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  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/softmurata/colab_notebooks/blob/main/object_detection/vision_transformer_finetuning.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N9AfJTM87RDH",
        "outputId": "5b7361fb-2e5f-474c-fa0f-4fed322e4eac"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ba71X15ROIcR"
      },
      "outputs": [],
      "source": [
        "!git clone https://github.com/lucidrains/vit-pytorch.git"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install vit-pytorch"
      ],
      "metadata": {
        "id": "Ko1-dywxOblT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# check code\n",
        "import torch\n",
        "from vit_pytorch import ViT\n",
        "\n",
        "v = ViT(\n",
        "    image_size = 256,\n",
        "    patch_size = 32,\n",
        "    num_classes = 1000,\n",
        "    dim = 1024,\n",
        "    depth = 6,\n",
        "    heads = 16,\n",
        "    mlp_dim = 2048,\n",
        "    dropout = 0.1,\n",
        "    emb_dropout = 0.1\n",
        ")\n",
        "\n",
        "img = torch.randn(1, 3, 256, 256)\n",
        "\n",
        "preds = v(img) # (1, 1000)"
      ],
      "metadata": {
        "id": "0_2Zn7SZOjnq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install scikit-learn timm tqdm "
      ],
      "metadata": {
        "id": "GKgGHeQ5Oo3e"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!unzip /content/drive/MyDrive/dogs-vs-cats-redux-kernels-edition/train.zip\n",
        "!unzip /content/drive/MyDrive/dogs-vs-cats-redux-kernels-edition/test.zip"
      ],
      "metadata": {
        "id": "lHdnBAx3P1-V"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!mkdir /content/vit-pytorch/examples/data\n",
        "!mv train /content/vit-pytorch/examples/data/\n",
        "!mv test /content/vit-pytorch/examples/data/"
      ],
      "metadata": {
        "id": "UW7nSEPG7uQv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install linformer"
      ],
      "metadata": {
        "id": "oPIH7RBY8dXF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from __future__ import print_function\n",
        "\n",
        "import glob\n",
        "from itertools import chain\n",
        "import os\n",
        "import random\n",
        "import zipfile\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torch.optim as optim\n",
        "from linformer import Linformer\n",
        "from PIL import Image\n",
        "from sklearn.model_selection import train_test_split\n",
        "from torch.optim.lr_scheduler import StepLR\n",
        "from torch.utils.data import DataLoader, Dataset\n",
        "from torchvision import datasets, transforms\n",
        "from tqdm.notebook import tqdm\n",
        "\n",
        "from vit_pytorch.efficient import ViT\n",
        "import seaborn as sns\n",
        "import timm"
      ],
      "metadata": {
        "id": "31gVGVj-8I_n"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Training settings\n",
        "batch_size = 64\n",
        "epochs = 20\n",
        "lr = 3e-5\n",
        "gamma = 0.7\n",
        "seed = 42"
      ],
      "metadata": {
        "id": "ijNY65AH8meb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def seed_everything(seed):\n",
        "    random.seed(seed)\n",
        "    os.environ['PYTHONHASHSEED'] = str(seed)\n",
        "    np.random.seed(seed)\n",
        "    torch.manual_seed(seed)\n",
        "    torch.cuda.manual_seed(seed)\n",
        "    torch.cuda.manual_seed_all(seed)\n",
        "    torch.backends.cudnn.deterministic = True\n",
        "\n",
        "seed_everything(seed)"
      ],
      "metadata": {
        "id": "LGJ-XdP38pQI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "device = 'cuda'"
      ],
      "metadata": {
        "id": "dad1VTKG8qk8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "os.chdir('/content/vit-pytorch/examples')\n",
        "train_dir = 'data/train'\n",
        "test_dir = 'data/test'\n",
        "train_list = glob.glob(os.path.join(train_dir,'*.jpg'))\n",
        "test_list = glob.glob(os.path.join(test_dir, '*.jpg'))\n",
        "print(f\"Train Data: {len(train_list)}\")\n",
        "print(f\"Test Data: {len(test_list)}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ck2oJtZB8-yw",
        "outputId": "d344c475-70b2-4b9f-eaac-2b655cbcebbe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Data: 25000\n",
            "Test Data: 12500\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "labels = [path.split('/')[-1].split('.')[0] for path in train_list]"
      ],
      "metadata": {
        "id": "MhMGIwnC9SUj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Random plots\n",
        "random_idx = np.random.randint(1, len(train_list), size=9)\n",
        "fig, axes = plt.subplots(3, 3, figsize=(16, 12))\n",
        "\n",
        "for idx, ax in enumerate(axes.ravel()):\n",
        "    img = Image.open(train_list[idx])\n",
        "    ax.set_title(labels[idx])\n",
        "    ax.imshow(img)"
      ],
      "metadata": {
        "id": "Dg6LTwp29Vwx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_list, valid_list = train_test_split(train_list, \n",
        "                                          test_size=0.2,\n",
        "                                          stratify=labels,\n",
        "                                          random_state=seed)"
      ],
      "metadata": {
        "id": "M9okdT2h9dGg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Image Augumentation\n",
        "train_transforms = transforms.Compose(\n",
        "    [\n",
        "        transforms.Resize((224, 224)),\n",
        "        transforms.RandomResizedCrop(224),\n",
        "        transforms.RandomHorizontalFlip(),\n",
        "        transforms.ToTensor(),\n",
        "    ]\n",
        ")\n",
        "\n",
        "val_transforms = transforms.Compose(\n",
        "    [\n",
        "        transforms.Resize(256),\n",
        "        transforms.CenterCrop(224),\n",
        "        transforms.ToTensor(),\n",
        "    ]\n",
        ")\n",
        "\n",
        "\n",
        "test_transforms = transforms.Compose(\n",
        "    [\n",
        "        transforms.Resize(256),\n",
        "        transforms.CenterCrop(224),\n",
        "        transforms.ToTensor(),\n",
        "    ]\n",
        ")"
      ],
      "metadata": {
        "id": "3eMpLnXS9geJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "img_path = \"data/train/dog.12208.jpg\"\n",
        "label = img_path.split(\"/\")[-1].split(\".\")[0]\n",
        "label = 1 if label == \"train/dog\" else 0 #ここを書換えてください\n",
        "print(label)"
      ],
      "metadata": {
        "id": "rhWCmdhvAsDm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class CatsDogsDataset(Dataset):\n",
        "    def __init__(self, file_list, transform=None):\n",
        "        self.file_list = file_list\n",
        "        self.transform = transform\n",
        "\n",
        "    def __len__(self):\n",
        "        self.filelength = len(self.file_list)\n",
        "        return self.filelength\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        img_path = self.file_list[idx]\n",
        "        img = Image.open(img_path)\n",
        "        img_transformed = self.transform(img)\n",
        "\n",
        "        label = img_path.split(\"/\")[-1].split(\".\")[0]\n",
        "        label = 1 if label == \"train/dog\" else 0 #ここを書換えてください\n",
        "\n",
        "        return img_transformed, label"
      ],
      "metadata": {
        "id": "fZetuy5l9lWV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Dataset and Datalaoder\n",
        "train_data = CatsDogsDataset(train_list, transform=train_transforms)\n",
        "valid_data = CatsDogsDataset(valid_list, transform=test_transforms)\n",
        "test_data = CatsDogsDataset(test_list, transform=test_transforms)\n",
        "train_loader = DataLoader(dataset = train_data, batch_size=batch_size, shuffle=True )\n",
        "valid_loader = DataLoader(dataset = valid_data, batch_size=batch_size, shuffle=True)\n",
        "test_loader = DataLoader(dataset = test_data, batch_size=batch_size, shuffle=True)"
      ],
      "metadata": {
        "id": "JEFqlhGK9r6H"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# efficient attention\n",
        "efficient_transformer = Linformer(\n",
        "    dim=128,\n",
        "    seq_len=49+1,  # 7x7 patches + 1 cls-token\n",
        "    depth=12,\n",
        "    heads=8,\n",
        "    k=64\n",
        ")"
      ],
      "metadata": {
        "id": "NqU5JxJS9zJ5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create model\n",
        "model = ViT(\n",
        "    dim=128,\n",
        "    image_size=224,\n",
        "    patch_size=32,\n",
        "    num_classes=2,\n",
        "    transformer=efficient_transformer,\n",
        "    channels=3,\n",
        ").to(device)"
      ],
      "metadata": {
        "id": "8zuLBlaV96LG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import timm\n",
        "model = timm.create_model('vit_small_patch16_224', pretrained=True, num_classes=2)\n",
        "model.to(device)"
      ],
      "metadata": {
        "id": "I2p0KBzjOPUA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Train settings\n",
        "# loss function\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "# optimizer\n",
        "optimizer = optim.Adam(model.parameters(), lr=lr)\n",
        "# scheduler\n",
        "scheduler = StepLR(optimizer, step_size=1, gamma=gamma)"
      ],
      "metadata": {
        "id": "DBZA4JX6-BTP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Training\n",
        "##ここを追記↓##\n",
        "train_acc_list = []\n",
        "val_acc_list = []\n",
        "train_loss_list = []\n",
        "val_loss_list = []\n",
        "##############\n",
        "\n",
        "for epoch in range(epochs):\n",
        "    epoch_loss = 0\n",
        "    epoch_accuracy = 0\n",
        "\n",
        "    for data, label in tqdm(train_loader):\n",
        "        data = data.to(device)\n",
        "        label = label.to(device)\n",
        "\n",
        "        output = model(data)\n",
        "        loss = criterion(output, label)\n",
        "\n",
        "        optimizer.zero_grad()\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "\n",
        "        acc = (output.argmax(dim=1) == label).float().mean()\n",
        "        epoch_accuracy += acc / len(train_loader)\n",
        "        epoch_loss += loss / len(train_loader)\n",
        "\n",
        "    with torch.no_grad():\n",
        "        epoch_val_accuracy = 0\n",
        "        epoch_val_loss = 0\n",
        "        for data, label in valid_loader:\n",
        "            data = data.to(device)\n",
        "            label = label.to(device)\n",
        "\n",
        "            val_output = model(data)\n",
        "            val_loss = criterion(val_output, label)\n",
        "\n",
        "            acc = (val_output.argmax(dim=1) == label).float().mean()\n",
        "            epoch_val_accuracy += acc / len(valid_loader)\n",
        "            epoch_val_loss += val_loss / len(valid_loader)\n",
        "\n",
        "    print(\n",
        "        f\"Epoch : {epoch+1} - loss : {epoch_loss:.4f} - acc: {epoch_accuracy:.4f} - val_loss : {epoch_val_loss:.4f} - val_acc: {epoch_val_accuracy:.4f}\\n\"\n",
        "    )\n",
        "\n",
        "##ここを追記↓##\n",
        "    train_acc_list.append(epoch_accuracy)\n",
        "    val_acc_list.append(epoch_val_accuracy)\n",
        "    train_loss_list.append(epoch_loss)\n",
        "    val_loss_list.append(epoch_val_loss)\n",
        "##############"
      ],
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            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/313 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b7889918ab5147b2ac52639cc171ca7f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch : 17 - loss : 0.0000 - acc: 1.0000 - val_loss : 0.0000 - val_acc: 1.0000\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/313 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8a307b184c374e4b9efe0d5a867abda3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch : 18 - loss : 0.0000 - acc: 1.0000 - val_loss : 0.0000 - val_acc: 1.0000\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/313 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0358982666bf42c4a8606eb23dfadf50"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch : 19 - loss : 0.0000 - acc: 1.0000 - val_loss : 0.0000 - val_acc: 1.0000\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/313 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5f2f52e747f445388aaab3dc643c2fdb"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch : 20 - loss : 0.0000 - acc: 1.0000 - val_loss : 0.0000 - val_acc: 1.0000\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualize Training results\n",
        "#出力したテンソルのデバイスをCPUへ切り替える\n",
        "device2 = torch.device('cpu')\n",
        "\n",
        "train_acc = []\n",
        "train_loss = []\n",
        "val_acc = []\n",
        "val_loss = []\n",
        "\n",
        "for i in range(epochs):\n",
        "    train_acc2 = train_acc_list[i].to(device2)\n",
        "    train_acc3 = train_acc2.clone().numpy()\n",
        "    train_acc.append(train_acc3)\n",
        "    \n",
        "    train_loss2 = train_loss_list[i].to(device2)\n",
        "    train_loss3 = train_loss2.clone().detach().numpy()\n",
        "    train_loss.append(train_loss3)\n",
        "    \n",
        "    val_acc2 = val_acc_list[i].to(device2)\n",
        "    val_acc3 = val_acc2.clone().numpy()\n",
        "    val_acc.append(val_acc3)\n",
        "    \n",
        "    val_loss2 = val_loss_list[i].to(device2)\n",
        "    val_loss3 = val_loss2.clone().numpy()\n",
        "    val_loss.append(val_loss3)\n",
        "\n",
        "#取得したデータをグラフ化する\n",
        "sns.set()\n",
        "num_epochs = epochs\n",
        "\n",
        "fig = plt.subplots(figsize=(12, 4), dpi=80)\n",
        "\n",
        "ax1 = plt.subplot(1,2,1)\n",
        "ax1.plot(range(num_epochs), train_acc, c='b', label='train acc')\n",
        "ax1.plot(range(num_epochs), val_acc, c='r', label='val acc')\n",
        "ax1.set_xlabel('epoch', fontsize='12')\n",
        "ax1.set_ylabel('accuracy', fontsize='12')\n",
        "ax1.set_title('training and val acc', fontsize='14')\n",
        "ax1.legend(fontsize='12')\n",
        "\n",
        "ax2 = plt.subplot(1,2,2)\n",
        "ax2.plot(range(num_epochs), train_loss, c='b', label='train loss')\n",
        "ax2.plot(range(num_epochs), val_loss, c='r', label='val loss')\n",
        "ax2.set_xlabel('epoch', fontsize='12')\n",
        "ax2.set_ylabel('loss', fontsize='12')\n",
        "ax2.set_title('training and val loss', fontsize='14')\n",
        "ax2.legend(fontsize='12')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        },
        "id": "FRtqEMcG-NNt",
        "outputId": "dca13d46-0e50-4510-badc-f585b25c88e2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 960x320 with 2 Axes>"
            ],
            "image/png": 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gNBoJCgoiMjKS9evXFyhudu7cSf369Rk4cCAAw4cPJz4+ngMHDtCjRw8aN25M48aNSUpKKva4p06d4qeffuLdd9+tkPOCvx/adba9s9tVJrVnVHs+UH/GqprPZrORkpKM1Wqp/FCFuHxZi83m/IiJlcXV+fR6N2rXDkCrLV2RU6nFzapVq0hISCAxMZHw8HDmz59/y7aHDh0iLi6OP/74g6ZNm/Lmm28SHBzssK8PPviA9PR0unTpwvTp06lRowYAFouF6dOn8+WXX6LT6YiMjGTcuHFo1Pr/RiGEEAWcPXsWRVEIDAy0LwsODmbHjh0F2iYmJtK8eXP7a41GQ1BQEImJifTo0cOp4yYkJNC5c2fuvPNOh+XvvPMOCxYs4J577mH06NH06tXLuRP6i5+fd6m2A6hd26fU21YWtWdUez5Qf8aqlu/ChQvo9TruuKO+fBZUOUVRSE29TG5uJnXq3Fn8BoWo1OKmTp06jB49mgMHDpCamnrLdsXdXvDdd9+xcOFCVqxYQYMGDYiNjWXKlCksWLAAgEWLFvHLL7/w1VdfkZ2dzVNPPcWdd97Jo48+WlmnKoQQoowyMzMxGo0Oy3x9fcnIKDj/QmZmpv0Lrnw+Pj6Fti2K1Wrliy++IDY21mH5Sy+9RJMmTXBzc2PPnj2MGzeOjz/+mJCQEKf2D5CamuH0PDcaTd4HtpQUkypvZQH1Z1R7PlB/xqqYT1EUrly5Su3adVEUjSpy6/Vap4eDr0yuzafB27smV65cRK/3LlCM6nTaYr8gqtTipnfv3gAcP368yOKmuNsLEhISGDx4MC1btgRg7Nix9OvXD5PJhI+PDwkJCcTFxVG7dm0Ann76adauXVvq4sbZIj9l8yZ+P3q4VJO05cuy5KJU8P8DtVp1d4uqPR+oP6Pa84H6M6o9H1R8xpphHblryOAK2/+teHl5FShOTCYT3t4FL2xeXl6YTCaHZenp6YW2LcqePXvIycnhH//4h8PyNm3a2H/v3bs3X3/9NTt27ChVcQM4/QHr8jUzZy9n0sDfSxUfzoqiKM6fX2VSez5Qf8aqlM9mUwAFnU7n0kyi5PL+rRRsNgWt1vmeNlU+c1Pc7QWnTp2iW7du9vUNGzbEYDBw5swZGjZsyKVLlxy2Dw4O5tSpU6XKUprbBzI93cggr7osjWxLLilpWaXaVgghylvqRRMh/pV/G0rDhg2BvGdgmjVrBuR9OZb/+40CAwNZu3at/bWiKJw8edLpAQDWr19P//79cXNzK7KdVqut8C+gbrT1+3N8++MFlozvhl4+pAlRCnI7WtVRtn8rVRY3xd1ekJmZia+vb6HrMzMz7a/z+fr6kp2djdVqRa937pRLc/uAd/iD1P9XZKm7bQ/8dJHlm3+hb6cGNLqzYj5QaAAfH09MJjNq/PJF7flA/RnVng/Un1Ht+aDiM2o1GoLuqcmVK6biG9+kJLcPFMXLy4uIiAjmz5/P7Nmz+fPPP1m3bh0zZswo0DY8PJzZs2ezadMmHnzwQVavXg1A586dgbxix2KxYLHkPVCc/7/u7u72fVy5coV9+/bx2WefOew7LS2NY8eOERYWhsFgYM+ePWzbto3ly5eX+tycZdBrybUpmDJz8POR4kYIIW5FlcVNcbcXFLXey8urQHuTyYS7u7vThU2+0n45V9pu2/TMHABaN6pFUH2/0h28GBoN+Pv7cOWKeu+bVXM+UH9GtecD9WdUez6ovIyuOv8pU6YwadIkunbtire3NyNHjrSPlBYaGsqyZcto3749fn5+LFq0iGnTpjF58mSaNWvGkiVL7D0w58+f54EHHrDvt3379gCcPHnSvuyLL76gadOm9lue81mtVhYuXMiLL76IVqulQYMGvPXWW7Rr166iT9/O6GkAIN2cg5+PR6UdVwhRtX388QqOHTvK/PmLSr2PLl3aM3/+Ijp0CCvHZBVHlcVNcbcXNGvWjOPHj/PQQw8BcO7cOSwWC40bN8bHx4c6depw/PhxAgICADhx4kShtzGoVbo5r7jJv5gJIcTtytfX95ZDMh87dszhdVhYGFu2bCm0bb169RwKmcKMGDGCESNGFFheq1Yt1q1bV8LEFcPnhuJGCFG9vfDCs4SEtOXZZ0eXeV9PPDGCJ54o+HetOivbLDlOslqt9tvDbDYb2dnZ5OQU/EMdHh7OuXPn2LRpExaLhY8++gj4+/aCwYMHk5CQwC+//EJGRgYLFiwgPDzcfiva4MGDWbx4MVevXuXChQusWLGCIUOGVN6JllF6lhQ3Qggh/ub91/Ugw2x1cRIhhBoU9vlZ5KnUnpslS5bw3nvv2V9v376dQYMGMWvWLKduL7j//vsZM2YMzz77LBkZGdx///28+eab9v0+//zzpKam0rt3b3Q6HQ8//HCVmlU6469v5ryluBFCCIHjbWlCiOprzpwZ/PjjD/z00498/vmnAOzcuZ8vv9zMsmVLiIoazqef/ofr16+zc+c+1q//jI0b15GcnIyHhwcdOtzHmDHjqVmzJgDLl3/AkSOHWLIk7xnBF154lqZNA7l2LZXvv/8Wb28jjz/+FIMGDS1xxgMHvuXDD9/n/Pk/8POrzUMPDeJf/3oMrVZLTk4O77wzl337vsFsNlOjRg3+9a/HGDr0X5hMJt5+ewZHjhwiJ8dKrVq1ee650fTs+Y/iD+qESi1uxowZw5gxYwpd58ztBQBRUVFERUUVus7NzY24uDji4uJKH9aF0s05eLjp0JdytDUhhBDVixQ3QpSfL779jUMnLlXa8e4LrsNDXRqVqO2ECa9x7tzZQm9Lu3o1hV9/PcXHH69Fq837jFi7dm3efHMO9erdw6VLyUye/Arz589m6tSCA6/k27ZtMzNmvM3rr09j375veP31V+nQIYx69e4pNt/x4z/z2msvMXnyNLp378mvv55i4sSx6PU6IiMfZdu2Lfz88498/PFaatasSUrKFa5cuQLA6tUfk5mZyeeff4GnpxfJyRcxm80lel+cIZ+eVSjDbJVb0oQQQthJcSOEAIiJGY+XlxceHnkDi/To8QD16zdAq9VSt+6dREUN5/Dhg0Xuo3v3XrRr1wGtVkuPHg/g6+vLyZPHS3T8zZs30qlTFx54IBy9Xk9wcHMeffRxNm5cD4DBYMBsNnP27BmsViu1a/sTFBRsX3f9+nXOnTuLoijUrXsnjRo1LsO7UThVDihwu0s352D0kuJGCCFEHiluhCg/D3VpVOKeFDWpWdPPXtTk27t3N59+uoqkpD+wWCwoig2z2Uxubu4tJy7197/D4bWHh6d9KpXiXLqUTMOGjgXJ3XffQ3LyRQB6936Q1NRUFi16h99/P0urVm0YOTL6ryLoCWw2G7NmTSc5+SLt29/HqFEvlKjHyBnSc6NC6Vk5GD2k7hRCCJHHzaDFTa+V4kaI20D+LWfFLc+/DW3IkEjWr9/Cjh17mTQp75GMippkuE6dAM6fT3JY9uefSQQE1AVAp9Px6KOPs2zZR2zYsI2GDRvy6qvjAfDw8OCZZ0bx0Uef8tlnG9HpdLz55pRyzyjFjcpYc21kW3JlMAEhhBB2Go0GH283KW6EuA3UqlWbP/74vdh2ZrMZm81GzZo1cXd3548/fmfVqpUVmq1fv3/y/fffsmfP1+Tm5pKYeILVq//DQw8NAuDo0cOcOPELOTk5uLm54enpZS/Kvv12L2fOnMZqteLu7oG7uzs6Xfl/mS/dAyojc9wIIYQojI+XFDdC3A7+9a8oZs6Mo0+fnoDC9u17Cm3XoEFDnnvuBaZPn0JmppkmTZrSu3dfjh//pcKytWzZiunT32LFiqXMmBGHn58fQ4cO4+GH80YlTk29yoIFc7h48QI6nZ6mTZsxffpbAPz555+8994CUlKuYDC40bJlK155ZXK5Z9QoFdVvVU2kpmZgtdqc2qYsM4YnXU7n9eWHeOj+hgzsWv4PWeVT+8zras8H6s+o9nyg/oxqzwfqzqjXa/Hz83Z1DNUp7XVl/uc/cjrpGgtf7FZBycpGzf8tgvrzgfozVsV8NpuNS5f+oE6de255u1dl0+u1Tv8NqEyuzlfUv1lJrivq+FcWdjLHjRBCiML4eLmRkWUl16beD0VCCOFqUtyojNyWJoQQojC+3nkTWWdkWV2cRAgh1EuKG5XJv2hJcSOEEOJGPvnFjTx3I4QQtyTFjcpIz40QQojC+HjlFTcyqIAQQtyaFDcqk3/R8pZ5boQQQtzA11sm8hRCiOJIcaMy0nMjhBCiMPaem0wpboQQ4lakuFGZDHMOWo0GT3fpuRFCCPG3/Gdu0rOkuBFCiFuR4kZl0s05eHvq0Wg0ro4ihBBCRfJHS5Pb0oQQ4takuFGZdHOO3JImhBCiAF8vGS1NCFG8N998g7i4yaVeX9VJcaMyGeYcmcBTCCFEAV4eBjQaSDfLPDdCCHErUtyoiKIoZGRZMXpIcSOEEMKRVqvB28Mgt6UJIUQR5Kl1Fcmy5JJrU/D2lH8WIYQQBRk9pbgRojrbtCmB1as/Zs2aDfbnr3Nychg06EEmTHiN7t178eGH77Nr1w5SUq7g4+ND167diY7+Nx4eHqU65uXLl3j33Xn88MP/A+Dee9sxZsw4/P3vAODrr3eycuUyLl1KxmDQ07RpEO+8sxiA9evXsnbtalJTU/Hw8KBjx87Exr5R9jeiDORTtIrIMNBCCCGKYvQ0cOma2dUxhKjSUjZvwnT4YKUdz6dDGLUH/LNEbcPDI3jvvfkcO3aUe+9tD8C+fd+g1eq4//5uANSrdw8LFiwmICCA3347w6uvjsfT04tRo15wOltubi4vv/wi9es35NNPE1AUhbffnsHEieNYujSenJwcpk2bzNy5C2nXrgPZ2dn89NOPAPzxx+8sXvwuy5Z9ROPGTcnMzCQx8YTTGcqb3JamIlLcCCGEo7S0NGJiYggNDaVLly7Ex8ffsu2hQ4fo378/bdq0YciQIZw48fdFNjExkaeffpqwsDCCgoLIzs522HbhwoW0bNmS0NBQ+8+RI0dKlaMiGb0MZJhzUBTFJccXQlQsLy9vevb8B1u2bLIv27x5I3369EWvz+uT6NOnH3Xr1kWj0dC4cRMGD36YQ4f+W6rjHT/+M7/+eorx41/BaDTi4+PDyy+/SmLiCY4f/wUAvV7P2bO/ce3aNdzd3WnXrgMAOp0ORYHffjtDRkY6Xl5etG17bxnfgbKTnhsVyR8BRwYUEEKIPHFxcVgsFvbv38/58+cZPnw4jRo1onv37g7tUlNTGT16NJMmTaJv37588sknREdH89VXX+Hm5oZer6dPnz5ERUUxatSoQo/Vu3dv5s+fX6YcFc3oaSDXppBlyZX50IQopdoD/lninhRXGDBgIDExoxk79mXS0038v/93hHHjJtrXb9y4ni++SODixYvk5lqxWq3UqFGzVMdKTk7G19cXX19f+7IaNWri4+NLcvJFWrVqzdy5C1mz5hOWL/8Af39/+vcfSGTkI9x1191MnfomGzasZ86cGdSrV59hwx4lPLxPmd+DspC/jCpi77mRAQWEEILMzEy2b99OQkICRqORoKAgIiMjWb9+fYGiYufOndSvX5+BAwcCMHz4cOLj4zlw4AA9evSgcePGNG7cmKSkpArNUVLOTmWW3z6/Zz8jKwcvD3VdwvMzqnWaNrXnA/VnrIr51Jq1KK1bt+Guu+5mx45tXL2aQuvWbahfvwEAP/30I/Pnz2b+/EWEhLRFr9ezdu0nrFnzSamOFRAQgMlkIi0tzV7gXL9+HZMpjYCAugC0aRNKmzahKIrCsWNHeemlf9OoUSM6dOhI16496Nq1B1arlb17v+GNN14jKKi5PW9ZaDSl+/dT11/G25zcliaEEH87e/YsiqIQGBhoXxYcHMyOHTsKtE1MTKR58+b21xqNhqCgIBITE+nRo0eJjrdv3z7CwsKoVasWgwYN4plnnkGr1TqVoyT8/LxLtR1Andp52+rdDfj7+5R6PxWpdm115sqn9nyg/oxVKV9ubi6XL2vR67Votep5GkOvLzrLQw8NZMuWjVy/fp1Ro563tzebM9DpdNSuXQsPDzdOnDhOQsLnDvvUaDRoNLc+xo3rW7duTePGTViwYA4TJ7761zM3s2jWLIjWrVtx7RuhTIsAACAASURBVNpVfvjhGPfd1xEfHx9q1qyBRqPBzc3A+fO/c/58Em3b3ouXlxc1auS9725u+mLPryg2G2i1WmrXNqLT6ZzeXoobFcnIypu7QIobIYTI6zExGo0Oy3x9fcnIyCi0bY0aNRyW+fj4FNq2MH369CEyMpI77riDX375hbFjx6LX6xkxYoRTOUoiNTWD3FybU9toNHkf2LRK3nZJf17HT2Uja+ZnTEkxocZHgtSeD9SfsSrms9ls2Gw2rFYbaqlt9HotVmvRfwN69+7LkiULcXd3p3v3Xvb27dqF8c9/DmH06JFYrVZatgyhT59+fPHFBnsbRVFQFG55DMf1Gt56az4LF85jyJC8W/Xuvbcds2bNRVE05OTYSEhYx6xZb2K15lCrVm1GjoymTZt2nD79KytXfsiZM6dRlLxeoEmTplK37t3Fnl9R8v/NUlLSCxSkOp222C+IKvUvY1paGpMnT2bfvn14e3vzzDPPMHz48ELbbty4kSVLlnDp0iVatGjBtGnTaNy4MZA3JN6CBQvYvHkzGRkZ9OrVizfeeANv77yTvXz5MtOmTePgwYPo9XqioqKIjo6urNMstXR55kYIIey8vLwKFBAmk8n+t/7mtiaTyWFZenp6oW0L06xZM/vvrVq1YtSoUaxdu5YRI0Y4laOkSvvBMP/6YDLnqPLDJeSdm1qzgfrzgfozVqV8as5ZlJo1a/LNN98XWK7VaomJGU9MzHiH5U89NdL+e3FDMd+8PiCgLtOnz7a/vrH48vf35513lhS6nyZNmrJkyYoij1UWpf3vrFJr2BsfyFy+fDkffPABe/fuLdDuyJEjzJgxg3nz5nH48GE6duxIdHQ0Vmtez8aHH37IwYMHSUhIYO/evVy7do3p06fbt3/55ZepUaMG+/btY/Xq1Xz22Wds3Lix0s6ztDLst6Wp69s4IYRwhYYNGwJw6tQp+7Ljx487FCL5AgMDOX78uP21oiicPHnS4VYyZ2i1WvuIZM7kqGg+fxU3MteNEEIUrtKKm/wHMseOHVvggcybff3110RERNCyZUv0ej3R0dGcP3/ePiznrl27ePLJJ/H398doNDJy5Ei2bt1KVlYWGRkZfP/997zwwgu4u7vToEEDhgwZwrp16yrrVEst3ZyDm0GLQe/8/YVCCFHdeHl5ERERwfz580lPTycxMZF169YxZMiQAm3Dw8M5d+4cmzZtwmKx8NFHHwHQuXNnIK/Yyc7OxmKxAGCxWByGg961axfXrl0D4MSJE7z//vuEh4c7naOi2QcUkOJGCCEKVWnFza0eyLzxm7Ab3TiGf/7vJ0+etL++eX12drb9GDeP/5//DV5p5Y/W4MxPabZLN+dg9DSU6niVlbEyf9SerypkVHu+qpBR7fnUnLE8TJkyBb1eT9euXRkxYgQjR460j1B241w0fn5+LFq0iGXLltG+fXs2b97MkiVLcHNzA+D8+fOEhITw4IMPAtC+fXtCQkLsx9m2bRsRERG0bduWMWPGMHDgQJ5++ukS5ahMRum5EUKIIlXa/U/OPJDZrVs3xowZQ2RkJMHBwSxatAir1UpWVhYAPXr0ID4+nrCwMDw8PFi6dCkAZrMZo9FIhw4deOedd5g8eTJ//vknCQkJmM2lm9G5LKPaODuaiNmSS02jR6WOgFOVRjxRK7VnVHs+UH9GteeDqpGxNHx9fXn33XcLXXfs2DGH12FhYWzZsqXQtvXq1SvyS665c+eWOkdl8pbiRgghilRpxY0zD2R26tSJCRMm8PLLL5OamsqgQYNo2rQpAQEBADz33HOYTCYiIyPRaDSMGDGCb7/9lrp188bjnjNnDtOmTaNXr174+/szcOBANm/eXKrcZRnVxtnRRNIysqnt686VK6biG5dRaTNWFrXnA/VnVHs+UH9GtecDdWcsyag2wjl6nRYPN50UN0IIcQuVVtw0bNgQyHsgM/8hzKIeyBw2bBjDhg0D8iYTWrt2rf0WAnd3d2JjY4mNjQXy5iaoW7euvfi58847Wbx4sX1fb7/9Nm3atCl19tJ+YHBmlAdrrg1zdi7eHoZK/YBSlUY8USu1Z1R7PlB/RrXng6qRUZQPo6dBihshnCZ/IKuOsv1bVWrPTf4DmbNnz+bPP/9k3bp1zJgxo0Bbi8XCmTNnCAwM5OrVq0ydOpXw8HD7UNDJycnYbDbq1q1LYmIis2bNYsyYMfaxsE+fPk2dOnXw9PRk3759fP7556xevbqyTrVUMmWOGyGEECVg9DRgyrS4OoYQVYJWq0Wn05OenobR6AuU0wOBZWCz5c3lolauzaeQnp6GTqcv9aSrlTrm8JQpU5g0aRJdu3bF29u7wIOh+Q+CWiwWJk6cyO+//467uzv9+vVjwoQJ9v0kJSUxYcIEUlJS8Pf356mnnmLo0KH29d9//z2LFi3CbDbTpEkTFixYQJMmTSrzVJ0mc9wIIYQoCaOngQspma6OIUSV4edXh9TUS2Rmprk6CpBXcKm5uHF1Pp1Oj59fnVJvX6nFTUkfDDUajWzatOmW+2nXrh27d+++5fqoqCiioqJKH9QF8osbo4fMcSOEEOLWjJ4GsnNyybHmytQBQpSAXm/gjjvuVkVBkfecpJGUlHRV3kqshnyl7bHJJ5+kVSJDem6EEEKUwN8jplnx85HiRoiSKuuH5vKg0YBOp/tromBXpylI7flKwvX/ygK4oedGihshhBBFkLluhBDi1qS4UYn0LCluhBBCFE+KGyGEuDUpblRCem6EEEKURP51IkOKGyGEKECKG5WQZ26EEEKUhPTcCCHErUlxoxIZZisawEtGSxNCCFEEKW6EEOLWpLhRiXRzDl4eerQa108uJYQQQr28PfO+BJPiRgghCpLiRiXSs3LkeRshhBDFkp4bIYS4NSluVCLdLMWNEEKI4rkbdOh1GiluhBCiEFLcqICiKGSYc2QwASGEEMXSaDQYPQ0yWpoQQhRCihsVyM7JxZqrSM+NEEKIEjF6GqTnRgghCiHFjQrIHDdCCCGcIcWNEEIUToobFcgwWwGZ40YIIUTJeHsayMyyYrMpro4ihBCqIsWNCqRnSc+NEEKIkjN6GlCAjCzpvRFCiBtJcaMC+Q+FessEnkIIIUpAhoMWQojCSXGjAvLMjRBCCGfkXy/yb2sWQgiRR4obFZDiRgghCpeWlkZMTAyhoaF06dKF+Pj4W7Y9dOgQ/fv3p02bNgwZMoQTJ07Y1yUmJvL0008TFhZGUFAQ2dnZDtu+9dZbREREEBoaSnh4eIHjPP7447Ru3ZrQ0FD7jytJz40QQhROihsVkOJGCCEKFxcXh8ViYf/+/SxfvpwPPviAvXv3FmiXmprK6NGjeeaZZzh8+DD9+/cnOjoai8UCgF6vp0+fPsyaNavQ47i7u/Pee+9x9OhRFi9ezIoVK/jyyy8d2rz22mscO3bM/uNK3lLcCCFEoaS4UQH7MzdS3AghhF1mZibbt29n7NixGI1GgoKCiIyMZP369QXa7ty5k/r16zNw4EDc3NwYPnw4NpuNAwcOANC4cWMefvhhmjVrVuixXnzxRZo1a4ZWq6VZs2b06tWLo0ePVuj5lYX03AghROGkuFGBdLMVg16Lu0Hn6ihCCKEaZ8+eRVEUAgMD7cuCg4M5depUgbaJiYk0b97c/lqj0RAUFERiYqLTx7XZbBw9erRAIfTOO+8QFhbG0KFD2b17t9P7vZFG4/zPjdv55Bc3WTml2ldF/ZT23CRf1cko+ap/RjXnKwkZnksF0s05ckuaEELcJDMzE6PR6LDM19eXjIyMQtvWqFHDYZmPj0+hbYsze/Zs9Ho9gwcPti976aWXaNKkCW5ubuzZs4dx48bx8ccfExIS4vT+/fy8nd4mX+3aPgC4eboDkKuAv79PqfdXEfIzqpXa84H6M0q+slN7RrXnK4oUNyqQkZWDt4cUN0IIcSMvL68CxYnJZMLbu2Bx4OXlhclkcliWnp5eaNuiLF68mG+++YZVq1bh5uZmX96mTRv777179+brr79mx44dpSpuUlMzyM21ObWNRpP3YSMlxYSigM2moNHAldRMrlwxFb+DSnBzRrVRez5Qf0bJV3Zqz6j2fDqdttgviKS4UYEMcw616ri7OoYQQqhKw4YNATh16pT9FrHjx48X+txMYGAga9eutb9WFIWTJ0/yyCOPlPh4S5cuZcOGDaxatYo77rijyLZarRalDFf+0m6qKHk/Go0Gbw8D6Zk5qvsAkp9RrdSeD9SfUfKVndozqj1fUeSZGxez2RQys6wymIAQQtzEy8uLiIgI5s+fT3p6OomJiaxbt44hQ4YUaBseHs65c+fYtGkTFouFjz76CIDOnTsDecVOdna2ffQ0i8XiMBz0smXLWLNmDfHx8QQEBDjsOy0tjb1795KVlUVubi5ff/0127Zto1evXhV16iXi7WmQAQWEEOImlVrcODNfwcaNG+1zDjz22GOcOXPGvi4nJ4c5c+bQrVs32rVrx4QJExxuXbh27Rrjxo2jY8eOhIWFMXr0aJKTkyvy1EotIysHBRkGWgghCjNlyhT0ej1du3ZlxIgRjBw5ku7duwMQGhrKkSNHAPDz82PRokUsW7aM9u3bs3nzZpYsWWK/tez8+fOEhITw4IMPAtC+fXuHW8refvttLl26RP/+/e3z2Lz++usAWK1WFi5cSKdOnbjvvvtYtGgRb731Fu3atavMt6IAo6deihshhLhJpd6WduN8BefPn2f48OE0atTIfqHKd+TIEWbMmMHKlSsJCgpiyZIlREdHs3XrVvR6PR9++CEHDx4kISEBDw8Pxo4dy/Tp05k5cyYAc+fO5erVq+zYsQM3NzdiY2OZOnUqixcvrszTLRGZ40YIIW7N19eXd999t9B1N881ExYWxpYtWwptW69ePU6ePHnL4xS1rlatWqxbt64EaSuX0cPAWbMJRVHQlHQYISGEqOYqrefGmfkKvv76ayIiImjZsiV6vZ7o6GjOnz9v/4Zu165dPPnkk/j7+2M0Ghk5ciRbt24lKysLgKSkJMLDw/H19cXDw4MBAwaUajjQypBhtgLIgAJCCCGcYvQ0kGtTyLLkujqKEEKoRqUVN87MVwA4PKiZ/3v+N2uKohRYn52dzdmzZwF47LHH2L17N6mpqWRkZLBp0ya6detW6uwVOUZ4RlZez42Pl0HGMa9i+apCRrXnqwoZ1Z5PzRlFxTJ65X0pliG3pgkhhF2l3ZbmzHwF3bp1Y8yYMURGRhIcHMyiRYuwWq32npkePXoQHx9PWFgYHh4eLF26FACz2QxA69at+eSTT+jYsSNarZbg4GDeeOONUuUuj/kIinQmFYC7AnxdMleB2scxV3s+UH9GtecD9WdUez6oGhlF+TLeMJGnP54uTiOEEOpQacWNM/MVdOrUiQkTJvDyyy+TmprKoEGDaNq0qX0Em+eeew6TyURkZCQajYYRI0bw7bffUrduXQBiYmJo0qQJR48exWAwsGTJEkaOHMlnn33mdO7ymI+gKMl/zU+Qm5NTqXMVOJPRFdSeD9SfUe35QP0Z1Z4P1J2xJPMRiNLLH2UzPVN6boQQIl+lFTcNGzYESjZfAcCwYcMYNmwYANevX2ft2rX2kW3c3d2JjY0lNjYWgH379lG3bl178XPy5EliY2PtPUVPPPEES5Ys4erVq9SqVcvp7GWdj6Aopr8uSt4eBpd8MFH7OOZqzwfqz6j2fKD+jGrPB1Ujoyhfxr+e1ZQR04QQ4m+V9syNM/MVWCwWTpw4gc1m48qVK0yaNInw8HAaN24MQHJyMhcuXLBP0jZr1izGjBmDVpt3OiEhIXz++edkZmZisVj45JNPCAgIKFVhU9Hy75WWeW6EEEI4w35bmhQ3QghhV6nz3JR0vgKLxcLEiRNp164d/fv3p06dOsTFxdn3k5SUxGOPPUbbtm0ZPXo0jz76KEOHDrWvnzFjBpcvX6Znz57cf//9HDx4kEWLFlXmqZZY/kXJ26NSR+UWQghRxUlxI4QQBVXqJ+qSzldgNBrZtGnTLffTrl07du/efcv1d999N0uWLCl90EqUbs7By12PTlupdaYQQogq7u/R0qwuTiKEEOohn6hdLN1slQk8hRBCOO3G0dKEEELkkeLGxTKycuR5GyGEEE7T67S4u+lIz7S4OooQQqiGFDculmHOkZ4bIYQQpWL0MJAut6UJIYSdFDcuZMnJxWK14e0pgwkIIYRwntHTIAMKCCHEDaS4caH8C1L+XAVCCCGEM4xeBnnmRgghbiDFjQvZixu5LU0IIUQpGD0NZFtyybHaXB1FCCFUQYobF5IJPIUQQpRFfs+/3JomhBB5nCpurl69WlE5bkvpWXkPgUrPjRBCiNLIf2YzQ4obIYQAnCxuunfvzvjx4zly5EhF5bmtyG1pQgghysI+140UN0IIAThZ3KxevRovLy9GjhxJ3759+fjjj0lLS6uobNWeFDdCCCHKQoobIYRw5FRx07p1a6ZNm8b+/fuJiooiISGBbt268eqrr/LDDz9UVMZq6+9nbmQoaCGEEM4zev1V3MiIaUIIAZRyQAGj0cijjz7K1KlTady4MRs2bOCJJ55g2LBhnDx5srwzVlv24kaGghZCCFEK+T038syNEELkcbq4SU9PZ/Xq1QwcOJBnnnmGe++9l82bN/Pdd99x3333ERMTUxE5q6V0cw46rQYPN52rowghhKiC8kdLM2VKcSOEEABO3Q/16quvsn37dho2bMgjjzzCgAED8PLysq9/8cUXWbVqVbmHrK7Ss3IwehrQaDSujiKEEKIK8paeGyGEcOBUz01ubi4rVqxgw4YNDBs2zKGwAdDpdKxdu7ZcA1Zn6WarDCYghBBFSEtLIyYmhtDQULp06UJ8fPwt2x46dIj+/fvTpk0bhgwZwokTJ+zrEhMTefrppwkLCyMoKIjs7GyHbS0WC6+//jrt27cnLCyMuXPnoiiKff2FCxcYMWIEbdu2pVevXmzdurXcz7U0PNx06LQaGVBACCH+4lRxM3v2bEJDQ4tsExgYWKZAt5MMc45M4CmEEEWIi4vDYrGwf/9+li9fzgcffMDevXsLtEtNTWX06NE888wzHD58mP79+xMdHY3FYgFAr9fTp08fZs2aVehxFi1axC+//MJXX33Fhg0b2LFjB59++ql9/bhx46hfvz7//e9/mTlzJpMmTSIxMbFiTtoJGo0Go6dBBhQQQoi/OFXcxMTE8N///tdh2ffff8/YsWPLNdTtwKYoZPx1W5oQQoiCMjMz2b59O2PHjsVoNBIUFERkZCTr168v0Hbnzp3Ur1+fgQMH4ubmxvDhw7HZbBw4cACAxo0b8/DDD9OsWbNCj5WQkMDzzz9P7dq1ueuuu3j66aftxzl79iw//vgjY8eOxcPDg7CwMHr16sWGDRsq7uSdYPQykG62ujqGEEKoglPFzcGDB7n33nsdlrVr165AwSOKl5llRVHAKMNACyFEoc6ePYuiKA53BAQHB3Pq1KkCbRMTE2nevLn9tUajISgoqES9K9evX+fSpUsO2994nFOnTnHXXXdRo0YN+/rmzZsXmqOkNBrnf261ndHTQIY5p1T7LO+f0p6b5Ks6GSVf9c+o5nwl4dQna5vNhlbrWA9pNBqsVvnGyFkZWflz3EjPjRBCFCYzMxOj0eiwzNfXl4yMjELb3lh8APj4+BTatrBt89vfeJzs7GysVisZGRkO64rKURJ+ft6l2g6gdm2fAstq1fAk8Y9r+NUyotOW8OpfgQrLqCZqzwfqzyj5yk7tGdWeryhOFTeNGjVi9+7d9O7d275sz549NGjQoNyDVXf5D38aZY4bIYQolJeXV4ECwmQy4e1dsDjw8vLCZDI5LEtPTy+0bWHb3tzeZDLh7u6OXq/H29ub9PT0EuUoidTUDHJzbU5to9HkfdhISTFxwzgHALjpNCgK/JGUap/U0xWKyqgGas8H6s8o+cpO7RnVnk+n0xb7BZFTxU10dDQxMTEcOnSIRo0a8dtvv7F+/Xrmzp1bpqC3I/sEntJzI4QQhWrYsCGQd1tY/rMyx48fL/S5mcDAQIfROhVF4eTJkzzyyCPFHqdGjRrUqVOH48ePExAQAMCJEyfsx2nWrBl//vknaWlp+Pr6FpmjpEr7oUFRCm6bPxG0SSWD1BSWUU3Ung/Un1HylZ3aM6o9X1GceuamR48eLF68mKSkJD799FP++OMP3nvvPXr16lVR+aote8+NCi5EQgihRl5eXkRERDB//nzS09NJTExk3bp1DBkypEDb8PBwzp07x6ZNm7BYLHz00UcAdO7cGcgrdrKzs+2jp1ksFofhoAcPHszixYu5evUqFy5cYMWKFfbjNGzYkFatWrFgwQKysrI4fPgwu3fvZtCgQRX9FpRI/nVEhoMWQggne24A7r//fu6///6KyHJbyR/ZRoobIYS4tSlTpjBp0iS6du2Kt7c3I0eOpHv37gCEhoaybNky2rdvj5+fH4sWLWLatGlMnjyZZs2asWTJEtzc3AA4f/48DzzwgH2/7du3B+DkyZMAPP/886SmptK7d290Oh0PP/ywQ6/P/Pnzee211wgLC6N27drExcWpZuoDKW6EEOJvpRqqKzMzk5SUFIdl99xzT7kEul2ky21pQghRLF9fX959991C1x07dszhdVhYGFu2bCm0bb169eyFTGHc3NyIi4sjLi6u0PV33nknK1euLGHqypVf3GRIcSOEEM4VN0lJSbz00kv83//9X4F1x48fL7dQt4MMuS1NCCFEOci/jpgypbgRQginnrl588038fPzY/369Xh5eZGQkEC3bt2YOXNmibZPS0sjJiaG0NBQunTpQnx8/C3bbty4kYiICEJDQ3nsscc4c+aMfV1OTg5z5syhW7dutGvXjgkTJjiMqNOvXz9CQ0PtPy1btmTAgAHOnGqFs/fceMg8N0KI20dmZqbDsy6i7Lz/mi8tf4oBIYS4nTlV3Pzwww/MnDmTFi1aoNFoaN68OXFxcUUWKTeKi4vDYrGwf/9+li9fzgcffMDevXsLtDty5AgzZsxg3rx5HD58mI4dOxIdHW2fT+fDDz/k4MGDJCQksHfvXq5du8b06dPt22/dupVjx47Zf1q0aMGDDz7ozKlWuIysHDzcdOh1Tv0TCCFElTJ//nx7b/+BAwfo2LEjYWFhfPfddy5OVn3IMzdCCPE3pz5Z5+bmUrNmTQA8PDwwm80EBATw+++/F7ttZmYm27dvZ+zYsRiNRoKCgoiMjGT9+vUF2n799ddERETQsmVL9Ho90dHRnD9/niNHjgCwa9cunnzySfz9/TEajYwcOZKtW7eSlZVVYF+nTp3ip59+KtOoNhUxu2u6OQejp8Hls7y6eqbZqpyvKmRUe76qkFHt+dScEWDTpk00atQIgCVLljBu3Dhee+01FixYUOq/ycKRt4cBDVLcCCEEOPnMTf4DmUFBQTRt2pQ1a9bg4+NjL3iKcvbsWRRFcRhdJjg4mB07dhTaXrlhcO3830+ePEnHjh1RFKXA+uzsbM6ePUtwcLDDfhISEujcuTN33nmnM6dqV94zSeczW3Kp6eOOv79rZ4BV+wy0as8H6s+o9nyg/oxqzwfqzWgymfD19SU7O5tffvmFFStWYDAYmDNnjqujVRtarQYvD70MKCCEEDhZ3Dz77LNcvnyZoKAgRo8ezahRo7BYLA63hN1KZmYmRqPRYZmvr2+B2acBunXrxpgxY4iMjCQ4OJhFixZhtVrtPTM9evQgPj6esLAwPDw8WLp0KQBms9lhP1arlS+++ILY2FhnTtNBec8knS8t3UKdmp5cuWIqvEEFK0lGV1J7PlB/RrXnA/VnVHs+UHdGnU6L0WgkOTmZkydP0qJFCwwGA9nZ2dhszv1dFUUzehowSXEjhBAlL24URaFDhw74+fkBcN9993Hw4EFycnLw8vIqdnsvL68ChYzJZMLbu2DPSKdOnZgwYQIvv/wyqampDBo0iKZNm9pnjn7uuecwmUxERkai0WgYMWIE3377LXXr1nXYz549e8jJyeEf//hHSU+zUOU5kzRAjtVGdk4uRk+Dyz+MqH0GWrXnA/VnVHs+UH9GtecD9WYcPHgwkZGRWCwWJkyYAMD//vc/GjZs6Npg1YzR08CVtIK3ZgshxO3GqeKmZ8+eHD16FK0271Edg8GAwVCyoYzzL2SnTp2iWbNmQN7w0fm/32zYsGEMGzYMgOvXr7N27VpCQkIAcHd3JzY21t4js2/fPurWrWsvfvKtX7+e/v372ydxU4v8+6KNHjIMtBCieouJiaFDhw4YDAY6dOgA5M0pk1/oiPLh7WngXLIJRVHQ5D/wJIQQt6ESDyig1Wq58847yczMLNWBvLy8iIiIYP78+aSnp5OYmMi6desYMmRIgbYWi4UTJ05gs9m4cuUKkyZNIjw8nMaNGwOQnJzMhQsXUBSFkydPMmvWLMaMGWMvugCuXLnCvn37Ct2/q2XYJ/CUYaCFENVf586d7YXNuXPn8PPzo2PHji5OVb0YPQ1YcxWyc3JdHUUIIVzKqdHSYmJimDRpEufOncNqtWKz2ew/JTFlyhT0ej1du3ZlxIgRjBw5ku7duwMQGhpqHw3NYrEwceJE2rVrR//+/alTp47DrNFJSUk89thjtG3bltGjR/Poo48ydOhQh2N98cUXNG3alJYtWzpzipUiXSbwFELcJl599VX73/atW7fSp08fIiIi2LJli4uTVS8yHLQQQuRxqutg3LhxQN5QzDc7fvx4sdv7+vry7rvvFrru2LFj9t+NRiObNm265X7atWvH7t27izzWiBEjGDFiRLGZXCF/ojVvKW6EENXc/v37ef311wGIj49n3rx5GI1GZs+eTf/+/V2crvrIL24yzFb8a7g4jBBCuJBTxc3HH39cUTluK9JzI4S4XZjNZjw9PUlPT+fs2bNERESg1WoZO3asq6NVK/nXE5PZAhZR4AAAIABJREFU4uIkQgjhWk4VN/fdd19F5bitSHEjhLhd1KpVi9OnT5OYmEibNm3QarVkZmbKQ+/lTG5LE0KIPE4VN99///0t13Xq1KnMYW4XGWYrILelCSGqvyeffNI+sMu8efMAOHz4ME2bNnVlrGrH+4bb0oQQ4nbmVHHz1FNPFViW/+1bSZ65EXlkKGghxO0iKiqKrl27otPpqFevHgD169dn6tSpLk5WvUjPjRBC5HGquDlx4oTD6+TkZObNm0d4eHi5hqru0s056LQaPN11ro4ihBAVrkGDBqSkpPC///2Pu+66i0aNGrk6UrUjxY0QQuQp00QrAQEBTJ48mSFDhvCPf/yjvDJVe+lZOXh76OWecyFEtZeens7EiRP5+uuvgbze/l69ejFr1ix8fHxcnK76MP41b1qGFDdCiNucU/PcFEZRFC5fvlweWW4bGeYced5GCHFbmD17NmlpaWzYsIGjR4+SkJCAyWRizpw5ro5WrRj0OtwNOkxS3AghbnNO9dysW7fO4bXZbGbr1q2EhoaWa6jqLsOcQ51aXq6OIYQQFW7fvn2sW7cOf39/AJo3b87cuXMLTLwsys7oqZfb0oQQtz2nipvFixc7vPb29qZVq1a8+OKL5RqqOlMUhYwsqwwmIIS4LWRlZeHr6+uwzNfXl6ysLBclqr68PQ1yW5oQ4rbnVHGze/fuispx2zBn55JrU2SOGyHEbaF169YsWLCAl156Ca1Wi81mY+HChbRq1crV0aodo6eBS6lmV8cQQgiXcqq4SU5Oxt3dnZo1a9qXXbt2jezsbAICAso9XHWUniUTeAohbh+vvPIKTz31FBs3buSuu+7iwoUL6HQ6Vq5c6epo1Y7R00CWJRdrrg29rsyP1AohRJXk1F+/mJgYkpKSHJYlJSURExNTrqGqs/xbBrw9yzRQnRBCVAlNmjRh27ZtxMbG0qdPH1577TW+/PJLmjRpUqLt09LSiImJITQ0lC5duhAfH3/LtocOHaJ///60adOGIUOGFJi+YNWqVXTt2pXQ0FDGjBnD9evX7ev69etHaGio/adly5YMGDDAvv7xxx+ndevWDm3URoaDFkIIJ3tuTp8+TcuWLR2WtWjRgjNnzpRrqOrMPoGn9NwIIaqpDz98HwCtVoPHTc8X/vrrr/z6668AJfpiLC4uDovFwv79+zl//jzDhw+nUaNGdO/e3aFdamoqo0ePZtKkSfTt25dPPvmE6OhovvrqK9zc3Pjuu+9YuHAhK1asoEGDBsTGxjJlyhQWLFgAwNatWx329/DDD9OzZ0+HZa+99hqPPPKIc29GJbqxuKlpdHdxGiGEcA2niht3d3cyMzPx9va2LzObzWi10v1dUlLcCCGqu//7v2NA3pw2en3h14eSzPOVmZnJ9u3bSUhIwGg0EhQURGRkJOvXry9Q3OzcuZP69eszcOBAAIYPH058fDwHDhygR48eJCQkMHjwYPsXdGPHjqVfv36YTKYC8+2cOnWKn376iXfffdfpc3el/CkGZFABIcTtzKnipnXr1ixbtsxhdLTly5cTEhJS7sGqK/ttaTJa2v9v7+7joij3/4+/dlnulgVFNG8qJZUb83bzBvM+j4ildQwNT2m/itTS8yXTMistT2SeslN69Gtm3mQeT+VJyZtK0zTRslLLk5Uo9CU07zIIdW+A3WXn9wfuCgLKAruM8Hk+HlvszDUz7xnA2Q9zzTVCiHpq0aKlAOh0WsLDQ67SunI5OTkoikJ0dLR7WmxsLNu2bSvXNjMzkw4dOrjfazQaYmJiyMzMZNCgQWRlZTFgwAD3/MjISPz9/cnOzqZr165l1pWWlkafPn1o2bJlmen//Oc/WbBgATfeeCOTJ09m8ODB1d43T5/h7Gp/peVCXVduCu0er782VCVjXVJ7PlB/RslXc2rPqPZ8VeFRcfPkk08yduxYduzYwU033cSxY8c4c+YM//73v72Vr96RKzdCCFE1VqsVg8FQZlpYWBgWi6XCto0aNSozLTQ01N3WarWWG5K69HwXh8PBpk2bmDlzZpnpTz75JO3atSMgIIBdu3Yxbdo0Vq9eXa0/7tWk4IuICK10XqsWVgA0fn40bVp5O2+7UkY1UHs+UH9GyVdzas+o9nxX4lFx065dOz7++GM2btzIiRMn6Nq1KyNHjiQiIsJb+eodS4EDuNR9QAghRMX0en254sNkMpXpGl26rclkKjPNbDa7215tvsuuXbuw2+0MGTKkzPTSV3eGDh3Kjh072LZtW7WKm/x8C8XFTo+W0WhKPmzk5ZlQlIrbFNtKzi9nfjeRm2uquJEXVSVjXVJ7PlB/RslXc2rPqPZ8fn5X7xHg8ZBdERERJCcnVztUQydDQQshRNVERkYCJffAREVFAZCRkeH+urTo6GjWrl3rfq8oCkePHnUPABAVFUVGRgZ33XUXAMeOHcNms9G2bdsy61m/fj0jRowgICDgitm0Wi1KDc781V1UUSpf1nVeMVntdfqh5EoZ1UDt+UD9GSVfzak9o9rzXYlHIwHMnTuXAwcOlJm2f/9+Xn755VoNVZ+ZC+wE+vvhX8lNtkIIIUro9XoSEhKYP38+ZrOZzMxM1q1bx6hRo8q1jY+P59ixY2zcuBGbzcY777wDQJ8+fQBITEwkLS2Nw4cPY7FYWLBgAfHx8WUGE8jNzWX37t3l1n/hwgXS09MpLCykuLiYHTt2sGXLlhrdc+MNMhS0EEJ4WNx88skn3HzzzWWm3XzzzXz00Ue1Gqo+MxfYMcgzboQQokpmz56NTqejf//+JCcnM2HCBPdIaUaj0f0Ht/DwcBYvXsyyZcvo0aMHmzdvZsmSJe4rMH379iUlJYWJEyfSr18/7HY7L7zwQpltbdq0ifbt25d75IHD4WDRokXceuut9OrVi8WLF/PKK6/QvXt3HxyBqgsK8MNPq3F3fxZCiIbIo0/ZhYWFBAUFlZkWHBxMQUFBrYaqzywFdrnfRgghqigsLKzSIZkPHjxY5n1cXNwV/9g2btw4xo0bV+n85OTkCrtdN2nShHXr1lUxcd3RaDSEBPvLlRshRIPm0ZWbG264gX379pWZtm/fPq6//vpaDVWflVy5keJGCCFE7TNIcSOEaOA8unLzwAMPMG3aNCZOnEhkZCQ5OTksW7aMadOmeStfveIodlJoK5Zn3AghhPAKQ5COU3nWuo4hhBB1xqPi5u6778bhcLB69WpOnDjB9ddfz5QpUyq8uVOUZyks6QctV26EEEJ4g0EfgOXkeZyKgvZafgqfEEJUk8dDdt1zzz1s3ryZb7/9lk2bNjF69GiczqqN13/hwgWmTJmC0WikX79+rFq1qtK2GzZsICEhAaPRyNixY8nOznbPs9vtvPrqqwwYMIDu3bszffr0cs9C2LdvH6NHj3Zva82aNZ7uaq1zdRWQe26EEEJ4gyFYh6KAtVAGFRBCNEweFTf5+fk88cQT9O7dm44dO5Z5VUVqaio2m409e/awYsUKli5dSnp6erl2Bw4cYO7cubz++uvs37+f3r17M2nSJByOkn+sly9fzjfffENaWhrp6emcO3eOOXPmuJfPzs5mypQp/PWvf2Xfvn1s2bKFW2+91ZNd9QpLgTzjRgghhPe4/nhmkftuhBANlEfFzd///nd+/fVX5syZQ1BQEIsWLaJTp07MnDnzqstarVa2bt3K1KlTMRgMxMTEkJSUxPr168u13bFjBwkJCXTs2BGdTsekSZM4efKke8jPzz77jAceeICmTZtiMBiYMGECH3/8MYWFhQC88cYbJCUlcdttt+Hv709oaCjt2rXzZFe9wuwubmQoaCGEELVPnnUjhGjoPPqUvXfvXj744ANatmyJn58fQ4YMoX379jzzzDNXHF4TICcnB0VRiI6Odk+LjY1l27ZtFbYv/eRn19dHjx6ld+/eKIpSbn5RURE5OTnExsby3//+lxtvvJG77rqL3NxcbrnlFp577jmaN2/uye66edpt2dX+8uUshZeu3NR1V+jKMqqF2vOB+jOqPR+oP6Pa88G1kVH4jiFIihshRMPmUXFTUFBAy5YtAQgICMButxMZGUlmZuZVl7VarRgMhjLTwsLCyt0rAzBgwABSUlJISkoiNjaWxYsX43A43FdmBg0axKpVq4iLiyMoKIi33nrLnQ/gzJkzfPjhh6xYsYJWrVrx4osv8uSTT/Kvf/3Lk90FIDw8xONlXCIiQsu8VzQlF8puaNmIpk1DK1rE5y7PqDZqzwfqz6j2fKD+jGrPB9dGRuF9Br0UN0KIhs2j4qZVq1YcO3aMNm3a0Lp1a7Zv3054eDjBwcFXXVav15crZEwmEyEh5YuHW2+9lenTp/PUU0+Rn5/P3XffTfv27d1XXh555BFMJhNJSUloNBqSk5P54osvaNGiBQBBQUGMHDnS3RVtypQpDBw4EKvVil6v92SXyc+3UFxctQETXDSakg8aeXkmSl1g4rc8MwCOIju5uSaP1lnbKsuoFmrPB+rPqPZ8oP6Mas8H6s7o56et0R+IhOekW5oQoqHzqLj5y1/+QmZmJm3atCE5OZnHH38cRVGq9JybyMhIALKysoiKigIgIyPD/fXlxowZw5gxYwA4f/48a9eupUuXLgAEBgYyc+ZM970+u3fvpkWLFu7iJyYmBk0FfTSUap75q/uBQVHKLuu6wVMf5K+aDyGXZ1QbtecD9WdUez5Qf0a154NrI6PwPiluhBANnUcDCowdO5b4+HgAhg4dyueff87HH3/MhAkTrrqsXq8nISGB+fPnYzabyczMZN26dRU+I8dms3HkyBGcTie5ubnMmjWL+Ph42rZtC8Bvv/3G6dOnURSFo0eP8vLLL5OSkoJWW7I799xzDxs2bODYsWPYbDYWL15MXFxchVeJfMlc4ECjAX2QDCgghBCi9sloaUKIhq5Gn7I9vUF/9uzZzJo1i/79+xMSEsKECRMYOHAgAEajkWXLltGjRw9sNhszZszg+PHjBAYGMnz4cKZPn+5ez4kTJ5g+fTp5eXk0bdqUhx56iNGjR7vnjxw5klOnTnHvvfficDjo2bMn8+bNq8mu1gpzgZ2QIH95sJoQQgivCLn4xzO5ciOEaKh8egkhLCyMhQsXVjjv4MGD7q8NBgMbN26sdD3du3dn586dV9zW5MmTmTx5cvWCeomlwC4P8BRCCOE1flot+kCdFDdCiAbLo25pombMBXZ5xo0QQgivMuj9pbgRQjRYUtz4iKIoJcVNkFy5EUII4T2GYCluhBANlxQ3PlJoK6bYqbhHshFCCCG8oaS4cVR7hFAhhLiWSXHjI66Ra+SeGyGEEN4UEuSPo9iJze7ZM9qEEKI+kOLGRyyFDkCKGyGEEN4lz7oRQjRkUtz4iOskI93ShBBCeJNr4BopboQQDZEUNz4ixY0QQghfMOgDACluhBANkxQ3PuIuboJkKGghhBDeI93ShBANmRQ3PiIDCgghhPAF1x/RpLgRQjREUtz4iHRLE0IIz124cIEpU6ZgNBrp168fq1atqrTtvn37GDFiBF27dmXUqFEcOXKkzPw1a9bQv39/jEYjKSkpnD9/3j1v0aJFdOzYEaPR6H4dOHCgWjnqmuuPaBYpboQQDZAUNz5iLpTiRgghPJWamorNZmPPnj2sWLGCpUuXkp6eXq5dfn4+kydPZvz48ezfv58RI0YwadIkbDYbAF9++SWLFi3izTffZM+ePWi1WmbPnl1mHUOHDuXgwYPuV48ePTzOoQbSLU0I0ZBJceMj5gI7/jotAf5+dR1FCCGuCVarla1btzJ16lQMBgMxMTEkJSWxfv36cm23b99O69atGTlyJAEBATz44IM4nU727t0LQFpaGomJiXTs2BGDwcDUqVPZvn07JpOpVnOogbu4KZTiRgjR8Ehx4yOWAodctRFCCA/k5OSgKArR0dHuabGxsWRlZZVrm5mZSYcOHdzvNRoNMTExZGZmApCVlUVsbKx7fmRkJP7+/mRnZ7un7d69m7i4OG6//XbeeustnE6nxzmqSqPx/FXV5QID/Ajw12K22qu1nZq8qrtvku/aySj56n9GNeerChm6y0csBXZCgqS4EUKIqrJarRgMhjLTwsLCsFgsFbZt1KhRmWmhoaHutlarlbCwsErnDxs2jKSkJJo1a8bhw4eZOnUqOp2O5ORkj3JURXh4SLWWA4iICK1Su7CQQAodTpo2rVr72lTVjHVF7flA/RklX82pPaPa812JFDc+Yi6w0zossK5jCCHENUOv15crIEwmEyEh5YsDvV5frouZ2Wx2t73a/KioKPf0Tp068eijj7J27VqSk5M9ylEV+fkWioudHi2j0ZR82MjLM6EoV2+vD/TjvKmQ3Nyrd7urLZ5m9DW15wP1Z5R8Naf2jGrP5+enveofiKRbmg8UO51Yi6RbmhBCeCIyMhKgTPevjIyMMoWIS3R0NBkZGe73iqJw9OhRd1eyqKioMvOPHTuGzWajbdu2FW5bq9WiXDyze5KjqhTF85cny4UE+WMusFdrOzV5VXffJN+1k1Hy1f+Mas5XFVLc+ICl0AHISGlCCOEJvV5PQkIC8+fPx2w2k5mZybp16xg1alS5tvHx8Rw7doyNGzdis9l45513AOjTpw8AiYmJpKWlcfjwYSwWCwsWLCA+Pp7Q0JKuF5999hnnzp0D4MiRI7z55pvEx8d7nEMtDMH+FBQV4/DwCpEQQlzrpLjxAXmApxBCVM/s2bPR6XT079+f5ORkJkyYwMCBAwHKPIsmPDycxYsXs2zZMnr06MHmzZtZsmQJAQEBAPTt25eUlBQmTpxIv379sNvtvPDCC+7tbNmyhYSEBLp160ZKSgojR47k4YcfrlIONTLIs26EEA2U3HPjA/IATyGEqJ6wsDAWLlxY4byDBw+WeR8XF8dHH31U6brGjRvHuHHjKpz32muvVTuHGpV+1k0jg9zvKYRoOOTKjQ9YCkq6pcloaUIIIXxBHuQphGiopLjxAblyI4QQwpcuFTeOOk4ihBC+JcWND0hxI4QQwpdc93haCuXKjRCiYZHixgdcJ5eQYLnFSQghhPdJtzQhREMlxY0PyJUbIYQQvmS4+Mc0s1WKGyFEw+LT4ubChQtMmTIFo9FIv379WLVqVaVtN2zYQEJCAkajkbFjx5Kdne2eZ7fbefXVVxkwYADdu3dn+vTpZZ4evWjRIjp27IjRaHS/XMOF1gVzgR0NMqCAEEII3zAElwyBLVduhBANjU+Lm9TUVGw2G3v27GHFihUsXbqU9PT0cu0OHDjA3Llzef3119m/fz+9e/dm0qRJOBwlN0YuX76cb775hrS0NNLT0zl37hxz5swps46hQ4dy8OBB96tHjx4+2ceKWArs6IN0aLWaOssghBCi4QgO9MNPq5HiRgjR4PisuLFarWzdupWpU6diMBiIiYkhKSmJ9evXl2u7Y8cOEhIS6NixIzqdjkmTJnHy5En31ZfPPvuMBx54gKZNm2IwGJgwYQIff/wxhYWFvtodj5gL7PIATyGEED6j0WgICdJhlgEFhBANjM/ucM/JyUFRFKKjo93TYmNj2bZtW4XtFUUp9/XRo0fp3bs3iqKUm19UVEROTg6xsbEA7N69m7i4OJo0acLdd9/N+PHj0WqrV8tpPLzg4mrv+r+l0EG4IdDj9XjT5RnVRu35QP0Z1Z4P1J9R7fng2sgo6kZIsD8WuXIjhGhgfFbcWK1WDAZDmWlhYWFl7pVxGTBgACkpKSQlJREbG8vixYtxOBzuKzODBg1i1apVxMXFERQUxFtvvQVAQUEBAMOGDSMpKYlmzZpx+PBhpk6dik6nIzk52ePc4eEhHi/jEhERCpR0S2t7fSOaNg2t9rq8xZVRrdSeD9SfUe35QP0Z1Z4Pro2MwrcMwf6c+cNa1zGEEMKnfFbc6PX6coWMyWQiJKR88XDrrbcyffp0nnrqKfLz87n77rtp3749zZs3B+CRRx7BZDKRlJSERqMhOTmZL774ghYtWgAQFRXlXlenTp149NFHWbt2bbWKm/x8C8XFTo+W0WhKPmjk5ZkotBVjczgJ8NOQm2vyePveUjpjqYtgqqH2fKD+jGrPB+rPqPZ8oO6Mfn7aGv2BSNSMIdgfc4Edp6KglUt7QogGwmfFTWRkJABZWVnu4iMjI6NMIVLamDFjGDNmDADnz59n7dq1dOnSBYDAwEBmzpzJzJkzgZIuaC1atHAXP5fTarVlurF5qrqLKsqlYThDgv1V98EDSjKqMZeL2vOB+jOqPR+oP6Pa88G1kVH4luHieaegyCGjdQohGgyfDSig1+tJSEhg/vz5mM1mMjMzWbduHaNGjSrX1mazceTIEZxOJ7m5ucyaNYv4+Hjatm0LwG+//cbp06dRFIWjR4/y8ssvk5KS4r6n5rPPPuPcuXMAHDlyhDfffJP4+Hhf7WoZ8owbIYQQdUEe5CmEaIh8duUGYPbs2cyaNYv+/fsTEhLChAkTGDhwIABGo5Fly5bRo0cPbDYbM2bM4Pjx4wQGBjJ8+HCmT5/uXs+JEyeYPn06eXl5NG3alIceeojRo0e752/ZsoWZM2dSVFREs2bNSExM5OGHH/blrrpJcSOEEKIulC5umofXcRghhPARnxY3YWFhLFy4sMJ5Bw8edH9tMBjYuHFjpevp3r07O3furHT+a6+9Vv2QtUyKGyGEEHXB9QgCGTFNCNGQ+PQhng2R66Qi/Z2FEEL4knRLE0I0RFLceJm50AHIlRshhBC+5S5urFLcCCEaDp92S2uI3FduguVQC1FdiuKsk5HANBooLi7G6ayb7VdFXWes7sORhfe5i5tCKW6EEA2HfOL2MrnnRojqczqd/PHHbzgctjrL8PvvWpxOz5515Wt1mVGj0RIa2hi9Xh4iqjaXuqU56jiJEEL4jhQ3XmYusKPz0xDo71fXUYS45phM59BqtTRrdgOaOnoIoU6nxeFQd3FTdxkVbDYb58/nAkiBozKuHgNyz40QoiGR4sbLLAV2QoL96+yDmRDXKkVRKCgwExHRAj+/uvvjgFarRe09r+oyY1BQMNCU8+fzpLhRGT+tluBAnYyWJoRoUFR+yr72mQvs0iVNiGpQFAVQ6rSwEVUTEBCAohSrvvteQ2QI1mGSAQWEEA2IFDdeZi6wY5BhoIWoAbnqqX7e+x5duHCBKVOmYDQa6devH6tWraq07b59+xgxYgRdu3Zl1KhRHDlypMz8NWvW0L9/f4xGIykpKZw/f94975VXXiEhIQGj0Uh8fHy57dx///107twZo9Hofl0LDMH+WGRAASFEAyLFjRc5nQrWQof7QWpCCCE8k5qais1mY8+ePaxYsYKlS5eSnp5erl1+fj6TJ09m/Pjx7N+/nxEjRjBp0iRstpLBKL788ksWLVrEm2++yZ49e9BqtcyePdu9fGBgIP/7v//Lt99+yxtvvMHKlSv55JNPymzj2Wef5eDBg+7XtcAQHCD33AghGhQpbrzIWuRAoaRbgBBCVNXq1SuZOvWvdR2jzlmtVrZu3crUqVMxGAzExMSQlJTE+vXry7Xdvn07rVu3ZuTIkQQEBPDggw/idDrZu3cvAGlpaSQmJtKxY0cMBgNTp05l+/btmEwmAB5//HGioqLQarVERUUxePBgvv32W5/urzcYgnXYHU6K7MV1HUUIIXxCihsvMrufcSNXboSo7/7nfyby1ltv1Mq6/t//S2b+/MW1sq5rWU5ODoqiEB0d7Z4WGxtLVlZWubaZmZl06NDB/V6j0RATE0NmZiYAWVlZxMbGuudHRkbi7+9PdnZ2uXU5nU6+/fZboqKiykz/5z//SVxcHKNHj2bnzp012jeNxvNXdZZz3fNpKbRXa5u+yOjLl9rzXQsZJV/9z6jmfFUhlxS8yCLPuBFClGK32/H3l38PqspqtWIwGMpMCwsLw2KxVNi2UaNGZaaFhoa621qtVsLCwiqdX9q8efPQ6XQkJia6pz355JO0a9eOgIAAdu3axbRp01i9ejVdunTxeL/Cw0M8XsYlIsKzEemua1py/PwDA2ja1Dej2Xma0dfUng/Un1Hy1ZzaM6o935VIceNF7gd4yoACQtRrr746l0OH/suPPx7igw/eA2D79j188slmli1bwrhxD/Lee//i/PnzbN++m/Xr/8OGDev47bffCAoKomfPXqSkPEHjxo0BWLFiKQcO7GPJkhVAyVWh9u2jOXcun6+++oKQEAP33/8Qd989utJMr7zyEvv3f8358+do3DicYcOG89BDE9BeHDO6qKiQd95Zyc6dn5GXl0vjxuEkJ0/g9ttHALB37xesWrWc48ePodVqueWW7syZM8+bh7EcvV5frvgwmUyEhJQvDvR6vbuLmYvZbHa3vdp8lzfeeIPPP/+cNWvWEBAQ4J7etWtX99dDhw5lx44dbNu2rVrFTX6+heJiz0aW02hKPmzk5ZlQlKovp704gt2vp84RFujdzhrVzegras8H6s8o+WpO7RnVns/PT3vVPxBJceNFZrlyI0St2vTFL+w7ctZn2+sVex2Jg9pdtd306c9y7FgOXbp0Y+LEyWXm/fFHHj//nMXq1WvdhUVERAQvvfQqN9xwI2fP/sZzzz3N/PnzeOGFuZVuY8uWzcyd+w+ef/5Fdu/+nOeff4aePeO44YYbK2x/880dmTDhUcLDm/DTTz/y1FOPExHRlJEjRwElxc+vvx7j5Zdfo02bSPLycsnLywNg//6vmTVrBrNmvUD//gNxOp388MP3VTpmtSkyMhIo6VLm6iKWkZFRrrsYQHR0NGvXrnW/VxSFo0ePcu+99wIQFRVFRkYGd911FwDHjh3DZrPRtm1b9zJvvfUWH374IWvWrKFZs2ZXzKbVai8OV1491V1UUTxb1tUt2lxg99kHFU8z+pra84H6M0q+mlN7RrXnuxK558aL5J4bIQTAlClPoNfrCQoKAmDQoD/RunUbtFotLVq0ZNy4B9m//5uxAEkdAAAgAElEQVQrrmPgwMF0794TrVbLoEF/IiwsjKNHMyptf+edI2nSJAKNRkOnTp0ZOvR29u//GoBz586xbdsWnnjiaSIjb0Kj0dC0aTNiYkruSfngg/cZMeIuBg8egr+/P4GBgfTo0auWjkbV6fV6EhISmD9/PmazmczMTNatW8eoUaPKtY2Pj+fYsWNs3LgRm83GO++8A0CfPn0ASExMJC0tjcOHD2OxWFiwYAHx8fGEhpZ0vVi2bBnvv/8+q1atonnz5mXWfeHCBdLT0yksLKS4uJgdO3awZcsWBg8e7OUjUHOGUsWNEEI0BHLlxovkyo0QteuufjdxV7+b6jqGRxo3DncXNS7p6Tt57701nDjxKzabDUVxUlBQQHFxcaUPLW3atOyVhKCgYKxWa4VtFUVh9eqVbN/+Kbm5vwMKNpuN2NibATh9+iQArVu3qXD5U6dO0atXb09202tmz57NrFmz6N+/PyEhIUyYMIGBAwcCYDQaWbZsGT169CA8PJzFixfz4osv8txzzxEVFcWSJUvcXcv69u1LSkoKEydOxGKx0LdvX1566SX3dv7xj3/g7+/PiBEj3NPuvPNOUlNTcTgcLFq0iMcffxytVkubNm145ZVX6N69u28PRjVIcSOEaGikuPEiS4EDkCs3QjQEri5nV5vu6ob23HOpDBhwG4GBgaSnf87MmdNr1M2ptM8++5QPPniPf/xjEVFR0fj5+bFgwT/cV3patGgFwPHjx4mN7VBu+ZYtW/Lrr8drJUtNhYWFsXDhwgrnXf6smbi4OD766KNK1zVu3DjGjRtX4byjR49WulyTJk1Yt25dFdKqjxQ3QoiGRrqleZH54lOhQ4KkhhSivmvSJKJKBUFBQQFOp5PGjRsTGBjIr78eZ82at2s1i8Vixs/Pj/DwcDQaDd99d4Bt27a454eHhzNkSAKvv/4Kx4/nAJCbm8vRo0cAuOeee/noo43s2rUDu91OUVERBw7sq9WMwjdcf1yzSHEjhGgg5FO3F1kK7AQH+qHzkxpSiPruL38Zx9//nsqwYbcBClu37qqwXZs2kTzyyP8wZ85srNYC2rVrz9Chd5CRcbjWstx++50cOvQ9DzzwFzQaLT17xjF06O1l7tGZMWMWb7+9jCeemMK5c3/QuHETHn54IjExsfTq1Zu//e0l3nlnJX//eyo6nY5bbulZJ/fdiJoJ9PfDX6fFJMWNEKKB0Ci11Q+insrPt+BweD5kZ9Omofx13g6shQ7mTerjpXTV58qYm6vOof7Ung/Un1Ht+eDKGZ1OJ2fP/sp1191YaZcvX9DptB7/G+BrdZ2xsu+VTnf1ITsbopqcV6rz+/zE4i9pbAjguQd6eragh9T+b47a84H6M0q+mlN7RrXnq8p5RS4peJG5wC6DCQghhKhThmB/uedGCNFgSHHjRVLcCCGEqGslxY2jrmMIIYRPSHHjJTZ7MTa7U4obIYQQdSok2J+CIgfFTnV3rxRCiNogxY2XmKw2AEKCpLgRQghRdwzuEdPk6o0Qov6T4sZLTNaLw0AHy4B0Qggh6o7h4nlIRkwTQjQEPi1uLly4wJQpUzAajfTr149Vq1ZV2nbDhg0kJCRgNBoZO3Ys2dnZ7nl2u51XX32VAQMG0L17d6ZPn47FYim3DpvNxrBhw+jbt683dueKTJaSKzfSLU0IIURdMgQHAPKsGyFEw+DT4iY1NRWbzcaePXtYsWIFS5cuJT09vVy7AwcOMHfuXF5//XX2799P7969mTRpEg5HySX15cuX880335CWlkZ6ejrnzp1jzpw55dbz1ltvERER4fX9qsgFqxQ3Qggh6p7ryo2MmCaEaAh8VtxYrVa2bt3K1KlTMRgMxMTEkJSUxPr168u13bFjBwkJCXTs2BGdTsekSZM4efIkBw4cAOCzzz7jgQceoGnTphgMBiZMmMDHH39MYWGhex2//PILn3zyCRMnTvTVLpYhV26EEEKoges8JMWNEKIh8NkNITk5OSiKQnR0tHtabGws27Ztq7B96WeLur4+evQovXv3RlGUcvOLiorIyckhNjYWgL/97W889dRTBAUF1Ti7RuN5e9eAAga9v8fL+4Irkxqzgfrzgfozqj0fXDmjmnN7w0sv/Y3i4mKef/7Fuo5SbRpNw/u+XQtC3AMKSHEjhKj/fFbcWK1WDAZDmWlhYWEV3iszYMAAUlJSSEpKIjY2lsWLF+NwONxXZgYNGsSqVauIi4sjKCiIt956C4CCggKg5H4dg8HAoEGD+Oabb2qUu7pP175gOQbAja0a0zRCvU/ojogIresIV6T2fKD+jGrPBxVnLC4u5vffteh02jJPva8LOp33t6/RaNBoqr8tX2SsjNMJWq2WiAgDfn5+dZZDVMx15UYGFBBCNAQ+K270en25QsZkMhESUv6D/6233sr06dN56qmnyM/P5+6776Z9+/Y0b94cgEceeQSTyURSUhIajYbk5GS++OILWrRowfnz51m4cCFr1qypldz5+RaKiz17NkDpKzf2Qhu5uep7toBGU/KBMi/PRKmLYKqh9nyg/oxqzwdXzuh0OnE6nTgcTuqyttHptDgc3v8dLrkiTbW25auMlXF9r/LyzGUKUT8/bbX/QCRqj3RLE0I0JD4rbiIjIwHIysoiKioKgIyMDPfXlxszZgxjxowB4Pz586xdu5YuXboAEBgYyMyZM5k5cyYAu3fvpkWLFjRv3pz9+/dz9uxZ7rnnHqBkZDWTyUTfvn1ZtWpVpdu7kup8MDRZ7PhpNQT6+6n2gyWU7Jvkqxm1Z1R7Pqg4o9ozl7ZxYxrvvrua99//EM3Ffll2u527776d6dOfZeDAwSxf/iaffbaNvLxcQkND6d9/IJMmPVblrrM7d37GmjWrOHXqJH5+Wjp16sKUKU/SqtX17jZ7937BqlXLOX78GFqtlltu6c6cOfMAOHv2N5YsWcR///sdFouFVq2u55lnnicmJrbWjsO18LPWEAUH6tBqNNItTQjRIPj0yk1CQgLz589n3rx5nDp1inXr1jF37txybW02G9nZ2URHR/PHH3/wwgsvEB8fT9u2bQH47bffcDqdtGjRgszMTF5++WVSUlLQarUYjUY+//xz97oOHjzI7Nmz2bBhA+Hh4b7aXUxWGyFBOvcHHSFEzeVt3ohpf826mnoitGccze+++6rt4uMT+N//nc/Bg99yyy09ANi9+3O0Wj/69h0AwA033MiCBW/QvHlzfvklm2eeeYLgYD2PPvo/Vcqi1+t55pnnadu2HSaTiblzX+CFF2axdOnbAOzf/zWzZs1g1qwX6N9/IE6nkx9++B6AoqJCHntsEl26dOXtt98lLCyMY8dyCA4Ors5hEdcYrUZDSLBOrtwIIRoEn3b2mD17Njqdjv79+5OcnMyECRMYOHAgAEaj0T0ams1mY8aMGXTv3p0RI0Zw3XXXkZqa6l7PiRMnGDt2LN26dWPy5Mncd999jB49GoCAgACaNWvmfjVq1AitVkuzZs3Q6Xz3QE2T1SYjpQnRQOj1Idx22xA++mije9rmzRsYNuwO9787w4YNp0WLFmg0Gtq2bUdi4j3s2/d1lbfRu3cfoqKi8fPzo3Hjxowf/wg//fQDVmtJd98PPnifESPuYvDgIfj7+xMYGEiPHr2Akis6Fy6c58knn6Fx48ZotVpuuqktLVq0rMWjINTMEOwvxY0QokHw3ad9SgYQWLhwYYXzDh486P7aYDCwcePGCtsBdO/enZ07d1Zpm3FxcXz55ZeeBa0FJquN6xrLX0WFqE0Rd/6ZiDv/XNcxKnTnnSOZMmUyU6c+hdls4rvvDjBt2gz3/A0b1rNpUxpnzpyhuNiBw+GgUaPGVV7/wYPfsmrVcnJysikouDTsfX5+PmFhoZw6dYpevXpXuOzp06do0aIlAQEB1d9BcU0LCfbn7B/Wuo4hhBBe59PipqFQFAWT1U7blmF1HUUI4SOdO3elVavr2bZtC3/8kUfnzl1p3boNAD/+eIj58+cxf/5iunTphk6nY+3af/P++/+u0rrtdjtPPTWVhx4az9///g/0+hAyM4+QnDzOPSx+y5Yt+fXX4xUu36JFK86cOY3dbsffX64oX4sufPM1p/fuwe/6GwluH01wVDR+l41AeiWGIH+yCxwoiiLdpYUQ9Vrdjq9aTxUUOXA6FemWJkQDM2LEXWzevIEtWz5ixIhLV5jMZrO7O5lOp+Po0SOsX/+fKq/XbrdjsxURGhqGXh9Cbu7vLFu2pEybe+65l48+2siuXTuw2+0UFRVx4MA+APr27UdoaCivvfYy586dQ1EUfvklmzNnTtfOjgufsOYcI//TrZxavJD/e/x/yHn+WX5bvYoLX+3Fnvt7mee/Xc4Q7I9TUSgocvgwsRBC+J5cufECV79mKW6EaFiGDRvB0qWLCQwMZPDgIe7pvXr15s9/HkVKyiM4HA46duzC7bePYNOmD6u0Xr1ez4wZs1i58i0WLnyNVq1uYMyY+/jqq0tdbnv16s3f/vYS77yzkr//PRWdTsctt/SkR49eBAYG8c9/vsmSJf/kgQfGUFBQyPXXl4yWJvfdXBvC4npz0+1/4tSho1izMinIyqQgK4vzu3dxfvcuAHThTQiOiiI4Kprg9tEEXH89motDcxv0JeejH7L/4KaWoYSHBuFfh89GEkIIb9EoV/pTjyA/3+Lx8yN+OX2BF985wOhB7bijdxsvJasZjQaaNg0lN1edz0BRez5Qf0a154MrZ3Q6nZw9+yvXXXdjnT7Es66fIVMVdZ2xsu+VTifPualIdc4rlf2u2PPyKPg5k4LMTAp+zsJ28oR7nlavJ7hde4KjovnJGc7qH6wUay49ZDUsJICIsECahAURERZEk7AgmoQGEtGo5Oswvb9HXdjU/m+O2vOB+jNKvppTe0a156vKeUWu3HjBpSs3cniFEKImLly4wHPPPcfu3bsJCQlh/PjxPPjggxW23bdvH6mpqfz666+0b9+el156idjYS8/xWbNmDUuXLsVsNtOvXz/mzJlDo0aNgJJROufMmcMnn3yCn58fSUlJTJs2zf3h/vTp08ycOZPvvvuOJk2a8MQTTzB8+HCv7//V+EdE4B9xK2FxtwJQbDZT8H8/X7yyk4nl8E9YfjhEc+BJNCj+ATh0Adj9/CnU+FOADovTD5vWn1ytPye1/tguvhy6AAJC9ASHhqBvZMDQ2EBAYCA6fx26AB06f390AToCAgLwD9QREKAjv8CB1VyIzk+Lv84Pf53W/dLKvT5CCB+QT99e4HpQWoh0SxNCiBpJTU3FZrOxZ88eTp48yYMPPshNN93kfoyAS35+PpMnT2bWrFnccccd/Pvf/2bSpEl8+umnBAQE8OWXX7Jo0SJWrlxJmzZtmDlzJrNnz2bBggUALF68mMOHD/Ppp59SVFTEQw89RMuWLbnvvvsAmDZtGjExMbzxxht8//33PProo0RFRREdHe3zY3IlfgYDhq7dMHTtBoDTZqMw5xcKMo9S9OtxnAUFOAsLcRYW4CwoxFl4AWdhYa08fbUYDac0GorRomi0FGu0ONHgvPi1otGiaDQoaKD0/zVa0ICi0cDFaZemX3yPBrSu+VxqR+nlcE+j9LqgpEjVgNZPi9OpuLetcS/PpXalllc0oHFvs4L1gzuPptx8V07Kti2VSXE3ce03+PvrsDuK3ctoSu2n5vL9vLitMtMv7le543DZdii1X5pSx/DSMdC61+naN41GQ2CQP0VFdvd6Sx8zzWXr1rjWgQaN1pVLc/GOb+3FuK5ltKAtyVgSp9SymtLrL/09LZmmKbWNU2HBmM1Fl3JrNRfXeekYufZXoy2dzbW+km2Xzqa5+L50Bs3l69dq3OtyrVtz8ef20jKg1Wjx9wPzeYv7+6PRXjr2l45ZyXuta32lfrRc329NqZ8PcYkUN14g99wIIUTNWa1Wtm7dSlpaGgaDgZiYGJKSkli/fn254mb79u20bt2akSNHAvDggw+yatUq9u7dy6BBg0hLSyMxMZGOHTsCMHXqVIYPH47JZCI0NJS0tDRSU1OJiIgA4OGHH2bt2rXcd9995OTkcOjQId58802CgoKIi4tj8ODBfPjhh8yYMYPq8PTzSLnPzFXkFxhASEwMITExlbZRFAWlqOhS0VNYSHFBQZlCqMhkxWoyU2xz4HS4XsUl/y8uRnEUoxQ70KLgKLKjOIuhuOSldRajLXaicRZfLKIUUJxoFAWcSsn/qfj/GhRQQKM40aK4Al+qNbj0tfCdoLoOcAWmUl+rsFcVANUZyuXib4X765Kffkr+MOCefum3wVW0u9rWdLp7e+7ZmkttS/0SujNU2kaDU+vHdeP+H+17db7qfleHFDde0LZVGF3aN6X1daF1HUUIIa5ZOTk5KIpS5upIbGws27ZtK9c2MzOTDh06uN9rNBpiYmLIzMxk0KBBZGVlMWDAAPf8yMhI/P39yc7OJjIykrNnz5ZZPjY2lqysLACysrJo1aqVuwsbQIcOHfj666o/hLW0mtyHFBEh55WKKIpSUjhdvAJV+r3iejkV93Tnxfeutq75KErJh0ins+RDmWs519el27qngaI4L85zTXZerOEU9yh2ijvPxfmXLX+pdiu9jYvLOhUUFHCCQql57uVd+3Zp3e6M5falVP5KjhVl1nnxPU4U961il9qXb+s6DpflUSi7HbisvbNk/9wFcKlsKGWXp+y6SpqX+hm4uP2KM13+M0LZ9ZfatoZL6y/Z7qWvS+3UZdsvu52y00raaUrvQ6X/L7vcpf9fYX6l67jUTlNZ+1JfaypbN1z840NF2y35j7uGKbUuzcWmmoslmhYNoSEBNG3qnX/PpLjxgnbXN+KlSX1VezOWEEJcC6xWK4bLnuUSFhaGxWKpsG3p4gMgNDTU3dZqtRIWFlbhfKvV6n5fejtFRUU4HA4sFkuZeVfKURX5+RaKiz0fUCAiIpS8PPWeV9SesfJ8pbqYuZQew8SPK6pg6Wq7do+hOqg9H6g/oy/z5eaart7oMn5+MqCAEOKap8J//UUZrr9M13bXb71eX66AMJlMhISUP7Hp9XpMprInSrPZ7G57pfl6vb5ce5PJRGBgIDqdjpCQEMxmc5VyVFV1PzSUujihWmrPqPZ8oP6Mkq/m1J5R7fmuRAa5F0Koklarxc9Ph9l8AaezGKfTKS/VvYqx222cO5eLThdQciNsLYqMjARwdw8DyMjIICoqqlzb6OhoMjIy3O8VReHo0aPuLm1RUVFl5h87dgybzUbbtm1p1KgR1113XZn5R44ccW8nKiqKU6dOceHChavmEEIIUbfkyo0QQrXCw68jP/8sVuuFqzf2Eq1Wi9Op7ufc1G1GDcHBBkJDG9f6mvV6PQkJCcyfP5958+Zx6tQp1q1bx9y5c8u1jY+PZ968eWzcuJHbb7+dd999F4A+ffoAkJiYyLRp07jzzjtp06YNCxYsID4+3t3dLDExkTfeeIMuXbpQVFTEypUruf/++4GSIqtTp04sWLCAp556ih9++IGdO3fy/vvv1/o+CyGEqBkpboQQqqXT+dOs2fV19sG9pO+xgbw8s2ovz9d1Rk3poWC9YPbs2cyaNYv+/fsTEhLChAkT3COlGY1Gli1bRo8ePQgPD2fx4sW8+OKLPPfcc0RFRbFkyRICAgIA6Nu3LykpKUycOBGLxULfvn156aWX3Nv561//Sn5+PkOHDsXPz4977rmHe++91z1//vz5PPvss8TFxREREUFqaqrqhoEWQggBGkVR6ylbHWrzSdJqovaMas8H6s+o9nyg/oxqzwfqzliVJ0k3RHJeqRtqzwfqzyj5ak7tGdWeryrnFbnnRgghhBBCCFEvSHEjhBBCCCGEqBekuBFCCCGEEELUCzKgwFX4+VW//qvJsr6i9oxqzwfqz6j2fKD+jGrPB+rMqMZMaiDnlbql9nyg/oySr+bUnlGt+aqSSwYUEEIIIYQQQtQL6izLhBBCCCGEEMJDUtwIIYQQQggh6gUpboQQQgghhBD1ghQ3QgghhBBCiHpBihshhBBCCCFEvSDFjRBCCCGEEKJekOJGCCGEEEIIUS9IcSOEEEIIIYSoF6S4EUIIIYQQQtQLUtwIIYQQQggh6gUpboQQQgghhBD1ghQ31XThwgWmTJmC0WikX79+rFq1qtK2+/btY8SIEXTt2pVRo0Zx5MgRr+ez2WzMnDmTwYMHYzQaGT58OJs3b660fUxMDN26dcNoNGI0Ghk/frzXMz799NN06tTJvU2j0cipU6cqbZ+ZmUlSUhJdu3bljjvu4KuvvvJqvtK5jEYjN998M48++mil7X1xDNesWUNiYiKdOnVi6tSpZeZ5enzWrFlD//79MRqNpKSkcP78ea/l++WXX5g8eTJ9+vShR48ejBs3jh9++KHS9XzzzTfExsaWOf5vvvlmjfNdKSPA4MGD6dKli3ubw4cPv+K6tm7dypAhQ+jatSsPPPAAJ0+e9Fq+AwcOlPuZjImJ4e23365wPd48hsI75LxSM2o/p4CcV2ozn5xXap6v3p5XFFEtTzzxhPLoo48qJpNJOXLkiNK7d29l165d5dr98ccfSvfu3ZUPP/xQKSoqUlauXKkMGjRIKSoq8mo+i8WiLFiwQDl+/LjidDqV/fv3K7fccovy3XffVdg+Ojpa+fnnn72a6XIzZsxQXn311Sq1tdlsym233aYsWbJEKSoqUj7++GPllltuUXJzc72csoTD4VD69eunbNiwodI2vjiGn376qbJ9+3blhRdeUB5//HH3dE+PzxdffKH06tVL+fHHHxWTyaQ89thjypQpU7yW7/vvv1fef/99JS8vT3E4HMrq1auVuLg4xWKxVLier7/+WunTp0+N83iSUVEU5bbbblPS09OrtJ6ff/5Z6datm7Jnzx6loKBAeemll5TRo0d7Nd/l24+NjVVOnjxZ4XxvHkPhHXJeqZlr6ZyiKHJeqWk+Oa/UTr7Lt18fzity5aYarFYrW7duZerUqRgMBmJiYkhKSmL9+vXl2m7fvp3WrVszcuRIAgICePDBB3E6nezdu9erGfV6PVOmTOHGG29Eo9HQo0cPbrnlFg4ePOjV7XrLvn37KCwsZOLEiQQEBHDHHXcQFRXF1q1bfbL9PXv2YLVaSUhI8Mn2KjN06FCGDBlCeHh4memeHp+0tDQSExPp2LEjBoOBqVOnsn37dkwmk1fydenShTFjxtCkSRP8/Py4//77KSgoIDs7u0bbq82Mntq0aRP9+/enX79+BAUF8dhjj3HkyBGysrJ8km/dunXceuuttGrVqkbbE+og5xXfqutzCsh5pab55LxS+/nqy3lFiptqyMnJQVEUoqOj3dNiY2Mr/OHLzMykQ4cO7vcajYaYmBgyMzN9ktXFarXy448/EhUVVWmbBx54gD59+jBx4sQa/yJV1X/+8x969erFXXfdxbp16yptl5WVRXR0NFrtpR/ZDh06+Ow4pqWlMXz4cIKCgq7Yri6OIXh+fLKysoiNjXW/j4yMxN/f32cnhUOHDuF0OmnTpk2lbc6dO0ffvn257bbbeP755zl37pxPsj399NP07t2b+++/n2+//bbSdpmZmWWOocFgoHXr1j75vjscDjZt2kRiYuIV29XVMRSek/NK7bhWzikg55XaJueVmqlP5xUpbqrBarViMBjKTAsLC8NisVTYNiwsrMy00NDQCtt6i9Pp5Omnn6Zz587069evwjb/+te/2LlzJ9u2baNDhw4kJydjNpu9muv+++9n69at7N27l2effZZXX32VTz/9tMK2Foul3HGs7JjXtj/++IOdO3cyatSoK7ari2Po4unxqcufy/z8fKZPn85jjz1GaGhohW3atm3Lhg0b2LNnD++++y5nzpzh6aef9nq2efPmsXPnTnbt2sXtt9/OhAkTKu3vXJfHMD09HbvdTnx8fKVt6uoYiuqR80rNXSvnFJDzSm2T80rN1afzihQ31aDX68v9oJlMJkJCQipse/klWbPZXGFbb1AUhdmzZ3P27Fnmz5+PRqOpsF2vXr0ICAhwX0rW6XR89913Xs3WsWNHmjRpgk6no3fv3owdO7bSy90hISHljmNlx7y2bd68mTZt2tC1a9crtquLY+ji6fGpq59Lk8nEww8/TP/+/ZkwYUKl7Zo1a0ZUVBRarZaWLVvy3HPPkZ6eTkFBgVfz9ejRg6CgIIKCgrjvvvu4+eab2b17d4Vt6/J32/UX38DAwErb1NUxFNUj55Wau1bOKSDnldok55XaUZ/OK1LcVENkZCRAmcuEGRkZFV6aj46OJiMjw/1eURSOHj1apuuBtyiKwgsvvEBGRgbLly/36JdDo9GgKIoX05Wn1Wor3WZUVBSZmZk4nU73tIyMDJ8cR1c/Yk/58hh6enyioqLK/FweO3YMm81G27ZtvZbRdQLq3Lkzs2bN8mhZ18+Gr38mr/Q9vPx322KxcPz48St20akNeXl5pKene/wzWVfHUFSNnFdqn1rPKSDnldoi55XaUd/OK1LcVINerychIYH58+djNpvJzMxk3bp1FV5ejo+P59ixY2zcuBGbzcY777wDQJ8+fbyeMzU1le+//54VK1aU6+5QWlZWFj/99BMOh4OCggIWLVpEUVERRqPRq/k++eQTzGYzTqeTAwcOsGbNmkovh/bq1YvAwECWL1+OzWZjy5YtZGZmMmzYMK9m/Omnn/j555/585//fMV2vjqGDoeDoqIiHA4HTqeToqIi7Ha7x8cnMTGRtLQ0Dh8+jMViYcGCBcTHx1d6Ob+m+cxmM+PHj6ddu3b87W9/u+p6vv76a06cOIGiKPz+++/MmTOHvn37otfra5TvShlPnTrFgQMHsNls2Gw2/vOf//Djjz9W2uXmrrvuYs+ePezdu5eioiIWLVpETExMjU9CleVz2bRpE23btqVz585XXI83j6GofXJeqblr4ZwCcl6prXxyXql5PjgtfbYAAAXBSURBVJd6d17xzaBs9c/58+eVlJQUpVu3bkrfvn2Vt99+2z2vW7duyv79+93vv/76a2X48OFK586dlcTEROXw4cNez3fixAklOjpa6dSpk9KtWzf3a8mSJeUyfvXVV0pCQoLStWtXpVevXkpycrKSkZHh9Yz33Xef0r17d6Vbt27KHXfcobz33ntl5t9xxx3Kxo0b3e+PHDmijB49WuncubMybNgwZe/evV7PmJqaqkyaNKnCeXVxDBcuXKhER0eXec2YMUNRlCsfn/379yvdunUrs65//etfSt++fZVu3bopf/3rX5Vz5855LV9aWpoSHR2tdO3atczPo+v4XZ5v5cqVyoABA5QuXboo/fr1U5555hklLy+vxvmulDErK0u56667lG7duik9e/ZU/vKXvyhff/21e7mTJ08q3bp1KzNE5ieffKIMHjxY6dKli3L//fcrJ06c8Fo+lxEjRpT598bFl8dQeIecV2rmWjinKIqcV2orn5xXap7Ppb6dVzSKorJrSUIIIYQQQghRDdItTQghhBBCCFEvSHEjhBBCCCGEqBekuBFCCCGEEELUC1LcCCGEEEIIIeoFKW6EEEIIIYQQ9YIUN0IIIYQQQoh6QYobIRqQtLQ0BgwYUNcxhBBC1BNyXhFqI8WNEEIIIYQQol6Q4kYIIYQQQghRL0hxI4SPFRUV8frrrzNkyBB69uzJ2LFjOXz4MACLFi3i3nvv5bXXXuPWW2+lb9++vPLKK9jtdvfy//d//8f48eOJi4tjwIABPP/885hMJvf8wsJC5s+fT0JCAkajkT/96U98+OGHZTK8//77DB48mO7du/PYY49hNpt9s/NCCCFqnZxXhLhEihshfGz27NkcOnSINWvW8NVXX3H77bfz8MMPc+HCBQAOHTqEn58f6enprF69mm3btrF8+XIAzGYzDz74IO3bt2fXrl2sW7eO7OxsZsyY4V7/rFmz+PLLL3njjTf47rvvePfdd4mOjnbPz83NJTs7m08++YQtW7Zw5MgRVq5c6duDIIQQotbIeUWIS6S4EcKH8vPz+fDDD3n++edp0aIFOp2OcePGERoayueffw5Ao0aNSElJISAggHbt2vHwww+zfv16AHbt2oXdbufJJ58kODiY6667jmeffZYdO3bw+++/88cff7B582Zmz55Nu3bt0Gg0NG/enI4dO7oz+Pn5MX36dIKCgrjuuuv405/+xKFDh+rkeAghhKgZOa8IUZaurgMI0ZAcP34cgKSkpDLT7XY7v/32GwAtWrTAz8/PPe+GG27gzJkzAJw+fZpWrVqh01361W3durV7nstNN91UaYbw8HD8/f3d7/V6PRaLpbq7JIQQog7JeUWIsqS4EcKHmjZtCsCmTZto1apVufmLFi3izJkzFBcXu09EJ0+epHnz5gC0bNmS06dP43A43CeiX3/91T3PtUxOTg6dOnXy+v4IIYSoW3JeEaIs6ZYmhA9df/31DBkyhNTUVE6ePAmU9HdOT0/n7NmzAJw/f57Fixdjs9nIzs5mxYoVJCYmAjBo0CD8/Px4/fXXKSws5Pfff2fu3LncdtttNGvWjCZNmjBixAhSU1PJzs4G4OzZs/z00091s8NCCCG8Ss4rQpQlxY0QPvbaa6/RsWNHkpOTMRqNDBs2jA8++ABFUQDo0qULdrudAQMGMG7cOIYMGcLEiRMBMBgMvP322xw9epSBAweSmJhImzZteOWVV9zrf/HFF+nZsycTJkzAaDRy7733kpWVVSf7KoQQwvvkvCLEJRrF9ZMvhKhzixYtYu/evbz33nt1HUUIIUQ9IOcV0dDIlRshhBBCCCFEvSDFjRBCCCGEEKJekG5pQgghhBBCiHpBrtwIIYQQQggh6gUpboQQQgghhBD1ghQ3QgghhBBCiHpBihshhBBCCCFEvSDFjRBCCCGEEKJekOJGCCGEEEIIUS9IcSOEEEIIIYSoF/4/rV2cluKQtJsAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}