notebooks/Facial_Keypoints_Detection.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Copy_of_Facial_Keypoints_Detection.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "machine_shape": "hm"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NfsegVsVf5N2",
        "colab_type": "text"
      },
      "source": [
        "# Facial Keypoints Detection"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qhzQOXUmf8t2",
        "colab_type": "text"
      },
      "source": [
        "## Info"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Npry0sK9gDOw",
        "colab_type": "text"
      },
      "source": [
        "# Header"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g6XwEkQIgFX7",
        "colab_type": "text"
      },
      "source": [
        "## Import Necessary Packages"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "41DCYHT1gChc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# low level\n",
        "from enum import Enum\n",
        "import os\n",
        "import random\n",
        "from time import time\n",
        "import datetime\n",
        "import math\n",
        "\n",
        "# middle level\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.image as mpimg\n",
        "import cv2\n",
        "from PIL import Image\n",
        "from mpl_toolkits.mplot3d import Axes3D \n",
        "\n",
        "# frameworks\n",
        "import torch \n",
        "from torch import nn, optim\n",
        "from torch.nn import functional as F\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "from torch.utils.data.sampler import SubsetRandomSampler\n",
        "from torchvision import transforms\n",
        "from torchvision.models import resnet18"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EqYXimOigjdE",
        "colab_type": "text"
      },
      "source": [
        "## Mout Google Dirve"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1PFGgKAggjKW",
        "colab_type": "code",
        "outputId": "f5985245-9b68-4637-f2e0-9cf5eacda001",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 132
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zGYu81DRgr1-",
        "colab_type": "text"
      },
      "source": [
        "# Data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mjBSlJDztQT1",
        "colab_type": "text"
      },
      "source": [
        "## Download and Extract Datas"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Htdg56FtTPSP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# \"\"\"Make directory for dataset\"\"\"\n",
        "# !mkdir \"/content/drive/My Drive/data\"\n",
        "\n",
        "# \"\"\"Download Data\"\"\"\n",
        "# os.environ['KAGGLE_USERNAME'] = \"\" # username from the json file\n",
        "# os.environ['KAGGLE_KEY'] = \"\" # key from the json file\n",
        "# !kaggle datasets download -d  selfishgene/youtube-faces-with-facial-keypoints # api copied from kaggle\n",
        "\n",
        "# \"\"\"Extract Data\"\"\"\n",
        "# !unzip /content/youtube-faces-with-facial-keypoints.zip -d \"/content/drive/My Drive/data\""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aest7VpDvXgV",
        "colab_type": "text"
      },
      "source": [
        "## Transformations"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fUakxINOdVkc",
        "colab_type": "text"
      },
      "source": [
        "### Basics"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "po95ZiifR_aC",
        "colab_type": "text"
      },
      "source": [
        "**Normalization**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c48YRQ4ih3J9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class Normalize(object):\n",
        "    \"\"\"Normalize the color range to [0,1] and convert a color image to grayscale if needed\"\"\"        \n",
        "    def __init__(self, color = False):\n",
        "        self.color = color\n",
        "    \n",
        "    def __call__(self, sample):\n",
        "        image, key_pts = sample['image'], sample['keypoints']\n",
        "        \n",
        "        image_copy = np.copy(image)\n",
        "        key_pts_copy = np.copy(key_pts)\n",
        "\n",
        "        # convert image to grayscale\n",
        "        if not self.color:\n",
        "            image_copy = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)\n",
        "        \n",
        "        # scale color range from [0, 255] to [0, 1]\n",
        "        image_copy=  image_copy/255.0\n",
        "            \n",
        "        # scale keypoints to be centered around 0 with a range of [-2, 2]\n",
        "        key_pts_copy = (key_pts_copy - image.shape[0]/key_pts_copy.shape[1])/(image.shape[0]/key_pts_copy.shape[1]*2)\n",
        "\n",
        "\n",
        "        return {'image': image_copy, 'keypoints': key_pts_copy}\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u63RF8nfXNcz",
        "colab_type": "text"
      },
      "source": [
        "**Rescale**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MbjKCjAyYjpc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class Rescale(object):\n",
        "    \"\"\"Rescale the image in a sample to a given size.\n",
        "    Args:\n",
        "        output_size (tuple or int): Desired output size. If tuple, output is\n",
        "            matched to output_size. If int, smaller of image edges is matched\n",
        "            to output_size keeping aspect ratio the same.\n",
        "    \"\"\"\n",
        "\n",
        "    def __init__(self, output_size):\n",
        "        assert isinstance(output_size, (int, tuple))\n",
        "        self.output_size = output_size\n",
        "\n",
        "    def __call__(self, sample):\n",
        "        image, key_pts = sample['image'], sample['keypoints']\n",
        "\n",
        "        h, w = image.shape[:2]\n",
        "        if isinstance(self.output_size, int):\n",
        "            if h > w:\n",
        "                new_h, new_w = self.output_size * h / w, self.output_size\n",
        "            else:\n",
        "                new_h, new_w = self.output_size, self.output_size * w / h\n",
        "        else:\n",
        "            new_h, new_w = self.output_size\n",
        "        new_h, new_w = int(new_h), int(new_w)\n",
        "            \n",
        "        img = cv2.resize(image, (new_w, new_h))\n",
        "                 \n",
        "        # scale the pts, too\n",
        "        if key_pts.shape[1] == 3:\n",
        "            key_pts = key_pts * [new_w / w, new_h / h, 1]\n",
        "        else:\n",
        "            key_pts = key_pts * [new_w / w, new_h / h]\n",
        "        \n",
        "        return {'image': img, 'keypoints': key_pts}"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c-AqYM1fcHjW",
        "colab_type": "text"
      },
      "source": [
        "**To Tensor**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9OZm-1uccHCQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class ToTensor(object):\n",
        "    \"\"\"Convert ndarrays in sample to Tensors.\"\"\"\n",
        "\n",
        "    def __call__(self, sample):\n",
        "        image, key_pts = sample['image'], sample['keypoints']\n",
        "         \n",
        "        # if image has no grayscale color channel, add one\n",
        "        if(len(image.shape) == 2):\n",
        "            # add that third color dim\n",
        "            image = image.reshape(image.shape[0], image.shape[1], 1)\n",
        "            \n",
        "        # swap color axis because\n",
        "        # numpy image: H x W x C\n",
        "        # torch image: C X H X W\n",
        "        image = image.transpose((2, 0, 1))\n",
        "        return {'image': torch.from_numpy(image),\n",
        "                'keypoints': torch.from_numpy(key_pts)}"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Gg_D2Wnub3jR",
        "colab_type": "text"
      },
      "source": [
        "### Cropping"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "khZS-xO7YprC",
        "colab_type": "text"
      },
      "source": [
        "**Random Cropping**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9VvQhdn4Yoc7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class RandomCrop(object):\n",
        "    \"\"\"Crop randomly the image in a sample.\n",
        "    Args:\n",
        "        output_size (tuple or int): Desired output size. If int, square crop\n",
        "            is made.\n",
        "    \"\"\"\n",
        "\n",
        "    def __init__(self, output_size):\n",
        "        assert isinstance(output_size, (int, tuple))\n",
        "        if isinstance(output_size, int):\n",
        "            self.output_size = (output_size, output_size)\n",
        "        else:\n",
        "            assert len(output_size) == 2\n",
        "            self.output_size = output_size\n",
        "\n",
        "    def __call__(self, sample):\n",
        "        image, key_pts = sample['image'], sample['keypoints']\n",
        "\n",
        "        h, w = image.shape[:2]\n",
        "        new_h, new_w = self.output_size\n",
        "\n",
        "        top = np.random.randint(0, h - new_h)\n",
        "        left = np.random.randint(0, w - new_w)\n",
        "\n",
        "        image = image[top: top + new_h,\n",
        "                      left: left + new_w]\n",
        "\n",
        "        key_pts = key_pts - [left, top]\n",
        "\n",
        "        return {'image': image, 'keypoints': key_pts}"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q5iH1ZJVZKZc",
        "colab_type": "text"
      },
      "source": [
        "**Face Cropping**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ObzvWWIuZH7o",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class FaceCrop(object):\n",
        "    \"\"\" Crop out face using the keypoints as reference\n",
        "    Args:\n",
        "        output_size (tuple or int): Desired output size. If int, square crop\n",
        "            is made.\n",
        "    \"\"\"       \n",
        "        \n",
        "    def __call__(self, sample):\n",
        "        image, key_pts = sample['image'], sample['keypoints']\n",
        "\n",
        "        image_copy = np.copy(image)\n",
        "        \n",
        "        h, w = image.shape[:2]\n",
        "        \n",
        "        x_max = 0\n",
        "        x_min = 10000\n",
        "        y_max = 0\n",
        "        y_min = 10000\n",
        "        \n",
        "        # Find the coordinates to keypoints at the far left, far right, top and bottom\n",
        "        # Also check that no keypoints are outside the image\n",
        "        for coord in key_pts:\n",
        "            if coord[0] > x_max:\n",
        "                if coord[0] >= w:\n",
        "                    x_max = w\n",
        "                else:\n",
        "                    x_max = coord[0]\n",
        "            if coord[0] < x_min:\n",
        "                if coord[0] < 0:\n",
        "                    x_min = 0\n",
        "                else:\n",
        "                    x_min = coord[0]\n",
        "            if coord[1] > y_max:\n",
        "                if coord[1] >= h:\n",
        "                    y_max = h\n",
        "                else:\n",
        "                    y_max = coord[1]\n",
        "            if coord[1] < y_min:\n",
        "                if coord[1] < 0:\n",
        "                    y_min = 0\n",
        "                else:\n",
        "                    y_min = coord[1]\n",
        "        \n",
        "        # Set the the left corner keypoint as out crop cooridnate\n",
        "        x = int(x_min)\n",
        "        y = int(y_min)\n",
        "        \n",
        "        # Get height and width of keypoint area\n",
        "        new_h = int(y_max - y_min)\n",
        "        new_w = int(x_max - x_min)\n",
        "        \n",
        "        #Set the smallest side equal to the largest since we want a square\n",
        "        if new_h > new_w:\n",
        "            new_w = new_h\n",
        "        else:\n",
        "            new_h = new_w       \n",
        "        \n",
        "        randsize1 = [2, 70]\n",
        "        randsize2 = [2, 30]\n",
        "        randsize3 = [1, 10]\n",
        "\n",
        "        # Check that padding dosent go outside the frame\n",
        "        padding_x_1 = 0\n",
        "        padding_x_2 = 0\n",
        "        padding_y_1 = 0\n",
        "        padding_y_2 = 0\n",
        "        \n",
        "        padding_size_x_1 = random.randint(randsize1[0],randsize1[1])\n",
        "        padding_size_x_2 = random.randint(randsize1[0],randsize1[1])\n",
        "        padding_size_y_1 = random.randint(randsize1[0],randsize1[1])\n",
        "        padding_size_y_2 = random.randint(randsize1[0],randsize1[1])\n",
        "        if(y - padding_size_y_1 > 0 and x - padding_size_x_1 > 0\n",
        "           and x + new_w + padding_size_x_2 < w and y + new_h + padding_size_y_2 < h):\n",
        "            padding_x_1 = padding_size_x_1\n",
        "            padding_x_2 = padding_size_x_2\n",
        "            padding_y_1 = padding_size_y_1\n",
        "            padding_y_2 = padding_size_y_2\n",
        "        else:\n",
        "            padding_size_x_1 = random.randint(randsize2[0],randsize2[1])\n",
        "            padding_size_x_2 = random.randint(randsize2[0],randsize2[1])\n",
        "            padding_size_y_1 = random.randint(randsize2[0],randsize2[1])\n",
        "            padding_size_y_2 = random.randint(randsize2[0],randsize2[1])\n",
        "         \n",
        "            if(y - padding_size_y_1 > 0 and x - padding_size_x_1 > 0\n",
        "               and x + new_w + padding_size_x_2 < w and y + new_h + padding_size_y_2 < h):\n",
        "                padding_x_1 = padding_size_x_1\n",
        "                padding_x_2 = padding_size_x_2\n",
        "                padding_y_1 = padding_size_y_1\n",
        "                padding_y_2 = padding_size_y_2\n",
        "                \n",
        "            else:\n",
        "                padding_size_x_1 = random.randint(randsize3[0],randsize3[1])\n",
        "                padding_size_x_2 = random.randint(randsize3[0],randsize3[1])\n",
        "                padding_size_y_1 = random.randint(randsize3[0],randsize3[1])\n",
        "                padding_size_y_2 = random.randint(randsize3[0],randsize3[1])\n",
        "         \n",
        "                if(y - padding_size_y_1 > 0 and x - padding_size_x_1 > 0\n",
        "                   and x + new_w + padding_size_x_2 < w and y + new_h + padding_size_y_2 < h):\n",
        "                    padding_x_1 = padding_size_x_1\n",
        "                    padding_x_2 = padding_size_x_2\n",
        "                    padding_y_1 = padding_size_y_1\n",
        "                    padding_y_2 = padding_size_y_2 \n",
        "        \n",
        "        \n",
        "        image_copy = image_copy[y - padding_y_1: y + new_h + padding_y_2, x - padding_x_1: x + new_w + padding_x_2]     \n",
        "\n",
        "        if key_pts.shape[1] == 3:\n",
        "            key_pts = key_pts - [x - padding_x_1, y - padding_y_1, 0] \n",
        "        else:\n",
        "            key_pts = key_pts - [x - padding_x_1, y - padding_y_1] \n",
        "\n",
        "        return {'image': image_copy, 'keypoints': key_pts}"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YveKioq5ZZ1C",
        "colab_type": "text"
      },
      "source": [
        "**Face Cropping Tight**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KAPMto1YZYH-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class FaceCropTight(object):\n",
        "    \"\"\" Crop out face using the keypoints as reference\n",
        "    Args:\n",
        "        output_size (tuple or int): Desired output size. If int, square crop\n",
        "            is made.\n",
        "    \"\"\"       \n",
        "        \n",
        "    def __call__(self, sample):\n",
        "        image, key_pts = sample['image'], sample['keypoints']\n",
        "\n",
        "        image_copy = np.copy(image)\n",
        "        \n",
        "        h, w = image.shape[:2]\n",
        "        \n",
        "        x_max = 0\n",
        "        x_min = 10000\n",
        "        y_max = 0\n",
        "        y_min = 10000\n",
        "        \n",
        "        # Find the coordinates to keypoints at the far left, far right, top and bottom\n",
        "        # Also check that no keypoints are outside the image\n",
        "        for coord in key_pts:\n",
        "            if coord[0] > x_max:\n",
        "                if coord[0] >= w:\n",
        "                    x_max = w\n",
        "                else:\n",
        "                    x_max = coord[0]\n",
        "            if coord[0] < x_min:\n",
        "                if coord[0] < 0:\n",
        "                    x_min = 0\n",
        "                else:\n",
        "                    x_min = coord[0]\n",
        "            if coord[1] > y_max:\n",
        "                if coord[1] >= h:\n",
        "                    y_max = h\n",
        "                else:\n",
        "                    y_max = coord[1]\n",
        "            if coord[1] < y_min:\n",
        "                if coord[1] < 0:\n",
        "                    y_min = 0\n",
        "                else:\n",
        "                    y_min = coord[1]\n",
        "        \n",
        "        # Set the the left corner keypoint as out crop cooridnate\n",
        "        x = int(x_min)\n",
        "        y = int(y_min)\n",
        "        \n",
        "        # Get height and width of keypoint area\n",
        "        new_h = int(y_max - y_min)\n",
        "        new_w = int(x_max - x_min)\n",
        "        \n",
        "        #Set the smallest side equal to the largest since we want a square\n",
        "        if new_h > new_w:\n",
        "            new_w = new_h\n",
        "        else:\n",
        "            new_h = new_w       \n",
        "        \n",
        "        randsize1 = [5, 10]\n",
        "\n",
        "        # Check that padding dosent go outside the frame\n",
        "        padding_x_1 = 0\n",
        "        padding_x_2 = 0\n",
        "        padding_y_1 = 0\n",
        "        padding_y_2 = 0\n",
        "        \n",
        "        padding_size_x_1 = random.randint(randsize1[0],randsize1[1])\n",
        "        padding_size_x_2 = random.randint(randsize1[0],randsize1[1])\n",
        "        padding_size_y_1 = random.randint(randsize1[0],randsize1[1])\n",
        "        padding_size_y_2 = random.randint(randsize1[0],randsize1[1])\n",
        "        \n",
        "        if(y - padding_size_y_1 > 0 and x - padding_size_x_1 > 0\n",
        "           and x + new_w + padding_size_x_2 < w and y + new_h + padding_size_y_2 < h):\n",
        "            padding_x_1 = padding_size_x_1\n",
        "            padding_x_2 = padding_size_x_2\n",
        "            padding_y_1 = padding_size_y_1\n",
        "            padding_y_2 = padding_size_y_2\n",
        "                 \n",
        "        image_copy = image_copy[y - padding_y_1: y + new_h + padding_y_2, x - padding_x_1: x + new_w + padding_x_2]     \n",
        "        \n",
        "        if key_pts.shape[1] == 3:\n",
        "            key_pts = key_pts - [x - padding_x_1, y - padding_y_1, 0] \n",
        "        else:\n",
        "            key_pts = key_pts - [x - padding_x_1, y - padding_y_1] \n",
        "        \n",
        "        return {'image': image_copy, 'keypoints': key_pts}"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qHNU4orScCQP",
        "colab_type": "text"
      },
      "source": [
        "### Flipping"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dBkG3MwaZjaI",
        "colab_type": "text"
      },
      "source": [
        "**Random 90 flipping**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kOizpqZ7ZtRW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class Random90DegFlip(object):\n",
        "    \"\"\"Random 90 degree flip of image in sample\"\"\"\n",
        "    def __call__(self, sample):\n",
        "        image, key_pts = sample['image'], sample['keypoints']\n",
        "        \n",
        "        image_copy = np.copy(image)\n",
        "        key_pts_copy = np.copy(key_pts)\n",
        "\n",
        "        if random.choice([0, 1]) < .25:\n",
        "            image_copy = np.rot90(image_copy,1)\n",
        "            image_copy = np.flipud(image_copy)\n",
        "            key_pts_copy = np.fliplr(key_pts_copy)\n",
        "            \n",
        "\n",
        "        return {'image': image_copy, 'keypoints': key_pts_copy}"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_6CVykOLauGt",
        "colab_type": "text"
      },
      "source": [
        "**Random horizontal flipping**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B9VKSw9BasiH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class RandomHorizontalFlip(object):\n",
        "    \"\"\"Random horizontal flip of image in sample\"\"\"\n",
        "    def __call__(self, sample):\n",
        "        image, key_pts = sample['image'], sample['keypoints']\n",
        "        \n",
        "        image_copy = np.copy(image)\n",
        "        key_pts_copy = np.copy(key_pts)\n",
        "        key_pts_copy_2 = np.copy(key_pts_copy)\n",
        "\n",
        "        if random.choice([0, 1]) <= 0.5:\n",
        "            # horizontally flip image\n",
        "            image_copy = np.fliplr(image_copy)          \n",
        "            # keypoints (x,y) = (-x,y)\n",
        "            key_pts_copy[:,0] = -key_pts_copy[:, 0]\n",
        "            # move keypoints form 2 kvadrant to 1 kvadrant\n",
        "            key_pts_copy[:,0] = key_pts_copy[:, 0] + image_copy.shape[1]\n",
        "\n",
        "            # since the keypoints are fliped around the y axis\n",
        "            # their placment are wrong int the keypoint array.\n",
        "            # E.g. the right eye and left eye is in the wrong place,\n",
        "            # so the keypoints need to be correctly mirrord in the list\n",
        "            \n",
        "            key_pts_copy_2 = np.copy(key_pts_copy)\n",
        "            \n",
        "            # mirror jawline \n",
        "            key_pts_copy_2[16] = key_pts_copy[0]\n",
        "            key_pts_copy_2[15] = key_pts_copy[1]\n",
        "            key_pts_copy_2[14] = key_pts_copy[2]\n",
        "            key_pts_copy_2[13] = key_pts_copy[3]\n",
        "            key_pts_copy_2[12] = key_pts_copy[4]\n",
        "            key_pts_copy_2[11] = key_pts_copy[5]\n",
        "            key_pts_copy_2[10] = key_pts_copy[6]\n",
        "            key_pts_copy_2[9]  = key_pts_copy[7]\n",
        "            key_pts_copy_2[8]  = key_pts_copy[8]\n",
        "            key_pts_copy_2[7] = key_pts_copy[9] \n",
        "            key_pts_copy_2[6] = key_pts_copy[10] \n",
        "            key_pts_copy_2[5] = key_pts_copy[11]\n",
        "            key_pts_copy_2[4] = key_pts_copy[12]\n",
        "            key_pts_copy_2[3] = key_pts_copy[13]\n",
        "            key_pts_copy_2[2] = key_pts_copy[14]\n",
        "            key_pts_copy_2[1] = key_pts_copy[15]\n",
        "            key_pts_copy_2[0]  = key_pts_copy[16]\n",
        "            \n",
        "            # mirror eyebrowns\n",
        "            key_pts_copy_2[26] = key_pts_copy[17] \n",
        "            key_pts_copy_2[25] = key_pts_copy[18] \n",
        "            key_pts_copy_2[24] = key_pts_copy[19]\n",
        "            key_pts_copy_2[23] = key_pts_copy[20]\n",
        "            key_pts_copy_2[22] = key_pts_copy[21]\n",
        "            key_pts_copy_2[21] = key_pts_copy[22]\n",
        "            key_pts_copy_2[20] = key_pts_copy[23]\n",
        "            key_pts_copy_2[19] = key_pts_copy[24]\n",
        "            key_pts_copy_2[18] = key_pts_copy[25] \n",
        "            key_pts_copy_2[17] = key_pts_copy[26]\n",
        "            \n",
        "            # mirror nose tip\n",
        "            key_pts_copy_2[35] = key_pts_copy[31] \n",
        "            key_pts_copy_2[34] = key_pts_copy[32] \n",
        "            key_pts_copy_2[33] = key_pts_copy[33]\n",
        "            key_pts_copy_2[32] = key_pts_copy[34]\n",
        "            key_pts_copy_2[31] = key_pts_copy[35]\n",
        "            \n",
        "            # mirror eyes\n",
        "            key_pts_copy_2[45] = key_pts_copy[36]\n",
        "            key_pts_copy_2[44] = key_pts_copy[37] \n",
        "            key_pts_copy_2[43] = key_pts_copy[38]\n",
        "            key_pts_copy_2[42] = key_pts_copy[39]\n",
        "            key_pts_copy_2[47] = key_pts_copy[40]\n",
        "            key_pts_copy_2[46] = key_pts_copy[41] \n",
        "            key_pts_copy_2[39] = key_pts_copy[42] \n",
        "            key_pts_copy_2[38] = key_pts_copy[43]\n",
        "            key_pts_copy_2[37] = key_pts_copy[44]\n",
        "            key_pts_copy_2[36] = key_pts_copy[45]\n",
        "            key_pts_copy_2[41] = key_pts_copy[46] \n",
        "            key_pts_copy_2[40] = key_pts_copy[47] \n",
        "   \n",
        "            # mirror lips\n",
        "            key_pts_copy_2[54] = key_pts_copy[48]\n",
        "            key_pts_copy_2[53] = key_pts_copy[49] \n",
        "            key_pts_copy_2[52] = key_pts_copy[50]\n",
        "            key_pts_copy_2[51] = key_pts_copy[51]\n",
        "            key_pts_copy_2[50] = key_pts_copy[52]\n",
        "            key_pts_copy_2[49] = key_pts_copy[53] \n",
        "            key_pts_copy_2[48] = key_pts_copy[54]\n",
        "            \n",
        "            key_pts_copy_2[59] = key_pts_copy[55]\n",
        "            key_pts_copy_2[58] = key_pts_copy[56]\n",
        "            key_pts_copy_2[57] = key_pts_copy[57]\n",
        "            key_pts_copy_2[56] = key_pts_copy[58] \n",
        "            key_pts_copy_2[55] = key_pts_copy[59]\n",
        "            \n",
        "            key_pts_copy_2[64] = key_pts_copy[60]\n",
        "            key_pts_copy_2[63] = key_pts_copy[61] \n",
        "            key_pts_copy_2[62] = key_pts_copy[62]\n",
        "            key_pts_copy_2[61] = key_pts_copy[63]\n",
        "            key_pts_copy_2[60] = key_pts_copy[64]\n",
        "                 \n",
        "            key_pts_copy_2[67] = key_pts_copy[65] \n",
        "            key_pts_copy_2[66] = key_pts_copy[66] \n",
        "            key_pts_copy_2[65] = key_pts_copy[67]\n",
        "\n",
        "            \n",
        "        return {'image': image_copy, 'keypoints': key_pts_copy_2}"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CFnEk7ITccxc",
        "colab_type": "text"
      },
      "source": [
        "### Rotate"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zAv_4uGpbKRN",
        "colab_type": "text"
      },
      "source": [
        "**Rotate**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HNraKgLNbB_e",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class Rotate(object):\n",
        "    \"\"\"Rotate image in sample by an angle\"\"\"\n",
        "    \n",
        "    def __init__(self, rotation):\n",
        "        self.rotation = rotation\n",
        "    \n",
        "    def __call__(self, sample):\n",
        "        image, key_pts = sample['image'], sample['keypoints']\n",
        "        \n",
        "        image_copy = np.copy(image)\n",
        "        key_pts_copy = np.copy(key_pts)\n",
        "        \n",
        "        rows = image.shape[0]\n",
        "        cols = image.shape[1]\n",
        "        \n",
        "        M = cv2.getRotationMatrix2D((rows/2,cols/2),90,1)\n",
        "        image_copy = cv2.warpAffine(image_copy,M,(cols,rows))\n",
        "                \n",
        "        \n",
        "        key_pts_copy = key_pts_copy.reshape((1,136))\n",
        "        new_keypoints = np.zeros(136)\n",
        "        \n",
        "        for i in range(68):\n",
        "            coord_idx = 2*i\n",
        "            old_coord = key_pts_copy[0][coord_idx:coord_idx+2]\n",
        "            new_coord = np.matmul(M,np.append(old_coord,1))\n",
        "            new_keypoints[coord_idx] += new_coord[0]\n",
        "            new_keypoints[coord_idx+1] += new_coord[1]\n",
        "        \n",
        "        new_keypoints = new_keypoints.reshape((68,2))\n",
        "        \n",
        "        return {'image': image_copy, 'keypoints': new_keypoints}"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AsuzLQdJbdX5",
        "colab_type": "text"
      },
      "source": [
        "**Random Rotate**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bp_I4BeXbcXt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class RandomRotate(object):\n",
        "    \"\"\"Rotate image in sample by an angle\"\"\"\n",
        "    \n",
        "    def __init__(self, rotation=30):\n",
        "        self.rotation = rotation\n",
        "    \n",
        "    def __call__(self, sample):\n",
        "        image, key_pts = sample['image'], sample['keypoints']\n",
        "        \n",
        "        image_copy = np.copy(image)\n",
        "        key_pts_copy = np.copy(key_pts)\n",
        "\n",
        "        factor = key_pts.shape[1]\n",
        "        \n",
        "        rows = image.shape[0]\n",
        "        cols = image.shape[1]\n",
        "        \n",
        "        M = cv2.getRotationMatrix2D((rows/2,cols/2),random.choice([-self.rotation, self.rotation]),1)\n",
        "        image_copy = cv2.warpAffine(image_copy,M,(cols,rows))\n",
        "                \n",
        "        \n",
        "        key_pts_copy = key_pts_copy.reshape((1,-1))\n",
        "        new_keypoints = np.zeros(key_pts_copy.shape[1])\n",
        "        \n",
        "        for i in range(68):\n",
        "            coord_idx = factor*i\n",
        "            old_coord = key_pts_copy[0][coord_idx:coord_idx+2]\n",
        "            new_coord = np.matmul(M,np.append(old_coord,1))\n",
        "            new_keypoints[coord_idx] += new_coord[0]\n",
        "            new_keypoints[coord_idx+1] += new_coord[1]\n",
        "        \n",
        "        new_keypoints = new_keypoints.reshape((68,-1))\n",
        "        \n",
        "        return {'image': image_copy, 'keypoints': new_keypoints}"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bTz9i3bZchr_",
        "colab_type": "text"
      },
      "source": [
        "### Other"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NIfmsEZgc674",
        "colab_type": "text"
      },
      "source": [
        "**ColorJitter**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8usVAO2OacoF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class ColorJitter(object):\n",
        "    \"\"\"ColorJitter image in sample\"\"\"\n",
        "    def __call__(self, sample):\n",
        "        image, key_pts = sample['image'], sample['keypoints']\n",
        "        \n",
        "        color_jitter = transforms.ColorJitter(\n",
        "            brightness=0.4,\n",
        "            contrast=0.4,\n",
        "            saturation=0.4,)\n",
        "        \n",
        "        image_copy = np.copy(image)\n",
        "        \n",
        "        key_pts_copy = np.copy(key_pts)\n",
        "\n",
        "        image_copy = color_jitter(Image.fromarray(image_copy)) \n",
        "       \n",
        "        image_copy = np.array(image_copy)\n",
        "        \n",
        "        return {'image': image_copy, 'keypoints': key_pts_copy}\n",
        "    "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZxzsovGfdGa2",
        "colab_type": "text"
      },
      "source": [
        "**Random Gamma**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_fbBSs_1dBms",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# helper\n",
        "def adjust_gamma(image, gamma=1.0):# build a lookup table mapping the pixel values [0, 255] to \n",
        "    # their adjusted gamma values\n",
        "    invGamma = 1.0 / gamma\n",
        "    table = np.array([((i / 255.0) ** invGamma) * 255\n",
        "    for i in np.arange(0, 256)]).astype(\"uint8\")\n",
        " \n",
        "    # apply gamma correction using the lookup table\n",
        "    return cv2.LUT(image, table)\n",
        "    \n",
        "class RandomGamma(object):\n",
        "    \"\"\"Random gamma of image in sample\"\"\"\n",
        "    def __call__(self, sample):\n",
        "        image, key_pts = sample['image'], sample['keypoints']\n",
        "        \n",
        "        image_copy = np.copy(image)\n",
        "        key_pts_copy = np.copy(key_pts)\n",
        "\n",
        "        image_copy = adjust_gamma(image_copy, gamma=random.uniform(0.8, 1.1)) \n",
        "        \n",
        "        return {'image': image_copy, 'keypoints': key_pts_copy}"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M1iIMJCwdg_H",
        "colab_type": "text"
      },
      "source": [
        "## Data Loader"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rjlrv1Ph0aA1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class Mode(Enum):\n",
        "    \n",
        "    bBox = \"boundingBox\"\n",
        "    lm3d = \"landmarks3D\"\n",
        "    lm2d = \"landmarks2D\"\n",
        "    cimage = \"colorImages\""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kOQiN1FYdgKC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class FacialKeypointsDataset(Dataset):\n",
        "    \"\"\"Face Landmarks dataset.\"\"\"\n",
        "    \n",
        "    def __init__(self, files, start_batch=0, batch_size=0, mode=Mode.lm2d,transform=None):\n",
        "       \n",
        "\n",
        "        self.files = files\n",
        "        self.start_batch = start_batch\n",
        "        self.batch_size = batch_size\n",
        "        self.mode = mode\n",
        "        self.transform = transform\n",
        "\n",
        "    def __len__(self):\n",
        "\n",
        "        return len(self.files)\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        if idx < self.start_batch * self.batch_size:\n",
        "            return {'image': np.array([]), 'keypoints': np.array([])}\n",
        "        \n",
        "        self.start_batch = 0\n",
        "        file, index = self.files[idx]\n",
        "        data = np.load(file)\n",
        "        pred = data[self.mode.value][:,:,index]\n",
        "\n",
        "        image = data[Mode.cimage.value][:,:,:,index]\n",
        "\n",
        "       \n",
        "        \n",
        "        # if image has an alpha color channel, get rid of it\n",
        "        if(image.shape[2] == 4):\n",
        "            image = image[:,:,0:3]\n",
        "        \n",
        "      \n",
        "        sample = {'image': image, 'keypoints': pred}\n",
        "\n",
        "        if self.transform:\n",
        "            sample = self.transform(sample)\n",
        "        # keypoints = sample['keypoints']\n",
        "        # keypoints = keypoints.view(-1)\n",
        "        # sample['keypoints'] = keypoints \n",
        "\n",
        "        return sample\n",
        "\n",
        "    "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iOTZdruYgCZ8",
        "colab_type": "text"
      },
      "source": [
        "## Data Creator"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d4Lu_lRYsfPV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\"\"\"Utils\"\"\"\n",
        "files = []\n",
        "\n",
        "def dfs_files(path):\n",
        "    global files \n",
        "    if path[-4] == \".\":\n",
        "        if path[-4::] == \".npz\":\n",
        "            files.append(path)\n",
        "        return \n",
        "    for p in os.listdir(path):\n",
        "        dfs_files(os.path.join(path, p))\n",
        "    return files    \n",
        "\n",
        "def files_of_files(files):\n",
        "    ff = []\n",
        "    for i, f in enumerate(files):\n",
        "        print(\"\\rfinished {} %\".format(int(((i+1)/len(files))*100)),end='')\n",
        "        data = np.load(f)\n",
        "\n",
        "        length = data[\"colorImages\"].shape[-1]\n",
        "        [ff.append([f, i]) for i in range(length)]\n",
        "    return ff"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4hSQcR5xxH07",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# global files\n",
        "# files = []\n",
        "# dfs_files(\"/content/drive/My Drive/data\")\n",
        "# data = files_of_files(files)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1R0d0TqY0OVF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# torch.save({\"files\":data},\"/content/drive/My Drive/data/files.pth\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cszIkLMcgBcd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def create_datasets(data_dir ,batch_size, start_batch, img_size, mode, valid_size = 0.1, test_size = 0.1, color=True):\n",
        "\n",
        "    data = torch.load(os.path.join(data_dir,\"files.pth\"))['files']\n",
        "    n_samples = len(data)\n",
        "    \n",
        "    np.random.seed(1)\n",
        "    np.random.shuffle(data)\n",
        "\n",
        "    valid_split = int(np.floor(valid_size * n_samples))\n",
        "    test_split = int(np.floor(test_size * n_samples))\n",
        "\n",
        "    valid_data = data[:valid_split]\n",
        "    test_data = data[valid_split:valid_split+test_split]\n",
        "    train_data = data[valid_split+test_split:]\n",
        "\n",
        "    print(f\"\\nTrain set: {len(train_data)} image\")\n",
        "    print(f\"Valid set: {len(valid_data)} image\")\n",
        "    print(f\"Test set: {len(test_data)} image\")\n",
        "\n",
        "\n",
        "    train_transform = transforms.Compose([\n",
        "                                        #   RandomRotate(5),\n",
        "                                          RandomHorizontalFlip(),\n",
        "                                          ColorJitter(),\n",
        "                                          FaceCrop(),\n",
        "                                          Rescale((img_size, img_size)),\n",
        "                                          Normalize(color),\n",
        "                                          ToTensor(),\n",
        "                                          ])\n",
        "    \n",
        "    valid_transform = transforms.Compose([\n",
        "                                          FaceCropTight(),\n",
        "                                          Rescale((img_size,img_size)),\n",
        "                                          Normalize(color),\n",
        "                                          ToTensor()\n",
        "                                          ])\n",
        "    \n",
        "    test_transform = transforms.Compose([\n",
        "                                         FaceCropTight(),\n",
        "                                          Rescale((img_size,img_size)),\n",
        "                                          Normalize(color),\n",
        "                                          ToTensor()\n",
        "                                          ])\n",
        "\n",
        "    test_dataset = FacialKeypointsDataset(test_data,\n",
        "                                          mode=mode,\n",
        "                                          transform=test_transform)\n",
        "\n",
        "    valid_dataset = FacialKeypointsDataset(valid_data,\n",
        "                                           mode=mode,\n",
        "                                           transform=valid_transform)\n",
        " \n",
        "    train_dataset = FacialKeypointsDataset(train_data,\n",
        "                                           start_batch,\n",
        "                                           batch_size,\n",
        "                                           mode=mode,\n",
        "                                           transform=train_transform)\n",
        "    \n",
        "    train_loader = DataLoader(train_dataset, batch_size=batch_size)\n",
        "    test_loader = DataLoader(test_dataset, batch_size=batch_size)\n",
        "    valid_loader = DataLoader(valid_dataset, batch_size=batch_size)\n",
        "    \n",
        "    return train_loader, valid_loader, test_loader"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uzs_11oof-vk",
        "colab_type": "text"
      },
      "source": [
        "## Data samples"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D2SRdNeRgOO6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def sample_output(data_loader, net=None):\n",
        "    if net is not None:\n",
        "        net.eval()\n",
        "        net.to('cpu')\n",
        "    \n",
        "    for i, data in enumerate(data_loader):\n",
        "        images = data['image']\n",
        "        key_pts = data['keypoints']\n",
        "\n",
        "        output_pts = None\n",
        "        if net is not None:\n",
        "            # print(images.shape)\n",
        "            output_pts = net(images.float())\n",
        "            # print(output_pts.shape)\n",
        "            # if output.shape:\n",
        "            \n",
        "            # output_pts = output_pts.view(output_pts.size()[0], 68, -1) #this line for 2d model\n",
        "\n",
        "        \n",
        "        if i == 0:\n",
        "            return images, output_pts, key_pts"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MUZqSKmzfvDr",
        "colab_type": "text"
      },
      "source": [
        "## Displayers"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ATPYhTEe0nTa",
        "colab_type": "text"
      },
      "source": [
        "**2D Facial LandMarks**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-AWfTO3AaDdE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# !mkdir \"/content/drive/My Drive/images\""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qRhf4afFfuww",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def show_all_keypoints(image, pred_key_pts=None, gt_key_pts=None):\n",
        "    image = np.moveaxis(image, 0, -1)\n",
        "\n",
        "    plt.imshow(image)\n",
        "    if pred_key_pts is not None:\n",
        "        plt.scatter(pred_key_pts[:, 0], pred_key_pts[:, 1], s=30, marker='.', c='r')\n",
        "  \n",
        "    if gt_key_pts is not None:\n",
        "        plt.scatter(gt_key_pts[:, 0], gt_key_pts[:, 1], s=30, marker='.', c='lightgreen')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "O5Qj3HIu-fii",
        "colab": {}
      },
      "source": [
        "def visualize_output(images, epoch, key_pts=None, gt_pts=None, batch_size=10):\n",
        "    plt.figure(figsize=(25,15))\n",
        "    for i in range(10):    \n",
        "        ax = plt.subplot(3, 5, i+1)\n",
        "\n",
        "        # de-transformacija slika\n",
        "        image = images[i].data   \n",
        "        image = image.numpy()   \n",
        "        # de-transformacija predvidjenih kljucnih tacaka\n",
        "        pred_key_pts = None\n",
        "        if key_pts is not None:\n",
        "            pred_key_pts = key_pts[i].data\n",
        "            pred_key_pts = pred_key_pts.numpy()\n",
        "            pred_key_pts = pred_key_pts*(image.shape[1]/4)+image.shape[1]/2\n",
        "\n",
        "\n",
        "        gt_key_pts = None\n",
        "        if gt_pts is not None:\n",
        "            gt_key_pts = gt_pts[i].data      \n",
        "            gt_key_pts = gt_key_pts*(image.shape[1]/4)+image.shape[1]/2\n",
        "        \n",
        "        show_all_keypoints(image, pred_key_pts, gt_key_pts)\n",
        "        plt.axis('off')\n",
        "    plt.savefig(f'/content/drive/My Drive/images/{epoch}.png')\n",
        "    plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ooQZikmJcmIw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def plot_3d(num, arr1, arr2=None):\n",
        "    fig = plt.figure()\n",
        "    ax = fig.add_subplot(111, projection='3d')\n",
        "\n",
        "    face = list(range(1,17))\n",
        "    l_eyebrow = list(range(18,22))\n",
        "    r_eyebrow = list(range(23,27))\n",
        "    top_nose = list(range(28,31))\n",
        "    bottom_nose = list(range(32,36))\n",
        "    l_eye = list(range(37,42))\n",
        "    r_eye = list(range(43, 48))\n",
        "    lips_1 = list(range(49,55))\n",
        "    lips_2 = list(range(55,61))\n",
        "    lips_3 = list(range(61,65))\n",
        "    lips_4 = list(range(65,68))\n",
        "\n",
        "\n",
        "\n",
        "    if arr2 is not None:\n",
        "        arr2 = arr2.view(-1, 3).detach().numpy()\n",
        "\n",
        "        for i ,(x,y,z) in enumerate(arr2):\n",
        "            if i in face:\n",
        "                ax.scatter(px, py, pz, color='r')\n",
        "                ax.scatter(x, y, z, color='r')\n",
        "                ax.plot([x,px],[y,py],[z,pz], color='r')\n",
        "            if i in l_eyebrow:\n",
        "                ax.scatter(px, py, pz, color='r')\n",
        "                ax.scatter(x, y, z, color='r')\n",
        "                ax.plot([x,px],[y,py],[z,pz], color='r')\n",
        "            \n",
        "            if i in r_eyebrow:\n",
        "                ax.scatter(px, py, pz, color='r')\n",
        "                ax.scatter(x, y, z, color='r')\n",
        "                ax.plot([x,px],[y,py],[z,pz], color='r')\n",
        "\n",
        "            if i in top_nose:\n",
        "                ax.scatter(px, py, pz, color='r')\n",
        "                ax.scatter(x, y, z, color='r')\n",
        "                ax.plot([x,px],[y,py],[z,pz], color='r')\n",
        "            if i in bottom_nose:\n",
        "                ax.scatter(px, py, pz, color='r')\n",
        "                ax.scatter(x, y, z, color='r')\n",
        "                ax.plot([x,px],[y,py],[z,pz], color='r')\n",
        "                \n",
        "            if i in l_eye:\n",
        "                ax.scatter(px, py, pz, color='r')\n",
        "                ax.scatter(x, y, z, color='r')\n",
        "                ax.plot([x,px],[y,py],[z,pz], color='r')\n",
        "            if i in r_eye:\n",
        "                ax.scatter(px, py, pz, color='r')\n",
        "                ax.scatter(x, y, z, color='r')\n",
        "                ax.plot([x,px],[y,py],[z,pz], color='r')\n",
        "            if i in lips_1:\n",
        "                ax.scatter(px, py, pz, color='r')\n",
        "                ax.scatter(x, y, z, color='r')\n",
        "                ax.plot([x,px],[y,py],[z,pz], color='r')\n",
        "            if i in lips_2:\n",
        "                ax.scatter(px, py, pz, color='r')\n",
        "                ax.scatter(x, y, z, color='r')\n",
        "                ax.plot([x,px],[y,py],[z,pz], color='r')\n",
        "            if i in lips_3:\n",
        "                ax.scatter(px, py, pz, color='r')\n",
        "                ax.scatter(x, y, z, color='r')\n",
        "                ax.plot([x,px],[y,py],[z,pz], color='r')\n",
        "            if i in lips_4:\n",
        "                ax.scatter(px, py, pz, color='r')\n",
        "                ax.scatter(x, y, z, color='r')\n",
        "                ax.plot([x,px],[y,py],[z,pz], color='r')\n",
        "            px,py,pz = x,y,z\n",
        "\n",
        "\n",
        "    arr1 = arr1.view(-1, 3)\n",
        "    for i ,(x,y,z) in enumerate(arr1):\n",
        "        if i in face:\n",
        "            ax.scatter(px, py, pz, color='g')\n",
        "            ax.scatter(x, y, z, color='g')\n",
        "            ax.plot([x,px],[y,py],[z,pz], color='g')\n",
        "        if i in l_eyebrow:\n",
        "            ax.scatter(px, py, pz, color='g')\n",
        "            ax.scatter(x, y, z, color='g')\n",
        "            ax.plot([x,px],[y,py],[z,pz], color='g')\n",
        "        \n",
        "        if i in r_eyebrow:\n",
        "            ax.scatter(px, py, pz, color='g')\n",
        "            ax.scatter(x, y, z, color='g')\n",
        "            ax.plot([x,px],[y,py],[z,pz], color='g')\n",
        "\n",
        "        if i in top_nose:\n",
        "            ax.scatter(px, py, pz, color='g')\n",
        "            ax.scatter(x, y, z, color='g')\n",
        "            ax.plot([x,px],[y,py],[z,pz], color='g')\n",
        "        if i in bottom_nose:\n",
        "            ax.scatter(px, py, pz, color='g')\n",
        "            ax.scatter(x, y, z, color='g')\n",
        "            ax.plot([x,px],[y,py],[z,pz], color='g')\n",
        "            \n",
        "        if i in l_eye:\n",
        "            ax.scatter(px, py, pz, color='g')\n",
        "            ax.scatter(x, y, z, color='g')\n",
        "            ax.plot([x,px],[y,py],[z,pz], color='g')\n",
        "        if i in r_eye:\n",
        "            ax.scatter(px, py, pz, color='g')\n",
        "            ax.scatter(x, y, z, color='g')\n",
        "            ax.plot([x,px],[y,py],[z,pz], color='g')\n",
        "        if i in lips_1:\n",
        "            ax.scatter(px, py, pz, color='g')\n",
        "            ax.scatter(x, y, z, color='g')\n",
        "            ax.plot([x,px],[y,py],[z,pz], color='g')\n",
        "        if i in lips_2:\n",
        "            ax.scatter(px, py, pz, color='g')\n",
        "            ax.scatter(x, y, z, color='g')\n",
        "            ax.plot([x,px],[y,py],[z,pz], color='g')\n",
        "        if i in lips_3:\n",
        "            ax.scatter(px, py, pz, color='g')\n",
        "            ax.scatter(x, y, z, color='g')\n",
        "            ax.plot([x,px],[y,py],[z,pz], color='g')\n",
        "        if i in lips_4:\n",
        "            ax.scatter(px, py, pz, color='g')\n",
        "            ax.scatter(x, y, z, color='g')\n",
        "            ax.plot([x,px],[y,py],[z,pz], color='g')\n",
        "        px,py,pz = x,y,z\n",
        "    \n",
        "    \n",
        "    elev = 100\n",
        "    azim = 90\n",
        "    ax.view_init(elev, azim)\n",
        "\n",
        "\n",
        "    ax.set_xlabel('X Label')\n",
        "    ax.set_ylabel('Y Label')\n",
        "    ax.set_zlabel('Z Label')\n",
        "    plt.savefig('3dimage{}.png'.format(num))\n",
        "\n",
        "    plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6MvI29Nsf6QG",
        "colab_type": "text"
      },
      "source": [
        "### Graph"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t9FH6HXTf4wj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def display_graph(train_losses, valid_losses):\n",
        "    plt.plot(train_losses, label='Training loss')\n",
        "    plt.plot(valid_losses, label='Validation loss')\n",
        "    plt.legend(frameon=False)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yigU3qY4BFY6",
        "colab_type": "text"
      },
      "source": [
        "# Models"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K-OsfMCjED4a",
        "colab_type": "text"
      },
      "source": [
        "## Utils"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hELUCWcBtvEk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def conv3x3(in_planes, out_planes, strd=1, padding=1, bias=False):\n",
        "    \"3x3 convolution with padding\"\n",
        "    return nn.Conv2d(in_planes, out_planes, kernel_size=3,\n",
        "                     stride=strd, padding=padding, bias=bias)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZrcGrqMUt0p8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class ConvBlock(nn.Module):\n",
        "    def __init__(self, in_planes, out_planes):\n",
        "        super(ConvBlock, self).__init__()\n",
        "        self.bn1 = nn.BatchNorm2d(in_planes)\n",
        "        self.conv1 = conv3x3(in_planes, int(out_planes / 2))\n",
        "        self.bn2 = nn.BatchNorm2d(int(out_planes / 2))\n",
        "        self.conv2 = conv3x3(int(out_planes / 2), int(out_planes / 4))\n",
        "        self.bn3 = nn.BatchNorm2d(int(out_planes / 4))\n",
        "        self.conv3 = conv3x3(int(out_planes / 4), int(out_planes / 4))\n",
        "\n",
        "        if in_planes != out_planes:\n",
        "            self.downsample = nn.Sequential(\n",
        "                nn.BatchNorm2d(in_planes),\n",
        "                nn.ReLU(True),\n",
        "                nn.Conv2d(in_planes, out_planes,\n",
        "                          kernel_size=1, stride=1, bias=False),\n",
        "            )\n",
        "        else:\n",
        "            self.downsample = None\n",
        "\n",
        "    def forward(self, x):\n",
        "        residual = x\n",
        "\n",
        "        out1 = self.bn1(x)\n",
        "        out1 = F.relu(out1, True)\n",
        "        out1 = self.conv1(out1)\n",
        "\n",
        "        out2 = self.bn2(out1)\n",
        "        out2 = F.relu(out2, True)\n",
        "        out2 = self.conv2(out2)\n",
        "\n",
        "        out3 = self.bn3(out2)\n",
        "        out3 = F.relu(out3, True)\n",
        "        out3 = self.conv3(out3)\n",
        "\n",
        "        out3 = torch.cat((out1, out2, out3), 1)\n",
        "\n",
        "        if self.downsample is not None:\n",
        "            residual = self.downsample(residual)\n",
        "\n",
        "        out3 += residual\n",
        "\n",
        "        return out3"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VFDpxCrCt5qW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class Bottleneck(nn.Module):\n",
        "\n",
        "    expansion = 4\n",
        "\n",
        "    def __init__(self, inplanes, planes, stride=1, downsample=None):\n",
        "        super(Bottleneck, self).__init__()\n",
        "        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)\n",
        "        self.bn1 = nn.BatchNorm2d(planes)\n",
        "        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,\n",
        "                               padding=1, bias=False)\n",
        "        self.bn2 = nn.BatchNorm2d(planes)\n",
        "        self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)\n",
        "        self.bn3 = nn.BatchNorm2d(planes * 4)\n",
        "        self.relu = nn.ReLU(inplace=True)\n",
        "        self.downsample = downsample\n",
        "        self.stride = stride\n",
        "\n",
        "    def forward(self, x):\n",
        "        residual = x\n",
        "\n",
        "        out = self.conv1(x)\n",
        "        out = self.bn1(out)\n",
        "        out = self.relu(out)\n",
        "\n",
        "        out = self.conv2(out)\n",
        "        out = self.bn2(out)\n",
        "        out = self.relu(out)\n",
        "\n",
        "        out = self.conv3(out)\n",
        "        out = self.bn3(out)\n",
        "\n",
        "        if self.downsample is not None:\n",
        "            residual = self.downsample(x)\n",
        "\n",
        "        out += residual\n",
        "        out = self.relu(out)\n",
        "\n",
        "        return out"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X7aD7Zg_uqFK",
        "colab_type": "text"
      },
      "source": [
        "## HourGlass"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZGhsC-3TuDwi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class HourGlass(nn.Module):\n",
        "    def __init__(self, num_modules, depth, num_features):\n",
        "        super(HourGlass, self).__init__()\n",
        "        self.num_modules = num_modules\n",
        "        self.depth = depth\n",
        "        self.features = num_features\n",
        "\n",
        "        self._generate_network(self.depth)\n",
        "\n",
        "    def _generate_network(self, level):\n",
        "        self.add_module('b1_' + str(level), ConvBlock(self.features, self.features))\n",
        "\n",
        "        self.add_module('b2_' + str(level), ConvBlock(self.features, self.features))\n",
        "\n",
        "        if level > 1:\n",
        "            self._generate_network(level - 1)\n",
        "        else:\n",
        "            self.add_module('b2_plus_' + str(level), ConvBlock(self.features, self.features))\n",
        "\n",
        "        self.add_module('b3_' + str(level), ConvBlock(self.features, self.features))\n",
        "\n",
        "    def _forward(self, level, inp):\n",
        "        # Upper branch\n",
        "        up1 = inp\n",
        "        up1 = self._modules['b1_' + str(level)](up1)\n",
        "\n",
        "        # Lower branch\n",
        "        low1 = F.avg_pool2d(inp, 2, stride=2)\n",
        "        low1 = self._modules['b2_' + str(level)](low1)\n",
        "\n",
        "        if level > 1:\n",
        "            low2 = self._forward(level - 1, low1)\n",
        "        else:\n",
        "            low2 = low1\n",
        "            low2 = self._modules['b2_plus_' + str(level)](low2)\n",
        "\n",
        "        low3 = low2\n",
        "        low3 = self._modules['b3_' + str(level)](low3)\n",
        "\n",
        "        up2 = F.interpolate(low3, scale_factor=2, mode='nearest')\n",
        "\n",
        "        return up1 + up2\n",
        "\n",
        "    def forward(self, x):\n",
        "        return self._forward(self.depth, x)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CLi2I25zulZ9",
        "colab_type": "text"
      },
      "source": [
        "## FAN"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8nMcI8lkuXe7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class FAN(nn.Module):\n",
        "\n",
        "    def __init__(self, num_modules=1):\n",
        "        super(FAN, self).__init__()\n",
        "        self.num_modules = num_modules\n",
        "\n",
        "        # Base part\n",
        "        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3)\n",
        "        self.bn1 = nn.BatchNorm2d(64)\n",
        "        self.conv2 = ConvBlock(64, 128)\n",
        "        self.conv3 = ConvBlock(128, 128)\n",
        "        self.conv4 = ConvBlock(128, 256)\n",
        "\n",
        "        # Stacking part\n",
        "        for hg_module in range(self.num_modules):\n",
        "            self.add_module('m' + str(hg_module), HourGlass(1, 4, 256))\n",
        "            self.add_module('top_m_' + str(hg_module), ConvBlock(256, 256))\n",
        "            self.add_module('conv_last' + str(hg_module),\n",
        "                            nn.Conv2d(256, 256, kernel_size=1, stride=1, padding=0))\n",
        "            self.add_module('bn_end' + str(hg_module), nn.BatchNorm2d(256))\n",
        "            self.add_module('l' + str(hg_module), nn.Conv2d(256,\n",
        "                                                            68, kernel_size=1, stride=1, padding=0))\n",
        "\n",
        "            if hg_module < self.num_modules - 1:\n",
        "                self.add_module(\n",
        "                    'bl' + str(hg_module), nn.Conv2d(256, 256, kernel_size=1, stride=1, padding=0))\n",
        "                self.add_module('al' + str(hg_module), nn.Conv2d(68,\n",
        "                                                                 256, kernel_size=1, stride=1, padding=0))\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = F.relu(self.bn1(self.conv1(x)), True)\n",
        "        x = F.avg_pool2d(self.conv2(x), 2, stride=2)\n",
        "        x = self.conv3(x)\n",
        "        x = self.conv4(x)\n",
        "\n",
        "        previous = x\n",
        "\n",
        "        output = None\n",
        "        for i in range(self.num_modules):\n",
        "            hg = self._modules['m' + str(i)](previous)\n",
        "\n",
        "            ll = hg\n",
        "            ll = self._modules['top_m_' + str(i)](ll)\n",
        "\n",
        "            ll = F.relu(self._modules['bn_end' + str(i)]\n",
        "                        (self._modules['conv_last' + str(i)](ll)), True)\n",
        "\n",
        "            # Predict heatmaps\n",
        "            tmp_out = self._modules['l' + str(i)](ll)\n",
        "            output = tmp_out \n",
        "\n",
        "            if i < self.num_modules - 1:\n",
        "                ll = self._modules['bl' + str(i)](ll)\n",
        "                tmp_out_ = self._modules['al' + str(i)](tmp_out)\n",
        "                previous = previous + ll + tmp_out_\n",
        "                \n",
        "        return output"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q_WMRq7hAiLK",
        "colab_type": "text"
      },
      "source": [
        "## Heat Map to 2D points"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6Km-4HTHzRLP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class HMTo2D(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(HMTo2D, self).__init__()\n",
        "        self.conv = nn.Conv2d(68, 68*2, 64)\n",
        "    def forward(self, hm):\n",
        "        pts = self.conv(hm)\n",
        "        pts = pts.view(pts.shape[0], -1)\n",
        "        return pts"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DFG8Dxr3AuPE",
        "colab_type": "text"
      },
      "source": [
        "## 2D LandMarks"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lBL19VTR0gYQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class LandMark2D(nn.Module):\n",
        "    def __init__(self, num_modules=1):\n",
        "        super(LandMark2D, self).__init__()\n",
        "        \n",
        "        self.fan = FAN(num_modules)\n",
        "        self.hm_2_2d = HMTo2D()\n",
        "    def forward(self, images):\n",
        "        hm = self.fan(images)\n",
        "        pts = self.hm_2_2d(hm)\n",
        "        return pts, hm"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X3XxFVVNaQyz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "outputId": "4f33a6a5-5e67-4ae1-f4f0-358824e9a05b"
      },
      "source": [
        "lm2d = LandMark2D()\n",
        "lm2d = lm2d.cuda()\n",
        "inp = torch.rand(1,3,256,256).cuda()\n",
        "start = time()\n",
        "i = 0\n",
        "while time() - start < 1:\n",
        "    i+=1\n",
        "    lm2d(inp)\n",
        "print(f\"FPS: {i}\")"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "FPS: 28\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mOQgGfpHufUJ",
        "colab_type": "text"
      },
      "source": [
        "## Depth"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Sq-a_u0U7wYA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class ResNetDepth(nn.Module):\n",
        "\n",
        "    def __init__(self, block=Bottleneck, layers=[3, 8, 36, 3], num_classes=68):\n",
        "        self.inplanes = 64\n",
        "        super(ResNetDepth, self).__init__()\n",
        "        self.conv1 = nn.Conv2d(3 + 68, 64, kernel_size=7, stride=2, padding=3,\n",
        "                               bias=False)\n",
        "        self.bn1 = nn.BatchNorm2d(64)\n",
        "        self.relu = nn.ReLU(inplace=True)\n",
        "        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)\n",
        "        self.layer1 = self._make_layer(block, 64, layers[0])\n",
        "        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)\n",
        "        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)\n",
        "        self.layer4 = self._make_layer(block, 512, layers[3], stride=2)\n",
        "        self.avgpool = nn.AvgPool2d(7)\n",
        "        self.fc = nn.Linear(512 * block.expansion, num_classes)\n",
        "\n",
        "        for m in self.modules():\n",
        "            if isinstance(m, nn.Conv2d):\n",
        "                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels\n",
        "                m.weight.data.normal_(0, math.sqrt(2. / n))\n",
        "            elif isinstance(m, nn.BatchNorm2d):\n",
        "                m.weight.data.fill_(1)\n",
        "                m.bias.data.zero_()\n",
        "\n",
        "    def _make_layer(self, block, planes, blocks, stride=1):\n",
        "        downsample = None\n",
        "        if stride != 1 or self.inplanes != planes * block.expansion:\n",
        "            downsample = nn.Sequential(\n",
        "                nn.Conv2d(self.inplanes, planes * block.expansion,\n",
        "                          kernel_size=1, stride=stride, bias=False),\n",
        "                nn.BatchNorm2d(planes * block.expansion),\n",
        "            )\n",
        "\n",
        "        layers = []\n",
        "        layers.append(block(self.inplanes, planes, stride, downsample))\n",
        "        self.inplanes = planes * block.expansion\n",
        "        for i in range(1, blocks):\n",
        "            layers.append(block(self.inplanes, planes))\n",
        "\n",
        "        return nn.Sequential(*layers)\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = self.conv1(x)\n",
        "        x = self.bn1(x)\n",
        "        x = self.relu(x)\n",
        "        x = self.maxpool(x)\n",
        "\n",
        "        x = self.layer1(x)\n",
        "        x = self.layer2(x)\n",
        "        x = self.layer3(x)\n",
        "        x = self.layer4(x)\n",
        "\n",
        "        x = self.avgpool(x)\n",
        "        x = x.view(x.size(0), -1)\n",
        "        x = self.fc(x)\n",
        "\n",
        "        return x"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YCzbW1LMA2Iq",
        "colab_type": "text"
      },
      "source": [
        "## 3D LandMark"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TbQhab8X6Fe8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class LandMark3D(nn.Module):\n",
        "    def __init__(self, num_modules=1):\n",
        "        super(LandMark3D, self).__init__()\n",
        "        self.LM2D = LandMark2D(num_modules)\n",
        "\n",
        "        self.conv = nn.ConvTranspose2d(68, 68, 4, 4)\n",
        "        self.depth = ResNetDepth()\n",
        "\n",
        "    def forward(self, images):\n",
        "        pts_2d, heatmaps = self.LM2D(images)\n",
        "        heatmaps = self.conv(heatmaps)\n",
        "        pts_depth = self.depth(torch.cat((images, heatmaps), 1))\n",
        "        pts_3d = torch.cat([pts_2d, pts_depth],1)\n",
        "        return pts_3d\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RhBFca5y6mQi",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "outputId": "0f697373-3f9f-4594-9fbc-8a13b28cdad4"
      },
      "source": [
        "lm3d = LandMark3D()\n",
        "lm3d.cuda()\n",
        "inp = torch.rand(1,3,256,256).cuda()\n",
        "start = time()\n",
        "i = 0\n",
        "while time() - start < 1:\n",
        "    i+=1\n",
        "    lm3d(inp)\n",
        "print(f\"FPS: {i}\")\n"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "FPS: 16\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Hsa1X3YQOjfx",
        "colab_type": "text"
      },
      "source": [
        "# Workers"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4kVLmdfKJfoc",
        "colab_type": "text"
      },
      "source": [
        "## Early Stopper"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "boAfEME2OuBm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class EarlyStopping:\n",
        "    def __init__(self,patience=15, path='/content'):\n",
        "        self.patience = patience\n",
        "        self.path = path\n",
        "        self.counter = 0\n",
        "        self.best_score = None\n",
        "        self.early_stop = False\n",
        "        self.val_loss_min = np.Inf\n",
        "    \n",
        "    def __call__(self, val_loss, model, epoch):\n",
        "        score = -val_loss\n",
        "        if self.best_score is None:\n",
        "            self.best_score = score\n",
        "        if score < self.best_score:\n",
        "            self.counter += 1\n",
        "            print(f'\\rEpoch {epoch} EarlyStopping counter: {self.counter} out of {self.patience}')\n",
        "            if self.counter >= self.patience:\n",
        "                self.early_stop = True\n",
        "        else:\n",
        "            print(\"\\rEpoch {} loss reduced from {} to {}\".format(epoch, -self.best_score, val_loss))\n",
        "            self.best_score = score\n",
        "            self.save_checkpoint(val_loss, model)\n",
        "            self.counter = 0\n",
        "    \n",
        "    def save_checkpoint(self, val_loss, model):\n",
        "        torch.save(model.state_dict(), self.path)\n",
        "        self.val_loss_min = val_loss"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AoFp53Jm3TH-",
        "colab_type": "text"
      },
      "source": [
        "## Controller"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nWvbursE93km",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# !mkdir \"/content/drive/My Drive/Controller3d\""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qLMn1Ego3SMe",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class Controller:\n",
        "\n",
        "    def __init__(self, path):\n",
        "        \n",
        "        self.path = path\n",
        "        self._initialize()\n",
        "        self._load()\n",
        "\n",
        "    def _initialize(self):\n",
        "        \n",
        "        self.epoch_n = 0\n",
        "        self.early_stopping_counter = 0\n",
        "        self.batch_n = 0\n",
        "        self.current_train_loss = 0\n",
        "        self.model_state_dict = None\n",
        "        self.optim_state_dict = None\n",
        "        self.best_score = None\n",
        "\n",
        "        self.train_loss = []\n",
        "        self.valid_loss = []\n",
        "\n",
        "\n",
        "    def _load(self):\n",
        "        self._load_epoch_info()\n",
        "        self._load_batch_info()\n",
        "\n",
        "    def _load_batch_info(self):\n",
        "        \n",
        "        path = os.path.join(self.path, \"batch_info.pth\")\n",
        "        \n",
        "        if not os.path.isfile(path):\n",
        "            return \n",
        "        \n",
        "        info = torch.load(path)\n",
        "\n",
        "        self.batch_n = info[\"batch_n\"]\n",
        "        self.model_state_dict = info[\"model_state_dict\"]\n",
        "        self.optim_state_dict = info[\"optim_state_dict\"]\n",
        "        self.current_train_loss = info['current_train_loss']\n",
        "\n",
        "    def _load_epoch_info(self):\n",
        "        \n",
        "        path = os.path.join(self.path, \"epoch_info.pth\")\n",
        "        \n",
        "        if not os.path.isfile(path):\n",
        "            return \n",
        "        \n",
        "        info = torch.load(path)\n",
        "\n",
        "        self.epoch_n = info[\"epoch_n\"]\n",
        "        self.early_stopping_counter = info[\"early_stopping_counter\"]\n",
        "        self.best_score = info[\"best_score\"]\n",
        "        self.train_loss = info[\"train_loss\"]\n",
        "        self.valid_loss = info[\"valid_loss\"]\n",
        "\n",
        "    def update_batch_info(self, batch_n, model, optim, current_train_loss):\n",
        "        self.batch_n = batch_n\n",
        "        self.model_state_dict = model.state_dict()\n",
        "        self.optim_state_dict = optim.state_dict()\n",
        "        self.current_train_loss = current_train_loss\n",
        "        self._save_batch_info()\n",
        "\n",
        "    def _save_batch_info(self):\n",
        "        torch.save({\n",
        "            'batch_n': self.batch_n,\n",
        "            'model_state_dict': self.model_state_dict,\n",
        "            'optim_state_dict': self.optim_state_dict,\n",
        "            'current_train_loss': self.current_train_loss,\n",
        "            }, os.path.join(self.path,\"batch_info.pth\"))\n",
        "    \n",
        "    \n",
        "    def update_epoch_info(self, epoch, best_score, train_loss, valid_loss, early_stopping_counter):\n",
        "        \n",
        "        self.epoch_n = epoch\n",
        "        self.early_stopping_counter =early_stopping_counter\n",
        "        self.best_score = best_score\n",
        "        self.train_loss.append(train_loss)\n",
        "        self.valid_loss.append(valid_loss)\n",
        "        self._save_epoch_info()\n",
        "        self.current_train_loss = 0\n",
        "        self.batch_n = 0\n",
        "        self._save_batch_info()\n",
        "\n",
        "    def _save_epoch_info(self):\n",
        "        torch.save({\n",
        "            'epoch_n': self.epoch_n,\n",
        "            'early_stopping_counter':self.early_stopping_counter,\n",
        "            'best_score': self.best_score,\n",
        "            'train_loss': self.train_loss,\n",
        "            'valid_loss': self.valid_loss,\n",
        "            }, os.path.join(self.path,\"epoch_info.pth\"))\n",
        "    "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pLmQ_B-7Jyn-",
        "colab_type": "text"
      },
      "source": [
        "## Trainer"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bvEqUKUnJ5vv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def train(train_loader, model, optimizer, criterion, controller):\n",
        "    model.train()\n",
        "    t = time()\n",
        "    train_loss = controller.current_train_loss\n",
        "    for i, data in enumerate(train_loader):\n",
        "        images = data['image']\n",
        "        if images.shape[1] == 0:\n",
        "            print(f\"\\rTaining finished batches {i+1}/{len(train_loader)}   {int(((i+1)/len(train_loader))*100)}%\", end='')\n",
        "            continue\n",
        "        targets = data['keypoints']\n",
        "        targets = targets.view(targets.size(0), -1)\n",
        "        \n",
        "        images = images.type(torch.FloatTensor)\n",
        "        targets = targets.type(torch.FloatTensor)\n",
        "        \n",
        "        images = images.to(device)\n",
        "        targets = targets.to(device)\n",
        "        \n",
        "        optimizer.zero_grad()\n",
        "        \n",
        "        output = model(images)#[0] for 2d model\n",
        "        loss = criterion(output, targets)\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "        \n",
        "        train_loss += 1/(i + 1) * (loss.data - train_loss)\n",
        "        \n",
        "        # print(targets.shape, output.shape)\n",
        "\n",
        "        del images\n",
        "        del targets\n",
        "        torch.cuda.empty_cache()\n",
        "        \n",
        "        delay = time() - t\n",
        "        hms = str(datetime.timedelta(seconds=int(delay* (len(train_loader)-(i+1)))))\n",
        "\n",
        "        print(f\"\\rTaining finished batches {i+1}/{len(train_loader)}   {int(((i+1)/len(train_loader))*100)}%   delay {int(delay)}s   time left {hms}   loss {loss.item()}\",end='')\n",
        "        if i%10 == 0:\n",
        "            controller.update_batch_info(i, model, optimizer, train_loss)\n",
        "        t = time()\n",
        "        \n",
        "    return train_loss"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cL61OCUxJqxj",
        "colab_type": "text"
      },
      "source": [
        "## Validater"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rZ-hgW0WJprQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def validation(valid_loader, model, criterion):\n",
        "    model.eval()\n",
        "    loss, running_loss = 0.0, 0.0\n",
        "    t = time()\n",
        "    with torch.no_grad():\n",
        "        for i, data in enumerate(valid_loader):\n",
        "            images = data['image']\n",
        "            targets = data['keypoints']\n",
        "            \n",
        "            targets = targets.view(targets.size(0), -1)\n",
        "            \n",
        "            images = images.type(torch.FloatTensor)\n",
        "            targets = targets.type(torch.FloatTensor)\n",
        "\n",
        "            images = images.to(device)\n",
        "            targets = targets.to(device)\n",
        "            \n",
        "            output = model(images)#[0] for 2d model\n",
        "            loss = criterion(output, targets)\n",
        "            running_loss += loss.item()\n",
        "            del images\n",
        "            del targets\n",
        "            torch.cuda.empty_cache()\n",
        "\n",
        "            delay = time() - t\n",
        "            hms = str(datetime.timedelta(seconds=int(delay * (len(valid_loader)-(i+1)))))\n",
        "            print(f\"\\rValidation finished batches {i+1}/{len(valid_loader)}   {int(((i+1)/len(valid_loader))*100)}%   delay {int(delay)}s   time left {hms}   loss { loss.item()} \",end='')\n",
        "            t = time()\n",
        "    avg_loss = running_loss / (i+1)\n",
        "    model.train()\n",
        "    return avg_loss"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fehFcth4Kepl",
        "colab_type": "text"
      },
      "source": [
        "## Worker"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mviJP2wGKhD6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def worker(n_epochs, train_loader, valid_loader, model, optimizer, criterion, early_stop, controller):\n",
        "    \n",
        "    all_train_loss, all_val_loss = controller.train_loss, controller.valid_loss\n",
        "    early_stop.counter = controller.early_stopping_counter\n",
        "    if controller.model_state_dict:\n",
        "        model.load_state_dict(controller.model_state_dict)\n",
        "        \n",
        "    if controller.optim_state_dict:\n",
        "        optimizer.load_state_dict(controller.optim_state_dict)\n",
        "    \n",
        "    for epoch in range(controller.epoch_n,n_epochs):\n",
        "        #visualize_output(test_images, epoch, test_outputs, gt_pts)\n",
        "        # if controller.batch_n != len(train_loader): \n",
        "        train_loss = train(train_loader, model, optimizer, criterion, controller)\n",
        "        controller.update_batch_info(len(train_loader), model, optimizer, train_loss)\n",
        "        val_loss = validation(valid_loader, model, criterion)\n",
        "        early_stop.best_score = controller.best_score\n",
        "        early_stop(val_loss, model, epoch)\n",
        "        all_train_loss.append(train_loss)\n",
        "        all_val_loss.append(val_loss)\n",
        "        controller.update_epoch_info(epoch+1, early_stop.best_score, train_loss, val_loss, early_stop.counter)     \n",
        "\n",
        "        if early_stop.early_stop:\n",
        "            break    \n",
        "            \n",
        "    return all_train_loss, all_val_loss  "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ezcLdBhFEOqB",
        "colab_type": "text"
      },
      "source": [
        "# Start Working"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yG88FEHnCSoA",
        "colab_type": "text"
      },
      "source": [
        "## Basic Configurations"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JwrKIYfjCdDL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "n_epochs = 1000\n",
        "device = torch.device('cuda')\n",
        "batch_size = 32\n",
        "image_size = 256\n",
        "base_dir = \"/content/drive/My Drive/data/\"\n",
        "valid_size = 0.1\n",
        "test_size = 0.1\n",
        "colored_image = True"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1NabtPT0c8pz",
        "colab_type": "text"
      },
      "source": [
        "## 2D Model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dcy13JEydXj5",
        "colab_type": "text"
      },
      "source": [
        "### Sub Config"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZSVvS-Kcvf8S",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# # !mkdir \"/content/drive/My Drive/Models\"\n",
        "criterion = nn.MSELoss()\n",
        "model = LandMark2D(1)\n",
        "model.to(device)\n",
        "controller = Controller(\"/content/drive/My Drive/Controller2d\")\n",
        "early_stop = EarlyStopping(5, \"/content/drive/My Drive/Models/lm2d.pt\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Nam8SmGGc8C_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 93
        },
        "outputId": "0565e2ec-315e-4ae0-d5da-991383dd90dd"
      },
      "source": [
        "mode = Mode.lm2d\n",
        "train_loader, test_loader, valid_loader = create_datasets(base_dir, batch_size, controller.batch_n, image_size, mode, valid_size, test_size, colored_image)"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Train set: 124448 image\n",
            "Valid set: 15556 image\n",
            "Test set: 15556 image\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_-_qS1K-LL1A",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "images, _, key_pts = sample_output(test_loader)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Dmadtbx7CtaP",
        "colab_type": "text"
      },
      "source": [
        "### Data Visualizing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hh7nhKTDR6cd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.load_state_dict(torch.load(\"/content/drive/My Drive/Models/lm2d.pt\"));"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N7zOe8hHebgS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# # model results\n",
        "test_images, test_outputs, gt_pts = sample_output(test_loader, model)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4Y0YDqa3LVJq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "visualize_output(test_images, controller.epoch_n, test_outputs, gt_pts)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B0MjvNKcj1pX",
        "colab_type": "text"
      },
      "source": [
        "### Train"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jWzYcmJL_GGJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.to(device);\n",
        "optimizer = optim.Adam(model.parameters(), lr=0.001)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-RkQJyn7j1MN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "all_train_loss, all_val_loss = worker(n_epochs, train_loader, valid_loader, model, optimizer, criterion, early_stop, controller)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vsHaE-oWj71R",
        "colab_type": "text"
      },
      "source": [
        "### Training Graph"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "noN8vOkbj_c-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "display_graph(all_train_loss, all_val_loss)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XiMb5hHxkCic",
        "colab_type": "text"
      },
      "source": [
        "### Resutls"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VFGESUmDC2X_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "test_images, test_outputs, gt_pts = sample_output(test_loader, model)\n",
        "visualize_output(test_images, test_outputs, gt_pts)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iDlQsPw5D2pq",
        "colab_type": "text"
      },
      "source": [
        "# 3D Model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xwh6t9RLkS8t",
        "colab_type": "text"
      },
      "source": [
        "### Sub Config"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ceA82ai3kVGU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "criterion = nn.MSELoss()\n",
        "model = LandMark3D(1)\n",
        "controller = Controller(\"/content/drive/My Drive/Controller3d\")\n",
        "early_stop = EarlyStopping(3, \"/content/drive/My Drive/Models/lm3d.pt\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UEItttIAaaJa",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.load_state_dict(torch.load(\"/content/drive/My Drive/Models/lm3d.pt\"));"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RyGIoOCEZ_TP",
        "colab_type": "code",
        "outputId": "b03254dc-d471-486c-ae97-b5ac7635f8a3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 93
        }
      },
      "source": [
        "mode = Mode.lm3d\n",
        "train_loader, test_loader, valid_loader = create_datasets(base_dir, batch_size, controller.batch_n, image_size, mode, valid_size, test_size, colored_image)"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Train set: 124448 image\n",
            "Valid set: 15556 image\n",
            "Test set: 15556 image\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oLpobDX5fKGj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "images, model_pts, key_pts = sample_output(test_loader, model)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l-g3FeBMZ1qJ",
        "colab_type": "text"
      },
      "source": [
        "## Data Visualizing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-bltXoM3aYqD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "3e789f00-1c6c-4b65-83fe-0ce8ba886291"
      },
      "source": [
        "# model training \n",
        "for i in range(10):\n",
        "    plot_3d(controller.epoch_n, key_pts[i], model_pts[i])"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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PtyZCGCK1C3d5uoAltKDT6VAqlRgMBjIyMsQKFE4QPV0PI5PJavVAWk0ZRNGbrl1UdyDN+g+gfv365ObmIggCRqORzMxM8Td3gmh0PUx1PF0Qc2hFPIM7PF1ro6tQKFAoFJbpyxKJBK1W656Dvc0Qja6HqWuebkpOCuuPryclJ8VjfdQEWcVZHLp0iKziisucOzrnW4HqZi9YfwaoV68e+fn5GAwGcaygHMSYroepzkBarXr1T0lBlpSEIToanEznnfvTXFYdXWX5HBsVy+KBi911mDXGluQtzPxhJgqpAp1Rx8ohKxnbYazD76lK5+zCd1mbceTpmgfV6tevz/Xr191WQfp2RDS6HkYul1dZr7a2eLqKuXORryo1LPrYWHSLbQ3LwcyDNsYHIP5IPNPrDSQ0wnPlhtxNVnEWM3+YiVqvRo1JtnD6d1P569TPaOr5kXEpg3XX16E1aMkuzmbX+V02s+zjD8czPWo6EUGOjakr32Vtx1Lr68a1Y32NqlQqS766iGNEo+thbgU93XL72LcP+apVpARBYhh0z4CI+HjUedn83lLGL96X2CNJI8mQ7qBhOPbUGISWd3N93gL79VlZSNPTMYaHQ3Cw286nOqTnpaOQKiwGF8C7xMADL33G2p7e/BrpjSrfB6Ugxe9qHjgQZ5v8bk96S1vSX92Ue0saU994QwgmL48zf2znYGfT99ghB+Tx8ejGjoWKBG5qkXdc1sMtG24IDAzk6tWrtTpV8mYiGl0PUytTxqxuYIn1LDtBQJKWhvSPP5Dt24d03z6kqanMGQLLY0o3m30Aioq+5BMvkOugRwbMvAwrumOnrRSTARF/7ePOpvcRvj2EsYq7uL/xvdQ7n8n1T1eQ1lBOi2tG6i2JxzB2rPvPtZKE1wtHZ7R9SOplEJENa7/QEv+jD8rcSwCcCoKOs+3bGHhGx8roM3xY/zRSI3TNVtA/05s0n2I2W20/+wB8kACqAQMQGjbE2L49xogIwpRKpIWFCBERCKGhKObNq1XesaPwgvVyuVyOn58f6enpdOjQ4aYcY21GlHYsB1f0dI8ePUpcXBz5+fnIZDJeeuklxo8fb9HTvXLlCl5eXgQFBdnMVHMFvV6P0Wh0q2Rem/ffp9m2bZbPl/r0IbdjRxqeOkX948dR5uQA8E9jFT/HhPJnIw2rQ9NtjakAE6X30KnjcDp7tcWvxIC8uJgllz/hi5JfLZuZjYoAfN7Nlxd7q7kQYCQ0D/67G2JHgJcBtDJYvUNG+Mtb0FUxFigIAmq1GpVKVaX9rfn56s+8e/odlCU6dDJYsw0ePgFGqZSrXbtSdOedqJs2RZBI+CjpDbsH0qPj1nK9WVNO5Z/i8PXDHMk7QnJ+sskolfkeTy6H9CEDaJ4vpen5S/j+8w+KGxMNAPRKJTKNBgkmI58YBt0yIH/RWoqrIcdZVFRUZQ3l4uJiy75SqRSZTIZcLkculyOTySxhh7Zt23pUp7mWI+rpVgVX9HTPnDmDRCKhbdu2ZGZm0rVrV06dOmUZSHj//feRyWRMnjy50v1fvXqVwsJCt8xlT8lJIenwt9wzdQEdc2zXaSVwKLIh+3qEsj9cwgHZZS6UmESAZBIZBqPBzlisGrqKCXdOsO/n4E6OPTWGmBuvzzc25/imTbS+/34S0/fx85cLWaz5GbWidD8fHZzut52gnlWrQeZuPd2sCylcGXo3La9oCC42LRN8fNi3YQNRVhKeinnzSP0i3hJ6aTvWsRe6+shqnt71tO1CAR45Bhs6mz4Gq4JpH9iOBkXe9PJvRfsc6JZwnJDd+3myzNtGnKoP787+vsrnV1XNXkEQ2Lt3L3K5HIVCgUQisTG4crkciUSCVCqlXbt2ldbrvY0QqwFXhW3btrFnzx7ApKfbt29fO6Pbrl07y/9DQkJo1KgRWVlZFqMrl8urHNN1V3jBZoR9dqkHCiaDGDdKytou2UA2Yd5hxIT0YnZId2JCYvCWedPjszI3pwSiQ6Id9hXRbSh39oxFHh9veWKrp0xB3aIFEomEmOa9kA97ig++tDW6CgOk1ReoWqlE9xPcLIIm/1mFV1wcQoACdDo0H35o54nrFi+mxfTptL4RrtE5ibf2Du9tv1ACD766ldGCgdM5p0nNTSX1Wip7Cw7yba5pJmT7CPjq6A2Da3Ubryzey4zPl9HuoTlQpvpDVnEW6XnphNcLJ1jl/li5UqmkuLjYYmDLhhmcKfKJmBCNbjm4oqdrTWJiIlqtltatW1uWmcWeq0pFRreiG2x/xn7brAKJ6QaeebDUEw2KGc7ngx+ie0h3QvxD7NqIjYol/ki8zWdno/NgMkS66dMtcePCsDA4f96yPrx5Z7Q+XiCUjnDrfLwIb9653HOtaQxjx6Lu29cy2GcIDIRDh+w3jIjAUMHgVkRQhMPvcUjrIQAMbzPcsnz//v20iWxD6rVUzuSe4X+FbwEXbBuUwL1/v0SveW/SpW0fuvR/lG7ebfgteQdxpxehkHnZpLu5E4lEgkKhQKvVolQq7YyueRvR8Dqmzhvd6urpmrl06RITJ05k3bp1NuWzPTk5omw+6Rt93iDMP4zU0/vISjnIz/zNccH+3AAOhEFEDhRNnsz4QbPo0N75gMfigYuZHjWdpMwkokOiyzW4FqwMkaRMkcVgVTArh39E3M5YFMjQYWDl0HiPeGXVJjgYozmzopqj8ZX5HhuqGtJQ1ZCeYT1JeT2GhWu72m3TL/AuzmuS+bFwJ+Pe2kmPnyBuDqgVoDaYHvRxCXH0bd7X7d+tl5cXRUVFKBQKy/VubXxFg+ucOm90q6unC6ailMOHD+fNN9+0i5NVV0/XGY7ySeftnsfoZPj8KzDIJbytF/g4EmaPwDbCJIGo6QtQ9xxBQdOmkJlZ4bFEBEW4ZmydUPbhMbbDWPo27+vR12BPUF1jUpXv0ZmXvHjgYhAENJs/J+CNWA43EfAyYBu2kSpIz0t3+/crkUgseroymcyhpyviGHEacDm4oqer1Wp54IEHmDRpEmPGjLFb76nJEeZ8Umu8pUqeT1Sg0oN/iYBSDzOPyYltZzvgFRsVS9vJz1nyPW/WdM1gVTBdm3a9ZQzuzWTxwMUcmnKIVUNWcWjKodJZbxIJPm07IPXzp8V1UyaINTqjjvB64W47DutrRaFQYDQaMRgMDuO6Io4RjW45uKKnu2XLFn799Vc+/fRTunTpQpcuXTh69KiljeqEF8C5QXSUTyoBWhWXSS9TqVjabIbjGxbx5riViAiKYMKdE+w8ZWN4OOh0BBeb0tt8dBBQAj4yb1YOWenRh5q3t7fNmIUYXqiYOh9eKA9X9HQnTJjAhAn2qVNmPOXpBquCWTlkJXEJcaUaAXe/TfBbz9tuqNNhDA8nIijY4WttbZlqfCtw02cHOiM4GO3KlXjFxfFQuoIBK7WkvjWfsH9N8fhbhOxG5oRGo0EuLzUn4kCac0Sj62GqW5iyPBzFRbUrA/CKiwOFKc1Ju3JlrZhiW1MhDE+nS9UEVTFW1pkWfuHhRNXgb+7t7W03MUU0uM4Rja6H8XQ14GBVsI1xKZvmVJHBvZ0k+H66/BPv7HnHTh3MnXjamFTrt7DOtKhBpFIpcrkctVqNQmEaZxCNrnPEmK6Hqa60Y1VfN41du9YKDxdq5gbMLs5m4cm3UOvV5GvzUevVxCXEuaSH6whHGrnOfouUgzvZ8NFMUg7uLL9NF7e7FQ2WecKEuUDlrXgONYXo6XqY6hamrPES7E7UrFJyUkz5pSVBdDqdU67alXnKcUwGtO05AqqhEeAqmd98ilKtRWM167Sq6VKV0ch99j/dWOmbbPrwyzpmfteRRa/sB6kUrAyP3XY7OrFoQaJde7U2buwC5gKVQUFBotEtB9HoehhPxnTdTVmtV8OYMeiee465J98lPv0LixLH7APw/hOgnzwZ3dKlpvjxDeb+NJdVR0rbmP3SAt4Jm4ZQBe0Jl8nKosMrS9DF2S7WGbSVTpdKyUlxqAt8Omkn96ZL+CvEm+K0cLzl3vifv8znvsk2OdArfJNhXAAGORglIEgkSAwCq6Ox3U51kqkHdxLUKbo0Bl0E0vPn8crLq+QXUHmsry13xcGVSiX5+fno9XpxIK0cRKPrYWQymcvZC2Uv/prKLBAEAVJSkK9aZaNm1f2XL+GXL4mfjclg3LiHzNOIIz75hFXHP+GanxSpXEG2n4xVnYttjMvyGIhb/jHe/fpBly7lnm9Vkaan01DvxZptJUwdZdJx0MkgvuP8SreblJlkv1CASdv/4bG/TB8397jAf3tLCM0ohHb2m6c0lXIi3BsJEiQC3JFeAti/7Xz04WN81kmLQuGNTq9hzTcCD53zIUajQb9qlcekLq1f/51Vyagq/v7+5ObmEhRUW1Q0ah+i0fUwroYXtiRvIS4hDrlUjkEwsHLISoaFD6uxQS5ZksnYlNXOvcPxLGISb0wjbpcDg+83AhrTCgeHmxgGLJvCgYPxdLpzIBH9H+L77H2mm10iR2fQsvKeRYyNmVqlYzeGh4NWy8MnYODfkFYfmpd443d4SqXbcibkE5NR+iwZv78IBfUIvfN+EvjObttFj20hottQy+eUgzvp+ov9xJkfmhShBtQ3BJGmDoOBS3UEa0AaF4e6b1+PxOXN15SjWY2uTBt2FrM1Go14e3tTXFyMWq12sKcIiEbXIQkJCTz11FMYDAamTZtmmRRhRqPRMGnSJA4dOkRQUBCbN2+mRYsWDtuSy+Xceeed7N+/32l/17XXiT0Yi8aosThET3z/BOuj1+Nj9Cl33+piNBrRaDQcUSgICLJXszrRxPF+3W8YoUHnIWdjO4wyKae9C7hnxEWH284ZpuHn+r/Bhd9g3X+QCCBIsNRniNv9JC13/42+/4gqncdfEyJ54tNEgoohSCsn+fl5nDh3Ds6dq3Rbo5uO5ptL35g+CKZwSocycpjbm+SxMewH2mXBGSunLiJHyi85afz4/fvoBT06o47QPxKR1gej1WwxiQCZDW1Ff0xKaxBcDAaJhJM7dlDgoSoRxcXF7PhjBxLB1nhKBAk7/thBhL/jfgVBQKvV4u3tbfcmZp6VVr9+fTIzM2nTpo1Hjv1WRzS6ZTAYDMyaNYuffvqJsLAwunXrxsiRI+nYsaNlmzVr1tCgQQPOnj3Lpk2beP7559m8ebNNO2YR86KiIrKysggPDycoKIhtVgLiZg5dOoTyiBKNVmNZppQrqRdeD798Pzp39pz6llqt5syZM0T268fGY58Be2w3cCEsV6AoobhFKE0VIfTJLWRvg9KY5H1nIeK6jHd7r0A5OIbk/dv58cTXfCo5gs6qbYUBvDev5I7pT1fau8suzmbEnyc48N8+bJq/F+2cObR68UWqqkLco0cPm4HDqFftvdS5+6CBWsd7PbD5jlKCjCzf8yxpNwxxwyL46hvwfxTyrIyun9IXrVFvE3XQyaDFddP/ZYJAp+HDPeLp6vV6jh49yvCOw5l3Yh4Ni0z9ptWHonoCw3sNd+rp6nQ69u7di1KptFtnXY7d29ubvLw8gmtJBk1tQkwZA3JySt2YxMRE2rRpQ6tWrfDy8uKhhx6yM5Tbtm3jscceA2DMmDHs3r3bLgyQkJBAUlIS69evZ+jQofz+++8ODS44ntLr7jnzzrD2VqLmLnbJyMKNkMENBvdJp2P0n7Tu/LPJ4JrjvxL4rQVk11OgbRpCi4ZtGXb/PF568mvkctubVieDFsUKpOkOaq1VQPzheIr1xQyPnIrQsCESq8oLVcUy5bbbUPSxsQhg+dNPm0b7Nd9yZ1BHh/vOOAz7Nvtz8OtG7NsZQtvrEjtNBL1Bz6L+i/CR+xDgFYCPxIs13ytoKPPHoFR6dFKLOTwQrArmW8Mj/LMUdn0G/yyDnZLHyg0tmIVutFotUqnUJsxgrb8QHh4uVgR2Qp02uhqNhj/++IMRI0ZYBrsuXrxIs2bNLNuEhYVx8aLtK7P1NnK5nHr16tkYbmtcqZFmntLbuFhKt4vQTFM6Z74mU4jMalbWjI8Y73CfbhkmA3T24aG8PHU9a4avYX6P+XjLbSsFKAxw6rW56K3KHAWrgll5z6JSjQCdSTMgON9gis9WggJtASsPr2R4q+G08muF0KgREgdSndVBt5tjCMYAACAASURBVHgx6kOHKFq+nEP/+x+6997D2L8/UTNec7j94De+oHPSZTruO0/od/torPe21UTQwcp7FjG1y1ROPXGK78Z9x6mZZxj1dSqF33zDwS++8Gi9OMs1lZXFgIUbUemhngZUOrj3jXWQ5Ty3WRAEvLy80Ol0Dq9Ns+G1ljcVsaVOhRcEQbAkb+fn5/P222+TkJDA8uXLPXaRuCp489BxgYeWgUYqwU8CuvZQ1LJm8nStcaT52sCnga2sYMtxtP7vANTR0YRGRPDAjeVZxVl8kPSBTXs6X29CRj/O1bSrNsvHxkxlwHnIfOM5WhQrCM43VNq7S8lJ4b+//5frmus8Hf00XAPB3x/JiROQkuLeqrkREehatqTk5MnSRd2GMnNHJ1aoSpfNLO5ERPdhpfvd0EV4KC6OAR/LSFPpCPn3IgJvDBrazChUgT4gAL1VH57A7OlK09MRFHIk1mNeCtPbhrOZbeZ9zUI3KpXKxqkQBW8qps4Y3TfeeAONpjRmum7dOiZOnMhff/1ls11oaCgXLpSq9GdkZBAaGupwm7CwMPR6PXl5eU5TZFzKXsjKwmvmTCRaI6aX7hK84uJQ3333TZmiW1bz1ZEhdnRGDkV4hqykoaohVwT7qhuBD00lcMBIpOnpqCtZhr3sBIbNKZt5df01pAcOAODTtatHquaWNSaLFiQy9eBODh/5lruiRthkLZgxT80OSE/nDhenZnsSs+E0NmuGRFfGIbghkFTRvkqlkry8PLt0SNHoVkydMboNGjRAp9NZ5om3bNmSgoICzpw5Q6tWrSwKSd26dSM1NZXz588TGhrKpk2b2LBhg01bZp3dnj178uWXX9K/f3+nF5kr4QVperppgoF1mo1CgezCBXBjJWBHuJoL7Kr4tiMRHuuHnR1V0AtwNIHh42Mf88xesC42JI+PRzd9uts8XmffU0S3oQ6NrQ0unmdNPWQlEgkEB1O0/H2kT0xHCigVPhW+bVi8ZKkUlUpFSUkJ3t7edmXZRaPrnDpjdGfNmmXzeebMmaxatYpRo0YxY8YMnnrqKYtBXr58OYMHD8ZgMDBlyhQ6derEK6+8QnR0NCNHjmTq1KlMnDiRNm3aEBgYyKZNm5z264qna9ZDtUGnQ2jeHMGFqg61jbIiPO6+AR1OYMA0uFe20rEsKanC+mW1jZrydAH0Y8fQNmU6b6U255Fleyv0wq2Nq1KpRK1WO0wbE2ekOafOGF1HPPHEEzz22GNMmjSJ6dOn4+fnB8CwYcMYNmyYzbavvVY6aOLt7c0XX3zhUh+ueLrmuB/TJqOVCvhJvS0eh3DRPu/VndyKKmPOJjB0z7BfZoh2vG1tpaa1FyQaLdm+cLFT5cI75jEQlUqFRqOxpJCJ4YWKqfNDjN7e3mzZssVicN2NS0YXU9xv2oKuzBgBmtWrMYwde0saRGe48zwcZVl0b9yd+n0ftE3tio1172AaNWNMatLTld6YPGJs2LBS+5p/Ty8vL/R6vSW2KxrdiqnTnm5N4KrRBbgeGkR6hulGMA9P1LjK2C2C9eDepuRN/HnxT3594hnGXL+ONDmZku++c7vBvZUVwMpiNoqy02cAMDZ0TSuhrFGVSqX4+Pig0Wjw8fERwwsuUGeMrrmAnqM/vV5P48aNPXKRVKZGmqCQI/XyQnrkCFBz3sKtWq7HPLg3oOUA7lpzF++ceocxRV4IDmZL3UrUpKcrO30avEAICqz0vuZ/zbFdo9GIVCoVB9IqoM4Y3bvvvpv09HRUKhVGo9HyJJbL5eTm5nLs2DEaN27s9n4r4+nmafK45ivj9Ln9tESsX+YqTf2a8uo9ryJ99hlkNyRqayplzN3UtDctTUmBSBDkrpkCa0/XfH2a83atvd3b4bryFHXG6Obn5/P9998TGRlJSUkJMpkMqVRqmcp4sydHzP1pLr9f+B2UcMeDamK3x/HuiBW3zSutp/t4XNGDeomQYpalzIAIN6eM1RQ16umeMhndv678RUpOSoVpgY48XfMstZKSErFyhAvUmYE0iUSCj4+PJQalVCpRKBQW4+spXEkZs8s7lUD86c84fe20x47L0tVtcnPIDx1izhDoOBseH23698khpZKV7qCmHk41ZnR1Op5seQqAHed20HVtV+btmlfhvmD7Fmb2epVKJRqNRjS6FVBnjK5MJrspI6uulGB3JpztLB/1VqMmvu/kiCA+NMtS3vhbHgMn24ti2o6QSCSkbf+UAgW0t5JaiD8Sb1MXztm+gOUt0TpUZzQaLZUjRBxTZ8ILY8aMwcfHx+XtK9LUXbJkCR9//DFyuZzg4GDWrl1Lcwe1wGQyGXfffXe5mriKIoXN5/ZZJtHsoEZZFDVr6lE9XYCioiKP9iEIAmq12qN9JFw57HD55sxEBu9v4HBdZTFrD3ta31ir1Xq0D4PBQJv33qPFjh18emPZ+93h6Rup6Zt/38zgxoMd7qvT6fD29kapVNqkj1kPqhUVFYlGtxwkFbwy3Xq5RE44e/YsCxcu5PLly7z44otER0dz4MABunTpgr+/v822BoOBdu3a2Wjqbty40UZT95dffiEmJgaVSsXKlSvZs2ePU03d5ORkIiIinOrpAszbNY/4I/G89z3MsapXeGH0aBp+/rn7vogyCILAgQMH6NGjh8f60Ov1HDlyhG7dunmsj+OZx+nxuf05HJpyyKXpy65QVFTEuXPniIyMdEt7jigoKCA9PZ1OnTp5ro/ERBr162ej4ikAHWbB6eDyv7PMzEzOnDlDYGCgTUzXYDCg1ZoE2YuLi7nrrrto1KgRMpnMYTt1AKdPnToTXpgxYwadOnVixowZvPLKKxQVFbF06VLOnj1rt60rmrr9+vVDpVIBJtHrjAz76VAJCQkcPHiQ9u3bl6unC6a80xO9v2ROos0bMs2++cakmOUhbodkf4B2Qe14MPRBGzchNirWbQa3pqiJeKj8sOO3gpiLFX9nSqUSiUSC7sa0detBNfP/fXx8SEtLc+9B30bUGaOrUqm4++67GTlyJAaDgfz8fJvMAmuP3xVNXWvWrFnD0KEVCJ64QJuzjjV53TkYdDszt81s/rcVWlyDtXcvdlo2vTZTE4N1uqgoh8vnz/nSpe/M19eXoqIioPQhYZ0BJJPJUCqVLqdK1jXqTEx31KhRrF27Fn9/f7y8vEhISLCkulSH9evXk5SUxN69e6t9jM50Am41/YCbRXBCAjEn4f5U8P/oRfTxQR4VA79V0bVpQ/aYMTT88kvLMn1sLC17VOw4CIKAQqGwTDaSy+V2DwpBEIiIiLAo94nYUmc83V27dvHtt9/ywAMPcPnyZb766iueeeYZS/0x61c6VzR1zW2++eabbN++3WHNqEoTEWFXGubC6NG3XJ6pIzw+ySM7m1ZLl3LNB1KD4JpUg1dcXLlVEKrUz20QjhEEgXOjBnJnLHz+8J2oDx1yeRKJ2bP18/OjsLAQwM7TvRWnldckdeZRVHaQy4y57Ii1x+uKpu6RI0d44oknSEhIoFGjRm47Tt3ixeimT2fh6/057pXH3Icm4ZoUSd1Gmp7OxjsEZgwFLwNoZbDmB4EHyqmCUFlupzzdH//ezskm0O7hlyv1UDd/B0qlkoKCAsvUX7B9WIjZC86pM0Y3ISGBs2fP4uXlhSAIaDQaZDIZMpmMvLw8Hn30UcLCTNUWXdHUfe655ygsLGTsjdfX8PBwtm/f7rT/St1MERE0+NdEvvl7OQ8f+xEGO07fuVWoiRvwaiM/ZgzVoVaA+kYG3tTBJdzdyBexHq09W0v2c1euhDuihlRqP+sHj9nbDQgIsBO4EY2uc+qM0T169Cjvvfce3bt3p3Xr1uzevRuVSkXPnj3R6XSUlJTYbF+Rpu6uXbtc6reqF9+Ygc/wworl/JD7E/dXqYW6xT+KQmRyJVBapULh5UO6vMitRrcmp+h6iuScZI4pr7EsKwwqmdJlPcHIx8eHwsJCh5OOxBCDc+qM0U1LS+Ojjz5ixIgRlmWTJ09m1KhR9OnT5yYemWOC6jVhxLVgtgaco+uxT+kR2uOWS3+yxtM3Ybh/ODoZYDX5TyejRsrYu5OaMFZbz21FYYDx/ndXel8bAXSJBJVKRXFxMX5+fhXOvBQxUWcG0rKzs8nLywNMydsAV69etSyrjRdMccN65HnDrB9muTQvvi7TUNWQFzq+gI/chwCvAHzkPpYy9hWSlYX00KEKB91qWuvWE5y4eoJNpzfS5zwEdqx8VkxZT9ycPmat3CdSPnXG6E6bNo0VK1YwceJEVq5cSe/evQkKCiL6RjqWJ0VvqkJKTgo7pbYTN1yZF18bqdKN6KIhtGZQk0GceuIU3437jlNPnGJsBwfpYikpyNavt0w4kW3Zgk+HDijvvx+fDh2QuViGyVN40rDP/WkuMetiKDIUs6s1POX7W6XbKHt81gUqRaPrGnUmvDBkyBC6du3K7t27SU9P59VXX6Vv3761ztiacSZ2k5SZdEuGGVw1JlnFWRSvX0P7F95BIpeD0Yh25Ur7fNuUFFPRyehom9H34PQcmiSdwhDtCxG2Xq5i7lzkq0rV3AyjRiHbuZNsuZY0PzUtrkPDuDjUffs6rRd2q6aMWSvZtc8yzT77JfRbl+Qcy0MQBPz8/MjKysLb21s0ui5QZ4wumDQVzEndRUVF5OfnU79+/Zt9WA6xLr5ovkkOhDovyng7sCV5C698FUfKuyVIdcCN0u1ekydj2LYNY3Q0xjvuQPbFF8jXr+fUDe3crr3GE/bGSlouWULa3q8serrtBoxDP24c0rQ0Th//hUNHdxATBB1uTPyTb9vGd21g3HjPpZlVFk95uuaHeFltjxNX5sG6HS63U/b4rGUdzTrV4ky08qkzRjclJYVZs2bRvHlz/vnnHzZs2MCoUaOYNm0aLVq08GjfVbmRzMUXO7wRz5yDpcv1htVur4bgaVzxfrKKs5j5w0w6ZZeglYLKap1eLuF48s9I//yaBkUQXmDSyl0eY95iM68+/C1XJcV8OLt0v9kHtvDBmC3MMW/bwrwcPkgwTT7ZFoHLaWa3ckw3OiSa9llYtD3M3PHlHtT/l+Jyrq6j2WcSiQR/f39ycnIICAhw41HfntTOd2sPsHz5cu6//37Wrl1LmzZteP/991EqlezevRvw7ECaVCqtUvuLw6Yz56CtAI48Pt6jAjg3i/S8dBRSBWn1TV6nNTrByOCReUTFwisD4FTQDSNq9cUcqlfsUE936qP+LO9hv/zUDZndP8Ns+zKnmd0sPGXYI4IieEHW1+G6ymh7OPJ0wZTbLpfLbaqkiKEGx9QZT9dcxwlMF87169ct4QZHVKSna2br1q2MGTOGgwcPWgblzJilHf/55x+io6OpV68eCxcudPmYm/zwAx0cLE/bvJnLbpww4Wk9XVf6uK69jkavId8XpoyCtdtMKV8qQcFvsx9lfkxHig3F+PpeIPH8Grv9D9jP0gZgfzPHl/iBMCjs3pPUpodA0FqWayRGslKz2P+P/bEaDAZ0Op1Hvyu9Xo/BYCA7O9vtbUd0mQTssVt+RKGg2MVzKikpoX79+g51Ffz9/bl+/brlPhNxTJ0xuiqVioKCAgCCg4NZvHgxd9xxB0OGmGbkWD+VDQYDs2bNstHTHTlypI2eLpi0T9977z1iYmJwREJCAgA9e/bku+++q5SIOgD168M779gtbjF+PC3cqMewf/9+j+rputrHquBVxCXEsbOrgoh2WlZEzKd/nyn0Dg6mt9V2584XArbTuq/Uw6H682sPr2bM12NsF0og8r0vieg2lI9OfUFcQhwKqQKdUcfKISsZ0sHxLK28vDwuXrxodx24k6tXr1JQUEDr1q3d33hMDJuWTeWhY6VflD42lsjx411u4tixY5ZZaGbM945CYYrRiDHd8qkzRnfWrFnk5uYCcN999xEYGMiECRMsFYCtja61ni5g0dMte7O9/PLLPP/88yxatKjcvqVSqctl2G2IiODiAw8Q+vXXSDDZlOXdYUATb3N48rZibIex9G3el/S8dMLrhTvNsW29aC2xXwjEp22xLJsWOY3ca7lszdhqWRZ7VyxD2wwlNiqW+CPxpcujYonoNrRSfdYknnotlxw/znvdBb6/pxmLwmcRMGhQpcWU/Pz8yM7Oxt/f327arzmTIT8/392HfltRZ4xueHg4ISEhaDQaYmJiiImJQaPRkJ2djV6vJyAgwCJK7khP98CBAzbtHT58mAsXLjB8+PAKjW5lyrCX5dxTT1H/uefwHTSIzN538WyvA0xMXML7971fpfZqO8GqYJcM3+KxnzA953mSMpOIDommpV9LkpOTefG+Fy3LzKlQiwcuZnrUdLvlle2zJvDkYJ1861YURkhtH0zhsAcJCAmpUjve3t6o1WpUKpWd4TVrmxQXF4thBifUGaP79ttv8/rrrxMSEmLxOs0XSWZmJosXL2batGkutWU0Gpk7dy6ffvqpS9u7WobdERKJBEPHjugfeYSQ9euZNOVhPjvxP57v+Tyh/k4CmXWEiKAIiwHV3Egvs17mbNvqcKvm6SIIyLZuRT6qPsWyqvchCAI+Pj7k5eVZnBRrzGMnaWlpBAYGVveob0vqjNF9+umniY2NRS6XI5FIkMlkFg1Q82czFenpFhQUcOLECfr27QvA5cuXGTlyJNu3b7cbTIPqebrmm8MwdiyK1at5NrcjnwpGliYu5d0B71apTZGqUVPSjp5AeuQI0vPnkTfqgN5Y9Wq9giBY1Pm0Wq1Db1ahUBAUJFZhdkadSRlTKpXUq1cPX19fVCoVSqUSLy8v5HK5XfE8az1drVbLpk2bGDlypGV9vXr1yM7OJi0tjbS0NHr06OHU4ALVShg3x8qMPXtiDAujzVc/83DHh1lzdA0fJn14S04LFikfT3i6si+/RJDLKWkQQLY6m3N556rUjvmhoFKpKCoqcphCJpVKLWMlIvbUGaNbGaz1dDt06MC4ceMserrlaeY6o7qzdARBAKkUw5gxSHfvRlucj9aoZf4v80UhnBqmJqQd3c6pU8g++4zXHwji9ysHyFRnct/2+6p83ZhL9hgMBpvr2jw7TczPLR/R6Dph2LBhnDlzhnPnzvHSSy8BJj1da4/XzJ49e5x6uYBd0nhlMHu6APpx40ipp2fz37ZVhW9VIZxqkZJC6ieLWP/TIptzT8lJYf3x9XbfR8rBnWz4aCYpB3fW9JFWGncaLcXcufhERyPJzeXlL66w7PvSdVW5bqwfCmZvV6Ry1JmY7s2kugNpZoTISPZHN6b91SvEZJomBJy+Meh+qwrhVAXF3Lk8888q0wy0LODIAh6/83HycvP4etfXlu1mdJnBa31e4+XX72W1/xnTwl/WMXNHJxYtSHTYdnnccuV6UlKQr1plM+33yURY2a3q1431d+Dt7U1OTo7dMTsq3yNSimh0awB3xHRvfOD+nIY8/sMVy/r3u8PTw24fIZyUnBSnqV2X8zNJ3rKcVl+uMmksWN3Tnx7/1PQfq2UfHfmI7375iMz6tstXqE4StGI8XYdMJbJxZxr72scfUw7u5PCRb7kraoQlp/dWw9n03piLpUa3MteNIAiWa1EQBKRSKd7e3hYRc0AML7iAaHRrgOpmL1iMbkoKDZNO2nkuWRMevC283LlfTGaV1YSHB9s9yD2S5iSm/sz+4tOc91Yz6Sj0CyunkTJEXcFkdMuwO/k7Xi/6DoAmRhWdfdtwZ/MYItvcw57PX2dtwA0t4zKe8a0U0zU4CXmZp0zHRsVW+rqxPj5BEFCpVOTm5uLr64tUKhXL9LiAaHRrALcMpOHccxmV16TKbVv3cTM9lHPPTWZV8BYbj3Trma1sBZoUwN25fsT5d6dP5yj8flvltJ2yTG0ynB3YSxe+HToFdfZVjl05yjFDJkcbHWN34THC/lhNWiB2nvHUgztp1NrxdG9347bfISICoVUr+Ptvy6JtA5rxyH3TuKfFPfRoU/mp32Zv13yMMpkMuVxuSR+zTsMUcYxodGuA6sZ0zUbXmefylX8Gnap8dDc/9qZJ2s+hP7bA6DIrBHirwXiefPQNsJo9pTiVy+wDW6ykHWF6+BiUX3xps2z2ARj85mvM3JTGCtVJy/KZGSHcNXMWRETQC0CrRXLyJPpDB4g/vYz5gaU52mY2715KbMv/ueeEy8GtnmJuLpKLF9E/9BCnO4cxJuNdXpryJiMkdxAcWLUZeNbhBfO/vr6+FBQUWHJ2b/b1VNsRsxdqAHN6TVWwuYAjItDHxiKA5e/7wa34WPsHBmP1REZuxmvh9WuZLF48mlcWDqB7hoMNJHDfv+bbGty5c5Fv2cL7CfDraph5tQVf/utL3n7gI94604qTy+GTb+Dkclh2oRNERLBoQSKH+n3JxyntObkclq/JxKdrVxTzTClTKQV/8z/5SVIf6MuAmUsdHuuW639wz+ZebPx7I0Vaz47Yu8toyb/6ColGg372bFbfqeXvxgoGtRxUrT6s47pmlEolBoPBZqaniHNET9cJrkg7btmyhQULFiCRSOjcuTMbNmxw2JZMJkOn06HT6Sp9HIIg2OyrW7gQ6eTJeC1ZgnLzZrInjCX73Nvsu7CPHiFVVwrT6XQeK10kPX2awi8/5NVj79EnvA9dzuSzLmM7b9U7SoGXwLTAADrk5DP7ADae6oxmD9Imzwtjyh9IMjORHjliGY0XgHsy4fChNMZ8NYZXD/pytV0Ru1qZBooW9oJBf5/kvaZNQSolSqcjqqjIJh4uj4/ncd8f2eBf+vo97Y4pxBV1ZKVvsmXZzBMqhh8r5q2BuSwqXsTeP1bxUkl3YrqMwmfAUP7UnOXnf36mf/P+9AitnlqbXm+aLVaVa6UsXuvXY+jQAU3Hjnz32Xfc2+xefKQ+lvzayvYhCIJFCrVsbNfPz4/CwkLq168vGt0KkFTg4dTJqLjBYKBdu3Y20o4bN260URlLTU1l3Lhx/PzzzzRo0ICrV6/SqFEjm3bMeroXLlwgICCAwMBA3n23clN3CwsL8fb2ttMvlZaU0H3sWC52jqDtgEOMaTaGmW1mVul88/PzPab433LpUkK//trhum87ysgcNowgn0bcse0nIo6lW0rwyI3wZzPI84br3pCnhH+dhKfLVD4QgA6zTCWNtnfAbuXDJyR810HK5IMGlv1gu9oINH4Wsv1s9wlUQ6ccOQ1LJEh96yFvFEZwsYTmx87SK7mAHpmlm+uBiQ/C5jtNn7s16MbiqKpX9jAXeFQqlVVuA8D7wgW6Pfoo52Nj+X3U3Uw6MIm57eYyOmw0RUVFKJVKh5q4FZGXl2cxrOb4rUwmQyqVcvnyZZo2bYogCHTq1KmuC944ffKInq4DXJF2XL16NbNmzaJBgwYAdgYXSvV0n3zySYYNG8a9995b6WNJTk4mNDSUevXq2a+cOZMWb71Fv4fuJjE/kY/v+rhKXsbBgwfp0qWL3XToapOSgs8NWUpHjEg20KHft5z2A/4F7XuX1oJLDYL6JbZ/wcWO24m5CEeczDqV6gSmHjTQ9aL9Or0UWuSVMbrAncZgAr0M5MgLyZZlk6XJJscP2raHebts7yYFsPZr2N0Ksn3hYO5BtI219AzrWf5344T09HTkcjkhVVQAsxzXjh0IUilNnn2W8+mfAxDXP44Q/5Dyr6lyEASBffv2odVq8fLyslkOpZMlfH19q3Xstzui0XWAK9KOZ86Yku179eqFwWBgwYIFFkH0srhrckRZdDNmIF+6lNGnBGYF/c2pnFN0bOg5ge3K4koZGOuc0dPBcCYYAiW+tJMF0FAVQCOvBjT0akBD7yAMd0rgOfvBrAOhJk/3uIMkjhcC76ft0IkYFQoYOxasYusyiZS0+mUqh0hgyZMJpalUWi2SixeRJOykZPVCIMe+Ewm0uG4yugC703ZX2ei6BaMR2YYNGPv3R2jalB27d3BXk7sI8S815FUNAXh7e1NQUGAxutZvyv7+/mRlZVlydkUcIxrdKqLX60lNTWXPnj1kZGRw7733cvz4cYfVhd2Wp1uWRo3QP/ooozb8j1lzYMfZHVUyuuX2UQ2cZVtYM6hZPx7q/ShZ9RVkF2cjSUmh3rHTHA6X81cDLSeLs8guSuVa9jUEBK53N+Umm3m/e6nRLhsTnn0AHmn7Aymnf0Bn1DF+NHyyzRSSkACTRxkthtKMFCmNL+Qi27keQ3Q0EkFA8eqryL/9lqwQfxz6hgKkWf3sA1oMcPEbctCUG1L3pL/9hvTCBTSvvcblwsscvHSQf/f6t1v6kMlkCIKAwWCweTMyq4/J5XKLzKaIY0Sj64CKpB3B5P3GxMSgUCho2bIl7dq1IzU1lW7dutm1Vx1PF8rPLNDPmUPI2rV0NzZly6ktNPVt6nA2V3X6qDI3si3k8fEOV6+9C6YF/ULkF7/w7D9hzL7aCMWhwwBMxlRKxlz5WG/Uk6POIfvxbN5O+JBTCetspkGfDoa4g6asBXMJ9p9aQ0rXPgxu3AW5VI6il4Llj5TQ8GohhSENaSovwPtwPCWGEssxfZggJWTBQMvna0q40kTF2//pzAbpSZbsgNlWRt8ggSkPlHq5A5oPqJaX647fQb5hA4K/P4b772ftkWUICDYP4+r0IQiCpdy6r6+vXTZDQECAWDmiAkSj6wBracfQ0FA2bdpkl5kwevRoNm7cyOTJk8nOzubMmTOWGHBZPObpAkLbthjuvx/p5R0kSy/xRMITgGm20eKBrg3oeHK0Wbd4Mbrp09n18Zv82d5A3+b96HvVB0N0NA+2bYXhtw9ZmvQ+b8oymLAzwy67QD9gAMLQocilchr7NqbxhVyCi1oyxMrgmnl6mCnMMPqUqdrvtg6wof/b9g+glBRkSUlk39GaVZLSiRYtcuCJ/XqbYwjUQK/7i0lXnGFC2wm0GTQOtbIxsl9+QWjcGGPv3kzXnKVV2m4GtKiewTVTrd/jyBFkX3yBYcgQ5v7xb1YdNZ3fw9setlwTXTACZgAAIABJREFU1fF0zQpjarUaHx8fu3bM6WMlJSV1fSDNKaLRdYC1tKPBYGDKlCkWacfo6GhGjhzJ4MGD+fHHH+nYsSMymYxFixY5FW521+QIZ5yY/gD7D31rsyz+SDzTo6a77PF6NE83IgK/MbN4+UZhSvPjxwuY0PcZHunzFH/8dwaw0W7X95eM5cwWJUENmzH2sJboA+k0B05RqjsB0DGwIx1UHbj3+63MueGJLvwZ9IbVFm8ZbuT5rjIZohBgfbQvDw43rUsLhCeHwAcJtscQcxE+fPJb2nq3NdXZa9sWg1VtsZ4Euy2GW53fwXxuEkC2bRsdLgHDStebr4nqGl0w5Z7rdDpLuKGst5uZmekw1CYiGl2nDBs2jGHDhtkse+211yz/l0gkLFmyhCVLllTYlkwmc1rqvSJcuTkSG5nyLdtnlY7+nw52XUHqZudVSiVSrt/Z3uG6HV19OVvfSFD2Wd4+gFPFrOScZNbuCqJ7mZQyeXw8kr//RmIwwKVLSJOTbdY/kFRE+5hSr3l5DMw8CB2sxssOhEKv3HO0bdq29pbrKaMoJsFeUQxM10QkkdU6D0EQ8PLyQq1WO0xt8/f3p2XLllVu/3ZHnJFWA8jl8ionu7vi6UaHRPPeTjj1IXz6jenfZd9XXkHqZtK21wje747NbLv3u8Oy+b9y9v+yOdjFcVw4xioV7Fry7w63kR49So76Ggf88ypsA+BAmO0xnA42fZc3+zsqj/IUxawxn0dVja7ZeTCL2xgMBrupwVKp1GMTbW4HxG+mBnCX4I0zIrJgzg0Pz/z3ZKJpuSvcbE8XTIUjU16KpcMseHy0acJDykulKlhGBwOUUKqYhQTmDXb8PT00sxFNB//FlGgHybrWbdxo5+DkIZZjeHpY1dS4qkpVDaKzK8T63MznUd2Hhzmc4OXlhUajsQkviNKOFSOGF2oATw6kgXMv59qvOwmMqAUxXRdZPHAxKTdKpT9VNgPDQSaEdbrY4BaD+YEfeN9BSllaUx9eaPkC97W8D51uA4qPPrKs/2V4J04Hl4rhxEbFsmjgYlL+5VzX15NU5XeQnD+P8sUXORbuTWR6aSaGPjaWDc/bl56vbkzXvL9CoaCwsNBmMsTNVqu7FRCNbg1QXRHzinCWD/vM9Y18bJiNQqaodh81RXml0s2ZELKkJAzR0fQOhlU3DMqf6X/yQ9oPPD3MFMc0x7ZnjF/Mnq6xljb0S7uhf+IJSxs9IiI45EA43dlx1LqYbkEBXuPG8lJMEW91L2Ga8h4WB4xD3qMXREQQAW49j7JG1SzrKJfLRW/XRUSjWwN42tN15AWmNoAvJScJ//01Xu/zeoX91AZP1yUiIiyZA9YGRacpjZmfDi71gPu26FtuG1C+obem1pVgNxph+hSmtE/hs84CkyMns3jQMuTS8m/r6nq61vuaB9RUKpVlmWhwy0eM6dYA1SlMCa7diLrFi1EfOoR21SrUhw7Rcvp8ph+CJYlL2H1+d7n73g43SbugdjwY9qDNspqMxboTV38P9ev/ZnT97/mss8DLvV7mg/s+qNDggnsfHuYBNevr+3a4njyJ6OnWAB73dM1YeXC6V15h0bM5/HF1DTO+eIQ/Zx2nka+9KI+ZW8bTLYdnIp7hxftevCmxWHfh6u9wZeNH/CvvPY63krBy8AomRU6qVD/uNIxKpdImfUw0uuUjGl0nVKSnm56ezmOPPcb169cxGAwsXLjQLq/XjFQqxcvLi+zs7EofR3FxMVqtFoWi/LisQ57/N2tfSqNvg91MWzGIjx//CanE/uVGq9WSm5tLcbETGS83oNfrq3T+rqLT6dBqtTQTmjGk6RAQcHt/+fn5lJSUePQ81Gp1hZKLaYnfM/HkM+QES/mk/8f0CRlQqWPSarVcu3atSqpyjkITCoWCgoICm5iuiHNEPV0HuKKnO2PGDKKiooiLiyM5OZlhw4aRlpZm045ZT9dcpjo0NJSVK1dW6ljy8vKQSqX4+/tX7WT0enYtfph57c7wb+lQxo5aaLfJ1atXqV+/vo1cn7u5ePGinX6FOzEb9SZNql8vzhlFRUVotVqLnKcnyMnJwcfHxyZGas1f537hqcRn8DJK+KDXCtq3qPxMuMzMTJo0aVLpXFpBELhw4YKddKNUKkWj0aBSqfD19SUgIIDw8PCqOQq3D6KebmVwRU9XIpFYhD3y8vIc6p+a9XQ///xzzp07x7PPPlvpY7lw4QJSqbRaBqv1+/v47T8RvB24k47721HQuaPN67daraZ58+Ye1UHNzs6mTZs2Hmtfo9GgVqs92seVK1coKipyqrHhDoxGIw0bNiQwMNCyzFyWPq8wm5eP/JsWRfD16C9o3sOxlGhFXLt2jdatW1fa0zUajVy4cAGj0WhjsAVBwNvb26KlK3q65SMaXQe4oqe7YMEC7rvvPj744AOKiorYtWuX0/Y8PTmiIiQ+Pnww/3d+Wt2BRzLfg0um5WYBFE9JO96O1HQJ9rk/zWXV0VWmKd4ZEF0ftgxcRf0qGlxzH1U9D4VCYan8a92e+Ro3GAyi0a0AMXuhimzcuJHHH3+cjIwMvv/+eyZOnOhUX0GhUFRLe8EdBvGqpIgiuWB56WmfBcWfxHN+/85qty3iXsxGKyUnhVVHV/He9zemeG+D39aBfFf52SgVUV2j6ygTRxAEfHx8KCoqEo1uBYhG1wGu6OmuWbOGcePGAdCzZ89yB1jMhSmrgrsu4KTM0llrlpv4G+g4YAzh774rerouUNN5ukmZSbR3MMU7eN0WSEmpVj/Vua4cqeZZG12R8hGNrgOs9XS1Wi2bNm1i5MiRNtuEh4f/f3vnHhZlmf7xzwwzMCCMA6gj4AHwhIiieVwz0/KwrlqW6a7+CnVXO1jtqmXZ2u/XdrW2Zlnu1ka67KprlmldZrqrW2a6amaiUqIcRI5yUBxAmGHO8/7+oBlBIAF5R6Hnc11zec287zP3+8wwX+/3fu7nvvnyyxqPIy0tDYvFQufOnRt6O++ljP0I7uI3Df2I9Tt2oPyh/ZDg1uMWxILvDtUrWOOmKa2Q5MJd1rE2bu/Z19dXCO8NEKLbALXr6fbv35/Zs2d76ul+9tlnAKxdu5a//e1vxMfHM2fOHDZt2tSo93CrY7pQs+vq8SGPN/ojVmzcAMLbvSHeiOkqjEZ2rpjGqqIPyG8kUaIprZDkwu1EXN+G3d2KXc6UuvaAWEhrhBvV042NjeXo0aNNeq/bwdOFmoIyOYET4NOH6h2b7Xifl+acYcQLiUjx8a1ir73hjfBC0LffkvzP/2X+RAN3OsLZ8eo3lLqerQkp/IDj8cehiYWM5MLt7dZuUOmuPGY0GrFarT/1lLFGEZ6uF5CrG3BLiBo1Bcfjj9epW3tu+nhyeoVwz5DvWPXiaBRPPgGXLrWqXcENqKpC/dvfUvLaYmZPKGNgxz5sfyaZAG0oge9urLPFu3YnjFuBu2VP7RBD7fKO4eHhN7Xtvb0jPF0voFQqb3n2Qm2ur9ZlkSQ+76Tlla//l1XKj/ii8J9sufsTIhc+h+Opp0D0upIV5YED+C5ezClnAVMXqukW3IOdD39BR79avYevK9Jzq3DHbt15ug3l7Op0Ollzvts6wtP1AjcTXgD5OvU6H34YYmJQKBR0UHfgvfv/wfv3vc/5SC13zLew5dOX8LtjCD47d4p47w+0yp1Hejo+778PJ0+i/u1v0UyfTloXJVOe7EhQh05sn76DzgENL8reDrhLN/r6+tbzdmsfFzSM8HS9wM1UGfP2xoUH+j3AiPARLPr3Ihbef4g9F8tJWvQwusQ7sb32GpK/v8dDvtVxRW/TGt9D7caYUNPiPem5MawNTUep8OPt4W8THlh/d+PtwPWi6uvrS1VVlafQTe0C54LGEZ6uF7hdFtKaaiMiKII9s/fw6rhX+VcPCwNXaPmy+gzV947Bf+hQfB97DP+hQ1E/84ys19XuqNU80v0IscJG0xFKzVcY230s3QO637aiJUkSSqXSI7oKhaJOZo47hHa7Xv/tgvB0vYDJZCIvLw+bzdbssQ6HA6fT2aKxTcXpdHqqdNXmifgnGBM+hkX7FvHb0Rmkna7fadc8axauO+64oQ1JkmSdg91ux+VyyWrjZr4Ln+Rk/JcubbAKysjCmqLrH2d8zIOhDxJhj5B1Hi39Lq53HNzers1mw9/f3/PeIrzw4wjR9QKnTp1i0qRJ5OXlNXusu8pYS8Y2lfLyco+t69Gi5Q+D/sDeA3MaHBs4aRKV48dT/otfUHXnnUgNtOSGmnKCcs7B6XRiNptltWEymbDb7c26a+lw8iT6DRsIOn4cR2AgEvXLT9VuHplyJYXI/MhGq4y1Bna7nezs7GYXvHHn5tYWVbVajdlsRpIkXC6XxxsWNI4QXRlxl3bMzMwkIiKCffv2sXHjxma9R2lpKd26dZM157GkpASdTteod5J/Kb9ux9zaY6dPpsvh4+j278cZFIRp8mSM06djHjUKfvhRu3+IgYGBck0Bu92Oj4+PrDaUSiUVFRU3tiFJ+B89SvBf/4r/iROYu4Sy6fcPsLH7ZWZuPMrT1zXOzKi1ZjY8rKbrsZzz6Nixo2cjQ3OQJImQkBAKCgo8fysKhcKzZiHycpuGEF0Z2bdvH4WFhcyaNYt9+/Y1+5bL4XCQn58vex1aPz8/9Hp9o+fc43MPz3emwU67L408yqKFv+FJ8yAidn5O0O7daD/+GJdej+PBB7HPmoV10CCK/P3p0qXxzhU3i9Vq5cqVK7LaMJvNVFZWNm5DkvDZtw+/11/HJzmZzJgubPjjOLaoz3LZvJMwYxgXX17Oeb8xdM8o4k+OA7xi2OEZ/ujgRxkTM4acnBxZ5+Hv709+fn6LbFgslgb7pFksFtRqtVhIawKiiLnMvPnmm0RERDRal+HHcMcp/Rq5ZW8NHA4HDoejTqm+hng7620+Lf60psRgIQQNvocBgx/iw4IPOWI4gq/Slyn6Kfyyy/0MSMlFf+AAnb75BqXdTnV4OIVjx2KYPJnqHj0ACMjLQ5ueTmVMDNU9e970PFwuFxaLRdbbckmSqK6u9uSgeubQty8dCgqI3LoVdW4W20Z15L1xWo6rClCiZFTIKKZ0ncLIkJH4KOre0ueZ8kg3phMTGEPPDj3r2ZBzHgEBAc0WSJfL5Ynfup87nU6MRiMBAQH4+/sTExODVqttUWeKdkSjH6wQXZlwhxbS09OJiopCr9ezc+fOZr1HWloa4eHhdOzY8cYnt5CCggJ8fHwaLMJ+PRmGDJJLkhnWdRj9Qvt5Xs8sy2TdiXVsS9uGJEnMipnFshHLiPHpimrPHvjgAzRff43C5cIZH4+k0eBTqz6x/dFHsb7xxk3Nw2q1kp6eTrzMW5hPnDjB8OHD8Xv2WdQbNnheN/jBKzM68n6sgzLJRE9tTxIGJvA/sf9DeFDzUsBOnjzJoEGDZL1dT0tLIywsDJ1O16xxbsG+cOGC57nT6fR4wFqtltjYWIKCgoToNnZAiK58ZGZmsmzZMjZv3tzssZIkcfr0aYYMGSLr7VpmZibh4eGtEkMsrCrk3ZR32XJ2C9WOaqZETWHJ0CV0V3YnK+sYRSn/Ztynp+h3KqfOOAkwvfQSjrFjcfbuDS3w8mw2G+fPn2fAgAE3PY962O0oc3PJOnuQA9/tZXqeRP/PDtf5VUnAwKd96D1yGo/EPsLY7mMb7EfXFC5cuEBoaGizBbE5GAwGqqqqiIyMbPZYk8nkieu6RdfhcGAymQgJCSEuLo7AwEAhuo0dEKLb+ri93KKiIvz9/enUqRNvNNOTczgcWCwWWRdUoCZjQavVtqqwV9gq+OTiJ3xy8ROMDiOhqlAMDgMACSk1dXyvF6zazy16PeYePaju2ZPqnj0x9+xJdY8e2IODoZHrdLlcmEymlveSA1SVlfjn5RFQUIB/Xh7+BQUE5OejKSzkdxOdvDOSa3PYVX8Op5f/DtP0mQ29dbOwWq2eFjhyIUkSlZWVLb6L0mq1mEwmT9aCw+HwhEXuuOMOOnToIES3sQNCdOXB5XIxdOhQdu/e3aI/7JycHPz9/WVttOh0Ojl9+jTDZCoTWGWrYs2xNfz55J89r/UrrSmgfr1gVW/bhtJuR5mRgTIz0/NQ1OpQLOl0uPr2xdmvH66+fT0PKTISm9NJWlqaJ7ygyMjw7JyT+l0LheBwoMjNRXn+fM0jM/PavwbDNVu+vrh69cLVpw/H+wZxV+DWG87BdOJEXVstxGg0kpeXJ4/XXouUlBRiYmKaLe6SJGEymcjNzfWIrt1u96xB3HnnnQQEBAjRbQSRvSATKSkpjB49Grvd3qL6opcuXSIyMlLW2qQmkwmVSiWrjQi/upkXGQ1kQfx7YjQngtKIC40jduQc/FU/LNS4XPiUlOB74QLqCxdQZ2Whzs5GvXcvvlu2eMZLvr7YIiPp27Urjvh4/E6dwr9W2U3rgAE4IyJqxubloahVL8AZGoo9OhrbxInYo6OxREWS1tWHZJ9iTpV9R0ppCuevnr/hHCoTEigLDYVW+CxdLheVlZWy16X18/MjLy+P0NDQZo+trvWfoRuVSuXxfgWNIzzdVsYdWrh48SIdOnRAo9Gg0+l49913m/weTqeTy5cvExYWJuOVQmVlJQqF4qZuyW9EdmU2Dx54sN7rb+gWEpx2nv36Sv7jn0eZtQwAJUqigqLor+tPrC6W/rr+xHSMuSbEP+BTWYlfTg5+OTlocnLwzclBfeECAYWF9VwMCbBGRGDt2xdrZCSWyEisPzyuaFycKT/D92Xfc6b8DKnlqZgcNZ0POqo7MjBkIN0DuvNhzof15vB5rz/TN7uC6oEDsbVyh+CioiLCwsJkjefb7XbKysp+NF3wx/D398dgMHg8XUmSsFgs9O/fnx49evzUN0mI8II3cTqdxMfHc+DAgTrpNU2luLgYq9XaokWO5pCenk5YWJis2REAj3zwCLtKdnmePzr4Ud6451qMW5IkioxFpFxK4fSl06RcTiHlUgqXqy8DoFQo6RvSlyH6IQzuMpgh+iEM7DKQDuprC242m41z584RcfQ/RL2wqt6tvyUxEfOvZpN6JZUTxSc8j+yKbAB8FD4M7DyQYWHDGB42nOFhw+ml6+URvWcPPMuGlGvZCtfPobXJyMiga9eusn833377LUOHDm12KECSJIxGI/n5+R7RdblcuFwuAgICGDlypBDdxg4I0a1PWVkZv/zlL8nNzSUyMpLt27cTHFy3b8pXX33F0qVLPc/T09PZtm0bM2bM4Oc//znHjx/3eBCrVq0iphkVudw7xORcSIGadLGIiAjZfxz5+fk4dA6+L/ueQSGDiNbe2CuUJInLlsucKz/H2fKznCs/x7mKc1yx1NxyK1ESpY0iVhdLbHAsMdoYtqdvJ/vc3gbjrQ+tjOXfmmwsTgsAnTSdiA+JJz40nkEhg4gNjiVA9eM5vtmV2SQXJxPtF82wSHnb5Vy9etVTm1ZOSktLCQgIaFFesMViwWazIUmSpy4F1OzcGz169E99h5oQ3ebw3HPPERISwooVK1i9ejXl5eW89tprjZ5fVlZG7969GTp0KOXl5aSmpqLT6ejcuTPBwcH8/e9/b7JtSZLIzs6mV69erTGVH7WTk5NDdCvfFjdkJzc3l6ioqFZ5v0vVl0gtSyW1LJWzZWc5W3aWUktpnXPW/btuvPWdEfCPRwYzuNNg4jvFM7jTYMIDwlt0626z2bh8+TLdunW72an8KGazmfLy8iblT98MJpOJqqqqFi/YqlQqSkpKPJtsAEaNGoWfn5/wdBs7IES3Pv369ePgwYOEhYVRXFzMuHHjyMjIaPT8DRs2cOjQIbZu3YrNZqNLly6sW7eOmTObnz5UUVFBSUlJszzjllBVVUVBQQGxsbGy2jGbzWRlZTFw4EDZbBQbi1n7zVo2fH/t9t+9c27glN8wf9Zq/FSts6vP4XDw3XffMXTo0FZ5v8ZwuVycPHmS4cOHy24nOTmZ4cOHt+g/IXemhcPh8BQ0F6ILCNFtOu6FhcjISCIjI/noo4+Ijo6moqLCc871oYUzZ86wYsUKVq1aRVJSEkuWLMFqtaLRaJg6dSpLly71NPC7ERUVFfj6+sq6nRVqfiwulwutViurHYvFQnV1NSEhIbLayarIYvZ/Z9d7/ZPxnxAV1Dpetpvi4mLZFznddvR6veziVVpaik6na1E4wGazeWK57nKRQnQBkTJWnwkTJlBSUlLv9T59+uDj48P58+dZvXo1r732Wj0PYPz48aSkpABw7tw54uLiOH78OMOGDeP7779Hp9MRHR1NaWkpGRkZbN++nd/97ndNuq7S0lIiIyNlz3F0C6GcmQtQUzbSx8enRWlJzUGr1TIjbAafFn/qeW1ezDxZYq+XL18mJCRE9sIu7noGcvcbc+8qa8l3JEkSPj4+spbUbG/8ZEV3//79dZ67F8/+9a9/oVKpSEtLY968eYwZM6ZONab8/HwWLlzo2QYZHR1NdHQ03bp1IysrC7vdjl6vJzExEbPZzPPPP8/Zs2frLcQ1hMVi8exgk5vs7GzCw8NRqeT9EzCZTGg0mibN/2aw2Wws67+MlZNWNlgfojXRaDRotVqvfHYKhUL2zy4gIKDJf6MNUV1d7amxK3J0b0y7FN3JkycTGBjIs88+y89+9rMmjVm9ejX33nsvJ06cYNCgQTz++OMcPHiQoqIinn76ac95CQkJrFy5kokTJ2I0GgkPD2fFihVkZmYyadIkdu7cyRtvvIFGo6G8vJwLFy4wcOBAMjMzb3gNRqMRSZKadO7N4C5akp2dLasdqFmF9/X1xWg0ymrH6XRiMpkIMAQwXD0cDJBpkOdztFqtZGRkyL46b7Vaqa6uxmQyyWoHagQ+PT29RSEBdyxXiG7TaJeiu27dOjZs2MCsWbNwOp088MADLF68mLi4OKDh0ML58+d57733AEhMTGTEiBH07dsXh8PBihUrANi+fTvp6elMnDgRqAktmEwmDh48yKlTp6iursbhcDBz5kwkSUKv1+NyuXjhhReadItYVVVFt27dZE8VM5vNVFdXtzgpvrm2OnfuLPstst1ux2g0emVONpsNrVYrezzc6XSSmZnplTm5y3u2xNuVJAmVSsX58+dvfLKgfYpu//79eeutt3jrrbdISUkhMTGRe++9F41GwyOPPNJgaEGv1/Pqq69it9tRKBSo1Wr++9//Mm7cOM8i0NWrV3E6nQQFBWG32xkyZAgLFixg5cqVDBs2DLPZjEKhYP78+bz66qukpKTwpz/9qUlpP06nE6fT6TUhDA4Olj3xHmp+kKGhobLWBIYaIVSr1V6ZU0VFhdds+fj4EBQU5JVFqcLCwhZvyJGzp1t7o91lL7gr1xcXF1NeXo4kSZ7CIUOGDKG4uLhezLRPnz7s3bsXi8XC2LFjUSgUfP/99zz//POUlZWxZs0aAAYMGEBubi5nzpwhJCSEqKgoFi5cSF5eHmq1mrvuuotXXnkFpVLJtGnTKC0tpU+fPsyeXX9V/XrMZjNms1n2VX6oWdzSaDQt2i3XXEpKSmQt2uPG6XRiMBhk7bjgxmQy4XQ6Zfd0Aa5cuULHjh29stHgZrIy3D3qHA4Ho0aNQqPR/NQ7SPx0shcUCgUmk4lly5ZhNpspLCxk7969+Pv7c/r06QbH9OvXj4iICIqLi0lMTGTYsGE4nU7279/P9u3bgZrQQnFxMcOGDSM6Oprc3FwAPvroI4qKiggICODUqVPodDpycnLYvHkz48ePZ8GCBU3y8i5evIher5c9mwCuefZyLwTBtZV+uXE4HFRWVnrFllqt9poth8OBSqXyiq2qqio0Gk2L0hXdpSjT0tJkuLL2RbsSXZfLhVKpZNOmTXTr1o05c+awbNkyOnXqRHJyMlu3buWtt96qM6asrIzs7GwCAwMZO3Ysx3/oaLBkyRKPhwswZ84cOnTowJEjR9BqtTz22GPcd999pKamUlRU5GnM99JLL7Fo0SLmz5/Pyy+/3KTrliSJrKws4uLiZL+NdN/ZeMMjdLlcqNVqrwiGzWajqKjIK7b8/PwwGo1esaVQKCgtLfWKLbvdjslkavFuO0mSxEJaE2hXousWrMLCQiZOnMhnn33m2RX2q1/9irKyMr744os6Y9x5uVlZWYwYMYJevXrVWTxLTk7mvffeQ61Wo1Ao2LhxI6+//jobNmwgLi6OXr16ebr9WiwWnnnmGRwOB3PmzKGgoKBJ1+3ev15YWNiKn0bDuKtBNfXabgb3fnxv2HK3jPGWrcrKSq/YcrlcXLlyxSuhIKfTSXFx8U2FMoTo3ph2Jbpu7rvvPpKSkvjPf/7DRx99RHl5OV27diUpKYlx48bVOdcdWrDZbBw5csSTl+v2LIYNG0ZSUhIffPABgwcPJiEhgYSEBLZs2cLKlSvJyMjAaDSiUCjQ6/U89dRTvPPOO81aZLl69SpBQUFeud03m834+/t7xZbb+/eGLbjWDlxufHx8cLlcXpuXewOC3DFSlUrl2ZTTkrlJkkTv3r1b+7LaHe1SdEeNGuUpj/jJJ59w7Ngxpk+fXk9wGwotXJ+XC9d+ZIcPH0an07Fp0yYOHDjAb37zG/7855quCHl5edx9993s3buXBx54oFkLEiUlJfTv31/2VDGoEd2wsLAWdSduLleuXEGSJK9smbXZbBgMBq/YgpoYvLdsGQwGOnbsKHvaHdR8jj4+Pi2em8vlEt7uDWg3ouuO52ZkZHDixAn0ej0TJ06kd+/ennju1q1b64xpamjB39+fjIwMzp07x9NPP83s2bOZNm0aZ86cYfTo0QwdOpS//vWvbNq0iaioKNasWYPL5WrSdburM/n6+jZ5zM1QWVlJ165dvWLLYrGgVqu9Ysv9Y/eGLagJZdntdq+0pAkMDKSystIrIYaQkBBycnJanHHi3lIsaJx2lTKWnZ3NggUL8PX1JTw8nODgYGJiYnjssccavDXlHnbpAAAICUlEQVTr168fDoeDI0eOADBmzBhUKlW9imKBgYF1dlTNnz+fqVOn8sQTT/DCCy8wZ84c0tPTWb58OV26dOHjjz9u8jW7XC4sFovsBW7cmEwmAgICvJLO487dbGqxn5vB259jdXU1Go3GK/mz3vwc3bsVW+pVu0MhImWs8ZQxz4pjI482QWpqqmS1WqU1a9ZIv//97yVJkqTq6mppz5490qhRo6R9+/bVG2MwGCSVSiXpdDqpd+/eksFgkDQajbR8+fJ65yqVSkmtVkt6vV7avHmz1Lt3b2nw4MGSWq2WYmNjpfj4eOnkyZNSaGio9Pzzz8s+X4FAcNvTqK62C093+fLlqNVqysrK8PX15f/+7/88GyB69OiB1WqtF8N0b4goLCxkxIgRlJWVYTQauXTpEiEhIZ7QQlJSEtOnT+fw4cOeHOCXX36ZPXv2kJeXR9++fSktLcVms3Hp0iUuXrwoe9t0gUBw29N+N0c4HA4mTJhAZmYm1dXVHD16lNWrVxMZGYkkSfTt25fExET69OlTZ1xTsxZOnjxJhw4dWLduHcnJyRiNRvr06YNarcZoNPL555+jUqk4duwYf/jDH4TgCgSCH6XNi65KpWLy5MlMnjyZyspKcnJyOHDgAGfOnOHYsWM89NBD9QS3qVkLLpeLJ598ktzcXE+d26NHjzJ16lSSk5M9NWmfffZZCgsLuf/++705dYFA0AZpF+EFqBHSQ4cOERUVxaBBg5AkiZEjR1JeXl5v1bepoYXBgwezYcOGmsaKDgd+fn6sWbOG9evX8+KLLzJgwADuuOMOLBYL8fHxHD58WPbCLgKBoE3QPtv1uNPEvvnmG/74xz8SHR3Nl19+iV6vZ8KECUyePLnBXlZNzVqYMmUKhw4dIiAgAKPRiK+vLwkJCezfv5/09HQAPvzwQ9avX09cXBzvvPOO/JMWCARtgUZFt003MXL/h/GPf/yDuXPn0qNHD8aNG8eiRYvYsmVLo114L126xIMPPsjmzZvp2rUrRUVF9UIDLpcLs9lMZmYmb7zxBnfeeScJCQnMnz+fiIgIrlypaQXetWtXMjIyPLV6BQKB4Mdo056um2nTpvHxxx8za9Ysli5dyj333EO3bt3w8/OrE1pwF2o+c+YMd911Fy6Xi+LiYnJzcz2hhd27d7NgwQI0Gg12u52XX34ZjUbDli1bSE9PR6VSYTAYcLlcREZGelqVZGVleSVRXiAQtAnab/aCxWLhxRdfxOVyMWjQIC5evEhRURE2m42zZ8/WqYHw3HPPERISgtlsZvTo0TidTpYsWVKnUPnkyZMpLCzk17/+NYcOHeKpp55Cq9XicDjQ6XSsX7+eJUuW1AkvHDx4UAiuQCBoEm1edDUaDaNGjQJqYrCPPfYYu3btYvny5fWKzuzatYuDBw9iMBgA+PTTTwkODq4TWnDv+tm6dSsGg4EhQ4awdOlSLly4QGpqKp06dUKr1fLNN98wcuRI/vnPf9bLehAIBILGaNPhBYfDAdSviLRjxw4mTZpUT3R1Oh0VFRUYDAZmzZrFoUOHGD9+PNu3b6+TtfDSSy8xdepUsrKyeP311+nQoQPJycmkpqZiMBiwWq1UVFQQGBjIL37xC95+++2f+pZHgUBQl/aVvZCVlVWvhJzNZsPX15dx48Zx4sQJoqKi6hxftWoV8+bNo6KiwvNacHAw5eXljdopKipixowZ7N69G71eT2FhIREREVRVVTFz5kwefvhhEhISWndyAoGgPdC+sheWLl2KWq1m7ty5HDp0CLgWFpg5cyZ33XUXqampdR73338/er2e4uJioKYf1I26J4SHhxMXF8fhw4cBiIiIACAoKIi5c+fy7bffyjVFgUDQTmmTort7924yMzPp3r078+bNQ6/Xs3DhQgoKCti1axdPPPFEg+Puu+8+Nm/eDMDmzZsb3EF28eJFzGYzUNPA8ciRI568XneamN1uZ8+ePSJNTCAQNJs2F15w10utXVLvu+++IzExkR07dlBeXt5oTVWDwcDs2bPJz8+nZ8+e9WK5SUlJfPHFFzzzzDMoFAokSeKpp57i0UcfxWQyMXbsWOx2O06nkwkTJvDmm2+KrAWBQNAQ7SumC9eKJV/fxiQvL4+ePXvewisTCASCdiK6VqsVpVJZr3Geu2uA8DoFAsFtQvtYSPvLX/5CYmIiQJ2WIEqlUgiuQCBoE7QpT7dnz56oVCp2795NbGxsnWPulDGBQCC4DWj7nu7hw4eJi4tj9erVzJ49m8uXL9c5npSUxNWrV2/R1QkEAkHTaDOi++GHHzJt2jRmzZrF3Xffzdq1az0hhpSUFDZu3FhvB5pAIBDcbrQZ0f3qq6+YMWMGULM5IjU1lS+++AKAbdu2MX369Ft5eQKBQNAk2oToFhYWsnjxYsLCwnC5XPTu3Zs5c+bw9NNPU15eztdff83cuXNv9WUKBALBDWkzC2nunFxJkjx5uWvXrmX9+vV06dLF0wVCIBAIbgPa/kKaOyWs9kaIxYsX06NHDx555JFbdVkCgUDQLNqMp9sYV65cITAwEI1Gc6svRSAQCNy0jx1pAoFA0EZo++EFgUAgaA8I0RUIBAIvIkRXIBAIvIgQXYFAIPAiQnQFAoHAiwjRFQgEAi8iRFcgEAi8iBBdgUAg8CJCdAUCgcCLCNEVCAQCL6K6wfFGt7IJBAKBoPkIT1cgEAi8iBBdgUAg8CJCdAUCgcCLCNEVCAQCLyJEVyAQCLyIEF2BQCDwIv8PHWBWSiV66pwAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Vu9JyY0IU3UqYM2cOe/bsITw8nL179zJnzhwA4uPjefLJJx2yIS6OqJybYSLtRhxzrY792oiuUqlEoVBQWFho3qfRaMjNzRUf1nZo9MMLleHv78++ffsqbI+KiuLLL7+ssP3xxx/n8ccft9rW0D1daPg/oRs6N5qol18gERAQQHJyMq6urlb2NBqNU5P1NFRET9fJNHRP92bwRJ1JY1wGXF50ZTIZfn5+qNVq8z5xWMo+oug6mZthIq2hC4uzudHGXJ1tv7zoAnh6epqTudfHOHRDRhRdJyOXy82xjI6SUZzB0bSjZBRnOKlXjlOfX57ErETW/buOxKzEOrV7o/08v9Hs1+T6lBddiUSCu7s7+fn5ouhWgTim62Sqm09306lNPP3L0yikCrQGLauiFzLKpRuGkBBw0lLQG4FZe2ax4sQK8+up3acSMzjGcQOJicji49FHRUFEhBN6+N9xoz00ynu6JhtyuRylUklWVhZNmjSp037eTIierpOpzkRaRnEG036ZhkqnIl+Tz8jjKkbc8yzK4cNxi4xE9t13Tu5tRRwdm7PyzjMykB49ChmOeep/Z/9tJbgdMqB49XIuHdrp0PGKWbNw69ED5ZQpuPXogeKFFxw6riFwI3q6toYXTOO4bm5u5OTkNPghNWcierpOpjpjusl5ycgkxiqrAUUQuxXcdYC2AADltGmo+ve/4TxeS+981HEVsdskyFxcQatFs2wZWfcNISkvict5l8v+8i8bt+VeplhXbLb16Q54Jq70xZbRXHtsDJ6fry4TBkEAlQoKCpAUFkJCAvIVK6yyi8iXL0c7eXK9ebw32phrdagr0YWyB3Tz5s3JyMgQs47ZQRRdJ1MdTzfEOwSdwRjpEJoLGlmp6JpQKJAmJ2OoZ9GtzNvKKM7g6V+eRqVT4VGkYtmPINcBamMyFP2Tj9N1JmRaZG70woVQwYcwgxcDNeFICzV8FnCaDplGwbWUgGZrv+N2xfcEl7gy8JLAgEQ1YVmCVRtbvVN88gnat94CJwvijeiJVpe6FF2DwUCTJk3Izs4mIyODVq1a1WlfbwZE0XUycrnc4THdQPdA3r3jXV769SXSA1xQGsrVn9JqjWO79UhVX8jkvGQUUgUqVDYfFBLg413gpoM2ucaHia9KjYRrwDUEpRKDXI4wVEm+YLuM+aNJPrzTu4SNYSUwBFrhTX9Fe+706MyZq3+z8IOK2a0UX3+NfN06mnfrRt7ttyN59FGEjh3LRLgOx4BvhEmjjOIMkvOSCfEOIdDd8YdyXUyk2QoTa9asmRijawdRdCvBkSTmJ06cYNq0aeTn5yOTyXj11VcZO3aseb9cLsff359Dhw45dM5WaqNncEe7B/l3VlN6fbgYAINCwelZs7h+4QJcuGB1TFFRkcP2a0Jl9nM1uah1xodDkg8oyzn1SqmSDk/FoPX3p1ihIEEuR1AoMCgUCDIZBkFArVZzv5sbOYkHYetrFc7RbVwMX4WEkKJK4XjucU7knWBb7gm+zj0C7tCyFzwbV9b+1PCBFI0YS8DBg/j/9RfNFi+GxYtRNW9OZp8+uKSnE3jwoLn9lVGjOP/MMzW6NhqNBqlUyqVLl2p0fFWoVCry8vKQSu1Pv+y7vo+YczHIJXJ0go4Xw19kYNOBDtkvKioiLi6u6oYWNGnSpIKnK5VKzZ6uIAgoFAqnVmFuyIhJzCvBkSTmZ8+eRSKREB4eztWrV+nRowenT5/m4YcfJjMzk8zMTHQ6HZGRkWzdurXKc14rukbbpW35ePDHPNX9KVz69QOZDPX339sdyz106JDTkpg7Yv+7098xbdc0FFIFI4+rWFVuTFc/ZozdYwsLC7l06RK33HILAIoXXkC+fLl5v27qVLQxFaMYDIKBDw5+wDt/vgMYJ9+iU+FwS9C3D+PRzo8yOHQwLSQt0FxKos3p08h27EC2dy9oNBWGJ9Rff41h0CAoFQqT5xiq9SAwo8hu9MjFixfx8PCgWbNmlV3CGpOQkEBoaKjdaICM4gwiV0Si0qnM29zkbpyecrqCx5uYlUj81XiigqKI8Dd694cOHSI6Otphb1cQBI4cOYJSqUStVqNQKPD19UUmk5GXl0dxcbE5415ISEhj9nbtXlDR062ErVu3sn//fsCYxLx///4VRNdysiAoKIimTZuSkZHBrl27AFi9ejXp6ek899xzDp1TKTOWZdHojT+1JYWFGLp2veEmzywZEzmG/q37G3/eTg5B8yHGsWcHwtzKf9m1MTFoJ0+u8qe/VCJlVIdRZtE9E2j8A+ggkTHvwDzmHZiHv6s/vQN7M6rXKAY9tITf3nqMxz4/YN0HwHX8eACEwEDW9/Nh8i0XefgkLNmqR6p0wUWQVvkAcQZV/fy3HN4xoZAqSM5LthLd8iF509z6s6g0JK+6wwutWrXi3LlzuLi4VBheMPVZJpNVy2ZjQhTdSnAkibklcXFxaDQaq9Iw1V0coZAqANAajOPAkpwcBDt12W4kAt0Dy77k7tRusi8iAr0D46wR/hFM7T6V5cfLPGNTfO+1omvsS9rHT6d/4sDVA/y842cAOgCPlbMjAB+9MoirLmry866zpslZfFSwxBQ9Ujp8Yi96xNnRBZmqTBLzE63HazMykCYnExroQaGm0OqYQk0hba9pkO1bh75dO06pr1gJLsCy4v3MGNqDsNtHQe/eRi/42HaiUyC8z312H3aCIODu7o5UKkWn01mJq6Xo3gjj3DcqjV50a5vE3ERaWhrjx49n7dq1VuNv1c29YBJdnUEHBgNkZ0MDEN3aUJsIgJjBMUzuPrnCz+ZmHs0Y12kcgwIGUVBYQIFHAYsOL2ILW1hcbgx4cS+Y7fIrSpkSqZ8Unc7x6BFnRy/sTtvN+/veNy+WWXrXUu4/UYLnMzMxKOS00GgYc6+BjbeUHWMQDOSNGExQVmm3A4DpVPjBeyQYJmzZwtuv9GOez3Hz9hmvzuNj9wfQfPSRzV8qgiDg4+PDtWvXUCgUVrG6pv0i9mn0olvbJOZgrAQ8fPhw5s+fX2Hss9qerswouhq9xhiLajA45Ok2Zu8iwj/CLLa2kEqkdG/endf7vc6Ws1t4/h5Y1rNsDHjd7CMUBBgLY5rGSJN8VBUmBes7eiSjOIP3Et5DbVCbhw+e+OkJBnwM3iUgLTG2W7UV9rW1Dss7EgwdS0W3S6ZxzPtMudu3V4rx34tJx8Gi7uiSaJi25AeadvkJz8VfVBhSMU2UKRSKCnXSTPtNE2siFRFXpFWCI0nMNRoN999/PxMmTGD06NEV9le3MKVUIkWChGPpxzhzOR6gStEVb27HMA1HgHH896tuMGDoVDoGlFUiDnQPZNmwZRR5uzH9QVeKFaDxcENwc0OzbJlNz89Z1z85Lxm5xNovcpUouOJvvU0rM3rmlpgEFYwO7qsHMI6jlP7NOAyRpaJ8uGXFc8cFw9w7NPzw6eQKKwtNnqybmxtqtdochy4OLzhGo/d0K2POnDk89NBDxMbG0rp1azZt2gQYk5gvX76cL7/8kk2bNvH777+TlZXFmjVrAFizZg3dunUDqi+6s/bMQkBg18Vd7Lq4ixnDYJGfX6XHmOIjxRu9auwNR1hiOTGYNc8YvaCyMynozJ/SId4h6ATre0cik9E+D6Bsu0JvDNczYSmoJsb/Cz2yXTg4sgd9Nv5FZJZRf08NH8iZpv9X4dy9UuD9fvBNVy39zv+Df+Ag8z7TvSaVSlEqleTl5eHm5iaKroOIolsJjiQxf/TRR3n00Uft2qjOMuDErMQKEx5LomGSSz5VLahsqONoll9OWyFN5XGkjWW7Nso2BCmDrPZVNRwB1hODhvpdj2LVhzkd5/BB4gfmMd1lw5bh3QqEadNAoUCnVjHxXi0rJ/zI9aLrRAVFcUv6SgTKJheRy0GhoN38ZYSOGQOTEtGURodcy81lamF7lh8raz/jMOxpZ/w14FUCST4C/hb9shzDVSgUFBcXo9PpRNF1EFF0nUx1RDf+arzV6w4ZEJ0CZ/yP0Z7/2T2uod/ggiCwcNUTJO3bxOGWxi+7rSxjjmYiK9/uf23/x5dtKlb6aAgMaT6ER3o/QnpJujl6QR8Jqv79kSYnMzHhbQ6rzxPbZoj5PtDOno08NhZ9795o3nwTqVJpHb5nGR1y6JDZ+//t0Lf8+f0i9rQTWFI6NaF1UxLSuqtVnyxDxCQSCV5eXmRmZuLj42PeL2IfUXSdTHWGF6KCosz/t0r8snUpulMGm4sEoOEXj/Rb8Cbztu8xv17cC55nOTvO76CFZwv83fyRSWRsP78dKFsI8WvGcp4z6PBQeqDWqVHpVFwrvMauS7us7G+4uIEXs140e7c1XTJrD2eHjAW4B9DSt9zAa2AgJX7ebDtwkHGdxln1QfHFF6DToV28GNq3x5Fp3Aj/CCKGz8OvbSd27JyKFzK06Fl29/IK16h8tIKbm5t5EZBpf1XRPo0ZUXSdjEwmczh6IcI/gh7NelCYcLRC4pfKMmc1ZNGVnj1L++17rN7rs3HG6ALPpgEIgsA/1/8hryQPKPcwAhYf+ZJXRrrjKnPFRe6CVl+W58JyldoHBz/gvf7v8Xvy71b5ipcNW8aYyJoveKiP625PvA6mHqRIW8Tg0MFlG4uLka9cif6eexBqkOXLaqGLnYdSedEFCAwMJDMzE7lcLg4vVIEouk6muuV63BRuDMlrAaRVtBUf79CigYaE4ljFZDVgFMuDHONcQNm2DhkVs5A9GwdT/zcfISwM3N05rUvn1oOP2BDnjYSd3mTMDyAYzCFY03ZNo3/r/rXyeJ3t6Z7JOsPfmX9XGMfee2kvcqmcO1vfad4mX78eSVYWumefrfE5rRa62KH8CjQPDw8kEgk6nU4U3SoQRdfJVGd4QaVVEZcWx7B+D8G6dRX266OibBzVsD1dXY8eNrdf85ZxLrDsYdUhA0aerthOArjMnGl+3R14p59tcU4fN4KFudsIKDKGWCX5gMa34pLZG4mPz3zMj//3o/m1eRw7MRHXDRsZ26ErJboSzmadJcQzmFZLlqC/9VYMffs6rU/2FkMEBASQkpKCwWAQRbcSRNF1MtXJp3vo6iE0eg2Rfe9HN7VJhcQvlaUgbKiia2jfnrQHH6TF5s3mbVm3duThxAv8EqoHScUhBUtMyWpo1gxUKiTXrjHnp5+AbRXazt5VwFVPGct+1KGRGTOiTb1fRcjkmocn1Pl1t0g5+WeTLH5M/dFq9/Ljy3nrh2wC127iXQBSWbq9La+MbIJWW8ImQcNd3SfBmTNOS+JuT3SVSiUymYyioiJRdCtBFF0gKysLf3//qhvWgOosAz5w5QAyiYy+wX3Rxgwzjs998w2quLhKv0AN2dMFSJo1C59XXjGLjXtEBF3OH4IfBtkcUrB8p2v7erJDuY2erj0ZnpBP5KsxaDFgK92Kz67/I1YCcqFsee+qbRI0HwLuznt/9igf/qaYNQv5irKoizX3A9aBA3S4DoFrN1ldj6cPG/gsKp8eV6FtDshjY5HHxlpnaCvN1VAXtfZsia5pSMHFxYWCgoJa2b/ZadSiq1ariY+P56WXXuKPP/6oNGdpTamOp/t78u/c2vxWPJWexg0+PuDiUqXHclN4FeWS3ESE9WbqrVMpXr3cZvN0D9gRDjtDSzgc/yO/8h3TPwaJDpSlbSzFuWDSBJSD78blscfAYumqzMW11tU4Kr3+dsSufFjbAM9u7FtxwkpMX/0d1nfB6okTnWr7NNEpxhV267sY42w/22WcfKWgADw8kK9da7yXHEi3WRW2soqZkEqluLm5kZ6e7jRHpqHTqERXEARzkuX8/HwWLlzIrl27WLJkiZXg7tq1i+eeew69Xs+TTz7JnDlzrOyo1WomTJjA0aNH8ff3Z+PGjYSGhto8p0wmo0OHDiQkJFTaN5VOxZGrRxjXZpy5bcj16wRClcfm5+eTmJiIXO6cj1OlUlXZh5qi0+nIz8+3af+J5k+QeV84bKlYaFL3vye5SyLh3uxsFPHZFFxPQi5YJxIvVEDMbbC1qyvPDR9AF8GXrhKJlRdsUKs5VVyMrobvLy8vj4KCAq5evVphn98vv9Bm/nwEuRyJTsel114je+hQLhVcqrAI5tf8E5z2L8uXAMZVZVY5EwRwD0dmqa0AACAASURBVAwBkiuc63AwZnFeEg1PH4GILJBt2IDUFD2jNmZLk0+ZwsnmzdH5+lb7sxUEARcXF6vcCraKU2ZkZKDValEoFA7bbiw0GtF99913UavLyt+sXbuW8ePH8/fff1u10+v1TJ8+nT179hAcHEzPnj0ZMWIEHTuWrc+PjY3F19eX8+fP8+233/Lyyy+zceNGKzvDhg0jMzOTkpIS0tLS+PHHH/Hz8+Pbb7+12b/9V/ajE3TcE3kPoSGhADTx8ECiUNgVdBNqtZpWrVo5LWF0Xl5elX2oKRqNhosXL9q1HxoaimriSdxWrTJvU02ciItFFjgd4J6ZiaJHD9CXmLfLBPi8FxR4CvTq0IsAtwAKP/0Ur+efR1AokGi1FH7yCcHdu1NTkpKS8PX1NVdJyFRlcqXgCqEaD9rNn49ErTaLXbv58/F+4AEOldhOERoXbC26gFlIx/1t9Hwjs4yCa+nFL+5VlkvY0lZEFhS8/z6eb72FtKiozKRSSSigCw2t9mer1WpJSEggKCjIbp00gLZt24oVge3QaETX19cXrVaLVCpFLpfTpk0bCgoKOHv2LG3btjV7iXFxcYSFhdG2bVsAHn74YbZu3Wolulu3bmXevHkAjB49mhkzZlQIkzElMT937hwvvfQS69evr7R/RzKOIJfKGRA2gCZKY5UAhUyGRC63WzXAhEKhwM3NDXd35wxMymSyKvtQU0pKSpBX9R4/+wzV9OlWic0rtG7SBM3y5SinTUMrBZ1GxfQHXCn0gnd6vE1oYKix3fjxqIYNM//kVwQGUhtfTKFQ4O7uTpMmTayqIndJVrNPWjbUAYBSiWdWFv3a9INfyxmSQI++YxH+Lnt4m8S0Qwas+7HiuPbcARCeBc/fXbFfvVIAuRyXDh2QlIsTl+h0uEZEIHh4VPuzNRgMuLq6UlRUZH7I26oI7OPjg6urq8N2GxONRnSnT59u9frpp59mxYoVjBw5kqeeeornnnsOqVRKamqqVQXT4OBgDh8+bHWsZRu5XI63tzdZWVkEBARQHkfjdH9P/p2o5lFmwQVArwcHx5kb8kSaQ313ILG5fswY8/LYrEAPJiqKeFrtirtQ7mEUGFhnFZVNfbesiqxCRaIn6LTlRLc0NWSEf6DN5OvtBsegmjTb/HBJTFkJx5fbHcdN84bFt8HEo7DKIvLOlPBGcFNg6NoVzbJlKEtzNZjGdAkMRKhhaJefnx8pKSk0b94cwDxkJ5PJzKIrpna0T6MRXVtMmTKFxx57jAkTJjB58mSneHOOTKQVaAo4mn6UWdGzrLZL9HoEB8qeNPTohTqlVFD9AX8gPT0dlUpV1VG1pnzZnEwPmD5SwZebtUg9PMBgsEoNaTfbmcXDJSYihl6SXrh1uAxb3qpwzsMtjRnGph6DFw8ahxR6pkBkiQeCW9n5LB9GlhN6NV3EIJfLcXFxqXBdTfbExRGV06hFF8DV1dWcshGgZcuWXLlyxfw6JSWFli2t172b2gQHB6PT6cjLy7M7U+uI6P6V8hd6Qc8dre6w3pGdDSoVJCZWGTIm8t8hkUgI8Q4xl1gysfEW+HAHNFm7FkNUVIVQLUeynbX2aE3vQWPRTb1mFbdtGnpw00LrXAgsNo7h6h57DPWkSRVDw+x49zW5dwRBwNfXl7S0NNzd3c2ebvky7CK2EZOYl6Nnz56cO3eOS5cuodFo+PbbbxkxYoRVG8vk5t9//z0DBw60e/M6IrpbzmxBihQ/17K8uYpZs5Bt24YkLw+3Hj1QvFBxBt+EeKP/95iSn8skxl8mbnI3VkhGEaACw+231zo2VhsTQ9GRI0x70JXOMyS8McoLN0FO7FbwXPsdmhUrUB09inbpUgw9ejh0vpp6pIIgmL3d8jHo4vBC1TQaT9dgMKDX623+6XQ6mjVrhkQiQS6Xs2TJEu666y70ej0TJ06kU6dOvPHGG0RFRTFixAgmTZrE+PHjCQsLqzQiAapeHDFrzyy+SvgKgL5f9zUu8wyejHzFCocT3jRkGvoX0/JhNyZyDL8n/87GUxs5OeUkQR8uNQ4PeXhUYsFx/vLMZsUtJXw29DO6urWhw11j8es3As0991CTOIHaiC6Aj48PaWlp5iQ3ICbUd4RGI7q33XYbycnJ5p9Dpp9CcrmcnJwc/vnnH5o1awbAPffcwz333GN1/Ntvv23+v6urK999951D563M07WVtHz58eW8fL45rW20t5fwRvR0K6c+BcBD6QESo+dLXh54e0Mdnf+HMz/gJnfjociH8Fm0GOX1IlSzZ9fYXk3vGdNxcrkcuVyOVltaudpieEEUXfs0GtHNz89nx44ddOnShZKSEmQyGVKp1PwzyBmr0aByT7d80nITh4OwKbr2Et5AI4heuIGxFBiZRIZBMIZoSXJzEUrjd2uL3qDnxzM/MqztMJqUGFB8/jm6e+9FuOWWqg+2Q208XdN3xt3dnfz8fNHTrQaNZkzXtFLGtEzRxcUFhUJhFl9nUVnImGXSckvC+96HbupUcwC8QOUJb0RP97+j/HWXSqRlopuaCjqdcSK0lvyZ8ifXi6/zQMQDxqGn3Fy05VZKVpfaDi8A5u9PUeniC1F0q6bRiK5MJrOZfNnZVFaC3bI6rYmp3acS4R+BNiYG9RdfAKBeutRu1Qho2OOiDbnvtshR5aDVa7nw0hNI//gDyZUrVU6EOsIPZ37AXeHOXc36oViyBP3QoQi1WElnorrXv/wD3pTkJicnxypkTMQ+jUZ0R48ejZubW72ft6rFETGDY4h/Ip5mHs2Iah5lVfNLKM2JWtXXQrzR/1tMwjVrzyxW/7sag2CgS+Amnh1m/OwklCafqaHHezLjJBtObuBhuuL77ItIMjNr7eVCzTxdU0YxKBvDlUqlKBQKCgsLzfZutodpXdJoRHfcuHG888473Hvvvfz1119oNBoOHDjg9DR0jtx8kQGRTOwykaPpR0krLKsYIQQFIUgkSFNSqrQhiu5/g+m6W02Klirtkmg4bRG+LYu3PYZfGYvPL6bXml7M31rIl68eRLZ5MwIgs4gtr23fa3qcpWh7e3uTmZkpiq0DNBrRfeqpp+jUqRNPPfUUb7zxBkVFRXz88cecP3/+v+4aAA9GPIiAwJazW8o2KpUIzZsjqUJ0xRv9v8fepGhccNn/K5sItUViViJb07Za5RSuC8/ZRG3HXi3TO8pkMpRKJSUlJVUcJdJoRNfd3Z3bbruNESNGoNfryc/Pt4os+K89xciASDoHdub7099bbRdatUJisULOFg19eKEh9x2M19/epGivlKonQu1hEnJ7uRdq4jmXpyaiazlHYZlpLCAggPz8/Fr36Wan0YjuyJEjWbVqFadOnUKpVLJr1y4EQUCpVFZ9cD0xOmI0h64e4kp+mcgKwcFVerrgfOFqqMJYX/22NSnaWe9PZBaoly+vdCLUHt6uxnCzwy1t76+u51ye2ni6thKZu7i4WNUEFH+B2abRxOnu3buXAwcOsH//flxdXfnhhx9444036NrVWA+l/A1SVSLzjz76iC+//BK5XE5gYCCrVq2idWtb0bUQHh7OmTNnquzjrcpbAVh+YDkTwiYAEOThQWByMmcSE+0G2efm5lJUVERWVvlkrHWDWq3m7NmzTrFtMBgoKipy6PrUhKKiIvR6vVUu5bokLy+PpKQklEolT7V6iiE+Q0jISeBs3lm+ufQN33eEPqdPc72a70+lU/HKgVdQoOBMoJbFvYzFNU1kjB1LqkRirIVWQzQaDQUFBdW+9qaJYUvRNj3cTDkZGupDuj5oNKJbPsm4Ca1WW8HjdSSReffu3YmPj8fd3Z1ly5Yxe/Zsu4nMz5w5Q2JiIn5+fqyzUeXXRGBgIF0DurLv+j5e6GMMMXIJC0OqVhP600+ob78dXXi4zffg4eFhTqRd12RmZhIQEOAUz0Wv15OXl0dgHaVaLI9MJkOj0TjNfmFhIX5+fubImMDAQHrRC51Bxz8F/zD9vpPEJ56u9vnn/jmXiwUXeS/yPYbsPEiC9mfiV7xH20IF2u7d0YWHU9t3VFxcjFarrVbfcnJyyM7Oxs3NzWbJHlNViezsbIKCgmrZw5uTRiO6u3bt4vz58yiVSgRBQK1WI5PJkMlk5OXl8cgjjxAcbJz1cCSR+YABA8z/7927t00xNQ1h3Hrrrfzxxx8OidbDnR9m7v65rD63muFhw2n+558A+Lz5JoB1scFScnJy8PDwwM/Pr4K9ukChUODr6+uURSSmki7O6rtarUYulzvN/tWrV/H29raZFnTl8JX0W9OHp/3+4L7UHdYpHCthy5ktrEtcx8xeMxlwPYgeX++g65OT0T76DAB1Ffgok8nIz8+v1rXx8vIiOTkZg8FQoWSPZVXgixcv0qJFizrq6c1FoxHdEydO8Omnn9KrVy/atWvHvn37cHd3p0+fPmi1WqtZV0cSmVsSGxvL3XfbSN9P9ce1EjKM9are+P0Nvtn8Bqd3l9op3W8r8U1DHztryD9FK+v7LU1voZukOT+FpPHTzikgMS5+sYzFLk9yXjLTf5lOVIso3uz3Jurh94GbW53E5dqiuveOVCrF1dWVwsJCfH19K3i6UqkUmUxGSEhIg/5cnUmjEd2kpCS++OIL7rvvPvO2J554gpEjR3LnnXfW2O66deuIj4/nt99+q3UfE7MS+ebkN+bXlc1a68uJrjNv8IYeHfFfkZiVSLyQZrW6Zfmx5UzuPtmmx6sz6Jj480T0gp7Vw1fjEn8M7wMH0Lz2GjRtWqF9banpRJqrqyt5eXk2Rdf0ulmzZk5dXt+QaTRXJTMzk7y8PMA4lgVw/fp18zbLMBhHEpmDcXJu/vz5bNu2rU6KQpaP9azOrLUoiv8d9oQr/tj2am1//6/3OZh6kE+HfEpbnzYoXnsNta8vumeeqbO+WlLTe0YikZjrpNkTXRH7NBrRffLJJ1m6dCnjx49n2bJl3H777fj7+xNVKmCWT2VHEpkfP36cKVOmsG3bNprWkRdSPtbzTKCxQoBA2Z+teM/6uNGdJeoN/Uta2XWJthPpZ2v7gSsHWHhoIY92fpSxHcci27ED2V9/cemxx8BJRUFrk/DGy8uLnJwcq21iohvHaDSiO2zYMLZu3crw4cPR6/W89dZbrFmzxuYMq2Ui88jISB566CFzIvNt27YB8NJLL1FYWMiYMWPo1q1bBVGuCbZiPRNfnYpmwQIkgPrrr23Ge9bH8IJI9Qnvcx8zDmP11Jxx2LjdkkOphxj741iCmwTzcfAUZGvXonj5ZQzh4aTZmSuoC2pbOUIul6NWq63uP/FeqZpGM6YLxvAkUwWJoqIi8vPz8fHxsdm2qkTme/fudUofYwbH4Cp35ZMjn/DbI78RFRSFoeNVmDsXaWoqtvOVNezFEQ19aMSu0ERE8LHfOKYtWU9csHF1WviYqWgtfqk8+8uzxP4TC8DbP+bh/8LtRpuAbuhQBLlzv6I1FV1TmfXs7GxzRFBtbDYmGo2nm5iYyCOPPMLevXvZu3cvr7zyCosWLSIpKcnp566uqDT1MA5XtPdvbzw+KAhD69ZIDx2y2d7ZN3lD/xI5s/9Vfbba559nVXdICPMkdPdRq18qiVmJZsEtn18BQLZ7N+6XLzup57XPp+vq6orBYDAXphS9XcdoNKK7ZMkS7r33XlatWkVYWBiLFy/GxcWFffv2AdjNeVsXSKXSGtm3vHkNffogO3gQbHzJG3J0wX/xBc0ozuBo2lEyijOcfi5JURFXvWFLjyYVxuItJ07tRap41UECdHvUJsuYZZ00tVpdId2j6f8iFWk0omuacQXjTZObm2sebnA2VeXULY+tL4OhTx8k164huXTJ4WPqioYs6uXZdGoTkSsiuXfTvUSuiOS7047VuquMSsUlNZU22eBfXHFXjxY9zP+3F6mS78RCpHVROcLDwwOdTmd1f4tiWzmNRnTd3d3NuXMDAwOJiYkhPT2dW0prTDnzRnGkDLstJBYBnvo+fQCQHjxYsZ14kztERnEGT//yNCqdinxNPh55Kr78cgpZyc7xJmWbNqGc+ARz/4D9C9ORVVLM1FakinbKFIrt5POoC2ojupberpubWwVvV8Q+jWYibfr06eYQl6FDh+Ln58ejjz5qrgDszBtFKpVWWobdEYTISAQfH2QHD6J/5BGrfRKJxOke+83g6SbnJSOVGP2Mh/+F2K2glanxWN0H/Yov0I8ZY/vAxERk8fGc7OBPvGuW1XLe8tclMSuR+Kvx9HZpR8epU5BqtKjcIclHIPj5yXj37w+luQ52XdwFwM8P/UxKfgq9DSeQxC1D/d576O+6C6FDByRxcTiTurjvXV1dKS4utqqyLWKfRiO6ISEhBAUFoVariY6OJjo6GrVaTWZmJjqdDi8vL9zd3Z1y7up6ugI2BE4qxdCrl93JtIYcMlZfgh7iHYJBMBBQZBRcdx2gA9Cge/JxvopfwfAejxDQsz+nPUuITztKn6/20XnpJp4ZBp9Hlxoqt5zXslyPqXpEVCrsAXZ2hkkjQakHjUzLR3sWMmHcIgB+ufgLnQM70791fxAEXB/7CH2/fuife854XerhQVqb1I6AVbme/Px8AgMDRdGtgkYjugsXLuSdd94hKCjIKt+nUqnk6tWrxMTE8OSTTzrl3JWVYa8MSbnqaPrbbkM5bx6y5cvR9+9vnphpyIsj6pNA90CWDVvGl19OQStTlwquEa0UYtUHmXHpIC2PQ7Kv6SAYNwrWd8V6Oe/x5eSp83A/mUz7VTkcaK7hJ0VZFZIkH8hzMQquSmH8A5iVtIx7v41EOupB/kr5i5m9ZgIgOXEC6ZkzqGfMsOrzfxl54eixpu+RKVucKLqV02hE9/nnn2fq1KnI5XIkEgkymcz8lDa9dhbV9nTtfBmkR48CoCytLGvKONaQF0fU9xd0TOQYBj53C03W3AaU5dhtInNj5ZPf8+mJZayR/mR1zPqutm3JN2xg5Y/G/7+EcUz2+dLQ7kwPeHIEKPRlggvG11fffYlLrSXoBT3D2g4z2tq4EUGhQD9qlLltfcRe19bThbKIBRcXFwoKCkTRrYJGI7ouLi7Vyo9QVRJzE5s3b2b06NEcOXLEvKS4PAEBAaSkpJCbm+vQuTMyjKFM586dw01uTOTncukSkduNa/YtM46dGzKE7KZN0ev1TqtPVVhYyIULF5A7IVBfEARUKpXTkpgXFhZiMBjKXRsJujfepPVbbyHI5Uh0Oi6//jqCogVtmvWEtJ/s2ivrOMz93bpS87NxsKyncVIMYG87cC33A0crg5ACGRt+/RZvd2+8C705c+oUnTdsIK9vXy5lZEDp5y8IAiUlJU69NqZzOIpcLreaSLMcwzUVp/Ty8nJKf28WGo3oVgdHkpgDFBQU8OmnnxIdHW3TjimJ+YULFzhx4gRNmzblm2++sdnWEtVFFQD5ynxaBxhnr93277fZtvnly+jDw9FoNHWWA6I8+fn5+Pv710lSn/IYDAays7Od1nfTJGYF+xMmcG34cGRXrqBv1Qqlvz9NgTsUd8CJinbG/W3h8UpgsqQXEVkVJ7miU+FMgPH/Mw7DbSnGIQaF3ii4sVuhaaGBnyUnGRgykBbNWuDyxx8oMjPRP/ywVT/1ej05OTlOuzamX0jVsX/58mX0er3NuFy5XF7jobTGhCi6NnAkiTnA66+/zssvv8yHH35o086uXcbZ6VGjRjF//nzatGlT5bln7ZnFyoSVANy95e6yCRs76Sdd77yTJk2aUFxcjK+vr802tUWpVOLt7W2ujlCXGAwG5HK50/quUqnQarW27fv6QliY1aZevr2Y2n0qy48vN2+b2uYhPgkaxAuW0QsZwLwelGf2M9/TtyCBfpPm0bG0etLgi3DJB0I17gQWCZx6bxbn8ufzSuQIfH19Uf78M4KnJ26jR1tdY51OZ04g7wyKiooAqmVfIpFw/Phxm8t+TcUp09LS6rajNxmNJk63OthKYp6aar1k6NixY1y5coXhw4dXac/Rp39iVqJ59tvE8uPLScxKhIgIdFOnmmM4AQzR0ebJNDH3Qs1JzEpk3b/rjNcZY/6LoxOPsmLYCo5OPErMmNXoH32UiJ538+gtjxrDxcp9HqYMcG16382jQ14ifEzZvoBi6D54Al4/7EJ1+jRfddQik8gYHDoYSkqQbd2KfuRIKPdQc3bWrppcdy8vLwRBQKfT2SxO6e7ujsFgQKVSiWO7dhA93RpgMBiYNWsWa9ascai9oyvSyufTtdwe4R+BNiYG7eTJyI4cQfbdd8h++w3psWNIgoMb7EQaiYk027ULsrORZWUZcwVXcxVWYlYi8ce2E51SmsGrGsdbhnlBWShYhH9ElaV1tDEx/N23L5H5+Uh697Y6r/mzio+3ek+JWYmsP7meLoFd8HPzQ7ZkCZL8fHS33Vat91wXCIJQrUTjpntMoVCgUqnw9va2smESYU9PTy5fvuy0EkkNHVF0bVBVEvOCggISEhLo378/AOnp6YwYMYJt27bZnExzNHqhfD5dE7e2uLXsRUQE+ogI9Pfei2vv3igffxxpabpJZ+IMUd/z8gNsT/qFkach8oOy7bbqwNlj1p5ZrDheJpozXp3HG80f5vRr08gpySEpLYmckhwkaRLy1HnkluQa/9S5pBWmmb1bE8uPL2fqleYOiXdiViI/KE8x8q6R3NLCRtvSz8qqr6UCn1KQwuHRvei/8yQC4PL00+j++cfqfddHftqa2FcoFBQXF1fon+kecXd3F+ujVYIoujawTGLesmVLvv32W9avX2/eb5qlNdG/f38WLVpkN3rBUU/XlE/XcjwRYP3J9bx757vWjX190cTG4jJsGH7z5pH72mvVeIfVwxlf/J7LunDK/wL4Q2wPuCUd/inVTvny5WT7uZPRzIMsQxHZhiKyDYVkGYrIMRSRJRSRLRSRrMvhiCHZKoRgSTRMW/ItT3l8a44iMOEiVeKj9MbHxRsfVx/kEhthggIcXzmPLjPmVRT/0pVp+qgoZl35wiygC04vYGroQ3yiHmTXU0/MSmT/nhVMSC3Ls9B/50mr6Adb9e+cSU1FXSKRoFAoKCoqQqFQVChOKZFI7KZMFRFF1yaWScz1ej0TJ040JzGPioqqdsLy6szoxgyOYXL3ycRfjadHix58cfwLPo77mFDvUJ7sZr14w9CvH7rZs2mycCHeUVFQLrD+RmXn+Z2cKrxgJZb/Noef2sG9F4yvZ5/6iK+UFY+VGsBPBf6q0rUN/hXbxAXDt99BkQv4loCvCnxKwE2nATJK/+C0P3ScAeXWoBCdYtwkX76clN6daDZqPIqXX+bc9yvMeXEj2wEW6ZaXX9rEM0s2EZkFuvHj0b7zDsjlIJOBXI7i+ec5/YNFH5vbvjaW9e9uxDFdE0qlkoKCAnx8fKxEV1wGXDWi6NqhqiTmluy3E85lQqFQVGtxhOV44oeDPiQ5P5mZe2cS7BVsDqY3oZ07F8OePQS/8w6aESMQQkIcPo+j1PXii+3nbdcI2xJZJrp64PFTLkRmS+l6SUVYtlFovdQg8WiCEBLCqfZ+3OL/RwU7vVKgzafrkDRrRlZqKrriYqReXqhVKlCrkVy+jPTCBdoFBvK09jBLFWUxYjMOQ2RWma13fniG5l8/Q74CPrd4ps04bMyBa+lNHwmGjlkg//pr+Ppr4lrCN7dA0yJ46JS1tvdKt31tLOvf3ciLI0yLijQajdWYLogJb6pCFN16oDaxi3KpnLX3rWXohqFM2DaB3f/bTbdm3coaKBRkffopzYYNQzlxIupdu4we1g3MfWH3sfbftRW2jzxtnO3/prc7P3bRUCwtXTHWDwLwoINbKyK03kTmyIhMUdPx7FWmayV8HlUmTjMOQ4TGE2H1agxt2+Li709Jy5YYwsIQ2rZF8frryFcYhwVkwCdTpzLplVjij223CvMyERXYhZycf1jQjwrDGCNOW4tuL4vaZ0+Ogq8sVrK9NdDYt8+MUYRWAmzqfX3Xv6vtijRPT09yc3MJCAgwbwNRdKvixv523iRIpdIapXY00UTZhM0Pbqb/uv6M/mE0+x/ZT7BXsHm/ITSUK3PnEvraa8g//BDd3Ll10W0r6tLrujvsbjr5d+Jk1slS4+Chhp+6KJgzVEtiYDEjPXsypvdkPH77k/MHt5MozeZ00AW2NJcT66mCSCASPBQeRLg1p3W2wPDLSsKCgzh9n4KwM9dRfn+MAIviiaWnItEf8zBBxPLldPbwoFNEBLJedyPs3Glue7JLC/pv+Ye4YGxyOBizYpb3kA8HUWHYYkk0PH3Euh0Arq6ov/gCw4MPWvf1BvR0TX0SBAFXV1dzXmrLfaLoVo4ouvWAXC6vderFFk1asPnBzQxZP4QHNj/A3nF78XIxLreUSCTk3HMPwQkJKN57D0P//hhK8+/WBc74EsVNjGPn+Z1sP7+dcF04Pbr0YNLPk7hedJ3bM1z5VX2EH/cdIap5D2a8uYiYZA/cV3yJbMUernvLSRjVj5NDunHaS82ZrDMkCGf4RX4RMEYjKCIUhPmG0VrRnTC9L1GSQPrtTuTjkv1l2cIo9T5jYoz5a2VQIodvO8GivnAm0BjkP+5v2+/hWulq17m/wfxfyzzWTR2pMIlnft/BNkRXIsFwxx02299o0QumMVuTwHp5eVFYWGjeJlYErhpJFU/Thp9a6gZg5syZDBo0iIEDB9ba1v8l/R/3b76fO0PuZPMDm1HIFOTm5pKenk5EUBCuffqAwUDJoUPg7V0HvYeTJ08SEhKCp6dnndgrz6FDh+jduzdZqixm/DKDbee2cae6BUMPpLH6NlfOu5fQ0rMlU7tPZaJnfwLXfIv866+R5Odj6NYN7ZQp6MeMoUCm42zWWRKzEjmTfYYzWWc4ee0klwsvYxAMhGZBkh8Vftu3z5NxzsdgO6VmaRurZcBQwYvtcB1Wbod+V+DUgpforLaxSlGAk0uMoisBBKkUXFzQ1GBQQQAAIABJREFULFtmM5dvcXEx586do2tXOxl3asn58+fx9vYmMNDOE6IcgiBgMBg4evQogiDg7e2NQqEgJSUFd3d3lEolgYGBqNVq2rVrZ67U0kix++QRV6TVAzWtHGGLgaEDWTx0MfuS9vH83uetf4J6eaFZvRpJairKZ5+1WU+tJtRXuR5/N3/Wj1zPZ0M/I84jl4+HNuHDHTq27PIlTBrI67+/Tvvdw3h2qJ6E+N1oPv0U1Gpcpk3DrX17/N5eSJQmgEc6P8Lbd7zNxvs3snvEbo4/eJy4x+MYOugpm+dt3W0gs/vM5s1+b/Jg+wdtthl6ySiYK7rP4/sHvq+w/0ygceUZQOshD+Euq5ibubcslNDdR9H37o3g7o56l3GFmt3k6dx4Y7rlF0PIZDJcXFwq5GMQsY8ouvVAXScBeeyWx3i5z8us+WcNiw4vshJFQ69eaF97Dfn33yOziC1uKEgkEiZ2ncifE/4kKKAt94/W8Uuojh2vJBDnPotR7UcR+3csXdf34QH/3ezeHINqxw70d9yBfPFiXDt3Rjl2LNL/+z/zQ0cpU9IpsBNTbp1S0f+QwPsD3+eNfm8wu89sXun3is1+9Swtn/7okJe4O8yYEwMwrwGecRgisoyTYRsNf1OsL+aVPq8wufMT9LymQGqA9ZP3QnAwsn//Rf/QQxj69jVXkbDFjTymC2XFXE3FKU1Vgauzyq0xIo7p1gN16emaeL3v61zKvcS8A/MIVATSy62XeZ/uhReQ7duHctYsSpo2RXrtWo2W11pS3/kROvh3YP8j+3nzwJt8xmf8HtKEDYs+Ys6wu+k64U0yzx6ncOsunj/4My6dujBj7gweeu8d3Fd/hXz1as4d/IlDUc1oH/0AgSMmARCRAdPc+rNMtd98nqndp1ot97W1QGWae39Cd8egtbh+pnjqTX9sYnTzHtwSlINqfhTqsDa8HduNrk27MrfvXIQPFtC2iZYR/n1p5tUC2bp1SIqK0JUruWSPG9XTtbyfTaXY6/oev1kRRbcecEa6O4lEwvJhy7lacJWZ+2fySudXOKY/Zq7fpYmNxbVzZ1wtkmJXZ3lt+XP9F7jIXXh/wPsMCh3EUzueouu0QgR2wgFjhMGMAjj9Hay7PZkJ15/idY9mTL1rKpei7uKr09+AcA1YxtPzvmDxpQikJ0/yOTDDHw6O70/3WTE28ytYLlCxrIdmRUYGHZOLuM+tNx2iBqGPlpGYlcjCXVNJzk9myV1LkKWls237IjJHwmODXgRA/s03GNq2dWii80ZcBlze0zWJsKurqzkvr7hAonLEiTQ7OJLEfNOmTcybNw+JRELXrl2tlgpb8u677xIREWGVuayuyNPk8cD+BygyFJm3jW41mtfcH6TL//5XIR70nw0bKHEgxaQl+fn5uLq6olTaWCJWB2RmZppjPe3xd/bfTImbYr1RgFNLjD/rv146l2WGXzmUaaOGXOkElmUMrgCcf/tt8vr0QV9ugtD10iWanDpFYceO5muVo8khTZVGC7cWtPs1jrbvvQdSKej1XHz1VeYG/8PmK5vNNsZ6D2XlqjSea/MvO6MD2DxwK27p1+l+//2kTJ5M6qRJVV4XrVZLUVGR05bUVvdzFQQBDw8PCgoKUKvVuLu74+npiUQiITs7m+zsbNq3b49UKiUkJMQp+ZcbEHafOqKnawNHkpifO3eOBQsW8Oeff+Lr68v169cr2DElMU9LS0OpVNKmTRs2btxYp309m3PWSnABvr/yPfNd2tts3yYjA/WgQdU6x8WLFwkMDHRa9EJBQYE5d7E94tS2q+LGBRtFd5SuBXc/vI1P4j/hvbj3bLazFF0JEP7GGwCcDPflUBc/bvXpTKeEdBSHD5vbaaOjWX+bJ097/mpMRC4xELtFILys0g9tF7zHb8+owaNs28bc3bxxFWL/hT+FEMInh+O+zViRwm3KFNo6sHKwqKiItLS0Kq9NTblw4QJNmzZ1+HMVBIF///3XHJVgGSImlUqRy+XmpcEi9hFF1waOJDFfuXIl06dPNyeAtpV935TEfOHChfj7+zNu3Lg67+vJiydtbt/TLJ9wG9tdMjKQe3jY2GMfhUKBq6srHtU8zlGkUmmVtvu26Qu/VtxuWgXmsX49iltv5cHOD9oUXcvVYmD0dNfNG81PnGGT8C+QA1xgRip8ZtFOcfgwi7qDyhtUpfNDk0YaE5MHlkYraGUQmmusi2aJaVlw3+3HUP32G66xsehvvRW3yMhK36sJvV6PXC532nWXy+W4ubk5bN+UpDwrK8tq8tY0nKBUKsnJyXFa0vWbBXGa0QaOJDE/e/Ys/9/emcdFWa7//z0wMwyLLCIiigiI4ZZabpj7lkvasUwtTW1RMyvbjkcrz/lap75Hf2mLdVL75lE7mWZ60vKklWsuiWIuuACiArIHzLAMs8/z+2OcEWRQMB4Uut+vl69ieJ77fp5nhmuu+7qv63OlpKTQt29f4uLiXAbWHXJspDmpTg5ybsYn7H2gcyWRballS7z+/nfUs2aBXu/2PHfUV8rYjXBucAFVMgbW9lJxQH8O73796DL378yOnFjp3MntJhMzflalZ7H6Pm+msZlNJDrc3qv/Pu7tEMKpSPfrdBJUNke3XyeS2VTpZycVDb33hAl45Ofj8euvqK42Fq0Jd9pGWkhICAaDwXX+9a3YFQoFJpPpRkP84RGe7i1itVq5cOEC+/btIzMzkwEDBpCYmOh2aVVTacdboX1we57q/BT/OvMv12vTOk9D7almuOJfdG/nx181Ixjw8DxU7TuiWrwY5T/+gebXXzGtX48UGyvLdclBpQ0uYzCdWhYS/6Kad5Lf5pLuEi+P683by3/ik28NPDdtDIcn9CGi+b3clWdD6p6FYfNml1D6Y7Gx5B/7kDf2v1Flnuurxk618aJi52CLt5o2Rg8sfgosJgOvTghgYHQPtuTudh1zfVmwosJ/ayrhWB9fdLU1us4+aDabrVKVpXOcwMBAioqKiLmuDZLgGsLouuFmIubg8H579+6NSqUiKiqKu+66iwsXLtCzZ88q48np6QIsGbiEYf7DKPUvrbTb/sy9z/D6vtd58PIWog+f4G3V2zz4+uvYevfG6+mn0fTvj/mjj7BNmnTD8e8ET9dJRQU2W0/oAhyOG83r+17nvVOr2flmLOvS78Vn42bUp7+nBeFE/ZrhOt86eza2xx9HAYxsO9Kt0e2ZWVmE5uWpcTy781lUHiosdgufjPiEnU1/4dLmTznXqRVLmo4ncu4KzrcJ5N3ZXQn77/5KZcHuqCjheCPuNE8XcG2maTSaKgURPj4+FBYWYrVaZdt4begIo+uGm4mYg6PZ5IYNG3jyyScpKCggJSWl2g0PpVLpWpLJgUKhINIvkq53Vy4X7disI1sf2cqPl3/k9X2vM3nbZPqG92Xx4MV0/+UX1NOm4fXUU1gOH8ayZAk00LJNP7Ufy+9fzuiY0czZOYeeARvgGa5avQyeb35N3Uu5ciXWBx5AGjjQbU7u7KiJtH2hI2bANtbRPWICEN4knN1puxkQMQDPl19i2M6rbdH3Z6FgOdZHHiFy6VJe8Cike1l3/tPR0Rk43xu+31D1mm3VCN5X5E7V03UK3bjzdMHR6DI3N1e2DcCGjjC6bqiJiPmIESP48ccf6dixI56enrz77rsEB7tR1EZ+T/dmnuj9UfczpM0QPk/8nLcOvkX/f/fn0Y6P8uam/yNq2WeoPvgAz4QETF98geQmnexO8nRvxMjokXz54JcM3XA1O+OqHbhe3ct77FgklQqpTRs+joxkZOwItjUvYNxZO2MXbXKNZ4uPxzZ+PC9nr2aF7RcA/nH4HzwPDK8wrwSYX3sNQkJoTwidm3XmDGdcojf7W8PAawsntxKOt4NbMep2ux2FQoFSqcRsNgOV83KdLd1Fu57qEUa3Gm4mYq5QKHjvvfd47733bjqWh4dHnRdHXM/NjKLSQ8lTXZ/ikfaPsCx+GR8lfMTWlK28OOpF5sV9TtPZc9H07Yt55UpsteyMcSeRqk11+3rFOO1zL8YS4R3KfVkerFecYFWzQrDDmg7w/MhrXrHnjh38lLSDFdOooqV7vUSjM1xQZi4joySD+6PuZ3zsw9w370M6SqUYDm9ylP/WojLwTi6OUKvVrmKIiuM4iyVEcUT1iOyFesDT0/N3SzveiNp8wP29/HlzwJucfPokY9uNZckvS+h0ZR4rv3gVS9tovB57DNX8+XDVi2loVJfN4YzT/jCiHTujLCzQ/MyAtvtYFV14w+yFjV1xm+Z+vcauM1yw4ewGSswlzO8zn+lJGjofOI/lb3+Drl2xPf54rTzcO1F7ARyfN09PTyRJwmKxVBrH6QkLo1s9wujWA0qlUlZP91aW/xEBEawds5a9U/YSGRDJnIS/0v0pMzvnjkH18cdcGj+AL35eTlJhUoMJL8B1qWVXmdp8LG3/dxWG48cZ8J+TJM5M5OKzF3nmnmfcjlHRoA4ZX7USEa4ZcYlr4QJJklh1YhXdQrvRO7gbqkWLsN99N7ZHH73l+7mTYroViyGczSlLSkqqGFlhcG+MCC/UA3KmjDm5VaPYq2Uvdk/ezX+S/8Nff/4ro5puJ+KtYDJsiXDkNYh/jcnBo/nQNAQGD74jYpE3o2JqWYRnBFFNorBdV4Ldwq8Fs+6ZVal9uxOnQbXOns2Eh//KkV26yptt984m8seZmBMSOKFS0eVq9sf+jP2cLzzPylErUX32GR5paRi3bXM0p7wF7kRPt2K2grtW7E5PV1A9wujWAyqV6o4JL1R3/vj243kg5gHeOvAWHyZ8WGlJ/WXB97zx8fd0+POti+bUN87UsqysrGqffbXZC/87FEOF2Gt1Aji29u0pP+LQekgqTOKNfW8Q4BXAxLJIVG+Nx9a7N/Zhw37XfdxpKWMVPV2FQoGXlxfl5eUor/blE50jbo4wuvWAp6cnFotFtvHravmvUWro2Kyj29+5Ot3WMLG/oeDOoLpbk1TMD76eV356hVUnr3rMEvzt3ZEs14NHfDyqV1+95S+pO7E44vq8XF9fX3Q6nasMXhjdmyNiuvWA3CljUHd/oNVtRFUsaVW/9x5U2Lm+k6nJc2kf3J7H7368WqN6I9L16dcMLrg245KCr1WfkZRU63Fdw93Bni44VnFms9m1mhAi5jdHPJ16QO6Ybl3+YVbZiJKqlrTu+mU9mo4dUL7/PpSU1NncDZGkMvcGteJmnGdCwi2NXR8x3Vs9p2KKmLM5pfNn4eneGGF064HbXRxRW5YNW8bxp46zauQqTv82ieU7r+3Uf9E/kFHToM8UA0f+byHeHTqgevNNcCNt+Ucgz5jn9vWKK4OaVJ9Vx53o6UJloxsQEEBxcXGl3wuqR4iYV8PNRMwzMjKYPn06Op0Om83G4sWLqxRTONmzZw+pqamyioDo9Xr5JABTUwlKSUHfqROlEeH8mPcja9PXUmAuYFhBU97bVETHYi+yR43iyoQJGFu0uGOu3ZlHKocOQHxRPAvPLiRYFcxv5t9crz8fD8uvFlhcGTeO1BdeuKXxrVYrNptNNjHw2j73ih2ADQYDBoMBX19f/P39yczMRKFQ0KRJE6Kjo13H/YGp9ttMGF032Gw27rrrrkoi5hs2bKikpztr1izuuecenn32Wc6dO8fo0aNJS0urNI5TxLy0tBStVkt4eDjBwcFs27atTq9XkiTi4+OJi4ur03GdXL58GR8fH0JDQ12vlVvKWfHrCpbFL6PUVMrUkij+viaN8BKwTZyI5ZVXkDq635S7HmcLdjnIzMxEkqQ679pxIvcEIzaOIEwdxqEh/6bgoaEc6R5Kt6Ubaa9TOirUfmdfuvz8fMrKymTTMDh69Cg9evSocQzWbDYTHx9PaGgoJpOJ8vJyfHx8CAgIoKysjN9++42mTZsSExODv7+/MLrVIMILbqgoYq5Wq10i5hVRKBSUXI1nFhcX07Jlyyrj7Ny5k4SEBFavXs24ceM4ePBgnRtc57XIibvwhY/Kh1d7v0rizESe6/EcG4KucNcrSua92oWSnVvx7tkT9cSJeBx13/GhIZOmS+PhLQ8T7B3M4rvepNlTs+lQ6sVj/9hB+xadoX37WlefuaM+BG9qM75SqcTLywuLxVIpvOBs42O1WkVFWg0QRtcNNRExX7RoEV988QXh4eGMHj2ajz766PphXMhdkXY7CfYOZvHgxZx8+iTjYh/iPZ+TxMxT8/8WDsVy9DCawYPxGjUKj59+crVEJykJzy+++F27+reLQkMhD215CIvdwjePfEPv1V/hceoU5k8/RZKhB56c3Er81enVVjS6gKtzhNFoFAb3Jgije4ts2LCBJ554gszMTL7//numTp1abRJ+faSMyc3N/kDbBLRh9QOrOTTtEN1b9mS+cjex83351zsTsF+8wOWnx/HVox24NGkI3t27o37mGby7d69VF4XbSVJhEmtOrWHsprGkFafx1UNf0WnfWcK//RbLiy9iqyae/3u4EwVvfH19KS8vd/1c8XOh0WgwGAzC6N4EURzhhpqImK9evdrVoqdPnz4YjUYKCgrc9krT6/Wkp6e7pPDkQJIk2ca32WxYrdYajd8hqANf/+lr9mfs538O/Q9Pl33Nn58LQmsGpCvAFZ63Vta39e7ZE/O998p27b/32czbO4/PTn/m+nloxFB6lwWjfm48xR06IL3xhiwCQRaLBZvNJtv7Wtvn4gwleHt7u86r6Ok6q9KMRqNsTUwbA8LTdUNFEXOz2czGjRt58Dq5w4iICHbvdrRnOX/+PEajkZCQELfjHT9+nMjISNeHtq7/GQwGVCqVbONbLBaUSmWtzhnQegC7J+3mrX5voTVrHQ+iGiWvqM8/R5GcjGS31/m1A7/r/OSC5EoGF2B3xm4yZj2M5OlJ4sKF2K8qbtX1P+dyXa731cvLC71eX+Pj7VffnyZNmri83YrP2VkWnJfnPo1O4EB4um6oiYj5smXLmDlzJu+//z4KhYK1a9e6XVZJksT27dv54IMPXJ5AXVNaWkrTpk1lG99gMNC6detbGj/E1/0XUUV920N5++k0Jo6YgGjso0ZjHT0aW1wc1MH9OGU1a3PtVruV47nH+fHyj3x1/quqB0hw3JpObO8REBGB3W6XJa0rMDCQ9PR02d7XkJAQdDod/v7+NTq+orE2m80ueUcnThGc3377rdLrgsqIlDGZycrKYvny5YwZM0a2WJezX5VcKTo6nY6AgIBbuv60sjSmxk+t/KIEZz92GN2VvT2ZM8oR7w40exKXYee+DImehd60ax2Hpc8gtL16YfPzu6VrN5lM2O12vL29b3hcgamA+MJ44gvjOVZ0jDJrGR54EO0bTar+OmF0Cc597OhEfGDVKmzt2t2Rz/5m2O12SktLCQgIqPE5BoOB0NBQCgsLsVgsaDQagoODUSqVaLVazGYzQUFBREVF4XeL71kjodo3THi6MuHM0XUutX744QeCg4PZtGnTTc6sPb/++iudO3eWpebdZrORmJhYKUe5NnSgAzPKZ/BZ4rUl+oyI8bReNAhd9+6MahnG+uTDFHgXcCz3GAnNj/JDzAUkDCikvXTO3UvcCg96ebeje5cHaDNyClKbNjWev7CwEL1eT0RERKXXLTYLR3OPsjt9N3sy9nCm4AwAoT6hPNjuQYb638PwJCvN9x3hVdL5Z7drgkXOsmgJaJObi2HAgGpDS7+X5ORkWrVqJZsBO3nyJDExMTX+0pAkifLyckwmE/n5+S4P35kmZrfbiY6ORtNA++3VB8LTlRFJkrjvvvv4/PPPaVHLKq2aYjAYSElJoWvXrjc/+BYoKysjIyPjlo2uk+TCZBJyE+jRogexwdfavut0OvLz87nrrruuvWbUcTz3OEez4jmW9BPHdIkUKxwbN03LoXexH72adqVnj4e4t/+j+GmqLo+d87X1aktzj+ZER0dzpeQKu9J2sSttF/sy9lFqLkXpoaRPyz4MCx/I/UVNuefwRZS79+B5/jwA9hYtsA0dypnYZpza8iG9M6+FRSQgfccO9K1bVzHqdYWz0uv6jdy64vLly3h7e9f48ylJEkVFRWi1WrKysvD19SUkJAS1Wo1Op0Ov19O7d2+8vLzwvEUd4UaCqEi7HaSkpLB8+XLmzJkj2xzFxcXY7XaCgoJkGb+0tBSLxULTpk1lGb+srAyTyVRtU08Au2TnUsklzqTs5uyFvfxqvEBSE4fKmYcdOpoC6BZ4N506DKNri178O+XfbLh0rQXvgJIQvFQafvJ2ZKSE+YTRL7Qfg4lh2Nlywg4l4JOQgIfJhF2lorxHD/R9+1LWty+mdu3g6tI+9J13aFqhK3TR5MmkvfIKpaWlbrNW6gKj0UhxcXGlasC6xGQyUVRUVKtGknq9HqVSSUFBAXa7nVatWqFWqykuLqasrIy4uDjUarUwutX9QhjduscZWsjOzkatVuPn50dQUBCrV6+u87mysrIIDg6WbTn322+/odFoZEsBcrbyrq1RL/vtCskHNnL60j6Omy8TH2ajpLpHcDWGrO3RBev9o+n6ywV8Dx1GlZMDgDkqCn2/fuj798fQqxfSDeK/qosX0Zw6hbFrVyxt22I2m8nLy6vzMmMndrud9PR0otx0aa4LJEni0qVLREdH1zhuLEkSfn5+pKamUlpaSps2bfDy8qKkpITS0lJhdB2ImG59snPnTux2O927d+e7776r1UZFbZAkiYyMDCIjI2XbpCssLCQ8PBwfHx9ZxrdYLKjV6tqHX8LCaNelF2MAzGYUBw+QumsDHxT+l3V36at85I+Fw7QfTqP44TRSQADWgQMxDh2KdehQpIgIlEDA1X83m5d+/XBGWO12O/n5+bK2HM/JyaF58+ayGbHS0lK8vb1rvFqSJMmVvQAOTQYvLy9RFFFDhNGViRMnThATE4OPj49sXSMMBgMajUbWEuPy8nJZO1+YTCZXPf8to1BA/wFE9B/A7MJk1n15X5VDnFKL5a+8gnH+/MrpaL/z3ux2u6ydQXx9fdFqtbJ9eQcFBZGfn1+rzTqn/oJaraasrIwmTZoI8fIaIoxuHeMMLWRmZuLr68uoUaN49913ZZnLZDIhSRJnzpyRZXxJchRenDt3TpbxwWHUdTod2dnZdTbmQ60e4pusb1zBsYrZBme7dcNQx5oPRqORxMRE2Tw9k8mETqeTLYQkSRKlpaXo9fpahRiCg4NdamPXhymF11s9IqYrAzabja5du7Jnz56b5of+Hs6cOUNkZKRs6UR6vZ7Lly/TuXNnWcYHxz1ERUXVuZ5ucmEypz74M32+2O/KNrDMmoVp6dI6nQcc9xAdHS1bCKasrIz09HQ6deoky/gAp0+fpm3btjV+H5x24/jx41itVpo3b44kSWi1WuLi4vDy8vqje74iplufHDx4kC5dushqcCVJklX8GxxeqJzjg0OoW47CgtjgWGL//h2mR06RunMnYePGIcXG3vzEW8DLywuj0Sib0fX19UWv18sytpNmzZpRUFBQ4/fbmZMrSZKrOaVcGTSNDWF0ZWDbtm1MmDCB81dzPeXA2VUgSUZ5xLKyMjw9PTHK2ISytLSU1NRU2ZajNk9Pivv1Q2u3g0zvh16vp6SkRFbNAbPZzNmzZ2XzHu12O1qttpKCWE2wWq2udDGn0RWhhRsjjO51FBUVMWnSJNLS0oiMjGTTpk1VvsH37t3Lyy+/7Po5KSmJjRs3snLlSjIzMzl//jz//Oc/CQgI4OjRo7K0isnLy8PHx0e2/FCAixcvEhYWJpsHB448Y7nSrcCx4WM0GgkPD7/5wbeI01jJVcAADqPo7+9PYGCgbHPo9XpatGhRK62HNm3acOXKFXx9fTEYDIDok3YzREz3Ov7yl7/QtGlTFixYwOLFi9FqtSxZsqTa44uKioiJiSEzMxMfHx/69euHl5cXW7du5aWXXqJz587MmDGjzq8zMTGR6OhoWZf/CQkJ3HvvvbLG5o4ePUqvXr1kG99isZCYmMi9MklHApSUlJCVlUWHDh1kmyMvL4/y8nLZ8nUB0tLS0Gg0tUrfs9vtJCcno1arKSwsxMPDQ8R0HYiYbk3Ztm0b+/btA2D69OkMGjTohkZ38+bNjBo1Ch8fHyRJIiEhgW+++Qar1crEiRNZsmQJTzzxRJ1eozOeq1arZUsXkyQJm82G3W6vVpy9LuYAZE15c16/nHMolUoMBoOsc/j4+JCTkyPrHIGBgWRkZNCsWbMan+P8nDg1duVqotmYEJ5uBYqKiggNDSUyMpLIyEi++uoroqOj0el0lY7LyMhgxowZXLlyhYyMDKKiotBoNFy8eBGdTueqO1+0aBErVqxg7dq1dXqdVqsVk8kkq5drs9kwGAyyKkXZ7Xb0er2sgtfOdKiayhfeyXOUlJTg7+8vW8zUeR9NmjSp9RwtW7YkNzeX8vJy+vfvLzzdP5qnO2LECPz8/Pjzn/9Mnz59qvx+2LBh5ObmVnm9Xbt2eHp6cuHCBRYvXsySJUvcfvimTZvGG2+8QefOnbn77rs5dOgQAQEB9OnTh9OnT7vGzszMZM2aNdxzzz11en9XrlzB09PTbTPMuqKwsBCdTkfbtm1lm6O8vJxLly7JmpLmXH3U9XtwPUePHqVbt26ybiKdPHmS9u3by6rglZycTEhISK3Ksu12OwUFBfj7+1dxUARVaZRG94MPPuDTTz9lwoQJ2Gw2HnroIebMmeP64961a5fb82JjY2nVqhU5OTlMnz6dfv36VdmoOnfuHFarleHDh/Phhx/y8MMPuyqFMjMdZU9WqxWlUklWVpYs5aFarZaYmJg6H7cier1e1g00uPac5KS+dtJVKpVs6W9O/P39XdrJcuFMHauN0VUoFNhsNte9O6sMBe5plEa3Q4cOvP/++7z//vucPHmSFStWMHToUDQaDVOnTuXtt992e15eXh4zZ85k3bp1zJ8/n+zsbF544YVKx6RYoFkqAAAM3UlEQVSkpBAYGMjDDz/MDz/8wJgxY7DZbBQXF7s+dJ06dWL48OHo9Xr69u3rMsZ1gXOZWVhYSFFRUZ2Nez35+fkEBgbW6bVfj16vx2g0yjoHONKt5J7DarWSnp4uq0E0m81kZWVhMplkm0OSJPLz8/H29q7VF1ZxcTGBgYGoVCpyc3NlDbU0dBpdTFeSHB1Uc3Jy0Gq1SJLkquTZuXMnc+fOrZLCZbVa0Wg0JCYm0r9/f+x2Ozk5OaSlpZGXl0fTpk1JSEhg6dKlnD9/njNnztC6dWvKy8srtShxbig4n2mLFi345ptv6jRlzGg0UlRUJGtoASA9PZ3WrVvLGpcrKSnBYrHcUNaxLnCm/8mJ3Gps4PicZmdny6bd6yQzM5OQkJBaeavOsuAzZ86gUCgYOHDgHz1f948T01UoFOj1el555RUMBgNZWVns2LEDb29vRo4cSUpKSpVznGliBoOB++67D5vNxksvvcSgQYNcy6wePXqQm5vL008/zZYtW/jvf/+Lh4cHW7Zs4c033yQmJoYvv/yS5cuX07lzZ2JjY1m+fHmd/7Gnp6fTqlUrWVWtJEkiMzNT1txWcMQCPTw8ZP8CycrKkn0OcNyP3PPk5uYSFhYmq0FTKBSYzeZa3YuzcaWnpyexsbEu50dQlUa1vehMbVq7di3h4eH87W9/w9fXl2bNmnH+/PlKBQ0V2bZtG9OnT3d1/N26dSvr1q3jT3/6k+sYZyz3ueeeQ6fTYTAY8PHxYffu3eTk5NCkSRO8vb157LHH2L59O9u3b//d3RbcUR/llmazWZaCjuuxWCyyxkDrE2cpsNx4e3u7ihDkIjg4mMLCwlqd4ywLBmTNsGgMNCpP17kUzsrKYvjw4Xz77beMHz8ecGyelZWVVTmnqKiIS5cuMWDAAFq1aoXNZiM1NZVdu3a5+pklJCTQs2dP/P39CQ4OxmKx0LFjR0JDQ11xyZycHLp160ZAQACFhYUolUrmzZtHYmJind2fJEnodDouXLhQZ2O6w2w2Yzab6/Ta3VFWVoZarXabSVKXGAwG2e/FarVSXl4uu0EsLy/n7NmzsvcgKysr49SpU7UKLzlXLoIb0+hiugBHjhzhs88+44cffuCrr76iQ4cOREVFERQUVCW3tV27duzYsQOj0eiqQPv000/RarWVjtNoNHh5eXHixAkiIiKYNGkSycnJZGdno9VqXUZ42bJlPPbYY/z88891fl+lpaXk5eXJnrmQm5uLQqGQrUWMkwsXLtCqVSvZsyROnz5Nly5dZJ3Dbrdz7tw5WdPfwLHS0Wq1slamgSOuq1ara1VmLkkSSqUSHx8fNBrNH93b/ePEdAHi4uLIycnBZDKxZcsWfvnlF+bPn89rr71W5diapImBw4vu1q0b0dHRAIwbN44jR46Qk5PDt99+y4EDB1CpVMTHx9OqVStZFMby8vIICQmRVb0MHJ5uixYtZJ8HHApacnttCoWiXoyAJEmyPzOVSkV2drbs84SFhXH58mXa1KLzMuBSHhNUT6Mxus6lTXJyMseOHSM0NJThw4cTExPDG2+8Ua1QSE3SxMBhiI4dO0aPHj1YuHAhe/bsoUePHhw6dAilUknXrl0ZO3Yser2e0aNHy1I6q9Vqad++vWxluU70ej0ajUb2eSwWC56enrLP48wjrY+lr9VqlXUeDw8PbDab7PN4e3tTXl5e63mcZcGC6mlU4YVLly7x5JNPolaradmyJUFBQbRv357NmzdXiRvWNE2sV69edOnSBYvFgr+/P1qtlkuXLjF48GC0Wi2pqancc889FBYWcvHiRcLDwzl06JAsyeFy6+fejnl8fHxk90DLy8trnXcq5nHMo9Foam10PT09RXihsXcDPnv2LO3atePDDz9Ep9PxzjvvYDAY2LNnD2+//TaLFi1ixIgRlc5xpomtWbOG8ePHV0oTS05Odh3n5+dXZQPuiSeeICUlhblz5/Loo48ye/ZsunbtSocOHVi6dCnbt2+vl/sWCAR3LI07prt27VpUKhVFRUWo1WoKCgpo1qwZDzzwALt37yYhIaGK0XWqiTlTY7Zu3UpQUFClNDEnWq0WHx8fvLy8KCgo4ODBgxQWFvLII4+4YsGLFi0iOTlZ9o0UgUDQsGnwRtdqtTJs2DBSUlIoLy/n0KFDLF68mMjISCRJ4vTp06xYsaLKeXl5eYSFhbFgwQImTJjgNk1s5cqVGI1G+vTpQ0ZGBs2bN6dJkyY8//zzfPzxxyiVSqZMmUJWVhbp6emEhISwcuXK+n4EAoGgAdEowgtOSkpKuHz5Mnv27CEpKYn169fj6+tLSEhIpeMWLFjAk08+6ZJw3LRpE9HR0VXSxMDRFWLYsGEMGTKEn3/+md27dzNt2jSOHTvmKn5Ys2YNU6dOla0rr0AgaHA07vACOIoc9u/fT1RUFC+++CKSJDFlyhS8vb3p2bNnpWP/8pe/EBgYyM8//8y6detYuHBhtfmIK1asYMCAAfj7+zNo0CBOnDiBSqXCx8eHhIQElEolv/zyi6ytWgQCQeOhQZePOFONjhw5wrRp09i7dy9Tpkxh2LBhLFmyBF9f3yoGFxzx3EceeYR169Yxffp0Nm3a5DaWu3fvXnJycrj//vsxGAwcOnTI5d12796dzZs3A1QpGRYIBILqaNDhBZvNhqenJ7NmzWLQoEFkZ2dz+fJl+vXrx1tvvcXgwYP55JNPXMc7m07u2bPHlSaWnZ1Nenp6pTSxlStXsnTpUpo3b46vry9+fn6Ul5czZ84ctm7dSmpqKhqNBqPRiLe3N0OGDGH9+vVCQ1QgEDhp3CljY8aMYfPmzUyYMIGXX36ZIUOGEB4ejpeXV6XKndzcXEaPHs23337LggULXE0ng4KCqsRzBw0aRHFxMX379qVHjx4kJCRw9OhRli9fTnh4ODNnzmTGjBmsWrWKxx9/nGnTptX3bQsEgjuXxhvTNRqNLFy4ELvdTpcuXcjMzCQ7Oxuz2czZs2ddXR3AUfK7ZMkS4uPjGTlyJJMmTeKll16qEs89fvw4ly9fpqysjIsXL7Jx40aMRiNeXl7ExcUBjpY9P/74I5MnT+bo0aPC6AoEghrR4I2uRqNxGcJRo0bxzDPPsG3bNubNm1fJ4MK1NLEHH3yQHTt2kJeXVyUea7fbefXVVzl06BC7du0iISGBHj168P3331NUVITVakWn0xEeHk5mZibbt29n2LBh9XrPAoGg4dKgja6zHbWzz1a/fv0ICwvj2LFjnD9/nnXr1rmOfeedd1z/v2DBAiZOnEhpaWmV3Ny5c+cybty4agW8TSYTI0aMQKfTkZ+fz1NPPcXMmTPlukWBQNDIaJAx3dTU1Cryhk7h7ZKSEh544AH27duHp6dnpWNiY2PZt28fYWFh5OTkVCn5BZgyZQoHDhzAw8ODsrIyzGYzc+bM4cUXX2Tw4MEkJSUBsGHDBvbt28eqVavkvVmBQNAQqTam2yBTxl5++WVUKhWTJ09m//79AK5OB+vWrcPX17eKwQV48MEHXd5vdWle69evJyMjg7S0NJYuXcq0adNYvHgxYWFh+Pv7c+TIESRJ4vPPPxdpYgKBoNY0SKP73XffkZKSQuvWrZk+fTqhoaHMmDGDK1eusG3bNp599lm35y1YsICffvqJdu3asWvXLhYsWAA4wgozZsy46byffPIJM2bMICYmhrZt2zJq1Kg6vS+BQND4aXDhBWdBREW5uVOnTrFixQq+/vprtFqt7PqsAoFAcBMaX56uUyzZ09Ozkm5nenp6rdXuBQKBoI5pHEbXZDLh4eFRpYOss0WIuziuQCAQ3AYax0ba8uXLXTKNFVuCeHh4CIMrEAgaBA3K023Tpg1KpZLvvvvOJTzjxJkyJhAIBHcADd/TPXDgAJ07d2bx4sVMnDiR/Pz8Sr//7LPPKC4uvk1XJxAIBDWjwRjdDRs2MGbMGCZMmMDAgQNZtmyZK8Rw8uRJ1qxZU6XsVyAQCO40GozR3bt3L+PGjQMcxRFnzpzhp59+AmDjxo2MHTv2dl6eQCAQ1IgGYXSzsrKYM2cOYWFh2O12YmJieOyxx3jhhRfQarUcPnyYyZMn3+7LFAgEgpvSYDbSnDm5kiS58nKXLVvGqlWraN68OQcPHrzNVygQCAQuGv5GmjMlrGIhxJw5c4iIiGDq1Km367IEAoGgVjQYT7c6CgoK8PPzQ6PR3O5LEQgEAieNoyJNIBAIGggNP7wgEAgEjQFhdAUCgaAeEUZXIBAI6hFhdAUCgaAeEUZXIBAI6hFhdAUCgaAeEUZXIBAI6hFhdAUCgaAeEUZXIBAI6hFhdAUCgaAeUd7k99WWsgkEAoGg9ghPVyAQCOoRYXQFAoGgHhFGVyAQCOoRYXQFAoGgHhFGVyAQCOoRYXQFAoGgHvn/y0OpwtO0yNgAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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VK2jVCsqPfUWxk+VG+btfPKwTafc+fdCMHo16+HC0ffpwuegyOVv20SMTAvoMg/Bwu1zHPUFqKrKkJDTdulX5vuj/X6l5qSRlJtEtqBvhfo55H50ldFKpFG9vb4qLi0XRrQX1XnQHDBhAdna22fb5lR61JRJJlR+irKwsxo8fz7p160wSOzsqn+43a2YQl71CV09JjllxkCPBFaLbO+Y0eS2u08i9EQqpggNXTW8ey3rA5KNQ3Lsjnr9totHXnzFvgIz2mRqeOapvNQ91XByq+PiKA3NykKanow0JAX//Ol+fXb+odu6bMbenTMR/3SbDa7P3pRJzD85lXeo6w+u4qDjiB1hvXxecJXTBwcEcO3YMjUaDQqEQRbcG1HvR3blzp9V9TZo0ISsri8DAQLKysqzW8CooKGDo0KHMnz+fmJgYk32OyL2QcyWVuOwVlFQxDhSdoStwd2D4fUT1iSavJI+bpTc5f+O8xfbzH4QNnQ/DfbrX7XI0LN9squXyxEQkaWloo6OR5OUhX7MGXFxArUaZkIBm9Gi7XWddkG3ahMuUKaBQgEpVdd9s8VgFAbKzkaalseDnf/LvdUfN3pct/YLJDm5IkaqIEnUJxapiilRFnMs6x47MHSanSzyeSGxUrN09XmcKnUwmw9vbm+vXrxMcHCyKbg2o96JbFcOHD2fdunXMnj2bdevWMWLECLM2SqWSkSNH8vTTTzNq1Ciz/fZcHKH/EF+5kISLFkzGqAUMChkXOoZW7/WnpFs3osLDiTJqlpqXStc1XSudGAbOWcdoNx9uld7il79+weXEVxb7sLbgN3y//YXHUsrNlRd3dJk8mYwenbikKCKkQQj+HjXzLu3p4bpMmYKkpATKR/FdnnsO1YkTCK1bIwQG4lFYiNTHB8W//oX8008Nh2rGjEE9dizSixeRpKUhuXQJaVoamstpJDcq5cuOcMtKUectG/7F510qXkslUjwVnvjfVIEHZk8ib+19i3/d/y86NO5gt2s3znPraARBwMvLi5ycnCpnM4iYI4puFcyePZsxY8awevVqWrRowaZNukfKpKQkEhMTWbVqFZs2bWLv3r3k5eXxWflsgM8++4wuXXTfwDp7ukaPyZLGjREEgeatu6E8aNrMVQ3ze8+jb8dhhPuFY81iuF84cVFxJB5PNGyLi4pjVPuKG0ZkQCRP7rUsuqti3NDeVjLgIjQ0KqarlAo8uaQXJ0NcUWlVJAxKYHR753u+eRdOkhUsJfQq+BeXb9RocPnoI0ObyPLfRvcpQOchyzdtQgBSQjzYGdWAnQMF9vhoKSjXsnY5lu3++8/GvLcrFw8VuPk1Rf5gHySZWZw7tYeIF83bb7mwhS0XttDatzXD2gxjRJsRdA3silRSN9F0dnIdf39/MjMz8fb2FkXXRsQS7A5m+fLlqFQqYmNjq2yXU5xDen66iZdoeEyWyUCjoWz5cg6EhBATE8O3a19lUlYiCi2opLAiMI7HJtoeJ6xuYOfVna8SPj+Rl49UbFPHxVG68D+cTtlN1wf+gaKsYtFHsRxaTIfc8gkB7nJ3Uial2OzxHjlyhG7dutXJU9t0ZhNTtk3GpagUpQxWb4YnToHg7k7JyZNItFokWVlcPniQFsnJuH7zjYnoCsCsUb6s7+5CVrEu7h3aIJS+LfrSp0UfgryCGPDlAD76CZP3JWfCGLw+WYMkLQ3p7t3I9uxBtnMnklu3AHhpkC5uDoAE4jq/wGu9ZrL1wla2nN/CnvQ9qLVqOtKEJ716ER0zmu6dhyKXVvhElj4flbl8+TIKhaLqaX7VxLqrsyMIAocPH6Z9+/Zcu3YNFxcXcnJykEgkREREcObMGaKiojh27Bj33XcfXl5e1vtybyOWYL9TyOXyateibzqziSm/TEEhVVR4iY37VDwml+M6ZQqK9esBeGxiPA9eieXKhSSat+6Gf/OaxQfD/cKrjCnGD4hno6Q7m4rSTWYvyIDIiP5oV6xCmDwZQS5HoyzlhWFacj0r/GuFVEF6fnqNwwy1Jac4hym/TKFEU0qJm27bcyOg1xWYP0TO5v/1RiqRIpFIUKMmPBT2WDjPliY36RjwMHPajqBPiz6ENjRdNBIXFcc0EkmIhh5XIXTAWGZOXAOAEBaGJiwMzbPPIvvvf3GJi0OCbmbIlKO6wc3uGRBesh7tfWeYHB3NpB6x5I5ayLlvE+j1zmpKJf9DofkfL4/ypGzUSIa3Gc7NkptM2znN9PNh4SmiOu+yuli3xc+hlacVY482ODjYsLDFeLtMJqviP1Z/EUXXwVS3DNggFuoSSsqjtJO3TaZfzEaCFQpDXBIAhQI3o5kW/s3Dayy2NaGFZwti+o+1uE8zejQlffogTU8nx9+T7769H9QVfVVpVYQ0CKmRvbpMqE/PT0chVRjeQwCtQs7s/wzAM6AZjwoCAgJaQUvejTy8Q735uu+vjP6tYibH0u5w1h9mhI/iqU5PWbQTPyCe2KjYaqd/aSqVQm+fB+F5oJw/H3VGBtKjR5EvW4Zk8WKaAUESCRJBQB8y/uS7Ujq02sz6U+sN5zD+fPRp0cfiDc2q6FqKdU+eTEmfPuDvb/VzaM2Osbi6uLjg6upKZmamGF6wAVF0HUx1+XQtiYVCquBSQ4HgylPNVCpKHbjaqMb4+6P198cPSBiUwORtk028JGd5uQAhDUJQaU3fL6lcwYJ/JJr1448//qBNmzak90qn/Qdd6XEVDjfTCS5At6BuVdqq7ilB1ygcdVwc8sSK2Lk6Lg7NtGkV8fbSUqQnTiD79lvdgJ7RzdnFzZMTA/7Hp7LjvLH7DZNrs/YUUZXQSdPTdR5upZu4ND0drb8/6fnpZvHkqp5WKourh4cH169fN0ytFFekWUdMeONgqlscYUksVFoVIS06o0xIQHB3RwAEuRxlQgKqhg0d3OPaMbr9aFImpfDjmB9JmZRS40G0unpF/h7+JAxKwF3ujo+LD+5y92qFP9wvnL6PxPF5lwrBjYuKs9tULlV8PGe++YZbixdTkpxsPpfXzQ1tTAyqmTN1gmhysApZaCtGtR9lEtuF2j1FaENCwMJNXBuiO09IgxDKNGWmuy3Y0YupJY+2ZcuWlJaWGl6Lnq5lRNF1MNWFF/Ri4SJzAcBF5mIQC83o0ZSkpKAND0fbvftdMw/WGv4e/nQN7OpUD9eYmgi/XhDiB8ST/GwyKwatIPnZZLsvWiht2ZKSMWOqXs3n719xg/XxQXB3R5mQAP7+NbqZVPlIr7chkSDIZCY2QPe/a+vbFgmSKu1YE11BEGjcuDFarZb8/PzavVn1BDG84GBsWQY8uv1oejbrSeSqSMa0H2MqFv7+CN27IyvP5SBSNf4e/jUWfZvCBQ7GOEZeeWbB6Paj6dOiT7WzF6p7pNeMHg2vvoqmVy+UH39sYkOlUXGp4BJPd3ya57o8V+XsBWvLfqVSKW5ubpw9exYQPV1riKLrYGwtTBnsE8yA0AHsvbLXzGPRduiA/PPPIcfKJFGRuxqbxac8Rm5xlw03k2oHr4qLkeTloe3a1Wy62LHsYxSrink47GG6Bna1coLqRVcmk+Hj41NtnpL6jBhecDA1qQY8MHQgl/Mvk5pnmldWW575S3rmjN37J+JY7qZyPZL0dACEFi3M9u3L2AdA7+DeVZ7fllSOrVu3RqlU2i15/72GKLoOpibLgAeGDQTgl4u/mGzXRkQAd1B0U1ORrV8PDk4y7vQRbydd191SOUJy+bKunQXR3X9lP+392hPgaTm/iMl5qvB0QZc608XFhVvli0NETBFF18HURHSDfYKJaBxhJro0aYLg54f09Om6daayyNggOooZM3Dv2hWXSZNw79oVxauv1q0Pdwkes2YZruvSI135cs5QsycMPal5qaz/c73V/ZXJKc4hOSuZnOKcu8rTlZZ7uvoZC3rUWjUHMw5yf/P7qz2/rakcFQoFjRs3ruVV3NuIMV0HU5PwAsCgVoNYcnQJ+WX5NHAtT0YtkaDt0AFJHTxdxYwZyFdU5N3VRkSYiLg6Lg7Vv/+N5MoVJFeuIL1yhbbbtyP/6SezjFqq2Fi759Z15qCL26VLuK1ejQTjJbq7YU1Xs7SLJgniqTotoyAIbDi9gZd3vIxcIkcjaPhnx38SG1L1EnB7UV1MV3L5MoKrKzRpYrL9xLUT3FbdrrPomtiqJhVqfUYUXQcjk8lqlMR8YNhA4g/H8+ulXxnZrqLahDYiAvnnn9N02zZo2LBmopeainzFChPxlJ4+bSamF75ONCxVbZ+nWyVlTIofHA6Gbu9NJ2zxevDzs70PdxFe5TebFL9ywTW6zMTjiag0Kpp4NeFm6U0TwdXvT7uVhlQqpbCskAJlgeG37MYtclw1CEbPj++efJeRnUfi7e3t8OuqNryQno4QEgKV8lvsu6KL594fXHvRBXG2gq2IoutgaprasUdQDxq6NuSXi7+YiK701CkkxcW0X7gQFi6sNnG2SR+Skqpt89Ig+MRIgOLaPsWE4j70eP55w35D0hb28uKUUD4Mm4rq5ZdBLq9zwnDFrVtIk5MhLMyuSccrJ/bJLc5lh/wCrdHlQjBDgDUnVpsIJ+iyi+lXriW7J9PMuxk+rj4Eewfj7edN/8PXifhuL4MfhwK3iuPkUjlXCq8QGmC9QKi9sMnTDTFfVLH/yn7aNmpLU6/qVztWjuGKy31rjii6Fti2bRuvvPIKGo2G559/ntmzZ5vsLysr4+mnnyY5ORk/Pz82btxIy5YtTdoMGjSI3Nxcbt++TXFxMb/99hsNGjRgwYIF1drv4t2FH8/+yIGGB5BKpHhcvkyP/fuBCqdMnphIcvfuFFsYFKmMh0JBjyr2p/jBzjB4+o+K5bCJ59azz/M48x4IpMOZLDOPcFlXDZOXLyV82TIAtK6uSLRaUl57jev9+lXbJ2MCdu2i+6JFCHI5Eo2mVuewxNILS9mctdnwupG8EbfUN9FKBFy7QM8rlo87uKMN0YfOc6YxdJoCS36Gl4yyip0Z2oncF6YhLypCXlSE2+VrdPr4LfIUGlSVcryoNCpuZ9zmUM6hOl9PdZSWlpKXl2c10cz9Fy6Q07s3Zw9V9EUjaNh3eR99/Ptw6FD1fdRoNDalcBSXAVtHFN1KaDQapk6dyo4dOwgODiY6Oprhw4fTwahg4+rVq/H19eXChQt89dVXzJo1i40bN5qcR1+Y8siRIyQkJPDJJ5/Y3IcnvZ5k98+7+U3zG4+1f4yICxcstotSqdBUqlRhkZgY1EeOmOQBOOEPXcqn/QpAyvKK5ku7w7QhkKPOYe4YPyTZpcBNGhdBy1twqaEuheOCuQP4bNpvSDUapCUl5HhA8YZFtBr/D9sT8eTk4P7oo0iUSlAqAYj48EPCYmPr5PGm5qWyeV+F4Oo81Ru0UHsy5Iyadqt+wrVBQybvfJWEkt2GdnH3xdFpZjxlmZm0/f573jqwgJeO5JmEYjps/Q3J1t/MbPqrdOkknxuBLuWmuyv/7PQvHuz6oFNSHKakpBAYGEhDS0vFi4txuXUL/+hofI0+M8ezj1O0v4jHuj5GTIfqP0sFBQUkJyejVqtxcXERPd1aIIoukJeXh195fPLIkSO0bt2asLAwAB5//HE2b95sIrqbN29m3rx5AIwaNYoXX3zR6oevumXAlth/RefVvnfwPd47+B5xLcew3EI7TbeqE7MYo4qPRxUbayhN82nGSn7bnsj/pcD7v5om/3z5CCREw3vNxzOo/WDOdsjjP8vHsnozKGXgooFnR8CxgJ0UyKGhBr7sqBMbF20Zyk29SBiywqb8C9UlYqktSZkVIZUlPxl7qkUc6dUWpUKKR1ISiwbE01+exg8XfmBY62EMbj0YACEoCPWUKczy8YH/TTI7v/qxx9D06wc+PgiCgGtsLJKyMp44BQMuQloTV5r8fIDsW/apj2cLVQmgtTm6+njuA80fsMmGRCLB09OTvLw8PD09rdoUhdg69Vp0y8rKSEpK4vXXX2f//v1IpVKuXr1K8+bNDW2Cg4M5fPiwyXHGbfQl0fPy8ixOkalp5YjUvFQ+P/W5ybbES5uYMmUsHZdXeNPquLiazyAID0dTfkx8eDypUbHkf7oEfv3crGmPDJjkHo/v5niaFsK2zeCh1v0ArAtt/dEAACAASURBVNkMyVtW4bEmjhwPNc+NgBJFeQkhbVmVaQH15BTnkOV6i+4qpWnGZ6NELLVFnymsXY5OcI3P3/3AOXigr+F1RndYNwTW/bmOuLZPEd/rXV3tNxcXtjbIYSTmKP/5T5P3X6nV4jJ5MigUNFap8Pl3Aprm4WTf+tOpAmRVdMvDU5Uf+vdn7KdVw1YEeVeR+NwIQRBwd3fn5s2bqNVqi6Irhhaqpl6JriAIaLVaBEGgoKCADz74gG3btrFs2TKH1Zaqqadr7KEZc/DpfoTfdkW2fj2lBw9Cx4517lu4XziMfAXeNRfdw8HQvEErWnk1xef8FZSydIPgAqhk4FWqQVixirS3JqHQlpnUbKsuibk+YbZcKuf/HtWw5luQSeWgUJgkYqnLtcVFxVG8NtHifkue/Vl/SDy7npzv1uNbBiVy2NgRFnU3rRRR4iIxm7lhLXeCMwXImtdpPF3QdeJE1IcP6zKg5Zzh17RfeSTskRrZkEqlNGzYkOvXr5vYrJwMR8Qy9UZ03333XcrKKlLXrVu3jvHjx/PHH3+YtGvWrBlXrlSMsGRkZNCsWTOLbYKDg1Gr1eTn5xvCE5Wp6ewFa7lcuwV1Q/ugDMXnn5tN+akT4eHkTBhjUlJcn8x73X1zGfnASG5dPY/LStP1+AotNG7fDU1IOE1iOqHa1Au0Fe9vVekHjRNmA/w3AnY3g6PZj+K94CO7zV6IHxBPmtcA+N68YGhlelwtT+8ogZ0d3fDEBSVq0BYzbQiGShGnG8PyPR5EWgp/VJE7wRlYFPhK0wUl6AZhX4q5wYpLuv/55vObeXXnqzZlWNMLqpeXF5mZmbi6upqIrpjAvHrqzYo0X19f/Pz88Pf3JzAwkNDQUAoLCzl37pyJJxodHc358+dJS0tDqVTy1VdfMXz4cJNz6asEA3zzzTf069fP6oespuEFvYdmjD7HqyEHQ11XplXCa/la3lo2lmf+D9pP1Q2ixUXF0cJTF//zCwnn6Lw4ihWQ7wrFCjg6Lw6/EN3jtX/zcBKGrLA5l60+cbsx+W5wLtq+08UAQmMGo46L0+UkxnrRv8NG99Vfn/2d869nkTT1DAqFbv7XWX/4vAucaQIt8zQ1Cn/cyfCCpemCKX4YBFdP4vFEm1fc6YU1MDCQkpISUXRrSL3xdKdOnWryesqUKaxYsYIRI0bwwgsv8MorryCVSpHL5SxbtoyBAwei0Wh49tlniYiIYO7cuXTr1o3hw4fz3HPPMX78eFq3bk2jRo346ivLlXOhdiXY4wfEk1mYyb4r+9g5bqch7aAQHo4glSI9cwbbl1vYxsyJa0gdPtNkTuvRo0cN3lP0tHjy/hFLbkoSjdt3IzrENJ5sa/pBsJK4XQbBoffZ+arKz11pEPHWe+8R+O23hv16zx5Mk5j7e/iTMDiRyVtfQFGiRCWD1T8paPBhIhobbw7ODi9UxtJgq8X5yehCW9WluDQW1kaNGnH58mXDE2Rl0RWF1zL1RnQtMWnSJCZMmMDTTz9NbGysYVrPkCFDGDJkiEnbt99+2/C3m5sbX3/9tU02aluCvbFHY1zlrqZfAldXips3x8XOnq6eynllK39p/ELCDd6tJWzNZatPzP3Czy+g1ChxRc7qzWoaPdGl9p2vDqNBxLTp0/F47TXcT55E060bD/jDCis1zww3k8t/0PKWBL/YSJsFV4+zy6KbEB6O5pFHkG/fbvDyu/YeC2ysfHi1ZYqMbehju66urqSlpZntEwXXOvVadEEnoJs2baq+YS2prejKpXLUgvkAXFFoKO6nT+OMiUiOrHU1uv1oNIKG57Y+x3cFQxl47mcKAwMdYssS2rZt0URGAhAOVXp4/h7++Lcf4KSe1Q2LCxVatUJwc0MZH48mJoZW4eHE7fQl8XjFIKOtZYoqC6tcLqewsNAwQC2RSNBqtaLoVkG9F11HU1vRlUllaLTmxxWHhSHdvRuKisDT0w49vHPoE/o0ysijtFkz+w4Q3iXc6fACgGzPHrS9eqF55hnDtvgB8WgFLZ+e+JSjE4/SoXEHi8daslFZWFu3bs3JkyfF8IKN1BvR1Wq1aDQaiz9qtZomTZo45ENSm8URUO7pas2PK27VCgBpSgraGiyOqC3OEA1pRgalzYKxvHjVMdwNc2edYisnB+mZMyjHjDFr29CtIVKJ1GbBBcshBD8/P7RaLcXFxWJ4wQbqjej26tWL9PR0PDw8DHdp/ePRzZs3OXnyJE0qpbyzB7X1dPNL8ylVl5Kal2ry2FcUqkucIjl9Ghwsuo7+4ugFXZKZSUlMDJ734KT6Oz1PV7ZPt+JM+9BDZu1VWpXZLBJbbFiy5ebmRlpamllCcxFz6o3oFhQU8NNPPxEZGUlpaSkymQypVIpUKkUikdw1iyNAl8NVvyqta6Ucr2XNmiF4eCA9fZqaS/ndibRMSWlwMH/vYIl17mTlCOnevQheXmijosz2qTS1E93K3qwgCCgUCsrKypDL5aKnWw315nYkkUhwd3dHKpXi7u6Oq6srCoXCIL6OoqZTxlLzUi3mcNXPoZTIZGhatEC6c6dTysw40lMTEGiXA42KQauo2Zf/78Id93T37kXbq5cuv0Ul1Fo1Cpl9RFcikRASEkJRUZG4DLga6o3oymSyisdZJ96FbSnBboy1ZcD67aEffogsJQXp2bNOKZ/jyC9Q5/dWk/IJtMyHNu+/j/vMmQ6zZczdVELHobb27UN69iya9u0ttlVpVcilNX/YtSa6DRo0QKvVcvv2bTG8UAX15p0ZNWoU7u7uTrdrz2XApKYS+N13SMDwI09MdLjH6xBSU2mz8ReTa3FdterveS13CcY3E8WMGbgPGqT7e8kSizfn2sZ0rYmuIAj4+PiQkZEhhheqoN6I7pNPPsk777zDo48+yoEDB1Aqlezbt4/CwkKH2q3ph6+qZcDWKkDYUhmiNjjyi7NpnWUP3VHXcqe4I161lXwLlW9oKo2qVuGFyhUjjEXXzc0NtVqNSuW8lJZ/N+qN6L7wwgtERETwwgsvMHfuXIqKili8eDEXrCQIv5PED4jnpa4vAXBg/AHDIJq1/Lk1yatbUxwhGql5qSzQ7La4z5HXcqdw9oo0W2/OeSV5lKhKbM65YGwDsCi6EomE4OBgCgoKankV9z71RnQ9PDzo1asXw4cPR6PRUFBQYDKz4G4L/jfx0k1fa92odcXG8HCyH3vMJHlLrfLq2oijxCIpM4mz/rqcB8bXcvKxBx12LZW5Fx9/9aJny815xo4ZbE/bzvXi63Rd05VXd9o2NmBpcURl0fX09EShUNRqqmR9oN6I7ogRI1izZg1nzpzBxcWFbdu2IQgCLi4ud7prFpFKdP8abaXUNumvv07evn1o/u//kACqWbMc2g9H3Iz0cetpQ3RZzfTZzUoWfGB3W3eaO7IiLTwcITTU6s25uhky1dmoztPVJ8ORy+vNjNQaUW/elZ07d7Jv3z52796Nm5sb3333HXPnzqVz586AuedTXXHKDz/8kFWrViGXy/H392fNmjW0sFIkMigoiIyMjBr1tzBfF2vOyMjA26WifHdJSQlZfn4UP/kkId9/z63vv+d2peQ89qKkpITs7Gy735i88GJcm3F8cf4LzvrD2cYwInAEPkqfGr9PtaG0tJSsrCyrBRztSVlZGVlZWU4ZzVcqlWRmZiLLzycsI4OCESMojomhtHNnVK1aQfl7u/3idovHbz+9Ha+wqmu53bp1C19fX4uern6bmHuhauqN6FYuHKlHpVKZeby2FKeMiooiKSkJDw8PEhISmDlzppkNfUXgCxcu8I9//ANfX19WrVplU38l5cMgGkFj5i1ptVpKOnRA6+GBx+HDFA4ebNM5a4MgCA7x1t7s+iYtyzyYn76S1W4TaNF6vCFpijNw1HVZs+NMW95btyJVqbj5zDOU6aeLGdmPbBRp8fjIRpHV9rO4uNjwd1Werii61qk3ortt2zYuXLhgqGBaVlaGTCZDJpORn5/PuHHjCA7WJRq1pThl374VNbZiYmJYv369RZuCIHDfffexf//+Gn0QG+U0AiAwKBA/94qqFAUFBTRp0gQfHx+E++/H59gxXIxqutmTW7du0bRpUzwdlFinew48fQJ69GlBvrs7gYGBTpnWl5OTQ1BQkFNCS9nZ2QQHBzvFq9bX7nPbuhVtx44EPGK5DE/z5s3pfqI7R7IqahDFRcXRp2Ofam2oVCquXr2Ku7t7lcnLRdG1Tr0R3RMnTrBkyRK6d+9Oq1at2LVrFx4eHvTs2ROVSkVpaamhrS3FKY1ZvXo1g614m7X98EnLw+1awTSma7xKTPPgg7hs3w5ZWeCgtIiO8tAUM2YwbMVKhgF8/zZZj52GFSuqO0ykGqSpqciSk1EuWFBlOxeZC+0atWNG9xkW8whbQ1+qp8SoenNl0RXDC1VTb0T30qVLfPrppwwbNsywbeLEiYwYMYKHLCQDsZX169eTlJTEnj177NFNA4aBNAuiq0efxES2bx8aC1mk6orDvjiV5pECBH77LbdefRXKY+yO5G5It+go5F98gSCToa7i85Bfls+hzENMi57GU52eqrGNxo0bc/78eathBTG8UDX1ZvZCbm4u+fn5QEVc6vr164Ztxkt1bSlOCbrBufnz57NlyxZcXV3t2l9rogsVX2Rt584IDRog27vXrrYdjbV5pPLkZCf3xDk4TYA0GmRffYVm4ECoImPenst7UGvVPBz6cI1NCIKAq6srUqmUW7duiaJbC+qN6D7//PMsX76c8ePHk5CQwAMPPICfnx/dyucuGo8u21Kc8vjx40yaNIktW7YQEBBg9/5eK7oGwPkb5022myShkcnQ9O6NdOdOZOvXO2QJrSM8NWvzSNVdu1rcLmIbjZKTkWZloRk3rsp2O9J24OPiQ4+gHjW2oRdUFxcXLl26JIpuLag3ojto0CA2b97M0KFD0Wg0vPXWW3z22WcEBQWZtTUuTtm+fXvGjBljKE65ZcsWAF5//XVu377N6NGj6dKli5ko14UZO2Yw/8B8AAZvHGwycb3yh1ly4wbSK1dwmTTJ7glwHPXFSbWwMGJpd0ht7BBzdxRnhhcCt29H8PVFU8VsFkEQ2J62nb4t+tZ4CbD+eIlEgkwmQ6vVmpTpEUXXNupNTBd0U8H0FSSKioooKCigYcOGFttWV5xy586dDumjtYnrsVGxFVWB9V/k1FSkhw4BGOKj8sREVLGxdlvZ5QjRSMpMYtoQSIiGHld15c/P+sPH15Lp3MzxMV24BytHHD1K4717UT/2GFQR6krNSyWjMIPZPWdbbVMVxoIaEhLC2bNnRdGtIfXG001NTWXcuHHs3LmTnTt38s9//pNFixZx6dIlh9uuiXBVl9rROLzg6AQ4jvridAvqRuMi8FbCT20qyp/fF+CYEux3Emd4uooZM3Dv0weZRoN806Yqn3Z2pO0AqFU8F0xF19fXF7VajVKpFEW3BtQb0V22bBmPPvooa9asoXXr1ixduhRXV1d27doFUKOctzVFKpXafP4qUztiKoTOSIDjCNGI2HWSyx/Bjs/h8mIY+yc81vwx2jZqa3dbdwMOFSAbM4rp2ZG2g/aN2xPsE1wrc5UF1d3dnWvXrlkUXVF4LVNvRFcikeDm5gboPji3bt0yhBscTU1y6lpK7Tiy7UhDaMFkIC08HHVcHHpZtHcCHId8aXJykE+Ow0MFDcvAQw0btroyO/A5+9uqB9Tkaed41nH2pO/hvia1f6KonNLR3d3dMANI9HRto96IroeHhyF3rr+/P/Hx8WRnZ9OpUyfAsd5ITYtTxg+IJ/nZZD4a8BEuUhf8PfxN9ht7n6r4eMrWrAFAuXAhqvh4+3Tagi17IE1Pp1Ri+l5IXFxxzcqyq536gq1POzN2zOD+9fejETR8cfoLm7OKVcZSHt2GDRtSWFgoiq6N1BvRnTp1KgMHDgTgkUceYeTIkbz11lvExMQAjhVdqVRa4+KU4X7hxEbFMrztcL5J/YYydRlguW6ZduRIBDc3pHaOTzviPbnYQItEVem9UKkoCwy0LvCpqdanxOXkIE1Ohpwcu/f1b0HLlqBQVJnusy5ZxSpjSXT9/PwoKCgwrEQTRbdq6o3ohoSEEBERQVlZGT169GDq1Km4ubmRm5tLdna2SSIPe1PbMuwAT0Y8yY3SG/xy8RfAihC6uKCNjkZ64EBdumkfKolgTnEOyVnJ5BTnkPPHQT5bNZk5/UHj5org44Pg7o4yIQG1r6/F0ylmzMC9a1eLU+Jkmzbh1j4c+ZDBuLUPR/b11zZ18W7LnVwXZN9/j0SloiwxkZSZMylJTjZ72qlucLYmVBZdqVSKTCbD3d2dwsJCUXRtoN5MGfvggw945513CAoKMnid+knemZmZxMfH8/zzzzvEdm3KsOvp37I/TTybsOH0Boa3HW61Qq+2Vy/kCxdCYSF4e1s4U+2oiUDJNm3CZfJkkMlArWbPpMEMafwzComcfyQVsbEjuHiCshsoJWU0HzCUIf2ncKjsL3wKL9GKVqYntLBcWJ6YyGbXi4QHRBA+bylSlcbgOcgmvYCmTx/wNw3H3MvI165F26oV2nHjyD5yhJYW4vmd/DtZPNbaoG11GIuu/nejRo3IysoiICBAzL1QDfVGdKdNm0ZcXBxyudwwuVsikSCVSg2vHUVdPF25VM7Y9mNJOJZAbnEuYFkINb17o/jgA6SHD6MdMKBO/dVToy9OTg4uU6YgMUoc1H3593hOhwYlZWzsCCUK3Q/A2q4QdOk73tj8naH9Y9cf4+2mT+J54hRFXTriuf8QIRZM/XBpOx8UbWeHFIxnWRehpPjcHzTwt8/13+1Izp5Ftn8/ynfegSry9VYOLUBF3b2aYsnT1adGlclkFBcXi55uNdQb0XV1dbV7fgRbqWlF4Mo8GfEkS5OW8k3qNwxqNMiyp9ujB4JMhuzAAbuJLtju6UrT00GhAKPsUyoZtLwFAbch16tCcAEUGmiXB38ZrUJ7cMm3tD/yreH1fzvCeAu2DgdDnge4VHpLFVq43ECC5Wyx9x7ytWsRFArUT1lPWrPu5DrW/bmOWT1nMab9GJIyk2qUVawylUVX/1sqldKwYUOuXbtGo0aNRNGtgnoT072T1FV0OwV0opN/Jzac3mD9w+zlhbZzZ7vGdWvyxdGGhEClCrAKLVxqCGf9QFnpQUIl023X0y4HXjqCSUn28afg4gORZsuFz/pDric8OwKK5ZDvqvsd938KAsNsk9y/vSiUliLfsAHNsGFgJffH8WvHmb5zOn1b9GVOrzmE+4XzVKenai24YNnT1YcTXF1d0Wg0qFSqv//760BE0XUCcrm81jFdPeM6jiM5O5mLhRetep/aXr2QHj0KZWV1smWMzTFdf3+UCQkI7u6GAbKj8+IoauBOTpAPY0+Buwp8SnW/R50y9XJ7XLV82uCnplKSnMzl+Hm0n6qrq6ZnYydo+6oL//ecJ+Gvu9F/1kqz6XX3KrItW5Dk5aGeONHi/hslNxi3eRyNPRqz9tG1yKT2CZ9Z8nShYlZN06ZNyc/PF0W3CupNeOFOUpeBND2jw0czZ/cctlzawmu+r1lso+3dG8myZUiPH0dbPhWuLtT0i6MZPZqSPn2QpqejDQkh2t+flOLZpOenExIbwrzzF9i9I5Fj3rd58O3n8by0k8TjiYAu/4LFc3brBuHhBISH03dnNmfL24MuLjm7V/n5G4TUG8GF8gG00FC0ffqY7dMKWmJ/iiWzMJPtT2y36/tiacqYVqtFJpMhCAINGzbk8uXL4mBaFYierhOoa3gBoKlXUwaEDmDLpS1otJbPpenZEwDp77/XyZYxNZ5e5e+PtmtXwwwCfw9/ugZ2xd/Dn/evf80zwjcsLdjGqP+NYl/6PsNhZ/1h1+DwKuebWuqL8fnrC5Lz55Ht3Yv6mWcsDqAtOrSIbRe38UHfD+ge1N1udvU12CwNpOnFVyqV4uXlxfXr1+1m915D9HStUF01YD3ffvsto0aN4ujRo4bcvHr0hSnT09M5fvw4vr6+LKimjEpVRLtE80vJL3z/x/d0v2r5y9SjaVO0n33G6eBgiq1UJ7aVsrIyrl27ZpdS2peLLpuNop/OO23y+uEeqbzW+1G6pJeR3iqQ3GAPJN/PRYqUG8obrL682qR94vFEuku608KzZtdZXFxMkp2SAlVHUVERh8ozwdmLVitW0Fwm40iHDiiNzl1UVMSyn5fx9qm36e/fn8iySLvbLioqQqPRmE1dNJ6f6+HhYVhyL2KOKLoWsKUaMEBhYSFLliyhRw/LyaC3bdsGwPjx45k6dSqRkXUbV++s6sySv5ZwqOQQL8e8bLZfMWMG8uxsAHo8+yzquLg6LQu+cOECOUIOF0ovGEa8U/NSazwCXqwqJv4HXT/a5Zimc6zMIvWPEASUAOfN91dGGaAkplPNQilHjx4lKirKLjeT6jh06JBh1aNdKCvD/ddf0Qwbxn1GqUcFQeCHvT+wMGUh4Y3D+fLJL/F0sW9BUUEQ+P3338nNzTWIrPFAGlSEpPzr0VzpmiKKrgVsqQYM8OabbzJr1iwWLlxY5fnsEdMFcFe4MzhkMD9e/pHbytt4uXhV7LSykKAuuXXfO/EeG9MqyspH+EWYeKdxUXHED4g32JclJRlisHp+uvATr/36GpfzL7PkJ90MBT1Lu5sOjLXLgW+CX8Pn/odRtW2FVtCiFbQozl3g1r4dPJm1xEyo3/v9PUrUJYyLGGd3kbnrSE1FkZCAJDfXbABNqVHydsrblKpL2TBig8PeC/1iIn0MV49xQUq9GItYRnxnLGCpGvDVq6bD68eOHePKlSsMHTq02vMpFIo6x3T1jAwbSbG6mC3nt5hst3du3dS8VBPBbZcDXXedpl2O7u+nT8Bv23Xr9ysv1f3r6SHsSv6GUZ8NZPT/RuNRrGZ/4RNmU8JePqI7F8CSnyDlE4h4YxHNHxhIyL8X0cy7GWHzFhP60KNE/WsJKZ/ARz9V9LF/y/74e/ozfed02q1ox9y9c8kszDS6COs5G6wN8hzMOMi7+9/lYMbBWr1vjkL/HstXrUIAZFu3mux/47c3SClMIXFwokNTZEokEuRyuaFihLGnK1YDtg3R060FWq2WGTNm8Nlnn9nU3l6eLkC3gG4Eewaz4fQGnox40rDd3rl1jdflV/ZQjVm/pytRpzDxsDt+u4fRTfZw3g8W7oJXDl1Fof0SS0NyX26Cb7q589KREjMvXbpnD9KUFJPtLx+B643ciFH6M2R7CUIDf35v0o8lTdJYfOhDlhxezBiX+1i0XUrT3RWdzpkwBq/la807UO6hq8PCeHXvLL4tPUauJ7x/8H36t+jPljFbzI9xNpaeYj79FNWkSRAezsYzG1lxYgWjg0czst1Ih3cnKCiICxcumCyOEJOY244ouhaorhpwYWEhp06dok/5dJ3s7GyGDx/Oli1bzAbTQJdlzF6erkQiYXiL4Sw/s5ylR5fySNgjuthqeW5deWLFlCohLKzWoQX9unzjRQuWeOqU5e09MuFsAMQPacQPj4cSUeDGJ/8yn1XxxBjokVFi4QxAXp7FzS2ulfJ6zytsUSqZ+6cfAZn5vFFWyGMusLGDlj99kmi627TP/us2MUCxGWWgP9elSkoPSZnz/Q1iDykBcAE+ksCHMt2ii42dYNflXRzMOEjP4J5Wrt45WHtaOX/wB767uZn/HPwPPZv1ZFLYJIf3RZ9nQaVSoVarDRn0RNG1HTG8YIHqqgE3aNCA3NxcLl26xKVLl4iJibEquIDhccweSCQS0gvTAXhj9xt0XdPVkBtVFR9PSXIyyhUrUE+YgCQtDcm5c7WyE+4XzhNhT1hdtFAd+nm3LVwDUMlgg/QUJ/xNi1Ge8NcNph22UsTgiUFFFrfHHoeU5dDx5DVCBp+hw8irdH+8gKf+IfBDOERZma0UnFvGflkG5yTXcb+STewhpUm4w03QJVVfsxkal5vedWlX7d4AO2LpaeWlQdAldx5v73+bUk0pYQ3CkEsd70PpvVs3Nzdu3rwpVgOuBaKnawHjasAajYZnn33WUA24W7duNa78a8/wwl/5f/HjlR9NtpkUrgwPRxMejuaRR3T1suLjUa4wT3hiC3Pum8Mtz/7wfdXZ14QmTeDaNcNr/VJdBGh9IJUXjkHTMlfa5Jh6n13K48Nny6sDv1wphBGX5sdvvX3p+3uGYVvlUENm6yZ00vjhVViGd0EZXvklNL5WCCjN+nk4CKYfgPEnQVHFv0OfMyLXUxc3vtNIr1xBAobwTIoffFJpwswXZ75goNdAool2aF/0ouvu7s6NGzfw9vYWRbeGiKJrheqqARuze/fuKs9Vlyxjlfkj7w+L25Myk0yncAUEoH7mGeQrVyKZMwchxFK+ruoJ7NLXLGxhzOb+zWm/fgfNMwrZlBDHAvdkzjYGBAi9Ad92gC86w0tHVSzZan58j6s60U2I1omo8Ve13750w983XMDXXEd56ZCGwAAf8PZGaOIDrb0RfHzIPbCLxkkVMy2WdteFO557aDov/PEVhflZpHxi+ZoVGl3OiP4t+t/x0IIkMxPX559HGxFB6cqVyP78k/1NsuDEPLO2KYUpTuuXTCZDoVBQWloqim4NEUXXCdjT0+3SuIvF7ZZyo6qnT0e+ahXyxYtRLV5cY1v6L44qPh5VbGzFlDCtFtm//smG7B282PMqktX3MfeBfzN5yW94X9zODxd+YFjzhxl61ZOSXT/zy7mt/FlmOU5x3V3321IYQwDmjQ7gp/u86XCxkM9WmscNZEsTKes72Gy7J/DW2mdJ27nRMCfYU+rOzJTFhBbB5BstKRp9H55fV6SWRC5HK5exZdogvpr40h0XXNRqXJ55BkpKKPvvf6FdOzSdO9M1N8Wi6Hbw6WC2zRHoRbVRo0bcunULb29vUXRrgCi6TsCenm4b3zY8Hvo4X6V9ZdjmKnXFz93PrK3QDKyuyAAAIABJREFUrBmaceOQr1uHauZMCAyssT3jIpgao0E5zXff8+TXX9Nn1iSmDtUwSz2L775/j3Zp+RxuBuv81+nm8X6wmOEspsncZ2CPeWWHgPIxNL34VkZz/Tp+p68jLYAyiS7uqkcpgWxlHg2M2hsv3nh02Gu810jJxQs/glZF97QyXj7mRrs+sQQnvINUoaBk9hzdzaRVK6QuLmhDQhh+l0zsV7z7LrLff6ds9WqEdu0M21PyzD3a2MhYWnq1dEq/9KLq7u5OdnY2Hh4eoujWAFF0nYA9PV2JRMIbXd7g9b6vk5SZhI+rDxN+nMAr21/hixFfmH3YVTNmIPv8cxRLl6J6/3279EGPZvRomrZtyw9PPM4fZelEZecb9i3tDtOoiDUHDBkL8eaiqx9wC7AygeHfoc+gefRR8tSFaH+eCEbZIzVyaNy+wsOfsWOGYamxfuXbX80VPF3SiFe2XKNDp74ov1rG4exsgvWT941uJo6vC2070h07UCxciPqZZ9A8/rhhe4mqhDm759DRvyNrh67lWPYxugV1I9QrlDNnzjilb8bi6uvrS1FRkZnoiosjrCO+M05AJpPZdfaCIAiG3KjD2w7nzd5vsvn8Zr4886VZe6FVKzSjRyNfvdrqFKyqbFWH0LkzZWvWEpVteeHDd6m6x/fQmMH8NjTCZPbCFw/4GlaYWcsypnrpJbSDB+M7bAxH58VRrCjPn6uAo/Pi8AvRCebhq4cNgqtfaPHZ93DiYxUr192g7duJlG3eXOvYtjMxjuMqFy0y2fdx0sekF6Tzn37/oYN/B0N+XGfWfTP2ZH19fQ31BUVP1zZET9cJyOVyVJUSfNeFyl+wV6Jf4ee/fubVna/yQPMHaO7T3GS/6rXXkG/ciGL5clRvvmmzHWv12Coju3DB4vYeV2H+gfkkZScxM2YmMW9/zqmwRaTmpeIzegL/eCQW9ws/88OFH+ji0oXi4pN4rK1YwFA5y1j0tHiS+g3gz6M/0Cl6GNGRgzl+7TifHv+UDac3AJbnFQsqFZroaDASgrtWFCrHcd0r4i5Zt7NYdHgRw9sM56GQh0wOc6bQGYurQqFALpeLJdhrgCi6TsAeqR31WBJCmVTGp0M+JeazGCb9PIkfx/yIVFLxECN06IB6+HDky5ahbdoUzQMP1HrRhCWsrXoLuQHvnPTnI8l+Ti37hb5HoCO6n6UXjvNqxkpDLod1rOPkkIksezHZYg4HqAgftMuBHr+u442wRhzwuYGHzJ3h6tZ8J0m1Oq9YlpRkEpO+W1G8847FOC7A3L1zUWlVvNfnPYvH3gnR1WcVy8rKMgkriKJrHVF0nYA9B9KsfZhDG4byQb8PmPrLVObtm0db37ammcDkciS3b+MybRqAzRnIbHpstbAaLuepUfzroYEolixh+uZTeGjM59gmRJ8GozGrtafXUnb2D+5LV3Huli+ZKT5IJVIkSChUFvLr5V8rLUm+we9t3emYXkKDsrO8ODGQXc2yLHaxtsuhnYl0+3YUixahnjjRJI4LkJSVxIbTG5jRfQahDUPNjr0T4QX9b7lcbggxiFSPKLpOwJ6eLlj/gk3oNIEPDnxA/OEKMY2LiiM+OBbZd7rYql74bMlAVhNvpfK0Mq/wcDSA5oknUMyZA0uWmB3TI8M0vaNOUI8ZXm+I8WDBkAZoNWpuaoppd8M8dND7XAmql16i9MUXWRgcTGpeKqeuvUrHb3Yb2lQOU9yNSK5exTU2VhfHrZS1ThAEZv46kwCPAGb2nGnx+DsVXjAu05Oenm6W4lHEHFF0nYC9PV1ronv2xlnDEmE9iccTibvS1GKFXFseuWvkQVWaVlbeYdRPP43CgugaD55ZisWOO1TMuEM6D+pMcw8+uM9KHzt2RAjWrSUO9wuHdVspecNyqsm7EuM47vr1JnFcgE0pmziceZjlg5bj7eJt9TR3WnT/+usvp9j/uyPOXnAC9ihMqaeqL5ZxZjBjrOU2qO6R225f4vLwQ+WqvvL2EYYm1hZHlC1aRPGtW7RMzSH04bEWT2/xOsLD0Tz11N0tuOWpJxXTpiE7cADl0qUIbU3TMh7POs6MnTMI9wtnfEdLBel13AlP1zilo1wuRyaTkZub65Q+/J0RPV0n4CxP19KqNIDOnQehjss2zUDm7Q02TJ+yV6xQH364vvsHDgfBA72HEWtUiSKkZTF8P93sOE3fvqBQADBz4hpyjgr4r9tk2F+b0IEz45/WUMyYgbw8J4YE0IaHm8Vxjecd3yq7xeu7Xq9IGn8Hsbbs18XFxSQ7n4hlRE/XCejT39kLa6IR7hdOXFSc2fb3DrxH8X8WGDKQla1YgbSwEEUd6rXVivBwAuJeZ9jw1w0DfPr5xv4dH6Bo4sQqC1MCeC1fa7iOkuTkOpUjumMY5cfV+6aS1FSTZOt7Lu8xqymXeFyXNN4Sd9rTlUgkyGQyVCqV3eak36uInq4VbClMuWnTJubNm4dEIqFz585s2LDB4rlkMhnBwcF2KYao0WgoLS21eq4nGj5Br+69OFNwhg4+HTh64ygfn/+YEf8dwdwOc5GXi1jbIUNo8tFHnIyIoKjV/7d37mFRlnkf/wzDcFYYQBQPqKCCJ8TE09qqm5rkId1M28h0LQ+lr6uVGh1eV1u3XMVTB9GkzV5rM9tLw9yycrcUTUPYNDUFUhRBQGA4DQwDM/O8f9CM4AwHZeZR8P5cl9cl89xz3889DN/5zf07hdicS6/XA5BXq4KYo6ioqKBk5kza/O53tPn5Z8r69EHXtSvU95qFhYFWW//1BigvLyclJUUWkbLVBDPgyy8JtTH2yqd7ODDEj4RrCRwrsN3R+dPvP+WhQOtaEwaDAb1e7/CGm1VVVXUE/ua43M6dO5OamiocaQ0gRNcGTWlMmZ6ezhtvvMGxY8dQq9U2W06buwFrNBr0ej29e/dm7969VuNuhfLycq5cuWLVr602EUQwhSkATGEKHVI68MqRV3gn5x12PLSjpu7qO+8gDR5MxDvvUP7NN6BUWs2Tk5OD0Wikc+d6DoXtSGpqKh06dMA7MhKmTHHoWsnJyQwYMECWVNWTJ08SEVGrSJHRiEs9H86LpP/j259yaOfRjtn9ZvPB2Q+sxjwy5BFC/awlu7S0lGvXrhHmwDNsg8HAsWPHrCzc2qLboUMHzp07J6zdBhCia4OmNKbcsWMHixYtQq1WAxAQEGA1j7kb8O7duzl//jwvvvhis+9NqVRaYiObytKhSzFh4n+P/C/OSmfiJ8SjDAiget06XJ96Cre//x3Ds8/aXMvsJHE0SqUSpVIpy1oKhQKVSuVwa6x2HCsAubm4Pv00yu++w9gjBKdfbnj73xwC1T1D2BnxBlN6TcFF6YK7yp1tP944h39m4DP0bd/35mUA+V4/Z2dnSkpKcHFxsSm65nvIz8+X5cO6JSJE1wa2GlP+8MMPdcak/dqRYcSIERiNRlatWkVUVJTN+VQqld1rL9wqzw99HqNkZFXiKpQKJdsf2g4zZmD8xz9QrVqFcfJkS9hVbe4Gp1NrwOk//8H1qacorSpjZ+xjvOt2BuN5GJ3rRuADU5k46QXm+df99rJh7AbmDZzXpJb3cv2eXFxc0Gg0BAYG1pv26+rqatMIEdQgRPc2MRgMpKen891335GVlcXIkSM5c+YMPj4+VmPNDgZ70BzrbPmw5RhNRv5y7C8onZTERcVRtWULbpGRqJ5/nqrVq1GmpFhiW8W5XPNxMplQrV7NmV3riIvy4eNeCsq1nxDhGcG8me8wPWx6g+3Sw/zCGhTb2shhuTs7O6PX6zGZTHW6/t68tnjv1I8QXRs01pgSaqzfoUOHolKp6N69O7169SI9PZ3Bg63bpcgVMtYUYn4Tg1Ey8vr3r+Ps5MxbD75F9auv4vLKK3XaehueeQaWLROW7i1Su55vUKmSn7fMIaZDFj8sAHdnPY+GPcq8iHkMChxk13XliF4wr+Ht7U1ZWRlOTk5CXG8DIbo2qN2YslOnTuzevdsqMmHq1Kl8/PHHzJkzh4KCAtLS0ixnwDcjVxpwU3n5Ny9jMBlYd2IdSoWSZ4aO5eyAmrTc3r9Wf3Tetg3V73+PvhWeyznqg6R2XC3U9GGrjoRezh1YN/J5ovtGo3ZTO2RtOT4ca4tuZmYm3t7eVqIrKow1jhBdGzSlMeX48eP5+uuv6dOnD0qlkvXr1+PnZ929wTzf3WLpmudYef9KDCYDG5M28p70HkytufY/P8BbNf4/3E6fpqxTPYVuHUBLtqovFF6wiqutVsKr/nOJ+eNm2axQOdYwO+3MrddvHiNoGCG69dBYY0qFQsHGjRvZuHFjo3PZu3OEveaJ7hvNxqSNdQoevD0UFp6EsELQDxhgl7Waej8tlRxtDku+XmJ9QQHVvn6y7U0u0TV3jCgsLLQpuqJrRMOIV0cGnJyc7nj0gi1SclJsPv5D5xodVsfF4dRKS/bZQ6C0VVrWHF1DeHw4x7OO3eiRXos+3vI1i5RjDfP7r23btlRVVdW5VnuMoH6E6MqAPY8XwH5/YPXVatAuW0LVypV47t9P9z/8AcV5+Vp7twQMJgPxp+L5/ZreZMe9wdwMP1K3SCzM8K8zbv6A+XT17CrLPcl5vCBJkiUet7i42OqaQqEQwtsAQnRlQK4qY7eKrVoNAR4BLL64hecHF5L1f++jLC7GbeRIlB9b91+715AkiX/98i+GvD8EnlvC0Q0adn4GW+Kv0q1LOOu3pJPyVArbo7aT8lQK6x9YL6v4yCm6CoUCV1dXcnNzbV4T1I8405WBu82RVpubA/CDfYJ5+buXeSflHU60O0Hsrq389o23cZ07l+rvv6d6/Xpwc7Pb+rW5m50wyTnJvPLdKxzNOkpUUYBV7V+nn36CS5cIC7sRV2vPvniNcSc6RyiVStGU8jYQoisD5noJtc/Abhej0YjRaLTLXGaC2wQTHPpruJsRXv/t60S2j2TxN4uZVrKQ99+K58EPInHbvBnFyZNUfPABpu7WLWOag8lkwmAw2HVf9SFJUpPXuVxymde+f419afsIkDx555gP8w9Z19kAkE6coLpW2KDBYLiltZqDubqXI9eqrq62qrkQEBBAdna2OF64BRSNfELevaZHC2Ljxo2o1WqC7ND+22g0Ul1djZuDrM3aXCy9yJq0NWRXZjOn6xwWZnWl/7r1KIxGfl6+nPKgINpeuEBpWBgVXZt3dllZWYlKpUJpo/COvamoqMDd3b1BYSipLuGjzI/Yn5OAyiix7JjE8qMS1X0Hkj9sGL3i4up2HAZOvPee1etQUVGBh4eHYzZSCzneFyaTCT8/P1xcXPD29iYjI4O+ffty/PhxRo0axenTp+nXrx/nz59nyJAhsvwu72LqfXMJS9eBmKuMpaWl0aVLFwIDA9m3b1+z5rx8+TJubm506NDBTndZP+p0NV+Ff8WrJ1/lvdT3yAmZwLLPt5Gx8SWGv72aYYU3xlbPn48+Nva21zp37hzdunXD07P+lFh78d///pfw8HCbxWEqDZVsP76Z2JMbKTNV8tSPsOq/bWk3ZSZV659G1bMnHYFqoxHVu+9anlc9fz79pk+3mi8pKYnBgwc73PIzmUwkJyczZMgQh61htmIvX75sqadr/kDR6XTieKGJCNF1IAcPHiQ7O5vp06dz8OBBu7wZNRoN/fr1s8PdNY5Wq6Vr1668P+F9hnUcxopvV/DFxS/gPmBg3UQK1bvvYggPxzhtGtyGcBoMBlS/dohwNOYMQWdnZ1ILU0nOTea+9vdx5uR+XvtpM5mqciamwevZven52GIMOx5Bf5O1qo+NpapWI86b26WbcXV1Ra/XO/ybiZOTE66uruh0Otxv6rFmL8yienNpR5VKRW5ubp3C5oL6EaLrYHbv3s3UqVPt8kY0GAyYTCZcXFzscGeNU11dbVnrd0G/q3tRcSORwpw6rIn5HwKeX4KyfwTGESMwjBiBcdgwUDee+lpdXS1LWUf4tWTl+fP889P/5W+mwzUdiSVAAfcVKthhepARC17BNHAgDcWcSKGhGOoRWzMeHh5UVFTIchykVqspKipymOiauVlYVSoV+fn5eHh4CEu3CQjRdSCSJHHo0CHWrl1riWdsDiUlJbi7u9tlrsYwh7iZ1zpy6YjNcUmdb4ju2NlwyV+il/Zn+mf+SL+336TPnyG0TQgd+/wGw5Bh6IcMwdS+vdU81dXVVKSk4HLqFFURERh69rTrfvRGPVdKr3C5+CIhqzbh/+0F/gj8kZpatkt/TT5cP3MvPYMGoanZvF3WLiwslCVLy9nZmevXrzv0DFmSJCorK+s8ZrayzY5DIboNI0TXQURFRZGVlUVeXh6PP/44Pj4+vPXWW82as7i4GDc3N5tdKuyNXq9HkiTLWkFOtp2Ag7NqjMTUqeN4YtxvuVR2iYtlFznS7hd2V+b8Ouoi7tUX6XN6F/2+gV5VPgT7hdElZDhtIkZR3akTPd98kw61qpxdnzGDrBUryCjL4GzxWfr59KN7m1oREwYDSq0W55ISnEtLUZaUIJUUkaPNIlOXxRVDHhloyHAu4ZJ7BVfdqzE51bR6P/9tXS/Hn5IgbjCktoMj+afwduuCvaisrKSyslK2jLGysjLy8vIcKnwqlcpiTZtFtn379ly8eFGIbhMQousgDh48yKpVq2jfvj0zZ860y5xJSUn069dPFqspOzsbtVptqf7fi17ML5nPu6duOI/md51O9zUPUB4ZSafQUJbeNEdZVRkXCi9wofACP18/y/krJ/mqYyofUAycAE7Q9sQmJmQ4849/GeoIYcCePcT5/MTqwBuNGBde8mNLohdZxiLSVaWk+0G6L6T5QbofZPiAwRP49UjZu8qJnuVuDC/35clyX3qoOjDsciVw3Gq/Q7NrRHdSxCR6+fWyun67VFZWkpaWRq9e9puzIfR6PUFBQQ49YqhdX8F81GBOCzYajUJ0G0GIroMwmUwkJCTw+eef22U+nU6Hq6urbMVEysvLrar/xz4Qy7wB80jOTSayQyShfqENnnm2cWnD4MDBDA6sW2NYo9NwoeBnzp/7lgvpx+hSchpszJSRdwE63vh5a3AhO7oXUl3rJfBQuBDi2pG+XkE8rA6hR0AfQjr2I8SvF/7u/tbFtVNT4V/WNY9/6ATzI+bb7D/WHFxdXa2+jjsSHx8f2c51zZhfYzc3NzQajRDdRhCi6yBOnTpFUFAQ3t7edplPo9Hg6+trl7maglarpbuNBIhQv9BmC5Ovuy+/6XI/v+lyP0RB9egzcHCE1bgfbi7lq4BR3cbycM+HCVGHEOITQqBX4C39kUuhoRQ+/jh+tdKaz0wbxYfLYu0uuIAlUcBkMsnygalWq8nMzKRjx46ND75NzPu5OZLB09OT/Px8u73nWytCdB3EP//5T6Kjo8nMzLTLfDk5Ofj5+dltvsaoqKggJyen8YF2oMrdHe2UKXRJSLA8ljFtIqnt/mU19k+hfyK4bTBIYCgycLXoqtWYxihetAj3SZMIyMhAFx6OMjgY93LILHfMa2symbh06ZIsUSeSJFFUVMSVK1ccanFKklTHcSZJEi4uLmi1WhE21ghCdG2g0Wh47LHHuHz5Mt26dWPPnj2Wrr+1yczMZO7cuVy9ehWFQsEXX3zBM888Q3JyMhqNBhcXF5ydnfnoo48abJneFAwGA15eXs2ao6kYjUZUKpVsoWlGo5ErL7yA6ZlncDt9msoBAzCGhDDzpJoP0z+0jJvZcyZh/s1vMe7i4kJ1SAi6X1srOXqX5q/6cr2erq6uKBQKh6/Xq1cvi+iahdbPz4+ysjKHrtvSEWnANlixYgW+vr7ExMSwdu1aioqK+Nvf/mY1bvTo0bzyyiuMGzcOrVaLk5MTHh4eREVFodVq+fLLL+1yPyUlJVy7do3evXvbZb7GKCoqIj8/XzbnT0FBAaWlpTbbHZmTF8xnyPbg+vXrVFRU0K1bN7vM1xjXrl3DaDTW6TDtSK5evYpSqXToEQPUWPBGo5HU1FS6d+9Obm4uPj4+nD9/nvHjx9/rxczrNfXv6VelPhISEpg9ezYAs2fP5rPPPrMa8/PPP2MwGBg3bhwAXl5elvjIjIwMhg8fbrf7kfs8t7y8XJZ0XDMGg6HexIhQv1Ce6PuEXc9b7VlUvil4eHig0+lkW8/sTHM0CoXC0rLHbOkqlUratm1r1/rRrQ0hujbIy8sjMDAQgA4dOpCXl2c1Ji0tDR8fHx555BEGDhzI8uXLLdW/8vLy2LdvH8OHDycmJga9Xt+s+7kToivXUQY0LLqOwN6NQhvDnJUmF15eXmi1WofHBtcW29pnu97e3vd6sZsGuWfPdMeOHWspwFybv/71r3V+rq9MncFgIDExkR9//JGgoCAee+wxdu7cSfv27Vm8eDHTpk2jurqa1157jZUrV7JgwYLbuk+TyYROp6vTEt7RFBQUoFQqKSwsbHywHSguLkalUskWWqXX6ykrK+PSpUuyrAfIvp4kSaSlpTm8noW5lKSop9t07lnRPXTokNVjZgdaRUUFI0eOJCEhgcrKSqt41czMTNavX091dTWTJk3iiy++ID09nRUrVlBSUoKTkxNvvfUW/fv3Z+HChezYsYO2bdve1n2WlJTg7e1928+/VSRJspzNyYVWq8XLy4s2bdrIsp5Op0On08n2mkLNtycvLy/ZzjnN364cvUdJkvDw8LDU2hWRC41zz4quLdauXcuYMWOIiIjgzJkzrF27FrVazZQpU+qMmzVrFqtXr+bFF1/ks88+IyAggEGDBlkcaYcOHWLdunX07duXxMREIiIi8Pf3r2fVhiksLKRz5842oyccgV6vx8PD47bv93bIz8/H399fVtEtLi6WdY95eXl4enrKdlbu5ubGlStXZNmjuXi6WWzvcQdao7RK0R0/fjxeXl4sW7bslhxaCQkJfPfdd7i4uDBlyhQ2bdrEyJEj2bNnDwDJycm88cYbGAwGoqKiUCqVTJkyBUmSuO+++7h48SJPP/008+fPJzMzk08++YSJEyeyefPm295LSUkJPe1c/KUh5HaiQes/04WasLGKigrZXltPT0/Lua4clmdth5qgYVrlR9LmzZsJCgpi+vTpBAYGsnDhQs6ePdvo88wOND8/PxITEy1Wq9mJFRkZyZNPPmlxoK1YsYLx48dz6tQpNmzYgMlk4rHHHuPAgQN88cUXBAUFER8ff9tOKXNJQDktB7mdaCC/6Do5OckuunJHMCgUCtkceLYcaoL6aZWWbu/evdm0aRObNm3i1KlTxMXFMWbMGIuA2bI2mutAGzlypCU4PDc3l/z8fAwGg01nXVPRaDQ4Ozs3a45bJT8/H7VaLeuaOp2OgoIC2f5YJUlCr9fLvseioiLZEiSgxqLPzMyU5WiquLgYLy+vOsVwBLZpdaJr/qTNycmhqKgIlUrF9u3b2b59OwcPHuTcuXO88MILdZ7TVAeaUqkkJCQEo9HI0qVL2b9/P+np6SxatAiDwYDRaCQ8PJzevXuzbNky2rVr16xGgWVlZfj7+8vS2NBMZWUlCoVC1jUlSZK1c655TTn3CDWvrZxrqlQqioqKZDnScHd3x8/Pj+LiYpycnIS12wCtTnQVCgXl5eU8//zz6HQ6srOz+fLLL3F3dycqKoqoqCir5zTVgebu7s758+e57777eO+99wAYNGgQ7u7ulJeXExgYSHR0NI8++ihLly5l2rRpt92M0mQykZubS3BwsKwWYG5urmyZWmby8vLs0rTzbl8zPz9f1jUlSSIpKYkuXbo4/D1kNnaKioqE4DZCq/oeYD7E37lzJ507d2blypV4enri7+/P+fPnee6552w+z5yBFhMTg1arZdOmTRw6dIiYmBigxoE2d+5coMbajY2NZcyYMfTv3x9JkujSpQtVVVXExsby9ttvM2DAADQaDbNmzbrtvZSWltK2bVtZ38B6vR5XV1fZ1gNkKe59t6yrVCotHTnkQKFQ4OnpKdu5riRJop5uE2hVlq75LCk7O5tx48axf/9+pk2bBtTE5Wq1WpvPq52BlpiYiFqtrhPHGxkZSXx8PDt37iQyMhJnZ2dee+01pk6dCsDgwYMpKiriiSeeYNSoUaxevbrZ4iV3FhrcmcgFo9F4z2Qvubu7o9PpZAuNgxspwXL9XkWcbuO0KtE18/DDDxMfH89XX33FJ598QlFRkeXI4OZOuk11oAFcuXKFTp06cenSJR544AH69+9PcHAwJpOJo0eP4uvry5IlS9i0aRMvvvhis/Zgjs+V0yLTarV4enrKumZVVRXOzs53xPKUWyDMYWNyRof4+PiQkZFBp06dHL5W7fq6gvpptVXG9u3bx969ewkICOD48eNMnjyZl156qc4YswPt8OHDDBs2zOJAGz16NKmpqZZxSqWS/v37AxAUFMT+/fvp1KkTSqWSgoICqqqqcHd3JzQ0lOjoaPbs2cPrr79+2/cuSRI6nc6hDQZtodPpcHFxkdXyNBqNVFdXy9IttzYVFRW4u7vLKhByd3M2I/c3mN69e9OhQ4d7XXzr3XyrEV1zZf7U1FROnjxJ+/btycnJoUePHoSFheHj42MVymIu4VhYWMiZM2cYMGAAarUajUbDunXrLOO8vLy4evUqHh4euLq6UlBQwPDhwwkJCcHLy4uBAwdy8eJF+vbtyy+//IKbmxurVq267b3IXXrQTEpKChEREbKKblFREYWFhfTo0UO2NQF++uknwsLCZBXAsrIysrKyZCvRaebcuXN069ZNFuGVJAmlUomXl5cQ3foutBbRBbh06RJz5szBxcWFjh07olarCQsLY8GCBTbfAKGhoXUy0JKSkiwZaL6+viQnJ7Nt2zZ2797N119/zYIFCyxlAZcsWUJMTAwvvfQSe/fu5dq1a+Tn5zNp0iS2bNnSrK+Q5rCi1m4RAZZQO7kdeHci8cRkMqHX6x3ev+xmKisrUalUsnyYmkXXzc1NiG59SJLV0TAkAAAG1UlEQVTU0L8WwdmzZyW9Xi+tW7dOevnllyVJkqSKigrpwIED0rBhw6SDBw/afJ63t7fl/yaTqc7PtVEqldKgQYOkoUOHSvv27ZMkSZLy8/OlkJAQy5jMzEypb9++9tqSQCBo2dSrq63CkbZz505UKpWlRU5BQQH+/v5MnDiRZ599llmzZtGuXbs6z2muA0003xMIBLdDixddg8HA2LFjSUtLo6KigmPHjrF27Vq6deuGJEn06tWLuLg4m0VjzOe+gYGB5OTkWGWgmTF7foODgxk9ejQ//vgj06ZNo7i42FI3ICsrSxYPsUAgaNm0qjPd0tJSMjIy+M9//sOFCxc4fvw4jz76KCtXrqwzzhy1kJycjL+/P0lJSWzfvt3KgQY1Vu6IESOYOnUqq1atYvjw4ajVakpKSsjLy8PHx4ekpCRWrlxJeHg4CxculHPLAoHg7qT1O9I0Gg2HDx+me/fuhIeHI0kSx44dw93dncG/dn01Y45amDdvHkOGDEGj0TBo0CArB1p8fDwzZszg66+/xtnZmcDAQJYuXcquXbuIjY3F19eXP/zhD2g0GgYOHMiHH34ou0NIIBDclbRO0TWHiZ04cYI1a9YQHBzMv//9b9q3b8/YsWMZP348gwYNsnqeOWrBfKxwc1yumZSUFNavX09UVBTJycm8/fbbQE0X4NjYWCIjIx2+R4FA0CJpnd2AzR8Yf//734mOjiYoKIjRo0czb948du3aZSlKczNNaTxpMpl44YUXiI2NtTnHnDlziIiI4C9/+csdqx8gEAhaHi1adM1xh9euXeORRx7h8OHDTJs2jccff5yysjK++uor+vXrV+dfQkJCnTnqi1rYunUrEyZMoHPnzlbXPvroI86cOUNiYiKJiYns2rXLMRsUCAStjhYfvVBZWcmrr76KyWQiPDycrKwsrl27RlVVFefOnasT2tXUurkAR44cYe/evaxZswYXFxeqqqrQarWkpKSg0+mYMGECW7ZsITo6mqSkpGZVFBMIBPcOLV503dzcGDZsGAAPPfQQCxYsICEhgeXLl1vF0ja1bi5AYGAgM2bMwNfXl8jISJKTk0lKSiIuLo7Q0FBmzZrFgQMHOHDgAGPHjpVlrwKBoOXTokXXXJvU3F/r/vvv59y5c3z66ac8+OCDVuOb0nhy27ZtPPvss+Tl5VkcaFCTIltaWsrAgQMZOXIk+fn5zJo1i1mzZjFv3jyZdiwQCFo8DaWryZs113TS09OtHtPr9ZIkSVJJSYl0//33SwaDwWpMU9J+jUajNGrUKOnq1avS+++/Ly1atEiSJEk6efKkNGbMGMu4I0eOSBMnTmz2XgQCQaukXl1tkY605557DpVKRXR0NIcPHwZuFIcZPHgwp06dYsCAAXZ3oAkEAkFzaZHHC59//jkZGRls27aN2bNno9PpmDx5Mn/+85/p0qUL69ats3lO25S03+PHj5OYmMjWrVvRarVUVVXh5eXFkiVLyMrKsowTab8CgeB2aHHJEeY+aLVL8p0+fZq4uDg+/fRTioqKLGNuZvny5fj5+RETE8PatWttpv3WZufOnXWSIoYMGcKbb77J0KFDmTBhAosXL2bChAl23J1AIGgltJ7kCCcnJ5ycnJAkCYPBgCRJDBgwgG3btlFYWEhGRka9z42JieGbb76hZ8+e9TaebIitW7cyd+5cevToQUhICA899JDd9iUQCO4NWpSlq9frcXJyQqVS1XncZDJZiicLBALBXUDrsHTffPNN4uLigJreWmacnJyE4AoEghZBi7J0u3btirOzM59//jl9+vSpc62qqkr29jYCgUBQDy3f0k1MTKRfv36sXbuWGTNmcP369TrX4+PjKSkpuUN3JxAIBE2jxYjuxx9/zKRJk5g+fTqjRo1iw4YNliOGU6dO8f7774sWOgKB4K6nxYjut99+y9SpU4Ga5IizZ8/yzTffALB7924mT558J29PIBAImkSLEN3s7GwWLlxIYGAgJpOJHj168Pjjj7N48WKKior4/vvviY6OvtO3KRAIBI3SYhxpRqMRpVKJJEmW9N0NGzawfft2AgICOHr06B2+Q4FAILDQ8h1p5pCw2vUSFi5cSFBQEE8++eSdui2BQCC4JVqMpVsfBQUFeHl54ebmdqdvRSAQCMy0zsaUAoFAcJfS8o8XBAKBoDUgRFcgEAhkRIiuQCAQyIgQXYFAIJARIboCgUAgI0J0BQKBQEaE6AoEAoGMCNEVCAQCGRGiKxAIBDIiRFcgEAhkxLmR6/WmsgkEAoHg1hGWrkAgEMiIEF2BQCCQESG6AoFAICNCdAUCgUBGhOgKBAKBjAjRFQgEAhn5f1gpMFvzCxZ8AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TVxBJMWQkZcr",
        "colab_type": "text"
      },
      "source": [
        "### Train"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r-W3v3hUCq1v",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# load 2d pretrained model as init weight\n",
        "# model.LM2D.load_state_dict(torch.load(\"/content/drive/My Drive/Models/lm2d.pt\"))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p74dKQOdB0EI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.to(device);\n",
        "optimizer = optim.Adam(model.parameters(), lr=0.0001, eps=1e-7)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NoqVu8Pykcun",
        "colab_type": "code",
        "outputId": "a470ed1b-6559-44e3-c1ea-f1d65985fd57",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "source": [
        "all_train_loss, all_val_loss = worker(n_epochs, train_loader, valid_loader, model, optimizer, criterion, early_stop, controller)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 15 EarlyStopping counter: 3 out of 3\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CNthAGbHkd6h",
        "colab_type": "text"
      },
      "source": [
        "### Training Graph"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qkrev4gAklp0",
        "colab_type": "code",
        "outputId": "b5c6518a-f86f-49b4-faff-4a7114285171",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        }
      },
      "source": [
        "display_graph(all_train_loss, all_val_loss)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NistHEseknn2",
        "colab_type": "text"
      },
      "source": [
        "### Results"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5szFTJb6D2FP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "images, model_pts, key_pts = sample_output(test_loader, model)\n",
        "for i in range(10):\n",
        "    plot_3d(controller.epoch_n, key_pts[i], model_pts[i])"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}