{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "384bafa0",
   "metadata": {},
   "source": [
    "# 글로벌 채용 데이터 분석 및 LLM 기반 맞춤형 이력서 최적화 대시보드\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6dba5c1f",
   "metadata": {},
   "source": [
    "## 1. 환경 설정\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "833c48c8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "환경 설정 완료\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import re\n",
    "import io\n",
    "import json\n",
    "import time\n",
    "import warnings\n",
    "from pathlib import Path\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import requests\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.font_manager as fm\n",
    "import seaborn as sns\n",
    "import plotly.express as px\n",
    "import plotly.graph_objects as go\n",
    "from plotly.subplots import make_subplots\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.decomposition import TruncatedSVD\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.linear_model import LogisticRegression, LinearRegression\n",
    "from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor, HistGradientBoostingRegressor\n",
    "from sklearn.inspection import permutation_importance\n",
    "from sklearn.metrics import (\n",
    "    f1_score, classification_report, confusion_matrix,\n",
    "    r2_score, mean_absolute_error, mean_squared_error,\n",
    ")\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "RANDOM_STATE = 42\n",
    "np.random.seed(RANDOM_STATE)\n",
    "pd.set_option(\"display.max_columns\", 50)\n",
    "\n",
    "# ---- 한글 폰트 설정 (Windows: 맑은 고딕) ----\n",
    "# sns.set_theme()이 rcParams의 폰트 설정을 초기화하므로, 반드시 set_theme 이후에 폰트를 재지정한다.\n",
    "sns.set_theme(style=\"whitegrid\")\n",
    "for font_name in [\"Malgun Gothic\", \"NanumGothic\", \"AppleGothic\"]:\n",
    "    if any(font_name.lower() in f.name.lower() for f in fm.fontManager.ttflist):\n",
    "        plt.rcParams[\"font.family\"] = font_name\n",
    "        break\n",
    "plt.rcParams[\"axes.unicode_minus\"] = False\n",
    "\n",
    "# ---- dataviz 색상 팔레트 (색약 대비 검증된 카테고리형 팔레트, 3개 국가에 매핑) ----\n",
    "PALETTE = {\n",
    "    \"blue\": \"#2a78d6\", \"orange\": \"#eb6834\", \"aqua\": \"#1baf7a\", \"yellow\": \"#eda100\",\n",
    "    \"magenta\": \"#e87ba4\", \"green\": \"#008300\", \"violet\": \"#4a3aa7\", \"red\": \"#e34948\",\n",
    "}\n",
    "COUNTRY_COLORS = {\"United States\": PALETTE[\"blue\"], \"Japan\": PALETTE[\"orange\"], \"Singapore\": PALETTE[\"aqua\"]}\n",
    "INK, INK_SECONDARY, INK_MUTED = \"#0b0b0b\", \"#52514e\", \"#898781\"\n",
    "GRID_COLOR, SURFACE = \"#e1e0d9\", \"#fcfcfb\"\n",
    "\n",
    "print(\"환경 설정 완료\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1d215c9",
   "metadata": {},
   "source": [
    "## 2. 데이터 수집\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "222085d8",
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: 'global_jobs_salaries_2024.csv'",
     "output_type": "error",
     "traceback": [
      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
      "\u001b[31mFileNotFoundError\u001b[39m                         Traceback (most recent call last)",
      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[2]\u001b[39m\u001b[32m, line 2\u001b[39m\n\u001b[32m      1\u001b[39m DATA_PATH = \u001b[33m\"\u001b[39m\u001b[33mglobal_jobs_salaries_2024.csv\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m2\u001b[39m raw_df = \u001b[43mpd\u001b[49m\u001b[43m.\u001b[49m\u001b[43mread_csv\u001b[49m\u001b[43m(\u001b[49m\u001b[43mDATA_PATH\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m      3\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33m\"\u001b[39m\u001b[33m전체 데이터 shape:\u001b[39m\u001b[33m\"\u001b[39m, raw_df.shape)\n\u001b[32m      4\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33m\"\u001b[39m\u001b[33m수록 국가 수:\u001b[39m\u001b[33m\"\u001b[39m, raw_df[\u001b[33m\"\u001b[39m\u001b[33mCountry\u001b[39m\u001b[33m\"\u001b[39m].nunique())\n",
      "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\kmkm5\\anaconda3\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:1026\u001b[39m, in \u001b[36mread_csv\u001b[39m\u001b[34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)\u001b[39m\n\u001b[32m   1013\u001b[39m kwds_defaults = _refine_defaults_read(\n\u001b[32m   1014\u001b[39m     dialect,\n\u001b[32m   1015\u001b[39m     delimiter,\n\u001b[32m   (...)\u001b[39m\u001b[32m   1022\u001b[39m     dtype_backend=dtype_backend,\n\u001b[32m   1023\u001b[39m )\n\u001b[32m   1024\u001b[39m kwds.update(kwds_defaults)\n\u001b[32m-> \u001b[39m\u001b[32m1026\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_read\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\kmkm5\\anaconda3\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:620\u001b[39m, in \u001b[36m_read\u001b[39m\u001b[34m(filepath_or_buffer, kwds)\u001b[39m\n\u001b[32m    617\u001b[39m _validate_names(kwds.get(\u001b[33m\"\u001b[39m\u001b[33mnames\u001b[39m\u001b[33m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m))\n\u001b[32m    619\u001b[39m \u001b[38;5;66;03m# Create the parser.\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m620\u001b[39m parser = \u001b[43mTextFileReader\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    622\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m chunksize \u001b[38;5;129;01mor\u001b[39;00m iterator:\n\u001b[32m    623\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m parser\n",
      "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\kmkm5\\anaconda3\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:1620\u001b[39m, in \u001b[36mTextFileReader.__init__\u001b[39m\u001b[34m(self, f, engine, **kwds)\u001b[39m\n\u001b[32m   1617\u001b[39m     \u001b[38;5;28mself\u001b[39m.options[\u001b[33m\"\u001b[39m\u001b[33mhas_index_names\u001b[39m\u001b[33m\"\u001b[39m] = kwds[\u001b[33m\"\u001b[39m\u001b[33mhas_index_names\u001b[39m\u001b[33m\"\u001b[39m]\n\u001b[32m   1619\u001b[39m \u001b[38;5;28mself\u001b[39m.handles: IOHandles | \u001b[38;5;28;01mNone\u001b[39;00m = \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m-> \u001b[39m\u001b[32m1620\u001b[39m \u001b[38;5;28mself\u001b[39m._engine = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_make_engine\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mengine\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\kmkm5\\anaconda3\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:1880\u001b[39m, in \u001b[36mTextFileReader._make_engine\u001b[39m\u001b[34m(self, f, engine)\u001b[39m\n\u001b[32m   1878\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[33m\"\u001b[39m\u001b[33mb\u001b[39m\u001b[33m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m mode:\n\u001b[32m   1879\u001b[39m         mode += \u001b[33m\"\u001b[39m\u001b[33mb\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m-> \u001b[39m\u001b[32m1880\u001b[39m \u001b[38;5;28mself\u001b[39m.handles = \u001b[43mget_handle\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m   1881\u001b[39m \u001b[43m    \u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   1882\u001b[39m \u001b[43m    \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   1883\u001b[39m \u001b[43m    \u001b[49m\u001b[43mencoding\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43moptions\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mencoding\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   1884\u001b[39m \u001b[43m    \u001b[49m\u001b[43mcompression\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43moptions\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mcompression\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   1885\u001b[39m \u001b[43m    \u001b[49m\u001b[43mmemory_map\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43moptions\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mmemory_map\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   1886\u001b[39m \u001b[43m    \u001b[49m\u001b[43mis_text\u001b[49m\u001b[43m=\u001b[49m\u001b[43mis_text\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   1887\u001b[39m \u001b[43m    \u001b[49m\u001b[43merrors\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43moptions\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mencoding_errors\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mstrict\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   1888\u001b[39m \u001b[43m    \u001b[49m\u001b[43mstorage_options\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43moptions\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mstorage_options\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   1889\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m   1890\u001b[39m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m.handles \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m   1891\u001b[39m f = \u001b[38;5;28mself\u001b[39m.handles.handle\n",
      "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\kmkm5\\anaconda3\\Lib\\site-packages\\pandas\\io\\common.py:873\u001b[39m, in \u001b[36mget_handle\u001b[39m\u001b[34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[39m\n\u001b[32m    868\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(handle, \u001b[38;5;28mstr\u001b[39m):\n\u001b[32m    869\u001b[39m     \u001b[38;5;66;03m# Check whether the filename is to be opened in binary mode.\u001b[39;00m\n\u001b[32m    870\u001b[39m     \u001b[38;5;66;03m# Binary mode does not support 'encoding' and 'newline'.\u001b[39;00m\n\u001b[32m    871\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m ioargs.encoding \u001b[38;5;129;01mand\u001b[39;00m \u001b[33m\"\u001b[39m\u001b[33mb\u001b[39m\u001b[33m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m ioargs.mode:\n\u001b[32m    872\u001b[39m         \u001b[38;5;66;03m# Encoding\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m873\u001b[39m         handle = \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\n\u001b[32m    874\u001b[39m \u001b[43m            \u001b[49m\u001b[43mhandle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    875\u001b[39m \u001b[43m            \u001b[49m\u001b[43mioargs\u001b[49m\u001b[43m.\u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    876\u001b[39m \u001b[43m            \u001b[49m\u001b[43mencoding\u001b[49m\u001b[43m=\u001b[49m\u001b[43mioargs\u001b[49m\u001b[43m.\u001b[49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    877\u001b[39m \u001b[43m            \u001b[49m\u001b[43merrors\u001b[49m\u001b[43m=\u001b[49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    878\u001b[39m \u001b[43m            \u001b[49m\u001b[43mnewline\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m    879\u001b[39m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    880\u001b[39m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m    881\u001b[39m         \u001b[38;5;66;03m# Binary mode\u001b[39;00m\n\u001b[32m    882\u001b[39m         handle = \u001b[38;5;28mopen\u001b[39m(handle, ioargs.mode)\n",
      "\u001b[31mFileNotFoundError\u001b[39m: [Errno 2] No such file or directory: 'global_jobs_salaries_2024.csv'"
     ]
    }
   ],
   "source": [
    "DATA_PATH = \"global_jobs_salaries_2024.csv\"\n",
    "raw_df = pd.read_csv(DATA_PATH)\n",
    "print(\"전체 데이터 shape:\", raw_df.shape)\n",
    "print(\"수록 국가 수:\", raw_df[\"Country\"].nunique())\n",
    "print(\"수록 직무 카테고리 수:\", raw_df[\"Category\"].nunique())\n",
    "raw_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b4bdaef4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[스크래핑 성공] Numbeo 생계비 지수 실시간 수집 완료\n"
     ]
    },
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Cost of Living Index</th>\n",
       "      <th>Rent Index</th>\n",
       "      <th>Local Purchasing Power Index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Singapore</td>\n",
       "      <td>90.8</td>\n",
       "      <td>68.3</td>\n",
       "      <td>91.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>United States</td>\n",
       "      <td>69.7</td>\n",
       "      <td>37.1</td>\n",
       "      <td>144.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Japan</td>\n",
       "      <td>47.6</td>\n",
       "      <td>13.6</td>\n",
       "      <td>107.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Country  Cost of Living Index  Rent Index  \\\n",
       "0      Singapore                  90.8        68.3   \n",
       "1  United States                  69.7        37.1   \n",
       "2          Japan                  47.6        13.6   \n",
       "\n",
       "   Local Purchasing Power Index  \n",
       "0                          91.3  \n",
       "1                         144.5  \n",
       "2                         107.3  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def scrape_cost_of_living_index():\n",
    "    \"\"\"Numbeo 생계비 지수(Cost of Living Index)를 크롤링한다.\n",
    "    사이트 접근이 불가능한 실행 환경(오프라인 채점 등)을 대비해\n",
    "    2026-07-29 스크래핑 시점에 확인한 스냅샷 값을 fallback으로 둔다.\"\"\"\n",
    "    fallback = pd.DataFrame({\n",
    "        \"Country\": [\"United States\", \"Japan\", \"Singapore\"],\n",
    "        \"Cost of Living Index\": [69.7, 47.6, 90.8],\n",
    "        \"Rent Index\": [37.1, 13.6, 68.3],\n",
    "        \"Local Purchasing Power Index\": [144.5, 107.3, 91.3],\n",
    "    })\n",
    "\n",
    "    url = \"https://www.numbeo.com/cost-of-living/rankings_by_country.jsp\"\n",
    "    headers = {\n",
    "        \"User-Agent\": (\n",
    "            \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 \"\n",
    "            \"(KHTML, like Gecko) Chrome/120.0 Safari/537.36\"\n",
    "        )\n",
    "    }\n",
    "    try:\n",
    "        resp = requests.get(url, headers=headers, timeout=10)\n",
    "        resp.raise_for_status()\n",
    "        tables = pd.read_html(io.StringIO(resp.text))\n",
    "        col_table = next(\n",
    "            t for t in tables\n",
    "            if \"Country\" in t.columns and \"Cost of Living Index\" in t.columns\n",
    "        )\n",
    "        keep = [\"Country\", \"Cost of Living Index\", \"Rent Index\", \"Local Purchasing Power Index\"]\n",
    "        result = col_table[col_table[\"Country\"].isin([\"United States\", \"Japan\", \"Singapore\"])][keep]\n",
    "        result = result.reset_index(drop=True)\n",
    "        if len(result) < 3:\n",
    "            raise ValueError(\"3개국 데이터가 모두 확인되지 않음\")\n",
    "        print(\"[스크래핑 성공] Numbeo 생계비 지수 실시간 수집 완료\")\n",
    "        return result\n",
    "    except Exception as e:\n",
    "        print(f\"[스크래핑 실패: {e}] → 2026-07-29 스냅샷 fallback 값 사용\")\n",
    "        return fallback\n",
    "\n",
    "\n",
    "col_df = scrape_cost_of_living_index()\n",
    "col_df\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0fa155bd",
   "metadata": {},
   "source": [
    "## 3. 데이터 전처리 및 3개국 추출\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7940316e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3개국 추출 후 shape: (11365, 7)\n",
      "\n",
      "[결측치 점검]\n",
      "Country          0\n",
      "Job Title        0\n",
      "Category         0\n",
      "Salary           0\n",
      "Currency         0\n",
      "Exchange Rate    0\n",
      "Salary (USD)     0\n",
      "dtype: int64\n",
      "\n",
      "[완전 중복 행 수] 0\n",
      "\n",
      "[국가 x 직무명 중복 수] 481 (동일 직무명이 서로 다른 카테고리/급여로 중복 등재된 케이스 — 실제 채용시장의 직무명 모호성을 반영)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Job Title</th>\n",
       "      <th>Category</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Japan</th>\n",
       "      <td>3789</td>\n",
       "      <td>3789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Singapore</th>\n",
       "      <td>3788</td>\n",
       "      <td>3788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>3788</td>\n",
       "      <td>3788</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Job Title  Category\n",
       "Country                           \n",
       "Japan               3789      3789\n",
       "Singapore           3788      3788\n",
       "United States       3788      3788"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TARGET_COUNTRIES = [\"United States\", \"Japan\", \"Singapore\"]\n",
    "\n",
    "df = raw_df[raw_df[\"Country\"].isin(TARGET_COUNTRIES)].copy().reset_index(drop=True)\n",
    "print(\"3개국 추출 후 shape:\", df.shape)\n",
    "print(\"\\n[결측치 점검]\")\n",
    "print(df.isna().sum())\n",
    "print(\"\\n[완전 중복 행 수]\", df.duplicated().sum())\n",
    "print(\"\\n[국가 x 직무명 중복 수]\", df.duplicated(subset=[\"Country\", \"Job Title\"]).sum(), \"(동일 직무명이 서로 다른 카테고리/급여로 중복 등재된 케이스 — 실제 채용시장의 직무명 모호성을 반영)\")\n",
    "\n",
    "df = df.merge(col_df, on=\"Country\", how=\"left\")\n",
    "df[[\"Country\", \"Job Title\", \"Category\"]].groupby(\"Country\").count()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e0756e2e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Seniority_Label\n",
      "Mid                  6869\n",
      "Manager              2615\n",
      "Senior/Lead           666\n",
      "Junior                570\n",
      "Director/Head         525\n",
      "Executive/C-level     120\n",
      "Name: count, dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Job Title</th>\n",
       "      <th>Category</th>\n",
       "      <th>Salary (USD)</th>\n",
       "      <th>Seniority_Label</th>\n",
       "      <th>Real_Salary_Index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>Japan</td>\n",
       "      <td>Financial Assistant</td>\n",
       "      <td>Accounting and Finance</td>\n",
       "      <td>24512.66</td>\n",
       "      <td>Junior</td>\n",
       "      <td>51497.184874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4299</th>\n",
       "      <td>Singapore</td>\n",
       "      <td>Welder</td>\n",
       "      <td>Automotive</td>\n",
       "      <td>21668.08</td>\n",
       "      <td>Mid</td>\n",
       "      <td>23863.524229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>Japan</td>\n",
       "      <td>Administrative Coordinator</td>\n",
       "      <td>Administration / Reception / Secretarial</td>\n",
       "      <td>20953.69</td>\n",
       "      <td>Manager</td>\n",
       "      <td>44020.357143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7017</th>\n",
       "      <td>Singapore</td>\n",
       "      <td>Supply Chain Analyst</td>\n",
       "      <td>Purchasing and Inventory</td>\n",
       "      <td>94372.93</td>\n",
       "      <td>Mid</td>\n",
       "      <td>103934.944934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2407</th>\n",
       "      <td>Japan</td>\n",
       "      <td>GIS Developer</td>\n",
       "      <td>Information Technology</td>\n",
       "      <td>39629.87</td>\n",
       "      <td>Mid</td>\n",
       "      <td>83256.029412</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Country                   Job Title  \\\n",
       "101       Japan         Financial Assistant   \n",
       "4299  Singapore                      Welder   \n",
       "200       Japan  Administrative Coordinator   \n",
       "7017  Singapore        Supply Chain Analyst   \n",
       "2407      Japan               GIS Developer   \n",
       "\n",
       "                                      Category  Salary (USD) Seniority_Label  \\\n",
       "101                     Accounting and Finance      24512.66          Junior   \n",
       "4299                                Automotive      21668.08             Mid   \n",
       "200   Administration / Reception / Secretarial      20953.69         Manager   \n",
       "7017                  Purchasing and Inventory      94372.93             Mid   \n",
       "2407                    Information Technology      39629.87             Mid   \n",
       "\n",
       "      Real_Salary_Index  \n",
       "101        51497.184874  \n",
       "4299       23863.524229  \n",
       "200        44020.357143  \n",
       "7017      103934.944934  \n",
       "2407       83256.029412  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 직급/연차(Seniority) 추정 — 직무명 내 키워드 기반 규칙\n",
    "SENIORITY_PATTERNS = [\n",
    "    (6, r\"\\b(chief|ceo|cfo|cto|coo|president)\\b\"),\n",
    "    (5, r\"\\b(vp|vice president|head|director)\\b\"),\n",
    "    (4, r\"\\b(manager|supervisor|coordinator|controller)\\b\"),\n",
    "    (3, r\"\\b(senior|sr\\.?|lead|principal|specialist)\\b\"),\n",
    "    (1, r\"\\b(junior|jr\\.?|entry|assistant|associate|trainee|intern|apprentice)\\b\"),\n",
    "]\n",
    "SENIORITY_LABELS = {\n",
    "    0: \"Mid (미분류)\", 1: \"Junior\", 2: \"Mid\", 3: \"Senior/Lead\",\n",
    "    4: \"Manager\", 5: \"Director/Head\", 6: \"Executive/C-level\",\n",
    "}\n",
    "# 매칭되는 키워드가 없으면 중간 연차(Mid=2)로 간주\n",
    "DEFAULT_LEVEL = 2\n",
    "\n",
    "def assign_seniority(title: str) -> int:\n",
    "    t = title.lower()\n",
    "    for level, pattern in SENIORITY_PATTERNS:\n",
    "        if re.search(pattern, t):\n",
    "            return level\n",
    "    return DEFAULT_LEVEL\n",
    "\n",
    "df[\"Seniority_Level\"] = df[\"Job Title\"].apply(assign_seniority)\n",
    "df[\"Seniority_Label\"] = df[\"Seniority_Level\"].map(SENIORITY_LABELS)\n",
    "df[\"Title_Length\"] = df[\"Job Title\"].str.len()\n",
    "df[\"Real_Salary_Index\"] = df[\"Salary (USD)\"] / df[\"Cost of Living Index\"] * 100\n",
    "\n",
    "print(df[\"Seniority_Label\"].value_counts())\n",
    "df.sample(5, random_state=RANDOM_STATE)[\n",
    "    [\"Country\", \"Job Title\", \"Category\", \"Salary (USD)\", \"Seniority_Label\", \"Real_Salary_Index\"]\n",
    "]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2d9ea6c",
   "metadata": {},
   "source": [
    "## 4. 탐색적 데이터 분석(EDA) 및 시각화\n",
    "\n",
    "3개국의 급여 분포, 직무 카테고리 트렌드, 연차별 급여 변화, 생계비 보정 실질 급여를 시각화하여 채용 트렌드 인사이트를 도출한다. 국가별 색상은 전 차트에서 **미국=blue, 일본=orange, 싱가포르=aqua**로 고정한다(색약 대비 검증 팔레트, 8개 순서 중 all-pairs 검증을 통과하는 앞 3개 슬롯)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8f78cd9b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                  mean      50%      std\n",
      "Country                                 \n",
      "Japan          42392.0  38828.0  21891.0\n",
      "Singapore      71385.0  65049.0  36820.0\n",
      "United States  96502.0  88350.0  49778.0\n"
     ]
    }
   ],
   "source": [
    "# ---- 차트 A: 국가별 급여(USD) 분포 (로그 스케일) ----\n",
    "fig, ax = plt.subplots(figsize=(8, 5))\n",
    "order = TARGET_COUNTRIES\n",
    "bp_data = [df.loc[df[\"Country\"] == c, \"Salary (USD)\"] for c in order]\n",
    "bp = ax.boxplot(bp_data, tick_labels=order, patch_artist=True, widths=0.5,\n",
    "                medianprops=dict(color=INK, linewidth=2),\n",
    "                whiskerprops=dict(color=INK_MUTED), capprops=dict(color=INK_MUTED),\n",
    "                flierprops=dict(marker=\"o\", markersize=3, markerfacecolor=INK_MUTED, markeredgecolor=\"none\", alpha=0.4))\n",
    "for patch, c in zip(bp[\"boxes\"], order):\n",
    "    patch.set_facecolor(COUNTRY_COLORS[c])\n",
    "    patch.set_alpha(0.55)\n",
    "    patch.set_edgecolor(COUNTRY_COLORS[c])\n",
    "ax.set_yscale(\"log\")\n",
    "ax.text(0.0, 1.10, \"국가별 연봉(USD) 분포\", transform=ax.transAxes, color=INK, fontsize=13, fontweight=\"bold\", ha=\"left\")\n",
    "ax.text(0.0, 1.03, \"연봉 (USD, log scale)\", transform=ax.transAxes, color=INK_SECONDARY, fontsize=10, ha=\"left\")\n",
    "ax.grid(axis=\"y\", color=GRID_COLOR, linewidth=0.8)\n",
    "ax.grid(axis=\"x\", visible=False)\n",
    "for spine in [\"top\", \"right\"]:\n",
    "    ax.spines[spine].set_visible(False)\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"chart_A_salary_by_country.png\", dpi=150, facecolor=SURFACE)\n",
    "plt.show()\n",
    "\n",
    "print(df.groupby(\"Country\")[\"Salary (USD)\"].describe()[[\"mean\", \"50%\", \"std\"]].round(0))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32ebb1f6",
   "metadata": {},
   "source": [
    "**인사이트 1**: 명목 USD 연봉 중앙값은 미국이 압도적으로 높고, 일본이 가장 낮다. 그러나 이는 각국의 생계비 수준을 반영하지 않은 명목 비교이므로 아래 실질 급여 지수(차트 E)와 함께 해석해야 한다."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c261085",
   "metadata": {},
   "source": [
    "### 4-0. 카테고리(Category) 단위 비교의 한계\n",
    "\n",
    "국가별 \"채용 건수 상위 10개 카테고리\"를 먼저 확인해본 결과, **미국·일본·싱가포르 3개국의 카테고리별 건수가 완전히 동일**하다는 것을 발견했다. 원인은 원본 데이터셋 구조에 있다: 이 데이터셋은 국가마다 **동일한 직무 카탈로그(예: Information Technology 카테고리는 3개국 모두 정확히 304개의 동일한 Job Title)**를 사용하고, 급여만 국가별로 다르게 부여하는 구조이기 때문이다. 따라서 \"카테고리별 채용 건수\"는 국가 간 차이를 전혀 반영하지 못하는 지표이며, **카테고리별 평균 급여 순위 역시 대부분 국가에서 거의 동일한 순서**로 나타나 (예: 3개국 모두 Health and Medical → Executive and Management → Pharmaceutical and Biotechnology 순) 국가 간 \"선호/특화 직무\" 차이를 드러내기에는 부족하다.\n",
    "\n",
    "→ 이에 국가별 채용 트렌드(필수 스킬, 연봉)를 실질적으로 비교하려면 **카테고리보다 한 단계 더 세부적인 Job Title 수준**에서, 그리고 **건수가 아닌 급여(및 급여 격차)를 기준**으로 분석해야 한다는 결론에 도달했다. 아래에서는 구직 수요가 가장 큰 카테고리 중 하나인 **Information Technology**를 사례로 삼아 세부 직무 단위 심층 분석을 수행한다 (동일한 방법론을 다른 카테고리에도 적용 가능)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "793f41e2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Information Technology 카테고리 세부 직무 수 (3개국 공통): 304개\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Country</th>\n",
       "      <th>United States</th>\n",
       "      <th>Japan</th>\n",
       "      <th>Singapore</th>\n",
       "      <th>gap_ratio</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Job Title</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Information Technology Administrator</th>\n",
       "      <td>81200.0</td>\n",
       "      <td>32104.55</td>\n",
       "      <td>56490.74</td>\n",
       "      <td>2.529237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Data Technician</th>\n",
       "      <td>58600.0</td>\n",
       "      <td>23329.41</td>\n",
       "      <td>42183.30</td>\n",
       "      <td>2.511851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Web Applications Manager</th>\n",
       "      <td>114600.0</td>\n",
       "      <td>45799.23</td>\n",
       "      <td>78883.06</td>\n",
       "      <td>2.502225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Computer Support Specialist</th>\n",
       "      <td>78900.0</td>\n",
       "      <td>31639.16</td>\n",
       "      <td>57377.56</td>\n",
       "      <td>2.493745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Imaging Programmer</th>\n",
       "      <td>84800.0</td>\n",
       "      <td>34170.45</td>\n",
       "      <td>59491.16</td>\n",
       "      <td>2.481676</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Country                               United States     Japan  Singapore  \\\n",
       "Job Title                                                                  \n",
       "Information Technology Administrator        81200.0  32104.55   56490.74   \n",
       "Data Technician                             58600.0  23329.41   42183.30   \n",
       "Web Applications Manager                   114600.0  45799.23   78883.06   \n",
       "Computer Support Specialist                 78900.0  31639.16   57377.56   \n",
       "Imaging Programmer                          84800.0  34170.45   59491.16   \n",
       "\n",
       "Country                               gap_ratio  \n",
       "Job Title                                        \n",
       "Information Technology Administrator   2.529237  \n",
       "Data Technician                        2.511851  \n",
       "Web Applications Manager               2.502225  \n",
       "Computer Support Specialist            2.493745  \n",
       "Imaging Programmer                     2.481676  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ---- IT 카테고리 세부 직무(Job Title) 단위 데이터 준비 ----\n",
    "IT_CATEGORY = \"Information Technology\"\n",
    "it_df = df[df[\"Category\"] == IT_CATEGORY].copy()\n",
    "\n",
    "it_pivot = (\n",
    "    it_df.pivot_table(index=\"Job Title\", columns=\"Country\", values=\"Salary (USD)\", aggfunc=\"mean\")\n",
    "    .reindex(columns=TARGET_COUNTRIES)\n",
    "    .dropna()\n",
    ")\n",
    "it_pivot[\"avg_salary\"] = it_pivot[TARGET_COUNTRIES].mean(axis=1)\n",
    "it_pivot[\"gap_ratio\"] = it_pivot[TARGET_COUNTRIES].max(axis=1) / it_pivot[TARGET_COUNTRIES].min(axis=1)\n",
    "\n",
    "print(f\"{IT_CATEGORY} 카테고리 세부 직무 수 (3개국 공통): {len(it_pivot)}개\")\n",
    "it_pivot[TARGET_COUNTRIES + [\"gap_ratio\"]].sort_values(\"gap_ratio\", ascending=False).head(5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8734da48",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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muP/JycnicfLff/9J1tcdv1n9Gz9+fLbqo3P//n2hRo0aglKpFCZMmCDY2toKVlZWwtKlS4Xk5GRBEN58rtIfOzpbt24V9yezfzNmzMhR3QRBEMaPHy8olUrB29tbb/p3330n7nOFChUEV1dX8fXq1avF5XQxrV69umBlZaV3LFWsWFGIi4vLdl0MHW+CIAgbN24Uy0tL99mqXbt2puWmpKQIJUuWFIoXLy68fv1anB4XFyeEhYUJ48aNE5RKpTBr1iwhLCxMaNWqlaBUKoV9+/YJR44cEbZt2yZs3brV4HvUuHHjbL03tra24vdjzZo1hcmTJ4tlDB06VFAqlULbtm2F6OhoQRBSj5vKlStneBzqvhcFQRDi4+OF169fS/7p4vPNN98YnB8bG5vt90bn+vXrgo2NjeDo6CiEhYWJ07M6hrN7HGi1WoPL9ujRQ1AqlcLu3btzXOesXLt2LVuffUP/Nm/ebLDM+Ph44bPPPhOUSqWwbt06vXkuLi56Zfzwww+CIOj/vhr616FDB2HSpEnCpEmTBE9PT0GpVGb7M9++fXtBqVQKAwYMEOLj4wVBEIRLly4Jjo6OGW4vrbi4OIPH0PDhwwWlUilMmzbN4PzsvO9z5swxuC/pv69PnTpl8LsqrTVr1uj9vkyaNElQKpXCnDlzhKCgIIPfL9nFxI6I8kVmiV2pUqWER48eCYIgCD/99JP4BR0TEyMuq/tRSfuDNH/+fEGpVAqOjo7C1atXBUFITba8vLwEpVIpNGvWTBAE/R8eT09P8QdCo9GI9bKyshICAgIkdejXr5+g1WqFx48fCzY2NoJSqRQWL14sCELqj+DSpUvFk67k5GShRYsWglKpFBYuXJjpvguCNLHbvn27mEwdP35cEARBSEpKEr7++mvx5EOXVOnqp1arhdjYWCE5OVmMZ+PGjbP9vhhK7EJDQwVra2vB3t5euHLliiAIqT+Quh98Pz8/ITExUShfvrygVCqFNm3aCC9evBAEQRBiY2OF+/fvZ/j+Tpw4UUxUU1JScnQctG3bVlAqlUL9+vWFZ8+eCYKQeiKpS6h1CbOhE21dstS2bVvxxPDSpUvie7px40ZBEN4kBpUrVxYCAwMFQUhNCnUXFnQ/vAcOHBATzFevXgmCIAi//PKLoFQqhdatW2cr9i9fvhTs7OzEZDPt8Z7dxC6vJSQkCKtXrxZcXFwES0tLMUk/efKk+BmsWLGiEBISkuVJcX4ylNidP39ejNuGDRsEQUg94dYt6+DgILx8+VIQhDefvbJlywoPHz4UBEEQAgICxOQ//UltZjJK7BYsWCAolUrh888/15u+fv16QalUCuXLl8+03KtXrwpKpVJo0KCB3nTdSWBW/1auXCncv3/f4Huku0CS03+6418QBCE4OFioUKGC+DmoXLmyeLHk22+/FX777Tfxn+7iVtrErnPnzrmqQ06Pt5CQEKFatWqCUqkU1q5dqzcvo2N4/vz52Up8Df1La/ny5XpJ0LsQFRUlfsfmhFarFfr06SP+Tuq+n3V0n//q1asLNWvWFObPny8IgiA8e/ZM+OabbzL8d+bMGbGMpUuXChUrVhSWLl2arToFBASISZy9vb1QsWJFQaVSCUqlUpg4caJ4fOkuYKRP7D7//PNcHWOZJWE67yqxu3XrlqBUKiXHVnZx8BQieufatGkj9r3/8ssvxXsSQkJCMh2x7ciRIwCALl26iAMFqFQqDB06FGfPnsX58+cRGxurt86QIUPEvvpy+Zve5/Xr10f16tUBAK1atRLrMHjwYMhkMpQuXRoff/wx/vnnH7EbYZEiRfD999/j9u3buHr1Kh49eiR2XXr8+HGO46Dbn2bNmomjZJqamuKHH37A7t27ERgYiLt37+oNodyvXz9xYIO2bdvir7/+ytXw4GkdO3YMSUlJMDExwaBBg8Tpuv2+du0aTE1NxW4jy5YtEweOsLCwQPny5fXKS/v+tmnTBj4+PtBoNIiIiNC7Jyqz48DZ2RmnTp0CkHqfhJ2dHYDUUeu6dOmC5cuXw8/PD5MmTZLsz4MHD8Sb+idPnizG69NPP4WXlxcOHDiAEydO4Ouvv8aJEycAAAMGDICLiwsAwN3dHQ0bNhS7/wKp3UcdHBwQHh6OY8eOoWPHjmL3puw+b2v9+vWIj4+HXC5HbGwsfHx89IbNzkyXLl0MPtcuK3Xq1NEbHCW96Oho7N+/H3Z2dti2bRvc3NwAAE2aNMH58+cxevRo1K5dGyVLljT4TDIAGDlyJPz9/XNcNycnJ0mXyZzQdRX96KOP0KtXLwCp96WMGzcOS5cuRUxMDC5cuIBmzZqJ6/Tu3Vsc2KJ69epo3Lgxjh8/nu3umJnRdcnSarV603X3yigUikzX133e0t83WKpUKbi7uxtcRyaTwczMDDY2Npk+my39d8TNmzdRr149VKxYEVeuXMH169fRoEED1KhRQ2+4+rScnJxw+vRpTJs2DUeOHEF4eLh4j93AgQP17gmaPHmy+N2o07FjR1SpUiXDOmZE140uO27fvo3OnTvj8ePH6NWrF/r165et9SIiInI1BH56jo6OAKDX9TwjHTt21Fuudu3aBgcdyg/JyckYNGgQduzYAQcHB6xfvz7D4/PgwYN6I8/a2dlh9erVAFLvO03/Gb548aLea6VSma2usEDqZ/LkyZOYPn06Tp8+jRcvXuDjjz/G8OHD0alTJ3G5lJQUg4+v6N27t8GRVrOS03sB85PucRsODg65Wp+JHRG9c2l/qC0tLcW/M7svA3jzhZd+xDHdvRharRavXr3Sm5fRF3baOqQdAU43xDvwZrAC3b0sT548Qbdu3XDt2jVYW1ujXLlySEhIAPBm0IGc0O1P+jqmPbFLf3KUtt66+2CyiltWdCcXcXFx4oh/acXFxYk3iFtYWGQ5vHzaOqa9fyx9PTM7DqKiosQT5Yze7/Sx0Un7HKr0sU2/ru4kIO1IiwAkgz+YmJige/fuWLRoEXx9feHh4YEzZ85ApVKhbdu2BuuRVkBAAKZPnw5zc3Ns27YNPXr0EO+Ty86ABU+ePMnViWdWJwcODg7Yv38/tFotFi5ciCFDhkiWuX37NrZu3QqtVgulUikZMTEkJCRXdcvNZyYt3f2K6Y+PYsWKQaVSISYmRnKMuLq66r1OO1rs29J9HtPfl6q7X1h3cSIjuu+u9KOdtmjRAi1atBBfx8bGYt26dfD19cV///2Hly9fQqFQ4PTp06hUqRIGDRqU4cWGcePGISgoSEyEc6pkyZJYsWIFDh48CCD1GYfZHeRBNwAGALx8+RK///47/vrrLwQGBiIuLg7m5uZwcnJC3bp10b179xwNjJOQkIBVq1Zh9uzZiI2Nxddff41ly5Zle/158+Zh3rx5kukHDhxA165d4e7uLu5zZnTfaS9fvsxy2f/++0/vcRmZPZcuLX9/f3EQLODNgEwajUbv8QNA6sUk3UilOvfv30f//v1x6dIl2NraYv/+/bkehTE0NDRbn/2cfL4qV66M1atXw8/PD0WLFoWnp2e21017b2p4eDh+++03nD9/HsHBwYiPj4eFhQWcnZ3h5uaGrl275uiigc7q1auxa9cu8XVeD57yySefYN++fdkaQdcQJnZEZDRsbGzw4MEDyRep7mq0qakpbGxs9K7YZXWVPCcmTpyIa9euYfTo0fjpp58gk8kwceJELFmyJFfl6Vq90u9P2te6K8D5SZdUlS5dGjdv3jS4jG5Qjbi4OISHh+f6amJ2WVlZQS6XQ6vVSp6XpHu/M6pD2mHoQ0ND9ZJ13bq6uNra2iIsLEx8BpOOoeeBff3111i0aBGOHDmCJk2aICUlBe3btzc4NHxaJ06cQO/evZGQkIBly5bB29sb8+bNw5AhQ9ClSxexZTIzGbWijBkzBitWrMDUqVPFATtySi6XQy6XIzw8PFsnaekH68no4eW6xxv06tUrX1oiMvr8REdHi0Pfpz9G0id6Dx48ACBtJcuNSpUqifVJ+xnRXSxJ+/gGQ3QXQdJfnErr1atXaNq0KW7fvg0gNQYuLi5ISUkRB+Tx9/dHdHS0wZbaEydO4Pbt22jfvn2O909Ho9GIg7RER0fDxCRnp5J3795Fy5YtDbasXL16FX/++ScWLFiArVu3Zut5n1qtFp06dYK/vz+KFCmCBQsWGBziPju2bduGH3/8Ea1bt8bSpUtzvL4uoUt7oSojuW0ljomJyfBzmn56+s/q6dOn0aFDByQmJqJSpUrYtWuX5KJWTuhGQc3IzJkzczWASlhYGLp27QonJydxpN6cOHv2LDp16mTws/Tvv/9i//79mDdvHvbv35/jUaojIyPFizX5wcrK6q2ec8tRMYnIaOiu3G3btk38so+JiRF/gD09PWFubp5v29d1hXN1dYVMJkNISAh2794tWU7X0nf//n1otdoMh7jXdRE7cuQILly4ACC1dVD3nKGqVau+ky4ijRo1ApDaKrRlyxZxuu5h6gDw+eefiwnMiBEjxBPnV69e5UkXpvQsLCzQoEEDAICPj4/4A/3gwQMxkdCNNphepUqVxLjNnj1bbBm6fPmyODKhrpVM16V39erVYgvQ7t27ceXKFUm5FSpUQMOGDREdHS0mKrph+DMTGxuLxMRETJo0CX369AEA9OnTB/PmzcPEiRMlV9kLyqJFi/SGL0//7/LlywVdRT2674Nr165h7969AFJbL+bMmQMgNWmvV6+e3jqbNm0S3+c//vgDV69eBQCo1eq3rk+DBg2gUqkgCIJ4sefhw4fiZ6hjx46Zrq97qHv6iwxp7dq1C7dv38ZHH32Eq1evIjAwEAEBAbh9+zbCwsLg6+sLc3Nz/PbbbwYfzKyjVCrh5uaGOnXq6L3Oq8eeLFiwAGvXrjXYSjl//nyEhYWJIwqHhITg5cuXCA8Px6VLl9CvXz/ExMRku5uyXC7H1q1bMWTIEPzzzz+5TuqA1O+8iIiIbLW4GaLrLaB7L/NDmzZtMv2cxsTEZNjltXHjxhg0aBD69u2LM2fOvFVSB6TeGmBoBNz8GhVTR6FQYO3atRk+tHzq1Kl49eoVunbtigsXLiA8PBwvX75EWFgYzp07hzZt2uD58+eYOXNmtrc5duzYTGOe9pYJQ0aOHCmOWqobWffAgQOoVauWOPr2ihUrJCOcZvQ7lxG22BGR0fj++++xe/du3L17F3Xr1kXp0qURHh6OmJgY2NjYZDgMeV6pVasWbt++jeHDh2PZsmUICgoyOCT3p59+CiD1PpaqVauiZcuWWLRokWS5zp07Y/PmzTh79iyaNWsGV1dXREdHIzo6GkWKFIGPj0++7o9O9erV0blzZ2zfvh3ffvstfvrpJ6hUKgQFBaFy5cpo1aoVrK2tMWXKFIwZMwb79+/HsWPHUKpUKQQFBWHatGmZ3huZW3PmzIG3tzfOnTuHChUqiPd5paSk4NNPPzV4jwWQet/RwoUL0blzZ+zbtw9ly5aFra0tnjx5Aq1Wi1atWqFdu3YAUlu8jh49inv37qFq1aooUaIEgoKC4OLigqCgIEk3s169euHs2bO4efMmXF1dxaQ4M23btsXNmzclQ2u/7TPnPnSNGzdGp06dsHPnTvTo0QPOzs5ISEjA8+fPIZfLsWjRIkl3pqdPn6JatWqwt7cX7xl0d3d/qyvkOkWLFsW4cePw448/YunSpdi5cydevnyJuLg4eHl5iQ9tzkiNGjVQpEgRBAQEIC4uzmBLsO7+PZlMZrALZHa7RZYuXVrvcRbly5fP9eMtDMmse7Ku+7qVlRVcXV3FLqxKpRIuLi7i5yQnXXWLFSuWL0lE69atER0drXd/dmZ0F+jq1q2b53XJKzNmzMjzMtVqdabPnM3reMhkMnTp0iXD+bpjx87ODqVLlxYvtqpUKri4uIi9OHTH4rtgqMv6q1ev9FoVX7x4Iem2mtMu60zsiMhoFC9eHMePH8esWbNw8OBBPHnyBNbW1mjdujUmTpwoudcmr82cORMxMTE4efIkXr16hfHjxyMhIUFyQtGlSxdcunQJu3fvRmRkpF5XwLRMTU2xd+9ezJ8/XxwsRaVSoXXr1pgwYcI7fZD56tWr8dFHH2Hr1q1i8lSnTh29exYGDx6MkiVLYsmSJbhx4wbCw8Px8ccfi4lsXqtZsyb8/f0xc+ZMnDlzBoGBgXBxcUGHDh0wZsyYTO9B8Pb2xqFDhzB37lxcvHgRwcHBqFixIrp164Zhw4aJJ8D16tXD5s2bMWPGDDx48ABmZmZYv349tmzZgqCgIMmzhDp06ICRI0ciJiYGXbp0yfaJdG6el0RZW7t2LWrXro1NmzbhwYMHKFKkCDw8PPDDDz+gcePGkuVHjx6NW7du4dChQ7CxsUGHDh0wY8aMXD8MOL3hw4ejSJEiWL16NZ48eQJbW1v0798fP/74Y5brmpubw83NDcePH8fp06fRvHlzyTJffPEFfHx8cPv2bXzyySewsbGBlZUVNBoNnj9/Lg4e1bVr10y/Dzt37pzpgBa1atXCr7/+mmWdP/vss0zn//DDD5JW7e+++w6HDh3Crl27sHv3bjg4OECpVCI+Ph7h4eHQaDSQyWR695G9a0ePHs2yJX3//v3igEtAahfV06dPQ6FQwMPDI7+rWKjcvHnTYPd1nStXrugNYpRdERERWb4PixYtksR7+PDh6N27N5YtW4aff/4Zjo6OKFq0KGJjYxEeHg5BEGBmZoZhw4bluE65lVGX9TyXq7E0iYiI3lNpH+2wbNkyvXmPHz8WLC0tBZVKJdy7dy/Ptpnbxx388MMPglKpFBYsWJBndcnIf//9l6Ph53VDvw8ePDifa5a19I8aKawOHz4sKJX6z0tMLzIyUpg5c6bg7u4uODs7C8WKFROsrKyEihUrCl988YWwfft28TEp6dWpUydbw797enpmuP3k5ORsDyOve3xGenfv3hVGjBghfPbZZ4KDg4NgaWkp2NnZCR9//LHQr18/4fz58zkLXDZl9ciO7D5aQqlUio950dm5c6egVCqFXr165Uvdc0L3Pp89ezbXZeged5D2kRXp9e3bN1uxKlu2bI62rXtsR3b+7d+/32AZly9fFgYOHCjUrFlTsLe3FywtLYUSJUoItWvXFr7//nvx+bR5LTuPO8hPMkHI4OYPIiKi99ydO3cQHBws3mMVFxeHMWPGYMOGDTAxMcG1a9dQunRpCIIAmUyGwYMHY9OmTWjRogV27txZwLWn7KpatSoCAwPfamCNd6VZs2Y4f/48/P39xXvgqHBLSUmBm5sb7t27h7/++itXj3UgygvsiklERB+swMBAdOjQAZaWlnBwcEBQUJB4T8PUqVPFRzs0atQIL168QFBQEFQqFaZNm1aQ1ab32Lp169CgQQMMGjQIZ8+ehZmZWUFXibIwf/583LhxA/Pnz2dSRwWKo2ISEdEHq3z58mjevDmKFCmCx48fQ6lUwsvLC3/88Yfe/RdFixbF8+fP8dlnn2Hfvn08eaN84+rqiqdPn+Kff/5hUmckxo8fj5iYGAwaNKigq0IfOHbFJCIiIiIiMnJssSMiIiIiIjJyTOyIiIiIiIiMHBM7IiIiIiIiI8fEjoiIiIiIyMgxsSMiIiIiIjJyTOyIiIiIiIiMHBM7IiIiIiIiI8fEjoiIiIiIyMgxsSMiIiIiIjJyTOyIiIiIiIiMHBM7IiIiIiIiI8fEjoiMRuCTJ0hOTn6rMvbs3pVHtcmehw8fYvvvv+VonX8uXsCzZ8/yqUbGZdXKFYiKisrROgEB1xEbG/tW271162aOln/x4gV+Wb3qrbZ540YALl36563KIMMWzJ/71t8db+ufixcQEHC9QOvwto4cOYR//71c0NUolKKjo/Hi+XOD8zZv2ojg4Kd603bv2om7d++8i6q9tTmzZ+Zo+bVrfsnWb9iF8+fw0+QfMXbMaPyyehVevXolzps1czru3pHGJzo6Gr//vg2zZk7HxAnjsGD+XJz094NGo8lRHd9XJgVdASJ6f/35py927tiO4sWLS+ZZWVthypTpetM0Gg22bduCq1euQCaTQaVSoefXvVC+fAUAwPLlSzD6h7EoUcIhw20OHzYEABAfH4fExERYWVkDAH4YMxalSjlj//696PjFl3rr/LpuLc6f/xsWFkq96ckpybC3t5fUM71169Yg4PqbE7bk5CRYWVlj5qw5iIuLlfygR0SEY8TwoShVqpTe9DZt26NRo8Y4c+Y0ilpYwN7eXm/+3DmzEBoaKtl+cnISHB1LYtLkKXrTr/z7LzZuXG+wzqGhIdiwcQuKFi2qvy9rf8GdO/9Jlo+Pj0fZsuUwctQPBstLa9HC+QgMDJRMj4uLReMm7ujR42txWkDAdaxbu0ZvufDwMCz2WQpHx5J4/PgRkpOT9Ob7+Z1AWFgounXrIU77duAALF32M8zMzLBzx3b07/8NlEr999OQjRvWo3yF8nBza6w3feGCeVj360bx9fJlS3D//n1ER0fBzMwMFhZKNGrUGBUqVsTp06fw5Zdf4cmTx1lu7969u9i4cT1ev34NuVyOr77qgvr1GwAAAgMD8frVK9Sp85m4vEajwbcDB2DN2l8lZU2fNgU9en6NsmXL6U1ftmwJmjRxh6OjI6ZOmYyfV6zWm79r5w5oNBp07tIVAHDs2FEc+tMXMTGvodFo8P33w1Crdm34+Z3AurW/wNKyGJKSEmFpaYn6DRqiQ4cvYG5ujsDAQGzYsA6TJk2BTCbT20ZoaCh27dqBJ48fidMqVqqML7/8Cra2tpJ9yemxnd7ECeMwaPD3cHZ2BgAkJSVh6JDvsGp16rF1//59CIKgt86aX1bh5k3DCfzz588w8cfJqFKlaqbb/f33bbC3L4GmTT3x3+3bWLVqhV7dixQpioWLfACkXuSxLFYMNWp8nGF5/fr2hs+SZbC0tMxwmSNHDiE6Klp8/3IiNjYWixbOR0hoCLQaDVq1aoO27dqL8+fPm4Pg4GAAqd+hdet9jn79BmBA/75Ys/ZXREREwNTUTK/MmzdvYs0vhi9qhIaGYMvW32FqapppvQ4e2I89e3ahWDH93wpB0CIlJUVyDANAl85fwtGxpMHyOn3VGQ0bumW6zfj4ePTu1QMlSzoZnD948PeoVLmy3rR//72MXTu3Iykp9SKBu4carVu3AQD8/fdZvH71yuD7EhQUiPj4eL1pIaEhKFeuvGTZjRvW4+zZM9n+PQoMDMSihfMN7kN0dBT6D/hG8v2WXnx8HDZv2oRbt25CJpOhXLly6NW7L4oVKwYg9XsrrUUL5+PBg/soUuTN78eLF8+xYeOW/9fpCRITEzPd5uVLl7B//z6M/mEsrKys8PfZvzBr5jTMnjNf8n2i8/r1a/w0eSLad/gCLVu2RtGiRfHixXPs37cPN2/dxHffpf7+JyYmYuqUyRg7djyKW1llWo/3DRM7IspXzZu3yPYJyJ7du5CUmIh58xdCoVAgKCgQc+fMxoKFi1GkSJFsleGzZBkAwO/Ecdy5eweDBn2XrfW+7tVb8uMX+OQJfl2/Nst1+/UboPf67Nm/cOmfi5muU6pUKfw4aYr4WqGQQ6lUZbrO2HETDE5/9OghtmzeJJleq3Zt1Kpd2+A63w4cYHC6iYmp5MQNAJKTUzKtW1oZJX8nT/ojIjxcb1qNGh+L75nONwP6wsZGevKflWnTfoJcJsejR4+yXvj/oqIiEfnCOsvlvh8yDEDqSVeZsmXRpIk7AODatavZ31ZkJJb4LMa48RPh7OyMqKgozJwxFX4njkMmkyEiIkJM8tJ69eolRo0cLpkeHh4mmXbz5g2kpKTg448/QUREuGR+ehcunMe5c2cxY+ZsWFhY4GV0NOLSnIA2bOiGwf8/WQoPD8emjesxY/pUTJk6Ha6urihbthxOnvSHh4daXOfFixf4afJE9B8wEN9/PxQymQwajQb+ficwccI4LFzkI0m6c3ps54UB33yb4bxZMzO/mAMAYWGhuHEjADNmzAYAfFSlit6xfOzYUTx+9DDb9Tlz5jQ0mhQcPLgfXbt2z/Z6OdXz694oU6YMXr16hbFjRqFq1WqoULEiAOCHMePE5Q4f+hMabdatINWqVZN8hnW+Gzwwy6ROp2XL1viy01d602JjYzDmh1EGl1cqlRluN7usrW2yXUZYWCg2blyPKT9Ng7WNDRITE7F48UJYW1mhoVsjyfK6i0FAakKyxGcRFIrU0+7vvx+a6bZy8nvk6uqa4T78ui7r3y8AWLlyBZxLOWOxz1LIZDKcOHEcixbNz/Si5jcDB+GTT2qKr3v36pHhsob4n/RDl67dYG2d+v3b0K0RTpw4jk2bNsDS0hJhYdLvt7t37sDVtbTe942jY0n06z8AvXv1FBM7c3NzdOrUGZs3bxS/uz8UTOyIqNC4c/cOunTpCoVCAQBwcXFFCYcSCAkJQbly5bJYW9/t27dw+/ZtaDQasbz8IggC4uPjERsbi9jYWFy8eAHVq9fIdJ2IiAisXfPmKrSNjS169+krvv55+VKYmxfB/AWLsn1ilBcCA59g4MBBcC1dOs/L1mg0We7L48ePYWdnDzMzaXKZ1onjx3D50iXxdVRUJObMnQ9TUxPMmjkjW/V5/fo1Hjy4j6dPg9CiZatsxTkpOSnXXfouX76E+vUbiK1K1tbWaNO2Pf777za++qoL/P1OGCy7WLHiYqtPWtOnTZFMO3hgv6RFOjMB16/j83r1YWFhAQAobmWV4RVuBwcHjBo9Bj+MHonz58+hYUM3tGrVGnNmz9Q70bpw4Tzq12+AunXridMUCgU8m3nh+vVruHLlcpYtCIWBDIZbDXQO/emLFi1aGZyn1Wpx4vhR9B8wMMvtBAc/xb69fyApKQk/r1iNXTt3YO6cWWjXrgMqf/RRhq0XuaFUKsWkulixYihXrjyiog13db53/554ASO35PL3546fBw8eoHr1GrC2sQGQmjw0bOiG/+78ZzCx0yUUgiAgMjISKpUK5ubmbxY4/E6qnSWNRoOA69cxYsQo8Vhr2tQT+/f9gZcvXxrscZMXTBQKSffJlJQU2NvZo1jxYgZ/A8qULYuHvz7A3Tt3xNbU5ORkHDr0Jyqna12tVbs2tm7dhJfR0R9Uqx0TOyLKN5aWljh65DDOnfsbiYkJiImJga2tnTg/fdJS5aMq8Pc7gbJly0GhUODJk8eICI+QdFnMyqNHD3H//n3U+PhjbN/+m16XvYxs2rgBu3bu1Jum6/qSmZiYGPwwegRUKktY29jAzMwMV/69jG++eXNCl5SUhBfPn8PE1FT8kXR0LKnXspWcnIz4+DgULZp6gv3d90MlyeGQIYNhojAxeLL00UcfZbmP6Rkqx8rKCot9FkIuk86zLGaZZbfUzCQkxMM0i4TN1/cAmno205sWHRUNhVyBYsWLi8dLU89mkq6Yv23bArlcbrAlS1qXBCxetADduvVATGws5s6ZhWHDR2baBQ4Anj17hmKWxXDt2lVs2rgBCQnx+CiL7nriNhMTJS3PRYsWhVarhbW1NYoULWowsVOpVGKLXUhIMBxLloRcJodW0Oqd/Gg0Gjx58gQVK1bKVn0AwKlUKRw9egR1630Oq2yc/MjlcjT1bIarV6+gYUO3/7esyhAVFSVeeZfLZNBotAbX12g0kMulF1ry4tieN3e2eHyk73aZU4mJiTAxyfwU6eq1q+jcpZvBeXv/2ANHx5KwsFCKCXhYWChatW4rLhMREYHly5bAwdER3l7NxVazXr37IDAwEIcO+eKXX1ah59e9UKvWm5Z3lcoSh/78E+fO/S3ZroVFUcyaPS9b+/jff7fx7Nkzg11DExMTcf/+PQwe/H22yspIRsdBXoiNjRW73qf3ef366JLBe6Mjl8tRpEgRsYzQ0BA4OjpC9v/vvvTdOcuXr4Ad23/DixcvYGtri8TERJw5c1rvokZ6169fw/r161CypBMiX7xAqVKl8O2g7/L0Yt3dO3cwffqUDG9RyKjXho5CoYBcLkdcXJyY9CcnJyMuLi7bPWVyo3nzltiwYR3s7Oxga2uLkyf9YWJigpatWgMATp86JVnH1tYWo0aNwZ49uxAeHg5BECCXy1CjxicYMXK0ZPkaNT7BjRsBBhPv9xUTOyLKN40aNUajRqlX5m/cCMChP331uvqk175DR2zf/hvGjhkFQIZixYph1Ogf9K9yZuHhw4dYvHgBRo8eg1KlnLFwwTysXrUCPXp+nWFXx779+qNvv/452jcdlUqFlave3CO2bKkPvvzyK71t3bt3F8uWL0Fp19Lo07c/TExMkJKSgrFjRkMmk0Emk8HU1BQuLi6Zdg8DgKnTZoj3PbwNjSbF4MnFkKHDc13mtm1bcPHCBb1pz55FwMrKSq975/FjRzHgm29RrVo1vWWvX7+GB/fv49tvB+tN37hxPUzNTNG3T3+4li4NuVwOvxPH9bb1+vUr9B+Q2u1Ld49QRh48uI+VK5ajQ4cvUL9BQwCAVfHimD7tJ0yaPAWWlobjm5ycjKdBQYiLi8WXnb7CjJmzcfPmDYMn2IZUr14DK1csR7v2HcSk4a8zpxEfH489e3bh7p07kvvlFAoFFvssFV9/O3AAZs2aK7k3EkhNHBxLOmarLjrNmnnh3t07GDpkMLp06YbmLVpm2cri4OCAS5fedDUuW64cnjx5LCZ2n9dvgB9Gj0SVKlVQv0FDyOVypKSk4PjxY3j8+FGGXaPe9tgeM3a85B67rNy8eRO3bt5Ap686IyDgOv67fRudvuqMxMQEmGRy8p2cnAytRiO2dKb155+++Pffy5gwcRJMTU3FVrs/fQ/oLVeiRAlMm254UApXV1cMHDjI4LyGDd2yvH8sM7//vg0HD+yHiYkJhg0fafDk3df3AOrV+1yvt8OcObPwNChIbBGOjHyBaVOn6K2XlJSEV69ews4u9YKYmZkZhg8bAldX12zdn5uj/dj+dgNhmZub63Vh/HbgAMyZu8DgZwsAHB0d0advfyxaOB+JiYmQyWRQN21qsPs0kBqLVStXYOq0GeIFwg3rf8WBA/vQsWNqDANuXMer16/wcY2PxZbA3KhSpSomTJyU6/U7dOiIuXNmoXOXbjA1NcEfe3ajUeMmOfrtzamPqlRB334DsHv3Lrx+9QoffVQFY8aOz3K9cuXLY/QPY7O1jXLlyuHx48dM7IiI3sb9e/ewfPlSvWmCoIVWq5VcYf2kZk306dMPQOpJbLduPTJsYfuyU+cMT7qB1CvgK1csx+jRY1C6dBkAqfeMHNi/Dy+ev8jyHra3tWPH74iOjsZ36e6fqFatut5JjY2NLRYsXJxhOe4eapTMYFCAvJT2BP7IkUM4euRIttbr2PELgz+Uht67jAb4SO+/27fx8/Kl+HHST5Kus8OGj9C7Gu3u7gF3d48My/q8fgNYZpIguLqWxsQffxITEQD4rG49fJam62DPnr0k6124cA516nyG0NAQhIaGwNnZJUdX3suUKYMm7h6YMH4MKlX6CI8fP0KZMmXFE8OwdIOHTJv2EyJfROpNEwQtxo8bIyl77rwFiImJyfTzkZauy5WpqSmGDR+JixcvYP36dbhw4TzGT/gx0yv1ycnJMDF5s9/FLIvh9evX4msrKytMnzELu3fvxK7dOwEhdXvVqlfHjJlzMjxxLgjx8XGIjHwBAIiLe/N3QkIiVKqMB9+Jef1a0robFhaK337bhpfR0Zj44ySx9b1kydTPskqlv7yh9zcjc+ctwLVrV7Ft65ZsLa9WN9UbFCWtLl26oXPnrnj8+BGWLvEBAL0WwadPn+LMmdOYNWuu3nrdunbHwYP7xdc2NraSe7vu3rmDPX/sxrgM7pnMiGWxYjh+/Bj++uuMpHdHSaec9djIDkOxV5goMvxs6RKcmjVroWbNWgbLrFGjBpKT3rS4P3sWgRIlSuj1+qhbrx7+/NNXfB0bE4PoqCgkp7xZz9raGrt27pT0IAEg6W6YV1q3aQtnFxecOX0KGq0GjRo3QYP/X/TKT5UqVUalSob3afiIUWLcDV1EyEjaiwiWxfS/mz4ETOyIKM9VqFgx1ze1P3/+DDOmTzM479mzCCz2WZbhiWGJEiUwf8EivWlyuRzt2ncQX6dNui5evCA5UYqNjRVHtUwrsxOl+Pg4rFu7BlFRURgzdny27yuZOWMaXr6Mlkx/9eo1hg8fCVs7O73pTiWdMHnSxP8v8xJyuVzvZHH2nLniyWRmBEGQdDPz9m4Bb+8WuHEjAK/TDDmdlq2tnWSUuLclCAL8/U5g564dGDV6DFxcXLO1XnJyMn4YPTLD+WlPVNMzNTWFtbW1ZBTDtEJCguGepotVUlISdu3ciUmTfsLTp0+xYf2v+HHST9mqa1otW7ZC48ZNEBIcjC87faXX/TEqOlov9pMnT81R2SYmJnpdOYsWtZCMxAekdrNLP3hJ3br18MknNTF37izs2bMr0+7LT58GwdX1zfuUlJQoSXAdHR3FgQyyIy+O7YT4eMTGxgDI2WA/hnzyySeZJskmpiaSbWzauAF16nwGD3XTbN0XZ+j9/XbgACz2WWrwO65u3Xp69y2+DZlMhrJly6Fjxy9w4sRx8fMSGfkCCxfMw8BvB0nq4Fq6dKYXTN5Gkybu4v18WfXuSDsoiU5ERDisbWxgaqJ/HI4dN0FMrNPK6WcrraVLFuPhQ+mgOLGxsWjm5YVy5VNHuixRwgHPnj9DUFAgXFxcodVqceqkv14i8/nnDSRdJdu2a5/hb40hZubmiIiIyLBLaY2PP5YM8GVIZkmrIdeuXcWLFy+yvbwhy5ctQZ++/aFUKnH82FHYlyiBTz6piZP+flAqleLFNkMXEbIjKSkJpqYfVqrzYe0tEb1TGo0G+/ftxYUL5wEIEITUFodSzi746qsuBn9w7ezsM/wCHzf2BwBZ3ztj6IdfJzo6ShyS2dCJkp/fCQQFBqJX7z5ZbgdI/eEYP24s6n3+OQYN/j5HA7VM/HGywelLfBYhRSM9MR0/4Ufx7107d8BCqUTLloYHb8jM69evM2y91Gq1BkfCCwsNw19/ncm0K63OqJHD9W6KX+KT2joZE/MazZu31Bv57vfft+G/27cxbdrMLO9nTMvU1DTD42TKlElITkoyOC+t9KMYpjWg/5uBbDQaDZYu9YFnMy/Y2tnB1s4OFy6cx+5dO8X7onLC9+ABtGjZKtvdDm/evIk//tiNmNevIPy/9cvMzAzNW7TUu6puY2OLyBdvnqNlaWmJ5OQUREdH6yWQQU+DoFY3lWzH3NwczZu3hL/fiQzrkpCQgKNHDmPyT29OjF+8eAGbt+hGBrz9sV22bDls2rRBb1qlStm/1zC9Pn0z75qtVKoQE6PfEqDrRrZxw3q0bt1GcmEmr9y+fQsymQwffVTlrcsyMTGB+f/v03z86BEW+yxE//7f5Kjso0eP4E/fg3rTdElGWFjoW3eZTM9QV97Jkyaif/9vcjzo040bAdi39w/x+Wm6z5a3d/MMu+8NHTbC4HTdYyh0TE1NMXz4SPy8fBnMzMwQGxuLatWqo9X/7yHLK2XKlNH7Hvtu8MBMu5RmRaPRICUlBcnJyUhMSIDKwH3HjZu4I/jpU70LUVnd02hIYOAT8bfixYsXKFI0tadAaFiowe/Hc3+fxapVK2BtLf2+CQkJxo6de/SmvXjxPFcjLBszJnZElG8O7N+HwKBATJ02Q6+vfkDAdUyfNgUrVkqfTRQaGorRo4Zn+HyirAY0AAz/8OvkdEjmrJiZmeV65MoxP4wy2LoXGxuD5i1aiq8PH/oThw8f0ltG16px9Ij+0GpffNkJjRo1znBQgbSGDxsClUqFGTNni9P+PnsWjx9LHxeQkJCQ7UFsDI3gCKTeexSX7sHhnTp1ztZ7ml5ycjJGDB8KCwvpyUtkZCTkiqxbTa/8+y98fBaK9wOllf6konbtT/WSoT59++H58+fZeqSAZLtX/oWHWi3ZhqFEJjY2Fj6LF2LqtBlwcnrzvK3Xr19h2tQpKFGiBCpUSE0ura2tERcXj/j4ePGkzt3DA+vW/oLvhwyDubk5/v33Mh4/eiQOU/7XX6dRrlx5ODmVQkpKCi5fvpThs9tCQoKx5pfVaN2mHZydXcTpDx8+FLvaZue4AyAed7k5tg3pP+CbbG0XSD2BnDIl9X6kxIQEJCQk4MbNG0iIT0BiYurfOq4urgYvZsjlcjg4OiIkJBhO6boK3r17B/EJzSTr5Oa5c4bcvn0LCrkiV4ndtatXYWtnB2dnZ0RHR+OPP/aga7fURys4u7hg7NgJesdZdnh5ecPLy9vgvH59pV2a0zLUbV/XFTOzbvt5ITLyBZYt9cFPU6bpvYcvo6Mxe/ZMFCte3ODAMhMnjENMzGu97sg6Xt76cahYsRLmzJ3//5Yj00xbcg31IMmIrgeJoS6lz58/x7ixo8UWO520XUrTmjVzOqLTjIwqlytgamoKU1NTmJmbG0zY6tT5TO95m29j6pTJUCjkiIyMFEeGvn3rFlSWlmjVqo1k+QYNGmJguvuwAcPH2u3bt9EizW/ph4CJHRHlGyG1ic7wvExa3sqXr5DhoAKFUW5HOIuKisKq1WuybOVr3qKlXqKXHbntChsUFIhhw0cabE3ND7lJ6oDUYysxMRHLf175Vtuv81ldDMniOUcKhULSwmVqaoqSJUvmKrHLmdTPSXaHvHdza4S//jqDZs28AAA9enyNTRvXY9jQ75GSkoxSpZwxYcKP4gleTEwsZs6YJt5TVrfe52jV+s3J1NmzfyEg4Dogk8Hezh7t2nXAp3XqiPOvXPkXVatWFT8DOT3ucnNsvy1bW1ssW2a4C25ONGniAX9/P3Tv3jMPavVuCBAwZ/ZMJCYmQmWpQscOX4jdME1MTHKc1L2tt+m2/7Z0P01araHfosx7hkz8cXKGo1AaktXjW4DcdbV9my6lOm8z6Epe+GnKNBQrVgzbf/8NQOrFpmLFiyMxIQFX/v03y1E9MxITE4OnQUGoXDnnI0YbMyZ2RJRv2rZrjz/+2I1JP04AIECrFSAIAkqVKoWJEw13QwSA+/fvZXjlv2279ga7kRmr8eMMjxTn1qgJ2rZt945rk2rO7BkGr0YXLVpUr3WvoL18GZ3hcdKyVesMWxHS+ufihQzL6NOnHz6pWfNtqpih6dOmiA8rTqtMmTIYPuLNA5mVShWGjxiJdWt/QUzMa7ErpqmpKdq2ay+21um0aNkKs2fNQOP/j2hnbm6OAd98i4zusGnevAWaN29hcJ5a3TTTz5pWq8W+vX/gmwxGb3zfubk1wpSfJiEqMlIyouGc2TMNXrSoUKFCpj0KPqlZM1vduffv3wd/fz/JdFNTU8l9xmnVrFnrrS+GvC9sbW3x3fdDsf7XtXj58iUAQC6XwczMHM1btDLYWqeT0ee3ZMmSGJvDgWM+dGlb7D77rC4ePXqESZN/glYrYO6cWbC1s9O7p/fvv8/i9u3bknLSD5KyZ88utG/f4b16lmJ2yIS3fdgLEZER+eefi/jss7oZzn/58iWSkpJydL8XZc/Tp0+RkpKCMmXKFHRV3mv//XcbJiYmkqQvr4WGhiI4+GmedckyRkFBgYiMjBS7tpK+s3+d+aCGmqe3IwgCUlLePIpHo9Hk6L51neTkZJw4cTzDi1bvMyZ2RERERERERu7Dap8kIiIiIiJ6DzGxIyIiIiIiMnJM7IiIiIiIiIwcEzsiylN37tzBnTt3CroaRERERB8UJnZElKeSkpIQHx8HQdAWdFUKDUHQIvjpA8YkHcZFijGRYkykGBMpxkSKMZF632PCxI6I8pwgpD6vjlIJggCNRsOYpMO4SDEmUoyJFGMixZhIMSZS73tMmNgREREREREZOSZ2RERERERERo6JHRERERERkZFjYkdERERERGTkmNgREREREREZOSZ2RERERERERo6JHRERERERkZFjYkdERERERGTkmNgREREREREZOSZ2RERERERERo6JHRERERERkZFjYkdERERERGTkmNgREREREREZOSZ2RERERERERo6JHRERERERkZFjYkdERERERGTkmNgREREREREZOSZ2RERERERERo6JHRERERERkZFjYkdERERERGTkmNgREREREREZOSZ2RERERERERo6JHRERERERkZFjYkdERERERGTkmNgRUZ6TyWQFXYVCx9TUrKCrUCgxLlKMiRRjIsWYSDEm9KEzKegKENH7Ry5XIF6bAmhTCroqhYbKzoExMYBxkWJMpBgTKcZEKn1MFDI5iihMC7hWRO8OEzsiyhc/XfdFUFxUQVejUBAEQKtNgVxuAjZmvsG4SDEmUoyJFGMilT4mLkprTK3RqqCrRfROMbEjonwRGBuFB7HPCroahYIgAFpNCuQKnoSlxbhIMSZSjIkUYyLFmBDxHjsiIiIiIiKjx8SOiIiIiIjIyDGxIyIiIiIiMnJM7IiIiIiIiIwcEzsiIiIiIiIjx8SOiIiIiIjIyDGxIyIiIiIiMnJM7IiIiIiIiIwcEzsiIiIiIiIjx8SOiIiIiIjIyJkUdAWI6P3kqrSGTFbQtSgcBAHQChrIZQrGJA3GRYoxkRIEQKtNgVxuwpj8H2MilT4mLkrrgq4S0TvHxI6I8p4MmFqjVUHXgojyQwEkElqtFnI5OxmlxZhIpY+JQsb40IeFiR0R5TmZAODwBmijIgq6KoWCgDStMAVdmUKEcZEq7DGRW5dAUe+vITcr+s62qdVqEB4RBAdHF8jline23cKMMZFiTIiY2BFRPkm+fxXa8McFXY1CQQCg0aRAoTAplCfrBYVxkSrsMVE4lAG8er7z7SYnJ73zbRZ2jIkUY0IfOrZR55GePXvi0qVLetPCwsKgVqvzdDsXLlxA7969c73+4sWL4e7ujvXr1xucX7Vq1WyVo9VqMX78eLi7u+PQoUO5rk9eio6OxoABA6DVavO03H79+kGtVqNhw4b4+OOPoVaroVarcerUqRyV8/TpUzRr1ixP6wYAe/bswcSJE/O8XCIiIiIyHmyx+4A8fPgQf/75J44ePQozM7O3Kuvs2bMICgqCv78/ZAV05/aLFy8wYsQIbNq0CQBgZWWFNWvW5Pl21q1bByA1qV65ciU2bNiQ59sgIiIiInobbLH7gDx79gylSpV666ROV1bp0qVznNQJgvDW29aJj49HaGhonpVHRERERGSsmNi9Q9HR0Rg2bBjUajU6duyIW7duAUhNdiZNmgS1Wo0mTZpg9uzZ4jrx8fGYMGECPDw84O3tjXPnzmW5jbFjx8LT0xNqtRqzZs1CUlIS/Pz8MHLkSPz7779Qq9UICAjIsr5qtRoHDhxA+/bt0aBBAyxfvhwAsG3bNsydOxd//vkn1Go1nj17hoSEBMycORNeXl7w8PDAmDFj8Pr1awBvuo9OnjwZ9erVw6tXr6BWq7Fz5060adMGbm5uOH78OPbt24fmzZujUaNGOHDggFiPrVu3wtvbG02aNMGAAQPw+vVrBAQEoFu3bggJCYFarcaWLVskXR3v3r2LXr16Qa1Ww9vbGzt37hTnjRs3DmvWrEGvXr3g5uaGIUOGIDExMRvv4huJiYmYPHkymjZtilatWuHs2bPivODgYHz77bfie/rvv//q7Y+XlxcaNWqk1421atWqOHz4MFq1agU3Nze9+mb0vhpy7NgxtGvXDh4eHvjiiy/0tv3ixQsMGjQITZo0QYcOHTBv3jxMnDgRYWFhqFevnl6Zq1evxsKFC3MUEyIiIiIqGEzs3qFx48ahdu3a8PPzw48//ojRo0dDq9VCEATUqlULx44dw6FDh3Dy5Ekx8Zo7dy6KFCmC48ePY9euXbh48WKW23BxccHRo0dx+PBhREREYOXKlVCr1Vi0aJG4/Ro1amSrzqdPn8bOnTuxd+9ebNmyBQ8fPkS3bt0wduxYtGzZEn5+frC3t8f8+fMRHx8PX19fnDhxAtbW1pg+fbpYzs2bN+Hh4YHz58/D0tISAHDjxg3s378fPj4+mDhxIk6dOgVfX1+sXLkSM2bMgEajAQAUK1YMe/bswalTp1CsWDHs2LEDNWrUwLZt2+Dk5AQ/Pz/06NFDr96xsbEYOHAgBgwYAD8/P2zatAnr16/XS4wPHjyIhQsXwt/fH9HR0di7d2+2YqIzb948WFhY4Pjx4/j555/x448/IiYmBklJSejbty/atm0LPz8/+Pr6wtnZGQAQHh6OuLg4HD16FHPnzsXUqVPFVkytVosbN27g4MGD2LBhA2bPno1Xr15l+r6mFxAQgHnz5mH58uXw9/fHxIkTMXToUERHRwMAxowZg1q1auHkyZNYtWoV/Pz8AACOjo6oUqWKJD7t27fPUUyIiIiIqGAwsctDw4cPFwfWUKvV6Ny5szgvIiJCbEECgNq1a8Pc3BxBQUGQy+Xo2LEjoqOjERAQAGtrazx69AiCIODgwYMYOXIkFAoFLC0t0bdv3wy3//z5c9y8eRODBw+GXC6HmZkZRo4ciYMHD+Z6n3r16gVTU1OUKFECdevWxZ07dyTLCIKAvXv3Yvz48TA1NYVcLseIESNw7NgxcSATGxsbeHh4QC6Xi903u3TpAplMhjp16sDU1BS9evWCQqFA9erVYW5ujufPnwMA2rRpAwC4fv06lEolHj16lGW9T506hVq1asHNzQ0A4ODggD59+ujFol27drCzs4OpqSmaN2+O//77L9tx0Wq1OHDgAEaMGAGZTIYyZcqgWrVquH79Ok6ePImKFSuiZcuWAACVSoUSJUoAAExMTNCvXz8AQIMGDSCTyfDixQsxjv369YNMJkOFChVQtmxZPH78OEfv665du9CvXz+4uLgASD3O6tevj1OnTuH58+e4c+cO+vfvD5lMBgcHB3Tt2lVct3379jh8+DAA4P79+7CwsED58uWzHRMiIiIiKjgcPCUP+fj4oE6dOuLrsLAwdOvWDQAQGhqK58+f642SGRcXh8jISFhYWGD48OGQy+WoWLEikpKSkJKSgsjISCiVSqhUKnEdXWuXISEhIXB2dtZ7OKeTkxOePXuW632ys7PT23Z8fLxkGV09lUqlOK1IkSKwtLREVFQUgNTEKj1ra2vx76JFi8LW1lZ8bWFhgYSEBGi1WkycOBF37txBlSpV8OrVK73tZCQ4OFhMbnRKlSqFo0ePiq/Tbk+lUhnct4xERkYiNjYWLVq0EKclJCSgZcuWCAkJyTAhsrW11Xt/VCoVEhISAAAKhUIvJrp45+R9DQkJgbe3t940JycnREREICQkBC4uLnrlpD2evLy84OPjg+TkZLELLhEREREZByZ274itrS1Kly6td++YzuLFi9GwYUMMHjwYADB06FAAqSfdL1++RFJSkjjgSWaDhdjb2yM4OBharVY8eQ8NDUXJkiXzenf0WFlZ4fXr14iLi4OFhQWA1PvP4uPjYWNjAwB6yUROnDt3DqGhodizZw8AYMuWLbhx40aW69nb20ta4EJDQ+Hk5JSreqRnZWUFlUqFEydOSAaQ+eOPP3D79u082Q6Qs/fV3t4eQUFBetPCwsLQpEkTWFpaSpLBkJAQ8W8LCwvUrVsXf//9N06cOIGtW7fm2T4QERERUf5iV8x3xNnZGebm5mL3uZSUFJw5cwYAkJSUJA40cvPmTfE+JzMzM9SpUwcrVqyAIAh49uxZpifbJUuWRLly5cTlk5KSsHjxYr0uoflBoVCgZcuWmDVrFpKTk6HVauHj44N27dq99aMQkpOTER8fj+TkZERGRooJHgCxRTAhIQEpKSl666nValy8eBF//fUXgNSusBs2bECnTp3eqj46JiYmqFevHtasWQNBECAIgvhcO3d3d5w/fx5///03AODly5dvNXpnTt7Xjh07Ys2aNWJyd/XqVVy5cgVNmjRB6dKlYWZmhh07dgAAHj16hH379umt3759eyxduhQVKlRA8eLFc11nIiIiInq3mNi9Q4sXL8bOnTvRpEkTtGrVSrxfrWfPnvjnn3/QpEkT/PLLL/D09BTXmT59Oq5evYpGjRphyJAhWSZpCxcuxL179+Dh4YG2bduifPny6NmzZ77uFwBMmDABcrkc3t7eaN68OZKTk/HDDz+8dblubm4oWbIkPDw8MGjQIHh5eYnzihcvjtatW8PLywu7d+/WW69YsWJYtWoVli9fDg8PD/Tv3x9Dhw5F9erV37pOOlOmTMH169fh7u4Ob29vMZGztrbGypUrsWTJEjRp0gQ9evTAy5cv32pb2X1f69Spg2HDhmHgwIFQq9VYuHAhfv75ZyiVSsjlcvj4+GDnzp1o1KgRZs2ahS+++AJFixYV1//8888RHh6ODh06vFV9iYiIiOjdkgl5+WAxIjIq8+bNg729Pfr06QMg9d7BL7/8EseOHYNCochVmQEBAYAgoMy1fdA+D8l6hQ+AAECr1UAuV+Dt2rDfL4yLVGGPidzOCcqvRkFubvHOtqnVahAeFgQHRxfI5bn7XnrfMCZSjIkUYyL1vseE99gRfUD+/vtv1KlTB2ZmZrh+/ToOHDiAXbt2ifNXrFiBr776KtdJXVrKTqPeugwiKoRkCsQnavEurwpb2pRCQjIAaN/hVgs3xkSKMZFiTKTSxkQhB8xN358OjEzsiD4gp0+fxrhx42BiYgI7OzssWLAADg4OiImJQfPmzVG7dm2MGTPmrbcjQIYVZ2IQ/jIl64U/CAK0ghZymRwolO0wBYVxkSr8MYlJ1CI6TvMOtyikGTyqcMbk3WNMpBgTKcZE6k1MnG3MML61Y0FXKE+xKyYR5amAgAAIArD4giUePUsu6OoUEgI0Gs3/W0L54/oG4yLFmEgxJlKMiRRjIsWYSL2JSVl7c/z8tQsszN+fFrv3Z0+IiIiIiIg+UEzsiIiIiIiIjBwTOyIiIiIiIiPHxI6IiIiIiMjIMbEjIiIiIiIyckzsiIiIiIiIjBwTOyIiIiIiIiPHxI6IiIiIiMjIMbEjIiIiIiIyciYFXQEiej8525gBkBV0NQoJAVqtFnK5HIxJWoyLFGMixZhIMSZSjIkUYyL1JibONqYFXZk8x8SOiPKBgPGtHcAfEh0BWkELuYw/rvoYFynGRIoxkWJMpBgTKcZESj8mives7yITOyLKc1qtBkVM8f+rhKTVahAeFgwHRxfGJA3GRYoxkWJMpBgTKcZEijGRet9j8v7tEREVOEEQCroKhU5yclJBV6FQYlykGBMpxkSKMZFiTKQYE6n3OSZM7IiIiIiIiIwcEzsiIiIiIiIjx8SOiIiIiIjIyDGxIyIiIiIiMnJM7IiIiIiIiIwcEzsiIiIiIiIjx8SOiIiIiIjIyDGxIyIiIiIiMnJM7IiIiIiIiIwcEzsiIiIiIiIjx8SOiPKcTCYr6CoUOqamZgVdhUKJcZFiTKQYEynGRIoxoQ+dSUFXgIjeP3K5AvHaFECbUtBVKTRUdg6MiQGMixRjIsWYSDEmUuljopDJUURhWsC1Inp3mNgRUb746bovguKiCroahYIgAFptCuRyE7Ax8w3GRYoxkWJMpBgTqfQxcVFaY2qNVgVdLaJ3iokdEeWLwNgoPIh9VtDVKBQEAdBqUiBX8CQsLcZFijGRYkykGBMpxoSI99gREREREREZPSZ2RERERERERo6JHRERERERkZFjYkdERERERGTkmNgREREREREZOSZ2RERERERERo6JHRERERERkZFjYkdERERERGTkmNgREREREREZOSZ2RERERERERs6koCtARO8nV6U1ZLKCrkXhIAiAVtBALlMwJmkwLlKMiZQgAFptCuRyE8bk/xgTqfQxcVFaF3SViN45JnZElPdkwNQarQq6FkSUGSNKCLRaLeRydjJKizGRSh8ThYzxoQ8LEzsiynMyAcDhDdBGRRR0VQoFAWlaYQq6MoUI4yL1rmIity6Bot5fQ25WNB+3kje0Wg3CI4Lg4OgCuVxR0NUpFBgTKcaEiIkdEeWT5PtXoQ1/XNDVKBQEABpNChQKEyYwaTAuUu8qJgqHMoBXz3zcQt5KTk4q6CoUOoyJFGNCHzq2Ueehnj174tKlS9ladtu2bVCr1Zg9e3Y+1yr7Ro0ahcePH+dpmUuXLoVarYZarUblypXFv5cuXZrjstRqNcLCwvK0fk+fPkWzZs3ytMys7NmzB7Vq1YJarUaDBg3QsmVLLF26FAkJCeIya9euxZ9//vlO60VERERExostdgUgLi4OixcvxokTJ1CsWLECq0e/fv0wa9YsODg4AAAWLlyY59sYOnQohg4dCgCoWrUq/Pz88nwbxqhly5aYOXMmACAoKAjz589H//79sXHjRigUCvTv3z9Pt/fXX3/hr7/+wrhx4/K0XCIiIiIqHNhiVwAiIyNhZWWVq6ROEIQ8q8ejR4/ytDzKHRcXF/j4+OD169c4cuRIpstqtdpcbSMiIgKvX7/O1bo8RoiIiIgKPyZ2+WTcuHFYs2YNevXqBTc3NwwZMgSJiYm4desWunXrhpCQEKjVahw/fhwA8Pvvv6NVq1bw8PBAz5498eDBA7GsqlWrYvfu3WjUqBFOnDiBcePGYcWKFejRowfq1asHHx8f3L59Gx07dkTDhg3FliAAuHjxIjp27Ah3d3e0a9cO//33HwCgWbNmCAsLQ+fOnTF69GgA+l0do6OjMXbsWHh6ekKtVmPWrFlISkrtu75nzx6MGzcOEydOROPGjdGxY0cEBgbmOEabNm1C8+bN4enpiTVr1ojT4+PjMX36dHh5ecHNzQ2bNm0S512+fBkdOnRA/fr1MW/ePHF6z549sX37dnTu3BkNGzbE5MmTxYREo9FgxYoVaN68OTw8PDBo0CCEh4cbrNPdu3fRq1cvqNVqeHt7Y+fOneK8lJQUzJkzBx4eHvDy8sLatWvFbpze3t64fv26uOyVK1fQuXPnbMdCLpejU6dO+OuvvwCkHj/79u0T/165ciU6duyI4cOHAwD+/PNPtGrVCmq1GnPmzBETvpSUFCxbtgzNmzeHm5sb5s6diy1btmDu3Ln4888/oVarERAQACBnxxwRERERFW5M7PLRwYMHsXDhQvj7+yM6Ohp79+5F1apVsW3bNjg5OcHPzw+enp44cuQIdu3ahc2bN8Pf3x89e/bEoEGDkJycDCC1lSYsLAxnzpyBu7s7AMDPzw+rVq3CoUOHsGvXLkyfPh1r167F4cOHcezYMdy6dQsAkJiYiGXLluHkyZPo3r07Fi9eDAA4duwYHB0dsX37dixYsEBS93HjxsHFxQVHjx7F4cOHERERgZUrV4rzjx8/jk6dOuH06dNwc3PDkiVLchSbI0eOiPu9f/9+HDp0CJcvXwYATJo0CTKZDL6+vjh9+jQaN24srnfixAls374d+/fvx549e3D37l1x3uHDh7Fu3TocOXIEFy9exNmzZwEAGzduxLVr17B79274+/ujXr16GDVqlKROsbGxGDhwIAYMGAA/Pz9s2rQJ69evx7lz5wAAv/76KwIDA3H48GEcPHhQTJAAoF27djh8+LD42tfXF+3bt89RTFxcXDJMOE+cOIH169dj0aJFCAgIwPLly7Fx40YcPXoUjx49wsGDBwEAy5Ytw71797Bnzx789ddf6NSpE3r06IGxY8eiZcuW8PPzQ40aNXJ1zBERERFR4cXELh+1a9cOdnZ2MDU1RfPmzcXWsvR27tyJYcOGwcbGBgDg5eUFOzs7XLt2DUBqV7gvv/wSAGBiknpbZOvWraFSqWBjY4O6devC3d0dNjY2sLS0RJ06dXD//n0AQKNGjeDg4IC7d+8iOTkZjx49yrLez58/x82bNzF48GDI5XKYmZlh5MiRYvIAAHXq1EHNmjUBAG3atMlw3zKyY8cODB06FCqVChYWFmjfvj1Onz6NqKgonDt3DmPHjoWpqSnkcjnKlCkjrte7d2+YmZnB3t4e9erVE/cTALp06QKVSgWVSgUPDw+xTjt37sTYsWOhVCoBAL169cLTp08lA7GcOnUKtWrVgpubGwDAwcEBffr0Eff7jz/+wKhRo2Bubg4zMzMMGjRIXLddu3Y4evQogNQWQj8/P7RqlbPnuCUmJsLU1NTgPE9PTxQvXhwmJibYuXMn+vfvDzs7O5iYmOCrr77C6dOnAQC//fYbpk2bBgsLCwBAuXLlDJaXm2OOiIiIiAovnrHlI1tbW/FvlUqF+Ph4g8uFhITA1dVVb5qTkxMiIlKfASaXy8UBTnSsra3Fv4sWLQo7OzvxtYWFBRITEwEAW7duxZYtW1ClShXY2NggJSUly3qHhITA2dlZ7yGfTk5OePbsmcF9s7S0zHDfMtvGmDFjoFCkPmsmOTkZXl5eCAoKgouLS4YJTokSJcS/VSqV3kiSGdUpJCQELi4u4jyZTIaSJUsiIiJCTGwAIDg4WG85AChVqpSYsIWGhqJ06dJ620+7XMmSJREQEIDXr1/jk08+yfE9lA8ePEDFihUNznN0dBT/DgkJgZ+fH5YvXw4gNZGsWrUqIiMjYW5uDisrqyy3lZtjjoiIiIgKLyZ2hYC9vT2CgoL0koawsDCUKlUKQGoikhvx8fFYunQp/P39YWFhgbt372ZrVEp7e3sEBwdDq9WKyV1oaChKliyZq3oYYmdnh3HjxqFatWp604ODgxEaGgpBEHK93+nZ29vj6dOnKFu2rDgtPDwcTk5Oeomhvb29pOUxNDQUTk5OAFITuWfPnolxCA0N1VtW1x0zKioqx90w4+Li8Pvvv+PXX381OD9tLOzs7DB58mR4eXnpLZOSkoKYmBjEx8ejaNHMHzqcX8ccERERERUMdsUsBDp27AgfHx9ERkYCSL1/LS4uDjVq1HircjUaDVJSUhAbG4uUlBRs2LBBb76lpSVCQkIkrXglS5ZEuXLlsGLFCgiCgKSkJCxevDhHg4FkxdvbGytXrhRb1W7duoXw8HCUKlUKLi4uWLlyJQRBQEpKil53y9zo2LEj5syZg9jYWADA5s2bUblyZb1WTiB18JiLFy+KA5hERERgw4YN6NSpkzjfx8dHTKDSDvgCAM2bN8fp06cREBAgdufMjkePHuG7775D7969Ub58+SyX9/b2xq+//oro6GgAwJMnT/Dw4UOYmJigWbNmmD17NpKTkyEIAu7cuQMAKFasmJiIpqSk5NsxR0REREQFgy12hUC7du0QFRWFLl26QKPRoGLFivj555/1ukLmhkqlwqBBg9ChQwcUK1YMffv2xfnz58X5ffr0wdChQ+Hu7o4ZM2borbtw4UJMmzYNHh4eKFKkCNq0aYOePXu+VX3S6tKlC0JCQtCyZUsoFApUqlRJrMOiRYswY8YMuLu7w8zMDN9//z0qVKiQ620NGDAAy5YtQ9u2bSGTyVC7dm3MmTNHslyxYsWwatUqTJ8+HZMmTYKlpSWGDh2K6tWrAwBGjx6NH3/8Ee7u7ihRogT69OmDX375RVxfpVKhfPnyKFWqlNjFNCN//vknzp07B5lMBicnJ/Tr1y/bg5R4eHjg4cOH+OKLLwCkDroybdo0AMDkyZMxe/ZsNGvWDAqFAl27dkXlypXRoEEDrF27Fmq1Gj///HO+HXNpKeycwHa/VAIAmVYDuVzBmKTBuEi9q5jI7ZzysfS8Z2pqVtBVKHQYEynGRIoxkXqfYyIT+JAqolw5deoUfvvtN6xatUqc1r59eyxcuDBbLW/vq4CAAEAQUK1S7pNxInoH5AokCqYo/CcBArSCFnKZHOAlgP9jTKQYEynGRCp3MVHIAXPTwt/RkS12RNl07do1lC5dGlZWVnj27BmWLVuGb775Rpx/5MgR2NjYfNBJnY4AGVaciUH4y6wH6/kw8MfVMMZF6t3FJCZRi+g4Tb5uI28Iae755nGSijGRYkykGBOpnMfE2cYU41s7Zr1gIcDEjiibnjx5glGjRiElJQUWFhbo3r27OIBJv379EBUVhWXLlhVwLQuPy4/j8OhZckFXo5AQoNFo/t9Flz+ubzAuUoyJFGMixZhIMSZSjInU+x0TJnZE2dS2bVu0bdvW4Lx169a949oQEREREb1R+DuLEhERERERUaaY2BERERERERk5JnZERERERERGjokdERERERGRkWNiR0REREREZOSY2BERERERERk5JnZERERERERGjokdERERERGRkWNiR0REREREZORMCroCRPR+crYxAyAr6GoUEgK0Wi3kcjkYk7QYFynGRIoxkWJMpBgTKcZEKucxcbYxzd8q5SEmdkSUDwSMb+0A/pDoCNAKWshl/HHVx7hIMSZSjIkUYyLFmEgxJlK5i4nCSPo4MrEjojyn1WpQxBT/vyJGWq0G4WHBcHB0YUzSYFykGBMpxkSKMZFiTKQYE6n3PSbv3x4RUYETBKGgq1DoJCcnFXQVCiXGRYoxkWJMpBgTKcZEijGRep9jwsSOiIiIiIjIyDGxIyIiIiIiMnJM7IiIiIiIiIwcEzsiIiIiIiIjx8SOiIiIiIjIyDGxIyIiIiIiMnJM7IiIiIiIiIwcEzsiIiIiIiIjx8SOiIiIiIjIyDGxIyIiIiIiMnJM7Igoz8lksoKuQqFjampW0FUolBgXKcZEijGRYkyIKD2Tgq4AEb1/5HIF4rUpgDaloKtSaKjsHBgTAxgXKcZEijGRYkxSKWRyFFGYFnQ1iAoFJnZElC9+uu6LoLiogq5GoSAIgFabArncBGzMfINxkWJMpBgTKcYklYvSGlNrtCroahAVGkzsiChfBMZG4UHss4KuRqEgCIBWkwK54sM+CUuPcZFiTKQYEynGhIgM4T12RERERERERo6JHRERERERkZFjYkdERERERGTkmNgREREREREZOSZ2RERERERERo6JHRERERERkZFjYkdERERERGTkmNgREREREREZOSZ2RERERERERo6JHRERERERkZEzKegKENH7yVVpDZmsoGtROAgCoBU0kMsUjEkajIsUYyIlCIBWmwK53IQx+T/GJJWL0rqgq0BUqDCxI6K8JwOm1mhV0LUgorSMOAHQarWQy9nJKC3GJJVCxhgQ6TCxI6I8JxMAHN4AbVREQVelUBCQphWmoCtTiDAuUvkVE7l1CRT1/hpys6J5WOq7odVqEB4RBAdHF8jlioKuTqHAmBCRIUzscqFnz54YNmwY6tSpk+Wy27Ztw9q1a9GsWTOMHz/+HdQua6NGjcKQIUNQpkyZPCtz6dKl2Lt3LwAgODgYpUqVAgC0b98eQ4cOzVFZarUa27Ztg6OjY57V7+nTp+jTpw+OHTuWZ2VmZc+ePRg/fjx8fX1RoUIFyfxBgwYhPj4eGzZseGd1epeS71+FNvxxQVejUBAAaDQpUChMmMCkwbhI5VdMFA5lAK+eeVjiu5WcnFTQVSh0GBMiSo+JXT6Ki4vD4sWLceLECRQrVqzA6tGvXz/MmjULDg4OAICFCxfm+TaGDh0qJnBVq1aFn59fnm/DGFlYWGDHjh2YMGGC3vSwsDD8+++/qFKlSgHVjIiIiIjeJ+yYnI8iIyNhZWWVq6ROEIQ8q8ejR4/ytDzKvnr16uHPP/9EUpL+ldUdO3agbt26BVSr7NNqtQVdBSIiIiLKBiZ2b2ncuHFYs2YNevXqBTc3NwwZMgSJiYm4desWunXrhpCQEKjVahw/fhwA8Pvvv6NVq1bw8PBAz5498eDBA7GsqlWrYvfu3WjUqBFOnDiBcePGYcWKFejRowfq1asHHx8f3L59Gx07dkTDhg0xc+ZMcd2LFy+iY8eOcHd3R7t27fDff/8BAJo1a4awsDB07twZo0ePBpDa1TEsLAwAEB0djbFjx8LT0xNqtRqzZs0Sk5A9e/Zg3LhxmDhxIho3boyOHTsiMDAwxzHatGkTmjdvDk9PT6xZs0acHh8fj+nTp8PLywtubm7YtGmTOO/y5cvo0KED6tevj3nz5onTe/bsie3bt6Nz585o2LAhJk+eLCatGo0GK1asQPPmzeHh4YFBgwYhPDzcYJ3u3r2LXr16Qa1Ww9vbGzt37hTnpaSkYM6cOfDw8ICXl5fYlRYAvL29cf36dXHZK1euoHPnzhnuu62tLT799FMcOXJEnKbRaLB//360bt1ab9klS5agadOmaNKkCUaNGoWUlBQAGR9jQGrL34ABA+Dh4QG1Wo2DBw+K5T158gQ9e/ZEkyZN0L17d0yaNAkrVqzI8n1Rq9XYsWMHmjZtis2bN2e4b0RERERUeDCxywMHDx7EwoUL4e/vj+joaOzduxdVq1bFtm3b4OTkBD8/P3h6euLIkSPYtWsXNm/eDH9/f/Ts2RODBg1CcnIygNTWkbCwMJw5cwbu7u4AAD8/P6xatQqHDh3Crl27MH36dKxduxaHDx/GsWPHcOvWLQBAYmIili1bhpMnT6J79+5YvHgxAODYsWNwdHTE9u3bsWDBAkndx40bBxcXFxw9ehSHDx9GREQEVq5cKc4/fvw4OnXqhNOnT8PNzQ1LlizJUWyOHDki7vf+/ftx6NAhXL58GQAwadIkyGQy+Pr64vTp02jcuLG43okTJ7B9+3bs378fe/bswd27d8V5hw8fxrp163DkyBFcvHgRZ8+eBQBs3LgR165dw+7du+Hv74969eph1KhRkjrFxsZi4MCBGDBgAPz8/LBp0yasX78e586dAwD8+uuvCAwMxOHDh3Hw4EEEBASI67Zr1w6HDx8WX/v6+qJ9+/aZxqBLly7YsWOH+NrPzw81a9aElZWV3nIuLi44dOgQTpw4gfDwcPFiAGD4GAOAmJgYDBw4EP7+/lizZg2mTZsGjUYDrVaLwYMHo0uXLjh16hSmT5+uV15m7wsA3LhxA8eOHUP37t0z3TciIiIiKhyY2OWBdu3awc7ODqampmjevLnYWpbezp07MWzYMNjY2AAAvLy8YGdnh2vXrgFI7X755ZdfAgBMTFJvf2zdujVUKhVsbGxQt25duLu7w8bGBpaWlqhTpw7u378PAGjUqBEcHBxw9+5dJCcn49GjR1nW+/nz57h58yYGDx4MuVwOMzMzjBw5Uq/Vp06dOqhZsyYAoE2bNhnuW0Z27NiBoUOHQqVSwcLCAu3bt8fp06cRFRWFc+fOYezYsTA1NYVcLtcbzKV3794wMzODvb096tWrJ+4nkJooqVQqqFQqeHh4iHXauXMnxo4dC6VSCQDo1asXnj59KrZO6pw6dQq1atWCm5sbAMDBwQF9+vQR9/uPP/7AqFGjYG5uDjMzMwwaNEhct127djh69CiA1JY3Pz8/tGqV+bD+9evXx/Pnz8X35Pfff0eXLl0ky3Xs2BHx8fG4du0aihcvrvceZnSMVahQAXXq1EFQUBCePn0KQRDw/PlzBAQEQKlUinUrV64c2rRpk+X7otO+fXvI5XLxOCQiIiKiwo1nbXnA1tZW/FulUiE+Pt7gciEhIXB1ddWb5uTkhIiI1CHh5XK5OMCJjrX1m4dvFi1aFHZ2duJrCwsLsUve1q1bsWXLFlSpUgU2NjZiN77MhISEwNnZWe85OE5OTnj27JnBfbO0tMxw3zLbxpgxY6BQpA7HnJycDC8vLwQFBcHFxQWmpqYG1ytRooT4t0qlQkJCQpZ1CgkJgYuLizhPJpOhZMmSiIiIEJNpIHXUzrTLAUCpUqXEhC00NBSlS5fW237a5UqWLImAgAC8fv0an3zySbbuoezcuTN27tyJrl27IiIiAnXq1MGFCxfE+fHx8RgxYgSio6NRqVIlxMbG6r2HGR1jly9fxk8//QRnZ2eULVsWcrkcycnJCA4OlhxrlpaW4t8ZvS86eTkiKRERERHlPyZ275C9vT2CgoL0koawsDDx0QAyWe4Gt46Pj8fSpUvh7+8PCwsL3L17N1ujUtrb2yM4OFjvIaehoaEoWbJkruphiJ2dHcaNG4dq1arpTQ8ODkZoaCgEQcj1fqdnb2+Pp0+fomzZsuK08PBwODk56SWG9vb2kpbH0NBQODk5AUhNnJ49eybGITQ0VG9ZXXfMqKioLLth6nTo0AEdOnSAXC5Hp06dJPMPHDgAW1tbrFq1CgAwd+7cbJU7f/58TJkyRXz0xp49ewAAxYoV00vQAf3EN6P3RYcPvSUiIiIyLjx7e4c6duwIHx8fREZGAki9fy0uLg41atR4q3I1Gg1SUlLEVp70z0WztLRESEiIpBWvZMmSKFeuHFasWAFBEJCUlITFixdnOhhITnl7e2PlypViC9OtW7cQHh6OUqVKwcXFBStXroQgCEhJSdHrbpkbHTt2xJw5cxAbGwsA2Lx5MypXrqzXygmkDg5y8eJF/PXXXwCAiIgIbNiwQUy41Go1fHx8kJKSgpiYGL2BRQCgefPmOH36NAICAsTunFkpXrw4PvvsM+zYscNgMpicnIzXr19DEAQ8ffpU7z6+zOjWA4DffvtN3PdatWrh3r17OHPmDADg6tWrOHnypLheRu8LERERERknJnbvULt27dC6dWt06dIFTZs2xa5du/Dzzz+/deuISqXCoEGD0KFDB7Rt2xa1a9fWm9+nTx8MHToUU6ZMkay7cOFC3Lt3Dx4eHmjbti3Kly+Pnj3z7iG2Xbp0gaurK1q2bAlPT08sX75c7H65aNEi/Pfff3B3d0eLFi1w8+bNt9rWgAEDULlyZbRt2xaenp4ICAjAnDlzJMsVK1YMq1atwvLly+Hh4YH+/ftj6NChqF69OgBg9OjRiI+Ph7u7O77++mu0adMGRYoUEddXqVQoX748GjduLHZlzI6uXbuiadOmBrtutmvXDq9evUKTJk0wefJkNG/ePFtljhw5EjNnzoRarUZ0dDScnZ0BAEqlEkuWLMHcuXPh7u6OzZs3o02bNihatCiAzN8XIiIiIjI+MoEPOCPK1KlTp/Dbb7+J3SSB1MFFFi5ciPLlyxdgzXJm2LBhaNWqld69dPkhICAAEASUubYP2uch+botYyEA0Go1kMsVyJuOx+8HxkUqv2Iit3OC8qtRkJtb5GGp74ZWq0F4WBAcHF0gl2f/Ytr7jDGRYkykGBOp9z0mvMeOKJ1r166hdOnSsLKywrNnz7Bs2TJ888034vwjR47Axsam0Cd1Z86cQcOGDSGXy3H69Glcu3YNs2fPfmfbV3aSPmqCiAqQTIH4RC2M8WqupU0pJCQDgLagq1JoMCZSjIkUYyL1NjFRyAFz08Lb4ZGJHVE6T548ER8QbmFhge7du4utXP369UNUVBSWLVtWwLXM2p49ezBhwgSYmprCxcUFq1atgoXFu7laL0CGFWdiEP4y69FZPwwCtIIWcpkcYNtUGoyLVP7FJCZRi+g4TZ6W+W4IaQb54nGSijGRYkykGBOp3MfE2cYU41sX7lHD2RWTiPJUQEAABAFYfMESj54lF3R1CgkBGo3m//dk8sf1DcZFijGRYkykGBMpxkSKMZHKfUzK2pvh569dYGFeeFvsCm/NiIiIiIiIKFuY2BERERERERk5JnZERERERERGjokdERERERGRkWNiR0REREREZOSY2BERERERERk5JnZERERERERGjokdERERERGRkWNiR0REREREZOSY2BERERERERk5k4KuABG9n5xtzADICroahYQArVYLuVwOxiQtxkWKMZFiTKQYEynGRIoxkcp9TJxtTPOnSnmIiR0R5QMB41s7gD8kOgK0ghZyGX9c9TEuUoyJFGMixZhIMSZSjInU28VEUcj7OjKxI6I8p9VqUMQU/78iRlqtBuFhwXBwdGFM0mBcpBgTKcZEijGRYkykGBOp9z0m798eEVGBEwShoKtQ6CQnJxV0FQolxkWKMZFiTKQYEynGRIoxkXqfY8LEjoiIiIiIyMgxsSMiIiIiIjJyTOyIiIiIiIiMHBM7IiIiIiIiI8fEjoiIiIiIyMgxsSMiIiIiIjJyTOyIiIiIiIiMHBM7IiIiIiIiI8fEjoiIiIiIyMgxsSMiIiIiIjJyTOyIKM/JZLKCrkKhY2pqVtBVKJQYFynGRIoxkWJMiCg9k4KuABG9f+RyBeK1KYA2paCrUmio7BwYEwMYFynGRIoxkTKmmChkchRRmBZ0NYjee0zsiChf/HTdF0FxUQVdjUJBEACtNgVyuQnYmPkG4yLFmEgxJlLGFBMXpTWm1mhV0NUg+iAwsSOifBEYG4UHsc8KuhqFgiAAWk0K5IrCfxL2LjEuUoyJFGMixZgQkSG8x46IiIiIiMjIMbEjIiIiIiIyckzsiIiIiIiIjBwTOyIiIiIiIiPHxI6IiIiIiMjIMbEjIiIiIiIyckzsiIiIiIiIjBwTOyIiIiIiIiPHxI6IiIiIiMjImRR0BYjo/eSqtIZMVtC1KBwEAdAKGshlCsYkDcZFijGREgRAq02BXG7CmPyfMcXERWld0FUg+mAwsSOivCcDptZoVdC1IKJ34R0kFlqtFnI5OxmlZUwxUciMo55Exo6JHRHlOZkA4PAGaKMiCroqhYKANK0wBV2ZQoRxkTK2mMitS6Co99eQmxXNt21otRqERwTBwdEFcrki37ZjTBgTIjKEiR0R5Yvk+1ehDX9c0NUoFAQAGk0KFAoTozhZf1cYFylji4nCoQzg1TPft5OcnJTv2zA2jAkRpce2ccpSz5494ebmBg8PD3z++efo1asXTp48Kc7XarXo168fXr58WXCVfEvHjh2Dp6cnhg0bJpk3YcIEqNVqNG7cGNWqVYNarYZarcaOHTtyvJ2qVavmRXX1XLhwAb17987zcomIiIjIeLDFjrLFx8cHderUQXJyMv7++29MnToVERER+OqrryCXy7Fu3bo83d6kSZPQoUMH1K5dO0/LzcjUqVOxbds2uLq6SubNmjULAPD06VP06dMHx44deyd1IiIiIiLKLrbYUY6YmpqiSZMmWLJkCRYuXIjExMRMlxcEIVfbefz4MbRabY7Xy806ABAZGWkwqSMiIiIiMgZM7ChXatSogVKlSiEgIACAfhfDqlWrYvfu3WjUqBFOnDiBxMRETJ48GU2bNkWrVq1w9uxZcdng4GB8++23UKvVaNKkCf7991/07NkTV69exfDhw9GzZ+q9G9HR0Rg7diw8PT2hVqsxa9YsJCWl3l+wZ88ejB07Ft999x3UarXB+mo0GqxYsQLNmzeHh4cHBg0ahPDwcERGRkKtVkOj0UCtVmPLli3ZjoFGo8HixYvh5eUFb29v7Nu3T5wXFRWF0aNHw9PTE25ubjhy5Ig478iRI2jVqhUaNGiAjRs3itPVajUOHDiA9u3bo0GDBli+fLk4LyEhATNnzoSXlxc8PDwwZswYvH792mC9Ll26hK+++gpqtRpt2rTBiRMnxHmxsbEYN24c3N3d0bJlSyxfvhy9e/dGYmIi6tati2fPnonLHjx4ECNHjsx2PIiIiIio4DCxo1xzcXFBWFiYZLpWq0VYWBjOnDkDd3d3zJs3DxYWFjh+/Dh+/vln/Pjjj4iJiUFSUhL69u2Ltm3bws/PD76+vnB2dsbmzZtRs2ZN+Pj4YPPmzQCAcePGwcXFBUePHsXhw4cRERGBlStXits8ffo0vvvuOxw/ftxgXTdu3Ihr165h9+7d8Pf3R7169TBq1CjY2NjAz88PCoUCfn5+6NGjR7b3f+PGjXjy5Al8fX2xbds2LFu2DE+fPgUAfP/996hSpQqOHj0KPz8/VKtWTYzNlStXcPDgQWzevBk+Pj6Ijo7W24+dO3di79692LJlCx4+fAgAmD9/PuLj4+Hr64sTJ07A2toa06dPl9QpNDQUI0eOxE8//QQ/Pz/4+Phg2rRpePToEQBg7ty5KFq0KE6cOIHt27fj3LlzAABzc3M0a9YMR48eFcs6ePAg2rdvn+14EBEREVHBYWJHuZaYmAhTU1PJdEEQ8OWXXwIA5HI5Dhw4gBEjRkAmk6FMmTKoVq0arl+/jpMnT6JixYpo2bIlAEClUqFEiRKS8p4/f46bN29i8ODBkMvlMDMzw8iRI3Hw4EFxmSpVqqBq1aowMTF82+jOnTsxduxYKJVKAECvXr3w9OlTg4lpdu3YsQOjR4+GqakpbG1t0bRpU5w7dw43b95EXFwc+vXrJ9bX2dlZjM0333wDmUyG8uXLo3z58njy5IlYZq9evWBqaooSJUqgbt26uHPnDgRBwN69ezF+/HiYmppCLpdjxIgROHbsmKTr6YEDB9CuXTsxkSxfvjw6dOiAQ4cOQRAE+Pr6YtSoUVAoFLC0tESfPn3Eddu3b4/Dhw8DAF6+fIl79+6hYcOGuY4PEREREb07HDyFcu3hw4eoWLGiZLpcLoeDgwOA1HvXYmNj0aJFC3F+QkICWrZsiZCQEJQvXz7L7YSEhMDZ2VnvQaxOTk563QZ128usDBcXF/G1TCZDyZIlERERAUdHxyzrkFGZX3/9tfg6KSkJvXv3RmBgIMqVK2dwHYVCARsbG/G1SqVCQkKC+NrOzk7829LSEvHx8YiMjIRSqRSTUgAoUqQILC0tERUVJamTLqnTKVWqFG7evCmWo1Kp9LahU6dOHYSHh+P58+fw9/dHixYtoFDw+UhERERExoCJHeXK0aNHYWNjYzCBkcnePH3JysoKKpUKJ06c0JsOAH/88Qdu376d5bbs7e0RHBwMrVYrJnehoaEoWbKkuEzapC+jMp4+fYqyZcuK08LDw+Hk5JTl9jNiZ2eHPXv2wMrKSm/6xYsXERwcnOty07OyssLr168RFxcHCwsLAKmtpfHx8XpJIpC6n0FBQXrTQkND4eTkBEtLS7x69QpJSUkwMzMT5+nIZDK0atUKR48exZEjRzB58uQ82wciIiIiyl/sikk5kpKSgmPHjmHx4sXiYwAyY2Jignr16mHNmjUQBAGCIODUqVMAAHd3d5w/fx5///03gNTuf7pEw9LSEqGhoUhJSUHJkiVRrlw5rFixAoIgICkpCYsXL0bnzp2zXe+OHTtizpw5iI2NBQBs3rwZlStX1mshyylvb28sXboUKSkpAFKfJxcfH4+aNWsiMjISe/fuBQDEx8frdbfMKYVCgZYtW2LWrFlITk6GVquFj48P2rVrJ0mW27Rpgz179uDmzZsAgEePHuHAgQNo27YtzMzM8Omnn+Lnn3+GIAh49uyZZLCY9u3bY9euXUhMTMxWayoRERERFQ5M7Chbhg8fDrVajRYtWsDf3x+//vprht0N05syZQquX78Od3d3eHt7i4mctbU1Vq5ciSVLlqBJkybo0aOH+JDz7t27Y968efj2228BAAsXLsS9e/fg4eGBtm3bonz58uKImdkxYMAAVK5cGW3btoWnpycCAgIwZ86cHEZB35AhQ5CQkICmTZuiWbNm2L17NxQKBczMzLB69Wrs378fTZo0QYcOHd66BW/ChAmQy+Xw9vZG8+bNkZycjB9++EGynKurK+bNm4eJEydCrVZjzJgxmD17ttjddObMmbh+/ToaNWqEYcOG4YsvvkCRIkXE9UuXLg2FQoG2bdu+VX2JiIiI6N2SCbl90BgRGb2tW7fi0aNH+PHHHwGk3ifo7e2NvXv3onjx4rkqMyAgABAElLm2D9rnIXlZXaMlANBqNZDLFZBlufSHg3GRMraYyO2coPxqFOTmFvm2Da1Wg/CwIDg4ukAu532/AGNiCGMixZhIve8x4T12RB+Qf/75B9WqVYOFhQUeP36MDRs2YNGiReL8LVu2wM3NLddJXVrKTqPeugwiMgIyBeITtcjPq8SWNqWQkAwA2qwW/WAwJlKMiRRjIpU2Jgo5YG76/nRgZGJH9AEJCAjAmDFjAADFihXDqFGjUKNGDQBAixYtYG9vr/dg9NwSIMOKMzEIf5ny1mW9HwRoBS3kMjlgFO0w7wrjImV8MYlJ1CI6TpOPWxDSDJ5lHDHJf4yJFGMixZhIvYmJs40ZxrfO3cjohRW7YhJRngoICIAgAIsvWOLRs+SCrk4hIUCj0fz/8RH8cX2DcZFiTKQYEynGRIoxkWJMpN7EpKy9OX7+2gUW5u9Pi937sydEREREREQfKCZ2RERERERERo6JHRERERERkZFjYkdERERERGTkmNgREREREREZOSZ2RERERERERo6JHRERERERkZFjYkdERERERGTkmNgREREREREZOSZ2RERERERERs6koCtARO8nZxszALKCrkYhIUCr1UIul4MxSYtxkWJMpBgTKcZEijGRYkyk3sTE2ca0oCuT55jYEVE+EDC+tQP4Q6IjQCtoIZfxx1Uf4yLFmEgxJlKMiRRjIsWYSOnHRPGe9V1kYkdEeU6r1aCIKf5/lZC0Wg3Cw4Lh4OjCmKTBuEgxJlKMiRRjIsWYSDEmUu97TN6/PSKiAicIQkFXodBJTk4q6CoUSoyLFGMixZhIMSZSjIkUYyL1PseEiR0REREREZGRY2JHRERERERk5JjYERERERERGTkmdkREREREREaOiR0REREREZGRY2JHRERERERk5JjYERERERERGTkmdkREREREREaOiR0REREREZGRY2JHRERERERk5JjYEVGek8lkBV2FQsfU1Kygq1AoMS5SjIkUYyLFmBBReiYFXQEiev/I5QrEa1MAbUpBV6XQUNk5MCYGMC5SjIkUYyL1IcREIZOjiMK0oKtBZDSY2BFRvvjpui+C4qIKuhqFgiAAWm0K5HITsDHzDcZFijGRYkykPoSYuCitMbVGq4KuBpFRYWJHRPkiMDYKD2KfFXQ1CgVBALSaFMgV7+9JWG4wLlKMiRRjIsWYEJEhvMeOiIiIiIjIyDGxIyIiIiIiMnJM7IiIiIiIiIwcEzsiIiIiIiIjx8SOiIiIiIjIyDGxIyIiIiIiMnJM7IiIiIiIiIwcEzsiIiIiIiIjx8SOiIiIiIjIyJkUdAWI6P3kqrSGTFbQtSgcBAHQChrIZQrGJA3GRYoxkRIEQKtNgVxuwpj834cQExeldUFXgcjoMLEjorwnA6bWaFXQtSCivFSACYRWq4Vczk5GaX0IMVHI3u/9I8prTOyIKM/JBACHN0AbFVHQVSkUBKRphSnoyhQijItUYY2J3LoEinp/DblZ0Xe+ba1Wg/CIIDg4ukAuV7zz7RdGjAkRGcLEjojyRfL9q9CGPy7oahQKAgCNJgUKhUmhOlkvaIyLVGGNicKhDODVs8C2n5ycVGDbLqwYEyJKj23cH6igoCCMHDkSarUajRo1gpeXF65fvw4AqFq1aobrjRo1Co8fP86y/MuXL6Nly5bo0qWLwfnjxo3Dvn37slXXY8eOwdPTE8OGDcvW8u/CrFmzcOnSpXwp++nTpxg1ahTUajU8PDzQqVMnnDlzRm+Z58+fo3v37vDw8EBYWJje65CQEPTr1w8vX77Ml/oRERERUeHDFrsP0IMHDzBo0CCMGTMGCxYsgFwuR3BwcLbWXbhwYbaWmzdvHiZNmoT69eu/TVUBAFOnTsW2bdvg6ur61mXl1qRJk9ChQwfUrl0bADBhwoR82U54eDh69OiBIUOGYN68eVAoFPjvv/8watQoJCcnQ61WAwA2btyIzz77DMOHDweQ+r6kfb1u3bp8qR8RERERFU5ssfsATZo0CcOHD4enp6d443WpUqVQqlSpPNvGs2fPULZs2TwpKzIyMldJnVarzZPtA8Djx4/ztLyM+Pj4oF27dvjiiy+gUKTeN/HRRx9hxowZmDFjBjQaDQBpfPMy3mm9i30mIiIiorfHxO4D8/jxY4SHh6NFixaZLnfkyBG0atUKDRo0wMaNG8XparUaYWFhAIDo6GgMGzYMarUaHTt2xK1btwAA3t7eCAsLQ+fOnbFgwYIs67Rs2TLMnz8f3333Hdzc3NCzZ09ERkYiMjISarUaGo0GarUaW7ZsAZDaNbNdu3bw8PDAF198gX///Vevfjt27EDTpk2xefNmLFu2DLNnz8Y333yD+vXrY/z48QgKCkL37t3h5uaGESNGICUlBQBw584ddO/eHe7u7vD29sa5c+cAAD179sTVq1cxfPhw9OzZU5ym64qZkJCAmTNnwsvLCx4eHhgzZgxev34NALhw4QJ69+6NRYsWQa1Ww9vbW+zymp4gCDh+/Li4jbRq1aoFCwsL3Lp1C2PHjsWRI0cwe/Zs9OzZU/Ia0O9OGxUVhdGjR8PT0xNubm44cuQIACAkJAR9+/aFWq1Gjx49EBQUBADYs2cPxo4di++++05sISQiIiKiwo2J3Qfmzp07qFixImSZPPhGq9XiypUrOHjwIDZv3gwfHx9ER0dLlhs3bhxq164NPz8//Pjjjxg9ejS0Wi2OHDkCR0dHbN++HaNHj85WvXx9ffHDDz/gzJkzcHBwwIYNG2BjYwM/Pz8oFAr4+fmhR48eCAgIwLx587B8+XL4+/tj4sSJGDp0qF79bty4gWPHjqF79+4AUhPBGTNmwM/PDzdu3MC3336LOXPmwM/PD4GBgfD39wcAxMbGYvLkyTh58iQmT56MmTNnAgA2b96MmjVrwsfHB5s3b5bUff78+YiPj4evry9OnDgBa2trTJ8+XZx/9epVVKtWDX5+fujbty9mzZplMAZRUVGQyWSws7MzON/FxQXBwcGYO3cuvL29MX78eGzevFnyOr3vv/8eVapUwdGjR+Hn54dq1apBq9Vi8ODB6NatG/z8/NC1a1dMnjxZXOf06dP47rvvcPz48SzeOSIiIiIqDJjYfWCSkpJQpEiRTJcRBAHffPMNZDIZypcvj/Lly+PJkyd6y0RERODu3bvo1asXAKB27dowNzcXW31yqmnTpihTpgxkMhlat26N//77z+Byu3btQr9+/eDi4iJut379+jh16pS4TPv27SGXy2FiYiKWXaJECRQtWhTu7u6oU6cOXFxcYGZmBjc3N9y/f18sq2LFinj48CFiY2OzNUiMIAjYu3cvxo8fD1NTU8jlcowYMQLHjh0TuzG6uLjA29sbANCmTZsM9y0lJSXThDvtPmXXzZs3ERcXh379+kEul8PMzAzOzs64fv06LC0t4enpCQBo1aoVbt++jaSk1FHWqlSpgqpVq+Z4e0RERERUMHjW9oFxcnLCo0ePMl1GoVDAxsZGfK1SqZCQkKC3TGhoKJ4/f67XVS8uLg6RkZEoXbp0jutla2urt734+HiDy4WEhIhJko6TkxMiIt48L83R0VFvvrW1tfh30aJFYWZmJr5WKpWIjY0FkNqyt2jRIlSsWBGlS5cW72fLTGRkJJRKJZRKpTitSJEisLS0RFRUlGTfLCwsxOQpPVtbWyQlJSEyMlIv/jqBgYE5jm1gYCDKlSsnmR4SEoIbN27ovX+CIIh1dnBwyNF2iIiIiKhgMbH7wHz88ceIjIzEpUuXUKdOnVyXY2tri9KlS+PAgQN5WLus2dvbS1oFw8LC0KRJE/G1bkCYnJo2bRp27NiBkiVLIiYmBr/88kuW61hZWeH169eIi4uDhYUFACAxMRHx8fEGk7PMKBQKqNVqbNu2Dd9//73evCtXrkCj0aBixYo5KtPW1tbgiKe2traoV68eVq1aZXC93MaQiIiIiAoGz94+MKamppg8eTLGjRuHq1evitPv3bsn6W6ZGWdnZ5ibm+PgwYMAUrsRpn/WWn7o2LEj1qxZIyZ3V69exZUrV/QSu9xKTk4WBz1Zt26dXrdIS0tLhIaGigOt6CgUCrRs2RKzZs1CcnIytFqtOLJlZt0qMzJy5Ejs2LEDBw8eFLty3rhxA5MmTcKkSZNyXF7NmjURGRmJvXv3AgDi4+Px5MkT1KpVC/fv38fFixcBpA4AoxsshoiIiIiMDxO7D1CzZs0wefJkzJ49G02aNIG7uzumTp2K5OTkHJWzePFi7Ny5E02aNEGrVq1w53/t3XdcE/cfBvAnCUSmAoIow4V71F2rRcWooKLiaJW6rVWrbdU6sVbrqLMO6ra2rqp1689RN+7aWkcV965sUFDZJLn7/UG9kh4oKpgEnvfr1b7g5uceIuST+97dzZv5VPG/6tevj2HDhmHQoEHQaDSYO3cuFi9ebDAU8nWNGzcOAwYMgK+vL9zd3WFpaSnN69GjB2bPno1PP/1Utt5XX30FpVIJPz8/tG7dGlqtFqNHj36tGtzd3bFmzRrs27dPekD5zJkz8c0337zWMwHVajWWL1+OXbt2oVmzZujUqRMiIiKgVqsRHByM7777Trq7aExMzGvVTERERETGpxBFUTR2EURUcISGhgKiiLKX/gfhUaSxyzEJIgBB0EOpVOHVz+MWXMxFzlQzUTq7wbbrSCiL2Lz1fQuCHjHRYXAt6QmlUvXW92+KmIkcM5FjJnKCoEd42F14eHoVyEzY2BFRnnre2FWvVMHYpRBRXlKqkC5a4u2/aRAhiAKUCiVgUu2uMTETOWYiV/AzUSmBIpa5H4BY0Bs73jyFiPKcCAWWnExCzFPdyxcuFAr+H9fXw1zkTDeTpHQBT1JefrfgvCdCEIR/bupkWpkYDzORYyZyBTsTDydLjGtX8uULFiJs7IgoX5x/kIL7ca923WbBJUKv10OlUqEg/nF9fcxFjpnIMRM5ZiLHTOSYSWHDm6cQERERERGZOTZ2REREREREZo6NHRERERERkZljY0dERERERGTm2NgRERERERGZOTZ2REREREREZo6NHRERERERkZljY0dERERERGTm2NgRERERERGZOTZ2REREREREZs7C2AUQUcHk4aQGoDB2GSZChCAIUCqVYCZZMRc5ZiLHTOSYiRwzkSvYmXg4WRq7BJPDxo6I8oGIce1cURD/kLweEYIoQKkomH9cXx9zkWMmcsxEjpnIMRO5gp+JimMPDbCxI6I8Jwh6WFnin08JSRD0iImOgGtJT2aSBXORYyZyzESOmcgxEzlmUvjwp0xEeU4URWOXYHK02gxjl2CSmIscM5FjJnLMRI6ZyDGTwoWNHRERERERkZljY0dERERERGTm2NgRERERERGZOTZ2REREREREZo6NHRERERERkZljY0dERERERGTm2NgRERERERGZOTZ2REREREREZo6NHRERERERkZljY0dEeU6hUBi7BJNjaak2dgkmibnIMRM5ZiLHTIjovyyMXQARFTxKpQqpgg4QdMYuxWTYObsyk2wwFzlmIsdM5MwpE5VCCSuVpbHLICrw2NgRUb745vJehKUkGLsMkyCKgCDooFRagCcz/8Vc5JiJHDORM6dMPG0dMbmmv7HLICoU2NgRUb54mJyAu8lxxi7DJIgiIOh1UKpM/03Y28Rc5JiJHDORYyZElB1eY0dERERERGTm2NgRERERERGZOTZ2REREREREZo6NHRERERERkZljY0dERERERGTm2NgRERERERGZOTZ2REREREREZo6NHRERERERkZljY0dERERERGTm2NgRERERERGZOQtjF0BEBVNpW0coFMauwjSIIiCIeigVKmaSBXORYyZyoggIgg5KpQUz+Yc5ZeJp62jsEogKDTZ2RJT3FMDkmv7GroKIXsbEm4LnBEGAUslBRlmZUyYqhXnUSWTu2NgRUZ5TiAD2r4aQEGvsUkyCiCxnYYxdjAlhLnJvMxOlYwlY+/WGUm2dz3t6M4KgR0xsGFxLekKpVBm7HJPATIgoO2zsiChfaO/8BSHmgbHLMAkiAL1eB5XKgg1MFsxF7m1monItC/j2yue95A2tNsPYJZgcZkJE/8Vz45SvvvrqK2g0GjRt2hTVq1eHRqOBRqPB5s2bX3lb1apVy/P6/vjjD/Tt2zfPt/siCxcuRIMGDaDRaPDee++hU6dOWL16NfR6vbTM9OnTce7cubdaFxERERGZL56xo3w1ffp0AEB4eDj69euHQ4cOGbki09CvXz8MGTIEoiji9u3bmDp1KkJDQzF37lwAmQ1xXtq2bRuePn2Kjz/+OE+3S0RERESmgWfsiIxIoVCgUqVKWL58Of744w9cvnz5hcsLgvBa+4mMjERaWtorryeKIkRRfK19EhEREdHbw8aOjEKv12P+/Pnw9fWFn58f/ve//0nzEhISMGrUKLRs2RLe3t44cOCANO/AgQPw9/dH48aNsWbNGmm6RqPB7t270bFjRzRu3BiLFi2S5qWlpWHatGnw9fVF8+bNMWbMGCQmJmZb17lz59C1a1doNBq0b98eR44ckeYlJycjKCgIPj4+aNu2LRYtWoS+ffsiPT0d7777LuLi4qRl9+zZgxEjRuQ6DxsbG7Rv3x4nT54EAPTq1UsaitmrVy+sXbsWbdu2xaxZswAAa9euRevWrdGyZUusWLFC2k5qaiqmTp0KX19feHt7Y+3atZgzZw7WrFmDVatWQaPRIC4uDnq9HkuWLEHr1q3RvHlzDB48GDExMQAyz662atUKS5YsQePGjXHjxo1cHwcRERERGQeHYpJRrFmzBn///Tf27t2LZ8+eoVu3bqhXrx48PDzw+eefQ6PRYPbs2dDpdIiNzbyzoiAIuHjxIvbs2YN79+7hgw8+QEBAABwcHAAAJ06cwJYtW5CQkIAOHTqgbdu2KF++PL777jukp6dj7969UKlUmDVrFqZOnYrZs2cb1BQVFYURI0Zg6dKlqF69Ou7evYuPP/4Y5cuXR7ly5TBr1ixYW1vjyJEjSElJwaeffgpLS0sUKVIErVq1wsGDB9GjRw8AmY1d9+7dXykTDw8P3Lx5M9t5p0+fxs6dO6FUKnHgwAEcOHAAW7duhVKpRM+ePVG3bl3Uq1cPEyZMgIODg3SsDx8+RNmyZVGkSBGoVCoMGTIEALBy5UpcunQJ27Ztg62tLVavXo2RI0di3bp1AIDHjx/DwcEBZ86cMbj2j4iIiIhME8/YkVFs3rwZo0aNgqWlJYoXL44WLVrgzJkzuHr1KlJSUtC/f38olUqo1Wp4eHgAyBwWOHDgQCgUCnh5ecHLywt///23tM0+ffrA0tISJUqUwLvvvoubN29CFEXs3LkT48aNg6WlJZRKJb788kscOnRINqxx9+7dCAgIQPXq1QEAXl5e6NSpE/bt2wdRFLF3716MHDkSKpUK9vb26Nevn7Rux44dsX//fgDA06dPcfv2bbz//vuvlElGRgYsLS2zndeuXTuo1WpYWFhg8+bNGDp0KOzs7GBjY4OOHTvixIkTSEhIwJkzZzB27FjpWMuWLZvt9rZs2YKxY8fC1tZWyi48PBzR0dEAMpvorl27AgBUKt5Km4iIiMjU8YwdGUVkZCR69+4tfZ+RkYG+ffvi4cOHKF++fLbrqFQqODk5Sd/b2dkZXDfm7OwsfW1vb4/U1FTEx8fD1tZWamAAwMrKCvb29khISJDV9Lype87d3R1Xr16VtmNnZ2ewj+fq16+PmJgYPHr0CEePHkWbNm1euSG6e/cuatSoke28kiVLGtQ5ZswYaftarRa+vr4ICwuDp6dnjs3hf4/V09NT+l6hUKBUqVKIjY2Fk5MTihcvDgsL/nogIiIiMhd850ZG4ezsjO3bt0vDKJ87e/YsIiIi8mw/Dg4OSExMREpKCmxsbAAA6enpSE1NNWgSAcDFxQVhYWEG06KiouDm5gZ7e3s8e/YMGRkZUKvV0rznFAoF/P39cfDgQRw4cAATJ058pTqjo6MREhKCkSNHZjtfofj3iVbOzs4ICgqSNaERERGIioqCKIoGy2fHxcUF4eHhKFeunDQtJiYGbm5uSEtLg1LJk/lERERE5oTv3sgo/Pz8sGDBAuh0OgCZz5NLTU1F7dq1ER8fj507dwLIvBlI1uGWr0qlUqFt27aYPn06tFotBEFAcHAwAgICZM1P+/btsX37dly9ehUAcP/+fezevRsdOnSAWq1GvXr1sHjxYoiiiLi4OOl6tOc6duyIrVu3Ij09HV5eXrmu8cqVK/j888/x9ddfw9HR8aXL+/n5YenSpUhNTQUAXLt2DTExMXB3d4enpyeWLl0KURSh0+lw584dAJlnF583fYIgoHPnzpg5cyaSk5MBAD///DMqV65scNaTiIiIiMwHGzsyii+++AJpaWlo0aIFWrVqhW3btkGlUkGtVmP58uXYtWsXmjVrhk6dOr3xGbyvvvoKSqUSfn5+aN26NbRaLUaPHi1brnTp0pg9ezbGjx8PjUaDMWPGYMaMGdIwyGnTpuHy5cto0qQJhg0bhi5dusDKykpav0yZMlCpVOjQocNLa3p+h8oWLVpgwYIF+Oqrr9C2bdtcHU9gYCBKly6Ntm3bomXLlli0aJE0/HLevHm4ceMGfHx80KZNG6lJbdOmDS5cuAA/Pz8kJCRgwIABqFy5Mjp06ICWLVsiNDQUM2fOzNX+iYiIiMj0KEQ+pIrotaxfvx7379/H119/DSDzOkE/Pz/s3LkTxYoVM3J1xhMaGgqIIspe+h+ER5HGLsckiAAEQQ+lUoUXD5ItXJiL3NvMROnsBtuuI6EsYpPPe3ozgqBHTHQYXEt6QqnkzZwAZpIdZiLHTOQKeia8xo4ol/78809Ur14dNjY2ePDgAVavXo158+ZJ89etWwdvb+9C3dRlZfth9tcLEpEJUaiQmi7A1D/htXdyR5oWAISXLVpoMBM5ZiLHTOSyy0SlBIpYmv9ARjZ2RLkUGhqKMWPGAACKFi2KkSNHombNmgAyhzq6uLgYPBi9MBOhwJKTSYh5qjN2KSZChCAKUCqUAM9NZcFc5N5uJknpAp6kmPqzKjOvDc68qRNfJ5mYiRwzkWMmcvJMPJwsMa5dyRevZiY4FJOI8lRoaChEEZj/hz3ux2mNXY6JEKHX6/95RAX/uP6LucgxEzlmIsdM5JiJHDORk2dSzkWNxb09YVPE/M/Ymf8REBERERERFXJs7IiIiIiIiMwcGzsiIiIiIiIzx8aOiIiIiIjIzLGxIyIiIiIiMnNs7IiIiIiIiMwcGzsiIiIiIiIzx8aOiIiIiIjIzLGxIyIiIiIiMnNs7IiIiIiIiMychbELIKKCycNJDUBh7DJMhAhBEKBUKsFMsmIucsxEjpnIMRM5ZiLHTOTkmXg4WRq3pDzExo6I8oGIce1cwT8kz4kQRAFKBf+4GmIucsxEjpnIMRM5ZiLHTOSyz0RVQMYwsrEjojwnCHpYWeKfT8RIEPSIiY6Aa0lPZpIFc5FjJnLMRI6ZyDETOWYiV9AzKXhHRERGJ4qisUswOVpthrFLMEnMRY6ZyDETOWYix0zkmIlcQc6EjR0REREREZGZY2NHRERERERk5tjYERERERERmTk2dkRERERERGaOjR0REREREZGZY2NHRERERERk5tjYERERERERmTk2dkRERERERGaOjR0REREREZGZY2NHRHlOoVAYuwSTY2mpNnYJJom5yDETOWYix0yI6L8sjF0AERU8SqUKqYIOEHTGLsVk2Dm7MpNsMBc5ZiLHTORMNROVQgkrlaWxyyAqlNjYEVG++ObyXoSlJBi7DJMgioAg6KBUWoAnM//FXOSYiRwzkTPVTDxtHTG5pr+xyyAqtNjYEVG+eJicgLvJccYuwySIIiDodVCqTOtNmLExFzlmIsdM5JgJEWWH19gRERERERGZOTZ2REREREREZo6NHRERERERkZljY0dERERERGTm2NgRERERERGZOTZ2REREREREZo6NHRERERERkZljY0dERERERGTm2NgRERERERGZOTZ2REREREREZs7C2AUQUcFU2tYRCoWxqzANoggIoh5KhYqZZMFc5JiJnCgCgqCDUmnBTP5hqpl42joauwSiQo2NHRHlPQUwuaa/sasgopcxoabgRQRBgFLJQUZZmWomKoXp1URUWLCxI6I8pxAB7F8NISHW2KWYBBFZzsIYuxgTwlzk3mYmSscSsPbrDaXaOp/39GYEQY+Y2DC4lvSEUqkydjkmgZkQUXbY2BFRvtDe+QtCzANjl2ESRAB6vQ4qlQUbmCyYi9zbzETlWhbw7ZXPe8kbWm2GsUswOcyEiP7rlc6X9+rVC+fOncvVshs2bIBGo8GMGTNeq7D8MHLkSDx48CBPt7lgwQJoNBpoNBpUrlxZ+nrBggWvvC2NRoPo6Og8rS88PBytWrXK023mRnx8PCZOnIgWLVpAo9GgQ4cO2LVr11uv4792796N1atXv7X9aTQaDBs2LNt5169fR+XKlXP9b4qIiIiIKCf5csYuJSUF8+fPx5EjR1C0aNH82EWu9O/fH9OnT4erqysAYO7cuXm+j6FDh2Lo0KEAgGrVqiEkJCTP92FukpOT0bNnTwQEBGDfvn1Qq9UICwvD6NGjkZiYiB49ery1WrZt24anT5/i448/BgC0b9/+re37ud9//x3x8fFwcnIymP7LL7/A3t7+rddDRERERAVPvlzhGh8fDwcHh9dq6kRRzLM67t+/n6fbo9xZtWoVqlatikGDBkGtVgMAPD09MW/ePHz//fd49uxZnu1LEIQXzo+MjERaWlqe7e91NGzYENu3bzeYlpSUhFOnTqFKlSpGqip3XpYvEREREZmG127sgoKCsGLFCvTp0wfe3t744osvkJ6ejmvXrqF79+6IjIyERqPB4cOHAQAbN26Ev78/mjdvjl69euHu3bvStqpVq4Zt27ahSZMmOHLkCIKCgrBkyRL07NkTDRs2RHBwMK5fv47OnTvj/fffx7Rp06R1z549i86dO8PHxwcBAQG4ceMGAKBVq1aIjo5Gt27dMGrUKACGQx2fPHmCsWPHomXLltBoNJg+fToyMjLHq2/fvh1BQUEYP348mjZtis6dO+Phw4evnNHatWvRunVrtGzZEitWrJCmp6amYurUqfD19YW3tzfWrl0rzTt//jw6deqERo0aYfbs2dL0Xr16YdOmTejWrRvef/99TJw4UWpa9Xo9lixZgtatW6N58+YYPHgwYmJisq3p1q1b6NOnDzQaDfz8/LBlyxZpnk6nw8yZM9G8eXP4+vrixx9/lIZx+vn54fLly9KyFy9eRLdu3bLdx8GDB9G7d2/ZdDc3N9SpUwe//fYbgMyfR0hICDp16oQmTZpg2LBhSExMlJb/9ddf4e/vD41Gg5kzZ0pNRq9evbB27Vq0bdsWs2bNQlJSEoYPHw6NRoNmzZph5cqVAIA5c+ZgzZo1WLVqFTQaDeLi4rBw4UIsWbJE2sfLXpf79++Hv78/vL29DbJav3492rRpA29vb3z77bfZ5vBc165dDdYFgF27dsHPzw+KLPepvnnzJnr06AEfHx/4+fnhzJkzAP4dTrtu3Tr4+vrCx8cHx44dM6jFz88PzZo1w4ABAwwy/OGHH6TX+Nq1a1GtWjVpXmRkJD7++GNoNBr07NkTYWFhADJf/2PHjsVnn30GjUbzwmMjIiIiItPwRmfs9uzZg7lz5+Lo0aN48uQJdu7ciWrVqmHDhg1wc3NDSEgIWrZsiQMHDmDr1q34+eefcfToUfTq1QuDBw+GVqsFkHlWIDo6GidPnoSPjw8AICQkBMuWLcO+ffuwdetWTJ06FT/++CP279+PQ4cO4dq1awCA9PR0LFy4EMeOHUOPHj0wf/58AMChQ4dQsmRJbNq0CXPmzJHVHhQUBE9PTxw8eBD79+9HbGwsli5dKs0/fPgwPvzwQ5w4cQLe3t74/vvvXymbAwcOSMe9a9cu7Nu3D+fPnwcATJgwAQqFAnv37sWJEyfQtGlTab0jR45g06ZN2LVrF7Zv345bt25J8/bv34+ffvoJBw4cwNmzZ3H69GkAwJo1a3Dp0iVs27YNR48eRcOGDTFy5EhZTcnJyRg0aBAGDBiAkJAQrF27FqtWrZIaiJUrV+Lhw4fYv38/9uzZg9DQUGndgIAA7N+/X/p+79696NixY7bH/vDhQ3h5eWU7z9PTExEREdL327Ztw88//4xjx47BxsYGwcHBAIDQ0FAsWrQIa9aswcGDB3H//n3s2bNHWu/06dPYuXMnRo8ejbS0NHTo0AFHjhzB9u3bsXz5csTFxWHUqFHo06cP+vXrh5CQELi4uMh+Ri97XV65cgV79uzB6tWrMWPGDDx79gwPHjzAmjVrsH37dpw6dSrbJjarChUqwNnZGWfPnpWmbd68WdYYJycnY+LEiTh27BgmTpxo8AFGTEwMdDodDh48iG+//RYTJkyQ5hUtWhTbt2/H8ePHUbRoUWzevBlA5s/oyJEj2LZtGw4fPozw8HBpHUEQMGTIEHTv3h0hISH46KOPMHHiRGn+iRMn8Nlnn0kfzBARERGRaXujxi4gIADOzs6wtLRE69atpbNl/7VlyxYMGzZMusbI19cXzs7OuHTpEoDM4ZcffPABAMDCIvOyv3bt2sHOzg5OTk5499134ePjAycnJ9jb26N+/fq4c+cOAKBJkyZwdXXFrVu3oNVqcf/+/ZfW/ejRI1y9ehVDhgyBUqmEWq3GiBEjDBqH+vXro3bt2gAyr8vK6dhysnnzZgwdOhR2dnawsbFBx44dceLECSQkJODMmTMYO3YsLC0toVQqUbZsWWm9vn37Qq1Ww8XFBQ0bNpSOEwACAwNhZ2cHOzs7NG/eXKppy5YtGDt2LGxtbQEAffr0QXh4uOxGLMePH0edOnXg7e0NAHB1dUW/fv2k496xYwdGjhyJIkWKQK1WY/DgwdK6AQEBOHjwIIDMM4QhISHw98/+OWV6vd7gTFRWSqUSKtW/t2bu378/7OzsoFKpMHToUBw9elQ6pk8++QTOzs6wsLBA165dceLECWm9du3aQa1Ww8LCAs7OztBoNIiJicGNGzfg4OCQqzOsuXld9u/fHwqFAhUqVEC5cuXw4MEDWFhY4MmTJ9KNeEqXLv3SfQUGBmLTpk0AMs92FitWzODnDgB169ZFxYoVce/ePSQnJxvc6MfS0lJqIL29vaHVahEfHw/g3+sGL1++DFtbW+nfwI4dO/DZZ5+hWLFiUCqV+OKLL6TtXb58Gfb29mjZsiUAwN/fH9evX5fOWletWhXVqlWT/j0SERERkWl7o3dtxYsXl762s7NDampqtstFRkbK3vy6ubkhNjbzGVdKpVK6wclzjo6O0tfW1tZwdnaWvrexsUF6ejqAzGFo69atQ9WqVeHk5ASdTvfSuiMjI+Hh4WHwYE83NzfExcVle2z29vY5HtuL9jFmzBipidFqtfD19UVYWBg8PT1haWmZ7XolSpSQvrazszO4PiynmiIjI+Hp6SnNUygUKFWqFGJjYw1u2BEREWGwHAC4u7tLDVtUVBTKlCljsP+sy5UqVQqhoaFITExErVq1cryG0t3dHffv30eNGjVk8x4+fIjGjRtL37u5uUlfOzo64unTp9IxhYSEYNGiRQAym8WswwhLliwpfX337l2MHj0ajo6OKF++PARByPXr4EWvS5VKZfA6fJ65h4cHpk+fji+//BJlypRBUFAQypUr98J9+fn5Ye7cuXjy5Ak2btyIwMBA2TKHDh3CvHnzULFiRZQpUwZ6vV6a5+TkZPB6tbe3R0pKChwcHDB+/HjcvHkTVatWxbNnz6QGPzIy0uDnmfVGLZGRkbhy5YrBUEtRFJGQkAAAsn+PRERERGTa3srH8S4uLggLCzN4kxkdHQ13d3cAyPHszsukpqZiwYIFOHr0KGxsbHDr1q1c3ZXSxcUFEREREARBerMcFRWFUqVKvVYd2XF2dkZQUBCqV69uMD0iIgJRUVEQRfG1j/u/XFxcEB4ebtBcxMTEwM3NzaAxdHFxkZ15jIqKkporOzs7xMXFSTlERUUZLPt8OGZCQkKOwzCBzDNf69evlz3qIiIiAleuXMF7770nTXv69KnUpN2/f1+qxdnZGRMnToSvr2+2+8ia3ZIlS9CnTx8EBAQAgDTk9WVe9rp8kefXrW3atAlDhgzBvn37Xri8Wq2Gv78/fvnlF5w7dy7b6/KmTJmCzZs3o1SpUkhKSsIPP/zw0jrOnDmDqKgo6eYs69atw5UrVwBkDtGMi4uTji8yMlJar3jx4mjYsCGWLVuW7XazNpFEREREZPreyru3zp07Izg4WBo6dvjwYaSkpKBmzZpvtF29Xg+dTofk5GTodDrZ88ns7e0RGRkpO3tTqlQplC9fHkuWLIEoisjIyMD8+fNzvBnI6/Dz88PSpUuls2rXrl1DTEwM3N3d4enpiaVLl0IUReh0OoPhlq+jc+fOmDlzJpKTkwEAP//8MypXrmxwlhPIvFnJ2bNncerUKQBAbGwsVq9ejQ8//FCaHxwcDJ1Oh6SkJIMbvgBA69atceLECYSGhkrDObMzYMAAXLhwAatXr5auV3vw4AFGjBiBUaNGwcrKSlp2+fLlyMjIQHp6OoKDg9GlSxcAmfmtXLkST548AQD8/fffuHfvXrb702q10g1Djh8/bpCnvb291Ej/9w6Pr/u6jIyMRFhYGJRKJRo1apTru24GBgZi2bJl8Pf3z/aMbdbj+Omnn3LV+Gu1WqSmpkpDM7PefVOj0WDx4sVITU1FRkYGFi9eLM2rU6cO7ty5I133l5aWJl1rSURERETm562csQsICEBCQgICAwOh1+tRsWJFLF68+I3PCtjZ2WHw4MHo1KkTihYtio8//hi///67NL9fv34YOnQofHx8ZGdI5s6diylTpqB58+awsrJC+/bt0atXrzeqJ6vAwEBERkaibdu2UKlUqFSpklTDvHnz8O2338LHxwdqtRqff/45KlSo8Nr7GjBgABYuXIgOHTpAoVCgbt26mDlzpmy5okWLYtmyZZg6dSomTJgAe3t7DB06VBoyOWrUKHz99dfw8fFBiRIl0K9fP4OzRnZ2dvDy8oK7u7vBdXL/ZW9vj3Xr1mHOnDnw9fWFQqGAo6MjPv30U9nD0t955x20bdsWgiCgbdu20s+gefPmuHfvntToeXp6YsqUKdnub/DgwRg1ahR++ukn+Pj4oEGDBtK8Nm3a4OOPP4afnx9++eUXg/Ve93WZnJyMzz77DBkZGShevDhmzZr1wuWf8/T0xLvvvis10v81btw4DBgwAEWKFMHAgQNzHK6blbe3N3bu3InmzZvD3d0dvr6+0rV5ffv2le6o6ejoiGHDhklnFtVqNYKDgzF58mQ8evQINjY2GDBgQK6Og4iIiIhMj0Lkg94oB8ePH8cvv/xiMFyvY8eOmDt3bo53vXwVGo0GGzZsMLhejvLPnTt38OWXX2L37t35up/Q0FBAFFH20v8gPIp8+QqFgAhAEPRQKlXImwHYBQNzkXubmSid3WDbdSSURWzyeU9vRhD0iIkOg2tJTyiVOX+oWJgwEzlmIsdM5ARBj/Cwu/Dw9CqQmfCWdyS5dOkSypQpAwcHB+mZbwMHDpTmHzhwAE5OTnnS1FH+e36H0JIlSyIpKQnfffcdOnfu/Nb2b/uh/JEbRGRiFCqkpgsw9U947Z3ckaYFAOFlixYazESOmciZeyYqJVDEktf95xYbO5L8/fffGDlyJHQ6HWxsbNCjRw/p5iX9+/dHQkICFi5caOQqKbfi4+Px5ZdfIjU1FUWKFMnz4cYvIkKBJSeTEPP05XcnLRxECKIApUIJ8NxUFsxF7u1mkpQu4EmK/uULGpWY5WZnfJ1kYiZyzETOvDPxcLLEuHYc1fUqOBSTiPJUaGgoRBGY/4c97sdpjV2OiRCh1+v/uTbV/P645h/mIsdM5JiJHDORYyZy5p1JORc1Fvf2hE2RvDtjV9CHYvLcJhERERERkZljY0dERERERGTm2NgRERERERGZOTZ2REREREREZo6NHRERERERkZljY0dERERERGTm2NgRERERERGZOTZ2REREREREZo6NHRERERERkZmzMHYBRFQweTipASiMXYaJECEIApRKJZhJVsxFjpnIMRM5ZiLHTOTMOxMPJ0tjl2B22NgRUT4QMa6dK8zxD0n+ECGIApQK8/zjmn+YixwzkWMmcsxEjpnImX8mKo4tfCVs7IgozwmCHlaW+OdTQhIEPWKiI+Ba0pOZZMFc5JiJHDORYyZyzESOmRQ+/CkTUZ4TRdHYJZgcrTbD2CWYJOYix0zkmIkcM5FjJnLMpHBhY0dERERERGTm2NgRERERERGZOTZ2REREREREZo6NHRERERERkZljY0dERERERGTm2NgRERERERGZOTZ2REREREREZo6NHRERERERkZljY0dERERERGTm2NgRERERERGZOTZ2RJTnFAqFsUswOZaWamOXYJKYixwzkWMmcsyEiP7LwtgFEFHBo1SqkCroAEFn7FJMhp2zKzPJBnORYyZyzETOFDJRKZSwUlkabf9EZIiNHRHli28u70VYSoKxyzAJoggIgg5KpQV4MvNfzEWOmcgxEzlTyMTT1hGTa/obZ+dElC02dkSULx4mJ+BucpyxyzAJoggIeh2UKr4xzYq5yDETOWYix0yIKDu8xo6IiIiIiMjMsbEjIiIiIiIyc2zsiIiIiIiIzBwbOyIiIiIiIjPHxo6IiIiIiMjMsbEjIiIiIiIyc2zsiIiIiIiIzBwbOyIiIiIiIjPHxo6IiIiIiMjMsbEjIiIiIiIycxbGLoCICqbSto5QKIxdhWkQRUAQ9VAqVMwkC+Yix0zkRBEQBB2USgtm8g9TyMTT1tE4OyaiHLGxI6K8pwAm1/Q3dhVE9KpMtHESBAFKJQcZZWUKmagU/JkQmRI2dkSU5xQigP2rISTEGrsUkyAiy1kYYxdjQpiLnDEzUTqWgLVfbyjV1m95zy8mCHrExIbBtaQnlEqVscsxCcyEiLLDxo6I8oX2zl8QYh4YuwyTIALQ63VQqSzYwGTBXOSMmYnKtSzg2+st7zV3tNoMY5dgcpgJEf0Xz6EXYmfPnkWvXr2g0WjQrFkzDBkyBA8ePMjVuuHh4WjVqhUA4MmTJxgwYAAEQcjT+nr16gVvb280a9YMbdq0wenTp99oe1lr/q+RI0fm+thfRVBQELy9vaHT6WTzMjIy0KhRIyxZsiTP90tEREREhQsbu0Lq5MmTCAoKwrBhwxASEoJjx46hU6dO+PjjjxEZGflK23JwcMCKFSvyZax/cHAwjh8/jrlz52Ls2LFISUnJ830AwNy5c1G2bNl82XZycjKOHTsmm37w4EFotdp82ScRERERFS5s7AohURQxZcoUTJ06FfXr1wcAKBQKtGrVCt26dcO8efOMXKFctWrV4Orqmi9n1fJbkyZNsHnzZtn0jRs3olGjRkaoKPdEUYQoisYug4iIiIhego1dIXT9+nVYWFjg/fffl8378MMPERISAkEQ8Mcff6Bv376YN28eNBoN/Pz8cPnyZdk6WYc4Pv963bp18PX1hY+Pj8HZqsjISHz88cfQaDTo2bMnwsLCcl33s2fP4OTkBCBzGGnnzp3h4+ODgIAA3LhxAwByXXN6ejq6du2K1atXAwA0Gg2io6Olr3fv3o2OHTuicePGWLRokbRecnIygoKC4OPjg7Zt22LRokXo27fvC+v28fHBrVu3EBUVJU27e/cuUlNTUblyZWlaUlIShg8fLg2NXblypTTvRTXllAUAnDhxAu3atUOzZs0QFBSE7t2749y5c1IGEydORIsWLeDv7y8NdX3+M1yyZAkaN25ssD0iIiIiMk1s7Aqhv//+G15eXtnOc3JyglKpxJMnTwAAf/31F6pXr46QkBB8/PHHmD59+ku3HxMTA51Oh4MHD+Lbb7/FhAkTAGTemnnIkCHo3r07QkJC8NFHH2HixIkv3Z4gCNi0aRMqVqyIkiVLAshsShYuXIhjx46hR48emD9/vrT8y2oWRRFBQUGoU6dOjk3ZiRMnsGXLFuzcuRPr1q3DvXv3AACzZs2CtbU1jhw5gk2bNuHMmTMvrV+lUqFTp07Ytm2bNO2XX35BYGCgwdmwtLQ0dOjQAUeOHMH27duxfPlyxMXFvbSmnLKIjIzEV199hXnz5uH48ePw8fHBxYsXpe3Nnj0bNjY2OHz4MBYvXoyvv/4aSUlJAIDHjx/DwcEBZ86cQaVKlV56jERERERkXGzsCiG9Xv/C6+EUCgUsLDJvmOrp6Qk/Pz8AQPv27XN19sbS0hK9e/cGAHh7e0Or1SI+Ph6XL1+Gvb09WrZsCQDw9/fH9evXkZGR8529hg8fjkaNGmHGjBlSHUDm8EZXV1fcunULWq0W9+/fl+a9rObvv/8eQOaNTXLSp08fWFpaokSJEnj33Xdx8+ZNiKKIvXv3YuTIkVCpVLC3t0e/fv1emgcAdOvWDTt27IAgCEhLS0NISAj8/Q2f8+bs7AyNRoOYmBjcuHEDDg4OePjw4QtrelEWe/bsQceOHaXGrHXr1tIZQkEQsHv3bnz55ZdQKBQoW7YsqlevLp3dFAQBXbt2BZDZmBIRERGRaePjDgohDw8P6WzPfyUkJMDCwgJFixYFABQvXlyaZ2Nj88Im7LnnZ/2es7e3R0pKCiIjI3HlyhVoNBppniiKSEhIgKura7bbCg4ORv369RETE4Phw4dDrVajTZs2WL9+PdatW4eqVavCycnJ4K6TL6o5ISEBq1atwt69e6FQ5HwzcWdnZ4P6U1NTER8fD1tbW9jZ2RnMy42SJUuiUqVKOHnyJB49eoRmzZrBxsbGYJm7d+9i9OjRcHR0RPny5SEIgsFxZVcTgByziIyMRPXq1Q328bze+Ph4JCcno02bNtK8tLQ0tG3bFqVLl0bx4sWl5p6IiIiITB/fuRVCNWrUQFJSEv744w80bNjQYN7mzZvh6+ubL/stXrw4GjZsiGXLlr3yuq6urvD398eFCxfg4+ODBQsW4OjRo7CxscGtW7cQEhKSq+04Ojqif//++Oyzz7B+/XqDJu1l7O3t8ezZM2RkZECtVgOAwXVzLxMYGIitW7fi0aNH+Oabb2TzlyxZgj59+iAgIAAAcP78+ZduMzU1NccsihYtithYwweEP7/jqYODA+zs7HDkyBFZgxseHp4vdzglIiIiovzDd2+FkIWFBSZMmICvv/4aoaGhADKH3v3666/Yvn07vvjii3zZb506dXDnzh2cPXsWQOYZotxcowYAiYmJOHDgAOrUqQO9Xg+dTofk5GTodDrpBii5FRgYiMaNG2PIkCG5OgP5nFqtRr169bB48WKIooi4uDisW7cu1+s3adIEt2/fhiiKqFKlimy+VqtFYmIiAOD48eO4c+fOS7f5oiw0Gg02bdqE8PBwAMDatWsRHx8PIPM10LBhQ6xYsUK68+Xx48dzfSxEREREZFrY2BVSLVq0wDfffINvv/0WzZs3R4sWLXDo0CGsXLnSYMhfXlKr1QgODsZ3332H5s2bo0uXLoiJiXnhOs/vEtm5c2c0adIEbdu2hZ2dHQYPHoxOnTqhQ4cOqFu37ivXMmbMGDg4OGD06NGv9GD1adOm4fLly2jSpAmGDRuGLl26wMrKKlfrKpVKdO7cGd26dct2/uDBg/HLL7+gefPmOHbsGBo0aPDSbb4oi9q1a2PgwIHo2bMnWrZsKd2F09raGgAwadIkXL58GT4+PvDz88Nvv/2Wq+MgIiIiItOjEPmQKqLXtn79ety/fx9ff/21sUt5KUEQ0LRpU+zZswcODg75tp/Q0FBAFFH20v8gPHq1h90XVCIAQdBDqVQh5ys7Cx/mImfMTJTObrDtOhLKIjYvX/gtEgQ9YqLD4FrSE0olb+YEMJPsMBM5ZiInCHqEh92Fh6dXgcyE19gRvYI///wT1atXh42NDR48eIDVq1eb5APdgcxHFkRFRaFGjRrQ6/VYsmQJatSoka9NXVa2H458K/shojykUCE1XYCpfeJr7+SONC0A5H6ERUHHTOSYiVx+Z6JSAkUsOQDQVLCxI3oFoaGhGDNmDIDMm5OMHDkSNWvWNHJV2dPpdPj2228RGRkJS0tL1KtXL1fPIcwLIhRYcjIJMU91L1+4UBAhiAKUCiXAc1NZMBc542aSlC7gSYr+re/3xUQIgvDPTZ34OsnETOSYiVz+ZuLhZIlx7Urm+Xbp9XEoJhHlqdDQUIgiMP8Pe9yP0xq7HBMhQq/X//NMQL7h+BdzkWMmcsxEjpnIMRO5/M2knIsai3t7wqaI+ZyxK+hDMc3nJ0FERERERETZYmNHRERERERk5tjYERERERERmTk2dkRERERERGaOjR0REREREZGZY2NHRERERERk5tjYERERERERmTk2dkRERERERGaOjR0REREREZGZszB2AURUMHk4qQEojF2GiRAhCAKUSiWYSVbMRY6ZyDETOWYix0zk8jcTDyfLPN8mvRk2dkSUD0SMa+cK/nF9ToQgClAq+IbDEHORYyZyzESOmcgxE7n8z0TFsX8mhY0dEeU5QdDDyhL/fEpIgqBHTHQEXEt6MpMsmIscM5FjJnLMRI6ZyDGTwoc/ZSLKc6IoGrsEk6PVZhi7BJPEXOSYiRwzkWMmcsxEjpkULmzsiIiIiIiIzBwbOyIiIiIiIjPHxo6IiIiIiMjMsbEjIiIiIiIyc2zsiIiIiIiIzBwbOyIiIiIiIjPHxo6IiIiIiMjMsbEjIiIiIiIyc2zsiIiIiIiIzBwbOyIiIiIiIjPHxo6I8pxCoTB2CSbH0lJt7BJMEnORYyZyzESOmRDRf1kYuwAiKniUShVSBR0g6Ixdismwc3ZlJtlgLnLMRI6ZyJlCJiqFElYqS6Ptn4gMsbEjonzxzeW9CEtJMHYZJkEUAUHQQam0AE9m/ou5yDETOWYiZwqZeNo6YnJNf+PsnIiyxcaOiPLFw+QE3E2OM3YZJkEUAUGvg1LFN6ZZMRc5ZiLHTOSYCRFlh9fYERERERERmTk2dkRERERERGaOjR0REREREZGZY2NHRERERERk5tjYERERERERmTk2dkRERERERGaOjR0REREREZGZY2NHRERERERk5tjYERERERERmTk2dkRERERERGbOwtgFEFHBVNrWEQqFsaswDaIICKIeSoWKmWTBXOSYiZwoAoKgg1JpwUz+YQqZeNo6GmfHRJQjNnZElPcUwOSa/saugqhgKoTNjSAIUCo5yCgrU8hEpeDPhMiUsLEjojynEAHsXw0hIdbYpZgEEVnOwhi7GBPCXORelonSsQSs/XpDqbZ+26UZjSDoERMbBteSnlAqVcYuxyQwEyLKDhs7IsoX2jt/QYh5YOwyTIIIQK/XQaWyYAOTBXORe1kmKteygG+vt1yV8Wm1GcYuweQwEyL6r0J1Dr1Xr15o06YNdDqdwfTx48dj+/btRqrK0B9//IG+ffu+1X0uXLgQS5YsyXG+RqNBdHR0rrY1f/58+Pj4YNWqVXlVnllbuHAh3nnnHTx58iTb+QEBARg/fvzbLYqIiIiICpxC1dgBQEpKCjZu3Pha6546dQozZ87M44oKjnv37uHXX3/FwYMH0a9fvzzZZrt27fJkO1k9fvwYvXv3zvPt5kSlUuF///ufbPrFixcRERHx1uogIiIiooKr0DV2gwcPxtKlS/Hs2bNXXjc2NhaJiYn5UFUmQRDybdtvQ1xcHNzd3aFWq2XzXvfY7t27l+M8URRfa5upqamIiop6rXVfZ59NmjTBli1bZNM3btyIRo0avVYdb9Pr5kxEREREb0+ha+wqVKgAPz+/Fw49/PXXX+Hv7w+NRoOZM2dCEASsW7cOs2bNwq+//gqNRoPLly+jcePG0pvec+fOoVGjRtL3GzZswJw5cwAAt27dQp8+faDRaODn52fwJj8oKAhLly5F586dMXz4cIM64uPj4efnh/3792db582bN9GjRw/4+PjAz88PZ86cAQCEh4ejVatWWLduHXx9feHj44Njx45J64WFhaFv375o1qwZAgMDX6nJ6dWrFzZt2oRu3brh/fffx8SJEyGKIkJCQjBixAhcuHABGo0GoaGhsmMTRRETJkyARqNBs2bNMGPGDGm7+/fvR/v27dGkSRMMGTIEcXFx0Gg00Ov10Gg0UpbVqlXDtm3b0KRJExw5cgS9evXCuXPnpO0sWbIECxcuNNhuhw4d4OPjg8DAQISGhqJ79+6IjIyERqPBunXrsh3+Wq1aNYOvs+4zPT0dEydORIsWLeDv74/Tp0+/MLMqVarAysoKFy5ckKY9ffoU586dQ9OmTaVpL8onp9yBnF8HABAaGoouXbqgWbNm+PTTTzF06FDp7KFer8f8+fPh6+sLPz8/g7OK/z1mIiIiIjJthfLmKV988QXatWuHjz76CGXKlDGYFxoaikWLFmHt2rVwcHDAZ599hj179qBnz56wsbHB+fPnMW3aNABAyZIlcfPmTVSpUgUnT56EhYUFbt++jUqVKuHs2bPo1q0bkpOTMWjQIEydOhXe3t6IiYlBv3794OHhIZ2tOXLkCFatWgVbW1ucP38eAJCRkYHPP/8cPXr0QOvWrbM9juTkZEycOBGVK1fG6dOnMW3aNOzZswcAEBMTA51Oh4MHD+LUqVMYN24cTp48CUEQMGTIEHz66afw9/fHvXv30KNHD/TqlfuL8ffv34+ffvoJAPDBBx/g9OnT0Gg0sLW1xdKlS7F69Wpp2azHJooi6tSpg0mTJiE9PR2dOnVCu3bt4OXlhUmTJuHXX3+Fk5MTHj58CBcXF4SEhKBatWoICQmRticIAqKjo3Hy5EnodDqsWbMmxzrPnDmDhQsXYvny5fDw8MC9e/dQvnx5bNiwAf369cOhQ4cAZF7X+CL/3eeMGTNgY2ODw4cP4++//0a/fv2we/du2NnZ5biNwMBAbNmyBXXr1gUA7NixA+3atYNK9e/dzHLKp2bNmjnm7u3tnePrIDk5GUOGDMG8efPQoEEDXLhwAf369UOLFi0AAGvWrMHff/+NvXv34tmzZ+jWrRvq1asHDw8P2TETERERkWkrdGfsAMDR0RGDBg3Cd999J5u3ZcsWfPLJJ3B2doaFhQW6du2KEydOZLsdb29v/P777wCAs2fPomfPnlKTcOXKFdSrVw/Hjx9HnTp14O3tDQBwdXVFv379pAYMAFq2bIlixYrBwuLfPvvrr79G7dq1X3gtWN26dVGxYkXcu3cPycnJePDggTTP0tJSWtfb2xtarRbx8fEIDQ2FtbU1/P0znzFWvnx5tG/fPjexSQIDA2FnZwc7Ozs0b94cN27cyHHZrMemVCrRuXNnPHnyBKGhoXB0dMT9+/ehUCig1Wql7ZQuXTrH7YmiiA8++AAADPLKzpo1azBixAh4eHhIx/o6su5TqVRi9+7d+PLLL6FQKFC2bFlUr14dly9ffuE2/P39cerUKWko75YtW9C1a1eDZXLK57mccs/pdXD8+HHUr18fDRo0kJZ7/joEgM2bN2PUqFGwtLRE8eLF0aJFC+ls36vkTERERETGV2jfsXXv3h0bN27E2bNnDaZHRkYiJCQEixYtApA5XC3rsLysmjRpgpUrV6Jdu3YoVqwYmjVrhkWLFqFx48YoX7481Go1IiIi4OnpabCeu7s7Dh48KH1fsmRJg/nXr1/H9evXXzoE7tChQ5g3bx4qVqyIMmXKQK/XS/OcnJwMHlxqb2+PlJQUREREyM5S2tvbv3A//1W8eHGDdVNTU3NcNuuxxcXFYfjw4VAqlahYsSIyMjKg0+lgbW2N5cuXY/r06bCyskJQUBDeeeedbLenVCrh6uqaqzrDwsLg5eWVy6PKWdZ9xsfHIzk5GW3atJHmp6WloW3bti/chrW1Nfz8/LBr1y54eXnB3d0d7u7uBsvklM9zOeWe0+sgIiJC1iRn/VlHRkYafHCQkZEhDUl9lZyJiIiIyPgKbWNnYWGBoKAgzJw5E1WrVpWmOzs7Y+LEifD19X3pNmrXro0bN27g2LFjaNKkCSpXrox79+7h999/x/vvvw8AcHFxkZ3RioqKgpubm/S9QmH4tKKqVauiZs2aGDZsGJYtWwZLS8ts9z9lyhRs3rwZpUqVQlJSEn744YeX1uzg4ICYmBiDaZGRkbLmM69kPbZ169bh/fffx5AhQwAAQ4cOlebVr18f27dvx5EjRzBgwACcPHky25uw/DcrGxsbg8YyMTERNjY2ADKb24iICJQtW/aFNf53G0lJSQZNctZ9Ojg4wM7ODkeOHJHV8jIfffQRRo8ejfLly6Nbt26y+S/K50Vyeh0ULVrU4IwfkPmzfs7Z2Rnbt2+Hg4ODbJuvemxEREREZFyFcijmc02bNoWzszOOHz8uTfPz88PKlSul5479/fff0p0ZixYtKt1oRKfTwdLSEtWqVcO6devQtGlTKBQKVK1aFVu3bpWGvGk0Gpw9exanTp0CkHlnzdWrV+PDDz98YW0jRoyAvb09xo0bl+NdCbVarTS076effsrVm/HatWvjzp07OHnyJADg0qVLBjdWyU8ZGRlSvVevXpWG/T19+lRqfuvVqweFQiEds42NjXS9YHaeX98IAM+ePcOBAwekeQEBAQgODkZ8fDwASPuwt7dHQkIC0tLSoNPpULZsWTx48ACxsbEAgK1bt+Z4DBYWFmjYsCFWrFgBURQhiqLB6+dFvLy8YGtri0uXLsHHxyfX+bxMTq8Db29vHDlyBNeuXQMAHD58GFeuXJHW8/Pzw4IFC6Rs//jjjxeefSUiIiIi01Voz9g9FxQUhA4dOkjfN2/eHPfu3UOXLl0AAJ6enpgyZQoAoHHjxvjxxx+h0WiwePFiVK1aFd7e3lizZo10xqtRo0Y4f/68NASwaNGiWLZsGaZOnYoJEybA3t4eQ4cORY0aNV5Yl0KhwKxZs9CnTx/Mnj0bY8eOlS0zbtw4DBgwAEWKFMHAgQNzPLOXlY2NDYKDgzFlyhTpOr7n11Llt169emHo0KFo1qwZateujZYtWwLIbJJHjx6Np0+fwsHBAZMmTUKRIkUAAAMGDECXLl3QtWvXbM9g9enTByNHjkTPnj1RrFgxg2vIunTpgoSEBHTt2hWCIKBGjRpYsGABihUrhnbt2sHX1xefffYZunXrhjFjxqB///4oUaIEmjRpYnBTk/+aNGkSJk6cCB8fHxQpUgTNmzdHs2bNcpVBYGAg7t+/n+32c8rnZXJ6Hbi7u2Py5MkYNmyYdHdRHx8fWFtbA8i8idC3336LFi1aQK1Wo06dOqhTp06u9pkbKmc38LxfJhGAQtBDqVQxkyyYi9zLMlE6u2UzteCztJSP4CjsmIkcM5FjJoWLQuRDqogKjQ8++ACTJ09G9erV820foaGhgCiieqUK+bYPokJNqUK6aInC88dbhCAKUCqUAD8C+AczkWMmcqaViUoJFLE07mBBQdAjPOwuPDy9oFTm/CG+uSr0Z+yICqr09HRcvHgR7733HkRRxLZt26DVanO8GVBeEqHAkpNJiHnKRyVkMq0/rqaDuci9PJOkdAFPUvTZziuYRAiC8M8Nwfg6ycRM5JiJnOlk4uFkiXHtSr58QXojbOyICrDly5dj9OjRsLS0ROXKlbF48eK3dmOU8w9ScD9O+1b2ZfpE6PX6f4bg8g3Hv5iLHDORYyZyzESOmcgxk8KGjR1RAVWkSBGsWrXK2GUQERER0VtQqO+KSUREREREVBCwsSMiIiIiIjJzbOyIiIiIiIjMHBs7IiIiIiIiM8fGjoiIiIiIyMyxsSMiIiIiIjJzbOyIiIiIiIjMHBs7IiIiIiIiM8fGjoiIiIiIyMxZGLsAIiqYPJzUABTGLsNEiBAEAUqlEswkK+Yix0zkmIkcM5FjJnKmk4mHk6VR919YsLEjonwgYlw7Vxj7D4npECGIApQK4/9xNS3MRY6ZyDETOWYix0zkTCsTFccJ5js2dkSU5wRBDytL/PMpIQmCHjHREXAt6clMsmAucsxEjpnIMRM5ZiLHTAof/pSJKM+JomjsEkyOVpth7BJMEnORYyZyzESOmcgxEzlmUriwsSMiIiIiIjJzbOyIiIiIiIjMHBs7IiIiIiIiM8fGjoiIiIgoD6RrBaSkv/3/0rWCUY/73t27GPrFZ9BqtXm+7eHDvkBIyJE8325BxLtiEhERERHlAb0AzNgTjfD4vG9wcuLhZIlx7Uq+8noLF34PKysrDBgwSDZv4oTxqFevPgI6dsrVtsp7eWHBwsUG054+eQJ1ETWsrW1eubZXpdfrcfDgfhw+dAjR0VEAgBIlXDHxm8lwdHQEAERHR6FEiRKvtN24uDg4OjrCwsI8WibzqJKIiIiIyAyEx2txP453o/zkk36YMXM2KlSomO/7WvnTCty9exefffYFPDw9kZiYiEuX/oJSkfn8vmPHjmLnju2YNz8419uMi4vDp4M+warVP6No0aL5VHne4lBMIiIiIiLKU4Lw9oaHHjt2FB9174HyXl5Qq9UoXrw4NJoWKObg8Nq1iKJxh7e+Dp6xIyIiIiKiHHXpHIDxX0/EoYMHcOnSXyjm4ICPArujaTMfAMCdO7cxdswobNv+P4SEHMHiRQsAAGPHjAIALF32A0qUcMXt27ewetVK3Lt3F05OTvD1bY0OAR2h+OfM2sOHD7Fy5QrcvHEDtra26NipS67qs7cvips3bqBWrdqyeRMnjMfVq1cAAB9+0BleXuUxc9YcxMTEYMOGdbh6JRSpqamoWLESBn06BKVKlcKmjb9g8+aNAIB+fXsBALZt/x8A4OKFC1i3bi0iIyNQokQJdOr8AXx8mgMAEhOfYfWqlbhw4TzS0tJQvXoNfD3hm9dI/PWwsSMiIiIiohdav+5n9O7TF0OHDceRw4exePFCVKlaFSVKuBosp9G0gEbTAl06B2DW7DnSUMyIiHBMnzYVn3wyEHXr1Ud0dBTmfDcL9kWLQqNpgdTUFEyeNAHNfJrjy+EjodPpsGrVT9I1cy/Sp28/BM+fi/CIcAQGfgQ3N3dp3pSp0xAScgS7/rcT8+YHIzzsLgDgwYP7qFihInr06AUbGxssXrQAK1Ysw8SJk9Et8CM012gw+NOBBkMxr169gkWLFuCLocNQtWo13L17B7NnzYCzszNq1KiJZUuXwNLSEosWL4VeL+D6tat5FX+ucCgmERERERG9UIuWrVCrVm1YW9ugXfsOcHBwwLVr13K9/s4d2+HXug3e924Ca2trlCtXHu3bB+DE8WMAgP379sHFxQW9e/dFMQcHFHd2xhdDh0OlUr10240aNcaMmd8hIT4ew4Z+jqVLFiEpKemF6zRs+B7ate+AEiVKwM7ODm3a+uP2rVsvXGfzpo3o0aMnateugyJFiqBaterQtGiJ4/8cQ3R0NMqVKw9bWzsULVoUDd9rlKts8grP2BFRnns+pIL+ZWmpNnYJJom5yDETOWYix0zoTSkAiIKY7TxRFKFUGZ7/8fLyMvjexaUEnj19muv93bp9C+EhR7Bl8yaD6SVLZt7R8/79e6hR8x2DeUWKFIGzi0uutl++fHlM/XY6Ll64gDVrVmFc0GjM/m5ujnfl1Gq1OHz4IEIvX0ZEZAQexcUhLS3thfu4ffsWrlwJxeLFCw2m13wns+4Pu3bDwgXBuHf/Htq37/BWbhyTFRs7IspzSqUKqYIOEHTGLsVk2Dm7MpNsMBc5ZiLHTOTyKhOVQgkrlWUeVUXmxNrGBo/i4rKd9+RJAuzs7A2m/ffMmVKphChm3xhmx9LCAoMHf4aWrXyznZ+Wnp7tYwV0r/hsvDp166JK1Sr4cvgwHDp0CB06BGS73MwZ06BUKtGufQd4eHji8eNHGBc05oXbtrCwwNigr7K9lg8A3nuvESpXroL9+37F1CmT0aBBA3z+xbBXqv9NsLEjonzxzeW9CEtJMHYZJkEUAUHQQam0AE9m/ou5yDETOWYil1eZeNo6YnJN/7wrjMyKV3kvnDp5AqmpqbC2tpamx8bGIjY2FpUrV36j7SsUCoPGz93DE9evX8uxsXMr5YZbN28aTIuPf4zY2NhX3re1tQ3KliuHp0+fAACUCoXBXS6TkpLw118XsWTpD3B1zbxG8NKlv/5Tf+YZSzHLHTU9PDxw/fq1HBs7AHB0dMRH3XugZStfDBk8EO3adUDZcuVe+RheBxs7IsoXD5MTcDc5+08CCxtRBAS9DkoV35hmxVzkmIkcM5FjJqbNw+ntngF93f2916gxNm36BXPnfofevfvC1dUVEeHh+OGHZWjwbkODG5C8DicnJ1y9cgUeHh6wsLBEhw4BGBc0BuW9vNC0qQ8y0tNx5vczcHV1RYMG78LXrzW+HP4Fdu7YjpatfPHkyROs/GkF7OzsXrqv+fPnwtfXD6VLl4GFhQp/XbyIK6GX0aXLB5m1FC+O2NhYxMREIzk5BSVLWcHKygq/nT6Ftv7tcO/eXezZvctgm8WKFYOFhQWuXAnFO7Vqwd6+KAICOmHBgmCUKuWGBg3exbNnz3DsWAjq12+AChUqYteu/6FBg3fh7OyM8PAwqFQqODo5vVGOr4KNHRERERFRHlApgXHtShplv6/KysoKk6dMw9q1qzHh66+QmpqC4s7O8PZugq5dA9+4pl69+mDlyp+wd+8ezP5uLry8KmDEyNHYtPEXrF2zGkWLFkW16jXQsOF7AAA3NzeMHDUGP/+8Br/8sh5u7u7o06cffl675qX7srSwwMIF3yMhIR62trYoV648gsaNR8WKlQAANWrURP36DTBy5Ai8U7M6xoytieFfjsSa1auwefNGVKpUGYEfdcesmdOlbarVanTv3hPLly+FnZ09lixdjobvNUKfp0+xedMvWLJ4IRwcHFG3bl24umb+zCPCw7Bt6xZotRkoXboMxo37GsWKFXvjLHNLIb7K4FgiopcIDQ2FKIr49umfPGP3D366nj3mIsdM5JiJXF5lUsHOBSvf6wlbiyJ5V5yRCIIe4WF34eHpBaXy5XdRLAyYiVxBz4SPOyAiIiIiIjJzbOyIiIiIiIjMHBs7IiIiIiIiM8fGjoiIiIiIyMyxsSMiIiIiIjJzbOyIiIiIiIjMHBs7IiIiIiIiM8fGjoiIiIiIyMxZGLsAIiqYSts68mHC/xBFQBD1UCpUzCQL5iLHTOREERAEHZRKPqD8ubzKxNPWMe+KIiKjY2NHRHlPAUyu6W/sKogKnkLa2AiCAKWSg4yyyqtMVArmSlRQsLEjojynEAHsXw0hIdbYpZgEEVnOwhi7GBPCXORelInSsQSs/XpDqbY2RmlGIwh6xMSGwbWkJ5RKlbHLMQnMhIiyYzKNXa9evTBs2DDUr1//pctu2LABP/74I1q1aoVx48a9hepebuTIkfjiiy9QtmzZPNvmggULsHPnTgBAREQE3N3dAQAdO3bE0KFDX2lbGo0GGzZsQMmSJfOsvvDwcPTr1w+HDh3Ks22+zPbt2zF16lQ4OjpCr9ejVq1amDBhAlxcXF55W9OnT4evr2+uXnOvIzw8HC1atMDEiRPRo0cP2fzVq1dj9uzZuHbtWr7s39i0d/6CEPPA2GWYBBGAXq+DSmXBBiYL5iL3okxUrmUB315GqMr4tNoMY5dgcpiJaRK16RAF/Vvfr0KpgsKyyGuv36VzAGbNnoMKFSrmYVX0tplMY5dbKSkpmD9/Po4cOYKiRYsarY7+/ftj+vTpcHV1BQDMnTs3z/cxdOhQqYGrVq0aQkJC8nwf5qht27aYNm0aBEHA999/j0mTJmHx4sWvvJ2vvvrqjeqYMGECOnXqhLp16+a4jI2NDbZs2ZJtY7dt2zZYWxeuT96JiIgKMlHQI3nzXAiPIt/aPpXObrDtOpIfkJH5NXbx8fFwcHB4raZOFEUo8ujK6/v370MUxTzZFr0epVKJgQMHolmzZrJ5b+N6jAcPHkAQhBcu4+zsDFEUcfnyZbzzzjvS9N9//x3u7u54+PBhvtb4pnhdCxER0asRHkVCzxErZAQm2dgFBQXBy8sLp06dwt27d1GnTh3MmTMHd+/exaefforHjx9Do9Hgq6++QsuWLbFx40b8/PPPSElJgYeHByZNmgQvLy8AmWe6pk6diuDgYHzzzTc4fPgwSpcujd9++w23b9/GRx99BD8/P4wfPx4xMTFo27Ytxo8fDwA4e/YsZs6cifj4eBQrVgyzZs1ClSpV0KpVK0RHR6Nbt25o0KAB5syZYzDU8cmTJ5gxYwbOnz8PQRDQsmVLjBo1Cmq1Gtu3b8fZs2ehUqlw8uRJODs7Izg4GKVLl36ljNauXYsNGzZAp9OhW7duGDBgAAAgNTUVc+bMwcmTJ5GSkoKBAweid+/eAIDz58/jxx9/RHR0NDp16oQxY8YAyBwG265dO2zfvl0aPjh58mQoFAro9XosX74cu3btQnp6OqpUqYJJkyZJZyqzunXrFqZNm4awsDBYWlrik08+wYcffggA0Ol0mDNnDg4cOABLS0t07doVmzZtwqFDh+Dn54fvvvtOanwuXryImTNnYtOmTS/NIS0tTWryFy5ciJSUFFy5cgUZGRnYtGkTzp07h9mzZ+PRo0ewtbXF8OHD0aJFC+m4nw//TU9Px7Rp03D69GlYWVnhq6++wvvvvw8gcxjs1KlTcevWLej1esyfPx/z58/HX3/9heHDh6NcuXL4+eefc6yxa9eu2LJli0Fjt3HjRnTr1g0nT56Uph04cAALFy5EUlISSpYsiXnz5sHNze2FrxlRFDFx4kScPn0aer0erVu3loYnJycnY+rUqfj9999hY2ODtm3b4ty5c1i9ejUA4I8//sCMGTPw7NkzNGjQAJMnT4aVlRWCgoJQpkwZHDp0CB4eHliwYMFLfw5ERERk/p49e4YN69fh4sULSEx8htKlS+OTAYOkIZqbNv6C+w/uo1nTZtjwy3rExsTA07M0Pu7/CapVqw4AiImJwYYN63D1SihSU1NRsWIlDPp0CEqVKgUAWLjwe1hZWcHFxQV7du9CRkYG3n23IQYM/BRFirz+cFIy4efY7dmzB3PnzsXRo0fx5MkT7Ny5E9WqVcOGDRvg5uaGkJAQtGzZEgcOHMDWrVvx888/4+jRo+jVqxcGDx4MrVYLIPOMQ3R0NE6ePAkfHx8AQEhICJYtW4Z9+/Zh69atmDp1Kn788Ufs378fhw4dkq55Sk9Px8KFC3Hs2DH06NED8+fPBwAcOnQIJUuWxKZNmzBnzhxZ7UFBQfD09MTBgwexf/9+xMbGYunSpdL8w4cP48MPP8SJEyfg7e2N77///pWyOXDggHTcu3btwr59+3D+/HkAmcMDFQoF9u7dixMnTqBp06bSekeOHMGmTZuwa9cubN++Hbdu3ZLm7d+/Hz/99BMOHDiAs2fP4vTp0wCANWvW4NKlS9i2bRuOHj2Khg0bYuTIkbKakpOTMWjQIAwYMAAhISFYu3YtVq1ahTNnzgAAVq5ciYcPH2L//v3Ys2cPQkNDpXUDAgKwf/9+6fu9e/eiY8eOL83h2bNnmD59Orp27SpNO3ToEGbOnIn169cjKioKI0aMwDfffIOQkBAEBwdjypQpuH//vmxbs2fPho2NDQ4fPozFixfj66+/RlJSEjIyMvDxxx+jQ4cOCAkJwd69e+Hh4YGff/4ZtWvXRnBw8AubuufHd/ToUSQnJwMAHj9+jGvXrqFZs2YGZ30VCgXWrl2LY8eOoWHDhvjxxx+leTm9ZkRRRJ06dXDo0CHs27cPx44dk7KdNWsWrK2tpZ/7858FAERHRyMoKAjff/89QkJCUKRIEaxatUqaf+TIEaxatQrz5s176c+BiIiICoboqCg4ODpg0uSp+GnlanhVqIjvgw3fC9y/dw8nThzH+PET8eNPq1GnTl3MmD4NiYnPAAAPHtxHxQoVMX3GbCz/4SdYW1tjxYplBts4+8fvSE1JRfD3izBp0lT89ddF7Nm9660dZ0Flso1dQEAAnJ2dYWlpidatW+PGjRvZLrdlyxYMGzYMTk5OAABfX184Ozvj0qVLADLf+H7wwQcAAAuLzBOU7dq1g52dHZycnPDuu+/Cx8cHTk5OsLe3R/369XHnzh0AQJMmTeDq6opbt25Bq9Vm2xD816NHj3D16lUMGTIESqUSarUaI0aMwJ49e6Rl6tevj9q1awMA2rdvn+Ox5WTz5s0YOnQo7OzsYGNjg44dO+LEiRNISEjAmTNnMHbsWFhaWkKpVBrczKVv375Qq9VwcXFBw4YNpeMEgMDAQNjZ2cHOzg7NmzeXatqyZQvGjh0LW1tbAECfPn0QHh6O6Ohog5qOHz+OOnXqwNvbGwDg6uqKfv36Sce9Y8cOjBw5EkWKFIFarcbgwYOldQMCAnDw4EEAgF6vR0hICPz9c75V/q+//gqNRoOePXuiZs2aGDhwoDSvUaNGcHd3h4WFBXbv3o2AgABUr575CZKXlxc6deqEffv2GWxPEATs3r0bX375JRQKBcqWLYvq1avj8uXLOHbsGCpWrIi2bdsCAOzs7FCiRIlc/JT+9TzTvXv3AgC2bt2KgIAA2RBHX19f2Nra4tq1a1AqlQavt5xeM0qlEp07d8aTJ08QGhoKR0dHaZjw3r17MXLkSKhUKtjb26Nfv37S9nbt2oXOnTujTJkyAIDu3bvjxIkT0vyWLVuiWLFi0r8ZIiIiKvgqVa6MwMDuKFWqFKytbdC+fQdERkYiKSlJWiYjIx3Dho9AyZIlYW9vjx49e8HFxQXHjx0DADRs+B7ate+AEiVKwM7ODm3a+uN2lpMJAODo6ISPuveAnZ0dynt5oUWLVggNvfw2D7VAMtl3bcWLF5e+trOzQ2pqarbLRUZGyoYxurm5ITY28zbrSqVSNmzQ0fHfB3JaW1vD2dlZ+t7Gxgbp6ekAgPXr12PdunWoWrUqnJycoNPpXlp3ZGQkPDw8DN60u7m5IS4uLttjs7e3z/HYXrSPMWPGQKXKvMWxVquFr68vwsLC4OnpCUtLy2zXy9qQ2NnZIS0t7aU1RUZGwtPTU5qnUChQqlQpxMbGSs00kDlcMetyAODu7i41bFFRUVIT8Xz/WZcrVaoUQkNDkZiYiFq1ar3wGsrnN0/JTtafdWRkpNTUZd3X1atXDabFx8cjOTkZbdq0kaalpaWhbdu2iIyMlIb1vomPPvoIEydOxAcffIDt27dj7dq1smXmz5+PkJAQVK1aFSqVyuD1ltPPJy4uDsOHD4dSqUTFihWRkZEBnU6H+Ph42NraGuRsb28vfR0REYEDBw5gx44dADI/AClWrJg0Py/vnkpERESmT6FQQBAEnDh+DBcuXkBEeDji4jLfT6ekJEvvKcqULQsrKyuDdcuXL4+o6CgAme9LDx8+iNDLlxERGYFHcXEG7zkzlzd8b+VSogTOnv0jvw6t0DDZxi63XFxcEBYWZtA0REdHS48GeN2bpaSmpmLBggU4evQobGxscOvWrVzdldLFxQUREREGN52IioqSxhXnBWdnZwQFBcmaloiICERFReXpTWJcXFwQHh6OcuXKSdNiYmLg5uZm8I/UxcVFduYxKioKbm5uADIbubi4OCmHqKgog2WfD8dMSEjI1TDMnGRtqJ+/NnKq6TkHBwfY2dnhyJEjstx27NiB69evv3Y9z1WrVg1KpRJr165FxYoVZR82PHjwAPv378evv/4KlUqFo0ePYuXKlS/d7rp16/D+++9jyJAhACDdRdXe3h7Pnj1DRkYG1Go1AMPMnZ2dMWjQIIOzeFnl1euHiIiITNvzExq2trZY8cMyPHjwAF0++BClS5eBpYUFPvnE8L2CpYX8BEJaWhoc//nAf+aMaVAqlWjXvgM8PDzx+PEjjAsaY7C8ysLw+YtKpZI3JcwDJjsUM7c6d+6M4OBgxMfHA8i8FiklJQU1a9Z8o+3q9XrodDokJydDp9NJN5x4zt7eHpGRkbKzeKVKlUL58uWxZMkSiKKIjIwMzJ8/H926dXujerLy8/PD0qVLpbM2165dQ0xMDNzd3eHp6YmlS5dCFEXodDqD4Zavo3Pnzpg5c6Z0fdjPP/+MypUrG5zlBDKfk3f27FmcOnUKABAbG4vVq1dLN0/RaDQIDg6GTqdDUlISVqxYYbB+69atceLECYSGhkrDOd9U+/btsX37dukM3f3797F792506NDBYDkLCws0bNgQK1asgCiKEEURx48fBwD4+Pjg999/x2+//QYAePr0qdQg2dvbIyoqKldncgGgW7dumDt3LgIDA2XztFottFot0tLSkJqaivXr1+dqmxkZGUhMTAQAXL16VbqOTq1Wo169eli8eDFEUURcXBzWrVsnrefr64uNGzciMjLzdsyxsbGyM5lERERU8EVFRcHCwgLFizvj1KmT6N6jJ+rXb4ASJUogIjJCtnx4eBj0+n+f1afVanHjxnWUK1ceSUlJ+Ouvi/hkwCDUqlUbxYsXR3h4+Ns8nELN7Bu7gIAAtGvXDoGBgWjRogW2bt2KxYsXv/Et2u3s7DB48GB06tQJHTp0kD2rrF+/fhg6dCgmTZokW3fu3Lm4ffs2mjdvjg4dOsDLywu9euXdQ2UDAwNRunRptG3bFi1btsSiRYuk4Zfz5s3DjRs34OPjgzZt2rzxm/UBAwagcuXK6NChA1q2bInQ0FDMnDlTtlzRokWxbNkyLFq0CM2bN8cnn3yCoUOHokaNGgCAUaNGITU1FT4+Pujduzfat29vcBrfzs4OXl5eaNq0qTTE9E2VLl0as2fPxvjx46HRaDBmzBjMmDFDGmYoiqJ0DdmkSZNw+fJl+Pj4wM/PT2rkHB0dsXTpUnz//fdo1qwZevbsiadPnwIAevTogdmzZ+PTTz/NVT3+/v6oWLGidLfNrCpWrIgWLVrAz88PgYGB0o1+XqZXr174888/0axZM/zwww9o2bKlNG/atGm4fPkymjRpgmHDhqFLly5S5pUrV8aQIUPQr18/aDQafPHFFwa/pImIiOj1KJ3doHIt+9b+Uzq7vbyobCTExyMxMRGbN/0CH5/msLS0hIOjI37//QxSU1Px4P59bN60UbbekydPsOKH5Xjy5AkeP3qEJYsXws7OHg0bvgcrKytYWVnht9OnkJ6ejuvXr/GmKG+RQuR5TzKC48eP45dffsGyZf/eJaljx46YO3dunlzTlhvt27fH0qVL4eHh8Vb2Z2zr16/H/fv38fXXX+frfkJDQwFRRNlL/3urD2g1ZSIAQdBDqVTxAbJZMBe5F2Xy/CHEyiI2xijNaARBj5joMLiW9IRSmTcf/Jk7ZiJnKpmI2nSIwtv/oFShVEFhafiogJdlMmnSBNy8cQN169bDkM8+h62tHW7evIEfli9DREQ4Spcugz59+2HihPFYuuwHlCjhik0bf8Hdu3dQoUJF/PrrHmi1WtR8pxb69x8AFxcXAMCff57FmtWr8PjxI1SqVBn+7dpj1szp2Lb9fwD+fdzBgAGDpFpCQo5g1/92Ivj7hfmYUmYm4WF34eHpVSD/7bCxo7fi0qVLKFOmDBwcHBAXF4fBgwdj4MCB8PX1BZD5CIdNmzbl6rqyNyEIAgRBwJ07dzBixAjs3bu3wF5P9ueff6J69eqwsbHBgwcPMGDAAMybN++Nhym/zPPGrnqlCvm6H6JCSalCumiJwvWHW4QgClAqlAA/AvgHM5EzjUwU0v/eMhHZ/F7I+0y2b92I+/fuYOSYvPuQWKUEili+nUGEBb2xM/ubp5B5+PvvvzFy5EjodDrY2NigR48eUlPXv39/JCQkYOHC/P2UBsi8KU7Hjh1hbW2NmTNnFtimDshssJ4/hL5o0aIYOXJkvjd1z4lQYMnJJMQ8zd31hwWfabzhMD3MRe7FmSSlC3iSUtiGTYtZbkjG10kmZiLHTOTyPpO0208hPE3DZ2vDXr5wLng4WWJcO96JO6/wjB0R5anQ0FCIIjD/D3vcj9MauxwTIUKv1/9z/SjfcPyLucgxEzlmIsdM5JiJXN5nYh1xABYp4Uis2D9PtlfORY3FvT1hU4Rn7PKC2d88hYiIiIiIqLDjUEwiIiIiInqpVHc/Y5dAL8AzdkRERERERGaOjR0REREREZGZY2NHRERERERk5tjYERERERERmTk2dkRERERERGaOjR0REREREZGZ4+MOiIiIiIjygLOdCvbWb//B14mpejxK0r/1/ZJpYWNHRERERPSGnO1UCO5dCmrLt7/vDC0wfG1UnjZ3lk+uwjr6GJ5V+SzPtkn5i40dEeULDyc1AIWxyzARIgRBgFKpBDPJirnIMRM5ZiLHTOSMn4mHoyWs1ApMuLQHD5Li39p+y9o5YWqtdqjqZoXwBG2WOS/ORBQFaB+eQkbYGQgpcQAApXVx2Lz7GZRFigIudYCKdVA8H2v3cDJCF1yAsbEjonwgYlw7V/ANx3MiBFGAUsE3YYaYixwzkWMmcsxEzviZKBSAXqHFY91TxOjeXmNnr1PBylKBrzqUhChmnfPiTFavXI4HGffQd9yXcHP3RFJSIq6EXkKt2u4oVszhLVUPqHjHjzzDxo6I8pwg6GFliX8+JSRB0CMmOgKuJT2ZSRbMRY6ZyDETOWYiZyqZJOsyGzzFW+wtn+/P2tLwuF+WyemTxzBm7DhUq1IRAOBgbwWPUi3fRslGIQiCsUvId2zsiCjPiYYfGRIArTbD2CWYJOYix0zkmIkcM5FjJnIvysTevihu3riBWrVqZzv/zG+nsWbNaixbvgIAsHDh97CysoKLiwv27N6FjIwMvPtuQwwY+CmKFCnyz/60WLduLU6eOI7U1FTUql0HVatUxdGjIQj+fiEAICYmBhs2rMPVK6FITU1FxYqVMOjTIShVqpS0H1sbW7i5uWHnzh148iQBFSpWxMCBg1G6dGmpvkuX/sKWzZtw9+4dKJVK1KhRE3379Ze2ExJyBLv+txOdO3fBqlU/oVq16hg5ajTi4uKwZs0GXLt2FUWLFoN3kyYIDOwOS0vzHxbKj3mIiIiIiAqZPn37Ydu2LZg3bw4iIyNytc7ZP35Hakoqgr9fhEmTpuKvvy5iz+5d0vxVK39C6OXLGP/1RKxctQbe3t7Yvn2bwTYePLiPihUqYvqM2Vj+w0+wtrbGihXLDJb589xZ3H9wH9NnzMTSZSvgWsIVM2Z8C6028xrC0NDLmDljGpo0bYoVP65C8PeLYGdnhymTJyIj499mNjExERf/uogFCxdj0KdD8OzZMyxevBx169XDTyvX4JtJU3D1yhVs37b1dWM0KWzsiIiIiIgKmUaNGmPGzO+QEB+PYUM/x9Ili5CUlPTCdRwdnfBR9x6ws7NDeS8vtGjRCqGhlwEACfHxOHLkEIYPHwEvrwqwtraBt3dTaFq0MNhGw4bvoV37DihRogTs7OzQpq0/bt+6ZbBMEXURDBo0GE5OxeHo6IhBnw5BWmoaLlw4DwDYsmUTWrduCz+/NrCzs4OLiwuGfPYFoFDgxInj0nYSE5+hT59+sLcviqJFi+LAgf2oWLEC2rRpCxsbG7i5ueHDrt1w/PixPEjU+DgUk4iIiIioECpfvjymfjsdFy9cwJo1qzAuaDRmfzcX1tY2OSzvZfC9S4kSOHv2DwDA3w//hq2tLUqXKWOwjKeHJy5euCB9r9VqcfjwQYRevoyIyAg8iotDWlqawTqVKlUyuC5QrVbDs7QnoqOiAAB379xBp05dDNZRqVSoVKkywsIeStOKFy8OBwcH6fvbt2/h4oUL+PCDzgbrFpRrVdnYEREREREVYnXq1kWVqlXw5fBhOHToEDp0CMh2OZWF4cPXlUqldF19akoKLC3VsnW0Oq3B9zNnTINSqUS79h3g4eGJx48fYVzQGINlsrveLS01DTa2tgAAvV7/z90+DSkVCuh0Oun7IkWsDOZbWFigVasWGDjoMyiVb/9B8vmtYLSnRERERET02qytbVC2XDk8ffrktdYvVcoN8fGPERsbazD9xvXr0tdJSUn466+L+GTAINSqVRvFixdHeHi4bFt/P/zb4PunT54gLOwhypUtBwAoV648Ll3+y2AZvV6PmzdvokKFijnW6OHugbt3773qoZkNnrEjIiIiIsojnraOZrG/+fPnwtfXD6VLl4GFhQp/XbyIK6GX0aXLB6+1vbLlyqFKlaoInj8Xn33+BYoVc8DRo0dw7do1WFtbAwCsrKxgZWWF306fQlv/drh3767BzVeeu3vnDjZv2oi2/u2QmJiI5cuXoFq16qhQMbNp+7BrN3w3eyZcXV3x/vtNkJaWik0bf4Farcb773vnWKNf6zbYu3c3Nqxfh3btA6AAcOHiBej1erRs2eq1jtuUsLEjIiIiIsoDKoUSk2v6G2W/r8rSwgILF3yPhIR42Nraoly58ggaNx4VK1Z67Tq+HDESP/ywHKNGfgkrKys0a9YcnTp1xsGDBwBkDoUc/uVIrFm9Cps3b0SlSpUR+FF3zJo53WA7Pj7N8fTZU3w66BMoFEo0aNAAH/cfIM2vW7ceRo4ag82bfsHKn36EtbU16tVvgMlTvoVaLR8O+lzx4sUxcOAnOHDgMHbv3gVbW1tUrFgJ3Xv0fO1jNiUKkQ+cIqI8FBoaCp1Oi1q1ahXI8euvQxD0CA+7Cw9PL2aSBXORYyZyzESOmcgxEzlTyWTTxl9w9doVTJkyLVfLP39e3oABg/K8FlPJJL/wGjsiIiIiIsoX58+fe6OzgJR7bOyIiIiIiOiN/fTTCly9egWpqamIjo7GsqVLEBUVhTZt2hq7tEKB19gREREREdEbs7W1xZLFC/H48WOo1WpUq1Yd06bNgLOzi7FLKxTY2BERERER0RsLDOyOwMDub7SNL74YlkfVFD4ciklERERERGTm2NgRERERERGZOTZ2REREREREZo6NHRERERERkZljY0dERERERGTm2NgRERERERGZOTZ2REREREREZo6NHRERERERkZljY0dERERERGTm2NgRERERERGZOTZ2REREREREZk4hiqJo7CKIqOC4cOECRFGEWq02dikmRafTwsLC0thlmBzmIsdM5JiJHDORYyZyzETOnDJRq9WoXLlyrpe3yMdaiKgQUigUxi7BJJnLH5G3jbnIMRM5ZiLHTOSYiRwzkSvImfCMHRERERERkZnjNXZERERERERmjo0dERERERGRmWNjR0REREREZObY2BEREREREZk5NnZERERERERmjo0dERERERGRmWNjR0REREREZObY2BEREREREZk5NnZERERERERmjo0dERERERGRmWNjR0REREREZObY2BEREREREZk5NnZERERERERmjo0dEeWpH3/8ET4+PnjnnXfQq1cv3L1719glvbZr167hk08+QZ06ddCwYUMMHz4cMTEx0vzLly+jW7dueOedd6DRaLBlyxaD9ZOSkvD111/j3XffRd26dTF27FgkJSUZLLNjxw74+fmhZs2a6Ny5My5evGgw/8GDB/j4449Rq1YteHt7Y+nSpfl3wK8oLCwMNWrUwE8//SRNK8yZpKamYvbs2WjatClq1KiBFi1aICIiAkDhzEWn02HOnDnw9vZG7dq1ERgYiHPnzknzC0sm4eHh6NevH/bv328w3RSOX6vV4rvvvkPjxo1Rq1YtDB48GLGxsXl49DnLLpeUlBTMmDEDTZs2Ra1atdC5c2ccP37cYL2CnEtOr5Ws+vbti3bt2hlMK6yZHDx4EJ07d8Y777yDd999F+vXr5fmFeRMXkgkIsoja9euFVu1aiXevHlTTExMFCdPnixqNBoxPT3d2KW9liFDhohbt24VExMTxYiICHHgwIHiBx98IIqiKEZGRor169cXN2/eLKamporHjx8Xa9WqJZ46dUpa//PPPxc//fRTMS4uToyKihK7desmjhgxQpp/+PBh8b333hPPnTsnpqSkiMuWLRPr168vPn78WBRFUUxOThabNWsmLly4UExOThYvXbokNmrUSNy0adPbDSIHX375pVi1alXxxx9/FEWxcGei1+vFvn37ikOGDBHv3bsnpqenizdu3BCfPXtWaHNZsGCB6OvrK964cUNMTk4W165dK9auXVuMiYkpFJn8/fff4vjx48U6deqINWrUEPft2yfNM5XjnzFjhti1a1cxLCxMfPz4sfjZZ5+JgYGBRstl8+bN4sSJE8WHDx+KiYmJ4oYNG8Tq1auL169fL9C5vCiTrE6cOCFWrVpV9Pf3N5heGDPZsWOH2KRJE/HYsWNiWlqaGBsbK96+fbtAZ5IbbOyIKE/o9Xrx/fffF0NCQqRpGRkZ4nvvvSceOHDAiJW9vsTERIPvo6KixEqVKomRkZHi7NmzxS+++MJg/rRp08RBgwaJoiiKd+7cEWvUqCH9kRBFUbx+/bpYpUoVadoHH3wg/vzzzwbbCAgIEFeuXCmKoiiuX79e7NSpk8H81atXi+3atcubA3wDx44dEz/88EOxe/fuUmNXmDPZsWOH2Lp1a1Gr1crmFdZcBgwYIAYHBxtMa9CggXjy5MlCkcmOHTvE0aNHi7du3RKbN29u8MbUFI7/6dOnYvXq1cWbN29K8x8/fixWr15dvHLlypsefo5elMt/f+eKoij26dNHXLRokSiKBTeXF2XyXHJysujr6ytOmzbNoLErjJk8e/ZMrF+/vvjnn39mu25BzSQ3OBSTiPLE7du3kZCQgMaNG0vTLC0t0aBBA9nwBnNhZ2dn8L2NjQ2AzCFmZ86cQZMmTQzmN2rUSDrW3377DTVr1oSTk5M0v0qVKihWrBguXbqEpKQkhIaGomnTpjlu48yZM9nOv3XrlmxIydv09OlTTJkyBVOnToVS+e+fkcKcydatW9G7d29YWFjI5hXWXAICArB3717cuHEDKSkpWL16NZycnFCnTp1CkUnHjh0xe/ZsVKxYUTbPFI7/3LlzKF68OCpVqiTNd3JyQqVKlfL1d/aLcvnv71wg8/euTqcDUHBzeVEmz3333Xfw9fVFlSpVDKYXxkwOHjyIcuXKoX79+tmuW1AzyQ02dkSUJ8LCwuDq6ooiRYoYTHd3d0dUVJSRqspbu3fvhoeHB9zd3fHw4UOULl3aYL67uzuePHmCtLS0bOcDgJubG6KiohAWFgaFQgF3d3fZNp7nldM+ABhc6/c2iaKIsWPHolu3bqhcubLBvMKaiU6nw+XLl1G0aFH06NEDtWrVQuvWraVrpgprLv7+/mjQoAECAgJQp04dzJ8/H/Pnz4etrW2hzeQ5Uzj+nPbh4eFhMr+zHz9+jDNnzqBhw4YAsj8moODnsn//fly8eBGff/65bF5hzOTChQuoWrUqZs6ciXfffReNGzfGuHHj8OzZMwCFM5Pn2NgRUZ5ITk6GlZWVbLqVlRUyMjKMUFHeOnLkCObMmYPJkydDqVQiJSUF1tbWBss8/z4jIwMpKSnZ5mFtbY2MjAwkJydDrVZDpVIZzM+aV3bbyLoPY1i6dCn0ej0GDBggm1dYM3ny5AnS09OxZs0aDB8+HL/99htGjRqF6dOn48iRI4U2l0WLFuH69ev43//+h/Pnz2PcuHHo378/wsLCCm0mz5nC8ee0D1P5nR0bG4sBAwbA19cX7733HoDsjwko2LncuXMH06dPR3BwsOyDU6BwZhITE4OQkBAUL14chw8fxoYNG/DgwQN89dVXAApnJs/Jx4wQEb0GS0tLaLVa2fT09HTZGxhzotVqERwcjO3bt2PJkiVo1KgRgMzj/e8v7/T0dACZv9hflIeNjQ3UarU0vOi/85/nld02nu/DGJn++uuv2LFjB7Zs2QKFQiGbXxgzASANR+3bty8aNGgAAGjZsiW6du2Kbdu2Fcpcnjx5guXLl2PHjh2oUKECACAwMBBXrlzB0qVLC2UmWZnC8b9sH8Z0+vRpjB07Fu3bt8eoUaOk6YUtl0ePHmHQoEGYMGECypYtm+0yhS0TIPN3buXKlaUPGIsWLYpvvvkGAQEBePbsWaHM5DmesSOiPFGyZEnExsZCr9cbTI+MjISnp6eRqnoziYmJ6NevHy5duoSdO3dKTR2QebzR0dEGy0dERKBkyZJQq9XZzgeAqKgoeHp6wtXVFTqdTnZr5Kx5ZbeNyMhIqFQquLm55dVh5trcuXMRHR2Nli1bon79+qhfvz7Onz+P77//Hu3bty+UmQCAo6Mj1Gq1bP9lypTBo0ePCmUuDx8+hF6vh5eXl8H0qlWr4vbt24Uyk6xM4fhLliyZ7ZCxyMhIeHh4vOkhvrZVq1ZhxIgRmDJlCsaOHWtwVqWw5bJx40ZERERg3Lhx0u/cyZMn4+7du6hfvz7OnTtX6DIBAFdXV9m/4eeNb1xcXKHM5Dk2dkSUJ6pXrw6lUonz589L07RaLf7880+DG6qYk/Hjx8Pe3h6rV6+Gq6urwbz69evj9OnTBtPOnDkjHWv9+vVx8eJFJCcnS/Nv3ryJlJQU1K5dG66urihduvRLt3Hq1CmD+b/99hvq1asHtVqdZ8eZW+vXr8e+ffuwc+dO6b8aNWqgX79+WLFiRaHMBAAUCgVq166NCxcuGEy/desWypcvXyhz8fDwgF6vlz3H8vr16/Dw8CiUmWRlCsdfv359PHz4EOHh4dL8hIQE3Lhxw+BDrLfp6NGjWLZsGTZs2ACNRiObX9hy6d27Nw4fPmzwO3fo0KEoXbo0du7ciZo1axa6TACgTp06uHjxIkRRlKbdunULlpaW8PT0LJSZSIx2P04iKnC+++47sX379uKDBw/ExMREcdKkSWL37t2NXdZriY+PFytXrmzw/KSsrl+/Lr7zzjvi3r17xfT0dPHYsWNigwYNDJ6j07VrV3HkyJFiQkKCGBkZKXbr1s3g9u8bNmwQmzZtKl65ckVMSUkRlyxZIrZq1Up67l9sbKxYr149cfXq1WJqaqp48eJFsXHjxuLp06fz9+BfQc+ePaXHHRTmTEJCQsT69euLx44dExMTE8Xdu3eLtWvXFm/dulVocwkKChI/+OAD6bmWmzdvFmvUqCFeunSp0GXy39u1m8rxDx8+XOzTp48YHR0tPYdr9OjRbyGRTP/NZdCgQeK0adNeuE5BzyWnxx08t23bNtlz7ApbJunp6WKTJk3EmTNnivHx8eLt27fFgIAAcd68edIyBT2TnLCxI6I8k5GRIX777bdigwYNxNq1a4vDhw83eI6MOblx44ZYqVKlbP9bvny5KIqieOjQIdHPz0+sXr262KFDB9kbxujoaHHQoEHiO++8IzZu3FicN2+eqNPpDJZZvHix2LhxY/Gdd94R+/fvLz58+NBg/rlz58SOHTuK1atXF319fcXdu3fn74G/oqyNnSgW7kw2btwoajQa6dhPnDghzSuMuaSlpYnz5s0TNRqNWLt2bbFbt27ib7/9Js0vTJlk92bdFI4/MTFRHDVqlFi7dm2xQYMG4sSJE8WUlJQ8PPIX+28unTp1yvZ3rp+fn7RMQc/ldRq7wpjJrVu3xJ49e4o1atQQGzduLM6ZM8fgOaIFPZOcKEQxy3lMIiIiIiIiMju8xo6IiIiIiMjMsbEjIiIiIiIyc2zsiIiIiIiIzBwbOyIiIiIiIjPHxo6IiIiIiMjMsbEjIiIiIiIyc2zsiIiIiIiIzBwbOyIiIiIiIjPHxo6IiIiIiMjMsbEjIiIiIiIyc2zsiIiIiIiIzBwbOyIiIiIiIjP3f397zC5JTtdWAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 900x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ---- 차트 B: IT 세부 직무(Job Title) Top 10 - 국가별 연봉 비교 (건수 대신 연봉 기준) ----\n",
    "top10_it = it_pivot.sort_values(\"avg_salary\", ascending=False).head(10).sort_values(\"avg_salary\")\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(9, 7))\n",
    "y_pos = np.arange(len(top10_it))\n",
    "bar_h = 0.25\n",
    "for i, c in enumerate(TARGET_COUNTRIES):\n",
    "    ax.barh(y_pos + (i - 1) * bar_h, top10_it[c].values, height=bar_h,\n",
    "            color=COUNTRY_COLORS[c], alpha=0.9, label=c)\n",
    "ax.set_yticks(y_pos)\n",
    "ax.set_yticklabels(top10_it.index, fontsize=9)\n",
    "ax.text(0.0, 1.06, f\"{IT_CATEGORY} 세부 직무 Top 10 (평균연봉 기준) - 국가별 연봉 비교\", transform=ax.transAxes,\n",
    "        color=INK, fontsize=12, fontweight=\"bold\", ha=\"left\")\n",
    "ax.text(0.0, 1.015, \"동일 직무 카탈로그를 국가별 실제 연봉(USD)으로 비교 (건수는 3개국 모두 동일하므로 의미 없음)\",\n",
    "        transform=ax.transAxes, color=INK_SECONDARY, fontsize=9, ha=\"left\")\n",
    "ax.legend(frameon=False, labelcolor=INK_SECONDARY, loc=\"lower right\")\n",
    "ax.grid(axis=\"x\", color=GRID_COLOR, linewidth=0.8)\n",
    "ax.grid(axis=\"y\", visible=False)\n",
    "for spine in [\"top\", \"right\"]:\n",
    "    ax.spines[spine].set_visible(False)\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"chart_B_it_top10_titles_by_country.png\", dpi=150, facecolor=SURFACE)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "03cb64eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>97178.0</td>\n",
       "      <td>93100.0</td>\n",
       "      <td>20724.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japan</th>\n",
       "      <td>42683.0</td>\n",
       "      <td>40783.0</td>\n",
       "      <td>9258.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Singapore</th>\n",
       "      <td>71811.0</td>\n",
       "      <td>68884.0</td>\n",
       "      <td>15459.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  mean   median      std\n",
       "Country                                 \n",
       "United States  97178.0  93100.0  20724.0\n",
       "Japan          42683.0  40783.0   9258.0\n",
       "Singapore      71811.0  68884.0  15459.0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ---- 차트 C: IT 연봉 분포 국가별 비교 (카테고리 평균 대신 전체 분포) ----\n",
    "fig, ax = plt.subplots(figsize=(8, 5))\n",
    "bp_data = [it_df.loc[it_df[\"Country\"] == c, \"Salary (USD)\"] for c in TARGET_COUNTRIES]\n",
    "bp = ax.boxplot(bp_data, tick_labels=TARGET_COUNTRIES, patch_artist=True, widths=0.5,\n",
    "                medianprops=dict(color=INK, linewidth=2),\n",
    "                whiskerprops=dict(color=INK_MUTED), capprops=dict(color=INK_MUTED),\n",
    "                flierprops=dict(marker=\"o\", markersize=3, markerfacecolor=INK_MUTED, markeredgecolor=\"none\", alpha=0.4))\n",
    "for patch, c in zip(bp[\"boxes\"], TARGET_COUNTRIES):\n",
    "    patch.set_facecolor(COUNTRY_COLORS[c])\n",
    "    patch.set_alpha(0.55)\n",
    "    patch.set_edgecolor(COUNTRY_COLORS[c])\n",
    "ax.text(0.0, 1.10, f\"{IT_CATEGORY} 연봉(USD) 분포 - 국가별 비교\", transform=ax.transAxes,\n",
    "        color=INK, fontsize=13, fontweight=\"bold\", ha=\"left\")\n",
    "ax.text(0.0, 1.03, f\"n = {len(it_df[it_df['Country']=='United States'])}개 직무 / 국가\", transform=ax.transAxes,\n",
    "        color=INK_SECONDARY, fontsize=10, ha=\"left\")\n",
    "ax.grid(axis=\"y\", color=GRID_COLOR, linewidth=0.8)\n",
    "ax.grid(axis=\"x\", visible=False)\n",
    "for spine in [\"top\", \"right\"]:\n",
    "    ax.spines[spine].set_visible(False)\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"chart_C_it_salary_distribution.png\", dpi=150, facecolor=SURFACE)\n",
    "plt.show()\n",
    "\n",
    "it_summary = it_df.groupby(\"Country\")[\"Salary (USD)\"].agg([\"mean\", \"median\", \"std\"]).round(0).reindex(TARGET_COUNTRIES)\n",
    "it_summary\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "874b3075",
   "metadata": {},
   "source": [
    "### 4-1. 국가별 IT 연봉 격차 심층 분석\n",
    "\n",
    "같은 IT 직무 카탈로그에서 **직무별로 국가 간 연봉 배율(최고국/최저국)이 얼마나 다른지**를 살펴보면, 임금 격차가 일부 특정 직무에 쏠려 있는지 아니면 카테고리 전반에 고르게 나타나는 구조적 격차인지 확인할 수 있다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "16f06524",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IT 세부 직무 연봉 배율(최고국/최저국) 분포 요약:\n",
      "min    2.04\n",
      "25%    2.21\n",
      "50%    2.28\n",
      "75%    2.34\n",
      "max    2.53\n",
      "Name: gap_ratio, dtype: float64\n",
      "\n",
      "[배율이 가장 큰 상위 5개 직무]\n",
      "Country                               United States    Japan  Singapore  \\\n",
      "Job Title                                                                 \n",
      "Information Technology Administrator        81200.0  32105.0    56491.0   \n",
      "Data Technician                             58600.0  23329.0    42183.0   \n",
      "Web Applications Manager                   114600.0  45799.0    78883.0   \n",
      "Computer Support Specialist                 78900.0  31639.0    57378.0   \n",
      "Imaging Programmer                          84800.0  34170.0    59491.0   \n",
      "\n",
      "Country                               gap_ratio  \n",
      "Job Title                                        \n",
      "Information Technology Administrator        3.0  \n",
      "Data Technician                             3.0  \n",
      "Web Applications Manager                    3.0  \n",
      "Computer Support Specialist                 2.0  \n",
      "Imaging Programmer                          2.0  \n",
      "\n",
      "[배율이 가장 작은 하위 5개 직무]\n",
      "Country                              United States    Japan  Singapore  \\\n",
      "Job Title                                                                \n",
      "Database Analyst                           92600.0  45320.0    73030.0   \n",
      "GIS Analyst                                79800.0  38683.0    62107.0   \n",
      "Director of Application Development       130400.0  62879.0   105828.0   \n",
      "Javascript Developer                       86800.0  41839.0    67236.0   \n",
      "Service Level Manager                     114300.0  55060.0    87485.0   \n",
      "\n",
      "Country                              gap_ratio  \n",
      "Job Title                                       \n",
      "Database Analyst                           2.0  \n",
      "GIS Analyst                                2.0  \n",
      "Director of Application Development        2.0  \n",
      "Javascript Developer                       2.0  \n",
      "Service Level Manager                      2.0  \n"
     ]
    }
   ],
   "source": [
    "# ---- 차트 C-2: IT 304개 직무의 국가 간 연봉 배율(최고국/최저국) 분포 ----\n",
    "fig, ax = plt.subplots(figsize=(8, 5))\n",
    "ax.hist(it_pivot[\"gap_ratio\"], bins=25, color=PALETTE[\"blue\"], alpha=0.85)\n",
    "ax.axvline(it_pivot[\"gap_ratio\"].mean(), color=INK, linewidth=1.5, linestyle=\"--\")\n",
    "ax.text(0.0, 1.10, f\"{IT_CATEGORY} 세부 직무별 국가 간 연봉 배율(최고국/최저국) 분포\", transform=ax.transAxes,\n",
    "        color=INK, fontsize=12, fontweight=\"bold\", ha=\"left\")\n",
    "ax.text(0.0, 1.03, f\"n=304개 직무, 평균 {it_pivot['gap_ratio'].mean():.2f}배 (점선), 표준편차 {it_pivot['gap_ratio'].std():.2f}\",\n",
    "        transform=ax.transAxes, color=INK_SECONDARY, fontsize=10, ha=\"left\")\n",
    "ax.grid(color=GRID_COLOR, linewidth=0.8)\n",
    "for spine in [\"top\", \"right\"]:\n",
    "    ax.spines[spine].set_visible(False)\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"chart_C2_it_salary_gap_distribution.png\", dpi=150, facecolor=SURFACE)\n",
    "plt.show()\n",
    "\n",
    "gap_desc = it_pivot[\"gap_ratio\"].describe()[[\"min\", \"25%\", \"50%\", \"75%\", \"max\"]].round(2)\n",
    "print(\"IT 세부 직무 연봉 배율(최고국/최저국) 분포 요약:\")\n",
    "print(gap_desc)\n",
    "print(\"\\n[배율이 가장 큰 상위 5개 직무]\")\n",
    "print(it_pivot.sort_values(\"gap_ratio\", ascending=False)[TARGET_COUNTRIES + [\"gap_ratio\"]].head(5).round(0))\n",
    "print(\"\\n[배율이 가장 작은 하위 5개 직무]\")\n",
    "print(it_pivot.sort_values(\"gap_ratio\")[TARGET_COUNTRIES + [\"gap_ratio\"]].head(5).round(0))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46f9bce3",
   "metadata": {},
   "source": [
    "**인사이트 2**: IT 카테고리는 3개국 공통으로 채용 규모가 가장 큰 축에 속하지만(4-0 참고, 카테고리 단위 건수·평균 순위는 3개국이 거의 동일), 세부 직무 단위로 들어가면 국가별 절대 연봉 수준(4-1 상단 Top10 차트, 분포 박스플롯)의 차이가 뚜렷하게 드러난다 — 미국이 일본의 약 2.3배 수준의 IT 연봉을 지급한다.\n",
    "\n",
    "다만 흥미로운 점은, **국가 간 연봉 배율(최고국/최저국) 자체는 직무마다 크게 다르지 않다**는 것이다(평균 2.28배, 표준편차 0.10, 범위 2.04~2.53배로 매우 좁음). 즉 \"이 직무는 유독 미국에서 프리미엄이 크고, 저 직무는 일본에서 프리미엄이 크다\"는 식의 직무별 특이 격차보다는, **국가 단위의 구조적·전반적인 임금 수준 차이**가 IT 카테고리 전체에 고르게 적용되는 패턴에 가깝다. 이는 6장 회귀 모델에서 `Country_Japan`이 가장 중요한 특성으로 나타난 것(인사이트 5)과도 일치하는 결과다.\n",
    "\n",
    "→ 따라서 LLM 리포트(7장)에서 구직자에게 국가별 연봉 기대치를 안내할 때는, \"특정 세부 직무를 잘 골라야 한다\"기보다 **\"어느 국가를 목표로 하느냐 자체가 연봉 수준을 가장 크게 좌우한다\"**는 메시지가 더 정확하며, 이는 목표 국가별 이력서 강조 포인트를 \"직무 선택\"보다 \"국가 선택과 그에 맞는 현지화\"에 두어야 한다는 근거가 된다 — 구직자의 정보 비대칭 문제를 해소하는 데 있어 이 구조적 인사이트 자체가 핵심 가치다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "94803f77",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ---- 차트 D: 연차(Seniority) 수준별 평균 급여 추이 ----\n",
    "fig, ax = plt.subplots(figsize=(8, 5))\n",
    "seniority_order = [0, 1, 2, 3, 4, 5, 6]\n",
    "for c in TARGET_COUNTRIES:\n",
    "    trend = (\n",
    "        df[df[\"Country\"] == c]\n",
    "        .groupby(\"Seniority_Level\")[\"Salary (USD)\"].mean()\n",
    "        .reindex(seniority_order)\n",
    "    )\n",
    "    ax.plot(trend.index, trend.values, marker=\"o\", markersize=6, linewidth=2,\n",
    "            color=COUNTRY_COLORS[c], label=c)\n",
    "ax.set_xticks(seniority_order)\n",
    "ax.set_xticklabels([SENIORITY_LABELS[i] for i in seniority_order], rotation=25, ha=\"right\", fontsize=9)\n",
    "ax.text(0.0, 1.10, \"직급/연차 단계별 평균 연봉 추이\", transform=ax.transAxes, color=INK, fontsize=13, fontweight=\"bold\", ha=\"left\")\n",
    "ax.text(0.0, 1.03, \"평균 연봉 (USD)\", transform=ax.transAxes, color=INK_SECONDARY, fontsize=10, ha=\"left\")\n",
    "ax.grid(color=GRID_COLOR, linewidth=0.8)\n",
    "ax.legend(frameon=False, labelcolor=INK_SECONDARY)\n",
    "for spine in [\"top\", \"right\"]:\n",
    "    ax.spines[spine].set_visible(False)\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"chart_D_seniority_salary_trend.png\", dpi=150, facecolor=SURFACE)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ae768316",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Salary (USD)</th>\n",
       "      <th>Real_Salary_Index</th>\n",
       "      <th>Cost of Living Index</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Japan</th>\n",
       "      <td>38827.6</td>\n",
       "      <td>81570.7</td>\n",
       "      <td>47.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Singapore</th>\n",
       "      <td>65048.6</td>\n",
       "      <td>71639.4</td>\n",
       "      <td>90.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>88350.0</td>\n",
       "      <td>126757.5</td>\n",
       "      <td>69.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Salary (USD)  Real_Salary_Index  Cost of Living Index\n",
       "Country                                                             \n",
       "Japan               38827.6            81570.7                  47.6\n",
       "Singapore           65048.6            71639.4                  90.8\n",
       "United States       88350.0           126757.5                  69.7"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ---- 차트 E: 생계비 보정 실질 급여 지수 비교 ----\n",
    "fig, ax = plt.subplots(figsize=(8, 5))\n",
    "bp_data = [df.loc[df[\"Country\"] == c, \"Real_Salary_Index\"] for c in TARGET_COUNTRIES]\n",
    "bp = ax.boxplot(bp_data, tick_labels=TARGET_COUNTRIES, patch_artist=True, widths=0.5,\n",
    "                medianprops=dict(color=INK, linewidth=2),\n",
    "                whiskerprops=dict(color=INK_MUTED), capprops=dict(color=INK_MUTED),\n",
    "                flierprops=dict(marker=\"o\", markersize=3, markerfacecolor=INK_MUTED, markeredgecolor=\"none\", alpha=0.4))\n",
    "for patch, c in zip(bp[\"boxes\"], TARGET_COUNTRIES):\n",
    "    patch.set_facecolor(COUNTRY_COLORS[c])\n",
    "    patch.set_alpha(0.55)\n",
    "    patch.set_edgecolor(COUNTRY_COLORS[c])\n",
    "ax.text(0.0, 1.10, \"국가별 생계비(Cost of Living) 보정 실질 급여 지수 비교\", transform=ax.transAxes, color=INK, fontsize=13, fontweight=\"bold\", ha=\"left\")\n",
    "ax.text(0.0, 1.03, \"생계비 보정 실질 급여 지수\", transform=ax.transAxes, color=INK_SECONDARY, fontsize=10, ha=\"left\")\n",
    "ax.grid(axis=\"y\", color=GRID_COLOR, linewidth=0.8)\n",
    "ax.grid(axis=\"x\", visible=False)\n",
    "for spine in [\"top\", \"right\"]:\n",
    "    ax.spines[spine].set_visible(False)\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"chart_E_real_salary_index.png\", dpi=150, facecolor=SURFACE)\n",
    "plt.show()\n",
    "\n",
    "real_summary = df.groupby(\"Country\")[[\"Salary (USD)\", \"Real_Salary_Index\", \"Cost of Living Index\"]].median().round(1)\n",
    "real_summary\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5a10985",
   "metadata": {},
   "source": [
    "**인사이트 3**: 생계비를 보정하면 명목 급여 순위와 실질 구매력 순위가 달라진다. 생계비 지수가 낮은 일본은 명목 급여는 가장 낮지만 실질 급여 지수 기준으로는 격차가 줄어들고, 생계비가 높은 싱가포르는 명목 급여 대비 실질 구매력 개선폭이 상대적으로 작다. → **구직자에게는 \"명목 연봉\"뿐 아니라 \"생계비 보정 실질 연봉\"을 함께 제시하는 것이 유의미한 인사이트**이며, 이는 7장 LLM 리포트에도 반영한다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4954a345",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x860 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ---- 차트 F: 카테고리(상위 15) x 국가 평균 급여 히트맵 ----\n",
    "top15_categories = df[\"Category\"].value_counts().head(15).index\n",
    "pivot = (\n",
    "    df[df[\"Category\"].isin(top15_categories)]\n",
    "    .pivot_table(index=\"Category\", columns=\"Country\", values=\"Salary (USD)\", aggfunc=\"mean\")\n",
    "    .loc[top15_categories, TARGET_COUNTRIES]\n",
    ")\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(7, 8.6))\n",
    "sns.heatmap(pivot, annot=True, fmt=\",.0f\", cmap=\"Blues\", linewidths=2, linecolor=SURFACE,\n",
    "            cbar_kws={\"label\": \"평균 연봉 (USD)\"}, ax=ax)\n",
    "fig.suptitle(\"직무 카테고리(상위 15) x 국가 평균 연봉 히트맵\", color=INK, fontsize=13, fontweight=\"bold\", x=0.02, ha=\"left\")\n",
    "ax.set_xlabel(\"\")\n",
    "ax.set_ylabel(\"\")\n",
    "plt.tight_layout(rect=[0, 0, 1, 0.96])\n",
    "plt.savefig(\"chart_F_category_country_heatmap.png\", dpi=150, facecolor=SURFACE)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a3eeb7d9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "대시보드 저장 완료 -> dashboard_category_explorer.html\n"
     ]
    },
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "marker": {
          "color": "#2a78d6"
         },
         "name": "United States",
         "showlegend": false,
         "type": "box",
         "visible": false,
         "xaxis": "x",
         "y": {
          "bdata": "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",
          "dtype": "f8"
         },
         "yaxis": "y"
        },
        {
         "marker": {
          "color": "#eb6834"
         },
         "name": "Japan",
         "showlegend": false,
         "type": "box",
         "visible": false,
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              ]
             }
            ],
            "label": "Telecommunication",
            "method": "update"
           }
          ],
          "x": 0,
          "xanchor": "left",
          "y": 1.18
         }
        ],
        "xaxis": {
         "anchor": "y",
         "domain": [
          0,
          0.45
         ],
         "gridcolor": "#e1e0d9",
         "zerolinecolor": "#e1e0d9"
        },
        "xaxis2": {
         "anchor": "y2",
         "domain": [
          0.55,
          1
         ],
         "gridcolor": "#e1e0d9",
         "zerolinecolor": "#e1e0d9"
        },
        "yaxis": {
         "anchor": "x",
         "domain": [
          0,
          1
         ],
         "gridcolor": "#e1e0d9",
         "zerolinecolor": "#e1e0d9"
        },
        "yaxis2": {
         "anchor": "x2",
         "domain": [
          0,
          1
         ],
         "gridcolor": "#e1e0d9",
         "zerolinecolor": "#e1e0d9"
        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ---- 인터랙티브 대시보드: 카테고리 선택 시 국가별 분포/평균 급여 갱신 ----\n",
    "categories_sorted = sorted(df[\"Category\"].unique())\n",
    "default_cat = df[\"Category\"].value_counts().idxmax()\n",
    "\n",
    "fig = make_subplots(\n",
    "    rows=1, cols=2,\n",
    "    subplot_titles=(\"카테고리별 국가 간 연봉 분포 (Box)\", \"카테고리별 국가 평균 연봉 (Bar)\"),\n",
    ")\n",
    "\n",
    "buttons = []\n",
    "n_countries = len(TARGET_COUNTRIES)\n",
    "for i, cat in enumerate(categories_sorted):\n",
    "    sub = df[df[\"Category\"] == cat]\n",
    "    visible_mask = []\n",
    "    for c in TARGET_COUNTRIES:\n",
    "        cd = sub[sub[\"Country\"] == c]\n",
    "        fig.add_trace(\n",
    "            go.Box(y=cd[\"Salary (USD)\"], name=c, marker_color=COUNTRY_COLORS[c],\n",
    "                   visible=(cat == default_cat), showlegend=(cat == default_cat)),\n",
    "            row=1, col=1,\n",
    "        )\n",
    "        visible_mask.append(cat == default_cat)\n",
    "    avg = sub.groupby(\"Country\")[\"Salary (USD)\"].mean().reindex(TARGET_COUNTRIES)\n",
    "    fig.add_trace(\n",
    "        go.Bar(x=TARGET_COUNTRIES, y=avg.values, marker_color=[COUNTRY_COLORS[c] for c in TARGET_COUNTRIES],\n",
    "               showlegend=False, visible=(cat == default_cat)),\n",
    "        row=1, col=2,\n",
    "    )\n",
    "    visible_mask.append(cat == default_cat)\n",
    "    full_visible = [False] * (len(categories_sorted) * (n_countries + 1))\n",
    "    start = i * (n_countries + 1)\n",
    "    for j in range(n_countries + 1):\n",
    "        full_visible[start + j] = True\n",
    "    buttons.append(dict(label=cat[:28], method=\"update\", args=[{\"visible\": full_visible}]))\n",
    "\n",
    "fig.update_layout(\n",
    "    updatemenus=[dict(active=categories_sorted.index(default_cat), buttons=buttons, x=0.0, y=1.18, xanchor=\"left\")],\n",
    "    title=dict(text=\"직무 카테고리별 국가 간 연봉 비교 대시보드 (드롭다운으로 카테고리 선택)\", x=0.02, font=dict(size=16, color=INK)),\n",
    "    paper_bgcolor=SURFACE, plot_bgcolor=SURFACE,\n",
    "    font=dict(color=INK_SECONDARY, family=\"system-ui, -apple-system, Segoe UI, sans-serif\"),\n",
    "    height=520, margin=dict(t=140),\n",
    ")\n",
    "fig.update_yaxes(gridcolor=GRID_COLOR, zerolinecolor=GRID_COLOR)\n",
    "fig.update_xaxes(gridcolor=GRID_COLOR, zerolinecolor=GRID_COLOR)\n",
    "fig.write_html(\"dashboard_category_explorer.html\", include_plotlyjs=\"cdn\")\n",
    "print(\"대시보드 저장 완료 -> dashboard_category_explorer.html\")\n",
    "fig.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "814b8bd3",
   "metadata": {},
   "source": [
    "## 5. 머신러닝 — 직무 카테고리 분류\n",
    "\n",
    "**목표**: 구직자가 입력한 직무명(Job Title) 텍스트만으로 50개 직무 카테고리 중 하나를 정확히 예측한다. 이 분류기는 이후 7장 LLM 파이프라인에서 \"이력서의 희망 직무 → 카테고리 자동 인식 → 해당 카테고리의 국가별 채용 트렌드 조회\"로 이어지는 핵심 연결고리로 사용된다.\n",
    "\n",
    "**성공 기준**: F1-score(macro) ≥ 0.85\n",
    "\n",
    "**방법**: 직무명 텍스트를 TF-IDF(1~2gram)로 벡터화한 뒤, Logistic Regression / Linear SVM / Random Forest 세 모델을 비교하여 최적 모델을 선정한다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "135e3a18",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "학습 셋: (9092, 5600)  테스트 셋: (2273, 5600)\n"
     ]
    }
   ],
   "source": [
    "X = df[\"Job Title\"]\n",
    "y = df[\"Category\"]\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X, y, test_size=0.2, random_state=RANDOM_STATE, stratify=y\n",
    ")\n",
    "\n",
    "tfidf_clf = TfidfVectorizer(ngram_range=(1, 2), min_df=1, max_features=8000)\n",
    "X_train_tfidf = tfidf_clf.fit_transform(X_train)\n",
    "X_test_tfidf = tfidf_clf.transform(X_test)\n",
    "\n",
    "print(\"학습 셋:\", X_train_tfidf.shape, \" 테스트 셋:\", X_test_tfidf.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f014f27d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>f1_macro</th>\n",
       "      <th>f1_weighted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Random Forest</td>\n",
       "      <td>0.932576</td>\n",
       "      <td>0.937154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Linear SVM</td>\n",
       "      <td>0.932148</td>\n",
       "      <td>0.939183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>0.835250</td>\n",
       "      <td>0.823071</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 model  f1_macro  f1_weighted\n",
       "0        Random Forest  0.932576     0.937154\n",
       "1           Linear SVM  0.932148     0.939183\n",
       "2  Logistic Regression  0.835250     0.823071"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "candidates = {\n",
    "    \"Logistic Regression\": LogisticRegression(max_iter=2000, class_weight=\"balanced\"),\n",
    "    \"Linear SVM\": LinearSVC(C=1.0, max_iter=5000, class_weight=\"balanced\"),\n",
    "    \"Random Forest\": RandomForestClassifier(n_estimators=300, n_jobs=-1, random_state=RANDOM_STATE, class_weight=\"balanced\"),\n",
    "}\n",
    "\n",
    "clf_results = []\n",
    "fitted_clfs = {}\n",
    "for name, model in candidates.items():\n",
    "    model.fit(X_train_tfidf, y_train)\n",
    "    pred = model.predict(X_test_tfidf)\n",
    "    f1_macro = f1_score(y_test, pred, average=\"macro\")\n",
    "    f1_weighted = f1_score(y_test, pred, average=\"weighted\")\n",
    "    clf_results.append({\"model\": name, \"f1_macro\": f1_macro, \"f1_weighted\": f1_weighted})\n",
    "    fitted_clfs[name] = model\n",
    "\n",
    "clf_result_df = pd.DataFrame(clf_results).sort_values(\"f1_macro\", ascending=False).reset_index(drop=True)\n",
    "clf_result_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "4f17dfd3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[선정 모델] Random Forest\n",
      "F1-score (macro)    : 0.9326  (목표 0.85 달성)\n",
      "F1-score (weighted) : 0.9372\n",
      "\n",
      "                                             precision    recall  f1-score   support\n",
      "\n",
      "                     Accounting and Finance       0.98      0.93      0.96       116\n",
      "   Administration / Reception / Secretarial       0.93      0.95      0.94        59\n",
      "      Advertising / Graphic Design / Events       0.91      0.85      0.88        46\n",
      "  Airlines / Aviation / Aerospace / Defense       1.00      1.00      1.00        38\n",
      "                               Architecture       0.94      1.00      0.97        17\n",
      "                                 Automotive       0.94      0.97      0.95        31\n",
      "                                    Banking       0.92      0.97      0.95        74\n",
      "                                  Bilingual       0.67      1.00      0.80         4\n",
      "                          Business Planning       0.80      0.92      0.85        38\n",
      "                 Care Giving and Child Care       0.94      0.84      0.89        19\n",
      "                  Cleaning and Housekeeping       1.00      1.00      1.00         8\n",
      "     Construction / Building / Installation       0.90      0.93      0.92        58\n",
      "                                 Counseling       0.93      1.00      0.97        14\n",
      "   Courier / Delivery / Transport / Drivers       0.76      1.00      0.86        16\n",
      "           Customer Service and Call Center       1.00      0.98      0.99        46\n",
      "          Electrical and Electronics Trades       0.79      0.79      0.79        24\n",
      "                                Engineering       0.98      0.89      0.93       104\n",
      "                              Environmental       1.00      1.00      1.00        17\n",
      "                   Executive and Management       0.85      0.81      0.83        89\n",
      "          Facilities / Maintenance / Repair       0.71      0.96      0.81        25\n",
      "                  Factory and Manufacturing       0.84      0.83      0.84        65\n",
      "                        Fashion and Apparel       0.89      1.00      0.94        17\n",
      "                    Fitness / Hair / Beauty       0.96      1.00      0.98        22\n",
      "    Food / Hospitality / Tourism / Catering       0.98      0.91      0.94        66\n",
      "                 Fundraising and Non Profit       0.91      1.00      0.95        20\n",
      "              Gardening / Farming / Fishing       1.00      1.00      1.00        18\n",
      "                     Government and Defence       1.00      1.00      1.00        13\n",
      "                         Health and Medical       0.99      0.97      0.98       225\n",
      "                            Human Resources       0.98      0.96      0.97        68\n",
      "                          Import and Export       0.95      1.00      0.97        18\n",
      "                     Information Technology       0.96      0.96      0.96       182\n",
      "                                  Insurance       0.90      0.95      0.93        40\n",
      "          Law Enforcement / Security / Fire       1.00      1.00      1.00        29\n",
      "                                      Legal       0.91      1.00      0.95        31\n",
      "                                  Marketing       1.00      0.97      0.98        59\n",
      "Media / Broadcasting / Arts / Entertainment       1.00      0.85      0.92        46\n",
      "                Oil / Gas / Energy / Mining       0.91      0.91      0.91        55\n",
      "                                   Pet Care       1.00      1.00      1.00        10\n",
      "           Pharmaceutical and Biotechnology       1.00      0.92      0.96        38\n",
      "                                Photography       0.83      1.00      0.91         5\n",
      "                           Public Relations       1.00      1.00      1.00        10\n",
      "                    Publishing and Printing       0.72      0.84      0.78        25\n",
      "                   Purchasing and Inventory       0.88      0.90      0.89        40\n",
      "             Quality Control and Compliance       0.93      0.93      0.93        29\n",
      "                                Real Estate       0.93      1.00      0.96        25\n",
      "                      Recreation and Sports       1.00      0.80      0.89        15\n",
      "                 Sales Retail and Wholesale       0.90      0.93      0.92        58\n",
      "             Science and Technical Services       0.94      0.94      0.94        67\n",
      "                       Teaching / Education       1.00      0.96      0.98       110\n",
      "                          Telecommunication       0.92      1.00      0.96        24\n",
      "\n",
      "                                   accuracy                           0.94      2273\n",
      "                                  macro avg       0.92      0.95      0.93      2273\n",
      "                               weighted avg       0.94      0.94      0.94      2273\n",
      "\n"
     ]
    }
   ],
   "source": [
    "BEST_CLF_NAME = clf_result_df.iloc[0][\"model\"]\n",
    "clf_best = fitted_clfs[BEST_CLF_NAME]\n",
    "y_pred_best = clf_best.predict(X_test_tfidf)\n",
    "best_f1_macro = f1_score(y_test, y_pred_best, average=\"macro\")\n",
    "best_f1_weighted = f1_score(y_test, y_pred_best, average=\"weighted\")\n",
    "\n",
    "print(f\"[선정 모델] {BEST_CLF_NAME}\")\n",
    "print(f\"F1-score (macro)    : {best_f1_macro:.4f}  (목표 0.85 {'달성' if best_f1_macro >= 0.85 else '미달'})\")\n",
    "print(f\"F1-score (weighted) : {best_f1_weighted:.4f}\")\n",
    "print()\n",
    "print(classification_report(y_test, y_pred_best, zero_division=0))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "36167105",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 상위 12개 빈도 카테고리에 대한 혼동행렬 (전체 50개는 가독성이 낮아 상위 카테고리만 표시)\n",
    "top_categories = df[\"Category\"].value_counts().head(12).index.tolist()\n",
    "mask = y_test.isin(top_categories)\n",
    "cm = confusion_matrix(y_test[mask], y_pred_best[mask], labels=top_categories)\n",
    "cm_df = pd.DataFrame(cm, index=top_categories, columns=top_categories)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(9, 8))\n",
    "sns.heatmap(cm_df, annot=True, fmt=\"d\", cmap=\"Blues\", linewidths=1.5, linecolor=SURFACE, ax=ax, cbar=False)\n",
    "fig.suptitle(f\"혼동행렬 - 상위 12개 카테고리 ({BEST_CLF_NAME}, 행=실제/열=예측)\", color=INK, fontsize=13, fontweight=\"bold\", x=0.02, ha=\"left\")\n",
    "ax.set_xlabel(\"예측 카테고리\", color=INK_SECONDARY)\n",
    "plt.xticks(rotation=45, ha=\"right\", fontsize=8)\n",
    "plt.yticks(fontsize=8)\n",
    "plt.tight_layout(rect=[0, 0, 1, 0.96])\n",
    "plt.savefig(\"chart_G_confusion_matrix.png\", dpi=150, facecolor=SURFACE)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17394d49",
   "metadata": {},
   "source": [
    "**인사이트 4**: TF-IDF + Linear SVM 조합은 직무명 텍스트만으로도 F1-macro 0.85 이상을 달성한다. 이는 직무명 자체가 카테고리 정보를 강하게 내포하고 있음을 의미하며(예: \"Accountant\" → Accounting and Finance), 실제 이력서 서비스에서 사용자가 희망 직무명만 입력해도 안정적으로 직무 카테고리를 자동 태깅할 수 있음을 시사한다. 다만 위 혼동행렬에서 보듯 \"Executive and Management\"처럼 여러 도메인에 걸친 범용 관리직 카테고리는 다른 카테고리와 혼동되는 경향이 있다 → 9장 한계점에서 재논의."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e326fdb",
   "metadata": {},
   "source": [
    "## 6. 머신러닝 — 연봉(USD) 예측 회귀\n",
    "\n",
    "**목표**: 직무명, 카테고리, 국가, 직급/연차 등의 정보로부터 적정 연봉(USD)을 예측한다. 구직자가 이력서에 희망 직무와 국가를 입력하면, 이 모델이 \"예상 적정 연봉 구간\"을 산출하여 LLM 리포트에 근거 데이터로 제공된다.\n",
    "\n",
    "**성공 기준**: 결정계수 R² ≥ 0.80\n",
    "\n",
    "**특성 구성**:\n",
    "- 직무명 TF-IDF → TruncatedSVD로 40차원 축약 (세부 직무 뉘앙스)\n",
    "- Category, Country One-Hot 인코딩\n",
    "- Seniority_Level (연차 서열), Title_Length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "7e19a0e0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TF-IDF+SVD 누적 설명분산: 0.138\n",
      "회귀 특성 shape: (9092, 95)\n"
     ]
    }
   ],
   "source": [
    "reg_df = df.reset_index(drop=True)\n",
    "y_reg = reg_df[\"Salary (USD)\"].values\n",
    "\n",
    "train_idx, test_idx = train_test_split(\n",
    "    np.arange(len(reg_df)), test_size=0.2, random_state=RANDOM_STATE,\n",
    "    stratify=reg_df[\"Country\"],\n",
    ")\n",
    "\n",
    "# ---- 텍스트 특성: 학습 데이터에만 fit하여 정보 누수 방지 ----\n",
    "tfidf_reg = TfidfVectorizer(ngram_range=(1, 2), min_df=2, max_features=5000)\n",
    "tfidf_train = tfidf_reg.fit_transform(reg_df.loc[train_idx, \"Job Title\"])\n",
    "tfidf_test = tfidf_reg.transform(reg_df.loc[test_idx, \"Job Title\"])\n",
    "\n",
    "svd = TruncatedSVD(n_components=40, random_state=RANDOM_STATE)\n",
    "svd_train = svd.fit_transform(tfidf_train)\n",
    "svd_test = svd.transform(tfidf_test)\n",
    "print(f\"TF-IDF+SVD 누적 설명분산: {svd.explained_variance_ratio_.sum():.3f}\")\n",
    "\n",
    "# ---- 범주형 특성 ----\n",
    "ohe_reg = OneHotEncoder(handle_unknown=\"ignore\", sparse_output=False)\n",
    "ohe_train = ohe_reg.fit_transform(reg_df.loc[train_idx, [\"Category\", \"Country\"]])\n",
    "ohe_test = ohe_reg.transform(reg_df.loc[test_idx, [\"Category\", \"Country\"]])\n",
    "\n",
    "# ---- 수치 특성 ----\n",
    "num_cols = [\"Seniority_Level\", \"Title_Length\"]\n",
    "num_train = reg_df.loc[train_idx, num_cols].values\n",
    "num_test = reg_df.loc[test_idx, num_cols].values\n",
    "\n",
    "X_reg_train = np.hstack([svd_train, ohe_train, num_train])\n",
    "X_reg_test = np.hstack([svd_test, ohe_test, num_test])\n",
    "y_reg_train, y_reg_test = y_reg[train_idx], y_reg[test_idx]\n",
    "\n",
    "feature_names = (\n",
    "    [f\"svd_{i}\" for i in range(svd_train.shape[1])]\n",
    "    + list(ohe_reg.get_feature_names_out([\"Category\", \"Country\"]))\n",
    "    + num_cols\n",
    ")\n",
    "print(\"회귀 특성 shape:\", X_reg_train.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "5860a22a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>r2</th>\n",
       "      <th>mae</th>\n",
       "      <th>rmse</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>HistGradientBoosting</td>\n",
       "      <td>0.905626</td>\n",
       "      <td>8231.337345</td>\n",
       "      <td>13766.799452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Random Forest</td>\n",
       "      <td>0.786652</td>\n",
       "      <td>14133.842163</td>\n",
       "      <td>20699.030054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Linear Regression</td>\n",
       "      <td>0.622271</td>\n",
       "      <td>18334.031221</td>\n",
       "      <td>27542.056060</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  model        r2           mae          rmse\n",
       "0  HistGradientBoosting  0.905626   8231.337345  13766.799452\n",
       "1         Random Forest  0.786652  14133.842163  20699.030054\n",
       "2     Linear Regression  0.622271  18334.031221  27542.056060"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg_candidates = {\n",
    "    \"Linear Regression\": LinearRegression(),\n",
    "    \"Random Forest\": RandomForestRegressor(n_estimators=300, n_jobs=-1, random_state=RANDOM_STATE),\n",
    "    \"HistGradientBoosting\": HistGradientBoostingRegressor(max_iter=300, random_state=RANDOM_STATE),\n",
    "}\n",
    "\n",
    "reg_results = []\n",
    "fitted_regs = {}\n",
    "for name, model in reg_candidates.items():\n",
    "    model.fit(X_reg_train, y_reg_train)\n",
    "    pred = model.predict(X_reg_test)\n",
    "    reg_results.append({\n",
    "        \"model\": name,\n",
    "        \"r2\": r2_score(y_reg_test, pred),\n",
    "        \"mae\": mean_absolute_error(y_reg_test, pred),\n",
    "        \"rmse\": mean_squared_error(y_reg_test, pred) ** 0.5,\n",
    "    })\n",
    "    fitted_regs[name] = model\n",
    "\n",
    "reg_result_df = pd.DataFrame(reg_results).sort_values(\"r2\", ascending=False).reset_index(drop=True)\n",
    "reg_result_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c651c709",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[선정 모델] HistGradientBoosting\n",
      "R^2  : 0.9056  (목표 0.80 달성)\n",
      "MAE  : 8,231 USD\n"
     ]
    },
    {
     "data": {
      "image/png": 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55hkWLVpEvXr1+Mc//sHu3bt54YUX6N+/Pz/99BNLlixhxIgR7Ny5kxdffNHtnsNoNDJ58mTsdjtvvPFG1R+kiIjUWeoyXE4PPPAA77//PgaDgWeeeYY777wTKHg6+Je//IUXXniBq666iptuuqnMvPbs2QPAzz//jJeXF+PGjQNg1apVZGVllbjdkSNHWLlyJZ9++ilbtmwBYMKECbz88svFBr/KIyAggBtuuIGVK1eyZs0aV+u93377zdUld9y4cRw9epQbbriBrKwsLBYLUVFRHDt2zHUsJSkMPJwfdCt8ff4NaWmCg4PZunUrAQEBJbYm7NOnD3369HHle359ZGdns3btWlavXs3SpUvJzc2ldevWfPjhh1x55ZXlKkNJEhISOHnyJN27dy/26f/p06fp3LkzUNC1xmazkZ2dTUZGBps3b3at16lTJz799FMAt27PAwcOZNeuXSxevNjV0rNVq1aMHTvWdWNfkv/+979kZGTwn//8x9V1tSoUdlHPy8tj0qRJPP/884wcORKjsfzPF/r06UOHDh3YsWOHWz2U5YknnuDTTz/l+PHjvPbaazz55JP8v//3/zhz5gwxMTH8+c9/rvDx2O12Ro8ezVdffYXFYmHhwoWuVhfBwcH84x//4Mknn6RHjx7lGqez8JrYuXMngYGBjBs3zjVb9MVYtmwZaWlpDB061K0L+YUmTJjAO++8Q3JyMv/73//o1q0bgGsSnltuuaXUQHJJquJa7tOnD7/99huNGjUqMs4nFATBx44dy9ixY4GCL9T16tVzW2fgwIEsWLCAL7/8stiucyIiFZWUlMSoUaNITU3lzTffZM2aNbz99tscPHiQ2bNnu7XGHzlypOuh1oUcDoerNfVXX31V4uy5hQ/PYmJi+PLLL8ss3wsvvOB6OHsxCrujnv9QqPC+s6IBwRMnTrBt2zYaN25M+/btXcvff/99/u///g+bzcbw4cN57bXXMJvNrFmzhjvvvJNPPvmE4OBg5syZU+Y+Cj9Pz5w5Q2JiIjfccAOtWrXihx9+uGwz8xa2FD1/7MLCeiztnv18rVq1Yvfu3TRo0KDY4KHRaGTIkCGucYHPnTtXpOtw4fA1a9eu5bnnnqv4gYiIiBRDAcFymjhxIj/99BOxsbFuY56NHTuWXr168fXXX7uCgcHBwYSFheHt7V0kn/T0dOLi4oCCp47x8fG88847QMENQ2k3F/v37+eJJ57AbrfTtWtXYmNjGTBgwEUf2+jRo1m5ciUbNmwgLy8Ps9nMF198ARTcrDZr1oxPPvnEFQw8cOAAgYGB5OTklNmqsTBgcP6T8sLX5R13LDw8nDNnzlT4uG6//XZXq8Jnn32W3bt3ExQUxPTp05k6dWqlgiIX+v777wHo2bNnsekOh4MDBw7g5eWF2WzG29sbPz8/QkJC3Lo0m83mEscCrFevXpFAl9PpdAUWSxpg+7HHHuPVV1/lwQcfrMyhlSgqKoqXX36Zhx9+mKNHj3LnnXfSsmVLpk+f7grklEfLli3ZsWNHqS1YL1SvXj2ee+45Jk6cyMKFCxk/fjwLFy7E29ub+fPnu4JMJQ3yfv4xFHYN8/Ly4r777uPUqVO8/PLLbkG/Bx98kIEDB7Jnzx7X8tDQUOx2e7FB+F9++YUff/wRKBheICsri3feeQe73X7RAcHCLmGFYxCVpHPnzq5g6+rVq+nWrRtOp9M1vuP5Y422bduWxMTEEvPatWuXayD3i72Wd+zYQa9evcpcrzgLFy50dcMrvNYKrz0Rkcqy2Wy89957vPjii6SkpHDvvfdy2223MXr0aOx2OytWrKBr167lagEN8J///MfVmrrwYV5xn4thYWH4+/uX2UPi1ltvLXdruvN169bNrcX8yJEjSUxMdH3ePv74464xB202G1DQM6LwAeamTZvKnLyjpPufYcOGsWjRIoYOHcrDDz/s+lzu0KED8fHxvPvuu9xzzz1AwWd6WFhYkQc/hT7++GPX63fffZfY2FgAJk2adNkCgoX1cP5nX+E9aVkPZqHgvr+yE6idP5nblVdeSWBgIHv37iUtLa3Ck7+IiIgURwHBcuratSvx8fEADB48uNggxj//+U/Xa39//2KfAr7wwgtkZ2fTs2dPfvjhBx555BG+/fbbcn2hvvbaa/noo49ITk6mRYsWxMTEALBhwwa8vb1d751OJ5MnTy53cGbIkCFYrVbOnTvHpk2buPbaa103v4WtF6+88koMBgM2m42///3vPPDAA7Rr167MccNatGgB4DYO244dO1zL6tevT35+fql5REdHV6rrTeHMwVarlcWLF7Nq1SpatGjBlVdeiY+PD3v37iU5OZlOnTrRoEEDDh48yF133UXLli1LnJzkQoXdQ5s0aeK2vGnTpuUa46ywJcGF1q5dy8MPP4zD4cDpdLpm27Pb7eTk5LiNbfTiiy8Wm8f5La1KU1xLrbLWu/vuu+nevTuzZs0iLi6OX3/9lYkTJ7J+/XoWLFhQrvwKA6LFBc5LM3bsWN59912+/fZbRo4cSUZGBv/4xz/cWnBYLBa3FgsXuvB8DR48mMGDB5Ofn+/6QnSh8wds9/f3L7beZs6cCeC6vh944IFyf5EsTeFkIpGRkeWaTORPf/oTjz76KKtXr2bmzJl8//33HD9+3O3vBhSM71haq5Dzu+sWXsuF1+/5rxcsWFBmNyZvb+9yz3J5ofO/LAYGBuLr68uZM2fKPcu7iEhxDAYD69atIyUlhYcfftj1d95isfDRRx/x6aefsn//fnr37l1mAGrPnj08/vjjGI1GnnrqKWJjY5k6dSpNmjRx9WAoNHfuXIYPH15m+Q4fPlypcaAvfMB48OBBDhw44Hp//Phx1/AkhbKzs137Ks/YfSXd/zRs2NA1zvCMGTOKnWzs/KFC/P39i+398dlnn7FlyxbatWvHoUOHePXVVxk1ahRXXXVVmWUryWeffcYzzzzjtmz+/Pl06dKlxG2io6OB4j/7PvvsszLvY00mU6U/+y68Fw0JCSE9PZ3jx48rICgiIlVCAcFKOHDgQLnG+7uwtd/y5cuZP38+ISEhLFmyhL/85S8sX76cMWPGuD0FLc3QoUMZNmwYGzZscLXeGTlyJOHh4a4WT0888QRHjhwp99hxvr6+3HjjjSxevJi1a9fStWtXNm/ejMVi4ZZbbgGgXbt2vPXWW8ycOZMPPviADz74gF69ejF//vxSb3Sio6Np3rw5Bw4c4I033mDEiBG8/vrrQMFT5IYNG7Js2bJSA34bN24s13GU5oorrqBRo0aMHTvW9cT11Vdf5f333ycuLo6+ffvy0Ucf8frrr7NgwYJytx4sfEpcUlDF4XC4gkQzZswoMnNqSRo3bozBYMBisWAymTCbza7z27dvX3x9ffH29sbHx4crrriiXHmW5PyAXGpqapH0wmO8cKzFq666iv/+97/s2bOHxx57jA0bNrBo0SJuvfVW+vXrV+Z+C2+oK3OjXBhILxwrsrC1QaEmTZq4AvgV4XQ6y/3lKy8vz+39a6+9xsqVK2nbti2rVq1i2LBhbN68mbvvvtv1O19Z508mUp6u2bfeeiszZsxg79697N+/n5UrVwIUGeNx6dKl5S7Dddddh9FoZOXKlWzZsgWj0ciqVatcM5xnZWWxZMmSErdv3bp1lc0SGRgYSHZ2NmfOnFFAUEQqzWw2s2jRIrZt20Z4eLhrJvXCFuGjRo0qVz579uzhlltuIT09ndjYWP7v//4PPz8//vrXvzJq1CiefPLJSg1xUNLn2OOPP87rr7/Ok08+Wa6Zgc8PZhUq7H2xb9++UoehKEnhfVtpD5WSk5PL9Zl64Rjcv/76K1OmTMFoNPLPf/6THTt28OijjzJ69Gg++eSTCpe1UFpaWpGJwsp6eHv11VdjtVr54YcfWL16NVdeeaVrwo+xY8fi4+PDBx98UOJEYgEBAVX22VcYBKxMrxkREZHiaFKRSvjll1/IyMgo8ad3795Ftvnoo49cM4O+/fbbBAUFsWDBAjp37syWLVvYtm1bhcvxwgsv8Pjjjxd5klsY1CkchLk8Ro8eDRS0TFu3bh15eXnccMMNBAUFuda59dZb2bVrF8uXL6dz585s3rzZNeZgaQonPPnrX/9Ky5Yt2bVrF927d+fjjz9m/vz55Z55r3DylbJ+Shqzp9BXX33F448/7hqHsVDhDWlF6q2wu0hJAU2Hw8HLL7/Myy+/7OqWUx6dOnVi165d7Nixg4SEBLeyLl26lE8//ZSPP/6Yf/3rXwwdOrTc+RYnODjYFey7cDy/pKQkV5fSklqDXnnllXzyySeuuvjpp5/K3OeyZcs4ePAggCvoXF5Op5OXX34ZKPgiYrfbi7SSTE5OJiYmpsSfkvbp5eVV6rWdkZFBVFRUke1eeOEF/va3vxEQEMC7776Ln58fH330EU2bNmX9+vVuMzpWVE5ODv/5z3/KnEzkfIGBgYwYMQIoaMGwatUqDAZDkYDgLbfcUmo9FbYAgYJu1hMnTiQrK4sBAwZw7bXXkpmZyQMPPMD8+fOLtLooyTfffFPua7mkFrSF11t5umuJiJTGYrHQo0cPvv32W8aPH89LL71Uoe2//fZb+vTpw5EjR3jwwQd57LHHgIKJNt544w3sdjtPP/10pbr+FnriiSeIiYkptrWdpxT2bCntge4777xT6ufp9OnTi2zz888/M2DAAE6fPs3f/vY3evXqxX333cfdd9/N0aNHL6oO7rjjjiJl6Nu3b6nb+Pn5MW3aNBwOB7feeisdO3bk+PHj3HzzzbzzzjvMnz+/XA/qDh8+XO7PvpImzapIV2UREZHyUAvBSmjVqlWFZgQGGDRoEL1792bChAn0798fKPhAX716NXv27OHqq6+ucDkWLVpUZNnPP//smpns22+/dQX6yjJw4EAaNGjA3r17Wb16NeDemujnn38GClqFDRw4kJMnT3LPPfeU6wZ39OjReHl58dZbb3HmzBl69uzJjBkzKjQJBZTdkuzUqVOu2f1K8+OPP7rGebtwORSM/VbeSTgaN24MUKFx8M7Xrl07lixZUiWz0F7ok08+Yd68eTz44IOMGTOmxPVMJhMDBgxg1apV/POf/6RJkyb069ePlJQU15iVVqvV1epv7dq1HDx4kDvuuMMVSNy5c6erRWxJYyFCQau6//73vzz66KNAwSQT5ZmI53yF3YXDwsJYsGABI0eOZMGCBdx222106NABKBgT6cJWAOcr6em63W6v1LkYN24ca9asYebMma6xC5s0acLatWtJS0ujTZs2Fc6z0LJly0hNTWXYsGGEhYWVe7u77rqLxYsX89FHH3Ho0CF69+7tGg+w0N69e0sdQ/DCIPacOXOIiopi5cqVGI1GRo4cWeExKv38/Mq8lhMTE926xZ8vIyODzMxM/Pz8yj0OqYjIxerfvz9LlixxDUdSqHfv3jz00EOEhoYWaa1+++23065dO44cOXJRXV0PHTrEzp07y3WPc759+/Zx6623Fpt27tw5oGDYmOKGuHnqqadcD5aKU577n0mTJlV4RuArr7ySMWPGYDAY+Pvf/+5aPm/ePEaMGMH111/PpEmTKpTnxfq///s/6tevz7///W9sNhsDBgwoV8vM85nN5jI/+5KTk0ttsVj4wLqw7kVERC6WAoIX4c477yz1Kd35XTCCg4Ndk4mcr379+pUKBsIfA/6f313j/Fno5syZw9ChQ/H19S0zL4vFwvDhw/nggw9Yvnw5gYGBbpOnbNu2jfvuu4/GjRtTv359V4unQYMGlausN910U4UDPxcqq8tFYReashR2GS6cORrg888/d7XS/Oqrr9i8eXO5JkAonMH1wtaG5dWwYUPXrHJV7cUXX2TPnj289NJLpQYEoWDsu2+++YYzZ84wZcqUIunPP/+8axy3M2fO8Ne//pXp06fTtGlT8vLyXGMrRUdHF/kCsXfvXmJiYsjLy+Po0aOum93CbtoGg4Gvv/6aESNG0KVLlxJbhUHBzfKTTz4JFLSYuO666xg4cCBfffUVDz30EOvWrcNoNJZ7DMfSFFcP5zv/2m/WrBnr168vsk54eDjh4eFuE8hUVHknE7lQ3759ad68Obt27QLcJxMpVNgNvby8vLx45JFHeOSRRyq03fm6detW5rUcExNTYkC38Frr3r17pcsgIlJRhX/PL2QwGFyfS8Xp1KkTnTp1qtKyPPfcczz99NPFjr13vtzc3DK77J4/tuD5yhq7ubBbdXnufwYMGFDqg7HzJ/MymUyuXgDnMxgMXH/99WXu61KZOHEiEydOrPT2YWFhZX723Xrrra6H8hf67bffOH36NM2aNaNRo0aVLoeIiMj5FBC8CN99912xM40WMplMPP/885ds/y+88AIBAQE4HA5sNhtTpkxh+fLl9O/fn6ioKN5//33XLKwXPtEuzpgxY/jggw/Iycnh1ltvdRtbrm3btsTExLB7926OHj1KixYtGDVqFP/3f/93yY7vUvnqq684c+aM6yZ25cqVfPzxx/j6+jJ79mz+8pe/cNttt/H2228zcODAUvPq2bMn9erVY+vWrWXO+nbhoOIX+uCDD0qdCKOixo4dy5w5c8o1sUjr1q3ZsGEDzz//PBs3buT06dMEBATQuXNnHnzwQbeb8K5du3LbbbexefNmjh49isVioV27dowcOZL77ruvSKut7Oxsdu7cidFoJCgoiJiYGG677TZGjRrlaiX6ww8/AAXjSpbm4Ycf5syZM7Ru3do16+xTTz3FunXr+PHHH/nXv/5VZEbmyvryyy9LTY+MjKzyGZwvdP5kIhX9ImQwGLj99tuZNWsWfn5+3HzzzZeolJfX119/DVDmtSkiUhnffvttiZNLQcF4ut99991lLNEfYmNjSx0funXr1vznP/9xve/QocNFPxwrSWRkJC1btuTXX39l9+7dtG3btsR1d+3axZEjR0pMT0hI8GiwryYo/Oy77rrrPFwSERGpTRQQvAiFY6CVpLD74qVyfpfh3Nxc1q5dS9u2bXn33Xfx9/fnl19+Ydu2bZw5c6ZcAcH+/fuXeOPYpUuXMgMkNcWFXYY3bNhAbm4u77//PsOGDSM7O5vHHnuM3bt3lxl0sFgsTJw4kVdeeYX33nuv1ABpWU/ps7OzK3YgZXjsscdcYxmVR8uWLV2t0UrTvHlz3nzzzTLXe+KJJ3jiiSfKte/C4GxZAcHiuh517NjR1fWpKpV1vk6ePFnl+7xQ4fmYMGFChbvYA66Wm8OHD68VYw5lZ2fzwQcf4O/vz5/+9CdPF0dEaqHMzMxS//5fOMHW5ZSSklJqF93yTohWVe6//34eeeQR3nzzTV555ZUS1yur3Joko3ROp5M333wTg8FQqclpRERESmJwljQtltQ4Bw4cIDQ01DXrZk5ODseOHaNFixYeLln1ZrPZOHz4sNvYLj///HO5x/tJSUmhe/fuWCwW/ve//5U6454U5XQ6iYqKIigoiO3bt3u6ONVGTk4OrVq14uzZs+zZs4cmTZqUazun04nBYOCXX36hV69e5OTklNnipab45z//6ZpVs7QueiIicunl5OTQo0cPjh8/ztatW4uddEsu3qeffspdd93F+PHjeeuttzxdHBERqUUUEBSpAp999hnjxo1jwoQJvPbaa54ujtQChw4d4qOPPiI8PLxcs3kX+vjjj5k9ezZHjhwhJyeHO++8kwULFly6gl4mhQHO9u3b8+WXX5Y6XIOIiFweP/30EwMHDqR3796sXLnS08WpdU6dOkX37t3x9/dn8+bNrvGcRUREqoICgiIitciqVau4//77MZvNjB8/ntjYWLfxQEVEREREREQUEBQREREREREREalDKj5KvYiIiIiIiIiIiNRYCgiKiIiIiIiIiIjUIQoIioiIiIiIiIiI1CEKCIqIiIiIiIiIiNQhCgiKiIiIiIiIiIjUIQoIioiIiIiIiIiI1CEKCIqIiIiIiIiIiNQhCgiKiIiIiIiIiIjUIQoIioiIiIiIiIiI1CEKCIqIiIiIiIiIiNQhCgiKiIiIiIiIiIjUIQoIioiIiIiIiIiI1CEKCIqIiIiIiIiIiNQhCgiKiIiIiIiIiIjUIQoIioiIiIiIiIiI1CEKCIqIiIiIiIiIiNQhCgiKiIiIiIiIiIjUIQoIioiIiIiIiIiI1CEKCIqIiIiIiIiIiNQhCgiWYd++fezbt8/TxRARERGRKqL7OxEREanrFBAsg81mIzs7C6fT4emiVBtOp4NjR/erTn6n+nCn+ihKdeJO9eFO9eFO9VHUpaiT/b/9wksvPF1l+dU0NpsNm8122fer3+/qSeelesnMzMRqtWK1WsnIOOfp4sh5dK1UPzon1VNNOS8KCJaD0+nE6XR6uhjVhtPpJD8/X3XyO9WHO9VHUaoTd6oPd6oPd6qPoqqqTvLz812vW0S34uZbxl1s0aSC9PtdPem8VF86J9WLrpXqR+ekeqop50UBQREREZFabt/e3bz95nzOnjnjWtayVWsPlkhEREREPEkBQREREZFaym638+XaOFYs/4QzZ9LZ8sNmTxdJRERERKoBL08XQERERESqXmrqaVYu/4QTJ1IA6Hn1NfTuc61nCyUiIiIi1YICgiIiIiK1zK5dO1n7xWry8vLw8/Nj6LCbaNY82tPFEhEREZFqQgFBERERkVpk166dfLZqGQCRUU0ZNvwWAgICPFwqEREREalOFBAUERERqUVat76SrVu+J7plK2J69cFo1JDRIiIiIuJOAUERERGRGszpdLL/t19o3qIlRqMRLy8v7vjTREwmk6eLJiIiIiLVlB4Zi4iIiNRQubm5rF61jKWf/pfv479zLVcwUERERERKoxaCIiIiIjVQyvFkVq74lLS0VAwGAyYv3daJiIiISPnozlFERESkBnE6nST8tJX1X68lPz+fgHr1GDFiFOERkZ4umoiIiIjUEJXuMnzkyBGuuuoq3nnnHdeyHTt2MG7cODp06MCAAQNYsmSJ2zYZGRnMmDGDHj160KVLF6ZNm0ZGRobbOsuWLWPQoEG0b9+eW265hYSEBLf0Q4cOMXHiRDp27Ejv3r1ZsGCBW3peXh6zZ8+mV69edOzYkfvvv58TJ05U9jBFREREqo2cnBxWLFvCV19+Tn5+PtHRrZhw970KBoqIiIhIhVQ6IPjPf/4Th8Phep+cnMykSZMYPXo0W7ZsYebMmTz77LNs2rTJtc706dM5ffo0cXFxxMXFcfjwYWJjY13p69at46WXXuK5555jy5YtDBo0iMmTJ5OamgpAVlYWEyZMoEuXLsTHx/P666+zaNEiFi9e7Mpj7ty5bN26lcWLF7N+/XpMJhMPPfRQZQ9TREREpNo4d/YMBw78htFoZMB1N3DzqHH4+vp6ulhl+uGHH2jdunWxP5MmTQKq5sGyiIiIiJRPpQKCGzdu5OjRo3Tu3Nm17MMPPyQmJoYxY8bg4+ND3759GTt2LIsWLQJg//79bNiwgWeffZbg4GBCQ0OZOXMmcXFxroDfwoULeeCBB+jatSu+vr7ce++9hIeHs2LFCgCWL19OUFAQU6dOxc/Pjw4dOnDvvfe69nH27Fk+/PBDZs2aRUREBEFBQTz99NPs3LmTXbt2XVRFiYiIiHhao8YhDLlxBLffeTfdul+NwWDwdJHKpWfPnuzbt8/tZ+fOnYSHhzN+/PgqebAsIiIiIuVX4TEEz5w5w9NPP83rr7/OM88841oeHx/P+PHj3daNiYlxBfM2b95M+/btCQoKcqW3adOG+vXrs337drp3787OnTuZO3dukTwSEhK4++67iY+Pp2/fvkXSn3vuOTIyMti6dSsNGzakVatWrvSgoCBatWpFQkIC7dq1q+jhupzfGrKuK6wL1UkB1Yc71UdRqhN3qg93qg93qg93WVlZrPniM5o1iyIsvBkArdtcCYDDkX/R+RuNnpuN+N///jcNGzZk4MCBzJ492/VgGXB7sHzNNde4Hixv3LjRdS85c+ZMbr75Zp544gm3+0sRERERKVuFAoJOp5Np06Yxbtw4Wrdu7ZaWmJhIVFSU27Lw8HDS09PJyckpNh0gLCyM5ORkjhw5gsFgIDw8vEgeW7dude2jf//+RdIBUlJSStxHREQEycnJFTnUIpKOHbyo7Wsj1Yk71Yc71UdRqhN3qg93qg93qg84ceIE8Zvjyc7O5kRKMkFBQRiNlR7tpVhRTVuVvdIlkJuby1tvvcWLL74IXPyD5QvvD8urKoKqFdufAt7Vkc5L9XL+delwOC77dSol07VS/eicVE+ePi/lfeBboYDgggULyM/P55577imSlpWVVWQMm8L3NpuNrKwsfHx8imzn6+uLzWYjMzMTi8WCyeRecB8fH2w2m2sfF+ZRnn2cn0dlhYU3q/Kb8JrK4XCQdOyg6uR3qg93qo+iVCfuVB/uVB/uVB8FdbDlh81s+u4bnE4nDRoE0aNndyIiW9SaOvnkk08ICgrimmuuAS7+wXJl5OfbOXpkf6W2vVgKeFdPOi/VQ1ZWtut1ctJhzvhV/3FS6xpdK9WPzkn15KnzUt4HvuUOCMbFxbFs2TKWLFlS7Hg1ZrO5SNAtNzcXKAjImc1m8vLyimyXm5uLn58fFosFu91ebHph0K+4PAr34evrW+Y+LobRaPRot5rqSHXiTvXhTvVRlOrEnerDnerDXV2tj8zMDFavWsbhQwU3kG3btWfg9YM5kXKk1tSJ0+nkgw8+cHvAfLEPlivDZPIiIrJFpbatLAW8qyedl+olMzPT9bpJWFMCAgI8WBo5n66V6kfnpHqqKeel3AHBuXPncuLECQYOHOhalpWVxfbt21m+fDmhoaEcP37cbZtjx44RGhqKxWIhNDSUH3/8sUi+ycnJREZGEhISgt1u58SJEzRu3NiVnpSURGRkJECx+0hKSsJkMhEWFkZoaGixT4mTkpLo3bt3eQ9VRERE5LI7cyadDz94h8zMTMxmMwOvH8JV7TvidNaubkDff/89p0+fZvjw4a5lF/tgubI8FWCtLcHd2kbnpXo4/xzonFRPOi/Vj85J9VTdz0u5Q5UfffQRn3/+OcuXL3f9XHXVVdx999289dZbdOvWzW0mOCgYD6ZXr14AdOvWjYSEBLcnPvv27SMrK4tOnToREhJCVFRUmXl89913bumbN2+ma9euWCwWunXrRmJiIkePHnWlp6WlsXfvXmJiYsp7qCIiIiKXXb169QltEk5wo8bcedefad+hU42ZRbgili9fTv/+/fH29nYtK8+D5QvT4Y8HyyJSvTlzsnCcSyvfT0a6p4srIlInlLuFYGhoaJFl3t7e1KtXj9DQUO68807GjRtHnz59GDhwIPHx8SxdupSPP/4YKAjmtWzZktjYWGbMmEF2djaxsbFMmDDB1SVk4sSJvPLKK7Rq1YrmzZvz3nvvkZGRwdChQwEYM2YM//rXv3j//fcZN24ce/fuZeHChcyePRuAJk2aMHjwYGbMmMGLL76I2WzmH//4B0OGDCEiIuKiK0tERESkKp07dxaLxRtvb28MBgNDh92EyWTCbDZ7umiXRH5+PuvWreOFF15wW174YHnYsGGuZRc+FH7zzTfJzMzE398fcH+wLCLVmzMvl6y4d3Cknyxz3azcoq2BRUSk6lVZZ+Y2bdowd+5c5s2bR5cuXXj55Zd55ZVXiI6Odq0zb948MjIy6NevH6NHj6Znz55MnTrVlT5+/HjGjRvH5MmTufrqq/nf//7HO++8g8ViAaBRo0a88cYbLF++nG7dujFt2jSmT5/uulkEmDVrFo0aNWLw4MEMHjyYhg0b8tRTT1XVYYqIiIhUif37f+W9f73Bl2vjcDqdwB/dY2urHTt2cO7cObp16+a2/M477yQuLo64uDhsNhsbN25k6dKlTJo0CXB/sJyenk5ycnKRB8siUr050k/iSE0u+yetaGtgERGpehWaZfhCixYtcns/cOBAtzEGLxQSEsLChQtLzXPKlClMmTKlxPSuXbuybNmyEtOtVqurxaCIiIhIdZOfn883G7/mxy3xAJw+dQqbzebWhba22rFjB5GRkQQGBrotL3ywPGfOHB5//HFatGhR7IPl2NhY+vXrh9VqZfTo0W4PlkVERESk/C4qICgiIiIi5XcmPZ2VKz8lOekYAF269uDa/gPx8qobt2R33XUXd911V7FpVfFgWURERETKp27cfYqIiIh42C/79vB53Cpyc3Pw9vZhyI0jaNW6jaeLJSIiIiJ1kAKCIiIiIpeYzWbjy7Wfk5ubQ5OwcEaMGEX9C7rNioiIiIhcLgoIioiIiFxiFouFYcNv4sCB/fTtNwCTyeTpIomIiIhIHaaAoIiIiMglsHv3z5iMRlq3aQtA0yua0/SK5h4ulYiIiIiIAoIiIiIiVSovL491X33Bju0JWCwWQkPD1D1YRERERKoVBQRFREREqsipUydZueJTTp08AUC37lcTUK+eh0slIiIiIuJOAUERERGRi+R0Ovl553a++vJz8vLy8Pe3Mmz4TeoiLCIiIiLVkgKCIiIiIhfB6XQS99kKdv28A4CmVzRj2PCb8fe3erhkIiIiIiLFU0BQRERE5CIYDAb8/PwwGAz07nMtV8f0xmAweLpYIiIiIiIlUkBQREREpIKcTic2mw1vb28A+va7jjZXtqNJk3APl0xEREREpGxGTxdAREREpCbJzclh5YpP+WTxR+Tn5wNgMpkUDBQRERGRGkMtBEVERETKKTn5GKtWLCU9PQ2j0UhS0lEiI5t6ulgiIiIiIhWigKCIiIhIGZxOJ//b+gMb1n+Fw+Ggfv1ARowcRZMwtQoUERERkZpHAUERERGRUmRnZ/P5Zyv47bdfAGjV+koGDxmOj4+Ph0smIiIiIlI5CgiKiIiIlCJu9XL27/8Vk8nEgOtuoFPnbppFWERERERqNAUERUREREpx7YDrOZdxjiE3jiAkJNTTxRERERERuWiaZVhERETkPJmZmeze/bPrfcOGwdw14R4FA0VERESk1lALQREREZHfHT50kNWrlpGVlYnVaiUq6goAdREWERERkVpFAUERERGp8xwOB5s3fcPmTd8A0DC4EX6+fh4ulYiIiIjIpaGAoIiIiNRp586dZfXKZRw5chiA9h06cd3AwVgsFg+XTERERETk0lBAUEREROqsAwd+47NVy8nOzsJsNnPDoKG0u6qDp4slIiIiInJJKSAoIiIiddbZM2fIzs6iceNQRtw0iqCghp4ukoiIiIjIJaeAoIiIiNQpTqfTNUlIx05dMBqNtG3XHi8v3RaJiIiISN1g9HQBRERERC6XX3/Zx/vvvUVOTjZQMHtwh46dFQwUERERkTpFd78iIiJS69ntdjZuWMf/tv4AwJYfNtO333UeLpWIiJTGmXUOB/nlXt9g9sbgoxniRUTKQwFBERERqdXS0lJZueJTUo4nA9C9x9Vc0/tazxZKRETKlL3mPcg+W651jYGN8LtxkgKCIiLlpICgiIiI1Fp79+zii89XYbPZ8PHx5cZhI4mObuXpYomISDk40k/hyDzt6WKIiNRKCgiKiIhIrbRt2/9Y+8VnAERERDJ8xCgC6tXzcKlERERERDxPAUERERGplVq3vpLvN39Hu6vac03vazEaa89cak6nkyybA28vI14mg6eLIyIiIiI1jAKCIiIiUmscOXKYiIgoDAYDvr5+TPzz/VgsFk8Xq0o5HE62HMzi9Dk7Fi8DV0f7E+Bj8nSxRERERKQGqT2PykVERKTOstlsxK1ewb8/ep+fd253La9twUCA1Mx8Tp+zA2CzOzl8yubhEomIiIhITaMWgiIiIlKjnTyRwooVn5J6+hQGg4Gs7CxPF+mS8jEbMBjA6Sx8r+e7IiIiIlIxCgiKiIhIjeR0OtmxPYF1X32B3W7Hag1g+IhbiIxq6umiXVJWHxOdmvpxLNVGgK+J5o1qXytIEREREbm0FBAUERGRGic3N5c1X6xm755dADRvHs2Nw0bi5+fv4ZJdHmGBZsICzZ4uhoiIiIjUUAoIioiISI2TcjyZvXt2YTQa6dNvAD16xGAwaLZdEREREZHyUEBQREREapyoplcw4LpBNAkLIzw80tPFERERERGpUTQKtYiIiFR7OTnZrF61jLS0VNeybt17KhgoIiIiIlIJaiEoIiIi1VrSsaOsXPEpZ8+e4Ux6Grfdcbe6B4uIiIiIXIQKtxBcu3YtN998M506daJPnz688MIL2Gw2V/q1115L69at3X62bdvmSs/IyGDGjBn06NGDLl26MG3aNDIyMtz2sWzZMgYNGkT79u255ZZbSEhIcEs/dOgQEydOpGPHjvTu3ZsFCxa4pefl5TF79mx69epFx44duf/++zlx4kRFD1VEREQ8yOl08sMPm/n4o/c4e/YMgYENuO76wQoGioiIiIhcpAoHBNPS0oiNjSU+Pp433niD9evXM3/+fFf6mTNnWLlyJfv27XP9dOrUyZU+ffp0Tp8+TVxcHHFxcRw+fJjY2FhX+rp163jppZd47rnn2LJlC4MGDWLy5MmkphZ0EcrKymLChAl06dKF+Ph4Xn/9dRYtWsTixYtdecydO5etW7eyePFi1q9fj8lk4qGHHqpM/YiIiIgHZGVl8umSf7Nx/Vc4HA7aXNmOu+6eTGhomKeLJiIiIiJS41U4IDhu3Dg6deqEr68vbdu2Zfz48Xz//fcA2Gw2srKyCAwMLHbb/fv3s2HDBp599lmCg4MJDQ1l5syZxMXFuQJ+Cxcu5IEHHqBr1674+vpy7733Eh4ezooVKwBYvnw5QUFBTJ06FT8/Pzp06MC9997LokWLADh79iwffvghs2bNIiIigqCgIJ5++ml27tzJrl27KlNHIiIichmdPXuWD957mwMHfsPLy4tBQ4YxfMQteHt7e7poIiIiIiK1wkWPIZiRkUFISAhQ0HoQoEGDBsWuu3nzZtq3b09QUJBrWZs2bahfvz7bt2+ne/fu7Ny5k7lz57ptFxMTQ0JCAnfffTfx8fH07du3SPpzzz1HRkYGW7dupWHDhrRq1cqVHhQURKtWrUhISKBdu3aVOk6Hw1Gp7WqjwrpQnRRQfbhTfRSlOnGn+nCn+nDncDjw9/fH3xqAxduH4SNuplGjxjidDpxOT5fOMy7l74jRaKryPEVERESk+qt0QPDs2bN8//33LFu2jNdeew2A9PR0ALp06YKfnx9t2rTh4YcfpkuXLgAkJiYSFRVVJK+wsDCSk5M5cuQIBoOB8PBwt/Tw8HC2bt3qyqN///5F0gFSUlJK3EdERATJycmVPVySjh2s9La1lerEnerDneqjKNWJO9WHu7peHznZOVi8LRiNRkwmE1f37IHZYiY35xxHj5zzdPGqhUvxOxLVtFXZK4mIiIhIrVOpgGD79u2x2Wz4+fnx6KOP0rp1awCaN2/Oli1b8PPz4+TJk/znP//hrrvuYtmyZURHR5OVlYWPj0+R/Hx9fbHZbGRmZmKxWDCZ3J9W+/j4uCYuKS4PX19f4I8uy8Xt4/w8KiMsvBlGY4V7WNdKDoeDpGMHVSe/U324U30UpTpxp/pwp/qAQwcPsGbtWtq370TvPv1IOnaQFi3b1tn6uJB+R0RERESkqlUqILhz504yMjLYt28fc+fO5YcffmDevHmYzWbq168PFLT6e+SRR9izZw+ffPIJf/vb3zCbzeTl5RXJLzc3Fz8/PywWC3a7vdj0wqBfcXnk5uYCBYHBsvZRWUajUd1qLqA6caf6cKf6KEp14k714a4u1ofD4eC7b9fzffwmAA4d3E+va/oAdbM+yqI6EREREZGqUunHzFarla5duzJ79mzWrFnDsWPHil0vOjqapKQkAEJDQzl+/HiRdZKTk4mMjCQkJAS73c6JEyfc0pOSkoiMjCwxj6SkJEwmE2FhYYSGhhbbNTgpKYmIiIhKHauIiIhUrbNnz/Dvj993BQM7de7K7XdOxMvrooc3FhERERGRMlx0v5PC7r0Gg6HY9B07drgCcd26dSMhIYHMzExX+r59+8jKyqJTp06EhIQQFRXFpk2b3PKIj4+nV69erjy+++47t/TNmzfTtWtXLBYL3bp1IzExkaNHj7rS09LS2Lt3LzExMRd7uCIiInKRfvt1H+/9602OHT2CxdubESNHccOgoZjNZk8XTS6x7OxsXnrpJfr27ctVV13Fdddd53qovGPHDsaNG0eHDh0YMGAAS5Yscds2IyODGTNm0KNHD7p06cK0adPIyMjwxGGIiIiI1HgVCghmZmYyZ84cDh48SE5ODnv37uWxxx6jX79+hIWFsXHjRr766ivOnj1LSkoKzzzzDPv27eO2224DCoJ5LVu2JDY2lvT0dJKTk4mNjWXChAmuLsETJ07klVdeYdeuXWRnZ7NgwQIyMjIYOnQoAGPGjGHPnj28//775OTksG3bNhYuXMj9998PQJMmTRg8eDAzZswgJSWF1NRUnnzySYYMGaIWgiIiIh6WlZXFqlXLyMnJJiS0CXdNuIc2V7bzdLHkMnA4HEyZMoXDhw/z/vvv89NPP/H6669Tr149kpOTmTRpEqNHj2bLli3MnDmTZ5991u0h8fTp0zl9+jRxcXHExcVx+PBhYmNjPXhEIiIiIjVXhfrlmM1mkpOTueOOOzh37hxhYWEMGzaMSZMmAQWtBZ977jmSk5OpV68enTt3ZsmSJW6BuHnz5hEbG0u/fv2wWq2MHj2aqVOnutLHjx9PWloakydPJiMjg+7du/POO+9gsVgAaNSoEW+88QbPPPMMs2fPJjw8nOnTp7taEALMmjWLp556isGDB2M2mxkyZAh/+9vfLqqiRERE5OL5+flxww03cvx4Mv2uvU5dhOuQlStXcvz4cVatWuU674UT0y1cuJCYmBjGjBkDQN++fRk7diyLFi3immuuYf/+/WzYsIGNGzcSFBQEwMyZM7n55pt54oknXMtEREREpHwqdBdusViYO3duiem9e/dmzZo1peYREhLCwoULS11nypQpTJkypcT0rl27smzZshLTrVYrs2fPLnUfIiIicnns27sbPz9/IqOaAtDuqg60u6qDh0sll9snn3zCn/70p2KDwPHx8YwfP95tWUxMDCtWrAAKhodp3769W+CvTZs21K9fn+3bt9O/f/9KlcnhyK/UdpXlcDjc/pfqQefl0nMCzt//lb2us9T3pW3n5PJf13WJrpXqR+ekevL0eSnvJHR6LC8iIiKXhN1u5+t1a9mWsBWrNYAJE+/Fz8/P08USD7Db7ezYsYPx48dz++238/PPP9OkSRMmTZrEmDFjSExMJCoqym2b8PBw0tPTycnJKTYdICwsrNjJ5MojP9/O0SP7K7XtxUo6dtAj+5XS6bxcGl5eZoL9/cnLs5Fvyy1zfZvN7nptz8/DVo5tAEx5NvLz8ziVnIjdnlfp8krZdK1UPzon1ZOnzktU01blWk8BQREREalyqamnWbn8E06cSAEKWgV6e3t7uFTiKenp6eTm5vL+++/z2GOP0bZtW+Lj43nssccICgoiKyvLNZ50ocL3NpuNrKwsfHx8iuTr6+uLzWarVJlMJi8iIltUatvKcjgcJB07SFh4M4zGi57bT6qIzsul58w8i9lswWQp+3PAzh8tW7xMZizl2AbAaLZgMpkJbVL04YFUDV0r1Y/OSfVUU86LAoIiIiJSpXbt2snaL1aTl5eHn58fQ4fdRLPm0Z4ulnhQ4c3whAkT6N69OwADBw5k7NixfPrpp5jN5iKBvdzcglZBPj4+mM1m8vKKtvjJzc29qFan5e1SU9WMRqPH9i0l03m5dByA4fd/ZblwnfJsU7ieAc9d13WJrpXqR+ekeqru50UBQREREakS+fn5rF3zGTt3bAMgKuoKhg6/mYCAAM8WTDyuQYMGWCwWwsLC3JY3bdqUhIQEQkNDOX78uFvasWPHCA0NxWKxEBoayo8//lgk3+TkZCIjIy9p2UVERERqo+rbdlFERERqFKPRiM1mw2AwcE3vfoy99Q4FAwUAg8FAp06d+Omnn9yW//LLLzRv3pxu3bqxadMmt7T4+Hh69eoFQLdu3UhISCAzM9OVvm/fPrKysujUqdMlL7+IiIhIbaMWgiIiIlJpTqeT/Px8vLy8MBgMDB48jC5durtmFBYpNHHiRB5//HFatGhB165d2bBhAytWrGDx4sXk5+czbtw4+vTpw8CBA4mPj2fp0qV8/PHHQEFAsGXLlsTGxjJjxgyys7OJjY1lwoQJRcYeFBEREZGyKSAoIiIilZKbm8vaNZ+Rn5/PyJtGYzAY8PbxUTBQitW/f38effRRnn76aVJSUmjRogXz5s2jZcuWAMydO5c5c+a4goavvPIK0dF/jD05b948YmNj6devH1arldGjRzN16lRPHY6IiIhIjaaAoIiIiFRYyvFkVqz4lPS0VAwGAydSjhMS2sTTxZJqbty4cYwbN67YtIEDBzJw4MAStw0JCWHhwoWXqmgiIiIidYoCgiIiIlJuTqeThJ+2sv7rteTn5xNQrx4jRoxSMFBEREREpAZRQFBERETKJScnhy/iVvLLL3sBiI5uxZChIzWGm4iIiIhIDaOAoIiIiJTL0k//w9EjiRiNRq7tfz1du/XAYDB4ulgiIiIiIlJBRk8XQERERGqGvn0H0CCoIbffOZFu3XsqGCgiIiIiUkMpICgiIiLFysrK4tDB/a73EZFRTPrz/TRpEubBUomIiIiIyMVSQFBERESKOHLkMO+9+wbLli7m9KmTruVGo24dRERERERqOo0hKCIiIi4Oh4Mfvt/Ed99uwOl0EhTUEIfT6eliiYiIiIhIFVJAUERERADIyMjgs9XLOHzoIADt2nXg+kE3YrFYPFwyERERERGpSgoIioiICIcPHWD1qmVkZmZiNpu5/oYbuap9R08XS0RERERELgEFBEVERITExENkZmYS3KgxI0eOomFwI08XSURERERELhEFBEVERIRrel+LxduHLl26YzabPV0cERERERG5hDRVoIiISB20/7dfWPLfj7Db7UDB7ME9e/ZSMFBEREREpA5QC0EREZE6JD8/n282fs2PW+IB+N/WH+h59TUeLpWIiIiIiFxOCgiKiIjUEWfS01m58lOSk44B0LVrD7p26+nhUomIiIiIyOWmgKCIiEgd8Mu+PXwet5Lc3Fy8vX24cegIWrZq4+liiYiIiIiIByggKCIiUstt/fF7vl63FoAmYeGMGDGK+oGBni2UiIiIiIh4jCYVERERqeWiW7bG29uHHj1juO32CQoGioiIiIjUcWohKCIiUgudOJFC48YhAAQGNuCeex/Az8/fw6USEREREZHqQC0ERUREapG8vDy++HwV7/3rDQ4e+M21XMFAEREREREppBaCIiIitcSpUydZufwTTp06CcDJkydp1jzaw6USEREREZHqRgFBERGRGs7pdPLzzu18uTYOu92Ov7+VYcNvoukVzT1dNBERERERqYYUEBQREakGDp+yceKcgyB/Ey1Dfcq9nc1m48s1cezatQOAK65oztDhN+Hvb71URRURERERkRpOAUEREREPS892kpiag8Fg5NQ5O74WIxFBlnJte+DAb+zatQODwUCfvv3pefU1GAyGS1xiERERERGpyRQQFBER8bDcPPf3OXnOcm/bpk1bjvfsRXR0KyIio6q4ZCIiIiIiUhtplmEREREPa+gP9XxNAPh5G4kIMpe4bm5ODmvXxJGVleVadm3/gQoGioiIiIhIuamFoIiIiId5mQz0ivYjN9+Ij9mAyVh8l9/k5GOsXP4pZ86kk5lxjptHjbvMJRURERERkdpAAUEREZFqwGg04O9VfMN9p9PJ1h9/YOOGr3A4HNSvH8jVMb0vcwlFRERERKS2UEBQRESkGsvOzibusxXs/+0XAFq1vpLBQ4bj41P+mYhFRERERETOp4CgiIhINXXy5Ak+WfIx586exWQyMeC6G+jUuZtmERYRERERkYuigKCIiEg1VS+gHkaDkQYNghhx02hCQkI9XSQREREREakFFBAUERGpRnJysvH29sFgMODt48PosbdhtQbg7e3t6aKJiIiIiEgtUfzo5aVYu3YtN998M506daJPnz688MIL2Gw2V/qOHTsYN24cHTp0YMCAASxZssRt+4yMDGbMmEGPHj3o0qUL06ZNIyMjw22dZcuWMWjQINq3b88tt9xCQkKCW/qhQ4eYOHEiHTt2pHfv3ixYsMAtPS8vj9mzZ9OrVy86duzI/fffz4kTJyp6qCIiIpfV4UMHeeetBWxL2Opa1rBhsIKBIiIiIiJSpSocEExLSyM2Npb4+HjeeOMN1q9fz/z58wFITk5m0qRJjB49mi1btjBz5kyeffZZNm3a5Np++vTpnD59mri4OOLi4jh8+DCxsbGu9HXr1vHSSy/x3HPPsWXLFgYNGsTkyZNJTU0FICsriwkTJtClSxfi4+N5/fXXWbRoEYsXL3blMXfuXLZu3crixYtZv349JpOJhx56qNKVJCIicik5HA42fbeR//5nEZmZGezYvg2Hw+HpYomIiIiISC1V4YDguHHj6NSpE76+vrRt25bx48fz/fffA/Dhhx8SExPDmDFj8PHxoW/fvowdO5ZFixYBsH//fjZs2MCzzz5LcHAwoaGhzJw5k7i4OFfAb+HChTzwwAN07doVX19f7r33XsLDw1mxYgUAy5cvJygoiKlTp+Ln50eHDh249957Xfs4e/YsH374IbNmzSIiIoKgoCCefvppdu7cya5du6qk0kRERKrKuXNnWb9+PfGbvwOgfYfO3HbHBIzGCn9Ei4iIiIiIlMtFjyGYkZFBSEgIAPHx8YwfP94tPSYmxhXM27x5M+3btycoKMiV3qZNG+rXr8/27dvp3r07O3fuZO7cuUXySEhI4O677yY+Pp6+ffsWSX/uuefIyMhg69atNGzYkFatWrnSg4KCaNWqFQkJCbRr165Sx6mWGn8orAvVSQHVhzvVR1GqE3eqjz8cOPAbn3+2kuzsbMxmC9ffMIS27a4CwOHI93DpPEO/H0VdyjoxGk1VnqeIiIiIVH+VDgiePXuW77//nmXLlvHaa68BkJiYSFRUlNt64eHhpKenk5OTU2w6QFhYGMnJyRw5cgSDwUB4eHiRPLZu3eraR//+/YukA6SkpJS4j4iICJKTkyt7uCQdO1jpbWsr1Yk71Yc71UdRqhN3db0+MjMzWb1qNU6nk8DAQHpd04t69Xw5emS/p4tWLdT134/iXIo6iWraquyVRERERKTWqVRAsH379thsNvz8/Hj00Udp3bo1UDC+n6+vr9u6he9tNhtZWVn4+PgUyc/X1xebzUZmZiYWiwWTyf1ptY+Pj2vikuLyKM8+zs+jMsLCm6n71u8cDgdJxw6qTn6n+nCn+ihKdeJO9fGHtLRznDt3llatWhAZFV3n6wP0+1Ec1YmIiIiIVLVKBQR37txJRkYG+/btY+7cufzwww/MmzcPs9lcJOiWm5sLFATkzGYzeXl5RfLLzc3Fz88Pi8WC3W4vNr0w6FdcHoX78PX1LXMflWU0GtWt5gKqE3eqD3eqj6JUJ+7qYn38+ss+GgQFERzcCICYXn1wOh0cPbK/TtZHaVQfRalORERERKSqVPoxs9VqpWvXrsyePZs1a9Zw7NgxQkNDOX78uNt6hcstFkux6VAwO3FkZCQhISHY7XZOnDjhlp6UlERkZCRAsXkkJSVhMpkICwsjNDS02K7BSUlJREREVPZwRUREKs1ut7PuqzUsW/pfVq741PXgymAweLhkIiIiIiJSF110v5PC7r0Gg4Fu3bqxadMmt/T4+Hh69eoFQLdu3UhISCAzM9OVvm/fPrKysujUqRMhISFERUWVmcd3333nlr5582a6du2KxWKhW7duJCYmcvToUVd6Wloae/fuJSYm5mIPV0REpELS0lL5aNG7/G/rDwA0a9Zc3T5FRERERMSjKvSNJDMzkzlz5nDw4EFycnLYu3cvjz32GP369SMsLIw777yTuLg44uLisNlsbNy4kaVLlzJp0iSgIJjXsmVLYmNjSU9PJzk5mdjYWCZMmODqEjxx4kReeeUVdu3aRXZ2NgsWLCAjI4OhQ4cCMGbMGPbs2cP7779PTk4O27ZtY+HChdx///0ANGnShMGDBzNjxgxSUlJITU3lySefZMiQIWohKCIil9WePbt4/903SUlJxsfHl1tG30r/ATcUGStXRERERETkcqrQGIJms5nk5GTuuOMOzp07R1hYGMOGDXMF/Nq0acPcuXOZM2cOjz/+OC1atOCVV14hOjralce8efOIjY2lX79+WK1WRo8ezdSpU13p48ePJy0tjcmTJ5ORkUH37t155513sFgsADRq1Ig33niDZ555htmzZxMeHs706dNdLQgBZs2axVNPPcXgwYMxm80MGTKEv/3tbxdVUSIiIuVV0EX4C7Zv+wmAiIhIho8YRUC9eh4umYiIiIiISAUDghaLhblz55a6zsCBAxk4cGCJ6SEhISxcuLDUPKZMmcKUKVNKTO/atSvLli0rMd1qtTJ79uxS9yEiInKpGI1GTp86CUBMr95c0/tadRMWEREREZFqo1KzDIuIiEhRTqcTg8GA0Whk+IhRnE49xRVXNPd0sURERERERNwoICgiInKRbDYbX639HG8fH64bOAiAgHr11EVYRERERESqJQUERURELsKJEymsXPEpqadPYTAY6NK1Ow0aBHm6WCIiIiIiIiXSgEYiIiKV4HQ62bbtf3z4wTuknj6F1RrAreP/pGCgSDHi4+Np3bq128/gwYNd6Tt27GDcuHF06NCBAQMGsGTJErftMzIymDFjBj169KBLly5MmzaNjIyMy30YIiIiIrWGWgiKiIhUUG5uLmu+WM3ePbsAaN48mhuHjcTPz9/DJROpns6cOUO7du1YunRpkbTk5GQmTZrE448/zvDhw9myZQt/+ctfCAsL45prrgFg+vTp2O124uLisNvtPPzww8TGxpY52Z2IiIiIFE8tBEVERCrA6XTyn39/wN49uzAajVzbfyCjxoxXMFCkFOnp6QQGBhab9uGHHxITE8OYMWPw8fGhb9++jB07lkWLFgGwf/9+NmzYwLPPPktwcDChoaHMnDmTuLg4UlNTL+NRiIiIiNQeaiEoIiJSAQaDgZ5XX8OGr79kxMhRhIVHeLpIItVeeno6DRo0KDYtPj6e8ePHuy2LiYlhxYoVAGzevJn27dsTFPRHd/w2bdpQv359tm/fTv/+/StdLocjv9LbVm5/Drf/pXrQebn0nIDz939lr+ss9X1p2zm5/Nd1XaJrpfrROamePH1ejEZTudZTQFBERKQM2dnZpKen0aRJGABt2rSlRYuWmM1mD5dMpGZIT0/n888/54svviAkJIS+ffvy0EMP0aBBAxITE4mKinJbPzw8nPT0dHJycopNBwgLCyM5ObnSZcrPt3P0yP5Kb38xko4d9Mh+pXQ6L5eGl5eZYH9/8vJs5Ntyy1zfZrO7Xtvz87CVYxsAU56N/Pw8TiUnYrfnVbq8UjZdK9WPzkn15KnzEtW0VbnWU0BQRESkFMeOHWHViqXY7XYmTJyM1RoAoGCgSAVMnTqVBx98EJPJxC+//MJzzz3HX/7yFz744AOysrLw9fV1W7/wvc1mIysrCx8fnyJ5+vr6YrPZKl0mk8mLiMgWld6+MhwOB0nHDhIW3gyjUSP3VBc6L5eeM/MsZrMFk8W7zHXt/NGyxctkxlKObQCMZgsmk5nQJkUfIEjV0LVS/eicVE815bwoICgiIlIMp9PJlh82883Gr3E6nQQGNiAnO9sVEBSR8rNara7XHTp04NVXX6VPnz788ssvmM3mIoG93NyCFkE+Pj6YzWby8oq29snNzcXPz++iylXeLjVVzWg0emzfUjKdl0vHARh+/1eWC9cpzzaF6xnw3HVdl+haqX50Tqqn6n5eFBAUERG5QFZWJp+tXs7BAwXdCdtc2Y5Bg4fh7V2+VgoiUrpGjRoRGBhIUlISoaGhHD9+3C392LFjhIaGYrFYCA0N5ccffyySR3JyMpGRkZeryCIiIiK1SvVtuygiIuIBiYmHeO9fb3LwwH68vLwYNGQYw0fcomCgSBVKTEwkLS2NyMhIunXrxqZNm9zS4+Pj6dWrFwDdunUjISGBzMxMV/q+ffvIysqiU6dOl7PYIiIiIrWGAoIiUmvlO8o3K53I+Xbt3EFGxjkaNgzmzj9NomPHLhgM5euuJCLFe/vtt9m7dy/Z2dls376dBx98kAEDBhAdHc2dd95JXFwccXFx2Gw2Nm7cyNKlS5k0aRJQEBBs2bIlsbGxpKenk5ycTGxsLBMmTCgy9qCIiIiIlI+6DItIrbQ9MZujqTb8vI30aO6Pv7eef0j5XHf9YPyt/lwd0weLxeLp4ojUCikpKdx9991kZGQQHh7OsGHDuOeeewBo06YNc+fOZc6cOTz++OO0aNGCV155hejoaNf28+bNIzY2ln79+mG1Whk9ejRTp0711OGIiIiI1HgKCIpIrZOWaedoasEA9Vm5Dg6ezOWqCLUiqevSMu2cPGengb8XjQL++Pg7eHA/e3b9zJChIzAYDFgsFvr2u86DJRWpfZ544gmeeOKJEtMHDhzIwIEDS0wPCQlh4cKFl6JoIiIiInWSAoIiUut4mQylvpe652x2Pt/vz8LhcAK59GzhT5C/ke++Xc/38QVjl0VERtGhY2fPFlREREREROQyUEBQRGqdAB8TV0X4knjahtXHSIvGmgyirjuTnf97MLBA0ok01sSv5tjRIwB06tyNK9te5aniiYiIiIiIXFYKCIpIrdQ02ELTYI3/JgUaWr0wexnIszvJPHmQ9Ru+JDcnG4u3N4OHDKdNm7aeLqKIiIiIiMhlo4CgiIjUen4WI31aWflu0yZ2/W89AKGhYYy4aRSBgQ08XDoREREREZHLS9NuiohIjZDvcLp1+60oX4uRq9o0x2Qy0bVbT267Y4KCgSIiIiIiUiephaCIiFR7+0/ksjcpBy+TgS5X+LnNElyWM+np1A8MBKBJk3D+fM8DrvciIiIiIiJ1kVoIiohIteZwONmXnAOAPf+P12Wx2+2sXRPH22/NJyXluGu5goEiIiIiIlLXqYWgiIhUawYDeJkKJgQBMJsMZW5z+vQpVq34lBMnUgA4kniYkJDQUrfJsjlISsvDz9tIWKD54gsuIiIiIiJSTSkgKCIi1ZrBYKDrFX7sSy7oMnxVhG+p6+/6eQdr13xGXl4efn5+DB12E82aR5e6TV6+k/hfM8nJcwBgC/fhikbeVXYMIiIiIiIi1YkCgiIiUu01tHrRq6W11HVsNhvrvvyCnTu3ARAVdQVDh99MQEBAmfln2RyuYCDA6cx8rmh0UUUWERERERGpthQQFBGRWmHXz9vZuXMbBoOBXtf0JaZXH4zG8g2Va/U2YvUxkpFTEBQMqaePRxERERERqb30jUdERGqFjp26knTsGO07dCKq6RUV2tZkNBATbeXE2Tz8LEaCrMV/PDqdTo6m5eF0QkQDM0Zj2eMZioiIiIiIVDcKCIqISI2Um5vLD99/R0yvvpjNZoxGI0OH31Tp/CxeBiKCLKWusz0xm2NpeQCcOGunWzO/Su9PRERERETEUxQQFBGRGifleDIrVnxKeloqOTk53DBo6GXZ76kM+x+vz9lLWfPyyMt3kpphx8/bSICPydPFERERERGRGkIBQRERqTGcTicJP21l/ddryc/PJ6BePdq163DZ9t8owMzRVNvvrz37EZrvKJgZ+VxOPgYDdGvmR+N6Zo+WSUREREREagYFBEVEpEbIycnhi7iV/PLLXgCio1sxZOhIfH19L1sZOkT6EBxgwuGE8EDPBt/OZOVzLicfAKcTktPtCgiKiIiIiEi5KCAoIiIVYrM7MRkLJuK4XE6kHGfZ0sWcOZOO0Wjk2v7X07VbDwyGyzuph8FgILxB6eMMXi7+3ka8TAbs+U4A6vuWb0ZlqT7yHc7Leh2JiIiIiBRSQFBERMptX3IOv6Xk4mUy0OUKv8vWbdbH15fc3FwCAxswfOQomjQJuyz7rc68zUZiov05lpaHv7eRqIbVI1ApZcvIyWfLgSyybQ6aBlu4KuLytXIVEREREQEFBEVEpJxsdie/peQCYM8veH0pA4J5eXmYzQVdYOvVq8+YsbcRFNQQbx+fS7bPmqaer4l6vppMpKbZf8JGts0BwOFTNqIaWnQeRUREROSyUv8iEREpF5MRvEx/dG+0eF26ro5HEg/z1puv8euv+1zLmoSFKxgotYLXebE/g+Hydr8XEREREQG1EBQRkXIyGQ10vcKP31JyMXsZuCq8csG54+l5/JzsJMucS+smvm7jADocDr6P/45N323E6XSy5ftNREe3uuxjBYpcSi1DfMjJc5KVW9Bl2N+7ap7PHjyZy97kXCwmA12b+RHop1aHIiIiIlI8BQRFRKTcggO8CL6IbsIZOfkkJGZzOsOJIyUXb7OJZo28C9IyMvhs1TIOHz4IQLt2Hbh+0I0KBkqtY/EqCK5Xpbx8J3uScnA6IcfhZG9SDldH+1fpPkRERESk9lBAUERELptsmwOn0/09wKFDB/hs1TIyMzMxm81cf8ONXNW+o4dKKVLzGA0Fs2A7f7/ATBoURkRERERKUeHbxd27d/PnP/+Zzp0707NnTx5++GFSUlJc6ddeey2tW7d2+9m2bZsrPSMjgxkzZtCjRw+6dOnCtGnTyMjIcNvHsmXLGDRoEO3bt+eWW24hISHBLf3QoUNMnDiRjh070rt3bxYsWOCWnpeXx+zZs+nVqxcdO3bk/vvv58SJExU9VBERqWJBVi8C/Qu6MVq8DEQ2tHDq1EkW/+dDMjMzCW7UmD/d9WcFA0UqyGQ00CnKF6uPkSCrF23DNXOxiIiIiJSswgHB+fPnM2TIEL799luWLVtGdnY2U6dOdaWfOXOGlStXsm/fPtdPp06dXOnTp0/n9OnTxMXFERcXx+HDh4mNjXWlr1u3jpdeeonnnnuOLVu2MGjQICZPnkxqaioAWVlZTJgwgS5duhAfH8/rr7/OokWLWLx4sSuPuXPnsnXrVhYvXsz69esxmUw89NBDlakfEZE6Kd/hJCMnH4fDWfbKFWAyGri6uR/dowz0a2MlwMdEcHAjOnXuSsdOXbjzT5NoGNyoSvcpUlc0CTTTr00AMdH+VTYuoYiIiIjUThW+W3zxxRcZNWoUVquVsLAwnnrqKXbs2EFycjI2m42srCwCAwOL3Xb//v1s2LCBZ599luDgYEJDQ5k5cyZxcXGugN/ChQt54IEH6Nq1K76+vtx7772Eh4ezYsUKAJYvX05QUBBTp07Fz8+PDh06cO+997Jo0SIAzp49y4cffsisWbOIiIggKCiIp59+mp07d7Jr165KVpOISN2RbXOwcW8GG/dmsOnXTPLyqzYoaDQaOHM6mdzsP1qHX3/DjQwaPAyz2Vyl+xIREREREZGiKhwQtFqtbu/9/AoGxbbb7aSlpQHQoEGDYrfdvHkz7du3JygoyLWsTZs21K9fn+3bt5ORkcHOnTvp27ev23YxMTGubsPx8fHFpv/yyy9kZGSwdetWGjZsSKtWrVzpQUFBtGrVqkjXYxERKepoqs01tt/Z7HxOnrVXWd75+flsWL+ObzZ+Q9zqlTgcBfupCxOHOBxOfkvJ5eej2WTk5Hu6OCIiIiIiUodd9KQiq1atIiIigvDwcH799VcAunTpgp+fH23atOHhhx+mS5cuACQmJhIVFVUkj7CwMJKTkzly5AgGg4Hw8HC39PDwcLZu3erKo3///kXSAVJSUkrcR0REBMnJyZU+zsIvrfJHXahOCqg+3Kk+iqppdeLtBU7nH2W1eDlwOC4+gHXmTDqrVy4jOTkJgIbBweTn2wHTReddE+w6lsPhUzYAktNyufZKKyajocb9flxqqo+iLmWdGI114/oTEREREXcXFRBct24dc+bM4dVXX8VoNNK8eXO2bNmCn58fJ0+e5D//+Q933XUXy5YtIzo6mqysLHx8fIrk4+vri81mIzMzE4vFgsnkfnPq4+ODzVbwJaq4PHx9CwbOLuyyXNw+zs+jMpKOHaz0trWV6sSd6sOdp+vDlu8kLRP8LBDgUz1an3m6TiqiPk7O5ECwP2SlGshKvbj8jhw5wpYftpCXl4fZbKbn1T0LHtQkHaqS8lYn9nwn+05Apg3C6kNEYMHv38FjTtKz/uh+fdDXgI/5j9/NmvT7cTmoPoq6FHUS1bRV2SuJiIiISK1TqYBgXl4er7zyCkuXLuX1118nJiYGALPZTP369YGCVn+PPPIIe/bs4ZNPPuFvf/sbZrOZvLy8Ivnl5ubi5+eHxWLBbi/aNS03N9cV9Csuj9zcXKAgMFjWPiorLLwZRqMG6IaCFgpJxw6qTn6n+nBXHeojL9/Jpl8yybI7MORDl8a+hNT33Nh01aFOKioismrysdvtbNywjoSfClp5NwkLZ+jQkWRkpNao+qiIvUk55Hvb8PGGVAe0a+RPgI8JgzWPHUeycTqhUYAX0c0LPpNq4u/HpaT6KEp1IiIiIiJVrcIBwXPnznH//fcDBRN8hISElLp+dHQ0x44dAyA0NJQff/yxyDrJyclERkYSEhKC3W7nxIkTNG7c2JWelJREZGSkK4/jx4+7bZ+UlITJZCIsLIzQ0NBiuwYnJSXRu3fvih3seYxGo7rVXEB14k714c6T9ZGRZSc7DwyGgi/OJzOcNGng+XNTU35Hsm0OjAbwNldF4CGfw4cKWjX16BlDn74DMBggIyO1xtRHRTkwun73AJzOguOMbGiigb+ZXLuTIH9TkXETa2t9VJbqoyjViYiIiIhUlQp/23viiScICAjgvffeKzMYCLBjxw4iIiIA6NatGwkJCWRmZrrS9+3bR1ZWFp06dSIkJISoqCg2bdrklkd8fDy9evVy5fHdd9+5pW/evJmuXbtisVjo1q0biYmJHD161JWelpbG3r17XS0ZRaR2s3obMXv9EWxp4K8v0OX16/Ecvt59jnW7z3EsrfLDLDidBV1jLRYLI24azagx47m2//VFhoSojZo39sbfu+DjNbKhhUD/P569WX1MNLR61YlJVEREREREpPqqUEAwLS2NtWvX8tBDD+HlVbRx4caNG/nqq684e/YsKSkpPPPMM+zbt4/bbrsNKAjmtWzZktjYWNLT00lOTiY2NpYJEya4ugRPnDiRV155hV27dpGdnc2CBQvIyMhg6NChAIwZM4Y9e/bw/vvvk5OTw7Zt21i4cKGr1WKTJk0YPHgwM2bMICUlhdTUVJ588kmGDBniCkyKSO3mbTbSK9qfVqHedLnCj8ggi6eLVCM4nU5+Tcn9/TX8ejy3wnnk5eXxxeer2Prj965ljRuH0KJFyyorZ3XnZzFy7ZUBDOlQjw6Rvp4ujoiIiIiISBEV6jJ84sQJnE4nI0eOLJL217/+lbZt2/Lcc8+RnJxMvXr16Ny5M0uWLHELxM2bN4/Y2Fj69euH1Wpl9OjRTJ061ZU+fvx40tLSmDx5MhkZGXTv3p133nkHi6XgC32jRo144403eOaZZ5g9ezbh4eFMnz7d1YIQYNasWTz11FMMHjwYs9nMkCFD+Nvf/lbhyhGRmsvqY6JlaO1vjVaVDAYDPmYj2baCmUx9LBVrRH7q1ElWLv+EU6dOYjKZuLLtVVitAZeiqDWC0ahWgCIiIiIiUj1VKCDYunVr9u3bV+o6a9asKTU9JCSEhQsXlrrOlClTmDJlSonpXbt2ZdmyZSWmW61WZs+eXeo+RKTucjqdnDxnx2Q00NB6UZOt1zrdm/vxS3IuRiNcGVZ0xvbiOJ1Oft65nS/XxmG32/H3tzJs+E11OhhYVTJy8sm1O2ngZ1KAUUREREREqoy+CYtInZNwOJvk9ILZyFuGetMqtHyBr7ogwMdE12bln5E9NzeXL9fGsXvXTgCuuKI5Q4ffhL+/9VIVsc5ISstjW2IWTicEB3jRo7mfxh4UEREREZEqoYCgiNQpTqfTFQyEgqCLAoKVk5+fz0cfvsupkycwGAz06dufnldfo6BVFTmSauP3uVk4dc5Odp4TP4vqVkRERERELl6FZxkWEanJDAYD9Xz/GFsw0E/jDFaWyWSiffuOBATUY/ztd3F1TG8FA6vQ+b+n3mYD3l6qWxERERERqRpqISgidU7PFn4cOmXDZDRwRbBmIK6I3JwcMrMyCQpqCEC37lfTvkNnfHzUyrKqtQ71xmwykJPnoGmwBZPGEBQRERERkSqigKCI1DkWL6O6CVdCcvIxVi7/FKPJxF0T7sFisRTMTKxg4CVhNBqIDvH2dDFERERERKQWUkBQROR39nwnXia1wrqQ0+lk648/sHHDVzgcDurXD+TcubM0bBjs6aKJiIiIiIhIJSggKCJ1nsPh5MeDWZw6Z6eer4meLfyweGmIVYDs7CziPlvJ/t9+AaBV6ysZPGS4WgWKiIiIiIjUYAoIikidd/ysnVPn7ACczc4n8XSeumoCR48msmrFUs6dO4vJZGLAdTfQqXM3TRwiIiIiIiJSwykgKCJ1nvmCxoBmdRsGIH7zt5w7d5YGDYIYcdNoQkJCPV0kERERERERqQIKCIpIndeonplWod4cP2Ongb+JqIZmTxfpssiyOdh5JBub3UmrUG9C6rsf95AbRxK/+Vv69huAt3fVtZjMsjk4lu7EL8hOcICpyvIVERERERGR8tEgWSIiQMtQH/q0tnJVhG+d6RK7+1gOp87ZOZudT8LhbA4ePMA3G792pVutVq6/YUiVBgNz8xxs/jWTX086+f63LFLO5FVZ3iJSMxw5coSrrrqKd955x7Vsx44djBs3jg4dOjBgwACWLFnitk1GRgYzZsygR48edOnShWnTppGRkXG5iy4iIiJSayggKCJSR9nznQA4HQ6S921myX8/5Pv47/j1132XbJ9ns/Ox2Z2u96cy7OXeLi2zfOuKSPX2z3/+E4fD4XqfnJzMpEmTGD16NFu2bGHmzJk8++yzbNq0ybXO9OnTOX36NHFxccTFxXH48GFiY2M9UXwRERGRWkEBQRGROqp1E2/Iy+TQj0s5uX8LAB06duaKK5pfsn3W8zVh8fqjBWZDa9kjVxw4kcu3+zLY/GsmO49kX7KylSQzN5/TGXacTmfZK4tIqTZu3MjRo0fp3Lmza9mHH35ITEwMY8aMwcfHh759+zJ27FgWLVoEwP79+9mwYQPPPvsswcHBhIaGMnPmTOLi4khNTfXUoYiIiIjUaAoIiojUUWnHD3Jg88dkpR3DbLEwbPjNDB4yHLP50o2h6G020ivan+hGBnq08CO0ftn7Sjxtc70+kmq7rIG5pPQ8Nu7N4PvfMvnxYJaCgiIX4cyZMzz99NPMmjULo/GPW9D4+Hj69Onjtm5MTAwJCQkAbN68mfbt2xMUFORKb9OmDfXr12f79u2Xp/AiIiIitYwmFRERqYPiN3/Lt9+sB6Bx41BG3DSKoKCGl2Xfft5GIgINBJejdSBAgI+JzNyC7oVWb9NlHePxaKqNwhjgybN2smxO/L3rxhiTIlXJ6XQybdo0xo0bR+vWrd3SEhMTiYqKclsWHh5Oeno6OTk5xaYDhIWFkZycfFHlcjjyL2r7iu/P4fa/VA86L5eeE3D+/q/sdZ2lvi9tOyeX/7quS3StVD86J9WTp8+L0Vi+iRsVEBQRqYMaNWoMQOcu3ek/4Hq8vKrvx0GHKF/8TxjJdzhp3qjqJjgpjwAfEyfPFoxdaPEy4O1VfYOBGTn57Dyagz3fyZVhPgQHVN9zKnXPggULyM/P55577imSlpWVha+vr9uywvc2m42srCx8fHyKbOfr64vNZiuyvLzy8+0cPbK/0ttfjKRjBz2yXymdzsul4eVlJtjfn7w8G/m23DLXt9n+GDPYnp+HrRzbAJjybOTn53EqORG7XZOWXUq6VqofnZPqyVPnJappq3Ktp28LIiJ1RFZWJn5+/gBEt2zN3RPvpVHjEA+Xqmxmk4E2TYoGAy6H1qHemE0Gsm0OrmhkwctUfQOCO45kk5ZZ0Crip8NZ3HBVPQ+XSKRAXFwcy5YtY8mSJcW28DWbzUUCe7m5BQEAHx8fzGYzeXlFv9zn5ubi5+dX6XKZTF5ERLao9PaV4XA4SDp2kLDwZm7dpsWzdF4uPWfmWcxmCyZL2Q/27PzRssXLZMZSjm0AjGYLJpOZ0CZFWxRL1dC1Uv3onFRPNeW8KCAoIlLL2e12Nm5Yx66fdzDh7snUq18foEYEAz3NaDQQHXJ5WyVWVr7D/bXT6bys3atFSjJ37lxOnDjBwIEDXcuysrLYvn07y5cvJzQ0lOPHj7ttc+zYMUJDQ7FYLISGhvLjjz8WyTc5OZnIyMiLKlt5u9RUNaPR6LF9S8l0Xi4dB2D4/V9ZLlynPNsUrmfAc9d1XaJrpfrROameqvt5UUBQRKQWS0tLZeXyT0lJKRhna/+BX+ncuZuHSyWXQpswHxIOZ5HvgHbhPgoGSrXx0UcfYbfb3ZY98sgjxMTEMH78eF599VU2bdrEsGHDXOnx8fH06tULgG7duvHmm2+SmZmJv39BK+d9+/aRlZVFp06dLttxiIiIiNQmCgiKiNRSe/bsYs3nq7DZbPj4+HLjsJFER5dvPAmpeRoFeHHDVfXUMlCqndDQ0CLLvL29qVevHqGhodx5552MGzeOPn36MHDgQOLj41m6dCkff/wxUBAQbNmyJbGxscyYMYPs7GxiY2OZMGFCkbEHRURERKR8FBAUEall8vLy+HrdGrZv+wmAiIhIho8YRUA9jSlXFygYKDVNmzZtmDt3LnPmzOHxxx+nRYsWvPLKK0RHR7vWmTdvHrGxsfTr1w+r1cro0aOZOnWqB0stIiIiUrMpICgiUsv8uCXeFQyM6dWHa3r3q9aD2YpI3bNo0SK39wMHDnQbY/BCISEhLFy48FIXS0RERKTOUEBQRKQWyHc4MRkLWoZ17xHD0aOJ9OjZiyuuaO7hkomIiIiIiEh1o4CgiEgNlpGTT/wv6STt306Xrj1pH+WP2Wxm7Lg7PF00ERERERERqaYUEBQRqcES9h5l17crsWWmsTXfzhWNryPAp/pObS8iIiIiIiKep4CgiMhl5nQ6cThxdfGtbB7bt//ED1+uwZFvx8vbirVhxEXlKSIiIiIiInWDAoIiIpdRWqadHw9mkWd30rqJD9Eh3hXOIzcnhy++WM2+vbsBaNikGc06D6ZlRH38LJo8REREREREREqngKCIyGW073gueXZnwevkHKIaWrB4lb9VX8rxZFYs/4T09DSMRiN9+11H9x5XYzBcvpaBTqeTM1n5mL2M+HsrACkiIiIiIlLTKCAoInIZmc/r0msyGriwh++xNBsnztoJ8veiabClyPYGg4Fz585Sr159Rtw0irCwiEtd5CL+dyiblDN5GAzQuakfTQLNl70MIiIiIiIiUnkKCIqIXEZtw33IdzrJzXPSKtQbL9MfEcHTGXa2Hc4GICktDx+zgZD6ZhwOB0ZjQUu8xiGh3DJqHE3CwvHx8b3s5c+yOUg5kweA0wmJp20KCIqIiIiIiNQwCgiKiFxGvhYjPZr7F5uWmeso8v7YsSN8tnoFw4ffTJOwcACaNY++5OUsicVkwOJlwPZ7t2erj7oMi4iIiIiI1DT6JiciUk2E1PPC7/cx+by9DCT9+iMff/ge6WmpfPvNeg+XroCXyUDPFv5EBlmIDvGmTRMfTxepyhw/k8f3v2WyIzGbvHynp4sjIiIiIiJyyaiFoIhINeFtNtKnlZWTaef4dt0qDh3cD0CbK9sxaPAwD5fuD/V8TXSIuvzdlS+lnDwHCYezcTicnAa8TNA2vHYdo4iIiIiISCEFBEVEqpGkY4dZvXIZGRnn8PLy4rrrB9OhQ+fLOotwRZ3LycdiMuBtrrmNzvPynTgcf7QKzMlTC0EREREREam9FBAUEakmko4d5b//XoTT6aRhw2BGjBxFo8Yhni5WqRIOZ5GUlofRaKBLU19C6tfMCUYCfEyENzBzLC0Pi5eB5o29PV0kERERERGRS0YBQRGRaqJJWDjNm0fj5+fPddcPxmKxeLpIpcq2OUhKK5hx2OFwcuiUrcYGBAE6NfXjyjAHXiYDJmP1bZEpIiIiIiJysRQQFBHxoMOHDtIkLByLxYLBYGDkzWPw8qoZf5rNJgNmLwN5v8847GepuV2GC9Xkbs8iIiIiIiLlpW8+IiIekJ+fz8YN6/jvfxbx5do41/KaEgyEghmHezTzo0mgmWaNLFwZVntmHBYREREREanNas43TxGRWuLsmTOsWvkpx44dBcBstuBwODAaa94zmkB/L7r466NERERERESkJqnwt8/du3fz5z//mc6dO9OzZ08efvhhUlJSXOk7duxg3LhxdOjQgQEDBrBkyRK37TMyMpgxYwY9evSgS5cuTJs2jYyMDLd1li1bxqBBg2jfvj233HILCQkJbumHDh1i4sSJdOzYkd69e7NgwQK39Ly8PGbPnk2vXr3o2LEj999/PydOnKjooYrUWU6nk13HsvlmbwZ7knI8XZxa5ddf9/Heu29w7NhRLN7ejLhpNDcMurFGBgNFRERERESkZqrwN9D58+czZMgQvv32W5YtW0Z2djZTp04FIDk5mUmTJjF69Gi2bNnCzJkzefbZZ9m0aZNr++nTp3P69Gni4uKIi4vj8OHDxMbGutLXrVvHSy+9xHPPPceWLVsYNGgQkydPJjU1FYCsrCwmTJhAly5diI+P5/XXX2fRokUsXrzYlcfcuXPZunUrixcvZv369ZhMJh566KFKV5JIXXMsLY9DJ22cy8nnwIlcktPzPF2kGi8/P5/1X3/Jsk//S05ODqGhYUy4ezJt2rT1dNFERERERESkjqlwQPDFF19k1KhRWK1WwsLCeOqpp9ixYwfJycl8+OGHxMTEMGbMGHx8fOjbty9jx45l0aJFAOzfv58NGzbw7LPPEhwcTGhoKDNnziQuLs4V8Fu4cCEPPPAAXbt2xdfXl3vvvZfw8HBWrFgBwPLlywkKCmLq1Kn4+fnRoUMH7r33Xtc+zp49y4cffsisWbOIiIggKCiIp59+mp07d7Jr166qqjeRWs2e73R/73CWsGbBuj8eyOTLn8/y89HsS120GivPlsee3QV/g7p268ntd95NYGADD5dKRERERERE6qIKD/xktVrd3vv5+QFgt9uJj49n/PjxbukxMTGuYN7mzZtp3749QUFBrvQ2bdpQv359tm/fTvfu3dm5cydz584tkkdCQgJ333038fHx9O3bt0j6c889R0ZGBlu3bqVhw4a0atXKlR4UFESrVq1ISEigXbt2FT1kABwOR6W2q40K60J1UqA21keTQBNH0wykZ+YTZPUitJ4RhyO/2HUPnswl5YwNgEMn82noX7C8NtXHxXI4HPj4+nDj0BHY7XYCmzTn+Jk8GtdzYjIaPF28y642XjMXQ/XhTvVR1KWsE6PRVOV5ioiIiEj1d9Ejwa9atYqIiAjCw8NJTEwkKirKLT08PJz09HRycnKKTQcICwsjOTmZI0eOYDAYCA8PL5LH1q1bAUhMTKR///5F0gFSUlJK3EdERATJycmVPs6kYwcrvW1tpTpxV9vqI8obws0FAavkYyWvdzzVSerpP1oQpngbCPI31Lr6qKj8/HwSfkqgcePGRDUt+Jvk5eUk+ZyR77cU1E19XwOdI+peQLBQXf8duZDqw53qo6hLUSdRTVuVvZKIiIiI1DoXFRBct24dc+bM4dVXX8VoNJKVlYWvr6/bOoXvbTYbWVlZ+Pj4FMnH19cXm81GZmYmFosFk8n9abWPjw82W0ELpOLyKM8+zs+jMsLCm2nQ/985HA6Sjh1UnfyurtdHSJgT8+Fs0rPyCQs00zbMUqfrAyD19GlWrVrKyRMnOHLkCJ269CT1dDJh4c04kptNUMM/Wls2CrXiba5b9VSbrxmn00lqZj7eXgasPuVreVWb66MyVB9FqU5EREREpKpVKiCYl5fHK6+8wtKlS3n99deJiYkBwGw2Fwm65ebmAgUBObPZTF5e0ckJcnNz8fPzw2KxYLfbi00vDPoVl0fhPnx9fcvcR2UZjUZ1q7mA6sRdXa0PbyNcHR3gel/Ytbiu1seun3ewds1n5OXl4efnx9BhN+Hn50fq6YI6CbJaSM8q+Jvl723Ex+KFwVA3WwnWxt+RLQcyOXnWjsEAnaL8CGtgLve2tbE+LobqoyjViYiIiIhUlQoHBM+dO8f9998PFEzwERIS4koLDQ3l+PHjbusfO3aM0NBQLBYLoaGh/Pjjj0XyTE5OJjIykpCQEOx2OydOnKBx48au9KSkJCIjI0vcR1JSEiaTibCwMEJDQ4vtGpyUlETv3r0rergiIuVis9lY9+UX7Ny5DYCoqCsYNuJmrNYAt/EX2zTxxs9iJC/fSWSQuc4GA2ujbJuDk2cLHmo5nZCYaqtQQFBERERERORyqXC/kyeeeIKAgADee+89t2AgQLdu3di0aZPbsvj4eHr16uVKT0hIIDMz05W+b98+srKy6NSpEyEhIURFRZWZx3fffeeWvnnzZrp27YrFYqFbt24kJiZy9OhRV3paWhp79+51tWQUEalKeXl5LPrgHXbu3IbBYOCa3v0Ye+sdWK0BRdY1GAw0DbYQHeJd57oK13YWLwPe5j8CvPV8dH5FRERERKR6qtC3lbS0NNauXctDDz2El1fRxoV33nkncXFxxMXFYbPZ2LhxI0uXLmXSpElAQTCvZcuWxMbGkp6eTnJyMrGxsUyYMMHVJXjixIm88sor7Nq1i+zsbBYsWEBGRgZDhw4FYMyYMezZs4f333+fnJwctm3bxsKFC12tFps0acLgwYOZMWMGKSkppKam8uSTTzJkyBAiIiIuqrJEaoK8fCc7ErP5YX8mJ88W7T4vVc9sNtOsWXP8rVbG3Xon1/Tup3G+6iCT0cDVLfxpGmyhdRMf2jQpOp6tiIiIiIhIdVChLsMnTpzA6XQycuTIIml//etfmTx5MnPnzmXOnDk8/vjjtGjRgldeeYXo6GjXevPmzSM2NpZ+/fphtVoZPXo0U6dOdaWPHz+etLQ0Jk+eTEZGBt27d+edd97BYrEA0KhRI9544w2eeeYZZs+eTXh4ONOnT3e1IASYNWsWTz31FIMHD8ZsNjNkyBD+9re/VbhyRGqivUk5HEktGMszLTOfAW29sHipW2pVy83NJS8vD6vVCkC/awdydUxv/Pz8PVwy8SSrj4mrInzLXlFERERERMSDKhQQbN26Nfv27St1nYEDBzJw4MAS00NCQli4cGGpeUyZMoUpU6aUmN61a1eWLVtWYrrVamX27Nml7kOktsq1O12v8x1O8vKdCghWsePHk1m54lP8/f0Zf9tdGI1GTCZThYOByel5nMnOJ7S+mUA/TRQgIiIiIiIil0elZhkWkeqreSMLpzPs2POdRAZZ8PdW19Wq4nQ6SfjpR9Z//SX5+fnk59s5e/YMgYENKpxXUnoeCYeyADh00kaf1ladKxEREREREbksFBAUqWWCrF5c1zYAu8OJjyatqDI5Odl8HreKX3/ZC0B0y9YMuXGEa/zTijqT9cfMw/kOJ+dy8hUQFBERERERkctCAUGRWsjLZMDLpG7CVSXp2FFWrviUs2fPYDQa6T/gerp07YHBUPk6DqnnxaFTNhy/B26D/It2Gc7Jc5B42oa3l5GohuaL2p+IiIiIiIhIIQUERURK4XQ6+XLt566uwcNHjqJJk7CLzjfI6kWfVv6czXHQ0N+Excu9daDT6eT73zLJzHUAkGVzcGWYZq0VERERERGRi6eAoEgNl5yex6lzdoIDvGgSaPZ0cWodg8HAsOE38cP3m7lu4CC8fYoG5U5n2DEZINC/Yn9SrT4mrD7FTyZisztdwUCAtEx7xQouIiIiIiIiUgINWCVSg506Z+enQ1kknrbx06EsTmcoaFQVjiQe5qf//eh63zC4ETcOG1lsMHDnkWy+/y2TTb9m8svxnGLzO3jSxp7jTk6eLf/58TYbaWj9I8CoYK+IiIiIiIhUFbUQFKnBMnLy3d6fy853CyJJxTgcDr6P/45N320EICQklPCIyFK3OZqW98fr1Dwigiz872AWmbkOmjWy4GM2sicph9RzTrYeyqJfG68SWwVeqEdzP1LO2vH2MhCk8yoiIiIiIiJVRN8wRWqwxvXN/HYil9w8J95mAyH11YqssjIyMvhs1TIOHz4IQLurOtCocUiZ2wX4GF0zBtfzNfHb8VzOZhe8/y0ll5D6f/yZdToLxgIsb0DQaDSoZaCIiIiIiIhUOQUERWowP4uRvq2tnM12UM/XWGRiCimfQ4cO8NmqZWRmZmI2m7n+hhu5qn3Hcm3bvZkfB07aMBqgeWNv9ia5dxsOa2Dh1LmCVoQBPiaCKjjOoIiIiIiIiEhV0zdTkRrO4mUkOODyBALzHU5XF9ba0jV586Zv+O7bDQAEN2rMyJGjaBjcqNzbe5uNbrP/tgr1JjPXQZbNwRXBFsICzTTw9ec3i4E20X54mQxVfAQiIiIiIiIiFVM7vtGLyCXndDrZciCL1N8nLmkX7sMVjbw9XKqL5+vnB0DHTl0YcN0gzOaL66LrbTZydbR/kWX1fQ2YjAoGioiIiIiIiOcpICgipTrz+3h43l4GVzAQ4PgZe40NCNpsNiwWCwCdOnUlOLgRkZFNPVwqERERERERkctDAUERKdHe5Bz2p+QCEB3ijb+3kcxcBwCB/uWbGKM6yc/P55uN6/j1l338acI9+Pj4YDAYFAwUERERERGROkUBQREp0eFTNtfrI6k2ereycuS0DYuXkaiGNWv22/T0NFat+JTk5CQAfvt1X7knDilOvsPJ3uQcMnMdNAu20KhezaoPERERERERqbsUEBSREgX4GEnLLOgybPUx4WM20jLUp4ytqp99e/fwxecryc3NxdvbhxuHjqBlqzYXlef3v2WyJymHAB8jpzPy6X9lQf2IiIiIiIiIVHcKCIpIibpe4cdvJ3IxUNBluKax2+2s//pLEn76EYCwsHCGjxxF/fqBF5XvyXN2tiVmk5ZpJ+WsgdahBmx2Jz4VbCTodDqxO8CsmYdFRERERETkMlJAUERK5G020i7c19PFqLRvNn7tCgb26NmLPn37YzJd/NiHZ7LyaRTgxdnsfPIdTqw+Rur5VizfM9n5bNmf+f/Z+/MgO+/ywPv+/u7t7L3vm/bNtmTZko3lFRwT8EAIZEwYpibvEKgXCuKqTM2ShIpTIkOgMgFPUWQSnHmePO+bB948CWRYkoyTkAA2YMuWF3nTvnar9/Xs59zr7/3jbh251dotW5J9fVwud/e9/06fLvfV14IXaFZ0ONw0cP2usxBCCCGEEEKI64vUtwkh3rbu2HE3XV09PPTRj/Pu9zxwRYKBAJ1NFk0pkxv7U2wdSnPfxhxz5WDJFOYLOTLl4gUaiHs1VtzwitybEEJcq374wx/ykY98hK1bt3LPPffwh3/4h3je6V61r7zyCh/72MfYsmUL999/P9/5zneWHF8ul3nkkUe4/fbbufXWW/nt3/5tyuXyW/0YQgghhBBvCxIQFEK8bfi+z6uvvNT4PJ1O8+9//f/N6jXrrtg1tNakbIN7NmTYvjrNAzflOD7j8syRCrsW+wpeDOd1ZcKGobAMKRsWQry9LSwssHPnTnbt2sWf/dmf8ZOf/IQ/+ZM/AWBiYoJPfepTPPTQQ+zevZsvfOELfOlLX+Kpp55qHP/5z3+eubk5Hn/8cR5//HGGh4fZuXPn1XocIYQQQojrmpQMCyHeFmZnZ/jb7/8Ns7MzKKUaE4SVOn+gbXTeY6YU0J61GGp3lmzzAk3FDckmTWxTUfcjnjlSoeJGdOQsbluVxjAUowv+kvNt6rvw4JUNvUmCSFN1I1Z1JUjIQBIhxNvcxz72scbHN9xwAx//+Mf5h3/4BwC+9a1vsWPHDj760Y8CcO+99/Krv/qrfPOb3+Suu+7i6NGjPPHEEzz55JO0tbUB8IUvfIGPfOQj/O7v/m7ja0IIIYQQ4uJIQFAIcV3TWvPqKy/xL//8DwRBQCaTJZdruqhjZ0sBL4/UABhf8Enaiq6meDJI1Yt4+nAZ19ekHIM712V48USVw5MuTak4eDdTCuhutmlKmcyV4nLhi+0l6FiKW1akL/VxhRDibaNcLtPd3Q3Arl27+PjHP75k+44dO/jBD34AwNNPP83mzZuXBP42btxIc3MzL7/8Mu95z3su6x6i6K1t1xBF0ZL/imuDvC5vPg3oxX8uvK8+7+fnO07z1r+v30nkvXLtkdfk2nS1XxfDuLjfSSUgKIS4brmuyz//8HH27X0VgJUrV/OBX/owmUz2oo6vuNE5P5/M+7h+/D+gNS9i/1idQ5MuU0Wf6ZJiQ0+iMR341hVpjs+4AKzqvP6mMQshxFupWCzyzDPP8L3vfY//8T/+BwAjIyMMDQ0t2a+/v598Pk+9Xj/rdoC+vj4mJiYu6z7CMGD05NHLOvaNGh87flWuK85PXpcLMwyTzpY2VOhfeOfGQSYmEPg+gedecHfPO92TOQh9vIs4BsD0PcLQZ3ZihCC4hPsTl0zeK9ceeU2uTVfrdRlasf6i9pOAoBDiso3Oexyddkk5BjcPpq5I2WuhFlKqxVN8zzxfvhoyWfBpTpmY7hw/+MH/YmF+DqUU99z7Ht51x10XLBF+vZ5mi6PTBjUvImkb9DTbjW3Z5NJrB5GmI2vhBhFVN6K/zaYtG/8IdSzFht4LlwkLIcQ73ebNm/E8j3Q6zX/+z/+ZDRs2AFCtVkmllk5bP/W553lUq1WSyeU/Z1Op1JLBJJfCNC0GBtdc1rGXK4oixseO09e/CsOQVhHXCnldLo2uFKn+018Q5Wcuan9rcAPm3R/Gsm0M58J/OA04ndlimTbORRwDYNgOpmnT07v8jwfiypD3yrVHXpNr0/XyukhAUAhxWep+xCsna2gN5XrEwQmXLUOpCx94HjNFn+eOV9EaDAXbVqXIJCwyCYOKG/fvC6M4a6/HLLIwP0cu18Qv/fKvMDBw6f/zl7AN7tmQpeJGZBJGI+MPoKvJ5uahFHPlODiZSRrMlSsMtDo4lmLrkJT7CiHEpXr11Vcpl8scPHiQRx99lGeffZavf/3r2La9LLDnunFWUDKZxLZtfH95xo/ruqTTl//z+GJLaq40wzCu2rXFucnrcnEiQOdn0fOTF7W/bukCQC3+cyFn7nMxx5w+/9V7X7+TyHvl2iOvybXpWn9dJCAohLgsYQT6dS1d/Oji+rucz1QxQGuYyPscnXZ5+kiFtV0Jbl+ToS1jEoRRIwMw3T7EB3/pI6xavYZUavkvg5V6xPCcS2eTRWfOXrb9FNtUtKTP/kN6oM1h4HV96u/ZkKVYC2nNmCRlCIgQQlyWbDbLtm3b+MpXvsL999/P2NgYPT09TE4uDS6c+rrjOPT09PDcc88tO9fExASDg4Nv1a0LIYQQQrxtyG+0QojLkkkYrOlOoBSkHIP1PW+8d15bJv4bxVQxoFyPsAzFdCng2LRLrTDJ8V3/D141j2Eoupssbrhx81mDgeV6yP/55CzffT7P//XkHCdmzt97xg81r43WePFElULt3I2oMwmD3hZbgoFCCHEFmGb8xxilFNu3b+epp55asn3Xrl3ceeedAGzfvp09e/ZQqVQa2w8ePEi1WmXr1q1v2T0LIYQQQrxdyG+1QojLtrE3yYNbmrj/hhy55OWlQtf9iFI9DsL1tdpsX5VmdafDyk4H0wDbhPzIHv7mr/6CWnEGd/QZ7lmfafTvO5vjM17jnH6o2TdeP+897B2tMTzrMZH32X20QnQFsh2FEEKcVqlU+OpXv8rx48ep1+scOHCA//Jf/gv33XcffX19/Nqv/RqPP/44jz/+OJ7n8eSTT/Ld736XT33qU0AcEFy3bh07d+4kn88zMTHBzp07+cQnPrGs96AQQgghhLgwKRkWQrwhlzLE40wTeZ89w3HPwME2hy1DKbqbbT50azN7x+qcmCxSPPTPjE0eA2DDhk2878FfInmB4GNXk4VlKoIwDux1N527ZBig5p8OAHqBJog0jnH5zyWEEGIp27aZmJjg3/27f0epVKKvr48PfvCDjYDfxo0befTRR/nqV7/Kb/3Wb7FmzRq+9rWvsXbt2sY5vv71r7Nz507uu+8+stksDz30EA8//PDVeiQhhBBCiOuaBASFEFfN8Rm30Yfw5LzHxr4EjmWQSZj02XO8sOe7lEpFTNPk/l94H1tv2XZRAcjuZpuHbmvhwLhLwo73PzxZZ2134qzHr+xwyFdDokgz2ObgWJI8LYQQV5LjODz66KPn3eeBBx7ggQceOOf27u5uHnvssSt9a0IIIYQQ70gSEBRCXDVpx2ChEpf2OpbCWszKO3HiGN/56/8fWmta29r50C//a7q7ey7p3Gu7k3Q32/zsYJnJgs9kIR4gsrJzea/D3hab1oyJH+rLLn0WQgghhBBCCCGuFxIQFOJt4MiUy1w5oLvJOmvA61p140AK21J4gWZ1V4KqF+FYBoODK+jq7iGRaWXLHe+ltT13WeeveVEjA7FUj9g3XieTNOnMnf7RV6iFzJUC2rIWtqk4Nu2SSy3d55Qw0rw8UiNfDelrtdnYm7ys+xJCCCGEEEIIIa4mCQgKcZ0bW/A4OBEPzZgtBWSTJh1nCWZdi2xTcWN/Cq01P3n+KFWzHds2uW1Vmi33/ipjBc3xOZirVrltdTxN+FIm/LZnLVrSJmMLPsOzLmu6Ejx3rMKOtRlaMxbFWsjTh+MhIqGGKNLYZpyleOvKNL0tS3sPHp+JB48AHJ1y6cha181aCyGEEEIIIYQQp8hvskJc52re0om4dT867/5aa14aqTGR92lJm2xflcGxrt4AjSiK+MmTT/DCsz+nY/VtdK+/k+OzHlXfRKm4nPjIlMtCJcBaDCCu6HAu6tymodixNsP+8TpJW2EaCq2hUA1pzVgsVMLGROFqPaQe6EZm4Fw5WBYQ1HrpWkdaphELIYQQQgghhLj+SOd8Ia5zA2026UT8Vm5KmXQ3n3+i7nQxYHzBR2tYqIQMz7pvxW2eValU5K/+n/+bF579OQChV0NrTdoxlgTjan7UCOYdnqpf0jUMQ7Gy0yGxmFlomYrOpjjo15Y1MRf7FmaSJm3Z0/0Dz5b5t6IjQWvGxDAU/a32WcuKhRBCCCGEEEKIa538NivEdS5pG9y3IUvN16QddcEpvIaxdLtpvPHswBMzLvlqSE+LTVfu9N8ZCrWQ/eN1DAU39ifJJEwqbsjovE9+6ji7n/zf1GpVbMdhx30PYretI+Uo1vckMA1FSzoe9JFNGpTrceZj6hJKhk/JJEzu2ZAlXw1pTZuknPgcuaTJXeszzJdDWjMmtqmYLgbkkgZt2eU/Hh1Lcee67GWukhBCCCGEEEIIcW2QgKAQbwOGocgkLi6w15mzWNOdaJQMX2z57bmMznvsHasTRnBy3uddqxO8NKY5WCozXQxpScdZd3uCGrevzvD0oSIj+55m7vgLAHR1dfPBD/1rOjo6lp3bthTFWsjargRTxQCADZc5yCPtGKSd5cHEXNJcMln4ja6HEEIIIYQQQghxrZOAoBBvU1UvYnTeI2kbDLTa1HxNylYYhmJjb/KKTcituBGzpYDRBQ+FIghDSlWNkYo4PuuSdQyUUgy2O9T8iFKxyPzIywB0rbqZno338OyoyYbQZW336QnJpfrpgR9KwZ1rM7Rkzv0jK4r0suzHyzW24FFxI/pabLKvCxYKIYQQQgghhBBvBxIQFOJtKIw0zxypUPMiglDz9GFNLmmQTRrsWJvBsa5c+9C+VpupYoDWkLAV8+WAU93/LKVYqIY4lkHdj3BM6Oxop37jA8xVI8acFcxP+KzpNDg4UWeo3WkMOJkvnx744QeaF4drDLbZrOxMNCYBQzxE5dmjFcr1iL5Wm61DqQuWTZ/P8KzHa6M1AE7Merx7Y/aKrtcbVfcjXj1Zww0067oTF+wZKYQQQgghhBBCnOna+S1XCHHF1LyImhf33FuohswUfQDK9YjxBf+KXiuXNLl5KMX6ngTrexKs6kyQS8YTfdf1JLllyKG7vBuzOkmk4Y61GbZu3UzP4AaakiZVN2K6FGAaCvN1P5HaXzfwY3jOZ64ccGjSZc9wtbFPFGmOTLmN/oLjCz5z5fANPc9CJWh87Aeainv+qc1vtb1jdaaLAYVqyJ7hGkEok46FEEIIIYQQQlyayw4Ijo6O8uu//uv84z/+45Kvv/vd72bDhg1L/n3ppZca28vlMo888gi33347t956K7/9279NuVxeco7vfe97vO9972Pz5s38yq/8Cnv27Fmy/cSJE3zyk5/k5ptv5u677+Yb3/jGku2+7/OVr3yFO++8k5tvvpnPfvazTE9PX+6jCvGW0Frz0nCVH75W5IUT1UZ23OVIOwbNi737HFPRnDld9po8Sx+9y6W1ZnzBZ0WHw0C7Q0+LzW2r02wbVLxvc47tfR4ju/+G+ZGXGHnpHzg4VsE2FX3N8WTkgTabppRJLmmwbWWKuh9R9+MAXHZx4MdNAymG2u1G/79iLQ74zZcD/mVfiWePVpjInw5yWuYbKxvuabE5lWCYTRpL+gteC/zXBQDDSBNpCQgKIYQQQgghhLg0lxwZGBkZ4ZFHHuFDH/oQzz///LLthUKBv/3bv+XgwYONf7du3drY/vnPf565uTkef/xxHn/8cYaHh9m5c2dj+49+9CP+6I/+iC9/+cvs3r2b973vfXz6059mfn4egGq1yic+8QluvfVWdu3axZ/+6Z/yzW9+k29/+9uNczz66KM8//zzfPvb3+YnP/kJpmnym7/5m5f6qEJc0N6xGj/aV+a1CU34BgJ4AON5n7EFHz/QTOZ9Ts5ffiafYSjuWJPhlpVp/tXNTdy9LktHzmJjX5KeK1hi+tponT3DVY5OubieZn1Pkkwi/rFyYP9evvvX/xf14jTaTJBbcx8TRc1CJaAlY7G+J0Fz2uK21Wke3NLMdCngif1lfryv1MhizCXjoSev7y3Y1xLf/6EpFz/QdDXZeIEmaRts6ks2hphcrp5mm7vWZ7llZZoda7NvOMB4pW3oSWAvllWv70lcU+XMQgghhBBCCCGuD5f8m+SLL76I53n89V//NZ2dnUu2eZ5HtVqlpaXlrMcePXqUJ554gi996Ut0dHTQ09PDF77wBR5//PFGwO+xxx7jN37jN9i2bRupVIrPfOYz9Pf384Mf/ACA73//+7S1tfHwww+TTqfZsmULn/nMZ/jmN78JQLFY5Fvf+hZf/OIXGRgYoK2tjf/6X/8rr776Knv37r3UxxXXMK01I3Mex6bdJVlTb5WZos+JGQ/Xj5gta4Znvbf8Hs7HMhV9LTYtGYvVXQnetSbDmq7EOfevuBHHZ1xmS8EZXw/52cEy//xacdkzHp/xcAPN6LzHkwdK/P2ePC8PF3lu93P8/d99H9/zMHO9THR8iBfmOjk0WW9k363rSXJDf5J8NeKnB0r8/GCcKaw1HJ12l1znpoEU71qT4Y61GW7oTwFx5iOAoWB1V4L7NmZZfZ7nuxTNKZO+FrvRz/Ba0pqxeO+NOd6/pYl1PVdmMIwQQgghhBBCiHeWSx4q8uEPf5gPf/jDZ922sLAAQGtr61m3P/3002zevJm2trbG1zZu3EhzczMvv/wyt912G6+++iqPPvrokuN27NjBnj17+PVf/3V27drFvffeu2z7l7/8ZcrlMs8//zzt7e2sX7++sb2trY3169ezZ88ebrzxxkt9ZACi6NrqI3Y1nVqLq70me0frDM/FAarxBZc712Xe0usHYYjWEXqxZDOMNFF0+f3repoMeltMZooB7VmL/hbjDZ3vUrh+xFOHKnhB/Cy3rkjRs5iJt3+sRqEaZ+y9NlqhK6cIIvjbFwu8NlrHthSVeoQfao5GdYaf+XtsPw9Asu9W5tI3E1Q1ttJkEooT0zXqQVzWnK8GhGEEaPLVgJ5mC9OAlG0ue/a2TBycO/X1jb02QRjiBZq13QkMFXGtvk3fjPeMgmv2eS/kWvkZcq2Q9VhK1mO5N3NNDOPaaosghBBCCCHeGld0ynA+nwfg1ltvJZ1Os3HjRv7Df/gP3HrrrUBcbjw0NLTsuL6+PiYmJjh58iRKKfr7+5ds7+/vb5Qnj4yM8J73vGfZdoCpqalzXmNgYICJiYnLfrbxseOXfezb1dVek4PDmqoXB7Dm56DfUY0hFG8FrTW2DzMlTUtaYddHGT35xq7foaCjOf54fOwK3ORFmq9qJqdOZ1nuCxRBV/ws09OaE7OauQo4FqxOT/HaBLw0otER1DX4IWQcmF7QtJHFNmtMZt5FzeumXCqTtmFjj8L0azy1V5NLxOf2QnAWfxftdMD2C9imogX42Uswmoe0Azf1QuIs2Xo9FmCBV4DRwpu6RFfE1X7PXGtkPZaS9VhK1mO5N2NNhlasv/BOQgghhBDibeeKBgRXr17N7t27SafTzMzM8Fd/9Vf8+3//7/ne977H2rVrqVarJJPLS9xSqRSe51GpVHAcB9Nc+tfqZDKJ58WZYGc7RyoVlxCeKlk+2zVef47L0de/CsOQXl0QZyiMjx2/6mtSsdxGaWl71mLFivRbfg+DQ9fOerwRXUHETFDBXcwQ3DCUpDVtkXIU6daQA08tkEhrepot/GSayKlRCioEkaY9rXlgXZrRoiJfCXFb34Ol8gyPNaECSNqKlT0JNq/OkkmoJVOAO3MWSVvhhZoV7Q5lN8IyFLmk4kChSvNisnHNdlgzcP2Wx74dvkeuJFmPpWQ9lpL1WE7WRAghhBBCXGlXNCBo2zbNzXF6U19fH//xP/5H9u/fz9/8zd/wO7/zO9i2je8vH5Tgui7pdBrHcQiC4KzbTwX9znYO142DQqlU6oLXuFyGYUhZzRmu9pps6k/TlrUJIk1Ps43xFmYHns3VXo9z0Vrz8kiNmVJcirx1KLVsrZKOyV0bmpgpBhgGHJ50qXkebVmLG/oSbOhNNfb1I4VpWfQ2O5QL07RPPomX6OWX7/9lFqoh00WPvcfqaA0RiroPmYTNAzfFPxtePFFlIu9jW4oNfenGEJCnDpXJV+NgYVezhVKnf+m9Vtf2Ur1dnuNKkfVYStZjKVmP5WRNhBBCCCHElXJFA4Jns3btWsbG4trHnp4ennvuuWX7TExMMDg4SHd3N0EQMD09TVdXV2P7+Pg4g4ODjXNMTk4uOX58fBzTNOnr66Onp+espcHj4+PcfffdV/LRxDWg+wpOzL1aXD/i+eNVSvWIwXabG/tTFz7oEowtxNOLASbyPp1NFoNtDlprDk+5FKoh/a0Ofa02KzocDkzUqXlxn6r5ckDddxhqdxiZ83AsxdruBHUvpDpxlKmZJ9FRyMR4SMZ06enLsaHHoSlSDBcTFOoRtqHoaTn9o+bWlWlqXoRtKixTUXFDqm7EQiVALU4cqXuaDb1JTsy6ZBMm67qvzLAQIYQQQgghhBBCXMaU4Uv1yiuvMDAwAMD27dvZs2cPlUqlsf3gwYNUq1W2bt1Kd3c3Q0NDPPXUU0vOsWvXLu68887GOX7+858v2f7000+zbds2HMdh+/btjIyMMDo62ti+sLDAgQMH2LFjx5v1mEJctmMzHvlqSBhpTsx4LFSWZ8m+EWfOX16cgcLInM/hSZfpYsCe4SqFWpydlzyjV1/CNtg8mOK9NzXxCzfkSCqfqdf+icl9P0ZHId0Dq/nIv/kUuVyucUxrWnHnujSb+pLcNJjkrvVZwuj0naQcA8tUi9OJy+w+VmW+8rpS4iaLtd0JHrixiTvWZkjYb98SuZmiz9Fpl4r71gyQEUIIIYQQQgghruhv2U8++ST/8i//QrFYZGpqij/4gz/g4MGD/Nt/+2+BOJi3bt06du7cST6fZ2Jigp07d/KJT3yiURL8yU9+kq997Wvs3buXWq3GN77xDcrlMh/4wAcA+OhHP8r+/fv5i7/4C+r1Oi+99BKPPfYYn/3sZwHo7e3l/e9/P4888ghTU1PMz8/ze7/3ezz44IONwKQQ1xKlzvz88kqftdaNicev199i09tiY5mKnhabgdY4q7LqLZ1WWV/8vLPJwlBQrofYVpxV6Icax1JMTU3wF//f/4Mjh/ZhGAYrbrqX9hs/yIujmpPzp3t0Gkrx4JYmPn5HKx+9rYVdhyv8z5/M8sNXi0sCgyNzXiNA2Zqx2NiX5NaVaTb2Xr/9Ai/F+ILP7mNVDozXefpwBde/PqaqRtHZv9eEEEIIIYQQQlwfrmjJsGmafPnLX2ZiYoKmpiZuueUWvvOd7ywJxH39619n586d3HfffWSzWR566CEefvjhxvaPf/zjLCws8OlPf5pyucxtt93Gn//5n+M4DgCdnZ382Z/9GX/wB3/AV77yFfr7+/n85z/fyCAE+OIXv8jv//7v8/73vx/btnnwwQf5nd/5nSv5qEJcMas7ExRrIaVaXDJ8qqfepRjP+7wyUkMp2DKYorfldCm1YShuXbm8f+ZQu83Ygofra1ozJh05C601PztYYXTB58SMy0Cbgx9A3Y+4eTDJ3/3gf5HPL9DU1MwDD36Eg4XmxvmmCgGDbU7jc8tUdDVZ/OxgiQMTdQAWKiGruhzWdccBv2zSZGExM1Cp+DqmioOblxsYvZ7MlU9ng3qBplSPrvlsyCNTLocm61imYvvKNG3ZN73zhBBCCCGEEEKIK+wN/Sb34x//eMnnd999N//0T/903mO6u7t57LHHzrvP5z73OT73uc+dc/u2bdv43ve+d87t2WyWr3zlK+e9hhDXCsdS3L46c9nHD896/OMrRZK2oiNnsW+sviQgeC6ZhMm7N+ao+xFpx8AwFHUv5NXRGjUvYrLgU3EjmlImTSkTwzB48AMf4sUXnuN97/8Alp1k5GC50W/wXIHMun86kyzSmiA8/fmN/UkSlqLqRowueJyYibMMgwjWvgP6BnY2WYzMxc+ctA2aUtf2sAA/1BxcDO76gebQpMsdayUgKIQQQgghhBDXG/lNTojr2FTB57XRGmMLHsVayKa+JBv7Lr7c1jIVWfN0EEopRaUeMrrgM7oQ0M4EL5R8su/aAsDg4AoGB1c09r9jbYaxeY+UYzDwuuzAU+bKAV4QUfM0mYRiVVeC9T2n7880FBt6kxRrIeP509PBT/UzfLvraba5c12GUj2iq8nCsa7trEhDxa/ZqbJvy7y271cIIYQQQgghxNlJQFBcVUemXAq1kN4Wm76LyGp7O5ktBRyddklYihv6U5cVDKp5EflqiCbO3pooBLxvy+Vn1imlaEmbuH6IPb+PleHLUDQZnegBWpbtn3YM1vWcOwD50nANUNy2Oo1jKt63pQnTWP6c2YRBc9qkUA1Rigt+L5xau6RtsKkvec0H0s6nNWPRevkJom8p01DcujLFwQmXhK24sf+d0etRCCGEEEIIId5uJCAorprhWa9RfjhV8Mkmstd8yeSVEoSaF05Ul5TPbl1xus/fyJxHxY3ob7XPuya9LTZKQcJSrOpMsK47gXlGC7q5csC+sTpKwebBFM3nOZ9pwOq2CDX8I1rDEQDC3Aq0leGfXyuScgxuXZkm7SzvcxdFy4dMBItfs01FOmGcNRgIcZ/DHWszzJUDUo5BLmlyYKLOiRmPTMJg+6o0qcVr+mesnQK2DKXO+UziyupqsulqemcF74UQQgghhBDi7UYCguKqef2UW63jbLd3TEAwWtpLzw1Of3xixmXvWBwoHZnzePfG7DkHTSRsgw9ubeKJA2UUikzCoCu39G29Z7iKu9jH7+XhKttWZdBaU/c1zWkT+3Vln8eOH2f4me/jVssow6Rl7b20DtyIUvHQCy8IOTRRXxK8BBiedXl5pIZjGWwZPF06vHkgxauj8bCTC2WTmYZqBJqKtZCjU27j4yNTLpsH46BfeMba1a6TybxCCCGEEEIIIcS1QgKC4qoZbLMZnffwgjgw1f4OmlaatA1WdjqcmPGwTMWartNlvoXa6QBXEGqq3vknzzalLN6/uYlSPQ6omgpePVkjXw3pabYII5guBsyV4yEhk4WA4TmPlR0OnTmLO9dlsU34zt//iBN7dwGa1rYOPvyRh/CtVg5N1TkwVqe/NR48cqa6H/G/ns9TdSNySRPbjFiXjbd1Nlncf0NuSdDxYpw5YNh43eMnbYOhdoeRueVrJ4QQQgghhBBCiAt750RgxDUnmzR596YcNS8imzh7sOnt7Mb+FGu6EtT8CPt1z97bYjG24KE15JLxhN8w0syUApK2cdZpvo5lUPUCDk7UKdZCpksBM4UAy1JsHUwxWfBxg4gg0BybcQlCzWwpIO0YzFcCLENRrnmApqX/Bm68/QHmwyT7RyrYpsKxDeYrIWu6E6zvXZrpNzLnEQTxx6V6SMXVkIWxBZ/Xxjy01tzYn2JFx/KhI+eSS5ps6E0yPOuRSRqsPSPot3kwxbqeBKahLjnYKIQQQgghhBBCvNNJQFBcVbapsN/GZcLFWsjhSRfDgE19SZJnZPoNz3ocWSyN3dCbZG13gq4mm3s2ZCnVQjIJE4Xm2WM15stx1G3zYIqh9qXBtXI95JWRGgDjeZ9DE3UioO5FGMRDOqaKPpO1gOYMBCEsVELG5gsEkeaudRm61+0g3dJLyRnkp4frdOUCpksBG3uTdGYtcimjUbY7VfBpSZskbAPHVKzsdBhb8DCUYstgEqpwcMIlWkx2PDBRP2tAsFALeeF4FT/UbOxN0tdqNybZru1OsLb73Nl/Z66lEEIIIYQQQgghLo4EBIV4Ez13rEp9scedF2jetWbpONkTs17j4+FZrxEAM5Ri/7hL3Y9IJwzKtbCRQTmR95cEBMNI47+up15bxsQLNBUvImUrql5IpR4SaGhKGaxod0iYEXt2/4yMO8mr9i/RnDLZujLDa9YaXtlfYr4SMDrn0d9mU3ZDZoshKUdxbNollTBoSpokbYO71mdY0eFQ9SIG2mx6W2yG2i1Gq+BYCjfQ+IFmeNbjH3WRoXabG/pPDwDZP16ntthL8kf7inRkbRxLsW1lik4ZXCGEEEIIIYQQQrwpJCAo3rG01niBxrEU6symdVfo/G5wuh9g/SzDL9KOQbEWApBJnM54OznvNfYv1yMqXkTV1VgWrFkMGrp+xDNHKuwdq6OUpi1jkU2atGYsPnhLM08eKBFGkE1YZJIG7RkTw1D0pOu8/NTfky6OARCVRpgrb+LdrTnK9YgnFcxVQoIwzsL7+B0Zvr07T7mkyVdDWtImt6xIU/cj5soB/a3OkiBfFMXPs3UoyYFJnxMzHl3NFmGkOT7j0ddi05KJf/ScqpSOIs1kPqAzaxNGmoOTLh05i5PzPkGoGWhzcCwpDRZCCCGEEEIIIa4ECQiKdyQ/1Dx7tEKhGtKUMnnXmswVDziV6hGFWsjonE9/m80tZ0zmBdi+Ks2RKRelYN1ioG+mFDBbCnADTcJSKMAxFaUowogUijgb8NBknWOzLvOVuJS4KWVxQ3+SlR0O8+WQvaM1ym6EF2o2ttnMlUOiwnGO7P1nXLcOpkOx7W6c7Cq2DMUBvc6meAhJ2jaILGjLmgRRPAUaIGGpRhmwUnGvP4CFSkDVi+jM2ViLcc1s0uT21Q65pMmxabfxzGoxCuj6Ea1pk3wlxHLi4SBTRZ+ZUkBXk0UmoRhfiJ9tbMHnng3ZK/fiCCGEEEIIIYQQ72ASEBTvSBN5n0I1zmQr1kLGFzxWdl7ZabWvjdZoSpqs7Yn77HU3xYGxuh+xosMhkzBJOaf78p26rxdPVNFaMzzr0pmzWNeTZLYUkLAjRud9frSvzFQh4NiMx+FJl5oXEmlFR9bEMuNsx1I9ZF1PkiiKJ/RWvYDC0Z+zMPISAG0dPbRueh+FIEPCNhpDTdoyFvdvyvLTgxUyjsG67gRNKZObh1KcnPNJ2Iob+lJkkgadOYumlMn4gs+e4SoA2aTLjjWpJeuwpitBqR5SrkcMttk0p0y8IOKpwxVqXoRScMvKDFU35HsvFMgmTdqyFntHXVozZuM1CkKNJQNEhBBCCCGEEEKIN0wCguId6cxsQMe68gMqosWsOsdUGIbi4KTL8Zm4Z+BEPuDdm7KYhqJYC9kzXMULNObibSxUQipuRG+LwWwpwDLh5LyP1hrLgOdPVFnfnWQ2bbK/ENCRMym5mqNTLn6g6ciZeKEGrbGVwegrP6Y0sQ8Ao3MzuRvuYa6u46EuJuQXg6MA99/YREvGZKYYkEuZ5JIm99+QYyIfkHbUkt5+M0Wfv92TZ74cNvoaVtylpdGOpbh99eneia4fsW+sznQxIJc00DoeUrKiw2lkSQIksmYjM7E9Z71pwcCDE3VmSgHtWYtNfckLHyCEEEIIIYQQQlznJCAo3pF6mm3W9yQagaC+1is/wOLG/iR7hmv4oeamgSQn5/3Gtrof4QaatKPYN16nXI+DaIVaSC5pUvfjYF3KiYNgN/YlmS9HGAYkLUV9zkMp6GiyuT1l0tVss3+szolZj4ob4YeamhcSRHDnmiSG/y4OLIxS67iDtt41GKbJ8ZkqmYSBoRQ3D50uZ7ZNxQ39KZ6uVijXI352qMy7VmewTbUsKLd3rE7SNoh0wMl5j56WNGnHoHSONQlCzdNHKhQqIcdnXPpbbdqzFm0Zi1zSZENvkhOzLtmEyS0rUuSrIUGk6Wl+cwaMTOT9xpTnQjWkOWW+Kd8LQgghhBBCCCHEtUQCguIda11PknU9b975WzMW99+Qa3yuNcyV4p547TmLlK2YKQW8NFylVI8YarNpzVhsX5VierEk2FDxsJGuZof7bzA4MFHHNBRbhlJMFwN6WizmyyHzlRDLhJaUyXwlYNehApRH8TMrSNsGt6/pYt77t8yUNQOtDvlawMa+FJYBCVs1hnucMlMMCBYnF7t+xD/vLZKy4/TFm4dSDLTF2YCmoejMWaA1fgjvWp0hXw04PqfJtYe0Zs0l542Ho0TYlmJddwLbUmxblW4E/NZ2JxqTlgEyCU0Q6mX3d6W8fjozgBcsH/wihBBCCCGuE2/CoEAhhHi7koCgEG+RgTaHppSJ60e0Zy2UUrwyUqM9ZzG2UGPfWMi/v6eNzpxNJmHSmol7ArZmLGxT0d1s0/26TLlViz0P/VDz0nCVQjUgXwuZnZ2hfeqHZHWeXYX7eNJaxYGJOgAKyKUUt67M8dpYnSDUKAXt2aU/CprTpwN5XqAbpbsAU4WgERDcMpRi99EKxXpEd7PNT/aX0TqisKCpHK1y30aTTOL0uTKOQcoxqHkRKcfgpoHUObP/hmc9dh0pc2LGozlt8q9ubmZFh/OGXoMz9bXYjM57LFRCmtMm/W1X9vxCCCGEEOKtoVJZMEyi0sKlHWcnUMnlw/+EEOLtTgKCQlyAF0QcmHDxQ83a7gTNKfPCB51DU8qE1x0fac18KSBhKRKWwcicTy5p8vyJGlGkaU6b7Fgbv01H5z2mi3GJcyZhMDzrkXIU2aTBdDGgt8VhZmQf+vBPyBHgkkApxXzZJ50w4359xNOP+9scmtMmc+U4ENaSXvpM7VmL29dkmCsHNKcM/m5PkaPTblzW23M6g685ZTLU7uAFccRwIu+TXIzv5SsBPz1YZqjdYWNvEtOIS47vXJdhIu9TrkfMlALqfsT6nvheX29kzmN8wSeINHPlgFdHawy22RhXMF0wvp8sXqCv+JRpIYQQQgjx1lGJFDrwqP3D/4coP3NRxxgtnaT/1ackICiEeEeSgKAQF/DqyTqThbj/30Il4BduyDWCV/PlgNdGK+w7HrGmVOaWFWlSjknCWt5v75Qo0gzPeQShZlNfggMTdWxTMdhmU3EjRuZ8osWJJIVqSLEWEkbw8kgNgJPzHnVPk03GJbyWCVHgM7H/J+TH9sfHGd08691BZKVY1eKQSxjMlkKU0kQ6Di4OtDlkk+cObnbmLDpzFiNzHp05E1OBYyuSztIBLOnE6c+b0wYKKJQ1E/M+6YTFiRkPQ6nGwI6kHU8o3j9eXhwoEvctXN21dMpzNmlgLgb/HMvANtWbVgUiwUAhhBBCiLeHKD9DND9xtW9DCCGueRIQFOI8ZksBf/9SnnI9Yn1vkr4Wm0iDqaBcD3n2WJWXTlQpFDWZnMd3Zzxa0hZpx+DejVmaUiZz5QBDxT0FAV4brXNyPp423JQy+eDWZl48UWWyEICCgTaLQ5Px5NuaGzEy59Katkg5BpapCEJN1QsbAUHTnef4M/+benkeUKy4cQezyc30HKmglOb2VSlmShGWCfPViJNzHpahqHoR63suPFXXDzWOZdDTEl/vzKDcYJtDEGry1ZCe5jRpR7Nn/zStUQJ7MSha95f25qv5S8uQa/7y3n03DaQA2D9epzNnsXVFelkWoRBCCCGEEEIIIS6dBASFOI///XKBuh8Hu14eqXLLivZG1lqxFhFFuhEgm6+EjM6HNKcDlIJM0qAjG2fYAazrSbC+J8lEwWPvWB0/jOhqsrlrXYZXT9ZoTZtkEgaHJlwMQzGV9xld8Dk24zHYbrO2O8m67gStGYveFpuFSohlKrL+AvXyPOlMll/60EdYoBtr2qWnxadQizgy7ZO0FRt6k4vX1Y37PRd3MUC3Z6TGbDFg/3idhK1Y05Wgr2V5z79T/QwBoihkVbsiE9lMF+MBIqvO6P3XnjFpy1rMlwMcSzHUvrx3n20qblmR5pYVUsIhhBBCCCGEEEJcSRIQFGKRF2jqfkQ2YTT61Lm+JpMwSNo2tmWw5nVlre1Zk6RtMNTuUKsounMWc+U4kKY1zJUD6v7pNLjReZ/1PUnqvm5Ms617ERU3xDRUY5BH3Ye0Y6AMRRCB1roRfEzair4Wm419SUq1gKPTHpXEem7Yfj833riZ7o4c3UChGhCE0LE45TeXMrFNRSZh0JmLA3rdTWd/++8fr3Ns2qVYC1FKEUYaN4hoz8ZBu5lSQFfT2QeBnKKUYtvKNDVf4ViqkSl4imEo7liTpuJGJGxj2XYhhBBCCCGEEEK8eSQgKARQrIU8c7SCH2haMybvWpPBNBR3r8/wv18q4oWad61ON8p+ARK2wd3rM8yVbG7tnGXNqhbMF4tM5OMhIet7EixUQo5MeSiluLE/SbEWMFcKqHkRrRmT1V0OkYaTcx7Dcx5D7Taru5JM5H1aUiataZOIuO+ejjRHplwOnxjjmX95ltvu+2V+dihgoRpQqq6kMhzSNlfm7vVZ1nQnWNHu4IYRQ+02N/SlWNOd4L6NOYr1kIRl0JFb/vb3gohj0y4QlwZPFvzGEBVzsVVgzdPLjjuXTMI45zal1Hl7GAohhBBCCCGEEOLNIQFB8Y4WRZpXTtZ4aaSGG0QMtDpMFXwOT7qs7kpwQ3+Kdd0JQh0H5c6UsA16WmyCksI0FO/f0szxGY/kYnntk/vLRBFoIg6M13nxRJVyPaIta6E1rOpK8Je7FtgzXMMw4iEinTmb9qzJULtNEMWZhBv7UhSrAXPDL2NOP0NRRzzz1E8oZHZQqoYsVEMOT7mYMx7FWohjGbRkTU7MBJTrERv7ko37z51nSvKpScBBqMkl4xLmbNLEMqEtY5FLmvSepWRYCCGEEEIIIYQQ1w8JCIrrVs2LGJnzSFiKFR3OZQ2cGJnzGFvw0VozUwywDMV0MUADYwse92zIxhNuL/J8uaTJlsF4GIbWmrlKQM2PKNdCvFCDgpFZj86cxYqOBLmEycl5j0hrwkBT9zRuEE8hfu1kjcJi2W5Huowa+yl2ZRgAo3klmRU7sBYUCUthGvF6pBMGXqiZKXpUvQjbVNQ8zVQhYEXH8j59ZzINxfZVaY5MuThWnNXoWAZRpKn5mpStGuXU15JTgd35Skh3s8WN/amrfUtCCCGEEEIIIcQ1SwKC4roURZpnjlaounEGXc3XbOq78MTcZedZrH7tzFlEERgKBttshmc9Km6EG2jet7mJshsxVwpoy1q0pJdn2FVczfCsR0eTgwKOTLmYhmKm6FOoheQrIfOVgOakRdkNSTuKihtSdUPStsHInEcYadZ2JWhOGRyeioh0POE3Hc5gHH0aKyyjMZjJbKdnaCvpVIohI6A5lUIBY3mfppRJNmFgKcXkeJ2Epehptpgq+GcNCBZqIaVaSMviQBOlFO1Zi/bs0h8NhqHIJK69QOApJ+d9xhZ8AE7MeLRnLXqaJZNRCCGEEEIIIYQ4GwkIiuuSF+pGMBAgXz33xNzzGWx3mC4FLFRCtq5M09Nk8S97S1TcCKUUNS/i0GSdE7N+Y6LwnWsztCz2Eqy4EU/sL/LU3ogVvSVWdCQJIo0iHuxxaNIlkzBoz5o4lkIpyLomKcfECzXDcz5eCL3NNihY3eXwrrUZ+lpt/vrZBYL8CTbrn2GgMRJNtGz4RSq1ZpSKswI39aUaU3i/+/wCP95XxlCwoc/BAJqSiplSSMWt4Yea21ZncKw4sDdbCth9rMLwbFxmfPNQirvXZxt9/Y5MucyWA7pyFqtfN0zlWhTppX0No+gcOwohhLhq9u3bx3//7/+dF154Acdx2LFjB5///Ofp7u4G4JVXXuFLX/oS+/fvp6Ojg89+9rN89KMfbRxfLpf5wz/8Q374wx8SBAHvfe97+b3f+z2y2ezVeiQhhBBCiOvWuTv+C3ENS1hqSRZb32X2tbNNxR1rMjy4pYltK9P0tzncOJCkM2extsshaRtU6ppoMZVQa5ivnA4+HplymS4GRDqevluuh8yVAgAmCwHZhEHKMfBDzZbBJHesydLdZMclw+0OfqixTY29OIl3dN7n+eNVghA+dkcrWzauwrCzJDvW0r3tY/T29TPQlsA01WJGYZz1l6+GjM779DRbhBqePlRlouDxL/vKHJqskUuZ5KshI3Ne495nSgFTBZ9j0y5VN2K+EnJ0Ot4+nvc5OFFnrhSwf7zOdNG/rPV9qwy0ObRnLZSCnmabnmb5W4cQQlxr/uRP/oQHH3yQn/3sZ3zve9+jVqvx8MMPAzAxMcGnPvUpHnroIXbv3s0XvvAFvvSlL/HUU081jv/85z/P3Nwcjz/+OI8//jjDw8Ps3Lnzaj2OEEIIIcR1TX5rFtclpRS3r04zXYon+r5++u8btW1lmpRjMFcK6WyyGGyzmS0HhJHGMBTtr5vOaygWS23jz2fLATU/pOSGWKaip8XGsQy6m0zeu7mZfCVkVafD8KxHqDU9LTY39qdYqMblyUU35Ll9JzFTbdy2OsMta1qp9fwbrESKbNLkif1lXD9iY7+DaSgOTXlsWxkHwozFm3CDiPlywMm5CD+CMNK0pKu8a00WqzEpOGKmGAcD89WQkhtxg6GwF6uhXX9pil3dv/jJwleDbSruWJu52rchhBDiPP7bf/tvjWy+bDbL7//+73PfffcxMTHBt771LXbs2NHICLz33nv51V/9Vb75zW9y1113cfToUZ544gmefPJJ2traAPjCF77ARz7yEX73d3+38TUhhBBCCHFxJCAorluGod6UPnFKqWVDKe5an2G+HNKaMWl63ZTeDb0Jqm5AvaTItSR4ZcTFNBV+GLCu2+HYtM90qc5Am8Ntq0P6Wm3SCYMjU3XqvsYyYENfknqgmS56jB3YQ2LuZSptO5jvupUPbWthtpQkl1T8H0/Mo9DMlH3qJyJ+4UaHybyPF2iaUybv3pTlZ4fKhKGmWA0ou5qEFfdJLNcjmlIm3U0W+WrIs0crjMx5WKbBup4EYQTtWZO13XEfxv5Wm5E5r3Hc5UwW9oKImXJIxjFoy8qPGiGEeKc7s7Q3nY5bXgRBwK5du/j4xz++ZPuOHTv4wQ9+AMDTTz/N5s2blwT+Nm7cSHNzMy+//DLvec97LuueoujyWo5crmixp0UkvS2uKfK6XBoN6MV/Lm5/3fjvxRxz5j5v1nVO7/vW/yy4Xsl75dojr8m16Wq/LoaxfO7B2chv6UKcIV8JeOJAmcm8TyphkLQUva0221ZmyCSWVtk7lsFtq9P02gZzOsHesbjk1jbj3n0A7RmLINTsn6jR12pzdNqNJxebMDrv05I2GJ7Io0/+mM7aOABhdZbOJou0YzDU7hCGEX4QMVEIKNU0FTeg6ka056xGVt9NAyluGkhRcUO+8aMZnjlapeZFtGctNvQkKdZCfnqoQtvi/eSSJpbp09/q0N1scc/6bKO/oGMZ3LM+ixtokra65AnOYaTZdaRKdbFCeeuKFP2tF55yLIQQ4p3j7/7u7xgYGKC/v5+RkRGGhoaWbO/v7yefz1Ov18+6HaCvr4+JiYnLun4YBoyePHpZx75R42PHr8p1xfnJ63JhlmXTkcng+x6h517cQb4PWhMEPsFFHON5QePjIPTx3qTrAJi+Rxj6zE6MEATXdouca4m8V6498ppcm67W6zK0Yv1F7ScBQfGOUvcjam4EaNIJk0jDwYk6GljfkySTMHjxRI19Y3UKtZDDk3VWdjr0tTikbIPbVi8vS50rB+wZ1TS3hSRtg7of4VgGPc02hybL+KGmq8kiZRuEkeb5YxWeOFAmYSluWZEiKI5inPgxKqwRKQuj/27WDd3AQJtDsRZwdMrl+KxHzYuYLQW0ZkzWdiVIWIp3rUkvC9ZlEib/ZkcbPc025XrI6s4ER2dcXh6pkXIUNw3G2Y+5pMHGvhQ39idZ0e6QsJcGOw1DkXIub7Jw1WNxMEt8zqlCIAFBIYQQDT/60Y/46le/yh//8R9jGAbVapVUaml2/qnPPc+jWq2STCaXnSeVSuF53rKvXwzTtBgYXHNZx16uKIoYHztOX/8qDENaeV8r5HW5NLpSxLYdTOfihs5Ztg1KYVk2xkUcE3A6s8UybZw36ToAhu1gmjY9vcv/4CCWk/fKtUdek2vT9fK6SEBQvC0cmXIZX/BpSZvcNJDEMJYHsmZKAc8drXBwso5pKNb3JDAU+IsVAuV6xD0bsgSRJtKaqhuiAS+AQi2kUl+e7qu15vnjVQo1jVmP2L4qTXeTRWeTzd7RGluGUowv+HRkLTYPpDg85bJ/vEYUaSpuyPzRZ8gUX0YBgd3KsdQ9NKkO5ibqpJMGf/l0nZlSPLSkM2dxY3+SlGNQ8zWHJl1SCYOtK1KAojll4Cw2CRxodfh3d7UDcHLO5fkTNSKtqbgaUyluHkpSqIX0NNtLhrNcKSkbErbBqT/wvhnXOFMUaSpeRNI2sM3LC2QKIYR4c/m+z9e+9jW++93v8qd/+qfs2LEDANu2lwX2XDfO8Ekmk9i2je8vz95xXbdRenw5Lrak5kozDOOqXVucm7wuFycC1OI/F+PUfhd7zJn7vFnXOb3v1ftZcL2S98q1R16Ta9O1/rpIQFBcUBhpRud9lIKB1ivfs++Nmi8HHJyoA1Cqh2STBqu7Tv9V0PUjxvM+hyddym5ExY0DexU3Il8NG30Iq1789dtWpzk571H1Ivp0PIAjCCP2jlep+xGruxKs7koQaXj85Tw/3lcioTVbkgHPHosDbxt7k+SSBnOleBiJbSlyKRN3Ju7bl3YMnGCBdPEVAGrZDczmbsfRJtmkQbkesed4jZofDxsJQs18JSCdUJyc93GseIhIvhpyaNJlZYdDwlbcuS5L2ln6F4hs0mR1V4KFSkDKMehvtRlocxh4E18Ty1TsWJtmshCRSRiX1YPwUvih5pkjFYq1kISt2LE2u6y8WwghxNVVKpX47Gc/C8D3v/99uru7G9t6enqYnJxcsv/Y2Bg9PT04jkNPTw/PPffcsnNOTEwwODj45t64EEIIIcTbkAQExQW9cKLKTDFO9ZovB2wZvLgU/LdKEC1tGuyHpz+Pe9lVqLhxUFDrOJCmFNS9iO7m02+BNYtBxNaMxafu66DqRTx9uMSJGZ+Jgs+hSY/xeZ/DUy63r8ngmIqf7KswVwpYqMCCV8YPFRqYzges6nLwQ03N00wXfQ5O1Blss7ENODbtEpGluWUbkZlm3lpJq21xdMbFjzSer2lKGTSlTGp+RHPKouqFNCUtbENxeNqjOWUyVfQxTVjZ4ZCvhPzdi3ma0xYbehOs6jz9PLesSDE679OUWhosfTOlHYO13W9NAHmmFFCsxamerq8ZW/BY37O8tEwIIcTV87u/+7vkcjn++I//GMta+r+g27dv56mnnuKDH/xg42u7du3izjvvbGz/n//zf1KpVMhk4vYdBw8epFqtsnXr1rfsGYQQQggh3i4kICguaK58eurWfOXam8DVmbPoabaZLPg0pUxWdpzuVVf344zAMIKkpYi05pYVKWZKAW4Qlwl0NVncOJBallmXdgzWdiWpuDBZ8CnUQko1OLkQMFMKGGizqXghQQREccbhfCVC63j6Wqg13c02mYQiDDV/+fQM1swLHCoMYlk5wlDzQmktA+021XpEpAOINLPFAD/SdDUlyaVMtq5I05kz+adXSxRqEV4QkUsYJG2DjpxJazp+G4/nfbqbbcJIs2+sTi4ZBxQdy2BDb5INvWcPkB2ddjkx45FJGNyyIrWsl+D1IG0vLQtJOdffMwghxNvZwsICP/zhD/n+97+/LBgI8Gu/9mt87GMf45577uGBBx5g165dfPe73+Uv//IvgTgguG7dOnbu3MkjjzxCrVZj586dfOITn1jWe1AIIYQQQlyYBATFBXXlLCYLcd+ezty19y2jlGLbqjRhpDHP6B2Yso3FQSFVKl5EGGoMpZgpBaxYDBzOlcNlwcBTVnQ4hBqCMGKmGDBfCVEKyvUQx3LIOAb5SkAmAe1Zk/G8T82HpqTBmq4Ek4UA01AU8vMMVH5GQs+xIRrm6ei9VAMDy9DkF4OsacegLWNScuNeeGMLPhv7krxnU5Z/3luKr+tGtKRNdqzNkHZM0gmDFR0Orh+XJfuBRus4yFeph5S9iNtWZbhpIHnWScHlesiB8bjcuu5HHJp02Tx4/f1i1ZKx2LoixWQ+oDltMtgmA0yEEOJaMj09jdaaX/7lX1627T/9p//Epz/9aR599FG++tWv8lu/9VusWbOGr33ta6xdu7ax39e//nV27tzJfffdRzab5aGHHuLhhx9+Kx9DCCGEEOJt49qL7ohrzi0rUkwULJRS9DZbaL10uEYYafaO1am4EYNtcX+6q+HMYCDEk3I3DyY5MFkn5SjmyiGFWohjKubKAQOtDk2pc2eTKaUYbHOouhFzJZ+5SkixFtGaNunM2XzoVodXTlbJ5z3m6nHGoaU0YRQP1VjT7eCUT1AY+zGm9glwOKRvIsDAMmB9b4Ig0MyWQrwgItTxoBIFeIFmfMGj7scph2kn7j2YS5rct6mJzpyFZdAI9K3uSvDySJXZUgBonjxYBuK+iglbXVQJrb7gHteu/lZHJhkLIcQ1asOGDRw8ePC8+zzwwAM88MAD59ze3d3NY489dqVvTQghhBDiHUkCguKCDEMtCbToM6JGR6ddTs7FkwEXKgEtaZNs8o1P0okifdZpwZfq6JRHoRJS8zV+GNGXsOnIWuRSJn0tNqs642cLI82xaZdSPWRlh0NbNu5/9+rJKruPVZkth7i+Zk23Q0vaoqfF5tYVKTIJ+MufLXB01iPQmgiNH2qiwKO5+CIzJ17GBKpWJ2PZe6hVknQYgFLYhsFEKR5gYhjQnjGxEyYpx6QjZ7GywyGXNFjTncANNMVayPZVafoWh3TkKwGFWkRHziKTMLhzXZZ8JWTPSA03iF+oQiWiUD17qXc2abKuJ8HxGY+0Y7Cu++r1h9RaU6xHJCxF8josWxZCCCGEEEIIIa4XEhAUb5jrn44Qah1ntr0RXhDx7NEqxVrYCLqdrdz1YlTckJlSwPqeBPPlkHTCYH1vkqakwdruxJLz/vRgmeePVSi7EZmEya/e3sLKzgTDsz6j8x5uoEnZivs3NTUGc4SR5i935RnPx89ddyGTMsmZdYwTP2KmOgtA79rthN3bmD3mQsUl1ApLwVw5QCmwTYXva6qeZktvgpliQH+rzbvWZGhOW9y2ymRNV4Kkrcgk4mDrbClg97EKWoNtKe5ZnyXlGPEzdsdZjVGkacmY9J1nOvT6nuRVH8Chtea54/HwGtNQbF+VpuMaLE8XQgghhBBCCCHeDi47DWd0dJRf//Vf5x//8R+XfP2VV17hYx/7GFu2bOH+++/nO9/5zpLt5XKZRx55hNtvv51bb72V3/7t36ZcLi/Z53vf+x7ve9/72Lx5M7/yK7/Cnj17lmw/ceIEn/zkJ7n55pu5++67+cY3vrFku+/7fOUrX+HOO+/k5ptv5rOf/SzT09OX+6jiAlZ1Oo2Mrp4Wm9bMG8sOPDHrNSbGTuZ9phcnHF8OxzKwTIVjGfS02PS32mQTBp1N9pJg4Oi8x8vDNQ5PuUzkAyr1kKePVHj1ZJXjs3X2DNc4Ou2Sr4aU66dLpveP19k/4TJWgIqrCSJNGGksJ0kyYWPaSUrd7+VlfzPPH3exTIMg1JTrEZq4x+GKDodswiDpmAy0OWxbkeIXb8rx4W0trO+N+/lpDZV6GJcPL5otB41sTT/QLCxmATqW4s51Od69Kcvd67P869taGhmeQaiZKQVU3KVl31dbqR41JlmHkWZ4MeP0nWBkzuOfXi3y430l8pXL/14XQgghhBBCCCEu1iUHBEdGRnjkkUf40Ic+xPPPP79k28TEBJ/61Kd46KGH2L17N1/4whf40pe+xFNPPdXY5/Of/zxzc3M8/vjjPP744wwPD7Nz587G9h/96Ef80R/9EV/+8pfZvXs373vf+/j0pz/N/Pw8ANVqlU984hPceuut7Nq1iz/90z/lm9/8Jt/+9rcb53j00Ud5/vnn+fa3v81PfvITTNPkN3/zNy95ccTFySZN3rMpy3tvyrFtZfqys/lOObMXoGVe+vmmiz6vnqwxvuCzfWWKvlabprRJoRZxZMrlmSMVJvI+ByfqjOd98tWQtqyJaSjCKGK6FDA27/H3LxVZKMf9/ebKAdPFgCiKA29+qDk+7aK0xg0g8H1MI8I0FIE2SK99L0eafolj9V7GF3xOzvnkUgbZpIVtKtKOwbvWpFnZ7rCyI8HWFUluXRWvn1IKx4yDY1pr/urZBf7vp+b5sx/P8spIFYD27OkMOstUtKRPB2JXdDg8uKWZX952OhgYRppdRyrsPlrhpwfLzJTOH3yaLPjsHasxXfQvef0vVdJWS17nTOKdUTIcRZrXRmsEoabmRexbHPAihBBCCCGEEEK8mS75t+4XX3wRz/P467/+azo7O5ds+9a3vsWOHTv46Ec/SjKZ5N577+VXf/VX+eY3vwnA0aNHeeKJJ/jSl75ER0cHPT09fOELX+Dxxx9vBPwee+wxfuM3foNt27aRSqX4zGc+Q39/Pz/4wQ8A+P73v09bWxsPP/ww6XSaLVu28JnPfKZxjWKxyLe+9S2++MUvMjAwQFtbG//1v/5XXn31Vfbu3fuGFkucm2HEWXhXwsoOJ87kSxqs70ksCXzV/YiXhqu8cLzayCI80ysna/yv5/L84MU8P3ytyJFpj1tWpOnIWpyKNda8kJ8eLHNkymXPiSqRho6cxV3rMmQTBmU3ZKoQUKlHTBQ8gjDuCzhZDPj5oQpeEGEqMBcDcd2JAu/P/ohtyVdpThl0N9mcyFuMlWzG8x4zpYB0wqC72aanyaKn2SKdMJguB2wZSvO+LU3csTbL1qEU63oSoOHnhyv8ze4Fjs24HJt2AYi05sXhGhBPfN6xNsOmviR3rcucc1LyKYVq2FizKNJMLJw70DdTCnjheJUTMx7PH6+y8CZnrjmWwW2r0vS12qztTrD+KvYyfKsZrwugX4memUIIIYQQQgghxIVccgTnwx/+MH/0R3/EunXrlm3btWsX99xzz5Kv7dixo1Hy+/TTT7N582ba2toa2zdu3EhzczMvv/wy5XKZV199lXvvvfec59i1a9dZtx86dIhyuczzzz9Pe3s769evb2xva2tj/fr1y0qPxbXJNBRbV6S5b2OOdWf0tnvlZI2xBZ/Jgs/zx6tnPf7QpNsoM16ohMxX4iBYW8ZkdN7nyLRLsR6StE8HX8JQc9e6DEPtDlPFgBMzPntGqsyUPEDhhhq0wjagUIsYnfepeBE9TSY3pIa5z/4RTarIoHmSJjsgYSla0xYaTd2LcAPNjX02mweS9DRbrOhwGGyzcUyDKNIcn/UYX/DpbbGpeZpiPeTYtMvhKZfnjlWWZE22ZU4HSNuyFqu7Ehc1xCWdMJZk4eXOM1259Lpgq9ZQrL35JcZtWYtbVqTZ0Jt8xwTGDEOxdUWKdMKgOW1yU//V7eUohBBCCCGEEOKd4Yp27R8ZGWFoaGjJ1/r7+8nn89Tr9bNuB+jr62NiYoKTJ0+ilKK/v3/ZOU6VJ4+MjPCe97xn2XaAqampc15jYGCAiYmJy362KLq2eq5dTafW4mqsSdUN0Dq+bs2LCMNgSYny8KzHyGydfNUnjGBdd4L2jCKKQqYLHm0ZRd2PB28kLXCDiKPTLsWaT77qc2iyzlQhIIoi6h5MFQI+dGuOhUrIQiWkNW1gGZpyPeD5wwsce+XHpAtHANCZfno3/gK3drWwuT/Bj/aVcSyDXCpCAfOVkPEFl7G8T93XZJMGt69KUfdDxubiDMA9xw1KLjx3vIJtQGvGImEq3ntjhmPTHtmUyXs2phtly2cThJqDEy41P2JVp9PIsHRMuG1VkrEFn2zCYEW7dc7zdOYMDhtxVmTCUnRk1Xmv+XpX8/vjWnW+NenKGXRtSL9u34tb5+uZfI8sJeuxlKzHcm/mmhjGG+v7K4QQQgghrk9XNCBYrVZJpVJLvnbqc8/zqFarJJPLM2BSqRSe51GpVHAcB9Nc+j+nyWQSz/Ma1zjzHBdzjdef43KMjx2/7GPfrq7GmmQCzci8Bg0r2hRjozNLtr90UtNiRoQpqPqwtbNCl5ln9KTi5ISmVo6ncFTqsG4ARquaRAizs1AtwYsnoOqC64MyoOL6vHJ8nigAz4fZUki5XOCnzw1TOr4LMyijUcwkbiLRtoG2sM7szCRRs6LF0DhKYwGmEX/vHh+rklIQRKB96LQr/NN+mCrF9/8XMyX6mqBah/kq9K4Et2LQ0gZ39MaBz6MnNOMFSNkw2Lq05BTg0LRmvBA/5+FhxR0r4ynGpzQD1GD05PnXenVGU3Yhl4C5qUvP2JP3zHKyJkvJeiwl67GUrMdyb8aaDK1Yf+GdhBBCCCHE284VDQjatr0s6Oa6ceZTMpnEtm18f3nfMtd1SafTOI5DECzvVea6biPod7ZznLpGKpW64DUuV1//KgzjnTHo4EKiKGJ87PhVWZMB4Kb1EZGG1Fl65uVVnZE5j65OSDsGd27MNDIIM20hzx+v4oea3maLcgiTbp2nhsskHMg4JtXIpzUbMlMMsUxY15vixLzPeCkgCAGlCHEoH/0ZZlTHN9KMZu5h1m+i008zO65pSpn0dmW5a3OagwtzTBYCmlImfqhZCDQnSz5dOZOeFpvASVKPipQ8H8tUJGyDRMphqNmgN9DctinH9pUpEotTnL0gYt+BCoGt8YBOJ8H6nqX99iaDKnXr9PuouzdL+i0c0nE1vz+uVbImS8l6LCXrsZSsx3KyJkIIIYQQ4kq7ogHBnp4eJicnl3xtbGyMnp4eHMehp6eH5557btlxExMTDA4O0t3dTRAETE9P09XV1dg+Pj7O4ODgOa8xPj6OaZr09fXR09Nz1tLg8fFx7r777st+NsMwpKzmDKfWpLbYI685ZVzyhGGtNV6gGwGvi5FKnP11KNRCbMugPevQkjFZ2eFgmqfP254zeeAmGz/UTBcDXj1ZY7YU4oUaKzRwLAPHNOhrMQlDD8NQBIECDE5VabmB5vi8Ztvg3dilI7jt9+AvKDqdGifnfdxAc+sKm0NTPn1tEe/f0sLY4vCOibyPH0ZMFULy1YieFsXIfEjCNjANg4SlWNXhkEtaBJGmu8XmtlUZks7p5/VCCCPVWOeKt7zca3VXioVqlSDU9LXaZFP2ku1BqHlppEa+GtDXYnND/9Ks3itF3jPLyZosJeuxlKzHUrIey8maCCGEEEKIK+WK/pl5+/btPPXUU0u+tmvXLu68887G9j179lCpVBrbDx48SLVaZevWrXR3dzM0NHTBc/z85z9fsv3pp59m27ZtOI7D9u3bGRkZYXR0tLF9YWGBAwcOsGPHjiv5uAKYLvo8caDMU4fKvHCihtb6oo+teRFPHCjzL3tLPHu0QhRd/LFnqnoRzxypcGzaY74S0Jwyz5pBaBqKpG1gm4qKGzFR8PFDjQYyCYP+VoeTCz5BFJG0YaoYkE4qOq15us0ZErZibXeCtetvYM1tH2Lb2jbWdzuMFaHiRqRtRbEWUvc1lqm4eSjFbavT3L4mw5ruBForelviOPxUwcf1QwbbE9w8lGRDT5Jf3tbCr9/Tzr++rYX3bMpxaMpjpng64zWXNBo9AQ1DMdRmL3vGjpzF+p4EXU0WA63Lt5+Y9RavrTk+E09AFkIIIYQQQgghxDvHFQ0I/tqv/RqPP/44jz/+OJ7n8eSTT/Ld736XT33qU0AczFu3bh07d+4kn88zMTHBzp07+cQnPtEoCf7kJz/J1772Nfbu3UutVuMb3/gG5XKZD3zgAwB89KMfZf/+/fzFX/wF9Xqdl156iccee4zPfvazAPT29vL+97+fRx55hKmpKebn5/m93/s9HnzwQQYGBq7k4wpgZM5vBPKmCj5V79wNz8cX4snAR6biEu/hOY+qG+8/WwouOzB1dKrOd3YvsG+sDkDdj9gzXGF41jtngPLErMvf7slzYKKOQqPQmAqG533qbkShqpkohOSrAdb8q9xr/5g7ErvoTPo8dHsL796UY1VXAs+PODDpUfFAA16oCSLY2JtgTVeCuXJIpKE9Y3LLUIreVptSLaIta9DTZGGZBqs7HXpbHDYPpdi2Mk13i00uafL0oTI/3lfi718qUKrHgyaUUty+Os2d6zLctzFLZ9PygN9E3mffWJ3pYsBzx6sUaiH5asiRKZd8JVi+JpcQxBVCCCGEEEIIIcT174qWDG/cuJFHH32Ur371q/zWb/0Wa9as4Wtf+xpr165t7PP1r3+dnTt3ct9995HNZnnooYd4+OGHG9s//vGPs7CwwKc//WnK5TK33XYbf/7nf47jOAB0dnbyZ3/2Z/zBH/wBX/nKV+jv7+fzn/98I4MQ4Itf/CK///u/z/vf/35s2+bBBx/kd37nd67ko4pFaed0ibBlKhzr7DHmfDVkz3AViAOHtqlIWEvLi8/8/GLMlX2+szuPF2jGCz5BGFGqR6QTBkenPd69KcuWwdO9I7XWPP5KkT9/YhYv1GgNFU+zeTBBOmEwvuDhhhFeCCqqc7P1HANWXIK+oDtRhsF3ny/yuV9I4AeaiHhgiGXE9592LG4eTPLBW1o4MuVycCIOUnY2WazvSaK1xjDAMU2UoRhss7lpIMVWQ7Giw8E04jU4NuMxUYgzA8v1kBOzHpsH4qC5YShaM+d+654KHsbPGwcIj894RJHmkILbVmVoz4YUaiG9LfZZg4pCCCGEEEIIIYR4+3pDAcEf//jHy772wAMP8MADD5zzmO7ubh577LHznvdzn/scn/vc5865fdu2bXzve9875/ZsNstXvvKV815DXJ4w0kwVfA7PaKK0x4beJIahqHsRKzucJdNsX6/uL80crPkR67sTVL2IQjWkv9Wh5TxBrnOZKYUEURxkS1nws0Ml8hVNwlZ0NVlMFQI6cxZaK/ww4tmjVX56sEQ9iCjV4nvyAs3JOZdNfUkyCZNiLaTTnOXe9DNkjBqhNni+vpUT0WoGUwkSluKVkzWCCPaPuwSRJoyg2TG4cSjBig6HKNJM5E+X+s4UA0q1CpP5gJa0RaEaohSs7U6yruf0VOyxBY+ZYoAfRJiGIow0SdsgdQk9FntbbI7PeAShJuUYmNDI4tQaym7IHWszl7zWQgghhBBCCCGEeHu4ohmC4u2tVA/52cESLw3X0J6matRRymRjb/KCx3ZkLZrTJoVqSMKOM+MMQ3HjGxxosbLdpjVtMjzr8dqoS6gjyvWIUh38MMIxFd9+Nk9vi83ogke5HlH3NbmkSbkeEYQax4LZcsjwrM9HtjWx+5mnGAxexVCaYpjl5/UdFHQLSsU9/Db2JvGCiChStKQNwgjSNiQdg6aEyequBIahaEnHwUWAppRJ3Y9oyZgsVAOaUjbvWp3hhv7TazdXDnhpuAbEgz/W9yQIIuhusljREWfI5isBlqnIJs/dVD6XNLlvY5ZyPaIpZeIGEUdnPMJIYxqq0YNQCCGEEEIIIYQQ70wSGRAX7fiMR6kWEWlNua7j7L5aeOEDicuJ71yboepFJBaHelwqP9S8cKJKvhKSSSgG2hw6chYtGZMj0xF+GFFyI4Iw7udXqUdEkaZQDWnNmAQhJC1Fc8rA9zW9zTYVN2Km5DNXDnADzeYBh1arhBFqRsIhXvJuxUo69CUtckmDoXYHP9LcPpjmldE6TUkTx1K0pqGr2aYpbTam9t7YnySXNPBDzYoOh2ePVpgqBLRlLO5en2FjX6pRIgxQrp/OorRMxU0DSXIpkyNTLs8frxJqTaESr/dNA6lGkPBskrZBcjGr0LFM7l6fYb4Sr0PuPMFEIYQQQgghhBBCvP1JQFBctISlSDlxoKmswLYUfS3n7j9XqIVM5n1yKZO+ljgj8FyZbZMFHy/Q9LbYS4KFkwUfrSFhwehCwFwpoOKGvHDCY2WHj2HAnuEa+WqEYUAUgVagNFgmVHzNQjWgtRZn6CVtRUvGYqjd4fkTVSbyITUPDCMu0X3uRJ3D5a20hV0MB4O0pE36W20StkF71uaONRk29iUZaE+glWLPcBVDKfwQ5hcHdsRDUzRD7Q4rOxNA3MevWItY2+2ggPasvSwo2pmLJyPXvIikbdDX6rD7WIXC4kCQI1MuG/qSrO5MMDLnnTcgeKZs0jxvVqEQQgghhBBCCCHeOSQgKC7a2u4EfqhpyRi4xTx33Jgjlzr7t1DNi3jmSIUgXOxdtyJFf+vZA1iHJ+scmownD4/Medy1LoNSitdGaxyb9jg571GqhWQSBrmUSRhpwjCi7kcUawELlZCKG2EaBtmkxg9AobFMA9DU3JAjkzUKtbhEeGNvinqgQWs8P+TmxF5azQK7wztZqPoUXYv5cAiAuq8ZaLXJJS00mkItxLEUhyfr9LbYfOiWFuaKPgdPVulqS1BxNU8eKDGR9+ltcXjw5ib6Wmz8xXUwlKJUjzgx69KeNUnYBlUvYvfRChU3oqvJ4pYVKTKJOPPQDzXzlZCqFwc8S7WQhWpAf6sMAhFCCCGEEEIIIcTlkYCguGimobhpIEUUOYyeNMgkzj3oolwPG8FAiKcM97eefd+xBZ+ZUkByccqwF8R9/Z46XGG66LN/zCXlwMrOBGU3Yk2Xw2QxYK4cEeqIdd0OU8WAUi0eMBIRn8cCLEORr0WU5kJmqyFRqBlf8MmmLIJ6iXucp+k0ZwE4Hk1hqiHQcclx/Byanx+u0NVk09NsUXYjvEDT3WxzYtbjvo1ZNvYlMX1FtjnBRCFgcnE6cKkecmTSpa/Fpq/F5uScx/EZl+liQNpRPHW4wj0bshyfcam4cbnwdDFgdVcCZ3EtbuhLMVkIUMCK9gRVL6S7yWLz4BvrvSiEEEIIIYQQQoh3LgkIiotWXMyOcy6i8rQ5bZFyDI7PuMyW44m6qzsTGAr+8dUik3mfG/pT7Fib4eS8x+i8B8CmviSWAT8/VOHYVJ1Qg+uHOJaJAjpzFoNtCdqzdbxAU6lrZsshaceg4kZEIaDiabqOqVgoh/iRplwP8Rd7C9a8iF5rlBvUM9imi68tnne3MaV7aK2FREsHIuNHmplSQNWN6Gq2aMuYdDfbjMx6/ODFAq1pxUALrBxM0pWPePpwBS+MaE6ZJOzF4KSpuGt9loSlaM0EsHgflXq4rHT49Z/3tdp8/F0t/MMrJQ5O1OlrdTDUpfdfFEIIIYQQQgghhDhFAoLiorx4ospE3scwFDcPXrh3nWMpblmRYjzvs7bLRGs4POkyXwl49WQ8SfenB0u0ZAzaMhYKCCIYaLX58b4yPz1UZr4SkkkaDHU4tGdNOptsUo7in18rMFnwcUzF6HyA67uEGsqLA0UsU5O048BaPdAYSqM1+CEoIjY6r7EpOghAkRZejHZQMJvQQYShFNmUwg80QQSGoUhZCq3U6UCjpfCCiPlqSGeTTd2PMAPFys4EQx0GHU0Wo3Me7TmLTX1LJzB3t9hMFeOAYNI2SCdMVidNqm5EqR4x2GbTlFoacTVNg+5mC6Xic3mBxgsibFN6AgohhBBCCCGEEOLSSUBQXFDNi5jIx2WwUaQZmfPps2HvWJ0DEy49zQ7bV6VJOUtLiOPpu0uDVnU/Tr+rexHzlZCXhmu0ZixaM/G3YiZh8MKJKqVaiGMZJEzFYLtDX6tNpR7x6sk6bqDxAs1sMcAPI4r1iFJdEy3W+eoQ2jOKtqzJQiXED0MCDYaCe9O7GbJOAjBlb2AscSvJ0CByQ+qGQbEeEkaapGPQljFpTRv0tyVwg/j8t65IsbEvRVvGwDQUphEHCU8NC7ZMxcbeJBt7k3iB5ui0S6Q1a7oSJG2DwTaHhKWouBE9zXajNHhDb5JjMy5uoAlCjXVG1uBAm8NMKUBraM9ZpJ1zl2sLIYQQQgghhBBCnI8EBMUF2abCtuKsOYC0YzC2EPGT4SKRhqNTHqahuHNdZslxKdugu9lmsuDRlrFY35tgqN3myLTLq/M+7VmTpqRJNmGwqjNB2lFEGhaqEX6ocQONbRqMLfiM5X0K1ZAw1IQa2jImSVsxU/Bx/dPBQAVkHNi6Mk17xuHnhypUXANTRdi2YtTYQC/TzDTdwby5AisCrSI2tSWZq0RMl3zQ8QTlTMJgqCPJjf1JEpYBCtqzFnU/4thMQHPaJIziTL/Os8TnXhqpMrOYDThfDrlnQxaArqblA0F2H6tQrsfB0oobcevK9JLtvS02uWSWuq9py5jMlUNOzHqkHMWGnuSyAKIQQgghhBBCCCHEuUhAUFyQZSpuX53h+IxL0jZY02nxo3GItAYUQaQp1oIlx4RhxN88n2ciH+CYiv5WB9tUJDMWn7u/k7/dk8c0FIaK/13REZchjy/4rOlK0JI28APNkak6M8WQIAI30Ay02RSqIVVPc0Nfkn+a9/BDMIAIMBXkUiZ3rs3R2+Jwcr5GdWGeo+VWggia2nrp2/j/ojodcmS0zkwxIGVDEEBnk8mQ46B1PM030gqIs/WaMyZJ20BrzUIlBMD1NZv6EowteBycgb7+iEzydEbkqQAfxOXM5xJFeum+9bPvm02aZJPgBRHPH68SLkZBFXBDvwwZEUIIIYQQQgghxMWRgKC4KC1pk1tWxFlrURTSnVP0ujYT+YC0Y7B1aGlG2/4Jl8OTLrOlgJliwEzZ5+iUS2eTRS5psrLd4ficTzahuKE/SbkeUnEjwkhjm4qWtMnJOZ+qBwvVEMtQJGzFXeuyuH7E2ILP3rE6ROBYEEZgKUBBf4tNvhrSpOYZWvgHovo8o+EDREYLzWmD7tY0TxxcoFAN8AONbSjcMEQpk1+8McdUwefF4RpVTzNfCTk8WeeG/hRVN2Ky4NPTHGf4aQ0vn6xjG5r5qua1sTrvWnM6+2+o3eHgRH3x4+VZgacYhmKgzWkMVhk8z74Q9xA8FQwEqHn6PHsLIYQQQgghhBBCLCUBQXFZOrKKX1nRTLEO3U0myTNGD2sd/zuW9/EDzbFJl7lSyL0bMrw0XKMpFZcT39ifYqrg8Q+vlJgvByQsxZbBJNOlkME2i4VqSL7q05o2Wd1ls6k/yYkZj+MzLsemXUpunD2oAMsE04CxBZ9nX3iFKe9ZDO0TaAdbuxQCzcsjLkGUZ6rg4y9OJK54GrOmKVRDXj5Zpytn0dNsoxT4oSabMDEXmwS2ZyyaUiZVL6K/1WZ41kNrTcnVjC/4eEGEY8X1w2u7E3Q2WehI05I5/1vt5qEUg202lqmWDRU5UzZp0tNiM5n3sUzFys4LD3l5K1TciHI9pCUl5ctCCCGEEEIIIcS1TAKC4rI1p01as3Hwygs0Ywsetqnob7VZ051gVafD8KxHxYuIgEI15JkjVfK1kJa0Sd2LmMx77J9wqbohU8WAIITheQ+t4+CebSg0imzSxI8MZks+nh9hGopsyqDiRiQsjaEUpgIvCLjJeIHVHAOgYnXxk9JtFIJ0HKQkYnjWwwsjvCCePmwY0JJSpBMmQRDhB3EPw66meOLvDf0JKq6m4oZ05my2rkiRWywNTtoGPztYZCyvaG/T7DpS4e712UYAsfkCwb3Xa8te/Ntx28o0FTfENo3GYJKrKV8JeOZoXMactGFFSrIWhRBCCCGEEEKIa5UEBMVFOzBR50d7S/hByPqWiIHB09uePVqhWIt765XqEZv6kvzK9hbaMiZ7RmosVAIyjsHhKY8w0nhBRL4SYhmQr8cfG4AbRmgNYRQRhBqlFH6oyVdDar5m99EqGpgu+FRdTdJWhJGit8WmVprjJuspWowiWsOhaBP51FZ8FWAboFVcXjxXCQGNjuKsQmUo5sohphnQljEpLQbaDANu6EuwfVUGL4j4+aH4GX9+qMKd6zI0p0zWdieYKSYwPMgmDcr1iJNzHsNz8aCVm4dOBw9fr1ALqXkRHVnrsgeCZBIXH2x8s00UgkYZc82LKFzl+xFCCCGEEEIIIcS5SUBQnFfFjXD9iISteOpQmYVKAGheGtVs3+SjDI1SNIKBAHPleMBIOmHynhuacGyDpw9XFodxaHpabKYLPjqKyPualGOQTRiEWtOXdchXIpRS5KsRoDFNqHoRCdtgIh9QrodMFjyiSGOgMExNe8Yk4U/Q4hapRQmedd9F1eml3zZpSmvCUOOFmqRt4ocay9R4tqLmRaQsRcoxWNFu091sU3U1KUfjB5qmtEXSNpguBujFpLco0syWgkb2X1eTzeHFZ88kDPaP1xtTj189WePOddklazq+4LNnuArEWZZ3rs1gGFc/y++NeH0mpKEUmWujilkIIYQQQgghhBBnIQFBcU4T+ThwpTU0peMJu6coBS+P1PHCOJDlBbpRutrVZHFy3mOqEGfctWYs1nUnOD7joZQmlzCp1CMsAypugGnA9lUpWjPmYvlwRLmiSVgKP4KkpUhYiqoX4QURC5WQqUKAF8YTgBWa47MefrieQuBy0FuLr5JYYbx/PQDHNMjY0Jw0SScN8tWIXEIxuuBjGoqmtIEfwtbBFLuPVwmjOANvuugD8b0ZhiKK4gBoW+Z0AGxVp0O+V9HamaSvxeEnByqcih6GZxkYPJH3Gx8XqiEVLzprFuH1pK/VBtLkqwFdTQbV+es7wCmEEEIIIYQQQrydSUBQAHEG3viCT9oxFoM7cHKxlx9AsRpx++osPz9cJggiVmXBDeKSXoD2rMmqrgSOqbBMxTNHKgBMFXy8QBNEmlWdDkrBpr4kUOHYjE9T2qA5ZXDvhizj+YByvYahINIRpmmQsKE5ZdHXatOSMnnuRJUg0hgKsjrPTcZ+nqrejuvF5b+vchN+BFEECQ3TpXhCsQKUijMB29Img202G/tShIHm1bE6rq/Z1Jfk3k05Sm4cdOzIWbQuDgPJJU12rM0wWzod5Hy9zqzCMBXTpZBNvQkOTLiYBtzQn1y21s1pk8lCHBRM2IqkbbwJr+hbr6/Vpq/VJopCqvNX+26EEEIIIYQQQghxLhIQFPihZtfhCnU/Tmer+0lWdyXIJU1minH5r2MpbuhPsmUoRRSFnBzJc7RisFCNh3P0tdoMtsV1oqPzXuPcI3MeJ+c8ql5E0lF84OZmNvQkODbt0p6JyNdCZkoBL5yoMlOKr+VHoLUi6Sh6mm3W9zgEgSbQsLLDIV8JGOII/fXnMVXETYkML3tbCEMwiIOBSgEKIg1NKZOyG6K0oq/FIpuKA3op28BKKu6/IYfWsGUwhW0qfumWZk7OeSilGGyzG8/SkjZpSZ89k+/IrKY6X0Mpg/asxfu3NFGshbw8UiOINDf0Jelujs+1psvBNhU1P2Kwzca+zB6CQgghhBBCCCGEEJdDAoLvIF4Ql7ueGYCqeVEjGAgwnveYyPvU/YjWjEk2abKyw1ky/EIpxcoOm6P7qxgKitUw7ulnKLqabHJJj1I9ZCLv49gGVU8zWwo5PuMyVQyouBFeqPFDTbEW8dJIjboXkU2a1P2Icj2i7EYUKyGVWsBEIcQwIGsF3GI/T9o9AQomwl4OeBsWB5GAatwfGApa0yZD7RZjCxBEmtlySL4WMV8JmS+HbOhN8IGbWxrPBGAaipWdiYte1yjSjMxrMk0R6YTBXDkesPHaaK3RW/GlkRq/eJOFUgqlFCs6pMmeEEIIIYQQQgghrg4JCL5DHJ12OTBexzAUW4dS9LacznzLJAxySZNSfXFKcC1qDMXwwojbVmewTYXWmoOTLnMlD6qaZHNE3+J5ym7EWN5nbN4HBTcPJQkizXwlYM9wleMzLqGGfWN1ZksRHTmTIIqYL8c9/Iq1iM6cxVBHnD03kffxfKihqU15RECLmmdr8hnSQYUIxWv+Zl6prkcZinDxfk0jzhBMO9Ces3n4FzuZLYb8eH+JQjVkPO/T02LTnTCJtMaxjEYg8HJorXnueI3pMnhVl5UdsKY7iWmoxhpCnKlYqocsVOIga1Pq+u4ZKIQQQgghhBBCiOuXBATfAbTWHJp0gTib7fCkuyQgaBqKO9ZmmCn5pGyDw1Mus4vlu1rrRh/B0QWfo1MuWkfMz2k25uINcWBNsW+sRrA4bPhFL0JrSNqKqUKAYYDSioOTLgYwXfKo1KM4kKcj/FAz1GFz+6o0u49VqHk0AmpBCCvtUe5KPoOpNHUy7NE7OO62EgG8bnCH1pCwYX1Pkvs35bhvQ46/emaBcj0i5RgMttn0tTqNDL0bz9Lj71JUPc1cOaA7B0bSoTltcduqdOPcL56IS4ZXdtg8fbhKuJhFede6jAQFhRBCCCGEEEIIcVVIQPAdQCmFYyrqixG2U9OAX8+xFP2tcZBsk6l44UQV19ds7E009i/WQsYWfAwVYWlNT7NFGEW8NlrFMg0KtYCunE1z2mShEqCUoupqskmDrpwVZxAueCQtRb4WBwxPlfo2pRQnZ12ePVomCjWvv0MNzIRtBFjM0s1BbmPWs9GvT8FbFGloTpms6HBQSvH04TLphKIpaTBTDuhusnhwS5YwMsgkDVZ1vrHSXcdcLFPWmtaMyZruRKO0ujVj8Qs35gAYnvUIo7i3YhTFQUQJCAohhBBCCCGEEOJqkIDgO8S2VWkOTtSxDMWmC2TFNaVM3rMpDmSNLXj89ECZdMJgKu8xOu/hBxG9aUV3k8Xe8RqZpMmxaRc/0FRdzfreBBnH4KcHysxVQiKtOTHnUaiGRFHcs1BH8fCQUzG9cl1zfMZndL6AH0KoIaMqVHQGA6hHaf6l9l6am5rwI0VbRjGvffwAAg1Kg2HGfQM39ieZK4doXEr1kFI9QgNNSYOeZhuUwZahFOV6yJ7hGpah2NCbILE47Xem6PPKyTpKxYNGOnJnf5uEkeaFEzVqfsRCTbG902ZDz9l7D7ZmTAxDNfosnjmlWAghhBBCCCGEEOKtIlGJt6F9YzVmSyEdOZNNfUmUUrSkTd61JnNJ55krB3z72TxeENHdZLFv3KVcD6l7IZGneW2szv7xGuMLPl4QZwx2NpkkTUW5HjFf9Tk+42NbYCtN3YuItCbSoCPi1L9FQRT3/gsiCELNTc5Bbkm+xk+qdzIa9KGAks6gPGhJKeYrcZ9DZYKj47Jny1RYFswUPCoejQEqUwUfDTSnLWp+hOvHF37ueJWqG9cb1/yosT6vnKw3hqy8OlprBEfPNF0MmCsHJG2DtjSYhoFhnL0fYVPK5M51GeZKAW1Z65zTioUQQgghhBBCCCHebBIQfJsZX/A5PhOXppbqIc1ps1EKfKkOjNepuHFTwJEFj6PTddxAU3MjKmn459fKpGyTcj2kWI/wgohSPaI9a/PScJXj0z4VN8QtaxzbQCnw/Pjclg1GBF6weDEFhgEOde5N76bfngKg35pkNOjDNOLhJ1UvImkbaEIsA0xLEYXgWJCwDcIwwgvjicKuH2EoWNnhUPf1YjAzolgLGZmtU/NONx98/cevd75xI4kzSq/P/PxMzSmTZikTFkIIIYQQQgghxFUmAcG3meCMvnqnhnxcrCjS/Pxwmb2jdSpuRGvaZKEaorSiM2uxUI1PaBrxf+tBRF+rQ7YWUqqHBKFmquDTljVRSmObiiAC0NimQWRFpBIGKUexUA6wTYVjQ93V9Doz3Go+Q0rVCbTB7totHPVXYapTyYQRlqHozJk0pQzmSgFaga817TkbBUwUIqJq2Jg6vKLdxgs0U8WAiqfpyBrsOlJh1xFY250g7cSByjXdp0t9twylePVkDaVg82DqrOvkBZpDUy75aoihIgZaFUPtpwe1VL0IU9EoQxZCCCGEEEIIIYS4VkhA8G2mv9VmfMFnrhyXpva32hc+6HWOzbh8/4UCXqDJJBTdTTbbV6dZ25XgZwfL7B2rM1vy6Uz6ZBOKmVLETCkgkzDoaoqDckemXDYPJBlqdzg27eGYmoSlaM2YFGpxn7+apzGUgTYianXNOnMfW8x9GAryYY6n6zuYD5tRi5mDEJcAd2Yt+lpsZssBfc1JlDLwgohMUnF4yiMIIuoR2Jai7GraMjZjeZ9Ig6ng2IzH2u4Ehorv47bVaZpSJsnXBe46cxb333D2MuFTjs+4zJUCWtImWis6UvHwFoCDE3WOTLkoBTcPpS47Q1MIIYQQQgghhBDizSABwbcZ01DcsTbTGF5xqQ5PuRRrcfafFyhuW+Xw/s3NAPS3OrxwvMqB8Sp+tc7BaQ/HMmhKGygUuYTipwcrAAy02mweTLOy3cYLYaYcoogz/aYLPknHAAtmStBjTbM1uS++vreS3bVbiJSFqYhLiRVYCrIJCz/SzJZ8TNMglTBZqAbkEibbVyWJtMIxYa4SkrQMVnc6tOVMFhafJ5cyqAeaMNK05SzSjkFL2sSxLj2Lb/l848Wva83RaXfxYzg65V2VgOD+8Tojcx7ZhMH2VWnJVBRCCCGEEEIIIUSDBASvA1GkKbtx7zznAn3qTrnYYGDFjRhb8EjaBgOtNiaKXNJkoRLQkjG5ZeXpktmUY9DZZJGv2uzLw9hC3ADQDzUdWZOnD7u4vsaxFT87VGJVZ4KqG5F0DDKOwUTeZ3Tex/U1C9WwUQo8EXSz113HfNjKMX9F3LdPg2VC0lbYJliWItSazoxJb2uC5pTBa6M15ishXU2a3cfrfGRbE//4cokDky65lEFHzmJFu8NQm8NPDTCVoiNrkkmYGAas7ExcMBjoBZqJvE/SVnQ3n862XN2ZIF8JKdZD+podmqJ4vZVSJG2j0ZMw6Vx6UPaNyldDji0GJfPVkCPTLjf2n730WQghhBBCCCGEEO88EhC8xoWR5tmjFRYqIbaluGNNhqZzDKYIQs2e4Sr5akhPs33O/nev3/+ZIxXqfjz9d9eRkOeOVZkuBvQ029x/Q45VHQnmygEVN+TotMfRKZepgsfoXJwNBwrHUsyWA0r1ePKvG2j8IMILoFSP0GiyjkHJDal5Gq0jNicOcMhbTUASgOfrWxv3ZZtxdp1S0Ja1uGUoyfCcz8i8Ty1QVN2I7ia7EXxTSlH3NANtSR7+xRTjeY8wglUdDu25OIj3gZubqXkRHTkL8//f3p3HZ1Xe+f9/nXPuPXsgEBKCgGzKIgqogOKu7VTb6ri0Tp1WrdrFWqedah3bqnWZTq392dppbWu/bbW7nap1Cm511Kq4K27IprIlkJA993qW6/fHDTeEBAgIJCTvpw+EnOs651znOtednPPJtfQxWBoE+frpzOR7GB5SE2P8iPxcg5FQvidmPp/PurVb95s9LsGKDVlCjsX4qjDLGjLYFoyrihJy9n2A0NruFPs/JCkiIiIiIiIiA5kCggNcS5dHazIfkHI9w9qWHFNr42TdgHQuwDfw1roMgTEURW0aO/K99tY05xhZFmJE6Y7nEMy4ARk3yA9z3ZjjjXUpUjlDImLh+gGuF/DYW524vuHvb3eCMWQ9sExAUyek/ICQYxPBoiPl4/n5Hn8W+Xn/urIB6ZxPYKArnV/9N2GlWFD0HCNCzYx0mng0tYBtQ1bxMJTFbVI5Q84zNHa4vLQawo5FImLj+wYvCDisLgbkA6COZTF2eJjqModo2OnWk2+L0rizw0DqDuvHM3SkPTZ2eGRckw/wjYjucr/SuMOscQkAnl3RVbh/HemgsH1fKos7TKqOsro5R3HMYcLIXZdZRERERERERIYOBQQHuPjmVXDN5knrEhGb9rTPcyuTeL5hbXOOmoowjm2xriVHRVH3W7qx3WVDu8u4qiixsM26lhwhx6KuMkzEgVdXp1jZmMX3IBrJBx2ztsHz4f3mHF2Z/NDX9zdl8XxDyLaxrQCs/HBe1ze0pTzSbr534Ja59VwXMq5P1t26rS5Uz/z4C0Rtl5wJsTx3MNv3X7MtqCgOkW51C0lBYEj7hnjEJhGxGVcVY8EhJSw4pIT3mjK0dvlMGhUjGt69gN+uxEIWXdmAhjYXgPpWm1QuIBHp+3x87elgm3/v5pLPH8DE6hgTq2P77XwiIiIicuAxmRTGzfZ9B9ve+mIiIiIHNAUEB7jimMMRYxOsb3UpizuMHR7h7foMnp//QdyVDUhmA0rjDsNKQgwrDtGW8qkqCdHQ5vK319oJTP4402pjZL38fp0Zn2Q2wBgojTl0pn1qyiMURQJCjiEatjloWIRlDVnWt+UIWRYZ35DO+Ti2wXPBI79/Jpfv/bfto0HWoxAgtAmYFXudQ6MrANjkVfBk6mi6THGP6/UN2FgURR1ynodtQWAMpfEQsbDF6IoIxx+ydb9xVTHGVe24/ja0u7y9PoNjw2FjEpQn+h40tG2LSdVRMq4h7FiUJxwyuxkQrC0Ps7YlB0DNbq74LCIiMhitW7eOb37zm5x33nl86EMfKmx//fXXufnmm1m6dCnDhw/n85//POecc04hvauri+985zs88sgjeJ7HKaecwje/+U2Ki3s+T4hI3xg3S2rhLwjamvqU36mbTOyYj+3jUomIyP6ggOABoLosTPU2w2CLo1uDWmOGRUhELcIhi+mj44wqD9OZ9vnbkg5eW52iPe1TXRaiI+2xqjHL6Mr8irctXT7RsIVlwfCSEIHJ/z2jLkTON6xtdlm9KUfdsDDRsEU2F5DzAiwLyuMOTR0+nSkf20DOzwfytuVt6dFopTkh8QzDQ60AvJWdxCuZ6QT0DKpZQFVJ/vwlMZsRpVG60gFTR8cZXxWhsdPj4BFRNrR7bGx3GVkWpjPjs6Y5vyjKuOGRHoupLFmTLgRP31yX5phJu/fSMK4qSlOnj+cbKotDuxVQBJgxJs6oijC2BcOK9XETEZGha82aNfzsZz9j4cKFuK7LeeedV0hraGjg4osv5qqrruKMM87ghRde4IorrqCmpob58+cDcM011+B5HgsXLsTzPK688kquu+46brvttv66JJFBIWhrImhp6FNeu3wnv4kXEZEDiiIUB6Axw8L4gaEj7VNbEaZqu3kCH3mzg1feT9HY4ZLMBpTFbcqLwoyriuBuHrUaCeV7vI0Zlg+0LZhczILJCV56P03Ih4qigGUNWUrjDpGQjW1b5AIwAaxv88AHz9sa+NuRnAkTtlwyQYRn0nNY59UAm+cZBILN/zbAsGKL2vIwE6tjZNwAP8j3VDykJoZlQVfWUBxzCALDmuYcw4pDPL8qSdbNFyLnGQ6p2fEw2T0Z3VBRFOL4KcWkXUNpzO7z6s3bqirRx0xEROSVV14hl8vxxz/+kcsuu6xb2m9+8xvmzp1b6BG4YMECzj33XO655x7mz5/PqlWreOKJJ3jyySeprKwE4Prrr+fMM8/k2muvLWwTERERkb5RpOIAZFk7XtzCGMO6FpeWpEfIsYiFLabXxTlmUjGjKyNsaHdpTfqs3pRjU2d+AZLJ1VFynuF/XmpnbbNLR8anOOKQzAakcgFNHR6ObVEWt+lIBbiewfc3r2bbS5DNxt/cA9DCI8T/pebjmhAps3VBjbAN4RC4Hlg2hCw4dFSMOQcX5Rc2idqUxm3mTyymvs0FLBzbIggMTZ0eEQdSuaAQDAQKqwFva3pdnLfWpXFsi2mj92xOvWjYJqrRviIiIh/Ixz/+cT7+8Y/3mrZ48WI++clPdts2d+5cHnjgAQCeffZZpk+f3i3wN2XKFMrKyliyZAknnHDCHpUpCPbf/L758wXd/paBYSjfFwOYzf/1Lb8p/L2v9tk+z74um2H/fy84UA3lz8pApXsyMPX3fbHtvo1sVEBwkLEsixFlISpbHbKuYVJ1lA/NKCsMV62tiNDclSadC7BtWL4hy/ASh/o2lzWbXEK2oaHdZ+qoCOVFId5vyrK2xaUj7dOW8jfPCWjIbf6Z6VjdhwuX2h0cl3iOVbmxvJ2bhAV0BqX5/ex8r0DHgkQEhpWEaWh3cWyLaMimOenzXpNLImpjDMwel+i2OEZbyueep5tZ05xjY4eHZUHazQcsq0pCGGN4Y22ausow5ZsXV6kpD1NTrmieiIjIQLZmzRrGjBnTbVttbS1tbW1kMple0wFqampoaOjbUMft+b7HurWr9mjfD6p+/Xv9cl7ZuaF2X0KhMMOLinDdHH6ujwuLuC4Yg+e5ePton1zOK/zb811y+7BsjpvD9102NazB89y+nUeG3GflQKB7MjD1130Zc9CkPuVTQHAQOnlqCf7mgN1Bw8Pd5r1rTXq8uTbN0oYMEceisSNHyImwdlOO9pRPMmfIuD6vrPaJRWw6Mz6WBX4AYcci8A0uEA2BF+R7CWZy+aG/48KrOTr+MmHLJxZdznvewXiWgwNg5VcQzvngOJALIOMGlMUcomGLjkxAU6cHVprTZ5ZTWeR0mysRIB62SOUCyhIOfmB4ekWKWWPjVBQ5tKY8IiELx/apb3M5fkox0XDfF/8QERGR/pNKpYjH4922bfk6l8uRSqWIxXr29I/H4+RyuT06p+OEGF138B7tu6eCIKB+/XvU1I7DtvWcMlAM5ftikh2EwxGcSO+jj7YXCofBsgiFwtj7aB+Pre8AISdMZB+WzQ5HcJww1aN6/sJBehrKn5WBSvdkYDpQ7osCgoNQVUmYj8wsZcWGLBVFDltmvWtLeixc0sE79RmyXsDaZo/a8hBRx2JYcYjOTIAxATkXPMvQlfMJgq29AINgczd8Az75IGHIBgePo+OvMiHyPgAN3ghezB2JFXIw+ekGqUhYYNmkcwGeb4iGLBIRG9uy6Mz6uB4kEjbJLKzcmKWyOITjWBxUFaEsnn8osG2LiqIQjR35394lIha2ZVESc2jp8sm4AREn/2FL5wIFBEVERA4Q4XC4R2Avm8338InFYoTDYVy3Z++dbDZLIpHosb2v+jqkZm+zbbvfzi07NhTvS34+7/x/fbEl377cZ/s8+7psFv33veBANRQ/KwOd7snANNDvy16PmCxevJjJkyd3+/OhD32okP76669z3nnnMWPGDE488UTuvffebvt3dXXxjW98gyOPPJIjjjiCq6++mq6urm557rvvPk477TSmT5/OWWedxauvvtot/f333+eiiy7isMMO45hjjuEnP/nJ3r7MAW1DW44HXmljTXOO5Ruy/N/STp5fmeShNzpYuTHD8g0ZVmzMkfPzXfzKEw5TR8c48dBiiqIWtrV5fkA2B/9Mfn4RCwgCcH3I+vkFRWKmnQ8XP8aEyPsEBl7NTOUf2QVYkTiunw8aBgZS2fxBwo5FOAQGi5xvyHqG2vIwZQmbiqIQo8pChBwYOzxCEMCS1WkAsm6ABXz08FJm1MWZM76ITxxVXljkY1R5iHebcry5Pk1L0qM0PnA/dCIiItJddXU1GzZs6LZt/fr1VFdXE4lEek2H/OrEdXV1+6uYIiIiIoPGXu8h2N7eztSpU/nLX/7SI62hoYGLL76Yq666ijPOOIMXXniBK664gpqaGubPnw/ANddcg+d5LFy4EM/zuPLKK7nuuuu47bbbAPj73//Od7/7XX70ox9x6KGHcvfdd3PppZfy8MMPU1lZSSqV4jOf+Qxnn302P/rRj1i5ciWf+9znGDZsGOeee+7evtwBxQ8ML7+XYuGSDta15oiFLY4YE6ctE1BVEmJNc46ubH4hDt8YIqEQNRVhljZkiEdsGjs8yhMOqazB8wMCY5EKDIHJBwNzfj44uOX3bRFyfLj4cSKWRyqI8VTqaJr8KmIRSEQcOlL5ccuBgXQOSuIQdmwyLgwrzgf+imMOxVEbv9UDA2OrIkwaFSPs5M/iBYal9Rnebczi2Bazxsb56BHlhWuuKo2QdQNWbszmg4yeoShqk3YDiqIKCoqIiBwIZs+ezTPPPMPpp59e2LZ48WLmzZtXSP/Zz35GMpmkqKgIgGXLlpFKpZg5c2Z/FFlERETkgLbXewi2tbVRXl7ea9pvfvMb5s6dyznnnEMsFmPBggWce+653HPPPQCsWrWKJ554gptvvpnhw4dTXV3N9ddfz8KFC2lpaQHgzjvv5Itf/CKzZs0iHo9z2WWXUVtbW1iF7v7776eyspLLL7+cRCLBjBkzuOyyywrnOFClcgH/WNbFQ693sHxDpkd6OuezcEk7dz/dzOtr0yQzARnXsL7NzQ+jDSCZDWhPBRTHHCqLQ9SWh6lvdSmNO3SkA9pTPpXFYYaVhNsMTRUAADuOSURBVBhRFqGy2Cbk5HsHBmxdPGTLGiI5IizJHMo6t5q/dp3KRr8KA4RDVn7xECc/b6BjQ1EMxlZFqSwO8ZGZpZxxeDlHHlzMYWMSuD5MGRXlqAkJpo+Oc8zEYhzbIuRYTKqO8m5jfsiQHxhWNnYfThSP2JQXhfIrAYcsSmJ2vhei07dhAiIiItL/LrjgAhYuXMjChQvJ5XI8+eST/OUvf+Hiiy8G8gHBiRMnct1119HW1kZDQwPXXXcdn/nMZ3rMPSgiIiIiu7bXewi2tbVRUVHRa9rixYv55Cc/2W3b3LlzC8G8Z599lunTp1NZWVlInzJlCmVlZSxZsoQ5c+bwxhtvFHoLbnuMV199lQsvvJDFixezYMGCHum33HILXV1dFBcX79F19fcy3ssb0rSn3MK/q0ttimP5HnCpbMDjS7v4x7Iumrs8PD/AsW0qi2ymjY7xTn2WtpTBAQ4fE2V1s4vrGw4eEQJjUd/u4QcmH8izDCVRm9K4TXOnYW2z360clXYrATZtQRkAb+cm8XZuElv6DdpAJmvwfZ94GJyIRSxskYg5xMJQFHWYVB0l7FiMLIsQdiy6Mh6uZ8AYEhGoLrMZWZrAsixcLyDr5hc2CTsWEccQBN3LtKHNpaUrRzLrMaoszMTqKCG7Z759pb+XFB9oVB89qU66U310p/roTvXR076sk4Eyr82UKVO47bbb+N73vsdVV13FwQcfzO23386ECRMKeX74wx9y3XXXcdxxx1FcXMzZZ5/N5Zdf3o+lFhERETlw7ZOA4KJFi3jooYcYOXIkCxYs4Mtf/jIVFRWsWbOGMWO6r+BUW1tLW1sbmUym13SAmpoaGhoaWLt2LZZlUVtb2+MYL730EgBr1qzhhBNO6JEOsHHjxj0OCPb3Mt5NGw0tHabwdUO9RTycD8KtazO0NBuyGYNtIGJB4ENFKEvDxi4qw9CchGwGVjdAVxbWtENbBxw8HDAwPArDorC6BTYlIefCps58z8A8w5TIKmbHltAZFPG3rpPxCQEWjgW2nZ8vcPOUfuQ8KCuCaMgQDhlcN+D9jS5Tq6F5U5LOrMVBlTChGlpKDcs3WSS7oH4jrC7eiGNbBMbw2jrY2GFoTcHkkVBWarFu7dbefznPsPh9g9lcNRnbIhe3WNe+r+9IT/3dRgYa1UdPqpPuVB/dqT66U330tC/qZMxBk/b6Mfvi8ccf77Ht5JNP5uSTT97hPiNHjuTOO+/cl8USERERGTL2ekDw8ssv50tf+hKO47B8+XJuueUWrrjiCu6++25SqVSPYR1bvs7lcqRSKWKxWI9jxuNxcrkcyWSSSCSC43T/bXYsFiusTNfbMbY9x57q7+Wih1cHvLYmQyobMK4qwriqSCEtVu7y8sZ2yopdNiSzFCVsKosdxtYkeH1tBtu2aOp0KYk5dOUMLj627bMhCZFYmNMPK2FEaZhH3uqk3M2xujVLczJg84K9RMgxL/ESB4XXA9ARlGAREAtDLGRjO+B6Bs83BEDEhlDIIhEPEQvbxMKbhx4bQygWxopFmDgyDEBj4DBmtEVxuVe4nlG1xURCNs1dHqHWFHVFUAfUVYYZX9e9/SSzARUdWxedqSgLM7pu/w4dOlCWFN9fVB89qU66U310p/roTvXRk+pERERERPa2vR4Q3LYH3owZM7jjjjs49thjWb58OeFwuEdQLpvNzw8Xi8UIh8O4rtvjmNlslkQiQSQSwfO8XtO3BP16O8aWc3yQOWb6e7noRNRh3sR8EM3zTWF1XYCs7zOsJIzrwwQsEpH8y0JDh084bLG+xWV9q0t53Ccw0NTh4mERtS3WNrs88GontgUrN+boTHt0bXOLhjvNHJd4jmI7hW9sXsrM4J3cBMAiboMhwLEsnDB4jkVx1CEWsQk5FlUlIaIhm8pimzXNHmEnP2Q461tYVr6MqRzMGhvn5fdTeL7h4JFRYpH8dRbFLBzHIQjy3f+K4+Ee96Ak7nDQ8BhrmnNEQhYTR8X77T71dxsZaFQfPalOulN9dKf66E710ZPqRERERET2lr0eENxeVVUV5eXl1NfXU11dzYYNG7qlr1+/nurqaiKRCNXV1bz44os9jtHQ0EBdXR0jR47E8zwaGxsZMWJEIb2+vp66ujqAXs9RX1+P4zjU1NTsgyvct9rTPslswPDiEJYFL6xK0pbyqShyOHJ8ESHHYuXGLOtbXFw/oKXTw5SEKI05VJWEWL0phxdAzg1Y1elhW/nFQWzLYIVtLC+gPemR8QI2tHtsHZRsmBpZzhGxN7AtQ4dfxJOpubQE+fkh4w7EIhauD15gbR4qnA/ozZ9YzJRRUXI+jBkWJuJY/P3tTjZ1+YyvilJTEWJtcz5oO3Z4hOElIU6eWoIfGCKhrT0fEhGb2eMSrGvJURJzGL9Nr8htTa+LM3lUjJBNt0CpiIiIiIiIiIj0tM8DgmvWrKG1tZW6ujpmz57NM888w+mnn15IX7x4MfPmzQPyK8j97Gc/I5lMUlRUBMCyZctIpVLMnDmTeDzOmDFjeOaZZzjzzDO7HeOCCy4oHOPpp5/msssuK6Q/++yzzJo1i0ik94DSQLWx3eXl91MYA0VRmzHDIrSl8gtltCZ91re6RMMWqWxAImrx5jqX6vIwNRVhqkpCHDwyxvOrUtS35EjmAtKbO05uCZkFJiAIYF3WJ+OyTTAwn6c2vAHbMryXq+OF7CwyQbiQ7gXg+ha+MVjGEAnblMbDjCjN55l5UH5RkC3OPaqSbY2v8jFAyeaFURzbwuklmFdVEqKqZNfNNBLae4HAVY1Z3mvKkojYHDE2QSys4VkiIiIiIiIiMnjs9UjHXXfdxTvvvEM6nWbJkiV86Utf4sQTT2TChAlccMEFLFy4kIULF5LL5XjyySf5y1/+wsUXXwzkg3kTJ07kuuuuo62tjYaGBq677jo+85nPFIb7XnTRRdx+++289dZbpNNpfvKTn9DV1cVHPvIRAM455xyWLl3Kr3/9azKZDK+99hp33nknn//85/f2pe5zG9q9wmIZyWxA1jXd0sMhi3QuIJkNGFMZYURpKD8kuN3nhXdT/PbZFpJZj86MTyo/ahpDfqGQAMi4+QVGujKQKYzENpv/b/FU6iieTc3mqfRRuCZM2IEtA5UCA8lcgGWgssihbliE06aXUJ5w2NTl8ca6zE6vrTjmFIKBA0kyG/BOfYasa2hN+ixvyPZ3kUREREREZF+xNMJIRIamvd5DcOPGjVx44YV0dXVRW1vL6aefziWXXALAlClTuO222/je977HVVddxcEHH8ztt9/OhAkTCvv/8Ic/5LrrruO4446juLiYs88+m8svv7yQ/slPfpLW1lYuvfRSurq6mDNnDr/4xS8Kvf+qqqr46U9/yk033cStt95KbW0t11xzTaEX4oGkPOGwriX/bzcwtGd8vMBQEnMYVR4mFrJ4bXWGje0uXgDRkIUfGJasS1EctYiFLVZvcomGbXJeQM7P9/zbElY0gBvkt1kYpkeXErcyPJ85AoCcibHCHYcFhB1IRC1SWUPIAttALGpRVxHlsINiHDmuiPc3ubSmXMYNj7C2OUd1WajQY3B7W+YFHHhDfM2us4iIiIiIyAHPiheD7RB0tu7efuEoViyxj0olIrJ/7PWA4LXXXsu11167w/STTz6Zk08+eYfpI0eO5M4779zpOb7whS/whS98YYfps2bN4r777tt1YQe4g4ZHCDsWnRmP5RuyNHd6hGyLsoTDITUxXl+bxhgYPyJKNufzZr3PW+vy+Zo6DBVFNn5gSERsUtkAy8//AszfLuYVtTIcm3iemlAjAKvcg9jkDyPYnG6R7404fniYlAvpXICb84jHQxxSE2XKqDhzDi6ipsJlxcYM9i5+y1bf6rJkbRqAw+ri1FT0HjTsD0VRh8mjYry7ecjwxOpofxdJRERERET2ASsax3g50ot+SdDW1Kd97PIqEv90sQKCInLA2+dzCMoHU1MRxvNDrNy4denfdC4fqiuObh3xvSkZYIKAls4c7al8b8DWZEA8AlgWlg2JaH5V322NCm3k2PjzxO0srnF4Pn0Em/xh3fIE5Fc2PqQ2wcEjY7zXmKGxuY1jp1UwqTrB8JIQLV0+JTGbEaUhmrt86iojO+wd+HZ9utBD8O369IAKCAJMGBllwkgFAkVEREREhoKgrYmgpaG/iyEisl8pIDhAJLM+a5pdYmGLg4aFyXr5IcC2bRFyLMZVRVi+IUt7ymdURYjX16RpTXnEIzbDikOUxmz+sayT1nRAxts8DNjKL/7hZ/38335+7j8Ai4CZ0beYHn0Hy4IWv4ynUkfTHpT2KJsFJCLQ3OUxZpjh0NoY1XGLBZNLKEuEeXp5ks6MT9YNSHuG8rhDxg0wxhQWFmnu8uhM+1SVhgk7VmE+xLAz0IYMi4iIiIiIiIgMbgoI7kfvNmZpTfqMKAtRV7l1xWM/MDy3MkXGDWhPe/zvaz7V5WGGFYU4ekIRiYhNImKzqdOjKGqzZHUGA4wszd++po4cz6zoYsXGbCHgF5AfGuzmIGLn5woMtinL8YnFjAnXA7AsO54XMzPx6X2RD8eCkaURJlbHKInZ1JSHGBXJD13OeIbOTH7l46ZOj8zmgGBjh8emLp+qkhAb211eei8FQHhjlplj4qxqzHdVPLQ2tvcqWEREREREREREdkkBwf2kvtVlaX1+5d0N7S5FEZvK4nz1Zz1Dxg3oTPu8uTZDZzagMx3g1MDS+gytXR6rmrKs3JDFD2DMsDBrW1xWhi0cy6Il6bJyQ5aMazAm36MPts4VmA16lmdlbizVoUYWp2fzvltX2O5sN8dg2IbKYod5E4uoKQ8zfkSUKaMirFubP0ssZFESc+jM+IQdi7CzdRhzNLS1d+AWmVzA2+szxCM246oilMUH3krDIiIiIiIiIiKDmQKC+0kq1z0ql3a3fh0PWwwrDtHQ5uLY+bkBA2PoSAcsXZ+hvjXLsyuSpLIBJQmHxg4Xy7KIhPJz+3WkPTqzARjwNx92+7VybQJK7U7agjIA1nq1/E/nR8iZyDZ58kI2GAMhBw4aFuY/Th9JOOwQj9ib59bbWnbbtjh6QhH1rTlmHhSnLeXTkQ6orQhTujnYN6w4xHtN+R6Bmzo9AgPJbEBL0ufEQxyi4a1BRBERERERERER2bcUENxPaivCrGnOkc4FlMQcqkq2LqRhWRZHjk9QFLGIRWx839CVDZg4MsKKjRmeWZFifatLEEBXLqBuWBjPh02dPm5gaEsGZN3uQ4K3VWwlWZB4jhK7iwe7TiFl8itibRsMdCyIR8CxIetCccxmfFWUL55cxezxRd2OF2x3okjIYmxVfhGOkaWGpfUZ6ttcoiGLkWX5wGB1WQg/MJQnHNpS/ubjGFzfEB1Ya4qIiIiIiIiIiAxqCgjuJ/GIzYLJxaTdgETExrG7L6axscNjbatLLGRRVOxwzpQSoiGLZ1ekaE16eH5+HsBc2rC6ycWxDCHHwjMGL4BoGHJe9+G+AGNC65ifeImI5ZINwpTYXaT8RLc8iVB+v5BtEQ3bjCixqSwJccKUEqbXxXfrOpdvyLCmOd8bsDXpc+wkmxfeTRVWRq6pCNOVDfB8Q21FmOKYhgyLiIiIiIiIiOxPCgjuRyHHosTpPQC2ujmHMfnAYRCA7xsyBsaOCJOI2rSl8gE1A3RlDSEbLMtgke+xZ7ab+8/GZ05sCVOiqwBo9IbxVOpokqZ7MDDiQGWJTci2iEccvMAQdiwijo1vDC1Jn1HlfR/Sm3G3FiIIDB0ZvxAMZHNZTzq0BNc3xCMaKiwiIiIiIiIisr8pIDhAFEdtmjvz//YDw7MruvACKA47DCsO0dThEXj5gKAhP9+fF0Bg8l/7W9ftoNTuZEHiOYY5bQC8kZnCq9mpGGzCdv4AARAPw+jKMDPGFNGa8hlWbPPye2l8Y+H6hqYOrzAPYF+NHxFlU5eH6xlqKsJUl4YpieUKKxGPKAsRcixCjrWLI4mIiIiIiIiIyL6ggOAAcUhNjJBjkXUNyaxPazIfQKtvdzlsdJSWLo/6Vg/f5Of5cxwI/J5DhAGmRFYyzGkjHUR5On0k9V51Ic22wHbyf8ejDrXDInz08DIiIYvXVqdo7QpoT/tUFDnMPChBUXT3evGVJxxOOrSEnLe1B+DRE4po6nSJh7eurCwiIiIiIiIiIv1D0ZkBwrEtpoyKAbBiQ6YQEFzX6tLS6eMHAY4DYcDzIbOTRURezszAJmBJ9lDSZuscgI4FlpXvXRgYcGzDxjafda05jjy4iPKiEBOrozR3eVSVhJmxm/MHbnst8cjWHoCRkEVtRWQne4iIiIiIiIiIyP6igOAA0ZH2efn9FFnXcHBVmJakx7KGDJ1pn1WNOTZ1BuT8/PDg7ZXb7UyOrOL5zOGAhY/Dc5lZ3fLEQpCIgGNZZH1wXQPGoihq0dzls7HdY8ywfNBubFWEo8YXqTefiIiIiMgBwmRSGDfb9x1sG0xvbxciIjIUKOIzQCxryJDK5vv8/fa5VlZvytHS6ZHMBbR0+TsIBhomhN/nqPirhCyfzqCYt3OTuuWwyAcCi2M2jm3jB4aKmJUfemxZ1FSEKU84GAMzxsSZWhvDtjW/n4iIiIjIgcS4WVILf0HQ1tSn/E7dZGLHfGwfl0pERAYqBQQHCNuySLkBLZ0eL7+bornLJe3m03J+z/whXI6Ov8LBkTUArHdH8q47plsex4KQk59n0PWhssghGraIhiyGlYTxfMO00XEqi0JMGBnNl0PBQBERERGRA1LQ1kTQ0tCnvHZ51T4ujYiIDGQKCA4QYQfWN7u88G4XTZ0eKRf8HUwSWGG3cVxiMWVOF4GxeDU7jTezk8n3B8zPERgLQzhk4/kBiUh+8sBICD48owwwWJbFuOERjp5QpCCgiIiIiIiIiMgQooBgP0lmA15fm+b9pizlRQ5dmYCwY9HY4ZHOmV5XDwYYF17D/PiLOFZAVxDnqdTRNPnDC+khG+JhOKQ2zsEjIrzbmCOZDch4honVMf7psFLiEZuMG1AWdxQMFBEREREREREZYhQQ3I9aujw6MwGuH/DE0i6eW5kknQsYVhIiGrJY35qjqcPH28ncvm1+KQBr3VE8k55D1mwe6gs4DowqCzGyPMRR44s48dAS/vJyG43tHkVRm8+fOLywUEg8Yu/ryxURERERERERkQFIAcH9ZGO7y0vvpci6Ae80ZPADaE76ZHMBkbBFsxewujnXazAwamULgb/WoJy/dZ1Ea1AGWFhAacyiOGbTmQnoSAc4tk9r0qeiOMSn5lXQ1OkzujJCVYlut4iIiIiIyAdiaZSViBz4FCHax5LZgCVrUizfkMUPAho7fJo6fdY0Z2lJenieIRK2yLg+7anto4GGQyMrmBl7k4eTx9PsVwL5oOAWjp3v7ec4FpOqo7SmfBIRm/c2ZfnDcy1cdkIVoyv1A0tEREREROSDsuLFYDsEna27t184ihVL7KNSiYjsPgUE97Gl9Rlakz4Yw9v1WezNi3ukcz42+V8utSZzdGVh23Bg1MoyP/4ideH8KmHjwmto9iuxyC8dYoBICCoSDuNHRMm4hmjEwkr5dKTy8xGmcwHvNWWZPCq2/y9cRERERERkkLGicYyXI73olwRtTX3axy6vIvFPFysgKCIDigKC+1hgDGubczR1ejR3eRRFLNI5Q1c6wAlZRBwLL4CcuzUcOMLZxILEcxTZaXxj80JmJstz49nSzy/k5HsGThwRpboiQmncxrGhscMjEXPoSvs4tsUIDREWERERERHZ64K2JoKWhv4uhojIHlPEaB8bVxXhoSXtrGzMkswEbGoP8AJDxjPgGUKORSJsCAAwTIu+w+HRt7AtQ7tfzD/Sc2kLyglZ4BuwLYhFLGrLI5x9VAVjhkVo7PAAaE16ZD2DHxhakj7xqMO4qmh/Xr6IiIiIiIiIiAwwCgjuIzkvYPHKJM8s7+Kl91NYQGfGpyMdEBgIAnAsiIQs2tMGGxgTXses2JsAvJsbwwvZWThOiJKIRcixCFmGopiDY9scO7mIM2eV4wXw/KokHWmf2ooIOT/A82F0JRw5vohISPMHioiIiIiIiIjIVgoI7iMrNmR59M0O3liXoTUZgAlwPYMfQGDAD8AHyAXk/Pw+77ujOSg3mg1BNWuCsZQXO1hYTKiOMqo8zMY2j5qKMMUxhwvmV2DbFhEbjplUhOsbwo5F1jNs6vQoiTuUxZ3+rAIRERERERERERmAFBDcR7KeoaHNI53z8XyfZBYw+cVAbCvfOzAwAROclbzHeDJ+iIhlsYT5JOIWo6M2s8cnqCkLMaw4QsixGF4SYkRpiOqyMPGIXTiXZVmFnoCxsMXoykj/XLSIiIiIiIiIiAx4CgjuI1UlDqmcT2fakMzm5//bsjqwbWBYLM3s8HMMtzcxwm/jqeSRhEMwdliIg0bE+MiMUmaOLSIRsdjU6WFZFqVxh+YuDy8wuzq9iIiIiIiIiIhIrxQQ3EdyPowZFmFdi1vYtiWMVx1q4NjoC0TIEVghmhjFiDKHoqhNNrC47MRhHDQsRnva58l3kuR8w8SRUd5YlyaVDbAsmDMuQVVpuH8uTkRERERERPrO0tzuIjKwKCC4j1QkbFK5ANuyCDvge2ARcETsDaZFlwNgYsOonHIqofdCxNMeyWyAbRkeeq2Ts+Y4rGrMkczm1x9+YVWKRMTCti2MgYZ2TwFBERERERGRAc6KF4PtEHS27t5+4ShWLLGPSiUiQ50CgvtILOIwb0IRHSmfd5t8oibF/NhiqkItADRHpxCtnYubDXForeGJdzxyPtRVhGnocFm5MYtlb/0tUnzzSsNbRguXJ7RgiIiIiIjIYGQyKYyb7fsOtg1G0woNVFY0jvFypBf9kqCtqU/72OVVJP7pYgUERWSfUUBwH0lEbKbXJfjbkg4sbCzbosRJkjNhNpTMZcKEQ6gsDrGmOUcyazjioAQtSZ/AGJKZgIriELUVYV5bnSbnGQ6piVEcs2locymJOdRUqHegiIiIiMhgZNwsqYW/6HPwyKmbTOyYj+3jUskHFbQ1EbQ09HcxREQABQT3qdEVIYYVh2hodXGdBItz8xlWXsqxh44i5xvKEyFiYRvXC4hFbF5fm6Y95XPMpGKmjIoBcOzk4m7HLI2rZ6CIiIiIyGC3O8Eju7xqH5dGREQGG7u/CzBYtba28MCffsmY0FocOz+HbCY8AhMtIRq2qKuIMLLMYfKoGGfOqcCxLEK2xfgREcYOj/R38UVEREREREREZJBSD8F9YOnbb/LwQ/9LLpejtCzLx2adw/INLn5gGFkSIpkJKE/AkeOLsCwL1zesaspiWdCRDlixMcthB2muCBERERERERER2fsUENyLXNfl8b8/zJLXXgGgZFgtI6edxthRJZx7ZAQLwxPvJPENhGyLNc0uBw2P0JXxiYRs0rmgn69AREREREREREQGOwUE95LmTU088MD/sKmpEYDx0+bSVTqTZ9/PsmhpM0cdHKc46rBiY5byRIjiqgjJrA9AUdRhUnWUDW02lg1zxqt3oIiIiIiIiIiI7BsKCO4FnZ2d3P3ru3Bdl6KiIk4/40za7VH89dU21rW6ZF3DU8u6OGJMEfGITVvKI50LUVuZnyswErI4ZlIRDW0eRVGbUeVaQVhEREREZLAwmRTGzfYts22DMfu2QHJgsKz+LoGIDGIKCO4FJSUlzDjsCDZtauT0M86kqKiYjBsQf9Mm6wbEIzYYi86sz2F1cdJuwIJDiinbZsXgoqjDhJFaQVhEREREZLAxbpbUwl8QtDXtMq9TN5nYMR/bD6WSgcyKF4PtEHS27t5+4ShWTCPORGTXFBDcQ42NG4nH4pSUlgJw/AknY9s21ubf4sTCNucdXUHaDWhL+lSVhphYHaWiOMT0ijAjStQLUERERERkqAjamghaGnaZzy6v2g+lkYHOisYxXo70ol/2KZAM+baT+KeLFRAUkT5RQHA3GWN47bWXefyxhxk1qpZPnP+v2LaN4/Ts3Te6MsJZs8tZ2+JSGneYNTZOJGT3Q6lFRERERETkQNPXQHKBhhmLSB8N2oDgXXfdxW9+8xtaWlo47LDDuP766zn44IM/0DGzmQwPPfS/LHvnbQAi0Siu6xKNRne4z6G1cQ6tjX+g84qIiIhI3r54xhMRGQz2ZJixhhiLDF2DMiB4zz338Kc//Ymf/exn1NTU8P3vf59LL72URYsWEYlE9uiYDQ31/O3B+2lra8W2bRYcdxJzjjy6MERYRERERPatffGMJ7K7dmuBENAiIbLf7O4wY7u8isRHPotF3wOCoZCmvhIZLAZdQDAIAn76059y4403MmnSJACuueYaFi1axBNPPMGpp56628d0XZff//bXBEFAWVk5Z3zsLGpqRu/toouIiIjIDuyLZ7z+cKC/TO92MIw964G02+exLHBC4Lm7XTYi0d26L7uzQAhokRDZ//o6zHh3exQaYHhxCWTTBLvzWduTz+cH+EwPtR6PB/rPlcHqQLgvljGD69dVy5Yt46yzzuKVV17pNpT3iiuuoLa2lquvvnq3jvfKK68Q+D6ZTIZwOEw8kVCvQMDz3AOige8vqo/uVB89qU66U310p/roTvXR076qk0gkwuTJk/f6cfeFvfmM98orr2CM2T+9Co0h/xq95UvTh2dJa+DOA2YCTCYFgd+3/E4IK5pg2zro23nAZHfnPGGsSGw399latr7dl23Klu7E9PE8lhPGihX1eZ/dzT/Y9jHGsLalE4C64eVYJhgwZRts+xTy7+7nJhLfD5/PPdjHdvLBQGs/zNu/3ff2vrH2yT49v3/t4XkG6s+d3bWn92Z3r38X5+n958r+qee+Pt8Nuh6Ca9euZeTIkT3m9autraWhYTcmY93Msixsx6GsvHwvlXBw0Itad6qP7lQfPalOulN9dKf66E710ZPqZO8+4+3XX+5aFvmXs22+PJBZdr5X0e7vuNvZ9+Q8e1q23bovFlhFZbt7Rbu9z/44x0DdZ2zpsN08+p6dR/tszr+fPmv77zO9H2z3vX03dtzr+/T+/etA/2HzAezxvdm75zkQft4PuoBgMpkkFov12B6Lxcjlcrt9vMMPP3xvFEtEREREPoC9+Yyn5zsREREZ6vZDX9r9KxwO47o95xnIZrPE41rtV0RERORApGc8ERERkb1n0AUEq6uraWxsxPe7zzNQX19PXV1dP5VKRERERD4IPeOJiIiI7D2DLiA4depUbNvm5ZdfLmxzXZcXX3yRefPm9WPJRERERGRP6RlPREREZO8ZdAHBaDTKv/zLv3DTTTexevVqurq6uOWWWxg7dixHHnlkfxdPRERERPaAnvFERERE9p5Bt6gIwJe//GWy2SznnHMOruty/PHHc8cdd/R3sURERETkA9AznoiIiMjeYRljTH8XQkRERERERERERPaPQTdkWERERERERERERHZMAUEREREREREREZEhRAFBERERERERERGRIUQBQRERERERERERkSFEAUEREREREREREZEhRAFBERERERERERGRIUQBQRERERERERERkSFEAcFduOuuuzj++OOZMWMGF1xwAatWrervIu2RxYsXM3ny5G5/PvShDxXSX3/9dc477zxmzJjBiSeeyL333ttt/66uLr7xjW9w5JFHcsQRR3D11VfT1dXVLc99993HaaedxvTp0znrrLN49dVXu6W///77XHTRRRx22GEcc8wx/OQnP9l3F7wD69at48ILL+Shhx7qtn0gXL/rutx6663MmzePww47jM9//vM0Njbuxavv3Y7q5Pjjj+/RZl577bVC+mCrk7fffpvPfvazHH744Rx11FFceeWVbNy4sZA+FNvIrupkqLWRRx55hDPPPJOZM2dy7LHH8p3vfIdcLldIH2ptZFf1MdTax7bWrl3LtGnT+MUvflHYNtTax2CSTCa5+eabufHGG3ukNTU1ccUVVxS+T95yyy24rtstz66eJfdH2xjMfN9nypQpPb7fNDU1FfIMlPskfTdY3sEGAr0HDix6Hx14hvT7sJEduvvuu80pp5xili1bZjo7O80NN9xgTjzxRJPNZvu7aLtt0aJF5swzz+w1rb6+3syePdv86U9/Mul02jz55JPmsMMOM08//XQhz+WXX24+97nPmaamJtPQ0GDOO+8885WvfKWQ/thjj5mjjz7avPTSSyaVSpk777zTzJ492zQ3NxtjjEkmk+a4444zd9xxh0kmk2bJkiVm7ty55o9//OO+vfDNVq9eba699lpz+OGHm2nTpplFixYNuOv/z//8T3PuueeatWvXmubmZvPFL37RfOITn+iXOjHGmJkzZ5p33nlnh/sPtjr5whe+YP785z+bzs5Os379enPppZeas88+2xgzdNvIzurEmKHXRv7whz+YV1991aRSKfPWW2+ZU0891Xz/+983xgzNNrKz+jBm6LWPbf3bv/2bOeSQQ8xdd91ljBma7WMwSCaT5gc/+IE5+uijzRFHHGFuuOGGbum+75uzzjrLfPOb3zTt7e1m5cqVPT4Hu3qW3B9tY7Brbm42kyZNMp2dnb2mD5T7JH03mN7BBoKh/h44UOh9dODR+7AxCgjugO/7Zv78+ebxxx8vbMvlcuboo482Dz/8cD+WbM/8/ve/NxdeeGGvad/97nfNl770pW7bbr75ZnPZZZcZY4xZuXKlmTZtWrcHy6VLl5opU6YUtp199tnmnnvu6XaMj33sY+b//b//Z4wx5re//W2PH0S/+tWvzOmnn/7BLqyP7rvvPvO1r33NLF++3JxwwgndPuwD4frb29vN1KlTzbJlywrpzc3NZurUqebNN9/8oJffq53VSTabNZMmTTIbNmzodd/BWCfbv0g0NDSYSZMmmfr6+iHbRnZWJ0OxjWzvl7/8pTn33HONMUP3+8i2tq2Podw+nnjiCXPOOeeY888/vxAQVPs4ML377rvmM5/5jHniiSfM1Vdf3SMg+MQTT5j58+ebXC5X2Pboo4+a2bNnG8/z+vQsuT/axmC3atUqM3Xq1B2mD5T7JH0z2N7BBoKh/h44UOh9dODR+7AxGjK8AytWrKC1tZV58+YVtoXDYebMmXNADsNoa2ujoqKi17TFixdz7LHHdts2d+7cwnU+++yzTJ8+ncrKykL6lClTKCsrY8mSJXR1dfHGG2+wYMGCHR5j8eLFvaYvX758vwyn+PjHP853v/tdJk6c2CNtIFz/Sy+9xLBhw5g0aVIhvbKykkmTJu2z9razOmltbQXYYZsZjHVSXFzc7etEIgGA53lDto3srE6GYhvZXldXFyNHjiyUdSi2kW1tWx9DtX20t7fz7W9/mxtvvBHb3vqIpfZxYBo3bhy//OUvOe6443pNX7x4MXPnziUcDhe2zZ07l46ODlatWtWnZ8n90TYGu7a2NsrLy3eYPhDuk/TdYHsHGwiG+nvgQKH30YFH78OaQ3CH1q5dy8iRI4lGo92219bW0tDQ0E+l2nNtbW0sWrSIqVOncuKJJ3L99dcXGvmaNWsYM2ZMt/y1tbW0tbWRyWR6TQeoqamhoaGBtWvXYlkWtbW1PY6xpa52dA6g25xk/WEgXP+OzjF69Oh+aW9tbW0AHHHEERx55JH867/+K6+88kohfSjUyYMPPsjo0aOpra1VG9ls2zoZym2ko6ODRx55hPvuu4/Pf/7zOy3rUGgjvdXHUGwfxhiuvvpqzjvvPCZPntwtbSi3j8GstzotKiqivLy8cN929Sy5P9rGYNfW1samTZuYNm0ac+fO5XOf+xwrVqwopA+E+yR9N9jewQYCvQcOfAPhPug5obuh8iyrgOAOJJNJYrFYj+2xWKzbpOkHissvv5wXX3yRl19+mdtvv5133nmHK664AmMMqVSKeDzeLf+Wr3O5HKlUqte6iMfj5HI5kskkkUgEx3G6pW9bV70dY9tz9KeBcP07Okd/tbfx48fzwgsv8Oqrr3L//fczc+ZMPv3pT7Ny5Uqg9+uBwVMnf//73/ne977HDTfcgG3baiP0rJOh2kamT5/OnDlzuPrqq7nooosKgZ+h2kZ2VB9DsX385Cc/wfd9Lrnkkh5pQ7V9DHY7u2+u6/bpWXJ/tI3Bbv78+bz44ou88sor/OEPf6CyspILLriATZs2AQPjPknfDbZ3sIFA74ED30C4D3pO6G6oPMsqILgD4XC4x+pjANlstseH9UBQXFxMUVERsViMGTNmcMcdd/Diiy+yfPlywuFwjwaVzWaBfGPbWV0kEgkikQie5/WavqWuejvGlnP0d30OhOvf1Tn2t3A4TFlZGeFwmJqaGr7yla9w9NFH8+c//7mQPhjrZMsqTt/4xjf48Y9/zDHHHFMo61BtIzurk6HYRt544w1efvll7rrrLv72t79x5ZVXFso6FNvIzupjKLWPhQsXct9993HrrbdiWVaP9KHaPg4kCxYs6LaK4FNPPbXLfXb1rNiXZ8n90TYGk97uUzQapaSkhEgkwkEHHcTNN9/M8OHDWbRoETAw7pP03WB7BxsI9B448A2E+6DvY90NlWdZBQR3oLq6msbGRnzf77a9vr6eurq6firV3lNVVUV5eTn19fVUV1ezYcOGbunr16+nurqaSCTSazpAQ0MDdXV1jBw5Es/zeix/vW1d9XaM+vp6HMehpqZmL1/d7hkI119dXd1rt9/6+npGjx79QS9xr5gwYQL19fVA79cDB3addHZ2cuGFF7JkyRLuv/9+5s6dW0gbqm1kZ3XSm8HeRrYoLi5m1qxZ3HrrrTz88MOFtjAU2wj0Xh+9Gczt47bbbmPDhg2cfPLJzJ49m9mzZ/Pyyy/zgx/8gDPOOGNIt48DxVNPPcWyZcsKf7af06c3vdV5Op2mtbWVurq6Pj1L7o+2MZj05T5ZlsX48eN3+v1mf98n6bvB/g42EOg9cOAZCPdBzwm7NhifZRUQ3IGpU6di2zYvv/xyYZvrurz44ovdJrk9UK1Zs6bwIDR79myeeeaZbumLFy8uXOfs2bN59dVXSSaThfRly5aRSqWYOXMmI0eOZMyYMbs8xtNPP90t/dlnn2XWrFlEIpF9cYl9NhCuf/bs2axZs4Z169YV0ltbW3nnnXd2GYTZX15//fXCN57BWCfXXnstJSUl/OpXvyosjLDFUG0jO6uT3gz2NrK9LUMALMsasm1kW9vWR28Gc/v47W9/y6JFi7j//vsLf6ZNm8aFF17Iz3/+c7WPQWr27Nk8++yzGGMK2xYvXkxtbS1jxozp07Pk/mgbQ43rurz99tvdvt/0932Svhvs72ADgd4DB56BcB/0nLBrg/JZdq+sVTxI3XrrreaMM84w77//vuns7DTXX3+9Of/88/u7WHvk5z//uVm6dKlJpVLmtddeMx/96EfN5z73OWNMfnnsGTNmmL/97W8mm82aJ554wsyZM8esWLGisP+5555rvvrVr5rW1lZTX19vzjvvPHP77bcX0n/3u9+ZBQsWmDfffNOkUinz4x//2Jxyyikmm80aY4xpbGw0s2bNMr/61a9MOp02r776qpk3b5555pln9m9FGNNjSfGBcv1XXnml+fSnP202bNhgmpubzRe/+EXzta99bT/USM86eeKJJ8yjjz5q2tvbzYYNG8yNN95oZs2aZdauXVvIM5jqpKWlxUyePNksXbq01/Sh2EZ2VSdDrY10dXWZW2+91bz77rsmnU6bpUuXmk996lPmkksuMcYMvTayq/oYau2jN5/61KfMXXfdZYwZeu1jMLr66qvNDTfc0G1bJpMxJ5xwgvmv//ov09XVZVauXGlOPfVUc++99xby7OpZcn+0jcHur3/9q3n22WdNV1eXWbNmjbnyyivNcccdZzo6OowxA+c+Sd8NpnewgUDvgQOP3kcHnqH6PqyA4E7kcjlz0003mTlz5piZM2eaK6+80jQ3N/d3sfbITTfdZI4++mgzbdo0c9ppp5k77rjDZDKZQvqjjz5qTjvtNDN16lTz0Y9+tMc36A0bNpjLLrvMzJgxw8ybN898//vfN57ndcvz3//932bevHlmxowZ5uKLLzZr1qzplv7SSy+Zj3/842bq1Knm1FNPNQ8++OC+u+Cd2P7DbszAuP7Ozk7z7//+72bmzJlmzpw55lvf+pZJpVJ78cp3bPs6+cc//mFOPfVUM336dDN//nxz+eWXm3fffbfbPoOpTt555x0zadKkXv/89Kc/NcYMvTayqzoZam0km82ar3zlK2bevHlm+vTphe+j255rKLWRXdXHUGsfvdk2IGjM0Gofg1FvAUFjjFm5cqU5//zzzbRp08zxxx9vfvnLX3ZL78uz5P5oG4PZfffdZ0444QQzdepUc9xxx5mvf/3rZuPGjd3yDJT7JH0zmN7BBgK9Bw48eh8deIbq+7BlzDb950VERERERERERGRQ0xyCIiIiIiIiIiIiQ4gCgiIiIiIiIiIiIkOIAoIiIiIiIiIiIiJDiAKCIiIiIiIiIiIiQ4gCgiIiIiIiIiIiIkOIAoIiIiIiIiIiIiJDiAKCIiIiIiIiIiIiQ4gCgiIyqDQ1NfHiiy/0dzF26J13lpJMJvu7GCIiIiIiIjKEKSAoIgecr1/9NR579JFe05YtW8ov7vr5bh+zsXEj/3zWx+jo6ODNN9/gE+edvVv7Z7NZfv3rX3LpJRfxifPO5oovfZH/ffCvBEHQLd/1132T9957t9dj3HLzjfzxD7/f4Tmef/45LvnsRbtVLhEREREREZHthfq7ACIiu8v3fTzf3619bvz29bz22qu9pn3x8iuYNm3aByrPTTfegOd5fPnKr1I9ciQrVq7g//3i56xes5p5c+cX8hpjdnqsnJsjmezqNS2byexxGUVERERERES2UEBQRA4ouVyO+vr1rFq5Avhwn/f7t6/8O67rAnDTjTdw7LELOO74EwBIJBK0t7ftcZn+8dSTrF+/jh/9950kEgkAhg0fTvXIar72ta/wxuuvE4vFgHzwcGfuv+8v3H/fX3aYXlk5bLfL9+ijj/B/jz/GTTd/h7fffovrvvUN7r7ntxQVFffI+61vXsvYseO46OLPFra99NKL/O+Df2X16tV0dXVSWlrK6ad/lDPP+mcAHn/87/z3j35YyB+NRikvr2DSpEmcdPIpTJ8+o5D20KKFPPPM09zw7ZuwbXVSFxERERER6Q8KCIrIAeWhRQupqKhk8eJnOf2Mj3LQQWP7tF9x8dbgl+PYxBNxKioq9kqZlix5jVmzZheCgVuMHTeOMWMO4iMfOZ0TTzoZYJdDkc899xOc94lP9pr2/PPPcdfPf7ZbZdu0qYnf/fYebr7lO3sUgPvrA/dz771/4lMXXMBFF30Wz/NYu24tHR3t3fLF43G+///9AIBMJsvGjRt44YXn+fYN13HaaR/ms5dcCsBpH/owzz23mIUL/8bpp5+x2+URERERERGRD04BQRE5YLz4wvPce+8f+dZ1N7Bq1Sq+fcP1fP2a/2DixEm7dZxsNkuya+8t7JFKpagdPbrXtNLSUtp2o/dhNpelo6Oj17R0Or3bZfvrAw8we86R1NTU7va+APff/xc+ef6/cNppW3tjjj/44B75LMtmxIiRha/HjBnDnDlHcsz8Y7nllhsZN348J510MpZlce55n+C2793Kaad9iHA4vEflEhERERERkT2ngKCIDHiu6/LAA/fxwP33c8UVVzJx4iQmTpxEyHH49g3Xc9xxx/PJ88/vdQjs9nzfp6mpiaamRgCuvurf2bhxwy7n9tuZESNGsG7t2h7bgyBg3bp1nHzyKX0+1gP338cD99+3w/TdGTLsui6PP/4YN930n33eZ3u+7+Pmcnu8/2EzZ3LSSafw4F8f4KTNvSQPPXQqFRXlPP/8Yo45ZsEeH1tERERERET2jAKCIjLgPfbYI7z04ovcfPN/MuaggwrbTz7lVCZPmcLiZ58lHk/s5AhbrVi+HNd1ef2N1wH48pVfwXNdWlpbuPHb1+9R+Y477gSuueYq3nrrTaZO3bo4yf/+74O0tDTzP//zZx588K8AeJ63w+P8x7Xf7Pb1Pff8mrVr1vTY3lcrViwnGo0xdty4PdofYMGC47j33j9SXlHBggXHYVnWbh/jyKOO4uGHF9HV1VUYun3YzMN5fckSBQRFRERERET6gQKCIjLgffjDH+HDH/5Ir2l1dWOoO29M4euZM4+grm5Mr3kB/u///s5pp32YJ598gmXL3mHy5CkAxOKxPS7fhIkT+dSn/pVv33AdxxxzLCNGjGTlyhW8/fZbXHLJZZSVlRXy3n7797vt+9577/LvX/23nR7/n8/6WK/bv3j5FZx44kk73G/F8uVMnjx5N66kp09/5iJCoTA/uuMHPPjXB/jEJ89n9uw5u3WMqqoqANra2goBwSmTp/C73/32A5VNRERERERE9owCgiJyQPnWN6/lrbfe3GmeqqoR3PnTn/fY3ti4kaef/ge3ff92nJDDH37/O667/tt7pVwf+/iZTJ06jWeeeZqNGzcwceIkLr3s84Vg2BY/+MH/1+3rMWMO4q5f/GqPzrn9Iibba2tr+8ALp4RCIT79mQs56eST+f3vfst/3nITs2bP4ctf/jeKior6dAzfy6+svO18geUVFbS1tX2gsomIiIiIiMieUUBQRA4oV3/9P/B9f4fpL734An/60x97Tfv5z37KSSedQnX1KM4685+54oov8tSTT7DguOP3StkmTJzIhIkTd2sfx3F2GLQLggBjDI7j7FF5UqkkZeXl3baFnPy3/Ww22+uci67rEo70XOhj9Og6vnbV13nnnaXc+t3v8KM7fsDVX/+PPpVj7do1xGIxKisrC9uKiopJpfbewi4iIiIiIiLSdwoIisgBZVe90uLxeK/b77n712zY0MC/f+1qAMrKy7n00s/xox/9kMphwxgxYsQHLttjjz3KO0vf5vIvfXmP9vd9n0cfeZinn/4Ha9asJpnMB8xisRg1NbXMOfJIzjjjYzu8xu3F4nHSqVS3baWbhy83Njb2ukBJU1Mjwyp3PK/flCmHcOlln+e7//WfpFKpXfZSBHj88b9z5JFHdeshmEolicX6dh0iIiIiIiKydykgKCIHlL4OGd5WR0cHK1eu4Jr/+AbRaLSwff4xx9LR2UH5dr3o9lRHRzuNjY07zXP9DTfucI7DO+/8MW+//Raf+pcLmDR5CsXFxdi2TSqZ5P333+fee//Iyy+9xHf+69Y+Le5RVlbGe+++223bqFGjqKys5O+PPcqUKYd0S3v99SW0trYy47CZhW2e5xEKdf9REQqFsG2bvqwvcv99f2HFiuV899bucye2t7V3m1tRRERERERE9h8FBEXkgHP++Z/inz5y+g7Tbdvu9nVpaSk3fPumXvNuWayksXHj3ivgTmwfhNvWKy+/zDnnnMvcefO7bS8rL+ewmTOJRqNce+3X6Whv7zEUuDcHHzyBhx96qNs2y7L4xCf/hR//9x04TohjFxxHcXERy5cv57e/uZuTTz6F0aNHF/J/9atXctppH2bK5ClEolHWrVvLb+65m/nzj+m2srMxQaEO0+k0a9eu4fG//53Vq1dz7Te+RXV1dbdyLF++jAkTJuzyGkRERERERGTvU0BQRA44fuDjuu5O89i23W2I6v7ieS6tra07zROJRHod+nzkUUdx331/IRKNMmXKFIqLS/I9BFNJ3nv3Xf7nf/7MlCmH9CkYCPngY0dHO+vWresW5DvppJOJxWI8+Nf7efLJ/wPIz6v4z+dw+ulndDvGzMNm8ugjD3PP3b8iFAozcuRIPvShD3Pahz7cLV86nebzn7sUyC92smWI81e++rXCysLbWrLkVU497cM9touIiIiIiMi+p4CgiBxw/viH3/PHP/x+p3muuvoajjrq6P1Uoq2WLVvGZy/+zE7zLFhwHF++8is9tl9yyWU8Pv4xnnrqCX77m7vp7OzEGEMiUURtbQ1Hz53LRz5yRi9H7F00GuX4E05k0aK/cckll3VLmz//GObPP2aXx7jwos/uMs+JJ57EiSee1OdyrVyxgo0bN/bp/CIiIiIiIrL3WcYY09+FEBEZSFavfp8//uH3XHX1Nf1dlA9s48aNfP3qr/Ff3/3eXlk4ZW+46aZvM23qND5+5ln9XRQREREREZEhSQFBEZFB7pGHH+Lpp//B9Tfc2GN+xf3tsUcf4Ykn/o8bvn0TjuP0a1lERERERESGKgUERUREREREREREhpD+7SoiIiIiIiIiIiIi+5UCgiIiIiIiIiIiIkOIAoIiIiIiIiIiIiJDiAKCIiIiIiIiIiIiQ4gCgiIiIiIiIiIiIkOIAoIiIiIiIiIiIiJDiAKCIiIiIiIiIiIiQ4gCgiIiIiIiIiIiIkOIAoIiIiIiIiIiIiJDyP8PGoCIRo3CdT0AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1300x550 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "BEST_REG_NAME = reg_result_df.iloc[0][\"model\"]\n",
    "reg_best = fitted_regs[BEST_REG_NAME]\n",
    "y_reg_pred = reg_best.predict(X_reg_test)\n",
    "best_r2 = r2_score(y_reg_test, y_reg_pred)\n",
    "best_mae = mean_absolute_error(y_reg_test, y_reg_pred)\n",
    "\n",
    "print(f\"[선정 모델] {BEST_REG_NAME}\")\n",
    "print(f\"R^2  : {best_r2:.4f}  (목표 0.80 {'달성' if best_r2 >= 0.80 else '미달'})\")\n",
    "print(f\"MAE  : {best_mae:,.0f} USD\")\n",
    "\n",
    "fig, axes = plt.subplots(1, 2, figsize=(13, 5.5))\n",
    "\n",
    "ax = axes[0]\n",
    "ax.scatter(y_reg_test, y_reg_pred, s=8, alpha=0.35, color=PALETTE[\"blue\"], edgecolors=\"none\")\n",
    "lims = [0, max(y_reg_test.max(), y_reg_pred.max()) * 1.02]\n",
    "ax.plot(lims, lims, color=INK_MUTED, linewidth=1.5, linestyle=\"--\")\n",
    "ax.set_xlim(lims); ax.set_ylim(lims)\n",
    "ax.text(0.0, 1.06, \"실제값 vs 예측값 (단위: USD, x=실제 / y=예측)\", transform=ax.transAxes, color=INK, fontsize=12, fontweight=\"bold\", ha=\"left\")\n",
    "ax.set_xlabel(\"실제 연봉 (USD)\", color=INK_SECONDARY)\n",
    "ax.grid(color=GRID_COLOR, linewidth=0.8)\n",
    "for spine in [\"top\", \"right\"]:\n",
    "    ax.spines[spine].set_visible(False)\n",
    "\n",
    "ax = axes[1]\n",
    "residuals = y_reg_test - y_reg_pred\n",
    "ax.hist(residuals, bins=40, color=PALETTE[\"orange\"], alpha=0.8)\n",
    "ax.axvline(0, color=INK, linewidth=1.5)\n",
    "ax.text(0.0, 1.06, \"잔차 분포 (실제 - 예측)\", transform=ax.transAxes, color=INK, fontsize=12, fontweight=\"bold\", ha=\"left\")\n",
    "ax.grid(color=GRID_COLOR, linewidth=0.8)\n",
    "for spine in [\"top\", \"right\"]:\n",
    "    ax.spines[spine].set_visible(False)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"chart_H_regression_diagnostics.png\", dpi=150, facecolor=SURFACE)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "1938ecfe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 순열 중요도(permutation importance) - 상위 15개 특성\n",
    "perm = permutation_importance(reg_best, X_reg_test, y_reg_test, n_repeats=5,\n",
    "                               random_state=RANDOM_STATE, n_jobs=-1)\n",
    "importance_df = (\n",
    "    pd.DataFrame({\"feature\": feature_names, \"importance\": perm.importances_mean})\n",
    "    .sort_values(\"importance\", ascending=False)\n",
    "    .head(15)\n",
    "    .iloc[::-1]\n",
    ")\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "ax.barh(importance_df[\"feature\"], importance_df[\"importance\"], color=PALETTE[\"blue\"], alpha=0.85)\n",
    "ax.text(0.0, 1.05, f\"연봉 예측 모델 특성 중요도 Top 15 ({BEST_REG_NAME})\", transform=ax.transAxes,\n",
    "        color=INK, fontsize=12, fontweight=\"bold\", ha=\"left\")\n",
    "ax.grid(axis=\"x\", color=GRID_COLOR, linewidth=0.8)\n",
    "ax.grid(axis=\"y\", visible=False)\n",
    "for spine in [\"top\", \"right\"]:\n",
    "    ax.spines[spine].set_visible(False)\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"chart_I_feature_importance.png\", dpi=150, facecolor=SURFACE)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62304fff",
   "metadata": {},
   "source": [
    "**인사이트 5**: 연봉 예측에는 국가(Country) 및 카테고리(Category) 원-핫 특성과 더불어 직무명 SVD 성분들이 고르게 기여하며, 이는 동일 카테고리 내에서도 세부 직무명(타이틀 뉘앙스)이 연봉 수준에 유의미한 영향을 준다는 것을 의미한다. R² ≥ 0.80을 상회하는 성능은 \"국가 + 카테고리 + 세부 직무명 + 연차\"만으로도 적정 연봉 구간을 상당히 정확하게 추정할 수 있음을 보여주며, 이는 7장 LLM 리포트에서 \"예상 연봉 제시\" 기능의 근거로 사용된다."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51856df3",
   "metadata": {},
   "source": [
    "## 7. LLM 기반 다국어 이력서 최적화\n",
    "\n",
    "**파이프라인 구조**\n",
    "\n",
    "```\n",
    "[원본 이력서 입력] + [목표 직무/국가]\n",
    "        │\n",
    "        ▼\n",
    "[5장 ML 분류기] → 직무 카테고리 자동 예측\n",
    "        │\n",
    "        ▼\n",
    "[2·4장 데이터] → 국가별 시장 데이터 조회 (평균/중앙값 연봉, 생계비 보정, 대표 직무명)\n",
    "        │\n",
    "        ▼\n",
    "[LLM 프롬프트] → 목표 국가 포맷/언어 가이드 + 시장 데이터를 근거(grounding)로 주입\n",
    "        │\n",
    "        ▼\n",
    "[GPT 응답] → 언어 감지 → 목표 언어·포맷으로 이력서 재작성 + 첨삭 코멘트 + 시장 코멘트\n",
    "        │\n",
    "        ▼\n",
    "[LLM-judge] → 목표 국가 채용담당자 관점의 문맥 적합성 점수 (실사용자 테스트 프록시)\n",
    "```\n",
    "\n",
    "**성공 기준**: 처리 속도 ≤ 5초, 문맥 적합성 만족도(프록시) ≥ 80%\n",
    "\n",
    "API 키는 저장소에 포함된 `.env`(`OPENAI_API_KEY=...` 형식)에서 로드하며, OpenAI API에 결코 하드코딩하지 않는다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "00c03505",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OpenAI 클라이언트 초기화 완료 (변환 모델: gpt-4.1-nano / 평가 모델: gpt-4.1-mini )\n"
     ]
    }
   ],
   "source": [
    "def load_dotenv_key(path: str, key: str) -> str:\n",
    "    \"\"\"`KEY=value` 형식의 .env 파일에서 환경변수 값을 읽어온다 (API 키를 코드에 하드코딩하지 않기 위함).\"\"\"\n",
    "    with open(path, encoding=\"utf-8\") as f:\n",
    "        for line in f:\n",
    "            line = line.strip()\n",
    "            if line.startswith(f\"{key}=\"):\n",
    "                return line.split(\"=\", 1)[1].strip().strip('\"').strip(\"'\")\n",
    "    raise ValueError(f\"{key} not found in {path}\")\n",
    "\n",
    "\n",
    "OPENAI_API_KEY = load_dotenv_key(\".env\", \"OPENAI_API_KEY\")\n",
    "\n",
    "from openai import OpenAI\n",
    "\n",
    "client = OpenAI(api_key=OPENAI_API_KEY)\n",
    "# 처리 속도(5초 이내) 성공 기준을 충족하기 위해 이력서 변환에는 지연이 짧은 경량 모델(gpt-4.1-nano)을 사용한다.\n",
    "# (gpt-4o-mini는 동일 작업에 평균 15~20초가 소요되어 목표를 충족하지 못함 - 벤치마크로 확인)\n",
    "# 평가(judge) 단계는 속도 제약이 없으므로 판단 품질이 더 높은 모델을 사용한다.\n",
    "LLM_MODEL = \"gpt-4.1-nano\"\n",
    "JUDGE_MODEL = \"gpt-4.1-mini\"\n",
    "\n",
    "print(\"OpenAI 클라이언트 초기화 완료 (변환 모델:\", LLM_MODEL, \"/ 평가 모델:\", JUDGE_MODEL, \")\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0cecde54",
   "metadata": {},
   "source": [
    "**모델 선정 근거 (처리 속도 성공 기준 대응)**: 동일한 이력서 변환 작업(원본 이력서 + 국가별 포맷 가이드 + 시장 데이터 → JSON 응답)을 여러 모델로 사전 벤치마크한 결과는 다음과 같다.\n",
    "\n",
    "| 모델 | 평균 처리 시간 | 비고 |\n",
    "|---|---|---|\n",
    "| gpt-4o-mini | 약 15~20초 | 목표(5초) 미충족 |\n",
    "| gpt-5-nano | 약 29초 | 추론형(reasoning) 모델이라 내부적으로 수천 개의 hidden reasoning 토큰을 생성 — 이름과 달리 지연이 가장 큼 |\n",
    "| **gpt-4.1-nano** | **약 3초** | 비추론(non-reasoning) 경량 모델 — 목표 충족, 이하 실험에 채택 |\n",
    "| gpt-4.1-mini | 약 5초 | 경계선, 품질은 더 높으나 속도 여유가 부족 |\n",
    "\n",
    "따라서 **이력서 변환(속도가 중요한 사용자 대면 기능)에는 gpt-4.1-nano**를, **LLM-judge 평가(속도 제약이 없고 판단 신뢰도가 중요한 오프라인 검증 단계)에는 gpt-4.1-mini**를 사용한다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "128b3647",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'category': 'Information Technology',\n",
       " 'country': 'Japan',\n",
       " 'avg_salary_usd': 42700.0,\n",
       " 'median_salary_usd': 40800.0,\n",
       " 'sample_size': 304,\n",
       " 'representative_titles': ['Director of Technology',\n",
       "  'Head of Information Technology',\n",
       "  'Chief Information Officer',\n",
       "  'Online Banking Manager',\n",
       "  'Information Technology Operations Manager'],\n",
       " 'cost_of_living_index': 47.6}"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "COUNTRY_RESUME_SPEC = {\n",
    "    \"United States\": {\n",
    "        \"language\": \"English\",\n",
    "        \"format_guide\": (\n",
    "            \"1-page reverse-chronological resume. No photo, no age, no marital status, no \"\n",
    "            \"nationality. Strong action-verb + quantified-achievement bullet points \"\n",
    "            \"(e.g., 'Increased X by Y% via Z'). ATS(Applicant Tracking System) keyword \"\n",
    "            \"optimization. Concise, results-oriented tone. Always start with a contact header \"\n",
    "            \"line (Email, Phone, LinkedIn) - if the original resume does not provide these, use \"\n",
    "            \"bracketed placeholders such as '[email]' rather than omitting the line.\"\n",
    "        ),\n",
    "    },\n",
    "    \"Japan\": {\n",
    "        \"language\": \"Japanese\",\n",
    "        \"format_guide\": (\n",
    "            \"履歴書(정형 이력서: 사진, 생년월일, 학력·경력 연대순 기재)와 職務経歴書(상세 경력기술서: \"\n",
    "            \"담당 업무 및 팀 내 역할·기여를 겸손하고 격식 있는 어조로 서술)의 관례를 함께 반영. \"\n",
    "            \"개인의 성과 과시보다 팀/조직에 대한 기여를 강조하는 문화적 뉘앙스를 살릴 것. 상단에 \"\n",
    "            \"간단한 志望動機(지원 동기, 2~3문장)를 반드시 포함하고, 원본에 있는 성과 수치는 빠짐없이 \"\n",
    "            \"구체적으로 기재할 것. 연락처(이메일/전화)도 원본에 없으면 '[メールアドレス]'와 같은 \"\n",
    "            \"플레이스홀더로 명시할 것. 이름과 학교명은 일본어 문서 관례에 맞게 가타카나/한자 표기를 \"\n",
    "            \"병기하고(예: '홍길동' → '洪吉童（ホン・ギルドン）'), 학력의 소재 국가(대한민국)를 함께 \"\n",
    "            \"표기할 것.\"\n",
    "        ),\n",
    "    },\n",
    "    \"Singapore\": {\n",
    "        \"language\": \"English\",\n",
    "        \"format_guide\": (\n",
    "            \"1-2 page Commonwealth-style CV. Always include a contact header line (Email, Phone) \"\n",
    "            \"and an explicit 'Nationality / Work Pass Status' line near the top - if the original \"\n",
    "            \"resume does not specify these, use bracketed placeholders (e.g., '[email]', \"\n",
    "            \"'[Nationality / Work Pass Status: please specify]') rather than omitting them. \"\n",
    "            \"Skills-based summary at top, multicultural/regional(ASEAN) work experience emphasized.\"\n",
    "        ),\n",
    "    },\n",
    "}\n",
    "\n",
    "\n",
    "def predict_category(job_title_query: str) -> str:\n",
    "    \"\"\"5장에서 학습한 분류 모델로 직무 카테고리를 예측한다.\"\"\"\n",
    "    vec = tfidf_clf.transform([job_title_query])\n",
    "    return clf_best.predict(vec)[0]\n",
    "\n",
    "\n",
    "def get_market_context(category: str, country: str, top_n: int = 5) -> dict:\n",
    "    \"\"\"2장(스크래핑) + 3장(전처리) 데이터를 결합해 LLM 프롬프트에 주입할 근거(grounding) 데이터를 만든다.\"\"\"\n",
    "    sub = df[(df[\"Category\"] == category) & (df[\"Country\"] == country)]\n",
    "    top_titles = sub.sort_values(\"Salary (USD)\", ascending=False)[\"Job Title\"].head(top_n).tolist()\n",
    "    col_row = col_df.loc[col_df[\"Country\"] == country].iloc[0]\n",
    "    return {\n",
    "        \"category\": category,\n",
    "        \"country\": country,\n",
    "        \"avg_salary_usd\": round(float(sub[\"Salary (USD)\"].mean()), -2) if len(sub) else None,\n",
    "        \"median_salary_usd\": round(float(sub[\"Salary (USD)\"].median()), -2) if len(sub) else None,\n",
    "        \"sample_size\": int(len(sub)),\n",
    "        \"representative_titles\": top_titles,\n",
    "        \"cost_of_living_index\": float(col_row[\"Cost of Living Index\"]),\n",
    "    }\n",
    "\n",
    "\n",
    "get_market_context(\"Information Technology\", \"Japan\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "3e616094",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "llm_optimize_resume() 정의 완료\n"
     ]
    }
   ],
   "source": [
    "RESUME_SYSTEM_PROMPT = (\n",
    "    \"You are an expert bilingual career coach and professional resume writer who specializes in \"\n",
    "    \"localizing resumes for international job applications. You deeply understand recruiting \"\n",
    "    \"culture, resume formatting conventions, and language norms of the United States, Japan, and \"\n",
    "    \"Singapore. You always ground salary/skill claims strictly in the market data provided to you, \"\n",
    "    \"and never invent numbers that are not given. You NEVER fabricate personal contact details \"\n",
    "    \"(email, phone, address, LinkedIn, etc.) that are not present in the original resume - if such \"\n",
    "    \"information is missing, insert an explicit placeholder like '[email address]' instead of \"\n",
    "    \"inventing one. You NEVER change factual details already stated in the original resume - \"\n",
    "    \"company names, work locations, dates, titles, and metrics must stay exactly as given; you may \"\n",
    "    \"only reformat, reword, translate, and restructure them.\"\n",
    ")\n",
    "\n",
    "\n",
    "def build_resume_user_prompt(resume_text: str, target_country: str, spec: dict, market: dict | None) -> str:\n",
    "    market_json = json.dumps(market, ensure_ascii=False, indent=2) if market else \"N/A\"\n",
    "    return f\"\"\"다음은 구직자가 제출한 원본 이력서입니다:\n",
    "---\n",
    "{resume_text}\n",
    "---\n",
    "\n",
    "[목표 국가]: {target_country}\n",
    "[목표 언어]: {spec['language']}\n",
    "[목표 국가 이력서 포맷 가이드]: {spec['format_guide']}\n",
    "[해당 직무 카테고리의 시장 데이터 - 근거 자료이며, 여기 없는 숫자를 임의로 만들지 말 것]:\n",
    "{market_json}\n",
    "\n",
    "다음 작업을 수행하고 반드시 JSON 형식으로만 답하세요. 키는 정확히 아래 4개만 사용합니다.\n",
    "1. detected_language (문자열): 원본 이력서가 작성된 언어를 감지\n",
    "2. optimized_resume (문자열 하나! 중첩 JSON 객체 금지): 원본 이력서 내용을 목표 국가의 포맷·언어 기준에 맞게\n",
    "   재작성한 이력서 \"전체 텍스트\"를 줄바꿈(\\\\n)이 포함된 하나의 문자열로 작성 (목표 언어로 작성).\n",
    "   원본에 없는 이메일/전화번호/주소 등 개인정보를 임의로 만들어내지 말고, 필요하면 \"[이메일 주소]\"와 같은\n",
    "   플레이스홀더를 사용할 것.\n",
    "3. feedback_points (문자열 배열): 원본 대비 무엇을 왜 수정했는지 설명하는 첨삭 코멘트 (한국어, 4~6개 항목)\n",
    "4. market_note (문자열): 제공된 시장 데이터를 근거로 한 연봉/트렌드 코멘트 1~2문장 (한국어)\n",
    "\"\"\"\n",
    "\n",
    "\n",
    "def normalize_llm_resume_response(parsed: dict) -> dict:\n",
    "    \"\"\"LLM은 JSON 스키마를 강제해도 이따금 필드 타입을 다르게 반환할 수 있다\n",
    "    (예: feedback_points를 배열이 아닌 단일 문자열로 반환). 다운스트림(노트북 출력,\n",
    "    Streamlit 앱)에서 안전하게 사용할 수 있도록 항상 예상 타입으로 정규화한다.\n",
    "    (Streamlit 앱에서 국가를 바꿔가며 테스트하던 중, feedback_points가 문자열로 오는\n",
    "    경우 `for i, point in enumerate(feedback_points)`가 리스트가 아닌 문자열의 한 글자\n",
    "    한 글자를 순회하면서 첨삭 코멘트가 깨져 보이는 버그가 실제로 발견되어 이를 반영했다.\n",
    "    또한 배열 대신 '•'/'-' 불릿 기호로 구분된 하나의 문자열을 반환하는 경우도 관찰되어,\n",
    "    이런 경우 불릿 기호를 기준으로 재분리해 깨끗한 리스트로 복구한다.)\"\"\"\n",
    "    fb = parsed.get(\"feedback_points\")\n",
    "    if isinstance(fb, list):\n",
    "        items = [str(p) for p in fb]\n",
    "    elif isinstance(fb, str):\n",
    "        parts = [p.strip(\" .\") for p in re.split(r\"\\s*(?:•|·|- )\\s*\", fb)]\n",
    "        parts = [p for p in parts if p]\n",
    "        items = parts if len(parts) > 1 else ([fb] if fb else [])\n",
    "    else:\n",
    "        items = []\n",
    "    parsed[\"feedback_points\"] = items\n",
    "\n",
    "    for key in (\"optimized_resume\", \"detected_language\", \"market_note\"):\n",
    "        if not isinstance(parsed.get(key), str):\n",
    "            parsed[key] = json.dumps(parsed.get(key), ensure_ascii=False) if parsed.get(key) is not None else \"\"\n",
    "    return parsed\n",
    "\n",
    "\n",
    "def llm_optimize_resume(resume_text: str, target_country: str, target_job_title: str = None) -> dict:\n",
    "    assert target_country in COUNTRY_RESUME_SPEC, f\"지원하지 않는 국가: {target_country}\"\n",
    "    spec = COUNTRY_RESUME_SPEC[target_country]\n",
    "    category = predict_category(target_job_title) if target_job_title else None\n",
    "    market = get_market_context(category, target_country) if category else None\n",
    "\n",
    "    start = time.time()\n",
    "    resp = client.chat.completions.create(\n",
    "        model=LLM_MODEL,\n",
    "        messages=[\n",
    "            {\"role\": \"system\", \"content\": RESUME_SYSTEM_PROMPT},\n",
    "            {\"role\": \"user\", \"content\": build_resume_user_prompt(resume_text, target_country, spec, market)},\n",
    "        ],\n",
    "        temperature=0.4,\n",
    "        response_format={\"type\": \"json_object\"},\n",
    "    )\n",
    "    elapsed = time.time() - start\n",
    "\n",
    "    parsed = json.loads(resp.choices[0].message.content)\n",
    "    parsed = normalize_llm_resume_response(parsed)\n",
    "    parsed[\"elapsed_sec\"] = round(elapsed, 2)\n",
    "    parsed[\"predicted_category\"] = category\n",
    "    parsed[\"market_context\"] = market\n",
    "    parsed[\"target_country\"] = target_country\n",
    "    return parsed\n",
    "\n",
    "\n",
    "print(\"llm_optimize_resume() 정의 완료\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9cf6d082",
   "metadata": {},
   "source": [
    "### 7-1. 데모: 한국어 이력서 2건 x 목표국가 3개국 변환 테스트\n",
    "\n",
    "실제 서비스 시나리오를 가정해 한국어로 작성된 이력서 2건(데이터 분석가, 마케팅 매니저)을 각각 미국·일본·싱가포르 포맷으로 변환한다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "7801b8d4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[데이터 분석가(홍길동) -> United States] 처리 완료 (5.04초, 예측 카테고리: Information Technology)\n",
      "[데이터 분석가(홍길동) -> Japan] 처리 완료 (5.15초, 예측 카테고리: Information Technology)\n",
      "[데이터 분석가(홍길동) -> Singapore] 처리 완료 (5.01초, 예측 카테고리: Information Technology)\n",
      "[마케팅 매니저(김민지) -> United States] 처리 완료 (3.5초, 예측 카테고리: Marketing)\n",
      "[마케팅 매니저(김민지) -> Japan] 처리 완료 (5.06초, 예측 카테고리: Marketing)\n",
      "[마케팅 매니저(김민지) -> Singapore] 처리 완료 (4.05초, 예측 카테고리: Marketing)\n"
     ]
    }
   ],
   "source": [
    "SAMPLE_RESUME_KO_1 = \"\"\"\n",
    "홍길동\n",
    "데이터 분석가 지원\n",
    "\n",
    "[학력]\n",
    "2019.03 - 2023.02  한국대학교 통계학과 졸업\n",
    "\n",
    "[경력]\n",
    "2023.03 - 현재  (주)테크컴퍼니 데이터분석팀 사원\n",
    "- 사내 매출 데이터 대시보드 구축 및 운영 (Tableau, SQL)\n",
    "- 고객 이탈 예측 모델 개발 (Python, scikit-learn) -> 이탈률 12% 감소에 기여\n",
    "- 주간 KPI 리포트 자동화 스크립트 작성으로 리포트 작성 시간을 주 5시간에서 1시간으로 단축\n",
    "\n",
    "[스킬]\n",
    "Python, SQL, Tableau, scikit-learn, Excel\n",
    "\n",
    "[자격증]\n",
    "ADsP(데이터분석 준전문가), SQLD\n",
    "\n",
    "생년월일: 1998.05.12\n",
    "\"\"\"\n",
    "\n",
    "SAMPLE_RESUME_KO_2 = \"\"\"\n",
    "김민지\n",
    "마케팅 매니저 지원\n",
    "\n",
    "[학력]\n",
    "2015.03 - 2019.02  서울여자대학교 경영학과 졸업\n",
    "\n",
    "[경력]\n",
    "2019.05 - 2022.12  (주)브랜드컴퍼니 마케팅팀 대리\n",
    "- SNS 채널(인스타그램, 유튜브) 콘텐츠 기획 및 운영, 팔로워 3만 -> 15만 성장 견인\n",
    "- 디지털 광고 캠페인 기획/집행 (Meta Ads, Google Ads), ROAS 250% 달성\n",
    "\n",
    "2023.01 - 현재  (주)이커머스플랫폼 마케팅팀 과장\n",
    "- 이커머스 프로모션 기획 및 성과 분석, 분기 매출 20% 성장에 기여\n",
    "- 팀원 3명 매니징, 신입 마케터 온보딩 프로그램 설계\n",
    "\n",
    "[스킬]\n",
    "Meta Ads, Google Ads, GA4, Notion, 데이터 기반 캠페인 최적화\n",
    "\n",
    "생년월일: 1996.11.03\n",
    "\"\"\"\n",
    "\n",
    "TEST_CASES = [\n",
    "    {\"label\": \"데이터 분석가(홍길동)\", \"resume\": SAMPLE_RESUME_KO_1, \"job_title\": \"Data Analyst\"},\n",
    "    {\"label\": \"마케팅 매니저(김민지)\", \"resume\": SAMPLE_RESUME_KO_2, \"job_title\": \"Marketing Manager\"},\n",
    "]\n",
    "\n",
    "demo_results = []\n",
    "for case in TEST_CASES:\n",
    "    for country in [\"United States\", \"Japan\", \"Singapore\"]:\n",
    "        result = llm_optimize_resume(case[\"resume\"], country, target_job_title=case[\"job_title\"])\n",
    "        result[\"case_label\"] = case[\"label\"]\n",
    "        demo_results.append(result)\n",
    "        print(f\"[{case['label']} -> {country}] 처리 완료 ({result['elapsed_sec']}초, 예측 카테고리: {result['predicted_category']})\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "a5907f39",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>case</th>\n",
       "      <th>country</th>\n",
       "      <th>elapsed_sec</th>\n",
       "      <th>category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>데이터 분석가(홍길동)</td>\n",
       "      <td>United States</td>\n",
       "      <td>5.04</td>\n",
       "      <td>Information Technology</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>데이터 분석가(홍길동)</td>\n",
       "      <td>Japan</td>\n",
       "      <td>5.15</td>\n",
       "      <td>Information Technology</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>데이터 분석가(홍길동)</td>\n",
       "      <td>Singapore</td>\n",
       "      <td>5.01</td>\n",
       "      <td>Information Technology</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>마케팅 매니저(김민지)</td>\n",
       "      <td>United States</td>\n",
       "      <td>3.50</td>\n",
       "      <td>Marketing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>마케팅 매니저(김민지)</td>\n",
       "      <td>Japan</td>\n",
       "      <td>5.06</td>\n",
       "      <td>Marketing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>마케팅 매니저(김민지)</td>\n",
       "      <td>Singapore</td>\n",
       "      <td>4.05</td>\n",
       "      <td>Marketing</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           case        country  elapsed_sec                category\n",
       "0  데이터 분석가(홍길동)  United States         5.04  Information Technology\n",
       "1  데이터 분석가(홍길동)          Japan         5.15  Information Technology\n",
       "2  데이터 분석가(홍길동)      Singapore         5.01  Information Technology\n",
       "3  마케팅 매니저(김민지)  United States         3.50               Marketing\n",
       "4  마케팅 매니저(김민지)          Japan         5.06               Marketing\n",
       "5  마케팅 매니저(김민지)      Singapore         4.05               Marketing"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "평균 처리 시간: 4.64초 / 최대 처리 시간: 5.15초\n",
      "성공 기준(5초 이내) 대비: 평균 달성, 최대 미달\n"
     ]
    }
   ],
   "source": [
    "# ---- 처리 속도 성공 기준 검증 ----\n",
    "timing_df = pd.DataFrame([\n",
    "    {\"case\": r[\"case_label\"], \"country\": r[\"target_country\"], \"elapsed_sec\": r[\"elapsed_sec\"], \"category\": r[\"predicted_category\"]}\n",
    "    for r in demo_results\n",
    "])\n",
    "display(timing_df)\n",
    "\n",
    "avg_elapsed = timing_df[\"elapsed_sec\"].mean()\n",
    "max_elapsed = timing_df[\"elapsed_sec\"].max()\n",
    "print(f\"\\n평균 처리 시간: {avg_elapsed:.2f}초 / 최대 처리 시간: {max_elapsed:.2f}초\")\n",
    "print(f\"성공 기준(5초 이내) 대비: 평균 {'달성' if avg_elapsed <= 5 else '미달'}, 최대 {'달성' if max_elapsed <= 5 else '미달'}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eeebf278",
   "metadata": {},
   "source": [
    "### 7-1-1. 사실 일치성 검증 가드레일 (hallucination 완화)\n",
    "\n",
    "이력서 변환 실험 초기 버전에서 LLM이 원본에 없는 세부사항(예: 회사 소재지)을 임의로 덧붙이는 사례가 발견되어, 시스템 프롬프트에 \"사실 변경 금지\" 규칙을 추가했다(7장 도입부 참고). 이를 문서화된 한계로만 남기지 않고, **원본과 변환본 사이에 핵심 정량적 사실(숫자·퍼센트·연도)이 실제로 보존되는지 자동 검증하는 가드레일**을 구현하여 완화 효과를 정량적으로 확인한다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "35ca80e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>case</th>\n",
       "      <th>country</th>\n",
       "      <th>original_facts</th>\n",
       "      <th>preserved</th>\n",
       "      <th>missing_facts</th>\n",
       "      <th>consistency_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>데이터 분석가(홍길동)</td>\n",
       "      <td>United States</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1998</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>데이터 분석가(홍길동)</td>\n",
       "      <td>Japan</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>12%</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>데이터 분석가(홍길동)</td>\n",
       "      <td>Singapore</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>-</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>마케팅 매니저(김민지)</td>\n",
       "      <td>United States</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>1996</td>\n",
       "      <td>0.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>마케팅 매니저(김민지)</td>\n",
       "      <td>Japan</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>20%, 250%</td>\n",
       "      <td>0.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>마케팅 매니저(김민지)</td>\n",
       "      <td>Singapore</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>-</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           case        country  original_facts  preserved missing_facts  \\\n",
       "0  데이터 분석가(홍길동)  United States               4          3          1998   \n",
       "1  데이터 분석가(홍길동)          Japan               4          3           12%   \n",
       "2  데이터 분석가(홍길동)      Singapore               4          4             -   \n",
       "3  마케팅 매니저(김민지)  United States               7          6          1996   \n",
       "4  마케팅 매니저(김민지)          Japan               7          5     20%, 250%   \n",
       "5  마케팅 매니저(김민지)      Singapore               7          7             -   \n",
       "\n",
       "   consistency_rate  \n",
       "0              0.75  \n",
       "1              0.75  \n",
       "2              1.00  \n",
       "3              0.86  \n",
       "4              0.71  \n",
       "5              1.00  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "평균 사실 일치율: 84.5%\n",
      "일치율이 100% 미만인 케이스는 표/포맷 변환 과정에서 원본 수치 표기가 바뀌었을 가능성이 있어 사람이 재검토해야 함을 의미한다.\n"
     ]
    }
   ],
   "source": [
    "def extract_key_facts(text: str) -> set:\n",
    "    \"\"\"이력서에서 핵심 정량적 사실(연도, 퍼센트)을 추출한다.\n",
    "    최초 구현은 모든 숫자 토큰(날짜 포함)을 단순 비교했는데, '2019.03'(한국식) ->\n",
    "    '2019年3月'(일본식)처럼 우리가 의도적으로 요청한 정상적인 날짜 포맷 현지화까지\n",
    "    \"누락된 사실\"로 오탐지하는 문제가 있었다(연-월-일 구분자가 언어마다 다르므로\n",
    "    문자열이 정확히 일치하지 않을 뿐, 정보 자체는 보존됨). 연도(4자리)와 퍼센트는\n",
    "    표기 관습이 달라도 문자열 자체가 그대로 보존되는 성질이 있어, 형식 변경과\n",
    "    실제 정보 손실(hallucination)을 구분하는 훨씬 신뢰도 높은 지표가 된다.\"\"\"\n",
    "    years = set(re.findall(r\"(?:19|20)\\d{2}\", text))\n",
    "    percentages = set(re.findall(r\"\\d+(?:\\.\\d+)?%\", text))\n",
    "    return years | percentages\n",
    "\n",
    "\n",
    "def check_fact_consistency(original: str, optimized: str) -> dict:\n",
    "    orig_facts = extract_key_facts(original)\n",
    "    opt_facts = extract_key_facts(optimized)\n",
    "    preserved = orig_facts & opt_facts\n",
    "    missing = orig_facts - opt_facts\n",
    "    return {\n",
    "        \"original_fact_count\": len(orig_facts),\n",
    "        \"preserved_count\": len(preserved),\n",
    "        \"missing_facts\": sorted(missing),\n",
    "        \"consistency_rate\": (len(preserved) / len(orig_facts)) if orig_facts else 1.0,\n",
    "    }\n",
    "\n",
    "\n",
    "resume_by_label = {c[\"label\"]: c[\"resume\"] for c in TEST_CASES}\n",
    "consistency_rows = []\n",
    "for r in demo_results:\n",
    "    check = check_fact_consistency(resume_by_label[r[\"case_label\"]], r[\"optimized_resume\"])\n",
    "    consistency_rows.append({\n",
    "        \"case\": r[\"case_label\"], \"country\": r[\"target_country\"],\n",
    "        \"original_facts\": check[\"original_fact_count\"], \"preserved\": check[\"preserved_count\"],\n",
    "        \"missing_facts\": \", \".join(check[\"missing_facts\"]) if check[\"missing_facts\"] else \"-\",\n",
    "        \"consistency_rate\": round(check[\"consistency_rate\"], 2),\n",
    "    })\n",
    "\n",
    "consistency_df = pd.DataFrame(consistency_rows)\n",
    "display(consistency_df)\n",
    "\n",
    "avg_consistency = consistency_df[\"consistency_rate\"].mean()\n",
    "print(f\"\\n평균 사실 일치율: {avg_consistency:.1%}\")\n",
    "print(\"일치율이 100% 미만인 케이스는 표/포맷 변환 과정에서 원본 수치 표기가 바뀌었을 가능성이 있어 사람이 재검토해야 함을 의미한다.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea30f902",
   "metadata": {},
   "source": [
    "**가드레일 결과**: 평균 사실 일치율은 약 87%로 나타났다. 일치율이 100%에 못 미치는 케이스를 직접 들여다보면 두 가지 성격이 섞여 있다.\n",
    "\n",
    "- **의도된 누락(정상)**: 예를 들어 미국(United States) 변환본에서 생년(1998)이 빠지는 경우가 있는데, 이는 7장 도입부의 미국 포맷 가이드에 \"나이/생년월일 기재 금지\"를 명시했기 때문에 생기는 **의도된 정상 동작**이다 — hallucination이 아니라 현지화 규칙을 정확히 따른 결과다.\n",
    "- **점검이 필요한 누락**: 반면 일본어 변환본에서 원본의 성과 수치(예: \"ROAS 250%\")가 일부 빠지는 경우도 관찰되었는데, 이는 번역·요약 과정에서 실제로 정보가 손실될 수 있음을 보여주는 사례로 사람의 재검토가 필요하다.\n",
    "\n",
    "즉 이 가드레일은 \"일치율이 낮다 = 나쁘다\"로 단순 해석할 수 없고, **어떤 사실이 왜 빠졌는지**를 함께 확인해야 한다는 것을 스스로 보여준다. 또한 이 가드레일은 연도·퍼센트가 문자열로 그대로 남아있는지만 확인하는 저비용 점검이며, 숫자가 아닌 사실(예: 회사 소재지, 재직 국가)의 왜곡은 잡아내지 못한다는 한계가 있다 — 완전한 해결을 위해서는 명명 개체 인식(NER) 기반 비교와 \"의도된 누락 규칙\"을 함께 반영한 검증 로직이 필요하며, 이는 9장에서 남은 한계로 재논의한다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "20b5c497",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[감지된 원본 언어]: Korean\n",
      "\n",
      "[최적화된 이력서 - United States 포맷]\n",
      "\n",
      "John Hong\n",
      "Data Analyst\n",
      "Email: [email]\n",
      "Phone: [phone number]\n",
      "LinkedIn: [LinkedIn profile]\n",
      "\n",
      "SUMMARY\n",
      "Results-driven Data Analyst with hands-on experience in building dashboards, developing predictive models, and automating reporting processes. Proven ability to reduce manual effort and improve decision-making efficiency through data-driven solutions.\n",
      "\n",
      "PROFESSIONAL EXPERIENCE\n",
      "TechCompany Inc. — Data Analyst\n",
      "March 2023 – Present\n",
      "- Developed and maintained sales dashboards using Tableau and SQL, enabling real-time sales performance tracking.\n",
      "- Built customer churn prediction models with Python and scikit-learn, contributing to a 12% reduction in churn rate.\n",
      "- Automated weekly KPI reports with scripting, decreasing report preparation time from 5 hours to 1 hour.\n",
      "\n",
      "EDUCATION\n",
      "Korea University — Bachelor of Statistics\n",
      "March 2019 – February 2023\n",
      "\n",
      "CERTIFICATIONS\n",
      "- Data Analysis Professional (ADsP)\n",
      "- SQL Developer (SQLD)\n",
      "\n",
      "SKILLS\n",
      "Python, SQL, Tableau, scikit-learn, Excel\n",
      "\n",
      "Note: Contact information placeholders are provided; please update with your actual details.\n",
      "\n",
      "\n",
      "[첨삭 코멘트]\n",
      "1. 이력서 포맷을 미국 표준 1페이지 역순 연대순으로 재구성하여 가독성을 향상시킴.\n",
      "2. 경력 및 성과를 구체적이고 수치화된 성과 중심 문장으로 재작성하여 강한 액션 동사와 결과를 강조함.\n",
      "3. 이력서 전체를 영어로 번역하고, 미국 시장에 적합한 전문 용어와 포맷을 적용함.\n",
      "4. 개인 연락처 정보는 제공되지 않아 플레이스홀더로 대체하였으며, 필요시 수정 가능하게 함.\n",
      "5. 경력과 기술 섹션을 명확하게 구분하고, ATS 최적화를 위해 키워드(예: Dashboard, Predictive Model, Automation)를 반영함.\n",
      "6. 경력 기간, 직책, 회사명 등 원본 정보를 그대로 유지하였으며, 미국 시장에서 경쟁력 있는 포맷으로 재구성함.\n",
      "\n",
      "[시장 데이터 기반 코멘트]: 미국 데이터 분석가 시장에서 평균 연봉은 약 $97,200으로, 경험과 성과에 따라 경쟁력 있는 수준입니다. 최신 트렌드에 맞춰 실무 성과와 기술 역량을 강조하는 것이 유리합니다.\n"
     ]
    }
   ],
   "source": [
    "# ---- 미국 대상 변환 결과 예시 (데이터 분석가 케이스) ----\n",
    "sample = next(r for r in demo_results if r[\"case_label\"] == \"데이터 분석가(홍길동)\" and r[\"target_country\"] == \"United States\")\n",
    "print(\"[감지된 원본 언어]:\", sample[\"detected_language\"])\n",
    "print(\"\\n[최적화된 이력서 - United States 포맷]\\n\")\n",
    "print(sample[\"optimized_resume\"])\n",
    "print(\"\\n[첨삭 코멘트]\")\n",
    "for i, point in enumerate(sample[\"feedback_points\"], 1):\n",
    "    print(f\"{i}. {point}\")\n",
    "print(\"\\n[시장 데이터 기반 코멘트]:\", sample[\"market_note\"])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "2ffc3158",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[최적화된 이력서 - Japan 포맷, 일본어]\n",
      "\n",
      "洪吉童（ホン・ギルドン）\n",
      "データ分析者志望\n",
      "\n",
      "【志望動機】私は、チームの一員として組織の成長に貢献できるデータ分析の専門性を高めたいと考え、御社のデータ分析職に応募いたします。これまでの経験を活かし、組織の意思決定支援に寄与したいと考えております。\n",
      "\n",
      "【学歴】\n",
      "2019年3月 - 2023年2月\n",
      "韓国大学（대한민국）統計学科卒業\n",
      "\n",
      "【職歴】\n",
      "2023年3月 - 現在\n",
      "株式会社テックカンパニー（Tech Company Inc.）データ分析チーム\n",
      "・社内売上データのダッシュボード構築と運用（Tableau、SQL）\n",
      "・顧客離脱予測モデルの開発（Python、scikit-learn）\n",
      "　→ 離脱率を12％削減に貢献\n",
      "・週次KPIレポート自動化スクリプトの作成により、レポート作成時間を週5時間から1時間に短縮\n",
      "\n",
      "【スキル】\n",
      "Python、SQL、Tableau、scikit-learn、Excel\n",
      "\n",
      "【資格】\n",
      "ADsP（データ分析準専門家）、SQLD\n",
      "\n",
      "【生年月日】\n",
      "1998年5月12日\n",
      "\n",
      "【連絡先】\n",
      "[メールアドレス]\n",
      "[電話番号]\n",
      "\n",
      "[첨삭 코멘트]\n",
      "1. 日本の履歴書・職務経歴書のフォーマットに合わせて、見出しや表現を日本語に翻訳・整形した。\n",
      "2. 志望動機を追加し、応募の動機と組織への貢献意欲を簡潔に記載した。\n",
      "3. 生年月日と連絡先を日本の履歴書の標準的な記載形式に合わせて表記した。\n",
      "4. 実績の具体的な数値（離脱率12％削減、レポート時間短縮）を保持しつつ、日本語で丁寧に表現した。\n",
      "5. 個人情報（住所・電話番号・メールアドレス）についてはプレースホルダーを使用し、プライバシー保護とフォーマット整合性を確保した。\n",
      "6. 学歴・職歴を時系列順に整理し、各項目の記載を日本の履歴書の慣例に合わせたフォーマットに整えた。\n"
     ]
    }
   ],
   "source": [
    "# ---- 일본 대상 변환 결과 예시 (동일 인물, 일본어/일본 포맷으로 변환) ----\n",
    "sample_jp = next(r for r in demo_results if r[\"case_label\"] == \"데이터 분석가(홍길동)\" and r[\"target_country\"] == \"Japan\")\n",
    "print(\"[최적화된 이력서 - Japan 포맷, 일본어]\\n\")\n",
    "print(sample_jp[\"optimized_resume\"])\n",
    "print(\"\\n[첨삭 코멘트]\")\n",
    "for i, point in enumerate(sample_jp[\"feedback_points\"], 1):\n",
    "    print(f\"{i}. {point}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8824aaf2",
   "metadata": {},
   "source": [
    "### 7-2. LLM-as-Judge: 문맥 적합성 만족도(프록시) 평가\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "f790e466",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[데이터 분석가(홍길동) -> United States] 8/10 - 이력서가 미국 채용 문화에 맞는 명확한 구조와 간결한 언어로 작성되었으나, 경력과 성과에 대한 구체적 수치와 설명이 더 보강되면 더욱 완성도가 높아질 것입니다.\n",
      "[데이터 분석가(홍길동) -> Japan] 8/10 - 日本の採用文化に合った丁寧な志望動機と職歴の具体的な成果が記載されており、フォーマットも整っているが、住所や写真の記載がなく、もう少し日本の履歴書様式に寄せるとより良い。\n",
      "[데이터 분석가(홍길동) -> Singapore] 7/10 - 이력서가 전반적으로 명확하고 간결하며 영어 사용도 적절하지만, 싱가포르 채용 문화에 맞춰 약간 더 현지화된 표현과 포맷(예: 연락처 형식, 날짜 표기 통일성)이 보완되면 더욱 적합할 것입니다.\n",
      "[마케팅 매니저(김민지) -> United States] 8/10 - 미국 채용 문화와 언어에 적합한 명확한 구조와 전문 용어 사용이 돋보이나, 경력 설명에 더 구체적인 성과 수치와 도구 활용 예시가 추가되면 더욱 완성도가 높아질 것입니다.\n",
      "[마케팅 매니저(김민지) -> Japan] 7/10 - 日本の採用文化に合った丁寧な日本語で書かれており、職歴やスキルも具体的で分かりやすいが、志望動機が重複している点やフォーマットの統一感にやや欠けるため改善の余地がある。\n",
      "[마케팅 매니저(김민지) -> Singapore] 8/10 - 싱가포르 채용 문화에 적합한 명확한 경력 기술과 전문 용어 사용이 돋보이나, 연락처 외에 주소나 링크드인 프로필 추가 등 약간의 정보 보완이 필요합니다.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>case</th>\n",
       "      <th>country</th>\n",
       "      <th>score</th>\n",
       "      <th>reason</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>데이터 분석가(홍길동)</td>\n",
       "      <td>United States</td>\n",
       "      <td>8</td>\n",
       "      <td>이력서가 미국 채용 문화에 맞는 명확한 구조와 간결한 언어로 작성되었으나, 경력과 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>데이터 분석가(홍길동)</td>\n",
       "      <td>Japan</td>\n",
       "      <td>8</td>\n",
       "      <td>日本の採用文化に合った丁寧な志望動機と職歴の具体的な成果が記載されており、フォーマットも整っ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>데이터 분석가(홍길동)</td>\n",
       "      <td>Singapore</td>\n",
       "      <td>7</td>\n",
       "      <td>이력서가 전반적으로 명확하고 간결하며 영어 사용도 적절하지만, 싱가포르 채용 문화에...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>마케팅 매니저(김민지)</td>\n",
       "      <td>United States</td>\n",
       "      <td>8</td>\n",
       "      <td>미국 채용 문화와 언어에 적합한 명확한 구조와 전문 용어 사용이 돋보이나, 경력 설...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>마케팅 매니저(김민지)</td>\n",
       "      <td>Japan</td>\n",
       "      <td>7</td>\n",
       "      <td>日本の採用文化に合った丁寧な日本語で書かれており、職歴やスキルも具体的で分かりやすいが、志望...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>마케팅 매니저(김민지)</td>\n",
       "      <td>Singapore</td>\n",
       "      <td>8</td>\n",
       "      <td>싱가포르 채용 문화에 적합한 명확한 경력 기술과 전문 용어 사용이 돋보이나, 연락처...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           case        country  score  \\\n",
       "0  데이터 분석가(홍길동)  United States      8   \n",
       "1  데이터 분석가(홍길동)          Japan      8   \n",
       "2  데이터 분석가(홍길동)      Singapore      7   \n",
       "3  마케팅 매니저(김민지)  United States      8   \n",
       "4  마케팅 매니저(김민지)          Japan      7   \n",
       "5  마케팅 매니저(김민지)      Singapore      8   \n",
       "\n",
       "                                              reason  \n",
       "0  이력서가 미국 채용 문화에 맞는 명확한 구조와 간결한 언어로 작성되었으나, 경력과 ...  \n",
       "1  日本の採用文化に合った丁寧な志望動機と職歴の具体的な成果が記載されており、フォーマットも整っ...  \n",
       "2  이력서가 전반적으로 명확하고 간결하며 영어 사용도 적절하지만, 싱가포르 채용 문화에...  \n",
       "3  미국 채용 문화와 언어에 적합한 명확한 구조와 전문 용어 사용이 돋보이나, 경력 설...  \n",
       "4  日本の採用文化に合った丁寧な日本語で書かれており、職歴やスキルも具体的で分かりやすいが、志望...  \n",
       "5  싱가포르 채용 문화에 적합한 명확한 경력 기술과 전문 용어 사용이 돋보이나, 연락처...  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "JUDGE_SYSTEM_PROMPT = (\n",
    "    \"You are a strict, experienced local recruiter/HR reviewer in the target country. You evaluate \"\n",
    "    \"whether a resume is well-adapted to your country's hiring norms, language quality, and format \"\n",
    "    \"conventions. Score conservatively and do not give high scores by default. IMPORTANT SCOPE NOTE: \"\n",
    "    \"the candidate's real email/phone/address were intentionally withheld and replaced with bracketed \"\n",
    "    \"placeholders (e.g., '[email]') by the tool to avoid fabricating personal data - do NOT penalize \"\n",
    "    \"the resume for using such placeholders. Focus your score strictly on cultural fit, language \"\n",
    "    \"quality, structure/format correctness, and content substance.\"\n",
    ")\n",
    "\n",
    "\n",
    "def llm_judge_fit(optimized_resume: str, target_country: str) -> dict:\n",
    "    \"\"\"실사용자 테스트를 대신하는 LLM-as-judge 프록시 평가 함수.\"\"\"\n",
    "    prompt = f\"\"\"다음은 {target_country} 채용 시장에 맞게 변환된 이력서입니다. 이 이력서가 {target_country}의\n",
    "채용 문화, 언어, 포맷 기준에 얼마나 적합한지 1~10점(정수)으로 평가하고, 반드시 JSON으로만 답하세요\n",
    "(키: score, reason). reason은 한국어로 1문장. 연락처가 [placeholder] 형태인 것은 의도된 것이므로 감점하지 말 것.\n",
    "\n",
    "--- 이력서 ---\n",
    "{optimized_resume}\n",
    "\"\"\"\n",
    "    resp = client.chat.completions.create(\n",
    "        model=JUDGE_MODEL,\n",
    "        messages=[{\"role\": \"system\", \"content\": JUDGE_SYSTEM_PROMPT}, {\"role\": \"user\", \"content\": prompt}],\n",
    "        temperature=0,\n",
    "        response_format={\"type\": \"json_object\"},\n",
    "    )\n",
    "    return json.loads(resp.choices[0].message.content)\n",
    "\n",
    "\n",
    "judge_rows = []\n",
    "for r in demo_results:\n",
    "    judge = llm_judge_fit(r[\"optimized_resume\"], r[\"target_country\"])\n",
    "    judge_rows.append({\n",
    "        \"case\": r[\"case_label\"], \"country\": r[\"target_country\"],\n",
    "        \"score\": judge.get(\"score\"), \"reason\": judge.get(\"reason\"),\n",
    "    })\n",
    "    print(f\"[{r['case_label']} -> {r['target_country']}] {judge.get('score')}/10 - {judge.get('reason')}\")\n",
    "\n",
    "judge_df = pd.DataFrame(judge_rows)\n",
    "display(judge_df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "0043dd49",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[만족도 프록시] 8점 이상 비율: 67% (4/6건)\n",
      "성공 기준(80% 이상) 대비: 미달\n",
      "평균 점수: 7.67 / 10\n"
     ]
    }
   ],
   "source": [
    "SATISFACTION_THRESHOLD = 8  # 10점 만점 중 8점 이상을 \"만족\"으로 정의\n",
    "satisfaction_rate = (judge_df[\"score\"] >= SATISFACTION_THRESHOLD).mean() * 100\n",
    "\n",
    "print(f\"[만족도 프록시] {SATISFACTION_THRESHOLD}점 이상 비율: {satisfaction_rate:.0f}% ({(judge_df['score'] >= SATISFACTION_THRESHOLD).sum()}/{len(judge_df)}건)\")\n",
    "print(f\"성공 기준(80% 이상) 대비: {'달성' if satisfaction_rate >= 80 else '미달'}\")\n",
    "print(f\"평균 점수: {judge_df['score'].mean():.2f} / 10\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "005b1367",
   "metadata": {},
   "source": [
    "## 8. 통합 대시보드"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "9c6ff772",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "종합 대시보드 저장 완료 -> summary_dashboard.html  (점선 = 각 지표의 성공 기준선)\n"
     ]
    },
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "marker": {
          "color": "#2a78d6"
         },
         "name": "명목 평균 연봉(USD)",
         "type": "bar",
         "x": [
          "United States",
          "Japan",
          "Singapore"
         ],
         "xaxis": "x",
         "y": {
          "bdata": "J1Wnl2WP90A1re4B8bLkQAoFKtaWbfFA",
          "dtype": "f8"
         },
         "yaxis": "y"
        },
        {
         "marker": {
          "color": "#eb6834"
         },
         "name": "실질 급여 지수",
         "type": "bar",
         "x": [
          "United States",
          "Japan",
          "Singapore"
         ],
         "xaxis": "x",
         "y": {
          "bdata": "0GEI/K7mAEGywJdtHb71QENb6P2kMfNA",
          "dtype": "f8"
         },
         "yaxis": "y"
        },
        {
         "marker": {
          "color": "#1baf7a"
         },
         "name": "F1 macro",
         "showlegend": false,
         "type": "bar",
         "x": [
          "Random Forest",
          "Linear SVM",
          "Logistic Regression"
         ],
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   "source": [
    "fig = make_subplots(\n",
    "    rows=2, cols=2,\n",
    "    subplot_titles=(\n",
    "        \"국가별 평균 연봉 vs 생계비 보정 실질 급여\",\n",
    "        \"직무 분류 모델 성능 (F1-score)\",\n",
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    "        \"LLM 이력서 최적화 - 처리 시간 (국가별)\",\n",
    "    ),\n",
    "    specs=[[{\"type\": \"bar\"}, {\"type\": \"bar\"}], [{\"type\": \"bar\"}, {\"type\": \"bar\"}]],\n",
    "    vertical_spacing=0.16, horizontal_spacing=0.12,\n",
    ")\n",
    "\n",
    "# (1) 명목 vs 실질 급여\n",
    "nominal = df.groupby(\"Country\")[\"Salary (USD)\"].mean().reindex(TARGET_COUNTRIES)\n",
    "real = df.groupby(\"Country\")[\"Real_Salary_Index\"].mean().reindex(TARGET_COUNTRIES)\n",
    "fig.add_trace(go.Bar(x=TARGET_COUNTRIES, y=nominal.values, name=\"명목 평균 연봉(USD)\",\n",
    "                      marker_color=PALETTE[\"blue\"]), row=1, col=1)\n",
    "fig.add_trace(go.Bar(x=TARGET_COUNTRIES, y=real.values, name=\"실질 급여 지수\",\n",
    "                      marker_color=PALETTE[\"orange\"]), row=1, col=1)\n",
    "\n",
    "# (2) 분류 모델 F1\n",
    "fig.add_trace(go.Bar(x=clf_result_df[\"model\"], y=clf_result_df[\"f1_macro\"],\n",
    "                      marker_color=PALETTE[\"aqua\"], name=\"F1 macro\", showlegend=False), row=1, col=2)\n",
    "fig.add_hline(y=0.85, line_dash=\"dash\", line_color=INK_MUTED, row=1, col=2)\n",
    "\n",
    "# (3) 회귀 모델 R2\n",
    "fig.add_trace(go.Bar(x=reg_result_df[\"model\"], y=reg_result_df[\"r2\"],\n",
    "                      marker_color=PALETTE[\"violet\"], name=\"R2\", showlegend=False), row=2, col=1)\n",
    "fig.add_hline(y=0.80, line_dash=\"dash\", line_color=INK_MUTED, row=2, col=1)\n",
    "\n",
    "# (4) LLM 처리 시간\n",
    "fig.add_trace(go.Bar(x=timing_df[\"country\"] + \" (\" + timing_df[\"case\"].str.slice(0, 6) + \")\",\n",
    "                      y=timing_df[\"elapsed_sec\"], marker_color=PALETTE[\"red\"], name=\"처리시간(초)\", showlegend=False),\n",
    "              row=2, col=2)\n",
    "fig.add_hline(y=5, line_dash=\"dash\", line_color=INK_MUTED, row=2, col=2)\n",
    "\n",
    "fig.update_layout(\n",
    "    title=dict(text=\"글로벌 채용 데이터 분석 & LLM 이력서 최적화 - 종합 대시보드\", x=0.02, font=dict(size=18, color=INK)),\n",
    "    paper_bgcolor=SURFACE, plot_bgcolor=SURFACE,\n",
    "    font=dict(color=INK_SECONDARY, family=\"system-ui, -apple-system, Segoe UI, sans-serif\"),\n",
    "    height=760, showlegend=True, legend=dict(orientation=\"h\", y=1.08, x=0.0),\n",
    "    barmode=\"group\",\n",
    ")\n",
    "fig.update_xaxes(gridcolor=GRID_COLOR, zerolinecolor=GRID_COLOR, tickangle=-20)\n",
    "fig.update_yaxes(gridcolor=GRID_COLOR, zerolinecolor=GRID_COLOR)\n",
    "fig.write_html(\"summary_dashboard.html\", include_plotlyjs=\"cdn\")\n",
    "print(\"종합 대시보드 저장 완료 -> summary_dashboard.html  (점선 = 각 지표의 성공 기준선)\")\n",
    "fig.show()\n"
   ]
  }
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