yolov8_custom_training.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dos8m2BebRel"
      },
      "source": [
        "- dataset: https://www.kaggle.com/datasets/outliersloop/footballyolov8\n",
        "- yolov8 github: https://github.com/ultralytics/ultralytics\n",
        "- yolov8 docs: https://docs.ultralytics.com/quickstart/"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kvJXfYgTfsBP"
      },
      "source": [
        "# Install YOLOv8:\n",
        "\n",
        "![image.png](data:image/png;base64,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)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "grULmxelVhf5",
        "outputId": "2dcda5af-c8e2-46d1-e27c-5eb10e7a1d75"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: ultralytics in /usr/local/lib/python3.8/dist-packages (8.0.16)\n",
            "Requirement already satisfied: sentry-sdk in /usr/local/lib/python3.8/dist-packages (from ultralytics) (1.13.0)\n",
            "Requirement already satisfied: matplotlib>=3.2.2 in /usr/local/lib/python3.8/dist-packages (from ultralytics) (3.2.2)\n",
            "Requirement already satisfied: requests>=2.23.0 in /usr/local/lib/python3.8/dist-packages (from ultralytics) (2.25.1)\n",
            "Requirement already satisfied: torchvision>=0.8.1 in /usr/local/lib/python3.8/dist-packages (from ultralytics) (0.14.1+cu116)\n",
            "Requirement already satisfied: psutil in /usr/local/lib/python3.8/dist-packages (from ultralytics) (5.4.8)\n",
            "Requirement already satisfied: thop>=0.1.1 in /usr/local/lib/python3.8/dist-packages (from ultralytics) (0.1.1.post2209072238)\n",
            "Requirement already satisfied: opencv-python>=4.1.1 in /usr/local/lib/python3.8/dist-packages (from ultralytics) (4.6.0.66)\n",
            "Requirement already satisfied: PyYAML>=5.3.1 in /usr/local/lib/python3.8/dist-packages (from ultralytics) (6.0)\n",
            "Requirement already satisfied: ipython in /usr/local/lib/python3.8/dist-packages (from ultralytics) (7.9.0)\n",
            "Requirement already satisfied: pandas>=1.1.4 in /usr/local/lib/python3.8/dist-packages (from ultralytics) (1.3.5)\n",
            "Requirement already satisfied: scipy>=1.4.1 in /usr/local/lib/python3.8/dist-packages (from ultralytics) (1.7.3)\n",
            "Requirement already satisfied: GitPython>=3.1.24 in /usr/local/lib/python3.8/dist-packages (from ultralytics) (3.1.30)\n",
            "Requirement already satisfied: Pillow>=7.1.2 in /usr/local/lib/python3.8/dist-packages (from ultralytics) (7.1.2)\n",
            "Requirement already satisfied: torch>=1.7.0 in /usr/local/lib/python3.8/dist-packages (from ultralytics) (1.13.1+cu116)\n",
            "Requirement already satisfied: numpy>=1.18.5 in /usr/local/lib/python3.8/dist-packages (from ultralytics) (1.21.6)\n",
            "Requirement already satisfied: tqdm>=4.64.0 in /usr/local/lib/python3.8/dist-packages (from ultralytics) (4.64.1)\n",
            "Requirement already satisfied: tensorboard>=2.4.1 in /usr/local/lib/python3.8/dist-packages (from ultralytics) (2.9.1)\n",
            "Requirement already satisfied: seaborn>=0.11.0 in /usr/local/lib/python3.8/dist-packages (from ultralytics) (0.11.2)\n",
            "Requirement already satisfied: gitdb<5,>=4.0.1 in /usr/local/lib/python3.8/dist-packages (from GitPython>=3.1.24->ultralytics) (4.0.10)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.8/dist-packages (from matplotlib>=3.2.2->ultralytics) (2.8.2)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.8/dist-packages (from matplotlib>=3.2.2->ultralytics) (3.0.9)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.8/dist-packages (from matplotlib>=3.2.2->ultralytics) (0.11.0)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.8/dist-packages (from matplotlib>=3.2.2->ultralytics) (1.4.4)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.8/dist-packages (from pandas>=1.1.4->ultralytics) (2022.7)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.8/dist-packages (from requests>=2.23.0->ultralytics) (1.26.14)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.8/dist-packages (from requests>=2.23.0->ultralytics) (2022.12.7)\n",
            "Requirement already satisfied: chardet<5,>=3.0.2 in /usr/local/lib/python3.8/dist-packages (from requests>=2.23.0->ultralytics) (4.0.0)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.8/dist-packages (from requests>=2.23.0->ultralytics) (2.10)\n",
            "Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in /usr/local/lib/python3.8/dist-packages (from tensorboard>=2.4.1->ultralytics) (0.4.6)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.8/dist-packages (from tensorboard>=2.4.1->ultralytics) (3.4.1)\n",
            "Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in /usr/local/lib/python3.8/dist-packages (from tensorboard>=2.4.1->ultralytics) (1.8.1)\n",
            "Requirement already satisfied: setuptools>=41.0.0 in /usr/local/lib/python3.8/dist-packages (from tensorboard>=2.4.1->ultralytics) (57.4.0)\n",
            "Requirement already satisfied: grpcio>=1.24.3 in /usr/local/lib/python3.8/dist-packages (from tensorboard>=2.4.1->ultralytics) (1.51.1)\n",
            "Requirement already satisfied: absl-py>=0.4 in /usr/local/lib/python3.8/dist-packages (from tensorboard>=2.4.1->ultralytics) (1.3.0)\n",
            "Requirement already satisfied: protobuf<3.20,>=3.9.2 in /usr/local/lib/python3.8/dist-packages (from tensorboard>=2.4.1->ultralytics) (3.19.6)\n",
            "Requirement already satisfied: werkzeug>=1.0.1 in /usr/local/lib/python3.8/dist-packages (from tensorboard>=2.4.1->ultralytics) (1.0.1)\n",
            "Requirement already satisfied: google-auth<3,>=1.6.3 in /usr/local/lib/python3.8/dist-packages (from tensorboard>=2.4.1->ultralytics) (2.16.0)\n",
            "Requirement already satisfied: tensorboard-data-server<0.7.0,>=0.6.0 in /usr/local/lib/python3.8/dist-packages (from tensorboard>=2.4.1->ultralytics) (0.6.1)\n",
            "Requirement already satisfied: wheel>=0.26 in /usr/local/lib/python3.8/dist-packages (from tensorboard>=2.4.1->ultralytics) (0.38.4)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.8/dist-packages (from torch>=1.7.0->ultralytics) (4.4.0)\n",
            "Requirement already satisfied: pexpect in /usr/local/lib/python3.8/dist-packages (from ipython->ultralytics) (4.8.0)\n",
            "Requirement already satisfied: prompt-toolkit<2.1.0,>=2.0.0 in /usr/local/lib/python3.8/dist-packages (from ipython->ultralytics) (2.0.10)\n",
            "Requirement already satisfied: jedi>=0.10 in /usr/local/lib/python3.8/dist-packages (from ipython->ultralytics) (0.18.2)\n",
            "Requirement already satisfied: backcall in /usr/local/lib/python3.8/dist-packages (from ipython->ultralytics) (0.2.0)\n",
            "Requirement already satisfied: decorator in /usr/local/lib/python3.8/dist-packages (from ipython->ultralytics) (4.4.2)\n",
            "Requirement already satisfied: pickleshare in /usr/local/lib/python3.8/dist-packages (from ipython->ultralytics) (0.7.5)\n",
            "Requirement already satisfied: traitlets>=4.2 in /usr/local/lib/python3.8/dist-packages (from ipython->ultralytics) (5.7.1)\n",
            "Requirement already satisfied: pygments in /usr/local/lib/python3.8/dist-packages (from ipython->ultralytics) (2.6.1)\n",
            "Requirement already satisfied: smmap<6,>=3.0.1 in /usr/local/lib/python3.8/dist-packages (from gitdb<5,>=4.0.1->GitPython>=3.1.24->ultralytics) (5.0.0)\n",
            "Requirement already satisfied: rsa<5,>=3.1.4 in /usr/local/lib/python3.8/dist-packages (from google-auth<3,>=1.6.3->tensorboard>=2.4.1->ultralytics) (4.9)\n",
            "Requirement already satisfied: cachetools<6.0,>=2.0.0 in /usr/local/lib/python3.8/dist-packages (from google-auth<3,>=1.6.3->tensorboard>=2.4.1->ultralytics) (5.2.1)\n",
            "Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.8/dist-packages (from google-auth<3,>=1.6.3->tensorboard>=2.4.1->ultralytics) (0.2.8)\n",
            "Requirement already satisfied: six>=1.9.0 in /usr/local/lib/python3.8/dist-packages (from google-auth<3,>=1.6.3->tensorboard>=2.4.1->ultralytics) (1.15.0)\n",
            "Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.8/dist-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard>=2.4.1->ultralytics) (1.3.1)\n",
            "Requirement already satisfied: parso<0.9.0,>=0.8.0 in /usr/local/lib/python3.8/dist-packages (from jedi>=0.10->ipython->ultralytics) (0.8.3)\n",
            "Requirement already satisfied: importlib-metadata>=4.4 in /usr/local/lib/python3.8/dist-packages (from markdown>=2.6.8->tensorboard>=2.4.1->ultralytics) (6.0.0)\n",
            "Requirement already satisfied: wcwidth in /usr/local/lib/python3.8/dist-packages (from prompt-toolkit<2.1.0,>=2.0.0->ipython->ultralytics) (0.2.5)\n",
            "Requirement already satisfied: ptyprocess>=0.5 in /usr/local/lib/python3.8/dist-packages (from pexpect->ipython->ultralytics) (0.7.0)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.8/dist-packages (from importlib-metadata>=4.4->markdown>=2.6.8->tensorboard>=2.4.1->ultralytics) (3.11.0)\n",
            "Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in /usr/local/lib/python3.8/dist-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard>=2.4.1->ultralytics) (0.4.8)\n",
            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.8/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard>=2.4.1->ultralytics) (3.2.2)\n"
          ]
        }
      ],
      "source": [
        "\n",
        "# Pip install (recommended)\n",
        "\n",
        "!pip install ultralytics"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SRXQH_20gqeO"
      },
      "source": [
        "# Creating train-val split"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7s5w4e8igLvA",
        "outputId": "d98e86f8-96d3-4d1a-888f-87a0dc2f1235"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.8/dist-packages (4.64.1)\n"
          ]
        }
      ],
      "source": [
        "## importing required libraries\n",
        "import os\n",
        "import shutil\n",
        "import random\n",
        "\n",
        "!pip install tqdm --upgrade\n",
        "from tqdm.notebook import tqdm"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C24nycBsg037",
        "outputId": "c038254e-7323-4389-881b-b2a56d43271c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "## connecting to the google drive\n",
        "\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HOTmdh2Mg1j6"
      },
      "outputs": [],
      "source": [
        "train_path_img = \"./yolo_data/images/train/\"\n",
        "train_path_label = \"./yolo_data/labels/train/\"\n",
        "val_path_img = \"./yolo_data/images/val/\"\n",
        "val_path_label = \"./yolo_data/labels/val/\"\n",
        "test_path = \"./yolo_data/test\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 168,
          "referenced_widgets": [
            "c38599454bbf46c284d2178c2b68ceae",
            "d8c66eb856f14b739e20674e13e875f6",
            "d46aa2324a294f87af12554782930b61",
            "9c1c7452c51549e0b628a9384bf8fb70",
            "68032b6b2c23415f9ef24bcb88e830f5",
            "40ad9f58a2484a2bb4dc08a160b3798d",
            "c7c806f71b57436a896584369ef4cd04",
            "98bfacd3cba549b8b4649149427b6fa5",
            "ef1344eba2a747efb4b0f7a2a9de0c5a",
            "0a93342ffcbe4165bb0c877ebe21be95",
            "aa36cc1b3c5b4b73b301231021dbe433",
            "ad93ed404c5547829bd76b0719c13e1a",
            "c23655c8106f4dadb965d51e9aabbf82",
            "2902efe0a2b444ea80d94bdee7679ebd",
            "f67ee2ce7de544ffac158d2374dd4341",
            "70e426ffb395402ebd54a61dfcfc84c0",
            "5d8ba823187a4fb7bf30b1d5600427ed",
            "f038e2324de24e808a66a8bcfac38938",
            "ad2da6e9fad547258e9c4c42ece1a931",
            "b2e899109ad848349cc7b6021c318ccd",
            "8c05b31f6183473ba0359f381f005e3b",
            "9cbe155f6f7c46998a18f19548f82699"
          ]
        },
        "id": "ADx6NEpng6jo",
        "outputId": "d393c9ff-3eb1-4414-d8d4-5bfdb38c1cf2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "------ PROCESS STARTED -------\n",
            "--- This folder has a total number of 123 images---\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/99 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c38599454bbf46c284d2178c2b68ceae"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "------ Training data created with 80% split 99 images -------\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/24 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ad93ed404c5547829bd76b0719c13e1a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "------ Testing data created with a total of 24 images ----------\n",
            "------ TASK COMPLETED -------\n"
          ]
        }
      ],
      "source": [
        "'''\n",
        "Split the dataset into train and test and creates the train.txt and test.tx with\n",
        "the respective path of the images in each folder\n",
        "'''\n",
        "\n",
        "def train_test_split(path,neg_path=None, split = 0.2):\n",
        "    print(\"------ PROCESS STARTED -------\")\n",
        "\n",
        "\n",
        "    files = list(set([name[:-4] for name in os.listdir(path)])) ## removing duplicate names i.e. counting only number of images\n",
        "    \n",
        "\n",
        "    print (f\"--- This folder has a total number of {len(files)} images---\")\n",
        "    random.seed(42)\n",
        "    random.shuffle(files)\n",
        "\n",
        "    test_size = int(len(files) * split)\n",
        "    train_size = len(files) - test_size\n",
        "\n",
        "    ## creating required directories\n",
        "\n",
        "    os.makedirs(train_path_img, exist_ok = True)\n",
        "    os.makedirs(train_path_label, exist_ok = True)\n",
        "    os.makedirs(val_path_img, exist_ok = True)\n",
        "    os.makedirs(val_path_label, exist_ok = True)\n",
        "\n",
        "    \n",
        "    ### ----------- copying images to train folder\n",
        "    for filex in tqdm(files[:train_size]):\n",
        "      if filex == 'classes':\n",
        "          continue\n",
        "      shutil.copy2(path + filex + '.jpg',f\"{train_path_img}/\" + filex + '.jpg' )\n",
        "      shutil.copy2(path + filex + '.txt', f\"{train_path_label}/\" + filex + '.txt')\n",
        "        \n",
        "    \n",
        "\n",
        "    print(f\"------ Training data created with 80% split {len(files[:train_size])} images -------\")\n",
        "    \n",
        "    if neg_path:\n",
        "        neg_images = list(set([name[:-4] for name in os.listdir(neg_path)])) ## removing duplicate names i.e. counting only number of images\n",
        "        for filex in tqdm(neg_images):\n",
        "            shutil.copy2(neg_path+filex+ \".jpg\", f\"{train_path_img}/\" + filex + '.jpg')\n",
        "            \n",
        "        print(f\"------ Total  {len(neg_images)} negative images added to the training data -------\")\n",
        "    \n",
        "        print(f\"------ TOTAL Training data created with {len(files[:train_size]) + len(neg_images)} images -------\")\n",
        "    \n",
        "\n",
        "\n",
        "    ### copytin images to validation folder\n",
        "    for filex in tqdm(files[train_size:]):\n",
        "      if filex == 'classes':\n",
        "          continue\n",
        "      # print(\"running\")\n",
        "      shutil.copy2(path + filex + '.jpg', f\"{val_path_img}/\" + filex + '.jpg' )\n",
        "      shutil.copy2(path + filex + '.txt', f\"{val_path_label}/\" + filex + '.txt')\n",
        "\n",
        "    print(f\"------ Testing data created with a total of {len(files[train_size:])} images ----------\")\n",
        "    \n",
        "    print(\"------ TASK COMPLETED -------\")\n",
        "\n",
        "## spliting the data into train-test and creating train.txt and test.txt files\n",
        "# train_test_split('/content/drive/MyDrive/custom_notebooks/yolo_data/')\n",
        "\n",
        "### for label_tag\n",
        "train_test_split('/content/drive/MyDrive/yolov8/data/') ### without negative images\n",
        "# train_test_split('./data/','./negative_images/') ### if you want to feed negative images"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "XLtCmg1V9ofb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "![image.png](data:image/png;base64,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)"
      ],
      "metadata": {
        "id": "jdGfDvTK7mQr"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "udzAp2TzhSwZ",
        "outputId": "13983955-cbb3-4885-c553-3cd8338375fb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Ultralytics YOLOv8.0.16 🚀 Python-3.8.10 torch-1.13.1+cu116 CUDA:0 (Tesla T4, 15110MiB)\n",
            "Setup complete ✅ (2 CPUs, 12.7 GB RAM, 23.8/78.2 GB disk)\n"
          ]
        }
      ],
      "source": [
        "import ultralytics\n",
        "ultralytics.checks()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9mNsS_ouh7DM"
      },
      "source": [
        "# Command Line Interface:\n",
        "\n",
        "\n",
        "![image.png](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAA4kAAAG0CAYAAACfVWIPAAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAMYFSURBVHhe7J0FmBtV24bfrSv1lhapO5QKLaXF3d2KU9zd/UN+oHzoh7tDgTpFWlooNeqUuru7e/65z2bCbEg2yW623W2fe69cyU4mM2eOzfuc9z1nMkIeJoQQQgghhBBCeBQKvwshhBBCCCGEEBKJQgghhBBCCCH+QSJRCCGEEEIIIUQEiUQhhBBCCCGEEBEkEoUQQgghhBBCRJBIFEIIIYQQQggRQSJRCCGEEEIIIUQEiUQhhBBCCCGEEBEkEoUQQgghhBBCRJBIFEIIIYQQQggRQSJRCCGEEEIIIUQEiUQhhBBCCCGEEBEkEoUQQgghhBBCRJBIFEIIIYQQQggRQSJRCCGEEEIIIUQEiUQhhBBCCCGEEBF2ukjcsWOHjRo1yv7zn//Y6aefbnXq1HEvPrON79gnO15//XU79dRTbcaMGeEtqbFy5Uq78sor7ZFHHrFNmzaFtwohhBBCCCGE2KkicenSpfbggw/aeeedZ3369LFGjRrZfffd5158Zhvf3XnnnbZw4cLwr4QQQgghhBBC7Cx2mkicNWuW3XPPPU4IPv/88+69U6dOdsMNN7gXn3/99Vd76aWXnDfxiSeesGXLloV/LYQQQgghhBBiZ7BTROKGDRvs3XffdeGhb7zxhvMWlihRIvztPxQrVszOOusse+GFF+yvv/5yv9myZUv4WyGEEEIIIYQQec1OEYmDBg2ynj17WseOHa1t27aWkZER/iY2bdq0cUKyR48eNmXKlPBWIYQQQgghhBB5TZ6LRLyIhJbWr1/fjjvuuIQCEQoXLmxHH320rV+/3oYNGxbeKoQQQgghhBAir8lzkchiNZMmTbKWLVva3nvvHd6aGPZlMRvmMm7evDm8VQghhBBCCCFEXpLnIpHFZ8aNG2dNmjSxokWLhrcmpnr16vb+++/bvffe6+YqCiGEEEIIIYTIe/JcJM6bN8+9V61a1b0nS6FChaxcuXJWtmzZpEJUhRBCCCGEEELknp2ycA2ULFky/EkIIYQQQgghRH5lp4lEIYQQQgghhBD5H4lEIYQQQgghhBARJBKFEEIIIYQQQkTYaSJx48aN4U9CCCGEEEIIIfIreS4S9913X/e+ZMkS954sO3bssNWrV9vatWstFAqFtwohhBBCCCGEyEvyXCRWrlzZDjjgAJs+fbpt3749vDUxCxcutGuuucY6depkW7ZsCW8VQgghhBBCCJGX5LlIrFKlijVq1MiGDBliCxYsCG9NzKJFi2zSpEnut8WLFw9vFUIIIYQQQgiRl+S5SCxVqpQdf/zxNnXqVOvbt29SoaN4HPv372/ly5e35s2bh7cKIYQQQgghhMhrdsrCNe3bt7fTTz/dPvzwQxs2bFh4a3xGjBhh3bt3t5NPPtnq1asX3iqEEEIIIYQQIq/ZKSIRbyLzC/fee2+75557rFu3bjHnGbKtR48e9vDDD1uTJk3suuuus2LFioW/FUIIIYQQQgiR12SEduLSoSxG89///te6dOliderUsXbt2lmNGjXcdzNmzLBRo0a59/PPP9+JSeYzxuL111+3L774ws4++2wrV65ceGt8ypYt6/ZFrMLKlSvtzjvvdCuvPvLII1aiRAm3XQghhBBCCCH2dHaqSAQebTFmzBjr1auXDRgwwIlCaNGihQtLPe2001yIaaFC8Z2ciMSXX345/F9ijjjiCLd/hQoV3P8SiUIIIYQQQggRm50uEoUQQgghhBBC5F92ypxEIYQQQgghhBAFA4lEIYQQQgghhBARJBKFEEIIIYQQQkSQSBRCCCGEEEIIEUEiUQghhBBCCCFEBIlEIYQQQgghhBARJBKFEEIIIYQQQkSQSBRCCCGEEEIIEUEiUQghhBBCCCFEBIlEIYQQQgghhBARJBKFEEIIIYQQQkSQSBRCCCGEEEIIEUEiUQghhBBCCCFEBIlEIYQQQgghhBARJBKFEEIIIYQQQkSQSBRCCCGEEEIIEUEiUQghhBBCCCFEBIlEIYQQQgghhBARJBKFEEIIIYQQQkSQSBRCCCGEEEIIEUEiUQghhBBCCCFEBIlEIYQQQgghhBARJBKFEEIIIYQQQkSQSBRCCCGEEEIIEUEiUQghhBBCCCFEBIlEIYQQQgghhBARJBKFEEIIIYQQQkSQSBRCCCGEEEIIEUEiUQghhBBCCCFEBIlEIYQQQgghhBARJBKFEEIIIYQQQkSQSBRCCCGEEEIIEUEiUQghhBBCCCFEBIlEIYQQQgghhBARJBKFEEIIIYQQQkSQSBRCCCGEEEIIEUEiUQghhBBCCCFEBIlEIYQQQgghhBARJBKFEEIIIYQQQkTIVyJxxowZduqpp9rrr78e3iIKEsmWX/fu3a1OnTo2cuTI8Baznj17WuvWra1///7hLSK3bN++3d599107/PDDbcKECeGt+Y+lS5fa//3f/7l0Hnjggfbjjz+Gv8lfrFy50q688kp75JFHbNOmTeGtIqfkl/pZkO47s2bNsnPOOceef/5527p1a3hr/iZWu1mxYoXdeOONdtttt9m6devctuygbCgjympPINX8iUdB6Vvzglh2RrLIFk0e2W6xod5R/6iHBZk8E4n+jYFMSvRiP/YXey6lSpWycuXK2V577RXesnOYNGmSnXDCCXbPPffEvRnH22fDhg3Wo0cPu/TSS109btmypd1yyy02ZMgQ27FjR3ivf0j1ppXq8aMpVKiQlShRwqpWreryNyfk5kabDFxjp06d7Msvv7TDDjvMbr75ZmvcuHH4290LicyspKN+5kfyss0ULVrU5VmFChVc/hVUChcu7K6jYsWKVqRIkfDWvCOv+7F0k4782ZP6VrHr2FW2W04oaP1AfiDP7jLFixe3448/3u67777I66yzznLfXX755Vm2sx/7iz2XY4891vr27WutWrUKb9k51K9f384++2z7+eefnfiKZsuWLdalSxdbs2aNXXDBBVamTBm3fdmyZa7u3nHHHe7/O++80y655BKbPn26e3/ppZfcTTqnpOP4GRkZrq19//33VqtWrfDW/MWiRYts/Pjx1rFjR3v66afthhtuyLdpFemlINTP/MY+++zjjP7rrrvOCYmCCkblyy+/bE888YQTQyIr6cgf9a1iZ7CrbDexc8gzkcjoAsYsHZP/OvLII913p59+epbt7Lc7jSSLggOGFvWxUaNGbpRp9erV4W8ymTx5sgvROeOMM6x58+ZuG16g//3vf/bXX3+5908//dRuvfVWu/vuu90x7r33XnvzzTedFzAUCrnfpEJeHz8/gXdt4sSJbnSvIBu9QgiRn1DfKoTILfk2XoU5KoTWEUdPPP0777xj69evD3/7DwsXLnSjZH7MPaLzzz//TMp4xhj/4osv3BwPOtLjjjvO3n77bVu1alV4j3+YN29e5DzsSwgg3ifm1fhwPELJrr76apd+f3/CBB966CGXVjxTxHD75+Sd/4PH8ePhEQJcC9fEtZG+jz/+2J2HOQv//e9/I9dNXsWa17N48WInNPgt50MQffTRR1m8UH66CYWbOXPmv46L9yoaroVr4tp4PfXUU7ZgwYLwt6kTHQaQkzRFlyfv/M/27GB0/txzz7XevXvb0KFDw1szj/fNN99YyZIl3bGKFSvmtiMcf/rpJ7v44ovtxBNPzBL2xT6XXXaZ2/+HH35wc0JSJZ3Hj57LE5xrEd3GXn311UgboxzIQzyYcP7552cpH6DO0gb8cNhY7SdYjiNGjHDzbDgf9ZrtHBc4D8dgm19eHH/AgAGR+k89u/32223cuHEx2zf5hveV/dg/Xl+QTLpTJXhuXnxmG/h5wEgr14MnqEmTJlmu1e8XOnTo4NJEedB/0M7iwW/+85//uFDoqVOnhrdmsmTJEldPGFjwy5T9qVf+dcc7R3bzcZKdG5bs9cSrn7nt+zgu10++JNNPBmFwpm3bts6TE+yXoXPnzu56Bg4cGN7yD4najH+t9DH0naSnV69e7rtk63qssvHP261bNxcNEazX1DXKIlnohzl/sJ0DaeA+Qr8zd+7c8NbM9ATvi5Q35Z7onPFCr8kHfs95ON6FF17o2mqs+ZfJ5FmiMoHc9gfJ2AaQXflHEyt/gmWfXd/t9zfZ9a2QXZ8F/nGi++7Ro0eH98hKutouJNtXAWUdrPcnn3yyu+9v3LgxvEdWSEdO7ATAbsJ+wo7it3lRV4L5nqzt4+O3U8qB8gjiH5dy9AfDk2lD4Lejb7/91uUV6Xnsscds8+bN/7LdfJItQ//YHCdIMB9oDz45KYNk+oFkbGUfyvHhhx/Othxjwfeklbymv/anDSV7v9wV5EuRSMXCuMF4J47+oIMOchP18agEC2HatGnue96p+A8++KCrWNdcc03czteHgn/22Wftueees4YNG9prr71mJ510kltI4f7773fhfj5jx46166+/3gYNGuQqA422fPny7pzvvffevyrGsGHD3P6EU+ElxdCnAlDhmUT+wQcf2NFHH+0qLHNMuGEgUKKhgnLdTZs2defad999nVHINrxJdCAcHzGBYcPxWdjAx88XQgGuuuoql394c7nJvPDCC//qFOl8mCg/Z84cd9yLLrrIBg8e7EJeMDp95s+f78qHcjrllFNc6BP5yZw9fptOkk2TX57k2VFHHeXKk3KlfHlFX2sQyomQZzoHQt+WL1/utlPu5B3hqISl+vgdCw051ggtIant2rVz9WXKlCnhrcmT18eHWG0MQ4P6T32mo8LA5IYBDALwP9uBfaj7tDk6PH5LHvJ7tmEMBGFe5wMPPOA6V25ECG/qLccFzsPx2Uab8I9P2qpUqeLK89FHH3U3N+r0qFGj3O98MEpo9+QHN9LHH3880hfQ9vwbXqrpTgb/3NRB0sj5SQfbKEuuh+vivHijEXWffPJJlmv98MMPXT9AOBjXyk3st99+c9cfbNNBGDA45phjXDuPNtww5MeMGWOHHHKIlS5d2uUFx6XfqFSp0r/OwTHSRU6vJ0hu+z7g+hGK0X0H/QlGUDzq1atn7du3d3XM7wuAPKRP4LsGDRqEt/5DojYDHI+0co+gHTDNItW6Ho/PPvvM9YEtWrRwfTF5xrkwRpKFNNHXYXQHjUQMMOoy9bdatWpuG/X+iiuucHXnrrvucvc18o5yRxRwXanA+TBA+T3tgvsjc+kYIP7qq6/Ce2WSbJ6lux+LJlXbIFb5p0qivtvvb+L1rZCozwoS3Xf7x4hHbttuqn1Vv3793HbylneM+z59+tgrr7wS3uMfcmMn8Fv2wX6iXuZ1XUnW9gmCLYPdQF78/fff4a2ZMIjPfeLggw928weTbUNBsCFJ9/777+/SHo+8ut/ktAwS9QOkh2tOxlZmX8qFUG72DZYjfWB0OfrQvzGw/8Ybb0TqKQ4A9s/t/TJP8RK+0/BuVqHatWuHRowYEd6SFa9RhDzREfKM8tDkyZPDW0OhtWvXhrxOMeQ1rJBX+bNs8zqv0Lp169w2WL9+fcirLG7fxYsXh7f+G9JywAEHhLp37x7y1Hx4ayjkNcKQZ1iFvvnmG/e/d3MMeQUVuuCCC0KzZ89222Dbtm2ht956yx3Da/Ru28aNG0MPP/yw+z3H8eH477//vrv2jh07hrwGHv4mFFq4cGHI6yxD3k02ch1+PkTvS1puuOEGdxyv8ro0+AwYMMClxbvJhreEQp9//nnI65Sy5CVpYTtp9DoRt81PN7/v2rVryKu0bjv7ejdnd74//vjDbeOcL730UsxrJC85hlfJw1tjE6seRG9LJU3+NvLMu6G5bcB2fhsso+z45Zdf3L6kxa9f5513XsgzuMN7hEJeZxHyOtJQhw4dQosWLQpv/TdcB2nkWD6xrjua3Bw/FpQF+UKdgkRtLPq88dJMffM66dDAgQNdPvtEt5945egT7zrIc68jdm0sWM9pL7RFr5MPbdmyxW1btmxZ6Nprrw1dd911oaVLl7ptEKsvSDbdsfBuQCHPKHbXw3WB337J582bN7ttQLul/Xo3SZcOiPV78G4CIe+GEfJuflmu1U+Td+MJb/k3XBfXFzwP10V/4908Q57h57b5ddsz3rOcY+rUqa4uBH/v15FY7Ti6PsUileuJVz9z2/dx3Fh1bujQoa78n3zyyUh5xbpe6gG/D/YbnrHm8or+L3j+aOK1GY7P9jfffDNLXUmlrsdKq9+GqP/8xifWvSURpIu8Ia/Jcx9PcLuy8wSK+5/9PCPcHTvYX5B+yp1+0xMVbluseh9rG3WVOkub9esi0Ka5tmA9SSXPILf9WCxSsQ0gXvnHIlb++GWfbN8dr29Nts/ivJw/VjuKRbrabip9ld//RZ+T77mPBsuc8k3WTojVzjzR5PbBfvLriv9bzuMJU7ctFqnUlXj57qefc/m2Tyz8/A72cUDbpU7TliGVNhSvj4FYbSuVMoxXT/18oB3QHiA3ZQDx+oFkbWWg/R599NGhcePGhbf8Y2sE+73o6/LvPdHtPzf3/51BvvQkouCDnhs8JzVr1nSjBF4DcNtQ14T84FZnpNyHuY1exrowI6/TCG/NCqMCw4cPdyMRnIvRFx9GE/H8eQ3BuYAZNfj9999dOCKjJz54eDg36WQkKxgOw/w2Xj4c35/PxiRfRm18GIHwKpIb+eF8QfhNcF8msxOmxmgDXq+gl4lRRdLHcXwYfcK1HRz1Ji2kjbzxKmp4ayac74gjjoiEN7KvXw7+cVnAxR9Nb9asmdsG7Os13ix5lA6SSRMrjnqdphsZZCTbh31JI2liNDQRhx56qCv77777znl2CR+g3MlbH6/NuBABvDh++Gl2pOpZzevj+8RrY4wieh1eeGtsqOu0iTZt2rjyIZ99/LrPqHSwTUSXYyIYfWaU0LvZZanntBfSTRv2bp5uG30Bng/ChipXruy2AX0B0QGsDkhYbk7SnQg8UvQVjGYGy4t2yzkYNQ+GysSC6+BFPxbMH9JJHWQEOR6M0jK6j9cMDz/QHhgF5vzUXfKK6+Z/QrGC+Vm3bl3Xj3nG2r/Ck3JKbq7Hh7Tmpu8DttH+g2nAy0ZZMcqeXag2+1WvXt15XPx65hkQ7r7C/SV4/lTAa4EnOVhXUqnr2UHftffee4f/y/7eEg/SxfXhcfCMyPDWzOiG/fbbz/XxwH6MyjOK7nsWgfTTjhjxj76/JIKwQ+63nnGeZY0C2rR/Xp905Flu+4Oc2Aaxyj9VctN3Q6p9FttS6btz03ZT7auoo/R90XYV9Sd6IZXc2gncl8lf8tuvK7yTJtoH9mQ8clJXovOdc/nlHt3XBcFLiLcw2MdxXGxe8sT3nuWkDUX3MbHIy/tNbsogO1KxlT0h5yKhePlQ3/CIv//++zHzh/LHk04EoSd6s7Q78phXbu6XeUlyrX4nQ0b5FSAe06dPd4XH6nhU+uCLMJVYIshn48aNLnSCDip4MwIq35NPPulugBQk+0GwU/ahU6Lzo1PlppgMQUGbU6icyd5kCDEgzhrXtp8/uLJjQWNOlO/cPMhbOvx0XEsikkkTHSFC4a233nLlFKwL3JSYvJ/MzZOyv+CCC5whSHgNN2M6uUTnL4gk08biwc2WTq9r165uUCWY3/68u+gbTDLlGA2GLTcbQqEQQhyf9kaYSBDaKHUxKOZ9uMkSWkToU07SnQjqFnUPwzZ4PF7MaUvGWK5Ro4Z7zhRhdffee68zZGhnlBEGVnQfFYR85Rox+vyQU8Qi6cJQ4Ld+f4dg4HhBKBNu5rRpriMd5OZ6EpFK3xcLfovgwBjkmuOBIOImjdjGIKNOkL/kVdDATJV47S7Zup4dQWMsN5A/XD8hkbB27VpngBHWyONKfBjQoj0988wzrp/1670/7ydVGPDiEQ3Un2TIbZ7ltj/IiW2Qm37XJ7fHSLXPyknfHYtk2m6qfRX7cm9HfCcit3YCaUIkYR8SrkiboG0QfssjabJbhTYndSWn+c5vGOhBQHNNQJ4R2ku9Llu2rNsGqbahZPqYvLzf5KYMEpGsrcxUH5wlhIJiW9CHIMK5r3G90XnEvZlH0bAdQR59/8vL+2U6yJciMVkYkWEkkwod60UhZwcFkCi+nhsX54mu7EG4iTDCkd+g8tL4R4wYYTfddJP98ssvrlFRsXdHuMZY9YAXYi8Z6MC4ecJ5550Xs5Oj8dK58kpEcNQwGfL6+OmEDjRWXvNivkmitpUdtCni9DE4Gd2jA8VoHTx4sCuXaPCoJduZpjvdjEKTvljH42YW9LTEghsb8xG4kXBjZ6SRm/m1117rPFkY49mBaGEEFQ8/Ri3CBoMMkQjccDCQMEaymwOVijjOjtxeT17DzRpjMLs+m2tgbg9eCq6BOeqIRIyvoMciHaRa1/MahCCC0DfA/EEHFvMJtg3SyJwcDL6HH37YGZv8hjlfeU068yyn/UFBtg1y22flFTujr8qpncAzK7E37777blfPqDcIBub/EmWQHTu7ruBZps9lkSQEDAKUfoyBL5+86nfysgxzUwbZkYqtTL5+9dVX7rzMWSQygLUBWLDJj3QMwpxGbLlFixa540ff//L7/bJAi0S/4XHzivViZCE7CM8IuvdjwfE5D/tGQ+HlprPKS7iur7/+2hmLTPSl88OYJE/wmO2O0CHFqge8kh39Z6TTrzfBETcfzoEXlVAJOsJ4zJ49270jXlIhr4+fTjCeENWx8pvRyGRGHeOBUcriF6y4x+M/GGkj7IxQmFij0Xh7kvXmpzvdtDXCaGIdD6GWjMedayI09ptvvnEjvp9//rnbzkISTKbPDj+8CBHDzQ5hQ7ik75GhPnPDpj7F8mrmRR+Wm+vJaxCI0aE9saAuMPJPKDPRBbwISc/J6H52pFrX8xraByF5pAuByOADIVR423wQjyzugGjGk0gYFd5H6hr3nLwmnXmW0/6goNoGkI4+Ky/YGX1VbuwEBiIJTWQ6CiLixRdfdH0u/Vr0QjFBdnZdwXZhShAh3AxwEWrK/3jifPKq38nrMsxpGcQjJ7Yyobos8kO+EnFAegg1JeQ0uowvvfRSF9bLSs14KmOJ2fx8vyywItE3jjGIUlXaVAYKmRtgtGHJSDzL/LIiIpWH/QADIRoaHwVOhcpvwovrYvSIToFGH4RwgJxCw2GkmRGPZI3yvIYbGiN0dIixOuF0wygPEBYQq8MjjIlRrnirICYir4+fW+jQaH/kd3ZCNjcQioKHAiM9aJSTH9H1lzaK4e/PyQvCjZC5BhwvL9LN8ThHrP4hWWin3GwwvIEbPKOULIGNkUo9yC5klfzBWEcos6ozq67hBfNDb/z+jvNEtw/6Tm6stGmMxnSQ2+vJSxjRxaBgrkkwdDIWCCNCsBj9ZUVJjOdkB5tSIZW6vrMgLVz/oEGDnEhk0AED0of1AbgH4LGIvvdlF7KXHb4hzYh7ItKRZ7ntDwqqbZCOPiuvSLWvYl/u/0w/SkRu7AQ8fISoE6WBjUidwxYi9JFBEgYaYj0Wx2dX1BX6LtoS3ijaMHZFUPznVb+TV/eb3JZBPFKxldkXYcpcZa6FASSulVVr8W4STRE915J8ZyAXkchAGoKR/tMnP98vocCKROYuEEfepUsXV2BB+B83OhUpFhhPjJpQoSjUoMhExb/22muuclDpaLjMTeMZLsHnANKQWM6WRo+rmX3zE4yWsfACHWLwpsvnzz77LPxf6tCICG1j9IS88iEPmRDPTX5ng/GCUcwkX0ZdgiEbGCzMQYglIHIK8x8Y9SE8hXMGz4cRSv5SL6kXjKilSl4fPydQ33240TAPjgGabt26ZQmL5TMehuweMZAM1F3Og4HuXz/vLG1OKEgQPK94eMgTbrg+dOiEhWDoMvqYF+lm9Jm+qHPnzlnaGe0BDxTnCeYd8H+wz8HA4Qby66+/ZtnOIBUvjNlEXi9ucAwasBQ4bZ+biw/9HX0Y1x3dPjg3/Rj54ofnY0QiEKKNKfI2mfxJx/WkA/oi0htMA95W6hBesURthz4djxp9HW0RkcjNPlmiyz0eqdT13MI9kXAwyiE76FO5R3IfZcSe9hX0qJEP1BMGJDieD/deFv7KCRi1GKwMdAQHIGPVu5zmWTr7sYJqG+Skz9pZpNpXUQbYI9wng0LfN+aD5MZOoL9iYJZQRH/utw8LmXA++t147Iq6wqALfRaPAuG6aF9B8qrfSbUMKRfmQWNPBtsg8xq5B/nktgyCBOt3KrYyv6NPZBGaoK0B9K3kZ7wy5H7D4+LoU2h7fhryy/0yHkXC7wUOblC4YonlZQLpGWec4YxrKiYuW1Y7yq6jY2UvOhGeOcM7yp3Co4NkPgYNFhh5omB5BguVk/NQoajo3Dxx1TOKn9/wGyqdEs9vQVDTAEmzX4lphL7XKlkYvTnzzDNd7Dr5zmpd/sgkjXxX3BAxXohNx2ChPOn06ISYMNyjRw/XwFj9LF2Qt8Sv00nwTt2hDpC/CBLqIHN1aPjBETofvNSMHEVDZ8CkaTqT3Bw/nTDax6gf8fbkK/M1EWW0H9LAQgeMghKXj4eTmwudHnH4wQ4vVfybKuEZdN60bUYQaaN0xNRdBnIoC+rf1Vdf7eYl+H0BopA2ym+YY+MLgmTTnWy+Is6YjM5zGXn3+weEBcYufQb9EHWUUWKuCzHLZ+YFMRhA+RKSQvgKfRF9D4YEEQ14Bwl/SdSuyBPqCItwEDoXPacI44i5DjynlZFc2gchyxj0GPz0cf6cTv7H8GJhANLNDRwjihs+eYaBmR3puJ50wfw42hp1CQ8i10tdYK4Z7S0ReOoR3xhxGFzJ1It4bSYeqdT13IKBzBwkyiK7vp9ypz5hEFGfoiMWGCykDlFHEInUFx5GTR0hnQj0oHhMBkbkeXA28xupL8yf4rp5Xhij7OSFT6p5ltt+LFa5F1TbINk+a1eRSl9FGVBn6PuZw+Xf53EARHuHc2snkE945bjH0H8wiILdQ7oI7fdtxljsirpCHtFnkT7mGCIag+Rlv5NKGZIPDFwwoMv5iE6g7yBfELf81ic3ZQDx+gHyIRlbGS8fi2X69Y36xHlZmRbtgD1MvsaD8iA0lTBfdAaDb/npfhmLAutJBG4iZDajvWQoBYwBjVFIhgdvKtFQQRGYuIipxPwWA5xOkxtDMLTGn+RNATORlQrKBFVuoFQUOp/8CJUdzwJGNA/wxFjiIaDEcNNQCBfKiSFP3tDJnn322e7h85yDbTQ88mpXwPkpN1Yl5WZPGTH6T+MnVjzdYZn++fwH9mJk8CBoOgzeMXT8TjAaRqj5bfSLvPRDDnJz/HSCIKDOYDCRj75niXPff//9rsxpC3fccYcbiWWFUTr73M7d4qZKuAUGADcuzs2kdfLutNNOcwZA0AjlBss+3GCY2I7xyv7kFfv7aUl3utmX43Me6hg3XG74jEIS5koYii9G6Oi5GZBWHrpLX8UoK2mi3vJCyCB4+S0hkVxTsgM5iDkMAY4fLYC40VOO/uR42gdpJlSH6w/e2LimDh062GOPPebCe6iHjNySV/w+Eem6ntxCXrCwAIKGewJ9IGmgXmV3Iw9C30m+Em6ZndALEq/NxCPVur6zII8QiHggyIcg1BGMJUKnGO2mfyJ8jbAvDGHAsEoFjsngLvcnjEXeGYxkoIw5U0FSzbO86scKom2QSp+1K0ilrwLsQOoK90zqIcY9YoJ7QDS5sRP4LaIHgUMEGoMLiBPm49K3JOofdkVdod8ivzgndT1IXvY7qZQhdQ3hTDoQ9+QlgpIypT0GyW0ZxOsHUrGVqW/sy72E9sL9jfucf8/LTlRTxgh2BOJLL73krjO/3C/jkeFdeM6H+4UQQuQLCGFhsIyBhViPA9mTQBwyj5D3RKtcZwdzdhhIxAjp2LFjeGvBBYMYYy23+SKEEGL3p0B7EoUQQpgbkWZklRFjRj5F7mH8FA8qIU+EQxV0GDX3F7zyF9IQQggh4iGRKIQQBRRC/PAKESLGCnbMsdiV8xd2Bwif7Nmzp5uyQOgkoUjJhqfmZwjhZE7NBRdcsEvDCYUQQhQMJBKFEKKAgnfIf+Av88OYPydyB/NEmS/K3KbLLrvMbr/99t1CVLFiLfN5dgfBK4QQIu/RnEQhhBBCCCGEEBHkSRRCCCGEEEIIEUEiUQghhBBCCCFEBIlEIYQQQgghhBARJBKFEEIIIYQQQkSQSBRCCCGEEEIIEUEiUQghhBBCCCFEBIlEIYQQQgghhBARJBKFEEIIIYQQQkSQSBRCCCGEEEIIEUEiUQghhBBCCCFEBIlEIYQQQgghhBARJBKFEEIIIYQQQkTY7URi9+7drU6dOjZy5MjwloJDz549rXXr1ta/f//wFrG7sWLFCrvxxhvttttus3Xr1oW3ij2VWbNm2TnnnGPPP/+8bd26Nbx156H6mF5mzJhhp556qr3++uvhLaIgsavbow/1h3pEfdoTib7+lStX2pVXXmmPPPKIbdq0yW3LD+zMclq6dKn93//9nx1++OF24IEH2o8//hj+pmCivrJgIE9iPqJUqVJWrlw522uvvcJbCiYYnm+//badfvrpTrC3bNnS7rvvPhszZoyFQqHwXpmkIup3hxtn4cKFrUSJElaxYkUrUqRIeKvYmeQng6No0aKuPlSoUMEKFcq77jjeDVn1sWCxfft2e/HFF52R+Mcff4S3ZiWZfURsdlZ7FHnH7mAnRLNhwwbr1KmTffnll3bYYYfZzTffbI0bNw5/m7+RGCzYqBfMRxx77LHWt29fa9WqVXhLwWPcuHF2zTXX2LvvvmsHHXSQE4eXXHKJ/f3333bZZZfZZ5995oyYPRUGAV5++WV74oknnDEi9mz22Wcfd+O/7rrrnGDb2ag+FiyoI3i69t9/f/vmm29s9erV4W/+YcKECW7w7cwzz3SRKSJ5dnV7FCIWixYtsvHjx1vHjh3t6aefthtuuMFq1aoV/laIvEMiUaQNwiEwOOHrr7+OdGZ33323fffdd3bWWWfZe++954wYIYQQqVO7dm0777zz7Pfff7dhw4aFt2ayZcsWN20BLrzwQgl/IXYDiH6ZOHGii7rS4IXYmeS5SMRrNGDAACcWCH8h9PD22293Hqdg6GHQJY2IuOWWW9z+xF+/+uqrtn79+vCemfDbIUOG2KWXXuoazsknn2xffPGFbdy4MbxHbPjd//73v5jhCISeEYLGHJ3gCO3kyZOdR4y0+6GTbPPxf0cI24gRI9zvSfvo0aPd9/6Nu0OHDi6tXBMCauHChe57n3ihl4QacG2MIPP9cccd58I5V61aFd4jE37H9926dcuSN+zP6CjpCMJxP/roo0hYaLzjJgtzKXldf/311qBBg/DWTEqXLm0XXXSRC+fBW5pX3sREZQHkO/nvx/ZTN//8889/hcKSD//9738j+1EnR40a5Y4fDFXMLpwiOvQlVqijX27ffvutK2fO99hjj9nmzZvd9+TVzz//nKU8Y5VTsvUsmmCezZw581/XPH369PCe/5BsnYwHeU2eB/uF6HaVSr4Cx6RfoX/heNFl618nnnr6JNpEkyZNspQF+3Ez9tt7vPrhH+vqq692/ZVfn/jNQw895PLcLw8/j3jn/2Ddj75Gv36wf6wX37GPz+LFi11/Rv7zPW2ZNk35gH88vue6GMRhv+jzBfMAUs2HVOpOLJKtT365Dx061OUz5+nVq1f429hwDI7l5xHn4FzB64Vkr9mHMiYN7MvrqaeesgULFoS/zUqybTgZMjIy3P2uWbNm1qVLlyz3KkL6e/To4byI1G0f6uJPP/0UOX+8viHVNheP4D0zu3xMVH8hHXWM82bXN/jEun4+n3HGGe4e4tcj/9y0/R07dtigQYOylO3HH3+cpX75x2WwNNG+0cydO9fVWfIzmC8wePBglxb6snjEazPBfA32KeDfk7BJIB1lAOSD31dyfO5VPb0+Mdo2yQm+/UQfRzsmb6Pra7LnT7a+xGLZsmXOBqLMpkyZEt6aM3vLz/fzzz/f/X/nnXe637KN7/x6hY1M/0K/wD3J7xOSbfdB2zHYTwXvWfPmzcvS3/n3uXgkuvcEibb533nnnX/Z/MD5/PJLpUxEzin8BHFGeQQVC88Rp8Awu+mmm+yQQw6xv/76y4UdEo5Yo0YNty8Viom406ZNc5WUCnDEEUe4eQGff/65my/Tpk2byDyBfv362R133OH+R3w0bdrUzb+g86NyXXDBBZFjB+EGW7x4cevcubMTMo0aNQp/YzZnzhz78MMPXdhn27Zt3b5UQCpvpUqVXIXnxkzHTKiPn/5t27Y5o5NRXd4Rqn6D5HcffPCBPffccy79dB78hmv89ddfXX6UL1/enZ8bK9uDaSdfMD588UAHwJzFTz/91DWsQw891M1lBBoQQmPJkiX222+/ue+OPPJIW7t2reuc9t13X5dPQIdFmsjbU045xS6//PLIcSdNmmTt2rWzkiVLun2TAUHz/fffOxHoHysa8p2FAdiH+sB7rGuOB/lL/SC9zBmJRXZlUbVqVfd7OlrqCPWG7aSBTmm//fazhg0buuMwr5JOEIOLTo5Ok/qMQUOHV69ePVc/qZd+3WUb5RkkOs107Fwv+eP/3i833vmO45Bm6gt1kDb0wgsv2DHHHBO3nEhbsvUsGj/PMIIQ+bQpzlW3bl375ZdfXD1jHgRCH1Kpk7GgQ+fGQ/ulTGhX/Ib2S7uijlJXU8lXfxs3dc5NaHN02dLWyZOjjz7aGV60ZcQ4xyYdXHefPn3s3nvvdYbfVVddleUYpIk+gzLx84x+iEGP+vXru+NUrlzZ9UH8hhDr3r17u7zk+ubPn+887IQK+vUs+hrJG27shAm2b98+8uIaMThoz6SfesP1k4cYaIR0k162Uw8wVOjDaHPUJc5Pmk466SRXr+m7mIcYqz5CqvmQbN2JRSr1iTKmTx44cKDLT/IRg4XzxYJ8ePDBB119w9Cn/bAgyfvvv+++I8/phyDZawbOfc899zjDmzpImXA8wux5p3/z66x/H0zUhlOhTJkyrqzeeOMNV695UZacBwOR6A3uPcB2DDPmNNG2uJ8F+wbSSl2AVNtcLPx7Jn0v7fDEE0905Ridj8nUX/5PRx1L1DfEa4/Ab8knzsV1U17+uRk0xHjm/ko7pX1y3Qi/smXLWvPmzV16/ePyG4Q8ddq/N1MPqCO0ed9LFMxr0sc5sDtIt1+u9BXcnxiYuPbaayPbo4nXZmrWrOnydc2aNa5fCNZB/57EdupWOsqANGADAmWNp5u6Sh2mn/LzCqLrWrx+Kgj5ffzxx7v2zCJcjz/+uGvzhGUWK1Ys5fMnU1+i04ldhYAePny4/ec//3H9LOTU3qKd0FaZf0i+kyZeBx98sLtnkX7qFfnP99x/aGOUBSTb7v3ypnwJbaXtUf8RoQxsUKfpW6hj2MfVqlVz56Xtxks7dTm7e4/fJsg/0hS0+Wk/9HHUU79M2C8Z202kGa+jyTM8Qyx02223hd56662Q18mEt4ZCXoUMeaIg5BkGIc8gctu8yhbyGk/o7LPPDnkF77aB14mGvBteqEOHDiGv8rptixcvDnlGe6hjx44hTxC5beBVntCjjz4aql27dmjEiBHhrf9m1apVoRtuuCH05JNPhjxxE94aCv30008hr2GEvE7c/U86L7744tBrr72WZT/Oybk9g8Kd02tAoYcffjh0wAEHhLp27RryOvzwnqGQJ4pCp59+esjrILLkgdfhu3N5N8TwllCoW7duWdLuGSshT7S645I2/vfxjJOQ1xGEXnrppchx+R2/v+6661zaffzruOuuu0Je5+m2eTc3d1yv04ocl3fSzzE8g8ltSxZPVIWuuOKKkNcJh7ybTnhrYqKvOTsoB+oIdSUe2ZWFX5ceeOCBSD4AZegZkq5OUbfAEysuXRwjmO8jR44MeTcidw7OBX7dJX3RRKfZz6fg7+OVG3g3ZlfO3g0+Szr8+kM6IZV6Fk28PON8X331lUubJ+Ai21Kpk7Hw84u2St77RLerVPLVMyLc8ehXgnnoHzNYL2OVAdBfnXfeea7PWr58eXjrP/0K5e4JNbfNzzPyljz2IT88AeLyLLp/itUOs7tGn6VLl7q6wW+D10bbvf7667P0l5yf7aTLE6nhrfHPEysvcpIPydSdWKRan0g/x3zzzTez9Mmx4DfsxzE4lg/nIK2cc9CgQW5bKtfMcUlTrLLv3r27O24wn5Ntw6nCfezmm28OeQIh5Ikqdx7OTX4Gz+MZ8W67Z3hlaZdTp05191u/vUEqbS4WpIP0UF+ptz4cP7qPTbb+5raOpdI3xLp+v85F3wuor2xnf/LSJ5bN4h83uo/36xfX69sdEJ3X1FOuP1hXfDsmWH6x8NMf3Wb8fKX90w8E8e9J3J8ht2XAeV955RXX9/l5AtRH7lm0PU8Ih7f++/rj9dmxiP4tpHL+VOpL8FwcC1uXtk7bDtaV3Npb0eXh49eraLsZUmn3/vFpo8G6NGfOHLcv3wXrf7LlDn4ayasg8dLutx/6Cr8vTsV2E+klT8NNGdHADY5LOBhHjUeDkXdGh7zOJrw1E0bX+M6H0QRGvBgx8SqE24YXgNE4RjSqVKnitgGjPoyOJIIRHEZiGOlnHh0wuszoD79n9AMIN/A6CTc6xUiUD+dk1ImwC0ZDfNjmj4T4cH28GGELbsfbw+gJIyLxIAyB0TtCCBg18UexoUWLFi5dXmfkRn6CMHK79957h//LzG+vMZvX4CJhFV4jd/lJ/vrH5Z3QBUIPvI7ObUsVRtP8kfldSayywIvJaCrXGBzxpN54N2nzOk/nhSVfGHVkZJhRsmC+k4+MxuUF0eVGnWTeEXWF6wmmw/ce4F1iv9zUM5/oPON8fluk7kBO62QQ2h3HO/fccyMeIqBdnXDCCc4r7RkF4a3J4fVlrk5zPEaFfRhZ9YwDe/bZZ7Md5QbqPaO6jDD7I6zAMUkrI+7eDTG8NRO/HHzID/IRovunWO0wEZQpo6qMFPOYimD9wPtCuBKeGR/OT3qox+RjTshJPiRTd2KRk/rEiDH1JNgnx4J0MtLPMTiWD+egvRMhQB8OqVwzn8eOHev6B7zRPhzXM8qcp9gnlTacKiw6RHqZWkDYKeF2pIe89M/DPZbzc262B+/DeIHoC8n/YEhebqCPJT2EqdH2fMhHPAl4gPx7bqr1N6d1LB19A54c8jZYfpyb7UcddZRr1z6xbBYf6kewDfv1C6iD8cCzyfV7xnwk5BQ7CM8PtgzHyY5k20wicloGnBcPGF42vFA+1EfKm2vKaX+VDKmcP6f15YcffnBecE98RSLRfPLK3vKJtptz2u7pF4N1ifsXYetcDx7BYNqTKfdkiGfzYwNwf4BkbTeRfv6xJvMIjCEqK65uXMcIMCpdvBh6Op9g44oFoRc0uJy6lzk+FYtOltBBoIIRBotIJGwB+I7Kecopp7h0B1/EV0d3bDTE6LQjKAgjwSVO54GhgbDkOrnJZ9e5sx/p4gYUvR+dHm57DJvoxhHsEOLBMRElTz75pAv1oqNau3atE3gIvYK+4EGsspg+fbrLK0I9osuTMAa+ozyps3R8dFQMKOwsosuNkBlCLLp27epCMYLppZ7SyfsDLbmpZz6x8iyanNbJIIR1Ex4SNCJ9MHoZWGKAKRWor7RpjCgGpQh9pp/g5kx7Jg98wyYepIvrYoXDaDD8MSboD5I1ZhIZnslAuOonn3ziDGqMwWgINSME2p/nwsufv5JTcpIPydSdWOSkPiVzjwD/2NQljhWkevXqrg8nXyGVa/aPSxhbojJOpQ3nBNo88w95fhqDQYRuB0XIxo0bXTvg2mgDQchDjEiuhWtLB5yLPImVj4gLwjL9KQ+QSv3NaR1LR9+QHRw/uu9OBfpCjOTs+haMda6BwXFCRoGBCu5PwQGQeCTbZhKR0zIAxBdt4ZlnnnEDNH55c+/dGSR7/pzUF+aZEtZ59tlnO3sxOo9of3lpb0WXb7rbPf1ndB+aLpKpm8nabiL95KlI5MbHHD8KkdFCDFhGdhkZPu+888J77RoQANykmVPCKC4jyohGRnyDMHpIuhG10S8afHBUKhY0fuKw6UA4/hVXXOHOyxwCvFV0XPHAGEGoZue5QizTeaUKo+WMqt19992uPLgx4zVjns3kyf8sHpIqdE45NXh2Bhh8XHes8uRFp+ND2ea2804HlE2stPK6+OKL3Y0mN/UsFdJVJzEkkxGuqXDaaae5ucYYXcyDxihlW6xFSqLhexbRoF1kNzDAdaUrLxOBQYNRzSgzc3ajDVG+Z/ANY4a5NswNwvjgNzllZ+dDXvZxPvHmavnsjGtOpg3nBNo9x2Z+EoYvI/5ByF88d4iMoFckmnT22eR3Mm07L+pvPHLTN+Q1GMi07ezqF/vgaceLTVQBbYK8wl7hmgoC2H7MBcSgf/jhh53zgGvAM7czSOX8qdQX+i/mIuOhoy7HEl55ZW/FY1e0+7wmFdtNpI88FYk0HkKlWJXr1ltvdaOeeA8Iu8qrUYlkYUSIcCE6XCbJE2rK/4y8BEFA0vi5+Ua/GNlPxlvAtRJqw6IceCs///xzt53nCbLoRTwYYSKcJbsV83IDN3JG0vFW0FnyAGZu3KSLkMBU4Fg0YsJzGT2PBeE3LCLx/PPPR0JmdjZ4DEhnrPLkRZ770Mnmh9EpDEhG/mKlF6+8Lx5yWs9SIV11EiMn3XUAQ4q0UY9ZUOKnn35yN3cWDHjppZeyDfHE2GZQgBAXDLFoMN525g2VvGGAjbZEmBRGRhD6JRbBYcEAro+QIkLSKB/CdXLKzs6HvO7jIFE41M645mTbcE7gXoYw414ULTbJXwzFeH1ZXtRp8jtR286r+huP3PQNeQ3n5pXIo4LnGq8htgqeFe7R2FTU3/wOXrNPP/3Ueejw5LHQEwKM8o616Em6SfX8qdQX2h3RO9g1HI8Q6lg2UDrtrUTsinaf16Riu4n0kacikREVRm0IpQh2flRQPE45hfAhGiYdZW4g/JW5LnhaCN3A8xIUfdx4EbrEO+cUPJR06nRSwA2dESSW8cU4ICwwnhCh0jNCzDGib7p0UsyZJM3B8KJkYMSSEC46P0bFKBvORTgEHSiGBvHfqcBoFfMt8MwSEhMLBCR5jZjJbnQrr/A9CqQvO48AN13C0fDI7SoxC+QTaWYgg84+O3JTz1IhHXWSjp68ZXAmGkZZEUeJrjca5i4wUsyNBKjDRC8wQHXppZe6ekn9yw7SxXX54VxBSC/9AKOVeW2UUTcR+qzIiaclVlg9eU9aGdSKXrU2N30r7Mx8yKs+DvxjE3YVNOqAkXaMRlYEhFSu2T9uMv1DKm04L8AA5n7JtTFIF4R6hnHKtTAQmg44FwNAsfKReykGNHZBXtbfaNLRN+Ql2CDMDUMEZndfJGwQUcg8RIQGYhobpiDAAAzthQiu6EEA6ktek8r5U60v9B3YPgw0IfiwnfBs+UIsL+ytROzsdp/XJGu7ifSTpyIRQxvRhQveDxfinaXGCS3JKYw4EoLFHIzgjZcbDyM0yULjxhvIZGRuatEdLqMTjCYRdhBcOIFKyuR8nimTaEQGIcs8K5YcDlZuRlJ5YUREx7f7cPNk9Iplgum0gr/nRkE+IgRSNaA4H8Y4IT4cJ8i2bdtcPgZvVqQzmZAc0srkZsJzWYwhCMek4+RYhEXlZuQ8p1CWnJtFHqLTx/+IE66TDpYRW8JReAXznU432utBB0YZYAgGO2REEEZuTqHtkKd0jNS1oKHLZ4xc//i5qWepkI46ydws8oy5HkEjm/xi/h03VK49lXzlRk+oLUZo8Ji0T8Qx9Tl62XS+C6YfMcLcM9IV9ChxPLYRikjIV15DvlIXmd9CfY3lXeB66F/Jm2DfxOfPPvss/N+/yS6kzWdn5kNe9XFAOgnH4xjBfo5zMGjC8vB+P5fKNZNm7j/MJ8Rj78NxqZe+cQmptGFItq9NFkQtC0NwfqIJ/Psw0Gew2Abp80O1ctuXIXS4B9DHBgeByMevvvrKzd3Co5LT+psTctI35BXkYXS+UL+AOpgI2gIimvBGPNMIk5xC3WDuKP1tsM6SLww4phPaD3Vr/PjxboDGh/suj1hIN9H9XCrnz019ob9hXjB1GLsXUrW30kGq7X5nkMy9Jx7J2m4i/eRpz4iYo6IyMZ2bASPijKbQUXLzZISXDi/VEWlG1GiIxHMz54pVBIHRmFRGa7lZIRLpJJgjiWgMwignE5d53g7vPHOHGxvGAc9Co9HTcWQHnTqjT48++qgTsDxbCJHBM2kIyyHMhpGkWGAcnnXWWS7v7rrrLicCuDEwCoTgYlSRZ/jkRHBxLXj1br75ZjcnhmMx0kteMD+IdAIdKuemMyMenNGneGBcEHPPtbKaJqu20THT6WIcMBrPyBmjbtHgNYl1Y0LcsKgBoRO5hbQw0sfcPa6bPKBO0pHiuWHlLL886ZAY6fPLDaOQeouhx4BCcO4qNyC+Z0I6ZcH1cc10zsw3oIPLKazMyDwKFtngWKxQRygLgyx09jx7jY43N/UsFdJRJ2lXPMeReSGkkXznRskzv0grc0Rom1xXsvlKneW8/jFZzINjUK8YdX/ggQciQoORZPombjh8Zt4xYbqMvNKfcD5WEiWvGTBg1UjylLrL7/ISRrwJ0ed6GaggwiEa6iwj0fStpO3GG2909RXRwbX65Uz/6huetB8MePIDIcJ2Qq5isTPzIS/7OH7D3B/aN/cKzkMdwmAjHxDhnAtSuWbSTP1C1NKP+P0c/SeiMbqdJduG8WAk29emAuKWOcosbkO+YhzOnj3b9fX0Xdddd51rK5Dbvox84DmX5Lffx3JsjFLu/czjpy6Sh6nU39yQSt+Q15AH5Dd1hqk3fhrwUsW6L0bjD2zjjSV0kvtjbqCsaWfYONhRlDlp5Pmv6cQfDKJeYVNwXuYBU6+w/xCpQfGWG6ijtDVeDPxjP5DXyZ4/N/WF/MOWRAwi5BFh1Pdk7a10kkq7z0tSuffEIxXbTaSXPPUkIuYId0PQ0fHwAGPm1jB6yiRgBF1OOwaMdGK6afx0BjReKg6dQCpgiHHzp+ONbjDcyPzJyoQbIHbpzBntZJQJQzdRJ80x6VR4EX/ODZTfMmpNfiS6CWIMcmNFfNHJcEMlPp6G4l9/TuB3dB50FHjLMGLoPGi8PFyWRg2MgtHBk45kRlvplBlF47iEcmEAIazIPzyydNjkazTUCfaNfjHK6odQpgPqDUY4y5YjoMhPOhryGIHF4AVQT8kf6u5vv/3mjB68iMxJoLMNwvV06NDBPZidUVnSzU2CB0UzIpkbqD/333+/vfnmm86I5JhvvfWWGwFmZN5fljq39SwVclsnSS/1gHRRp8g30oqHkbLxBXgq+cq+tFVuzAg/+gHKDAOX/CI/fKGBEYqgxshiyXLK3x9pxaDnGBjoXAvn5nh4OOPV3XRCGWOw8CJ/MSiiX/6y5RjWpJUbKG2WQRYEDukm/YRX+SO3hB9zk2Vf6lL0aGw0OzMf8qqPA37LXCHmAuGt5NgIMwYiaFfBPj+Va+a4LGiB0KSP4ndsI28ZdAiSbBtOta9NFoxg6gVthjpBvnJPYxCMdAQFP+nIbV9Gu6JtE9qH2KUt0p9yTtqon4+p1N/cwPmS7RvyGvod6rlfF8lj8pr7TDJpoC7RTzK4wTSC3IIwpYzJF+woBs78gfPcDG5GQxmwMiUrVyMUOBdCl8EX1ikABgXSAQOmzONmMIjBCRwRqZw/t/WFfqBjx46ujyWqCg8X25Kxt9JJKu0+L0n13hOPZG03kV4yvE44972wEHsIDGqwWi+eh0ceeSRlL7gQQog9CwZ2WLyPiAXecwoRFwxO4Ali4CEd0SFCCBGPPPUkCiGEEEKI3IMXhrlurMQugSiEyGskEoUQQggh8ilM3SBcES8kYaYFZVVTIUTBRiJRCCGEECKfggeRBWZYjIRFZljvQQgh8hrNSRRCCCGEEEIIEUGeRCGEEEIIIYQQESQShRBCCCGEEEJEkEgUQgghhBBCCBFBIlEIIYQQQgghRASJRCGEEEIIIYQQESQShRBCCCGEEEJEkEgUQgghhBBCCBFBIlEIIYQQQgghRASJRCGEEEIIIYQQETJCHuHPQhQ4duzYYVu2bHHvBZVChQpZsWLF3LsQQgghhBC7GlmlokBT0AUi+EJXCCGEEEKI/IBEoijQFHSB6LO7XIcQQgghhCj4SCR69OzZ01q3bm39+/cPb8k7Xn/9dTv11FNtxowZ4S3pZcWKFXbjjTfabbfdZuvWrQtvjU9epYfjcVyOL0R+o3v37lanTh0bOXJkeIsQQgghhPDJc5FYEIyxUqVKWbly5WyvvfYKbym4FC5c2EqUKGEVK1a0IkWKhLfmXTmsXLnSrrzySnvkkUds06ZN4a1CCCGEEEKIgoo8iR7HHnus9e3b11q1ahXeUnBB7L788sv2xBNPOLEo0sPmzZvthf+9Y5ffcnfkxf9sF0IIIYQQYndCIlGIJGDl0fp1almbFgdZy2ZNbfOWLbZi1SrT2sBCCCGEEGJ3I89EImGNhDfeeeed7v/zzz//X+GO27dvt59//tkuvfRS991xxx1nb7/9tq3yjG8fQhgJZSSkccSIEW6+3YEHHmijR4+OzHt788037c8//7QbbrjBfcdxPv74Y/db5uj997//tcMPP9x9d8stt9iECRPCR88kOhQzeM6ZM2f+6/fTp093+wUhLU8//bTbj2N16NDBzXVMddXKtWvX2s0332wPPvigbdy4MbzVbMOGDXbffffZ3XffbevXrw9vNbcP+/Ibfhsd/plMOQD7kmfkHd9fc801Lo/jPSHFzyO8rwMGDLAvv/zSmjRpEjPslPwm38g/8uedd97Jcg0+CxcujOQh+1KelGt+eEpL0aJF7exTTrRbrr7cbrj8Equ9/77hb3YOtAnahl8+55xzjn3xxRf/ymvyauLEia6utGzZMm4+pqvt+PWrW7duWdoy6aP+08bnzZtnDz30kEsPLz5T1tEk04aCc12j69Wrr776r3rFNQ8ZMiSSrpNPPtnlW7BtCSGEEEKIrBR+grjEPKBkyZJ21FFH2b777uuM0KeeesquvfZaq1+/vvsO4/G9996zF154wY455hi7/PLL3ZzATz/91CZNmmTt2rVz+23bts2JkGHDhrl3jDuMPYxCDPcff/zRBg0aZH///be1adPGvRBLHIeFW7777jv3jvHLuQcOHOjSc8ghh1j58uVdWidPnuwM3AsuuMBq1KgROSciicVs8CKRxrp169ovv/zijNPDDjvMSpcu7X7P8W666Sb3+aqrrrILL7zQVq9ebW+88YYVL17cmjdvHnkGHtcxbdo0O+WUU6xChQpuWxCel4cw5dxHHnlkZJ7k3Llz7ZNPPrFly5bZEUccEfntkiVLnEA7+OCDXZow7rkWfsd+ZcuWzbYcSA/bBw8e7EQB10l6EeTkwaGHHhoznRkZGS6vjj76aJe2Zs2a2WOPPebytWrVqu76KRuulfRgyJMe8gERUqZMGScY/HxhP4QsRv5FF13kypdyQVDut99+1rBhQ7dfNFu3bg1/yj3bvDq53hPjxbx6xfXFY9u27fbb4KHu81Ht2maZ+xkEgbLOux7mifrXmR3U51hQ5gwEIJjOOOMM11a47vfff999R577v+3Tp4/de++9brVU6mIwH6kDDRo0cNfGYEI62g5i79tvv3X1ddGiRda2bVv3PWKO39NeaeeVKlVyYd3VqlVz52WgxW/jkGwb8tMdq159/vnnriy4Dj+/+/XrZ3fccYf7n3rVtGlT++OPP6xXr16urvltXgghhBBCBPAM2TylW7duodq1a4c80RHekoknQEKeqAl5hmfIM2jDW0MhT6yEPCMz9M0337j/PSMz9PDDD4cOOOCAUNeuXUOeuHTbwTM0Q57YCnXs2DHkiaXw1lBo1apVoRtuuMGd9/XXXw95oi/8TeZ5OdYXX3wR3vLvNMY7J+n86quv3L6eoem2bd68OfTKK6+E7rrrrpBnJLttwDmfe+650HnnnReaN29eeGso9Nprr7k0k/Z4cGzO4RnO4S2h0E8//RTyhKBLE+n1Ic1s89PjCb3QFVdc4dLPdfjEKwfSw/Y333zTXYuPn0/ff/99eEts4p3PL5uzzz475ImU8NZQyBMhobvvvjt06aWXhpYvX55l2wMPPBDyRInbBp4RH/LEkdt38eLF4a1ZYZ/o1/DRY0LHnH1R6Nvuvf713cChw0KHn35+6Ic+v0a2LV6yNPTECy+H2p18dqjNCWe471995wN3bcHf+q/ly1eEbn3wMffic/T3K7369+aHn7rjcDyO+9hz/w3NX7DwX/sGX7GgHlE2tJWhQ4eGt2bWReomZeQJPbfNE+uuvt12222RvAWO/eijj4aOP/740JQpU9y2dLUd6hP7Uk7Ba5gzZ44re74jnX4bz20bSlSvOnToEDkGdYa6E32Nfn6Qhuj2IIQQQgghQqE8CzfNDrwgv//+uxvxx0MQ9No0atTIvTxjNouXiP18j0E0fFelSpXwf5mLtxD6WKtWLecFwZPjs88++9j+++9vnhEd3hKf6HOSTjwq4P8ez9/tt9/uwvLwkvhwTq6DMNFUFzfxjFcjjHPcuHHuf/Jh+PDhduaZZ9q5557rPD9+3uAl4jz8JqfgpTvhhBPctfj4+TR//vzwlpyBN9TPM8CDWLNmTee19MOKZ82a5bxUhBH63llg1Vm8UlOnTnUe02SpV7uWtTjwAPvltz9s7bp/wg/xXvcdMMj236eGNT+gidu2adNme+61t+zviZPthccftC4fvW3333K99erTz977/BvnXUwFzvG+97uuvX92x+F4HHf85KnW6Y13vPqQWpjjmjVrnLfXE3jWokWL8NbMukjeUL/xPAOhn3jh8cKxuq0P+Ui94Vh40YKkq+2QFs7jwzE5Dp5FvNF+G09XG4pXr/A+eiLQbcPDPWbMGOfBDF4j6dwdFqkSQgghhMgrdolIJISNcLGuXbu6cDHCR/2XP8eNsEkMbh8MxqCYTAZC2YLCJ1WSPacntt31PPPMM86w9q/FnweYKoTmIdzGjx/vDF4EFSGuhI1icPOZbeQRn9mX3+QURHCqeZssyRx7+vTpTgQSRhmsC34e8l0qQptw0bNPOcEmTZthY8b9M4du/qLF9ueoMXZ0+7ZWPhzGO3biJBvx19925/UdPWHZ1CqUL2dHH3aoXXdZB/u5/+82feZst1+yzJo7zxOnA9zvOQ7H47gcf+TY8d4rU/gnC+GVXD+hotF1uXr16m4l20suucT9P2fOHDdYgJiLBnGH4EKQJ5OXuW07PhwjmeOk2oaSqVfMhaT9xAtVFkIIIYQQsdklItGHRVSYTxfrdfHFF8edo5XfwNPDPCqM+Ycffth5SfHqPPvss+E9UoNHV+CFwejH24aIwnOIAMB7grcE7xrvzP1i34L+uAtEDJ6kWHWBF4IhFRrWrWNNGtSzPr8PtC1hr+uIMWOtuCdYEG8+kz0hWaNaVatdc7/wlkyaNW1spTyhhOhLhXkLFjkB06TBP14uqFe7ptXeb1+bOHVaeEtqJBoEYMBg8eLFzoOY3fM+d+zY4QRZfiPdbUgIIYQQQuScXSoSEYGEuxGSFv1C+ARD3fIr/kIfhNvhBWEhFxZaYbEXf1GOnMBCMITLIQJHjRoV8RayyAZhh4TREQrqLxpT0EEQIxRj1QVesRbPyY5SpUraGScdZ3+Nn2AzZ891YZ4D/xxhR7RtY9WqVA7vZbZs+QqrWqWSlShWPLwlk9Je2ZUoXtwtZJMKy1eutLKlS1u5vcqGt2RStEhRK1eurK1fn9rxfBKFRzNIQKgmHmbCSqNBGAY98/mJvGpDQgghhBAiZ+wSkUj4GYKHUMmlS5eGtxZMMMoRasyHYl5UEH9uVE4gZLBx48Zu+X7CTlu3bu2EgO9lZBsrU9arVy9meGFBwveSIXzT6eVi3mG1KlXst0FDbfL0Gc4reFT7tlnCFEuXLmVLli63TVuyhmCu37jRNm3enPJARelSpWytV+6r16wNb8lk67attnr1WitSJLXjIZRYLZbQyejHqRCKirhiBV5AZDM/MdY8Uuoo3mc8svnN65xXbYgQXea44okXQgghhBDJs9NEYtCLgeHGgjCIAp6vFjR++Yzh6y/akt8htA+Rg2jDaPdh4R0eAZBTOC5ikGfjsThNcF4Vj47A88bzHdknu/DCaPLKm8RxcyrwEMPMQ+vSpYvLtyD8/+GHH7pwylRh3uFJxxxhA4YOs8++7epEY3RYKdsWLF7ivI1BpniicoMnFBvVqxvekhyElRLSOWHK1PCWTGbPm2/zFi70ztc0vCU5KFsWeOLRFjxmwoe8ZhEanhfIIyKA+bzMO/z++++zeBNZ+IVtHItHYuQ38qoNMYDCY2F4VEZwMIr8iH5OKJCniFLKTwghhBBiTybPRSLPFsQTwkO7ebFwBrBaI3OQWHjjrrvush49eri5Z9ddd517rhohaOn0KuUVPC8OwcvzE3kgOUY7D5RnERY8JDzYPGj4JgseLH81S0RU0FvIZ1afJBSVfZLxdsUrh9yC5wdjHEP8pZdecs/yC65KmwwIBB7ez4IqN998s7322mvueM8//7x7th3esZyK28PaHOzmCI6fNMVOP+FYt6hNkGaNG9nBBx1or7//iU2bOdulgYVs3vzoczvx6COtrif6gPNzjMHDR9nQUaNtxcpV7sVntk2dkbnCaK399rUTjjrCPvzqWzcHkuNNnzXbXnrrA2vWpJG1anaA2y9ZKFvm7jIYcM8997h5m+QNjzflxXMTCdkGPGc8A5P5fbfddptrTywOxfMHeefB85RVfiOv2hArtV555ZVObJIvHJcX/Q6r6UZDH8RCWrkRpkIIIYQQuwN5LhIROBishHzx8G8WWwGWob///vudYGFxFh54/dZbbzkB9NVXX2VZNj8/QxoxZl999VUnjhC9CDDmVvEAdCBUMCcQGkj+4SHiofg+fGYb3yW7oEu8csgtzCu99NJL3XyyDz74wHmHc+KJwVvGQ/ZZwZWHs994443uWE8++aQ9+uijWR6NkQpVKley5k0bW9NGDdxiNtGUKFHc7rnpWqtbq6Zdf89DdtJFV9qDTz3vCb3D7dpLL7QiYQG+des2++y7rvbws53syU6veqJwlnvxmW29funn9kPUXX3x+e73Dz79gjvedXc/5I5/783Xu7mSqVK5cmUnmFnFlAfJkzeUI4u60IaCj55g8OXdd991AwIvvviiPfbYY07If/LJJ3b66afnyzaVl22IekU+kIcct3fv3k5YP/XUU+E9/oF8YsXY6JBXIYQQQog9jYxQQXDXCREHQgezY9GSpXb348/YJeeeaaccd3R4a2wQKOvWb7AypUulZWVdnrG4bv16tygOYjQRQbEnhBBCCCHErmKXrm4qRF6CSPuu549WskQJa9PioPDW+CAMea5huh69gheSeZHJCEQhhBBCCCHyC/IkigJNPE/iF993t54/97W169bbI3fdYoce3DL8Tf5FnkQhhBBCCJEfkEgUBRpWPY2eA8n/P/cfYHMXLLRjD2/n5gPmd1hcJ789mkIIIYQQQuyZSCSKAg2CkMemFOTHFiAQeXYo70IIIYQQQuxqJBKFEEIIIYQQQkSQ60IIIYQQQgghRASJRCGEEEIIIYQQESQShRBCCCGEEEJEkEgUQgghhBBCCBFBIlEIIYQQQgghRASJRCGEEEIIIYQQESQShRBCCCGEEEJEkEgUQgghhBBCCBFBIlEIIYQQQgghRASJRCGEEEIIIYQQESQShRBCCCGEEEJEkEgUQgghhBBCCBFBIlEIIYQQQgghRASJRCGEEEIIIYQQESQShRBCCCGEEEJEkEgUQgghhBBCCBFBIlEIIYQQQgghRASJRCGEEEIIIYQQESQShRBCCCGEEEJEkEgUQgghhBBCCBFBIlEIIYQQQgghRASJRCGEEEIIIYQQETJCHuHPO41dcEohhBBCCCGEKJBkZGSEP+0cdopIlCgUQgghhBBCiPSQ16Ixz0SihKEQQgghhBBC5C15IRjzRCTm9JASlkIIIYQQQog9lZwKvnQLxbSLxESHkxAUQgghhBBCiNRIJATTKRTTJhKzO8z27dvd9/4rVdKURCGEEEIIIYTY5eRE0PEb/1W4cOHw1n+TDrGYFpEY7xA7duxwArHv+B328/giNnVJYdu6Pb2uUCGEEEIIIYTY3SlaOGT1q263E5tus+OaFnJCsVCh2E80zK1QzLVIjPdztm/bts3e7BeyiYuK2jkHF7Jm+5oVKxLeQQghhBBCCCFEUmzZZjZ+vtl3I3ZYk7232g1HZ1iRIkXiCsLcCMU8EYlsw4v4y9/brPtfxeyJswpJHAohhBBCCCFEGnjk+x12erMtdsKBRZw3MZYgzI1IjO2fTJJ4AtF/J8QUD6IEohBCCCGEEEKkh8vaF3JaK6i9oom1LVlyLBITJQRPInMQCTEVQgghhBBCCJEeWtUyp7XQXD7pFIq58iQGCSaAz7xYpEZeRCGEEEIIIYRIH8U9jYXW8nWXT/BzbkibSPRJV8KEEEIIIYQQQiQm3RosRyIxOhGxEiWxKIQQQgghhBB5RzI6LCe6LK2eRD8BEohCCCGEEEIIkffkhQZLe7ipEEIIIYQQQoiCS8oiMVqhxvs/nUpWCCGEEEIIIURW4mmvRP8nIm2eRIlCIYQQQgghhNh1pEuTZXgHSulI0bv7/wffeW3dutXOfKOUfX2j25xrJiwwm+i9sqNyWbPWtc1KFQtvEEIIIYQQQojdjEplzI7vZNb95g1WtGhRy8jIcC+IfveJ/j87CoRIfPFHsxGzwv8kAIH4+JlmNSuHNwghhBBCCCHEbkRei8Q8Wd0Ugp9zAx7EZAUibNhi9mR3s9nLwhuEEEIIIYQQYjckL/QXpNWTGPy8ZcsWO+vN0rn2JH473Oz7EZmfEx0LYfiEJxA3ekJRHkUhhBBCCCHE7ojvSex203orVqxYFu9hvvMk7moQhE94wrCkJxDlURRCCCGEEEKI1MmVSAx6Dn2Cn3cFeSUUZ8+aaRede6q1OrCO/fiDd9AA/ne8+CyEEEIIIYQQO4tYeiw3uqzAexIRgtHktUex6/ff2JIli8P/5Zz33n7diU7ehRBCCCGEECI/UKBF4n3fmnX8wOyTQeENAfJSKI4cPtR6df/eduzYEd4ihBBCCCGEELsHBVYkIvzmhEXf+DjPT8xLodi7VzebPm1K+L9/s337dvv5x552xcXnOG8h7z/17mFbt26xzZs32TP/ecTefuNlty/vt1x/pa1etdL9v2jRAnvphafttBMOt6MPa2lPP/mQ2yaEEEIIIYQQeU2BFYmsXnpyM7P9PSF4qvcej3QLxXr1G9q5F1xiM2dMsy7ffe0EXzR4GL/49EN76L7bbc6cWda8ZWv3/vD9d7jtxAe3OriNHXb40W5/3o8+9gQrVqy4LV++zF549gn74rMPba9y5a169X2sq3eeF597ylauXOH2F0IIIYQQQoi8okCHm17R3uyF882ObBjeEId0CsWMjEJ28imn25FHH2e9unexUSOHh7/5h7lzZjtPY+069eyt9z6zDz75xj78tLPVb9DYevXoYvPnzbWTTjnDDmjW3O3P+7kXXGwlS5Wygb/3s9/797Wzz7vI/e6jz7+1Czpcbv1//dnGjhnl9hdCCCGEEEKIvCLfi8Sqe4U/eOTGA5hOoVixUhU7/6LL3OfvO39pK5ZnPdCsmdNt6pSJdlCLVlardh23rcY++9qBB7VwHsh4Yap4JceNG+s+tzmknRONxYuXsGbe72DK5InuXQghhBBCCCHyinwvElvXzhR2kNtQ0WihyIP6c0rLVq3tpFPPcB6+H3v3sFDon0VsNmxY794LFSoceWglYq9q1WruM/MVY7Fl82Zbsmih+/zgvbe5uYy8HnngTrcNEanFcoQQQgghhBB5Sb4Xicw9TOecwip7mZUpEf4nFyD6zj73Ajdn8PvOX9i0qZPD33hpLlXave/YsT3yfBIEnv/YjMKFC7v3aIoVL25V967uPp9z/sV26x33ZXkxH3L79m3ueyGEEEIIIYTICwrEnMR0hYr6v126JvP/81tnvueURo0P8MRch/B//1Crdl03//Cv0SNt1swZbtuC+fPs779Gu3mKdes1cNt88A4iJhGefnhqoUKF7IKLLrMrr77BLrm8o7U6+BA7/sRTrWjRsFtVCCGEEEIIIfKAArNwTW6FYvRvbjwm85i5ASHHAjQHtTg4vCWT/favaaecdpabf3jjtZfZ1VdcaB0vv8DNUzztjHOciIR999vfvX/95Sd2zx03uAVvOB6L4nz3zed2wdkn2Y3XXGpnn3qs3XTdFfbn0BgPhBRCCCGEEEKINFJgRCLkVCjGEoiJVkRNFhakOf/CS8L/ZYJ4xPv37Auv2v7717Ixo4a792eef8Vt98NN2x92pF108RW2bes2W7VqlZu/WKlSZXv0yefsmutvceGnw/4c7ETnk890skPatne/E0IIIYQQQoi8IiPkT5pLkuDu/ufgu/95y5Ytdtabpe3rG92/aQWx94Qn+jZ64o85i497wjGeVzAvBaIQQgghhBBC7GwqlTE7vpNZt5vWW7FimdPRcDj5i2ZGv0PwcyIKlCfRJ1mPogSiEEIIIYQQQqRGgRSJkEgoSiAKIYQQQgghROoUWJEI8YSiBKIQQgghhBBC5IwCLRIhllC8/1sJRCGEEEIIIYTICQVeJEK0UPSfgyiBKIQQQgghhBCpsVuIRPCF4sG1Ml/Pny+BKIQQQgghhBCpstuIREAo3nNy5ovPQgghhBBCCCFSY7cSiUIIIYQQQgghcodEohBCCCGEEEKICBKJQgghhBBCCCEiSCQKIYQQQgghhIggkSiEEEIIIYQQIoJEohBCCCGEEEKICBKJQgghhBBCCCEiSCQKIYQQQgghhIggkSiEEEIIIYQQIoJEohBCCCGEEEKICBkhj/DnpAju7n8Ovvuft2zZYme9Wdr63Ov+FUIIIYQQQgiRJo7vZNbtpvVWrFgx939GRoZ7+Z+D7xD8nAh5EoUQQgghhBBCRJBIFEIIIYQQQggRQSJRCCGEEEIIIUQEiUQhhBBCCCGEEBEkEoUQQgghhBBCRJBIFEIIIYQQQggRQSJRCCGEEEIIIUQEiUQhhBBCCCGEEBEkEoUQQgghhBBCRJBIFEIIIYQQQggRQSJRCCGEEEIIIUQEiUQhhBBCCCGEEBEkEoUQQgghhBBCRJBIFEIIIYQQQggRQSJRCCGEEEIIIUSEjJBH+HNSBHf3Pwff/c9btmyxs94sbX3udf/miO07zNZvMNu2PbxBpI0ihc1KlzIrrGECIYQQQgghChzHdzLrdtN6K1asmPs/IyPDvfzPwXcIfk5EvpYIEoh5B/lK/gohhBBCCCFEkHwtEiUQ8xblrxBCCCGEECIaBRsKIYQQQgghhIggkSiEEEIIIYQQIoJEohBCCCGEEEKICBKJQgghhBBCCCEiSCQKIYQQQgghhIggkSiEEEIIIYQQIoJEohBCCCGEEEKICBKJQgghhBBCCCEiSCQKIYQQQgghhIggkSiEEEIIIYQQIoJEohBCCCGEEEKICBKJQgghhBBCCCEiSCQKIYQQQgghhIggkSiEEEIIIYQQIoJEohD5nFAoZMuXL7dVq1a5z8myevVqW7p0aUq/ESJdbNmyxRYsWGBbt24Nb9kzyUk7VN4JIYTY1WR4N66ULMjg7v7n4Lv/mZvcWW+Wtj73un9zxIrV4Q9pZIt3z/3tT7N+Q0Pu+BkZZtWrmJ1/klmTehnu/z2JiuXCH9IExtDnn39uCxcuDG/JSocOHaxFixbh//Inffv2tV9++cVOOOEEO+6448Jbdx0Yi5988omVLl3aLrnkEqtUqVL4m/ggKL/++mubP3++XXvttbb//vuHv9n98csvFg0aNLCLL77YSpUqFd6S//DbEFx66aVWpYrXQaVIsB3WrVvXtbu99tor/G0mEyZMsI8//th9btu2rZ1++ulWtGhR9392JJO+7du3288//2y//fabnXzyyXb00UeHv4nP+vXrbdCgQfbXX3+5c+yzzz6ur2jTpo2VKFHC7ROvf6lXr54deuih1rhxYytSpIht2LDBvvzyS5syZYrddNNNVqtWrfCeO7d956Qd5iTvhBBC7Jkc38ms203rrVixYu7/DE/I8PI/B98h+DkRe5Qn0bv32ufdQ/btTyFbvsqsdEmzIoXN5i82e+0zs/6eeEwXM+eZ3fZUyL34vKdRvHhxq127tjPKgy+/Eu/uzJo1y+677z57//33ncGaG0qWLGkVK1Z04tA3lhNB/pcrV86qV6/uxOWeCOIluv6VL18+pQ5yd2D69Om2eLHXyQVAiLA9XSC8qO+8Q6FChVxeI0yTGdRA/DEQwu/XrFlje++9txNWvXr1ss6dOzsBGSTYvyAmp02bZp999pkNHDjQduzYEd4rZ5CWl19+2b34nBty0g5TzTshhBAiL9ijROK8RWZ/TTYr7F31Vedm2EsPZtjrj2bYce3MMywQiSFbsy68s8gViJrzzjvPrrnmmiyvpk2bhvcQyVKhQgW7/vrrnRcxWUMTYYn3CC/Knmpo4oWKrn/USfJmT2PSpElOGPogxObMmRP+L/0gxNu1a2ePPPKINWvWLLw1Ntu2bbM///zTDawccsgh9uCDD9pdd91lDz/8sPMMjhs3znkEgwT7l9tuu82uu+46104QiXje8ws5aYep5J0QQgiRVxR+wiP8Oa1gkHw9vJhd3j68IQds3Bz+kCbmLgrZkNFmOLOOa5fhQi1xKlSpkGHbtmd472abt2TYs2+HrN9QswMaZNheZcyWLDd7/LWQffezWWVv3x8HhOztr81Wr82wgSND9sF3Zr1/M1u/0axxnQwbPTFkL3/sGT+eTcbrjxGeGvdO1KB25jV980PI3vnGrFvfzO/KljLbd+8MT6CGvHObfdnLrETxDKu7v2fczTB74vWQ/TTArH4tL83lwxeTJkom55hKGrxmY8eOdZ8xcKJFDeHIhJT973//s40bN7pQOEKTv//+e/v000+dIYUXgeP079/fvv32WxcahuFXuXJlK1u2rDsOHoe3337b/Y8X4auvvnIGIuFyZcqUsd69e9s333xjo0aNciPyeJUwRrt3724fffSR88j9/vvv9sUXX7jQNb73jz1jxgznZSFtderUcdsIG+vTp4916dLFfv31VzdHsEaNGjE9e6NHj3bnAPYjbKx+/fo2YsSISJrHjBljP/30kzs+oY9cA3lAmvmOfML7ULhwYefNePfdd23o0KEurI684X+MZ0Lrunbt6vKJ68CrwvHYB88MoW6cm3Lwr518/OOPP9x3nMvPH4xTzjt+/HjnvenZs6c797x589z5+N7Pj/xMrPILsnLlSpcPhPThjcLTgwh55ZVXbPbs2S6P8XhT53r06OHyFhFDnlLm1DG/TMgr8pu8og5SBuwzc+bMSB5yvpo1a7pjIoSeffZZ9/slS5a48qbekrd+ecdqQ3jHOFe3bt3cbyZPnuyOV7VqVffbaPxjUD8pb0QhXjdfIJM+6j91Y8WKFbbvvvtaw4YNbe7cuS59a9eudflAevw0kzeNGjVy8+T89DVp0sS1C44F5Lu/H9uo74nqDfkzYIDXwXmceuqpLr1A2jdv3mwTJ050ApD00GdE5w3Hp02Rp5Q95+IYf//9t2t/rVu3dp45n+zqB9f66quv2rp169xr8ODB7li0M7+8yXdCXvFuUn+yqyc5bYd+/+bnnf8/eUJ+cP4hQ4a4c3Au9qPtIvypd9QR2i3zId98880s5SmEEGL34bPBZhe13hrp37kf8PI/B98h+DkRe5Qncf/qGVa5oifUNpl92i1kk2eGnAexmmeTXHam2SVnZFizhp5orGS2dn1myCjMXWS2xvsfUdkwYFMMGM4xzEp5OgEx+OsQs2Ge/VLJs0cObJDpseTF5333zjzv/z4P2e/Dzcp42qnOft5x15EWs1ETzMqVzbATDsucF8mxl6ww++kPhKtZm2bmRGNBh8p54IEHOgMNYw+jGoMNwYPRh+cAwwrBhBjbb7/93DaMYgTasmXLwkfKBOOJOUwY6hib/P/ee+85YxfDctGiRfbjjz86gzwIxjuGM0YZ58b45vexwFjEsMOg4zwcF2MQwzA6DA4QungCACP++OOPd8LVhzRiFPueHdKPQMag43c0dI6NgZpd6NzUqVOdQCQ8jTRxHf369XOiOzsQM+QHRmp0/nBMDEyMzWrVqrl3jrk7gWBo1aqVK38EPeU7bNgw9x3z3xAcCB3KBPHOvvyG+siARXAxEfajDDZt2uTqBmXAPD/CJClHXtQVBkaCZYko4JwIVOo75c1+/pzuIGyjjlAuCBKECe2ANuILpnjgcUPIkU5/Hp8fakrdzK1nn/aMiEEUAu8HHHCAE1XJgoBB0FDfSG8Q5kq+8MILbr7kzhA4tNOjjjrKCTZefA56AMlH+iHajf9/MvUkFtm1w3jQb9EX8hv6qx9++MGJXuAYCFXSwmAR9ZvBMiGEECIn7FEisVxZsw6nmvMOMg/xxQ/M7ng2ZN9491Hfa8l3DWplquwxE0O23bPrJkzDUMsUaRUCaz/gaXzx/swXQpB9ps0xq7VPhp1+TIYVK2ruxefmjc3meDYawrOcd467r8qw+65lO15Ms5HjMo3DVp7NVrOG2WJPC73fOWSTpodcuo9rX7AW1cEg7dSpk5un5L8QR4BhTCggRgwGFZ4UwEDH04EhjLHdsmVLO//8811YGcISwYL4C8Jxbr75ZhfOhUHJMdn31ltvdSGafI/hhUchyFlnneV+x34Yd3gQML5iwQIfeBAIhbvxxhvduRBzpBEjMRoWp2jevLn7jNHYvn17Z9T5sI3wuBtuuMEZpYQC4jEhTbzOOOMMN5cJwYb4iAfHJNyO67z88svd/3hTYgnXIP61R+cPRi3XRFpOOeUUd5233367u+6CCIZ6sP75c8wQNgw84NlBZOHR5R1xw+AFYg5vDUb4SSed5PLrnHPOcaIKDxxeZR8E5WWXXebqxZVXXunKjeNfccUVbttFF13k9mNOYFC8I/Qof/KY3/M76hiCKRq24YVmHxaK4bhXXXWV+x9vWXZ1BPBiknaEIQKRNkJbop4G62VOQLhR1/0FWXinHaYy99gXU+Qbr1RBRNN+aS8Iu9xcE7/F84jHjxefg4vN0DZoD4TCHnbYYUnXk1jEa4fZceSRR0b6OwQs6aGvJQ/opzgGC/hQR+jbTjzxxPAvhRBCiNTYo0QiNK2fYU/exjzEDCtdKtO713dwyB55OWQzwvoDrx2L/DGHcZ4n7KbMCjmB1rJpVqFWsnjIiUAGuEsUz9y2cdO/PQE+DWubvfm4JyofyLCqlTK9jPvtHf4yDOGfJ3iCkO8QlIjUo9pk2N65s+V2Ohiw/sIS/is4FwzvBi8MG4xXjCT2B4x3vAcY2BwHgzPaw+DD93gtCLvCQAQ8EnjXMGD5HjCmgpAWDFLCtwjDAgREcN4WYMDi5QA/DJFzYXgDgjZV8LZwLNLAi+vkerluQETHu94gpMO/Pj9dCBHCarPDv/bo/CGUjzzAUCb0kDzkuKSnIIKXOFj/MN59bxR15eCDD3aCCY8e/yMIqA9cN6tKUiYIHkAMIu6jCZYBooJyYz8/zzgekL9BT2Lwd3h9ECLkPemJhnBQBBD78QKujbBsPIqxhKUP33Eejo8w5PiICgY3WPEz0YBCOmGQKCja8cjF87Yl2jc4CHX//fe76AHEGsLNz6O8gJBgBq8oX8o22XoSi3jtMDvYj/7Ob5vAb2j31B9gsIPv2CeZfkQIIYSIxR4nEqGMJw4vPMXsJU+s3d0xw4WbEvb5Va+QE42Ehlb3tq3y7LWhf5lbCRVRVy+X4Z54GidONzfn8cbHQ3btIyHr/uu/RSUhrXtXyVSjZUubtWgSX3jmVzBOoheuwaPmg0GN4e6DcMJwBkbFEY7Mu2HKLIYgXqG8AmMKMEKjwzsRXISBAvMXfaOVUDGI9ZtUwVBn/iVzoTg2xq8fGrgzwdgk9JFySCVcML/CwEOw/rHwT9BoZlDCF/t8RgD4YHQz54t5YJQJ9TB68ZR04XvQEHCxBL5f//BaPf7441nSQ/1LNCiAsEAQIgzxxNO2uFZ/UGZngSgKinb6AK7bb/cM0PjX4u8bT/BxTcFBKLyZV199tROJfnveGezMepId5B1h0+DnpxBCCJEb9iiRyEIx93cK2budQ55xhTjwxIknyBCMeO6YA8giNTwaA6GGF6//0Mx9CUElFDU3TJtt9sYXIZvr2f9HHWJ2zfmZ4aXRDBsbsgVLMoUhcyP7D80UmLsThGIxL8uHBWbwZAFGLPO6GBknJItwrLwMefRFHoap72nyQSz5XiG8BYRhBl8Y27HmkSULRibzipiPxNwuwlBZDTG3YYA5gTl1hL9yPbm5poIA1xcMFybk15/bRX1gsSHmGuJh5Pl2hJIGww7TiZ/flLnvUQri1z/Oz7P9gvWPEO1kRIEvggntJqwacZysxytdMEgUFO2EQvrti/TQ3n1B7O97xBFHuP+jiR6E4tmXeL8TDW5Qtr5HMlZep8LOrifZQR3wy9Pvz4QQQojcsEeJxGqVzFauyZxjOHN+phGMLeyHdRb3bC08d9CicYYVL5a5HQF5UCNG/DO/SwnvN769zSI3LEKzfw2zM47NsDbNMqxCuawHXb02ZAOGZ35u0cRLk5cGFsOZNT9z2+4ABjHzr5hHRMgfcwiZE4ahDmzHq0XY1EEHHeQMXARcOkGQkg7mc7HQA7BARbQXAuOL0D7A68F8H+YC4bFA1LHYTrSwDOILgHhgFPM8OEIHEcKEoRIWuSsgRI08YAEMBBPpRsT6hvvuBIMU1DkW/GFhIRg+fLjzxlAnCM0EBgYIA8ajlV05pwpCxQ8tpPw5H2nBuxaNL6IIHcUjyLw06iB1j/aRzIACAoJj0LaYt0abyk5cItjwKlMHCHdNlkT1PRZcH3NB8aSySJJf3ziOP3CUKtRlvx0xb5N6DFw7/QwCMeg5jkWia9kZ9SRZEMfUH2AQAI8sYjGVshNCCCGC7FEikcVj8Byu32DW6X2zO58N2e3PhKxnePFGVjatEJ5+5UJOM7WBW+209r6Zn5OFxWZKebqG8NVXPgnZZ93Nau6T+fB+RClzIO95PmS/Dsk0QrZszRSkf4zIsIVLzfbz7JdLTs9wcyhZVKf37yGLmi6Xr8E4+e6779zD5IMvxCHzqDDIMeIOP/xwJ7ww2pgbhoGI8cb/rAjJsvMs4c6IPSSas5MsrGb6xhtv2Ouvv24jR450xrc/JzAawhZZBZKwspdeesmliZBQ5kExVywWGPtcB9+z3D2Gaizw3CFCEWaEsHLMTz/91OVRMPxuZ4BoIGwPY5M5YOQPIbCsulkQYeXS6PpHnSS8Fy82gomyRfAzUOEvUEQ+INqBFUT5HWXOYiTJhHcmA8ciRJG6/dlnn7l6zWI6zGmLBoHHQkmINtJBufAImeeee84tupNMm+Ca/PBSPF1+mG00eOhoB3jz/XPxiIZE+CuA0mapv4kWYAnCwAxlQBsj/5955hl77bXX7Mknn3TtFPBypxJGimhCuHGtrErMIzxefPFF134J5Waxqnh5wLnwDPptkt/HAiGa1/UkWRjAog5zvYSu+30bj3kRQgghcsIeJRJZFOaWSzPc4y6YY8hzDTd59hWPrDj/5AzrcNo/C9Owb539M//JSagpq6CeelTmcTgHA9IHeBrk8rMyvZWEkVYqn/kgf1i52mzOgpD9NixzkZzjDs1wQvOkw71jFDcbN8VswvTMfQsCGK4YS8zPCb4YfUeUYajhCWGRGYw1jHUMU8QU8xPPPfdcZ6ixH941wuyAxSnSEU7lL3OPxwSPJWGt/kh8NAg5QtuOO+44Z3ziecQovvDCC+MKS74nVI5jIvgw4mKBh/S0005zHlX2w7DkWjHoEcw7c2ER4LzkBe+kB8PTX5RjV3hIcgNlG13/8CCy0ih1kEEK6h1lQP5TH9iON/GYY45xL/IfDxeDGdRX8sSf+5UbyF/OSRkjSljRFk9yrHrCNsQ7K9gSUkn68SqSJhZOSTZsknaGiODFtcaC7YSBkj7yj/85byKvG+kiRJSBDcRsvPoeD7+NcT1441gsiuvCU8pcQ9peqvWP8mUeKvnEsfAi4v1nBVKexxjPk0p58Bt/YaB43kT6gryuJ6nA9Z555plusIHzU2Z+v0l5pFomQggh9mwyvBtgSrFBwd39z8F3/zPhPWe9Wdr63Ov+zRErdmGU27oNZi9/HLL5i8xuvpRnHe6eN1ie/binwOg+zxnjofTM+0MgiKwgwBEHGJwYlRi6eEnw8DDfihVpRc7Be4n3kMVWmEeHIBEiHXDvpe3iDUbA+vOdiYDgOZMIWCGEELsXx3cy63bT+siK18FBweh3CH5OxB7lSUwWHonBAjdzFpjV3o9VTXdPgShENKygSSgj4X6Ez7311ltOIOJtjReeJ4TY9TC39YMPPnDPA/XbLgIRzzHeRSGEECIVJBJjwPzArduYh0hoaoYLGRViTwAPF6F4hPYRngmEABIKGGtRFSFE/qBGjRrOY8hCQLRdwn7xHl566aW7ZLVkIYQQBRuFm+7h7EnhpkIIIYQQQuwuKNxUCCGEEEIIIcROQSJRCCGEEEIIIUQEiUQhhBBCCCGEEBEkEoUQQgghhBBCRJBIFEIIIYQQQggRQSJRCCGEEEIIIUQEiUQhhBBCCCGEEBEkEoUQQgghhBBCRJBIFEIIIYQQQggRQSJRCCGEEEIIIUQEiUQhhBBCCCGEEBEkEoUQQgghhBBCRJBIFEIIIYQQQggRQSJRCCGEEEIIIUSEjJBH+HNSBHf3Pwff/c9btmyxs94sbX3udf/miHNvTSlpIgd88/L28CchhBBCCCHEzqJIkSLhTznj+E5m3W5ab8WKFXP/Z2RkuJf/OfgOwc+JkEjcw3nvyZXhT0IIIYQQQoidRcWKFcOfcsYeKxI3bNgQ/iSEEEIIIYQQuw+lSpUKf8oZeSkSNSdRCCGEEEIIIUQEiUQhhBBCCCGEEBEkEoUQQgghhBBCRJBIFEIIIYQQQggRQSJRCCGEEEIIIUQEiUQhhBBCCCGEEBEkEoUQQgghhBBCRJBIFEIIIYQQQggRQSJRCCGEEEIIIUQEiUQhhBBCCCGEEBEkEoUQQgghhBBCRJBIFEIIIYQQQggRQSJRCCGEEEIIIUSEjJBH+HNSBHf3Pwff/c9btmyxs94sbX3udf/miA0bNoQ/7TqmzphpS5evdJ+LFClsTRrUtzKlS7n/CwI7duywxYsXW8mSJa18+fLhrbln3sJFtnHjRqtfp3Z4i9gVLF261IoWLZrWshU7n82bN9uyZcusWrVqXj9TJLw1PpT7unXr3OdChQrZ3nvvbcWLF3f/7wy2b99uk6ZOt9VrM9NQongxO6BxQyvm1UXxDwX9/iGEECJvKVUqd/eE4zuZdbtpvRUrVsz9n5GR4V7+5+A7BD8nQp7EBMyeN99GjR1n/QYOtk5vvGuLPeMsL0BU80o3GHPDhg2z2bNnh7ekh2GjxlivX/qF/0uOrVu32uo1a7MMNKQTjrvWM5w3bdoc3rL7M27cuLSXbV6XE6xbv8Fue+gJ+7bHD+EtezZr1661gQMHJt0HrFixwubOnWtTpkyxn376yf1+Z8Lg0xRPANE39u7b31599yNb75VpOtnm9V3Uj5MuutKOPruDXXTdre58BYmddf8QQggh0o1EYgKOO+Iwu+Xqy63D2adbyRIlwlvTz6BBg9xrd2bMuIlOGKxavSa8Jb1s3rzFnnnlDev9a//wFpET8rqcoHSpknb3Tde69iVSp2HDhnbEEUdYq1atdqoH0Qfv9dmnnOj6xjNPOj68Nb0MGT7KOvfoba88/aj16/Kl3dzxMnvFE6PzFy4K75H/2Vn3DyGEECLdFH7CI/w5reDB+np4Mbu8fXhDDsCjkRtmzJpji5cts0oVKkTcq3hHJk+fYStXrXbbYe269fbH0GE2dORoK1u6jJXbq+y/3LHs//vgP+2ododYxajQPka8x06YZD/1+922bdtmlStVtMKFC4e/zZ5Vq1bZwoULnTeIkDMMPrZx/hJho4I0r1mzxiZPnhwJHWW/YBr57axZs2zatGlue+nSpd07I/5Tp0514YiEpAFhagsWLHDHTya0DUjD3941/jpwsBXKKGRLl6+wNWvX2qEHt4x8v3DxEuv5y6+2YNESL2/LR4wivEZjxk+wiVOm2bjJU6x6taq2eOly93v2I1wOuIZho/6y37x8LuLlXxUvH4PXuGHjRhs0fKRXVsP/9T1hXROnTnO/L1yIvM+wuQsWevlQMl8YZ4ROEyKIB4hyKlOmjPMEUQ6UjV9fKK/58+fbxIkT3WfK0c8foH5RV2bOnOlCCxYtWuRCFfyy9evK+PHjbfXq1e73GPTJkGw5kdcbN212bfyX/n/YqL/H2T7V947kM+U4+u8J1uf3P7w27LWHiuUj9WyFV7f5bt6CRa7dlfLE4l5ly7jvgO9pt2W8axo+eqwN9oQCn2O1yV0B+U975RonTZrk8pay5TPlEGyzhI9SDoRlU97RfYJfTuvXr3d5O2fOHCf+/PLiHNOnT3flTXun3UfnAefGm9igQYOkQ1Y2bNhof0+c7MIei4fDUwAvPNv3KlvWq1tF3TVMmznLevf9zVZ6dalq5Uox69KCRYtt2Oi/7KRjjvxXW1vm9RN9fh9oE7w6VaF8Oe+cpd12BiBor5UqVsiSL9St1avXun1H/vW3K/vTTzjWXfdeXh7+7PWxjRvU9+pbNRfyjmeuWNFirv9mcCN4juygHP8aP9HVSwYsfMjzMd72EiW8/Pauxe/X+vw+yEvvdFcPOT7poa5Omznb5sxbYENGjLIqXv6MGvu36yf3rVE9S15ld//YHciuzQN9C+VdodxeNmHyVOvVp79339xme1et4uq+fw/F48r9ZZN3vCXhe7cPdemPP4d7dW2MlfLaQoVy5fJFnyCEELkhWRstHp8NNruo9dbIvZR+0e8bo98hlX5ztxaJs+bOs/979U1r0/Igd3OHpcuW26PPv2wN69axmvvu4xkls+yOR59yIYrbvJvbB192tuUrVlmLZk2zGOfxbvIYXP/576s28M8RVn6vvezn3wY4sXhIy+ZZjI94IBwwAhF/3Gg5J8YjBl857yaIkfL333/bzz//bJUqVXL7/PLLL84YZf4SLF++3Dp37uzyi8IfOXKkzZs3z2rXru3+D4pEztelSxfbd999rWrVqu57X6jyHnxhSCEyuIG/9dHn9vHX3zuxgOE+aNgIZwghEklj196/2AtvvOPyZvrsOfbR199Z86ZN3P6rvOsh72bMnuu8AKW8tCMyt27bavXr1HIVm//veeL/bKZXZsxr+q5nb5vvGZ8tDzrA5QlhW/c++X/e7xdHvp/piaxWBx3ofj/67/HOyJjmCQzq3pYtW51xt/++NZwxsatBDH7//fcurYjEP//8013XjBkznKG///77u1DDH3/80QkDyn/ChAnuVatWLScI+Z7QQgQk3yMwEIs1a9Z0ZUs5jB071h2jrGfoI1IIYeTYrhy98kSAIlSD5Ux9Q4BgyCUqJ/j0my72c/8B1rn7D27AZeGiJda6eTMr7xmA7P/g0528Y8yxWvvt69pCtx/7WLs2rTzDroRNnT7TXnv/Exs+xkvnr795RmJlz+iv544L4yZOsU5vvuvVr5FuUIA68P7n37jzI0R3NZs2bbJevXq59or469u3rxN5fKas6tWr58qVfCfMG3FI+x4yZIgrJ1/IUW5ff/21a8fkP22Wtti4cWN3w6Cd8j0DBdTnAQMGuDpAmw928PFEIuUar02X8sThu599bbPnzvfazwGR43Xp9ZMr12MPb2cZ3jXQ5r/u2tMJrxGeYPuscxdrcWDTf4mceCIR4fTQM52c4Fy+cqW99fHnXp0qaQ3r1bHNXl1+8Y33bD+vD2EwAhCpz77yljVpWM9tW7ZipQ0ZOdoOO+RgJ2bnLVjo6sVpJxzj+vO+Awbah19+6wb3ECbjPfHxVdcedvBBzVyas4Nr7vFTX+8Yg6y9d3y/bg8ZMdql87jD23v5WdLt86JXH/f16t5ar5xfe+9jd42N6td1dfXR515y5TPir7H2TbdeXnmFXHpIO32Tz+4sEhO1eZjr9X//99pbLm8IT0bgU6bk0Xavjr/98Rf2yTf/3F/oWzZs3BQZhGTA6JH/e9F95l79wRed3Xn9+4MQQhRUJBJzSG5FYjnPUMZ4IS0HNm7otg30xA0G8MXnnGnbt223F/73jp3sGTc3XHGJHdq6pWcENbHPvu1qzRo3ssoV/xnFjHeT/67nj85z9uxD9zpj45j2h9pQz9DAwPXPmR0IQYxHDHc+H3XUUe5/PgNGHQbpIYcc4rwMGPwVK1Z0AqFu3bputBYRyI3yhBNOcMIQQxWPYpUqVZzx74tEKlCPHj2sbdu21qhRo0hF8YUqxmrwxf6VK1f2jKHJnvHV01588iFnCB512KFOgFnI3E18imf4f/TVt/bcI/fZid73Rx56iBM0CIG2rZpb2TKlnTFa1jOY8VQ8ePtNdqSXj43r13PnoHze8oyEerX2t/tvvcH7TQt33K+79bRG9eq60ef3v/jG+76mPXDbjZHvv/i+h9WvXdOqVa1idWru70TpX+MnWHvPOLniwnPd4EB+EIiAVw+Bf/zxx7uyQFTwmTJAQFKWDAZgyJ9xxhlWp04dZ/gjJmgHNWrUcGKAcjrvvPNcGfOiblSoUCEyAEDI8jnnnOPEBt8jHPkNQpN8pl5Q14LljMihrlTw0pVdOflQv2lDLz7xsF163lnOaEcgwtz5C5yH6nqvPVH/MfAHDB3mjD8MSMqKMMVTjj3KJk2b7oRfUCQiOBiAeOahe+z4ow53dWnduvU2bcYsV+apdG55AWWBsKed1q9f33ly+cygiy/o2Ub7pBwoQ9qt773ne+oB4rJdu3buRdlTJ/AsU26UGd8TSnrooYe6Ns3xEZ18DoaXxhOJ2bXpalWrun6j20997DCvrZT2yguBRhs797ST3GJUiDEE4/OPPWDHeKKR1wKvro2fNNW16aBhHkskLlm6zPWthIjS1x52SGvbf58arh9p55UjHkQ81Hjt/OPhYcLjeN7pJ7uBIOoGQpZ08R3CocM5Z1irZpnCFs8U537qgbvsiEPb2FHt2zovOPW8acP6Lh3x4Pf0S9/3+tGaH9DE9fWU7aeeEG7Topm1PbiF68MQnpeed7Yde0R7T3weaNW9vOvd7zc7om1rJ1IQvw/ceqNV3xtRu8J9Dnl/4yZN8fK2dcSbtjuLxERtHrj+H/r0s9OOP9YeuesWO+fUEyMDFNH3F/KavKUcfZHYuXsvO8C7J996zRVuW0uvDuB1bN60cVKeYyGEyK/kZ5G4Ww/BMRKMQTpgyDDnzdriGQEYPyd4xicGwpJly235ipV2kCcu/EzjptbAM5LGT57i/s8OjL1xkybbwc2buXMB7xhUf02YmJYFVKg8zZs3d2IPDxSiAc8hRic3UUDIYRCOGTPGVnpGC8bi2WefHfE0gu/JatGihTVp8s/1wn777efmN0W/MG5h2qzZzvNa3TPwgVBPDD4f8oqV+/AgMgrMaDzeR8IGEcuJWLN2nTMUuFZ+yzEQEIA3mBF8jD8MAz/d1apUtteffTyLwCgoxGqgDAZQRgwQ+CtU8U4Z4BXGgEV88D11wf/eH0wAPEd0EtQP6gn1BU8UggGDl/3btGnzr3JGqODxSoWTPZFHuG80eFjOOPE45zGkHBkoQBBjTCcLHoYSYSFEXlWpXNGWegY4c07zE6QtVllSXtWrV3feW0AA+YIST6TfdvfZZx/3Pey1116RGwViDiFPmVGOvBg8QBDyXTIkatMILYQRHkIgbJIwPwQTjBk33hp7ZemXMW2eAbDJ02a49poIPEVcY6P6/7TPZk0aubKdPW+B+59BtZlz5jqvG8L49yF/unSVDotdhMWEKVPdtvZtDnZ9MyGiGzduct8D/W3RIpn5hrBkQAmPYzLUq13TebJ+GzTUnX/hkqU23yujwz3BSbkixs8/4xQXDv3nqDGuPtMfrfLSRaRCkMwQ9z2TZNt8jb2r2dGekI9uM9H3F74nDDUI/fyvfwxy0QeLvHKi7Do9/uC/9hNCCJE+dvs4jQObZBpFf0+YbDNnz3U3GEaEYfuO7TjDvJuS+9eBoRacI5Udnl3hQmUKF86ajekcKcbIx4OAwMNQjAXG5gUXXOA8Ql27drX//e9/LuyQ3/rg2UAI4AXB+AyCB4NwtugXcyB9yJPsRju2eiKH8CtW8uPFfLNDW7dI2nji2Mwh9H/PsZo1aeyM1NCOzFU2ixb9Z44LhgQer+zSVJDASKW8okOngt4hSCTmMMwRm5QpLwYy8FRxXMqduhRdzoMHD3bCJR0QFnzdPQ+5uam5jQQoqPjlGDSGERzBuspnfzAgFvyWcGG/HJcsWeK8hf6cx0QkatOIq+OOaO/CLfEi8o4HiJB52LZt+7+uAY8jc/WSYfv2zL6nUOD3iLly5TLD/mEfTzTU3n8/N/eQaQDTZ85xwhFoD198380ObNTQLfyCR/vB2290Qm6k1z+kAwZUyAMEIOcf9OcI50UlXUBbwrN433+es7nzkxOeeyLpaPOJ7i8nHn2EPX7PbS7C4daHnrDzOt7kIg6oJ0IIIfKG3V4kYvScfuKxLnTqx36/uVAiFhgAwlSKFimcZVR4/YYNNsu76fmj2dmBaCnnHX/9hqzeMrxgJYuX+Jd4zAkY74STnXjiic6jSLiZv0gJcJPEO4HX4vDDD7eOHTvaxRdf7MQA8898+O788893xirz4YICkvlreB6iX4S1AnlBnpA3scDYIqyzY4fznUHnvy6/4FxnjCYio1CmIUloYfD3vPAeMqeprGdE8FgGHwy48ZOmuMUjdgfIQzyEiLoghJNiPGGwIyr4P1h2QdiHekA4cdB7hPeQ3/I9Hq7ocmaQIV1iG699g7p17N6br3OGfduWLaxihd0rvC4RlAHGcrCcGODxxSMhiHha4nkFEWbsd+CBB2YpR17MS06GRG0amDOGd/aX3/5wXn9Eog+Lb23assW1Mx8WGyJEn6iBRBB+yH5Eb/isXrvWzV8t7rVn8EUa3ieEH/OHfYHGgAbCDS+yL1RZrATP0dLly93/6aBpowZubvXPXh6QDqJMSBfg4Rww5E+7/5brXQhsu9Yt3XMORVZy2+YT3V+4x+G9xpN8n1cWnd/7n91/2w329idfunBkIYQQecNuLxIBz+Gc+axAN9pOOPqIiNFBuFWrg5q5OYgs2sHNiMUW1no3pBbhsKvswJhgkQdCYPzwSOZn/PrHYBdymorhTcgoYYIIPvCNMwxKd5P0DEre8RaOHj3afefDohcsVuILDH7jCwsfPBAIxKOPPtqFwzH/jeMB86EQn9Ev5qlBkwb1XFguI+38Bm8s1+zDIj3MIUGE8z2vEWPGuvk+/jkAr+AO739C0YCQVL5n7ihhSJ92/t4dGygP5iIROoYRceShbaz7T33cqoj8hkVqXnn3Qy8//jEs8HJgZE6ePtMZp+xHGGdBgLIiFJByQQgCocMsUsN26hrz2QghRfxzbewX9PYSiko9YW4c3/NixUzCkPlMvUAoRJczvwvOc4tXTsnAYk2UIQMn/GbgsOFu1ck9CTy3ePIoK/IAQfjXX39FPIF4gwkFp6wRQ9RRPP3sB8wxZQ7i0KFDI22awSIWw/H3SUSiNg0MoCEM3/30KxdmiljyIbyTVTpZaIRroN3R/pj7hwc/EXgIq3vXyFwy2iJ1qO/vA91cxHq1a4X3MhdmyCqXH371rROMvkCjPh7QqKHrs+kLYOrMWS5NdWvVdP8nC2H/z732ln3xffcsohcIUT3+yMPsc+8eUKli+Szh6wwCUu0XLlni8oA+kAWzRFZy2+YT3V+IUvnvW+97ovALV5e4fzP3lbpSJBBdAj/06W+PPvdfF6ocC8Kr73rsaef9jMXylavcYku/9B8Q3iKEEHsuu/XCNT54s1atXu28XScedXhEPPHOAgdjJ06KGBHcPO6+8RqrXXM/t8/Lb3/gbho9fu7rFlrgnVXYCI/BoGCeBTfJZ15+w/3+h779rcM5p7vJ90GRlghG/gkFZUXDfv36OYMe4x2xh2eid+/eblVF5iexqAnzzBAPeJ8wOJmPxqqlCMjhw4e7BS/8hUuCq5viUcJIZWEMVjfFIE0EKwkyivvyOx+6GzXLkNf3DE48fCwigEeW+UaEZr350ef2VZcebpVSFplAAPowD5QV6154/W230A3L1h9+SGtXPsxrYbT46Zf/5x6gzUqBLGjRusVBLh/r1q7pHsfwxAsvu0UOEKF3XNfR/S4InoauP/xkr7//iRP/lFN+GP1n4RrmpDE3jQef+58pR7xMfKYsEBHdu3d3q2EiLJgvSDljGFFHCD+lHvBijhrb8A5RthjWiAs8xayAi8jgvAcffHBkHmMyxC2n8DFYuIaFamLNB2VVyhFj/nIPDif/ixQu4o5HGTP/i1UKL73pDhdKyOI3PLKE9sQgCwt9ME84ehEUFihhldOj2rV17WJXQp+EZ582SNv0P4O/gAwDPogxBm4I8SSct2nTptayZUtn2FKfWYgIMf/VV185rz9tmGNT1rRRyjHYphH6RBL4KxL379/f3n//fVfGzF/knX6DcwcjDRKRuXKpVybnne28hz5sR+i98Po7rnxYNAaByPP+KAOE1yOeMU6/xyMu8BJ+4wlC9m3asIHV3G8fO8gTnr369LNXvH7jS69vxMDH2xT0MpGHRHIgEi4441T3v0+DOrXcIzieful1J85+7jfArrr4fFcXyYNY9YK6Cf6CJ7DCM/w/985P3rdv3SoiRH24LzDXkUdt1An3+8C8WPqvl976wN7//GsXkntwi2Y2c848O7J9W0+4r3dp4PzUW/8zYtZPF/1QdveP3YFEbR4QbQw4HHPYoVlWwIVE9xfKC48v5c/9mIGH/oOG2I3hhXL8QV9sDvKXxY8YdCSPo2HxIMrxaK9seKRLNAzyfv5dV7doEiv5CiFEXhO87+WEvFy4JsMTESkF9Qd39z8H3/3PjJCf9WZp63Ov+zdHYASnA8IUGV28qsP5cTt+DDSezYTgSSUDfbjudevXO2OcRR5yAsfAc0BBR1ca/zuEQLz0cZMk3xEaObmGROANwJORXR7xSBCM4OzmLmFg8liFWMdJdA6+5/cI83hpIK8QnCy/ntvGtyvwy9oPEY2Gcqa+ZlcXqAd8l5vrz66cEsFvIdk5bLsjfjlSBtHCBBJ9D8mUdW7gcQUY7w/dcbPzqkVDGunXEEw5rUvUBUJtkwk9j0Uy/U4iXESB99tYfTMCkcesPPXgXZE5mUH8PMjN+fcEctPmqedYDpQzkSOvvf+xFfb6vluvuTJzhzCJ+iTKisGI4PM/g7jvvb6R9hQPHs9CW1BZCyF2BtFrT6TK8Z3Mut203tmMQN/l91/R75BK37Zbh5tyQ2AEkxFuRskZjYwHBhBhVKlkXhB+x+9zKhCBYyDwYhlj/nfZpQ9DE5Ga02tIBNeWKI8wBBMZCXwf7ziJzsH3zHfKLg18x+h0To3aXQ3pp6zjeaIp50R1gc4it9efXTklgt/mxFjcnfDLMZ4ATPQ9JFPWOQFjmzm99I2sAB1LIALnpQ7kpi5RD3IqECGZficReBqj+2bEN5EjCMRTjz86pkAEPw/SXQa7Gzlt89yjn3rpdRdKzIJJePuGDB9lRxx6SHiPf+D42ZUF2+MJRHDfZyMQgd+rrIUQYjcXiTzL7+YHHrPpM2fbzR0vj2sICSHEnsSXXbrbQ892ciGehIrvibDgyvX3PGytWzSz4448LLxV7GwIXT/fq4dfdulhV995v33bs7fdcX3HPbZeCiFEfmGPCDcVQgghhBBCiPyEwk2FEEIIIYQQQhQIJBKFEEIIIYQQQkSQSBRCCCGEEEIIEUEiUQghhBBCCCFEBC1cI3LNuvUbbMKUqW75cqhSqYLVr1Pbfc5vUD952DIP6581d54dfNCBds0lF+ZqiX6RO6bOmOke3bJv9eQfAi92LQWpzecUnt03aep0W712nfu/RPFidkDjhgVqlezd4RqEEGJ3RgvXiN2aDRs32l/jJtqosePsvc+/sl6/9At/s/PBeL3toSfs2x4/hLdkZcr0mfb4Cy9bw7q17Yl7bre2B7eI+zxCsXOgvgwbNSb8XyY8/Pz/XnvL/vvW+5GBp1TgGXir16zN0W9FYnZGm+ch+rTjky660o4+u4NddN2t7nw7ix07dtiUGTPdOXv37W+vvvuRrff6l4LE7nANQgghdg2yjkWuqVq5kl172UV2y9WXW7PGu/bZVqVLlbS7b7rWjjsi9nPPxk6YZK0OOtAuOvt0q1Nrf2vT4qAcPQBa5C08/Pyqi86zS849M6VRL58xnoBhsGDV6jXhLSKd7Iw2zwPVO/foba88/aj16/Kl3dzxMnvFEznzFy4K75G3FC1a1M4+5UR3jWeedHx4a8Fid7gGIYQQu4bCT3iEP6cVwly+Hl7MLm8f3pAD8AaI/AEemdlz51vvX/vbgkVLrFKF8layRInwt/8wdMRo937owS3duw+/X7h4ifX5fZBNnDrdyu1V1sqULp1FACxbvsL++HO4DRs9xkqVLGkVypXL8v3adettyIhR9sfQ4c77V6lihYgXcMWqVTb67wk2b8Eitx/ho3uVLeO+A0IaJ0+baf0HDrZNmzZbmTJlbO6ChZZRKMM7Vwn7a/xEJ0wQmT6bN2+2Md52RGSsa93d2OK1t3GTplixYkVdXv42eKhXRqX+VQ7k5UYvD2njv/T/w0b9Pc72qb53JI8o62kzZ1nvvr/ZytWrnaDAWPXhPH+O+ssGDx9pZUqV8urDNPdA7cYN6jlP8JjxE2z2vAXeb9d4ZVLYq2sVwr/MJLu66P9+4pRpNm7yFKteraotXrrc1qxd6/aT15gw/o3298TJrmyLh8NTYO26dW77XmXLujqQqBx94rV5oE33+X2gC/GuUL6ca/OAeKfcacOFCxd224C6tXr1WrfvyL/+dvXj9BOOdfVvL6/N/tzvd6+e1PfqWzWb54nFxUuXWrGixbw+YZgbGAieIxmS7dcWLFrs9Ut/2UnHHPmv7xP1W/FIthzoh2iPfX7/w7snbrPKFcu7vor2Rz1fv2GD/TpgsG3YtMmdt+cvfa1E8eJWsXz5LOnI7hpyS7w0+tA/z5g1x8qWKW3DR4+1X377w4oXL+bqFOA1ZgCvn9c/F8ooZJu84y1ZtixL289pPgshRH4m1n01FT4bbHZR662Reyn9ot83Rr9DKv2mRKJICGX52bdd7X8ffuoMj+mz59h7n31tzZo0siqVKob3yiSWwYgh1uOnvvbim++6eWdr16+319772BkqjerXdftgODzyfy+6z4i4D77obEs9o6DlQQc4w37WnHl256NPOREA3/f80UaN/dsObdXSNbCp02faa+9/YsPHjLUff/3N9q5a2YkOn9F/j3eG17RZs2z9xo3mNSEnWit6RmWNvau59PUdMMjaH3JwpKEN8a7lrY8/t+MOb2+lPUNud2fN2nX29Mv/sx4/9/XKYJMtW7HC3v74C2es+uUEn37TxX7uP8A6d//BJk+fYQs947p182ZO6GHsvfXR5/Z1157OYB/hGfrdf+zjwnoR4BjGT730mifWh7jj9u73m431hPhBTRu78lq4ZIm94dWzwcNHOUOSsNNgXUpUF1etWWO/D/7TZsye6zxOpbw6Rj3aum2r1a9TK4sg2VMhAPddL88QR6289uXfMLr0+smV67GHt7MMr81Fl+NnnbtYiwObunwPEk8kMqDz0DOdnNBZvnKla0ulSpS0hvXq2OYtW+zFN96z/WpUd0IeEEfPvvKWNWlYz21btmKlDRk52g7z2iQiat6ChTZo2Eg77YRj3CBT3wED7cMvv7Wh3j4Ik/GTp9pXXXvYwQc1c2lORCr9WjyBlajfyo5kymHVmrX24NOdvPo8x2rtt6/95Inkbl57atemlRUuVNjr8z62fn8Mtu07drjyGTt+kjvutz162yGtmmcZKMsrkcj1xksjA3AwbuIUe/mdD1wfO2jYCJszf4G7FzSoW9v1GfQzn3zzvRPptH36lg0bN0XqVG7yWQgh8jMSiTlEIjF/MHXGLPvUM0Cef+wBZ2AceeghznjHo9ceYyVgeMcyGFnECAPu0vPOtmOPaO8Wi6letaoTCEe0be0WUejcvZcd0LiR3XrNFe63LZsd4EaVm3viAc8An72abQ/dcbP7/rBDWtsA7/x1a+9vlStWtGpVq7iwqlOOPcomTZvuPFtBkVin5v7WpuVBNtMTD9WqVLbbr7vK/Y8xSoNhhPv7Xj9a8wOaeMer4Ooe19ymRTMncFJpVAUVjC/mLV118fl22fnn2FHt2tp++1R3Btvhh7aOGJaUMQLsxSce9sr0LGe0IxABI/63QUPtxScftiPbHWLHeIYuC2cs9wz+Axs39MpxiCc4xtlLTz3ivj+ybRvnvdy3RmZ5ld9rLzv1+GNcWS5euswdM1iXEtVFjGIM7rJlyrhBgQdvv8mdp3H9ehKIYcgHvDzdfupjh3l5xgAIAu39L76xc087yS1AQzkiVMhnypDXgsWLbfykqdbWEx9BwzxWm1/ild0L/3vHhYhefM6Zrr3uv08NT8T1tHbefngQ8e7iqfePN8HrI/A4nnf6ya5PoA0joEgX31EPO5xzhrXy+gbaI55IhM9TD9xlRxzaxo5q39Z51rj3NG1YP5yS+KTSr8UTWIn6rexIphzmemIKT+P1V1zi2g+CecDQYU5MIbLw9l9yzll2xknHuXZ0ybln2QlHHW6/eGXXJOxx9ckrkZhdGhGNwLl79ulnd3j9Ln0v7RuBCOO8dkq9ePHJh1zauEcgAClHv07lJp+FECI/k59FoobgREIQXdX3rmpVK2WGBlHBLjjzVLvrxquzhBTFo3jx4nb+Gac4A/7PUWPcSDEri65atdoTkJkDAQiEX/8Y5LyAi5YstXq1a1qnxx+0vT3xB3Vr1bS/J0yy73r2dkYJngK+b1TvHw9XbuB8zFVE4OD5XOilYf6iRZ44apNSg9odKFv6H+8D4oqQzxUrV4e3ZHKyJ8ajvS0wYsxYZzAyKEA5Dxv1lxXxOi4WDEJ4Y+wf3PxAJwaBsGC/jJMht3VRZILQYjAEDyFMmznbhfkxSAJjxo33yr5upIwpw2PaH2qTp81wHudEEAqKkd/Iqz8+eOjwCBJKDHjtZ86Z6zyGtLnfh/zp0lU6vNLbSq9/YAVVtrVvc7ATHISFb9y4yX0P1J+iRTJvsAjLCuX2ch5HYOGie5/8P7v8lrsjr5vuf9Q7/3z3fTrqUqJ+KxGJygEP/hknHuciJWhPREps3LjReWaDEBlRuPCuuZ0nm8bmBzS2Fgc0Df/3D9NmzbaGdetY9XCeUQ7R+ZfbfBZCCJE6EokiIRh7zPspFDBCGPnAW5OMgOL3jNjf95/nPIGXacBFc+LRR9jj99zmvD+3PvSEndfxJheWhPEIhCO+9J9HPLGyyh58ppOdc+UN1rX3zy5UKR0wAnPcEe2diF26bLkN+nOEG8nfZ+9/RuL3RDC8t2zd5rw+ycJ8IlZT9F8MEjRr+s/iJhjFOSW3dVFkgriivhP+h/eKdzxAvnjn0RZ494J5iqcr2UWetm/f4d4LBX6PmCtXrmz4P3Ntq/b++7m5h7S56TPnOOEItPsvvu9mBzZq6BZdwRv84O03usGbkV6dSgYGpR658xZ79enHIq9nH77XeSghHXUpUb+ViETlgKC97p6HrOcvv+bbyJp0pJGyym40Pbf5LIQQInUkEkVCWDxgtScSggYACxGMnzTFGVqJwFMwYMifdv8t17tQsnatW7pQKB9u9Hgn8BTc5+3T+b3/2f233WBvf/KlCzfjewwo0nHTVZfZ52++bK889aibd8N8tnTRtFED27dGdfv5tz/ciDhhW8GQsz0Ryhmi56HFA68jeXjjlZc4495/EV6GEYjQmDx9ZlL1Jha5rYviH5gztnnzFjf3k0VFECc+lStVtE1btmTJUxaF2u6JR8o4EXiT2Y9FinwoN+avFi+WKQb8gRnaGsJv/31rRAZlCFFHOFapXDEi2FisBM/R0uXL3f+J4HfMXSTqwH8hvvCKQm7rUqJ+K1myK4cBQ4ZZg7p17N6br3NCuW3LFlaxQnJtcWeR2zTiOZ7lCU0W4IlFuvJZCCFEakgkioQwv2eLZ8SwEAU3bJ6RxgIP/J/MogFFixbxfmduURJ+z/w0wkZ9tm7b5p6H9/YnXzijEuOOOTNuzo73W/jy+x72n/++Flm4pljxYsaDQ32DLx3gNTv+yMPs82+7WqWK5bPMadyTmDJ9hvPQUhbMUWQRoGTDuphPhDeWxUQoa14sZMFCNXBIy+Y2ZtwEt5Ih3xHyN2j4SPddMiRbFwmT3OF9T9gjcD3sL/4BwYQgeffTr1x4I+Leh/BOwrv9cmQ10u4/9XFz//C0JQIPYfVq1dxcMuoR+d/394FuLmK92rXCe5kLM9y2fZt9+NW3TjD6gzJ4nw9o1NAr19GRNj915iyXJkLP00Fu+7Vk+i0fwknveuzpSKhrkOzKgcWeCK9cv2GjS+PAYcPdKq75idymsYnXz3JPIHqD33Mswkp9UsnnH/r0t0ef+68LVY5FduUAy1eucostMadTCCH2dCQSRULwKvDswa+69LCTL7rShXoizliQghv29Fmz7awrrnMPvGZlTF58vuKWu93NmqXKLzv/bHvprQ/smHMutuvufsg9oxDDkZVGEWd4m/BUnH7J1Xb6pVe7leyuvPBc51ngHJecd5bzBJx71Q125uXX2jV3PmCnHneUNWnUwKWR1e8458kdrrQ/R46x19//xP3/wNPPuwVZkoWwVkbFjz28vUvXnggLYJx/9U2uLBB0119+cdJhhsxhfODWG1z+n3pxR/ciLIxVMYF5aZTro8+95OoCD/dmQROfRHUpUV30YcEdPMF3PfqU+/21dz3gQpVFVphzW6N6NbfoSzD/6tTcz+7x8pk2SxleeO0tbu7ZOaee6L5PVE7UlztvuNozxhe4enRqh6vcXDWOSYilD5+PaNvGrXKKYAzCufb3ytFv8/c8/qxdfO6Zrg6lg0R1iX6D/oPruu8//+dW5DzHSwv/098k6reCMF+PRXnWrVsf3pKVeOVwzGHtrIyXR1z/ceddakOGj3aiiuiMZEh0Dekgt2ncz2v/d3l15R1PJNMn3P7If7xy3yf8bebgXTL5jPeXOazTZ81xgj8WicqBx2z8PXFSZHBJCCH2ZDJCDN2lQHB3/3Pw3f9MuNBZb5a2Pve6f3PEhjjhJ2LXwSMM8AxmN38kHtSNdevXu9XogoZQEIwaHlcQbx9Cw1ganTks8Y6RG1gY4/3Pv7GnHrwrMi9oTwHj/g7PQLvl6iusRbOmrizwEuQkn/2y5nltseoK5cjcxezqQiKSqYuJ6tOeDl5evIWsGhxrUCRROSYDZbBjx44s4jAVGExiIZS8LMPc9GuQqJ6Rj9wT8ZDGIlE5cHxIdrBmV5CbNCLwsBwoZ8JPebRH4UKF7NZrrszcIUxS+ez1LcHnTgZJVA7A41koA/UXQoidQanwYm055fhOZt1uWu+i64C+y++/ot8hlb5NnkSREhh6OTWkqJiJFoXAwMhuH87NPKNUKnkyIFoIQUIgnnr80XucQIwGjwrzynKaz35Zx6srbE9lgZBYJFMXE9WnPRWMbebefdO9l5svGkuYQKJyTAbKIKcCEaiLeV2GuenXIFE9Y3ssYZJsOXB8XvmZnKaRwamnXnrdhRKzYNLvg/+0IcNH2RGHHhLe4x+Syuc4AhHilUMQfh/v+EIIsSchkSiEB4svXH/Pw9a6RTM77sjDwlv3LJiHVbVK5cjCImL35csu3e2hZzvZBWecmrbwTZE6Kgdzz1g937v+L7v0sKvvvN++7dnb7ri+o+qlEELsYhRuKoQQQgghhBA7GYWbCiGEEEIIIYQoEEgkCiGEEEIIIYSIIJEohBBCCCGEECKCRKIQQgghhBBCiAgSiUKItLFi1SobPHxU5KWHUu+erFu/wYaN/itSzlNnzAx/I/ITPH+QR2z45TRq7Dj3HEEhhBAiERKJYo+BlXfXrlsXeeizSD+r16x1huiw0WPs1fc+smGjxoS/EbsTGzZutL/GTXRl/d7nX1mvX/qFv0kfPET/2x4/2EkXXWlHn93BLrruVnc+kTw7duywKZ6AJ9969+1vr777ka33BL4QQgiRCIlEscewefMWe+aVN6z3r/3DW0S6qb3/fnbL1ZfbDZdf4n3eN7xV7G5UrVzJrr3sIlfWzRrnzfPseKB65x697ZWnH7V+Xb60mzteZq94Ime+vNNJU7RoUfeQfsrpzJOOD28VQgghElP4CY/w57RCmMvXw4vZ5e3DG3LAVoXF5Bvwws2YPcd+6ve7LVi0xMrvVdZKlSoZ/jbz+4WLl1jPX35131eqUN5KligR/vaf73v3/c2WrVhhpbzvps2abRW9/RYuWWrzFi60SVOn298TJlmVSpXs1z8G2yJve/W9q1rhwoXdMTZv3mzDRv1lvw3+04p426pUqhh53gthjYuXLrViRYvZH0OH2ZhxE61C+XJWpnRp9z3hcBOnTnO/L1yI42XY3AULrXTpklnSuSdD+cyZN9+VHQ/WB9rxxCnT3HvZMpl5uWz5Cvvjz+HOW1iqZEmrUK7cv567s23bdq+chto+1fe2xg3qhbeK/MCGDRvt74mTvbZRyoqHn6sEeNnZvlfZslasWFHXZqfNnOXa7MrVq50wRHREM3TEaPd+6MEt3XsQ6kqf3wfaBK8OBdvjqtVrXHusVLFCpH0D7XT16rVu35F//W1lSpWy00841tWvvcqUsZ+9/qdxg/pevaqWsM0nQ6J+DdauW++OP3TkaCtbuoyV8/bx63t2/VrwumKRbDnQ743+e4KXj39498RtVrlieStSpEikba7fsMF+HTDYNmza5NLV85e+VqJ4catYvnyWdrlg0WIXInzSMUemvc+Ll0YfwtBnzJrj+pDho8faL7/9YcWLF3N1CvAaj/X6/n4DB1uhjEK2yTvekmXLvL6ogvsekul3hBCioBHrvpoKnw02u6j11sg9h37R7xuj3yGVflMiUSSEsnzvs6/tiy49rJxnuGA4vvnR51a75n62X43qzlDq2vsXe+GNd5xhMt0zuj76+jtr3rSJExyAgXXfk885w2DR4qX26bddbdTY8XZUu7Y2aNgIe/Xdj50wwXggLAoDo5cnODHI6tWuaUs9A+GeJ/7PZs6d5xmFRe27nr1tvmf0tDzoAPe7vgMG2odffuvOg5EyfvJU+6prDzv4oGbOcBz993hnhEzzDBWuZ8uWrc6423/fGs7YEOaEQCevDFsc2NTlOyzw8uiF/71jhx1ysNtG/j763EvOEKdDev/zbzxjbrm1bNY0IixBIjH/EvJe73rtefbc+dbKaz/+DaNLr5/s5/4D7NjD21mGV5ZveW386649XfsZ4Qm2zzp3cXWDNh4knkgcMmKUPfRMJyd0lq9caW99/Lknokpaw3p1bPOWLfbiG++5/qN6tapuf8TRs6+8ZU0a1nPblq1YaUO8+kbdQ0TNW7DQ6ytG2mknHOPqYqI2n4hE/RpMnTHL7nj0KReivs07xwdfdraiRYpao/p1Xb5l168lEmLJlMOqNWvtwac7OSFba799nZjt9mMfa9emlRvseu39j63fH4Nt+44drnzGjp/kjvttj952SKvmntAs444JeSUS6ZvjpbFUyczzjJs4xV5+5wOvzAa5/n7O/AW2r9c3NKhb2wnEtz/+wj755nt3v2DuZOfuP9iGjZsidQph+cj/veg+UxYffNHZndfv/4UQoqAikZhDJBLzB8wz+/TbLnbXDVfb6Sce6wygEiWKO88fxs20mbPto6++teceuc9O9AyQIw89xBNhW2z4mLHW1jNUGOnGILz0vLPs2ksv8oy+1lbTE2cYniccdbjN8oQfRtpdN15jO3aErKwnQO6+6VqbM2+BMwTatDjIMzC/sHq19rf7b73BO2YLZzx83a2nNapX1ypXrOC8EhhBTz1wlx1xaBs7qn3biAesacP6Vqfm/k60/jV+grX3jJcrLjzX2rQ8SAIxAB6NKdPx5KyxAxs3dNsG/jncNnvt8GSvXGHcpCl27BHtXQhb8wOaWJMG9e37Xr3t0Nat3O99JBLzL9xIGITp9lMfO8xrC6W9ckOgvf/FN3buaSdZ/Tq1nRhDqDz/2AN2jCdWeC1YvNjGT5rq2nTQMI8lEpcsXeYGFwgRvficM12b33+fGp6I62ntvP3wIK5Zu9bGjJ8YOd4ET+ThcTzv9JPdQBB1BwFFuvgO4dDhnDOsVbNMQZWozSciUb+GGHnxzffs3FNPsmsuudBd3wGNG9jn33Wzti2b247Qjmz7tURCLJlymOuJKdrV9Vdc4tokgnnA0GFOTCGyaGOXnHOWnXHSca5tXnLuWe7cv3hlR9vE4+qTVyIxuzQiGoFz9+zTz+647iq73XvRfyAQYdzEya5evPjkQy5t9C/0+5SjX6c6d+/l5X0ju/WaK9y2ll4dwOvYvGljN2AlhBAFlfwsEjUEJxKC8bJvjer2xffd3Yguhsw5nuF027VXOiNn/OQp3nth50FkFHiIZzQyOkx4EQtc4Gnaum2bNfNu6D6M9GOQRVO48L+r5Jq165whQUPi2Jxj0rTp7jsEpg9hYozyA0ZmhXJ7Oe+DSA46mOM8A23k2HFO2GMk/zF0uGfAHuy+w5A/+dijnHFHPaAcMNQ3evutW78+fBRREEBoMbiCoAEGegjzQ/jDmHHjrXH9ui6kGwjvPqb9oTZ52gzXHhNBKChGfqP6/wwQNGvSyHkEZ89b4P5v74mJmXPmOo8h0Qi/D/nTpat0qczBhpWrVnuicarb1t6rgwiOvzxRuXHjJvc9ZNfmZ8+bbzfd/6hdfsvdkde9T/6fE4eQqF9buGSJEzf0YdR1XvMWLHJ1nVD4VPq1eCQqBzyWZ5x4nE2dPtOdn4G3jV568MwG8UyCmH3nziDZNDY/oLG1OKBp+L9/IDy3Yd06Vr1qFfc/Bsze4c8+DDT9+scg+/HX31zeE13S6fEH/7WfEEKI9CGRKBKC8fXAbTe6UV7CuTpcf7vd8ch/nAHlg7FESBGr6PFiHs+hrVuE5/+ZM5xKl8w61ycVEIjMIfSPz7maNWkcMWJFesBYK+yJQTyKGNkbN22yA5tkehUBI+2m+x51xrsouCCuGBAg/A9xxDseoPJ77eW+xxPMoEBwxBFRlawA2r59h3svFPg9Yq5cucwwZthn72puoSPmHi71BNf0mXOccARE4xffd7MDGzV0i64c2e4Qe/D2G938ZQYxkgFP5LMP32uvPv1Y5PXInbdEQjCT6de4/pmz50b6HdpFu9atrGz4GLnt1xKVA23wunsecnO982tkTTrSSJlkN5p+4tFH2OP33Obmat760BN2XsebXNgq9UQIIUTeIJEoEsKNn9H7w9u2tpf+84h1+fhtO6BRA3vjw0/dggV4mQjb7NjhfGfQ+a/LLzjXGUHFixe3tWvXuRHgnJBRKNPQJIw1eHxehB2J9EF5YZDj1flz1Bg7pGXziMGKd/Gn/gPs+ssvduG67Vq3tIOaNnZeF1HwYM4YK/4yDxivP+LEp3KlirZpyxbnDfTBi7bdE49EDSSC8EP2Cz6Tb/XatbZw0RIrXixTDPiea7xPCD/mByMcgXB1hGOVyv8sTsViJXiOli5f7v5PBN5P6i7ePf8VXHQmYb9WKNN7ftoJx/6r30Hc5rZf88muHAYMGWYN6taxe2++zrXLti1buEVx8hO5TSOe41me0KR/iQVCEO81nuT7brneOr/3P7v/thvs7U++dOHIQggh8gaJRJEQRu8J2xo8fKS7YRf1REGxYsWMVQUxohASzCH5sd9v7nteI8aMte97/eg+s4Jd3do1rcfPfV3oFoYji9NgYCUD8xWPbt/WPu38fcQg42HezN1JJZyUUX+M0MnTZ7o0kLZt27aFvxU+Bx90oHsAd7+BQyKeHcBoLunlIZ4D8o6y7Nb7Fze3TBQ8EFAIknc//cqFNxJ66UN4JysNszALZc1qpN1/6uPm/pUt889iKPFARFWvVs3NJaOtEX7e9/eBbi5ivdq1wntleq63bd9mH371rROM/pwKBNgBjRq68HLaOkydOculqW6tmu7/3JKoX9t3n+pONLKgip8GQkzf/uQLF7KaSr9GOOldjz3t2k402ZVD6VIlXZ+3fsNGl8aBw4a7VVzzE7lNY5MG9Wz5ipU26M9MzyDHImLBhyiV/771vst38hiRz5xK6kqRolkHqH7o098efe6/LlQ5FtmVAyT6Xggh9iQkEkVCWOnvzus72uvvf2KnXtzRTu5wlTMer770QhcixOj+4/fcbj/3/8N9xz4YfYe0auFu6IR13dzxcick+e5Ubx/CUQlfSwaOcf6Zp3qGVGu78rZ77czLr7ULrrnZE49lbO/wyojJcsZJx9u0GTPtxAsut+POu9QZviIrVTzjlxUiWfzD9+wAIpuFMX7wDOFjzrnYK4Nb3DY8PP4DupnbxYPPT+5wpf05coyrM/z/wNPPuzmOIn9xuCf6alSv5hZ98T1sUKfmfnbPTdfaS2994Nrshdfe4uaenXPqie776bNm21lXXOfKFpHEi89X3HK3M9CpF3fecLVnbC+w0y+52rV55qpxTLzVPnw+om0b18cgGINwrv09oXbuVTe4Nn/P48/axeee6eY2poNE/RqeSPotPJB+Gq696wG3WBbhkan0a8zXY1Gedd73sYhXDscc1s7KeHnEuemvhgwf7UQV8ziTgTZH26Ns7vvP/7lVRc/xroX/aavpILdp3G+fGm7xoHc8kUy/cvsj//HKfZ/wt5lhwXhv8WRTl06/9Gq30umVF56bpX/C600Y/PTwXPhYJCqHRN8LIcSeREaIobsUCO7ufw6++58JFzrrzdLW5173b47YECf8ROwaKFsWbWBuEUZgLHj2F6Pw0d/79YLf8wyv0eMmuJFzVrTzwxmTAY8EiyKwol3QmEoF0kL4EsuzZzcPRsSG/MNrQBljSIuCC48rQBg9dMfNzhiPxm/ztNmcthWEyo4dO7KIw1RIR5vPjmT6NUJTeXZfdBqS7dfYj3siHtJYJCoHf4AlXvryA7lJIwKPnKScCT/l0R7Mjb71miszdwjDObZu2xq3Lrh89soq+NzJIInKIdH3QgiRbkqFF2vLKcd3Mut203oXCQP0jX7/GP0OsfrOeMiTKJKGikWoWXZGAIZg9PfceHlERpcffvJu4NvcaPZnnbu6pe8JJU0FRAlpSKWSR8NvmZskgZgzyD8350wCscCCsU1I8Tfde7nHEcQSJuC3+dy0FfqDnApESEebzw7/GrPr17j+6DSk0q/xu1jCI9lyIG3ZpS8/kNM04nl+6qXXXSgxCyb9PvhPGzJ8lB1x6CHhPf6B42dXF1w+xxGIEK8cfBJ9L4QQexLyJIqdAnNOvuzSwwYOG+Hm/pxw1GF24Zmn6YYsxC7gwy87u1DrG664xK3uGc/oFtmT235N5ZBpNxDi+fHX37kwZqYvXHb+2e55uKqXQojdnfzsSZRIFEIIIYQQQoidjMJNhRBCCCGEEEIUCCQShRBCCCGEEEJEkEgUQgghhBBCCBFBIlEIIYQQQgghRASJRCGEECmxbv0GGzb6Lxs8fJR7TZ0xM/yN2FNh0bpFixbZDz/8YB9//LH9/vvvbgE7IYQQBROJRCFEgWPzxh3uJXYNGzZutL/GTbRRY8fZe59/Zb1+6Rf+Jn3wEP1ve/xgJ110pR19dge76Lpb3fl2NjxIf/WatZGVu0Vsli5dat26dbOqVavaaaedZrVq1UppFb28ZseOHe5h/bwLIYRIjESiEKLA8eP3q91L7BqqVq5k1152kd1y9eXWrHGj8Nb0wgPVO/foba88/aj16/Kl3dzxMnvl3Y9s/sJF4T12DmM8MXzbQ0/YqtVrwltELObPn2916tSxVq1aWeXKla1mzZpWtGjR8Le7nhUrVtg333zj3oUQQiSm8BMe4c9pZfv27fb18GJ2efvwhhzACK4QYueyccMOGz9yo036e5OVLF3ISpcpbDgE8NzNmLLZihTNsBIlM8eXtm8P2ZzpW2zTxpCV8vabM2OLFfL2XTB3q40ctMH9rnzFIu7dh9/MmrrFRg7eYNu3haxchcJWqPA/O2zyzj/XO06ZvbzjeccePsDbz/tNhcpFbPmSbTbb2zZtwmZvv5BLx7LF29zxS5XRmFcybNiw0f6eONnKlC5lxcPPVYK169a57XuVLWvFihV1nrNpM2dZ776/2crVq50wjGX0Dx0x2r0fenBL9x5k2fIV1uf3ge5h6RXKl/POWdptX7h4ic2ZN98qVShvhQr5dWm7TfT2471smdI28q+/rUypUnb6Ccc6j9ReZcrYz/1+t8YN6ts+1avZPE8sLl661IoVLWZ/DB3mxFzwHIlYsWqVzZg1x6uvGS6Nk6fNsGpVKlvJEiXc94TUjhk/waVp3OQpVr1aVe98y23N2rVZ0r2ngwdxyZIlNnnyZHfPLuHl3yovbykzPgPeO0TkxIkT3efSXhkF82/z5s22ePFiK1mypAtZHT9+vNuvjFfmbCdsdcqUKZHwVb6nLvJ8Mc5DXV2zZo1Lg3+c4sWLu++2bdvmzk0aZ82aZeXKlXMexXVefY9OR3bg2R47YZL95NVBjlm5UkUrXLiw+86vS9TX4aPHuhBsPpfbq6xLQ6LvGYAYO3GS1/bKRNokeTJm/ET3Pe0hHXDM0X9P8Or7H15ZeddQsbwVKVIk/K25Oj9xqtdWy+1lEyZPtV59+nvXvc32rlrF5ZOfB/0GDvbaTSHb5B1vybJlXnuoED5CZpv/48/hNmz0GCvllUMFL7+5BiFEbHI7mPbZYLOLWm+N9Ee0N7/NRb9DKu1RIlEIEWHBnK323gtLbeuWkBNwfbquscJFMmzf2sXc+xRPOPbttsaatCjpCYkMGz1kg/3ac40dfFhpr4PKsJ5frrIBP6+zNau225bNIevbfa2tX7fD6jUu4XVMmULzq3dW2ITRm6x02ULu94jJBgeWiAjPpYu22bcfrrBJYzfZiIHrbbknAot6wrRekxK2yBOfU8ZtsoVeOjd7wpRjrly2zcqWK2wVq/xj7Ij4EDT57mdf2+y5863VQQdEbhhdev1kP/cfYMce3s4yPIPwrY8+t6+79nTCa4Qn2D7r3MVaHNjUKpYv7/b3iScSh4wYZQ8908kJzuUrV9pbH39upUqUtIb16niic411euMddzwMZVjgCccX/veOHXbIwW7bshUrbcjI0e5/DOd5CxbaoGEj7bQTjnHf9x0w0D788lsb6u2DwTveM2q/6trDDj6omUtzIsZNnGKPvfCy/TnqL3d8jvN1t152cPPM36/yRMfvg/+0GbPnOu9lKU/wLPUM4K3btlr9OrUiN+Q9nXnz5tmCBQucOEOEICYQbAg4BBnC7scff7Tp06e7bRMmTHAvwlH9hz8jKn/66SebPXu2jR071lZ69YXvqlev7uY2Iv4QjQMGDHDnQRSOGDHCeS4Rg3///bf9/PPPVqlSJZeGX375xQnUatWqOUE3bdo0W+aJGQQoAnLDhg22adMm9z31n+18TzqCL46NAcfAyn/++6oN/HOEld9rL/v5twFOLB7SsrmVLlXS1aVOb77r6ieh2PMXLbb3P//G1ZN9qu+d8PsiRYtY9x/7WP9BQ+zQ1i1dHn789XeuDR1/5OG5NiKBuvvg0528+jzHau23r0t/N++c7dq08sRcppif65Xj/732lmsLvfv2dwMxtI1WBx1o2738f/vjL+yTb753gyQI3c7df/CuZ1Ok7SOAH/m/F93nTZs22wdfdHbnben1M8mKcSH2NCQSc4hEohA7DwRcl09XWrtjy9gJZ5ezRs1KWs16xa1/7zXW8MCSVrJUIatRs6jNmbHV5s3c4ryMPb9abadeUN72qVnMa/Nmf4/YaI08wXf6xRWsacuSVqdRcfv9h7XuHSE3qO86W7F0u11xayUnNJu1LunE4OZNIatVv7hLx7o1O2z4HxuszRGl7aJrK7n0IBDp1xCCDQ7wxOL8rVa+UmE7wzsP/0sgJg83ErwH3X7qY4d5BmLp0qWcF/H9L76xc087yTNcaztjFsH4/GMP2DGeaOS1wDPOx0+aam1bNc9i8MUSiUuWLnOCjxDRi8850xN6rW3/fWp4Iq6ntfP2w2M3ZfpMW+2JxQMbN3S/GfjncNvs9fknH3OkOz7GM0KWdOHVwCDtcM4Z1qpZprDF47HAM7afeuAuO+LQNnZU+7YRT2TThvXdMbOD344YM9aee/R+O+6I9naSd17ON33WbGf842lFRJctU8Z5WB+8/SY7st0h1rh+PQnEAH5oKWGciMKjjjrK/c9nGDNmjBNcZ5xxhhN1DRo0sJkzZ7r7e40aNdw+iDb2O+igg+zkk0+2Fi1a2H777eeE4dSpU61169bWrFkz5w3kc+PGjZ3QRETiDUQ4HnLIIdawYUPbf//9rWLFis7bWLduXSf09tlnHytfvrzNmTPHpY/fs41y5Bx4GDk24jb4qlChghO23/X80asvS+zZh+619occbMe0P9TVewQf9Ze6NGjYCHvmoXvs+KMOtyMPPcTWrVtv02bMcu2C32b3fWGvvjduUM8Jt02bt3j5tdq+8er7PTddZ1WrVHJ5lFvmzl/g6vT1V1zi0szgy4Chw5zgQzTCSu+8P/TpZ6cdf6w9ctctds6pJ0YGksZ5bYD2++KTD7m2cqzXZhCAtDe/7Xfu3ssOaNzIbr3mCretpddW8To2b9o4aQ+/EHsa+VkkamhHCOFYsWy7LV+y3Qm2iX9tci+8eBvXh5y3DvAWHn/WXi4c9Iu3llv748pY/aaZo9A+iEe/D6pavYjtVaGwrV653XknZ0/b7PYvHvYa8n5Qm1I2c8pm53n0qVSlsBOQKfRlIgUQWpUrVnAeQpg2c7YLH2t+QBP3/5hx4z0xVNeqVKro/i/i3XwwjAnJXLN2nduWHXggMB4beYLKp1mTRs4rMXveAnczQ5iNHDvO1juvzmb7Y+hwT7QeHLnRYbBOmDLVpbW9tx1D9q/xE23jxk3ueyhVqqQVLZJ5gy3m3WgJk8PjmCykp4QnIoBrbNuqhc1fuNiF0YncgxcPLyOi0fca8o6YwwMZHAjGC1i/fv1sDZhY3igMrObNmzsPIWIPAbp8+XIXTkodTAT1DQF6xBFH/OvleybHTZrsPMzUN+CdgZO/Jkx0dReCdYlrqFK5oi31hPNmT/RBou8JKb3xykuta++f7bX3P3ZCq07N/dx36aCR157POPE4mzp9pvMCDh8z1oXd4uUPUmPvanZ0+7b/Kodps2Zbw7p1rHrVKu5/vicMNQhC99c/BtmPv/5mi5YstXq1a1qnxx/8135CiIKBRKIQIgLzCfHSTZ+4yb3mz95iTZqXsFKe8PPBI0j46TZP9NWqx7yf8BcxKOQdsFjxDFu1YpuxOOSOHWwLfxmmTDl1QzsbjFxEWt8Bg5wXkXc8C4TSwbZt251BHjQU8TiWKJFp5CZi+/bMFSSZ7+eDmCtXLjO0FDA48aDgUZw9b75t3LTJDmyS6VUknPCL77vZgY0ausVx8OA9ePuNttAzPBGWeQVzqJavWGmb9eiGtEA54qmLFnd459IFxx82bJh9//33zmOZKghJQlwJZY1+ITbptwi1LFw46zVEh12ng/1qVHfzbamHdWvVDG9ND7Sx6+55yHr+8muOo7SYM5md1+PEo4+wx++5zXneb33oCTuv403Og0o9EEIUPGSdCSEchQqbZXg9AmGep11UPsur2j7/GAbMCWRBmRaHlrJf/r+9e4+RqrzDOP5bmF0xUPHCusvNSEtEidbWtKk0tVUUMaUCSpUULV5WBIG2uIq2BrUR9R+imNYg2pIGWtlYqeFiVutCK4pugCpttYpKLUkRKEh1oSu3Zel53p13OBxm58zszOBevp9kcy5zzsyZ2Tmz59n3fX+ztCHjV1Hs39ccBMRDVl5R6greaBzivsj26rqqIKnHx/GjsUhqxXjp5VddUQ2FRE9FOfYFQSncErNl63Y7FITHRCL+F6VubdruQOhitGHPHtu2fYedUNbyXlJQVfhbXb/W1r75V9fF04dUjWPb+fEu19rig6ounNUisTO4cC8WjcnqW1FuJyZbfJAftdKphU+tcWHqHqqwka5lMFdqMVRRm5EjR7oWxUGDBlllZWXy1niuVa+83HVvjf7o2EtLE9Y7eF82frY3uUeLjZv+GbxPehwTHttKQap21cvuHzRq9Vv83DJXKKZQXqlfZ2d96Ys2c9qt7ry78IKv2qmnZB90ewbBfnMQNNXyn46OX70M1OJ/1/TJ9vtfPW53/3iKzV+42HXjBtDxEBIBOH0qEm4M4qrlu12FUWkIAl7tsw2u+IyosM2Kmk/t0tEn2YixvYMLpBKrW7Y7CBNH/lO87d8HXddS/fP47+v3upbD8sqEm6prqQrVbNnc0lKjIjV/W7fXrU8kMjRJRlQOKLUdW5vss+RxNTXxn+pcKZApGD61qMZ1Mx3Qr2/yFnPdO996Z6MrYKGLP1VfXPZinRv7pzF6cQadMTAIWxVujJKCoi52V65eY6edeooNHnRmciuzr51/nv1j4/v2pzX1bqyXp3Fk5549xOr/ssFVXJQP/rXZHVOuLSy7PvnUFdB56c+vJNccoeCqFhZR9zh1k1ORDj2+py63zcFroC60oudCy0h2FALVtVSFZRQMRUVpVOVU633X4nxofK0LKLt3u2ljY6Nt2NAyTjZMYxe1rcZOaju1QPpWTo1tVLiM/qjFU8eoYk56bygYisb3rXr1ddflNN/xRJ7OtSUram3yxAlWdd14e++DD235iysL9l5TgR29xxV2dZ9r1q13FYGzNfSswa6V/bW1LS2D/nzxDjY12SNP/DoIhU+7c17hW5WC9fqpME+YurlX3/dg6tyLirs90zkNoHAIiQAcBb7vje9tvXp3t4fu2GazZ2y1XzzwHxtwZpnrbqrgWLe0wY0VHHJeD9f6N3rCyfbhe/vt7Td04dFyP5/sCi4WZm23+6d9ZKtf2OMK22hcomg84iWjvmALHvnYfj79I5v30A678OKedvb5R49rjHPuBSe6UKlj/NktW6z2mYbU4yN7FwWhr1/fClf0Jdy1VGOh7pw6yR59YoGNmnCzjZ803bVuqJCFqLjL2BtudV9yv/yPK92P5m+YfocbS6huqbdPqQou8rbalddV2agf3OTGQOk+/bguKe9zmqtGqkIz/Ssrkmtb6LHO6N/Xxt00xcZMnGR33v+wTRg3xo1tzIVK8r/17sZUyIv63ZKl7jlOnFbtivZcccl3kre0GBgcw+UXX2TV9852z3FS9U/tv8FFKrKjQjLDhg2zmpoamzt3ri1cuNAVplEIKwS19un+a2trbdasWbZo0SLXMqhWcP+VGaLt9B2Ofrv58+e77qTZ+PpXvmw/vOYq9x7Ue+W2u+61MVdc5iqRFoIC129qltjEa692FU9VTKb6tir7w/Mv2Nsb309ulZ/h3/qm9QrOPZ1Ll33/eqtfv8EFP1URzsbA/v2sOjinn1xUY8OvnmA/mfVAcH72T97aMiZYXcPV40Dn/JXXV7lKpzeOH3fMua1xkfpaHBXvSSfu9rhzGkBhlBzO8d9U4c39fHjq5/XhPHZeT6ub6RbbRBXPABx/aplTIRlVNA1lh4y0/eL5u1y10WHDe7nvW2xtf31M6PYTepS4cNoW/j4UFhVYkTtVU1QLxj0zprmLvCh9nv+vsdEV3Ghri4kKe6jFJhwOc6GWOxXYUHXEcJDNhcYY6vmF91e5/scXLLTHHrwv9d2ImcZc6nno6y/yOY6uTO8ldTtV4ZpCdDON8vevVuBMvx8fHvUVGbn+Hv35oMCpQkcdkS+0k+344jC9drrC0/mo7qcqsKNxxT+65caWDZLizhW9jvodhFvsw+Jul3TnNNAR5TtGe8Qcs6VTG1PFwXRO+PMiOpVczhlaEgEcQ8FLrYc5fJYcRftl2t/f3taAKP4+CIi500Wcunk+s+x5u+q7I9MGRNEfE3UvzadLnS5G2xoQRRfjOoZc/rBFqbJkpv11jHEXzbo93+PoyvS6KZgVIyCKv/+4348fJ9mW36P20XugowZEyea9no56CMx+9Jeuy7fGTeo7ROvXv2nfHvaN5BZHxJ0rWp8pAMbdLnHnNID8ERIBFIT+XqvyqVoH0b6pKMY9D8+xa0ePyrn7ZmehAjqnl/cpWmgBOpOTe59k1wSfF4ufW25Vt99tz66otRmTb+6ynx9AV0B3UwAAAAA4zuhuCgAAAADoEAiJAAAAAIAUQiIAAAAAIIWQCAAAAABIISQCAAAAAFIIiQAAAACAFEIiAAAAACCFkAgAAAAASGm3ITH8pf0AAAAAgOOjXbckJhKJ5BwAAAAAdA7du3dPzrVP7TYklpSUuBevWzd6xAIAAADoHJRxSktLk0vtU8nhHPt1hjf38+Gpnz9w4ICNndfT6ma6RQAAAABAgYyYY7Z0aqOVlZW5ZTWy6cfPh6cSno9DMx0AAAAAIIWQCAAAAABIISQCAAAAAFIIiQAAAACAFEIiAAAAACCFkAgAAAAASCEkAgAAAABSCIkAAAAAgBRCIgAAAAAgJa+QmO83+QMAAAAA8pMuj+WTy2hJBAAAAACkFC0kKrmWdj9s+5uSKwAAAAAAeVPGUtYqVi/OgobE8EF269bNBpc32RubkysAAAAAAHlTxlLWUubyChkYi9qSePnQg/bb15ppTQQAAACAAlC2UsZS1uoQLYmeDlapdvg5ZkNO328znm621ze1PCEAAAAAQG6UpZSplK2UsZS1lLmKERRLDgeS81mJbh5e1rxf9vNNTU226p3DVvdumW3ambCDh4qTdgEAAACgs9IYRHUxHXHOAbt0aIklEgkXEH1IDM9LNDxGlzPJOSRKdBe/HJ6G55ubm92Pn/fro9KtAwAAAICuIF2Q8+t8q6Gm4RZETcPz4akXXY5TtJCYzVTC8wAAAACAo4NdayEwbupFl+MUNCSKn892mk6m2wAAAACgM8kU4loLgK1NJTwv0eU4bQqJEt3NL4fXR9el28ZLtw4AAAAAupJ0gS4aBKNTSbdOosvZKHhIFD8ft06iywAAAADQ1bUW9sLrM63zosvZKFhIlPC6dPPRfdLdBwAAAACg9cDXWihMFwjTrYvT5pAo6XYNr2ttXvJ4WAAAAADoEqIhr9gBUfIKiZJu90yBMO7h8jwcAAAAAOhw4gJdpkBYyIAoRQmJEl2f7XYAAAAA0NW1FvKyCYjyuYZEyTUAEgwBAAAAIDu5BsF8AqIUJCRKprvJ5SEIkAAAAAC6mlyCXaZt8w2IUrCQ6MXdHSEQAAAAAHITF/4KEQ69godEaetdEiABAAAAdFVtDXqFDIhSlJAoBD4AAAAAKK5CB0QpWkgMIzACAAAAQGEUIxiGHZeQGEVoBAAAAIDsFDsURn0uIREAAAAA0B6Z/R+Nvf51RIYjcgAAAABJRU5ErkJggg==)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2WmjglsViQ1q"
      },
      "source": [
        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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qfYFm0eZiVYL"
      },
      "source": [
        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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n5bwE3zckX3i"
      },
      "source": [
        "## Training"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p4Eng64Fj1SJ",
        "outputId": "1c578680-7526-4f36-cf1d-1510db156cb1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s.pt to yolov8s.pt...\n",
            "100% 21.5M/21.5M [00:00<00:00, 142MB/s]\n",
            "\n",
            "Ultralytics YOLOv8.0.16 🚀 Python-3.8.10 torch-1.13.1+cu116 CUDA:0 (Tesla T4, 15110MiB)\n",
            "\u001b[34m\u001b[1myolo/engine/trainer: \u001b[0mtask=detect, mode=train, model=yolov8s.pt, data=/content/drive/MyDrive/yolov8/dataset.yaml, epochs=10, patience=50, batch=8, imgsz=640, save=True, cache=False, device=, workers=8, project=/content/drive/MyDrive/yolov8/training_results, name=football, exist_ok=False, pretrained=False, optimizer=SGD, verbose=False, seed=0, deterministic=True, single_cls=False, image_weights=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, save_json=False, save_hybrid=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, show=False, save_txt=False, save_conf=False, save_crop=False, hide_labels=False, hide_conf=False, vid_stride=1, line_thickness=3, visualize=False, augment=False, agnostic_nms=False, retina_masks=False, classes=None, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=False, opset=17, workspace=4, nms=False, lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, fl_gamma=0.0, label_smoothing=0.0, nbs=64, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0, cfg=None, v5loader=False, save_dir=/content/drive/MyDrive/yolov8/training_results/football\n",
            "Downloading https://ultralytics.com/assets/Arial.ttf to /root/.config/Ultralytics/Arial.ttf...\n",
            "100% 755k/755k [00:00<00:00, 79.8MB/s]\n",
            "Overriding model.yaml nc=80 with nc=2\n",
            "\n",
            "                   from  n    params  module                                       arguments                     \n",
            "  0                  -1  1       928  ultralytics.nn.modules.Conv                  [3, 32, 3, 2]                 \n",
            "  1                  -1  1     18560  ultralytics.nn.modules.Conv                  [32, 64, 3, 2]                \n",
            "  2                  -1  1     29056  ultralytics.nn.modules.C2f                   [64, 64, 1, True]             \n",
            "  3                  -1  1     73984  ultralytics.nn.modules.Conv                  [64, 128, 3, 2]               \n",
            "  4                  -1  2    197632  ultralytics.nn.modules.C2f                   [128, 128, 2, True]           \n",
            "  5                  -1  1    295424  ultralytics.nn.modules.Conv                  [128, 256, 3, 2]              \n",
            "  6                  -1  2    788480  ultralytics.nn.modules.C2f                   [256, 256, 2, True]           \n",
            "  7                  -1  1   1180672  ultralytics.nn.modules.Conv                  [256, 512, 3, 2]              \n",
            "  8                  -1  1   1838080  ultralytics.nn.modules.C2f                   [512, 512, 1, True]           \n",
            "  9                  -1  1    656896  ultralytics.nn.modules.SPPF                  [512, 512, 5]                 \n",
            " 10                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          \n",
            " 11             [-1, 6]  1         0  ultralytics.nn.modules.Concat                [1]                           \n",
            " 12                  -1  1    591360  ultralytics.nn.modules.C2f                   [768, 256, 1]                 \n",
            " 13                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          \n",
            " 14             [-1, 4]  1         0  ultralytics.nn.modules.Concat                [1]                           \n",
            " 15                  -1  1    148224  ultralytics.nn.modules.C2f                   [384, 128, 1]                 \n",
            " 16                  -1  1    147712  ultralytics.nn.modules.Conv                  [128, 128, 3, 2]              \n",
            " 17            [-1, 12]  1         0  ultralytics.nn.modules.Concat                [1]                           \n",
            " 18                  -1  1    493056  ultralytics.nn.modules.C2f                   [384, 256, 1]                 \n",
            " 19                  -1  1    590336  ultralytics.nn.modules.Conv                  [256, 256, 3, 2]              \n",
            " 20             [-1, 9]  1         0  ultralytics.nn.modules.Concat                [1]                           \n",
            " 21                  -1  1   1969152  ultralytics.nn.modules.C2f                   [768, 512, 1]                 \n",
            " 22        [15, 18, 21]  1   2116822  ultralytics.nn.modules.Detect                [2, [128, 256, 512]]          \n",
            "Model summary: 225 layers, 11136374 parameters, 11136358 gradients, 28.6 GFLOPs\n",
            "\n",
            "Transferred 349/355 items from pretrained weights\n",
            "\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 64 weight(decay=0.0005), 63 bias\n",
            "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /content/yolo_data/labels/train... 99 images, 0 backgrounds, 0 corrupt: 100% 99/99 [00:00<00:00, 1927.48it/s]\n",
            "\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: /content/yolo_data/labels/train.cache\n",
            "\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))\n",
            "\u001b[34m\u001b[1mval: \u001b[0mScanning /content/yolo_data/labels/val... 24 images, 0 backgrounds, 0 corrupt: 100% 24/24 [00:00<00:00, 2474.70it/s]\n",
            "\u001b[34m\u001b[1mval: \u001b[0mNew cache created: /content/yolo_data/labels/val.cache\n",
            "Image sizes 640 train, 640 val\n",
            "Using 2 dataloader workers\n",
            "Logging results to \u001b[1m/content/drive/MyDrive/yolov8/training_results/football\u001b[0m\n",
            "Starting training for 10 epochs...\n",
            "Closing dataloader mosaic\n",
            "\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "       1/10      2.11G      1.659      2.179      1.264         17        640: 100% 13/13 [00:21<00:00,  1.62s/it]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 2/2 [00:03<00:00,  1.96s/it]\n",
            "                   all         24        190      0.688      0.593      0.589      0.331\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "       2/10      2.72G      1.389      1.434      1.127          6        640: 100% 13/13 [00:10<00:00,  1.18it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 2/2 [00:02<00:00,  1.41s/it]\n",
            "                   all         24        190      0.942      0.626      0.661      0.429\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "       3/10      2.72G      1.264      1.076      1.036         26        640: 100% 13/13 [00:10<00:00,  1.20it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 2/2 [00:02<00:00,  1.39s/it]\n",
            "                   all         24        190      0.925       0.64      0.672      0.447\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "       4/10      2.72G      1.228     0.9498      1.036         18        640: 100% 13/13 [00:10<00:00,  1.21it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 2/2 [00:02<00:00,  1.38s/it]\n",
            "                   all         24        190      0.887      0.747      0.811      0.512\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "       5/10      2.72G      1.169     0.8475     0.9703         22        640: 100% 13/13 [00:10<00:00,  1.20it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 2/2 [00:02<00:00,  1.39s/it]\n",
            "                   all         24        190      0.902      0.839      0.856      0.554\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "       6/10      2.72G      1.176     0.7833     0.9874         25        640: 100% 13/13 [00:10<00:00,  1.18it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 2/2 [00:02<00:00,  1.40s/it]\n",
            "                   all         24        190      0.943      0.805      0.845      0.525\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "       7/10      2.72G      1.193     0.6543     0.9772         22        640: 100% 13/13 [00:10<00:00,  1.23it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 2/2 [00:02<00:00,  1.41s/it]\n",
            "                   all         24        190      0.988       0.85      0.884      0.575\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "       8/10      2.72G       1.09     0.6459     0.9562          6        640: 100% 13/13 [00:10<00:00,  1.22it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 2/2 [00:02<00:00,  1.40s/it]\n",
            "                   all         24        190      0.973      0.835      0.882      0.553\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "       9/10      2.72G      1.193      0.679     0.9815         19        640: 100% 13/13 [00:10<00:00,  1.27it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 2/2 [00:02<00:00,  1.39s/it]\n",
            "                   all         24        190      0.969      0.849      0.895      0.536\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      10/10      2.72G      1.073     0.6211     0.9545          6        640: 100% 13/13 [00:10<00:00,  1.25it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 2/2 [00:03<00:00,  1.59s/it]\n",
            "                   all         24        190       0.99      0.839      0.869      0.568\n",
            "\n",
            "10 epochs completed in 0.044 hours.\n",
            "Optimizer stripped from /content/drive/MyDrive/yolov8/training_results/football/weights/last.pt, 22.5MB\n",
            "Optimizer stripped from /content/drive/MyDrive/yolov8/training_results/football/weights/best.pt, 22.5MB\n",
            "\n",
            "Validating /content/drive/MyDrive/yolov8/training_results/football/weights/best.pt...\n",
            "Ultralytics YOLOv8.0.16 🚀 Python-3.8.10 torch-1.13.1+cu116 CUDA:0 (Tesla T4, 15110MiB)\n",
            "Model summary (fused): 168 layers, 11126358 parameters, 0 gradients, 28.4 GFLOPs\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 2/2 [00:03<00:00,  1.80s/it]\n",
            "                   all         24        190      0.988       0.85      0.884      0.575\n",
            "                player         24        170      0.976      0.974      0.993      0.595\n",
            "              football         24         20          1      0.726      0.775      0.556\n",
            "Speed: 0.2ms pre-process, 11.2ms inference, 0.0ms loss, 1.5ms post-process per image\n",
            "Saving /content/drive/MyDrive/yolov8/training_results/football/predictions.json...\n",
            "Results saved to \u001b[1m/content/drive/MyDrive/yolov8/training_results/football\u001b[0m\n"
          ]
        }
      ],
      "source": [
        "!yolo task=detect mode=train model=yolov8s.pt data=/content/drive/MyDrive/yolov8/dataset.yaml epochs=10 imgsz=640 batch=8 project=/content/drive/MyDrive/yolov8/training_results name=football "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xRq4HW-Nopo9"
      },
      "source": [
        "## Inferencing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0YYuevqfnyak",
        "outputId": "3184eacc-5999-40b9-8571-aa96922585ab"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Ultralytics YOLOv8.0.16 🚀 Python-3.8.10 torch-1.13.1+cu116 CUDA:0 (Tesla T4, 15110MiB)\n",
            "Model summary (fused): 168 layers, 11126358 parameters, 0 gradients, 28.4 GFLOPs\n",
            "image 1/9 /content/drive/MyDrive/yolov8/test_images/22d_f_195_6ty.jpg: 384x640 12 players, 15.5ms\n",
            "image 2/9 /content/drive/MyDrive/yolov8/test_images/22d_f_440_54r.jpg: 384x640 1 player, 11.9ms\n",
            "image 3/9 /content/drive/MyDrive/yolov8/test_images/22d_f_590_54r.jpg: 384x640 11.6ms\n",
            "image 4/9 /content/drive/MyDrive/yolov8/test_images/54r_f_125_6ty.jpg: 384x640 13 players, 11.6ms\n",
            "image 5/9 /content/drive/MyDrive/yolov8/test_images/54r_f_225_fre.jpg: 384x640 1 player, 11.6ms\n",
            "image 6/9 /content/drive/MyDrive/yolov8/test_images/54r_f_95_rt3.jpg: 384x640 11 players, 11.6ms\n",
            "image 7/9 /content/drive/MyDrive/yolov8/test_images/6ty_f_405_6ty.jpg: 384x640 3 players, 11.6ms\n",
            "image 8/9 /content/drive/MyDrive/yolov8/test_images/fr_f_10_fre.jpg: 384x640 11 players, 1 football, 11.6ms\n",
            "image 9/9 /content/drive/MyDrive/yolov8/test_images/we_f_20_22d.jpg: 352x640 1 player, 1 football, 14.2ms\n",
            "Speed: 0.5ms pre-process, 12.3ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
            "Results saved to \u001b[1mruns/detect/predict\u001b[0m\n"
          ]
        }
      ],
      "source": [
        "!yolo task=detect mode=predict model=/content/drive/MyDrive/yolov8/training_results/football/weights/best.pt conf=0.55 source=/content/drive/MyDrive/yolov8/test_images"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!cp -r /content/runs/detect/predict /content/drive/MyDrive/yolov8/output"
      ],
      "metadata": {
        "id": "yozuGgMMBBWy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jmYPAUhIx9Wz"
      },
      "source": [
        "### inferencing on video\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Cj7O6UlHyENs",
        "outputId": "a48bc67c-cbad-4d05-bab1-ac83ff43c4c5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Ultralytics YOLOv8.0.16 🚀 Python-3.8.10 torch-1.13.1+cu116 CUDA:0 (Tesla T4, 15110MiB)\n",
            "Model summary (fused): 168 layers, 11126358 parameters, 0 gradients, 28.4 GFLOPs\n",
            "video 1/4 (1/149) /content/drive/MyDrive/yolov8/vid2/5.mp4: 352x640 1 player, 1 football, 15.4ms\n",
            "video 1/4 (2/149) /content/drive/MyDrive/yolov8/vid2/5.mp4: 352x640 1 player, 1 football, 13.6ms\n",
            "video 1/4 (3/149) /content/drive/MyDrive/yolov8/vid2/5.mp4: 352x640 1 player, 1 football, 13.1ms\n",
            "video 1/4 (4/149) /content/drive/MyDrive/yolov8/vid2/5.mp4: 352x640 1 player, 1 football, 14.6ms\n",
            "video 1/4 (5/149) /content/drive/MyDrive/yolov8/vid2/5.mp4: 352x640 1 player, 1 football, 24.4ms\n",
            "video 1/4 (6/149) /content/drive/MyDrive/yolov8/vid2/5.mp4: 352x640 1 player, 1 football, 13.4ms\n",
            "video 1/4 (7/149) /content/drive/MyDrive/yolov8/vid2/5.mp4: 352x640 1 player, 1 football, 13.0ms\n",
            "video 1/4 (8/149) /content/drive/MyDrive/yolov8/vid2/5.mp4: 352x640 1 player, 1 football, 13.3ms\n",
            "video 1/4 (9/149) /content/drive/MyDrive/yolov8/vid2/5.mp4: 352x640 1 player, 1 football, 13.6ms\n",
            "video 1/4 (10/149) /content/drive/MyDrive/yolov8/vid2/5.mp4: 352x640 1 player, 1 football, 13.5ms\n",
            "video 1/4 (11/149) /content/drive/MyDrive/yolov8/vid2/5.mp4: 352x640 1 player, 1 football, 13.5ms\n",
            "video 1/4 (12/149) /content/drive/MyDrive/yolov8/vid2/5.mp4: 352x640 1 player, 1 football, 13.6ms\n",
            "video 1/4 (13/149) /content/drive/MyDrive/yolov8/vid2/5.mp4: 352x640 1 player, 1 football, 15.8ms\n",
            "video 1/4 (14/149) /content/drive/MyDrive/yolov8/vid2/5.mp4: 352x640 1 player, 1 football, 13.7ms\n",
            "video 1/4 (15/149) /content/drive/MyDrive/yolov8/vid2/5.mp4: 352x640 1 player, 1 football, 13.4ms\n",
            "video 1/4 (16/149) /content/drive/MyDrive/yolov8/vid2/5.mp4: 352x640 1 player, 1 football, 13.6ms\n",
            "video 1/4 (17/149) /content/drive/MyDrive/yolov8/vid2/5.mp4: 352x640 1 player, 1 football, 13.5ms\n",
            "video 1/4 (18/149) /content/drive/MyDrive/yolov8/vid2/5.mp4: 352x640 1 player, 1 football, 13.9ms\n",
            "video 1/4 (19/149) /content/drive/MyDrive/yolov8/vid2/5.mp4: 352x640 1 player, 1 football, 13.5ms\n",
            "video 1/4 (20/149) /content/drive/MyDrive/yolov8/vid2/5.mp4: 352x640 1 player, 1 football, 13.3ms\n",
            "video 1/4 (21/149) /content/drive/MyDrive/yolov8/vid2/5.mp4: 352x640 1 player, 1 football, 13.3ms\n",
            "video 1/4 (22/149) /content/drive/MyDrive/yolov8/vid2/5.mp4: 352x640 1 player, 1 football, 14.2ms\n",
            "video 1/4 (23/149) /content/drive/MyDrive/yolov8/vid2/5.mp4: 352x640 1 player, 1 football, 15.0ms\n",
            "video 1/4 (24/149) /content/drive/MyDrive/yolov8/vid2/5.mp4: 352x640 1 player, 1 football, 14.5ms\n",
            "video 1/4 (25/149) /content/drive/MyDrive/yolov8/vid2/5.mp4: 352x640 1 player, 1 football, 13.5ms\n",
            "video 1/4 (26/149) /content/drive/MyDrive/yolov8/vid2/5.mp4: 352x640 1 player, 1 football, 13.8ms\n",
            "video 1/4 (27/149) /content/drive/MyDrive/yolov8/vid2/5.mp4: 352x640 1 player, 1 football, 14.5ms\n",
            "video 1/4 (28/149) /content/drive/MyDrive/yolov8/vid2/5.mp4: 352x640 1 player, 1 football, 14.0ms\n",
            "video 1/4 (29/149) /content/drive/MyDrive/yolov8/vid2/5.mp4: 352x640 1 player, 1 football, 13.8ms\n",
            "video 1/4 (30/149) /content/drive/MyDrive/yolov8/vid2/5.mp4: 352x640 1 player, 1 football, 18.6ms\n",
            "video 1/4 (31/149) /content/drive/MyDrive/yolov8/vid2/5.mp4: 352x640 1 player, 1 football, 15.8ms\n",
            "video 1/4 (32/149) /content/drive/MyDrive/yolov8/vid2/5.mp4: 352x640 1 player, 1 football, 13.5ms\n",
            "video 1/4 (33/149) /content/drive/MyDrive/yolov8/vid2/5.mp4: 352x640 1 player, 1 football, 14.0ms\n",
            "video 1/4 (34/149) /content/drive/MyDrive/yolov8/vid2/5.mp4: 352x640 1 player, 1 football, 14.7ms\n",
            "video 1/4 (35/149) /content/drive/MyDrive/yolov8/vid2/5.mp4: 352x640 1 player, 1 football, 14.0ms\n",
            "video 1/4 (36/149) /content/drive/MyDrive/yolov8/vid2/5.mp4: 352x640 1 player, 1 football, 13.3ms\n",
            "video 1/4 (37/149) /content/drive/MyDrive/yolov8/vid2/5.mp4: 352x640 1 player, 1 football, 13.4ms\n",
            "video 1/4 (38/149) /content/drive/MyDrive/yolov8/vid2/5.mp4: 352x640 1 player, 1 football, 14.1ms\n",
            "video 1/4 (39/149) /content/drive/MyDrive/yolov8/vid2/5.mp4: 352x640 1 player, 1 football, 13.1ms\n",
            "video 1/4 (40/149) /content/drive/MyDrive/yolov8/vid2/5.mp4: 352x640 1 player, 1 football, 13.6ms\n",
            "video 1/4 (41/149) /content/drive/MyDrive/yolov8/vid2/5.mp4: 352x640 1 player, 1 football, 13.9ms\n",
            "video 1/4 (42/149) /content/drive/MyDrive/yolov8/vid2/5.mp4: 352x640 1 player, 1 football, 13.5ms\n",
            "video 1/4 (43/149) /content/drive/MyDrive/yolov8/vid2/5.mp4: 352x640 1 player, 1 football, 13.6ms\n",
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            "video 4/4 (501/519) /content/drive/MyDrive/yolov8/vid2/8.mp4: 640x384 1 player, 11.0ms\n",
            "video 4/4 (502/519) /content/drive/MyDrive/yolov8/vid2/8.mp4: 640x384 1 player, 14.0ms\n",
            "video 4/4 (503/519) /content/drive/MyDrive/yolov8/vid2/8.mp4: 640x384 1 player, 10.2ms\n",
            "video 4/4 (504/519) /content/drive/MyDrive/yolov8/vid2/8.mp4: 640x384 1 player, 10.9ms\n",
            "video 4/4 (505/519) /content/drive/MyDrive/yolov8/vid2/8.mp4: 640x384 1 player, 10.7ms\n",
            "video 4/4 (506/519) /content/drive/MyDrive/yolov8/vid2/8.mp4: 640x384 1 player, 10.2ms\n",
            "video 4/4 (507/519) /content/drive/MyDrive/yolov8/vid2/8.mp4: 640x384 1 player, 10.1ms\n",
            "video 4/4 (508/519) /content/drive/MyDrive/yolov8/vid2/8.mp4: 640x384 1 player, 10.8ms\n",
            "video 4/4 (509/519) /content/drive/MyDrive/yolov8/vid2/8.mp4: 640x384 1 player, 10.7ms\n",
            "video 4/4 (510/519) /content/drive/MyDrive/yolov8/vid2/8.mp4: 640x384 1 player, 10.6ms\n",
            "video 4/4 (511/519) /content/drive/MyDrive/yolov8/vid2/8.mp4: 640x384 1 player, 11.2ms\n",
            "video 4/4 (512/519) /content/drive/MyDrive/yolov8/vid2/8.mp4: 640x384 1 player, 10.2ms\n",
            "video 4/4 (513/519) /content/drive/MyDrive/yolov8/vid2/8.mp4: 640x384 1 player, 10.2ms\n",
            "video 4/4 (514/519) /content/drive/MyDrive/yolov8/vid2/8.mp4: 640x384 1 player, 12.2ms\n",
            "video 4/4 (515/519) /content/drive/MyDrive/yolov8/vid2/8.mp4: 640x384 1 player, 12.4ms\n",
            "video 4/4 (516/519) /content/drive/MyDrive/yolov8/vid2/8.mp4: 640x384 1 player, 10.7ms\n",
            "video 4/4 (517/519) /content/drive/MyDrive/yolov8/vid2/8.mp4: 640x384 1 player, 9.6ms\n",
            "video 4/4 (518/519) /content/drive/MyDrive/yolov8/vid2/8.mp4: 640x384 1 player, 10.0ms\n",
            "video 4/4 (519/519) /content/drive/MyDrive/yolov8/vid2/8.mp4: 640x384 1 player, 14.0ms\n",
            "Speed: 0.5ms pre-process, 13.3ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 640)\n",
            "Results saved to \u001b[1mruns/detect/predict2\u001b[0m\n"
          ]
        }
      ],
      "source": [
        "!yolo task=detect mode=predict model=/content/drive/MyDrive/yolov8/training_results/football/weights/best.pt conf=0.77 source=/content/drive/MyDrive/yolov8/vid2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vLoiY90U0nNs"
      },
      "outputs": [],
      "source": [
        "!cp -r /content/runs/detect/predict2 /content/drive/MyDrive/yolov8/output"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# !yolo task=detect mode=predict model=/content/drive/MyDrive/yolov8/training_results/football2/weights/best.pt conf=0.5 source=/content/drive/MyDrive/yolov8/videos"
      ],
      "metadata": {
        "id": "aiJX4prsf3sA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# !cp -r /content/runs/detect/predict3 /content/drive/MyDrive/yolov8/output"
      ],
      "metadata": {
        "id": "pzZaNOnUf3jL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xbiG25BSveB6"
      },
      "source": [
        "## Export the model -- In progress\n",
        "\n",
        "Export a YOLOv8 model to any supported format with the `format` argument, i.e. `format=onnx`.\n",
        "\n",
        "Format                  | `format=...**bold text**`         | Model\n",
        "---                     | ---                       | ---\n",
        "PyTorch                 | -                         | yolov8n.pt\n",
        "TorchScript             | `torchscript`             | yolov8n.torchscript\n",
        "ONNX                    | `onnx`                    | yolov8n.onnx\n",
        "OpenVINO                | `openvino`                | yolov8n_openvino_model/\n",
        "TensorRT                | `engine`                  | yolov8n.engine\n",
        "CoreML                  | `coreml`                  | yolov8n.mlmodel\n",
        "TensorFlow SavedModel   | `saved_model`             | yolov8n_saved_model/\n",
        "TensorFlow GraphDef     | `pb`                      | yolov8n.pb\n",
        "TensorFlow Lite         | `tflite`                  | yolov8n.tflite\n",
        "TensorFlow Edge TPU     | `edgetpu`                 | yolov8n_edgetpu.tflite\n",
        "TensorFlow.js           | `tfjs`                    | yolov8n_web_model/\n",
        "PaddlePaddle            | `paddle`                  | yolov8n_paddle_model/"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eA_r1BCfsTdP",
        "outputId": "be4bcfa4-c51d-4681-f96c-164888b266a3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'task': 'detect', 'mode': 'export', 'model': '/content/drive/MyDrive/yolov8/training_results/football2/weights/best.pt', 'data': None, 'epochs': 100, 'patience': 50, 'batch': 16, 'imgsz': 640, 'save': True, 'cache': False, 'device': None, 'workers': 8, 'project': None, 'name': None, 'exist_ok': False, 'pretrained': False, 'optimizer': 'SGD', 'verbose': False, 'seed': 0, 'deterministic': True, 'single_cls': False, 'image_weights': False, 'rect': False, 'cos_lr': False, 'close_mosaic': 10, 'resume': False, 'overlap_mask': True, 'mask_ratio': 4, 'dropout': 0.0, 'val': True, 'save_json': False, 'save_hybrid': False, 'conf': None, 'iou': 0.7, 'max_det': 300, 'half': False, 'dnn': False, 'plots': True, 'source': None, 'show': False, 'save_txt': False, 'save_conf': False, 'save_crop': False, 'hide_labels': False, 'hide_conf': False, 'vid_stride': 1, 'line_thickness': 3, 'visualize': False, 'augment': False, 'agnostic_nms': False, 'retina_masks': False, 'format': 'onnx', 'keras': False, 'optimize': False, 'int8': False, 'dynamic': False, 'simplify': False, 'opset': 17, 'workspace': 4, 'nms': False, 'lr0': 0.01, 'lrf': 0.01, 'momentum': 0.937, 'weight_decay': 0.0005, 'warmup_epochs': 3.0, 'warmup_momentum': 0.8, 'warmup_bias_lr': 0.1, 'box': 7.5, 'cls': 0.5, 'dfl': 1.5, 'fl_gamma': 0.0, 'label_smoothing': 0.0, 'nbs': 64, 'hsv_h': 0.015, 'hsv_s': 0.7, 'hsv_v': 0.4, 'degrees': 0.0, 'translate': 0.1, 'scale': 0.5, 'shear': 0.0, 'perspective': 0.0, 'flipud': 0.0, 'fliplr': 0.5, 'mosaic': 1.0, 'mixup': 0.0, 'copy_paste': 0.0, 'cfg': None, 'hydra': {'output_subdir': None, 'run': {'dir': '.'}}, 'v5loader': False}\n",
            "Ultralytics YOLOv8.0.11 🚀 Python-3.8.10 torch-1.13.1+cu116 CPU\n",
            "Fusing... \n",
            "Model summary: 168 layers, 11126358 parameters, 0 gradients, 28.4 GFLOPs\n",
            "\n",
            "\u001b[34m\u001b[1mPyTorch:\u001b[0m starting from /content/drive/MyDrive/yolov8/training_results/football2/weights/best.pt with output shape (1, 6, 8400) (21.5 MB)\n",
            "\u001b[31m\u001b[1mrequirements:\u001b[0m YOLOv8 requirement \"onnx>=1.12.0\" not found, attempting AutoUpdate...\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "tensorflow 2.9.2 requires protobuf<3.20,>=3.9.2, but you have protobuf 3.20.3 which is incompatible.\n",
            "tensorboard 2.9.1 requires protobuf<3.20,>=3.9.2, but you have protobuf 3.20.3 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mLooking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting onnx>=1.12.0\n",
            "  Downloading onnx-1.13.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (13.5 MB)\n",
            "     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 13.5/13.5 MB 94.6 MB/s eta 0:00:00\n",
            "Requirement already satisfied: numpy>=1.16.6 in /usr/local/lib/python3.8/dist-packages (from onnx>=1.12.0) (1.21.6)\n",
            "Collecting protobuf<4,>=3.20.2\n",
            "  Downloading protobuf-3.20.3-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.whl (1.0 MB)\n",
            "     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 1.0/1.0 MB 66.7 MB/s eta 0:00:00\n",
            "Requirement already satisfied: typing-extensions>=3.6.2.1 in /usr/local/lib/python3.8/dist-packages (from onnx>=1.12.0) (4.4.0)\n",
            "Installing collected packages: protobuf, onnx\n",
            "  Attempting uninstall: protobuf\n",
            "    Found existing installation: protobuf 3.19.6\n",
            "    Uninstalling protobuf-3.19.6:\n",
            "      Successfully uninstalled protobuf-3.19.6\n",
            "Successfully installed onnx-1.13.0 protobuf-3.20.3\n",
            "\n",
            "\u001b[31m\u001b[1mrequirements:\u001b[0m 1 package updated per ['onnx>=1.12.0']\n",
            "\u001b[31m\u001b[1mrequirements:\u001b[0m ⚠️ \u001b[1mRestart runtime or rerun command for updates to take effect\u001b[0m\n",
            "\n",
            "\n",
            "\u001b[34m\u001b[1mONNX:\u001b[0m starting export with onnx 1.13.0...\n",
            "\u001b[34m\u001b[1mONNX:\u001b[0m export success ✅ 12.9s, saved as /content/drive/MyDrive/yolov8/training_results/football2/weights/best.onnx (42.6 MB)\n",
            "\n",
            "Export complete (14.1s)\n",
            "Results saved to \u001b[1m/content/drive/MyDrive/yolov8/training_results/football2/weights\u001b[0m\n",
            "Predict:         yolo task=detect mode=predict model=/content/drive/MyDrive/yolov8/training_results/football2/weights/best.onnx -WARNING ⚠️ not yet supported for YOLOv8 exported models\n",
            "Validate:        yolo task=detect mode=val model=/content/drive/MyDrive/yolov8/training_results/football2/weights/best.onnx -WARNING ⚠️ not yet supported for YOLOv8 exported models\n",
            "Visualize:       https://netron.app\n"
          ]
        }
      ],
      "source": [
        "!yolo mode=export model=/content/drive/MyDrive/yolov8/training_results/football2/weights/best.pt format=onnx"
      ]
    },
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