Serendipidity.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU",
    "gpuClass": "standard"
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dr8pewGpBoER"
      },
      "outputs": [],
      "source": [
        "from math import ceil, floor\n",
        "from tqdm import tqdm\n",
        "import random\n",
        "import pickle\n",
        "import copy\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import torch\n",
        "from torch.utils.data import TensorDataset, DataLoader\n",
        "from sklearn.cluster import KMeans\n",
        "\n",
        "from google.colab import output as colab_output\n",
        "colab_output.enable_custom_widget_manager()\n",
        "import gdown"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!gdown --id 1-JdHF_fHRJtS9Yr-KOiSqSZIK0kotoz_\n",
        "!gdown --id 1-H5xzRDJo92H5sFUJ2WK_1b4E4u5iIIi"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CPQCrQ2gQpGa",
        "outputId": "330e71aa-22b1-47ed-f643-156e539e16aa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/gdown/cli.py:127: FutureWarning: Option `--id` was deprecated in version 4.3.1 and will be removed in 5.0. You don't need to pass it anymore to use a file ID.\n",
            "  warnings.warn(\n",
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1-JdHF_fHRJtS9Yr-KOiSqSZIK0kotoz_\n",
            "To: /content/movies_ml_1m.pickle\n",
            "100% 591k/591k [00:00<00:00, 48.6MB/s]\n",
            "/usr/local/lib/python3.8/dist-packages/gdown/cli.py:127: FutureWarning: Option `--id` was deprecated in version 4.3.1 and will be removed in 5.0. You don't need to pass it anymore to use a file ID.\n",
            "  warnings.warn(\n",
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1-H5xzRDJo92H5sFUJ2WK_1b4E4u5iIIi\n",
            "To: /content/ratings_ml_1m.pickle\n",
            "100% 24.0M/24.0M [00:00<00:00, 48.3MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class NeuralMatrixFactorization(torch.nn.Module):\n",
        "    def __init__(self, n_users, n_items, n_factors):\n",
        "        super().__init__()\n",
        "        self.n_items = n_items\n",
        "        self.n_factors = n_factors\n",
        "        self.user_factors = torch.nn.Embedding(n_users, n_factors, sparse=True)\n",
        "        self.item_factors = torch.nn.Embedding(n_items, n_factors, sparse=True)\n",
        "        self.fc = torch.nn.Linear(n_factors, 1)\n",
        "        self.sigmoid = torch.nn.Sigmoid()\n",
        "\n",
        "    def get_user_embedding(self, user_id):\n",
        "        return self.user_factors(user_id)\n",
        "    \n",
        "    def get_movie_embedding(self, item_id):\n",
        "        return self.item_factors(item_id)\n",
        "\n",
        "    def forward(self, users, items, last_layer = False):\n",
        "        users = self.user_factors(users)\n",
        "        items = self.item_factors(items)\n",
        "        mul = torch.mul(users, items)\n",
        "        if last_layer:\n",
        "            return torch.mul(torch.sign(self.fc.weight), mul)\n",
        "        return torch.squeeze(self.sigmoid(self.fc(mul)))\n",
        "\n",
        "class NeuralColabrativeFiltering(torch.nn.Module):\n",
        "    def __init__(self, n_user, n_items, n_factors, num_features, num_layers, last_layer, catogories_from_movieId):\n",
        "        super().__init__()\n",
        "        self.catogories_from_movieId = catogories_from_movieId\n",
        "\n",
        "        self.user_factors = torch.nn.Embedding(n_user, n_factors, sparse=True)\n",
        "        self.item_factors = torch.nn.Embedding(n_items, n_factors-num_features, sparse=True)\n",
        "        self.catogories = torch.nn.Sequential(\n",
        "            torch.nn.Linear(num_features, num_features),\n",
        "            torch.nn.ReLU()\n",
        "        )\n",
        "\n",
        "        self.last_layer = last_layer\n",
        "        self.fc_layers = torch.nn.ModuleList()\n",
        "        layer_size = 2*n_factors\n",
        "        ratio = (last_layer/layer_size)**(1/(num_layers-1))\n",
        "        for i in range(num_layers-1):\n",
        "          out_layer = ceil(ratio*layer_size) if i != num_layers - 2 else last_layer\n",
        "          self.fc_layers.append(torch.nn.Linear(layer_size, out_layer))\n",
        "          self.fc_layers.append(torch.nn.ReLU())\n",
        "          layer_size = out_layer\n",
        "\n",
        "        self.fc = torch.nn.Linear(last_layer, 1)\n",
        "        self.sigmoid = torch.nn.Sigmoid()\n",
        "\n",
        "    def get_user_embedding(self, user_id):\n",
        "        return self.user_factors(user_id)\n",
        "    \n",
        "    def get_movie_embedding(self, item_id):\n",
        "        return torch.cat([self.item_factors(item_id), self.catogories(self.catogories_from_movieId[item_id])], dim=-1)\n",
        "\n",
        "    def forward(self, userIds, itemIds, last_layer = False):\n",
        "        users = self.user_factors(userIds)\n",
        "        items = self.item_factors(itemIds)\n",
        "        features = self.catogories(self.catogories_from_movieId[itemIds])\n",
        "\n",
        "        x = torch.cat([users,items,features],dim=-1)\n",
        "\n",
        "        for layer in self.fc_layers:\n",
        "            x = layer(x)\n",
        "        if last_layer:\n",
        "          return torch.mul(torch.sign(self.fc.weight), x)\n",
        "        return torch.squeeze(self.sigmoid(self.fc(x)))\n",
        "\n",
        "class NeuMF(torch.nn.Module):\n",
        "    def __init__(self, model_mf, model_ncf, last_layer):\n",
        "        super().__init__()\n",
        "        self.model_mf = model_mf\n",
        "        self.model_ncf = model_ncf\n",
        "\n",
        "        self.fc_last_layer = torch.nn.Sequential(\n",
        "            torch.nn.Linear(self.model_ncf.last_layer + self.model_mf.n_factors, last_layer),\n",
        "            torch.nn.ReLU()\n",
        "        )\n",
        "        self.last = torch.nn.Linear(last_layer, 1)\n",
        "        self.sigmoid = torch.nn.Sigmoid()\n",
        "\n",
        "    def get_user_embedding(self, user_id):\n",
        "        return torch.cat([self.model_mf.user_factors(user_id), self.model_ncf.user_factors(user_id)],dim=-1)\n",
        "    \n",
        "    def get_movie_embedding(self, item_id):\n",
        "        return torch.cat([self.model_mf.get_movie_embedding(item_id), self.model_ncf.get_movie_embedding(item_id)],dim=-1)\n",
        "        \n",
        "    def forward(self, userIds, itemIds, last_layer=False):\n",
        "        x = self.fc_last_layer(torch.cat([self.model_mf(userIds, itemIds,True), self.model_ncf(userIds, itemIds,True)], dim=-1)) \n",
        "        if last_layer:\n",
        "            return torch.mul(torch.sign(self.last.weight), x)\n",
        "        return torch.squeeze(self.sigmoid(self.last(x)))"
      ],
      "metadata": {
        "id": "eOog8BU_RbKh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "NUM_USERS = 6040 \n",
        "NUM_MOVIES = 3883"
      ],
      "metadata": {
        "id": "IkhLw42hQvAG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ratings = None\n",
        "with open('ratings_ml_1m.pickle', 'rb') as handle:\n",
        "    ratings = pickle.load(handle)\n",
        "ratings = ratings[ratings[\"userId\"] < NUM_USERS]\n",
        "ratings = ratings[ratings[\"movieId\"] < NUM_MOVIES]\n",
        "\n",
        "movies = None\n",
        "with open('movies_ml_1m.pickle', 'rb') as handle:\n",
        "    movies = pickle.load(handle)\n",
        "movies = movies[:NUM_MOVIES]\n",
        "\n",
        "movies_tensor = torch.from_numpy(movies.values).to(torch.float32).cuda()"
      ],
      "metadata": {
        "id": "LeQ-egKWQwKg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def make_dataset(ratings):\n",
        "  u_tensor = torch.from_numpy(ratings['userId'].values.astype('long')).to(torch.long).cuda()\n",
        "  m_tensor = torch.from_numpy(ratings['movieId'].values.astype('long')).to(torch.long).cuda()\n",
        "  y_tensor = torch.from_numpy(ratings['rating'].values.astype('long')).to(torch.float32).cuda()\n",
        "  dataset = TensorDataset(u_tensor, m_tensor, y_tensor)\n",
        "  return dataset"
      ],
      "metadata": {
        "id": "e6hWDIuZQw1f"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class make_top_k():\n",
        "  def __init__(self, model):\n",
        "    self.model = model\n",
        "  \n",
        "  def train(self, dataset, lr, epoch, loss_func, batch_size, extra):\n",
        "    if extra is not None:\n",
        "      self.train_(self.model.model_mf, dataset, *extra[0])\n",
        "      self.train_(self.model.model_ncf, dataset, *extra[1])\n",
        "    self.train_(self.model, dataset, lr, epoch, loss_func, batch_size)\n",
        "\n",
        "  def train_(self, model, dataset, lr, epoch, loss_func, batch_size):\n",
        "    print(\"Starting Training\")\n",
        "    loss_func = loss_func.cuda()\n",
        "    optimizer = torch.optim.SGD(model.parameters(), lr=lr)\n",
        "    for e in tqdm(range(epoch), leave=False):\n",
        "      second_last_iter = floor(len(dataset)/batch_size)\n",
        "      for i in range(ceil(len(dataset)/batch_size)):\n",
        "        if i <= second_last_iter: \n",
        "          userIds, movieIds, ratings = dataset[i*batch_size:(i+1)*batch_size]\n",
        "        else:\n",
        "          userIds, movieIds, ratings = dataset[i*batch_size:]\n",
        "        \n",
        "        optimizer.zero_grad()\n",
        "        prediction = model(userIds, movieIds)\n",
        "        loss = loss_func(prediction, ratings)\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "  \n",
        "  def make_ranking(self, k, dataset):\n",
        "    top_k = torch.zeros((NUM_USERS,k)).to(torch.long)\n",
        "    print(\"Making top k from Ranking\")\n",
        "    for u in tqdm(range(dataset.shape[0]), leave=False):\n",
        "      movies_for_user = dataset[u].cuda()\n",
        "      user = torch.full(movies_for_user.shape, u).to(torch.long).cuda()\n",
        "      rankings = self.model(user, movies_for_user)\n",
        "      top_k[u] = movies_for_user[torch.topk(rankings, k)[1]].detach().cpu()\n",
        "    return top_k\n",
        "  \n",
        "  def make_lexicase(self, k, dataset):\n",
        "    torch.manual_seed(100)\n",
        "\n",
        "    top_k = torch.zeros((NUM_USERS,k)).to(torch.long)\n",
        "    print(\"Making top k from Lexicase\")\n",
        "\n",
        "    for u in tqdm(range(dataset.shape[0]), leave=False):\n",
        "      movies_for_user = dataset[u].cuda()\n",
        "      user = torch.full(movies_for_user.shape, u).to(torch.long).cuda()\n",
        "      last_layer_movies = self.model(user,movies_for_user,True).detach().cpu()\n",
        "\n",
        "      num_features = last_layer_movies.shape[1]\n",
        "\n",
        "      not_selected_yet = torch.ones(movies_for_user.shape, dtype=torch.bool)\n",
        "      movie_idx = []\n",
        "\n",
        "      for _ in range(k):\n",
        "        select_feature = torch.randint(num_features,(10,))\n",
        "        selected = torch.ones(movies_for_user.shape, dtype=torch.bool)\n",
        "        selected = torch.logical_and(not_selected_yet, selected)\n",
        "\n",
        "        for selected_feature in select_feature:\n",
        "          movies_selected_feature = last_layer_movies[selected][:, selected_feature]\n",
        "          max_val = torch.max(movies_selected_feature)\n",
        "          filter = torch.nonzero(selected)[movies_selected_feature < max_val-0.05]\n",
        "          selected[filter] = False\n",
        "          if torch.sum(selected) == 1:\n",
        "            break\n",
        "        selected_idx = torch.nonzero(selected)\n",
        "        selected_idx = selected_idx[0]\n",
        "        not_selected_yet[selected_idx] = False\n",
        "        movie_idx.append(selected_idx)\n",
        "      top_k[u] = movies_for_user[movie_idx].detach().cpu()\n",
        "    return top_k\n",
        "  \n",
        "  def make_mix(self, k, dataset, per_lex=0.5, ranking_k=None, lexicase_k=None):\n",
        "    print(\"Making top k from Mix\")\n",
        "    top_k = torch.zeros((NUM_USERS,k)).to(torch.long)\n",
        "\n",
        "    k_lexicase = floor(per_lex*k)\n",
        "    top_ranking = self.make_ranking(k) if ranking_k is None else ranking_k\n",
        "    if k_lexicase == 0:\n",
        "      return top_ranking\n",
        "    top_lexicase = self.make_lexicase(k) if lexicase_k is None else lexicase_k\n",
        "\n",
        "    for u in tqdm(range(dataset.shape[0]), leave=False):\n",
        "      inter = torch.full((k,), -1).to(torch.long)\n",
        "      ranking_to_add = []\n",
        "      lexicase_to_add = top_lexicase[u][:k_lexicase]\n",
        "      index = 0\n",
        "      for i in range(k-k_lexicase):\n",
        "        while top_ranking[u][index] in lexicase_to_add:\n",
        "          index += 1\n",
        "        ranking_to_add.append(top_ranking[u][index])\n",
        "      \n",
        "      i_r = 0\n",
        "      i_l = 0\n",
        "      index = 0\n",
        "\n",
        "      while index < k:\n",
        "        if i_r < (k-k_lexicase):\n",
        "          inter[index] = ranking_to_add[i_r]\n",
        "          index += 1\n",
        "          i_r += 1\n",
        "        if i_l < k_lexicase:\n",
        "          inter[index] = lexicase_to_add[i_l]\n",
        "          index += 1\n",
        "          i_l += 1\n",
        "      \n",
        "      assert(-1 not in top_k)\n",
        "      top_k[u] = inter\n",
        "\n",
        "      # lexicase_index = 0\n",
        "      # ranking_index = 0\n",
        "      # index = 0\n",
        "      # while index < k:\n",
        "      #   while top_ranking[u][ranking_index] in inter:\n",
        "      #     if ranking_index == k:\n",
        "      #       break\n",
        "      #     ranking_index += 1\n",
        "      #   inter[index] = top_ranking[u][ranking_index]\n",
        "      #   index += 1\n",
        "      #   while top_lexicase[u][lexicase_index] in inter:\n",
        "      #     if top_lexicase == k:\n",
        "      #       break\n",
        "      #     lexicase_index += 1\n",
        "      #   if index <= 2*k_lexicase:\n",
        "      #     inter[index] = top_ranking[u][lexicase_index]\n",
        "      #     index += 1\n",
        "      # assert(-1 not in inter)\n",
        "      # top_k[u] = inter\n",
        "      # unique = torch.unique(inter_2k)\n",
        "      # unique = unique[unique != -1]\n",
        "      # idx = torch.tensor([torch.nonzero(inter_2k == v)[0] for v in unique]).sort()[0]\n",
        "      # top_k[u] = inter_2k[idx][:k].detach().cpu()\n",
        "    \n",
        "    return top_k\n",
        "  \n",
        "  def give_all_top_k(self,k,dataset,per_lex=0.5):\n",
        "    # top_k = {'r':None,'l':None,'m':None}\n",
        "    # top_k['r'] = self.make_ranking(k,dataset)\n",
        "    # top_k['l'] = self.make_lexicase(k,dataset)\n",
        "    # top_k['m'] = self.make_mix(k, dataset, per_lex, top_k['r'], top_k['l'])\n",
        "\n",
        "    top_k = {'r':None, 'm-50':None, 'l':None}\n",
        "    # top_k = {'r':None,'m-20':None, 'm-40':None, 'm-60':None, 'm-80':None, 'l':None}\n",
        "    top_k['r'] = self.make_ranking(k,dataset)\n",
        "    top_k['l'] = self.make_lexicase(k,dataset)\n",
        "    top_k['m-50'] = self.make_mix(k, dataset, 0.5, top_k['r'], top_k['l'])\n",
        "    # top_k['m-20'] = self.make_mix(k, dataset, 0.2, top_k['r'], top_k['l'])\n",
        "    # top_k['m-40'] = self.make_mix(k, dataset, 0.4, top_k['r'], top_k['l'])\n",
        "    # top_k['m-60'] = self.make_mix(k, dataset, 0.6, top_k['r'], top_k['l'])\n",
        "    # top_k['m-80'] = self.make_mix(k, dataset, 0.8, top_k['r'], top_k['l'])\n",
        "\n",
        "    return top_k"
      ],
      "metadata": {
        "id": "VROPqs89Qzrq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class metric():\n",
        "  def __init__(self, models, names, train_info, test_k=[5,10,20,50,100]):\n",
        "    self.models = models\n",
        "    self.names = names\n",
        "    self.test_k = test_k\n",
        "    self.train_info = train_info\n",
        "    self.results = {}\n",
        "  \n",
        "  def get_results(self):\n",
        "    return self.results\n",
        "  \n",
        "  def run_all(self):\n",
        "    train_dataset = make_dataset(ratings)\n",
        "    models_make_top_k = [make_top_k(copy.deepcopy(model)) for model in self.models]\n",
        "    [mtk.train(train_dataset, *ti) for ti, mtk in zip(self.train_info,models_make_top_k)]\n",
        "    movie_dataset = torch.zeros((NUM_USERS,NUM_MOVIES)).to(torch.long)\n",
        "    all_movies = torch.arange(NUM_MOVIES).to(torch.long)\n",
        "    dataset = []\n",
        "    for u in range(NUM_USERS):\n",
        "      movie_dataset[u] = all_movies\n",
        "      user_ratings = ratings[ratings['userId'] == u]\n",
        "      dataset.append(user_ratings.sort_values('rating', ascending=False)[\"movieId\"][10:].values)\n",
        "    top_max_k = [mtk.give_all_top_k(max(self.test_k),movie_dataset) for mtk in models_make_top_k]\n",
        "    embeddings = [mtk.model.get_movie_embedding(all_movies.cuda()).detach().cpu() for mtk in models_make_top_k]\n",
        "      \n",
        "    self.coverage(top_max_k)\n",
        "    self.personalization(top_max_k)\n",
        "    self.diversity(embeddings, dataset, top_max_k)\n",
        "\n",
        "    train = pd.DataFrame(columns = ['userId', 'movieId', 'rating'])\n",
        "    test = torch.zeros((NUM_USERS,max(self.test_k))).to(torch.long)\n",
        "    for u in tqdm(range(NUM_USERS), leave=False):\n",
        "      for_user = ratings[ratings[\"userId\"] == u]\n",
        "      r = 1\n",
        "      filter = for_user[\"rating\"] >= r\n",
        "      while filter.sum() == 0:\n",
        "        r -= 0.2\n",
        "        filter = for_user[\"rating\"] >= r\n",
        "      remove_index = for_user.index[filter][0]\n",
        "      remove_movie = for_user[for_user.index == remove_index][\"movieId\"].values[0]\n",
        "      perm = torch.randperm(NUM_MOVIES)\n",
        "      test[u] = perm[perm != remove_movie][:100]\n",
        "      test[u][0] = remove_movie\n",
        "      for_user = for_user[for_user.index != remove_index]\n",
        "      train = train.append(for_user,ignore_index=False)\n",
        "    train = train.sort_index()\n",
        "    train_dataset = make_dataset(train)\n",
        "    selected_movies = test[:,0]\n",
        "    models_make_top_k = [make_top_k(copy.deepcopy(model)) for model in self.models]\n",
        "    [mtk.train(train_dataset, *ti) for ti, mtk in zip(self.train_info,models_make_top_k)]\n",
        "    top_max_k = [mtk.give_all_top_k(max(self.test_k),test) for mtk in models_make_top_k]\n",
        "\n",
        "    self.hit(selected_movies, top_max_k)\n",
        "\n",
        "    return self.results\n",
        "  \n",
        "  def coverage(self, top_max_k):\n",
        "    print(\"Started Coverage Caclculations\")\n",
        "    results = {}\n",
        "    for top_k_model, _name in tqdm(zip(top_max_k, self.names), leave=False):\n",
        "      for rank_type in [\"r\",\"l\",\"m-50\"]:\n",
        "      # for rank_type in [\"r\",\"m-20\",\"m-40\",\"m-60\",\"m-80\",\"l\"]:\n",
        "        name = _name + \"(\" + rank_type + \")\"\n",
        "        top_k_model_rt = top_k_model[rank_type]\n",
        "        res = []\n",
        "        for k in self.test_k:\n",
        "          top_k = top_k_model_rt[:,:k]\n",
        "          unique = len(torch.unique(top_k))\n",
        "          res.append(unique/NUM_MOVIES)\n",
        "        results[name] = res\n",
        "    self.results['coverage'] = results\n",
        "\n",
        "    fig, ax = plt.subplots(figsize=(2+2*len(self.test_k),5))\n",
        "\n",
        "    plt.title(\"Coverage\", fontsize=24)\n",
        "\n",
        "    names = results.keys()\n",
        "\n",
        "    x = np.arange(len(self.test_k))\n",
        "    width = 0.9/len(names)\n",
        "    shift = np.array([round(i*width,4) for i in range(len(names))])\n",
        "    shift = shift - np.mean(shift)\n",
        "\n",
        "    for i,name in enumerate(names):\n",
        "      ax.bar(x+shift[i], results[name], width, label=name)\n",
        "\n",
        "    plt.xlabel(\"Top K\", fontsize=20)\n",
        "    plt.xticks(x, [\"top - \"+str(i) for i in self.test_k], fontsize=17)\n",
        "    plt.ylabel(\"Coverange Rate\", fontsize=20)\n",
        "    plt.yticks(fontsize=17)\n",
        "    ax.grid(False)\n",
        "    ax.tick_params(bottom=False, left=True)\n",
        "    box = ax.get_position()\n",
        "    ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])\n",
        "    ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n",
        "    plt.show()\n",
        "  \n",
        "  def personalization(self, top_max_k):\n",
        "    print(\"Started Coverage Caclculations\")\n",
        "    results = {}\n",
        "    for top_k_model, _name in tqdm(zip(top_max_k, self.names), leave=False):\n",
        "      for rank_type in [\"r\",\"l\",\"m-50\"]:\n",
        "      # for rank_type in [\"r\",\"m-20\",\"m-40\",\"m-60\",\"m-80\",\"l\"]:\n",
        "        name = _name + \"(\" + rank_type + \")\"\n",
        "        top_k_model_rt = top_k_model[rank_type]\n",
        "        res = []\n",
        "        for k in self.test_k:\n",
        "          top_k = torch.clone(top_k_model_rt[:,:k]).type(torch.LongTensor)\n",
        "\n",
        "          all_res = torch.zeros((NUM_USERS, NUM_MOVIES))\n",
        "          for i in range(NUM_USERS):\n",
        "            all_res[i][top_k[i]] = 1.0\n",
        "\n",
        "          a_norm = all_res / all_res.norm(dim=1)[:, None]\n",
        "          res_mat = torch.mm(a_norm, a_norm.transpose(0,1))\n",
        "          res.append(1 - (torch.sum(res_mat)-NUM_USERS)/(NUM_USERS*(NUM_USERS-1)))\n",
        "        results[name] = res\n",
        "    self.results['personalization'] = results\n",
        "\n",
        "    fig, ax = plt.subplots(figsize=(2+2*len(self.test_k),5))\n",
        "\n",
        "    plt.title(\"Personalization\", fontsize=24)\n",
        "\n",
        "    names = results.keys()\n",
        "\n",
        "    x = np.arange(len(self.test_k))\n",
        "    width = 0.8/len(names)\n",
        "    shift = np.array([round(i*width,4) for i in range(len(names))])\n",
        "    shift = shift - np.mean(shift)\n",
        "\n",
        "    for i,name in enumerate(names):\n",
        "      ax.bar(x+shift[i], results[name], width, label=name)\n",
        "    \n",
        "    plt.xlabel(\"Top K\", fontsize=20)\n",
        "    plt.xticks(x, [\"top - \"+str(i) for i in self.test_k], fontsize=17)\n",
        "    plt.ylabel(\"Personalization Rate\", fontsize=20)\n",
        "    plt.yticks(fontsize=17)\n",
        "    ax.grid(False)\n",
        "    ax.tick_params(bottom=False, left=True)\n",
        "    box = ax.get_position()\n",
        "    ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])\n",
        "    ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n",
        "    plt.show()\n",
        "  \n",
        "  def hit(self,selected_movie,top_max_k):\n",
        "    print(\"Started Hit Rate Calculations\")\n",
        "    if 'hit' not in self.results.keys() or self.results['hit'] is None:\n",
        "      results = {}\n",
        "      for top_k_model, _name in tqdm(zip(top_max_k, self.names), leave=False):\n",
        "        for rank_type in [\"r\",\"l\",\"m-50\"]:\n",
        "        # for rank_type in [\"r\",\"m-20\",\"m-40\",\"m-60\",\"m-80\",\"l\"]:\n",
        "          name = _name + \"(\" + rank_type + \")\"\n",
        "          top_k_model_rt = top_k_model[rank_type]\n",
        "          res = []\n",
        "          for k in self.test_k:\n",
        "            top_k = top_k_model_rt[:,:k]\n",
        "            hit = sum([selected_movie[u] in top_k[u] for u in range(len(selected_movie))])/len(selected_movie)\n",
        "            res.append(hit)\n",
        "          results[name] = res\n",
        "      self.results['hit'] = results\n",
        "    results = self.results['hit']\n",
        "\n",
        "    fig, ax = plt.subplots(figsize=(2+2*len(self.test_k),5))\n",
        "\n",
        "    plt.title(\"Hit Rate\", fontsize=24)\n",
        "\n",
        "    names = results.keys()\n",
        "\n",
        "    x = np.arange(len(self.test_k))\n",
        "    width = 0.8/len(names)\n",
        "    shift = np.array([round(i*width,4) for i in range(len(names))])\n",
        "    shift = shift - np.mean(shift)\n",
        "\n",
        "    for i,name in enumerate(names):\n",
        "      ax.bar(x+shift[i], results[name], width, label=name)\n",
        "    \n",
        "    plt.xlabel(\"Top K\", fontsize=20)\n",
        "    plt.xticks(x, [\"top - \"+str(i) for i in self.test_k], fontsize=17)\n",
        "    plt.ylabel(\"Hit Rate\", fontsize=20)\n",
        "    plt.yticks(fontsize=17)\n",
        "    ax.grid(False)\n",
        "    ax.tick_params(bottom=False, left=True)\n",
        "    box = ax.get_position()\n",
        "    ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])\n",
        "    ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n",
        "    plt.show()\n",
        "  \n",
        "  def diversity(self, movie_embeddings, dataset, top_max_k):\n",
        "    print(\"Started Diversity Calculations\")\n",
        "    results_min = {}\n",
        "    results_max = {}\n",
        "    results = {}\n",
        "    for embeddings, top_k_model, _name in tqdm(zip(movie_embeddings, top_max_k, self.names), leave=False):\n",
        "      clustering = KMeans(n_clusters=int(len(embeddings)**0.5)).fit(embeddings)\n",
        "      labels = clustering.labels_\n",
        "      cluster_centroids = torch.zeros((len(np.unique(labels)),embeddings.shape[1]))\n",
        "      for c in np.unique(labels):\n",
        "        cluster_centroids[c] = torch.mean(embeddings[labels == c],axis=0)\n",
        "      centroids_clustering = KMeans(n_clusters=int(len(cluster_centroids)**0.5)).fit(cluster_centroids)\n",
        "      centroids_labels = centroids_clustering.labels_\n",
        "      unique_cl_counts = np.unique(centroids_labels, return_counts=True)[1]\n",
        "      for rank_type in [\"r\",\"l\",\"m-50\"]:\n",
        "      # for rank_type in [\"r\",\"m-20\",\"m-40\",\"m-60\",\"m-80\",\"l\"]:\n",
        "        name = _name + \"(\" + rank_type + \")\"\n",
        "        top_k_model_rt = top_k_model[rank_type]\n",
        "        res = []\n",
        "        for k in self.test_k:\n",
        "          top_k = top_k_model_rt[:,:k]\n",
        "          ret = 0\n",
        "          rets = []\n",
        "          for u in range(NUM_USERS):\n",
        "            relevant_centroid_clusters = np.unique(centroids_labels[labels[dataset[u]]])\n",
        "            n_relevant_cluster = sum(unique_cl_counts[relevant_centroid_clusters])\n",
        "            top_k_labels = labels[top_k[u]]\n",
        "            unique_c = []\n",
        "            num_ruc = 0\n",
        "            for c in top_k_labels:\n",
        "              cl = centroids_labels[c]\n",
        "              if cl in relevant_centroid_clusters and c not in unique_c:\n",
        "                num_ruc += 1\n",
        "                unique_c.append(c)\n",
        "            min_val = min(n_relevant_cluster, k)\n",
        "            val = num_ruc / min_val\n",
        "            rets.append(val)\n",
        "            ret += val\n",
        "          ret = ret/NUM_USERS\n",
        "          res.append(ret)\n",
        "          rets.sort()\n",
        "        results[name] = res\n",
        "    self.results['diversity'] = results\n",
        "\n",
        "    fig, ax = plt.subplots(figsize=(2+2*len(self.test_k),5))\n",
        "\n",
        "    plt.title(\"Set Difference Diversity Rate\", fontsize=24)\n",
        "\n",
        "    names = results.keys()\n",
        "\n",
        "    x = np.arange(len(self.test_k))\n",
        "    width = 0.8/len(names)\n",
        "    shift = np.array([round(i*width,4) for i in range(len(names))])\n",
        "    shift = shift - np.mean(shift)\n",
        "\n",
        "    for i,name in enumerate(names):\n",
        "      ax.bar(x+shift[i], results[name], width, label=name)\n",
        "    \n",
        "    plt.xlabel(\"Top K\", fontsize=20)\n",
        "    plt.xticks(x, [\"top - \"+str(i) for i in self.test_k], fontsize=17)\n",
        "    plt.ylabel(\"Diversity Rate\", fontsize=20)\n",
        "    plt.yticks(fontsize=17)\n",
        "    ax.grid(False)\n",
        "    ax.tick_params(bottom=False, left=True)\n",
        "    box = ax.get_position()\n",
        "    ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])\n",
        "    ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n",
        "    plt.show()"
      ],
      "metadata": {
        "id": "jIHvio7zQ10D"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "nmf_model_ = NeuralMatrixFactorization(NUM_USERS, NUM_MOVIES, 75).cuda()\n",
        "\n",
        "ncf_model = NeuralColabrativeFiltering(NUM_USERS, NUM_MOVIES, 70, 18, 4, 50, movies_tensor).cuda()\n",
        "\n",
        "ncf_model_ = NeuralColabrativeFiltering(NUM_USERS, NUM_MOVIES, 70, 18, 4, 50, movies_tensor).cuda()\n",
        "\n",
        "neumf_model = NeuMF(nmf_model_, ncf_model_, 80).cuda()"
      ],
      "metadata": {
        "id": "eXCLOaoTQ4YK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "models = [ncf_model,neumf_model]\n",
        "names = [\"NCF\",\"NeuMF\"]\n",
        "train_info = [[1e-2,1,torch.nn.CrossEntropyLoss(),1024,None],[1e-1, 1, torch.nn.MSELoss(),1024,[[1e-1,1,torch.nn.MSELoss(),1024],[1e-2,1,torch.nn.CrossEntropyLoss(),1024]]]]\n",
        "\n",
        "met = metric(models,names,train_info)"
      ],
      "metadata": {
        "id": "8imGpTZURslL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "results = met.run_all()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "dz4liBqCVEu6",
        "outputId": "ff411c6f-b644-4d67-f310-375f836f86c7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Starting Training\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Starting Training\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Starting Training\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Starting Training\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Making top k from Ranking\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Making top k from Lexicase\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Making top k from Mix\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Making top k from Ranking\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Making top k from Lexicase\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Making top k from Mix\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Started Coverage Caclculations\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Started Coverage Caclculations\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Started Diversity Calculations\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Starting Training\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Starting Training\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Starting Training\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Starting Training\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Making top k from Ranking\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Making top k from Lexicase\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Making top k from Mix\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Making top k from Ranking\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Making top k from Lexicase\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Making top k from Mix\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Started Hit Rate Calculations\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x360 with 1 Axes>"
            ],
            "image/png": 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cDh06XLhp06b0IUOG7J8/f37zKnkzpxEJMwAAQC0ybty4XfPnz88sKChIiy7fuHFjRu/evYvKajdx4sQ2eXl5nfPy8jqvWrUqQ5JWr17dqHfv3gcjx2zZsqX+HXfcsfujjz76Z4cOHY5kZWWVHDlyxPLz89PK6rcmSHgOs5l9SdJ/SOok6Sx37xsuz5V0iaSl7r4vmUECAAAgOTIzM0sHDRpUMGXKlJYZGRknzTsuy0MPPbR1xIgRJ+R4hYWFdZs1a3a8j5ycnCN9+vQ5GH1M8+bNj23ZsiW9VatWxacefWokNMJsZg9KWiPpXkkDJH0zpq+XJA1LWnQAAABIuvHjx++cO3dui4MHDx7PBc8///ziFStWNEykn7S0NC8p+eJ+voYNG56UgB8+fNjildckFU6YzWyIpImSlkrqJumR6Hp3/1jSOypjhxQAAABUD9nZ2SUDBgzYN3fu3BaRsnvvvTd//PjxbbZs2VJXkg4dOmTTpk1rUXYv0rnnnnto7dq19cuqLy0t1e7du+t17NjxcPKiP/0SmZJxl6SPJH3H3Y+Y2TVxjlkr6RvJCAwAAOBMVpFl4KrShAkT8mfNmnV8BYvBgwcX5ufn1+3Tp09Hd5eZ6frrrw+86e+KK64oXLJkSeMuXbrETYiXL1/e8KKLLjpYr169ZId/WiWSMHeVNNPdjwQcs13SSXddAkBF5P5kUcJtNjf4j8qdbHJh5doBQA1WVFT0buTntm3bHisuLn43un706NEFo0ePLoht9+qrr26O198dd9yxZ+jQobljxozZ07FjxyMbN278Z3T9Cy+80Py2227blaTwUyaROcwmqbz5J9mSDlU+HAAAANQU7du3Pzpy5Mg9e/fujZtTdunSpfg73/nO56c7rmRLZIR5o6ReZVWaWR1Jl0r6Z1nHAEBNtjavU6XadVq3NsmRAED1MWrUqDJXRytvHeeaIpER5nmSupvZ2DLqfyrpfElzTzkqAAAAoJpIZIT5l5IGSXrMzK6T5JJkZlMlfU3SxZLelvRskmMEAAAAUqbCCbO7F5vZNyU9Iel6SZEdW8YoNLf5RUl3uPuxpEcJAAAApEhCO/25e6Gk75vZGElfkdRcUqGkVe6+uwriAwAAAFIq4a2xJcnd90r6Y5JjAQAAqD0mN+mR3P4Ky13X2cx6jBo1aueMGTO2StL999+ffeDAgbRp06Ztl6Snn366+RNPPNHKzDwtLU2DBg0qePDBB3dee+21uW+//Xbjxo0bl0jSsGHD9kycOHFXaWmpevXq1WHx4sUfZWZmljZs2PCioqKid7dv31538ODB5/71r3/dmNT3mCKJ7PRXYmb3lXPMBDNjSgYAAEA1lJ6e7osXL262Y8eOkwZN582bd/b06dNbLl26dMOGDRs+XLNmzdomTZoc3/f6oYce2rpu3boP161b9+HEiRN3hds0ufDCC4szMzNPWHq4devWx7Kzs48uWbLkrKp/V1Uv0XWYrYLHVbxTs3Qz+5mZbTGzQ2b2vpkNTaB9ppk9YWafmNlhM9tmZq+a2dmJxAEAAHCmS0tL8+HDh+9++OGHT9po7rHHHsuZMmXK1tzc3KOSlJGR4eUtCzdnzpzMa665Zn+8uquvvnr/7Nmzmycl8BRLJGGuiGZKfOOS3yi0JN1CSXdK2iZprpldX15DM8uR9HdJ35M0U9Ktkp6UVE9SwwTjAAAAOOONGzdu1/z58zMLCgrSoss3btyY0bt376Ky2k2cOLFNXl5e57y8vM6rVq3KkKTVq1c36t2798F4x/fu3fvgqlWrGiU3+tQInMNsZpfFFOXGKZNCK2a0U2j1jPUVPbmZ9ZA0TNID7j45XPacpL9Immpm89z9aEAXvw6fuxs3HQIAAJQvMzOzdNCgQQVTpkxpmZGRUd4uzsc99NBDW0eMGHHCJiWFhYV1mzVrFreP1q1bH9u1a1f6qcZbHZR309+fFV5vOfx8Y/gRT2Tr7LI2Noknsp7zM5ECd3czm67QBiiXSfpT3JOZXSBpoEJL2e02s/rh9ocTOD8AAECtM378+J3du3fvPGTIkONTLs4///ziFStWNBw4cGCFt7JOS0vzkpISpaWlnVRXVFRk9evXr3BCXp2VNyXjwfDjZwolxG9FlUU/Jkm6XVIXd09kp7/ukjbHGR1eFVVflivCz9vN7I+SiiUVm9lfzaxbAjEAAADUKtnZ2SUDBgzYN3fu3BaRsnvvvTd//PjxbbZs2VJXkg4dOmTTpk1rUXYv0rnnnnto7dq19ePVffDBBw06dOhQnNzIUyNwhDkyTUKSzOxGSa+5+5NJPH+OpB1xyiNlrQPaXhB+flbSe5KGSmoh6X5Jy8zsS+7+abyGZnaTpJskqV27dolHDQAAcKoqsAxcVZowYUL+rFmzsiKvBw8eXJifn1+3T58+Hd1dZqbrr78+8Ka/K664onDJkiWNu3TpctJf+JcuXdq4X79+hVUR++mWyE5/51bB+TMk7YpTfiiqviyRSeS7JfVz9xJJMrN3FNqi+0cK7UJ4End/VuEtvC+++GKPdwwAAMCZpqio6N3Iz23btj1WXFz8bnT96NGjC0aPHl0Q2+7VV1/dHK+/O+64Y8/QoUNzx4wZsye2/8WLFzf9wx/+8FHSgk+hZK+SkahiSfGG8RtE1Qe1laTfRZJlSXL3v0n6SNKlSYkQAAAAcbVv3/7oyJEj9+zdu/eEnHL79u11R48evTMrK6ukrLY1SUI7/ZmZKbSE25WSzlH8ZNfdvU8Fu9wh6fw45Tnh5+0BbSN1O+PU7ZR00vqCAAAASK5Ro0btiy1r3br1sRtuuGF/CsKpEhVOmMOrUCyW9A2FbgB0nbhJiUeVV9QaSX3MLCvmxr+eUfVlicz7aROn7hzFnxsNAAAAJCSRKRk/lvRNSQ8pdHOdSZqs0I15/yHpU0m/k5TIenu/D/dze6QgPIp9i0KjxH8JlzUxszwzaxLV9s+S8iUNM7OMqPbfkpQr6Y8JxAEAAADElUjCPEjSGnef5O57I4Xunu/uv5N0uaT+ku6uaIfu/neF1lu+z8yeNLNRkhZJ+pqke6M2LblG0trwc6TtEYVu6suVtNzM7jKzn0maL+lfkp5I4L0BAAAAcSWSMJ8naUXUa1doC+rQC/ePFUp2v59gDCMlTVEoGX5GUltJw9x9dnkN3f0lSd8Nx/KYpNskvSbpUnffn2AcAAAAwEkSuenvqL5Y7k2SPpeUFXPMJwrtvldh4Z35JoQfZR0zU9LMMuoWSFqQyDkBAABSreusrj2S2d8/bvxHues6m1mPUaNG7ZwxY8ZWSbr//vuzDxw4kDZt2rTtkvT00083f+KJJ1qZmaelpWnQoEEFDz744M5rr7029+23327cuHHjEkkaNmzYnokTJ+4qLS1Vr169OixevPijzMzMSu3qF9v3888//69evXoVl5aWauTIkW3ffPPNJg0aNCh9/vnnN1966aVF27dvrzt48OBz//rXv26szPkqI5GEeatCN9NFbJD01ZhjLpK0VwAAAKh20tPTffHixc127NiRn5OTcyy6bt68eWdPnz695dKlSzfk5uYeLS4utunTpzeP1D/00ENbR4wYsS+mTZMLL7ywuLLJclDfr7zySpOPP/64webNmz9YtmzZWbfddlu7999/f13r1q2PZWdnH12yZMlZV1xxxcFTOW9FJTIlY4WkXlGvX5PU1cyeM7Nvm9njkvoqdDMeAAAAqpm0tDQfPnz47ocffvik5Xcfe+yxnClTpmzNzc09KkkZGRk+duzYwJ3+5syZk3nNNdfsl6T169enn3vuuRdee+21ubm5uV0GDhx47muvvda4e/fuee3bt++ybNmyhonEunDhwqbXX399QZ06ddSnT5+Dn332Wd1PPvmkniRdffXV+2fPnt28vD6SJZGEea6k9WaWG379S0l/V2gO8n9JGitpk6SfJDE+AAAAJNG4ceN2zZ8/P7OgoCAtunzjxo0ZvXv3Liqr3cSJE9vk5eV1zsvL67xq1aoMSVq9enWj3r17Hx/l/fTTTxv8+Mc/3rlp06YPNm3a1GDOnDnN33nnnXU///nPt/785z/PKavvBx544JwOHTp0/sEPftC2uLjYJGnHjh31cnNzj0SOycnJORJJmHv37n1w1apVjcrqL9kqnDC7+5/d/Sp33xx+XSSpt0KrZ/xU0lBJ3dx9W1UECgAAgFOXmZlZOmjQoIIpU6a0TKTdQw89tHXdunUfrlu37sNLLrmkWJIKCwvrNmvW7Ph0jHPOOefwJZdcUpyWlqYOHToUX3755Z/VqVNH3bt3L9q6dWu8De80bdq0bR9//PEH//u//7t23759affdd1+r8mJp3br1sV27diWylPEpOaWtsd39mLu/6u6PuvvL7l5kZqcteAAAACRu/PjxO+fOndvi4MGDx3PB888/v3jFihUJTZtIS0vzkpIvdr9OT08/voFdnTp11KBBAw8fp5KSEpOkSy+99IK8vLzOgwcPbi+FtteuU6eOMjIyfOTIkQWrV68+S5JycnKObt68+XheuWPHjvT27dsflaSioiKrX7/+Kc2bTkRCW2MHMbM6kkYptNpF+2T1CwBAdfGLwf0TbjP25TeqIBLg1GRnZ5cMGDBg39y5c1sMHTq0QJLuvffe/PHjx7fp1q3bxnbt2h07dOiQTZ8+vfmYMWPKnMd87rnnHlq7dm39Ll26HK7ouZcvX37C6haffPJJvfbt2x8tLS3V/Pnzm3bq1KlYkgYOHLh/+vTpLX/4wx/uXbZs2VmNGzcuiSTMH3zwQYMOHToUV+7dJ65CCbOZNZDUWaGl5daHNw2Jrh8q6QGF1mpOZGtsAACAWqkiy8BVpQkTJuTPmjXr+BLBgwcPLszPz6/bp0+fju4uM9P1118feNPfFVdcUbhkyZLGiSTMsQYPHnzu3r1767q7de7cuWj27NmfSNJ1111XuGjRoibt27fvkpGRUfrcc89tjrRZunRp4379+hVW9pyJKjdhNrMxCiXDkSH6XWZ2s7v/l5nlSZotqYdCW1wvlDSpqoIFAABA5RUVFb0b+blt27bHiouL342uHz16dMHo0aMLYtu9+uqrm+P1d8cdd+wZOnRo7pgxY/Z07NjxyMaNG/8Zr01sXbS33357Q7zyOnXq6Le//e2WeHWLFy9u+oc//OGjeHVVIXAOs5kNlDRV0lmSisKPbEkvm9m3JL0t6WJJf5T0FXe/xt3fr9qQAQAAUB20b9/+6MiRI/fs3bv3lO6LS8T27dvrjh49emdWVlZJ+UcnR3lv7lZJpZKGuHtjd28sabikdIVGk0sl9Xf3f3f3lP5ZAQAAAKffqFGj9p3qxiWJaN269bEbbrhh/+k6n1R+wtxd0hvuPi9S4O4vSlokqb6kH7j74iqMDwAAAEip8hLmTEnx5pt8EH5emtxwAAAAgOqlvIQ5TVK8ux4PS5K7H0h6RAAAAEA1UpFl5VgmDmec3J8sqlS7zVO+neRIAABAdVeRhPlHZjYipqypJJnZx3GOd3c/71QDAwAAOJOtzevUI5n9dVq3ttwFGMysx6hRo3bOmDFjqyTdf//92QcOHEibNm3a9sqc08x6DBw4cO/ChQv/JUlHjx5Vy5Ytv9ytW7eDy5Yt++jJJ59sPmnSpDbZ2dlHJalTp05FCxYs2CxJI0eObDto0KB9V1111UkzFm666aY2/fv3Lxw4cODnlYkr2SqSMDcNP+LJjVPGiDQAAEA1lJ6e7osXL262Y8eO/JycnGOn2l9GRkbp+vXrMw4cOGCNGjXyBQsWnB1JjiMGDBiwb/bs2Sesp5yfn5+2evXqs55//vlPY/s8duyY7rnnnl0jRoxoX1MS5nNPSxQAcJp1ndU14Tbzyj8EAKq1tLQ0Hz58+O6HH344+6mnntoWXbd9+/a6I0aMaL9t27Z0SZo2bdqWK6644uCYMWNaN2rUqOTBBx/cKUkXXHDBhW+88cbGjh07HpGkvn37Fr7yyitNR4wYse+ll17KvPbaa/euXLmyUVAcL774YrM+ffp8Fnl9zjnndB04cODet9566+y77747/6abbtq3f//+ulu2bKnbrl27U07sT1Vgwuzun5yuQIAaYXKTSrQ5bTt3AgBQrnHjxu3q2rXrhZMnT86PLr/55pvbjhkzZueVV155YOPGjelXXnnlBR9//HHc3fmi3XDDDXsnTZqUM3jw4P1r165t+IMf/KAgOmF+/fXXm+Xl5TWSpFtvvXXn6NGjC1auXNnoe9/73r7ofpo3b37sww8/XBt53bVr16I333yz0fe///39p/ymT1FFpmQAAADgDJGZmVk6aNCggilTprTMyMg4vuHIihUrzt64cWNG5PWBAwfSCgsLy93Br2fPnsVbt26tP2PGjMy+ffueNEoUb0rGzp0762VnZ58wcjx8+PATEuisrKxjkdHuVCNhBgAAqGXGjx+/s3v37p2HDBmyJ1Lm7lqzZs3ahg0bnnA/Wt26db209IuN/A4fPmyx/fXr12//pEmT2i5ZsmT9rl27ys0vGzRoUFpcXHxCMt64ceMTdgs8dOiQRSf0qXTa9v0GAABA9ZCdnV0yYMCAfXPnzm0RKbv00ks/e+SRR1pGXq9cuTJDknJzcw+/9957Z0nS8uXLG27btq1+bH+33nrrnnvuuWf7JZdcUlyR83fs2PHQhg0bTuon2qZNmxp8+ctfrlB/VY0RZgAAgBSoyDJwVWnChAn5s2bNyoq8fvbZZz8dNWpUuw4dOnQuKSmxnj17ft6rV68tw4cP3zdnzpzm559//oUXXXTRwfbt2x+K7eu88847OnHixF0VPffAgQMLf/WrX2WNGTNmT7z6w4cP2+bNm+tfdtllByv37pKLhBkAAKCWKCoqejfyc9u2bY8VFxcff52Tk3Ns0aJFJ+2x0ahRI1+xYsXG8vqL6N+//+f9+/f/XJLuuuuuAkkFscf069fvwIQJE87Zs2dPWosWLUq2bdv2j+j6l19+ucmAAQP21atXL6H3V1VImIFqam1ep4TbdFq3tvyDAACoBh5//PGtmzZtSm/RosVJ0y6OHTtm9913385UxBUPCTMAAABOu8svv7zM6RYjR47cV1ZdKlT4pj8zKzGz+8o5ZoKZpXxxaQAAACBZElklw8KPihwHAAAAnBGSvaxcM0kn3TkJAAAA1FSBc5jN7LKYotw4ZZKUJqmdpOslrU9SbAAAAEDKlXfT358lRXZ7cUk3hh/xmKRSSWOTEhkAAMAZ7Jlb3uyRzP5u//Xl5a7rbGY9Ro0atXPGjBlbJen+++/PPnDgQNq0adO2V+acZtZj4MCBexcuXPgvSTp69Khatmz55W7duh1ctmzZR08++WTzSZMmtcnOzj4qSZ06dSpasGDBZkkaOXJk20GDBu276qqrDlxyySUdp06d+ulll11W1KtXrw4LFy7clJWVVVKZmKpCeQnzgwolyibpfoUS6LfiHFei0Bp7y9x9XTIDBAAAQHKkp6f74sWLm+3YsSM/JyfnlBdqyMjIKF2/fn3GgQMHrFGjRr5gwYKzI8lxxIABA/bNnj17S3RZfn5+2urVq896/vnnP43tc+jQoQVTp07NevTRR/NPNb5kCUyY3X1y5Gczu1HSa+7+ZFUHBQBnkmdueTPhNrf/+vIqiARAbZeWlubDhw/f/fDDD2c/9dRT26Lrtm/fXnfEiBHtt23bli5J06ZN23LFFVccHDNmTOtGjRqVPPjggzsl6YILLrjwjTfe2NixY8cjktS3b9/CV155pemIESP2vfTSS5nXXnvt3pUrVzYKiuPFF19s1qdPn8/i1Q0ZMmR/r1698qpTwlzhm/7c/VySZQAAgJpt3Lhxu+bPn59ZUFCQFl1+8803tx0zZszODz74YO2CBQs23XLLLbkV6e+GG27Y+/LLLzcrKiqytWvXNvzqV796wvrKr7/+erO8vLzOeXl5nZ944onmkrRy5cpGF198cdx1mLOyskqOHDli+fn5afHqU4GNSwAAAGqRzMzM0kGDBhVMmTKlZUZGRmmkfMWKFWdv3LgxI/L6wIEDaYWFheUOrvbs2bN469at9WfMmJHZt2/fwtj6eFMydu7cWS87O7vMKSHNmzc/tmXLlvRWrVqdtAtgKpSZMJvZmwrf6OfuW8OvK8LdvU9SogMAAEDSjR8/fmf37t07DxkyZE+kzN21Zs2atQ0bNvToY+vWreulpcfzah0+fPikPTf69eu3f9KkSW2XLFmyfteuXeUOyDZo0KC0uLi4zGT88OHD1rBhw9Ky6k+3oN8avhF+NIx5XZEHAAAAqqns7OySAQMG7Js7d26LSNmll1762SOPPNIy8nrlypUZkpSbm3v4vffeO0uSli9f3nDbtm31Y/u79dZb99xzzz3bL7nkkgqNCHfs2PHQhg0bTupHkkpLS7V79+56HTt2PJzo+6oqZf4G4O51gl4DAACg8iqyDFxVmjBhQv6sWbOyIq+fffbZT0eNGtWuQ4cOnUtKSqxnz56f9+rVa8vw4cP3zZkzp/n5559/4UUXXXSwffv2J21Sd9555x2dOHHiroqee+DAgYW/+tWvssaMGbMntm758uUNL7roooP16tWr/JtLspTPYTazdEn3KbS+c0tJGyQ94u4vJdhPs3DbFpJucPcXkx0rAABATVZUVPRu5Oe2bdseKy4uPv46Jyfn2KJFiz6ObdOoUSNfsWLFxvL6i+jfv//n/fv3/1yS7rrrrgKFlh4+Qb9+/Q5MmDDhnD179qS1aNGiZNWqVcc3vnvhhRea33bbbRVOvk+H6jBq/BtJP5W0UNKdkrZJmmtm1yfYz8OSMso9CgAAACn3+OOPb920aVN6bHmXLl2Kv/Od73yeipjKUt7W2O0q06m7byn/qNDuMJKGSXogsuazmT0n6S+SpprZPHc/GtBFdD8/VGhzlZ9XJmYAAACcPpdffnncZeXGjh170jSNVCtvSsZmfbE1dkV5BfqNuC58/DPHG7u7mU2XNFfSZZL+FNSBmVm4/e8krUwwVgAAACBQeYntFp2cMDeV1ETSJ0k4f3dJm919d0z5qqj6wIRZ0ihJXSRdK+mCJMQEAAAAHFfe1ti5sWVmNlnSfe5+bhLOnyNpR5zySFnroMZmlinpEUk/d/dtZkbCDAAAgKSqzE1/iU7RCJIhKd4ae4ei6oM8Imm/pGmJnNTMbjKzd8zsnd27Ywe3AQAAgC+kelm5YknxFq1uEFUfl5l9RaHpGFe7e0ILW7v7s5KelaSLL744mb8AAAAAVMgvBvfvkcz+xr78RrnrOptZj1GjRu2cMWPGVkm6//77sw8cOJA2bdq07ZU5p5n1GDhw4N6FCxf+S5KOHj2qli1bfrlbt24Hly1b9tGTTz7ZfNKkSW2ys7OPSlKnTp2KFixYsFmSRo4c2XbQoEH7rrrqqgOVOXds3zfddNOuyLrOTz31VPOpU6fmSNI999yz48477yyQpF69enVYuHDhpqysrJJEzpXqhHmHpPPjlOeEn4Mu3i8kvSPpH2aWGy5rFX5uES7b6u5l7lMOAABQm6Snp/vixYub7dixIz8nJ+eUc6SMjIzS9evXZxw4cMAaNWrkCxYsODuSwEYMGDBg3+zZs09YQS0/Pz9t9erVZz3//POfnsr54/W9c+fOtEcffbT16tWrP6xTp44uuuiizkOGDNmflZVVMnTo0IKpU6dmPfroo/mJnCfV6zCvkdTezLJiyntG1ZelnaRLJP0r6hHZ7OQ/w6/bJC9UAACAmi0tLc2HDx++++GHH86Ordu+fXvdK6+88rwuXbp06tKlS6clS5acJUljxoxpff/99x8//oILLrhw/fr1x9dP7tu3b+Err7zSVJJeeumlzGuvvXZveXG8+OKLzfr06fNZ5PU555zT9fbbbz8nLy+vc5cuXTotX7684aWXXnpB27Ztuzz22GOxeWKg1157rclll132WXZ2dklWVlbJZZdd9tn8+fObSNKQIUP2z58/v3ki/UmpT5h/L8kk3R4pCC8Td4uknQqtxywza2JmeWbWJKrtTZKuiXncF677Zfh1tdolBgAAINXGjRu3a/78+ZkFBQVp0eU333xz2zFjxuz84IMP1i5YsGDTLbfckluR/m644Ya9L7/8crOioiJbu3Ztw69+9asnrK/8+uuvN8vLy+ucl5fX+YknnmguSStXrmx08cUXn3Bcu3btjqxbt+7Dnj17Hhg5cmTu66+/vulvf/vbukcffbTMRSD+8Ic/NO3QoUPnfv36/dtHH31UT5K2bdtWr02bNkcix5xzzjlHtm3bVk+SsrKySo4cOWL5+flpZfUZT0qnZLj7381srqT7witevC/pu5K+JunGqE1LrpH0gqQRkmaG2y6J7c/M9od/XO3ur1Vp8AAAADVQZmZm6aBBgwqmTJnSMiMjozRSvmLFirM3btx4fMGFAwcOpBUWFpY7uNqzZ8/irVu31p8xY0Zm3759C2Pry5g2US87O/uEKSHXXXfdfknq2rVr0cGDB+s0a9astFmzZqXp6emlkS20Y4//4Q9/uDcjI8Mff/zxFsOGDTv37bff3lBevM2bNz+2ZcuW9FatWpV5r1ys8nb6K3NCdECdu3siifhIhTZIGS7pZkkbJA1z9zkJ9FGt5f5kUcJtNk/5dhVEAgAAII0fP35n9+7dOw8ZMuT4rnrurjVr1qxt2LDhCQsi1K1b10tLj+fVOnz4sMX2169fv/2TJk1qu2TJkvW7du0qNw9s0KBBaXFxcZ2YMpekOnXqKD09/XgMderU0dGjR+3OO+88Z+nSpU0kad26dR+2atXqeC76ox/9aM+DDz7YRpLOOeeco2+99VbjSN22bdvSv/71rx/favvw4cPWsGHDL95QBZT3W4NV4pHQNA93P+zuE9y9rbvXd/euscmyu890d3P3meX09efwcS8mEgMAAEBtkp2dXTJgwIB9c+fObREpu/TSSz975JFHWkZer1y5MkOScnNzD7/33ntnSdLy5csbbtu27aQVzm699dY999xzz/ZLLrmkQqO2HTt2PLRhw4Z4K6WV6amnntq2bt26D9etW/ehJH3yySf1InVz585t+m//9m+HJOnqq68ufOutt87evXt32u7du9Peeuuts6+++upCSSotLdXu3bvrdezYMaEV1srbuCTVc5wBAADOSBVZBq4qTZgwIX/WrFnHb6h79tlnPx01alS7Dh06dC4pKbGePXt+3qtXry3Dhw/fN2fOnObnn3/+hRdddNHB9u3bH4rt67zzzjs6ceLECt87NnDgwMJf/epXWZFl4Crjsccea/nHP/6xaVpamjdt2vTYzJkzN0uhXwbGjRu3vUePHp0k6d57792enZ1dIoUS/osuuuhgvXr1Ano+WaqXlQMAAMBpUlRU9G7k57Zt2x4rLi4+/jonJ+fYokWLPo5t06hRI1+xYsXG8vqL6N+//+f9+/f/XJLuuuuuAkkFscf069fvwIQJE86JzE3etm3bPyJ1sW2i66I988wz2yRti1d39913F9x9990nnfeFF15ofttttyW8KAQjyAAAADjtHn/88a2bNm1KL//I5OnSpUvxd77znc/LP/JEjDADAADgtLv88ssPln9Uco0dO7ZSU0AYYQYAADg9SktLS09aYQKpF74uZa6cwQgzUMW6zupaqXbzkhwHACDlPti9e3fnrKyswjp16nj5h+N0KC0ttd27dzeR9EFZx5AwV1eTm5R/TNx2J60XDgAAqoFjx46Nys/Pfy4/P7+L+Ct/dVIq6YNjx46NKusAEmYAAIDToEePHrskDUx1HEgcv90AAAAAAUiYAQAAgAAkzAAAAEAAEmYAAAAgAAkzAAAAEICEGQAAAAhAwgwAAAAEIGEGAAAAApAwAwAAAAHY6Q9am9epUu06rVub5EgAAACqH0aYAQAAgAAkzAAAAEAAEmYAAAAgAAkzAAAAEICb/gCgGvrF4P6Vajf25TeSHAkAgBFmAAAAIAAJMwAAABCAhBkAAAAIQMIMAAAABCBhBgAAAAKwSgZwBnnmljcr1e72X1+e5EgAADhzMMIMAAAABCBhBgAAAAKQMAMAAAABSJgBAACAACTMAAAAQAASZgAAACAACTMAAAAQgIQZAAAACEDCDAAAAARIecJsZulm9jMz22Jmh8zsfTMbWoF2bczsPjP7HzMrMLN94Z8Hn464AQAAUDukPGGW9BtJP5W0UNKdkrZJmmtm15fT7upwu08kTZJ0n6RDkn5nZo9UWbQAAACoVeqm8uRm1kPSMEkPuPvkcNlzkv4iaaqZzXP3o2U0XyapnbvvjurvGUlLJN1jZo+7+94qfQMAAAA446V6hPk6SS7pmUiBu7uk6ZJaSbqsrIbu/s/oZDmq7XyFfhHoUBUBAwAAoHZJdcLcXdLm2MRX0qqo+kS1Dj8XVDoqAAAAICzVCXOOpB1xyiNlrePUlcnMMiXdJGmNu288xdgAAACAlCfMGZIOxyk/FFVfIWaWJul3kppJuqWcY28ys3fM7J3du2MHtwEAAIAvpDphLpZUP055g6j6ipohqa+kUe7+96AD3f1Zd7/Y3S/OyspK4BQAAACobVKdMO9Q/GkXOeHn7RXpxMx+IWmEpHHuPjtJsQEAAAApT5jXSGpvZrHDvD2j6gOZ2SRJYyQ94u6/SHJ8AAAAqOVSnTD/XpJJuj1SYGam0BzknQqtxywza2JmeWbWJLqxmY2WNFnSr939p6craAAAANQeKd24xN3/bmZzJd0XXuHifUnflfQ1STdGbVpyjaQXFJp2MVOSzOxqSf8paYuk/zGzYTHdr3T3j6v8TVQzXWd1TbjNvCqIAwAA4EyR0oQ5bKSkzZKGS7pZ0gZJw9x9Tjntuik0Ot1O0qw49SMk1bqEGQAAAMmV8oTZ3Q9LmhB+lHXMTIVHlqPKJis0HQMAAACoMqmewwwAAABUaykfYQaQer8Y3D/hNmNffqMKIgEAoPphhBkAAAAIQMIMAAAABCBhBgAAAAKQMAMAAAABSJgBAACAAKySgUp75pY3E25z+68vr4JIAAAAqg4jzAAAAEAAEmYAAAAgAAkzAAAAEICEGQAAAAhAwgwAAAAEIGEGAAAAApAwAwAAAAFImAEAAIAAJMwAAABAABJmAAAAIAAJMwAAABCAhBkAAAAIQMIMAAAABKib6gBQu/xicP9KtRv78htJjgQAAKBiGGEGAAAAApAwAwAAAAFImAEAAIAAJMwAAABAABJmAAAAIAAJMwAAABCAhBkAAAAIQMIMAAAABCBhBgAAAAKQMAMAAAABSJgBAACAACTMAAAAQAASZgAAACAACTMAAAAQgIQZAAAACEDCDAAAAARIecJsZulm9jMz22Jmh8zsfTMbmkD7G8JtDpnZJ2b2gJnVq8qYAQAAUHukPGGW9BtJP5W0UNKdkrZJmmtm15fX0Mx+IGm2pE/DbV+XdJ+k6VUWLQAAAGqVuqk8uZn1kDRM0gPuPjlc9pykv0iaambz3P1oGW0bSJoi6c+S+ru7h8v3S/qpmT3p7v+o8jcBAACAM1qqR5ivk+SSnokUhBPf6ZJaSbosoO03JbWQ9EwkWQ6bLsnCfQMAAACnJNUJc3dJm919d0z5qqj6oLbRx0qS3H27pK3ltAUAAAAqJNUJc46kHXHKI2Wty2kbfWxs+6C2AAAAQIXYibMZTvPJzTZJ+sTdL48pryOpRNL/dfdbymj7G0kj3P2kpN/M/iKppbvnldH2Jkk3hV92lLS+8u8CAADgJO3dPSvVQSA5UnrTn6RiSfXjlDeIqg9qa2ZWL86NgQ2C2rr7s5KeTSRQAAAA1E6pnpJR1tSJyHSL7eW0VUD7oLYAAABAhaQ6YV4jqb2Zxf7JomdUfVBbSfpKdKGZtZbUppy2AAAAQIWkOmH+vUJLwN0eKTAzk3SLpJ0KrccsM2tiZnlm1iSq7TJJBZJuD7eJuC38/EpVBg4AAIDaIaVzmN3972Y2V9J9ZpYp6X1J35X0NUk3Rs1NvkbSC5JGSJoZbnvIzMYrNBf5dTN7TVI3hRLm5939/dP4VgAAAHCGSvVNf5I0UtJmScMl3Sxpg6Rh7j6nvIbuPsPMjkgap9DmJ7skPSTpZ1UWLQAAAGqVlC4rBwAAAFR3qZ7DjABm1s3MJptZu1THUlFmNtPMPM7jWKpjS1QN/fwvMbNnzGyVmR0Of/a55Ry/zMwOmFmBmc02s5anMeSE1LRrYmZ1zOxGM1tgZp+YWZGZrTOzqWbWtIw2NeqaSDXvukiJf1eZ2VVm9jczKzazfDN70swane6446mhn3+VfVeZ2Q1m9r6ZHQr/u3vAzOpV2ZtBrVAdpmSgbN0kTZL035K2pDaUhJRKujFOWU3TTTXv8/93hTbl+aekdZK+VNaBZtZVoZtnN0v6saRmksZK6mZml7j7oSqPNnHdVLOuSUOF7rtYJek5hZbD/LKkOyUNNLMe7v555OAaek2kmnddIir0XWVm35L0hqS/SbpbUq6kH0nKk3RFlUZYMd1U8z7/KvmuMrMfKPRvbbGkpxT693afQkvQ/rAq3ghqBxJmVAV39xdTHUQt9StJj7p7sZlNVMB/QpIelnRI0tfdfY8kmdnbkpZKGiXp6aoOthY4IulSd18RXWhmf5P0W4VuZH4yqoprcnpV9LtqmqSPJH3T3Q9Lx3eqnWFm/d39jaoM8gyV9O8qM2sgaYqkP0vq7+E5p2a2X9JPzexJd/9H1bwdnOmYklFNmdlkhVYGkaS/Rv258BtRx4w0s/8N/9lpt5n91szaxPQz08yOmVkbM1toZp+H/5z1q6r8c2L4T9Fnm52w5F+NUVM/f3ff6e5BO2RG4jpb0pWSXor8BxRu/98K3Xg7ONmxnaqaeE3c/Uhsshz2avi5c1RcNe6aSDXzusScN/C7ysw6SeoiaUYkWQ6bLemAUnxdaurnX0XfVd+U1ELSM5FkOWy6QkvYXpeM2FE7kTBXX/P1xfbdP5N0Q/ixVpLM7CeSfiPpM0n3Snpe0vckrbDQEn3RTNIfJB1T6M9ZCxVa63peFcWeJqkw/PjMQnPNsqvoXFWlJn/+FdFVUj2FpgrEWqXQnzqr2y87Z9I1iexQWhBVVhOviVSzr0tFvqu6h59PuC7ufkTSe1H1qVKTP/+KSOTfRVnXarukrUr9tUJN5u48qulD0vcluUJ/0o0ub6HQn6f+IqluVPm3w8c/FlU2M1w2K6aPn4fLr0xyzI9IelzSEEmDFPpT2TFJGyU1SfVneqZ//jHnmBg+R26cuu+F674Vp+6xcF21u141/ZpEnes5hebKfqmmX5Oael0q+l0l6Z7w+S+I08c8Sfv4/E85/qR8V4WvoUuqF+fYVZLeTfW14lFzH4ww10x9JdWX9Et3P35Ht7svkvShpP5x2vyyjNfxjq00dx/v7uPc/Xfu/oq736HQPM3zJY1O5rlSqNp+/gnICD8fjlN3KOaYmqDGXBMzGy7pB5Ke8BM3WDrTrolUja9LAt9V5V2X6nxNqu3nn4BE/l1kKDQv/WgZx1bna4VqjoS5ZsoNP6+LU/ehpHPjlK+PfuHuuyXtK+NYSZKZpZlZq5hHwl847v5bSfmSvpVo22oqN/xcIz7/MkTmDtaPU9cg5piaIDf8XK2viYVWW5gh6Y8K/Xk82pl2TaQacl2izhXvu6q861Kdr0lu+LlGfP5lSOTfRXEonLhLyFX3a4VqjoQZQdoqtAxW9KOyN7h8Kql5kuKqLZL5+cfaEX5uHacuR9JBheY84kSVviZm9lVJCyStkXRtnFEwrknlVeV3VXnXZXslz3MmqS7fVVwrVBmWlaveytqGcXP4OU+hUYJonST9K06bjpLejbwwsyyF1rKMd2xEvFHhfwYcH1f4hoxz48Ra3Z0Rn38Z/qHQfM2vSJoVU3eJQnP9quM2oDXympjZlxVaF3azpG+7+8E4h9XUayLV0OsSq4zvqjXh569Ieivq2HSF1j9+LdHzVIEz4vMvQyL/LqKv1SeRg8ystaQ2Cs3TBiqFEebqLfKfatOY8qUKzecabWbHf+kxs6skXSjp9Th93V3G60VlndzdD7n7f8c8dpR1vJk1CC8BFOsOhW4+WVxW22qqRn3+iXD3zyQtkTTEzI6PpplZX0kdJL2SjPNUgRp3Tcysg0Kf9V6FblzaW0bfNfWaSDXsuiTyXeXuaxVKNn9oZtHTAoZLaqTqcV1q1OefiAT/XSxTaOWZ22NWlLkt/FwdrhVqKEaYq7c1Co0cjA9/URyW9Ka77wqvvfmIpD+Z2e8lnSPpLoV2eXo0pp9SSd3N7FVJf5J0sUI3tyxx9/+XxHhbSfpfM3tJoTlzxyRdptAd6O+p5m26UNM+f5lZe4WWlJKkr4ef77DQwv373T36GvxU0v9I+ouZTVfoP9t7FBoZmpHMuJKoRl0TM2usUNKSJekZSX1iVobb6e5Lo17XxGsi1bDrosS/q8YqlDC+aWazFJobPCYcY7yk83SraZ9/lXxXufshMxuv0DJ7r5vZawr9FeA2Sc/H3GQLJCbVy3TwCH4oNOLxL4W+0F3SN6LqfiDpfYW+HPdIelFSm5j2M8Nt2yi0pubnCo10/V9JjZMca1OFFvNfr9CC/ofDPz8sqVGqP8sz/fMPn+8b4TjjPTbHOf7/KLQr1sFwXC9Kyk71536mXBOFEquyrodL+vOZcE1q4HVJ+LtKoa2c/67Qags7FUqqk/5vuDZ8/uHzVdl3lULbnX8Qfr+fSnpQcZaa48EjkYe5V9cpcUgGM5spaZi789eEFODzr364JtUT1yW1+PyBYMxhBgAAAAKQMAMAAAABSJgBAACAAMxhBgAAAAIwwgwAAAAEIGEGAAAAApAwAwAAAAFImAEAAIAAJMwAajQz8wQf309RnDPLOr+Z1TWz34Tr15hZdgpCBACUgR19ANR0D8Qpu1tSE0lPSNofU/de1YaTGDPLkDRPUn9Jf5J0jbt/ntqoAADRSJgB1GjuPjm2LDyK20TSL91982kOqcLMLFPSG5K+KullScPd/UhqowIAxGJKBoBaxcyuM7O/mFmhmRWb2T/MbLyZ1Y9z7Obwo4mZPW1m28zskJl9aGZ3mZmdQhxtJf1VoWT5SUlDSZYBoHoiYQZQa5jZwwqN5HaSNFfS05JM0sOS/mhm6XGapUv6b0lXSvqdpBmSmio03ePpSsbRWdJKSZ0l/dTdRzu7SAFAtUXCDKBWMLOvShov6VNJXd39VncfJ6mbQtMivi7pnjhNcyQdltTF3ce6+52Sukj6WNJtZnZZgqF8VdJySa0kjXD3RyrzfgAApw8JM4DaYmT4+SF3z48UuvsxSWMllUoaVUbb8e5+OKrNXkk/C78ckWAcN0lqJukBd5+ZYFsAQAqQMAOoLbqHn9+MrXD3DZK2SjrXzJrEVB9TaPpErD+Hny9KMI6lCiXn482sT4JtAQApQMIMoLaIJMI7yqiPlDeNKd/j7iVxjo+MUscm2OWZK2m4pPqS3jCzf0+wPQDgNCNhBlBbFIafW5VRnxNzXEQLM0uLc3ykn9jjy+XucyQNkZQmaYGZfTfRPgAApw8JM4Da4t3w8zdiK8zsfEltJP3L3ffHVNeV1CtOf5F+3o1TVy53/72k70pySS+b2dDK9AMAqHokzABqi+fDzxPNLCtSGB49nqrQ9+Fvymj7SPQ6zeENRyaGX75Q2YDc/Q2Fdvg7IulFM0v0BkIAwGnATn8AagV3X2lmj0m6V9IHZvZ7SQclXaXQMnHLJT0ep+kOheYbf2Bm/yWpnqTvKTSFY7q7/+UU4/pvM+snaZGk35hZhrtPP5U+AQDJxQgzgFrD3X8saaikjQrdeHeXQt+DEyV9q4yd9o5I6itpiULzjm9WaN7yaEl3JCmuv4bPsV/SM2Y2Nhn9AgCSw9hcCgDiM7PNkuTuuamNBACQSowwAwAAAAFImAEAAIAAJMwAAABAAOYwAwAAAAEYYQYAAAACkDADAAAAAUiYAQAAgAAkzAAAAEAAEmYAAAAgAAkzAAAAEOD/A7/B03cpUB1ZAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "results = met.run_all()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "DnQOdbaJRu23",
        "outputId": "e9cfceee-022f-412a-941d-a1ec7b9c81ac"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Starting Training\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Starting Training\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Starting Training\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Starting Training\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Making top k from Ranking\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Making top k from Lexicase\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Making top k from Mix\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Making top k from Ranking\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Making top k from Lexicase\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Making top k from Mix\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Started Coverage Caclculations\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Started Coverage Caclculations\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Started Diversity Calculations\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Starting Training\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Starting Training\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Starting Training\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Starting Training\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Making top k from Ranking\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Making top k from Lexicase\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Making top k from Mix\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Making top k from Ranking\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Making top k from Lexicase\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Making top k from Mix\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Started Hit Rate Calculations\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "results = met.run_all()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "uV9iJ7ltXeMt",
        "outputId": "27c58cd2-c214-4bb7-d81d-5d1cbd030123"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Starting Training\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Starting Training\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Starting Training\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Starting Training\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Making top k from Ranking\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Making top k from Lexicase\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Making top k from Mix\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Making top k from Ranking\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Making top k from Lexicase\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Making top k from Mix\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Started Coverage Caclculations\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Started Coverage Caclculations\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Started Diversity Calculations\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x360 with 1 Axes>"
            ],
            "image/png": 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BEvnj5NHwZszINtZ9CNYMTzZ6XOnPJXf/M8F/EgyYbWajzawwkm/BFvMXmdn/I8FSjyJSBYkWaNahI9sHcAEHNwCIHLsIRmGj0/YDP07Qxkkc3DQhUnYjh26X7MAVMfVacHAr53JgLcFmCSvS6P+3o9qP3sb2Mw7dEtgJpi58MUlbWdu4JCxzYcz7ujt8n/bG9LNDOueOKvdlgiXgIuX3hu3viWn/qzH1VlD5BhXJ3pvBHLqN9q7wuibbGrtKn5kUPg+R1xK9rfGGOO/BTuB2Em/pfBsJNvSIKtMopr9bgPqV9C9rn4GYdveH1yC63Q3EfP6TvU6CtY4jdfcAn4bv7+8TnL9Z1OdgD3E2wanK93Ul5ZpycER5TFR6z5hrsyPq+SaCOc5x2yfFn0sEK2zMjjpHRfgZ+CzmWj5W1fdBhw4dhx4aYZaccfe/AJ0Ibpx5BvgwzGpE8Av4LYI7zr/k7nclaOND4DSCZaxeIvil0YTgl/a7wMPA/xCs5RxdbyPwNYKRmg0ENyV1CI+qKAqPyHJlq8P+3AP0cvfT3f3dKrZdbe7+PMEI8J0E7+segn/7fkawI9pEoIdXcc1ad38DKAF+GLa3I2x/F8EfCw8QBMt/r87riHPe3xMsp/Yg8EGYXJdgNPsRYFicOlX6zKThGA5+Ho4leI/fJ9hM4hqgjbtPcPf9VWwfD252ezYqaZYHo57J6mTzM/ANgq275xPsGNiIIGB+N2z3lHQ+/+7+IsG67H8nmNLRluB7s1WC8pvDsgBzPP4mOBnlwWh5ZGT7B+H0MTxYF/xMgp9pWwimiESWzexGsOtlojZT+rnkwfzwiwnma88ieM8bhuf6kGCe+5Ukn/4hImkwd891H0RERKosDFbXEvyBcqEH0xZERDJGI8wiIlLbDSEIlj8B5uW4LyJyBFLALCIitZaZFRPMhwZ4wN0rEpcWEakaTckQEZFax8x+D5xFsKpHHYI57F/y7GxWIiJHOY0wi4hIbdSK4GbArQRrgZ+vYFlEskUjzCIiIiIiSdTNdQdyrUWLFl5cXJzrboiIiMgRZOHChRvdvbDyklIbHPUBc3FxMW+++WauuyEiIiJHEDOr0rr2UjNpDrOIiIiISBIKmEVEREREklDALCIiIiKShAJmEREREZEkFDCLiIiIiCShgFlEREREJAkFzCIiIiIiSShgFhERERFJQgGziIiIiEgSCphFRERERJJQwCwiIiIikkTdXHdAEritSRXrbctsP0RERESOcgqYpcoeuubFtOtc/5tzs9ATERERkezRlAwRERERkSQUMIuIiIiIJKGAWUREREQkCQXMIiIiIiJJKGAWEREREUlCAbOIiIiISBIKmEVEREREklDALCIiIiKShAJmEREREZEktNPfYVD8o+fSrrOiQRY6IiIiIiJp0wiziIiIiEgSGmEWOYI8dM2LVap3/W/OzXBPREREjhwaYRYRERERSUIjzMLiks5Vq3jOQ5ntiIiIiEgNpIBZJB23NalCnW2Z74eIiIgcNpqSISIiIiKShAJmEREREZEkFDCLiIiIiCShgFlEREREJAkFzCIiIiIiSShgFhERERFJQgGziIiIiEgSCphFRERERJJQwCwiIiIikoQCZhERERGRJBQwi4iIiIgkoYBZRERERCSJurnugEguFP/ouSrVW9Egwx0RERGRGk8jzCIiIiIiSShgFhERERFJQgGziIiIiEgSCphFRERERJLIecBsZvlm9hMzW2lmu83sXTMbkmLdAjP7sZm9b2a7zGyNmf0/M+uZ7X6LiIiIyNEh5wEz8Fvgx8Ac4EZgNTDDzC5Loe5M4A7gFWAk8CDQDfiHmX05K70VERERkaNKTpeVM7MewFDgdne/LUx7hCAAvs/MZrr7vgR1vwD0Aya5+01R6c8A74XtvpHVFyAikoKHrnkx7TrX/+bcLPRERESqItcjzJcCDjwUSXB3ByYDrYCzk9Q9NnxcG5Meeb4rQ30UERERkaNYrjcu6Q6scPcNMekLovL/lqDuEmAdcLOZfQi8CbQEbgc2AA9nvrsih8/iks7pVzrnocrLiIiISFpyPcLcms+PEBOV1iZRRXffDXwT2AHMBj4FFgInA2e6+8eZ7aqIiIiIHI1yHTAXAHvipO+Oyk9mK/AWcCdwEcGNf42B58ysMFElM7vKzN40szc3bIgd3BYREREROSjXAXMZUD9OeoOo/LjM7Hjgn8Bz7n6ru89x9weA84ATCVbeiMvdH3b309399MLChHG1iIiIiEjOA+a1xJ920Tp8XJOk7ncIbvybHZ3o7ouBxcBXMtFBERERETm65fqmv7eAPmZWGHPjX8+o/EQigXZenLy6QEUG+iciNd1tTapYb1tm+yEiIkesXI8w/wEw4PpIgpkZcA3BChivhGlNzKzEzKJ/My4NH4dGN2hmpwOdCG4AFBERERGplpyOMLv7G2Y2A7jVzJoB7xKsfPEV4IqoTUsuBh4DrgSmhmlTgVHAL8zsiwSblBQTBN9lwH2H51WIiIiIyJEs11MyAIYDK4BhwNXAB8BQd5+erJK7bw5Hk28Fvg5cDuwEXgLGh3OZRURERESqJecBs7vvAcaGR6IyUzk4shydXkrUdA4RERERkUzL9RxmEREREZEaTQGziIiIiEgSOZ+SISIiIgLw0DUvpl3n+t+cm4WeiBxKI8wiIiIiIklohFlEaoziHz2Xdp0VDbLQERERkSgaYRYRERERSaJKI8xmVgJ0Bhq5++8y2yURERERkZojrRFmM+tmZm8C7xFsaz01Ku+rZrbLzPpntosiIiIiIrmTcsBsZh2Bl4FOwP3A8zFFXgE2A9/KVOdERERERHItnSkZE4B84HR3f9/MJgAXRjLd3c3sn8CXM9xHERERqa7bmlSx3rbM9kOkFkpnSkYfYJa7v5+kzKdAm+p1SURERESk5khnhLkpsKqSMkYwCi0iIiJZUJXlF0FLMIpURzoB8zrgpErKnEIwyiwicsRZXNK5ahXPeSizHRERkcMqnSkZLwL9zaxTvEwz+zLBtI2/ZKJjIiIiIiI1QToB893AfuAVM7uWcK6ymZ0SPn8W2A7cl/FeioiIiIjkSMpTMtx9qZldAjwJPBgmG/Bu+LgV+Ka7r8x0J0VEREREciWtnf7c/c9mdgJwBfBfQHNgG/A68Ji7b858F0VERKQ20Xx/OdKkvTW2u28l2Ljk/oz3RkRERESkhklnp79HzWxAJWX6mdmj1e+WiIiIiEjNkM5Nf98GulVS5ksE0zVERERERI4I6QTMqagPlGe4TRERERGRnEk3YPZEGWZWHzgbKK1Wj0REREREapCkN/2Z2UcxST8wsyvjFM0DCglGmH+Tob6JiIiIiORcZatk1OHgqLITrLdsccrtA/4D/A24M2O9ExERERHJsaQBs7sXR742swrgF+5+R7Y7JSIiIiJSU6SzDvPXgBVZ6oeIiIiISI2UztbYf89mR0REREREaqK0d/oLV8P4MtCW4Ca/z3H3x6vZLxERERGRGiGtgNnMhgP3AE0TFSG4OVABs4iIiEiUhQsXtqxbt+4jQFcyvxeGVF0FsGj//v0jevTosT5egZQDZjPrCzwCvAf8FPg58AywADgHOB94GphbrS6LiIiIHIHq1q37SKtWrToXFhZuqVOnTsK9LeTwqqiosA0bNnQpLS19BBgQr0w6f93cBGwCern7L8K0d9x9orv3Bb4LfBNYXp1Oi4iIiByhuhYWFn6mYLlmqVOnjhcWFm4jGPmPXyaN9roDz7r79nj13f23wHxgbLodFRERETkK1FGwXDOF1yVhXJxOwHwMsDbq+W7g2JgybwI902hTRERERKRGS+emv1KC7a8j1gKdYso0IdgmW0RERESSKP7Rcz0y2d6Kif+zsLIyZtZjxIgR66ZMmbIKYPz48UU7duzImzRp0hqABx98sPn999/fysw8Ly+PgQMHbrrjjjvWXXLJJcWvv/5648aNG5cDDB06dOO4cePWV1RU0KtXr45z5879sFmzZhWx5+vVq1fHOXPmLC8sLCzP5Gs93NIZYX6PQwPkfwB9zOwrAGbWFbg0LCciIiIiNUx+fr7PnTu36dq1az83aDpz5sxjJ0+e3PKFF1744IMPPnj/rbfeWtykSZMDge6dd965asmSJe8vWbLk/XHjxq0P6zQ55ZRTymKD5YqKCsrLyxkyZMim++67rzD2XLVNOgHz80BvM2sTPr8HKAdeNrMNwL+BxsCdme2iiIiIiGRCXl6eDxs2bMNdd91VFJt3zz33tJ44ceKq4uLifQAFBQV+0003bUzW3vTp05tdfPHFWwGWLl2aX1xc3PXiiy8u7tix4ynLly/PHzx48NZZs2Y1z8qLOYzSCZj/j2Czko0A7v4+0IcgkN4IzAMudHctKyciIiJSQ40ePXr9rFmzmm3atOmQabTLli0r6N27965E9caNG3d8SUlJl5KSki4LFiwoAFi4cGGj3r1774yUWblyZf0bbrhhw4cffvhex44d9xYWFpbv3bvXSktLa/WU3XS2xt4HrItJex3ol+lOiYiIiEh2NGvWrGLgwIGbJk6c2LKgoOBz844TufPOO1ddeeWVW6LTtm3bVrdp06YH2mjduvXePn367Iwu07x58/0rV67Mb9WqVVn1e58baW+NnYyZNQPGuPvoTLYrIiKSSQ9d82KV6l3/m3Mz3BOR3BgzZsy67t27dxk8ePCBKRcnnXRS2fz58xsOGDBge7K60fLy8ry8vJy8vGAAuWHDhp8LwPfs2WPx0muTjGzLaGaNzGwC8BEwKhNtioiIiEh2FBUVlffv33/LjBkzWkTSbrnlltIxY8Ycv3LlyroAu3fvtkmTJrVI3AqccMIJuxcvXlw/UX5FRQUbNmyo16lTpz2Z6/3hV+kIs5mdCPwY+DKwj2B1jDvdfWOYfwMwHmhOsDbzLxI0JSIiR4vbmlShzrbM90OkBktlGbhsGjt2bOm0adMOrGAxaNCgbaWlpXX79OnTyd0xMy677LKkN/2df/752+bNm9e4a9eucQPiV199teFpp522s169epnu/mGVNGA2sw7Av4CmgIXJ3YBzzaw3MBM4H9gLPATc5e6l6XTAzPKBW4ErgJbAB8Dd7v5kivWbAROAi4BWBDcgvg5c6e6fpdMXERERkSPZrl273o583a5du/1lZWVvR+ePHDly08iRIzfF1vvjH/+4Il57N9xww8YhQ4YUjxo1amOnTp32Llu27JDlhR977LHm11133foMdT9nKhth/jHQDJgLTA3TRhAEyS8DpwF/AG5y90+r2IffAv8LTAbeBb4JzDCzOu4+PVlFM2sNvAo0AB4BPiHYXKU30BBQwCwiUg3FP3quSvVWNMhwR0SkRurQocO+4cOHb9y8eXOdeBuXdO3atewb3/hGynOia6rKAuZzgffc/cBKGGY2C1hEMNJ8v7v/oKonN7MewFDgdne/LUx7BHgFuM/MZoarcyTyG4KdBbu5+4aq9kNEREREqmbEiBFbEuVVto5zbVHZTX/HA4fcSuzuFcAL4dP7qnn+SwEnmM4Rad8JRptbAWcnqmhmJwMDgHvdfYOZ1TezhJPORURERESqorKAuT7wuXkswGYAd19dzfN3B1bEGR1eEJWfyPnh4xoz+wtQBpSZ2T/MrFs1+yUiIiIiAmRoWblqaA2sjZMeSWsTJy/i5PDxYYLXMQS4EegIvGRm7TLVSRERERE5eqWycck5Zva5NAAzu5WDq2dEuLv/JMXzFwDx7pzcHZWfSKPwcQPQ193Lwz69SbBKxg9IsCa0mV0FXAXQvn37FLsqIiIiIkejlALm8Ijn9qivnSB4diDVgLmMYNpHrAZR+cnqAvw+EiwDuPu/zOxD4KxEFd39YYKRaU4//XRPsa8iIiIimXNbkx6ZbW9bpes6m1mPESNGrJsyZcoqgPHjxxft2LEjb9KkSWsAHnzwweb3339/KzPzvLw8Bg4cuOmOO+5Yd8kllxS//vrrjRs3blwOMHTo0I3jxo1bX1FRQa9evTrOnTv3w2bNmlU0bNjwtF27dr29Zs2auoMGDTrhH//4x7KMvsYcqSxgvr2S/OpaC5wUJ711+LgmSd1I3ro4eeuAomr0S0REROSIk5+f73Pnzm26du3a0tatW++Pzps5c+axkydPbvnCCy98UFxcvK+srMwmT57cPJJ/5513rrryyiu3xNRpcsopp5TFLinXpk2b/UVFRfvmzZt3zPnnn78zu68q+5IGzO6e7YD5LaCPmRXG3PjXMyo/kchfUcfHyWtL/LnRIiIiIketvLw8HzZs2Ia77rqr6Fe/+tUhizfcc889rSdOnLiquLh4H0BBQYFXtizc9OnTm1199dVxy1x00UVbH3/88eZHQsCc65v+/kAwjeP6SIIFE6avIRglfiVMa2JmJWYWvdfqy0ApMNTMCqLqfx0oBv6S7c6LiIiI1DajR49eP2vWrGabNm3Ki05ftmxZQe/evXclqjdu3LjjS0pKupSUlHRZsGBBAcDChQsb9e7dO25A3Lt3750LFixoFC+vtkllDnPWuPsbZjYDuDXc4jqy099XgCuiNi25GHgMuJJwx0F332tmo4AZwKtmNo1gl7/vAx8D9x/Gl1JjnDrt1LTrzMxCP0RERKRmatasWcXAgQM3TZw4sWVBQcHndudLJN6UjG3bttVt2rRp3DbatGmzf/369fnV7W9NkNOAOTQcWAEMA64GPgCGVrYtNoC7P2lmu4GxwD3ATuAZ4IfuvjVL/ZVq+PmgfpUXiuOmp/6U4Z6IiIgcvcaMGbOue/fuXQYPHnxgOsVJJ51UNn/+/IYDBgxIeSvrvLw8Ly8vJy8v73N5u3btsvr166cckNdkOQ+Y3X0PQcA7NkmZqYQjy3HyZgOzs9E3ERGp+RaXdE6/0jkPVV5G5AhWVFRU3r9//y0zZsxoMWTIkE0At9xyS+mYMWOO79at27L27dvv3717t02ePLn5qFGjEs5jPuGEE3YvXry4fteuXffE5i1atKhBx44dk614VmvkPGAWEREROSqlsAxcNo0dO7Z02rRphZHngwYN2lZaWlq3T58+ndwdM+Oyyy5LetPf+eefv23evHmN4wXML7zwQuO+fftuy0bfDzcFzCIiIiJHiV27dr0d+bpdu3b7y8rK3o7OHzly5KaRI0duiq33xz/+cUW89m644YaNQ4YMKY6MQke3P3fu3OOef/75DzPW+RxSwCySZVW5ERN0M6aIiNR8HTp02Dd8+PCNmzdvrhO9FvOaNWvqjhw5cl1hYWF5svq1RcoBs5l9E5gTvaueiBwZqnIzpm7EFBERgBEjRmyJTWvTps3+yy+/fGsOupMV6azD/AfgEzO7w8zaZ6tDIiIiIiI1SToB80NAQ2AcsNzMnjWzfuFGIyIiIiIiR6SUA2Z3vxFoQ7Bu8pvA/wBzCEadx5tZm+x0UUREREQkd9K66c/ddxOshzzVzLoSbDRyGXAbMM7MngP+z93/nOF+ioiIiHyONsSSw6HKq2S4+yLgRjMbDQwC7gQGAAPMbCXBFI5fu3vc/cVFREREjmanTju1Rybb+88V/6l0XWcz6zFixIh1U6ZMWQUwfvz4oh07duRNmjRpDcCDDz7Y/P77729lZp6Xl8fAgQM33XHHHesuueSS4tdff71x48aNywGGDh26cdy4cesrKiro1atXx7lz534YvUpGOmLbfvTRRz/u1atXWUVFBcOHD2/34osvNmnQoEHFo48+uuKss87atWbNmrqDBg064R//+MeyqpyvKqq1rJyZHUMwwnwV0DZMfgc4iWCr6pFm1t/d36nOeURERESk+vLz833u3LlN165dW9q6dev90XkzZ848dvLkyS1feOGFD4qLi/eVlZXZ5MmTm0fy77zzzlVXXnnllpg6TU455ZSyqgbLydp++umnm3z00UcNVqxYseill1465rrrrmv/7rvvLmnTps3+oqKiffPmzTvm/PPPPywDs+nc9HeAmZ1mZr8B1gC/AUqAR4Du7t6dYK7zj4AWwAMZ6quIiIiIVENeXp4PGzZsw1133VUUm3fPPfe0njhx4qri4uJ9AAUFBX7TTTcl3elv+vTpzS6++OKtAEuXLs0/4YQTTrnkkkuKi4uLuw4YMOCEZ555pnH37t1LOnTo0PWll15qmE5f58yZc9xll122qU6dOvTp02fnZ599VveTTz6pB3DRRRdtffzxx5tX1kampBwwm1lDM/uOmS0guOnvKmAVMBJo4+5XRUaS3X2Hu99DEESfnvlui4iIiEhVjB49ev2sWbOabdq0KS86fdmyZQW9e/felajeuHHjji8pKelSUlLSZcGCBQUACxcubNS7d+8Do7yffvppgx/+8Ifrli9fvmj58uUNpk+f3vzNN99c8tOf/nTVT3/609aJ2r799tvbduzYsct3vvOddmVlZQawdu3aesXFxXsjZVq3br03EjD37t1754IFCxpV/V1ITzojzGuAh4FuwB+Bc939FHf/lbt/lqDOaqBB9booIiIiIpnSrFmzioEDB26aOHFiy3Tq3XnnnauWLFny/pIlS94/44wzygC2bdtWt2nTpgemY7Rt23bPGWecUZaXl0fHjh3Lzj333M/q1KlD9+7dd61atap+vHYnTZq0+qOPPlr073//e/GWLVvybr311laV9aVNmzb7169fn59O/6sjnYB5O8FqGO3d/VJ3fzmFOpOBE6rQLxERERHJkjFjxqybMWNGi507dx6IBU866aSy+fPnpzVtIi8vz8vLD24CnZ+f75Gv69SpQ4MGDTwsR3l5uQGcddZZJ5eUlHQZNGhQBwi2165Tpw4FBQU+fPjwTQsXLjwGoHXr1vtWrFhxICheu3ZtfocOHfYB7Nq1y+rXr1+tedPpSOemvw7unlbHwpHnRKPPIiIiUsOdOu3UtOvMzEI/JLOKiorK+/fvv2XGjBkthgwZsgnglltuKR0zZszx3bp1W9a+ffv9u3fvtsmTJzcfNWpUwnnMJ5xwwu7FixfX79q1655Uz/3qq68esrrFJ598Uq9Dhw77KioqmDVr1nGdO3cuAxgwYMDWyZMnt/zud7+7+aWXXjqmcePG5ZGAedGiRQ06duxYVrVXn750Aua/mtlUd388UQEzGwoMd/dzq981ERERkSNXKsvAZdPYsWNLp02bVhh5PmjQoG2lpaV1+/Tp08ndMTMuu+yypDf9nX/++dvmzZvXOJ2AOdagQYNO2Lx5c113ty5duux6/PHHPwG49NJLtz333HNNOnTo0LWgoKDikUceWRGp88ILLzTu27fvtqqeM13pBMznAC9XUqYD8NWqdkZEREREsmfXrl1vR75u167d/rKysrej80eOHLlp5MiRm2Lr/fGPf1wRr70bbrhh45AhQ4pHjRq1sVOnTnuXLVv2Xrw6sXnRXn/99Q/ipdepU4ff/e53K+PlzZ0797jnn3/+w3h52VClZeWSKAD2V1pKRERERGq9Dh067Bs+fPjGzZs3ZzqmTGjNmjV1R44cua6wsLC88tKZke7GJR4v0cwMaA/8N/BpdTslIiIiIrXDiBEjtlReKnPatGmz//LLL996OM+Z9K8BM6sws3Izi0Twt0WeRx8Eo8ofESw59/vsdllERERE5PCpbIT5FQ6OKp8NrARWxClXDmwC/kawWYmIiIiIyBEhacDs7udEvjazCuAxd78j250SEREREakp0pnDfAKwNUv9EBGRKD8f1K9K9W566k8Z7omIiKQcMLv7J9nsiIjI4aTNGEQk1xaXdO6RyfY6L1lc6brOZtZjxIgR66ZMmbIKYPz48UU7duzImzRp0pqqnNPMegwYMGDznDlzPgbYt28fLVu2/FK3bt12vvTSSx8+8MADzSdMmHB8UVHRPoDOnTvvmj179gqA4cOHtxs4cOCWCy+8cEdsu1ddddXx/fr12zZgwIDtVelXpiUMmM1sPMH85YfcfXP4PBXu7j/JSO9EREREJGPy8/N97ty5TdeuXVvaunXrai8FXFBQULF06dKCHTt2WKNGjXz27NnHRoLjiP79+295/PHHD1lPubS0NG/hwoXHPProo59bXW3//v3cfPPN66+88soONSVgTrZKxm3h0SLmeSqHiIiIiNQweXl5PmzYsA133XVXUWzemjVr6l5wwQUndu3atXPXrl07z5s37xiAUaNGtRk/fvyB8ieffPIpS5cuzY88P++887Y9/fTTxwE8+eSTzS655JLNlfXjiSeeaNqnT5/PIs/btm176rXXXtu2S5cunR999NGmHTt23Lt169a6K1euTHcJ5KxIFjB/DTiXYGWMyPNUDm2LLSIiIlJDjR49ev2sWbOabdq0KS86/eqrr243atSodYsWLVo8e/bs5ddcc01xKu1dfvnlm5966qmmu3btssWLFzc888wzd0bnP/vss01LSkq6lJSUdLn//vubA7z22muNTj/99EPKNW/efP/777+/+KqrrtoCcOqpp+568cUXG1XrxWZIwqjd3f+e7LmIiIiI1D7NmjWrGDhw4KaJEye2LCgoqIikz58//9hly5YVRJ7v2LEjb9u2bZXu4NezZ8+yVatW1Z8yZUqz8847b1tsfrwpGevWratXVFR0yJSQYcOGHbIBSmFh4f7Vq1fnUwPUiGFuERERETl8xowZs6579+5dBg8evDGS5u689dZbixs2bHjIzs5169b1iooDcTV79uyx2Pb69u27dcKECe3mzZu3dP369ZXGlw0aNKgoKys7JBhv3LhxRfTz3bt3W3RAn0sp7/ttZsVm9t9mdkxUWl0zu93M/m1mr5nZxdnppoiIiIhkSlFRUXn//v23zJgxI3KvGmedddZnd999d8vI89dee60AoLi4eM8777xzDMCrr77acPXq1fVj27v22ms33nzzzWvOOOOMslTO36lTp90ffPDB59qJtnz58gZf+tKXUmov29IZYZ4ADACiJ4mPA26Nej7TzL7i7q9nonMiIiIiR6pUloHLprFjx5ZOmzatMPL84Ycf/nTEiBHtO3bs2KW8vNx69uy5vVevXiuHDRu2Zfr06c1POumkU0477bSdHTp02B3b1oknnrhv3Lhx61M994ABA7b9+te/Lhw1atTGePl79uyxFStW1D/77LN3xss/3NIJmM8E/ubu+wHMrA5wHbAEOB9oBfwV+AEwKMP9FBEREZFq2rVr19uRr9u1a7e/rKzswPPWrVvvf+655z6KrdOoUSOfP3/+ssrai+jXr9/2fv36bQf43ve+twnYFFumb9++O8aOHdt248aNeS1atChfvXr1f6Lzn3rqqSb9+/ffUq9evbReX7akPCWDYGQ5evOSbgRLzj3k7qvc/U1gDvDlzHVPRERERI5E995776rly5fHvalv//79duutt6473H1KJJ0R5noEG5lE9A6fvxiVtgponYF+iYiIiMgR7Nxzz0043WL48OFbEuXlQjojzKuAL0Y9/29go7svjkprCXyGiIiIiMgRIp0R5j8BPzCz+4DdwNeBx2LKdOTQaRsiIiIiIrVaOgHzPcBFwKjw+WqClTMAMLOWBDcGPpCpzomIiIiI5FrKAbO7rzezU4E+YdLf3X17VJEWwGjgLxnsn4iIiIhITqUcMJvZMGCdu/8pXr67vw+8n6mOiYiIiBzJHrrmxR6ZbO/635xb6brOZtZjxIgR66ZMmbIKYPz48UU7duzImzRp0pqqnNPMegwYMGDznDlzPgbYt28fLVu2/FK3bt12vvTSSx8+8MADzSdMmHB8UVHRPoDOnTvvmj179gqA4cOHtxs4cOCWCy+8cMcZZ5zR6b777vv07LPP3tWrV6+Oc+bMWV5YWFhelT5lQzo3/T0K9M1WR0REREQku/Lz833u3LlN165dm8603IQKCgoqli5dWrBjxw4DmD179rGR4Diif//+W5YsWfL+kiVL3o8Ey6WlpXkLFy485sILL9wR2+aQIUM23XfffYWx6bmUTsBcmmZ5EREREalB8vLyfNiwYRvuuuuuoti8NWvW1L3gggtO7Nq1a+euXbt2njdv3jEAo0aNajN+/PgD5U8++eRTli5demD95PPOO2/b008/fRzAk08+2eySSy7ZXFk/nnjiiaZ9+vSJu7La4MGDt86aNat5FV5e1qQTAP8Z+Fq4w1/GmFm+mf3EzFaa2W4ze9fMhlShnaZmtsHM3MyGZrKPIiIiIkeK0aNHr581a1azTZs25UWnX3311e1GjRq1btGiRYtnz569/JprrilOpb3LL79881NPPdV0165dtnjx4oZnnnnmIesrP/vss01LSkq6lJSUdLn//vubA7z22muNTj/99LjrMBcWFpbv3bvXSktL8+Ll50I6w/FjgdeB35rZaHePu/d3FfwW+F9gMvAu8E1ghpnVcffpabRzF1CQoT6JiIiIHJGaNWtWMXDgwE0TJ05sWVBQUBFJnz9//rHLli07EEvt2LEjb9u2bZUOlPbs2bNs1apV9adMmdLsvPPO2xab379//y2PP/74yui0devW1SsqKtqfqM3mzZvvX7lyZX6rVq3KUn9l2ZNOwPwksA0YBgw2sxUE0zQ8ppy7ex9SYGY9gKHA7e5+W5j2CPAKcJ+ZzXT3fUmaiG7nu8B44KcpvRoRERGRo9SYMWPWde/evcvgwYMPDIC6O2+99dbihg0bHhLb1a1b1ysqDsTV7Nmzx2Lb69u379YJEya0mzdv3tL169dXGl82aNCgoqysLGEwvmfPHmvYsGFFovzDLZ3pFecQ7PRnQH2gE/DVMD32SNWlBAH3Q5EEd3eC0eZWwNmVNWBmFtb/PfBaGucWEREROSoVFRWV9+/ff8uMGTNaRNLOOuusz+6+++6WkeevvfZaAUBxcfGed9555xiAV199teHq1avrx7Z37bXXbrz55pvXnHHGGSmNCHfq1Gn3Bx988Ll2ACoqKtiwYUO9Tp067Un3dWVLOuswZ+OGv+7ACnffEJO+ICr/b5W0MQLoClwCnJzZ7omIiIhkRyrLwGXT2LFjS6dNm3ZgNYqHH3740xEjRrTv2LFjl/LycuvZs+f2Xr16rRw2bNiW6dOnNz/ppJNOOe2003Z26NBhd2xbJ5544r5x48atT/XcAwYM2PbrX/+6cNSoUZ+b4vvqq682PO2003bWq1ev6i8uwzKypEg1tAbWxkmPpLVJVtnMmgF3Az9199VmllLAbGZXAVcBtG/fPvXeiojIUe3ng/qlXeemp+JuXyCSE7t27Xo78nW7du32l5WVHXjeunXr/c8999xHsXUaNWrk8+fPX1ZZexH9+vXb3q9fv+0A3/ve9zYBm2LL9O3bd8fYsWPbbty4Ma9FixblCxYsWBrJe+yxx5pfd911KQffh0Oul4krAOINt++Oyk/mbmArMCmdk7r7w+5+urufXlhYo5b5ExERETkq3HvvvauWL1+eH5vetWvXsm984xvb49XJlbRGmMMl5a4HLgM6A8e4e90w7zSCG+9+6e4fpNhkGcF86FgNovIT9eXLBNMxLnL3GjPHRUREREQqd+6558ZdVu6mm27K1EpsGZPyCLOZ5QMvAL8ETgS2E9wAGPExMJwgmE7VWuJPu2gdPibbpvHnwJvAf8ys2MyKCW4UBGgRpuV6yomIiIiI1HLpBJSjga8BtwF3Eizhdmsk0923mtkrwAXAhBTbfAvoY2aFMTf+9YzKT6Q90IEgUI/1i/A4AViRYl9ERCSHTp12apXqzcxwP0REYqUzh/kyYL673+HuFXx+/WUIgtd07qL7A8Eo9fWRhHCZuGuAdQTrMWNmTcysxMyaRNW9Crg45ogE8L8Mn9eoCeMiIiIiUvukM8J8AvBcJWU2A81SbdDd3zCzGcCt4YoXkZ3+vgJcEbVpycXAY8CVwNSw7rzY9sxsa/jlQnd/JtV+iIiIiIgkkk7AvBs4rpIy7QlWrUjHcIJpE8OAq4EPgKFpbostIiIiUqv8fFC/Hpls76an/lTpus5m1mPEiBHrpkyZsgpg/PjxRTt27MibNGlSsvvGkrY3YMCAzXPmzPkYYN++fbRs2fJL3bp12/nSSy99+MADDzSfMGHC8UVFRfsAOnfuvGv27NkrAIYPH95u4MCBWy688MIdVTl3bNtXXXXV+si6zr/61a+a33fffa0Bbr755rU33njjJoBevXp1nDNnzvLCwsLydM6VTsD8DnC+meW7+97YzHC6xAWkudteuMLF2PBIVGYq4chyJW29zKE3IoqIiIhIKD8/3+fOndt07dq1pa1bt95f3fYKCgoqli5dWrBjxw5r1KiRz549+9hIABvRv3//LY8//vjK6LTS0tK8hQsXHvPoo49+Wp3zx2t73bp1eT/72c/aLFy48P06depw2mmndRk8ePDWwsLC8iFDhmy67777Cn/2s5+VpnOedOYwPwy0A6ab2bHRGWZ2HEFA2xT4TTodEBEREZHDIy8vz4cNG7bhrrvuKorNW7NmTd0LLrjgxK5du3bu2rVr53nz5h0DMGrUqDbjx48/UP7kk08+ZenSpQfWTz7vvPO2Pf3008cBPPnkk80uueSSzZX144knnmjap0+fzyLP27Zte+r111/ftqSkpEvXrl07v/rqqw3POuusk9u1a9f1nnvuSWvTjGeeeabJ2Wef/VlRUVF5YWFh+dlnn/3ZrFmzmgAMHjx466xZs5qn0x6kETC7+5MEQfElwAbgWgAze5NgebhvAJPdfW66nRARERGRw2P06NHrZ82a1WzTpk150elXX311u1GjRq1btGjR4tmzZy+/5pprilNp7/LLL9/81FNPNd21a5ctXry44ZlnnnnI+srPPvts05KSki4lJSVd7r///uYAr732WqPTTz/9kHLt27ffu2TJkvd79uy5Y/jw4cXPPvvs8n/9619LfvaznyXc+fn5558/rmPHjl369u37hQ8//LAewOrVq+sdf/zxB2ZDtG3bdu/q1avrARQWFpbv3bvXSktL8xK1GU9a6xS7+/Bw6biRwBcJpj90B94DJrn7Y+m0JyIiIiKHV7NmzSoGDhy4aeLEiS0LCgoqIunz588/dtmyZQd2Wd6xY0fetm3bKh1c7dmzZ9mqVavqT5kypdl55523LTY/wbSJekVFRYdMCbn00ku3Apx66qm7du7cWadp06YVTZs2rcjPz6+IbKEdW/673/3u5oKCAr/33ntbDB069ITXX3+90s3zmjdvvn/lypX5rVq1SrhBXqy0t8Z296nufhrQCDgeaOzupypYFhEREakdxowZs27GjBktdu7ceSAWdHfeeuutxUuWLHl/yZIl769fv/7dJk2aVNStW9crKg7E1ezZs+dz94v17dt364QJE9oNGzas0ukYAA0aNKgoKyurE5PmAHXq1CE/P//A8sV16tRh3759duONN7aNjFQDtGrVqrygoMABfvCDH2x87733GgK0bdt236pVqw5MGVm9enV+27ZtD8yr3rNnjzVs2PDgC0pBOjv9HVLW3cvcfY27x93WUERERERqpqKiovL+/ftvmTFjRotI2llnnfXZ3Xff3TLy/LXXXisAKC4u3vPOO+8cA/Dqq682XL16df3Y9q699tqNN99885ozzjgjpVHbTp067f7ggw8+104yv/rVr1ZHgnmATz75pF4kb8aMGcd94Qtf2A1w0UUXbfv73/9+7IYNG/I2bNiQ9/e///3Yiy66aBtARUUFGzZsqNepU6c96Zw7nSkZn5rZE8Dj7v5eOicRERERkUOlsgxcNo0dO7Z02rRpB26oe/jhhz8dMWJE+44dO3YpLy+3nj17bu/Vq9fKYcOGbZk+fXrzk0466ZTTTjttZ4cOHXbHtnXiiSfuGzduXMobxg0YMGDbr3/968LIMnBVcc8997T8y1/+clxeXp4fd9xx+6dOnboCgj8GRo8evaZHjx6dAW655ZY1RUVF5RAE/KeddtrOevXqJWn589IJmBsSbI99s5m9RXAD4JPuntLQu4iIiIjk1q5du96OfN2uXbv9ZWVlB563bt16/3PPPfdRbJ1GjRr5/Pnzl1XWXkS/fv229+vXbzvA9773vU3Aptgyffv23TF27Ni2kbnJq1ev/k8kL7ZOdF60hx56aDWwOl7e97///U3f//73P3fexx57rPl1112X9k7Q6cxhLgIGA38GugEPAGvM7I9mNsDM0rrbUERERESOXvfee++q5cuX51deMnO6du1a9o1vfGN7uvVSHmEONyuZCcw0syJgKHAFwbbVFwEbw22uH3f3z/21ISIiIiISce655x72++BuuummKk0BSXuVDAB3X+fuP3f3LwI9gF8BTrDc3BtVaVNERETkCFdRUVGhHYlroPC6JFw5o0oBc7RwNPkhYBqwD21NLSIiIhLPog0bNjRR0FyzVFRU2IYNG5oAixKVSWvjkmhm1oRgTvMVQM8weTvwdFXbFBERETlS7d+/f0RpaekjpaWlXcnAoKVkTAWwaP/+/SMSFUgrYA7XYu5LECT3B+oTTMX4G8GqGbPdPeVdU0RERESOFj169FgPDMh1PyR9KQfMZvZz4H+BlgTTLj4gmIbxO3dflZ3uiYiIiIjkVjojzD8AtgFTgGnu/s/sdElEREREpOZIJ2AeAjzj7mltJSgiIiIiUpulsw7zU9nsiIiIiIhITaQ7NEVEREREkkg4wmxmFQTLbHRx9w/C555Cm+7uVV6uTkRERESkJkkW2L5CECDvinkuIiIiInLUSBgwu/s5yZ6LiIiIiBwNNIdZRERERCSJtOcam1kHoJBgesYGd1+Z8V6JiIiIiNQQKY0wm1kLM5tkZmuBj4B/AQuAj81sjZnda2bNstlREREREZFcqDRgNrOTgTeBkUARUA6sBzaEX7cCRgFvmtkXstdVEREREZHDL2nAbGZ1gOlAe+DvwHlAI3dv7e6tgMbA+QQraBQDT2S1tyIiIiIih1llI8znA6cDM4E+7v6iu++NZLr7Hnf/K3Au8Aegp5l9PWu9FRERERE5zCoLmC8B9gA3unvCNZjDvBuAfcC3Mtc9EREREZHcqixg7g7Md/cNlTXk7uuBV8M6IiIiIiJHhMoC5nbAe2m09x7QoerdERERERGpWSoLmI8FtqbR3laCGwFFRERERI4IlQXM+QRLx6WqIqwjIiIiInJESGXjkoQ3+4mIiIiIHOlS2Rr7NjO7LdsdERERERGpiVIJmC3NNjUiLSIiIiJHjKQBs7unMmVDREREROSIpYBYRERERCQJBcwiIiIiIkkoYBYRERERSUIBs4iIiIhIEjkPmM0s38x+YmYrzWy3mb1rZkNSqHe8md1qZv80s01mtiX8etDh6LeIiIiIHB1yHjADvwV+DMwBbgRWAzPM7LJK6l0U1vsEmADcCuwGfm9md2ettyIiIiJyVEllHeasMbMewFDgdne/LUx7BHgFuM/MZrr7vgTVXwLau/uGqPYeAuYBN5vZve6+OasvQERERESOeLkeYb6UYKOThyIJ7u7AZKAVcHaiiu7+XnSwHFV3FsEfAh2z0WERERERObrkOmDuDqyIDXyBBVH56WoTPm6qcq9EREREREK5DphbA2vjpEfS2sTJS8jMmgFXAW+5+7Jq9k1EREREJOcBcwGwJ0767qj8lJhZHvB7oClwTSVlrzKzN83szQ0bYge3RUREREQOynXAXAbUj5PeICo/VVOA84AR7v5GsoLu/rC7n+7upxcWFqZxChERERE52uQ6YF5L/GkXrcPHNak0YmY/B64ERrv74xnqm4iIiIhIzgPmt4AOZhY7zNszKj8pM5sAjALudvefZ7h/IiIiInKUy3XA/AfAgOsjCWZmBHOQ1xGsx4yZNTGzEjNrEl3ZzEYCtwG/cfcfH65Oi4iIiMjRI6cbl7j7G2Y2A7g1XOHiXeCbwFeAK6I2LbkYeIxg2sVUADO7CPgFsBL4p5kNjWn+NXf/KOsvQkRERESOaDkNmEPDgRXAMOBq4ANgqLtPr6ReN4LR6fbAtDj5VwIKmEVERESkWnIeMLv7HmBseCQqM5VwZDkq7TaC6RgiIiIiIlmT6znMIiIiIiI1mgJmEREREZEkFDCLiIiIiCShgFlEREREJAkFzCIiIiIiSShgFhERERFJQgGziIiIiEgSCphFRERERJJQwCwiIiIikoQCZhERERGRJBQwi4iIiIgkoYBZRERERCQJBcwiIiIiIkkoYBYRERERSUIBs4iIiIhIEgqYRURERESSUMAsIiIiIpKEAmYRERERkSQUMIuIiIiIJKGAWUREREQkCQXMIiIiIiJJKGAWEREREUlCAbOIiIiISBIKmEVEREREklDALCIiIiKShAJmEREREZEkFDCLiIiIiCShgFlEREREJAkFzCIiIiIiSShgFhERERFJQgGziIiIiEgSCphFRERERJJQwCwiIiIikoQCZhERERGRJBQwi4iIiIgkoYBZRERERCQJBcwiIiIiIkkoYBYRERERSUIBs4iIiIhIEgqYRURERESSUMAsIiIiIpJEzgNmM8s3s5+Y2Uoz221m75rZkDTqXx7W2W1mn5jZ7WZWL5t9FhEREZGjR84DZuC3wI+BOcCNwGpghpldVllFM/sO8DjwaVj3WeBWYHLWeisiIiIiR5W6uTy5mfUAhgK3u/ttYdojwCvAfWY20933JajbAJgIvAz0c3cP07cCPzazB9z9P1l/ESIiIiJyRMv1CPOlgAMPRRLCwHcy0Ao4O0ndrwEtgIciwXJoMmBh2yIiIiIi1ZLrgLk7sMLdN8SkL4jKT1Y3uiwA7r4GWFVJXRERERGRlOQ6YG4NrI2THklrU0nd6LKx9ZPVFRERERFJiR06m+Ewn9xsOfCJu58bk14HKAf+z92vSVD3t8CV7v65oN/MXgFauntJgrpXAVeFTzsBS6v+KkREREQ+p4O7F+a6E5IZOb3pDygD6sdJbxCVn6yumVm9ODcGNkhW190fBh5Op6MiIiIicnTK9ZSMRFMnItMt1lRSlyT1k9UVEREREUlJrgPmt4AOZhb7L4ueUfnJ6gJ8OTrRzNoAx1dSV0REREQkJbkOmP9AsATc9ZEEMzPgGmAdwXrMmFkTMysxsyZRdV8CNgHXh3Uirgsfn85mx0VERETk6JDTOczu/oaZzQBuNbNmwLvAN4GvAFdEzU2+GHgMuBKYGtbdbWZjCOYiP2tmzwDdCALmR9393cP4UkRERETkCJXrm/4AhgMrgGHA1cAHwFB3n15ZRXefYmZ7gdEEm5+sB+4EfpK13oqIiIjIUSWny8qJiIiIiNR0uZ7DLEmYWTczu83M2ue6L6kys6lm5nGO/bnuW7pq6ft/hpk9ZGYLzGxP+N4XV1L+JTPbYWabzOxxM2t5GLucltp2TcysjpldYWazzewTM9tlZkvM7D4zOy5BnVp1TaD2XRdI/2eVmV1oZv8yszIzKzWzB8ys0eHudzy19P3P2s8qM7vczN41s93h993tZlYvay9Gjgo1YUqGJNYNmAD8FViZ266kpQK4Ik5abdON2vf+/zfBpjzvAUuALyYqaGanEtw8uwL4IdAUuAnoZmZnuPvurPc2fd2oXdekIcF9FwuARwiWw/wScCMwwMx6uPv2SOFaek2g9l2XiJR+VpnZ14E/Af8Cvg8UAz8ASoDzs9rD1HSj9r3/WflZZWbfIfhemwv8iuD77VaCJWi/m40XIkcHBcySDe7uT+S6E0epXwM/c/cyMxtHkl9CwF3AbuCr7r4RwMxeB14ARgAPZruzR4G9wFnuPj860cz+BfyO4EbmB6KydE0Or1R/Vk0CPgS+5u574MBOtVPMrJ+7/ymbnTxCZfxnlZk1ACYCLwP9PJxzamZbgR+b2QPu/p/svBw50mlKRg1lZrcRrAwC8I+ofxeeE1VmuJn9O/y30wYz+52ZHR/TzlQz229mx5vZHDPbHv4769fZ/Hdi+K/oY80OWfKv1qit77+7r3P3ZDtkRvp1LHAB8GTkF1BY/68EN94OynTfqqs2XhN33xsbLIf+GD52iepXrbsmUDuvS8x5k/6sMrPOQFdgSiRYDj0O7CDH16W2vv9Z+ln1NaAF8FAkWA5NJljC9tJM9F2OTgqYa65ZHNy++yfA5eGxGMDMfgT8FvgMuAV4FPgWMN+CJfqiGfA8sJ/g31lzCNa6npmlvucB28LjMwvmmhVl6VzZUpvf/1ScCtQjmCoQawHBvzpr2h87R9I1iexQuikqrTZeE6jd1yWVn1Xdw8dDrou77wXeicrPldr8/qcine+LRNdqDbCK3F8rqc3cXUcNPYBvA07wL93o9BYE/556Bagblf4/Yfl7otKmhmnTYtr4aZh+QYb7fDdwLzAYGEjwr7L9wDKgSa7f0yP9/Y85x7jwHMVx8r4V5n09Tt49YV6Nu161/ZpEnesRgrmyX6zt16S2XpdUf1YBN4fnPzlOGzOBLXr/q93/jPysCq+hA/XilF0AvJ3ra6Wj9h4aYa6dzgPqA7909wN3dLv7c8D7QL84dX6Z4Hm8slXm7mPcfbS7/97dn3b3GwjmaZ4EjMzkuXKoxr7/aSgIH/fEydsdU6Y2qDXXxMyGAd8B7vdDN1g60q4J1ODrksbPqsquS02+JjX2/U9DOt8XBQTz0vclKFuTr5XUcAqYa6fi8HFJnLz3gRPipC+NfuLuG4AtCcoCYGZ5ZtYq5kj7B467/w4oBb6ebt0aqjh8rBXvfwKRuYP14+Q1iClTGxSHjzX6mliw2sIU4C8E/x6PdqRdE6gl1yXqXPF+VlV2XWryNSkOH2vF+59AOt8XZUF34i4hV9OvldRwCpglmXYEy2BFH1W9weVToHmG+nW0yOT7H2tt+NgmTl5rYCfBnEc5VJWviZmdCcwG3gIuiTMKpmtSddn8WVXZdVlTxfMcSWrKzypdK8kaLStXsyXahnFF+FhCMEoQrTPwcZw6nYC3I0/MrJBgLct4ZSPijQq/l6R8XOENGSfE6WtNd0S8/wn8h2C+5peBaTF5ZxDM9auJ24DWymtiZl8iWBd2BfA/7r4zTrHaek2gll6XWAl+Vr0VPn4Z+HtU2XyC9Y+fSfc8WXBEvP8JpPN9EX2tPokUMrM2wPEE87RFqkQjzDVb5JfqcTHpLxDM5xppZgf+6DGzC4FTgGfjtPX9BM+fS3Ryd9/t7n+NOdYmKm9mDcIlgGLdQHDzydxEdWuoWvX+p8PdPwPmAYPN7MBompmdB3QEns7EebKg1l0TM+tI8F5vJrhxaXOCtmvrNYFadl3S+Vnl7osJgs3vmln0tIBhQCNqxnWpVe9/OtL8vniJYOWZ62NWlLkufKwJ10pqKY0w12xvEYwcjAl/UOwBXnT39eHam3cDfzOzPwBtge8R7PL0s5h2KoDuZvZH4G/A6QQ3t8xz9z9nsL+tgH+b2ZMEc+b2A2cT3IH+DrVv04Xa9v5jZh0IlpQC+Gr4eIMFC/dvdffoa/Bj4J/AK2Y2meCX7c0EI0NTMtmvDKpV18TMGhMELYXAQ0CfmJXh1rn7C1HPa+M1gVp2XUj/Z9VNBAHji2Y2jWBu8Kiwj/GCzsOttr3/WflZ5e67zWwMwTJ7z5rZMwT/BbgOeDTmJluR9OR6mQ4dyQ+CEY+PCX6gO3BOVN53gHcJfjhuBJ4Ajo+pPzWsezzBmprbCUa6/g9onOG+HkewmP9SggX994Rf3wU0yvV7eaS//+H5zgn7Ge9YEaf8fxHsirUz7NcTQFGu3/cj5ZoQBFaJrocDLx8J16QWXpe0f1YRbOX8BsFqC+sIguqMfw8fDe9/eL6s/awi2O58Ufh6PwXuIM5Sczp0pHOYe02dEieZYGZTgaHurv8m5IDe/5pH16Rm0nXJLb3/IslpDrOIiIiISBIKmEVEREREklDALCIiIiKShOYwi4iIiIgkoRFmEREREZEkFDCLiIiIiCShgFlEREREJAkFzCIiIiIiSShgFpFazcw8zePbOern1ETnN7O6ZvbbMP8tMyvKQRdFRCQB7egjIrXd7XHSvg80Ae4HtsbkvZPd7qTHzAqAmUA/4G/Axe6+Pbe9EhGRaAqYRaRWc/fbYtPCUdwmwC/dfcVh7lLKzKwZ8CfgTOApYJi7781tr0REJJamZIjIUcXMLjWzV8xsm5mVmdl/zGyMmdWPU3ZFeDQxswfNbLWZ7Taz983se2Zm1ehHO+AfBMHyA8AQBcsiIjWTAmYROWqY2V0EI7mdgRnAg4ABdwF/MbP8ONXygb8CFwC/B6YAxxFM93iwiv3oArwGdAF+7O4jXbtIiYjUWAqYReSoYGZnAmOAT4FT3f1adx8NdCOYFvFV4OY4VVsDe4Cu7n6Tu98IdAU+Aq4zs7PT7MqZwKtAK+BKd7+7Kq9HREQOHwXMInK0GB4+3unupZFEd98P3ARUACMS1B3j7nui6mwGfhI+vTLNflwFNAVud/epadYVEZEcUMAsIkeL7uHji7EZ7v4BsAo4wcyaxGTvJ5g+Eevl8PG0NPvxAkFwPsbM+qRZV0REckABs4gcLSKB8NoE+ZH042LSN7p7eZzykVHq2AC7MjOAYUB94E9m9t9p1hcRkcNMAbOIHC22hY+tEuS3jikX0cLM8uKUj7QTW75S7j4dGAzkAbPN7JvptiEiIoePAmYROVq8HT6eE5thZicBxwMfu/vWmOy6QK847UXaeTtOXqXc/Q/ANwEHnjKzIVVpR0REsk8Bs4gcLR4NH8eZWWEkMRw9vo/g5+FvE9S9O3qd5nDDkXHh08eq2iF3/xPBDn97gSfMLN0bCEVE5DDQTn8iclRw99fM7B7gFmCRmf0B2AlcSLBM3KvAvXGqriWYb7zIzP4fUA/4FsEUjsnu/ko1+/VXM+sLPAf81swK3H1yddoUEZHM0giziBw13P2HwBBgGcGNd98j+Dk4Dvh6gp329gLnAfMI5h1fTTBveSRwQ4b69Y/wHFuBh8zspky0KyIimWHaXEpEJD4zWwHg7sW57YmIiOSSRphFRERERJJQwCwiIiIikoQCZhERERGRJDSHWUREREQkCY0wi4iIiIgkoYBZRERERCQJBcwiIiIiIkkoYBYRERERSUIBs4iIiIhIEgqYRURERESS+P8yDTSPccqiAgAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Starting Training\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Starting Training\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Starting Training\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Starting Training\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Making top k from Ranking\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Making top k from Lexicase\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Making top k from Mix\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Making top k from Ranking\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Making top k from Lexicase\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Making top k from Mix\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Started Hit Rate Calculations\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "nmf_model_ = NeuralMatrixFactorization(NUM_USERS, NUM_MOVIES, 75).cuda()\n",
        "\n",
        "ncf_model_ = NeuralColabrativeFiltering(NUM_USERS, NUM_MOVIES, 70, 18, 4, 50, movies_tensor).cuda()\n",
        "\n",
        "neumf_model = NeuMF(nmf_model_, ncf_model_, 80).cuda()"
      ],
      "metadata": {
        "id": "ZCUVlr9be6pZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "models = [neumf_model]\n",
        "names = [\"NeuMF\"]\n",
        "train_info = [[1e-1, 1, torch.nn.MSELoss(),1024,[[1e-1,1,torch.nn.MSELoss(),1024],[1e-2,1,torch.nn.CrossEntropyLoss(),1024]]]]\n",
        "\n",
        "met = metric(models,names,train_info)"
      ],
      "metadata": {
        "id": "JrULS5-1e20P"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "results = met.run_all()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "OuZc5KQYezar",
        "outputId": "db59200f-db69-4c97-f0df-81f376430de9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Starting Training\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Starting Training\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Starting Training\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Making top k from Ranking\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Making top k from Lexicase\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Making top k from Mix\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Making top k from Mix\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Making top k from Mix\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Making top k from Mix\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Started Coverage Caclculations\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Started Coverage Caclculations\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Started Diversity Calculations\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Starting Training\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Starting Training\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Starting Training\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Making top k from Ranking\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Making top k from Lexicase\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Making top k from Mix\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Making top k from Mix\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Making top k from Mix\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Making top k from Mix\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Started Hit Rate Calculations\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x360 with 1 Axes>"
            ],
            "image/png": 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w4403nvSdp6Sk9J4yZUpKr169er744osJ3bt3P3vixImYffv21YvZEATMAAAAjciMGTMOrV27ts3Ro0ej/dPvvffe1MzMzPyPP/54Z1ZW1p777rsvrTrt3XXXXcfWrFmTUFRUZDt37mx23XXXnfbPX79+fUJ6enqv9PT0Xs8880xbSdq6dWuLAQMGnFeubdu25z755JOd99xzz3FJ6t27d9Fbb73V4qLebC0JO2o3s6sl/auknpKaO+eGe9PTJA2U9KZz7nhtdhIAAAC1o02bNuXjx48/On/+/Pbx8fHlvvQtW7a03L17d7zvvLCwMLqgoKDKwdVBgwYV79+/v+nSpUvbDB8+vCAwf8yYMcdXrFixzz8tPz+/SVJS0jn/tIkTJ54XPyYmJp7Lzc2NVT0QVsBsZj+T9FN9NTLtvzRKlKRXJD0s6dna6BwAAABq38yZM/P79evXa8KECUd8ac45bd++fWezZs3OW/ouJibGlZdXxNUqKSmxwPZGjhx5Ys6cOambNm3KOXToUJXxZVxcXHlxcfF5wfgVV1xR7n9+5swZ8w/oI6naUzLMbIKkRyS9KamPpHn++c65zyT9XZXskAIAAID6ISkpqWzMmDHHV69e3c6XNmTIkJPz5s1r7zvfunVrvCSlpaWVfPjhh80lafPmzc1yc3ObBrY3ZcqUI9OnTz8wcODA4upcv0ePHmd27dp1QTv+9uzZE3fNNddUq726Fs4I80OSPpV0k3PurJl9O0iZnZKG1UbHAAAALmfVWQauLs2aNStv+fLlib7zJUuWfDl58uTO3bt371VWVmaDBg06NXjw4H0TJ048vmrVqrZXXnnlVX379j3dpUuXM4Ftde3atfSRRx45VN1rjx07tuCFF15IzMzMPBIsv6SkxPbu3dt06NChp4PlX2rhBMy9Jb3snDsboswBSRc8dQkAAIDIKyoq+ofv59TU1HPFxcUV58nJyec2bNjwWWCdFi1auC1btuyuqj2f0aNHnxo9evQpSXrooYeOSjoaWGbkyJGFs2bNSjly5Eh0u3btynJzc3f4569Zs6bVmDFjjjdp0iSs91dXwlklwyRVNY8kSdIFv3UAAAAA/p566qn9e/bsCfpQ37lz5+zRRx/Nv9R9qkw4I8y7JQ2uLNPMoiQNkfTPi+0UAAAALm833HBDpdMtJk2aVK9WXAtnhPl3kvqZ2bRK8n8q6UpJqy+6VwAAAEA9Ec4I8y8kjZf0pJndJu+Scma2UNLXJQ2Q9D+SltRyHwEAAICIqXbA7JwrNrNvSHpG0nck+XaHyZRnbvNKSQ86585V0gQAAADQ4IS1cYlzrkDSd80sU9K1ktpKKpD0gXPucB30DwCAWvfcfW9FugsAGpCwt8aWJOfcMUn/Xct9AQAAaDzmtupfu+0VVLmus5n1nzx5cv7SpUv3S9Ls2bOTCgsLoxctWnSgJpc0s/5jx449tm7dus8lqbS0VO3bt7+mT58+p99+++1PFy9e3HbOnDmdkpKSSiWpZ8+eRVlZWXsladKkSanjx48/PmrUqMKaXHvu3LlJv/3tb9tFR0e7tm3bnlu+fPne7t27n5WkZ599tu3ChQuTJWn69OkHp06delSSBg8e3H3dunV7EhMTy8K5VrUDZjMrkzTXOfd4iDKzJD3mnKtRIA6gcUv7yYaw6+yd/6066AkAXJ5iY2Pdxo0bEw4ePJiXnJx80dNo4+Pjy3NycuILCwutRYsWLisrq6UvOPYZM2bM8RUrVuzzT8vLy4vetm1b8xdffPHLml67f//+RdOmTdt5xRVXlC9YsCDx3/7t3zpt2LDhs/z8/OgFCxZ03LZt2ydRUVHq27dvrwkTJpxITEwsu+OOO44uXLgwccGCBXnhXCvcdZgv2Du8knLVb9Qs1sweN7N9ZnbGzD4yszvCqN/GzJ4xsy/MrMTMcs3sP82sZTj9AAAAuNxFR0e7iRMnHn7iiScu2GjuwIEDMSNGjOiakZHRMyMjo+emTZuaS1JmZmbH2bNnV5Tv1q3bVTk5ORXrJw8fPrzgtddeay1Jr7zySptx48Ydq6ofK1euTLjxxhtP+s5TUlJ6P/DAAynp6em9MjIyem7evLnZkCFDuqWmpmY8+eSTicHaGDNmzKkrrriiXJKGDBlSePDgwVhJeuONN1oNHTr0ZFJSUlliYmLZ0KFDT65du7aVJE2YMOHE2rVr21bz46oQTsBcHQkKf+OS38izJN06SVMl5UpabWbfqaqimSVL+pukWyW9LGmKpMWSmkhqFmY/AAAALnszZsw4tHbt2jZHjx6N9k+/9957UzMzM/M//vjjnVlZWXvuu+++tOq0d9dddx1bs2ZNQlFRke3cubPZddddd976yuvXr09IT0/vlZ6e3uuZZ55pK0lbt25tMWDAgPPKde7c+Wx2dvYngwYNKpw0aVLa+vXr97z//vvZCxYs6FhVH379618nDh8+vECScnNzm3Tq1KliZ+qUlJSzubm5TSQpMTGx7OzZs5aXlxddWVvBhJw6YWZDA5LSgqRJnhUzOsuzekZOdS9uZv0l3SnPNI653rRlkv4iaaGZ/c45VxqiiV95r92Hhw4BAACq1qZNm/Lx48cfnT9/fvv4+PiKXZy3bNnScvfu3fG+88LCwuiCgoIqB1cHDRpUvH///qZLly5t4wta/QWbkpGfn98kKSnpvCkht9122wlJ6t27d9Hp06ejEhISyhMSEspjY2PLfVtoB7v+888/3+Z///d/m/3617+uVgzatm3bc/v27Yvt0KFDcXXKS1XPYX5H3vWWva93e49gfFtnV7axSTC+9Zyf8yU455yZPS/PBihDJf056MXMukkaK89SdofNrKm3fkkY1wcAAGh0Zs6cmd+vX79eEyZMOOJLc85p+/btO5s1a+b8y8bExLjy8oq4WiUlJRdMvx05cuSJOXPmpG7atCnn0KFDVT7LFhcXV15cXBwVkOYkKSoqSrGxsRV9iIqKUmlpqU2dOjXlzTffbCVJ2dnZn0jSG2+8ccXChQuT//rXv+bEx8c7SUpJSSl99913r/DVz83Njb3++utP+fe/WbNmX72haqjqt4afeY/H5QmI3/VL8z/mSHpAUoZzLpyd/vpJ2htkdPgDv/zK/F/v6wEz+29JxZKKzeyvZtYnjD4AAAA0KklJSWVjxow5vnr16na+tCFDhpycN29ee9/51q1b4yUpLS2t5MMPP2wuSZs3b26Wm5vbNLC9KVOmHJk+ffqBgQMHVmvUtkePHmd27dp1QTuhPPvss7nZ2dmf+ILlLVu2xE+dOrXLunXrPk1JSakYrb755psL3n333ZaHDx+OPnz4cPS7777b8uabby6QpPLych0+fLhJjx49whpgDfkbgG+ahCSZ2d2S3nDOLQ7nAlVIlnQwSLovLdSclW7e1yWSPpR0h6R2kmZLetvMrnbOBX3y0szukXSPJHXu3Dn8XgMAAFysaiwDV5dmzZqVt3z58ooH6pYsWfLl5MmTO3fv3r1XWVmZDRo06NTgwYP3TZw48fiqVavaXnnllVf17dv3dJcuXS54Xq1r166ljzzyyKHqXnvs2LEFL7zwQmJmZuaRqksHN2PGjNSioqLo8ePHd5Wkjh07nn3rrbc+TUpKKpsxY8aB/v3795SkH/3oRweSkpLKJE/A37dv39NNmjQJ61rh7PT3tbBarp54ScE+3DN++ZVp4X09LGmkc65Mkszs7/Js0f1v8uxCeAHn3BJ5t/AeMGCAC1YGAADgclNUVPQP38+pqanniouLK86Tk5PPbdiw4bPAOi1atHBbtmzZXVV7PqNHjz41evToU5L00EMPHZV0NLDMyJEjC2fNmpXim5ucm5u7w5cXWMc/z9/WrVt3VfY+H3744aMPP/zwBdd96aWX2t5///3VDux9anuVjHAVSwo2HB/nlx+qriS96guWJck5976kTyUNqZUeAgAAoNY99dRT+/fs2RNbdcnak5GRUXzTTTedqrrk+cLaYMTMTJ4l3EZISlHwYNc5526sZpMHJV0ZJD3Z+xpq1xlfXn6QvHxJF6wvCACov3ov712jejvuDjr4BKCeu+GGG05XXap2TZs2rUZTQMLZ6a+ppI2ShsnzAKDT+ZuUOL/06tou6UYzSwx48G+QX35lfPN+OgXJS1HwudEAAABAWMIZYf6xpG/Is2LGM5KOSJorz1zgYZLmS9oq6a4w2nxd0o/kWWFjrlQxin2fPKPEf/GmtZL3AUHnnG99v3ck5Um608yecM4Ve8t+U1KaPBuZAAAuczvTe4ZfadhzVZcBAK9w5jCPl7TdOTfHOVex5aFzLs8596qkGySNlvRwdRt0zv1NnvWWHzWzxWY2WdIGSV+X9CO/TUu+LWmn99VX96w8D/WlSdpsZg+Z2eOS1kr6XJ6gHgAAALgo4QTMXSVt8Tt38mxB7Tlx7jN5gt3vhtmHSfKMTn9bng1MUiXd6ZxbUVVF59wrkm7x9uVJSfdLekPSEOfciTD7AQAAAFwgnCkZpfpquTdJOiUpMaDMF/Lsvldt3p35ZnmPysq8rEqmWDjnsiRlhXNNAACASOu9vHf/2mxvx907qlzX2cz6T548OX/p0qX7JWn27NlJhYWF0YsWLQq10ELI9saOHXts3bp1n0tSaWmp2rdvf02fPn1Ov/32258uXry47Zw5czolJSWVSlLPnj2LsrKy9krSpEmTUsePH3981KhRhTW5ts/LL7/c+nvf+17Xd999d+fQoUOLJGnmzJkdVq1a1S4qKkpPP/30vnHjxp08c+aMDRkypPt7772XE+46zOGMMO+X52E6n12Srgso01fSMQEAAKDeiY2NdRs3bkw4ePBgWCulVSY+Pr48JycnvrCw0CQpKyurpS849hkzZsxx3w59vmA5Ly8vetu2bc0vNlg+fvx41C9/+cukq6++umLFjW3btsWtXbu2TU5Ozj//+Mc/7nr44Yc7nzt3TnFxce76668/uWzZsjbhXiecgHmLpMF+529I6m1my8zsW2b2lKTh8jyMBwAAgHomOjraTZw48fATTzxxwfK7Bw4ciBkxYkTXjIyMnhkZGT03bdrUXJIyMzM7zp49u6J8t27drsrJyalYP3n48OEFr732WmtJeuWVV9qMGzeuysHTlStXJtx4440nfecpKSm9H3jggZT09PReGRkZPTdv3txsyJAh3VJTUzOefPLJwBkNFaZNm5Yyffr0vKZNm1as0vb666+3vuWWW47Fx8e79PT0s126dCl55513mkvSrbfeeuLVV18NO2AO57eL1ZJSzSzNObdX0i8k3STPHOTvybOk3KeSfhJuJwAAl5m5rcKv87XOtd8PfKUm90TivlyGZsyYcah3795XzZ07N88//d57703NzMzMHzFiROHu3btjR4wY0e2zzz77Z1Xt3XXXXcfmzJmTfPvtt5/YuXNns+9///tHt27d6tuRWevXr09IT09vIUlTpkzJ/+EPf3h069atLW699dbj/u107tz5bHZ29iff//73UydNmpT2/vvvZxcXF0f17t37qh/96EeHA6+7efPmZrm5ubETJkwoWLRoUQdfem5ubuy//Mu/VIxcd+zY8eyXX34ZK+n0tddeW/zRRx81D+sDU3hbY78jv9Fj51yRmf0feYLmKyXtlbTeOVcUbicAAABwabRp06Z8/PjxR+fPn98+Pj6+3Je+ZcuWlrt37473nRcWFkYXFBRUORth0KBBxfv372+6dOnSNsOHDy8IzB8zZszxFStW7PNPy8/Pb5KUlHTOP+222247IUm9e/cuOn36dFRCQkJ5QkJCeWxsbLlvC21f2bKyMmVmZqb+9re//Tyc9x4TE6MmTZq448ePRyUkJJRXXcNbL5yLBHLOnZP0n/5pZhbrXfINAAAA9dDMmTPz+/Xr12vChAkVO98557R9+/adzZo1O28TupiYGFde/lVsWVJS4r9xnSRp5MiRJ+bMmZO6adOmnEOHDlUZX8bFxZUXFxdHBaQ5SYqKilJsbGxFH6KiolRaWmpTp05NefPNN1tJ0pYtW3J2794dd8MNN/SQpCNHjjS59dZbr3z99dc/TUlJ8Y0oS5IOHDgQm5qaWhGblpaWWuB7rEo4c5hDMrMoM7tH0u7aahMAAAC1LykpqWzMmDHHV69e3c6XNmTIkJPz5s1r7zvfunVrvCSlpaWVfPjhh82limkQTQPbmzJlypHp06cfGDhwYHF1rt+jR48zu3btuqCdUJ599tlc38ODbdu2LTt+/Pj/5ubm7sjNzd1xzTXXnH799dc/HTp0aNG4ceNOrF27tk1xcbFlZ2fH7t27N27YsGGnJc/Dhq1btz7nP+e5Oqo1wmxmcZJ6ybO0XE7gCLKZ3SHpMXnWag6rAwAAAI1RdZaBq0uzZs3KW758ecUDdUuWLPly8uTJnbt3796rrKzMBg0adGrw4MH7Jk6ceHzVqlVtr7zyyqv69u17ukuXLmcC2+ratWvpI488cqi61x47dmzBCy+8kJiZmXmk6tLhGTBgwJmbb775WPfu3a+Kjo7WokWLvoiJ8YS8f/jDH1oGmzZSlSoDZjPLlCcYbuZNOmRm9zrn/svM0iWtkNRfnof+1kmaE24nAAAAUPeKior+4fs5NTX1XHFxccV5cnLyuQ0bNnwWWKdFixZuy5YtQWcQ+LfnM3r06FOjR48+JUkPPfTQUUlHA8uMHDmycNasWSm+ucm5ubk7fHmBdfzzKvPBBx/k+J8vWLAgb8GCBXmB5V555ZU2Cxcu3F9Ve4FCTskws7GSFkpqLqnIeyRJWmNm35T0P5IGSPpvSdc6577tnPso3E4AAACgcXnqqaf279mzJ7bqkrXjzJkzNnbs2BNXX311Sbh1qxphniKpXNK/Oud+J0lmdqek5fKMJp+RNNo5tzHcCwMAAKDxuuGGG05XXar2xMXFuQcffPCC0e7qqOqhv36Sfu8LliXJObdS0gZJTSV9n2AZAAAAl7OqAuY2koItWP2x9/XN2u0OAAAAUL9UFTBHSwo2z6NEkpxzF7X/NwAAAFDfVWdZOZaJw2Un7ScbalRv7/xv1XJPAABAfVedgPnfzOx7AWmtJcnMLlh6RJJzznW92I4BQGP29O2ja1Rv2prf13JPANSVnek9+9dmez2zd1a5rrOZ9Z88eXL+0qVL90vS7NmzkwoLC6MXLVp0oCbXNLP+Y8eOPbZu3brPJam0tFTt27e/pk+fPqfffvvtTxcvXtx2zpw5nZKSkkolqWfPnkVZWVl7JWnSpEmp48ePPz5q1Kgaz1hYtmxZwrx58zqamXr27Fm0fv36zyXp2Wefbbtw4cJkSZo+ffrBqVOnHpWkwYMHd1+3bt2exMTEslDtBqpOwNzaewSTFiSNEWkAAIB6KDY21m3cuDHh4MGDecnJyecutr34+PjynJyc+MLCQmvRooXLyspq6QuOfcaMGXN8xYoV+/zT8vLyordt29b8xRdf/LKm196xY0fTp59+Ovl//ud/shMTE8tyc3NjJCk/Pz96wYIFHbdt2/ZJVFSU+vbt22vChAknEhMTy+64446jCxcuTAy2RnMoVc1h/loNjv8vnA4AAADg0oiOjnYTJ048/MQTTyQF5h04cCBmxIgRXTMyMnpmZGT03LRpU3NJyszM7Dh79uyK8t26dbsqJyenYv3k4cOHF7z22mutJc/GIOPGjTtWVT9WrlyZcOONN570naekpPR+4IEHUtLT03tlZGT03Lx5c7MhQ4Z0S01NzXjyyScTg7Xx3HPPJf7gBz845BstTklJOSdJb7zxRquhQ4eeTEpKKktMTCwbOnToybVr17aSpAkTJpxYu3Zt22p+XBVCBszOuS9qcoTbCQAAAFwaM2bMOLR27do2R48ejfZPv/fee1MzMzPzP/74451ZWVl77rvvvrTqtHfXXXcdW7NmTUJRUZHt3Lmz2XXXXXfe+srr169PSE9P75Went7rmWeeaStJW7dubTFgwIDzynXu3Plsdnb2J4MGDSqcNGlS2vr16/e8//772QsWLOgY7Lqffvpp0127dsX169cv/Zprrkl//fXXW0pSbm5uk06dOp31lUtJSTmbm5vbRJISExPLzp49a3l5edHB2qxMdaZkAAAA4DLRpk2b8vHjxx+dP39++/j4+HJf+pYtW1ru3r073ndeWFgYXVBQUNVsBA0aNKh4//79TZcuXdpm+PDhBYH5waZk5OfnN0lKSjpvSshtt912QpJ69+5ddPr06aiEhITyhISE8tjY2HLfFtr+5cvKymzPnj1N33vvvZzPP/+8ybBhw9KHDRsWbDnk87Rt2/bcvn37Yjt06FBcVVkfAmYAAIBGZubMmfn9+vXrNWHChCO+NOectm/fvrNZs2bnPY8WExPjyssr4mqVlJRYYHsjR448MWfOnNRNmzblHDp0qMr4Mi4urry4uDgqIM1JUlRUlGJjYyv6EBUVpdLSUps6dWrKm2++2UqSsrOzP0lOTj47aNCg002bNnXp6elnv/a1r5355z//2TQlJaX03XffvcJXPzc3N/b6668/5d//Zs2alSsMVf7WAAAAgMtLUlJS2ZgxY46vXr26nS9tyJAhJ+fNm9fed75169Z4SUpLSyv58MMPm0vS5s2bm+Xm5jYNbG/KlClHpk+ffmDgwIHVGrXt0aPHmV27dl3QTijPPvtsbnZ29ifZ2dmfSNItt9xywhcYHzx4MObzzz+P69GjR8nNN99c8O6777Y8fPhw9OHDh6PffffdljfffHOBJJWXl+vw4cNNevToEWyfkUoxwgwAABAB1VkGri7NmjUrb/ny5RUP1C1ZsuTLyZMnd+7evXuvsrIyGzRo0KnBgwfvmzhx4vFVq1a1vfLKK6/q27fv6S5dupwJbKtr166ljzzyyKHqXnvs2LEFL7zwQmJmZuaRqksHd8stt5z84x//2LJr165XRUdHu5/97GdfdujQoUySZsyYcaB///49JelHP/rRgaSkpDLJE/D37dv3dJMmTcK6FgEzAABAI1FUVPQP38+pqanniouLK86Tk5PPbdiw4YI9Nlq0aOG2bNmyu6r2fEaPHn1q9OjRpyTpoYceOirpaGCZkSNHFs6aNSvFNzc5Nzd3hy8vsI5/nr+oqCgtW7Zsv6T9gXkPP/zw0YcffviC67700ktt77///moH9hXXCrcCAAAAcLGeeuqp/Xv27ImtumTtycjIKL7ppptOVV3yfIwwAwAA4JK74YYbTlddqnZNmzatRlNAqj3CbGZlZvZoFWVmmdlF7xoDAAAA1BfhTMkw71GdcgAAAMBlobbnMCdIuuDJSQAAAKChCjmH2cyGBiSlBUmTpGhJnSV9R1JOLfUNAAAAiLiqHvp7R5JvpxUn6W7vEYxJKpc0rVZ6BgAAcBl77r63+tdmew/86oYq13U2s/6TJ0/OX7p06X5Jmj17dlJhYWH0okWLDtTkmmbWf+zYscfWrVv3uSSVlpaqffv21/Tp0+f022+//enixYvbzpkzp1NSUlKpJPXs2bMoKytrryRNmjQpdfz48cdHjRpVWJNr7969O/bOO+9MO3nyZExZWZkef/zx3Ntvv71AkmbOnNlh1apV7aKiovT000/vGzdu3MkzZ87YkCFDur/33ns5tb0O88/kCZRN0mx5Auh3g5Qrk2e9vLedc9lh9QAAAACXRGxsrNu4cWPCwYMH85KTky96oYb4+PjynJyc+MLCQmvRooXLyspq6QuOfcaMGXN8xYoV+/zT8vLyordt29b8xRdf/LKm1549e3byLbfccvzHP/7x4W3btsWNHTu22+23375j27ZtcWvXrm2Tk5Pzzy+++KLJN7/5ze433XTTx3Fxce76668/uWzZsjZTpkw5Fs61Qs5hds7Ndc495pybK+kLSW94zwOPf3fOvUCwDAAAUH9FR0e7iRMnHn7iiSeSAvMOHDgQM2LEiK4ZGRk9MzIyem7atKm5JGVmZnacPXt2Rflu3bpdlZOTU7F+8vDhwwtee+211pL0yiuvtBk3blyVwejKlSsTbrzxxpO+85SUlN4PPPBASnp6eq+MjIyemzdvbjZkyJBuqampGU8++WRisDbMTCdPnoyWpOPHj0e3b9++VJJef/311rfccsux+Ph4l56efrZLly4l77zzTnNJuvXWW0+8+uqrbar5cVWo9kN/zrmvOecWh3sBAAAA1B8zZsw4tHbt2jZHjx6N9k+/9957UzMzM/M//vjjnVlZWXvuu+++tOq0d9dddx1bs2ZNQlFRke3cubPZddddd976yuvXr09IT0/vlZ6e3uuZZ55pK0lbt25tMWDAgPPKde7c+Wx2dvYngwYNKpw0aVLa+vXr97z//vvZCxYs6BjsuvPmzTvw2muvtUlKSrr6lltu6bZ48eJ9kpSbmxubmpp61leuY8eOZ7/88stYSbr22muLP/roo+bVeV/+2LgEAACgEWnTpk35+PHjj86fP799fHx8uS99y5YtLXfv3h3vOy8sLIwuKCiocnB10KBBxfv372+6dOnSNsOHDy8IzA82JSM/P79JUlLSeVNCbrvtthOS1Lt376LTp09HJSQklCckJJTHxsaW+7bQ9i//0ksvtbnjjjuOPvbYY/l/+tOfmn/3u9/92q5du/4Zqq8xMTFq0qSJO378eFRCQkJ5qLLn1assw8zekvdBP+fcfu95dTjn3I3V7QAAAAAurZkzZ+b369ev14QJEyp2vnPOafv27TubNWvm/MvGxMS48vKvYsuSkpIL9twYOXLkiTlz5qRu2rQp59ChQ1UOyMbFxZUXFxdHBaQ5SYqKilJsbGxFH6KiolRaWmpTp05NefPNN1tJUnZ29icrV65s98c//nGXJA0fPvx0SUlJVF5eXkxKSkrFiLIkHThw4LwR59LSUgt8j1UJ9VvDMO/RLOC8OgcAAADqqaSkpLIxY8YcX716dTtf2pAhQ07Omzevve9869at8ZKUlpZW8uGHHzaXpM2bNzfLzc1tGtjelClTjkyfPv3AwIEDi6tz/R49epzZtWvXBe2E8uyzz+ZmZ2d/kp2d/YnkmWqxcePGlpK0ffv2uLNnz1pycvK5cePGnVi7dm2b4uJiy87Ojt27d2/csGHDTkuehw1bt259rmnTpmEFzJX+BuCciwp1DgAAgJqrzjJwdWnWrFl5y5cvr3igbsmSJV9Onjy5c/fu3XuVlZXZoEGDTg0ePHjfxIkTj69atartlVdeeVXfvn1Pd+nS5YJN6rp27Vr6yCOPHKrutceOHVvwwgsvJGZmZh6punRwP//5z7/8wQ9+kPbcc88lmZl+9atf7Y2KitKAAQPO3Hzzzce6d+9+VXR0tBYtWvRFTIwn5P3DH/7QMti0kapEfA6zmcVKelSe9Z3bS9olaZ5z7pUw20nw1m0n6S7n3Mra7isAAEBDVlRU9A/fz6mpqeeKi4srzpOTk89t2LDhs8A6LVq0cFu2bNldVXs+o0ePPjV69OhTkvTQQw8dlWfp4fOMHDmycNasWSm+ucm5ubk7fHmBdfzz/PXv3//M9u3bg67QtmDBgrwFCxbkBaa/8sorbRYuXLg/WJ1Q6sOo8W8k/VTSOklTJeVKWm1m3wmznSckxVdZCgAAABH31FNP7d+zZ09s1SVrx5kzZ2zs2LEnrr766pJw61a1NXbnmnTIObev6lKe3WEk3SnJt9azzGyZpL9IWmhmv3POlYZowr+dH8izucp/1KTPAAAAuHRuuOGG01WXqj1xcXHuwQcfvGC0uzqqmpKxV19tjV1drhrt+tzmLf9cRWXnnJk9L2m1pKGS/hyqATMzb/1XJW0Ns68AAABASFUFtvt0YcDcWlIreXb+u1j9JO11zh0OSP/ALz9kwCxpsqQMSeMkdauFPtW6tJ9sCLvO3vnfqoOeAAAAIFwhA2bnXFpgmpnNlfSoc+5rtXD9ZEkHg6T70oLu7OLXlzaS5kn6D+dcrpnVy4AZAAAADVdNHvoLd4pGKPGSgk28PuOXH8o8SSckLQrnomZ2j5n93cz+fvhw4OA2AAAA8JVILytXLCnYotVxfvlBmdm18kzHuNk5F9bTjs65JZKWSNKAAQNq8xcAAACAann69tH9a7O9aWt+X+W6zmbWf/LkyflLly7dL0mzZ89OKiwsjF60aNGBmlzTzPqPHTv22Lp16z6XpNLSUrVv3/6aPn36nH777bc/Xbx4cds5c+Z0SkpKKpWknj17FmVlZe2VpEmTJqWOHz/++KhRowoHDhzYY+HChV8OHTq0aPDgwd3XrVu3JzExsSzEpS+pSC8rd1DBp10ke19D3bynJf1d0g4zSzOzNEkdvHntvGmR/oUAAACg3oiNjXUbN25MOHjwYK3ESPHx8eU5OTnxhYWFJklZWVktfcGxz5gxY477dujzBct5eXnR27Ztaz5q1KjCwDbvuOOOowsXLkwMTI+kSAfM2yV1MbPAD2WQX35lOksaKOlzv8O32cnPveedaq+rAAAADVt0dLSbOHHi4SeeeCIpMO/AgQMxI0aM6JqRkdEzIyOj56ZNm5pLUmZmZsfZs2dXlO/WrdtVOTk5FesnDx8+vOC1115rLXk2Bhk3btyxqvqxcuXKhBtvvPFksLwJEyacWLt2bdsavL06E+mA+XVJJukBX4J3mbj7JOXLsx6zzKyVmaWbWSu/uvdI+nbA8ag37xfe82pv0QgAANAYzJgx49DatWvbHD16NNo//d57703NzMzM//jjj3dmZWXtue+++9Kq095dd911bM2aNQlFRUW2c+fOZtddd9156yuvX78+IT09vVd6enqvZ555pq0kbd26tcWAAQOCrsOcmJhYdvbsWcvLy4sOlh8JEZ2y4Jz7m5mtlvSod8WLjyTdIunrku7227Tk25JekvQ9SS97624KbM/MTnh/3Oace6NOOw+gfpjbquoyQesV1G4/AKCBaNOmTfn48eOPzp8/v318fHy5L33Lli0td+/eXbHgQmFhYXRBQUGVg6uDBg0q3r9/f9OlS5e2GT58+AVfrmPGjDm+YsWK8za1y8/Pb5KUlHSusjbbtm17bt++fbEdOnSo9Hm2S6mqnf4qnWwdIs8558IJxCfJs0HKREn3Stol6U7n3Kow2gAAAEA1zZw5M79fv369JkyYcMSX5pzT9u3bdzZr1uy8BRFiYmJceXlFXK2SkhILbG/kyJEn5syZk7pp06acQ4cOVRkHxsXFlRcXF1cajJeUlFizZs3KK8u/1Kr6rcFqcIQ1zcM5V+Kcm+WcS3XONXXO9Q4Mlp1zLzvnzDn3chVtveMttzKcPgAAADQmSUlJZWPGjDm+evXqdr60IUOGnJw3b1573/nWrVvjJSktLa3kww8/bC5Jmzdvbpabm3vBCmdTpkw5Mn369AMDBw6s1ohwjx49zuzatSvYSmkqLy/X4cOHm/To0SOsVdDqUlUbl0R6jjMAAMBlqTrLwNWlWbNm5S1fvrxi4YUlS5Z8OXny5M7du3fvVVZWZoMGDTo1ePDgfRMnTjy+atWqtldeeeVVffv2Pd2lS5czgW117dq19JFHHqn2s2Njx44teOGFFxIzMzOPBOZt3ry5Wd++fU83adKk5m+ulrHsGgAAQCNRVFT0D9/Pqamp54qLiyvOk5OTz23YsOGzwDotWrRwW7Zs2V1Vez6jR48+NXr06FOS9NBDDx2VdDSwzMiRIwtnzZqVcuTIkeh27dqVffDBBzm+vJdeeqnt/fffX68WbmAEGQAAAJfcU089tX/Pnj2xgekZGRnFN91006lI9KkyjDADAADgkrvhhhuCLis3bdq0C6ZpRBojzAAAAJdGeXl5+QUrTCDyvPel0lU5GGEG0Cj1Xt477Do77t5RBz0B0Ih8fPjw4V6JiYkFUVFRruriuBTKy8vt8OHDrSR9XFkZAmYAAIBL4Ny5c5Pz8vKW5eXlZYi/8tcn5ZI+Pnfu3OTKChAwA0A17UzvWaN6bw17rpZ7AqAh6t+//yFJYyPdD4SP324AAACAEBhhBqCnbx8ddp1pa35fBz0BAKD+YYQZAAAACIGAGQAAAAiBgBkAAAAIgYAZAAAACIGAGQAAAAiBgBkAAAAIgYAZAAAACIGAGQAAAAiBgBkAAAAIgYAZAAAACIGAGQAAAAiBgBkAAAAIgYAZAAAACIGAGQAAAAiBgBkAAAAIgYAZAAAACIGAGQAAAAiBgBkAAAAIgYAZAAAACIGAGQAAAAiBgBkAAAAIgYAZAAAACIGAGQAAAAghJtIdABqUua1qUKeg9vsBAAAuGUaYAQAAgBAImAEAAIAQCJgBAACAEAiYAQAAgBAiHjCbWayZPW5m+8zsjJl9ZGZ3VKNeJzN71MzeM7OjZnbc+/Ptl6LfAAAAaBwiHjBL+o2kn0paJ2mqpFxJq83sO1XUu9lb7wtJcyQ9KumMpFfNbF6d9RYAAACNSkSXlTOz/pLulPSYc26uN22ZpL9IWmhmv3POlVZS/W1JnZ1zh/3ae07SJknTzewp59yxOn0DAAAAuOxFeoT5NklO0nO+BOeck/S8pA6ShlZW0Tn3T/9g2a/uWnl+EeheFx0GAABA4xLpjUv6SdobGPhK+sAv/89httnR+3r0YjoG1Jbey3vXqN6Ou3fUck8AAEBNRHqEOVnSwSDpvrSOQfIqZWZtJN0jabtzbvdF9g0AAACI+AhzvKRDQdLP+OVXi5lFS3pVUoKk0VWUvUeewFqdO3eu7iWAS2pnes+w67w17LmqCwEAgLBEeoS5WFLTIOlxfvnVtVTScEmTnXN/C1XQObfEOTfAOTcgMTExjEsAAACgsYn0CPNBSVcGSU/2vh6oTiNm9rSk70ma7pxbUUt9Qx14+vaQg/+Vmrbm97XcEwAAgOqJ9AjzdkldzCxwmHeQX35IZjZHUqakec65p2u5fwAAAGjkIh0wvy7JJD3gSzAzk3SfpHx51mOWmbUys3Qza+Vf2cx+KGmupF855356qToNAACAxiOiUzKcc38zs9WSHvWucPGRpFskfV3S3X6blnxb0kvyTLt4WZLM7GZJP5e0T9J7ZnZnQPNbnXOf1fmbAAAAwGUt0nOYJWmSpL2SJkq6V9IuSXc651ZVUa+PPKPTnSUtD5L/PUkEzAAAALgoEQ+YnXMlkmZ5j8rKvCzvyLJf2lx5pmMAAAAAdSbSc5gBAACAeo2AGQAAAAiBgBkAAAAIgYAZAAAACIGAGQAAAAiBgBkAAAAIgYAZAAAACIGAGQAAAAiBgBkAAAAIIeI7/aESc1vVsF5B7fYDAACgkWOEGQAAAAiBgBkAAAAIgYAZAAAACIGAGQAAAAiBgBkAAAAIgYAZAAAACIGAGQAAAAiBgBkAAAAIgY1LLjO9l/cOu86Ou3fUQU8AAAAuD4wwAwAAACEwwgztTO9Zo3pvDXuulnsCAABQ/zDCDAAAAIRAwAwAAACEQMAMAAAAhEDADAAAAIRAwAwAAACEQMAMAAAAhEDADAAAAIRAwAwAAACEQMAMAAAAhEDADAAAAIRAwAwAAACEQMAMAAAAhEDADAAAAIRAwAwAAACEQMAMAAAAhEDADAAAAIRAwAwAAACEEPGA2cxizexxM9tnZmfM7CMzuyOM+nd565wxsy/M7DEza1KXfQYAAEDjEfGAWdJvJP1U0jpJUyXlSlptZt+pqqKZfV/SCklfeuuul/SopOfrrLcAAABoVGIieXEz6y/pTkmPOefmetOWSfqLpIVm9jvnXGkldeMkzZf0jqTRzjnnTT8h6admttg5t6PO3wQAAAAua5EeYb5NkpP0nC/BG/g+L6mDpKEh6n5DUjtJz/mCZa/nJZm3bQAAAOCiRDpg7idpr3PucED6B375oer6l5UkOecOSNpfRV0AAACgWiIdMCdLOhgk3ZfWsYq6/mUD64eqCwAAAFSLnT+b4RJf3GyPpC+cczcEpEdJKpP0a+fcfZXU/Y2k7znnLgj6zewvkto759IrqXuPpHu8pz0k5dT8XQAAAFygi3MuMdKdQO2I6EN/koolNQ2SHueXH6qumVmTIA8GxoWq65xbImlJOB0FAABA4xTpKRmVTZ3wTbc4UEVdhagfqi4AAABQLZEOmLdL6mJmgX+yGOSXH6quJF3rn2hmHSV1qqIuAAAAUC2RDphfl2cJuAd8CWZmku6TlC/Peswys1Zmlm5mrfzqvi3pqKQHvHV87ve+vlaXHQcAAEDjENE5zM65v5nZakmPmlkbSR9JukXS1yXd7Tc3+duSXpL0PUkve+ueMbOZ8sxFXm9mb0jqI0/A/KJz7qNL+FYAAABwmYr0Q3+SNEnSXkkTJd0raZekO51zq6qq6JxbamZnJc2QZ/OTQ5L+XdLjddZbAAAANCoRXVYOAAAAqO8iPYcZIZhZHzOba2adI92X6jKzl83MBTnORbpv4Wqgn/9AM3vOzD4wsxLvZ59WRfm3zazQzI6a2Qoza38JuxyWhnZPzCzKzO42sywz+8LMisws28wWmlnrSuo0qHsiNbz7IoX/XWVmo8zsfTMrNrM8M1tsZi0udb+DaaCff519V5nZXWb2kZmd8f67e8zMmtTZm0GjUB+mZKByfSTNkfQnSfsi25WwlEu6O0haQ9NHDe/z/3/ybMrzT0nZkq6urKCZ9Zbn4dm9kn4sKUHSNEl9zGygc+5Mnfc2fH3UsO5JM3meu/hA0jJ5lsO8RtJUSWPNrL9z7pSvcAO9J1LDuy8+1fquMrNvSvq9pPclPSwpTdK/SUqX9H/rtIfV00cN7/Ovk+8qM/u+PP/WNkp6Vp5/b4/KswTtD+rijaBxIGBGXXDOuZWR7kQj9YKkBc65YjN7RCH+E5L0hKQzkq53zh2RJDP7H0lvSpos6Zd13dlG4KykIc65Lf6JZva+pN/K8yDzYr8s7smlVd3vqkWSPpX0DedciVSxU+1SMxvtnPt9XXbyMlXr31VmFidpvqR3JI123jmnZnZC0k/NbLFzbkfdvB1c7piSUU+Z2Vx5VgaRpL/6/blwmF+ZSWb2v94/Ox02s9+aWaeAdl42s3Nm1snM1pnZKe+fs16oyz8nev8U3dLsvCX/GoyG+vk75/Kdc6F2yPT1q6WkEZJe8f0H5K3/J3kevL29tvt2sRriPXHOnQ0Mlr3+0/vay69fDe6eSA3zvgRcN+R3lZn1lJQhaakvWPZaIalQEb4vDfXzr6Pvqm9IaifpOV+w7PW8PEvY3lYbfUfjRMBcf63VV9t3Py7pLu+xU5LM7CeSfiPppKQfSXpR0q2StphniT5/JukPks7J8+esdfKsdf27Oup7tKQC73HSPHPNkuroWnWlIX/+1dFbUhN5pgoE+kCeP3XWt192Lqd74tuh9KhfWkO8J1LDvi/V+a7q53097744585K+tAvP1Ia8udfHeH8u6jsXh2QtF+Rv1doyJxzHPX0kPRdSU6eP+n6p7eT589Tf5EU45f+LW/5J/3SXvamLQ9o4z+86SNquc/zJD0laYKk8fL8qeycpN2SWkX6M73cP/+AazzivUZakLxbvXnfDJL3pDev3t2vhn5P/K61TJ65slc39HvSUO9Ldb+rJE33Xr9bkDZ+J+k4n/9F979Wvqu899BJahKk7AeS/hHpe8XRcA9GmBum4ZKaSvqFc67iiW7n3AZJn0gaHaTOLyo5D1a2xpxzM51zM5xzrzrnXnPOPSjPPM0rJf2wNq8VQfX28w9DvPe1JEjemYAyDUGDuSdmNlHS9yU9487fYOlyuydSPb4vYXxXVXVf6vM9qbeffxjC+XcRL8+89NJKytbne4V6joC5YUrzvmYHyftE0teCpOf4nzjnDks6XklZSZKZRZtZh4Aj7C8c59xvJeVJ+ma4deupNO9rg/j8K+GbO9g0SF5cQJmGIM37Wq/viXlWW1gq6b/l+fO4v8vtnkgN5L74XSvYd1VV96U+35M072uD+PwrEc6/i2JPd4IuIVff7xXqOQJmhJIqzzJY/kdNH3D5UlLbWupXY1Gbn3+gg97XjkHykiWdlmfOI85X43tiZtdJypK0XdK4IKNg3JOaq8vvqqruy4EaXudyUl++q7hXqDMsK1e/VbYN417va7o8owT+ekr6PEidHpL+4Tsxs0R51rIMVtYn2KjwP0OUD8r7QMbXgvS1vrssPv9K7JBnvua1kpYH5A2UZ65ffdwGtEHeEzO7Rp51YfdK+pZz7nSQYg31nkgN9L4EquS7arv39VpJ7/qVjZVn/eM3wr1OHbgsPv9KhPPvwv9efeErZGYdJXWSZ542UCOMMNdvvv9UWwekvynPfK4fmlnFLz1mNkrSVZLWB2nr4UrON1R2cefcGefcnwKOg5WVN7M47xJAgR6U5+GTjZXVraca1OcfDufcSUmbJE0ws4rRNDMbLqm7pNdq4zp1oMHdEzPrLs9nfUyeB5eOVdJ2Q70nUgO7L+F8VznndsoTbP7AzPynBUyU1EL14740qM8/HGH+u3hbnpVnHghYUeZ+72t9uFdooBhhrt+2yzNyMNP7RVEi6S3n3CHv2pvzJP3ZzF6XlCLpIXl2eVoQ0E65pH5m9p+S/ixpgDwPt2xyzv2xFvvbQdL/mtkr8syZOydpqDxPoH+ohrfpQkP7/GVmXeRZUkqSrve+PmiehftPOOf878FPJb0n6S9m9rw8/9lOl2dkaGlt9qsWNah7YmZXyBO0JEp6TtKNASvD5Tvn3vQ7b4j3RGpg90Xhf1dNkydgfMvMlsszNzjT28dgQeel1tA+/zr5rnLOnTGzmfIss7fezN6Q568A90t6MeAhWyA8kV6mgyP0Ic+Ix+fyfKE7ScP88r4v6SN5vhyPSFopqVNA/Ze9dTvJs6bmKXlGun4t6Ypa7mtreRbzz5FnQf8S789PSGoR6c/ycv/8vdcb5u1nsGNvkPL/Is+uWKe9/VopKSnSn/vlck/kCawqux9O0juXwz1pgPcl7O8qebZy/ps8qy3kyxNU1/q/4cbw+XuvV2ffVfJsd/6x9/1+KelnCrLUHAdHOIc5V1+nxKE2mNnLku50zvHXhAjg869/uCf1E/clsvj8gdCYwwwAAACEQMAMAAAAhEDADAAAAITAHGYAAAAgBEaYAQAAgBAImAEAAIAQCJgBAACAEAiYAQAAgBAImAE0aGbmwjy+G6F+vlzZ9c0sxsx+483fbmZJEegiAKAS7OgDoKF7LEjaw5JaSXpG0omAvA/rtjvhMbN4Sb+TNFrSnyV92zl3KrK9AgD4I2AG0KA55+YGpnlHcVtJ+oVzbu8l7lK1mVkbSb+XdJ2kNZImOufORrZXAIBATMkA0KiY2W1m9hczKzCzYjPbYWYzzaxpkLJ7vUcrM/ulmeWa2Rkz+8TMHjIzu4h+pEr6qzzB8mJJdxAsA0D9RMAMoNEwsyfkGcntKWm1pF9KMklPSPpvM4sNUi1W0p8kjZD0qqSlklrLM93jlzXsRy9JWyX1kvRT59wPHbtIAUC9RcAMoFEws+skzZT0paTezrkpzrkZkvrIMy3ieknTg1RNllQiKcM5N805N1VShqTPJN1vZkPD7Mp1kjZL6iDpe865eTV5PwCAS4eAGUBjMcn7+u/OuTxfonPunKRpksolTa6k7kznXIlfnWOSHveefi/MftwjKUHSY865l8OsCwCIAAJmAI1FP+/rW4EZzrldkvZL+pqZtQrIPifP9IlA73hf+4bZjzflCc5nmtmNYdYFAEQAATOAxsIXCB+sJN+X3jog/YhzrixIed8odWCAXZXVkiZKairp92b2/8KsDwC4xAiYATQWBd7XDpXkJweU82lnZtFByvvaCSxfJefcKkkTJEVLyjKzW8JtAwBw6RAwA2gs/uF9HRaYYWZXSuok6XPn3ImA7BhJg4O052vnH0HyquSce13SLZKcpDVmdkdN2gEA1D0CZgCNxYve10fMLNGX6B09XijP9+FvKqk7z3+dZu+GI494T1+qaYecc7+XZ4e/s5JWmlm4DxACAC4BdvoD0Cg457aa2ZOSfiTpYzN7XdJpSaPkWSZus6SnglQ9KM9844/N7L8kNZF0qzxTOJ53zv3lIvv1JzMbKWmDpN+YWbxz7vmLaRMAULsYYQbQaDjnfizpDkm75Xnw7iF5vgcfkfTNSnbaOytpuKRN8sw7vleeecs/lPRgLfXrr95rnJD0nJlNq412AQC1w9hcCgCCM7O9kuScS4tsTwAAkcQIMwAAABACATMAAAAQAgEzAAAAEAJzmAEAAIAQGGEGAAAAQiBgBgAAAEIgYAYAAABCIGAGAAAAQiBgBgAAAEIgYAYAAABC+P8BzL/DtWfSUMYAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}