shap.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from captum.attr import *\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import pandas as pd\n",
    "from json import load\n",
    "from tqdm import tqdm\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "matplotlib.rcParams['font.family'] = 'Liberation Serif'\n",
    "matplotlib.rcParams['font.size'] = 15.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "DEVICE = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\") # Setting the device for running the model and generate importance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"COVIDiSTRESS_April_May_Combined.csv\", encoding= 'unicode_escape')\n",
    "df = df.drop([\"Unnamed: 0\", \"1\", \"Uninformative response\", \"PSS10_avg\"], axis=\"columns\")\n",
    "features = list(df.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading the trained model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "class StressNN(nn.Module):\n",
    "    def __init__(self, input_size = 54):\n",
    "        super(StressNN, self).__init__()\n",
    "        self.LinBlock = nn.Sequential(\n",
    "            nn.Linear(input_size, 256),\n",
    "            nn.LeakyReLU(),\n",
    "            nn.Linear(256, 128),\n",
    "            nn.LeakyReLU(),\n",
    "            nn.Linear(128, 64),\n",
    "            nn.LeakyReLU(),\n",
    "            nn.Linear(64, 32),\n",
    "            nn.LeakyReLU(),\n",
    "            nn.Linear(32, 16),\n",
    "            nn.LeakyReLU(),\n",
    "            nn.Linear(16, 1)\n",
    "        )\n",
    "    def forward(self, x):\n",
    "        x = self.LinBlock(x)\n",
    "        x = torch.sigmoid(x)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = StressNN(df.shape[1])\n",
    "model.load_state_dict(torch.load(\"var/model_april_may_mse.pt\"))\n",
    "model = model.to(DEVICE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Defining the object for SHAP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "svs = ShapleyValueSampling(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Generating general SHAP values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Shapley Value Sampling attribution: 100%|██████████| 1351/1351 [00:24<00:00, 55.79it/s]\n"
     ]
    }
   ],
   "source": [
    "world = torch.from_numpy(df.to_numpy()).type(torch.FloatTensor).to(DEVICE)\n",
    "f_imp = svs.attribute(world, target=0, show_progress=True).mean(axis=0).cpu()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Creating visualizations for the SHAP feature importance values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "imp_df = pd.DataFrame().from_dict({\n",
    "    \"Features\": features,\n",
    "    \"Importance\": f_imp,\n",
    "    \"Absolute Importance\": torch.abs(f_imp),\n",
    "    \"Direction\": f_imp/torch.abs(f_imp)\n",
    "})\n",
    "imp_df.sort_values(\"Absolute Importance\", inplace=True)\n",
    "imp_df[\"Direction\"] = imp_df[\"Direction\"].apply(lambda x: \"teal\" if x==1 else \"crimson\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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qWbKkXn75ZZ04ceKW5+3WrZsqVaqksLAw/fbbb2Ztbr5/BWH58uVGYCZJq1evliS1b98+X/1UqlRJfn5+2V5Z3xdvb2+zRcsBAAAAAEUDARPypHz58tq6davmzZunH374QbVq1VKtWrXk5+cnLy8v9e3bV87Oznr11VclSf3799fSpUs1depU+fr6ysfHR2FhYZo7d65GjhwpSYqJidHw4cMlSZs3b9bgwYN16tQp45zu7u7auXOnbG1t5evrq4CAAAUFBemxxx7Txo0bZW9vL0kaPny49u7dK0l69tlndezYMUVGRmrAgAGSbjylLWuETXh4uEqWLClvb2+1bNlSYWFhqlmzpqQbQdC/Q6LcBAcHa9OmTUpJSZGvr69q1KihFi1aqEuXLlqzZk2hXUdAQIB27twpNzc3NWjQQNWrV1ezZs3UuXNnff7555KkkiVLauvWrQoODlb79u3l7e2t5s2bq3Pnzlq7dq0kKSwszFh/aujQocax0o1RSVlPuBswYIAiIiIUFxenZ599VpK0d+9e4/4FBQWpb9++qlixonx8fPTee+9pwYIFxpPyAAAAAAD3N1PmrVYfBoD7QEJCgpydnXXlypV79olyqampWrNmjdq2bSs7Oztrl/OfxX2wvtTUVBWfNMnaZRS6zDFjrF1CjvhbKBq4D9bHPSgauA/Wxz0oGvJ6Hwr79xEjmAAAAAAAAGARHscE3GT06NFatWpVntquWbNGbm5uhVzRva9Xr16KiIjIU1sW8AYAAACAexMBE3CTcePGady4cdYu474yb948a5cAAAAAAChkTJEDAAAAAACARQiYAAAAAAAAYBGmyAEAgHvCisBAnlIDAABQRDGCCQAAAAAAABYhYAIAAAAAAIBFCJgAAAAAAABgEQImAAAAAAAAWISACQAAAAAAABYhYAIAAAAAAIBFCJgAAAAAAABgEVtrFwAAAArG0YebW7uEwjXvdWtXAAAAgNtgBBMAAAAAAAAsQsAEAAAAAAAAixAwAQAAAAAAwCIETAAAAAAAALAIARMAAAAAAAAsQsAEAAAAAAAAixAwAQAAAAAAwCIETMiz1NRULV68WCEhIfL29lZAQIACAgLk4eGhWrVqqXPnzlq9erW1y8Q9buHChTKZTLd89evXz9rlAQAAAABuwdbaBeDecOLECYWEhOivv/7S9OnT1alTJ9nb20uS0tPTtWPHDr377rvasGGDnnzySStXi3tdmTJl5OLikm17uXLlrFANAAAAACA3BEzIVVJSktq1a6c//vhDv/32m2rWrGm238bGRs2bN9eoUaO0cuVKK1WJ+8nAgQP17rvvWrsMAAAAAEAeETAhV3PnztX+/fvVv3//bOHSzerUqaOUlJS7WBkAAAAAACgKWIMJuVqyZIkkKSQkJMd2zs7OatOmjfF+2bJlaty4sfz8/OTj46M6depo/vz5xv49e/aofv36MplMev311xUbG6uXX35Z1apVM9ocOXJEzz33nKpXr66AgABVqVJFAwYM0IULF4w206ZNk5ubmxwdHRUWFqYnnnhCVatW1UMPPaS+ffsqPT3drM527dqpcuXK8vf3l7u7u9r+P/buPaqqOv//+POIqEjIFzVBFLyRAgHiDUfTBs1SUSszyTEVnfBS3ijHxluUpGiKZZpWBl4YL5U642WigfJCdhkNDUWgTBO1vGeIouBB+f3hjz2dUAQPeLBej7X2WpyzP/uz33tvcS1e6/P57JAQDh8+fNv3Z8+ePfTt2xcvLy/8/Pxo0KABAQEBfPrppxV6HYcOHWLw4MHcd999+Pv74+Hhga+vLytXrjTanDp1imHDhuHp6Wmsm7V48WJj//jx43FxccFkMpGdnU1sbCxBQUEsWrSI+Ph4vL29MZlMxMTE8Oijj+Lr60utWrXo378/Fy5cMPrJz89n4sSJ+Pv74+/vj6enJ4899tht31MRERERERG5uyhgkhIVFBSwZ88eAPz9/Ut93Ntvv82AAQN48cUX2b9/P99++y2TJ09m+PDhvPrqqwC0a9eOtWvXArBhwwZGjx5N7dq1uXLlCgA//vgjf/rTn7h69SppaWns27eP5ORktm7dSnBwMJcvXwYgIiKC5s2bc/HiRUaPHs3HH3/MwYMHmTBhAkuWLDECsiKZmZns2rWLtLQ09uzZw+eff86QIUNu6/58+eWXdOzYkbp165KRkcH+/fs5dOgQfn5+/PjjjxV2HQcPHqRdu3acO3eOb775hrS0NA4fPkzv3r05ePAgAHl5eXTt2pV9+/aRlpbGgQMHCA8PZ/To0axYsQKAN9980wiCHnvsMT7//HPc3Nwwm80MGTKEP/3pT8D1UG7Tpk1kZGSwaNEi1q1bxxtvvGHUUzQ98ssvvyQtLY2UlBSrQrvblZ+fT05OjsUmIiIiIiIiFU8Bk5Tol19+oaCgAIDatWuX6pj8/HymTp1KUFAQffv2Nb7v378/nTt3Jjo6mnPnzlkc07FjRz755BPmzp3L+PHjAYiOjubnn39m+vTpVKtWDYCGDRsaodWyZcuKndvBwcHifAApKSkWbZYsWWIsIF2vXj38/f3ZuXNnqa7ttyIiInBwcGDBggVGjTVq1KBfv35Gm4q4jilTpnD+/HmWLFnCPffcA0DVqlV56qmnjDZr1qwhIyODSZMm4ezsDMCIESOoUaMGb731VrFzLlu2jOXLlxMbG8u9995bpnqSkpLw8fHByckJuH5f33333ZvfuFtYuXIlbdu2JTAwkBYtWhAcHMxrr73GpUuXSjxu1qxZODs7G5uHh8dt1yAiIiIiIiKlp4BJSvTraVlVqpTun8vXX3/NL7/8QqdOnYrt69q1K3l5eezYscPi+1/3/cILLwDXQ4s6derg7e1drA+ATz75pMQ6atSoAVBsFEu3bt0sPtvb22M2m0vs60ZOnjzJ119/TadOnSwCGIC+ffsyaNCgCrmOq1evkpCQgK+vLw0aNLBo26ZNG6ZMmQJAcnIyAH5+fhZ9ubm5kZGRUew8Rc+gXr16PP3006WuB8DV1ZXExETee+8941526NChxOu6mSeffJLk5GS+/vprUlNTSU9PZ+LEicyePZtOnTqVuM7X5MmTOX/+vLEdO3bstmoQERERERGRslHAJCWqU6cOdnZ2ABbrBZWk6I96FxeXYvvq1atn0eZW/Vjbx2/l5OTw4osv0qZNG3x8fAgMDCw2wqm0jhw5AoC7u3uxfVWqVKFq1apGneV5HWfPniU3N/eG5wWMUVInTpwAro84CgwMNLaCggJq1apVpnPeysyZM3FycmLEiBHUr1+fMWPG3Ha4c88999CgQQNMJhNwfWRWr169eOGFF/jmm2/44IMPbnps9erVqVWrlsUmIiIiIiIiFU9vkZMS2dvb06pVK1JSUti3bx9ubm63PKYoGCgsLCy279q1axZtbtWPtX38Vv/+/UlOTuaTTz6hc+fOAAQHBxujfcqiqLaiKYQ3U97XUdrzFrVLTEwsNtKpvLVr144DBw6watUq/vGPf7Bo0SJWrVrFnj17aNKkSbmco1WrVgCkpaWVS38iIiIiIiJSfjSCSW5p6NChACWOHCkyYsQIY92bX375pdj+06dPA+Dp6XnLvjw8PKzu49cuXrxIUlIS3bt3N8IlaxSd//jx4yW2K+/ruPfee3FwcLjleYvCwDNnzpSp/9vl4uLCmDFj2LlzJ2+++SbZ2dksX768zP0UjQz7raLpmkUjtERERERERKTyUMAktxQeHk7r1q2Jj49n165dN2137NgxDhw4QNu2balduzafffZZsTZbtmzBwcGhVAFP9+7dOXfuHOnp6cX6KNpfFnl5eQDF1ku6Xe7u7vj7+5OcnFxs0fJfK+/rsLOzo1u3bnz77bdkZmbetN0DDzwAXF8DqqLNmzfP4nNRKHnhwoUy9/XUU08Zz+rXdu/eDcCDDz5Y9gJFRERERESkQilgkluqXr06//73v2nfvj3du3dn2bJlXL582dh/7do1/vvf//Lcc88Z7WfMmEFKSgpr16412q1Zs4Yvv/ySl156if/7v/+75XknTZpEnTp1iIyM5MqVK8D1EGvOnDkEBAQQFhZWpuuoW7cujRs35quvvuLixYsA/Pjjj5w6dapM/fza3LlzMZvN/PWvf+Xs2bMW+4pCkvK+DoAZM2bg6OjIM888w9GjR2943kGDBtGkSRNiYmKMcKbIr59feVi7dq0RmAFs3rwZgN69e5e5r7y8PD744AOLaYWfffYZCxcupE+fPmUO5ERERERERKTiKWCSUqlfvz6fffYZsbGx/Pvf/6Zly5a0bNkSPz8/GjduzMiRI3F2dmbcuHEAPPvss6xZs4a5c+fi6+uLt7c3MTExvPfee0yePBmAjIwM/v73vwOwfft2xo8fz48//mics2HDhnz11VdUrVoVX19fAgICCA4O5uGHH2br1q1Ur14dgL///e/s3bsXgL/85S/88MMP7Nmzh9GjRwPX39JWNMJm2bJlODo60rx5c7p06UJMTAz3338/cD0I+m1IdCvdu3dn27Zt5Ofn4+vri4+PD3/+858JDQ0lISGhwq4jICCAr776Cnd3d4KCgmjRogWdOnWiX79+rFixAgBHR0c+++wzunfvTu/evWnevDmdO3emX79+JCYmAhATE2OsP/XCCy8Yx8L1UUlFb7gbPXo0KSkpZGVl8Ze//AWAvXv3Gs8vODiYkSNH0qhRI7y9vXnttddYunSp8aa8st7T1157jcaNG9OyZUu8vb158cUXefXVV/nXv/5V5v5ERERERESk4pkKb7T6sIjI70BOTg7Ozs6cP3/+rn2jnNlsJiEhgZCQEOzt7W1dzh/W3fIcDt1r/fpyldn+2L9V+mfwe3e3/C783uk52J6eQeWg52B7egaVQ2mfQ0X/faQRTCIiIiIiIiIiYpWqti5ApDKJjIxk06ZNpWqbkJCAu7t7BVd09wsPDyclJaVUbVNTUyu2GBEREREREakQCphEfiUqKoqoqChbl/G7Ehsba+sSREREREREpIJpipyIiIiIiIiIiFhFAZOIiIiIiIiIiFhFU+RERER+J5qd2WHrEiqM2Wxmf0KCrcsQERERkZvQCCYREREREREREbGKAiYREREREREREbGKAiYREREREREREbGKAiYREREREREREbGKAiYREREREREREbGKAiYREREREREREbFKVVsXICIiIlIaj6emQmqqrcuoMIUvv2zrEkRERERum0YwiYiIiIiIiIiIVRQwiYiIiIiIiIiIVRQwiYiIiIiIiIiIVRQwiYiIiIiIiIiIVRQwiYiIiIiIiIiIVRQwiYiIiIiIiIiIVRQwyR2TlpZGREQETZs2xWQy4enpSWBgoMXm7e1N48aNK6yGadOm0bFjR0wmE8uXLy/z8Vu3bmXYsGHUqVMHk8mEl5cXgYGB+Pj40LBhQ5o1a0b//v1Zs2YNZrO52PEHDhzAzc2NcePGlcPV3F0WLVpEmzZt8PLyIiAggEaNGvHwww/z0Ucf3fSYn3/+mdGjR9OtW7c7WKmIiIiIiIiUlQImuWP8/f2ZP38+Dz74IABRUVGkpqZabLt378ZkMlVYDTNmzGDEiBG3fXzXrl1ZtmwZ/v7+AMTGxpKamkpmZibHjh1j3bp12NvbM3DgQNq2bcuhQ4csjs/JyeHs2bNkZWVZcxl3pR07djBgwAC+++479u3bx8GDB/Hy8qJ3796sXLnSom1BQQELFy4kICCAxYsXU1BQYKOqRUREREREpDQUMEml4ujoSN26dW1dxm0xmUy0atWK1atXExMTw759++jevTu5ublGm7Zt23L8+HHWr19f5v779+/P9u3by7HiOy80NBQ7OzsA7O3tiYmJwc7OjhkzZli0mz9/Pg4ODmRkZNiiTBERERERESkjBUxS6UyZMsXWJVhtwoQJdOnShUOHDrFgwQKLffXq1cPe3r5M/e3Zs+e2QqnKZPXq1TRq1MjiO0dHR+69995iI7r+9re/ER4ejrOz8x2sUERERERERG6XAiapdPr27QvAkiVLaN68OSaTiYCAAFavXg3A2LFjqV27Nt26dWPHjh2kp6fTo0cPTCYTISEhPPzww/j7++Pi4kLfvn354YcfbHIdI0eOBODDDz8E4OjRo4SFheHg4FBsnan9+/fTs2dPWrZsibe3N15eXsTFxQHwzjvvEBoaSmFhIeHh4QQGBrJq1Spmz56Nm5sbJpOJtLQ01q1bR3BwMJMmTTL6/eCDD2jdujVeXl40bdqUJ554wiLMyc/PZ+LEifj7++Pv74+npyePPfaYsb+wsJBZs2YREBBAQEAAnp6edOjQ4YbrS91KlSo3/u/m3LlzNGzYsMz9iYiIiIiISOVR1dYFiBRp3749O3fuND6PGDGCbt264e/vj52dHaGhocD1aVYZGRkkJiYa060mTZpEYmIivXr1YvTo0QAcP36crl27EhwcTFpa2h0fDdOxY0cA9u7di9lsxtPTkxUrVpCcnGzRLj8/nx49ejBo0CA+/vhjAJYvX863334LwKhRozh58iTTp08nNjaW4OBg49i8vDymT5/O8OHDadKkCR4eHuTl5QEQHx9PWFgY8fHxDB48mHPnzhEUFMQjjzxCeno69vb2TJs2jY0bN7J7926cnJw4ffq0xYLaixcvZu7cuaSnp1O/fn1ycnLo1KkTV69eLfMorBvJzMzkypUrdO/e3eq+RERERERExHY0gklsJjIy0uINcikpKcXaNG3alFdffZXU1FRiYmI4d+4c48ePJz4+3giXfs3R0dH42d3dnRkzZnDs2DFjNNCdVK9ePeD6KKBz587dtF1GRgY//fQT7du3N74bNGgQTz75ZKnPFR0dzZo1a4iLi+O+++4Drt9fX19fBg8eDEDt2rUZPHgw33//PUlJSQAkJSXh4+ODk5OTUfO7775r9JuUlISHhwf169cHoFatWsTGxpZLuAQQFxeHk5MTEydOLJf+8vPzycnJsdhERERERESk4ilgEpv57Vvkfrs+T5GIiAiCgoKYPn06/fr1Y+bMmTRo0KBU5+jcuTMA27ZtK7e6S6uwsND4+WbTwwBcXV0BeOWVV9izZw8AVatWpW3btqU+V1H/1apVY/To0WRlZXHkyBH8/Pws2hVNzStaPNvV1ZXExETee+89Y9pbhw4dLGpLS0sjOjqaS5cuARAUFHTDcK+sdu/ezVtvvcWyZcuKTRm8XbNmzcLZ2dnYPDw8yqVfERERERERKZkCJqk0WrRoccPvq1SpQlxcHNeuXeP06dP06NGj1H3WqVMHuD5d7k4rGrVkb2+Pi4vLTdu5u7sTGRlJWloabdq0ITAwkLfffpurV6/e9rlPnDgBwJYtWyxGic2ePRtXV1cjkJo5cyZOTk6MGDGC+vXrM2bMGI4dO2b0M2XKFBo1asTUqVNxdXUlLCysXN7s9v333/P4448TGxtLv379rO6vyOTJkzl//ryx/fpaREREREREpOIoYJJKo2j9oRspKCjAycmJjIyMMk13y83NBSiXETdllZ6eDkDr1q2pWrXk5c6mT59Oamoqzz//PCdOnOC5554r0xS53yoaPdW3b1+LUWKZmZmcPHmSCRMmANCuXTsOHDjAwoULadasGYsWLSIgIIDDhw8D10c8paens3TpUtq3b098fDytW7e2WCurrNLS0ujduzeLFy9m0KBBt93PjVSvXp1atWpZbCIiIiIiIlLxFDBJpWM2m5k/f77xOTc3l+HDh7N9+3ZatGjBxIkTOXnyZKn6KhrJU15TsMpiw4YNAAwcOLBU7QMCAnj99dfJysrigQceYMOGDUbQU1Zubm4AnDlz5pZtXVxcGDNmDDt37uTNN98kOzub5cuXG/tr1qzJsGHD+PTTT9m4cSP5+fksXrz4tupKTk6mf//+rF69mj59+txWHyIiIiIiIlL5KGCSSmfv3r2kpqYan8ePH8/YsWPx8/PjnXfeITs7m3HjxpWqr6JRUT179qyIUm8qMzOTuLg4fHx8CA8PL7HtwYMH2bhxo/HZwcHBeGPehQsXgP+tsVTaaXNNmjTB3d2d5ORkrly5ctN28+bNs/g8dOhQi/P+dv+jjz6Ki4uLsb8s1q5dy7hx4/j4449p06ZNmY8XERERERGRyksBk1Q6X3zxhfHz2rVrycvLY8iQIQAEBwcTFhbG2rVr2bx5c4n9ZGVl8cYbb9CqVatyn4p1MwUFBWzYsIFu3brh5eXFRx99RM2aNUs8Ji8vj+joaE6fPg3AlStXSEpKwsvLCx8fHwAaNmwIXL8fR48e5bvvviuxT5PJxLRp08jOziYiIoL8/Hxjn9lsNoKqtWvXsmXLFmNf0T3t3bs3AF999ZXFaKbPP/+c7OzsMo8+On78OAMGDODnn3+mZ8+eeHt7F9t27dpVpj5FRERERESk8ih5YRiRcrRz505mz57Njh07ABg+fDijRo2yaHP16lUKCgoYMGAAERERrFixgi5duvDNN9/QqlUrdu3aZQQx4eHhDB06lNdee804fubMmSxcuBCz2UxOTg59+vRhxowZ2NvbA/DSSy8ZgcqSJUs4dOgQUVFRmEymUl3D1q1bWblyJfv37zdquOeee4z9ly9fxsvLi+nTpzNkyBCqVatm7NuyZQtLly7l1KlTADz99NMMHToUPz8/nJycaNmyJc7OzuTn5+Pr68umTZuMukNDQ1mzZg3Lly8nISGBUaNG4e7uzvr1643r3rNnDy+88IJxvmeffRYHBwcWLFhAo0aNcHd3p169ejRu3Jh58+bh6OhIcHAwI0eOxGw24+DgQLVq1Vi6dCldu3YFoFOnTsyZM4epU6fi7OzMtWvXmDVrFsOGDSvV/Spy5coVrl27xk8//XTTNkVvqQN47bXX2LVrl7E4e0pKCsHBwbi7uxMaGsrjjz9epvOLiIiIiIhIxTIV/vpd6iJ3qe3bt9OlSxdWrFhhjHYSycnJwdnZmfPnz9+1C36bzWYSEhIICQkxAke58/QcbM9sNlMtOtrWZVSowpdftnUJt6TfhcpBz8H29AwqBz0H29MzqBxK+xwq+u8jTZGT35Vr167ZugQRERERERGRPxxNkZM/vPDwcFJSUkrV9teLj//RRUZGsmnTplK1TUhIwN3dvYIrEhEREREREVtRwCR/eLGxsbYu4a4UFRVFVFSUrcsQERERERGRSkBT5OSul5CQwNixYwF45ZVXeOqpp8jNzbVxVSIiIiIiIiJ/HBrBJHe9kJAQQkJCbF2GiIiIiIiIyB+WRjCJiIiIiIiIiIhVNIJJRERE7gobAgP1GmQRERGRSkojmERERERERERExCoKmERERERERERExCoKmERERERERERExCoKmERERERERERExCoKmERERERERERExCoKmERERERERERExCpVbV2AiIiIVJxD93a2dQnlJ/Zvtq5ARERERG5CI5hERERERERERMQqCphERERERERERMQqCphERERERERERMQqCphERERERERERMQqCphERERERERERMQqCphERERERERERMQqCpik1NLS0oiIiKBp06aYTCY8PT0JDAy02Ly9vWncuHGZ+t2xYwejRo3C0dHR4tgVK1bQv39/TCYTQ4cOve26P/jgA5ydnXn//fdvu4/fq/379zN27Fjq1auHyWSydTk3VFhYyJw5c3jkkUcwmUxs377d1iWJiIiIiIjIb1S1dQFy9/D392f+/PlkZ2dz+PBhoqKiigU/ly5dws/Pr0z9du7cmc6dO/Of//zH4vuwsDD+/Oc/s27dOqvqPn78ODk5ORw/ftyqfn6P/Pz8WLhwIWlpaSQnJ9u6nBsymUy8+OKL1KtXj08++cTW5YiIiIiIiMgNaASTlKuaNWtSv359m50/KCiIrKwsi++ef/55fvzxR1544QXbFCUiIiIiIiLyO6eAScpdfHy8Tc77r3/9i6+//vqG+xo0aHCHq5HyVqWK/rsSERERERGprPQXm5S7Zs2akZuby7Bhw6hRo4bFukobNmygR48emEwmXnnllXI7Z1RUFM899xwAISEhBAYGsm3bNrZu3UqvXr2KreP0xhtv4OHhgbOzM3PmzKF79+54eXnh7OzMn//8ZxYvXsywYcPo3bs3DRs2xNPTkw8//LDYeRcuXIivry/NmzenWbNmDB06lJ9//vmW9Z46dYphw4bh6elJ8+bNCQgIYPHixcb+5cuX4+fnh8lkIiYmhj59+uDj44OTkxNBQUEsWrSIESNG8Oijj9KsWTNcXV158803jePj4uJo3LgxNWrUICwsjM6dO+Ph4UGjRo145ZVXuHr1aqnu65YtW3jooYfw8fHh/vvv5/7772fOnDnG8WlpabRv3x6TyYSrqyvDhg0jJycHgLlz59KqVSvq1q3LvHnzjD4/+OADWrdujZeXF02bNuWJJ54oNurs8OHDPP300zRq1IjAwECCgoKIjo4uVc0iIiIiIiJy52kNJik3cXFx1KlTh8cffxxHR0eWLVvGtm3bLNo8/vjjBAYG0qRJk3I9d2RkJD/88AMrVqwgISHBItRq2rRpsfM9//zzbNy4keTkZMaOHcuLL74IwMyZM5k2bRqBgYHExsZiZ2dHfn4+nTt35plnniEkJIR77rkHuB5qvfrqqyQlJdGlSxcOHz5M27ZtOXLkSLHr/rW8vDy6du1KjRo1SEtLw9nZmQULFjB69GgcHR0JCwtj6NChbN++nfT0dHr16sXf/vY3AFatWsWgQYNo2LAh77//PtWqVePatWv069ePF154gT59+tC0aVOeeeYZduzYwYoVK1i8eDGOjo4ArFu3jtDQUM6dO8eCBQtKvKf//ve/eeyxx3j99dcZP348cH1B9u7du/Pdd98RFxeHv78/GzduxMPDg1q1arFs2TLj+IkTJ3L69GmcnJyYMGECcH10W1hYGPHx8QwePJhz584RFBTEI488Qnp6Ovb29pw8eZIOHTrg4eHB7t27qVu3LnA9dBs2bNgt/y2IiIiIiIjInacRTFJuvvvuO1uXcFscHByMn/v16wdcX6zczs4OgOrVq/PQQw9x8eJFDhw4AMDFixeNN5t16dIFgCZNmvDoo48awdDNrFmzhoyMDCZNmoSzszMAI0aMoEaNGrz11lulqu/ChQtUq1YNuD51rEePHly7do3U1NRixxeFSwBPPvkkISEhvP3225w+fbrE+/Liiy/i6urKuHHjjO86d+5M//79Wbp0KRkZGQC4ubnRvXt3Dh48yK5du4y2hYWFrF27liFDhhjfRUZG4uvry+DBgwGoXbs2gwcP5vvvvycpKQmAOXPmcOrUKebNm2eES6WVn59PTk6OxSYiIiIiIiIVTwGT3LbIyEgCAwONLS4uztYlWa1GjRoAmM1mi++rV68OYAQWKSkp5ObmFntjXtHIqaLw5UaK3tb262Nr1KiBm5tbiceVpb6SdO7cmYKCAnbs2HHTNj/++COZmZl07NgRk8lksa9r164AFm90K5p+uHLlSuO7L7/8ksaNGxv3JCsriyNHjtzynv3nP//BwcGBTp063fJafmvWrFk4Ozsbm4eHR5n7EBERERERkbLTFDm5bVFRURbrGs2dO9d2xdxhJ06cAOAf//gHiYmJxveXLl3C1dWVwsLCWx7bv39/qlb9369gQUEBtWrVqqCK/6dOnToAHD9+/KZtjh07BoCLi0uxffXq1bNoA9CnTx9q167NBx98wBtvvIGdnR3vv/8+YWFhRpui696yZQuBgYHG9/n5+bi6uhqLeB8+fJiGDRve1qLekydPtnhbYE5OjkImERERERGRO0ABk5SbiRMn2rqEO6YoQBo9ejRTp069rWMTExNt8na73NxcAGMK4I0UjVq6UVB27do1izZwfQTVgAEDWLx4MZ988gmPPPIIH3/8scXC3EV99e3bl/fee++m5y4oKLAI3sqievXqxmguERERERERuXM0RU7kNri5uQFw5syZO3pseSgaSfTrhdB/q2jUzy+//FJsX9HaTZ6enhbfF41WWrVqFdu3b+dPf/oTTk5Oxv7SXrerqytnz569xVWIiIiIiIhIZaKASSqUk5MTV65cuSPnKppSdfXq1Qo/V5s2bahevbqxMHVZPPDAAwC3dWx5+Pjjj3F0dKRz5843bdOgQQPuv/9+vvjiC2PEUpEtW7YA8Mgjj1h8HxQUhK+vLxs2bGDp0qUW0+Pg+iLo7u7uJCcnl/hvolOnTpw9e/aW61GJiIiIiIhI5aGASSpUYGAgJ0+eZPfu3cZ3Fy5cqJBzNWzYEIDVq1dz7Ngxfvjhhwo5D4CzszPjx48nMzOT6Ohoi1Dr8uXLJR47aNAgmjRpQkxMjMV9Kc2x1lq6dCn79u1j+vTpFqOLbuS1117jzJkzvPHGG8Z3ycnJrFu3jhEjRtCiRYtix4SFhXHx4kWSk5N56KGHLPaZTCamTZtGdnY2ERER5OfnG/vMZrNxDydOnIi9vT3jxo2zWLT8559/vq1rFhERERERkYqngElK7dtvv6Vr166sX78euP4a+wcffJDFixff9Jjo6Gj+/Oc/07VrVx588EH++te/8v777wPXR/Bs2LCBzz//nGeffZYzZ85w5swZnn32WT777DPi4+N58cUXgevBxoQJEzhy5MhNzzVy5EjatWvHjBkz6Nq1K9u2beOdd97h2WefBa6/9Wz48OH88MMPjB07lr179wIQGhrKrl27SEpKsmj73HPPUVhYSGRkJKtWrQKuvzlv69atwPU3ls2ZM4eVK1fi4eFBu3bt6NWr1y3XZHJ0dOSzzz6je/fu9O7dm+bNm9O5c2f69etnLBj+6quvGm9pe/bZZ/n4449JSUkhNDQUgL179/LMM8/wyy+/sHDhQubPnw/AggUL+PDDD41zValShfbt29O6dWuaN2/OggULWLp0KRMmTABg//79jBs3zhgtFB4ezsaNGwHo1asX//nPf9i8eTMtWrTAz8+P0aNHExUVddNnPmjQIOzs7Bg0aNANF+l+9tlnWbZsGf/9739p1KgRrVu3pkePHowdO5a8vDzg+uiwxMRELl++jKenJy1btqRHjx5GYPj666/z9ddfl3iPRURERERE5M4yFZb0uisRuWsNHTqU+Pj4YlPcKlpQUBDx8fF4e3vf0fPeSE5ODs7Ozpw/f/6OvKGvIpjNZhISEggJCcHe3t7W5fxh3c3P4dC9N58Oe7fZH/u3u/IZ/J7czb8Lvyd6DranZ1A56DnYnp5B5VDa51DRfx9pBJPI79idzo9//vlnnJ2dK0W4JCIiIiIiIneOAiYRuW0FBQVcvHjR+LxkyRKGDx9uw4pERERERETEFhQwicht+/HHH3nppZcA+Prrr9m+fTv9+/e3cVUiIiIiIiJypylgEvkdev7559m0aRMADzzwgLEIeHmzt7cnNjYWHx8fXnrpJVavXo3JZKqQc4mIiIiIiEjlVdXWBYhI+XvjjTd44403Kvw8DRo04MKFCxV+HhEREREREancNIJJRERERERERESsooBJRERERERERESsoilyIiIiv2PNzuywdQnlwmw2sz8hwdZliIiIiMhNaASTiIiIiIiIiIhYRQGTiIiIiIiIiIhYRQGTiIiIiIiIiIhYRQGTiIiIiIiIiIhYRQGTiIiIiIiIiIhYRQGTiIiIiIiIiIhYpaqtCxAREREpjcdTUyE11dZllFrhyy/bugQRERGRO0YjmERERERERERExCoKmERERERERERExCoKmERERERERERExCoKmERERERERERExCoKmERERERERERExCoKmER+xzIzM+nTpw/3338/LVu2JDc319YliYiIiIiIyO+QAiaR/y81NZURI0bg6emJyWTC09OTwMBA7r//fjw9PWnUqBGPPvoo77333l0R1OTk5PDQQw/RtWtX9u/fj4uLC2az2dZlGTZv3kx4eDgmk4ng4GBblyMiIiIiIiJWUMAk8v8FBgayZMkSunbtCkBUVBSpqamkp6dz9OhRkpKSaNSoEaNGjcLPz4+UlBQbV1yy9evXc+LECR5++GFMJhPvvvsujo6Oti7L0KdPH2JjY21dhoiIiIiIiJQDBUwipdSiRQsWLlzI+++/z5EjR+jRowc//vijrcu6qW+++QbACJVatGiBvb29LUsSERERERGR3ykFTCJl1L9/f4YOHcrPP//Mq6++autyburChQsAmEwmG1ciIiIiIiIiv3cKmERuw8iRI4Hr09CuXbsGwJ49e+jevTuNGjWiWbNmdO7cma1btwJw+fJlZs2aRa1atWjRogWRkZE89NBDeHh4UKdOHXr37s1bb73F4MGD6dGjB/Xq1aNFixZs27atzLUdPHiQp59+ms2bNwMQEhJCYGAgkyZNMtrciVrXr19PYGAgzZs3x9vbm6ZNm/Lmm2+W+joWLlyIr68vzZs3p1mzZkaoJyIiIiIiIpWPAiaR29C2bVuqVavGzz//zOHDh8nMzKRz5854enpy+PBhvv/+e1xdXenVqxffffcdDg4OTJ48mdq1a3Pp0iVeeukltmzZwtGjR+nXrx8fffQR+fn5xMfH85///Ifvv/+ey5cv89e//rXMtXl5ebFq1Sp69+4NQEJCAqmpqcyePRvgjtWamZnJ008/zXfffce3335L//79iYiIYMuWLbe8hqioKF544QUWLVrEgQMH+PTTT9m8eTNPPvlkme+HiIiIiIiIVDwFTCK3wc7Ojtq1awNw9uxZZs+eTV5eHjNmzKBKlSpUqVKFsWPHkpeXV2whazs7O2MtJJPJxBNPPAFAXl6eMZ3N2dmZBx54gKysLM6dO1eutd+pWkNCQoy3xAF069YNgJ07d5ZY38WLF5kzZw6PPPIIXbp0AaBJkyY8+uijbN++nfT09Jsem5+fT05OjsUmIiIiIiIiFU8Bk8htKiwsBKBKlSokJydTt25dXF1djf2NGzcGICMjo8R+atSoAYDZbLb4vnr16gDlHpLcqVpbt26Ni4uL8bkoqLpy5UqJ50hJSSE3Nxc/Pz+L70tT46xZs3B2djY2Dw+PEs8lIiIiIiIi5aOqrQsQuRtdu3aN7OxsAFxdXTlx4gQmk4nAwECjTWFhIa6urkYoU1ncqVqTkpJYuHAhBw4coHr16ly6dKnU9QH84x//IDEx0fj+0qVLuLq6GsHejUyePJkXXnjB+JyTk6OQSURERERE5A5QwCRyGw4dOkR+fj6urq54enpSWFiIp6cnqampti7tlu5ErV988QU9evSgf//+7N69m3vuuYft27cbU95uVR/A6NGjmTp1apnOW716dWM0lYiIiIiIiNw5miInchs2bNgAwMCBAwFwc3Pj7NmzNqyo9O5Erf/6178oLCxk0qRJ3HPPPWU61s3NDYAzZ85URGkiIiIiIiJSARQwiZTRqVOnmDt3Lq6urvz9738H4IEHHiA7O5tdu3bZuLpbuxO15uXlAeDg4FDmY9u0aUP16tVJSkoq77JERERERESkgihgEimlwsJCtm3bRnBwMNWqVSMhIcFYKPvFF1+kevXqREREcOrUKYtj8vPzbVXyDd2JWtu2bQtghETXrl3jm2++KdWxzs7OjB8/nszMTKKjo7l69aqx7/Lly+VSn4iIiIiIiJQvBUwi/19qaiojR45k69atAERGRhIYGGhs3t7ezJgxgxEjRvDtt9/SunVr49hWrVqxdetWHB0d8fPzw9/fn65duzJo0CAOHTpEbm4uf/3rXzl58iQnT55k8ODBHDx4kNWrVzNt2jQAVq1aRWRkJIWFhTz33HN88sknAIwZM6ZM6yWZzWaGDRvGv//9bwD69+/PyJEjjSlnd6LWgQMHMmzYMKZPn07btm15/PHHjdFMCQkJ/POf/2Tz5s2Eh4cD198MN27cOPbt2wdcfxvcnDlzWLlyJR4eHrRr145evXqVeU0mERERERERuTNMhSW9kklE5C6Wk5ODs7Mz58+fp1atWrYu57aYzWYSEhIICQnB3t7e1uX8Yek52J7ZbKZadLStyyiTwpdftnUJ5U6/C5WDnoPt6RlUDnoOtqdnUDmU9jlU9N9HGsEkIiIiIiIiIiJWqWrrAkTk1t555x3eeeedUrWNjY011kASERERERERuRMUMIncBUaNGsWoUaNsXYaIiIiIiIjIDWmKnIiIiIiIiIiIWEUBk4iIiIiIiIiIWEUBk4iIiIiIiIiIWEVrMImIiMhdYUNgoF6DLCIiIlJJaQSTiIiIiIiIiIhYRQGTiIiIiIiIiIhYRQGTiIiIiIiIiIhYRQGTiIiIiIiIiIhYRQGTiIiIiIiIiIhYRQGTiIiIiIiIiIhYpaqtCxAREREpjcdTUyE11dZllFrhyy/bugQRERGRO0YjmERERERERERExCoKmERERERERERExCoKmERERERERERExCoKmERERERERERExCoKmERERERERERExCoKmERERERERERExCoKmKTSSUtLIyIigqZNm2IymfD09CQwMNBi8/b2pnHjxhVWw7Rp0+jYsSMmk4nly5eX+fitW7cybNgw6tSpg8lkwsvLi8DAQHx8fGjYsCHNmjWjf//+rFmzBrPZXOz4AwcO4Obmxrhx48rhau4uixYtok2bNnh5eREQEECjRo14+OGH+eijj2xdmoiIiIiIiNyEAiapdPz9/Zk/fz4PPvggAFFRUaSmplpsu3fvxmQyVVgNM2bMYMSIEbd9fNeuXVm2bBn+/v4AxMbGkpqaSmZmJseOHWPdunXY29szcOBA2rZty6FDhyyOz8nJ4ezZs2RlZVlzGXelHTt2MGDAAL777jv27dvHwYMH8fLyonfv3qxcudLW5YmIiIiIiMgNKGCSu5KjoyN169a1dRm3xWQy0apVK1avXk1MTAz79u2je/fu5ObmGm3atm3L8ePHWb9+fZn779+/P9u3by/Hiu+80NBQ7OzsALC3tycmJgY7OztmzJhh48pERERERETkRhQwyV1rypQpti7BahMmTKBLly4cOnSIBQsWWOyrV68e9vb2Zepvz549txVKVSarV6+mUaNGFt85Ojpy7733/iFHdImIiIiIiNwNFDDJXatv374ALFmyhObNm2MymQgICGD16tUAjB07ltq1a9OtWzd27NhBeno6PXr0wGQyERISwsMPP4y/vz8uLi707duXH374wSbXMXLkSAA+/PBDAI4ePUpYWBgODg7F1pnav38/PXv2pGXLlnh7e+Pl5UVcXBwA77zzDqGhoRQWFhIeHk5gYCCrVq1i9uzZuLm5YTKZSEtLY926dQQHBzNp0iSj3w8++IDWrVvj5eVF06ZNeeKJJyzCnPz8fCZOnIi/vz/+/v54enry2GOPGfsLCwuZNWsWAQEBBAQE4OnpSYcOHW64vtStVKly4/+Wzp07R8OGDcvcn4iIiIiIiFS8qrYuQKSs2rdvz86dO43PI0aMoFu3bvj7+2NnZ0doaChwfZpVRkYGiYmJxnSrSZMmkZiYSK9evRg9ejQAx48fp2vXrgQHB5OWloazs/MdvZ6OHTsCsHfvXsxmM56enqxYsYLk5GSLdvn5+fTo0YNBgwbx8ccfA7B8+XK+/fZbAEaNGsXJkyeZPn06sbGxBAcHG8fm5eUxffp0hg8fTpMmTfDw8CAvLw+A+Ph4wsLCiI+PZ/DgwZw7d46goCAeeeQR0tPTsbe3Z9q0aWzcuJHdu3fj5OTE6dOn6datm9H/4sWLmTt3Lunp6dSvX5+cnBw6derE1atXyzwK60YyMzO5cuUK3bt3t7ovERERERERKX8awSSVXmRkpMUb5FJSUoq1adq0Ka+++iqpqanExMRw7tw5xo8fT3x8vBEu/Zqjo6Pxs7u7OzNmzODYsWPGaKA7qV69esD1UUDnzp27abuMjAx++ukn2rdvb3w3aNAgnnzyyVKfKzo6mjVr1hAXF8d9990HXL+/vr6+DB48GIDatWszePBgvv/+e5KSkgBISkrCx8cHJycno+Z3333X6DcpKQkPDw/q168PQK1atYiNjS2XcAkgLi4OJycnJk6cWGK7/Px8cnJyLDYRERERERGpeAqYpNL77Vvkfrs+T5GIiAiCgoKYPn06/fr1Y+bMmTRo0KBU5+jcuTMA27ZtK7e6S6uwsND4+WbTwwBcXV0BeOWVV9izZw8AVatWpW3btqU+V1H/1apVY/To0WRlZXHkyBH8/Pws2hVNzcvIyDDOnZiYyHvvvWdMe+vQoYNFbWlpaURHR3Pp0iUAgoKCbhjuldXu3bt56623WLZsWbEpg781a9YsnJ2djc3Dw8Pq84uIiIiIiMitKWCSu06LFi1u+H2VKlWIi4vj2rVrnD59mh49epS6zzp16gDXp8vdaUWjluzt7XFxcblpO3d3dyIjI0lLS6NNmzYEBgby9ttvc/Xq1ds+94kTJwDYsmWLxSix2bNn4+rqagRSM2fOxMnJiREjRlC/fn3GjBnDsWPHjH6mTJlCo0aNmDp1Kq6uroSFhRnhlDW+//57Hn/8cWJjY+nXr98t20+ePJnz588b269rFBERERERkYqjgEnuOkXrD91IQUEBTk5OZGRklGm6W25uLkC5jLgpq/T0dABat25N1aolL4s2ffp0UlNTef755zlx4gTPPfdcmabI/VbR6Km+fftajBLLzMzk5MmTTJgwAYB27dpx4MABFi5cSLNmzVi0aBEBAQEcPnwYuD7iKT09naVLl9K+fXvi4+Np3bq1xVpZZZWWlkbv3r1ZvHgxgwYNKtUx1atXp1atWhabiIiIiIiIVDwFTHLXMpvNzJ8/3/icm5vL8OHD2b59Oy1atGDixImcPHmyVH0VjeS51RSsirBhwwYABg4cWKr2AQEBvP7662RlZfHAAw+wYcMGI+gpKzc3NwDOnDlzy7YuLi6MGTOGnTt38uabb5Kdnc3y5cuN/TVr1mTYsGF8+umnbNy4kfz8fBYvXnxbdSUnJ9O/f39Wr15Nnz59bqsPERERERERuXMUMMlda+/evaSmphqfx48fz9ixY/Hz8+Odd94hOzubcePGlaqvolFRPXv2rIhSbyozM5O4uDh8fHwIDw8vse3BgwfZuHGj8dnBwcF4Y96FCxeA/62xVNppc02aNMHd3Z3k5GSuXLly03bz5s2z+Dx06FCL8/52/6OPPoqLi4uxvyzWrl3LuHHj+Pjjj2nTpk2ZjxcREREREZE7TwGT3LW++OIL4+e1a9eSl5fHkCFDAAgODiYsLIy1a9eyefPmEvvJysrijTfeoFWrVqWeimWtgoICNmzYQLdu3fDy8uKjjz6iZs2aJR6Tl5dHdHQ0p0+fBuDKlSskJSXh5eWFj48PAA0bNgSu34+jR4/y3XffldinyWRi2rRpZGdnExERQX5+vrHPbDYbQdXatWvZsmWLsa/onvbu3RuAr776ymI00+eff052dnaZRx8dP36cAQMG8PPPP9OzZ0+8vb2Lbbt27SpTnyIiIiIiIlLxSl7wRcQGdu7cyezZs9mxYwcAw4cPZ9SoURZtrl69SkFBAQMGDCAiIoIVK1bQpUsXvvnmG1q1asWuXbuMICY8PJyhQ4fy2muvGcfPnDmThQsXYjabycnJoU+fPsyYMQN7e3sAXnrpJSNQWbJkCYcOHSIqKgqTyVSqa9i6dSsrV65k//79Rg333HOPsf/y5ct4eXkxffp0hgwZQrVq1Yx9W7ZsYenSpZw6dQqAp59+mqFDh+Ln54eTkxMtW7bE2dmZ/Px8fH192bRpk1F3aGgoa9asYfny5SQkJDBq1Cjc3d1Zv369cd179uzhhRdeMM737LPP4uDgwIIFC2jUqBHu7u7Uq1ePxo0bM2/ePBwdHQkODmbkyJGYzWYcHByoVq0aS5cupWvXrgB06tSJOXPmMHXqVJydnbl27RqzZs1i2LBhpbpfRa5cucK1a9f46aefbtqm6C11IiIiIiIiUnmYCn/9jnSR37nt27fTpUsXVqxYYYx2kt+vnJwcnJ2dOX/+/F274LfZbCYhIYGQkBAjSJQ7T8/B9sxmM9Wio21dRpkUvvyyrUsod/pdqBz0HGxPz6By0HOwPT2DyqG0z6Gi/z7SFDn5Q7p27ZqtSxARERERERH53dAUOZFSCg8PJyUlpVRtf734+B9dZGQkmzZtKlXbhIQE3N3dK7giERERERERKW8KmERKKTY21tYl3JWioqKIioqydRkiIiIiIiJSgTRFTv4wEhISGDt2LACvvPIKTz31FLm5uTauSkREREREROTupxFM8ocREhJCSEiIrcsQERERERER+d3RCCYREREREREREbGKRjCJiIjIXWFDYKBegywiIiJSSWkEk4iIiIiIiIiIWEUBk4iIiIiIiIiIWEUBk4iIiIiIiIiIWEUBk4iIiIiIiIiIWEUBk4iIiIiIiIiIWEUBk4iIiIiIiIiIWKWqrQsQERERKY3HU1MhNdXWZZRa4csv27oEERERkTtGI5hERERERERERMQqCphERERERERERMQqCphERERERERERMQqCphERERERERERMQqCphERERERERERMQqCphERERERERERMQqCphERERERERERMQqCpjkjklLSyMiIoKmTZtiMpnw9PQkMDDQYvP29qZx48YVVsO0adPo2LEjJpOJ5cuXl/n4rVu3MmzYMOrUqYPJZMLLy4vAwEB8fHxo2LAhzZo1o3///qxZswaz2Vzs+AMHDuDm5sa4cePK4WruLosWLaJNmzZ4eXkREBBAo0aNePjhh/noo48s2h05coSHHnoIDw8PfH198fLyonnz5jz//POcO3fORtWLiIiIiIhISRQwyR3j7+/P/PnzefDBBwGIiooiNTXVYtu9ezcmk6nCapgxYwYjRoy47eO7du3KsmXL8Pf3ByA2NpbU1FQyMzM5duwY69atw97enoEDB9K2bVsOHTpkcXxOTg5nz54lKyvLmsu4K+3YsYMBAwbw3XffsW/fPg4ePIiXlxe9e/dm5cqVRrszZ85w8uRJUlJSyMjI4ODBg/zjH//g3Xff5aGHHuLq1as2vAoRERERERG5EQVMUqk4OjpSt25dW5dxW0wmE61atWL16tXExMSwb98+unfvTm5urtGmbdu2HD9+nPXr15e5//79+7N9+/ZyrPjOCw0Nxc7ODgB7e3tiYmKws7NjxowZFu26du2Kq6ur8bl9+/Y8+eSTpKamsnPnzjtas4iIiIiIiNyaAiapdKZMmWLrEqw2YcIEunTpwqFDh1iwYIHFvnr16mFvb1+m/vbs2XNboVRlsnr1aho1amTxnaOjI/fee6/FiK62bduycOHCYse7u7sD10eBiYiIiIiISOWigEkqnb59+wKwZMkSmjdvjslkIiAggNWrVwMwduxYateuTbdu3dixYwfp6en06NEDk8lESEgIDz/8MP7+/ri4uNC3b19++OEHm1zHyJEjAfjwww8BOHr0KGFhYTg4OBRbZ2r//v307NmTli1b4u3tjZeXF3FxcQC88847hIaGUlhYSHh4OIGBgaxatYrZs2fj5uaGyWQiLS2NdevWERwczKRJk4x+P/jgA1q3bo2XlxdNmzbliSeesAhz8vPzmThxIv7+/vj7++Pp6cljjz1m7C8sLGTWrFkEBAQQEBCAp6cnHTp0uOH6UrdSpcqN/7s5d+4cDRs2vOXxR44cwd7enoCAgDKfW0RERERERCpWVVsXIFKkffv2FtOfRowYQbdu3fD398fOzo7Q0FDg+jSrjIwMEhMTjelWkyZNIjExkV69ejF69GgAjh8/TteuXQkODiYtLQ1nZ+c7ej0dO3YEYO/evZjNZjw9PVmxYgXJyckW7fLz8+nRoweDBg3i448/BmD58uV8++23AIwaNYqTJ08yffp0YmNjCQ4ONo7Ny8tj+vTpDB8+nCZNmuDh4UFeXh4A8fHxhIWFER8fz+DBgzl37hxBQUE88sgjpKenY29vz7Rp09i4cSO7d+/GycmJ06dP061bN6P/xYsXM3fuXNLT06lfvz45OTl06tSJq1evlnkU1o1kZmZy5coVunfvXmK7LVu2sGnTJubOnWuMZBIREREREZHKQyOYxGYiIyMt3iCXkpJSrE3Tpk159dVXSU1NJSYmhnPnzjF+/Hji4+ONcOnXHB0djZ/d3d2ZMWMGx44dM0YD3Un16tUDro8CKuntZxkZGfz000+0b9/e+G7QoEE8+eSTpT5XdHQ0a9asIS4ujvvuuw+4fn99fX0ZPHgwALVr12bw4MF8//33JCUlAZCUlISPjw9OTk5Gze+++67Rb1JSEh4eHtSvXx+AWrVqERsbWy7hEkBcXBxOTk5MnDix2L6MjAwCAwNp2LAhgwYN4o033mDMmDEl9pefn09OTo7FJiIiIiIiIhVPAZPYzG/fIvfb9XmKREREEBQUxPTp0+nXrx8zZ86kQYMGpTpH586dAdi2bVu51V1ahYWFxs83mx4GGItZv/LKK+zZsweAqlWr0rZt21Kfq6j/atWqMXr0aLKysjhy5Ah+fn4W7Yqm5mVkZBjnTkxM5L333jOmvXXo0MGitrS0NKKjo7l06RIAQUFBNwz3ymr37t289dZbLFu2rNiUQQBfX19SU1P58ccf2b59OytWrCAoKIgff/zxpn3OmjULZ2dnY/Pw8LC6ThEREREREbk1BUxSabRo0eKG31epUoW4uDiuXbvG6dOn6dGjR6n7rFOnDnB9utydVjRqyd7eHhcXl5u2c3d3JzIykrS0NNq0aUNgYCBvv/02V69eve1znzhxArg+tezXo8Rmz56Nq6urEUjNnDkTJycnRowYQf369RkzZgzHjh0z+pkyZQqNGjVi6tSpuLq6EhYWZoRT1vj+++95/PHHiY2NpV+/frds36JFCz788EP27t3L888/f9N2kydP5vz588b262sRERERERGRiqOASSqNovWHbqSgoAAnJycyMjLKNN0tNzcXoFxG3JRVeno6AK1bt6Zq1ZKXO5s+fTqpqak8//zznDhxgueee65MU+R+q2j0VN++fS1GiWVmZnLy5EkmTJgAQLt27Thw4AALFy6kWbNmLFq0iICAAA4fPgxcH/GUnp7O0qVLad++PfHx8bRu3dpirayySktLo3fv3ixevJhBgwaV+rgGDRrQpEkTtm/fftM21atXp1atWhabiIiIiIiIVDwFTFLpmM1m5s+fb3zOzc1l+PDhbN++nRYtWjBx4kROnjxZqr6KRvLcaApWRduwYQMAAwcOLFX7gIAAXn/9dbKysnjggQfYsGGDEfSUlZubGwBnzpy5ZVsXFxfGjBnDzp07efPNN8nOzmb58uXG/po1azJs2DA+/fRTNm7cSH5+PosXL76tupKTk+nfvz+rV6+mT58+N2zz3//+15iO91u5ubnGIuYiIiIiIiJSeShgkkpn7969pKamGp/Hjx/P2LFj8fPz45133iE7O5tx48aVqq+iUVE9e/asiFJvKjMzk7i4OHx8fAgPDy+x7cGDB9m4caPx2cHBwXhj3oULF4D/rbFU2mlzTZo0wd3dneTkZK5cuXLTdvPmzbP4PHToUIvz/nb/o48+iouLi7G/LNauXcu4ceP4+OOPadOmzU3bvf/++3z11VfFvj9y5AinTp2iU6dOZT63iIiIiIiIVCwFTFLpfPHFF8bPa9euJS8vjyFDhgAQHBxMWFgYa9euZfPmzSX2k5WVxRtvvEGrVq3KNBXLGgUFBWzYsIFu3brh5eXFRx99RM2aNUs8Ji8vj+joaE6fPg3AlStXSEpKwsvLCx8fHwAaNmwIXL8fR48e5bvvviuxT5PJxLRp08jOziYiIoL8/Hxjn9lsNoKqtWvXsmXLFmNf0T3t3bs3AF999ZXFaKbPP/+c7Ozsm44+upnjx48zYMAAfv75Z3r27Im3t3exbdeuXUb7999/n4sXLxqfz507x+jRo7nnnnt47bXXynRuERERERERqXglLwwjUo527tzJ7Nmz2bFjBwDDhw9n1KhRFm2uXr1KQUEBAwYMICIighUrVtClSxe++eYbWrVqxa5du4wgJjw8nKFDh1oEDjNnzmThwoWYzWZycnLo06cPM2bMwN7eHoCXXnrJCFSWLFnCoUOHiIqKwmQyleoatm7dysqVK9m/f79Rwz333GPsv3z5Ml5eXkyfPp0hQ4ZQrVo1Y9+WLVtYunQpp06dAuDpp59m6NCh+Pn54eTkRMuWLXF2diY/Px9fX182bdpk1B0aGsqaNWtYvnw5CQkJjBo1Cnd3d9avX29c9549e3jhhReM8z377LM4ODiwYMECGjVqhLu7O/Xq1aNx48bMmzcPR0dHgoODGTlyJGazGQcHB6pVq8bSpUvp2rUrAJ06dWLOnDlMnToVZ2dnrl27xqxZsxg2bFip7leRK1eucO3aNX766aebtvn1W+reeustfHx8jMXR8/LyePDBB9mzZw/NmjUr07lFRERERESk4pkKf/0udZG71Pbt2+nSpQsrVqwwRjuJ5OTk4OzszPnz5+/aBb/NZjMJCQmEhIQYgaPceXoOtmc2m6kWHW3rMsqk8OWXbV1CudPvQuWg52B7egaVg56D7ekZVA6lfQ4V/feRpsjJ78q1a9dsXYKIiIiIiIjIH46myMkfXnh4OCkpKaVq++vFx//oIiMj2bRpU6naJiQk4O7uXsEViYiIiIiIiK0oYJI/vNjYWFuXcFeKiooiKirK1mWIiIiIiIhIJaApcnLXS0hIYOzYsQC88sorPPXUU+Tm5tq4KhEREREREZE/Do1gkrteSEgIISEhti5DRERERERE5A9LI5hERERERERERMQqGsEkIiIid4UNgYF6DbKIiIhIJaURTCIiIiIiIiIiYhUFTCIiIiIiIiIiYhUFTCIiIiIiIiIiYhUFTCIiIiIiIiIiYhUFTCIiIiIiIiIiYhUFTCIiIiIiIiIiYpWqti5AREREbO/QvZ1tXcKtxf7N1hWIiIiIyE1oBJOIiIiIiIiIiFhFAZOIiIiIiIiIiFhFAZOIiIiIiIiIiFhFAZOIiIiIiIiIiFhFAZOIiIiIiIiIiFhFAZOIiIiIiIiIiFhFAZPIXWzz5s2Eh4djMpkIDg4ulz4PHDiAm5sb48aNK5f+bqawsJA5c+bwyCOPYDKZ2L59e4WeT0RERERERCqOAiaRu1ifPn2IjY0t1z5zcnI4e/YsWVlZ5drvb5lMJl588UUGDhxYoecRERERERGRiqeASeQPbOLEiSxfvtziu7Zt23L8+HHWr19frH1QUFCFB08iIiIiIiJy91HAJPIHdfz4cRYtWnTDffXq1cPe3t7iu3/96198/fXX5V5HlSr6b0hERERERORup7/sRH6HPv/8c4KCgvDy8uL+++/H09OTqVOnUlhYCMC6devo2bMnly9fJjIyksDAQObOncvRo0cJCwvDwcGBxo0bG/1FRUXx3HPPARASEkJgYCDbtm1jzJgxuLi4YDKZjLY7duygf//+mEwmhg4dalHX4cOHefrpp2nUqBGBgYEEBQURHR19w2tYuHAhvr6+NG/enGbNmjF06FB+/vnn8r1RIiIiIiIiUi6q2roAESl/WVlZtG/fnvnz52NnZ8fbb7/Nc889R9OmTXnmmWd48sknuXjxIsOGDSMqKsoiCFqxYgXJyckW/UVGRvLDDz+wYsUKEhISjPCpS5cu7N+/36J9586d6dy5s0XoBHDy5Ek6dOiAh4cHu3fvpm7dugAsX76cYcOGWbSNiori1VdfJSkpiS5dunD48GHatm3LkSNH2LZtWzneKRERERERESkPGsEk8jvUvn17Jk6ciJ2dHQDdunUDYOfOnTarac6cOZw6dYp58+YZ4dKNXLx40Xi7XJcuXQBo0qQJjz76KNu3byc9Pf2mx+bn55OTk2OxiYiIiIiISMVTwCTyO3Tffffh6elpfC5aT+nKlSu2Kon//Oc/ODg40KlTpxLbpaSkkJubi5+fn8X3RaOmMjIybnrsrFmzcHZ2NjYPDw+r6xYREREREZFb0xQ5kd+hXbt2ERMTQ1paGlWrVuXq1au2LonDhw/TsGHDWy7qfeLECQD+8Y9/kJiYaHx/6dIlXF1djXWkbmTy5Mm88MILxuecnByFTCIiIiIiIneAAiaR35nDhw/z4IMP0r59e3bs2EHdunXJysqiSZMmNq2roKCAqlVv/V9OUYA0evRopk6dWqZzVK9enerVq99WfSIiIiIiInL7NEVO5Hfmo48+Ij8/n4iIiBLXOrrTXF1dOXv27C3bubm5AXDmzJmKLklERERERETKiQImkd+ZvLw8ABwcHEpsVzRVrbTT527W3snJCbj1+k6dOnXi7NmzJa6hBNCmTRuqV69OUlJSqeoSERERERER21PAJPI707ZtWwCLgGb37t3F2jVs2BCAjRs3cuTIkRLfzvbr9qtXr+bYsWP88MMPAAQGBgLw73//22h74cKFYsdPnDgRe3t7xo0bZ/F2t59//tminbOzM+PHjyczM5Po6GiLQOvy5csl1igiIiIiIiK2oYBJ5C62efNmwsPDgetvVxs3bhy1a9fmxRdfZOXKlQQEBNCzZ0/Onj1LzZo1+e9//0tcXBwAwcHBPPXUU3z66ae0b9+et99+my1btvD0009z6tQpTp06xdNPP80nn3wCwMiRI2nXrh0zZsyga9eubNu2DYC//e1vPPbYYwwdOpQOHTowZMgQ5s2bR40aNfj6669ZtmwZcH1kUmJiIpcvX8bT05OWLVvSo0cPI6h6/fXX+frrr4Hrb4ObM2cOK1euxMPDg3bt2tGrV68yr8kkIiIiIiIid4YW+Ra5i/Xp04c+ffoQGxtr8X1AQACvvfaaxXcjR460+FylShXef//9Yn0+9NBDNzxXgwYN2LVrV7HvnZ2d2bBhQ7HvX3nllWLfdenShS+++KLY94sWLSpW28SJE5k4ceINaxEREREREZHKRSOYRERERERERETEKgqYRERERERERETEKgqYRERERERERETEKgqYRERERERERETEKgqYRERERERERETEKgqYRERERERERETEKlVtXYCIiIjYXrMzO2xdQonMZjP7ExJsXYaIiIiI3IRGMImIiIiIiIiIiFUUMImIiIiIiIiIiFUUMImIiIiIiIiIiFUUMImIiIiIiIiIiFUUMImIiIiIiIiIiFUUMImIiIiIiIiIiFWq2roAERERkdJ4PDUVUlNtXUaJCl9+2dYliIiIiNiERjCJiIiIiIiIiIhVFDCJiIiIiIiIiIhVFDCJiIiIiIiIiIhVFDCJiIiIiIiIiIhVFDCJiIiIiIiIiIhVFDCJiIiIiIiIiIhVFDDJHZOWlkZERARNmzbFZDLh6elJYGCgxebt7U3jxo0rrIZp06bRsWNHTCYTy5cvL/PxW7duZdiwYdSpUweTyYSXlxeBgYH4+PjQsGFDmjVrRv/+/VmzZg1ms7nY8QcOHMDNzY1x48aVw9Xcvb788kvatWt3w2ewfft2TCZTiVtycvKdL1pERERERERuqqqtC5A/Dn9/f+bPn092djaHDx8mKiqKoUOHWrTJzc3Fz8+vwmqYMWMGy5cv56uvvrqt47t27UrXrl0JDg4mOTmZ2NhYgoODASgsLCQ1NZW5c+cycOBAZs+ezT//+U+aNWtmHJ+Tk8PZs2fJysoqh6u5+/z000/8/e9/55///CeXL1++aTsHBwc8PT2LfX/58mUuXrxImzZtKrJMERERERERKSMFTFKpODo6UrduXVuXcVtMJhOtWrVi9erVtGnThr/97W90796dvXv34ujoCEDbtm05fvw4Li4uZe6/f//+jB492gi07ja//PILs2fP5plnnuGhhx7ir3/9603b9uvXj3/84x/Fvn/ppZcwmUzcc889FVmqiIiIiIiIlJGmyEmlM2XKFFuXYLUJEybQpUsXDh06xIIFCyz21atXD3t7+zL1t2fPHtavX1+eJd5xLi4uLFy4kC5dumAymW7aztPTk2eeeabY99nZ2cTFxf3hpxeKiIiIiIhURgqYpNLp27cvAEuWLKF58+aYTCYCAgJYvXo1AGPHjqV27dp069aNHTt2kJ6eTo8ePTCZTISEhPDwww/j7++Pi4sLffv25YcffrDJdYwcORKADz/8EICjR48SFhaGg4NDsXWm9u/fT8+ePWnZsiXe3t54eXkRFxcHwDvvvENoaCiFhYWEh4cTGBjIqlWrmD17Nm5ubphMJtLS0li3bh3BwcFMmjTJ6PeDDz6gdevWeHl50bRpU5544gmL6Xn5+flMnDgRf39//P398fT05LHHHjP2FxYWMmvWLAICAggICMDT05MOHTrccH2p8tK0adMbjtKaP38+Tz755F07wk1EREREROT3TFPkpNJo3749O3fuND6PGDGCbt264e/vj52dHaGhoQCEhoaSkZFBYmIidnZ2AEyaNInExER69erF6NGjATh+/LixXlJaWhrOzs539Ho6duwIwN69ezGbzXh6erJixYpiC1Tn5+fTo0cPBg0axMcffwzA8uXL+fbbbwEYNWoUJ0+eZPr06RZrPgHk5eUxffp0hg8fTpMmTfDw8CAvLw+A+Ph4wsLCiI+PZ/DgwZw7d46goCAeeeQR0tPTsbe3Z9q0aWzcuJHdu3fj5OTE6dOn6datm9H/4sWLmTt3Lunp6dSvX5+cnBw6derE1atXyzwKyxrnz59n8eLFfP3113fsnCIiIiIiIlJ6GsEkNhMZGWnxBrmUlJRibZo2bcqrr75KamoqMTExnDt3jvHjxxMfH2+ES79WtNYRgLu7OzNmzODYsWPGaKA7qV69esD1UUDnzp27abuMjAx++ukn2rdvb3w3aNAgnnzyyVKfKzo6mjVr1hAXF8d9990HXL+/vr6+DB48GIDatWszePBgvv/+e5KSkgBISkrCx8cHJycno+Z3333X6DcpKQkPDw/q168PQK1atYiNjb2j4RLAggUL6N69O40aNSqxXX5+Pjk5ORabiIiIiIiIVDwFTGIzUVFRpKamGtvNwoOIiAiCgoKYPn06/fr1Y+bMmTRo0KBU5+jcuTMA27ZtK7e6S6uwsND4uUqVm/+qubq6AvDKK6+wZ88eAKpWrUrbtm1Lfa6i/qtVq8bo0aPJysriyJEjxd7IVzQ1LyMjwzh3YmIi7733njHtrUOHDha1paWlER0dzaVLlwAICgq6YbhXUS5cuMCbb77J3//+91u2nTVrFs7Ozsbm4eFxByoUERERERERBUxSabRo0eKG31epUoW4uDiuXbvG6dOn6dGjR6n7rFOnDnB9utydVjRqyd7evsS3xrm7uxMZGUlaWhpt2rQhMDCQt99+m6tXr972uU+cOAHAli1bLEaJzZ49G1dXVyOQmjlzJk5OTowYMYL69eszZswYjh07ZvQzZcoUGjVqxNSpU3F1dSUsLMwIp+6Ut956iw4dOhQLy25k8uTJnD9/3th+fS0iIiIiIiJScRQwSaVRtP7QjRQUFODk5ERGRkaZprvl5uYC3NERN0XS09MBaN26NVWrlrzc2fTp00lNTeX555/nxIkTPPfcc2WaIvdbRaOn+vbtazFKLDMzk5MnTzJhwgQA2rVrx4EDB1i4cCHNmjVj0aJFBAQEcPjwYeD6iKf09HSWLl1K+/btiY+Pp3Xr1hZrZVWk3NxcXn/9dYuFy0tSvXp1atWqZbGJiIiIiIhIxVPAJJWO2Wxm/vz5xufc3FyGDx/O9u3badGiBRMnTuTkyZOl6qtoJM9v39p2J2zYsAGAgQMHlqp9QEAAr7/+OllZWTzwwANs2LDBCHrKys3NDYAzZ87csq2Liwtjxoxh586dvPnmm2RnZ7N8+XJjf82aNRk2bBiffvopGzduJD8/n8WLF99WXWW1aNEivL29eeCBB+7I+UREREREROT2KGCSSmfv3r2kpqYan8ePH8/YsWPx8/PjnXfeITs7m3HjxpWqr6JRUT179qyIUm8qMzOTuLg4fHx8CA8PL7HtwYMH2bhxo/HZwcHBeGPehQsXgP+tsVTaaXNNmjTB3d2d5ORkrly5ctN28+bNs/g8dOhQi/P+dv+jjz6Ki4uLsb8iXbp0iXnz5pV69JKIiIiIiIjYjgImqXS++OIL4+e1a9eSl5fHkCFDAAgODiYsLIy1a9eyefPmEvvJysrijTfeoFWrVgwaNKhCay5SUFDAhg0b6NatG15eXnz00UfUrFmzxGPy8vKIjo7m9OnTAFy5coWkpCS8vLzw8fEBoGHDhsD1+3H06FG+++67Evs0mUxMmzaN7OxsIiIiyM/PN/aZzWYjqFq7di1btmwx9hXd0969ewPw1VdfWYxm+vzzz8nOzqZPnz6luR1Wefvtt3F1daVXr14Vfi4RERERERGxTskLw4iUo507dzJ79mx27NgBwPDhwxk1apRFm6tXr1JQUMCAAQOIiIhgxYoVdOnShW+++YZWrVqxa9cuI4gJDw9n6NChvPbaa8bxM2fOZOHChZjNZnJycujTpw8zZszA3t4egJdeeskIVJYsWcKhQ4eIiorCZDKV6hq2bt3KypUr2b9/v1HDPffcY+y/fPkyXl5eTJ8+nSFDhlCtWjVj35YtW1i6dCmnTp0C4Omnn2bo0KH4+fnh5OREy5YtcXZ2Jj8/H19fXzZt2mTUHRoaypo1a1i+fDkJCQmMGjUKd3d31q9fb1z3nj17eOGFF4zzPfvsszg4OLBgwQIaNWqEu7s79erVo3HjxsybNw9HR0eCg4MZOXIkZrMZBwcHqlWrxtKlS+natSsAnTp1Ys6cOUydOhVnZ2euXbvGrFmzGDZsWKnu16+dPHmSqVOncuLECTIzM4Hra099+OGH1K9fn5kzZxpT+y5fvkxMTAwxMTFlPo+IiIiIiIjceabCX79LXeQutX37drp06cKKFSuM0U4iOTk5ODs7c/78+bt2wW+z2UxCQgIhISFG4Ch3np6D7ZnNZqpFR9u6jFsqfPllW5dQofS7UDnoOdienkHloOdge3oGlUNpn0NF/32kKXLyu3Lt2jVblyAiIiIiIiLyh6MpcvKHFx4eTkpKSqna/nrx8T+6yMhINm3aVKq2CQkJuLu7V3BFIiIiIiIiYisKmOQPLzY21tYl3JWioqKIioqydRkiIiIiIiJSCWiKnNz1EhISGDt2LACvvPIKTz31FLm5uTauSkREREREROSPQyOY5K4XEhJCSEiIrcsQERERERER+cPSCCYREREREREREbGKAiYREREREREREbGKpsiJiIjIXWFDYCAhISHY29vbuhQRERER+Q2NYBIREREREREREasoYBIREREREREREasoYBIREREREREREasoYBIREREREREREasoYBIREREREREREasoYBIREREREREREatUtXUBIiIiUnkcurezrUu4udi/2boCEREREbkJjWASERERERERERGrKGASERERERERERGrKGASERERERERERGrKGASERERERERERGrKGASERERERERERGrKGASERERERERERGrKGASERERERERERGrVLV1AXL3MJvNfPDBB6xbt46MjAxq1KgBwC+//ELt2rXx8vJi6NCh9OnTx8aVyt3stdde4x//+AcXL17knnvuITs7mxYtWhAREaF/WyIiIiIiIpWURjBJqRw9epT27dszYcIEBgwYQFpaGvv27WPfvn1kZWXx1ltvkZ2dzZYtW2xdqtzlduzYwXPPPccPP/zA/v37OXjwIG5ubjz66KOsW7fO1uWJiIiIiIjIDShgklu6fPkyvXr1IiMjg61btzJgwACqV69u7Lezs6Nz585MmzbNhlXK74XJZCI0NJQqVa7/91SjRg1effVVAJYsWWLL0kREREREROQmNEVObum9995j//79PPvss9x///03bde6dWvy8/PvYGXye7R58+Zi37m7uwOQk5Nzp8sRERERERGRUtAIJrmlVatWAfD444+X2M7Z2ZkePXoYnz/88EM6dOiAn58f3t7etG7dmri4OGP/119/Tbt27TCZTPztb3/jwIEDPPPMM9x3331Gm4MHDzJw4EBatGhBQEAAzZo1Y/To0fz8889Gm/nz5+Pu7o6TkxMxMTH07NkTLy8v/u///o+RI0dy9epVizp79epF06ZN8ff3p2HDhoSEhHD48OHbvj979uyhb9++eHl54efnR4MGDQgICODTTz+t0Os4dOgQgwcP5r777sPf3x8PDw98fX1ZuXKl0ebUqVMMGzYMT09PmjdvTkBAAIsXLzb2jx8/HhcXF0wmE9nZ2cTGxhIUFMSiRYuIj4/H29sbk8lETEwMjz76KL6+vtSqVYv+/ftz4cIFo5/8/HwmTpyIv78//v7+eHp68thjj932Pf2tI0eOANC2bdty61NERERERETKjwImKVFBQQF79uwBwN/fv9THvf322wwYMIAXX3yR/fv38+233zJ58mSGDx9uTHdq164da9euBWDDhg2MHj2a2rVrc+XKFQB+/PFH/vSnP3H16lVjzafk5GS2bt1KcHAwly9fBiAiIoLmzZtz8eJFRo8ezccff8zBgweZMGECS5YsMQKyIpmZmezatYu0tDT27NnD559/zpAhQ27r/nz55Zd07NiRunXrkpGRwf79+zl06BB+fn78+OOPFXYdBw8epF27dpw7d45vvvmGtLQ0Dh8+TO/evTl48CAAeXl5dO3alX379pGWlsaBAwcIDw9n9OjRrFixAoA333zTCIIee+wxPv/8c9zc3DCbzQwZMoQ//elPwPVQbtOmTWRkZLBo0SLWrVvHG2+8YdQzbdo0Nm7cyJdffklaWhopKSlWhXa/duHCBSZPnoyfn98tp2Hm5+eTk5NjsYmIiIiIiEjFU8AkJfrll18oKCgAoHbt2qU6Jj8/n6lTpxIUFETfvn2N7/v370/nzp2Jjo7m3LlzFsd07NiRTz75hLlz5zJ+/HgAoqOj+fnnn5k+fTrVqlUDoGHDhkZotWzZsmLndnBwsDgfQEpKikWbJUuWULduXQDq1auHv78/O3fuLNW1/VZERAQODg4sWLDAqLFGjRr069fPaFMR1zFlyhTOnz/PkiVLuOeeewCoWrUqTz31lNFmzZo1ZGRkMGnSJJydnQEYMWIENWrU4K233ip2zmXLlrF8+XJiY2O59957y1RPUlISPj4+ODk5Adfv67vvvnvzG1cK48aNIyAgwHhW//rXv3BzcyvxmFmzZuHs7GxsHh4eVtUgIiIiIiIipaOASUr062lZRYsu38rXX3/NL7/8QqdOnYrt69q1K3l5eezYscPi+1/3/cILLwDXQ4s6derg7e1drA+ATz75pMQ6atSoARRft6dbt24Wn+3t7TGbzSX2dSMnT57k66+/plOnThYBDEDfvn0ZNGhQhVzH1atXSUhIwNfXlwYNGli0bdOmDVOmTAEgOTkZAD8/P4u+3NzcyMjIKHaeomdQr149nn766VLXA+Dq6kpiYiLvvfeecS87dOhQ4nXdyoIFC9i3bx8XLlzgkUceISAgwGLU1I1MnjyZ8+fPG9uxY8esqkFERERERERKRwGTlKhOnTrY2dkBWKwXVJKiP+pdXFyK7atXr55Fm1v1Y20fv5WTk8OLL75ImzZt8PHxITAwsNgIp9IqWheoaAHqX6tSpQpVq1Y16izP6zh79iy5ubk3PC9gjJI6ceIEcH3EUWBgoLEVFBRQq1atMp3zVmbOnImTkxMjRoygfv36jBkzptzCnWrVqjFq1CjCwsL429/+xr59+27atnr16tSqVctiExERERERkYqngElKZG9vT6tWrQBK/MP+10wmEwCFhYXF9l27ds2iza36sbaP3+rfvz8LFixg/vz5ZGZmkpqaetsLRxfVVjSF8GbK+zpKe96idomJiaSmphrbsWPHjPCpvLRr144DBw6wcOFCmjVrxqJFiwgICCi3dZgAOnXqxLVr14qNfhMRERERERHbU8AktzR06FAAPvjgg1u2HTFihLHuzS+//FJs/+nTpwHw9PS8ZV8eHh5W9/FrFy9eJCkpie7du9O5c+cyHXsjRec/fvx4ie3K+zruvfdeHBwcbnneovWKzpw5U6b+b5eLiwtjxoxh586dvPnmm2RnZ7N8+fIy9XHlyhU+//zzG+7Lzc0Fri9eLiIiIiIiIpWLAia5pfDwcFq3bk18fDy7du26abtjx45x4MAB2rZtS+3atfnss8+KtdmyZQsODg6lCni6d+/OuXPnSE9PL9ZH0f6yKAomfrte0u1yd3fH39+f5OTkYouW/1p5X4ednR3dunXj22+/JTMz86btHnjgAeD6GlAVbd68eRafi0LJCxculKmfS5cuMXny5BvuK1qI/UZre4mIiIiIiIhtKWCSW6pevTr//ve/ad++Pd27d2fZsmVcvnzZ2H/t2jX++9//8txzzxntZ8yYQUpKCmvXrjXarVmzhi+//JKXXnqJ//u//7vleSdNmkSdOnWIjIzkypUrwPUQa86cOQQEBBAWFlam66hbty6NGzfmq6++4uLFiwD8+OOPnDp1qkz9/NrcuXMxm8389a9/5ezZsxb7igKt8r4OgBkzZuDo6MgzzzzD0aNHb3jeQYMG0aRJE2JiYti9e7dFm18/v/Kwdu1aIzAD2Lx5MwC9e/cuc18pKSl88803Ft9t3ryZVatWERYWRvv27a0rVkRERERERMqdAiYplfr16/PZZ58RGxvLv//9b1q2bEnLli3x8/OjcePGjBw5EmdnZ8aNGwfAs88+y5o1a5g7dy6+vr54e3sTExPDe++9Z4xQycjI4O9//zsA27dvZ/z48fz444/GORs2bMhXX31F1apV8fX1JSAggODgYB5++GG2bt1K9erVAfj73//O3r17AfjLX/7CDz/8wJ49exg9ejRw/S1tRSNsli1bhqOjI82bN6dLly7ExMRw//33A9eDoN+GRLfSvXt3tm3bRn5+Pr6+vvj4+PDnP/+Z0NBQEhISKuw6AgIC+Oqrr3B3dycoKIgWLVrQqVMn+vXrx4oVKwBwdHTks88+o3v37vTu3ZvmzZvTuXNn+vXrR2JiIgAxMTHG2+ZeeOEF41i4Piqp6A13o0ePJiUlhaysLP7yl78AsHfvXuP5BQcHM3LkSBo1aoS3tzevvfYaS5cuNd6UV1rVq1enZ8+ehIaG4uXlRWBgIM2bN2fOnDksWrSIZcuWlak/ERERERERuTNMhTdafVhE5HcgJycHZ2dnzp8/f9e+Uc5sNpOQkEBISAj29va2LucP64/0HA7da/0adRVlf+zf/hDPoDL7I/0uVGZ6DranZ1A56DnYnp5B5VDa51DRfx9pBJOIiIiIiIiIiFilqq0LEKlMIiMj2bRpU6naJiQk4O7uXsEV3f3Cw8NJSUkpVdvU1NSKLUZEREREREQqhAImkV+JiooiKirK1mX8rsTGxtq6BBEREREREalgmiInIiIiIiIiIiJWUcAkIiIiIiIiIiJW0RQ5ERERMTQ7s8PWJdyQ2Wxmf0KCrcsQERERkZvQCCYREREREREREbGKAiYREREREREREbGKAiYREREREREREbGKAiYREREREREREbGKAiYREREREREREbGKAiYREREREREREbFKVVsXICIiIlIaj6emQmqqrcu4ocKXX7Z1CSIiIiI2pRFMIiIiIiIiIiJiFQVMIiIiIiIiIiJiFQVMIiIiIiIiIiJiFQVMIiIiIiIiIiJiFQVMIiIiIiIiIiJiFQVMIiIiIiIiIiJiFQVMcsekpaURERFB06ZNMZlMeHp6EhgYaLF5e3vTuHHjCqth2rRpdOzYEZPJxPLly8t8/NatWxk2bBh16tTBZDLh5eVFYGAgPj4+NGzYkGbNmtG/f3/WrFmD2WwudvyBAwdwc3Nj3Lhx5XA1d68vv/ySdu3a3fQZ7N69m759+9KwYUP8/Pzw9vYmKirqhvdUREREREREbE8Bk9wx/v7+zJ8/nwcffBCAqKgoUlNTLbbdu3djMpkqrIYZM2YwYsSI2z6+a9euLFu2DH9/fwBiY2NJTU0lMzOTY8eOsW7dOuzt7Rk4cCBt27bl0KFDFsfn5ORw9uxZsrKyrLmMu9ZPP/3EoEGD6NatGykpKTds89VXX9GxY0cAvv/+e/bv38+6deuYP38+I0eOvJPlioiIiIiISCkpYJJKxdHRkbp169q6jNtiMplo1aoVq1evJiYmhn379tG9e3dyc3ONNm3btuX48eOsX7++zP3379+f7du3l2PFd9Yvv/zC7NmzeeaZZ1i0aNFN27300ksUFBSwcOFCHBwcAPDz82P8+PEsW7aM1NTUO1SxiIiIiIiIlJYCJql0pkyZYusSrDZhwgS6dOnCoUOHWLBggcW+evXqYW9vX6b+9uzZc1uhVGXi4uLCwoUL6dKlS4mj1P773//i6upKw4YNLb7v0aMHwF1/H0RERERERH6PFDBJpdO3b18AlixZQvPmzTGZTAQEBLB69WoAxo4dS+3atenWrRs7duwgPT2dHj16YDKZCAkJ4eGHH8bf3x8XFxf69u3LDz/8YJPrKJrO9eGHHwJw9OhRwsLCcHBwKLbO1P79++nZsyctW7bE29sbLy8v4uLiAHjnnXcIDQ2lsLCQ8PBwAgMDWbVqFbNnz8bNzQ2TyURaWhrr1q0jODiYSZMmGf1+8MEHtG7dGi8vL5o2bcoTTzxhMT0vPz+fiRMn4u/vj7+/P56enjz22GPG/sLCQmbNmkVAQAABAQF4enrSoUOHCl0L6erVqzg5ORX7vnnz5gAawSQiIiIiIlIJKWCSSqN9+/YWn0eMGMF//vMfatasiZ2dHaGhoQCEhobSqlUrEhMT6dy5M/fff78RqvTq1YtPPvmEtLQ00tPTyczMJDg4mPPnz9/x6ylaR2jv3r2YzWY8PT1ZsWIFrq6uFu3y8/Pp0aMHLVu2ZO/evXz77bdMmzaN77//HoBRo0YxaNAg4H9rPj399NNMmjSJUaNGATB8+HDWr1+Ph4cHeXl5AMTHxzNgwACef/55Dh48SEpKCvv27eORRx4xAqJp06axceNGvvzyS9LS0khJSeHw4cNGbYsXL2bu3LkkJiayb98+9u/fT25uLlevXq2w+9a6dWuOHDlS7Jm5uLhgZ2fHsWPHKuzcIiIiIiIicnsUMInNREZGWrxB7kaLPjdt2pRXX32V1NRUYmJiOHfuHOPHjyc+Ph47O7ti7R0dHY2f3d3dmTFjBseOHTNGA91J9erVA66PAjp37txN22VkZPDTTz9ZBGyDBg3iySefLPW5oqOjWbNmDXFxcdx3333A9fvr6+vL4MGDAahduzaDBw/m+++/JykpCYCkpCR8fHyMEUP16tXj3XffNfpNSkrCw8OD+vXrA1CrVi1iY2PLPMWvLGJiYigoKGDkyJFGyHTlyhW2bt1KlSpVLNa0+q38/HxycnIsNhEREREREal4CpjEZn77FrlGjRrdsF1ERARBQUFMnz6dfv36MXPmTBo0aFCqc3Tu3BmAbdu2lVvdpVVYWGj8XKXKzX/VikY0vfLKK+zZsweAqlWr0rZt21Kfq6j/atWqMXr0aLKysjhy5Ah+fn4W7Yqm5mVkZBjnTkxM5L333jNGNXXo0MGitrS0NKKjo7l06RIAQUFBNwz3ykuHDh3473//y5UrV2jfvj2dO3fmL3/5C0eOHMFsNpd4L2fNmoWzs7OxeXh4VFidIiIiIiIi8j8KmKTSaNGixQ2/r1KlCnFxcVy7do3Tp08biz2XRp06dQA4fvx4udRYFkWjluzt7XFxcblpO3d3dyIjI0lLS6NNmzYEBgby9ttvWzUN7cSJEwBs2bLFYpTY7NmzcXV1NUKamTNn4uTkxIgRI6hfvz5jxoyxmII2ZcoUGjVqxNSpU3F1dSUsLMwIpypS27Zt+ec//8m3337Ljh07WL9+PY8//jjwv2d6I5MnT+b8+fPGpul0IiIiIiIid4YCJqk0Pv7445vuKygowMnJiYyMjDJNdyuaTlWRI25uJj09Hbi+plDVqlVLbDt9+nRSU1N5/vnnOXHiBM8991yZpsj9VtHoqb59+1qMEsvMzOTkyZNMmDABgHbt2nHgwAEWLlxIs2bNWLRoEQEBAcY6TI0bNyY9PZ2lS5fSvn174uPjad26NTt37rzt2m7XoUOHAPD3979pm+rVq1OrVi2LTURERERERCqeAiapdMxmM/Pnzzc+5+bmMnz4cLZv306LFi2YOHEiJ0+eLFVfRSN5fvvWtjthw4YNAAwcOLBU7QMCAnj99dfJysrigQceYMOGDRYLbpeFm5sbAGfOnLllWxcXF8aMGcPOnTt58803yc7OZvny5cb+mjVrMmzYMD799FM2btxIfn4+ixcvvq26rJGcnAxcX8hdREREREREKhcFTFLp7N271+JV9OPHj2fs2LH4+fnxzjvvkJ2dzbhx40rVV9GoqJ49e1ZEqTeVmZlJXFwcPj4+hIeHl9j24MGDbNy40fjs4OBgvDHvwoULwP/WWCrttLkmTZrg7u5OcnIyV65cuWm7efPmWXweOnSoxXl/u//RRx/FxcXF2F8R3n77bb744guL7woKCliyZAktWrSge/fuFXZuERERERERuT0KmKTS+XW4sHbtWvLy8hgyZAgAwcHBhIWFsXbtWjZv3lxiP1lZWbzxxhu0atWKQYMGVWjNRQoKCtiwYQPdunXDy8uLjz76iJo1a5Z4TF5eHtHR0Zw+fRq4/sa0pKQkvLy88PHxAaBhw4bA9ftx9OhRvvvuuxL7NJlMTJs2jezsbCIiIsjPzzf2mc1mI6hau3YtW7ZsMfYV3dPevXsD8NVXX1mMZvr888/Jzs6mT58+pbkdt+X8+fNs2rTJmOZ36dIlwsPDOX36NCtWrKB69eoVdm4RERERERG5PSUvDCNSjnbu3Mns2bPZsWMHAMOHD2fUqFEWba5evUpBQQEDBgwgIiKCFStW0KVLF7755htatWrFrl27jCAmPDycoUOH8tprrxnHz5w5k4ULF2I2m8nJyaFPnz7MmDEDe3t7AF566SUjUFmyZAmHDh0iKioKk8lUqmvYunUrK1euZP/+/UYN99xzj7H/8uXLeHl5MX36dIYMGUK1atWMfVu2bGHp0qWcOnUKgKeffpqhQ4fi5+eHk5MTLVu2xNnZmfz8fHx9fdm0aZNRd2hoKGvWrGH58uUkJCQwatQo3N3dWb9+vXHde/bs4YUXXjDO9+yzz+Lg4MCCBQto1KgR7u7u1KtXj8aNGzNv3jwcHR0JDg5m5MiRmM1mHBwcqFatGkuXLqVr164AdOrUiTlz5jB16lScnZ25du0as2bNYtiwYaW6X7928uRJpk6dyokTJ8jMzASurz314YcfUr9+fWbOnImbmxvNmjXjpZde4oMPPsDFxYVLly7RsWNHvv76a7y8vMp8XhEREREREal4psJfv0td5C61fft2unTpwooVK4zRTiI5OTk4Oztz/vz5u3bBb7PZTEJCAiEhIUbgKHeenoPtmc1mqkVH27qMmyp8+WVbl3BH6HehctBzsD09g8pBz8H29Awqh9I+h4r++0hT5OR35dq1a7YuQUREREREROQPR1Pk5A8vPDyclJSUUrX99eLjf3SRkZFs2rSpVG0TEhJwd3ev4IpERERERETEVhQwyR9ebGysrUu4K0VFRREVFWXrMkRERERERKQS0BQ5ueslJCQwduxYAF555RWeeuopcnNzbVyViIiIiIiIyB+HRjDJXS8kJISQkBBblyEiIiIiIiLyh6URTCIiIiIiIiIiYhWNYBIREZG7wobAQL0GWURERKSS0ggmERERERERERGxigImERERERERERGxigImERERERERERGxigImERERERERERGxigImERERERERERGxigImERERERERERGxSlVbFyAiIiJSGo+npkJqqq3LKKbw5ZdtXYKIiIiIzWkEk4iIiIiIiIiIWEUBk4iIiIiIiIiIWEUBk4iIiIiIiIiIWEUBk4iIiIiIiIiIWEUBk4iIiIiIiIiIWEUBk4iIiIiIiIiIWEUBk1Q6aWlpRERE0LRpU0wmE56engQGBlps3t7eNG7cuMJqmDZtGh07dsRkMrF8+fIyH79161aGDRtGnTp1MJlMeHl5ERgYiI+PDw0bNqRZs2b079+fNWvWYDabix1/4MAB3NzcGDduXDlczd0nKSmJLl260KhRI7y8vGjRogVRUVFcuXLF1qWJiIiIiIjIDShgkkrH39+f+fPn8+CDDwIQFRVFamqqxbZ7925MJlOF1TBjxgxGjBhx28d37dqVZcuW4e/vD0BsbCypqalkZmZy7Ngx1q1bh729PQMHDqRt27YcOnTI4vicnBzOnj1LVlaWNZdxV/r000/p2bMn3t7eZGVlcfDgQd58801eeeUVnnnmGVuXJyIiIiIiIjeggEnuSo6OjtStW9fWZdwWk8lEq1atWL16NTExMezbt4/u3buTm5trtGnbti3Hjx9n/fr1Ze6/f//+bN++vRwrvrMWLFjAtWvXmDJlihEi9ujRg86dO7Nq1SrOnj1r4wpFRERERETktxQwyV1rypQpti7BahMmTKBLly4cOnSIBQsWWOyrV68e9vb2Zepvz549txVKVSbHjx8HwM3NzeL7Bg0aUFhYyMmTJ21RloiIiIiIiJRAAZPctfr27QvAkiVLaN68OSaTiYCAAFavXg3A2LFjqV27Nt26dWPHjh2kp6fTo0cPTCYTISEhPPzww/j7++Pi4kLfvn354YcfbHIdI0eOBODDDz8E4OjRo4SFheHg4FBsnan9+/fTs2dPWrZsibe3N15eXsTFxQHwzjvvEBoaSmFhIeHh4QQGBrJq1Spmz56Nm5sbJpOJtLQ01q1bR3BwMJMmTTL6/eCDD2jdujVeXl40bdqUJ554wmJ6Xn5+PhMnTsTf3x9/f388PT157LHHjP2FhYXMmjWLgIAAAgIC8PT0pEOHDjdcX+pWWrRoAVAsSPrxxx9xcHCgUaNGZe5TREREREREKpYCJrnrtG/f3uLziBEj+M9//kPNmjWxs7MjNDQUgNDQUFq1akViYiKdO3fm/vvvN0KVXr168cknn5CWlkZ6ejqZmZkEBwdz/vz5O349HTt2BGDv3r2YzWY8PT1ZsWIFrq6uFu3y8/Pp0aMHLVu2ZO/evXz77bdMmzaN77//HoBRo0YxaNAg4H9rPj399NNMmjSJUaNGATB8+HDWr1+Ph4cHeXl5AMTHxzNgwACef/55Dh48SEpKCvv27eORRx4xAqJp06axceNGvvzyS9LS0khJSeHw4cNGbYsXL2bu3LkkJiayb98+9u/fT25uLlevXi3z/Zg6dSr/93//x/Tp08nPz6ewsJB//vOffP7550RGRuLk5FTmPkVERERERKRiKWCSSi8yMtLiDXIpKSnF2jRt2pRXX32V1NRUYmJiOHfuHOPHjyc+Ph47O7ti7R0dHY2f3d3dmTFjBseOHTNGA91J9erVA66PAjp37txN22VkZPDTTz9ZBGyDBg3iySefLPW5oqOjWbNmDXFxcdx3333A9fvr6+vL4MGDAahduzaDBw/m+++/JykpCbj+VjcfHx8j3KlXrx7vvvuu0W9SUhIeHh7Ur18fgFq1ahEbG1vmKX4Avr6+rFq1ihUrVnDvvfdSt25d+vXrx5AhQyxGXd1Ifn4+OTk5FpuIiIiIiIhUPAVMUun99i1yN5siFRERQVBQENOnT6dfv37MnDmTBg0alOocnTt3BmDbtm3lVndpFRYWGj9XqXLzX8miEU2vvPIKe/bsAaBq1aq0bdu21Ocq6r9atWqMHj2arKwsjhw5gp+fn0W7oql5GRkZxrkTExN57733jFFNHTp0sKgtLS2N6OhoLl26BEBQUNANw71b2bhxI4899hhvvPEGv/zyC6dPnyYxMZHNmzcTHh5e4rGzZs3C2dnZ2Dw8PMp8fhERERERESk7BUxy1ylao+e3qlSpQlxcHNeuXeP06dP06NGj1H3WqVMH+N8C03dS0agle3t7XFxcbtrO3d2dyMhI0tLSaNOmDYGBgbz99tu3NQ2tyIkTJwDYsmWLxSix2bNn4+rqagRSM2fOxMnJiREjRlC/fn3GjBnDsWPHjH6mTJlCo0aNmDp1Kq6uroSFhRnhVFlcuXKFUaNG0apVK8aMGYOdnR12dnY88sgjjB07lri4OJKTk296/OTJkzl//ryx/bpGERERERERqTgKmOSu8/HHH990X0FBAU5OTmRkZJRpultubi7AbY24sVZ6ejoArVu3pmrVqiW2nT59OqmpqTz//POcOHGC5557rkxT5H6raPRU3759LUaJZWZmcvLkSSZMmABAu3btOHDgAAsXLqRZs2YsWrSIgIAAYx2mxo0bk56eztKlS2nfvj3x8fG0bt2anTt3lqme7777jpMnT3L//fcX29eqVSsAtm/fftPjq1evTq1atSw2ERERERERqXgKmOSuZTabmT9/vvE5NzeX4cOHs337dlq0aMHEiRNL/Ur7opE8v31r252wYcMGAAYOHFiq9gEBAbz++utkZWXxwAMPsGHDBosFt8vCzc0NgDNnztyyrYuLC2PGjGHnzp28+eabZGdns3z5cmN/zZo1GTZsGJ9++ikbN24kPz+fxYsXl6meoqDvRlMFi8K/ixcvlqlPERERERERqXgKmOSutXfvXlJTU43P48ePZ+zYsfj5+fHOO++QnZ3NuHHjStVX0aionj17VkSpN5WZmUlcXBw+Pj63XF/o4MGDbNy40fjs4OBgvDHvwoULwP+CmdJOm2vSpAnu7u4kJydz5cqVm7abN2+exeehQ4danPe3+x999FFcXFyM/aXVokUL7OzsOHToULF9RVPufrtelIiIiIiIiNieAia5a33xxRfGz2vXriUvL48hQ4YAEBwcTFhYGGvXrmXz5s0l9pOVlcUbb7xBq1atGDRoUIXWXKSgoIANGzbQrVs3vLy8+Oijj6hZs2aJx+Tl5REdHc3p06eB6+sVJSUl4eXlhY+PDwANGzYErt+Po0eP8t1335XYp8lkYtq0aWRnZxMREUF+fr6xz2w2G0HV2rVr2bJli7Gv6J727t0bgK+++spiNNPnn39OdnY2ffr0Kc3tMLi4uDBixAg+++wzY2QXXJ9GuGDBAu677z4jVBMREREREZHKo+QFX0RsYOfOncyePZsdO3YAMHz4cEaNGmXR5urVqxQUFDBgwAAiIiJYsWIFXbp04ZtvvqFVq1bs2rXLCGLCw8MZOnQor732mnH8zJkzWbhwIWazmZycHPr06cOMGTOwt7cH4KWXXjIClSVLlnDo0CGioqIwmUyluoatW7eycuVK9u/fb9Rwzz33GPsvX77M/2vv3uOiLPP/j7+Hg0aEhJoHRPEwJiggHtI8lOBaKeWWlYctFdzw0OYpW/frKRVCMU1zdXPL9VzplrnrYaXVTcUsW0oNQ6RMEzUVzUwxFBzg/v3hj1lHQIFhGNDX8/GYx4O57+u+rs99fxhv5uN137fZbFZMTIwGDx6satWqWddt27ZNy5Yt05kzZyRJzz//vKKiohQUFCQvLy+1bt1a3t7eysnJUcuWLbVx40Zr3P369dOaNWu0YsUKJSQkaMSIEfL19dW6deus+71v3z6NGzfOOt6LL74oDw8PLViwQP7+/vL19VWdOnXUuHFjzZ07V56engoLC9Pw4cNlsVjk4eGhatWqadmyZerevbskqWvXrpo9e7YmT54sb29v5efnKz4+XkOGDCnR8breggUL1KxZM02bNk0TJ05Ubm6u8vLy1Lt3b02fPl0eHh6l7hMAAAAA4Fgm4/pnpAO3ucTERIWHh2vlypXW2U64fWVmZsrb21sXL16ssjf8tlgsSkhIUEREhLWQiIpHHpzPYrGo2syZzg6jSMa0ac4OocLwWagcyIPzkYPKgTw4HzmoHEqaB0d/P+ISOdyR8vPznR0CAAAAAAC3DS6RA0ooOjpae/bsKVHb628+fqebOnWqNm7cWKK2CQkJ8vX1dXBEAAAAAIDyRoEJKKElS5Y4O4QqKTY2VrGxsc4OAwAAAADgQFwihztGQkKCRo0aJUmaPn26+vfvr6ysLCdHBQAAAABA1ccMJtwxIiIiFBER4ewwAAAAAAC47TCDCQAAAAAAAHahwAQAAAAAAAC7cIkcAACoEtaHhioiIkLu7u7ODgUAAAA3YAYTAAAAAAAA7EKBCQAAAAAAAHahwAQAAAAAAAC7UGACAAAAAACAXSgwAQAAAAAAwC4UmAAAAAAAAGAXN2cHAAAAKr8j9z3k7BCkJX90dgQAAAAoBjOYAAAAAAAAYBcKTAAAAAAAALALBSYAAAAAAADYhQITAAAAAAAA7EKBCQAAAAAAAHahwAQAAAAAAAC7UGBChUlJSdHYsWPVtGlTmUwmNWrUSKGhoTavgIAANW7c2GExTJkyRZ07d5bJZNKKFStKvf327ds1ZMgQ1apVSyaTSWazWaGhoQoMDJSfn5+aNWumvn37as2aNbJYLIW2P3TokOrVq6fRo0eXw95UXbt379YDDzxQ4hxkZ2crICBAUVFRDo0LAAAAAFA2FJhQYYKDgzV//nw9/PDDkqTY2FglJyfbvPbu3SuTyeSwGOLi4jRs2LAyb9+9e3ctX75cwcHBkqQlS5YoOTlZaWlpOnHihD766CO5u7vrueeeU/v27XXkyBGb7TMzM3Xu3Dmlp6fbsxtV1smTJzVw4ED16NFDe/bsKfF2U6ZM0XfffefAyAAAAAAA9qDAhErF09NTtWvXdnYYZWIymdSmTRutXr1ab7zxhr755hs99thjysrKsrZp3769Tp06pXXr1pW6/759+yoxMbEcI65Yv/zyi2bNmqUXXnhBb731Vom3++9//6tVq1Y5MDIAAAAAgL0oMKHSmTRpkrNDsNsrr7yi8PBwHTlyRAsWLLBZV6dOHbm7u5eqv3379pWpKFWZ+Pj4aOHChQoPDy/xLLXs7Gy98MILmjdvnoOjAwAAAADYgwITKp0+ffpIkhYvXqz7779fJpNJISEhWr16tSRp1KhRqlmzpnr06KFdu3YpNTVVPXv2lMlkUkREhB555BEFBwfLx8dHffr00Q8//OCU/Rg+fLgk6cMPP5QkHT9+XJGRkfLw8Ch0n6kDBw6oV69eat26tQICAmQ2m7V06VJJ0ttvv61+/frJMAxFR0crNDRU77//vmbNmqV69erJZDIpJSVFH330kcLCwjRhwgRrvx988IHatm0rs9mspk2b6umnn7a5PC8nJ0fjx49XcHCwgoOD1ahRIz355JPW9YZhKD4+XiEhIQoJCVGjRo3UqVOnIu8v5QhTp07VI488oq5du1bIeAAAAACAsnFzdgBAgY4dOyopKcn6ftiwYerRo4eCg4Pl6uqqfv36SZL69eungwcPasuWLXJ1dZUkTZgwQVu2bNHjjz+ul156SZJ06tQpde/eXWFhYUpJSZG3t3eF7k/nzp0lSfv375fFYlGjRo20cuVK7dy506ZdTk6OevbsqYEDB+rjjz+WJK1YsULffvutJGnEiBHKyMhQTEyMlixZorCwMOu22dnZiomJ0dChQ9WkSRM1bNhQ2dnZkqRVq1YpMjJSq1at0qBBg3T+/Hl16NBBjz76qFJTU+Xu7q4pU6Zow4YN2rt3r7y8vHT27Fn16NHD2v+iRYs0Z84cpaamqn79+srMzFTXrl2Vl5dX6llYpZWUlKSNGzdq3759Onv2rEPHAgAAAADYhxlMcJqpU6faPEGuqJs+N23aVK+99pqSk5P1xhtv6Pz58xozZoxWrVplLS5dz9PT0/qzr6+v4uLidOLECetsoIpUp04dSddmAZ0/f77YdgcPHtTJkyfVsWNH67KBAwfq2WefLfFYM2fO1Jo1a7R06VI1b95c0rXj27JlSw0aNEiSVLNmTQ0aNEjff/+9tm7dKknaunWrAgMD5eXlZY35nXfesfa7detWNWzYUPXr15ck1ahRQ0uWLHF4cSknJ0cvvPCCFi9erLvvvrtU22VmZtq8AAAAAACOR4EJTnPjU+T8/f2LbDd27Fh16NBBMTExeuaZZzRjxgw1aNCgRGM89NBDkqQdO3aUW9wlZRiG9WcXl+I/anXr1pUkTZ8+Xfv27ZMkubm5qX379iUeq6D/atWq6aWXXlJ6erqOHTumoKAgm3YFl+YdPHjQOvaWLVv0t7/9zXrZW6dOnWxiS0lJ0cyZM3X58mVJUocOHYos7pWnadOmqXv37tYnDpZUfHy8vL29ra+GDRs6KEIAAAAAwPUoMKHSaNGiRZHLXVxctHTpUuXn5+vs2bPq2bNnifusVauWpGuXy1W0gllL7u7u8vHxKbadr6+vpk6dqpSUFLVr106hoaH661//qry8vDKPffr0aUnStm3bbGaJzZo1S3Xr1rUWpGbMmCEvLy8NGzZM9evX18iRI3XixAlrP5MmTZK/v78mT56sunXrKjIy0lqccpSvvvpK//jHPxQfH1/qbSdOnKiLFy9aX9fvCwAAAADAcSgwodIouP9QUXJzc+Xl5aWDBw+W6nK3rKwsSXL4jJuipKamSpLatm0rN7eb3+4sJiZGycnJevnll3X69Gn94Q9/KNUlcjcqmD3Vp08fm1liaWlpysjI0CuvvCJJeuCBB3To0CEtXLhQzZo101tvvaWQkBAdPXpU0rUZT6mpqVq2bJk6duyoVatWqW3btjb3yipPV69e1e9//3stXrzY5nLHkqpevbpq1Khh8wIAAAAAOB4FJlQ6FotF8+fPt77PysrS0KFDlZiYqBYtWmj8+PHKyMgoUV8FM3lufGpbRVi/fr0k6bnnnitR+5CQEM2bN0/p6enq0qWL1q9fby30lFa9evUkST/99NMt2/r4+GjkyJFKSkrSn//8Z124cEErVqywrr/77rs1ZMgQffLJJ9qwYYNycnK0aNGiMsV1K1988YUOHDigHj16yM3Nzfoym82Srt243M3NTbGxsQ4ZHwAAAABQNhSYUOns379fycnJ1vdjxozRqFGjFBQUpLffflsXLlzQ6NGjS9RXwayoXr16OSLUYqWlpWnp0qUKDAxUdHT0TdsePnxYGzZssL738PCwPjHv0qVLkv53j6WSXjbXpEkT+fr6aufOnbp69Wqx7ebOnWvzPioqymbcG9f/9re/lY+Pj3V9eWvfvr1SUlJsZl0lJycrISHBOn5ycrJGjBjhkPEBAAAAAGVDgQmVzueff279ee3atcrOztbgwYMlSWFhYYqMjNTatWu1adOmm/aTnp6uN998U23atNHAgQMdGnOB3NxcrV+/Xj169JDZbNbmzZtv+RS07OxszZw5U2fPnpV07TKxrVu3ymw2KzAwUJLk5+cn6drxOH78uL777rub9mkymTRlyhRduHBBY8eOVU5OjnWdxWKxFqrWrl2rbdu2WdcVHNMnnnhC0rUZRdfPZvrss8904cIF9e7duySHo9Q8PT0VFBRU6HX//fdLku69914FBQVZn9AHAAAAAKgcbn5jGKAcJSUladasWdq1a5ckaejQoYVmouTl5Sk3N1cDBgzQ2LFjtXLlSoWHh+vrr79WmzZt9OWXX1oLMdHR0YqKitLrr79u3X7GjBlauHChLBaLMjMz1bt3b8XFxcnd3V2S9Oqrr1oLKosXL9aRI0cUGxsrk8lUon3Yvn273nvvPR04cMAawz33gxUWtQAAOA5JREFU3GNdf+XKFZnNZsXExGjw4MGqVq2add22bdu0bNkynTlzRpL0/PPPKyoqSkFBQfLy8lLr1q3l7e2tnJwctWzZUhs3brTG3a9fP61Zs0YrVqxQQkKCRowYIV9fX61bt8663/v27dO4ceOs47344ovy8PDQggUL5O/vL19fX9WpU0eNGzfW3Llz5enpqbCwMA0fPlwWi0UeHh6qVq2ali1bpu7du0uSunbtqtmzZ2vy5Mny9vZWfn6+4uPjNWTIkBIdr+tlZGRo8uTJOn36tNLS0iRdu/fUhx9+qPr162vGjBnWS/sAAAAAAFWLybj+WepAFZWYmKjw8HCtXLnSOtsJyMzMlLe3ty5evFhlb/htsViUkJCgiIgIa8ERFY88SEfue8jZIejAkj/e0TmoDPgsVA7kwfnIQeVAHpyPHFQOJc2Do78fcYkcbiv5+fnODgEAAAAAgDsOl8jhjhcdHa09e/aUqO31Nx+/002dOlUbN24sUduEhAT5+vo6OCIAAAAAgLNQYMIdb8mSJc4OoUqKjY1VbGyss8MAAAAAAFQCXCKHKi8hIUGjRo2SJE2fPl39+/dXVlaWk6MCAAAAAODOwQwmVHkRERGKiIhwdhgAAAAAANyxmMEEAAAAAAAAuzCDCQAA3FKzn3Y5dXyLxaIDCQlOjQEAAADFYwYTAAAAAAAA7EKBCQAAAAAAAHahwAQAAAAAAAC7UGACAAAAAACAXSgwAQAAAAAAwC4UmAAAAAAAAGAXN2cHAAAAUBJPJSdLyclOG9+YNs1pYwMAAFR2zGACAAAAAACAXSgwAQAAAAAAwC4UmAAAAAAAAGAXCkwAAAAAAACwCwUmAAAAAAAA2IUCEwAAAAAAAOxCgQkVJiUlRWPHjlXTpk1lMpnUqFEjhYaG2rwCAgLUuHFjh8UwZcoUde7cWSaTSStWrCj19tu3b9eQIUNUq1YtmUwmmc1mhYaGKjAwUH5+fmrWrJn69u2rNWvWyGKxFNr+0KFDqlevnkaPHl0Oe1N17d69Ww888ECxOcjIyNALL7ygxo0bW49t//79dejQoYoNFAAAAABQIhSYUGGCg4M1f/58Pfzww5Kk2NhYJScn27z27t0rk8nksBji4uI0bNiwMm/fvXt3LV++XMHBwZKkJUuWKDk5WWlpaTpx4oQ++ugjubu767nnnlP79u115MgRm+0zMzN17tw5paen27MbVdbJkyc1cOBA9ejRQ3v27CmyTU5Ojh5++GHt2LFDu3fvVlpampKTk/Xtt9+qS5cuysjIqOCoAQAAAAC3QoEJlYqnp6dq167t7DDKxGQyqU2bNlq9erXeeOMNffPNN3rssceUlZVlbdO+fXudOnVK69atK3X/ffv2VWJiYjlGXLF++eUXzZo1Sy+88ILeeuutYttt3rxZ33//vYYMGSJfX19JUu3atTV27FidO3dO77//fkWFDAAAAAAoIQpMqHQmTZrk7BDs9sorryg8PFxHjhzRggULbNbVqVNH7u7upepv3759ZSpKVSY+Pj5auHChwsPDbzpL7dSpU5KkevXq2Sxv0KCBJOn06dOOCxIAAAAAUCYUmFDp9OnTR5K0ePFi3X///TKZTAoJCdHq1aslSaNGjVLNmjXVo0cP7dq1S6mpqerZs6dMJpMiIiL0yCOPKDg4WD4+PurTp49++OEHp+zH8OHDJUkffvihJOn48eOKjIyUh4dHoftMHThwQL169VLr1q0VEBAgs9mspUuXSpLefvtt9evXT4ZhKDo6WqGhoXr//fc1a9Ys1atXTyaTSSkpKfroo48UFhamCRMmWPv94IMP1LZtW5nNZjVt2lRPP/20zeV5OTk5Gj9+vIKDgxUcHKxGjRrpySeftK43DEPx8fEKCQlRSEiIGjVqpE6dOhV5f6ny0qJFC0kqdCncjz/+KEkKDAx02NgAAAAAgLKhwIRKo2PHjjbvhw0bpn//+9+6++675erqqn79+kmS+vXrpzZt2mjLli166KGH1KpVK2tR5fHHH9d//vMfpaSkKDU1VWlpaQoLC9PFixcrfH86d+4sSdq/f78sFosaNWqklStXqm7dujbtcnJy1LNnT7Vu3Vr79+/Xt99+qylTpuj777+XJI0YMUIDBw6U9L97Pj3//POaMGGCRowYIUkaOnSo1q1bp4YNGyo7O1uStGrVKg0YMEAvv/yyDh8+rD179uibb77Ro48+ai0QTZkyRRs2bNDu3buVkpKiPXv26OjRo9bYFi1apDlz5mjLli365ptvdODAAWVlZSkvL89hx61Hjx567LHHtHTpUh0+fFjSteLcvHnz1KZNGz333HMOGxsAAAAAUDYUmOA0U6dOtXmCXFE3fW7atKlee+01JScn64033tD58+c1ZswYrVq1Sq6uroXae3p6Wn/29fVVXFycTpw4YZ0NVJHq1Kkj6dosoPPnzxfb7uDBgzp58qRNgW3gwIF69tlnSzzWzJkztWbNGi1dulTNmzeXdO34tmzZUoMGDZIk1axZU4MGDdL333+vrVu3SpK2bt2qwMBAeXl5WWN+5513rP1u3bpVDRs2VP369SVJNWrU0JIlS0p9iV9pmEwmrVmzRvfcc48CAgLUqFEj+fv768KFC9q4caM8PDyK3TYnJ0eZmZk2LwAAAACA41FggtPc+BQ5f3//ItuNHTtWHTp0UExMjJ555hnNmDHDej+eW3nooYckSTt27Ci3uEvKMAzrzy4uxX/UCmY0TZ8+Xfv27ZMkubm5qX379iUeq6D/atWq6aWXXlJ6erqOHTumoKAgm3YFl+YdPHjQOvaWLVv0t7/9zTqrqVOnTjaxpaSkaObMmbp8+bIkqUOHDkUW98rLuXPn1KVLF9WvX19nzpzR8ePH9eOPP6ply5bq2rWr9VK5osTHx8vb29v6atiwocPiBAAAAAD8DwUmVBoF9965kYuLi5YuXar8/HydPXtWPXv2LHGftWrVkvS/G0dXpIJZS+7u7vLx8Sm2na+vr6ZOnaqUlBS1a9dOoaGh+utf/2rXZWgFN8Letm2bzSyxWbNmqW7dutaC1IwZM+Tl5aVhw4apfv36GjlypE6cOGHtZ9KkSfL399fkyZNVt25dRUZGWotTjhIfH6+0tDT9+c9/tuavQYMGWrx4sY4dO6ZXX3212G0nTpyoixcvWl/X7wsAAAAAwHEoMKHS+Pjjj4tdl5ubKy8vLx08eLBUl7tlZWVJkkNn3BQnNTVVktS2bVu5ubndtG1MTIySk5P18ssv6/Tp0/rDH/5QqkvkblQwe6pPnz42s8TS0tKUkZGhV155RZL0wAMP6NChQ1q4cKGaNWumt956SyEhIdb7MDVu3FipqalatmyZOnbsqFWrVqlt27ZKSkoqc2y3kpiYKFdXVwUEBNgsb9y4se69914lJiYWu2316tVVo0YNmxcAAAAAwPEoMKHSsVgsmj9/vvV9VlaWhg4dqsTERLVo0ULjx48v9ISx4hTM5LnxqW0VYf369ZJU4ptSh4SEaN68eUpPT1eXLl20fv16mxtul0a9evUkST/99NMt2/r4+GjkyJFKSkrSn//8Z124cEErVqywrr/77rs1ZMgQffLJJ9qwYYNycnK0aNGiMsVVEllZWTKZTDKZTIXWubq66tdff3XY2AAAAACAsqHAhEpn//79Sk5Otr4fM2aMRo0apaCgIL399tu6cOGCRo8eXaK+CmZF9erVyxGhFistLU1Lly5VYGCgoqOjb9r28OHD2rBhg/W9h4eH9Yl5ly5dkvS/eyyV9LK5Jk2ayNfXVzt37tTVq1eLbTd37lyb91FRUTbj3rj+t7/9rXx8fKzrHaFVq1bKzc3VsWPHbJafOXNGP//8c6H7SgEAAAAAnI8CEyqdzz//3Prz2rVrlZ2drcGDB0uSwsLCFBkZqbVr12rTpk037Sc9PV1vvvmm2rRpo4EDBzo05gK5ublav369evToIbPZrM2bN+vuu+++6TbZ2dmaOXOmzp49K0m6evWqtm7dKrPZrMDAQEmSn5+fpGvH4/jx4/ruu+9u2qfJZNKUKVN04cIFjR07Vjk5OdZ1FovFWqhau3attm3bZl1XcEyfeOIJSdIXX3xhM5vps88+04ULF9S7d++SHI4yGTdunNzc3DRhwgRrcSw7O1tjx46Vi4uLJk6c6LCxAQAAAABlc/MbwwDlKCkpSbNmzdKuXbskSUOHDtWIESNs2uTl5Sk3N1cDBgzQ2LFjtXLlSoWHh+vrr79WmzZt9OWXX1oLMdHR0YqKitLrr79u3X7GjBlauHChLBaLMjMz1bt3b8XFxcnd3V2S9Oqrr1oLKosXL9aRI0cUGxtb5OVYRdm+fbvee+89HThwwBrDPffcY11/5coVmc1mxcTEaPDgwapWrZp13bZt27Rs2TKdOXNGkvT8888rKipKQUFB8vLyUuvWreXt7a2cnBy1bNlSGzdutMbdr18/rVmzRitWrFBCQoJGjBghX19frVu3zrrf+/bt07hx46zjvfjii/Lw8NCCBQvk7+8vX19f1alTR40bN9bcuXPl6empsLAwDR8+XBaLRR4eHqpWrZqWLVum7t27S5K6du2q2bNna/LkyfL29lZ+fr7i4+M1ZMiQEh2v62VkZGjy5Mk6ffq00tLSJF2799SHH36o+vXra8aMGapXr566dOmi7du3Kz4+XoGBgfLw8NAvv/yioKAgbd26Vb/5zW9KPTYAAAAAwLFMxvXPUgeqqMTERIWHh2vlypXW2U5AZmamvL29dfHixSp7w2+LxaKEhARFRERYC46oeOTB+SwWi6rNnOnUGIxp05w6fmXAZ6FyIA/ORw4qB/LgfOSgcihpHhz9/YhL5HBbyc/Pd3YIAAAAAADccbhEDne86Oho7dmzp0Rtr7/5+J1u6tSp2rhxY4naJiQkyNfX18ERAQAAAACchQIT7nhLlixxdghVUmxsrGJjY50dBgAAAACgEuASOVR5CQkJGjVqlCRp+vTp6t+/v7KyspwcFQAAAAAAdw5mMKHKi4iIUEREhLPDAAAAAADgjsUMJgAAAAAAANiFGUwAAKBKWB8aymOQAQAAKilmMAEAAAAAAMAuFJgAAAAAAABgFwpMAAAAAAAAsAsFJgAAAAAAANiFAhMAAAAAAADsQoEJAAAAAAAAdnFzdgAAAAAl8VRyspScXGHjGdOmVdhYAAAAVR0zmAAAAAAAAGAXCkwAAAAAAACwCwUmAAAAAAAA2IUCEwAAAAAAAOxCgQkAAAAAAAB2ocAEAAAAAAAAu1BgAgAAAAAAgF0oMAFOtGnTJkVHR8tkMiksLMzZ4UiSDh06pHr16mn06NEOHccwDM2ePVuPPvqoTCaTEhMTHToeAAAAAMBxKDABTtS7d28tWbLE2WHYyMzM1Llz55Senu7QcUwmk/70pz/pueeec+g4AAAAAADHo8AEwEb79u116tQprVu3rtC6Dh06OLzwBAAAAACoeigwAXe4r776Sv3799evv/5qXVanTh25u7vbtPvnP/+pr776qtzHd3HhnyEAAAAAqOr4ZgdUUosWLVKbNm0UEhKi5s2bq0uXLtqwYYN1fUpKivr37y+TyaQhQ4ZoyJAhevDBB+Xt7a2goCB99tlnNv1lZ2crLi5OwcHBCg4Olp+fn1q1aqUuXbro+eef1z333KPjx48rMjJSHh4eaty4sXXb2NhY/eEPf5AkRUREKDQ0VDt27NDIkSPl4+Mjk8lkbbtr1y717dtXJpNJUVFRNjEcPXpUzz//vPz9/RUaGqoOHTpo5syZRe7/woUL1bJlS91///1q1qyZoqKi9PPPP9t5VAEAAAAAjkCBCaiE/u///k+vvPKKFi1apG+++UaHDh3SM888o6eeekqrVq2SJAUHB+vFF1+UdG3G0fLly/Xf//5Xhw8f1rlz5zR48GCbPl988UW99tprevfdd5WSkqK0tDR5eHjI29vbeoPxRo0aaeXKlapbt67NtlOnTtVjjz0mSUpISFBycrLCw8P1l7/8Ra1bt7Zp+9BDD2nt2rWF9ikjI0OdOnXSoUOHtHfvXiUnJ+vLL7/UhAkTCrWNjY3VuHHj9NZbb+nQoUP65JNPtGnTJj377LNlO6AAAAAAAIeiwARUMqdOndLcuXP15JNPqlOnTpKu3RB73Lhx8vf314QJE5SXl2ezjYeHh/Xn++67T2FhYTp69KjOnz8vSTp79qxWrFihsLAwhYaGSpK8vLwUGRmpc+fO2cyMcpTZs2frzJkzmjt3rmrXrl1su19//dX6dLnw8HBJUpMmTfTb3/5WiYmJSk1NLXbbnJwcZWZm2rwAAAAAAI5HgQmoZLZv3668vDx17dq10Lrw8HCdPn1aBw4cuGkfd911lyRZCywZGRmSVKiwU6tWLZv1jvTvf/9bHh4eRe7X9fbs2aOsrCwFBQXZLC+4ZO/gwYPFbhsfHy9vb2/rq2HDhnbHDQAAAAC4NTdnBwDA1okTJyRJPj4+hdbVqVPH2ubGS9NupkGDBnJxcdHJkydtlv/444+SJLPZXNZwS+zo0aPy8/O75U29T58+LUl69913tWXLFuvyy5cvq27dujIMo9htJ06cqHHjxlnfZ2ZmUmQCAAAAgApAgQmoZApumF1UISU/P9+mTUnVqlVLAwYM0AcffKAdO3YoPDxcP/74o5YsWaJWrVopIiLC/sBvITc3V25ut/4np2C/X3rpJU2ePLlUY1SvXl3Vq1cvU3wAAAAAgLKjwARUMgUzbn755ZdC686ePSvp2s24S2vJkiXav3+/xo4dK4vFIk9PTz3++OOaNGlShRRl6tatq3Pnzt2yXb169SRJP/30k6NDAgAAAACUE+7BBFQy3bt3l5ubmz799FOb5YZhaPv27fLz81OrVq1K1afFYtGIESN0//33a//+/Tp48KC++uorvfnmm7rvvvtK1EfBpW033mDcy8tLknT16tWbbt+1a1edO3fupvdQkqR27dqpevXq2rp1a4niAgAAAAA4HwUmoJKpX7++xo0bp02bNunzzz+XdK24NGfOHP344496/fXXb3kfoxudP39eq1ev1oYNG9S4cWMFBAQoICBAQUFBioiI0KZNm27Zh5+fnyRp9erVOnHihH744QdJsj6V7l//+pe17aVLlwptP378eLm7u2v06NE2T3f7+eefbdp5e3trzJgxSktL08yZM20KWleuXCn5TgMAAAAAKgyXyAFOtGnTJm3YsEHStaejjR49WtHR0Zo1a5YaNmyoF198UYZhKDs7W3Xr1tX69ev15JNPSpKSkpI0bdo0SdeKPj4+PhozZoymTp2q//znP5KkUaNGad68eWrevLneffddvfDCCzp27JhNDKmpqfr3v/+tzz77TFeuXNGyZct05swZSdLzzz+vqKgoPfLIIxo+fLj+/e9/Ky4uTqtWrdKECRPUtGlT/fGPf1RKSoqioqI0Z84cNW/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      "text/plain": [
       "<Figure size 1200x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12, 10))\n",
    "imp_df = imp_df.iloc[-24:, :]\n",
    "plt.barh(imp_df[\"Features\"], imp_df[\"Importance\"], color=imp_df[\"Direction\"], zorder=2)\n",
    "plt.grid(axis=\"x\", zorder=1, which=\"major\")\n",
    "plt.legend([plt.Rectangle((0,0),1,1, color=\"teal\"), plt.Rectangle((0,0),1,1, color=\"crimson\")], [\"Positive\", \"Negative\"])\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"resources/world_importance_nn.png\", dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Infering countries from their locations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Inverse scaling the longituda and latitude values\n",
    "df[\"latitudex\"] = df[\"latitude\"]*1000\n",
    "df[\"longitudex\"] = df[\"longitude\"]*1000\n",
    "df[\"latitudex\"] = df[\"latitudex\"].apply(int)\n",
    "df[\"longitudex\"] = df[\"longitudex\"].apply(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Rounding off the latitude and longitude values to facilitate merging\n",
    "with open(\"country-codes-lat-long-alpha3.json\", \"r\") as f:\n",
    "    c_j = load(f)\n",
    "country_df = pd.DataFrame().from_dict(c_j[\"ref_country_codes\"]).loc[:, [\"latitude\", \"longitude\", \"country\"]]\n",
    "country_df[\"latitudex\"] = country_df[\"latitude\"]/90.0*1000\n",
    "country_df[\"longitudex\"] = country_df[\"longitude\"]/180.0*1000\n",
    "country_df[\"latitudex\"] = country_df[\"latitudex\"].apply(int)\n",
    "country_df[\"longitudex\"] = country_df[\"longitudex\"].apply(int)\n",
    "country_df.drop(columns=[\"latitude\", \"longitude\"], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\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>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>Trust_countrymeasure</th>\n",
       "      <th>Lon_avg</th>\n",
       "      <th>Dem_age</th>\n",
       "      <th>Dem_edu</th>\n",
       "      <th>Dem_Expat</th>\n",
       "      <th>Dem_dependents</th>\n",
       "      <th>Dem_riskgroup</th>\n",
       "      <th>Corona_concerns_1</th>\n",
       "      <th>...</th>\n",
       "      <th>Self-employed</th>\n",
       "      <th>Student</th>\n",
       "      <th>Divorced/widowed</th>\n",
       "      <th>Married/cohabiting</th>\n",
       "      <th>Single</th>\n",
       "      <th>Isolated</th>\n",
       "      <th>Isolated in medical facility of similar location</th>\n",
       "      <th>Life carries on as usual</th>\n",
       "      <th>Life carries on with minor changes</th>\n",
       "      <th>country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.622222</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.466667</td>\n",
       "      <td>-0.416931</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Denmark</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.540741</td>\n",
       "      <td>0.108333</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.733333</td>\n",
       "      <td>0.989318</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Slovakia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.540741</td>\n",
       "      <td>0.108333</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>0.619253</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.582783</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Slovakia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.511111</td>\n",
       "      <td>0.011111</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.266667</td>\n",
       "      <td>0.545240</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.321513</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.540741</td>\n",
       "      <td>0.108333</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.466667</td>\n",
       "      <td>1.581423</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Slovakia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173009</th>\n",
       "      <td>0.622222</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.466667</td>\n",
       "      <td>-0.786996</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Denmark</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173010</th>\n",
       "      <td>0.622222</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.466667</td>\n",
       "      <td>-0.416931</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Denmark</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173011</th>\n",
       "      <td>0.622222</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.266667</td>\n",
       "      <td>1.359384</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.321513</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Denmark</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173012</th>\n",
       "      <td>0.622222</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>1.137345</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Denmark</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173013</th>\n",
       "      <td>0.622222</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.866667</td>\n",
       "      <td>1.359384</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Denmark</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>173014 rows × 55 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        latitude  longitude  Trust_countrymeasure   Lon_avg   Dem_age  \\\n",
       "0       0.622222   0.055556                   0.5  0.466667 -0.416931   \n",
       "1       0.540741   0.108333                   0.5  0.733333  0.989318   \n",
       "2       0.540741   0.108333                   0.5  0.533333  0.619253   \n",
       "3       0.511111   0.011111                   0.2  0.266667  0.545240   \n",
       "4       0.540741   0.108333                   0.8  0.466667  1.581423   \n",
       "...          ...        ...                   ...       ...       ...   \n",
       "173009  0.622222   0.055556                   0.6  0.466667 -0.786996   \n",
       "173010  0.622222   0.055556                   0.4  0.466667 -0.416931   \n",
       "173011  0.622222   0.055556                   0.4  0.266667  1.359384   \n",
       "173012  0.622222   0.055556                   0.8  0.333333  1.137345   \n",
       "173013  0.622222   0.055556                   1.0  0.866667  1.359384   \n",
       "\n",
       "         Dem_edu  Dem_Expat  Dem_dependents  Dem_riskgroup  Corona_concerns_1  \\\n",
       "0       0.833333        1.0        0.000000            1.0           0.333333   \n",
       "1       0.833333        0.0        0.000000            1.0           0.666667   \n",
       "2       0.666667        0.0        0.582783            1.0           0.666667   \n",
       "3       0.833333        0.0        0.321513            1.0           0.333333   \n",
       "4       1.000000        0.0        0.000000            1.0           0.666667   \n",
       "...          ...        ...             ...            ...                ...   \n",
       "173009  0.833333        1.0        0.000000            0.5           0.166667   \n",
       "173010  0.666667        1.0        0.000000            0.5           0.666667   \n",
       "173011  0.500000        1.0        0.321513            1.0           0.500000   \n",
       "173012  0.666667        1.0        0.000000            0.5           0.500000   \n",
       "173013  0.500000        1.0        0.000000            1.0           0.833333   \n",
       "\n",
       "        ...  Self-employed  Student  Divorced/widowed  Married/cohabiting  \\\n",
       "0       ...            0.0      0.0               0.0                 1.0   \n",
       "1       ...            0.0      0.0               1.0                 0.0   \n",
       "2       ...            1.0      0.0               1.0                 0.0   \n",
       "3       ...            0.0      0.0               0.0                 0.0   \n",
       "4       ...            1.0      0.0               1.0                 0.0   \n",
       "...     ...            ...      ...               ...                 ...   \n",
       "173009  ...            0.0      0.0               0.0                 0.0   \n",
       "173010  ...            0.0      0.0               0.0                 0.0   \n",
       "173011  ...            0.0      0.0               0.0                 0.0   \n",
       "173012  ...            0.0      0.0               0.0                 1.0   \n",
       "173013  ...            0.0      0.0               0.0                 0.0   \n",
       "\n",
       "        Single  Isolated  Isolated in medical facility of similar location  \\\n",
       "0          0.0       0.0                                               0.0   \n",
       "1          0.0       0.0                                               0.0   \n",
       "2          0.0       0.0                                               0.0   \n",
       "3          1.0       0.0                                               0.0   \n",
       "4          0.0       0.0                                               0.0   \n",
       "...        ...       ...                                               ...   \n",
       "173009     1.0       1.0                                               0.0   \n",
       "173010     1.0       0.0                                               0.0   \n",
       "173011     1.0       0.0                                               0.0   \n",
       "173012     0.0       0.0                                               0.0   \n",
       "173013     1.0       0.0                                               0.0   \n",
       "\n",
       "        Life carries on as usual  Life carries on with minor changes   country  \n",
       "0                            0.0                                 1.0   Denmark  \n",
       "1                            0.0                                 1.0  Slovakia  \n",
       "2                            0.0                                 1.0  Slovakia  \n",
       "3                            0.0                                 1.0    France  \n",
       "4                            0.0                                 1.0  Slovakia  \n",
       "...                          ...                                 ...       ...  \n",
       "173009                       0.0                                 0.0   Denmark  \n",
       "173010                       0.0                                 1.0   Denmark  \n",
       "173011                       0.0                                 1.0   Denmark  \n",
       "173012                       0.0                                 1.0   Denmark  \n",
       "173013                       0.0                                 1.0   Denmark  \n",
       "\n",
       "[173014 rows x 55 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merging the reference table to get countries from their locations\n",
    "df=df.merge(country_df, how=\"left\", on=[\"latitudex\", \"longitudex\"])\n",
    "df.drop(columns=[\"latitudex\", \"longitudex\"], inplace=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Generating the SHAP feature importance values for each country"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 159/159 [04:29<00:00,  1.70s/it]\n"
     ]
    }
   ],
   "source": [
    "for country in tqdm(df[\"country\"].unique()):\n",
    "    coun_df = df[df[\"country\"]==country].drop(columns=[\"country\"])\n",
    "    coun = torch.from_numpy(coun_df.to_numpy()).type(torch.FloatTensor).to(DEVICE)\n",
    "    f_imp_coun = svs.attribute(coun, target=0).mean(axis=0).cpu()\n",
    "\n",
    "    imp_df_coun = pd.DataFrame().from_dict({\n",
    "        \"Features\": features,\n",
    "        \"Importance\": f_imp_coun,\n",
    "        \"Absolute Importance\": torch.abs(f_imp_coun),\n",
    "        \"Direction\": f_imp_coun/torch.abs(f_imp_coun)\n",
    "    })\n",
    "    imp_df_coun.sort_values(\"Absolute Importance\", inplace=True)\n",
    "    imp_df_coun[\"Direction\"] = imp_df_coun[\"Direction\"].apply(lambda x: \"teal\" if x==1 else \"crimson\")\n",
    "    fig = plt.figure(figsize=(10, 12))\n",
    "    \n",
    "    imp_df_coun = imp_df_coun.iloc[-24:, :]\n",
    "    plt.barh(imp_df_coun[\"Features\"], imp_df_coun[\"Importance\"], color=imp_df_coun[\"Direction\"], zorder=2)\n",
    "    plt.grid(axis=\"x\", zorder=1, which=\"major\")\n",
    "    plt.legend([plt.Rectangle((0,0),1,1, color=\"teal\"), plt.Rectangle((0,0),1,1, color=\"crimson\")], [\"Positive\", \"Negative\"])\n",
    "    plt.tight_layout()\n",
    "    plt.savefig(f\"resources/countries/{country}.png\", dpi=300)\n",
    "    plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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