SSL+OOD.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": [
        "kXsHiy9rxnim",
        "3-56TiGtxxbP",
        "QDTDwrm3xvEH",
        "tAr87gKMxt-i",
        "CiGOAbWHym3q",
        "MnUvZkR2x4cq",
        "6SuQsZqdyDLN",
        "REfGcOB2yHDy",
        "ZKKH7LkAyYEC",
        "qvXR5mfJ7xLK",
        "RjMvT6R1y2SG",
        "l9baRjorzLC7",
        "IoDavBeH65mE",
        "kN0ZvgII68iT"
      ],
      "gpuType": "T4"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Imports"
      ],
      "metadata": {
        "id": "kXsHiy9rxnim"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Common"
      ],
      "metadata": {
        "id": "3-56TiGtxxbP"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KhbGoT8Exd_W",
        "outputId": "7872378a-7dc0-4951-fbf3-9360d122956d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting prv-accountant\n",
            "  Downloading prv_accountant-0.2.0-py3-none-any.whl (21 kB)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from prv-accountant) (1.23.5)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from prv-accountant) (1.10.1)\n",
            "Installing collected packages: prv-accountant\n",
            "Successfully installed prv-accountant-0.2.0\n"
          ]
        }
      ],
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torchvision\n",
        "from torch.utils.data import random_split, Dataset, DataLoader, ConcatDataset, Subset, TensorDataset\n",
        "from sklearn.model_selection import train_test_split\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from PIL import Image\n",
        "from tqdm import tqdm\n",
        "!pip install prv-accountant\n",
        "\n",
        "from IPython.display import clear_output"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## HF"
      ],
      "metadata": {
        "id": "QDTDwrm3xvEH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install datasets\n",
        "!pip install transformers\n",
        "!pip install timm\n",
        "\n",
        "import transformers\n",
        "import datasets\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cj5ElM-gxplv",
        "outputId": "e4b88729-f910-48d5-d55d-4ec96d053f97"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting datasets\n",
            "  Downloading datasets-2.14.5-py3-none-any.whl (519 kB)\n",
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            "Collecting xxhash (from datasets)\n",
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            "Installing collected packages: xxhash, dill, multiprocess, huggingface-hub, datasets\n",
            "Successfully installed datasets-2.14.5 dill-0.3.7 huggingface-hub-0.16.4 multiprocess-0.70.15 xxhash-3.3.0\n",
            "Collecting transformers\n",
            "  Downloading transformers-4.33.1-py3-none-any.whl (7.6 MB)\n",
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            "Collecting tokenizers!=0.11.3,<0.14,>=0.11.1 (from transformers)\n",
            "  Downloading tokenizers-0.13.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.8 MB)\n",
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            "\u001b[?25hCollecting safetensors>=0.3.1 (from transformers)\n",
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            "Installing collected packages: tokenizers, safetensors, transformers\n",
            "Successfully installed safetensors-0.3.3 tokenizers-0.13.3 transformers-4.33.1\n",
            "Collecting timm\n",
            "  Downloading timm-0.9.7-py3-none-any.whl (2.2 MB)\n",
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            "Installing collected packages: timm\n",
            "Successfully installed timm-0.9.7\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## drive"
      ],
      "metadata": {
        "id": "tAr87gKMxt-i"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4ohdb6RxxrlT",
        "outputId": "dad2d9f8-59cc-49d2-9cec-2f835160a2c6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Utils"
      ],
      "metadata": {
        "id": "SUxXIay0xyvl"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Deep Hypersphere"
      ],
      "metadata": {
        "id": "CiGOAbWHym3q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# model\n",
        "\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "\n",
        "\n",
        "class CentroidClassifier(nn.Module):\n",
        "    def __init__(self, embedding_size, class_number):\n",
        "        super().__init__()\n",
        "        self.embedding_projection = nn.Linear(embedding_size, embedding_size)\n",
        "        self.classifier = nn.Linear(embedding_size, class_number)\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = F.normalize(x)\n",
        "        x = self.embedding_projection(x)\n",
        "        x = F.relu(x)\n",
        "        x = F.normalize(x)\n",
        "        cls = self.classifier(x)\n",
        "        return {'cls' : cls, 'embeddings': x}\n",
        "\n",
        "\n",
        "class NormalClassifier(nn.Module):\n",
        "    def __init__(self, embedding_size, class_number):\n",
        "        super().__init__()\n",
        "        self.classifier = nn.Linear(embedding_size, class_number)\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = F.dropout(x, p=0.5, training = self.training)\n",
        "        x = self.classifier(x)\n",
        "        cls = x\n",
        "        return {'cls' : cls, 'embeddings': x}\n",
        "\n"
      ],
      "metadata": {
        "id": "n6djRtDdyr5x"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Deep Models"
      ],
      "metadata": {
        "id": "MnUvZkR2x4cq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class ModelExperiment():\n",
        "  def __init__(self, model_name = 'dino_vitb16'):\n",
        "    self.model_name = model_name\n",
        "    self.models = dict()\n",
        "\n",
        "    self.download()\n",
        "\n",
        "  def download(self):\n",
        "    models = dict()\n",
        "\n",
        "    match self.model_name:\n",
        "\n",
        "      # Vision Models\n",
        "      case 'resnet50':\n",
        "        models['model'] = torchvision.models.resnet50(weights = 'DEFAULT')\n",
        "\n",
        "      case 'resnet152':\n",
        "        models['model'] = torchvision.models.resnet152(weights = 'DEFAULT')\n",
        "\n",
        "      case 'dino_vitb16':\n",
        "        models['model'] = transformers.ViTModel.from_pretrained('facebook/dino-vitb16')\n",
        "\n",
        "      case 'msn_vitb16':\n",
        "        models['model'] = transformers.ViTMSNModel.from_pretrained('facebook/vit-msn-base')\n",
        "\n",
        "      case 'vgg19':\n",
        "        models['model'] = torchvision.models.vgg19(weights = 'DEFAULT')\n",
        "\n",
        "      # NLP Models\n",
        "      case 'data2vec':\n",
        "        hf_path = 'facebook/data2vec-text-base'\n",
        "        models['autotokenizer'] = transformers.AutoTokenizer.from_pretrained(hf_path)\n",
        "        models['model'] = transformers.AutoModel.from_pretrained(hf_path)\n",
        "\n",
        "      case 'unsup_simcse_roberta_base':\n",
        "        hf_path = 'princeton-nlp/unsup-simcse-roberta-base'\n",
        "        models['autotokenizer'] = transformers.AutoTokenizer.from_pretrained(hf_path)\n",
        "        models['model'] = transformers.AutoModel.from_pretrained(hf_path)\n",
        "\n",
        "      case 'unsup_simcse_roberta_large':\n",
        "        hf_path = 'princeton-nlp/unsup-simcse-roberta-large'\n",
        "        models['autotokenizer'] = transformers.AutoTokenizer.from_pretrained(hf_path)\n",
        "        models['model'] = transformers.AutoModel.from_pretrained(hf_path)\n",
        "\n",
        "      case 'sup_simcse_roberta_base':\n",
        "        hf_path = 'princeton-nlp/sup-simcse-roberta-base'\n",
        "        models['autotokenizer'] = transformers.AutoTokenizer.from_pretrained(hf_path)\n",
        "        models['model'] = transformers.AutoModel.from_pretrained(hf_path)\n",
        "\n",
        "      case 'sup_simcse_roberta_large':\n",
        "        hf_path = 'princeton-nlp/sup-simcse-roberta-large'\n",
        "        models['autotokenizer'] = transformers.AutoTokenizer.from_pretrained(hf_path)\n",
        "        models['model'] = transformers.AutoModel.from_pretrained(hf_path)\n",
        "\n",
        "      case 'sup_simcse_bert_base':\n",
        "        hf_path = 'princeton-nlp/sup-simcse-bert-large-uncased'\n",
        "        models['autotokenizer'] = transformers.AutoTokenizer.from_pretrained(hf_path)\n",
        "        models['model'] = transformers.AutoModel.from_pretrained(hf_path)\n",
        "\n",
        "      case 'sup_simcse_bert_large':\n",
        "        hf_path = 'princeton-nlp/sup-simcse-bert-large-uncased'\n",
        "        models['autotokenizer'] = transformers.AutoTokenizer.from_pretrained(hf_path)\n",
        "        models['model'] = transformers.AutoModel.from_pretrained(hf_path)\n",
        "\n",
        "      case other:\n",
        "        print('Model identifier not found!, possible models: ')\n",
        "        print(self.get_default_models())\n",
        "\n",
        "    self.models = models\n",
        "\n",
        "  def get_model(self):\n",
        "    return self.models\n",
        "\n",
        "\n",
        "  def get_default_models(self):\n",
        "    default_models = dict()\n",
        "    vision_models = ['dino', 'msn', 'resnet (152, 50)', 'simclrv2', 'vgg19']\n",
        "    nlp_models = ['data2vec', 'simcse sup roberta large, base', 'simcse unsup roberta large, base', 'simcse sup bert large, base']\n",
        "    default_models['vision'] = vision_models\n",
        "    default_models['nlp'] = nlp_models\n",
        "    return default_models\n",
        "\n"
      ],
      "metadata": {
        "id": "1y0AR-Q0x0p4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Datasets"
      ],
      "metadata": {
        "id": "XXx4B_XSx-45"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from torchvision import datasets, transforms\n",
        "\n",
        "class DatasetExperiment():\n",
        "  def __init__(self, name = 'cifar10_torch', task_name = None, preprocessing_args = {'type' : None}):\n",
        "    self.name = name\n",
        "    self.task_name = task_name\n",
        "    self.class_descriptions = None\n",
        "\n",
        "    self.transform = self.create_transform(preprocessing_args)\n",
        "    self.download()\n",
        "\n",
        "\n",
        "  def download(self):\n",
        "    # pytorch\n",
        "    match self.name:\n",
        "      case 'cifar10_torch':\n",
        "        self.train_data = torchvision.datasets.CIFAR10(root='./data', train=True,\n",
        "                                          download=True, transform=self.transform)\n",
        "        self.test_data = torchvision.datasets.CIFAR10(root='./data', train=False,\n",
        "                                              download=True, transform=self.transform)\n",
        "\n",
        "        self.classes = ('plane', 'car', 'bird', 'cat',\n",
        "            'deer', 'dog', 'frog', 'horse', 'ship', 'truck')\n",
        "\n",
        "      # simclrv2\n",
        "      case 'cifar10_tf':\n",
        "        (xtrain, ytrain), (xtest, ytest) = tf.keras.datasets.cifar10.load_data()\n",
        "        xtrain = xtrain.astype(np.float32) / 255.0\n",
        "        xtest = xtest.astype(np.float32) / 255.0\n",
        "        y_train = ytrain.reshape(-1)\n",
        "        y_test = ytest.reshape(-1)\n",
        "\n",
        "      case 'mnist_torch':\n",
        "        self.train_data = torchvision.datasets.MNIST(root='./data', train=True,\n",
        "                                          download=True, transform=self.transform)\n",
        "        self.test_data = torchvision.datasets.MNIST(root='./data', train=False,\n",
        "                                              download=True, transform=self.transform)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "      case 'svhn_torch':\n",
        "        self.train_data = torchvision.datasets.SVHN(root='./data', split = 'train',\n",
        "                                          download=True, transform=self.transform)\n",
        "        self.test_data = torchvision.datasets.SVHN(root='./data', split = 'test',\n",
        "                                              download=True, transform=self.transform)\n",
        "\n",
        "\n",
        "\n",
        "      # huggingface nlp data\n",
        "      case 'glue':\n",
        "        task = self.task_name\n",
        "        dataset = datasets.load_dataset('glue', task)\n",
        "        train_dataset = dataset['train']\n",
        "        test_dataset = dataset['validation']\n",
        "        train_dataset.set_format(type = 'torch')\n",
        "        test_dataset.set_format(type = 'torch')\n",
        "\n",
        "\n",
        "      case other:\n",
        "        print('Dataset identifier not found!')\n",
        "\n",
        "\n",
        "  # preprocess data\n",
        "  # -- preprocessing arguments --\n",
        "  # type : 'imagenet' (normal without resize), 'imagenet_resize' (normal with resize for transformers)\n",
        "  # size : 224 for vit-b/16 transformers\n",
        "  def create_transform(self, preprocessing_args):\n",
        "    transform = None\n",
        "\n",
        "    preprocessing_type = preprocessing_args['type']\n",
        "\n",
        "    grayscale_transform = None\n",
        "    if self.name in ['mnist_torch', 'fashion_mnist_torch']:\n",
        "      grayscale_transform = transforms.Grayscale(3)\n",
        "\n",
        "    resize_transform = None\n",
        "    if preprocessing_type.endswith('resize'):\n",
        "      size = preprocessing_args['resize_size']\n",
        "      resize_transform = transforms.Resize(size)\n",
        "\n",
        "    match preprocessing_type:\n",
        "\n",
        "      # imagenet pretrained\n",
        "      case 'imagenet' | 'imagenet_resize':\n",
        "        transforms_list = [   grayscale_transform,\n",
        "            resize_transform,\n",
        "            transforms.ToTensor(),\n",
        "        transforms.Normalize((0.5, 0.5, 0.5), (0.2, 0.2, 0.2))]\n",
        "\n",
        "        transforms_composed = transforms.Compose([transform for transform in transforms_list if transform is not None])\n",
        "\n",
        "      ## TODO\n",
        "      case 'imagenet_simclr':\n",
        "        pass\n",
        "\n",
        "      case None:\n",
        "        pass\n",
        "\n",
        "      case other:\n",
        "        print('Preprocessing pipeline identifier not found!')\n",
        "\n",
        "    return transforms_composed\n",
        "\n",
        "\n",
        "\n",
        "  # return dataloaders for train and test data.\n",
        "  # -- dataloader args --\n",
        "  # batch_size\n",
        "  def get_dataloaders(self, dataloader_args = dict()):\n",
        "    train_dataloader = DataLoader(self.train_data, **dataloader_args)\n",
        "    test_dataloader = DataLoader(self.test_data, **dataloader_args)\n",
        "\n",
        "    return train_dataloader, test_dataloader\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "# stores features\n",
        "def store_features(features, root, experiment_identifier = '_'):\n",
        "\n",
        "  parent_path = root\n",
        "\n",
        "  if not root.endswith('/'):\n",
        "    parent_path += '/'\n",
        "\n",
        "  for key in features.keys():\n",
        "    path = parent_path + experiment_identifier + '_' + key\n",
        "    np.save(path, features[key])\n",
        "\n",
        "\n",
        "# loads features as a dictionary\n",
        "def load_features(root, experiment_identifier, keys):\n",
        "  features = dict()\n",
        "\n",
        "  if root.endswith('/'):\n",
        "    parent_path = root + experiment_identifier + '_'\n",
        "\n",
        "  else:\n",
        "    parent_path = root + '/' + experiment_identifier + '_'\n",
        "\n",
        "  for key in keys:\n",
        "    path = parent_path + key + \".npy\"\n",
        "    features[key] = np.load(path)\n",
        "\n",
        "  return features\n",
        "\n",
        "\n",
        "\n",
        "def get_dataloader_from_xy(X, y, dataloader_args):\n",
        "    dataset = TensorDataset(torch.from_numpy(X), torch.from_numpy(y))\n",
        "    dataloader = DataLoader(dataset, **dataloader_args)\n",
        "\n",
        "    return dataloader\n"
      ],
      "metadata": {
        "id": "PXYIIPkdx_oJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Feature Extraction"
      ],
      "metadata": {
        "id": "6SuQsZqdyDLN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from tqdm import tqdm\n",
        "\n",
        "class ImageFeatureExtractor_Torch():\n",
        "  def __init__(self, model, model_name):\n",
        "    self.model = model\n",
        "    self.model_name = model_name\n",
        "\n",
        "    # a dict to store the activations\n",
        "    self.activation = None\n",
        "    self.hook = None\n",
        "    self.hook_models = ['resnet50', 'resnet152', 'vgg19']\n",
        "\n",
        "  # extract features\n",
        "  def extract(self, dataloader, device, verbose = True):\n",
        "\n",
        "    # possibly register hook. the function checks model name to make sure a hook is needed\n",
        "    self.register_hook()\n",
        "\n",
        "\n",
        "    # initializations\n",
        "    X, y = [], []\n",
        "    self.model.eval()\n",
        "\n",
        "    # feature extraction loop\n",
        "    with torch.no_grad():\n",
        "      # train features\n",
        "      print(\"###Extracting features###\")\n",
        "\n",
        "      if verbose:\n",
        "        dataloader_iterator = enumerate(tqdm(dataloader))\n",
        "\n",
        "      else:\n",
        "        dataloader_iterator = enumerate(dataloader)\n",
        "\n",
        "      for i, (input, target) in dataloader_iterator:\n",
        "\n",
        "        outputs = self.model(input.to(device))\n",
        "        embeddings = self.get_embeddings(outputs)\n",
        "        X.append(embeddings.cpu().numpy().squeeze())\n",
        "        y.append(target.cpu().numpy())\n",
        "\n",
        "    print(\"Done\")\n",
        "\n",
        "\n",
        "    # numpyify the features!\n",
        "    X = np.concatenate(X)\n",
        "    y = np.concatenate(y)\n",
        "\n",
        "    if self.hook:\n",
        "      self.hook.remove()\n",
        "\n",
        "    return X, y\n",
        "\n",
        "\n",
        "  # TODO\n",
        "  def get_embeddings(self, outputs):\n",
        "\n",
        "    match self.model_name:\n",
        "      case 'dino_vitb16':\n",
        "        ## DINO ViTb16\n",
        "        embeddings = outputs.last_hidden_state[:,0]\n",
        "\n",
        "      case 'msn_vitb16':\n",
        "        embeddings = outputs.last_hidden_state[:,0]\n",
        "\n",
        "      case 'resnet50' | 'resnet152' | 'vgg19':\n",
        "        embeddings = self.activation['feats']\n",
        "\n",
        "      case 'deep_centroids':\n",
        "        embeddings = outputs['embeddings']\n",
        "\n",
        "      case other:\n",
        "        print(\"embeddings not found for model!\")\n",
        "        return\n",
        "\n",
        "    return embeddings\n",
        "\n",
        "\n",
        "\n",
        "  # torch method for restoring activations\n",
        "  def getActivation(self, name):\n",
        "    self.activation = dict()\n",
        "    # the hook signature\n",
        "    def hook(model, input, output):\n",
        "      self.activation[name] = output.detach()\n",
        "    return hook\n",
        "\n",
        "\n",
        "  # register hook\n",
        "  def register_hook(self):\n",
        "\n",
        "    if self.model_name in self.hook_models:\n",
        "      match self.model_name:\n",
        "        case 'resnet50' | 'resnet152':\n",
        "          final_embedding_layer = model.avgpool\n",
        "        case 'vgg19':\n",
        "          final_embedding_layer = model.classifier[0]\n",
        "        case other:\n",
        "            print(\"error while registering hook!\")\n",
        "\n",
        "      # register forward hooks on the final embedding layer\n",
        "      self.hook = final_embedding_layer.register_forward_hook(self.getActivation('feats'))\n",
        "\n",
        "    else:\n",
        "      return\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "  class SentenceClassificationFeatureExtractor_HF():\n",
        "    def __init__(self, model, model_name, tokenizer):\n",
        "      self.model = model\n",
        "      self.model_name = model_name\n",
        "      self.tokenizer = tokenizer\n",
        "      self.embedding_type = None\n",
        "\n",
        "      match model_name:\n",
        "        case 'simcse_roberta':\n",
        "          self.embedding_type = 'last_hidden_states'\n",
        "        case other:\n",
        "          print('model name not defined')\n",
        "          return\n",
        "\n",
        "    def extract(self, dataloader, device, dataset_name, task_name):\n",
        "      # find correct keys to access text features using dataset and task name\n",
        "      input_id, output_id, idx_id = None, None, None\n",
        "\n",
        "      match dataset_name:\n",
        "        case 'glue':\n",
        "\n",
        "          match task_name:\n",
        "            case 'sst2':\n",
        "              input_id = 'sentence'\n",
        "              output_id = 'label'\n",
        "              idx_id = 'idx'\n",
        "\n",
        "            case other:\n",
        "              print('task identifier not found')\n",
        "              return\n",
        "\n",
        "        case other:\n",
        "          print('dataset identifier not found')\n",
        "          return\n",
        "\n",
        "\n",
        "      X, y, idx = [], [], []\n",
        "      dataloader_iterable = enumerate(tqdm(dataloader))\n",
        "\n",
        "      self.model.eval()\n",
        "      with torch.no_grad():\n",
        "        print(\"Extracting embeddings!\")\n",
        "\n",
        "        for input, features in dataloader_iterable:\n",
        "          idx.append((features[idx_id]).cpu().numpy())\n",
        "          y.append(features[output_id].cpu().numpy())\n",
        "\n",
        "          sentences = features[input_id]\n",
        "          inputs = self.tokenizer(sentences, padding=True, truncation=True, return_tensors=\"pt\").to(device)\n",
        "\n",
        "          match self.embedding_type:\n",
        "            case 'last_hidden_states':\n",
        "              embeddings = self.model(**inputs, output_hidden_states=True, return_dict=True).last_hidden_state[:,0].to(device)\n",
        "\n",
        "            case 'pooler_output':\n",
        "              embeddings = model(**inputs, output_hidden_states=True, return_dict=True).pooler_output.to(device)\n",
        "\n",
        "            case other:\n",
        "              print('embedding type not defined')\n",
        "              return\n",
        "\n",
        "          X.append(embeddings.cpu().numpy())\n",
        "\n",
        "      print('Done')\n",
        "      return np.concatenate(X), np.concatenate(y), np.concatenate(idx)\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "eBgMTfVjyC3F"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Evaluations"
      ],
      "metadata": {
        "id": "REfGcOB2yHDy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "sns.set_theme()\n",
        "\n",
        "from tqdm import tqdm\n",
        "\n",
        "\n",
        "\n",
        "def plot_dict(y_dict = dict(), x = [], title='', ylabel = 'Accuracy', xlabel = 'Epsilon'):\n",
        "\n",
        "  keys = y_dict.keys()\n",
        "  legends = []\n",
        "  for key in keys:\n",
        "    plt.scatter(x, y_dict[key])\n",
        "    legends.append(key)\n",
        "\n",
        "  plt.legend(legends)\n",
        "  plt.xlabel(xlabel)\n",
        "  plt.ylabel(ylabel)\n",
        "  plt.title(title)\n",
        "\n",
        "\n",
        "def plot_hist_dict(data_dict = dict(), title = '', histogram_args = dict()):\n",
        "  for key in data_dict.keys():\n",
        "    plt.hist(data_dict[key], **histogram_args)\n",
        "\n",
        "  plt.title(title)\n",
        "  plt.legend(data_dict.keys())\n",
        "\n",
        "\n",
        "def sample_dot_products(X, y, iterations):\n",
        "\n",
        "  from tqdm import tqdm\n",
        "\n",
        "  intra_class_dot_products = []\n",
        "  inter_class_dot_products = []\n",
        "\n",
        "  classes = np.unique(y)\n",
        "  n_classes = len(classes)\n",
        "\n",
        "  class_features = []\n",
        "  features_per_class_count = []\n",
        "\n",
        "  for i in range(n_classes):\n",
        "    class_features.append(X[np.nonzero(y == classes[i])])\n",
        "    features_per_class_count.append(class_features[i].shape[0])\n",
        "\n",
        "\n",
        "\n",
        "  print('Sampling Intra Class Dot Products!')\n",
        "\n",
        "  for i in tqdm(range(iterations)):\n",
        "\n",
        "    #choose a class\n",
        "    sampled_class = np.random.randint(n_classes)\n",
        "\n",
        "    #sample embeddings\n",
        "    emb1 =  class_features[sampled_class][np.random.randint(features_per_class_count[sampled_class]),:]\n",
        "    emb2 =  class_features[sampled_class][np.random.randint(features_per_class_count[sampled_class]),:]\n",
        "\n",
        "    #normalize embeddings\n",
        "    emb1 /= np.linalg.norm(emb1)\n",
        "    emb2 /= np.linalg.norm(emb2)\n",
        "\n",
        "    intra_class_dot_products.append(np.dot(emb1, emb2))\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "  print('Sampling Inter Class Dot Products!')\n",
        "\n",
        "  for i in tqdm(range(iterations)):\n",
        "\n",
        "    #choose two different classes\n",
        "    sampled_classes = np.random.choice(n_classes, size = 2, replace = False)\n",
        "\n",
        "    #sample embeddings\n",
        "    emb1 =  class_features[sampled_classes[0]][np.random.randint(features_per_class_count[sampled_classes[0]]),:]\n",
        "    emb2 =  class_features[sampled_classes[1]][np.random.randint(features_per_class_count[sampled_classes[1]]),:]\n",
        "\n",
        "    #normalize embeddings\n",
        "    emb1 /= np.linalg.norm(emb1)\n",
        "    emb2 /= np.linalg.norm(emb2)\n",
        "\n",
        "    inter_class_dot_products.append(np.dot(emb1, emb2))\n",
        "\n",
        "  dot_products = {'intra class': intra_class_dot_products, 'inter class': inter_class_dot_products}\n",
        "\n",
        "  return dot_products\n",
        "\n",
        "\n",
        "def sample_per_class_dot_products(X, y, iterations, class_names):\n",
        "  from sklearn.preprocessing import normalize\n",
        "  from tqdm import tqdm\n",
        "\n",
        "  classes = np.unique(y)\n",
        "  n_classes = len(classes)\n",
        "\n",
        "  class_features = []\n",
        "  features_per_class_count = []\n",
        "\n",
        "  for i in range(n_classes):\n",
        "    class_features.append(X[np.nonzero(y == classes[i])])\n",
        "    features_per_class_count.append(class_features[i].shape[0])\n",
        "\n",
        "\n",
        "\n",
        "  print('Sampling Per Class Dot Products')\n",
        "\n",
        "  dot_products_per_class = dict()\n",
        "\n",
        "  for i in tqdm(range(n_classes)):\n",
        "    class_centroid = np.sum(X[y == classes[i]], axis=0)\n",
        "    class_centroid = normalize(class_centroid.reshape(1,-1))\n",
        "    class_dot_products = []\n",
        "\n",
        "    for j in range(iterations):\n",
        "\n",
        "      #sample embeddings\n",
        "      emb_sample = class_features[i][np.random.randint(features_per_class_count[i]),:]\n",
        "\n",
        "      # normalize sample\n",
        "      emb_sample = normalize(emb_sample.reshape(1,-1))\n",
        "\n",
        "      class_dot_products.append(emb_sample@class_centroid.T)\n",
        "\n",
        "    dot_products_per_class[class_names[classes[i]]] = np.array(class_dot_products).squeeze()\n",
        "\n",
        "  return dot_products_per_class\n",
        "\n",
        "\n",
        "def plot_dot_products(X, y, iterations_full, iterations_per_class, class_names = dict(),\n",
        "                      histogram_args = dict(), suptitle = 'Distribution of Dot Products'):\n",
        "\n",
        "  dot_products_per_class = sample_per_class_dot_products(X, y, iterations_per_class, class_names = class_names)\n",
        "  dot_products = sample_dot_products(X, y, iterations_full)\n",
        "\n",
        "\n",
        "  plt.figure(figsize=(14,6))\n",
        "  plt.suptitle(suptitle)\n",
        "\n",
        "  plt.subplot(1,2,1)\n",
        "  plot_hist_dict(dot_products, title = 'Overall', histogram_args=histogram_args)\n",
        "\n",
        "  plt.subplot(1,2,2)\n",
        "  plot_hist_dict(dot_products_per_class, title='Per Class', histogram_args=histogram_args)\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "DNe5Y73tyHtm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Trainer"
      ],
      "metadata": {
        "id": "ZKKH7LkAyYEC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def training_loop(model, train_dataloader, test_dataloader, num_epochs, optimizer, criterion, device):\n",
        "\n",
        "  from tqdm import tqdm\n",
        "\n",
        "  model.train()\n",
        "  training_loss = []\n",
        "  print('Training')\n",
        "  for epoch in tqdm(range(num_epochs)):\n",
        "\n",
        "    epoch_loss = 0\n",
        "    count_batches = 0\n",
        "    for i, (input, target) in enumerate(train_dataloader):\n",
        "      input = input.to(device).to(torch.float32)\n",
        "\n",
        "      #compute output\n",
        "      output = model(input)\n",
        "      output = output['cls']\n",
        "      target = target.type(torch.LongTensor).to(device)\n",
        "\n",
        "      loss = criterion(output, target)\n",
        "      count_batches += 1\n",
        "      epoch_loss += loss.item()\n",
        "\n",
        "      # backpropagate\n",
        "      optimizer.zero_grad()\n",
        "      loss.backward()\n",
        "      optimizer.step()\n",
        "\n",
        "    epoch_loss /= count_batches\n",
        "    training_loss.append(epoch_loss)\n",
        "\n",
        "  with torch.no_grad():\n",
        "    correct = 0\n",
        "    total = 0\n",
        "    for i, (input, target) in enumerate(test_dataloader):\n",
        "      input = input.to(device).to(torch.float32)\n",
        "\n",
        "      # compute output\n",
        "      output = model(input)\n",
        "      output = output['cls']\n",
        "      target = target.type(torch.LongTensor).to(device)\n",
        "\n",
        "      loss = criterion(output, target)\n",
        "      total += target.size(0)\n",
        "      _, predicted = torch.max(output.data, 1)\n",
        "      correct += (predicted == target).sum().item()\n",
        "\n",
        "    accuracy = correct/total\n",
        "    print(f'\\n Accuracy on the test images: {accuracy}')\n",
        "\n",
        "\n",
        "  return training_loss\n"
      ],
      "metadata": {
        "id": "YqwQEpSJyZNg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## OOD Score"
      ],
      "metadata": {
        "id": "qvXR5mfJ7xLK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import normalize\n",
        "from scipy.special import softmax\n",
        "\n",
        "\n",
        "class CentroidOOD():\n",
        "  def __init__(self):\n",
        "    self.centroids = None\n",
        "    self.classes = None\n",
        "    self.centroids_labels = None\n",
        "\n",
        "  def fit_labeled(self, X_train, y_train):\n",
        "\n",
        "    self.classes = np.unique(y_train)\n",
        "    centroids = []\n",
        "    centroids_labels = []\n",
        "\n",
        "    for c in self.classes:\n",
        "      #calculate sum\n",
        "      class_centroid = np.sum(X_train[y_train == c], axis=0)\n",
        "\n",
        "      #perturb class mean\n",
        "      centroids.append(class_centroid)\n",
        "      centroids_labels.append(c)\n",
        "\n",
        "    self.centroids_labels = np.array(centroids_labels)\n",
        "    centroids = np.array(centroids)\n",
        "\n",
        "    self.centroids = normalize(centroids)\n",
        "\n",
        "\n",
        "  def predict_prob(self, X_test, apply_softmax = True):\n",
        "    n_samples = X_test.shape[0]\n",
        "    y_test_probs = np.zeros((n_samples, len(self.classes)))\n",
        "\n",
        "\n",
        "    for i in range(n_samples):\n",
        "      #calculate distance from centroids\n",
        "      dists = -X_test[i,:]@(self.centroids.T)\n",
        "\n",
        "\n",
        "      #use softmax to transform dists to probabilities\n",
        "      if apply_softmax:\n",
        "        y_test_probs[i,:] = 1 - softmax(dists)\n",
        "      else:\n",
        "        y_test_probs[i,:] = dists/np.sum(dists)\n",
        "\n",
        "    return y_test_probs\n",
        "\n",
        "\n",
        "  def predict_ood(self, X_test, apply_softmax = True):\n",
        "    from scipy.stats import entropy\n",
        "    centroids = self.centroids\n",
        "    classes = self.classes\n",
        "\n",
        "\n",
        "    probs = self.predict_prob(X_test, apply_softmax)\n",
        "    ents = entropy(probs, axis=1)\n",
        "\n",
        "    return ents\n",
        "\n",
        "  # y_test are 0-1 labels corresponding to ood and id\n",
        "  def score_ood_f1(self, X_test, y_test, ood_threshold = 0.5):\n",
        "    scores = self.predict_ood(X_test)\n",
        "    print(scores.shape)\n",
        "\n"
      ],
      "metadata": {
        "id": "_bK1t9Nt72g1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Experiments"
      ],
      "metadata": {
        "id": "zWBcHRDqyPQZ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Experiment loop"
      ],
      "metadata": {
        "id": "RjMvT6R1y2SG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# experiment loop\n",
        "\n",
        "def deep_centroids_experiment(data, dataloader_args, optimizer_args, classifier_type = 'centroids', optimizer_type = 'SGD'):\n",
        "\n",
        "  #data\n",
        "  X_train = data['X_train']\n",
        "  X_test = data['X_test']\n",
        "  y_train = data['y_train']\n",
        "  y_test = data['y_test']\n",
        "\n",
        "\n",
        "  # dataloader\n",
        "  train_dataloader = get_dataloader_from_xy(X_train, y_train, dataloader_args)\n",
        "  test_dataloader = get_dataloader_from_xy(X_test, y_test, dataloader_args)\n",
        "\n",
        "\n",
        "  # classifier\n",
        "  embedding_size = X_train.shape[1]\n",
        "  class_number = len(np.unique(y_train))\n",
        "\n",
        "\n",
        "  match classifier_type:\n",
        "    case 'centroids':\n",
        "      centroid_classifier = CentroidClassifier(embedding_size = embedding_size, class_number = class_number)\n",
        "\n",
        "    case 'linear':\n",
        "      centroid_classifier = NormalClassifier(embedding_size = embedding_size, class_number = class_number)\n",
        "\n",
        "    case other:\n",
        "      print('non default classifier')\n",
        "      return\n",
        "\n",
        "  criterion = nn.CrossEntropyLoss()\n",
        "\n",
        "  match optimizer_type:\n",
        "    case 'SGD':\n",
        "      optimizer = torch.optim.SGD(centroid_classifier.parameters(), **optimizer_args)\n",
        "\n",
        "    case 'Adam':\n",
        "      optimizer = torch.optim.Adam(centroid_classifier.parameters(), **optimizer_args)\n",
        "\n",
        "    case other:\n",
        "      print('non default optimizer')\n",
        "      return\n",
        "\n",
        "  return train_dataloader, test_dataloader, centroid_classifier, optimizer, criterion\n",
        "\n"
      ],
      "metadata": {
        "id": "IQCm4e9Yy4Ew"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Dots"
      ],
      "metadata": {
        "id": "TibKiO2JFVD6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### CIFAR-10"
      ],
      "metadata": {
        "id": "IoDavBeH65mE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "root = '/content/drive/MyDrive'\n",
        "dataset_name = 'mnist_torch'\n",
        "model_name = 'dino_vitb16'\n",
        "\n",
        "experiment_identifier = model_name + '_' + dataset_name\n",
        "keys = ['X_train', 'y_train', 'X_test', 'y_test']\n",
        "data = load_features(root, experiment_identifier, keys)"
      ],
      "metadata": {
        "id": "Vkmh_F5T3ly7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# non private projection\n",
        "dataloader_args = {'batch_size': 1000, 'shuffle': True}\n",
        "optimizer_args = {'lr' : 4, 'momentum': 0.9}\n",
        "\n",
        "train_dataloader, test_dataloader, centroid_classifier, optimizer, criterion = deep_centroids_experiment(data, dataloader_args, optimizer_args)\n",
        "\n",
        "\n",
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "\n",
        "centroid_classifier.to(device)\n",
        "num_epochs = 3\n",
        "train_loss = training_loop(centroid_classifier, train_dataloader,\n",
        "                           test_dataloader, num_epochs, optimizer, criterion, device)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8ERbmbtRzL7r",
        "outputId": "58ae3f01-1b01-4b8a-d001-7e0cfd217694"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 3/3 [00:05<00:00,  1.83s/it]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " Accuracy on the test images: 0.9555\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(train_loss)\n",
        "plt.xlabel('epoch')\n",
        "plt.ylabel('training loss')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "id": "sxQdHSwG3wVo",
        "outputId": "b08cc2d2-4bfb-4f8b-82ac-3564f10070a8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# feature extraction\n",
        "feature_extractor = ImageFeatureExtractor_Torch(centroid_classifier, 'deep_centroids')\n",
        "\n",
        "# get train embeddings\n",
        "print('train dataset')\n",
        "X_train, y_train = feature_extractor.extract(train_dataloader, device, verbose = False)\n",
        "\n",
        "\n",
        "X = X_train\n",
        "y = y_train\n",
        "\n",
        "\n",
        "classes = {0: 'plane', 1: 'car', 2: 'bird', 3: 'cat', 4: 'deer', 5: 'dog',\n",
        "           6: 'frog', 7 : 'horse', 8 : 'ship', 9 : 'truck'}\n",
        "\n",
        "iterations_full = 5000\n",
        "iterations_per_class = 10000\n",
        "\n",
        "model_name = 'Centroids Dino ViTb/16'\n",
        "dataset_name = 'MNIST'\n",
        "suptitle = f'Distribution of Dot Products in {model_name} Emebeddings ({dataset_name})'\n",
        "histogram_args = {'bins' : 100, 'density' : True, 'alpha' : 0.6}\n",
        "\n",
        "plot_dot_products(X, y, iterations_full = iterations_full, iterations_per_class = iterations_per_class,\n",
        "                  class_names = classes, histogram_args = histogram_args, suptitle = suptitle)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 743
        },
        "id": "MzaGvz1r6Vh1",
        "outputId": "96326df3-874d-4f61-9b8a-5b6b9f25900e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "train dataset\n",
            "###Extracting features###\n",
            "Done\n",
            "Sampling Per Class Dot Products\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 10/10 [00:15<00:00,  1.57s/it]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sampling Intra Class Dot Products!\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 5000/5000 [00:00<00:00, 37455.43it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sampling Inter Class Dot Products!\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 5000/5000 [00:00<00:00, 20920.33it/s]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# feature extraction\n",
        "feature_extractor = ImageFeatureExtractor_Torch(centroid_classifier, 'deep_centroids')\n",
        "\n",
        "# get train embeddings\n",
        "print('train dataset')\n",
        "X_train, y_train = feature_extractor.extract(train_dataloader, device, verbose = False)\n",
        "\n",
        "\n",
        "X = X_train\n",
        "y = y_train\n",
        "\n",
        "\n",
        "classes = {0: 'plane', 1: 'car', 2: 'bird', 3: 'cat', 4: 'deer', 5: 'dog',\n",
        "           6: 'frog', 7 : 'horse', 8 : 'ship', 9 : 'truck'}\n",
        "\n",
        "iterations_full = 5000\n",
        "iterations_per_class = 10000\n",
        "\n",
        "model_name = 'Centroids Dino ViTb/16'\n",
        "dataset_name = 'MNIST'\n",
        "suptitle = f'Distribution of Dot Products in {model_name} Emebeddings ({dataset_name})'\n",
        "histogram_args = {'bins' : 100, 'density' : True, 'alpha' : 0.6}\n",
        "\n",
        "plot_dot_products(X, y, iterations_full = iterations_full, iterations_per_class = iterations_per_class,\n",
        "                  class_names = classes, histogram_args = histogram_args, suptitle = suptitle)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 743
        },
        "id": "qis0iQC_zXIM",
        "outputId": "b3eb2b44-fd82-47ee-c35d-fb5f220efa07"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "train dataset\n",
            "###Extracting features###\n",
            "Done\n",
            "Sampling Per Class Dot Products\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 10/10 [00:16<00:00,  1.62s/it]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sampling Intra Class Dot Products!\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 5000/5000 [00:00<00:00, 39093.08it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sampling Inter Class Dot Products!\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 5000/5000 [00:00<00:00, 22640.36it/s]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# feature extraction\n",
        "feature_extractor = ImageFeatureExtractor_Torch(centroid_classifier, 'deep_centroids')\n",
        "\n",
        "# get train embeddings\n",
        "print('train dataset')\n",
        "X_train, y_train = feature_extractor.extract(train_dataloader, device, verbose = False)\n",
        "\n",
        "\n",
        "X = X_train\n",
        "y = y_train\n",
        "\n",
        "\n",
        "classes = {0: 'plane', 1: 'car', 2: 'bird', 3: 'cat', 4: 'deer', 5: 'dog',\n",
        "           6: 'frog', 7 : 'horse', 8 : 'ship', 9 : 'truck'}\n",
        "\n",
        "iterations_full = 5000\n",
        "iterations_per_class = 10000\n",
        "\n",
        "model_name = 'Centroids Dino ViTb/16'\n",
        "dataset_name = 'MNIST'\n",
        "suptitle = f'Distribution of Dot Products in {model_name} Emebeddings ({dataset_name})'\n",
        "histogram_args = {'bins' : 100, 'density' : True, 'alpha' : 0.6}\n",
        "\n",
        "plot_dot_products(X, y, iterations_full = iterations_full, iterations_per_class = iterations_per_class,\n",
        "                  class_names = classes, histogram_args = histogram_args, suptitle = suptitle)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 743
        },
        "id": "IFIfylkb5dkJ",
        "outputId": "623dcb71-25e9-4d21-a0ec-0a80601aa987"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "train dataset\n",
            "###Extracting features###\n",
            "Done\n",
            "Sampling Per Class Dot Products\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 10/10 [00:15<00:00,  1.59s/it]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sampling Intra Class Dot Products!\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 5000/5000 [00:00<00:00, 23031.71it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sampling Inter Class Dot Products!\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 5000/5000 [00:00<00:00, 14518.71it/s]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x600 with 2 Axes>"
            ],
            "image/png": 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rV66gQYMGaNSoEU6dOoXVq1cjOjoac+bM0ci7detWTJ8+HTY2Nvjoo49QqFAhXLx4EatWrcKJEyfw66+/6vXlqXPnzjh58iR27Nih87NE9aVJ9R5vCocPH8aff/6J4OBg+Pv74/bt2zh06BAuXLiAX3/9FZ6enlrrHDlyBD4+Pnnq8XLmzBlER0ejT58+kEgkOHnyJO7duwcrKytUrVpVKzhqKsePH8fJkyfRuHFjdOvWDdeuXcPu3bvx9OlTjB8/Hn379kWtWrXQqVMn3Lt3DydOnMDTp0+xd+9eSCT//U6b22vfkPaXy+Xo06cPbt26hcqVK6Njx45QKpU4ffo0xo8fj/DwcIwdO1adXxRFjBkzBseOHUPp0qXRs2dPpKWlYdeuXbh3757O9jD2Z0pYWBhWr16N6tWro3Pnznj27BkOHz6Mvn374rffftPYv6ioKHTr1g0RERHqoFlkZCRmzJihs+xXr16hU6dOiI+PR8OGDdG8eXOkpKSoj0/Pnj31enzy3LlzAIBatWplm69169aYO3cuduzYoRHAPH78OKKiojBhwgQ8evQo220UKVIE/fr1w08//YTVq1dj1KhROdZPF2dnZwAZbZCYmAhbW9tcbUcfVlZWqFy5Mv7++2/ExcWZvCcs0TtDJPoAeHl5iV5eXtnmSUlJEX18fEQvLy/x8ePH6vSePXtqrevv7y82aNBATExM1NpOVFSUxusmTZqITZo0ybFun3/+uZiWlqa1/MsvvxS9vLzEJ0+eqNOePHmiXm/u3Lka+a9fvy76+PiItWvXFuPi4tTpixcvFr28vMTz589rlaHa3pdffplj2Znpapt9+/aJXl5eYvv27cX4+Hh1ekJCgtihQwfRy8tL3Lt3r842mDx5spienq5ODw8PFytVqiS2bNlSZ/lvi4+PF/39/cWKFSuKly5d0li2YsUK0cvLS+zXr59G+vnz50UvLy9x8eLFepXxdp2zo+ucUiqV4kcffSR6eXmJv//+u0b+AwcOiF5eXmKLFi1EhUKhTs/u2GVHdVx1nX87duwQvby8xOrVq4tJSUmiKP53vOvXr69xDeS23l999ZXo5eUlzp49WyO/6hzV1e7ZXS+62iEqKkqsWbOmWLlyZfHChQta6zx//lzjtZeXl9izZ0+d2+/QoYNYuXJlMTIyUmvZ29d1Vnbt2iV6eXmJu3bt0khv0qSJ6OXlJQ4cOFDd3qIoipGRkWKtWrXEWrVqiampqTluf8+ePaKXl5fYrVs3veqTWc+ePUVvb29x//79Gulv3rwRP/74Y7FKlSri69evtfalUqVK4tmzZzXWmT9/vujl5SWuXLlSI13f94w2bdpotWlurl9d5V25ckX08vISg4ODxZiYGHV6cnKy2KVLF61rQi6Xi97e3mKHDh003n9UoqOjde7L21TnZ07vJQsXLhS9vLzEL774Qp2W1fuQqr06dOigsS8JCQlicHCwWLFiRfHVq1fq9KdPn4qVK1cWa9SoIf7vf//T2Na0adNELy8v8auvvtJrf9LT08X69euL3t7eWu8HKSkpYkBAgOjj46NRvj7vp/q0k+rc8/LyEo8fP66xbP369aKXl5fYu3dvrfXu3bsnenl5icuWLct237y8vMQGDRpkuXzRokWil5eXOH/+fLFNmzbquqj+evToofd7gij+d/3PmjVLXLx4sc6/5ORkrf2vVKmSxvuaQqEQ+/btK3p5eYl+fn5a78WTJk0Svby8xCNHjmik5/baN6T9Vdfi2+8JycnJYv/+/UVvb2/x1q1b6vS9e/eKXl5eYpcuXTT2PSYmRmzatKnO9+rcfKbouk9Rnae63qt//fVX0cvLS5w2bZpGuqpt582bp5F++/ZtsXLlylplb9y4UfTy8hLXr18vvi0hIUHjcyA7devWFWvWrCkqlUqdyzOfy5MnTxYrVaqk8bnXv39/sWbNmmJiYqK4YMECnfusOnZnzpwR4+Pjxbp164rVq1cXX758qc6jum63b9+uc93M21QqlWLHjh1FLy8v8eOPPxY3b94s3rx5U0xJSdFrn0Xxv+Omz33P7NmzRS8vL/HkyZN6b5/I3PFxKqJ/WVpaqn890Kebq0wm0znwm+pXaUNYWFjgyy+/1PlYRHYcHBwwfPhwjbQqVaqgbdu2kMvl2T77bCqqx5/Gjx8POzs7dbqtrS0+//xzAP89n52ZjY0NJk2apNGm5cuXR82aNXH//n0kJCTkWPaxY8cQGxuLVq1aaU0T3r9/f5QsWRJnzpzBs2fPcrVvhtJ1Tl29ehUPHjxAjRo18PHHH2vkb9WqFWrVqoV//vkHV65cMVo95HI5lixZgiVLlmD+/PkYNGgQpkyZAgAYO3YsrK2tNfIPHDhQq7eDofVOS0vDvn37YGdnpzXuheocNYbffvsN8fHx6Natm84eVFmNIZAVmUym8zrMzXWty1dffaXR3oUKFULTpk0RFxeHf/75J8f1X79+DcDw/bpz5w4uXryI5s2bo3Xr1hrLVGMmpaSk4NChQ1rrtmrVSqtLvOpX+xs3bhhUD5XRo0drtamxrl/Vo2xDhw5VX39Axi+2ugb9FAQBoijC0tJSo/eCirEHG1YNwhwdHa33OhMmTNDYF1tbW7Rt2xZKpRJhYWHq9L179yItLQ09e/bUenRi7NixsLOzw++//67XlLxSqRQdO3aEKIpajyocO3YM0dHRaNKkCQoXLqz3fhgqICAATZo00Ujr2bMnSpcujfPnz2vNaKP6zGvWrFmeyo2KigIArFmzBgDwyy+/4OrVq9i7dy/q16+PS5cuYfTo0QZvd+PGjVi6dKnOv5SUFK38rVu31nhfk0gkaNeuHQCgQoUKWu/F7du3B5Bxvavk5drXt/1jYmKwd+9e+Pr6YtCgQRr5rays8Pnnn0MURezbt0+drrpOx44dqzEjpLOzM4YNG6ZVF1N8ptSsWVNrRqWOHTtCJpPh+vXr6rTU1FQcOHAADg4O+OyzzzTyV6xYUd3uurz9+QpkXL+60t+WmpqKyMhIuLm56dXbrUuXLlAoFOrrNSIiAmfPnkXbtm31HvdO1b6JiYnqxz8NJQgCFi9eDH9/f9y5cwczZ85Ehw4dULNmTXTu3BkrV6406nhtbm5uAJBv93ZE7wI+TkWUiajnuBRt27bFpk2b0KpVK7Rs2VLdtTa33ThLliyZq4FvfXx8YG9vr5Xu7++PPXv24NatW3o/hmQst27dgkQi0fmF2s/PD1KpFLdv39ZaVqZMGZ37ovqyKpfLNYJCWZUNZNx4vk0mk8HPzw8RERG4detWvg12+PY5papjVgP1BQQE4MqVK7h16xb8/PyMUoe4uDj1uDpSqRROTk5o2LAhevbsqXN2lcyPROW23g8ePEBSUhJq166t87pQnaN59ddffwEAGjZsmOdttW3bFnPnzkXr1q3RqlUr+Pv7o2bNmkYL4Dg4OKBMmTJa6ZnPcVO5du0aACA+Pl7neCWqgMKDBw+0lvn6+mqlqaa5ffPmTa7qk905ltfrV7UdXe9BtWrV0gq+29vbo0mTJjhx4gTatWuH5s2bq8fHMMWA32IWjzFmR99jkF0bOjk5wcfHB5cuXcKDBw9QsWLFHMvt3LkzVqxYoR4wVdV2qkC8qQcS1fUeKJVKUatWLTx+/Bi3b9/WeMT06NGjKFu2LCpUqJCnclXHSCqVYvny5epHs7y9vbF06VJ89NFHuHjxIq5du2bQo1XHjh0z6DEvXcddNZC3rlnnVAHCFy9eqNPycu3r2/43btyAQqGAIAg6y1DNiJS5DNW9gq7HhHRdu6b4TNHVvhYWFihUqJDG+/E///yD5ORk+Pr66rxPqVWrltaPU0FBQViwYAFmzpyJ06dPo379+qhZsybKly+v97UfGxsLAHqPHVOtWjV4eXlh9+7dGDZsGHbs2AGlUmnwIMWdO3fGpk2bsGfPHvTp0wdeXl4GrQ9kDCi9adMm3L9/H2fOnEFYWBiuX7+u/vv111+xceNGvR6PzYkhP8ASvS8YxCH6V0pKivpmOKcvbZMmTYK7uzt2796NlStXYuXKlZDJZGjYsCEmTpyo84tadnL7S6bq14es0gtiZpq4uDg4OTnpHAhSJpOpxxl5W1Y3KapeEQqFQq+ygazbU5Wu7+CeeaXrnFKVndWMOqaoY8mSJXH8+HG98+s6rwytt+rfrIKTWZ27hlKVY4wppvv16wcXFxds2bIFmzZtwoYNGyAIAvz8/PDFF1+gSpUqedq+Mc5xVTu/fPnSoLJVXwbOnDmDM2fOZJlP12CWuuqtqrOugcr1oesaNdb1m925p3oPetuPP/6IVatWYf/+/eovoVZWVmjRogW+/PJLo52vwH8z4hkSHNR1DFQBlczHQN821DdgWLJkSdStWxenT59GaGgogoKC8PTpU5w9exYlS5ZEgwYN9N6H3MjpMy7zuRAREYGbN2/qNVZUTlRBAl1j69jY2KB+/frYuXMnrl+/btLxcXQFK1THPbtlmaeRzsu1r2/7q8q4ceNGtr3zMveoVd0r6BogOrv3B2N+pmT3nqzrusqqbF3pJUuWxM6dO7FkyRL8+eefOHz4MICM4Gv//v3Ru3fvHOun6q2jq5dWVrp06YJZs2bh1KlT2L17NypXrgwfHx+91wcyzqPPP/8cQ4YMwbx587B69WqD1s+sXLlyGr0C79+/jylTpuDatWuYM2cOfvrpp1xvWyU5ORmA7l5PRO8rBnGI/nXlyhWkp6fDzc0tx1/KpFIp+vbti759+yIqKgpXrlzBgQMHcPDgQfzvf//DgQMH9J4FAzDsF9nMIiMjs03P/IuRqgxdXxSNGTBwcHDAmzdvkJaWpnVzlp6ejpiYGJ2/ZBmrbOC/R07epkrPr4HvdJ1T+tbRVG2kD13no6H1VuXXFbADsj53BUHIcupeXV88VeW8fPkyzzN2ARmPI7Rv3x5yuRzXrl3DkSNHsGvXLgwcOBB//PGH0Xrl5JbqV+uwsDCDBnFU5ZsyZYpeXx5MLS/nWE77nPnce3tATdV70NuPo1lbW2PkyJEYOXIknj9/jkuXLmHPnj3Yu3cvIiIi1LOcGYNqdjVdvZHySrXvkZGROnuj5OY9sGvXrjh9+jS2b9+OoKAg7Ny5E6IoomPHjjofPzOmnD7jMu+H6lGq5s2b57lcDw8Pre1npvryb8iX64KSl2tf3/ZX/du3b19MmjRJ73plda+g6z0gt58pxqD6XMuq7KzSy5Urhx9//BHp6em4c+cOzp49i82bN2P27NmwsbHJsSebo6MjLCws1EEyfbRr1w7z58/HtGnT8PLlS61H7vXVuHFj1KlTB3/++adRZuhTKVeuHObNm4dmzZrh/PnzRtmmqn0K+vOZKD9xTBwiZPySuXz5cgBAmzZtDFq3UKFCaN68ORYtWoSAgAA8fvxYY2YFiUSi1y/suXHr1i2dvW0uXrwIABq/vjg5OQEAnj9/rpU/85gKmalu0A35tb1SpUpQKpW4fPmy1rJLly5BoVAY/KuQIWUD/+1/Zunp6eo6mar8zLI6p7KrI/DfF7zMXeVzcxyMzdB6e3p6wsbGBrdv39YZJMxqO05OToiKitIZyNF1nlavXh0AcOrUqZx3Avpfj46OjmjUqBFmzZqFDh06IDY2FpcuXdKrDFNSTeeckpKi16+jqrFPqlWrBgA6r0tjysu5aqzrV7Vc13auXLmS4/EvXrw4Pv74Y6xZswZlypTBlStXjNZN/9y5c7h69Sqsra3zPG6LLqo2VF2Pmcnlcty+fRtWVlY5TjWcWVBQEAoXLoxTp07h+fPn2L17N6RSKTp16mS0emdF1zWnUCjUY29lnunx6NGjKFKkiFGCY4GBgRAEAffv39d5LoeHhwNAnmbAyi95ufb1bf+qVatCIpEYVIaPjw+USqXO8d90Xbu5/UwxBk9PT1hbW+Pu3bs677lyGsNOJpPB19cXgwcPxoIFCwBkPFqnDy8vL7x+/VrvntWOjo5o0aIFXrx4AVtbW61xkAwxceJECIKA7777zqj3H6pH4/UdwiAnqsf03p75leh9xiAOffCioqIwduxYXLx4ESVKlMCQIUOyzZ+amqrzAzstLU396EzmcRScnZ0RHR2t7u5pTHFxcVi2bJlG2o0bN7Bv3z44ODhofElQ3dju3r1bo6v18+fPtbaRue6AYYPFdezYEQDwww8/ICkpSZ2elJSknlrcVDf/wcHBcHZ2xoEDB9Rjpahs2LABT58+Rd26dU0+Hk5251StWrXg4eGBK1eu4ODBgxrrHTx4EJcvX0bZsmU1xgnIzXEwNkPrbWFhgbZt2yIhIUFrjATVOapLlSpVkJ6erh70UmX37t24evWqVv727dvD3t4eW7du1fmFI/PYEEBGW76dpnL+/HmdN5WqMSPela7aX331Fezt7bFy5UqsXbtW43pWefbsGcaOHaseD6NKlSqoXbs2jhw5ojVIrcrdu3ez/EVZX3k5V411/arGAfv55581fsFOSUlRf4HKLDo6Gnfv3tVKT0xMRGJiImQymc5HPgwhiiIOHz6MMWPGAABGjhxpkgGBP/74Y1hYWGDz5s1a0wkvWrQI8fHx+Pjjjw3qKSqTydChQwcoFApMmDABL1++RMOGDY3yCGNOzp8/jxMnTmikbd68GY8fP0adOnXU4+FER0fj6tWrCA4ONsp05yVLlkSTJk3w7NkzbNy4UWPZ6dOncfr0aTg6Opr8cTJjyMu1r2/7FypUCG3btkVYWBiWLVumM1D6+PFjPHnyRP1aNaDwjz/+qNGjKTY2Vv0DSGa5/UwxBktLS7Rq1QpxcXFadbtz5w5+++03rXVUvSXfpuoxpO/nSZ06daBUKjUGWs7JmDFjsGzZMqxevTpPvXp9fHzw8ccf486dOzhw4IDe6z158gQbN27Uuf+iKKrb0Fjj/v39999wcXHJ1dg9ROaKj1PRB0X1wa9UKhEXF4fw8HBcuXIFaWlpqFq1KubPn59jd8zk5GR8+umnKFOmDCpXrowSJUogJSUFZ8+exf379xEUFKTxK2dgYCBu3LiBgQMHonbt2rC0tETFihURFBSU5/3x8/NTP5dfs2ZNvH79GiEhIVAqlZg5c6bGh3e1atXg5+eHS5cuoXPnzggICEBkZCROnDiB+vXr6+yhExgYiDVr1uDrr79G8+bNYWdnB0dHR/Ts2TPLOrVt2xbHjh3DH3/8gdatW6tvqo8ePYqnT5+iVatWWjNqGIudnR1mz56NMWPGoGfPnvjoo49QokQJ3Lx5E6dPn0bhwoUxc+ZMo5Zp6Dml+lWrX79+GDt2LPbv3w9PT0/8888/OHr0KOzs7DBv3jyNxxQCAgIgkUiwYMEChIeHq7vy65rBw1RyU++xY8fi3Llz2LBhA8LCwlCrVi31OdqwYUOd4/T06tULu3fvxvTp03Hu3DkUL14ct2/fxl9//aUefDYzV1dX/PDDDxg1ahR69+6Nhg0bwtvbG/Hx8bh79y6eP3+uUU5gYCAOHDiAoUOHwsfHRz1grp+fH0aMGAFbW1tUr14dJUuWhCiKuHz5Mm7cuIHKlSujbt26pmtgA5QrVw5r1qzBqFGj8N1332Hjxo0IDAxEkSJFkJiYiDt37qiDN5lnivnhhx/Qp08fTJkyBZs2bUK1atXg4OCAFy9e4N69e7h37x62bduWq0HWVXLznqFirOu3Vq1a6NWrFzZt2oQ2bdrgo48+gkwmw7Fjx+Do6KgVPHn58iXat28PLy8veHt7o3jx4oiPj8fJkyfx+vVr9OrVy6AvQhcvXlS/LyQnJ+PVq1e4evUqnj59CktLS0yYMMEo47bo4u7ujkmTJqlng2nZsiVcXV1x6dIlXLt2DZ6enpgwYYLB2+3SpQtWrVql7mnRtWtXY1ddpyZNmmDEiBEIDg5GmTJlcPv2bZw6dQrOzs6YNm2aOt+xY8egUCiy7N10//59rFq1SiNNLpdj4sSJ6tdffPGFxnv1tGnTcPv2bcyZMwcnT55EpUqVEBERgaNHj0IqlWLWrFkGP5q7YcOGLMdh8ff3z3Lg+LzK7bWvb/sDwNSpU/Ho0SMsXrwYe/fuRc2aNeHm5oZXr17h/v37uHHjBhYsWKAeyLZNmzYICQnB8ePH0aZNGzRt2hTp6ek4ePAgqlSpgsePH2vtR24+U4xl/PjxOH/+PFavXq0eC+n169f4448/0KhRIxw9elQjgPj7779j27ZtqFWrFkqVKgUnJyc8fvwYJ06cgKWlJfr06aNXuc2bN8fatWvx559/6v0ZVKJECaP9WDV27FgcPHhQKyicnfj4eMyePRvz5s1DzZo14eXlBTs7O0RFReH8+fN48uQJChUqhC+//DLP9Xvw4AGePXuGrl27GiWAS2QuGMShD4pqhh4LCwvY2dmhZMmSaN++PZo3b4769evr9Xy/jY0NJkyYgAsXLuDatWvqL7ClS5fG9OnT1T1RVD777DPI5XKcOHECV69ehUKhQIcOHYwSxHF3d8eMGTMwf/58bN26FampqfDx8cHw4cN1/kL4008/Yd68eTh27Bg2bdqEsmXL4vPPP0e9evXwxx9/aOVv0KABJk6ciO3bt2PDhg1IS0tDyZIlc/xCtmDBAvj5+WHXrl3Ytm0bgIwvnv3790f37t3zvN/ZCQ4OxpYtW7BixQqcPn0a8fHxcHNzQ7du3TBs2DCj/3qcm3OqWrVq2LlzJ5YvX45z587hxIkTcHFxQevWrTFs2DB4enpq5C9Xrhzmzp2LtWvXYsuWLepfLfMziJOberu6uuLXX3/FggULcOLECYSFhcHDwwPTp0/PcrDl8uXLY926dVi4cCFOnDgBqVSK2rVrY+vWrThy5IhWEAfIeHZ/165dWLVqFc6dO4czZ87A0dERnp6eWj3rpkyZAkEQcO7cOYSGhkKpVGLEiBHw8/PD+PHjcfr0ady8eROhoaGwsrJCiRIlMGHCBHTv3j3PvTGMqXr16vjjjz+wY8cOHDt2DCdPnoRcLoe1tTXKlCmDfv36oUuXLhozfxQrVgy7du3C5s2bcfjwYezbtw8KhQJubm4oX748evbsmedfMnP7nqFirOt3ypQpKFu2LH755Rds3boVzs7OaNasGcaNG6eeolmlZMmSGDlyJC5evIgLFy4gJiYGzs7O8PDwwPjx4w1+HOHixYu4ePEiBEGAjY0NnJ2dUb58eXTt2hXt2rUzeQ+WHj16oEyZMli7di0OHz6MpKQkFC9eHAMGDMDQoUP1nukms1KlSiEwMBBnz55FsWLFjDIbnD6aN2+Orl274ueff0ZoaChkMhmaN2+OcePGqcetATLGw3FyctI5qxGQ0fvh7ZmLkpKSNNJGjBihEcQpVqwYdu/ejWXLluH48eO4fPky7Ozs0KRJEwwZMiRXj2293asnsxEjRpgsiJPba1/f9gcyxo3ZtGkTtm/fjv379+Pw4cNISUmBm5sbypQpg0mTJmkEIQRBwKJFi7By5Urs2bMHmzdvRpEiRdCxY0cMHz5c50DyuflMMRY3Nzds3boVCxYsQGhoKP7++294eHhg2rRpsLGxwdGjRzWCvW3atEFqaiquXbuGmzdvIjk5GUWLFkXr1q3Rr18/vd9ra9SogUqVKmHfvn2YMGGC1ux6pla8eHH06dMHK1eu1HudcuXKYdmyZTh9+jT+/vtvhISE4M2bN+rPp6FDh6JPnz5GGcNG1QvK1PeWRO8aQTTWA4lERERERPkoPj4egYGBaNWqFb777ruCrg59gBYuXIiff/4Zq1evNskjdvv378f48eOxdOlSk4ylZa5SU1PRtGlTlCtXDuvXry/o6hDlK46JQ0RERERm6dSpU0hNTeWXWzK5ly9faqXdvXsXGzduhLOzc5Y9wfKqdevWqFatGpYsWWK0wYDfB1u2bEFkZKTGY5FEHwr2xCEiIiIiIspG/fr1UaZMGVSoUAE2NjZ49OiR+rHcefPmoW3btiYr+86dOzhy5Ai6dOmSL4OKm4MtW7bA2tpaPUg20YeEQRwiIiIiIqJsLF26FEePHkVERAQSEhLg4OCA6tWro3///iYbz4iISBcGcYiIiIiIiIiIzADHxCEiIiIiIiIiMgMM4hARERERERERmQEGcYiIiIiIiIiIzACDOEREREREREREZoBBHCIiIiIiIiIiM8AgDhERERERERGRGWAQh4iIiIiIiIjIDDCIQ0RERERERERkBhjEISIiIiIiIiIyAwziEBERERERERGZAQZxiIiIiIiIiIjMAIM4RERERERERERmgEEcIiIiIiIiIiIzwCAOEREREREREZEZYBCHiIiIiIiIiMgMMIhDRERERERERGQGGMQhIiIiIiIiIjIDDOIQEREREREREZkBBnGI6L1z4cIFeHt748KFC+q0iRMnIigoqABrRURERGR63t7eWLJkSUFXg4hMRFbQFSCid1N4eDhWrFiBCxcuICYmBs7OzqhTpw6GDh2KChUqFHT1iIiIiPLF7t27MWnSJPVrS0tLlChRAvXq1cOwYcPg5uaWL/W4ffs21qxZg0uXLiEqKgq2trbw8fFB27Zt0b59e0il0nypBxEVLAZxiEjL4cOHMW7cODg7O6Njx45wd3dHREQEdu7ciUOHDmHhwoVo1qxZQVeTiIiIKN+MGjUK7u7uSE1NxZUrV/Drr78iNDQU+/fvh42NjUnL3rFjB6ZNm4ZChQqhXbt2KFOmDBISEnD+/HlMmTIFr1+/xtChQ01aByJ6NzCIQ0QaHj9+jC+++AKlSpXCL7/8AldXV/Wy3r17o0ePHvjiiy+wd+9elCpVKl/qlJiYCFtb23wpi4iIiEiXhg0bokqVKgCAzp07w9nZGevWrcOxY8fQpk2bPG07KSkpy0DQX3/9hWnTpqF69epYuXIl7O3t1cv69u2LGzduIDw8PE/lE5H54Jg4RKRh9erVSEpKwjfffKMRwAEAV1dXzJw5E4mJiVi1ahUOHjwIb29vXLx4UWs7W7duhbe3N+7du6dOu3//PkaNGgV/f39UqVIFn3zyCY4dO6ax3u7du9XbnD59OgIDA9GoUSMAQEREBKZPn44WLVqgatWqqFOnDkaNGoWnT5+aoCWIiIiIshYQEAAAGvchv//+Oz755BNUrVoV/v7+GDt2LJ4/f66xXq9evdCmTRuEhYWhR48eqFatGhYsWJBlOUuXLoUgCJg/f75GAEdFdU+VFX3vn9LS0rB06VI0b94cVapUQZ06ddC9e3ecOXNGnef169eYNGkSGjZsCF9fX9SvXx+fffYZ78WI8hF74hCRhhMnTqBkyZKoXbu2zuV+fn4oWbIkQkNDMXnyZNja2uKPP/6Av7+/Rr6QkBBUqFABXl5eADLG2OnevTuKFi2KQYMGqdcbPnw4lixZovV41owZM+Dq6orhw4cjMTERAHDjxg1cu3YNrVu3RrFixRAREYFff/0VvXv3xoEDB0zelZmIiIhI5fHjxwAAZ2dnAMDy5cuxaNEitGzZEp06dUJ0dDQ2b96MHj164LfffoOjo6N63djYWAwaNAitW7fGxx9/jEKFCuksIykpCefPn0ft2rVRokSJXNVT3/unpUuXYsWKFejcuTOqVq2K+Ph4hIWF4ebNm6hXrx4AYOTIkfjf//6Hnj17omTJkoiOjsaZM2fw/PlzuLu756p+RGQYBnGISC0uLg6vXr1C06ZNs83n7e2N48ePIz09HUFBQTh06BC++uor9YB6r1+/xqVLlzBixAj1OrNnz0bx4sWxa9cuWFpaAgA+/fRTdO/eHfPnz9cK4jg5OWH9+vUag/Q1btwYH330kUa+Jk2aoGvXrjh06BDat2+fl90nIiIiylJ8fDyio6ORmpqKq1evYtmyZbC2tkaTJk0QERGBJUuWYMyYMRpj0zRv3hwdOnTAli1bNNJfv36NGTNmoFu3btmW+ejRI6Slpal/FMsNfe+fTp48iUaNGuGbb77RuR25XI5r167hiy++wIABA9TpQ4YMyXXdiMhwfJyKiNQSEhIAAHZ2dtnmUy1PSEhAy5YtERUVpfFI1aFDh6BUKtGqVSsAGb82nT9/Hi1btlTfAEVHRyMmJgb169fHw4cP8fLlS40yunTpojXLgrW1tfr/aWlpiImJQenSpeHo6Ihbt27lfseJiIiIctC3b1/1Y95jx46FnZ0dli5diqJFi+LIkSNQKpVo2bKl+j4nOjoabm5uKFOmDC5cuKCxLUtLy2wfgVKJj48HkPO9WXb0vX9ydHREeHg4Hj58mOV2LCwscPHiRbx58ybX9SGivGFPHCJSyxycyU7mYE/Dhg3h4OCAkJAQBAYGAsh4lKpSpUrw8PAAkNHdWBRFLFq0CIsWLdK5zaioKBQtWlT9WleX3OTkZKxYsQK7d+/Gy5cvIYqiellcXJwBe0pERERkmKlTp8LDwwNSqRRubm7w8PCARJLxm/jDhw8hiiKaN2+uc12ZTPNrV9GiRdU9k7OjGgMnp3uz7Oh7/zRq1CgMGzYMLVq0gJeXF+rXr4927dqhYsWKADICTxMmTMB3332HevXqoVq1amjcuDHat2+PwoUL57p+RGQYBnGISM3BwQGFCxfG3bt3s8139+5dFC1aVH1jERwcjCNHjmDatGmIiorC1atXMW7cOHV+pVIJAOjfvz8aNGigc5ulS5fWeG1lZaWV55tvvsHu3bvRp08fVK9eHQ4ODhAEAWPHjtW4ISEiIiIytqpVq6pnp3qbUqmEIAhYtWqVVk9iAFqzbGbuHZOdMmXKQCaTaUwUYSh975/8/Pxw5MgRHDt2DGfOnMHOnTuxYcMGzJgxA507dwaQ0RspKCgIR48exenTp7Fo0SKsXLkSGzZsgI+PT67rSET6YxCHiDQ0adIE27dvx+XLl3UObnz58mVERESga9eu6rSWLVtiz549OHfuHO7fvw9RFNGyZUv1ctVU5BYWFqhbt26u66Z6bnvixInqtJSUFPbCISIiogJVunRpiKIId3d3dU9kY7CxsUFAQADOnz+P58+fo3jx4gZvw5D7J2dnZ3Ts2BEdO3ZEQkICevbsiSVLlqiDOEDGvvbv3x/9+/fHw4cP0b59e6xduxbz58/P3U4SkUE4Jg4RaRgwYACsra0xbdo0xMTEaCyLjY3FtGnTYGNjg4EDB6rT69atC2dnZ4SEhOCPP/5A1apV1YEbAChUqBD8/f2xbds2vHr1SqvM6Ohoveqm65etTZs2QaFQ6Lt7REREREbXvHlzSKVSLF26VKt3sCiKWvdUhhg+fDhEUcQXX3yh87GqsLAw7NmzJ8v19b1/eruOdnZ2KF26NFJTUwFkzJSVkpKikad06dKws7NT5yEi02NPHCLSULZsWcydOxeff/452rZti06dOsHd3R0RERHYuXMnYmJisGDBAo3HnywsLNCsWTMcOHAASUlJ+PLLL7W2O23aNHz66ado27YtunTpglKlSiEyMhJ//fUXXrx4gb179+ZYt8aNG+P333+Hvb09ypcvj7/++gtnz55VT+1JREREVBBKly6NMWPG4IcffkBERASCg4NhZ2eHp0+f4ujRo+jSpYvGjE6GqFmzJqZOnYoZM2agZcuWaNeuHcqUKYOEhARcvHgRx48fx5gxY7JcX9/7p9atW8Pf3x+VK1eGs7Mzbty4gUOHDqFnz54AMsb96du3Lz766COUL18eUqkUR48eRWRkJFq3bp2rfSMiwzGIQ0RaWrZsCU9PT6xcuRI7d+5EbGwsnJ2dUadOHQwZMkTnNJetWrXCjh07IAiCxqNUKuXLl8euXbuwdOlS7NmzB7GxsXB1dYWPjw+GDx+uV72mTJkCiUSCffv2ISUlBTVr1sS6des0egURERERFYTBgwejbNmyWL9+PZYtWwYAKFasGOrVq4egoKA8bbtbt26oUqUK1q5di99++w0xMTGwtbWFj48P5syZg48//jjLdfW9f+rVqxeOHz+OM2fOIDU1FSVKlMCYMWPUwadixYqhdevWOHfuHPbu3QupVApPT0/8+OOPaNGiRZ72j4j0J4gcDZSIiIiIiIiI6J3HMXGIiIiIiIiIiMwAgzhERERERERERGaAQRwiIiIiIiIiIjPAIA4RERERERERkRlgEIeIiIiIiIiIyAwwiENEREREREREZAYYxCEiIiIiIiIiMgOygq6AIURRhFIpGn27Eolgku2SJraz6bGN8wfbOX+wnU3PVG0skQgQBMHo2yXDmereKT/xvcB88FiZDx4r88DjZD6Mcaz0vX8yqyCOUikiOjrBqNuUySRwcbGDXJ6I9HSlUbdN/2E7mx7bOH+wnfMH29n0TNnGrq52kEoZxHkXmOLeKT/xvcB88FiZDx4r88DjZD6Mdaz0vX/i41RERERERERERGaAQRwiIiIiIiIiIjPAIA4RERERERERkRlgEIeIiIiIiIiIyAyY1cDGRERUcJRKBRQKRT6VJSA5WYrU1BQoFJyVwRRy28ZSqRQSidSENaOCkJ/XtyHe9/cCXk9ERGQoBnGIiChboihCLo9GUlICgPz7EhUZKYFSydkYTCl3bSzAxsYOjo6unEb8PVBQ17ch3u/3Al5PRERkGAZxiIgoW0lJCUhKioe9vTOsrKwB5M8XDalUeC9/eX+XGN7GIlJSkhEfHwsLCyvY2tqbrG6UPwrq+jbE+/tewOuJiIgMxyAOERFlSRRFxMfHwtraDvb2TvlatkwmQXr6+/rr+7shN21sYWGF9PQ0xMfHwsbGjr0HzFhBXt+GeJ/fC3g9ERGRoTiwMRERZUmpVEKpVMDa2ragq0LvEGtrWyiVivf4EZcPA6/vdwOvJyIiMgSDOERElCWlMmOgUw68SZmpzgfV+UHmidf3u4HXExERGYJBHCIiyhG7+FNmPB/eLzyeBYvtT0REhjB4TJxHjx5hzZo1+PvvvxEeHg5PT0/s379fK59cLsfixYtx8OBBvHnzBkWLFsWnn36K/v37G6XiREREREREREQfEoODOOHh4QgNDUW1atWgVCohitqzBSQmJqJXr16QSqWYPHkyChUqhIcPHyI+Pt4olSYiooInCKb9BVkQBEgkurcviiJ0fPxka/bs6bhz5xY2bdpu0HohIfsgk1mgefOPDCvQBHK7D0SGMvX1nZ3cXN/66NSpLerWrY9x4740/saJiIjyicFBnKCgIAQHBwMAJk6ciLCwMK08K1euREJCAvbu3Qtb24zB8urUqZPHqhIR0btCEABIpUhKSTddGemizh8KAMDaSgZBoTDoi17fvgORlJRkcD1CQvbB1tb2nQjiEOWH/Li+s5Ob65uIiOhDYXAQRyLJeRidnTt3okePHuoADhERvV8EQUBSSjpOXnuKhMQ0k5QhEQQodXyLs7O1QOMa7rCxkGQZ5NGlZEl3Y1ZPi0KhgCiKkMkM/mgleqfkx/Wdldxe30RERB8Ko99pPn36FK9fv4aLiwuGDh2K06dP//sLZnNMmjQJdnZ2xi6SiIgKSEJiGuISU02y7ayCOLn19qNIISH78O23M7B27WasWPET/v77KtzcCqNPnwFo2bINAGDEiMH466+rAID69WsDAPr1G4QBA4ZgxIjBsLW1RZMmwdi4cS2ePYvAihXr4OZWBCtXLsO1a1cRFRWJIkWKoEmTYPTrNwiWlpbZ1jE1NRXr16/GkSOHEBn5Cs7OLqhd2x9TpkzXmT8yMlKvsvbv/x1bt/6CZ88iYG1tjTJlymLUqHGoUqVKtssrVaqcpzYn82bK69vYVNf3sGGj8dNPixAR8RRly3pi3Lgv4etbRec6YWHXsWnTOty5cxsJCfFwdy+Nbt164KOPWqvzXL16GaNGDcXChUtx4MA+nDnzJxwdHfHJJ53Ro0cfre2tXPkTbt0Kg1QqRWBgfYwePR4uLq4m3XciIvqwGD2IExkZCQD47rvv0Lx5c6xatQoPHz7EDz/8gMTERCxYsCBP25fJjDuhllQq0fiXTIPtbHps4/zxobWzUvn+z5oyc+bXaNu2Pbp1+xR79/6Gb7+dgUqVKqNsWQ+MHz8R33zzNaysrDF8+BgAQJEiRdTr3rlzG8+fP8PAgUPh4OCIIkWKIiYmBo6OThg5ciwcHBzw5MljrF27ElFRkZg8eVq2dfnqqy9w5col9OrVD5UrV0FsbAxCQ09kmf/Nm9gcy/rrr6uYO/cbdO/eC4GB9ZCcnIzbt28iPj4OAPD339kvz45UKhj9c5kot6KiorBgwXfo338wHBwcsHnzBowfPwJbt+7RGUh58eI5qlSphvbtO8LS0go3bvyNuXO/gSiK6kCuyvffz0GLFq3w7bff488/T2L58iUoV64CAgLqAsgI4IwcOQQBAfUwY8YcJCcnYdWq5Zg4cTxWrFiXD3tPREQfCqMHcZRKJQDAw8MD3333HQAgMDAQMpkMX331FcaOHYtSpUrlatsSiQAXF9P05HF0tDHJdkkT29n02Mb540Np5+RkKSIjJVpf1gVBgJAuQiIIkJhw8FNd25YIAgRBgFQqgUSif08d4d/1VPuhGjS5U6eu6NSpCwCgevUaOHfuNE6dOoHy5cuhQoXysLOzh62tDapXr6a1Pbn8Ddat24SiRYup04sUKQxv73Hq1zVq1ICdnS1mzpyGL76YCGtr3efOhQvncfbsacyc+a3G+DstW7bKch+8vb1yLOvu3VtwdHTC6NFj1fkaNmyo/v+dO9kv10WpFCCRSODkZAtra+ts8xLlF7n8Db75Zi5q1fIDAFSvXguffNIa27ZtwdChI7TyBwe3UP9fFEVUq1YDr169xO+/79YK4jRuHIQBA4YAAGrX9se5c2dw8uQxdRDn55+XomLFSvj22+/VA0J7epZH795dce7caQQG1jfJPhMR0YfH6EEcJycnANoDGQcEBADImN0qt0EcpVKEXJ6Ytwq+RSqVwNHRBnJ5EhQKpVG3Tf9hO5se2zh/fGjtnJqaAqVSCYVCRHr6f/srkQgQRRHKf/9MIavHqZRixoDHCoUSSqX+ZYv/rqfaD9W6tWvXUadZWFihWLHiePnyhTpNNVNO5v1XpZcrVwGFChXRWCaKInbs+BV79+7Bs2fPkJqaol72+PETeHqW11m/ixcvwNraGk2aNNMqK6t90Kes8uW9IZe/wYwZU9G8+UeoUqU6rK2tIQgZ53OFCrqXZ0ehEKFUKvHmTSKSkhRayx0dbT6Y3mr07rC3t1cHcFSva9f2x61b2pNwAIBcLsfatSvw55+hiIx8DYUi41xW3ctm5ucXoP6/IAgoU6YsXr16BQBITk7GjRt/Y/jw0eptAECpUqVRpEhR3L59i0EcIiIyGqMHcUqVKpXtM/8pKSlZLtNHVje2eaVUiuobelNNbUmAQqE02TGkDGzj/PGhtLNC8f6/GdrbO2i8lskskJqq3zggrq7aj2hs374Fy5Ytwqef9kbNmrXh4OCA27dvYcGC77Ldrlz+BoUKuRk0rbM+ZdWq5Yevv56JHTu2Yty4kbC0tETjxk0xevR4uLq6oGbNrJc7Omp/mc3s7eAeUUFydnbRSnN1dcWjR//ozP/tt9MRFnYdffsOhIdHOdjZ2WHPnp04fvyIVl4HB833CQsLC8TFZTxyGBcnh0KhwOLFC7B4sfawAa9evczN7hARUT4QBKjvvcwlDmD0II6lpSXq1auHc+fOaaSfPXsWAFC58rs1SKIgAIqkeFimx6kPmGBhg8R0qVkcQCIiKji6Ai4nThxDvXoNNR7fePhQ95fIzBwdnRAVFQlRFPUO5OhbVosWrdCiRSvExsbi9OmTWLx4IWQyGb7+enq2yydNmqpXPYjeBbGxMVpp0dHRKFTITSs9JSUFZ8+exogRY9GpUzd1em5mxLK3d4AgCOjVqx8aNmystdzJydngbRIRkekJAmArU0BMS8p4bSZxAIODOElJSQgNDQUAREREID4+HgcPHgQA+Pv7w9XVFSNGjEC3bt0wfvx4dOjQAY8ePcIPP/yAtm3bonTp0sbdgzwToExJQHzYaaQnxkNqbQf7Kg0gSBw4tSUREcHCwgIpKfrP0JOSkgwLCwuNtMOH/8hxvdq1/fHLLxtw/PgRNG3a3CRlOTs7o02b9jh37gwePXpo8HKid1l8fDyuXLmkfqQqPj4ely9fxCefdNbKm5aWBqVSqXH9JCYm4PTpUwaXa2NjA1/fKnj06B9UrDgs9ztARET5ShAEiGlJiL/xJwCYTRzA4CBOVFQURo8erZGmer1x40bUqVMHvr6+WLVqFebPn4/PPvsMTk5O6Nq1K8aOHatrk+8ERXICFEnxBV0NIiJ6x5Qp44GDB/fj9OlTcHNzg5tbYbi5Fc4yv59fHezYsRW7dm1DqVJlcOhQCJ4+fZpjOX5+dRAYWA9z5sxERMRT+Pj4Qi6X4+TJY5g5c06uy1qzZgXevIlFjRq14OLiivv3/4cLF86ha9ceei0nMheOjk6YO/cbjdmpRFFEly7dtfLa29ujUiUfbN68Hs7OzpBKZdi8eT3s7OwRGxttcNnDho3G6NGfYerUSWjatDkcHBzw+vUrXLp0Aa1atUXNmrWNsYtERGQCiuSEgq6CQQwO4ri7u+Pu3bs55gsMDMSuXbtyVSkiIjIPdrYWOWfKpawGNjZlmbr06NEbERFPMGvWNMTHx6Ffv0HqWWp06dt3EGJjY7F69QoAQOPGTTFmzAR8+WXOP2TMmjUP69atwu+/78batSvh6loIfn51ssyvT1kVK/pg+/Zfcfz4USQmJqBw4SLo3r0X+vQZoNdy+s8ff/yBvXv34ubNm5DL5ShTpgx69eqFjh07ajwCt2PHDqxevRrPnj2Dh4cHxo4diyZNmuS4/ZcvX2LWrFk4ffo0LCws0KxZM0yaNAn29vam3K0s5fe1ltcyCxUqhM8+G4WfflqEiIin8PDwxIIFS+DqWkhn/mnTZuP777/F7NnT4ejohE6duiEpKRFbt242uOwqVarhp59WY82aFZgzZwbS0tJQuHBR1K7tB3f33E3oQUREpIsgvut9hTJRKJSIjjZulMzCQgo7xCPyfAjSE+IgtbGHk99HSJY4GDTrCWVPJpPAxcUOMTEJHATTRNjG+eNDa+e0tFRERT1HoULFYWHx36D1ggBAKkVySrrJyhYEIcvurNZWMkCheOefWX7XyWSSXJ3HWZ0XKq6udu/l7FRdu3ZFyZIlERwcDBcXF5w9exarV6/G8OHDMWJExrhEBw4cwPjx4zF06FAEBAQgJCQEu3btwi+//ILq1atnue20tDR88sknAICxY8ciOTkZ3333HSpWrIgVK1bkus7Z3TsV5PWdnbevb33O09mzp+POnVvYtGl7PtTQuHK6nszFh/b5aM54rMwDj5PpSSQCrJVxeHMpY3iY3MYBjHWs9L1/MvrAxkRE9P4TRUBQKGBjYbov6lKpJMtp3EUGcKgALF++XGNGssDAQMTGxmLdunUYNmwYJBIJFi9ejNatW2PMmDEAgICAANy7dw/Lli3DqlWrstz2oUOHEB4ejpCQEHh6egIAHB0dMWDAAFy/fh1Vq1Y16b5llh/Xd7bl8/omIiLK0vv3MxkREeULUQSUStFkf6KY3bKC3nv6EOmaUr5SpUqIj49HYmIinjx5gocPH6Jly5YaeVq1aoVz585lO8X8qVOn4O3trQ7gAEC9evXg7OysnlAiP5n6+s7+2s/33SUiIjIb7IlDRERElEtXrlxB0aJFYW9vjytXrgAAPDw8NPKUK1cOaWlpePLkCcqVK6dzOw8ePNAI4AAZjxR6eHjgwYMHpqn8e2TKlOkFXQUiIqJ8wSAOERERUS5cvnwZISEh+PLLLwEAb968AZDxGFRmqteq5brI5XI4ODhopTs5OWW7nj5kMt0dr5VKQWf6u0Q1XrQg4L3voSOVClkeK3OgGsfhfRwP633DY2UeeJxMTxAECCIgSIR/X2e0t0Ri2AdOfh8rBnGIiIiIDPTixQuMHTsWderUQe/evQu6OlmSSAS4uNjpXJacLEVkpMQsggfv85cYpVKARCKBk5MtrK2tC7o6eeboaFPQVSA98ViZBx4n00qLjYelZUZYxEImha2zba63lV/HikEcIiIiIgPI5XIMGjQIzs7OWLJkCSSSjACDk5MTACAuLg6FCxfWyJ95uS6Ojo6Ij4/XSn/z5g2KFy+e67oqlSLk8kSdy1JTU6BUKqFQiO/szCeqX0UVCuV72xNHoRChVCrx5k0ikpIUBV2dXJNKJXB0tIFcnpTloPT0buCxMg88TqYnCAIs0xVITc2YjTEtXYGE2MQsZ0fNirGOlaOjDWenIiIiIjKm5ORkDBkyBHFxcdi2bZvGI1CqMW3eHt/mwYMHsLCwQKlSpbLcrqenJ+7du6eRJooi/vnnH9SrVy9Pdc4qQKNQvPtREdV99PsawMnsXQ6mGUKhUL4X+/Eh4LEyDzxOpiORCBBFQPx3SnFRzGhvQ6cYV8mvY/X+9k0lIiIiMqL09HSMGTMGDx48wOrVq1G0aFGN5aVKlULZsmVx8OBBjfSQkBAEBgbC0tIyy203bNgQd+7cwcOHD9Vp586dQ2xsLBo1amTU/SAiIiLzxSAOERERkR5mzJiBEydOYOjQoYiPj8dff/2l/lNNHz5y5Ejs378fixcvxoULFzBt2jRcv34dw4YNU28nIiICPj4+WLp0qTqtRYsWqFChAkaOHIkTJ04gJCQEkydPRuPGjVG1atV831ciIiJ6N/FxKiIi+iDMnj0dd+7cwqZN2w1aLyRkH2QyCzRv/pGJaqa/NWtWYOvWzThy5M+CrsoH6cyZMwCAuXPnai07duwY3N3d0aZNGyQlJWHVqlVYuXIlPDw8sHTpUtSoUUOdVxRFKBQKjWfuLSwssHr1asyaNQvjxo2DTCZDs2bNMHnyZNPvGBEREZkNBnGIiChXBCFjQDjTbV+ARKJ7+6IoGjxGRt++A5GUlGRwPUJC9sHW1vadCOJQwTp+/Lhe+Tp37ozOnTtnudzd3R13797VSi9atCiWLFmS6/oZk6mv7+zk5vomIiL6UDCIQ0REBhMEwFamgJhmeFBE7zLErAczFSxskJguNeiLXsmS7sapWB6lpCTDysr8pxGm91d+XN/Zlp+L69tUVD2mZDLeMhMR0buBn0hERGQwQRAgpiUh/safUCQnmKYMiaCeLSAzqbUd7Ks0gCBxMGgKyLcfpwoJ2Ydvv52BtWs3Y8WKn/D331fh5lYYffoMQMuWbQAAI0YMxl9/XQUA1K9fGwDQr98gDBgwBABw9uxprFu3Cvfv/w+2tjZo3Lgphg8fAxsbGwDA1auXMWrUUMyb9yNCQvbi4sULqF69BubN+1FnHZVKJbZv34J9+37Ds2cRcHBwRNWq1TFx4tewt7fXyp+UlITlyxfj0qULePXqJVxcXFGnTiA++2yURv7Tp0Oxbt1qPH78EFKpFCVLlsLAgUPQoEHDbJcHBtbXu33p/ZEf13dWcnt9q4SFXceaNStw82YYRFFE2bIeGDz4M/j5BWD58iU4d+40nj9/Bjs7e1SrVgMjR46Dm5ubev0RIwbD1tYWTZoEY+PGtXj2LAIrVqxDxYo+xtxNIiKiXGMQh4iIck2RnABFUrxJtp1VEMfYZs78Gm3btke3bp9i797f8O23M1CpUmWULeuB8eMn4ptvvoaVlTWGDx8DAChSpAgA4MSJo5g2bTJatWqLAQOGICoqEj//vBRxcXLMmDFHo4x582ajefOW+PbbTpBIsp5TYOHC77F372506fIp/PzqIDExAWfPnkZSUqLOIE5ycjKUSiUGDx4GZ2cXvHr1Ehs3rsWkSeOxZMkKAEBExFN89dWXCA5ugaFDh0OpFPG//91DXFycXsvpw2XK69sUrl//C6NHf4bKlavgyy+/goODA+7cuYWXL18AAGJiotGrVz+4uRVGbGwMtm79BSNGDMbmzds1etrcuXMbz58/w8CBQ+Hg4IgiRYpmVSQREVG+YxCHiIg+aJ980gWffJIxfomvbzWcO3caJ08eQ9++A+Hh4QlbWzvY2trC17eKeh1RFLFs2SIEBTXDxIlfq9MLFXLD55+PRp8+A+HpWU6dXr9+QwwbNirbejx+/Ai//bYTgwcPQ69e/dTpjRs3zXIdFxcXTJgwSf06PT0dxYuXwLBhA/H48SOULl0G9+7dQXp6OsaN+wK2tnYAgDp1AtXr5LScyFwsX74YJUuWwqJFyyGVSgEA/v4B6uWTJ09T/1+hUMDXtyo6dGiFq1cva+STy99g1aoNKFq0WP5VnoiISE8M4hAR0Qct85c3GxsbFCtWHK9fv8p2nSdPHuHFi+cYNWo80tPT1ek1atSERCLB3bu3NYI4+jyWdPXqJYiiiDZt2hlU/4MHD2Dbtl/w9OkTjYGbnzx5jNKly6BcuQqQSqWYPv0rfPxxB1SvXlOjV09Oy4nMQXJyMm7eDMOQIcPVAZy3nTt3Bhs2rME//9xHQsJ/j4k9efJI432gXLkKDOAQEb2nMg/cL5EIgLKAK5QLDOIQEdEHzd7eQeO1TGaB1NTUbNeJjY0FAEyePEHnctXjGyqurq451uPNmzeQSqVwcck5r0po6AnMmjUNH3/cAYMHD4OjozOioiIxefIEpKamAABKly6D775biE2b1mHKlM8hCALq1AnE2LFfwt29RLbLixXjF1kyD3FxciiVSri5Fda5/Pbtm5g4cRwaNGiEnj37wNnZFYIgYMiQvkhJ0bze9bleiYjI/Lw9cL8ACSSCCAgA3oHB9PXFIA4REZGBHB2dAABjx36BypV9tZa//UVSn6manZycoFAoEBMTrXcg58SJo6hQwQtffDFFnXbt2hWtfAEBdREQUBcJCfE4f/4clixZgDlzZmDZshXZLl+0aLle9SAqaPb2DpBIJIiMfK1z+alTJ2Fvb4+ZM+eqx6V68eK5zrwFNbU6ERGZ1tsD91s4FYaDVzVzi+Eg69EViYiICBYWFlq/1JcpUxZFihTFs2cRqFjRR+svq94A2alZ0w+CIODAgb16r5OSkgKZzEIj7fDhg1nmt7OzR9OmzdC0aXM8fPiPwcuJ3lU2NjaoXLkKDh48AIVCobU8JSUZMplMI0Bz+PAf+VlFIiJ6R6gG7lemJBZ0VXKFPXGIiIiyUaaMBw4e3I/Tp0/Bzc0Nbm6F4eZWGCNGjMWMGVOQnJyEwMD6sLGxwYsXz3Hu3GkMHjwcpUuXMaic0qXLoF27jli1ajnkcjlq1/ZHcnIyzp07jf79B6Nw4SJa6/j51cGCBd9h/frVqFy5Cs6fP4MrVy5q5Pntt124efMG6tQJRKFCbnj+/BkOH/4D/v519FpOZC6GDh2J0aOHYsyYYejQoTMcHBxw794dODk5w8+vDrZv/xULF85Dw4ZNEBZ2HYcOhRR0lYmIiAzGIA4REeWa1NrOZNvOaopxU5apS48evRER8QSzZk1DfHwc+vUbhAEDhiAoKBgODvbYsGGt+hf9YsWKo06dunB1LZSrssaN+wIlSpTA3r2/Yfv2LXByckL16jVha2urM3+7dp/g2bMI7Ny5DVu2bIK/fwCmTZuNIUP6qvOUL18BZ8/+iSVLFkIufwNX10IIDm6BQYOG6rWcPlz5fa3ltcxq1apjyZIVWLVqOb79djokEik8PDwxaNBnqF3bH599NhK7dm1HSMg+VKlSDfPm/Yju3T8xYu2JiIhMTxBF0Wwe/1IolIiOTsg5owEsLKSwQzwiz4cgPSEOUht7OPl9hGSJA5Q6vjxQ7shkEri42CEmJgHp6WY4BLgZYBvnjw+tndPSUhEV9RyFChWHhYWlOv3tgeFMQRCArD6hBAsbJKZLs1xO+pHJJLk6j7M6L1RcXe0glfKJ7XdBdvdOBXl9Z+ft6zu356m5yOl6Mhcf2uejOeOxMg88TsYnkQiwVsbhzaWDUCTFw9KlKJyqBCLm8lGIInIdBzDWsdL3/ok9cYiIyGCiCCSmSyFIHHLOnEtSqQQKhe4PQjFdZACHyETy4/rOtnxe30RERFliEIeIiHJFFAFTduaUSET2iCQqIKa+vomIiCh32NeZiIiIiIiIiMgMMIhDRERERERERGQGGMQhIiIiIiIiIjIDDOIQEVGOODYGZcbzgYiIiKhgMIhDRERZkkqlAIDU1JQCrgm9S1Tng1TK+RGIiIiI8hPvvoiIKEsSiRQ2NvaIj48BAFhaWkEQhHwpW6kUoFCwx4cpGdrGoigiNTUF8fExsLGxh0TC34KIiIiI8hODOERElC1HR1cAUAdy8otEIoFSqczXMj80uW1jGxt79XlBRERERPmHQRwiIsqWIAhwcioEBwcXKBTp+VKmVCrAyckWb94ksjeOieS2jaVSGXvgEBERERUQBnGIiEgvEokEEollvpQlk0lgbW2NpCQF0tPZG8cU2MaUHUFAvj06+TZRFGHo2NmzZ0/HnTu3sGnT9izzdOrUFnXr1se4cV/mql4ffdQYnTt3x4ABQ3K1PhERkTEwiENEREREaoIACJZKJCuSC6R8a6k1kCoxOJCTk2+//R4ODo7G3SgREVE+YxCHiIiIiNQEQUCyIhl/PrqExNSkfC3b1tIGDcr4wVqwM/pU9l5eFbNdLooi0tLSYGmZPz0OiYiIcoNBHCIiIiLSkpiahLiUhIKuhkHOnTuDn35ahIiIpyhb1hPjxn0JX98qALQfp1I9gjVs2Cj8/PMyPHr0D6ZNm4UmTYLx558nsXz5Erx48RzlypXP9SNYRERExsYgDhERERGZvaioKCxY8B369x8MBwcHbN68AePHj8DWrXvg4qJ7NrXIyEj8+ON89OkzAEWLFkPRosUQHn4XX331JerUqYuRI8fi2bNnmDp1ElJT0/J5j4iIiLQxiENEREREZk8uf4NvvpmLWrX8AADVq9fCJ5+0xrZtWzB06Aid68TFyTF//mJUruyrTps2bRKKFCmGOXPmQyqVAgCsrKwwd+43pt8JIiKiHHCOUCIiIiIye/b29uoAjup17dr+uHUrLMt1nJycNAI4AHDr1k3Uq9dAHcABgCZNmhq/wkRERLnAIA4RERERmT1nZxetNFdXV0RFRWa5jotLIa20qKhIuLhobsvOzh6WllZ5ryQREVEeMYhDRERERGYvNjZGKy06OhqFCrlluY4gaKcVKuSGmBjNbSUkxCM1NSXPdSQiIsorg4M4jx49wtSpU9GuXTv4+PigTZs22eY/evQovL29c8xHRERERJRb8fHxuHLlksbry5cvwsfHN5u1tFWqVBlnzvwJhUKhTjtx4pjR6klERJQXBg9sHB4ejtDQUFSrVg1KpRKiKGaZNzk5Gd9++y3c3LL+BYSIiIiIKK8cHZ0wd+43GrNTiaKILl26G7Sdnj37YNCgPpg0aQI6dOiEZ88isHXrZj5ORURE7wSDgzhBQUEIDg4GAEycOBFhYVkPFrdixQqUKFEC7u7u2eYjIiIioneLraWNWZVZqFAhfPbZKPz00yJERDyFh4cnFixYAldX7XFvsuPlVREzZ87Fzz8vwZQpn8PDoxymT/8W48frnuGKiIgoPxkcxJFI9HsC6/Hjx1i3bh22bt2K9evXG1oMERERERUAURRhLbVGgzJ+OWc2AWupNURF1j29dZkyZbr6/3Xr1teZZ+fOfVmu87ZGjZqgUaMmGmkHD540qE5ERESmYHAQR1+zZ89Gu3btULFiRVMVQURERERGJooAUiWwFuwKpnyFiGye1iciIvqgmSSIc/z4cVy7dg0HDx40+rZlMuNOqCWVSgAFIBEAQSJk/AkZ6RIJ7yCMRSqVaPxLxsc2zh9s5/zBdjY9tjFlRxSR7biHREREVDCMHsRJSUnBt99+i5EjR8LV1dWo25ZIBLi4GP9XobRYOWQWMkisZJBYymAhk8LW2dbo5RDg6Jj/z9d/aNjG+YPtnD/YzqbHNiYiIiIyH0YP4mzYsAESiQStW7eGXC4HAKSlpUGpVEIul8Pa2hqWlpa52rZSKUIuTzRmdSGTSWENID0tHWkp6ZBK0pGWrkBCbCJ/gTIiqVQCR0cbyOVJUCiUBV2d9xLbOH+wnfMH29n0TNnGjo427OFDREREZAJGD+I8ePAAjx49QmBgoNYyPz8/TJ8+Hd27GzbVY2bp6ca90RQEAQCgFAFRKWb8iYBCoYRSySCOsSkUSqMfQ9LENs4fbOf8wXY2PbYxERERkfkwehBn0KBB6NChg0baypUr8c8//2DOnDkoW7assYskIiIiIiIiIso9QYBEktHJQxTf3UH2DQ7iJCUlITQ0FAAQERGB+Ph49QDG/v7+KFeuHMqVK6exzp49e/Dy5UvUqVPHCFUmIiIiIiIiIjIOwcISMpkUQpocIgDBwgaJ6dJ3MpBjcBAnKioKo0eP1khTvd64cSMDNURERPReevToEdasWYO///4b4eHh8PT0xP79+9XLnz59iqZNm+pc19LSEjdu3Mhy2xcuXEDv3r210lu1aoWFCxfmvfJERESUJYnUAmJaMuLDzkEUAfsqDSBIHN7JcXINDuK4u7vj7t27Bq0zd+5cQ4shIiIieqeEh4cjNDQU1apVg1Kp1LqxK1KkCLZt26aRJooiBg4ciICAAL3KmDNnDjw9PdWvXVxc8l5xIiIi0osiOeGd7H2TmdHHxCEiIiJ6HwUFBSE4OBgAMHHiRISFhWkst7S0RPXq1TXSLly4gPj4eLRp00avMipUqIAqVaoYpb5ERET0/mEQh4iIiEgPEonh06bv378f9vb2CAoKMkGNTEcQ/pvBM78V5GCSISH7IJNZoHnzjwqmAkRERDlgEIeIiIjIBNLS0nD48GE0a9YMVlZWeq0zePBgxMbGonDhwmjdujVGjx4Na2vrPNVDJtMdfFIqdQdpBAGwhgLKpKQ8lZtbEmsbJAsZg0mq4kiCgHwJ7ISE7IOtrW2BBHGkUiHLY2UOpFKJxr/07uKxMg88TsYnCAIEERAkAgSJAPw7E5UgvPV/IeNzRyqVQCLJ+cMnv48VgzhEREREJnDq1CnExsbq9SiVg4MDBg4cCD8/P1hZWeH8+fNYu3YtHjx4gBUrVuS6DhKJABcXO53LkpOliIyUaAUPBEGAmBCPyD9PIz0hIddl54bMzg6FG9aHzM5JY8yh/LoxFgQBgpB14MsUlEoBEokETk62eQ7YvQscHW0KugqkJx4r88DjZFxpsfGwtJRBqZRBZiGFRBBgYSmDJNP/AcBCJoWts61B286vY8UgDhEREZEJ7Nu3D25ubggMDMwxr4+PD3x8fNSvAwMDUaRIEcycORPXr19H1apVc1UHpVKEXJ6oc1lqagqUSiUUChHp6Up1ukQiQCICaXHxSIuLz1W5uSUqMx6lUiiUUCpF9S+hCoVSr544YWHXsWbNCty8GQZRFFG2rAcGD/4Mfn4BWL58Cc6dO43nz5/Bzs4e1arVwMiR4+Dm5gYAGDFiMP766yoAICCgJgCgX79BGDBgiMn2FwAUChFKpRJv3iQiKUlh0rJMSSqVwNHRBnJ5EhQKZc4rUIHhsTIPPE7GJwgCLNMVSE1NhyIlHco0BWxEEWmp6RAk//1fBJCWrkBCbKJes1MZ61g5Otro9aMFgzhERERERpaQkIATJ06gc+fOkEqludpGy5YtMXPmTISFheU6iANAI0CTmULxjk+/gf8eodIngHP9+l8YPfozVK5cBV9++RUcHBxw584tvHz5AgAQExONXr36wc2tMGJjY7B16y8YMWIwNm/eDplMhvHjJ+Kbb76GlZU1hg8fAyBjxrH88nYwzVwpFMr3Yj8+BDxW5oHHyXgkEgGi+O8PBkoRUGZ8uIiiCCHT/0URGj8o6Cu/jhWDOERERERGduTIESQnJ6Nt27YFXZUPxvLli1GyZCksWrRcHTjz9/9vavfJk6ep/69QKODrWxUdOrTC1auX4e8fAA8PT9ja2sHW1ha+vpwhjIiI3k0M4hAREREZ2f79+1G6dGlUq1Yt19s4cOAAAHDKcT0kJyfj5s0wDBkyPMueT+fOncGGDWvwzz/3kZBprJ8nTx5pBHuIiIjeZQziEBEREekhKSkJoaGhAICIiAjEx8fj4MGDAAB/f3+4uroCAKKjo3Hu3DkMGjRI53YiIiLQrFkzDBs2DCNGjAAATJgwAWXKlIGPj496YOP169cjODiYQRw9xMXJoVQq4eZWWOfy27dvYuLEcWjQoBF69uwDZ2dXCIKAIUP6IiUlNZ9rS0RElHsM4hARERHpISoqCqNHj9ZIU73euHEj6tSpAwD4448/kJ6enuWjVKIoQqFQaAyWWKFCBezbtw9r165FWloaSpYsiaFDh2Lw4MEm2pv3i729AyQSCSIjX+tcfurUSdjb22PmzLmQSDIGjXzx4nl+VpGIiMgoGMQhIiIi0oO7uzvu3r2bY74ePXqgR48eBm1nyJAhGDLEtLMgvc9sbGxQuXIVHDx4AN269dR6pColJRkymQyCIKjTDh/+Q2s7FhYW7JlDRETvNAZxiIiIiEiLzM7OrMocOnQkRo8eijFjhqFDh85wcHDAvXt34OTkDD+/Oti+/VcsXDgPDRs2QVjYdRw6FKK1jTJlPHDw4H6cPn0Kbm5ucHMrnOUjWkRERAWBQRwiIiIiUhNFERJrGxRu1KBAypdY22g8aqavatWqY8mSFVi1ajm+/XY6JBIpPDw8MWjQZ6hd2x+ffTYSu3ZtR0jIPlSpUg3z5v2I7t0/0dhGjx69ERHxBLNmTUN8fBz69RuEAQPYQ4qI6H0mWFhCYmULSKSQWNtBmZ5W0FXKFoM4RERERKQmikCyIIVg41BA5YvIRQwHAFClSjUsXvyzzmU9evRBjx59NNJOn76s8bpw4SL4/vtFuSuciIjMjyCBXfmqgEwGUQbYlKsEQQkg0+O37xoGcYiIiIhIgygiV71hiIiIzIogQJQA0RGXIdjYQJkQh0Ila2uMofauYRCHiIiIiIiIiD5Y6anxkMiUUKTEF3RVciQp6AoQEREREREREVHOGMQhIiIiIiIiIjIDDOIQEREREREREZkBBnGIiIiIiIiIiMwAgzhERERERERERGaAQRwiIiIiIiIiIjPAIA4RERERERERkRlgEIeIiIiINAgCIJEIBfInCMbbj7i4ONSvXxshIfuMt1EiIjIrmT/T3geygq4AEREREb07BAGQSaVITUkvkPItrWRIVyggigVSPBERvUcEAbCVKSCmJUGABBJBBMw8lsMgDhERERGpCYKA1JR0hF2LQFJiWr6WbWNrAd8aJSG1kEA0syhOamoqZDIZJBJ2dCcielcIggAxLQnxN/6ExMoWDl7VzD2GwyAOEREREWlLSkzL9yBOXu3duwcbN65FTEw0fH2r4rPPRmrlCQnZh23bfsGTJ4/h6OiEli3bYODAoZBKpeo8r169xM8/L8WFC2eRlJSMSpV8MHLkOFSsWEmdp1Ontqhbtz6KFi2G3bt34NWrl9i37wicnZ3zY1eJiMgAiuSEgq6C0TCIQ0RERERm78yZPzFv3my0atUWTZs2x927t/H11xM18mzduhnLly9Bly6fYsSIMXj48CFWrvwJSqVSHfCRy+UYNmwgbGxsMGbM57C3t8fOndsxevRQbN26By4ururthYYeh7t7aYwePQESiQQ2Ntb5us9ERPThYRCHiIiIiMzehg1rUK1aDUyePA0AUKdOIFJTU7F+/WoAQGJiAtasWYlPP+2NIUOGAwD8/AJgYSHDkiUL8emnveDk5IwdO35FfHwcVq3aoA7Y1Krlj+7dP8Gvv27CsGGj1WWmp6dj/vzFsLGxyee9JSKiDxUf2iUiIiIis6ZQKHD37m00bNhYI71x46bq/9+4cR1JSYlo0qQp0tPT1X+1a9dBSkoKHjy4DwC4ePE8atSoDQcHR3UeiUSC6tVr4vbtWxrbr1GjFgM4RESUr9gTh4iIiIjMWmxsDBQKhcajTgDg6vrf6zdvYgEA/fv31LmNV69eqvPdvHkDjRsHaOUpWdJd47WLS6G8VJuIiMhgDOIQERERkVlzdnaBVCpFTEy0Rnp09H+vHRwcAQCzZ3+PokWLam2jePES6nx16tTFoEFDtfJYWFhqvBbMfYoTIiIyOwziEBEREZFZk0ql8PKqiFOnTqJr1x7q9JMnj6n/7+tbFdbW1nj9+iUaNWqS5bZq1/bH4cN/oEwZDz4qRURE7xwGcYiIiIjI7PXp0x8TJ47Ht9/OUM9OdehQiHq5g4MDBgwYip9+WoJXr16hRo1akEqlePbsKf788xRmz54Ha2trdOvWA0eOHMSIEYPRuXM3FC1aDLGxMbh16ybc3Nw0gkRERET5jUEcIiIiItJiY2thVmXWr98IEyZMwsaNa3H06GH4+FTGjBnfYvDgvuo83bv3ROHChbFt2y/YtWsbZDIZSpZ0R926DSCTZdwWOzk5Y8WKdVi1ajmWL18CufwNXFxc4ePjqzVwMhERUX5jEIeIiIiI1ERRhKWVDL41ShZI+ZZWMqQrFLlat337jmjfvqNG2unTlzVeBwe3QHBwi2y3U6iQGyZO/DrbPDt37stVHYmIiPKCQRwiIiIiUhNFIF2hgNRCUiDlpysUEMUCKZqIiOidxyAOEREREWkQxYweOURERPRuKZifWIiIiIiIiIiIyCAM4hARERERERERmQEGcYiIiIiIiIiIzACDOEREREREREREZoBBHCIiIiIiIiIiM2Dw7FSPHj3CmjVr8PfffyM8PByenp7Yv3+/enl8fDzWrVuH0NBQPHz4EJaWlqhatSrGjh0Lb29vo1aeiIiIiIiIiOhDYXBPnPDwcISGhqJMmTIoV66c1vJnz55h27ZtqFevHn788Ud88803iIuLQ9euXXH//n2jVJqIiIiIiIiI6ENjcE+coKAgBAcHAwAmTpyIsLAwjeXu7u44cuQIbGxs1GkBAQEICgrCli1b8PXXX+exykREREREREREBhIkkFjbQSoCEitbQBAKukYGM7gnjkSS/Sq2trYaARwAsLOzQ+nSpfHq1StDiyMiIiKifCYIgEQiFMifMe+n4+LiUL9+bYSE7DPeRomIyCwJMkukWdkjvVxtKCvVR1qZKkiztAck0oKumkEM7omTG3K5HOHh4ahbt25+FEdEREREuSQIgIVUibTUpAIp38LSBmkKCUSxQIonIqL3lCCVIUkJXIyIQkJSMuzsHVDPwRU20nwJixhNvtT2+++/hyAI6N69e563JZMZd0ItqVQCKACJAAgSIeNPyEiXSHj3YCxSqUTjXzI+tnH+YDvnD7az6bGNKSuCICAtNQl3/zqPlKTEfC3bysYW3tUDIMjsITKKQ0RERicgxcIWKaIUMpklIABSGztAMJ/7IZMHcXbt2oXt27dj7ty5KFasWJ62JZEIcHGxM1LN/pMWK4fMQgaJlQwSSxksZFLYOtsavRwCHB1tcs5EecI2zh9s5/zBdjY9tjFlJSUpEcmJCQVdDYPs3bsHGzeuRUxMNHx9q+Kzz0ZqLFcqldi4cS327/8dUVGRKF68BLp0+RTt23fUyBcaegI//7wEL1++QPnyXhg/fiJGjx6Kzp27Y8CAIfm5S0REZGwCkJzwGlKFJdJTXQAZIDCIkyE0NBRTp07FsGHD0KFDhzxvT6kUIZcb9xchmUwKawDpaelIS0mHVJKOtHQFEmIT+QuQEUmlEjg62kAuT4JCoSzo6ryX2Mb5g+2cP9jOpmfKNnZ0tGEPH8p3Z878iXnzZqNVq7Zo2rQ57t69ja+/nqiRZ9myRdi5cyt69+6PKlWq4ezZPzF//hwoFOno2LErAODevTuYOnUi6tZtgFGjxuHFixeYNm0SUlPTCmK3iIjIBJSKNIgKCURRAcC8Bjc2WRDnr7/+wujRo9G+fXuMHj3aaNtNTzfujabw7+h5ShEQlWLGnwgoFEoolQziGJtCoTT6MSRNbOP8wXbOH2xn02Mb0/tiw4Y1qFatBiZPngYAqFMnEKmpqVi/fjUAIDY2Frt2bUP37r3UvWn8/QMQGxuLdetWo337TpBKpdi0aT2KFy+B2bPnqSf0sLW1xTffTC2YHSMiIsrEJD+T/e9//8OQIUMQEBCAGTNmmKIIIiIionz16NEjTJ06Fe3atYOPjw/atGmjladXr17w9vbW+rt//36O23/58iVGjhyJGjVqwN/fH1OmTEF8fLwpduW9o1AocPfubTRs2FgjvXHjpur/37oVhvT0dDRpEqyRp2nTZoiNjcGTJ48BAHfu3ELduvU1ZmRt0KCR6SpPRERkAIN74iQlJSE0NBQAEBERgfj4eBw8eBAA4O/vD1EUMWDAAFhZWaFPnz4ICwtTr2tvb4/y5csbqepERERE+Sc8PByhoaGoVq0alEpllo9d16xZE19++aVGmru7e7bbTktLw8CBAwEAP/zwA5KTk/Hdd99h/PjxWLFihXF24D0WGxsDhUIBFxdXjXRX1/9ex8XJtdIAwMWlEABALn8DAIiKioSzs4tGHltbO1haWhm93kRERIYyOIgTFRWl9XiU6vXGjRsBAC9evAAA9O3bVyOfv78/Nm3alJt6EhERERWooKAgBAdn9OKYOHGixg9VmTk6OqJ69eoGbfvQoUMIDw9HSEgIPD091dsZMGAArl+/jqpVq+ap7u87Z2cXSKVSxMREa6RHR//32tHREQAQExONwoWLqNNjYqL+Xe4EAChUyA2xsTEa20lMTEBqaopJ6k5ERGQIg4M47u7uuHv3brZ5clpOREREZG4yP15jbKdOnYK3t7c6gAMA9erVg7OzM0JDQxnEyYFUKoWXV0WcOnUSXbv2UKefPHlM/f9KlXwhk8lw4sQxeHlVVKcfP34ULi6uKFWqNACgYkUfnD17GiNGjFUf81OnTubPjhAREeXA5FOMExEREX1ILl68iOrVq0OhUKBatWoYPXo0/Pz8sl3nwYMHGgEcIGPyBQ8PDzx48CBP9ZHJdAeflMp3fzaOf+efgCAAOU0a2qdPf0ycOB7ffjtDPTvVoUMh6uXOzs7o2LErtmzZCEtLS1SuXAXnzp3BkSMHMXbs55BKpQCAXr36YtCgPpgy5Qt8/HEHvHz5HL/+uhmWllYmDeRJpUKWx8ocqGak48x07z4eK/PA42QcgiBAEAFBIvw3IrCujz9BvQIgCBAEQCqRQCLJebKj/D5WDOIQERERGYmfnx/atWuHsmXL4tWrV1izZg369euHTZs2oUaNGlmuJ5fL4eDgoJXu5OSEN2/e5Lo+EokAFxc7ncuSk6WIjJRoBQ8EQYBCIcDG1g4SSf4GeqysbSEIAqRSzRtnfW6MGzdugi+/nIz169fi2LHD8PHxxaxZczFgQG9IJBn7OHr0WDg5OWLv3j3YsGENihcvgS+/nIwOHTqpt+Pj44NZs+Zi+fKlmDLlc3h6lsPUqTMxbNhgODo6GD3QolQKkEgkcHKyhbW1tVG3XRAcHW0KugqkJx4r88DjlHdpsfGwtJRBKpMiDRnxGolEUP9SIECARBAAISNdIgiQSaWwcbI1qJz8OlYM4hAREREZyahRozReN27cGG3atMFPP/2EVatW5Xt9lEoRcnmizmWpqSlQKpVQKESNaeYFAbCwsEaFanXyq5oaZBbWSEtXQBQz6iKVSqBQKHPsiQMAbdt+grZtP9FIO336MgCo97F37wHo3XuARp7M+w8ADRo0QYMGTdSvL1++CIUiHZ6eFbTy5pVCIUKpVOLNm0QkJSmMuu38JJVK4OhoA7k8CQqFcduIjIvHyjzwOBmHIAiwTFcgNTUdsnQFRAAiMj4fVR8sIkQoRRGCmJGuFEWkKxRIiE3MchKDzIx1rBwdbfT60YJBHCIiIiITsbW1RaNGjXDo0KFs8zk6OuqcTvzNmzcoXrx4nuqQVdBBodB9YyqKQJpCAkFmn6dycytNIaoDNm//m1/mz5+L2rX94OjohH/+eYANG9bAy8sb1apl3Zsqr94OppkrhUL5XuzHh4DHyjzwOOWNRCJAFAFRKQKqZtT1maJKEzOCO6KY0fZKpf4fQPl1rBjEISIiIipgnp6euHfvnkaaKIr4559/UK9evXyvT8Y9bD5HTt4hcXFyLFz4Pd68iYW9vT3q1AnE8OFjTDomDhERkT4YxCEiIiIykcTERJw8eRJVqlTJNl/Dhg2xd+9ePHz4EGXLlgUAnDt3DrGxsWjUqFE+1JQymzHj24KuAhERkU4M4hARERHpISkpCaGhoQCAiIgIxMfH4+DBgwAAf39/PHjwAKtXr0azZs1QsmRJvHr1CuvWrcPr16+xaNEi9XYiIiLQrFkzDBs2DCNGjAAAtGjRAitWrMDIkSMxbtw4JCUlYd68eWjcuDGnFyciIiI1BnGIiIiI9BAVFYXRo0drpKleb9y4EcWKFUNaWhoWLlyI2NhY2NjYoEaNGpgxY4ZGIEYURSgUCo3HlSwsLLB69WrMmjUL48aNg0wmQ7NmzTB58uT82TkiIiIyCwziEBEREenB3d0dd+/ezTbPmjVrcr2dokWLYsmSJbmuHxEREb3/ODobEREREREREZEZYBCHiIiIiIiIiMgMMIhDRERERERERO8nQYDU2g4SS2sAAiAUdIXyhmPiEBEREREREdF7RxAAUWYFoYwvYGEFJQBBZpGxwEwxiENEREREGgQBEAroBlcURWSauEtv27b9gm3btiAy8jXq1WuAOXN+MH7liIjIrAiCgNTUNNy+eROCRAr3WvUgouA+44yBQRwiIiIiUhMEwEYmA9IUBVMBCxmS0tMNCuQ8efIYS5f+iB49+qBevQZwcnI2WfWIiMj8JCcmQJDKoFQqC7oqecYgDhERERGpCYIApCkgv/0aipS0fC1bamUBx0qFIUgEiAZEcR4/fgRRFNG2bXuULOmuM09KSjKsrKyNVVUiIqICwSAOEREREWlRpKRBkZRe0NXI0ezZ0/HHH/sBAF27tgcATJ48Dd9+OwPz5v2IkJC9uHjxAqpXr4F5837EixfPsXTpQly6dAEKhQJVq1bH8OFjUK5cefU209LSsGzZjzh06A+IohJBQc1QvXotzJz5FXbs2IvixUsUxK4SERExiENERERE5qtv34EoW9YDy5cvwezZ36NQITe8evUSADBv3mw0b94S337bCRKJBImJCRg5cggEQcCECZNgaWmFjRvXYvjwQdiw4VcULVoMAPDzz0vw+++7MWDAEFSo4I2TJ4/h55+XFORuEhERAWAQh4iIiIjMWMmS7ihVqgwAwMvLG8WLl8DVqykAgPr1G2LYsFHqvDt2bMWLF8+xadN2lC3rAQCoUaMmOnZsg+3bf8XIkWMhl7/Bnj270KfPAPTs2RcAUKdOIEaPHqYODhERERUUSUFXgIiIiIjIFAID62u8/vvva/D0LKcO4ACAo6MTateug+vX/wIA3L//P6SmpqBevYYa6zZooPmaiIioIDCIo4sgQCL578+MZx8jIiIi+mC5urpqvI6Li4OLi6vOfHFxcgBAVFQkAMDFxUUjj671iIiI8huDOG8RLCwhk0lhlS6HtTIO1so42MoUDOQQERERmRnhrRs4R0dHxMTEaOWLjo6Gg4MjAKBQITcA0MoXExNtoloSERHpj0Gct0ikFhDTkhEf9ifeXDqI+Bt/QkxL0roJICIiIiLzUrVqdTx48D88fvxQnSaXy3H58kVUrVodAODpWQ6WllY4fTpUY90//9R8TUREVBA4sHEWFMkJUCTFF3Q1iIiIiAqE1MrivSuzdeu22L59Cz7/fAwGDfpMPTuVVCpFly7dAQBOTs7o0KEjNm5cC0tLS1So4I0TJ47iyZPHAABB4G+gRERUcBjEISIiIiI1URQBCxkcKxUumApYSCGmp5tk07a2dliyZAWWLFmAefO+hVKpQJUq1bBs2Sr19OIAMHToSKSnp2PTpvUQRSUaNmyCHj36YOHCebC3tzdJ3YiIiPTBIA4RERERqYkikJSeDkFSMI+Si+npEEXD1mnYsDFOn76sfl2zZm2N15kVK1Ycs2d/n+32LCwsMHbsFxg79gt12jfffI3ixUswiENERAWKQRwiIiIi0iCK//bI+UBdu3YFN278DW/vSlAqlTh79jQOHz6IkSPHFnTViIjoA8cgDhERERFRJjY2tjh79jR++WUDUlJSULx4CYwcORZdunxa0FUjIqIPHIM4RERERESZVKxYCT//vLagq0FERKSFw+sTEREREREREZkB9sQhIiIiIiIioveOIACCRAJrWzsIEikEwfz7sTCIQ0RERERERETvFUEARJkUyYIVivnWgkSQwNLGBoC8oKuWJwziEBEREREREdF7RRAEpCiUuBARjadPIuBmb4t6zq4QCrpiecQgDhERERERERG9lxLT0iFPSoatxfsR/jD/B8KIiIiIiIiIiD4A70coioiIiIiMRhAyuqEXBFEUIYqGrTN79nTcuXMLmzZtN02liIiI3hEM4hARERGRmiAAFhZAampqgZRvaWmBtDQYHMghIiL6EDCIQ0RERERqgiAgNTUVt2/fRkpKcr6WbWVljUqVKkEisYT4DkRxRFFEWloaLC0tC7oqREREABjEISIiIiIdUlKSkZSUv0GcvLp69TKWLFmAJ08ew8PDE+PHT0LFipUAACkpKVi5chmOHj2MuDg5Spcui379BqFRoybq9VWPZQ0bNgo//7wMjx79g2nTZqFBg8ZYsWIZjh07jJiYaDg6OsLb2wdTp34De3t7AEBcXBxWrFiGP/88AblcDg+Pchg6dAT8/QMKpC2IiOj9xCAOEREREZm96OgoLFo0Hz169IW9vT1WrFiKyZMnYPv23yGTyTBz5le4cOEcBg8ehtKly+LgwQP46qsvMGfOfNSv30i9ncjISPz443z06TMARYsWQ9GixbBp0zr89tsufPbZSHh4eOLNm1hcvHgeaWkZj5ylpaVh7NjhiI6OwqBBw1C4cBEcPhyCzz8fjbVrf0G5cuULqlmIiOg9wyAOEREREZk9uVyOJUtWwtOzHADA2toao0YNxc2bYbCzs0No6AlMmDAJ7dt3BAAEBNTFixfPsXbtKo0gTlycHPPnL0blyr7qtNu3b8Lfvw4++aSzOq1x46bq/x8+/AfCw+9i/fpf4eHhCQCoUycQT548wfr1q/HNN3NNuu9ERPTh4BTjRERERGT23NwKqwM4ANTBlNevX+Lvv68BAIKCgjXWCQpqhvDwu0hKSlKnOTk5aQRwAMDLqyLOnTuLNWtW4Pbtm1AqlRrLL148j3LlyqNUqdJIT09X//n51cGdO7eMup9ERGRiEikEiQQSiQCJREABTdaYJYN74jx69Ahr1qzB33//jfDwcHh6emL//v1a+Xbs2IHVq1fj2bNn8PDwwNixY9GkSRMdWyQiIiIiyhvV2DQqMpkFgIxZtuLi5JDJZHB0dNLI4+rqClEUER8fBxsbGwCAi0shrW337t0fgiDg4MEDWLduFZydXfDJJ53Rr98gCIKAN29ice/eXTRurD3+jVQqNdYuEhGRiUlkVrCwd4YoKGCBRIgSQJBYITlFeGdmTTQ4iBMeHo7Q0FBUq1YNSqVS58wBBw4cwNdff42hQ4ciICAAISEhGDFiBH755RdUr17dGPUmIiIiItKLo6MT0tPTIZfL4ejoqE6Pjo6GIAiwt3dQp+n6xdXS0hIDBgzBgAFD8PTpExw4sBdr165EiRIl8dFHreHo6IRy5Spg0qSv82N3iIjIRCRSCyiV6Yh5dRMpaamQyWzgWqIGBMHmnZg1EchFECcoKAjBwRldUSdOnIiwsDCtPIsXL0br1q0xZswYAEBAQADu3buHZcuWYdWqVXmrMRERERGRAapWrQ4AOHHiKNq1+0SdfuLEUVSo4K3uhaMPd/dSGDJkOH7/fTcePXoIAKhd2x/nzp2Bm1thuLkVNmbViYioACjSEpGelgq8G3EbDQYHcSSS7IfRefLkCR4+fIjPP/9cI71Vq1aYN28eUlNTYWlpaWixRERERES5Ur58BTRq1ARLly5ESkoKSpcug8OH/0BY2HXMmfNDjutPmjQe3t6V1AGfM2dOIS5Ojpo1awMAPvqoNX7/fTdGjBiC7t17olSp0oiPj0d4+F2kpaVh6NARpt5FIiL6QBh9dqoHDx4AADw8PDTSy5Urh7S0NDx58gTlypXTtSoRERERvSOsrKzfqzKnTv0GK1Yswy+/rIdcLkfp0mUxa9Z3qF+/YY7rVqlSDcePH8XWrZuhUChQqlQZTJ36Dfz86gDIeNxq8eLlWLt2JTZuXIuoqEg4OTnDy8sbHTp0zmHrRERE+jN6EOfNmzcAoPG8cebXquW5JZMZd0ItqVQCKACJAAgSIeM/AARBgCD590/IyCeRvIN9qcyEVCrR+JeMj22cP9jO+YPtbHpsY8qKKIqwtLRApUqVCqR8S0sLpKUZds81Zcp0rTQHBwecPn1Z/drKyhqjRo3HqFHjDdoOAHz6aW98+mnvbOtgZ2ePkSPHYeTIcXrVmYiIKDeMHsQxJYlEgIuLndG3mxYrh8xCBomVDDILKSSCAAtLGaRKGSSWMljIpLB1tjV6uR8iR0f9nzmn3GEb5w+2c/5gO5se25jeJopAWhogkRTM4+9paeI7MwMIERHRu8boQRwnp4ypG+Pi4lC48H8Du8nlco3luaFUipDLE/NWwbfIZFJYA0hPS0daSjqUaQrYiCLSUtORnpIOqSQdaekKJMQmvjOjUZsjqVQCR0cbyOVJUCiUBV2d9xLbOH+wnfMH29n0TNnGjo4272UPn0ePHmHNmjX4+++/ER4eDk9PT+zfv1+9PD4+HuvWrUNoaCgePnwIS0tLVK1aFWPHjoW3t3e2275w4QJ699bu6dGqVSssXLjQ6PuSE1EE73uIiIjeQUYP4nh6egLIGBtH9X/VawsLC5QqVSpP209PN+6NpvDvPJJKERCVYsZ/kHHjIir//RMBhUIJpZI3M3mlUCiNfgxJE9s4f7Cd8wfb2fTYxvoLDw9HaGgoqlWrBqVSqRXkePbsGbZt24aOHTtizJgxSElJwdq1a9G1a1fs2rVLrzEB58yZo3H/5OLiYvT9ICIiIvNl9CBOqVKlULZsWRw8eFA9FTkAhISEIDAwkDNTERERkVkKCgpS39tMnDgRYWFhGsvd3d1x5MgRjemqAwICEBQUhC1btuDrr7/OsYwKFSqgSpUqxq04ERERvTcMDuIkJSUhNDQUABAREYH4+HgcPHgQAODv7w9XV1eMHDkSEyZMQOnSpVGnTh2EhITg+vXr2Lx5s3FrT0RERJRPJJLsHxGztdUeP8/Ozg6lS5fGq1evTFUtIiIi+oAYHMSJiorC6NGjNdJUrzdu3Ig6deqgTZs2SEpKwqpVq7By5Up4eHhg6dKlqFGjhnFqTURERGQG5HI5wsPDUbduXb3yDx48GLGxsShcuDBat26N0aNHw9o6/6f6JiIioneTwUEcd3d33L17N8d8nTt3RufOnXNVKSIiIqL3wffffw9BENC9e/ds8zk4OGDgwIHw8/ODlZUVzp8/j7Vr1+LBgwdYsWJFnuogk+nuQaRUCnnabn74d+hCCALe+xmrpFIhy2NlDlSDmb+Pg5q/b3iszAOPU94JgoA0QYAAIMdPPFUGQVD/XyIIECQCBCHjOEgkuj+I8vtYmdUU40RERETmYteuXdi+fTvmzp2LYsWKZZvXx8cHPj4+6teBgYEoUqQIZs6cievXr6Nq1aq5qoNEIsDFxU7nsuRkKSIjJWYRPHifv8QolQIkEgmcnGzfi15Xjo42OWeidwKPlXngccqbqMSUjKCMIPz3ywD+neDo39cCBEj+Dd5IJAIEZARvLK1kkMqksLCQwd5G+5Hpt+XXsWIQh4iIiMjIQkNDMXXqVAwbNgwdOnTI1TZatmyJmTNnIiwsLNdBHKVShFyeqHNZamoKlEolFArxnZ2hTPXrp0KhfG974igUIpRKJd68SURSkqKgq5NrUqkEjo42kMuToFC8m+cTZeCxMg88TnknCALSJAIgIqM7Z6YPEjHTaxEilKIIQcz43BSRMUt1ano6pEoF0tLSkZSSqDUrpYqxjpWjo41eP1owiENERERkRH/99RdGjx6N9u3ba40jWBCyCtAoFO9+VER1v/y+BnAye5eDaYZQKJTvxX58CHiszAOPU+5JJAIgSDJiODllVmUQRfX/laIIiVKEKGYcB6Uy+63k17F6f/umEhEREeWz//3vfxgyZAgCAgIwY8aMPG3rwIEDAFAgU44L/3YpL4g/wQRD9cyePR29enXJMV/9+rWxZcsm41eAiIjISNgTh4iIiEgPSUlJCA0NBQBEREQgPj4eBw8eBAD4+/tDFEUMGDAAVlZW6NOnD8LCwtTr2tvbo3z58up1mzVrhmHDhmHEiBEAgAkTJqBMmTLw8fFRD2y8fv16BAcH53sQRxAAaysRojI5X8tVly+xQnKKUCC9b37+eR2KFSue/wUTEZHJCACkMhmkMhn0GOL4nccgDhEREZEeoqKitB6PUr3euHEjAODFixcAgL59+2rk8/f3x6ZNGT08RFGEQqHQeLa+QoUK2LdvH9auXYu0tDSULFkSQ4cOxeDBg021O1kSBAGiMhnRz64hPS0pX8uWWdjAtUQNCIJNlmMPmJKvb/73eiIiIuPKGLNY+PdxKkAqkcDJ1QX2djaQSCQZgxqbcTCHQRwiIiIiPbi7u+Pu3bvZ5slpeVbbGTJkCIYMGZKn+hlbeloS0tN0D4r8Lnrw4D5++mkRbt26iZSUZBQpUhRt2rRDjx591HmuXr2MJUsW4MmTx/Dw8MT48ZNQsWIl9fL69Wtj2LDR+PTTXgCAESMGw9bWFk2aBGPdulWIjIyEj09lfPHFZJQuXTa/d5GI6P/s3XmYXVd55/vvWmsPZ6xJk63BlizZGmxZsi0PYBuDCZ0Q6OQ+yU13kn4SCCSBzoUACU+H0AkhfbkkfTvNTcBhJh1CSHfTT3KbGwLpkISAwTZhMrY84gEsy7ZmqarOuId1/zhVpapSlVTTqUm/j59y1T7D3uvsXaqzz7vf9b5yAcZAGEK73QYsuBDvM5q1YzR9Ee9HukWu3BiOgjgiIiIisvL9xm/8GgMDA7zjHb9NpVLh2WcPcezY0bH7T548wR/90R/wb/7Na6lUKnzkI3fxzne+nc985rMEwfSnxI899iiHDz/LG9/4ZgA+9rEP8mu/9mb+4i/+kiiKuv66RERk5owxtNttHnnkEYzxbL5qFx7IspQsT5cky3OhKYgjIiIiIiva6dOnef75w7zlLb/Obbe9BIDrrz8w4TGDg4N84AMf5YortgNQKBT41V99Iw89dJB9+/ZPu+5Tp05y110fZcuWywC46qqd/OzP/iSf//xf87/9bz/ZnRckIiLz0mp16rrl+err7KXuVCIiIiKyovX29nLJJZfykY/cxRe+8DmOHj1yzmPWrl03FsAB2LbtCgCOHTv3seNt27Z9LIADsHnzFnbsuJKHHz54nmeJiIh0h4I4IiIiIrKiGWN43/vu4vLLt/G+9/1HfuInXsXrX/9z3H//t8ceU6lUJjwnCEJgtG7C9Pr7+6e4bQ0nTpxYgJGLiIjMjoI4IiIiIrLiXXbZ5bznPf+RL3zhS3zgAx8hikJ+4zfeRr0+v+LMp06dmuK2E6xZs2Ze6xUREZkLBXFEREREZNUIgoDrrruBf/NvXkutVuP48WPzWt/TTz/Js88eGlt+9tlDPPHE99iz55r5DlVERLrAGEOhEFMoxCPtxFcXFTYWERERkRXtiSe+x113/T+8/OX/gk2bNjM8PMynPvVfuPTSjWzatHle6+7vH+A3fuNtvP71bwTg4x//EGvXruNHf/RfLsTQRURkARkDJopZv3U71kAUF8AMLvWwFpSCOCIiIiJyjiAsrphtrlmzhjVr1vCpT/0Xjh8/RrlcYd++/bzrXf8nzrl5jWnnzl3cccedfOhD7+fEiePs2XMNb3/7b6q9uIjIMmSMoZnn3PfsceLAcuuOCqstF0dBHBEREREZ473H2JiBjdctyfaNjfHez+o5/f0D/PZv/5/T3v/v//27z7mtWq3y1a9+c8Jtk5dHvepVP8arXvVjsxqTiIgsneFWmyydXxB/uVIQR0RERETGeA/NlsGYxc/E6WzfM8sYjoiIyEVDQRwRERERmcB7Zp0NIyIiIt2nII6IiIiIyBTuuuujSz0EERGRCdRiXERERERERERkBVAmjoiIiIiIiIhcdIwxGOMIojKEEeStpR7SBSmIIyIiIiIiIiIXlSgIiOIqQx7sppvBWqK8DWZ5T1hSEEdERERERERELiqhC2jlnm8/d5KTx5+lXCpw86XrMMYs9dDOS0EcEREREREREbkoNZKU4VYT3PLOwBm1MkYpIiIiIiIiInKRUyaOiIiIiIiIiKxIxjA2BWq5T4VaCMrEEREREZEJjAFrzZJ8zeX8+ytf+Sf+6q/+x8LviPN4/vnnuO22A3zpS3+/qNsVEZGzjAEbB7QDQzswtKwBa1Z1MEeZOCIiIiIyZvSEuJXnS7L92DryVor3M3/O3Xf/E48++jA/8RM/1a1hiYjIMmSMoZXn3Hf4JPUkY20p5poNvRirII6IiIiIXAQmnxAvplLouGXTAJEx+NlEcWbAe0+SJERRtKDrFRGRpVdPMmrtlHK0+kMcq/8VioiIiMisjZ4QL3f/1//1br7whc8BcNttBwB45StfDcCjjz7Mr/zKr/LhD/8xP/jB0/zO77yHRqPBe9/7u3zuc39PX1/f2Hpe+9qf5corr+Lf//t3j9128OADfOITH+Ghhw7ivWfr1m388i//W2688ZYpx/LYY4/y67/+Jl784tt5xzt+G2tVuUBERBaWgjgiIiIismK99rW/yOnTp/jBD77Pu971HgD6+/v50z/9OMePH+cP//APeM1rXs+GDZewYcMlPPDA/TNa7wMP3M9b3vJvufrqvfzGb/wW1WqVRx99mCNHXpj28f/u372VH/mRV/GWt7x9VddjEBGRpaMgjoiIiIisWJs2baavr58XXniea67ZO+G+oaFB/uAP3s/VV18zdttMgzgf+tD72bRpC3/0Rx/COQfATTdNnYHzjW98nXe+8+387//7T/OGN/wfc3shIiIiM6AgjoiIiIisSr29vRMCODPVbDZ56KGDvOEN/8dYAGc699zzVf7hH/6OX/iFX+Lnfu4X5jpUERGRGdFEXRERERFZlfr718zpeUNDg+R5ztq16y742K997W7iuMDLX/4v5rQtERGR2VAQR0RERERWpanK0ox2p0rTZMLtQ0ODYz9XKlWstRw/fuyC23jzm9/G1q3beMtbfoWjR4/Mb8AiIiIXoCCOiIiIiKxoQRDSbrdn9Nh16zYA8P3vPz122/e///SEAEyxWOTqq/fyt3/7N2TZ+dusFwoF/uAP/oje3l7e8pZ/y8mTJ+bwCkRERGZGQRwREREROUcpdJSjYFG/SuH5689MZ+vWrbzwwvN88Yt/y6OPPszzzz837WOvvvoa1q/fwAc+8D7uueerfPGLf8vv/M476e3tnfC4N77xzRw69Axvfeuv8I//+Pd84xtf59Of/iSf+9xnz1lnuVzhfe+7izgu8Na3/gpnzpye0+sQERG5EBU2FhEREZEx3nti67hl08CSbD+2ltyns3rOq1/94zz88EP84R/+J86cOcMrX/nqaR8bBAHvfe8f8J//8+/x27/9G2zevIU3v/nXuOuuP5zwuH379vOBD3yEj33sQ7z3ve/GWse2bVfwS7/0b6dcb09PD//P//PHvPnNv8zb3vYm3v/+D1OpVGb1OkRERC5EQRwRERERGeM95K2UaKqCMosg9ynez+455XKF3/3d98748bt27eZjH/uzCbf96Z/+xTmP27t3H+9//4enXMell27kq1/95oTb+vv7+fM//x8zHoeIiMhsKYgjIiIiIhN438nIERERkeVFNXFEREREREREZFUzGGBpskwXkoI4IiIiIiIiIrJqGWvBrI7wR9dexT/8wz/wUz/1U1x33XXcdtttvOUtb+HQoUPd2pyIiIiIiIiIyIiRrBsPPvOrZppwV4I4X//613nTm97Ejh07+OM//mPe+c538uijj/K6172OZrPZjU2KiIiIyByslpPalUr7X0SkSybMnPKwSv7cdqWw8d/8zd+wceNG3vve92JGOhsMDAzwmte8hoMHD3LgwIFubFZEREREZsg5B0C73SKK4iUezcWr3W4B4Jz6jYiILJRO9Zt8qYfRFV15t0jTlHK5PBbAAahWq4CuNoiIiIgsB9Y6isUKw8OnAIiieMK523KR54YsW33nj9572u0Ww8OnKBYrWLs6ajWIiCy1sXeyVRp76EoQ5yd+4if47Gc/y6c//Wl+7Md+jNOnT/O+972PPXv2cP3113djkyIiIiIySz09AwBjgZzlyFpLnq/Oq6kAxWJl7DiIiMj8GDqzpnyS4/3qfO/oShDnwIED3HXXXfz6r/86/+E//AcAdu/ezcc//vGx1N25CoKFvUrhnIUMrAFjTecHwBiDsSNfpvM4a1dnJG8xOGcnfJeFp328OLSfF4f2c/dpHwt0znd6e9dQrfaTZelSD+cczhl6e0ucOVNfldk4zgXKwBER6QbvlYkzG9/+9rf5d//u3/Gv/tW/4qUvfSmnT5/mgx/8IL/8y7/MX/zFX1AoFOa0XmsN/f3lBR4tJKcHCcIAGwcEocMaQxgFuDzARgFh4Cj1lRZ8uxejnp7iUg9h1dM+Xhzaz4tD+7n7tI9n7gc/+AGf+MQn+O53v8v3vvc9rrjiCj73uc+d87j/8T/+Bx//+Md57rnn2LZtG29729t42ctedsH1HzlyhPe85z189atfJQxDXvGKV/Cbv/mbVCqVbrycCay1WBt1fTuzFQSWQqFAo5GRpqvziqqIiMhsdCWI8573vIdbbrmFd7zjHWO37d+/n5e+9KV89rOf5V//6389p/XmuWdwsL5QwwQgCBwFIE1SklZKnmQUvSdpp6StFGdTkjSjdrquej7z4Jylp6fI4GCDLNNJWDdoHy8O7efFof3cfd3cxz09xVWZ4fO9732PL3/5y+zbt488z6c8L/ibv/kbfvu3f5s3vvGN3HLLLXz+85/nTW96E5/+9KfZv3//tOtOkoRf/MVfBOA//+f/TLPZ5D/+x//Ir//6r/ORj3ykWy9JREREVpiuBHGefPJJXv7yl0+47ZJLLqG/v59nnnlmXute6KswowX8cg8+950f6BSb8/nIl4csy8lzBXHmK8tyXUnrMu3jxaH9vDi0n7tP+3jm7rzzTn7oh34IgHe84x0cPHjwnMe8//3v51WvehVvfetbAbjlllt4/PHH+eM//mM+9rGPTbvu//W//hff+973+PznP88VV1wBQE9PD69//et54IEHuPbaaxf+BYmIiMiK05XLZBs3buThhx+ecNvhw4c5deoUmzZt6sYmRURERLrqQrVLDh06xPe//31e+cpXTrj9R3/0R7n33ntpt9vTPvcrX/kKO3fuHAvgANx666309fXx5S9/eX4DFxERkVWjK0Gcn/7pn+bv//7vec973sM999zD5z//ed74xjeyZs2ac05sRERERFaDp556CoBt27ZNuH379u0kScKhQ4fO+9zxARzoZAtv27ZtbL0iIiIiXZlO9fM///NEUcR//a//lb/8y7+kXC6zf/9+/vAP/5D+/v5ubFJERERkSZ05cwboTIMab3R59P6pDA4OUq1Wz7m9t7f3vM+biYXu7LmY1EVt5dCxWjl0rFYGHaeZMcZgAGsMduRnM68Vnv3Rmpl1q17sY9WVII4xhp/5mZ/hZ37mZ7qxehERERGZgW519lxs6qK2cuhYrRw6ViuDjtOFnai3iKKA1BiCcLoQx8TQjjFmymiPGf3PGqI4wAWOMAyoFC/crXqxjlVXgjgiIiIiF5ve3l4AhoaGWLdu3djtg4ODE+6fSk9PD8PDw+fcfubMGS699NI5j6kbnT0XkzrVrRw6ViuHjtXKoOM0M8YYEgPtdkqrnZJOm306MYvGez/5ppFHjfyXe9ppisszkiSl0Zq+W/VCHauZdvdUEEdERERkAYzWtJlc3+app54iDEO2bNly3uc+/vjjE27z3vP0009z6623zmtcq6H7mLqorRw6ViuHjtXKoON0ftYafGDIvSf3Hs+UsZlzGGOnnnc17sm599hZdKterGOlCXYiIiIiC2DLli1s3bqVv/3bv51w++c//3le9KIXEUXRtM99yUtewqOPPsr3v//9sdvuvfdeTp8+zR133NGtIYuIiFw8zNkfTBBhXMw8K+gsCWXiiIiIiMxAo9EYa/d9+PBhhoeHxwI2N910EwMDA7z5zW/m7W9/O5dddhk333wzn//853nggQf48z//87H1HD58mFe84hX8yq/8Cm9605sA+OEf/mE+8pGP8OY3v5lf+7Vfo9Fo8H//3/83L33pS7n22msX/8WKiIisWp68nZGH6VIPZE4UxBERERGZgRMnTvCWt7xlwm2jy3/2Z3/GzTffzKtf/WoajQYf+9jH+OhHP8q2bdu46667uO6668ae470ny7IJc+vDMOTjH/8473nPe/i1X/s1giDgFa94Be985zsX58WJiIhcRHzu4QLTo5YrBXFEREREZmDz5s089thjF3zcT/3UT/FTP/VTs17Phg0b+MAHPjCvMYqIiMjqpiDOTBiDtWfnynnfKW4kIiIiIiIiIrJYFMS5ABNGBIHDJINjhapNWKSeOgVyRERERERERJYRAzjnsJiVWLf4ghTEuQDrQnzSZPjgvaSNGq5QprL3doytTtsnXkRERERERESWgIFytUQhMFhjMcZgzOqJ5iiIM0NZs0bWGF7qYYiIiIiIiIgI0InNmLFAjQHwnlbzFJDj/QbAMzklx1gDdvxt5wZ5jOlk8pix7SwPdqkHICIiIiIiIiIyG8ZAEEAnWJOBz/AjRVB8npLlKUw5ecZgQocJg3ErCifcj7W4vE3gUywpRZctm0COMnFEREREREREZEUxxpAkKcPNFqdPnWKw2aZiB4ABRvJnzn3OyPd2O6EdtMhzTz5VmZQ8Jzn1AnlQJBs4jStajCkvi5IqCuKIiIiIiIiIyIqU5zlZlpOlKUmSAp2O0ucrauz9aPBm+qCMTxM8IT5PF3jE86MgjoiIiIiIiIisCMZ0snCsNYzGaowHg8HZ1V8xRkEcEREREREREVn2jIFiEECSYYD2SLZN3k7Jmgl5li3p+BaDgjgiIiIiIiIisuwZYyDJGHzkGBhPdEW1c8fozKh8KUe3OBTEEREREREREZEVI2slAPh86QsNL7bVP2FMRERERERERFYlYwwudLggwAZuqYfTdcrEEREREREREZEVx1hDqVigt7+CK8WUShGMFD4+X+eplUyZOCIiIiIiIiKy4owGa1qNk9QGn6PdHBy9ZymH1VUK4oiIiIiIiIjIipWlKVma4L26U4mIiIiIiIiILBkzMkXKWrNaZ0nNmDJxRERERERERGRZMgYC58iSnCzJwXusuXhDGcrEEREREREREZFlyRhDu5Vy8DuH8R6u3rmePMsxdvXWvTkfBXFEREREREREZFlr1BMA8ixf4pEsrYs3B0lEREREREREZAVREEdEREREREREZAVQEEdEREREREREZAVQTZy5GG1tNsJ7j7/I25yJiIiIiIiISHcpiDNLJowIAodJBsfa05uwSD11CuSIiIiIiIiISNcoiDNL1oX4pMnwwXtJGzVcoUxl7+0YW8UriiMiIiIiIiIiXaIgzhxlzRpZY3iphyEiIiIiIiIiFwkVNhYRERERERGRlcOAMWBGfr6YKIgjIiIiIiIiIiuCGWk0lOe+U6f2IqtqoiCOiIiIiIiIiKwIxhryzHP6ZIM0zQEuqvq0CuKIiIiIiIiIyIqSZflFl4UDCuKIiIiIiIiIyGpmDKuleI6COCIiIiIiIiKy6owl6qyi6VYK4oiIiIiIiIjIqrV6QjgK4oiIiIiIiIiIrAgK4oiIiIiIiIiIrAAK4oiIiIiIiIiIrADBUg9ARERERERERGQqxoC1hkIxHFu+mHU1iPP//r//L5/85Cd58sknKZVK7N27l7vuuotCodDNzYqIiIiIiIjICmcMFJzDZxm7d6wFYygVI84s9cCWUNeCOB/60If42Mc+xhvf+Eb279/PqVOnuPfee8myrFubFBEREREREZEVzhgwxmCtgTRn8JFjHDt0mkJfkZ7rN8FFnI3TlSDOU089xV133cUHP/hB7rjjjrHbf/iHf7gbmxMRERERERGRVcAYCJyj3Urx1uCNIWsmtIfb2Ngt9fCWXFeCOH/1V3/F5s2bJwRwRERERERERETOxxhDu5Vy8DuH8R727t4AfqlHtXx0JYjz3e9+l6uuuooPfvCDfOpTn2JoaIhrrrmG3/zN32Tfvn3d2KSIiIjIkvu5n/s5/vmf/3nK+973vvfxqle9asr77rzzTg4fPnzO7Q888ABxHC/oGEVERFaCRj0BIM8VwRmvK0GcY8eOcfDgQR5//HF+53d+h2KxyIc//GFe97rX8Xd/93esWbNmzusOgoXtiu6chQysAWNN5wc60b+ZLBtrMKazHmv1yzUd5+yE77LwtI8Xh/bz4tB+7j7t4+74nd/5HYaHhyfc9slPfpK/+7u/40UvetF5n/vDP/zDvO51r5twWxRFCz5GERGR1Wv1fybvShDHe0+9XueP/uiP2LVrFwD79u3jzjvv5M///M95y1veMqf1Wmvo7y8v5FABSE4PEoQBNg4IQoc1hjAKcPmFl20UEAaOUl9pwce1GvX0FJd6CKue9vHi0H5eHNrP3ad9vLB27Nhxzm2//uu/zq233srAwMB5n7t27Vr279/fpZGJiIjIatCVIE5PTw99fX1jARyAvr4+9uzZwxNPPDHn9ea5Z3CwvhBDHBMEjgKQJilJKyVPMorek7RT0hksO5uSpBm103W8X/1Rv7lyztLTU2RwsEGW5Us9nFVJ+3hxaD8vDu3n7uvmPu7pKSrDZ8S3v/1tnn32Wd761rcu9VBERERkFehKEGfHjh0888wzU97XarXmte40XdgTTWM606NyDz73nR/oZBPNZNnnHu8hy3LN1ZuBLMsX/BjKRNrHi0P7eXFoP3ef9nF3fe5zn6NUKvHyl7/8go/967/+az7zmc8QhiEHDhzg7W9/Ozt37lyEUYqIiCwvxkChGI79LGd1JYjzspe9jL/6q7/ikUceYffu3QCcOnWKhx56iNe+9rXd2KSIiIjIspKmKV/4whe48847KZXOP+36zjvv5Nprr2Xjxo0cOnSID3/4w/zsz/4s//N//k+2bNkyr3EsdD3BxaTaTSuHjtXKoWO1MlzMx8laQ+Acu3esBWMolSLOmNEEjLMRHcPCR3estRjjcGERGxU7JXbMSB1cO3Ud3MU+Vl0J4vzQD/0Qe/fu5Vd/9Vd529veRhzHfPSjHyWKIn72Z3+2G5sUERERWVa+9rWvcfLkSV796ldf8LG/9Vu/NfbzgQMHuPXWW3nlK1/JJz7xCd797nfPeQzdqie42FS7aeXQsVo5dKxWhov1ONWP1xh85Bg2cvRetxFjDNYa3EhjobHGQwsYx4mCgLjYx5CxMHANPaWNNJ+5D2s6QaVi7/kvyCzWsepKEMday0c/+lF+7/d+j3e9610kScKBAwf49Kc/zbp167qxSREREZFl5XOf+xx9fX3cdttts37u+vXrueGGG3jooYfmNYZu1BNcTKqPtXLoWK0cOlYrw8V8nKy1BN7THm5jYof3nXImee7JRsqZjJU7WcCKJqF1NHP4zgsnaDVOcuO6Ep6A3HvSbPo6uAt1rGZaU7ArQRyAgYEB/tN/+k/dWr2IiIjIstVsNvn7v/97fuzHfowwDJd0LKuh5pFqN60cOlYrh47VynAxHqdgJErh8ZhxUZpOAGXccpfaiTeSlGa7Db7U2ZyfWR3cxTpWF98EOxEREZEu+8d//Efq9Tr/8l/+yzk9/8iRI3zrW99i7969CzwyERGRlcuFDmMNrhAQlsIFnU61UnQtE+eiMjI/b5QfidSJiIjIxemv//qv2bhxIzfccMM5973mNa/hueee44tf/CLQmXb1pS99iTvuuIP169dz6NAhPvrRj+Kc4xd+4RcWe+giIiLLkosdA7sHaJk2dnORvktiwmJAstQDW2QK4syTCSOCwGGSwbFkLhMWqadOgRwREZGL0JkzZ7j77rt5zWteM9JJY6I8z8mybGx58+bNHD16lPe+970MDQ1RrVa55ZZb+NVf/dV5d6YSERFZLVzoyMh55NFHGHrhFIVChWtfdO1SD2vRKYgzT9aF+KTJ8MF7SRs1XKFMZe/tGFudsuiRiIiIrG69vb0cPHhw2vs/9alPTVjev3//ObeJiIjI1LI07fxgRluCL+14FpuCOAska9bIGsNLPQwRERERERGRVclYw+bt22luybAuoBUEI+3H3VIPbdEoiCMiIiIiIiIiy561ltQa7j10hEaSc2UK12zom3L68mqlII6IiIiIiIiIrBhDjRb1xNNI0qUeyqJTi3ERERERERERkRVAQRwRERERERERkRVA06lEREREREREZFkbrXpjDARhgPP52RsvIgriiIiIiIiIiMgyZzDGUCoV6O2vECQpznSCOsZePNEcBXFEREREREREZFnzeLzp/NSsn6DWqNMuA6xd2oEtMtXEEREREREREZEVI89S8jQB75d6KItOQRwRERERERERkRVA06m6wRjsyJw87/3FGBwUERERERERkQWmIM4CM2FEEDhMMogHTFiknjoFckRERERERERkXhTEWWDWhfikyfDBe/EeKntvx9gqXlEcEREREREREZkHBXG6JGvWlH0jIiIiIiIi0i0X4WduFTYWEREREREREVkBlIkjIiIiIiIiIkvCGDDGjFs2XJQpNjOkII6IiIiIiIiILDpjIHCOdis9e9uE+E0nuGMCS1gKsc6SjQv4XIwUxBERERERERGRRWeMIWmnHPzOYRr1BID1l1bZs2MdBoP3nhxPtH0NxTQlLxewzmKsW+KRLx3VxOk2Y7D27NdFHjQUERERERERARj5fGyo1RKGh9s0GimtZjrxMRZaFu47dILvvHCK3E+cfnWxUSZOF5kwIggcJhkcm9FnwiL11KlzlYiIiIiIiFzUjDFkec6RU3WGhlpsXFOe9rG1ZkKcZIs4uuVJQZwusi7EJ02GD95L2qjhCmUqe2/H2CpeURwRERERERERfO4xIxk2xpjRUjgyBQVxFkHWrJE1hpd6GCIiIiIiIiLLijHQ1xsRBjm5TcnJRmayKPFhKgriiIiIiIiIiMgSynl+6ChhE+IBj/cb8Xgl5ExBQRwRERERERERWRDGnC087L2fcT3YdpbiLTjnMYaRLlQK40ymII6IiIiIiIiITGt8YAamD84YAwUy8kYDAFso0jQzb+xTiEN2bd+I8TmVisMWYozycSZQi3ERERERERERmdJoYCZsDI19FciYqsu3MYa82eDYl+/m2JfvJm82zmkHbgxYa7DWjBQyPntfFAZYn1IbOs3gyeNA3t0XtwIpE0dEREREREREpmSMIW90AjNprUZQLrPujtsxxem7Lqe12jTrgtDlJO0GBshzQxQViAsBzp7NMcmznDRdvHbiK6l7tII4IiIiIiIiInJeaa1GMjTSddl0Mmk6PKM9wa09/+QnYwxJu8Hj372PtFEjjgvs2P9iSs6xuTJAwza6+RJWBQVxRERERERERGRGbBwRhg5TH8RjCAsxaaPZCeU4i8V3YjrTJLcYoN1s0B4epH3iJPnVCX7wNNVSRBgbgiDAZCsnM2axKYgjIiIiIiIiIjNig5Cs2eTYV+/FFUusuWEfx756L8lwjcK6day5Yd+0MRxjOv/LM0/mLa5cwQYBO/dvw2PJrKevVMblflymj4ynII6IiIiIiIiIzEpaq411nRqdahWUy+d9jjGGLPecPtVg+NQQ1XIJrCMwnqMPHKHRbBJtX0ffjrVYqz5MU9FeEREREREREZFFk6c5aZKTZxkGsM6StzPyZkrWSpd6eMuaMnFEREREREREZNGFcUwQhbiKp7cvphwbgsAt9bCWNQVxRERERERERGTRBWFImrRpDtY4efR5fJJTWV9d6mEtawriiIiIiIiIiEhXGMNYkWJjDBgICzHlniqFchlrLT73pO0Ev2hdqcxIleWVR0EcEREREREREcGYkUAL4L0fK1w8nxU6Y2m1O3VujO+s/9rrb4QU4jgiMgVMr8U5R5p1tx6OH/u+cluYK4gjIiIiIiIicpGzFgKXk7ab4D1hVCTJ5tcLyVhDq51x8DuHadQT1l9aZfuOMoEJOPbAM9jc43ZZSlt6CEoxNgpxcYCxhrAcYwP1YppMQRwRERERERGRi5gxUDA5rcYw37v/Xoxz7Nj3IkxQWZD1N+oJjXpCq3k206Y93MBmkLcSMIYN120l9Tm2UqJlMnr2XEqxWIKVOeupaxYlrFWr1XjJS17Czp07efDBBxdjkyIiIiIiIiIyA8YY8laT9slTDH7/BzSHhi5YM8bGEUGphHGOoFohrFbGlm0czW771pAbz8MHH+I73/oW99//HR5+6GEycqyzGGtGsnIU0VmUTJwPfvCDZFm2GJsSERERERERkTnweUaeJRd+oLX07t9L5iyJ86x5yYupHzuKCULaNqd3/15Of2f2CRz1Wh0XBmQhkGRElQJtEkzBEvVG2GAkiHMRx3K6nonz5JNP8hd/8Re8+c1v7vamRERERERERKTLjDGkxvP4t+/h6Ufup522eew79/LAV/4Xj99/H6nx2CCc1zaCMCBJE773xPcYPDPI4Jmhs3eu3LrE89b1IM573vMefvqnf5pt27Z1e1MiIiIiS+qv/uqv2Llz5zlff/AHf3De53nv+ehHP8pLX/pSrr32Wv71v/7X3H///YszaBERkTlqDg/RbjRGfh6kMXSGVr22oNtot1rkWUau2T1Al6dT/e3f/i2PP/44H/jAB3jooYe6uSkRERGRZePjH/841Wp1bHnDhg3nffzHPvYx3v/+9/P2t7+dnTt38ulPf5rXve51fPazn2XLli3dHq6IiMgyMN0cqc7tFyjRc9HoWhCn0Wjw+7//+7ztbW+jUlmYitYAwQK3GHPOQgbWdIopYUd/Qcy8lyffZ6zBmM42rb248r+csxO+y8LTPl4c2s+LQ/u5+7SPu+vqq69mYGBgRo9ttVp85CMf4XWvex2vfe1rAbjhhhv4kR/5ET7xiU/w7ne/u3sDFRERWS4M2HGBnNGgzeTvF7uuBXE+9KEPsWbNGn7yJ39ywdZpraG/v7xg6xuVnB4kCANsHBCEDmsMYRTg8vkt20n32SggDBylvtKCv4aVoqenuNRDWPW0jxeH9vPi0H7uPu3jpfftb3+b4eFhXvnKV47dFkURr3jFK/jiF7+4hCMTERGZO2NmGXjx4H2OwXWePxLQ8T7H5zl5nndhlCtPV4I4hw8f5k/+5E/44z/+Y4aGOsWH6vX62PdarUa5PPtgTJ57BgfrCzrWIHAUgDRJSVopeZJR9J6knZLOc9nYifc5m5KkGbXTdbz3GAOxSfBJY2w8JizS8iF+lSXqOGfp6SkyONggy/SPrxu0jxeH9vPi0H7uvm7u456e4kWf4fPqV7+aU6dOsXHjRv7Vv/pX/OIv/iLOuSkf+9RTTwFwxRVXTLh9+/btfPKTn6TZbFIoFLo+ZhERkXkznS/jLJRjvIWcHCyEkSOOA6y1WHv+84QwDInDEGstURxjrJn4GdlM+n4R6UoQ59lnnyVJEn75l3/5nPt+/ud/nn379vGZz3xmTutO04U90TQjocHcg8995wc6BQbnu2wm3edzj8fgvSfPPdYa8qTB8IN3kzVruEKZyt7byWxAnq+yKM6ILMsX/BjKRNrHi0P7eXFoP3ef9vHCWrduHW9+85vZt28fxhj+8R//kT/8wz/kyJEjvOtd75ryOYODg0RRRBzHE27v6enBe8+ZM2fmFcRZ6Knoi0nT/lYOHauVQ8dqZVjs42SMGTdtyYxl0diR7ZuR8iNjX2NPnLQOa8EFmHIVt30X9dyRkxOEhj27LqVSCIlMgOnpwwUBPkvPGUsYhWzdtZPchLTDmDVbLscFEdYdO/sgvwgNqszo10hZFDt1WZTFPlZdCeLs3r2bP/uzP5tw2yOPPMLv/d7v8bu/+7vs3bu3G5td9kwYEQQOkwziAYPFGk/WqpE1hpd6eCIiIjJPt99+O7fffvvY8m233UYcx3zyk5/kjW98I+vXr1/U8XRrKvpi07S/lUPHauXQsVoZFvM4NY/XsdZgR2q6BoGjZ6QUSLM9TBR1SpCEkcNY03nsSECnUxLWYANHlkGt1ubxBw+zZotl65XrKceOCDhz8Aj5ugKlLT0Ya/FTZNIEYUBmA77+zDGCM03itM2Byy9dlJo44xN8DCP1bI0hcI5i7/nLoizWsepKEKenp4ebb755yvuuvvpqrr766m5sdtmzLsQnTYYP3kvaqBH2rqN61b6LMQNMRETkovHKV76SP/mTP+GRRx6ZMojT09NDu92m1WpNyMYZHBzEGENvb++ct92NqeiLSVMrVw4dq5VDx2plmM1x6gQ3xn+q9LMuz2GMIUgz8tyTj8wkybKcM2c6pT9c7kmNpdVKse0Mn3dml+S5xwPeQz4y4yQIIyq9Fbbt3kBUDCCvsWljkTxtkzTa5K2ZhSGGWwlhOyVLk9m9mHnw4757OhnLufek2dmyKJMt1L+pmU5H72qLcZla1uxk3rjCyr8yJiIiIvMzWgvn6aefZteuXWO3P/XUU2zcuHHe9XBWw3Q5TftbOXSsVg4dq5XhQsfJGAhdTtI+W2c1jIqkmZ1VIMdagxt5vPd+ZPqSI20NdsbhU3qv38fpb96PKxTBuc7UqXHbGA1wuCAgS9s88q17yPFsuHQNGy+/FvxobbgVUjpkNJLjO0GxLMvPW/Zksf5NLVoQ5+abb+axxx5brM2JiIiILAuf//zncc6xZ8+eKe+//vrrqVQqfOELXxgL4iRJwt/93d/xkpe8ZDGHKiIiK4wxhqTd4LH776PVqBMXS+zcfwsmqEyZNTJTLgxJ200e/+43aTXrVEoltu+5gcr112Cso21ygmqlU8B4GvWhYXJymn2daUajMRGZH2XiiIiIiCyQ17/+9dx8883s3LkTgH/4h3/gM5/5DD//8z/PunXrAHjNa17Dc889N9Y+PI5j3vCGN/CBD3yAgYEBrrrqKv7rf/2vnD59mte//vVL9lpERGTlaDXqNOu1BVmXsQ5rDXhIaoO068NkQUSaJjz2ra/RbjRYt2sXm7fvmWUP8YlcHBAEFhsH81rPgljq7c+CgjgiIiIiC2Tbtm385V/+JS+88AJ5nrN161be+c538nM/93Njj8nznCzLJjzvl37pl/De8yd/8iecPHmS3bt384lPfIItW7Ys9ksQEZGLnDEWn+X4JKH5wlGajRq9/Z0LEc3hQVr1Ou1G4wJrueBGWHvDlbStIa2WyYrRWCesxWdGulabse7Vy5mCOCIiIiIL5Ld+67cu+JhPfepT59xmjOENb3gDb3jDG7oxLBERkVnz3pNnCT49tw34fBlrSALHfd8/Sqm3wYuv2oix0wRQbCe4Yl3QpYSZTiHnPFsZk70UxBERERERERGRRVdrtqEQT3u/AcqlEkHoqPYUsaYz82nBM2Y8cJ6ixcvJUuUriYiIrCrGdDo7jH6tgGxcERERkWXOYIwnz1Ka9eO022c6t17E51nKxBEREZknYwDnaLTOphsX4gCTZbNq7ykiIiKyWMZntFhrWN5xEd+pJ6cTKwVxlrvJqWJ+pEe9iIgsrcknPrV2yj9951lq9YRyKeSl122mGNp5tfcUERERmY/pPk8aAwUy8mYTYw3GWpy1GNudyTrWWZxxOLe8Q0UrgYI4y5gxUAoyfHK28rcJi9RTp0COiMgSmpx542yno0GtnjBUby/p2ERERERgXKBmXCcpWyjSNA5jDHmzSfvMSZIswYYRcX8fQbWCuVCXKDPNz9MIwohKfwWXONKCm7KejTHgAkexXKLcU8UFDutsJwi1zHOEFpuCOMuYMQafNBh+8G6yZg1XKFPZezvGVnVlV0RkCRljaLTOZt6sGyhyYNclE89pRmrkjFImpYiIiCwmYwx5o8GxL99NWqsRlMusu+N2TLHaud8akizhka9/mTRJWLdrF5u374HpukSN8eN+uvDJjQsCsiShcWaIoTMniSobJtwfBRYXOJo9VbbedD29/T2YIKCnt0wLjwtUync8BXFWgKxZI2sML/UwRERkktHMm0oxnHB7HDrC0FFrZ2PnOaqRIyIiIkshrdVIhqb/PNkcGiJJWrRHMnbGB2ZMJ9n47DIjjZzIyX1OO01mPI4sS8mzs/UDjTXYwBKEAa0855+fPcmzLxxh+2UZey/t7xQyNg5KG86z1ouPgjjLjTFjV26tNZAv8XhERGTWwsDSbGXcff+zDKtGjoiIiKwg7TQh950gTT5dpo0/54fZMYaop0g8UIHYAdAgZ6jZppEkgCfPMrw73yq6PM1qmc7iUhBnGTFhRBA4TDKIBwwWa/yy/eUREZHzU40cERERWXn8jKZJTTb5Y6sf+Z8/58ZOho8Hzpw+Q95ukmVrOp+BZxiYWbRrYsvws7iCOMuIdSE+aTJ88F7SRo2wdx3Vq/Ytx98bEZFlTZ39RERERBbALM6fcuMnPCGKY1wYEBeKWOew5tzaNmmakqXnSbdZAvNNMuo2BXGWodEaOK5QXuqhiIisOJM7R4Hq0YiIiIjM1Pg8nCy/UNZLp0pO7nMa7dZYzRtjLdfdchuhiyiWixSDImFfwGn3fJdHv/opiCMiIqvK5M5RqkcjIiIiMnuzPWvy5GPnWsYYQhdx6sHnqNkAf/kmipf1YVCnqflSEEdERFYl1aMRERERWVpJrYmxAdm4DGmZHwVxRERERERERGTWjLVgDMYYXBCyLCsBrzIK4oiIyKpnDFg78aRCxY5FRETkomPM2DmRsbbzBXOLvRiIewfIcNggpLTmElqtxvTrMoawFGPjAGMNRidic6IgjoiIrGpx6AhDR62dTZjcfaFix+pwJSIiIquJjSNsISBJhgFI8QTVCt4ZfDqXkxwDBgbPNEiSjJPHh0nbTQYuneKRgcUUHZXd6zGFmMR6jHEzbikuZymIIyIiq1oYWJqtjLvvf5bhegJwwWLH6nAlIiIiK5UZn23jLDaOYAhsENJut3jswW/SatTp6+lh8/Y9tPM2jbRJTj66hvOtfOSHsydE+UgLqzRJSZNsyqdZZ2knCQ8ffBhPzoYrNrNu23bNvpoDBXFEROSiMJtCx+pwJSIiIitREEYEoSMdybZJSOm9bi8n7r5v7DHtZp1Wo0Y7igDIPfg8n3J9wMRAi7FgXee7MUyOwlzoLKlZb5D7nHZbzSfmSkEcERGRaajDlYiIiKwkLghI2y0e/+43aTXrFMOQyzZfiQ1DbBxircGmCTZLLrguAzjbmXY1ekPYP0ASx+TGEYQRxUovtmWwzhEVCrTbre6+QFGTdhEREREREZHVYHS2U1IbpD10mla9PnafdSE+z2kePUbzyDEmzBE3Z7/bIOjUqul0hqCZtsh9jveeDM/J48PUhlLiqMz2Hdexa/d+ymGV61/0EqJCYfFe7EVKmTgiIiIiIiIiq4IBD63jJ2gODVK4vHTOI3ya4NN04m2jyTZBQFztx5sAG4RE/Wtg6BQePzZVKk0yjHWQeM489DytoRZsvpRwTUQ4MkVr6az+IjsK4oiIyIo3vpOUtWZG79+T246r+5SIiIisFj7NzgnUXFjnvOjMqQatJCJLc7zptCGfTlJv0R5ukjYTFF5YHNrLIiKyok3uJOXsuUX2Jpuq7bi6T4mIiMhqZJwjKJdxxQIzudKVZRlZli9aM4fRIJHB4AI3rgOWTEVBHBERWdEmd5JaN1DkwK5LznuKMrntuLpPiYiIyEpl6JwPdb4m3heEEXFfH/7anbi4ANZ0ukstE9ZaevqqWGtx1tPTXyUIls/4liMVNhYRkWVn9ATEGIO1556QTGW0k1SjMfPU4dHn1OoX7tCw0Eanc43/0oUnERERmQ1jwOI7HafSNjYbOQ8aOadwQUiaJjz2ra/x9IPfJPf5eadHLTZrLRhPnqe0Gidp1U+MnA/ppGg6ysQREZFlxRjIMRw9WSdJMrz3izLVaXyNnJnW1ZnPtsZPARulKV0iIiIyE6P1AK215HlO6+gx6qeOU3IxY3PFx2kOD2Ldcv743wnk5Pls6/hcfJbzURQRkYtQZ3pUxj0Hn+f0mSbFYnDOVKe5FDI+n8k1cmZSV2e2JgeJau2zU8AATekSERGRGTEGQpeTJk28txhn8T4nT1J8lo174KQfF+DUJohiyj09FIrFCSu0oYMwoFSpEBWKGLN8sn1WGwVxRERkWao1OlOd8kkBjbkUMr6QyTVyZlJXZzamCxKNTucSEREROZ+JF7AgTRs8cv/XiAslrrhqL8aaKQI1Z8+hcuOZKkNnRtse/W4tV159gK1X5BQrFZxzGGNxcUjxigHSOOeq62+kUCoQFYu0XG1O25PzUxBnpRmpDzFKLXFF5GIzl0LGMzUaVKkUwwVY21ndDhKJiIjI6mUMFMjIGw0AgkqZOp7nTz5HsVRhm78GDOTk+JFAjR/5KScn9zmtpEVpjtsfDf8YY3CEHHvgaZI1ffTePIA1Fhc6UjIefvgRDh/6PmvWr+HATS9eVrV3VhMFcVYQE0YEgcMkg2MxVBMWqadOgRwRueh0K+DSTStxzCIiIrK0jDHkjQbHvnw3Noq49GW3Y9OcXleg4AoYY/DeU08atLJOhm+SJ4AfS77J5/h50RhwQdDJAhrJBGrXmkSlczOJm80mw0NDFCvFuW1MZkRBnBXEuhCfNBk+eC9po4YrlKnsvR1jq6qfICLSZYtR+Hj8NkDZliIiInJWWqsRAHmrRfvMSYafPYxZu27k3k4gx+d5Z3Ehzh8MuCgm6lmDNwHOBWQ2ZbqTIGMgCAOsCzDWEoYhUblAaANsFCzvhlOGTvv1FUBBnBUoa9bIGsNLPQwRkTkbP68bln+wYjEKH0/eBqhblYiIiEzNZxl50iZPu9fNyWDIc8/J48O4uE6a5ed7MGFcYM3aS+jr7SeMIkyPYeC6rRgPplro1O1xBpZhAypjLVjAmLNZR2Z5BnUUxBERkUU1VXvtCwUrFrP991QWo6bN5G2oW5WIiIh0zSxOYtIkI8vy82b3GGMgh9bJOllQpt1s8fhjj3P8yecwwPrLNnLlvt0USiXyLCWKonm/hIXlSdpt8jwnX+YXFxXEERGR81rorJnJhYkvFKxYjCyYmVqMmjbqWCUiIiLnMIagXCYolTDOdVpUzebpnE0s8WNljxmZMhXiknD0UfMaZp5m+JGAT7PZpD5cwwBZlmLiApuuvRp8TrFSxDiHWUZTmFbKRTMFcUREZFpzyZqZbj1n22J2MmlmGqxQZycRERG5mBkDthBQunYnxjpaJiesVjHBzD/OG3M2SNHK2jTzBO8cJggoDWyAuIy3DmstpWqVuFTGOkcQnpsx4+KQsBhhrCEoxZC6C049cs7RynPuPXSMoXqTLZeu4eYryhMuFM6WmXZhdVMQR0REpjXbrJmp18GEQNBcM2nU2UlEREQuBuMvfpmRGi1Jo8Vj3/oa7UaDdbt2sXn7HoyZWwtvjwcDWQ5ZknPy2BBnTp+hunYLhajCvutejA1CSkGZ7bv284PHv392bNZyyY07MHFAI29T3rUeMNhCQBAFlCplCqUC1lqCKYJMw802g40W9XYyp7FPfB3TLaxuCuKIiMgFzWeKz+RAkDJpRERERM6aGLQBH2bUswZJrUFCRmAcoYHW8CDNRp12o7Fg2/Ye0iQlbacYY/Gp5/RDL5A1M8zlCW5TSDCufo2xhtx6Hnv4YYJKRO2F01R7e7j65v1s3r6dgY2XEUUR7TBm8/btHH/iMElrMaaJT47izCaqs7LOShXEERGRRaFMGhEREZGJjAET5TSzJgDOWLI852s/+CYJbdrtjK39m9lX2UJuxtWyWUCTk6vbtSZZIyNrJjgbU6pUKZY706u86Ty4Wa8TuJz6cI0wDDHGkFnHvc88R4Jh46aMq3tCgjA8J4hjrcVY13m97sJTsRbHchjDzCiIs8qstLa9IrLyjO8U1eEZ/8anvzsiIiIiUxv/ec17jzGGZtbk7h98g3q7wdpyP9dtuppau0FCi1Y7JcsT8J5W0qKRNsk5T6vvsQ3Nf6w2dJR7ernuxttwQUjRFfElT/M82dnDjRYtDH1JCpx74c5aS//afkqFCGOg3FMiCNz8BztvK+f8VUGcVcQYKAUZPjmbXmfCIvXUrZhfSBFZ3iZ3ijIGCnFIo5WMZa3OpfCxiIiIyGpnDBTIyEemQ9liidQFOCwGaGcJjaQ57fNzDz6/QABntAPVpPOwc0/LRqZvjRQyzj3ExeLERzgLGZx+8Hnydk5wlaeyfc35tz+OtYZiuYQBojgGDMYajPW0m6fI87W0m4OY4szXKV0K4nzhC1/g//v//j8eeughBgcHufzyy/m5n/s5fvInf3Je1adlCsaMXRG31uCTYYYfvJusWcMVylT23o6x1RXTLk1ElrfpOkWNLs+l8LFMb3LWk7KcREREVi5jDHmjwbEv342NItbdeRtZu4kj5dq+rbT6cp4een5e2/Ae8JDlZydeeToBoAmPwROEIeVSlRtuuYM0San09eKswxpHRjb2+HatSd7KycZ1K72QyFkq5QpbrttLlmWUK2WwdqwYc+5z8B6fzXyd0tGVIM6f/umfsmnTJt7xjnfQ39/PPffcw2//9m/zwgsv8KY3vakbm7womTAiCBwmGRyZzGCxxpO1amSN4aUenoisYpPr28yn8LFMbXLWEyjLaSWY64WsO++8k8OHD59z+wMPPEAcx90csoiILKCZlLdIazUCoN1u8fiD36TWHGQwa3DlvpsJbJemFo2L6OR5Tp57rAsg9Zx68Hnqp4bIr9hE700Dk6bNz01gLe3cc8+h45wZqnP5xrXc0lNRUscC6EoQ50Mf+hADAwNjyy960Ys4ffo0/+W//Bd+5Vd+BWvn1gpNJrIuxCdNhg/eS9qoEfauo3rVvonTH8dl6oCu4orI8jQ+48RaA0Z/qCZnPSnLaWWYz4WsH/7hH+Z1r3vdhNuicR1BRERkeTMGQpeTtM+WtwijIklmJ34GMyPBHqDVrNOsD1PP6oswQAcGbBBgg7P1apJak9ZgnbS58BfkhhstztSb1FrzbykuHV0J4owP4IzavXs3n/nMZ6jX61QqlW5s9qKVNTuZN65QnnD75Eydzm2qkSMi5zf+ClInoNLd7U3OOHELcPVnNVGW08oynwtZa9euZf/+/YswShER6QZjDEm7wWP330erUSculti5/xZMUBm7AGMAYz2pyfB4cpNPea4VuZBKVMBHhp64Ou8OTsY5gp4ewFDqX4+Nh5dHUyiZtUUrbPytb32LDRs2zDuAEwQLm8XjRoo1WdPpec/IhwdjzLyXF3Jdc1m2YdTJ1HnovrM1cq65jSDoXfSruM7ZCd9l4WkfL47Vvp+NgRxDo9WZB+1s52+KtQZrTOdv28jfy6mWR29zzmLtyMmKMZjUT7h//HOi0NFsZ3z1/sPU6gnr+otcv3vD2edeYJuzXR5d70KuczHGNNW+nY/V/ru8VHQhS0REWo06zXpt6jsNpD5nsH6KnjzhVOMM0aS3dWsMN67bSX9UIU2SkRbeFuvOnWo10zMCay25hzTNOXlsiFazzqXbZ/e6ZHlYlCDON7/5TT7/+c/zG7/xG/Naj7WG/v7yhR84S8npQYIwwMYBQeiwxhBGAS6f37JdwHXNZ9nlLUzexOYBYRhQ6i0t+D6cqZ6e4oUfJPOifbw4VvN+Pnqyzj0Hn6fWSFjbV2TflesIQ0cUB4Shwxgz/XLUyarp65v4d6Z9sk4YOaJs+nW005xWlpNkfiwTaHR9593mLJeBBV/nooxpmn07X6v5d3m5mOmFrL/+67/mM5/5DGEYcuDAAd7+9rezc+fORRqliIgsptTneO/J8wyYGJwxGOIU6qeP8NDX/4megfXsfvHLzgniZHlOPsuL89570jQjS7MLP3iBOGdHLhoZXODIlAI0L10P4rzwwgu87W1v4+abb+bnf/7n57WuPPcMDi7sXMEgcBSANElJWil5klH0nqSdks5z2diFW9dCLAdxZy5m4/jznULIYZGWDxdlapVzlp6eIoODDbLsAm3xZE60jxfHat/PxhiSJOP0mSZD9TaR69RfSZKMdislSbLzLzvbef7p+tm04ZF1Ju0ZrmNkGSBpZzN+/EyXgQVf56KMaYp9Ox/d/F3u6Skqw2fETC9k3XnnnVx77bVs3LiRQ4cO8eEPf5if/dmf5X/+z//Jli1b5jWGhc5iXkzKGFs5dKxWDh2r7jHGkGUjGcwjX+OzaI0xnelUZ5/RWfCdnycXRc7TlOGTx4jCePTR50y9msspweQaqlGlQNxOCIoRWIOLAqA1+xVPwTrLwIY1lMoFDJ5ypUgjS2eeQrQcuADrLIFxI+dgE+vMLva/qa4GcQYHB/mlX/ol+vr6+MAHPrAgBY3TdGFPNEf/keQefO7Heq957+e9bBZwXQuxbExAnjQZOngv3kNl7+1kNiDPF+9fUJblC34MZSLt48WxWveztaZzVWjky3sPI38fZ7Kcj72x+bG/LdbSmfM9m3WO8HMYw4WWR9e7kOtcjDGN3pZl+YL+3V6tv8vLwWwuZP3Wb/3W2M8HDhzg1ltv5ZWvfCWf+MQnePe73z3nMXQri3mxKWNs5dCxWjl0rLpj8FSdKArwWUAUdTJre8Zl0Tbbw1hrcCNBG2sshk45jMA6KoUSgXNk1pLTycoZi7oYzqmNM37R2NmVMnRBCJFlYP/l9LRT4nKBRt5i7fXbOPrtH+DiAGMNLg7JW3ML6lhrMRbazVPk+TrarTOYsLwIPSzmn+1jrSWoDhBVB8BCwbUAjwsKBOG5/34W699U14I4zWaTN7zhDQwNDfHf//t/p1qtdmtTMktZs6bCxiIXkQu1ulzIQsZTtcXuFCpW2qxcXOZ7IWv9+vXccMMNPPTQQ/MaRzeymBfTas9+XE10rFYOHauFZcalxxhjyNKMdjul1UoxLiVLEoZfOILPcoyzmCwj95CNnI/lPsd7Ax56oyo9WYlCPSOKypyxg3j8hPbgkz/IjV/0+cQEF2MtjNXUC84JADnnaCcJDz34EMMnz1DoLbF+22Z2bN/OwHWbMYUCLZOx/rptvHDfk1O+/iiOKJSKnWDN2PmkHYk3GazrRJbyPAfv8VkG4ZSrmqeFPdeMnCMur6Vtr+eML5A2hgmokZx+ir4Ne0lrZy88LtS/qZlmMncliJOmKW9961t56qmn+PSnP82GDRu6sRlZYBf6oCciK48xgHM0RqbsABTiAJNleH/u/fMNuExuiw2wbqDIgV2XKIwjF43ldiFrNWRaKWNs5dCxWjl0rObPGDBRTjNrAuCwhCNBl7HPUVnOifu+RfPYMQrr1rH2hn1jWSjGgDMOg6FcLFOMCgwfOULDNYn6+ij09WNdMCGGM9NpSNZZgv415KbTTrwwsA7rgrPrGadRr1MbGiZ3kGYpSZby8MGH8AYq6/vYun4Thb7SWGaOGbkwEYYhl998Pa5YpB1EFEoOFwYUe6tY67HWUu2t0Mxz8AvfaXPqc0s/6fvchM7RzuG+w8fJojYuHeKmjWvwaYr3TJkZvVj/proSxPnd3/1dvvSlL/GOd7yD4eFh7r///rH79uzZQxRF3diszIMxUAoyfNI4e5vakYuseMYYGq2Uf/rOs9TqCeVSyEuv20wx7NS6mXz/QgVcxrfFrhS7crlFZFlaqAtZR44c4Vvf+hY//uM/vsAjFBGRhWKMoZk1ufsH36DebnBpz3puXHMVqUnJTEJG1ukiPP7EaiQAEkQxoQvpsTFps0lvpZ8sSXjkG18mbzRZe+VuNu/YjbFnixlPVRNn+rFZcm8YPFMnTTKGzjQo9fTN+LU1ag28gWJaodhfxV+zEVMo0DY5UU8RYywucPgg5N5Dx7kkNexZ24MLAozxJK0z5Hkf7dYgRF3szNjlz6r1VouUlCBd+CDUXHUliPO1r30NgN///d8/575/+Id/YPPmzd3YrMzWSNtgGKmDkQwz/ODdZ9uR770dY6uL3o5cRBbe+KDK+e5XwEVkfmZyIes1r3kNzz33HF/84hcB+NznPseXvvQl7rjjDtavX8+hQ4f46Ec/inOOX/iFX1iiVyIiIjNVbzcYatVYlyXk5BwbPsGZoZNcEjlsGFK4aivh5ZdioxhfiLBxhAsyyHOaR47SHjxDMBKdaQ0Pk9ZrtJvnToWdYjbVBeXZaE29uXWjcoEjSZOxzJyBLRu4bPPmCcGk4WabRjJx/T5PO9On8pRu6d6n1JEXZyzLsSRAV4I4//iP/9iN1coCMmFEEDhMMtjpVIXFGk/WqpE1hpd6eCIiMgVjGAu+g6a9LkczuZCV5/mEk+nNmzdz9OhR3vve9zI0NES1WuWWW27hV3/1V+fdmUpERBZf5jPaWYqxjqTd4rFv38PwsWNU1q5j7w/9KDYIgc77QJYm5O2EfAZBljz3ZDNobmCMoVipQhhj4tLY9Kf5GM3Mabea817X8nd2Ptxy7Ibe9RbjsjxZF+KTJsMH7yVt1Ah711G9at8yjDOKyEIbHwiYbyFjWTxTFY0eX99IloeZXMj61Kc+NWF5//7959wmIiKLazaNIBi9DG4Nzlgid/5M5ubwEI3B0wSFAsYYglKJCEc3TsLCKKYQV9h93UvBWIwLqZZ6CON4wbc1anIhY7McIx8LwSyP82YFcS5yWbOTeeMKK7/9qMjFaPIJB5w/O2NyIECdo1aOyUWjJ9c3EhERkbkxBkKXk7TP1gcNoyJJZscaQRTIyBsNMIawEJM2mmDAknHTul3887FHKYbxtGdVBggKBWwUUbhyG4Gl061qXM2b+bJBQFQo4lPPmYdeIKm3CatVqrf1E4QLM2U+CAKssxRKReJCjA0c1b7KWCHjnt4KrZHm6KuKMRifY32KM35JM3QUxBERWaGm6jwFUCwE2DzH+3MzbSYHAtQ5auW5UH0jERERmR1jDEm7wePfvY9Wo0FcLLFz/y3YoEyWdRpB5I0Gx758N0GpxJob9nHsq/eS1mq4gR4GbruFG9bsIA5iLBZnHZELCF0AGDyenBwXBmPTq0wh5Orb/gV2AaY6QaedePXSLUTVfsCQtXOSegtcBMZQKJeJCkWsc0SFwpQ1dy4kjEI2XHY5SVxiy769hFFAGBewp+sTCxnHXSxkvFS8J09a+KET4LORi6hLcxFNQRwRkRVqcmcpgP7emJdev4V2kp8300aFjEVERETOMkCrVaPWGCQzKTkpQZhj8rPnUWmtNuHnZGgYKgXStM0j37mHSqmH6/ffzkDUQyHKqLoCAO2sTa1dpyfrXIRpDg9hsnlObzLgwnCkvg7YMMAkMDTYwOc56Uih4SAKCKOQ3XsOEEYBJVfi+hfdzn1f+l+z3mQQBmTW8a0XTnG6VmNtpcC1l5ZxYYBP610vZLzkvMdnS//6FMQREVnhJrfzVqaNiIiIyCwZQ+ZzXhg6RiGrs6UxSG8UEZrijJ5erw/hTGdqlPWexnPP0QpLgMcDuc/xeb5www0CymsvoVCqAAZb6cHV62RZZxujU61t6PBpzpmDz2NST3rJBsKBiDCK5rZdY6glbY6ffA6blsk3DLBUGSkXKwVxZM4uVPxLRGbv3Bo3ncJ5Y0sz/HemTJuLg7pViYiILKw0T2lnKbk/2y3KmE79mqBcJiiVYNIUKIPBGYcbKfCLMaRJGz/Htt4X1nnvHzxVp16HJMkYPtPEhgYzzaW7pNbEpJ6smQDnr8NjjKFYLuINRHE8ZaHiPE/wWTL7nucybwriyJwYA6Ugwydni3+ZsEg9dfp3LDJHk2vcGAOFOKTRSsYucJyv3o1cXNStSkREpPuMAWczElJK1+7sFCIuRNg4giGwUUTgAtaEZYquAB5CLMYsTK2b80nTHGNT8JBmOeVygUKphLGGsFzABxYbnvuR3zpLqVIljGKstROycoIgoFQK2X3ddgCiYkSpHBFGujC4XCiII3NijMEnDYYfvJusWcMVylT23o6xVXVJEZmjyTVuRqdCjU6Nmmm9G7k4qFuViIhI91hjO1/WkrZrPHH/vZx68glKvQPs/aEfPVuLJggg9zSOHMUXh/Fpp07hQp6hzWRdYRyzY98BjItpkdF37WbIcuJiCeMMYTnGpBBUY8JL1rDj1pdSqpZpBCE7briWoRNf6bweZ/E+4fSxh0ladcr9A5TLB6j0VPBTdEWVxacgjszLaItyEZnaXKYdTp4KNX5Z9W5kMnWrEhERWVjOWnqjCsVWiqWGzzNa9TqNwTNYN3VGSpYkZEEy5YWUOZ2rjZxD2iAA5wCDdQEuDEfOLSeuNQhDvLE8/NBBypVehg6foNpb4brbbqGRt+m5ZhPWWMrr+xk2hgeHEtqDZ6iUWuwMQsIwJAecc+RBmdKmfUBOb+9aaiZkw9W7Oq3Y4wJwZi6vSBaIgjgiIl0yVQvw8dOhOs7WvJnp9CjVuxERERHpnmJcohDF1I+fAA9BpQz4kXo3Iw/qXKmb8zbGTgWnWoV1GBdigpDSwCXE1R4wUBxYRx5G5MZhjJmw+TCKsM7RbrVwrkl9uEahGJNkKU889gTHnzxM39oBru7fT62dcOi5F0iDkC2XXoKNQ4qVEt5BoVCgmXvuPXSUYui4qbqe+184wTOHjrKmXODW3l5dQFxiCuLI9IxRwUyReZg8PWrydKjJNW80PUpERERk6QVBSNJu8+g37iZ0IVfedmcn+8WM9pqCnIycdNpTtzTLybLOYyd/hArjGFMoY42jUO7BtFKC6GzLcWMNxoV4b2g0ISw6rHE0GjlHnztNqbfZWedIFMdYy1V7r6dQqVKt9pN5PyHA1Gq1qA/XKJZKAGR5RpIm5MYSOUtPXw9bb74egFJfFW8c9SQlzzuFmetJymCjRTHofp0fuTAFcWRKJowIAodJBsf+6FywcLGCPiJTutB0KE2PEhEREemumU5xD2yAMw4DNIeHyIN47NzMA62sjccwaNrUXUpPoTDtNqf6KGSMYesNt+KDgMw6rrjpJeRJSqFUxAUhhUoP1ho27dgNQcDOAzcSRhGZsey8/gBHD79ANqlVuTGG0AYkQy3aQ01cOZpx7ZrAGto53PvMMYZaLbZddgk3bS1O+Fwny4uCODIl60J80mT44L2kjQsXLp4y6BOVaGadoM9Uf0TUolxWo/G/19NNjzpfzRsRERERWVjGgIlymlmzU2cGQ+xifGLwvnMh2ucQYlgXVugPS2PTlXJy/Fj+DQRxjItC1lx9DX07mhR6BjDOYt3523afHYvFW8fDDz9Mpe8Y7UaDcrnENddtxec52268HWsgLJR49JFHefqxR4kKMZu3XcH6/oEJnaQm83mOz/Jp7z+foWaLM40W9XFlAGR5UhBHzmumhYsnB32CSj+9195KIUs7FT88ZI187MOtWpTLajS5Bo6mR4mIiIgsjPEXymZ78dcYQzNr8vVn72dPdTNxBqViH2FcIm00wVlSk2LSjMbzz1Ny8ch2oJW2yL3H+04wJwgj0qTNY9/+KkNHj7Bp+26ufPFLMW52U41azQau3mDo8HFMfy/tZosnnniSZ596lN6eXvbfso5ms8nw0BBxmtBqNme1/vPuDwzWWvyMT1MNLnCdIssTCgPJUlAQR2Zu3HQpaw1MEeQdDfq4QnliUKdUIdx/B1AZWZValMvSW+hssOlahOttTkRERGTujIECGXmjcwHYFoo0zewv/iZZgmm1ee4rX4X1l7H+uus59tV78R4qB/aChzxJyNOz2Sijn00mb6oxPETtzCla9fl16s3TlCzJwHtarSa1oUGiYOqP6S5wlKtVCqXS3Ft9m07NnSgu0GZmgSdrLT39FarlAtbYkXI7OsNdKgriyIxMni5lsFjjL/hvdzSoY6aZU6kW5bJUpuocVYgDTJbNOxtM06NEREREzm82mTXGGPJGg2NfvhuAdXfcjinO/eJvc3iIfCDFOAfGYPAsl+kAU43CWoezAdWefm689WWEcUwxqhDFMSO1h2ctb2fkdmRr5/1MZ/A+o1k7RtMX8X4D4OfTmEvmSUEcmZHJ06XC3nVUr9q3sPHXLhZGVv0dmWxy1ky5FPKy6zdTihx5PnLFZQa/JzOpgSMiIiIiZxkDoctJ2p3MmjAqkmR2yvMuYzrnWMZAVq91bpvjdiMXEgQh1bXr6b3malo2p3jNlRhjCcql+Z/Hdek80NjOvkmHmxz91g+o9FTpfckAxXKFPAfrHD5w5PnU9XCCIKBUKVMoFbDWds5d89l9GMqylCyf/8VOmT8FcWRWxk+XmjVjRuZfmnOmY82pG9bMN6v6OzKt0ayZOHSEoaPWzsYugVwoM0c1cERERERmzxhD0m7w2P33AbBz/y2YoHJOZs1oQeJW3sSSkZoMG8eYkfO2zoU3T5bljMYvprp4O7rNPWu2UcoD4utvhFKBR++/hyOPPUx1zXr23vEj839h5/tscd7TxLN3GmNh5MJ2EIZUqlWKlQpxIe7cN/oMZ9h5/Q2kWY63FlsqEMb+nBkQxhg2XXEF/ZdsJowi2kFEoeQ6NXwugs9CZuz76jlHVxBHFoUJIzCGqH2awJ87HWu23bBmtW3V35EZCAM7of13uRTy0us2UwzttL8nqoEjIiIiMnetRv28948WJP7Wcw9ydbSBY+1Btt94Bykpee0k4AmjmNCFNOlcoB1fOwfAFkukLiAwjiDzPH7/vVBrsOfWl9Oq1xk+c4ogiuf+IkynK5V1Dlw4cpXv3LNBP82MLYOZUBTZRTE2CDHGcNn2K+m/dDPWOUrlMsWeHi65eQfWWNo+o9jTy9e/9jXKhw9TKZW5YvsORuc5RXHUaVvuHJkNuOeZwyQYtm+FPWt7sMZOGFDgAoIww9rZFWhe7lbjpz0FcWRRGBfi202GDt5LWh+edjrWdDVyFmI6lOrvyGzaf8+GauCIiIiIdE8jaZIGKTiDt5bHvv01jj31BC6Ouebml9G77hJcobfzGaE+zPGv3E1aqxH197PhJbdCs4514F2BrN0iqQ2dZ2tmwgf/qcsad1gXEPT0AIZC33rCOMa4ABMEWDfxo3aa5eTTpnd3tuC9p91KSNoZxhoy57j32ePU2ymXrFvDneUyT3zvCYZOD3HprsvZvHkjzUYDG0UU4xjrLIVSkbhaZNuOqzCFAkkYU3AhrdzTyDKaSXbOObALHH3renGtNnEhHNeBajWGQFY+BXFkUeWzmY41UiPHGCjYVNOhZF4WYurT+CCQMQZrZ70KERERkYvW6LmUtYbcmJHzqtmf0LfqNQZPHCGIC2Rp0snYoQ7Gd6ZdNYdpDJ+BSoGs2eSFe75Gjqd47R6Mn3hx+Fy+E2zxjI1tus8c1llyD2mac/LYIGFUIEszrIuo9A9QKFew1hHGhYkv0wBmUsaL7+wg6wJcGGJdgCGj7Q2NDIbbaWeGwcjD83EVjcMwZOMV20miIpuuuZowDgijmH969Bk2pIa9G/rpW9NLmCRY63HWYZ2FPOscE6DVOEVtaJCkCPgNKly8jCmII8vS+Bo5WItNM84c/CrZAk+1ktXlfBlb8536NBoEqrUy2ifrJEk2Ml1Z73AiIiIiFzJa36aZNXHG4kjJTIozdsLZ1DmZ0xdcbyewcfDoIzSSFteVtpDmGS8MHaVcDVmbJRw7/hy9hSp5NrNWTuM/YtggGBmPwQYhdqT9t3MBdqRkhPeeNM2xNgNj2bLzGtZtupwwLpIZy1U33cGJoy9gg3BknSEuCAGDca4zpcqANZb+gXX0DfRhMPjUkzVT8nZKHDjCIGDjNXtIk5RKNR4LtARBQGYcXz98gkPPvsAla6rctHUTtXbSybwB2s1T1GpDJCUPrMOFAc7D6LlsnmVkaRs/13ZXsmgUxJFlaXyNHBOVqF61byyLRy5OkwM0MDlIwzktw4uFAJvneH92+tRcpz6NBoG+8p3DtLOcpJ2xpr+gGjgiIiKyKi10d9fR+jZ3/+AbGODavq0cGz7BusqasTou4wM9AM5YMGe7SsWV6ljR37PrtYQuYIcbIHOekosYNJDmGVmW4PFk03RtuiDrKK9Zhw1CvA0I1qyjNLAeMMR968iDiKhQ7NSsqVYJwwgXBGAsBx98kDTzbNq+g00b1rHmsu0UK52pVxhLkubE3pNnntFYivee9qk6aVQaGYDH5znee0JnaWU59x46RtJOuPPqrVgHblyQqZ6kDDZaVJuFc15KlmXkaYL3OcZAT28ZmyRY4zvPV+rNiqEgjixrWbOGncObxTkR/Dn+3Za5m+8b/8SpS+CNnRCggYndoyZn2vT3xrz0+i20kxz8wnWOqjUSWllOu5VSKuhPqIiIiKw+xkxRILhQpGnmX86gPtJWPM0zMj8x62N8oKfebrC23M8Nm68Z6yp16b4biKsjgZDR51hL3mrxwj99GQoRA7feeW4nojmeAhrbCbhkac6pEzUap+r0XWI606eOD+KzjI1X7MK4gN033YQxFuMCCuUqzUaDVjuh1WwBhlPHazSbdiygdHZGwcQdmicZPpv+w8tws02aJDhjCMOA9RsuJYjDzvSombymkWlc7cZp6rVB2iWAtboouYLoE4isOpNbik/uhCXdN1VWzIXadZ/v+c4aMp+NBWiAabtHjc+0Gd9tai6do4w5m8Y7XSFkERERkdXGGEPeaHDsy50CwUG5zLo7bscUz1/OYPxFuNELeOPr4Jy/Fs1ZadY5BzRAYBw2y3ns/nvJh4a55rZXTDgns85hnAMDWTKxOYWNY6wLiMpVgmL5nM5LQRhR6umnUOnBOkcQRlOOx3tPnuZkaTa2nCY5gXPkwCOPPML3H38c5xw79uzm8q1bJ3ScstYSF4tEcQFj3Iz3w3TiKKBULtH0nvVbdxCGAXGxhDHDuCCY0TS03KdkWTJ9wR9ZthTEkZVp5I3gLM/oX3NrDT4ZHmspPmUnrEnPn296qEw0OStmJu26z/f80QBMfRE7R8WhIwwdtXY2MZNHgRwRERG5SKS1GsnQhcsZjF74cjYjbTXwQBgVybwl9hl5s3NxtVIMiVxIO0umXU9gHTdeuoe01SK0AS7JqIRFWs06WX38WAxRoUixr5+Wz7j0+hsx1mJGAjVBscimfTeROcO6666nGMbYauVspre1bNm1j7WbtxNEET6I2bxrH8898uCs91Oz2WR4aAjnHK1Wa8J9QRBS6e1nzy0vwrkArKVQqXZafM9R5Bzt3PPN507y7KHnuWLLBvb39eIs9K2pUqmUNDtqFVMQR1ac8UWPO/NHDWEUk7Q6c2dHM2+y1tSdsM55Pud2u1roOcAXq7m0657q+UvRujsM7JSZPCIiIiJy1ujUK99qkZLyxIP/TI7lqv234Mol0tPDHP3K3bgoZu1tL6K3UKGeNM8JYhgDsU8JGgnehzz88Dc5NnScbRu3s+XKa6bIXvEEUUSaJjz90Lc5/dwhdt34krHaLi6KMc7y+Hfv49iTT7Du0k3suPkljF6RM8aQYzn44AMkacqm7Tu5bNOlBNHU2ThTOd/ng9HsIxc40izloQcP0k4Srtp7DZdvuYxKby9xVKARz3x7k9WTlOFWQjNNyPOUVuMkzeFBTPVyjOlsWxcgVx8FcWTFGV/0OG2czbSpTVqe7u/V5OdP7nY1eToWqKX5crUY052WMpAkIiIismyMy2Sf3AE0bzQ49c1vUbhqK83hQdLAkZucLG9Tb57huVPPc8WLbif1OXt6tpAD5SDGGTdu9Z31nPjmP+Ou2Ey7NkyjNkRr5ELtZFl+NojSatRoDJ0Zm1oVlMtUwnWU+vppPtGGQpEsiHBBRFwsY9IM6xzWGOq1YdrthFZz6u1ceLd0pokFUYi1jpH+4QRRgXSstI2h2WrS07+GTZeso1CAa6+9Cjy0tmzkuaefx1pLuVymd6CPQrGAtXaW0648eZ7ifU4QBLjAUamWcGb0AvWcXp4sQwriyIqVNSdm2kxenunzJzPG4JPG2HSs+bY0X61ZPUv9uqad7iQiIiIiC8YVYqKBPqI4JGkMgfdjRY7HN33K6nXyLCXJU07WBxlqDVMtDHTOn53BW8v3vnsfg0OnqPStoe/qAwzEPcDZQIhxFg/4fDRCNP24Jp+Xh3GRUv8aMuOpXL2b2IALIwZPNzh6+DTF8jqCQpFLdlxDnibkNuxMa7IOmHp61wSmU8Bh/FbDOKZYrOCMY+26SzEGnOsEptqtlGYzHYs0GWOJwgjyhBOHH+Hoc4coVge4bMeLKZZC4oLnij1Xcen2HTTqTVouoFgpd9aXpOeOZ7phWgvk5HlKs36cQtADrFUQZxVREEdkGtMFeS5kclelgk1XXVbPTNt5z3adEzqKXeD500130vuTiIiIyMKwcUTfDfvwxYhG0iAfGmb4yafov/46XKUCrclPsAQuoJrGFAmIGyk2LGOtw1hLa3iIE997nGTdBvKr9mG9J4piXODIkiESn1LeuQOKhc6F1fOcL0fFEsWRgsRxsQwDa8l9zjOPPcjgMz8gqFTZ9aKXkeeetJ1ijKWdJDz80EGGT59m6649XL51G8a6c9YdRjHWOgrlKlGxNFZn59zHRYAhGWzSODpIEIZwaec0Ns9H24NPepKHNKnTrA8SFyt4n3L82W/RqvdA39V880iLYycGuaINu9dUcIEjCIPO9DMDLnAzKlyM92RZqsLFq5CCOCIwIT10Pi3Jz+mMZS02zThz8KtkU0zdWqkWup33VN2oZvp8TXcSERER6Q4bhGTW88S376F0yQa27bketlxCI2thG54oLJPl44IgxkCWUT9yhMbRYyTHT1G58sqxttYAeZKQt9sjwQVDEIQk7SaPPfDPpEODhDlsv/E2vDekKeBHJyidFYQR1975KqJSGR/EXHrVXkrlKt5YBjZtp6dvAy4MJmx3VKNeozY8RLs1OQLVEcZFdl13G5EpcuW1t2GjmHKphzCOR7YdYp2jVK1SLJc7GTqpJ08ysjkWK263aqRJiM0zGklKPcloZCnGGPrW9RO0WhSLEQbo6atQcOcGnuQC5nild8IntmVytVhBHLnoTS50PJOW5FO1T+zcPnEq1mh9nvw8WT1LPS1pPhaqnfd03aiWyd9JkRVjfJ0oWFl/T0REZPFNdR4KnfcS4yxBqZOF0hweIm4PkCZtHv3m3ZhKiXLvGnbtvxUbVs5Zb54mZEkCjcY594E/21zEdJbSPOXIqeeJGgllAjAQByHrohJB6gmMJTQBKZ0giQtCjAt56OBBSn3P4/Ds3ruf7z3xCEef/QEuz9l34CZcEFAsl6n09lKodDpTWdcJwoztA2c6LcpHxKUygYsZeuQox5/6PsV1A1Ru7qfS2wtpwhVX7aMclrnx1pcRhCFBEFIoFylVy7gwJAg743dhSJCmGGswmJGgksFYSxAWiYtVwqg0IdBkjCGMQiq9JSwe6xxh5GidOk4zapPna2k1TlPsWTOPoy6zkeee5XYqpSCOXPSmK5Q8XQDhnGybKaZHzbQ+z2orojzfrBhl1YjM3eQ6UQCFOMBk2Yr8eyIicrHr9oW+6abHBz4hTeokpBR2X4ErFiZMJ2oMD2JMRlAojnRgstjA4srTTzsaL8sh7Vw5JbQhLslxWKquQGazkYx4Az6n/tzzNExEPk1NmHptGOIS4cgbX6vZpDY8TGQs5Z5+YlNg77U3kexKWLvpUhJyKtV+jA1gJHgSREUi7zDW4YKI9Tv2EJdKJNQJykWqV11Ki4zLd++BPKfY10vtVB2fG0wQEBcsV117OemuTRhrCYslnAtZs249WZZjjcO6gL7+NRSKJYLKAC66hoHydoIwxkdVXBCNHBOLx9OsHaMdJ/i8H2Mgz1LykalReZ529o+hExwKnKoWd9FyPIdSEEdkxMwDL2ezbYDZT4+aNHXLJ8MLVkRZRC5ek+tElUshL71uM8XQ6u+JiMgKM9q2Ox+XyTJaTHjyn/TpsmnG12js3D6xGLAxljRJ+c73jjI41KZYDHjxNZeCb/LY/feRDJ0hymHHTbfBpBosxtiRWjaWtH2ajJzCzm24cvmCAQXv/cjFBoPPM1onTpC327ROniQYmbI0Kkva5Gky6/cx4zrZL0NPnODowz8gKIesu3QjWbNN+3QDVwjBWgwGj6NcHSAKYqp9AxSKFbAOs9nQs+EybCXikUce4dD3n6Kvr5fetet45KGHOX74BTZvv4w9AzvJA2h5h3MhpXIVWinpcBtTCMgGW/g0Ix1q46o9tDzc9+wxjp86RX9PLy/d2Y91Ey9g5llKnmdMlQNirSWMQqyxOOvp6a8SBJpedUGr6FRIQRyRmZiiZk7WrM1+NdNM3cpacyuivFBmcqVntoWHRWRpjGa0iYjI0pl8bgWzy6QZbbd97Mt3k9ZqBOUy6+64HVOsTgrEgIlyWnkLO3JyVrQx1nvarWbnfp8TupC8mWLjApm1NBs1svYwUZ5zy/Yqp04MY4CSy0hSaDfqtBvDMEUCjHGOKAgJckPSbPDo/feQ1OuYdsbVt72c0ZNE6wJcsdgpPFyunBMIGpVnnbbYeZrApCDObIzucxeEBEFn6lLWSmkPN8jJgE7XK595XBBjsVgX0Nu/lizJ8I2UVr3FQ99+AB9ZktNN4jjk6pv30Wp1MnziqBNsaTYa1IdqtJotDIZ2brnn+4dwYcTLrx6g6CxxHGOLEVEQY6whLsTEcYwBau02Q80GUVyYwyu1eJ/j85RW4ySteg3TP7PuvBe1VfTZRUEckQs4b80cz6yKIs9o6ta49cG5b/jnnhR0RjX5asuMX98UqbSTp2DMp/Dw+O1MrKTvx9ahoJCIiIisFqOBlWbWnHB7wRWgbSecX10o0JPWa6TDwyOPPfc8zxhDK29RHzqFabaJgoi+/g3UGzW+d/+9tJp1CoUSWy/fRfOx79N/4w0QxrSbNR6//+uQttm48UqOfOXbBNZyyU++EuMA5/F48rHt+QnbJMtpnThB1m5z+tlDuCiCJBt7TFgo0rNhA3m1Sh4GbLz+RuJKhbhYISqWLrwPraVQrFBds55ib/+EGjZTsp3gjTUB1Z61FAsxLgimikF1gknekw02Oxkywy1MIcBnnddZH65higHtWgOfdaY5BUFAuVKhWCphraVYKlGqlokLMcZYjLEMN5uUjCMMQhrGsvnaa7CBY01Pmdg02XXtNqJSCb9ARYk9nalVnelV0i1mJp3AFpmCOCIXcN7AyxyKIsP0U7cmB4w6t52tkXNODR1jCKOYpNXEeEhODxObiHSKVNvpTC4qPNUUjPkWHp5cq8MYKMQhjVYyp25WIiIiIotltlk1xhiaWZO7f/AN6u3OOVspKnL75TdSMGW899MGeoquCKkdKcBrcYWQ5nCKMRk5GSbMMfnE1tsWg2m2efYf/55q7xoGbn85tZNHee7Rg2RpwoYrrjp7Xhk4rDMkaUa9OYRLUhw5A4WczEIQWJK0hUk62TGtJCXzGe1spE3UOOMzaFwUTbgvCCOyNOGpg99k094byIEoiNlw5dVU117CU9/9xtnHRjHWBRQrPYSlEtViD4VKL5t3X8fGK6/FBQFxpZf+SzbjsFjniApFbBBgrMPQmWIEkA62aB4bwvV6jLEE5YiezWsJe2Jc5HBpQBiFZECed46jc46oWCS2MdZ1piqlk4JWm7fvoG/TFsIwIIkidr7oBtadOE0Ux9SJCUu9GBcQOkcr83zzuVM8e/gIvdUiL+8p0z75JMcPP03v2s2US/0qYbOEzNj3mR0EG4RkF37YolIQR2SGpgq8zLYo8oVMXt/kGjlj9XgO3k0+0v3KXbmP2kP3kjXrFKo9xDtfhLXVCW/uoycaoycho99H14mZ2RSMNOmkGZmRwEsczuxKwuRaHaNBoLl2sxIRERGZqem6is74uTPIqpn8HGfs2HlNO0ugffY+aztZ1628ydefvZ/TjUEA+oo9vOyyA+RpJ4SQkFLdt5dn/vaz+KEGxeYZSpUCkSuS52e7SI1qDg0SB53pSHmWkrabZHlGEEbEfX2Y3VeQmRTqpwFP3u5MBUrylNynnGontOo16oMnaZ84AWEwFsqYa221tN0mzXIef+xRKn1rSJoNtmzcSKnaM9KJKWbznhswUYGt191KEEZU1l5CzQTUbIwPoegcGMvG7Xu5ZOtufOBYu/UqgqhIWChjrB0ZpyEIAgqFApVqFReHcOVaBq5cA3gGDRT6S+y5fjePPvYkgQtwgeOaG/YQRAbjoRDn7Lz2Sh5//GnajDQxMZbMOL7xwmkSDDuygN3renl4uE3zTJMNrZSda8pjnaeMgWaSYIsRcSEmimLq1a1ULltLUCjhrRsrqiyLbeadps7+ys803LN4FMQRWQAzLYo8rZHAyuhfiAk1ciZN1zI5ZK067doQPirhfWc5bw6TRwEGMNZRbyZjqy8WApzPiW0K7QZ0smAhLFBLAyZnwUye+mStwVi4ZVcvLm0SRwE9DHPzrl7+/jszr70xufuUulGJiIhIN00uEDxdceDpn3/hrJrJ24t9iqu1OFC+nLSY0g4d3z31FMYY4jwlazQ651pk7Klu5p+Gv0srbVOOiqStJt+7/5u0GnVKUchlm3fgw4A0SzBYojCgnTTILRjvscZiRwr0Tjl+Ou240zThse/cA1FIbAKuOPBiWqdOUerrPxuoCS24kS5NLliw7ANrHZfvugZTKJNnGVGlzBU9vbgwwoQFiAo8+tjjPP/9JymVy1x1oMpDtZxnnj1MOYq5/fJLSBptHvzG/aQmJSoViUPLFVftolAsUSxXCa0lDAOu2H8NtWabIAppYEmynK8fOsKpMy9QKcTctm0TYWiIiwU2bttOEhUxQUCzNUjkEwZPPEEcXUqpUqZ9qjn2O2CspVZvk1hHI0nI85QTp56n6T095csZO5ceCc60W6epDx4jj9bTynK+fvgYx44fZ+Oaddy4fWCKtuIlgjCYcLt02XKLzMyCgjgiS2k0aJN70txj89GrQyNtA6crhJznPHeiRpE61SzHe0gyD+2UGEM7y/nnR16g0UgplwJefO0m2pkn8J0snrRZ41TL0nPNS3j6jOWqLQNUiiHWQl+lQBwHNJOMfGQ8zhqssUS+zYkHvoJNmwQb1xJvOkA4rhp+HDqqpYhSIZhVpg50pieXi50GkaVCMF3tu0UThRZnzdiYolBvqiIiIivN+ALBwJTFgWei3m4w1LpwUwtjDHmzwdGv3M0zLzxNUCqy8c6X01uoEBhH3uyMJavXMJUC8U376C/2UGs3KIdFMIZ2s06rUcNlEcY54moPhcDSX+nHnKnRbA5RKVUZqg0SuYBKsYewWCUqlXFRjAkColKZgUu3kLRbuDAg9zknTx0jLpdpZR58TuAC4mIZF0bE/QNctXsPUVDG9kRceeMdPP7Q18loYozFBsHIRUczoT6NsZZipYegWIQkn7LFuAsCPAH3HTrOYL3O5vXrOLC+irGOIOqhVOnBZ4eJClWCuECe59STlMFGizxnJCXC06jViXpLbNq6HcIIXyhz/UteRuY9Bk8ShMR9vXzzqec4eug42xPD7rVV6u2UU0ND5HnSCXy5gFKpRO4C7nvuBMP1OgVqvOTKHbSjEtgi668osXb9Rl54/EkKxYBi6Ohb00MjHclix5NlCfk0v0be52RpQt4ptEO9nTDUaFBPJl78jIKAwIUUN+0jCAwmrGBoTLVKkTEK4ohcyNjEyYnZMgvFezh+psHgySGqVKmmOXneeYOwwcj0qofuJa2Pn65lSNOcNOu0aMxyz/PHh4nTIj3eUMoHuW6jI80MrlgmS3O++8Qxdq2D00eO0xumtJMCfdUCV7kWPQxz25VFKtU11Gt14tYZ6kNN6nnAfY+eoacacfOeSzFAqz6Ma9dJG0WstVSKIaVCQOAML97dB0lj1pk6UWjpiTNevD0mzcLO8+NsQhBofJBn9DkXWudcgzCFyHHdFVV6GB4bk4lLSx5YEhERkblJa7PvKjpZHERELqQcdc6B7LiTwsnTtJJajeaZ0xRDx9rKGva3CxRbKc5ZfJaQDNdwhYC1pR4O5JeTlz1r+9bj0xSbJtgsIQgrxH39bLnpRbgwJCcnH65x7MHvUr7uRtxwnSTPaTQTgmqV7be+lGLcQ1bqwUYldv/Iz+B9jkkbWHJyP1IMF4+xll033EGhUKVQrNCzrwdXqjD0yDEynxNuLBOW+qj2x0TFMtVLNuMKFVwYU+hdgyGjXKgSV3rZfuAOjHPgc8JShUKpQv3UKYy1BIUixXIPjVZGrZXSzC02KlDp7aeVpWzcdwNpELDxsm2cfOYItuQwzmFIO+fedArLWmsplktEpRKpt/zz949wyYac3WurfOv5Y5w6fYQNvVWu3byFRpox1EpopOmk4xcQFaq0g5w1lxUJ4wKNk3WG2wnWZrRz+Pqzx2hmMWQZB9b1UenrwedtkmaD+uAR2sbh+yss1AeCTg2dnPuePUalELBvS+/su5TIRUdBHLm4jA/CzCQoYzqP8/5stkzYhT+sWeZJRoIyns6bVZLnY5k5WatO3homb08/XStNcyLrIGky9OjXOXroCGG1jx23/wuMH2bPBke1YDg10j3LhSEma3Hmga+QhSlDVOjbfxNDD96ND1NONAyVPbfRSjIajXOr3tswotpT5EXbcwJn6TF1SuWcJ796N7QbU2bqTCdwFtNucvrBr1AbHKS3r4/+W/8FQdAJvEwO8gDnDarMNwgTBo7Itxl66J85eugoNi6x4cCd5xQ1FBERkZVvus6fcHZ6eeRCrl+/E5d7oiCiTECSDI9l9IRRkcy7c84V4koVGwTYU02aKYSVKr037OfYl7+CDQN8s80L//RPuFKRdbe/gtbwII3nnqPVbtPbv44sz8ispZ1mZMNDlEoVLrlmH2FcoNWKefL++8iTNsW167hs227CuMTwUJOvHzvNcKtNuRRx06V9xA56123AWrCZxwUhYVjk9MHniW0BazMGbujp1NoxBoxj85XXdNp0G8sl2/ZgXYCNCsSlKlu3X00hLJJncOi5I6SZJ3SW3oFL2LbnAEFYICgUKa/dgDWGPM3JmikGCJ2hlWX886Gj1FptestldhYCrLG4KMIaR6e+TUgQhpQqJcIw4aprLseFIWlcpvb0kbEgTa3Z5vTwEJVCCN53ppA518lqH3c8Audo5fCNwyfIWgk3XbHpnN+FepqSmAJxGFDtLbO2uIkgcHjvybOU3MytNtCF1FotrDlPi1uRcboWxHnyySd5z3vew3e+8x3K5TI//uM/zlvf+laiSZXLRRbNSEAmGw2MzCAoY4Asy0nTnCMn67RqNbb2z+EP7OSA0djyuds2jGbW1Cj4s9OlRqdbzaQaV9as0RoexBbKmJGgzmADevffdE4gI6kPk0UpycjgRpfbtfMHLIwLMWmLMw9+hbxZJ952GcWt15I2auSN4XMydZw1VEvR2HSpqaZbtWvDtIYGaUcT/zRNDvLEpcp5gyoLFYRJGyP7MdObqojM3FzPgbz3fOxjH+Mv/uIvOHnyJLt37+Y3f/M32b9//+IMXGQVGK3rN/qeP9K/YVrWgo08rayJN51OT3EY0U7aeDyGzrqiIMDlnke+8zWq5V7WXPdSvvfdb9Js1CkUS1x13c1kUedCkzM5GAiLRYq9/fg8x7iIPDWk9YR4YC3N3iJ9xSLWOdIkIR3uBCR8lpEnGT5LR8ZvyPKcRx5+mKBQYt0lG9m2bRu+VMEWy2y8uYxL25z4wfeorN1AkEfUE0+tnXDo+aNUKzG13ohqT4Xt191KlqWE1mGiIuV1RZrPDGIThwsCbBgQ7VxPM/fkRUvoe2gND/L0009z8uhRiqUyA+vXsW7jFgrFCs0fnCF2Ma0zDVy5gG95TJpT7R3gip3XE9sSG7fswgUR1rSBkSYdPh+rKTPUahGEl+PKZYrlEnEUd46XB+tNp06MhdNHH+K5HzxOuXc9fVfcQRAG4w7suCNsDEEcUu0tYfFYawkiN+Gcu95Oydpna0dO+P3BADm+PUTgNlC3Fp/HREXbmSrWnRiOyKx0JYhz5swZXvOa17B161Y+8IEPcOTIEX7/93+fZrPJu971rm5sUmQGDFmWc2qwSSUZF5QZGPmAPkWgZfSd33tIR4I5E++fblMj6xqZwTNW88Z3ruyMLrvpJtICSZoTTJguNS6oM8tXnjVrJHOYXjs5CGOnSGVp14Y7QZtmfeJzz8nUqfEjN6zh5MlBwsDNqTDyaJBnpsYHYaxduho386kVJDIfk4uUz7Yri8zefM6BPvaxj/H+97+ft7/97ezcuZNPf/rTvO51r+Ozn/0sW7ZsWaRXIDI/ZlxDhm7+zZmcPTOaEWMjT5C28PVOUVoXhBjjO+dsI99Gx2cMBC6j1RhmqDXMmvJa0nZKlFpM6sgDON2s01upUAyKeA/DtUFCF3VW127isgSbpQRA4/RJsjwjdAWCSoWrrv+XuEqVzAYElR6SoSY2t0SFMpddfxuBcURxLztf8i94/omD2CjChhEucljc2LmmDUKu2HsdPioQxUWSsEA783zz+dMk7YxbNq1nW1gmDorgDT5JAEOapGRZp+lF7j2HnnkWLOzcdx11Y3DWMXDzVcRkJPU6tpCShgHfPnya+ilPObQcuHQNxd4+Nu64kmIU085z+nZcQ5pDubcfm6Xs3r+bMLYEpV7aNiS1Flft5XStTR4WyIOQYk91Ur0cT55nkGdEgaPSU+Wy6/cRxSGFcgV7qkZPf4VqIcY5Sytt0GoMUaiswQUB5UoBS6c9uJsUpAFPs3aMdpzg8/5OYeJJvzzW2bEA3VQX+ZyBVua595kjrOnP2bmm2ik6rDfR1WOmh9JMu7BkuhLE+W//7b9Rq9W466676OvrAyDLMn73d3+XN7zhDWzYsKEbm5XlaLSd9dhyt7fHhCDL+KCMGflfluV4PGmW4/NOhH4sM8d3MnNGl7PcT5jv7Ebm5I7e70eCMGaaoI3LJ9a8WRP20e89J840ODOy3DfDlzY+qLMYXBSzpq/Ei7Z3MmEq1CiUA56bYTbLdJk6gwfvJm/WLzzdyhhKhYBqKaIYB+f87swmKBNEMT3VAsYPjU2vcsUyoTv7/HIxGFvfyOYXZFdbAzdP6ur1oj393POIV1BHuioOHWHoqLWzsV/mYiHA5p3sPk0P7I65ngO1Wi0+8pGP8LrXvY7Xvva1ANxwww38yI/8CJ/4xCd497vfvTgvQOZtPu2slyNrO++50Hk9o00PJjMGsmZKlHn8aAJrFNDKzvY48v7s/hndN+fbRxO3ncPYh24I82Ss4xR0uk4lLqTlG9SGTvLCV79K2myyZdsuBnbtJqxUOgMIDIltkZu8M82n3eax797HcNbikutfSfPRU5xsnWaokdC7Zz2HfnCSyjUFdvdupd5q4IIy1VIVZwO2XrkPk3hsFBKHJZ554B5qg6fovf3lrN9/A+1SkUcff5zsB8+SZx7TTLn62muA/7+9+4+RqrofPv4+5/7aH7OzsLCLgFoEA0WfIOAPQJr90j5f0odoQlp/0UprW1NoulWC2qqNURNqY2ltU4hKbCVCU22amKamqUSapvJVqq2htj7kiY+ySJ8vKosL7C92Zu6P8/xxZ2d3Ztllmd1Z9i6flzHh3Lk/PufM3bmfOXPuuYrwk4jsyZOwqBZneiOXzvifhJZNzcxZTF+4mBPHP8RN1WE58Zw0mVCx/9/HiWyHy2fN4IqmKfSGAUaDW+VCb5aentPY1S6qSqNsDQqqXYepU6eSjUIuW7wYS2si2+W/3n0XrQzLZ12EW23T9u+/4U2phalX0hP59OQMrnbIhRFvHf0EK+xh6ZxL+duR/6Y3cvAiaL78Yozloeunkgt6CAy8efgD6munsLCpgb+1d3A6m6NxSi2fbqjDcW2C0BRG2pgwAjSupQlQ/O/OXmqrQhalUvjZDk53HsczU9BqJpZdjVcdz98Tz9F4Ak9VEUVT+wbbF4nCgCgKz/hAacu2cR0LbcUPDLEdm6EexdWTzVETjNVzukSSqEJnb/zDvkGhbAf0+c/bK9KJs2/fPlasWFFIXgDWrFnDI488wuuvv84Xv/jFShxWTCRneOpS1HeFHsvvDQM7afI96X23S/Xd0xvmk4PQ9N/f3EcrVXS7VJjpZfaUgeXTzJ5ihlzfS1k0EN/qNFSnzRT657wJw3hfQUl5IirMmVPSCaO14lxuLiodqVMoD7jdqrSTxnI9HMfi6ksdshcpampsbB0Vfjk7U6fMcHPeWI5buK2sdK6g6+d5RMZj2rQ0Vr6M0qRrbEY65qd0pM3A28ZsS1OtfNryT/VyLp3FjAUraJ5fE49QKmNE0mjIU7cuHI6tyWRD/uvt/6b7tM/Ueo9VSy8h50fxr9GBwe7NyfyJY6zcHOjAgQN0d3ezZs2awjLXdVm9ejV79+6tdNgXhMHznpy5A2Gk6w11jGrbBj//pc+x6Q2Csx5jrDp7Bu63b/eF9Kuk3DdypbTDaeA+tAbP1oR+EG/vaAIFxlfxU4OKYldE2SCeGDfjY6c86hc2oo3CYAgdQ7fJUBtpyObQKp4/RrkeOSyiKO6kia/1Cq3BIiDs6UZpjVPlEQW5eD2lIAhpf/MNek+0lOy7iAAAFWNJREFUY9fWMmPVf0AqRbX20DX1NF19PSYKqJraSFBTTd11KzCArk4RdB4jF+SwLYeqqpp47hQMykDPh21YuobT3RnqTSMqcxodZmmsm0bGy/KZZZ/HtjSRtkjNuIjutpMYyyIThjResYjcO2+hbBvl2kSZiNypXoxSeFNqmHnZPIJUik6tCOY04l7SQGCF+GEGfB/bcfBStSy4djknjrdjuw44Lump08h29+Kj8SPFad8nDH26Oz7Gcxy0ns0ppQhyORxlcKvip1zZrkNNdRXZ0PDG4Q9o7zjBjPqpXDt3Hj3ZDIoQrUApjdKKyEREvk+Q8+nq6qXBawDi249U0AsGTvtZctqhxrMJFBz4qJ3uXIaqoJtr5tTSk8ugbZ8wDDn2yUlOBwHpuiosW5OeksIKwqJzXymd7xwMaD/5MUFtLcycXni6U/zUcwemzGeqOwvPq8FoC0MUj+Q5x5/c4mNH5DKniKLp+LkulHLlhw1Rov9z0c/5GM8Q+H48W9YEOFcq0onT2trKTTfdVLQsnU7T2NhIa2trJQ45+Qy6rWe8jjvU3C0jLA8wsDMjnX/qUph/6lL/9qXHp2T/DN0O+W73whw3Ju5I6czfLnWiO0NKR3R15MtdGVL6zB/0fbdLRUF0xvJQ6zv5Y5eOrCnttEmyoW6XGq2Bt1t5XnEnTd9TuTr+tY+OU6eYPnM2DVcvQ+c/NEs7ZbRXw8zr/hPb0sOOpDnTXEFt/+8YVVMbabpmRVF5+jUrin7WGTjyxwsNWUuf+alcJbeNFUYw9fZAtofIn140QsmeNZ3qS65jal3VkCNzznY7Vt/rUDyiqHSEketYXD0vXTThc+mIpNJOnbHu9KlyrRHX0z3L62ebY+lcb2Mb+AS0ydS51XPap+t0jlS1U9SpU1fj8p/LPoUrj10bU+XmQH2vzZ07t2j5vHnz2LVrF5lMhqqqqrEP+DyqVEfGUMdyHMjlckW3T7iuje+bQodEfHuNRS5bPKG/69kEYXjW+JRS4Id0/p/jAKSvaMK2dX67+MckpQxaR4S5TGE7y/UAC2N0fj9RUSdJ3+gTY+J7guIRLAM7PfKjW5SiNxOAMjgeRATxiGKtcFCYIEAZMChsx8XSCkIfwhCjHTKhwnMNJsoWYtPKobu7lyAK8VIe2rZwbQsryreR7eFH+ZHKuRCTyRL2ZLGqFSaIOH3kBMrS6Itr6Al78Kob6On1cTyPCEWN5WDCENdxiEKfyLLIRBEK8ByPHpXFdqvQSuNUe2Rzfv4Nhbrly/H/70Hs2lpMegrZ0BAFhp4c6JpaPvr3EWqzPvPmXo5VV4MxhsCEpGqncqr9OJayUMrm0sv+B47jYHsO1U31KOUxdVoNDRelqWtYgFujCMIsOspQU2VjIs2HRz9i9sWzydgWOQOeH1A9/SLm/Mf/wkQheBrXdrh48SJQ8WgZx/X420cnCYzGzgZcN6uBWksRhAEfH/470+YsIdvr4HoetU0X0ZvL0ms0juNQW2eTrkvRa6I4PTGGMAzQjk02jNj/7zZOdHQxu3EKK+Z/Cksbpkyro7amGqWgJ5eh43Q3ddU1fWcrnuvg1tTRhcL91NVopTB2LZDte/P7c/3Cua/AROQyJ4nCaXR2nyCDiwn9wnloWRrL0qTSVSg/RBF30vT2fEI2NJgpqTN8dzDx+2+KR73YfU9vOnqc4598wqxpjVw7rwHF6K7TURT/8mpMOFHukBHjbdD7fuYTweT/m0iUMWN/ubzyyivZtGkTGzZsKFp+4403smTJErZs2VLWfvuGcI4lpUAZQ5TrjS+SWqMcD5PLjLqMUqPaF1qjHY8ol4lnWh9QNqMsDxVb37FKjz3Scun+gkwvURShLQvb9TB+tqhuZohYi15Xath2iNDYrkuYy+SnIlPYrkeQzaDV8GVjongiN9stq4zS2N7AetqjLMftFPlZwiBEjWE5Moy4Xca6nYYt57Ko/NMIym6n/CMsgcJnhFaKyM+MTTuW7F9pTZRPMLWOe4uC/PlpWRbKdghy2eLyUO1g2WjX649bK8IIcn6EyndkmHyi0Te/yVCvo8DSmvg+8/hyM7BcaJcgSxRGhfO3r17xYDWF0ppsLsQYQ5Wj0Xro13U+hqHKcQzxspwf4lgKhRlVPfvneYkne1Tn2G4mv/7AOONOnvh9LdRTaTK5gMgYLK3xHKtiZfLvVSWPUVrWSlHt2fkvhmN6aS2aVPRCU24O9PTTT/PUU0/xzjvvFC3fs2cPmzZtYt++fWXdjl6J3OlcDTwVhhoVUrreQDr/mTtaxhiCIIifWoPJP7pYU5q4GxM/8XFgsLatRz5qzYDJ/wgU39LSV8l4JGvf71MmivqfgaR10VeE+JMyP1KGOFct/LGqvqX99SpsV1in//WBLdf/21h/TCZf6v9KbDCh3z+pr+WAgcKzFRRopfOv9++3sHUQxRP52vFIm779k/8tzgC5MCqE6VoaZeJjoeJ4c2GERmFbCn/Auo6lyYX50YT5bQc0e/9+MbiWFXd05TvtVF8l8p2HQRAWOsh0fN9WYUdxv1jxOxKZ/HG1yneixf/GQC4MC7/Qu1rlOy0MSltkg/hR2VopHEuTDaJC21XZVhyXMQRBDmU5+JFBKY2jNdkwiOPRcTkT+BgTP3nJ1ZqM76MUeI4z6LM+6wdEJp4T0LNtsn5AGIVY2sJzbDK+j1YK17LJRfGoFkspHCu+BS6+pse3OmWDEEwU7yfI1xUzZFlpHcc3KN5cnBdY9pBlpTSeMzje4eLPhmEcv9ZDlsMwzF/rnXOqTyYI0Hrs4z9TfZQCz3KGj992yAR+f0yBH+cxfTEUlRUZP4hzHjs+RhRF2JY1bNmyrLjufvzYE8/uj0krNWS57+8uEwSAQiuKynGbxvHHZfJtPEx98qNf4nNoZPUpatNziP9s9QFDlaUhzKEdD9BF17S+a9VocqqR5k+JesR43LNbmaTQqk4VLxjL8ij3pUvKpbGOqnyWY51ruXR/bm1d8eu2M+z6w70PZ2sHXbJvdxzLpfUcbVnbTtEf51iUR1KP810ebTtB8W2q2k6NaTsO3H/8JWBgPUrO/XNsh6K4rfgX4wJLD1p3uNdL9zdo//bgj/7S9e3qwccc7vWzlSGeW6l0n6OvpzXi9Qe9foY4dcn7mrLdcS2fr2OKyauSudN46psXZbRKP7uHXm+Uxxti+6KlZ63T2LxvZdXkDHM+DN7PEPG5Zz+iPUzddcnrpesOt+3g1/SQRdc9t3kt9DAtWXp9A+g71Wyr+DPXHqJ9XCu+Ng+8TJVum7K8IcupqpLjeMOXU17/tnH8dkn5zDHbJX9Dw5UHx1s14vK5x++MvHwO9UkV1Wfs4h9NfYre92HOiUHxeyWvnUN5ZG08+JhnLg9zHleoPud8jgxTn0HfZwcYq2vV2VTkKOl0mq6urkHLOzo6qK+vr8QhhRBCCCHOu3JzoHQ6TS6XI5vNFi3v7OxEKSX5kxBCCCGACnXizJ07d9B9311dXRw/fnzQvd5CCCGEEJNFuTlQ32uHDx8uWt7a2sqsWbMm3Xw4QgghhChPRTpxmpub2b9/P52d/c922bNnD1prVq5cWYlDCiGEEEKcd+XmQEuXLiWVSvHyyy8Xlvm+zyuvvEJzc3NFYxZCCCFEclSkE2fdunXU1tbS0tLCa6+9xosvvsjWrVtZt25dWZPyCSGEEEIkwUhzoDvuuIPVq1cXyp7nsXHjRnbu3MmuXbv461//yr333supU6e48847z0dVhBBCCDEBVeTpVACHDh1iy5Yt/OMf/6C2tpa1a9eyefNmXFcmVBRCCCHE5DWSHOgrX/kKR48e5c9//nNhmTGGZ555hueff54TJ06wcOFCHnzwQZYsWXI+qiGEEEKICahinThCCCGEEEIIIYQQYuyMzzOwhBBCCCGEEEIIIcSoSCeOEEIIIYQQQgghRAJIJ44QQgghhBBCCCFEAkgnjhBCCCGEEEIIIUQCSCeOEEIIIYQQQgghRAJIJ44QQgghhBBCCCFEAkzqTpxDhw7x9a9/ncWLF7Ny5Uq2bt1KLpc763bGGJ555hlWrVrFokWLuO2223j77bcrH3BCldPObW1tbN26lbVr17JkyRKam5u59957OXr06DhFnSzlnssDPffccyxYsICNGzdWKMrkG007Hzt2jPvvv5/ly5ezaNEi1qxZw0svvVThiJOp3HY+efIkDz/8MKtWrWLx4sXceOONvPDCC+MQcfIcOXKEhx9+mLVr13LFFVdw4403jmg7uf6J8ST5Q3JIHpIMksckh+RCyTERcyp7TPYyAXV0dHDHHXcwZ84ctm/fzrFjx3j88cfJZDI8/PDDw277i1/8gm3btnHfffexYMECfv3rX/ONb3yD3//+91xyySXjVINkKLedDx48yN69e7npppu46qqrOHnyJE8//TS33HILf/jDH2hoaBjHWkxsozmX+xw/fpwnn3ySadOmVTja5BpNO7e1tXHbbbdx2WWXsWXLFlKpFO+99945J7gXgtG086ZNm2htbeWee+5h5syZ7Nu3j0cffRTLsrj11lvHqQbJ8N577/Hqq69y1VVXEUURxpgRbSfXPzFeJH9IDslDkkHymOSQXChZJmROZSapHTt2mMWLF5uTJ08Wlv3mN78xCxcuNB9//PGQ22UyGbN06VLzxBNPFJZls1nz2c9+1jzyyCMVjDiZym3njo4O4/t+0bKPPvrILFiwwDz77LOVCjeRym3jgb773e+a733ve2b9+vVmw4YNFYo02UbTzvfdd5+57bbbTBAEFY4y+cpt57a2NjN//nzz4osvFi2//fbbzVe/+tVKhZtYYRgW/n3//febG2644azbyPVPjCfJH5JD8pBkkDwmOSQXSpaJmFNN2tup9u3bx4oVK5gyZUph2Zo1a4iiiNdff33I7Q4cOEB3dzdr1qwpLHNdl9WrV7Nv375KhpxI5bZzOp3GtosHgl100UU0NDTQ1tZWqXATqdw27vPWW2/xpz/9iXvvvbeCUSZfue3c3d3Nyy+/zJe//GUsyxqHSJOt3HYOggCAurq6ouWpVGrEv4hcSLQ+98u7XP/EeJL8ITkkD0kGyWOSQ3KhZJmIOdWk7cRpbW1l7ty5RcvS6TSNjY20trYOux0waNt58+bx4Ycfkslkxj7YBCu3nc/k8OHDtLe3M2/evLEMMfFG08ZhGLJlyxa+9a1v0dTUVMkwE6/cdj548CC+72PbNuvXr+fKK69k5cqV/PjHP8b3/UqHnTjltvPMmTP5zGc+w44dO3j//ffp7u7mj3/8I6+//jq33357pcO+IMj1T4wnyR+SQ/KQZJA8JjkkF5r8Kp1TTdo5cTo7O0mn04OW19fX09HRMex2ruvieV7R8nQ6jTGGjo4OqqqqxjzepCq3nUsZY/jBD35AU1MTN9xww1iGmHijaePnn3+e3t5evva1r1Uousmj3Hb+5JNPAHjooYe49dZb+c53vsO//vUvtm3bhtZafnksMZrzefv27WzevLnwGWFZFg899BCf//znKxLrhUauf2I8Sf6QHJKHJIPkMckhudDkV+mcatJ24ohk2b59O2+88Qa//OUvqampOd/hTArt7e1s27aNH/3oR7iue77DmbSiKALg+uuv54EHHgBg+fLl9PT0sHPnTlpaWuSL7xgwxvDggw/ywQcf8MQTT9DY2Mj+/fv54Q9/SH19vXx5E+ICJfnDxCV5SDJIHpMckguJPpO2EyedTtPV1TVoeUdHB/X19cNul8vlyGazRT1nnZ2dKKWG3fZCVG47D/Tb3/6WJ598kscee4wVK1aMdYiJV24b//znP2fBggVcc801dHZ2AvG9tEEQ0NnZSU1NzaB5BS5ko/nMgDjhGWjFihXs2LGDI0eOsGDBgrENNsHKbee//OUv7Nmzh5deeqnQnsuWLaO9vZ3HH39cEpcxINc/MZ4kf0gOyUOSQfKY5JBcaPKrdE41aefEmTt37qB7Cru6ujh+/Pige9NKt4P4/uqBWltbmTVrlvRElyi3nfvs3buXRx99lLvvvpubb765UmEmWrltfPjwYf7+979z7bXXFv4/cOAAr732Gtdeey379++vdOiJUm47X3755cPuN5vNjkl8k0W57fz+++9jWRbz588vWr5w4ULa2tro7e2tSLwXErn+ifEk+UNySB6SDJLHJIfkQpNfpXOqSduJ09zczP79+ws9/wB79uxBa83KlSuH3G7p0qWkUilefvnlwjLf93nllVdobm6uaMxJVG47A7z55pvcc8893HLLLbS0tFQ61MQqt42///3vs3v37qL/P/3pT7N48WJ2797NokWLxiP8xCi3nWfPns38+fMHJaP79++nqqrqrMnRhWY07RyGIe+++27R8oMHDzJt2jSqq6srFvOFQq5/YjxJ/pAckockg+QxySG50ORX6Zxq0o5hXLduHb/61a9oaWlh48aNHDt2jK1bt7Ju3TpmzJhRWO+OO+7gww8/ZO/evQB4nsfGjRvZvn07DQ0NzJ8/nxdeeIFTp05x5513nq/qTFjltvOhQ4doaWlhzpw5rF27lrfffruwbkNDA5deeul4V2XCKreNFy5cOGhf6XSampoali1bNm7xJ0W57QywefNmvv3tb/PYY4+xatUq3nnnHXbu3Mmdd94pczSUKLedm5ubmTVrFnfffTctLS00NTXx2muv8bvf/Y677rrrfFVnwurt7eXVV18F4OjRo3R3d7Nnzx4ArrvuOhoaGuT6J84ryR+SQ/KQZJA8JjkkF0qWiZhTTdpOnPr6enbt2sWWLVtoaWmhtraWm2++mc2bNxetF0URYRgWLfvmN7+JMYadO3dy4sQJFi5cyLPPPssll1wynlVIhHLb+Z///CddXV10dXXxpS99qWjdL3zhCzz++OPjEn8SjOZcFiM3mnb+3Oc+x09/+lOeeuopXnjhBZqamrjrrrvYsGHDeFYhEcpt51QqxXPPPcfPfvYzfvKTn9DV1cXFF1/MAw88wPr168e7GhNee3s7mzZtKlrWV969ezfLli2T6584ryR/SA7JQ5JB8pjkkFwoWSZiTqWMMWbUexFCCCGEEEIIIYQQFTVp58QRQgghhBBCCCGEmEykE0cIIYQQQgghhBAiAaQTRwghhBBCCCGEECIBpBNHCCGEEEIIIYQQIgGkE0cIIYQQQgghhBAiAaQTRwghhBBCCCGEECIBpBNHCCGEEEIIIYQQIgGkE0cIIYQQQgghhBAiAaQTRwghhBBCCCGEECIBpBNHCCGEEEIIIYQQIgGkE0cIIYQQQgghhBAiAaQTRwghhBBCCCGEECIB/j9ZGdxziLOfdAAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# feature extraction\n",
        "feature_extractor = ImageFeatureExtractor_Torch(centroid_classifier, 'deep_centroids')\n",
        "\n",
        "# get train embeddings\n",
        "print('train dataset')\n",
        "X_train, y_train = feature_extractor.extract(train_dataloader, device, verbose = False)\n",
        "\n",
        "\n",
        "X = X_train\n",
        "y = y_train\n",
        "\n",
        "\n",
        "classes = {0: 'plane', 1: 'car', 2: 'bird', 3: 'cat', 4: 'deer', 5: 'dog',\n",
        "           6: 'frog', 7 : 'horse', 8 : 'ship', 9 : 'truck'}\n",
        "\n",
        "iterations_full = 5000\n",
        "iterations_per_class = 10000\n",
        "\n",
        "model_name = 'Centroids Dino ViTb/16'\n",
        "dataset_name = 'MNIST'\n",
        "suptitle = f'Distribution of Dot Products in {model_name} Emebeddings ({dataset_name})'\n",
        "histogram_args = {'bins' : 100, 'density' : True, 'alpha' : 0.6}\n",
        "\n",
        "plot_dot_products(X, y, iterations_full = iterations_full, iterations_per_class = iterations_per_class,\n",
        "                  class_names = classes, histogram_args = histogram_args, suptitle = suptitle)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 743
        },
        "id": "5b0s9Ciw6DGT",
        "outputId": "ca0504a5-8cd8-40e6-bdb2-96822937dc5c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "train dataset\n",
            "###Extracting features###\n",
            "Done\n",
            "Sampling Per Class Dot Products\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 10/10 [00:15<00:00,  1.60s/it]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sampling Intra Class Dot Products!\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 5000/5000 [00:00<00:00, 38028.88it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sampling Inter Class Dot Products!\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 5000/5000 [00:00<00:00, 22138.88it/s]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### MNIST"
      ],
      "metadata": {
        "id": "kN0ZvgII68iT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "root = '/content/drive/MyDrive'\n",
        "dataset_name = 'mnist_torch'\n",
        "model_name = 'dino_vitb16'\n",
        "\n",
        "experiment_identifier = model_name + '_' + dataset_name\n",
        "keys = ['X_train', 'y_train', 'X_test', 'y_test']\n",
        "data = load_features(root, experiment_identifier, keys)"
      ],
      "metadata": {
        "id": "yiGtu7a_7Hce"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# non private projection\n",
        "dataloader_args = {'batch_size': 1000, 'shuffle': True}\n",
        "optimizer_args = {'lr' : 4, 'momentum': 0.9}\n",
        "\n",
        "train_dataloader, test_dataloader, centroid_classifier, optimizer, criterion = deep_centroids_experiment(data, dataloader_args, optimizer_args)\n",
        "\n",
        "\n",
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "\n",
        "centroid_classifier.to(device)\n",
        "num_epochs = 50\n",
        "train_loss = training_loop(centroid_classifier, train_dataloader,\n",
        "                           test_dataloader, num_epochs, optimizer, criterion, device)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8KC1kUck7C7V",
        "outputId": "5b517699-eb2e-4e25-d2eb-84c8498d1b4a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 50/50 [00:52<00:00,  1.05s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " Accuracy on the test images: 0.9787\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# feature extraction\n",
        "feature_extractor = ImageFeatureExtractor_Torch(centroid_classifier, 'deep_centroids')\n",
        "\n",
        "# get train embeddings\n",
        "print('train dataset')\n",
        "X_train, y_train = feature_extractor.extract(train_dataloader, device, verbose = False)\n",
        "\n",
        "\n",
        "X = X_train\n",
        "y = y_train\n",
        "\n",
        "\n",
        "\n",
        "classes = {0: 'one', 1: 'two', 2: 'three', 3: 'four', 4: 'five', 5: 'six',\n",
        "           6: 'seven', 7 : 'eight', 8 : 'nine', 9 : 'ten'}\n",
        "\n",
        "iterations_full = 5000\n",
        "iterations_per_class = 10000\n",
        "\n",
        "model_name = 'Centroids Dino ViTb/16'\n",
        "dataset_name = 'MNIST'\n",
        "suptitle = f'Distribution of Dot Products in {model_name} Emebeddings ({dataset_name})'\n",
        "histogram_args = {'bins' : 100, 'density' : True, 'alpha' : 0.6}\n",
        "\n",
        "plot_dot_products(X, y, iterations_full = iterations_full, iterations_per_class = iterations_per_class,\n",
        "                  class_names = classes, histogram_args = histogram_args, suptitle = suptitle)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 743
        },
        "id": "sbiOq16269R8",
        "outputId": "6a9d380a-c7da-4765-9d6b-8c155cad8ae9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "train dataset\n",
            "###Extracting features###\n",
            "Done\n",
            "Sampling Per Class Dot Products\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 10/10 [00:13<00:00,  1.31s/it]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sampling Intra Class Dot Products!\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 5000/5000 [00:00<00:00, 39212.30it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sampling Inter Class Dot Products!\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 5000/5000 [00:00<00:00, 23782.60it/s]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### R152"
      ],
      "metadata": {
        "id": "ZuaxzSJP-jyL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# model\n",
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "\n",
        "model_name = 'resnet152'\n",
        "model_dict = ModelExperiment(model_name = model_name).get_model()\n",
        "model = model_dict['model']\n",
        "model.to(device)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "# data\n",
        "preprocessing_args = dict()\n",
        "preprocessing_args['type'] = 'imagenet'\n",
        "\n",
        "dataloader_args = dict()\n",
        "dataloader_args['batch_size'] = 128\n",
        "\n",
        "dataset_name = 'cifar10_torch'\n",
        "\n",
        "dataset = DatasetExperiment(name = dataset_name, preprocessing_args = preprocessing_args)\n",
        "train_dataloader, test_dataloader = dataset.get_dataloaders(dataloader_args)\n",
        "\n",
        "\n",
        "\n",
        "# feature extraction\n",
        "feature_extractor = ImageFeatureExtractor_Torch(model, model_name)\n",
        "\n",
        "# get train embeddings\n",
        "print('train dataset')\n",
        "X_train, y_train = feature_extractor.extract(train_dataloader, device, verbose = True)\n",
        "\n",
        "# get test embeddings\n",
        "print('test dataset')\n",
        "X_test, y_test = feature_extractor.extract(test_dataloader, device, verbose = True)\n",
        "\n",
        "\n",
        "## store features\n",
        "data = {'X_train' : X_train, 'y_train' : y_train, 'X_test': X_test, 'y_test': y_test}\n",
        "root = '/content/drive/MyDrive'\n",
        "experiment_identifier = model_name + '_' + dataset_name\n",
        "\n",
        "store_features(data, root = root, experiment_identifier=experiment_identifier)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LwJnstg7-xmj",
        "outputId": "ab98a8f2-d0ef-4167-ce96-cc31a99820aa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Files already downloaded and verified\n",
            "Files already downloaded and verified\n",
            "train dataset\n",
            "###Extracting features###\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 391/391 [00:52<00:00,  7.41it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Done\n",
            "test dataset\n",
            "###Extracting features###\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 79/79 [00:05<00:00, 14.12it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Done\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# non private projection\n",
        "dataloader_args = {'batch_size': 1000, 'shuffle': True}\n",
        "optimizer_args = {'lr' : 1e-2}\n",
        "\n",
        "train_dataloader, test_dataloader, centroid_classifier, optimizer, criterion = deep_centroids_experiment(data, dataloader_args, optimizer_args)\n",
        "\n",
        "\n",
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "\n",
        "centroid_classifier.to(device)\n",
        "num_epochs = 10\n",
        "train_loss = training_loop(centroid_classifier, train_dataloader,\n",
        "                           test_dataloader, num_epochs, optimizer, criterion, device)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lD8j6lNk-mXu",
        "outputId": "74322cd2-a0ee-4e70-c94d-3f9bb899f253"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 10/10 [00:10<00:00,  1.02s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " Accuracy on the test images: 0.6637\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(train_loss)\n",
        "plt.xlabel('epoch')\n",
        "plt.ylabel('training loss')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "id": "Yi24YItTBhZI",
        "outputId": "7e2743cd-fbb3-4311-9f88-b6e1aa5ca6ab"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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iV+dt2bIFCoUCxcXF+Pzzzx1YofMYFt65fUZpVSPqm9rg7ekqckVERETSIqk7QQqFbeXYet5gpvF0RbCfJ4DOXeWJiIioO0ndCZIilcr+AUupVHT73VGGR/jifHUTcksMmDgyyKGfy5kNVD+od9gPaWE/pIX9sC+GoGtQKAT4+qoddn2NxsNh1waAccODsOtwCXJLjQ79OgYLR/eD+ob9kBb2Q1rYD/tgCLoGs9kCo7HJ7tdVKhXQaDxgNDbDZDLb/fpdwi48DssvNqC0zAAPN7b7SgaqH9Q77Ie0sB/Swn70TKPx6PWdMv5U7EFHh+P+kplMZodeX6t2hZ/GHdXGFmQV1iIhys9hn2swcHQ/qG/YD2lhP6SF/bAPPlQc5GKtW2hwcDQREdGlGIIGudgLU+W5XhAREVF3knoc1tzcjPT0dABASUkJGhoarKs/T5w4ETqdDqmpqSgtLcXOnTut5x0/fhwlJSWoqakBABw7dgwAoNPpMHHixAH+KqSl605QfqkR7R1muDhgthsREZEzklQIqq6uxvLly7sd6/p406ZNSEpKgtlshslk6vaeDz74AJ999pn14/feew9AZ3DavHmzg6uWNr3OExpPFxib2nH2vNEaioiIiOROsFgslr6edPr0aeTl5XXb2+u///0vNmzYgLa2Ntxxxx1ITU21a6FiMJnMqKlptPt1VSoFfH3VqK1tHJCBba9/dhyHsioxZ0o07pgc6fDP52wGuh90beyHtLAf0sJ+9EynU/d6dphNz0b+8Y9/YMeOHdaPi4qK8Oijj6K4uBgAsHbtWnz00Ue2XJocwDo4urhO1DqIiIikxKYQdObMGYwfP9768bZt26BQKPDZZ5/hk08+wfTp07Flyxa7FUn9E3thM9XcYgPM5j7f+CMiIhqUbApB9fX18PHxsX6cnp6OlJQU6HQ6AEBKSgoKCgrsUiD1X3igFzzclGhpM6GookHscoiIiCTBphAUEBCAvLw8AEBFRQVOnjyJlJQU6+uNjY3c1FRCFAoBQ0N9AABZnCpPREQEwMbZYdOmTcO///1vtLW14dixY3B1dcWtt95qfT0rKwvh4eF2K5L6LzZci+P51cgpqsOvJrA3RERENoWgFStWoKamBtu2bYO3tzdefPFF+Pv7A4B1bZ/77rvProVS/1w6ONpisUAQBHELIiIiEplNIUitVuPll1++4muenp7YvXs33N3d+1UY2VekXgMXlQL1Te0oq2lCsB93lSciInmz68CdtrY2tLS0wNvbGy4uLva8NPWTi0qB6GANAI4LIiIiAmwMQV999RVeeOGFbsfWr1+PcePGYcKECVi2bBkaG+2/yCD1T9cjsRyGICIiIttC0HvvvYfm5mbrx4cPH8b69etx/fXXIzU11bp6NEnLxR3l60Stg4iISApsGhNUVFSE2bNnWz/evn07/P39sX79eqhUKlgsFnz77bd4/PHH7VYo9V9MqAYKQUC1sRVVhmb4az3ELomIiEg0Nt0Jamtrg5ubm/XjPXv2YMqUKVCpOjNVTEwMysrK7FMh2Y27qwoRem8AQE6RQeRqiIiIxGVTCAoLC0NGRgYA4Pjx4ygoKMANN9xgfb26uhqenp72qZDsKjZcC4CDo4mIiGx6HDZ//nz87W9/Q25uLsrLy6HX63HzzTdbXz98+DCGDh1qtyLJfmLDffDN/iLkcDNVIiKSOZtC0KJFi+Dm5ob09HQkJCTgt7/9rXVdoLq6OlRWVmLhwoV2LZTsY9iFzVTPVzfB2NgGjdpV3IKIiIhEYlMIAoB58+Zh3rx5lx338fHB1q1b+1UUOY6XhwtCA9QoqWxETnEdxscFil0SERGRKGwOQV1yc3NRUlICAAgNDeVjMCcQG+aDkspGZBUxBBERkXzZHIK+++47rF271hqAuoSFhWHVqlWYNm1av4sjx4gN98GuIyWcIUZERLJmUwhKT0/HH//4R4SEhGDlypWIiYkBAOTl5eHjjz/GH/7wB2zYsAFTpkyxa7FkH12LJhZW1KO5tQMebv2+IUhEROR0bPrp98YbbyAuLg4ffPBBt6nw06ZNw29+8xvce++9eP311xmCJMrX2w0BPu6orGtBbokBo6L9xC6JiIhowNm0TlBWVhbuvvvuK64F5OnpidmzZyMrK6vfxZHjxF6YJcYtNIiISK5sCkFubm4wGK4+nsRgMHRbUZqkh/uIERGR3NkUgpKSkrBp0yYcOXLksteOHTuGzZs3Izk5ud/FkeN0haCz541oazeJWwwREZEIbBoT9OSTT2LBggW49957MXr0aERFRQEAzp49i59//hl+fn544okn7Foo2Vegrwe0alcYGttw9rwRcUN8xS6JiIhoQNl0Jyg8PBxffPEFFi1aBIPBgB07dmDHjh0wGAxYvHgxtm3bhrCwMHvXSnYkCAIfiRERkazZPDfaz88Pf/7zn/HnP//ZnvXQAIoN98GBMxUMQUREJEs23QmiwaHrTlBuiREms1ncYoiIiAZYr+4ErV69us8XFgQBL7zwQp/Po4ETGqCGp5sKTa0dKCxvQFSwRuySiIiIBkyvQlBmZmafLywIQp/PoYGlEAQMC9PiWF41sovqGIKIiEhWehWCfvjhB0fXQSKJDfexhqDpE4eIXQ4REdGA4ZggmesaF5RTbIDZYhG3GCIiogHEECRzEXpvuKoUaGhux/mqRrHLISIiGjAMQTKnUioQE6oFAGQXX30rFCIiosGGIYgwLOxCCOJ6QUREJCMMQYS4S1aOtnBcEBERyQRDECE6VAulQkBtfSuqDC1il0NERDQgGIIIbi5KROq9AfCRGBERyYdNe4ctXrz4mq8LggA3Nzfo9XokJSVh+vTpUKls3qaMBkBsuA/ySo3ILqpDyqhgscshIiJyOJvuBFksFpSVlWH//v3IyspCQ0MDGhoakJWVhf3796OsrAzV1dX45ptv8Pjjj2Pu3Lmoqamxd+1kR8O4ozwREcmMTSFo+fLlMBgMWLt2LTIyMrB161Zs3boVGRkZePHFF2EwGPD0009j3759eOGFF5Cbm4tXXnnF3rWTHQ0L00IAUF7bDENDq9jlEBEROZxNIeill17CnDlzcPfdd0OpVFqPK5VKzJ49G7Nnz8aLL74IQRAwZ84czJ07Fz/++KO9aiYHULu7IDTACwDXCyIiInmwKQRlZWUhLCzsqq+HhYXhzJkz1o/j4+NhMPT8g7WgoABr1qzBrFmzMHLkSNxxxx29qsdiseDtt9/GTTfdhNGjR2P+/Pk4evRor86li+L4SIyIiGTEphAUEBCAtLQ0mM3my14zm834+uuv4e/vbz1WV1cHrVbb43VzcnKQnp6OiIgIxMTE9Lqed955B//6179w//3346233kJAQACWLFmCoqKiXl+DgGHhXDSRiIjkw6YQ9MADD+DAgQNYuHAhPv30U+zfvx/79+/HJ598ggULFuDQoUNYsmSJ9f1paWkYPXp0j9edOnUq0tPT8a9//Qvx8fG9qqW1tRVvvfUWlixZgvvvvx/Jycl45ZVX4OPjg3fffdeWL0+2ujZTLa5oQFNLu7jFEBEROZhN89bvu+8+CIKAf/3rX3jqqacgCAKAzsdSPj4+eOqpp3DfffcBANra2rB69WqEhob2eF2Fou+Z7PDhw2hoaMCMGTOsx1xdXXHrrbdi586dfb6enPl4uSHQ1wMVtc3IKTbguqH+PZ9ERETkpGxevOfee+/FPffcgxMnTqC0tBQAEBISgoSEBLi4uFjf5+rqiokTJ/a/0qvIz88HAERHR3c7HhMTg40bN6KlpQXu7u42X1+lsv96kkqlotvvUjJ8iC8qapuRW2rA+OGBYpczIKTcDzliP6SF/ZAW9sO++rWCoYuLC8aOHYuxY8faq54+MxqNcHV1hZubW7fjGo0GFosFBoPB5hCkUAjw9VXbo8wr0mg8HHZtW40dHoTdx0qRV2J06NcuRVLsh5yxH9LCfkgL+2Ef/QpBubm5KCoquurMr7vvvrs/lxed2WyB0dhk9+sqlQpoNB4wGpthMl0+uFxM4f6d31g5RXUoqzDCzUXZwxnOT8r9kCP2Q1rYD2lhP3qm0Xj0+k6ZTSGosLAQTz75JH7++eer7jouCMKAhCCNRoO2tja0trZ2uxtkNBohCEKvZqVdS0eH4/6SmUxmh17fFr5ebvDxckVdQxuyC+swIsJX7JIGjBT7IWfsh7SwH9LCftiHTSFozZo1yM7Oxp///GckJiZCo9HYu65e6xoLdPbsWQwfPtx6PD8/HyEhIf0aDyRHgiAgNtwH+09XILtIXiGIiIjkxaYQdPjwYSxduhSLFi2ydz19Nm7cOHh5eeHrr7+2hqD29nZ8++23mDJlisjVOae4S0IQERHRYGVTCPL19YW3t7e9a0FzczPS09MBACUlJWhoaEBaWhoAYOLEidDpdEhNTUVpaal1+rubmxuWLl2KdevWQafTITY2Fh9++CHq6urw4IMP2r1GOejaTDWv1IAOkxkqzkIgIqJByKYQtGDBAnzxxRe47777uu0d1l/V1dVYvnx5t2NdH2/atAlJSUkwm80wmUzd3vPQQw/BYrHgvffeQ01NDUaMGIF3330X4eHhdqtNTkL81VC7q9DY0oGC8nrEhPRvXBUREZEU2RSCIiMjYTabMWvWLMydOxd6vf6KYehXv/pVn64bFhaGrKysa75n8+bNlx0TBAFLly7F0qVL+/T56MoUgoBhYT44mluF7KI6hiAiIhqUbApBK1eutP733//+9yu+RxAEnD592raqSHSx4Z0hKKfIgBlJYldDRERkfzaFoE2bNtm7DpKYrn3EcorrYLZYoLiwNQoREdFgYVMIcuQ2GCQNQ4K84OaiRGNLB0orGxEW6CV2SURERHbFaT90RSqlAjGhnes/ZXGqPBERDUK9uhO0aNEiKBQKvPvuu1CpVFi8eHGP5wiCgI0bN/a7QBJPbLgPTp2rRU5xHaaNDxO7HCIiIrvq9eMws/ni8txX2yrjUr15D0lbbJgPgM47QRaLBQLHBRER0SDSqxD0y2npV5qmToNPdIgGSoUAQ0MbKuqaEeTrKXZJREREdsMxQXRVri5KRAV3jgviFhpERDTY2DQ7rEtDQwNKS0thNBqv+PhrwoQJ/bk8SUBsuA9ySwzILqrDDaNDxC6HiIjIbmwKQbW1tXjuuefw7bffXraFBQDr+BEuluj8YsN9sGNfAXKKDGKXQkREZFc2haCnn34au3btwqJFi5CYmAiNRmPvukgihoZqIQCoqGtGbX0rfL3dxC6JiIjILmwKQXv27EFqair+9Kc/2bsekhhPdxXCg7xQWN6AnOI6TBwRJHZJREREdmHTwGh3d3eEhobauxaSqEunyhMREQ0WNoWgu+66C9999529ayGJsu4jxhBERESDiE2Pw6ZPn44DBw7gwQcfxPz586HX66FUKi97X3x8fL8LJPENuxCCiisb0dDcDi8PF3ELIiIisgObQtC9995r/e+MjIzLXufssMFFq3aFXueJspom5BYbMGaYv9glERER9ZtNIejFF1+0dx0kcbHhWpTVNCG7qI4hiIiIBgWbQtDs2bPtXQdJXGy4D3YfO4/s4jqxSyEiIrILbptBvdI1Q6ygrB4tbR3iFkNERGQHvboTtHr1agiCgOeeew5KpRKrV6/u8RxBEPDCCy/0u0CSBj+tO3QaN9QYW5FXakR8pE7skoiIiPqlVyEoMzMTgiDAbDZDqVQiMzOzx3MEQeh3cSQdgiAgNtwH+06WI6eojiGIiIicXq9C0A8//HDNj0keYsM6QxB3lCciosGAY4Ko17oWTcwrNaLDZBa3GCIion5iCKJeC/bzhJeHC9o7zDh3vl7scoiIiPrFpinyAJCeno73338fp06dQn19PSwWy2Xv4WKJg0vXuKDD2ZXILq7D0DCt2CURERHZzKY7Qd988w0eeeQRVFVV4fbbb4fZbMbMmTNx++23w93dHXFxcVi2bJm9ayUJiL0QfDguiIiInJ1Nd4LeeustjB49Gv/3f/8Hg8GADz/8EHPnzkVycjKKi4sxf/58hIWF2btWkoDYIT4AgJxiA8xmCxQKzgIkIiLnZNOdoLy8PNx+++1QKpVQqTpzVEdH5wJ6YWFhWLhwId555x37VUmSER7oBTdXJZpbO1Bc2SB2OURERDazKQS5u7vDxaVzJ3GNRgNXV1dUVlZaX/f390dxcbF9KiRJUSoUGBbKR2JEROT8bApBUVFRyMvLs348YsQIbNu2DR0dHWhtbcX27dsRHBxstyJJWoZdmCrPEERERM7MphB066234vvvv0dbWxsA4JFHHsH+/fsxYcIETJo0CQcPHsTDDz9s10JJOuK6QlCx4YqzAomIiJyBTQOjH3zwQTz44IPWj2+++WZs3rwZ3377LZRKJW688UZMmjTJbkWStEQFe0OlFGBsbEN5bTP0Ok+xSyIiIuqzPoegtrY2/Pe//0VoaCiGDx9uPZ6YmIjExES7FkfS5KJSIjpYg+xiA7KL6hiCiIjIKfX5cZiLiwuWL1+OI0eOOKIechJdU+W/O1iEitomcYshIiKyQZ9DkCAIiIyMRG1trSPqISeRkhAMTzcViisb8Zf/dwB7T5SJXRIREVGf2DQweunSpfjggw+Qn59v73rISQTpPPHskomIDdOipc2Ed7afwjtfnkRza4fYpREREfWKTQOjjx07Bh8fH9x5552YOHEiQkND4e7uftn7nnrqqX4XSNLlp3XHn+4dh+0Z57Btz1nsPVmO3BIDHr4rHjEh3FeMiIikTbDYMMf50gHRV72wIDj9Bqomkxk1NY12v65KpYCvrxq1tY3o6DDb/fpiyCmuw9tfnEK1sQVKhYC7b4jCjKQIp9hWYzD2w5mxH9LCfkgL+9EznU4NpbJ3D7psuhN05swZW06jQWxYmA+eXTIBm77Jwv7TFfhPej5Onq3BQ3fGw9fbTezyiIiILmPTmKDS0lK0tLRc9fWWlhaUlpbaXBQ5J093Fyy9Kx4P3D4cbi5KnCmswzPv7ceR7MqeTyYiIhpgNoWgadOmYefOnVd9/YcffsC0adNsLoqclyAIuGF0CJ55YAIigrzR0NyOdVuPY/O3WWhrN4ldHhERkZVNIainYUTt7e1QKGy6NPLy8vDAAw9gzJgxSElJwUsvvWTdnuNa6uvr8fTTTyMpKQnXXXcdFi1a5PRjkpyZXueJPy8aj+kTwwEAuw6X4LlNB7nzPBERSUavxwQ1NDTAaDRaP66rq7viIy+j0YgdO3YgICCgz8UYDAakpqYiMjIS69atQ3l5OdauXYuWlhasWbPmmuc+9thjOHHiBJ588kn4+/vj/fffR2pqKrZt28bNXEXiolJg/tRhiI/U4X+/Oo2Sykb89f2DmD91KKaOC4UgSH/QNBERDV69nh22fv16vP766726qMViwYoVK/DII4/0qZi33noLGzZswK5du+Dj4wMA+Oijj/Dss89i165dCAoKuuJ5R48exfz58/Hmm29i6tSpAIDm5mZMmzYNt99+u81T9Tk7zH6MjW1496vTOJ5fDQAYM9QfD9w+HN6eriJXJs9+SBn7IS3sh7SwHz1zyOywlJQUeHp6wmKx4B//+AdmzpyJ+Pj4bu8RBAEeHh6Ij4/HqFGj+lY1gN27dyM5OdkagABgxowZeOaZZ7Bnzx7MmTPniuedOnUKgiAgJSXFeszDwwOJiYnYtWsX1yuSAI3aFSvuGY3vDhbjkx9zcTS3Cs+8tx8P3TESIyJ1YpdHREQy1OsQNHbsWIwdOxZA512WX/3qV4iNjbVrMfn5+Zg7d263YxqNBgEBAddcnbqtrQ0KhQJKpbLbcRcXF5SUlKClpeWKiznSwBIEAbdOCEfcEB+89cVJnK9uwj+3HMWMSRG4+4YoqHqZ3ImIiOzBpnWCHn30UXvXAaBzPJFGo7nsuFarhcFguOp5ERERMJlMOHXqFEaPHg0AMJvNOHHiBCwWC4xGo80hSKWy/w/mrtt0vb1dN9hEh2rx1weT8H87s7HrSAl27CvAmcJa/O7uBASJsCO93PshNeyHtLAf0sJ+2JdNIUhqUlJSMGTIEDzzzDP4+9//Dj8/P7z99tsoKioCAJsH4CoUAnx91fYstRuNxsNh13YGj/0mEUmjQ7Du46PILzVizbuZeGTOdZiaGC5KPXLvh9SwH9LCfkgL+2EfkgpBGo0G9fX1lx03GAzQaq++F5WrqyteffVVPP7447jzzjsBALGxsUhNTcXmzZu7jTHqC7PZAqOxyaZzr0WpVECj8YDR2AyTSd4D20aGa/H8b5OwYdsJZBXW4dUPDyPzeClSZwyHh9vA/PVkP6SF/ZAW9kNa2I+eaTQejt02w1Gio6MvG/tTX1+PyspKREdHX/PchIQEpKWloaCgABaLBZGRkfjrX/+K+Ph4uLi42FyTI0ffm0xmju4HoFW74skFY7F97zl88dM5ZJwoQ3ZRHZbeFY+Y0IHbiJX9kBb2Q1rYD2lhP+xDUg8Vp0yZgoyMjG7rEaWlpUGhUHSb+XU1giAgMjISUVFRqK2txY4dO3DPPfc4smSyE4VCwF0pUVh13zj4adxRZWjBi/8+jO0Z52A293mPXyIioh5JKgQtWLAAarUay5Ytw08//YT//Oc/eOmll7BgwYJuawSlpqbi1ltv7Xbum2++iR07diAzMxNbtmzB3LlzkZCQcNVp9SRNQ8O0eHbJBEwcEQizxYKtu/Pxzy1HUFvfKnZpREQ0yEjqcZhWq8XGjRvx3HPPYdmyZVCr1fj1r3+NlStXdnuf2WyGydR9Hyqj0Yi///3vqK6uRmBgIO666y78/ve/t3n7DhJP10asCVF++GBnNs4U1mHNu5lYcvsIjI3t+0rkREREV9LrFaPliCtGi6+spglvbTuJgvLOAfM3jw3F/KlD4eqi7OHM3mM/pIX9kBb2Q1rYj571ZcVo3iYhSdPrPPE/i8fjtolDAAC7jpTgrxsPoriCG7ESEVH/MASR5KmUCsybOhSPzb8OGrUrSqsa8deNB/H9oWLwRiYREdmKIYicRkKUH/66ZCJGx/ihw2TGBzuzse4/x1Hf1CZ2aURE5IQYgsipaNSuWP7r0Vh4yzColAKO5lZhzXv7cepcjdilERGRk2EIIqcjCAJuTQzHU4sTEeznCUNDG17echSf/JiLDq6gSkREvcQQRE5rSJA31tw/ATeOCYEFwNf7CvHC5kMor7X/VidERDT4MASRU3NzUSL1tuFYNjsBancVzpXV4y//7wD2HD/PQdNERHRNDEE0KIyPC8SzSyYiNtwHrW0mvPvVabzz5Sk0tXSIXRoREUkUQxANGjqNO/60cCxm3xAFhSBg36ly/OX/7UdeiUHs0oiISIIYgmhQUSgE3HlhI1Z/7cWNWL/kRqxERPQLDEE0KA0N0+IvD0y0bsT62YWNWGuMLWKXRkREEsEQRIOWp7sKS++Kx5LbR8DNRYkzhXV45r39OJxdKXZpREQkAQxBNKgJgoDrRwfjmQcmIELvjcaWDqzfehybvslCa7tJ7PKIiEhEDEEkC3qdJ/5n0XjcltS5EeuPR0rw3MaDKOJGrEREssUQRLKhUiow7+aheHz+GGgvbMT63MaD+PZAIdcUIiKSIYYgkp34KB2effDiRqz//iYbq17/CT/nVTMMERHJiErsAojEoPHs3Ij1+0PF+GRXHk6drcGpszWI1HvjjsmRGDPMHwpBELtMIiJyIIYgki1BEHBLYjgmjgzCD0dLkZZxDufK6rF+63GE+qsxMzkCE0YEQqngDVMiosFIsPD+/1WZTGbU1DTa/boqlQK+vmrU1jaio4O7noutqx8FxbX4el8BfjhcjObWzpljgT4euD05ApMT9FApGYYGAr8/pIX9kBb2o2c6nRrKXv57zRB0DQxB8vDLfjS1tOP7Q8XYebAYDc3tAABfbzfcljQEU64LgZuLUuSKBzd+f0gL+yEt7EfP+hKC+DiM6Bc83V1wZ0oUbp0QjvSjpUjbX4ja+lZ8+F0Otmecw68mhGPquDB4uPHbh4jImfFfcaKrcHdVYfrEIZg6LhQ/HS/D1/sKUGVowX/S8/H1vkLckhiGWxLD4eXhInapRERkA4Ygoh64qJS4eWwobhgdjMxT5dixrwDnq5vwxZ5z+GZ/EW4eG4pfTQyHj5eb2KUSEVEfMAQR9ZJKqUDKqGAkx+txKLsSX2WcQ2FFA9L2F+K7Q8W44bpgzEgaAn+th9ilEhFRLzAEEfWRQiFgwvBAJMYF4Hh+Nb7MOIe8EiN2HS7B7qOlmBQfhNsnRSDYTy12qUREdA0MQUQ2EgQBo2P8MSraD2cK67A94xxOF9Riz/EyZBwvQ+LwQMxMjsCQIG+xSyUioitgCCLqJ0EQMCLCFyMifJFXasBXGQU4mluFA2cqcOBMBcYM9cfMyRGICdGKXSoREV2CIYjIjmJCtPjjr0ejqKIBX+09hwOnK3A0twpHc6swIsIXd0yOxPAhPhC4JQcRkegYgogcIDzQC4/MSsDdNzRhx94C7D1ZhtMFtThdUIuYUA3uSI7E6Bg/hiEiIhFxxehr4IrR8jAQ/agyNOPrzEL899h5dJg6P8eQQC/MnByJ8bEBUCgYhrrw+0Na2A9pYT96xhWjiSTGX+uBRb+Kw52TI/Ht/iLsOlKCwooGvPn5Ceh1npiZHIGkkUHcn4yIaADxTtA18E6QPIjRj4bmdnx3sAjfHSxGU2sHAMBf644ZkyJw/Sg9XFTy3Z+M3x/Swn5IC/vRM26gaicMQfIgZj+aWzuw60gJvt1fCGNT52atWi9X3DZxCG4aEwo3V/mFIX5/SAv7IS3sR8/4OIzISXi4qXD7pAhMGx+G/x4rxdeZnZu1fvRDLr7aW4BbE8MwbXwYPN25PxkRkb0xBBFJgJuLErckhuOmsaHIOFGGHfsKUFHbjM/+exZp+wsxdVwYbp0QDo2nq9ilEhENGgxBRBKiUiow5boQpIzS48CZCnyVUYCSqkZ8tbcAOw8UYcqYENw2cQh0GnexSyUicnoMQUQSpFQoMGmkHhNHBOFoThW2Z5zDubJ6fHewGLsOl+D60cGYMSkCgT7crJWIyFYMQUQSphAEjIsNwNhh/jh5rgbbMwqQXVSH9KOl+O+x80gaGYjbkyMR6s/NWomI+oohiMgJCIKAhCg/JET5IbuoDtv3nsOJ/BrsPVmOvSfLMT42AHdMjkSEnpu1EhH1FkMQkZOJDffBY+FjcK7MiK8yCnAou9L6a/gQHyTH6zE+LhCe7vz2JiK6Fq4TdA1cJ0genL0fJZUN+GpfATJPlaPru1mlVGDMMH8kxwdhVLSfU61E7ez9GGzYD2lhP3rm1OsE5eXl4fnnn8eRI0egVqsxa9YsrFixAq6u154aXFtbi1dffRW7d+9GXV0dwsLCcN9992HhwoUDVDmROEIDvPDwnfGYOyUG+06VIeNEGc5XN+HgmQocPFMBtbsKE0YEITk+CENDtdy0lYjoAkmFIIPBgNTUVERGRmLdunUoLy/H2rVr0dLSgjVr1lzz3OXLlyM/Px+PPfYYgoODsXv3bvzlL3+BUqnEvHnzBugrIBKPn9YdM5MjcfukCBSWN2DvyTJkniqHobENPx4pwY9HSuCvdcekeD2S44MQ7MfB1EQkb5IKQVu2bEFjYyPWr18PHx8fAIDJZMKzzz6LpUuXIigo6IrnVVZWIjMzEy+++CLmzJkDAEhOTsbx48fx1VdfMQSRrAiCgAi9NyL03ph381CcLqjF3pNlOJRdiSpDC7ZnnMP2jHOI1HsjOV6PiSODoFVzEUYikh9JDRTYvXs3kpOTrQEIAGbMmAGz2Yw9e/Zc9byOjs4NKL29u8+M8fLyAoc8kZwpFALio3T47R0j8f89ej0evmskRsf4QSEIOFdWjw+/z8Hj6/fglY+PYu/JMrS2mcQumYhowEjqTlB+fj7mzp3b7ZhGo0FAQADy8/Ovel5wcDCuv/56bNiwAVFRUdDr9di9ezf27NmDf/7zn44um8gpuLkqMWmkHpNG6mFsbMOBMxXYe7IM+aVGnMivwYn8Gri5KDEu1h/J8XqMiPSFUiGp/08iIrIrSYUgo9EIjUZz2XGtVguDwXDNc9etW4eVK1di5syZAAClUomnnnoK06dP71dNKpX9fwh0jVrv7eh1ciw59kOndcf0pCGYnjQEZTVNyDh+HhknylBR22xde0irdsWk+CBMHhWMSL33gA2olmM/pIz9kBb2w74kFYJsZbFYsHr1apw7dw4vv/wyAgICkJGRgRdeeAFardYajPpKoRDg6+u4waMaDbc8kBK59sPXV40RMQFYMmsUsgpr8eOhYuw+UgJDYxu+2V+Eb/YXISzQCzeND8ONY8OgH6AB1XLth1SxH9LCftiHpEKQRqNBfX39ZccNBgO0Wu1Vz/vxxx+RlpaGL774AnFxcQCApKQkVFdXY+3atTaHILPZAqOxyaZzr0WpVECj8YDR2AyTies8iI39uChI44b5N8dg7pQoHM+vRsbxMhzOrkRxRQP+/fUZ/PvrMxgWpkXKqGBMGBEIbwfsas9+SAv7IS3sR880Gg/nXCcoOjr6srE/9fX1qKysRHR09FXPy83NhVKpRGxsbLfjI0aMwCeffILm5mZ4eNiWmh25GJXJZOZiVxLCfnQ3KsoPo6L80NzagUNZldh7sgxnCmqRU2xATrEBm7/JwugYPyTH63HdUD+4qJR2/fzsh7SwH9LCftiHpELQlClTsGHDhm5jg9LS0qBQKJCSknLV80JDQ2EymZCVlYXhw4dbj588eRJ+fn42ByAiAjzcVLh+dDCuHx2M2vpWZJ4qx76TZSisaMCRnCocyamCh5sKiXEBSI7XI3aIDxRckJGInICkts0wGAyYOXMmoqKisHTpUutiiXfeeWe3xRJTU1NRWlqKnTt3AgAaGhpw5513wsXFBcuWLUNgYCB++uknvPfee/jDH/6A3//+9zbVw20z5IH9sE1xZQP2nSzHvlNlqDG2Wo/rNG5IGhmE5Hg9wgK8+nxd9kNa2A9pYT961pdtMyQVgoDObTOee+65bttmrFy5stu2GYsWLUJJSQl++OEH67GCggK8+uqrOHToEOrr6xEWFoZ77rkHv/nNb6BU2nabniFIHtiP/jFbLMgpqsPek2U4cKYSza0d1tfCA72QHK9H0sgg+Hq79ep67Ie0sB/Swn70zKlDkJQwBMkD+2E/7R0mHMutxt6TZfg5rxomc+c/LwKA4RG+F3a4D4CH29WfxLMf0sJ+SAv70TOn3kCViJyXi0qJxOGBSBweiIbmdhy8sCBjTrEBpwtqcbqgFpu/zcLYYf6YFK9HQpTOqXa4J6LBhSGIiBzCy8MFN40NxU1jQ1FZ14x9p8qx90QZymqasP90BfafroCXhwsmjgjEpHg9YkI03OGeiAYUQxAROVyAjwfunByJO5IjUFBej70nypF5uhzGxjb8cLgEPxwuQaCPBybFB+H660IcukgpEVEXjgm6Bo4Jkgf2Qxwmsxmnz3XucH84uwqt7Rc3bx0a7oOxQ/0xbpg/gnSeIlZJ/P6QFvajZxwTRESSp1QokBDth4RoP7S2mXAkpxJ7T5bj5Nka5BbVIbeoDp/sykVYgBrj4wIxPi4Aof5qPjIjIrvhnaBr4J0geWA/pKWptQOnigzYfagIp87VwnzJP1FBOk8kxgVgfFwAIoIGblNXOeP3h7SwHz3jFHk7YQiSB/ZDWi7tR119K47mVOFQVgVOnqtBh+niP1f+WneMiw1AYlwgokM1XKXaQfj9IS3sR8/4OIyIBgUvDxfrlh3NrR04lleFQ1mVOJ5fjSpDC749UIRvDxTBx8sV42IDMD4uELHhWigVnHZPRD1jCCIip+DhpsKkkXpMGqlHa7sJJ/KrcSirEkdzq1DXcHGWmbenC8YOC0BiXACGR/hyHSIiuiqGICJyOm4uyguDpQPR3mHGqXM1OJRViSM5lahvasfuY6XYfawUnm4qjBnmj/FxAUiI0tl9p3sicm4MQUTk1FxUClw31B/XDfVHhykOWUV1OJRVicNZFTA2tSPjRBkyTpTBzVWJ62L8MD4uEKOidXB35T9/RHLHfwWIaNBQKRWIj9QhPlKH39wai5zizkB0KLsStfWt1pWqXVQKJETpkBgXiOuG+sPTnf8UEskRv/OJaFBSKATEDfFF3BBfLLhlGM6eN3YGoqwKVNa14EhOFY7kVEGpEDAyUofxcQEYO8wf3p6uYpdORAOEU+SvgVPk5YH9kBZH98NisaCoogEHLwSi89VN1tcUgoC4IT4YHxeAcbEB8PFys/vndzb8/pAW9qNnXCfIThiC5IH9kJaB7kdpVSMOZVXgUFYlCisarMcFADFhWiTGBmBcXAD8tR4Or0WK+P0hLexHz7hOEBFRL4X4qxHiH4U7U6JQUduEQ9mVOJRVifxSI3KLDcgtNmDLD7mI1HtjfFzn4ozcz4xocOCdoGvgnSB5YD+kRSr9qDG2WANRTlEdLv2Hsms/s8S4AIQM8v3MpNIP6sR+9Ix3goiI+kmnccetieG4NTEchsY2HMmuxMGsCpwpqENxZSOKK89i209nodd5Yjz3MyNySrwTdA28EyQP7Ie0SL0fDc3tOJLTeYfo1BX2M+sMRIGIDhkc+5lJvR9yw370jHeCiIgcxMvDBTeMDsENo0M69zPL7b6f2Tf7i/DN/s79zBKi/RAW4IVQfzVC/NXw8XLlnSIiCWEIIiKykYebCpPi9ZgUr0drmwnH86txKLsSxy7sZ/bTz+e7vd/TTYWQALU1FIX5qxES4AWNpwvDEZEIGIKIiOzAzVWJxOGBSBweiPYOE06dq0VeqRGlVY0oqWpERW0Tmlo7rDPOLuXl4YIQ/4vhKNRfjZAANTRcuJHIoRiCiIjszEWltO5n1qW9w4Tz1U3WUFRS2YjSqkZU1jWjobkd2UV1yC6q63Ydb08XhPqrEerv1e0OkpeHywB/RUSDE0MQEdEAcFEpMSTIG0OCvLsdb203oay6CSVVDSipakRpZWdIqjK0oL6pHWcK63CmsK7bOVq1a7c7RqEX/tvTneGIqC8YgoiIROTmokSE3hsR+l+EozYTSqsbf3HnqAHVxlYYGttgaGzD6YLabuf4ertd/ljNXw0PN/5TT3Ql/M4gIpIgN1clooI1iArWdDve3NrRGY4u3DHqCkm19a3WXyfP1nQ7R6dxQ6j/xVlqoQFqBPt5wt2VPwJI3vgdQETkRDzcVIgJ0SImRNvteFNLh/XOUXFlgzUcGRraUGNsRY2xFcfzq7ud4691737nKECNYD813FyUA/klAQBMZjPaOy7+6jBd+G9T9+NXPGYyo+NaxztMaO8ww9VVicS4QCTGBcLTnT/+iIslXhMXS5QH9kNa2A/7amxptw7CvvTOkbGx7YrvFwAE+HhYQ1F4oBeGhPqgtrYJLW0d6DBZrKHiaoGkvaMrfFzy+tWOX/hlHsAfRS4qBcbFBmBygh4jI32hVPRuYT0p4PdHz7iLvJ0wBMkD+yEt7MfAqG9quzje6JIB2Q3N7WKXBqVCgItKcfGXsvN3lfLyYxc/Vna+RyVceE152XvLa5uQcaIM56ubrJ9Lq3bFpPggpCQEIyzQS8Svunf4/dEzhiA7YQiSB/ZDWtgPcRkb27rdMTpf1YjG1g4oBFwMI5eGkEuPdQsnF0KJUugWUq4UYn4ZbhQKxy0cabFYcK6sHhknypB5qrxb6BsS6IXJCXokxeuhVUtzjSZ+f/SMIchOGILkgf2QFvZDWgZzPzpMZhzPr0bG8TIcza2Cydz541AhCEiI1mFygh5jh/nDRTXwY6SuZjD3w164dxgREVEPVEoFxg4LwNhhAWhobseB0+XYc6IM+aVG/JxXjZ/zquHhpsKE4YFIGaXH0FAttzcZZBiCiIhI9rw8XHDzuDDcPC4M56sbsfdkGfaeKEO1sRW7j5Vi97FSBPp4IDlBj+QEPQJ9PMQumeyAj8OugY/D5IH9kBb2Q1rk3A+zxYKswjpknDiPg1mVaG0zWV+LDdNi8qjgAZ9uL+d+9BbHBNkJQ5A8sB/Swn5IC/vRqbXNhMPZlcg4cR6nztWi6weni0qBscP8MTkhGPFRjp9uz370jGOCiIiI7MjNVWl9FFZb34p9J8uw50QZSqsasf90BfafroBG7YpJI4MwOUF/2R5xJE0MQURERH3g6+2GGZMicFvSEBSU1yPjeBn2nSqHsbEN3x4owrcHihB+Ybr9pJFB0Hq5iV0yXQUfh10DH4fJA/shLeyHtLAfvWOdbn+iDMdyq9BhujjdPj5Kh5RReowZ6g/Xfm5Jwn70jI/DiIiIBtCVpttnnChDXqkRx/OrcTy/Gh5uSkwYHojJCcEYFsbp9lLAEERERGRHl063L6vp3Kpj74nzF6bbn8fuY+fhr3XH5AQ9JifoEejrKXbJssXHYdfAx2HywH5IC/shLeyHfZgtFmQX1iHjRBkOZFV0m24/NEyLlAQ9JgwPhKe7yzWvw370zKmnyOfl5eH555/HkSNHoFarMWvWLKxYsQKurlffxyUzMxOLFy++4mtRUVFIS0uzqRaGIHlgP6SF/ZAW9sP+Wtu7ptuX4dS5GnT9FO58pOaPyQl6JETrrjjdnv3omdOOCTIYDEhNTUVkZCTWrVuH8vJyrF27Fi0tLVizZs1Vz4uPj8dHH33U7VhDQwMeeughTJkyxdFlExER9ZqbixLJ8Xokx18+3f7AmQocOFMBjacLJsXrOd3ewSQVgrZs2YLGxkasX78ePj4+AACTyYRnn30WS5cuRVBQ0BXP8/LywpgxY7od27p1K8xmM+644w4HV01ERGSbS6fbF5Y3YM+J88g8VQ5jU7t1un1YwIXp9vFB8Od2HXbl2KUt+2j37t1ITk62BiAAmDFjBsxmM/bs2dOna23fvh2RkZEYPXq0naskIiKyL0EQEKH3xr23xOLlZSn449zRSIwLgEopoLiyAR/vysXjr+/BPz88gh8OFqG4sgHtfBzWb5K6E5Sfn4+5c+d2O6bRaBAQEID8/PxeX6eqqgr79u3D7373O3uXSERE5FAqpQJjhvljzDB/NLa0Y//pCmScOI+8kou72wOAIAABWg/o/Tyh13ki2M8TwX5q6HWe8PZ04RT8XpBUCDIajdBoNJcd12q1MBgMvb7Ojh07YDKZ7PIoTKWy/82yrgFbvR24RY7FfkgL+yEt7Ie4tF5uuHVCOG6dEN453f54GU6eq0FxRQOaWztQUdeMirpmazDqonZXQe+nRoifZ+fv/p2/B/l6QMVeWkkqBNnLl19+ifj4eERFRfXrOgqFAF9ftZ2qupxGw2e7UsJ+SAv7IS3sh/h8fdUYERMAALBYLKitb0VJRQOKK+pRXNGA4soGFFc0oLK2CY0tHcgrMSCvpPsNBIVCgF7nibBAb4QGeiHswq/QAC9Zbu8hqRCk0WhQX19/2XGDwQCtVturaxQWFuLnn3/G6tWr+12P2WyB0djU7+v8klKpgEbjAaOxGSYTn+mKjf2QFvZDWtgPaenqR319CwSTGWF+Hgjz8wBGBFrf09ZuQllNE8qqm1Ba3djt95Y2E0qrGlFa1Qic6n5tLw8X6yO1YD9PBPt3/h7g41x3jzQaD+ecIh8dHX3Z2J/6+npUVlYiOjq6V9f48ssvoVAocPvtt9ulJkeuw2AymbnOg4SwH9LCfkgL+yEt1+qHQhAQ4qdGiJ8a4xBgPW6xWFDX0Iay6kacr2nC+eqmC2GpEdXGVjQ0tyOn2ICc4u53j5QKAQE+Hgj287xk/FHn2CMvj2sv7ih1kgpBU6ZMwYYNG7qNDUpLS4NCoUBKSkqvrvHVV19h4sSJCAwM7PnNREREMiEIAny93eDr7YYRkbpur7W2mVBe2xmKusLR+epGlNU0oa3d3BmWapqAnO7X9PZ0sQ7K1uvU0Pt5IljnCX8f9ysu9ig1kgpBCxYswObNm7Fs2TIsXboU5eXleOmll7BgwYJuawSlpqaitLQUO3fu7Hb+qVOnkJeXhwceeGCgSyciInJabq5KDAnyvmxhRrPFgrr6Vpy/8HitKxidr25CbX0r6pvaUd905btHQTrPSwKSpzUg9bQ1yECSVAjSarXYuHEjnnvuOSxbtgxqtRq//vWvsXLlym7vM5vNMJlMl53/5ZdfwtXVFdOnTx+okomIiAYthSBAp3GHTuOO+F/cPWpp60B5TTPO13SON+oKR+U1TWjrMF8ce/QLGrUr9DpPDAn0wp0pkfD2vPq2WI4mub3DpIR7h8kD+yEt7Ie0sB/S4gz9MFssqDG2XHy0dsnjtbqGtm7vTb0tDjeOCbXr53favcOIiIjIuSkEAf5aD/hrPZAQ5dfttebWDuv4oubWDiSNvPJ2WAOFIYiIiIgGhIebClHBGkQFX74wshikP3SbiIiIyAEYgoiIiEiWGIKIiIhIlhiCiIiISJYYgoiIiEiWGIKIiIhIlhiCiIiISJYYgoiIiEiWGIKIiIhIlhiCiIiISJYYgoiIiEiWGIKIiIhIlhiCiIiISJYEi8ViEbsIqbJYLDCbHfPHo1QqYDKZHXJt6jv2Q1rYD2lhP6SF/bg2hUKAIAi9ei9DEBEREckSH4cRERGRLDEEERERkSwxBBEREZEsMQQRERGRLDEEERERkSwxBBEREZEsMQQRERGRLDEEERERkSwxBBEREZEsMQQRERGRLDEEERERkSwxBBEREZEsMQQRERGRLDEEDaC8vDw88MADGDNmDFJSUvDSSy+hra1N7LJk6euvv8bvfvc7TJkyBWPGjMGsWbPw6aefwmKxiF0aAWhsbMSUKVMQFxeH48ePi12ObH322We4++67MWrUKCQlJeG3v/0tWlpaxC5Ltr7//nvcc889GDt2LK6//nosX74cRUVFYpfl1FRiFyAXBoMBqampiIyMxLp161BeXo61a9eipaUFa9asEbs82Xn//fcRGhqKVatWwdfXFxkZGXj66adRVlaGRx99VOzyZO+NN96AyWQSuwxZe/PNN/HOO+/gkUcewZgxY1BbW4u9e/eyLyLJzMzEo48+irvvvhsrV65EXV0dXnvtNSxZsgRffvkl3N3dxS7ROVloQGzYsMEyZswYS21trfXYli1bLCNGjLCUlZWJV5hMVVdXX3bsqaeesowbN85iMplEqIi65ObmWsaMGWP58MMPLbGxsZaff/5Z7JJkJy8vzzJy5EjLjz/+KHYpdMHTTz9tmTp1qsVsNluP7d271xIbG2s5cOCAiJU5Nz4OGyC7d+9GcnIyfHx8rMdmzJgBs9mMPXv2iFeYTOl0usuOjRgxAg0NDWhqahKhIury/PPPY8GCBYiKihK7FNnaunUrwsLCcOONN4pdCl3Q0dEBtVoNQRCsx7y9vQGAj/H7gSFogOTn5yM6OrrbMY1Gg4CAAOTn54tUFV3q0KFDCAoKgpeXl9ilyFZaWhqys7OxbNkysUuRtWPHjiE2NhZvvPEGkpOTkZCQgAULFuDYsWNilyZbc+bMQV5eHj744APU19ejqKgIr7zyCkaOHIlx48aJXZ7TYggaIEajERqN5rLjWq0WBoNBhIroUgcPHsSOHTuwZMkSsUuRrebmZqxduxYrV65kEBVZZWUlfvrpJ2zbtg3PPPMMXn/9dQiCgCVLlqC6ulrs8mQpMTER69evx8svv4zExETccsstqK6uxjvvvAOlUil2eU6LIYhkr6ysDCtXrkRSUhIWL14sdjmy9eabb8LPzw9z584VuxTZs1gsaGpqwmuvvYbbbrsNN954I958801YLBb8+9//Frs8WTp8+DD+9Kc/Yd68edi4cSNee+01mM1mPPzww5yx1w+cHTZANBoN6uvrLztuMBig1WpFqIiAzjt0Dz30EHx8fLBu3TooFPz/AjGUlJTgvffew+uvv279Pukam9XU1ITGxkao1WoxS5QVjUYDHx8fDB8+3HrMx8cHI0eORG5uroiVydfzzz+PSZMmYdWqVdZjY8aMwU033YRt27Zh/vz5IlbnvBiCBkh0dPRlY3/q6+tRWVl52VghGhgtLS1YunQp6uvr8dFHH1kHGdLAKy4uRnt7Ox5++OHLXlu8eDGuu+46fPzxxyJUJk9Dhw5FYWHhFV9rbW0d4GoI6Fxnbtq0ad2O6fV6+Pr6XrVX1DOGoAEyZcoUbNiwodvYoLS0NCgUCqSkpIhcnfx0dHRgxYoVyM/PxwcffICgoCCxS5K1ESNGYNOmTd2OnT59Gi+++CKeffZZjBo1SqTK5Onmm2/G1q1bcfr0aYwYMQIAUFtbi5MnT+L+++8XtziZCgkJwalTp7odKykpQW1tLUJDQ0WqyvkJFs6tGxAGgwEzZ85EVFQUli5dal0s8c477+RiiSJ4+umn8fHHH2PVqlUYO3Zst9dGjhwJV1dXkSqjLpmZmVi8eDE+/fRThqABZjabMW/ePBgMBqxcuRJubm54++23ce7cOWzfvh0BAQFilyg7GzduxAsvvIBFixZh6tSpqKurw5tvvomamhps374dvr6+YpfolBiCBlBeXh6ee+45HDlyBGq1GrNmzcLKlSv5A1cEU6dORUlJyRVf+/777xEWFjbAFdEvMQSJq6amBi+++CJ27dqF9vZ2JCYmYvXq1Rg6dKjYpcmSxWLBli1b8OGHH6KoqAhqtRpjxozBypUrERMTI3Z5ToshiIiIiGSJU2GIiIhIlhiCiIiISJYYgoiIiEiWGIKIiIhIlhiCiIiISJYYgoiIiEiWGIKIiIhIlhiCiIhssG7dOsTFxaGmpkbsUojIRgxBREREJEsMQURERCRLDEFEREQkSwxBRCRp5eXlWL16NSZPnoyEhATMnDkTn376qfX1zMxMxMXFYceOHXjllVeQkpKCMWPG4JFHHsH58+cvu97XX3+NOXPmYPTo0UhKSsITTzyB8vLyy96Xl5eH5cuXY9KkSRg9ejSmT5+OV1999bL31dfXY9WqVUhMTMT48eOxevVqNDc32/cPgYgcQiV2AUREV1NVVYV58+ZBEATcd9990Ol02L17N/7nf/4HDQ0NuP/++63vffPNNyEIAh566CFUV1dj48aNuP/++7Ft2za4u7sDALZu3YrVq1dj1KhReOyxx1BdXY1Nmzbh8OHD+Pzzz6HRaAAAZ86cwX333QeVSoX58+cjNDQUhYWF+OGHH7By5cpuNa5YsQJhYWF47LHHcOrUKXzyySfQ6XR48sknB+zPiYhswxBERJL16quvwmQy4csvv4Svry8AYOHChXjsscewfv16LFiwwPpeg8GAHTt2wMvLCwAwcuRIrFixAh9//DEWL16M9vZ2/POf/0RsbCw++OADuLm5AQDGjx+PpUuX4v3338cf//hHAMDzzz8Pi8WCzz77DCEhIdbP8cQTT1xW44gRI/DCCy9YP66rq8Onn37KEETkBPg4jIgkyWKx4Ntvv8XUqVNhsVhQU1Nj/XX99dejvr4eJ0+etL7/7rvvtgYgALjtttsQEBCA9PR0AMCJEydQXV2NhQsXWgMQANx0002Ijo7Gjz/+CACoqanBgQMHMHfu3G4BCAAEQbiszkuDGAAkJiairq4ODQ0N/f4zICLH4p0gIpKkmpoaGI1GfPTRR/joo4+u+p6uR1gRERHdXhMEARERESgpKQEAlJaWAgCioqIuu050dDQOHToEACgqKgIAxMbG9qrOXwalrnoMBkO3UEZE0sMQRESSZDabAQB33XUXZs+efcX3xMXFITc3dyDLuoxCceUb6haLZYArIaK+YggiIknS6XRQq9Uwm82YPHnyVd/XFYIKCgq6HbdYLCgoKEBcXByAi3dszp49i+Tk5G7vPXv2rPX18PBwAEB2drZ9vhAikiyOCSIiSVIqlZg+fTq++eabKwaSX25X8fnnn3cbh5OWlobKykpMmTIFAJCQkAA/Pz9s2bIFbW1t1velp6cjLy8PN910E4DO8DVhwgT85z//sT5C68K7O0SDC+8EEZFkPf7448jMzMS8efNwzz33YOjQoTAYDDh58iT27t2L/fv3W9+r1Wpx7733Ys6cOdYp8hEREZg3bx4AwMXFBU888QRWr16N3/zmN5g5c6Z1inxoaGi36fZPPfUUFi5ciNmzZ2P+/PkICwtDSUkJfvzxR2zbtm2g/xiIyEEYgohIsvz9/fHJJ5/g9ddfx86dO/Hhhx/Cx8cHQ4cOvWy6+iOPPIKsrCy8/fbbaGxsRHJyMp555hl4eHhY3zNnzhy4u7vjnXfewT//+U94enrilltuwZNPPmkd0AwAw4cPx8cff4zXXnsNH374IVpbWxESEoIZM2YM2NdORI4nWHh/l4icWGZmJhYvXozXXnsNt912m9jlEJET4ZggIiIikiWGICIiIpIlhiAiIiKSJY4JIiIiIlninSAiIiKSJYYgIiIikiWGICIiIpIlhiAiIiKSJYYgIiIikiWGICIiIpIlhiAiIiKSJYYgIiIikiWGICIiIpKl/x9aBLod0bE+uwAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# feature extraction\n",
        "feature_extractor = ImageFeatureExtractor_Torch(centroid_classifier, 'deep_centroids')\n",
        "\n",
        "# get train embeddings\n",
        "print('train dataset')\n",
        "X_train, y_train = feature_extractor.extract(train_dataloader, device, verbose = False)\n",
        "\n",
        "\n",
        "X = X_train\n",
        "y = y_train\n",
        "\n",
        "\n",
        "classes = {0: 'plane', 1: 'car', 2: 'bird', 3: 'cat', 4: 'deer', 5: 'dog',\n",
        "           6: 'frog', 7 : 'horse', 8 : 'ship', 9 : 'truck'}\n",
        "\n",
        "iterations_full = 5000\n",
        "iterations_per_class = 10000\n",
        "\n",
        "model_name = 'Centroids Resnet152'\n",
        "dataset_name = 'CIFAR-10'\n",
        "suptitle = f'Distribution of Dot Products in {model_name} Emebeddings ({dataset_name})'\n",
        "histogram_args = {'bins' : 100, 'density' : True, 'alpha' : 0.6}\n",
        "\n",
        "plot_dot_products(X, y, iterations_full = iterations_full, iterations_per_class = iterations_per_class,\n",
        "                  class_names = classes, histogram_args = histogram_args, suptitle = suptitle)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 743
        },
        "id": "fGW1uFd_Byqt",
        "outputId": "1d30d0ca-6574-4196-c394-9830fbf0f02b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "train dataset\n",
            "###Extracting features###\n",
            "Done\n",
            "Sampling Per Class Dot Products\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 10/10 [00:17<00:00,  1.71s/it]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sampling Intra Class Dot Products!\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 5000/5000 [00:00<00:00, 30520.50it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sampling Inter Class Dot Products!\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 5000/5000 [00:00<00:00, 19102.63it/s]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## OOD"
      ],
      "metadata": {
        "id": "r6M9Qs0eFfah"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Dino"
      ],
      "metadata": {
        "id": "Bh3MibXjmOrX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# model\n",
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "\n",
        "model_name = 'dino_vitb16'\n",
        "model_dict = ModelExperiment(model_name = model_name).get_model()\n",
        "model = model_dict['model']\n",
        "model.to(device)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "# data\n",
        "preprocessing_args = dict()\n",
        "preprocessing_args['type'] = 'imagenet_resize'\n",
        "preprocessing_args['resize_size'] = 224\n",
        "\n",
        "dataloader_args = dict()\n",
        "dataloader_args['batch_size'] = 32\n",
        "\n",
        "dataset_name = 'cifar10_torch'\n",
        "dataset = DatasetExperiment(name = dataset_name, preprocessing_args = preprocessing_args)\n",
        "train_dataloader, test_dataloader = dataset.get_dataloaders(dataloader_args)\n",
        "\n",
        "dataset_name = 'svhn_torch'\n",
        "_, test_dataloader_ood = dataset.get_dataloaders(dataloader_args)\n",
        "\n",
        "\n",
        "# feature extraction\n",
        "feature_extractor = ImageFeatureExtractor_Torch(model, model_name)\n",
        "\n",
        "# get train embeddings\n",
        "print('train dataset')\n",
        "X_train, y_train = feature_extractor.extract(train_dataloader, device, verbose = False)\n",
        "\n",
        "# get test embeddings\n",
        "print('test dataset')\n",
        "X_test, y_test = feature_extractor.extract(test_dataloader, device, verbose = False)\n",
        "\n",
        "\n",
        "# get ood test embeddings\n",
        "print('ood test dataset')\n",
        "ood_X_test, ood_y_test = feature_extractor.extract(test_dataloader_ood, device, verbose = False)\n",
        "\n",
        "\n",
        "data = {'X_train': X_train, 'y_train': y_train, 'X_test': X_test, 'y_test': y_test}"
      ],
      "metadata": {
        "id": "_gRGwjSmFgon"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataloader_args = {'batch_size': 1000, 'shuffle': True}\n",
        "optimizer_args = {'lr' : 4, 'momentum': 0.9}\n",
        "\n",
        "train_dataloader, test_dataloader, centroid_classifier, optimizer, criterion = deep_centroids_experiment(data, dataloader_args, optimizer_args)\n",
        "\n",
        "\n",
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "\n",
        "centroid_classifier.to(device)\n",
        "num_epochs = 30\n",
        "train_loss = training_loop(centroid_classifier, train_dataloader,\n",
        "                           test_dataloader, num_epochs, optimizer, criterion, device)"
      ],
      "metadata": {
        "id": "ni4xN8BBXlT9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# feature extraction\n",
        "feature_extractor = ImageFeatureExtractor_Torch(centroid_classifier, 'deep_centroids')\n",
        "\n",
        "dataloader_args = {'batch_size':1000, 'shuffle':True}\n",
        "train_dataloader = get_dataloader_from_xy(X_train, y_train, dataloader_args)\n",
        "\n",
        "# get train embeddings\n",
        "print('train dataset')\n",
        "X_train, y_train = feature_extractor.extract(train_dataloader, device, verbose = False)\n",
        "\n",
        "\n",
        "\n",
        "data_id = {'X_train': X_train, 'y_train': y_train, 'X_test': X_test, 'y_test': np.zeros((X_test.shape[0],1))}\n",
        "data_ood = {'X_test' : ood_X_test, 'y_test' : np.ones((ood_X_test.shape[0],1))}\n",
        "\n",
        "ood_exp_X_test = np.vstack((X_test, ood_X_test))\n",
        "ood_exp_y_test = np.vstack((data_id['y_test'], data_ood['y_test']))\n",
        "\n",
        "\n",
        "\n",
        "# get test embeddings\n",
        "test_dataloader = get_dataloader_from_xy(ood_exp_X_test, ood_exp_y_test, dataloader_args)\n",
        "ood_exp_X_test, ood_exp_y_test = feature_extractor.extract(train_dataloader, device, verbose = False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MMdgLuB1YLCW",
        "outputId": "665b0ca5-a8b2-46ed-c9a4-9caa357e3943"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "train dataset\n",
            "###Extracting features###\n",
            "Done\n",
            "###Extracting features###\n",
            "Done\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ood = CentroidOOD()\n",
        "ood.fit_labeled(data_id['X_train'], data_id['y_train'])\n",
        "\n",
        "ents = ood.predict_ood(ood_exp_X_test)\n",
        "ents_dict = {'ID': ents[(ood_exp_y_test == 0).squeeze()], 'OOD' : ents[(ood_exp_y_test == 1).squeeze()]}\n",
        "\n",
        "hist_args = {'bins' : 50, 'density' : True}\n",
        "results = plot_hist_dict(ents_dict, title='OOD Scores', histogram_args = hist_args)\n",
        "\n",
        "plt.xlabel('Entropy')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "SK7DKN20bJo1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### vgg19"
      ],
      "metadata": {
        "id": "DkR162pqmROo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# model\n",
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "\n",
        "model_name = 'vgg19'\n",
        "model_dict = ModelExperiment(model_name = model_name).get_model()\n",
        "model = model_dict['model']\n",
        "model.to(device)"
      ],
      "metadata": {
        "id": "weGDeooMmSH0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# data\n",
        "preprocessing_args = dict()\n",
        "preprocessing_args['type'] = 'imagenet'\n",
        "\n",
        "dataloader_args = dict()\n",
        "dataloader_args['batch_size'] = 128\n",
        "\n",
        "dataset_name = 'cifar10_torch'\n",
        "dataset = DatasetExperiment(name = dataset_name, preprocessing_args = preprocessing_args)\n",
        "train_dataloader, test_dataloader = dataset.get_dataloaders(dataloader_args)\n",
        "\n",
        "dataset_name = 'svhn_torch'\n",
        "_, test_dataloader_ood = dataset.get_dataloaders(dataloader_args)\n",
        "\n",
        "\n",
        "# feature extraction\n",
        "feature_extractor = ImageFeatureExtractor_Torch(model, model_name)\n",
        "\n",
        "# get train embeddings\n",
        "print('train dataset')\n",
        "X_train, y_train = feature_extractor.extract(train_dataloader, device, verbose = False)\n",
        "\n",
        "# get test embeddings\n",
        "print('test dataset')\n",
        "X_test, y_test = feature_extractor.extract(test_dataloader, device, verbose = False)\n",
        "\n",
        "\n",
        "# get ood test embeddings\n",
        "print('ood test dataset')\n",
        "ood_X_test, ood_y_test = feature_extractor.extract(test_dataloader_ood, device, verbose = False)\n",
        "\n",
        "\n",
        "data = {'X_train': X_train, 'y_train': y_train, 'X_test': X_test, 'y_test': y_test}"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AwqM2ZiVmf-_",
        "outputId": "71fdc62c-16bd-4753-f653-08c5cf60d0bf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Files already downloaded and verified\n",
            "Files already downloaded and verified\n",
            "train dataset\n",
            "###Extracting features###\n",
            "Done\n",
            "test dataset\n",
            "###Extracting features###\n",
            "Done\n",
            "ood test dataset\n",
            "###Extracting features###\n",
            "Done\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# non private projection\n",
        "dataloader_args = {'batch_size': 1000, 'shuffle': True}\n",
        "optimizer_args = {'lr' : 1e-4}\n",
        "\n",
        "train_dataloader, test_dataloader, centroid_classifier, optimizer, criterion = deep_centroids_experiment(data, dataloader_args, optimizer_args,\n",
        "                                                                                                         classifier_type = 'linear', optimizer_type = 'Adam')\n",
        "\n",
        "\n",
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "\n",
        "centroid_classifier.to(device)\n",
        "num_epochs = 15\n",
        "train_loss = training_loop(centroid_classifier, train_dataloader,\n",
        "                           test_dataloader, num_epochs, optimizer, criterion, device)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zF2weGApnL80",
        "outputId": "29dbd63a-14af-45e3-deb9-5970ab171454"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 15/15 [00:11<00:00,  1.28it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " Accuracy on the test images: 0.6104\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(train_loss)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 434
        },
        "id": "76LugKKxq_g5",
        "outputId": "86baad4e-b368-43e5-bd88-552119fc3ebe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# feature extraction\n",
        "feature_extractor = ImageFeatureExtractor_Torch(centroid_classifier, 'deep_centroids')\n",
        "\n",
        "dataloader_args = {'batch_size':1000, 'shuffle':True}\n",
        "train_dataloader = get_dataloader_from_xy(X_train, y_train, dataloader_args)\n",
        "\n",
        "# get train embeddings\n",
        "print('train dataset')\n",
        "X_train, y_train = feature_extractor.extract(train_dataloader, device, verbose = False)\n",
        "\n",
        "\n",
        "\n",
        "data_id = {'X_train': X_train, 'y_train': y_train, 'X_test': X_test, 'y_test': np.zeros((X_test.shape[0],1))}\n",
        "data_ood = {'X_test' : ood_X_test, 'y_test' : np.ones((ood_X_test.shape[0],1))}\n",
        "\n",
        "ood_exp_X_test = np.vstack((X_test, ood_X_test))\n",
        "ood_exp_y_test = np.vstack((data_id['y_test'], data_ood['y_test']))\n",
        "\n",
        "\n",
        "\n",
        "# get test embeddings\n",
        "test_dataloader = get_dataloader_from_xy(ood_exp_X_test, ood_exp_y_test, dataloader_args)\n",
        "ood_exp_X_test, ood_exp_y_test = feature_extractor.extract(train_dataloader, device, verbose = False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CRRqr5wanWa-",
        "outputId": "6578ffa8-23dd-483a-a588-840d4965426b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "train dataset\n",
            "###Extracting features###\n",
            "Done\n",
            "###Extracting features###\n",
            "Done\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ood = CentroidOOD()\n",
        "ood.fit_labeled(data_id['X_train'], data_id['y_train'])\n",
        "\n",
        "ents = ood.predict_ood(ood_exp_X_test)\n",
        "ents_dict = {'ID': ents[(ood_exp_y_test == 0).squeeze()], 'OOD' : ents[(ood_exp_y_test == 1).squeeze()]}\n",
        "\n",
        "hist_args = {'bins' : 50, 'density' : True}\n",
        "results = plot_hist_dict(ents_dict, title='OOD Scores', histogram_args = hist_args)\n",
        "\n",
        "plt.xlabel('Entropy')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 480
        },
        "id": "n7zamV24naKa",
        "outputId": "9dddca70-f059-45aa-e0d2-0dfc521289a3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}