Data_Preprocessing_and_cleaning.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyPZoGHWadDq9+/myiWCoLFW",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/MansiGit/Amazon-Gourmet-Groceries-Recommendar-System/blob/main/Data_Preprocessing_and_cleaning.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-nS_gMBXJHjD",
        "outputId": "7297b387-c678-4366-9448-f91bca89a0ae"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!ls drive/MyDrive/MDM_Project_Team12/data\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "miydL18oJO7Q",
        "outputId": "bbf5ae16-850f-4c6b-bc36-0821006f688b"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Grocery_and_Gourmet_Food_5.json\n",
            "meta_Grocery_and_Gourmet_Food.json\n",
            "meta_Grocery_and_Gourmet_Food.json.to_pickle\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import json\n",
        "import pandas as pd\n",
        "import missingno as msno"
      ],
      "metadata": {
        "id": "LKckOWxyJdhQ"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Loading Data and cleaning Reviews dataset\n",
        "\n"
      ],
      "metadata": {
        "id": "1taYX1o8JkZs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_json('/content/drive/MyDrive/MDM_Project_Team12/data/Grocery_and_Gourmet_Food_5.json',lines=True)"
      ],
      "metadata": {
        "id": "VFELHKU3JkGd"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Shape of the dataset\n",
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "buuPAfMBJeVv",
        "outputId": "58fcb481-b64b-4361-dbcb-aab5d334441d"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1143860, 12)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#How many dimensions\n",
        "df.ndim"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NwHJLtMWJoLU",
        "outputId": "50c5edd9-1295-4b20-8cf9-18a135d4be3d"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# describe the dataset\n",
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q7VDOsSWJqmD",
        "outputId": "9ddee69b-ce1c-4f67-bc46-45da69b68241"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1143860 entries, 0 to 1143859\n",
            "Data columns (total 12 columns):\n",
            " #   Column          Non-Null Count    Dtype \n",
            "---  ------          --------------    ----- \n",
            " 0   overall         1143860 non-null  int64 \n",
            " 1   verified        1143860 non-null  bool  \n",
            " 2   reviewTime      1143860 non-null  object\n",
            " 3   reviewerID      1143860 non-null  object\n",
            " 4   asin            1143860 non-null  object\n",
            " 5   reviewerName    1143722 non-null  object\n",
            " 6   reviewText      1143470 non-null  object\n",
            " 7   summary         1143641 non-null  object\n",
            " 8   unixReviewTime  1143860 non-null  int64 \n",
            " 9   vote            158202 non-null   object\n",
            " 10  style           592086 non-null   object\n",
            " 11  image           9510 non-null     object\n",
            "dtypes: bool(1), int64(2), object(9)\n",
            "memory usage: 97.1+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#finding how many missing values\n",
        "missing = df.isna().sum()\n",
        "msno.bar(df, color=\"dodgerblue\", sort=\"ascending\", figsize=(10,5), fontsize=12)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 632
        },
        "id": "E4ruk3OAJtS7",
        "outputId": "0020af32-d923-4a6b-e31b-8bdedbfd699a"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 3 Axes>"
            ],
            "image/png": 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2249dPyIiQr28vMwZnPfv368NGzZUDw8P/fvvv5Oiy4lu5syZmjlzZg0ICFAXFxf18vLScePGmcsvX76sBQoUUMMwtHTp0vFO/Z4yZYr6+/vr9u3bk7rricLRx69KDRx9/KrUQJUaJCWCJFK05cuXq5ubm7766qs6efJk/fzzz7VMmTLq4uKi3bt3N2dqjPvl+MKFC+rm5qaTJ0/W0NBQbdCggfr4+KTISUUWLVqkHh4e+tprr+mCBQt0/vz5mjNnTm3ZsqW5zv379/Wnn37SLFmyqGEY+tprr+nt27c1JiZG//zzT33xxRf1xRdftDulL6W5ffu2FilSRF988UV1c3PTbt26aVhYWILrxn0ulC1bVosWLZqiT2E8fPiw5s+fX9944414py4/rHnz5tqoUSNt3ry5+vn56Y8//qiqD0LVyy+/rIcOHUqKLic6221eBgwYoAcPHtSSJUtqgQIFzGs8Hw7HMTExeufOHU2XLp2OHDlSQ0NDtX79+urj45Nib/GxdOlS9fT01F69eun69ev1ypUr2rBhQ82fP7/GxMSYp31fvnxZS5cubc5Iu2HDBj137pz+8ccfWr16dS1UqJC5Ey4lcfTxq1IDRx+/KjVQpQZJjSCJFMl2DeDrr7+uZcuWjXc/n9dee03d3Nz0pZdesjvKGB0drVevXlVPT0/t3r27Nm7cOEXeHy42NlYvX76slSpV0nbt2umpU6fMZS+//LK2atUq3jbbt28398Blz55dS5Ysqbly5VJ/f/8UewQmrkqVKmnjxo115MiR6uzsrD169HhkmLQdfbTdImTMmDFJ2dVEtXHjRvXz89Ply5ebbR999JG2bt1a69atq5MmTdIjR46o6j/XhqZLl07nz59vztiqmnInGpo4caI6Ozvr22+/rWfOnFHVf2ZcjXvrm7iio6M1PDxcM2bMqM2bN9cGDRqkyPcB1Qd/t7t372rLli21QYMGevLkSXPZF198oZUqVTJ/tj3vr169qp06dVI/Pz91c3NTf39/DQoK0qCgoBQ3Q62jj1+VGjj6+FWpgSo1SC4uyX1qLfAsbNcAhoaGiouLi2TPnl1ERKKjo8XFxUW+++47CQgIkHHjxom3t7eMGjVKMmfOLM7OzuLv7y8ZMmSQiRMnipeXl2zcuFGKFSuWzCOyxjAMiYiIkL1790qjRo0kW7Zs5jI/Pz+5ePGidOnSRdzc3KRr164SEhIiL7zwgmzevFkmT54smzdvlmvXrknZsmWlT58+kjdv3mQczb8TGxsrTk5O0qZNG1m+fLl06NBBwsLC5NNPPxURkZEjR4qvr6+5vqqa182VL19eihUrJg0aNEiWvieGgwcPSkxMjNSvX19ERGrVqiWbNm2SHDlySEREhLzxxhtSqVIlGTt2rLz00ksyYcIEyZgxo9SsWVPc3d3NxzEMI7mG8MwOHz4svXr1km7dusk777wjGTNmFBGR4sWLS/HixWX8+PFSt25dyZcvn912zs7O4uzsLFmyZJFFixaJj49PinwfEHnwdzMMQ3bt2iUVKlSQHDlymMsuXrwoFy9elAoVKoiISLNmzaRp06aSJ08e+d///ifdunWTX3/9Vc6fPy+5cuWSVq1aSc6cOZNpJM/G0ccvQg0cffwi1ECEGiSb5E6ywLOKiorSjh07as6cOTU0NNTcwxR3lsZevXqpYRj66aefalRUlEZHR+u9e/c0e/bsahiGeVP6lOjEiRPq6uqq7733nsbExOj9+/d14sSJ5mkaZcqU0eDgYPXz89Nx48bpjRs34j1GSj0KlZDVq1erp6enHj9+XK9fv67vv/++Ojk5ac+ePfXSpUvatm1b3bx5s7m+bey2W0WkVBs3blQfHx9dtGiRTpgwQdOnT6+///67ea+roUOHamBgoJYrV848cv+0s9v+14WFhemmTZv0woUL8ZbZroG13dom7jWwsbGxGhERoVWrVlUfH58Uf4uPGzduaL58+bRixYq6c+dOvXTpkv7vf/9TwzC0du3a2rVrV61Vq5YahqEtWrQwT/lPLRx9/KrUwNHHr0oNVKlBciBIplCpKQBYYRu37f9r165VZ2dnHTFihN06ccNkkyZNNDg4WK9du2a2XblyRY8ePZpEvU48tnFHR0drbGysvvXWW2oYhhYrVkyrVKmihmFo//79zWvDjh8/rvXr19eAgADdunWrqqaeEPGwmzdvarFixczr/k6fPq0ffPCBOjk5aZ48edTV1VV/+umneNul9NfSmTNnNGPGjNqhQwd9++23tUqVKvFuZTNy5Eh1cnLSUaNGqWrKH3NcD0+SZBvbrVu3tFSpUpovX754EynY1rl+/Xq80+JTGttYFi5cqG5uburj46P58+dXwzC0Z8+edtf49O3bVw3DMG/GHfe9IKU+Jxx9/KrUwNHHr0oNVKlBciFIpgB79uzR33//XadPn66nT5/WO3fuqGrquffdvxEWFqZdu3a1O/Kgah8mN23apO7u7uY9A1PyxCoPO3TokH722WdapkwZffHFF7VAgQJ68OBBc3lsbKzu2bNH/fz8tG3btsnY08QT97o+VfvXQd26dbVRo0bmzydOnNB8+fKpk5OT1q1bVy9fvpxk/XxeHh6/quqkSZPUxcVFnZyctEaNGmZ7RESE+e9KlSpp+fLlk6SPz9vjngM2sbGxOnToUDUMQydOnGi22aT0+2Um9DxYs2aNtm3bVjt27KglSpTQLVu2qKp9fcqVK6fFixdP8Z8fjj5+VWrg6ONXpQaq1CC5EST/46ZPn65BQUHq7e2thmFoUFCQduzY0Zyd0VFeAH/++ad+99132rVrV501a5aePXvWXLZjxw6tWrWqOjs765w5c8x225fGsLAwDQgI0MGDByd5vxPLypUrdeDAgdqiRQsdO3asbtq0yW753bt39eDBg9qtWzezzRYi7t69qzly5NCmTZsmaZ8T28qVK7V79+5auHBhbdeund3f2rY38ZtvvtFSpUppZGSk3rt3T5s2baq+vr7avHlzdXd3106dOj1yAp7/useN//Tp09qjRw/18PBQV1dXu2U2devW1TJlyiRllxPd42oQl+21f/fuXc2VK5fWrFkz3rKU6uEa2I7A29gmIuvSpYs5eVDc0FytWjUtVKhQig3Sjj5+VWrg6ONXpQaq1OC/giD5H7Z27Vr19vbWvn376m+//aZHjhzRTp06aWBgoAYHB+uuXbtUNfWHyZkzZ2q2bNk0KChIfX191TAMbd68ud0Mi2vWrNGKFSuqYRj6v//9z+40hW3btmn27NnNGRxT2hfJGTNmqJeXl+bNm1eLFi2qbm5umi1bNn3nnXfs1tu3b58ahhHvy/X69es1ODhY3333XVVNeeNXfbBDJSAgQEuUKKENGjRQX19fzZEjh65atcpuvR07dqiHh4euWLFCW7durX5+frpo0SINDQ3V/v37a0BAQILX0/3XPc34d+3apa+++qq6uLhouXLldPHixeayPXv2aLFixbRTp07madEpzdM+B2xsXw5GjRqlhmHoDz/8kJTdfS6epgaxsbF67do1TZs2rQ4cONBu+x07dmi+fPm0ffv2KfIUd0cfvyo1cPTxq1IDVWrwX0KQ/A/74osvNHPmzPFuzTB9+nTNnz+/+vr6mte9pdYwuXTpUk2TJo12795dN2/erKGhoTpu3Dhzkpm4Nm3apM2aNVPDMLRJkyb6zTff6IQJE7RmzZoaFBT0xHvs/RcdOnRIM2XKpD179jSv6dy2bZs2btxYnZ2d7e4Xef36da1evbpmzJhR582bpzdv3tTly5drvXr1NCgoSI8fP55cw/hXfvrpJ/X29tZevXqZk6Ls3LlTDcPQzz77TFX/CcfXrl3TMmXKqK+vr6ZPn17nzp1rHpm9ePFiijy19WnGb/P333/rgAED1NXVVdOmTatt2rTRN998U0uVKqX+/v4p9j6RVp4DD78X/vXXX5ohQwatU6dOvGslUxIrNVBVHThwoBqGoe+++66uXbtWp02bpjVr1tSAgIAU+Txw9PGrUgNHH78qNVClBv81BMn/sO7du2vGjBnNn+OeB758+XItXLiwpkuXznwhpLYwefbsWa1evbq+9NJLdjNrxcTE6Kuvvqq+vr7xJsoIDQ3Vb7/9VjNnzqze3t4aGBioJUuWTLH3A9q2bZt6enrqihUr7NrPnDmjgwcPVmdnZ7tTVmfNmqUhISFqGIZ6eHhounTpNH/+/Cl2/CdOnNAXX3xRW7RoYReEjx8/riEhIfr+++/rrVu3zBlKVVWHDRumBQoU0Pnz56f4GVmfdvxxJxG4deuW/vbbb1qhQgUNDAzULFmyaM2aNXXfvn3JMYR/7WlrEHcyrYdPVerbt68GBgbq9evXk6zfielZarB7927t2LGjurq6mvcOLVmyZIq8Z6yjj1+VGjj6+FWpgSo1+C8iSP6HzZ07V93c3OzO+477BWnhwoWaPXt2rVChQoo80vIk69evj3dKmm0v0/fff69ubm66e/duu3aby5cv699//6379+9PsV8eVR+c3mwYhq5fv15VH1wLaBvrlStX9KOPPlLDMLRz585224wePVo7deqk//vf//TUqVPJ0vfE8Pfff2ubNm3sTtNUVR03bpwahqEhISFao0YNLVq0qL799tvm8oMHD+q9e/eSuLeJz8r4+/XrZ7fOvXv39OrVq3r58uV4s7imJFZqEPd075iYGPO1snfv3hQ9O+uzPg/Onj2rK1eu1KFDh+qSJUv0/PnzSdzzxOHo41elBo4+flVqoEoN/osIkv9hBw4c0IwZM2rt2rXtZuKMGyY/+ugj9fb2NsNmajgqGTcUDhs2zAyCsbGx5vi2bdumHh4eunDhQlVN+TMwPsrp06c1Z86c2qJFC/OoU2xsrFmjixcvardu3TRNmjSp4hqwhMR97quqTp48WZ2cnLRbt266ZMkS3bBhgzZv3lwNw9CuXbsmUy+fHyvj7969ezL18vmiBtZqEHfSrdTC0cevSg0cffyq1ECVGvzXECT/42bOnKmGYeibb75p3htQ9Z9ZKmNjYzVfvnzaqlWr5Oric/WoSUEOHjyorq6uOmPGDLv2lDojp+qjx9qrVy91c3PT7777zjxVM26YPHLkiGbJkkVbtGjx2MdJKWz9f/gCeNtOhA4dOuj777+vN27cMJedOnVKa9eurWnSpEnxN5f/t+M/cOBAkvX1eaEGvA4cffyq1MDRx69KDVSpwX8dQfI/YOfOnY+9hs12L7Q+ffroyZMnzXbbi6tKlSpap06d593N52779u06YcIE7dq1q65evfqxR1dPnDihnp6e5v3hVB8cwe3Tp4/OmzcvKbqb6O7du6fXrl3Tmzdvxru2r2rVqhoUFKSLFy82r5WNjY0174n5ySefqJeXl91tUVKi77//Xl966SW7MT4sNjbWrj62GixevFgNw9B169YlTWefA0cfvyo1UKUGjj5+VWrg6ONXpQaq1CAlIEgmszlz5qhhGNquXbt4N5K3CQsL08GDB6thGNq6dWvzejlV1f3792vhwoW1R48edkepUpqZM2dq5syZNTAwUN3c3NTV1VVnzZqlqgm/cdy4cUP9/Pz0888/V9UH583XrVtXvb299fDhw0na98SwdOlSrV27tqZNm1aDg4O1ffv2djsNjhw5oqVKldLg4GD98ccf9datW3bbDxw4UDNnzpyij8ieP39eDcMwZ921zbYa9+//8HMh7s6GDz74QNOmTZsiZ+dVZfyq1ECVGjj6+FWpgaOPX5UaqFKDlIIgmYz+/PNPzZ07t/r7+6u7u7u2bt3abiriuC+QyMhI/frrr9XFxUWzZMmiXbp00b59+2qlSpXsZm5NiZYtW6Y+Pj7avXt33bBhg+7atUsbNmyohQoV0rt37yYYJG/duqXp06fXYcOGaWhoqNavX1/TpEljTr6TksyaNUt9fX21bt26OmDAAG3evLmmSZNGa9eurRcvXjTX27dvn5YvX17TpEmjAwcONGey3bNnj9asWVOrVKmSooPkjRs3tEiRIlqtWjXNkCGD1q5dO8EPDpu4Hxh//vmnVqhQQWvVqhUvZKcUjj5+VWqgSg0cffyq1MDRx69KDVSpQUpBkEwmd+7c0ddff10Nw9CZM2fqd999p87OztqmTRu7UPjw6Z1//PGHdurUSbNmzap58uTR2rVrp9hp/WNjY/XevXvaqFEjbdiwod2sioMGDdIqVaqo6j+nKdjExMTozZs3NX369Nq+fXtt0aKFent76549e5Ky+4li69atmj17dn3zzTftxt+vXz91d3ePNzPZjRs3tGXLlurp6al+fn5avHhxzZkzpwYEBKSKqaw7dOigFStW1E8++UTd3Ny0bt265gdH3NdC3H//9ttvWqtWLU2XLl2KvzbO0cevSg1UqYGjj1+VGjj6+FWpgSo1SAkIksnoo48+0ldeecX8edy4cers7PzII5Nxb7h97949vXv3boq/T15YWJgGBQVpz5497doHDhyopUqV0qpVq2rVqlV1ypQpdtM1x8TEaP78+dUwDE2bNm2KPBIZERGhgwYN0uzZs+uWLVtU9Z+/8d27dzVr1qz6+uuv27XbLFiwQAcPHqwtW7bUwYMH65EjR5K284nM9iEwfvx4rV69ul67dk0//fRT84PDdn1E3J0KYWFh2rBhQy1SpIiGhISk2HtlqjJ+VWqgSg0cffyq1MDRx69KDVSpQUpCkEwGtltVxMbGxvv3119/nWCYjCs13OLDJjo6WgsUKKA1atTQy5cv64ULF3TixIlqGIbWrFlTW7ZsqdWrVzdv7RB3Mpm6deuqYRgpdo/T3bt3tUOHDtq3b1+7dtuR2rJly2qtWrXslqXW25zYHDt2TH18fHTr1q168+ZNHTlypLq7u2u9evX05s2b2qlTJ50+fbq5fu/evbVPnz6p5hoIRx+/KjVQpQaOPn5VauDo41elBqrUICUgSCahzZs3P/EatujoaB0/fry6uLho69at7aYtPnToULzpj1My21gmTZqkvr6+6u3trQUKFFDDMPTtt9/WS5cuqeqDwDVs2DA1DEO/++47c/vr16/rmTNnkqXvz+rhCZGOHz+uR48eVdX4OwjatWunRYoU0ejo6Hh/99QYKGNiYvTGjRtarFgx/fbbb1VV9fLlyzp69Gh1d3fXoKAgTZs2rf7yyy92R+Jtp7mkdI4+flVqoEoNHH38qtTA0cevSg1UqUFKQZBMIpMnT1ZfX19dsGDBE9eNGybbtGmjhw8f1tWrV2tISIj269cvCXr7/MyYMUPHjh1r13bz5k397bfftEOHDtqhQwfNnz+/bt261VweGRmpV65c0WLFimmRIkX07t27qeqo7KPG8vrrr2tISIjdqRuHDh2yu5YypXrcDpHu3btrxYoVzVNXzp07p4UKFVJnZ2ctXry4eeF8Sg7Tjj5+VWqgSg0cffyq1MDRx69KDVSpQUrmJHjupk6dKq+99pp07txZKlasaLaraoLrOzs7y+uvvy5ff/21zJ8/X958803p1auXnDt3Ttq1a5dU3U50U6dOlY4dO8qlS5fk/v37ZnvatGmldu3aMn36dBk7dqyULVtW8uXLJyIi4eHh4urqKunTpxcfHx/x9PQULy8vcXJKeU/dP/74Q4YOHSpdu3aVTz/9VE6ePClRUVHi5OQkMTEx5nqxsbEiIuLm5iYxMTHi6uoqIiIHDx6U119/XcqXLy8RERGPfP78l23dulVERFxcXCQ6OtpumW08JUuWlIsXL4qLi4tERkZKz5495ezZs9K6dWs5duyY1KtXTyIiIsTZ2TnJ+/9vOfr4RaiBCDVw9PGLUANHH78INRChBqlCMgVYhzFlyhQ1DEP79Omjp0+ffqpt4k6u06dPHzUMQ/38/FL0rJy2Orz99tt21znajsbZ9iTZ7g/58OQ7W7du1Tx58minTp00IiIixd0vc/r06erj46PZs2fX7Nmzq5OTk2bPnl0HDx5snu788N40270hb968qfv379cGDRqon5+f7ty5MzmG8K/Nnz9fDcPQ+vXrm20J7YU8f/68BgUF6fTp07VZs2aaNm1a/fnnn/X8+fM6dOhQzZgxo546dSopu54oHH38qtRAlRo4+vhVqYGjj1+VGqhSg9SCIPkcLVq0SA3D0LfeesvuPjY//vijjh49Wt955x3dunWr3rhxI8HtV61apaVKlVI/P78UO6GM6oMQZRiG9uvXT0+ePGm2R0dHxxt7eHi49ujRQz09PbVPnz56+PBhnT59utarV08DAgL08OHDSdv5RLB582ZNly6ddu/eXffv369hYWG6atUqc9bZZs2amXWIGyY/+eQT9fPz01WrVmmTJk1S7C1OVFW3b9+uOXLk0ICAADUMQxs2bGgue/iD48aNG1q8eHE1DEMzZcqk8+bN03v37pnLrl69mqR9TwyOPn5VaqBKDRx9/KrUwNHHr0oNVKlBakKQfE6io6O1ffv26uTkpC+99JLZ3qRJE3V1dVVPT091cXFRNzc3feONN/TgwYN221+8eFGrVKmiXl5eunfv3qTufqLZtGmTGoahpUuXtrsYetCgQVq5cmXNlCmTNm/eXGfNmqV3795VVdU9e/ZovXr11MnJSQ3DUC8vLy1evHiKPSL7xRdfaKZMmeLdomTTpk0aHByshmGYM5Cp/vMmOnr0aPX19dXixYurt7d3irzFiarq7du39a233lLDMPTbb7/VDz74QA3D0EaNGpnrPPzBsXz5cs2TJ4/Onj3bfN6ktKPQNo4+flVqoEoNHH38qtTA0cevSg1UqUFqQ5B8jm7duqWvv/66pk2bVjt27KhNmjTRDBky6FdffaX79+/X7du3a69evdQwDH311Vf14sWLdtsvWLBA9+3bl0y9Txzbtm3T2rVrq5ubm3711VeqqtqgQQN1d3fXihUraqNGjTRjxozq4eGh/fr109u3b6uqamhoqP7yyy86YsQIXbp0qZ47dy45h/FMbG9yPXv21KCgIHMWWlv7pUuXNEeOHBoUFGSe9mu7mFz1nyO5adOmTdE7E27duqV9+vQxJ4q6ceOGvvvuu4/84LCd7nz58mVz50JK5ujjV6UGqtTA0cevSg0cffyq1ECVGqQ2BMnnxPYCCAsL086dO6uXl5dmzZpVf/75Z7uwoKr67rvvqpOTk/7yyy+qmvpmntq9e7c2bNhQDcPQfPnyaebMmXXx4sXmG8Lt27e1Ro0a6uHhod98802qGH/cv/H333+vhmHovHnz7Na5d++eVq1aVefMmaOlS5fWwMBAu1NX//77b23QoEG8o9Up0blz5+xqcuXKFX3vvffifXCk1mm7HX38qtRAlRo4+vhVqYGjj1+VGqhSg9SEIJmIIiMjzSNqcYWFhekrr7yiXbp00evXr5vttr0sJ0+eVF9fX23Xrp2qpp7D9XED4a5du7Rx48aaLVs2nTBhgvnmYPt/eHi45siRQ6tUqZLixz9p0iTt3bu3OYnOhQsXtHLlyurn56c//fSTnj9/Xq9fv25OpHTr1i09evSoenh46JAhQ1T1n+fAwzsdUpqEbm1iG9ujPjhiYmL0wIEDqWLPo6OPX5UaqFIDRx+/KjVw9PGrUgNVapAaESQTyU8//aSNGzfWnDlzao0aNXTAgAHmxcCqD8Lkw7NKxZ2dNWPGjNqpU6ck7fPzsHbtWl2xYkW8UxJUH8y82q9fPz1z5ozdNrbz3fv27avu7u66b9++FBsmp06dqoZh6Jtvvmk3wdIvv/yiFStWVBcXF82aNatmyZJFvby8dNq0aar64F6aRYoU0fbt26uq/XMjpXn4OZDQGBL64GjcuLGqPrh2tGLFitq9e3fGnwLHr0oNVKmBo49flRo4+vhVqYEqNUjtCJKJYNasWerm5qbly5fXzp07a4kSJdQwDC1atKiuXLnSPDKlGj8gxMbG6tKlSzUgIEC/+OILu2UpzcKFC9UwDC1RooSuWrXKPCIZN0zeuXPHbpu4Y+3QoYPmypXLrl4pie0WJ/369TNv9RJ3fMeOHdPRo0dr27Zt9dVXX9U///zTbnmJEiW0RYsWSd7vxPSo50BC4l4rOnjwYDUMQ1988UUtU6aMent7p8jJlRx9/KrUQJUaOPr4VamBo49flRqoUgNHQJD8l06cOKG5cuXSzp0764kTJ1RV9fr16zpgwAA1DEMLFCigM2bMsDvlNW6wsk1GkyNHjhR9H5y//vpLixYtqtmzZ9egoCAtVKiQrly5MsEwafPw0cqCBQtqy5Yt44XNlGDq1Knq5OQU7z6Zly9f1oMHD9odnUxoR8GGDRs0R44cOmbMmCTp7/PwpOdAQmzLrl27pu3bt1fDMNTf3z9FTi7k6ONXpQaq1MDRx69KDRx9/KrUQJUaOAqC5L+0Y8cOdXNz08WLF6vqP+Hozp07Wq5cOTUMQ3PkyKFLlixR1X9CRHR0tA4YMEArV66sgYGBKfpFEhkZae49mjJliu7evVuzZcumhQsXfuKbhuqD0x5q1aql/v7+eujQoSTqdeI5ceKEOjk5aaZMmXTjxo1me7du3TRXrlxqGIZmy5ZN27Ztq5GRkapqfwH5unXrtEGDBpo5c2ZzZ0RK82+fA+vXr9fixYun2HumOvr4VamBKjVw9PGrUgNHH78qNVClBo6EIPkv/fHHH2oYhhkUY2JizLDQq1cvLVeunBYqVEjz5ctnTrQTHh6ua9as0Zw5c2rVqlVTxYvkl19+0R49epg/b9iwQbNly/bYPVD379/XRo0aaY4cOTRnzpz6119/JWWXE01ERIR+9dVX6u3trZ06ddJz585pvXr11MfHR1u3bq2fffaZ1q5dW52dnbV06dLmtbOxsbE6dOhQLVmypGbNmjVF70xQfbbngOqDo7Z16tRRwzBS7HNAlfGrUgNVauDo41elBo4+flVqoEoNHAVB8l+6evWqZsqUSatXrx7v/O1atWpphw4ddOLEiWoYhr711lvmssjISD18+LBevXo1qbv83NjeFGxHXTdt2vTEN42RI0dq586d9dixY0na18QWGRmp48aNU1dXVw0MDNTcuXPrihUrzNN0b926pV9++aV6eHiYF5Crqq5Zs0Y//fTTFHsk8mHP8hyIiIjQn3/+OVXsUHH08atSA1Vq4OjjV6UGjj5+VWqgSg0cAUEyEdiOStaoUUOXLFmiGzZs0B49eqiLi4u5N6VcuXJatmzZBK8VTM3ivmn8/vvvZvu2bdv0/Pnzqpryb3FhExUVpd98841myJBBhwwZYo7L9kZ5+/Ztbdu2rfr4+NjdGzI13DfzcZ7mOZCaOfr4VamBKjVw9PGrUgNHH78qNVClBqkNQTKRrF+/XgMCAtQwDHVzc1MfHx+dPn26GSa+/PJL9fPz09OnTztsmCxcuLCuXr1af//9d82VK5dWrVrVPA04tQgPD9cFCxbEO8Jqm/b6119/VcMwdOXKlcnRvWST0HMgd+7cqfI5kBBHH78qNVClBo4+flVq4OjjV6UGqtQgNTFUVQWJ4ty5c7J9+3a5deuWVK9eXbJly2Yu69atm6xdu1b2798vrq6uydjL5LF9+3Zp2bKluLu7S0xMjFy/fl1Wr14tJUuWTO6uJbrY2FhxcnISVRXDMMz/i4gMHTpURo8eLXv37pXcuXMnc0+TliM9BxLi6OMXoQYi1MDRxy9CDRx9/CLUQIQapBrJm2Mdw65du7RcuXLavn17DQ8PT7H3iXwWccc6btw4NQxD06VL5zD3A4p79HnXrl1asWJFrVy5st64cSP5OpXEHP054OjjV6UGqtTA0cevSg0cffyq1ECVGqQ2LskdZFMjjXMEatq0aTJ//nw5evSoTJkyRdzd3ZO5d0nLVodVq1bJjBkzJE2aNLJx40YpWLBgMvcsaTg5OYmIyPz582XSpEmyf/9+2bRpk/j5+SVvx5KQoz8HHH38ItRAhBo4+vhFqIGjj1+EGohQg9SGIPkc2F4ka9eulc8++0wMw5B169ZJ/vz5k7lnyeP+/fvyww8/yF9//SXbt293qDeLqKgo6d69u6xatUq8vLxkw4YNDjV+G0d+DogwfhFqIEINHH38ItTA0ccvQg1EqEFqwjWSz1FERITs3btXsmTJIsHBwcndnWR1/PhxiY2NlZCQkOTuSpL7+++/ZcWKFdKmTRu762YdjSM/B0QYvwg1EKEGjj5+EWrg6OMXoQYi1CC1IEgCScA2AQ8AAACQGhAkAQAAAACWcIgEAAAAAGAJQRIAAAAAYAlBEgAAAABgCUESAAAAAGAJQRIAAAAAYInlIHnnzh358MMPpW7duuLv7y+GYcjUqVOfevubN29K165dJTAwULy9vaVatWqya9cuq90AAAAAgGfytJlm+/bt0qNHDylVqpS4urqKYRhP9fgbN24UwzDEMAy5evWq3bLFixdLnTp1JDg4WNzd3SVLlizSokUL2bdvX7zHCQ8Pl5EjR0rBggXFy8tLMmfOLC1btpT9+/cn+HtXrVol1atXl7Rp00qaNGmkVKlSMnfu3HjrLVmyREqWLCkeHh6SLVs2+fDDDyU6OvqpxmZjOUhevXpVhg0bJgcPHpRixYpZ2jY2NlYaNGggs2fPlp49e8pnn30mly9flqpVq8rRo0etdgUAAAAALHvaTLNixQr5/vvvxTAMyZUr11M9dmxsrLz11lvi7e2d4PK///5b0qVLJ71795ZvvvlGunfvLrt375YyZcrI3r177dZt166dfPDBB1K1alUZN26cdOvWTf744w8pX768nDp1ym7dKVOmSO3atcXV1VU++eQTGT16tFSuXFnOnDljt94vv/wiTZs2FT8/P/n666+ladOmMnz4cHnrrbeeanwmtSg8PFwvXLigqqo7duxQEdEpU6Y81bZz585VEdH58+ebbZcvX1Y/Pz9t06aN1a4AAAAAgGVPm2kuXryo9+7dU1XVN998U58mPn377bcaEBCgvXv3VhHRK1euPHGbixcvqouLi3br1s1sO3v2rIqI9u/f327dNWvWqIjoF198YbadPHlSPT09tVevXk/8XQULFtRixYppVFSU2TZ48GA1DEMPHjz4xO1tLB+RdHd3l4wZM1rdTEREFixYIEFBQdKsWTOzLTAwUF5++WX5+eefJSIi4pkeFwAAAACe1tNmmqCgIPH09Hzqx71+/bq8//77MmzYMPHz83vq7TJkyCBeXl5y8+ZNs+327dtmH+LKlCmTiIhdvyZMmCAxMTEybNgwEXlw6q6qxvs9Bw4ckAMHDkjXrl3FxcXFbO/Ro4eoqixYsOCp+5ykk+3s3r1bSpYsKU5O9r+2TJkycu/ePTly5EhSdgcAAAAAEs2QIUMkY8aM0q1btyeue/PmTbly5Yr8/fff8tprr0lYWJjUqFHDXJ47d27JkiWLfP7557J06VI5e/asbN++Xd544w3JmTOntG7d2lx31apVkj9/flmxYoVkyZJF0qRJIwEBATJkyBCJjY0119u9e7eIiJQuXdquL8HBwZIlSxZz+dNwefIqiefChQtSuXLleO22VH3+/HkpUqRIUnYJAODgsn+VvL//VO/k/f3JPX4RauDo4xehBsk9fhFqkBj++usvmThxoqxYsUKcnZ2fuH65cuXk8OHDIiLi4+Mj77//vnTp0sVc7urqKgsXLpS2bdtK48aNzfZSpUrJ5s2b7Y54Hj16VJydneXVV1+VAQMGSLFixWTRokUyfPhwiY6OlpEjR4rIgzwm8k/+iitTpkxy/vz5px5vkgbJ+/fvi7u7e7x2Dw8PczmApMOHBjVw9PEDAJBYevXqJfXq1ZPatWs/1fpTpkyRsLAwOXHihEyZMkXu378vMTExdmdvpkuXTooXLy4tW7aUcuXKybFjx2TkyJHSsmVLWblypZmj7ty5I7GxsTJq1Ch59913RUSkefPmcv36dfnqq69k0KBBkiZNGjNvPSqThYWFPfV4kzRIenp6JngdZHh4uLkcAAAAAFKSuXPnyubNmxO8hcejlC9f3vx369atpUCBAiIiMmbMGBERuXXrlrz44ovyzjvvSL9+/cx1S5cuLVWrVpUpU6ZI9+7dReRBjrp79660adPG7ne0adNGfv31V9m9e7dUrlzZzFuPymRW8liSXiOZKVMm83BqXLa24ODgpOwOAAAAAPxr77zzjrRs2VLc3NwkNDRUQkNDzYlzzpw588RTRtOlSyfVq1eXWbNmmW0LFy6US5cu2Z3WKiJSpUoV8fX1lU2bNpltthz18MQ8GTJkEBGRGzduiMg/p7Q+KpNZyWNJGiSLFy8uu3btsrvgU0Rk27Zt4uXlJXnz5k3K7gAAAADAv3bmzBmZPXu25MyZ0/zvq68eXD9SsmRJqV+//hMf4/79+3Lr1i3z50uXLomISExMjN16qioxMTESHR1ttpUqVUpERM6dO2e3ri3ABgYGisiDPCYisnPnznjrnT171lz+NJ5bkLxw4YIcOnRIoqKizLYWLVrIpUuXZNGiRWbb1atXZf78+dKoUaMEz9UFAAAAgP+yxYsXx/uvVatWIiIyffp0+fLLL811L1++HG/70NBQWb16td1sqraDbHPmzLFbd8mSJXL37l0pUaKE2Wb7XZMnTzbbYmNjZcqUKeLv728GzUKFCkn+/Pll0qRJdgH122+/FcMwpEWLFk895me6RnL8+PFy8+ZNM+HapqMVEXnrrbckbdq0MnDgQJk2bZqcPHlScuTIISIPgmS5cuXk1VdflQMHDkj69Onlm2++kZiYGBk6dOizdAUAAAAALHuaTHPq1CmZMWOGiPxzFG/48OEiIpI9e3bp0KGDiIg0bdo03uPv2bNHRETq1asn6dOnN9uLFCkiNWrUkOLFi0u6dOnk6NGjMnnyZImKipJRo0aZ6zVq1EgKFSokw4YNk1OnTpmT7YwfP14yZcpkN8NrkyZNpEaNGjJy5Ei5evWqFCtWTH766SfZuHGjTJw40e6A3ejRo6Vx48ZSu3Ztad26tezbt0/Gjx8vr732mnmd5tN4piA5ZswYOXXqlPnzokWLzKOM7du3l7Rp0ya4nbOzs6xYsULeeecdGTdunNy/f19eeOEFmTp1quTLl+9ZugIAAAAAlj1Npjl58qQMGTLEbjvbz1WqVDGDpBXdu3eX5cuXy6+//iq3b9+WDBkySO3atWXQoEF2t0J0c3OTDRs2yMcffyzLly+XH3/8UdKkSSNNmzaVTz75xC6cGoYhP/30k7z//vsyd+5cM1/NnDlT2rVrZ/f7GzZsKIsWLZKhQ4fKW2+9JYGBgTJo0CD54IMPLI3DUFW1PHoAqQK3fqAGjj5+EWqQ3OMXoQaOPn4RapDc4xehBrAuSSfbAQAAAACkfARJAAAAAIAlBEkAAAAAgCUESQAAAACAJQRJAAAAAIAlBEkAAAAAgCUESQAAAACAJQRJAAAAAIAlBEkAAAAAgCUESQAAAACAJQRJAAAAAIAlBEkAAAAAgCUESQAAAACAJQRJAAAAAIAlBEkAAAAAgCUESQAAAACAJQRJAAAAAIAlBEkAAAAAgCUESQAAAACAJQRJAAAAAIAlBEkAAAAAgCUESQAAAACAJQRJAAAAAIAlBEkAAAAAgCUESQAAAACAJQRJAAAAAIAlBEkAAAAAgCUESQAAAACAJQRJAAAAAIAlBEkAAAAAgCUESQAAAACAJQRJAAAAAIAlBEkAAAAAgCUESQAAAACAJQRJAAAAAIAlBEkAAAAAgCUESQAAAACAJQRJAAAAAIAlBEkAAAAAgCUESQAAAACAJQRJAAAAAIAlBEkAAAAAgCUESQAAAACAJQRJAAAAAIAlBEkAAAAAgCUESQAAAACAJQRJAAAAAIAlBEkAAAAAgCUESQAAAACAJQRJAAAAAIAlBEkAAAAAgCUESQAAAACAJQRJAAAAAIAlBEkAAAAAgCUESQAAAACAJQRJAAAAAIAlBEkAAAAAgCUESQAAAACAJQRJAAAAAIAlBEkAAAAAgCUESQAAAACAJQRJAAAAAIAlBEkAAAAAgCWWg2RERIS8++67EhwcLJ6enlK2bFlZuXLlU227atUqqVatmqRPn178/PykTJkyMmPGDMudBgAAAIBn9eeff0rdunXF19dX0qRJI7Vr15Y9e/bEW+/333+XLl26SOHChcXZ2Vly5MiR4OOFhoaKYRgJ/jdnzpx46x88eFDq1q0rPj4+4u/vLx06dJArV67EW+/ChQvStWtXyZkzp3h6ekru3Lnl7bfflmvXrsVbd968eVKuXDnx8/OTgIAAqVKliixfvtxybZ6Wi9UNOnXqJAsWLJA+ffpISEiITJ06VerXry9r166VSpUqPXK7JUuWSNOmTaV8+fLy0UcfiWEYMm/ePOnYsaNcvXpV+vbt+68GAgAAAABPsmvXLqlUqZJkzZpVPvzwQ4mNjZVvvvlGqlSpItu3b5d8+fKZ686ePVvmzp0rJUuWlODg4Cc+dps2baR+/fp2beXLl7f7+ezZs1K5cmVJmzatfPLJJ3Lnzh0ZM2aM/P3337J9+3Zxc3MTEZE7d+5I+fLl5e7du9KjRw/JmjWr7N27V8aPHy9r166VP//8U5ycHhwX/Prrr6VXr17SoEEDGTVqlISHh8vUqVOlYcOGsnDhQmnWrNm/LVs8loLk9u3bZc6cOTJ69Gjp37+/iIh07NhRChcuLAMGDJDNmzc/ctvx48dLpkyZZM2aNeLu7i4iIt26dZP8+fPL1KlTCZIAAAAAnrshQ4aIp6enbNmyRQICAkREpH379pI3b14ZNGiQLFy40Fz3k08+ke+++05cXV2lYcOGsm/fvsc+dsmSJaV9+/aPXeeTTz6Ru3fvyp9//inZsmUTEZEyZcpIrVq1ZOrUqdK1a1cReXAg7tSpU7Js2TJp0KCBub2/v78MGzZM9u7dKyVKlBCRB0HyhRdekKVLl4phGCIi0rlzZ8mcObNMmzbtuQRJS6e2LliwQJydnc3BiYh4eHhIly5dZMuWLXLmzJlHbhsWFibp0qUzQ6SIiIuLi6RPn148PT2foesAAAAAYM2GDRukZs2aZogUEcmUKZNUqVJFli1bJnfu3DHbg4ODxdXV1dLj3717VyIjIx+5fOHChdKwYUMzRIqI1KxZU/LmzSvz5s0z28LCwkREJCgoyG77TJkyiYjYZaiwsDDJkCGDGSJFRHx9fcXHx+e5ZS1LQXL37t2SN29e8fX1tWsvU6aMiEiC5xXbVK1aVfbv3y9DhgyRY8eOyfHjx+Xjjz+WnTt3yoABA6z3HAAAAAAsioiISDBceXl5SWRk5BOPOj7O0KFDxcfHRzw8POSFF16Q33//3W75uXPn5PLly1K6dOl425YpU0Z2795t/ly5cmVxcnKS3r17y9atW+Xs2bOyYsUKGTFihDRt2lTy589vrlu1alX59ddf5euvv5bQ0FA5dOiQvPnmm3Lr1i3p3bv3M4/ncSyd2nrhwgUzAcdlazt//vwjtx0yZIicPHlSRowYIcOHDxeRB3+shQsXSpMmTax0AwAAAACeSb58+WTr1q0SExMjzs7OIiISGRkp27ZtE5EHYc8qJycnqV27trz00kuSOXNmOXHihHzxxRdSr149WbJkiXlq6oULF0REHpmprl+/LhEREeLu7i4FCxaUSZMmSf/+/e2us3zllVfk+++/t9t23LhxcvXqVenVq5f06tVLRETSp08vq1evjneNZmKxFCTv379vd2qqjYeHh7n8Udzd3SVv3rzSokULadasmcTExMikSZOkffv2snLlSilXrpzFrgMAAACANT169JDu3btLly5dZMCAARIbGyvDhw83Q97jMs2jZMuWTX777Te7tg4dOkjBggWlX79+ZpC0PfaTMpVteebMmaVMmTJSv359yZ49u2zYsEHGjRsn6dOnlzFjxpjbenl5Sb58+SRLlizSsGFDuX37tnz55ZfSrFkz2bBhg+TJk8fymJ7EUpD09PSUiIiIeO3h4eHm8kfp2bOnbN26VXbt2mXOLvTyyy9LoUKFpHfv3uYeAAAAAAB4Xt544w05c+aMjB49WqZNmyYiIqVLl5YBAwbIiBEjxMfHJ1F+j7+/v7z66qsyatQoOXv2rGTJksXMS0+TqTZt2iQNGzaUrVu3mqfCNm3aVHx9fWXo0KHSuXNnKViwoIiItGzZUlxcXGTp0qXm4zVp0kRCQkJk8ODBMnfu3EQZU1yWrpHMlCmTmdTjsrU9akrcyMhImTx5sjRo0MAMkSIirq6uUq9ePdm5c+djL0gFAAAAgMQyYsQIuXTpkmzYsEH++usv2bFjh8TGxoqISN68eRPt92TNmlVERK5fvy4i/5zS+qhM5e/vbx6NnDhxogQFBcW7nrJx48aiquYdM06cOCG//vqrNG7c2G49f39/qVSpkmzatCnRxhOXpSOSxYsXl7Vr10pYWJjdhDu2o4nFixdPcLtr165JdHS0xMTExFsWFRUlsbGxCS4DAAAAgOchXbp0UqlSJfPnVatWSZYsWewmsfm3Tpw4ISIigYGBIvLgVNXAwEDZuXNnvHW3b99ul6cuXbr0yPwkIhIdHW2uJyKPXNe2XmKzdESyRYsW5rWNNhERETJlyhQpW7asmbhPnz4thw4dMtfJkCGD+Pn5yeLFi+2OPN65c0eWLl0q+fPn5xYgAAAAAJLF3LlzZceOHdKnTx+7Myif1pUrV+K1nTt3Tn744QcpWrSo3eQ6zZs3l2XLltndOnH16tVy5MgRadmypdmWN29euXTpkqxbt87ucX/88UcREfMeknny5BEnJyeZO3euqKq53tmzZ2XDhg3meonN0hHJsmXLSsuWLWXgwIFy+fJlyZMnj0ybNk1CQ0Nl8uTJ5nodO3aU9evXmwNxdnaW/v37y/vvvy/lypWTjh07SkxMjEyePFnOnj0rM2fOTNxRAQAAAEAC/vjjDxk2bJjUrl1bAgICZOvWrTJlyhSpW7duvFtl/PXXX7JkyRIRETl27JjcunXLvANFsWLFpFGjRiIiMmDAADl+/LjUqFFDgoODJTQ0VCZOnCh3796Vr776yu4xBw0aJPPnz5dq1apJ79695c6dOzJ69GgpUqSIvPrqq+Z6PXv2lClTpkijRo3krbfekuzZs8v69evlxx9/lFq1aknZsmVF5MHRzs6dO8v3338vNWrUkGbNmsnt27flm2++kfv378vAgQOfSx0tBUkRkenTp8uQIUNkxowZcuPGDSlatKgsW7ZMKleu/NjtBg8eLDlz5pSvvvpKhg4dKhEREVK0aFFZsGCBNG/e/JkHAAAAAABPK3PmzOLs7CyjR4+W27dvS86cOWX48OHy9ttvi4uLfTzatWuXDBkyxK7N9vMrr7xiBsnatWvLhAkT5H//+5/cuHFD/Pz8pHLlyvL+++9LyZIl7bbPmjWrrF+/Xt5++2157733xM3NTRo0aCCff/653Wyu+fLlkz///FPef/99mTlzply8eFGCg4Olf//+MnToULvH/Pbbb6VYsWIyefJkMzi+8MILMn369CfmtGdlaNzjnwAcSvavnrzO83Tq+dwf1xJHr4Gjj1+EGiT3+EWogaOPX4QaJPf4RagBrLN+AjAAAAAAwKERJAEAAAAAlhAkAQAAAACWECQBAAAAAJYQJAEAAAAAlhAkAQAAAACWECQBAAAAAJYQJAEAAAAAlhAkAQAAAACWECQBAAAAAJYQJAEAAAAAlhAkAQAAAACWECQBAAAAAJYQJAEAAAAAlhAkAQAAAACWECQBAAAAAJYQJAEAAAAAlhAkAQAAAACWECQBAAAAAJYQJAEAAAAAlhAkAQAAAACWECQBAAAAAJYQJAEAAAAAlhAkAQAAAACWECQBAAAAAJYQJAEAAAAAlhAkAQAAAACWECQBAAAAAJYQJAEAAAAAlhAkAQAAAACWECQBAAAAAJYQJAEAAAAAlhAkAQAAAACWECQBAAAAAJYQJAEAAAAAlhAkAQAAAACWECQBAAAAAJYQJAEAAAAAlhAkAQAAAACWECQBAAAAAJYQJAEAAAAAlhAkAQAAAACWECQBAAAAAJYQJAEAAAAAlhAkAQAAAACWECQBAAAAAJYQJAEAAAAAlhAkAQAAAACWECQBAAAAAJYQJAEAAAAAlhAkAQAAAACWECQBAAAAAJYQJAEAAAAAlhAkAQAAAACWECQBAAAAAJYQJAEAAAAAlhAkAQAAAACWECQBAAAAAJYQJAEAAAAAlhAkAQAAAACWECQBAAAAAJYQJAEAAAAAlhAkAQAAAACWWA6SERER8u6770pwcLB4enpK2bJlZeXKlU+9/dy5c6V8+fLi7e0tfn5+UqFCBVmzZo3VbgAAAADAM9u1a5c0btxY/P39xcvLSwoXLizjxo0zl8fGxsqECROkePHi4uPjI0FBQVKvXj3ZvHnzMz2eiMgnn3wi5cqVk8DAQPHw8JCQkBDp06ePXLlyJd7jjRgxQho3bixBQUFiGIZ89NFHjx1PUucsF6sbdOrUSRYsWCB9+vSRkJAQmTp1qtSvX1/Wrl0rlSpVeuy2H330kQwbNkxatGghnTp1kqioKNm3b5+cO3fumQcAAAAAAFb8/vvv0qhRIylRooQMGTJEfHx85Pjx43L27FlznXfeeUe++OILad++vfTo0UNu3rwpEydOlCpVqsimTZukTJkylh5PROTPP/+U4sWLS+vWrSVNmjRy8OBB+e6772T58uWyZ88e8fb2Ntd9//33JWPGjFKiRAn57bffHjue5MhZloLk9u3bZc6cOTJ69Gjp37+/iIh07NhRChcuLAMGDHhkOhcR2bp1qwwbNkw+//xz6du377/rNQAAAAA8g7CwMOnYsaM0aNBAFixYIE5O8U/SjI6Olm+//VZatGghM2bMMNtbtmwpuXLlklmzZplB8mkez2bhwoXx2sqXLy8tWrSQpUuXSuvWrc32kydPSo4cOeTq1asSGBj4yMdMrpxl6dTWBQsWiLOzs3Tt2tVs8/DwkC5dusiWLVvkzJkzj9x27NixkjFjRundu7eoqty5c+fZew0AAAAAz2D27Nly6dIlGTFihDg5Ocndu3clNjbWbp2oqCi5f/++BAUF2bVnyJBBnJycxNPT09LjPU6OHDlEROTmzZsJtj9JcuUsS0Fy9+7dkjdvXvH19bVrt6XxPXv2PHLb1atXywsvvCDjxo2TwMBASZMmjWTKlEnGjx9vvdcAAAAA8AxWrVolvr6+cu7cOcmXL5/4+PiIr6+vdO/eXcLDw0VEzLlgpk6dKrNmzZLTp0/LX3/9JZ06dZJ06dLZHVh7mseLS1Xl6tWrcvHiRdmwYYP06tVLnJ2dpWrVqs80nuTKWZZObb1w4YJkypQpXrut7fz58wlud+PGDbl69aps2rRJ1qxZIx9++KFky5ZNpkyZIm+99Za4urpKt27dnqH7AAAAAPD0jh49KtHR0dKkSRPp0qWLjBw5UtatWydff/213Lx5U3788UcREZk5c6a0atVK2rdvb26bK1cu2bRpk+TKlcvy49lcunTJLlNlyZJFZs+eLfnz57c8luTMWZaC5P3798Xd3T1eu4eHh7k8IbbDq9euXZM5c+ZIq1atRESkRYsWUqRIERk+fDhBEgAAAMBzd+fOHbl375688cYb5qyqzZo1k8jISJk4caIMGzZMQkJCJE2aNFKoUCEpX7681KhRQy5evCijRo2Spk2byoYNGyR9+vSWHs/G399fVq5cKeHh4bJ7925ZtGjRM5+Ompw5y9KprZ6enhIRERGvPe4h4EdtJyLi6uoqLVq0+OeXOzlJq1at5OzZs3L69GkrXQEAAAAAy2zZpE2bNnbtbdu2FRGRLVu2SHR0tNSsWVPSpk0r48ePl5deekm6d+8uq1atkuPHj8vo0aMtPV5cbm5uUrNmTWnYsKEMGTJE/ve//0mXLl1k2bJlzzyW5MhZloJkpkyZ5MKFC/HabW3BwcEJbufv7y8eHh4SEBAgzs7OdssyZMggIg8OywIAAADA82TLLAlNpCPyIJf88ccfsm/fPmncuLHdOiEhIVKgQAHZtGmTpcd7nAoVKkimTJlk1qxZlseSnDnLUpAsXry4HDlyRMLCwuzat23bZi5P8Jc4OUnx4sXlypUrEhkZabfMdl3l46a0BQAAAIDEUKpUKRGRePdYjJtLLl26JCIiMTEx8baPioqS6OhoS4/3JOHh4XLr1q2nHYIpOXOWpSDZokULiYmJkUmTJpltERERMmXKFClbtqxkzZpVREROnz4thw4dstu2VatWEhMTI9OmTTPbwsPDZdasWVKwYMFHHs0EAAAAgMTy8ssvi4jI5MmT7dq///57cXFxkapVq0revHlFRGTOnDl26+zatUsOHz4sJUqUsPR4IiJ3796Ve/fuxevPwoUL5caNG1K6dOlnGk9y5SxLk+2ULVtWWrZsKQMHDpTLly9Lnjx5ZNq0aRIaGmpXuI4dO8r69etFVc22bt26yffffy9vvvmmHDlyRLJlyyYzZsyQU6dOydKlSxNvRAAAAADwCCVKlJDOnTvLDz/8INHR0VKlShVZt26dzJ8/XwYOHCjBwcESHBwstWrVkmnTpklYWJjUrl1bLly4IF9//bV4enpKnz59LD2eyIPZXWvWrCmtWrWS/Pnzi5OTk+zcuVNmzpwpOXLkkN69e9v105aVbOHzjz/+kOHDh4uISIcOHSR79uwiknw5y1KQFBGZPn26DBkyRGbMmCE3btyQokWLyrJly6Ry5cqP3c7T01PWrFkjAwYMkB9++EHu3r0rxYsXl+XLl0udOnWeeQAAAAAAYMWECRPM22QsXrxYsmfPLl9++aVdQPz5559lzJgxMmfOHPn111/Fzc1NXnzxRfn4448lX758lh8vS5Ys0rx5c1mzZo1MmzZNoqKiJHv27NKzZ08ZPHiwBAQE2D3m5MmTZf369ebPa9eulbVr14qISKVKlcwgmVw5y9C4hw0BOJTsXyXv7z/V+8nrPG+OXgNHH78INUju8YtQA0cfvwg1SO7xi1ADWGfpGkkAAAAAAAiSAAAAAABLCJIAAAAAAEsIkgAAAAAASwiSAAAAAABLCJIAAAAAAEsIkgAAAAAASwiSAAAAAABLCJIAAAAAAEsIkgAAAAAASwiSAAAAAABLCJIAAAAAAEsIkgAAAAAASwiSAAAAAABLCJIAAAAAAEsIkgAAAAAASwiSAAAAAABLCJIAAAAAAEsIkgAAAAAASwiSAAAAAABLCJIAAAAAAEsIkgAAAAAASwiSAAAAAABLXJK7A0Byyf5VcvdA5FTv5O4BAAAAYB1HJAEAAAAAlhAkAQAAAACWECQBAAAAAJYQJAEAAAAAlhAkAQAAAACWECQBAAAAAJYQJAEAAAAAlhAkAQAAAACWECQBAAAAAJYQJAEAAAAAlhAkAQAAAACWECQBAAAAAJYQJAEAAAAAlhAkAQAAAACWECQBAAAAAJYQJAEAAAAAlhAkAQAAAACWECQBAAAAAJYQJAEAAAAAlhAkAQAAAACWECQBAAAAAJYQJAEAAAAAlhAkAQAAAACWECQBAAAAAJYQJAEAAAAAlhAkAQAAAACWECQBAAAAAJYQJAEAAAAAlhAkAQAAAACWECQBAAAAAJYQJAEAAAAAlhAkAQAAAACWECQBAAAAAJYQJAEAAAAAlhAkAQAAAACWECQBAAAAAJYQJAEAAAAAlhAkAQAAAACWECQBAAAAAJZYDpIRERHy7rvvSnBwsHh6ekrZsmVl5cqVln9xrVq1xDAM6dmzp+VtAQAAACCxjBgxQgzDkMKFC9u1V61aVQzDiPdf3bp17dbr1KlTguvZ/jt37pzlx3zaPj7s5s2bkiFDBjEMQxYsWGCxEk/PxeoGnTp1kgULFkifPn0kJCREpk6dKvXr15e1a9dKpUqVnuoxFi1aJFu2bLHcWQAAAABITGfPnpVPPvlEvL29E1yeJUsWGTlypF1bcHCw3c/dunWTmjVr2rWpqrzxxhuSI0cOyZw5s+XHtNLHuD744AO5d+/eE9f7tywFye3bt8ucOXNk9OjR0r9/fxER6dixoxQuXFgGDBggmzdvfuJjhIeHS79+/eTdd9+VDz744Nl6DQAAAACJoH///lKuXDmJiYmRq1evxlueNm1aad++/WMfo3z58lK+fHm7to0bN8q9e/ekXbt2z/SYVvpos2/fPvn222/lgw8+eO5Zy9KprQsWLBBnZ2fp2rWr2ebh4SFdunSRLVu2yJkzZ574GJ999pnExsaaQRQAAAAAksMff/whCxYskLFjxz52vejoaLlz546lx549e7YYhiFt27b9V4/5tH0UEendu7e89NJL8uKLL1rq67OwFCR3794tefPmFV9fX7v2MmXKiIjInj17Hrv96dOnZdSoUfLpp5+Kp6entZ4CAAAAQCKJiYmRt956S1577TUpUqTII9c7cuSIeHt7S5o0aSRjxowyZMgQiYqKeuxjR0VFybx586RChQqSI0eOZ37Mp+2jiMj8+fNl8+bN8tlnnz12vcRi6dTWCxcuSKZMmeK129rOnz//2O379esnJUqUkNatW1v5tQAAAACQqCZMmCCnTp2SVatWPXKd3LlzS7Vq1aRIkSJy9+5dWbBggQwfPlyOHDkic+fOfeR2v/32m1y7di3B01qtPObT9FFE5P79+9K/f3/p27ev5MiRQ0JDQx8/+ERgKUjev39f3N3d47V7eHiYyx9l7dq1snDhQtm2bZvFLgIAAABA4rl27Zp88MEHMmTIEAkMDHzkepMnT7b7uUOHDtK1a1f57rvvpG/fvlKuXLkEt5s9e7a4urrKyy+//MyP+bR9FBEZNWqUREVFyaBBgx67XmKydGqrp6enRERExGsPDw83lyckOjpaevXqJR06dJAXXnjhGboJAAAAAInj/fffF39/f3nrrbcsb9uvXz8RkUceJbxz5478/PPPUqdOHQkICHjmx3zaPoaGhsro0aNlxIgR4uPj81S/LzFYOiKZKVMmu3ug2Fy4cEFEHj1l7fTp0+Xw4cMyceLEeIdZb9++LaGhoZIhQwbx8vKy0h0AAAAAsOTo0aMyadIkGTt2rN2leeHh4RIVFSWhoaHi6+sr/v7+CW6fNWtWERG5fv16gst/+umnR87W+igPP6aVPn7wwQeSOXNmqVq1qpm1Ll68KCIiV65ckdDQUMmWLZs4OVk6hvhEloJk8eLFZe3atRIWFmY34Y7tdNXixYsnuN3p06clKipKKlasGG/Z9OnTZfr06bJ48WJp2rSple4AAAAAgCXnzp2T2NhY6dWrl/Tq1Sve8pw5c0rv3r0fOUvqiRMnREQeebrprFmzxMfHRxo3bvzUfXr4Ma308fTp03Ls2DHJlStXvPV69OghIiI3btwQPz+/p+7P07AUJFu0aCFjxoyRSZMmmbfviIiIkClTpkjZsmXNJH369Gm5d++e5M+fX0REWrdunWDIfOmll6R+/fry+uuvS9myZf/lUAAAAADg8QoXLiyLFy+O1/7+++/L7du35auvvpLcuXNLWFiYuLu7280Ro6oyfPhwERGpU6dOvMe4cuWKrFq1Stq0aZPg2ZZP+5hP20cRkeHDh8e7t+S+fftkyJAhMmDAAClfvrx4e3s/sS5WWQqSZcuWlZYtW8rAgQPl8uXLkidPHpk2bZqEhobaXTTasWNHWb9+vaiqiIjkz5/fDJUPy5kzJ0ciAQAAACSJ9OnTJ5g/bEcgbcvWrVsnbdq0kTZt2kiePHnk/v37snjxYtm0aZN07dpVSpYsGe8x5s6dK9HR0Y88rXXXrl1P9ZhP20cRkUqVKsVbz3b08YUXXnhuWctSkBR5cCrqkCFDZMaMGXLjxg0pWrSoLFu2TCpXrvw8+gcAAAAASS579uzy4osvyuLFi+XixYvi5OQkBQoUkAkTJkjXrl0T3GbWrFmSIUMGqVmzZqI95n+V5SDp4eEho0ePltGjRz9ynXXr1j3VY9mOWAIAAABAcno4w+TMmVPmzZtn6TG2bNny2OXP8phxPW3Oqlq16nPPWok7dQ8AAAAAINUjSAIAAAAALCFIAgAAAAAsIUgCAAAAACwhSAIAAAAALCFIAgAAAAAsIUgCAAAAACwhSAIAAAAALCFIAgAAAAAsIUgCAAAAACwhSAIAAAAALCFIAgAAAAAsIUgCAAAAACwhSAIAAAAALCFIAgAAAAAsIUgCAAAAACwhSAIAAAAALCFIAgAAAAAsIUgCAAAAACwhSAIAAAAALCFIAgAAAAAsIUgCAAAAACwhSAIAAAAALCFIAgAAAAAsIUgCAAAAACwhSAIAAAAALCFIAgAAAAAsIUgCAAAAACwhSAIAAAAALCFIAgAAAAAsIUgCAAAAACwhSAIAAAAALCFIAgAAAAAsIUgCAAAAACwhSAIAAAAALCFIAgAAAAAsIUgCAAAAACwhSAIAAAAALCFIAgAAAAAsIUgCAAAAACwhSAIAAAAALCFIAgAAAAAsIUgCAAAAACwhSAIAAAAALCFIAgAAAAAsIUgCAAAAACwhSAIAAAAALCFIAgAAAAAsIUgCAAAAACwhSAIAAAAALCFIAgAAAAAsIUgCAAAAACwhSAIAAAAALCFIAgAAAAAsIUgCAAAAACwhSAIAAAAALCFIAgAAAAAsIUgCAAAAACwhSAIAAAAALCFIAgAAAAAsIUgCAAAAACwhSAIAAAAALCFIAgAAAAAssRwkIyIi5N1335Xg4GDx9PSUsmXLysqVK5+43aJFi6RVq1aSK1cu8fLyknz58km/fv3k5s2bz9JvAAAAALBsx44d0rNnTylUqJB4e3tLtmzZ5OWXX5YjR46Y68TGxsrUqVOlcePGkjVrVvH29pbChQvL8OHDJTw8PN5j3rp1SwYMGCAhISHi6ekp2bNnly5dusjp06ft1suRI4cYhpHgfyEhIeZ6U6dOfeR6hmHIrFmzzHWTK2e5WN2gU6dOsmDBAunTp4+EhITI1KlTpX79+rJ27VqpVKnSI7fr2rWrBAcHS/v27SVbtmzy999/y/jx42XFihWya9cu8fT0/FcDAQAAAIAn+fTTT2XTpk3SsmVLKVq0qFy8eFHGjx8vJUuWlK1bt0rhwoXl3r178uqrr0q5cuXkjTfekAwZMsiWLVvkww8/lNWrV8uaNWvEMAwReRA6a9WqJQcOHJAePXpI3rx55dixY/LNN9/Ib7/9JgcPHpQ0adKIiMjYsWPlzp07dv05deqUvP/++1K7dm2zrXLlyjJjxox4ff/yyy9l7969UqNGDbMtuXKWpSC5fft2mTNnjowePVr69+8vIiIdO3aUwoULy4ABA2Tz5s2P3HbBggVStWpVu7ZSpUrJK6+8IrNmzZLXXnvNeu8BAAAAwIK3335bZs+eLW5ubmZbq1atpEiRIjJq1CiZOXOmuLm5yaZNm6RChQrmOq+//rrkyJHDDJM1a9YUEZGtW7fKjh07ZPz48fLmm2+a6+fLl086d+4sq1atkpdeeklERJo2bRqvP8OHDxcRkXbt2pltuXLlkly5ctmtd//+fenRo4dUr15dMmbMaLYnV86ydGrrggULxNnZWbp27Wq2eXh4SJcuXWTLli1y5syZR2778OBExCzowYMHrXQDAAAAAJ5JhQoV7EKkiEhISIgUKlTIzCVubm52IdImofwSFhYmIiJBQUF262bKlElE5IlHBGfPni05c+ZM8PfFtXTpUrl9+7Zd4BRJvpxlKUju3r1b8ubNK76+vnbtZcqUERGRPXv2WPrlFy9eFBGR9OnTW9oOAAAAABKLqsqlS5eemEsSyi+lS5cWb29vGTJkiKxZs0bOnTsn69evlwEDBsgLL7xgHrlMyO7du+XgwYPStm3bJ/Zx1qxZ4unpKc2aNXviukmRsywFyQsXLpjJOi5b2/nz5y398k8//VScnZ2lRYsWlrYDAAAAgMQya9YsOXfunLRq1eqx63322Wfi6+sr9erVM9vSp08vc+fOlVu3bkmNGjUkS5YsUrVqVQkODpY1a9aIi8ujrya0TZrz8FHGh12/fl1+/fVXadSokXm95eMkRc6yFCTv378v7u7u8do9PDzM5U9r9uzZMnnyZOnXr5/dDEUAAAAAkFQOHTokb775ppQvX15eeeWVR673ySefyKpVq2TUqFHi5+dntywwMFBKlCghI0aMkJ9++kk++ugj2bBhg7z66quPfLzY2FiZM2eOlChRQgoUKPDYPi5YsEAiIyOfGDhFki5nWZpsx9PTUyIiIuK126bAfdoZgTZs2CBdunSROnXqyIgRI6x0AQAAAAASxcWLF6VBgwaSNm1acz6YhMydO1fef/996dKli3Tv3t1u2YkTJ6RatWoyffp0ad68uYiINGnSRHLkyCGdOnWSX375xe4Ips369evl3Llz0rdv3yf2c9asWeLv75/g48SVlDnL0hHJTJkyyYULF+K129qCg4Of+Bh79+6Vxo0bS+HChWXBggWPPdQLAAAAAM/DrVu3pF69enLz5k359ddfH5llVq5cKR07dpQGDRrIhAkT4i2fOnWqhIeHS8OGDe3aGzduLCIimzZtSvBxZ82aJU5OTtKmTZvH9vP06dOyYcMGadmypbi6uj5yvaTOWZaCZPHixeXIkSPmzEQ227ZtM5c/zvHjx6Vu3bqSIUMGWbFihfj4+FjrLQAAAAD8S+Hh4dKoUSM5cuSILFu2TAoWLJjgetu2bZOXXnpJSpcuLfPmzUswnF26dElUVWJiYuzao6KiREQkOjo63jYRERGycOFC81rKx/nxxx9FVR97Wmty5CxLQbJFixYSExMjkyZNMtsiIiJkypQpUrZsWcmaNauIPEjNhw4dstv24sWLUrt2bXFycpLffvtNAgMDE6H7AAAAAPD0YmJipFWrVrJlyxaZP3++lC9fPsH1Dh48KA0aNJAcOXLIsmXLHnkZX968eUVVZd68eXbtP/74o4iIlChRIt42K1askJs3bz71NY/ZsmWTSpUqJbg8uXKWpeOdZcuWlZYtW8rAgQPl8uXLkidPHpk2bZqEhobK5MmTzfU6duwo69evF1U12+rWrSsnTpyQAQMGyMaNG2Xjxo3msqCgIKlVq1YiDAcAAAAAHq1fv36yZMkSadSokVy/fl1mzpxpt7x9+/Zy+/ZtqVOnjty4cUPeeecdWb58ud06uXPnNgNop06dZMyYMdKtWzfZvXu3FCpUSHbt2iXff/+9FCpUyLynY1yzZs0Sd3d385rKR9m3b5/89ddf8t5774lhGAmuk1w5y/KJs9OnT5chQ4bIjBkz5MaNG1K0aFFZtmyZVK5c+bHb7d27V0QeTJn7sCpVqhAkAQAAADx3e/bsERGRpUuXytKlS+Mtb9++vVy7dk3OnDkjIiLvvfdevHVeeeUVM0gGBATIzp075YMPPpClS5fKhAkTJCAgQDp37iyffPKJuLm52W0bFhYmy5cvNyf5eRzb7UEed5/J5MpZloOkh4eHjB49WkaPHv3IddatWxevLe7RSQAAAABIDglllYflyJHDUn7JnDmz3Rmaj+Pr6/vUt00cOXKkjBw58rHrJFfOsnSNJAAAAAAABEkAAAAAgCUESQAAAACAJQRJAAAAAIAlBEkAAAAAgCUESQAAAACAJQRJAAAAAIAlBEkAAAAAgCUESQAAAACAJQRJAAAAAIAlBEkAAAAAgCUESQAAAACAJQRJAAAAAIAlBEkAAAAAgCUESQAAAACAJS7J3QEkn+xfJe/vP9U7eX8/AAAAgGfDEUkAAAAAgCUESQAAAACAJQRJAAAAAIAlBEkAAAAAgCUESQAAAACAJQRJAAAAAIAlBEkAAAAAgCUESQAAAACAJQRJAAAAAIAlBEkAAAAAgCUESQAAAACAJQRJAAAAAIAlBEkAAAAAgCUESQAAAACAJQRJAAAAAIAlBEkAAAAAgCUESQAAAACAJQRJAAAAAIAlBEkAAAAAgCUESQAAAACAJQRJAAAAAIAlBEkAAAAAgCUESQAAAACAJQRJAAAAAIAlBEkAAAAAgCUESQAAAACAJQRJAAAAAIAlBEkAAAAAgCUESQAAAACAJQRJAAAAAIAlBEkAAAAAgCUESQAAAACAJQRJAAAAAIAlBEkAAAAAgCUESQAAAACAJQRJAAAAAIAlBEkAAAAAgCUESQAAAACAJQRJAAAAAIAlBEkAAAAAgCUESQAAAACAJQRJAAAAAIAlBEkAAAAAgCUESQAAAACAJQRJAAAAAIAlBEkAAAAAgCUESQAAAACAJZaDZEREhLz77rsSHBwsnp6eUrZsWVm5cuVTbXvu3Dl5+eWXxc/PT3x9faVJkyZy4sQJy50GAAAAgGf1bzINHrAcJDt16iRffPGFtGvXTr766itxdnaW+vXry8aNGx+73Z07d6RatWqyfv16GTRokAwdOlR2794tVapUkWvXrj3zAAAAAADAimfNNPiHi5WVt2/fLnPmzJHRo0dL//79RUSkY8eOUrhwYRkwYIBs3rz5kdt+8803cvToUdm+fbu88MILIiJSr149KVy4sHz++efyySef/IthWJf9qyT9dQk61Tu5ewAAAAA4ln+TafAPS0ckFyxYIM7OztK1a1ezzcPDQ7p06SJbtmyRM2fOPHbbF154wQyRIiL58+eXGjVqyLx5856h6wAAAABgzb/JNPiHpSC5e/duyZs3r/j6+tq1lylTRkRE9uzZk+B2sbGx8tdff0np0qXjLStTpowcP35cbt++baUrAAAAAGDZs2Ya2LN0auuFCxckU6ZM8dptbefPn09wu+vXr0tERMQTt82XL5+V7vwrnFZKDRx9/CLUQIQaOPr4RaiBo49fhBo4+vhFqIGIY9XgWTMN7Fk6Inn//n1xd3eP1+7h4WEuf9R2IvJM2wIAAABAYnnWTAN7loKkp6enRERExGsPDw83lz9qOxF5pm0BAAAAILE8a6aBPUtBMlOmTHLhwoV47ba24ODgBLfz9/cXd3f3Z9oWAAAAABLLs2Ya2LMUJIsXLy5HjhyRsLAwu/Zt27aZyxP8JU5OUqRIEdm5c2e8Zdu2bZNcuXJJmjRprHQFAAAAACx71kwDe5aCZIsWLSQmJkYmTZpktkVERMiUKVOkbNmykjVrVhEROX36tBw6dCjetjt27LALk4cPH5Y1a9ZIy5Yt/80YAAAAAOCpPG2mweMZqqpWNnj55Zdl8eLF0rdvX8mTJ49MmzZNtm/fLqtXr5bKlSuLiEjVqlVl/fr1Evehb9++LSVKlJDbt29L//79xdXVVb744guJiYmRPXv2SGBgYOKODAAAAAAS8DSZBo9nOUiGh4fLkCFDZObMmXLjxg0pWrSofPzxx1KnTh1znYSCpIjI2bNnpW/fvvL7779LbGysVK1aVb788kvJkydP4owGAAAAAJ7gaTINHs9ykAQAAAAAODZL10gCAAAAAECQBAAAAABYQpAEAOD/xcTEJHcXAABIEQiSAACH17dvX7l//744Ozs7bJg8depUcncBAJCCECQBAA5t7dq18tVXX0n58uUlPDzcIcNkmzZt5OWXX5a///47ubsCAEghCJIQEbG7VQsT+TqW2NhYERGJjo4WkQfTYQOOpHz58jJlyhS5dOmSlClTxiHDZIsWLeTAgQMycOBA+euvv5K7O0gGts8CG74LAHgSgqSDs31RMgzDbLP9++EPldTKkT8sT548Kb/88oucOXNGXFxcZN++fdKzZ085cuRIcnct2TRr1kwmTpyY3N1IVo7ymli3bp3cunVLPDw8pHXr1jJy5EiHDZPNmzeXuXPnypo1a+S9994jTDogJycnuXv3rpw+fVouXLhg973AUd4TAFjj0EHycV8QHOFNMyYmRpydneX48ePSp08fqVOnjrRq1UqmTp0qt27dEicnp1QfJmNiYswPy5iYGLl//745Zkd4DuzcuVM6dOggkyZNktWrV0upUqXk4sWL4uXlldxdSxYnT56UXbt2yapVq+T27dvJ3Z0kY3svjIiIEFWVW7duJXOPnr/hw4dL9erV5eeff5bbt2+Lu7u7tGnTRj799FOHDJMxMTFSv359+fnnn2XDhg0ydOhQ2bVrV3J3K0nZ/s6RkZFy9+5dCQ8PN8/USO2fhdu2bZP+/ftLSEiIhISESJ48eaRdu3aycOFCEXmwg9kRPhNtz4GoqCi5f/9+MvcmeTjC3xmJx1AHfcZER0eLi4uLiIgsWrRIzp49K25ublKmTBkpWbKkiDz44HBySp1ZW1XFMAw5cOCAVK1aVdKmTSuZM2eWK1euyJUrV6RUqVIyc+ZMCQgISO6uPje2IC0i8t5778nu3bvlzJkzUqZMGencubNUrlw5mXuYNN58802ZMGGCuLq6SsWKFWXSpEmSO3fu5O5Wsvn000/lo48+ko0bN0qpUqVS9fuAyD+vgyNHjsjw4cNl//79oqrSuHFj6dy5s2TLli25u/hchIWFSZs2bWTLli0yduxYadq0qfj6+kpERIT8+OOP8u6770pQUJBs375dPDw87N4vUhvb2M6ePSuhoaEyYcIEmT17trRq1UreffddKV68eHJ38bmz1eDw4cMyaNAgOXDggLi4uEjx4sVlxIgRki1bNvNzM7WZO3euDB48WFxdXaVcuXISEBAgoaGhsmjRInFxcZEPP/xQBg8eLCKSamsg8s93vsOHD8uHH34oZ86ckapVq0qjRo2kXLlyyd29JGF7HYSFhcmdO3fk1q1bkjlzZvH19RWR1P33xzNSBxQTE2P+u0mTJpouXTr18fFRZ2dnzZ07t/br1y8Ze5d0rl27pqVKldJq1arptm3bzPb8+fNr+vTpddWqVcnYu6RTv3599ff31/r16+vLL7+s+fPnV8MwdM6cOcndtecqNjZWVVWPHTumhmGoi4uLvvrqq3r27Fm75Y4gNjbWfF+4cOGCFipUSOvVq6f3799P5p49X7a/8YEDBzR9+vRarFgxbdasmbZp00Y9PDz0xRdf1HXr1iVzL5+f27dva7169TRt2rQ6bdo0vXXrlqqqhoeH65QpUzRDhgxapEgR83kQHR2dnN19LmzPgf3792v27Nm1UKFCWr9+fc2ZM6c6OTlpgwYNdM+ePcncy+fLVoNDhw5pQECAFitWTLt27apt27bVbNmyqaenp/7999/J3MvnY8qUKerq6qpdu3bVjRs32i1bsWKF5s2bVw3D0Pfffz+Zepi0jh49qgEBAZolSxYtVaqUenp6atGiRXXmzJnJ3bXnzvb+duDAAS1XrpymT59eDcPQF154Qb/44otk7h3+qxwySNq88sorGhQUpFOmTNHQ0FC9evWqlihRQg3D0Hnz5iV39567P//8UzNlymT3BvnBBx+oi4uL/vDDDxoWFqaqqTtQjBw5UjNkyKDz5s3T27dvq6rql19+qYZhaM+ePfXevXvJ3MPnw/aBERUVpXv27NFOnTppkyZN1DAMHTBggJ4+fdpcNzX//R8WGxursbGx+vbbb2v69OnNL9Bxdz6lNrdu3dLq1atr9erV7XYoNWrUSH18fHT16tWp+jlAmFS9fv26lihRQsuWLasbNmxQVdVTp07p5MmT1dPTU+vXr5/qw+Tdu3e1Tp06WrFiRd25c6fZ3rhxY/M7QWp7HUyfPl0Nw9D+/fvbvefHxMSYY92yZYsWK1ZMDcPQSZMmJVdXE13cv2Xcz8OJEydq7dq1ddeuXaqq+vfff2tISIjmy5dPv/vuu2Tpa1I6cuSIpk+fXqtUqaKjRo3SOXPmaI0aNdQwDB02bFhydw//QQ4bJI8dO6YFChTQ4cOHmwFi9+7d6uHhoW+88Ybdm6pq6vgy/fAXoBUrVqi3t7ceOnRIVVXfeecddXV11UmTJundu3dVVTUyMlJ//vlnjYiISPL+JpaFCxfqjh07ElzWunVrrVu3rt65c0dVVdevX69eXl7auXNnDQ0NTcpuJrkDBw7o4MGDVVXNv+/rr7+uhmHoO++8Yx6ZtAkPD0/yPj5vL7/8statW1eXLFli9xy/fv26+vv7a5cuXZKxd0nj4sWLmjFjRv3888/Ntv79+6u7u7tOmjTJDFapmaOHydOnT6ufn5++++678ZbNmzdPPTw89KWXXtI///wzGXqXNK5cuaLZs2fXTz/91GwbOHCguWPV9pywfRdI6TuXNmzYoIZhaL169fTEiRNmu218cb/zrFu3Tt3c3LRIkSJ266ZUf/31l6ra/w0PHTqk/fv31xo1auibb75pt/7hw4e1aNGimjdv3lQdJqOjo7Vr165aokQJ3b59u9neuXNn9fX11e+//14jIyOTsYf4L3LYILl161Z1dnbW33//XVVV16xZo56entq2bVs9d+6cud7evXuTq4uJIjw83O4L8tGjR/XYsWOq+qAGhmHoihUr9OOPP1YXFxedNGmS3VG4t956S+vWratXr15N8r4nhsuXL2vGjBk1b968unv3brtlUVFRWq5cOW3fvr2qqq5du1a9vLy0Xbt2ev78eXO977//PtXtjY+OjtZOnTppcHCwRkREaFRUlLmsS5cuZpi8cOGCqj74IP3888/t9tSnZLGxsTpx4kQdOnSo5sqVS9OnT68VK1bUtWvXmjuRBgwYoMHBwan61M7Y2FjzfcB26l6/fv3ivRfcv39fZ8yYkZxdfW5sXybDwsIeGyYzZ86sWbJkSXWnO8fGxuru3bvVyclJR4wYoapqN8aIiAh988031TAM7dixo90XzNTk5MmT6uPjY56NFHfHqu11EBkZqR988IFev349ObuaKPbu3avly5fXoKAgXbRo0RN3FI4ZM0YNw9CVK1cmUQ+fj6FDh6phGLp27Vq79v79+6thGJo5c2adMmWKqj74nLR9NsYNk5MnT07iXieN8PBwLV68uHbq1Mls69+/vzo7O+v3339vvifadrwDqg4SJON+SbbtZdu2bZt6e3vr1q1bdefOnerp6RkvQMyYMUObNGkS78hMShEZGamDBw/Wtm3bqqrqwYMH1TAM7dGjh7lXqXHjxuru7m6euhP36MOWLVu0QoUK+tprr6XoUzw3bdqkBQoU0CJFipinq6g+eF40b95cK1WqpAsWLDCfA3F3JPzxxx+aP39+nTVrVnJ0/bkaPny4pkmTxjwiH3cP9GuvvaaGYWi3bt30hx9+ML9gnzp1Krm6m6gaN26sFSpU0H379umtW7f0008/1bJly6qLi4tWqFBBFy1apKtWrVJfX18dNWqUqqaOsxIScuvWLQ0JCdFevXrpO++8oy4uLjphwgS71/ywYcPUz89Pjx8/now9/fdsRxIjIiL03r178f6mjwuT3377rYaEhKT4GqgmfNTpxRdf1JCQEHPHY1RUlLl80aJF6uvrq4ZhaPPmzVP0GSoJiYmJ0UuXLmmWLFm0b9++5s6U7777zjw7R1X1s88+06JFi+r+/fuTsbf/ju37UExMjO7fv1/Lly9vXt6R0E4S23Ng1apVahiGfv3110na38T2+++/a/369XXLli3xltnCZLFixfTIkSOq+mD8ccNkyZIlNTAwUKdPn56k/U4Kt2/f1hdeeEHfe+89VX1Qj4QOMPTt2zdV72CFNQ4RJG0e3pNasWJFzZo1q3p6emqXLl3sviSHhobqSy+9pHXq1NFLly4ldVcTxd27d3Xs2LFqGIbWqFFDfX19tWHDhnZH137++WctXbq0enh42O2hW7VqldatW1ezZs2qR48eTYbeJ64tW7ZoSEiIGSZtH45r1qwxg3TLli3tdiScP39ee/furQULFnzkqbEpRUKn4q1Zs0YNw9A//vhDVf+5PtDGdhTCy8tLs2TJYhfCU7I9e/ZotmzZdNasWXrjxg1V/efL0oQJE7RevXpqGIY2aNBA06ZNq/7+/uaXipTM9hx4+JS88PBw7dSpk7q5uam7u7t+8803dss3b96s1apV0xYtWpjXTadEtvEfPnxYW7Roofnz59fixYvr6NGj7S5leDhM2sYcHh6uN2/eTJa+J5a414KpqvnlMCYmRidOnKiurq7apEmTeEenxo0bpy1atNC1a9eal0KkVLYaJLRj6JNPPlHDMNQwDP3f//5nV4ft27dr5cqVtWnTpin2ebBo0SIdO3as3fvevn37tEKFCpohQwadP39+vDBpq9fJkyfVMAz99ttvk7rbic6283T//v3xjkz269dPDcPQzp07253Ga3vNHDx4UCtVqmSe2ZXaNG3aVAsWLKi9evVSNzc3nThxot3OlIkTJ2rOnDl1zZo1ydhL/Jek6iAZ98uz7bSMFStWqOqDD84ZM2Zo3rx51cfHx24P48mTJ/WDDz7QdOnSpfjTuWJiYsw3xkyZMiUYiGbMmKGFCxdWwzC0XLlyWqJECc2WLZsGBweb1xKkVHG/NGzdulVz585thsmYmBiNjo7WUaNGqaenp7Zp08Y8dXPv3r06ePBgdXd31/HjxyfnEBLNiRMndNmyZXrkyBGNiorSa9euqWEY8U7TiXsNxC+//KLLli2Ld81wStWrVy8dNWqU1q1b1/xwjDuxhKrqzZs3dfXq1VqtWjXNnTu3GoahQ4cOtZvZNaWxvQ6OHj2qgwYN0l69eum6devMo+/Xrl3TIkWKqGEYOnDgQL1z547GxMTor7/+qrVq1dLg4OAUHSBsf7cDBw5oYGCgFixYUNu0aaN169bVoKAgbdq0qd0XQ1uYTJ8+vU6cODFFB2ibuM+BHj16aLVq1bRmzZr6ww8/6N27dzU8PFx79Oihnp6eWq1aNT1y5IhevXpVN27cqLVq1dKmTZum+OujbM+DI0eOaJcuXfS1117Tb775xgwJV65c0a5du6qTk5N++OGHeuDAAVVVXbx4sdapU0eDgoJS7OsgMjJSy5cvr05OTvrtt9+aYfhxYTLu++LgwYM1MDAwRe9Yjjuee/fumRPMPXy6bs+ePdUwDO3SpUuCYTKlvw5s7wW2z8C4Zxjs2LFDc+TIoYZh6Jdffmm3M+XPP//U2rVra5UqVfTy5ctJ22n8Z6XaIBn3dNY//vhD+/Tpo0FBQWoYhv7666+q+uBFNGLECA0ICNBs2bLpe++9pwMHDtSaNWuqm5ubeUqbaso8rS02Nlajo6O1devWmi9fPnVzc9PGjRsnOGHE33//rSNHjtS6detqw4YNdfjw4Sn+FK64fzPbF+a4RyZt10xeuHBBhw8fru7u7urj46PZs2fX9OnTq7+/v93ECynxOWBz8+ZNLVq0qLm3PTg4WEuXLq2+vr7aqlUr/eOPP/TatWvJ3c3nasWKFerq6qru7u5ao0YNu+tfVOOf7nf9+nX9888/tWbNmpo7d267dVOiw4cPa2BgoHp6eqqzs7O6uLhop06dzGsjL1++rGXLllVvb28NDg7WPHnyaObMmTVnzpwp/lpx1QeTCpUpU0br1q1rNzttxYoV1dPTU+vVq2f3JTksLEwrVKig2bNnT7FHoGzi3ubFFqSrVq2q1atXV8Mw9KWXXtITJ07o7du39Z133tH06dOrl5eXZsqUSQMCAtTf31/37duXzKNIHEePHjXf3zNkyKCGYWiLFi308OHD5vKuXbuqYRjq7e2t6dKl03Tp0mmePHlS/Ovg6tWrWqtWLU2TJo2OHz/+sWEy7lGozZs3a/ny5bVdu3apYqeKzZo1a7RWrVrq5ORkzpdhYwuTr732mp48edJuWUr+LmDbmXLw4EGtWrWqVqxYUZs1a2buNLlz546OHTtW06dPr+XLl9eff/5ZT58+rd9//71Wq1ZNAwICUvSp3Uh8qTJIxn2R169fXwsVKqSFChXSRo0amV+kf/rpJ1V9sFdqwYIF2rBhQw0MDNSAgACtV6+e/vDDD+ZjpNSjEDZ//fWX7t27V0ePHq0eHh7aqFEj89SOh/espeRrIR+lWbNm2rdvX1V9sIPBFiYLFy5sNwHP9u3btX///tqhQwcdM2aM3V7KlP4cuH//vp48eVK3bNmis2fP1nfeeUfr16+vadOmVcMwzABdqVIl7du3r44ZMybVTaoRGxurY8eO1ZCQEPX19TVP8U7otN+47yF//vmnent762effZZkfU1MtrH069dPGzRooGvXrtWbN2/qW2+9pe7u7lq/fn3zdXDz5k394YcftHv37tqhQwf9+uuvU/x1sbbxz5kzRzNmzKg///yzuWzQoEHq4uKijRs3Vl9fX61fv77dDrTbt2+n2KPxD79nXb9+XStUqKA1a9a0uz6sUqVKmj59evOap/DwcN2+fbsOGjRI27dvrwMGDEjRR6FsbKfuf/zxx1q3bl3duXOnHjt2TMeNG6e+vr5arVo1u3tFLl++XIcNG6Z9+/bV2bNn65kzZ5Kx9/+e7flw7do1rVat2hPD5MKFCzU8PFyPHDmiDRo00KxZs6boU/xt7/N37tyxO5q2adMmrVat2mPDZKtWrVLVLO6nTp3STJkyaUhIiJYtW1YDAgI0TZo0unjxYlX953MgJCTE/M6cLl06LVGiRKq9nyqeXaoMkjaDBg1ST09PnTt3rjnr6OLFi7V8+fJ2YdL2ReP48eN6/vx5uz1uKS1AxD2V8+EvyJcvX9bPPvvMDJNxx3nixAm7SWZS8h63h7Vp00YDAwPN8cXExOjWrVs1JCRECxUqpLt27XrsdTMp7Tmg+vR9bt26tQYFBenMmTO1d+/eWq1aNc2aNaumS5cuVUzz/rDY2Fj9+uuvNSAgQLNmzWqGhMfdzuHGjRuaK1cu7datW1J1M1E8/Bxo2LBhvPuADRkyRL29vVPlfQJtf1PbzMMHDhzQfv36mctHjRqlzs7O5k7DV155RQ3D0IYNG5p751Oi6dOn68WLF+O1Hz16VAMDA3XixIlmmyPc5uXh1/agQYO0T58+5s/37t3TuXPnatq0abV69eq6Z8+eVPX5p2p/nWNYWJgZJn19fRMMk7YJeL766iutV6+e+vj4pOijsbbxHzlyRF966SXt2bOn3WU7jwuTnTp1Ui8vL7v5E1Iq22fCjBkztHz58ua8B6tWrdLq1aurl5eXzp4921w/PDxcFyxYoNOnT9ctW7ZwOisSlGqDZExMjDZu3FiLFSsW71SMP/74Q8uUKaOGYehvv/1mtj/84ZHSPkxsb5bHjh3T7t27a8WKFbVOnTp205VfunTJPDLZtGlTvXDhgu7evVurV6+u5cqVS3Wz8ak+2LOcPn16HTZsmN2XCluYLFy4sN0EPCmVrf+2UzDPnDmj06dP18GDB+vChQvtTk2zXfcwceJETZMmjd1Rl+vXr5uTMaR0p0+f1qNHj9pNV24Lk4GBgRoSEmIeaXhUmDx+/LgWKlRIX375ZbuZLP/LbGM5deqUTpo0SRcsWKAdO3bUJUuWqKr99O1DhgxRLy8vrV+/vvkcSWhWz5Ro//79ahiGTp06VVX/OQNj/fr1mj59ev3oo4/MnYyrVq3SwMBADQwM1BYtWqTI66B++OEHNQxDBw0apNHR0XZ/vxUrVqiTk5MZrOPe5sV2GuO9e/fiXS+Wkp8DttfBiRMndOzYsfrBBx9o27ZtzedD3PdMW5isUaNGqtupovrgPomGYejHH3+skZGRjw2T+/fv16pVq6phGJomTZoUXQ9beNq/f79mypRJS5curV9++WW89TZu3PjIMJnQjpmU5OGJ1n788Ue7HaMxMTG6a9curV27tnp5eemcOXNS3b1y8fyk2iAZGxurNWrU0IIFC5ptca9xmj17tnnIftmyZeY2KdXD18DkypVLX3zxRc2XL586Oztr3rx5zckkLl26pGPGjNE0adKov7+/5syZU/39/VP8zKQPH4GJ+0ZYq1YtLVy4cLy/8datW7VAgQKaL1++FH0q5/Lly3X58uXml999+/Zp9uzZNV26dOrn56fOzs76wgsvxJtYxzale9wdKqnF66+/rrly5dI0adJogQIFdNmyZeYOldjYWB03bpxmyJBB8+TJY4bJh6+DjI2N1Y8++khdXV1T3HUhtvcCZ2dn872uXr165vK4kygMGTJE/fz8tGLFinrw4MHk6G6isb3u7927p6+88opWqVIlXjiaPn26pkuX7v/au++oKs6tDeB74NAF6R0iXQSNINgrAnqjEMUWI8aYqLFhBWtsEWPvHRXFWIMae6Ix1mgiaiyJCnYloViwAUp9vj/4ZnJGMLneGI9z2L+17splzsH1DqfMPG/Zr2xq+7Rp09CyZUvMmDFDkVM5ly1bBgDo16+f9F2u/p34+++/w9bWFtOnT8fo0aMr3OZl2LBhcHR0VPyNs7pLly7Bzs4O+vr6MDQ0hCAIqFevXrnXWAyT1tbWCAoK0oo1oerb3YwdOxYhISFSlW4Afxkmz549i86dOyu+4B5QVoHdx8cHYWFhsvXRLzpy5AhatGgBQ0ND6b5Q6dQLTH388cfo1q0bgoOD0bFjx3LPFYvpmJiY4Ouvv1b0PTF7c7QySIofnM8//xwqlQrr1q2THlMfcWvQoAG8vLygr69frgdKiR4/foyGDRuifv360pdlQUEB5syZAwcHBzg7O0t7Yj548AB79+5Fly5d0KNHD8XfPKpTr74ovheOHz8OAwMDWfEc0U8//QQbGxvFVugVRyHE/b1+//13uLm5ITw8HN9++y1KSkqwb98+GBgYwMfHR3YDdefOHVhbW2PmzJmaav6/om3btrC2tkbfvn0xdepUtGnTBmZmZliwYIF0kyyGSUdHR7i6ur50DcyTJ08UU3hKfYp2REQEwsPDsX37dpw+fRqNGjWCIAiIiYmRnq8eJocPHw4nJyfFrglUd/PmTezbtw9NmjTB4sWLpePi98HKlSthYmIi7Q97+vRphIWFYdCgQRpp7z/VqlUr2Nvby96nqamp+Pzzz6XpaA8ePEDbtm1RpUoVGBoaYsWKFbJ/49ixY2jWrBmio6NlhVaUQj00q29zEhMTg/feew/79+/H0aNH8dlnn0EQBAwZMqTc+t+ioiKsXbsWrq6uil8bLLp69SqWLFmCpk2bYvz48dJxMSS8LEyWlJRozQylr7/+Gra2trJweOfOHfzwww+YPXs2Nm/eLB0/duwYAgICYGVlhby8PK0IU9euXYOVlRXs7Ozg4uICKysr2Nra4vDhw+XO78yZM9IWWNu2bdNQi5mSKD5I/tXw+9WrV2FpaYmAgIByQfHChQuoVq0a5s6diyZNmqBmzZqK3BdI/Uvg5s2bsLKywoQJE2TPKS4uxurVq2FhYYGWLVvKprUBKLdn2NvuxS8+9VGkdu3awcHBAWPHjkVmZqY0QvfHH3+gSZMmaNy4Me7evVtuz0Sl9sCLIXLo0KFSAFi3bh3c3d1lozCTJ0+GoaEh1qxZU+71d3R0xIcffvhG2/1vmjhxItzc3LB582bpvf3tt99CEASYm5tj1qxZsr1h582bB319fWm6mzolTu9JS0vDgQMH0Lt3b1nnyK1bt9CuXTuYmZnJApP6518b1sAUFBSgadOmUnXiM2fOAJAHjStXrsDPzw8uLi6oW7cuatSoASsrK0WOQi1evBgWFhZITk6WiqgBQExMDARBQFxcnPS6XrhwATY2NjA1NcX8+fOl5x48eBDh4eGKLagivrbq14Jr165h+fLlaNiwoexcCwoKMHjwYAiCgGHDhlUYJrWpMmn37t0hCAJsbGykuhAvzt4Rw6SVlRVmzZqldetlv/rqKxgbG0tBcu3atVLFYpVKBUEQMG7cOOn5J0+eVGxxHfG1VR+Nnjt3Llq1aoUzZ87g1q1bWLNmDezt7REcHIyTJ0+Wu6c6efIkoqKitGqAgf17FB0k1S8aiYmJ+OKLLzBu3DjcunVLmq6zf/9+mJiYoGbNmlKRgcuXL2PSpEnw8/NDeno6li1bBkEQsGfPHo2cx/9K/PCLa3zE9UDqo0vil0lxcTF69+4NIyMjqZCEEovIiAoLC2U3TUBZD/z333+PDz/8EKampvDy8sLAgQOlUdjvv/8egiDg66+/BqD8wjqbN2+GIAgYMWKErBDAmDFj4OTkJIXo2NhYaS2U+Dd78uSJdNPcp08franEdv/+fXTp0gUff/yx9Lk4evQojI2NER0djc6dO8PIyAhz5syR1ooBUHQhCXV5eXnS2iZra2scP34cwJ8zMdLT06UwqT4yqS0jD6KUlBSEhYVJI0/qN8bi5/7cuXPo2bMnmjVrhi5duij2pmn06NGwtraWzvHatWvSd3zPnj1hbW2NoUOHSp1lZ86cgZubG0xMTPDuu+8iICAAbm5ucHZ2VuTn4JNPPoGpqams06ekpARBQUEQBAHe3t7Sa6u+7nXQoEFSmNSGUfiXyc3NRc+ePSEIAsLCwl5affbBgwcICAiAi4uLtARAW4h7SLu6usLf3x96enoIDw/H+vXrce3aNURERMDLy6vcNh9KcuTIkXLHUlNTMXbsWERGRmLYsGHS8efPn2Pv3r2ws7NDvXr1KgyTShtgYJqj2CCpfsP//vvvw9zcHBYWFjA1NYWzszMSEhKkL8ODBw9Ke0haWFjAysoKgiBIFQzz8vIgCEK5kTwlyM7OhpWVFdatW4fi4mI4OzujcePGsilO4k3i2bNnIQgCtmzZoqnmvhYlJSVYv349unTpIvW0N2vWDL6+vigoKEBubi4uX76Mjh07wsXFBSYmJhg6dCi2b9+ODh06IDg4WFahVonWrl0LQRCgo6NTbn3jlClT4OzsDADSWqiEhATZWqiePXvi888/B1B+XaCSFRQUYPr06dKN9KVLl2BqaoouXbogMzMTV69ehZOTExwdHTFz5sxyI9FK6kh4mS1btqBFixYQBAGzZ88GUBae1IswtWvXDlZWVujZs6cmm/paVNQhVFRUhDNnzqBBgwawsbFBcnKy9D1YWloqvc7inrrif5VEPO9vvvkGgiBg0qRJuHnzJlQqlWwKY/fu3cuFyWvXrmHGjBmIjIxEREQE4uPjFXsTPXXqVHh5eZULwffv38e7774LQRDQu3dv6fVX/4wPGjQIenp66NOnj+K39wBeXuchNzcXH374IQRBwMSJE3Hv3r0Kn5eTk6PYkTjgr7+/v/nmG3zyyScIDQ3Fpk2bZDPQ+vfvDw8PD8WOxE6ZMgWCIMiWcQHAl19+CUEQULVqVcybN0/2WElJCfbs2SOFyZSUFK2YxsvePMUGSVH37t3h6OiIlStXIi0tDXl5eahVqxY8PT0xd+5cqfrkzZs3sXjxYvTo0QOxsbGyEscbN26Enp6etF5GSW7cuAE/Pz+0atUKADB//nypat+L5apXrFgBIyOjv1xsrhQrVqyAmZkZ6tSpgyZNmsDBwUF2swiUfVFevnwZsbGxUkEhIyMjODg44MCBAwCUOXVx9erV0jYFvr6+cHJyksp4A2U3iUZGRvD394e+vj5Wr14tm6r1ww8/wNvbG1OmTNGqECkSbyby8/PRuXNnNGzYUDZdr2XLllJnkjjtUclerNYLALt370ZAQAAEQZAqtb4YJkNCQuDq6qrYad3An5/fR48eIS0tDRcuXJBNWz537hwCAgLg7OyMnTt3ysKk+n+VRv2GOT09XdrvzsjICO+99x7S0tJkIwoVhUltcPjwYWzdulW61t24cUP2nf7o0SPUqFEDRkZGWLRokTQiqf73++STT2Bubi573yiReN53797Fjz/+iN27d5er1B0VFQV9fX1MnTr1pWFSqdQr9I4bNw6dOnXChAkTZEUECwoKyl3zTp8+jaZNm6J9+/blln0oxa5duxAVFYXTp0+Xe2zGjBkQBAG1a9cuN/OouLgYe/fuhbOzM7y9vSv8fcb+jqKD5O7du+Hh4YElS5ZIPUk//fQTTExMYGlpiSpVqmDOnDl48ODBS/+NU6dOITw8HO+8845iF9d/8cUX0NXVxY8//ggA6NWrF3R1dTFgwABp4+lDhw4hLCwMvr6+WnMjMXPmTOjr60NfXx8JCQmyx17smUxJScHy5cvh4eEBQRAQHh7+Jpv62ohrIuPi4pCeno69e/fC19cXDg4O0kUgNzcXcXFxsLCwQMOGDWU3yz/99BNatWqF6tWrK7rnWd25c+cqnJr7/Plz1KxZE127dpWOXb58GUFBQTh8+LBsU3YlEm+cxMDwYm/6nj17EBAQAENDwwrD5B9//KHoURjx/C9duoT69evD1tYWpqamcHJywrp166SiIS8Lk0rVrVs3hISEyG6Iv/nmG+jr60NXVxc9evSQjqvPQhDD5LBhw6QQIX5PKjFQi9+FixcvRlFRER4+fAgPDw+4u7vL/jY5OTnw8vKCubm59FxAfo1Q+jVRfYuLmjVrwtLSEgYGBtDX18fo0aOl0dpnz56hQ4cOUpgUp/8rnfr5Ozo6wsHBAT4+PqhSpQpq1KghVTQG5J3H27dvR2hoKKytrRW7d2xSUhJWrlwpzc5KTU3Fxo0bpcdLS0sRHx8PQRDQq1evctWKS0pKsH37dnh7eyt2VgLTLEUHyW3btqFt27bSm//06dMwNjZGz549kZmZiXr16sHe3l42MikqKSlBdHQ0GjVqBFtbW0Xsk/SyIjOFhYWoUaMGWrRoAaBsqm5sbCwEQYCenh4cHBykil1KL+WtfhGIi4uDtbU1TE1NERAQIK2FfHGtjLqsrCwMGzYMNjY20s21Uvz666/Q0dHB0KFDpXMtLS3Fzp07pTAp9r5evXoVn376KQRBQKNGjfD555+jV69e8Pf3h7W1teLfB6KoqCgYGhrC0NAQDRs2xNmzZ6XX/9mzZ6hevTqaNm2K27dv49KlS5g8eTJcXFxkvdRKnM6qvsF2ly5dEBgYiHfffRdr1qyRrW/avXs3AgMDXxomle7q1auwtrZGo0aNMHv2bKxcuRIdOnSAIAiYP3++NAJ19uxZaS3gli1bFBsm8/LyMHbsWFhbW8um5vXu3Rs1atRA27ZtIQgChg8fLj2mPm23e/fucHBwwGefffaXHaxvO/UiY+IyhefPn2PhwoWws7NDnTp1XjlMKt2NGzdgb2+P5s2bIykpCUeOHMHEiROlSs1iaMzPz0fHjh1hYmKCzz//XGvC5O3bt+Ht7Y2wsDAcOnQIAJCZmQlLS0vUqFFDqmoOlP0NWrVqBVdXV/j5+Sm2RsCaNWsgCAJiY2Px5MkTqaNAEASsXbtW9twJEyZI07wrCpNKHY1lmqfoIJmVlSWFyDt37sDV1RWRkZG4ceMGgLJ9tXR0dGBra4upU6eW22C6V69eeP/99xVRZEEMkffu3ZONPBQWFqK4uBjx8fEwNzfH1q1bpcd2796NMWPGoEePHpg2bZpitjB4GfWAeOrUKWzbtg0nTpxAfHw8bG1tERgYKL32Fd0oi8eys7Nha2uL/v37v5mGv0aHDh2SisSI74mSkhLs2rVLCpPifph37tzBypUrUatWLdjb28Pb2xvdunVTxPv9vzFy5Eg4OztjxIgR0vRlFxcXbNu2Tboo7t27F2ZmZrCxsYGrqysMDAwwY8YMDbf8nxFvfi9dugRra2v4+fmhTZs2UogYPHiw7DUWw6Spqani10eLSktLpQJigYGBso6BAQMGSOuF1APjuXPn4ObmBj8/v3KFupTk0aNH0neA2KEkHr9y5Qqio6OlkCVSD5Pvv/8+PDw8FDuVU73I2Itr3Z89e4aVK1fC0tLypWHSxsYGs2bN0prOFPE6MGbMGPj7+8tmWowePRq6urpYu3atbKpzfn4+wsLCYGtrqxVBUrwHqlGjBg4fPiwdnzx5MgRBgIuLCxwcHLB06VLpsQ0bNmD69OmKLbQkdqYMGzZM9jk4dOgQ3nvvPVhbW5erRK4eJpW4SwF7OykiSP43X/gbN26Eg4MDDh48KB1buXIlWrZsicDAQFn5b/VeSCX1wty9exc2NjZo0KAB5syZI3vs999/h729PTp16qSh1v271ENkt27d4OzsjHHjxkmv5ZdffimFSfXA/Pjx4wqna3Tp0gUBAQHS9Le33cvWclYUJu3t7aUwCZStC7l58yYePXqkNZXYiouL0bFjR4wfP176G6SkpKB+/fqws7PD1q1bpXM9ffo0unbtisGDB5eb8qNUGRkZCA4ORuvWrfHzzz9Lx1u0aAFdXV307dtXti50z549cHNzg729PZ4+faroc1cXFBSETz/9VPp5xIgRUKlUWLlypbQuWH1PwV9//VXqbFK6Bw8ewNbWFpGRkbJrWmpqqlRYRT1Mqn/2lVps7K+KjInv6b8Lk1ZWVqhWrZrWVSZt3bo12rRpI/0cFxcHlUqFFStWSJ8F9Y6V/Px8RU9tV1daWorFixejb9++0rFJkyZBpVJh48aNuH79OqytreHq6iq7F1RijQSgbF9MccuSirZs+vHHHxEeHv7SMKlSqdC1a1fFDy6wt8NbHyTVP+iTJ0/GoEGDMH36dGRlZcluhqZPnw5DQ0PpBjozMxPdunVDbGxshf+uEm+kcnNzkZSUhMDAQBgbGyMwMBBr1qyRgtLSpUuhr68vG5XUNm3btoWzszOmT59ebk3r1KlTYW1tjcDAQGRlZeHZs2eYMWMG7OzscP36dek1F6fAhIaGytYQKUFaWpq03kN983mg/Mik+iiNNsnJyUF6ejqGDRsmKzIEAL/99pu0Xm7r1q3S6/viFDYlTWkTRwzUv7O+/vpr1KxZU7aRdlxcHAwNDdGpUycIgoDPPvsMqamp0uP79u1TdIh68f1eWlqKGjVqSHtijhw5Enp6erIKxcXFxWjbtq3s76Bk4t9AnJUSHx8PExMT9OjRQzbj5sqVK38bJpXm74qMAf9dmHz48KHiR2PE94H691jr1q3xn//8B0DZSOSLnwUACA0NLRfAtcXTp0+lz0BycjIMDQ0xa9YsqcMgNjYWZmZmcHJykraCUyJxOqu4tZGoqKhIdo34qzAZGxuLqlWrlivIyNj/4q0PkqKIiAjo6urC3NwcgiCgXr162Ldvn3SBOHz4MARBQKdOnTBr1izExMTAyMgIK1askP4NJYbHimRnZ2PTpk1o3LgxDA0N4enpiY0bN2LXrl2oXbs2unfvrlUbKotmz54NR0dHbNq0STa9V72zYdGiRbCwsICFhQVatmwJXV1dTJo0SXq8pKQE3377Lby8vHD27Nk32fzXYvjw4RAEQVrj8GKxDPUw6erqKhut0gbR0dFwc3ODhYUFBEFAfHx8ueeIYdLJyQlff/214joL1O3btw/6+vo4evQoAPmWD6NGjZKeFx8fD11dXSxfvhx5eXno1asXdHR0MGzYMFy8eFEjbf83pKWlISkpCUDZ6ErPnj0RGBiIHj16SCOR6tNWN2zYAAcHB+zatUtTTX5txNf+/PnzsLOzw6lTp5Cfn4958+ZBX1//pWFSpVKhT58+mmr2a/HfFBkTiX+n58+fY9WqVbCyskLdunXLLW1ROvVORaCs+Nw777yDiIgIqFQqJCYmyq6TO3bsgLOzM1avXq2B1r5ZQ4cORe3atWVB6eOPP0ZERARCQ0NlszWUROxM+eijj6QOwzFjxkiPFxcX/9dhUtuq9jLNUUSQPHbsGN59911s3boVly9fxt69e+Hq6goPDw8kJydLvaxJSUkwNTWFgYEB7O3tpT3UtNmKFSvQpk0b6OjooE2bNnB0dISurq5iF4//lT59+sDHx0cqEpGZmYm1a9ciPDwcjRs3xrhx4wCUraGJjIxEvXr1ZBdaMXTl5OQodl3IhQsXUKtWLbRq1arc1Cz1MLl7927Y29vD19dX0aMQ6j766CPY2tqiY8eO6Nq1K0xMTFCnTh0pZKm7ePEiAgMDoa+vr+jRh507d8LT0xPW1tY4fvy47DHxxvjgwYOwsrLClClTpBvHpKQkGBsbQxAEDBgwQCtuoktKStC9e3cYGBhIYfHIkSNQqVQQBAGjR4+WPf/06dMICQlB06ZNFbseUCR+dz1+/BgtW7ZE48aN8f333wMo+z6bO3fuS8NkREQEzM3NFVuZ9O+KjDk6Ov5lmBRvvps1a/amm/6vEjsVxVCUmpoKd3d3CIJQruPg9OnTCA0NRVBQkGKnNb+Krl27wtPTU/rcnzp1Cg0aNMC2bds03LL/3ZIlS6BSqTBs2DA8efIE165dQ7du3cp9970sTDo4OMjuhxh7Xd7KIPnivPU9e/bAz89PduN88eJFuLu7w93dXbZ/4KVLl8ptB6CkaWz/LfVzKigowJYtWxASEgJTU1MIgqDom+cXiYU1evbsCXd3d2zZsgUHDx5E3bp1YW9vD39/f9SpUwcqlQpz586Vfk+9Uq8S3wPi5+Dp06eyUZZJkybB2toaq1evLjfKrh4mv/vuO615H1y7dg3t27fHqlWrUFRUhIKCAmzYsAGGhoZo3ry5tPWNuvPnz8umfirV3r17UatWLVStWlUKk+qv+5o1a2BlZYUTJ05IxyZPnowePXpgw4YNWjUiuX37dlhYWGDKlClSYNq4cSMEQUD9+vWxatUq3L9/HwkJCQgJCYGVlZViy/q/KCMjA5cuXUKDBg2QnJws+057+PDhS0cmr127pvjw8HdFxv4qTD579gxfffUV0tLS3myj/2Vip2J4eLg0unT69GlUrVoVDg4OiI2NxalTpzB16lQ0adIElpaWsn0ltVlKSoo0Q23IkCFo0qQJ7O3ty1UrVYLS0lLk5+fD1dUVvXv3ln2WL1++/F+FyePHj6NevXrw9PTEo0ePtGZ2Hns7vHVBUj1Erlu3DvPnz8fcuXOldTDqzxHDpFjSvaKRF23+wLx4bhkZGTh06JCi10H9FbFKpaGhIQRBQPPmzWU9bF5eXujQoUO531Pye+Dq1avw8fFBXFycbESqfv368Pf3l4pFqd9UKvl8K9KuXTv85z//QbVq1aTed/F8v/nmGylMvjhip06JHQnqYWDPnj3lwqR6oSlBEHD8+HEUFhbi/PnzCAkJQZ8+fbTmvSCeR1FRERo2bIiAgADk5eVJj2/duhWOjo7S2iFTU1MEBQVpzcyMx48fo1q1ajA3N4e3t7dUYEP9eimGSQMDA/Ts2VMrRqFfpchYRWFSiZ/7ivxdp2JiYqJsq5uwsDCYmZlBEARYWFigRYsWlSZEio4cOQJXV1fY2tqibt26iv8uePLkiWxmhfgZSEtLkyo1qy93eDFMnjx5UrF7pbO321sXJEURERHSTYH4P/WKrOob0Hp7e8PJyQnr16/XmhsnVrGbN29i8+bN2LNnj+xGMj09HYGBgRg4cCAA7QlT06dPhyAI8PT0REBAAGbOnAkAOHPmDKytrWUVK7XlnF8k7oulr68vFRAqLS2Vznfbtm0wNDREaGgojh07psmm/mM3btzArVu3Knxs165d5cIkUFa9s2bNmnB2dkZoaCj8/f1hZWWl6G1eXiysA/xZcfLIkSMwMDDAl19+Kfud69ev49ixY1ixYgVSUlIqrGaoVLm5uVi6dCmqV68OQRCQlJQk1QdQ/xs9fPgQCxcuhCAI6Nevn6aa+9r9t0XGKgqT2uLvOhXVA2ZOTg5u3bqFvXv34vr16+X20a4sHjx4gPT0dEXvmQr8+d0nvvdf7GD5b8MkY/+GtyZIqldV2759O9zd3bFq1SocPXoUs2bNgiAICA0NlW1rIIbJ3377DZaWlkhMTHzj7Waad/PmTXzxxRcwMzNT9BoIoHwPenFxMRo2bAhnZ2fMnTsXRkZGiIqKwpYtWzBy5Ej4+vpi06ZNGmrtv0v9O2Ho0KEQBAFRUVGyoCVeJLdv3w5BEFCnTh3FVqITC4p4e3ujf//+2LFjR7ntafbs2YMaNWqUC5PXrl1Du3btUKdOHURGRmrFdM7U1FRMmzYNJ0+elB3PyMhAeHi4bN/YynCz9PDhQ6xevRoODg6oX7++rGCI+vnn5ORg2bJlWvEeEFX2ImMAdypWdteuXZPNuHrxXuFlYVJbRuXZ20tjQXLNmjXliiMAZTdTcXFx6Nq1q2yq6rJlyyAIAlq1alVhmFR6jxP736xbtw6ffPIJjIyMMH36dE035x8RL/7i1iWin3/+GU5OTli4cCHS0tJQr149hIWFoUGDBnB1dcWnn36q2OJBr6Jv377SSIv6FB3x77Z582YsWrRIU837RwoKCvDee+9BEATY2NjAyMgIVapUgbOzMwYOHIjk5GTpPXHkyBHUrl0bpqam5UZgCwoKZHvFKVVJSQliY2MhCAKcnJwwYsQIXLlyReqJ37p1KwRBkCoRatPNkniO+fn5ePDgAe7fvy9NW3z06BFWr14tTVd82VQ1bQsSlbHIGHcqMnViZ+mUKVNe+hz1MBkTE/MGW8cqM40EycLCQnz22WcQBEFWJGPatGkQBAFeXl7StKWXhcmX7ZGnTTcU7K/dunUL5ubm8Pf3R0JCgnRcye+B7Oxs1KpVCzVr1sTFixdRUFCAoqIiDB8+HCEhIcjKysK9e/cwZ84chIaGStO+v/76a003/bUZO3YsevbsiQkTJpRb19O7d++/DJMv+1kJ7t27h/fffx/6+vqYMmUK5s6di379+klbHnl5eeHTTz/FkSNHkJCQgEaNGsHCwkIasVPiOaur6HN74sQJ9OrVC4aGhnBzc0OvXr2QmZmJ4uJifPjhh6hWrZpWrfsRQ+Tly5fRpk0buLu7w9PTE2FhYdJU5dzcXFmYfNlUaKXiImPcqcjKf59nZWXhgw8+QM2aNf+yHkBaWhoiIyNhZGSEu3fvKv66wN5+GhuRfPToEQ4cOCA7VlBQgKioKAiCgHfffVea169eNEAMkyEhIbIqhaxyunHjhmwtmJJDJADk5eVh2rRpqFOnDszMzDB27Fhp3dw777yDCRMmACgbrcjMzESfPn1gYmKiNdPYIiMjoVKpYG9vD0NDQ5ibm5f7nhDD5IABA7TmJlp8396/f1+qMLhw4UIAZR0mO3fuRJs2beDs7AxDQ0M4OTlJ6+V0dXXLbcyuNGJ4SE9PR1JSEsaMGYPExEQpEFy+fBmdOnWCra0tzM3NMWrUKPTp0wfVq1fHypUrNdn01y41NRWWlpYIDAzEkCFDMHDgQPj4+MDY2FiqE/D06VMpTIaFhWldgTUuMsadipWZ+L7Ozs5GTk6O9POxY8dgYmKC/v37A3j5e/7q1auKr9TMlOOtWCMZGRmJoUOHAihbF9WlSxdpLyRxjZB6mFy8eDEEQcD27ds10l72dlLijURFwbe4uBh3797FgAEDYGFhAV9fXxw6dAiJiYnQ09OTTe0GtGda98GDB+Hn54cNGzbgyZMnSE5ORt26daFSqfDNN9/InivOaPjoo49koxZKJIaomzdv4smTJ3jw4AGaN28OExMTLF68WPoOfPbsGR4/foylS5eib9++qFq1qnTzqOStDdQLp7m6usLU1FTaG9LIyEiabVBQUIDz58+jV69ecHZ2hr29PQRBQLt27WTraZXs2bNniIyMRMOGDXHmzBnp+AcffABBEJCcnCwbsUtKSoIgCIiMjNSavwHA6wEB7lSs7G7evAldXV00a9YMK1eulK4Ds2fPho6ODvbu3QtAe9//TDk0HiTz8/PRq1cv6OjoYOLEiQDKQmP79u1RpUoV9O/fv8IwqfRSzoyJN4QZGRnYtm0bFi9ejP3790u97UDZur+2bdtCEAR07twZTk5OaNmypVb2Nv7www9o06YN8vPzpWNHjhxB06ZNoaenVy5Mdu3aFXPmzHnDrfx3pKamQhAETJ48GYWFhXjw4AFatGgBU1NTLFq0qMKqi9evX8fWrVtx8+bNN97e1y0jIwNeXl4IDQ3F7t27UVRUhE2bNqF169bQ0dHBjBkzZM/ft28fxo8fDxcXF5w9e1YzjX5N1G8Ec3Nz4e7uLoUEABg5ciT09PSwatUqPH78WPY7OTk5WL9+PVJTU99om183Xg/InYpM7qeffoIgCFCpVIiMjESLFi1w8+ZNZGdn47333oO/v7+iOxCZ9tB4kATK9sgSCyuMHz8eQFlobNeunRQmxQuoWEhCfU0EY0qjPgrj6ekJS0tLGBsbS5VJd+zYIT33jz/+QGJiIszNzaGrqwtBELBq1Sqt6YlcsGAB5s2bh+7du+O9994r97gYJlUqlezvok6JfwuxI6GgoABjx45FSEgIjh49Kj0uhkkzMzMsWrRIFiK07Xvv22+/hZGRETZs2CA7/ttvv6Fr164QBAG7du0q93vqnQ5KJL5v79+/j9LSUmRkZKBKlSpSBfIRI0ZAT08PCQkJ0rkWFBRg0KBBUgerEt/76ng9IHcqsorvZRcsWAADAwOMHTsWHTt2hJmZGRISEjBmzBj4+voiPj5e8d+BTPneiiAJlIVJscT3i2HS3Nwcffr0qbR7ITHlq+hm7/bt23BxcUHLli2xfft2XLt2DcnJydJ0vRfX/6WlpaFLly7w8PCQyuAr3XvvvQc9PT3o6emhatWqsLCwwE8//VTueUeOHEGLFi0qXAOk5Bvpq1evYsmSJWjatKn0vQf8eU5/FSa1yaJFiyAIAjIzMwHIi6ydPHkSbm5uaNiwIZ4+fap1556dnQ0rKyt89dVXAIDatWsjOjoaAwcOhEqlwooVK2SBYtGiRXB3d39pwTklqszrAblTkYnS09Pxww8/SD/fvHkTHTp0wMcff4zMzEyMHz8e/v7+6NChA3R0dBAQEKD4wlJM+d6aIAm8PEy2adMGgiDg0KFDmm0gY/+De/fuASi/99ns2bPh7e0tG4WKj4+HSqVCUlKSrKdR/J2HDx+WK3+vVJs3b4avry/Wrl2LkydPYsqUKbCzs0NwcLBsjzzR4cOHUbt2bakIjTbo3r27tOWHuOb7xZ5pMUxaWVlh1qxZePLkiSaa+q86ePAgBEHA1KlTpWPqm24PGDAAVatWxd27dzXRvH/VjRs34Ofnh1atWqGkpEQK1YIgYNasWbL3w6lTp9C0aVO0adNGqzpWK9N6QO5UZBV5+vQpgoKCYGpqiokTJ0qdaStXroSvr69UdG779u0YNmwYHB0dIQgCPvzwQ002m7G3K0gC8jA5adIkAGVhcvfu3RpuGWOvrn///qhZsybu3LlT7rGePXsiKChI+jkuLk6axiYWkHn27Bny8vIAaNco1JYtWzBhwgRERERI5/r48WNs3LgRdnZ2qFu3boVhUtumceXm5qJnz54QBAFhYWFIT0+v8HkPHjxAQEAAXFxctKYjQV1GRgY8PT3h4+ODffv2lXt8yJAhcHJy0pqpjC/64osvoKurK22HNXToUAiCgP79+0vbuyQnJyM8PBw2NjayStVKVFnXA3KnInuR+nX9+++/x9ChQ2FgYIBGjRpJnYvR0dHw8fGRnpednY3vv/8eAQEBil8jzpTvrQuSQNkNZVxcHARBQFxcnOwxbVsbxLRb37594evrKyuGIY60fPbZZ/Dz8wNQtnfii2uhAKBt27ayPTK1wbp16yAIAuzs7DBkyBDZY/n5+bIw+bLediWG6pe1OTc3Fx9++CEEQcDEiROlm80X5eTkaM12JxU5ceIETExMEBwcjC1btkjHf/nlFwQFBSEsLEzqVFGqF98DYqXVwsJC+Pn5oUWLFgDKRifEa6BKpYKJiQnMzc1RvXp1nD9//o23+3WqrOsBuVORqXvZvezjx49x9OhR+Pj4wN7eHh999BHOnTsHLy8vDB8+XPZc9VkbjGnKWxkkgbIPU79+/TB//nxNN4WxV6Z+oRdHme7cuSMbcdqxYwfs7OzQoEED6OrqYs2aNVIBDaCsMqWHhwfmzZunVR0o9+7dQ3x8PHR0dODj41Nuelp+fj42bdoEJycnBAQEKH70Bfjzgn/37l38+OOP2L17N3777Tfp8efPnyMqKgr6+vqYOnXqS8Oktvv2229hamqKKlWqoHXr1ujatSv8/f1hYWEh+3spkfidcO/ePWmtK1AWIouLixEfHw9zc3Ns3bpVeuzAgQNYsmQJRo8ejW+++UbRQQqo3OsBuVORicTX/datW5g5cyYGDRqEUaNG4fbt29LuBDk5ORg1ahTeeecdWFpawtfXF3Xq1JGtoVTqZ4Fpl7c2SAJ/VmhlTInUewvv3r2LmjVrolmzZrh9+zYAIDMzE61bt5b2gVN38uRJhIeHv7QHW4nUL4D37t3D5MmTIQgCBgwYUO4GOT8/Hxs2bICBgYHiy/yr3zzXrFkTlpaWMDAwgL6+PkaPHi2NMD179gwdOnSQwqS2TuP8O5cvX0b37t3h4+OD6tWro2PHjopcC1eRu3fvwsbGBg0aNCi3dc3vv/8Oe3t7dOrUSUOte/14PSB3KjI59euBo6MjXF1d4eLiAisrK9jZ2WHOnDnIysoCUHYPfPr0afTo0UNaNz1s2DAeiWRvlbc6SDKmZC9+2Q8dOhTu7u6IiIjAjRs3AABXrlxBcHAwjI2N8f7772PDhg2Ii4tD3bp1YWlpqTX7pY4aNQpmZmbStgZA2RqncePGQRAEDBo0qMIwWdE6SSW6ceMG7O3t0bx5cyQlJeHIkSOYOHEiBEFATEyMFBrz8/PRsWNHmJiY4PPPP6+0YbKoqAgFBQXIz8/Xqg7F3NxcJCUlITAwEMbGxggMDMSaNWukvUCXLl0KfX192aikUvF6wD9xp2Ll9mL4z8zMhK+vL8LDw3H48GE8e/YMd+7cgaWlJWrXro1Lly6V+x2xuvfFixffZNMZ+1scJBn7F6WmpmLJkiXSz+Im6hEREbh+/TqAso3lY2Ji4OHhAZVKBScnJ0RGRmrNKAxQVpWzYcOG8PHxwapVq6TjOTk5+Pzzz18aJkVK7YUXb4THjBkDf39/2dYmo0ePhq6uLtauXSvb7iI/Px9hYWGwtbWttEFS22VnZ2PTpk1o3LgxDA0N4enpiY0bN2LXrl2oXbs2unfvrujqvLweUI47FSun48ePV3h8z549sLOzk03lHjlyJAwMDGSfA+DPddQAFL9GnGknDpKM/UsKCgrQsWNHODg4yKZkTZgwoVyYzMvLw9OnT3Hq1Cncu3dPdiFRMvUAePz4cdSvXx9eXl4Vhkl9fX3079//pZVLlax169Zo06aN9HNcXJy0R6AYGNRH3vLz87Xy78DKW7FiBdq0aQMdHR20adMGjo6O0NXVVXRw4PWA5XGnYuUyePBgGBoaygqHiaZNmwYbGxvp59jYWKhUKiQkJEhhMT8/XxqF56ms7G3GQZKxf9GGDRsgCALWrl0rOz5x4kS4uLigbdu2Uo80oB297yIxDKtfBMUw6e3tLZvmmpOTg9GjR0MQBBw5cuSNt/V1Es9XPUS3bt0a//nPfwCUjURWdPMcGhpa4dYXTDupvz8KCgqwZcsWhISEwNTUFIIgKHKjcV4PWDHuVKx8jhw5gtq1a1dYZXnHjh0wNjbGqVOnMH78eKhUKixfvlx2PWjXrh0iIiI4RLK3HgdJxv4F6jdUbdu2hbu7e7lpimKYbN++vXQToS169OiB3r17S3u9qV8Mf/zxR9SqVQsuLi5Yt26ddPzBgweyqZ9KlpaWhmXLlkk/z5w5E++88w4iIiKgUqmQmJgoq9y5Y8cOODs7Y/Xq1RpoLdOUFzuOMjIycOjQIVnnktLwesCKVeZOxcpG7AARt7S5fv26bN3zmTNn4OPjA09PTxgYGGDt2rWy7W9++OEH1K9fH7GxsVq1RpxpJw6SjL0GFfUaiheTNWvWwNTUVJrWJJb3BoDJkyfDxMQEXbt2la2FULq2bdvC2toacXFxFYbJvXv3QhAEeHl5yQKXSOkjEcOHD4cgCFKxoNTUVLi7u0MQBPTp00f23NOnTyM0NBRBQUGK396BMV4PKFfZOxUrs8LCQjx79gxBQUFwdnaWVSCfNm0aBEFAQEAAfv75Z+n4Tz/9hFatWsHLy4vfC0wROEgy9pqkpqZi4sSJuHv3brlQGBQUhODgYOln9TA5bdo0ralOun//fikMde/eHdbW1oiNjZXCpPp5161bF56enrC3t9e6SnQXLlxArVq1EB4eLlWvPH36NKpWrQoHBwfExsbi1KlTmDp1Kpo0aQJLS0vF75PImKgyrwfkTkUmvgfE/x47dgy+vr6oVauWbBbO2LFjoVKpUK1aNQwaNAgdOnRArVq1YGNjgwsXLmik7Yy9Kg6SjL0GBQUF6NevHwRBQPXq1REbG4vs7Gzp8d27d8PAwACzZs2Sjmnb2odu3brB1dUVcXFxsmNimLx79650/Ndff0VQUBCWLFmC5ORkTTT3tRFfx6dPn8rWM02aNAnW1tZITEyUbhjPnj2LsLAwmJmZQRAEWFhYoEWLFhwimdbg9YDcqciAq1evonPnztLoc0pKCjw9PVGrVi189dVX0vNWr16Njh07wt7eHv7+/ujTpw+/B5iicJBk7DXJysrCxYsX0bp1a1hZWcHKygqTJ0/GL7/8guLiYjRo0AAtWrRAZmamppv62rVp0wbOzs6YOXOmtBZK1K1bN9jY2OCTTz7B/fv3cefOHUyYMAE1atSQTfFS8nTWq1evwsfHB3FxcbKS7/Xr14e/v7/sBjknJwe3bt3C3r17cf36dTx8+FADLWbs31OZ1wNypyIDykYbBUGQrfs/depUhWGytLRUqt7No9FMaThIMvY/EC/8hYWFKC4ulvUqFxcX4+DBg+jZsyeMjY1hbW2NBQsWSBvQV1QOXMlmz54NR0dHbNq0SVZARv2COGrUKJibm8PAwACOjo4QBAEzZszQRHP/FdOnT4cgCPD09ERAQABmzpwJoKyogrW1NT799FPpudp008yYOl4PWKYydyqyMo8ePYKTkxM6deokO64eJtevXy8dF6+XfH1gSsNBkrFXJIbItLQ0dO7cGf7+/vD29saoUaOQkpIie+63336Lfv36wcDAANWqVYMgCKhXrx4KCgq05oLRp08f+Pj4SOsgMzMzsXbtWoSHh6NRo0aYOHEiAODAgQMYMWIEevfujY0bN0q/r8S/w4ujp8XFxWjYsCGcnZ0xd+5cGBkZISoqClu2bMHIkSPh6+srK7TAmDbg9YDcqcjKqF/HxPfElClTYG5uju3btwP487MhhsnAwECsWbPmzTeWsddIAABijP1XAJAgCJSamkqNGzcmV1dX8vPzIwC0f/9+qlKlCiUlJVGTJk1kv3Px4kWaNGkSpaen06pVq8jPz0+DZ/F6AKDS0lLq3bs3HTlyhGbMmEGWlpY0atQounPnDllbW5OBgQGdP3+eZsyYQUOHDiUiouLiYlKpVEREVFpaSjo6Opo8jVcmvgeys7OpatWqZGhoSEREJ0+epA4dOtCoUaMoPDycPvroIzIzM6Pc3Fz6448/KCwsjKZPn05WVlYaPgPGXp+0tDTatGkT9e/fnywsLKTPNhFRcHAwCYJAKSkpRERUVFREenp6REQ0ffp0ioqKIi8vL420+3UoKSkhXV1dunLlCo0bN44uXbpEhYWFFBUVRVFRURQcHCw997vvvqOdO3dSYmIiOTg40O3bt6lu3bp09OhR0tPTI0EQNHgm7H8lvgfE/6r77bffqGXLlhQREUErV66k0tJSAkC6urr0yy+/UHh4OFWvXp327t1LZmZmGjoDxv4hzWVYxpTpyZMnCA0NRZMmTXD69GnpeEhICCwsLLBt27YKeyefPn0qm/qpLS5dugRra2sYGhpCEAQ0b95ctqWHl5cXOnbsqMEWvn7Z2dmoVasWatasiYsXL6KgoABFRUUYPnw4QkJCkJWVhXv37mHOnDkIDQ2FIAgQBAFff/21ppvO2GtTmdcDit/xly9fhpWVFQICAhAdHS2tCXdzc8PRo0fL/c6vv/6Kjh07ol69elxkS0tcvXoVzZo1w549e5CTkyN7TNzmQ1w7X1paKo1Mnj17FteuXXvj7WXsdeIgydh/QX0q49OnT2Fvb4/Zs2dLx0aNGgWVSoVVq1ZJYVF9ipMSp2++ips3b2Lz5s3Ys2cP8vLypOPp6ekIDAzEwIEDAWjP3yEvLw/Tpk1DnTp1YGZmhrFjx+LGjRu4desW3nnnHUyYMAEAkJ+fj8zMTPTp0wcmJiaK39qAsRdV5vWA3KnIAGDRokXw8vKCjo4OWrVqhYSEBOmxq1evolq1aujatavsNdeWayFjHCQZ+xvqvYcffvghDh8+DDs7O+zfvx8AEBsbCz09PSQkJCA/P1/6veXLl8t65yubmzdv4osvvoCZmRm2bdum6eb8IxVVlC0uLsbdu3cxYMAAWFhYwNfXF4cOHUJiYiL09PTKrZcV15AyplS8HpA7FVnFr+GjR4+wYsUKvPvuu9DT00NISIi0Z+TEiRNhYWGB1NRUAMquUM7YizhIMvYXxAtGZmYmvL29ERoail27dsHS0hIjRoxAfHw8VCoVli1bJguR48aNQ61atbS2KuHfWbduHT755BMYGRlh+vTpmm7OPyLePGdkZGDbtm1YvHgx9u/fj9zcXOk5mzdvRtu2bSEIAjp37gwnJye0bNkSf/zxh6aazdhrxUXGuFOR/fk5ePz4Me7du4fz588D+PNeIS0tDRs3bkT16tVhbGyM4OBgJCQkQF9fv1wFV8a0AQdJxl5CvGnIzc3FtWvXUKdOHfzwww8AgC+//BJ6enrSujf16ZwnT55ESEgIOnbsWCmnL926dQvm5ubw9/eXTfFRYi+s2OaLFy/C09MTlpaWMDY2hiAIiIqKwo4dO6Tn/vHHH0hMTIS5uTl0dXUhCAJWrVql6BtnxgBeDwhwpyL7M0ReunQJzZo1g62tLVQqFZo3b47ExETZyPOzZ8+wZMkSNGvWTFoj7+XlVW4NJWNKx0GSsb/wxx9/wNzcHPXr10fz5s2l47/88gvatWsHQRAQHx8vXRx27dqF8PBwODg4SNNYKqMbN27g8uXL0s9KCZEVhb7bt2/DxcUFLVu2xPbt23Ht2jUkJydDEAS0a9cOt27dkj0/LS0NXbp0gYeHB65evfqmms7Yv6oyrwfkTkUmSktLg42NDZo0aYJx48Zh/fr1qFGjBoyMjBAfHy8rpiNasWIF3n//fdk1kTFtofr7uq6MVV4qlYoaN25Mhw8fJjs7O7p+/Tq5ublRQEAAjRo1ivT09GjcuHG0YMECqXy7kZER7du3j3x8fDTces1xc3OT/j8ARWzxcf/+fbK2tpa2JMH/b/OxZcsWMjIyogkTJkjbumzatIl0dXWpffv2ZGtrK/0bAMjb25uWLVtGAMjCwkJTp8PYP6a+PY8gCPTbb79RXFwc1alTh4iIRo8eTUePHqXly5dTy5YtSRAEaYsPXV1dAkBVqlTR5Cm8Fjo6OpSRkUF+fn5UvXp1MjU1pZCQECIiat26NaWkpNCOHTvoypUrFBoaSsbGxrR7925auHAhXb58mQ4dOsTbO2iBwsJC+vLLL8nDw4NmzZpFdevWJSKilJQUWr16Nfn4+FBJSUm57a169epF0dHR0lZRjGkVjcZYxhQgIyMD0dHREAQBkyZNkj2WlZWF77//HjExMRg4cCBWrVqFO3fuaKil7H/Vv39/1KxZs8LXrmfPnggKCpJ+jouLk9ZBPX36FEDZNCZxJIKnsjJtwOsB5bKzs9G2bVtUqVIFHh4euHbtmvQ3+vnnn9GpUycIggBbW1vY2dnBzs4O1apVw4ULFzTccva6FBcXw8/PD71795aODRs2DCqVCgkJCXj06BEAyNbP8/WAaTsekWTsbzg4ONDMmTOpsLCQJk6cSGZmZjRkyBAiIrK1taXQ0FAKDQ3VbCPZP1JaWkrFxcWUn58vHRM3mNbX16dnz54REdHnn39O8+bNo8WLF1N0dDQZGRkREVGnTp0oMjKSevfuzRuLM8XD/88iyMrKoi5dupCrqys9ffqUioqK6MCBA5SSkkLz5s2jRYsWyT4H48ePpx07dlBoaKhspF4b2NraUkJCAo0YMYLWr19P69evp/HjxxMRUb169WjhwoXUp08f2rlzJwGggIAACgsLIxcXFw23nL2qZ8+eka6uLhUVFZGJiQkRERUXF1NOTg49e/aM/P39iYho+PDhtGDBAlqyZInsczBjxgzq2rUrVa9ena8HTPtpOskyphRZWVlSr/PcuXOl4yUlJVKvI/c+Kov665Weng4AuHPnjvT/AWDHjh2ws7NDgwYNoKurizVr1kg9zwCwb98+eHh4YN68eYpZC8rYy/B6wL+WmZmJzp07l7sO8He/dti3bx+io6NRv359tG/fHt9++63s8U6dOsHLywvDhg2Dnp5eueJKCQkJsLKywrFjx9500xnTCA6SjL0C9TC5YMECTTeHvQZiURAAuHv3LmrWrIlmzZrh9u3bAMpuHFu3bg1BEBAZGSn73ZMnTyI8PPyl02IZUyIuMvbXuFNRO61ZswaGhoZwcXGBv7+/VG1VfR/kbdu2wdHREYIgYNasWbLfP3XqFMLDw9GiRQvcv3//TTefMY3gIMnYK8rKysIHH3wAQRCwdOlSTTeH/UPqQRIAhg4dCnd3d0RERODGjRsAgCtXriA4OBjGxsZ4//33sWHDBsTFxaFu3bqwtLTEr7/+qommM/av4PWAf487FbXL6tWrIQgCBg8eLL2Pk5KSoK+vDxcXF6nianFxMeLi4mBhYYHatWvj1KlTuHHjBr766is0b94cNjY2uHjxoiZPhbE3ioMkY/+DjIwMfPzxx3zB0BKpqalYsmSJ9PP48ePh4uKCiIgIaf+369evIyYmBh4eHlCpVHByckJkZCQuXbqkqWYz9q/hImN/jzsVtcOGDRsgCAKGDh0qW7YAAH379oWxsTHOnTsnHSssLMSkSZPg5uYGQRBgYGAAGxsb1KpVq1J1pjAGAAIAaHqdJmNKJBZjYcpWWFhI3bp1o+PHj9PRo0fJ09OTiIgmTpxIiYmJVLt2bZo3bx65u7tTfn4+lZaWUmpqKlWrVo0MDQ21YnsDxiqSlZVFgwcPpuTkZJozZ45UZAz/vzUOI8rMzKQxY8ZQXFwc1ahRQ9PNYa8AABUWFlKjRo3ol19+obi4OJo+fToREeXn55OxsTGNHDmSNm/eTGPGjKGoqCgiIrK2tqaSkhLKzs6m/fv305MnT8jHx4dq165NdnZ2mjwlxt44DpKMsUpv48aN1K1bN0pKSqLu3btLxydNmkSrVq2id999lxYsWCDtj8k30qyyyM7OppiYGNqyZYssTJaWlpIgCCQIQqX/PHCnorJlZGTQBx98QJcvX6aYmBgaPXo06enpUUZGBjVr1oyuX79OdnZ2VFxcTO7u7vTBBx9Q06ZNpf1UGavMOEgyxiot9RvgiIgIunTpEqWkpJCVlZX0HDFMBgUF0axZs8jd3V1TzWVMI9TD5Pz58ykmJkbTTWLstRA7ATIyMqhDhw509epVio2NpY8//phatmxJKpWKBg4cSG5ubnTw4EE6cOAAnT17loyMjMjDw4PGjh1LUVFRUqcKY5UNB0nGWKVR0chBaWkp6ejoUFJSEsXExND06dOpX79+VFRURHp6ekREFB8fT9OmTaPIyEhau3YtqVS8BS+rXLKzs2nIkCG0efNmWrJkCfXt21fTTWLsHxGvB4WFhaSvry+FybS0NNLT0yNnZ2dKTk4mNzc3KSQ+fPiQrly5QitWrKBz587R+vXrycfHR8NnwpjmcJBkjFUqaWlptGnTJurfvz9ZWFjIQmFwcDAJgkApKSlERLIwOX36dIqKiiIvLy+NtJsxTeP1gEzbXLlyhU6cOEEfffQR6ejoUGZmJnXq1IlOnDhB/fr1o4ULF5KOjg4VFBSQgYGB1PFYWFhIpaWlZGhoqOlTYEyjOEgyxiqNwsJCGjJkCC1btox8fHyobdu2FBcXR7a2tkREtGfPHurQoQNNmTKFhg8fTkS8/okxdfx5YNqiqKiIWrRoQYIg0LFjx6i4uJhUKhVlZmZShw4d6MqVKzR48GAaO3Ys6ejoSCGSMfYn/kQwxioNfX19mjBhAv32229UrVo1Wr16NdWoUYPi4+Pp7Nmz1Lp1awoMDKQ9e/ZQVlYWERHfNDOmhj8PTFsIgkAuLi6Um5tLREQqlYpKSkrIwcGBtmzZQl5eXjR//nyKj48nIpLCJGPsTxwkGWNaq6SkhIjKep5LSkqoqKiI7OzsqEaNGrR7925KTk6myMhImjp1KoWHh9OSJUuoVatWdPjwYTp+/LiGW88YY+x1eTEEqlQqatiwIaWmptKVK1eIqKyjpKSkhBwdHWnr1q3k5eVFS5cupZEjRxIR8YgkYy/gqa2MMa0kTsG7cuUKjRs3ji5dukSFhYUUFRVFUVFRFBwcLD33u+++o507d1JiYiI5ODjQ7du3qW7dunT06FHS09PjanyMMaYw4rpGoj8rdGdkZNDvv/9OAQEBpKenR4cPH6bw8HA6duwY1atXT5reKq6Pz8zMpJCQECoqKqKff/6ZrK2tNXxWjL1dOEgyxrSOeNOQmppKjRs3JldXV/Lz8yMAtH//fqpSpQolJSVRkyZNZL9z8eJFmjRpEqWnp9OqVavIz89Pg2fBGGPsf9GzZ086ffo0nTx5koyNjYmI6N69e+Tr60uPHz8me3t7cnV1JXd3d9q8eTMNGTKEJk2aRERERkZGsn8rIyODCgsLqVq1am/6NBh763GQZIxppadPn1JUVBQVFBTQ3Llzpc2jW7ZsSWfPnqVVq1ZRu3btpNFGcQQzNzeXSktLyczMTJPNZ4wx9j8oKCigRYsW0dy5c+mHH36Qtud4/PgxnThxgnJzc+nIkSN0584dun79Ol2/fp0KCwvJzs6O3NzcqEGDBuTt7U0eHh7k4+NDLi4uGj4jxt5eHCQZY1pDvapebm4ueXl5UVxcHA0bNoyIiEaPHk2zZs2i5cuXU8eOHcnMzEy2xYc4kskYY0y58vLyKD8/n2xsbCg9PZ2sra3LjTQSEV29epU6depEpqam1LBhQ7p06RKlpKTQvXv3yNLSks6ePctBkrG/wKuGGWNaQQyR586do27dutGZM2cIANWsWZOIiOLi4mj27Nm0ZMkS6tq1qzTiuHr1arp79y4REYdIxhjTAiYmJmRjY0OPHz+mpk2bUlhYmFSdVSzCRkTk5eVFzZo1o9u3b9PEiRNp165ddPPmTfr999/p/PnzHCIZ+xscJBljigeAdHR0KCsri7p06UJ3796lp0+fUlFRER04cICmTJlC8+bNo4ULF1J0dLTUMz1+/HhavHixdIPBGGNMe+jp6dHIkSMpNTWVPvjgA8rLy5O2sBGruLq7u9P9+/fp4cOHRERkbGxMjo6O5OTkpLF2M6YUHCQZY4pWWlpKgiBQXl4e5eXlkampKY0ePZratm1LsbGxNHfuXBo3bhxt2LCBunfvLoXIlJQUOn78OHl7e3MlPsYY00LGxsYUHR1Ns2bNomPHjlGXLl0oLy+PiP6cgRIaGkrPnz+nlJQUTTaVMUXiIMkYUzQdHR3KyMggZ2dnio6OJlNTUwoJCSEiotatW1ObNm2IiOjKlStUUFBARES7d++mcePG0eXLlyk+Pp4L6zDGmMKJU1afP39Ojx49ko5XqVKFoqKiaP78+bIwKQZJBwcHIiK6ffv2G28zY0qn0nQDGGPsn1KpVNS4cWM6fPgw2dnZ0fXr18nNzY0CAgJo1KhRpKenR+PGjaMFCxZINw9GRka0b98+qaIfY4wxZSotLSVdXV1KTU2lmJgYSk9PJ39/f/roo48oPDyczMzMqH379kRENHjwYOrSpQtt3ryZTExMSFdXl1q3bk1hYWEaPgvGlIertjLGtEJmZiaNGDGC1q9fTxMnTqTx48dLj2VnZ9Ovv/5KO3fuJAAUEBBAYWFhXEiBMca0xK1btyg4OJisrKzIy8uLfv31VyooKKChQ4fSgAEDyMTEhB4/fkzffPMNDR48mFq0aEHr1q2jKlWq0NOnT8nU1FTTp8CY4nCQZIxpjaysLBo8eDAlJyfTnDlzaMiQIUTE23owxpg2Evf/LSwspN27d9PSpUtp9uzZVKtWLcrPz6eIiAi6fPky9enTh+Li4qQwuWPHDvr444+pQ4cOlJycrOnTYEyxeGorY0xr2Nvb04IFCwiAtHfkkCFDSBAEqSiPIAgcLBljTAuI01knT55MOTk5ZGNjQ7Vq1SIAZGxsTLt376ZOnTrR8uXLSRAEio2NpapVq1JkZCStW7eO6tSpo+lTYEzROEgyxrSKnZ0dLVy4kIiIhg0bRrq6uhQTE0M6On/WFuMQyRhjyiXuG0xEdPToUdq4cSNVrVqVBg4cSERl3/FFRUVkZGREW7ZsoQ4dOtCyZctIR0eHhg4dSubm5tS1a1e+FjD2D3HVVsaY1hHDZJcuXWjw4MG0bNkyTTeJMcbYa6Kjo0O3b98mANSnTx9avnw5PX78mJYtW0Y//vgjEZXtIVlUVESGhoa0detWqlu3LsXHx9PixYt5VgpjrwkHScaYVrKzs6M5c+ZQjx49qGnTpppuDmOMsdfk8ePHFBISQl5eXgSAevfuTUuXLqUHDx7Q5MmT6dSpU0QkD5ObNm2idu3aUfv27TlEMvaacLEdxphWE4sxMMYY0w7Pnz+nhIQEio+Pp3feeYdSUlJIEARauHAhDR48mMLDw2ny5MkUHBxMRESFhYWkr6+v4VYzpn14jSRjTKtxiGSMMeUTp6OWlJSQoaEh9e7dmwwNDWnkyJFUt25dSklJoZiYGCIq2yuSiCg+Pp6CgoI4RDL2L+EgyRhjjDHG3kpiYZ38/HwyMTEhXV1dKikpISMjI+revTsR0UvD5OPHj2nx4sUUGBioyVNgTGvxGknGGGOMMfZW0tHRofT0dAoODqbNmzcTEZULk9OmTaNLly5RkyZNCADFxMTQtGnT6MKFC2Rtba3hM2BMe3GQZIwxxhhjb62SkhJSqVQUFxdH27dvJyJ5mOzWrRt17dqVTpw4QS1btiQANGLECPr999/J1dVVs41nTItxkGSMMcYYY2+tatWq0fbt26latWrUr18/WZgsLCykKlWq0KBBg8jDw4MOHz5MLVq0ICKiqlWrarDVjGk/DpKMMcYYY+yt5u7uTmvWrCFvb29ZmBQL6Xz33Xdka2tLq1atohUrVhBR2bRYxti/hz9hjDHGGGPsrefu7k6JiYnk7e1Nn3zyCa1bt45KS0vp5MmTdPToUapfvz5FR0eTl5eXppvKWKXA+0gyxhhjjDHFuH37Ng0aNIh27dpF9vb2VFhYSADo6NGj5Ofnp+nmMVZpcJBkjDHGGGOKkpOTQ/v27aPt27eTg4MD9evXj3x8fDTdLMYqFQ6SjDHGGGNMkUpKSkhHR4cEQdB0UxirdDhIMsYYY4wxxhh7JVxshzHGGGOMMcbYK+EgyRhjjDHGGGPslXCQZIwxxhhjjDH2SjhIMsYYY4wxxhh7JRwkGWOMMcYYY4y9Eg6SjDHGGGOMMcZeCQdJxhhjjDHGGGOvhIMkY4wxxhhjjLFXwkGSMcYYY4wxxtgr4SDJGGOMMcYYY+yVcJBkjDHGGGOMMfZK/g9DUHXQMBbQ0QAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#drop irrelevant variables - 'image','vote','style','reviewText','summary','reviewTime','reviewerName','unixReviewTime','verified'\n",
        "drop_list = ['image','vote','style','reviewText','summary','reviewTime','reviewerName','unixReviewTime','verified']\n",
        "df.drop(df[drop_list], axis=1, inplace=True)\n",
        "df.head(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "P4mv8egdJ_d9",
        "outputId": "edc32d88-2e66-4b7d-9d46-b05cb51ed2f4"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   overall      reviewerID        asin\n",
              "0        5  A1QVBUH9E1V6I8  4639725183\n",
              "1        5  A3GEOILWLK86XM  4639725183\n",
              "2        5  A32RD6L701BIGP  4639725183\n",
              "3        5  A2UY1O1FBGKIE6  4639725183\n",
              "4        5  A3QHVBQYDV7Z6U  4639725183"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-7f76a217-216e-4a30-ada1-f7f89d928441\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>overall</th>\n",
              "      <th>reviewerID</th>\n",
              "      <th>asin</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5</td>\n",
              "      <td>A1QVBUH9E1V6I8</td>\n",
              "      <td>4639725183</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5</td>\n",
              "      <td>A3GEOILWLK86XM</td>\n",
              "      <td>4639725183</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5</td>\n",
              "      <td>A32RD6L701BIGP</td>\n",
              "      <td>4639725183</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5</td>\n",
              "      <td>A2UY1O1FBGKIE6</td>\n",
              "      <td>4639725183</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>A3QHVBQYDV7Z6U</td>\n",
              "      <td>4639725183</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7f76a217-216e-4a30-ada1-f7f89d928441')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-7f76a217-216e-4a30-ada1-f7f89d928441 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-7f76a217-216e-4a30-ada1-f7f89d928441');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#numeric data information \n",
        "numeric_features = df.select_dtypes(include=['int64','float64']).columns\n",
        "\n",
        "#nominaldata information \n",
        "nominal_features = df.select_dtypes(include=['object'])\n",
        "print(numeric_features,nominal_features)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SmfSywZDKHg-",
        "outputId": "ad249179-fa28-4187-dd47-b38126416602"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Index(['overall'], dtype='object')              reviewerID        asin\n",
            "0        A1QVBUH9E1V6I8  4639725183\n",
            "1        A3GEOILWLK86XM  4639725183\n",
            "2        A32RD6L701BIGP  4639725183\n",
            "3        A2UY1O1FBGKIE6  4639725183\n",
            "4        A3QHVBQYDV7Z6U  4639725183\n",
            "...                 ...         ...\n",
            "1143855  A223YRQH2Z5T1D  B01HJF6FRA\n",
            "1143856  A38GDA4TB9EILT  B01HJF6FRA\n",
            "1143857  A2025PN7HDC5BO  B01HJF6FRA\n",
            "1143858  A1NY7XWC7EPQOA  B01HJF6FRA\n",
            "1143859  A1P0X9E6F99J4T  B01HJF6FRA\n",
            "\n",
            "[1143860 rows x 2 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Loading Data and cleaning Meta data dataset\n"
      ],
      "metadata": {
        "id": "YDPnGT1TKTiv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "meta_df = pd.read_json('/content/drive/MyDrive/MDM_Project_Team12/data/meta_Grocery_and_Gourmet_Food.json',lines=True)"
      ],
      "metadata": {
        "id": "2-mczWDEKTPq"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "meta_df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I0Yqjk8CKKat",
        "outputId": "359b5bf0-a1ba-4d94-fe65-81c1f607384d"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 287051 entries, 0 to 287050\n",
            "Data columns (total 19 columns):\n",
            " #   Column           Non-Null Count   Dtype \n",
            "---  ------           --------------   ----- \n",
            " 0   category         287051 non-null  object\n",
            " 1   tech1            287051 non-null  object\n",
            " 2   description      287051 non-null  object\n",
            " 3   fit              287051 non-null  object\n",
            " 4   title            287051 non-null  object\n",
            " 5   also_buy         287051 non-null  object\n",
            " 6   tech2            287051 non-null  object\n",
            " 7   brand            287051 non-null  object\n",
            " 8   feature          287051 non-null  object\n",
            " 9   rank             287051 non-null  object\n",
            " 10  also_view        287051 non-null  object\n",
            " 11  main_cat         287051 non-null  object\n",
            " 12  similar_item     287051 non-null  object\n",
            " 13  date             287051 non-null  object\n",
            " 14  price            287051 non-null  object\n",
            " 15  asin             287051 non-null  object\n",
            " 16  imageURL         287051 non-null  object\n",
            " 17  imageURLHighRes  287051 non-null  object\n",
            " 18  details          287027 non-null  object\n",
            "dtypes: object(19)\n",
            "memory usage: 41.6+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#drop irrelvant variables - 'tech1','fit','tech2','main_cat','similar_item','date','price'\n",
        "drop_list = ['tech1','feature','also_view','fit','tech2','main_cat','similar_item','date','price','also_buy','brand','rank','imageURL','imageURLHighRes','details']\n",
        "meta_df.drop(meta_df[drop_list], axis=1, inplace=True)\n",
        "meta_df.head(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "A-pyEws1KgP7",
        "outputId": "b625ce9a-99b0-40d7-86ce-1a2fc28782c6"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                            category  \\\n",
              "0  [Grocery & Gourmet Food, Dairy, Cheese & Eggs,...   \n",
              "1  [Grocery & Gourmet Food, Cooking & Baking, Sug...   \n",
              "2  [Grocery & Gourmet Food, Cooking & Baking, Fro...   \n",
              "3  [Grocery & Gourmet Food, Cooking & Baking, Fro...   \n",
              "4  [Grocery & Gourmet Food, Cooking & Baking, Fro...   \n",
              "\n",
              "                                         description  \\\n",
              "0  [BEEMSTER GOUDA CHEESE AGED 18/24 MONTHS, Stat...   \n",
              "1  [Shipped from UK, please allow 10 to 21 busine...   \n",
              "2  [Jazz up your cakes with a sparkling monogram ...   \n",
              "3                      [Large Letter - Height 4.75\"]   \n",
              "4                                            [4.75\"]   \n",
              "\n",
              "                                               title        asin  \n",
              "0  Beemster Gouda - Aged 18/24 Months - App. 1.5 Lbs  0681727810  \n",
              "1                          Trim Healthy Mama Xylitol  0853347867  \n",
              "2  Letter C - Swarovski Crystal Monogram Wedding ...  1888861118  \n",
              "3  Letter H - Swarovski Crystal Monogram Wedding ...  1888861517  \n",
              "4  Letter S - Swarovski Crystal Monogram Wedding ...  1888861614  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2b962eb7-b203-4645-ad82-cd769b2133bb\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>category</th>\n",
              "      <th>description</th>\n",
              "      <th>title</th>\n",
              "      <th>asin</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>[Grocery &amp; Gourmet Food, Dairy, Cheese &amp; Eggs,...</td>\n",
              "      <td>[BEEMSTER GOUDA CHEESE AGED 18/24 MONTHS, Stat...</td>\n",
              "      <td>Beemster Gouda - Aged 18/24 Months - App. 1.5 Lbs</td>\n",
              "      <td>0681727810</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>[Grocery &amp; Gourmet Food, Cooking &amp; Baking, Sug...</td>\n",
              "      <td>[Shipped from UK, please allow 10 to 21 busine...</td>\n",
              "      <td>Trim Healthy Mama Xylitol</td>\n",
              "      <td>0853347867</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>[Grocery &amp; Gourmet Food, Cooking &amp; Baking, Fro...</td>\n",
              "      <td>[Jazz up your cakes with a sparkling monogram ...</td>\n",
              "      <td>Letter C - Swarovski Crystal Monogram Wedding ...</td>\n",
              "      <td>1888861118</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>[Grocery &amp; Gourmet Food, Cooking &amp; Baking, Fro...</td>\n",
              "      <td>[Large Letter - Height 4.75\"]</td>\n",
              "      <td>Letter H - Swarovski Crystal Monogram Wedding ...</td>\n",
              "      <td>1888861517</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>[Grocery &amp; Gourmet Food, Cooking &amp; Baking, Fro...</td>\n",
              "      <td>[4.75\"]</td>\n",
              "      <td>Letter S - Swarovski Crystal Monogram Wedding ...</td>\n",
              "      <td>1888861614</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2b962eb7-b203-4645-ad82-cd769b2133bb')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-2b962eb7-b203-4645-ad82-cd769b2133bb button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-2b962eb7-b203-4645-ad82-cd769b2133bb');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#filter out the category\n",
        "# Transform dataframe\n",
        "meta_df['category'] = meta_df['category'].apply(lambda x: x[1])"
      ],
      "metadata": {
        "id": "CzBagmJzKmhk"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Look for non unique values and remove the duplicates\n",
        "print(len(meta_df['asin'].unique())-len(meta_df['asin']))\n",
        "non_unique_asins = meta_df[meta_df.duplicated(['asin'], keep=False)]['asin'].unique()\n",
        "#print(meta_df[meta_df['asin'].isin([\"B0005XMOK6\"])])\n",
        "\n",
        "\n",
        "#dropping duplicate rows in metadata\n",
        "meta_df=meta_df.drop_duplicates(subset='asin', keep='first')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EIO6WVZ9KpGt",
        "outputId": "95683020-e81f-4d4b-aad4-dd2ee8cfc2fc"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-3697\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Merge the two dataframes"
      ],
      "metadata": {
        "id": "9kF_ywH6K0WS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#merge metadata and reviews using an inner join\n",
        "merged_df = df.merge(meta_df, on='asin', how='inner')"
      ],
      "metadata": {
        "id": "ttJlWMLaKv38"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "merged_df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wGHN_GUlK3jZ",
        "outputId": "6c44935a-9ccb-40bb-ad9d-dc086c0e688f"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 1143063 entries, 0 to 1143062\n",
            "Data columns (total 6 columns):\n",
            " #   Column       Non-Null Count    Dtype \n",
            "---  ------       --------------    ----- \n",
            " 0   overall      1143063 non-null  int64 \n",
            " 1   reviewerID   1143063 non-null  object\n",
            " 2   asin         1143063 non-null  object\n",
            " 3   category     1143063 non-null  object\n",
            " 4   description  1143063 non-null  object\n",
            " 5   title        1143063 non-null  object\n",
            "dtypes: int64(1), object(5)\n",
            "memory usage: 61.0+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Storing the merged dataset into a pickle\n"
      ],
      "metadata": {
        "id": "hQr4-qYIK68V"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "merged_df.to_pickle('/content/drive/MyDrive/MDM_Project_Team12/data/meta_Grocery_and_Gourmet_Food.json.to_pickle')\n"
      ],
      "metadata": {
        "id": "w7oyH108K4u6"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "faBW0Z4RK9bA"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}