TrabajoFinal_Fundamentos.ipynb

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  "metadata": {
    "colab": {
      "name": "TrabajoFinal_Fundamentos.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "TPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CmzTpzRQwz2b"
      },
      "source": [
        "**Trabajo Final Fundamentos de aprendizaje de máquina**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tWpgCIetuVg_"
      },
      "source": [
        "*Integrantes*\r\n",
        "\r\n",
        "*   Antayhua, Miguel\r\n",
        "*   Barrueta, Marleny\r\n",
        "*   Fuertes, Sandro"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 321
        },
        "id": "RYedb6CzxDhv",
        "outputId": "a1a2539f-105f-4f51-ced5-faef0eeba5ac"
      },
      "source": [
        "import pandas as pd, numpy as np, matplotlib.pyplot as plt, seaborn as sb\r\n",
        "from sklearn.preprocessing import LabelEncoder\r\n",
        "from sklearn.model_selection import train_test_split\r\n",
        "plt.rcParams['figure.figsize'] = (18, 10)\r\n",
        "plt.style.use('seaborn')\r\n",
        "df = pd.read_csv(\"https://archive.ics.uci.edu/ml/machine-learning-databases/00468/online_shoppers_intention.csv\")\r\n",
        "df['Revenue'] = LabelEncoder().fit_transform(df.Revenue.values)\r\n",
        "l={'OperatingSystems','Browser','Region','TrafficType','VisitorType','Weekend','Month'}\r\n",
        "for s in l:\r\n",
        "  df = df.astype({s: object})\r\n",
        "df.describe()"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Administrative</th>\n",
              "      <th>Administrative_Duration</th>\n",
              "      <th>Informational</th>\n",
              "      <th>Informational_Duration</th>\n",
              "      <th>ProductRelated</th>\n",
              "      <th>ProductRelated_Duration</th>\n",
              "      <th>BounceRates</th>\n",
              "      <th>ExitRates</th>\n",
              "      <th>PageValues</th>\n",
              "      <th>SpecialDay</th>\n",
              "      <th>Revenue</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>12330.000000</td>\n",
              "      <td>12330.000000</td>\n",
              "      <td>12330.000000</td>\n",
              "      <td>12330.000000</td>\n",
              "      <td>12330.000000</td>\n",
              "      <td>12330.000000</td>\n",
              "      <td>12330.000000</td>\n",
              "      <td>12330.000000</td>\n",
              "      <td>12330.000000</td>\n",
              "      <td>12330.000000</td>\n",
              "      <td>12330.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2.315166</td>\n",
              "      <td>80.818611</td>\n",
              "      <td>0.503569</td>\n",
              "      <td>34.472398</td>\n",
              "      <td>31.731468</td>\n",
              "      <td>1194.746220</td>\n",
              "      <td>0.022191</td>\n",
              "      <td>0.043073</td>\n",
              "      <td>5.889258</td>\n",
              "      <td>0.061427</td>\n",
              "      <td>0.154745</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>3.321784</td>\n",
              "      <td>176.779107</td>\n",
              "      <td>1.270156</td>\n",
              "      <td>140.749294</td>\n",
              "      <td>44.475503</td>\n",
              "      <td>1913.669288</td>\n",
              "      <td>0.048488</td>\n",
              "      <td>0.048597</td>\n",
              "      <td>18.568437</td>\n",
              "      <td>0.198917</td>\n",
              "      <td>0.361676</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>184.137500</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.014286</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>7.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>598.936905</td>\n",
              "      <td>0.003112</td>\n",
              "      <td>0.025156</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>4.000000</td>\n",
              "      <td>93.256250</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>38.000000</td>\n",
              "      <td>1464.157213</td>\n",
              "      <td>0.016813</td>\n",
              "      <td>0.050000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>27.000000</td>\n",
              "      <td>3398.750000</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>2549.375000</td>\n",
              "      <td>705.000000</td>\n",
              "      <td>63973.522230</td>\n",
              "      <td>0.200000</td>\n",
              "      <td>0.200000</td>\n",
              "      <td>361.763742</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Administrative  Administrative_Duration  ...    SpecialDay       Revenue\n",
              "count    12330.000000             12330.000000  ...  12330.000000  12330.000000\n",
              "mean         2.315166                80.818611  ...      0.061427      0.154745\n",
              "std          3.321784               176.779107  ...      0.198917      0.361676\n",
              "min          0.000000                 0.000000  ...      0.000000      0.000000\n",
              "25%          0.000000                 0.000000  ...      0.000000      0.000000\n",
              "50%          1.000000                 7.500000  ...      0.000000      0.000000\n",
              "75%          4.000000                93.256250  ...      0.000000      0.000000\n",
              "max         27.000000              3398.750000  ...      1.000000      1.000000\n",
              "\n",
              "[8 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GT-yxH-u5DAf",
        "outputId": "c1381280-ec49-446c-c878-c18e75ae0ef1"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 12330 entries, 0 to 12329\n",
            "Data columns (total 18 columns):\n",
            " #   Column                   Non-Null Count  Dtype  \n",
            "---  ------                   --------------  -----  \n",
            " 0   Administrative           12330 non-null  int64  \n",
            " 1   Administrative_Duration  12330 non-null  float64\n",
            " 2   Informational            12330 non-null  int64  \n",
            " 3   Informational_Duration   12330 non-null  float64\n",
            " 4   ProductRelated           12330 non-null  int64  \n",
            " 5   ProductRelated_Duration  12330 non-null  float64\n",
            " 6   BounceRates              12330 non-null  float64\n",
            " 7   ExitRates                12330 non-null  float64\n",
            " 8   PageValues               12330 non-null  float64\n",
            " 9   SpecialDay               12330 non-null  float64\n",
            " 10  Month                    12330 non-null  object \n",
            " 11  OperatingSystems         12330 non-null  object \n",
            " 12  Browser                  12330 non-null  object \n",
            " 13  Region                   12330 non-null  object \n",
            " 14  TrafficType              12330 non-null  object \n",
            " 15  VisitorType              12330 non-null  object \n",
            " 16  Weekend                  12330 non-null  object \n",
            " 17  Revenue                  12330 non-null  int64  \n",
            "dtypes: float64(7), int64(4), object(7)\n",
            "memory usage: 1.7+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JKnQXHCLck4z"
      },
      "source": [
        "**Gráfica de Barras - variables cualitativas**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 401
        },
        "id": "JXRkxzvulgtx",
        "outputId": "d1f0e0f9-91db-44a1-bf10-68d9d31352f9"
      },
      "source": [
        "columnas_object = df.select_dtypes(include=['object']).columns\r\n",
        "fig, axes = plt.subplots(nrows=1, ncols=len(columnas_object), figsize=(18, 5))\r\n",
        "axes = axes.flat\r\n",
        "print(columnas_object, len(columnas_object))\r\n",
        "for i, colum in enumerate(columnas_object):\r\n",
        "    df[colum].value_counts().plot.barh(ax = axes[i])\r\n",
        "    axes[i].set_title(colum, fontsize = 10, fontweight = \"bold\")\r\n",
        "    axes[i].tick_params(labelsize = 9)\r\n",
        "    axes[i].set_xlabel(\"\")\r\n",
        "fig.tight_layout()\r\n",
        "plt.subplots_adjust(top=0.9)\r\n",
        "fig.suptitle('Distribución variables cualitativas', fontsize = 10, fontweight = \"bold\");"
      ],
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Index(['Month', 'OperatingSystems', 'Browser', 'Region', 'TrafficType',\n",
            "       'VisitorType', 'Weekend'],\n",
            "      dtype='object') 7\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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0adOoX78+BoOBF154gdWrVzN06FB69erFyJEjMRgMvP322wAYjUaGDx+OwWDg6aef5tixY8C1/fH111/H19eX1NRUZs+ejb+/P61atSI6Oprjx48zfPhwTpw4QWBgIL/88gu+vr78/vvvzJ8/n0cffZSZM2diMBiYMmUKAGfOnGHAgAEYDAbeffddczX+xIkTANjY2ODs7MyMGTPYsWMHWVlZGAwGVqxYAcCGDRto2rQpRqORhQsX0qZNG3x9fZkxYwYmk4lu3bqZs8O1Vm1t2rQhICCAL7/8Erh2VSEkJIRhw4ZhMBiIjY1lxIgRtGjRguXLlwOwZcsWHn30Uby9vRk8eDBnzpwppd+gWKMqVapQvXp1XF1dAahevTqenp6EhoZiMBgAWLFiBYGBgbRq1YqXX36ZjIwMxowZw2uvvQbAsGHDaN68OdnZ2WzcuBF/f3/OnDnDkCFD8PHxoVOnTmzZsgW49sf/ySefpEWLFkyYMIFLly6ZW3iGhYXxxBNPWOaNkAqlSpUqVKtWDT8/P+rVq8fhw4fzPaYmJyfTvXt3WrZsyaxZs8wtkdesWYOvry/nz58nPT2d8ePH07JlSzp16mRetqC/FSJ3K3f/tbe3x9bWlmrVqjFixAhGjRpFYGAg69atY82aNQQGBmIwGJgwYQI5OTk888wzAIwfP57AwECSk5P5/PPPmT9/Ps8//zzjxo0D4KeffuKxxx7D19eXV155hczMTKpVq0a1atWws7Mzb3PRokU8/PDD5pbLwcHBzJw5kw8//JB27doxdepUfHx8GD9+PFevXuXKlSu8/PLL+Pr6EhwczL59+yz2HkrZ8Oijj3Lp0iV+/fVXALZv346npyfTp08376/5nTPnHqMTExPznM/DtWPzww8/TJs2bZgxYwbZ2dk3nYfk9xlbv349Dz30EOnp6QDmz9xXX32Fp6cn7733HgaDgeeee46MjAxMJhNvvfUWLVu25NFHHzWfB4ncjubNm5OYmIjJZGL//v34+/tz9OhR0tPTOXjwIG5ubhw+fJguXbrQqlUrwsLCyMnJKdL+N2bMGAYMGEB2djZff/01AQEBtG3blgULFgAFn8cYjUb69++PwWBg0aJFxf6ay2UBDaBVq1b8+OOP7Nmzhxo1auDu7k5aWhoTJkwgODiYNWvWcPToUebOnWte5pdffmHp0qVcvXqVpUuXMnz4cNzc3Bg+fDjDhw8H4MiRI/Ts2ZN+/frx6aefcvr0aUu9RLEyf/zxB3Xr1qVmzZrmaU2bNiUnJ4fMzEwSExMZPHgwY8aMITo6mqSkJD744APOnDnDhg0baNSoEXPmzDEve+jQIdavXw/A+fPnCQ8PZ9y4ccyZMwdnZ2fz/pnfFdYTJ07g6+vLyJEj+eqrr/jzzz+JiYkhMTGRL7/8Mk83OH9/f2rVqsXzzz9Pp06deOuttzh27BjVqlUjKCiIrVu3AtdOClq3bo29vT3vvvsus2bNYtWqVZw8eZKUlBSmTZsGXLsKvGfPHubOnUt4eDizZs0iNDQUo9EIwJ49exg7diweHh5MmzaNoUOH0q5dOz7++GMA5s2bR9euXdm8eTP169fnv//9bzH/pqQsCQoKwt/fn6FDh9K7d2+qVq0KQFpaGt9//z0HDx5k2rRpjBo1ihUrVrBt2zaioqLMJ6Ymk4m9e/dSv359Dh48yP79+zEYDKxevZrk5GQ2bdrE6NGjzV31X3nlFVq2bElcXBxJSUk3tfzJLfSKlIa//vqLlJQU6tSpk+8xNTw8nIyMDFatWmW+IHij+fPnk5CQQGxsLIMGDWLq1KnmizX5/a0QKSk7d+4kMjKSoKAgDhw4wAsvvMDnn3/OunXr2L59u/mLzrx581i/fj1ubm7079/ffA4OkJmZycsvv0yHDh1YtWoVW7duZeXKlflur0ePHpw6dYrExEROnjzJoUOHeOKJJ7CxsSEtLQ0/Pz9mzZrFN998w/bt21m6dCkJCQmsW7eOrl27llgPAik/GjZsiIeHBz/++CNw7Vw5KCjI/Pz58+fzPWfOlfs9M/d8/ocffjAf5xcsWMBnn33GmjVr8myzoPOQLl26YG9vz08//URWVhbx8fHm/R2gWrVqfPLJJ/z888+sWbOGuLg4YmNj+eyzzxgxYgSTJ082994SKSqDwUB6ejrHjh1j//79BAUFUaVKFQ4cOEBiYiLe3t5MmDCBp556ipUrV7Jhwwb+85//3HL/i46OZu/evXzwwQecPHmS119/nYkTJxIdHc2CBQvMrdvyO49ZunQp//vf/1izZg3Ozs7F/prti32NVqJt27Z88sknODo60rZtW7Kzs9m7dy+XL1/mqaeeok6dOnTs2DFPn9xHHnmE+vXr07RpU06dOoWjoyM2NjY4ODjg6OgIQN26dfHz8yMrK4slS5Zw6tQpatWqZamXKVbE1ta2wPGZjhw5Qr169WjZsiUNGjRg1qxZHD58mF27dnH8+HGCg4PNV1BzBQYGUrt2bS5fvsylS5cYN24cFy9e5OrVq5w5cwYHBwdsbGzy7Z5sb2/PY489xv79+3n33Xc5deoUBw4cwMfHhwcffJDu3bubT1SdnZ1Zt24dX3/9NVu3buWLL77gm2++4bvvvqNHjx6MHj2azMxMduzYwahRo6hWrRr16tUjLCyMgIAAhgwZgru7u/kzUq1aNXbt2oXJZGLkyJEAZGVl8dtvvwHg7u5O06ZNeeihh0hJScHPz499+/bx3XffAeDl5cVnn31GSkoKQUFBPPzww8X3S5IyZ/ny5VSvXp0///yTF198kQceeAC4dqJYq1Yt1q1bh729Pb1798bGxgZ/f3927tzJK6+8wrvvvsvvv/+Oq6sr7dq1Y+/evRw4cIBWrVrh6elJcnIy48aNo127dgwdOpTTp0/zxx9/kJqaytq1a7l48SJ79+41t6p84oknqFGjhiXfDqkgcr+AXbhwgU6dOnH16tV8j6kHDhygXbt21KtXjyeeeIK1a9fetK74+Hgefvhh7r//fmrXrs3MmTPNrSXy+1tx//33l9rrlIrF29ubRo0aAVCjRg0++ugjMjIygGsXRf7xj38AUKlSJfM5uL29vfn8Aq5drExLS6Nr1640bNjQfPEjPw0aNMDX15ctW7ZQt25d3N3d8fHxYdu2bdjb2xMcHGw+z889Jzt58iQ9e/YkOzubjIwMMjMz82xf5EZBQUFs2rSJfv368eeffxIUFGQe/qegc+Zcuftf7vl8fHw8DRo0MJ/7enp6smvXLvMyhZ2HVK9enc6dO7NlyxZq1qyJyWSiY8eO5ovx3bt357777qNBgwYcPnyYnJwcrly5Qv/+/TGZTJw7d47jx4+bP4ciReHl5UWlSpU4cOAAv//+O82aNaNx48bmAlqvXr1Yv349H3/8MYsWLSI9PZ29e/dy/vz5fPc/uHbxcO7cubz44ovUqlWLb7/9lqysLHNjjaysLPbu3Qvkfx5z8OBBmjVrRv369Xn88ceJiIgo1tdcrgtouTcNeP7559m8ebO5Ap/LZDJha/t/jfBy/0AWVgipXLmyeZ7cdYgA/POf/yQmJoZTp06Zu5sdOHAAe3t7PD09OXr0KPB/+4ydnR2Ojo4EBQXxyiuvAOTZR3P3tbVr1xIXF8fy5ctJTk7m1VdfveV+5+DgYN4GQE5Ojrm57PUZ4NqAp+fOnWPw4MEMHjyYTZs2MXLkSH777TfatGnDPffcw8qVK0lNTSUoKAg7OztWrVpFXFwc27dvp1+/fjddDXN0dMTe3p7Vq1ebp9WoUYN169bl+Zzl5rz+Mzdz5kx++OEHtm/fzuTJkzl69CjDhg0r2i9Byp3atWtzzz33cN9999GsWTPzSWlu4big43qTJk2wtbXliy++wGAw4OPjw6ZNm/jtt98YMWIEfn5+rFq1io0bN7JmzRpWrVrFF198AcC4cePMBYxKlSpx6NChPNsUKWnLly/HxsaGf/3rX3h4eODk5JTvMbWg4/r1rv+M5OTk5JmW398KkZKSe15z9OhR3n//fSZPnkxgYCBBQUFFPp++cX+/cOGCuQtbfnr27MmqVavMX6Ry5X4Gbjwn8/b2zjOeq719uf2qJMUkKCiIiIgIvv32W+rVq4eXl5f5uaKcM18vv3Oa66fd6jykZ8+eTJkyBRcXFzp27GhutX+9nJwcbG1tsbe357777uPf//63+Tk1CpHb5ejoSJMmTYiLiyMrK4tGjRrRtGlTdu7cyYkTJ2jVqhUAb7/9tnlIrapVq/LRRx8VuP/l5OTw2GOP8emnnxISEmL+7rh48WLzd2wnJyeioqIKPI/JPbZnZ2cX+2sut104nZ2d8fDwIDExkbZt2wLX+uhWqVKF2NhYc9ed3OcKYmdnx7Fjx7h06VJpxJYyrFu3btSsWZM33niDY8eOkZCQwEcffUSfPn1wcXEhOTmZhIQE1q9fj42NDV5eXvj4+LB3716ysrJYsWJFvn9UMzIysLGx4Z577mH79u0AnD59Gjs7O86cOUNqamqR8nl6erJv3z6SkpLytFKIjo6md+/exMfHc/bsWY4dO4adnR0NGzbE1taW7t27M3/+fNq3b4+TkxPJyclMmDABf39/Xn/9dWrVqsXRo0fNB65Dhw7h4+NDdnY2CQkJJCUl8dprr3Hx4sVbZszJyeGVV17Bzs6OCRMm0Lp1a44cOVKk1yfl06VLl8jIyOB///sfBw4cuOnk0d/fn+zsbFauXMnhw4eJj4+nXbt22Nvb4+3tzddff42Pjw8+Pj5s2bKFCxcu4O3tTWxsLKtWrSIkJIQRI0aQnJxMlSpVePDBB9m5cyc5OTnMnj2bn376yUKvXCqy2rVr8+CDDzJ8+HAWL15M48aN8z2menp6smPHDk6cOMG3336b77pat27N1q1bOXr0KF988QUODg60bNmylF+RyP/JbXV2zz33sGnTJhwcHDAajUUqov3jH/+gZs2afPvttxw7doxHH33UfPEjP4899hgHDx5k69atecawzMrKIi4ujo0bN5KVlUXjxo3x8fHh0KFDXLhwgfXr1/PBBx/kudAukp/GjRvj7u7OsmXL8nTfBAo8Z77e9efzrVu35tixY2zfvp1ff/2VgwcP0q5duyJnadOmDSaTidjY2JvGbF23bh379u0jOTmZJk2a4OPjw19//cXx48fZuXMn06dPv/M3QSo0Hx8fNm/ejIeHB46OjjRt2pTt27dTqVIlmjRpQo0aNYiPjyczM5OpU6eSmJhY6P7n7u5OaGgoV69eJSoqioceeggHBwfi4+M5c+YMkydP5q+//iowj6enJ3v37iUpKSnPhcfiUq7/KrRr145mzZqZ70JVq1Yt5s6dy9q1a+nRowdeXl7m26wWpHPnzsTFxbFkyZLSiCxlWJUqVVi+fDmXLl2iW7dujBo1io4dOzJ58mQAHnjgAcLDw/nwww8ZPnw49evX58UXX+T++++ne/fubNmyJc/V0VyPPfYYDzzwAP3796d9+/Z4eXkRGhpK27Ztsbe3Z8CAAUXKFxISwv33389TTz1l7mNub2/PsGHD6NmzJ+PHj6dDhw7ExsYyZ84c6tWrB0DXrl1JS0sz/yF2d3enbt269O7dm65du9KyZUu6du2Kr68vrq6uPPPMMzz44IO89NJLzJgxg5dffpn27dsXqQ+6ra0trVq1IjQ0lPbt25tvsiAVV4cOHfD19aVv3760adPG3IUtV+4NBT766CMGDBhAly5dGDRoEAC+vr5kZGTg4+ND3bp1qVy5Mk2bNsXR0RE/Pz927dpFu3btmDt3LqGhodjb2zNr1iz+/PNPnnjiCS5evEj79u0t8bJFABg0aBA1a9YkJiYm32Pq6NGjcXBwoFevXjg5OQE3t5gZPXo0fn5+9O7dmxUrVjBnzpw8XYhESpuXlxedOnVi+vTp/PXXXzz99NPMnz+/SDcNqly5Mu+88w7btm2jZ8+ePPzww+abD+THyckJf39/atasSePGjfNM37ZtG6+++iq9evWiVatWPPPMM3To0IG+ffuyYsUKunfvXiyvV8q/oKAgTp06RefOnfNML+ic+XrXn8937NiR0aNHM3HiRMaMGcPQoUPz/W5QEFtbWx599FGuXLlChw4d8jz3999/ExtPGDIAACAASURBVBISQkBAAI8//jhdu3alf//+jBo1infffZfHH3/c3JpH5Hb4+PiQmZnJQw89BMBDDz1EZmYmTZs2pXr16syZM4ctW7bQo0cPatSoga+v7y33v2rVqjFs2DCWLFmCg4MDoaGh5hZpXl5e/POf/ywwT0hICP/4xz946qmnqFSpUrG/XhuT+iCKlLhJkyaRmJhYIlXwotq3bx8bN27kX//6F9u3byc0NJSNGzdSv379Apc5efIkS5YsYe3atfznP/8pkYOQiIjcmR9//JGEhAT69u3Lv//9b6Kjo/n555/V3VgEuHz5MklJSQwePJgRI0bw3HPPAddurLF06VJ++eUXCycUKT7Z2dkcPXqUUaNG0b59e15//XUAVq5cyeTJk9m1a5e5UYmI3Dl17BepINzc3Ni/fz/Lli3DycmJKVOmFFo8A5gyZQr//e9/mTlzpopnIiJWxs3NjQ8//JDFixfj4uLC22+/reKZyP934MABnn32Wfz9/QttpSZSHpw6dcrcw2r06NGWjiNSbqkFmoiIiIiIiIiISCHK9RhoIiIiIiIiIiIid0sFNBERERERERERkUKogCYiIiIiIiIiIlIIq7mJQHb2Vc6cuWjpGHnUrFm1VDO5ujqV2rbEsqxxfy+q4vpcaH+vGKx5Xy/NY7z294rBmvf30qJ9veKw5v29tI7v2t8rBmve14tbYZ8d7e8VgzXs76Vdh7lRYfu61bRAs7e3s3SEm1hjJikfyvK+VZazS+mz5v3FmrNJ2aR9SioSa97frTmblD0VaX+qSK9V8mcN+4A1ZCiI1RTQRERERERERERErJEKaCIiIiIiIiIiIoWwmjHQRERERESk5OXk5BAdHU1kZCRxcXFs376duLg4ANLS0mjdujXjx4+3cEoRERHrYlUFNDs7GwCuXjVZOImIiIiISPlkNBpp0qQJHh4eAAQHBxMcHAzA2LFjCQkJsWQ8ERERq2Q1BbSpC3eQevoidWpV5aU+3iqiiYiISJmTezEQdEFQrJeLiwsuLi6Eh4fnmb5hwwYMBgMuLi5FWo8135XPmrOJiEjZZDUFtNTTF0lJy7B0DBEREZE7knsxENAFQSmTYmJiWLJkSZHnP3XqQgmmuXOurk6lkk1FOhGRisVqCmgiIlI2nDhxgjfffBMnJyeqVq1KWFgYtra6J42ILgZKWXbkyBFcXV1xdHS0dBQRERGrpG88IiJyW2JiYujfvz/vvfceDRo0YNu2bZaOJCIit2H37t2MGjWKQ4cOMXHiRFasWEFSUhINGza0dDQRERGrVWIt0Lp168a6detKavUiImIhlStX5ty5c8C1O7kdPnyYgICAAue35i4u1pxNRKSkGAwGIiIibpreuXNnC6QREREpG267gBYfH8/kyZNp1KiReVpYWBi1a9cu1mAiImKdnnvuOWbPns3OnTtxcHDI8/cgPxV9jJzcbYmIiIiISNl1Ry3QHnnkEaZOnWp+vGbNGr777jscHBzo0qULXbt2xWQyMWfOHJKTk/Hw8ODFF18sttAiImI5RqORwYMH8+CDDzJ9+nS8vb0tHUmkRKxcuZKoqCgaNGgAwJgxY/Dy8rJwKhERERGxhLvuwnn16lUWLVrE119/jY2NDf369aNr165cunSJAQMG4ObmxpNPPsnw4cOLPCips3P1u41VbNRqQEQkL0dHR1599VWcnZ1p2LAhzZs3t3QkkRLTs2dPhgwZYukYIiIiImJhd1RA+89//sPRo0fNj8+ePctrr70GgJ2dHRkZGTg4OODm5gaAi4sLZ8+eLXI3T6Mx3Spu+16a3Xtytydlk1opSEXi5uZGTEyMpWOIlIrNmzeTmJiIg4MDkyZN4t5777V0JBERERGxgDsqoHXs2NHchfPq1as8/fTTzJo1C4CkpCSqVatGVlYWJ0+epHbt2qSlpVGjRo3iSy1ihdRKQUSkfAkICMDf3x93d3diY2OJjo5m7NixRV7emlrUi4iIiMjduesunHZ2djzzzDOMGTMGGxsbPD09GTVqFE5OTixfvpxjx47RpUuXInffFCmr1EpBRKR8SUxMpH79+gA4OTmRlZV1W8tbS4v60qTW9CIiIlJe3XYBzd/fH39//zzTevToQY8ePfJMW7169d0lEylD7qSVQln+klGWs4uIFJWLiwvTpk2jevXqXLlyhbCwMEtHEhERERELuesWaCJyZ60USnN8veJUXGMDqggnItbOy8uLqKgoS8cQKXY5OTlER0cTGRlJXFwcJpOJ1157DUdHR86fP8/cuXNxcXGxdEwRERGrYjUFtDq1qub5X6QsUSsFERERKSuMRiNNmjTBw8MDgIMHDzJo0CDatGnDJ598wg8//EDfvn0tnFJERMS6WE0BbfqwthiN6QAVbrwQKfvUSkFERETKChcXF1xcXAgPDwegXbt2wLWbg+3atYsJEyYUaT3W3JrcmrOJiEjZZDUFNFDhTERERMqu61vRq0W9lDVGo5Hp06czaNAgHnzwwSItY63DURTXcBNF2Y6IiFQcVlVAExERESmrrm9ND7owKGXH+fPnmTJlCm+88Qbu7u6WjiNiVcJj99Cv0z91TBcRFdBEREREiou+YElZsHv3bhYtWsShQ4eYOHEiBw4cwNXVlZkzZwLQuXNnevXqZeGUItbh9LnLlo4gIlZCBTQRERERkQrEYDAQERFh6RgiIiJliq2lA4iIiIiIiIiIiFgzFdBEREREREREREQKoQKaiIiIiIiIiIhIIVRAExERERERERERKYQKaCIiUqCcnByioqLw9/fHaDTe9FhERERERKQiUAFNREQKZDQaadKkCR4eHvk+FhERERERqQjsLR1ARESsl4uLCy4uLoSHh+f7uChcXZ1KKt5ds+ZsIiIlJScnh+joaCIjI4mLi8PZ2Zl58+aRlJTEhQsXCAsLo2HDhpaOKSIiYlWsqoBmZ2eT5/HVqyYLJRERkeJy6tQFS0fIl6urU6llU6FORKzJja2J9+7dy8mTJwkPDyc5OdnC6URExJJurMvcqfJYz7GaAtrUhTtIPX3R/LhOraq81Me7XL7pIiIiIiKWcmNr4n379uHo6EhoaCjp6em88sorRVqPNV8csOZsIiLW6sa6zJ0qr/UcqymgpZ6+SEpahqVjiIjIdXbv3s2iRYs4dOgQEydOxM/Pj71795ofBwYG0q9fP0vHFBGRu3D58mVq1KjB+PHj2bJlCxEREUybNu2Wy1X0FsYq0olIeaO6TOGspoAmIiLWx2AwEBERYekYIiJSgjw8PNi1axcA1apVIzs728KJRERErI8KaCIiIiLFJL9xQ8pb9wUp+25sXdyxY0fOnz/PuHHjOH/+PFOmTLF0RBEREaujApqIiIhIMchv3JDyOgaIlG1qXSwiInL7bllAS05OZsaMGZw4cYJ169aVRiYRERGRMkfjhoiIiIiUX7a3mqF+/fpERkYC0K1bN/P03J8HDhxIZGQks2fPNt+xZ8eOHYwcOZJXX32VTz/9tCRyi4iIiIiIiIiIlIpi6cIZEBCAl5cX//rXv7h48SJz587l008/pVq1agwePJgePXrg5HT7d6lxdq5eHPHuiu6uIyIiIiIiIiJSsRVLAa127doAVKpUicuXL/P3338TFhYGwNWrV0lLS7ujAprRmG7RMUNK6xbY129PRERERERE7ky3bt009JCIlIhCC2gffvghISEhmEwmnJycSE9Px2QykZWVhdFoLHC5Bg0aMH36dBwdHfnjjz+4//77izu3iIiISIlKSkrinXfewcXFBaPRyOuvv46bm5ulY4mIVGhffPEF33zzDZUrV+bKlSsMGjSIixcvYmdnR9euXS0dT0TKsUILaA899BAjR47E1taWESNGcODAAaZMmYK7uzu1atUqcLnRo0czbtw4KleuTI0aNXjjjTeKPbiIiIhISfrxxx9p3749zzzzDMuWLWPnzp306NHD0rFERCqsn3/+mc2bN7N48WIcHBzIyMhg8ODB/PnnnzRt2hRvb28yMzOZNWsWv//+OwEBATz77LMsWbKE/fv3c/XqVUJCQvDz86N79+40atSI4cOH4+HhYemXJiJlQKEFtMDAQAIDA82PAwICzD+/8MILAHluEpD7c/v27Wnfvn2xBhUREREpTT179iQkJISdO3eSkpLC0qVLLR1JRKRC27JlC/3798fBwQGAatWq0atXL9577z3+9a9/4ebmxrlz5xg/fjxZWVkMHDiQxx57jM2bN/Ppp5+SkZHByJEjWbZsGX/99RdfffWVeV0iIrdSLGOgiYiIiJQ3y5cv59lnn6VHjx6sXr2amJgYhg0bdtvrsYabIolcLycnh+joaCIjI4mLi+PMmTPqrixlhsmUd4zsnJwc7OzszI9dXV1xdHTE0dGRzMxM/vrrL06ePMmkSZMAsLW1Ba6N413U4llFOo5rXG6RgllNAa1OraqFPhYREREpTefOnaNJkyYA1KhRg99+++2O1mPpmyKVJn3xKhuMRiNNmjQxd1tTd2UpKx555BGWLFmCv78/jo6OpKens3r1avr160dOTk6+y7i5uXH//fcza9YsTCYTf/zxBwA2NjZF3m5FOY4XdhM9Hd9FrKiANn1YW4zG9DzTKsJBSkRERKzTgAEDmDlzJt999x1nz55l8uTJlo4kUixcXFxwcXEhPDwcuPPuytb8hdqas8md8/f3Jzk5meeff57KlSuTmZnJyJEjyczMZMGCBTzwwAM3LVO7dm1atGjBhAkTuHTpEoGBgTz44IMWSC8iZZ3VFNBABTMpu9auXUtcXBwAaWlptG7dmvHjx1s4lYiI3I169eqxYMECS8cQKXF32l25oJYqllZYK5ri3o6Uvj59+tCnT5+bpgcFBQGwbt0687Tcn/Pbn6+fT0SkKGwtHUCkPAgODiYiIoKIiAjq1q1LSEiIpSOJFIucnByioqLw9/fHaDRy+vRpRowYwZgxY3j22WdJS0uzdEQREblL586do2bNmsC17sqnT5+2cCIRERHrY1Ut0ETKug0bNmAwGHBxcbnlvGX5qmVZzi6358Zxcg4ePMigQYNo06YNn3zyCT/88AN9+/a1cEoREbkdu3fvZtGiRRw6dIiJEycSGBjIZ599pu7KIiIihVABTaQYxcTEsGTJkiLNa63dHm6luLpFqAhXNtw4Tk67du0AuHr1Krt27WLChAm3XIc1/66tOZuUPfndAEk3RRJrZDAYiIiIyDOtX79+FkojIiJSNqiAJlJMjhw5Yr5ttkh5ZjQamT59OoMGDSrSILzWWiwurTFycrcl5V9+N0QCjfEqIiIiUh6ogCZSTJKSkmjYsKGlY4iUqPPnzzNlyhTeeOMN3N3dLR1HxOqoWCYiIiJSPqmAJlJMOnfuTOfOnS0dQ6RY3ThOzoEDB3B1dWXmzJnAtf2+V69eFk4pIiIiIiJSslRAExGRAuU3To6IiIiIiEhFY2vpACIiIiIiIiIiItZMBTQREREREREREZFCqIAmIiIiIlKB5OTkEBUVhb+/P0aj0Tx98+bNdOrUyYLJRERErJdVjYFmZ2dT4HO6q5WIiIiIyN0zGo00adIEDw8P87SzZ8+yatUq6tata8FkIiIi1stqCmhTF+4g9fTFfJ+rU6sqL/XxVhFNREREROQuubi44OLiQnh4uHna7NmzGTduHG+88UaR1+Pq6lQS8YqFNWcTEZGyyWoKaKmnL5KSlmHpGCIiIiIiFUpcXByenp7cf//9t7XcqVMXSibQXXJ1dSqVbCrSiYhULBoDTURERESkAvv+++9JTExk0qRJ/PHHHyxcuNDSkURERKyO1bRAExERERGRkrd7924WLVrEoUOHmDhxIoGBgfTr1w+AgQMHMmzYMAsnFBERsT4lXkAbOHAgH3zwAc7OziW9KRERERERuQWDwUBERES+z3366aelnEZERKRsuOsCWnJyMjNmzODEiROsW7euODKJiIiIiIiIiMj/9+6773Lo0CEOHjyIu7s7Tk5ODBs2jBYtWlg6WoVx12Og1a9fn8jISAC6detmnn79zyIiIiIVgZ2dTb7/RESkbKp1b2VLRxABYMKECURGRtKmTRsmT57M4MGDiYiI4KWXXuKnn35i+vTpAMTHx5t/njVrFq+++ipjxowhKSnJkvHLhTIzBpqzc3WLbFd31xERuXPhsXvo1+mfXL1qsnQUkRI3deEOUk9fvGl6nVpVeamPtz4HIiJl0OinfKz2jrMidnZ2vP/++8THx9/03N69ezlz5gxz5swhOTmZefPm8d5775Vatrup4VhrHabMFNCMxvRSP/EsrVtgX789EZHy5PS5y5aOIFJqUk9fJCUtw9IxREREpIJwd3cv8Lnjx49z+PBhJk2aVIqJ/s+d1nBKuw6T3/YLclcFtA8//JCQkBBMJhNOTk6kp6djMpnIysrCaDTezapFRERERERERKQANjbXholwdHQkMzMTgL///hu4NtyWt7c3oaGhZGZmkpqaarGc5cVdFdAeeughRo4cia2tLSNGjODAgQNMmTIFd3d3atWqVVwZRUTEiqxdu5a4uDgA0tLSaN26NePHj7dwKhERERGRisnLy4uUlBTmzZvH5cvXeoB4e3uzdu1aJk2axJkzZ+jfvz/169e3cNKy7a4KaIGBgQQGBpofBwQEmH9+4YUXAN0KW0SkvAkODiY4OBiAsWPHEhISYuFEIiIiIiIVw6xZs8w/+/v7A1ClShWioqJumve1114rtVwVQZkZA01ERKzLhg0bMBgMuLi4FDqfpW4CUxQae1IKc+LECd58802cnJyoWrUqYWFh2Nre9Q3MRSwuJyeH6OhoIiMjiYuL49KlS9rXRUREbsFqCmh1alW9o+dERMQyYmJiWLJkyS3ns8RNYIqiNAcoVaGubIqJiaF///4EBgYSGRnJtm3b8rS2FymrjEYjTZo0wcPDA9C+LiIiUhRWU0CbPqwtRmN6gc9b45cvEZGK6siRI7i6uuLo6GjpKCIlpnLlypw7dw641mLn8OHDd1xUsOaWmFLxuLi44OLiQnh4OHDn+7o1Xxyw5mwiIlI2WU0BDVQkExEpK5KSkmjYsKGlY4iUqOeee47Zs2ezc+dOHBwcaNSo0R2vy1pbYhY3FS3Kpjvd10urFe/tKq0WxtrfRUQqFg1uICIit61z586MHTv2lvPVurdyKaQRKRlGo5HBgwfz9ttvY29vj7e3t6UjiZQI7esiIiK3ZlUt0EREpHwZ/ZSP1bZQELkVR0dHXn31VZydnWnYsCHNmze3dCSRYrF7924WLVrEoUOHmDhxIs2bN+fnn3/Wvi4iIlIIFdBERERE8uHm5kZMTIylY4gUO4PBQERERJ5pL7zwgoXSiIiIlA3qwikiIiIiIiIiIlIIFdBEREREREREREQKoS6cIiIiIsWgTq2qtzVdRERERMoOFdBEism8efNISkriwoULhIWF0bBhQ0tHEhGRUjR9WFuMxvR8n7t61VTKaURERESkOKmAJlIM9u7dy8mTJwkPDyc5OdnScURExEJUKBMREREpn1RAEykG+/btw9HRkdDQUNLT03nllVduuYyrq1MpJCsZZTm7iIiIiIiIyO1SAU2kGFy+fJkaNWowfvx4tmzZQkREBNOmTSt0mVOnLpRSuuLl6upULNlVhBMREbGMnJwcoqOjiYyMJC4ujs2bNxMVFUWDBg0AGDNmDF5eXhZOKSIiYl2sqoBmZ2dT4HPqEiHWzMPDg127dgFQrVo1srOzLZxIREREJH9Go5EmTZrg4eFhntazZ0+GDBliwVQiIiLWzWoKaFMX7iD19MV8n6tTqyov9fFWEU2sVocOHfjhhx8YN24c58+fZ8qUKZaOJCIiIpIvFxcXXFxcCA8PN0/bvHkziYmJODg4MGnSJO69995brseaW5NbczYRESmbrKaAlnr6IilpGZaOIXJHbGxsmD59uqVjiFid8Ng99Ov0T10AERGxYgEBAfj7++Pu7k5sbCzR0dGMHTv2lstZ63AUxTXcRFG2IyIiFYetpQOIiEj5dfrcZUtHEBGRW0hMTDQPP+Hk5ERWVpaFE4mIiFgfq2mBJiIiIiIiJW/37t0sWrSIQ4cOMXHiRNq3b09UVBTVq1fnypUrhIWFWTqiiIiI1bnjAprJZMLGpuBB/0VEpOy78U5tFy5cICwsjCpVqnDu3DnCwsJo2LChpWOKiMhtMBgMRERE5JkWEhJioTQiIiJlwx114fzggw949913izuLiIhYmRvv1HblyhWmTZvG/Pnzad26NQkJCRZOKCIiIiIiUvJuuwVaamoqVatWxc7OjuTkZHbt2sWZM2cYMmQIK1eu5MyZM/Tt25dx48bh5uaGg4MDOTk5TJ06tSTyi4hICbrxTm0eHh6kpKSYWyosXrz4lutwdq5eohnvhgaAFhERERGRorjtAlpsbCy9e/fGzs6O5cuX59t1Z+PGjbRs2ZJhw4axZMkSTpw4cddBLfUFTF+uRETycnNzY9myZSxfvpxPPvmE4cOHFzq/0ZhulXfhLK27tOVuS0REREREyq7bKqBlZWXx/fffk5SUBEBSUhL9+/e/ab7U1FTq168PQKNGjYqlgGaJL2Cl+eUqd3siItZs4cKFNG7cmPbt21OnTh2OHz9u6UgiIiIiIiIl7rYKaOvXr2fQoEH07NkTgNWrV2MymThz5gwAf//9N5UqVaJWrVqkpqYCmIttIiJS9tx4pzYfHx+WLVtGbGwsGRkZulObyA3s7G59gyVrbJEpIiIiIoW7rQLa559/bh4HByAwMJA+ffpw3333ceXKFVJTU/nnP/9JUFAQ48aN4+jRo5hMJhwcHIo9uIiIlLz87tR2O2rdW7kY04hYt6kLd5B6+mKh89SpVZWX+niriCYiIiJSxtxWAS0mJibPYycnJzZs2HDTfOfOnWPs2LH4+Pjw9ddfk5KScncpRUSkTBr9lE+pdoUXsaTU0xdJScuwdAyRO7J27Vri4uIASEtLo3Xr1owfP97CqURERKzHbd9EoCgcHByIiIjg3nvv5fz58+riIyIiIiJixYKDgwkODgZg7Nix5rsti4iIyDUlUkCrWrUqCxcuvK1l6tSqekfPiYiIiIhI8diwYQMGgwEXFxdLRxERkVJWXLWX8lrDKZEC2p2YPqwtRmN6gc9rrBARERERkZIVExPDkiVLijSvNd9B3pqziYhYq1vVZW5HeazhWE0BDcrnGywiIiJlk8aEkormyJEjuLq64ujoWKT5rXWMS1dXp1LJpiKdiJRHqssUzKoKaCIiIiLWQmNCSUWTlJREw4YNLR1DRETEKqmAJiIiIlKI4h4Tytm5erGsR6S4de7cmc6dO1s6hoiIiFVSAU1ERESkELczJlRRGI3p5bZ7hLq0iYiISHlla+kAIiIiItbqdseEEhEREZHySQU0ERERkQJoTCgRERERARXQRESkBIXH7sHOzsbSMUTuWOfOnRk7dqylY4iIiIiIhamAJiIiJeb0ucuWjiAiIiIiInLXVEATEREREREREREphO7CKSIit+Xo0aOEhYVRpUoVzp07R1hYmMaIEgHq1KpaLPOIiIh1KcpwFOX17soi8n9UQBMRkdty5coVpk2bhru7OxERESQkJKiAJgJMH9YWozH9lvPpS5ZYoxMnTvDmm2/i5ORE1apVCQsLw9ZWnVVEpi7cQerpi4XOU6dWVV7q463ju0g5Z1UFtMIq+zoYiYhYBw8PD1JSUggJCQFg8eLFFk4kYj10viJlVUxMDP379ycwMJDIyEi2bdtGQECApWOJWFzq6YukpGVYOoaIWAGrKaAVVtlXRV9ExLq4ubmxbNkyli9fzieffMLw4cMLnNfZuXopJrs9rq5Olo4gImIVKleuzLlz5wDIycnh8OHDtyygWfMx1JqziYhI2WQ1BTRV9qUsS0pK4p133sHFxQWj0cjrr7+Om5ubpWOJlIiFCxfSuHFj2rdvT506dTh+/Hih8xuN6VZ5AcTV1YlTpy6U2rZERKzZc889x+zZs9m5cycODg40atTolsuU1jH0dpXW8V3HdhGRikUDG4gUgx9//JH27dszY8YMWrduzc6dOy0dSaTEBAcHs2zZMsaMGcOKFSsYOHCgpSOJiMhdMhqNDB48mLfffht7e3u8vb0tHUlERMSqWE0LNJGyrGfPnoSEhLBz505SUlJYunTpLZcpy1cty3J2uXt169Zl0aJFlo4hIiLFyNHRkVdffRVnZ2caNmxI8+bNLR1JRETEqqiAJlIMli9fzrPPPkuPHj1YvXo1MTExDBs2rNBlrLXbw60UV7cIFeEqhlr3VrZ0BBERKQI3NzdiYmIsHUNERMRq3VYB7cqVK0ydOpVLly6Zp7311ltUr269A0SLlIZz587RpEkTAGrUqMFvv/1m4UQi1mH0Uz5ltlgsIiIiIiKS67YKaAkJCVSrVo3Zs2cDsH37do4fP05ERAT33HMPmZmZvPnmm2zYsIHvv/+eBx98kMOHDzN16lQNqC7l2oABA5g5cybfffcdZ8+eZfLkyZaOJCIiIiIiIiLF5LYKaM2aNWPp0qVMnz4dPz8/WrduzZdffklAQAC9e/fmyy+/ZM2aNVSqVAlXV1cmTJjAxo0b+eqrr3jxxRfvKqizs2VauambmRRFvXr1WLBggaVjiIiIiIiIiEgJuK0CWvXq1fn4449JTU3l119/5aWXXiIlJYUHHniAXbt2cfHiRZo3b06lSpXMLc5cXFw4derUXQc1GtO5etV01+u5HaV1C+zrtyciIiIiIiIiItbltgpoW7ZswcHBgbZt2/L4449z9epVXn/9dcaPH8/jjz/O6dOnsbOzY9OmTRw/fhyAlJT/1979R0VZ5/0ffw0IKmAqDJJQ6R4Vje1wsttC8nYp7dx27m5LTTM2Re9cNWUjXS0VlVXIX7tZ+QM6oClp6q2kZW2H1mKTvd0Stdi0vTX31jINM2SUzwblGwAAIABJREFUXyYk8P2jL9xSgAzMzHXNzPNxDuc4M9dc13vwMxfXvObzo0g9evRwSvEAAAAAjOHra5Ekl3/JDQCAEewK0O644w4tWbJEr7/+ujp06KDa2lq9//77Wr58uf7617/q0qVLWrRokSTpwoULev755/XPf/5TK1eudErxAAAAAFwvJesjXSi5orCQAM0aG02IBgDweHYFaCEhIVq3bt3P7n/55Zcb3T58+LBiYmI0ZcqU9lUHAAAAwKFKSkq0cOFC+fv7q6ysTMuWLdOqVavk4+Oj6upqrVixQl27dm1xHxdKrqjoYqWLKgYAwHg+RhcAAADgKXx9LTf8AYx24sQJTZo0SWvXrlVcXJzy8vI0aNAgvfzyyxoxYoS2b99udIkAAJiOXT3QWmvMmDF2PycsJKBNjwEAAJhB/ZC2ljDcDWYwZMgQSVJNTY0OHz6sqqoqzZgxQ5IUFRWlvLw8I8sDAMCUnBKgtUXqtHtls1U0+zgXmgAAwMwY0gZ3YrPZlJqaqkmTJundd99VXV3br7WDg4McWJljsLo9AMDRTBOgSYRkAOBp0nP+rvjhfTm/A4CJlJWVKTk5WYsXL1ZERIS+/PJLHT16VHfffbeOHTum6Ohou/Zns1WY6jwfGtpFxcXlLjkOAMB7mCpAAwB4lpLSq0aXAAD4iezsbJ0/f17Lli2TJA0dOlSff/65kpKSJEnLly+/4T7qp1hhqhUAgLcgQAMAAAC8SFJSUkNYVi8+Pt6ufVw//YqZep8BAOAsrMIJALDLqVOn9NRTT2nRokWaOXOmioqKjC4JcJoXX3xRiYmJSkhI0JkzZ4wuBzCVmpo6wjMYJicnRwcPHmzysSVLlqi83PnDeAF4FwI0AIBdDhw4oKFDh+r555/X4MGDdejQIaNLApzis88+03fffaf09HQtW7ZMPj5cNgFAcwoKCnTXXXfp/PnzDfdNnDix3fudPHmyzp4923D77NmzmjhxosaNG6fBgwc3+ZwlS5aoS5cuysrK0rFjx9pdAwBIDOEEANhp9OjRSkhI0KFDh1RUVKTXXnutxe3NuDpbPSaARkuOHj0qf39/LVmyRBUVFXr22Wcdsl8zvycAoD3uv/9+rVy5UmvWrGm47+2339a+ffvk5+enESNGqKLix6G/Y8eO1eOPP66nnnpK9913nyZOnKitW7f+bJ/x8fHauXOn5s6dK0natWuX4uPjtW7dOvXr10+9e/fWmjVr1K1bN127dk2rVq3SpEmTtGrVKu3evVvHjh3TqlWr9Mc//lHff/+9SktL9dRTTykkJETPPfecQkNDtXjxYlmt1na9dk85t3NtBDSPAA0AYJdt27Zp8uTJGjVqlPbu3avXX39d06ZNa3Z7s63OVs9Vq7TVHwvu5+rVq+rWrZt+97vfKT8/XxkZGVq6dGm792vW94Qj0NYB79a7d2/5+Pjovffe04MPPqiamhpt2LBBb731liwWi+Lj4/XSSy9p3bp1GjFihCIiInTkyBH17dtXvXr1anKfw4cPV2Zmpqqrq2WxWHTgwAE988wzeuWVVyT92PNt0KBBevLJJ/Xll1/q2rVrkqROnTpp4MCBeuKJJ3Ty5EldvXpVK1eu1Lfffqvk5GSlpqaqrKxM27dvd8hr94Rze0vXRpzfAYZwAgDsVFpaqu7du0uSunXrppKSEoMrApwjMjJStbW1kqTAwMCGD2UAgOZNnTpVO3fuVHl5uWw2my5fvqyFCxcqOTlZvr6+6tq1q4qLi3XkyBENHz5cxcXFOnTokIYMGdLk/jp06KAHHnhA77//vt5//30NHz5cHTr8Xz+QcePGqaysTE888YRycnIaPVbvm2++aQjobr75ZhUXF0uSwsPDnfAbAOCpCNAAAHaZMGGC/uu//ksLFy7Url27HDK/CWBGv/rVr1RWVqbZs2frlVde0ZNPPml0SYCp+Ppa5OtrMboMmIy/v78SExO1evVqBQcH6+abb9bKlSu1cuVKpaWlKTAwULfeeqv+9Kc/adCgQerYsaP+9re/KTY2ttl9PvbYY9q7d6/efvttjR8/vtFjX375paZOnart27fru+++0/Hjxxses1gsqqur06233qqvv/5aknT+/HmFhYU1PA4ArcUQTgCAXW655ZaGYRM3EtK1k5OrAZzHYrEoNTXV6DIAU0rJ+kgXSq4oLCRAs8ZGu/3QNTjWoEGD9M4778jX11ePPfaYkpKSZLFY1L9/f82cOVOxsbFas2aNevTooejoaL3xxhvq1q1bs/uzWq0Nj4eGhjZ6rKysTM8880zD/X379m14bMCAAVq5cqVeeuklBQQEaMGCBQ1fjACAvSx1dXWm+WvnqrloWsuV8+PUHw/ew2ztvbUc9b6gvXsPs7Z15kCDo01f8YGKLla2uE24NVArpg/22LCBtu4eamtrlZ2drczMTOXm5qpbt26NbgcHB99wH/Xt3Yxt2lXnd9q7d/CmcztzoEEy/trd1TlMU8dvjql6oLWmC7i7n5QAAAAAI9lsNkVFRSkyMrLJ24ARbDabXn755Z/dP3fuXN10000GVAQAjZkmQKvvBt4SuogDAACzCgsJcMg2gLNZrVZZrValp6c3edtewcFBjizPIegt436Cg4MZNg/A1EwToF0ouXLDrrGAJ7m+xyWhMAC4v9Rp98pmq7jhdpzz4WlstgpTtWuGcAIAnME0ARrgTa7vcUnPSgDwHJzL4S3qe1PSqxIA4C0I0AAD0OMSAAAYpbCwUBs2bNDJkyc1b948DRo0SJ999lnD7WHDhik+Pr7FfVzf45LgGADgDQjQAAAAAC8ycOBAZWRktHs/BGcAAG/i09Yn7tq1S5MmTdL06dM1efJkffjhh01ul5SU1ObiAAAAAAAAAKO1qQfawYMHtX//fm3cuFF+fn6qrKzUb37zG/3iF7/Q8uXL5efnp759+yoqKkoHDx7Utm3b9MQTTzi6dgAAAAAAAMDp2hSg5efn69e//rX8/PwkSYGBgRo1apRGjBihnTt36s4771ROTo6GDx+udevWOTQ8c/Uy2ayuAwAAAAAA4N3aPAdaXV3jOQ9qa2vl5+en8PBwSdK4cePaV1kzXLlMtquWwL7+eHBv+/fvV1pamvLy8owuBQAAAAAAOEib5kC77777tHXrVlVXV0uSKioqtHfvXsXExOirr76SJL366quqrq5WbW2tw4oFzOzy5ct688031bNnT6NLAUwjPefv8vW1GF0GAMAJfH0tDT8AAHi6NvVAi4mJ0dmzZzV16lR16tRJ1dXVmjFjhn7xi1/o+eef12uvvaY+ffrI399fQUFBeumllzR79mxH1w6YyqpVqzR79mwtXrzY7ue6emhye9FbEq1VUnrV6BIAAE6QkvWRLpRckSSFhQRo1thoVuUEAHi0Ng/hHDt2rMaOHfuz+7Oyshrd3rVrV1sPAbiN3Nxc9e/fX717927T8105NLm9HDW0mRAOAADz2LNnjzZt2qTbbrtNkpSUlKQBAwY0u/2FkisquljpqvIAADBcmwM0RwsLCXDINoARPvjgA3Xo0EHz58/X6dOnlZWVpWnTphldFtButbW1ys7OVmZmpnJzcxUQEKDnnntOPj4+qq6u1ooVK9S1a1ejywQAOMDo0aM1ZcoUo8sAAMCUTBOgpU67VzZbxQ23c5deOvAuq1evbvj3xIkTCc/gMWw2m6KiohQZGSlJ2r17twYNGqSEhATt3btX27dv14wZMwyuEgDgCPv379fx48fl5+en+fPn2/UFidmmo6CnOwDA0UwToEmEY/AMW7duNboEwGGsVqusVqvS09MlSV988YUefvhhSVJUVFSrVpw124eq6/EBCwB+FBcXp5iYGEVERCgnJ0fZ2dl65plnWv18M01H4ajpJlpzHACA9zBVgAYAML+6Ovs+IJnpQ9X1XPUBq/5Y8A5tXY3QjO8ReJfjx4/r1ltvlSR16dJFP/zwg8EVAebAVEMA6hGgAQBaLSoqSkePHtXdd9+tY8eOKTo62uiSANO4flVCe7CCIczAarVq6dKlCgoKUlVVldLS0lrc/vrAgPAAnoyphgDUI0ADDMBFJ9xFYWGhNmzYoJMnT2revHkaNmyYPv/8cyUlJUmSli9fbnCFgHmwKiHc2YABA7Rp06ZWb//TUIHwAJ6M9g1AIkADDMFFJ9zFwIEDlZGR0ei++Pj4Vj8/pGsnR5cEADAJrl8AAN7Ex+gCAG9VU1PX8AN4qsRxd9LGAQAAALg9AjQAAAAAAACgBQzhBAAAaEJJSYkWLlwof39/lZWV6YUXXpDVajW6LAAAABiAHmgAAABNOHHihCZNmqS1a9cqLi5OeXl5RpcEAAAAg9ADDQAAoAlDhgyRJNXU1Ojw4cOaM2eO044VHBzktH0DzuLra2l0mzkvAQCejAANAACgGTabTampqZo0aZL69OnjxONUeET4EBraxegS4CIpWR/pQsmVhtthIQGaNTbaI9oxAABNIUADAABoQllZmZKTk7V48WJFREQYXQ7gVKdOndIf//hHWa1W2Ww2LVq0SOHh4c1uf6HkioouVrqwQgAAjGWqAO2n3cBdiW/LAADA9bKzs3X+/HktW7ZMkvTAAw9ozJgxBlcFOMeBAwc0dOhQPfHEE9qyZYsOHTqkUaNGGV0WAMDFjMxljKqhtXmQaQK0n3YDdyW6nAMAgJ9KSkpSUlKS0WUALjF69GglJCTo0KFDKioq0muvvWb3Psw0lx/DiQHAfkbmMkaxJw8yTYBGN3AA8Ey+vha+oAAAk9u2bZsmT56sUaNGae/evXr99dc1bdo0u/Zhlrn8QkO7qLi43CXHAQBPQi7TMtMEaAAAz5OS9ZEk0csXAEyutLRUUVFRkqRu3brpf/7nf1rcPiwkoMXbAAB4GgI0AIDTeFsXcHi3tgYIBA8wgwkTJmjZsmXat2+fLl++rAULFrS4feq0e2WzVTS6jy9KAACezO4AraCgQMnJyerfv79qamoUEhKiuXPnKjg42Bn1AQAAuIWmAoXWIniA0W655Ra98sordj2HdgsA8CZt6oEWFxenlJQUSVJ+fr6Sk5P17//+79q3b5/8/Pw0YsQIPfjgg9q5c6c+/PBDVVdXKzExUf/yL//i0OIBAMZ48cUXderUKZWXlystLU29evUyuiTAFAgUAAAAPFO7h3DGxcXphRde0Pr165WbmyuLxaL4+HgNGzZMO3fu1J49e/Tdd9/p2LFjjqgXAGCwzz77TN99953S09N19uxZo8sBAAAAAKdzyBxolZWVqq6u1sKFCyVJvr6+KikpUffu3SVJPXr00PDhwx1xKKdpbtltVteBs/j6Wpy2b3pAwJmOHj0qf39/LVmyRBUVFXr22WeNLgkAAAAAnKrdAVpeXp7uuOMOnT9/XitXrpQknTp1SlarVRcvXlRtba2Ki4t14MABPfroo+0u2FmaWnbbVUtgX388eIeUrI+cNrl6WEgAKx7Cqa5evapu3brpd7/7nfLz85WRkaGlS5e2+JzmvqQwGuddAAAAAK3RpgAtPz9fM2fOVE1NjW666SalpqZq//79SkpKksViUf/+/TVz5kxNmDBBiYmJ+v777/X00087unbAbV0ouaKii5VGlwG0SWRkpA4fPixJCgwM1LVr1274nKa+pDCaK78kIagD4Ima601vtvM9AACOYHeAFhMTo7y8vJ/dP2rUKI0aNarRfePGjdO4cePaXh0AwHR+9atfKS8vT7Nnz1ZZWZmSk5ONLgkA4GLN9aanJzwAwFM5ZA40AID3sFgsSk1NNboMAICD2bPCMr3pAQDexjQBWlhIgFceGwA8GedXAHAPrLAMAEDLTBOgpU67VzZbhWHHp5s5ADhe/bmdcywAmJsjV1g2w8IxzD0JAHA00wRoEiEWAHgizu0AYH5tWWG5OUZ/ceKqRWII6QDAu/gYXQDgCc6cOaPf/OY3evrpp5WQkKAzZ84YXRIAAECrRUZGqra2VlLrV1gGAMCbmKoHGuCuqqqqtHTpUkVERCgjI0OffvppixPvAgAAmIm9Kyw3N8clc18CADwVARrgAJGRkSoqKlJCQoIkaePGjYbW4+y5RxiyAACAZ7F3heWW5i9m6D4AwBMRoAEOEh4eri1btmjbtm3avHmzpk+fblgtzpx7xFHzihDCAfBEvr6WNj2PwAHuiHYLAPAmBGiAA2RlZen222/X0KFDFRYWpnPnzhldEgDAxVKyPtKFkit2Py8sJECzxkYTRgAAAJgYARrgACNHjlRKSopycnJUWVmptLQ0o0sCALjYhZIrKrpYaXQZAAAAcAICNMABevbsqQ0bNhhdBgAAAAAAcAICNACAU10/JxRD1ADAczQ35x/negCAJyJAAwA4zfVzQjHPEwB4jubm/ONcDwDwVARogAHCQgLcct+AvZgTCp5g//79SktLU15entGlAKbB+R0A4G0I0AADpE67VzZbhdP2z7e+AOAYly9f1ptvvqmePXsaXQrgMLW1tcrOzlZmZqZyc3MVEBCg5557Tj4+PqqurtaKFSvUtWtXo8sEAMBUTBWgNTePgqsRPsAVaGdwV3v27NGmTZt02223SZKSkpI0YMAAg6sCnGPVqlWaPXu2Fi9e7NTjBAcHOXX/wPVsNpuioqIUGRkpSdq9e7cGDRqkhIQE7d27V9u3b9eMGTMMrhIAAHMxTYDW3DwKrsa8DQBwY6NHj9aUKVOMLgNwqtzcXPXv31+9e/d2+rFstgqPuPYIDe1idAloBavVKqvVqvT0dEnSF198oYcffliSFBUV1e7hymYIhGmLAABHM02AxjwKAOA+9u/fr+PHj8vPz0/z589v9VAfM3youh4fsNCSDz74QB06dND8+fN1+vRpZWVladq0aUaXBThFXZ3jAlyjA+HQ0C4qLi53yXEAAN7DNAEaAMA9xMXFKSYmRhEREcrJyVF2draeeeaZVj3X6A9V13PVB6z6Y8H9rF69uuHfEydOJDyDx4qKitLRo0d1991369ixY4qOjr7hc5pbtIjFjAAAnooADQBgl+PHj+vWW2+VJHXp0kU//PCDwRUBzrd161ajSwAcprCwUBs2bNDJkyc1b948DRs2TJ9//rmSkpIkScuXL7/hPlpaEMksX5QAgLf4j//4D/3pT38yugyPZ3eAVlBQoKlTpyo/P1/du3eXJK1fv14FBQVcXAKAF7BarVq6dKmCgoJUVVWltLQ0o0sCANhh4MCBysjIaHRffHy83fshKAMA5yooKNCCBQvUr1+/hvvS0tLUo0cPA6vyXm3qgRYdHa13331XEyZMUF1dnc6dOydJ2rZtmz7++GNdvXpVjzzyiEaOHKnx48erX79+euihhxQbG+vQ4gEArjdgwABt2rTJ6DIAAAAAj3ffffcpJSWl4XZTuUu95cuXq7y8XGVlZXr88ccVGxurRYsWyWKx6Pvvv1dycjLhWzu0KUC75557dOTIEU2YMEGffPKJ7rrrLn3zzTfq2LGj1q1bpytXrujJJ5/UyJEjVVxcrIyMDIWEhDi6dgCAyV0/Fw7z4sDTtbWN894AAHPz9bUYXYLLXP9a6WVqTk3lLvUOHjyo1157TYGBgfrmm2+0b98+hYeHKykpSR9//LG2bNmiuXPnGli9e2tTgGaxWNSvXz+dPn1a+/btU1JSkt555x1VV1crJSVFnTp1UnV1tSSpc+fObheeXb9KHBM/A0Db/XSOHC7E4MlamhPqRnhvAIA5pWR9pAslV4wuw+XCQgI0a2w0f59M4MMPP9SZM2cabtf3SLs+d6m3YMECLV68WFeuXNGsWbN07tw5HTp0SPPnz9cPP/wgq9Xq6vLdwvUZUEvavIjAo48+qh07dqiurk5BQUEqKyvTG2+8oT179uj8+fMqKCiQ9GPY5m7qV4lz5QptEmEdAM/EhRe8Ce0dADzLhZIrKrpYaXQZ8GL3339/wxDOsrIyTZ48+We5iyRdvXpVAQEBWr9+vb766iutXLlSDz/8sOLi4jR16lSVl5fr+++/N+plmFp9BiS1nMu0OUC7+eab9fXXX+vxxx+XJAUGBqpTp05asGCBevXqpYCAAP3lL39p6+4BAAAAmFhrh7URLAOAYwQGBqpbt25N5i7+/v7avn27srOzde3aNY0dO1ZxcXFatGiRFixYoJKSEj399NPMgdYOlrq6OlP8RZu+4gNTJPvh1kCtmD6YHmhwOle2LUdy1PuC9u49zNrWXXmOp717D7O2d1ehrXuP1g5rM2IYmKvO77R372CWz6mudv3nYon27i28sb3b09bb3AMNAAAAgPupra1Vdna2MjMzlZubq/LycqWlpalz584qLS1VWlqaevXq1eI+GNYGAPA2pgnQzLIClVnqAAAAAJzBZrMpKipKkZGRkqSqqiotXbpUERERysjI0KeffnrDAA0AAG9jmgCtPStXORrzNAAAAMBTWa1WWa1WpaenS5IiIyNVVFSkhIQESdLGjRsderzWrm7mSAw3AwA4mmkCNIngCt6ltRPvtgXvJQAAYI/w8HBt2bJF27Zt0+bNmzV9+nSH7fv61c1cgTnQAADOYKoADfAWrZ14ty2MmKwXAAC4r6ysLN1+++0aOnSowsLCdO7cOaNLAgDAdAjQAAMw8S68SVO9LQl4AcA4hYWF2rBhg06ePKl58+bpzjvv1JYtW5STk6PKykqlpaXdcB+tnTeY+YUBAJ6CAA0A4DRN9baklyQAGGvgwIHKyMho1z7smb+Y8z0AwBMQoAEAnIbelgDguQjGAADexMfoAgAAAAAAAAAzI0ADANht//79Gj58uNFlAKbj62sx5AcAAADOxRBOwAFqa2uVnZ2tzMxM5ebmKjg42OiSAKe5fPmy3nzzTfXs2dPoUgBTceYKyy1hXkEAAADnI0ADHMBmsykqKkqRkZFGlwI43apVqzR79mwtXry4zfsIDg5yYEVtFxraxegS4EGY8w/epr29Hwl9AQDuhAANcACr1Sqr1ar09HSjS5Hk/HCC0MF75ebmqn///urdu3e79mOzVRj+wSk0tIuKi8tddiwA8CTt7XFJz0kAgLshQAM8kDPDCUeFDgQK7umDDz5Qhw4dNH/+fJ0+fVpZWVmaNm2a0WUBAFyMHpcAAG9DgAYAaLXVq1c3/HvixImEZwDgYfbv36+0tDTl5eUZXQoAAKZiqlU4jVq5qqUVrVgZC61RWFiomTNn6uTJk5o3b5527NhhdEmA023duvWG24SFBCjcGtjoJywkwAXVAQDsxSIxcKR//OMf2rRpk8v3nZOTo4MHDzrluAC8m2l6oBm1cpWzMb+Ddxg4cKAyMjKMLgMwndRp98pmq/jZ/ZwT4Q7OnDmjtLQ0de7cWaWlpUpLS1OvXr2MLgtwGkcsEmMPZ87ZylQRzSsoKFBycrL69+8vSSovL9eoUaP06KOP/mzbpKQkrV27tk3H+eUvf6lf/vKX7ap1/fr1Cg8P15gxYyRJdXV1euSRR7R9+/Zm9z1u3DhJ0t///nd98sknmjJlSrtqAIB6pgnQmEcBADwTYRncVVVVlZYuXaqIiAhlZGTo008/JUCDx3LUIjH2cNacra5aJMadQ7q4uDilpKRIkoqKipSYmCg/Pz/t27dPfn5+GjFihLp3766DBw8qPT1dPXv21KVLlzRlyhTt2bNHly5d0h133KGsrCx16dJF48eP18aNG3XnnXfq2LFjmjFjhqqrq/XnP/9ZTz75pObMmaMhQ4bon//8p0aOHKnhw4dr9uzZuummmxQSEqITJ04oMzPzZ3WOHz9es2bNagjQDhw4oLvuukv/+Mc/9Oc//1lz5szR/Pnz1bVrV128eFG///3v9cYbb6hfv3566623dOnSJcXGxur48ePKz8+XxWLRkCFD9Nhjj2n8+PHq16+fHnroIcXGxrr09w/APZkmQAMAADCTyMhIFRUVKSEhQZK0ceNGgytqnrNXX4bns3eRmPYOx2c4v7Hy8/M1c+ZMlZaWysfHR4sWLdKSJUv01ltvyWKxKD4+Xjt37lSPHj2UmJioPXv2NLkfX19fvfzyyyooKFDnzp319NNP669//av27dun++67r2G76upqJSUl6dSpU1qzZo26deum7t27a+nSpXrvvfd04sSJJvcfGhqqsLAwHT9+XLfffrt27typWbNmqaSkRJJ09uxZ1dXVKSUlRaWlpfL19W147r/927/p0qVLioyM1Pz587V3715ZLBY9/PDDGj16tIqLi5WRkaGQkBDH/WI9DH9bgMYI0AAAAJoRHh6uLVu2aNu2bdq8ebOmT59udElNcubqy/Zw5x453s7eRWKaG6JvDzO0WW9V3wPtq6++0pIlS3Tbbbfp8uXLWrhwoaQfg7HKyhuPDoqIiGj4d48ePSRJHTt2VFVVVaPtfvrYhQsXGp7br1+/Fo/x61//Wjt27FBiYqK+//579e3btyFAGzBggP71X/9V06dPV9euXZscfnzp0iVZrVZZLD/OTR0SEqLLly+rc+fOhGc3cP3fFs7vQBsCtIKCAs2YMUPvvvtuwwSjEydObNVk0gB+5MxvXflGFwAcIysrS7fffruGDh2qsLAwnTt3zuiSAJdo7XU9AZj76927t3r27KnPPvtMN998s1auXClJOnXqlAIDA1VbWytJ8vf3V3V1tSTp22+/VceOHSWpIZSyV0hIiP7+9783HKslgwYN0osvvqidO3dq/PjxjR779ttvFRsbq8cff1y7du3S22+/3fCYxWJRbW2tgoODZbPZVFf3Y3u9dOmSunfv3ubaAXivNvVAu//++7Vy5UqtWbOm4b7du3c3GlceGhqqI0eO6Nlnn1VZWZmeeuopbd++3WGFA+7MEd/atoQLWgBov5EjRyolJUU5OTmqrKxUWlqa0SUBgMMlJibqt7/9rSZNmqSkpCRZLBb1799fM2fOVJ8+fTR//nzNnj1bOTk5Db3H+vbt265j3n333dqyZYsWL17u/38qAAANQElEQVSsoKCgG4ZZjzzyiF599VUlJiY2ut9isWjZsmXq2rWrKioqNG/ePL3zzjuSpD59+ujVV1/VHXfcocmTJ2v27Nmqq6vT5MmT1aEDA7EA2M9SVx/Ft1JBQYEOHTokHx8f9enTRw8++KDi4+NVWVnZaFz57t279cQTT2jXrl168803VV5e3jCHSFOmr/jAIxcRCLcGKnPBA0aXARNyxeS2zuCoiXnpBu49zNrWXTXJdP2x4PmMupYJtwZqxfTBpvjyhLbuXbz9/E57b7uqqioVFhZq8ODBOnLkiN58800tW7bM6LKa5KmfU2/kp39baO/ewRvbuz1tvc3R+9SpUzV9+nQNGTJEly5dUnh4+M/Glffv319ffPGF3n//faWmprb1UG6vtfOScFICAAAA4On8/f21a9cu7dq1S+Xl5Xruuee0evVqlZaWNtpu7Nixio6ONqhKAGiszQGav7+/EhMTtXr16mbHlT/00EPau3ev6urqZLVaHVY0AACA2Rg1ByVzXwJwNxaLRS+++GKj++bMmWNQNQDQOu0a/D1o0CC988478vX1bXJc+T333KNFixbpt7/9raPqBQAAMCVnz2/ZEjMM34T38fU1bhJ22jwAwNXsDtBiYmIUExPTcHvp0qUN/x41alSjbX18fPTBBx+0ozwAgLtz1AcsPizBHdBO4S1Ssj7ShZIrhhw7LCRAs8ZG834DALgUy48AAJzGUR+w+LAEAM515swZpaWlqXPnziotLVVaWpp69erV7PYXSq543UTTAADvZpoAzVPn7/DU1wUArcEHLABwD1VVVVq6dKkiIiKUkZGhTz/9tMUADQDgebwxv7DnNZsmQDNy3pDmBAcHOaQmekwAAADAzCIjI1VUVKSEhARJ0saNGw2uqGXBwUEtPs7q9gBgPzPkMo7KYezR2szGNAGaZM6gyYw1AYCRamtrlZ2drczMTOXm5io4ONjokgAADhAeHq4tW7Zo27Zt2rx5s6ZPn250Sc2y2SqavU4PDe2i4uJyp9dASAfAE5khAzFDDU3xMboAAIB7sdlsioqKUmRkpNGlAAAcJCsrS//93/8tSQoLC9Ply5cNrggAAHMxVQ80AID5Wa1WWa1Wpaenu/S4Nxqu0xb0HgCAH40cOVIpKSnKyclRZWWl0tLSWtzeyHlyvHGOHgCA8QjQAABuoaXhOm3hqiE+9ccCADPr2bOnNmzY0OrtjZ4nx6zDewAAnosADQAAAIDdCLEAAN6EOdAAAHYpLCzUzJkzdfLkSc2bN087duwwuiQAAAAAcCp6oAEA7DJw4EBlZGS0altHzVPDfDcAAAAAjESABhjE19didAlt1tbaGerhfRw5Rw7tBwAAAIBRCNAAA6RkfaQLJVeMLsOlwkICNGtsNCGIF+L/HAA8kzt/GQgAgL0I0AADXCi5oqKLlUaXAQBwMG8MFAjJvZNZvwwMCwlQ6rR7jS4DHsRbp5Hw1tcNtIQADQAAwAHMGig4E72LPUdtba2ys7OVmZmp3NxcBQcHt7g9XwbCWzhyOgqzCw4OavRaObcDjRGgAQAAOACBAtyZzWZTVFSUIiMjjS4FMB1vCpK86bUC9jJVgGbGYQ/OrokTFAAAAIxmtVpltVqVnp5udCkAAJiSaQI0hj0AAAAAcITQ0C5GlwAA8DCmCdAY9gB3VlJSooULF8rf319lZWV64YUXZLVajS4LAADAKxUXlzv9GIR0AOBdTBOgAe7sxIkTmjRpkmJjY7V582bl5eVp/PjxRpcFAADQKoWFhdqwYYNOnjypefPmadiwYYqPj292e7Ou0GfWugAA7o8ADXCAIUOGSJJqamp0+PBhzZkzx+CKzCk4OMjoEmAAM85vWc+ZtTE83zPYuzIh4K4GDhyojIyMVm/vTSsTAgAg2RmgFRQUaOrUqcrPz1f37t0lSevXr1dBQYG2bt3qlAIBd2Gz2ZSamqpJkyapT58+RpdjSjZbRUOowLAH7+CN81tKzHHpSVi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            "text/plain": [
              "<Figure size 1296x360 with 7 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mFbWKQcDcutq"
      },
      "source": [
        "**Gráfica de cajas - variables numéricas**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 486
        },
        "id": "6kBryxglRL9M",
        "outputId": "f5aa5624-2e0c-4c03-e9b9-1c9f4d846214"
      },
      "source": [
        "columnas_num = df.select_dtypes(include=['int64', 'float64']).columns\r\n",
        "fig, axes = plt.subplots(nrows=1, ncols=len(columnas_num), figsize=(18, 7))\r\n",
        "for i, colum in enumerate(columnas_num):\r\n",
        "    df.boxplot(colum, ax=axes.flatten()[i])\r\n",
        "fig.tight_layout()\r\n",
        "plt.subplots_adjust(top=0.9)\r\n",
        "fig.suptitle('Diagrama de caja variables cuantitativas', fontsize = 10, fontweight = \"bold\");"
      ],
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1296x504 with 11 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dGRvoGgQAPsm",
        "outputId": "552fd86c-efc9-4bb6-86c5-f1154605dde6"
      },
      "source": [
        "print('Valores únicos')\r\n",
        "for i in range(df.shape[1]):\r\n",
        "  print(df.columns.values[i])\r\n",
        "  print(df.iloc[:,i].unique())"
      ],
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Valores únicos\n",
            "Administrative\n",
            "[ 0  1  2  4 12  3 10  6  5  9  8 16 13 11  7 18 14 17 19 15 24 22 21 20\n",
            " 23 27 26]\n",
            "Administrative_Duration\n",
            "[  0.         53.         64.6       ... 167.9107143 305.125\n",
            " 150.3571429]\n",
            "Informational\n",
            "[ 0  1  2  4 16  5  3 14  6 12  7  9 10  8 11 24 13]\n",
            "Informational_Duration\n",
            "[  0.   120.    16.   ... 547.75 368.25 211.25]\n",
            "ProductRelated\n",
            "[  1   2  10  19   0   3  16   7   6  23  13  20   8   5  32   4  45  14\n",
            "  52   9  46  15  22  11  12  36  42  27  90  18  38  17 128  25  30  21\n",
            "  51  26  28  31  24  50  96  49  68  98  67  55  35  37  29  34  71  63\n",
            "  87  40  33  54  64  75  39 111  81  61  47  44  88 149  41  79  66  43\n",
            " 258  80  62  83 173  48  58  57  56  69  82  59 109 287  53  84  78 137\n",
            " 113  89  65  60 104 129  77  74  93  76  72 194 140 110 132 115  73 328\n",
            " 160  86 150  95 130 151 117 124 127 125 116 105  92 157 154 220 187 112\n",
            " 131 159  94 204 142 206 102 313 145  85  97 198 181 126 106 101 108 119\n",
            "  70 122  91 276 100 291 114 172 217 141 133 156 136 180 135 195  99 362\n",
            " 179 118 175 148 440 103 178 184 705 134 176 146 189 120 193 222 121 107\n",
            " 305 199 439 223 230 280 377 310 158 486 153 139 182 221 229 216 170 202\n",
            " 346 274 240 162 123 211 227 168 161 429 686 167 518 256 255 358 213 191\n",
            " 282 155 138 246 237 271 171 414 219 262 409 243 241 197 449 143 188 391\n",
            " 238 152 165 293 174 584 164 311 340 250 200 385 292 232 251 517 225 169\n",
            " 309 235 501 224 275 318 144 397 343 245 186 337 351 166 349 423 359 163\n",
            " 147 264 312 226 324 266 260 338 272 534 470 207 218 326 190 304 205 233\n",
            " 401 177 330 286 247 357 315 231 339 283 374 248 279 281 234 261 290 336\n",
            " 378 254 183 210 192]\n",
            "ProductRelated_Duration\n",
            "[  0.          64.           2.66666667 ... 465.75       184.25\n",
            " 346.        ]\n",
            "BounceRates\n",
            "[0.2        0.         0.05       ... 0.00382775 0.00086956 0.01114899]\n",
            "ExitRates\n",
            "[0.2        0.1        0.14       ... 0.01276133 0.01395349 0.02903061]\n",
            "PageValues\n",
            "[ 0.         54.17976426 19.44707913 ... 78.81172527  3.68540082\n",
            " 12.24171745]\n",
            "SpecialDay\n",
            "[0.  0.4 0.8 1.  0.2 0.6]\n",
            "Month\n",
            "['Feb' 'Mar' 'May' 'Oct' 'June' 'Jul' 'Aug' 'Nov' 'Sep' 'Dec']\n",
            "OperatingSystems\n",
            "[1 2 4 3 7 6 8 5]\n",
            "Browser\n",
            "[1 2 3 4 5 6 7 10 8 9 12 13 11]\n",
            "Region\n",
            "[1 9 2 3 4 5 6 7 8]\n",
            "TrafficType\n",
            "[1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 18 19 16 17 20]\n",
            "VisitorType\n",
            "['Returning_Visitor' 'New_Visitor' 'Other']\n",
            "Weekend\n",
            "[False True]\n",
            "Revenue\n",
            "[0 1]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 760
        },
        "id": "V2erUsd4r07h",
        "outputId": "e85d5af8-d58a-4f72-8f0c-33cad9b35f1f"
      },
      "source": [
        "sb.set()\r\n",
        "plt.rcParams['figure.figsize'] = (18, 10)\r\n",
        "pearsoncorr = df.corr(method='pearson')\r\n",
        "pearsoncorr\r\n",
        "mask = np.zeros_like(pearsoncorr)\r\n",
        "mask[np.triu_indices_from(mask)] = True\r\n",
        "sb.heatmap(pearsoncorr, annot=True, cmap='Spectral_r', mask=mask)\r\n",
        "plt.title('Matriz de Correlación Pearson')"
      ],
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Matriz de Correlación Pearson')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 77
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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XO3fulJubmwICAhQSEqLvvvtO6enpGjlypIYNGyZJCggI0Ntvvy0fHx8tXLhQCQkJcnZ2lsFg0PLlyzV//nxJ0pAhQ2Q0GvXBBx9o9uzZMplMOnLkiC5duqS4uDhNmDBBR44cUV5enho1aqTZs2erevXqmjFjhpKTkxUSEqLGjRtrwYIF1jkzMjI0c+ZMrV271vqM/fv315QpU9SxY0etWbNGK1askNlslru7u1588UXdfvvtFfgTAgAAAADgxpD0kJSUlKTz58+rc+fOSk9PV2xsrHr37q3JkycrIiJCYWFh2r17tx588MEi96Wmpio6Olrp6enq1auXHnnkEcXExGjPnj0aO3asNenxR9nZ2Vq5cqWSk5MVHByssLAwubm5Wa+fP39eS5cu1TfffKMqVaooMzNTVapU0fTp07VixQrFxMQU6f/rr78qOjparq6ukqSpU6eqZs2akqT58+dr0aJFmjhxoiIjIzV37lytXr26WEzt27dXVlaWkpKS1KJFC+3fv18ZGRnq0KGDEhMTtWHDBn344YdycnLSli1b9MILLygmJuZPrz0AAAAA4MYZjRxkWhokPSStWrVKISEhMhgM6tWrl2bOnKmUlBQdOHBAISEhkqS2bdvKx8enyH2BgYEyGo3y8vJSjRo11KNHD0nSHXfcodOnTysnJ0fOzs7F5gsKCpIkNWjQQNWqVVNqaqqaNm1qvV61alU1atRIkydP1r333qv77rtP7u7u14w/MDDQmvCQCrfTxMfHKy8vT1lZWfL29i7VOoSGhmrNmjV6/vnntWbNGoWGhspgMOirr75SUlKSBg4cKEmyWCzKyMgo1ZgAAAAAAFSWv3zSIzc3VwkJCXJyclJcXJwkKS8vT2vWrLnuvVcmNEwmk/W7yWSSVHjGxtWSHn+8z2w2F7luMpn08ccfa+fOnfr+++/Vv39/LV68WC1atLhqHFcmPBITE/XRRx8pJiZGNWvWVHx8vD7++OPrPotUmPQYNGiQnn32WSUkJFjP/7BYLAoPD9dTTz1VqnEAAAAAAOXLaKrsCGzDX/4g002bNqlJkybaunWrvvrqK3311Vd6//33tW7dOvn4+Cg+Pl6StGfPHh04cKBCYsrMzNS5c+fUsWNHjR8/Xj4+Pjp48KAkyc3NTZmZmde8NyMjQ+7u7qpRo4Zyc3MVGxtrvebu7l7ivfXq1VOzZs00c+ZMNWvWTPXr15dUeNZIXFycUlNTJUlms1k///xzWTwqAAAAAADl5i9f6REbG6vg4OAibX5+fiooKNCUKVP0z3/+U4sWLZKPj4/atGlTITFlZmZq3Lhxys7OlsViUatWrdSrVy9J0siRI/Xwww+rSpUq+uCDD4rd27VrV61bt04PPPCAPDw81L59e+3du1dS4YGqTZo0Ud++fXX77bdrwYIFxe4PCwvT5MmT9eqrr1rbOnTooKefflpjxoyR2WxWXl6eAgMD1bp163JaAQAAAABASTjTo3QMFovFUtlB4K8nPf1iZYcAAAAA4C/I07NqZYdQJr5oElSh8/U8sr5C5ysrf/lKDwAAAAAAbI3xL39YRemwTAAAAAAAwC5R6QEAAAAAgI0xmjjTozSo9AAAAAAAAHaJSg8AAAAAAGyMiRKGUmGZAAAAAACAXaLSAwAAAAAAG8OZHqVDpQcAAAAAALBLVHoAAAAAAGBjjJQwlArLBAAAAAAA7BJJDwAAAAAAYJfY3gIAAAAAgI0xGjnItDSo9AAAAAAAAHaJSg8AAAAAAGyM0VTZEdgGKj0AAAAAAIBdotIDAAAAAAAbw5kepUPSAwAAAAAAlIsjR45oypQpOn/+vGrUqKG5c+fK29u7SJ+zZ8/q+eef16lTp5Sfn69OnTpp2rRpcnD48ykLtrcAAAAAAGBjjKaK/dys6dOna+jQofr88881dOhQRUZGFuvz9ttvq2nTpoqPj9e6dev0yy+/aOPGjX9idf6HpAcAAAAAAChzZ8+e1b59+9S3b19JUt++fbVv3z6dO3euSD+DwaBLly6poKBAubm5ysvLk5eXV5nEwPYWVIrq0Y9Udgi3vAvDllZ2CAAAAABuUaYKPtMjIyNDGRkZxdqrVaumatWqXfWeU6dOycvLSyZTYamIyWRS7dq1derUKdWsWdPa74knntC4ceN077336vLly3rooYfUrl27MombSg8AAAAAAFCiZcuW6f777y/2WbZs2Z8e+7PPPpOvr6+++eYbbd26VYmJifrss8/KIGoqPQAAAAAAsDnGCi5hGD58uMLCwoq1X6vKQ5Lq1q2r06dPy2w2y2QyyWw2Ky0tTXXr1i3SLzo6WrNnz5bRaFTVqlUVEBCg7du3KzAw8E/HTaUHAAAAAAAoUbVq1dSgQYNin5KSHrVq1VLLli2VkJAgSUpISFDLli2LbG2RpAYNGmjr1q2SpNzcXG3btk3Nmzcvk7hJegAAAAAAYGOMJkOFfm7Wiy++qOjoaD3wwAOKjo7WSy+9JEkaNWqU9u7dK0l64YUX9OOPPyo4OFihoaHy9vbWoEGDymSdDBaLxVImIwE3IHd+eGWHcMvjIFMAAACg7Hl6Vq3sEMrEL91CK3S+O7aurdD5ygpnegAAAAAAYGMq+kwPW8UyAQAAAAAAu0TSAwAAAAAA2CW2twAAAAAAYGMMRo7nLA0qPQAAAAAAgF2i0gMAAAAAABtjoIShVFgmAAAAAABgl6j0AAAAAADAxhgMnOlRGlR6AAAAAAAAu0SlBwAAAAAANoYzPUqHZQIAAAAAAHaJSg8AAAAAAGyM0ciZHqVBpQcAAAAAALBLVHoAAAAAAGBjONOjdEh64K/D2V0OvZ6QofFd0uWLMn8TrYL931y1q6F2E5n8R8pQu4mUlyPzD6tVsOvTCg4YAAAAAPBnkPTAX4YpYJQs5nzlv/OoDJ7ecgh9QZYzx2Q5e6JoxypV5RD2D5m3LFHBwW2S0UGqWqtyggYAAACAqzBwpkep3FRBzIULF3TnnXdq5syZ1+wTERGhr7/++obHDgkJUXZ29nX7jRo1SsePHy+xT1RUlHJzc284BklKTk7WypUrb3jOmxEREaH7779fISEh6tmzp0aMGKHNmzeX+TwZGRlatGhRkbapU6cqMTGxzOe65Tg4y9i8k8zffSTlZctyMkkF/0mUsaV/sa6mdsEqOLZbBUn/lsz5Ul62dC6lEoIGAAAAAPwZN5X0SEhI0F133aVPP/30ppMK1xIXF6cqVapct9+iRYvUqFGjEvssXLhQeXl5V72Wn59f4r0pKSnFkh6lmfNmTZs2TXFxcfriiy80evRoTZ06VRs3bryhMQoKCmSxXDvbl5GRocWLFxdpmzVrltq3b39TMdsSg0c9qaBAOn/K2mZJPypDrYbF+9bxkbIz5TB4lhwff18OIc9LVW+ryHABAAAAoEQGY8V+bNVNhR4bG6snnnhCvr6+2rRpkyTp0KFDGjhwoPr06aNnnnlGOTk51v4RERF65ZVXNHToUPn7++u9995TQkKChgwZooCAAG3YsMHa19fXV5cuXZIkBQQE6I033tDgwYMVEBCg6Ohoa7+AgAAdOHBAUmFyIzAwUCEhIQoNDVVGRoZeeuklSdKQIUMUEhKijIwMTZkyRVOnTtXQoUMVHh4uSZowYYL69++v4OBgPfnkk7pw4YIkacaMGTp8+LBCQkI0fvz4InMmJiYqNDS0yJr0799fO3bskCStWbNGAwcOVP/+/fXwww/rP//5zw2tb6dOnTR27Fi98847kgorVubOnWu9fuX3qKgojR8/XiNHjlRQUJAyMjI0d+5chYeHq1+/fho+fLhSUlKsz3Tx4kWFhIRoyJAh1p/N7xU5Z86c0ZNPPqng4GAFBwdr7dq1Rdb7Wj8Lm+BURcrNKtqWkyU5Fk+wGarWkrHlfTJvfl95ix+X5cJpOQQ9U0GBAgAAAADKyg2f6ZGUlKTz58+rc+fOSk9PV2xsrHr37q3JkycrIiJCYWFh2r17tx588MEi96Wmpio6Olrp6enq1auXHnnkEcXExGjPnj0aO3asevfufdX5srOztXLlSiUnJys4OFhhYWFyc3OzXj9//ryWLl2qb775RlWqVFFmZqaqVKmi6dOna8WKFYqJiSnS/9dff1V0dLRcXV0lFW7vqFmzpiRp/vz5WrRokSZOnKjIyEjNnTtXq1evLhZT+/btlZWVpaSkJLVo0UL79+9XRkaGOnTooMTERG3YsEEffvihnJyctGXLFr3wwguKiYm5oXW+66679Morr5Sq7549e7R69Wrrc4waNUrPPfecJOmTTz7RvHnzNH/+fEVGRio8PFxxcXFXHWfmzJlq3ry53nzzTaWlpal///5q1aqVfHx8JF3/Z3FLy82WnFyLtjm5FG5d+QNLfq4sh7fLcvqwJMn8/cdyGrOs8P4/Jk4AAAAAALesG056rFq1SiEhITIYDOrVq5dmzpyplJQUHThwQCEhIZKktm3bWn9R/l1gYKCMRqO8vLxUo0YN9ejRQ5J0xx136PTp08rJyZGzs3Ox+YKCgiRJDRo0ULVq1ZSamqqmTZtar1etWlWNGjXS5MmTde+99+q+++6Tu7v7NeMPDAy0Jjykwu008fHxysvLU1ZWlry9vUu1DqGhoVqzZo2ef/55rVmzRqGhoTIYDPrqq6+UlJSkgQMHSpIsFosyMjJKNeaVStqm8kfdunWzJjwkaevWrVqxYoWysrKuu43nStu2bdOUKVMkSbVr15a/v7+2b99u/Vle72dxK7P8dlIyGqUada1bXAye3sUPMZVkOXNMunL9OR8IAAAAwC3GaMNbTirSDSU9cnNzlZCQICcnJ2u1QF5entasWXPde69MaJhMJut3k8kkqfCMjaslPf54n9lsLnLdZDLp448/1s6dO/X999+rf//+Wrx4sVq0aHHVOK5MeCQmJuqjjz5STEyMatasqfj4eH388cfXfRapMOkxaNAgPfvss0pISLCe/2GxWBQeHq6nnnqqVONcy969e9W8eXPrMxYUFFivXbl1SFKRaouUlBTNmTNHq1atUsOGDbVz505NnDjxT8Xyu+v9LG5p+TkqOLRdpi5DZP7iLRlqe8vYtIPyV04t1rXgl6/k0HeSCnavl+XsCZk6D1BByj6qPAAAAADAxtxQbmjTpk1q0qSJtm7dqq+++kpfffWV3n//fa1bt04+Pj6Kj4+XVLjd4vfzNspbZmamzp07p44dO2r8+PHy8fHRwYMHJRUmAzIzM695b0ZGhtzd3VWjRg3l5uYqNjbWes3d3b3Ee+vVq6dmzZpp5syZatasmerXry+p8OyLuLg4paamSpLMZrN+/vnnG3qmxMRELVy4UKNGjZIkNW7cWL/88osKCgqUmZlZ4ptdMjMz5ejoKE9PTxUUFBTZVuPu7q7s7OxrVn906dLFmvRJT0/Xli1b1Llz5xuK/VZm3rRIBgcnOY5+Xw69n5F507uynD0hQ/2Wcnzyf2eUWE78LPO3K+QQ+oIcH39fhhp1lb/+9UqMHAAAAACKMhgsFfqxVTdU6REbG6vg4OAibX5+fiooKNCUKVP0z3/+U4sWLZKPj4/atGlTpoFeS2ZmpsaNG6fs7GxZLBa1atVKvXr1kiSNHDlSDz/8sKpUqaIPPvig2L1du3bVunXr9MADD8jDw0Pt27fX3r17JRUeqNqkSRP17dtXt99+uxYsWFDs/rCwME2ePFmvvvqqta1Dhw56+umnNWbMGJnNZuXl5SkwMFCtW7cu8Tlmzpyp119/XZcvX1a9evX08ssvq3v37pKknj17av369erdu7fq1aunO+6445rj+Pr6KjAwUEFBQfLw8JC/v7/1lbQ1atSwHlJavXr1YueMTJs2TZGRkdaf8cSJE63VJnYhJ1P58XOLNVtSflXem8OKtBXs+VwFez6vqMgAAAAAAOXAYLmRwyOAMpI7P7yyQ7jlXRi2tLJDAAAAAOyOp2fVyg6hTCQP6FOh8zVY9WmFzldWOPoEAAAAAADYpRt+ewtuzieffKLo6Ohi7a+88opatmxZCREBAAAAAGyVwcimjdIg6VFBBg4caH2NLQAAAAAAKH8kPQAAAAAAsDEGDqsoFZYJAAAAAADYJSo9AAAAAACwMUbO9CgVKj0AAOEOeGMAACAASURBVAAAAIBdotIDAAAAAAAbw5kepcMyAQAAAAAAu0SlBwAAAAAANsZg4EyP0qDSAwAAAAAA2CWSHgAAAAAAwC6xvQUAAAAAABvDQaalwzIBAAAAAAC7RKUHAAAAAAA2xmDkINPSoNIDAAAAAADYJSo9AAAAAACwMUZKGEqFZQIAAAAAAHaJSg9UCseIPpUdwi3NUKuJPJVY2WHc8tLPtK/sEAAAAIBKwZkepUOlBwAAAAAAsEtUegAAAAAAYGMMlDCUCssEAAAAAADsEpUeAAAAAADYGIOBMz1Kg0oPAAAAAABgl6j0AAAAAADAxhgpYSgVlgkAAAAAANglKj0AAAAAALAxBiNnepQGlR4AAAAAAMAuUekBAAAAAICNMVDCUCosEwAAAAAAsEskPQAAAAAAgF1iewsAAAAAALbGZKjsCGwClR4AAAAAAMAuUekBAAAAAICNMRip9CgNKj0AAAAAAIBdotIDAAAAAABbY6KGoTRYJQAAAAAAYJeo9AAAAAAAwNZwpkepkPSA3TqfcVnT5mzQtzuOyqO6i54Z7a/gXq2K9Vsa84OiY3/Ub+cvy9XFSUH3t9CkJ7vLwaGwEOrXA6c1c/6X2n84TW6uThoc0lZPjPhbRT9OhTh//pKmTluub7/9VR4e7nr2mVAFB3cs1m/p0i/1QfRm/fZbplxdnRUU1E6TJ4XLwcFUCVEDAAAAwNWR9IDdmvGvL+ToYNI38WOVdDBNj0/6RC2aear57Z5F+gXc20z9+7RRtapVdD7jsp6aulYfrErUiCGFv+xPfClePbr5aPnCB5Vy6oIeeuJDtWhWWwFdm1fGY5WrGTM+kqOjg7795lX9mpSsxx9fqBYtGqh583pF+gUE3KX+/e9RtWquOn/+ksY/9a4++OBrjRjRo5IiBwAAAP5aDCYqPUqDMz2uIyAgQAcOHCixz9GjRxUaGqrQ0FCtW7eugiL7n4yMDC1atKhI29SpU5WYmFgu8yUnJ6tTp07lMnZZybqcqy8279f4UV3l5uqkdnc1UMC9zbXu81+K9W3UwEPVqlaRJFksktFo0PHk89brKacuKLhXK5lMRjVq4KG772ygg0fOVNizVJSsrBxt/GKXnhrfT25uVdS+XTMFBNyluHXbi/Vt1MhT1aq5SpIsFouMRoOOHU+r6JABAAAAoEQkPcrAxo0b5efnp7Vr16pfv36lvi8/P79M5s/IyNDixYuLtM2aNUvt27cvk/Ft0dETv8lkMqpJo5rWNt9mntdMVsRv3Kd2PeerS9ACJR1K0+CQttZrDw9qr7jPflZevln/OXZWu39O0T0dvMv7ESrc0aOnC9esiZe1rYVvfR06ePKq/ePjd+judk+rc5eJSkpK1pDB3SoqVAAAAABGQ8V+bBTbW0opIiJCrVu31u7du5WWlqbevXtr4sSJWrdunZYtW6aCggLt3LlTUVFRslgsioyM1Llz5+Tg4KBnnnlG3boV/kLo6+ursWPHavPmzeratatSU1Pl5OSko0eP6sSJE+rZs6e6d++uqKgopaamavjw4Ro+fLgkae7cudqxY4fy8vLk4eGh2bNnq379+poxY4YuXryokJAQubi4KCYmRhERERo5cqS6d++uM2fOaPr06Tp+/Lgk6dFHH1VoaKikwkqWkJAQfffdd0pPT9fIkSM1bNiwEuezBVlZuXJ3cy7SVtXdWZeycq/aP7hXKwX3aqWjJ84pbsPPqlXT1Xrtvr811ZSXP9X7H+2Q2WzREyPuUZuWdcs1/sqQlZUjd3eXIm1Vq7ro0qXsq/YPDu6o4OCOOnr0tNbGbVetWlUrIkwAAAAAKDWSHjfg1KlT+vDDD3Xp0iX16NFDAwYMUL9+/XTs2DFlZWXpueeekyQNHDhQgwYN0sCBA3Xo0CE99NBD2rBhg2rWLKw6cHZ2VmxsrCRpypQpOnjwoJYtWyaz2ayAgABdvHhR0dHRSk9PV2BgoAYMGCA3NzeNGjXKOscnn3yiefPmaf78+YqMjFR4eLji4uKuGvfMmTPVvHlzvfnmm0pLS1P//v3VqlUr+fj4SJKys7O1cuVKJScnKzg4WGFhYSXOZwtcXZ2UeSmnSFvmpVy5uTqVeJ93w5pqdvttmjHvC0XNCdP5jMsa9ewn+sezPdW3ZyudOZepp6au1W013TS0/93l+QgVztXVWZmZl4u0ZWZmy82tSon3eXt7qXmzunppxkdaGDW6PEMEAAAA8DvO9CgVkh43IDAwUEajUVWrVlXTpk11/PhxeXt7F+mTmZmpX3/9VeHh4ZKkZs2aqWXLltq9e7cCAgIkSWFhYUXu6dGjh5ycCn8Zb9Kkifz9/WU0GuXl5aVq1aopNTVVTZs21datW7VixQplZWXd0NaYbdu2acqUKZKk2rVry9/fX9u3b7cmPYKCgiRJDRo0KJP5bgXeDT1kNhfo6Ilz8m5YmGzafyhNzZvcdt17zWaLjp/8TZJ0IuW8TCaDQnu3liTVqV1NQT1aasu2/9hd0sPb26twzY6elrd34RaXpP3JavaHQ0yvJt9coOPH08s7RAAAAAC4IZzpcQOcnf+3XcJkMslsNt/UOK6urkW+/3Hcq82TkpKiOXPm6F//+pcSEhI0e/Zs5eZefavGjaro+SqCq4uTevr7aMHib5R1OVc79yRr078Pqt8DdxTr+8m6n3T2t0uSpENHzujd5dvUuV1jSVKTRjVlsRSe+VFQYFH62Uyt35Qk36aexcaxda6uzurZ008LFsQrKytHP+48pE2bflJIv+KH1n7yyTc6ezZDknTo0Em9++5n6tK5RUWHDAAAAPxlGYyGCv3YKpIeZczd3V0tW7bUmjVrJEmHDx9WUlKS2rZte507S5aZmSlHR0d5enqqoKBAMTExRebMzs6+ZjVGly5d9PHHH0uS0tPTtWXLFnXu3Pmm57MVkRN7KScnX3/ru1ATXlyn6RMfUPPbPZW4+4Tu7vGatd/OvcnqF/G+/O5/TY9N/ETdujTVs4/7S5Lc3ZwVNTtMy1b+oI6BryvskaXyuf02jXnknsp6rHI1PfJBZefk6Z6/TdKECe/pxelD1bx5PSUmHpTf3U9Z++3ceVjB/V5WW7/xeuyxherWrbWefTa0EiMHAAAAgOLY3lIO5s2bp8jISC1dulQODg569dVXred53CxfX18FBgYqKChIHh4e8vf3t76StkaNGgoODlZwcLCqV69eLEExbdo0RUZGKjg4WJI0ceJENW/e/KbnsxU1qrnozVf6F2tv37ahdn75rPX7nKl9Shync7vGWvXe8DKP71ZUo4ab3npzTLH29u2ba9fON6zf58z5a6wHAAAAANtmsFgslsoOAn89ljPvV3YItzRDrSaVHYJNSD/z130tMwAAAG6Op6d9vHUwe1ZIhc5XZerVX5xxq2N7CwAAAAAAsEtsbwEAAAAAwMbY8uGiFYlKDwAAAAAAYJeo9AAAAAAAwNaYqPQoDSo9AAAAAACAXaLSAwAAAAAAW0OlR6lQ6QEAAAAAAMrFkSNHNHjwYD3wwAMaPHiwjh49etV+69evV3BwsPr27avg4GCdOXOmTOan0gMAAAAAABtjK29vmT59uoYOHaqQkBDFxcUpMjJSy5cvL9Jn7969WrhwoZYtWyZPT09dvHhRTk5OZTI/lR4AAAAAAKBEGRkZSk5OLvbJyMi45j1nz57Vvn371LdvX0lS3759tW/fPp07d65Iv6VLl2rkyJHy9PSUJFWtWlXOzs5lEjeVHgAAAAAA2JoKPtNj2bJlWrhwYbH2sWPHaty4cVe959SpU/Ly8pLJZJIkmUwm1a5dW6dOnVLNmjWt/Q4fPqwGDRrooYceUlZWlnr27KkxY8bIYPjzz0jSAwAAAAAAlGj48OEKCwsr1l6tWrU/PbbZbNb+/fu1ZMkS5ebm6u9//7vq1aun0NDQPz02SQ8AAAAAAGyNsWJPq6hWrdoNJzjq1q2r06dPy2w2y2QyyWw2Ky0tTXXr1i3Sr169egoMDJSTk5OcnJx0//33a8+ePWWS9OBMDwAAAAAAUOZq1aqlli1bKiEhQZKUkJCgli1bFtnaIhWe9fHNN9/IYrEoLy9P33//vVq0aFEmMZD0AAAAAADAxhhMhgr93KwXX3xR0dHReuCBBxQdHa2XXnpJkjRq1Cjt3btXktSnTx/VqlVLQUFBCg0NVbNmzTRgwICyWSeLxWIpk5GAG2A5835lh3BLM9RqUtkh2IT0M+0rOwQAAADYGE/PqpUdQpnI+7/BFTqf45iVFTpfWaHSAwAAAAAA2CUOMgUAAAAAwNYYK/aVtbaKSg8AAAAAAGCXqPRApcjx8KzsEG5p+fnnKjuEW57jvA/0598Ibv8y/v5BZYcAAACA8vAnDhf9K6HSAwAAAAAA2CUqPQAAAAAAsDEGzvQoFSo9AAAAAACAXaLSAwAAAAAAW2OihqE0WCUAAAAAAGCXqPQAAAAAAMDW8PaWUqHSAwAAAAAA2CUqPQAAAAAAsDG8vaV0qPQAAAAAAAB2iUoPAAAAAABsDWd6lAqVHgAAAAAAwC5R6QEAAAAAgK3hTI9SodIDAAAAAADYJZIeAAAAAADALrG9BQAAAAAAG2PgINNSodIDAAAAAADYJSo9AAAAAACwNUZqGEqDVQIAAAAAAHaJSg8AAAAAAGwNr6wtFSo9AAAAAACAXaLSA3brwvksTf/Hx9r23X551HDT+GeCFNT37mL9lr73tdbFJerUyfOq4eGqwUPu0SOPdi/S58MP/q3o5Vt17lym6tb10OsLR8jb27OiHqXcXLiQpRn/WK3vtx1UjRpuGvt0L/Xu07ZYv+Xvb1XCup3/XSM3DRzcSQ+P7Fas348//EePjVisRx+7T0+M71URj1D+qrjLIWisjE3aSpczlL85WgX7tl61q8Hrdjn0eFSGOrdLeTkyf7dK5sQEybW6HHo+KmPD1pKjsyxnjit/0/uynDxYwQ8DAAAAu8GZHqVC0gN2a/bM1XJ0NOnrrS8qKemkxo15Tz6+9dSseZ0i/SySZr3yoJr71FXyibMa/fd35VW3hnoH+UmSVq/arjWxO7Tw/x7V7U29lHzirKpVc62EJyp7c2euk6OjSV9seUH7k07pqSeWyce3rpo28yrSzyLppdkD1dynjpJPnNOTjy2RV53qeiDoLmufvDyz5r3yqVrf2bCCn6J8OTzwmGTOV+6CR2TwaiLHgdOUl3ZEljMninZ0qSrHwZHK3/S+CpK+k0wOMlS9rfCaUxVZTh5S7pdLpKwLMt7VQ44D/6Hctx6T8rIr/qEAAACAv4gySw0FBATowIEDJfY5evSoQkNDFRoaqnXr1pXV1KWWkZGhRYsWFWmbOnWqEhMTy2W+5ORkderUqcQ+q1evVvv27RUaGqrevXurX79+WrhwobKzy/4XodWrV+vIkSPW75s2bdLcuXPLfJ5bQVZWjr7cuFdPjg+Uq5uz7m7XRP7dWykh/sdifUc82l0tWzWQg4NJ3k1q676A1tq986gkqaCgQG+/tVGTnuunps3qyGAwqGGj21S9hu0nPS5n5WrTF79ozLiecnV1lt/d3vK/r6U+jd9VrO/wkd3UslX9/66Rp/y7t9RPu48V6RO97Bt1vqeZvJvYfgWMlaOzjL5dZP73CikvW5bkX1Vw6AcZW99XrKupY4gKjuxWwS9bJXO+lJsty9nkwovnT8v8wzrp0m+SpUAFuzcWJkVq1a/Y5wEAAID9MBor9mOjKjTyjRs3ys/PT2vXrlW/fv1KfV9+fn6ZzJ+RkaHFixcXaZs1a5bat29fJuPfrHvuuUdr167Vhg0btGTJEv388896+umnb3ic663TmjVrdPToUev3+++/X88999wNz2MLjh09IwcHY5EtKL6+9XT4UGqJ91ksFu388T/WSofTqRd0OvWCDh1KVa+Al9W75yy9FfW5CgoKyjX+inDs2BmZHIxq7H2bta25b13951BaifdZLBbt3nlUtzf9XzXIqZO/ad2aRI0aE1Bu8VYGQ816UkGBLOdOWtssp4/IcFujYn2N9XxkuXxRjhGvyGn8UjkMmCpVu61YP0ky1G4imRxk+e1UucUOAAAAoBy2t0RERKh169bavXu30tLS1Lt3b02cOFHr1q3TsmXLVFBQoJ07dyoqKkoWi0WRkZE6d+6cHBwc9Mwzz6hbt8JzAnx9fTV27Fht3rxZXbt2VWpqqpycnHT06FGdOHFCPXv2VPfu3RUVFaXU1FQNHz5cw4cPlyTNnTtXO3bsUF5enjw8PDR79mzVr19fM2bM0MWLFxUSEiIXFxfFxMQoIiJCI0eOVPfu3XXmzBlNnz5dx48flyQ9+uijCg0NlVRYyRISEqLvvvtO6enpGjlypIYNG1bifDejVq1amjt3rrp166aDBw/KxcVF4eHh2r59u6TC6pHfv//+5/79++v777/XoEGD5O3trddff105OTkym80aPXq0+vTpo9jYWP3888+aOXOmXn/9dT333HNKTU3V5s2btWDBAknSu+++a63AadOmjaZNmyY3NzdFRUXpyJEjunjxok6cOKFGjRrpjTfekIuLy03+LSl/l7Ny5OZWpUibe9UqyrqUU+J9/7dwoywWi0L7d5QknT59QZK07dv9WrV2oi5evFy4/aVOdYUP7Fw+wVeQy1k5cndzLtJWmjV6581NKiiwqF9YO2vbP+ckaMzYwooRu+LoIuVkFWmy5GTJ6HSVv/vVaslUp6nyYqbLknZMpoDhcgyZoLwPni/az8lFDsFPy/zNymJjAwAAAKXG21tKpVzO9Dh16pQ+/PBDXbp0ST169NCAAQPUr18/HTt2TFlZWdbqgoEDB2rQoEEaOHCgDh06pIceekgbNmxQzZo1JUnOzs6KjY2VJE2ZMkUHDx7UsmXLZDabFRAQoIsXLyo6Olrp6ekKDAzUgAED5ObmplGjRlnn+OSTTzRv3jzNnz9fkZGRCg8PV1xc3FXjnjlzppo3b64333xTaWlp6t+/v1q1aiUfHx9JUnZ2tlauXKnk5GQFBwcrLCysxPluVvXq1dW4cWMdPHhQd955Z4l9z58/rzZt2ljnv3DhglasWCGTyaQzZ86of//+uvfeexUeHq61a9daEzxS4XaX323ZskXr1q1TTEyM3Nzc9Nxzz+mtt97SpEmTJEk///yzVq1apapVq+rRRx9VfHy8Bg0adNPPWN5cXJ116VLRLUKZmdlydbv2L+UfffiN4tclaukHT8rJqfCfhrOzoyTpkUe7q1o1F1Wr5qIBgzrr31uTbD7p4eLqrMw/JDguXWeNVq7Ypk/jd2nxssesa7R186+6dClHvXqX/HfVJuVdlpyLbmUyOLvKknv5Kn1zVXDge1lOHZIkmb9ZKYenPyi8//fkhoOTHAdOleXkfpm3xZZ39AAAAMBfXrkkPQIDA2U0GlW1alU1bdpUx48fl7e3d5E+mZmZ+vXXXxUeHi5JatasmVq2bKndu3crIKCwRD4sLKzIPT169JCTk5MkqUmTJvL395fRaJSXl5eqVaum1NRUNW3aVFu3btWKFSuUlZV1Q1tjtm3bpilTpkiSateuLX9/f23fvt2a9AgKCpIkNWjQoEzmK4nFYilVP2dnZ/Xu3dv6/dy5c3rhhRd07NgxmUwmXbhwQUeOHFHbtsXfyHGlbdu2KSgoSO7u7pKkQYMGafbs2dbr9957r6pVqyZJuvPOO63VMLeqxt63KT+/QMeOpqvxf7e4HNh/Sk2b1blq/zWxO/T+4q+1ZPkT8qpTw9ru3cRTjo4mGa5IohoM9pFRbdz4NpnzC3T82Bk1aly4DePg/lTd3qz2VfvHrU7U0ve2aNHSx+RVp7q1fcf3h/XrLynq5V/49yUzM1tGo1GHDp7Wa1ER5f8g5chy7qRkNMrgUde6FcVQ21uWM8X//lvSj0lX/rv9479hk4Mcw5+X5eJZ5W/4v/IMGwAAAH8FNnzORkUql1Vydv7ffyk2mUwym803NY6ra9H/wvrHca82T0pKiubMmaN//etfSkhI0OzZs5Wbm3tT8/9RRc134cIFHT9+XD4+PnJwcCiSAMnJKfpf5l1cXIr8Ev7iiy+qY8eOio+PV1xcnOrUqVPsnptRVj/TiuLq6qz7e7bRWws/V1ZWjnbtPKLNX/2ivsHtivX9NH6nol7foHcWP6YGDWsVuebi4qQHerfVkvc269KlbJ1OPa/YT75Xt/taVtSjlBsXVycF9Giltxd+qctZudq985g2f71PfYL9ivVdn7Bbb76xUW+9O1INGtYscm3MuJ5a/emzWhE7Titix6nbfS0VNqCDps8Mr6hHKT95OSrY/71M3R6UHJ1lqN9CxuYdVfDz5mJdzXs2yejTufC8DqNJpr8NUsGJfYVVHkaTHMKekyU/V/nxb6jwfTgAAAAAylulpYbc3d3VsmVLrVmzRpJ0+PBhJSUlXbci4XoyMzPl6OgoT09PFRQUKCYmpsic2dnZ16zG6NKliz7++GNJUnp6urZs2aLOnUvewlDSfDfj90qNLl26qFmzZrrtttuUl5enY8cK35SRkJBQ4v0XL15U/fr1ZTAY9O2331rvkyQ3NzddvHjxqvd16dJFGzZsUGZmpiwWi1atWqV77rnnTz1LZZv6j/7Kyc5T964vasrEDzU1sr+aNa+jnYn/Ued2L1j7LVywQRcuXNLQQW+oc7sX1LndC3r5xVXW689PC5Orq7N6+L+siKFR6t3HT2H/PfPD1k35R4iyc/LUw3+WXpgco+f/EaKmzby068cjurfDi9Z+/xf1hc5fyFLEkLd0b4cXdW+HFzX7pbWSJDc3Z912W1Xrx7mKo1xcHFW9uu2/4UaS8j9/R3JwltP4ZXIMmaD8z9+R5cwJGRq0ktOEj6z9LMf2Kn9LtBwHTZPT+GUyeNRVXtxrkiRD/RYyNe8gY5O2cnr2QzlN+EhOEz6SoUGrynosAAAA2Dre3lIq5bK9pbTmzZunyMhILV26VA4ODnr11Vet53ncLF9fXwUGBiooKEgeHh7y9/e3vpK2Ro0aCg4OVnBwsKpXr14sQTFt2jRFRkYqODhYkjRx4kQ1b978pucrre+++06hoaHKzs6Wk5OTevbsqVGjRkmSHBwcNHXqVI0YMUI1a9bUfffdV+JYEyZM0EsvvaSoqCi1adNGvr6+1muDBw/WK6+8ovfee6/YW1v8/f21f/9+DRkyRJLUunVrjRkz5oae41ZTvYarXl84olj73e1v1/c//m/rzoYvppY4jrt7Fb36r2FlHt+toHp1V722oPgWFL92TfTNDy9av8d/PqnUY740a0BZhHbryM5UfuycYs2W5H3K/deDRdoKdn2m3F2fFe974hflzAkttxABAAAAXJ3BUtrDI4AylG2Or+wQbmn5BWWzJcueOc77oLJDsAkZf2edAAAAruTpWbWyQygTBV+Oq9D5jD2iKnS+slKplR4AAAAAAOAm8MraUiHpUQHOnj2rkSNHFmvv2bOnxo4dWwkRAQAAAABg/0h6VIBatWopLi6ussMAAAAAANgLGz5ctCKxSgAAAAAAwC5R6QEAAAAAgK2h0qNUWCUAAAAAAGCXqPQAAAAAAMDW8PaWUqHSAwAAAAAA2CUqPQAAAAAAsDWc6VEqrBIAAAAAALBLVHoAAAAAAGBrqPQoFVYJAAAAAADYJSo9AAAAAACwMQYDb28pDSo9AAAAAACAXaLSAwAAAAAAW8OZHqXCKgEAAAAAALtE0gMAAAAAANgltrcAAAAAAGBr2N5SKiQ9UCmcj+6r7BBuac63d6jsEG59j4VWdgS3vIKa9VVT31d2GLe8c2c6V3YIAAAAKCckPQAAAAAAsDXG/2fvvqOjqtY+jn8nk5CekIReQyCELhCULhBagITQFMR2RVGxYAEFAUHAC6LYQrleuPiqoKAUCVUEVBQIIB2kQxIILZDeSJ33j8hAnABRU5j4+6yVtWbO2eecvTc7Q86eZz9Hj6wtDMXDiIiIiIiIiEiZpEgPEREREREREWujnB6Fol4SERERERERkTJJkR4iIiIiIiIi1kY5PQpFkR4iIiIiIiIiUiYp0kNERERERETE2iinR6Gol0RERERERESkTFKkh4iIiIiIiIi1UaRHoaiXRERERERERKRMUqSHiIiIiIiIiLXR01sKRZEeIiIiIiIiIlImKdJDRERERERExNoop0ehqJdEREREREREpEzSpIeIiIiIiIiIlEla3iIiIiIiIiJibbS8pVDUSyIiIiIiIiJSJinSQ0RERERERMTa6JG1haJIDxEREREREREpkxTpIWVWQnIGEz76hW17L+DhZs8r/2pFcJe6tyyfmZVDvxdWkpqWxZaFQwCIT7zGc1M2cSY6kdxcEz413Rnz5H20bFy5pJpRrBISUpkwYSHbth3Fw8OFV14JITj4PotyCxZ8z8qVOzh/Pg4PDxeGDr2fJ5/sYd6/d+9ppk9fyunTl6hRowKTJg3B379eSTal2CQkpTNh2jq27YrAw92RV0Z0JrhHY4tyny3ZxaJle4hPSMPJqRy9uzbktecDsLW14cKlRIIenp+vfFp6Fq+/EMCwoa1LqinFJiEhlTcnfMn2bcco7+HMK6/0JSj4XotyCxZsImzlTi78Po6GDO3Ik092M+8/ejSaf7+9lBPHz+Ps7MCDg9sz4rleJdkUEREREeuhnB6FokkPKbOmzN2Ona2RrV89xLEzsTwzaSMNfDzxre1RYPlPlx/C082B1LQs8zYnR1v+/UpHvKu5YTDA5vCzjJi8kW2Lh2JrtP4PmSlTlmBnZ8vWrTM4diyaZ56ZQ4MGNfD1rZavnMkE77zzL/z8qnP27BWeemoWVap40KfPvSQkpDJixH+YPPkhundvwdq1vzJixFw2bpyKu7tzKbWs6EyZuQE7OyNb14zk2MnLPDN6KQ3qVcLXp2K+cgEdfBnQpxlurg4kJKXz0rhvWbh0N088dB/VqrizMgN/xgAAIABJREFUd/Noc9noCwn0ePATenTxK+nmFIu3p3yDnZ0tP2+dzrFj0Yx45j/4NaiBr2/V/AVNJt555zHq+1Xj3NmrPPXUbKpWKU/vPq0AeG30Z3Tr1ozPv3iJ8+djeeThD/FrUJ2AgGal0CoRERERKQus/67tLwoICCAwMJC+ffsSFBTE2rVr/9b5Zs2axYwZM/7y8Tt37mTr1q35tvn5+REcHEzfvn0JDg5m8+bNdzxPdHQ0rVvf+Zvj6Ohovv76679cXz8/P1JTU//y8cUt7VoWG7dFMfLRljg72uHfuAoBrWux6odTBZaPvpTMqh9P8/SD+W+u7MvZ4lPDHRsbAyYT2BgNJKZkkpicURLNKFZpaRls3LiPkSODcXZ2wN+/HgEBzVi1aqdF2aee6kHjxrWwtTXi41OFgIBm7N17BoB9+85QsaIbgYH+GI029O3bGg8PVzZu3F/STSpyaemZbPzpOCOHd8TZqRz+99QkoEM9Vn132KJsrRoeuLk6AGAymbCxMXA2Oq7A865cf4hWzWtSo2r5Yq1/SUhLy+D7jfsZObIPzs72+PvXpUtAU1av2mVR9smnutOocU1sbY3U8amcbxwBXDgfS1DwvRiNNtSqVZGWLety6uTFkmyOiIiIiPUw2JTsj5Wy3poXgdDQUFatWsW7777LG2+8QVzcjRuU7OzsEq3Lrl272LZtm8X2JUuWsGrVKkaPHs3o0aOLrF7nz5//W5Med7vI80kYjQbq1HA3b/Pz8eRkVEKB5af+ZwevPO6PvX3BwU99n/uWe/p9znOTN/FAz/p4lXcslnqXpMjIGIxGG+rUubFUx8+vBifvcJNpMpnYs+dUvm/xTSbTH0tx8uSFoqxuqYg8G5fXR7W8zNv8fCtzMuJqgeVXf/8b/t3ep22vjzl26jKD+7WwKGMymQhbf5h+vZoWW71LUmRkDLZGG7zzjaPqd5ysyBtHp6l30zh69LEurArbRVZWDhFnLnNgfwRt2zUotrqLiIiISNmn5S1Ao0aNcHZ2ZuzYsVSsWJGIiAhSU1MJCwtj3rx5rFq1CoCmTZsyYcIEnJ2dSU5OZvz48Zw4cYKKFStSpUoVKlSoAMDYsWNp0qQJjzzyiMX75ORkpk2bxuHDhzEYDLRq1YrBgwezZMkScnNz2b59O3369OHpp5/OV8fWrVuTlpZGUlISnp6eHDhwgJkzZ5qjLUaOHEnnzp0t2jZq1CgiIiLIysqiVq1aTJs2DXd3d6ZMmUJ0dDQhISHUrl2b0NBQzpw5w7Rp04iPjycrK4vHH3+cgQMHAvD999/zwQcfYG9vT48ePSyuc7dJS8/Cxalcvm2uzuVITc+yKLtxeyS5ubl0b+fNzoMF36itmtufjMxsNm6PIis7t1jqXNLS0q7h4pJ/8sbV1ZHU1Gu3PW7WrDXk5poYMKAtAM2b1yEmJpE1a36lZ8+WrFmzi7Nnr5KenllsdS8paelZuDjb59vm6mxPalrBbQvu0ZjgHo2JPBdH2PpDeHlaLu/ZcyCa2PhUenYpGzfzaWkZOLs45NtWmHE0e9Y6TLkmBgxoY97WuXMTxo79gv/7dDM5ObmMeK4XTZvWLpZ6i4iIiFg9K46+KEma9AB27NhBRkYGtra2HD16lEWLFuHk5MSWLVtYtWoVS5YswdnZmTFjxjB37lxee+015syZg7OzM9999x1xcXEMGDCAXr3unHBv2rRpODk5ERYWho2NDXFxcXh6ejJkyBDS0tIYM2ZMgcdt3LiRNm3a4OnpSVJSEpMmTWLevHlUqlSJmJgYBg0axJo1ayyOGz9+PJ6engB8+OGHzJ8/n9GjRzNx4kRmzJjBihUrgLzIltGjR/Pee+9Rt25dUlJSGDhwIM2bN8fd3Z0333yTxYsX4+Pjw/z58y2uc7dxcrQj5Q83pilpmTg72uXblnYti5mf7ua/k7vf8Zz25WwJ6lyX3s8sp6GPJw18vO54zN3MycmBlJT0fNtSUq7h7OxwiyNg0aKfCAvbyZdfjqJcuby+9PBwYc6cZ3n33RVMnbqE9u0b0bZtA6pUKTh3ijVxcrQjJTX/UqaU1Ayc/zCh9kfeNT2pV6ciU2ZuYNb0gfn2rVx/iO6d/e54Dmvh5GRPakr+CY47jaMvF21hVdguFn75snkcJSSk8vTwuUx48wH6BLXi6tUkXn5pARUquPLQ0PuLtQ0iIiIiUnb9oyc9Ro4cib29PS4uLsyaNYvVq1fTvHlznJycAAgPD6d37964uLgA8OCDDzJt2jQgLwfHhAkTAPD09KR79zvfNAP8+OOPrFixApvfM+1en5C4lSFDhpCamsrVq1f5/PPPAdi3bx/R0dEMHz7cXM5gMBAVFYWHR/4bzbCwMFavXk1WVhZpaWl4e3sXeJ3IyEhOnz7Nq6++at6WlZXFmTNnsLGxoVGjRvj4+AAwePBgZs6cWaj2lhbv6m7k5JiIPJ+Id/W8JS7Hz8ThWzt/DoWo80mcv5zMI6+vAyArK4fktCw6PLyYJR8EUaOyq8W5s7NzOXcp2eonPby9K5GTk0tkZAze3pUAOH482jL55O+WL9/O/PkbWLToVYsJjfvuq8+yZWMByM7OoXv3N3niiW4FncaqeNfyzOujc3F418z7XT1+KgbfOhXueGxOTi5nz+dfTnUtI4vvfjjG7OkDiqW+pcHbuxLZFuPofL5lKzdbvjyc/83fyBeLXs43jqLPXcVoNBDSLy8nUZUqHvTu7c/PW37TpIeIiIhIQazk6S0RERGMHTuWhIQEypcvz4wZM255X3rmzBn69+/P0KFDbxkQ8Gf9oyc9QkNDqV+/vvn96tWrzRMef4fRaCQ398YSiIyMv5708nqUyYIFC3j11Vf57rvvMJlM+Pn58eWXX1qUj46ONr/evXs3ixcvZsmSJXh6erJ69Wq++eabAq9jMpnw8PAgLCzMYl9hEqjebZwc7Ojerjahi/by9ksdOHY6js07zrL4/aB85Xy9Pfjx88Hm9/uOxjD1P+GsCA3B092B/cdiyMnJpWn9iuTmmli46ghXE9Jp5leppJtU5Jyc7OnevTmhoat5++1HOHYsms2bD7B48WsWZVev3sWHH4bx+ecvU7NmRYv9R46cw9e3GhkZmYSGrqZKFQ86dmxUEs0oVk6O5ejeyY/Q+T/z9hu9OXYyhs2/nGTxfx+1KLt01X4COvji5enMqYirzFsYTvv76uQrs3HLCdxcHWjtX3aWbOSNo3uYHbqWKW8P5dixaH7YfJAvF4+yKLt69a989OEqPvv8JWrWzD9x5F2nEiYTrFn9K737+BMbm8L6dXu4r3V9i/OIiIiIiPWYNGkSQ4cOJSQkhLCwMCZOnMgXX3xhUS4nJ4dJkybRrVvRfnlqHVNDpaRt27asX7+elJQUTCYTy5Yto127dgC0adPGvDQkPj6eTZs2mY+rXbs2hw4dAiAmJoadO288DaNLly4sWLDAnPjxevJUFxcXkpOTb1mXYcOG4eXlxeLFi2nRogVRUVHs2LHDvP/gwYMWySSTkpJwcXGhfPnyZGZmsnz5cvM+FxcXUlJSzO/r1KmDg4MDK1euNG87ffo0KSkpNG/enCNHjhAZGQnA0qVLC9F7pW/i8+3IyMih/UOLGfXuT0x6vh2+tT3YffgSLQfk/ZLZGm2o6Olk/nF3tcfGYKCipxNGow2ZWTlMmRtOmyFf0unRJWz5NZr/vtWDyl5/f3LsbjBx4kNkZGTRvv3rjBq1gEmTHsLXtxq7d5+kZcuXzeU++mgVCQkpPPDADFq2fJmWLV9m0qSvzPv/97/vadt2NJ07j+PKlSRmz36mNJpTLCa+1pOMjGza9wll1KQwJr3WE1+fiuzef46WXW9EPO09GE3fR/9Hi4CZPD3qG+5vW5dXn+2c71wr1x0iJLAxBoOhhFtRvN6cOJhrGVl0bP8Go0d9xsRJg/H1rcru3afwb3kjeiz0ozUkJqTy4APv4t/yVfxbvspbkxYD4OLiyMeznuKLz3+kzX2vM6D/dHzrV+PZEYGl1SwRERGRu5sVPL0lNjaWI0eOEBSU9+VzUFAQR44cyfcQkevmzZtH586dbxkF8lcZTJaPXfhHCAgI4JNPPskX6fHHBKRAvkSmTZo04c033zQnMh03bhwnT57Ml8h0zJgxxMfHM3LkSGJjY/H29iY3N5cOHTrwyCOPkJSUxLRp0zh06BBGo5H77ruPCRMmcO7cOV544QUAcyJTPz8/9u7di7NzXjLEX3/9lVdeeYVNmzZx4sQJ3nvvPRITE8nKyqJmzZp88sknXLhwgYEDB7Jz506ysrJ47bXX+O233/Dw8KBVq1YcOnSIhQsXkp2dzfPPP8/58+fx8fEhNDSUyMhIpk2bxsWLF8nNzcXLy4uPPvoIT09PcyJTBwcHevTowccff5yvbn+W6fRff7zvP4LPvaVdg7tf3NnSrsFdL9ezemlXwSrEXW1z50IiIiJSZlSsaLmE3RqZLswu0esluzxGUlKSxXY3Nzfc3NwKPObw4cOMGTOGtWvXmrf17t2b9957j8aNG5u3HTt2jKlTp/LFF18wd+7c2+a7/LP+sZMeUro06XEHmvS4M0163JEmPQpHkx4iIiL/LGVm0uPi3BK93uxlOcyebTnR8sILL/Diiy8WeExhJj2ysrIYOnQo06dPp169esyaNatIJz3+0Tk9REREREREROTOHn/8cfr372+x/VZRHgBVq1bl8uXL5OTkYDQaycnJISYmhqpVbyS9v3LlCmfPnuXpp58G8tI0mEwmUlJSmDp16t+utyY9REREREREROS2breM5Va8vLxo2LAha9asISQkhDVr1tCwYcN8TzGtVq1avjyYRR3poUSmIiIiIiIiItbGChKZArz11lssWrSInj17smjRIiZPngzA8OHDzQ8AKU7K6SGlQjk97kA5Pe5MOT3uSDk9Ckc5PURERP5ZykxOj0uflOj1DFWeLdHrFRUtbxERERERERGxNjZauFEY6iURERERERERKZMU6SEiIiIiIiJibf5Gno1/EvWSiIiIiIiIiJRJivQQERERERERsTaK9CgU9ZKIiIiIiIiIlEmK9BARERERERGxNor0KBT1koiIiIiIiIiUSYr0EBEREREREbE2ivQoFPWSiIiIiIiIiJRJivQQERERERERsTY2imEoDPWSiIiIiIiIiJRJivQQERERERERsTIGg7G0q2AVFOkhIiIiIiIiImWSIj2kdJSvXNo1uKsZkq+UdhXueqaczNKuwl3PmJJQ2lW46631W1DaVbAK9x1YWtpVEBEREflLNOkhIiIiIiIiYm30yNpCUS+JiIiIiIiISJmkSA8RERERERERa6NIj0JRL4mIiIiIiIhImaRIDxERERERERFro0iPQlEviYiIiIiIiEiZpEgPEREREREREWtjoxiGwlAviYiIiIiIiEiZpEgPEREREREREWujnB6Fol4SERERERERkTJJkR4iIiIiIiIi1kaRHoWiXhIRERERERGRMkmRHiIiIiIiIiLWRpEehaJeEhEREREREZEySZEeIiIiIiIiItbGRjEMhaFeEhEREREREZEySZMeIiIiIiIiIlImaXmLiIiIiIiIiLVRItNCUS+JiIiIiIiISJmkSA8psxKS0pkwbR3bdkXg4e7IKyM6E9yjsUW5z5bsYtGyPcQnpOHkVI7eXRvy2vMB2NracOFSIkEPz89XPi09i9dfCGDY0NYl1ZRik5CYxvi3w9i24xQe5Z149fnuBAc2syj32VfbWfj1TuIT03ByLEfv7k14fWQPbG2N5jKfLw7n8yXhxMWlUrWKO3NnDqVO7Qol2ZxikZCUzoR3vmfbr5F54+iZjgR3b2hR7rOv97Bo+T7iE9NxcrSjd4Afrz3XCVvbvLnlvYfOM33WT5yOjKVGVXcmjeqKf7MaJd2cYpGQmMb4qd/eGEcv9CA48B6Lcp99uY2F3+zI+11zLEfv7k15/aWe5nEUEDyTq3EpGH9PytWiWU0+nfNEibaluNiVd6HZ+69QoZM/mXGJHJ/+f1z49ieLcjbl7Gg05Vmq9GqHwdaW+N2/cWjMLDIuxZrLVA3pRP1XH8aheiUyYuI48PL7xO/6rQRbIyIiIncFRXoUiiY9pMyaMnMDdnZGtq4ZybGTl3lm9FIa1KuEr0/FfOUCOvgyoE8z3FwdSEhK56Vx37Jw6W6eeOg+qlVxZ+/m0eay0RcS6PHgJ/To4lfSzSkWU95di52tkW0bXufoiUs88/IiGvhWwbdupXzlAu5vwIDgFri5OpKQmMbIsV+z8OsdPPFwewCWrtzDslV7mffhI9StU5Fz5+Nxc3UojSYVuSkfbMbOzoatYSM4diqGZ17/lgb1KuJbJ/+ETkCHugzo3fjGOHpzNQuX7eWJIa1ISEpnxNiVTB7dje73+7J20zFGjFnJxm+ewr0M9NOUGauxszOy7fuxHD1xkWdeWvj7OKqcr1xApwYM6Nvyxjgas5iFS3bwxCPtzWU++eAR2rWuV9JNKHaNp71AblY2m5oNwa1JXe79YgpJv0WQciIqXznvp0Lw8G/Iz11HkJ2cStN3X6LJ28+x56mpAFS4vwUNxg9j37PTSdh3HPvKnqXRHBERERGroakhKZPS0jPZ+NNxRg7viLNTOfzvqUlAh3qs+u6wRdlaNTzMN+gmkwkbGwNno+MKPO/K9Ydo1bwmNaqWL9b6l4S09Ey+/+EILz0bgLOTPa2a1ybg/gaErdtvUbZWDU/cXB0BMJnAxmAg6lxeH+Xm5jJ7/o+MeyWQej6VMBgM1KrhSXl3pxJtT3FIS89i45aTjHyyfd44alaDgPZ1WbXhiEXZWtXL3zSO8vro7PkEAPYdukBFT2cCu/hhNNrQt2cjPMo7sXHLyRJtT3G4MY66/T6OvG8zjrzyjyMbA1HRsRblyhqjoz1Ve7fnxLtfkJN2jfhdv3H5+x1UHxRgUdapZhWubNlD5tUEcjOyuLjqZ1z8apv31x/9KKc+/IqEvcfAZCLjUmy+KBARERH5BzHYlOyPlbpjzQMCAggMDKRv374EBQWxdu3av3XBWbNmMWPGjL98/M6dO9m6dWu+bX5+fgQHB9O3b1+Cg4PZvHnzHc8THR1N69Z3Xp4QHR3N119//Zfr6+fnR2pq6i33r1ixglatWtGvXz969epF3759mT17NteuXfvL17zdtSIiIszvN2/e/Lf+Le5mkWfjMBptqFPLy7zNz7cyJyOuFlh+9fe/4d/tfdr2+phjpy4zuF8LizImk4mw9Yfp16tpsdW7JEWejc3ro5uWoDTwrcypMzEFll/93UFadv43bbq/w7GTlxgy4F4ALsUkcSkmiROnY+jUZyYBIR8Q+t8fyM3NLZF2FKfIc9fH0Y1v0/3qVeRkRME3mas3HsW/5yzaBs3l2KkrDA65sVTIZDL9obSJk2cKHo/WJDLqquU4ql/1NuPoAC07TaVNt2kcO3FjHF03+s2ltOk2jWHP/x/HTlws1rqXFOe6NTDl5JB65rx5W/KRM7jeNJlx3bnFG/C4txH2lT2xcbSn2oAuXPnh17ydNja4N/OlnJc7nbd9SsDuhTT+93PYOJQrqaaIiIiIWJ1CLW8JDQ2lfv36HDlyhCFDhtC2bVs8PfNuArKzs7G1LblVMrt27SItLY0OHTrk275kyRKcnZ3ZsmULL7/8Mr/++muR1Ov8+fN8/fXXDB48+G+f61batWtHaGgoALGxsYwfP56XX36ZTz755E+d507/Ft9++y0eHh7UqVMHgK5du9K1a9e/XvG7WFp6Fi7O9vm2uTrbk5qWWWD54B6NCe7RmMhzcYStP4SXp7NFmT0HoomNT6VnlwbFUueSlpaWYdlHLg637qPAZgQHNiPybCwr1+4399Gly0kAbNt5itVLnicp+RpPvvgFVSq58WD/VsXbiGKWN47y31Dedhx1b0hw94ZEnosnbMMRvDzy+qh5k2rExKayZtNRenauz5qNxzh7PoH0jOxib0NxS0vPxMXlj+PIntTUjALLBwfeQ3DgPUSevfr7OHIx73tv6gM0blANEya+WBzOky9+zvplL5mjQ6yV0cmBrOS0fNuyklKxdbaMhkqNOM+1C1fotu8rcrNzSD4Wwc7xcwCwr1gem3J2VOnTgfD+o8nNzqbV/03C96WHOD7j8xJpi4iIiNxFrDj6oiT9qV5q1KgRzs7OjB07lvHjxzN06FAGDhwIwLx58wgKCiIoKIg33njDHN2QnJzMyJEjCQwM5NFHH+Xs2bPm840dO5ZFixYV+D45OZk33njDHMExZcoUjh8/zpIlS1i5ciUhISHMmzfPoo6tW7cmLS2NpKS8G7EDBw7w6KOPMmDAAAYMGMBPP/1UYNtGjRrFgAEDCA4O5vnnnycxMRGAKVOmcPr0aUJCQhg5ciQAZ86c4amnnmLgwIH07duX5cuXm8/z/fffExgYSEhICHPmzPkz3QuAl5cXM2bMIDw8nJMnT1pEpNz8/vrrGTNm0L9/f5YuXUp4eDiDBw+mX79+BAcHmyNzli9fzuHDh3n77bcJCQlh+/btrFixwtwmuPW/4axZs3j11VcZPnw4gYGBPP3006Snp//ptpUkJ0c7Uv5w05WSmoGz0+2/EfWu6Um9OhWZMnODxb6V6w/RvbPfHc9hLZyc7P9aH9XywrduJSbPWAOAg4MdAE892gE3V0dqVPNgcP9WbNlu/Us38sZR/gmOlLTMQowjD+p5ezHlg00AeLg7MmdaCJ99vYcOfT/hl50RtG1VmyoVXW57Hmvg5FiOlJQCxtEfJtT+yLtWBXx9KjF5xmrzNv/mtXFwsMPRoRzPPNEJVxcHdu+Lus1ZrENO2jXsXPNPcNi6OpGdmmZRtsm0F7ApZ8f3jQaxoV4/Lq3bzr2L3s47z7W8sRj56SoyYuLIiksi4r8rqNj1XovziIiIiEiePxUKsWPHDjIyMrC1teXo0aMsWrQIJycntmzZwqpVq8zRFmPGjGHu3Lm89tprzJkzB2dnZ7777jvi4uIYMGAAvXr1uuO1pk2bhpOTE2FhYdjY2BAXF4enpydDhgwhLS2NMWPGFHjcxo0badOmDZ6eniQlJTFp0iTmzZtHpUqViImJYdCgQaxZs8biuPHjx5ujVz788EPmz5/P6NGjmThxIjNmzGDFihVAXjTF6NGjee+996hbty4pKSkMHDiQ5s2b4+7uzptvvsnixYvx8fFh/vz5FtcpDHd3d2rXrs3Jkydp1szySRo3S0hIoGnTpub+SExM5KuvvsJoNHL16lUGDBhAhw4dGDhwICtXrmTYsGF06dIFwNwm4Lb/hgCHDx9m2bJluLq68uSTT7J69WoefPDBv9S+kuBdy5OcnFwiz8XhXTPv3/X4qRiL5JMFycnJNediuO5aRhbf/XCM2dMHFEt9S4N3La+8Pjobi/fvy4COnbxEPZ9KdzgSsrNzOXs+L6dHndpe2NkZMRgM5v03vbRq3jWvj6N4vGt6AHD81BV863jd4cjr4yjR/P6+FjVZNv8RIK//ug/+H08Mtu5IGADv2hV+H0dX8a6V9/t17EQhx1FO7i3z5wAYDIYClgVZn9TT0RiMRpzqVCMt4gIAbo18SD5uOaHj1tiH4zM+IyshBYDIT8Pwe/0x7DzdyIpLIv3ClbyEKNdZf/eIiIjIX1VG/uYuboWK9Bg5ciQhISHMmjWLWbNm4ebmRmBgIE5Oed9chYeH07t3b1xcXDAYDDz44IOEh4cDeTk4Bg0aBICnpyfdu3cvVMV+/PFHnnzySWx+f3Th9QmJWxkyZAgBAQHmpSEA+/btIzo6muHDhxMSEsLw4cMxGAxERVn+oRkWFmaO9FizZg1Hjx4t8DqRkZGcPn2aV199lZCQEB5++GGysrI4c+YMBw4coFGjRvj4+AD8rSUxhf1D397ePt8kUlxcHCNHjiQoKIgnn3ySxMTEfHk8buV2/4YAHTp0wM3NDYPBQLNmzfJF7NyNnBzL0b2TH6HzfyYtPZO9B6PZ/MtJ+gY2sSi7dNV+YuPyolpORVxl3sJw2vjnX2u/ccsJ3FwdaO1vuQbfWjk5lqN7l4aE/vcH0tIz2XMgis1bjhHSu7lF2aUr9xAbl3cTdupMDPM++5m29+aNc0eHvEfY/m/hVlJSM7h0OZGvv91D5w71S7Q9xcHJ0Y7u9/sSumAbaelZ7D14ns1bT9G3ZyOLsktXHyQ2Pu+b+1MRscxbtIs2/rXM+4+cuExWdg4pqRm8O3cLVSq50rG1d0k1pdjkjaNGhH6yOW8c7Y9i85ajtxhHu285ji5cSmDP/igys7LJyMjif1/8QnxCKi3vsf7fuZz0DC6t30b91x7D6GiPx72NqNyzLeeX/WBRNuHACaoP6oatqxMGWyO1Hw/i2sWrZMXlRS9Gf/093sNCKOfljq27C3We7k/Mxl0l3SQRERERq/Gncnpct3r1avOEx99hNBrzJTvMyCh4DXhhXI9QWLBgAa+++irfffcdJpMJPz8/vvzyS4vy0dHR5te7d+9m8eLFLFmyBE9PT1avXs0333xT4HVMJhMeHh6EhYVZ7CtMAtXCSExM5OzZs9SvXx9bW9t8EyB/7CNHR8d837C/9dZbBAQEMHv2bAwGAz179vxb/Xqdvf2NUHWj0Vgk5yxuE1/ryfh/r6V9n1DKuzsy6bWe+PpUZPf+czw96mvzo2j3Hozmo/9uIS09C4/yTgQGNOCl4ffnO9fKdYcICWycr6/Lgkljghg3dSXtesygvLsTb40NxrduJXbvi2T4S4vY9/MEAPYeOMuH/9lEWlomnh7O9OzamJefvfHkiYmv9eHNaavo2Ps93FwceKCfP4P6tiytZhWpiaO6Mn76Btr3nUt5N0cmjeqGb50K7D4QzdOvrWDv93lLxPYeusBH87eRlp6ZN4461+elp248ivV/X/3KzzvyJiA7tvZm9rSQUmlPcZg0NphxU76F9lNnAAAgAElEQVSlXffpeePojb741q2cN45GfsG+XyYCsPdAFB/O3XhjHHVrwsvP5uUVSk3N4K13VnEuOg57e1sa1K/K/NDH8Shv/U8BAjj8xmyaffAq3Q59TVZ8EoffmEXKiSg87mvMfV++zQbf/gAcnTKfxlNH0Hnbp9jY2ZJ8PIrdT04xn+fkh19RzsONzlsXkJORycXVv3AqdHFpNUtERERKUUlHxFrrrVCRZCBt27YtM2fO5LHHHsPZ2Zlly5bRrl07ANq0acOKFSvw9/cnPj6eTZs2ERgYCEDt2rU5dOgQADExMezcuRN/f38AunTpwoIFC5gwYQIGg8G8vMXFxYXLly/fsi7Dhg1j/fr1LF68mP79+xMVFcWOHTto06YNAAcPHqRp0/xP30hKSsLFxYXy5cuTmZmZL0eHi4sLKSkp5vd16tTBwcGBlStX0q9fPwBOnz5N5cqVad68OePGjSMyMhJvb2+WLl36p/syLi6ON998k7Zt21KvXj2ys7PJysoiKiqK2rVrF7g052bJyclUr14dg8HAtm3b8kW1ODs7k5ycXOBxt/s3tFbl3RyZM2OQxfZWzWuaJzwApk8IuuO5Fnw0pEjrdrco7+7E3JlDLba3auFtnvAAmD6p/23P4+LiwIfT7t7lTn9HeTdH5kzvZ7G91T01zBMeANPHBd72PB+8dedxZq3Kuzsx9/2HLba3auFtnvAAmD5p4C3P4Vu3MquXvFgs9bsbZCWksGfYFIvt8bt+M094AGTFJ7P/hXdveR5Tdg6Hx83h8Lg/nzNKRERE5J+oSNK9durUieDgYIYMGUJwcDAAI0aMAOC5554jKSmJwMBARo4cSatWN9awP/DAA1y6dInevXvz1ltv5ctfcT2RZlBQEH379mXu3LkAdOvWjUOHDt0ykanBYGDMmDH873//w97enrlz5zJnzhz69u1Lr169mD17tsWMWMeOHalVqxY9e/bkkUceoVGjG6Hrfn5+1KlTh6CgIEaOHImtrS2ffPIJ69atIzg4mD59+jB58mQyMzPx8vJi6tSpPPvss/Tr16/Q0RDbt2+nX79+BAYG8q9//YsGDRrw0UcfAWBra8v48eN54oknGDRoEEaj8bbnGjVqFO+++y4hISGsX78ePz8/877BgwczZ84ccyLTm93u31BERERERETuLiZyS/THWhlMZSFLnFgdU+xnpV2Fu5rBzrof0VkSTNcS71zoH87g6FHaVbjrrfVbUNpVsAr3HfjzkYsiIiJ3q4oVXUu7CkUi17SpRK9nY+hWotcrKkWyvEVERERERERESo5Jj3ErFE16lIDY2FiGDRtmsb179+688MILpVAjERERERERkbJPkx4lwMvLq8CnvYiIiIiIiIj8FSZTCefZsNKntxRJIlMRERERERERkbuNJj1EREREREREpEzS8hYRERERERERK6NEpoWjSA8RERERERERKZMU6SEiIiIiIiJiZUo8kamVUqSHiIiIiIiIiJRJivQQERERERERsTLK6VE4ivQQERERERERkTJJkR4iIiIiIiIiVsaEcnoUhiI9RERERERERKRMUqSHiIiIiIiIiJXR01sKR5EeIiIiIiIiIlImKdJDRERERERExMro6S2Fo0gPERERERERESmTFOkhIiIiIiIiYmWU06NwNOkhpaLT+5VLuwp3NQdffYDdiatrudKuwl3Ps+K10q7CXe/giFGlXYW7nsnRBj7fVdrVuOutffy+0q6CiIiIFECTHiIiIiIiIiJWRjk9Ckc5PURERERERESkTNKkh4iIiIiIiIiUSVreIiIiIiIiImJlTCgPYGEo0kNEREREREREyiRFeoiIiIiIiIhYGZNJiUwLQ5EeIiIiIiIiIlImKdJDRERERERExMoop0fhKNJDRERERERERMokRXqIiIiIiIiIWBkTyulRGIr0EBEREREREZEySZEeIiIiIiIiIlbGZFJOj8JQpIeIiIiIiIiIlEmK9BARERERERGxMsrpUTia9BARERERERGRYhEREcHYsWNJSEigfPnyzJgxA29v73xl5syZw7p167CxscHOzo5XXnmFjh07Fsn1NekhIiIiIiIiYmWsJafHpEmTGDp0KCEhIYSFhTFx4kS++OKLfGWaNWvGsGHDcHR05NixYzzyyCNs3boVBweHv3195fQQERERERERkSIXGxvLkSNHCAoKAiAoKIgjR44QFxeXr1zHjh1xdHQEwM/PD5PJREJCQpHUQZEeIiIiIiIiIlampHN6JCUlkZSUZLHdzc0NNze3Ao+5ePEilStXxmg0AmA0GqlUqRIXL17E09OzwGNWrlxJrVq1qFKlSpHUW5MeIiIiIiIiInJbn3/+ObNnz7bY/sILL/Diiy8WyTV27drFxx9/zKefflok5wNNekgZ90B7b4be74ODnZGfDl/ig7DfyMqxXPvWqGZ5nuzmi191N3JNsO9MLKFrjhKbnAHAkI51CGxZnSrlHUlMzeTbnWdZ8ktESTen2AxoVIcHm/pgb2vkl8hLzAo/TFauZT/Z2hh4o1MLfL3cqeLqxOj14Ry8FFdguU9C7sfRzsjD3/xQEk0odkG+dennVx97o5Hw6AvM27ef7IL6yGDg5db3UtejPJWcnZm45Rd+u3LVvD+kvi+da9eiopMjSZmZbDgdQdiJkyXZlCITvTGccxu2kpuZRYWWjfB9OAgbu4L/W4k/eoZTX60lIy4R1zo18HuiHw5e5QHYPWkO1+JuhC/mZmXj2cSXJi8MzXeOy+H7Of5/K/F9NJiqHf2Lr2ElZEjrWjzWzhsHOyM/HL3MjHVHycqx/MamSXV3nu5clwZV3cjNNbE3Kp73NxwjNiWzFGpd/B7yr8lj99XG3tbIDydimLHpWIH9YmtjYGpQExpWdqWauyPPfr2HvedujCM7o4FRXerTybcStjYGDl5I4J2Nx7mSklGSzRERESkzHn/8cfr372+x/VZRHgBVq1bl8uXL5OTkYDQaycnJISYmhqpVq1qU3bdvH6+99hpz587Fx8enyOqtnB6FFBAQQGBgICEhIQQGBjJhwgSysrJKu1pmjz76KF27diUkJISePXsyd+7cQh23YsUKIiLKzs37ze71rcDDnXx4ZcEuHnj3R6p5OjKsW70Cy7o62rL613M8+N4WHnj3R9Iychg7sKl5vwGYtvQgfaZuYvRnuxnQpjYBzSx/Ua2Rf7UKDG5WlzEbdvLo0h+o6urEoy18b1n+8OU4Zvy8n9i0a7cs80CTuiReKzs3Fs0rV6K/X30m/7yVZ9dtoLKLE0MaNbxl+aNXY/l41x7i0wvuo9Bfd/PYqrW8/ct2etX1oX2N6sVV9WIT99spzn23lWavPs5901/h2tV4olb/WGDZrORUjvzna7xDAmj30RhcvatxdN5S8/5Wk5+nw6zxdJg1nvah47D3cKeif6P850hN5+y6X3CqVrFY21VSWvt48Xg7b55ftIeQ0F+oVt6R4Z3qFljW1cGWlXuj6R/6CyGhv5CWmc2bwY1LuMYlo423J4/d583z3+wjZN42qpd35Ol2t/6j50B0ApPW/cbVAiYyhrSsSdNq7jz8+U76fLKV5GvZjA6oX5zVFxERKVEmckv0x83NjRo1alj83G7Sw8vLi4YNG7JmzRoA1qxZQ8OGDS2Wthw8eJBXXnmF0NBQGjcu2r9zNOnxJ4SGhhIWFsbatWs5deoUGzduLO0q5TNhwgTCwsJYuHAhn376KQcOHLjjMd9++y2RkZHFX7lSENiyOmt3RxMZk0LKtWw+//E0gS1rFFh254mr/HT4EmkZ2WRk5fLtjiia1vYw71/8SwQnLiSRk2vi3NVUth69TNPa5UuqKcWqe70afHfiHFEJKaRkZvPlgZP0qFdwP2Xnmvj2SCS/xcSTayp4DWEVF0e61q3OkoOni7PaJapz7VpsjoziXFIyqVlZLDt6nM7etQosm20ysfbUaY7FxhbYR2EnThKRkEiuycSFlBR2XbhIgwpexd2EInc5fD9VOrTAuVol7JwdqdWnE5e27y+w7NV9R3GuVpGKrRpjY2dH7eDOpEZfJu3iFYuyiSeiyE5Jo0LL/JMekd9uonrX1ti5OBVLe0pan3uqsmr/BSKupJJ8LZtPf4kg6J5qBZYNPx3LD0djSM3MISM7l6W/nqNZzbLx+fNHfRpXZdWhC5yJTSU5I5tPwyMIalLwBHN2rokle89x4HwiOQX8rlVzd2RHZBxxaZlk5uSy8fhlfCo4F3cTRERE5A/eeustFi1aRM+ePVm0aBGTJ08GYPjw4Rw6dAiAyZMnc+3aNSZOnEhISAghISEcP368SK6vSY+/ICMjg4yMDNzc3EhNTeWNN94gKCiIoKAg5s+fby4XEBDAiRMnCnwfEBDAxx9/zODBgwkICGDRokXmcqdPn2bYsGEEBwcTHBzMt99+C0BMTAwjR45k0KBBBAcH88knnxRYv0qVKlGnTh0uXLgAQHh4OIMHD6Zfv34EBwezdu1aAJYvX87hw4d5++23CQkJYfv27QDMmzePQYMG0b9/f5599lmuXMm7Mdm0aRPBwcGEhIQQFBTEzp07i6pLi0WdSi6cvngj0c7pi0l4udrj5mh3x2Pv8fYk4nLKLfc3u8N+a1Lbw5Uz8Tf66UxcEp5ODrja37mfCvJcm8b8355jZOTkFFUVS11NNzciExLN7yMTEvFwcMClXLm/fe6GFbw4V0BCqLtd2oUrONe4kVzKpUZlspJSyUpJsyibeuEKzjVvlDXal8OhogepFywnPS6H76dCy4YY7W/0bVJENMlRF6h6f6sibkXp8anowsnLyeb3Jy8n4+VSuM+nFrU8iLhSNj5//sjHy5mTV270y4krKXg52+Pu8OdX4646dIFm1d2p4FwOe1sbAhtWYXtEbFFWV0REpFSZTKYS/fmr6taty9KlS9mwYQNLly41L12ZP38+TZvmRdcvX76cHTt2EBYWZv7x8/Mrkn5STo8/YeTIkdjb23P27Fk6dOhAhw4deO+998jNzWX16tWkpqYyePBg6tevT6dOne54vmvXrvH1118THR1NcHAw/fv3x97enueee46XX36ZXr16ARAfHw/AmDFjeO6557j33nvJzMzkX//6F02bNqV9+/b5zhsREUFCQgKtW7cGoFGjRnz11VcYjUauXr3KgAED6NChAwMHDmTlypUMGzaMLl26ABAWFsa5c+f45ptvsLGx4auvvuKdd97h/fffJzQ0lClTptCiRQtycnJIT08vyu4tco7lbEm5lm1+f/21k70tSem3XprkU8WVxwPqMm7R3gL3P9G1HjYGWL/nfNFWuJQ42hpJzbzRT9dfO9nZkpzx55Zwta9VGaPBwLazl2lWpeBszNbIwdZI2k3L2a6/drS1JSXzr+dVGNyoATYGAz9Env3bdSxpOdcysXW0N783Ojr8vj3DIhojJyOTcq75t9k6OpCTkWFR7ureIzR+/iHzNlNuLqe+Wku9h3pjsCk78/SOdkZSMm76fPr9tXM5420/n+pVcmHY/T68/nXBUTXWzrFcwf3iVM6WxJs+zwvjbHwal5MzWDeiI9m5uZy+ksp7m4vmGyMRERGxHpr0+BNCQ0OpX78+GRkZvPjii3z22WeEh4czbtw4DAYDLi4u9OnTh/Dw8EJNevTu3RvAvA7q0qVL5Obmkp2dbZ7wAPDw8CAtLY1du3ble55xamoqp0+fNk96vP3228ycOZMzZ84wZswY8zqpuLg4xo0bR1RUFEajkcTERCIiImjevLlFnX744QcOHz5sTlCTk5ODi4sLAG3atGH69On06NGD+++/n/r176610d3vqcaofnnrvw5GxpOemY3zTd8OOtvnvU7LuPUfztU9nXjv8VaErjnKwch4i/0D2tQisEV1Xpi3s8CEqNYgwKcaL7XLm1E9fDmO9OwcnG5KPulU7vd+yvpzNxgOtkaeurch4zfuKrrKlpKONWvwjH8LAI5evcq17Byc7G58A+/4++v07D/XRzfrVdeHTrVrMeGnnwtMiHq3ubzzICcXrQbAvV5tjA7lyLkpb8v110YHe4tjjfblyE7PP8GRnZ6B0T5/2av7jmLr7Ih7fW/ztgs//Ypz9cq4+dQsqqaUip5NqjC2T14emP1nE0jPyjF/JsGNz6fUzFtHSNXwcOTDh1rw4Ybj7D9XNM+tL209G1bmje4NANh/PoH0zBxcbuoXl3J5j7dLy/zzv2uvd/OjnNFAt9lbSM/K4dF7a/PRwOYM+3J30VReRESklJm4+/+GvBto0uMvsLe3p3Pnzvz000+3LWc0Gsm96WYm4w/fatrf9Af/9Uy2BoOhwHPl5uZiMBhYtmwZdnYFhz9PmDCBLl26sGfPHoYNG0bbtm3x8/PjrbfeIiAggNmzZ2MwGOjZs6dFXa4zmUyMGDGCQYMGWewbN24cx48fZ8eOHbz00ks88cQTPPjgg7ftg5K08cAFNh64YH7/5uB7qFvFlR8PXQKgXlVXYpMzbvktauXyDnzw5L18/uMpvt9/wWJ/b/8aDO1Ulxfn7eBK0q2TeN7tfjhzgR/O3Gjf2Pub4+Ppxs+RFwGo6+lGXNq1Px3lUc3NmcoujnzQqy0AtkYbnO3sWDK4Ky+t3c7llLs7Muhmv5yL5pdz0eb3L9/XCm93d7ZH50X3eLu7EX/t2l+O8gjwrk1/v/q8ueVn4m6R7PRuU7l1Myq3bmZ+f/R/y0g5d5mKrZoAkBJ9CTs35wJzbjhXq8jl8Bs5hnIyMrl2JQ7nPyQlvRx+gMpt7sn3OZhw9AyJJ6MIH/0eANmp6aScvUTquUvUG9qnSNtYnDYcvsSGw5fM76f0b4JvZRc2H7kMgG9lF2JTbv35VMXdgVmP+PPpLxGsP3SxROpcEjYcvcyGo5fN76f2aYxvRRc2HY8BwLeiK7GpGX86ygOgfiVX/vPLaZJ+P/abfdE826Eu7o52JN4mmkZERETKlrITK1yCcnNz+fXXX/H29qZt27YsX74ck8lESkoK69ato127dgDUqlXLnJglPDycq1ev3u60ANSpUwdbW1vWr19v3hYfH4+Liwv+/v7MmzfPvP3ixYvmfBs38/f3Z+jQoXz88ccAJCcnU716dQwGA9u2bSMqKspc1tnZmeTkG+unAwIC+Oqrr0hMzMtfkJmZybFjxwA4c+YMfn5+PP744/Tt29fctrvVhr3n6dOqJrUrueDiYMtjXerx3d7oAstWcLPnoydb8234WVbtOmexv/s91Rjeoz6jPt3FxXjruXkvjE2nzxPoW4Na7i44l7Nl6D31+P5Uwf0EYGdjg53RxuJ1ZHwyD3/zAyNWbWXEqq18uO0QCdcyGLFqK1dSrbvPfoo6S0Cd2tRwdcXJzo5BDRvw022WpNja2GD3+1KMm19DXhTJ0CaNmPzLNi6nWua/sBaV29zDpW17Sb0QQ3ZaOmfX/kyVdpbRYwBeLRqSej6GK3uOkJuVRdSaLTjXqIxT1RuTHhnxiSQcj6By2/zn8HuiH60mP4//m8/i/+azuNauRu3gTnj361qs7Stu6w5epG/z6tSp4IyLvS3DOviw5oDlZCtARVd75jziz7Jfz/HtLT7Dyoq1v12kb9Nq1PH6vV/aerPm8K0neeyMBsrd9Hl0/TXAkUtJ9G5cFedyRow2BgY1r05M8jVNeIiISJlhLTk9SpsiPf6E6zk9srKy8PX15fnnn8fW1papU6cSHBwMQN++fbn//vsBeOmllxg7diyLFi2iTZs2VKtWcGb+m9na2jJ37lymTJnC3LlzMRgMDBs2jH79+jFz5kymT59uvpazszP//ve/qVjR8hGOI0aMoHv37hw5coRRo0YxefJkZs2aRdOmTfMlhBk8eDDvvPMOCxYsYMyYMfTr14+EhAQeeeQRIO8X6aGHHqJBgwa8//775iUybm5u/Pvf//7bfVqcdp28yuKfz/DxU/dhb2tky2+X+HTTKfP+z1/qwKKfzrDxwAWCWtWkupcT/+paj391vfFY28DJeU/oeaq7L+5Odvz3uXbmfRv3X+D9sN9KrkHFZPf5Kyw9fIb3erWhnNGGrVGXWLjvpHn/vH73s+TgKXN0yIIBnajye36G6T3z8sY8uvQHLqekE3/TEobkjExyTaZ826zV/ssxhB0/yeROHShnNLLj/AWWHDlq3v9R964sP3bcHB0yq2c3KjnnPSViYse85WfPrtvAlbQ0HmrSCNdy5ZjRtbP5+J+jzjFvn3XlaPBs4kvNnu05+P7n5GZlUaFlI2oHdzHv3z1pDjV7d6Ry62aUc3Wm0bMPcmrxOo5/ugLXOtVpODx/NNnlHQdx86mJY6X8uWBsnRzzvTfYGjE62GPr5FB8jSsBO07HsnB7JHMf9cfezsiPRy8zf8uNJx4tfrYtn22NYMPhS/RtUZ0ank481cmHpzrdeHxrlxkFPyLYmu2IjGPhrijmPtgSe1sbfjwZw7ztZ8z7l/yrNf+3M9IcHbJ0WFuqueeNkVkP5C1JC5m3jYtJ1wj96SSjAvxY/lQ77GwMnL6ayuthd/dkvYiIiBQ9g8map2zEat0/bv2dC/2DOfhqfd6duLr+9QSi/xSeFa1j6UxpOvhzhdKuwl3P5Kig0MJY+/h9pV0FEREppIoVXUu7CkXiYtp/S/R6VZ2eKdHrFRX9JSMiIiIiIiIiZZKWt4iIiIiIiIhYmVyt2SgURXqIiIiIiIiISJmkSA8RERERERERK5NjMpR2FayCIj1EREREREREpExSpIeIiIiIiIiIlVFOj8JRpIeIiIiIiIiIlEma9BARERERERGRMknLW0RERERERESsTK4SmRaKIj1EREREREREpExSpIeIiIiIiIiIlclRItNCUaSHiIiIiIiIiJRJivQQERERERERsTLK6VE4ivQQERERERERkTJJkR4iIiIiIiIiViZXOT0KRZEeIiIiIiIiIlImKdJDRERERERExMrkKKdHoSjSQ0RERERERETKJEV6iIiIiIiIiFiZ3NKugJXQpIeUijpHrpZ2Fe5qxxyrlHYV7nrXvPTxdSe2dspudSf6LLqzxApOpV2Fu96Dc0JZ83pp18I6BF1eWdpVEBGRfxjdNYiIiIiIiIhYmVzl9CgU5fQQERERERERkTJJkR4iIiIiIiIiViZXK5kLRZEeIiIiIiIiIlImadJDRERERERERMokLW8RERERERERsTI5SmRaKIr0EBEREREREZEySZEeIiIiIiIiIlZGiUwLR5EeIiIiIiIiIlImKdJDRERERERExMrkKqdHoSjSQ0RERERERETKJEV6iIiIiIiIiFiZHOX0KBRFeoiIiIiIiIhImaRIDxEREREREREro5wehaNIDxEREREREREpkxTpISIiIiIiImJlcpXTo1AU6SEiIiIiIiIiZZIiPURERERERESsjJ7eUjiK9BARERERERGRMkmRHlKm9QxuSO8BjbG3N/Lr9rN8/slOsrNzLcpVq+HO0y+3p1IVFwAiT8exaP6vXIhOBKBXv0Z06FIXr0rOpCRlsHn9cdavPFKibSkuQ1rX4rF23jjYGfnh6GVmrDtKVgHTxk2qu/N057o0qOpGbq6JvVHxvL/hGLEpmQD41/bgyft98KviStK1bPrP2vr/7N13eFRV+sDx72Qmk0lPSO8k9ACBEJq0BATpBAF7AQs2FBV1FXVF9ue61kUBG4qLCCICIl06hBKk9xAggfSeQPqUzPz+mDAhJigr6ft+nofnmTv3zJ1zXs6d3DnznnMbuin16q6wIO7vEYxGpWRXQib/3n0WfS0TKVVWCt4aFkYHD2d8nGyZ/stBjqcXVCvT3t2R5wZ0pJ2HE+X6CpYcTWTlyeSGakq9GRXSluh27VErlfyWnsY3J49jMNY835QKBdN79ibExQVPO3tm743hbF6uZX9nd3cmtu9EsIsLxXodz23d3JDNqFejRnZg3LhOqNUqfjuYwsKFh2r9TGrb1o277w4jJNgVo9HE2bPZLPruCFeulANgZ2fN5MkRdO/mA8DWrRdYuep0g7alvowfHMJdQ9uhsVay93g68386ib6WGHVs7cpDozvSLsAFo9HEyYu5fLHyFAWFWgAeGNmBe4e3r/baZ/61k8y80gZrS11Tuzoy4JuX8b0jAm1uIUde/4bEZTtqlnO2p88nz+I3shcA575Yy/HZiy37w/8xhaDo/jh3CuLEP5dU2yeEEKL5kLu33BzJ9BAtVpfuPoye2JkP3trKjKmr8fB24M77utVa9kpBKfM/2M0zD/7EtIdXcOxgKs+8PNCyX6GABZ/u45kHlvPR7O0MHdWBPgNaN1BL6k+fEDcm92vNtCVHiJ67B18XW6ZGtqm1rKNGxS9HU7lz7h6i5+6hVGfg72M7W/aX6StYdzydedsuNFT1G0yvADce6BHMi2sOcdf3u/F1suPR3m1vWP5UxhXe2XaSvBJtjX3OGms+HBPB2jOpjF24g/uW7uFQSl59Vr9BdPPwJLpde/5v/x6e3fornvb23NWx0w3Lx+flMv/IYQrKy2vsKzdUsDP5MkvOnKrPKje4sDBvxkWH8s4/d/Dc9DV4edpz16SutZZ1sFezfftFnpu+lmefW0tZuYGnnupr2f/wQz2wUSt5bvpa3nxzCwMHtiYyMrihmlJvenT04O6h7Zg5bz+TZ23F292eB0d1rLWsg501m/YlMeXtrUyetZWycgMzHgivVibmaBoTXt5g+decBzwAbps/HaNOz4/ek9j94Lvc9vnzuIQG1SjX+9/PoLSzYUXwA6zvM402Dw6j7ZThlv1FF9M59OrXpG440JDVF0IIIRqFDHo0kiFDhjBixAiio6Mt/1JTU29YftmyZSxatAiAuLg4Nm7cWOvxxo0bx8iRI1mxYsVN1WPRokXk5TX/L1y1GTCkDTHbLpKWcpXSEh1rfzrFgCG1f6EvLdGTm10CgAIwGo14+jha9m9cfZakxGQQSloAACAASURBVHyMRhOZ6YUcPZhKu04eDdGMejW6mw9rj6dzKaeEonID3+65xJhuvrWWjU3IY0dcNiW6CrQGIysOpRAW4GLZfza9kE2nMki7UtZQ1W8wIzr4sSEujcsFJRRrDXx3OIERHf1qLWswmlhxMolTmVeoMNXMBLm7W2sOpuSx9UIGeqOJMn0FSQUl9d2EejcoMIidyUmkFhVRotfzc/w5ogJqfhkDqDCZ2JiYQHx+HsZaYpRwpYA9qSlklzb/uFwvclAwu3YmkJpaSEmJnp9/PnPDgYrjJzL47bcUysoM6HQVbN58ng7t3S37e0T4sXZdHDpdBTm5JezcmcjgqJCGakq9GdonkM0HkknOLKK4TM+yX+MZ2ieg1rKHz2az93g6peUGtPoK1sZcIjTErYFr3HBUdhqCJg7k6FuLMJSUk73vNMlrY2nz0LAaZQPG3sbpD5dTUaalOCmLC99uot0jIy37Ly7eQtqvB9EXtbzPayGEEOL3ZHpLI5o7dy7t27e/qbL33Xef5XFcXBy7du1i1KhRtR7v/PnzTJgwgUGDBuHl5fWHx128eDH9+vXDza3lXSj6BThz9LcUy3bypQJcXG2xd1RTUqSr9TWfL70HjUaFQqFg9bITNzx2h1BPdm4+X+d1bmghHg7ExOdYti9kFeHmYIOTrTWFZfo/fG14oCuXcorru4pNQnArB/ZdzrZsJ+QV4WZng5ONNYXaP47T73X2ciYxv5jPJ/TGz8mOs9lXmRMTR3ZxzYyH5iTA0ZHDGemW7aTCq7hoNDhYqynW136+/a/x93fm8OE0y3ZScgEuLrY4OKgpLv7jGHXq5Elq5XS7axTXZ7QqwN/fheYuyMeRA6cyLNuJaYW0ctLgaGdNUekfn2td27qRnFFY7bk+Xbz56b2R5BeWsy7mEhv2Xq6PajcIp/b+mAwVFF6o+oGk4GQCXoNqz2Dkd/3DtUvreq2fEEKIhie3rL05kunRhCQkJBAZGUlamvmieP78+bz44osAzJs3j/fff5+CggLmzp3L/v37iY6O5p133qlxnPbt2+Pk5ERWVhYA69at46677mL8+PGMHz+e2NhYAL744guys7OZPn060dHRXLx4EZ1Ox/vvv8+kSZMYN24cr7zyCiUl5l9bly9fzsiRI4mOjmbs2LEkJCQ0RFj+Mo2tNWXXXSSXlZq/VNjaWt/wNc88sJyn7l/O918fJCkxv9Yyd94bhkIBe7Y37fbfDFtrJcVag2X72mN7tfIPX9fW04FHB4W0yKkstakRJ535sd2fxKk2Hg4ahnfwZe7ec9z1fQwZhWXMGhZWZ3VtLDYqFaWGqhiV6s3nnq1Kxtav0WhUlF43mFha+flkq7nxZxJAYKALEyd0ZunS45bnTpzIIHpcKBqNCi8vBwZHhWBj89/3x6bGVq2ipKyqH5WUXYvRH/ej1r5O3D+iA9+sOWN5bs+xdJ54Zwf3ztzEp8uOc/+IDkRG1J6h1RxYO9iiK6w+PUd3tQRrR9saZdN+PUTYq/ehcrDFsY0v7R4ZicrOpqGqKoQQQjQpcjXaiKZPn46NjfkiRKlU8vPPP/Piiy/y4osvMn36dNatW8eqVauqvcbV1ZXp06eza9cu5s6dW+txjxw5gqurKx07mudBDxgwgDFjxqBQKEhMTGTKlCnExMTw9NNPs2LFimoZJ59//jmOjo6sXLkSgA8//JAFCxbw4osv8sEHH7Bp0yY8PT3R6XRUVFTUV2j+ktsGBTPl6T4AnD+bTXmZHlu7qi8TmsrHZX+SwaDTGtj563nmf3c3rz23lqKrVb/ADx3Vgf6D2/DP1zfXuvhgUze8izevjTavs3A8+Qpl+grsbao+Bq49LtHd+P/W39WWOfeFM2dzPMdTrtRvhRvJsHY+vBQVCsDJ9AJznK4b4LC3Nsep9A/idCNag5E9idmcyzb/Ir3oUALrHxuCvVpFic7wJ69uOgb4BzC1m3n9hLi8XLQGQ7UBDltV5flmaD5tqmv9+wcx9fHKhSTP5VBebsDW9roYVQ7AlpXf+DPJy8uB116N5LvvjnLuuqysRYuO8MiUCD6ZM4aiYh379ifRv1/t04massE9/XnuXnOmwumEPMp0BuyuG+C49ris/Mb9yMfdnv97ui9frjrFmYSqwerkzCLL47hLBfyyO5EB3X3ZfSSttsM0efriMtROdtWes3ayr3WKyoHn59N37rNMPL8YbV4hl37cQfC9QxqqqkIIIRpIhSxkelNk0KMR1Ta9Zfz48Rw4cIBp06axdOlSHBwcbvp406dPx2QykZyczKeffoparQYgJSWFl156iaysLFQqFbm5ueTk5ODhUXNNih07dlBcXMzmzea7Jeh0OsvgSd++fXnttdcYPHgwUVFRBATUPs+6scTGXCI25pJl+6kZAwho7crBfUkABLZuxZWCshtObbmeQqFAbaPEtZWtZdBj4O1tGD2hM+++sYWCZroY3ubTmWw+nWnZ/sedXWjn5cD2s+asoHZeDuQVa284tcXbWcO8ByP4ds8lNl2Xgt7SbL2QwdYLVe37+9Aw2rg7sjPBHKe27o7klWr/66ktYJ4aY6IqF7G5ZiXuTU1hb2rV9LHnInoR5OzMgXTzF8ogZ2eulJf/T09t2bcviX2Vnz8Azz17G0GBrhw4YI5bUJArV66U3XBqi7u7HW++MZifV59hz++mZZSU6Jj/Waxl+957wriYUHt2WlO283AqOw9XTdf42+QIQvyc2XPMPFUqxN+Z/MLyG05t8XS15V/P9mPZr+fZcejG62IBYDJVnxLUzBSeT0WhUuLU1o/Ci+bzrFVYCFfOXq5RVldQRMxD/7Js9/jnY+QePNdQVRVCCCGaFJne0sTodDouXLiAo6Pjf73A6Ny5c9m8eTP//ve/mTlzJrm55ltAzpgxg/vvv58NGzawevVqlEolWm3Nu0oAmEwmZs2axZo1a1izZg2bNm1izpw5gHm6zQsvvEBZWRkPP/wwu3fvvrXG1rN9OxMZNLQtvv7O2NlbM+6uruzdUfuUlM7dfAgMdkVhpUBja819j/akpERHRuUc+tsGBTPpwXA+eHsbOVktZx2LjSczGNfdj2B3exxsVDw6IIT1J9JrLevhaMNnD0aw8lAKq4/W/HKhANRKK1RWimqPW4LN8emM7uRPkKs9DmoVD0eE8Ou5G/9abG2lQK00f7xaK60sjwE2nUtjULAXbd0cUVopmNwzhBPpBc0qy6M2MSnJDAlsjZ+jI3Yqaya078CulKQblldZWWFtZVXjMZj7krWVFUqFFQoUlY+bf1+K2XOZwYND8PNzws7Omjvv7Mzu3ZdqLevqasvf3xzC5s0X2LbtYo39Xp4OODioUSgUdO/mw5Db27J6dfO/Ze32gynccVsggd6O2NuquHd4e7ZdtzbT9dycNbz3XH/WxSSycd/lGvv7dvXGoTKbpn2QC+MiQzhwMrNGuebCUFpO0s97CZ89BZWdBs9+nQmM7kfC91trlHUM8cGmlRMKKyv8RvSmw9TRnPjnEst+hUqJ0sYarBRYVT5WWMkloRBCNDdGU8P+a64k06OJ+eCDD+jcuTPvvfceU6dO5ccff8Tb27taGQcHB4qKim5wBBg5ciSbNm3iq6++4o033qCoqAh/f38AVq1ahU5X9auivb19tWMNGTKERYsWER4ejkajobi4mKysLIKCgkhPTycsLIywsDCSk5OJi4sjMjKyjiNQd04dS2fj6jO89s4w1Golh2OTqy1O+u7csaxbeZrYmEvY2Vvz4NRetHKzQ6erIPFCLh/P3o5eb57CMvGB7jg42vD2h1WLx+7ffYnvvvytwdtVlw4k5PH9/st8/lAENtZKdsZl8fXuqoGhZU/dxqK9l9h8OpNx4X74t7Lj8cgQHo+sukvE4Pd3AhAe5MoXD/e0PL/n9ds5cjmfZ74/0nANqicHU3JZduwSn0b3wkalZHdCFt8erPoi+t29/VlyJNGSHbLk/oH4OJnn2X881hyTu7/fTWZROUfT8lnw2wXeH90DjUrJycwC/m/byYZvVB07kZ3F2ovneavfQNRKJQcz0llxLs6y/6PBQ/nlQrwlO2TO7cPwtLMH4I1+AwB4dsuv5JSV0snNnVkDBlleu2TseM7k5vCPfXsasEV178SJDNaui+Pvf78dtbWSgwdTWLGy6ra8H344il9+OcO+fUkMGdIGLy9HJk3qwqRJXSxlpjxinnoYHOLKww/3wN5OTUZGEfPn7yc1tbDGezY3R+KyWbntIu891w8bayV7T2SwZGNVhsKXrw9m+ZYL7DycyvB+Qfh42PPAqI48cN1tbSe8vAGAyAg/XnwgHGuVFblXylix7SLbDtY+gNJcxE77lAELX+HerJVo8wqJfeZTrpxNwmtAV4Zt/BdLnMYA4BbRnj5znkHt4sDV86nsfvBdrpytGoTsv+Al2l13C9tubzzInkc+4OJ3mxu8TUIIIUR9U5hMtdwvUNS7IUOGoFarLWt6AIwbN441a9awYsUKbGxsWLFiBatXr2bx4sV88cUXlJaW8uqrr1JUVMTUqVMpLS2ld+/evPnmmwwZMoQvv/zSMl0mKSmJCRMmsGnTJvbv38/cuXNxdnZm4MCBLF++nFWrVuHv78+KFSv45ptv0Gg0fPzxxwQFBTF//ny2b9+OQqFAoVDw7LPPEhUVxZQpUygqKkKhUODj48N7772Hq6vrX2r/5PHf10kcW6pzEd5/Xuh/nI1b81tTpaH5+LWsW77WB8WPtWe9iSpX3e3+vND/uLs/q32NLVHTmKxfGrsKQgiBh4djY1ehTqy9vOTPC9Whca0fbND3qysy6CEahQx6/DEZ9PhzMujx52TQ48/JoMefk0GPPyeDHjdPBj2EEE2BDHr8Nc110EOmtwghhBBCCCGEEM1MhaQv3BRZtUoIIYQQQgghhBAtkmR6CCGEEEIIIYQQzYzR1PzvbtcQJNNDCCGEEEIIIYQQLZJkegghhBBCCCGEEM2MUdb0uCmS6SGEEEIIIYQQQogWSTI9hBBCCCGEEEKIZkbu3nJzJNNDCCGEEEIIIYQQLZIMegghhBBCCCGEEKJFkuktQgghhBBCCCFEMyPTW26OZHoIIYQQQgghhBCiRZJMDyGEEEIIIYQQopmRW9beHMn0EEIIIYQQQgghRIskmR5CCCGEEEIIIUQzI2t63BzJ9BBCCCGEEEIIIUSLJJkeQgghhBBCCCFEMyOZHjdHMj2EEEIIIYQQQgjRIkmmhxBCCCGEEEII0cxIpsfNkUwPIYQQQgghhBBCtEgKk8kk40OiwcVkfNfYVWjSrmiVjV2FJk9boWjsKjR5JYbGrkHT52jd2DVo+oxylfCnfjxm19hVaBYy0hwauwrNwpr7+zZ2FYRo8Tw8HBu7CnXiy7NLG/T9ngp9oEHfr65IpocQQgghhBBCCCFaJFnTQwghhBBCCCGEaGZkTY+bI5keQgghhBBCCCGEaJEk00MIIYQQQgghhGhmJNPj5kimhxBCCCGEEEIIIerFpUuXuOeeexg+fDj33HMPly9frlGmoqKC2bNnM3ToUIYNG8aKFSvq7P1l0EMIIYQQQgghhBD1YtasWdx///1s3ryZ+++/n7feeqtGmXXr1pGcnMyWLVtYvnw58+bNIzU1tU7eXwY9hBBCCCGEEEKIZsZoath/hYWFpKam1vhXWFh4wzrm5eVx9uxZxowZA8CYMWM4e/Ys+fn51cpt3LiRu+66CysrK1q1asXQoUP59ddf6yROsqaHEEIIIYQQQggh/tB3333H/Pnzazz/7LPP8txzz9X6moyMDLy8vFAqlQAolUo8PT3JyMigVatW1cr5+vpatn18fMjMzKyTesughxBCCCGEEEII0cw09EKmj06ezJ133lnjeScnp4atyH9JBj2EEEIIIYQQQgjxh5ycnP7rAQ4fHx+ysrKoqKhAqVRSUVFBdnY2Pj4+Ncqlp6cTFhYG1Mz8uBWypocQQgghhBBCCNHMVJga9t9f4ebmRqdOnVi/fj0A69evp1OnTtWmtgCMGDGCFStWYDQayc/PZ9u2bQwfPvxWQwTIoIcQQgghhBBCCCHqydtvv82SJUsYPnw4S5YsYfbs2QBMnTqVU6dOARAdHY2/vz933HEHd999N9OmTSMgIKBO3l+mtwghhBBCCCGEEM1MhUnR2FW4KW3atGHFihU1nv/6668tj5VKpWUwpK5JpocQQgghhBBCCCFaJMn0EEIIIYQQQgghmhljA9+9pbmSTA8hhBBCCCGEEEK0SJLpIYQQQgghhBBCNDN/9Y4q/2sk00MIIYQQQgghhBAtkmR6CCGEEEIIIYQQzYxketwcGfQQLUZJYRmLPtjA2cOXcHC2ZcLUwfQZ2rlGOZPJxKoFO9m74QQAA0Z3Y+ITg1EozLd8OrH/Aj8v2Ele5lX823jy8Cuj8G3tAYBeZ+DnBTs5tDMOvdZA79tDuee5YahUyoZr6C0oLSzjpznrOH8kEXtnO0Y9MpjwIV1rlDOZTGxcuJ2Dvx4HoPeI7ox67HZLjIwVRrZ8v5tDm4+jLdPh5uvKUx88jK2DBpPJxObvdnFoywl0ZTp823pz57QReLf2bNC2/lWlRWWs/mQtF48mYudkxx1ThtBtcO0x2vKf7RzefAyAnsPDueORqhhdc2z7CVZ9vIbx08fQc0QPAMqKy9nw1a9cOHwRgN6je3L7g1H127A6VFZUxsZ5a7l0LBFbJzuiHh5C58jaY7Tru+0c32qOUfdh4URNNseotLCUle/8SF5aHiajETd/d25/ZBj+oYEA5CRls/3bLWRezKCsqIyZa99q0DbeqtKiMn6es5YLR83n2h1ThtD9Bv1o87fbOVTZj3oND2f4o1X96PWR/8Daxppr3SossgsTXhhreX3axQw2fLWZ9IsZWGvURN0zgP7j+9R/A+tAXZ1rb46qHqOug7pwZ2WM9q0+wIF1Bym9WoraVk3XQZ0Z/tgwlMrmneg6pl0bxndoj41SSWxqOguOHcdgNNYop1IoeKFPL9q4uuBpb89bu/dwJifXsj+6fTuiggLxsLOlUKdjc8Il1py/0JBNqTf3hAfyYERrNColOy9m8eHOOPS1fDtQWSmYPaIrHb2c8HGyZdrKwxxLK7Ds/zg6nG6+LpZta6UVyQUlPLT0QIO0QwghxK2TQY86NGTIENRqNWq1GqPRyNNPP83o0aPr7PharZaBAweybNky2rRpY3k+JSWF8ePHs2fPHuzs7G5Yty+//JL27dvXWX2amqWfbEZlreTjn58n5WIW82b+hH8bT/yCPaqVi1l3jON7z/PWN4+hUCiY8/IPuHu7EBXdg6zUfL55Zw3T37uHkFA/Ni8/wPzXV/J/i59EqbJi0w+xXI7PYPZ/pmI0Gpk3cwUbvt9H9CODGqnV/53Vn21CpVIya/kM0hMy+fbvP+IT4lVjQOLAxqOciY3nxS+eQKGABTOX0srbldvGRACw5fvdXD6byrOfPIKLpzNZSTmo1OaPk5MxZzm0+TjP/HsKrp7O/PrdTn78cA0vfDa1wdv7V6z7fCNKlZLXfniJjMRMvp+1DO8QL7yCqsfo0KajxMXG8+xnT6IA/vPGEly9XOg9uqelTFlRGbuX78UzqHof3LhgM3qtnpf+8zwlV0v4dub3uHi6EHFH94Zo4i3b/KU5RtMXv0TWpUxW/GMZnsFeeARWj9HxzUc5/1s8j336JAoFLHtrCc5eLvQY2RO1Rs3o6eNo5esGCrjwWzwr3vmR579/GSulFVZKKzr170yPkb1Y9e7yRmrpX7f2s40orZW8vuwlMhIy+W7WMnxq6UcHNx3lbGw80z97EhTw7etLcPV2oc91/Wj650/i5tuqxnuUXC1l0ZtLGf3EcLoM6ESFoYKruYX13ra6Upfn2rOf1R6jjn3b02NYd2wdNJQWlbHsnys4sOY3+k+4rb6bV2+6e3lyZ4f2vB2zl/yycv7Wrw/3hnZiyekztZaPy81j/YUEXu7bu9b9cw8dJulqId729rw1sD+5paXsS02rzybUuz6BbjzUszXPrTpCbomWf43pxuN92/DFvou1lj+RfoXlx5N5Z1RYjX0vrTlWbXv+xAiOpBTUKCeEEI2hlvFuUYvm/VNHEzR37lzWrl3LBx98wMyZM8nPz6+zY9vY2DB69GhWr15d7fnVq1czfPjwGw54/C/Qluk4GnOO6EcHobFT0y4sgG792nFgy+kaZWM3n+KOu/vQytMJVw9Hht3dh/2/ngTgzMFE2oUF0C4sAKXKihH33caV3CLOn0gC4OT+C9w+sRf2TrY4uthz+8Re7Nt4okHb+lfpynWc2hvH8MlR2NiqCe4SSOht7Tm6/VSNske2nmTQxNtw8XDC2d2JyIl9ObzV3M7SojL2rP6NSS+MxtXLBYVCgXdrT6wrBz3yM6/QunMAbj6uWCmt6DGkK1lJOQ3a1r9KV67j7L44hj40GBtbNa07B9KxT3uO7zhZo+yx7SfoP6Evzu5OOLk70X/CbRzdVr0vbFm0g9vG9cbOqfq5GX/wPAMn9UOtscbVy4WI4d05urX6hXVTpSvXER8bx8AHBqO2VRMQGkjb3u05vbNmjE7tOEHv6L44uTvh6OZEn+jbOLXdHCOVWoWbvzsKKwWYQGGloLy4nLKiMgDc/N3pdkc47oEeNY7b1OnKdZzZF8ewa/2oSyCd+rbn2PZa+tG2EwyY0BfnynNt4MTbOLr15j5T9q6OpV1EG7oP6YpKrcLGzgbPZhKvuj7XbsTNpxW2DhrzhsmEwkpBXkbz/sIaFRTI9stJpBQWUaLXszIunqjWgbWWNZhMbLiYwLm8PIymmlkOa85f4NKVqxhNJtKLizmYnkFHd7f6bkK9Gxnqw7oz6VzKL6FIa+A/By8xqpNvrWUNRhM/HU/mZPoVjH9y70dvRw3dfF3ZFJdeH9UWQghRTyTTo56EhoZib2/Piy++SHFxMXq9HldXV9599138/PwAWLJkCYsXL8bR0ZHIyEiWLl3Kb7/9BsDu3bv54osv0Ol0WFtbM3PmTLp3787EiRN5+umnefHFF1EqlZhMJn755Rc+/PBD1q1bx+LFi9Hr9QC8+uqr3HZbzV+zfp/1cf12YmIi7777LgUFBej1eiZPnszEiRMpKyvj1Vdf5eLFi6hUKoKDg/n0008bKJp/Lis1H6XSCu+Aqos1/zaenD+RXKNs+uVc/NtU/ZIY0MaL9MtV6b7VrgtNJkwmE2mXcugUEVz5lOm63SYKcoooLS7H7tqFdROVk5qHldIKD/+qGPkEe5F4KqlG2aykHHxDvKrKhXhZBi4yL2djpbTi1J44Ylb/hsbOhgHje9N/XC8Aukd15kTMWXJS82jl7cLhbSfp0LNNjfdoinLTzDFyvz5GIV5cqiVG2Uk5eAdfF6NgL7KTqwZ3UuPTSLuQzthpozi152zNNzNVf9xcBoby0/KwsrLCza8qRl7BXiSfrhmj3OQcPK+LkWewF7kp1dv5zXNfkpeWi9FgpNsd4di72Ndf5RtIbmot/Si49n6UlZSDz3XnmnewF1nJ1WO04JVFmEwmAjsFMPqJO3D1Mqfap5xLw6u1J1/O+Ja89HwCOvgxbtooXDyd66lldacuzzWAb/5WFaORU6tiBHBi5ynWzt+AtkyHnZMdIx8fVg8tajgBTk4cTM+wbF++chVXjQYHtZpine6Wjt3J3Y2tly7dahUbXXArB/YkVPWRizlFuNnb4KSxprBc/5ePO7KTLyfSC8gsKq+LagohhGggMuhRTw4cOIBWq2XOnDm0amVOuV2xYgUfffQRc+bM4dy5c3z11VesWbOGVq1a8c4771hem5yczOeff87ChQtxcHDgwoULTJ06lV27dtGlSxdcXV3Zu3cvkZGRHDhwALVaTUREBCEhIYwZMwaFQkFiYiJTpkwhJibmputsMBh4+eWX+fDDD2nTpg3FxcVMnDiR7t27k5iYSElJCRs3bgTg6tWrdRuwW1RepkNjZ1PtOVsHG8pLa14AlpfpsLW3qVZOW6bDZDLRKaI1qxbsJP5YEm26+PPrslgqDBXotAYAOvcOYfuqw3QMD8JYYWLHz4cB0GkN2DnUYwPrgLZMj83vY2RvbnuNsuXV46mxr4rR1ZxCyku05KTl8/p3z5GTls+C15bg4edG+4gQHFs5EtwlkA8e+xwrKwXOHs489cGD9d6+uqAr09WIkY29ptYY6cp1aOw115WzQVcZI5PRxNrPNjL2mZFYWSlqvLZdRFtiVuxj4oxoiq+UcGTLcfS3cCHekPTltcTIToPuv4zRtfUYHp/3FAadgfjYc1QYKuq38g1EW0uMNH/Qj2yui5HmdzGa+sFkAjr6o9fq2bp4J9/NWsZznz2JUmnF1dxC0i9m8Og/H8Qr2ItfF27jx/dX8dTHj9Z7G29VXZ1rCoWCx96vitG2xTv5/u1lTJv/pGXdjm6Du9JtcFdy0/I4vv0kDi5N/MP6T2hUSkr1VZ8X1x7bqlS3NOhxT2hHrBQKdlyu+WNBc2NnraRYZ7BsX3tsZ628xUEPHxYdTLzl+gkhRF2RhUxvjgx61LHp06djY2ODg4MD8+bNIyYmhh9++IHS0lIMhqo/wAcPHiQyMtIyIDJp0iTWrVsHwJ49e0hOTuaBBx6wlDcYDOTm5uLu7s7EiRNZtWoVkZGRrFq1igkTJgDmtT1eeuklsrKyUKlU5ObmkpOTg4fHzaU7X758mYSEBGbMmGF5Tq/Xk5iYSMeOHUlISGD27Nn07t2bqKioWw1VndLYqikv1VZ7rrxEh8ZOfYOyuuvKabGxVaNQKPAJcufR18bww6ebuZpfQt9hnfEJcsfVwxGA0Q/1p6xYyz8eX4jKWsXAMd1JvpCJk2vT/3XaxtYa7e9jVGpue42ymurx1JbqLDFS2VgDMOyBgVjbWOMb4kX3yM6cO3SR9hEhbFsaQ0p8Om8seR7HVg4c3X6KuLBVEQAAIABJREFUL/+2hJcXPIVaY12/jbxFalt1jRhpbxAjtaZ6WW2pFnVljA5sOIR3sBcBHf1rfZ/RT41g/RebmDN1PnaOdoRFdubk7trn4zc11praY6T+L2N0PZVaRefILix45nO8QrzwCvaun8o3EJtaYnSjc+3PYhTcNQgAlbWSMU8OZ/bE98lJNmc+WKutCe3XEf8O5uzB2x8YxDv3fER5SXm1QYKmqK7ONageo9FPDuf/JlXF6Hrufm54Bnmw7vON3P/m3XXdpHozMMCfJyPCAYjLzaXcUIGdddVnqW3l47LrrjH+WyPbhBAZFMibu2JqXRC1qbujgzd/G9IJMK/PUaqvwF5ddYl77XGp/q8PrIb5utDKTs3Oi9m3VlkhhBANTgY96tjcuXMt00bS0tKYMWMGK1euJCAggKNHj/Lyyy/f1HEGDhzIBx98UOu+cePG8emnn5KSksKOHTv429/+BsCMGTN47bXXGDp0KEajkW7duqHVamu8XqlUYrzuouZaGZPJhKurK2vWrKn1fdevX8+BAweIiYlhzpw5rFu3Dhsbm1rLNjQv/1ZUVBjJSs3Hy988kJSSkGW568r1fFu7k5KQRXDl/N6UhGx8W7tb9kdEdSIiynzxVFpUzt4NJ2jdwQcAtY01978wnPtfGA6YF0UNau9T66/5TY2HvxvGCiM5aXl4VE5NSE/MwjuoZoy8gjzISMwisKOfpZxXZTmf4MqpQdd/cb3uYXpCFt0iQ3HxcAKg1x3dWPvlZrKScwhoX/uc6qbC3c8co9y0PNwrY5SZmFVjIVIAzyAPMi5lWb5wZl7KsqynkHj8EpdOJ3H+sPkuCGVFZWQkZJKRmMXYZ0Zi52jL3X+bYDnWlkXb8e/QtGNzTSs/N4xGI/npeeZFSIHsy1m1rr3hHuhB9qUsfNubY5R9KQv3gBsPwlYYKriSeaXZD3q4+9fSjy7V3o+8gjzITMwioLIfZSRm4fUH63IoFFUzo7yDPVFcf/Ipmv7n0DV1da7V5o/CYKwwkt/M1vTYk5LKnpRUy/YLvXvS2tmZ/ZWLjbZ2dqKgvPwvZ3kMaR3EnR3a8/fdMeSXNc9pG1viM9kSn2nZfntEF9q5O7DjQhYA7dwdyCvR3lKWx6hOPuxOyKbsFgZOhBCirkmmx82RhUzrUXFxMdbW1nh4eGA0Gvnxxx8t+3r37k1MTIxlodPrFyft378/e/bs4cKFqtvGnTxZtbibq6srAwYM4IUXXqB37954epq/hBYVFeHvb/5ledWqVehucAEUGBjIqVPmxStjY2PJzTWvZxEcHIxGo+GXX36xlE1ISKC4uJjMzEyUSiVDhw61LNB65cqVW4pPXbKxVdNjYAfWfBuDtkzHxVMpnNh3gb53dKlRtu8dXdn600EKcoq4klvElp9+o9+IqhXbk+IzMFYYKbpSwuKPN9Ktfzt8gsyDItdeYzKZSDiTxvrFexn3yMAGa+etUGvUdOnfkS2Ld6Mr13HpTApnY8/T4/aat4iMGBpGzM8HuJpbyNW8ImJWxtJzWDcA3H1bEdwlkO3L9mLQGchKzuH4rjN06tMOAP8OPpzcE0dRQTFGo4kj205SYTDiXsudFZoatUZNaL9ObF+yC125jqQzycQdiKf7kJor+ocPCWP/6gMU5hZSmFfEvp8P0GOoOUYTZkTz/JfPMG3ek0yb9yS+7XwZcv8ghk0eDEBeRj6lhaUYK4ycP3SBw78eJere5tOPOtzWiZil5hilnk3mwm/xdBlcM0ZdBodxcM0BivIKKcor4uAvB+h6uzlGaedSSTmbTIW+Ar1WT+yqfZReLbEMkJhMJgw6A8bKKS8GnQGD/q//kt2QrvWjbd9X9aOzsfGE315LP7o9jL2rzedaYV4Re38+QI/Kcy0rKZv0hEyMFUa0ZTo2fr0VJzcnPAPMn0cRw7pzJvYc6QmZVBgq2PlDDEGdA5p8lgfU3bmWlZRNxnUx2vSNOUYelTE6/OtRiq+UAJCdnEPMT/sI6da6wdpZH3YlJTMkOAh/R0fsrK2Z1Kkju/5gSorKygprK6saj8GcRXJ/l1Bm79lHVklpvde9oWyKy2BMZz9at7LHQa1iSu8QNv7B4qPWSgXqyulQ1z++Rq20Ykg7LzaelQVMhRCiOZJMj3rUoUMHRowYwahRo3B1dSUyMpLDh81rQHTs2JHHH3+ce++9FwcHB/r27Yujo3kKRevWrfnwww954403KC8vR6/X06NHD8LCqi4GJ06cyBNPPMH8+fMtz82cOZNnnnkGZ2dnBg4ciIuLC7V5/vnnee2111iyZAl9+/bF19f8C7NKpeLLL7/k3XffZeHChRiNRtzc3Pjkk0+Ij4/n448/BsBoNPLEE0/g5eVV6/EbywMvjmDR+xuYceenODjZ8sCLI/AL9uD8yWTm/m058399BYDIceHkZlzh7Ue/BmDg6O5Ejgu3HOfHeVtJSchGqbKiZ1RH7n5mqGVfTnoB3767jqIrJbh6OjHhicF07hXSsA29BROeHcVP/17L23f/G3snWyY8NxLv1p4knkpm4Zs/8M81rwHQd3QP8jIL+PjJrwDoPTKcvqN7WI7zwMw7+enf65l110c4uNgzfHIU7cLNC70Ovrs/xVdKmfPM1+jKdbj7tuLhv0+quoNCEzd22ihWz1nLv+77GDsnW8ZNG4VXkCeXTyex+K0feOvnmQD0GhVBfmYB8575EoCI4eH0GmW+pe/v26pUKbGxs7F8GU2/kMHGBZspLynHzc+Nu165s8ZtOpuy4U+NYsPctcx96GNsHW0Z/vQoPAI9STmTxPLZP/DyT+YYhY+I4EpmAd88Z45Rt2HhhI8wx6jCYGDrgs1cySowL7Ab5Mldf78PRzfz5+DV7Kt8MXWu5T0/nPQuzp7OPPPN8w3c2r8m+tlRrJqzln/ea+5H0c+a+9Gl00l89/cfeHu1OUa9K/vR3KfNMeo5Ipzelf2ouKCENfM3cjW3ELXGmsDQAB6efS9KlRKANt2DuWPyEBbPWoZOq6d1aAD3vDqh9go1QXVxrhUXlLDus+ti1CmAB9+uilHS2RS2Lt6JrkyHvbMdXQaGcvtDgxunwXXkeFY2a+IvMDtyAGqlkgNp6fx4Ns6y/5Nht7PqXLwlO2Te8KF42punYL41sD8AT23cTE5pKfd1CcVRreb926Msr49JSmHBseMN16B68FtSHkuPXGb+xAhslEp2JWTxzYEEy/4lD97G4kOXLNkhPz7cHx8nWwA+udPctyZ8u8eyYGlkG0+KtQaOpDavLCEhRMv3JzedEpUUJlMt9zATDaK4uBgHB/OCavPmzSMpKYmPPvqokWvVMGIyvmvsKjRpV7TKxq5Ck6etaD6p/I2lpHkkRjQqx6a9zEyTIBdUf+7HY/+7t4z/b2SkNe9FZBvKmvv7NnYVhGjxPCrX62vuXjnwQ4O+34d972/Q96srkunRiD7++GOOHj2KXq8nICCAf/zjH41dJSGEEEIIIYQQzYCs6XFzZNCjEc2aNauxqyCEEEIIIYQQQrRYMughhBBCCCGEEEI0M5LpcXPk7i1CCCGEEEIIIYRokSTTQwghhBBCCCGEaGYk0+PmSKaHEEIIIYQQQgghWiTJ9BBCCCGEEEIIIZoZua38zZFMDyGEEEIIIYQQQrRIkukhhBBCCCGEEEI0M7Kmx82RTA8hhBBCCCGEEEK0SDLoIYQQQgghhBBCiBZJprcIIYQQQgghhBDNjNGoaOwqNAuS6SGEEEIIIYQQQogWSTI9hBBCCCGEEEKIZsZYIZkeN0MyPYQQQgghhBBCCNEiSaaHEEIIIYQQQgjRzMiaHjdHMj2EEEIIIYQQQgjRIkmmhxBCCCGEEEII0cxIpsfNkUwPIYQQQgghhBBCtEiS6SEaxdcn7Bq7Ck2axtbQ2FVo8hzVpsauQpNnL5/wf8pKhv7/lL6isWvQ9D3Wu7ixq9AsOKmvNnYVmrwfLqqZFb+usavR5M3uMLaxqyBEkyCZHjdHLveEEEIIIYQQQgjRIsnvgEIIIYQQQgghRDNjrJBMj5shmR5CCCGEEEIIIYRokSTTQwghhBBCCCGEaGZkTY+bI5keQgghhBBCCCGEaJEk00MIIYQQQgghhGhmJNPj5kimhxBCCCGEEEIIIVokyfQQQgghhBBCCCGaGaOxsWvQPEimhxBCCCGEEEIIIVokGfQQQgghhBBCCCFEiyTTW4QQQgghhBBCiGbGWCELmd4MyfQQQgghhBBCCCFEiySZHkIIIYQQQgghRDMjt6y9OZLpIYQQQgghhBBCiBZJMj2EEEIIIYQQQohmRjI9bo5kegghhBBCCCGEEKJFkkwPIYQQQgghhBCimamQu7fcFMn0EEIIIYQQQgghRIskmR5CCCGEEEIIIUQzI2t63BwZ9BAtzoigdowO6YiNUsnBzFQWnTmKwWSstWxoK08mh4bjZmtHwpV8Fpw6RF55KQCuNhomh/agg6sHOqOBNQlx7EhJtLz2+xF3oTUYMFVuH8hIZuGZI/XdvDqRujWWlM17Mer0uPcIpd0DY7Cyrv3joCAukYs/bECbfxXHYH86PDIejZsLAIdnfUZ5/hVLWaPeQKsu7ejy7P3oi0o48/mPlGbmYjIasfPxIGTSHTi3DWyQNt6qxM2xJG7YR4VOj3evULo8PBrlDWKUezaR04s3UpZ/FZcQf7o9Ho2du0u1MrriMnbPnI+9txv93njU8nz6wTOcX72L8oJCbFs50WHi7XhHdKzXttWV+E2xxK/fh0Grx793KBGP3DhGWacTOfrdRkrzrtKqjT+9n4zGvjJGJ5ZtJTn2NPrSctT2toQMiSA0eqDltYcXriMnLomirDx6TY0meFD3BmlfXYjfGEvcun0YdHoCeofS89EbxyjzdCJH/mOOkVtbf/o8GY29hzlGx5ZuIe1wPOVXi7F1dSQ0eiDBg7rVOMalmBP89uUv9Jo6ljaDe9Rr2+rKhU2xxG/YR4VWj1/vUMKn3DhG2WcSOfbdRsoq+1HEE1X96NSyraQcqOpHwUMi6Diuqh+ZjEbOrtrF5ZhjGMp12Hu1YtDMyajtNQ3Szr+ipLCM5f9eT/yRROydbBn92BAihnSpUc5kMrH+mx0c2HQcgL4juzPm8SEoFAoSTiWz4PVl1crryvVMeWsi3QZ24ujOM/y6eDdF+SWo1Eo69mrDxGkj0NjbNEgb61txYRmL3t/ImcOXcHC2ZeLUKPoO61yj3LmjSaz9bi/JF7Kwc9TwwfJnGqG29St5SyxJm8x/1zwjQun40Ogb/u3PP5tI/NKNlOdfxSnYn9BHo7G97u9a/tlELq7YSklmHtb2GtrdMxyvXtXjmrH/BGcX/kLHyWPxG9Q8Po+EEC2TDHqIFqWruxdjQjryr0O7KSgv44Ue/ZjQrjM/nT9Vo6yDtZrnw/ux8PRhjuWkM7FdF57t3pfZB3YA8FRYH5KLrjDveCx+Dk7M7BVFRkkRcfk5lmO8vn8L2aUlDda+upB/5iIpv+4l7KXJqJ0dOfvFjySt20nwhGE1yuqLSjj7xXLaPzwOt27tubxmJ3ELVhA+cyoAPWdPs5Q1mUwcfP1TPCJCAVBq1LSfHI2tZytQKMg7fo4z83/gto9fQaFUNkxj/6KcUxdJ2LCPvq8+jMbFkcNzl3Nh9S463j20RlldUSlH5v1E2CNj8ezegfM/7+DY5yvp/9bj1cqd+2krDj7umEwmy3PlBYUc/+pnej5/Lx5d25J94gJHP1/BkI9ewMbJvt7beSsyT17k3Lp9RL3+MLaujuybs5wzq3YRdm/NGGmLStn/6U/0fHwsvuEdOL1yB7HzVjJ0tjlGwZHhdL4zEpVGTWl+ITHvL8HJ1x3/Xp0AcA70IqBPZ04u39agbbxVGScucnbtPoa8+TC2Lo7smbOc0yt30e2+WmJUWMq+OT/Ra+pY/Hp04NSKHeyft5Jh/zDHSGVjzaCX78PRx438xDR2vbcUR+9WuLcPsBxDV1zG2TV7cPb3aLA23qrMkxeJX7+PQTMfRuPqSOwnyzn78y663lN7P4r99CciHhuLT3gHzqzawcH5Kxn8tjlGraPC6VTZj8ryC9nzwRIcfdzxq+xHZ1ftIu9CClGzHsPOzZnC1JwbDq40FavmbUKpUvKPn14kLSGTr99Yjm+IFz6tq/8fx244yqn98bzy1VRQwJev/kArbxf6j42gTddA3l/3qqXsxROX+ebvP9GxZxsAgjv7M/2TKTg426Et0/HTJxvYuGgXE6YNb9C21pelczajslYyZ/V0Ui5m8elrKwho64lfcPUYqm2tGTAqDL3WwIalsY1U2/qTd/oilzfto8fLD2Pj4sjJz5aTuGYXbSfV/nft5Oc/0WnyWNy7dyBx9Q5Of7WSXm+Yz7Xi9BxOL1hF6GPjaRXahoqycvSl5dWOoS8p4/KGPdj7Np/PIyGaI8n0uDmypkc92LRpE+PHjyc6OpoRI0bw0ksv1cv7REdHU15e/odlUlNT6dOnj2V7yJAhjBgxgnHjxjFs2DCefvppjh49Wi/1awwDfFuzO/USacWFlBr0/HIxjoF+rWst28vLj7TiqxzMSkVvNLL64hkCHV3wsXfERqkk1M2TtQlxVJhMJBdd5VBWKoP8ghu2QfUgK/Y43gPCsff1xNrelsDRkWTuP15r2dxjcdj7euDRszNW1tYEjY2iJDWL0oycGmWvnk/CUFyKew/zoIeVtTV23u4orKzAZEJhZYWhtBx9SVm9tq8upO47QcCgcBz9zDFqFz2I1L21xyjzSBwOfh749O6MUq2i3Z1RFKZkUZyeaymTfyGForQc/AdWz1Aoyy/E2k6DZ1g7FAoFXt3bo7JRU5qdX6/tqwuX95wgODIcZ39P1Pa2hI4fxOU9tcco9VAcTv4eBPQxx6jzhCiuJmdRWBkjJ193VBq1pbxCoaA4qyoG7Yb1xqtLSJP/gvp7l/acICSqMkYOtnS+cxCXYmqPUUpljAL7mmPUZWIUV5KyKEwzx6jrpME4+bmjsFLg1tYfj46B5F5IqXaME8u3035EH9SOdvXetrqSvPcErSPDcarsR53GDyLpBv0o7VAcTn4e+Ff2o9A7o7hyXT9y9LlxP9KVlHFh8wF6PDYWe3cXFAoFzgGeKNVNt09py3Sc3HuOkVMisbFVE9IlkM63tePwtpqD+Ie2niJqUl9cPJxwcXcialIfDm05WetxD205SbeBHbGxNcfK1dMZB+eqPmNlZUVuWtP/DLoZ2jIdR2LiGf/YQDR2atqFBdCtX1tit5yuUTakky/9hnfFw9elliM1fxn7T+A7IByHyr9rwWMGkbGv9nMt52gcDr4eePXqjNJaRUh0FMUpWZRkmM+1y+tj8IuMwL1rO6yUVlg72GHn2araMRJWbSfg9j5YN6PPIyFEy9V0/9o3U9nZ2cyePZvVq1fj4+ODyWQiLi6uXt5rzZo1f+l1c+fOpX379gBs2bKFJ554goULF9KtW81U6ebG38GJo9lplu3koiu42GhwsFZTrNdVK+vn4Exy0VXLtraiguzSYvwcnCgoN38xVyiqj576OzpV236z92AUCgUXCnL5If4EuWWldd2kOleanoNbt6rpEw7+XugLS9AXl2LtUP3ipCQ9B/sAb8u20kaNxsOVkvQc7Hyq/3qTFXsc9x6dUNqoqz1/ZPbn5ikuFUa8B/RA7eRQD62qW0VpOXiFd7BsOwV4oy0sQVdcivp3MSpKy8YpwMuyrbJRY+fpSlFaNg6+7piMRs4s2UjYI2MpTMmu9lqXYF8cfD3IOhaPZ7d2ZB07j5VKieN1x2uqrqbl4BtRFSOXIG/Kr5agLSrF5ncXuYWp2bgEXhcjjRp7L1cKU7Nx8nUHIG7tXuLWxGDQ6rH3cCHwtq4N05B6VJiag/91MXIN/OMYuf4uRg5erlxNy8bJz71aWYNOT35iOu2G9bI8l3cxjfzEdHo+MprkA2fqqUV1rzA1B58eVTFyDvRGe6MYpWXj/PsYVZ5r1/pR/DpzP6rQ6rHzcCGgn7kfXU3JxkppRdqhs1z49QDWtja0vaMPbYb1boBW/jU5aflYKa3w9HezPOfXxouLJ5NrlM28nINfSFVsfEO8yEyqOTitLdNxYs85Hv+/u6s9n3g6ma/fWE55qRa1xppHZk2qw5Y0nsyUfJRKK7wDqmIY0NaL88drxrClK0nLwaN71bnmEOCN7gZ/+4vTsnG47u+Q0kaNrYcrJWnZ2Pu4czUhFVsPVw689QX64lJadQqm/X0jsXawBeBqYhqFl9Pp8OBosg43n88jIZojyfS4OTLoUcdyc3NRqVS4uJh/KVAoFISGmn/57tChA9OmTWP79u2Ul5czY8YMhg83p4+eOHGCjz76iJIS81SJ6dOnExUVBcDOnTuZN28eBoMBKysr3nvvPTp27EiHDh04evQo9vb2vP/++xw8eBC9Xo+rqyvvvvsufn5+f1rfO+64g5MnT7Jw4ULmzp1LbGwsn3zyCVqtloqKCp566ilGjx7NyZMnef3111m/fr3ltePGjePtt9+mR4+mM0/TRqWi1KC3bJdVPtaoVDUGPTQqFYU6bbXnSg16bFUqyisMxBfkEt2mEz/Gn8TXwYleXv4U6avKv/PbTi5eycNGqWJSuy681GMAb+zfivG66QtNUUW5DpVt1Vxtpa2m8nltjQufCq2uxq/GKlsNFVptjXK5R8/Sedp9Nd4vYtYzGPV6co+dw2ioqKtm1CtzjKrm+V+Ll6FMV2PQw1Cuw8ax+lQUa1sNhnJzf7u09TdcQvxwbu1bY9BDYWWFX78wjn25CqPegEKlpMczd6H63cBRU2Qo12F9XYysr8WoXFfjy6pBW3uM9OVV52SncQPoOLY/V5IySTtyDmu75r+egL5ch7XddTGqbJO+lhjptTo0v4+RnQZ9WfXPLYDDCzfgEuiFd5h5eoLRaOTwfzYQMWUUCqvmdfFj0P4uRn/QjyrKdaid/jhGHcYOoP2Y/lxNyiT9yDnL8cryC9GXainOyGPkv5+nODOfmPcW4+DthlfXNvXVvFuiLdOh+d15oLG3QVuqrVm2XFdtDQ5bew3aMh0mk6na4P3Jveewd7alTVhQtdeHdAnkX2te4UpuIQc2HqOVd8vIdtCW6WusTWJrb0N5LedVS1ehvcHftXLdDf72Vz/XVHZVf9e0BYVkxp4kfMZDqF0cObvwF+J/2ESXJyZgMhqJX7KBDg80v88jIUTLJYMedaxjx46EhYURFRVFnz596NGjB9HR0bi6ugLmtNE1a9aQmJjIfffdR8+ePbG2tmbWrFksWLAAT09PsrOzmTRpEuvXrycvL48333yTpUuX0rp1a3Q6HTpdzT/WU6dO5dVXzXN2V6xYwUcffcScOXNuqs7dunVjxw7zOhahoaH88MMPKJVKcnNzmTBhAgMGDCAsLAw7OzsOHjxI7969OXz4MFZWVo0+4NHPJ5BHOkcAEF+Qg9ZgwFZlbdl/7XG5wVDjteUGA7aq6qeArcqassqyX5z4jcmh4XwSNYac0mL2ZyTh51CV6RFfYE7zLDXo+T7uGF8PvRNfe0dSiwvrtpG3KOu3k1xYsg4A57ZBKDVqKsqrLpqvPVZqan7JVNqoMZRVv8A2lGlR2lQvm3ssDpW9Lc7tW9daBytrazx7d+XQW/NxCPDG4brskaYgbf9JTn1nHtBr1d4cI8N1Mbr2WGVbczBCpVGjL/9djMq1qDRqyguKuLz1IAPefqLW9809k8i5n7bR97UpOAf5cPVyOoc//RHNjAdwDmpaMUrad5Ij35pj5N4hCJWmet/QVz6+fnrBNSobtWX/NYYyLda/K6tQKHBt7UPmyQTO/D979x3W1Pn2Afwb9hYHgmwE3OKiLtx7oQJaatUud2tr/dXW1rbW1do62lpbRYta7etoleHWWrG498Kt7D1EIEAgIcn7RzQQiYKtcAJ8P9flVXJyCPd5ejLOnfu5n5B/0H5CzeopEHfyOi5uUI2RTQsXGJpoHveTn58+bgAw1DJGMkkxDJ86565u/Qu5iRno98Wb6ovZB0cuwNrZFo08HV/q8VSFhFPXcXlTmfPIuPLnkb5J+dcjbWMkEolg7doE6VHRuBX6D9qNH6yextLCrzf0jQxVfWK6tkbatfs6m/QwNjVC0VMJjqICKYy1JASNTTT3LSoshrGpUblqxQtHrsN7gFe57U9YN7JCC293bPk6DHPWTta6T01ibGqIooKnxrCwGCZaXstrm7Sz13Fni+q5Zu3pono/1/Ler/W59oz3/if76hkZoolPe5jZqSpoXIf3wJWVvwMAko5dgIWjLeq56/7rEVFtoJAzuVgZTHq8ZHp6elizZg3u3buHCxcu4O+//8aGDRuwd6/qonPs2LEAgKZNm6JVq1a4evUqDAwMkJSUhClTpqgfRyQSIT4+HtevX0evXr3g6uoKADAyMoKRUfk3qOPHj2Pbtm0oLCxEiZYL/Ocp21gxOzsb8+bNQ3x8PPT19ZGbm4vY2Fi0b98eEydOxLZt29C5c2ds3boV48ePf9HheelOpybgdGppmeoMry5wtrTG+bQkAICzZT3kFBeVq/IAgOT8XPQo0+/DWF8fjc3Mkfw4afGwqBDfXz6l8dgxuc+e56wEIILuvfDYdvGCbRcv9e3bwbuQn5gOG2/VCgD5SWkwtDIv900PAJjb2yD9zDX1bXmxFEWZ2eUak6WfuQbbru2e+UH6CaVcjqKsRzqX9HDo7gWH7qVjdCUoBHkJabDvrOpEn5eQDmMr83JVHgBg6dAYSSdLx6ikWIqCjGxYOjRGTkwyinPEOD7vFwCAXFYCuVSGvz9Ygf4//g+5CWlo0NwF1m72AADrpg6wbuqArFsxOpf0cPHxgotP6Rid/SUEOQlpcOqqGqOchHSY1DMv9+08AFg5NkbciTJjVCRFfkY2rBwba/1bSoUC+RmPXvIRVD3XHl5w7VE6Rqd/DkFOfBrLw/bNAAAgAElEQVSc/+0YpWejnkPpGEXtOobUaw/Q78u3NCph0m/EIuN2PFKv3gegamiaE5eGnLg0dHp72Es/zv/C2ccLzmXOo/NrVOeRYxfVGOUmpMP4WWPk0BgJJzXH6MlzTRuFXIGCx+dRvcel+pqv0br3el2WjUMDKOQKZCZlw8ZR1S8hJSYddi7lG0PaudogJSYdLi0cnrnfo4xcRF+Lx6sfPv+cUCgUeJhS855/2tg5NYBcrkB6UjZsH49h4oMM2Ls1quA3az67rl6w61r6XLuxPgT5iWnqFVbEiekwesZ7v4VDY6Se1nzvl2Rmw/zxc83C0RYo+35f5ufsW7HIuRePE7NVr0eyAgnECWnIT0xD8/G69XpERHUHG5lWkWbNmmH8+PHYtGkTLC0tcf78+Wfuq1Qq0bx5c+zevVv9LzIyEm3bVm5Oe3JyMpYuXYqVK1di3759+Oabb7RWgzxLVFQUPD09AQALFixA586dsXfvXuzevRt2dnYofjyVYciQIbh27Rpu3bqFc+fOYcSIEZX+G9XlZEoceju6wd7cEmYGhhjl3gonkuO07nsxPRmOFvXgbesAQz09jHZvhURxLlILxAAAe3NLmOgbQF8kQvcmzmjbyBYHY+8BABwsrOBsWQ8iqJIlr7doh0fFEqQU6FaVhza2Xdsh7dRlFKRkoKRQgoT9x2HXXfsSoA07tERBcgYyL92CQiZD/L5ImDvaavTzKH6Ui5y7sbDtpvkYeTGJyL0fD0WJ6kI/8dBJyPIKYOlW8bQroTl0b4ek41cgTs6ErKAID/Yeh2MP7WNk27EFxMkZSL1wC3JpCe7vjoSVoy0s7BvBxssDfVd8iB6LpqPHoulo5tcH9VyaoMei6RDp6cHazR7Z9+KRG58GAMiNT0X2vQSNHiG6yqVHO8RGXkFuciakBUW4vfs4XHtqHyMH7xbIS8pA0nnVGN0Mj0Q9J1tY2TeCUqFE9NGLkBZIoFQq8TA6GQ+OXEDj1qVNg+UlcsilJVAqAYX88c8K3Z5GBgBuPdsh5p8ryE1SjdHNsOPPXG7X8ZUWyE3MQOLjMboRFglrZ1t1P49bu08g/tQN9Jn3RrmEQJfpozFsxXsYvHQ6Bi+djvpN7dHavzfaBvar8mP8r5x7tENc5BXkPT6P7uw+DpfnnEe5SRlIfvxcu/3UeRQTUXoeZUcnI+bvC2jcSnUeWdg2QKPmzriz5zjkshLkJWci6ewNNOnQrDoP94UYmxrBq0cLHNz8D4olUsTcSMSN0/fgPaD8ZwPvAW3xz65zyMnKQ26WGMd2ncUrg7w09rn4dxRcWzuikb1mw8lLR6PwKEPV3yo7PQcHNv4Dzw6uVXZc1cnY1AgdezVH+IbjKJZIcT8qCVdP3Ue3QeWX/VUolJAVl0BeooBSqfq5RFYzpmRWRpNu7ZBy4gryUzIhKyxC3L7jaOKj/blm07EF8pMzkHHxFuSyEsTuiYSFoy3Mm6hej+x92iP11FVIMh9BXixD/IGTaOSlei61mjQaXZe8h84LpqPzgumwcrVHU9/ecPfT/dcjIqq9WOnxkqWnpyMlJQUdOnQAAKSlpSE7OxuOjqoyv5CQELz77ruIi4vDrVu30L59exgYGCA+Ph5nz55F165dAQDXr19H27Zt4ePjgzVr1iAuLk5jeouFRWkzyPz8fBgaGsLGxgYKhQI7duyodLx///03tm/fjg0bNgAAxGIxHBwcIBKJcOrUKcTHx6v3NTQ0REBAAGbMmAFfX1+Ympr+5/F62aKy0rE/9g7mde4DI319XEhLQuj90iZaS30GYW/MHZxOTYBYJsVPV0/jjZYdMMOrC6JzH+KXa2fV+7ZtZIeR7i1hrKePOHEOll88AfHjipF6RsZ4q3UnNDA2RbG8BPdzHmLlpZOQ63g/DwBo0MYTToN9cH3lZihkMjTq2Aouvn3V91/86hc4DesJ2y5eMLI0R6vpr+LB9gO4uzEUlm4OaDlFs8Fd+tnrsGrqpFqatgyFTI7oPw6iKPMRRPp6MHewRev3X4extWYzWF3U2MsDTYf54Ox3m6GQymDn3Qqefn3U90fOWwOPET3g0N0Lxlbm6DTzVdz4/QCurg+DdVMHdJihGiN9QwPoW5c+Vw1MTSDS14PJ420NW7ii2eg+uPzLnyjOK4CRpRk8RvSATRvdLLcvq0k7DzQf7oN/vt4MuVQGx1daoXVAH/X9h+auQcuRPeDi4wUTK3N0n/UqLm8+gHNrw9DA3QHdZpaeR8kX7yDqz6NQlMhhUt8SHoM6w3NQaYPJ49/+jsw7qteih/cTcWnDPvSZ9yYat3KtrsP9V5q080CLET6IWLIZcpkMTq+0QpsxfdT3H/h4DVqN6gHXHqox8vnwVVz67QDO/hKGBh4O6PZ+6Rhd/yMCegb62D/7J/W2lqN6ovXonjAyNyn7Z6FnoA9DM2MYmWlu10V2Xh5oNtwHx79RnUcOr7RCK/8+6vv/+nQNWvj2gLOP6rnW9YNXcXXLAZx/fB51fq90jFIu3sGNx+eRaX1LuA/qDPcy51HndwNwKXgP9s5YBmMrc7Qa0xeNWzetzsN9YQHvD8WOlXsx/9UfYGZpijGzhqKJqw2ioxKwft529VK03Ud0xMO0HCyfuh4A0GVoB3QfoTn99OLfUeg7tmu5v5EWn4W9wRGQ5BfB1MIELTt7YMSkvuX2q6kmzB6MTd/tx4ejf4KFlSkmzB4MBzcb3LuWiB/n/oE1h+YAAO5dS8DyD7epf2/6oOVo3t4Zn6wSvqr1ZWjY1gMuQ3xwebnqfa1xp1ZoOqqP+v6zX66B6/AesOuqeu/3evdV3N16ADeDw2DV1AFtppU+1+x7dkDRwxxcWBKseuw2Hmj2+hAA0OjRAwAiA33omxrDoAa8HhHVRGxkWjkipbIGXKXVIMnJyfjyyy+RnJwMExMTKBQKjB8/Hq+99hqaN2+OmTNn4ujRo5BIJBqNTK9fv47ly5cjNzcXMpkMTk5OCAoKgp6eHiIiIrB69WrI5XLo6+vj22+/RfPmzTUamS5ZsgQRERGoX78+evfujfDwcERERCApKQkBAQE4d+4cANWStU+myEgkEri7u2Pq1Knq3hynTp3CwoULYWpqirZt2yIqKgrz5s1TL3ubnp6O3r1749ChQ+opN//GxEM7/9tA13Impi82RakusjTiS1dFzJnWrpAe6x0rVIu+7K4yPew4SJVhZcRxqsi2B7W/38jLsLC5r9AhUA1nY2MpdAgvRb/1B6v170VMHVqtf+9lYdKjGpVNUtRUu3fvxv79+7F+/fr/9DhMejwfkx4VY9KjYkx6VIxJj4ox6VExJj0qh0mPijHpUTlMetB/VVuSHn2CDlXr3/tn+pBq/XsvCz8SU6VNmjQJCQkJWLt2rdChEBEREREREVWISY9qdPfuXaFD+E+e9P0gIiIiIiIiYbGnR+WwsJeIiIiIiIiIaiVWehARERERERHVMAo5Kz0qg5UeRERERERERFQrsdKDiIiIiIiIqIZhT4/KYaUHEREREREREdVKrPQgIiIiIiIiqmFY6VE5rPQgIiIiIiIiolqJlR5ERERERERENQxXb6kcVnoQERERERERUa3ESg8iIiIiIiKiGoY9PSqHlR5EREREREREVCux0oOIiIiIiIiIBCGRSPDZZ5/h5s2b0NfXx9y5c9G3b99y+/39999Ys2YNpFIplEolAgIC8M4771T4+Ex6EBEREREREdU0CqXQEbwUGzZsgIWFBY4cOYK4uDiMHz8ef/31F8zNzTX2s7Gxwdq1a2FrawuxWAx/f394eXnB29v7uY/P6S1EREREREREJIiDBw8iMDAQAODq6oo2bdrg+PHj5fZr164dbG1tAQCWlpZwd3dHcnJyhY/PSg8iIiIiIiKiGkavmis98vLykJeXV267lZUVrKys/vXjpqSkwMHBQX27SZMmSEtLe+7vREdH4+rVq1i4cGGFj8+kBxERERERERE91+bNm/Hzzz+X2z5z5ky8//77z/w9Pz8/pKSkaL3v9OnTLxxHRkYG3n33XXz11Vfqyo/nYdKDBDG3c/kMIZXKKuJTk/67fBlnMFakVf0ioUPQefLaMV24ys3Y6VDxTnVcQTLf2ypyqv9hoUPQeZOKOuOTaweEDkPnLWs3TOgQqBqIqvlN+s0334Sfn1+57RVVeYSFhT33fnt7eyQnJ6NBgwYAgNTUVHTp0kXrvg8fPsTbb7+NyZMnY+jQoZWKm+8+RERERP8BEx5ERFQX/NdpLM8yZMgQ/PHHH2jbti3i4uIQFRWFlStXltvv0aNHePvttzF+/HiMHTu20o/PrwGJiIiIiIiIahg9hbJa/1WVSZMmIS8vDwMHDsS0adOwaNEiWFhYAABWrVqF7du3AwDWr1+PuLg4/PHHHxg1ahRGjRqFkJCQCh+flR5EREREREREJAgzMzP89NNPWu+bNWuW+ue5c+di7ty5L/z4THoQERERERER1TDVvXpLTcXpLURERERERERUK7HSg4iIiIiIiKiGEbHSo1JY6UFEREREREREtRIrPYiIiIiIiIhqGD05Kz0qg5UeRERERERERFQrsdKDiIiIiIiIqIbh6i2Vw0oPIiIiIiIiIqqVmPQgIiIiIiIiolqJ01uIiIiIiIiIahhOb6kcVnoQERERERERUa3ESg8iIiIiIiKiGkbESo9KYaUHEREREREREdVKrPQgIiIiIiIiqmHY06NyWOlBRERERERERLUSKz2IiIiIiIiIahg9OSs9KoOVHkRERERERERUK7HSg2otca4Ea745hGvn42BpbYoJ03uh5+BW5faLuhSPnRtPI/ZuOswtTRAUNl3r4928nID57+1AwFvd8Pq0nlUdfpUoyJNgy/L9uHUxFhb1TOE3uS86D2hdbj+lUonQ9cdw6sA1AIDPsHbwn9oXIpEIAHDt9H2E/3oMD9Ny4eDeGBPnDIO9q025x/n+f1tx90o81vz9KfT1a0aOtTrG6PSh69iyfD+MjEpfgt9b+iqat3ephiN8+QrzJPjj+324dykG5vVMMeydfujYr025/ZRKJfZviMC5g1cBAF2GtsfwSf0gEokQE5WAXz/frrG/tEiGN78MgFfPltVyHFVJnCvBD0sO4/LZONSzNsVb7/VC3yHlj+vaxQRsCz6DB3fSYWFlgs17ppbbJ3z7JYTvuISc7EI0trPC/BWj4ejSoDoOo0qJcyVYteQwrpyLg5W1Kd58txf6aBmj6xcTsH3DGUQ/HqONuzXH6J1R65GTXQg9PdVzsaWXPRavHlstx1BdAtq4ItDLHcYGejgRm4ZVp25CplCU289AT4R5fdujWaN6sLM0w0f7z+Jaarb6/jc6euL19u6QyUt/d2roCaSKJdVyHFXpta7OmODjBhNDfRy7lY5l+29BpuUb0dYO9TC1rwda2FtBrlDiSlw2vj90Bw/zpQCAjq718U4vdzRvYglxUQn8V52o7kN56XLypfjit2s4fTML1hZG+F9AC4zo6vDM/aUlCvgtOI6CohL8s2KAevuxq+n4PuQOUh4WopmjFRa/5QUPe8vqOIQqkxJxGsl/nYRCJkPD9q3Q9DVf6Blqv1zKuRON2D/3ozg7FxauDvCY6A+ThtYAAFlBIWJ27EPu3WgAIli39EDT10bAwNQEUnE+4nYeRO6DOCiKpTCzbwxX/yGwdHOqxiOl2oI9PSqHSQ8d0a9fPxgZGcHIyAgymQzvvPMOxo6tXR/SqtuvK4/AwFAPG/a/h7j7Gfjmo11w8WwM56aNNPYzMTFCvxFekA6UIXTzWa2PVVIix8YfI+DZukl1hF5ltq86DH0DfSwPnYWkB+lY/dmfcHRvDHs3zYTFib1XcO3UPXwZPAmACKs+3oZGTazRe2RHpCdlY+PXu/H+t4Fwa+WAv3acxZrPd2HhlmkaiY1zR25ALi//IVzXVdcYNW3lgE9WvyHAEb58IT8fhL6hPhb8ORvJ0WnY8MUfsG9qC7unEmFn91/GjdN38VHQFIhEwLpPt6GBnTW6j+iEpm2dsXTPXPW+D67FYeP8P9Hc2726D6dK/LLsKAwN9LH98LuIvpeBrz4MRVNPG7i4P/V6ZGqIQSPboPegFvjjt3PlHudQ+HUc3hOFhT/4w9mtIVKTc2FpaVxdh1Gl1i4/CkNDffzfoXcRcy8DC2eHwk3LGBmbGmKgr2qM/tQyRgAwf6Uf2neumUnEing7NMJr7dwxZ/85PCwswsKBnfBmJ08EX7irdf8baY8QeiMOX/bvoPX+f2JS8e0/16oy5GrXxb0hJvq4YeaWi8gSF+PbwPaY3McDa4/eL7evpakhdl9Owmd/ZkGuUGLOsJb4YlQbzN56GQBQJJVj39VkHLmhhzd7Nq3uQ6kSi7fegKGBHk78MBB3EvMwfdV5NHeygqeD9oTFxkPRqG9hhIKiEvW2uPR8fPzrFaz7sDPaNbXGxkMxeG/1Bexf0gcGNeRLjqc9unUfyX+dQOsP3oaRtSXurN+OxP0RcBk9qNy+svwC3P11B9zHj0KDts2RsDcC9zb+Ca+PVUnYhL1HUVIoQceFswEAd3/dgcQDx+AWMBSKYiksXOzhGjAEhpbmSD99GbfXbkWnRbOhb1I7Xs+JdE3NfFWqpX766Sfs2bMHq1atwsKFC5Geni50SDVWkUSKc8fuYdzUnjA1M0LLdo7w7umByEM3y+3r2boJ+gxtDVt762c+3p5tF9CusyscXBpWZdhVqlgixeXjdzDqnV4wMTWCR1sntOvuibNHbpTb98xfURgwtgvq21ihvo0lBoztgjOHrgMAbl2IgUdbJ3i0dYK+vh4Gj+uGnCwx7l2NV/++JL8I+7acRMC0ftV2fC9DdY5RbVEskSLq5B0MfbM3jE2N0LSNM1p388TFo1Hl9r3wdxR6B3SFtY0V6jWyQu+ALrjw13Wtj3vxyHV49WwBY1Ojqj6EKlckkeJUxD1MnO4DUzMjtGnviK69PHD0wK1y+zZv3QT9h7VGE4fyr0cKhRJbfz2DqbP7wqVpI4hEItg7WsOynml1HEaVKpJIcTriHiZMU41R6/aO6NLLA8cOah+jfsNaw07LGNUFgzwdcfBuIuJz8pEvLcH/XXmAQZ6OWvctUSgRejMON9IfQaGsO98GDmtnj71XkhGbWQBxUQk2HY/B8Pb2Wvc9+yALEbfSUSiVo7hEgZ3nE9DWqfTcupWSh0PXU5HyqOZXvwBAYXEJjlxKxQejm8PcxACdPBugbztb7DmTpHX/pMxC7D2bjKnDPTS2n7qRiU6eDdDJswEM9PUweag70h8V4cLdbK2PUxNknruKxt06wsy+MQzMTOE4pDcyzl7Vuu/Dq7dh1qQxGnVsAz1DQzgN74vC5DQUpmUCAIofPkKDdi1hYGoCA1MTNGjXEpLUDACASaMGsO/vA6N6lhDp6cGuhzeUcjkkGQ+r7Vip9hAplNX6r6Zi0kMHNWvWDFZWVkhPT0dMTAwmT56MgIAAjBw5EiEhIQCANWvW4JtvvlH/zqNHj9ClSxcUFhZCKpXiu+++w5gxYzBy5Eh8/PHHKCgoAAB8+umnmD9/Pt544w0MGjQIn3zyCZSPPwhNnDgRx44dUz9m2dsZGRn44IMPMGbMGPj6+iIoKKi6huNfSUl4BD19Pdg7l5Z8u3o0RmJM1gs/VkZqLiL2RWHsO91fZojVLj0pG3r6erB1Kk3cOLo3RkpcZrl9U+Ky4OTeuHQ/D1ukxD1j7JRKKJVKpMSWPk5Y8D/oPbIjrBqYv7wDqAbVOUaJD9Lxv1E/4MuJQdi/5WSNrIoBgMxk1ZjZOJaOWZOmtkjXMmbpcZmwb2qrvm3vbov0+PL7FUukuH7iDrwHelVN0NUsKeER9PX1NKaguHnaIP4FX4+yMsTIyhAjPjoLE4evw1uj1uP3daegqMEfQp5IfjxGDv9xjJ5YMX8/Xh/0C758fydi7mW8rDB1gkt9C8Rki9W3ox/moYGZMayMDf/V43VzbozQCQMQHNATvi2dX1aYgnKzscD99NIxup8mRkMLY1iZVjxGHVzqIzYzvyrDE1RcWgH09UVws7NQb2vhZIUHKWKt+y/ZdgMf+jeHiWH5SwYllGV+BpRK4H5y3kuPuboUpmbA3NFOfdvc0Q4ycT5k+YXl9pWkZsDMoXRffWMjGDdqoE5s2PXqgkdRd1FSKEFJoQQPr96CdStPrX+3IDEVihI5TGxq/jRFIl3FpIcOunTpEurXr48WLVpgzpw5+OyzzxASEoJt27Zh/fr1iI6OxujRo3HgwAGUlKhKDfft24d+/frBzMwMwcHBsLS0xK5du7Bnzx40btwY69evVz/+/fv38euvv2Lfvn24efMmTp8+XWFMc+fOxcSJE7Fr1y6EhITg+PHjOHXqVJWNwX9VJJHCzFyzRNDMwhhFhdIXfqyNPxzFuKk9YGpWs79xLpZIYWqmOSam5sYo1jImxRIpTC2MNfeTSKFUKtGykyvuXUvA3avxKJHJcXDrachL5JAWq87FuLupiL6RhL7+3lV7QFWgusbI08sJX22cghVhH2L6Qn9ciLiJv3Zon1ql66QSKUy0jZmkuNy+xUVSmJR5XpqYm6jHrKyoU3dgbmUKd6/aMT2hqFAGM3PN1w9zC2NIXvD1KOvxRdzlc3FYu+NNfLc2EJF/3cHh3eWramoaSaEMpk+Nkdm/GCMAmLNoODaET8HGPVPRtpMz5n+wC/niopcVquBMDfVRIJWpbxdISx5vf/EZy//EpOKdXccxZuvf+P5EFCZ08EDfpjV7GicAmBrpa0zFyH/82mtmpP/c33NvbIF3ervj5yP3qjQ+IRUWl8DCRDP5Y2FqoDFeTxy5nAqFQomBHcufE91a2eDi3Wycv5MFaYkC6/bfh0yuQJG0ZibwAUBeLIW+iYn6tr6pyePt5d/P5MVSGJhqvvcZmBpDXqR6zbJwagKlXI7zn3yL8598C5GeCHa9Opd7nBJJEe5vCYHTsD4wMDUpdz9RRfQUymr9V1Oxp4cO+eCDD6BUKpGQkIBVq1YhISEB0dHR+N///qfeRyaTISYmBgMHDoSHhwciIyPRv39/hIWF4bPPPgMAREREID8/H4cPHwYASKVStGjRQv0YAwYMgLGx6oW6VatWSEhIgI+PzzPjKiwsxPnz55GdXVqyWFBQgOjo6Of+npBMTI1QWKD5JiUpKIbJCyYuLpx4AEmhFD4Dan4jRWNTI0gKNcekqFAKYy1jYmxqBElB6cVGUUExjE2NIBKJYOfcCG99OgI7Vh1GbnYBugxojSYujVDfxhIKhRLbfzyEwJkDa0zj0rKqY4wAwMa+vvr3HJo2xvA3euCvHecwdHzNqyYyMjVCkbYxMy0/L9nYxAjFZfYtO2ZlXTxyHZ0GepXbXlOZmBmisEDz4r2woPiFE6lGJqq37DETO8PC0gQWliYY6ueFC6djMNSvZlfFmJoZajyfgH83RgDQql1pQ8ZX3+qCiP03cfNqMrr0rJn9Yfq522N2D1Vj4Ki0bEhkcpiVSXCYP26ILJGVv2itSEJOaUXDrYwchN2IQy+3JjgWk/ofo65eg9raYe4IVaPya/E5kEjlMDcuM0aPfy6Uyp/5GI71TfHD+I744dAdXEvIqdqABWRmbID8IpnGtoKiEpibaF4SFBaXYMXO21j3YfkLdQBo2sQCSye1w+KtN5GZW4SRXR3g3sQStvVrzoV75vlriN6+FwBg5eEMfWMjyItK36Pkj5P3+sbl38/0jY1QUqT53icvKoa+ieo16+6GP2HmYIsW014HlErEhR3G/d9C0HxyYOn+UhnuBG2FhasTHAf3eunHR0SlmPTQIT/99BOaNWuGgwcP4rPPPsPatWtRv3597N69W+v+fn5+CA8Ph6OjI8RiMby9Vd+sK5VKfPXVV+jWrZvW3zMu8+Ktr68PuVyu/llRpvt78ePMtkKhgEgkwq5du2Bo+O/KZ6ubvXN9KOQKpCRmw95JVS4Ydz8TTk81Ma1I1MV4RN9Ow6ThvwAACvOLoacvQkJ0Jj5d5v/S465Kto4NoJArkJ6UDVtH1ZgkPUjXuuqKvWsjJEWnw62lag50UnQG7F1Lx65T75bo1FuVCCrML8KpA9fg2qIJigqLEX83Fb8uCgcA9fn06djVmLrAD55eul06XR1jpJ1Io0y4JrFxUI1ZZnI2bBxUY5YSkw5bLWNm62qDlJh0OLdwKN3PRXO/Rxm5iL4WjzGzhlV98NXE0bk+5HIFkhMewcFZlfCKvZ8Jlxd8PXJ0aQADQ32UzQXVlsSQg7YxuvfiY6SVCKq6+xoqIjoFEdEp6tvz+rRH04ZWiIxNAwA0bWCF7MJi5BXLnvUQlaYEVONVw/wVlYa/otLUtxf6t4WHnSWO3lL1RvO0tcTD/GLkSbSPkV09E/z0hjc2HY/Boes1K+HzolztzCGXKxGXng9XW9UUlzuJeeVWXYlPL0DKQwkmfnsGACArUUAskaHn7CPY8bkPHBqZYbC3PQZ7q94D8wplCDn5N9q61ZxeOzad28Gmczv17XubdqIgOQ2NOqmSjIXJaTC0tIChhVm53zVt0hiZ566ob8uLpSjKfATTJqpprwVJaWgaOAL6xqokiF2PVxD1/Qb1/gpZCe6u3wYj63pwH+dbJcdHdYOellWpqLya91VsHTB06FD4+Pjg0KFDMDExQXh4uPq+6Oho5OervpkZNGgQLly4gE2bNsHPz0/94bdfv3747bffUFSkKufNz89HdHR0hX/X2dkZUVGqMukHDx7g9u3bAAALCwt06tRJY4pMamoqMjPLz8XXFSamRujSpxl2/HoSRRIp7lxLwoUT99F7SPmlRxUKJaTFJZDLFVACkBaXQCZTJYLGTe2B1X9Oxootb2LFljfh3dMdA0Z64b3Ph1bzEf13xqZG6NCzOfZuOo5iiRQPohJx9fR9dB1YfmnRroPa4u+d5/EoU4ycLDGO/HkO3YaUfi8QNEoAACAASURBVJMcfzcVCrkC4pwC/N+KA/Dq7gk750YwNTfGd7s+wBfBk/BF8CS8/63qG415696BW8tnL4enK6pjjADgxrlo5GWrnsdpCVk48PtJtPNpVj0H+ZIZmxqhrU8LHNr8D4olUsTeTMTN0/fg3b9tuX29B7RFZMg55GblIfehGJEhZ/HKIM0KhUtHo+DayhGN7GvP3GYTUyN07+uJ39edQpFEipvXknEm8gH6Dyu/hPaT16OSEjmgVGq8HpmYGKL3wObY9fsFFBZIkZkuxsGw6+jSo2ZWMJRlYmqEbn09sXW9aoxuXUvGueMP0Hfo88dI+dQYZaTl4da1ZMhkqulkIb+fR16OBC29dP/1p7KOPEjC0GZOcLa2gLmRAcZ3cMdf97U3oQQAQz09GD6uvDMo8zMAdHduDIvHlSLNberBr7UrTsfX/CbqB6+lwLeDA1wbmcPC2ABv9WqK/VdTtO5rY2mM1W94Y9f5BIRdKj+OIgBG+nrq6kUjfT0Y6NXAzNBjZsYGGNCxCVaH30NhcQku389GxNV0jOym2QzX08ESEcv7I3RBT4Qu6IlFb3mhoZUxQhf0hF0DVfPkm3E5kCuUyBYX46vN19G3vS2aNrHQ9mdrBJvO7ZFx+jIKUzNQUihB4qFINO7aXuu+Ddu1RGFKBh5euQmFTIbEg//AzMEWZnaqRL6Fiz3ST1+CXCqDXCpD2qmLMHdQ9bRSyOW4G7wDeoaG8HzDDyI9Xo4RVTWR8unJ1CSIfv36ISgoCM2aqS584uPj4e/vj3Xr1mH9+vVITU2FQqFAw4YN8eOPP6JBA9UFweeff47Q0FAcPXoU9vaqbLtMJsPPP/+Mo0ePQiQSQSQSYebMmRg0aBA+/fRTtGnTBhMmTAAAjduJiYmYNWsWZDIZWrVqhbi4OEyfPh19+/ZFZmYmli5divv3Vcu9mZub4+uvv4a7+7/7sH0je0PFO/1H4lwJfvnmIK6fj4dlPRNMmNEbPQe3wq2rifj6f7uwNUK1jNiNywn46r0dGr/buoMTFq0ZV+4xVy8+gIaNLfH6tJ5VGntWUdUUYRXkSbB52X7cvhQLcytT+E/pi84DWuP+9QSsnvsHfjr4MQBVtVDoumM4eUDVtbzHsPbwn9ZXnVhb9v4WJEVnQN9AD516t8DYdwdoXWUjKy0Hn49bgzV/f1pjprtUxxjtWnsUZ49EoVgig1V9c3QZ0BrD3+gBfYPnzzd/Ufmy6hnzwjwJdqzci/uXY2FmZYrhk/qhY782iIlKwK+fb1cvRatUKrEvOALnDqm+HesypANGTO6nUa3w7Ttr0XdsV3QZqn15zZetVf3q6fUgzpXgh8WHcflcHKzqmeLtmb3Qd0hL3LiShC9nhSDs+CwAwPVLCZg7/U+N323b0RHL1r0GACjIL8ZP3/yFC6diYG5hgiGj2+L1yd2qtOKjur5EEudKsGrxYVw5rxqjN9/rhT6Px2jBhyHYFVk6RvNmaI5Rm46O+DboNcRHZ2H5l/uQmpQDIyMDuDVrjLdn9oJnKzttf/KlmbGzepMqAW3c8Fq7pjDS18OJuHSsOnkDsseVdcEBPbHtarS6OuT/AvvAzlLzm+rxO44hPV+CeX3bw9uhEQz19ZBZUIQ9t+MRfrPqVpkqSK6+AuPXurpgoo8rjA31cexWOpbtvwXZ45N564zu2HwyBn9FpeGd3k0xpY8HCqWa04P6L40AoGpsuuatVzTuuxyXjfc2X6ySuE/1P1wlj1tWTr4UX2y6htO3smBtYYj/BbTEiK4OuHjvIab9eB6X1pT/Yuf8nSx8EnwV/6wYoN42fukp3EnMg6G+Hga/0gRzA1vBzLjq/x9PKtI+5eZlSDl6CslHTkIhK0GD9q3g/pov9B5PJ7uyeDUcB/dSV4fk3IlG7J/7UZydAwtXR3hM9INJQ1WlWlHWI8Tu3A9xbCKgBCxcHOD26nCYNm6I3PuxuPnjJugZGqJs6V6r9ybAysP1pR3Lsna1p2KyKtjYaF+iuaYZOTW0Wv/envU1q9L9CSY9SBDVkfSoyaoq6UF1S3UlPWqy6kp61GSsnK1YdSc9aqrqTHrUVNWR9KjpqjLpUZsw6fF8tSXpMWpySLX+vd3BAdX6914WfiImIiIiIiIiolqJKXciIiIiIiKiGkZPUXOXia5OrPQgIiIiIiIiolqJlR5ERERERERENYyego23KoOVHkRERERERERUK7HSg4iIiIiIiKiG0eMSa5XCSg8iIiIiIiIiqpVY6UFERERERERUw4jY06NSWOlBRERERERERLUSKz2IiIiIiIiIahiu3lI5rPQgIiIiIiIiolqJlR5ERERERERENQwrPSqHlR5EREREREREVCux0oOIiIiIiIiohtGTs9KjMljpQURERERERES1EpMeRERERERERFQrcXoLERERERERUQ0jYiPTSmGlBxERERERERHVSqz0ICIiIiIiIqphuGRt5YiUSiVHioiIiIiIiIhqHU5vISIiIiIiIqJaiUkPIiIiIiIiIqqVmPQgIiIiIiIiolqJSQ8iIiIiIiIiqpWY9CAiIiIiIiKiWolJDyIiIiIiIiKqlZj0ICIiIiIiIqJaiUkPIiIiIiIiIqqVmPQgIiIiIiIiolqJSQ8iIiIiIiIiqpWY9CAiIiIiIiKiWolJDyIiIiIiIiKqlZj0ICIiIiIiIqJaiUkPIiIiIiIiIqqVmPQgIiIiIiIiolqJSQ+qk+Li4jBu3Dj069cPAHDz5k2sXr1a4Kh0k0QiQWxsLB48eKD+R0RERFRXXb16VegQiOgFiJRKpVLoIIiq21tvvYV33nkHK1euxO7du6FQKODr64v9+/cLHZpO2bp1K1asWAFra2uIRCIAgEgkwtGjRwWOjGqa4uJi7NmzB4mJiSgpKVFv/+STTwSMSjdIJJLn3m9qalpNkeg+nkcVu3DhAlq1agVzc3Ps3LkTUVFRmDJlCpycnIQOTWfExsbC3t4exsbGOHHiBG7fvo3AwEDUq1dP6NB0klQqRW5uLmxsbIQORWeMGjUK+vr6eP311+Hr6wtjY2OhQ9JZZ86cQXR0NCZMmICsrCyIxWK4ubkJHRbVMQZCB0AkBLFYjF69euH7778HAOjp6cHQ0FDgqHTPxo0bsW/fPjg4OAgdis7p2rWrOhFUllKphEgkwpkzZwSISnfNmjULMpkMXl5eMDIyEjocndKhQwet59ITt2/frsZodBvPo4otWrQIe/bswf3797Fp0yaMHDkSn3/+ObZs2SJ0aDrjww8/xK5du5CYmIivvvoKPj4+mDt3LoKCgoQOTWfMnj0bixYtgqGhIUaNGoVHjx5h2rRpmDRpktCh6YTdu3fj4sWL2LZtG3788Uf4+vri9ddfZ3LxKevXr0dkZCQyMzMxYcIElJSUYN68edi+fbvQoVEdw6QH1Un6+vqQyWTqC4309HTo6XG219NsbGyY8HiGkJAQoUOoUeLj43Hw4EGhw9BJd+7cAQCsWbMGRkZGCAwMhFKpxM6dOyGTyQSOTrfwPKqYgYEBRCIRjh8/jnHjxmHixIk4dOiQ0GHplCdfdERGRmLcuHGYMmUKRo0aJXRYOiU2NhaWlpY4dOgQunTpgs8++wyvvvoqkx5leHt7w9vbG7dv38aMGTOwefNm9OrVCx9//DHc3d2FDk8n7Nu3DyEhIRg7diwAwM7ODvn5+QJHRXURkx5UJ73++uuYOXMmHj16hNWrVyM8PByzZ88WOiyd0717dyxbtgzDhw/XKN308PAQMCrdwGTQi3FyckJ+fj4sLCyEDkVnHTlyBGFhYerbkyZNgr+/P6ZPny5gVLqF51HFSkpKcO3aNRw5cgSLFy8GAMjlcoGj0i3FxcXIysrCsWPH8OGHHwJQVelRqSfTxy5cuIDevXvD1NSUXw495eLFi9i6dSuuXbuGMWPGYOzYsTh79izeffddHD58WOjwdIKJiUm5SurnVTYSVRUmPahOGj16NBwdHXHs2DFIJBJ899138Pb2FjosnRMeHg4AGt8SsqeHptTUVCxfvhx37txBcXGxejvHSJOlpSUCAgLQs2dPjWkJ7MVQqqioCPHx8XBxcQEAJCQkVNjvo67heVSxWbNmYf78+ejatSs8PT0RGxurPqdI5c0338SQIUPQrVs3tG3bFomJibC0tBQ6LJ3i7u6OyZMnIyYmBh999BGKioqEDkmn+Pr6wtzcHBMmTMDy5cthYKC6pBo1ahT27NkjcHS6w87ODhcvXoRIJIJCoUBQUBA8PT2FDovqIDYypTrp4sWLTHLQS/H2229j2LBh2LhxI7755hts374dzs7OmDlzptCh6ZSff/5Z63aOU6m//voLX375Jdq0aQMAuHXrFhYvXowBAwYIHJnu4HlEVUEul0Mul7NPTBlFRUU4efIkmjdvDicnJ6Snp+Pu3bvo1auX0KHphKioKLRt21boMHReZmYm5s6di/Pnz0MkEsHb2xsrVqxAw4YNhQ6N6hgmPahOCggIgFgsxujRo+Hv7w87OzuhQ9JZDx48wLlz5wComndynqqm0aNHIzw8HL6+vti7dy8UCgUCAwOxc+dOoUOjGujhw4e4du0aAKB9+/Zo0KCBwBFRTfPw4UMsXboUqamp2Lp1K+7cuYMrV65g3LhxQoemMyQSCdatW4fExESsXLkS0dHRiI2NZYLxKbGxsYiOjsaAAQOQn5+PkpISWFtbCx2WzhCLxYiNjdWo8nzllVcEjEh3SSQSKBQKmJubCx0K1VGc3kJ1UkhICO7evYvw8HCMHTsWnp6e8Pf3x4gRI4QOTaeEh4dj5cqV6N27NwBg3bp1mDNnDkaOHClwZLrjyVxVMzMzpKSkoFGjRsjOzhY4Kt0jkUiwZs0anD59GgDQo0cPTJ8+ncuxPiUvLw8KhQIDBgxAQUEBcnJyeJFRBs+jin3xxRfo1asXtm3bBgBo2rQpPv74YyY9yliwYAFsbGzUTYTt7Ozw0UcfMelRRlhYGNatWweZTIYBAwYgIyMDixYtwm+//SZ0aDrhwIED+O6775CXl4fGjRsjISEBLVq00OjLREBkZKTW7U8+VxJVF3YkojqrefPmmDt3Lo4ePQpHR0d8/PHHQoekczZu3IjQ0FAsWbIES5YsQUhICIKDg4UOS6d4e3sjJycH48aNg7+/PwYMGIB+/foJHZbOWbx4MTIyMjBv3jzMmzdP/QGaSoWFhWHGjBlYunQpANWqUk+aLJIKz6OKpaenY9y4cdDX1wcAGBkZsQHlU+7evYs5c+aok9bm5uZQKBQCR6VbNm/ejJCQEHWvk6ZNmyIrK0vgqHRHUFAQQkND4eLigsOHDyM4OJjTXbQIDg5W/1uzZg3ef/99rF27VuiwqA5ipQfVWffu3UNYWBj2798PDw8PfPfdd0KHpJNsbGy0/kwqc+fOBaCa5tK5c2fk5+ejWbNmAkele6KiorB371717Y4dO7Ji6ClPLjLGjx8PgBcZ2vA8qtiThopP5OXlcWWSpzzdu6O4uJhj9BRDQ8NyUxGeJNJI9Txr2LChemUkHx8frFixQuCodM/vv/+ucfvBgwfYsGGDQNFQXcakB9VJfn5+KCwsxOjRo/HHH3+gSZMmQoekk5ydnfHTTz8hMDAQALBz5044OTkJHJXukUgkSEtLg1wuh56eHh48eMBlfbUoLCyEmZkZAHBVEi14kVE5PI+eb+DAgZg/fz4KCgoQGhqKbdu2ISAgQOiwdIq3tzeCgoIglUpx7tw5bNq0iRV6T7G2tkZsbKx6edHdu3ez/1kZRkZGUCqVcHFxwe+//w4HBwcUFhYKHZbO8/DwwM2bN4UOg+ogJj2oTvriiy/QqVMnocPQeQsXLsSSJUswcuRIiEQidO/enaXkT9m6dStWrFgBa2tr9YdDLutbnq+vLwIDAzF8+HAAqvnQo0aNEjgq3cKLjIrxPKrYlClTsGfPHuTl5SEyMhITJ07kGD1l9uzZCA4Ohrm5OZYvX45+/fph2rRpQoelU+bNm4ePPvoIsbGx6NevH0xMTBAUFCR0WDpj1qxZyM/Px5w5c7BgwQKIxWJ89dVXQoelc8r29FAoFIiKiipXjUZUHbh6C9UpiYmJcHJywoMHD7Tez2/n6UX1798fW7ZsgYODg9Ch6LzIyEicPXsWANCtWzcuffiUmJgYzJkzBzExMWjQoIH6IsPZ2Vno0HQKz6PnO3PmDLp161bhtrosOjq63Epk2rbVdXK5HHFxcVAqlXBzc2PlGb2wiRMnqn82MDCAs7MzJk+ezKphqnZMelCdMm3aNKxbt05rGSu/nS916dIldOrUiV23K+G1117Djh07hA6DagleZNB/5efnV24FCW3b6jKOUcX45dCzXb9+HRs3blSPkaenJ9555x02MiXSYawvojpl3bp1AICIiAiBI9FtYWFh6NSpk9aVWkQiEZMeZXTv3h3Lli3D8OHDYWxsrN7OD4Yqy5cvx8cff4wPPvhAPW2jrFWrVgkQlW6aNWsWVq1apfFt85NtdR3Po4rFx8cjLi4O+fn5GglrsVjM3iePZWdnIzs7G8XFxYiOjlY3LxWLxezH8JSpU6eqf5ZKpcjKyoK9vX2d//x05coVTJ06Fa+99hpGjBgBpVKJ69evY9KkSfj111/Rrl07oUPUOWfOnEFCQgJKSkrU25407CaqLkx6UJ2k7UKCFxellixZAqB8120qLzw8HABw6NAh9TZWDZV60junb9++Akei+xISEspti4mJESAS3cPzqGKXL19GaGgosrKyNBLWFhYW+PTTTwWMTHfs3bsXmzdvRkZGBqZMmaLebmlpicmTJwsYme55Orlx5swZHD9+XKBodEdwcDC++eYbDBw4UL1t4MCBaNeuHdatW4c1a9YIGJ3umTt3Lm7evIlWrVqxcpEExektVCdpK2P19fXVWAqRgHHjxmH79u0VbiOqCPsMPNuff/6JP/74AzExMRpVHmKxGG5ubmweWAbPo4qFhobC399f6DB0WlBQEKZPny50GDUOpwABgwcPxuHDh1/4vrpq8ODB2LdvHwwNDYUOheo4VnpQnfLk4iIuLg5jxoxRb39ycUGaioqKNG7L5XLk5uYKFI3uevDgAc6dOwcA6Nq1K5vhabFs2bJyH5a1bauLfHx84OLigsWLF+OTTz5Rb7ewsEDz5s0FjEz38DyqmL+/P8RiMWJjY1FcXKze/sorrwgYlW55kvB4+PChxhjZ29sLFZLOKdvT48mqG1KpVMCIdIOJicm/uq+u4gpkpCuY9KA6hRcXlRMcHIzg4GDk5+drfINaVFQEX19fASPTPeHh4Vi5cqW6z8m6deswZ84cjBw5UuDIdAP7DFTMwcEBDg4O2Ldvn9Ch6CyeR5V34MABfPfdd8jLy0Pjxo2RkJCAFi1aMDFUxtmzZzF37lw8fPgQenp6kMlksLa2xpkzZ4QOTWeU7elhYGAAFxcXfPvttwJGpBtkMplGP5in7yNNrq6ueOuttzBgwAAYGRmpt7OnB1U3Jj2oTuHFReUEBgZiyJAhWLx4MebPn6/ebmFhgXr16gkYme7ZuHEjQkNDYWNjAwDIzMzEpEmTmPR4jH0GKk8sFuPXX3/F7du3Nb593rJli4BR6QaeR5UXFBSE0NBQTJo0CeHh4Th16hRL7p+ybNky/Pbbb5g9ezbCwsKwa9cuJCUlCR2WTqnrDUufpaioSKMfTFnamizXdVKpFM7Ozrh3757QoVAdx6QH1Um8uHg+S0tLWFpaqle7oed7kvB4+mdSzQH38/Njn4FKmDdvHtzd3REXF4dZs2YhJCQErVu3FjosncDzqPIMDAzQsGFDyOVyAKoKxxUrVggcle5xc3NDSUkJRCIRxo4dC39/f8yePVvosAT3rKVqn6jrK5MxGfRili5dKnQIRACY9KA6ihcXlZOamorly5fjzp07GskhrkxSytnZGT/99BMCAwMBADt37oSTk5PAUeke9hmoWHx8PFavXo2jR49ixIgRGDRoEN544w2hw9IpPI8qZmRkBKVSCRcXF/z+++9wcHDgcqxPMTBQffy1tbVFREQEHBwc2K/qsbLTWp7GlcnoRUkkEqxbtw6JiYlYuXIloqOjERsbiwEDBggdGtUxTHpQncSLi8qZN28ehg0bhtu3b2PFihXYvn07nJ2dhQ5LpyxcuBBLlizByJEjIRKJ0L17dyxatEjosHQO+wxU7Ml8Z0NDQ+Tk5KBevXrIzs4WOCrdwvOoYrNmzUJ+fj7mzJmDBQsWQCwW46uvvhI6LJ3yxhtvIDc3F7NmzcJHH30EsViMzz77TOiwdAIrGZ6va9euWqexKJVKiEQi9oV5yoIFC2BjY4M7d+4AUDU2/eijj5j0oGrHpAfVSby4qJxHjx5h7Nix2LJlCzp06IB27dohMDAQM2fOFDo0ndGwYUP88MMPQoeh89hnoGKurq7IycmBr68vAgMDYWlpyQq0p/A8qtiT5tOWlpb47bffAEB9wUEqI0aMAAB4eXnhyJEjAICsrCwhQ9JZXOFGU0hIiNAh1Ch3797Fd999h5MnTwIAzM3NoVAoBI6K6iImPahO4sVF5TxZV93MzAwpKSlo1KgRk0OPXbp0CZ06ddJYSaKsJ6u5kAr7DFTsyXi8/fbbaNu2LcRiMXr27ClwVLqF59HzXb9+HampqejcuTPq16+P+/fv48cff8Tly5f5DfRjmZmZSE9PR4sWLWBgYIDs7GysW7cOoaGhuHDhgtDh6YwzZ87g008/5Qo3T3FwcBA6hBql7IotAFBcXKx15RuiqsakB9VJvLioHG9vb+Tk5GDcuHHw9/eHkZERhgwZInRYOiEsLAydOnXSWEniCZFIxKTHU9hn4MV4e3sLHYJO4nn0bGvXrsXGjRvh5uaG77//HuPHj1f/l0uNquzcuRMLFy5EvXr10KBBA8yaNQuffvopevTowW/wn7J8+XKucPMc7HlWOd7e3ggKCoJUKsW5c+ewadMm9OvXT+iwqA4SKZluozpGLpdjzJgxnANeCWKxGJaWlgCAlJQU5Ofno1mzZgJHRTXRmTNn0KZNGzx8+FDdZ+Cjjz5C9+7dhQ5NcJwjXnk8j55t6NCh2LJlC2xsbBAbG4sRI0bg999/R8eOHYUOTWcMHz4cP/74Izw9PXHp0iW88cYbWLlyJZP5Wvj7+yM0NBQjRozAvn37NLaR6kuzYcOGYePGjfjmm2/UPc84/VeTTCZDcHAwIiIioFQq0a9fP0ydOlXdTJiouvCMozpHX18fZmZmKC4uhrGxsdDh6CylUonAwEAcOHAAAOfxPsu4ceOwffv2CrfVZXK5HAkJCejWrZtGnwFS4TfMlcPz6PlMTEzUS2a7ubnBzc2NCY+nGBgYwNPTEwDQqVMnODk5MeHxDFzh5vnY86xy5HI5ZsyYgRkzZggdCtVxTHpQneTm5obx48dj8ODBMDMzU28fP368gFHpFpFIhCZNmiA3Nxf16tUTOhydVVRUpHFbLpfzg+FT9PX18ccff6iX9SVNZeeI5+fnIz4+nj2GtOB59HxisVijx1BxcbHGbU65U33rHB0dre4poKenp3Hbw8NDyPB0QlFREUxMTLSucDNv3jyhw9MZ7HlWOX369EH//v3h7++PTp06CR0O1WFMelCdJJfL4enpiZiYGKFD0WkWFhbw8/NDr169NJJDn3zyiYBR6Ybg4GAEBwcjPz9fvVoCoPrA6OvrK2BkuqlLly44dOgQv1V9jsjISMyfPx/6+vqIiIhAVFQUfvnlFwQFBQkdms7gefRsTZo00egxZGdnp77NPkMqRUVFmDJlisa2J7dFIhH7MQDo0aMHhgwZgoCAANSrV09jhRsqxZ5nlXPo0CHs27cPX3/9NQoKCuDn54fRo0fDzs5O6NCojmFPD6qT8vPzYWFhUeG2uu7nn3/Wup3lm6pvVXNzc7F48WLMnz9fvd3CwoKVMVp07doVOTk5MDExgampKftVaBEQEICgoCBMmTIF4eHhAIBhw4app5gRzyOiqpaYmIjw8HCEhYXBxMQE/v7+8PPzQ8OGDYUOTWex51nl3Lt3D5s2bcKePXtw8+ZNocOhOoZJD6qT/Pz8yjUy1baNiF6O5ORkrdu5/F+pgIAAhISEYPTo0eqkR9mfiedRZZ05cwbR0dGYMGECHj58iLy8PLi5uQkdls74+uuv8fnnn1e4ra47c+YMQkNDERERgc6dO2PMmDHo37+/0GEJSiqVwsjICBKJROv9pqam1RyR7lMoFIiMjERYWBguXLiA/v37Y8mSJUKHRXUMp7dQnVJSUgKZTAaFQoGioiL1PF6xWPzMN7C6bNmyZVq3c3pLKS5bVzm8KK2Yubk5srKy1Cu5nDt3Tr16EqnwPKrY+vXrERkZiczMTEyYMAEymQzz5s1jc+UyLl68WG7bhQsXBIhEt3Xr1g3dunVDUlISPvzwQ8ycORO3b98WOixBBQYGIiwsDB06dIBIJFJXmz35b10fn6ctXboU+/fvR7NmzTB69GgsW7YMJiYmQodFdRCTHlSnBAUF4eeff4ZIJEL79u3V2y0sLPD2228LGJluKtvHo7i4GP/88w/atGkjYES6Z968eRg2bBhu376NFStWqJetI03PWpaV0xJKzZkzB1OmTEFSUhImTpyIuLg4rF27VuiwdArPo4rt27cPISEhGDt2LABVb4/8/HyBo9INBw8exMGDB5GcnIxZs2apt+fn5/NC7ClKpRInTpxAaGgoTp48CR8fH3zwwQdChyW4JxXBd+7cETiSmsHa2ho7d+5EkyZNhA6F6jgmPahOmTlzJmbOnIlFixZp9GEg7Z7u3TFt2jSND4rEZesqq+yyrMXFxdi7d696SURS8fLywpYtW3D58mUAQIcOHXgh9hSeRxUzMTFRryzxhLZEUV3k5uaGPn36ICoqCn369FFvt7Cw0GhIXZfFxsYiLCwM4eHhaNCgAfz9/bFgwQJYpyv7iQAAIABJREFUW1sLHZpOuXfvHhwdHdVfDhUWFiI5OVm9JDKpzJgxA7Gxsfj7778xYMAA5Of/f3t3HlVVuf4B/LtBUKZAC0MNSVGCe521xOEnhZCGIoimImGrFNMknHDC9BpYJs6iKErc1Fg5MSjKMhIMyFlbV6mrMiii1xRJIUDgwOH8/iBOHAaFBt99PN/PWq519t7/fBdry2E/+32fpwRVVVW8n+ip418KpJPmzZuH6upq6OnpITMzE1lZWXB1dYWhoaHoaLJmYmKCO3fuiI4hKxxb1zz1tyXMnj0bEyZMwKxZswQlkpfq6moUFRWhbdu2cHJygkKhwN69exEZGYm0tDTR8WSD99GTWVlZ4cKFC5AkCdXV1di+fTsfxH5jb28Pe3t7ODs786GrCZMmTcLo0aOxbds2js5+jMWLF2Pfvn3q41atWmHRokWIjY0VmEp+4uLiEBERgcrKSri4uCA/Px/BwcH48ssvRUcjHcOiB+mkKVOm4KuvvkJpaSmmTp0KOzs7pKen4/PPPxcdTVbq9vRQqVT48ccfYWtrKzCR/DQ2tm7EiBGiY8nerVu38Msvv4iOIQspKSkIDAxEWVkZHB0dMWfOHMydOxcvvPAC1q9fLzqerPE+amjZsmVYtGgRsrKy0Lt3bwwYMABr1qwRHUsWdu3ahXfffRc7duxo9Dr7VQHp6ekaL4AePHiAdu3aCUwkT0qlUmNFlaGhIZRKpcBE8rRr1y7ExMTAx8cHANC1a1cUFBQITkW6iEUP0kkqlQrGxsY4evQoJkyYgI8++gju7u6iY8lO3Z4e+vr68Pb2hqurq8BE8rNo0SIANVM2XnvtNY6ta0LdXgzV1dWoqqripITfbNq0CRs3bsTAgQORlJSEd999FwEBAXj//fdFR5Md3kdPZmlpiaioKJSVlaG6uhomJiaiI8lG69atAWh+t5Gm2oLHpUuXMGfOHPXkjYyMDOzfvx8hISGCE8pDq1atcOvWLVhbWwMA8vLyoK+vLziV/BgYGDT4HcSfE4nAogfppIqKCigUCpw8eRLvvPMOAEBPT09wKvlhX4rmKSsrw927d6FUKqGnp4fs7Gx069ZNdCxZqduLoVWrVnjhhRf4h08dw4YNAwC4u7tj48aNLHg0gffRk8XHx+ONN96Aubk5AKCwsBBpaWkYM2aM4GTiTZo0CUDj323V1dVPO46srVq1Cjt37kRgYCAAoGfPnli8eLHgVPLh7+8Pb29vODk5AQBSU1M5hrURFhYWuHHjhrpYfejQIVhZWQlORbqIRQ/SSW5ubhgyZAhsbGzQr18/3L9/X/0GiGocP34cERERyM7OBgB0794d06dPh4uLi+Bk8rJ7925s2LABFhYW6i91SZI4srae8vJyZGVlAQDs7Ozw4osvCk4kL3VHaJuammocGxkZiYwmK7yPniwqKgqenp7qYwsLC0RFRbHoUcf8+fMREhKiXvFx7949zJ8/H1999ZXgZPJRWVnZoHhfv0GuLnvjjTewZ88enDp1CgAwffp02NjYCE4lP0FBQZg/fz5u3LgBZ2dntGnThquFSAgWPUgn+fv7w9fXF2ZmZtDT04OxsTHCwsJEx5KN1NRUrFixAv7+/ujduzdUKhUuX76MFStWwMDAQP1mg2r2qx47dowPX02oqKjAnDlzcPr0adjY2EClUiEvLw9DhgzBhg0b2DwYwLVr19C3b191kQOA+liSJFy5ckVgOnngffTnsNeApi5dumDcuHFYv3498vPz1d939DtDQ0OUlpaqi/nZ2dl8OVSPpaUl+vTpw4avTbh//z5KS0uxf/9+3Lx5Ew8ePMCxY8cwY8YMnD9/XnQ80jEsepBOqd1/Wbt64f79+xrX+eBaY/fu3Vi/fj1ee+019bl//OMf6NatG8LDw1n0qMPKyor3zWPs3LkTAJCWlobnnnsOAFBUVISlS5dix44dfNAAcPXqVdERZI/3UfNZWloiKSkJb775JgDgm2++wfPPPy84lbz4+/ujb9++8Pb2xnPPPYd///vfbNJdz4wZMzB16lTk5+dj8eLFSE9PZ0PcOlJTU7F8+XLo6+sjJSUFGRkZ2Lp1K7Zv3y46miwcOHAAn3zyCczNzdGuXTvMnj0bixcvxtChQzW2KRI9LZKq7qslomfcBx98gIiICDg7Oze4xi0JvxsxYgS++eabFl/TRWfOnEFCQgKcnJw03oKxMFTD09MTUVFRDbr/P3jwAO+//z7i4+MFJZOfnTt3ws/P74nndBHvo+bLycnBhx9+qO5Roa+vj/DwcHTt2lVwMvn4+eefMWfOHHTt2hVZWVno0aMHgoKCuGKonlu3biE9PR0qlQpDhw7l9o06xo0bh+3bt8PPz0/9+8fNzQ2JiYmCk8nDqFGjsHHjRnTv3h0XL17ElClTsG7dOowcOVJ0NNJRXOlBOiUiIgJAzYhIatrjOtuz672mEydO4MSJE8jNzVU3w5UkiUWP3ygUikbHHbZr1w4VFRUCEslXYmJigwJHY+d0Ee+j5rO1tUViYiJu3LgBoGYrB5u9avL29sbcuXPh4eGByspKrF69GhMmTGDxrB5ra2tMnjxZdAzZsrS01Dhm0ex3rVq1Qvfu3QEA/fv3h7W1NQseJBSLHqSz6k7cqMWJGzWKi4uRmpra6LWSkpKnnEbevv32W6SkpKBNmzaio8jS434ubNBZ4+TJk/j++++Rn5+P0NBQ9fmSkhJwMWYN3kcto1AooK+vD6VSqS5+8PsNuHPnDjp27IioqCj1yhcDAwN8/PHHiIqKEpxOXuqOh65lZmaGPn36YMGCBQ0e+HWNiYkJCgoK1D+js2fPwszMTHAq+aisrEROTo76O0xPT0/jmL+P6Glj0YN0UnR0NNauXQtzc3ONt/Pc3lKjQ4cOiIyMbPQaR41psra2RqtW/FXalFu3bmH27NkNzqtUKty+fVtAIvkxMDCAiYkJJEnSWEnVvn17TJ8+XWAy+eB91Hy132+cKNXQrFmzEBcXh65du2L8+PE4ePCg+lpCQgJHRdfh4+ODX3/9FePGjQNQMwpZX18fRkZGWLZsmc73rggMDISfnx9u374NX19f5ObmYtu2baJjyUZ5eXmDVYq1x/x9RCKwpwfppOHDh2P37t3o1KmT6Cha7erVq7C3txcdQ6jly5cjJycHLi4uGktbfXx8BKaSj7i4uMdeHzt27FNKIn+ZmZmws7MTHUOWeB81H7/fmubp6anewlL3c2PHuu7tt9/GgQMHNM6NGzcOMTExGDVqFI4ePSoomXwUFxfjhx9+AFAzcau2yTIRyQ9fT5JOsrS05B+Ef4ElS5Y88WHkWVdZWYnOnTsjMzNTdBRZau7DaHh4OD788MO/OY287d+/H/7+/rCwsAAAPHz4EOHh4Vi6dKngZOLxPmo+fr81re52jfpbN+of67pff/0VhYWFGr+Pare3GhgYiIwmG5WVleqGwVVVVYLTENHjsOhBOmnw4MEIDQ3FqFGjNCZucI9hy+j6QjGlUokOHTogICBAdBSt9+233+r8w+qFCxfUDxgA0LZtW5w/f15gIu3D+4jfb49TUVGh7itQ93PtNfqdr68vPDw81E2509LSMG3aNJSWlqJfv36C04mXlJSEZcuWoUePHlCpVAgKCkJISAhcXFxERyOiRrDoQTqpdgnrsWPH1Oe4x7DldP3NmL6+PtLS0lj0+AvoegENgEZT5Vp8e9gyvI/4/fY49fsM1P2s699n9b3zzjsYMGCAuvA6efJk9XbW5cuXi4wmCxs2bMDevXvRpUsXAEBubi5mzpzJogeRTLHoQTqJI2vpr/L666/jiy++gKenp0YTSk6UaBk+cAA9e/bEypUr4efnB5VKhcjISPTs2VN0LK3C+4jfb4/Dn03L2Nvb63zfrqa0bt1aXfAAgJdffplT3IhkjEUP0ikKhQKGhoYoKytr9DofVFuGb1WBLVu2AADWrFmjPidJEq5cuSIqEmmpoKAgfPrpp/D09IQkSXj99dcRFBQkOhZpmezs7EbPc3sLtcTPP/+MNWvW4OrVqxpbf7hiqMbw4cOxbds2jB8/HiqVCrGxsRg+fDjKy8uhUqn49ySRzLDoQTpl4sSJiIuLQ9++fSFJksZDOx9UW44TSmom2NCfxwIaYGpqilWrVomOodV4H0FjzLFCoUBBQQE6duzIVQ7UIkFBQXBzc8OVK1ewdu1afP311+jcubPoWLKxdetWAMCmTZs0/p7csmUL/54kkiGOrCWiJuXm5mLJkiW4d+8eUlJS8NNPPyElJQUfffSR6Giy8vDhQ1y6dAkA0KdPH41mlNQ8Dx48QLt27UTHECo6OrrR8ywuosnVebVq36ryPmro9OnTSEtLw6JFi0RHIS1SO8LX3d0dCQkJqK6uxsSJExuMsdV1RUVFOHfuHKytrbkViEjGuNKDdJpCodBoHsjliJpWrFiBmTNnYt26dQAABwcHLFy4kEWPOtLT07FgwQI4ODgAqHk7tmbNGgwZMkRwMnlwdHRstM+CSqWCJEk4ffo0APBBFcCPP/6o/lxRUYGzZ8+id+/eLHoA6tV5Tal9q8r7qKFBgwYhNDRUdAzSMrVjaY2NjXHnzh288MILePDggeBU4gUGBmLatGmwt7dHYWEhPDw8YGpqiocPH2Lu3Ll4++23RUckokaw6EE66dixY1i1ahXu3bunXpbI5YgNFRcXY9iwYVi/fj0AQE9PT/2HENXYsGEDoqOjYWtrCwDIycnBggULWPT4TUxMjOgIWqP+1pb8/HwEBwcLSiMvtdvIwsPDYWhoiIkTJ0KlUuHAgQOorKwUnE5e6vb0qK6uRkZGBhQKhcBEpG0KCwthamqKoqIiTJ48GV5eXjA0NMTIkSNFRxPuv//9r3pFx6FDh2Bra4uoqCjcvXsXH3zwAYseRDLFogfppDVr1iAsLAw9evSAnp6e6Diypa+vj8rKSvUb1nv37vHnVU9VVZW64AEAtra2HDNaR6dOnURH0Frt27dHbm6u6Biy8u233yIuLk59PHXqVHh5eWHGjBkCU8lL3Z4erVq1go2NDT7//HOBiUibJCYmYsmSJTAxMcHw4cMRFhaG2NhYlJSUwM7OTnQ84Vq3bq3+fPHiRfWIWisrK06PIpIxFj1IJ1laWqJXr16iY8je5MmT4e/vj4cPHyIsLAzx8fGYO3eu6Fiy0q5dO8TGxsLLywsAEBcXxyX2jeAkgCer29NDpVIhIyOD91I95eXluHnzJmxsbAAAeXl5T+z3oWvYsJT+jG3btmHv3r1wcHDAmTNnsHXrVuzZs0d0LFm5d+8ezM3Nce7cOQQEBKjP1/1uIyJ5YdGDdJKvry82btwIV1dXjao9R/pp8vT0xEsvvYQTJ06grKwMq1evxoABA0THkpXg4GAEBgZixYoVAGr6nqxdu1ZsKBniJIAnq9vTQ19fH7a2tliyZInARPIzd+5cTJgwAT169ABQs9Q8JCREcCr5SU9Px6lTpwAAQ4cO5XY7ajY9PT11jypHR0euEqpn+vTp8PT0hIGBAfr376/+u/E///kPOnbsKDgdETWF01tIJ0VFRWHz5s2wsLBQb9eQJIlvneu5cOECixxNOHLkCEaPHo1bt27B2toapaWlAAATExPByeSJkwDor/LLL79oTEviahhNkZGRiI+Px6hRowDUbFfw9PTE1KlTBScjbeDm5oawsDD1CNaAgACNY74cAu7fv4+CggLY29trbP9VKpUsfBDJFFd6kE7as2cPkpKS0L59e9FRZG3VqlUoLi6Gp6cnvLy8YGVlJTqSbERFRWH06NEICAhAXFwcix1PwEkATXvSZI2FCxc+pSTa4ddff0V1dTVcXFxQWlqKwsJCjomu49ChQ9i7dy9MTU0B1Kxs9Pb2ZtGDmqW8vBx+fn4a52qP+XKohqWlJSwtLTXOvfjii4LSEFFzsOhBOqljx44seDRDTEwMrl27hvj4eLz99tvo3r07vLy8MHr0aNHRhFOpVAgJCUF+fn6jD618UNU0YMAAFBYWwtvbWz0JYMSIEaJjyYKxsTGAmv4U58+fh6urKwDg+PHjePXVV0VGk524uDhERESgsrISLi4uuHfvHoKDg/Hll1+KjiYrtQWP+p+JnoQ9YYjoWcTtLaSTVq9ejXv37mHkyJEaPT2cnJwEppI3hUKBlStX4sCBAxztC+DmzZtISkrCrl27MGnSpAbX/f39BaTSDnfu3OEkgEZMmTIFmzZtQtu2bQEADx8+xOzZs7F7927ByeTD09MT0dHR8PHxQXx8PABg9OjROHLkiOBk8lHbB6Z2dGbtFrL6I5GJiIh0BVd6kE6qbRhYtyO5JEksejQiMzMTcXFxOHr0KLp164bVq1eLjiQLNjY28PPzg5WVFdzd3UXH0QplZWW4e/culEol9PT0kJ2dzf3hdRQUFKgLHgDQtm1bFBQUCEwkPwYGBg22kunr6wtKI0/Lli3D1q1bsXLlSkiShMGDB2PmzJmiYxEREQnDogfpJI5fa56xY8fi0aNH8PT0xL59+9ChQwfRkWTH3d0deXl5yMvLg1KpVJ9nAU1TdHQ01q5dCwsLC3XjN+4P19StWzcsXboU48ePBwDExsayKFSPhYUFbty4ob6HDh06xF5Dv1EqlUhKSoK5uTkWLFiAXbt24dSpU6isrNT43URERKRruL2FdEpqaupjr/NBVdPFixfRv39/0TFkbf369di/fz9sbW01JgFxS4Km4cOHY/fu3ejUqZPoKLJVUlKCLVu24Ny5cwCAgQMHYtasWezJUMf169cRGBiI69evo127dmjTpg22b9/O8ccAli9fjszMTCgUCrz00ktQKBRwcnLC+fPnoVKpsGHDBtERiYiIhGDRg3SKr68vgJr+FBkZGeqeApmZmejVqxf27t0rMp5s1I5hzc7ObvQ63z7/ztXVFXFxcXwwfYJJkybx/xf9JZRKJXJzc6FSqdClSxdub/mNm5sbjh49irKyMgwdOhRnzpyBoaEhqqurMWbMGPY9ISIincXtLaRTare1zJs3D0FBQejduzcA4PLly9i1a5fIaLKycuVKREREYPr06Q2ucUuCJktLSxY8mmHw4MEIDQ3FqFGjNJoHs4D2u7KyMoSHh+PUqVMAgKFDh2LGjBkwMjISnEw+Zs+ejU2bNsHW1rbBOV1naGgISZJgbGyMzp07w9DQEACgp6enHhlNRESki1j0IJ2UlZWlLngAQK9evZCZmSkwkbxEREQA4Oi65ujTpw/mzZvHSUBPUDtp49ixY+pzLKBpCgkJgVKpRFBQEADg4MGDCA4O5tSNOvLy8hqcu379uoAk8qNQKJCTkwOVSqXxGQAqKioEpyMiIhKHRQ/SSUZGRjh06BA8PDwAAIcPH+bb1EY09gaVb1U1ZWRkAOAkoCdhAe3JMjIykJCQoD7u168fxowZIzCRfOzfvx/79u1Dbm6uutErABQXF6NLly4Ck8lHeXk5/Pz81Md1P9c2fiUiItJFLHqQTvrss8+wcOFCfPzxx5AkCXZ2dli8eLHoWLLDt6pNq+138q9//UtwEu2RnZ2Ns2fPAgAcHR01tihQjUePHsHY2BhAzXYXqjFkyBDY2NggJCQECxcuVJ83NTXFK6+8IjCZfLCwSERE1Dg2MiWddufOHcTGxiIhIQEqlQpJSUmiI8lC7VvV69evazyY1r5V3b59u8B08uDs7Ayg4RtUlUrFbRuNiI+Px7p169QrYNLS0hAYGMiVDHXs2LEDCQkJGDVqFAAgMTERY8aMwbRp0wQnIyIiItJeLHqQzqmqqkJycjJiYmJw6dIlVFVVISoqSqPHh6773//+h9u3byMkJATLly9Xn699q8ppCdRSY8aMwRdffAFLS0sAwP379zF16lQcPnxYcDJ5SUtLw+nTpwEAgwYNwrBhwwQnkpfi4mLs3LkTV65c0ehTwRHRRERE1BRubyGd8tlnn+Ho0aN45ZVXMHbsWGzevBlubm4seNTTqVMndOrUiSMO6S9VW/Co/5lqxrCOHz8ecXFxLHQ8RlBQEGxtbZGbm4vZs2cjJiYG//znP0XHIiIiIhlj0YN0yr59+9CnTx9Mnz4djo6OANjg7XH4VpX+Kp07d8bmzZsxceJEAMCBAwdgbW0tOJV86Ovrw9jYGBUVFRpTgEjTzZs3ERYWhuTkZIwePRpvvvkmpkyZIjoWERERyRiLHqRT0tPTkZCQgNDQUBQVFcHT0xNKpVJ0LNniW1X6q3zyySdYuXIlxowZA0mSMHjwYAQHB4uOJStdunSBj48PRowYoW5mCgA+Pj4CU8mLoaEhAMDAwACFhYUwNzfHgwcPBKciIiIiOWPRg3TKc889Bx8fH/j4+ODq1auIiYlBRUUFfHx84O7ujkmTJomOKCt8q0p/leeffx4bNmwQHUO2CgsLcf/+fVhZWXFC0mO8/PLLKCwshLu7OyZOnAgzMzMWYomIiOix2MiUdF5lZSWOHz+O2NhY7Ny5U3QcWRk/fjwOHjwILy8vREVFwdzcHCNGjOCUG2q2ixcvon///khNTW30eu00F12WmJiIJUuWwMTEBAqFAmFhYRg0aJDoWLJ34cIFFBcX4//+7//QqhXf4RAREVHj+FcC6TwDAwO89dZbeOutt0RHkR2+VaU/Ky4uDv3790dkZGSDa5IksegBYNu2bdi7dy8cHBxw5swZbN26lUWPZhgwYIDoCERERKQFuNKDiJqFb1WJ/h4eHh44dOiQ+tjT0xPx8fECE8mPo6Njo02nVSoVJElSj/klIiIiqo9PLkTUqLojNAG+VaU/x9vbG19//fUTz+miyspK5OTkoPYdhEKh0Dju1q2byHiyEBMTIzoCERERaSkWPYioURyhSX+l8vJyjWOlUomioiJBaeSlvLwcfn5+GudqjyVJQnJysohYstKpUyf155KSEty8eZNb7YiIiKhZWPQgoiZxhCb9WZGRkYiMjERJSYlGn4ry8nK4u7sLTCYfKSkpoiNojdTUVCxfvhz6+vpISUlBRkYGtm7diu3bt4uORkRERDLFogcRNUmpVKJ79+4coUl/2MSJEzFy5EiEhIRg+fLl6vOmpqYwNzcXmIy00ebNm3Hw4EH1SpiePXsiLy9PcCoiIiKSMxY9iKhJS5cuhampqca5kpISQWlIG5mZmcHMzAwRERGio9AzwtLSUuPY0NBQUBIiIiLSBnqiAxCRfPn6+jbrHNGT/Pzzz5g3bx7c3NwwfPhw9T+iljAxMUFBQYF6ksvZs2dhZmYmOBURERHJGVd6EFEDVVVVqKysRHV1NcrLy9VTJIqLi1FWViY4HWmjoKAguLm54cqVK1i7di2+/vprdO7cWXQs0jKBgYHw8/PD7du34evri9zcXGzbtk10LCIiIpIxSVX7NENE9JstW7Zgy5YtkCQJdX9FmJqa4r333sOsWbMEpiNt5Onpifj4eLi7uyMhIQHV1dWYOHEiDhw4IDoaaZni4mL88MMPAIC+ffuiTZs23OJCRERETeL2FiJqwN/fH1evXoW3tzeuXr2q/nfhwgUWPOgPMTAwAAAYGxvjzp07qKqqwoMHDwSnIm1SXV2Nhw8fwszMDE5OThg0aBDi4+Ph4uIiOhoRERHJGIseRNSkefPmobq6GgCQmZmJo0ePQqFQCE5F2mjAgAEoLCyEt7c3vLy84OLiAmdnZ9GxSEukpKRgwIABGDx4MN577z1cunQJI0eOxJEjR7B+/XrR8YiIiEjGuL2FiJrk5eWFr776CqWlpfDy8oKdnR0sLS3x+eefi45GWuzOnTsoKSmBnZ2d6CikJTw8PDB//nwMHDgQSUlJWLZsGQICAvD++++LjkZEREQyx5UeRNQklUoFY2NjfPfdd5gwYQK++OIL/PTTT6JjkZYqKyvDjRs38OjRI+jp6SE7O1t0JNIiw4YNQ+vWreHu7o7nn3+eBQ8iIiJqFk5vIaImVVRUQKFQ4OTJk3jnnXcAAHp6rJVSy+3evRsbNmyAhYWFetyoJElITk4WnIy0Rd1JUqamphrHRkZGIqMRERGRjLHoQURNcnNzw5AhQ2BjY4N+/frh/v37aN26tehYpIV27dqFY8eO4cUXXxQdhbTQtWvX0LdvX41pUrXHkiThypUrAtMRERGRnLGnBxE9VlFREczMzKCnp4fS0lKUlJTwwZVazMfHB9HR0aJjEBEREZGOYdGDiBq4desWrK2tm+y50K1bt6eciLTdmTNnkJCQACcnJ43VQk5OTgJTkbbZuXMn/Pz8nniOiIiIqBa3txBRAytXrkRERASmT5/e4Br7MNAfceLECZw4cQK5ubnqvjCSJLHoQS2SmJjYoMDR2DkiIiKiWlzpQUREfztnZ2ckJiaiTZs2oqOQFjp58iS+//57HD58GB4eHurzJSUluHz5MuLj4wWmIyIiIjnjSg8ieqyysjLcvXsXSqVSfY7bW6ilrK2t0aoVv3LojzEwMICJiQkkSYKxsbH6fPv27RtdkUZERERUiys9iKhJ0dHRWLt2LczNzTW2JHB7C7XU8uXLkZOTAxcXFxgaGqrP+/j4CExF2iYzMxN2dnaiYxAREZEW4Ws3ImpSVFQUjhw5gk6dOomOQlqusrISnTt3RmZmpugopMX2798Pf39/WFhYAAAePnyI8PBwLF26VHAyIiIikisWPYioSZaWlix40J+mVCrRoUMHBAQEiI5CWu7ChQvqggcAtG3bFufPnxeYiIiIiOROT3QAIpKvwYMHIzQ0FD/99BOys7PV/4haQl9fH2lpaaJj0DOgbm+hWlVVVQKSEBERkbZgTw8iapKzs3ODc+zpQX/Eli1bYGRkBE9PT41GlEZGRgJTkbYJCgqCsbEx/Pz8oFKpEBkZidLSUqxatUp0NCIiIpIpFj2IiOhvZ29v3+CcJEm4cuWKgDSkrUpKSvDpp5/iu+++gyRJeP3117FkyRKYmZmJjkZEREQyxaIHETWgUChgaGiwG42UAAAGr0lEQVSIsrKyRq/z7TwREREREWkDFj2IqIGxY8ciLi4O9vb2kCQJdX9N8O08/VEPHz7EpUuXAAB9+vTRaEhJ1BzR0dGNnufoYyIiImoKix5ERPS3S09Px4IFC+Dg4AAAuHbtGtasWYMhQ4YITkbaZMmSJerPFRUVOHv2LHr37o3w8HCBqYiIiEjOOLKWiJ5IoVBoTE3g9hZqqQ0bNiA6Ohq2trYAgJycHCxYsIBFD2qR+g1L8/PzERwcLCgNERERaQMWPYioSceOHcOqVatw79499TYXbm+hP6Kqqkpd8AAAW1tbjhqlP619+/bIzc0VHYOIiIhkjEUPImrSmjVrEBYWhh49ekBPT090HNJi7dq1Q2xsLLy8vAAAcXFxaNeuneBUpG3q9vRQqVTIyMjgfURERESPxaIHETXJ0tISvXr1Eh2DngHBwcEIDAzEihUrAAAODg5Yu3at2FCkdX788Uf1Z319fdja2mr0+SAiIiKqj41MiahJR48eRVZWFlxdXdG6dWv1+W7duglMRdrkyJEjGD16NG7dugVra2uUlpYCAExMTAQnIyIiIiJdwKIHETUpKioKmzdvhoWFhXp7iyRJSE5OFpyMtIWXlxdiY2PVY5CJ/ojQ0NDHXl+4cOFTSkJERETahttbiKhJe/bsQVJSEtq3by86CmkplUqFkJAQ5OfnN/rgyodVag5jY2MAQF5eHs6fPw9XV1cAwPHjx/Hqq6+KjEZEREQyx6IHETWpY8eOLHjQn7Jx40YkJSVBkiT1gytRS/n7+wMApkyZgtjYWLRt2xYAMHPmTMyePVtkNCIiIpI5Fj2IqEm9evXCvHnzMHLkSI2eHk5OTgJTkTaxsbGBn58frKys4O7uLjoOabmCggJ1wQMA2rZti4KCAoGJiIiISO7Y04OImuTr69vgnCRJ2L17t4A0pO3y8vKQl5cHpVKpPscCGrVEQEAAzMzMMH78eABAbGwsioqKsHnzZsHJiIiISK5Y9CAior/d+vXrsX//ftja2mo0xWUBjVqipKQEW7Zswblz5wAAAwcOxKxZs2Bqaio4GREREckVix5E1EBqaupjr/PtPLWUq6sr4uLi+HBKRERERE8Ve3oQUQORkZEAAIVCgYyMDNjZ2QEAMjMz0atXLxY9qMUsLS1Z8KA/raysDOHh4Th16hQAYOjQoZgxYwaMjIwEJyMiIiK54koPImrSvHnz8O6776J3794AgMuXL2PXrl1Yt26d4GSkbUJDQ3H37l02xaU/JSgoCEqlEhMmTAAAHDx4EACwatUqkbGIiIhIxrjSg4ialJWVpS54ADXTXDIzMwUmIm2VkZEBANizZ4/6nCRJLHpQi2RkZCAhIUF93K9fP4wZM0ZgIiIiIpI7Fj2IqElGRkY4dOgQPDw8AACHDx/mMnJqkezsbADAv/71L8FJ6Fnx6NEjGBsbA6jZ7kJERET0ONzeQkRNys7OxsKFC5GVlQVJkmBnZ4fFixdjwIABoqORlnB2dgZQs6qjLpVKBUmSkJycLCIWaakdO3YgISEBo0aNAgAkJiZizJgxmDZtmuBkREREJFcsehDRE925cwexsbFISEiASqVCUlKS6EhEpKPS0tJw+vRpAMCgQYMwbNgwwYmIiIhIzlj0IKJGVVVVITk5GTExMbh06RKqqqoQFRWl0eODiOhpUSqVGD9+POLi4kRHISIiIi2iJzoAEcnPZ599BicnJ+zbtw/u7u5ITU2Fubk5Cx5EJIy+vj6MjY1RUVEhOgoRERFpETYyJaIG9u3bhz59+mD69OlwdHQE0LAnAxHR09alSxf4+PhgxIgR6mamAODj4yMwFREREckZix5E1EB6ejoSEhIQGhqKoqIieHp6QqlUio5FRDqssLAQ9+/fh5WVFa5fvy46DhEREWkJ9vQgose6evUqYmJicOTIEXTt2hXu7u6YNGmS6FhEpEMSExOxZMkSmJiYQKFQICwsDIMGDRIdi4iIiLQAix5E1CyVlZU4fvw4YmNjsXPnTtFxiEiHuLu7IzQ0FA4ODjhz5gy2bt2KPXv2iI5FREREWoCNTImoWQwMDPDWW2+x4EFET52enh4cHBwAAI6OjiguLhaciIiIiLQFe3oQERGRrFVWViInJwe1i1MVCoXGcbdu3UTGIyIiIhnj9hYiIiKSNWdn5yavSZKE5OTkp5iGiIiItAmLHkRERERERET0TGJPDyIiIiIiIiJ6JrHoQURERERERETPJBY9iIiIiIiIiOiZxKIHERERERERET2TWPQgIiIiIiIiomfS/wOYpR9fblHvVAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1296x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RhXLymt1bLJR"
      },
      "source": [
        "**Tablas de Contingencia**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 395
        },
        "id": "en2_B6iVbKTC",
        "outputId": "0ffef6fb-9980-4a0c-ab45-0a64eb110abd"
      },
      "source": [
        "pd.crosstab(df['Month'], df['Revenue'])"
      ],
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>Revenue</th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Month</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Aug</th>\n",
              "      <td>357</td>\n",
              "      <td>76</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Dec</th>\n",
              "      <td>1511</td>\n",
              "      <td>216</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Feb</th>\n",
              "      <td>181</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Jul</th>\n",
              "      <td>366</td>\n",
              "      <td>66</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>June</th>\n",
              "      <td>259</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mar</th>\n",
              "      <td>1715</td>\n",
              "      <td>192</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>May</th>\n",
              "      <td>2999</td>\n",
              "      <td>365</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Nov</th>\n",
              "      <td>2238</td>\n",
              "      <td>760</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Oct</th>\n",
              "      <td>434</td>\n",
              "      <td>115</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sep</th>\n",
              "      <td>362</td>\n",
              "      <td>86</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Revenue     0    1\n",
              "Month             \n",
              "Aug       357   76\n",
              "Dec      1511  216\n",
              "Feb       181    3\n",
              "Jul       366   66\n",
              "June      259   29\n",
              "Mar      1715  192\n",
              "May      2999  365\n",
              "Nov      2238  760\n",
              "Oct       434  115\n",
              "Sep       362   86"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "F49T5Tv-bXsa",
        "outputId": "b5304c96-bdba-4c9f-949c-fe58a0e82483"
      },
      "source": [
        "pd.crosstab(df['Browser'], df['Revenue'])"
      ],
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>Revenue</th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Browser</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2097</td>\n",
              "      <td>365</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>6738</td>\n",
              "      <td>1223</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>100</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>606</td>\n",
              "      <td>130</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>381</td>\n",
              "      <td>86</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>154</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>43</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>114</td>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>131</td>\n",
              "      <td>32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>7</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>45</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Revenue     0     1\n",
              "Browser            \n",
              "1        2097   365\n",
              "2        6738  1223\n",
              "3         100     5\n",
              "4         606   130\n",
              "5         381    86\n",
              "6         154    20\n",
              "7          43     6\n",
              "8         114    21\n",
              "9           1     0\n",
              "10        131    32\n",
              "11          5     1\n",
              "12          7     3\n",
              "13         45    16"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 708
        },
        "id": "9qj5Xm59bntT",
        "outputId": "abf61d9d-eff3-4692-bc59-8db56ac9f66a"
      },
      "source": [
        "pd.crosstab(df['TrafficType'], df['Revenue'])"
      ],
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>Revenue</th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TrafficType</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2189</td>\n",
              "      <td>262</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3066</td>\n",
              "      <td>847</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1872</td>\n",
              "      <td>180</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>904</td>\n",
              "      <td>165</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>204</td>\n",
              "      <td>56</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>391</td>\n",
              "      <td>53</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>28</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>248</td>\n",
              "      <td>95</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>38</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>360</td>\n",
              "      <td>90</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>200</td>\n",
              "      <td>47</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>695</td>\n",
              "      <td>43</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>11</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>38</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>16</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>148</td>\n",
              "      <td>50</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Revenue         0    1\n",
              "TrafficType           \n",
              "1            2189  262\n",
              "2            3066  847\n",
              "3            1872  180\n",
              "4             904  165\n",
              "5             204   56\n",
              "6             391   53\n",
              "7              28   12\n",
              "8             248   95\n",
              "9              38    4\n",
              "10            360   90\n",
              "11            200   47\n",
              "12              1    0\n",
              "13            695   43\n",
              "14             11    2\n",
              "15             38    0\n",
              "16              2    1\n",
              "17              1    0\n",
              "18             10    0\n",
              "19             16    1\n",
              "20            148   50"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uKcCrt7Y4ypz"
      },
      "source": [
        "**Modelando**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dH2Z55Gp41bC"
      },
      "source": [
        "df['VisitorType_T'] = df[['VisitorType']].replace({'VisitorType' : { 'New_Visitor' : 0, 'Returning_Visitor' : 1, 'Other' : 2 }})\r\n",
        "df['Weekend_T'] = df['Weekend'].astype(int)\r\n",
        "df['Month_T'] = df[['Month']].replace({'Month' : {'Feb' : 4, 'Mar' : 2, 'May' : 2, 'June' : 2\r\n",
        "    , 'Jul' : 1, 'Aug' : 1, 'Sep' : 3, 'Oct' : 3, 'Nov' : 3, 'Nov' : 3, 'Dec' : 1}})\r\n",
        "\r\n",
        "df['Browser_T'] = df['Browser'].replace(1,1)\r\n",
        "df['Browser_T'] = df['Browser'].replace(2,1)\r\n",
        "df['Browser_T'] = df['Browser'].replace(6,1)\r\n",
        "df['Browser_T'] = df['Browser'].replace(7,1)\r\n",
        "df['Browser_T'] = df['Browser'].replace(8,1)\r\n",
        "\r\n",
        "df['Browser_T'] = df['Browser'].replace(4,2)\r\n",
        "df['Browser_T'] = df['Browser'].replace(5,2)\r\n",
        "df['Browser_T'] = df['Browser'].replace(10,2)\r\n",
        "\r\n",
        "df['Browser_T'] = df['Browser'].replace(12,3)\r\n",
        "df['Browser_T'] = df['Browser'].replace(13,3)\r\n",
        "df['Browser_T'] = df['Browser'].replace(11,3)\r\n",
        "\r\n",
        "df['Browser_T'] = df['Browser'].replace(3,4)\r\n",
        "\r\n",
        "df['TrafficType_T']=df['TrafficType']\r\n",
        "\r\n",
        "\r\n",
        "df['TrafficType_T'] = df['TrafficType_T'].replace(1,1)\r\n",
        "df['TrafficType_T'] = df['TrafficType_T'].replace(3,1)\r\n",
        "df['TrafficType_T'] = df['TrafficType_T'].replace(9,1)\r\n",
        "df['TrafficType_T'] = df['TrafficType_T'].replace(13,1)\r\n",
        "df['TrafficType_T'] = df['TrafficType_T'].replace(14,1)\r\n",
        "df['TrafficType_T'] = df['TrafficType_T'].replace(19,1)\r\n",
        "\r\n",
        "df['TrafficType_T'] = df['TrafficType_T'].replace(2,2)\r\n",
        "df['TrafficType_T'] = df['TrafficType_T'].replace(7,2)\r\n",
        "df['TrafficType_T'] = df['TrafficType_T'].replace(8,2)\r\n",
        "df['TrafficType_T'] = df['TrafficType_T'].replace(16,2)\r\n",
        "df['TrafficType_T'] = df['TrafficType_T'].replace(20,2)\r\n",
        "\r\n",
        "df['TrafficType_T'] = df['TrafficType_T'].replace(4,3)\r\n",
        "df['TrafficType_T'] = df['TrafficType_T'].replace(5,3)\r\n",
        "df['TrafficType_T'] = df['TrafficType_T'].replace(6,3)\r\n",
        "df['TrafficType_T'] = df['TrafficType_T'].replace(10,3)\r\n",
        "df['TrafficType_T'] = df['TrafficType_T'].replace(11,3)\r\n",
        "\r\n",
        "df['TrafficType_T'] = df['TrafficType_T'].replace(15,4)\r\n",
        "df['TrafficType_T'] = df['TrafficType_T'].replace(18,4)\r\n",
        "df1 = df.copy()"
      ],
      "execution_count": 82,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bBbqCMPpXRlJ"
      },
      "source": [
        "**Eliminando Outliers**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NrRFeFxmXU_L",
        "outputId": "922edf44-a175-48ce-e64c-6a38aa7e6103"
      },
      "source": [
        "print('Tamaño inicial', df1.shape)\r\n",
        "df1 = df.drop(df[df['Administrative_Duration']>1600].index)\r\n",
        "print('Paso 1',df1.shape)\r\n",
        "df1 = df1.drop(df1[df1['ProductRelated_Duration']>2500].index)\r\n",
        "print('Paso 2', df1.shape)"
      ],
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Tamaño inicial (12330, 23)\n",
            "Paso 1 (12308, 23)\n",
            "Paso 2 (10758, 23)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "evLdyjMx_jfi"
      },
      "source": [
        "**Cantidad de valores del Target Revenue**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 598
        },
        "id": "9dZNno_Q_F-o",
        "outputId": "8255b596-be0d-4152-a4d8-de78176ef988"
      },
      "source": [
        "x = ['False','True']\r\n",
        "y = list(df1.Revenue.value_counts())\r\n",
        "plt.barh(x, y)\r\n",
        "for index, value in enumerate(y):\r\n",
        "    plt.text(value, index, str(value))"
      ],
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1296x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ai9tVU2vYHLR",
        "outputId": "0f4df27f-0030-4abc-9a9b-f6f2382f7f37"
      },
      "source": [
        "X = df1[[\"Administrative_Duration\",\"Informational_Duration\",\"ProductRelated_Duration\",\"PageValues\",\r\n",
        "                   \"SpecialDay\",\"VisitorType_T\",\"TrafficType_T\",\"Weekend_T\",\"Browser_T\",\"Month_T\"]]\r\n",
        "y = df1['Revenue']\r\n",
        "\r\n",
        "X = pd.get_dummies(X, columns = [\"VisitorType_T\", \"Weekend_T\",\"TrafficType_T\",\"Browser_T\",\"Month_T\"], drop_first = True)\r\n",
        "\r\n",
        "X_train, X_test, y_train, y_test = train_test_split(X,y, test_size=0.3, stratify=y, random_state=42)\r\n",
        "print(X_train.shape, X_test.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(7530, 27) (3228, 27)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XH3NdgFkEKoA"
      },
      "source": [
        "**Agrupamiento con Kmeans**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 649
        },
        "id": "gp6MTWtEgbNg",
        "outputId": "d3b9fa74-ba14-4278-97a8-1964e4aba1c7"
      },
      "source": [
        "from sklearn.cluster import KMeans\r\n",
        "print('Obtener el valor K')\r\n",
        "Nc = range(1, 10)\r\n",
        "kmeans = [KMeans(n_clusters = i) for i in Nc]\r\n",
        "score = [kmeans[i].fit(X_train).score(X_test) for i in range(len(kmeans))]\r\n",
        "plt.plot(Nc,score)\r\n",
        "plt.xlabel('Nro de Clusters')\r\n",
        "plt.ylabel('Score')\r\n",
        "plt.title('Curva de codo')\r\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Obtener el valor K\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1296x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nDPxnGZcPMGI"
      },
      "source": [
        "**LIbrerias para los modelos**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lcuv-F4APMhv"
      },
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\r\n",
        "from sklearn.svm import SVC\r\n",
        "from sklearn.linear_model import LogisticRegression\r\n",
        "from sklearn.model_selection import GridSearchCV\r\n",
        "from sklearn.metrics import confusion_matrix, accuracy_score, classification_report\r\n",
        "from sklearn.ensemble import RandomForestClassifier, VotingClassifier"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HQdAexB6ZTZK"
      },
      "source": [
        "**Modelo KNN**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fwpUt06Afton",
        "outputId": "cba31416-35e5-4567-8b1e-22882b2fa2f2"
      },
      "source": [
        "knn = KNeighborsClassifier(n_neighbors=4, leaf_size=1, p=5)\r\n",
        "knn.fit(X_train, y_train)\r\n",
        "print('Con KNN')\r\n",
        "print('SCORE del test', knn.score(X_test, y_test))\r\n",
        "print('SCORE del train', knn.score(X_test, y_test))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Con KNN\n",
            "SCORE del test 0.8921933085501859\n",
            "SCORE del train 0.9094289508632138\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 673
        },
        "id": "gy4FU7rdM7VA",
        "outputId": "57b4fca4-b359-4486-a68d-cc7c7ec5f7b5"
      },
      "source": [
        "y_predknn = knn.predict(X_test)\r\n",
        "cmkmm = confusion_matrix(y_test, y_predknn)\r\n",
        "sb.set(font_scale=3.0)\r\n",
        "sb.heatmap(cmkmm, annot=True, fmt='d')\r\n",
        "plt.title('Matriz de confusión del KNN')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Matriz de confusión del KNN')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 66
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1296x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gJsObGb-ZOOq"
      },
      "source": [
        "**Modelo - Random Forest**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nv7CkUAyUZpd",
        "outputId": "d6d0e7a3-1806-4216-9732-562854c4740d"
      },
      "source": [
        "hiper1 = {'n_estimators': np.array([100,150,200]), 'min_samples_leaf': np.arange(4,7)}\r\n",
        "grid = GridSearchCV(RandomForestClassifier(random_state=42, n_jobs = -1), hiper1, cv=10)\r\n",
        "grid.fit(X_train, y_train)\r\n",
        "grid.best_params_"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'min_samples_leaf': 6, 'n_estimators': 100}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OSHg-IQ7oquV",
        "outputId": "f34ab87f-0120-4860-f5d6-73817aef9f10"
      },
      "source": [
        "rfc = RandomForestClassifier(random_state=42, n_jobs = -1, n_estimators= 100, min_samples_leaf=6)\r\n",
        "rfc.fit(X_train, y_train)\r\n",
        "print('Con random forestt')\r\n",
        "print('SCORE para el test', rfc.score(X_test, y_test))\r\n",
        "print('SCORE para el train', rfc.score(X_train, y_train))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Con random forestt\n",
            "SCORE para el test 0.9107806691449815\n",
            "SCORE para el train 0.9390438247011952\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 673
        },
        "id": "9rG1AIQLMg2n",
        "outputId": "1d3f6912-eaa9-4494-c19f-3a63ffc88e3d"
      },
      "source": [
        "y_predrf = rfc.predict(X_test)\r\n",
        "cmrf = confusion_matrix(y_test, y_predrf)\r\n",
        "sb.set(font_scale=3.0)\r\n",
        "sb.heatmap(cmrf, annot=True, fmt='d')\r\n",
        "plt.title('Matriz de confusión del Random Forest')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Matriz de confusión del Random Forest')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 65
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1296x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZgV49x0eZkUs"
      },
      "source": [
        "**Modelo - Supoort Vector Classifier**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Zh3fumHOaNbh",
        "outputId": "b5c9055e-721e-4ac0-8035-eb80171fe567"
      },
      "source": [
        "hiper1 = {'C': np.array([0.1,1,10]), 'gamma': np.array([0.01,0.001,0.0001])}\r\n",
        "grid = GridSearchCV(SVC(kernel='rbf'), hiper1, cv=10)\r\n",
        "grid.fit(X_train, y_train)\r\n",
        "grid.best_params_"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'C': 1.0, 'gamma': 0.0001}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_MAznmampMR5",
        "outputId": "633356b7-d255-4d6e-b9ba-8ccc7e68350f"
      },
      "source": [
        "svm = SVC(kernel='rbf', probability=True, C=1, gamma=0.0001)\r\n",
        "svm.fit(X_train, y_train)\r\n",
        "print('Con SVC')\r\n",
        "print('SCORE del test', svm.score(X_test, y_test))\r\n",
        "print('SCORE del train', svm.score(X_train, y_train))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Con SVC\n",
            "SCORE del test 0.8983890954151177\n",
            "SCORE del train 0.9159362549800797\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 673
        },
        "id": "i-_C5-QAOZDx",
        "outputId": "c0d6117e-a9c8-4c87-af38-cd83468b2af1"
      },
      "source": [
        "y_predsvc = svm.predict(X_test)\r\n",
        "cmsvc = confusion_matrix(y_test, y_predsvc)\r\n",
        "sb.set(font_scale=3.0)\r\n",
        "sb.heatmap(cmsvc, annot=True, fmt='d')\r\n",
        "plt.title('Matriz de confusión SVC')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Matriz de confusión SVC')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 64
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1296x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5fGkSvZquA6l"
      },
      "source": [
        "**Modelo - Logistic regression**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IDiHeLMNuGQ1",
        "outputId": "ea2f8b32-0ef7-4be9-8ca5-73b190edd86b"
      },
      "source": [
        "hiper1 = {'C': np.array([3,4,5,6]), 'tol': np.array([5,1,0.1])}\r\n",
        "grid = GridSearchCV(LogisticRegression(random_state=42, n_jobs=-1), hiper1, cv=10)\r\n",
        "grid.fit(X_train, y_train)\r\n",
        "grid.best_params_"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'C': 5, 'tol': 5.0}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7RlEysJwvWqQ",
        "outputId": "ca3b2951-9410-413c-e122-f0904b8bba7b"
      },
      "source": [
        "lrg = LogisticRegression(random_state=42, n_jobs=-1, C=5, tol=5)\r\n",
        "lrg.fit(X_train, y_train)\r\n",
        "print('Con Logistic Regression')\r\n",
        "print('SCORE del test', lrg.score(X_test, y_test))\r\n",
        "print('SCORE del train', lrg.score(X_train, y_train))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Con Logistic Regression\n",
            "SCORE del test 0.9021065675340768\n",
            "SCORE del train 0.9042496679946879\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 674
        },
        "id": "DWNL-Y8kQAW1",
        "outputId": "4d9af66d-f327-4579-d938-67cc97b19681"
      },
      "source": [
        "y_predlrg = lrg.predict(X_test)\r\n",
        "cmlrg = confusion_matrix(y_test, y_predlrg)\r\n",
        "sb.set(font_scale=3.0)\r\n",
        "sb.heatmap(cmlrg, annot=True, fmt='d')\r\n",
        "plt.title('Matriz de confusión Regresión logística')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Matriz de confusión Regresión logística')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1296x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Bqsgy4FRuGpG"
      },
      "source": [
        "**Modelo de ensamble Voting Classifier**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OowZI8hAqS_O",
        "outputId": "190518ea-cc22-44d2-871a-c09026bb1532"
      },
      "source": [
        "evc1 = VotingClassifier(estimators=[('knn', knn),('rfc', rfc),('svc', svm),('lr', lrg)], voting='hard')\r\n",
        "evc1.fit(X_train, y_train)\r\n",
        "print('COn ensemble Voting hard')\r\n",
        "print('SCORE del test', evc1.score(X_test, y_test))\r\n",
        "print('SCORE del train', evc1.score(X_train, y_train))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "COn ensemble Voting hard\n",
            "SCORE del test 0.8996282527881041\n",
            "SCORE del train 0.9107569721115538\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "09VBdr3TeXp0",
        "outputId": "b84219e7-0b3d-45ea-deb0-e836cf2957b1"
      },
      "source": [
        "evc2 = VotingClassifier(estimators=[('lr', lrg),('rfc', rfc), ('knn', knn), ('svc', svm)], voting='soft'\r\n",
        "    , weights=[3,4,2,1])\r\n",
        "evc2.fit(X_train, y_train)\r\n",
        "print('COn ensemble Voting')\r\n",
        "print('SCORE del test', evc2.score(X_test, y_test))\r\n",
        "print('SCORE del train', evc2.score(X_train, y_train))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "COn ensemble Voting\n",
            "SCORE del test 0.904275092936803\n",
            "SCORE del train 0.9200531208499336\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ymllzHF9yuXS",
        "outputId": "e98b154e-dda2-4b61-f46b-46670f347bbd"
      },
      "source": [
        "for clf in (lrg, rfc, svm, knn, evc1, evc2):\r\n",
        "    clf.fit(X_train, y_train)\r\n",
        "    y_predclf = clf.predict(X_test)\r\n",
        "    print(clf.__class__.__name__, accuracy_score(y_test, y_predclf))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "LogisticRegression 0.9021065675340768\n",
            "RandomForestClassifier 0.9107806691449815\n",
            "SVC 0.8983890954151177\n",
            "KNeighborsClassifier 0.8921933085501859\n",
            "VotingClassifier 0.8996282527881041\n",
            "VotingClassifier 0.9045848822800495\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 673
        },
        "id": "78b-XmJRfOV6",
        "outputId": "d7042f18-09e2-41b3-90a9-c2544448d002"
      },
      "source": [
        "y_predvf2 = evc2.predict(X_test)\r\n",
        "cmevf2 = confusion_matrix(y_test, y_predvf2)\r\n",
        "sb.set(font_scale=3.0)\r\n",
        "sb.heatmap(cmevf2, annot=True, fmt='d')\r\n",
        "plt.title('Matriz de confusión')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Matriz de confusión')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 62
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1296x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "perzoEFoEha4",
        "outputId": "12418c71-7df6-4ebf-af66-80ee9ffc605f"
      },
      "source": [
        "print(classification_report(y_test, y_pred))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.93      0.97      0.95      2785\n",
            "           1       0.73      0.55      0.63       443\n",
            "\n",
            "    accuracy                           0.91      3228\n",
            "   macro avg       0.83      0.76      0.79      3228\n",
            "weighted avg       0.90      0.91      0.91      3228\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QbAoJSu9zjES"
      },
      "source": [
        "**Comprobando resultado**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1f88Ip7Ozn4h",
        "outputId": "6e19317b-17e3-4a74-bbec-fd659b22e70a"
      },
      "source": [
        "n_row = 5\r\n",
        "y_pred1 = evc2.predict([X_test.iloc[n_row].values])\r\n",
        "if y_pred1[0] == y_test.iloc[n_row]:\r\n",
        "  print('El modelo ha predecido el valor de', y_test.iloc[n_row])\r\n",
        "else:\r\n",
        "  print('El modelo no ha predecido', y_pred1[0] , '!=', y_test.iloc[n_row])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "El modelo ha predecido el valor de 0\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}