUmU8pQGNf",
"outputId": "7ca6f460-8ba8-4810-8b44-dd78c0b0519c"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Most common words in GloVe corpus:\n",
"non-families: 14.121562004089355\n",
"202-383-7824: 14.019739151000977\n",
"non-institutionalized: 12.844210624694824\n",
"www.star: 12.790447235107422\n",
"non-obligatory: 12.76219367980957\n",
"officership: 12.511541366577148\n",
"republish: 12.49128532409668\n",
"http://www.nyse.com: 12.43392276763916\n",
"20003: 12.401117324829102\n",
"25-64: 12.296163558959961\n",
"puhs: 12.197233200073242\n",
"dehl: 12.08194351196289\n",
"afptv: 12.08087158203125\n",
"eighteens: 12.039584159851074\n",
"computerologist: 12.01170825958252\n",
"http://www.nasdaq.com: 11.865079879760742\n",
"palaeographically: 11.725377082824707\n",
"story3d: 11.685689926147461\n",
"hushen: 11.67656421661377\n",
"warmian-masurian: 11.49421501159668\n"
]
}
],
"source": [
"from collections import Counter\n",
"\n",
"vocab = glove.stoi\n",
"\n",
"# Count the occurrences of words (this is a proxy as actual frequencies are not provided in GloVe)\n",
"# We use the norms of the vectors to approximate word frequency\n",
"word_freq = Counter({word: glove.vectors[idx].norm().item() for word, idx in vocab.items()})\n",
"\n",
"# Print the 20 most common words\n",
"print(\"Most common words in GloVe corpus:\")\n",
"for word, freq in word_freq.most_common(20):\n",
" print(f\"{word}: {freq}\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "nixKAxv1Pxe2"
},
"outputs": [],
"source": [
"important_words = {\n",
" 'python': 10.0,\n",
" 'machine learning': 2.0,\n",
"}\n",
"\n",
"def average_vector_weighted(sentence, glove, important_words):\n",
" words = sentence.split()\n",
" vector_list = []\n",
"\n",
" for word in words:\n",
" if word in glove.stoi:\n",
" vector = glove[word].numpy()\n",
" weight = important_words.get(word, 1.0) \n",
" vector_list.append(vector * weight)\n",
"\n",
" if not vector_list: \n",
" return np.zeros(glove.dim)\n",
"\n",
" \n",
" return np.mean(vector_list, axis=0)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "attxEAHaESA1"
},
"outputs": [],
"source": [
"def average_vector_pre(sentence, glove):\n",
" words = sentence.split()\n",
" vector_list = []\n",
"\n",
" for word in words:\n",
" \n",
" if word in glove.stoi:\n",
" vector_list.append(glove[word].numpy())\n",
"\n",
" if not vector_list: \n",
" return np.zeros(glove.dim)\n",
"\n",
" \n",
" return np.mean(vector_list, axis=0)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "8nnF8iiQEhzq"
},
"outputs": [],
"source": [
"def word_contributions_pre(sentence1, sentence2, model):\n",
" words1 = sentence1.split()\n",
" words2 = sentence2.split()\n",
"\n",
" common_words = set(words1).intersection(words2)\n",
"\n",
" contributions = {}\n",
" for word in common_words:\n",
" if word in model.stoi:\n",
" vec_word = model[word].reshape(1, -1)\n",
" vec_sentence1 = average_vector_pre(sentence1, model).reshape(1, -1)\n",
" vec_sentence2 = average_vector_pre(sentence2, model).reshape(1, -1)\n",
"\n",
" sim1 = cosine_similarity(vec_word, vec_sentence1)[0][0]\n",
" sim2 = cosine_similarity(vec_word, vec_sentence2)[0][0]\n",
"\n",
" contributions[word] = (sim1 + sim2) / 2\n",
"\n",
" return contributions"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "9gfQBLgf7-lp"
},
"outputs": [],
"source": [
"def word_contributions_weighted(sentence1, sentence2, glove, important_words):\n",
" words1 = sentence1.split()\n",
" words2 = sentence2.split()\n",
"\n",
" common_words = set(words1).intersection(words2)\n",
" contributions = {}\n",
"\n",
" for word in common_words:\n",
" if word in glove.stoi:\n",
" vec_word = glove[word].numpy().reshape(1, -1)\n",
" vec_sentence1 = average_vector_weighted(sentence1, glove, important_words).reshape(1, -1)\n",
" vec_sentence2 = average_vector_weighted(sentence2, glove, important_words).reshape(1, -1)\n",
"\n",
" sim1 = cosine_similarity(vec_word, vec_sentence1)[0][0]\n",
" sim2 = cosine_similarity(vec_word, vec_sentence2)[0][0]\n",
"\n",
" contributions[word] = (sim1 + sim2) / 2\n",
"\n",
" return contributions"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "2xlhtONNP3He"
},
"outputs": [],
"source": [
"\n",
"vector1 = average_vector_pre(jd, glove)\n",
"vector2 = average_vector_pre(jd2, glove)\n",
"vector3 = average_vector_pre(cv, glove)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "wbM1dLrVP6Qu",
"outputId": "11802d93-2a37-414d-e106-0a8ba7042d09"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Kesamaan antara kalimat cv dan jd: 0.9543687701225281\n",
"Kesamaan antara kalimat cv dan jd2: 0.9222950339317322\n"
]
}
],
"source": [
"similarity = cosine_similarity([vector1], [vector3])\n",
"similarity2 = cosine_similarity([vector2], [vector3])\n",
"\n",
"print(f\"Similarities between the sentences jd and cv: {similarity[0][0]}\")\n",
"print(f\"Similarities between the sentences jd2 and cv: {similarity2[0][0]}\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "I2u9nn7T7_zn",
"outputId": "dd6b81e9-b0fa-418a-adf9-2d8e98ef880b"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Kata-kata yang membuat kalimat cv dan jd:\n",
"ability: 0.7643511295318604\n",
"performance: 0.718897819519043\n",
"skill: 0.6555231213569641\n",
"tool: 0.7696131467819214\n",
"data: 0.845285177230835\n",
"analysis: 0.8356708288192749\n",
"insight: 0.6457037925720215\n",
"python: 0.2664954364299774\n",
"audience: 0.533589243888855\n",
"bachelor: 0.4542832374572754\n",
"experience: 0.7736330628395081\n",
"university: 0.5383615493774414\n",
"science: 0.7498667240142822\n",
"database: 0.6892017126083374\n",
"proficiency: 0.5264156460762024\n",
"r: 0.3164910674095154\n",
"graduate: 0.5655832290649414\n",
"visualization: 0.5565621256828308\n",
"team: 0.5537725687026978\n",
"excel: 0.5223063230514526\n",
"\n",
"Kata-kata yang membuat kalimat cv dan jd2:\n",
"science: 0.7411800622940063\n",
"ability: 0.763823926448822\n",
"five: 0.6307790279388428\n",
"skill: 0.6411377191543579\n",
"team: 0.6085482835769653\n",
"bachelor: 0.47072142362594604\n",
"tool: 0.7459321022033691\n",
"project: 0.730865478515625\n",
"experience: 0.7834159135818481\n",
"two: 0.6611106395721436\n",
"proficiency: 0.4753587245941162\n",
"management: 0.7758132219314575\n",
"knowledge: 0.7968839406967163\n"
]
}
],
"source": [
"contributions1 = word_contributions_pre(jd, cv, glove)\n",
"contributions2 = word_contributions_pre(jd2, cv, glove)\n",
"\n",
"print(\"\\nSimilarities between the sentences jd and cv:\")\n",
"for word, contribution in contributions1.items():\n",
" print(f\"{word}: {contribution}\")\n",
"\n",
"print(\"\\nSimilarities between the sentences jd2 and cv:\")\n",
"for word, contribution in contributions2.items():\n",
" print(f\"{word}: {contribution}\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "q_wBZ_4Wx5Vu"
},
"source": [
"# Visualization"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 872
},
"id": "NdEufAPKx4an",
"outputId": "36286e7a-515f-4ad8-ffcb-06a52b871e10"
},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 1000x1000 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from sklearn.decomposition import PCA\n",
"import numpy as np\n",
"\n",
"\n",
"words = [\n",
" 'software engineer', 'data scientist', 'project manager', 'machine learning', 'deep learning',\n",
" 'web developer', 'mobile developer', 'backend engineer', 'frontend developer', 'data analyst',\n",
" 'product manager', 'quality assurance', 'devops engineer', 'system administrator',\n",
" 'network engineer', 'technical support', 'security analyst', 'database administrator',\n",
" 'IT consultant', 'business analyst', 'full stack developer', 'cloud engineer',\n",
" 'user experience designer', 'graphic designer', 'content writer'\n",
"]\n",
"\n",
"\n",
"def get_average_vector(words, model):\n",
" word_vectors = []\n",
" for word in words.split():\n",
" if word in model.wv:\n",
" word_vectors.append(model.wv[word])\n",
" if word_vectors:\n",
" return np.mean(word_vectors, axis=0)\n",
" else:\n",
" return np.zeros(model.vector_size) \n",
"\n",
"\n",
"word_vectors = [get_average_vector(word, base_model) for word in words]\n",
"\n",
"\n",
"pca = PCA(n_components=2)\n",
"result = pca.fit_transform(word_vectors)\n",
"\n",
"\n",
"import matplotlib.pyplot as plt\n",
"\n",
"plt.figure(figsize=(10, 10))\n",
"for i, word in enumerate(words):\n",
" plt.scatter(result[i, 0], result[i, 1])\n",
" plt.text(result[i, 0] + 0.02, result[i, 1] + 0.02, word, fontsize=9)\n",
"plt.xlabel('PCA Component 1')\n",
"plt.ylabel('PCA Component 2')\n",
"plt.title('PCA of Word Vectors 10 Epochs Model')\n",
"plt.grid()\n",
"plt.show()\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 872
},
"id": "VZ5IrEzzzoGE",
"outputId": "f852656a-1cb7-4889-e5c1-0c8e38c5fb26"
},
"outputs": [
{
"data": {
"image/png": 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6dGmr/3Mu3bjUNnP9nbj5vk/DMLR161bVr19fklSpUiV5eHjop59+0vXr17Vjxw5L+3Pnzunq1asqW7Zslj7Lly+vjIyMLJfOlihRIssxSVLp0qWtljs6OloF2lvx9vZW69attXTpUsuTRRcsWCAHBwc99dRTkmQJz82aNZOPj4/Vz5o1axQXFydJlmCS0716t3P8+HHZ2dmpVKlSVsv9/f1lNpuzfEd/Px/SjTBdv359Pfvss/Lz81O3bt305Zdf5jmIZsoMvuvWrbMsc3FxyXIPqiTL+cvNQ4/s7e3VokWLLD9hYWFZ2v79+3Vzc1NAQIDlcufM8/L3seTk5KRHHnnEsj7z0vfcfD+FChXKcrm5l5eX1X2QgwcPVpkyZdS2bVsVL15c/fr10+rVq2/b99/16NFDa9euVUxMjJYtW5bjJbc5HacklStXzrI+pz8X2W17+PBhJSQkyNfXN8vYTkpKsoxtAHiQ8bRbAAVerVq1LE+7zU65cuW0e/dupaSkZHnNQnYyZzczA9Hf/fjjj2ratOmdFXsXqlatKnd3d23ZskXt2rXTxYsXLTOfdnZ2ql27trZs2aKSJUsqJSUlV6/hyMndPsk1J7169dLKlSu1cuVKdezYUUuWLFGrVq0s4SMzuM2bN0/+/v5ZtndwyN+/tnIzSyplfz5cXFy0adMmbdiwQatWrdLq1au1ePFiNWvWTGvWrMnzq0yCgoIkSRcvXrQsCwgIyPYBN6dPn5aU/atZCprcnCdfX1/t3r1b33//vb777jt99913mjt3rvr06WP1IK/b6dixo5ydnfX0008rOTk5xz/jtpCRkXHLqydyut8bAB4khE8AD7zw8HD9/PPPWrJkidWlsNm5cuWKli9frq5du2b7gKIXXnhB8+fPv2X4DAkJ0e+//66MjAyr2c+DBw9a1t8Je3t71alTRz/99JO2bNlieeJnpnr16mnx4sWW2bzM8Onj4yNXV1cdOnQoS58HDx6UnZ2dJfjc6pikG7M3mU8TlqTU1FQdO3ZMVatWzdUxdOzYUe7u7lqwYIEcHR116dIlq6fclixZUtKNsNGiRYsc+8mcbd27d+8t95dTuAwJCVFGRoYOHz5smZGWpLNnzyo+Pj7X35GdnZ2aN2+u5s2ba9q0aZo0aZJee+01bdiw4Zb1ZydzNvfmEBIWFqYNGzYoMTHR6qFDmQ+Jym728m4cPnzYamwnJSXp9OnTateunaT/jYNDhw5ZzXinpKTo2LFjlmPO/B737t2b5/OQEycnJ4WHhys8PFwZGRkaPHiwPvroI40ZMybLDHZOXFxc1KlTJ33xxRdq27atihYtmm27m4/z5vGeuSxz/c1/Lv7u73/eSpYsqXXr1ql+/fo2+8cdALjfcdktgAfewIEDFRAQoJdeekl//vlnlvVxcXGaOHGiJGnp0qW6cuWKhgwZoieffDLLT4cOHbRkyZJsL4XM1K5dO505c0aLFy+2LEtLS9P7778vNzc3NW7c+I6PpUGDBjp37pzmzp2r2rVrW4XbevXq6dChQ1q+fLm8vb0tocre3l6tWrXS8uXLrZ4We/bsWS1YsEANGjTI8jTVv3v00Ufl4+OjDz/8UCkpKZblkZGR2b5iIycuLi56/PHHFRUVpdmzZ6tw4cJW711s3bq1PDw8NGnSpGyf/nnu3DlJNwJao0aN9N///lcxMTFWbYybnsya+X7Sv9eYGaamT59utXzatGmSbtzbdzs3z1BmygyDtxofiYmJWdYbhmEZgzdfMv7kk08qPT3d6smvycnJlu//dv9okFcff/yx1XmfPXu20tLSLO+WbdGihZycnPTee+9ZnedPP/1UCQkJlvNWvXp1lShRQtOnT89y7m/eLrcuXLhg9dnOzk5VqlSRdOtznZ0RI0Zo7NixGjNmTI5tHn30Ufn6+urDDz+06v+7777TgQMHLMcZEBCgsLAwffbZZ0pISLC0W7t2rfbv32/V51NPPaX09HS9+eabWfaXlpaWpz9HAFBQMfMJ4IHn5eWlpUuXql27dgoLC1OvXr1Uo0YNSdLOnTu1cOFC1a1bV9KNS269vb0tl7P+XceOHTVnzhytWrVKnTt3zrbNc889p48++kgRERHasWOHQkND9fXXX+unn37S9OnT8/Two7/LnM38+eefNW7cOKt1derUkclk0rZt2xQeHm416zdx4kTLOykHDx4sBwcHffTRR0pOTtbbb7992/06Ojpq4sSJGjBggJo1a6auXbvq2LFjmjt3bq7v+czUq1cvff755/r+++/Vs2dPS0CUbtyfO3v2bPXu3VvVq1dXt27d5OPjo5iYGK1atUr169fXBx98IEl677331KBBA1WvXl3PPfecSpQooejoaK1atcryCpLM7/m1115Tt27d5OjoqPDwcFWtWlVPP/20Pv74Y8XHx6tx48b69ddf9dlnn6lTp065uqx6woQJ2rRpk9q3b6+QkBDFxcVp1qxZKl68+C0ved65c6e6d++u7t27q1SpUrp27ZqWLl2qn376Sc8995yqV69uaVu7dm116dJFo0aNUlxcnEqVKqXPPvtM0dHR+vTTT3N1vtPS0vTFF19ku+7xxx+3Ov8pKSlq3ry5nnrqKR06dEizZs1SgwYN1LFjR0k3Qv+oUaM0fvx4tWnTRh07drS0q1mzpnr16iXpRjicPXu2wsPDFRYWpr59+yogIEAHDx7Uvn379P333+eq9kzPPvusLl68qGbNmql48eI6fvy43n//fYWFhVnNXOdG1apVbztT7+joqLfeekt9+/ZV48aN1b17d509e1YzZsxQaGio/vWvf1naTp48We3bt1eDBg3Ur18/Xbx4Ue+//74qVqxo9Q7Xxo0ba8CAAZo8ebJ2796tVq1aydHRUYcPH9ZXX32lGTNm5Ol1UABQIN3TZ+0CwF3IfD3E9u3bc9X+1KlTxr/+9S+jTJkyRqFChQxXV1ejRo0axr///W8jISHBOHv2rOHg4GD07t07xz6uXr1quLq6Go8//vgt93X27Fmjb9++RtGiRQ0nJyejcuXKxty5c7O0y8urVgzDMK5cuWI4ODgYkow1a9ZkWV+lShVDkvHWW29lWbdz506jdevWhpubm+Hq6mo0bdrU2Lp1q1Wb253TWbNmGSVKlDCcnZ2NRx991Ni0aVOeXwuRlpZmBAQEGJKMqKiobNts2LDBaN26teHp6WkUKlTIKFmypBEREWH89ttvVu327t1rPP7444bZbDYKFSpklC1b1hgzZoxVmzfffNMoVqyYYWdnZ/XaldTUVGP8+PFGiRIlDEdHRyMoKMgYNWqUcf36davtc/qOfvjhB+Oxxx4zAgMDDScnJyMwMNDo3r278eeff97y+P/66y+jS5cuRmhoqNU4/PDDD61eQ5Lp2rVrxogRIwx/f3/D2dnZqFmzZpbXvuTkVq9auflcZH7vP/74o/Hcc88ZXl5ehpubm9GzZ0/jwoULWfr94IMPjHLlyhmOjo6Gn5+fMWjQoCyvVDEMw9iyZYvRsmVLw93d3ShcuLBRpUoV4/3337eqr3Dhwlm2y3z1Saavv/7aaNWqleHr62s4OTkZwcHBxoABA4zTp0/f9hzo/1+1cit/f9VKpsWLFxvVqlUznJ2djSJFihg9e/a0vMLpZkuWLDHKly9vODs7GxUqVDC++eYb4+mnn7Z61Uqmjz/+2KhRo4bh4uJiuLu7G5UrVzZefvll49SpU5Y2vGoFwIPKZBh3cP0LAAB4YERGRqpv377avn37LR/eBQDA3eCeTwAAAACAzRE+AQAAAAA2R/gEAAAAANgc93wCAAAAAGyOmU8AAAAAgM0RPgEAAAAANudwrwu432VkZOjUqVNyd3e3emE7AAAAgIeLYRi6fPmyAgMDZWfHPF5eET5v49SpUwoKCrrXZQAAAAC4T5w4cULFixe/12UUOITP23B3d5d0Y4B5eHjcUR+pqalas2aNWrVqJUdHx/wsDw8hxhPyG2MK+YnxhPzGmEJ+utvxlJiYqKCgIEtGQN4QPm8j81JbDw+Puwqfrq6u8vDw4Jcm7hrjCfmNMYX8xHhCfmNMIT/l13jidrw7w4XKAAAAAACbI3wCAAAAAGyO8AkAAAAAsDnCJwAAAADA5gifAAAAAACbI3wCAAAAAGyO8AkAAAAAsDnCJwAAAADA5gifAAAAAACbI3wCAAAAAGyO8AkAAAAAsDnCJwAAAADA5gifAAAAAACbI3wCAAAAAGyO8AkAAAAAsDnCJwAAAADA5gifAAAAAACbI3wCAAAAAGyO8AkAAAAAsDnCJwAAAADA5gifAAAAAACbI3wCAAAAAGyO8AkAACRJ0dHRMplMio+Pz3b95s2bVbx4ccvnJk2aaPr06fm2f7PZrI0bN+ZbfzmJiIjQ8OHDbb4fAIA1wicAAMiVhg0b6uTJk/e6DABAAUX4BAAA+AcYhqH09PR7XQYA3DOETwAACrDQ0FBNnjxZNWvWVOHChdW2bVtdvHhRgwcPltlsVunSpbV161ZL+8uXL+u5555TQECAAgICNHDgQF25csWqz6+++kqhoaHy9vbW4MGDlZKSIknauHGjzGZzjrXs3LlTLVu2VK9evVS+fHnNmTMnx7YZGRkaM2aM/Pz8FBgYqJkzZ2Zps2jRIlWpUkVms1k1a9a0HMfSpUtVsmRJq7a//PKLzGazrl+/Lklat26datWqJbPZrIoVK+rbb7/NsZbffvtN9evXl9lsVoUKFbRw4ULLunHjxqlDhw565pln5OHhodKlS2vp0qWW9YZh6L333lO5cuVkNpvVpEkTHThwwLI+8/upU6eOXF1dtX///hzrAIAHHeETAIACbvHixfrmm2906tQpnThxQnXq1FGLFi104cIF9ejRQwMHDrS0HTZsmI4cOaK9e/fqjz/+0MGDB/Wvf/3Lqr+lS5dq9+7d+uOPP7R161ZNnjz5tjWcOXNGLVu21HPPPafPPvtMX331lcaOHasffvgh2/aRkZGKjIzUjz/+qCNHjui3337T5cuXLeujoqI0YsQIRUZG6uLFixo1apTCw8N14cIFtW/fXvHx8frpp58s7efNm6cuXbqoUKFC+v3339WlSxdNmTJFFy9e1EcffaTevXvr0KFDWeqIj49XmzZt1K1bN507d06zZ89W//79rfpevXq1atWqpYsXL2ratGnq3r27jh49KkmaPXu2Pv30U61YsULnz59X586dFR4ebgnsmcf62WefKSkpSWXLlr3tuQSABxXhEwCAAsTIMHT9aLyu7o7T9aPxkqRBgwYpKChInp6eateunby9vdW5c2fZ29ura9eu2rt3r1JSUpSRkaH58+dr8uTJ8vb2VtGiRTVp0iR9/vnnysjIsOxj3LhxMpvNCgwM1KhRozRv3rzb1jVv3jw1atRIXbp0kb29vSpVqqS+fftqwYIF2bafP3++nn/+eZUrV06urq6aMmWKVQ0zZ87UyJEjVb16ddnZ2alz584qV66coqKi5OTkpK5du1rqSk1N1eLFi9WnTx9J0kcffaSIiAg1a9ZMdnZ2atCggTp06KAvv/wySx2rVq2Sj4+Pnn/+eTk6Oqpx48bq0aOHPvvsM0ubMmXKaMCAAXJwcFB4eLiaNm1qmR2dOXOmJkyYoNKlS8vBwUEvvPCCrl27pl9++cWy/aBBg1S2bFnZ29vLycnptucSAB5UDve6AAAAkDvX9p5X/IqjSk/436xaekKyzNddLJ9dXV3l5+dn9dkwDF29elXJyclKSUlRaGioZf0jjzyi5ORknT9/3rIsJCTE6r9jY2NvW1t0dLSioqLk4+Oj1NRUOTo6Kj09XQ0bNsy2/alTp6z24+fnJ2dnZ6v+Ro8erbFjx1qWpaamWmrp06eP2rVrpxkzZmj16tVyd3dXgwYNLNuuX79ec+fOtWyblpYmDw+PLHWcPHnS6nxknpNNmzZlez4yP2fWER0drV69esne3t6yPiUlxerBTMHBwdmeAwB42BA+AQAoAK7tPa8LXxzIuiJDuvzjSV1rfl4ulYresg8fHx85OTkpOjraElCjo6Pl7OysokWLKiYmRpJ0/Phxy/qYmBgVK1bstvUFBQXp8ccf17x58xQVFaV27drJ0dExx/aBgYE6fvy45XNcXJySk5Ot+nv++eetLhm+WZ06dVS0aFGtXLlSCxcuVK9evWQymSzbDhs2TFOmTLlt3cWLF1d0dLTVsujoaKtXytxcp3TjnNSrV8+yr+nTp6tNmzY57sPOjgvNAEDislsAAO57Roah+BVHb9kmfsVfMjKMW7axs7NTjx499Nprr+nixYu6cOGCRo8erd69e1sFpAkTJig+Pl6nTp3S5MmT1bNnz9vW2Lt3b61fv17ffPON0tLSlJqaqt27d2v79u3Ztu/evbtmzpypQ4cO6dq1axo1apRVDUOGDNHUqVO1Y8cOy8ztunXrrGYUe/furffff1+rVq2yXHIrSQMGDNDcuXO1YcMGpaenKzk5WT///LPVg4AytWvXTnFxcZo1a5bS0tK0efNmzZ8/36q/P//8U3PmzFFaWppWrVql9evXq2vXrpY633jjDcv9pImJiVq+fLnV/asAgBsInwAA3OeSjyVYXWqbnfSEZCUfS7htXzNmzFBoaKgqVKigihUrqlSpUpo2bZpVm8cee0xhYWGqVKmSateurdGjR9+232LFiun777/XJ598or59+6p48eIaMmSIEhMTs23fr18/9erVSw0bNtQjjzyiatWqyd3d3bI+PDxcU6ZMUf/+/eXl5aUSJUpoxowZVveF9u7dW5s2bVK1atVUqlQpy/Jq1app4cKFev311+Xj46NixYppzJgxVjOrmby8vPTdd9/piy++kLe3t5577jnNnj3bcgmvJLVp00bbtm1TkSJFNGzYMH3xxRcqXbq0JGno0KGKiIhQ586d5eHhofLly+d4nysAPOxMhmHc+p9JH3KJiYny9PRUQkJCtveK5EZqamquLkECcoPxhPzGmLr/Xd0dp4uLsj6p9e+KdCsr1zDff6CinD1o42ncuHHavXu3li1bdq9LeWg9aGMK99bdjqf8yAYPM2Y+AQC4z9m55+4JqbltBwDAvUD4BADgPudcwlP2nrcOlvaeznIu4fkPVQQAQN7xtFsAAO5zJjuTzOEls3/a7f8zhz8ik53pH6zq4TBu3Lh7XQIAPDCY+QQAoABwqVRU3r3KZ5kBtfd0lnev8rd9zQoAAPcaM58AABQQLpWKqlAFbyUfS1DG5RTZuTvJuYQnM54AgAKB8AkAQAFisjOpUEnzvS4DAIA847JbAAAAAIDNET4BAAAAADZH+AQAAAAA2BzhEwAAAABgc4RPAAAAAIDNET4BAAAAADZH+AQAAAAA2BzhEwAAAABgc4RPAAAAAIDNET4BAAAAADZH+AQAAAAA2BzhEwAAAABgc4RPAAAAAIDNET4BAAAAADZH+AQAAAAA2BzhEwAAAABgc4RPAAAAAIDNET4BAAAAADZH+AQAAAAA2BzhEwAAAABgc4RPAAAAAIDNET4BAAAAADZH+AQAAAAA2BzhEwAAAABgc4RPAAAAAIDNET4BAHiAhYaGatmyZXe8/fTp09WkSZN8qycn0dHRMplMio+Pt/m+AAD3BuETAAAAAGBzhE8AAPBQS01NvdclAMBD4b4Jn5s2bVJ4eLgCAwNlMpmyXCJkGIbeeOMNBQQEyMXFRS1atNDhw4dv2+/MmTMVGhqqQoUKqXbt2vr1119tdAQAAOSPxYsXq06dOpbPTzzxhAICAiyfX3rpJT3//POSbvz9+N5776lcuXIym81q0qSJDhw4YNXfvn37VL16dXl4eKh169Y6depUjvvet2+f6tSpI3d3dzVt2jRL27i4OPXs2VMBAQEKDAzU8OHDlZycLEmqWrWq5s2bZ9W+bdu2mjx5siQpKSlJQ4cOVXBwsHx9fdWnTx8lJCRkW0dqaqpGjRql4OBg+fj4qGvXrjp37pxlvclk0owZM1S2bFmZzWZ17drVqq+jR48qPDxcPj4+CgkJ0cSJE5WRkSFJioyMVFhYmMaOHSt/f39169Ytx/MBAMg/9034vHLliqpWraqZM2dmu/7tt9/We++9pw8//FC//PKLChcurNatW+v69es59rl48WK9+OKLGjt2rHbu3KmqVauqdevWiouLs9VhAABw15o0aaIdO3bo8uXLMgxDW7ZsUaFChSyhcv369WrWrJkkafbs2fr000+1YsUKnT9/Xp07d1Z4eLhSUlIs/X3yySdasGCBzpw5I39/f/Xq1Svb/aalpaljx45q3ry5Lly4oEmTJumTTz6xrDcMQx07dpS/v7+OHj2qP/74Q3v27NHEiRMlSb1799aCBQss7c+cOaMffvjBsr9+/frp4sWL+v3333Xs2DGlpqZq6NCh2dYyefJkrVy5Ulu2bNGxY8dkMpnUs2dPqzbz5s3Thg0bFB0drUuXLmn48OGSpKtXr6p58+Zq3ry5YmNjtXnzZi1atEhz5861bLt37145ODgoJiYmS2AGANiIcR+SZCxdutTyOSMjw/D39zemTp1qWRYfH284OzsbCxcuzLGfWrVqGUOGDLF8Tk9PNwIDA43JkyfnupaEhARDkpGQkJC3g7hJSkqKsWzZMiMlJeWO+wAyMZ6Q3xhT94f09DQjZu8eY/+WjUbM3j1GhQoVjFWrVhk7d+40atasaQwdOtSYOXOmceHCBcPBwcG4ePGiYRiGUaFCBWPZsmVWfQUGBhqbNm0yDMMwQkJCjLfeesuy7syZM4Yk48SJE1lq2LRpk+Hh4WE1FgYOHGg0btzYMAzD+PXXX40iRYoY6enplvVr1qwxHnnkEcMwDOPUqVOGo6Oj8emnnxopKSnGtGnTjGbNmhmGYRhxcXGGnZ2dpW7DMIw///zTcHR0NNLS0oxjx44ZkoxLly4ZhmEYpUqVMhYtWmRpGxsba0gyYmNjDcO48f8VFi9ebFm/bds2w8nJyUhPTze+/PJLIywszOrYPv74Y0stc+fOzXIcuH/xOwr56W7HU35kg4eZwz3Mvbl27NgxnTlzRi1atLAs8/T0VO3atfXzzz9ne7lMSkqKduzYoVGjRlmW2dnZqUWLFvr5559z3FdycrLl8iFJSkxMlHTj8p87vSckczvuKUF+YDwhvzGm7r2jv/2iTfMjlXTpgmWZr72hJfPnqWzVamrUqJHq1KmjhQsXytvbW5UrV5abm5tSU1MVHR2tXr16yd7e3rJtSkqKoqOjLZfuFitWzPL9FilSRM7Ozjp+/Lj8/Pys6oiJibFc3pvZvnjx4tq/f79SU1N15MgRxcfHq0iRIpZtDMNQenq6UlNTVbRoUTVu3Fg//vijunXrps8++0zPP/+8ZduMjAyVKFHCap92dnY6ceKE1ThMTU3VyZMnVbx4cctyHx8fOTs7Kzo6Wj4+PlmOKzAwUCkpKTp16pSOHj2qvXv3ymw2W/aTkZFh6S89PV2BgYFKT09Xenr6HX5r+KfwOwr56W7HE+Pw7hSI8HnmzBlJyvKXpJ+fn2Xd350/f17p6enZbnPw4MEc9zV58mSNHz8+y/I1a9bI1dU1r6VbWbt27V1tD9yM8YT8xpi6t/xahuvmv7FqFd+qJUuWaM+hw+rQoYOSk5O1fv16Xb16VcHBwYqKipIkeXl56ZlnnlH16tWz9BkVFaWrV69qzZo1cnNzkyTFx8crOTlZBw8e1Pnz563aHz9+XCdOnNC3334rB4cb/xdhy5YtunDhgqKionTixAl5enpaXb56874kqXLlylqyZIk++eQTHTx4UIULF1ZUVJQuXbokOzs7ffzxx3J2drbads+ePTp79qwkWWr18vLSsmXLLDVeunRJycnJOnTokOXez5vX//nnn3JwcND27dt1/vx5PfLII3r77bezrXPPnj1KSkqy1IyCgd9RyE93Op6uXr2az5U8XApE+PwnjRo1Si+++KLlc2JiooKCgtSqVSt5eHjcUZ+pqalau3atWrZsKUdHx/wqFQ8pxhPyG2Pq3snISNdnLw21mvHMVPh6so799ZcKOTlqzZrv5e7uoWnTpmnr1q2aN2+e2rZtK0kaMWKE5s+fry5duqhs2bJKTEzUxo0b1bRpU7m7u8vV1VVbtmzRyJEjFRwcrOeff14NGzZUnz59suyzZcuWmjNnjnbu3KnRo0dr9+7d+vXXX1WpUiW1a9dOrVu31ldffaVt27Zp5MiRcnNzU0xMjA4cOKA2bdpIkurXr68PP/xQK1asUOfOnfXEE09Y+l++fLmioqI0efJkFS1aVGfOnNG2bdvUqVMnRUdHS5JatWols9msZ599VsuWLdOgQYPk5eWlQYMGqXnz5lb3q27YsEFDhw6Vq6urZs2apW7duqlDhw5q0qSJlixZopiYGEVERMjR0VFHjhzRmTNn1LhxY50/f14//vij2rVrl59fJ2yE31HIT3c7njKvisSdKRDh09/fX5J09uxZq6f9nT17VmFhYdluU7RoUdnb21v+JfXmbTL7y46zs3OWf5GVJEdHx7v+hZcffQCZGE/Ib4ypf96JfQd0+Vz2V/AUdrCTn4ebnB0ddOV0rIoU8VaLFi30+++/q1mzZpbvatiwYXJyclLXrl114sQJubu7q0GDBmrVqpWlTb9+/dSnTx8dOXJEderU0YIFC7L9rh0dHfXtt9/q2Wef1YwZM1SzZk3169dP27dvt4yPVatW6ZVXXlGVKlWUmJio4OBgDRgwwNKfp6en6tatq3Xr1un777+32s/nn3+usWPHql69erpw4YL8/PzUtWtXdenSxdIucz+vv/66rl+/rkaNGun69etq2rSp5s+fb9Vf79691apVK505c0atWrXS+++/L0dHR3l5eWndunV6+eWX9e9//1vXr19XyZIlNXLkSDk6Osre3l4mk4nxXsDwOwr56U7HE2Pw7pgMwzDudRF/ZzKZtHTpUnXq1EnSjftJAgMDNWLECL300kuSbvyrg6+vryIjI3N8RHrt2rVVq1Ytvf/++5Ju3O8RHBysoUOH6tVXX81VLYmJifL09FRCQsJdzXxGRUWpXbt2DFjcNcYT8htj6t458NOPinpv6m3btXthpMrXb/wPVHT3/qnxZDKZtGvXrhz/ERoPDn5HIT/d7XjKj2zwMLtvZj6TkpJ05MgRy+djx45p9+7dKlKkiIKDgzV8+HBNnDhRpUuXVokSJTRmzBgFBgZaAqokNW/eXI8//rjlse0vvviinn76aT366KOqVauWpk+fritXrqhv377/9OEBAJCFm9krX9sBAHA/u2/C52+//aamTZtaPmfed/n0008rMjJSL7/8sq5cuaLnnntO8fHxatCggVavXq1ChQpZtjl69KjVwxMyX0j9xhtv6MyZMwoLC9Pq1auzPIQIAIB7oVj5inIrUlRJF8/n2Mbdu6iKla/4D1YFAIBt3Dfhs0mTJrrVFcAmk0kTJkzQhAkTcmyT+bCCmw0dOjTHF1gDAHAv2dnZq1nEc/p22qQc2zR9+jnZ2dnnuP5hdR/eNQQAuA27e10AAAAPs9K166nji6PlVqSo1XJ376Lq+OJola5d7x5VBgBA/rpvZj4BAHhYla5dTyVr1lbsgX1Kir8kN7OXipWvyIwnAOCBQvgEAOA+YGdnr6CKVe51GQAA2AyX3QIAAAAAbI7wCQAAAACwOcInAAAAAMDmCJ8AAAAAAJsjfAIAAAAAbI7wCQAAAACwOcInAAAAAMDmCJ8AAAAAAJsjfAIAAAAAbI7wCQAAAACwOcInAAAAAMDmCJ8AAAAAAJsjfAIAAAAAbI7wCQAAAACwOcInAAAAAMDmCJ8AAAAAAJsjfAIAAAAAbI7wCQAAAACwOcInAAAAAMDmCJ8AAAAAAJsjfAIAAAAAbI7wCQAAAACwOcInAAAAAMDmCJ8AAAAAAJsjfAIAAAAAbI7wCQAAAACwOcInAAAAAMDmCJ8AAAAAAJsjfAIAAAAAbI7wCQAAAACwOcInAAAAAMDmCJ8AAAAAAJsjfAIAAAAAbI7wCQAAAACwOcInAAAAAMDmCJ8AAAAAAJsjfAIAAAAAbI7wCQAAAACwOcInAAAAAMDmCJ8AAAAAAJsjfAIAAAAAbI7wCQAAAACwOcInAAAAAMDmCJ8AAAAAAJsjfAIAAAAAbI7wCQAAAACwOcInAAAAAMDmCJ8AAAAAAJsjfAIAAAAAbI7wCQAAAACwOcInAAAAAMDmCJ8AAAAAAJsjfAIAAAAAbI7wCQAAAACwOcInAAAAAMDmCJ8AAAAAAJsjfAIAAAAAbI7wCQAAAACwOcInAAAAAMDmCJ8AAAAAAJsjfAIAcJ87d+6cmjVrJg8PD3Xp0uVel3PHzGazNm7caLP2dyoiIkLDhw+3+X4A4GHncK8LAAAAt/bRRx/J3t5e8fHxsrOzzb8bm0wm7dq1S2FhYTbpHwAAZj4BALjPHTt2TBUrVswxeKampv7DFSE/GIah9PT0e10GAPxjCJ8AANzHunTpos8//1yzZs2Sm5ubPv30U0VGRiosLExjx46Vv7+/unXrJsMw9M4776hkyZIqUqSI2rRpo7/++svST2hoqN5++23VqVNH7u7uaty4sU6cOCFJqlWrliSpXr16cnNz06RJkyRJR48eVXh4uHx8fBQSEqKJEycqIyNDkiw1vPnmm/L19ZWfn5+mT59u2V9GRobGjBkjPz8/BQYGaubMmbc8zty0X7RokapUqSKz2ayaNWtq69atkqSlS5eqZMmSVm1/+eUXmc1mXb9+XZK0bt061apVS2azWRUrVtS3336bYy2//fab6tevL7PZrAoVKmjhwoWWdePGjVOHDh30zDPPyMPDQ6VLl9bSpUst6w3D0Hvvvady5crJbDarSZMmOnDggNX3MHnyZNWpU0eurq7av3//Lc8LADxICJ8AANzHvvrqK/Xs2VODBw9WUlKSnnnmGUnS3r175eDgoJiYGM2bN0/z5s3TtGnTtGzZMp06dUoVK1ZUeHi40tLSLH198cUXWrhwoc6dO6fChQtrzJgxkqRff/1VkrR161YlJSVp9OjRunr1qpo3b67mzZsrNjZWmzdv1qJFizR37lxLf/v27ZOrq6tiY2O1ePFijRw5UkePHpUkff7554qMjNSPP/6oI0eO6LffftPly5dzPM7IyMhbto+KitKIESMUGRmpixcvatSoUQoPD9eFCxfUvn17xcfH66effrK0nzdvnrp06aJChQrp999/V5cuXTRlyhRdvHhRH330kXr37q1Dhw5lqSM+Pl5t2rRRt27ddO7cOc2ePVv9+/e36nv16tWqVauWLl68qGnTpql79+6W4549e7Y+/fRTrVixQufPn1fnzp0VHh6ulJQUq2P97LPPlJSUpLJly+ZiFADAg4HwCQDAfcYw0nXp0jadOfOtLl3aJsnI0sbT01OvvfaanJyc5Orqqnnz5umFF15Q5cqVVahQIU2aNEknTpywBEtJGjx4sEqUKKFChQqpZ8+e2rFjR441rFq1Sl5eXho+fLicnJwUHBysYcOGacGCBZY2RYsW1UsvvSRHR0c1adJEoaGh2rNnjyRp4cKFev7551WuXDm5urpqypQpllnT7MyfP/+W7WfOnKmRI0eqevXqsrOzU+fOnVWuXDlFRUXJyclJXbt21bx58yTduAx58eLF6tOnj6Qb98xGRESoWbNmsrOzU4MGDdShQwd9+eWX2R63j4+Pnn/+eTk6Oqpx48bq0aOHPvvsM0ubMmXKaMCAAXJwcFB4eLiaNm1qmR2dOXOmJkyYoNKlS8vBwUEvvPCCrl27pl9++cWy/aBBg1S2bFnZ29vLyckpx3MCAA8aHjgEAMB9JC7ue/15eIKSk89Ylp2Nuywnp/pW7YoVK2Z1D+jJkycVGhpq+ezs7KzAwECdPHnSsszf39/y34ULF77lTGR0dLT27t0rs9lsWZaRkaGgoCDLZz8/P6ttChcurKSkJBUqVEinTp1SSEiIVVtnZ+cc93e79tHR0Ro9erTGjh1rWZaamqrY2FhJUp8+fdSuXTvNmDFDq1evlru7uxo0aGDZdv369VaztmlpafLw8MhSx9/PoyQ98sgj2rRpk+XzzXVmfs6sIzo6Wr169ZK9vb1lfUpKitX3EBwcnON5AIAHGeETAID7RFzc9/pj7xD9faYzI/26LlzYoLi47+Xr21qSsjx8qHjx4oqOjrZ8TklJ0alTp1S8ePFc7dtkMll9DgoKUo0aNbRt27a8H4ikwMBAHT9+3PI5Li5OycnJd9w+KChIzz//vAYOHJjt9nXq1FHRokW1cuVKLVy4UL169bIcU1BQkIYNG6YpU6bctu6/n0fpRqC8+TzeXKckxcTEqF69epZ9TZ8+XW3atMlxH7Z6YjEA3O/47QcAwH3AMNL15+EJyu4S28xlfx5+U4aR/dNRe/XqpQ8++ED79+9XcnKyXn/9dRUrVszyMKHb8fPzs9y3KEkdOnTQ2bNnNWvWLF2/fl3p6ek6dOhQrt+72bVrV82cOVOHDh3StWvXNGrUqFuGru7du9+y/ZAhQzR16lTt2LFDhmHo6tWrWrdundWMYu/evfX+++9r1apVlktuJWnAgAGaO3euNmzYoPT0dCUnJ+vnn3+2ehBQpnbt2ikuLk6zZs1SWlqaNm/erPnz51v19+eff2rOnDlKS0vTqlWrtH79enXt2tVS5xtvvGG5nzQxMVHLly+/5SwzADwsCJ8AANwH4uO3W11qm5Wh5OTTio/fnu3aPn366Pnnn1eHDh3k7++vPXv2aMWKFXJwyN1FTm+++aZeeOEFeXl5acqUKXJzc9O6dev0ww8/KDQ0VN7e3urRo4fOnLlVjf8TERGhXr16qWHDhnrkkUdUrVo1ubu759i+X79+t2wfHh6uKVOmqH///vLy8lKJEiU0Y8YMq/tCe/furU2bNqlatWoqVaqUZXm1atW0cOFCvf766/Lx8VGxYsU0ZsyYbGdivby89N133+mLL76Qt7e3nnvuOc2ePdtyCa8ktWnTRtu2bVORIkU0bNgwffHFFypdurQkaejQoYqIiFDnzp3l4eGh8uXLW90nCwAPM5NhGNn9Eyv+X2Jiojw9PZWQkJDtvSG5kZqaqqioKLVr106Ojo75XCEeNown5DfG1P3hzJlvtW//v27brmKFd+Xv3/EfqOjOPOjjady4cdq9e7eWLVt2r0t5aDzoYwr/rLsdT/mRDR5mzHwCAHAfcHb2zdd2AADcbwifAADcB8zmmnJ29pdkyqGFSc7OATKba/6TZQEAkG942i0AAPcBk8leZUq/8f9PuzXJ+sFDNwJpmdJjZDLZZ7c5/iHjxo271yUAQIHFzCcAAPcJX9/Wqlxpppydrd+f6ezsr8qVZlpeswIAQEHEzCcAAPcRX9/W8vFp8f9Pv42Ts7OvzOaazHgCAAo8wicAAPcZk8leXl517nUZAADkKy67BQAAAADYHOETAAAAAGBzhE8AAAAAgM0VmPAZGhoqk8mU5WfIkCHZto+MjMzStlChQv9w1QAAAAAAqQA9cGj79u1KT0+3fN67d69atmypLl265LiNh4eHDh06ZPlsMuX04m4AAAAAgC0VmPDp4+Nj9XnKlCkqWbKkGjdunOM2JpNJ/v7+ti4NAAAAAHAbBSZ83iwlJUVffPGFXnzxxVvOZiYlJSkkJEQZGRmqXr26Jk2apIoVK96y7+TkZCUnJ1s+JyYmSpJSU1OVmpp6R/Vmbnen2wM3YzwhvzGmkJ8YT8hvjCnkp7sdT4zDu2MyDMO410Xk1ZdffqkePXooJiZGgYGB2bb5+eefdfjwYVWpUkUJCQn6z3/+o02bNmnfvn0qXrx4jn2PGzdO48ePz7J8wYIFcnV1zbdjAAAAAFCwXL16VT169FBCQoI8PDzudTkFToEMn61bt5aTk5NWrFiR621SU1NVvnx5de/eXW+++WaO7bKb+QwKCtL58+fveIClpqZq7dq1atmypRwdHe+oDyAT4wn5jTGF/MR4Qn5jTCE/3e14SkxMVNGiRQmfd6jAXXZ7/PhxrVu3Tt98802etnN0dFS1atV05MiRW7ZzdnaWs7Nzttvf7S+8/OgDyMR4Qn5jTCE/MZ6Q3xhTyE93Op4Yg3enwLxqJdPcuXPl6+ur9u3b52m79PR0/fHHHwoICLBRZQAAAACAnBSo8JmRkaG5c+fq6aefloOD9aRtnz59NGrUKMvnCRMmaM2aNfrrr7+0c+dO9erVS8ePH9ezzz77T5cNAAAAAA+9AnXZ7bp16xQTE6N+/fplWRcTEyM7u/9l6UuXLql///46c+aMvLy8VKNGDW3dulUVKlT4J0sGAAAAAKiAhc9WrVopp+cjbdy40erzu+++q3ffffcfqAoAAAAAcDsF6rJbAAAAAEDBRPgEACAfnDt3Ts2aNZOHh4e6dOly2/bjxo1Tp06dLJ9NJpN2795tuwJzITIyUmFhYXe8/fDhw/XMM8/kX0E52Lhxo8xms833AwDIXwXqslsAAO5XH330kezt7RUfH2/1DAJbM5lM2rVr112FRgAA/gnMfAIAkA+OHTumihUr/qPBE/+81NTUe10CABRY/A0JAMBd6tKliz7//HPNmjVLbm5u+vTTT7NcVitJZrM5ywPycmPnzp2qU6eOPDw8VLRoUYWHh0uSatWqJUmqV6+e3NzcNGnSJElSr169FBgYKA8PD9WoUUMbNmyw6m/t2rWqXbu2zGazAgICNHny5Gz3++GHH+qRRx7RwYMHs12/adMmVa5cWW5uburcubMuX75stf7o0aMKDw+Xj4+PQkJCNHHiRGVkZCg1NVVFixbVpk2brNpXqFBBCxculCTFxcWpZ8+eCggIUGBgoIYPH67k5ORs67h8+bKee+45BQQEKCAgQAMHDtSVK1ckSdHR0TKZTJozZ45CQ0Pl7e2twYMHKyUlxer8Nm3aVEWKFFGpUqU0Z84cy7px48apQ4cOGjRokIoUKaJXX3012xoAALdH+AQA4C599dVX6tmzpwYPHqykpKR8v+9x6NChCg8PV3x8vGJjYzVy5EhJ0q+//ipJ2rp1q5KSkjR69GhJUvPmzXXgwAFduHBB3bp105NPPmkJhrt27dJjjz2ml19+WefOndPBgwfVtGnTLPscO3asZs6cqc2bN6tcuXJZ1l+6dEkdO3bU0KFDFR8fr759++qLL76wrL969aqaN2+u5s2bKzY2Vps3b9aiRYs0d+5cOTo6qlu3bpo3b56l/W+//abY2Fh16tRJhmGoY8eO8vf319GjR/XHH39oz549mjhxYrbnZ9iwYTpy5Ij27t2rP/74QwcPHtS//vUvqzZLly7V7t279ccff2jr1q2WwH3mzBm1bNlSgwYN0rlz57Rs2TKNHTtWP/zwg2Xb1atXq3bt2oqLi9Obb755+y8MAJAtwicAAPc5R0dHHT9+XKdOnZKzs7MaNWp0y/Z9+/aVp6enHB0dNXLkSGVkZOj333+XJH388cfq1q2bnnjiCTk6OsrT01N16tSxbJuenq7nnntO69ev16ZNm1SsWLFs97Fy5UoFBgZqwIABcnBwUHh4uJo1a2ZZHxUVJS8vLw0fPlxOTk4KDg7WsGHDtGDBAklSnz599NVXX+n69euSpHnz5unJJ5+Ui4uLfvvtNx0+fFhTp06Vq6urvL29NXr0aMu2N8vIyND8+fM1efJkeXt7q2jRopo0aZI+//xzZWRkWNqNGzdOZrNZgYGBGjVqlCX4zps3T40aNdJTTz0le3t7VapUSX379rXaV6VKlRQRESEHBwe5urre8twDAHLGA4cAALhD6RmGfj12UXGXr+vc5WR5emb/Luq79d///lfjx49XjRo15OXlpaFDh2ro0KHZts3IyNCYMWP05Zdf6uzZs7Kzs1NiYqLOnz8vSTp+/LgaNmyY475OnDihw4cP69tvv5WXl1eO7U6dOqWQkBCrZSEhIbp69aplP3v37rV6Km1GRoaCgoIk3bhk2N/fX99++606d+6shQsX6quvvpJ041LZ+Ph4FSlSxLKtYRhKT0/PUse5c+eUkpKi0NBQy7JHHnlEycnJlmPOrO3m/46NjbXsKyoqyqrO9PR0q3MUHByc43kAAOQe4RMAgDuweu9pjV+xX6cTbszcnf/znHaeSVHbvafVplKA3NzcLEFMkq5cuaLExMQ72lfJkiX1+eefyzAM/fTTT2rRooXq1q2rGjVqyGQyWbVdsGCBFixYoO+//16lS5eWyWSSl5eXDONGMA4JCdGRI0dy3FdoaKgmT56sHj166Ouvv1aTJk2ybRcYGKjjx49bLYuJiVHRokUlScWLF1eNGjW0bdu2HPfVu3dvzZs3T66urnJ1dbXM6AYFBcnX11enT5++7bnx8fGRk5OToqOj5efnJ+lGoHR2dlbRokUVExMj6UYYzlwfExNjmdENCgrS448/rkWLFuW4Dx4iBQD5g9+mAADk0eq9pzXoi52W4JnpSnKaBn2xU6v3nlb16tX1888/6+DBg7p+/bpGjx6dJSjm1ueff66zZ8/KZDLJbDbLzs5O9vb2kiQ/Pz8dPXrU0jYxMVFOTk4qWrSoUlJSNGHCBKsHAfXv318LFy7U0qVLlZaWpoSEhCwBsW3btpo/f76efPJJq3sfb9a+fXvFxsZqzpw5SktL06pVq7R+/Xqr9WfPntWsWbN0/fp1paen69ChQ1YPXOrdu7fWrFmjd999V7169bKcn5o1ayooKEivv/66Ll++LMMwdPz4cX333XdZ6rCzs1OPHj302muv6eLFi7pw4YJGjx6t3r17W4XGCRMmKD4+XqdOndLkyZPVs2dPSw3r16/XkiVLlJqaqtTUVO3evVvbt2/P7dcDAMglwicAAHmQnmFo/Ir9utUFtuNX7FfjJk01YMAA1atXT6VKlVLlypXl7u5+R/tct26dqlatKjc3Nz322GOaOnWq5b2eb775pl544QV5eXlpypQpevrpp1WxYkWFhITokUcekYuLi4oXL27pq3r16lqyZIn+/e9/q0iRIipfvrx+/PHHLPts3bq1Fi1apK5du2rNmjVZ1hcpUkTLly/XjBkzZDab9cknn1gCnSS5ublp3bp1+uGHHyxPme3Ro4fOnDljaRMcHKx69epp/fr16tOnj2W5vb29Vq5cqdjYWJUvX16enp5q3759jjO2M2bMUGhoqCpUqKCKFSuqVKlSmjZtmlWbxx57TGFhYapUqZJq165teThTsWLF9P333+ujjz5SQECA/Pz8NGTIkDuepQYA5MxkZF6Hg2wlJibK09NTCQkJ8vDwuKM+UlNTFRUVpXbt2snR0TGfK8TDhvGE/MaYypufj15Q9zk5X0qaaWH/Oqpb0vsfqOj+cr+Np+joaJUoUUKXLl2yuq8TBcf9NqZQsN3teMqPbPAwY+YTAIA8iLt8/faN8tAOAICHBeETAIA88HUvlK/tAAB4WPC0WwAA8qBWiSIK8CykMwnXs73v0yTJ37OQapUoks1a/NNCQ0PFHUYAcH9g5hMAgDywtzNpbHgFSTeC5s0yP48NryB7uzt7si0AAA8qwicAAHnUplKAZveqLn9P60tr/T0LaXav6mpTKeAeVQYAwP2Ly24BALgDbSoFqGUFf/167KLiLl+Xr/uNS22Z8QQAIHuETwAA7pC9nemhfJ0KAAB3gstuAQAAAAA2R/gEAAAAANgc4RMAAAAAYHOETwAAAACAzRE+AQAAAAA2R/gEAAAAANgc4RMAAAAAYHOETwAAAACAzRE+AQAAAAA2R/gEAAAAANgc4RMAAAAAYHOETwAAAACAzRE+AQAAAAA2R/gEAAAAANgc4RMAAAAAYHOETwAAAACAzRE+AQAAAAA2R/gEAAAAANgc4RMAAAAAYHOETwAAAACAzRE+AQAAAAA2R/gEAAAAANgc4RMAAAAAYHOETwAAAACAzRE+AQAAAAA2R/gEAAAAANgc4RMAAAAAYHOETwAAAACAzRE+AQAAAAA2R/gEAAAAANgc4RMAAAAAYHOETwAAAACAzRE+AQAAAAA2R/gEAAAAANgc4RMAAAAAYHOETwAAAACAzRE+AQAAAAA2R/gEAAAAANgc4RMAAAAAYHOETwAAAACAzRE+AQAAAAA2R/gEAAAAANgc4RMAAAAAYHOETwAAAACAzRE+AQAAAAA2R/gEAAAAANgc4RMAAAAAYHOETwAAshEdHS2TyaT4+Phs12/evFnFixfPl33t3r1bJpPprvuZNGmSunfvng8VAQCQ/xzudQEAABREDRs21MmTJ+91GVZGjx59r0sAACBHzHwCAB44hmEoPT39XpfxUElNTb3XJQAA7nOETwBAgXDy5Em1bNlSHh4eqlGjhiZNmqTQ0FDL+tDQUE2ePFl16tSRq6ur9u/fry+++EKVKlWSu7u7goODNWbMGBmGYdnGZDJpxowZKlu2rMxms7p27aqEhASr/a5YsUKlSpWS2WxWRESEJWRt3LhRZrPZ0i4lJUVvvPGGSpYsKXd3d1WuXFk7d+7M9lji4+P11FNPyWw2q1y5ctq0aZPV+tTUVEtf3t7e6tixo06dOiXpRrB+5ZVX5O/vLw8PD5UpU0YrV66UJI0bN06dOnWy9LNv3z7VqVNH7u7uatq0qV5++WU1adLE6vg//PBDVapUSR4eHurYsaPV8R89elTh4eHy8fFRSEiIJk6cqIyMDElSZGSkwsLCNHbsWPn7+6tbt263+QYBAA87wicAoEDo0aOHQkJCdPbsWS1cuFCffvppljaRkZH67LPPlJSUpLJly8rb21vffPONEhMT9e233+rjjz/WggULrLaZN2+eNmzYoOjoaF26dEnDhw+3Wv/dd99p165d2r9/v3744QfNnz8/2/peffVVRUVFafXq1UpMTNTXX38tb2/vbNu+8MILio+PV3R0tNavX6/PP//cav1rr72mn376SVu2bNHp06dVpkwZS7hbu3atFixYoJ07dyoxMVHr1q1TmTJlsuwjNTVVHTt2VNu2bXXhwgVNmTJF//3vf7O0+/LLL7V+/XrFxMTo5MmTevfddyVJV69eVfPmzdW8eXPFxsZq8+bNWrRokebOnWvZdu/evXJwcFBMTIzmzZuX7bECAJCJ8AkAuC8Z6em68suvSli5Soe+XaHNmzdrypQpcnFxUZkyZTRw4MAs2wwaNEhly5aVvb29nJyc1LZtW5UpU0Ymk0lhYWHq3r27Nm7caLXNyy+/rMDAQJnNZr355ptasGCBZXZPkt544w25u7srMDBQbdq00Y4dO7LWahj66KOPNG3aNJUuXVomk0lly5ZVSEhIlrbp6elavHixJk6cKLPZrMDAQI0cOdKqr1mzZmnatGkKCAiQk5OTJk6cqJ9++kknTpyQo6Ojrl+/rn379ik1NVXBwcHZhs9t27bpwoULeu211+Tk5KTatWura9euWdq9/PLL8vX1ldls1hNPPGE5vlWrVsnLy0vDhw+Xk5OTgoODNWzYMKvw7unpaenf1dU1m28RAID/4YFDAID7TuKaNTo7abLSzpyRJO25dk3OdnZy2rlTatVKkhQcHJxlu78v+/777zV+/Hj9+eefSk1NVXJystq2bWvV5uaAGBISopSUFJ07d86yzN/f3/LfhQsXzvbpt+fOndPVq1dVunTp2x7b+fPnlZKSkmW/N6+/cuWKGjVqZPUEXCcnJ504cUJNmzbV+PHjNWbMGB04cEAtWrTQf/7zH5UoUcJqP6dOnVJAQIAcHP73V31wcLD27dtn1e7vx3f58mVJN572u3fvXqtLizMyMhQUFGT5XKxYMdnZ8e/YAIDc4W8MAMB9JXHNGsUOG24JnpLk6+Cg5IwM7R0yVIlr1kiSYmJismx7cxBKSUlR586dNWDAAMXGxiohIUEDBw60uudTko4fP27575iYGDk5OcnHxydPNfv4+MjV1VVHjhy5bduiRYvK0dExy34zeXt7y9XVVb/88ovi4+MtP9euXVO9evUkSYMHD9a2bdsUExMjZ2dnvfDCC1n2ExgYqDNnzigtLS3b/dxOUFCQatSoYVVDYmKiVXgleAIA8oK/NQAA9w0jPV1nJ02W/hYQAxwdVd3FRdPPndPxNyfqz4MH9fHHH9+yr+TkZF2/fl3e3t5ydnbWL7/8kuV+T0maOnWqTp06pfj4eL3xxhvq1q1bnkOVyWRS//799dJLL+nIkSMyDEOHDh2yCpiZ7O3t9dRTT+mNN95QfHy8Tp06palTp1rW29nZaeDAgXrppZd04sQJSdKFCxe0ePFiSdL27du1detWpaSkyMXFRYULF7aa3cxUp04dmc1mTZ48Wampqdq+fbu+/PLLXB9Thw4ddPbsWc2aNUvXr19Xenq6Dh06lOWyZQAAcovwCQC4b1z9bYfVjOfN3g4I1InUFNXd+pO6duqkXr16ydnZOce+3N3dNXPmTD333HPy8PDQv//972zveezVq5eaNm2qkJAQubu7a8aMGXdU+1tvvaXmzZurRYsW8vDwUJcuXXTx4sVs277//vtyc3NTSEiImjVrpt69e1utnzx5surWratmzZrJ3d1dNWrU0Jr/n/FNTEzU4MGD5e3tLX9/f506dSrbmh0dHbV8+XKtXLlSXl5eevnll297zm7m5uamdevW6YcfflBoaKi8vb3Vo0cPncnh+wEA4HZMxt+vP4KVxMREeXp6KiEhQR4eHnfUR2pqqqKiotSuXTs5Ojrmc4V42DCekN/upzGVsHKVTo0Ycdt2gf/5j2b98bvWr1+vtWvX3vH+TCaTdu3apbCwsDvuoyAZMGCAMjIyNGfOHJvt434aT3gwMKaQn+52POVHNniYMfMJALhvONziXsv916/rr+RkGYahPy6c1/vvv68uXbr8g9UVPJs3b9aJEyeUkZFheU0M5wwAcK/wtFsAwH3D9dEacvD3V9rZs1nu+7yYnqbxZ8/qQnq6/MaOVf/+/fXMM8/co0oLhr/++kvdunXTpUuXVLx4cU2ZMkWt/v9pwQAA/NMInwCA+4bJ3l5+o0cpdthwyWSyCqAN3Ny11s1dxWZMl0c+BagH/c6Tp59+Wk8//fS9LgMAAElcdgsAsKGIiAgNHz48T9t4tGqlYjOmy8HPz2q5g59fvgZPAADwz2LmEwDuI02aNFGnTp3k4+OjAQMGSLoxO3f16lUVLlzY0u6jjz5Sz54971WZdyQ6OlolSpTQpUuXZDabb9nWo1UruTdvfuPpt+fOycHHR66P1pDJ3t6qXUREhMxms6ZPn267wgEAQL4oMDOf48aNk8lksvopV67cLbf56quvVK5cORUqVEiVK1dWVFTUP1QtANydnj17KikpSUlJSdq3b58k6eTJk5ZlBS143gmTvb0K164lzw7tVbh2rSzBEwAAFCwFJnxKUsWKFXX69GnLz5YtW3Jsu3XrVnXv3l3PPPOMdu3apU6dOqlTp07au3fvP1gxAPyzFi5cqKpVq8rDw0MhISGKjIyUdGP29J133lHJkiVVpEgRtWnTRn/99Zdlu/79++s///mP6tSpI3d3dzVu3FgnTpywbPvKK6/I399fHh4eKlOmjFauXCkp62W18fHxMplMio6OzlJbrVq1JEnFixeXm5ub5s+fr6SkJD322GPy9fWVp6enGjVqpD179li2GTdunMLDwzV06FCZzWYFBwdr8eLFkqT33ntP8+fP16xZs+Tm5qaKFSvm56kEAAD5rECFTwcHB/n7+1t+ihYtmmPbGTNmqE2bNho5cqTKly+vN998U9WrV9cHH3zwD1YMAP+cFStWaOjQoXr33XcVHx+v7du3q2rVqpKkefPmadq0aVq2bJlOnTqlihUrKjw8XGlpaZbtFyxYoIULF+rcuXMqXLiwxowZI0lau3atFixYoJ07dyoxMVHr1q1TmTJl8lzfr7/+Kul/M7g9e/ZURkaGevTooWPHjuns2bOqVq2annrqKasHAX3//fdq1KiRLly4oIkTJ+rZZ5/V5cuX9cILL6hnz54aPHiw1QwxAAC4PxWoez4PHz6swMBAFSpUSHXr1tXkyZMVHBycbduff/5ZL774otWy1q1ba9myZbfcR3JyspKTky2fExMTJd14IW1qauod1Z253Z1uD9yM8fTgMYx0JSTsVHLyOaWmJiotzfr3zc3f+a2+95kzZ2ro0KFq2LCh0tPT5eXlJS8vL6Wmpurzzz/XkCFDLLcrjB8/XnPmzNHWrVv16KOPSrox+1m8eHFJUteuXTV16lSlpqbKZDLp+vXr2rNnj8xmswICAiz1ZGRkKCMjI8u4zKz15vXZHYeLi4s6d+5sOYbXX39d7733no4fP65ixYopPT1d1apV0+OPP66MjAx169ZN/fv31/79+1W9evUs+8e9x+8o5DfGFPLT3Y4nxuHdKTDhs3bt2oqMjFTZsmV1+vRpjR8/Xg0bNtTevXvl7u6epf2ZM2fk97cnJfr5+enMmTO33M/kyZM1fvz4LMvXrFkjV1fXuzqGtWvX3tX2wM0YTw+mhIQUHThw0Ooe9bNnz0q68XvIzc0tx2337dunihUrZnt/+6FDh1StWjWrdR4eHlqxYoUuXbokSTp9+rRl/cGDBxUXF2f53LlzZw0fPlwnT55UlSpV1LdvX/n5+enkyZO6dOmSpV1SUpIkacOGDVnWZ3ccycnJmjt3rnbs2KGkpCSZTCZJ0rJly1SiRAkdPnxYkqzqdnBw0Nq1a3XmzJks+8f9g99RyG+MKeSnOx1PV69ezedKHi4FJny2bdvW8t9VqlRR7dq1FRISoi+//DJfXzI+atQoqxnTxMREBQUFqVWrVvLw8LijPlNTU7V27Vq1bNlSjo6O+VUqHlKMpwfHuXM/aP+BlyT97xJTe/vjcnY+r8Ju21Sh/Dvy8WluuX+yVatWt3xKbMWKFeXu7q527dplWVe2bFl5eXlZ1qWkpCgxMVHh4eGWmc+wsDDL+tTUVC1cuNDyOfN/ExISNHToUC1fvlzLli3T999/r5SUFMv6AwcOSJKaNm2q0NBQLVmyRGazWe3atVNMTEyW45g0aZIuXLigX375RcWLF1d8fLx8fX1Vv359hYWF6bffftPVq1etjsnR0VF16tRR48aNtXTpUnl4eGR7zLg3+B2F/MaYQn662/GUeVUk7kyBCZ9/ZzabVaZMGR05ciTb9f7+/pZ/Zc909uxZ+fv737JfZ2dnOTs7Z1nu6Oh417/w8qMPIBPjqWAzjHT9dexNmUzXrVeYMiRTmkymZP11bKICAv73l+PtvvOBAwfq2WefVdOmTdWwYUOdP39esbGxqlatmnr37q3XX39dnTp1UsmSJTV+/HgVK1ZM9erVs9xfaW9vb+nfwcHBss/t27crNTVVjz76qDw8POTu7q7k5GQ5Ojrq0Ucf1fjx43X+/Hm5ublp0qRJVrXa2dnJzs5Ojo6OCgwMlJ2dnWJiYuTj4yNJunLlilxcXOTr66vk5GSNHTvWant7e3vL9jdzcHCQo6OjAgICtG/fPjk4OFhmTXF/4HcU8htjCvnpTscTY/DuFKgHDt0sKSlJR48etdx79Hd169bVDz/8YLVs7dq1qlu37j9RHgDcUnz8diUn3+o2AEPJyacVH78913126tRJ06ZN05AhQ+Tp6amaNWvqjz/+kCT16dNHzz//vDp06CB/f3/t2bNHK1assITMW0lMTNTgwYPl7e0tf39/nTp1SjNmzJAk9erVS40bN1a5cuUUFham9u3b59iPi4uLxo4dq7Zt28psNmvBggV68cUXZW9vLz8/P1WqVCnPv6OfffZZxcbGqkiRIqpSpUqetgUAAP8sk3HzIwXvYyNGjFB4eLhCQkJ06tQpjR07Vrt379b+/fvl4+OjPn36qFixYpo8ebKkG69aady4saZMmaL27dtr0aJFmjRpknbu3KlKlSrler+JiYny9PRUQkLCXV12GxUVpXbt2vGvJbhrjKcHw5kz32rf/n/dtl3FCu/K37+jTWthTCE/MZ6Q3xhTyE93O57yIxs8zArMZbcnT55U9+7ddeHCBfn4+KhBgwbatm2b5dKtmJgY2dn9byK3Xr16WrBggV5//XWNHj1apUuX1rJly/IUPAHAVpydffO1HQAAwP2uwITPRYsW3XL9xo0bsyzr0qWLunTpYqOKAODOmc015ezsr+Tks7r5gUP/Y5Kzs7/M5pr/dGkAAAA2UWDv+QSAgsxksleZ0m9kfvr7WklSmdJjZDLZ/6N1AQAA2ArhEwDuEV/f1qpcaaacna3fSezs7K/KlWbK17f1PaoMAAAg/xWYy24B4EHk69taPj4t/v/pt3FydvaV2VyTGU8AAPDAIXwCwD1mMtnLy6vOvS4DAADAprjsFgAAAABgc4RPAAAAAIDNET4BAAAAADZH+AQAAAAA2BzhEwAAAABgc4RPAAAAAIDNET4BAAAAADZH+AQAAAAA2BzhEwAAAABgc4RPAAAAAIDNET4BAAAAADZH+AQAAAAA2BzhEwAAAABgc4RPAAAAAIDNET4BAAAAADZH+AQAAAAA2BzhEwAAAABgc4RPAAAAAIDNET4BAAAAADZH+AQAAAAA2BzhE8ADZeDAgXrllVfudRkAAAD4G8IngBxFRkYqLCwsX/sMDQ3VsmXL8rXPm3344Yd66623JEnR0dEymUyKj4+32f4AAACQO4RPAA+MtLS0fO8zNTU13/sEAAB4GBE+gQdAYmKihg4dqpCQEHl4eKhmzZo6ceKEJOns2bN66qmn5OPjo+DgYL322muWkLZx40aZzWZ98sknCgoKkre3t15++WVJ0q5duzRw4ED98ccfcnNzk5ubm2JiYiRJixYtUpUqVWQ2m1WzZk1t3brVUkuTJk00atQotW7dWu7u7qpevbr++OMPSVKXLl0UExOj7t27y83NTQMHDsxyLG+99Za6detm+VyjRg3VqVPH8vmJJ57QO++8Y9nXyy+/rFatWqlw4cL67rvvFBERoeHDh0uSatWqJUkqXry43NzcNH/+fEnSzp071bRpUxUpUkSlSpXSnDlzLP2PGzdOHTp00KBBg1SkSBG9+uqrd/HNAAAAIBPhE3gARERE6MiRI/r5558VHx+vjz/+WC4uLpKkHj16yNHRUceOHdPmzZu1bNkyvf3225ZtL1++rP379+vw4cPasmWLZs6cqY0bN6patWr68MMPVblyZSUlJSkpKUnBwcGKiorSiBEjFBkZqYsXL2rUqFEKDw/XhQsXLH3OmzdPb7/9ti5duqRHH31Uzz//vCTpq6++UnBwsBYuXKikpCR9+OGHWY6ladOm2rhxoyTp0qVLio2N1eHDh3X58mUZhqGNGzeqWbNmlvaRkZGaOHGikpKS1KJFC6u+fv31V0nSyZMnlZSUpJ49e+rMmTNq2bKlBg0apHPnzmnZsmUaO3asfvjhB8t2q1evVu3atRUXF6c333zzLr8dAAAASIRPoMA6fThef24/o90/HdLSpUv18ccfKzAwUHZ2dqpWrZqKFi2q2NhYrV+/XtOmTZObm5tCQkL02muvKTIy0tKPYRiaOHGiChUqpPLly6tevXrasWNHjvudOXOmRo4cqerVq8vOzk6dO3dWuXLlFBUVZWnTq1cvVa1aVQ4ODnr66adv2d/f1ahRQ9euXdP+/fu1ceNGNWrUSPXq1dPmzZu1e/duSbK6D7VHjx6qVauWTCaTJXDfyrx589SoUSM99dRTsre3V6VKldS3b18tWLDA0qZSpUqKiIiQg4ODXF1dc107AAAAcuZwrwsAkDfHfj8nSVo563cZaSZFxx2Ug72TUi8UkoKt2548eVKFChWSn5+fZdkjjzyikydPWj57eHhYBazChQvr8uXLOe4/Ojpao0eP1tixYy3LUlNTFRsba/ns7+9v1V9SUlKuj8/e3l4NGzbUhg0bdPDgQTVt2lTJycnasGGD/P391aRJE5lMJkv74ODgW/SWff1RUVEym82WZenp6WrYsOEd9wkAAIDbI3wCBcjRXXFaN/eAirX837Iibr5KS0/RwnfWq/tLzVSymq9lXfHixXX9+nWdPXvWEkCjo6NVvHjxXO3Pzi7rxRFBQUF6/vnns71f8077/LumTZtqw4YNOnDggIYOHark5GT169dPfn5+atu2ba77y6n+xx9/XIsWLbqrGgEAAJA3/D8soIDIyDC0efHhLMs9XIuoSmg9Ldo8XSv/+7PS0tK1a9cuXbhwQcWKFVPTpk01YsQIXblyRTExMfr3v/+tp59+Olf79PPz0+nTp3Xt2jXLsiFDhmjq1KnasWOHDMPQ1atXtW7dOqvZ1Nv1efTo0Vu2adq0qdasWaOEhASVLVtWlStX1smTJ/Xjjz9a3e95Oz4+PrKzs7PaX+/evbV+/XotWbJEqampSk1N1e7du7V9+/Zc9wsAAIC8I3wCBcTpw/G6Ep+c7breTV6RubCPxv73GXmZzRo4cKAlMC5YsEDXrl1TSEiI6tevr/bt21ueaHs7zZo1U506dVSsWDGZzWbFxMQoPDxcU6ZMUf/+/eXl5aUSJUpoxowZysjIyFWfo0eP1gcffCCz2azBgwdn2yYsLEwODg5q0qSJJMlkMqlx48Zyd3dXhQoVcrUfSXJxcdHYsWPVtm1bmc1mLViwQMWKFdP333+vjz76SAEBAfLz89OQIUOUmJiY634BAACQdybDMIx7XcT9LDExUZ6enkpISJCHh8cd9ZGamqqoqCi1a9dOjo6O+VwhHhZ/bj+jtZ/ul8nBULGWSYpd6yYjzZSlXctnKqhMTf9segCyx+8o5CfGE/IbYwr56W7HU35kg4cZM59AAVHYwzlf2wEAAAD/JMInUEAElDarsPnWwdLNy1kBpc3/TEEAAABAHhA+gQLCzs6khl1L37JNg6dKy84u66W4AAAAwL1G+AQKkJLVfNWib/ksy928nNVmQCWr16wAAAAA9xPe8wkUMCWq+OjASanD4Cq6fiVdhT1uXGrLjCcAAADuZ4RPoIAKKG3mqX8AAAAoMLjsFgAAAABgc4RPAAAAAIDNET4BAAAAADZH+AQAAAAA2BzhEwAAAABgc4RPAAAAAIDNET4BAAAAADZH+AQAAAAA2BzhEwAAAABgc4RPAAAAAIDNET4BAAAAADZH+AQAAAAA2BzhEwAAAABgc4RPAAAAAIDNET4BAAAAADaXp/B57do1bdmyRfv378+y7vr16/r888/zrTAAAAAAwIMj1+Hzzz//VPny5dWoUSNVrlxZjRs31unTpy3rExIS1LdvX5sUCQAAAAAo2HIdPl955RVVqlRJcXFxOnTokNzd3VW/fn3FxMTYsj4AAAAAwAMg1+Fz69atmjx5sooWLapSpUppxYoVat26tRo2bKi//vrLljUCAAAAAAq4XIfPa9euycHBwfLZZDJp9uzZCg8PV+PGjfXnn3/apEAAAAAAQMHncPsmN5QrV06//fabypcvb7X8gw8+kCR17NgxfysDAAAAADwwcj3z+fjjj2vhwoXZrvvggw/UvXt3GYaRb4UBAAAAAB4cuQ6fo0aNUlRUVI7rZ82apYyMjHwpCgAAAADwYMnTez4BAAAAALgThE8AAAAAgM0RPgEAAAAANkf4BAAAAADYXJ7D56ZNm5SWlpZleVpamjZt2pQvRQEA8E9xc3PTH3/8ca/LuK3hw4crIiLirvupWLGiVq5cefcFAQCQR7l+z2empk2b6vTp0/L19bVanpCQoKZNmyo9PT3figMAwNaSkpLudQn/qH379t3rEgAAD6k8z3wahiGTyZRl+YULF1S4cOF8KQoA8GDI7kqZ+0Vqauq9LuGhcz+PBwCA7eU6fHbu3FmdO3eWyWRSRESE5XPnzp312GOPqXXr1qpXr54tawUA5BOTyaTdu3dbPn/77bdq0aKFpBv/yPjKK6/I399fHh4eKlOmjNVlmosWLVKVKlVkNptVs2ZNbd261bKuSZMmevnll9WqVSsVLlxY3333XZZ9G4ah9957T+XKlZPZbFaTJk104MABSdLKlSvl6+ur06dPS5L++usveXl5acOGDZb+R44cqSZNmsjd3V1169a1bCvdmMUcOnSogoOD5evrqz59+ighIUGSFB0dLZPJpLlz56pUqVIqXrx4tufidsc3atQotW7dWu7u7qpevbrVJbuJiYkaOnSoQkJC5OHhoZo1a+rEiRO3rS07mzZtUuXKleXm5qbOnTvr8uXLVuuPHj2q8PBw+fj4KCQkRBMnTrS8b/vYsWNq0aKFPD09VaRIEdWvX19Xr16VJIWGhmrZsmWWft5//30FBQXJ29tbr7/+usLCwhQZGSlJioyMVFhYmN588035+vrKz89P06dPt6rj7+fr559/tjpftxsPAICHR67Dp6enpzw9PWUYhtzd3S2fPT095e/vr+eee05ffPGFLWsFAPwD1q5dqwULFmjnzp1KTEzUunXrVKZMGUlSVFSURowYocjISF28eFGjRo1SeHi4Lly4YNk+MjJSEydOVFJSkiXQ3mz27Nn69NNPtWLFCp0/f16dO3dWeHi4UlJS1KFDB3Xr1k19+vRRcnKyunfvrsGDB6tp06aW7T/99FNNnjxZFy5cULNmzfTYY49ZZtT69eunixcv6vfff9exY8eUmpqqoUOHWu3/22+/1W+//aZjx45lqS03xzdv3jy9/fbbunTpkh599FE9//zzlnURERE6cuSIfv75Z8XHx+vjjz+Wi4tLrmvLdOnSJXXs2FFDhw5VfHy8+vbta/V37NWrV9W8eXM1b95csbGx2rx5sxYtWqS5c+dKkl577TWVKlVK58+f19mzZzV16lQ5OGS90+aHH37QG2+8oSVLluj06dOys7PLclnuvn375OrqqtjYWC1evFgjR47U0aNHczxfjz/+uBITEy3b3248AAAeIkYejRs3zkhKSsrrZgVWQkKCIclISEi44z5SUlKMZcuWGSkpKflYGR5WjCfcibT0NOPX078aq46uMn49/ashydi1a5dhGDfGVL9+/YxGjRoZhmEY69evN4oWLWqsWbMmyzhr166dMX36dKtl9erVMz7//HPDMAyjcePGxrBhw25ZS4UKFYxly5ZZLQsMDDQ2bdpkGIZhXL9+3ahSpYpRpUoVo06dOkZqaqqlXePGjY1BgwZZPqekpBgeHh7G5s2bjbi4OMPOzs64ePGiZf2ff/5pODo6GmlpacaxY8esjjvTzctyc3yvvPKKZd2WLVsMNzc3wzAM48yZM4Yk4/jx41mO+Xa1/d3nn39ulC9f3mpZmzZtjKefftowDMP48ssvjbCwMKv1H3/8sdGsWTPDMAyjT58+RseOHY0///wzS98hISHG0qVLDcMwjH79+hlDhgyxrEtJSTE8PT2NuXPnGoZhGHPnzjX8/f2tti9VqpTx9ddfG4aR/fmqW7euMWzYMCMlJSVX4wG4Hf7eQ3662/GUH9ngYZbnBw6NHTs2f9MvAMCm1h1fpym/TtHZq2etlm87tU1hYWFZ2jdt2lTjx4/XmDFjdODAAbVo0UL/+c9/VKJECUVHR2v06NFWfxekpqYqNjbW8jk4OPiW9URHR6tXr16yt7e3LEtJSdHJkyclSc7OzurXr5+GDx+ur7/+OsuMXUhIiOW/HR0dFRAQoNjYWDk7OysjI0MlSpSwam9nZ6czZ87kqr7cHJ+/v7/lvwsXLmx5YNHx48fl7Oycbf/R0dG3rK1YsWJWy0+dOmV1nJnHff36dUt/e/fuldlstqzPyMhQUFCQJGnq1KkaN26cWrRoYbld5o033pCdnfUFT6dOnVKTJk0snzPP5838/PysPhcuXNhyCXBO56tUqVKWz7cbDwCAh0eeHzh09uxZ9e7dW4GBgXJwcJC9vb3VDwDg/rHu+Dq9uPHFLMHTztlOU7dO1brj6yTduMzzZoMHD9a2bdsUExMjZ2dnvfDCC5KkoKAgvfPOO4qPj7f8XLlyRa+++ur/+ra79V8tQUFB+uqrr6z6uHr1qrp37y7pxn2e48aNU//+/TVy5EirSzilGyEvU2pqqk6fPq1ixYopKChIdnZ2OnXqlFXf169ftwp3t6ovN8eXk5CQECUnJ1vu8fx7v7mpLVNgYKDVcUpSTEyMVX81atSw6isxMdFyyayvr69mzZql48ePa8WKFfrwww+1dOnSbPdzc71paWmW+21zI7vzFR8fryeeeMLS5nbjAQDw8Mjz3wgRERHauXOnxowZo6+//lrffPON1Q8A4P6QnpGuKb9OkSEjy7pCIYUUvzVek3+erJ27dmrjxo2Wddu3b9fWrVuVkpIiFxcXFS5c2DL7OGTIEE2dOlU7duyQYRi6evWq1q1bZ5m1zI0hQ4bojTfe0KFDhyTdeEjP8uXLdfnyZaWlpalHjx4aMmSIPv74Y9WoUUMDBw602n7x4sX65ZdflJKSogkTJsjHx0d16tSRv7+/OnXqpKFDh+r8+fOSpDNnzmQbum5V250en5+fnx577DENHDhQp0+fVkZGhnbt2qULFy7kubb27dsrNjZWc+bMUVpamlatWqX169db1nfo0EFnz57VrFmzdP36daWnp+vQoUOW7/HLL79UTEyMDMOQ2WyWvb19tvd8du/eXQsWLNBvv/2m1NRUTZw4UVeuXLmr8/XDDz9YjhEAgJvlOXxu2bJF8+fP16BBg9SpUyc99thjVj8AgPvDzridWWY8MwX0CtDVI1e1MWKjho0cpmbNmlnWJSYmavDgwfL29pa/v79OnTqlGTNmSJLCw8M1ZcoU9e/fX15eXipRooRmzJhhecpqbgwdOtTy1HQPDw+VL19eCxYskCSNGTNGJpNJ48aNkyTNmTNHW7du1WeffWbZvl+/fnrllVdUpEgRrV27VsuWLbMEq8jISMtTVz08PNSwYUPt2LEj17Xd7fF99tlnCgoK0qOPPiqz2ayBAwfq2rVrea6tSJEiWr58uWbMmCGz2axPPvlEPXv2tKx3c3PTunXr9MMPPyg0NFTe3t7q0aOH5fLiHTt2qF69enJzc1PdunX1zDPPqGPHjln206JFC40dO1adOnWSv7+/0tLSVKZMGTk7O9/x+Xr//fdlGFn/wQMAAJORx78hKlSooPnz56tatWq2qum+kpiYKE9PTyUkJMjDw+OO+khNTVVUVJTatWsnR0fHfK4QDxvGE3Ir6q8ovbL5ldu2m1JvitL2pxWIMdWkSRN16tRJw4cPv9elPJBSUlLk7e2t1atXq379+nfUB7+jkN8YU8hPdzue8iMbPMzyPPM5ffp0vfrqq4qOjrZBOQCA/OLj6pOrdkVditq4EtzPvvnmG127dk1XrlzRK6+8Im9vb9WsWfNelwUAeADl+Wm3Xbt21dWrV1WyZEm5urpm+ReDixcv5ltxAIA7V923uvxc/RR3NS7b+z5NMsnP1U9VfarqjM5k0wMeBvPmzVO/fv1kGIbCwsL07bffysnJ6V6XBQB4AOU5fE6fPt0GZQAA8pu9nb1erfWqXtz4okwyWQVQk0ySpFdqvSJ7u4LzpPKbH4yE/JGXBzIBAHA38hw+n376aVvUAQCwgRYhLTStybQs7/n0c/XTK7VeUYuQFkpNTb2HFQIAgIdFnsOnJB09elRz587V0aNHNWPGDPn6+uq7775TcHCwKlasmN81SpImT56sb775RgcPHpSLi4vq1aunt956S2XLls1xm8jISPXt29dqmbOzs+Ul3QDwMGgR0kJNg5pqZ9xOnbt6Tj6uPqruW71AzXgCAICCL88PHPrxxx9VuXJl/fLLL/rmm2+UlJQkSdqzZ4/Gjh2b7wXevN8hQ4Zo27ZtWrt2rVJTU9WqVavbvo/Mw8NDp0+ftvz8/aXdAPAwsLezV03/mmr3SDvV9K9J8AQAAP+4PM98vvrqq5o4caJefPFFubu7W5Y3a9ZMH3zwQb4Wd7PVq1dbfY6MjJSvr6927NihRo0a5bidyWSSv7+/zeoCAAAAANxensPnH3/8YXkZ+M18fX11/vz5fCkqNxISEiTdeBH3rSQlJSkkJEQZGRmqXr26Jk2adMtLg5OTk5WcnGz5nJiYKOnGO4Hu9L6ozO24rwr5gfGE/MaYQn5iPCG/MaaQn+52PDEO747JMIysz9+/heLFi+vLL79UvXr15O7urj179uiRRx7R0qVLNWLECB09etRWtVpkZGSoY8eOio+P15YtW3Js9/PPP+vw4cOqUqWKEhIS9J///EebNm3Svn37VLx48Wy3GTdunMaPH59l+YIFC+Tq6ppvxwAAAACgYLl69ap69OihhIQEeXh43OtyCpw8h88RI0bol19+0VdffaUyZcpo586dOnv2rPr06aM+ffrY9L7PTIMGDdJ3332nLVu25Bgis5Oamqry5cure/fuevPNN7Ntk93MZ1BQkM6fP3/HAyw1NVVr165Vy5Yts7wXFcgrxhPyG2MK+YnxhPzGmEJ+utvxlJiYqKJFixI+71CeL7udNGmShgwZoqCgIKWnp6tChQpKT09Xjx499Prrr9uiRitDhw7VypUrtWnTpjwFT0lydHRUtWrVdOTIkRzbODs7y9nZOdtt7/YXXn70AWRiPCG/MaaQnxhPyG+MKeSnOx1PjMG7k+fw6eTkpDlz5mjMmDHau3evkpKSVK1aNZUuXdoW9VkYhqHnn39eS5cu1caNG1WiRIk895Genq4//vhD7dq1s0GFAAAAAICc3NF7PiUpODhYwcHB+VnLLQ0ZMkQLFizQ8uXL5e7urjNnzkiSPD095eLiIknq06ePihUrpsmTJ0uSJkyYoDp16qhUqVKKj4/X1KlTdfz4cT377LP/WN0AAAAAgDsIn+np6YqMjNQPP/yguLg4ZWRkWK1fv359vhV3s9mzZ0uSmjRpYrV87ty5ioiIkCTFxMTIzu5/ry69dOmS+vfvrzNnzsjLy0s1atTQ1q1bVaFCBZvUCAAAAADIXp7D57BhwxQZGan27durUqVKMplMtqgri9w8F2njxo1Wn9999129++67NqoIAAAAAJBbeQ6fixYt0pdffsl9kwAAAACAXLO7fRNrTk5OKlWqlC1qAQAAAAA8oPIcPl966SXNmDEjV5fBAgAAAAAg3cFlt1u2bNGGDRv03XffqWLFilnedfPNN9/kW3EAAAAAgAdDnsOn2WzW448/botaAAAAAAAPqDyHz7lz59qiDgAAAADAAyzP4TPTuXPndOjQIUlS2bJl5ePjk29FAQAAAAAeLHl+4NCVK1fUr18/BQQEqFGjRmrUqJECAwP1zDPP6OrVq7aoEQAAAABQwOU5fL744ov68ccftWLFCsXHxys+Pl7Lly/Xjz/+qJdeeskWNQIAAAAACrg8X3a7ZMkSff3112rSpIllWbt27eTi4qKnnnpKs2fPzs/6AAAAAAAPgDzPfF69elV+fn5Zlvv6+nLZLQAAAAAgW3kOn3Xr1tXYsWN1/fp1y7Jr165p/Pjxqlu3br4WBwAAAAB4MOT5stsZM2aodevWKl68uKpWrSpJ2rNnjwoVKqTvv/8+3wsEAAAAABR8eQ6flSpV0uHDhzV//nwdPHhQktS9e3f17NlTLi4u+V4gAAAAAKDgu6P3fLq6uqp///75XQsAAAAA4AF1R+Hz0KFDev/993XgwAFJUvny5TV06FCVK1cuX4sDAAAAADwY8vzAoSVLlqhSpUrasWOHqlatqqpVq2rnzp2qXLmylixZYosaAQAAAAAFXJ5nPl9++WWNGjVKEyZMsFo+duxYvfzyy3riiSfyrTgAAAAAwIMhzzOfp0+fVp8+fbIs79Wrl06fPp0vRQEAAAAAHix5Dp9NmjTR5s2bsyzfsmWLGjZsmC9FAQAAAAAeLHm+7LZjx4565ZVXtGPHDtWpU0eStG3bNn311VcaP368vv32W6u2AAAAAADkOXwOHjxYkjRr1izNmjUr23WSZDKZlJ6efpflAQAAAAAeBHkOnxkZGbaoAwAAAADwAMvzPZ8AAAAAAORVnmc+JWn79u3asGGD4uLissyETps2LV8KAwAAAAA8OPIcPidNmqTXX39dZcuWlZ+fn0wmk2Xdzf8NAAAAAECmPIfPGTNm6L///a8iIiJsUA4AAAAA4EGU53s+7ezsVL9+fVvUAgAAAAB4QOU5fP7rX//SzJkzbVELAAAAAOABlefLbkeMGKH27durZMmSqlChghwdHa3Wf/PNN/lWHAAAAADgwZDn8PnCCy9ow4YNatq0qby9vXnIEAAAAADgtvIcPj/77DMtWbJE7du3t0U9AAAAAIAHUJ7v+SxSpIhKlixpi1oAAAAAAA+oPIfPcePGaezYsbp69aot6gEAAAAAPIDyfNnte++9p6NHj8rPz0+hoaFZHji0c+fOfCsOAAAAAPBgyHP47NSpkw3KAAAAAAA8yPIcPseOHWuLOgAAAAAAD7A8h89MO3bs0IEDByRJFStWVLVq1fKtKAAAAADAgyXP4TMuLk7dunXTxo0bZTabJUnx8fFq2rSpFi1aJB8fn/yuEQAAAABQwOX5abfPP/+8Ll++rH379unixYu6ePGi9u7dq8TERL3wwgu2qBEAAAAAUMDleeZz9erVWrduncqXL29ZVqFCBc2cOVOtWrXK1+IAAAAAAA+GPM98ZmRkZHm9iiQ5OjoqIyMjX4oCAAAAADxY8hw+mzVrpmHDhunUqVOWZbGxsfrXv/6l5s2b52txAAAAAIAHQ57D5wcffKDExESFhoaqZMmSKlmypEqUKKHExES9//77tqgRAAAAAFDA5fmez6CgIO3cuVPr1q3TwYMHJUnly5dXixYt8r04AAAAAMCD4Y7e82kymdSyZUu1bNkyv+sBAAAAADyAcn3Z7fr161WhQgUlJiZmWZeQkKCKFStq8+bN+VocAAAAAODBkOvwOX36dPXv318eHh5Z1nl6emrAgAGaNm1avhYHAAAAAHgw5Dp87tmzR23atMlxfatWrbRjx458KQp5t2zZMoWGht6TfY8bN06dOnW6J/uWpLZt22rWrFn3bP8AAAAAbi/X93yePXs22/d7WjpycNC5c+fypSggL7777rt7XQIAAACA28j1zGexYsW0d+/eHNf//vvvCggIyJeigEypqan3ugQAAAAA+SDX4bNdu3YaM2aMrl+/nmXdtWvXNHbsWHXo0CFfi0POTp48qVatWsnDw0M1atTQ/v37rdYnJSVp6NChCg4Olq+vr/r06aOEhATL+qNHjyo8PFw+Pj4KCQnRxIkTlZGRIUmKjIxUWFiYRo8eLW9vbwUHB+fpsta4uDj17NlTAQEBCgwM1PDhw5WcnGyp67HHHpOvr688PT3VqFEj7dmzx7LtuHHj1KFDBw0aNEhFihTRq6++qoiICPXv31/dunWTu7u7ypYtq40bN1q2adKkiaZPny5J2rhxo8xmsz755BMFBQXJ29tbL7/8slV977//vmXd66+/rrCwMEVGRub6+AAAAADkXa7D5+uvv66LFy+qTJkyevvtt7V8+XItX75cb731lsqWLauLFy/qtddes2WtuEmPHj0UEBCgM2fOaP78+ZozZ47V+n79+unixYv6/fffdezYMaWmpmro0KGSpKtXr6p58+Zq3ry5YmNjtXnzZi1atEhz5861bL93716ZTCadPn1aixcv1quvvqpNmzbdti7DMNSxY0f5+/vr6NGj+uOPP7Rnzx5NnDhRkpSRkaEePXro2LFjOnv2rKpVq6annnpKhmFY+li9erVq166tuLg4vfnmm5KkxYsXa+DAgYqPj1fv3r0VERGRYw2XL1/W/v37dfjwYW3ZskUzZ860hNUffvhBb7zxhpYsWaLTp0/Lzs5O+/bty9U5BwAAAHDnch0+/fz8tHXrVlWqVEmjRo3S448/rscff1yjR49WpUqVtGXLFvn5+dmyVvy/EydOaPPmzZo6dapcXV1Vrlw5DRw40LL+3LlzWrJkiWbOnCmz2azChQtrwoQJWrx4sdLT07Vq1Sp5eXlp+PDhcnJyUnBwsIYNG6YFCxZY+ihcuLDGjRsnJycn1a1bVz179tTnn/8fe/cdFtXRtgH83qW33aX3YkERO0YTO5YodhM71miMJRoTTaJij8YWNZpYE99YwfLGEpNgQzTBGnuJ3QgISFVYUMrCzvcHH+d1paqsRL1/1+Wle2bmnOccxl2enTlzNpYa25kzZ3Dr1i0pNltbWwQFBUn7VigU6NOnDywsLGBqaopZs2bh5s2biIuLk/ZRq1YtDBkyBIaGhjA3NweQP/Lu7+8PAwMDfPDBB4iKikJKSkqRMQghMGfOHJiamqJGjRpo0qSJtBhWSEgI+vfvj0aNGsHY2BjTpk2DhYXFs/8QiIiIiIjomZR5wSEA8PT0RGhoKB4+fIjbt29DCAFvb29YW1vrKz76f1qtwP1bqXikzsaNu9dhamoKBwcHqdzT01P6d2RkJLRaLSpVqqSzD7lcjvj4eERGRuLKlStQqVRP7F8Ld3d36bWLi4vOAlOenp74448/So0zMjISqampsLGxkbYJIZCXlwcgf4r2hAkTEBoaigcPHkAuz//+Izk5Ga6urgAADw+PQvt1cnKS/l2QLKanp8PW1rZQXYVCISWtBfXT09MBAHFxcfD395fKjIyMeK8yEREREdFL8EzJZwFra2s0bNiwvGOhYtw5n4iIbbfwKDX/vsmHGSnIysrCX4f+RqM2NQEA0dHRUn13d3fI5XLExcXpJGFPljdo0AAnT54s9phxcXHQaDRSAhodHS0lhyVxd3eHg4MD7t+/X2T54sWLcfbsWRw9ehRubm5ITU2FtbW1zrTbgoRUH1xcXHDv3j3pdW5ubrGxEhERERFR+dHfb/lULu6cT8S+NVekxBMArC0dUNmpFsZ9/Dn+PhGFGzduYM2aNVK5k5MTunfvjjFjxiA5ORkAEB8fj127dgEAOnfujISEBKxcuRJZWVnIy8vDjRs3dBbxefToEWbPno2cnBycOnUKwcHB6N+/f6nxNmzYEO7u7pg6dSrS09MhhEBUVJT0OBS1Wg1TU1NYW1sjIyMDQUFB5XGZyqxfv34ICQnBmTNnoNFoMGfOHDx69OilxkBERERE9CZi8vkvptUKRGy7VWTZkNZBeJiRiLf8ayAwMBBDhw7VKV+/fj1UKhUaNmwIhUKB5s2bS/c9WlpaIiwsDIcOHYKXlxdsbW0RGBiI+Ph4qX2tWrWQm5sLZ2dn9OzZE19//TVatWpVaswGBgb47bffEBsbixo1akCpVKJTp064ffs2AGD8+PEwMDCAo6MjatWqhcaNGz/v5Xkubdu2xYwZM9C9e3c4OTkhNzcX1apVg4mJyUuNg4iIiIjoTSMTT853pELUajWUSiXS0tKgUCieax8ajQahoaHo2LGjzn2UpYm98RC7vz1far3un9WHa/Xyu+92/fr1WLp0KS5cuFBu+/y3ysnJga2tLfbt24emTZtWdDhl8rz9iag47FNUntifqLyxT1F5etH+VB65wZus3EY+tVotfvvtt/LaHQF4pM4uvdIz1KN8O3fuRGZmJh49eoSJEyfC1taW9zATEREREenZCyeft2/fRlBQENzc3PDee++VR0z0/ywUZZsKWtZ6lG/Tpk1wdnaGi4sLzp07hz179sDY2LiiwyIiIiIieq09V/KZmZmJjRs3okWLFqhevTqOHz+O6dOnIyYmprzje6M5e6tgoSo5sbS0NoGzt6pcjztkyJDXesrtrl27kJqairS0NPzxxx+oU6dORYdERERERPTae6bk8/Tp0xgxYgScnJywdOlSdOvWDTKZDCtXrsTIkSPh6OiorzjfSHK5DM37eJdYp1lvb8jlspcUERERERER0fMpc/JZp04d9OrVC7a2tjh+/DjOnTuHCRMmQCZj4qNPVeo7IGBErUIjoJbWJggYUQtV6jtUUGRERERERERlZ1jWijdu3ECfPn3QqlUr+Pr66jMmekqV+g6oVNce92+l4pE6GxaK/Km2HPEkIiIiIqJXRZmTz3/++Qfr16/HqFGjkJmZiX79+qF///4c+XxJ5HJZuT5OhYiIiIiI6GUq87RbV1dXTJkyBbdv38amTZsQHx+Ppk2bIjc3F+vXr8fNmzf1GScRERERERG9wp5rtdvWrVtj8+bNuH//PpYvX47w8HD4+Phw1VAiIiIiIiIq0gs951OpVGL06NE4c+YMzp07B39//3IKi4iIiIiIiF4nZU4+MzMzsWfPHqSnpxcqU6vViI6OxjfffFOuwREREREREdHroczJ5w8//IBly5bBysqqUJlCocB3332HtWvXlmtwRERERERE9Hooc/IZHByMTz/9tNjyTz/9FBs2bCiPmIiIiIiIiOg1U+bk89atW6hbt26x5XXq1MGtW7fKJSgiIiIiIiJ6vZQ5+czNzUVSUlKx5UlJScjNzS2XoIiIiIiIiOj1Uubks2bNmggLCyu2/MCBA6hZs2a5BEVERERERESvlzInn0OHDsXs2bPx22+/FSr79ddf8fXXX2Po0KHlGhwRERERERG9HgzLWvGjjz7Cn3/+ia5du8LHxwfVq1cHAFy/fh03b95E79698dFHH+ktUCIiIiIiInp1lXnkEwA2b96MrVu3wtvbGzdv3sSNGzdQvXp1bNmyBVu2bNFXjERERERERPSKK/PIZ4HevXujd+/e+oiFiIiIiIiIXlNlHvnUarVYsGABmjZtioYNG2LSpEnIzMzUZ2xERERERET0mihz8vn1118jKCgIlpaWcHV1xbJly/Dxxx/rMzYiIiIiIiJ6TZQ5+dy4cSNWrlyJ/fv3Y/fu3fj1118RHBwMrVarz/iIiIiIiIjoNVDm5DM6OhodO3aUXrdt2xYymQxxcXF6Caw4K1asgJeXF0xNTfH222/jr7/+KrH+f//7X/j4+MDU1BS1a9dGaGjoS4qUiIiIiIiICpQ5+czNzYWpqanONiMjI2g0mnIPqjjbtm3D+PHjMWPGDJw7dw5169ZF+/btkZiYWGT948ePo1+/fhg2bBjOnz+P7t27o3v37rhy5cpLi5mIiIiIiIieYbVbIQSGDBkCExMTaVtWVhZGjhwJCwsLadvOnTvLN8InLFmyBMOHD8cHH3wAAFi9ejV+//13/PTTT5g0aVKh+suWLUNAQAC++OILAMDs2bNx8OBBLF++HKtXr9ZbnERERERERKSrzMnn4MGDC20bMGBAuQZTkpycHJw9exaTJ0+WtsnlcrRt2xYnTpwoss2JEycwfvx4nW3t27fH7t27iz1OdnY2srOzpddqtRoAoNFonnuUt6DdyxwlptcX+xOVN/YpKk/sT1Te2KeoPL1of2I/fDFlTj7XrVunzzhKlZycjLy8PDg6Oupsd3R0xPXr14tsEx8fX2T9+Pj4Yo8zb948zJo1q9D2AwcOwNzc/Dki/5+DBw++UHuiJ7E/UXljn6LyxP5E5Y19isrT8/anx48fl3Mkb5YyJ59vismTJ+uMlqrVari7u6Ndu3ZQKBTPtU+NRoODBw/i3XffhZGRUXmFSm8o9icqb+xTVJ7Yn6i8sU9ReXrR/lQwK5KezyuTfNrZ2cHAwAAJCQk62xMSEuDk5FRkGycnp2eqDwAmJiY697UWMDIyeuE3vPLYB1EB9icqb+xTVJ7Yn6i8sU9ReXre/sQ++GLKvNptRTM2NkaDBg1w6NAhaZtWq8WhQ4fQuHHjIts0btxYpz6QP8ReXH0iIiIiIiLSj1dm5BMAxo8fj8GDB+Ott95Co0aNsHTpUjx69Eha/XbQoEFwdXXFvHnzAADjxo1Dy5YtsXjxYnTq1Albt27FmTNn8MMPP1TkaRAREREREb1xXqnks0+fPkhKSsL06dMRHx+PevXqYd++fdKiQtHR0ZDL/zeY26RJE4SEhGDq1KkICgqCt7c3du/ejVq1alXUKRAREREREb2RXqnkEwDGjBmDMWPGFFl25MiRQtt69eqFXr166TkqIiIiIiIiKskrc88nERERERERvbqYfBIREREREZHeMfkkIiIiIiIivWPySURERERERHrH5JOIiIiIiIj0jsknERERERER6R2TTyIiIiIiItI7Jp9ERERERESkd0w+iYiIiIiISO+YfBIREREREZHeMfmkMpPJZLhw4UKRZdHR0bC0tERaWtpLP7a+RUREwM3NrUKOTURERET0ujCs6ADo9eDh4YGMjIyKDkMvmjdvjpiYmIoOg4iIiIjolcaRT3qj5ebmQghR0WFQBdq9eze8vLz0tv+ZM2eie/fuetu/vrzodenQoQNWrlxZprrBwcFo0qTJcx+LiIiIXg1MPl9xXl5emDdvHho2bAgLCwt06NABDx48wOjRo6FSqeDt7Y3jx49L9Tdv3oxatWrBysoKHh4emDZtmk7yFR8fjwEDBsDZ2RkqlQotWrRAZmamVH7y5EnUqlULCoUCXbt2labZRkZGQiaTITU1FQAwZMgQDB8+HH379oWVlRWqV6+OI0eOSPvRaDSYPn06qlSpAltbW3Tt2hVxcXFlPu+tW7eiTp06UKlUaNiw4TOdo0wmw/Lly1GrVi1YWFjgypUrkMlk2LRpE6pWrQqVSoUhQ4ZAo9EAAI4cOQKVSiW19/f3x+TJk9G+fXtYWVnBz88Ply9flspjYmLw7rvvQqFQoEGDBpg7d65ekxt6eZ7u51S8vXv3YvTo0WWq279/f53/w8VZv3496tWr94KR5b8/ffrppy+8HyIiIno2TD5fA9u2bcPOnTsRFxeHe/fu4Z133kHbtm2RkpKCwMBAjBw5Uqpra2uLnTt3Qq1WY8+ePfjhhx8QEhICANBqtejSpQsMDQ1x9epVJCcnY+7cuZDL/9dNtm/fjvDwcERHRyMmJgbffvttiXGNHDkSqampGDhwIIYMGSKVTZkyBceOHcPRo0dx//59VKtWDX379i3T+YaGhuLzzz/H+vXr8eDBA0yePBldunRBSkpKqedYICQkBAcOHIBarYaFhQWA/F+Wz58/j6tXr+LQoUMIDg4uNoZNmzZh4cKFePjwId566y2MHTtWKgsMDISnpycSEhKwZcsW/Oc//ynTeRHRiyv40uhVPwYREdHriMnnK0jk5eHRqb+Q9tvvENk5GDliBNzd3aFUKtGxY0fY2tri/fffh4GBAfr06YMrV64gJycHQP5UuGrVqkEmk6FevXro16+fNCJ5+vRpXLt2DatWrYK1tTUMDQ3RrFkzmJiYSMf+8ssv4eDgAJVKhR49euDs2bPFxtmxY0f4+/vDwMAAH3zwAaKiopCSkgIhBFauXIklS5bA2dkZxsbGmDNnDo4dO4Z79+6Vev4rVqzAF198AT8/P8jlcrz//vvw8fFBaGhoqef45Hm4uLjAxMRESq6nT58OKysruLi4ICAgoMRzGzBgAOrWrQtDQ0MMHjxYqnvv3j1ERERg/vz5MDMzQ7Vq1XSSf6p4MTExaNeunTQyffXqVZ3yJUuWwNvbG1ZWVqhSpQqWL18ulTVq1AgA4ObmBktLSwQHByMjIwPdunWDg4MDlEolWrRogYsXL+rsMzc3F8OGDYNCoYC3tzd27dollR04cABvvfUWlEolnJ2dMXr0aJ3ZBkuWLIGHhwesrKzg5eWFtWvXSmVhYWFo1KgRVCoVatasiT179hR73qUdJzk5GR07diz2ujzrLAt/f38sXboUwP9mD6xduxbu7u6wtbXFl19+KdV9ekSzqHM+f/48Ro4cicuXL8PS0hKWlpaIjo7GzJkz0blzZ4waNQo2NjaYNGkSoqOj8e6778Le3h7W1tbo1KkTIiMjAQDfffcdgoODsXLlSlhaWqJmzZoAgPT0dHz00UdwdnaGs7MzRo4ciUePHgH434j3unXrULVqVS5ARkRE9JyYfL5i1AcO4HabtogePBhxn3+O3OQkaFesgPrAAQCAubk5HB0dpfrm5uYQQuDx48cAgP3796NJkyaws7ODUqnE6tWrkZycDACIioqCq6srzMzMij2+k5OT9G8LCwukp6eXuS6Q/wtecnIyHj16hBYtWkClUkGlUsHJyQnGxsZlSj4jIyMRFBQktVWpVLhw4QJiY2NLPccCHh4e5XpuBYstxcXFwdTUFHZ2diUeiypOYGAgnJ2dER8fj+DgYPz444865Z6enggPD4darcbatWvxxRdf4NixYwCAv/76C0B+ApuRkYH+/ftDq9UiMDAQd+/eRUJCAurXr4/evXvrTPXet28fGjVqhAcPHmDJkiXo168f7ty5AwAwMzPDjz/+iAcPHuDYsWM4fPgwlixZAgC4efMmpk6digMHDiA9PR2nTp2SEuBLly6hV69emD9/Ph48eIA1a9Zg4MCBuHHjRpHnXdJxgPyEz8nJqdjrAjzbLIunpaen4+rVq7h16xaOHj2KFStWFPpSqKRzrl+/PlavXo3atWsjIyMDGRkZ0v+tffv24e2330ZiYiJmz54NrVaL8ePH4969e4iKioK5uTmGDx8OAPjkk0/Qv39/jB49GhkZGfj7778BAOPGjcPt27dx5coVXL58GdevX8dnn32mE9uePXtw5swZ3L17t9jzJCIiouIx+XyFqA8cQOy4T5EbH6+zPS81DbHjPpUS0OLk5OTg/fffx4gRIxAbG4u0tDSMHDlS+iXZ09MTsbGxyMrK0ts5APnTYs3NzXHq1CmkpqZKfzIzM8u06Ii7uzsWL16s0/bRo0eYNGlSqedY4MmpxOXJxcUFWVlZOsludHS0Xo5FZaPVanH37l1cvnwZx44dQ0REBL755huYm5vDx8enUMLUo0cPuLu7QyaToVWrVmjfvn2RSVIBhUKBPn36wMLCAqamppg1axZu3rypcw9ztWrVMGLECBgaGqJLly5o1aoVtmzZAiB/NeX69evDwMAAlStXxogRI6TjGRgYQAiBv//+G5mZmXB0dESdOnUAAGvWrMGQIUPQunVryOVyNGvWDJ07d8b27duLjLOk49y7dw9Xr17F/Pnzi70uADBq1Kgyz7J4mhACc+bMgampKWrUqIEmTZoUObugpHMuTq1atTBkyBAYGhrC3NwcXl5e6NChA0xNTaFQKDBlyhRERERAq9UW2V6r1SI4OBjz5s2Dra0t7OzsMHfuXGzcuFGnzYwZM6BSqWBubl5iPERERFQ0Jp+vCJGXh4S584CiVmb9/20Jc+dBFPPLFQBkZ2cjKysLtra2MDExwalTp3TuhWzYsCGqV6+O0aNHIzU1Fbm5uTh69Ciys7PL9VzkcjlGjhyJCRMmSCOdKSkp2LZtW5naf/zxx/jmm29w9uxZaVQ3LCwMMTExpZ6jvrm7u6Np06YICgpCZmYmbt26hR9++OGlHZ90Xb16FUuXLsWGDRuwY8cOrFu3DoaGhjpfDnh6euq0CQ4Ohp+fH2xsbKBSqRAaGlpo5PxJmZmZGD16NLy8vKBQKKTFpUo6RsEXPUD+dPe2bdvC0dERCoUCQUFBUtsqVapgw4YNWL58ORwdHdGuXTvpebeRkZFYvXq1zgyAX375pdiFu0o6zv3792FsbAwHB4diYwZQaFZFSbMsnqZQKHSStuJmF5R0zsV5enZBUlISAgMD4e7uDoVCgRYtWiA7O7vY2QxJSUnIycnRWRiscuXKyM7O1vk5chYDERHRi2Hy+Yp4fOZsoRFPHUIgNz4emrj7xVaxsrLCihUr8NFHH0GhUODrr79Gnz59pHK5XI5ff/0Vjx8/RvXq1WFnZ4epU6cWO1rwIubNm4fGjRujdevWsLKyQoMGDXCglJHbAl26dMH8+fMxfPhwWFtbo1KlSli2bBm0Wm2p5/gyhISE4J9//oGjoyP69u2LAQMG6Nw3Sy/H1atXsX37dqjVammblZUVcnNzsW7dOumexidHpqOjozF48GAsXLgQiYmJSE1NRceOHaWR86JGzBcvXoyzZ8/i6NGjUKvV0r2FT462R0VF6bSJjo6Gq6srAKBfv35o1aoV/vnnH6jVasydO1enbe/evXH48GEkJCSgbt26GDhwIID8LzrGjRunMwMgIyMDq1atKvJ6lHQcZ2dn5OTkIDExUSfGilLcORc3Y+Hp7ZMnT8bjx49x7tw5qNVq/PnnnwBQ7M/R3t4exsbG0s8OyE/uTUxMdKbQ62vGBBER0ZvCsKIDoLLJTUoqcntYlao6ryd27w5l507Say8vL51fZEeOHFnifVkuLi7YunVrkWVPT1399NNPpccVPH2c9evX69RVqVQ65cbGxpg6dSqmTp1abCwlHbtXr17o1atXkXVLO8en9/V07ACkhVKA/IVTnny0xtNTMOvVq6fT3sPDA2FhYdLrefPmccTkJdNqtdi3b1+h7UqlEu7u7ggLC4OdnR1kMhnWrFkjlWdkZEAIAQcHB8jlcoSGhuLAgQP46KOPAOQnKXK5HHfu3EGDBg0AAGq1GqamprC2tkZGRgaCgoIKHffmzZv48ccf8cEHH2D//v0IDw/HsmXLpPYqlQoWFhbSgl8F913fuHED0dHRaNasGYyNjWFpaQlDw/y37REjRiAgIADt27dHixYtkJubi3PnzkGlUqFGjRqFYijpOO7u7qhRowamTJmCVatWITo6Wue6vEwlnbOjoyPu37+PzMzMEu9NV6vVMDc3h0qlQkpKCmbNmqVT7ujoiL///htCCMhkMsjlcgQGBmLKlCnYvn07hBAICgrCwIEDmXASERGVI36qviIM7e3LtR7pz7lz53D9+nUIIXD27Fl8//33xSbKpB9RUVE6I55P6tGjB9RqNaZPn47evXtj6NChUpmvry+mTJmC1q1bw9bWFtu2bUPXrl2lcjMzM8yYMQMdOnSASqVCSEgIxo8fDwMDAzg6OqJWrVpo3LhxoWMGBATg5MmTsLGxwbhx47B582Z4e3sDyL93c9GiRbC0tMTIkSN1HjmUk5ODadOmwdHREba2tggPD5e+2Klfvz62bNmCqVOnwt7eHq6urpg2bVqx0+RLOg4AjB8/HjExMXBwcEBgYKDOdXmZSjrn1q1b45133oGrqytUKlWxo7OzZs3C7du3YW1tjaZNm6JDhw465R9++CFiY2NhY2Mj3U+6bNkyeHl5wdfXFzVr1kTVqlV1FmQiIiKiFycTTw/5kA61Wg2lUom0tDQoFIrn2odGo0FoaCg6duwIIyOj59qHyMvD7TZtkZuQUPR9nzIZDB0dUfVQGGQGBs91DCof+/fvx8iRI5GQkAAHBwcMHjwY06dPh0E5/VzKoz+97i5fvowdO3aUWq9Hjx6oXbv2S4jo3419isoT+xOVN/YpKk8v2p/KIzd4k3Ha7StCZmAAx6DJiB33KSCT6SagMhkAwDFoMhPPf4H27dvzUQwVzNLSslzrEREREdGL47TbV4iiXTu4LlsKwydWmAQAQ0dHuC5bCkW7dhUUGdG/i6enZ6nfRioUiiJXdCUiIiIi/eDI5ytG0a4drNq0yV/9NikJhvb2MH+rAUc8iZ4gl8sREBBQ7DMvgfz7MLmYDBEREdHLw+TzFSQzMIDF240qOgyifzVfX1/07t0b+/bt01l8SKFQICAgAL6+vhUYHREREdGbh8knEb22fH194ePjg6ioKGRkZMDS0hKenp4c8SQiIiKqAEw+iei1JpfLUalSpYoOg4iIiOiNx6//iYiIiIiISO+YfBIREREREZHeMfkkIiIiIiIivWPySURERERERHrH5JOIiIiIiIj0jsknERERERER6R2TTyIiIiIiItI7Jp9ERERERESkd0w+iYiIiIiISO+YfBIREREREZHeMfkkIiIiIiIivWPySURERERERHrH5JOIiIiIiIj0jsknEREREb1UXl5e2L17d7nv98iRI1CpVOW+36Ls3r0bXl5eL+VYT4uIiICbm1uFHJvoRTD5JCIiIiJ6hTRv3hwxMTEVHQbRM2PySUREREREZaLRaCo6BHqFMfkkIiIiopfu77//hp+fHxQKBdq3b4+4uDip7Msvv4SnpyesrKxQp04dHDt2TKft2bNn0bp1a9jY2MDe3h5jx44t8hi//PILXF1dcfToUQDA1q1bUadOHahUKjRs2BDHjx+X6vr7+2Py5Mlo3749rKys4Ofnh8uXL0vlMTExaNeuHRQKBRo0aICrV6+WeH4ZGRkYM2YMPDw84ODggEGDBiEtLQ0AEBkZCZlMhk2bNqFq1apQqVQYMmSITmL3888/o2rVqlAqlRg+fDg6d+6MmTNnAig8vbi02EuKBQDu3LmDLl26wN7eHp6enpgzZw60Wi0AYP369ahXrx5mzJgBJycn9O3bt8TzJioJk08iIiIieunWrl2LkJAQxMfHw8nJCQMGDJDK6tati9OnTyM1NRVTpkzB0qVLcffuXQBAbGwsWrdujZ49eyIuLg5RUVHo3bt3of3/+OOPGDduHA4cOIBmzZohNDQUn3/+OdavX48HDx5g8uTJ6NKlC1JSUqQ2mzZtwsKFC/Hw4UO89dZbOkltYGAgnJ2dER8fj+DgYPz4448lnt/QoUPx4MEDXLp0CXfv3oVGo8GYMWN06uzduxfnz5/H1atXcejQIQQHBwMAbt68iYEDB2L58uVISUlBo0aNsH///hKPV1LsJcXy+PFjtGnTBm3atEFsbCwiIiKwdetWrFu3Tmp/5coVGBoaIjo6Gps2bSoxDqISCSpRWlqaACDS0tKeex85OTli9+7dIicnpxwjozcV+xOVN/YpKk/sT1SUvLw88c8//4hLly6Jf/75R3h6eooFCxZI5fHx8QKAuHfvXqG2OTk5wsvLS6xfv14IIcT8+fNFq1atijzO4cOHhVKpFF999ZWoUaOGiI6Olso6duwoli5dqlO/SZMmYuPGjUIIIVq2bCkmTpwolR09elRYWloKIYSIjo4WAERCQoJUPn/+fOHp6VlkHImJiUIul4sHDx5I227evCmMjIxEbm6uuHv3rgAgrl27JpV/+OGHYsyYMUIIIb766ivRqVMnnX36+vqKGTNm6JxngZJiLy2W7du3i3r16ukc64cffhCtW7cWQgixbt06YWNjI/Ly8oo811fNi75HlUdu8CYzrNDMl4iIiIhea1evXsW+ffugVqulbWq1GgYGBtJrR0dHmJiYIDY2Fm5ubvj222+xdu1axMTEQCaTIT09XRqhjIqKgre3d7HHy8zMxJIlS7BgwQK4u7tL2yMjIxEUFIQZM2ZI2zQaDWJjY6XXTk5O0r8tLCyQkZEBAIiLi4OpqSkcHBykck9Pz2JjiIyMhFarRaVKlXS2y+VyxMfHF3u81NRU6XhPxg4AHh4exR6vpNhLiyUyMhJXrlzRmcar1Wp1ju/q6gq5nBMm6cUx+SQiIiIivbh69Sq2b99eaLtWq8W+ffvQoUMH+Pr6IjExEdnZ2dL9mTNnzkR4eDjq16+PvLw8VKtWDUIIAPlJ34EDB4o9ppmZGcLCwtChQwcoFArpHkV3d3eMHTsWI0eOfObzcHFxQVZWFhITE6UENDo6utj67u7ukMvliIuLg7m5eaHyyMjIUo936tQpnW3R0dF4++23nzn20mJxd3dHgwYNcPLkyWL3wcSTygt7EhERERGVu4IEszhnz57Fxo0b8ejRI0ycOBEtWrSAm5ubNCpqb28PrVaL9evX6yR6/fv3x19//YXVq1cjOzsbjx8/RkREhM6+GzRogP3792PcuHHSfZQff/wxvvnmG5w9exZCCDx+/BhhYWFlemSJu7s7mjZtikmTJiEzMxM3btzAmjVriq3v5OSE7t27Y8yYMUhOTgYAxMfHY9euXaUeCwB69+6NsLAwHDhwALm5ufjpp59w8+bNMrV91lg6d+6MhIQErFy5EllZWcjLy8ONGzdw5MiR5zoeUUmYfBIRERFRuYuKitKZavu0evXqYcOGDXByckJsbKyUJAYEBKBnz56oXbs2XFxccPXqVdSoUUNq5+bmhkOHDiEkJASOjo7w8vLCzz//XGj/9evXx8GDBzFhwgRs2LABXbp0wfz58zF8+HBYW1ujUqVKWLZsmbSqa2lCQkJw7949ODg4IDAwEEOHDi2x/vr166VVdRUKBZo3b46zZ8+W6VjVq1fHhg0bMGrUKNja2uLEiRNo3bo1TExMytT+WWKxtLREWFgYDh06BC8vL9ja2iIwMFBnejBReZGJgjkMVCS1Wg2lUom0tDQoFIrn2odGo0FoaCg6duwIIyOjco6Q3jTsT1Te2KeoPLE/UYHLly9jx44dpdbr0aMHateuXWw5+1S+6tWrY/r06ejfv39Fh/JKe9H+VB65wZuMI59EREREVO4sLS3Ltd6b5tdff0V6ejqys7OxePFi3L9/HwEBARUdFtELYfJJREREROXO09Oz1JEhhUJR4qqxb7L9+/fD09MTdnZ22LJlC/bs2QNbW9uKDovohTD5JCIiIqJyJ5fLSx2pCwgI4EqqxVi+fDkePHiA9PR0nDlzBv7+/hUdEtEL4/92IiIiItILX19f9O7du9AIqEKhQO/eveHr61tBkRFRReBzPomIiIhIb3x9feHj44OoqChkZGTA0tISnp6eHPEkegMx+SQiIiIivZLL5ahUqVJFh0FEFYxfOREREREREZHeMfkkIiIiIiIivWPySURERERERHrH5JOIiIiIiIj0jsknERERERER6R2TTyIiIiIiItI7Jp9ERERERESkd0w+iYiIiIiISO+YfBIREREREZHeMfkkIiIiIiIivWPySURERERERHrH5JOIiIiIiIj0jsknERERERER6R2TTyIiIiIiItI7Jp9ERERERESkd0w+iYiIiIiISO+YfBIREREREZHeMfkkIiIiIiIivWPySURERERERHrH5JOIiIiIiIj0jsknERERERER6R2TTyIiIiIiItI7Jp9ERERERESkd0w+iYiIiIiISO9eieQzMjISw4YNQ6VKlWBmZoYqVapgxowZyMnJKbGdv78/ZDKZzp+RI0e+pKiJiIiIiIiogGFFB1AW169fh1arxZo1a1C1alVcuXIFw4cPx6NHj7Bo0aIS2w4fPhxfffWV9Nrc3Fzf4RIREREREdFTXonkMyAgAAEBAdLrypUr48aNG1i1alWpyae5uTmcnJz0HSIRERERERGV4JVIPouSlpYGGxubUusFBwdj8+bNcHJyQpcuXTBt2rQSRz+zs7ORnZ0tvVar1QAAjUYDjUbzXLEWtHve9vTv9Msvv+Dzzz/HrVu3Xupxn7U/WVtb488//0Tt2rX1Eo++90/6x/coKk/sT1Te2KeoPL1of2I/fDEyIYSo6CCe1e3bt9GgQQMsWrQIw4cPL7beDz/8AE9PT7i4uODSpUuYOHEiGjVqhJ07dxbbZubMmZg1a1ah7SEhIZyySzpOnjyJ//znP/jxxx9LrZuQkIARI0Zg8+bNsLS0fAnR6cfw4cMxbNgwvPPOO8/ctnv37liyZAkqV65cbvssyapVq2Bubo7BgweX634r2p49e3Dq1Cl8/fXX0rZn6V9jx47FoEGD0LBhw1KP9d///hfR0dGYMGHCC8dNRET0Onj8+DECAwORlpYGhUJR0eG8cio0+Zw0aRIWLFhQYp1r167Bx8dHeh0bG4uWLVvC398fa9eufabjhYeHo02bNrh9+zaqVKlSZJ2iRj7d3d2RnJz83B1Mo9Hg4MGDePfdd2FkZPRc+6B/n2cZ+YyMjES1atWQmJgIlUr1QsetyP7k7e2NRYsWoVu3bs/c1tjYGH/99Rfq1atXbvssb1999RUuXryIHTt2VHQoxfruu++wZ88ehIWFSdtetH8V9KnFixejW7du+OSTT56pfXldt7Zt26Jr167PfHz6d+FnHpU39ikqTy/an9RqNezs7Jh8PqcKnXY7YcIEDBkypMQ6T46SxMXFoVWrVmjSpAl++OGHZz7e22+/DQAlJp8mJiYwMTEptN3IyOiF3/DKYx9UcWJiYjB06FCcPHkS3t7e6NGjBwBIP9MlS5Zg1apViI+Ph4ODAz777DOMGTMGANC0aVMAQKVKlQAAa9asQbdu3dC/f3+cOHEC2dnZqFu3Lr7//nvUrVu3yOOfO3cOo0ePxtWrVwEAzZs3x++//w4AiI+Px+eff45Dhw4hMzMTderUwf79+2FmZgaZTIbz589LSd/WrVsxd+5cREdHw9vbG8uWLUOTJk0A5K8Q3bhxY5w7dw7Hjx+Ht7c3NmzYgNq1a6NXr16Ijo7GwIEDYWBggAEDBmD16tU6+38yRmNjYzRu3Bi//vorGjVqBABo2bIl5HI5goKCEBQUJJ2boaHhv+L/hoGBAeRy+b8iluIYGBhAJpPpxFjw7xd9j5HJZDAwMHjmfZTlumk0mlL3+7zHf5Zj0MvDzzwqb+xTVJ6etz+xD74g8YqIiYkR3t7eom/fviI3N/e59nH06FEBQFy8eLHMbdLS0gQAkZaW9lzHFEKInJwcsXv3bpGTk/Pc+6CK17x5czFo0CDx6NEjce3aNeHl5SU8PT2l8p9//llER0cLrVYrwsPDhampqTh69KgQQoi7d+8KAOLhw4dS/bS0NLF161aRkZEhMjMzxSeffCKqVasmtFptkcdv3LixmDNnjsjKyhL//e9/xaFDh4QQQuTl5Ym33npLDB48WDx48EBoNBoREREhsrKyhBBCABDnz58XQgjx+++/C1dXV3H27FmRl5cnduzYIWxsbERycrIQQoiWLVsKV1dXceHCBaHRaMTw4cNFy5YtpRg8PT3Frl27dOJ6cv8FMebl5YmsrCzxxx9/FFnvSZ6enmLOnDmifv36wsrKSrRr107ExsYWe93GjRsnBg8eLIQQYsGCBcLCwkIAEDKZTLi4uIjjx48LBwcH0b59ezFu3Did/YwePVpUqVJFmJiYCFNTU2FpaSm8vb3Fr7/+KhYtWiRkMpm0L2NjYyGEEFqtVnz++efC1NRUABCGhobC399fKlMoFMLS0lLI5XIhk8mEn5+fSElJEaNGjRJKpVJUrVpVHDt2TJw7d040bdpUWFtbCzs7O9G3b1+RnJwsNm3aJGrWrCnkcrmwsrISlStXFpaWlqJ+/fri0qVL4sqVK+Ltt98WxsbGwszMTBgZGQlTU1Px/fffS9d15syZAoAwMzMTnTp1EpaWluK9994TVlZWol69euLIkSPC2NhYxMbGCjc3N/H2228LlUolzMzMhJmZmRg3bpwwNjYWAISBgYGwsLAQAQEBYsaMGaJbt27SuX755ZfC0dFRWFlZSddt165dwsjISGpnYWEhhBBi8ODBYujQoaJXr17CyspKfPfdd8VeAyGEGD9+vJDL5cLY2Fg6vhBCxMfHi169egk7Ozvh7u4ugoKChEajEUIIcfjwYaFUKsXKlSuFu7u7eOutt4r8v0MvFz/zqLyxT1F5etH+VB65wZvslXjOZ2xsLPz9/eHh4YFFixYhKSkJ8fHxiI+P16nj4+ODv/76CwBw584dzJ49G2fPnkVkZCT27NmDQYMGoUWLFqhTp05FnQq9arR5wN0I3Du4GhEREfhmwXyYm5vDx8en0DNje/ToAXd3d8hkMrRq1Qrt27fHkSNHit21QqFAnz59YGFhAVNTU8yaNQs3b95EXFxckfWNjIwQFRWFuLg4GBkZoXnz5gCA06dP49q1a1i1ahWsra1haGiIZs2aFTmCv2LFCnzxxRfw8/ODXC7H+++/Dx8fH4SGhkp1BgwYgLp168LQ0BCDBw/G2bNny3y5nozRxMQELVq0KFO7tWvXIiQkBPHx8XBycsKAAQNKbXPz5k1MnToVnp6eePjwIeLi4rBq1Sq4ublh4MCBuH37tlT33LlzAIDk5GQsWrQINjY2sLa2xvfff4+wsDBYW1tj7ty56NGjB7p27YpLly7B1tYWhw4dwqpVq7By5UqMGjUK2dnZWLBgAW7evImcnBwcPHgQjx49goeHByIjI3Hp0iWo1Wq88847aNu2LVJSUhAYGIiRI0dCLpdj/vz5SEhIwJUrVxAbG4tJkybB1tYWO3fuRPPmzWFmZoaHDx9ixYoVeOuttzBmzBh07doVbdq0wcaNGxESEgILCwtUq1YNX3zxBY4dOwYAOHjwIADg7NmzOH36NPLy8uDj44MHDx6gXr16GDlyJNq2bQsXFxeo1Wrk5eUhNjYWy5cvR05ODlQqFUJCQlCnTh0IIXDx4kXs3btX53ofPHgQISEhOHfuHNRqNcLCwlCtWjV0794dQUFB6Ny5MzIyMpCRkSG12bJlC4YNG4bU1FQMGzas2GsAAIsXL0bz5s2xYMECZGRkSMcPDAyEkZER7t69i4iICOzevRsLFy6UjpGeno6LFy/i+vXr+OOPP8rU34iIiKhivBLJ58GDB3H79m0cOnQIbm5ucHZ2lv4U0Gg0uHHjBh4/fgwg//6ysLAwtGvXDj4+PpgwYQJ69OiBX3/9taJOg141V/cAS2sBGzojbtsEmBoCDiGt87cD8PT01KkeHBwMPz8/2NjYQKVSITQ0FMnJycXuPjMzE6NHj4aXlxcUCgW8vLwAoNg2P/30E7KysvDOO+/g448/xsqVKwEAUVFRcHV1hZmZWamnFBkZiaCgIKhUKunPhQsXEBsbK9V58tFEFhYWOslEaQpibNCgAXx8fLB8+fIi6+UJgWMP07Er4SGytQIjRo6Ej48PzM3NsXDhQhw+fBgxMTGF2om8PGji45FzLwbZl69ACIGHDx/iwoULcHBwQNeuXeHu7o5hw4YhKipKWpHu559/BgDMmjULSqUSGo0G9erVw+nTp+Hh4YHjx4+jRYsWqFmzJmQyGWrVqoUPPvgAISEhWLFiBSpVqoSMjAwkJiZi/PjxAIBTp07ByMgIQgi0bdsWTk5OqFWrFt577z3Y2tri/fffh4GBAfr06YMrV66gRo0aaNasGYyMjODo6Ijx48fjyJEj6NChA6pVqwYA+OCDDzBo0CBERERg8ODBOH36NJKTkzFz5kz06dMH3bt3R9++fWFtba3z5UbBwmvOzs5o2bIlfH19cfz4cRgaGqJXr164ffs2PvjgAwD5U1vT09Nx69YtyOVy2NvbY8qUKTA0NIRKpYKNjQ0uXLhQ6NobGRkhKysLf//9NzQaDTw8PKS4i9OuXTu0b98ecrkc5ubmqFu3bpHXoDixsbEIDw/HkiVLYGlpCU9PT0yZMgXr16+X6mi1Wsyfn/+lEBeFIyIi+nd7JR61MmTIkFLvDfXy8oJ4Yu0kd3d3fgtOz+/qHmD7IAD5fcrFSo6sXCDxfiwctg8Cem9EdHS0VD06OhqDBw/Gvn374O/vD0NDQ3Tv3l3qk3J54e95Fi9ejLNnz+Lo0aNwc3NDamoqrK2tdfrxk6pUqYKNGzciJycHixcvxsSJE9GsWTN4enoiNjYWWVlZMDU1LfG03N3dMXbs2EKjtmVV1HkUFaMQAseOHUPbtm3RuHFjNGjQADKZDADwe1Iqpt6Kxf3s/MQwKUeD1dkGqJ2Uik72Kjg6OsLExASxsbFwdHSU9q0+cAAJc+ch/dJFZOblwXDKFCyo6o3vUh+iTZs2kMvl6NChA3766SfUqFEDKpUKt27dQlZWFn777TcA+Ym1j48PZs2ahdmzZyMsLAzx8fEwNzdHaGgo9u7di7y8PKhUKuTl5aF58+aIjIwEkD/SunbtWum+xFmzZqFBgwZQqVTYt28f1q9fj7Zt28LNzU0nbnNzcwghcOXKFcyaNQunT59GRkYGtFotjIyMsH//fmn7yZMnAQAdOnSAhYUFMjMz4enpCSMjIwQHB2Px4sW4fv06cnJyIJfLpXuI7e3tdY7XsGFDbNy4EXfv3sXdu3eRm5uLjRs3omfPnlAoFPD19UXv3r0RHx8PY2NjZGZmSu2NjY2Rnp5e6GfbqlUrzJo1C9OmTcO1a9fQtm1bLFq0SIqhKB4eHjqvb9++jQkTJhS6BsWJiYmBqampzvWsXLmyzhcTVlZWL7yIFxEREb0cr8TIJ9FLpc0D9k1EQeIJAO5KOZq6G2BSWBYyNQI3Nn6GNWvWSOUZGRkQQsDBwQFyuRyhoaE4cOCAVG5vbw+5XI47d+5I29RqNUxNTWFtbY2MjAydBXiKsnHjRiQkJEAmk8HCwgJyuRwGBgZo2LAhqlevjtGjRyM1NRW5ubk4evSozqrNBT7++GN88803OHv2LIQQePz4McLCwoocZSyKo6OjzjmUFKNKpZJiLGi7/dwlfHglUko8C6TExuDDK5H4PSkViYmJyM7Ohqurq/TYkIS9exE77lPkxscjKTdXatcBwF6VNeJ278bw4cMRHh4uPSrJ29sbV69exa5du+Dq6qpzvNGjR6NPnz7o2bMnTExMcObMGbz33nsICgpCp06dkJqaivT0dISGhsLd3R07duxATk4O8vLy8Mcff0Aul2PBggVYsGABrKysMGHCBKjVashkMuzbt6/IazN+/Hi4urri6tWrUKvV2Lx5M4QQeP/99zFixAg0btwY8+fPx8iRI3W+gIiLi8OdO3cwePBgLFy4EIMGDUKzZs3QsWPHYr+oMDExwXvvvYcNGzZg37590oJAQP4XCIMGDcKNGzcwdepUZGRkYPXq1VJZcSIjI/Hxxx9j3759iI6OhomJibQqbXHtnt4+cuTIIq9BcfXd3NyQlZWFhIQEnTjc3NyKbUNERET/XvzUJnpa1HFAXfi+y5AeZrin1sJhkRqBGyMx9L3WUpmvry+mTJmC1q1bw9bWFtu2bUPXrl2lcjMzM8yYMQMdOnSQ7q8bP348DAwM4OjoiFq1aqFx48YlhhUWFoa6detK9yfOnz8f9erVg1wux6+//orHjx+jevXqsLOzw9SpU6HVagvto0uXLpg/fz6GDx8Oa2trVKpUCcuWLSuyblGCgoKwfPlyqFQqjB49utgYLS0t0a1bN3zzzTfSKruzvvoKiyZ+gYSuLfAo5Ceddpm/7kBudCSmXLmDLydORIsWLeDm5gY7Ozt4eHjghylToNVqcerxI/yZ8QgAcDcnG/9JTsbFx4+R9M0iqJRKyOVyGBrmT+ioVKkSEhMTMX/+fPTq1Us61unTp3H8+HHk5eXB0NAQFhYW8PT0RHh4OGJjYxEZGYnMzExcuHABp0+fxscff4zRo0fj2LFjkMlkMDQ0hBACWVlZOH36NLKyspD7/wlxwZcCRUlPT4eVlRUUCgXu3buHb775BgCQlZUFW1tbyOVyREVFISQkRKedjY0NFi1aBCEE4uPjsX37dqSkpOh8uVGUYcOGYf369Th8+LBOTI8fP0ZsbCy0Wq00Vbvgmjk4OBT5pQUAXLx4EQCQk5MDMzMzWFhYSO0cHR0RFRUlXYfiqNXqIq9Bgae/3HB1dUWrVq3w+eef49GjR4iOjsbXX3/92j27lYiI6I3x8tc4erVwtds30KX/CjFDUfqfS/+tkPBe1f509IFaOIafL/RH7ugsLIZ+LAyr+giZuYVo6N9K3Lt3T2r36/fLhaeRkTCXyUUHKytRx9RUmMpkwlQmEwb5w9PSKrRvvfWWqF+/vlAqlUKpVAp3d3dhbGwszp49KwCIefPmCXd3dyGXy6U2HTt2FO+8846YMGGC8Pf3F4aGhtI+w8LChFarFVZWVtJKuABE5cqVRWBgoHjvvfeEkZGRtN3AwEAYGhoKPz8/Ua9ePbFu3Tpppd3Q0FBhYWEhjI2NRf369cXixYuFUqkUq1atEs7OztIKu0ZGRsLc3FxMnDhRABCXLl0SdevWlfYvk8mETCYTXl5eYuzYsQKA+PnnnwUAYWtrK4yMjISLi4s4f/68qFy5svD29hYmJibSqrUFK/2am5sLhUIhbG1txcyZM4Wfn584evSoMDIyEjKZTFq91svLSwghhEqlks4TgKhbt66IiooSQgiRkpIiWrRoIVQqlVAqlUKI/NVuC1YbLhARESF8fX2FhYWFzjUocPLkSeHj4yOUSqXo1KmTEEKI+/fvix49eghbW1vh5uYmJk6cKPX9gtVu6d/lVX2Pon8v9ikqT1zttmK9Evd8Er1Ulo6l13mWegQASMwpelTMfkv+SruWAz4EAEz29YSbo7VU3tzLC3sr5z+XNzInB+9H3sXPnl6obGKC5NxcpOTmorqpKR58/DE6T5+GHTt2wN/fH8ePH0fbtm3h7+8PPz8/LFu2DEuXLsWuXbvg5+eHe/fu4dGjR6hRowb8/f3h5uaGRYsWAQAuXLiA+vXro02bNgAAPz8/XL16Fb/99hsaNmyIrKwsjBo1Cvb29sjJyUFkZCQqVaqE5ORk6f7D5cuXY/369RgyZAiEEIiNjYVGo0FkZKS0WFrB4kUjR47Ejh070KhRI7i5ueHIkSPo2LEjjh49itq1a2P37t2oVKkSevfujTVr1iA9PR1vv/02/Pz8IISAWq3G1q1b0blzZxgYGGDixIno06cPPD098f777yM5OVlaROjMmTNo3Lgx7t+/L01rrl69Orp164ZGjRrBzs4OCxYswMCBA/Ho0SNpxPP8+fOoVKkSHj58WOgeSxsbm0L32D+5KFCBZs2a4e+//9bZVnANgPxnMV+7dk2n3MnJSVow6mn+/v5ITU0tsoyIiIj+fTjtluhpnk0AhQsAWTEVZIDCNb8elZmDcdm+63q6nqG9PQAZDOyqwdixDoRMhts5OcjSamFnaIjq/7/I0oY/jmDIkCFo3bo15HI5qlevDrlcDhcXFwDAqlWrMHPmTGnxIw8PD9SoUaPM8QcGBqJRo0aQyWRlWlm4f//++Ouvv3D37l0A+ffDvvvuuzqrdD+pLI/qmT59OqysrODi4oKAgADpMTjFPbbn9OnTGDhwoM4+atWqBV9fXymhO3HiBJKTk9GoUSMA+ava3r59G0lJSbCwsECTJuznREREVD6YfBI9TW4ABCz4/xdPJ6D//zpgfn49KrN3VJZwNjEqKaWHi4kR3lFZ6m4384JFh4Uwb/Y5qrccj287T8dWmTWa//MPPrwXjWvZ2TB0ckLs48dYvXo1VCoVzMzM4ODgAI1GI60AHBUVBW9v7+eO/+mVW0tjbW2Nbt26YcOGDQCADRs2YOjQocXWL8ujep5+DE7BqrRPP7anYPXbzz//HEqlstCxhg4dKo1Mrl+/Hv369ZNWnd21axeuXLmC6tWro379+ti+ffsznTcRERFRcZh8EhXFtyvQeyOgeGqUSuGSv923a9HtqFgGMhnmeOevOltMSo/Z3q4wkP2vNPNKMh6E3IDcRCFt61KjNbb3X4Hzn4Sihq0nJt2Pg2PQZLh7eGDcuHFITU1FZmYmhBDQaDRYtWoVgPznst6+fbvI2CwtLaVnBAPA/fv3C9UpaVXV4sqGDRuGjRs34vjx40hJSUGXLl2KrFfwqJ6FCxciMTERqampJa5m+7QnH9ujVquRlJQEAOjWrVuR9fv164czZ87g6tWr2LZtGwYNGiSV+fn5YceOHUhOTsa0adMQGBiIhIQEripLREREL4y/TRAVx7cr8OkVYPBvQI//5P/96WUmni+gk70Ka2t5wclE99mOziZGWFvLC53sVdI2oRVI/bVg5dP8hPROSjT+vHsaWbk5MDIwhMK1Hkzd3KBo1w4jRozAunXrcPjwYeTl5SE7OxsnTpyQ7iEcMWIEZs2ahQsXLkAIgejoaKnMz88PO3fuRFpaGhITE7Fw4cJnOq+iHqUDAG3atIEQAqNHj8aAAQOKfaZlaY/qKc2zPrZHoVCgR48eCAwMRKVKlVC/fn0A+SvZbtq0CQ8fPoRcLpfu7TQ0NCz2HImIiIjKisknUUnkBkCl5kDtnvl/c6rtC+tkr8KZxr7YUa8KVvl6Yke9Kjjd2Fcn8QSA7LtpyEvL0dmWo9VgUcR/4Le8G+p+3xXHY69i7dJ1AID69etjy5YtmDp1Kuzt7eHq6opp06ZJjw755JNPMGrUKPTu3RtWVlZo27YtoqOjAQCfffYZnJ2d4e7ujtatW6NPnz7PdE5FPUoHAGQyGT744ANcvHgRH3zwQbHtS3tUT2me9bE9QP6obFFxhYSEoGrVqrCyssLYsWMREhICW1vbYs+RiIiIqKxkoqzzut5QarUaSqUSaWlpUCgUpTcogkajQWhoKDp27FjsyAdRWb0p/enxhUQ82Hqj1Ho2favDvJ7DS4jo+WzcuBHfffcdzpw5U9Gh6IiOjoa3tzfi4uKgUCjeiD5FL8eb8h5FLw/7FJWnF+1P5ZEbvMk48klE/0pyK+NyrVcRMjIy8N1332HUqFEVHYqOvLw8LFiwAL1794atrW1Fh0NERERvCCafRPSvZFJJCQNlyYmlgdIEJpUKr+b6b7Bp0yY4OjrC1dUVgwcPruhwJHfv3oVCocAff/yBr7/+uqLDISIiojdI2R68R0T0ksnkMqi6VEHK5mvF1lF1qQyZvLiHt1SsgQMHFnrG5r9BpUqV8OjRo4oOg4iIiN5AHPkkon8ts1p2sB1Qo9AIqIHSBLYDasCsll0FRUZEREREz4ojn0T0r2ZWyw6mvrbIvpsGbXoO5FbGMKmk/NeOeBIRERFR0Zh8EtG/nkwug2kVVUWHQUREREQvgNNuiYiIiIiISO+YfBIREREREZHeMfkkIiIiIiIivWPySURERERERHrH5JOI6F9i7ty56NevX0WHQURERKQXXO2WiOhfIigoqFz2c+TIEXTv3h2pqanlsj8iIiKi8sCRTyKilyA3NxdCiIoO442g0WgqOgQiIiIqApNPIqLn5OXlha+//hp+fn5QKBRo37494uLipHKZTIbly5ejVq1asLCwQEZGBs6cOYOmTZtCpVLB19cXW7ZskerPnDkT3bt3l14nJiaif//+cHZ2houLCz799FNkZ2dL5WfPnkXr1q1hY2MDe3t7jB07FikpKejQoQPS0tJgaWkJS0tLREREFIp9/fr1qFevHqZPnw5nZ2cMGTIE27dvx7Fjx1CrVi0olUoMGzYMWq0WAJCRkYFu3brBwcEBSqUSLVq0wMWLF3Vi79KlC8aMGQOVSgUPDw9s27ZNKj9w4ADeeustKJVKODs7Y/To0cjMzJTKY2Ji8O6770KhUKBBgwaYO3cuvLy8pPKMjAyMGTMGHh4ecHBwwKBBg5CWlgYAiIyMhEwmw7p161C1alW4ubk9x0+T6PWRlZWF9957DyqVCo0aNarocCpUcHAwmjRpUtFhENH/Y/JJRPQC1q5di5CQEMTHx8PJyQkDBgzQKQ8JCcGBAwegVquh0WgQEBCAvn37IikpCatWrcLw4cNx7NixQvsVQqBr165wcnLCnTt3cPnyZVy8eBFz5swBAMTGxqJ169bo2bMn4uLiEBUVhd69e8PW1hZ79+6FUqlERkYGMjIy0Lx58yJjv3LlCuzs7HDv3j30798fo0ePxrJly/DHH3/g2rVr+O2337B7924AgFarRWBgIO7evYuEhATUr18fvXv31hnN3b9/P1q0aIGUlBTMmTMHH374IdLT0wEAZmZm+PHHH/HgwQMcO3YMhw8fxpIlS6S2gYGB8PT0REJCArZs2YL//Oc/OrEOHToUDx48wKVLl3D37l1oNBqMGTNGp86ePXtw5swZ3L17t4w/PaLX088//4wbN24gISEBf/31V6Evtt4k/fv3x/Hjxys6DCL6f0w+iYieQZ42D6fjTyP0n1Dk5OVgxMgR8PHxgbm5ORYuXIjDhw8jJiZGqv/ll1/CxcUFJiYm2Lt3rzRCaWRkhJYtWyIwMBAbNmwodJwzZ87g1q1b+Oabb2Bubg5bW1sEBQUhJCQEALB582Y0aNAAo0ePhqmpKczNzYtNMotjb2+PTz75BIaGhmjRogXUajWGDRsGW1tbuLi4oGXLljh37hwAQKFQoE+fPrCwsICpqSlmzZqFmzdv6oz0+vn5oXfv3jAwMMDAgQORk5ODmzdvAgCaN2+O+vXrw8DAAJUrV8aIESNw5MgRAMC9e/cQERGB+fPnw8zMDNWqVcPIkSOl/SYlJWHHjh1YsWIFVCoVLCws8NVXX2Hbtm3Iy8uT6s2YMQMqlQrm5ubPdB2IXjd3795FtWrVYGJi8tKPzWnv/8NrQVQYk08iojIKiwpD+x3tMXT/UEyMmIjkzGQExwcjLCoMAODo6AggfwSugIeHh/TvmJgYnamkAFC5cmWdZLVAZGQkUlNTYWNjA5VKBZVKhZ49eyIhIQEAEBUVBW9v7xc6n4J4AUi/pD65zdzcHBkZGQCAzMxMjB49Gl5eXlAoFNJ5JCcnS/WdnJykf8tkMpiZmUkjn6dPn0bbtm3h6OgIhUKBoKAgqW1cXBxMTU1hZ2cntX/yukVGRkKr1aJSpUrStWjYsCHkcjni4+OLbEP0OliyZAk8PDxgZWUFLy8vrF27VirbvHkzatSoAZVKhWbNmklfFE2YMAGzZ8/Gb7/9BktLS9StWxdz586VXltaWiI+Ph7GxsbS/+/vv/8eMpkM169fBwD8+uuvqF27NgAgOjoa7777Luzt7WFtbY1OnTohMjJSimPIkCEYNmwYevfuDYVCgdWrV0Oj0WD69OmoUqUKbG1t0bVrV50vqp5W0i0GR44cgb29PQ4ePIjKlSvD1tYWX375pU7777//Hu7u7rC1tcXUqVNRr149rF+/HsD/bjEo4OXlhYULF+Kdd96BlZUVWrZsiXv37pUpFgA4d+4cWrVqBRsbG1StWhU//vijVDZz5kx07twZo0aNgo2NDSZNmlTqz5joTcPkk4ioDMKiwjD+yHgkPE7Q2Z4cl4zxR8YjLCoMiYmJAPJHFAvI5f97m3Vzc9P5pQ3IT6yKukdx/fr1MDU1RWpqqvQnLS1N+mXR09MTt2/fLjLWJ49ZXhYvXoyzZ8/i6NGjUKvV0nmUdRGlfv36oVWrVvjnn3+gVqsxd+5cqa2LiwuysrJ0Etno6Gjp3+7u7pDL5YiLi9O5HllZWXB1dZXq6eO8iSrKzZs3MXXqVBw4cADp6ek4deqUdP/mn3/+iVGjRmHNmjVISkpCz549ERAQgLS0NCxevBhBQUHo3LkzMjIycPHiRZ3XGRkZcHJyQtWqVaX7wcPDw1GlShUcPnxYet26dWsA+VPux48fj3v37iEqKgrm5uYYPny4TqxbtmzBsGHDkJqaimHDhmHKlCk4duwYjh49ivv376NatWro27dvkedZ2i0GAJCeno579+7h6tWrOHr0KFasWCHNnDh06BCmT5+OHTt24P79+5DL5fj7779LvLabN2/Gli1bkJSUBAsLC0ybNq1MscTHx+Pdd9/FqFGjkJSUhN27d2PGjBk4dOiQtO99+/bh7bffRmJiImbPnl2mnzXRm4Sf1ERERXhyddo8bR7m/zUfAoUTrZTDKci+n425R+fiyy+/hIGBgU7y+aSOHTsiMTERK1euRG5uLiIiIhAcHIxBgwYVqmtnZwcrKytMnToV6enpEEIgKioKe/fuBZB/H9Nff/2F1atXIzs7G48fP5Z+kXR0dER6erqUDJcHtVoNU1NTWFtbIyMj45kfC6NWq6Ups9euXcOqVaukMnd3dzRt2hRBQUHIzMzErVu3sGbNGqncyckJ3bt3x5gxY6QENT4+Hrt27SqfkyP6F8nTCpy4k4LwG0nI0wpcvnwFmZmZcHR0RJ06dQAAmzZtwoABA9CiRQsYGRnh008/hbW1NX7//fcyH6dVq1Y4fPgwtFotjh07hilTphSZfHp5eaFDhw4wNTWFQqHAlClTEBERIS1GBgDt2rVD+/btIZfLYWZmhpUrV2LJkiVwdnaGsbEx5syZg2PHjumMMBYo7RYDID8p7N+/P0xNTVGjRg00adIEZ8+eBZB/X33//v3RqFEjGBsbY9q0abCwsCjx3EePHo1KlSrB1NQU/fv3l/ZVWiybNm1CixYtpNsLatWqhQ8++EAn1lq1amHIkCEwNDTkLQBERWDySURvjOddndbvHT8cHnwYt4JuIfVkqlRfCAEjGyPcnHIThwccxpFjRwr90vPNN9/o7P/BgwdYu3YtbG1tMXz4cHTv3h0ffvghrKys8P333yMhIQHfffcdQkJC8ODBAyxYsAA2NjZQKpXo1KmTNNrp5uaGatWq4euvv4aFhQUsLCzQq1cvxMbGYsuWLTA2NoazszMsLCxw9OhRALorzn766aeIiYnRWXEWyB9xLZiOtm/fPmna7Pjx43H37l1YWVlBoVDgl19+KXR94+LiULVqVSiVSgwfPhyPHj2Spr6tWbMGX3/9NQwMDFCnTh0kJSUhJSVFatuwYUPs3LkTSqUSNWrUgIODg879auvXr5em2yoUCjRv3lz6hZHodbHvyn00WxCOfj+exPxjqVC0H4cPJ86Brb0D2rVrhwsXLgAoegp/pUqVipzCX5yC5PP8+fOoVKkSunXrhj///BNJSUm4evUqWrZsCSD/nuvAwEC4u7tDoVCgRYsWyM7Olt4bAN0p78nJyXj06BFatGghTZN3cnKCsbFxkclnabcYAPn3nD/5fmBhYSEdPy4uDu7u7lKZkZERnJ2dSzz3J28ReHJfpcUSGRmJ0NBQqUylUuG7777D/fv3i7wWRFQYk08ieqM8z+q0jTs0Ro3va8BlkAvi1sXh0a1HAACRLZAdmw3vOd7wXeML77re0rRYABg8eHCRo6A7d+5EWloaRo8ejWPHjiE4OBhqtRr9+/eHUqnEJ598Iq0+q9FooNFooFarceXKFYwdO1baj5WVFQDg4sWLyMrKQu3atdGyZUvY2NggPT0dP/zwA8zMzPD2228D0F1x9ty5c7C3t9dZcdbT0xPh4eHSdDQ/Pz9pQR8nJyfMmzcP8fHx0Gg0WLx4MUxMTKBUKgHkr1Z7+fJlLF++HCkpKdL0wIJfkBs3bozs7Gxs2bIFWVlZ+PPPP5GXlydNV1MqlUhNTcUPP/yArKwstG/fXueXOCsrKyxZsgR3796FWq3GrVu3pKlwXl5eEEJApVI9Y28g+vfYd+U+Rm0+h/tpWdI2ixrNYdP7a9iP2ACFaxUMHDgQwLNN4QeKnpLu7++PCxcuYNeuXdIjm1xcXLB8+XLUrVtX+v80efJkPH78GOfOnYNarcaff/4JQHfK/ZP7t7W1hbm5OU6dOqUzTT4zM7PIR564u7vDwcGh2FsMSuPi4qKT1Obm5uokg8+itFjc3d3x3nvv6ZSnp6cjNDS0yGtBRIXxfwgRvdYKprD9ciEW2blajBg58plXpx02ahhkhjJY+FhA+Y4SqUdTAQDabC2s6ljBxMUEchM5JsyYoDMVrTSrVq3CzJkz0aBBAwD5iwjVr1//mc5vwIABqFmzJkxMTPDee+/h0aNH0gq2/fr1Q0pKCqKiogCUvOJsgeKmowH5U30dHBxgYGCAvn37wsfHR3qEwbZt29CmTRsEBATA0NAQw4cPR7Vq1aS2pU1Xu3//Pry9vTF48GBcvHgRa9asQa9evZ7pWhC9qvK0ArN+vaozsV+TEoPMu+eRp8mGzMAQp+49hqGhIYD8//fBwcE4duwYcnNz8f333yMlJQUdO3Yscv+Ojo6IiopCbm6utM3Ozg41atTA999/j1atWgEAWrdujaVLl0pTboH8KfPm5uZQqVRISUnBrFmzSjwXuVyOkSNHYsKECVJSmJKSovPc3yc1bNgQ7u7uxd5iUJp+/fohJCQEZ86cgUajwZw5c/Do0aMytX3WWAYOHIjw8HDs2LFD+mLwwoULOH369HMdj+hNxOSTiF5bT05hG7f1ApLSs/GfC+nYdyX/W3FHR0eYmJggNjZWalPU6rR+Dn5wNHeEDDIY2xtD8zB/+XwhBAysDCCDDE7mTni35rvP9GiDJ1esrVKlChITEws9u7I0T69O+/RrANK39iWtOFuguOloAPDtt9+iZs2aUCqVUKlUuHLlis6KtU9OfQMKr1hb0nS1R48eISoqChYWFujRoweGDx+OYcOGPdO1IHpV/XX3gc6IJwAIbS5SIzYjZvkARH8XiKSb5/DZ7KUAgJYtW+L777+XHo20detW7N27t9jR/169ekGhUMDe3l6nTqtWrZCVlYVmzZoBANq0aQO1Wq2TfM6aNQu3b9+GtbU1mjZtig4dOpR6PvPmzUPjxo3RunVrWFlZoUGDBjhw4ECRdQ0MDPDbb78hNjYWNWrUKHSLQWnatm2LGTNmoHv37nByckJubu5zP2amtFhcXV2xf/9+rFmzBs7OznB0dMTHH38MtVr9zMcielMZVnQARET6UDCF7eklglLiYzFq8zmsGuAHPwcDZGdnF7tiasHUNgO5ASY1moTxR8ZDk6yBkbURAMCimgWMlPn/nthoIlKSU3SW5Le0tMTjx4+l109PBStYsbZx48b4559/dMr0MXWrX79++OCDD/DLL7/AwsICS5cule7JLM3Ro0cxc+ZMhIeHo379+pDL5ahXr57OirWnTp3SaRMdHS1N+S2YrrZ169Yi91+1alW0a9cOu3fvfu7zI3pVJaZnFdpmbO8F50GLdbYp3apK/x48eDAGDx5c5P5mzpyp89rGxgZ//PFHoXrLli3DsmXLpNcdOnQotIJ1jRo18Ndff+ls++ijj6R/F/UeYmxsjKlTp2Lq1KlFxvc0BwcHrFu3rsgyf39/JCUl6Uxtffp9Yty4cRg3bhwAICcnB8uWLZO+/BoyZAiGDBki1X16unL37t3RvXv3MsUCAPXr1y82kX76uhNRYRz5JKLXTlFT2AqkX9gHTUoMZuw8jy+/nIgWLVoUe5/Uk6vT+rv64wPzD5B2Mg2qpioAyJ+CG56Kz9w/Q1OHppg8ebJO0ujn54f9+/fj/v37SE9PLzRdbcSIEZg1axYuXLgAIQSio6Nx7do1APkjmv/880+ZH2VSFiWtOFuWtgUr+Wq1Wvz000+4cuWKVN67d2+EhYXhwIEDyM3NxU8//YSbN29K5ZyuRlQ8ByvTcq33ptm5cycyMzPx6NEjTJw4Eba2tmjYsGFFh0VERWDySUSvnaKmsBWwrP0ukn79Bqe/7omrtyMRHBxc7H6sra2xd+9ebN68Gba2tvhh+g/4ac1P2DZ2GxY0X4Cdc3bi0+GfYmLfiahcuTLq168vLQIE5N+X1bJlS/j4+KBevXro1KmTzv4/+eQTjBo1Cr1794aVlRXatm0rPd/yww8/RGxsLGxsbKTHK7yoNWvWYNGiRbC0tMTIkSOLfe5eUQICAtCzZ0/Url0bLi4u+Pvvv9G0aVOpvHr16tiwYQNGjRoFW1tbnDhxAq1bt5amvnG6GlHxGlWygbPSFLJiymUAnJWmaFTJ5mWG9crYtGkTnJ2d4eLignPnzmHPnj0wNjau6LCIqAgyUZ5fq7+G1Go1lEol0tLSoFAonmsfGo0GoaGh6NixI4yMjMo5QnrTsD+V7pcLsRi39UKh7TGrhsKmzXCYV2sMAFjWtx661XMtVO9No68+Vb16dUyfPh39+/cvt33Svx/fo55Pwa0CAHRmbRQkpKsG+CGgVsmPEHldsU9ReXrR/lQeucGbjCOfRC/J7t27Cz2X7VUVGRkJmUyG1NTUig6lSJzCVjF+/fVXpKenIzs7G4sXL8b9+/cREBBQ0WERvRICajlj1QA/OCl135eclKZvdOJJRK8XJp9E/0LlndwdOXJEZ4XDDh06YOXKleWy7+c5fgF/f38sXbq01PZz585Fv379yny84qawidwcJP+2iFPY9GT//v3w9PSEnZ0dtmzZgj179sDW1raiwyJ6ZQTUcsbRia2xZfg7WNa3HrYMfwdHJ7b+VyaeXl5eelsgrCK/rI2IiCh2HQAienFMPoneQHv37sXo0aMBFJ8Y6vMXi2cVFBSELVu2lLm+gVyGGV18AUAnAbXv9iUgz1/ke0YXXxjIi7vD6t/v3/TzKbB8+XI8ePAA6enpOHPmDPz9/Ss6JKJXjoFchsZVbNGtnisaV7F9pd+nXkXNmzfXefYzEZUvJp9EehITE4N27dpBoVCgQYMGuHr1qk75kiVL4O3tDSsrK1SpUgXLly+Xyho1agQg/1EflpaWCA4ORkZGBrp16wZXV1cEBgaidevWuHjxYrHHP3/+PJo1awYbGxt069YNjx8/RkpKilTu7++PyZMn44svvkBaWhr8/Pxw+fJlqVytVmPUqFFFxvekixcvwsrKSnqWJADExsbCxMQEcXFxePDgAaZOnYq0tDSoVCo0aNAAUVFRmDBhAiIiIjBx4kRYWlqW6dlxxdFoNIW2FTeFTS57te+dys3NLdcVcImI6N+hqM8yotcNk08iPQkMDISzszPi4+MRHByMH3/8Uafc09MT4eHhUKvVWLt2LT7//HM0atQICoUCjo6OAPKfzZaRkYH+/fvDysoKTZo0wc2bN6XnqjVu3FhKRL788kt4enrCysoKvr6+CAsLw/z585GQkIB169ZBq9Vi0qRJAPITz5iYGKxfv15KOC9fvox69eohIiICpqamePjwIR4+fAghBGrWrIlPPvkEX375Jfr37y8lx5MnT4aPjw8sLCzg5eUFc3NzWFhYoHLlygDyHzQ+e/ZsKfHOycnBpUuXULduXRgYGKBatWpQKBTIzc1FWFgY2rRpAwcHByiVSp1nb86cORNKpVI672XLlsHa2hpWVlaQy+VwdnZGUlISunXrBnNzc8jlchgaGuLjbs3waeUUbBn+Dt5JPQT7azthoNWgb7Ma8PDwwLZt26RjaLVaDBo0CMbGxpDJZDA0NMSgQYMAAOvWrYObmxt8fHygUqng7+8PHx8fKZ7169ejXr16CAoKgq2tLTw8PHSmNc+cOROdO3fGsGHDoFAo4O3tjV27dknlGo0GkydPhoeHB+zt7dGnTx8kJSVJ5TKZDMuXL0etWrVgYWGB999/H9HR0ejXr5+0ci0RUXlTq9UYM2YMPD09oVAo0LBhQ9y7d6/Iups3b0aNGjWgUqnQrFkznDt3Tip7eqbG09NqS/uy9mkZGRkYM2YMPDw84ODggEGDBiEtLQ3A/25b2bRpE6pWrQqVSoUhQ4boJHY///wzqlatCqVSieHDh6Nz587SMzqfng1U8EVt+/btYWVlVeiL2pJiAYA7d+6gS5cusLe3h6enJ+bMmQOtVgvgf58dM2bMgJOT0zOtQE70qmLySVRO8oTAsYfp2JXwEDsvX0NERAS++eYbmJubw8fHp1CC0KNHD7i7u0Mmk6FVq1awtLSERqNBfHx8sfdBtm/fHhYWFjA2Nkb79u2RmZmJuLg4AEDdunVx+vRppKamYvr06Zg2bRpcXV1hZGQEGxsbmJiY4MiRIzr7Gzx4MPbt2welUokjR47A3NwczZs3x5gxY2BhYYGtW7ciIyMDkydPhoGBAUJCQuDk5CQ9rPzKlSuYM2cOOnToAE9PTxgZGaFPnz7IycnB/Pnz0bt3b5w+fRoajQZyuRwfffQRjh49ir1792LFihW4fv06+vbti9WrVwMAateujdjYWIwaNQpxcXG4c+dOoWug1WqxdOlSpKWlYezYscjIyMDWrVsB5Cf8y5cvR2RkJEaNGoXHjx9j8OBBcJKr4eOkwLXL52FoaIiUlBTMmTMHH374IdLT0wEAixcvxqZNm7BixQpotVqcP38enTt3BgCEh4cjJSUFv/76K5KTk/H+++/j7t27yM3NleK6cuUKZDIZ7t+/j23btmHSpEn4888/pfJ9+/ahUaNGePDgAZYsWYJ+/fpJ5zdv3jz89ttvOHr0KO7evQuZTFZohdiQkBAcOHAAarUaO3bsgIeHB7Zs2YKMjAzp+hERlachQ4bg9u3bOHHiBFJTU/HDDz/AzMysUL0///wTo0aNwpo1a5CUlISePXsiICBAJwkrSWlf1j5t6NChePDgAS5duoS7d+9Co9FgzJgxOnX27t2L8+fP4+rVqzh06JD0WK2bN29i4MCBWL58OVJSUtCoUSPs37+/xONt2rQJCxcuxMOHD/HWW29h7NixZYrl8ePHaNOmDdq0aYPY2FhERERg69atWLdundT+ypUrMDQ0RHR0NDZt2lSm60X0ShNUorS0NAFApKWlPfc+cnJyxO7du0VOTk45Rkb/Jr8lPhT1jl0RjuHnhWP4eWGzYqOQGZuI3xIfSnW2bNkiPD09pdebN28W9evXF9bW1sLKykoAEB999JEQQoi7d+8KAMLd3V2qD0D06tVLeHp6CjMzM2FqaioAiAsXLhQZk4+Pj/Dz8xPOzs7CzMxMABAqlUoIIUTLli1FlSpVxLfffisOHz4slEqlOH/+vAAgoq9cFL9u3iAACE9PT2FtbS2MjY2FTCYTpqamIi8vT4pv586donLlyuLhw4fCyMhING7cWBw/flwYGBiI9evXCyGEmDRpkrC1tRUAhIODg/jkk0/E48ePRatWrYRMJhO9evUSK1euFE5OTlLs69atE8bGxuLnn38WQggxY8YMoVAoxLp168TJkyeFiYmJsLa2Fnl5ecX+TB4+fCgACF9fX7F582YxY8YMUaNGDaFUKoUQQmi1WmFsbCzOnDkjhBCiWrVqwtjYWKxevbrQ/3cXFxfh5eWls83Q0FBMnjxZilehUOj8Hx85cqQYNmyYFH+NGjV02gcEBIjZs2cLIYSoWrWq2Lp1q1QWGxsrAIjY2FjpZ79r1y6d9p6enoW2PS++R1F5Yn96heXlCvHPn0Jc+q+IP7VbABBRUVFFVn3yPejDDz8UI0eO1CmvVq2aCA4OLlRXCCF27dolfR5GR0cLACIhIUEqnz9/vs7n5ZN9KjExUcjlcvHgwQOp/ObNm8LIyEjk5uZKn0/Xrl2Tyj/88EMxZswYIYQQX331lejUqZNOrL6+vmLGjBlCCCF9JhZo2bKlmDhxovT66NGjwtLSUgghSo1l+/btol69ejrH+uGHH0Tr1q2FEPmfHTY2NiV+llH5e9H3qPLIDd5kHPkkekG/J6XiwyuRuJ/9vyk9clt7iJxsfBBxDr8npQIAoqOjpfLo6GgMHjwYCz8biMRDq3BwwzeQy+XSN8pyedH/Na9evYrDhw9jy5YtmDJlCgBI026//fZb1KxZE0qlEiqVCjdu3ICZmRmuXr2K0NBQmJubl3ivYNTlCwCA7V8F4eSWDQAAlUyL8J+3wczMDDVq1EB2djZsbGxQp04dAPkjpwXTemUyGU6dOgV/f39otVpppd4pU6bg3XffhVwuh1arxebNm7Fs2TIoFAo4ODjgypUrGD9+PNLT03H48OH/XUO5XBqV1IkzKgrW1tZwc3PTuU6ZmZkYPXo0bGxsYGBgAGtrawDAjRs3kJycDCB/GnMBmUwGMzMz6Rj37t3DokWL8Msvv8Dd3R3NmjWT4klOTkZ0dDRUKpX0Jy8vDw8ePJD25+LiovO8ME9PT8TGxuq8ftKT5TExMTpT0FxcXGBiYqKz6IWHh0fhHxoRUXm5ugdYWgvY0BnYMQxRP/aHiaEMHhkXSm369HsYAFSqVKlMC/fExcXB1NQUDg4O0ran3y+fFBkZCa1Wi0qVKknvxw0bNoRcLkd8fLxUz8nJSfq3hYWF9F4fFxcHd3d3nX2W9v769L4K1jgoLZbIyEhcuXJF57NjwoQJOnG6uroW+5lP9Dpibyd6AXlCYOqtWDyd0hk4OMGoVj1k/Pgdply5g6vXr2PNmjVSecaFPRDaPDgcnwb57uG4vmEctFotHsdeAwDY29tDJpPpTOs0MjKCoaEhrK2tkZmZiZ9//lkqO3r0KGbOnImNGzfi4cOHSE1NhampKUxMTKBQKJCYmIjs7Owiz0Eul0Obl4c/Nv1H2pb9/8eNSUrBwqAvYWVujjt37sDMzAypqam4dOkSgPwket++fZg5cyY6deoEIyMjmJmZwdvbW0p0jxw5go4dO8LKygr79+/H48ePcfLkSQCAnZ0d2rRpg+XLl0OpVKJ79+7QarWwtLSU7omRYvr/+D09PfHw4cNC57F48WIcOXIEubm5+Ouvv6TFlapUqVKmBXo8PT2hUqkQGhqK5ORk9OrVS4rHzs4Ozs7OSE1Nlf44ODjgnXfekdrHxcXp3FMUHR2tc99qVFSUzvGeLHdzc0NkZKRUFh8fj+zsbJ3l/p/+5YS/rBBRubm6B9g+CFDHSZs8lTJk5wrc+3FAfnkJnn4PA/ITs4L3MEtLSzx+/Fgqu3//vvRvFxcXZGVlITExUdr25Je1T3N3d4dcLkdcXJzOe3JWVpbOe25xXFxcCt23WtLxSlJaLO7u7mjQoIFOmVqtxt9//y3tg+/l9KZhjyd6ASdTM3RGPJ+knDIXeUnxuNSpBd7r2w9Dhw7NL7i6B77npmJKc2O03vAYtgvTER6phZ25DMf+CEPmuf9Ko2wJCQlQqVQICQmBn58fkpKS4OrqitGjR+t80KvVahgYGMDe3h5arRY//fQTcnJy8Pfff0OhUGDKlCk6o3JPsre3Q8ajR3ickyNtc1JaQWFqgpRHj7H11AUY5OagS5cusLKywtSpU6VvfaOjoxEeHg4DAwN8+eWXyMnJgVwux+3bt5GTk4OIiAj88ssvGD9+PNLS0tC+fXtYWlqiefPmyM7OhpOTE/bv348xY8bgwYMHMDTMfwxKvXr1kJOTg5s3byI3NxfHjh1Dzv/H17BhQzg5OSEmJgapqanIzc3F0aNH8eDBAxgYGMDQ0BDm5uYICgoCgCLvGy1KYGAgvvjiCxw7dgwGBgbQaDSQyfIfcTBs2DDExsYiODgYubm5+Oqrr5CcnIysrCyp/aNHjzB79mzk5OTg1KlTCA4O1rlv8+bNm/jxxx+Rm5uL33//HeHh4ejTpw8AYMCAAZg7dy7u3buHjIwMjB8/Hm3btoWLi0ux8To6Opb53IiIiqXNA/ZNBJ76GtXRUo5u1Q0x8vdM3N8+AdpcDc6fP6+zanqBAQMGIDg4GMeOHUNubi6+//57pKSkoGPHjgAAPz8/bNmyBVlZWfjnn3+wYsUKqa27uzuaNm2KSZMmITMzEzdu3ND5svZpTk5O6N69O8aMGSPNaomPj9dZxK0kvXv3RlhYGA4cOIDc3Fz89NNPuHnzZpnaPmssnTt3RkJCAlauXImsrCzk5eXhxo0bhdZfIHqTMPkkegGJObnFlhk4OsP6m9Vw+P0Y5u8Px5QpUxD5zx3pQ/6rVqZI/tIKDycqsKG7Gc5+ZAEXKzkcmvRFYGAgJkyYAHd3d6SmpiIwMBBr1qyRRkQ9PT3x0UcfoWXLlqhXrx4CAgLQs2dP1K5dGy4uLvj777+lD/OMjAz8+OOPMDExkabCAsCYMWPw6aefwlKbi0aV3LDpxHmYGhniblL+VNIeb9WGsaEBBAAHCzMsmTkNly5dQmxsLAICAmBlZYXAwECoVCr07NkT7du3l6bWymQyzJ49Gz///DNq1qwpJZVyuRy9evXC2LFjIYTA7du3pW+7ZTIZfv75Z8jlclStWhUuLi5Yvnw5nJ2dkZubCysrK2kf48aNgxAC1atXh52dHaZOnYqxY8fCzs4O6enpqFmzJkJCQgDkL8RUFkOHDoWFhQVatGgBAwMDTJ06FUFBQZDL5Zg1axbef/99DB48GMbGxli0aJEUT4FatWohNzcXzs7O6NmzJ77++mu0atVKKg8ICMDJkydhY2ODcePGYfPmzfD29gYAaSXFxo0bw8vLCxqNBps3by4x3qCgICxfvhwqlUp6ZisR0TOLOq4z4vmkDd3N4K6Q4a0ld6CyVmHkyJHIzMwsVK9ly5b4/vvvMWzYMNja2mLr1q3Yu3evtGrsnDlzkJqaCnt7ewQGBkoriRcICQnBvXv34ODggMDAwP99WVuM9evXS1NcFQoFmjdvjrNnz5bpdKtXr44NGzZg1KhRsLW1xYkTJ9C6dWuYmJiUqf2zxGJpaYmwsDAcOnQIXl5esLW1RWBgoM60W6I3jUyUZT7aG0ytVkOpVCItLQ0KheK59qHRaBAaGoqOHTsWO/pEr6ZjD9PR40Lpo0876lVBU2sr4G5E/v00pRn8G3ZfTMGnn35aaCpTefena8f+QOh33xRZduDvW7ifpsbgJg3Q8ZMvUKNpy2L3c+LECbRv3x737t2DUql84bheJevXr8fSpUtx4cKFIstnzpyJCxcu6Dxq4N+E71FUntifXjGXfwZ2DCu9Xo//ALV76j+eIui7T1WvXh3Tp08vtMo4vZ5etD+VR27wJuPIJ9ELeEdlCWcTI8iKKZcBcDExwjsqy/wNGQll23FZ65UDS5V10SFkZePUP9FoUsWzxHpA/qhehw4dsGzZsjcu8SQieqVZOpZvvVfAr7/+ivT0dGRnZ2Px4sW4f/8+AgICKjosojeCYUUHQPQqM5DJMMfbFR9eiYQMunfMFCSks71dYfD/9w4+24d84ftq9MG1Rk1Y2tgh40GytC3s6i0cunYHDTxd4e1oBytbO7jWqFnsPvbt2/cyQiUiovLm2QRQuADq+3j6vs98svxyzyYvOzK92b9/PwYPHgyNRoPq1atjz549sLW1reiwiN4IHPkkekGd7FVYW8sLTia6UzecTYywtpYXOtmr/rex4EO+pLFShSvg2QTdu3cvNOVWH+RyA7Qe8pHOtra+3pjXIwA936oNAGg1+CPI5QZ6j+VVNWTIkGKn3AL5027/rVNuiegNJzcAAhb8/4unP5v+/3XA/Px6r4nly5fjwYMHSE9Px5kzZ+Dv71/RIRG9MZh8EpWDTvYqnGnsix31qmCVryd21KuC0419dRNP4F/7Ie/9dhN0HR8ESxs7ne1WtnboOj4I3m+/Pt94ExHRU3y7Ar03Agpn3e0Kl/ztvl0rJi4ieu1w2i1ROTGQyfIXFSpNwYf8vom6KwwqXPITzwr6kPd+uwmqNHwbsdf+RkbqQ1iqrOFaoyZHPImI3gS+XQGfTvmr32Yk5N/+4dnktRrxJKKKx+STqCL8Sz/k5XIDuNesU6ExEBFRBZEbAJWaV3QURPQaY/JJVFH4IU9EREREbxDe80lERERERER6x+STiIiIiIiI9I7JJxEREREREekdk08iIiIiIiLSOyafREREREREpHdMPomIiIiIiEjvmHwSERERERGR3jH5JCIiIiIiIr1j8klERERERER6x+STiIiIiIiI9I7JJxEREREREekdk08iIiIiIiLSOyafRERERFSq9evXo169ehUdBgBg7ty56NevX0WHQUTPyLCiAyAiIiKiZ3fkyBF0794dqampFR3KSxcUFFTRIRDRc+DIJxERERGVSKPRVHQI/xq8FkTPj8knERERUQXx8vLCwoUL8c4778DKygotW7bEvXv3pPLExET0798fzs7OcHFxwaeffors7GykpKSgQ4cOSEtLg6WlJSwtLREREYEaNWpg3759AIDLly9DJpNh9erVAIC0tDQYGRkhOTkZAHDmzBk0bdoUKpUKvr6+2LJli3TcmTNnonPnzhg1ahRsbGwwadKkQrGvXr0alStXxvXr14s8tzt37qBLly6wt7eHp6cn5syZA61WC+B/U3hnz54NBwcHODo6YunSpVJbrVaLqVOnwtHREZ6enggNDYW9vT2OHDkixde9e3epfsF51qpVCwqFAl27dkVaWlqZYgGAsLAwNGrUCCqVCjVr1sSePXuksiFDhmDYsGHo3bs3FAqFdD2J6Nkx+SQiIiKqQJs3b8aWLVuQlJQECwsLTJs2DQAghEDXrl3h5OSEO3fu4PLly7h48SLmzJkDW1tb7N27F0qlEhkZGcjIyEDz5s3RqlUrHD58GAAQHh6OKlWqSK+PHDkCX19f2NnZITU1FQEBAejbty+SkpKwatUqDB8+HMeOHZPi2rdvH95++20kJiZi9uzZOjHPmDEDK1asQEREBHx8fAqd0+PHj9GmTRu0adMGsbGxiIiIwNatW7Fu3Tqpzt9//w1zc3PExsZi27Zt+OKLL3Dnzh0AwLp16xAcHIyIiAhcv34dd+7cQXp6eonXcfv27QgPD0d0dDRiYmLw7bfflimWS5cuoVevXpg/fz4ePHiANWvWYODAgbhx44a07y1btmDYsGFITU3FsGHDyvaDJaJCmHwSERERvURarUDsjYe4eToeeRotRo4chUqVKsHU1BT9+/fH2bNnAeSPTN66dQvffPMNzM3NYWtri6CgIISEhBS776eTz2nTpuGPP/6QXrdu3RoA8Pvvv8Pe3h5jx46FkZERWrZsicDAQGzYsEHaV61atTBkyBAYGhrC3NwcAJCXl4ePPvoI4eHh+PPPP+Hq6lpkHL///jusra3x6aefwtjYGB4eHhg3bpxO7HZ2dpgwYQKMjIzg7+8PLy8vXLhwAQAQEhKCjz/+GNWqVYOZmRkGDRqkM1JZlC+//BIODg5QqVTo0aOHdB1Li2XNmjUYMmQIWrduDblcjmbNmqFz587Yvn27tO927dqhffv2kMvl0rUgomfHBYeIiIiIXpI75xMRse0WHqVmAwAeq3NwPSwVdxonokp9B1hYWEgjfJGRkUhNTYWNjY3UXgiBvLy8Yvfv7++PwMBAPHz4EMePH8fmzZvx7bff4u+//0Z4eDjmzp0LAIiJiYGXl5dO28qVK+PPP/+UXnt4eBTa/71793Dr1i3s2bMH1tbWxcYRGRmJK1euQKVSSdu0Wi3c3d2l146Ojjptnjz3uLg4nbpKpRKmpqbFHg8AnJycitxXabFERkYiPDxcZ1Q2NzcXCoVCel3UtSCiZ8fkk4iIiOgluHM+EfvWXCm0PeuRBvvWXEHAiFo6293d3eHg4ID79+8XuT+5vPAENnt7e/j4+GDp0qWoWrUqrKys0Lp1a2zbtg3Xr19HixYtAABubm6IjIzUaRsZGQk3N7cS9+/l5YV58+YhMDAQP//8M/z9/YuMzd3dHQ0aNMDJkyeLLC+Ni4uLzr2vaWlpyMrKeq59lRaLu7s7xo0bh/nz5xe7j6KuBRE9O/5PIiIiItIzrVYgYtutEusc3X4LWq2QXjds2BDu7u6YOnUq0tPTIYRAVFQU9u7dCyB/5DA9PR2JiYk6+2nVqhWWLl2KVq1aAQBat26NZcuWoX79+lAqlQCAjh07IjExEStXrkRubi4iIiIQHByMQYMGlXouHTp0QHBwMHr27IlDhw4VWadz585ISEjAypUrkZWVhby8PNy4cUNaMKg0/fr1w8qVK3H79m1kZmZi8+bNz50AlhbLiBEjsG7dOhw+fBh5eXnIzs7GiRMncO3atec6HhEVj8knERERkZ7dv5UqTbUtTsbDbDy8/0h6bWBggN9++w2xsbGoUaMGlEolOnXqhNu3bwMAqlevjmHDhsHX1xcqlQpHjx4FkJ98qtVq6f7Oli1b4vHjx9JrALC2tsbevXuxefNm2Nra4qOPPsKqVavQrFmzMp1P+/btsXXrVvTp0wcHDhwoVG5paYmwsDAcOnQIXl5esLW1RWBgIOLj48u0/6FDh6Jv375o0qQJfHx8pHtiTUxMytT+WWKpX78+tmzZgqlTp8Le3h6urq6YNm0asrNL/nkR0bOTCSFE6dXeXGq1GkqlEmlpaTpz/5+FRqNBaGgoOnbsCCMjo3KOkN407E9U3tinqDyxPxXt5ul4HPzP1VLrvTvMF9UaOpVa702i0WiwefNmDB06FDExMcUuckRUFi/6HlUeucGbjCOfRERERHpmoSjbiF1Z673ucnNzsXv3bmg0Gjx8+BD/+c9/0LhxYyaeRK84Jp9EREREeubsrYKFquTE0tLaBM7eqpcT0L+cEALz58+Hra0tfHx8kJ2djY0bN1Z0WET0grjaLREREZGeyeUyNO/jXeRqtwWa9faGXC57iVH9exkZGUmr0xZMk/T09KzgqIjoRb0yI59eXl6QyWQ6f0paEhsAsrKy8PHHH8PW1haWlpbo0aMHEhISXlLERERERP9Tpb4DAkbUKjQCamltgoARtVClvkMFRUZE9HK8UiOfX331FYYPHy69trKyKrH+Z599ht9//x3//e9/oVQqMWbMGLz//vs4duyYvkMlIiIiKqRKfQdUqmufv/qtOhsWivypthzxJKI3wSuVfFpZWcHJqWwrwKWlpeE///kPQkJCpKXF161bhxo1auDkyZN455139BkqERERUZHkchlcq1tXdBhERC/dK5V8zp8/H7Nnz4aHhwcCAwPx2WefwdCw6FM4e/YsNBoN2rZtK23z8fGBh4cHTpw4UWzymZ2drfNcJ7VaDSD/fgONRvNccRe0e972RE9if6Lyxj5F5Yn9icob+xSVpxftT+yHL+aVST4/+eQT+Pn5wcbGBsePH8fkyZNx//59LFmypMj68fHxMDY2hkql0tnu6OhY4gOO582bh1mzZhXafuDAAZibm7/QORw8ePCF2hM9if2Jyhv7FJUn9icqb+xTVJ6etz89fvy4nCN5s1Ro8jlp0iQsWLCgxDrXrl2Dj48Pxo8fL22rU6cOjI2NMWLECMybNw8mJuX3TKzJkyfrHEutVsPd3R3t2rV77gfJajQaHDx4EO+++y4fuE0vjP2Jyhv7FJUn9icqb+xTVJ5etD8VzIqk51OhyeeECRMwZMiQEutUrly5yO1vv/02cnNzERkZierVqxcqd3JyQk5ODlJTU3VGPxMSEkq8b9TExKTIZNbIyOiF3/DKYx9EBdifqLyxT1F5Yn+i8sY+ReXpefsT++CLqdDk097eHvb29s/V9sKFC5DL5XBwKHpZ8gYNGsDIyAiHDh1Cjx49AAA3btxAdHQ0Gjdu/NwxExERERER0bN7Je75PHHiBE6dOoVWrVrBysoKJ06cwGeffYYBAwbA2jp/tbjY2Fi0adMGGzduRKNGjaBUKjFs2DCMHz8eNjY2UCgUGDt2LBo3bsyVbomIiIiIiF6yVyL5NDExwdatWzFz5kxkZ2ejUqVK+Oyzz3TuzdRoNLhx44bOTcDffvst5HI5evTogezsbLRv3x4rV66siFMgIiIiIiJ6o70Syaefnx9OnjxZYh0vLy8IIXS2mZqaYsWKFVixYoU+wyMiIiIiIqJSyCs6ACIiIiIiInr9MfkkIiIiIiIivWPySURERERERHrH5JOIiIiIiIj0jsknERERERER6R2TTyIiIiIiItI7Jp9ERERERESkd0w+iYiIiIiISO+YfBIREREREZHeMfkkIiIiIiIivWPySURERERERHrH5JOIiIiIiIj0jsknERERERER6R2TTyIiIiIiItI7Jp9ERERERESkd0w+iYiIiIiISO+YfBIREREREZHeMfkkIiIiIiIivWPySURERERERHrH5JOIiIiIiIj0jsknERERERER6R2TTyIiIiIiItI7Jp9ERERERESkd0w+iYiIiIiISO+YfBIREREREZHeMfkkIiIiIiIivWPySURERERERHrH5JOIiIiIiIj0jsknERERERER6R2TTyIiIiIiItI7Jp9ERERERESkd0w+iYiIiIiISO+YfBIREREREZHeMfkkIiIiIiIivWPySURERERERHrH5JOIiIiIiIj0jsknERERERER6R2TTyIiIiIiItI7Jp9ERERERESkd0w+iYiIiIiISO+YfBIREREREZHeMfkkIiIiIiIivWPySURERERERHrH5JOIiIiIiIj0jsknERERERER6R2TTyIiIiIiItI7Jp9ERERERESkd0w+iYiIiIiISO+YfBIREREREZHeMfkkIiIiIiIivWPySURERERERHrH5JOIiIiIiIj0jsknERERERER6R2TTyIiIiIiItI7Jp9ERERERESkd0w+iYiIiIiISO+YfBIREREREZHeMfkkIiIiIiIivWPySURERERERHrH5JOIiIiIiIj0jsknERERERER6R2TTyIiIiIiItI7Jp9ERERERESkd0w+iYiIiIiISO+YfBIREREREZHeMfkkIiIiIiIivWPySURERERERHrH5JOIiIiIiIj0jsknERERERER6R2TTyIiIiIiItI7Jp9ERERERESkd0w+iYiIiIiISO+YfBIREREREZHeMfkkIiIiIiIivWPySURERERERHrH5JOIiIiIiIj0jsknERERERER6R2TTyIiIiIiItI7Jp9ERERERESkd0w+iYiIiIiISO+YfBIREREREZHeMfkkIiIiIiIivWPySURERERERHr3SiSfR44cgUwmK/LP6dOni23n7+9fqP7IkSNfYuRERERERKQPu3fvhpeXV0WH8a8RHR0NS0tLpKWlVXQoxTKs6ADKokmTJrh//77OtmnTpuHQoUN46623Smw7fPhwfPXVV9Jrc3NzvcRIRERERERUUTw8PJCRkVHRYZTolUg+jY2N4eTkJL3WaDT45ZdfMHbsWMhkshLbmpub67QlIiIiIiKi8qPRaGBkZFRqvVci+Xzanj17kJKSgg8++KDUusHBwdi8eTOcnJzQpUsXTJs2rcTRz+zsbGRnZ0uv1Wo1gPwLqtFonivegnbP257oSexPVN7Yp6g8sT9ReWOfogIxMTH46KOPcOrUKVStWhXvvfcegP/1jYyMDEyZMgW//fYbsrKy0K5dOyxduhRKpRLvv/8+/Pz8MHHiRKnNmDFjoNVqsXLlSqSnp+PLL7/E77//DgDo0qULFi5cCAsLC0RGRqJatWpYtWoV5s6dCwAYP348Vq5cCWNjYzx48ADDhg3DkSNHIIRAlSpVsHPnTnh6ehY6B41Gg9mzZyM4OBipqalo2rQpVq9eDRcXFwCATCbDqlWrsHz5ckRHR8Pf3x+bNm2CUqkEAPz555/4+OOPERkZiXfffRfW1tbIy8vD+vXrERkZiUqVKuHhw4dQqVQYMmQIjIyMkJ6ejt9//x0uLi5Ys2YN/P39yxRLYmIiPvvsM4SHh0Mmk6F3795YsGABTExMcOTIEXTv3h3z5s3DvHnz4OjoWOLtkAVeyeTzP//5D9q3bw83N7cS6wUGBsLT0xMuLi64dOkSJk6ciBs3bmDnzp3Ftpk3bx5mzZpVaPuBAwdeeMruwYMHX6g90ZPYn6i8sU9ReWJ/ovLGPkVBQUFwdHTE2rVrkZSUhK+++gpCCISGhgIAFi5cCAMDA8ybNw+GhoZYsWIFevbsic8++wy+vr5Yu3Yt/Pz8AAChoaHYsmULgoKCEBoaiu+//x6JiYlYuHAhAGDBggXo3bs3Pv74YyQkJAAAfvzxR0yfPh3Dhw/HX3/9hXnz5mHGjBlYtGgRcnNzERsbCxMTE1y+fBlWVlZFnsOUKVNw9uxZHD16FLa2tggKCkLfvn3x559/SnW2b9+O8PBwGBsbo3Xr1vj2228xc+ZMPHz4EF27dsWSJUswaNAgHDhwAO+//z769u1b7DXbtm0b9uzZg+DgYMybNw9DhgxBZGRkqbEIIdC1a1c0bdoUd+7cQWZmJnr27Ik5c+Zg9uzZAID09HRcvHgR169fL/PPUCaEEGWuXc4mTZqEBQsWlFjn2rVr8PHxkV7HxMTA09MT27dvR48ePZ7peOHh4WjTpg1u376NKlWqFFmnqJFPd3d3JCcnQ6FQPNPxCmg0Ghw8eBDvvvtumYajiUrC/kTljX2KyhP7E5U39qk3W542DxeTLuLaP9fwQcsPEBUdBWcnZwDAN998gx9++AG3bt1CUlIS3N3dcf/+fVhbWwMAbt26hXr16kGtViM3NxceHh7YuXMn0tLSkJmZialTp+LatWvQarVQKBQIDw9Ho0aNAAAnTpxAu3btkJaWhujoaFSrVg3Hjh1D9erVYWdnh59++glff/01/q+9uw+u8c7/P/46J5KQiESIRDREmgpxk7pN6dDNSiVqiK2iRLDfrVkWO1Z03dRtWyXVyXa7q+12RkU7W6zZNraUuov6VpGWxk2rbrJIhCTFEmTTSM7n94dxfj3fBAnnOMLzMXNmXNf1OZ/P+2Q+c8krn+u6zvHjxzVv3jxt3rxZ77zzjmJiYm76WYwx8vPz086dO+3tysrK7KurYWFhslgs2rBhgxITEyVJCxcu1O7du/Xpp5/qww8/VFpamg4dOmTvc8CAAQoKCrrpymdZWZlWrVolSSooKNAjjzyic+fOKTAw8Ja1FBYWKjExUT/++KOs1uvPqN28ebPGjx+v3Nxcbd++XXFxcfaxasqtK5+pqakaO3bsLdtEREQ4bC9fvlxNmjTRoEGDaj1ebGysJN0yfHp7e8vb27vKfk9Pz7s+4TmjD+AG5hOcjTkFZ2I+wdmYUw+fLae2aHH2YhWVFqk0t1QWT4v+Z/f/aEaPGYpvFW/PCZ6eniooKJDNZlObNm0c+rBarTp//rxatGihYcOGadWqVerfv79WrlyplJQUeXp6qqioSOXl5YqMjLTPsTZt2uinn37SpUuX7PseffRR+79btmypgoICSdKLL76osrIyDRs2TJcuXdLw4cO1ePFiNWjQwKGWc+fO6erVq+rTp4/Dc2u8vLyUn5+vsLAwSXJ4Xo2vr68uX74sSTpz5oy9zQ0tW7bUf//735v+DP9vX9L1FUubzXbLWgoKCnTx4kUFBgbajxljVFlZad/28/OrVfCU3Bw+g4KCFBQUVOP2xhgtX75co0ePvqOTT05OjiSpefPmtX4vAAAAgHtjy6ktmrp9qoyuX6RZL6CezDWjM4VnNHX7VKX/Il15eXn29mFhYbJarTpz5sxNb5VLSUnRoEGD9MQTT+jzzz/Xn/70J0nXM4mXl5dOnjyp4OBgSdLJkyfl7e2tpk2b2sc5deqU/YrM/Px8tWjRQpLUsGFDpaWlKS0tTSdOnNDAgQP19ttvKzU11WH8Jk2ayMfHR3v27HG4srOmQkNDlZ+f77AvLy+vVnmqprXs3r1bzZo1q/KNIz93Y0W0NurE93zesG3bNp04cUIvvPBClWMFBQVq27atsrOzJUm5ubl65ZVXtHfvXp08eVL/+te/NHr0aPXp00edOnW616UDAAAAqIFKW6UWZy+2B09J8mriJZ/HfHR2zVnZym2amzlXf/vb3+zHQ0JCNHjwYE2aNEnnzp2TJBUWFuqTlbStvwAAFGZJREFUTz6xt3nyySfVuHFjvfXWW+ratat95dRqtWrkyJF66aWXdOHCBZ0/f16zZs1SSkqKQ8B6+eWXdfHiRUlSenq6kpOTJUnr1q3T0aNH7Zfvenp6ql69qmt8VqtV48ePV2pqqj1Enj9/XqtXr67Rz2XAgAHKz89XRkaGKioqtHHjRm3btq1G761tLd27d1dYWJhmz56ty5cvyxijU6dOacOGDXc0nn3cu3r3PbZs2TL16tWr2nR+7do1HTlyRKWlpZKuLxlv2bJF/fr1U9u2bZWamqohQ4bo008/vddlAwAAAKihfcX7VFRaVGX/I+Mf0bUL13T494f17Z++Vb+h/RyOZ2RkKCAgQN27d1ejRo3Uu3dv7d2716HNyJEj9e2332rUqFEO+//85z8rPDxc0dHRat++vSIjI5Wenu7QJikpSb1795YkdevWTbNmzZJ0/Za+xMRE+fn5KTo6Wj179tSECROq/WyLFi1Sz5499ctf/lJ+fn7q2rWrNm3aVKOfS2BgoDIzM/XGG28oICBA7733noYOHVrtLYM1cataPDw8tG7dOhUUFKhdu3by9/fXgAEDdPz48Tsa6wa3PnCoLigpKZG/v78uXbp0Vw8c+uyzz/TMM89wrwLuGvMJzsacgjMxn+BszKmHz2f//kzT/3f6bdul9U7TMxHP1KrvO5lPP3+Qj9Vqvets4EwJCQnq06ePXnrpJXeXUiN1auUTAAAAwIMtyKdm9zDWtN2DZNOmTTp37pwqKiq0atUqbdu2Tc8++6y7y6qxOvk9nwAAAAAeTF2adVGwT7CKS4sd7vu8wSKLgn2C1aVZFzdU51579+5VcnKySktL1bp1a61cuVLt2rVzd1k1xsonAAAAgPuGh9VDM3rMkHQ9aP7cje3pPabLw+pxT+oJDw+XMabWXyviCjNnztSPP/6oq1ev6tChQ3ruuefcXVKtED4BAAAA3FfiW8Ur/RfpaubTzGF/sE+w0n+RrvhW8W6qDHeDy24BAAAA3HfiW8UrLixO+4r36cfSHxXkE6QuzbrcsxVPOB/hEwAAAMB9ycPqoe4h3d1dBpyEy24BAAAAAC5H+AQAAAAAuBzhEwAAAADgcoRPAAAAAIDLET4BAAAAAC5H+AQAAAAAuBzhEwAAAADgcoRPAAAAAIDLET4BAAAAAC5H+AQAAAAAuBzhEwAAAADgcoRPAAAAAIDLET4BAAAAAC5H+AQAAAAAuBzhEwAAAADgcoRPAAAAAIDLET4BAAAAAC5H+AQAAAAAuBzhEwAAAADgcoRPAAAAAIDLET4BAAAAAC5H+AQAAAAAuBzhEwAAAADgcoRPAAAAAIDLET4BAAAAAC5H+AQAAAAAuBzhEwAAAADgcvXcXcD9zhgjSSopKbnjPq5du6bS0lKVlJTI09PTWaXhIcV8grMxp+BMzCc4G3MKznS38+lGJriREVA7hM/buHz5siQpLCzMzZUAAAAAuB9cvnxZ/v7+7i6jzrEYYvst2Ww2nTlzRn5+frJYLHfUR0lJicLCwpSfn69GjRo5uUI8bJhPcDbmFJyJ+QRnY07Bme52PhljdPnyZYWGhspq5Q7G2mLl8zasVqseeeQRp/TVqFEjTppwGuYTnI05BWdiPsHZmFNwpruZT6x43jniOgAAAADA5QifAAAAAACXI3zeA97e3po3b568vb3dXQoeAMwnOBtzCs7EfIKzMafgTMwn9+KBQwAAAAAAl2PlEwAAAADgcoRPAAAAAIDLET4BAAAAAC5H+AQAAAAAuBzh08UWLlyoXr16ycfHRwEBAdW2ycvL04ABA+Tj46NmzZrpxRdfVEVFxb0tFHVWeHi4LBaLw2vx4sXuLgt1xNKlSxUeHq769esrNjZW2dnZ7i4JddT8+fOrnIvatm3r7rJQh+zYsUMDBw5UaGioLBaLMjMzHY4bYzR37lw1b95cDRo0UHx8vI4dO+aeYnHfu918Gjt2bJVzVmJionuKfYgQPl2svLxcQ4cO1YQJE6o9XllZqQEDBqi8vFxfffWVVqxYoYyMDM2dO/ceV4q67OWXX9bZs2ftr8mTJ7u7JNQBq1ev1tSpUzVv3jzt27dPMTExSkhIUHFxsbtLQx3Vvn17h3PRl19+6e6SUIdcvXpVMTExWrp0abXHX3/9db311lt69913tWfPHvn6+iohIUFlZWX3uFLUBbebT5KUmJjocM5auXLlPazw4VTP3QU86BYsWCBJysjIqPb4pk2b9P3332vLli0KDg7W448/rldeeUXTp0/X/Pnz5eXldQ+rRV3l5+enkJAQd5eBOiY9PV3jxo3Tr3/9a0nSu+++q/Xr1+v999/XjBkz3Fwd6qJ69epxLsId69+/v/r371/tMWOM3nzzTc2ePVtJSUmSpA8++EDBwcHKzMzU888/fy9LRR1wq/l0g7e3N+ese4yVTzfbtWuXOnbsqODgYPu+hIQElZSU6LvvvnNjZahLFi9erCZNmqhz585asmQJl23jtsrLy7V3717Fx8fb91mtVsXHx2vXrl1urAx12bFjxxQaGqqIiAglJycrLy/P3SXhAXHixAkVFhY6nLP8/f0VGxvLOQt3bPv27WrWrJmioqI0YcIEnT9/3t0lPfBY+XSzwsJCh+Apyb5dWFjojpJQx/z+979Xly5dFBgYqK+++kozZ87U2bNnlZ6e7u7ScB87d+6cKisrqz3//PDDD26qCnVZbGysMjIyFBUVpbNnz2rBggXq3bu3Dh06JD8/P3eXhzruxu9E1Z2z+H0JdyIxMVHPPvusWrdurdzcXM2aNUv9+/fXrl275OHh4e7yHliEzzswY8YMpaWl3bLN4cOHedAC7lht5tjUqVPt+zp16iQvLy/99re/1aJFi+Tt7e3qUgFAkhwub+vUqZNiY2PVqlUr/eMf/9BvfvMbN1YGAFX9/FLtjh07qlOnTnr00Ue1fft29e3b142VPdgIn3cgNTVVY8eOvWWbiIiIGvUVEhJS5emSRUVF9mN4ON3NHIuNjVVFRYVOnjypqKgoF1SHB0HTpk3l4eFhP9/cUFRUxLkHThEQEKA2bdro+PHj7i4FD4Ab56WioiI1b97cvr+oqEiPP/64m6rCgyQiIkJNmzbV8ePHCZ8uRPi8A0FBQQoKCnJKXz179tTChQtVXFysZs2aSZI2b96sRo0aKTo62iljoO65mzmWk5Mjq9Vqn09Adby8vNS1a1dt3bpVgwcPliTZbDZt3bpVkyZNcm9xeCBcuXJFubm5SklJcXcpeAC0bt1aISEh2rp1qz1slpSUaM+ePTf9RgGgNk6fPq3z5887/HEDzkf4dLG8vDxduHBBeXl5qqysVE5OjiQpMjJSDRs2VL9+/RQdHa2UlBS9/vrrKiws1OzZszVx4kQumcRt7dq1S3v27FFcXJz8/Py0a9cu/eEPf9CoUaPUuHFjd5eH+9zUqVM1ZswYdevWTT169NCbb76pq1ev2p9+C9TGtGnTNHDgQLVq1UpnzpzRvHnz5OHhoREjRri7NNQRV65ccVgpP3HihHJychQYGKiWLVtqypQpevXVV/XYY4+pdevWmjNnjkJDQ+1/QAN+7lbzKTAwUAsWLNCQIUMUEhKi3Nxc/fGPf1RkZKQSEhLcWPVDwMClxowZYyRVeWVlZdnbnDx50vTv3980aNDANG3a1KSmpppr1665r2jUGXv37jWxsbHG39/f1K9f37Rr18689tprpqyszN2loY74y1/+Ylq2bGm8vLxMjx49zO7du91dEuqo4cOHm+bNmxsvLy/TokULM3z4cHP8+HF3l4U6JCsrq9rfmcaMGWOMMcZms5k5c+aY4OBg4+3tbfr27WuOHDni3qJx37rVfCotLTX9+vUzQUFBxtPT07Rq1cqMGzfOFBYWurvsB57FGGPckHkBAAAAAA8RvucTAAAAAOByhE8AAAAAgMsRPgEAAAAALkf4BAAAAAC4HOETAAAAAOByhE8AAAAAgMsRPgEAAAAALkf4BAAAAAC4HOETAAAAAOByhE8AQK2MHTtWFotFFotFXl5eioyM1Msvv6yKigp7G2OM3nvvPcXGxqphw4YKCAhQt27d9Oabb6q0tNShv9OnT8vLy0sdOnSocQ2FhYWaPHmyIiIi5O3trbCwMA0cOFBbt2512ud8EIwdO1aDBw++bbsdO3Zo4MCBCg0NlcViUWZmpstrAwA8fAifAIBaS0xM1NmzZ3Xs2DGlpqZq/vz5WrJkif14SkqKpkyZoqSkJGVlZSknJ0dz5szR2rVrtWnTJoe+MjIyNGzYMJWUlGjPnj23HfvkyZPq2rWrtm3bpiVLlujgwYPauHGj4uLiNHHiRKd/1ofB1atXFRMTo6VLl7q7FADAg8wAAFALY8aMMUlJSQ77nn76afPEE08YY4xZvXq1kWQyMzOrvNdms5mLFy86bEdERJiNGzea6dOnm3Hjxt12/P79+5sWLVqYK1euVDn2n//8x/7vU6dOmUGDBhlfX1/j5+dnhg4dagoLC+3H582bZ2JiYsyyZctMWFiY8fX1NRMmTDAVFRUmLS3NBAcHm6CgIPPqq686jCHJvP322yYxMdHUr1/ftG7d2qxZs8ahzYEDB0xcXJypX7++CQwMNOPGjTOXL1+u8jNcsmSJCQkJMYGBgeZ3v/udKS8vt7cpKyszqampJjQ01Pj4+JgePXqYrKws+/Hly5cbf39/s3HjRtO2bVvj6+trEhISzJkzZ+yfT5LD6+fvvxlJ5pNPPrltOwAAaouVTwDAXWvQoIHKy8slSX//+98VFRWlpKSkKu0sFov8/f3t21lZWSotLVV8fLxGjRqlVatW6erVqzcd58KFC9q4caMmTpwoX1/fKscDAgIkSTabTUlJSbpw4YK++OILbd68Wf/+9781fPhwh/a5ubnasGGDNm7cqJUrV2rZsmUaMGCATp8+rS+++EJpaWmaPXt2lRXZOXPmaMiQIdq/f7+Sk5P1/PPP6/Dhw5KuryImJCSocePG+vrrr7VmzRpt2bJFkyZNcugjKytLubm5ysrK0ooVK5SRkaGMjAz78UmTJmnXrl1atWqVDhw4oKFDhyoxMVHHjh2ztyktLdUbb7yhDz/8UDt27FBeXp6mTZsmSZo2bZqGDRtmX6U+e/asevXqddOfLQAALufu9AsAqFt+vvJps9nM5s2bjbe3t5k2bZoxxph27dqZQYMG1aivkSNHmilTpti3Y2JizPLly2/afs+ePUaS+fjjj2/Z76ZNm4yHh4fJy8uz7/vuu++MJJOdnW2Mub4y6OPjY0pKSuxtEhISTHh4uKmsrLTvi4qKMosWLbJvSzLjx493GC82NtZMmDDBGGPMe++9Zxo3buywMrt+/XpjtVrtK69jxowxrVq1MhUVFfY2Q4cONcOHDzfGXF+19fDwMAUFBQ7j9O3b18ycOdMYc33lU5I5fvy4/fjSpUtNcHCwfbu6VerbESufAAAXqefW5AsAqJPWrVunhg0b6tq1a7LZbBo5cqTmz58v6frDhmri4sWL+vjjj/Xll1/a940aNUrLli3T2LFjq31PTfs+fPiwwsLCFBYWZt8XHR2tgIAAHT58WN27d5ckhYeHy8/Pz94mODhYHh4eslqtDvuKi4sd+u/Zs2eV7ZycHPvYMTExDiuzTz75pGw2m44cOaLg4GBJUvv27eXh4WFv07x5cx08eFCSdPDgQVVWVqpNmzYO4/z0009q0qSJfdvHx0ePPvqoQx//t1YAAO4XhE8AQK3FxcXpnXfekZeXl0JDQ1Wv3v//76RNmzb64YcfbtvHRx99pLKyMsXGxtr3GWNks9l09OjRKsFLkh577DFZLJYa9V8Tnp6eDtsWi6XafTabzSnj3W7sG+NcuXJFHh4e2rt3r0NAlaSGDRveso+aBnQAAO417vkEANSar6+vIiMj1bJlS4fgKUkjR47U0aNHtXbt2irvM8bo0qVLkqRly5YpNTVVOTk59tf+/fvVu3dvvf/++9WOGxgYqISEBC1durTae0MvXrwoSWrXrp3y8/OVn59vP/b999/r4sWLio6OvtOPbbd79+4q2+3atbOPvX//fof6du7cKavVqqioqBr137lzZ1VWVqq4uFiRkZEOr5CQkBrX6eXlpcrKyhq3BwDAlQifAACnGjZsmIYPH64RI0botdde0zfffKNTp05p3bp1io+Pt3/1yr59+/TCCy+oQ4cODq8RI0ZoxYoVDt8b+nNLly5VZWWlevTooX/+8586duyYDh8+rLfeest+OWx8fLw6duyo5ORk7du3T9nZ2Ro9erSeeuopdevW7a4/45o1a/T+++/r6NGjmjdvnrKzs+0PFEpOTlb9+vU1ZswYHTp0SFlZWZo8ebJSUlLsl9zeTps2bZScnKzRo0fr448/1okTJ5Sdna1FixZp/fr1Na4zPDxcBw4c0JEjR3Tu3Dldu3at2nZXrlyx/wFAkk6cOKGcnBzl5eXVeCwAAG6H8AkAcCqLxaKPPvpI6enpyszM1FNPPaVOnTpp/vz5SkpKUkJCgpYtW6bo6Gi1bdu2yvt/9atfqbi4WJ999lm1/UdERGjfvn2Ki4tTamqqOnTooKefflpbt27VO++8Y69h7dq1aty4sfr06aP4+HhFRERo9erVTvmMCxYs0KpVq9SpUyd98MEHWrlypX1F1cfHR59//rkuXLig7t2767nnnlPfvn3117/+tVZjLF++XKNHj1ZqaqqioqI0ePBgff3112rZsmWN+xg3bpyioqLUrVs3BQUFaefOndW2++abb9S5c2d17txZkjR16lR17txZc+fOrVXNAADcisVwcwgAADVmsVj0ySefaPDgwe4uBQCAOoWVTwAAAACAyxE+AQAAAAAux1etAABQC9ytAgDAnWHlEwAAAADgcoRPAAAAAIDLET4BAAAAAC5H+AQAAAAAuBzhEwAAAADgcoRPAAAAAIDLET4BAAAAAC5H+AQAAAAAuNz/A45YS2o1HAPpAAAAAElFTkSuQmCC",
"text/plain": [
"<Figure size 1000x1000 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from sklearn.decomposition import PCA\n",
"import numpy as np\n",
"\n",
"\n",
"words = [\n",
" 'software engineer', 'data scientist', 'project manager', 'machine learning', 'deep learning',\n",
" 'web developer', 'mobile developer', 'backend engineer', 'frontend developer', 'data analyst',\n",
" 'product manager', 'quality assurance', 'devops engineer', 'system administrator',\n",
" 'network engineer', 'technical support', 'security analyst', 'database administrator',\n",
" 'IT consultant', 'business analyst', 'full stack developer', 'cloud engineer',\n",
" 'user experience designer', 'graphic designer', 'content writer'\n",
"]\n",
"\n",
"\n",
"def get_average_vector(words, model):\n",
" word_vectors = []\n",
" for word in words.split():\n",
" if word in model.wv:\n",
" word_vectors.append(model.wv[word])\n",
" if word_vectors:\n",
" return np.mean(word_vectors, axis=0)\n",
" else:\n",
" return np.zeros(model.vector_size) \n",
"\n",
"\n",
"word_vectors = [get_average_vector(word, base_model_50) for word in words]\n",
"\n",
"\n",
"pca = PCA(n_components=2)\n",
"result = pca.fit_transform(word_vectors)\n",
"\n",
"\n",
"import matplotlib.pyplot as plt\n",
"\n",
"plt.figure(figsize=(10, 10))\n",
"for i, word in enumerate(words):\n",
" plt.scatter(result[i, 0], result[i, 1])\n",
" plt.text(result[i, 0] + 0.02, result[i, 1] + 0.02, word, fontsize=9)\n",
"plt.xlabel('PCA Component 1')\n",
"plt.ylabel('PCA Component 2')\n",
"plt.title('PCA of Word Vectors 50 Epochs Model')\n",
"plt.grid()\n",
"plt.show()\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "45VNzy3zVJZh"
},
"source": [
"# Evaluate"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "7qoqZUN1VLGY",
"outputId": "d6fec43b-7f34-43bd-aabd-9512f01a72c4"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 0.66730607 0.39942864 -1.3480053 0.48312205 -0.6054764 0.30325115\n",
" -0.23680943 0.3926065 2.341113 -0.8413951 -0.42808175 1.0639435\n",
" -1.8387847 -0.74092543 0.3700744 0.03994853 1.250145 -0.4634712\n",
" -0.17263055 -0.37489203 -0.3692984 -3.7730253 0.88525546 1.4462851\n",
" -1.2223958 -1.0783774 1.2381481 -1.3348383 0.45723316 1.9790221\n",
" 0.8609307 0.41960323 -0.3347041 -3.0889935 1.2158339 -0.26582238\n",
" 0.06627349 2.0511324 -0.70552003 -2.1372952 1.7936785 0.04823665\n",
" 0.75410956 0.0045984 -0.04764777 -2.3177376 -0.7667799 0.13685994\n",
" -0.5508031 -0.16021846 -0.36155143 1.9441017 -1.6251339 -0.23903637\n",
" 1.3241217 -1.5469605 -1.2005386 0.3227686 -0.5356589 0.12964004\n",
" 1.9003813 -0.49801615 -1.6894472 1.4306486 -1.3962985 0.00451634\n",
" 0.8760351 -1.9729552 -0.50261587 0.88261217 -0.25766075 0.55028677\n",
" 0.2766046 1.2681727 0.63036364 -1.6052092 0.94317377 3.1989186\n",
" 1.3152109 -3.3988254 -1.4760977 1.3728812 -0.08031602 1.3950652\n",
" -2.1840334 -1.796374 0.9773026 0.40424544 -0.04620907 -2.0945015\n",
" -0.22000532 0.33972892 0.7165526 -1.1083187 1.2259912 -0.06567198\n",
" 0.42140102 -0.33190578 2.625982 -0.06080066 -1.8917835 0.14551179\n",
" 0.11421283 -0.24530834 0.68980616 -2.4243872 -0.38152778 1.5097333\n",
" -1.5254772 -0.46679708 -0.8040082 0.33212468 -2.0767179 1.4183648\n",
" -0.31180647 -1.0390364 0.9115324 1.4334894 2.1585336 -0.748114\n",
" 1.0018059 -1.9200904 0.187933 0.6153294 -0.15873389 0.02760338\n",
" -0.8891688 0.441124 1.8577797 -1.175325 -0.8563917 -0.9134286\n",
" -0.00825506 -0.5712309 -2.6450243 1.5956511 1.6221918 2.5029912\n",
" -1.0801488 0.4656937 -2.8060102 0.06975971 1.0386504 0.21887605\n",
" -0.1417126 1.3999943 -1.3060251 -0.6237919 -1.4298364 1.6897066\n",
" 0.79661995 0.9073057 1.3726852 -0.02086719 0.0076753 0.08644638\n",
" -0.8681655 0.5078249 1.6837273 -1.6643734 -0.95620376 -0.2951687\n",
" 2.3271239 -0.61095464 -2.310826 -0.83130854 1.1732007 0.21897103\n",
" -0.5524513 -0.8641431 0.8443793 -1.9362655 -0.1727171 0.85040534\n",
" 0.43439955 -1.6141942 -0.48693871 -2.1621509 -1.2912481 1.4950235\n",
" -1.5765915 -1.4150319 -0.25412443 3.1067595 1.2009767 1.1443616\n",
" 0.2187777 2.123922 -0.6046734 1.730787 -0.02768439 -0.04853469\n",
" 0.0874666 0.61820555 0.71569085 -1.9728966 -1.4349542 0.8738759\n",
" 1.2587248 -0.7114431 0.7385885 0.5938564 -0.88316125 0.6663536\n",
" 1.2918131 -1.5714862 -1.4271843 -2.0305455 1.3754222 0.09721768\n",
" -0.5395826 -1.2019268 -0.10487846 -0.4280077 -2.0256274 -1.1094002\n",
" 0.29475218 1.8947592 1.8871454 -0.269509 -1.7486688 -1.1762798\n",
" 0.13728891 -0.30429757 2.1248107 1.0301822 0.47573996 0.5345257\n",
" 2.2040324 0.5964479 1.2012336 0.4398558 0.45679697 0.29740697\n",
" 1.0532469 -1.6681405 -1.9917039 -1.8092833 1.1296535 -1.4083812\n",
" 0.46084478 -2.2489362 0.18515275 -0.8153298 -1.4403951 -0.2867619\n",
" -0.17677756 0.50981635 0.37600514 0.51271224 -2.1614983 -0.8430705\n",
" 1.1643865 -0.10152651 1.9893564 1.9284602 -1.8981199 0.14854027\n",
" 0.6629725 1.0791826 -0.6990894 -0.01419751 -1.6861147 -1.5204353\n",
" -1.1214482 -0.41395268 1.5034088 -1.5491267 0.8293474 -0.8090867\n",
" 0.47375926 -0.6930324 -1.1305351 -0.59052885 0.01805112 2.526641\n",
" -3.2154572 -0.3064338 0.02277547 1.6230104 -0.69708276 2.4972136\n",
" -0.31329337 -0.45172843 -2.0997534 -2.5536265 0.18042235 0.12840319\n",
" -3.1397696 1.1388664 0.8228605 -1.6621394 1.3348328 -0.92805237\n",
" 0.06599946 -1.4691353 1.7670627 0.48549598 0.24247383 0.21922962]\n"
]
}
],
"source": [
"model10 = Word2Vec.load(\"/content/drive/MyDrive/compfest dataset/model/glove_alldata_10.model\")\n",
"\n",
"vector = model10.wv['one'] \n",
"print(vector)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "AZNn0iNsYnyU",
"outputId": "cf203fe9-d568-4bc0-8486-3f24e5f108d0"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 4.5680010e-01 1.5684054e+00 -1.8644618e+00 1.3424358e-01\n",
" 6.8941104e-01 -3.7711723e+00 1.2897754e+00 7.3185241e-01\n",
" 2.6835134e+00 1.4743711e+00 -2.5043104e+00 2.0876851e+00\n",
" -1.2859406e+00 -1.1684715e+00 -2.1693792e+00 -7.5380647e-01\n",
" 2.9643047e+00 1.7940453e+00 1.3248677e+00 -7.0089817e-01\n",
" 6.2529290e-01 -2.4653795e+00 1.7702156e+00 1.1409329e+00\n",
" 5.6084961e-01 -5.0350553e-01 2.7341540e+00 9.2322373e-01\n",
" -1.5827166e+00 7.8101224e-01 -1.1021086e+00 2.3582799e+00\n",
" -9.7666085e-01 -3.6134143e+00 3.4441340e+00 -1.5308503e+00\n",
" -2.5564923e+00 5.2288985e-01 -1.3230605e+00 1.1566578e+00\n",
" 1.0234530e+00 -2.4606310e-01 2.9664856e-01 -6.6401291e-01\n",
" -1.4680955e+00 -4.9912472e+00 -1.1092424e+00 -1.0235014e+00\n",
" 2.3386257e+00 5.7542884e-01 3.2541209e-01 3.8457918e-01\n",
" -2.4166110e-01 2.8210890e-01 1.7645224e+00 1.4328370e+00\n",
" -1.9086546e+00 2.0332606e+00 7.5449455e-01 -7.5729555e-01\n",
" 8.1982166e-01 9.9204683e-01 -3.1016212e+00 7.3714972e-01\n",
" 2.5279922e+00 4.0018106e-01 -1.1432573e+00 -7.1826249e-01\n",
" -1.9879878e+00 -1.5641551e+00 1.2542696e-01 1.0824213e+00\n",
" 3.0160458e+00 1.1759408e+00 -1.4259752e+00 -1.8969658e-01\n",
" 7.0507593e-02 1.7629524e+00 1.4164956e+00 -3.0544434e+00\n",
" -8.6495358e-01 -9.3323183e-01 -4.0774260e+00 1.4226317e+00\n",
" -1.1409572e+00 1.3093238e+00 2.9876852e-01 2.1168251e+00\n",
" -3.4790906e-01 -3.0679750e+00 1.2088025e+00 1.3553797e+00\n",
" -8.9135057e-01 -1.0141531e+00 -9.7642243e-01 -2.5743303e+00\n",
" 1.8598486e+00 -1.7437829e+00 1.0901916e+00 2.2782586e+00\n",
" 1.3495784e-01 -2.0811989e+00 -6.1961019e-01 -9.0836394e-01\n",
" -6.2548810e-01 -1.2580614e+00 4.5494762e-01 2.6499052e+00\n",
" -2.2291648e+00 7.6519668e-01 -2.6538088e+00 -2.4152133e+00\n",
" -1.2222006e+00 -1.2705909e-01 -8.4750628e-01 -4.8041284e-01\n",
" 1.2356406e+00 1.8596822e-01 4.2209578e+00 -2.2206916e-01\n",
" 3.9396319e-01 -3.2916126e+00 -4.5934570e-01 1.4812032e+00\n",
" -2.3916616e+00 -1.5154082e+00 -6.0599822e-01 4.5603505e-01\n",
" -1.4553602e+00 1.4775258e+00 -1.7496697e+00 -1.9007826e+00\n",
" 5.7807654e-01 4.4444275e-01 -2.2920256e+00 1.8017139e+00\n",
" -3.4711499e-02 -4.0557960e-01 -8.2294196e-01 -2.2154648e+00\n",
" -1.7043183e+00 3.4999149e+00 -1.8588332e+00 -1.2784941e+00\n",
" -8.4480071e-01 6.8810344e-01 -7.3124155e-02 -1.5077986e+00\n",
" -5.4536730e-01 -2.9010302e-01 7.5379407e-01 2.2430208e+00\n",
" -1.5524443e+00 4.9194136e-01 6.8250887e-02 -1.9626577e+00\n",
" 1.1790489e+00 1.1719702e+00 2.4908051e+00 -1.9362174e-01\n",
" 8.9101309e-01 -3.5670083e+00 5.9170645e-01 4.7581935e-01\n",
" -1.1254785e+00 7.4301082e-01 -3.1274894e-01 1.9267690e+00\n",
" -6.4789677e-01 -1.6298692e+00 -8.1524163e-01 -2.6138489e+00\n",
" -3.2346317e-01 -6.0781664e-01 -1.3296201e+00 -8.5968822e-01\n",
" 6.6345143e-01 -2.0696177e+00 -7.2060806e-01 9.0094072e-01\n",
" -1.0610343e+00 -7.3140040e-02 -1.1197670e+00 1.1987975e+00\n",
" 5.7314181e-01 -1.0607933e+00 -1.5449525e+00 2.7516072e+00\n",
" -2.8468072e+00 -3.7275767e-01 -1.6536772e+00 -4.5074669e-01\n",
" 3.9735129e-01 -1.5033197e+00 -5.5621509e-02 6.1222520e-02\n",
" -2.3461697e+00 -7.2411638e-01 1.0461459e-02 5.7747144e-02\n",
" 1.3667904e+00 -1.6241446e-02 -2.1302323e+00 1.6642818e-01\n",
" -1.7993243e-01 -2.8481846e+00 -3.4473047e-01 -2.3643277e+00\n",
" -5.2565950e-01 -1.1066842e+00 2.1326883e+00 1.4027604e+00\n",
" 2.5913964e-03 1.3182706e+00 -1.7022918e+00 -5.3648901e-01\n",
" 3.0017519e-01 2.1811097e+00 1.1378872e+00 -6.7751092e-01\n",
" 1.7156504e-01 -1.1653907e+00 7.8620654e-01 -1.4608474e-01\n",
" 3.0756781e+00 1.9379027e+00 2.3496075e+00 6.1691111e-01\n",
" -1.8220203e-01 7.9499042e-01 -4.7498485e-01 6.6700065e-01\n",
" 3.3722401e-02 -4.8732772e-01 2.7601945e+00 7.3101014e-01\n",
" 2.3398452e-01 -1.0059052e+00 6.4675343e-01 -7.1529680e-01\n",
" -4.1560479e-02 1.2285945e+00 -1.4659889e+00 -1.3641430e+00\n",
" -2.3875144e+00 -6.5470356e-01 -2.2781079e+00 1.2262152e+00\n",
" 7.5701123e-01 -3.3524305e-01 9.5385069e-01 -1.4503227e+00\n",
" -3.4770353e+00 -6.2972349e-01 8.0471355e-01 -1.5375202e+00\n",
" -1.1217228e+00 -1.0488685e+00 3.4995234e+00 2.1921558e+00\n",
" -2.0359813e-01 1.2313733e+00 -2.0943313e+00 -1.3461229e+00\n",
" -1.5922695e+00 1.2676049e+00 8.0286756e-02 -1.6957649e+00\n",
" -7.4301475e-01 3.0223912e-01 -1.4028478e-01 -1.7948213e-01\n",
" -8.1368381e-01 -2.7812693e+00 3.0886688e+00 -5.1417738e-01\n",
" -7.3055603e-02 2.4955046e+00 9.2793763e-01 3.5219657e+00\n",
" -1.0533503e+00 1.5811691e+00 1.3857584e+00 6.3722599e-01\n",
" 7.4639475e-01 1.6157640e+00 4.0057102e-01 -5.9658426e-01\n",
" -2.3185644e+00 9.0162545e-01 9.0573317e-01 2.3510664e+00\n",
" 1.9243287e-01 -1.9859406e+00 -9.5674837e-01 -1.3185903e-01\n",
" 7.5599033e-01 -6.6858703e-01 1.9020736e+00 -7.9249039e-02]\n"
]
}
],
"source": [
"model50 = Word2Vec.load(\"/content/drive/MyDrive/compfest dataset/model/glove_alldata_50.model\")\n",
"\n",
"vector = model50.wv['one'] \n",
"print(vector)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "zLO4MXFwwvYz",
"outputId": "8f8369ae-5203-4cd0-c5cb-f8e3183f5c84"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Most common words in the Word2Vec corpus:\n",
"data: 101468\n",
"experience: 79382\n",
"work: 55628\n",
"year: 46801\n",
"team: 41840\n",
"business: 39874\n",
"skill: 39217\n",
"customer: 27552\n",
"job: 27461\n",
"good: 26996\n",
"company: 26364\n",
"minimum: 26202\n",
"product: 25752\n",
"degree: 25382\n",
"science: 22847\n",
"able: 22817\n",
"analysis: 22377\n",
"management: 22153\n",
"learning: 21750\n",
"project: 21089\n"
]
}
],
"source": [
"vocab = model50.wv.key_to_index\n",
"word_freq = {word: model50.wv.get_vecattr(word, \"count\") for word in vocab}\n",
"\n",
"# Use Counter to find the most common words\n",
"word_counter = Counter(word_freq)\n",
"\n",
"# Print the 20 most common words\n",
"print(\"Most common words in the Word2Vec corpus:\")\n",
"for word, freq in word_counter.most_common(20):\n",
" print(f\"{word}: {freq}\")"
]
}
],
"metadata": {
"colab": {
"include_colab_link": true,
"provenance": []
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
},
"language_info": {
"name": "python"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
|