notebooks/transferability_evaluation.ipynb

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  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "!pip install pymoo"
      ],
      "metadata": {
        "id": "Q57DaD-czrnW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pickle as p\n",
        "import numpy as np\n",
        "from pymoo.indicators.hv import HV\n",
        "from pymoo.indicators.igd import IGD\n",
        "import matplotlib.pyplot as plt\n",
        "from matplotlib.ticker import FormatStrFormatter\n",
        "import os\n",
        "from scipy import stats\n",
        "\n",
        "plt.rc('legend', fontsize=13)\n",
        "plt.rc('xtick', labelsize=10)\n",
        "plt.rc('ytick', labelsize=11)\n",
        "plt.rc('axes', labelsize=13)\n",
        "plt.rcParams[\"font.family\"] = \"serif\""
      ],
      "metadata": {
        "id": "fLDsibkT1ZJW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "smwFnWEQzc-h"
      },
      "outputs": [],
      "source": [
        "def get_hashKey(arch):\n",
        "    \"\"\"\n",
        "    Get the key which is used to represent the architecture in \"self.data\".\n",
        "    \"\"\"\n",
        "    return ''.join(map(str, arch))\n",
        "\n",
        "def find_the_better(x, y):\n",
        "    if isinstance(x, list):\n",
        "        x = np.array(x)\n",
        "    if isinstance(y, list):\n",
        "        y = np.array(y)\n",
        "\n",
        "    sub_ = x - y\n",
        "    x_better = np.all(sub_ <= 0)\n",
        "    y_better = np.all(sub_ >= 0)\n",
        "    if x_better == y_better:  # True - True\n",
        "        return -1\n",
        "    if y_better:  # False - True\n",
        "        return 1\n",
        "    return 0  # True - False\n",
        "\n",
        "def get_front_0(F):\n",
        "    l = len(F)\n",
        "    r = np.zeros(l, dtype=np.int8)\n",
        "    for i in range(l):\n",
        "        if r[i] == 0:\n",
        "            for j in range(i + 1, l):\n",
        "                better_sol = find_the_better(F[i], F[j])\n",
        "                if better_sol == 0:\n",
        "                    r[j] += 1\n",
        "                elif better_sol == 1:\n",
        "                    r[i] += 1\n",
        "                    break\n",
        "    return r == 0\n",
        "\n",
        "def calculate_IGD(af):\n",
        "    af = np.array(af)\n",
        "    af[:, 1] = (af[:, 1] - min_FLOPs) / (max_FLOPs - min_FLOPs)\n",
        "    af = np.round(af, 4)\n",
        "    igd_value = IGD_computing(af)\n",
        "    return igd_value\n",
        "\n",
        "\n",
        "def calculate_HV(af):\n",
        "    hv_value = HV_computing(af)\n",
        "    return np.round(hv_value / np.prod(reference_point), 6)\n",
        "\n",
        "\n",
        "def evaluate(list_arch):\n",
        "    F = []\n",
        "    for arch in list_arch:\n",
        "        hashKey = get_hashKey(arch)\n",
        "        test_error = np.round(1 - benchmark_data['200'][hashKey]['test_acc'][-1], 4)\n",
        "        efficiency_metric = np.round(benchmark_data['200'][hashKey]['FLOPs'], 4)\n",
        "        f = [test_error, efficiency_metric]\n",
        "        F.append(f)\n",
        "\n",
        "    F = np.array(F)\n",
        "    list_arch = np.array(list_arch)\n",
        "    fr0 = get_front_0(F)\n",
        "    AF = F[fr0]\n",
        "    AF = np.unique(AF, axis=0)\n",
        "    AS = list_arch[fr0]\n",
        "    igd_value = calculate_IGD(AF)\n",
        "    hv_value = calculate_HV(AF)\n",
        "    best_test_acc = np.round((1 - np.min(AF[:, 0])) * 100, 2)\n",
        "    opt_results = {\n",
        "        'Approximation Set': AS,\n",
        "        'Approximation Front': AF,\n",
        "        'IGD': igd_value,\n",
        "        'HV': hv_value,\n",
        "        'Best testing accuracy': best_test_acc,\n",
        "    }\n",
        "    return opt_results\n",
        "\n",
        "\n",
        "\n",
        "def summarize_results():\n",
        "    list_variant = [\n",
        "        'train_loss', 'synflow',\n",
        "        'ENAS_TFI', 'MOENAS_PSI', 'MOENAS_TF_PSI',\n",
        "        'E-TF-MOENAS',\n",
        "    ]\n",
        "    for variant in list_variant:\n",
        "        print('Algorithm:', variant)\n",
        "        algo_res_path = f'{RES_PATH}/NAS201-C10_{variant}'\n",
        "\n",
        "        list_rt = []\n",
        "        list_approximation_front = []\n",
        "        list_IGD = []\n",
        "        list_HV = []\n",
        "        list_exps = list(os.listdir(algo_res_path))\n",
        "        for exp in list_exps:\n",
        "            if 'txt' in exp:\n",
        "                continue\n",
        "\n",
        "            p_file_ = algo_res_path + f'/{exp}'\n",
        "\n",
        "            E_Archive_search_each_gen = p.load(open(p_file_ + '/E_Archive_search_each_gen.p', 'rb'))\n",
        "            final_ea = E_Archive_search_each_gen[-1]\n",
        "            rs_evaluation = evaluate(final_ea['X'])\n",
        "\n",
        "            ea = {\n",
        "                'X_lst': rs_evaluation['Approximation Set'],\n",
        "                'F_lst': rs_evaluation['Approximation Front'],\n",
        "            }\n",
        "\n",
        "            igd_value = rs_evaluation['IGD']\n",
        "            hv_value = rs_evaluation['HV']\n",
        "\n",
        "            list_approximation_front.append(ea)\n",
        "            list_IGD.append(igd_value)\n",
        "            list_HV.append(hv_value)\n",
        "\n",
        "        rs = {\n",
        "            'EA_all': list_approximation_front,\n",
        "            'IGD_all': list_IGD,\n",
        "            'HV_all': list_HV,\n",
        "        }\n",
        "        p.dump(rs, open(f'{RES_PATH}/raw_results_{variant}_{dataset_abbrev}_T.p', 'wb'))\n",
        "\n",
        "\n",
        "def processing_results():\n",
        "    list_variant = [\n",
        "        'train_loss', 'synflow',\n",
        "        'ENAS_TFI', 'MOENAS_PSI', 'MOENAS_TF_PSI',\n",
        "        'E-TF-MOENAS',\n",
        "    ]\n",
        "\n",
        "    for variant in list_variant:\n",
        "        res = p.load(open(f'{RES_PATH}/raw_results_{variant}_{dataset_abbrev}_T.p', 'rb'))\n",
        "\n",
        "        best_arch_found = []\n",
        "        final_EA = []\n",
        "        final_IGD = []\n",
        "        final_HV = []\n",
        "        final_running_time = []\n",
        "\n",
        "        n_runs = len(res['EA_all'])\n",
        "        for i in range(n_runs):\n",
        "            EA = res['EA_all'][i]\n",
        "            F = EA['F_lst']\n",
        "            best_arch = np.round((1 - np.min(F[:, 0])) * 100, 2)\n",
        "            igd_value = res['IGD_all'][i]\n",
        "            hv_value = res['HV_all'][i]\n",
        "\n",
        "            best_arch_found.append(best_arch)\n",
        "            final_IGD.append(igd_value)\n",
        "            final_HV.append(hv_value)\n",
        "            final_EA.append(EA)\n",
        "\n",
        "        rs_final = {\n",
        "            'best_arch_found': best_arch_found,\n",
        "            'final_EA': final_EA,\n",
        "            'final_IGD': final_IGD,\n",
        "            'final_HV': final_HV,\n",
        "        }\n",
        "        p.dump(rs_final, open(f'{RES_PATH}/{variant}_{dataset_abbrev}_T.p', 'wb'))"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Summarize"
      ],
      "metadata": {
        "id": "Tauhm0_q0uYe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "RES_PATH = '/content/drive/MyDrive/Information_Sciences/nas201'  # Experiment Results Path\n",
        "DATABASE_PATH = '/content/drive/MyDrive/Benchmark_Data/NASBench201'  # Database Path"
      ],
      "metadata": {
        "id": "bpN-TRlx0veG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = 'ImageNet16-120'\n",
        "if dataset == 'CIFAR-100':\n",
        "    dataset_abbrev = 'C100'\n",
        "else:\n",
        "    dataset_abbrev = 'IN16'"
      ],
      "metadata": {
        "id": "6jiCQbe_0yUI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "benchmark_data = p.load(open(DATABASE_PATH + f'/[{dataset}]_data.p', 'rb'))\n",
        "if dataset == 'CIFAR-100':\n",
        "    opt_pareto_front = p.load(open(DATABASE_PATH + '/[POF_TestAcc_FLOPs]_[NAS201_cifar100].p', 'rb'))\n",
        "    min_FLOPs, max_FLOPs = 7.78890, 220.12554  # CIFAR-100\n",
        "    reference_point = [0.99 + 1e-4, 220.126 + 1e-4]\n",
        "else:\n",
        "    opt_pareto_front = p.load(open(DATABASE_PATH + '/[POF_TestAcc_FLOPs]_[NAS201_ImageNet16-120].p', 'rb'))\n",
        "    min_FLOPs, max_FLOPs = 1.95340, 55.037560  # ImageNet16-120\n",
        "    reference_point = [0.9917 + 1e-4, 47.173 + 1e-4]\n",
        "\n",
        "opt_pareto_front[:, 0] /= 100\n",
        "opt_pareto_front[:, 1] = np.round((opt_pareto_front[:, 1] - min_FLOPs) / (max_FLOPs - min_FLOPs), 4)\n",
        "opt_pareto_front = np.round(opt_pareto_front, 6)\n",
        "\n",
        "HV_computing = HV(reference_point)\n",
        "IGD_computing = IGD(opt_pareto_front)"
      ],
      "metadata": {
        "id": "uPyjManF00az"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "summarize_results()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iw4LPvCW3Ajx",
        "outputId": "229edb72-445b-45b7-d7a7-a619f5fdf666"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Algorithm: train_loss\n",
            "Algorithm: synflow\n",
            "Algorithm: ENAS_TFI\n",
            "Algorithm: MOENAS_PSI\n",
            "Algorithm: MOENAS_TF_PSI\n",
            "Algorithm: E-TF-MOENAS\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "processing_results()"
      ],
      "metadata": {
        "id": "WfKoGYzR4Ovf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Visualize"
      ],
      "metadata": {
        "id": "Gqdih7SG4FUH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def draw(metric):\n",
        "    list_algo = ['train_loss', 'synflow', 'MOENAS_PSI', 'MOENAS_TF_PSI', 'ENAS_TFI', 'TF-MOENAS', 'E-TF-MOENAS']\n",
        "    list_xticks = ['NSGA-II (train_loss)', 'NSGA-II (synflow)', 'MOENAS_PSI', 'MOENAS_TF_PSI', 'ENAS_TFI', 'TF-MOENAS', 'E-TF-MOENAS (Ours)']\n",
        "    list_color = ['green', 'tab:orange', 'brown', 'black', 'tab:olive', 'tab:cyan', 'red']\n",
        "\n",
        "    plt.grid(True, axis='y', linestyle='--', zorder=0)\n",
        "\n",
        "    for i, algo in enumerate(list_algo):\n",
        "        mean, std = algo_perf[dataset_abbrev][algo][metric]['mean'], algo_perf[dataset_abbrev][algo][metric]['std']\n",
        "        (_, caps, _) = plt.errorbar(i, mean, yerr=std, fmt='.', capsize=10, linewidth=4, color=list_color[i], zorder=i+2, markersize=18, label=list_xticks[i])\n",
        "        for cap in caps:\n",
        "            cap.set_markeredgewidth(2)\n",
        "\n",
        "    ax = plt.gca()\n",
        "    ax.get_xaxis().set_visible(False)\n",
        "    plt.title(metric + f' ({dataset})', fontsize=20)\n",
        "    box = ax.get_position()\n",
        "    ax.set_position([box.x0, box.y0 + box.height * 0.1, box.width, box.height * 0.9])\n",
        "    plt.legend(loc='upper center', bbox_to_anchor=(0.5, -0.05), fancybox=True, shadow=True, ncol=4)\n",
        "\n",
        "    plt.savefig(f'{RES_PATH}/201_{dataset_abbrev}_transfer_{metric}.jpg', bbox_inches='tight', pad_inches=0.1, dpi=300)\n"
      ],
      "metadata": {
        "id": "uXXkiRVk3Bza"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "algo_perf = {\n",
        "    'C100': {\n",
        "        'TF-MOENAS': {\n",
        "            'IGD': {'mean': 0.0413, 'std': 0.0152},\n",
        "            'HV': {'mean': 0.6808, 'std': 0.0030}\n",
        "        }\n",
        "    },\n",
        "    'IN16': {\n",
        "        'TF-MOENAS': {\n",
        "            'IGD': {'mean': 0.0252, 'std': 0.0134},\n",
        "            'HV': {'mean': 0.4170, 'std': 0.0035}\n",
        "        }\n",
        "    },\n",
        "}\n",
        "list_algo = ['train_loss', 'synflow', 'MOENAS_PSI', 'MOENAS_TF_PSI', 'ENAS_TFI', 'E-TF-MOENAS']\n",
        "for algo in list_algo:\n",
        "    res = p.load(open(RES_PATH + f'/{algo}_{dataset_abbrev}_T.p', 'rb'))\n",
        "    igd, hv = res['final_IGD'], res['final_HV']\n",
        "    mean_igd, std_igd = np.round(np.mean(igd), 4), np.round(np.std(igd), 4)\n",
        "    mean_hv, std_hv = np.round(np.mean(hv), 4), np.round(np.std(hv), 4)\n",
        "    algo_perf[dataset_abbrev][algo] = {'IGD': {'mean': {}, 'std': {}}, 'HV': {'mean': {}, 'std': {}}}\n",
        "    algo_perf[dataset_abbrev][algo]['IGD']['mean'] = mean_igd\n",
        "    algo_perf[dataset_abbrev][algo]['IGD']['std'] = std_igd\n",
        "    algo_perf[dataset_abbrev][algo]['HV']['mean'] = mean_hv\n",
        "    algo_perf[dataset_abbrev][algo]['HV']['std'] = std_hv"
      ],
      "metadata": {
        "id": "6B-SNmVB0qr5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "draw('IGD')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 486
        },
        "id": "yeLxb0nCANK1",
        "outputId": "9db250b6-84d0-439d-cf69-fcd083f5a604"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "draw('HV')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 486
        },
        "id": "nVy7-2ugCVuq",
        "outputId": "e4b97952-6897-4901-f395-744f60b402f0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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nTgbLdSc51Gq1ydEJmZmZ0jPZ+/btqzf5nDG638fmzZuXWI+IiGouJu5ERGQVil5Z37RpEzZu3IiQkBD4+vqWy/bbtWsHubxwt1faBGj29vbYvHkz7Ozs8Mcff+CTTz4pdfvXrl3DwoULAQBz5szRm52+KqxZswZ169Y1a2b3Dh06oHXr1gAKn8VuySgDU9566y0pYV2yZAk0Gs1Db/Nh+Pn5Se/FDz/8YPRq+eXLl6VkfNasWZDJZAZ1Ro4cKZ3U+e6774y2tX79emRnZ0Mul2PWrFklxqXRaHD9+nUAKJdbQoiI6NHExJ2IiKyCv78/nnzySQCFz1JPSUkpt2HyQOEw5ICAAADAn3/+WWr9jh07YuPGjXBwcMDbb7+NDz/8EAUFBUbrnjx5Et27d0daWhpeeukl6R7vqnLp0iWcPn0aw4cPN3voddHH8JXHcHlPT0+MGjUKABAXF4eNGzc+9DYf1ieffAJXV1fcuHEDs2fP1luWk5ODCRMmQAiBwMBATJgwweg2atWqhWXLlkEmk2Hjxo3YuXOn3vLr16/j/fffB1B48uKJJ54oMaYrV65ArVYDAJ555pmydo2IiB5xfI47ERHpuXDhAi5cuAAAyMrKkv7VPZ89KCjI4Fnf9+7dw/79+wFAunoIAFu3boWbmxuaNm2KwMDAUtseOXIkpk2bBgBwdHQs9+eADx8+HGfOnMGhQ4fMqv/CCy/gyJEjGD9+PN5//30sX74c/fv3R7NmzeDo6Ig7d+7g0KFDiIyMhI2NDd577z3MmzfP4Ert9evXER0dDUB/tnHde+rv7w9/f38Apb//ANC/f384OjoaxKtrZ9euXQAK769eu3at0c9MR/fZFY35119/xcqVK2FjY4P+/fvjwoULuHbtmsF6xuIvasaMGfj++++h0Wjw0UcfSbccdO7cWZoA0Fz79++X3rtLly5J5RcuXNB7b0q6taJRo0bYsWMH+vfvj8WLF+PPP/9E3759kZ2djZUrV+Lvv/9Gu3btsHXr1hIfGdivXz98+eWXCA8Px4ABAzB69Gi0b98e//zzD77++mskJydj9OjRWLRoUan90g3N9/LyQs+ePUutT0RENZQgIiIqYs6cOQKAydfKlSsN1jl06FCJ64waNcqstu/evSuUSqUAIIYPH16+HRNCpKamChcXFwFAHDp0yOz1tFqt2LFjh3jllVeEn5+fcHZ2FjY2NsLd3V106dJFzJkzR8TFxZlcf+XKlSW+P3PmzJHqlvb+AzDZlql2jH1mOqV9dnFxcWLUqFFmx1/c4MGDDer/8MMPJb/hRgQHB5f6vph7WHPv3j3xzjvviJYtWwoHBwdRp04d0alTJ/HFF1+IvLw8s2M6f/68GDNmjGjYsKGws7MTKpVKhIWFie3bt5u9jQ4dOggA4j//+Y/Z6xARUc0jE6KCpm8lIiKyQl988QXefPNN9OjRQ7raSVQV9u7di969e6NJkya4ePEin+FOREQmMXEnIqIaRavVol+/fti1axe+/PJLvPbaa1UdEtVA9+/fR4cOHXD//n0cPHjQ6Az2REREOpycjoiIahS5XI4ff/wRPXr0wJtvvomVK1dWdUhUw9y6dQvPPPMMkpOTsXnzZibtRERUKibuRERU4zg6OmLv3r2YOXMmr7hTpYuIiICdnR2OHz+O0NDQqg6HiIiqAQ6VJyKiGu3GjRto1KhRVYdBNciNGzfQsGFDo8+JJyIiMoaJOxEREREREZEV41B5IiIiIiIiIiumrOoAiKjyabVa3L59G05OThyqSUREVE0IIZCRkQFvb2/I5bz+RlSTMHEnqoFu376NBg0aVHUYREREVAa3bt1C/fr1qzoMIqpETNyJaiAnJycAhTt+Z2fnKo6GiIiIzJGeno4GDRpI+3EiqjmYuBPVQLrh8c7OzkzciYiIqhne5kZU8/DmGCIiIiIiIiIrxsSdiIiIiIiIyIoxcSciIiIiIiKyYkzciYiIiIiIiKwYE3ciIiIiIiIiK8bEnYiIiIiIiMiKMXEnIiIiIiIismJM3ImIiIiIiIisGBN3IiIiIiIiIivGxJ2IiIiIiIjIijFxJyIiIiIiIrJiyqoOgIiIiIjIakVEFL4qy9SphS8ioiKYuBMRERERmZKeDiQkVG57RETFMHEnIiIiIjLF2Rnw8TGvbmIikJenX2ZrC7i7W9YeEVExMiGEqOogiKqLK1euIDw8HA8ePIBarUZQUBAWLlyI2rVrl7pudHQ0PvzwQ6jVaiQnJ0MIgUmTJmHChAlSncjISMybNw/5+fmwsbEBAMydOxchISFSnVWrVuHjjz+Gp6en3vZHjx6N0aNHm9WP9PR0uLi4IC0tDc48QCAiIiofgYHA8eP6ZZ06AceOlcvmuf8mqrk4OR2RmZKTk9GtWzd06dIFx48fx6lTpxAbG4thw4aVum5kZCSGDBmCTz/9FJGRkTh37hxCQkJw+PBhqc61a9cQFhaGsLAwREdH47fffsPrr7+O0NBQXLp0SW97s2bNQlRUlN7L3KSdiIiIiIiqFw6VJzLT559/jqysLEybNg0AoFQqMXv2bAQHByM6OhpBQUFG1xNCYOLEiZgxYwaaN28ulc+ePRvx8fHSz+fPn4darUZYWJhUFhYWhtzcXOzbtw8tW7asoJ4REREREZE14xV3IjPt2rULbdu2hZ2dnVTWqVMnyOVy7Ny50+R6J0+exNWrV9GrVy+9cjc3N7Rp00b6uWvXrqhfvz5WrFgBjUYDAPjuu+8AAF5eXuXYEyIiIiIiqk54xZ3ITLGxsejTp49ema2tLdzc3HDlyhWT6507dw4AkJCQgJkzZyIpKQn29vYYNGgQxo8fD7m88PyZm5sboqOjMXz4cHh5eaFWrVq4ffs2xo4di4EDB+ptc/fu3Vi7di3UajUcHBzwwgsvYPz48VAq+SdNRERERPSo4VE+kZkyMzP1rrbr2NnZITMz0+R6ycnJAIDw8HDs2rULvr6+OHv2LHr06IGYmBhE/Pts2Lt37yIkJAQ9e/bEgQMHoFQqsW3bNiQlJUnJPQCoVCr4+vpi5cqVcHR0xMWLFxEaGordu3djx44dRmNQq9VQq9XSz+n/PmomPz8f+fn5AAC5XA6FQgGNRgOtVivV1ZUXFBSg6FyWCoUCcrncZLluuzq6kwoFBQVmldvY2ECr1UqjDwBAJpNBqVSaLDcVO/vEPrFP7BP7xD5VRp8EABkMlWefiKhmYuJOZKbatWvrJb86arW6xFnlFQoFAOCNN96Ar68vAKBt27YYN24clixZgrlz58LZ2RmLFy/GP//8g08++USaUb5fv35o1KgR0tPTMXXqVABA79690bt3b2n7rVu3xrvvvovXXnsNJ0+eRIcOHQxiWLBgAT744AOD8n379sHBwQEA4Ovri4CAAFy4cAE3b96U6vj5+aFFixY4efIkEhMTpfI2bdqgYcOGOHz4MDIyMqTywMBAeHh4YN++fXoHGCEhIbC3t8fu3bv1YggNDUVOTg4OHToklSmVSoSFhSEpKQnHiszE6+TkhO7du+PWrVvSSAYAcHd3R1BQEGJjYxETEyOVs0/sE/vEPrFP7FNl9ilPrYbBKX61utz6dPTo0eJbJ6Iago+DIzJTu3bt4OTkpLcDz8vLg729Pd5++2189NFHRtfbsGEDhg4dir179+LZZ5+Vyr/++mu89tprOHPmDNq2bYuwsDCcP39eb8I6AOjWrRsyMjJw5swZk7EdOnQI3bt3x/r16zFkyBCD5cauuDdo0ABJSUnS42Sq0xWNR/EqDfvEPrFP7BP7VM37dPYsxLPPQpaaiuJEnz7QvPsuRLt2D9WnlJQUuLq68nFwRDUQr7gTmSk0NBSfffYZ8vLyYGtrCwA4ceIEtFqt3kzwxYWEhEChUBgk5Ldv3wYA6XnsDRo0QGRkpN72gcJ744tOTjd27Fh8/vnncHR0lMpu3boFAPDx8TEag52dndFh/jY2NtLVfR2FQiGNEijK1P3zpsqLb7cs5XK5XO82gdLKTcXOPrFPlpazT+wTwD6ZitHS8hrRpy1bgMGDITMxlF22Zw+Ue/cCP/4IDBgglZdXn4jo0cdZ5YnMFB4eDgcHB+me9IKCAsyfPx99+/ZF586dpXpjxoyBv78/cnNzARQm5pMmTcLSpUuR+u9Z+ISEBKxYsQLDhw+Ht7c3AGDChAnQaDT4+OOPpW2tWbMGV69exYgRI6SyyMhILFu2TPr5/v37WLx4MTp06ICnn366wvpPRERERpw8CQweDBS5Am9Aoyl8DR4MnDpVebER0SODQ+WJLBATE4Pw8HCkpaUhNzcXgYGBWLRokd497kOGDMGpU6fw559/wt7eHkDhpDTz5s3DL7/8AhcXF6jVagwcOBBTpkzRu7oeFRWFefPmITs7GzKZDPn5+Zg0aRLGjBkj1fnxxx+xevVqpKamQqlUIisrC8888wxmzZoFFxcXs/qRnp4OFxcXDrUjIiJ6WP36Abt3l5y46ygUQFgYsG1bmZri/puo5mLiTlQDccdPRERUDm7eBBo1Aiw5nJbJgBs3gH8nrLUE999ENReHyhMRERERlUVkpGVJO1BY/+DBiomHiB5ZTNyJiIiIiMoiIwMwMsFeieRyID29YuIhokcWE3ciIiIiorJwcgKKPM7NLFotwGHuRGQhPlOCiEyKOBaBiGMRldbe1MCpmBo4tdLaIyIieig9ehTes27pPe7du1dcTET0SGLiTkQmpavTkZCRUKntERERVRu+vkCfPpbPKl+GiemIqGZj4k5EJjnbOcPHycesuonZicjT5OmV2cht4OHoYVF7RERE1cr77wN79pR+5V0mK3zNnl15sRHRI4OPgyOqgcr7cTInE07i2bXPIjU31WBZ3+Z98X7X99Hep/1Dt0NERGSVtmwBBg8GCgqML1coCpP2TZuA/v3L3AwfB0dUczFxJ6qBynPHv+XSFgzePBgFWuMHKwqZAjKZDD8O/BEDWg54qLaIiOjhfX3zPr6JT6y09l6t746JvuaPvqq2Tp0CnnkGSE01XNavX+GV9vYPdxKbiTtRzcWh8kRUZicTTmLw5sHQaE3f16cRGsiEDIM3D0b0K9G88k5EVMUyNBrcUedXans1Qvv2QIsWwPHj+uUBAcC2bVUTExE9Mpi4E1GZfXj4QwghIFDywB0BASEEPjzyIba9zIMXIqKq5KRQwMvOxqy6SXkFyC82ONNGJoObrfmHkE4KhUXxPXLs7Ko6AiJ6BDBxJ6IyuZl2Ezuv7Cw1adfRCA12xOzAzbSb8HXhbLpERFVloq+H2UPXw85cwZn0bL0yfyd77HqqeUWERkREJsirOgAiqp4ir0eanbTrCAgcjDtYQRERERERET2amLgTUZlk5GVALrPsK0Quk/NZ7UREREREFmLiTkRl4mTrBK3QWrSOVmj5rHYiIiIiIgvxHnciKpMeTXpABplFw+VlkKF74+4VGBUREVE5i4gofJkj0chj9s6eBerXN7+9qVMLX0RERTBxJ6Iy8XXxRZ/mfbA7djc0ovRH/ShkCoQ1D+PEdEREVL2kpwMJCWVfPy/PsvXTeUsZERli4k5EZfZ+1/ex5+oeyETJV95lkEEmk2F2l9mVGB0REVE5cHYGfHwqtz0iomKYuBNRmbX3aY8fB/6IwZsHo0BbYLSOQqaATCbDpoGb0N6nfSVHSERE9JA4dJ2IrAATdyIyKeJYBCKOlX5fX91adZGUnWT0qruN3AZOdk54Y88beGPPGyVuZ2rgVEwN5MEREREREVFRTNyJyKR0dToSMh7ivj4AuZpc5Gbnmt0eERERERHpY+JORCY52znDx6ny7uvjo+KIiIiIiAwxcScikzh0nYiIiIio6smrOgAiIiIiIiIiMo2JOxEREREREZEVY+JOREREREREZMWYuBMRERERERFZMSbuRERERERERFaMiTsRERERERGRFWPiTkRERERERGTFmLgTERERERERWTEm7kRERERERERWjIk7ERERERERkRVj4k5ERERERERkxZi4ExEREREREVkxJu5EREREREREVoyJOxEREREREZEVY+JOREREREREZMWYuBMRERERERFZMSbuRERERGS2PK22qkMgIqpxmLgTERERkYGz6Vm4mp1rUP5nZi5GXriOP9KzqyAqIqKaiYk7EREREenZlZiKfmdjkVZg/Op6ZHI6+p69gl2JqZUbGBFRDcXEnYiIiIgkZ9Oz8OpfN6ARputoAGgE8OpfN3jlnYioEjBxJyIiIiLJpzfuQQighLwdQOFyIYBPb9ytjLCIiGo0Ju5EREREBACIz83D/uR0aMysrwGwLzkd8bl5FRkWEVGNx8SdiIiIiAAARx5klHqlvTgB4PcHGRURDhER/YuJOxEREREBALI0WosPDuUAMjV8RBwRUUVi4k5EREREAABHhRyWpuBaALUVPKQkIqpI/JYlIiIiIgBAl7pOkFm4jgzA03WdKiIcIiL6FxN3IiIiIgIA1K9li16uzlCYWV8B4BlXZ9SvZVuRYRER1XhM3IkscOXKFfTu3RudOnVCQEAAJk2ahMzMTLPWjY6ORmhoKHr06IE2bdrgySefxPLly/XqREZGIjg4GEFBQQgODkZwcDAOHTpksK0TJ06gS5cuePrpp9GmTRvMnj0bBQUF5dJHIiKq2d5q5AmZDKVeeZcBkMmAKY08KyMsIqIajYk7kZmSk5PRrVs3dOnSBcePH8epU6cQGxuLYcOGlbpuZGQkhgwZgk8//RSRkZE4d+4cQkJCcPjwYanOtWvXEBYWhrCwMERHR+O3337D66+/jtDQUFy6dEmqd+XKFfTs2ROvvfYafv/9d/z+++/Yvn07pk2bViH9JiKimiXA2QHfPN4IihIydwUAhQxY/ngjBDg7VFpsREQ1FRN3IjN9/vnnyMrKkhJkpVKJ2bNnY/v27YiOjja5nhACEydOxIwZM9C8eXOpfPbs2Zg+fbr08/nz56FWqxEWFiaVhYWFITc3F/v27ZPK5s+fD29vbwwdOhQAULt2bUyfPh3Lli3DzZs3y62/RERUc4W518GOts3hojR+qNjT1Rk72jZHqHudyg2MiKiGYuJOZKZdu3ahbdu2sLOzk8o6deoEuVyOnTt3mlzv5MmTuHr1Knr16qVX7ubmhjZt2kg/d+3aFfXr18eKFSug0WgAAN999x0AwMvLSy+OoKAgvW0FBQVBo9Fg7969Ze4fERFRUQHODnjMoZZB+RO1a2G1fxNeaSciqkTKqg6AqLqIjY1Fnz599MpsbW3h5uaGK1eumFzv3LlzAICEhATMnDkTSUlJsLe3x6BBgzB+/HjI5YXnz9zc3BAdHY3hw4fDy8sLtWrVwu3btzF27FgMHDgQAPDgwQMkJyfD29tbrw0fHx8AMBmHWq2GWq2Wfk5PTwcA5OfnIz8/HwAgl8uhUCig0Wig1f7vYUC68oKCAgghpHKFQgG5XG6yXLddHaWy8Oum+L34psptbGyg1WqlkxgAIJPJoFQqTZabip19Yp/YJ/aJfSpbn4qW6dha0Fdr7FN1/5yIqGZi4k5kpszMTL2r7Tp2dnYlTlCXnJwMAAgPD8euXbvg6+uLs2fPokePHoiJiUFERAQA4O7duwgJCUHPnj1x4MABKJVKbNu2DUlJSVJyr2uneBy6n03FsWDBAnzwwQcG5fv27YODQ+EVE19fXwQEBODChQt6Q+79/PzQokULnDx5EomJiVJ5mzZt0LBhQxw+fBgZGRlSeWBgIDw8PLBv3z69A4yQkBDY29tj9+7dejGEhoYiJydHbxI+pVKJsLAwJCUl4dixY1K5k5MTunfvjlu3bkknRADA3d0dQUFBiI2NRUxMjFTOPrFP7BP7xD49XJ9SU9MAhf6M8UKIat2n6vw5HT16FERUM8mEsVOpRGTAxcUFffr0wbp16/TKVSoVunTpgs2bNxtdb+HChZg1axa++eYbTJgwQSqfMWMGlixZgtTUVDg7O2PatGn4/PPP8eDBA9SuXRsAoNVq0ahRI0yZMgVTp07FgwcPUK9ePbz77ruYP3++tK2cnBw4ODhg2rRp+OSTTwxiMHbFvUGDBkhKSoKzszMAXtFgn9gn9ol9Yp8My0NPx+BsRo5enE85O2Crf+Nq26fq/DmlpKTA1dUVaWlp0v6biGoGXnEnMlOzZs1w+/ZtvbK8vDwkJSXpTTpXnK+vLwCgYcOGeuVNmzaFEAJXr15F27ZtcfnyZahUKilpBwp34E2aNMG6deswdepU1K1bF66urgZx6H42FYednZ3R0QI2NjawsbHRK1MoFFAoDJ/gqzuAMbe8+HbLUi6Xy6XRBuaUm4qdfWKfLC1nn9gngH0CChNPY6pznx7Fz4mIHn2cnI7ITKGhoTh79izy8vKkshMnTkCr1erNBF9cSEgIFAoF4uPj9cp1ybanZ+Hzb3VXwItuHyi8N97R0VEvjqLD84DCZ8TL5XI899xzZescERERERFZLSbuRGYKDw+Hg4ODdE96QUEB5s+fj759+6Jz585SvTFjxsDf3x+5ubkAChPzSZMmYenSpUhNTQVQmIyvWLECw4cPlyaamzBhAjQaDT7++GNpW2vWrMHVq1cxYsQIqWz27NlISEjAxo0bARTe175kyRJMmjRJurpPRERERESPDt7jTmSBmJgYhIeHIy0tDbm5uQgMDMSiRYv0hrcPGTIEp06dwp9//gl7e3sAgEajwbx58/DLL7/AxcUFarUaAwcOxJQpU2Br+79Jf6KiojBv3jxkZ2dDJpMhPz8fkyZNwpgxY/TiOHHiBKZPnw4hBDIzMxEWFoYPPvjA7CF06enpcHFx4T1yRERUorAzV3AmPVuv7ClnB+x6yvQtYlRxuP8mqrmYuBPVQNzxExGROZi4Wxfuv4lqLg6VJyIiIiIiIrJiTNyJiIiIiIiIrBgTdyIiIiIiIiIrxsSdiIiIiIiIyIoxcSciIiIiIiKyYkzciYiIiIiIiKwYE3ciIiIiIiIiK8bEnYiIiIiIiMiKMXEnIiIiIiIismJM3ImIiIiIiIisGBN3IiIiIiIiIivGxJ2IiIiIiIjIijFxJyIiIiIiIrJiTNyJiIiIiIiIrBgTdyIiIiIiIiIrxsSdiIiIiIiIyIoxcSciIiIiIiKyYkzciYiIiIiIiKwYE3ciIiIiIiIiK6as6gCIiIiIqPJ8ffM+volPNKtuUl6BQdmFjBwERP9ldnuv1nfHRF8Ps+sTEZEhJu5ERERENUiGRoM76vwyr58vhEXrZ2g0ZW6LiIgKMXEnIiIiqkGcFAp42dlUantERPRwmLgTERER1SATfT04dJ2IqJrh5HREREREREREVoyJOxEREREREZEVY+JOREREREREZMWYuBMRERERERFZMSbuRERERERERFaMiTsRERERERGRFWPiTkRERERERGTFmLgTERERERERWTEm7kRERERERERWjIk7ERERERERkRVj4k5ERERERERkxZi4ExEREREREVkxJu5EREREREREVoyJOxEREREREZEVY+JOREREREREZMWYuBMRERERERFZMSbuRERERERERFaMiTsRERERERGRFWPiTkRERERERGTFmLgTERERERERWTEm7kRERERERERWjIk7ERERERERkRVj4k5ERERERERkxZi4ExEREREREVkxJu5EREREREREVoyJOxEREREREZEVU1Z1AETVyZUrVxAeHo4HDx5ArVYjKCgICxcuRO3atUtdNzo6Gh9++CHUajWSk5MhhMCkSZMwYcIEAMDcuXOxceNGeHp66q137do1NG3aFFFRUQCAVatW4eOPPzaoN3r0aIwePbpc+klERERERNaDiTuRmZKTk9GtWzdMnjwZ7777LgoKChAaGophw4Zh27ZtJa4bGRmJV155Bfv370fz5s0BAFOmTMHhw4elxB0AZs2aZZB8P/XUUxg5cqRembF6RERERET0aGLiTmSmzz//HFlZWZg2bRoAQKlUYvbs2QgODkZ0dDSCgoKMrieEwMSJEzFjxgwpaQeA2bNnIz4+Xvr59ddfh62trd66p06dwvXr1/Hyyy9XQI+IiIiIiKg64D3uRGbatWsX2rZtCzs7O6msU6dOkMvl2Llzp8n1Tp48iatXr6JXr1565W5ubmjTpo30s4eHB+rUqaNX5+uvv8bo0aPh4OBQLn0gIiIiIqLqh1fcicwUGxuLPn366JXZ2trCzc0NV65cMbneuXPnAAAJCQmYOXMmkpKSYG9vj0GDBmH8+PGQy42fP0tLS8OPP/6IM2fOGCzbvXs31q5dC7VaDQcHB7zwwgsYP348lErjf9JqtRpqtVr6OT09HQCQn5+P/Px8AIBcLodCoYBGo4FWq5Xq6soLCgoghJDKFQoF5HK5yXLddnV0sRUUFJhVbmNjA61WC41GI5XJZDIolUqT5aZiZ5/YJ/aJfWKf2KdHpU9EVDMxcScyU2Zmpt7Vdh07OztkZmaaXC85ORkAEB4ejl27dsHX1xdnz55Fjx49EBMTg4iICKPrrV27Fp06dYKfn59euUqlgq+vL1auXAlHR0dcvHgRoaGh2L17N3bs2GF0WwsWLMAHH3xgUL5v3z7par6vry8CAgJw4cIF3Lx5U6rj5+eHFi1a4OTJk0hMTJTK27Rpg4YNG+Lw4cPIyMiQygMDA+Hh4YF9+/bpHWCEhITA3t4eu3fv1oshNDQUOTk5OHTokFSmVCoRFhaGpKQkHDt2TCp3cnJC9+7dcevWLemECAC4u7sjKCgIsbGxiImJkcrZJ/aJfWKf2Cf26VHq09GjR0FENZNMFD2dR0Qmubi4oE+fPli3bp1euUqlQpcuXbB582aj6y1cuBCzZs3CN998ozcR3YwZM7BkyRKkpqbC2dnZYD1/f3/MnTsXAwYMKDW2r7/+Gq+99hpOnDiBDh06GCw3dsW9QYMGSEpKktrmFQ32iX1in9gn9ol9su4+paSkwNXVFWlpaUaPHYjo0cUr7kRmatasGW7fvq1XlpeXh6SkJL1J54rz9fUFADRs2FCvvGnTphBC4OrVq2jbtq3esujoaKSkpKBfv35mxaa7Kn/t2jWjibudnZ3R0QI2NjawsbHRK1MoFFAoFAZ1TQ3DN1VefLtlKZfL5UZvJTBVbip29ol9srScfWKfAPbJVIyWlrNPFd8nInr0cXI6IjOFhobi7NmzyMvLk8pOnDgBrVaLsLAwk+uFhIRAoVDozSAPQDoJUPx57EDhFXRT96yPHTsWWVlZemW3bt0CAPj4+JjfISIiIiIiqhaYuBOZKTw8HA4ODtI96QUFBZg/fz769u2Lzp07S/XGjBkDf39/5ObmAihMzCdNmoSlS5ciNTUVQOFEdStWrMDw4cPh7e2t186DBw+wdetWjB8/3mgckZGRWLZsmfTz/fv3sXjxYnTo0AFPP/10eXaZiIiIiIisAMfbEJnJ1dUVUVFRCA8Px7Zt25Cbm4vAwEAsWrRIr15ubi6ys7P17k2LiIjAvHnz0LVrV7i4uECtVuPNN9/ElClTDNpZvXo1nnnmGYOEXmfhwoVYvXo1tm7dCqVSiaysLPTp0wezZs0yOUM9ERERERFVX5ycjqgGSk9Ph4uLCye3ISIiqka4/yaquXh5joiIiIiIiMiKMXEnIiIiIiIismK8x52IiCrVpVWrcHnNmkprr8XIkWg5enSltUdERERU3pi4ExFRpcrPykLOvXuV2h4RERFRdcbEnYiIKpWNoyPsVSqz6qpTUqDNz9crk9vYwK5ePYvaIyIiIqrOOKs8UQ3EWWmpuvh16FAknz+vV+b65JN4dv36KoqIiKjqcP9NVHNxcjoiIiIiIiIiK8bEnYiIiIiIiMiKMXEnIiIiIiIismJM3ImIiIiIiIisGBN3IiIiIiIiIivGxJ2IiIiIiIjIijFxJyIiIiIiIrJiTNyJiIiIiIiIrBgTdyIiIiIiIiIrxsSdiIiIiIiIyIoxcSciIiIiIiKyYkzciYiIiIiIiKyYsqoDICIiIuMiIiIQERFRae1NnToVU6dOrbT2iIiIyDxM3ImIiKxUeno6EhISKrW96urmze9x89aKSmvPt8Er8PUdW2ntERFRzcbEnYiIyEo5OzvDx8fHrLqJiYnIy8vTK7O1tYW7u7tF7VVXBQWZUKvvVmp7RERElYWJOxERkZWyZOh6YGAgjh8/rlfWtm1bHDt2rCJCszpKZW3Y2XmaVTcvLxlC5OuVyWQ2sLV1tag9IiKiysLEnYioPEQvBY4tq7z2AicBQZMrrz0iK+frO9bsoeunTg9EevofemVOTq3Rvt3migiNiIjooTFxJyIqD+oMION25bZHRERERDUCE3ciovJg5wQ4eZtXNzsR0OgP04XCBnAw/15k2DmZX5eIiIiIqjUm7kRE5SFosvlD17/rCcSf0i/zagOMO1DuYRERERFR9Sev6gCIiIiIiIiIyDQm7kRERERERERWjIk7ERERERERkRVj4k5ERERERERkxZi4ExEREREREVkxJu5EREREREREVoyJOxEREREREZEVY+JOREREREREZMWYuBMRERERERFZMSbuRERERERERFaMiTsRERERERGRFWPiTkRERERERGTFmLgTERERERERWTEm7kRERERERERWjIk7ERERERERkRVj4k5ERERERERkxZi4ExEREREREVkxJu5EREREREREVoyJOxEREREREZEVY+JOREREREREZMWYuBMRERERERFZMSbuRERERERERFaMiTsRERERERGRFVNWdQBE1cmVK1cQHh6OBw8eQK1WIygoCAsXLkTt2rVLXTc6Ohoffvgh1Go1kpOTIYTApEmTMGHCBADA3LlzsXHjRnh6euqtd+3aNTRt2hRRUVFS2YkTJzB9+nQIIZCZmYk+ffpg7ty5UCr5J01ERERE9KjhFXciMyUnJ6Nbt27o0qULjh8/jlOnTiE2NhbDhg0rdd3IyEgMGTIEn376KSIjI3Hu3DmEhITg8OHDevVmzZqFqKgovZeHhwdGjhwp1bly5Qp69uyJ1157Db///jt+//13bN++HdOmTSv3PlMlKlBXdQREREREZKWYuBOZ6fPPP0dWVpaUICuVSsyePRvbt29HdHS0yfWEEJg4cSJmzJiB5s2bS+WzZ8/G9OnTpZ9ff/11vPDCC3rrnjp1CtevX8fLL78slc2fPx/e3t4YOnQoAKB27dqYPn06li1bhps3b5ZHV6kixZ8Bkq4Ylt+9AKx/GUg4U/kxEREREZFVY+JOZKZdu3ahbdu2sLOzk8o6deoEuVyOnTt3mlzv5MmTuHr1Knr16qVX7ubmhjZt2kg/e3h4oE6dOnp1vv76a4wePRoODg56cQQFBenVCwoKgkajwd69e8vQM6o0f28HVjwD5KYZXx67D/j+mcJ6RERERET/4g2xRGaKjY1Fnz599MpsbW3h5uaGK1eMXEH917lz5wAACQkJmDlzJpKSkmBvb49BgwZh/PjxkMuNnz9LS0vDjz/+iDNn/ncF9sGDB0hOToa3t7deXR8fHwAwGYdarYZa/b+h2Onp6QCA/Px85OfnAwDkcjkUCgU0Gg20Wq1UV1deUFAAIYRUrlAoIJfLTZbrtquju/++oKDArHIbGxtotVpoNBqpTCaTQalUmiw3Fbs19EmWcBbKzWMgtBrIYILQQAgZsHkMNKP3QOnbwar7BFT852T8fRJSLNWxTxX1ORVdr2hZfn5+te1TSeUP0ychtChOCK3B92F16pPOo/Q5sU/G+0RENRMTdyIzZWZm6l1t17Gzs0NmZqbJ9ZKTkwEA4eHh2LVrF3x9fXH27Fn06NEDMTExiIiIMLre2rVr0alTJ/j5+enFoGuzeAxFlxe3YMECfPDBBwbl+/btk67m+/r6IiAgABcuXNAbcu/n54cWLVrg5MmTSExMlMrbtGmDhg0b4vDhw8jIyJDKAwMD4eHhgX379ukdYISEhMDe3h67d+/WiyE0NBQ5OTk4dOiQVKZUKhEWFoakpCQcO3ZMKndyckL37t1x69Yt6YQIALi7uyMoKAixsbGIiYmRyq2pTx2vfwpPISCDYXJVlAwCWq0W6TveR71Jv1p1nyrjczImLy8PAKptnyrqc0pNTTV4r1JTU7Fv375q2yegYj4ne4dUKBSG75Wu7erYJ51H6XNinwz7dPToURBRzSQTxk7RE5EBFxcX9OnTB+vWrdMrV6lU6NKlCzZv3mx0vYULF2LWrFn45ptvpBnkAWDGjBlYsmQJUlNT4ezsbLCev78/5s6diwEDBkhlDx48QL169fDuu+9i/vz5UnlOTg4cHBwwbdo0fPLJJwbbMnbFvUGDBkhKSpLa5hWNCuxTWjyUSwNKTdqLEpBBNuVPaJy8rbNP/6roz+nAiBFIPn9er01Xf388u2FDte1TRX1OXbp0wYkTJ/TqdezYEUeOHKm2fSqp/GH69Me5wcjI0P+9cnJ6EgFtfqy2fdJ5lD4n9skw9pSUFLi6uiItLc3osQMRPbp4xZ3ITM2aNcPt27f1yvLy8pCUlKQ36Vxxvr6+AICGDRvqlTdt2hRCCFy9ehVt27bVWxYdHY2UlBT069dPr7xu3bpwdXU1iEP3s6k47OzsjI4WsLGxgY2NjV6ZQqGAovilKPzvAMbc8uLbLUu5XC43OlzaVLmp2Ku8T7eOAhYk7UDhlXfEHYYiYJh19qmIiv6cDMgKbzaozn2qiM9JJjO8CUMmk0l1qmOfSisva59kMsNlMpncoI3q1Cdzy9mnR7NPRPTo4+R0RGYKDQ3F2bNnpWG6QOHz1LVaLcLCwkyuFxISAoVCgfj4eL1yXbJd/LntQOGkdOPHjze6gw4NDdUbngcUJvpyuRzPPfecRX2iSqLOBIwkCiWSyQF1Run1iIiIiOiRx8SdyEzh4eFwcHCQ7kkvKCjA/Pnz0bdvX3Tu3FmqN2bMGPj7+yM3NxdAYWI+adIkLF26VLoHNSEhAStWrMDw4cMNJpp78OABtm7divHjxxuNY/bs2UhISMDGjRsBFN7XvmTJEkyaNEm6uk9Wxq42YGQyrBIJLWDnVDHxEBEREVG1wsSdyEyurq6IiopCVFQUAgMD0b59ezRp0gTr16/Xq5ebm4vs7Gy9e9MiIiLQr18/dO3aFV26dEH//v3x5ptv4vvvvzdoZ/Xq1XjmmWcMEnqd5s2b48CBA1i2bBmefvppPP300wgLCzM5yR1ZgcbBgOm55E2QAY27VkQ0RERERFTN8EYZIgv4+fmV+qz0DRs2GJQpFAp88MEHRmd2L27KlCmYMmVKiXV0E05RNVGnAdD8ucLntAtN6fVlCqD5s4XrEREREVGNxyvuRESVIXjGv5OqlXblXVZYr+v0yoiKiIiIiKoBJu5ERJXB5ylg4EpAXsKs6TJF4fJBqwrrE1Gl0WrzSq9ERERURZi4ExFVllb9gLH7gFouxpc3f7Zwecu+lRsXUQ2Sln4e2dnXDMozM//C+fMTkJ5+oQqiIiIiKhkTdyKiyuTzFODW3LDc0x8YsoFX2okq0P37v+LMmZdQUJBudHlSchROnxmE+/d/reTIiIiISsbEnYjIGijtqjoCokdaWvp5XPzrTYgSJ4jUQAgNLv71Jq+8ExGRVWHiTkRERI+8G3HL/n1MpyilpoAQAnE3llZGWERERGZh4k5ERESPtNzc20hKPgjAjMcxAgA0SEo6iNzc2xUZFhERkdmYuBMREdEjLSUlGqVfaS9OIOVBdEWEQ0REZDEm7kRERPRI02gyYfkhjxyagsyKCIeIiMhiTNyJiIjokaZQ1AagtXAtLRTK2hURDhERkcWYuBMRET2i1Gp1VYdgFerVCwIgs3AtGerVDaqIcIiIiCzGxJ2IiKiaO3nyJC5fvmxQ/scff6Bfv344depUFURlPWrV8oaba3cACjPXUMDNrTtq1fKuyLCIiIjMxsSdiIioGtuyZQs6d+6M1NRUo8t3796NoKAgbNmypXIDszKNG0+GTCZD6VfeZZDJZGjcaHJlhEVERGQWJu5ERETV1MmTJzF48GBoNKYfc6bRaKDRaDB48OAafeXd2dkfrR//HDJZSVfdFZDJFGjd+nM4O/tXWmxERESlYeJORERUTX344YcQQkCIkh91pqvz4YcfVlJk1snD41m0e+onKJXORpe7uXVDu6d+gof7s5UcGRERUcmYuBMREVVDN2/exM6dO0u82l6URqPBjh07cPPmzQqOzLo5O/vDwaGpQXnt2o/jSf/lvNJORERWiYk7ERFRNRQZGVnqlfbihBA4ePBgBUVUvcnltlUdAhERkUlM3ImIqFrR5OVVdQhWISMjA3K5ZbtxuVyO9PT0CoqIiIiIKgoTdyIiskpJFy4g/fp1g/LUS5fw26RJSP7zzyqIyno4OTlBq9VatI5Wq4Wzs/H7u4mIiMh6MXEnIiKrc2v/fuwfMQL5GRlGl98+cgT7hg/Hrf37Kzky69GjR49/H29mPplMhu7du1dQRERERFRRmLgTEZFVSbpwAb9Pnw5RwqRrQqOB0Gjw+/TpNfbKu6+vL/r06QOFoqTHm/2PQqFA37594evrW8GRERERUXlj4k5ERFblr2++AYQofJXk3zoXv/mmcgKzQu+//z5kMlmpV951dWbPnl1JkREREVF5YuJORERWI+v2bST89luJV9uLEhoNEqKikHX7dgVHZp3at2+PH3/8scSr7gqFAgqFAps2bUL79u0rMToiIiIqL0zciYjIatw9caL0K+3FCYF7J05UTEDVwIABAxAdHY06deoYXR4WFobo6Gj079+/cgMjIiKicsPEnYiIrEZBVhZg4SPOIJMhPyurYgKqJtq3b48WLVoYlAcEBGDbtm280k5ERFTNMXEnIiKroXR0BCx8xBmEgI2jY8UEVM3Z2dlVdQhERERUDpi4ExGR1fDs2BGw8BFnkMmg6tixYgIiIiIisgJM3ImIyGo4envDJzgYMjMfcSZTKODTrRscvb0rODIiIiKiqsPEnYiIrErriRMLr7qXduX93zqtX321cgIjIiIiqiLKqg6AiOiREL0UOLbMvLrZiYZld84BS1qa317gJCBosvn1qxHXJ57A0598gt+nT4coKDBaR6ZQADIZnl6yBK5PPFHJERIRERFVLibuRETlQZ0BZDzEs8Q1+Zatr84oe1vVQINevfDM2rU4OGEC8tPTDZZ7d+2K1q++yqSdiIiIagQm7kRE5cHOCXCqxPus7Zwqr60q4vrEE3Bu3BjJ58/rlddp2RLBS5dWUVRERERElY+JOxFReQia/MgOXbc2Clvbqg6BiIiIqFJxcjoiIiIiIiIiK8bEnYiIiIiIiMiKMXEnIiIiIiIismJM3ImIiIiIiIisGBN3IiIiIiIiIivGxJ2IiIiIiIjIijFxJyIiIiIiIrJiTNyJiIiIiIiIrBgTdyIiIiIiIiIrxsSdiIiIiIiIyIoxcSciIiIiIiKyYkzciYiIiIiIiKwYE3ciIiIiIiIiK8bEnYiIiIiIiMiKMXEnIiIiIiIismJM3ImIiIiIiIismLKqAyCqTq5cuYLw8HA8ePAAarUaQUFBWLhwIWrXrl3qutHR0fjwww+hVquRnJwMIQQmTZqECRMm6NVbtWoVVq1aBZlMhrt378LNzQ0ff/wxOnfuDACIiorC6NGj0ahRI731nnvuOcyaNavc+kpERERERNaBiTuRmZKTk9GtWzdMnjwZ7777LgoKChAaGophw4Zh27ZtJa4bGRmJV155Bfv370fz5s0BAFOmTMHhw4f1Evf//Oc/OHjwILZv3w5nZ2fk5uYiODgYly5dkhJ3ABg9ejTmzp1bIf0kIiIiIiLrwsSdyEyff/45srKyMG3aNACAUqnE7NmzERwcjOjoaAQFBRldTwiBiRMnYsaMGVLSDgCzZ89GfHy89HNcXBzmzZuHP/74A87OzgCAWrVqYc2aNXBwcKjAnhERERERkTXjPe5EZtq1axfatm0LOzs7qaxTp06Qy+XYuXOnyfVOnjyJq1evolevXnrlbm5uaNOmjfTzpk2b4ObmhtatW+vV8/PzQ4MGDcqnE0REREREVO3wijuRmWJjY9GnTx+9MltbW7i5ueHKlSsm1zt37hwAICEhATNnzkRSUhLs7e0xaNAgjB8/HnK5XKpXv359rF27FqtWrUJ2djbc3Nzw1ltvISQkRG+bx48fR2hoKDIzM2FjY4OePXtiypQpsLe3NxqDWq2GWq2Wfk5PTwcA5OfnIz8/HwAgl8uhUCig0Wig1WqlurrygoICCCGkcoVCAblcbrJct10dpbLw66agoMCschsbG2i1Wmg0GqlMJpNBqVSaLDcVO/tUfftk1L/9qK59qqjPqeh6Rcvy8/OrbZ9KKn+YPgmhRXFCaA2+D6tTn3Qepc+JfTLeJyKqmZi4E5kpMzNT72q7jp2dHTIzM02ul5ycDAAIDw/Hrl274Ovri7Nnz6JHjx6IiYlBRESEVO/8+fP45ZdfsGPHDtjb22PVqlXo3r07tm7diueffx4A4OLiAh8fHyxatAiurq64efMm+vXrh82bN+P48eOwsbExiGHBggX44IMPDMr37dsnDcP39fVFQEAALly4gJs3b0p1/Pz80KJFC5w8eRKJiYlSeZs2bdCwYUMcPnwYGRkZUnlgYCA8PDywb98+vQOMkJAQ2NvbY/fu3XoxhIaGIicnB4cOHZLKlEolwsLCkJSUhGPHjknlTk5O6N69O27duiWdEAEAd3d3BAUFITY2FjExMVI5+1T9+2RMXl4eAFTbPlXU55SammrwXqWmpmLfvn3Vtk9AxXxO9g6pUCgM3ytd29WxTzqP0ufEPhn26ejRoyCimkkmjJ2iJyIDLi4u6NOnD9atW6dXrlKp0KVLF2zevNnoegsXLsSsWbPwzTff6E1EN2PGDCxZsgSpqalwdnZG7969sXfvXsTExOjdC9++fXsIIXD69GmTse3duxe9e/fGjz/+iJdeeslgubEr7g0aNEBSUpJ0Pz2vaLBP1tinAyNGIPn8eb02Xf398eyGDdW2TxX1OXXp0gUnTpzQq9exY0ccOXKk2vappPKH6dMf5wYjI0P/98rJ6UkEtPmx2vZJ51H6nNgnw9hTUlLg6uqKtLQ0af9NRDUDr7gTmalZs2a4ffu2XlleXh6SkpL0Eu3ifH19AQANGzbUK2/atCmEELh69Sratm1bYr3iVwGK012ZvHbtmtHldnZ2RkcL2NjYGFyhVygUUBS/FIX/HcCYW27syr+l5XK53OhwaVPlpmJnn6p3nwzIZACqd58q4nOS/fu+FCWTyaQ61bFPpZUXjf3mze9x89YKo+sWl5eXbFCWmfk3TpzsZtb6AODb4BX4+o4tNUZLyx/1z8mccvbJ8j4R0aOPk9MRmSk0NBRnz56VhukCwIkTJ6DVahEWFmZyvZCQECgUCr0Z5AFIJwE8PT0BAD179gQAo/W8vLykn9955x3ExcXp1bl16xYAwMfHx9JuERE9EgoKMqFW3zXrJUS+wfpC5Ju9vlp9FwUFpm+RIiIiKm9M3InMFB4eDgcHB+me9IKCAsyfPx99+/bVe8b6mDFj4O/vj9zcXACFifmkSZOwdOlS6R7UhIQErFixAsOHD4e3tzcAoH///mjbti0++ugjadjcoUOH8Pvvv2PGjBnS9o8dO4aIiAhpKF5GRgbmzZuHRo0aYcCAARX+PhARWSOlsjbs7Dwr7aVU1q7qLhMRUQ3C8TZEZnJ1dUVUVBTCw8Oxbds25ObmIjAwEIsWLdKrl5ubi+zsbL170yIiIjBv3jx07doVLi4uUKvVePPNNzFlyhSpjlKpxN69ezFt2jS0adMGderUgVarxZYtW/DCCy9I9d59910sX74cQUFBqFWrFrKystC+fXusXbsWtWvzQJKIaiZf37EGQ9eJiIgeFZycjqgGSk9Ph4uLCye3Iav369ChhpPTPfkknl2/vooisl6BgYE4fvy4XlmnTp30Zr4mouqN+2+imotD5YmIiIiIiIisGBN3IiIiIiIiIivGxJ2IiIiIiIjIijFxJyIiIiIiIrJiTNyJiIiIiIiIrBgTdyIiIiIiIiIrxsSdiIiIiIiIyIoxcSciIiIiIiKyYkzciYiIiIiIiKwYE3ciIiIiIiIiK8bEnYiIiIiIiMiKMXEnIiIiIiIismJM3ImIiIiIiIismLKqAyAiIiLjIiIiEBERYVbdxMREg7KzZ8+ifv36Zrc3depUTJ061ez6REREVDmYuBMREVmp9PR0JCQklHn9vLw8i9ZPT08vc1tERERUcZi4ExERWSlnZ2f4+PhUantERERkfZi4ExERWSkOXSciIiKAk9MRERERERERWTUm7kRERERERERWjIk7ERERERERkRVj4k5ERERERERkxZi4ExEREREREVkxJu5EREREREREVoyJOxEREREREZEVY+JOREREREREZMWYuBMRERERERFZMSbuRERERERERFaMiTsRERERERGRFWPiTkRERERERGTFmLgTERERERERWTEm7kRERERERERWjIk7ERERERERkRVj4k5ERERERERkxZi4ExEREREREVkxJu5EREREREREVoyJOxEREREREZEVY+JOREREREREZMWYuBMRERERERFZMSbuRERERERERFaMiTsRERERERGRFWPiTkRERERERGTFmLgTERERERERWTEm7kRERERERERWjIk7ERERERERkRVTVnUARERUs1xatQqX16wxq646JcWg7MHff+OX7t3Nbq/FyJFoOXq02fWJiIiIrA0TdyIiqlT5WVnIuXevzOtr8/MtWj8/K6vMbRERERFZAybuRERUqWwcHWGvUlVqe0RERETVmUwIIao6CCKqXOnp6XBxcUFaWhqcnZ2rOhwiIiIyA/ffRDUXJ6cjIiIiIiIismJM3IkscOXKFfTu3RudOnVCQEAAJk2ahMzMTLPWjY6ORmhoKHr06IE2bdrgySefxPLlyw3qrVq1Ct26dUNISAhatmyJLl264OjRo+UWBxERERERVS9M3InMlJycjG7duqFLly44fvw4Tp06hdjYWAwbNqzUdSMjIzFkyBB8+umniIyMxLlz5xASEoLDhw/r1fvPf/6D1atXY/v27Th06BD++OMP5OXl4dKlS+USBxERERERVT+8x53ITHPmzMGnn36K+/fvw87ODgBw+PBhBAcH4+jRowgKCjK6nhACzZs3R3h4OCZPniyVJyUlIT4+Hm3atAEAxMXFoXnz5vjjjz/QunVrqV5MTAwcHBzQoEGDh4qjKN4jR0REVP1w/01Uc/GKO5GZdu3ahbZt20rJMgB06tQJcrkcO3fuNLneyZMncfXqVfTq1Uuv3M3NTUraAWDTpk1wc3PTS9oBwM/PT0raHyYOIiIiIiKqnvg4OCIzxcbGok+fPnpltra2cHNzw5UrV0yud+7cOQBAQkICZs6ciaSkJNjb22PQoEEYP3485HK5VK9+/fpYu3YtVq1ahezsbLi5ueGtt95CSEjIQ8WhVquhVquln9PT0wEA+fn5yM/PBwDI5XIoFApoNBpotVqprq68oKAARQfoKBQKyOVyk+W67eoolYVfNwUFBWaV29jYQKvVQqPRSGUymQxKpdJkuanY2Sf2iX1in9gn9ulR6RMR1UxM3InMlJmZqXeVW8fOzq7EieGSk5MBAOHh4di1axd8fX1x9uxZ9OjRAzExMYiIiJDqnT9/Hr/88gt27NgBe3t7rFq1Ct27d8fWrVvx/PPPlzmOBQsW4IMPPjAo37dvHxwcHAAAvr6+CAgIwIULF3Dz5k2pjp+fH1q0aIGTJ08iMTFRKm/Tpg0aNmyIw4cPIyMjQyoPDAyEh4cH9u3bp3eAERISAnt7e+zevVsvhtDQUOTk5ODQoUNSmVKpRFhYGJKSknDs2DGp3MnJCd27d8etW7ekEyIA4O7ujqCgIMTGxiImJkYqZ5/YJ/aJfWKf2KdHqU/FJ6slopqD97gTmcnFxQV9+vTBunXr9MpVKhW6dOmCzZs3G11v4cKFmDVrFr755htMmDBBKp8xYwaWLFmC1NRUODs7o3fv3ti7dy9iYmLQvHlzqV779u0hhMDp06fLHIexK+4NGjRAUlKSdI8cr2iwT+wT+8Q+sU/sk3X3KSUlBa6urrzHnagG4hV3IjM1a9YMt2/f1ivLy8tDUlKSXqJdnK+vLwCgYcOGeuVNmzaFEAJXr15F27ZtS6xX9CpAWeKws7MzepXexsYGNjY2emUKhQIKhcKgru4Axtzy4tstS7lcLpduJTCn3FTs7BP7ZGk5+8Q+AeyTqRgtLWefKr5PRPTo4+R0RGYKDQ3F2bNnkZeXJ5WdOHECWq0WYWFhJtcLCQmBQqFAfHy8Xrku+fb09AQA9OzZEwCM1vPy8nroOIiIiIiIqHpi4k5kpvDwcDg4OEj3pBcUFGD+/Pno27cvOnfuLNUbM2YM/P39kZubC6AwMZ80aRKWLl2K1NRUAIUT1a1YsQLDhw+Ht7c3AKB///5o27YtPvroI2nY3KFDh/D7779jxowZFsdBRERERESPBt7jTmSBmJgYhIeHIy0tDbm5uQgMDMSiRYtQu3Ztqc6QIUNw6tQp/Pnnn7C3twcAaDQazJs3D7/88gtcXFygVqsxcOBATJkyBba2ttK6iYmJmDZtGs6dO4c6depAq9Vi+vTpeOGFFyyOoyRpaWmoU6cObt26xXvkiIiIqgndHDWpqalwcXGp6nCIqBIxcSeqgeLj4/WeDU9ERETVx61bt1C/fv2qDoOIKhETd6IaSKvV4vbt23BycoJMJqvqcIiIiMgMQghkZGTA29vb6CR4RPToYuJOREREREREZMV4qo6IiIiIiIjIijFxJyIiIiIiIrJiTNyJiIiIiIiIrBgTdyIiIiIiIiIrxsSdiIiIiIiIyIoxcSciIiIiIiKyYkzciYiIiIiIiKwYE3ciIiIiIiIiK8bEnYiIiIiIiMiKMXEnIiIiIiIismJM3ImIiIiIiIisGBN3IiIiIiIiIivGxJ2IiIiIiIjIijFxJyIiIiIiIrJiTNyJiIiIiIiIrBgTdyIiIiIiIiIrxsSdiIiIiIiIyIoxcSciIiIiIiKyYkzciYiIiIiIiKwYE3ciIiIiIiIiK8bEnYiIiIiIiMiKKcuyUl5eHgoKCso7FiIiIiIiIqJHnq2tLZRK89NxixL3lJQU3L17Fzk5ORYHRkRERERERESATCaDq6srfH19IZPJSq1vduKekpKCuLg4ODs7w8vLC7a2tmY1QERERERERESFhBBIT0/H7du34ejoCDc3t1LXMTtxv3v3LpydnfHYY48xYSciIiIiIiIqI0dHR+Tk5ODmzZuws7ODk5NTifXNmpwuLy8POTk5cHNzY9JORERERERE9JDq1asHIQQ2btyIy5cvl1jXrMRdNxGdra3tw0dHREREREREVMPZ2NgAADQaDQ4cOIDExESTdS2anM7cq+0RxyIQcSzCkk0/lKmBUzE1cGqltUdE5e/SqlW4vGZNpbXXYuRItBw9utLaI6rJbt78Hjdvrai09nwbvAJf37GV1h5RuYqIKHxVlqlTC19EVOl0+bWbmxtiY2Nx8+ZNuLu7G61bpsfBlSZdnY6EjISK2LTJ9oioesvPykLOvXuV2h4RVY6Cgkyo1XcrtT2iais9HUiovONopPM4mqiq6RL49BL+HiskcXe2c4aPk49ZdROzE5GnydMrs1XYwt3B+JkGU+2RdUtPT4ezMz+nypCRkVHq5BbWyMbREfYqlVl11Skp0Obn65XJbWxgV6+eRe0RUeVQKmvDzs7TrLp5eckQQv/vWyazga2tq0XtEVVbzs6Aj3nH0UhMBPL0j6NhawuYuGJnsj0iKyGEQFZWFmrXrp7f4w9zHC6TyaDVak1XEGbIysoSp0+fFllZWeZUt0in7zoJzIXeq9N3ncq9HXPFxcUJlUolnJ2dBQAxZswYo/XefPNNoVKphFwuF3Xr1hUqlUoUFBRIy+Pj48XkyZNFixYthEqlEiqVSrRq1UqMGDFCfPvtt0KtVhvdbmRkpHjppZeEr6+vUKlUwsnJSTz++ONi1KhRYt26dSIjI8Nk7Ldv3xYKhUJ07drV4n6PGzdOqFQqAUDY2NgIlUolVqxYUeoycyxdulSEhoZaHFN52Lt3r3BxcRF79+6tsDaysrKESqUSjo6OAoCIi4ursLbMsWrVKvH888+L1NTUKo2jIu0dMkSsa9VK77V3yJCqDouIysHJUy+KA5FN9F4nT71Y1WERWadOnYQA9F+dquY4Wnc8pDuGdnZ2lo6Bi75q1aol5syZU+K2AAg3Nze99Yoeh+pejo6OYtSoUWL//v3Sto2ta27bkyZNko7vAYiPP/641H4HBgYKAFI+UFx+fr749ttvRdeuXaU4GjduLMaOHSsuXbpkUH/FihVCpVIJGxsbAcBoP3Tvh+6Yc926dXr9X716tdFY27VrJ9zc3KTt9uvXz2i93NxcUbduXdGwYUOh1WpL7P+pU6fEgAEDROPGjYVKpRLe3t6iXbt2YvLkyWLnzp2lvHv64uPjRefOnUVUVJTBspSUFDFnzhzh7+8vvQdPPPGEeO+990RKSopF7VSksLAwsWzZMrPr6/LszZs3iwULFogDBw6YrGuViXvA1wHl3o6lVq5cKQAIAGLz5s0m6zVs2FAcOnRIrywuLk64urqKHj16iOvXrwshhNBoNCI6Olq0bNlSABB37tzRWyc/P1+MHz9e1K1bV3z33XdSgq5Wq8X+/fuFv7+/ACCmTZtmMpaIiAgBQMhkMvHPP/+Uqd8ARHBwsMXLTFm0aJFo1qyZuH37tl55w4YNLd5WWWzatEnIZDKxadOmCm9rzpw5VpG4CyHE66+/Ljp06CCys7OrOpQKYSxx3/Vi1R7YFz/p5+rqWup35t69e/UORBYvXqy3PCYmRkyYMEHaGapUKtGlSxfx9ddfi/z8fIPttWrVStStW1cAEI6OjkZ39LqDHJ2AgABpJ96qVSuRk5NjsN2NGzdKBwS67R4+fNhonxYuXCgAlHpyLysrSyxevFgEBAQIT09PoVKpRNOmTUW/fv3E4sWLDb4zSmPsBKNKpRIODg7C29tbjB8/Xty7d89gvQMHDojnnntOOlnq4+MjAgMDxfTp0/X6aOyg8Nlnn7UoRjKPscT9+Im+VRaPpclIQUGBUKlU0t9ijx49jB74Ll68WDo4123T1P7jtddeEwDEwYMHS4w1JSVFvPfee6J169ZSXH5+fmLQoEHiiy++sOiE7qhRowy+R4wlRbrvDyHM+w7SJQNCGE9Qxo0bZ3aMJIwn7gFVexytO4ZeuXKl0eWjRo0yK3Ev/vdg7Dh0zpw5evu0UaNGlXgsZk7bQhQepwIQXl5eJi+2CSHE4cOHpXyheD4ghBCJiYkiKChItG7dWkRGRgqNRiOEEOLu3bvivffeE7a2tuL77783uu3g4GDpb8sYY/3U9d/Z2VnKQYqLi4srcbtCCLFlyxapX6b290IIsXXrViGXy8Vbb70lfb/k5eWJjRs3CkdHR+Hn51diO0Xdu3dPNG7c2OBYSIjCkwNeXl7ihRde0DvZ8ffff4t+/foJDw8PcerUKbPbqkgJCQmiadOm4pNPPjGrfrVJ3E/EnxB1Pq5jkLhjLkTf9X3FyfiT5dqeJVauXCm8vb2FjY2NqFevnoiPjzdaz1ji/sYbbwgA4tq1awb1T5w4YTRxnz59upDL5eL333832s7du3eFm5tbiYl727ZtxRNPPCEAiAULFpTSQ+PKM3E/f/68UCgUYs+ePQbLKitxF0KIzMzMSmnHmhL33Nxc4e3tLd55552qDqXcJZ4/LzZ17GiQuK9r1UpEvf66SLpwoUrjK3rS7/PPPy+xbvfu3U3+Xa1cuVLUqlVLzJ49W9y9e1cIUXgCMDIyUrRq1Up06NBBJCYmGqx36NAhAcDkgUnxgxwh/rcTByAmT55sMl5zDnh0Jxm7d+9usk5+fr7o2LGjaNCggTh69KiU1Fy/fl08//zzAoDYsGFDie2YUvz9LCgoEFu2bBGOjo6icePGemflv/jiCwFALFq0SDrJlZ2dLZYtWybkcrnRxLy0g0J6OKlp50TUb20MEvcDkU3EuXPjRVra+SqLzdJkRPe3CKDEA7jg4GCT2xSi8CDY1dVVABCvvPKKyXqpqamiSZMm4sknnxQXinwP/vnnn6Jz584CgDh27JjJ9Uvrj66s+O9/8SSgtO+gQ4cOSYm7TmkJCplw4oQQdeoYJu6AEH37CnGyao6jH5XEXXdM/d1335ms16dPH2m/VzwfKCgoEN26dRM+Pj4iKSnJ6PoffvihkMlkRkeGljVxb9SokQAgOnfurDcaWMecxH3AgAFS/1999VWT9Z544gnh6+tr9OTkwoULLUrcBw0aJDp27GhQnpCQIFQqlQgNDZVOfBSl0WjEM888I9zc3ERCQoLZ7VWk7du3C6VSKS5evFhqXUsSd7MeB1cRtlzags4rOiM1N9Xo8t2xuxG0IghbLm2p3MCKaNasGebPn4+UlBSMHj0aQgiz1ouJiQEAozMCtm/fHuPHj4e9vb1UFhsbi4iICDz//PPo3Lmz0W2qVCqMGDFCemRAcZcvX0ZcXBy+//57AMC6devMirUizZ8/Hy1btsRzzz1XpXE41sB7me3s7PDmm2/is88+Q1paWlWHU25u7d+P/SNGID8jw+jy20eOYN/w4bi1f38lR6bPz88Ptra2iIiIgEajMVrnzJkzSEpKMrps//79GDt2LObOnYv//Oc/UP17779cLkf37t0RFRWFW7duYcCAASa3XxaNGjXC0qVLsWfPnjKtf/HiRdy+fRteXl6IiorC7du3jdbbvn07Tpw4gblz5yIoKEiakKVx48ZYv349XF3Nv5e5NAqFAv3798fkyZMRFxeHZcuWASh87Mr//d//oUuXLpgxY4b0nWxvb4/XX38dr776arnFQOa5f/9XnDnzEgoKjE/Mk5QchdNnBuH+/V8rObKyq1OnDurUqYN3330X58+fL9M29u7dC1dXV9jZ2eHnn3+GWq02Wu/777/H9evXERERgSeeeEIqb926NX766SfI5VV2yEcVYcsWoHNnIDXV+PLdu4GgoMJ6VmbFihX4v//7vxLrXLp0CT5m3Oc/efJkLFiwoFzb1pkyZQoUCgU++eQToznA33//jZiYGDz//PNG11+3bh2ioqIwc+ZMk/u1GTNmwNXVFZMnT7Z4f56fn49GjRoZlI8aNQphYWE4evQoPvroI4u2CQBpaWnYu3cv1q9fDxsbG/z000/IKz6Pwr9iYmLg5uZm9MljL774Ivr3729Wm3///Td++uknzJgxw2DZ7Nmzce/ePSxYsMDo95hcLsfHH3+MpKQkvPfee2a1V9H69u2Lxx57rEzvf0mq5Fv8ZMJJDN48GBqt6V9QjdBAo9Vg8ObBOJVwqhKj0zd9+nT06tULBw4cwH//+1+z1vHy8gIArF692mCZTCbD8uXL4eLiIpWtWrUKWq0Wffv2LXG7ERERJr+c1q1bh4EDB6J9+/Zo3bo1Ll68iHPnzpkVb0XIycnBtm3b0KNHD73yAwcOwNPTE7du3UJ0dDQ8PT3h6emJsLAwAMDjjz8OFxcXyGQy/Prrr5g2bRqaNm0KhUIhfSlcv34dU6ZMQcuWLeHp6Yl69eohNDQUf/zxh15bb7/9Njw9PSGTyTC6yGO/xo8fD3d3d8hkMsydOxdffvklWrVqBRcXF3Tu3NlgOw8rLy8P//nPf9C8eXOoVCr4+Phg7NixBklNdnY23nnnHTRr1gxeXl5o1KgRBgwYgC1FdroajQYLFy5Ey5Yt4eXlBV9fX/Tu3Vs6YVNUz549kZ2dja1bt5Zrf6pK0oUL+H36dIgSdmxCo4HQaPD79OlI/vPPSoxOn6enJ4YOHYobN25g06ZNRussWrQI06ZNMyjXaDSYNGkS3NzcMNXE43nc3d0xY8YMHDlyBGvXrrUotv/7v//DihXGH8u1fv16KJVKjBkzpsTniJqydu1avPTSS3j55Zeh1Wqxfv16o/VKOrnp4OCAd955B40bN7a4/ZJ06tQJAHDs2DEAwP379/HgwQOTj1wZNWoUevXqVa4xkGlp6edx8a83IURJB64aCKHBxb/eRHr6hUqLzVzGEgIXFxcsX74ceXl5GDZsGHJzcy3e7rp16zBu3DiEhoYiLS0NO3bsMFqvpL8rLy8vzJw50+TvuzELFizA5MmTS63n4+ODS5cumb3d4OBgXLt2zez6ZMTJk8DgwUBJiZ5GU/gaPBg4VXXH0UWtWrUK3bp1g1wuL/VEUosWLUxerCrKzc1NOu4ur7Z1mjRpggEDBuDy5cvYvn27wfLFixfjrbfeMrm9b7/9FgBKPL63tbVF7969cfXqVURFRZkVV7du3bBq1SoolabnGF+5ciU8PT0xb948nDx50qzt6mzevBmdO3dG69at8eyzzyIlJQW7d+82WtfLywsXLlwweuzctGlTs0+qbNy4EQAM8obs7GysW7cOvr6+8Pf3N7l+QEAAGjRogPXr1yMnJwcpKSnw9PSEvb293kmFmJgYeHp6wtbWVu+kh65cVz8+Ph4DBgyAj48PZDIZunXrBgC4ceMGhg0bhoYNG8LT0xN+fn549dVXcebMGYOYevbsiV9++cXkydayqJLE/cPDH0IIAYGSr2ALCAgh8OGRDyspMkMymQxr1qyBu7s73n33XVy4UPrBwuDBgwEAb775Jvr374+dO3eW+KFFR0cDKExay2rdunUYNmwYAEj/VuVV91OnTkGtVqNFixZ65T179sTdu3fRoEEDBAUF4e7du7h79y527doFAPjrr7/w2WefAQDef/99dO3aFVevXtW7Arh7925s2rQJmzZtwt27d3H9+nW4uLiga9euuH79ulRv4cKFuHvX8PFD3377LU79uxPbvHkzcnJycOHCBdy4cQP5+fl47rnnyu2PTKvVon///li6dCnWr1+Pe/fu4cyZM7h8+TI6duyoF9/rr7+O3bt347fffsOdO3dw5swZ5OXl6SVv//nPf/DFF19g27ZtuHPnjnRGety4cQZtt2zZEgBw5MiRculLVfvrm2/+NwiwJP/WufjNN5UTmAnTp0+HTCbD4sWLDZZdv34dJ06cwJAhQwyWRUVFITY2Fs8991yJBy26M/zfffedWfFERUWhUaNGJR60BAYGYs6cObh3757R36mSCCGwYcMGDBs2TPoOMnVSQXeQtWbNGqNXMaZNm4aOHTta1H5pdLO06vru5uYGpVKJgwcPIj4+3qB+x44djZ5YoYpxI27Zv78LpY1sKzwuiLuxtDLCMktpCcGgQYMwduxY/PXXX5g5c6ZF287IyMDOnTsxZMgQs/+ujF00AAoT8aZNm5rdtpeXF9zc3EqtZ2NjY7CvN0Umk+Gff/6BQqEwOw4y4sMPLdof4sOqO46u7qZPnw6g8GR7UQkJCdi3b5/ehaGiCgoKcOLECTg4OJR6IrpVq1YA/pcPlAd3d3esWbMGGo0Gw4YNQ5YFj8Bdu3at2TnF4MGDUVBQgM6dO+ONN97A8ePHzR6hXNTvv/8OlUqFOnXq6JWfPn0aeXl5ZuVIrVq1Ql5eHk6fPo169erh7t27Uk6m4+fnh7t37yIoKMhoua7+pEmT8OGHHyIhIQHvvPMOgMIRDs888wxsbGxw6dIl3L17F1u3bsWBAwfwxRdfGMTTsmVL5OTkSDlHeaj0xP1m2k3svLITmhLPqv+PRmiwI2YHbqbdrODITPP09MTKlSuhVqvNOmPeu3dvLF68GHZ2dti6dSv69u0LNzc3vPTSS/j1V8MhfrrkrW7dumWKLzo6GgUFBejatSsAYOjQoZDJZNiwYUPJjxSoQFeuXAFQ+N6VVbt27fD8889DJpOhR48e0ogHDw8PzJkzRxoKWKdOHXz77bfIycnBl19+aVEbSqUS06ZNg1KpRN26dfHqq6/i/v375fbluX79euzevRszZsxAu3btABS+J5999hni4+Mxa9Ysqe62bdvQs2dPeHt7AwBcXV3x8ccf6x1obdu2DR07dkTz5s0BFN4GsHjxYvj5+Rm07eDgAGdnZ+mzqM6ybt9Gwm+/lXi1vSih0SAhKgpZJoZqV4bHH38czz33HP744w/sLzZ0f8mSJZg0aZLRxPzo0aPS+iVp3Lgx7O3tcfLkyXIdLv/uu+8iODgY27dvxzcWnPw4fPgwFAoFgoKC8NRTT8HPzw/nz5/HX3/9ZVA3NDQUTk5O2Lx5M9q1a4cVK1YgOTm53PpgjO5vWnc7ko2NDQYMGIDU1FT4+/vj3XffxZ9VOEqjJsvNvY2k5IMAzP091iAp6SByc6vu79tSn332Gfz8/LB06VLs3bvX7PW2bNmC9u3bo379+ujTpw9cXFywZ88ePHjwwKDuwIEDoVAosGTJEnTv3h0//vgjMkzcVkTV2M2bwM6dJV9tL0qjAXbsKFyvkoWHh0sjKz09PREeHl5pbbdv375c2u7QoQO6dOmC6Ohoaf8MAJ9++inGjRund+trUSkpKcjPzzdIRI2p9++jbO/cuWN0edF+eHp6mn2M2qtXL0yfPh1Xr141u//x8fE4deoUBgwYAADo168fnJycsHPnTqO3Xs6dOxfDhw9HTk4Oli5disDAQPj4+GDy5Mn4+++/zWoTKMwbjOUMluRIpb2Plhg2bJh0QuXVV1+V+hMbG4sXX3wRDg4OAAqT8/fee8/oqA9dWXkeh1d64h55PbLUK+3FCQgcjDtYQRGZJywsDOHh4bh48SLefvvtUutPnz4dN27cwCeffIJOnTohKysLP/30E5577jkMHDjQ6IG2sTNU58+fl/5QXVxcjD7TcN26dVKyDgC+vr7o0qULEhIScOjQoTL09uHpDsIf5v5y3YkIoPA+1SlTpgAAXnrpJYP7T2vXrg1vb2+jSUJJip9xa9CgAQCYvDfXUj/++CMAoE+fPnrl7dq1g5eXF7Zs2SL9Lnh4eGD16tX46aefkP/vM8pbt26NyMhIaT0PDw/s3r1bOlEBFH6ZXb582Wj7jo6OJu+jrk7unjhR+pWF4oTAvRMnKiYgMxk7U5+UlISff/4ZEyZMMLqOuTspmUyGOnXqIC8vz2jS+8knn+jt6HU74dLI5XKsXbsW9erVw9SpU6Xht6XRfQ/plHR10MPDAz///DO8vb1x9uxZjB07FiqVCt26dcOKFStM3ktXFjk5OVi/fj2+/PJLtGjRAq+//rq07Msvv8Rzzz2HBw8eYMGCBfD390eTJk3w9ttv42YVHOTWVCkp0Sj9SntxAikPyu/qlCXKkow4Ojpiw4YNsLGxwZgxY8z+Xi46ms7Ozg4vvvgi8vLyjN6C07p1a6xevRp16tTBoUOH8PLLL8Pd3R1hYWH46aefKv1EfvHvoIc5kU9FREaWaX+Ig5V/HP3ZZ59JIyvv3r0rjaisDKdOnSq3tnX3Xev25Wlpafjhhx/MupXEEsbuEweg1w9jV4tLMn/+fLRr1w7ff/89fvnll1Lrb9iwAX369JGeQ+7g4ID+/fsjNzcXmzdvNqhvb2+PH374ARcvXsTMmTPx2GOP4c6dO1i2bBn8/f3xySefmBVncnJyuc1JZep9tETRHKRhw4YYOHAgXF1doVAoMGfOHBw/flxa/sorrxi9JUDXn/I8Dq/0xD0jLwNymWXNymVypKuNT1ZTmRYuXIgnn3wSX3zxhdEr58WpVCpMmzYNx44dQ3x8PBYsWIA6derg559/1hveqtuZpaSkGGzjySeflP5QAwICDIa65OfnY9OmTdKOXcfU0JbyOgNZGt2Bd0n335TGw8PDaLlarcann36Kjh07wtvbW+pLQkKClMyaq/gwQFtbWwCQEueHdfXqVQAweibO29sbGRkZuHfvHoDC+5Hs7e3x0ksvwdPTE2PGjNE7uwsU7gSbNGmCCRMmwMPDAy+99BL27NljcliSUqks1ySoqhRkZQGWTqwkkyHfgqFhFaF79+5o27YtDhw4IN3/9cUXX2D48OF681w8LGM7qenTp+vt6LdYMEFR/fr18d133yE7OxvDhg0r9e8hLy8PmzdvxvDhw6Uy3XfQ+vXrjf5+9urVC9euXcOaNWvQp08f2NjY4LfffsPYsWMREBCAhIQEs+Mtruj8Gd7e3pg7dy5effVVREdHw9nZWarn6uqKPXv24NixY5g0aRJ8fHwQFxeHRYsWwc/PDxs2bChzDGQ+jSYTlh+OyKEpyKyIcEpV1mQkICBAuoXLnFtR7t69i6NHj2LgwIFSme5vzNRw+WHDhuHGjRv46quv0L17d2g0GuzevRsvvfQSgoODK/UKfPHvIGO3rlEZZGRYvj+Uy4H0qj+ONmXAgAFlOtFcWW326dMHLVq0wI4dO3D58mV8/fXXeOGFF0qcM6JevXqwsbFBqqnJA4vQHf+bc6++pWxsbLBhwwbUrl0b48ePL/VqdNFh8jrm3IL7+OOPY+HChYiNjcX58+fx2muvQQiBWbNmmXXlPS8vz2jOoMuRjI0yKq4830djOUj9+vWxdOlSxMTEIDAwEI899hhmzZqld5tuUbr+lOdxeKUn7k62TtAKy876aoUWznbOpVesYHZ2dtiwYQPs7e0xevRoi86geHt7Y9asWdJZ8gMHDkjLdGfOLl68aHFMv/76K1JTU9GrVy+9L6DZs2dDJpPh559/1hvaX55nIEuiG0LyMAmwqftwhw0bhhkzZuCtt97CjRs3pL7orpaXRxtVISgoCNevX8fPP/+MHj16YP369Xj66aelkQZA4WQtFy9exK+//opBgwZhz549CA0NxYABA4wmR3l5edJnUZ0pHR0BS68WCQEbK3iiQNGr7tnZ2Vi+fLneZ1qcuTspIQRSU1Nha2srDQ8rT/3798fEiRNx5swZzJkzp8S6u3btQuPGjfXuc23SpAkCAwNx8+ZNk/Ms1KpVCyNGjMCOHTuQmJiINWvWoHnz5vj777+l960sis6f8eDBA1y5cgX//e9/TY5i6NSpE5YuXSpNmjl48GDk5ubi1VdffSRGrFg7haI2AEuvBmuhUBqOQLN24eHh6N27N7Zt24bly5eXWHfDhg147rnn9IbaBgcHw8fHB0ePHsU///xjdD0XFxdMnDgRkZGRuHfvHpYtWwZvb2/8/vvv5T7DMVUBJyfL94daLeBc9cfRo0ePNjoB25YtW8p8ormsbVvSpkwmw9SpUyGEwPz58/HFF1+YnDxWR6lUomPHjsjOzkZcXFyJdXWJ7dNPP21WX6KiokzeW2/MY489hmXLliE5ORmjRo0yebHn4sWL+OuvvzBu3Di9nGLEiBFQKBT47bffjM4JU5y/vz++/PJLzJo1CxqNxqzRvw4ODkZzhnbt2sHW1tasEbV///03atWqJd2aWpLSRiCZyg8mTpyI+Ph4fPXVV/D29pYmjNaNsC1Kl7CX53F4pWctPZr0gAyWDWGQQYbujbtXUESWadmyJT799NMSz5hPnDjR6CQFwP/ur8zOzpbKRo0aBblcbtYQluLWrVuHJUuWGJzVvn//Pl588UWkp6cbnQmzounu0zbnDJklHjx4gC1btqBXr154+eWXpSvk1qpZs2YAjA+9v337NpycnKRHfRUUFEj33W7atAk3btxAp06d8Nlnn0kz8Go0GshkMjzzzDNYsWIF4uPj0a9fP2zduhUHjQyDS01NNetxKtbOs2NHwNKhTzIZVOU8wVlZDBo0CA0bNsRPP/2E999/Hz169ED9+vVN1td9R5S2k4qLi0NOTg4CAwPNmuipW7duuHHjhkWxR0REoFWrVli4cCEOHz5sst66desQGxtrMCxW1wdzZr6vXbs2RowYgejoaDg5Oemd3KwsMpkMgYGB2LhxI4YOHYqMjAycqOLbLWqCevWCAAuPCwAZ6tU1f7hoRTKVjBgjk8mwatUqqFQqvPXWWyXe+7hu3TocPHhQ72/K29sbqampEEKYNQFtvXr18Prrr+PgwYOQyWRV8ndVlBDC6OOryAI9epRpf4ju1nEcXV2NHDkSHh4eWLt2Ldq1ayfNNVSS8ePHA4DJJ0EAhcndnj170LJlS73h2eVt5MiRGDp0KPbv349PP/3UaJ1169bhzTffxL179/Ryinv37uGtt94y+rSYxx9/3ORTrIzlPKZ4e3sbzRkcHBwwbNgw3Lx5s8QJwv/44w/cunULI0eORK1ataRy3VXvgoICvfr3798vNabihBDQaDSoW7cuJk6ciMOHD+PUqVNwcnIyerFB15/yPA6v9MTd18UXfZr3gUJm3oyiCpkCff36wtfFt4IjM9/48eMxcOBAbNu2zeh9kLm5uUaTKOB/B+NFzwY1b94cU6ZMwe7duy3aqWZkZGDXrl0GMybq6GasrorZ5Vu3bg0AJs/MOTo6Sn9EBQUFmDx5sln3lNra2kImkxkMDc7Ly5OGnFuTl156CQCkWfN1Tp8+jTt37uDFF1+Uki4bGxu9R3B5eXlJw5N0Q62aNm2qNzuli4uLtGMoPhwrMTERarVa+iyqM0dvb/gEB0Nm5kzEMoUCPt26wfHfE0hVSalUIjw8HBqNBhEREaVeSe7WrRuaNWuGvXv3ljhiZdu2bQBQoc8bt7e3x8aNG2Fra4sRI0YYnZgmLS0N+/btQ1xcnMEJxBs3bsDW1habN2/We1LDunXrpL+N4lxdXdGiRQuzdvQPq3bt2iZPLlpywEEPp1Ytb7i5dgdg7kzjCri5dUetWlX/910WHh4eWLNmDXJycjB06FCjf+cxMTG4desWEhMTDf6udCeTiu/bP/roI6PPQAYKZ0x2c3Pj7/OjwNcX6NMHMHdmfoUC6Nu3cD0rMW/ePIwdO7aqw7CInZ0d3njjDQAw+XdW3LBhwxASEoJFixaZnIB18eLFePDgAZYtW2bxKNDHHnsMv/32m9n1v/rqKzRp0sToxUUhBNavX28wTF7HVE6RlZVlMgZjOY8prVu3NnmL3Pz58+Hp6Yl33nnH6GgBrVaLWbNmwdvbG/PmzdNbpkuab926JZXdu3fP5PD2kvz2228Gj6Rr164dunXrZvSWCF0OVJ7H4VUyTvj9ru8XJl+lnGGXoTBBm91ldiVFZr5vv/0Wvr6+JoebbN26FXPmzJHut9BqtTh69ChGjx4NX19f6Y9fZ+HChRg9ejT69++PL7/8Eun/3ouk1Wpx8uRJjBo1CocPH9Y7w/fLL78gMDBQumJbXGhoKJydnbFnzx6j989XpNatW0OlUuH8+fNGl7ds2RLXr19Hbm4ujh07huXLl5ucmbMoR0dH9O7dG/v27ZOeT56dnY033nijTM/HrWhDhw6VnjJw+vRpAIX3LU6ZMgX169c3mMzi7bfflu5BvHfvHjZs2IAWLVrgySeflOrMmTNHSvDT0tKwcuVKuLu7o3uxs+m6M6CPyrOoW0+cWHjVoLQrDf/WaV2BCa2lxo8fjzp16qBnz55o06ZNiXUVCgW+/PJLJCcnIyIiwmidpKQkLF68GD179jR54s6UNWvWGDwntSRPPPEEPvnkE9y8eVM6WVDU5s2bERwcDFdXV4NldevWxbPPPosHDx7oPQM2Pz8fR48eNTonhVqtxrVr18za0T+srKwskyMJ/vrrL8hkMjz11FMVHgcBjRtP/veEbGlXEguPCxo3Kt9JocqDJcnIM888g2nTpuHMmTM4duyYwfK1a9fipZdeMnrP5+OPP44nnngCf//9t96zk/Py8nDw4EGjxyX3799HUlJSpfxdUSV4/32L9oeYbV3H0VqttlyfhlJZ3nvvPeTn50sndkujUCjw008/oVGjRujWrRsOHTokDdG+f/8+Zs+ejQ8//BCrV69GSEiIxfEUFBRY9Og1Z2dnrF+/3ugovSNHjsDe3t7kPq9t27Zo3rw5Lly4YHBr75w5c7B+/XrpODwvLw8bN27EvHnzEBYWZlbfevbsiYyMDKMJtZeXF3bv3o1z587hxRdf1JuQ+fLly3jxxRdx8eJF7N692yAn0k0OvWTJEhQUFCAjIwOzZs0qceRjSf7++298/fXX0u/vuXPnEBUVhZdfftmg7rlz5+Dt7f1Qj/s2IMyQlZUlTp8+LbKyssypbpaf//5ZKOcpBebC6EvxgUIo5ynFlr+3lFub5oiLixMqlUo4OzsLGxsboVKpxMcff2y07pEjR4RCoRCHDh3SK4+NjRULFy4UPXr0EI0aNRIqlUrUrl1b+Pn5ibfeekvcv3/fZPu//vqrGDBggPDx8REqlUq4urqKFi1aiOHDh4tffvlFFBQUCCGEGDRokHBwcBCOjo6iYcOGBts5c+aMUKlUwsbGRgAQ9erVEx988IHJdseNGydUKpUAIPV7xYoVpS4ryTvvvCPc3d1Fdna2wbJLly6JwMBA4ebmJho1aiS+/fZbIYQQwcHBwtnZWQAQdevWFSqVSiQnJ+utm5ycLCZMmCC8vb2Fu7u7CAgIEEuWLBENGjSQ4rt48aKYOXOmFHetWrWESqUSsbGx4p133hFubm4CgHB0dBStWrUSQgjx6quvirp16woAwtnZWfTo0aPUPgpR+PehUqmEo6OjACDc3NzEq6++Ki1Xq9Xigw8+EI899pjw8PAQXl5e4pVXXhEJCQl621m9erXo3bu3aNCggfD09BSNGjUSr732mrhz545UZ/v27WLAgAGiUaNGwtPTU/j6+oqhQ4eKK1euGMQ1YcIE0ahRI6HRaMzqR3Vwc98+sd7fX6xr1croa/0TT4j1/v7i5v79VRbjypUrRXBwsEF5QUGB0c8CgNH6a9asEbVq1RLvv/++9J2h0WjEwYMHxeOPPy6efvppkZKSYrDeoUOHBAAxZ84cs+OLi4sTpe0O+vXrZ3S7ISEhYv369SbXW79+vQAgBgwYoBcDANG/f38RGxsrlV+9elW88MILws7OThw5cqTEeEwx9X6aqlu/fn2xc+dOkZeXJ4QQIjMzUyxdulQoFArx+uuvG6wzatQoAUDExcWVKT4y7d69vSLyYHNxILKJiVczEXmwubh3f2+VxKf7vV25cqXR5XPmzBGjRo2Sfj506JDR/bOOWq0WTz31lNFtNmnSRERHR5tc96OPPhIAxNSpU/XaByAmTJgg7V+0Wq04f/68ePrpp0XdunVFTExMqf0siTm//6V9BxkTHBxc6ncQFfPzz0IolbontRu+FIrC5Vsq7zg6MzNT2NnZCaVSKQAIpVIp7OzsDF4KhULvb6U0o0ePNjgO3bhxo16d/fv3C5VKJWrVqiUdi6lUKnHixAmL+vDBBx8IlUol5HK5qFu3rnSMaMzly5f1jv/q1q0rmjdvblAvPz9ffPvtt6Jr165CpVIJlUolGjduLMaNGycuX75sUP+rr74SdnZ2QiaTCQBG30M7OzsBQMpB1q1bJ/Xf0dFRqFQqcfjwYaNx674/dKZOnSqcnJykY+Xiecr9+/f13lsXFxcxbtw4IYQQv//+u5g5c6bo0KGD8PHxER4eHsLZ2Vm0b99eRERESPvW0qSkpAgHBwexePFik3UePHgg/u///k/4+/sLlUolPDw8xBNPPCHef/99o8dDOj/88INo0aKFqFOnjujYsaM4cuSICA4OFnK5XKhUKrF69WqRnJys10eVSmVwLJGWliYWLVokOnXqJLy9vYWnp6do2bKlWLBggVCr1Xp1MzMzRb169cT7779fat91efbmzZvFggULxIEDB0zWrbLEXQghTsafFHU+rmM0ce+3oZ84GX+yXNujyvXgwQOhUqnEokWLqjqUGufGjRvC1tZWbNq0qapDKXdJFy6ITZ06GU3coyZNEkkXLlRJXLoTOEVP+pV0gsvYCbHly5fr1YmJiRHjx48XTZo0kXb2Xbp0Ed98843Iz8832KaPj490sk6hUBjd0SuVSr2dUUBAgHQiS6VSiX79+hmNNykpSXh7e0sH47dv3xYqlUrIZDLh5uYmZs6cabDOzJkzpW0rFAqhUqnE5cuXRXJysvj222/FgAEDRLNmzYRKpRIuLi6ifv36YsiQIeLcuXNmvOOlv5+hoaElrrN7927xxhtviICAAOHl5SXc3NxEnTp1RJcuXcSKFSuEVquV6ho7KHz22WctjpNKlpZ2XkT91sZo4n7u/HiRlna+0mOyNBkpKCgQKpVK1K1bVzownDRpktFtX7lyRdSuXVtK3E+cOCE8PDykv8cvvvjCYJ0hQ4ZIJ5ltbW2FSqUSWVlZIj4+Xnz++eciNDRU+s5wdnYWTZo0EePGjRPXr18v83uwZ88eg9///v37G9Rr1aqVFJsueSh6cqG4FStW6F1kUKlUUkJAZjh5Uog6dYwn7v36FS4nqiYWLFggPDw8REZGRlWH8tAWLFggvLy8RFpaWql1LUncZUKUPsYiOzsbly5dQsuWLct9hurA7wNxPP64XlmAZwDOvnq2XNuhqnHixAn07dsX33//Pfr27VvV4dQIiYmJ6N69O/r06WP0uZKPgl+HDkVysdsw6rRsiVAjzxglourl1OmBSE//Q6+sdu3H0bFD5U+0SmT1AgOB4/rH0QgIAM7yOJqqF61Wi+HDh+PevXvYuXOnWbfQWqPt27dj3Lhx2LlzJzp06FBqfV2efePGDcTGxqJ9+/Ymb2mskMQ94lgEIo4ZvzezuMTsRORp9J9vZ6uwhbuD6WcjFjc1cCqmBpb8WAaqOjExMfjqq69MzmJJ5WvDhg0oKCjAiBEjqjoUi1xatQqX16wxq646JQXaYhM6yW1sYGfBY9FajByJlhY8ToWIyu7mze9x89YKs+rm5SVDCP2/b5nMBra2hvMomOLb4BX4+lavya+IJBERhS9zJCYCxZ8TbWsLlPCMcQNTpxa+iKqYEAIREREICAgwmLupunjrrbcwceJE+Pn5mVXfksTdcNaTcpCuTkdChvGZAc2Rp8mzaP10dXqZ26KK5+fnx6S9Eulm/qxu8rOykPMQTwbQ5udbtH5+VlaZ2yIiyxQUZEKtvlvm9YXIt2j9goLMMrdFVOXS0wETM2ybJS/PsvXTeRxN1kEmk2HatGlVHcZD+e9//1th266QxN3Zzhk+TpX37GhnO+dKa4tqlvDwcPz4448l1vnss88sntmbDNk4OsLexBMSKqo9IqocSmVt2Nl5Vmp7RNWWszNQjs9+Nqs9IrJ6VX6POxERkSk8eUZU/gYMGIDo6OgS62zZsgVBQUGVFBERUc1U5UPliYiIysNnn32Gzz77rKrDIHqkbNmypapDICIiC8mrOgAiIiIiIiIiMo2JOxEREREREZEVY+JOREREREREZMWYuBMRERERERFZMU5OR0RWISIiAhEREZXW3tSpUzF16tRKa4+oJvv65n18E59Yae29Wt8dE309Kq09IiKiilYxiXv0UuDYsgrZtFGBk4CgyZXXHhGVu/T0dCQkJFRqe0RUOTI0GtxR51dqe0RERI+Siknc1RlAxu0K2bTJ9siqpaenw9nZuarDsGoZGRlwcnKq6jCqjLOzM3x8fMyqm5iYiLy8PL0yW1tbuLu7W9QeEVUOJ4UCXnY2ZtVNyitAvhB6ZTYyGdxszT9kcVIoLIqPiIjI2lXMPe52ToCTt3kvhZEducLG/PWdvAvbKyc3btyAp6cnXFxcIJPJ8MorrxitFx4eDk9PTygUCtSrVw+enp7QFDnDn5CQgDfeeAMtW7aEp6cnPD098fjjj2PkyJH47rvvDJIOnYMHD2Lw4MFo2LAhPD094ezsjNatW2P06NFYv349MjMzTcZ+584dKJVKBAcHW9zv8ePHw9PTEzKZDLa2tvD09MTKlStLXWaOZcuWYciQIRbHVJE+//xzNG/eHB4eHvDx8cGYMWPg7u4OmUyGuXPnVklMISEhNfrZulOnTkV8fLxZr7Zt2xqs37ZtW7PXj4+P5zB5oko00dcDfwQ9btbL38neYH1/J3uz1/8j6PFyHSbfqFEjaT+veykUCigUCr0yFxcXeHl5wdPTE7Vr14ZMJjNYT/eqXbs2Ro8ebbLNhQsXwtPTE7a2tpDJZHjttddKjXPIkCGQyWRwcXGBp6cnbt26pbdcCIHNmzejd+/eUpy+vr4YOnQoTp48abC9AwcOwNPTE/b29pDJZHB3dzfaF4VCgaioKADAkSNH9Po/b948o7EOGDBAOq5wd3c3+p2u8/jjj8PR0bHE4x8AuHLlCkaPHo3mzZvD09MTXl5eaNOmDSZMmID169eX8u4REVUDwgxZWVni9OnTIisry5zqlvm2hxBznPVfXz1d/u1YaOXKlQKAACA2b95ssl7Dhg3FoUOH9Mri4uKEq6ur6NGjh7h+/boQQgiNRiOio6NFy5YtBQBx584dvXXy8/PF+PHjRd26dcV3330nMjIyhBBCqNVqsX//fuHv7y8AiGnTppmMJSIiQgAQMplM/PPPP2XqNwARHBxs8TJTFi1aJJo1ayZu375dpngqwo4dOwQAsW7dOiGEEEePHhUNGzYUcXFxAoCYM2dOlcT1119/CXd3d/HTTz9VSfvVSadOnaS/T90rICCgSmOKi4sTKpVKODs7CwDC1dW11O/MvXv3CgDCxsZGqFQqsXjxYr3lMTExYsKECaJx48ZCpVIJlUolunTpIr7++muRn59vsL1WrVqJunXrCgDC0dFRWqfoy9HRUYwaNUpaJyAgQLi5uQkAolWrViInJ8dguxs3bhQqlUrUqlVL2u7hw4eN9mnhwoUCgFixYkWJfc/KyhKLFy8WAQEBwtPTU6hUKtG0aVPRr18/sXjxYou+M+bMmSNq1apl0E8Aom7dunrlAERcXJx49tlnpX4XX1f30i031/79+6X3CYBwc3MTKpVK1K1bVzg5OYmQkBBx4MABg/Xi4+PF5MmTRYv/b+/Ow6Kq9z+Av4dhDWSHg6iAmrK4XStLSEUFQQwxSLQUlB5FK7vpdelGTwo+1sM1FdfrvaJAkWAlLoiCC2543VBADbfEIDMlFBAIZZv5/v7gd04McwZmEBjUz+t55unpnO855ztn8Jzv+vm6uAjXdnNzY6GhoWzr1q2strZWSCv2G9+4cUPtPD4LJly8ybhjeQof7+zrWsuP2Hve0dGROTo6KmxLSEgQ3pGRkZEMgNJxvMjISIV/h6p4enoKf6MlJSUq0xUWFjKpVMoAsISEBKX9jx8/ZpMmTWK9evVie/bsYXV1dYwxxsrLy9maNWuYoaEhi4qKEj33zJkzhX83YsTuD//9dXV12blz51Tmu6XzMsZYbm6u8IxPTExUmS47O5sZGRmx9957jxUXFzPGGGtoaGAHDx5k3bt3ZwYGBiqPJYQQbeLr2SkpKSw6Olq0nMDTblT5uznAw5+VtxdfAZLfBX7P6fw8NWFvbw89PT3MmTNHo7m3MTExKC0tRWxsLHr37g0A0NHRgbu7O7755hvRYyIiIhAXF4e0tDTMmjULJiYmABqH/3p7e+Pw4cOwtrZu8brbt2/HoEGDwBjrEq3LV65cQUREBDZs2IDu3btrOzuCo0ePAgACAwMBAB4eHjh37pw2swQAcHNzw9KlSzF79myUlJRoOztdVnZ2Nm7cuKG0PS8vDwEBAbhw4YIWctXYK1dcXIz169cDAEpLSxEXF9fiMV9//TWAxr/B4uJiLF68WNj3zTffYMiQIbC1tcXZs2dRXFyMe/fuISoqChs2bMCbb76Jhw8fKpzv6tWrwqiNxYsXo7i4WOnT9BoAkJubK9yza9euYcmSJUr5nDp1KoqLizF16lThvCNHjhT9TklJSQAan0eqNDQ0YOzYsdiwYQM2bdqEe/fuobi4GEeOHIFEIsGSJUtw8uTJFu+dqjw2/567d+9W2M47ePCg8L2bH8t/NP1b8vb2Fu4TAFy4cAHFxcUoKyvDpUuX8OTJE4wbNw579uwRjikqKsKQIUNw/fp1pKenC7/ztm3bcPHiRYSHh6OsrExIL/YbOzs7a5TPriy3shoFj2uUtv/0Zw1mXPkFeZWPtZAr7Ro0aBBqamqwYcMGlWnWrFmDAQMGqNw/Z84cZGZm4vjx43j77behp9c42tHc3BwLFy7Etm3bEBUVhdjY2HbLt5OTExoaGhASEtJqb7kqfLkG+OvZImbFihWQyWSIj48Hx3EAAKlUCl9fX6xbt65N1yaEkK5GexX3a/uAeB+gpkJ8/63DQJxPYzot6devH7766iuUlZUhLCwMrNmcO1Vu3rwJAKLzbYcNG4bw8HAYGf01FPDWrVuIiYnBpEmT8Oabb4qek+M4hIaGCi/b5m7cuIHCwkKhotDSC66zfPXVV3B1dcX48eO1nRUF5eXlAKDwG9jZ2WkrOwrmzJkDAELljyjavXs33nzzTTx69Eh0f3p6Ojw8PLQ+5cDZ2Rn6+vqIiYlRmELTVE5OjlLFm3fkyBHMmjULUVFRWLFihVAQ1dHRwdixY3HixAn89ttvCAoKUnn+tnBycsKmTZuQkZHRpuPz8/Nx7949dO/eHSdOnMC9e+KxTvbt24fz588jKioKHh4ekEgkAIDevXsjOTkZVlZWbf4OXVWfPn2QkJAAxphC40hbGnqfVwcePEJA7i1UNMhF9x8trcTE3J9x4MGjTs3X0aNH8frrr7eaLjAwEImJiWqdc9myZYiPj1cr7ezZs2FqaorNmzejurpaaX9paSl2796N2bNnix6flZWF7du3Y86cOejbt69omunTp2Pw4MH49NNPNQ7cefv2bdEpep6envjoo49QUFCA+fPna3ROAJDL5fj++++xZcsW2NjYIDMzE3/88Ydo2ps3b8LExASGhoZK+3x9fREaGqrx9QkhpKvRTsX9bg6Q8j4gb6HAyWSN+1Pe12rP++LFizFu3DhkZmZi7dq1ah3D9y5/++23SvskEgliY2NhZmYmbPvmm28gl8sxceLEFs8bExOD6Oho0X1JSUmYPHkyhg0bhoEDByI/Px+XLl1SK78d4cmTJ0hNTYWXl5fSPplMhpUrV8LV1RXdu3eHg4MD/Pz8hEaHSZMmwdDQEBKJBBzHCXPkHjx4ADs7OxgYGMDc3ByJiYkYMGCAEI/g4MGD+OSTT+Do6AgrKysEBwcrVIxu3rwJOzs7/PDDDwAgzM9TNQevqczMTIwePRq2trawsbHBqFGjcOjQIWH//v37YWlpKcwvbDpf7+DBg5BIJLh69aqwbeTIkTAxMYGJiYnC9Q0MDDBy5Ejs2LFD3Vv9wsjOzsbUqVNbrKjKZDLIZDJMnTpVaz3vQOPf1rRp01BUVIQff/xRNM3XX3+NRYsWKW2XyWSYN28erK2tVc7Dt7GxwZIlS3Dq1KkWe7bFtFRhSE5Ohq6uLt5//308eKD50l3bt2/HlClT8O6770Iul6sc+dNS4+ZLL72EiIgIoRKrjo8//ljls7G569evqx0E0cnJCfX17RcJ3cXFBWZmZrh9+7ZwfzVt6H1e5VZWY+7VIshaaB+XAZAxYO7Vok7tee/bty9eeumlVtOZmZnBwcGhxTQnTpyAk5MTdHR0oKOjXhHM1NQUc+fORVlZGbZt26a0f9OmTQgJCVEZ3HTr1q0A0GoZY9KkSaioqMDOnTvVyldYWBiioqIglUqFxrfm1qxZg4EDByI+Pl7jBtXjx4/D2NgY7u7uwrNf1buxe/fuKCsrw/79+5X2mZmZCfeAEEKeZdqpuGetAhg/baklrDFd1urOyJUoiUSCxMRE2NjY4PPPP8eVK1daPYYfJvnJJ58gMDAQ+/fvR21trcr0Z86cAYAWh7m1JikpCdOnTwcA4b/a7HW/cOECamtr4eLiorRvxYoV2LhxI1JTU3H//n2hIM33FqSmpgqV2e+++w7Lli0D0Fiw/e2332BhYYHc3FzMmDEDV69eFXqno6KiEBAQgF9//RUnT55EZmYmZs2aJVzX2dlZYRgrPxyWP78qycnJ8PX1ha+vL+7du4f79+/Dz88Pfn5++O677wAA/v7++OmnnyCRSDBr1izk5uYKx6empgJo7GXknTp1Cn5+fkhISFC6vqurKwoLC3H37l017vSL48svvwRjrNWRL3yaL7/8spNyJm7x4sWQSCRYtWqV0r5ffvkF58+fFw3aeOLECdy6dQvjx49XOcIGaCxkAxAtyItRp8Lg7u6OyMhI/PHHHyp771RhjGHHjh2YPn268AxS1ajAN24mJiaK/p6LFi3CG2+8ofa1ra2t1Z6O4+Li0uJ95Tk5OeHEiRPQ1W3fxVf478v/Bpo29D6v1hX9AcbUKhWAMWBdUXErKZ8v8+fPh56eHtauXYuGhgZh+5MnTxAbG4tPPvlE5bGnT58G0HoZw83NDcBfZZL2YGhoiB07dsDQ0BBz5sxROQpHzPbt29Uu1/Dv9cDAQMycORPHjh1TuE+EEPI86PyK+6PfgJ8PNvaoq4PJgJsZjcdpCR9Fvba2FtOnT0dNjfL8u6b8/PywatUqGBgYYO/evZg4cSKsra0xZcoUhV5aHj/v0sLCok35O3PmDBoaGjBq1CgAwLRp0yCRSLBjxw7I5eJDDjvazz83xi4QG4KempqKN954A/379wcAGBsbY9WqVQrzNGfMmAFdXV2lnsH9+/fDzc0Nffr0UTrv8OHD4e3tDQAYOHAg/P39kZGRoTKCvzqqqqowb948uLq6IiIiArq6utDV1UVERAQGDBiAjz/+WBhW2KNHD7zyyitIS0sTjmeMIT09HTY2NkIFHgDq6upw4sQJ0WkEfEGev4cEuHPnDvbv36/2sHCZTIa0tDTcuXOng3Om2oABAzB+/Hjk5eXhyJEjCvvWrFmDefPmiVYg1S1k9+7dG0ZGRsjOzm7X4fKff/45PD09sW/fPmzZskXt47KysiCVSuHh4YFXX30Vzs7OuHz5ssJIE96ECRPQrVs3pKSk4LXXXkN8fDxKS0vb7Tt0Vfn5+aisrISzs7MwHUDTht7n0d2aOhwprYS6f8UyAIdLK3G3pu3P9s7CR0/nP0FBQW06T48ePfDuu+/i119/FUaNAUB8fDy8vb1bHEWibhnD0tISQOMKNWKGDRum8F2a5qMlAwcOxJo1a1BaWqr2tMOamhrs3r1bqLAPHz4cffv2xcWLF4VRKk3NnTsXixYtAmMMiYmJ8PLygq2tLcLCwrpEDBtCCGkPnV9xLzyJ1tvUm2NAYVZH5EZtb731FubPn4/8/Hz885//bDX94sWLUVRUhNWrV2P48OGorq7Gzp07MX78eEyePFm0oC32Mrt8+bLCUjN80LqmkpKShMo6ADg4OGDkyJH4/fffcfz48TZ826fHF8KNjY2V9tna2iI9PR1bt27FkydPADQWKJoGHLOzs4Ofnx/27t0rzEkHGgspqpbo8/DwUPj/Xr16ob6+vk1DfnmHDx/Go0eP4O/vr7Rv4sSJqKysxMGDB4VtAQEBKCgowLVr1wAAFy9eRO/evREYGIjs7GyhAHXs2DG8+uqrokMb+Xumav7zi+jo0aNqx5jgMcZw7NixDsqRevgAaXwQOqDxd921a5cQz6A5dQvZEokE5ubmqKurE630rl69uk0VBh0dHWzfvh2WlpZYuHChaCFZDP8c4rXU625ra4tdu3bB3t4eubm5mDVrFjiOw+jRoxEfH/9UjW1t8cMPPygtcdV8Ka2nwRhDfn4+wsLCIJVKFaZdadrQ+zw6VV7VllIB/lde1RHZaVfNgyM+TfwN/nnCj+KRyWRYu3atUsDJp6Vq2DsfbJH/8I1O6vjoo48wadIkHDlyRK1gcWlpaXB1dcXLL78sbGvpmaKjo4PVq1ejoKAAy5cvx+DBg1FeXo5vv/0W7u7ubZpjTwghXU3nV9xr/wQkGl5WogPUav8FvXLlSgwZMgQbN25Uq0DFcRwWLVqEs2fP4u7du4iOjoa5uTl27dqlMLyV75VuGjmYN2TIEOElOXToUKXANPX19fjxxx+FFxpP1bCy5i3mHfUy4wveYsNM169fjz59+mDOnDmwtbXFlClTkJGRoVQxe//991FbWyt8h+LiYpw9exbvvPOO6DWbR93X19cHgKeao1pQUAAAosNw7e3tFdIAf80h5IfFp6amIiAgAAEBAWCMCb3x+/btQ0BAgOg1+XvW2ZWXrqyqqkrt+aA8HR0djYMstbexY8filVdeQWZmJvLy8gAAGzduREhISLsOfxYraDePKq9JhaFnz57Ytm0bHj9+jOnTp7f6b6iurg4pKSkICQkRtvHPoOTkZNFGl3HjxuH27dtITEyEv78/9PT0cPLkScyaNQtDhw7VaCWPpyUWVb5Xr15PfV7+eWtjY4Nx48ahe/fuyMrKgp+fn0K6tjT0Pk+qZXKNCyM6AP6UaWdEWXuaP3++wjt52LBhKtMOHjwYPj4+uHz5Mg4dOoSUlBS8/PLLQtR1VfgyRtNGcDF8GaSjVoGJi4tDjx49EBERgZ9++qnFtE2HyfOaPlNUcXJywrJly3D58mUUFBTgs88+g4GBATZs2PDCNIQRQp5fnV9xNzABmIYvWyYHDMSDrnQmAwMD7NixA0ZGRggLC9OoR9Te3h6fffaZEKgqMzNT2Mf3FOfn52ucp0OHDuHRo0cYN26cwsv/iy++gEQiwa5duxSG9jdvMe+o6OV8IB+xAr+Liwvy8/Nx6NAhBAcHIyMjAxMmTEBQUJBCAd/f3x+2trbCcPlvv/0WwcHBKgM1aVqx6whDhw5Fr169lCruXl5eMDY2RmpqqlCBV1Vx5yvs6gRDelF069ZN42kfcrkcpqamHZQj9TXtdX/8+DFiY2OxYMEClenVLWQzxvDo0SPo6+sLQ1zbU2BgID744APk5OQgMjKyxbQHDhxA7969FWJa9OnTB+7u7rhz5w5OnTolepyhoSFCQ0ORlpaGBw8eIDExEf3798e1a9favRdRG/jn7cOHD3H//n2kpaUpjQziqdvQ+zwylupA0yq4HICJVPvPfE2NHj0aRUVFwv+vX79eoyUImz5PVq1apda/E361GrFpK03xI8VGjBjR6jmBxsC6UVFRaqUFACsrK3z33Xeor6/HtGnTVE47LCsrw6FDh7BixQqFcs2oUaOgp6eHX375Ra15+H379kV0dDQ2btwIQLHcRQghz6LOf+v19gQgPgxLNQnQe1RH5EZjrq6uWLduHYqLi1UGb/rggw+EF0Vz/Av08eO/IuLOnDkTOjo6Cmv7qispKQlr1qxR6i0qKSnBO++8g8rKSoWgaJ2F740Wq3zIZDJIJBL4+PggPj4ed+/eRUBAAPbu3aswtFlPTw8hISHIy8tDXl5ei8PkO0q/fv0AQDSgDr+NT8ObOHEizp8/j7Nnz0Iul+Pll1+GoaEhfHx8cPToUWRlZcHW1hY9e/YUvSZ/z9SNfP0i8PLyUjl8UxWJRIKxY8d2UI7UFxwcDEdHR+zcuRNLly6Fl5eXyt8eUL+QXVhYiCdPnsDd3R1SqbTVfDSvMKgjJiYGbm5uWLlyJbKyVE9XSkpKwq1bt5SGm/PfQZ3I9yYmJggNDcWZM2fQrVs3rReyi4qKMHr0aK1cu6WG3ufRSItubSkVYISF9hv0O9u4ceMwZMgQHDt2DDKZTIjr0pLw8HAAUIi/IiY1NRVWVlaYPHlyu+RVzJgxY/DZZ5+1OO1w586dmDBhAkpKSpTKNvw0k+bPFD8/P5VlHbFyFyGEPIs6v+Ju3gvoPx6QtF7QBNCYztmv8bguIjw8HJMnT0Zqaqpo8KuamhqVc2v5guxrr70mbOvfvz8WLFiA9PR0jQpoVVVVOHDggMp5ZnzEam1Elx84cCAAiEZG79u3r0KvgpmZmVCwaL4+N19R//DDD2FgYNDiMMKO4OPjA3Nzcxw4cEBpX1paGkxNTeHr66uwPSAgAHK5HB9++KFCr3pAQABqamqwYMEClb3tQOM9k0qlohH5X1QODg7w9/dXq4IKAFKpFBMnTmx1aabOoKuri/nz50MmkyEmJqbVHrLRo0ejX79+OHjwYItD1Plgh3Pnzm3X/DZlZGSE77//Hvr6+ggNDUVFRYVSmoqKChw+fBiFhYVKheyioiLo6+sjJSVFIeBaUlISpkyZInpNKysruLi4vDCFbE0bep9HPQ31Mc7KFGqWCiAF4GNlip6G+h2ZrS6Lf4aoOypl1KhRmDFjBmJjY3H79m3RNElJSbhy5QrWrl0rGkunJV5eXmqvXw8Ay5cvx/Dhw1X+3YsNk+cFBwdDKpXixx9/VHg+PnnyRGVMH7FyFyGEPIu0M87McwkgkaD1nndJY7pRXW/I5NatW+Hg4KAyYNbevXsRGRkpzBmTy+U4ffo0wsLC4ODggL///e8K6VeuXImwsDAEBgZi8+bNwtxcuVyO7OxszJw5E1lZWUIkdgDYs2cP3N3dwXGcaB4mTJgAU1NTZGRkiM6f70gDBw4Ex3G4fPmy6P7IyEghaFxFRQUSEhJgY2Oj1EM6YMAAvP766zh//nyn97YDjb2A//73v3Ht2jVER0ejoaEBDQ0NiI6OxtWrV7Fp0yal4dhjxoxBt27dcPnyZYUKur+/P3R0dHDp0qUWK+6XLl3C66+//kIsAaWJpUuXQiKRtNrzzqf54osvOilnrQsPD4e5uTm8vb3xt7/9rcW0UqkUmzdvRmlpKWJiYkTTPHz4EKtWrYK3t7dGAaIACBGX1TVo0CCsXr0ad+7cUVgZgZeSkgJPT08hSnpTFhYW8PX1RXl5OdLT04Xt9fX1OH36tBCcsqna2lrcvn37hSlka9rQ+7z6h5MdJBK1SgWQSIAFTsorlrwoQkJCUF9fr7JyK2bLli3w8fHB2LFjkZqaKlR6KyoqsHbtWoSHh2PlypUIDQ3VOD8ymUyjqUy6urpITk4WDc7666+/Ij8/H2+99Zbosba2tvDy8kJpaalCYFigcT37TZs24c8//wQANDQ0ICMjAwsWLMDQoUMVgmcSQsgziamhurqaXbx4kVVXV6uTXD1XUxlbbslYpKn4J8qicf+1fe13TTUUFhYyjuOYqakp09PTYxzHsX/961+iaU+dOsWkUik7fvy4wvZbt26xlStXMi8vL+bk5MQ4jmMmJibM2dmZ/eMf/2AlJSUqr3/o0CEWFBTEevTowTiOY1ZWVszFxYWFhISwPXv2sIaGBsYYY8HBweyll15ixsbGzNHRUek8OTk5jOM4pqenxwAwS0tLtnz5cpXXnT17NuM4jgEQvnd8fHyr+1oSERHBbGxs2OPHjxW279u3jwUFBTEnJydmZ2fHHBwc2LRp09jPP/8sep7//ve/TF9fnz148EBpn6enJzM1NWUAmIWFBZs9ezZjjLFBgwYxY2NjBoBZW1uzpUuXshs3bjCO45ihoSEDwDiOYxzHsZqaGjZ79mxmbW3NADBjY2NhO+/w4cNs1KhRzMbGhllbW7ORI0eyjIwMld998uTJzNbWlslkMoXtI0aMYL169VJ53J07d5iuri6Li4tTmeZFtmvXLqarq8vw/8s5N/9IpVKmq6vLdu/erbU8JiQkME9PT6XtDQ0NSn8PjDEGQDR9YmIiMzQ0ZEuXLhWeGTKZjB07dowNGDCAjRgxgpWVlSkdd/z4cQaARUZGqp2/wsJC1trrICAgQPS8Y8aMYcnJySqPS05OZgBYUFCQQh4AsMDAQHbr1i1he0FBAXv77beZgYEBO3XqVIv5aU1kZCQDoPR8bor/3jNnznyqazU3c+ZMBoAVFhaqnXbZsmWstLSUMdb4O//vf/9jbm5uzMHBQenZ19pv/KzaX1LOehzPY9wx8Y/9sTzW43geO1BSrrU8Ll26lHEcx3R0dJiOjg7jOI6tWrVK2M+/Z/j3j4WFBeM4jqWkpGh0nfj4eOEdbmpqyjiOU1kGq66uFsotAIT0FRUVCunkcjnbuXMn8/X1ZXZ2dozjONarVy/23nvvsQsXLiidNz09nRkYGDAdHR0GgOnr6zMDAwOlj0QiYQkJCYwxxrKysoTvb2hoyDiOY0lJSaL55p8N/L+TtWvXMnNzc6GckZOTo3SMo6OjcG9NTEyYr68vY4yxS5cusaioKOEda2try7p168YGDx7MIiMjWVVVlbq3nhBCOhVfz05JSWHR0dEsMzNTZVrtVdwZY+zuRcaie4lX3JPfbdxPnlnl5eWM4zj29ddfP9V54uLi2OTJk9spV13b3Llz2ZAhQ1h9fb22s9JlZWdnM3Nzc9GKe0BAAMvOztZKvpoWntVp4BJrEIuNjVVIc/PmTRYeHs769OkjNDSNHDmSbdmyRfRvpEePHkJjnVQqFS1k6+rqKlTchw4dKjRacRzHAgICRPP78OFDZm9vL1QW7927xziOYxKJhFlbW7NPP/1U6ZhPP/1UOLdUKmUcx7EbN26w0tJStnXrVhYUFMT69evHOI5jZmZmrGfPnuy9995jly5dUuOOi8vPz1eqOL322mtK6Xx9fYW88RWM4ODgNl+XMcaOHDmi0DhobW3NOI5TaJxoTtOGXjc3N2ZhYaHQyHjjxo2nyndXkltRzfpnXRatuM+4fJvlVrRzOYQQQgjRIk0q7hLGWl8c+fHjx7h+/TpcXV3bP9L1Nm/gbrMoqnaDgQ/EoxCTZ8v58+cxceJExMXFCUultUYul+PPP/8UhqCPGjUKERERSksoPW82b96M6OhonDx5En369NF2dro0d3d3nDt3TmHb0KFDkZubq6UcEULay1s5PyOnUnFe/yATQxwZRnE/CCGEPF/4enZRURFu3bqFYcOGqZzSqLzIdns4swk4+2/10j5+oLztwXVgjav613OfB3h8rH560mneeOMNnDp1Cv/5z3/UrrjfuXMHPj4+yM/Px+nTp/Hw4UOlAHDPo5ycHGRnZ3fYGrpdXUxMjMo53c3x8RGaunr1aovR2ptbuHAhFi5cqHZ6Qkjb/fdOCbbcFXnfi3hY16C07UZ1LYaeaXmlhabm9rTBBw62aqcnhBBCurqOqbjXVgFVystnqU1Wr9nxtVVtvxbpcM7Ozli3bp3a6Q0NDVFXVwc7OzvY29sjMTGxS6zR3tHi4uK0nQWtqqysxO+//97m4+vq6jQ6ng8ASQjpeFUyGe7Xql4loTX1jGl0fJVM1uZrEUIIIV1Rx1TcDboB3ew75NQqr0eeG3Z2dhqvN02efaampp26dn3z1QAIIR2nm1SK7gZ6nXo9Qggh5Hmi/TnuhBBCiArz58/HDz/80GKa9evXa7wsnibOnDmDoKCgFtN4eHhg9+7dHZYHQgghhDx/tD/HnRBCCGkH69evx/r167WaBw8PDxQXF2s1D4QQQgh5sT3/E4cJIYQQQgghhJBnmEYVdzVG1RNCCCGEEEIIIaQVmtSv1aq46+o2jqivq6trW44IIYQQQgghhBAiqK9vXDGloUF5KdTm1Kq46+vrw8jICA8fPqRed0IIIYQQQggh5CmVlZVBJpNBpsYypmoHp7Ozs0NhYSEKCgpgbW0NfX19SCSSp8ooIYQQQgghhBDyImGMobKyEuXl5Xjw4IGwTdrCcqZqV9wtLS0BAAUFBaisrHzKrBJCCCGEEEIIIS8mxhgqKipQUVEhjGo3MzNTmV6j5eAsLS1hZWWFjIwMGBoawszMDDo6FJieEEIIIYQQQghRV319PeRyOerr61FSUgJzc3M4OjqqTC9hGk5aZ4whLy8P586dQ1VVFeRyOQ2ZJ4QQQgghhBBCNMAPj7e0tMSYMWPQt29flWk1rrjzqqurcf/+fdTV1VHAOkIIIYQQQgghRAMSiQRGRkawt7eHgYFBy2nbWnEnhBBCCCGEEEJIx6MJ6oQQQgghhBBCSBdGFXdCCCGEEEIIIaQLo4o7IYQQQgghhBDShVHFnRBCCCGEEEII6cKo4k4IIYQQQgghhHRh/wcUeHtLzlXXkAAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "draw('IGD')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 486
        },
        "id": "ByGKBFNiCfHF",
        "outputId": "f95e42a4-d9b4-4d2d-cdd2-a51f1816b4d9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "draw('HV')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 486
        },
        "id": "huInbZ_fCffv",
        "outputId": "608738e5-9efe-4567-8c38-d92bd8aaed50"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## T-tests"
      ],
      "metadata": {
        "id": "bBSfdHOe5eWZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "alpha = 0.05\n",
        "\n",
        "\n",
        "def return_IGD_HV(rs):\n",
        "    return rs['final_IGD'], rs['final_HV']\n",
        "\n",
        "def compare(x, y, name_x, name_y, metric='IGD', a=0.05):\n",
        "    p_value = stats.ttest_ind(x, y)[-1]\n",
        "\n",
        "    print(metric, name_x, name_y)\n",
        "    print('p_value:', p_value)\n",
        "\n",
        "    if np.isnan(p_value):\n",
        "        p_value = 1\n",
        "    if p_value <= a:\n",
        "        mean_1 = np.mean(x)\n",
        "        mean_2 = np.mean(y)\n",
        "        std_1 = np.std(x)\n",
        "        std_2 = np.std(y)\n",
        "        cohen_d = (abs(mean_1 - mean_2)) / ((std_1 ** 2 + std_2 ** 2) / 2) ** (1 / 2)\n",
        "        if cohen_d >= 0.8:\n",
        "            effect_size = 'large'\n",
        "        elif cohen_d >= 0.5:\n",
        "            effect_size = 'medium'\n",
        "        elif cohen_d >= 0.2:\n",
        "            effect_size = 'small'\n",
        "        else:\n",
        "            effect_size = 'trivial'\n",
        "        rs = np.mean(y) - np.mean(x)\n",
        "        if metric in ['HV', 'best_arch_found']:\n",
        "            rs *= -1\n",
        "        if rs > 0:\n",
        "            rs_compare = 'worse'\n",
        "        else:\n",
        "            rs_compare = 'better'\n",
        "        print('Reject |', rs_compare, effect_size)\n",
        "    print()\n",
        "\n",
        "def print_mean_std(_all, round_pre=4):\n",
        "    _mean = np.mean(_all, axis=0)\n",
        "    _std = np.std(_all, axis=0)\n",
        "    print(f'{np.round(_mean, round_pre)} ({np.round(_std, round_pre)})')\n",
        "\n",
        "def print_best_arch_mean_std(rs):\n",
        "    _mean = np.round(np.mean(rs['best_arch_found']), 2)\n",
        "    _std = np.round(np.std(rs['best_arch_found']), 2)\n",
        "    print(f'Best (mean & std): {_mean} ({_std})')\n",
        "\n",
        "def run_t_test():\n",
        "    print('Dataset:', dataset)\n",
        "\n",
        "    list_variant = [\n",
        "        'val_error', 'val_loss', 'train_loss',\n",
        "        'moead-val_error', 'moead-val_loss', 'moead-train_loss',\n",
        "        'synflow', 'jacov', 'snip', 'grad_norm', 'grasp', 'fisher',\n",
        "        'ENAS_TFI', 'MOENAS_PSI', 'MOENAS_TF_PSI',\n",
        "        'E-TF-MOENAS', 'E-TF-MOENAS-C'\n",
        "    ]\n",
        "\n",
        "    list_competitor = []\n",
        "    for i, variant in enumerate(list_variant):\n",
        "        if os.path.exists(RES_PATH + f'/{variant}_{dataset_abbrev}_T.p'):\n",
        "            print(variant)\n",
        "            res = p.load(open(RES_PATH + f'/{variant}_{dataset_abbrev}_T.p', 'rb'))\n",
        "\n",
        "            igd, hv = return_IGD_HV(res)\n",
        "            print('IGD: ', end='')\n",
        "            print_mean_std(igd)\n",
        "            print('HV: ', end='')\n",
        "            print_mean_std(hv)\n",
        "            print_best_arch_mean_std(res)\n",
        "            performance = {\n",
        "                'variant': variant,\n",
        "                'IGD': igd,\n",
        "                'HV': hv,\n",
        "                'best_arch_found': res['best_arch_found']\n",
        "            }\n",
        "            list_competitor.append(performance)\n",
        "            print('-' * 100)\n",
        "    print('='*20)\n",
        "    print()\n",
        "    n_competitor = len(list_competitor)\n",
        "    # n_competitors = 2\n",
        "    print(f'Alpha: {alpha/n_competitor}\\n')\n",
        "    for metric in ['IGD', 'HV']:\n",
        "        print('Metric:', metric)\n",
        "        for i in range(len(list_competitor)):\n",
        "            for j in range(i + 1, len(list_competitor)):\n",
        "                pair = [list_competitor[i][metric], list_competitor[j][metric],\n",
        "                        list_competitor[i]['variant'], list_competitor[j]['variant'], metric, alpha/n_competitor]\n",
        "                compare(*pair)\n",
        "        print('='*100)\n",
        "        print()"
      ],
      "metadata": {
        "id": "Unt-cExC5fn_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "run_t_test()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-jhIczO75xsL",
        "outputId": "1dd639e3-1635-4b48-d708-4d0d80c7c457"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset: CIFAR-100\n",
            "train_loss\n",
            "IGD: 0.0298 (0.0096)\n",
            "HV: 0.6835 (0.0006)\n",
            "Best (mean & std): 73.14 (0.12)\n",
            "----------------------------------------------------------------------------------------------------\n",
            "synflow\n",
            "IGD: 0.0328 (0.0146)\n",
            "HV: 0.6698 (0.0028)\n",
            "Best (mean & std): 73.42 (0.28)\n",
            "----------------------------------------------------------------------------------------------------\n",
            "ENAS_TFI\n",
            "IGD: 0.0754 (0.0226)\n",
            "HV: 0.6688 (0.004)\n",
            "Best (mean & std): 71.3 (0.7)\n",
            "----------------------------------------------------------------------------------------------------\n",
            "MOENAS_PSI\n",
            "IGD: 0.0761 (0.0264)\n",
            "HV: 0.6675 (0.0067)\n",
            "Best (mean & std): 71.09 (1.04)\n",
            "----------------------------------------------------------------------------------------------------\n",
            "MOENAS_TF_PSI\n",
            "IGD: 0.0749 (0.0246)\n",
            "HV: 0.6681 (0.0048)\n",
            "Best (mean & std): 71.13 (0.81)\n",
            "----------------------------------------------------------------------------------------------------\n",
            "E-TF-MOENAS\n",
            "IGD: 0.0101 (0.0076)\n",
            "HV: 0.6844 (0.0012)\n",
            "Best (mean & std): 73.5 (0.07)\n",
            "----------------------------------------------------------------------------------------------------\n",
            "====================\n",
            "\n",
            "Alpha: 0.008333333333333333\n",
            "\n",
            "Metric: IGD\n",
            "IGD train_loss synflow\n",
            "p_value: 0.3565304502785881\n",
            "\n",
            "IGD train_loss ENAS_TFI\n",
            "p_value: 3.106707708147606e-13\n",
            "Reject | worse large\n",
            "\n",
            "IGD train_loss MOENAS_PSI\n",
            "p_value: 8.79780701615647e-13\n",
            "Reject | worse large\n",
            "\n",
            "IGD train_loss MOENAS_TF_PSI\n",
            "p_value: 4.073873521616047e-12\n",
            "Reject | worse large\n",
            "\n",
            "IGD train_loss E-TF-MOENAS\n",
            "p_value: 1.8260102851022764e-12\n",
            "Reject | better large\n",
            "\n",
            "IGD synflow ENAS_TFI\n",
            "p_value: 1.1283585851096715e-10\n",
            "Reject | worse large\n",
            "\n",
            "IGD synflow MOENAS_PSI\n",
            "p_value: 8.229157432500336e-11\n",
            "Reject | worse large\n",
            "\n",
            "IGD synflow MOENAS_TF_PSI\n",
            "p_value: 7.210618144841182e-10\n",
            "Reject | worse large\n",
            "\n",
            "IGD synflow E-TF-MOENAS\n",
            "p_value: 2.9012820711031246e-10\n",
            "Reject | better large\n",
            "\n",
            "IGD ENAS_TFI MOENAS_PSI\n",
            "p_value: 0.922280547814599\n",
            "\n",
            "IGD ENAS_TFI MOENAS_TF_PSI\n",
            "p_value: 0.9470514254626172\n",
            "\n",
            "IGD ENAS_TFI E-TF-MOENAS\n",
            "p_value: 1.1077971892971226e-19\n",
            "Reject | better large\n",
            "\n",
            "IGD MOENAS_PSI MOENAS_TF_PSI\n",
            "p_value: 0.8715351254454394\n",
            "\n",
            "IGD MOENAS_PSI E-TF-MOENAS\n",
            "p_value: 2.464509120765399e-19\n",
            "Reject | better large\n",
            "\n",
            "IGD MOENAS_TF_PSI E-TF-MOENAS\n",
            "p_value: 2.909300855916565e-18\n",
            "Reject | better large\n",
            "\n",
            "====================================================================================================\n",
            "\n",
            "Metric: HV\n",
            "HV train_loss synflow\n",
            "p_value: 6.998936943871073e-35\n",
            "Reject | worse large\n",
            "\n",
            "HV train_loss ENAS_TFI\n",
            "p_value: 4.427961510437608e-25\n",
            "Reject | worse large\n",
            "\n",
            "HV train_loss MOENAS_PSI\n",
            "p_value: 4.1168703690840665e-19\n",
            "Reject | worse large\n",
            "\n",
            "HV train_loss MOENAS_TF_PSI\n",
            "p_value: 6.972346968820338e-23\n",
            "Reject | worse large\n",
            "\n",
            "HV train_loss E-TF-MOENAS\n",
            "p_value: 0.0005077977500857553\n",
            "Reject | better large\n",
            "\n",
            "HV synflow ENAS_TFI\n",
            "p_value: 0.27917776719783677\n",
            "\n",
            "HV synflow MOENAS_PSI\n",
            "p_value: 0.07888062813075096\n",
            "\n",
            "HV synflow MOENAS_TF_PSI\n",
            "p_value: 0.11646510061631893\n",
            "\n",
            "HV synflow E-TF-MOENAS\n",
            "p_value: 8.241311374486162e-35\n",
            "Reject | better large\n",
            "\n",
            "HV ENAS_TFI MOENAS_PSI\n",
            "p_value: 0.43303033425283965\n",
            "\n",
            "HV ENAS_TFI MOENAS_TF_PSI\n",
            "p_value: 0.643793678274738\n",
            "\n",
            "HV ENAS_TFI E-TF-MOENAS\n",
            "p_value: 2.5873250833080604e-25\n",
            "Reject | better large\n",
            "\n",
            "HV MOENAS_PSI MOENAS_TF_PSI\n",
            "p_value: 0.700961877576944\n",
            "\n",
            "HV MOENAS_PSI E-TF-MOENAS\n",
            "p_value: 6.1196152412697e-20\n",
            "Reject | better large\n",
            "\n",
            "HV MOENAS_TF_PSI E-TF-MOENAS\n",
            "p_value: 2.68425579338825e-23\n",
            "Reject | better large\n",
            "\n",
            "====================================================================================================\n",
            "\n"
          ]
        }
      ]
    }
  ]
}