EmotionDetector.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S3OT1sMRdm9I"
      },
      "source": [
        "# ML-CV-EmotionDetector\n",
        "\n",
        "Aufgabe im Rahmen des KI-Labors der [HKA](https://www.h-ka.de/).\n",
        "\n",
        "Gruppe der Overfitter:\n",
        "- [Tr33Bug](https://github.com/Tr33Bug)\n",
        "- [gusse-dev](https://github.com/Gusse-dev)\n",
        "- [CronJorian](https://github.com/CronJorian)\n",
        "- [BFuertsch](https://github.com/BenJosh95)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j8kOf270fARO"
      },
      "source": [
        "--- \n",
        "## Vorgehen\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FO7q176ee0FD"
      },
      "source": [
        "\n",
        "1. Recherche: \n",
        "    - Framework: Tensorflow/Keras\n",
        "    - Welches Modell? --> EfficientNet \n",
        "        - in Keras/TF vorhanden (`tf.keras.applications.EfficientNetB0`)\n",
        "        - Rechenauffwand ist verhältnissmäßig gering --> Mehr Finetunig möglich!\n",
        "    - Welcher Datensatz? \n",
        "        - AffectNetHQ: \n",
        "            - 8 Emotionen (\"ANGER\", \"CONTEMPT\", \"DISGUST\", \"FEAR\", \"HAPPY\", \"NEUTRAL\", \"SAD\", \"SUPRISE\")\n",
        "            - ca. 31000 Bilder\n",
        "            - Farbbilder, verschiedene Bildauflösungen und Formate\n",
        "            - *Nicht einwandfrei gelabled!*\n",
        "            - https://www.kaggle.com/datasets/tom99763/affectnethq\n",
        "        - FER13\n",
        "            - 7 Emotionen (\"Angry\", \"disgust\", \"fear\", \"happy\", \"neutral\", \"sad\", \"surprise\")\n",
        "            - ca. 35000 Bilder\n",
        "            - Schwarzweiß-Bilder, 48x48pixel Auflösung\n",
        "            - *Nicht einwandfrei gelabled!* minimal besser als AffectNet\n",
        "            - https://www.kaggle.com/datasets/msambare/fer2013\n",
        "        - Weitere Datensätze nicht erreichbar oder schlechter geeignet!\n",
        "    - Rechenkapazität\n",
        "        - Google Colab Free mit Google Drive anbindung\n",
        "        - Michis GTX 1070, Henriks RTX 3070\n",
        "2. Arbeitsschritte:\n",
        "    1. Data-Engineering, EDA\n",
        "    2. Modellauswahl + Modellvorbereitung\n",
        "    3. BaseLine-Model(CNN) konzipieren\n",
        "    4. Hyperparameter testen\n",
        "    5. Modelle trainieren\n",
        "    6. Metriken und Modelle extrahieren\n",
        "    7. Demo/Showcase konzipieren\n",
        "    8. Dokumentation und Präsentation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_ZdL3ryNgdpW"
      },
      "source": [
        "## Imports"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7v3zgnkee0FF"
      },
      "outputs": [],
      "source": [
        "## Imports \n",
        "import pandas as pd\n",
        "import os\n",
        "from PIL import Image\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "import matplotlib.image as mpimg\n",
        "from tqdm import tqdm\n",
        "\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "from tensorflow.keras.applications import EfficientNetB0\n",
        "from tensorflow.keras.layers import Dense, Flatten, Dropout,BatchNormalization, Activation, RandomBrightness, RandomContrast, RandomFlip, RandomCrop, Resizing, Conv2D, MaxPooling2D\n",
        "from tensorflow.keras.models import Model, Sequential\n",
        "from keras.applications.nasnet import NASNetLarge\n",
        "from tensorflow.keras.callbacks import EarlyStopping\n",
        "from tensorflow.keras.optimizers import Adam"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bw1OawhYXMgc",
        "outputId": "47ad5ae9-41b2-4355-d6c9-d6af03351397"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2.9.2\n",
            "[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]\n"
          ]
        }
      ],
      "source": [
        "## =========================== Function Definitions =========================== ##\n",
        "\n",
        "## image count function definition\n",
        "def imgCount(dir):\n",
        "    jpgCount = 0\n",
        "    pngCount = 0\n",
        "\n",
        "    for path,_,files in os.walk(dir):\n",
        "        \n",
        "        for file in files:\n",
        "            _,ext = os.path.splitext(file)\n",
        "            if ext == \".jpg\":\n",
        "                jpgCount += 1\n",
        "            else:\n",
        "                pngCount += 1\n",
        "    return (jpgCount, pngCount)\n",
        "\n",
        "\n",
        "## =========================== Define Lables =========================== ##\n",
        "#CLASSES = [\"ANGER\", \"CONTEMPT\", \"DISGUST\", \"FEAR\", \"HAPPY\", \"NEUTRAL\", \"SAD\", \"SUPRISE\"]\n",
        "# CLASSES = [\"ANGER\", \"DISGUST\", \"FEAR\", \"HAPPY\", \"NEUTRAL\", \"SAD\", \"SUPRISE\"]\n",
        "CLASSES = [\"ANGER\", \"HAPPY\", \"NEUTRAL\", \"SAD\"]\n",
        "NUM_CLASSES = len(CLASSES)\n",
        "\n",
        "## =========================== Image and Dataset Variables =========================== ##\n",
        "## constants def\n",
        "IMG_WIDTH = 224\n",
        "IMG_HEIGHT = 224\n",
        "\n",
        "BATCH_SIZE = 32\n",
        "SPLIT = (80,20)\n",
        "\n",
        "## =========================== TF-Test GPU and Version =========================== ##\n",
        "print(tf.__version__)\n",
        "print(tf.config.list_physical_devices(\"GPU\"))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        },
        "id": "FbxTilDVe0FH",
        "outputId": "02ed544f-6ac3-42ca-a92f-3d6822404be6"
      },
      "outputs": [
        {
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-4-d34e24439d9f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m## Mount GDrive in GColab\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mgoogle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolab\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mdrive\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mdrive\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmount\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'/content/drive'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/google/colab/drive.py\u001b[0m in \u001b[0;36mmount\u001b[0;34m(mountpoint, force_remount, timeout_ms, readonly)\u001b[0m\n\u001b[1;32m    104\u001b[0m       \u001b[0mtimeout_ms\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtimeout_ms\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    105\u001b[0m       \u001b[0mephemeral\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 106\u001b[0;31m       readonly=readonly)\n\u001b[0m\u001b[1;32m    107\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    108\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/google/colab/drive.py\u001b[0m in \u001b[0;36m_mount\u001b[0;34m(mountpoint, force_remount, timeout_ms, ephemeral, readonly)\u001b[0m\n\u001b[1;32m    123\u001b[0m   \u001b[0;32mif\u001b[0m \u001b[0mephemeral\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    124\u001b[0m     _message.blocking_request(\n\u001b[0;32m--> 125\u001b[0;31m         'request_auth', request={'authType': 'dfs_ephemeral'}, timeout_sec=None)\n\u001b[0m\u001b[1;32m    126\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    127\u001b[0m   \u001b[0mmountpoint\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_os\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexpanduser\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmountpoint\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/google/colab/_message.py\u001b[0m in \u001b[0;36mblocking_request\u001b[0;34m(request_type, request, timeout_sec, parent)\u001b[0m\n\u001b[1;32m    169\u001b[0m   request_id = send_request(\n\u001b[1;32m    170\u001b[0m       request_type, request, parent=parent, expect_reply=True)\n\u001b[0;32m--> 171\u001b[0;31m   \u001b[0;32mreturn\u001b[0m \u001b[0mread_reply_from_input\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrequest_id\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout_sec\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/google/colab/_message.py\u001b[0m in \u001b[0;36mread_reply_from_input\u001b[0;34m(message_id, timeout_sec)\u001b[0m\n\u001b[1;32m     95\u001b[0m     \u001b[0mreply\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_read_next_input_message\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     96\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mreply\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0m_NOT_READY\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreply\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 97\u001b[0;31m       \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msleep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0.025\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     98\u001b[0m       \u001b[0;32mcontinue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     99\u001b[0m     if (reply.get('type') == 'colab_reply' and\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ],
      "source": [
        "## Mount GDrive in GColab\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5Bd3nfFsmYUk"
      },
      "source": [
        "---\n",
        "## Data-Engenieering"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ifsem8Lkg9ea"
      },
      "source": [
        "--- \n",
        "### Datenvorverarbeitung I - Ordnerstruktur Herstellen und Image Resize"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MLOnjl7SUOSK"
      },
      "outputs": [],
      "source": [
        "## =========================== Initial Folder-Paths =========================== ##\n",
        "## dataset directory\n",
        "root_directory = \"/content/drive/MyDrive/KaggleAPI/Datasets/affectnethq/\"\n",
        "root_directory = \"./Datasets/\"\n",
        "\n",
        "## affectnet dataset\n",
        "directory = root_directory + \"affectnet_split/\"\n",
        "## fer13 Dataset\n",
        "# directory = root_directory + \"fer13_split/\"\n",
        "train_directory = directory + \"train\"\n",
        "test_directory = directory + \"test\"\n",
        "\n",
        "## model directory\n",
        "model_folder = root_directory + \"models/\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7WiW-KSxe6OE"
      },
      "outputs": [],
      "source": [
        "df = pd.read_csv(root_directory + \"labels.csv\")\n",
        "df = df.rename(columns={'pth': 'image_path', 'label': 'label'})\n",
        "df[\"image_shape\"] = None\n",
        "\n",
        "# Create image size entries in the dataframe\n",
        "for idx, rows in df.iterrows():\n",
        "    image_path = root_directory + \"/\" + rows.image_path\n",
        "    image = Image.open(image_path)\n",
        "    df.loc[idx, \"image_shape\"] = image.size"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zJfcEQJ3hFds"
      },
      "outputs": [],
      "source": [
        "print(df[\"image_shape\"].min())\n",
        "print(df[\"image_shape\"].max())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "J41kTP9YZdxp",
        "outputId": "48758a86-1979-45aa-ec4d-bfc48fcd4736"
      },
      "outputs": [
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "x = []\n",
        "y = []\n",
        "\n",
        "for item in df[\"image_shape\"].iteritems():\n",
        "    x.append(item[1][0])\n",
        "    y.append(item[1][1])\n",
        "\n",
        "# Plot the distribution of the image sizes\n",
        "bins = np.arange(100,4000,2)\n",
        "plt.hist(x,bins=bins, log=True)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "i4kGsxbIZdsN"
      },
      "outputs": [],
      "source": [
        "# Create directory on same level\n",
        "new_dir = root_directory + \"affectnet_resized\"\n",
        "new_width = 224\n",
        "new_height = 224\n",
        "\n",
        "if not os.path.exists(new_dir):\n",
        "    os.mkdir(new_dir)\n",
        "\n",
        "for _,dirs,_ in os.walk(root_directory):\n",
        "    for directory in dirs:\n",
        "        # Create new directory\n",
        "        dir_path = new_dir + \"/\" + directory\n",
        "        if not os.path.exists(dir_path):\n",
        "            os.mkdir(dir_path)\n",
        "            \n",
        "        # Iterate over images, resize the to 224x224 and save them, keep the name\n",
        "        for root,_,files in os.walk(root_directory + \"/\" + directory):\n",
        "            \n",
        "            new_image_directory_path = new_dir + \"/\" + directory\n",
        "        \n",
        "            for file in files:\n",
        "                image_path = root + \"/\" + file\n",
        "                if os.path.isfile(image_path):\n",
        "                    image = Image.open(image_path)\n",
        "                    image = image.resize((new_width, new_height))\n",
        "                    image.save(new_image_directory_path + \"/\" + file)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b2jMKmnNq9HJ"
      },
      "source": [
        "--- \n",
        "### Datenvorverarbeitung II - JPG to PNG Conversion\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Xa10Nx6NZdmy"
      },
      "outputs": [],
      "source": [
        "## TEST image conversion\n",
        "\n",
        "imgPath = \"/content/drive/MyDrive/KaggleAPI/Datasets/affectnethq_resized_manual/affecnet_resized_order/happy/\"\n",
        "# read the image in \n",
        "photoJPG = plt.imread(imgPath + \"image0041721.jpg\")\n",
        "photoPNG = plt.imread(imgPath + \"ffhq_4054.png\")\n",
        "\n",
        "# Print image values\n",
        "print(photoPNG.min(), \"bis\", photoPNG.max())\n",
        "print(photoJPG.min(), \"bis\", photoJPG.max())\n",
        "\n",
        "img = Image.open(imgPath + \"image0041721.jpg\")\n",
        "img.save(imgPath + \"image0041721.png\")\n",
        "\n",
        "photoNewPNG = plt.imread(imgPath +\"image0041721.png\")\n",
        "print(photoNewPNG.min(),photoNewPNG.max() )\n",
        "plt.imshow(photoNewPNG)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FNHWcHeBy_OO"
      },
      "outputs": [],
      "source": [
        "print(\"Number of JPG:\",imgCount(directory)[0], \"\\nNumber of PNG:\", imgCount(directory)[1])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "s_MqFD1fZdji"
      },
      "outputs": [],
      "source": [
        "# Convert every JPG to PNG\n",
        "for path,_,files in os.walk(directory):\n",
        "    \n",
        "    for file in files:\n",
        "        _,ext = os.path.splitext(file)\n",
        "        if ext == \".jpg\":\n",
        "            image_path = path + \"/\" + file\n",
        "            filename = file[:-3]\n",
        "            image = Image.open(image_path)  \n",
        "            image.save(path + '/' + filename + \"png\")\n",
        "            os.remove(image_path)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Nho_ARQLu7Id"
      },
      "source": [
        "**Ergebnis: Ein directory \"affectnet_resized\" mit 8 subdirectories für die 8 Klassen. Alle Bilder das .png-Format**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nAUJYtwivA_l"
      },
      "source": [
        "--- \n",
        "### Datenvorverarbeitung III - Split dataset into train and test directories"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iadUy_15vHnQ"
      },
      "outputs": [],
      "source": [
        "import random\n",
        "\n",
        "current_directory = \"affecnet_resized_order\"\n",
        "new_directory = \"affectnet_split\"\n",
        "train_directory = new_directory + \"/\" + \"train\"\n",
        "test_directory = new_directory + \"/\" + \"test\"\n",
        "\n",
        "\n",
        "if not os.path.exists(new_directory):\n",
        "    os.mkdir(new_directory)\n",
        "if not os.path.exists(train_directory):\n",
        "    os.mkdir(train_directory)\n",
        "if not os.path.exists(test_directory):\n",
        "    os.mkdir(test_directory)\n",
        "\n",
        "for _,sub_directories,_ in os.walk(current_directory):\n",
        "    for sub_dir in sub_directories:\n",
        "        \n",
        "        # Create subdirectories\n",
        "        train_subdir = train_directory + \"/\" + sub_dir\n",
        "        test_subdir = test_directory + \"/\" + sub_dir\n",
        "        if not os.path.exists(train_subdir):\n",
        "            os.mkdir(train_subdir)\n",
        "        if not os.path.exists(test_subdir):\n",
        "            os.mkdir(test_subdir)\n",
        "            \n",
        "        # Iterate over images\n",
        "        original_directory = current_directory + \"/\" + sub_dir \n",
        "        for _,_,files in os.walk(original_directory):\n",
        "            \n",
        "            size = len(files)\n",
        "            cut = int(0.8 * size)\n",
        "            random.seed(0)\n",
        "            random.shuffle(files)\n",
        "            train_list = files[:cut]\n",
        "            test_list = files[cut:]\n",
        "            \n",
        "            for file in train_list:\n",
        "                original_image_path = original_directory + \"/\" + file \n",
        "                if os.path.isfile(original_image_path):\n",
        "                    new_image_path = train_subdir + \"/\" + file\n",
        "                    image = Image.open(original_image_path)\n",
        "                    image.save(new_image_path)\n",
        "                \n",
        "                \n",
        "            for file in test_list:\n",
        "                original_image_path = original_directory + \"/\" + file \n",
        "                if os.path.isfile(original_image_path):\n",
        "                    new_image_path = test_subdir + \"/\" + file\n",
        "                    image = Image.open(original_image_path)\n",
        "                    image.save(new_image_path)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3rxgGzN8vOyF"
      },
      "source": [
        "***Ergebnis: Ein directory \"affectnet_split\" mit zwei Subdirectories train (80%) und test (20%), Daten jeweils aus den 8 Klassen, subdirectories für jede Klasse***"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iRhIsghczv86"
      },
      "source": [
        "--- \n",
        "## ML-Data-Prep. - dataset and batch generation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YMGPuFf4e0FN"
      },
      "source": [
        "**Daten Annahme:**\n",
        " - Test und Train Datensatz in verschiedenen Ordnern\n",
        " - Jede Klasse hat eigenen Ordner mit den entsprechenden Bildern\n",
        " - 4 Klassen (\"ANGER\", \"HAPPY\", \"NEUTRAL\", \"SAD\")\n",
        " - Alle Bilder gleiche Größe und gleiches Format "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T4lZmshHe0FN"
      },
      "source": [
        "### GDrive Mounten\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JhIDfV93e0FN"
      },
      "outputs": [],
      "source": [
        "## Mount GDrive in GColab\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_93kpIkme0FO"
      },
      "source": [
        "### Load Dataset Directorys"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Sg71xwmWe0FO"
      },
      "outputs": [],
      "source": [
        "## Dataset directory\n",
        "# root_directory = \"./Datasets/\"\n",
        "\n",
        "## Google Drive Root-Dir\n",
        "root_directory = \"/content/drive/MyDrive/KaggleAPI/Datasets/\"\n",
        "\n",
        "\n",
        "## affectnet dir\n",
        "directory = root_directory + \"affectnet_split/\"\n",
        "## fer13 dir\n",
        "# directory = root_directory + \"fer13_split/\"\n",
        "\n",
        "train_directory = directory + \"train\"\n",
        "test_directory = directory + \"test\"\n",
        "\n",
        "## Model directory\n",
        "model_folder = root_directory + \"models/\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dR2HE4Tee0FO"
      },
      "source": [
        "### Datenverteilung"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "AdyCTeUNe0FO"
      },
      "outputs": [],
      "source": [
        "## Count all files in the train dataset folder\n",
        "train_distibution = []\n",
        "for _,dirs,_ in os.walk(train_directory):\n",
        "  for x in dirs:\n",
        "    train_distibution.append(len(os.listdir(train_directory + \"/\" + x)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PnRKf6nKe0FP",
        "outputId": "d770a641-0064-4ce0-bc7b-00fbc02218de"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[3319, 3464, 2468, 2675]\n"
          ]
        }
      ],
      "source": [
        "## Print number of Files in train-dataset\n",
        "print(train_distibution)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "O6V0dlLfe0FP",
        "outputId": "c2911c40-c2b9-458e-a038-ba4d31bd2680"
      },
      "outputs": [
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "## Plot train-dataset distribution\n",
        "plt.bar(CLASSES, train_distibution)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vNqIdV4gZdW6",
        "outputId": "cdb90da6-3561-4f05-e655-2bbe9ef0079b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Number of JPG: 0 \n",
            "Number of PNG: 14909\n"
          ]
        }
      ],
      "source": [
        "## Check for file-format (PNG or JPG)\n",
        "print(\"Number of JPG:\",imgCount(directory)[0], \"\\nNumber of PNG:\", imgCount(directory)[1])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 547
        },
        "id": "xr897_vC1XPE",
        "outputId": "fdca4eee-0cef-4f30-ce6a-a0cd08828d9f"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "## Display the value distribution of one test image\n",
        "\n",
        "## Load image\n",
        "imgPath = train_directory + \"/happy/ffhq_10.png\"\n",
        "\n",
        "\n",
        "plt.xlabel(\"Value\")\n",
        "# y co-ordinate denotation.\n",
        "plt.ylabel(\"pixels Frequency\")\n",
        "plt.title(\"Histogramm for given Image\")\n",
        "plt.xlabel(\"Value\")\n",
        "plt.ylabel(\"pixels Frequency\")\n",
        "img=mpimg.imread(imgPath)\n",
        "x=img[:,:,0]\n",
        "plt.hist(x)\n",
        "plt.show()\n",
        "\n",
        "plt.imshow(img)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tp0UJ4Y9e0FX"
      },
      "source": [
        "### Load Datasets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KyBTLkuBZAYd",
        "outputId": "d4965523-bc47-4d01-9134-0b4b167d9b16"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Found 11926 files belonging to 4 classes.\n",
            "Using 9541 files for training.\n",
            "Found 11926 files belonging to 4 classes.\n",
            "Using 2385 files for validation.\n",
            "Found 2983 files belonging to 4 classes.\n",
            "<class 'tensorflow.python.data.ops.dataset_ops.BatchDataset'>\n"
          ]
        }
      ],
      "source": [
        "# Load datasets from directory - lables from folder\n",
        "train_dataset = tf.keras.utils.image_dataset_from_directory(train_directory, image_size=(IMG_HEIGHT, IMG_WIDTH), \n",
        "                                                            seed=0, shuffle=True, batch_size=BATCH_SIZE, label_mode=\"categorical\", \n",
        "                                                            subset=\"training\", validation_split=0.2)\n",
        "\n",
        "valid_dataset = tf.keras.utils.image_dataset_from_directory(train_directory, image_size=(IMG_HEIGHT, IMG_WIDTH), \n",
        "                                                            seed=0, shuffle=True, batch_size=BATCH_SIZE, label_mode=\"categorical\", \n",
        "                                                            subset=\"validation\", validation_split=0.2)\n",
        "\n",
        "test_dataset = tf.keras.utils.image_dataset_from_directory(test_directory, image_size=(IMG_HEIGHT, IMG_WIDTH), \n",
        "                                                            seed=0, shuffle=True, batch_size=BATCH_SIZE, label_mode=\"categorical\")\n",
        "\n",
        "print(type(train_dataset))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vbs766H-Y9YJ",
        "outputId": "44997791-d1fb-4fdc-fbda-821d94f45513"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(<tf.Tensor: shape=(32, 224, 224, 3), dtype=float32, numpy=\n",
            "array([[[[ 27.,  13.,  12.],\n",
            "         [ 27.,  13.,  12.],\n",
            "         [ 28.,  14.,  13.],\n",
            "         ...,\n",
            "         [ 36.,  15.,  10.],\n",
            "         [ 39.,  15.,  13.],\n",
            "         [ 40.,  16.,  14.]],\n",
            "\n",
            "        [[ 27.,  13.,  12.],\n",
            "         [ 27.,  13.,  12.],\n",
            "         [ 28.,  14.,  13.],\n",
            "         ...,\n",
            "         [ 36.,  15.,  10.],\n",
            "         [ 37.,  16.,  13.],\n",
            "         [ 38.,  17.,  14.]],\n",
            "\n",
            "        [[ 27.,  13.,  12.],\n",
            "         [ 27.,  13.,  12.],\n",
            "         [ 28.,  14.,  13.],\n",
            "         ...,\n",
            "         [ 36.,  17.,  11.],\n",
            "         [ 38.,  19.,  13.],\n",
            "         [ 38.,  19.,  13.]],\n",
            "\n",
            "        ...,\n",
            "\n",
            "        [[208., 204., 205.],\n",
            "         [225., 221., 222.],\n",
            "         [240., 238., 241.],\n",
            "         ...,\n",
            "         [190., 188., 189.],\n",
            "         [192., 190., 191.],\n",
            "         [197., 195., 196.]],\n",
            "\n",
            "        [[225., 223., 226.],\n",
            "         [232., 230., 233.],\n",
            "         [244., 242., 247.],\n",
            "         ...,\n",
            "         [190., 188., 189.],\n",
            "         [190., 188., 189.],\n",
            "         [196., 194., 195.]],\n",
            "\n",
            "        [[241., 239., 242.],\n",
            "         [241., 239., 242.],\n",
            "         [248., 246., 251.],\n",
            "         ...,\n",
            "         [191., 189., 190.],\n",
            "         [190., 188., 189.],\n",
            "         [195., 193., 194.]]],\n",
            "\n",
            "\n",
            "       [[[164.,  93.,  91.],\n",
            "         [164.,  93.,  91.],\n",
            "         [164.,  93.,  91.],\n",
            "         ...,\n",
            "         [191., 133., 131.],\n",
            "         [191., 133., 131.],\n",
            "         [191., 133., 131.]],\n",
            "\n",
            "        [[164.,  93.,  91.],\n",
            "         [164.,  93.,  91.],\n",
            "         [164.,  93.,  91.],\n",
            "         ...,\n",
            "         [191., 133., 131.],\n",
            "         [191., 133., 131.],\n",
            "         [191., 133., 131.]],\n",
            "\n",
            "        [[164.,  93.,  91.],\n",
            "         [164.,  93.,  91.],\n",
            "         [164.,  93.,  91.],\n",
            "         ...,\n",
            "         [191., 133., 131.],\n",
            "         [191., 133., 131.],\n",
            "         [191., 133., 131.]],\n",
            "\n",
            "        ...,\n",
            "\n",
            "        [[197., 146., 143.],\n",
            "         [197., 146., 143.],\n",
            "         [197., 146., 143.],\n",
            "         ...,\n",
            "         [207., 166., 164.],\n",
            "         [207., 166., 164.],\n",
            "         [207., 166., 164.]],\n",
            "\n",
            "        [[197., 146., 143.],\n",
            "         [197., 146., 143.],\n",
            "         [197., 146., 143.],\n",
            "         ...,\n",
            "         [207., 166., 164.],\n",
            "         [207., 166., 164.],\n",
            "         [207., 166., 164.]],\n",
            "\n",
            "        [[197., 146., 143.],\n",
            "         [197., 146., 143.],\n",
            "         [197., 146., 143.],\n",
            "         ...,\n",
            "         [207., 166., 164.],\n",
            "         [207., 166., 164.],\n",
            "         [207., 166., 164.]]],\n",
            "\n",
            "\n",
            "       [[[ 19.,  20.,   4.],\n",
            "         [ 20.,  21.,   5.],\n",
            "         [ 20.,  21.,   5.],\n",
            "         ...,\n",
            "         [ 18.,  21.,   4.],\n",
            "         [ 14.,  17.,   0.],\n",
            "         [  8.,  11.,   0.]],\n",
            "\n",
            "        [[ 17.,  18.,   2.],\n",
            "         [ 17.,  18.,   2.],\n",
            "         [ 18.,  19.,   3.],\n",
            "         ...,\n",
            "         [ 25.,  28.,  11.],\n",
            "         [ 21.,  24.,   7.],\n",
            "         [ 15.,  18.,   1.]],\n",
            "\n",
            "        [[ 14.,  15.,   1.],\n",
            "         [ 14.,  15.,   1.],\n",
            "         [ 15.,  16.,   2.],\n",
            "         ...,\n",
            "         [ 24.,  27.,  10.],\n",
            "         [ 21.,  24.,   7.],\n",
            "         [ 15.,  18.,   1.]],\n",
            "\n",
            "        ...,\n",
            "\n",
            "        [[ 74.,  46.,  22.],\n",
            "         [ 74.,  46.,  22.],\n",
            "         [ 74.,  46.,  22.],\n",
            "         ...,\n",
            "         [ 98., 105.,  97.],\n",
            "         [ 94., 101.,  93.],\n",
            "         [ 87.,  94.,  86.]],\n",
            "\n",
            "        [[ 74.,  46.,  22.],\n",
            "         [ 74.,  46.,  22.],\n",
            "         [ 74.,  46.,  22.],\n",
            "         ...,\n",
            "         [ 96., 103.,  95.],\n",
            "         [ 92.,  99.,  91.],\n",
            "         [ 85.,  92.,  84.]],\n",
            "\n",
            "        [[ 74.,  46.,  22.],\n",
            "         [ 73.,  45.,  21.],\n",
            "         [ 73.,  45.,  21.],\n",
            "         ...,\n",
            "         [ 84.,  91.,  83.],\n",
            "         [ 80.,  87.,  79.],\n",
            "         [ 73.,  80.,  72.]]],\n",
            "\n",
            "\n",
            "       ...,\n",
            "\n",
            "\n",
            "       [[[153., 151., 138.],\n",
            "         [154., 152., 139.],\n",
            "         [153., 151., 138.],\n",
            "         ...,\n",
            "         [177., 176., 160.],\n",
            "         [191., 199., 182.],\n",
            "         [218., 229., 220.]],\n",
            "\n",
            "        [[154., 152., 139.],\n",
            "         [155., 153., 140.],\n",
            "         [154., 152., 139.],\n",
            "         ...,\n",
            "         [178., 176., 162.],\n",
            "         [193., 200., 186.],\n",
            "         [219., 230., 223.]],\n",
            "\n",
            "        [[154., 152., 139.],\n",
            "         [154., 152., 139.],\n",
            "         [154., 152., 139.],\n",
            "         ...,\n",
            "         [178., 177., 163.],\n",
            "         [194., 203., 187.],\n",
            "         [221., 230., 227.]],\n",
            "\n",
            "        ...,\n",
            "\n",
            "        [[  5.,   5.,   5.],\n",
            "         [  4.,   5.,   5.],\n",
            "         [  7.,   7.,   7.],\n",
            "         ...,\n",
            "         [255., 255., 255.],\n",
            "         [255., 255., 255.],\n",
            "         [255., 255., 255.]],\n",
            "\n",
            "        [[  5.,   5.,   5.],\n",
            "         [  5.,   5.,   5.],\n",
            "         [  8.,   7.,   8.],\n",
            "         ...,\n",
            "         [255., 255., 255.],\n",
            "         [255., 255., 255.],\n",
            "         [255., 255., 255.]],\n",
            "\n",
            "        [[  5.,   5.,   5.],\n",
            "         [  5.,   6.,   6.],\n",
            "         [  9.,   8.,   9.],\n",
            "         ...,\n",
            "         [255., 255., 255.],\n",
            "         [255., 255., 255.],\n",
            "         [255., 255., 255.]]],\n",
            "\n",
            "\n",
            "       [[[160., 130., 106.],\n",
            "         [164., 136., 114.],\n",
            "         [141., 116.,  94.],\n",
            "         ...,\n",
            "         [ 63.,  61.,  62.],\n",
            "         [ 60.,  58.,  59.],\n",
            "         [ 51.,  49.,  50.]],\n",
            "\n",
            "        [[155., 124., 103.],\n",
            "         [160., 132., 111.],\n",
            "         [149., 124., 104.],\n",
            "         ...,\n",
            "         [ 53.,  51.,  52.],\n",
            "         [ 57.,  55.,  56.],\n",
            "         [ 57.,  55.,  56.]],\n",
            "\n",
            "        [[172., 143., 125.],\n",
            "         [168., 141., 124.],\n",
            "         [161., 135., 120.],\n",
            "         ...,\n",
            "         [ 54.,  52.,  53.],\n",
            "         [ 58.,  56.,  57.],\n",
            "         [ 60.,  58.,  59.]],\n",
            "\n",
            "        ...,\n",
            "\n",
            "        [[163., 164., 166.],\n",
            "         [168., 169., 171.],\n",
            "         [169., 170., 172.],\n",
            "         ...,\n",
            "         [142.,  20.,  31.],\n",
            "         [142.,  18.,  28.],\n",
            "         [142.,  18.,  28.]],\n",
            "\n",
            "        [[163., 164., 166.],\n",
            "         [168., 169., 171.],\n",
            "         [169., 170., 172.],\n",
            "         ...,\n",
            "         [142.,  20.,  31.],\n",
            "         [141.,  17.,  27.],\n",
            "         [143.,  17.,  28.]],\n",
            "\n",
            "        [[156., 157., 159.],\n",
            "         [160., 161., 163.],\n",
            "         [161., 162., 164.],\n",
            "         ...,\n",
            "         [142.,  20.,  31.],\n",
            "         [141.,  17.,  27.],\n",
            "         [143.,  17.,  28.]]],\n",
            "\n",
            "\n",
            "       [[[104.,  65.,  24.],\n",
            "         [130.,  86.,  47.],\n",
            "         [123.,  66.,  36.],\n",
            "         ...,\n",
            "         [ 86.,  93.,  51.],\n",
            "         [ 85.,  92.,  48.],\n",
            "         [ 84.,  91.,  47.]],\n",
            "\n",
            "        [[101.,  64.,  22.],\n",
            "         [127.,  83.,  44.],\n",
            "         [120.,  65.,  34.],\n",
            "         ...,\n",
            "         [ 85.,  92.,  50.],\n",
            "         [ 85.,  92.,  48.],\n",
            "         [ 84.,  91.,  47.]],\n",
            "\n",
            "        [[ 97.,  62.,  20.],\n",
            "         [121.,  82.,  41.],\n",
            "         [116.,  63.,  31.],\n",
            "         ...,\n",
            "         [ 83.,  90.,  48.],\n",
            "         [ 83.,  90.,  46.],\n",
            "         [ 82.,  89.,  45.]],\n",
            "\n",
            "        ...,\n",
            "\n",
            "        [[ 33.,  31.,  19.],\n",
            "         [ 33.,  31.,  19.],\n",
            "         [ 32.,  30.,  18.],\n",
            "         ...,\n",
            "         [ 78.,  78.,  54.],\n",
            "         [ 75.,  75.,  51.],\n",
            "         [ 70.,  70.,  46.]],\n",
            "\n",
            "        [[ 35.,  33.,  21.],\n",
            "         [ 34.,  32.,  20.],\n",
            "         [ 33.,  31.,  19.],\n",
            "         ...,\n",
            "         [ 77.,  75.,  52.],\n",
            "         [ 77.,  75.,  52.],\n",
            "         [ 73.,  71.,  48.]],\n",
            "\n",
            "        [[ 37.,  35.,  23.],\n",
            "         [ 36.,  34.,  22.],\n",
            "         [ 34.,  32.,  20.],\n",
            "         ...,\n",
            "         [ 65.,  63.,  40.],\n",
            "         [ 66.,  64.,  41.],\n",
            "         [ 63.,  61.,  38.]]]], dtype=float32)>, <tf.Tensor: shape=(32, 4), dtype=float32, numpy=\n",
            "array([[1., 0., 0., 0.],\n",
            "       [0., 0., 0., 1.],\n",
            "       [1., 0., 0., 0.],\n",
            "       [1., 0., 0., 0.],\n",
            "       [0., 0., 1., 0.],\n",
            "       [0., 0., 0., 1.],\n",
            "       [0., 0., 1., 0.],\n",
            "       [1., 0., 0., 0.],\n",
            "       [1., 0., 0., 0.],\n",
            "       [1., 0., 0., 0.],\n",
            "       [0., 0., 0., 1.],\n",
            "       [1., 0., 0., 0.],\n",
            "       [0., 0., 1., 0.],\n",
            "       [0., 0., 0., 1.],\n",
            "       [0., 0., 1., 0.],\n",
            "       [0., 0., 1., 0.],\n",
            "       [0., 1., 0., 0.],\n",
            "       [0., 0., 1., 0.],\n",
            "       [0., 1., 0., 0.],\n",
            "       [0., 0., 1., 0.],\n",
            "       [0., 0., 0., 1.],\n",
            "       [0., 1., 0., 0.],\n",
            "       [1., 0., 0., 0.],\n",
            "       [0., 0., 0., 1.],\n",
            "       [0., 0., 0., 1.],\n",
            "       [0., 1., 0., 0.],\n",
            "       [0., 1., 0., 0.],\n",
            "       [0., 1., 0., 0.],\n",
            "       [0., 0., 1., 0.],\n",
            "       [0., 1., 0., 0.],\n",
            "       [0., 1., 0., 0.],\n",
            "       [1., 0., 0., 0.]], dtype=float32)>)\n"
          ]
        }
      ],
      "source": [
        "## Print one batch of the train-dataset\n",
        "print(next(iter(train_dataset))) "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fdsthAeBAOm0"
      },
      "source": [
        "### Normalisierung und One-Hot Encoding\n",
        "\n",
        "**Normalisierung:**\n",
        "    - \"EfficientNet models expect their inputs to be float tensors of pixels with values in the [0-255] range.\" Zitat: https://keras.io/api/applications/efficientnet/#efficientnetb0-function\n",
        "\n",
        "\n",
        "**One-Hot-Encoding:**\n",
        "    - Die Funktion `tf.keras.utils.image_dataset_from_directory` macht das mit `label_mode=\"categorical\"` selbstständig."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bDxndfJf0WrP"
      },
      "source": [
        "### Data Augmentation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Q91ubZHE0V_S"
      },
      "outputs": [],
      "source": [
        "AUTOTUNE = tf.data.AUTOTUNE\n",
        "\n",
        "## Define augmentation-functions\n",
        "# BUG: https://github.com/keras-team/keras-cv/issues/581\n",
        "augmentation = Sequential([\n",
        "  RandomBrightness(0.15),\n",
        "  RandomContrast(0.15), \n",
        "  RandomFlip(\"horizontal\"),\n",
        "  RandomCrop(200, 200),\n",
        "  Resizing(IMG_HEIGHT, IMG_WIDTH)\n",
        "])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 668
        },
        "id": "cG-n7z3h07-P",
        "outputId": "f28c6889-763c-44e2-8c28-c8b887ef8946"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:Using a while_loop for converting RngReadAndSkip\n",
            "WARNING:tensorflow:Using a while_loop for converting Bitcast\n",
            "WARNING:tensorflow:Using a while_loop for converting Bitcast\n",
            "WARNING:tensorflow:Using a while_loop for converting StatelessRandomUniformV2\n",
            "WARNING:tensorflow:Using a while_loop for converting RngReadAndSkip\n",
            "WARNING:tensorflow:Using a while_loop for converting Bitcast\n",
            "WARNING:tensorflow:Using a while_loop for converting Bitcast\n",
            "WARNING:tensorflow:Using a while_loop for converting StatelessRandomUniformFullIntV2\n",
            "WARNING:tensorflow:Using a while_loop for converting StatelessRandomGetKeyCounter\n",
            "WARNING:tensorflow:Using a while_loop for converting StatelessRandomUniformV2\n",
            "WARNING:tensorflow:Using a while_loop for converting AdjustContrastv2\n",
            "WARNING:tensorflow:Using a while_loop for converting RngReadAndSkip\n",
            "WARNING:tensorflow:Using a while_loop for converting Bitcast\n",
            "WARNING:tensorflow:Using a while_loop for converting Bitcast\n",
            "WARNING:tensorflow:Using a while_loop for converting StatelessRandomUniformV2\n",
            "WARNING:tensorflow:Using a while_loop for converting RngReadAndSkip\n",
            "WARNING:tensorflow:Using a while_loop for converting Bitcast\n",
            "WARNING:tensorflow:Using a while_loop for converting Bitcast\n",
            "WARNING:tensorflow:Using a while_loop for converting StatelessRandomUniformFullIntV2\n",
            "WARNING:tensorflow:Using a while_loop for converting StatelessRandomGetKeyCounter\n",
            "WARNING:tensorflow:Using a while_loop for converting StatelessRandomUniformV2\n",
            "WARNING:tensorflow:Using a while_loop for converting AdjustContrastv2\n",
            "WARNING:tensorflow:Using a while_loop for converting RngReadAndSkip\n",
            "WARNING:tensorflow:Using a while_loop for converting Bitcast\n",
            "WARNING:tensorflow:Using a while_loop for converting Bitcast\n",
            "WARNING:tensorflow:Using a while_loop for converting StatelessRandomUniformV2\n",
            "WARNING:tensorflow:Using a while_loop for converting RngReadAndSkip\n",
            "WARNING:tensorflow:Using a while_loop for converting Bitcast\n",
            "WARNING:tensorflow:Using a while_loop for converting Bitcast\n",
            "WARNING:tensorflow:Using a while_loop for converting StatelessRandomUniformFullIntV2\n",
            "WARNING:tensorflow:Using a while_loop for converting StatelessRandomGetKeyCounter\n",
            "WARNING:tensorflow:Using a while_loop for converting StatelessRandomUniformV2\n",
            "WARNING:tensorflow:Using a while_loop for converting AdjustContrastv2\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 9 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Data augmentation option 1: modify the datasets\n",
        "train_dataset_augmented_concat = train_dataset.concatenate(train_dataset.map(lambda image, label: (augmentation(image, training=True), label), num_parallel_calls=AUTOTUNE).prefetch(buffer_size=AUTOTUNE))\n",
        "train_dataset_augmented = train_dataset.map(lambda image, label: (augmentation(image, training=True), label), num_parallel_calls=AUTOTUNE).prefetch(buffer_size=AUTOTUNE)\n",
        "\n",
        "## Print 9 sample images from the augmented dataset\n",
        "for images, labels in train_dataset_augmented.take(1):\n",
        "  for i in range(9):\n",
        "    ax = plt.subplot(3, 3, i + 1)\n",
        "    ax.axis(\"off\")\n",
        "    plt.imshow(images[i].numpy().astype(\"uint8\"))\n",
        "    plt.title(CLASSES[np.argmax(labels[i])])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ysv4vwM2NB3r"
      },
      "source": [
        "## BaseLine-Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "T-1yTN5_e0FZ"
      },
      "outputs": [],
      "source": [
        "BASELINE_TRAIN_EPOCHS = 20\n",
        "BASELINE_PATIENCE = 5"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qwq2hu6he0FZ"
      },
      "source": [
        "### BaseLine-Model Definition"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8syhALzZw5FE"
      },
      "outputs": [],
      "source": [
        "tf.keras.backend.clear_session()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fS8RliUDe0FZ"
      },
      "outputs": [],
      "source": [
        "## BaseLine Model\n",
        "baseLine_model = Sequential()\n",
        "\n",
        "baseLine_model.add(Conv2D(64, (3, 3), padding='same', input_shape=(224, 224, 3)))\n",
        "baseLine_model.add(Activation('relu'))\n",
        "baseLine_model.add(BatchNormalization())\n",
        "\n",
        "baseLine_model.add(Conv2D(32, (3,3)))\n",
        "baseLine_model.add(Activation('relu'))\n",
        "baseLine_model.add(BatchNormalization())\n",
        "\n",
        "baseLine_model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "\n",
        "baseLine_model.add(Conv2D(16, (3,3)))\n",
        "baseLine_model.add(Activation('relu'))\n",
        "baseLine_model.add(BatchNormalization())\n",
        "\n",
        "baseLine_model.add(Conv2D(8, (3,3)))\n",
        "baseLine_model.add(Activation('relu'))\n",
        "baseLine_model.add(BatchNormalization())\n",
        "\n",
        "baseLine_model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "baseLine_model.add(BatchNormalization())\n",
        "\n",
        "baseLine_model.add(Flatten())\n",
        "baseLine_model.add(Dropout(rate=0.5))\n",
        "baseLine_model.add(Dense(512)) \n",
        "baseLine_model.add(Activation('relu'))\n",
        "baseLine_model.add(Dropout(rate=0.5))\n",
        "baseLine_model.add(Dense(256))\n",
        "baseLine_model.add(Activation('relu'))\n",
        "baseLine_model.add(Dropout(rate=0.5))\n",
        "baseLine_model.add(Dense(256))\n",
        "baseLine_model.add(Activation('relu'))\n",
        "\n",
        "\n",
        "baseLine_model.add(Dense(NUM_CLASSES))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZyKIoJq_e0FZ"
      },
      "outputs": [],
      "source": [
        "# defining metrics for training\n",
        "BASELINE_METRICS = [tf.keras.metrics.CategoricalAccuracy(name='categorical_accuracy')]\n",
        "\n",
        "# defining callbacks for early stopping\n",
        "BASELINE_EARLY_STOPPING = EarlyStopping(verbose=1, patience=BASELINE_PATIENCE, restore_best_weights=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z5NS5ahMe0FZ",
        "outputId": "efdda2c3-d670-43fb-9ed6-ecf2b5a71ba4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d (Conv2D)             (None, 224, 224, 64)      1792      \n",
            "                                                                 \n",
            " activation (Activation)     (None, 224, 224, 64)      0         \n",
            "                                                                 \n",
            " batch_normalization (BatchN  (None, 224, 224, 64)     256       \n",
            " ormalization)                                                   \n",
            "                                                                 \n",
            " conv2d_1 (Conv2D)           (None, 222, 222, 32)      18464     \n",
            "                                                                 \n",
            " activation_1 (Activation)   (None, 222, 222, 32)      0         \n",
            "                                                                 \n",
            " batch_normalization_1 (Batc  (None, 222, 222, 32)     128       \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " max_pooling2d (MaxPooling2D  (None, 111, 111, 32)     0         \n",
            " )                                                               \n",
            "                                                                 \n",
            " conv2d_2 (Conv2D)           (None, 109, 109, 16)      4624      \n",
            "                                                                 \n",
            " activation_2 (Activation)   (None, 109, 109, 16)      0         \n",
            "                                                                 \n",
            " batch_normalization_2 (Batc  (None, 109, 109, 16)     64        \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " conv2d_3 (Conv2D)           (None, 107, 107, 8)       1160      \n",
            "                                                                 \n",
            " activation_3 (Activation)   (None, 107, 107, 8)       0         \n",
            "                                                                 \n",
            " batch_normalization_3 (Batc  (None, 107, 107, 8)      32        \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " max_pooling2d_1 (MaxPooling  (None, 53, 53, 8)        0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " batch_normalization_4 (Batc  (None, 53, 53, 8)        32        \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 22472)             0         \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 22472)             0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 512)               11506176  \n",
            "                                                                 \n",
            " activation_4 (Activation)   (None, 512)               0         \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 512)               0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 256)               131328    \n",
            "                                                                 \n",
            " activation_5 (Activation)   (None, 256)               0         \n",
            "                                                                 \n",
            " dropout_2 (Dropout)         (None, 256)               0         \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 256)               65792     \n",
            "                                                                 \n",
            " activation_6 (Activation)   (None, 256)               0         \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 4)                 1028      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 11,730,876\n",
            "Trainable params: 11,730,620\n",
            "Non-trainable params: 256\n",
            "_________________________________________________________________\n",
            "None\n"
          ]
        }
      ],
      "source": [
        "# compile the model \n",
        "baseLine_model.compile(optimizer=Adam(learning_rate=0.0001), loss='categorical_crossentropy',metrics=BASELINE_METRICS)\n",
        "\n",
        "# print summary of the model\n",
        "print(baseLine_model.summary())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PuWpH6gge0Fb"
      },
      "source": [
        "### BaseLine-Model Training"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 182
        },
        "id": "2rDkPXswe0Fb",
        "outputId": "7cca8fa5-d6a9-4699-caab-a4eff2b603ec"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/20\n",
            "598/598 [==============================] - 88s 145ms/step - loss: 2.6505 - categorical_accuracy: 0.2831 - val_loss: 1.3360 - val_categorical_accuracy: 0.3753\n",
            "Epoch 2/20\n",
            "598/598 [==============================] - 81s 135ms/step - loss: 1.3620 - categorical_accuracy: 0.3572 - val_loss: 1.2869 - val_categorical_accuracy: 0.4428\n",
            "Epoch 3/20\n",
            "598/598 [==============================] - 80s 133ms/step - loss: 1.3200 - categorical_accuracy: 0.4104 - val_loss: 1.2282 - val_categorical_accuracy: 0.4532\n",
            "Epoch 4/20\n",
            "598/598 [==============================] - 82s 136ms/step - loss: 1.4000 - categorical_accuracy: 0.4218 - val_loss: 1.6634 - val_categorical_accuracy: 0.4335\n",
            "Epoch 5/20\n",
            "598/598 [==============================] - 80s 133ms/step - loss: 2.0140 - categorical_accuracy: 0.3613 - val_loss: 1.5871 - val_categorical_accuracy: 0.2994\n",
            "Epoch 6/20\n",
            "598/598 [==============================] - 82s 137ms/step - loss: 1.8431 - categorical_accuracy: 0.2717 - val_loss: 1.5445 - val_categorical_accuracy: 0.2734\n",
            "Epoch 7/20\n",
            "598/598 [==============================] - 80s 133ms/step - loss: 1.7780 - categorical_accuracy: 0.2638 - val_loss: 1.6502 - val_categorical_accuracy: 0.2759\n",
            "Epoch 8/20\n",
            "597/598 [============================>.] - ETA: 0s - loss: 1.6941 - categorical_accuracy: 0.2561Restoring model weights from the end of the best epoch: 3.\n",
            "598/598 [==============================] - 81s 135ms/step - loss: 1.6939 - categorical_accuracy: 0.2560 - val_loss: 1.4767 - val_categorical_accuracy: 0.2168\n",
            "Epoch 8: early stopping\n"
          ]
        }
      ],
      "source": [
        "# train model\n",
        "baseLine_train_history = baseLine_model.fit(train_dataset_augmented_concat, validation_data=valid_dataset, epochs=BASELINE_TRAIN_EPOCHS, verbose=1, callbacks=[BASELINE_EARLY_STOPPING])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JhVHck-wxQvd"
      },
      "source": [
        "**Results (1. Epoche):**\n",
        "Results of various test for hyperparameters and architecture!\n",
        "```\n",
        "Used: train_dataset_augmented\n",
        "\n",
        "  Changes to: Learning_Rate\n",
        "  Architecture: conv 64,32 + Dense 128,64,32\n",
        "\n",
        "      - learning_rate= 0.01\n",
        "      Epoch 1/10 299/299 [==============================] - 71s 133ms/step - loss: 1.6015 - categorical_accuracy: 0.2552 - val_loss: 1.3412 - val_categorical_accuracy: 0.2475 \n",
        "\n",
        "      - learning_rate= 0.001\n",
        "      Epoch 1/10 299/299 [==============================] - 101s 273ms/step - loss: 2.0015 - categorical_accuracy: 0.2352 - val_loss: 2.1132 - val_categorical_accuracy: 0.2175 \n",
        "\n",
        "      - learning_rate= 0.0001\n",
        "      Epoch 1/10 299/299 [==============================] - 75s 188ms/step - loss: 1.4115 - categorical_accuracy: 0.2800 - val_loss: 1.2998 - val_categorical_accuracy: 0.2533 \n",
        "\n",
        "  Changes to: Architecture\n",
        "  learning_rate= 0.0001\n",
        "\n",
        "      - Arch: conv 64,32,16,8 + Dense 512,128,64,32\n",
        "      Epoch 1/10 299/299 [==============================] - 122s 217ms/step - loss: 7.7305 - categorical_accuracy: 0.2773 - val_loss: 7.9984 - val_categorical_accuracy: 0.2677 \n",
        "\n",
        "      - Arch: conv 64,32,16 + Dense 256,256,128\n",
        "      Epoch 1/10 299/299 [==============================] - 99s 141ms/step - loss: 7.2115 - categorical_accuracy: 0.2881 - val_loss: 7.8462 - val_categorical_accuracy: 0.2948\n",
        "\n",
        "      - Arch: conv 32,64,128 + Dense 256,256,128\n",
        "      Epoch 1/10 299/299 [==============================] - 94s 300ms/step - loss: 7.4523 - categorical_accuracy: 0.2549 - val_loss: 7.4745 - val_categorical_accuracy: 0.2717\n",
        "\n",
        "      - Arch: conv 64,32,16,8 + Dense 256, 128, 64\n",
        "      Epoch 1/10 299/299 [==============================] - 130s 240ms/step - loss: 3.9697 - categorical_accuracy: 0.2754 - val_loss: 2.8383 - val_categorical_accuracy: 0.3166\n",
        "\n",
        "\n",
        "FINAL ARCH: \n",
        "    train_dataset_augmented_concat\n",
        "      - Arch: conv 64,32,16,8 + Dense 512, 256, 256\n",
        "      Epoch 1/10 299/299 [==============================] - 74s 242ms/step - loss: 1.5975 - categorical_accuracy: 0.3753 - val_loss: 1.3568 - val_categorical_accuracy: 0.4658\n",
        "\n",
        "\n",
        "    Epoch 1/25\n",
        "    598/598 [==============================] - 105s 166ms/step - loss: 1.7295 - categorical_accuracy: 0.3610 - val_loss: 1.2201 - val_categorical_accuracy: 0.4717\n",
        "    Epoch 2/25\n",
        "    598/598 [==============================] - 54s 91ms/step - loss: 1.2756 - categorical_accuracy: 0.4475 - val_loss: 1.1908 - val_categorical_accuracy: 0.4809\n",
        "    Epoch 3/25\n",
        "    598/598 [==============================] - 54s 91ms/step - loss: 1.2639 - categorical_accuracy: 0.4591 - val_loss: 1.2412 - val_categorical_accuracy: 0.4839\n",
        "    Epoch 4/25\n",
        "    598/598 [==============================] - 54s 91ms/step - loss: 1.2912 - categorical_accuracy: 0.4707 - val_loss: 1.2863 - val_categorical_accuracy: 0.5082\n",
        "    Epoch 5/25\n",
        "    598/598 [==============================] - 54s 90ms/step - loss: 2.4386 - categorical_accuracy: 0.4569 - val_loss: 1.1240 - val_categorical_accuracy: 0.4998\n",
        "    Epoch 6/25\n",
        "    598/598 [==============================] - 54s 90ms/step - loss: 3.3485 - categorical_accuracy: 0.4697 - val_loss: 5.0774 - val_categorical_accuracy: 0.5036\n",
        "    Epoch 7/25\n",
        "    598/598 [==============================] - 54s 90ms/step - loss: 2.6635 - categorical_accuracy: 0.4523 - val_loss: 2.0948 - val_categorical_accuracy: 0.4478\n",
        "    Epoch 8/25\n",
        "    598/598 [==============================] - 54s 90ms/step - loss: 2.4465 - categorical_accuracy: 0.4590 - val_loss: 3.3772 - val_categorical_accuracy: 0.4952\n",
        "    Epoch 9/25\n",
        "    598/598 [==============================] - 54s 90ms/step - loss: 1.8603 - categorical_accuracy: 0.4304 - val_loss: 2.2337 - val_categorical_accuracy: 0.4621\n",
        "    Epoch 10/25\n",
        "    598/598 [==============================] - 54s 90ms/step - loss: 2.0291 - categorical_accuracy: 0.4395 - val_loss: 2.4065 - val_categorical_accuracy: 0.4713\n",
        "    Epoch 11/25\n",
        "    598/598 [==============================] - 56s 93ms/step - loss: 2.2854 - categorical_accuracy: 0.4531 - val_loss: 2.3398 - val_categorical_accuracy: 0.4683\n",
        "    Epoch 12/25\n",
        "    597/598 [============================>.] - ETA: 0s - loss: 2.3921 - categorical_accuracy: 0.4557Restoring model weights from the end of the best epoch: 5.\n",
        "    598/598 [==============================] - 55s 92ms/step - loss: 2.3925 - categorical_accuracy: 0.4556 - val_loss: 2.2445 - val_categorical_accuracy: 0.4461\n",
        "    Epoch 12: early stopping\n",
        "\n",
        "    --> BASELINE_50_affAHNS_retrain0_lr0001_es7.h5\n",
        "    \n",
        "    \n",
        "    \n",
        "    train_dataset_augmented\n",
        "    - Arch: conv 64,32,16,8 + Dense 512, 256, 256\n",
        "    Epoch 1/15\n",
        "    299/299 [==============================] - 77s 234ms/step - loss: 2.2446 - categorical_accuracy: 0.2937 - val_loss: 1.3020 - val_categorical_accuracy: 0.4340\n",
        "    Epoch 2/15\n",
        "    299/299 [==============================] - 71s 234ms/step - loss: 1.3619 - categorical_accuracy: 0.3619 - val_loss: 1.2785 - val_categorical_accuracy: 0.4419\n",
        "    Epoch 3/15\n",
        "    299/299 [==============================] - 69s 229ms/step - loss: 1.3223 - categorical_accuracy: 0.3893 - val_loss: 1.2344 - val_categorical_accuracy: 0.4537\n",
        "    Epoch 4/15\n",
        "    299/299 [==============================] - 69s 228ms/step - loss: 1.2990 - categorical_accuracy: 0.4153 - val_loss: 1.2182 - val_categorical_accuracy: 0.4549\n",
        "    Epoch 5/15\n",
        "    299/299 [==============================] - 68s 224ms/step - loss: 1.2752 - categorical_accuracy: 0.4365 - val_loss: 1.2376 - val_categorical_accuracy: 0.4553\n",
        "    Epoch 6/15\n",
        "    299/299 [==============================] - 69s 227ms/step - loss: 1.2843 - categorical_accuracy: 0.4415 - val_loss: 1.2724 - val_categorical_accuracy: 0.4667\n",
        "    Epoch 7/15\n",
        "    299/299 [==============================] - 68s 223ms/step - loss: 1.2688 - categorical_accuracy: 0.4527 - val_loss: 1.2558 - val_categorical_accuracy: 0.4746\n",
        "    Epoch 8/15\n",
        "    299/299 [==============================] - 68s 225ms/step - loss: 1.2977 - categorical_accuracy: 0.4609 - val_loss: 1.2013 - val_categorical_accuracy: 0.4813\n",
        "    Epoch 9/15\n",
        "    299/299 [==============================] - 68s 225ms/step - loss: 1.3775 - categorical_accuracy: 0.4582 - val_loss: 2.8880 - val_categorical_accuracy: 0.4759\n",
        "    Epoch 10/15\n",
        "    299/299 [==============================] - 68s 225ms/step - loss: 2.2842 - categorical_accuracy: 0.4079 - val_loss: 1.7842 - val_categorical_accuracy: 0.3933\n",
        "    Epoch 11/15\n",
        "    299/299 [==============================] - 69s 228ms/step - loss: 2.3266 - categorical_accuracy: 0.4212 - val_loss: 4.1684 - val_categorical_accuracy: 0.4834\n",
        "    Epoch 12/15\n",
        "    299/299 [==============================] - 68s 225ms/step - loss: 2.9248 - categorical_accuracy: 0.3834 - val_loss: 1.6619 - val_categorical_accuracy: 0.3212\n",
        "    Epoch 13/15\n",
        "    298/299 [============================>.] - ETA: 0s - loss: 1.8934 - categorical_accuracy: 0.3175Restoring model weights from the end of the best epoch: 8.\n",
        "    299/299 [==============================] - 68s 226ms/step - loss: 1.8932 - categorical_accuracy: 0.3175 - val_loss: 1.6054 - val_categorical_accuracy: 0.2956\n",
        "    Epoch 13: early stopping\n",
        "\n",
        "    --> BASELINE_47_affAHNS_retrain0_lr0001_es7.h5\n",
        "\n",
        "\n",
        "\n",
        "    train_dataset_augmented_concat\n",
        "    - Arch: conv 64,32,16,8 + Dense 512, 256, 256\n",
        "    Epoch 1/20\n",
        "    598/598 [==============================] - 88s 145ms/step - loss: 2.6505 - categorical_accuracy: 0.2831 - val_loss: 1.3360 - val_categorical_accuracy: 0.3753\n",
        "    Epoch 2/20\n",
        "    598/598 [==============================] - 81s 135ms/step - loss: 1.3620 - categorical_accuracy: 0.3572 - val_loss: 1.2869 - val_categorical_accuracy: 0.4428\n",
        "    Epoch 3/20\n",
        "    598/598 [==============================] - 80s 133ms/step - loss: 1.3200 - categorical_accuracy: 0.4104 - val_loss: 1.2282 - val_categorical_accuracy: 0.4532\n",
        "    Epoch 4/20\n",
        "    598/598 [==============================] - 82s 136ms/step - loss: 1.4000 - categorical_accuracy: 0.4218 - val_loss: 1.6634 - val_categorical_accuracy: 0.4335\n",
        "    Epoch 5/20\n",
        "    598/598 [==============================] - 80s 133ms/step - loss: 2.0140 - categorical_accuracy: 0.3613 - val_loss: 1.5871 - val_categorical_accuracy: 0.2994\n",
        "    Epoch 6/20\n",
        "    598/598 [==============================] - 82s 137ms/step - loss: 1.8431 - categorical_accuracy: 0.2717 - val_loss: 1.5445 - val_categorical_accuracy: 0.2734\n",
        "    Epoch 7/20\n",
        "    598/598 [==============================] - 80s 133ms/step - loss: 1.7780 - categorical_accuracy: 0.2638 - val_loss: 1.6502 - val_categorical_accuracy: 0.2759\n",
        "    Epoch 8/20\n",
        "    597/598 [============================>.] - ETA: 0s - loss: 1.6941 - categorical_accuracy: 0.2561Restoring model weights from the end of the best epoch: 3.\n",
        "    598/598 [==============================] - 81s 135ms/step - loss: 1.6939 - categorical_accuracy: 0.2560 - val_loss: 1.4767 - val_categorical_accuracy: 0.2168\n",
        "    Epoch 8: early stopping\n",
        "\n",
        "\n",
        "    --> BASELINE_44_affAHNS_retrain0_lr0001_es7.h5\n",
        "```\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kxbMfPQbe0Fb"
      },
      "source": [
        "### BaseLine-Model Evaluation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cp0i0k_ke0Fb",
        "outputId": "d4588545-b68c-4f47-8a39-4f4d641564d4"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Plot the train- and validation-accuracy of last training\n",
        "plt.plot(baseLine_train_history.history['categorical_accuracy'])\n",
        "plt.plot(baseLine_train_history.history['val_categorical_accuracy'])\n",
        "plt.title('model accuracy')\n",
        "plt.ylabel('accuracy')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'val'], loc='upper left')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iFYh_Qc3e0Fb",
        "outputId": "63dc5995-2520-40d9-8d73-bdd7bd52c68d"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Plot the train- and validation-loss of last training\n",
        "plt.plot(baseLine_train_history.history['loss'])\n",
        "plt.plot(baseLine_train_history.history['val_loss'])\n",
        "plt.title('model loss')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'val'], loc='upper left')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uvSbhAgBe0Fb",
        "outputId": "a33dfc5f-bf0a-49ad-f2ff-2a9fe86b6701"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "94/94 - 9s - loss: 1.2339 - categorical_accuracy: 0.4482 - 9s/epoch - 101ms/step\n"
          ]
        }
      ],
      "source": [
        "# evaluate training with test dataset\n",
        "loss_test, acc_test = baseLine_model.evaluate(test_dataset, verbose=2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SXNRUYPye0Fb"
      },
      "source": [
        "### Save Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WLAdM7Hoe0Fb"
      },
      "outputs": [],
      "source": [
        "## save model \n",
        "# baseLine_model.save(model_folder + \"BASELINE_44_affAHNS_retrain0_lr0001_es7.h5\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0rSb5Elte0Fc"
      },
      "outputs": [],
      "source": [
        "## Load a saved model\n",
        "baseLine_model = tf.keras.models.load_model(model_folder + \"BASELINE_50_affAHNS_retrain0_lr0001_es7.h5\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iHZqRsSXnG8J"
      },
      "source": [
        "---\n",
        "## Transferlearning"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0zISMTfDe0Fc"
      },
      "source": [
        "---\n",
        "### Base-Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dIVKlrKMe0Fc"
      },
      "outputs": [],
      "source": [
        "## ML-Model Constants\n",
        "BASE_LEARNING_RATE = 0.0005 # default = 0.001\n",
        "BASE_TRAIN_EPOCHS = 15\n",
        "BASE_PATIENCE = 7"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TKRVw5Fgrim1"
      },
      "source": [
        "#### Base-Model Definition\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "McqarDu7e0Fd"
      },
      "outputs": [],
      "source": [
        "## Clear Keras-session\n",
        "tf.keras.backend.clear_session()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Oc7n_QdJrrFc",
        "outputId": "fc8331e7-0042-436d-ddaf-eb3f7dcc8b66"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/keras-applications/efficientnetb0_notop.h5\n",
            "16705208/16705208 [==============================] - 0s 0us/step\n"
          ]
        }
      ],
      "source": [
        "## Load EfficientNet-Model from Keras-API\n",
        "base_model_freeze = tf.keras.applications.EfficientNetB0(\n",
        "    include_top=False,\n",
        "    weights=\"imagenet\",\n",
        "    input_shape=(IMG_HEIGHT, IMG_WIDTH, 3),\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7jRKNV7NMx-Y"
      },
      "outputs": [],
      "source": [
        "## Freezing Layers\n",
        "for layer in base_model_freeze.layers:\n",
        "    layer.trainable=False"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "F0wcaPHe7aQo"
      },
      "outputs": [],
      "source": [
        "## Classifier-model to add to base_model\n",
        "base_model_classifier=Sequential()\n",
        "\n",
        "base_model_classifier.add(base_model_freeze)\n",
        "base_model_classifier.add(Dropout(0.5))\n",
        "base_model_classifier.add(Flatten())\n",
        "base_model_classifier.add(BatchNormalization())\n",
        "base_model_classifier.add(Dense(256, kernel_initializer='he_uniform'))\n",
        "base_model_classifier.add(BatchNormalization())\n",
        "base_model_classifier.add(Activation('relu'))\n",
        "base_model_classifier.add(Dropout(0.5))\n",
        "base_model_classifier.add(Dense(64, kernel_initializer='he_uniform'))\n",
        "base_model_classifier.add(BatchNormalization())\n",
        "base_model_classifier.add(Activation('relu'))\n",
        "base_model_classifier.add(Dropout(0.5))\n",
        "base_model_classifier.add(Dense(32, kernel_initializer='he_uniform'))\n",
        "base_model_classifier.add(BatchNormalization())\n",
        "base_model_classifier.add(Activation('relu'))\n",
        "base_model_classifier.add(Dense(NUM_CLASSES,activation='softmax'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "AmxmBTOrNJ9S"
      },
      "outputs": [],
      "source": [
        "# defining metrics for training\n",
        "METRICS = [tf.keras.metrics.CategoricalAccuracy(name='categorical_accuracy')]\n",
        "\n",
        "# defining callbacks for early stopping\n",
        "EARLY_STOPPING = EarlyStopping(verbose=1, patience=BASE_PATIENCE, restore_best_weights=True, monitor=\"val_categorical_accuracy\")\n",
        "\n",
        "# define learningrate for optimizer\n",
        "OPTIMIZER = tf.keras.optimizers.Adam(learning_rate=BASE_LEARNING_RATE) "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "p0wBdqVP7mjP"
      },
      "outputs": [],
      "source": [
        "# Compile model\n",
        "base_model_classifier.compile(optimizer=OPTIMIZER, loss='categorical_crossentropy',metrics=METRICS)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NNCL_Tz_c08F",
        "outputId": "11252e02-437f-447c-d37b-d3a119e70439"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " efficientnetb0 (Functional)  (None, 7, 7, 1280)       4049571   \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 7, 7, 1280)        0         \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 62720)             0         \n",
            "                                                                 \n",
            " batch_normalization (BatchN  (None, 62720)            250880    \n",
            " ormalization)                                                   \n",
            "                                                                 \n",
            " dense (Dense)               (None, 256)               16056576  \n",
            "                                                                 \n",
            " batch_normalization_1 (Batc  (None, 256)              1024      \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " activation (Activation)     (None, 256)               0         \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 256)               0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 64)                16448     \n",
            "                                                                 \n",
            " batch_normalization_2 (Batc  (None, 64)               256       \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " activation_1 (Activation)   (None, 64)                0         \n",
            "                                                                 \n",
            " dropout_2 (Dropout)         (None, 64)                0         \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 32)                2080      \n",
            "                                                                 \n",
            " batch_normalization_3 (Batc  (None, 32)               128       \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " activation_2 (Activation)   (None, 32)                0         \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 4)                 132       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 20,377,095\n",
            "Trainable params: 16,201,380\n",
            "Non-trainable params: 4,175,715\n",
            "_________________________________________________________________\n",
            "None\n"
          ]
        }
      ],
      "source": [
        "## Print model summary\n",
        "print(base_model_classifier.summary())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P7w2VNvve0Ff"
      },
      "source": [
        "#### Base-Model Training"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PiYKsrt69wLK",
        "outputId": "59333aa2-fb27-42a3-f27d-7455c47ba6cf"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/15\n",
            "598/598 [==============================] - 66s 103ms/step - loss: 1.0316 - categorical_accuracy: 0.5633 - val_loss: 0.8523 - val_categorical_accuracy: 0.6419\n",
            "Epoch 2/15\n",
            "598/598 [==============================] - 59s 98ms/step - loss: 0.8276 - categorical_accuracy: 0.6594 - val_loss: 0.7866 - val_categorical_accuracy: 0.6826\n",
            "Epoch 3/15\n",
            "598/598 [==============================] - 58s 97ms/step - loss: 0.7295 - categorical_accuracy: 0.7029 - val_loss: 0.7749 - val_categorical_accuracy: 0.6860\n",
            "Epoch 4/15\n",
            "598/598 [==============================] - 61s 101ms/step - loss: 0.6677 - categorical_accuracy: 0.7357 - val_loss: 0.7578 - val_categorical_accuracy: 0.6872\n",
            "Epoch 5/15\n",
            "598/598 [==============================] - 85s 143ms/step - loss: 0.6295 - categorical_accuracy: 0.7518 - val_loss: 0.7535 - val_categorical_accuracy: 0.6998\n",
            "Epoch 6/15\n",
            "598/598 [==============================] - 58s 96ms/step - loss: 0.5872 - categorical_accuracy: 0.7734 - val_loss: 0.7464 - val_categorical_accuracy: 0.7040\n",
            "Epoch 7/15\n",
            "598/598 [==============================] - 58s 97ms/step - loss: 0.5638 - categorical_accuracy: 0.7824 - val_loss: 0.7560 - val_categorical_accuracy: 0.6998\n",
            "Epoch 8/15\n",
            "598/598 [==============================] - 58s 97ms/step - loss: 0.5371 - categorical_accuracy: 0.7950 - val_loss: 0.7583 - val_categorical_accuracy: 0.7031\n",
            "Epoch 9/15\n",
            "598/598 [==============================] - 59s 98ms/step - loss: 0.5119 - categorical_accuracy: 0.8073 - val_loss: 0.7615 - val_categorical_accuracy: 0.7040\n",
            "Epoch 10/15\n",
            "598/598 [==============================] - 59s 99ms/step - loss: 0.4834 - categorical_accuracy: 0.8177 - val_loss: 0.7726 - val_categorical_accuracy: 0.7078\n",
            "Epoch 11/15\n",
            "598/598 [==============================] - 70s 118ms/step - loss: 0.4834 - categorical_accuracy: 0.8172 - val_loss: 0.7652 - val_categorical_accuracy: 0.7015\n",
            "Epoch 12/15\n",
            "598/598 [==============================] - 71s 118ms/step - loss: 0.4630 - categorical_accuracy: 0.8262 - val_loss: 0.7673 - val_categorical_accuracy: 0.6994\n",
            "Epoch 13/15\n",
            "598/598 [==============================] - 85s 142ms/step - loss: 0.4462 - categorical_accuracy: 0.8353 - val_loss: 0.7781 - val_categorical_accuracy: 0.6956\n",
            "Epoch 14/15\n",
            "598/598 [==============================] - 94s 156ms/step - loss: 0.4343 - categorical_accuracy: 0.8386 - val_loss: 0.7879 - val_categorical_accuracy: 0.7115\n",
            "Epoch 15/15\n",
            "598/598 [==============================] - 179s 298ms/step - loss: 0.4136 - categorical_accuracy: 0.8506 - val_loss: 0.8280 - val_categorical_accuracy: 0.6910\n"
          ]
        }
      ],
      "source": [
        "## Train model\n",
        "base_train_history = base_model_classifier.fit(train_dataset_augmented_concat, validation_data=valid_dataset, epochs=BASE_TRAIN_EPOCHS, verbose=1, callbacks=[EARLY_STOPPING])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Yya2jOUue0Ff"
      },
      "source": [
        "#### Base-Model Evaluation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "dOwKd21T_02s",
        "outputId": "abec4b72-df8a-41cc-d2a6-019266f3c043"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Plot the train- and validation-accuracy of last training\n",
        "plt.plot(base_train_history.history['categorical_accuracy'])\n",
        "plt.plot(base_train_history.history['val_categorical_accuracy'])\n",
        "plt.title('model accuracy')\n",
        "plt.ylabel('accuracy')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'val'], loc='upper left')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "MHZ1quzyFHAo",
        "outputId": "36c3e692-4edc-4596-b739-3d675a6c1952"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Plot the train- and validation-loss of last training\n",
        "plt.plot(base_train_history.history['loss'])\n",
        "plt.plot(base_train_history.history['val_loss'])\n",
        "plt.title('model loss')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'val'], loc='upper left')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bLma9l68HMg9",
        "outputId": "45242949-6105-4fb0-ba43-1f85cc0ebb26"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "94/94 - 11s - loss: 0.8228 - categorical_accuracy: 0.7000 - 11s/epoch - 122ms/step\n"
          ]
        }
      ],
      "source": [
        "# evaluate training with test dataset\n",
        "loss_test, acc_test = base_model_classifier.evaluate(test_dataset, verbose=2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qgyf9K2se0Fg"
      },
      "source": [
        "#### Save model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "m4kMAHZiNq8N"
      },
      "outputs": [],
      "source": [
        "## save model \n",
        "# base_model_classifier.save(model_folder + \"BASE_70_affAHNS_retrain0_lr0005_es7.h5\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0Ma8SmDKNqx6"
      },
      "outputs": [],
      "source": [
        "## Load a saved model\n",
        "base_model_classifier = tf.keras.models.load_model(model_folder + \"BASE_70_affAHNS_retrain0_lr0005_es7.h5\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nvHpjpd0e0Fh"
      },
      "source": [
        "---\n",
        "### Fine-Tuning\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0KtCjXELe0Fh"
      },
      "outputs": [],
      "source": [
        "## ML-Model Constants\n",
        "LEARNING_RATE = 0.0005 # default = 0.001\n",
        "TRAIN_EPOCHS = 15\n",
        "PATIENCE = 7"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1dUo8b2De0Fh"
      },
      "source": [
        "#### FineTune-Model Definition"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HDpcdpI_e0Fh"
      },
      "outputs": [],
      "source": [
        "tf.keras.backend.clear_session()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "k0rymLd7e0Fh"
      },
      "outputs": [],
      "source": [
        "# Get base model from Keras api\n",
        "base_model = tf.keras.applications.EfficientNetB0(\n",
        "    include_top=False,\n",
        "    weights=\"imagenet\",\n",
        "    input_shape=(IMG_HEIGHT, IMG_WIDTH, 3),\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NG1Nac1O4c_Q",
        "outputId": "4227768d-41cf-4d66-df7d-154c6bc184e3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"efficientnetb0\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                   Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            " input_1 (InputLayer)           [(None, 224, 224, 3  0           []                               \n",
            "                                )]                                                                \n",
            "                                                                                                  \n",
            " rescaling (Rescaling)          (None, 224, 224, 3)  0           ['input_1[0][0]']                \n",
            "                                                                                                  \n",
            " normalization (Normalization)  (None, 224, 224, 3)  7           ['rescaling[0][0]']              \n",
            "                                                                                                  \n",
            " tf.math.truediv (TFOpLambda)   (None, 224, 224, 3)  0           ['normalization[0][0]']          \n",
            "                                                                                                  \n",
            " stem_conv_pad (ZeroPadding2D)  (None, 225, 225, 3)  0           ['tf.math.truediv[0][0]']        \n",
            "                                                                                                  \n",
            " stem_conv (Conv2D)             (None, 112, 112, 32  864         ['stem_conv_pad[0][0]']          \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " stem_bn (BatchNormalization)   (None, 112, 112, 32  128         ['stem_conv[0][0]']              \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " stem_activation (Activation)   (None, 112, 112, 32  0           ['stem_bn[0][0]']                \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " block1a_dwconv (DepthwiseConv2  (None, 112, 112, 32  288        ['stem_activation[0][0]']        \n",
            " D)                             )                                                                 \n",
            "                                                                                                  \n",
            " block1a_bn (BatchNormalization  (None, 112, 112, 32  128        ['block1a_dwconv[0][0]']         \n",
            " )                              )                                                                 \n",
            "                                                                                                  \n",
            " block1a_activation (Activation  (None, 112, 112, 32  0          ['block1a_bn[0][0]']             \n",
            " )                              )                                                                 \n",
            "                                                                                                  \n",
            " block1a_se_squeeze (GlobalAver  (None, 32)          0           ['block1a_activation[0][0]']     \n",
            " agePooling2D)                                                                                    \n",
            "                                                                                                  \n",
            " block1a_se_reshape (Reshape)   (None, 1, 1, 32)     0           ['block1a_se_squeeze[0][0]']     \n",
            "                                                                                                  \n",
            " block1a_se_reduce (Conv2D)     (None, 1, 1, 8)      264         ['block1a_se_reshape[0][0]']     \n",
            "                                                                                                  \n",
            " block1a_se_expand (Conv2D)     (None, 1, 1, 32)     288         ['block1a_se_reduce[0][0]']      \n",
            "                                                                                                  \n",
            " block1a_se_excite (Multiply)   (None, 112, 112, 32  0           ['block1a_activation[0][0]',     \n",
            "                                )                                 'block1a_se_expand[0][0]']      \n",
            "                                                                                                  \n",
            " block1a_project_conv (Conv2D)  (None, 112, 112, 16  512         ['block1a_se_excite[0][0]']      \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " block1a_project_bn (BatchNorma  (None, 112, 112, 16  64         ['block1a_project_conv[0][0]']   \n",
            " lization)                      )                                                                 \n",
            "                                                                                                  \n",
            " block2a_expand_conv (Conv2D)   (None, 112, 112, 96  1536        ['block1a_project_bn[0][0]']     \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " block2a_expand_bn (BatchNormal  (None, 112, 112, 96  384        ['block2a_expand_conv[0][0]']    \n",
            " ization)                       )                                                                 \n",
            "                                                                                                  \n",
            " block2a_expand_activation (Act  (None, 112, 112, 96  0          ['block2a_expand_bn[0][0]']      \n",
            " ivation)                       )                                                                 \n",
            "                                                                                                  \n",
            " block2a_dwconv_pad (ZeroPaddin  (None, 113, 113, 96  0          ['block2a_expand_activation[0][0]\n",
            " g2D)                           )                                ']                               \n",
            "                                                                                                  \n",
            " block2a_dwconv (DepthwiseConv2  (None, 56, 56, 96)  864         ['block2a_dwconv_pad[0][0]']     \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " block2a_bn (BatchNormalization  (None, 56, 56, 96)  384         ['block2a_dwconv[0][0]']         \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block2a_activation (Activation  (None, 56, 56, 96)  0           ['block2a_bn[0][0]']             \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block2a_se_squeeze (GlobalAver  (None, 96)          0           ['block2a_activation[0][0]']     \n",
            " agePooling2D)                                                                                    \n",
            "                                                                                                  \n",
            " block2a_se_reshape (Reshape)   (None, 1, 1, 96)     0           ['block2a_se_squeeze[0][0]']     \n",
            "                                                                                                  \n",
            " block2a_se_reduce (Conv2D)     (None, 1, 1, 4)      388         ['block2a_se_reshape[0][0]']     \n",
            "                                                                                                  \n",
            " block2a_se_expand (Conv2D)     (None, 1, 1, 96)     480         ['block2a_se_reduce[0][0]']      \n",
            "                                                                                                  \n",
            " block2a_se_excite (Multiply)   (None, 56, 56, 96)   0           ['block2a_activation[0][0]',     \n",
            "                                                                  'block2a_se_expand[0][0]']      \n",
            "                                                                                                  \n",
            " block2a_project_conv (Conv2D)  (None, 56, 56, 24)   2304        ['block2a_se_excite[0][0]']      \n",
            "                                                                                                  \n",
            " block2a_project_bn (BatchNorma  (None, 56, 56, 24)  96          ['block2a_project_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block2b_expand_conv (Conv2D)   (None, 56, 56, 144)  3456        ['block2a_project_bn[0][0]']     \n",
            "                                                                                                  \n",
            " block2b_expand_bn (BatchNormal  (None, 56, 56, 144)  576        ['block2b_expand_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " block2b_expand_activation (Act  (None, 56, 56, 144)  0          ['block2b_expand_bn[0][0]']      \n",
            " ivation)                                                                                         \n",
            "                                                                                                  \n",
            " block2b_dwconv (DepthwiseConv2  (None, 56, 56, 144)  1296       ['block2b_expand_activation[0][0]\n",
            " D)                                                              ']                               \n",
            "                                                                                                  \n",
            " block2b_bn (BatchNormalization  (None, 56, 56, 144)  576        ['block2b_dwconv[0][0]']         \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block2b_activation (Activation  (None, 56, 56, 144)  0          ['block2b_bn[0][0]']             \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block2b_se_squeeze (GlobalAver  (None, 144)         0           ['block2b_activation[0][0]']     \n",
            " agePooling2D)                                                                                    \n",
            "                                                                                                  \n",
            " block2b_se_reshape (Reshape)   (None, 1, 1, 144)    0           ['block2b_se_squeeze[0][0]']     \n",
            "                                                                                                  \n",
            " block2b_se_reduce (Conv2D)     (None, 1, 1, 6)      870         ['block2b_se_reshape[0][0]']     \n",
            "                                                                                                  \n",
            " block2b_se_expand (Conv2D)     (None, 1, 1, 144)    1008        ['block2b_se_reduce[0][0]']      \n",
            "                                                                                                  \n",
            " block2b_se_excite (Multiply)   (None, 56, 56, 144)  0           ['block2b_activation[0][0]',     \n",
            "                                                                  'block2b_se_expand[0][0]']      \n",
            "                                                                                                  \n",
            " block2b_project_conv (Conv2D)  (None, 56, 56, 24)   3456        ['block2b_se_excite[0][0]']      \n",
            "                                                                                                  \n",
            " block2b_project_bn (BatchNorma  (None, 56, 56, 24)  96          ['block2b_project_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block2b_drop (Dropout)         (None, 56, 56, 24)   0           ['block2b_project_bn[0][0]']     \n",
            "                                                                                                  \n",
            " block2b_add (Add)              (None, 56, 56, 24)   0           ['block2b_drop[0][0]',           \n",
            "                                                                  'block2a_project_bn[0][0]']     \n",
            "                                                                                                  \n",
            " block3a_expand_conv (Conv2D)   (None, 56, 56, 144)  3456        ['block2b_add[0][0]']            \n",
            "                                                                                                  \n",
            " block3a_expand_bn (BatchNormal  (None, 56, 56, 144)  576        ['block3a_expand_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " block3a_expand_activation (Act  (None, 56, 56, 144)  0          ['block3a_expand_bn[0][0]']      \n",
            " ivation)                                                                                         \n",
            "                                                                                                  \n",
            " block3a_dwconv_pad (ZeroPaddin  (None, 59, 59, 144)  0          ['block3a_expand_activation[0][0]\n",
            " g2D)                                                            ']                               \n",
            "                                                                                                  \n",
            " block3a_dwconv (DepthwiseConv2  (None, 28, 28, 144)  3600       ['block3a_dwconv_pad[0][0]']     \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " block3a_bn (BatchNormalization  (None, 28, 28, 144)  576        ['block3a_dwconv[0][0]']         \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block3a_activation (Activation  (None, 28, 28, 144)  0          ['block3a_bn[0][0]']             \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block3a_se_squeeze (GlobalAver  (None, 144)         0           ['block3a_activation[0][0]']     \n",
            " agePooling2D)                                                                                    \n",
            "                                                                                                  \n",
            " block3a_se_reshape (Reshape)   (None, 1, 1, 144)    0           ['block3a_se_squeeze[0][0]']     \n",
            "                                                                                                  \n",
            " block3a_se_reduce (Conv2D)     (None, 1, 1, 6)      870         ['block3a_se_reshape[0][0]']     \n",
            "                                                                                                  \n",
            " block3a_se_expand (Conv2D)     (None, 1, 1, 144)    1008        ['block3a_se_reduce[0][0]']      \n",
            "                                                                                                  \n",
            " block3a_se_excite (Multiply)   (None, 28, 28, 144)  0           ['block3a_activation[0][0]',     \n",
            "                                                                  'block3a_se_expand[0][0]']      \n",
            "                                                                                                  \n",
            " block3a_project_conv (Conv2D)  (None, 28, 28, 40)   5760        ['block3a_se_excite[0][0]']      \n",
            "                                                                                                  \n",
            " block3a_project_bn (BatchNorma  (None, 28, 28, 40)  160         ['block3a_project_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block3b_expand_conv (Conv2D)   (None, 28, 28, 240)  9600        ['block3a_project_bn[0][0]']     \n",
            "                                                                                                  \n",
            " block3b_expand_bn (BatchNormal  (None, 28, 28, 240)  960        ['block3b_expand_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " block3b_expand_activation (Act  (None, 28, 28, 240)  0          ['block3b_expand_bn[0][0]']      \n",
            " ivation)                                                                                         \n",
            "                                                                                                  \n",
            " block3b_dwconv (DepthwiseConv2  (None, 28, 28, 240)  6000       ['block3b_expand_activation[0][0]\n",
            " D)                                                              ']                               \n",
            "                                                                                                  \n",
            " block3b_bn (BatchNormalization  (None, 28, 28, 240)  960        ['block3b_dwconv[0][0]']         \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block3b_activation (Activation  (None, 28, 28, 240)  0          ['block3b_bn[0][0]']             \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block3b_se_squeeze (GlobalAver  (None, 240)         0           ['block3b_activation[0][0]']     \n",
            " agePooling2D)                                                                                    \n",
            "                                                                                                  \n",
            " block3b_se_reshape (Reshape)   (None, 1, 1, 240)    0           ['block3b_se_squeeze[0][0]']     \n",
            "                                                                                                  \n",
            " block3b_se_reduce (Conv2D)     (None, 1, 1, 10)     2410        ['block3b_se_reshape[0][0]']     \n",
            "                                                                                                  \n",
            " block3b_se_expand (Conv2D)     (None, 1, 1, 240)    2640        ['block3b_se_reduce[0][0]']      \n",
            "                                                                                                  \n",
            " block3b_se_excite (Multiply)   (None, 28, 28, 240)  0           ['block3b_activation[0][0]',     \n",
            "                                                                  'block3b_se_expand[0][0]']      \n",
            "                                                                                                  \n",
            " block3b_project_conv (Conv2D)  (None, 28, 28, 40)   9600        ['block3b_se_excite[0][0]']      \n",
            "                                                                                                  \n",
            " block3b_project_bn (BatchNorma  (None, 28, 28, 40)  160         ['block3b_project_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block3b_drop (Dropout)         (None, 28, 28, 40)   0           ['block3b_project_bn[0][0]']     \n",
            "                                                                                                  \n",
            " block3b_add (Add)              (None, 28, 28, 40)   0           ['block3b_drop[0][0]',           \n",
            "                                                                  'block3a_project_bn[0][0]']     \n",
            "                                                                                                  \n",
            " block4a_expand_conv (Conv2D)   (None, 28, 28, 240)  9600        ['block3b_add[0][0]']            \n",
            "                                                                                                  \n",
            " block4a_expand_bn (BatchNormal  (None, 28, 28, 240)  960        ['block4a_expand_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " block4a_expand_activation (Act  (None, 28, 28, 240)  0          ['block4a_expand_bn[0][0]']      \n",
            " ivation)                                                                                         \n",
            "                                                                                                  \n",
            " block4a_dwconv_pad (ZeroPaddin  (None, 29, 29, 240)  0          ['block4a_expand_activation[0][0]\n",
            " g2D)                                                            ']                               \n",
            "                                                                                                  \n",
            " block4a_dwconv (DepthwiseConv2  (None, 14, 14, 240)  2160       ['block4a_dwconv_pad[0][0]']     \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " block4a_bn (BatchNormalization  (None, 14, 14, 240)  960        ['block4a_dwconv[0][0]']         \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block4a_activation (Activation  (None, 14, 14, 240)  0          ['block4a_bn[0][0]']             \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block4a_se_squeeze (GlobalAver  (None, 240)         0           ['block4a_activation[0][0]']     \n",
            " agePooling2D)                                                                                    \n",
            "                                                                                                  \n",
            " block4a_se_reshape (Reshape)   (None, 1, 1, 240)    0           ['block4a_se_squeeze[0][0]']     \n",
            "                                                                                                  \n",
            " block4a_se_reduce (Conv2D)     (None, 1, 1, 10)     2410        ['block4a_se_reshape[0][0]']     \n",
            "                                                                                                  \n",
            " block4a_se_expand (Conv2D)     (None, 1, 1, 240)    2640        ['block4a_se_reduce[0][0]']      \n",
            "                                                                                                  \n",
            " block4a_se_excite (Multiply)   (None, 14, 14, 240)  0           ['block4a_activation[0][0]',     \n",
            "                                                                  'block4a_se_expand[0][0]']      \n",
            "                                                                                                  \n",
            " block4a_project_conv (Conv2D)  (None, 14, 14, 80)   19200       ['block4a_se_excite[0][0]']      \n",
            "                                                                                                  \n",
            " block4a_project_bn (BatchNorma  (None, 14, 14, 80)  320         ['block4a_project_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block4b_expand_conv (Conv2D)   (None, 14, 14, 480)  38400       ['block4a_project_bn[0][0]']     \n",
            "                                                                                                  \n",
            " block4b_expand_bn (BatchNormal  (None, 14, 14, 480)  1920       ['block4b_expand_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " block4b_expand_activation (Act  (None, 14, 14, 480)  0          ['block4b_expand_bn[0][0]']      \n",
            " ivation)                                                                                         \n",
            "                                                                                                  \n",
            " block4b_dwconv (DepthwiseConv2  (None, 14, 14, 480)  4320       ['block4b_expand_activation[0][0]\n",
            " D)                                                              ']                               \n",
            "                                                                                                  \n",
            " block4b_bn (BatchNormalization  (None, 14, 14, 480)  1920       ['block4b_dwconv[0][0]']         \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block4b_activation (Activation  (None, 14, 14, 480)  0          ['block4b_bn[0][0]']             \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block4b_se_squeeze (GlobalAver  (None, 480)         0           ['block4b_activation[0][0]']     \n",
            " agePooling2D)                                                                                    \n",
            "                                                                                                  \n",
            " block4b_se_reshape (Reshape)   (None, 1, 1, 480)    0           ['block4b_se_squeeze[0][0]']     \n",
            "                                                                                                  \n",
            " block4b_se_reduce (Conv2D)     (None, 1, 1, 20)     9620        ['block4b_se_reshape[0][0]']     \n",
            "                                                                                                  \n",
            " block4b_se_expand (Conv2D)     (None, 1, 1, 480)    10080       ['block4b_se_reduce[0][0]']      \n",
            "                                                                                                  \n",
            " block4b_se_excite (Multiply)   (None, 14, 14, 480)  0           ['block4b_activation[0][0]',     \n",
            "                                                                  'block4b_se_expand[0][0]']      \n",
            "                                                                                                  \n",
            " block4b_project_conv (Conv2D)  (None, 14, 14, 80)   38400       ['block4b_se_excite[0][0]']      \n",
            "                                                                                                  \n",
            " block4b_project_bn (BatchNorma  (None, 14, 14, 80)  320         ['block4b_project_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block4b_drop (Dropout)         (None, 14, 14, 80)   0           ['block4b_project_bn[0][0]']     \n",
            "                                                                                                  \n",
            " block4b_add (Add)              (None, 14, 14, 80)   0           ['block4b_drop[0][0]',           \n",
            "                                                                  'block4a_project_bn[0][0]']     \n",
            "                                                                                                  \n",
            " block4c_expand_conv (Conv2D)   (None, 14, 14, 480)  38400       ['block4b_add[0][0]']            \n",
            "                                                                                                  \n",
            " block4c_expand_bn (BatchNormal  (None, 14, 14, 480)  1920       ['block4c_expand_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " block4c_expand_activation (Act  (None, 14, 14, 480)  0          ['block4c_expand_bn[0][0]']      \n",
            " ivation)                                                                                         \n",
            "                                                                                                  \n",
            " block4c_dwconv (DepthwiseConv2  (None, 14, 14, 480)  4320       ['block4c_expand_activation[0][0]\n",
            " D)                                                              ']                               \n",
            "                                                                                                  \n",
            " block4c_bn (BatchNormalization  (None, 14, 14, 480)  1920       ['block4c_dwconv[0][0]']         \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block4c_activation (Activation  (None, 14, 14, 480)  0          ['block4c_bn[0][0]']             \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block4c_se_squeeze (GlobalAver  (None, 480)         0           ['block4c_activation[0][0]']     \n",
            " agePooling2D)                                                                                    \n",
            "                                                                                                  \n",
            " block4c_se_reshape (Reshape)   (None, 1, 1, 480)    0           ['block4c_se_squeeze[0][0]']     \n",
            "                                                                                                  \n",
            " block4c_se_reduce (Conv2D)     (None, 1, 1, 20)     9620        ['block4c_se_reshape[0][0]']     \n",
            "                                                                                                  \n",
            " block4c_se_expand (Conv2D)     (None, 1, 1, 480)    10080       ['block4c_se_reduce[0][0]']      \n",
            "                                                                                                  \n",
            " block4c_se_excite (Multiply)   (None, 14, 14, 480)  0           ['block4c_activation[0][0]',     \n",
            "                                                                  'block4c_se_expand[0][0]']      \n",
            "                                                                                                  \n",
            " block4c_project_conv (Conv2D)  (None, 14, 14, 80)   38400       ['block4c_se_excite[0][0]']      \n",
            "                                                                                                  \n",
            " block4c_project_bn (BatchNorma  (None, 14, 14, 80)  320         ['block4c_project_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block4c_drop (Dropout)         (None, 14, 14, 80)   0           ['block4c_project_bn[0][0]']     \n",
            "                                                                                                  \n",
            " block4c_add (Add)              (None, 14, 14, 80)   0           ['block4c_drop[0][0]',           \n",
            "                                                                  'block4b_add[0][0]']            \n",
            "                                                                                                  \n",
            " block5a_expand_conv (Conv2D)   (None, 14, 14, 480)  38400       ['block4c_add[0][0]']            \n",
            "                                                                                                  \n",
            " block5a_expand_bn (BatchNormal  (None, 14, 14, 480)  1920       ['block5a_expand_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " block5a_expand_activation (Act  (None, 14, 14, 480)  0          ['block5a_expand_bn[0][0]']      \n",
            " ivation)                                                                                         \n",
            "                                                                                                  \n",
            " block5a_dwconv (DepthwiseConv2  (None, 14, 14, 480)  12000      ['block5a_expand_activation[0][0]\n",
            " D)                                                              ']                               \n",
            "                                                                                                  \n",
            " block5a_bn (BatchNormalization  (None, 14, 14, 480)  1920       ['block5a_dwconv[0][0]']         \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block5a_activation (Activation  (None, 14, 14, 480)  0          ['block5a_bn[0][0]']             \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block5a_se_squeeze (GlobalAver  (None, 480)         0           ['block5a_activation[0][0]']     \n",
            " agePooling2D)                                                                                    \n",
            "                                                                                                  \n",
            " block5a_se_reshape (Reshape)   (None, 1, 1, 480)    0           ['block5a_se_squeeze[0][0]']     \n",
            "                                                                                                  \n",
            " block5a_se_reduce (Conv2D)     (None, 1, 1, 20)     9620        ['block5a_se_reshape[0][0]']     \n",
            "                                                                                                  \n",
            " block5a_se_expand (Conv2D)     (None, 1, 1, 480)    10080       ['block5a_se_reduce[0][0]']      \n",
            "                                                                                                  \n",
            " block5a_se_excite (Multiply)   (None, 14, 14, 480)  0           ['block5a_activation[0][0]',     \n",
            "                                                                  'block5a_se_expand[0][0]']      \n",
            "                                                                                                  \n",
            " block5a_project_conv (Conv2D)  (None, 14, 14, 112)  53760       ['block5a_se_excite[0][0]']      \n",
            "                                                                                                  \n",
            " block5a_project_bn (BatchNorma  (None, 14, 14, 112)  448        ['block5a_project_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block5b_expand_conv (Conv2D)   (None, 14, 14, 672)  75264       ['block5a_project_bn[0][0]']     \n",
            "                                                                                                  \n",
            " block5b_expand_bn (BatchNormal  (None, 14, 14, 672)  2688       ['block5b_expand_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " block5b_expand_activation (Act  (None, 14, 14, 672)  0          ['block5b_expand_bn[0][0]']      \n",
            " ivation)                                                                                         \n",
            "                                                                                                  \n",
            " block5b_dwconv (DepthwiseConv2  (None, 14, 14, 672)  16800      ['block5b_expand_activation[0][0]\n",
            " D)                                                              ']                               \n",
            "                                                                                                  \n",
            " block5b_bn (BatchNormalization  (None, 14, 14, 672)  2688       ['block5b_dwconv[0][0]']         \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block5b_activation (Activation  (None, 14, 14, 672)  0          ['block5b_bn[0][0]']             \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block5b_se_squeeze (GlobalAver  (None, 672)         0           ['block5b_activation[0][0]']     \n",
            " agePooling2D)                                                                                    \n",
            "                                                                                                  \n",
            " block5b_se_reshape (Reshape)   (None, 1, 1, 672)    0           ['block5b_se_squeeze[0][0]']     \n",
            "                                                                                                  \n",
            " block5b_se_reduce (Conv2D)     (None, 1, 1, 28)     18844       ['block5b_se_reshape[0][0]']     \n",
            "                                                                                                  \n",
            " block5b_se_expand (Conv2D)     (None, 1, 1, 672)    19488       ['block5b_se_reduce[0][0]']      \n",
            "                                                                                                  \n",
            " block5b_se_excite (Multiply)   (None, 14, 14, 672)  0           ['block5b_activation[0][0]',     \n",
            "                                                                  'block5b_se_expand[0][0]']      \n",
            "                                                                                                  \n",
            " block5b_project_conv (Conv2D)  (None, 14, 14, 112)  75264       ['block5b_se_excite[0][0]']      \n",
            "                                                                                                  \n",
            " block5b_project_bn (BatchNorma  (None, 14, 14, 112)  448        ['block5b_project_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block5b_drop (Dropout)         (None, 14, 14, 112)  0           ['block5b_project_bn[0][0]']     \n",
            "                                                                                                  \n",
            " block5b_add (Add)              (None, 14, 14, 112)  0           ['block5b_drop[0][0]',           \n",
            "                                                                  'block5a_project_bn[0][0]']     \n",
            "                                                                                                  \n",
            " block5c_expand_conv (Conv2D)   (None, 14, 14, 672)  75264       ['block5b_add[0][0]']            \n",
            "                                                                                                  \n",
            " block5c_expand_bn (BatchNormal  (None, 14, 14, 672)  2688       ['block5c_expand_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " block5c_expand_activation (Act  (None, 14, 14, 672)  0          ['block5c_expand_bn[0][0]']      \n",
            " ivation)                                                                                         \n",
            "                                                                                                  \n",
            " block5c_dwconv (DepthwiseConv2  (None, 14, 14, 672)  16800      ['block5c_expand_activation[0][0]\n",
            " D)                                                              ']                               \n",
            "                                                                                                  \n",
            " block5c_bn (BatchNormalization  (None, 14, 14, 672)  2688       ['block5c_dwconv[0][0]']         \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block5c_activation (Activation  (None, 14, 14, 672)  0          ['block5c_bn[0][0]']             \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block5c_se_squeeze (GlobalAver  (None, 672)         0           ['block5c_activation[0][0]']     \n",
            " agePooling2D)                                                                                    \n",
            "                                                                                                  \n",
            " block5c_se_reshape (Reshape)   (None, 1, 1, 672)    0           ['block5c_se_squeeze[0][0]']     \n",
            "                                                                                                  \n",
            " block5c_se_reduce (Conv2D)     (None, 1, 1, 28)     18844       ['block5c_se_reshape[0][0]']     \n",
            "                                                                                                  \n",
            " block5c_se_expand (Conv2D)     (None, 1, 1, 672)    19488       ['block5c_se_reduce[0][0]']      \n",
            "                                                                                                  \n",
            " block5c_se_excite (Multiply)   (None, 14, 14, 672)  0           ['block5c_activation[0][0]',     \n",
            "                                                                  'block5c_se_expand[0][0]']      \n",
            "                                                                                                  \n",
            " block5c_project_conv (Conv2D)  (None, 14, 14, 112)  75264       ['block5c_se_excite[0][0]']      \n",
            "                                                                                                  \n",
            " block5c_project_bn (BatchNorma  (None, 14, 14, 112)  448        ['block5c_project_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block5c_drop (Dropout)         (None, 14, 14, 112)  0           ['block5c_project_bn[0][0]']     \n",
            "                                                                                                  \n",
            " block5c_add (Add)              (None, 14, 14, 112)  0           ['block5c_drop[0][0]',           \n",
            "                                                                  'block5b_add[0][0]']            \n",
            "                                                                                                  \n",
            " block6a_expand_conv (Conv2D)   (None, 14, 14, 672)  75264       ['block5c_add[0][0]']            \n",
            "                                                                                                  \n",
            " block6a_expand_bn (BatchNormal  (None, 14, 14, 672)  2688       ['block6a_expand_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " block6a_expand_activation (Act  (None, 14, 14, 672)  0          ['block6a_expand_bn[0][0]']      \n",
            " ivation)                                                                                         \n",
            "                                                                                                  \n",
            " block6a_dwconv_pad (ZeroPaddin  (None, 17, 17, 672)  0          ['block6a_expand_activation[0][0]\n",
            " g2D)                                                            ']                               \n",
            "                                                                                                  \n",
            " block6a_dwconv (DepthwiseConv2  (None, 7, 7, 672)   16800       ['block6a_dwconv_pad[0][0]']     \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " block6a_bn (BatchNormalization  (None, 7, 7, 672)   2688        ['block6a_dwconv[0][0]']         \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block6a_activation (Activation  (None, 7, 7, 672)   0           ['block6a_bn[0][0]']             \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block6a_se_squeeze (GlobalAver  (None, 672)         0           ['block6a_activation[0][0]']     \n",
            " agePooling2D)                                                                                    \n",
            "                                                                                                  \n",
            " block6a_se_reshape (Reshape)   (None, 1, 1, 672)    0           ['block6a_se_squeeze[0][0]']     \n",
            "                                                                                                  \n",
            " block6a_se_reduce (Conv2D)     (None, 1, 1, 28)     18844       ['block6a_se_reshape[0][0]']     \n",
            "                                                                                                  \n",
            " block6a_se_expand (Conv2D)     (None, 1, 1, 672)    19488       ['block6a_se_reduce[0][0]']      \n",
            "                                                                                                  \n",
            " block6a_se_excite (Multiply)   (None, 7, 7, 672)    0           ['block6a_activation[0][0]',     \n",
            "                                                                  'block6a_se_expand[0][0]']      \n",
            "                                                                                                  \n",
            " block6a_project_conv (Conv2D)  (None, 7, 7, 192)    129024      ['block6a_se_excite[0][0]']      \n",
            "                                                                                                  \n",
            " block6a_project_bn (BatchNorma  (None, 7, 7, 192)   768         ['block6a_project_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block6b_expand_conv (Conv2D)   (None, 7, 7, 1152)   221184      ['block6a_project_bn[0][0]']     \n",
            "                                                                                                  \n",
            " block6b_expand_bn (BatchNormal  (None, 7, 7, 1152)  4608        ['block6b_expand_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " block6b_expand_activation (Act  (None, 7, 7, 1152)  0           ['block6b_expand_bn[0][0]']      \n",
            " ivation)                                                                                         \n",
            "                                                                                                  \n",
            " block6b_dwconv (DepthwiseConv2  (None, 7, 7, 1152)  28800       ['block6b_expand_activation[0][0]\n",
            " D)                                                              ']                               \n",
            "                                                                                                  \n",
            " block6b_bn (BatchNormalization  (None, 7, 7, 1152)  4608        ['block6b_dwconv[0][0]']         \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block6b_activation (Activation  (None, 7, 7, 1152)  0           ['block6b_bn[0][0]']             \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block6b_se_squeeze (GlobalAver  (None, 1152)        0           ['block6b_activation[0][0]']     \n",
            " agePooling2D)                                                                                    \n",
            "                                                                                                  \n",
            " block6b_se_reshape (Reshape)   (None, 1, 1, 1152)   0           ['block6b_se_squeeze[0][0]']     \n",
            "                                                                                                  \n",
            " block6b_se_reduce (Conv2D)     (None, 1, 1, 48)     55344       ['block6b_se_reshape[0][0]']     \n",
            "                                                                                                  \n",
            " block6b_se_expand (Conv2D)     (None, 1, 1, 1152)   56448       ['block6b_se_reduce[0][0]']      \n",
            "                                                                                                  \n",
            " block6b_se_excite (Multiply)   (None, 7, 7, 1152)   0           ['block6b_activation[0][0]',     \n",
            "                                                                  'block6b_se_expand[0][0]']      \n",
            "                                                                                                  \n",
            " block6b_project_conv (Conv2D)  (None, 7, 7, 192)    221184      ['block6b_se_excite[0][0]']      \n",
            "                                                                                                  \n",
            " block6b_project_bn (BatchNorma  (None, 7, 7, 192)   768         ['block6b_project_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block6b_drop (Dropout)         (None, 7, 7, 192)    0           ['block6b_project_bn[0][0]']     \n",
            "                                                                                                  \n",
            " block6b_add (Add)              (None, 7, 7, 192)    0           ['block6b_drop[0][0]',           \n",
            "                                                                  'block6a_project_bn[0][0]']     \n",
            "                                                                                                  \n",
            " block6c_expand_conv (Conv2D)   (None, 7, 7, 1152)   221184      ['block6b_add[0][0]']            \n",
            "                                                                                                  \n",
            " block6c_expand_bn (BatchNormal  (None, 7, 7, 1152)  4608        ['block6c_expand_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " block6c_expand_activation (Act  (None, 7, 7, 1152)  0           ['block6c_expand_bn[0][0]']      \n",
            " ivation)                                                                                         \n",
            "                                                                                                  \n",
            " block6c_dwconv (DepthwiseConv2  (None, 7, 7, 1152)  28800       ['block6c_expand_activation[0][0]\n",
            " D)                                                              ']                               \n",
            "                                                                                                  \n",
            " block6c_bn (BatchNormalization  (None, 7, 7, 1152)  4608        ['block6c_dwconv[0][0]']         \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block6c_activation (Activation  (None, 7, 7, 1152)  0           ['block6c_bn[0][0]']             \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block6c_se_squeeze (GlobalAver  (None, 1152)        0           ['block6c_activation[0][0]']     \n",
            " agePooling2D)                                                                                    \n",
            "                                                                                                  \n",
            " block6c_se_reshape (Reshape)   (None, 1, 1, 1152)   0           ['block6c_se_squeeze[0][0]']     \n",
            "                                                                                                  \n",
            " block6c_se_reduce (Conv2D)     (None, 1, 1, 48)     55344       ['block6c_se_reshape[0][0]']     \n",
            "                                                                                                  \n",
            " block6c_se_expand (Conv2D)     (None, 1, 1, 1152)   56448       ['block6c_se_reduce[0][0]']      \n",
            "                                                                                                  \n",
            " block6c_se_excite (Multiply)   (None, 7, 7, 1152)   0           ['block6c_activation[0][0]',     \n",
            "                                                                  'block6c_se_expand[0][0]']      \n",
            "                                                                                                  \n",
            " block6c_project_conv (Conv2D)  (None, 7, 7, 192)    221184      ['block6c_se_excite[0][0]']      \n",
            "                                                                                                  \n",
            " block6c_project_bn (BatchNorma  (None, 7, 7, 192)   768         ['block6c_project_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block6c_drop (Dropout)         (None, 7, 7, 192)    0           ['block6c_project_bn[0][0]']     \n",
            "                                                                                                  \n",
            " block6c_add (Add)              (None, 7, 7, 192)    0           ['block6c_drop[0][0]',           \n",
            "                                                                  'block6b_add[0][0]']            \n",
            "                                                                                                  \n",
            " block6d_expand_conv (Conv2D)   (None, 7, 7, 1152)   221184      ['block6c_add[0][0]']            \n",
            "                                                                                                  \n",
            " block6d_expand_bn (BatchNormal  (None, 7, 7, 1152)  4608        ['block6d_expand_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " block6d_expand_activation (Act  (None, 7, 7, 1152)  0           ['block6d_expand_bn[0][0]']      \n",
            " ivation)                                                                                         \n",
            "                                                                                                  \n",
            " block6d_dwconv (DepthwiseConv2  (None, 7, 7, 1152)  28800       ['block6d_expand_activation[0][0]\n",
            " D)                                                              ']                               \n",
            "                                                                                                  \n",
            " block6d_bn (BatchNormalization  (None, 7, 7, 1152)  4608        ['block6d_dwconv[0][0]']         \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block6d_activation (Activation  (None, 7, 7, 1152)  0           ['block6d_bn[0][0]']             \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block6d_se_squeeze (GlobalAver  (None, 1152)        0           ['block6d_activation[0][0]']     \n",
            " agePooling2D)                                                                                    \n",
            "                                                                                                  \n",
            " block6d_se_reshape (Reshape)   (None, 1, 1, 1152)   0           ['block6d_se_squeeze[0][0]']     \n",
            "                                                                                                  \n",
            " block6d_se_reduce (Conv2D)     (None, 1, 1, 48)     55344       ['block6d_se_reshape[0][0]']     \n",
            "                                                                                                  \n",
            " block6d_se_expand (Conv2D)     (None, 1, 1, 1152)   56448       ['block6d_se_reduce[0][0]']      \n",
            "                                                                                                  \n",
            " block6d_se_excite (Multiply)   (None, 7, 7, 1152)   0           ['block6d_activation[0][0]',     \n",
            "                                                                  'block6d_se_expand[0][0]']      \n",
            "                                                                                                  \n",
            " block6d_project_conv (Conv2D)  (None, 7, 7, 192)    221184      ['block6d_se_excite[0][0]']      \n",
            "                                                                                                  \n",
            " block6d_project_bn (BatchNorma  (None, 7, 7, 192)   768         ['block6d_project_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block6d_drop (Dropout)         (None, 7, 7, 192)    0           ['block6d_project_bn[0][0]']     \n",
            "                                                                                                  \n",
            " block6d_add (Add)              (None, 7, 7, 192)    0           ['block6d_drop[0][0]',           \n",
            "                                                                  'block6c_add[0][0]']            \n",
            "                                                                                                  \n",
            " block7a_expand_conv (Conv2D)   (None, 7, 7, 1152)   221184      ['block6d_add[0][0]']            \n",
            "                                                                                                  \n",
            " block7a_expand_bn (BatchNormal  (None, 7, 7, 1152)  4608        ['block7a_expand_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " block7a_expand_activation (Act  (None, 7, 7, 1152)  0           ['block7a_expand_bn[0][0]']      \n",
            " ivation)                                                                                         \n",
            "                                                                                                  \n",
            " block7a_dwconv (DepthwiseConv2  (None, 7, 7, 1152)  10368       ['block7a_expand_activation[0][0]\n",
            " D)                                                              ']                               \n",
            "                                                                                                  \n",
            " block7a_bn (BatchNormalization  (None, 7, 7, 1152)  4608        ['block7a_dwconv[0][0]']         \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block7a_activation (Activation  (None, 7, 7, 1152)  0           ['block7a_bn[0][0]']             \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block7a_se_squeeze (GlobalAver  (None, 1152)        0           ['block7a_activation[0][0]']     \n",
            " agePooling2D)                                                                                    \n",
            "                                                                                                  \n",
            " block7a_se_reshape (Reshape)   (None, 1, 1, 1152)   0           ['block7a_se_squeeze[0][0]']     \n",
            "                                                                                                  \n",
            " block7a_se_reduce (Conv2D)     (None, 1, 1, 48)     55344       ['block7a_se_reshape[0][0]']     \n",
            "                                                                                                  \n",
            " block7a_se_expand (Conv2D)     (None, 1, 1, 1152)   56448       ['block7a_se_reduce[0][0]']      \n",
            "                                                                                                  \n",
            " block7a_se_excite (Multiply)   (None, 7, 7, 1152)   0           ['block7a_activation[0][0]',     \n",
            "                                                                  'block7a_se_expand[0][0]']      \n",
            "                                                                                                  \n",
            " block7a_project_conv (Conv2D)  (None, 7, 7, 320)    368640      ['block7a_se_excite[0][0]']      \n",
            "                                                                                                  \n",
            " block7a_project_bn (BatchNorma  (None, 7, 7, 320)   1280        ['block7a_project_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " top_conv (Conv2D)              (None, 7, 7, 1280)   409600      ['block7a_project_bn[0][0]']     \n",
            "                                                                                                  \n",
            " top_bn (BatchNormalization)    (None, 7, 7, 1280)   5120        ['top_conv[0][0]']               \n",
            "                                                                                                  \n",
            " top_activation (Activation)    (None, 7, 7, 1280)   0           ['top_bn[0][0]']                 \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 4,049,571\n",
            "Trainable params: 4,007,548\n",
            "Non-trainable params: 42,023\n",
            "__________________________________________________________________________________________________\n",
            "None\n"
          ]
        }
      ],
      "source": [
        "# Print summary of the pretrained model\n",
        "print(base_model.summary())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DHU8NlQ8e0Fh"
      },
      "outputs": [],
      "source": [
        "# Freezing Layers\n",
        "#for layer in base_model.layers[:-76]:\n",
        "for layer in base_model.layers[:-104]:\n",
        "    layer.trainable=False"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WuwhBMCIe0Fh"
      },
      "outputs": [],
      "source": [
        "# Building model to add to base_model\n",
        "fine_model=Sequential()\n",
        "\n",
        "\n",
        "fine_model.add(base_model)\n",
        "fine_model.add(Dropout(0.5))\n",
        "fine_model.add(Flatten())\n",
        "fine_model.add(BatchNormalization())\n",
        "fine_model.add(Dense(256, kernel_initializer='he_uniform'))\n",
        "fine_model.add(BatchNormalization())\n",
        "fine_model.add(Activation('relu'))\n",
        "fine_model.add(Dropout(0.5))\n",
        "fine_model.add(Dense(64, kernel_initializer='he_uniform'))\n",
        "fine_model.add(BatchNormalization())\n",
        "fine_model.add(Activation('relu'))\n",
        "fine_model.add(Dropout(0.5))\n",
        "fine_model.add(Dense(32, kernel_initializer='he_uniform'))\n",
        "fine_model.add(BatchNormalization())\n",
        "fine_model.add(Activation('relu'))\n",
        "fine_model.add(Dense(NUM_CLASSES,activation='softmax'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-9RvM1Yie0Fh"
      },
      "outputs": [],
      "source": [
        "# Defining metrics for training\n",
        "METRICS = [tf.keras.metrics.CategoricalAccuracy(name='categorical_accuracy')]\n",
        "\n",
        "# Defining callbacks for early stopping\n",
        "EARLY_STOPPING = EarlyStopping(verbose=1, patience=PATIENCE, restore_best_weights=True, monitor=\"val_categorical_accuracy\")\n",
        "\n",
        "# Define learningrate for optimizer\n",
        "OPTIMIZER = tf.keras.optimizers.Adam(learning_rate=LEARNING_RATE) "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HBI9xwvfe0Fj"
      },
      "outputs": [],
      "source": [
        "# Compile the model \n",
        "fine_model.compile(optimizer=OPTIMIZER, loss='categorical_crossentropy',metrics=METRICS)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NdQw7-Ure0Fj",
        "outputId": "d1132cfb-b045-4700-f6e5-204a5a3b4344"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " efficientnetb0 (Functional)  (None, 7, 7, 1280)       4049571   \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 7, 7, 1280)        0         \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 62720)             0         \n",
            "                                                                 \n",
            " batch_normalization (BatchN  (None, 62720)            250880    \n",
            " ormalization)                                                   \n",
            "                                                                 \n",
            " dense (Dense)               (None, 256)               16056576  \n",
            "                                                                 \n",
            " batch_normalization_1 (Batc  (None, 256)              1024      \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " activation (Activation)     (None, 256)               0         \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 256)               0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 64)                16448     \n",
            "                                                                 \n",
            " batch_normalization_2 (Batc  (None, 64)               256       \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " activation_1 (Activation)   (None, 64)                0         \n",
            "                                                                 \n",
            " dropout_2 (Dropout)         (None, 64)                0         \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 32)                2080      \n",
            "                                                                 \n",
            " batch_normalization_3 (Batc  (None, 32)               128       \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " activation_2 (Activation)   (None, 32)                0         \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 4)                 132       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 20,377,095\n",
            "Trainable params: 19,699,000\n",
            "Non-trainable params: 678,095\n",
            "_________________________________________________________________\n",
            "None\n"
          ]
        }
      ],
      "source": [
        "## Print summary of the model\n",
        "print(fine_model.summary())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "XbIWIIZF0lMf",
        "outputId": "5a9efb6a-5b2a-4004-c89f-f23bb143f1f0"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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LFy6Ehoay2B/QpzIyMmJjY5lvq9JCx2OGoBg/8sef9qXe3vdRgh55d44HgiCOHTsGn3oGmut4zMBlPgAAsACCKQAAsACCKQAAsACCKQAAsACCKQAAsIDTMYn+hBEA6J05HhYsWNDx5cEAaK6TYKrdJ2XA2+fGjRuJiYnvwvGwYMGC4OBgDw8PfXcE9Bsd/+vtJJjCbDtAS0xMfBeOhwULFnh4eLwLIwVs6RhM4Z4pAACwAIIpAACwAIIpAACwAIIpAACwAIIpAACwwHCD6W+//TZ8+HD8bUIbG5uYmBidNX3q1CkXFxf8slFbW9tFixbprGlgsJYtW0a/glblkLh06VJYWBjzsFm8eDGzgI+Pj0gkMjY2HjFiRKffLNKB6OhoNzc3sVjM4/FkMtn69euZ77RWn8vU3Nzs6uq6ceNGA283Li7O1dWVz+cLhUJXV9fIyEj6M+xnzpyJi4tjvpU8IyOD3rlWVlYaNqGK+T4+A3yf6bRp0xBC1dXVum9aKpWamZnpvl3DYYDHQx9Bmn22xNLSMisrq6ioiPlpr6ioqFmzZikUCrwolUoHDBiAEMrMzGSunpWVxfwGlO55eXmlpKRUVlYqFIpjx45xudyPPvpIw1wm/E065reYDLPdmTNn7ty5s6ysrK6u7vjx41wu99///d/p3MTERC8vLzqwtLe3l5SUXLt2bcaMGW/tN6B0FkwbGxs9PDyYKRBMdXA8dNzseqlKw2Cq8g0oiqK2bds2dOjQpqYmOkUqlR46dMjIyMjOzq6mpoZO13swnTlzJvN1yHhSLf40Ybe5tF9//dXHx6enQU0v7fr5+TH3y/z58xFCL168oFPkcrmHh0draytzLfgGFAvS0tLKysr03Yt3DoubXfd78MGDB5GRkZs3byZJkpnu6ekZHBxcWlq6bt06XfZHvczMTObnpvHFbGNjoya5WFNTU0hISGJiYr9oNz09nblf7OzsEELMewibNm3Ky8vrabVq9KdgmpqaKhQKBQLB6dOnp0+fLhaLBw8efOTIEZyblJREkuTAgQOXLVsmkUhIkvT09Lx58ybOlcvlJiYm+CvECKEVK1YIhUKCICoqKhBCwcHBa9euffjwIUEQMplMw/788ssvbm5uZmZmJEmOGjXqwoULCKHPP/8c33mRSqW5ubkIoaVLlwoEAjMzszNnziCElEplVFSUg4MDn88fPXo0PvvbsWOHQCAQiURlZWVr1661s7MrKipic9v1JYqiEhIShg8fzuPxLCws5syZc+/ePZzVo83O7h48f/68WCzeunVr3w08KSmJoihfX9+OWTExMUOHDt23b9+lS5c6XVfNRlN/nKMuDqGeKi0t5fP5zs7OmudGRESsWLHC2tpai+b03m5xcbG5ubmjoyOdYmFh4eXllZiYSLH1LQnmaarhX+ZHREQghC5fvlxbW1tWVjZ58mShUNjS0oJzg4KChELh3bt3m5ubCwsL3d3dRSIRfckQGBhoY2ND1xwfH48QKi8vx4v+/v5SqZTZdLeX+SdOnNi0aVNVVVVlZeXEiRPpqwN/f39jY+PS0lK65MKFC8+cOYP/vW7dOh6Pd/Lkyerq6vDwcCMjo1u3btFDW7VqVXJy8ty5c//44w9ttxlrNDweoqKiTExMDhw4UFNTk5+fP378eCsrq1evXuHcHm12FvdgZmamSCSKjo7WZKRIq8t8FxcXNzc3lWJSqfTx48cURV2/ft3IyMjJyam+vp7qcJmvfqOpP867OoQ019DQIBKJ5HK55rk5OTm+vr5UZ9+vN+R2W1paSkpKkpOTeTxex49yh4WFIYRyc3PplN5c5vfLYErfCklJSUEIPXjwAC8GBQUxw9+tW7cQQps3b8aLrAdTptjYWIRQWVkZRVH4ZCQmJgZn1dbWDhkyBN8YampqEggEAQEBOKuxsZHH43311Vcdh2YINDkeGhsbTU1N6RFRFPXf//3fCCE6ivU0mLK4BzWnRTCtr68nCGLWrFkqxehgSlHU2rVrEUIrV66k/m8w7XajqTnO1RxCmouIiBg6dCj90Kzb3MbGxgkTJpSUlFC9C6a6b9fGxgYhNGDAgP/8z/+k/zei/fDDDwihv/3tb3TKu3vPFH/bsrW1tdPcCRMmCAQC+uqpT+EPxuLJFh9++OHQoUN/+OEHvMWPHj0aEBCAbwwVFRU1NjaOHDkSr8Xn821tbXXTwz5SWFhYX18/YcIEOsXd3d3ExIS+PO8NXe7BnsL/car/znBMTMywYcNSUlJycnKY6T3daMzjvPeHUHp6+vHjxy9cuCASiTTMDQ8P//LLL/FtR63ppd3nz5+XlZUdPnz4xx9/HDdunMpddbz7Xr9+rXX9TP07mHaLx+Ph/9D6wtmzZ6dMmWJtbc3j8davX0+nEwSxbNmyR48eXb58GSH0t7/97bPPPsNZDQ0NCKGNGzfSk9qePn2qcru9f6mpqUEImZqaMhPNzc3r6upYqb9P92BvNDc3I4R4PJ6aMiRJ7t+/nyCITz/9tKmpiU7vzUbr5SF09OjR7du3Z2dnOzk5aZibk5NTUFDw+eefa9iEQbXL5XKtra19fHyOHj1aWFiIryBpfD4f/WtX9t7bHExbW1tramoGDx7MYp3Xrl3btWsXQujZs2d+fn62trY3b96sra2Ni4tjFluyZAlJkvv27SsqKhKLxfRtb3wTfdeuXcyrgxs3brDYQx0zNzdHCKlEAbY2e1/sQbbgv0PmxO9OeXh4rFmzpri4eMuWLXRibzZabw6h5OTkgwcPXrlyZdCgQZrnpqWlXb58Gf98hiAI3IGtW7cSBPH7778bcrtMMpnM2Ni4sLCQmdjS0oL+tSt7720OptnZ2RRFTZw4ES9yOJyubgho7n/+53+EQiFCqKCgoLW19auvvnJxcSFJUuV19BYWFgsWLMjIyNi5c+cXX3xBp9vb25MkmZeX18tuGI6RI0eampoyD+6bN2+2tLS89957eLE3m70v9iBbBg4cSBBEbW1ttyW3bNni6uqKp3Zg3W40NbQ7hCiKCg0NLSgoyMjIUDkj7jZ3//79zMDNvHfJvFNhUO1WVlYuXLiQmVJcXKxUKu3t7ZmJePfh+6q997YF0/b29urq6ra2tvz8/ODgYAcHhyVLluAsmUxWVVWVkZHR2tpaXl7+9OlT5oqWlpYvXrx48uRJXV1dp3+xra2tr1+/zs7OxsHUwcEBIXTp0qXm5ubi4uKOd7uWL1/+5s2bzMzMWbNm0YkkSS5duvTIkSOpqakKhUKpVJaUlLx8+ZLVbaBTJEmuXbs2PT394MGDCoWioKBg+fLlEokkKCgIF+jpZmdrD2ZlZfXp1CiBQODi4lJSUtJtSXyxz5xN2e1GU19bV4dQQECAjY1Npz9XvXv37o4dO/bu3cvlcgmGnTt3dpvbLQNsVygUXrx48cqVKwqForW1NTc395NPPhEKhfhnVDS8+0aNGqVJc91jxn6Depr/22+/jRgxwsjICCFka2u7devWlJQUfMN4yJAhDx8+3LNnj1gsRgg5Ojrev3+foqigoCAul2tnZ8fhcMRi8Zw5cx4+fEhXWFlZOXXqVJIknZ2dv/7665CQEISQTCbDM29u377t6OjI5/M/+OCDv/71r1KptKstlp6ejisMDQ21tLQ0NzefP3/+7t27EUJSqZT5441x48aFhYWpjOvNmzehoaEODg4cDsfa2trf37+wsDAuLg5fa9jb23ecwKEvGh4P7e3t8fHxQ4YM4XK5FhYWfn5+RUVFdK7mm/3Vq1ds7cFXr16dO3dOJBLRcyrUQ1pNjZLL5Vwut7GxES+mp6fjw8bKygo/wWcKCQlhTo1Ss9G6Pc47PYQoivLz80MIRUVFdex8QUFBpwdzfHx8t7kqOj5VN8B2KYry9fV1dnY2NTXl8XhSqTQgIKCgoEClzMyZM+3s7Nrb2+mUt3lqVI/gX0/ruxf/a8aMGY8ePdJ3L7Sn++NBX3tQu2BaXFzM4XAM5z8/pVI5efLktLQ0aFcTFRUVJEnu3LmTmfjuTo3qqNsHAn2NvkWQn5+Pz6H0259+R+97UI2mpqYLFy4UFxfjBxcymSw6Ojo6OrqrFx3pklKpzMjIqKurCwgIgHY1sWnTprFjx8rlcoQQRVEvXrzIycl58OCB1l1624Kp3oWGhhYXF9+/f3/p0qXMB7jgLVBVVfXRRx8NHTr0008/xSlhYWHz588PCAjQ5ElUn8rOzj516lRWVpb6qa/QLpaQkJCXl3fu3Dk8Q/z06dN2dnaTJ08+e/as9n1inqb268v8sLAwPLfZycnpxIkT+upGRESEkZGRvb09/fvR/kvHx4Me9yDS4DJfjQsXLoSGhrLYH9CnMjIyYmNjmW+r0kLHY4agGD/yP378+IIFCyi2fvYP+rl353ggCOLYsWPwqWeguY7HDFzmAwAACyCYAgAACyCYAgAACyCYAgAACzgdk44fP677fgADhF+f8Y4cD/36dTPAIDAf7Wv3/QMAAHgHqZsaBYABwrNP3pETZNB/wT1TAABgAQRTAABgAQRTAABgAQRTAABgAQRTAABgAQRTAABgAQRTAABgAQRTAABgAQRTAABgAQRTAABgAQRTAABgAQRTAABgAQRTAABgAQRTAABgAQRTAABgAQRTAABgAQRTAABgAQRTAABgAQRTAABgAQRTAABgAQRTAABgAQRTAABgAQRTAABgAQRTAABgAQRTAABgAQRTAABgAQRTAABgAQRTAABgAQRTAABgAQRTAABgAQRTAABgAQRTAABgAQRTAABgAUFRlL77AMD/cejQobS0tPb2drz4+PFjhJCzszNeNDIy+uyzzwIDA/XWPwA6A8EUGJz8/PwxY8aoKfDPf/5z9OjROusPAJqAYAoMkaura1FRUadZMpmsuLhYx/0BoFtwzxQYosWLF3O53I7pXC536dKluu8PAN2CM1NgiB49eiSTyTo9OIuLi2Uyme67BIB6cGYKDJGLi8v48eMJgmAmEgQxYcIEiKTAMEEwBQbqP/7jP4yNjZkpxsbG//Ef/6Gv/gCgHlzmAwNVVlYmkUjoCVIIISMjoxcvXtjY2OixVwB0Bc5MgYEaOHCgl5cXfXJqbGw8ZcoUiKTAYEEwBYZr8eLFzCunxYsX67EzAKgHl/nAcCkUCmtr65aWFoQQl8stKyszNzfXd6cA6BycmQLDJRaLP/roIw6Hw+FwZsyYAZEUGDIIpsCgLVq0SKlUKpVK+DE+MHBwmQ8MWnNzs5WVFUVRFRUVfD5f390BoGuUrsybN0/fYwUAvFvmzZunsxDH0eXAJk6cuHr1al22CLAFCxYEBwd7eHjouyPayMvLIwhC/XuksF27diGE4BgDGD4edEanwXTw4MEff/yxLlsE2IIFCzw8PPrpxp87dy5CiMPp/lg9ceIEQqifDhOwDh8POqPTYAqAFjQJowDoHTzNBwAAFkAwBQAAFkAwBQAAFkAwBQAAFvSnYPrmzZtVq1bZ2toKBILz5893TDl37pyZmdlPP/2kvh4Ni+mYu7u7sbHx2LFj1ZTJycmZNGmSQCCQSCShoaFv3rzp0y4Z5oZi0aVLl8LCwk6dOuXi4kIQBEEQKu9S8fHxEYlExsbGI0aMuH37tl46GR0d7ebmJhaLeTyeTCZbv359fX29hrlMzc3Nrq6uGzduNPB24+LiXF1d+Xy+UCh0dXWNjIxUKBQ468yZM3FxcUqlUsOqdKw/BdNvv/32/Pnz9+7dS0xMxHtOJYXS7NdcGhbTsVu3bk2dOlVNgcLCQh8fH29v7/Ly8vT09B9++GH58uV92iXD3FBs+eabb5KSksLDw/39/R89eiSVSgcMGHDw4MGzZ8/SZS5evHjixIlZs2YVFhaOHz9eL/28cuXKypUrnzx5UlFRERsbm5iYOH/+fA1zmSIiIrr6RqFBtfvLL7988cUXz549e/369ZYtW+Li4ujf+/j6+pIk6e3tXVNTo3mFuqOznwfMmzevl79GcHd3X7hwofoUnWlsbPTw8GC3Em9v7zExJq0AACAASURBVLFjx3ZVeMGCBc7Ozu3t7XgxPj6eIIg//vhDk4YQQseOHetlb/sOKxuT6skxtm3btqFDhzY1NdEpUqn00KFDRkZGdnZ2NTU1dHpWVtbs2bN73zetzZw5s62tjV7Es2ifPXumSS7t119/9fHxQQhFREQYeLt+fn7M/YJj9IsXL+gUuVzu4eHR2trabVW9jzk90p/OTEtKSlS+WNkxRWfS0tLKyspYr6Sr4bS1tZ09e9bLy4v+LNL06dMpijp9+nQv+2AIWNmYmnvw4EFkZOTmzZtJkmSme3p6BgcHl5aWrlu3Tmed6VZmZibz8y1WVlYIocbGRk1ysaamppCQkMTExH7Rbnp6OnO/2NnZIYSY9xA2bdqUl5fX02p1wOCCqVKpjIqKcnBw4PP5o0ePPnbsGELoH//4h0wme/ny5Y8//kgQhKmpaceUnJwcBwcHgiB2795N13bgwIEJEyaQJCkUCp2cnLZs2dKxWKctpqamCoVCgUBw+vTp6dOni8XiwYMHHzlyBK8SHBy8du3ahw8fEgQhk8nUF+6qCZVKcMkHDx64uroKhUI+nz958uScnByc/ujRo/r6egcHB7pOqVSKEMrPz++jHaGyodSPMSkpiSTJgQMHLlu2TCKRkCTp6el58+ZNnCuXy01MTGxtbfHiihUrhEIhQRAVFRWdbofz58+LxeKtW7f20dCSkpIoivL19e2YFRMTM3To0H379l26dKnTdSmKSkhIGD58OI/Hs7CwmDNnzr1793CWdodBT5WWlvL5fGdnZ81zIyIiVqxYYW1trUVzem+3uLjY3Nzc0dGRTrGwsPDy8kpMTKQM7TaUzs6BNTzlXrduHY/HO3nyZHV1dXh4uJGR0a1bt3CWjY3NJ598wiyskvL8+XOEUHJyMl7Ev8zdtm1bZWVlVVXV999/HxgY2LFYVy1GREQghC5fvlxbW1tWVjZ58mShUNjS0oLX8vf3l0qldNPqC3fVhEol3t7eLi4ujx8/bm1tvXPnzvvvv0+S5P379ymKunr1KkIoPj6eOXw+n+/t7d3tJqW0vcxX2VDqxxgUFCQUCu/evdvc3FxYWOju7i4SiejrvsDAQBsbG7rm+Ph4hFB5eTleVNkOmZmZIpEoOjq6px3W8BhzcXFxc3NTSZRKpY8fP6Yo6vr160ZGRk5OTvguvMplflRUlImJyYEDB2pqavLz88ePH29lZfXq1Sucq91hoLmGhgaRSCSXyzXPzcnJ8fX1pSiqvLwc9eRyW7/ttrS0lJSUJCcn83i8AwcOqOSGhYUhhHJzc9VX8k5f5jc3N6empvr5+fn7+5ubm2/cuJHL5e7fv1+LqlpbWzdv3jx16tQNGzZYWlpaWFh89tln7u7uPW3R09NTLBZbW1sHBAQ0NDQ8e/ZMTaOdFu7RoEQikZOTE4fDGTFixN69e5ubm/fs2YMQwg/uVb7WyeVym5qatNg4vaFmg3A4HHzK5ubmlpqaWldXp92+mzlzpkKhiIyMZK/X/6uhoeHx48f4vL5THh4eq1evfvLkyYYNG1SympqaEhIS5s6du2jRIjMzs1GjRn333XcVFRV4H9F6fxh0JTY2ViKRxMTEaJjb1NQUHBycmprao1YMoV17e/vBgwdv2rRpx44dCxYsUMkdMmQIQqigoEDr+vuCYQXToqKixsbGkSNH4kU+n29ra0tfRvVIfn5+TU3NtGnT6BRjY+NVq1Zp3aKJiQlCqLW1VZPWmYW1HtSoUaPMzMzwhTy+i9TW1sYs0NLSosdXfKrfIBMmTBAIBNrtuz5VVlZGUZRAIFBTJiYmZtiwYSkpKfRtFqywsLC+vn7ChAl0iru7u4mJCX1DQwUrhwEtPT39+PHjFy5cEIlEGuaGh4d/+eWX+Laj1vTS7vPnz8vKyg4fPvzjjz+OGzdO5ZY63n2vX7/Wuv6+YFjBtKGhASG0ceNG4l+ePn2qcldbQ3huWrcfumCxxb5ogsvl4j9FfMORnnCHEGpsbGxubpZIJCx2lV08Hg9f4hmU5uZmhBCPx1NThiTJ/fv3EwTx6aefMs/98YwcU1NTZmFzc/O6urpu2+3lkXb06NHt27dnZ2c7OTlpmJuTk1NQUPD5559r2IRBtcvlcq2trX18fI4ePVpYWBgbG8vMxecQeFcaDsMKpvhe9a5du5h3Im7cuKFFVYMGDUII4UccummR9Sba2tqqqqrwQydnZ2eRSPT06VM698GDBwih0aNHs9hVFrW2ttbU1AwePFjfHVGF/w67nfjt4eGxZs2a4uLiLVu20In4/2aV0KnhMHtzpCUnJx88ePDKlSv4qNYwNy0t7fLly0ZGRjh24w5s3bqVIIjff//dkNtlkslkxsbGhYWFzET8jUVD+/KCYQVTe3t7kiTz8vJ6X5WTk5OlpeXFixd11iLrTfz888/t7e14rjj+oty1a9fa29txblZWFkEQnT6SNgTZ2dkURU2cOBEvcjgcDe+Q9LWBAwcSBFFbW9ttyS1btri6uubm5tIpI0eONDU1ZUaEmzdvtrS0vPfee93Wpt1hQFFUaGhoQUFBRkaGyhlxt7n79+9nBm7mgyDmnQqDareysnLhwoXMlOLiYqVSaW9vz0zEu8/GxkZ9bTpmWMGUJMmlS5ceOXIkNTVVoVAolcqSkpKXL19qURWPxwsPD7927ZpcLi8tLW1vb6+rq7t79y5bLVpaWr548eLJkyd1dXXqw4SaJjpW0tLSUltb29bWdvv2bblc7ujouGTJElxPZGTk69evv/nmm4aGhhs3bsTHxy9ZsmTYsGFabJw+0t7eXl1d3dbWlp+fHxwc7ODgQHdeJpNVVVVlZGS0traWl5czT7FRh+2QlZXVd1OjBAKBi4tLSUlJtyXxxT7zoR9JkmvXrk1PTz948KBCoSgoKFi+fLlEIgkKCtKktq4Og4CAABsbm05/rnr37t0dO3bs3buXy+USDDt37uw2t1sG2K5QKLx48eKVK1cUCkVra2tubu4nn3wiFArXrFnDLIZ336hRozRpTnd6MxWgRzScpvDmzZvQ0FAHBwcOh2Ntbe3v719YWPjkyZNx48YhhDgczvjx40+ePNkxJTk5Gd9YFAgEeE4GRVG7d+8eNWoUSZIkSY4bNy4lJaVjsU5bTElJwTe5hwwZ8vDhwz179ojFYoSQo6Mjnqt0+/ZtR0dHPp//wQcfREVFqS/caRMqlbx69Wr//v1Tp04dOHAgh8MZMGDAX/7yl6dPnzI3ztWrV//0pz/xeDyJRBISEtLc3Kzhxkc9nxqlsqG63SBBQUFcLtfOzo7D4YjF4jlz5jx8+JCurbKycurUqSRJOjs7f/311yEhIQghmUyG506pbIdz586JRKKYmJgedZjS+BiTy+VcLrexsREvpqen44f7VlZWK1euVCkcEhLCnBrV3t4eHx8/ZMgQLpdrYWHh5+dXVFSEs7rdRF0dBn5+fgihqKiojl3t6oE1niSnPldFxylKBtguRVG+vr7Ozs6mpqY8Hk8qlQYEBBQUFKiUmTlzpp2dHf1rwK7oeGqUwQVT0Be0CKY9FRQUZGlp2adNdEvDY6y4uJjD4XScvagvSqVy8uTJaWlp0K4mKioqSJLcuXNntyXf6XmmoF8z2Nf5qJDJZNHR0dHR0V296EiXlEplRkZGXV1dQEAAtKuJTZs2jR07Vi6Xs9ux3oNgCt5FYWFh8+fPDwgI0ORJVJ/Kzs4+depUVlaW+qmv0C6WkJCQl5d37tw5fb2UQw0IpoAF4eHh+/fvr62tdXZ2PnnypL67o5GtW7fK5fJt27bptxve3t6HDh2iX1wA7apx+vTpN2/eZGdnW1hYsN6x3oPvPgIWxMbGqkyr7hd8fHzwC+JAvzB79uzZs2fruxddgjNTAABgAQRTAABgAQRTAABgAQRTAABggU4fQJWUlBw/flyXLQIau29vMUz4V4ZwjAGspKREp6/a0dnPA+hPDAIAgG7o8hdQOj0znTdv3okTJ3TZIsAIgjh27Bj+hORbDH/JEo4xgHX19ek+AvdMAQCABRBMAQCABRBMAQCABRBMAQCABRBMAQCABRBMAQCABf0smJ46dcrFxYXoTKefou0j586dMzMz++mnn/qo/p07d+Lvvn333Xd91ARQcenSpbCwMOYBtnjxYmYBHx8fkUhkbGw8YsSITr9fpANTpkzpeOR3/KQd6+ti7e3tu3bt8vT0VEmPjo52c3MTi8U8Hk8mk61fv17lrduHDx92d3cXiUSOjo5Lly599eoVMzcnJ2fSpEkCgUAikYSGhr558wannzlzJi4urr+8cRz1u2Dq7+//6NEjqVRqZmaGJ8q2tbU1Nja+fv1al++4pSiqT+tft27d9evX+7QJwPTNN98kJSWFh4fTB9iAAQMOHjx49uxZuszFixdPnDgxa9aswsJC/MlYA/HBBx/oYN3i4uJ/+7d/W7NmTWNjo0rWlStXVq5c+eTJk4qKitjY2MTEROYEz2PHjgUGBs6fP7+kpOT06dPXrl2bPn16W1sbzi0sLPTx8fH29i4vL09PT//hhx+WL1+Os3x9fUmS9Pb2rqmp0XqAutTPgmlHxsbGfD5/4MCBQ4cO7btWmpqamP8hz5w5s7a2dtasWX3XYv+isn0MpCoNbd++/ejRo8ePHxeJRHRiUlKSkZFRUFCQ3l/Fz0SSpEKhYP7qJigoaP369X297j//+c8NGzYsX7587NixHXNNTU3xF8BEItHHH3/s5+d3/vz558+f49zvv/9+0KBBISEhZmZmY8eOXbNmTV5e3s2bN3Huli1bbG1tN2/eLBQKPTw8QkND/+u//uvevXs4d9WqVWPGjJkxYwYdfA1Zvw+mtIyMjL6rPC0traysrO/q7+9Y3D463tQPHjyIjIzcvHkzSZLMdE9Pz+Dg4NLS0nXr1umsM906f/48M+I/f/78zp07H374YV+vO2bMmFOnTgUGBvJ4vI65mZmZzA9iW1lZIYToE9jnz59LJBKCIPCivb09Qgh/67utre3s2bNeXl507vTp0ymKOn36NF3bpk2b8vLyEhMTNemnfr09wRSTy+UmJib0RxFWrFghFAoJgqioqEAIpaamCoVCgUBw+vTp6dOni8XiwYMHHzlyhFnDgQMHJkyYQJKkUCh0cnLasmVLcHDw2rVrHz58SBCETCbLyclxcHAgCGL37t14FYqiEhIShg8fzuPxLCws5syZQ//X2m2Lv/zyi5ubm5mZGUmSo0aNunDhgi42U2fUjEL9VlXZPklJSSRJDhw4cNmyZRKJhCRJT09P+kykR1UhhM6fPy8Wi7du3dpHo05KSqIoytfXt2NWTEzM0KFD9+3bd+nSpU7X7c1+VyqVUVFRDg4OfD5/9OjRx44d06Lz27dvX7VqlRYr9nJd9UpLS/l8vrOzM150cXFh/u+Ib5i6uLgghB49elRfX+/g4EDn4m9u5+fn0ykWFhZeXl6JiYl9fW+NBTr4/T/G4mdXmfdMKYq6fPky84PdgYGBNjY29GJ8fDxCqLy8HC9GREQghC5fvlxbW1tWVjZ58mShUNjS0oJzd+3ahRDatm1bZWVlVVXV999/HxgYSFGUv7+/VCql68SXMMnJyXgxKirKxMTkwIEDNTU1+fn548ePt7KyevXqlSYtnjhxYtOmTVVVVZWVlRMnThwwYABOLy4uRgj99a9/ZWWLIQ0+9ax+FOq3qsr2CQoKEgqFd+/ebW5uLiwsxA8fnj17pkVVmZmZIpEoOjpak2FqcYy5uLi4ubmpJEql0sePH1MUdf36dSMjIycnp/r6eoqisrKyZs+eTRfrzX5ft24dj8c7efJkdXV1eHi4kZHRrVu3etTzkpISNzc3pVLZo7V6ue77778/ZswYNQUaGhpEIpFcLqdTsrOzuVxuUlKSQqG4c+fO8OHDp02bhrOuXr2KEGL+8VIUxefzvb29mSlhYWEIodzc3J72Fj71rJHa2lr6iaS3t3dPV/f09BSLxdbW1gEBAQ0NDc+ePUMItba2bt68eerUqRs2bLC0tLSwsPjss8/c3d3VV9XU1JSQkDB37txFixaZmZmNGjXqu+++q6io2LNnT7ctIoTmzZv3zTffWFhYWFpa+vr6VlZWlpeX93Q4vafhKDTH4XDwKZubm1tqampdXd3+/fu1qGfmzJkKhSIyMlK7bqjX0NDw+PFjfDbUKQ8Pj9WrVz958mTDhg0qWb3Z783NzampqX5+fv7+/ubm5hs3buRyuT3dPtu3b//666+NjLT5E+7NuurFxsZKJJKYmBg6xcvLKzQ0VC6Xi8XikSNH1tXV7du3D2fhB/fMWwQIIS6X29TUxEwZMmQIQqigoID13rKrvwZT5pnpzz//rHU9JiYmCKHW1laEUH5+fk1NzbRp0+hcY2Pjbi+FCgsL6+vrJ0yYQKe4u7ubmJjQF7ZqWlSBv16rl7kgPR1Fj0yYMEEgENCXwIajrKyMoij180BiYmKGDRuWkpKSk5PDTO/Nfi8qKmpsbBw5ciTO4vP5tra2Pdo+L168OHPmzJIlSzRfhZV11UtPTz9+/PiFCxeYN2cjIiL27Nlz+fLl+vr6R48eeXp6enh44Gs7fJ9a5eFSS0sLn89npuAd9Pr1a9Y7zK7+GkyZpkyZwspTAoVCgRAyNzfv0Vp43obKfD1zc/O6ujpNVj979uyUKVOsra15PJ6Gj1b7Qi9H0S0ej6eXM271mpubEUKdPlShkSS5f/9+giA+/fRT5hlTb7ZYQ0MDQmjjxo301dXTp087TjlSIy4u7osvvlB5aKaDddU4evTo9u3bs7OzmTO+X758GRcX9+WXX3744YdCodDZ2Xnv3r0vXrzA93bwrXP8d4c1NjY2NzdLJBJmzTi24p1lyN6GYMqWQYMGIYTwkxDN4eCr8idUU1OjySu+nz175ufnZ2tre/Pmzdra2ri4uB41zaLejKJbra2tbFXFLvxX2u2lgIeHx5o1a4qLi7ds2UIn9maLWVtbI4R27drFvOOm+acQXr16dfjw4a+++krD8mytq0ZycvLBgwevXLmC/4hoxcXFSqWSmSgWiy0tLQsLCxFCzs7OIpEIP9nHHjx4gBAaPXo0s5KWlhb0r51lyN7CYMrhcDq9iO6Wk5OTpaXlxYsXe7TWyJEjTU1Nf//9dzrl5s2bLS0t7733XrfrFhQUtLa2fvXVVy4uLiRJ0hNEdK/bUWi9VRFC2dnZFEVNnDix91WxC//MTJOZpFu2bHF1dc3NzaVTerPf7e3tSZLMy8vTrttxcXGLFi2ytLTU8bqdoigqNDS0oKAgIyOj4++p8H8tL1++pFPq6uqqqqrwBCkOhzNjxoxr1661t7fj3KysLIIgVCZX4B1kY2PDVp/7yFsYTGUyWVVVVUZGRmtra3l5OfP/PfV4PF54ePi1a9fkcnlpaWl7e3tdXd3du3cRQpaWli9evHjy5EldXZ1KICBJcu3atenp6QcPHlQoFAUFBcuXL5dIJEFBQd22iCeFXLp0qbm5ubi4mJUblNrpdhTqt2rH7dPe3l5dXd3W1pafnx8cHOzg4EDfpOtRVVlZWX03NUogELi4uOAvR6mHL/aZj0p6s99Jkly6dOmRI0dSU1MVCoVSqSwpKcERJyAgwMbGRs3PVV+/fv3DDz+sXr26Y1afrtuVu3fv7tixY+/evVwul/lD1Z07dyKEnJ2dp06dunfv3mvXrjU1NT1//hxvn88++wyvHhkZ+fr162+++aahoeHGjRvx8fFLliwZNmwYswm8g0aNGtXTvumazuYNsDJN4ddff6V/6WRra6syhQKrrKycOnUqSZLOzs5ff/11SEgIQkgmkz179iwlJQXfzB4yZMjDhw/37NkjFosRQo6Ojvfv38er7969e9SoUSRJkiQ5bty4lJQUiqJu377t6OjI5/M/+OCDjRs34ns9AoHA19eXoqj29vb4+PghQ4ZwuVwLCws/P7+ioiJcW7cthoaGWlpampubz58/H09clUqlwcHB+P9hoVA4d+7cXm40SrOpUWpGoX6rqmyfV69eBQUFcblcOzs7DocjFovnzJnz8OFD7ao6d+6cSCSKiYnRZJhaHGNyuZzL5TY2NuLF9PR0/HDfyspq5cqVKoVDQkKYU6N6s9/fvHkTGhrq4ODA4XCsra39/f0LCwspivLz80MIRUVFddXhNWvWLFq0qNOsvlv3xo0bkyZNom9l2traenp6Xr16laKorh6y0xOeKioqgoODZTIZj8czNTWdNGnS3//+d2blV69e/dOf/sTj8SQSSUhISHNzs0rrM2fOtLOza29v72pcXdHx1Kh+FkyBdjQJpizCPy7UWXM0LY6x4uJiDodz4MCBPupSTymVysmTJ6elpfWjdftURUUFSZI7d+7UYl2YZwreBv3lZT8ymSw6Ojo6OlrlRUd6oVQqMzIy6urqAgIC+su6fW3Tpk1jx46Vy+X67kj3IJiCd11YWNj8+fMDAgL0/k6T7OzsU6dOZWVlafEKNH2t26cSEhLy8vLOnTuHp2AbOAimgGXh4eH79++vra11dnY+efKkvrujka1bt8rl8m3btum3G97e3ocOHaJfXNAv1u07p0+ffvPmTXZ2toWFhb77ohGOvjsA3jaxsbGxsbH67kWP+fj4+Pj46LsX4H/Nnj179uzZ+u5FD8CZKQAAsACCKQAAsACCKQAAsACCKQAAsECnD6B+++035pe2gC7t2rXrxIkT+u5F3/rtt98QQnCMAey3336j3wihA7oLph4eHjprC6iYN2+evrugPfx6kXHjxnVbUpd/OcDwTZw4UZdhh6AM/8sq4N328ccfI4SOHz+u744AoA7cMwUAABZAMAUAABZAMAUAABZAMAUAABZAMAUAABZAMAUAABZAMAUAABZAMAUAABZAMAUAABZAMAUAABZAMAUAABZAMAUAABZAMAUAABZAMAUAABZAMAUAABZAMAUAABZAMAUAABZAMAUAABZAMAUAABZAMAUAABZAMAUAABZAMAUAABZAMAUAABZAMAUAABZAMAUAABZAMAUAABZAMAUAABZAMAUAABZAMAUAABZAMAUAABZAMAUAABZAMAUAABZw9N0BAFQ1Nja+efOGXmxpaUEIVVdX0yk8Hk8gEOihZwB0jaAoSt99AOD/SE1NXbFihZoCKSkpX331lc76A4AmIJgCg1NeXi6RSJRKZae5xsbGL1++tLa21nGvAFAP7pkCg2Ntbe3t7W1sbNwxy9jY+M9//jNEUmCAIJgCQ7Ro0aJOr5koilq0aJHu+wNAt+AyHxiiuro6a2tr5mMozMTEpLy8XCwW66VXAKgBZ6bAEIlEolmzZnG5XGYih8OZPXs2RFJgmCCYAgMVGBjY1tbGTFEqlYGBgfrqDwDqwWU+MFAtLS1WVlZ1dXV0iqmpaUVFBY/H02OvAOgKnJkCA2ViYjJ//nwTExO8yOVyFyxYAJEUGCwIpsBwLVy4EP/8CSHU2tq6cOFC/fYHADXgMh8Yrvb2dltb2/LycoSQlZXVq1evOp18CoAhgDNTYLiMjIwWLlxoYmLC5XIDAwMhkgJDBsEUGLS//OUvLS0tcI0PDF+/fGvU8ePH9d0FoCMURQ0YMAAh9Pjx4ydPnui7O0BHPv74Y313ocf65T1TgiD03QUAQB/qj3Gpv17mHzt2jHqHHTt2DCGk717oAkLo22+/LSws1HdHgI7gY7s/6q/BFLw7Bg8e7Obmpu9eANANCKYAAMACCKYAAMACCKYAAMACCKYAAMACCKYAAMCCdyiYfv755yKRiCCIvLw8ffdFP86dO2dmZvbTTz/puyN95dKlS2FhYadOnXJxcSEIgiCIxYsXMwv4+PiIRCJjY+MRI0bcvn1bL52cMmUK0YGpqWlfr4u1t7fv2rXL09NTJT06OtrNzU0sFvN4PJlMtn79+vr6emaBw4cPu7u7i0QiR0fHpUuXvnr1ipmbk5MzadIkgUAgkUhCQ0PpTyScOXMmLi6uq28jvm30PatMG0jbeaZHjhxBCOXm5rLeJR3Tbp5pZmamWCw+c+ZMX3Spj2i+r6OiombNmqVQKPCiVCrFP53KzMxkFsvKypo9ezb7HdWYl5dXxz/DadOm9fW6FEXdv39/0qRJCKExY8Z0rDklJaWyslKhUBw7dozL5X700Ud07tGjRxFCcXFxNTU1ubm5Li4uY8eObW1txbl37tzh8/mRkZH19fXXr1+3srJaunQpvW5iYqKXl1d1dbWGney/c6j7Z6ffumDa2Njo4eGheXkDP+B6Ohw1NNzX27ZtGzp0aFNTE50ilUoPHTpkZGRkZ2dXU1NDp+s9mE6bNo2O+FhQUNDly5f7et28vLy5c+cePHhw7NixHYPpzJkz29ra6EX8a85nz57hxalTpw4aNKi9vR0v7t69GyGUk5ODFxcsWODs7EznxsfHEwTxxx9/0LXJ5XIPDw86+Kpn4Me2Gu/QZT4y4N+hpqWllZWV6bsXrNHxcB48eBAZGbl582aSJJnpnp6ewcHBpaWl69at01lnunX+/HmRSEQvPn/+/M6dOx9++GFfrztmzJhTp04FBgZ2+oLtzMxM5ku5rKysEEKNjY10QxKJhP7zsbe3Rwg9ffoUIdTW1nb27FkvLy86d/r06RRFnT59mq5t06ZNeXl5iYmJmvSz/3rLgylFUfHx8cOGDePxeGZmZiEhIXTWjh07BAKBSCQqKytbu3atnZ1dUVERRVEJCQnDhw/n8XgWFhZz5sy5d+8eLp+UlESS5MCBA5ctWyaRSEiS9PT0vHnzJrOtrtaVy+UmJia2trZ4ccWKFUKhkCCIiooKhFBwcPDatWsfPnxIEIRMJuujTZGTk+Pg4EAQBD6tSE1NFQqFAoHg9OnT06dPF4vFgwcPxmfu3Q62p8M5f/68WCzeunVrHw0tKSmJoihfX9+OWTExMUOHDt23b9+lS5c6XVfNXlO/iRBCSqUyKirKwcGBuR6MzwAAIABJREFUz+ePHj1au99Bbt++fdWqVVqs2Mt11SstLeXz+c7OznjRxcWF+b8jvmHq4uKCEHr06FF9fb2DgwOdK5VKEUL5+fl0ioWFhZeXV2JiItUPf3HfA3o8K9Ya0vgyPyIigiCIb7/9trq6urGxMSUlBTEu8yMiIhBCq1atSk5Onjt37h9//BEVFWViYnLgwIGampr8/Pzx48fjdxLj8kFBQUKh8O7du83NzYWFhfh+PH0ppH7dwMBAGxsbumPx8fEIofLycrzo7+8vlUo13wLaXQo9f/4cIZScnMwc/uXLl2tra8vKyiZPniwUCltaWjQZbI+Gk5mZKRKJoqOje9phSrN97eLi4ubmppIolUofP35MUdT169eNjIycnJzq6+upDpf56vea+k20bt06Ho938uTJ6urq8PBwIyOjW7du9Wh0JSUlbm5uSqWyR2v1ct3333+/42U+U0NDg0gkksvldEp2djaXy01KSlIoFHfu3Bk+fDh9o/bq1asIofj4eGYNfD7f29ubmRIWFoY0u8MGl/mGqKmpadeuXX/+85/XrFljbm7O5/MtLS07Ftu+ffvKlStPnTrl6OiYkJAwd+7cRYsWmZmZjRo16rvvvquoqNizZw9dmMPh4LMYNze31NTUurq6/fv347a6XdcweXp6isVia2vrgICAhoaGZ8+e0VldDbanZs6cqVAoIiMj2ev1/2poaHj8+DE+G+qUh4fH6tWrnzx5smHDBpUsDfdap5uoubk5NTXVz8/P39/f3Nx848aNXC63p9tn+/btX3/9tZGRNn+GvVlXvdjYWIlEEhMTQ6d4eXmFhobK5XKxWDxy5Mi6urp9+/bhLPzgXuW93Vwut6mpiZkyZMgQhFBBQQHrvTUcb3MwffDgQWNjo7e3t4blCwsL6+vrJ0yYQKe4u7ubmJgwr+WZJkyYIBAI8FVhT9c1QPjTda2trZ3mMgdrUMrKyiiKEggEasrExMQMGzYsJSUlJyeHmd7TvcbcREVFRY2NjSNHjsRZfD7f1ta2R9vnxYsXZ86cWbJkiearsLKueunp6cePH79w4QLz5mxERMSePXsuX75cX1//6NEjT09PDw8PfKGD71OrfJS7paWFz+czU/AOev36NesdNhxvczAtKSlBCFlbW2tYvqamBiGkMmvP3Nyc+bVhFTweD3+hSIt1+x16sAalubkZIaT+q6UkSe7fv58giE8//ZR5xtSbvdbQ0IAQ2rhxIz3f8+nTp/QTG03ExcV98cUXKg/NdLCuGkePHt2+fXt2draTkxOd+PLly7i4uC+//PLDDz8UCoXOzs579+598eIFvreDb50rFAq6fGNjY3Nzs0QiYdaMYyveWW+rfvmmfQ3hQ42eP9wtc3NzhJDKH1JNTc3gwYM7Ld/a2krn9nTdfoc5WIOC/0q7nRbu4eGxZs2anTt3btmyhX5a0pu9hv+T3rVrV3BwsBbdfvXq1eHDh4uKinS8rhrJyckXLly4cuWKyv8uxcXFSqVy0KBBdIpYLLa0tCwsLEQIOTs7i0Qi/GQfe/DgAUJo9OjRzErwV2ZVTlffMm/zmenIkSONjIzwDXINy5uamv7+++90ys2bN1taWt57771Oy2dnZ1MUNXHiRE3W5XA4XV1B9wvMwSJDGs7AgQMJgqitre225JYtW1xdXXNzc+mUnu5xJnt7e5Iktf41XVxc3KJFizq9id+n63aKoqjQ0NCCgoKMjIyOv6fC/7W8fPmSTqmrq6uqqsITpDgczowZM65du9be3o5zs7KyCIJQmVyBd5CNjQ1bfTZAb3Mwtba29vf3P3nyZFpamkKhyM/PV/84iCTJtWvXpqenHzx4UKFQFBQULF++XCKRBAUF0WXa29urq6vb2try8/ODg4MdHBzwfatu15XJZFVVVRkZGa2treXl5cz/yRFClpaWL168ePLkSV1dnYEEKdT1YFEPh5OVldV3U6MEAoGLiwu+paMevthnPirRZI+rqW3p0qVHjhxJTU1VKBRKpbKkpARHnICAABsbGzU/V339+vUPP/ywevXqjll9um5X7t69u2PHjr1793K5XOYPVXfu3IkQcnZ2njp16t69e69du9bU1PT8+XO8fT777DO8emRk5OvXr7/55puGhoYbN27Ex8cvWbJk2LBhzCbwDho1alRP+9af6HcygXaQxlOj6urqPv/88wEDBpiamn7wwQdRUVEIocGDB//zn/+Mi4vDFx329vYHDhzA5dvb2+Pj44cMGcLlci0sLPz8/PDkUywoKIjL5drZ2XE4HLFYPGfOnIcPH9K56tetrKycOnUqSZLOzs5ff/01nvEqk8nwZKPbt287Ojry+fwPPviAnpejhhbTR5KTk/HtLYFA4Ovrm5KSgp8JDBky5OHDh3v27BGLxQghR0fH+/fvdzvYHg3n3LlzIpEoJiamRx3GNNnXcrmcy+U2NjbixfT0dPxw38rKauXKlSqFQ0JCmFOj1Oy1bjfRmzdvQkNDHRwcOBwO/p8bf17Fz88PIRQVFdVVh9esWbNo0aJOs/pu3Rs3bkyaNIm+lWlra+vp6Xn16lWKorp6yE5PeKqoqAgODpbJZDwez9TUdNKkSX//+9+ZlV+9evVPf/oTj8eTSCQhISHNzc0qrc+cOdPOzo7+lZQa/XdqVP/stJ6+ARUUFGRpaan7djvSwQFnIIPVZF8XFxdzOBz6f0S9UyqVkydPTktL60fr9qmKigqSJHfu3KlJ4f4bTN/my/y+8K68/wYh1H8GK5PJoqOjo6OjVV50pBdKpTIjI6Ouri4gIKC/rNvXNm3aNHbsWLlcru+O9C0IpuBtEBYWNn/+/ICAAE2eRPWp7OzsU6dOZWVlqZ/6alDr9qmEhIS8vLxz585xuVx996WP6fvUWBtIH5f5YWFheM62k5PTiRMndNy6ir6+FDKcwfZoX1+4cCE0NLRP+wN6JCMjIzY2lvk+qm7138t8guqHrx4gCOLYsWP4LWHvpuPHjy9YsKA/7ruegn39rum/xzZc5gMAAAsgmAIAAAsgmAIAAAsgmAIAAAv66wOoiRMnGuBLN3SmpKTkt99+mzdvnr470udOnjz5ju/rdw0+tvtjXIIzUwAAYEF/PTN9x6fL9N/pIz0F+/pd03+PbTgzBQAAFkAwBQAAFkAwBQAAFkAwBQAAFkAwBQAAFrydwfTUqVMuLi7MDzCYmJgMHDhwypQp8fHx1dXV+u4g6BOXLl0KCwtj7v3FixczC/j4+IhEImNj4xEjRmjxeQ9WTJkyheig45eXWF8Xa29v37Vrl6enp0p6dHS0m5ubWCzm8XgymWz9+vUqL4c9fPiwu7u7SCRydHRcunTpq1evmLk5OTmTJk0SCAQSiSQ0NJT+iuWZM2fi4uL6y4txe0t/L6zSHtLstWxSqdTMzIyiKPwto59//nnJkiUEQUgkklu3bvV9N/tQ/31NWU9puK8pioqKipo1a5ZCocCLUql0wIABCKHMzExmsaysLOZnS3TPy8ur45/htGnT+npdiqLu378/adIkhNCYMWM61pySklJZWalQKI4dO8blcj/66CM69+jRowihuLi4mpqa3NxcFxeXsWPHtra24tw7d+7w+fzIyMj6+vrr169bWVktXbqUXjcxMdHLy6u6ulrDTvbfY7t/drqHwZTpxIkTRkZGAwcOrKmp6Zveaa+xsdHDw0OTkjo44DTvTJ9WpeG+3rZt29ChQ5uamugUqVR66NAhIyMjOzs75r7WezCdNm0aHfGxoKCgy5cv9/W6eXl5c+fOPXjw4NixYzsG05kzZzLfOoon9uIvelEUNXXq1EGDBtFfcNq9ezdCKCcnBy8uWLDA2dmZzo2PjycI4o8//qBrk8vlHh4edPBVr/8G07fzMl+NefPmLVmypKys7LvvvtN3X1SlpaWVlZXpuxf/H4ud6etxPXjwIDIycvPmzSRJMtM9PT2Dg4NLS0vXrVvXd6331Pnz50UiEb34/PnzO3fufPjhh3297pgxY06dOhUYGMjj8TrmZmZmMr/bamVlhRBqbGykG5JIJARB4EX8kWf8Sdq2trazZ896eXnRudOnT6co6vTp03RtmzZtysvLS0xM1KSf/dc7F0wRQvh7xVlZWQihHTt2CAQCkUhUVla2du1aOzs7/HHKhISE4cOH83g8CwuLOXPm3Lt3D6+blJREkuTAgQOXLVsmkUhIkvT09Lx58yZduZp15XK5iYkJ/kQoQmjFihVCoZAgiIqKCoRQcHDw2rVrHz58SBCETCZjZaRsdUb9qHs6rvPnz7P75eekpCSKolQ+1I7FxMQMHTp03759ly5d6ukmSk1NFQqFAoHg9OnT06dPF4vFgwcPPnLkCL2uUqmMiopycHDg8/mjR4/Gp1Q9tX379lWrVmmxYi/XVa+0tJTP5zs7O+NFFxcX5n+H+Iapi4sLQujRo0f19fUODg50Lv40bH5+Pp1iYWHh5eWVmJhI9cPfNfWAHs+KtYZ6cZlPUZRCoUAI2dvb48WIiAiE0KpVq5KTk+fOnfvHH39ERUWZmJgcOHCgpqYmPz9//PjxVlZW9BeYg4KChELh3bt3m5ubCwsL8V15+oJI/bqBgYE2NjZ0T+Lj4xFC5eXleNHf318qlWqyBTS8FGKxM+pH3aOqMjMzRSJRdHS0JiPVZF+7uLi4ubmpJEql0sePH1MUdf36dSMjIycnp/r6eqrDZb76TYSPjcuXL9fW1paVlU2ePFkoFLa0tODcdevW8Xi8kydPVldXh4eHGxkZ9fRefElJiZubm1Kp7NFavVz3/fff73iZz9TQ0CASieRyOZ2SnZ3N5XKTkpIUCsWdO3eGDx9O36i9evUqYnwUGuPz+d7e3syUsLAwhFBubm633ft/7N17XBNX2jjwE8g9JFwEIUW5BrUIllp1BesLli5Wfb0gumDFrnbfLmo14gUpKqiICMUqHxXsapVPKxavFC2IdrWL1lVZ+yoL4qWIIiByFwKESwjz++O8O79pCCHAhAR8vn8xZ+YcnjMzeTKXkxk4zR9KhEIhg8FoamqiFsbFxa1Zs+b8+fP29vb79u1buHBhcHCwqampu7v7119/XVtbe+TIEXJhJpOJj2VcXV2Tk5ObmppSUlIQQq2trb3WHTS0B9NTr/tqzpw5MpksMjKyf2GoaGlpef78OT4aUsvT03P9+vUlJSVffPGFyiwtV5GXl5dIJLKysgoKCmppaSktLUUItbW1JScn+/v7BwQEmJmZbdu2jcVi9XWFxMXFrV271sioPx/DgdTVLDY2ViwWx8TEkCXe3t7h4eFSqVQkErm5uTU1NX3zzTd4Fr5xT71EgBBisVitra3UEhcXF4RQQUEB7dEajjcxmba0tBAEIRKJ1M4tLCxsbm6eNGkSWTJ58mQ2m009l6eaNGkSn8/H54Z9ratTOg2G2mv9qq6uJghC8ys5Y2Jixo4dm5SUdPPmTWp5X1cRfsmgQqFACD158kQul7u5ueFZPB7PxsamTyukoqLi4sWL+KJTXw2krmbp6elnzpy5cuUK9eLs1q1bjxw5cu3atebm5mfPnnl5eXl6epaVlSGE8HXqzs5OaiMdHR08Ho9agjdQVVUV7QEbjjcxmf72228IoXHjxqmd29DQgBBSGbtnZmamciRLxeFwampq+ldXd3QdDNlr/Wpra8PBaFiGy+WmpKQwGIxPP/2UesQ0kFXU0tKCENq2bRs53vPFixfkHRttxMfHf/bZZyo3zQahrganTp2Ki4vLyclxcHAgC1+9ehUfH//Xv/71gw8+EAgEjo6OR48eraiowBdz8LVyfOkMk8vlbW1tYrGY2jLOrXhjDVdMfQegB5cvX0YIzZo1S+1cMzMzhJDKx6mhoaGn5xMrFApybl/r6pROg6H2Wr/wp7TXYeGenp4bNmzYu3fvrl27yLslA1lFVlZWCKH9+/eHhob2I+zKysrvv//+yZMng1xXg4MHD165cuXnn39W+XYpKipSKpVvvfUWWSISiSwsLAoLCxFCjo6OQqEQ39nHnj59ihCaMGECtZGOjg70n401XL1xR6aVlZX79+8fNWrUp59+qnYBNzc3ExOTX3/9lSzJzc3t6Oh477331C6fk5NDEMTUqVO1qctkMvFJ4iDQaTDUXg+wqQEaOXIkg8FobGzsdcldu3aNGzfu/v37ZElftzXV6NGjuVxuXl5e/8KOj48PDg62sLAY5LpqEQQRHh5eUFCQkZHR/fdU+Kvl1atXZElTU1N9fT0eIMVkMmfPnn3jxo2uri48Nzs7m8FgqAyuwBvI2tqarpgN0DBPpgRBNDc34+HENTU1p0+fnjZtmrGxcUZGRk/XTLlc7saNG9PT01NTU2UyWUFBwapVq8RicUhICLkM/klVZ2dnfn5+aGionZ0dvnrVa12JRFJfX5+RkaFQKGpqaqjf5wghCwuLioqKkpKSpqamgecm2oPpqdd9bSo7O5vGoVF8Pt/Jyam8vFybFZKSkkK9VaLNttbQ2ooVK9LS0pKTk2UymVKpLC8vxxknKCjI2tpaw89Vq6qqjh8/vn79+u6zdFq3Jw8fPvzyyy+PHj3KYrGoP1Tdu3cvQsjR0XHGjBlHjx69ceNGa2trWVkZXj9/+ctfcPXIyMiqqqrt27e3tLTcvn07ISFh+fLlY8eOpf4LvIHc3d37GttQoseRBP2Gehsuc/HixQkTJvD5fDabjW93MhgMMzOzKVOmREdH19XVkUvGx8fjU4/Ro0efOHECF3Z1dSUkJLi4uLBYLHNzc39/fzz4FAsJCWGxWLa2tkwmUyQSLViwoLi4mJyruW5dXd2MGTO4XK6jo+PatWvDwsIQQhKJBI8xunfvnr29PY/He//998nROWppOXyExmA097pPTV26dEkoFMbExPQaP6Hd0CipVMpiseRyOZ5MT0/HN/ctLS3XrFmjsnBYWBh1aJSGVZSUlIRvm7i4uBQXFx85cgR/Advb2//2228EQbS3t4eHh9vZ2TGZTCsrq4CAgMLCQoIg/P39EUJRUVE9Bbxhw4bg4GC1s3RX9/bt29OmTSMvZdrY2Hh5eV2/fp0giJ5uspMDnmpra0NDQyUSCYfDMTExmTZt2g8//EBt/Pr161OmTOFwOGKxOCwsrK2tTeW/z5kzx9bWlvyVlAZDd2jU0Axa699r60JISIiFhYW+/js2+DucvnqtzbYuKipiMpnkd6HeKZXK6dOnHzt2bAjV1ana2loul7t3715tFh66yXSYn+bryJvyFJzfM9heSySS6Ojo6OholQcd6YVSqczIyGhqagoKChoqdXVtx44dHh4eUqlU34HoFiRTMBxEREQsXrw4KChImztROpWTk3P+/Pns7GzNQ18Nqq5O7du3Ly8v79KlSywWS9+x6Ji+D437A+nvND8iIgKP3HZwcDh79qxeYiAG/VRIj73u07a+cuVKeHi4TuMBfZKRkREbG0t9HlWvhu5pPrzqeUgauq/D7SvY1m+aobtvw2k+AADQAJIpAADQAJIpAADQAJIpAADQAJIpAADQYKjezdd3CAAAHRqKeWlIPoKvfy/bAUPU/v37EUJqH+0BgOEYkkem4I2CB5meOXNG34EAoAlcMwUAABpAMgUAABpAMgUAABpAMgUAABpAMgUAABpAMgUAABpAMgUAABpAMgUAABpAMgUAABpAMgUAABpAMgUAABpAMgUAABpAMgUAABpAMgUAABpAMgUAABpAMgUAABpAMgUAABpAMgUAABpAMgUAABpAMgUAABpAMgUAABpAMgUAABpAMgUAABpAMgUAABpAMgUAABpAMgUAABpAMgUAABpAMgUAABpAMgUAABpAMgUAABpAMgUAABpAMgUAABow9R0AAKpyc3P//e9/k5PPnj1DCB05coQseeedd/7whz/oITIAesYgCELfMQDwO5mZmXPnzjU2NjYyMkII4V2UwWAghLq6upRK5Y8//vjf//3feo4SgN+DZAoMjkKhsLS0lMlkaueKRKKamho2mz3IUQGgGVwzBQaHxWItWbJEbbrUMAsA/YJkCgzRkiVLOjo6upcrFIqPP/548OMBoFdwmg8MUVdX11tvvVVVVaVSbmVlVVlZia+lAmBQYKcEhsjIyGjZsmUqp/NsNnv58uWQSYFhgv0SGKjuZ/odHR1LlizRVzwAaAan+cBwubi4PH36lJx0cnIqLi7WYzwAaABHpsBwBQcHs1gs/Debzf7zn/+s33gA0ACOTIHhevr0qYuLCzn55MmTMWPG6DEeADSAI1NguCQSyTvvvMNgMBgMxjvvvAOZFBgySKbAoH3yySfGxsbGxsaffPKJvmMBQBM4zQcGraKiYvTo0QRBlJWV2dra6jscAHo0TJLp4sWL9R0C0JWcnByEkI+Pj57jADpz9uxZfYdAg2Fymn/u3Lny8nJ9R6Er5eXl586d03cUg0HtdrSzs7O3t9dLPEDXhtO+PUyOTBkMxunTp//0pz/pOxCdOHPmTGBg4PDYUpqp3Y719fUIIQsLCz0FBXRoOO3b8HBoYOggjYIhYZic5gMAgH5BMgUAABpAMgUAABpAMgUAABq8ocm0vb193bp1NjY2fD7/ww8/HDlyJIPB+Prrr/UdF50uXbpkamr6448/6jsQXbl69WpERMT58+ednJzwT06XLVtGXcDPz08oFBobG48fP/7evXt6CdLHx4fRjYmJia7rYl1dXfv37/fy8lIpj46OdnV1FYlEHA5HIpFs3ry5ubmZusD3338/efJkoVBob2+/YsWKyspK6tybN29OmzaNz+eLxeLw8PD29nZcfvHixfj4eKVSqX2Ew8kbmky/+uqry5cvP378ODExceXKlbdu3dJ3RPQbHsNNerJ9+/YDBw5s2bIlICDg2bNnzs7OI0aMSE1NzcrKIpf56aefzp49O3fu3MLCwokTJ+oxWhXvv//+INQtKir6r//6rw0bNsjlcpVZP//885o1a0pKSmpra2NjYxMTE6k/ezl9+vTSpUsXL15cXl5+4cKFGzduzJo1q7OzE88tLCz08/Pz9fWtqalJT08/fvz4qlWr8Kx58+ZxuVxfX9+GhoZ+d3AII4YFhNDp06e1X37y5Mkff/wxOVlUVIQQOnz4sOZacrnc09Ozp0ndOX36tCFvKRrXg5bbcc+ePWPGjGltbSVLnJ2dT548aWRkZGtr29DQQJZnZ2fPnz+fltj6Z+bMmTKZjFoSEhJy7do1XdfNy8tbuHBhamqqh4fHO++8ozJ3zpw5nZ2d5CQe2FtaWoonZ8yY8dZbb3V1deHJQ4cOIYRu3ryJJwMDAx0dHcm5CQkJDAbj0aNHZGtSqdTT01OhUGgTp4Hv233yhh6ZlpeXkw/K1N6xY8eqq6t7mnxjDfJ6ePr0aWRk5M6dO7lcLrXcy8srNDT05cuXmzZtGrRgenX58mWhUEhOlpWVPXjw4IMPPtB13Xfeeef8+fNLly7lcDjd52ZmZhobG5OTlpaWCCHyALasrEwsFjMYDDw5evRohNCLFy8QQp2dnVlZWd7e3uTcWbNmEQRx4cIFsrUdO3bk5eUlJiZqE+dw8sYl07///e8SieTVq1fffvttT1egfvnlF1dXV1NTUy6X6+7ufuXKFYRQaGjoxo0bi4uLGQyGRCJRmUQIKZXKqKgoOzs7Ho83YcIE/JWbnJwsEAj4fP6FCxdmzZolEolGjRqVlpam627evHnTzs6OwWDgwwrNYRw4cIDL5Y4cOXLlypVisZjL5Xp5eeXm5uK5UqmUzWbb2Njgyc8//1wgEDAYjNra2u6rBSF0+fJlkUi0e/duHXXtwIEDBEHMmzev+6yYmJgxY8Z88803V69eVVuXIIh9+/a9/fbbHA7H3Nx8wYIFjx8/xrN63VJqt29fxcXFrVu3rh8VB1hXs5cvX/J4PEdHRzzp5ORE/XbEF0ydnJwQQs+ePWtubrazsyPnOjs7I4Ty8/PJEnNzc29v78TERGJYX2hSQ78HxnRBfTzNt7a2/vOf/0xOqpzmnz17dseOHfX19XV1dVOnTh0xYgQuDwgIcHZ2JmupTG7atInD4Zw7d+7169dbtmwxMjK6e/cuQRBbt25FCF27dq2xsbG6unr69OkCgaCjo0P7aPt3KlRWVoYQOnjwIJ7UHEZISIhAIHj48GFbW1thYSG++UCe9y1dutTa2ppsOSEhASFUU1Ojdj1kZmYKhcLo6Oi+Bkxotx2dnJxcXV1VCp2dnZ8/f04QxK1bt4yMjBwcHJqbm4lup/lRUVFsNvvEiRMNDQ35+fkTJ060tLSsrKzEczWvop62r/bKy8tdXV2VSmWfag2w7h/+8Ifup/lULS0tQqFQKpWSJTk5OSwW68CBAzKZ7MGDB2+//fbMmTPxrOvXryOEEhISqC3weDxfX19qSUREBELo/v37vYYHp/nD3KJFi7Zv325ubm5hYTFv3ry6urqamhrNVdra2pKTk/39/QMCAszMzLZt28ZisVJSUsgFvLy8RCKRlZVVUFBQS0tLaWmpjjuhnoYwmEwmPmRzdXVNTk5uamqixq+9OXPmyGSyyMhI+qL+/1paWp4/f46PhtTy9PRcv359SUnJF198oTKrtbV13759CxcuDA4ONjU1dXd3//rrr2tra48cOUJdTO0q6nX7aiMuLm7t2rX9e7vqQOpqFhsbKxaLY2JiyBJvb+/w8HCpVCoSidzc3Jqamr755hs8C9+4p14iQAixWKzW1lZqCX4/QkFBAe3RGjJIpr3Al1Z7He3x5MkTuVzu5uaGJ3k8no2NDXkKSYVfX6xQKOiOtG80hzFp0iQ+n682fv2qrq4mCILP52tYJiYmZuzYsUlJSTdv3qSWFxYWNjc3T5o0iSyZPHkym80mL2iooK4i7bdvTyoqKi5evLh8+XLtq9BSV7P09PQzZ85cuXKFenF269atR44cuXbtWnNz87Nnz7y8vDw9PfGJDr5OTd7Zxzo6OniIct33AAAgAElEQVQ8HrUEb6CqqiraAzZkkEzVyMrK8vHxsbKy4nA4mzdv1qZKS0sLQmjbtm3keMAXL150H5IyhHA4nF6PxwdfW1sbQkjtTRUSl8tNSUlhMBiffvop9YgJj9dRuUpuZmbW1NTU6/8d+PaNj4//7LPPVG6aDUJdDU6dOhUXF5eTk+Pg4EAWvnr1Kj4+/q9//esHH3wgEAgcHR2PHj1aUVGBr+3gS+cymYxcXi6Xt7W1icViass4t+KN9eaAZKqqtLTU39/fxsYmNze3sbExPj5em1pWVlYIof3791Ovody+fVvHweqKQqFoaGgYNWqUvgNRhT+lvZ4oeHp6btiwoaioaNeuXWShmZkZQkgldWrZzQFu38rKyu+//3716tVaLk9XXQ0OHjyYmpr6888/v/XWW9TyoqIipVJJLRSJRBYWFoWFhQghR0dHoVCI7+xj+F3cEyZMoDbS0dGB/rOx3hyQTFUVFBQoFIrVq1c7OTlxuVxyCIhmo0eP5nK5eXl5ug5vcOTk5BAEMXXqVDzJZDL1fl0Cw79Va2xs7HXJXbt2jRs37v79+2SJm5ubiYnJr7/+Spbk5uZ2dHS89957vbY2wO0bHx8fHBzcv2cJDqSuWgRBhIeHFxQUZGRkdB/Ngr9aXr16RZY0NTXV19fjAVJMJnP27Nk3btzo6urCc7OzsxkMhsrgCryBrK2t6Yp5SIBkqgoP+7h69WpbW1tRURH1gpqFhUVFRUVJSUlTU5NCoaBOGhsbr1ixIi0tLTk5WSaTKZXK8vJy6h5p+Lq6ul6/ft3Z2Zmfnx8aGmpnZ0depJNIJPX19RkZGQqFoqamhnpggrqtluzsbN0NjeLz+U5OTtq8VQGf7FNvlXC53I0bN6anp6empspksoKCglWrVonF4pCQEG1a62n7BgUFWVtba/i5alVV1fHjx9evX999lk7r9uThw4dffvnl0aNHWSwW9Yeqe/fuRQg5OjrOmDHj6NGjN27caG1tLSsrw+vnL3/5C64eGRlZVVW1ffv2lpaW27dvJyQkLF++fOzYsdR/gTeQu7t7X2Mb2gZx5IAOIa2HRpWUlLz77rsIISaTOXHixHPnzn311Vf4K1QgECxcuJAgiPDwcAsLCzMzs8WLF+Nxms7OzqWlpffu3bO3t+fxeO+//35lZaXKZHt7e3h4uJ2dHZPJtLKyCggIKCwsTEpKwhfjXVxciouLjxw5IhKJEEL29va//fablr3rx/CRgwcP4stbfD5/3rx5vYYREhLCYrFsbW2ZTKZIJFqwYEFxcTHZWl1d3YwZM7hcrqOj49q1a8PCwhBCEokEj51SWQ+XLl0SCoUxMTF9ChjTZjtKpVIWiyWXy/Fkeno6vrlvaWm5Zs0alYXDwsKoQ6O6uroSEhJcXFxYLJa5ubm/v/+TJ0/wrF5XkdrtSxCEv78/QigqKqqngDds2BAcHKx2lu7q3r59e9q0aeSlTBsbGy8vr+vXrxME0dNNdnLAU21tbWhoqEQi4XA4JiYm06ZN++GHH6iNX79+fcqUKRwORywWh4WFtbW1qfz3OXPm2Nrakr+S0mA4DY0aLt3o4zjToWUQdriQkBALCwud/gttaLMdi4qKmEzmiRMnBiekXimVyunTpx87dmwI1dWp2tpaLpe7d+9ebRYeTskUTvPB/xkqD/uRSCTR0dHR0dEqDzrSC6VSmZGR0dTUFBQUNFTq6tqOHTs8PDykUqm+AxlskEzB0BMREbF48eKgoCBt7kTpVE5Ozvnz57OzszUPfTWoujq1b9++vLy8S5cu9ePZF0MdJFOAtmzZkpKS0tjY6OjoOFTeu7t7926pVLpnzx79huHr63vy5EnywQVDoq7uXLhwob29PScnx9zcXN+x6AG8nRSg2NjY2NhYfUfRZ35+fn5+fvqOAvx/8+fPnz9/vr6j0Bs4MgUAABpAMgUAABpAMgUAABpAMgUAABpAMgUAABowiGHxagEtH0cCADBAwyMLDZ+hUaGhoZ6envqOQidu376dmJjYv5cODS2BgYHDeDuC7vC+re8o6DF8kqmnpyd+Y+2wlJiYOIx7RwoMDBze2xF0N2ySKVwzBQAAGkAyBQAAGkAyBQAAGkAyBQAAGkAyBQAAGrwpyfT8+fNOTk4MdfB7bvfu3Ytf1vb111/rO1igE1evXo2IiKDuCcuWLaMu4OfnJxQKjY2Nx48f3493K9FFoVDExsZKJBI2m21mZubm5lZSUoJnRUdHu7q6ikQiDocjkUg2b95MfUK2j49P992bfGWe5roIoZs3b06bNo3P54vF4vDw8Pb2dlx+8eLF+Pj4ofLscH3S96P+6YG0e22Js7Ozqakp/ruzs1Mul1dVVb399tu4pKioCCF0+PBhHQbaL8Pp1Q6aabkd+yEqKmru3LkymQxPOjs7jxgxAiGUmZlJXSw7O5v6zii98Pf3Hzt27J07dxQKRUVFxbx58woKCvAsb2/vpKSkuro6mUx2+vRpFov10UcfkRW9vb27f8BnzpypTd0HDx7weLzIyMjm5uZbt25ZWlquWLGCnJuYmOjt7f369WvaOzuc9u3h0o2+J1MS+eHRMpnK5XJPT8+eJnVhEHY4GnsxkKZ0lEz37NkzZsyY1tZWssTZ2fnkyZNGRka2trYNDQ1kud6TaVpaGoPByM/PVzt3zpw5nZ2d5CQekItfa0gQxMyZM8lvCywkJOTatWva1A0MDHR0dCRfgZeQkMBgMB49ekQuL5VKPT09FQrFQHv4e8Mpmb4pp/kaZGRk9Gn5Y8eOVVdX9zQ5RNHYC0NbIU+fPo2MjNy5cyeXy6WWe3l5hYaGvnz5ctOmTfqKrbvDhw9PnDixp5ckZ2ZmUl9ebWlpiRCSy+V48vLly0KhkJxbVlb24MGDDz74oNe6nZ2dWVlZ3t7e5M+yZ82aRRDEhQsXyOV37NiRl5c3bAbY6wIk0x798ssvrq6upqamXC7X3d39ypUrCKHQ0NCNGzcWFxczGAyJRKIyiRBSKpVRUVF2dnY8Hm/ChAn4izc5OVkgEPD5/AsXLsyaNUskEo0aNSotLY3egAmC2Ldv39tvv83hcMzNzRcsWPD48WM8SyqVstls8i0Xn3/+uUAgYDAYtbW13Tt14MABLpc7cuTIlStXisViLpfr5eWVm5vbj6YQQpcvXxaJRLt376a3s9o7cOAAQRDz5s3rPismJmbMmDHffPPN1atX1dbVsEp73aZq9wTNOjo67ty54+HhoWXXXr58yePxHB0d1c6Ni4tbt26dNnWfPXvW3NxsZ2dHzsVvz87PzydLzM3Nvb29ExMTiWHxO3qd0OtxMW1Qv07zr127Rr4rnOh2mn/27NkdO3bU19fX1dVNnTp1xIgRuDwgIMDZ2ZmspTK5adMmDodz7ty5169fb9myxcjI6O7duwRBbN26FSF07dq1xsbG6urq6dOnCwSCjo4ObXqn5alQVFQUm80+ceJEQ0NDfn7+xIkTLS0tKysr8dylS5daW1uTCyckJCCEampq1PYiJCREIBA8fPiwra2tsLBw8uTJQqGQPCXsU1OZmZlCoTA6Olqbnmq5HfvEycnJ1dVVpdDZ2fn58+cEQdy6dcvIyMjBwaG5uZnodpqveZVq3qY97QkaPH/+HCHk4eHh4+NjY2PD4XDGjRt36NAhtS+gb2lpEQqFUqlUbVPl5eWurq5KpVLtXJW6169fRwhRPwsEQfB4PF9fX2pJREQEQuj+/fuae9EncJo/hDU2NpI3On19fTUsuWjRou3bt5ubm1tYWMybN6+urq6mpkZz421tbcnJyf7+/gEBAWZmZtu2bWOxWCkpKeQCXl5eIpHIysoqKCiopaWltLSUnl4h1Nraum/fvoULFwYHB5uamrq7u3/99de1tbVHjhzpX4NMJhMfkbm6uiYnJzc1NVE7or05c+bIZLLIyMj+hTFALS0tz58/x8dZanl6eq5fv76kpOSLL75QmaXlKlW7TXvdE9TCt9etrKx2795dWFhYVVW1YMGCNWvWfP/9990Xjo2NFYvFMTExapuKi4tbu3atkZH6D7hKXXzjnnoRACHEYrFaW1upJS4uLgihgoICzb14Y71xyZR6ZPqPf/xDy1r4vbW9jg558uSJXC53c3PDkzwez8bGhjwxpGKz2QghhULRh9A1KiwsbG5unjRpElkyefJkNptNnp4PxKRJk/h8vtqOGLjq6mqCIDS/DzkmJmbs2LFJSUk3b96klvd1lVK3qfZ7AhWHw0EIjR8/3svLy8LCwtTUdOfOnaampt2/EdPT08+cOXPlyhXqRVJSRUXFxYsXly9frva/dK+LryZ3dnZSF+vo6ODxeNQSvBqrqqo09+KN9cYlUyofHx8NNx+ysrJ8fHysrKw4HM7mzZu1abClpQUhtG3bNvLg98WLF+T9AZ1qaGhACJGDCjEzM7OmpiZa2udwOL0emBugtrY29J8k1RMul5uSksJgMD799FPqsdhAVmn/9gSxWIwQwlefMTabbW9vX1xcTF3s1KlTcXFxOTk5eIh0d/Hx8Z999pnKDTcNdfEVcJlMRpbI5fK2tjYcDwnnVrxKQXdvdDLVoLS01N/f38bGJjc3t7GxMT4+XptaVlZWCKH9+/dTr6Tcvn1bx8EihJCZmRlCSOVz3tDQMGrUqIE3rlAo6GpqkOHPf6+nFJ6enhs2bCgqKtq1axdZOJBV2r89wcTExMXF5eHDh9TCzs5OU1NTcvLgwYOpqak///zzW2+9pbaRysrK77//fvXq1d1n9VTX0dFRKBS+ePGCLHn69ClCaMKECdTFOjo60H9WKegOkql6BQUFCoVi9erVTk5OXC5Xyyf5jx49msvl5uXl6Tq87tzc3ExMTH799VeyJDc3t6Oj47333sOTTCaz31cVcnJyCIKYOnXqwJsaZPhXbY2Njb0uuWvXrnHjxt2/f58s6XWVatDvPSEwMPD+/fvPnj3Dk3K5/MWLF3ikFEEQ4eHhBQUFGRkZKsfLVPHx8cHBwRYWFtRCzXWZTObs2bNv3LjR1dWFS7KzsxkMhsoQCLwara2t+9qpNwQkU/XwMJGrV6+2tbUVFRVRL5NZWFhUVFSUlJQ0NTUpFArqpLGx8YoVK9LS0pKTk2UymVKpLC8vf/Xq1SAEzOVyN27cmJ6enpqaKpPJCgoKVq1aJRaLQ0JC8AISiaS+vj4jI0OhUNTU1FAPQ7p3CiHU1dX1+vXrzs7O/Pz80NBQOzs78hpcn5rKzs7W49AoPp/v5ORUXl7e65L4ZJ96E6bXVaq5tZ72hKCgIGtr655+rrphwwZ7e/vly5eXlpbW1dWFh4e3trbim2MPHz788ssvjx49ymKxqD8Y3bt3L1m9qqrq+PHj69evV2m217qRkZFVVVXbt29vaWm5fft2QkLC8uXLx44dS20Er8aexsCCYTIoAfU2pOaf//znmDFjcJdtbGxUxnwQBPHVV1/hr1yBQLBw4UKCIMLDwy0sLMzMzBYvXnzo0CGEkLOzc2lp6b179+zt7Xk83vvvv19ZWaky2d7eHh4ebmdnx2QyraysAgICCgsLk5KS8MV7FxeX4uLiI0eOiEQihJC9vf1vv/3Wa++0HD7S1dWVkJDg4uLCYrHMzc39/f2fPHlCzq2rq5sxYwaXy3V0dFy7dm1YWBhCSCKR4AFPKr0ICQlhsVi2trZMJlMkEi1YsKC4uLh/TV26dEkoFMbExPQaP6GboVFSqZTFYsnlcjyZnp6Ob+5bWlquWbNGZeGwsDDq0CgNq7TXbap2TyAIwt/fHyEUFRXVU8BlZWVLliwxNzfncDhTpkzJzs7G5T3dRqcOadqwYUNwcHD3NrWpe/369SlTpnA4HLFYHBYW1tbWptLInDlzbG1t1Y7T6rfhNDRquHRDZ7/pNgSDv8OFhIRYWFgM5n/EdLEdi4qKmEzmiRMn6G2235RK5fTp048dO6bvQPqmtraWy+Xu3buX3maHUzKF03yg3rB5SpBEIomOjo6OjlZ5SJJeKJXKjIyMpqamoKAgfcfSNzt27PDw8JBKpfoOxHBBMgXDX0RExOLFi4OCgrS5E6VTOTk558+fz87O1jz01dDs27cvLy/v0qVLeMA1UAuSKVC1ZcuWlJSUxsZGR0fHc+fO6TsceuzevVsqle7Zs0e/Yfj6+p48eZJ8ssGQcOHChfb29pycHHNzc33HYtCGz6ueAV1iY2NjY2P1HQX9/Pz8/Pz89B3F0DN//vz58+frO4ohAI5MAQCABpBMAQCABpBMAQCABpBMAQCABsPnBtTgPE9EL3DXzpw5o+9ABsMw3o6gu+G0uRnEsHgJgZYPIgEAGKBhkoWGRzfAMIbfo/mGHJiDoQuumQIAAA0gmQIAAA0gmQIAAA0gmQIAAA0gmQIAAA0gmQIAAA0gmQIAAA0gmQIAAA0gmQIAAA0gmQIAAA0gmQIAAA0gmQIAAA0gmQIAAA0gmQIAAA0gmQIAAA0gmQIAAA0gmQIAAA0gmQIAAA0gmQIAAA0gmQIAAA0gmQIAAA0gmQIAAA0gmQIAAA0gmQIAAA0gmQIAAA0gmQIAAA0gmQIAAA0gmQIAAA0gmQIAAA0gmQIAAA0gmQIAAA0gmQIAAA0gmQIAAA0YBEHoOwYAfufkyZPHjh3r6urCk8+fP0cIOTo64kkjI6O//OUvS5cu1Vt8AKgDyRQYnPz8/HfeeUfDAv/+978nTJgwaPEAoA1IpsAQjRs37smTJ2pnSSSSoqKiQY4HgF7BNVNgiJYtW8ZisbqXs1isFStWDH48APQKjkyBIXr27JlEIlG7cxYVFUkkksEPCQDN4MgUGCInJ6eJEycyGAxqIYPBmDRpEmRSYJggmQID9cknnxgbG1NLjI2NP/nkE33FA4BmcJoPDFR1dbVYLCYHSCGEjIyMKioqrK2t9RgVAD2BI1NgoEaOHOnt7U0enBobG/v4+EAmBQYLkikwXMuWLaOeOS1btkyPwQCgGZzmA8Mlk8msrKw6OjoQQiwWq7q62szMTN9BAaAeHJkCwyUSiT766CMmk8lkMmfPng2ZFBgySKbAoAUHByuVSqVSCT/GBwYOTvOBQWtra7O0tCQIora2lsfj6TscAHpG6MaiRYv03TMAAFC1aNEiHSU9pu6Cnjp16vr163XXPhiKAgMDQ0NDPT09ta+Sl5fHYDA0P0fK0Ozfvx8hBPu/ocHbRUd0mExHjRr1pz/9SXftg6EoMDDQ09OzTzvGwoULEUJMpg73VdqdPXsWIQT7v6HB20VHhtIOCt5MQyuNgjcW3M0HAAAaQDIFAAAaQDIFAAAaQDIFAAAa6DOZTp482djY2MPDQxeNr1ixgsvlMhiMtrY2XbQ/yPbu3Tty5EgGg/H111/jkkuXLpmamv7444+0tE9va7Qz8PAG7urVqxEREefPn3dycmIwGAwGQ+WpLn5+fkKh0NjYePz48ffu3dNXnAqFIjY2ViKRsNlsMzMzNze3kpISPCs6OtrV1VUkEnE4HIlEsnnz5ubmZrKij48PoxsTExNt6iKEbt68OW3aND6fLxaLw8PD29vbcfnFixfj4+OVSuVgdL43+kymd+/enTFjho4aT0lJ2bRpk44aH3ybNm26desWtYSg9adr9LZGOwMPb4C2b99+4MCBLVu2BAQEPHv2zNnZecSIEampqVlZWeQyP/3009mzZ+fOnVtYWDhx4kR9hRoYGPjdd9+dPHlSLpc/evTI2dmZzHo///zzmjVrSkpKamtrY2NjExMTFy9erLm1999/X5u6hYWFfn5+vr6+NTU16enpx48fX7VqFZ41b948Lpfr6+vb0NCgg+72kY5+DLBo0SJtfmng6+vr4eHR18blcrmnp2evi23duhUh1Nra2tf2DRN+Jefhw4dpaU3LdUg7hNDp06cH//9qia7VouX+TxDEnj17xowZQ91LnZ2dT548aWRkZGtr29DQQJZnZ2fPnz9/4LH1W1paGoPByM/PVzt3zpw5nZ2d5CQeY1taWoonZ86cKZPJqMuHhIRcu3ZNm7qBgYGOjo5dXV14MiEhgcFgPHr0iFxeKpV6enoqFIpeu6D9dukH/V8zVfsSSs2OHTtWXV2t5cIq7xECWJ/W4ZtjkFfL06dPIyMjd+7cyeVyqeVeXl6hoaEvX740qLOrw4cPT5w40d3dXe3czMxM6mtmLC0tEUJyuRxPXr58WSgUknPLysoePHjwwQcf9Fq3s7MzKyvL29ub/CDPmjWLIIgLFy6Qy+/YsSMvLy8xMZGOXvaf/pPp06dPx40bJxAIeDze9OnTb968Sc765ZdfXF1dTU1NuVyuu7v7lStXEEKhoaEbN24sLi5mMBjku9VOnDgxadIkLpcrEAgcHBx27dqFy42MjLKysmbNmmVqaioWi48fP65NSMnJyQKBgM/nX7hwYdasWSKRaNSoUWlpaeQCBEHs27fv7bff5nA45ubmCxYsePz4MZ715Zdf8vl8oVBYXV29ceNGW1vbVatWCQQCIyOj9957z9ramsViCQSCiRMnTp8+ffTo0Vwu18zMbPPmzZp7reLmzZt2dnYMBuPQoUN4HXa/IPX3v/9dy3Wo0prmDva6cminEp7mAA4cOMDlckeOHLly5UqxWMzlcr28vHJzc/FcqVTKZrNtbGzw5Oeffy4QCBgMRm1tbffVghC6fPmySCTavXu3jrp24MABgiDmzZvXfVZMTMyYMWO++eabq1evqq07kG2kVCqjoqLs7Ox4PN6ECRNOnz7da6gdHR137tzR/g7Hy5cveTyeo6Oj2rlxcXHr1q3Tpu6zZ8+am5vt7OzIuc7Ozgih/Px8ssTc3Nzb2zsxMZHQ7+UgHR3xan+a7+Tk9Pz5c4VC8eDBgz/84Q9cLve3337Dc8+ePbtjx476+vq6urqpU6eOGDEClwcEBDg7O5ON4N/b7tmzp66urr6+/m9/+9vSpUuJ/5zmX7t2raGhob6+fvbs2RwOp6WlRZv4ybqNjY3V1dXTp08XCAQdHR14blRUFJvNPnHiRENDQ35+/sSJEy0tLSsrK6l1161bd/DgwYULFz569Gj79u0Iodzc3JaWltra2o8++gghlJWVVVNT09LSIpVKEUJ5eXmae61yml9WVoYQOnjwIJ71xRdf4K69evXK3Nzcy8tLqVRqvw6prWnZwZ5WjmaoX6f5KuFpDiAkJEQgEDx8+LCtra2wsHDy5MlCoZA8Z1y6dKm1tTXZckJCAkKopqZG7WrJzMwUCoXR0dF9DVjL/d/JycnV1VWl0NnZ+fnz5wRB3Lp1y8jIyMHBobm5meh2mj+QbbRp0yYOh3Pu3LnXr19v2bLFyMjo7t27mkN9/vw5QsjDw8PHx8fGxobD4YwbN+7QoUPk2TdVS0uLUCiUSqVqmyovL3d1dcX7Z691r1+/jhBKSEigLsPj8Xx9faklERERCKH79+9r7oVOT/P1n0zfeecdchJ/22zatKn7krGxsQih6upq4vd7fEdHh5mZ2YwZM8glOzs78XeUyjXT7777DiH04MEDbeJXqZuUlIQQevr0KUEQcrncxMQkKCiIXPhf//oXQoj8yHW/VouTaVNTE5789ttvEUIFBQXU6qdOndLcaw3JlMrf35/L5T5+/FhzaxqSaV87SF05vaIxmfYUQEhIiKmpKVn37t27CKGdO3fiyT4l037TZv9vbm5mMBhz585VKSeTKUEQGzduRAitWbOG+H0yHcg2am1t5fP5ZF25XM7hcFavXq052oKCAoTQH//4x3/+8591dXUNDQ1ffPEFQig1NbX7wlu3bh0zZozKRVLSmjVrNFz6V6n7008/IYT27dtHXUYkEnl5eVFL8Ennd999p7kXw/yaKZW7u7upqSn1AJ6EL612HwORn5/f0NAwc+ZMssTY2FjtGQRuQaFQ9CMwNptN1i0sLGxubp40aRI5d/LkyWw2mzyX1LK1zs7OXgPrqdc9OXPmzA8//LBz586xY8f2u7W+dpC6cvRCcwCTJk3i8/nkKbDhwN9qfD5fwzIxMTFjx45NSkqiXv5CA9tGT548kcvlbm5ueBaPx7Oxsel1/XA4HITQ+PHjvby8LCwsTE1Nd+7caWpqeuTIEZUl09PTz5w5c+XKFepFUlJFRcXFixeXL1+u9r90r4uvJpMfFqyjo0Pl4bZ4NVZVVWnuhU4ZVjJFCLFYLPJTkZWV5ePjY2VlxeFwqFcVqWQyGUJoMF9ogQdhkEPkMDMzs6amJlra16bXatXV1a1du3by5Mn4cKbfrem6g4OPw+HU1NToOwpVeAQ0TlI94XK5KSkpDAbj008/bW1tJcsHso1aWloQQtu2bSMvr7948YK8U9QTsViMEMIXlzE2m21vb19cXExd7NSpU3FxcTk5OQ4ODmrbiY+P/+yzz1RuuGmoiy9w4485JpfL29racDwknFv1O6jcsJJpZ2dnfX09vthcWlrq7+9vY2OTm5vb2NgYHx+vtspbb72Ffr+NdQ0nbpW9tqGhYdSoUQNvXMteq7Vu3bqGhoaUlBTyxmj/WtNpBwefQqEwzODx57/XEwVPT88NGzYUFRWRt1XRwLaRlZUVQmj//v3UU9Tbt29rrmViYuLi4vLw4UNqYWdnp6mpKTl58ODB1NTUn3/+GX8qu6usrPz+++9Xr17dfVZPdR0dHYVC4YsXL8iSp0+fIoQmTJhAXQy/dVG/72IwrGT6j3/8o6urC49JLigoUCgUq1evdnJywr9lUlvFwcHBwsICX1gZHG5ubiYmJr/++itZkpub29HR8d577w28cS173V1WVtbJkycjIyPHjx+PS8LCwvrXmk47OPhycnIIgpg6dSqeZDKZerwiQYV/0tbY2Njrkrt27Ro3btz9+/fJkoFsIzyGJC8vr68BBwYG3r9//9mzZ3hSLpe/ePECj5QiCCI8PLygoCAjI0PleJkqPj4+ODjYwsKCWqi5Ln6X4o0bN7q6unBJdnY2g8FQGQKBV/AXsMQAACAASURBVKO1tXVfO0Uj/SfTjo6OxsbGzs7Oe/fuSaVSe3t7fD0FH59evXq1ra2tqKiIejHIwsKioqKipKSkqanJyMhoy5YtN27ckEqlL1++7OrqampqUvn+pBeXy924cWN6enpqaqpMJisoKFi1apVYLA4JCRl44xp6rYFMJlu5cqWHhwe+J9DW1vbrr7/m5eVpuQ5VkotOOzg4urq6Xr9+3dnZmZ+fHxoaamdnR16kk0gk9fX1GRkZCoWipqaGesiDuq2W7Oxs3Q2N4vP5Tk5O5eXlvS6JT/apIzEHso24XO6KFSvS0tKSk5NlMplSqSwvL3/16hVCKCgoyNrauqefq27YsAF/PEtLS+vq6sLDw1tbW/Eu9/Dhwy+//PLo0aMsFos6Pm/v3r1k9aqqquPHj3d/+0CvdSMjI6uqqrZv397S0nL79u2EhITly5er3BXAq7GnMbCDREc3trS8a5aSkjJjxoyRI0cymcwRI0YsWbLkxYsX5Nzw8HALCwszM7PFixfjMYbOzs6lpaX37t2zt7fn8Xjvv/8+Hgty6NAhd3d3LpfL5XLffffdpKSk+Ph4fMzv4uJSXFycmppqbm6OEBo1alSvN/STkpLw9Wxc98iRIyKRCCFkb2+Ph211dXUlJCS4uLiwWCxzc3N/f/8nT57guuT/HT169IkTJwiCSExMxK05ODj88ssvcXFx+MzI2tr65MmTp06dwl+n5ubmaWlpPfU6NDQULyYQCBYuXHjw4EF8LYnP58+bN4+6y5Jmz56t5Trctm0btTXNHex15WiG+n43X6WzvQYQEhLCYrFsbW2ZTKZIJFqwYEFxcTHZWl1d3YwZM7hcrqOj49q1a8PCwhBCEokEj51S2bUuXbokFApjYmL6FDCh9f4vlUpZLJZcLseT6enpeBClpaUlvoNPFRYWRh0aNZBt1N7eHh4ebmdnx2QyraysAgICCgsLCYLw9/dHCEVFRfUUcFlZ2ZIlS8zNzTkczpQpU7Kzs3E5vtffHXVI04YNG4KDg7u3qU3d69evT5kyhcPhiMXisLCwtrY2lUbmzJlja2urdpwW1XAeGgXeNP1Ipn0VEhJiYWGh03/RKy33/6KiIiaTib90DYFSqZw+ffqxY8f0HUjf1NbWcrncvXv39rrkGzQ0CgBaGMhjhHolkUiio6Ojo6NVHpKkF0qlMiMjo6mpKSgoSN+x9M2OHTs8PDzwj1/06E1Mpo8fP+7+40vSkNuTwJAWERGxePHioKAgbe5E6VROTs758+ezs7M1D301NPv27cvLy7t06VI/nvJBrzcxmY4bN07DsfqpU6f0HSDovy1btqSkpDQ2Njo6Op47d07f4Whl9+7dUql0z549+g3D19f35MmT5IMLhoQLFy60t7fn5OTgOyL6Be99BMNKbGws/tXs0OLn5+fn56fvKIae+fPnz58/X99R/J838cgUAABoB8kUAABoAMkUAABoAMkUAABooMMbUOXl5WfOnNFd+2CI6vWZGsMA/nUj7P+Gpry8XIePvNHRjwEWLVqkq4gBAKC/dPcLKB0emS5atOjs2bO6ax8MRQwG4/Tp0/j1k8MYflMx7P+Gpte3Tw8EXDMFAAAaQDIFAAAaQDIFAAAaQDIFAAAaQDIFAAAaQDIFAAAa6D+Znj9/3snJifpEUSaTaWlp+eGHH6anp9P1X1asWIHfKKf2ZbDUGJYtW0ad5efnJxQKjY2Nx48f39O7cXRt7969+OVrX3/9NS65dOmSqanpjz/+SEv79LYG+uHq1asREREGvh8ihBQKRWxsrEQiYbPZZmZmbm5uJSUleFZ0dLSrq6tIJOJwOBKJZPPmzdQnXvv4+HR/djD5+jzNdRFCN2/enDZtGp/PF4vF4eHh7e3tuPzixYvx8fGG8ixwHY1f7evrAZydnU1NTfHf9fX1V69eHTduHELo1KlTdIW0detWhFBra6uGGEaMGIEQyszMpJZnZ2dT372jF0VFRQihw4cP48nMzEyRSHTx4kVaGqe3Nc2Q7l9bYgj6tP9HRUXNnTtXJpPhSUPeD/39/ceOHXvnzh2FQlFRUTFv3ryCggI8y9vbOykpqa6uTiaTnT59msViffTRR2RFb2/v7sln5syZ2tR98OABj8eLjIxsbm6+deuWpaXlihUryLmJiYne3t6vX7/WJv434h1Q1GSKXblyBSG0cOHCXuvK5XJPT89eF9MmmZ48edLIyMjW1rahoYEsN4SdWCWZDpCWa0wXdJ1MaezaQJrSfv/fs2fPmDFjqLulwe6HaWlpDAYjPz9f7dw5c+Z0dnaSk/h3Gfg1hQRBzJw5k/y2wEJCQq5du6ZN3cDAQEdHR/JleQkJCQwG49GjR+TyUqnU09NToVD02oU39B1QDg4OCKGGhoZelzx27Fh1dbWWzWp+d7yXl1doaOjLly83bdqkZYNDUZ/W2NBCY9cGYS09ffo0MjJy586dXC6XWm6Y++Hhw4cnTpzY0+uUMzMzqS+jtrS0RAjJ5XI8efnyZaFQSM4tKyt78ODBBx980Gvdzs7OrKwsb29v8pM7a9YsgiAuXLhALr9jx468vLzExEQ6etl/hptM8/PzEULUs4NffvnF1dXV1NSUy+W6u7vjQ9fQ0NCNGzcWFxczGAyJRIKXPHHixKRJk7hcrkAgcHBw2LVrFy43MjLKysqaNWuWqampWCw+fvx49/8bExMzZsyYb7755urVq2oDIwhi3759b7/9NofDMTc3X7BgwePHj/GsL7/8ks/nC4XC6urqjRs32trarlq1SiAQGBkZvffee9bW1iwWSyAQTJw4cfr06aNHj+ZyuWZmZps3b9bcRxU3b960s7NjMBj41c1Pnz7tfjXq73//u5ZrTKU1zR1MTk4WCAR8Pv/ChQuzZs0SiUSjRo1KS0vTdqNqR0MAUqmUzWaTr9b4/PPPBQIBg8Gora3t3rUDBw5wudyRI0euXLlSLBZzuVwvL6/c3Nx+NIUQunz5skgk2r17N409PXDgAEEQ8+bN6z5rIPthr5tJqVRGRUXZ2dnxeLwJEyacPn2611A7Ojru3Lnj4eGhZddevnzJ4/EcHR3Vzo2Li1u3bp02dZ89e9bc3GxnZ0fOxW/DxvkBMzc39/b2TkxMJAhCy/B0QkdHvAM5zZfL5dnZ2fb29n5+fs3NzeQyZ8+e3bFjR319fV1d3dSpU0eMGIHLAwICnJ2dycX279+PENqzZ09dXV19ff3f/va3pUuXEv85zb927VpDQ0N9ff3s2bM5HE5LSws1hufPnxMEcevWLSMjIwcHB/zfVU6voqKi2Gz2iRMnGhoa8vPzJ06caGlpWVlZiefi/7Ju3bqDBw8uXLjw0aNH27dvRwjl5ua2tLTU1tZ+9NFHCKGsrKyampqWlhb8SsW8vDzNfVQ5zS8rK0MIHTx4EM/64osvcEdevXplbm7u5eWlVCq1X2PU1rTs4LVr1xobG6urq6dPny4QCDo6OrTc0EiL03zNASxdutTa2ppcOCEhASFUU1OjtmshISECgeDhw4dtbW2FhYWTJ08WCoXk+WOfmsrMzBQKhdHR0dp0U8v938nJydXVVaWQxv2wp820adMmDodz7ty5169fb9myxcjI6O7du5pDff78OULIw8PDx8fHxsaGw+GMGzfu0KFDal9V39LSIhQKpVKp2qbKy8tdXV3xLtpr3evXryOEEhISqMvweDxfX19qSUREBELo/v37mnvxplwzVcny7u7u3377bXt7u9rl8Xt+qqurid/v9B0dHWZmZjNmzCCX7OzsxF9ZKtdMv/vuO4TQgwcPqDHgnZggiI0bNyKE1qxZQ/x+J5bL5SYmJkFBQWStf/3rXwgh8jPW/cosTqZNTU148ttvv0UIkZftcXW199mofdSQTKn8/f25XO7jx4+1X2MqrfW1g0lJSQihp0+fdv+PavWaTHsNoK/JlHot/u7duwihnTt39qOpPtFm/29ubmYwGHPnzlUp18V+SN1Mra2tfD6frCuXyzkczurVqzVHW1BQgBD64x//+M9//rOurq6hoeGLL75ACKWmpnZfeOvWrWPGjFG5SEpas2aNhqv/KnV/+uknhNC+ffuoy4hEIi8vL2oJPsv87rvvNPfiTblmSu70CoWivLx8/fr1Uql0woQJ+LRLBX6ta/chEfn5+Q0NDTNnziRLjI2N1Z5Q4BYUCoXaYGJiYsaOHZuUlHTz5k1qeWFhYXNz86RJk8iSyZMns9ls8uSxV2w2GyHU2dnZaxg99bEnZ86c+eGHH3bu3Dl27Nh+t9bXDuLu9LQa+2Hga1iDSZMm8fl88nRYv/AXm+b3KtO1H1I305MnT+RyuZubG57F4/FsbGx6XSccDgchNH78eC8vLwsLC1NT0507d5qamh45ckRlyfT09DNnzly5coV6kZRUUVFx8eLF5cuXq/0v3eviq8nk5wXr6Ojg8XjUErwaq6qqNPdCpwwomZKYTKatre2KFSv27t375MkT8hW4WVlZPj4+VlZWHA6Hep2RSiaTIYTMzMwGGAOXy01JSWEwGJ9++mlraytZjm+IkePjMDMzs6ampgH+R0ybPqpVV1e3du3ayZMn42OZfrem6w7qPQAOh1NTU0NLUwOEhzzjJNUTXeyHLS0tCKFt27aRV9hfvHhB3inqiVgsRghRj2zYbLa9vX1xcTF1sVOnTsXFxeXk5OAbyN3Fx8d/9tlnKjfcNNTFF7Xx5xqTy+VtbW04HhLOrWpHkQ8aQ0ymJHzf8OHDhwih0tJSf39/Gxub3NzcxsbG+Ph4tVXeeust9PtN3m+enp4bNmwoKioi71+h/6RplV22oaGBlsd3a9lHtdatW9fQ0JCSkkLeFe1fazrtoN4DUCgUg9kXzfDnv9dzBdr3QysrK4TQ/v37qaeovb7+wMTExMXFBX8YSZ2dnaampuTkwYMHU1NTf/75Z/wx7K6ysvL7779fvXp191k91XV0dBQKhS9evCBLnj59ihCaMGECdbGOjg70n1WqLwadTP/3f/8XIYRPWgsKChQKxerVq52cnPBvmdRWcXBwsLCwwNdZBm7Xrl3jxo27f/8+WeLm5mZiYvLrr7+SJbm5uR0dHe+9997A/52WfewuKyvr5MmTkZGR48ePxyVhYWH9a02nHaQlACaT2e+rCjk5OQRBTJ06deBNDRz+VVtjY2OvS9K7H+JhJHl5eX0NODAw8P79+8+ePcOTcrn8xYsX+IiHIIjw8PCCgoKMjAyV42Wq+Pj44OBgCwsLaqHmukwmc/bs2Tdu3Ojq6sIl2dnZDAZDZQgEXo3W1tZ97RSNDCuZtra24puDFRUVKSkp27Zts7S0XL9+PUIIj424evVqW1tbUVER9dqQhYVFRUVFSUlJU1OTkZHRli1bbty4IZVKX7582dXV1dTUpPJ1qj18kkUdAcflcjdu3Jienp6amiqTyQoKClatWiUWi0NCQgbWdYQ09lEDmUy2cuVKDw8PfEOgra3t119/zcvL03KNqWQTnXZQG70GIJFI6uvrMzIyFApFTU0N9ZhFbde6urpev37d2dmZn58fGhpqZ2dHXrDrU1PZ2dn0Do3i8/lOTk74bVGa0bsfcrncFStWpKWlJScny2QypVJZXl7+6tUrhFBQUJC1tXVPP1fdsGGDvb398uXLS0tL6+rqwsPDW1tb8V738OHDL7/88ujRoywWizpEb+/evWT1qqqq48eP448zVa91IyMjq6qqtm/f3tLScvv27YSEhOXLl6vcGMCrsacxsINERze2tL9rlp6e3v1WPofDcXFxWb16NTmKBX99WVhYmJmZLV68GA+KdHZ2Li0tvXfvnr29PY/He//99/HQkEOHDrm7u3O5XC6X++677yYlJcXHx+NTABcXl+Li4tTUVHNzc4TQqFGjHjx4QMZgaWmJ75xShYWFUYekdHV1JSQkuLi4sFgsc3Nzf3//J0+e4Fnkfxk9evSJEycIgkhMTMSXxh0cHH755Ze4uDh8WmRtbX3y5MlTp07h71Jzc/O0tLSe+hgaGooXEwgECxcuPHjwIL6QxOfz582bR91fSbNnz9ZyjW3bto3amuYOJiUl4e7g1XjkyBGRSIQQsre3/+2337TZ3EiLoVEaAiAIoq6ubsaMGVwu19HRce3atWFhYQghiUSCdxWVnSEkJITFYtna2jKZTJFItGDBguLi4v41denSJaFQGBMTo003tdz/pVIpi8WSy+V4kq79sNfN1N7eHh4ebmdnx2QyraysAgICCgsLCYLw9/dHCEVFRfUUcFlZ2ZIlS8zNzTkczpQpU7Kzs3E5vtffHXVI04YNG4KDg7u3qU3d69evT5kyhcPhiMXisLCwtrY2lUbmzJlja2urdpwW1RsxNAq8IbRJpjQKCQmxsLAYtH9H0nL/LyoqYjKZ+HvXECiVyunTpx87dkzfgfRNbW0tl8vdu3dvr0u+KUOjANAFQ3mkkDoSiSQ6Ojo6OlrlIUl6oVQqMzIympqagoKC9B1L3+zYscPDwwP//kWPIJkCoE8RERGLFy8OCgrS5k6UTuXk5Jw/fz47O1vz0FdDs2/fvry8vEuXLuGR1HoEyRQMW1u2bElJSWlsbHR0dDx37py+w+nR7t27pVIpOZ5aX3x9fU+ePEk+rGBIuHDhQnt7e05ODr4Fol9MfQcAgK7ExsbiH9EaPj8/Pz8/P31HMfTMnz9//vz5+o7i/8CRKQAA0ACSKQAA0ACSKQAA0ACSKQAA0ECHN6Du3LmzePFi3bUPhqj9+/efPXtW31Ho1p07dxBCsP8bmjt37pBPZqCdrpKpp6enjloGQ9qiRYv6WgU/4OPdd9/VQTi6ortPLBiIqVOn6i41MQj9vjUFgN7gd1WeOXNG34EAoAlcMwUAABpAMgUAABpAMgUAABpAMgUAABpAMgUAABpAMgUAABpAMgUAABpAMgUAABpAMgUAABpAMgUAABpAMgUAABpAMgUAABpAMgUAABpAMgUAABpAMgUAABpAMgUAABpAMgUAABpAMgUAABpAMgUAABpAMgUAABpAMgUAABpAMgUAABpAMgUAABpAMgUAABpAMgUAABpAMgUAABpAMgUAABpAMgUAABpAMgUAABpAMgUAABpAMgUAABpAMgUAABow9R0AAKrkcnl7ezs52dHRgRB6/fo1WcLhcPh8vh4iA6BnDIIg9B0DAL+TnJz8+eefa1ggKSlp9erVgxYPANqAZAoMTk1NjVgsViqVaucaGxu/evXKyspqkKMCQDO4ZgoMjpWVla+vr7GxcfdZxsbGH374IWRSYIAgmQJDFBwcrPaciSCI4ODgwY8HgF7BaT4wRE1NTVZWVtTbUBibza6pqRGJRHqJCgAN4MgUGCKhUDh37lwWi0UtZDKZ8+fPh0wKDBMkU2Cgli5d2tnZSS1RKpVLly7VVzwAaAan+cBAdXR0WFpaNjU1kSUmJia1tbUcDkePUQHQEzgyBQaKzWYvXryYzWbjSRaLFRgYCJkUGCxIpsBwffzxx/jnTwghhULx8ccf6zceADSA03xguLq6umxsbGpqahBClpaWlZWVagefAmAI4MgUGC4jI6OPP/6YzWazWKylS5dCJgWGDJIpMGhLlizp6OiAc3xg+N7op0adOXNG3yGAXhAEMWLECITQ8+fPS0pK9B0O6MWf/vQnfYegN2/0NVMGg6HvEAAYVt7kfPJGH5kihE6fPj1svksXL16MEDp79qy+A6HZw4cPEUKurq548syZM4GBgW/yh9Yw4e2i7yj06U1PpsDwkWkUAEMGN6AAAIAGkEwBAIAGkEwBAIAGkEwBAIAGkEwBAIAGkEz77H/+53+EQiGDwcjLy9N3LDS4dOmSqanpjz/+qO9AdOXq1asRERHnz593cnJiMBgMBmPZsmXUBfz8/IRCobGx8fjx4+/du6evOBUKRWxsrEQiYbPZZmZmbm5u5I8UoqOjXV1dRSIRh8ORSCSbN29ubm4mK/r4+DC6MTEx0aYuQujmzZvTpk3j8/lisTg8PJx8u8HFixfj4+N7eq0hUIN4gyGETp8+3Y+KaWlpCKH79+/THtJALFq0aNGiRX2tlZmZKRKJLl68qIuQdOH06dPa77dRUVFz586VyWR40tnZGf+eKjMzk7pYdnb2/PnzaQ60j/z9/ceOHXvnzh2FQlFRUTFv3ryCggI8y9vbOykpqa6uTiaTnT59msViffTRR2RFb2/v7p/rmTNnalP3wYMHPB4vMjKyubn51q1blpaWK1asIOcmJiZ6e3u/fv1am/j7tF2GpTe785BMB5FcLvf09Bx4O9p/aPfs2TNmzJjW1layxNnZ+eTJk0ZGRra2tg0NDWS53pNpWloag8HIz89XO3fOnDmdnZ3kJP6ZSWlpKZ6cOXMm+W2BhYSEXLt2TZu6gYGBjo6OXV1deDIhIYHBYDx69IhcXiqVenp6KhSKXrsAyRRO8/sDfofaD8eOHauurh60f/f06dPIyMidO3dyuVxquZeXV2ho6MuXLzdt2jRowfTq8OHDEydOdHd3Vzs3MzOT+sQsS0tLhJBcLseTly9fFgqF5NyysrIHDx588MEHvdbt7OzMysry9vYm9+dZs2YRBHHhwgVy+R07duTl5SUmJtLRy2EOkqlWCIJISEgYO3Ysh8MxNTUNCwujzlUqlVFRUXZ2djweb8KECfgrOjk5WSAQ8Pn8CxcuzJo1SyQSjRo1Ch/SYtevX58yZQqfzxeJRO7u7jKZrKemdOfmzZt2dnYMBuPQoUO9xnzgwAEulzty5MiVK1eKxWIul+vl5ZWbm4vnSqVSNpttY2ODJz///HOBQMBgMGpraxFCoaGhGzduLC4uZjAYEokEIXT58mWRSLR7924dde3AgQMEQcybN6/7rJiYmDFjxnzzzTdXr15VW5cgiH379r399tscDsfc3HzBggWPHz/Gs3rdrP3Ygh0dHXfu3PHw8NCyay9fvuTxeI6OjmrnxsXFrVu3Tpu6z549a25utrOzI+c6OzsjhPLz88kSc3Nzb2/vxMREAn6/2yu9HhfrGdL6NH/r1q0MBuOrr756/fq1XC5PSkpClNP8TZs2cTicc+fOvX79esuWLUZGRnfv3sW1EELXrl1rbGysrq6ePn26QCDo6OggCKK5uVkkEsXHx7e2tlZWVi5cuLCmpkZDU9ro32l+WVkZQujgwYNkT3uKmSCIkJAQgUDw8OHDtra2wsLCyZMnC4VC8pxx6dKl1tbWZMsJCQkIIdwvgiACAgKcnZ3JuZmZmUKhMDo6uq8Ba3k66eTk5OrqqlLo7Oz8/PlzgiBu3bplZGTk4ODQ3NxMdDvNj4qKYrPZJ06caGhoyM/PnzhxIn40NZ6reRX1Yws+f/4cIeTh4eHj42NjY8PhcMaNG3fo0CHy7JuqpaVFKBRKpVK1TZWXl7u6uiqVSrVzVepev34dIZSQkEBdhsfj+fr6UksiIiKQFhe14DQfjkx719raun///g8//HDDhg1mZmY8Hs/CwoKc29bWlpyc7O/vHxAQYGZmtm3bNhaLlZKSQi7g5eUlEomsrKyCgoJaWlpKS0sRQiUlJTKZbPz48Vwu19ra+vz585aWlr02NWjUxowxmUx8yObq6pqcnNzU1NS/COfM+X/s3WtcE9e6MPAVyGVyJwgCCuEW1AooIrbIrm91e0q3ehQRFTzWHu1uN2oVUVQUb9zEC/6UA6KtrWVvhYqgbGxV1G09nNaKaI9QEKoCCgio3Ay3BAhk3g/r7bwxQAiQENTn/4lZM7PmmTWTh7msmZnX3Ny8a9cu3UX9/7W1tT158gQfZ/Vq+vTpGzduLC8v37Ztm9oouVx++PDhRYsWffzxx0Kh0NXV9csvv6yvrz9x4oTqZL020eC2IL69bm5uvnfv3qKiohcvXixcuHDdunXfffddz4ljYmKsrKyio6N7rWr//v3r1683Mur9d602L75xr/bKbQaDIZfLVUucnJwQQoWFhZrXAkAy7V9paalMJps9e3avYx8+fCiTyVxcXPAgm822tLSkzgpV4W/DKRQKhJCDg8Po0aM//vjj8PBwqgeM9lUNG9WYe/Lw8OBwOIaNsFe1tbUkSXI4HA3TREdHjx8/PjEx8ebNm6rlRUVFra2tHh4eVMm0adOYTCZ1QUONahMNbgvirwQ6Ozt7eXmZmpoKhcKIiAihUKiWvhFCGRkZaWlpV69eVb1ISqmpqfn+++9XrlzZ61J6zouvJqt9T7uzs5PNZquW4GZ88eKF5rUAkEz7V1VVhRAyNzfvdWxbWxtCaOfOnVQXv4qKCurmQF/YbPaNGzfef//9vXv3Ojg4BAQEyOXywVVlWCwWC3+jaURpb29HfySpvhAEkZSURKPRPv30U9VjMalUihCi+mliJiYmqh+d7svgtqCVlRVCCF9cxphMpq2tbVlZmepkqamp+/fvz87OtrOz67WeAwcOfP7552o33DTMiy9w44v1mEwma29vx/FQcG7FTQo0gGTaP7x3Up2Z1eAke+TIEdWrJzk5Of1W6+zs/MMPP9TU1ISGhp49e/bQoUODrspQFAqFVCq1trY2dCDq8O+/3w7n06dP37RpU0lJSVRUFFVoYmKCEFJLnVqu5uC2II/Hc3Jywq9tpXR1dQmFQmowISEhOTn5xo0bY8aM6bWS58+ff/fdd2vXru05qq957e3t+Xx+RUUFVVJaWooQmjRpkupk+AOxaoeroCdIpv1zcXExMjLCV+t7srGxIQhioE9D1dTU4B+Pubn5vn373N3di4uLB1eVAWVnZ5Mk6enpiQfpdHpfFwSG2ejRo2k0WlNTU79TRkVFTZgwIS8vjypxcXHh8Xi//vorVZKbm9vZ2Tl16tR+axv0FvT398/Ly3v8+DEelMlkFRUVuKcUSZKhoaGFhYWZmZlqx8uqDhw48PHHH6teze93XjqdPnfu3J9++kmpVOKSrKwsGo2m1gUCN6OFhcVAV+ptP9EFwgAAIABJREFUA8m0f+bm5n5+fufOnTt58mRzc3NBQYHqxSyCIFatWnXmzJljx441Nzd3d3dXVVU9e/ZMc501NTWrV69+8OBBZ2dnXl5eRUWFp6fn4KoaZkql8uXLl11dXQUFBcHBwWKxmLpIJ5FIGhsbMzMzFQpFXV2d6iEPQsjU1LSmpqa8vLylpUWhUGRlZemvaxSHw3FwcMDXZzTDJ/uqN2EIgggJCcnIyEhOTm5ubi4sLFyzZo2VlVVgYKA2tfW1BQMCAiwsLPp6XHXTpk22trYrV66srKxsaGgIDQ2Vy+X45lhxcfHBgwe//vprBoOh+sDooUOHqNlfvHjx7bffbty4Ua3afufdtWvXixcv9uzZ09bWlpOTExsbu3LlyvHjx6tWgpuxrz6w4P/Td3eBkQxp3TWqpaXls88+GzVqFI/He//993fv3o0Qsra2/u2330iS7OjoCA0NFYvFdDodZ96ioqLExER85d7JyamsrOzEiRMCgQAhZGtr++jRo/Lyci8vL5FIZGxsPGbMmB07duDHVHqtSsvVGUTXqISEBHzhjMPhLFiwQHPMJEkGBgYyGIyxY8fS6XSBQLBw4cKysjKqtoaGhlmzZhEEYW9vv379etwbVyKR4L5T9+7ds7W1ZbPZ77///vPnzy9fvszn86OjowcUMKl1F5ygoCAGgyGTyfBgRkYGvrlvZma2bt06tYm3bNmi2jVKqVTGxsY6OTkxGAyRSOTr6/vw4UM8qt8m6msL+vr6IoR2797dV8BPnz5dtmyZSCRisVjvvvtuVlYWLu/rNrpql6ZNmzZ9/PHHPevUZl7c35nFYllZWW3ZsqW9vV2tknnz5o0dO7bXflqqoGvU273yg32cdGQahsdJAwMDTU1N9bqIfmn5oy0pKaHT6adPnx6GkLTR3d09Y8aMkydPGjqQgamvrycI4tChQ/1OCckUTvPBwLwurxGSSCSRkZGRkZFqL0kyiO7u7szMzJaWloCAAEPHMjDh4eFubm5BQUGGDuQ1AMkUvLG2b9++ZMmSgIAAbe5E6VV2dvb58+ezsrI0d30daQ4fPpyfn3/58mUGg2HoWF4DkEyBtsLCwpKSkpqamuzt7c+dO2focLSyd+/eoKCgffv2GTaM2bNnp6SkUC8ueC1cuHCho6MjOztbJBIZOpbXA3zqGWgrJiYmJibG0FEMmLe3t7e3t6GjeP34+Pj4+PgYOorXCRyZAgCADkAyBQAAHYBkCgAAOgDJFAAAdIBGvsUv0KbRaJ6eniPwPR2Dc/v2bYQQ9aT8m6qqqur27duLFy82dCDgFXi7vM35BI5MAQBAB972I9OzZ8/i7zW+AZYsWYIQSk9PN3Qg+pWWlubv7/8277cjE2wXODIFAAAdgGQKAAA6AMkUAAB0AJIpAADoACRTAADQAUimmpw/f97BwUH1ew9MJnP06NEzZ86MjY19+fKloQMEQ3X9+vXt27erbugVK1aoTuDt7c3n842NjZ2dnfv66Ii+RUZGTpw4USAQsFgsiUSydetW1Ze0RkdH015FfWsaUygUMTExEomEyWSamJi4uLjgr4t///33Bw4ceF1eUDvyQTLVxM/P7/Hjx46OjkKhkCRJpVJZW1ublpZmb28fGhrq7Oys+tk18NrZs2dPfHx8WFgYtaFHjRqVnJx86dIlappr166lp6fPnz+/qKjI3d3dIHHeuHFj3bp15eXl9fX1MTExcXFxuBuclvz9/U+dOpWSkiKTyX7//XdHR0ecixcsWEAQxOzZs/HXrcEQQTIdABqNZmJiMnPmzKSkpLS0tBcvXsybN8/gLx4eTnK53MvLa6RVNTj79+9PTU1NS0vj8/lUYXx8vJGRUWBg4IjarDweD38whs/nL1261NfX98qVK0+fPqUmUPs6y/3796lRqampmZmZ6enp7733Hp1Ot7KyunDhAnXoumHDhsmTJ8+dO7erq2u41+qNA8l0kBYvXrxy5cra2tovv/zS0LEMn5MnT9bW1o60qgahtLR0165dERERBEGolnt5eQUHB1dXV2/evNlQsfV08eJF1e+nmpmZIYRkMpk28x4/ftzd3V3Dt0XDw8Pz8/Pj4uKGHudbDpLp4OFPHGdlZeHB7u7u3bt3i8ViNps9adIk/H2xY8eOcblcDodz4cKFOXPmCAQCa2vrM2fOUJXgb0NyOByBQODq6trc3NxXVbpCkuThw4ffeecdFoslEokWLlz44MEDPCooKIjJZFIvhP/iiy+4XC6NRquvr0cIBQcHh4SElJWV0Wg0iUQSHx9PEMTo0aNXr15tZWVFEISXl1dubu4gqkIIXblyRX9ffu4pPj6eJEm1D8Rj0dHR48aN++abb65fv97rvBoasN/NrZMtW11dzWaz7e3t+52ys7Pz9u3bbm5uGqYRiUQffPBBXFzc2/zwkm4M7/f7Rhak3ddJqWumanDis7GxwYObN29msVjnzp17+fJlWFiYkZHR3bt3SZLcsWMHQujHH39samqqra2dMWMGl8vt7OwkSbK1tVUgEBw4cEAulz9//nzRokV1dXUaqtJMy6+T7t69m8lknj59WiqVFhQUuLu7m5mZPX/+HI9dvny5hYUFNXFsbCxCCEdFkqSfn5+joyM1NjAwkMvlFhcXt7e3FxUVTZs2jc/n4w87D7Sqixcv8vn8yMjIfuPXyVcwHRwcJk6cqFbo6Oj45MkTkiRv3bplZGRkZ2fX2tpKkmRWVpbqh6A1N6CGzU0Odsuqamtr4/P5QUFBVElUVJS1tbWJiQmDwbCzs/Px8blz5w4e9eTJE4SQm5vbzJkzLS0tWSzWhAkTjh49qvbd5u3btyOE8vLyBhSJGvg66du98kNLpiRJ4quoJEnK5XIOhxMQEIDLZTIZi8Vau3Yt+cevSy6X41GJiYkIodLSUvKPa1sXL15UrVNDVZppk0xlMhmPx6MqJ0nyzp07CCEqiw00maq2zN27dxFCERERg6hKe0P/0ba2ttJotPnz56uVU8mUJMmQkBCE0Lp168hXk2m/Dahhcw96y6rasWPHuHHjmpubqZLKysp79+61tLR0dHTk5ORMmTKFzWbfv3+fJMnCwkKE0IcffvjLL780NDRIpdJt27YhhJKTk1Xr/PbbbxFCp06dGlAkaiCZwmn+4LW1tZEkKRAIEEIPHz6UyWTUdX02m21paUmd/aliMpkIIYVCgRBycHAYPXr0xx9/HB4ejnurDKiqQSgqKmptbfXw8KBKpk2bxmQyqdPzofDw8OBwOLoKVX9qa2tJktT8odDo6Ojx48cnJibevHlTtXygDai6uYe+ZTMyMtLS0q5evap608zGxmbKlCk8Ho/JZHp6eiYlJcnlcpzEWSwWQsjZ2dnLy8vU1FQoFEZERAiFwhMnTqhWi5vixYsX2kcCeoJkOniPHj1CCE2YMAEh1NbWhhDauXMn1devoqKi31sEbDb7xo0b77///t69ex0cHAICAuRy+eCq0hLuBMPj8VQLTUxMWlpadFI/i8Wqq6vTSVX6097ejv5INH0hCCIpKYlGo3366adyuZwqH0oDDnHLpqam7t+/Pzs7287OTsNkrq6uxsbGeOe0srJCCOHr1BiTybS1tS0rK1Odhc1moz+aBQwaJNPBu3LlCkJozpw5CCFzc3OE0JEjR1QP+3NycvqtxNnZ+YcffqipqQkNDT179uyhQ4cGXZU2TExMEEJqv3ypVKqTN2QrFApdVaVXOHf021l9+vTpmzZtKikpiYqKogqH0oBD2bIJCQnJyck3btwYM2aM5imVSqVSqcT/Kng8npOTU3FxseoEXV1dQqFQtaSzsxP90Sxg0CCZDtLz58+PHDlibW396aefIoRsbGwIgsjPzx9QJTU1NXhHNzc337dvn7u7e3Fx8eCq0pKLiwuPx1N91iA3N7ezs3Pq1Kl4kE6n43PSQcjOziZJknrV/1Cq0qvRo0fTaDRtepJGRUVNmDAhLy+PKum3ATUY3JYlSTI0NLSwsDAzM1PtiBj76KOPVAfxHa3p06fjQX9//7y8vMePH+NBmUxWUVGh1lMKN4WFhcWAAgNqIJlqhSTJ1tZWfA+0rq7u7Nmzf/rTn4yNjTMzM/E1U4IgVq1adebMmWPHjjU3N3d3d1dVVT179kxztTU1NatXr37w4EFnZ2deXl5FRYWnp+fgqtISQRAhISEZGRnJycnNzc2FhYVr1qyxsrIKDAzEE0gkksbGxszMTIVCUVdXV1FRoTq7qalpTU1NeXl5S0sLTpRKpfLly5ddXV0FBQXBwcFisRj3GBtoVVlZWcPWNYrD4Tg4OFRVVfU7JT7ZV+3j2W8Daq6try0bEBBgYWHR6+OqxcXFBw8e/PrrrxkMhuozo4cOHcITVFdXp6amSqVShUKRk5Pz2WeficXiNWvW4LGbNm2ytbVduXJlZWVlQ0NDaGioXC7Ht6EouCk09EUFWtHr7a0RDvV3N//777+fNGkSh8NhMplGRkboj4eg3n333cjIyIaGBtWJOzo6QkNDxWIxnU43Nzf38/MrKipKTEzEV/ednJzKyspOnDiBk6+tre2jR4/Ky8u9vLxEIpGxsfGYMWN27NjR1dXVV1X9ro6WXaOUSmVsbKyTkxODwRCJRL6+vg8fPqTGNjQ0zJo1iyAIe3v79evXb9myBSEkkUhwh6d79+7Z2tqy2ez333//+fPngYGBDAZj7NixdDpdIBAsXLiwrKxscFVdvnyZz+dHR0f3G79O7hoHBQUxGAyZTIYHMzIyHB0dEUJmZmb4Dr6qLVu2qHaN0tCAmjc32feW9fX1RQjt3r27Z6j4jnxPsbGxeIKQkBBHR0cul0un062trT///POamhrVGp4+fbps2TKRSMRisd59992srCy1RcybN2/s2LFq/aUGCu7mv90rr13XqNeFlslUh/AzjsO5RFJHP9qSkhI6na72FKYBdXd3z5gx4+TJk8O/6Pr6eoIgDh06NMR6IJnCaT4Yktf0nUMSiSQyMjIyMlL19UuG0t3dnZmZ2dLSEhAQMPxLDw8Pd3NzCwoKGv5Fv2EgmYK31Pbt25csWRIQEGDwd5pkZ2efP38+KytLc9dXfTh8+HB+fv7ly5cZDMYwL/rNA8kUDFJYWFhSUlJTU5O9vf25c+cMHc5g7N27NygoaN++fYYNY/bs2SkpKdR7DIbNhQsXOjo6srOzRSLRMC/6jUQ3dADgdRUTExMTE2PoKIbK29vb29vb0FEYho+Pj4+Pj6GjeHPAkSkAAOgAJFMAANABSKYAAKADkEwBAEAHIJkCAIAO0Mi3+FsFNBrN0CEA8EZ5m/PJW901SrffVgJ6cuTIEYTQxo0bDR0IAJq81Uem4LWwdOlShFBaWpqhAwFAE7hmCgAAOgDJFAAAdACSKQAA6AAkUwAA0AFIpgAAoAOQTAEAQAcgmQIAgA5AMgUAAB2AZAoAADoAyRQAAHQAkikAAOgAJFMAANABSKYAAKADkEwBAEAHIJkCAIAOQDIFAAAdgGQKAAA6AMkUAAB0AJIpAADoACRTAADQAUimAACgA5BMAQBAByCZAgCADkAyBQAAHYBkCgAAOgDJFAAAdACSKQAA6AAkUwAA0AFIpgAAoAOQTAEAQAcgmQIAgA5AMgUAAB2gGzoAANTl5ub+9ttv1ODjx48RQidOnKBKJk+e/N577xkgMgD6RiNJ0tAxAPCKixcvzp8/39jY2MjICCGEd1EajYYQUiqV3d3dP/zww7//+78bOEoAXgXJFIw4CoXCzMysubm517ECgaCuro7JZA5zVABoBtdMwYjDYDCWLVvWa7rUMAoAw4JkCkaiZcuWdXZ29ixXKBT/8R//MfzxANAvOM0HI5FSqRwzZsyLFy/Uys3NzZ8/f46vpQIwosBOCUYiIyOjFStWqJ3OM5nMlStXQiYFIxPsl2CE6nmm39nZuWzZMkPFA4BmcJoPRi4nJ6fS0lJq0MHBoayszIDxAKABHJmCkevjjz9mMBj4byaT+Z//+Z+GjQcADeDIFIxcpaWlTk5O1ODDhw/HjRtnwHgA0ACOTMHIJZFIJk+eTKPRaDTa5MmTIZOCkQySKRjRPvnkE2NjY2Nj408++cTQsQCgCZzmgxGtpqbGxsaGJMmnT5+OHTvW0OEA0Cd9JdPDhw/n5OToo2bwtsnOzkYIzZw508BxgDfC9OnTN23apI+a9XWan5OTc/v2bT1VDt4qYrHY1tYWIXTu3LmqqipDh6N3t2/fht+Onty+fVt/B3l6fJ+pp6dnenq6/uoHb4nGxkaEkKmpKY1G27hx49KlSw0dkX4tWbIEIQS/HX3Abasn8HJoMNKZmpoaOgQA+gd38wEAQAcgmQIAgA5AMgUAAB2AZAoAADpgyGQ6bdo0Y2NjNzc3fVS+atUqgiBoNFp7e7s+6h9mhw4dGj16NI1G+/LLL3HJ5cuXhULhDz/8oJP6dVtbv5RK5ZEjR7y8vIZhWcO8asPv+vXr27dvP3/+vIODA370dsWKFaoTeHt78/l8Y2NjZ2fne/fuGSTIyMjIiRMnCgQCFoslkUi2bt3a2tpKjY2Ojqa9ysXFRXV2hUIRExMjkUiYTKaJiYmLi0t5eTlC6Pvvvz9w4EB3d/cwr06vDJlM7969O2vWLD1VnpSUtHnzZj1VPvw2b95869Yt1RLdPm0xnA/ClZSU/J//8382bdokk8mGYXFv9jN+e/bsiY+PDwsL8/Pze/z4saOj46hRo5KTky9dukRNc+3atfT09Pnz5xcVFbm7uxskzhs3bqxbt668vLy+vj4mJiYuLm5AvZT8/f1PnTqVkpIik8l+//13R0dHnIsXLFhAEMTs2bOlUqneYteW4U/z8Sd8B0Qulw/PQc1INm/evKampvnz5w9udrU2HGJt2vvtt9+2bdu2Zs0aPZ2R9DRsqzb8u+X+/ftTU1PT0tL4fD5VGB8fb2RkFBgY2NTUNJzBaMbj8QIDA01NTfl8/tKlS319fa9cufL06VNqgtOnT5Mq7t+/T41KTU3NzMxMT09/77336HS6lZXVhQsXqEPXDRs2TJ48ee7cuV1dXcO9Vq8yfDKlXlipvZMnT9bW1mo58SCS9dtgQG2oQ5MnTz5//vzy5ctZLNbwL12vhrlJS0tLd+3aFRERQRCEarmXl1dwcHB1dfWIOjO7ePGisbExNWhmZoYQ0vLU5Pjx4+7u7q6urn1NEB4enp+fHxcXN/Q4h8LwybS0tHTChAlcLpfNZs+YMePmzZvUqJ9//nnixIlCoZAgCFdX16tXryKEgoODQ0JCysrKaDSaRCLBU54+fdrDw4MgCC6Xa2dnFxUVhcuNjIwuXbo0Z84coVBoZWX17bffahPSsWPHuFwuh8O5cOHCnDlzBAKBtbX1mTNnqAlIkjx8+PA777zDYrFEItHChQsfPHiARx08eJDD4fD5/Nra2pCQkLFjx65Zs4bL5RoZGU2dOtXCwoLBYHC5XHd39xkzZtjY2BAEYWJisnXrVs1rrebmzZtisZhGox09ehS3Ia2Hf/3rX1q2oVptmlew38YZUdRWTXPw8fHxBEGMHj169erVVlZWBEF4eXnl5ubisUFBQUwm09LSEg9+8cUXXC6XRqPV19ej3nbLK1euCASCvXv36mnV4uPjSZJcsGBBz1HR0dHjxo375ptvrl+/3uu8Q9m+3d3du3fvFovFbDZ70qRJZ8+eHUTw1dXVbDbb3t6+3yk7Oztv376t+TxGJBJ98MEHcXFxBr6kQ+rH4sWLFy9e3O9ks2fPdnBwePLkiUKhuH///nvvvUcQxKNHj/DY9PT08PDwxsbGhoYGT0/PUaNG4XI/Pz9HR0eqkiNHjiCE9u3b19DQ0NjY+NVXXy1fvpwkyR07diCEfvzxR6lU2tjYOHfuXBaL1dbWpk381LxNTU21tbUzZszgcrmdnZ147O7du5lM5unTp6VSaUFBgbu7u5mZ2fPnz1Xn3bBhQ0JCwqJFi37//fc9e/YghHJzc9va2urr6//yl78ghC5dulRXV9fW1hYUFIQQys/P17zWJSUlCKHjx4/jQXyKlJCQgEdt27YNr9qzZ89EIpGXl1d3d7f2baham5Yr2FfjaOm9996bPHnygGZBCJ09e3ZAs5A9Vk1z8IGBgVwut7i4uL29vaioaNq0aXw+v7KyEo9dvny5hYUFVXNsbCxCqK6uDg+qNenFixf5fH5kZORAA9byt+Pg4DBx4kS1QkdHxydPnpAkeevWLSMjIzs7u9bWVpIks7KyfHx8qMmGsn03b97MYrHOnTv38uXLsLAwIyOju3fvDmgF29ra+Hx+UFAQVRIVFWVtbW1iYsJgMOzs7Hx8fO7cuYNHPXnyBCHk5uY2c+ZMS0tLFos1YcKEo0ePKpVK1Tq3b9+OEMrLy9O8aC3bdnAMn0xVf1EFBQUIoc2bN/ecMiYmBiFUW1tLvrrXdnZ2mpiYzJo1i5qyq6sL/4/C+4RcLsflp06dQgjdv39fm/jV5k1MTEQIlZaWkiQpk8l4PF5AQAA18Z07dxBC1M9GbV6SJHEybWlpwYP/+Mc/EEKFhYWqs6empmpeaw3JVJWvry9BEA8ePNBcm4ZkOtAVVG0c7Rk2mfYVfGBgoFAopOa9e/cuQigiIgIPDiiZDpo2v53W1lYajTZ//ny1ciqZkiQZEhKCEFq3bh35ajIdyvaVy+UcDoeaVyaTsVistWvXDmgFd+zYMW7cuObmZqqksrLy3r17LS0tHR0dOTk5U6ZMYbPZ+KdaWFiIEPrwww9/+eWXhoYGqVS6bds2hFBycrJqnfik89SpU5oXrddkavjTfFWurq5CoRCnVDX40mrPPhAFBQVSqfSjjz6iSoyNjTds2NBXDQqFYhCB4W8O43mLiopaW1s9PDyosdOmTWMymdT5oJa1UdfLNQTW11r3JS0t7Z///GdERMT48eMHXdtAV1C1cV47moP38PDgcDjUKfDIgf8jcjgcDdNER0ePHz8+MTFR9dIZGtr2ffjwoUwmo27+sNlsS0vLAbVPRkZGWlra1atXVW+a2djYTJkyhcfjMZlMT0/PpKQkuVyOkzi+tu7s7Ozl5WVqaioUCiMiIoRC4YkTJ1SrxU3x4sUL7SPRuZGVTBFCDAaD2rMvXbo0c+ZMc3NzFoulelVRVXNzM0LIxMRk2CLEnTB4PJ5qoYmJSUtLi07q12ate9XQ0LB+/fpp06bhQ5JB16bvFXy9sFisuro6Q0ehDvee1nwTjyCIpKQkGo326aefyuVyqnwo27etrQ0htHPnTurSfEVFhfZd3FJTU/fv35+dnW1nZ6dhMldXV2Nj40ePHiGErKysEEL4wjTGZDJtbW3VvlPLZrPRH81iKCMrmXZ1dTU2NorFYoRQZWWlr6+vpaVlbm5uU1PTgQMHep1lzJgx6NW21jecuNX2PKlUam1tPfTKtVzrXm3YsEEqlSYlJVG3TQdXm15X8PWiUChG5orj3NHvSQZ+EXJJSQl1SxYNbfuam5sjhI4cOaJ6eqvlG0ITEhKSk5Nv3LiBf7MaKJVKpVKJ/1XweDwnJ6fi4mLVCbq6uoRCoWpJZ2cn+qNZDGVkJdP//u//ViqVuF9xYWGhQqFYu3atg4MDfpap11ns7OxMTU2vXbs2bEG6uLjweLxff/2VKsnNze3s7Jw6derQK9dyrXu6dOlSSkrKrl27nJ2dccmWLVsGV5teV/D1kp2dTZKkp6cnHqTT6SPkagZ+HE6bnqRRUVETJkzIy8ujSoayfXH/k/z8/AFFS5JkaGhoYWFhZmam2hExpnqZDiGE72hNnz4dD/r7++fl5T1+/BgPymSyiooKtZ5SuCksLCwGFJhuGT6ZdnZ2NjU1dXV13bt3LygoyNbWduXKlQghfHx6/fr19vb2kpIS1Qs6pqamNTU15eXlLS0tRkZGYWFhP/30U1BQUHV1tVKpbGlpUfs/plsEQYSEhGRkZCQnJzc3NxcWFq5Zs8bKyiowMHDolWtYaw2am5tXr17t5uaGr823t7f/+uuv+fn5WrahWoLQ6wqOfEql8uXLl11dXQUFBcHBwWKxGO+QCCGJRNLY2JiZmalQKOrq6ioqKlRnVGvSrKws/XWN4nA4Dg4O2nx3AJ/sq/bxHMr2JQhi1apVZ86cOXbsWHNzc3d3d1VV1bNnzxBCAQEBFhYWvT6uWlxcfPDgwa+//prBYKj23jt06BCeoLq6OjU1VSqVKhSKnJyczz77TCwWr1mzBo/dtGkTTguVlZUNDQ2hoaFyuRzv6hTcFBr6og4HPd3Y0vKuWVJS0qxZs0aPHk2n00eNGrVs2bKKigpqbGhoqKmpqYmJyZIlS3A/QUdHR3zjz9bWls1mv//++7g/x9GjR11dXQmCIAhiypQpiYmJBw4cwMf8Tk5OZWVlycnJIpEIIWRtbd3vDf3ExER8PRvPe+LECYFAgBCytbXF3baUSmVsbKyTkxODwRCJRL6+vg8fPsTzUsu1sbHBD3XExcXh2uzs7H7++ef9+/fjMxQLC4uUlJTU1FT871QkEp05c6avtQ4ODsaTcbncRYsWJSQk4A6PHA5nwYIF1E6pau7cuVq24c6dO1Vr07yC/TaOZjk5OX/605/whTCEkKWlpZeX1//8z//0OyM5qLv5ag3Vb/CBgYEMBmPs2LF0Ol0gECxcuLCsrIyqraGhYdasWQRB2Nvbr1+/fsuWLQghiUSC+06p7ZaXL1/m8/nR0dEDCpjU+rcTFBTEYDBkMhkezMjIcHR0RAiZmZnhO/iqtmzZoto1aijbt6OjIzQ0VCwW0+l0c3NzPz+/oqIikiR9fX0RQrt37+4ZKr4j31NsbCyeICQkxNHRkcvl0ul0a2vrzz//vKamRrWGp0+fLlu2TCQSsVisd999NysrS20R8+bNGzt2rFp/qZ7e5K5RAGhvEMl0oPAjj3pdRL+0/O2UlJTQ6XS1pzANqLu7e8aMGSdPnhz+RdfX1xMEcejRSqJcAAAgAElEQVTQoX6nfIu6RgFgcCPkFUT9kkgkkZGRkZGRqq9fMpTu7u7MzMyWlpaAgIDhX3p4eLibmxt++MWA3sZk+uDBg54PX1IMsje8GaBhh9n27duXLFkSEBBg8HeaZGdnnz9/PisrS3PXV304fPhwfn7+5cuXB/GWD916Gz+oN2HCBPKNfi2bobzuDRsWFpaUlNTZ2Wlvbx8bG7t48WJDR9S/vXv3Xrt2bd++ffv37zdgGLNnz549e/bwL/fChQsdHR3Z2dmqd9gM5W1MpgD0KiYmBj9x+3rx9vb29vY2dBSG4ePj4+PjY+go/p+38TQfAAB0DpIpAADoACRTAADQAUimAACgA5BMAQBAF/T0MMBr0a0EAPC20d8TUHrsGuXp6blx40b91Q/eNv7+/sHBwdTLhN5U+DM88NvRB9y2eqLHZGptbb106VL91Q/eNv7+/tOnT3/jd6r09HSE0Bu/mgaB21ZP4JopAADoACRTAADQAUimAACgA5BMAQBAByCZAgCADoz0ZHr+/HkHBwfV12LS6XQzM7N/+7d/y8jI0NVSVq1ahb831+unYlVjWLFiheoob29vPp9vbGzs7Ozc69dvhsGhQ4fw59W+/PJLXHL58mWhUPjDDz/opH7d1gZ07vr169u3bx/he2lkZOTEiRMFAgGLxZJIJFu3blV9p3V0dLTa229dXFxUZ1coFDExMRKJhMlkmpiYuLi4lJeXI4S+//77AwcOjJD3eY/0ZOrn5/f48WNHR0ehUIh7xtbV1Z09e7a6utrPz+/s2bM6WUpSUtLmzZv7jWHUqFHJycmXLl2iRl27di09PX3+/PlFRUX4o6rDb/Pmzbdu3VItIXX6UlHd1gZ0a8+ePfHx8WFhYSN8L71x48a6devKy8vr6+tjYmLi4uKWLFmi/ez+/v6nTp1KSUmRyWS///67o6MjzsULFiwgCGL27NlSqVRvsWtrpCfTnkQi0ezZs//rv/4LIZSWltbv9HK53MvLSyeLjo+PNzIyCgwMNPiLzTWbN29eU1PT/PnzBze7WosNsbbXiA53FR1WpcH+/ftTU1PT0tL4fD5VODL3Uh6Ph7+vxefzly5d6uvre+XKladPn1ITqH3M6v79+9So1NTUzMzM9PT09957j06nW1lZXbhwgTp03bBhw+TJk+fOndvV1TXca/Wq1y+ZYnZ2dgghbf4dnTx5sra2VstqNX9Z3svLKzg4uLq6WsNh7BtgQC32JtHhig9DG5aWlu7atSsiIoIgCNXykbmXXrx4UfVl+GZmZgghmUymzbzHjx93d3fX8Bnn8PDw/Pz8uLi4occ5FK9rMi0oKEAIffDBB1TJzz//PHHiRKFQSBCEq6vr1atXEULBwcEhISFlZWU0Gk0ikeApT58+7eHhQRAEl8u1s7OLiorC5UZGRpcuXZozZ45QKLSysvr22297Ljc6OnrcuHHffPPN9evXew2MJMnDhw+/8847LBZLJBItXLjwwYMHeNTBgwc5HA6fz6+trQ0JCRk7duyaNWu4XK6RkdHUqVMtLCwYDAaXy3V3d58xY4aNjQ1BECYmJlu3btW8jmpu3rwpFotpNBr+sHNpaWnPzzH961//0rLF1GrTvILHjh3jcrkcDufChQtz5swRCATW1tZnzpzRdqPqgobwgoKCmEwm/vIzQuiLL77gcrk0Gq2+vr7nisfHxxMEMXr06NWrV1tZWREE4eXllZubO4iqEEJXrlwRCAR79+7V4ZrGx8eTJLlgwYKeo4ayl/a7Ebu7u3fv3i0Wi9ls9qRJkwZ3qa26uprNZtvb2/c7ZWdn5+3bt93c3DRMIxKJPvjgg7i4OANfktLTM/+6/aSq6jVTmUyWlZVla2vr7e3d2tpKTZOenh4eHt7Y2NjQ0ODp6Tlq1Chc7ufn5+joSE2GH87dt29fQ0NDY2PjV199tXz5cpIkd+zYgRD68ccfpVJpY2Pj3LlzWSxWW1ubagxPnjwhSfLWrVtGRkZ2dnZ46VlZWapfJN+9ezeTyTx9+rRUKi0oKHB3dzczM3v+/Dkei5eyYcOGhISERYsW/f7773v27EEI5ebmtrW11dfX/+Uvf0EIXbp0qa6urq2tDX9wMT8/X/M6lpSUIISOHz+OB/HZU0JCAh61bds2vCLPnj0TiUReXl7d3d3at5hqbVqu4I8//tjU1FRbWztjxgwul9vZ2TmYrd4D0uJTz5rDW758uYWFBTVxbGwsQqiurq7XFQ8MDORyucXFxe3t7UVFRdOmTePz+ZWVlYOo6uLFi3w+PzIyUpvV1PK34+DgMHHiRLVCHe6lfW3EzZs3s1isc+fOvXz5MiwszMjI6O7du9qsF6WtrY3P5wcFBVElUVFR1tbWJiYmDAbDzs7Ox8fnzp07eNSTJ08QQm5ubjNnzrS0tGSxWBMmTDh69KhSqVStc/v27QihvLw8zYvW66eeX5tkqvY/wNXV9R//+EdHR0ev0+Mv+dTW1pKv7tadnZ0mJiazZs2ipuzq6sL/0PAOJJfLcfmpU6cQQvfv31eNAe+mJEmGhIQghNatW0e+upvKZDIejxcQEEDNdefOHYQQ9StSWwpJkjiZtrS04MF//OMfCKHCwkLV2VNTUzWvo4ZkqsrX15cgiAcPHmjfYmq1DXQFExMTEUKlpaU9lzgI/SbTfsMbaDKl/oWTJHn37l2EUERExCCqGhBtfjutra00Gm3+/Plq5frYS1U3olwu53A41LwymYzFYq1du3ZAK7hjx45x48Y1NzdTJZWVlffu3Wtpaeno6MjJyZkyZQqbzca/vsLCQoTQhx9++MsvvzQ0NEil0m3btiGEkpOTVevE55GnTp3SvGi9JtPX5jSf2q0VCkVVVdXGjRuDgoImTZqET6zU4I++9uwwUVBQIJVKP/roI6rE2Nh4w4YNfdWgUCh6DSY6Onr8+PGJiYk3b95ULS8qKmptbfXw8KBKpk2bxmQyqdPDfjGZTIQQdSldQxh9rWNf0tLS/vnPf0ZERIwfP37QtQ10BfHq9NWMOjf09tfAw8ODw+FQp8OGhf/taf6usq72UtWN+PDhQ5lMRt38YbPZlpaWA2qTjIyMtLS0q1evqt40s7GxmTJlCo/HYzKZnp6eSUlJcrkcJ3EWi4UQcnZ29vLyMjU1FQqFERERQqHwxIkTqtXipnjx4oX2kejca5NMKXQ6fezYsatWrTp06NDDhw/37duHyy9dujRz5kxzc3MWi6V6nVFVc3MzQsjExGSIMRAEkZSURKPRPv30U7lcTpXjG2I8Hk91YhMTk5aWliEuEdNmHXvV0NCwfv36adOm4aOVQdem7xUcIn2Hx2Kx6urqdFLVEOEO0TjR9EUfe2lbWxtCaOfOndT194qKCi3vIyGEUlNT9+/fn52djW8g98XV1dXY2PjRo0cIISsrK4SQ6jETk8m0tbUtKytTnYXNZqM/msVQXr9kSsF394qLixFClZWVvr6+lpaWubm5TU1NBw4c6HWWMWPGoFc3zKBNnz5906ZNJSUl1P0r9EeaVtsppVKptbX10Jeo5Tr2asOGDVKpNCkpibqjOrja9LqCQ6fX8BQKxchZU5w7+j2T0Pleam5ujhA6cuSI6ultTk6ONjEnJCQkJyffuHED/ww1UCqVSqUS/6vg8XhOTk74Z07p6uoSCoWqJZ2dneiPZjGU1ziZ/u///i9CCJ+0FhYWKhSKtWvXOjg44GeZep3Fzs7O1NT02rVrOgkgKipqwoQJeXl5VImLiwuPx/v111+pktzc3M7OzqlTpw59cVquY0+XLl1KSUnZtWuXs7MzLtmyZcvgatPrCg5dv+HR6fRBX3PIzs4mSdLT03PoVQ0dfuZNm56kut1LcSeT/Pz8AUVLkmRoaGhhYWFmZqbaETGmeuUNIYTvaFFvAff398/Ly3v8+DEelMlkFRUVaj2lcFNYWFgMKDDdep2SqVwux7fwampqkpKSdu7caWZmhl9ILhaLEULXr19vb28vKSlRvfpjampaU1NTXl7e0tJiZGQUFhb2008/BQUFVVdXK5XKlpYWtX962sOnUaq95wiCCAkJycjISE5Obm5uLiwsXLNmjZWVVWBg4NBWHSGN66hBc3Pz6tWr3dzc8GX79vb2X3/9NT8/X8sWU8sXel3Boes3PIlE0tjYmJmZqVAo6urqKioqVGfvueJKpfLly5ddXV0FBQXBwcFisXjlypWDqCorK0u3XaM4HI6Dg0NVVVW/U+p2LyUIYtWqVWfOnDl27Fhzc3N3d3dVVdWzZ88QQgEBARYWFr0+rlpcXHzw4MGvv/6awWCodtE7dOgQnqC6ujo1NVUqlSoUipycnM8++0wsFq9ZswaP3bRpk62t7cqVKysrKxsaGkJDQ+VyOd6fKbgpNPRFHQ56urGlq7tmGRkZPW/ls1gsJyentWvXUv1USJIMDQ01NTU1MTFZsmQJ7hTp6OiI7xLa2tqy2ez3338fd/44evSoq6srQRAEQUyZMiUxMfHAgQP4BMHJyamsrCw5OVkkEiGErK2t79+/T8VgZmaG742q2rJli2qnE6VSGRsb6+TkxGAwRCKRr6/vw4cP8ShqKTY2Nvh5j7i4OHzh3M7O7ueff96/fz8+ebGwsEhJSUlNTcX/aUUi0ZkzZ/pax+DgYDwZl8tdtGhRQkIC7v/I4XAWLFhA7a+q5s6dq2WL7dy5U7U2zSuYmJiIVwc344kTJwQCAULI1tb20aNHQ98ZkBZdozSER5JkQ0PDrFmzCIKwt7dfv379li1bEEISiQTvSGq7SmBgIIPBGDt2LJ1OFwgECxcuLCsrG1xVly9f5vP50dHR2qymlr+doKAgBoMhk8nwoK720n43YkdHR2hoqFgsptPp5ubmfn5+RUVFJEn6+voihHbv3t0zVHxHvqfY2Fg8QUhIiKOjI5fLpdPp1tbWn3/+eU1NjWoNT58+XbZsmUgkYrFY7777blZWltoi5s2bN3bsWLX+Uj1B1ygASFK7ZKpD+PHHYVscRcvfTklJCZ1OV3sK04C6u7tnzJhx8uTJ4V90fX09QRCHDh3qd0roGgWAYYyQ1xH1SiKRREZGRkZGqr5+yVC6u7szMzNbWloCAgKGf+nh4eFubm74CRcDgmQKwOtq+/btS5YsCQgIMPg7TbKzs8+fP5+VlaW566s+HD58OD8///Lly7ivtAFBMgWgF2FhYUlJSU1NTfb29ufOnTN0OH3au3dvUFAQ1dvaUGbPnp2SkkK9rGDYXLhwoaOjIzs7G9/kMCw9fuoZgNdXTEwMfsR25PP29vb29jZ0FIbh4+Pj4+Nj6Cj+HzgyBQAAHYBkCgAAOgDJFAAAdACSKQAA6IAeb0BVVVVp840mALSn5Ts1Xmv4yUj47ehDVVWVHl9Vo6eHARYvXqyviAEAYLD09wQUjYQP+YKRbenSpQiO1MCIB9dMAQBAByCZAgCADkAyBQAAHYBkCgAAOgDJFAAAdACSKQAA6AAkUwAA0AFIpgAAoAOQTAEAQAcgmQIAgA5AMgUAAB2AZAoAADoAyRQAAHQAkikAAOgAJFMAANABSKYAAKADkEwBAEAHIJkCAIAOQDIFAAAdgGQKAAA6AMkUAAB0AJIpAADoACRTAADQAUimAACgA5BMAQBAByCZAgCADkAyBQAAHYBkCgAAOgDJFAAAdACSKQAA6AAkUwAA0AFIpgAAoAOQTAEAQAdoJEkaOgYAXpGSknLy5EmlUokHnzx5ghCyt7fHg0ZGRn/961+XL19usPgA6A0kUzDiFBQUTJ48WcMEv/3226RJk4YtHgC0AckUjEQTJkx4+PBhr6MkEklJSckwxwNAv+CaKRiJVqxYwWAwepYzGIxVq1YNfzwA9AuOTMFI9PjxY4lE0uvOWVJSIpFIhj8kADSDI1MwEjk4OLi7u9NoNNVCGo3m4eEBmRSMTJBMwQj1ySefGBsbq5YYGxt/8sknhooHAM3gNB+MULW1tVZWVlQHKYSQkZFRTU2NhYWFAaMCoC9wZApGqNGjR3/wwQfUwamxsfHMmTMhk4IRC5IpGLlWrFiheua0YsUKAwYDgGZwmg9GrubmZnNz887OToQQg8Gora01MTExdFAA9A6OTMHIJRAI/vKXv9DpdDqdPnfuXMikYCSDZApGtI8//ri7u7u7uxsexgcjHJzmgxGtvb3dzMyMJMn6+no2m23ocADoG/l2WLx4saFbGoC30eLFiw396x8mdEM39fDx9PTcuHGjoaN4nfj7+wcHB0+fPt2wYeTn59NoNM3vkRqKI0eOIIRg39AH3LZvibcomVpbWy9dutTQUbxO/P39p0+fbvBGW7RoEUKITtfXvpqeno4QMvhqvpFw274l3qJkCl5T+kujAOgQ3M0HAAAdgGQKAAA6AMkUAAB0AJIpAADoACRTvbh8+bJQKPzhhx9GeJ368LrEOWjXr1/fvn37+fPnHRwcaDQajUZTewOLt7c3n883NjZ2dna+d++eQYKMjIycOHGiQCBgsVgSiWTr1q2tra3U2OjoaNqrXFxcVGdXKBQxMTESiYTJZJqYmLi4uJSXlyOEvv/++wMHDnR3dw/z6rwWIJnqBamH58r0Uac+vC5xDs6ePXvi4+PDwsL8/PweP37s6Og4atSo5OTkS5cuUdNcu3YtPT19/vz5RUVF7u7uBonzxo0b69atKy8vr6+vj4mJiYuLW7Jkifaz+/v7nzp1KiUlRSaT/f77746OjjgXL1iwgCCI2bNnS6VSvcX+2jLwQwPDZfHixXp9EkMmk02fPn3k1zkgCKGzZ88aMADNdNU+2u8b+/btGzdunFwup0ocHR1TUlKMjIzGjh0rlUqp8qysLB8fn6HHNmjz5s3r6uqiBnEv2srKSjwYFRV1+vTpvuY9c+YMjUYrKCjoa4KgoKDp06crFIp+w9D3725EgSNT3Th58mRtbe3Ir/NNMsztU1paumvXroiICIIgVMu9vLyCg4Orq6s3b948bMH06+LFi6offTEzM0MIyWQybeY9fvy4u7u7q6trXxOEh4fn5+fHxcUNPc43CSTT3v38888TJ04UCoUEQbi6ul69epUadfr0aQ8PD4IguFyunZ1dVFRUcHBwSEhIWVkZjUaTSCQ3b94Ui8U0Gu3o0aMIoXfeeYdGoxkZGU2dOhXvzVu3bsU1//3vf+9rWZrrRAiRJHn48OF33nmHxWKJRKKFCxc+ePAAjzp27BiXy+VwOBcuXJgzZ45AILC2tj5z5swwtJtanJojiY+PJwhi9OjRq1evtrKyIgjCy8srNzcXjw0KCmIymZaWlnjwiy++4HK5NBqtvr6+Z/sghK5cuSIQCPbu3aunVYuPjydJcsGCBT1HRUdHjxs37ptvvrl+/Xqv8w5lY3V3d+/evVssFrPZ7EmTJp09e3YQwVdXV7PZbHt7+36n7OzsvH37tpubm4ZpRCLRBx98EBcXR77Rl3QGzLAHxsNmoKcb6enp4eHhjY2NDQ0Nnp6eo0aNwuX4WeN9+/Y1NDQ0NjZ+9dVXy5cvJ0nSz8/P0dGRmv3p06cIoYSEBJIku7q67OzsxGKx6mnXxo0bjxw5onlZGuokSXL37t1MJvP06dNSqbSgoMDd3d3MzOz58+d47I4dOxBCP/74Y1NTU21t7YwZM7hcbmdn54AaDQ3qNF8tTs2RBAYGcrnc4uLi9vb2oqKiadOm8fl86mx0+fLlFhYWVM2xsbEIobq6ul7b5+LFi3w+PzIycqABa7lvODg4TJw4Ua3Q0dHxyZMnJEneunXLyMjIzs6utbWV7HGaP5SNtXnzZhaLde7cuZcvX4aFhRkZGd29e3dAK9jW1sbn84OCgqiSqKgoa2trExMTBoNhZ2fn4+Nz584dPOrJkycIITc3t5kzZ1paWrJYrAkTJhw9elSpVKrWuX37doRQXl6e5kW/Vaf5kEz7FxMTgxCqra3t7Ow0MTGZNWsWNaqrqwv/f9ac+HAKTktLw4NtbW1isbipqUnDsjTXKZPJeDxeQEAANfbOnTsIISqV4N8ndXUvMTERIVRaWjqgFddhMu0rksDAQKFQSM179+5dhFBERAQeHFAyHTRt9o3W1lYajTZ//ny1ciqZkiQZEhKCEFq3bh35ajIdysaSy+UcDoeaVyaTsVistWvXDmgFd+zYMW7cuObmZqqksrLy3r17LS0tHR0dOTk5U6ZMYbPZ9+/fJ0mysLAQIfThhx/+8ssvDQ0NUql027ZtCKHk5GTVOr/99luE0KlTpzQv+q1KpnCa3z8Gg4EQ6u7uLigokEqlH330ETXK2Nh4w4YN/dbw2WefCYVC6hpTcnLywoULBQKBhmVprrCoqKi1tdXDw4MqmTZtGpPJpM6R1TCZTISQQqHoN1R90xyJh4cHh8OhToFHDvzvjcPhaJgmOjp6/PjxiYmJN2/eVC0fysZ6+PChTCaj+i2x2WxLS8sBtU9GRkZaWtrVq1f5fD5VaGNjM2XKFB6Px2QyPT09k5KS5HI5TuIsFgsh5Ozs7OXlZWpqKhQKIyIihELhiRMnVKvFTfHixQvtI3njQTLt3aVLl2bOnGlubs5isbZu3YoLm5ubEUKD+HgGj8f729/+duvWLXxIcvz48aCgIM3L0gx3TOHxeKqFJiYmLS0tA41tpGGxWHV1dYaOQl17ezv6I9H0hSCIpKQkGo326aefyuVyqnwoG6utrQ0htHPnTqpDaEVFhZb3kRBCqamp+/fvz87OtrOz0zCZq6ursbHxo0ePEEJWVlYIIXxhGmMymba2tmVlZaqz4Bd142YBGCTTXlRWVvr6+lpaWubm5jY1NR04cACXjxkzBr26n2kvKCiIwWAcOXLkp59+srGxcXR01LwszXBCV/s1SqVSa2vrQcQ2cigUipG5Fjh39HvGMH369E2bNpWUlERFRVGFQ9lY5ubmCCHq8jqWk5OjTcwJCQnJyck3btzA+60GSqVSqVTifxU8Hs/Jyam4uFh1gq6uLqFQqFqCv3II3z5QBcm0F4WFhQqFYu3atQ4ODgRB0Gg0XG5nZ2dqanrt2rVB1Infpnru3Lldu3YFBwf3uyzNXFxceDzer7/+SpXk5uZ2dnZOnTp1ELGNHNnZ2SRJenp64kE6nT4SLk0ghEaPHk2j0ZqamvqdMioqasKECXl5eVTJUDaWjY0NQRD5+fkDipYkydDQ0MLCwszMTLUjYkz1UhVCCN/Rot4C7u/vn5eX9/jxYzwok8kqKirUekrhprCwsBhQYG82SKa9EIvFCKHr16+3t7eXlJRQ17ZYLFZYWNhPP/0UFBRUXV2tVCpbWlrw/3BTU9Oampry8vKWlpa+fv8hISFdXV0vX77885//3O+yNNdJEERISEhGRkZycnJzc3NhYeGaNWusrKwCAwN13Rh6p1QqX7582dXVVVBQEBwcLBaLV65ciUdJJJLGxsbMzEyFQlFXV1dRUaE6o1r7ZGVl6a9rFIfDcXBwqKqq6ndKfLKv2sdzKBuLIIhVq1adOXPm2LFjzc3N3d3dVVVVz549QwgFBARYWFj0+rhqcXHxwYMHv/76awaDofrM6KFDh/AE1dXVqampUqlUoVDk5OR89tlnYrF4zZo1eOymTZtsbW1XrlxZWVnZ0NAQGhoql8vxbSgKbgoNfVHfRoa462UAA72rGBoaampqamJismTJEtxl0tHREXfZOXr0qKurK0EQBEFMmTIlMTGRJMl79+7Z2tqy2ez3339/586duHckh8NZsGCBarWzZs365ptvtFyW5jqVSmVsbKyTkxODwRCJRL6+vg8fPsQVJiYm4vsDTk5OZWVlJ06cwDe7bG1tHz16pH0joIHfzU9ISFCNs99IAgMDGQzG2LFj6XS6QCBYuHBhWVkZVVtDQ8OsWbMIgrC3t1+/fv2WLVsQQhKJBG8I1fZ5/vz55cuX+Xx+dHT0gAImtd438IUamUyGBzMyMvC1GjMzM3wHX9WWLVtUu0YNZWN1dHSEhoaKxWI6nW5ubu7n51dUVESSpK+vL0Jo9+7dPUPFd+R7io2NxROEhIQ4OjpyuVw6nW5tbf3555/X1NSo1vD06dNly5aJRCIWi/Xuu+9mZWWpLWLevHljx45V6y/V01t1Nx+SKejTIJLpQAUGBpqamup1Ef3Sct8oKSmh0+kansIcZt3d3TNmzDh58uTwL7q+vp4giEOHDvU75Vv1u4PTfGBgr8sriCQSSWRkZGRkpOrrlwylu7s7MzOzpaUlICBg+JceHh7u5uam2iMFILhmCoD2tm/fvmTJkoCAAG3uROlVdnb2+fPns7KyNHd91YfDhw/n5+dfvnwZ94kGFEimwGDCwsKSkpKamprs7e3PnTtn6HC0snfv3qCgoH379hk2jNmzZ6ekpFAvLhg2Fy5c6OjoyM7OFolEw7zokQ+++wgMJiYmBj8++3rx9vb29vY2dBSG4ePj4+PjY+goRig4MgUAAB2AZAoAADoAyRQAAHQAkikAAOjAW3QDqqqqKi0tzdBRvGa0fKfGaw0/GQn7hj5UVVWNwNfW6IuhnxoYJosXLzZ0SwPwNnp7noB6i45MFy9enJ6ebugoXic0Gu3s2bP4w5ZvMPwNZNg39GFA35d+3cE1UwAA0AFIpgAAoAOQTAEAQAcgmQIAgA5AMgUAAB2AZAoAADoAybRP3333HY1G8/LyGkolly9fFgqFP/zwg66i0lOdQHvXr1/fvn37+fPnHRwc8LeVVqxYoTqBt7c3n883NjZ2dnbu9RtNwyAyMnLixIkCgYDFYkkkkq1bt6q+0zo6Opr2KhcXF9XZFQpFTEyMRCJhMpkmJiYuLi7l5eUIoe+///7AgQOvy/u8hxkk0z599913jo6OOTk5paWlg66EJEkdhqS/OoGW9uzZEx8fHxYW5ufn9/jxY0dHx1GjRiUnJ1+6dIma5tq1a+np6fPnzy8qKnJ3dzdInDdu3Fi3bl15eXl9fX1MTExcXNyAunz6+/ufOpTTjNYAACAASURBVHUqJSVFJpP9/vvvjo6OOBcvWLCAIIjZs2dLpVK9xf66gmTau4aGhuLi4oiICITQqVOntJ9RLperHszOmzevqalp/vz5QwlGH3WOBGrrNUKq0mD//v2pqalpaWl8Pp8qjI+PNzIyCgwMNPjr91XxeDz8fS0+n7906VJfX98rV648ffqUmkDtY1b379+nRqWmpmZmZqanp7/33nt0Ot3KyurChQvUoeuGDRsmT548d+7crq6u4V6rkQ2Sae/S0tLmzZuH/w/j3U7LGU+ePFlbW6vbYPRR50igw/UahiYqLS3dtWtXREQEQRCq5V5eXsHBwdXV1Zs3b9ZrAANy8eJF1c9Nm5mZIYRkMpk28x4/ftzd3V3DZ5zDw8Pz8/Pj4uKGHuebBJJp77777rtFixbx+Xxvb+/y8vKff/655zSnT5/28PAgCILL5drZ2UVFRQUHB4eEhJSVldFoNIlEcvPmTbFYTKPR8Aec33nnHRqNZmRkNHXqVLxbb926VSgUEgTx97//HSH0888/T5w4EZe4urpevXoVIaS5ToQQSZKHDx9+5513WCyWSCRauHDhgwcP8Khjx45xuVwOh3PhwoU5c+YIBAJra+szZ87osKE0LD0oKIjJZFKf1vjiiy+4XC6NRquvr++5XvHx8QRBjB49evXq1VZWVgRBeHl55ebmDqIqhNCVK1cEAsHevXt1uKbx8fEkSS5YsKDnqOjo6HHjxn3zzTfXr18faCv1u426u7t3794tFovZbPakSZPOnj07iOCrq6vZbLa9vX2/U3Z2dt6+fdvNzU3DNCKR6IMPPoiLi4MrTq8Y7pcBGMiAPjlbUVFhbm7e1dVFkuTp06cRQn/961/Vpjly5AhCaN++fQ0NDY2NjV999dXy5ctJkvTz83N0dKQmwydWCQkJJEl2dXXZ2dmJxWJcM7Zx48YjR47gv9PT08PDwxsbGxsaGjw9PUeNGoXLNdRJkuTu3buZTObp06elUmlBQYG7u7uZmdnz58/x2B07diCEfvzxx6amptra2hkzZnC53M7OTm3aAWnxqWfNS1++fLmFhQU1cWxsLEKorq6u1/UKDAzkcrnFxcXt7e1FRUXTpk3j8/mVlZWDqOrixYt8Pj8yMlKb1dRy33BwcJg4caJaoaOj45MnT0iSvHXrlpGRkZ2dXWtrK0mSWVlZPj4+1GRD2UabN29msVjnzp17+fJlWFiYkZHR3bt3tVkvSltbG5/PDwoKokqioqKsra1NTEwYDIadnZ2Pj8+dO3fwqCdPniCE3NzcZs6caWlpyWKxJkyYcPToUaVSqVrn9u3bEUJ5eXmaF/1WfeoZkmkv9u3bt2rVKvx3U1MTi8USCAQymYyaoLOz08TEZNasWVRJV1cX/ketOfHhFJyWloYH29raxGJxU1NTzxjwx5Fqa2s11ymTyXg8XkBAADX2zp07CCEqj+Afqlwux4OJiYkIodLSUm3aod9k2u/SB5pMhUIhNXj37l2EUERExCCqGhBt9o3W1lYajTZ//ny1ciqZkiQZEhKCEFq3bh35ajIdyjaSy+UcDoeaVyaTsVistWvXDmgFd+zYMW7cuObmZqqksrLy3r17LS0tHR0dOTk5U6ZMYbPZ9+/fJ0mysLAQIfThhx/+8ssvDQ0NUql027ZtCKHk5GTVOr/99luE0KlTpzQv+q1KpnCa3wt8jo//FggE3t7ezc3NFy5coCYoKCiQSqUfffQRVWJsbLxhw4Z+a/7ss8+EQiF1sSk5OXnhwoUCgaDnlPg7uv32QSkqKmptbfXw8KBKpk2bxmQyqRNkNUwmEyGkUCj6DVUbA136gHh4eHA4HOp02LDwfzXN31WOjo4eP358YmLizZs3VcuHso0ePnwok8momz9sNtvS0nJAbZKRkZGWlnb16lXVm2Y2NjZTpkzh8XhMJtPT0zMpKUkul+MkzmKxEELOzs5eXl6mpqZCoTAiIkIoFJ44cUK1WtwUL1680D6SNx4kU3X3798vLCycP38+1QUP9+hUvaff3NyMEDIxMRlo5Twe729/+9utW7fwscnx48eDgoKosZcuXZo5c6a5uTmLxdq6das2FeIeKjweT7XQxMSkpaVloLENgr6XzmKx6urqdFLVELW3t6M/Ek1fCIJISkqi0WiffvqpXC6nyofSSm1tbQihnTt3UntjRUWFlveREEKpqan79+/Pzs62s7PTMJmrq6uxsfGjR48QQlZWVgghfDEaYzKZtra2ZWVlqrOw2Wz0R7MADJKpupSUlGXLlqkevTc2NrLZ7GvXrj1//hxPM2bMGPTqDqe9oKAgBoNx5MiRn376ycbGxtHREZdXVlb6+vpaWlrm5uY2NTUdOHBAm9pwQlf7WUql0uF5vblel65QKIZtRfqFc0e/JwrTp0/ftGlTSUlJVFQUVTiUVjI3N0cIUVfVMS0/f5CQkJCcnHzjxg28u2qgVCqVSiX+V8Hj8ZycnIqLi1Un6OrqEgqFqiWdnZ3oj2YBGCTTV5AkmZqa+sUXX6gWikSiJUuWdHd3f/fdd7jEzs7O1NT02rVrg1iEtbX10qVLz507t2vXruDgYKq8sLBQoVCsXbvWwcGBIAgajaZNbS4uLjwe79dff6VKcnNzOzs7p06dOojYBqrfpdPp9EFfUsjOziZJ0tPTc+hVDd3o0aNpNJo2PUmjoqImTJiQl5dHlQxlG9nY2BAEkZ+fP6BoSZIMDQ0tLCzMzMxUOyLGVK9QIYTwHa3p06fjQX9//7y8vMePH+NBmUxWUVGh1lMKN4WFhcWAAnuzQTJ9xa1btwQCwZ/+9Ce18jVr1iCVM30WixUWFvbTTz8FBQVVV1crlcqWlhb8z9zU1LSmpqa8vLylpaWvH39ISEhXV9fLly///Oc/U4VisRghdP369fb29pKSEtULahrqJAgiJCQkIyMjOTm5ubm5sLBwzZo1VlZWgYGBOmiO/vS7dIlE0tjYmJmZqVAo6urqKioqVGfvuV5KpfLly5ddXV0FBQXBwcFisXjlypWDqCorK0u3XaM4HI6DgwP+WpRm+GRftY/nULYRQRCrVq06c+bMsWPHmpubu7u7q6qqnj17hhAKCAiwsLDo9XHV4uLigwcPfv311wwGQ/WZ0f/L3r2HNXHmiwN/J+QySUhCkGu5k6BUQCj1mtWjPuzaVY8g4gWrdrVrF60WqUgRUIoIeMEVDgr2WFmeo7hyEQ5YBdu1HvpbV7T2EQqCF0QuKlUuck+AEOb3xzzNpgghwJBE+X7+Ymbeeec770y+zOWdmWPHjpEFnj9/npGR0dbWJpfLi4uLt27damtrS+7kCKHdu3fb2dlt3ry5vr6+paUlNDRUJpORt6GUyKZQ0xd1MtLarS7d0uSu4p///Gcul0un093d3e/evascf/DgQfJCEkLIysoqOTmZHH/y5Ek3Nzccx3Ecf++998jxd+/etbOzY7PZ8+fP37dvH9k1ksPheHt7qy5r8eLFZ86cGRRAaGiosbGxkZHRmjVryG6kIpGIvPGqps6BgYH4+HgnJycGgyEUCn19fR8+fEhWmJycTN4ocHJyqq6uPn36NHmzy87O7tGjRyM2GtKga5SapRME0dLSsnjxYhzHHRwcPvvss5CQEISQWCwmOzyprteLFy8CAgIYDIaVlRWdTufz+StXrqyurh5bVQUFBTweLyYmZsR1JDS+40xen1F26sjNzSUv0ZiYmJB38FWFhISodo0azzbq7e0NDQ21tbWl0+mmpqZ+fn4VFRUEQfj6+iKEIiMjXw+VvCP/uvj4eLJAcHCwSCQi93Zra+tPPvmkoaFBtYanT5+uX79eKBSyWKzZs2cXFhYOWsTy5cutrKwG9Zd63aS6mw/JFAxLk2RKIfLxR60tTknDfaOqqopOpw96ClOHFArFggULUlNTtb/o5uZmHMePHTs2YslJ9buD03ygR/T5dURisTg6Ojo6Olr19Uu6olAo8vLyOjs7/f39tb/0qKgoDw8P1Y4oAME1UwA0FxYWtmbNGn9/f52/06SoqCgnJ6ewsFB919eJcPz48dLS0oKCArIrNFCCZAr0Qnh4eFpaWnt7u4ODw8WLF3UdzrBiY2MDAwMPHTqk2zC8vLzOnz+vfFmB1uTn5/f29hYVFQmFQi0vWv/RdR0AAAghFBcXRz5Bq/+WLFmyZMkSXUehGz4+Pj4+PrqOQk/BkSkAAFAAkikAAFAAkikAAFAAkikAAFBgEt2AunXr1qi+KQYQQgkJCdnZ2bqOYmLdunULIQT7xkS4deuW8u0Kb73JkkyVL3EAmlu9erWuQ0AIIfKlIe+9994E1T95fu3aN3fu3Mnz08MI+IoL0G9r165FCGVlZek6EADUgWumAABAAUimAABAAUimAABAAUimAABAAUimAABAAUimAABAAUimAABAAUimAABAAUimAABAAUimAABAAUimAABAAUimAABAAUimAABAAUimAABAAUimAABAAUimAABAAUimAABAAUimAABAAUimAABAAUimAABAAUimAABAAUimAABAAUimAABAAUimAABAAUimAABAAUimAABAAUimAABAAUimAABAAUimAABAAUimAABAAUimAABAAUimAABAAbquAwBgMKlU2tvbqxzs6+tDCLW2tirHsFgsDoejg8gAGB5GEISuYwDgN1JSUnbs2KGmQHJy8qeffqq1eADQBCRToHeamposLS0VCsWQUw0MDH755RdTU1MtRwWAenDNFOgdU1NTLy8vAwOD1ycZGBj8/ve/h0wK9BAkU6CPNm7cOOQ5E0EQGzdu1H48AIwITvOBPurs7DQ1NVW9DUViMplNTU18Pl8nUQGgBhyZAn3E4/FWrFjBYDBUR9LpdB8fH8ikQD9BMgV6asOGDf39/apjFArFhg0bdBUPAOrBaT7QU319fSYmJp2dncoxhoaGzc3NLBZLh1EBMBw4MgV6islkrlmzhslkkoMMBmPdunWQSYHegmQK9NeHH35IPv6EEJLL5R9++KFu4wFADTjNB/prYGDAwsKiqakJIWRiYvLixYshO58CoA/gyBToLxqN9uGHHzKZTAaDsWHDBsikQJ9BMgV6bf369X19fXCOD/TfZHlrVHFx8dOnT3UdBRg1giCmTJmCEKqpqamtrdV1OGDUbGxs5s2bp+sotIKYHFavXq3rlgZgMlq9erWuf/1aMlmOTBFCq1evzs7O1nUUbxIMwzIzM9euXavbMCorKxFC06dPn6D616xZgxCCfWMikG07SUyiZAreUBOXRgGgENyAAgAACkAyBQAACkAyBQAACkAyBQAACkAyBQAACkAyHdbWrVt5PB6GYaWlpbqOhQIDAwMJCQkSiWSiF1RQUCAQCL755puJXpCuXLt2LSwsLCcnx9HREcMwDMM2bdqkWmDJkiU8Hs/AwMDFxeXu3bs6CTI6Onr69Ol8Pp/FYonF4i+++KKrq0s5NSYmBvstV1dX1dnlcnlcXJxYLGYymUZGRq6uruQTE5cuXTpy5Mhw3zqc5CCZDuvMmTNff/21rqOgRlVV1X/8x3/s3r1bKpVO9LKIt/rVOV9++WVSUlJ4eLifn9+TJ09EItGUKVPS09OvXLmiLPPdd99lZ2evWLGioqLC09NTJ3Fev359586dtbW1zc3NcXFxiYmJo+ryuW7durNnz54/f14qld6/f18kEpG52NvbG8dxLy+vtra2CYv9TQXJ9I0kk8k0P8b8+eef9+7du337dg8PjwmNirR8+fL29vYVK1ZM9IJG1QiUOHz4cEZGRlZWFo/HU45MSkqi0WgBAQHt7e3aDEY9Q0PDgIAAY2NjHo+3du1aX1/fq1evqj5Rfe7cOdWnd+7du6eclJGRkZeXl52dPWfOHDqdbmlpmZ+frzx03bVrl7u7+7JlywZ9BwFAMlUHwzBdhzC01NTUxsZGDQu7u7vn5ORs2LDhLXuz8qgaYfweP368f//+AwcO4DiuOl4ikQQFBT1//nzPnj1aC2ZEly9fVn3JlomJCUJIw/OSU6dOeXp6urm5DVcgKiqqtLQ0MTFx/HG+TSCZ/gZBEPHx8dOmTWOxWAKBICQkRDnp6NGjHA6Hx+M1NjYGBwdbWVk9fPiQIIjjx4+/++67LBZLKBSuXLnywYMHZPmkpCQcx83MzLZt22ZpaYnjuEQiuX37tuqyhps3MDCQyWRaWFiQgzt27OByuRiGNTc3I4SCgoKCg4Orq6sxDBOLxVpqGs3cuHHD1tYWw7CTJ08ihFJSUrhcLofDyc/PX7p0KZ/Pt7a2vnDhAllYfRONthGuXr3K5/NjY2MnaNWSkpIIgvD29n59UkxMzNSpU8+cOXPt2rUh51WzrdU3EUJIoVBERkba2tqy2ewZM2ZkZmaOIfjnz5+z2WwHB4cRS/b19d26dUv9SYxQKFy4cGFiYuLbfUln1LT+NgDdWL16tSYvXIiIiMAw7K9//Wtra6tUKk1OTkYIlZSUKKcihHbt2nXixIlVq1bdv38/MjKSyWSeO3eura2trKzM09OTfIcxWT4gIIDL5VZWVvb09FRUVMyaNYvH49XX15NT1c+7YcMGc3NzZWDx8fEIoaamJnLQz89PJBKNthHmzJnj7u6ueXmEUGZm5miXQp5LnjhxghwkG+37779vb29vbGxcsGABl8vt6+sjp6pvolE1wuXLl3k8XnR09GgD1nDfcHR0nD59+qCRIpGopqaGIIibN2/SaDR7e/uuri6CIAoLC318fJTF1G9r9U20Z88eFot18eLF1tbW8PBwGo12586dUa1gd3c3j8cLDAxUjjl48KC1tbWRkRGDwbC3t/fx8fnxxx/JSTU1NQghDw+PRYsWWVhYsFgsZ2fnkydPDgwMqNYZFham+tMYjoZt+3aAI9N/k8lkCQkJv//973fv3m1kZMRms42NjV8vdvjw4Z07d+bk5NjZ2R0/fnzVqlUbN24UCARubm5fffVVc3Pz6dOnlYXpdDp5PDJ9+vSUlJTOzs60tDRyWSPO+zaRSCR8Pt/U1NTf37+7u7u+vl45abgmGq3ly5d3dHTs37+fuqj/rbu7u6amRiQSDVdg3rx5n3/+eW1t7d69ewdN0nBbD9lEPT09KSkpvr6+fn5+RkZG+/btYzAYo22fuLg4S0vLmJgY5Zg//elPly5devr0aVdX14ULF+rr6xcuXFhRUYEQIm80mZqaxsbGVlRUvHz5cuXKlTt37vz73/+uWqeTkxNCqLy8fFSRvN0gmf7b48ePpVKpl5eXhuUrKiq6urpmzpypHDNr1iwmk6l6Lq9q5syZHA6HPL8b7bxvDfIDeXK5fMipqk2kVxobGwmC4HA4asrExMRMmzYtOTn5xo0bquNHu61Vm+jhw4dSqVR584fNZltYWIyqfXJzc7Oysr799lvVm2Y2NjbvvfeeoaEhk8mcO3duWlqaTCYjz8PIC+suLi4SicTY2FggEBw4cEAgEAxK/WRTvHz5UvNI3nqQTP/t2bNnCCFTU1MNy5O9QwwNDVVHGhkZqX6deBAWi0V+0WgM804SyibSKz09PejXRDMcHMfT0tIwDPv4449lMply/Hi2dXd3N0Jo3759yg6hdXV1mvdvy8jIOHz4cFFRkb29vZpibm5uBgYGjx49QghZWloihMgL0yQmk2lnZ1ddXa06C5vNRr82CyBBMv038i5tb2+vhuWNjIwQQoN+Em1tbdbW1kOWl8vlyqmjnXeSUG0ivULmjhE7q8+bN2/37t1VVVUHDx5UjhzPtib/tSckJKhemysuLtYk5hMnTqSnp1+/fv2dd95RX3JgYGBgYID8V2FoaOjk5ES+Q1apv79fIBCojiG/Gks2CyBBMv03V1dXGo32ww8/aF7e0NDwp59+Uo65fft2X1/f+++/P2T5oqIigiDmzp2rybx0On24c+G3mGoTIX1qBDMzMwzDNOlJevDgQWdn55KSEuWY0e4nqmxsbHAcH+0zeARBhIaGlpeX5+XlDToiJn3wwQeqg+QdLeXHRdatW1dSUvLkyRNyUCqV1tXVDeopRTaFubn5qAJ7u0Ey/TdTU1M/P7+LFy+mpqZ2dHSUlZWpvx2E43hwcHBubm56enpHR0d5efn27dstLS0DAgKUZQYGBlpbW/v7+8vKyoKCgmxtbTdv3qzJvGKx+NWrV3l5eXK5vKmpqa6uTnXRxsbGDQ0NtbW1nZ2depJuxmy4JkKjbITCwsKJ6xrF4XAcHR3JC0HqkSf7qn08NdlP1NS2ZcuWCxcupKSkdHR0KBSKZ8+e/fLLLwghf39/c3PzIR9XraysPHr06Ndff81gMFSfGT127BhZ4Pnz5xkZGW1tbXK5vLi4eOvWrba2ttu3byen7t69287ObvPmzfX19S0tLaGhoTKZbNCNNbIp1PRFnYx00INAFzTsotHZ2bl169YpU6YYGhrOnz8/MjISIWRtbf3zzz8fOXKEPKmxsbFRPj0yMDAQHx/v5OTEYDCEQqGvry/Z+ZQUEBDAYDCsrKzodDqfz1+5cmV1dbVyqvp5W1paFi9ejOO4g4PDZ599RvZ4FYvFZLehu3fv2tnZsdns+fPnK3vYDKe4uPh3v/sdeS0MIWRhYSGRSH744YcRWwONvmvUiRMnyJ6hHA7H29s7OTmZvFPh5ORUXV19+vRpPp+PELKzs3v06NGITTSqRigoKODxeDExMaMKmNB43wgMDGQwGFKplBzMzc0lb+6bmJjs3LlzUOGQkBDVrlFqtvWITdTb2xsaGmpra0un08n/9xUVFQRB+Pr6IoQiIyNfD3W4m+zx8fFkgeDgYJFIxOVy6XS6tbX1J5980tDQoFrD06dP169fLxQKWSzW7NmzCwsLBy1i+fLlVlZWg/pLvW5SdY2CZDqByOf5tLxQCo0hmY6WPjSRhvtGVVUVnU4f9BSmDikUigULFqSmpmp/0c3NzTiOHzt2bMSSkyqZwmn+xIL364zoTWkisVgcHR0dHR2t+volXVEoFHl5eZ2dnf7+/tpfelRUlIeHR2BgoPYXrc8gmb7ZHjx4gA1PJ7+0t1hYWNiaNWv8/f11/k6ToqKinJycwsJC9V1fJ8Lx48dLS0sLCgoYDIaWF63nIJlOlPDw8LS0tPb2dgcHh4sXL07QUpydndWcd2RkZEzQcimhnSaiVmxsbGBg4KFDh3QbhpeX1/nz55UvLtCa/Pz83t7eoqIioVCo5UXrP4yYHK8qgG+jjwGGYZmZmWvXrtV1IBML9o2JM6naFo5MAQCAApBMAQCAApBMAQCAApBMAQCAAnRdB6A9t27dGtU3xQBCKCEh4a2/e3Dr1i30660SQK1bt24p37Tw1oMjUwAAoMAkOjKdO3fuW3+QRS0Mwz7//HPoGgXGbFId78ORKQAAUACSKQAAUACSKQAAUACSKQAAUACSKQAAUACS6chycnIcHR1VX23HZDLNzMwWLVoUHx/f2tqq6wCBVl27di0sLEx1r9i0aZNqgSVLlvB4PAMDAxcXlyE/K6IF0dHR06dP5/P5LBZLLBZ/8cUXqq9hjYmJGfS2RuXXpElyuTwuLk4sFjOZTCMjI1dX19raWoTQpUuXjhw58qa8glbLIJmOzM/P78mTJyKRSCAQEAQxMDDQ2NiYlZXl4OAQGhrq4uKi+q008Hb78ssvk5KSwsPDlXvFlClT0tPTr1y5oizz3XffZWdnr1ixoqKiwtPTUydxXr9+fefOnbW1tc3NzXFxcYmJiaPqpbRu3bqzZ8+eP39eKpXev39fJBKRudjb2xvHcS8vL/L71UAVJNNRwzDMyMho0aJFaWlpWVlZL1++XL58uc7fFvw6mUwmkUh0HYU6FEaonZU9fPhwRkZGVlYWj8dTjkxKSqLRaAEBAXq1DxgaGpKfhOHxeGvXrvX19b169erTp0+VBQZ9f+XevXvKSRkZGXl5ednZ2XPmzKHT6ZaWlvn5+cpD1127drm7uy9btqy/v1/ba6XfIJmOy+rVqzdv3tzY2PjVV1/pOpbBUlNTGxsbdR2FOhRGqIWVffz48f79+w8cOIDjuOp4iUQSFBT0/PnzPXv2TGgAo3L58mXVL6SamJgghKRSqSbznjp1ytPTU82XR6OiokpLSxMTE8cf59sEkul4kd8lLiwsRAgdPXqUw+HweLzGxsbg4GArKyvyI5THjx9/9913WSyWUChcuXLlgwcPyHmTkpJwHDczM9u2bZulpSWO4xKJ5Pbt28rK1cwbGBjIZDKV71rfsWMHl8vFMKy5uRkhFBQUFBwcXF1djWGYWCyeuNWnKkL1TTHalb169Srln31OSkoiCMLb2/v1STExMVOnTj1z5sy1a9dG20opKSlcLpfD4eTn5y9dupTP51tbW1+4cEE5r0KhiIyMtLW1ZbPZM2bMyMzMHEPwz58/Z7PZDg4OI5bs6+u7deuWh4eHmjJCoXDhwoWJiYmT5NXympqgD/Xpm/F/JVF5zXSQjo4OhJCNjQ05GBERgRDatWvXiRMnVq1adf/+/cjISCaTee7cuba2trKyMk9PTxMTE+X3mQMCArhcbmVlZU9PT0VFxaxZs3g8HvkpY4Ig1M+7YcMGc3NzZSTx8fEIoaamJnLQz89PJBKNZ5WRBl8npTBC9U0xqqouX77M4/Gio6M1WU0N9w1HR8fp06cPGikSiWpqagiCuHnzJo1Gs7e37+rqIgiisLBQ9VPP6luJ3Ge+//779vb2xsbGBQsWcLncvr4+cuqePXtYLNbFixdbW1vDw8NpNNqdO3c0WS+l7u5uHo8XGBioHHPw4EFra2sjIyMGg2Fvb+/j4/Pjjz+Sk2pqahBCHh4eixYtsrCwYLFYzs7OJ0+eHPRV57CwMIRQSUmJ+kXD10nBKPB4PAzDOjs7VUcePnx4586dOTk5dnZ2x48fX7Vq1caNGwUCgZub21dffdXc3Hz69GllYTqdTh6zTJ8+PSUlpbOzMy0tDSEkk8lGnFe3Zw3VUAAAIABJREFUKI9wuKYYreXLl3d0dOzfv39sYbyuu7u7pqZGJBINV2DevHmff/55bW3t3r17B03SsJUkEgmfzzc1NfX39+/u7q6vr0cI9fT0pKSk+Pr6+vn5GRkZ7du3j8FgjLZN4uLiLC0tY2JilGP+9Kc/Xbp06enTp11dXRcuXKivr1+4cGFFRQVCiLzRZGpqGhsbW1FR8fLly5UrV+7cufPvf/+7ap1OTk4IofLy8lFF8naDZDpe3d3dBEHw+fwhp1ZUVHR1dc2cOVM5ZtasWUwmU/VcXtXMmTM5HA55DjjaebVvQiNUbQqda2xsJAhC/adAY2Jipk2blpycfOPGDdXxo20lJpOJEJLL5Qihhw8fSqVS5c0fNpttYWExqjbJzc3Nysr69ttvVW+a2djYvPfee4aGhkwmc+7cuWlpaTKZLDk5GSHEYrEQQi4uLhKJxNjYWCAQHDhwQCAQDEr9ZFO8fPlS80jeepBMx+vRo0cIIWdn5yGnkj1IDA0NVUcaGRkNOpJVxWKxmpqaxjavlk10hMqm0Lmenh70a6IZDo7jaWlpGIZ9/PHHMplMOX48rdTd3Y0Q2rdvn7JDaF1dnYb3kRBCGRkZhw8fLioqsre3V1PMzc3NwMCA3JMtLS0RQuTFaBKTybSzs6uurladhc1mo1+bBZAgmY7X1atXEUJLly4dcqqRkRFCaNDPpq2tzdraesjycrlcOXW082rfhEao2hQ6R+aOETurz5s3b/fu3VVVVQcPHlSOHE8rmZqaIoQSEhJUr80VFxdrEvOJEyfS09OvX7/+zjvvqC85MDAwMDBA/qswNDR0cnKqrKxULdDf3y8QCFTH9PX1oV+bBZAgmY7LixcvEhISrK2tP/744yELuLq6Ghoaqvbqv337dl9f3/vvvz9k+aKiIoIgyJeTjzgvnU4nTwZ1ZUIjVG2KcVY1fmZmZhiGadKT9ODBg87OziUlJcoxo90HVNnY2OA4XlpaOqpoCYIIDQ0tLy/Py8sbdERM+uCDD1QHyTta8+bNIwfXrVtXUlLy5MkTclAqldbV1Q3qKUU2hbm5+agCe7tBMh0FgiC6urrI25pNTU2ZmZm/+93vDAwM8vLyhrtmiuN4cHBwbm5uenp6R0dHeXn59u3bLS0tAwIClGUGBgZaW1v7+/vLysqCgoJsbW3J7lYjzisWi1+9epWXlyeXy5uamurq6lQXbWxs3NDQUFtb29nZOUFpiPIIh2uK0VZVWFhIbdcoDofj6Oj47NmzEUuSJ/uqfTw12QfU1LZly5YLFy6kpKR0dHQoFIpnz5798ssvCCF/f39zc/MhH1etrKw8evTo119/zWAwVJ8ZPXbsGFng+fPnGRkZbW1tcrm8uLh469attra227dvJ6fu3r3bzs5u8+bN9fX1LS0toaGhMpls0I01sinU9EWdjLTdfUBHxtNF49KlSzNmzOBwOEwmk0ajoV8fgpo9e3Z0dHRLS4uy5JEjR8gTHxsbG+UTJgMDA/Hx8U5OTgwGQygU+vr6kp1PSQEBAQwGw8rKik6n8/n8lStXVldXK6eqn7elpWXx4sU4jjs4OHz22WchISEIIbFYTHYnunv3rp2dHZvNnj9/vrIXzqggDbpGURih+qYYVVUFBQU8Hi8mJkaT1dRw3wgMDGQwGFKplBzMzc0lb+6bmJjs3LlzUOGQkBDVrlFqWik5OZm8mePk5FRdXX369GnyH7Odnd2jR48Igujt7Q0NDbW1taXT6aampn5+fhUVFQRB+Pr6IoQiIyNfD3W4m+zx8fFkgeDgYJFIxOVy6XS6tbX1J5980tDQoFrD06dP169fLxQKWSzW7NmzCwsLBy1i+fLlVlZWg/pLvW5SdY2CZKpj5DN/uo5iaJokUwrpqik03DeqqqrodPqgpzB1SKFQLFiwIDU1VfuLbm5uxnH82LFjI5bU29/dRIDTfN2Dd/Ao6XNTiMXi6Ojo6Oho1dcv6YpCocjLy+vs7PT399f+0qOiojw8PAIDA7W/aH0GyRQATYWFha1Zs8bf31/n7zQpKirKyckpLCxU3/V1Ihw/fry0tLSgoIDBYGh50XoOkqkuhYeHp6Wltbe3Ozg4XLx4Udfh6NKb0hSxsbGBgYGHDh3SbRheXl7nz59XvqxAa/Lz83t7e4uKioRCoZYXrf8wYnK8qgA+5zsGGIZlZmbCp57BmE2qtoUjUwAAoAAkUwAAoAAkUwAAoAAkUwAAoAAkUwAAoIKunxrQktWrV+u6pQGYjCbPE1CTpWtUcXGx6qcZwRskISEBIfT555/rOhAwFjY2Nsr3Ub3dJksyBW8usqNrVlaWrgMBQB24ZgoAABSAZAoAABSAZAoAABSAZAoAABSAZAoAABSAZAoAABSAZAoAABSAZAoAABSAZAoAABSAZAoAABSAZAoAABSAZAoAABSAZAoAABSAZAoAABSAZAoAABSAZAoAABSAZAoAABSAZAoAABSAZAoAABSAZAoAABSAZAoAABSAZAoAABSAZAoAABSAZAoAABSAZAoAABSAZAoAABSAZAoAABSAZAoAABSAZAoAABSAZAoAABSAZAoAABSAZAoAABSg6zoAAAa7ffv2zz//rBx88uQJQuj06dPKMe7u7nPmzNFBZAAMDyMIQtcxAPAbly9fXrFihYGBAY1GQwiRuyiGYQihgYEBhULxzTff/Od//qeOowTgtyCZAr0jl8tNTEw6OjqGnMrn85uamphMppajAkA9uGYK9A6DwVi/fv2Q6VLNJAB0C5Ip0Efr16/v6+t7fbxcLv/www+1Hw8AI4LTfKCPBgYG3nnnnZcvXw4ab2pq+uLFC/JaKgB6BXZKoI9oNNqmTZsGnc4zmczNmzdDJgX6CfZLoKdeP9Pv6+tbv369ruIBQD04zQf6y8nJ6fHjx8pBR0fH6upqHcYDgBpwZAr018aNGxkMBvk3k8n805/+pNt4AFADjkyB/nr8+LGTk5Ny8OHDh1OnTtVhPACoAUemQH+JxWJ3d3cMwzAMc3d3h0wK9BkkU6DXPvroIwMDAwMDg48++kjXsQCgDpzmA73W0NBgY2NDEMTTp0+trKx0HQ4Aw4JkitasWaPrEIA6RUVFCKFFixbpOA6gVnZ2tq5D0DE4zUcXL1589uyZrqPQmVu3bt26dUvXUahja2trZ2c3zkqePXt28eJFSuIBg0DbkuDIFGEYlpmZuXbtWl0Hohvkgbk+H1a8evUKIWRsbDyeSrKystatWwd7+0SAtiXBy6GBvhtnGgVAO+A0HwAAKADJFAAAKADJFAAAKADJFAAAKADJdNS2bt3K4/EwDCstLdV1LL8xMDCQkJAgkUi0sKyCggKBQPDNN99oYVk6ce3atbCwsJycHEdHR/J51k2bNqkWWLJkCY/HMzAwcHFxuXv3rk6CjI6Onj59Op/PZ7FYYrH4iy++6OrqUk6NiYnBfsvV1VV1drlcHhcXJxaLmUymkZGRq6trbW0tQujSpUtHjhxRKBRaXp03HSTTUTtz5szXX3+t6ygGq6qq+o//+I/du3dLpVItLO7t7gfz5ZdfJiUlhYeH+/n5PXnyRCQSTZkyJT09/cqVK8oy3333XXZ29ooVKyoqKjw9PXUS5/Xr13fu3FlbW9vc3BwXF5eYmDiqJ1DWrVt39uzZ8+fPS6XS+/fvi0QiMhd7e3vjOO7l5dXW1jZhsb+FIJm+DX7++ee9e/du377dw8NDO0tcvnx5e3v7ihUrJnpBMplMO8faSocPH87IyMjKyuLxeMqRSUlJNBotICCgvb1dm8GoZ2hoGBAQYGxszOPx1q5d6+vre/Xq1adPnyoLnDt3jlBx79495aSMjIy8vLzs7Ow5c+bQ6XRLS8v8/HzloeuuXbvc3d2XLVvW39+v7bV6Y0EyHQvyG+76w93dPScnZ8OGDSwWS9exUCw1NbWxsVFri3v8+PH+/fsPHDiA47jqeIlEEhQU9Pz58z179mgtmBFdvnzZwMBAOWhiYoIQ0vDU5NSpU56enm5ubsMViIqKKi0tTUxMHH+ckwQkU40QBBEfHz9t2jQWiyUQCEJCQlSnKhSKyMhIW1tbNps9Y8aMzMxMhFBKSgqXy+VwOPn5+UuXLuXz+dbW1hcuXFDO9cMPP8yePZvD4fD5fDc3N/Iz8UNWpW9u3Lhha2uLYdjJkyfRSGualJSE47iZmdm2bdssLS1xHJdIJLdv3yanBgYGMplMCwsLcnDHjh1cLhfDsObmZoRQUFBQcHBwdXU1hmFisRghdPXqVT6fHxsbO0GrlpSURBCEt7f365NiYmKmTp165syZa9euDTkvQRDHjx9/9913WSyWUChcuXLlgwcPyEkj7gyUbPfnz5+z2WwHB4cRS/b19d26dUv9eYxQKFy4cGFiYuLbfUmHSsSkhxDKzMxUXyYiIgLDsL/+9a+tra1SqTQ5ORkhVFJSQk7ds2cPi8W6ePFia2treHg4jUa7c+cOORdC6Pvvv29vb29sbFywYAGXy+3r6yMIoquri8/nHzlyRCaTvXjxYtWqVU1NTWqq0tCcOXPc3d1HtfqrV69evXr1qGYhCII8lzxx4gQ5qGZNCYIICAjgcrmVlZU9PT0VFRWzZs3i8Xj19fXk1A0bNpibmytrjo+PRwiRrUEQhJ+fn0gkUk69fPkyj8eLjo4ebcBkehqxmKOj4/Tp0weNFIlENTU1BEHcvHmTRqPZ29t3dXURBFFYWOjj46MsFhkZyWQyz50719bWVlZW5unpaWJi8uLFC3Kq+iYa53YnCKK7u5vH4wUGBirHHDx40Nra2sjIiMFg2Nvb+/j4/Pjjj+SkmpoahJCHh8eiRYssLCxYLJazs/PJkycHBgZU6wwLC1Pdz4ejYdu+9aAJRk6mUqmUw+H84Q9/UI4hjynInUwmk3E4HH9/f2VhFov16aefEr/+fmQyGTmJTMGPHz8mfr16dfnyZdUFqalKQ7pNpkOuKUEQAQEBAoFAOe+dO3cQQgcOHCAHR5VMx0yTH3xXVxeGYStWrBg0XplMCYIIDg5GCO3cuZP4bTKVSqWGhobKbUcQxI8//ogQUuZ9NU00/u1O1j916tSOjg7lmPr6+rt373Z2dvb29hYXF7/33ntsNvvevXsEQZSXlyOE/vCHP/zrX/9qaWlpa2vbu3cvQig9PV21zr/97W8IobNnz6pfNCRTEpzmj+zx48dSqdTLy2vIqQ8fPpRKpcor92w228LCQnl+p4r8cLFcLkcIOTo6mpmZbdy4MSoqiuyPMqqq9Jzqmr5u5syZHA5HD9ersbGRIAgOh6OmTExMzLRp05KTk2/cuKE6vqKioqura+bMmcoxs2bNYjKZygsag6g20fi3e25ublZW1rfffqt608zGxua9994zNDRkMplz585NS0uTyWRkEievrbu4uEgkEmNjY4FAcODAAYFAcPr0adVqyaZ4+fKl5pFMZpBMR0a+oM/U1HTIqd3d3Qihffv2KXvz1dXVjXgTgM1mX79+ff78+bGxsY6Ojv7+/jKZbGxVvYlYLFZTU5Ouoxisp6cH/ZpohoPjeFpaGoZhH3/8sUwmU44nexEZGhqqFjYyMurs7BxxuePc7hkZGYcPHy4qKrK3t1dTzM3NzcDA4NGjRwghS0tLhBB5YZrEZDLt7OwGffyVzWajX5sFjAiS6cjIG7u9vb1DTiWTbEJCguoBf3Fx8YjVuri4fPPNNw0NDaGhoZmZmceOHRtzVW8WuVze1tZmbW2t60AGI3PHiJ3V582bt3v37qqqqoMHDypHGhkZIYQGpU4NV3M82/3EiRPp6enXr19/55131JccGBgYGBgg/1UYGho6OTlVVlaqFujv7xcIBKpj+vr60K/NAkYEyXRkrq6uNBrthx9+GHKqjY0NjuOjfRqqoaGB3JVNTU0PHTrk6elZWVk5tqreOEVFRQRBzJ07lxyk0+nDXRDQMjMzMwzDNOlJevDgQWdn55KSEuUYV1dXQ0PDn376STnm9u3bfX1977///oi1jW27EwQRGhpaXl6el5c36IiY9MEHH6gOkne05s2bRw6uW7eupKTkyZMn5KBUKq2rqxvUU4psCnNz81EFNmlBMh2Zqampn5/fxYsXU1NTOzo6ysrKVC8t4Ti+ZcuWCxcupKSkdHR0KBSKZ8+e/fLLL+rrbGho2LZt24MHD/r6+kpKSurq6ubOnTu2qt4IAwMDra2t/f39ZWVlQUFBtra2mzdvJieJxeJXr17l5eXJ5fKmpqa6ujrVGY2NjRsaGmprazs7O+VyeWFh4cR1jeJwOI6Ojpp8doE82Vft44njeHBwcG5ubnp6ekdHR3l5+fbt2y0tLQMCAjSpbbjt7u/vb25uPuTjqpWVlUePHv36668ZDIbqM6PHjh0jCzx//jwjI6OtrU0ulxcXF2/dutXW1nb79u3k1N27d9vZ2W3evLm+vr6lpSU0NFQmk5G3oZTIplDTFxX8xkTf4dJ/SIOuUZ2dnVu3bp0yZYqhoeH8+fMjIyMRQtbW1j///DNBEL29vaGhoba2tnQ6ncy8FRUVycnJ5PV7Jyen6urq06dP8/l8hJCdnd2jR49qa2slEolQKDQwMHjnnXciIiL6+/uHq2rEVSguLv7d735HXghDCFlYWEgkkh9++EGT1R/D3fwTJ06QPUM5HI63t7f6NSUIIiAggMFgWFlZ0el0Pp+/cuXK6upqZW0tLS2LFy/GcdzBweGzzz4j+/CKxWKy79Tdu3ft7OzYbPb8+fNfvHhRUFDA4/FiYmJGFTCh8R3nwMBABoMhlUrJwdzcXJFIhBAyMTEh7+CrCgkJUe0aNTAwEB8f7+TkxGAwhEKhr6/vw4cPyUkjNtFw293X1xchFBkZ+Xqo5B3518XHx5MFgoODRSIRl8ul0+nW1taffPJJQ0ODag1Pnz5dv369UChksVizZ88uLCwctIjly5dbWVkN6i/1OribT4Im0CiZvsXG1jVqVMhHHid0ESPS8AdfVVVFp9MHPYWpQwqFYsGCBampqdpfdHNzM47jx44dG7EkJFMSnOYDbXhTXkEkFoujo6Ojo6NVX7+kKwqFIi8vr7Oz09/fX/tLj4qK8vDwCAwM1P6i31CQTPXdgwcPsOHp5Gf2dgsLC1uzZo2/v7/O32lSVFSUk5NTWFiovuvrRDh+/HhpaWlBQQGDwdDyot9ckEz1nbOzs5ozi4yMDF0HOILw8PC0tLT29nYHB4c35YPAsbGxgYGBhw4d0m0YXl5e58+fV764QGvy8/N7e3uLioqEQqGWF/1Gg6+TgokVFxcXFxen6yhGbcmSJUuWLNF1FLrh4+Pj4+Oj6yjePHBkCgAAFIBkCgAAFIBkCgAAFIBkCgAAFIBkCgAAFMCISf9NAn37oBMAbyLIJNA1CiGEgoKClG/TmWwSEhIQQp9//rmuA5lYxcXFiYmJ+vlNrTcd2ba6jkL3IJkihNC8efPWrl2r6yh0Izs7GyE0GVY/MTFxMqymTkAyRXDNFAAAKAHJFAAAKADJFAAAKADJFAAAKADJFAAAKADJdOxycnIcHR1V3y7KZDLNzMwWLVoUHx/f2tqq6wABZa5duxYWFqa6xTdt2qRaYMmSJTwez8DAwMXFZchPNmmHXC6Pi4sTi8VMJtPIyMjV1bW2tvb1Yj09Pc7Ozvv27SMHL126dOTIkTflBd56C5Lp2Pn5+T158kQkEgkEAoIgBgYGGhsbs7KyHBwcQkNDXVxcVL9VCd5cX375ZVJSUnh4uHKLT5kyJT09/cqVK8oy3333XXZ29ooVKyoqKjw9PXUV6rp1686ePXv+/HmpVHr//n2RSDTkJwMiIiIePnyoHPT29sZx3MvLq62tTYvBvm0gmVIGwzAjI6NFixalpaVlZWW9fPly+fLlOn9buz6QyWQSiUTfqtLQ4cOHMzIysrKyeDyecmRSUhKNRgsICNCr7ZuRkZGXl5ednT1nzhw6nW5paZmfn+/q6jqo2M2bN+/duzdo5K5du9zd3ZctW9bf36+teN82kEwnxOrVqzdv3tzY2PjVV1/pOhbdS01NbWxs1LeqNPH48eP9+/cfOHAAx3HV8RKJJCgo6Pnz53v27NFaMCM6deqUp6en+i8zy2SykJCQIfvYR0VFlZaWQvf7MYNkOlHI78IXFhaSgwqFIjIy0tbWls1mz5gxg3yuMSUlhcvlcjic/Pz8pUuX8vl8a2vrCxcuKCv54YcfZs+ezeFw+Hy+m5tbR0fHcFVNNIIgjh8//u6777JYLKFQuHLlygcPHpCTAgMDmUym8usaO3bs4HK5GIY1NzcjhIKCgoKDg6urqzEME4vFSUlJOI6bmZlt27bN0tISx3GJRHL79u0xVIUQunr1Kp/Pj42NnaC1TkpKIgjC29v79UkxMTFTp049c+bMtWvXhpxXTYuNuN3HsIn7+vpu3brl4eGhvlhERMSOHTtMTU1fnyQUChcuXJiYmAhP2Y/RxH8AVd+h8X3qWXnNdBAy8dnY2JCDe/bsYbFYFy9ebG1tDQ8Pp9Fod+7cIQgiIiICIfT999+3t7c3NjYuWLCAy+X29fURBNHV1cXn848cOSKTyV68eLFq1aqmpiY1VY2Nhp96joyMZDKZ586da2trKysr8/T0NDExefHiBTl1w4YN5ubmysLx8fEIITJagiD8/PxEIpFyakBAAJfLrays7OnpqaiomDVrFo/Hq6+vH0NVly9f5vF40dHRI8Y/ts8ROzo6Tp8+fdBIkUhUU1NDEMTNmzdpNJq9vX1XVxdBEIWFhT4+Pspi6ltMzXYnxrSJa2pqEEIeHh6LFi2ysLBgsVjOzs4nT55U/er9jRs3vL29CYJoampCCEVERAyqJCwsDCFUUlIyqlaCTz2T4Mh0ovB4PAzDOjs7EUI9PT0pKSm+vr5+fn5GRkb79u1jMBhpaWnKwhKJhM/nm5qa+vv7d3d319fXI4Rqa2s7OjpcXFxwHDc3N8/JyTExMRmxqokgk8mOHz++atWqjRs3CgQCNze3r776qrm5+fTp02OrkE6nk4ds06dPT0lJ6ezsHNsqLF++vKOjY//+/WMLQ73u7u6amhqRSDRcgXnz5n3++ee1tbV79+4dNEnDFhtyu49tE5M3mkxNTWNjYysqKl6+fLly5cqdO3f+/e9/V4YUFBSUkpKiphInJyeEUHl5ufplgSFBMp0o3d3dBEHw+XyE0MOHD6VSqfJWAJvNtrCwUJ70qWIymQghuVyOEHJ0dDQzM9u4cWNUVJSyg4vmVVGooqKiq6tr5syZyjGzZs1iMpnK0/PxmDlzJofDmehVGIPGxkaCINR/ZjkmJmbatGnJyck3btxQHT/aFlPd7mPbxCwWCyHk4uIikUiMjY0FAsGBAwcEAoEyfYeHh//lL3+xsrJSUwm5si9fvlS/LDAkSKYT5dGjRwghZ2dnhFB3dzdCaN++fcoeqXV1dVKpVH0NbDb7+vXr8+fPj42NdXR09Pf3l8lkY6tqnMgeM4aGhqojjYyMyOPu8WOxWOSJp17p6elBvyap4eA4npaWhmHYxx9/LJPJlOPH02Jj28SWlpYIIfLiMonJZNrZ2VVXVyOEbty4UV5evnXrVvWVsNls9OuKg9GCZDpRrl69ihBaunQpQoi83p+QkKB6haW4uHjESlxcXL755puGhobQ0NDMzMxjx46NuarxMDIyQggNSgRtbW3W1tbjr1wul1NVFbXIzDJiV/Z58+bt3r27qqrq4MGDypHjabGxbWJDQ0MnJ6fKykrVkf39/QKBACGUmpr6/fff02g0MjuTi4iNjcUwTLU3dF9fn3LFwWhBMp0QL168SEhIsLa2/vjjjxFCNjY2OI6XlpaOqpKGhgbyt2Fqanro0CFPT8/KysqxVTVOrq6uhoaGqr+627dv9/X1vf/+++QgnU4nT1HHoKioiCCIuXPnjr8qapmZmWEYpklP0oMHDzo7O5eUlCjHjNhiaox5E69bt66kpOTJkyfkoFQqraurI3tKpaWlqaZm1RtQqtciyJU1Nzcf7aIBgmRKCYIgurq6yNumTU1NmZmZv/vd7wwMDPLy8shrpjiOb9my5cKFCykpKR0dHQqF4tmzZ7/88ov6ahsaGrZt2/bgwYO+vr6SkpK6urq5c+eOrapxwnE8ODg4Nzc3PT29o6OjvLx8+/btlpaWAQEBZAGxWPzq1au8vDy5XN7U1FRXV6c6u7GxcUNDQ21tbWdnJ5koBwYGWltb+/v7y8rKgoKCbG1tyZ5ko62qsLBw4rpGcTgcR0fHZ8+ejViSPNk3MDBQHaO+xdTXNtwm9vf3Nzc3H+5x1d27d9vZ2W3evLm+vr6lpSU0NFQmk71+c0wNcmXV91QFw5qAHgJvGDTWrlGXLl2aMWMGh8NhMpk0Gg39+hDU7Nmzo6OjW1paVAv39vaGhoba2trS6XRTU1M/P7+Kiork5GTykr+Tk1N1dfXp06fJ5GtnZ/fo0aPa2lqJRCIUCg0MDN55552IiIj+/v7hqhrz6mvYNWpgYCA+Pt7JyYnBYAiFQl9f34cPHyqntrS0LF68GMdxBweHzz77LCQkBCEkFovJDk937961s7Njs9nz589/8eJFQEAAg8GwsrKi0+l8Pn/lypXV1dVjq6qgoIDH48XExIwY/9i67wQGBjIYDKlUSg7m5uaSN/dNTEx27tw5qHBISIhq1yg1LaZ+uxPDb2JfX1+EUGRk5HABP336dP369UKhkMVizZ49u7CwcMhiw3WNWr58uZWVlWpvKk1A1ygSNMF4+5m+6TRMphQKCAgwNjbW5hKJsf7gq6qq6HT6uXPnJiKkMVAoFAsWLEhNTZ2Iypubm3EcP3bs2GhnhGRKgtN8oANvyguKxGJxdHR0dHT0kK8L0TKFQpGXl9fZ2env7z8R9UdFRXl4eAQGBk5E5ZMBJFMA1AkLC1uzZo2/v7/O32lSVFSUk5NTWFiovuvr2Bw/fry0tLSgoIAhbsJKAAAgAElEQVTBYFBe+SQByRRoVXh4eFpaWnt7u4ODw8WLF3UdjkZiY2MDAwMPHTqk2zC8vLzOnz+vfHEBhfLz83t7e4uKioRCIeWVTx7wqWegVXFxcXFxcbqOYtSWLFmyZMkSXUcxUXx8fHx8fHQdxRsPjkwBAIACkEwBAIACkEwBAIACkEwBAIACcAMKIYQm+kUh+ox8gjArK0vXgUwschO/9aupE5P556MKIyb9JwowDNN1CAC88SCTQDIF+m7t2rUIDiqB3oNrpgAAQAFIpgAAQAFIpgAAQAFIpgAAQAFIpgAAQAFIpgAAQAFIpgAAQAFIpgAAQAFIpgAAQAFIpgAAQAFIpgAAQAFIpgAAQAFIpgAAQAFIpgAAQAFIpgAAQAFIpgAAQAFIpgAAQAFIpgAAQAFIpgAAQAFIpgAAQAFIpgAAQAFIpgAAQAFIpgAAQAFIpgAAQAFIpgAAQAFIpgAAQAFIpgAAQAFIpgAAQAFIpgAAQAFIpgAAQAFIpgAAQAFIpgAAQAFIpgAAQAGMIAhdxwDAb5w/fz41NXVgYIAcrKmpQQg5ODiQgzQa7c9//vOGDRt0Fh8AQ4FkCvROWVmZu7u7mgI///zzjBkztBYPAJqAZAr0kbOz88OHD4ecJBaLq6qqtBwPACOCa6ZAH23atInBYLw+nsFgbNmyRfvxADAiODIF+ujJkydisXjInbOqqkosFms/JADUgyNToI8cHR09PT0xDFMdiWHYzJkzIZMC/QTJFOipjz76yMDAQHWMgYHBRx99pKt4AFAPTvOBnmpsbLS0tFR2kEII0Wi0hoYGc3NzHUYFwHDgyBToKTMzs4ULFyoPTg0MDBYtWgSZFOgtSKZAf23atEn1zGnTpk06DAYA9eA0H+ivjo4OU1PTvr4+hBCDwWhsbDQyMtJ1UAAMDY5Mgf7i8/l//OMf6XQ6nU5ftmwZZFKgzyCZAr22ceNGhUKhUCjgYXyg5+A0H+i1np4eExMTgiCam5vZbLauwwFgWFpKpoN6XwMAgNZoJ8vRtbAMUlBQ0Lx587S2OPDWKC0txTBs0HukiouLExMTMzMzdRWV1qxbtw5+O2NG7ifaWZb2jkwzMzPXrl2rhWWBt0x/fz9CiE7/zT/+rKysdevWTYaLVPDbGQ9t7ifaOzIFYGwGpVEA9BPczQcAAApAMgUAAApAMgUAAApAMgUAAAroSzKdNWuWgYGBh4fHRFS+ZcsWHMcxDOvp6ZmI+rXs2LFjZmZmGIZ99dVX5JiCggKBQPDNN99QUj+1takRHR09ffp0Pp/PYrHEYvEXX3zR1dU1oUvU2qrpyrVr18LCwnJychwdHTEMwzBs0NthlixZwuPxDAwMXFxc7t69q6s45XJ5XFycWCxmMplGRkaurq61tbWvF+vp6XF2dt63bx85eOnSpSNHjigUCq3GqjF9SaZ37txZvHjxBFWelpa2Z8+eCapc+/bs2XPz5k3VMdT2/NBaf6Pr16/v3Lmztra2ubk5Li4uMTFxzZo1E7rEt7sr1ZdffpmUlBQeHu7n5/fkyRORSDRlypT09PQrV64oy3z33XfZ2dkrVqyoqKjw9PTUVajr1q07e/bs+fPnpVLp/fv3RSLRkP9HIyIiVL+r6O3tjeO4l5dXW1ubFoPVlL4kU9IYHpSSyWQSiWQignmDLF++vL29fcWKFWObfVAbjrM2zRkaGgYEBBgbG/N4vLVr1/r6+l69evXp06cTt0StrZr2d8vDhw9nZGRkZWXxeDzlyKSkJBqNFhAQ0N7ers1g1MvIyMjLy8vOzp4zZw6dTre0tMzPz3d1dR1U7ObNm/fu3Rs0cteuXe7u7suWLSN7H+sV/UqmQ36QUr3U1NTGxkYNC8NTrUMaVRtS6PLly6ofJjExMUEISaVS7UdCOS036ePHj/fv33/gwAEcx1XHSySSoKCg58+f69WZ2alTpzw9Pd3c3NSUkclkISEhQz68FBUVVVpaqrXnmjSnX8n08ePHzs7OXC6XzWYvWLDgxo0bykn//Oc/p0+fLhAIcBx3c3P79ttvEUJBQUHBwcHV1dUYhim/s3bu3LmZM2fiOM7lcu3t7Q8ePEiOp9FoV65cWbp0qUAgsLS0/Nvf/qZJSCkpKVwul8Ph5OfnL126lM/nW1tbX7hwQVmAIIjjx4+/++67LBZLKBSuXLnywYMH5KSjR49yOBwej9fY2BgcHGxlZbV9+3Yul0uj0d5//31zc3MGg8Hlcj09PRcsWGBjY4PjuJGR0RdffKF+rQe5ceOGra0thmEnT54k2xB7zT/+8Q8N23BQbepXcMTGGZXnz5+z2WwHB4exzT6iQaumPvikpCQcx83MzLZt22ZpaYnjuEQiuX37Njk1MDCQyWRaWFiQgzt27OByuRiGNTc3o6F2y6tXr/L5/NjY2AlataSkJIIgvL29X58UExMzderUM2fOXLt2bch5x7N9FQpFZGSkra0tm82eMWOGJk/39vX13bp1a8S7IxERETt27DA1NX19klAoXLhwYWJiot5dtCG0AiGUmZmpvoyXl5ejo2NNTY1cLr93796cOXNwHH/06BE5NTs7Oyoq6tWrVy0tLXPnzp0yZQo53s/PTyQSKStJSEhACB06dKilpeXVq1f//d//vWHDBoIgIiIiEELff/99W1vbq1evli1bxmKxuru7NQleOW97e3tjY+OCBQu4XG5fXx85NTIykslknjt3rq2trayszNPT08TE5MWLF6rz7tq168SJE6tWrbp///6XX36JELp9+3Z3d3dzc/Mf//hHhNCVK1eampq6u7sDAwMRQqWlperXuqqqCiF06tQpcpA8NT5x4gQ5ae/eveSq/fLLL0KhUCKRKBQKzdtQtTYNV3C4xtFcd3c3j8cLDAzUsDz5ux3tUgatmvrgAwICuFxuZWVlT09PRUXFrFmzeDxefX09OXXDhg3m5ubKmuPj4xFCTU1N5OCgJr18+TKPx4uOjh5twIRmvx1HR8fp06cPGikSiWpqagiCuHnzJo1Gs7e37+rqIgiisLDQx8dHWWw823fPnj0sFuvixYutra3h4eE0Gu3OnTvqQ62pqUEIeXh4LFq0yMLCgsViOTs7nzx5cmBgQFnmxo0b3t7eBEE0NTUhhCIiIgZVEhYWhhAqKSlRvyxirPvJ2OhXMnV3d1cOlpWVIYT27Nnzesm4uDiEUGNjI/Hbvbavr8/IyGjx4sXKkv39/eR/MHKfkMlk5PizZ88ihO7du6dJ8IPmTU5ORgg9fvyYIAipVGpoaOjv768s/OOPPyKElD+bQfMSBEEm087OTnLwf/7nfxBC5eXlqrNnZGSoX2s1yVSVr68vjuMPHjxQX5uaZDraFVRtnFGJiIiYOnVqR0eHhuUpTKbDBR8QECAQCJTz3rlzByF04MABcnBUyXQ8RvztdHV1YRi2YsWKQeOVyZQgiODgYITQzp07id8m0/FsX5lMxuFwlPNKpVIWi/Xpp5+qX53y8nKE0B/+8Id//etfLS0tbW1te/fuRQilp6cr65k5c+azZ8+I4ZMpeVp59uxZ9csitJtM9es0X5Wbm5tAICBT6iDkpdXXe0iUlZW1tbV98MEHyjEGBga7du0arga5XD6GwJhMpnLeioqKrq6umTNnKqfOmjWLyWQqzwc1rE15NV1NYMOt9XCysrL+93//98CBA9OmTRtzbaNdQdXG0Vxubm5WVta3336revNE+9QHP3PmTA6HozwF1h/kf0QOh6OmTExMzLRp05KTk1UvnaHxbd+HDx9KpVLljSM2m21hYTFi+7BYLISQi4uLRCIxNjYWCAQHDhwQCASnT58mC4SHh//lL3+xsrJSUwm5si9fvlS/LC3T32SKEGIwGMo9+8qVK4sWLTI1NWWxWKpXFVV1dHQghLT5cQuyi4ahoaHqSCMjo87OTkrq12Sth9TS0vLZZ5/NmjWLPCQZc20TvYIIoYyMjMOHDxcVFdnb21NV5wRhsVjksZJeIXtPk0lqODiOp6WlYRj28ccfy2Qy5fjxbN/u7m6E0L59+5SX5uvq6ka8f2hpaYkQIi8uk5hMpp2dXXV1NULoxo0b5eXlW7duVV8J+Zpwfes2rr/JtL+//9WrV7a2tgih+vp6X19fCwuL27dvt7e3HzlyZMhZ3nnnHfTb7TTRyMQ9aM9ra2uztrYef+UarvWQdu3a1dbWlpaWprxdPrbaJnQFEUInTpxIT0+/fv06ue30mVwup3DFKURmlhFPMubNm7d79+6qqirlLVk0vu1L3h1KSEhQPdUtLi5WP5ehoaGTk1NlZaXqyP7+foFAgBBKTU39/vvvaTQamZ3JRcTGxmIY9tNPPynLk99Y1LcvL+hvMv2///u/gYEBsl9xeXm5XC7/9NNPHR0dyWeZhpzF3t7e2Nj4u+++01qQrq6uhoaGqpv59u3bfX1977///vgr13CtX3flypXz58/v37/fxcWFHBMSEjK22iZuBQmCCA0NLS8vz8vLG3RkpJ+KiooIgpg7dy45SKfTx3aZiHLk43Ca9CQ9ePCgs7NzSUmJcsx4ti/Z/6S0tHS0Aa9bt66kpOTJkyfkoFQqraurI3tKpaWlqaZm1WumqtciyJU1Nzcf7aInlH4l076+vvb29v7+/rt37wYGBtrZ2W3evBkhRB6fXrt2raenp6qqSvWCjrGxcUNDQ21tbWdnJ41GCw8P/3//7/8FBgY+f/58YGCgs7Nz0P9AauE4HhwcnJubm56e3tHRUV5evn37dktLy4CAgPFXrmat1ejo6Ni2bZuHhwd5Xb+np+enn34qLS3VsA0HJYiJW8HKysqjR49+/fXXDAZDtRfXsWPHxlkzhQYGBlpbW/v7+8vKyoKCgmxtbckdEiEkFotfvXqVl5cnl8ubmprq6upUZxzUpIWFhRPXNYrD4Tg6Oj579mzEkuTJvmrf3vFsXxzHt2zZcuHChZSUlI6ODoVC8ezZs19++QUh5O/vb25uPtzjqrt37yZ/2vX19S0tLaGhoTKZjNxdNUSurPqeqjqgnftcSIO7+WlpaYsXLzYzM6PT6VOmTFm/fn1dXZ1yamhoqLGxsZGR0Zo1a8h+giKRqL6+/u7du3Z2dmw2e/78+WR/jpMnT7q5ueE4juP4e++9l5ycfOTIEfKMwMnJqbq6Oj09XSgUIoSsra1HvKGfnJxMXu0m5z19+jSfz0cI2dnZkd22BgYG4uPjnZycGAyGUCj09fV9+PAhOa9yuTY2NufOnSMIIjExkazN3t7+n//85+HDh8mzG3Nz8/Pnz2dkZJD/bIVC4YULF4Zb66CgILIYl8tdtWrViRMnyA6PHA7H29t7yGS0bNkyDdtw3759qrWpX8ERG0cN8q7u6+Lj49XPSBrDXdpBDTVi8AEBAQwGw8rKik6n8/n8lStXVldXK2traWlZvHgxjuMODg6fffZZSEgIQkgsFpN9pwbtlgUFBTweLyYmZlQBkzT57QQGBjIYDKlUSg7m5uaKRCKEkImJCXkHX1VISIhq16jxbN/e3t7Q0FBbW1s6nW5qaurn51dRUUEQhK+vL0IoMjJyuICfPn26fv16oVDIYrFmz55dWFg4ZLHh7uYvX77cyspKtTfVcCZp1ygANKeFHwn5qOuELkITmvx2qqqq6HQ6+Q9bHygUigULFqSmpk5E5c3NzTiOHzt2TJPC0DUKAL2gty8oGkQsFkdHR0dHR0/0a7c0oVAo8vLyOjs7/f39J6L+qKgoDw8P8vEWvTLZk+mDBw9ef/hSaYL2hskAGlbLwsLC1qxZ4+/vr/N3mhQVFeXk5BQWFqrv+jo2x48fLy0tLSgoGMN7PCbaZP9UmbOzM6FvT/i+Fd70hg0PD09LS+vr63NwcIiPj1+9erWuIxpZbGzsd999d+jQocOHD+swDC8vLy8vr4moOT8/v7e3t6ioSPUemv6Y7MkUgCHFxcWRT9y+WZYsWbJkyRJdRzFRfHx8fHx8dB3FsCb7aT4AAFACkikAAFAAkikAAFAAkikAAFBAezegRnwDAgCaI3enrKwsXQeiDfDbGTNtNh2mnf4r8PElAICuaCfLae/INDMzc+3atVpbHHi7ZWVlrVu37o3uyqohDMPgtzNm5H6inWXBNVMAAKAAJFMAAKAAJFMAAKAAJFMAAKAAJFMAAKAAJFMAAKDAm5RMc3JyHB0dVV+LSafTTUxMfv/73+fm5lK1lC1btpDfmxvyQ7KqMWzatEl10pIlS3g8noGBgYuLy3Bfv5lox44dIz+v9tVXX5FjCgoKBALBN998Q0n91NYGKHft2rWwsDA930sRQnK5PC4uTiwWM5lMIyMjV1fX2tra14v19PQ4Ozvv27ePHLx06dKRI0f0943d2nmhP6LusyUikUggEJB/v3r16tq1a87OzgihjIwMSuonCCIiIgIhJJPJ1MQwZcoUhNDly5dVxxcWFqp+XUcnqqqqEEKnTp0iBy9fvszn8y9dukRJ5dTWNh7a/ByFbmn+24mMjFyxYkVHRwc5qM97qa+v77Rp027duiWXyxsaGry9vcvLy18vtnv3bvTbb0AlJiYuXLiwtbVVwwXBZ0s0JRQKvby8/uu//gtp9mShTCaTSCSULDopKYlGowUEBOj8xebqLV++vL29fcWKFWObfVCLjbO2NwuFewuFVQ3n8OHDGRkZWVlZPB5POVI/99KMjIy8vLzs7Ow5c+bQ6XRLS8v8/HxXV9dBxW7evHnv3r1BI3ft2uXu7r5s2bL+/n5txaupNzuZkuzt7RFCbW1tI5ZMTU1tbGzUsFr1j8BKJJKgoKDnz5/v2bNHwwrfRKNqsbcMhes+0c34+PHj/fv3HzhwAMdx1fH6uZeeOnXK09NT/YeaZTJZSEhIYmLi65OioqJKS0uHnKRbb0MyLSsrQwgtXLhQOeaf//zn9OnTBQIBjuNubm7ffvstQigoKCg4OLi6uhrDMLFYTJY8d+7czJkzcRzncrn29vYHDx4kx9NotCtXrixdulQgEFhaWv7tb397fbkxMTFTp049c+bMtWvXhgyMIIjjx4+/++67LBZLKBSuXLnywYMH5KSjR49yOBwej9fY2BgcHGxlZbV9+3Yul0uj0d5//31zc3MGg8Hlcj09PRcsWGBjY4PjuJGR0RdffKF+HQe5ceOGra0thmHkh50fP378+ueY/vGPf2jYYoNqU7+CKSkpXC6Xw+Hk5+cvXbqUz+dbW1tfuHBB041KETURBgYGMplM8uPPCKEdO3ZwuVwMw5qbm19f96SkJBzHzczMtm3bZmlpieO4RCK5ffv2GKpCCF29epXP58fGxlK1mklJSQRBeHt7vz5pPHvpiBtRoVBERkba2tqy2ewZM2aQ59Tq9fX13bp1y8PDQ32xiIiIHTt2mJqavj5JKBQuXLgwMTGR0LeHibVzNQFNzDVTqVRaWFhoZ2e3ZMmSrq4uZZns7OyoqKhXr161tLTMnTt3ypQp5Hg/Pz+RSKQslpCQgBA6dOj/s3fnYVFc6cLATzW9FA3dTSMIBGRtIkFUBtEIo190nGEmMiKICEbNVeMjMRpEkSCLKAiowVEfFJIxcplRUGS7kCgYH81DEq5LzBUCghJEFnFjkb0baJr6/qibuh2WpoFquoX39xd96vSp95yufqnldNWRlpaW169f//Of/9ywYQPx2znTmzdvtrW1vX79euXKlRwOp7u7Wz6GmpoagiBu3brFYDAsLS3JtQ86GxUZGclmsy9cuNDW1lZaWurk5GRgYPDy5UtyKbmW3bt3nz59es2aNQ8fPjx48CBC6O7du93d3c3NzX/7298QQlevXm1qauru7iYfx1hSUqK4j4POmT59+hQhdPr0aXLR/v37yY68ePFCKBS6urrKZDLlR0y+NSU7ePPmzfb29sbGxqVLl+ro6PT19Y3nUx9CyXNhiiPcsGGDkZERVTk+Ph4h1NTUNGzf/f39dXR0Kioqenp6ysvLFy5cyOPx6uvrx9HUlStXeDxedHS0Mj1V5rtjbW1tb28/qJDGrXSkD3Hfvn0cDicrK6u1tTUsLIzBYNy7d09xqDU1NQghR0fHZcuWGRsbczgcOzu7M2fODAwMUHWKioo8PDwIgmhqakK/P2dKCg0NRQgVFxcrXhcxuedM38hkOuj/wdy5c//973/39vYOW598kk9jYyPx+226r69PT09v+fLlVM3+/n7y392gC1Dnz59HCD148EA+BnIzJQgiKCgIIbRr1y7i95upWCzW1dX18/Oj3vXTTz8hhKiv0NDLXGQy7ezsJF/++9//RghRJ+bJtw97nU2+jwqSqTwvLy8cxx89eqT8iA1qbawdTExMRAg9fvx46BrHQZkvyagRjjWZUv/FCYK4d+8eQigqKmocTY3JqN+drq4uDMNWrVo1qFwVW6n8hyiRSLhcLvVesVjM4XA++eQTxd0pKytDCP3lL3/57//+75aWlra2tv379yOEUlNTqXacnZ0bGhqIkZMpeaR4/vx5xesi4ALUqKhtWiqVNjQ07NmzJyAgYN68eeRR1SDkI2GHTqcoLS1ta2v761//SpVoaWnt3r17pBakUumwwcTExMyePTsxMbGoqEi+vLy8vKury9nZmSpZuHAhm82mjg1HxWazEULUiXYFYYzUx5FkZGT813/9V1RU1OzZs8fd2lg7SHZnpGFUhYl/BAo4OztzuVzqiFiNyH97ip+rTNdWKv8hVlZWisVi6sKRtra2sbHxqAPC4XAQQnPmzHF1ddXX1xcIBFFRUQKB4OzZs2SFsLCw7du3m5qaKmiE7OyrV68Ur2uSvZHJlMJkMk1NTbds2XL8+PHKysojR46Q5VevXl22bJmhoSGHw5E/zyivo6MDIaSnpzfBGHAcT0lJwTBs69atEomEKicviOnq6spX1tPT6+zsnOAaScr0cVgtLS2ffvrpwoULyb2Vcbem6g5OnKoj5HA45K6TepEToskkNRJVbKXd3d0IoYiICOr8e11dnVgsVvwuExMThJD8fg+bzbawsKiurkYIFRUVlZWVbdu2TXEj2tra6LeOa443O5lSyCuDFRUVCKH6+novLy9jY+O7d++2t7cfO3Zs2Le89dZb6Pcf6ri5uLjs3bu3qqqKun6FfkvTgzbKtrY2MzOzia9RyT4Oa/fu3W1tbSkpKdTDx8fXmko7SAuVRiiVSjWks2RmGfVIgvatlLw6dPLkSflD3VHvbK+rq2tra0t+VSn9/f0CgQAhlJycfPPmTQaDQWZnchWxsbEYhv38889U/b6+PqrjmmOKJNP/+Z//QQiRB61lZWVSqfSTTz6xtrYmf8s07FssLS319fWvX79OSwCHDx+2s7MrLi6mShwcHHR1deW3gLt37/b19S1YsGDiq1Oyj0NdvXo1LS3twIEDc+bMIUuCg4PH15pKO0iLUSNkMpnjPu1QWFhIEMTixYsn3tQEkb95U2YmKb1bKTnJpKSkZKwB+/r6FhcXP3nyhHwpFovr6urI/aGUlBT51Cx/zlT+XATZWSMjo7GuWqXe1GQqkUjIy3/Pnz9PSUmJiIgwMDDYs2cPQsjc3BwhdOPGjZ6enqqqKvmzP/r6+s+fP6+tre3s7GQwGGFhYT/88ENAQMCzZ88GBgY6OzsH/cNUHnkYRe3rkSVBQUE5OTmpqakdHR1lZWU7duwwMTHx9/efWNcRUthHBTo6Oj7++GNHR0fylH9PT8/PP/9cUlKi5IgNShYq7SAtRo1QJBK9fv06NzdXKpU2NTXV1dXJv31o3wcGBlpbW/v7+0tLSwMDA83NzTdv3jyOpgoKCmicGsXlcq2trRsaGpQZEBq3UhzHt2zZcunSpaSkpI6ODplM1tDQ8OLFC4SQn5+fkZHRSD9X3bt3r4WFxebNm+vr61taWkJCQiQSCblNKonsrOKZqmowKZe56Lman5OTM/RSPofDsbW1/eSTT6hJKgRBhISE6Ovr6+np+fj4kJMibWxs6uvr79+/b2Fhoa2tvWTJEnLyx5kzZ+bOnYvjOI7jf/jDHxITE48dO0YePtja2lZXV6empgqFQoSQmZnZgwcPqBgMDAzIa6PygoOD5SedDAwMxMfH29raslgsoVDo5eVVWVlJLqLWMmvWrAsXLhAEcerUKfK0uqWl5Y8//nj06FHywMfIyCgtLS09PZ38PywUCi9dujRSHwMDA8lqOjo6a9asOX36NDn5kcvlenh4HD9+fOgGsHLlSiVHLCIiQr41xR1MTEwku0MO49mzZ/l8PkLIwsLi119/neCWQCh9lVZBhARBtLS0LF++HMdxKyurTz/9NDg4GCEkEonIbWnQ1uLv789isUxNTZlMJp/P9/T0rK6uHl9T+fn5PB4vJiZGmZ4q890JCAhgsVhisZh8SddWOuqH2NvbGxISYm5uzmQyDQ0Nvb29y8vLCYLw8vJCCEVGRo4U8NOnT9evXy8UCjkczqJFiwoKCoatNtLVfHd3d1NTU/nZVCOBqVEAjGLyf5vv7++vr68/mWskKfPdqaqqYjKZ5H9lTSCTyZYuXZqcnKyKxpubm3EcP378uDKVYWoUAJpIY+9XJBKJoqOjo6Oju7q61B0Lkslkubm5nZ2dfn5+qmj/0KFDjo6O5G9YNAokUwCmgtDQUB8fHz8/P7Xf06SwsDA7O7ugoEDx1NfxOXHiRElJSX5+PjkbWqNAMgVgdGFhYSkpKe3t7VZWVllZWeoOZ3ixsbEBAQHUbGt1WbFiRVpaGnWnAhrl5eX19vYWFhaSlzE0DVPdAQDwBoiLiyN/Zavh3Nzc3Nzc1B2FqqxevXr16tXqjmJEsGcKAAA0gGQKAAA0gGQKAAA0gGQKAAA0wIhJuVs1hmGLFy/WhLtCgKmhoaHhzp07a9euVXcgKpeVlQXfnXEjt5NJynKTsxofH59JWAuYksgbc/zhD39QdyDgTZWZmTkJa5mkZArAuK1btw4p9/RZANQIzpkCAAANIJkCAAANIImMNSEAACAASURBVJkCAAANIJkCAAANIJkCAAANIJkCAAANIJkCAAANIJkCAAANIJkCAAANIJkCAAANIJkCAAANIJkCAAANIJkCAAANIJkCAAANIJkCAAANIJkCAAANIJkCAAANIJkCAAANIJkCAAANIJkCAAANIJkCAAANIJkCAAANIJkCAAANIJkCAAANIJkCAAANIJkCAAANIJkCAAANIJkCAAANIJkCAAANIJkCAAANIJkCAAANIJkCAAANmOoOAIDBxGJxb28v9bKvrw8h1NraSpVwOBwul6uGyAAYGUYQhLpjAOB3kpKSdu7cqaBCYmLiJ598MmnxAKAMSKZA4zQ1NZmYmMhksmGXamlpvXjxwtDQcJKjAkAxOGcKNI6hoeGKFSu0tLSGLtLS0vrzn/8MmRRoIEimQBNt3Lhx2GMmgiA2btw4+fEAMCo4zAeaqLOz09DQUP4yFInNZjc1NfH5fLVEBYACsGcKNBGPx1u1ahWLxZIvZDKZq1evhkwKNBMkU6ChNmzY0N/fL18ik8k2bNigrngAUAwO84GG6uvrMzAw6OzspEp0dXWbm5s5HI4aowJgJLBnCjQUm8328fFhs9nkSxaL5evrC5kUaCxIpkBzffDBB+TPnxBCUqn0gw8+UG88ACgAh/lAcw0MDBgbGzc1NSGEDAwMXr58OezkUwA0AeyZAs3FYDA++OADNpvNYrE2bNgAmRRoMkimQKOtX7++r68PjvGB5pv6d426ffv206dP1R0FGCeCIGbMmIEQqqmpqa2tVXc4YJxmzZrl4uKi7ihUjJjq1q5dq+4xBmC6W7t2rbozgcpN/T1ThNDatWszMzPVHcUU5OPjgxBS9dhWVFQghOzt7VW6FgUyMjJ8fX0JuFQ7XuR2MuVNi2QK3mhqTKMAKA8uQAEAAA0gmQIAAA0gmQIAAA0gmQIAAA0gmQIAAA0gmdIjPz9fIBB88803Gt6mAgMDAydPnnR1dVX1iia5X5Pvxo0boaGh2dnZ1tbWGIZhGLZp0yb5Cm5ubjweT0tLa86cOffv31dXnFKpNC4uTiQSsdlsPT09BweHYX8W0dPTY2dnFxERQb78+uuvjx07NtLjDqczSKb0UMUkxMmc2FhVVfX//t//27t3r1gsVvW6pvaEzYMHDyYkJISFhXl7ez958sTGxmbGjBmpqalXr16l6ly/fj0zM3PVqlXl5eVOTk7qCtXX1/f8+fNpaWlisfjhw4c2NjZdXV1Dq4WHh1dWVlIvPTw8cBxfsWJFW1vbJAb7BoB5puMkkUhWrFhx69Yt8qW7u3t7e7sGtqmMX375JTo6eseOHd3d3ZOQ6SatX4PGcxIcPXo0PT39l19+wXGcKkxISNi0aZO/v395eblAIJi0YBRLT0/Pzc395Zdf5s6dixAyMTHJy8sbWu3WrVsPHjwYVLh79+4nT56sXLnyhx9+YDIhh/wv2DMdp+Tk5MbGRs1vUxnz58/Pzs7esGHDFLv18iSP5+PHjw8cOBAVFSWfSRFCrq6ugYGBz54927dv36QFM6ovvvjCycmJzKQjkUgkwcHBp06dGrro0KFDJSUlwy6atiCZ/q8ff/zR3t5eIBDgOD537txvv/2WWnThwgVnZ2ccx3V0dCwtLQ8fPhwYGBgUFFRdXY1hmEgkKioqMjc3xzDszJkzCKF33nkHwzAGg7FgwQLyqPmzzz4jW/7Xv/410roUt4kQIgjixIkT77zzDofDEQqFnp6ejx49IhclJSXp6Ohwudy8vLz333+fz+ebmZldunRpcodQKYP6pTjyhIQEHMdnzpz58ccfm5iY4Dju6up69+5dcmlAQACbzTY2NiZf7ty5U0dHB8Ow5uZmNGQ8EULXrl3j8/mxsbEq6lpCQgJBEB4eHkMXxcTEvP322+fOnbtx48aw753IhyuTySIjI83NzbW1tefNm3f58uVRQ+3r67tz546jo6PiauHh4Tt37jQ0NBy6SCgUvvfee6dOnZraJ23GRm13BZgsa9euVeYmC5mZmYcOHXr9+nVLS8vixYtnzJhBlp88eRIhdOTIkZaWltevX//zn//csGEDQRDe3t42NjbU28kbU50+fZogiP7+fktLS3Nz8/7+fqrCnj17Tp48qXhdCtokCCIyMpLNZl+4cKGtra20tNTJyYm8XzK5NDw8HCF08+bN9vb2xsbGpUuX6ujo9PX1jWms3n333fnz5ytfX8mxHWRQvxRH7u/vr6OjU1FR0dPTU15evnDhQh6PV19fTy7dsGGDkZER1XJ8fDxCqKmpiXw5aDyvXLnC4/Gio6PHGjCZnkatZm1tbW9vP6jQxsampqaGIIhbt24xGAxLS8uuri6CIAoKClavXk1Vm8iHu2/fPg6Hk5WV1draGhYWxmAw7t27pzjUmpoahJCjo+OyZcuMjY05HI6dnd2ZM2cGBgaoOkVFRR4eHgRBkDfnDg8PH9RIaGgoQqi4uHjUkRnfdvLGgT3T/7V27dqDBw8KhUJ9fX0PD4+WlpampiapVBoVFbV8+fL9+/fr6+sLhcKPPvpo4cKFipvS0tLavXt3fX19Tk4OWSIWi7Ozs7du3apgXYrblEgkJ06cWLNmzcaNGwUCwdy5c7/88svm5uazZ8/KV3N1deXz+YaGhn5+ft3d3fX19eMdj8mmIHImk0nustnb2yclJXV2dqakpIxjFe7u7h0dHQcOHKAv6v/T3d1dU1NjY2MzUgUXF5c9e/bU1tbu379/0KKJfLg9PT1JSUleXl7e3t56enoREREsFmvU8SEvNBkaGsbGxpaXl7969crT03PXrl0XL16kQgoMDExKSlLQiK2tLUKorKxM8bqmD0imwyAf1y6TyUpLS9va2v76179Si8hEOWoL27ZtEwgE1Bml1NRUT0/PYR/4Tq1LcYPl5eVdXV3Ozs5UycKFC9lsNnXMOwj5HDqpVDpqqJpGceTOzs5cLpc6BNYcjY2NBEFwuVwFdWJiYmbPnp2YmFhUVCRfPpEPt7KyUiwWOzg4kIu0tbWNjY1HHR/y5PicOXNcXV319fUFAkFUVJRAIKDSd1hY2Pbt201NTRU0Qnb21atXitc1fUAy/V9Xr15dtmyZoaEhh8P57LPPyMKOjg6EkJ6e3lhb09XV3b59+61bt3766SeE0BdffBEQEKB4XYqR01B0dXXlC/X09OSfhDxNcDicUXfkJ19PTw/6LUmNBMfxlJQUDMO2bt0qkUio8ol8uN3d3QihiIgI7Dd1dXWjzm8zMTFBCJEnl0lsNtvCwqK6uhohVFRUVFZWtm3bNsWNaGtro986DhAkU1J9fb2Xl5exsfHdu3fb29uPHTtGlr/11lvo99uc8gICAlgs1smTJ3/44YdZs2ZRB4AjrUsxMqEP+na1tbWZmZmNI7Y3l1Qq1cxek5ll1CMMFxeXvXv3VlVVHT58mCqcyIdLXh2iTseTbt++rfhdurq6tra25I1iKf39/eTMreTk5Js3bzIYDDI7k6uIjY3FMOznn3+m6pMPjiU7DhAkU1JZWZlUKv3kk0+sra1xHMcwjCy3tLTU19e/fv36ONo0MzNbt25dVlbWgQMHAgMDR12XYg4ODrq6uvKb8t27d/v6+hYsWDCO2N5chYWFBEEsXryYfMlkMjXkVMbMmTMxDFNm/uzhw4ft7OyKi4upkol8uLNmzcJxvKSkZKwB+/r6FhcXP3nyhHwpFovr6urImVIpKSnyqVn+ApT8uQiys0ZGRmNd9VQFyRQhhMzNzRFCN27c6Onpqaqqos5VcTicsLCwH374ISAg4NmzZwMDA52dneT/c319/efPn9fW1nZ2do70fQ4KCurv729tbf3Tn/406roUt4njeFBQUE5OTmpqakdHR1lZ2Y4dO0xMTPz9/ekeDI0zMDDQ2tra399fWloaGBhobm6+efNmcpFIJHr9+nVubq5UKm1qaqqrq5N/46DxLCgoUN3UKC6Xa21t3dDQMGpN8mBf/kmrE/lwcRzfsmXLpUuXkpKSOjo6ZDJZQ0PDixcvEEJ+fn5GRkYj/Vx17969FhYWmzdvrq+vb2lpCQkJkUgkQy+OKUB2VvFM1ell8iYOqImS0zJCQkL09fX19PR8fHzIKZA2NjbkFJwzZ87MnTsXx3Ecx//whz8kJiYSBHH//n0LCwttbe0lS5ZERESQsx25XC45m4SyfPnyc+fOKbkuxW0ODAzEx8fb2tqyWCyhUOjl5VVZWUk2mJiYSF4NsLW1ra6uPnv2LHmxy8LC4tdffx2177dv3/7jH/9InkdDCBkbG7u6un7//fd0ja2806dPy/dr1Mj9/f1ZLJapqSmTyeTz+Z6entXV1VRrLS0ty5cvx3Hcysrq008/DQ4ORgiJRCLyg5Mfz5cvX+bn5/N4vJiYmDEFTCg9NYo8sSMWi8mXOTk55LkdAwODXbt2DaocHBwsPzVqIh9ub29vSEiIubk5k8k0NDT09vYuLy8nCMLLywshFBkZOVLAT58+Xb9+vVAo5HA4ixYtKigoGLbaSFOj3N3dTU1N5WdTjWSaTI2CZArGbxLG1t/fX19fX6WrGJWSybSqqorJZF64cGESQlKGTCZbunRpcnKyKhpvbm7Gcfz48ePKVJ4m30E4zAea7k25QZFIJIqOjo6Ojh72diGTTCaT5ebmdnZ2+vn5qaL9Q4cOOTo6ys9RAZBMp7hHjx5hI1PRN23aCg0N9fHx8fPzm5w7uShQWFiYnZ1dUFCgeOrr+Jw4caKkpCQ/P5+cJQ1IkEynODs7OwUHJunp6eoOUJGwsLCUlJT29nYrK6usrCx1h6OU2NjYgICAI0eOqDeMFStWpKWlUTcuoFFeXl5vb29hYaFQKKS98Tca3D4LaK64uLi4uDh1RzFmbm5ubm5u6o5CVVavXr169Wp1R6GJYM8UAABoAMkUAABoAMkUAABoAMkUAABoMC0uQN25c8fHx0fdUUxBd+7cQQhN+bElfzc55bupOnfu3KFupzCFwZ4pAADQYFrsmS5evDgzM1PdUUxB5M7alB/bjIwMX1/fKd9N1ZkmO/WwZwoAADSAZAoAADSAZAoAADSAZAoAADSAZAoAADSAZKqUixcvYhjm6uo6kUby8/MFAsE333xDV1QqahNM0I0bN0JDQ7Ozs62trclbHW7atEm+gpubG4/H09LSmjNnzkiPFZkEUqk0Li5OJBKx2Ww9PT0HB4fa2tqh1Xp6euzs7CIiIsiXX3/99bFjx96Um8xOJkimSrl48aKNjc3t27cfP3487kYIgqAxJNW1CSbi4MGDCQkJYWFh3t7eT548sbGxmTFjRmpq6tWrV6k6169fz8zMXLVqVXl5uZOTk7pC9fX1PX/+fFpamlgsfvjwoY2NzbC3tQ4PD6+srKReenh44Di+YsUK8gnVgALJdHQtLS0VFRVRUVEIofPnzyv/RolEIr8z6+7u3t7evmrVqokEo4o2Ndmg/mpIUyM5evRoenp6RkYGj8ejChMSEhgMhr+/v9rvGC0vPT09Nzc3MzPz3XffZTKZJiYmeXl5Dg4Og6rdunXrwYMHgwp37949f/78lStX9vf3T1a8bwBIpqPLyMhwd3cn/yGTT/hR8o3JycmNjY30BqOKNjUZjf1V9dA9fvz4wIEDUVFROI7Ll7u6ugYGBj579mzfvn2qW/tYffHFF05OToqfLSqRSIKDg0+dOjV00aFDh0pKSoZdNG1BMh3dxYsX16xZw+Px3Nzcamtrf/zxx6F1Lly44OzsjOO4jo6OpaXl4cOHAwMDg4KCqqurMQwTiURFRUXm5uYYhpGPI33nnXcwDGMwGAsWLBCLxQihzz77TCAQ4Dj+r3/9CyH0448/2tvbkyVz58799ttvEUKK20QIEQRx4sSJd955h8PhCIVCT0/PR48ekYuSkpJ0dHS4XG5eXt7777/P5/PNzMwuXbo0CQOoIKqAgAA2m03dEH7nzp06OjoYhjU3Nw/tb0JCAo7jM2fO/Pjjj01MTHAcd3V1pZ6VPaamEELXrl2j98nPCQkJBEF4eHgMXRQTE/P222+fO3fuxo0bYx2iUT84mUwWGRlpbm6ura09b9488vF/ivX19d25c8fR0VFxtfDw8J07dxoaGg5dJBQK33vvvVOnTsGJpv+j8kf2qdsEn4xYV1dnaGjY399PEMSFCxcQQh999NGgOidPnkQIHTlypKWl5fXr1//85z83bNhAEIS3t7eNjQ1V7enTpwih06dPEwTR399vaWlpbm5Otkzas2fPyZMnyb8zMzMPHTr0+vXrlpaWxYsXz5gxgyxX0CZBEJGRkWw2+8KFC21tbaWlpU5OTgYGBi9fviSXhoeHI4Ru3rzZ3t7e2Ni4dOlSHR2dvr6+cQ+OkmOrOKoNGzYYGRlRlePj4xFCTU1Nw/bX399fR0enoqKip6envLx84cKFPB6PfLDzWJu6cuUKj8eLjo4eNX4ln05qbW1tb28/qNDGxqampoYgiFu3bjEYDEtLy66uLoIgCgoK5B/1PJEPbt++fRwOJysrq7W1NSwsjMFg3Lt3T3GoNTU1CCFHR8dly5YZGxtzOBw7O7szZ87IP7e5qKiIfMb4SI96Dg0NRQgVFxePOjLwdFKAEEIXL178+9//rqWlhRDy8PDgcDiZmZkSiYSqIJVKo6Kili9fvn//fn19faFQ+NFHHy1cuFBxs1paWrt3766vr8/JySFLxGJxdnb21q1byZdr1649ePCgUCjU19f38PBoaWkht2kFJBLJiRMn1qxZs3HjRoFAMHfu3C+//LK5ufns2bPy1VxdXfl8vqGhoZ+fX3d3d319/VjHZEyUjEp5TCaT3IOzt7dPSkrq7OxMSUkZRzvu7u4dHR0HDhwYXxiDdHd319TU2NjYjFTBxcVlz549tbW1+/fvH7RoIh9cT09PUlKSl5eXt7e3np5eREQEi8UadUDIC02GhoaxsbHl5eWvXr3y9PTctWvXxYsXqZACAwOTkpIUNGJra4sQKisrU7yu6QOS6SjIY3zybz6f7+bm1tHRkZeXR1UoLS1ta2v761//SpWQiXLUlrdt2yYQCKizTqmpqZ6ennw+f2hN8hmQo05GKS8v7+rqcnZ2pkoWLlzIZrOpA+FB2Gw2QkgqlY4a6kSMNaoxcXZ25nK51BGxGjU2NhIEofhRoDExMbNnz05MTCwqKpIvn8gHV1lZKRaLqQtH2traxsbGow4Ih8NBCM2ZM8fV1VVfX18gEERFRQkEAip9h4WFbd++3dTUVEEjZGdfvXqleF3TByRTRR48eFBWVrZq1Srq2cjkjE75a/odHR0IIT09vbE2rquru3379lu3bv30008IoS+++EL+KeRXr15dtmyZoaEhh8P57LPPlGmQnKqiq6srX6inp9fZ2TnW2Gik6qg4HM6o++yToKenB/2WpEaC43hKSgqGYVu3bpU/uJnIEHV3dyOEIiIiqE20rq6OPAuvgImJCUKIPJtMYrPZFhYW1dXVCKGioqKysrJt27YpbkRbWxv91nGAIJkqlpaWtn79evnTIq9fv9bW1r5+/frLly/JOm+99Rb6/XapvICAABaLdfLkyR9++GHWrFnUQWJ9fb2Xl5exsfHdu3fb29uPHTumTGtkQh/0DWxrazMzMxtHbHRRaVRSqVTtHSSRmWXUowcXF5e9e/dWVVUdPnyYKpzIEJFXh6hT7aTbt28rfpeurq6trW1FRYV8YX9/v0AgQAglJyffvHmTwWCQ2ZlcRWxsLIZhP//8M1W/r6+P6jhAkEwVIAgiPT19586d8oVCodDHx0cmk1FnlywtLfX19a9fvz6OVZiZma1bty4rK+vAgQOBgYFUeVlZmVQq/eSTT6ytrXEcxzBMmdYcHBx0dXXlN/e7d+/29fUtWLBgHLHRZdSomEzmuE81FBYWEgRB3cV9Ik1N0MyZMzEMU2Ym6eHDh+3s7IqLi6mSiXxws2bNwnG8pKRkrAH7+voWFxc/efKEfCkWi+vq6siZUikpKfKpWf4ClPy5CLKzRkZGY131VAXJdES3bt3i8/l//OMfB5Xv2LEDyR3pczicsLCwH374ISAg4NmzZwMDA52dneT/fH19/efPn9fW1nZ2do70JQ8KCurv729tbf3Tn/5EFZqbmyOEbty40dPTU1VVJX/uTEGbOI4HBQXl5OSkpqZ2dHSUlZXt2LHDxMTE39+fhuEYr1GjEolEr1+/zs3NlUqlTU1NdXV18m8f2t+BgYHW1tb+/v7S0tLAwEBzc/PNmzePo6mCggIap0ZxuVxra2vyASejDkhKSgp5SZMqGfcHh+P4li1bLl26lJSU1NHRIZPJGhoaXrx4gRDy8/MzMjIa6eeqe/futbCw2Lx5c319fUtLS0hIiEQiGXpxTAGys4pnqk4vKp4toH7jm5bx0Ucf6ejoMJnM+fPn379/nyo/fPgweb4JIWRqapqYmEiWnzlzZu7cuTiO4zj+hz/8gSy/f/++hYWFtrb2kiVLIiIiyCmQXC6XnHFCWb58+blz5wYFEBISoq+vr6en5+PjQ04jtbGxqa+vV9zmwMBAfHy8ra0ti8USCoVeXl6VlZVkg4mJieQVA1tb2+rq6rNnz5IXuywsLH799dexjg9JybFVEBVBEC0tLcuXL8dx3MrK6tNPPw0ODkYIiUQicsKTfH9fvnzp7+/PYrFMTU2ZTCafz/f09Kyurh5fU/n5+TweLyYmZtT4lZwaRZ60EYvF5MucnBzyvI2BgcGuXbsGVQ4ODpafGjWRD663tzckJMTc3JzJZBoaGnp7e5eXlxME4eXlhRCKjIwcKeCnT5+uX79eKBRyOJxFixYVFBQMW22kqVHu7u6mpqbys6lGMk2mRkEyBeM3+WPr7++vr68/mWsklE6mVVVVTCaT/I2cJpDJZEuXLk1OTlZF483NzTiOHz9+XJnK0+Q7CIf54A2jsfcrEolE0dHR0dHRw94uZJLJZLLc3NzOzk4/Pz9VtH/o0CFHR0f5+ScAkikAtAkNDfXx8fHz81P7PU0KCwuzs7MLCgoUT30dnxMnTpSUlOTn55MzoAEJkil4Y4SFhaWkpLS3t1tZWWVlZak7nOHFxsYGBAQcOXJEvWGsWLEiLS2NulMBjfLy8np7ewsLC4VCIe2Nv9GmxaOewdQQFxcXFxen7ihG5+bm5ubmpu4oVGX16tWrV69WdxSaCPZMAQCABpBMAQCABpBMAQCABpBMAQCABpBMAQCABtPian5WVpaS9woB4zBNxnaadFNF1q5dq+4QVA4jpvojXG7fvk0+2wO8ocinwuzZs0fdgYDxmzVrlouLi7qjUK2pn0zBm27dunUIoYyMDHUHAoAicM4UAABoAMkUAABoAMkUAABoAMkUAABoAMkUAABoAMkUAABoAMkUAABoAMkUAABoAMkUAABoAMkUAABoAMkUAABoAMkUAABoAMkUAABoAMkUAABoAMkUAABoAMkUAABoAMkUAABoAMkUAABoAMkUAABoAMkUAABoAMkUAABoAMkUAABoAMkUAABoAMkUAABoAMkUAABoAMkUAABoAMkUAABoAMkUAABoAMkUAABoAMkUAABoAMkUAABoAMkUAABowFR3AAAMdvfu3V9++YV6+eTJE4TQ2bNnqZL58+e/++67aogMgJFhBEGoOwYAfufKlSurVq3S0tJiMBgIIXITxTAMITQwMCCTyb755pu///3vao4SgN+DZAo0jlQqNTAw6OjoGHYpn89vampis9mTHBUAisE5U6BxWCzW+vXrh02XChYBoF6QTIEmWr9+fV9f39ByqVT6wQcfTH48AIwKDvOBJhoYGHjrrbdevXo1qNzQ0PDly5fkuVQANApslEATMRiMTZs2DTqcZ7PZmzdvhkwKNBNsl0BDDT3S7+vrW79+vbriAUAxOMwHmsvW1vbx48fUS2tr6+rqajXGA4ACsGcKNNfGjRtZLBb5N5vN/o//+A/1xgOAArBnCjTX48ePbW1tqZeVlZVvv/22GuMBQAHYMwWaSyQSzZ8/H8MwDMPmz58PmRRoMkimQKN9+OGHWlpaWlpaH374obpjAUAROMwHGu358+ezZs0iCOLp06empqbqDgeAEU3TZOrj46PuEICyCgsLEULLli1TcxxAaZmZmeoOQQ2m6WF+VlZWQ0ODuqN4kzQ0NGRlZall1ebm5hYWFpO2Otg2JkKN24naTdM9UwzDLl++vG7dOnUH8sbIyMjw9fVVy9by+vVrhJC+vv7krA62jYlQ43aidnBzaKDpJi2NAjAR0/QwHwAA6AXJFAAAaADJFAAAaADJFAAAaADJVCnbtm3j8XgYhpWUlKg7lgmJjo62t7fn8/kcDkckEn322WddXV2qW11+fr5AIPjmm29Utwr1unHjRmhoaHZ2trW1Nfmz102bNslXcHNz4/F4Wlpac+bMuX//vrrilEqlcXFxIpGIzWbr6ek5ODjU1tYOrdbT02NnZxcREUG+/Prrr48dOyaTySY11jcWJFOlnDt37quvvlJ3FDT47rvvdu3aVVtb29zcHBcXd+rUKZX+fmFqT5E5ePBgQkJCWFiYt7f3kydPbGxsZsyYkZqaevXqVarO9evXMzMzV61aVV5e7uTkpK5QfX19z58/n5aWJhaLHz58aGNjM+w/0fDw8MrKSuqlh4cHjuMrVqxoa2ubxGDfVJBM33gSicTV1VXJyrq6uv7+/vr6+jweb926dV5eXteuXXv69KmKYnN3d29vb1+1apWK2qeMaRBocfTo0fT09IyMDB6PRxUmJCQwGAx/f//29vbJDEax9PT03NzczMzMd999l8lkmpiY5OXlOTg4DKp269atBw8eDCrcvXv3/PnzV65c2d/fP1nxvqkgmSqLfG67BkpOTm5sbFSy8pUrV7S0tKiXBgYGCCGxWKySyCbRmAZh4h4/fnzgwIGoqCgcx+XLXV1dAwMDnz17tm/fvkkLZlRffPGFk5PT3LlzFdSRTa1dEwAAIABJREFUSCTBwcGnTp0auujQoUMlJSXDLgLyIJmOiCCI+Pj42bNnczgcgUAQHBxMLfr888+5XC6Px2tsbAwKCjI1Na2srCQI4sSJE++88w6HwxEKhZ6eno8ePSLrJyQk4Dg+c+bMjz/+2MTEBMdxV1fXu3fvyq9rpPcGBASw2WxjY2Py5c6dO3V0dDAMa25uRggFBgYGBQVVV1djGCYSicbax2fPnmlra1tZWY17lBQoKioyNzfHMOzMmTMIoaSkJB0dHS6Xm5eX9/777/P5fDMzs0uXLpGVFQ/RWAfh2rVrfD4/NjZWFf0ioyUIwsPDY+iimJiYt99++9y5czdu3Bj2vQo+a8VDhBCSyWSRkZHm5uba2trz5s27fPnyqKH29fXduXPH0dFRcbXw8PCdO3caGhoOXSQUCt97771Tp05N7ZM2NCCmJYTQ5cuXFdcJDw/HMOwf//hHa2urWCxOTExECBUXF1NLEUK7d+8+ffr0mjVrHj58GBkZyWazL1y40NbWVlpa6uTkZGBg8PLlS7K+v7+/jo5ORUVFT09PeXn5woULeTxefX09uVTxezds2GBkZEQFFh8fjxBqamoiX3p7e9vY2IxjELq7u3k8XkBAgDKVye/tWFdBnkA4ffo0+ZIctJs3b7a3tzc2Ni5dulRHR6evr49cqniIxjQIV65c4fF40dHRYw2YUG7bsLa2tre3H1RoY2NTU1NDEMStW7cYDIalpWVXVxdBEAUFBatXr6aqKf6sFQ/Rvn37OBxOVlZWa2trWFgYg8G4d++e4lBramoQQo6OjsuWLTM2NuZwOHZ2dmfOnBkYGKDqFBUVeXh4EATR1NSEEAoPDx/USGhoqPzGr8D4tpOpAfZMhyeRSE6ePPnnP/957969enp62traw/6o8ejRo7t27crOzrawsDhx4sSaNWs2btwoEAjmzp375ZdfNjc3nz17lqrMZDLJ/RF7e/ukpKTOzs6UlBRyXaO+VxXi4uJMTExiYmJUupahXF1d+Xy+oaGhn59fd3d3fX09tWikIRord3f3jo6OAwcO0Bf1/+nu7q6pqbGxsRmpgouLy549e2pra/fv3z9okZKf9bBD1NPTk5SU5OXl5e3traenFxERwWKxRh0f8kKToaFhbGxseXn5q1evPD09d+3adfHiRSqkwMDApKQkBY2QzzsoKytTvK5pDpLp8B4/fiwWi1esWKFk/fLy8q6uLmdnZ6pk4cKFbDZb/lhenrOzM5fLJY/vxvpeWuTk5GRkZHz77bfy108mGfkkZ6lUOuxS+SHSKI2NjQRBcLlcBXViYmJmz56dmJhYVFQkXz7Wz1p+iCorK8ViMXXhSFtb29jYeNTx4XA4CKE5c+a4urrq6+sLBIKoqCiBQECl77CwsO3btyu+VyzZ2VevXile1zQHyXR45E3Yhj2FNCxy7oiurq58oZ6eXmdn50hv4XA45FHVON47Qenp6UePHi0sLLS0tFTRKmhBDZFG6enpQb8lqZHgOJ6SkoJh2NatWyUSCVU+kc+6u7sbIRQREYH9pq6ubtSLhyYmJggh8uQyic1mW1hYkM95LSoqKisr27Ztm+JGtLW10W8dByOBZDo88iptb2+vkvX19PQQQoO+Em1tbWZmZsPWl0ql1NKxvneCTp8+nZqa+t1337311luqaJ8u8kOkUcjMMupUdhcXl71791ZVVR0+fJgqnMhnTf5rP3nypPx5utu3byt+l66urq2tbUVFhXxhf3+/QCBACCUnJ9+8eZPBYJDZmVxFbGwshmE///wzVb+vr4/qOBgJJNPhOTg4MBiM77//Xvn6urq68tvf3bt3+/r6FixYMGz9wsJCgiAWL16szHuZTOZIx8JjQhBESEhIWVlZbm7uoJ0jDSQ/RIi+QZi4mTNnYhimzEzSw4cP29nZFRcXUyVj3U7kzZo1C8fxcfwGz9fXt7i4+MmTJ+RLsVhcV1dHzpRKSUmRT83yF6Dkz0WQnTUyMhrrqqcVSKbDMzQ09Pb2zsrKSk5O7ujoKC0tVXw5CMfxoKCgnJyc1NTUjo6OsrKyHTt2mJiY+Pv7U3UGBgZaW1v7+/tLS0sDAwPNzc03b96szHtFItHr169zc3OlUmlTU1NdXZ38qvX19Z8/f15bW9vZ2ak43VRUVHz++edfffUVi8XC5Bw/fnzcA0WvkYYIjXEQCgoKVDc1isvlWltbK3M3fvJgX35irzLbiYLWtmzZcunSpaSkpI6ODplM1tDQ8OLFC4SQn5+fkZHRSD9X3bt3r4WFxebNm+vr61taWkJCQiQSydCLYwqQnVU8UxVM00kMSInpL52dndu2bZsxY4auru6SJUsiIyMRQmZmZr/88suxY8fIQ55Zs2ZduHCBrD8wMBAfH29ra8tisYRCoZeXFzn5lOTv789isUxNTZlMJp/P9/T0rK6uppYqfm9LS8vy5ctxHLeysvr000/JGa8ikYicNnT//n0LCwttbe0lS5ZQM2yGNdLV2Pj4+FFHbBxTXk6fPk3ODOVyuR4eHomJieR1DFtb2+rq6rNnz/L5fISQhYXFr7/+OuoQjWkQ8vPzeTxeTEzMmAImKbNtBAQEsFgssVhMvszJySEv7hsYGOzatWtQ5eDgYPmpUQo+61GHqLe3NyQkxNzcnMlkkv/vy8vLCYLw8vJCCEVGRo4U8NOnT9evXy8UCjkczqJFiwoKCoatNtLUKHd3d1NTU/nZVCOZzlOjpmu3lfjC0Iv8EedkrpFek/Al0ZAhUmbbqKqqYjKZ1P9RtZPJZEuXLk1OTlZF483NzTiOHz9+XJnK0zmZwmH+5IG774zqTRkikUgUHR0dHR2t0ntuKUkmk+Xm5nZ2dvr5+ami/UOHDjk6OgYEBKii8akEkunU8ejRI2xkKvqmTVuhoaE+Pj5+fn5qv6dJYWFhdnZ2QUGB4qmv43PixImSkpL8/HwWi0V741MMJNPJEBYWlpKS0t7ebmVlpboH4drZ2Sk4BklPT1fRemkxOUNEr9jY2ICAgCNHjqg3jBUrVqSlpVE3LqBRXl5eb29vYWGhUCikvfGpBx71DJQyfR7hC9vGREyf7WQo2DMFAAAaQDIFAAAaQDIFAAAaQDIFAAAaQDIFAAAaTN+r+eoOAYApa3pmFaa6A1CbwMBAFxcXdUfxxrh9+/apU6eUeejQm87X1xe2jXEjtxN1R6Ee0zeZuri4wFzCMTl16tR0GDFfX1/YNiZi2iZTOGcKAAA0gGQKAAA0gGQKAAA0gGQKAAA0gGQKAAA0gGQ6NtnZ2dbW1vL3CWWz2TNnzly2bFl8fHxra6u6AwST6saNG6GhofJbxaZNm+QruLm58Xg8LS2tOXPmjPSMpkkglUrj4uJEIhGbzdbT03NwcKitrR1araenx87OLiIignz59ddfHzt27E25Y7faQTIdG29v7ydPntjY2AgEAoIgBgYGGhsbMzIyrKysQkJC5syZI//gSTC1HTx4MCEhISwsjNoqZsyYkZqaevXqVarO9evXMzMzV61aVV5e7uTkpK5QfX19z58/n5aWJhaLHz58aGNjM+wzAsLDwysrK6mXHh4eOI6vWLGira1tEoN9U0EynRAMw/T09JYtW5aSkpKRkfHq1St3d3e133p9KIlE4urqqu4oRkFjkJPQ36NHj6anp2dkZPB4PKowISGBwWD4+/tr1DaQnp6em5ubmZn57rvvMplMExOTvLw8BweHQdVu3br14MGDQYW7d++eP3/+ypUr+/v7JyveNxUkU9qsXbt28+bNjY2NX375pbpjGSw5ObmxsVHdUYyCxiBV3d/Hjx8fOHAgKioKx3H5cldX18DAwGfPnu3bt091ax+rL774wsnJSfGDmiUSSXBw8LDz7Q8dOlRSUjJtp+IrD5IpnciHvBcUFCCEPv/8cy6Xy+PxGhsbg4KCTE1NySf6njhx4p133uFwOEKh0NPT89GjR+R7ExIScByfOXPmxx9/bGJiguO4q6vr3bt3qcYVvDcgIIDNZlMPrti5c6eOjg6GYc3NzQihwMDAoKCg6upqDMNEIpFKR4CuIBWPxlj7e+3aNT6fHxsbS1c3ExISCILw8PAYuigmJubtt98+d+7cjRs3xjpESUlJOjo6XC43Ly/v/fff5/P5ZmZmly5dot4rk8kiIyPNzc21tbXnzZunzK97+/r67ty54+joqLhaeHj4zp07DQ0Nhy4SCoXvvffeqVOnpucv7sdApc8+1VhoYo96ps6ZDtLR0YEQmjVrFvkyPDwcIbR79+7Tp0+vWbPm4cOHkZGRbDb7woULbW1tpaWlTk5OBgYG1MPu/f39dXR0Kioqenp6ysvLFy5cyOPxyOfCEwSh+L0bNmwwMjKiIomPj0cINTU1kS+9vb1tbGzG3V9C6Uf40hik4tEYU1NXrlzh8XjR0dHK9FSZbcPa2tre3n5QoY2NTU1NDUEQt27dYjAYlpaWXV1dBEEUFBSsXr2aqqZ4iMht5ubNm+3t7Y2NjUuXLtXR0enr6yOX7tu3j8PhZGVltba2hoWFMRiMe/fuKQ61pqYGIeTo6Lhs2TJjY2MOh2NnZ3fmzJmBgQGqTlFRkYeHB0EQTU1NCKHw8PBBjYSGhiKEiouLFa+LgEc9A7rweDwMwzo7O+ULjx49umvXruzsbAsLixMnTqxZs2bjxo0CgWDu3Llffvllc3Pz2bNnqcpMJpPcZ7G3t09KSurs7ExJSUEISSSSUd+rdrQHOdJojJW7u3tHR8eBAwfGF8Yg3d3dNTU1NjY2I1VwcXHZs2dPbW3t/v37By1ScohcXV35fL6hoaGfn193d3d9fT1CqKenJykpycvLy9vbW09PLyIigsVijTog5IUmQ0PD2NjY8vLyV69eeXp67tq16+LFi1RIgYGBSUlJChqxtbVFCJWVlSle1zQHyZRO3d3dBEHw+fxhl5aXl3d1dTk7O1MlCxcuZLPZ8sfy8pydnblcLnkMONb3qoVKg5QfDfVqbGwkCELxc5VjYmJmz56dmJhYVFQkXz7WIWKz2QghqVSKEKqsrBSLxdSFI21tbWNj41EHhMPhIITmzJnj6uqqr68vEAiioqIEAgGVvsPCwrZv325qaqqgEbKzr169UryuaQ6SKZ1+/fVXhJCdnd2wS8n5Jbq6uvKFenp6g/Zk5XE4HPLIaxzvnXyqDpIaDfXq6elBvyWpkeA4npKSgmHY1q1bJRIJVT6RIeru7kYIRUREUHOc6+rqxGKx4neZmJgghMizySQ2m21hYVFdXY0QKioqKisr27Ztm+JGtLW10W8dByOBZEqna9euIYTef//9YZfq6ekhhAZ9bdra2szMzIatL5VKqaVjfa9aqDRI+dFQLzKzjDqV3cXFZe/evVVVVYcPH6YKJzJE5NWhkydPyp+nu337tuJ36erq2traVlRUyBf29/cLBAKEUHJy8s2bNxkMBpmdyVXExsZiGCY/Y7qvr4/qOBgJJFPavHz58uTJk2ZmZlu3bh22goODg66urvw2evfu3b6+vgULFgxbv7CwkCCIxYsXK/NeJpNJHgyqkUqDlB+NCTY1QTNnzsQwTJmZpIcPH7azsysuLqZKxroNyJs1axaO4yUlJWMN2NfXt7i4+MmTJ+RLsVhcV1dHzpRKSUmRT83yF6Dkz0WQnTUyMhrrqqcVSKbjRBBEV1cXeUm0qanp8uXLf/zjH7W0tHJzc0c6Z4rjeFBQUE5OTmpqakdHR1lZ2Y4dO0xMTPz9/ak6AwMDra2t/f39paWlgYGB5ubm5HSrUd8rEolev36dm5srlUqbmprq6urkV62vr//8+fPa2trOzk7V5SDagxxpNMbaVEFBAY1To7hcrrW1dUNDgzIDkpKSoqWlJV8y6jagoLUtW7ZcunQpKSmpo6NDJpM1NDS8ePECIeTn52dkZDTSz1X37t1rYWGxefPm+vr6lpaWkJAQiUQy9OKYAmRnFc9UBdN0EgMa79Sor7/+et68eVwul81mMxgM9NuPoBYtWhQdHd3S0kLVPHbsGHlYNGvWrAsXLpCFAwMD8fHxtra2LBZLKBR6eXmRk09J/v7+LBbL1NSUyWTy+XxPT8/q6mpqqeL3trS0LF++HMdxKyurTz/9NDg4GCEkEonIuUT379+3sLDQ1tZesmQJNQtnTJSc8kJjkIpHY0xN5efn83i8mJgYZXqqzLYREBDAYrHEYjH5Micnh7y4b2BgsGvXrkGVg4OD5adGKRiixMRE8lKPra1tdXX12bNnyX/MFhYWv/76K0EQvb29ISEh5ubmTCbT0NDQ29u7vLycIAgvLy+EUGRk5EgBP336dP369UKhkMPhLFq0qKCgYNhqI02Ncnd3NzU1lZ9NNZLpPDVqunZ7YvNMVcTf319fX1/dUQxv8r8k6hoNZbaNqqoqJpNJ/Y9UO5lMtnTp0uTkZFU03tzcjOP48ePHlak8nZMpHOZrFrhDjzyNHQ2RSBQdHR0dHT3s7UImmUwmy83N7ezs9PPzU0X7hw4dcnR0DAgIUEXjUwkkUwDGIzQ01MfHx8/PT+33NCksLMzOzi4oKFA89XV8Tpw4UVJSkp+fz2KxaG98ioFkqinCwsJSUlLa29utrKyysrLUHY6avRGjERsbGxAQcOTIEfWGsWLFirS0NOpOBTTKy8vr7e0tLCwUCoW0Nz71YMS0vHkBhmGXL1+Gx/kqLyMjw9fXdzpsLbBtTMT02U6Ggj1TAACgASRTAACgASRTAACgASRTAACgAVPdAajNqHeIAPLI4crIyFB3IJMBto1xm85DN32v5qs7BACmrGmaVaZnt8EbhJylNE12isGbC86ZAgAADSCZAgAADSCZAgAADSCZAgAADSCZAgAADSCZAgAADSCZAgAADSCZAgAADSCZAgAADSCZAgAADSCZAgAADSCZAgAADSCZAgAADSCZAgAADSCZAgAADSCZAgAADSCZAgAADSCZAgAADSCZAgAADSCZAgAADSCZAgAADSCZAgAADSCZAgAADSCZAgAADSCZAgAADSCZAgAADSCZAgAADSCZAgAADSCZAgAADSCZAgAADSCZAgAADSCZAgAADSCZAgAADTCCINQdAwC/k5aWlpycPDAwQL6sqalBCFlZWZEvGQzGRx99tGHDBrXFB8BwIJkCjVNaWjp//nwFFX755Zd58+ZNWjwAKAOSKdBEdnZ2lZWVwy4SiURVVVWTHA8Ao4JzpkATbdq0icViDS1nsVhbtmyZ/HgAGBXsmQJN9OTJE5FINOzGWVVVJRKJJj8kABSDPVOgiaytrZ2cnDAMky/EMMzZ2RkyKdBMkEyBhvrwww+1tLTkS7S0tD788EN1xQOAYnCYDzRUY2OjiYkJNUEKIcRgMJ4/f25kZKTGqAAYCeyZAg01c+bM9957j9o51dLSWrZsGWRSoLEgmQLNtWnTJvkjp02bNqkxGAAUg8N8oLk6OjoMDQ37+voQQiwWq7GxUU9PT91BATA82DMFmovP5//tb39jMplMJnPlypWQSYEmg2QKNNrGjRtlMplMJoMf4wMNB4f5QKP19PQYGBgQBNHc3Kytra3ucAAYESTT38nIyPD19VV3FAC8AS5fvrxu3Tp1R6FBmOoOQBNdvnxZ3SGoja+vb2BgoIuLi7oD+T8lJSUYhim+j9RYnTx5EiG0Z88eGtucVmCfYyhIpsOYzv9vfX19XVxcNGoE1qxZgxBiMuncVjMzM9H0/qAnCJLpUJBMgaajN40CoCJwNR8AAGgAyRQAAGgAyRQAAGgAyRQAAGgAyXSitm3bxuPxMAwrKSlRdyz/Kzo62t7ens/nczgckUj02WefdXV1qXSN+fn5AoHgm2++Uela1OjGjRuhoaHZ2dnW1tYYhmEYNuiuK25ubjweT0tLa86cOffv31dXnFKpNC4uTiQSsdlsPT09BweH2traodV6enrs7OwiIiLIl19//fWxY8dkMtmkxjrlQDKdqHPnzn311VfqjuJ3vvvuu127dtXW1jY3N8fFxZ06dcrHx0ela5zaP/04ePBgQkJCWFiYt7f3kydPbGxsZsyYkZqaevXqVarO9evXMzMzV61aVV5e7uTkpK5QfX19z58/n5aWJhaLHz58aGNjM+z/0fDwcPnnFXp4eOA4vmLFira2tkkMdqqBZDoF6erq+vv76+vr83i8devWeXl5Xbt27enTp6pbo7u7e3t7+6pVq1S3CpJEInF1dVX1WuQdPXo0PT09IyODx+NRhQkJCQwGw9/fv729fTKDUSw9PT03NzczM/Pdd99lMpkmJiZ5eXkODg6Dqt26devBgweDCnfv3j1//vyVK1f29/dPVrxTDSRTGgx6VJHaXblyRf6BHwYGBgghsVisvohok5yc3NjYOGmre/z48YEDB6KionAcly93dXUNDAx89uzZvn37Ji2YUX3xxRdOTk5z585VUEcikQQHB586dWrookOHDpWUlAy7CCgDkul4EAQRHx8/e/ZsDocjEAiCg4Pll8pkssjISHNzc21t7Xnz5pE/Tk1KStLR0eFyuXl5ee+//z6fzzczM7t06RL1ru+//37RokVcLpfP58+dO7ejo2Okpsbq2bNn2traVlZWE+v0iIqKiszNzTEMO3PmDBqtpwkJCTiOz5w58+OPPzYxMcFx3NXV9e7du+TSgIAANpttbGxMvty5c6eOjg6GYc3NzQihwMDAoKCg6upqDMPIx+pdu3aNz+fHxsaqqGsJCQkEQXh4eAxdFBMT8/bbb587d+7GjRvDvpcgiBMnTrzzzjscDkcoFHp6ej569IhcNOrGMI7Pva+v786dO46OjoqrhYeH79y509DQcOgioVD43nvvnTp1amqftFEhAsght9pRq4WHh2MY9o9//KO1tVUsFicmJiKEiouLyaX79u3jcDhZWVmtra1hYWEMBuPevXvkuxBCN2/ebG9vb2xsXLp0qY6OTl9fH0EQXV1dfD7/2LFjEonk5cuXa9asaWpqUtCU8rq7u3k8XkBAgJL1EUKXL18e0yoIgiDPIZw+fZp8qaCnBEH4+/vr6OhUVFT09PSUl5cvXLiQx+PV19eTSzds2GBkZES1HB8fjxAiR4MgCG9vbxsbG2rplStXeDxedHT0WANeu3bt2rVrR61mbW1tb28/qNDGxqampoYgiFu3bjEYDEtLy66uLoIgCgoKVq9eTVWLjIxks9kXLlxoa2srLS11cnIyMDB4+fIluVTxEI3jc6+pqUEIOTo6Llu2zNjYmMPh2NnZnTlzZmBggKpTVFTk4eFBEERTUxNCKDw8fFAjoaGh8luyAuPbTqY2SKa/o0wyFYvFXC73L3/5C1VC7lOQm6BEIuFyuX5+flRlDofzySefEL99fyQSCbmITMGPHz8mCII8h3XlyhX5FSloSnnh4eFvv/12R0eHkvVpTKbD9pQgCH9/f4FAQL333r17CKGoqCjy5ZiS6bgpk0y7urowDFu1atWgciqZEgQRFBSEENq1axfx+2QqFot1dXWpz44giJ9++gkhROV9BUM0vs+9rKwMIfSXv/zlv//7v1taWtra2vbv348QSk1NpdpxdnZuaGggRk6m//mf/4kQOn/+vOJ1EZBMhwOH+WP2+PFjsVi8YsWKYZdWVlaKxWLqrL+2traxsTF1fCePzWYjhKRSKULI2tp65syZGzduPHToEDWXRfmmRpKTk5ORkfHtt9/KXzyZfPI9HcrZ2ZnL5Y6pX5OjsbGRIAgul6ugTkxMzOzZsxMTE4uKiuTLy8vLu7q6nJ2dqZKFCxey2WzqhMYg8kM0vs+dw+EghObMmePq6qqvry8QCKKiogQCwdmzZ8kKYWFh27dvNzU1VdAI2dlXr14pXhcYFiTTMWtoaEAIDXvWCSHU3d2NEIqIiMB+U1dXN+rFH21t7e+++27JkiWxsbHW1tZ+fn4SiWR8TVHS09OPHj1aWFhoaWmpfO/UgsPhkPtKGqWnpwf9lqRGguN4SkoKhmFbt26VSCRUOTnHSFdXV76ynp5eZ2fnqOsd3+duYmKCECJPLpPYbLaFhUV1dTVCqKioqKysbNu2bYobIW+/TXYcjBUk0zEjL+z29vYOu5RMsidPnpTf/799+/aozc6ZM+ebb755/vx5SEjI5cuXjx8/Pu6mEEKnT59OTU397rvv3nrrrTH0TR2kUmlbW5uZmZm6AxmMzCyjTmV3cXHZu3dvVVXV4cOHqULycVWDUqeS3Rzf566rq2tra1tRUSFf2N/fLxAIEELJyck3b95kMBhkdiZXERsbi2HYzz//TNUnn10ITzQYH0imY+bg4MBgML7//vthl86aNQvH8bH+Gur58+fk18DQ0PDIkSNOTk4VFRXja4ogiJCQkLKystzc3EF7RpqpsLCQIIjFixeTL5lM5kgnBCbZzJkzMQxTZibp4cOH7ezsiouLqRIHBwddXV35PHX37t2+vr4FCxaM2tr4PneEkK+vb3Fx8ZMnT8iXYrG4rq6OnCmVkpIin5rlz5nKn4sgO2tkZDTWVQMEyXQcDA0Nvb29s7KykpOTOzo6SktLqdNSCCEcx7ds2XLp0qWkpKSOjg6ZTNbQ0PDixQvFbT5//vzjjz9+9OhRX19fcXFxXV3d4sWLx9dURUXF559//tVXX7FYLEzO8ePHaeg8TQYGBlpbW/v7+0tLSwMDA83NzTdv3kwuEolEr1+/zs3NlUqlTU1NdXV18m/U19d//vx5bW1tZ2enVCotKChQ3dQoLpdrbW1NntVRjDzYl5/bi+N4UFBQTk5OampqR0dHWVnZjh07TExM/P39lWltpM/dz8/PyMhopJ+r7t2718LCYvPmzfX19S0tLSEhIRKJhLwMpSSys4pnqoIRqeSy1htLyalRnZ2d27ZtmzFjhq6u7pIlSyIjIxFCZmZmv/zyC0EQvb29ISEh5ubmTCaTzLzl5eWJiYnk2X1bW9vq6uqzZ8/y+XyEkIWFxa+//lpbW+vrRsNOAAAgAElEQVTq6ioUCrW0tN56663w8PD+/v6RmlIcG3lVd6j4+HhlRgCN/Srt6dOnyZmhXC7Xw8NDcU8JgvD392exWKampkwmk8/ne3p6VldXU621tLQsX74cx3ErK6tPP/2UnMMrEonIuVP379+3sLDQ1tZesmTJy5cv8/PzeTxeTEzMmAImlJ4aFRAQwGKxxGIx+TInJ8fGxgYhZGBgQF7BlxccHCw/NWpgYCA+Pt7W1pbFYgmFQi8vr8rKSnLRqEM00ufu5eWFEIqMjBwp4KdPn65fv14oFHI4nEWLFhUUFAxbbaSr+e7u7qampvKzqUYyju1kyoNk+jtKJtMpbBK+JORPXVW6ilEpmUyrqqqYTOaFCxcmISRlyGSypUuXJicnq6Lx5uZmHMePHz+uTGVIpkPBYT5QgzflBkUikSg6Ojo6OlrVt91Shkwmy83N7ezs9PPzU0X7hw4dcnR0DAgIUEXj0wEk0zfMo0ePsJGp6Gs2nYWGhvr4+Pj5+an9niaFhYXZ2dkFBQWKp76Oz4kTJ0pKSvLz81ksFu2NTxOQTN8wdnZ2Cg400tPT1R3gKMLCwlJSUtrb262srLKystQdjlJiY2MDAgKOHDmi3jBWrFiRlpZG3biARnl5eb29vYWFhUKhkPbGpw947iOYVHFxcXFxceqOYszc3Nzc3NzUHYWqrF69evXq1eqO4o0He6YAAEADSKYAAEADSKYAAEADSKYAAEADuAA1jIyMDHWHoE5K3kvljUb+bnKaf9CAXhgBjyiQk5GR4evrq+4oAHgDXL58ed26deqOQoPAnukwpvM/GAzDpsOXhHz2dWZmproDeVNp2kMkNQGcMwUAABpAMgUAABpAMgUAABpAMgUAABpAMgUAABpAMgUAABpAMqVNdna2tbW1/N1F2Wz2zJkzly1bFh8f39raqu4AwfjduHEjNDRU/iPetGmTfAU3Nzcej6elpTVnzpyRntGkaseOHbOzs9PW1tbR0bGzszvw/9m797gmrrRx4CeBJJOEhGtEilwTFVG8wq6y9WNdd2mVBUFFqda2truLt0YqXgoK5eYF4VUKxd60rIqKqCxaFLXUZasfLbWvsFBYLVAVwWq4BwiYEOb3x3k7v5RLCDC5qM/3L3Jm5sxzZiYPczmZEx0tl8upqfHx8Z6enkKhkMPhSCSSbdu2UW+8Pn/+fFJS0rPyxm7TZaA3+j8jRj9siVgstrS0JEkSjxn3r3/96+2332YwGA4ODrdu3aIpTD1CL8ZwFDoOW4LFxMQEBATI5XL8USwW29raIoTy8/M1ZysoKNAcA8rw/P39U1JSZDJZe3t7Tk4Oi8X685//TE2dN29eRkZGU1OTXC4/deoUi8V67bXXqKmpqanz5s1raWnRcV0vyHEyLHBmqi8MBsPKyuqVV17JzMzMycl58uSJv7+/0d/WbnRdXV2+vr6mVpUWe/fuzc7OzsnJEQgEVGFaWhqTyQwLCzOpHcpmszds2CASiSwsLEJCQoKCgr7++mtqOFsLCws8+pZAIFi+fHlwcPClS5cePnyIp27atGnatGmLFi3q6ekxXguebZBMDWHZsmVvv/22TCb79NNPjR2LkR0+fFgmk5laVYOprq6Ojo6Oi4sjCEKz3NfXNzw8vL6+fsuWLXoNYFhyc3M143R0dEQIUdfy+fn5moNR29nZIYQUCgVVEhsbW1pampqaaqBwnzuQTA0EjwtfUFCAP6rV6piYGGdnZy6XO3XqVHx74eDBg3w+n8fjnTt3buHChUKhcNy4cSdPnqQq+fe///273/2Ox+MJhUIvLy98R2zAqvSKJMn9+/dPmjSJw+FYW1sHBQXduXMHT5JKpWw2mxpaY8OGDXw+n8FgNDY2IoTCw8MjIiJqamoYDIZEIklLSyMIYsyYMWvXrnVwcCAIwtfXt7i4eARVIYQuXbokFAp37dpFY0vT0tJIkgwMDOw/KTExccKECYcOHSosLBzuVhpyR9OyT6uqqqysrFxcXAacWl9fz+Vy3dzcqBJra+t58+alpqaSL/DPqUfFuHcZTA2N90z7wInPyckJf9yyZQuHwzlz5kxLS0tUVBSTycR3VHfs2IEQ+uabb9ra2mQy2dy5c/l8vlKpJEmyo6NDKBQmJSV1dXU9fvx4yZIlDQ0NWqoaGaTDvbCYmBg2m33s2LHW1taysrKZM2fa2dk9fvwYT121apW9vT01c3JyMkIIh0qS5NKlS8ViMTU1LCyMz+dXVlZ2d3dXVFT4+PgIBILa2toRVJWfny8QCOLj43Vppo73TN3d3T09PfsUisXie/fukSR548YNJpPp6ura0dFB9rtnqn0radnR5Oj2qVKprKurS09P53A4gw1S3dnZKRAIpFJpn/LIyEiEUElJyZBr0eU4edFAMv0N/SVTkiTxXVSSJLu6ung8XmhoKC5XKBQcDmf9+vXkr9+xrq4uPCkjIwMhVF1dTZLkjz/+iPo99NBS1cgM+SVRKBQWFhbUGkmS/P777xFCVBYbbjLV3Fy3bt1CCMXFxY2gqmHRJZl2dHQwGIyAgIA+5VQyJUkyIiICIbRx40byt8l0yK2kZUePcp/a29sjhGxtbT/66CMqO/exY8eOCRMmUI/UKF9++SVC6OjRo0OuBZJpf3CZbyCdnZ0kSQqFQoTQ3bt3FQrFlClT8CQulzt27FjqGlATm81GCKlUKoSQu7v7mDFj3njjjdjY2Pv37+MZdK+KLhUVFR0dHd7e3lSJj48Pm82mLs9Hw9vbm8fj6TV+3clkMpIktY+rnJiYOHHixIyMjOvXr2uWD3crae7oUe7Thw8fymSyEydOHDlyZMaMGf1vK+fm5ubk5Fy+fFnzkRqGG/vkyRMd1wU0QTI1kJ9++gkh5OHhgRDq7OxECO3cuZPqkfrgwQPNRwED4nK5V69effnll3ft2uXu7h4aGtrV1TWyqkajtbUVIWRhYaFZaGVl1d7eTkv9HA6noaGBlqpGqbu7GyHE4XC0zEMQRGZmJoPBeOedd7q6uqjy0WylUe5TFoslEon8/Pyys7MrKir6jAWbnZ29d+/eoqIiV1fX/styuVz0a8PBcEEyNZBLly4hhBYuXIgQEolECKEDBw5oXiPo8n77yZMnf/XVV48ePdq+ffupU6dSUlJGXNWIWVlZIYT6JIXW1tZx48aNvnKVSkVXVaOHM8uQXdnnzJmzefPmqqqqhIQEqnA0W4mufSqRSMzMzCoqKqiS9PT0rKysq1evvvTSSwMuolQq0a8NB8MFydQQHj9+fODAgXHjxr3zzjsIIScnJ4IgSktLh1XJo0ePKisrEUIikWjPnj0zZ86srKwcWVWjMWXKFAsLix9++IEqKS4uViqVs2bNwh/Nzc3x5eoIFBUVkSQ5e/bs0Vc1emPGjGEwGLr0JE1ISPDw8CgpKaFKhtxKWoxsnzY1Na1cuVKzpKqqSq1WOzk5IYRIkty+fXt5eXleXl6f82VNuLH4risYLkim9CNJsqOjo7e3lyTJhoaGU6dO/eEPfzAzM8vLy8P3TAmCWLNmzcmTJw8ePCiXy9VqdV1dHdW5ejCPHj1au3btnTt3lEplSUnJgwcPZs+ePbKqRoMgiIiIiNzc3KysLLlcXl5evm7dOgcHh7CwMDyDRCJpbm7Oy8tTqVQNDQ0PHjzQXNzGxubRo0f3799vb2/HiRL/VKynp6esrCw8PNzZ2Rl3IxtuVQUFBfR2jeLxeO7u7ni0KO3wxb5mL84ht5L22gbbp6Ghofb29gP+XJXP51+5cuXq1atyuVylUpWUlLz11lt8Pn/z5s0IocrKyn379n3xxRcsFkvzF88pKSmaleDGenl5DRkkGIB+n289a0bzNP/8+fNTp07l8XhsNpvJZKJffwT1u9/9Lj4+vqmpSXPmp0+fbt++3dnZ2dzcXCQSLV26tKKiIiMjAz8BGD9+fE1Nzeeff46Tr4uLy08//XT//n1fX19ra2szM7OXXnppx44dPT09g1U14i2AdHhK29vbm5ycPH78eBaLZW1tHRwcfPfuXWpqU1PT/PnzCYJwc3N77733tm7dihCSSCS4w9Pt27ddXFy4XO7LL7/8+PHjsLAwFovl6Ohobm4uFAqDgoJqampGVtXFixcFAkFiYqIuzdSxa5RUKmWxWAqFAn/Mzc0Vi8UIITs7O/wEX9PWrVs1u0Zp2UradzQ5+D4NDg5GCMXExAwYbWBgoJubm4WFBYfDEYvFoaGh5eXleFJ5efmAX//k5GTNGvz9/R0dHfF5gHa6HCcvGkimvzH6rlHPOgN/SfAPHA22OoqOybSqqsrc3Hyw3pqGp1ar586de/jwYX1U3tjYSBBESkqKLjNDMu0PLvOBkZnyy4okEkl8fHx8fDz1o0wjUqvVeXl57e3toaGh+qg/NjZ2+vTpUqlUH5W/CCCZAqBNZGRkSEhIaGio0d9pUlRUdPbs2YKCAu1dX0dm//79paWlFy9eZLFYtFf+goBkCowmKioqMzOzra3Nzc3tzJkzxg5nULt27ZJKpXv27DFuGAsWLDh+/Dj1sgIanTt37unTp0VFRdbW1rRX/uIwN3YA4MW1e/fuPl3KTZafn5+fn5+xo9CXxYsXL1682NhRPPPgzBQAAGgAyRQAAGgAyRQAAGgAyRQAAGgAD6AGEBISYuwQjOnAgQOnT582dhT69d1336EXfkcDejFIGKJAw82bN/fv32/sKMBv4BeIzJgxw9iBgN/YvHnznDlzjB2FCYFkCkzd8uXLEUI5OTnGDgQAbeCeKQAA0ACSKQAA0ACSKQAA0ACSKQAA0ACSKQAA0ACSKQAA0ACSKQAA0ACSKQAA0ACSKQAA0ACSKQAA0ACSKQAA0ACSKQAA0ACSKQAA0ACSKQAA0ACSKQAA0ACSKQAA0ACSKQAA0ACSKQAA0ACSKQAA0ACSKQAA0ACSKQAA0ACSKQAA0ACSKQAA0ACSKQAA0ACSKQAA0ACSKQAA0ACSKQAA0ACSKQAA0ACSKQAA0ACSKQAA0ACSKQAA0ACSKQAA0MDc2AEA0JdCoXj69Cn1UalUIoRaWlqoEg6Hw+PxjBAZAINjkCRp7BgA+I2DBw9u2LBBywwZGRnr1683WDwA6AKSKTA5DQ0NDg4OarV6wKlmZma//PKLSCQycFQAaAf3TIHJEYlECxYsMDMz6z/JzMzsT3/6E2RSYIIgmQJT9MYbbwx4zUSS5BtvvGH4eAAYElzmA1PU3t4uEok0H0NhbDa7oaFBKBQaJSoAtIAzU2CKBAJBQEAAi8XSLDQ3N1+8eDFkUmCaIJkCE7Vq1aqenh7NErVavWrVKmPFA4B2cJkPTJRSqbSzs2tvb6dKLCwsGhsbORyOEaMCYDBwZgpMFJvNDgkJYbPZ+COLxVqxYgVkUmCyIJkC07Vy5Ur88yeEkEqlWrlypXHjAUALuMwHpqu3t3fs2LENDQ0IITs7u8ePHw/Y+RQAUwBnpsB0MZnMlStXstlsFou1atUqyKTAlEEyBSbt9ddfVyqVcI0PTJ+B3hqVk5NjmBWB5wxJkra2tgihe/fu3b9/39jhgGfS8uXLDbAWA90zZTAYBlgLAAD0Z5gsZ7j3mZ46dcow/x/Ac6ayshIh5OnpqVmYk5OzYsWKF+HxKYPBgO/OiOHjxDDrgpdDA1PXJ40CYJrgARQAANAAkikAANAAkikAANAAkikAANAAkikAANDAVJKpj4+PmZnZ9OnT9VH5mjVrCIJgMBjd3d36qN/AUlJSxowZw2AwPv30U1xy8eJFS0vLr776ipb66a1Ni6SkJA8PDy6Xy+fzPTw8oqOj5XK5XtdosKYZS2FhYWRk5NmzZ93d3RkMBoPBWL16teYMfn5+AoHAzMxs8uTJt2/fNkqQ2vd7fHy8p6enUCjkcDgSiWTbtm0dHR140vnz55OSkgYbadHoTCWZ3rp1a/78+XqqPDMzc8uWLXqq3PC2bNly48YNzRJ6u1sarPPmtWvX/va3v9XW1j558iQhISEpKWnZsmV6XePz3S/1ww8/TEtLi4qKWrp06c8//ywWi21tbbOysi5cuEDNc+XKldOnTwcEBFRUVMycOdMocWrf71evXt24ceP9+/cbGxt3796dmpoaEhKCJwUGBhIEsWDBgtbWVqNEPgTSIBBCp06d0j7PggULpk+fPtyaFQrFnDlzhpxtx44dCKGurq7h1m+aqqqqEEKffPIJLbXpuA1pFxwcrLlH8Hfm0aNHuix76tQpgx29I0DjJtXlu0OS5J49eyZMmKC5PcVi8fHjx5lMpqOjY2trK1VeUFCwePFiWmIbGe373d/fv6enh5qKf61QW1tLlUil0jlz5qhUKl3WZcjjxFTOTLE+Y/7o4vDhwzKZTMeZ4VetAxrWNqRRbm4uQRDUR0dHR4QQdU33TDPwJq2uro6Ojo6Li9PcngghX1/f8PDw+vp6k7oy077f8/PzNV8PZmdnhxBSKBRUSWxsbGlpaWpqqoHC1ZlpJdPq6moPDw8+n8/lcufOnXv9+nVq0rVr1zw9PS0tLQmC8PLyunz5MkIoPDw8IiKipqaGwWBIJBI857Fjx7y9vQmC4PP5rq6uCQkJuJzJZF64cGHhwoWWlpYODg5ffvmlLiEdPHiQz+fzeLxz584tXLhQKBSOGzfu5MmT1AwkSe7fv3/SpEkcDsfa2jooKOjOnTt40r59+3g8nkAgkMlkERERjo6O69at4/P5TCZz1qxZ9vb2LBaLz+fPnDlz7ty5Tk5OBEFYWVlt27ZNe6v7uH79urOzM4PB+Pjjj/E2ZPTz9ddf67gN+9SmvYFDbpxhqaqqsrKycnFxGdniQ+rTNO3Bp6WlEQQxZsyYtWvXOjg4EATh6+tbXFyMp0qlUjabPXbsWPxxw4YNfD6fwWA0NjaigQ7LS5cuCYXCXbt26alpaWlpJEkGBgb2n5SYmDhhwoRDhw4VFhYOuOxo9q9arY6JiXF2duZyuVOnTsWngcOlfb/X19dzuVw3NzeqxNraet68eampqaSp3bQxzAkw0u0y393d/d69eyqV6scff/z9739PEMRPP/2Ep54+fTo2Nra5ubmpqWn27Nm2tra4fOnSpWKxmKrkwIEDCKE9e/Y0NTU1Nzd/9tlnq1atIn+9zP/mm29aW1ubm5sXLVrE4XA6Ozt1CZ5atq2tTSaTzZ07l8/nK5VKPDUmJobNZh87dqy1tbWsrGzmzJn4Ncaay27atCk9PX3JkiX//e9/P/zwQ4RQcXFxZ2dnY2Pja6+9hhC6cOFCQ0NDZ2enVCpFCJWWlmpvdZ/L/IcPHyKE0tPT8aQPPvgAN+2XX36xtrb29fVVq9W6b0PN2nRs4GAbRxdKpbKuri49PZ3D4Rw7dkzHpUZ2+danadqDDwsL4/P5lZWV3d3dFRUVPj4+AoGAut5ctWqVvb09VXNycjJCqKGhAX/ss0nz8/MFAkF8fPxwAyZ1++64u7t7enr2KRSLxffu3SNJ8saNG0wm09XVtaOjg+x3mT+a/btlyxYOh3PmzJmWlpaoqCgmk3nr1i0d26XLfu/s7BQIBFKptE95ZGQkQqikpGTItRjyMt+0kum0adOoj2VlZQihLVu29J9z9+7dCCGZTEb+9qhVKpVWVlbz58+n5uzp6cH/wfrcMz169ChC6Mcff9Ql+D7LZmRkIISqq6tJklQoFBYWFqGhodTM33//PUKI+tr0v1eLk2l7ezv+eOTIEYRQeXm55uLZ2dnaW60lmWoKDg4mCOLOnTvaa9OSTIfbQM2NoyN7e3uEkK2t7UcffaR7FqYxmQ4WfFhYmKWlJbXsrVu3EEJxcXH447CS6WgM+d3p6OhgMBgBAQF9yqlkSpJkREQEQmjjxo3kb5PpaPZvV1cXj8ejllUoFBwOZ/369Tq2S5f9vmPHjgkTJsjl8j7l+LLy6NGjQ67lxb1nqsnLy8vS0hKn1D7wrdX+PSTKyspaW1tfffVVqsTMzGzTpk2D1aBSqUYQGB7iDS9bUVHR0dHh7e1NTfXx8WGz2dT1oI61UWMaawlssFYPJicn55///GdcXNzEiRNHXNtwG6i5cXT08OFDmUx24sSJI0eOzJgxwyh3bzHtwXt7e/N4POoS2HTg/4g8Hk/LPImJiRMnTszIyNC8dYZGt3/v3r2rUCimTJmCJ3G53LFjx+q+fYbc77m5uTk5OZcvXxYIBH0m4cY+efJEx3UZhukmU4QQi8WijuwLFy688sorIpGIw+Fo3lXUhHurWVlZGSxC3EXDwsJCs9DKykpzgOLR0KXVA2pqanrvvfd8fHzwKcmIa9N3AxFCLBZLJBL5+fllZ2dXVFTgU2bTxOFw8IBUJgX3ntY+bitBEJmZmQwG45133unq6qLKR7N/Ozs7EUI7d+6kbs0/ePBA80mRdtr3e3Z29t69e4uKilxdXfsvy+Vy0a8NNx2mm0x7enqam5udnZ0RQrW1tcHBwWPHji0uLm5ra0tKShpwkZdeegkhhB8CGAZO3H2OvNbW1nHjxo2+ch1bPaBNmza1trZmZmZSD0ZHVpteG9iHRCIxMzOrqKigvWZaqFQqPTV8lHBmGfIiY86cOZs3b66qqqIeyaLR7V+RSIQQOnDggOal7s2bN4cbf//9np6enpWVdfXqVfyN7g+PWYsbbjpMN5n+61//6u3txf2Ky8vLVSrV+vXr3d3d8W+ZBlzE1dXVxsbmypUrBgtyypQpFhYWP/zwA1VSXFysVCpnzZo1+sp1bHV/Fy5cOH78eHR09OTJk3HJ1q1bR1ab/hrY1NTUZ1inqqoqtVrt5OQ0ypr1pKioiCTJ2bNn44/m5uYju01EO/xzuLa2tiHnTEhI8PDwKCkpoUpGs39x/5PS0tJhRat9v5MkuX379vLy8ry8vD7ny5pwY/FdV9NhWslUqVS2tbX19PTcvn1bKpW6uLi8/fbbCCF8flpYWNjd3V1VVaV5Q8fGxubRo0f3799vb29nMplRUVHffvutVCqtr6/v7e1tb2/H72nXE4IgIiIicnNzs7Ky5HJ5eXn5unXrHBwcwsLCRl+5llZrIZfL165dO3369A8++AAh1N3d/cMPP5SWluq4DfskCP01kM/nX7ly5erVq3K5XKVSlZSUvPXWW3w+f/PmzaOsmUa9vb0tLS09PT1lZWXh4eHOzs74gEQISSSS5ubmvLw8lUrV0NDw4MEDzQX7bNKCggL9dY3i8Xju7u51dXVDzokv9jV7cY5m/xIEsWbNmpMnTx48eFAul6vV6rq6ul9++QUhFBoaam9vP+DPVbXv98rKyn379n3xxRcsFkuzb19KSopmJbixXl5eQwZpUIZ5zoV0eJqfmZk5f/78MWPGmJub29ravv766w8ePKCmbt++3cbGxsrKKiQkBPcTFIvFtbW1t2/fdnFx4XK5L7/8Mu7P8fHHH3t5eREEQRDEjBkzMjIykpKS8BXB+PHja2pqsrKyrK2tEULjxo0b8oF+RkYGvtuNl/3888+FQiFCyMXFBXfb6u3tTU5OHj9+PIvFsra2Dg4Ovnv3Ll6WWq+TkxPu/JGamoprc3V1vXbt2t69ey0tLRFC9vb2x48fz87Oxv9sra2tT548OVirw8PD8Wx8Pn/JkiXp6em4wyOPxwsMDOxz2GGLFi3ScRvu3LlTszbtDRxy42gXGBjo5uZmYWHB4XDEYnFoaCjVq2FII3hK22dDDRl8WFgYi8VydHQ0NzcXCoVBQUE1NTVUbU1NTfPnzycIws3N7b333tu6dStCSCKR4L5TfQ7LixcvCgSCxMTEYQWM6fLdkUqlLBZLoVDgj7m5uWKxGCFkZ2eHn+Br2rp1q2bXqNHs36dPn27fvt3Z2dnc3FwkEi1durSiooIkyeDgYIRQTEzMgNFq2e/l5eUDpqnk5GTNGvz9/R0dHXt7e4fcei9o1ygAdGeAL0lYWJiNjY1eV6ELXb47VVVV5ubmuvfS1Te1Wj137tzDhw/ro/LGxkaCIFJSUnSZGbpGAWASTPYFRX1IJJL4+Pj4+HhT+DGuWq3Oy8trb28PDQ3VR/2xsbHTp0/HP28xKS96Mr1z507/H19S9HQ0vAhgwxpYZGRkSEhIaGioLk+i9KqoqOjs2bMFBQXau76OzP79+0tLSy9evDiC93jo24s+OqmHhwdpar/wfS486xs2KioqMzNTqVS6ubklJyfr+92AtNi1a9eVK1f27Nmzd+9eI4axYMGCBQsW6KPmc+fOPX36tKioSPMZmul40ZMpAAPavXu3Kf98YDB+fn5+fn7GjkJfFi9evHjxYmNHMagX/TIfAABoAckUAABoAMkUAABoAMkUAABowDDMI1cGgzF79mwTfEkEeEbV1dV99913z8RD9lE6c+YMfHdGDB8nhslycGYKAAA0MNyZ6alTp/BAgwCMXk5OzooVK57prqw6gu/OaBjyOIEzUwAAoAEkUwAAoAEkUwAAoAEkUwAAoAEkUwAAoMGzlEzPnj3r7u6u+SY3c3NzOzu7P/3pT7m5uXStZc2aNXiIpAHHPtSMYfXq1ZqT/Pz8BAKBmZnZ5MmTBxywwQBSUlLwiECffvopLrl48aKlpeVXX31FS/301gZoV1hYGBkZaeJHaVJSkoeHB5fL5fP5Hh4e0dHReFxhLD4+3tPTUygUcjgciUSybds26iWt58+fT0pKMt2XzBrmHdSIvjfti8ViS0tL/Hdzc3NhYaGHhwdCKDs7m5b6SZLcsWMHQqirq0tLDLa2tgih/Px8zfKCggLNASGMoqqqCiH0ySef4I/5+flCofD8+fO0VE5vbaNhyDeoG5fu352YmJiAgAC5XI4/muxR6u/vn5KSIpPJ2tvbc3JyWCzWn//8Z2rqvHnzMjIympqa5HL5qVOnWCzWa6+9Rk1NTU2dN29eS0uLjuuCN+3rytraesGCBR999BFCKCcnZ8j5u7q6fH19aVl1Wloak8kMCwsz+rt4tfP3929rawsICBjZ4qXt2V8AACAASURBVH222Chre7bQeLTQWNVg9u7dm52dnZOTIxAIqELTPErZbPaGDRtEIpGFhUVISEhQUNDXX3+NR+JDCFlYWOABYwQCwfLly4ODgy9duvTw4UM8ddOmTdOmTVu0aFFPT4/xWjCwZzuZYq6urgih1tbWIec8fPiwTCbTsVrtgyH7+vqGh4fX19dv2bJFxwqfRcPaYs8ZGtuu781YXV0dHR0dFxdHEIRmuWkepbm5uZpxOjo6IoSoa/n8/HzNdz/b2dkhhBQKBVUSGxtbWlqamppqoHB19jwk07KyMoTQvHnzqJJr1655enpaWloSBOHl5XX58mWEUHh4eERERE1NDYPBkEgkeM5jx455e3sTBMHn811dXRMSEnA5k8m8cOHCwoULLS0tHRwcvvzyy/7rTUxMnDBhwqFDhwoLCwcMjCTJ/fv3T5o0icPhWFtbBwUF3blzB0/at28fj8cTCAQymSwiIsLR0XHdunV8Pp/JZM6aNcve3p7FYvH5/JkzZ86dOxcPUG5lZbVt2zbtbezj+vXrzs7ODAYDj0VaXV3dfwSRr7/+Wsct1qc27Q08ePAgn8/n8Xjnzp1buHChUCgcN27cyZMndd2pNNESoVQqZbPZeLxShNCGDRv4fD6DwWhsbOzf9rS0NIIgxowZs3btWgcHB4IgfH19qeGyh1UVQujSpUv0jvyclpZGkmRgYGD/SaM5SofciWq1OiYmxtnZmcvlTp06FV9TD1dVVZWVlZWLi8uAU+vr67lcrpubG1VibW09b9681NRU0tR+/2aYuwlIP/dMFQpFQUGBi4uLn59fR0cHNc/p06djY2Obm5ubmppmz55ta2uLy5cuXSoWi6nZDhw4gBDas2dPU1NTc3PzZ599tmrVKvLXe6bffPNNa2trc3PzokWLOBxOZ2enZgz37t0jSfLGjRtMJtPV1RWvvc/dqJiYGDabfezYsdbW1rKyspkzZ9rZ2eHxqKm1bNq0KT09fcmSJf/9738//PBDhFBxcXFnZ2djY+Nrr72GELpw4UJDQ0NnZyceQay0tFR7G/vcM8XXR+np6XjSBx98gBvyyy+/WFtb+/r6qtVq3beYZm06NvCbb75pa2uTyWRz587l8/lKpXIke70fHe+FaY9w1apV9vb21MzJyckIoYaGhgHbHhYWxufzKysru7u7KyoqfHx8BAIBHth5uFXl5+cLBIL4+HhdWqrLd8fd3d3T07NPIY1H6WA7ccuWLRwO58yZMy0tLVFRUUwm89atW7o0iiRJpVJZV1eXnp7O4XAGG1e1s7NTIBBIpdI+5ZGRkQihkpKSIdcCQz1rgwcE1+Tl5XXkyJGnT58OOD8efEImk5G/PaaVSqWVldX8+fOpOXt6evC/uz4PoI4ePYoQ+vHHHzVjwIcpSZIREREIITw6ueZhqlAoLCwsQkNDqaW+//57hBD1Fer/mAsn0/b2dvzxyJEjCCFqSHG8+IDP2TTbqCWZagoODiYI4s6dO7pvsT61DbeBGRkZCKHq6ur+axwBXb4kQ0Y43GRK/RcnSfLWrVsIobi4uBFUNSxDfnc6OjoYDEZAQECfcn0cpZo7sauri8fjUcsqFAoOh7N+/Xod22Vvb48QsrW1/eijjwb7F7tjx44JEyZQj9Qo+Erx6NGjQ64FHkANgTqmVSpVXV3d+++/L5VKp06diq+q+sCjGPbvTlFWVtba2vrqq69SJWZmZps2bRqsBpVKNWAwiYmJEydOzMjIuH79umZ5RUVFR0eHt7c3VeLj48Nms6lrwyGx2WyEEHWjXUsYg7VxMDk5Of/85z/j4uImTpw44tqG20DcnME2oz6Mfhdo4e3tzePxqCtiI8L/9rQPBUrXUaq5E+/evatQKKZMmYIncbncsWPH6r5BHj58KJPJTpw4ceTIkRkzZvS/p5ybm5uTk3P58mXNR2oYbuyTJ090XJdhPJPJlGJubu7o6LhmzZqUlJS7d+/u2bMHl1+4cOGVV14RiUQcDkfzPqMm3LXNyspqlDEQBJGZmclgMN55552uri6qHD8Qs7Cw0JzZysqqvb19lGvEdGnjgJqamt577z0fHx98tjLi2vTdwNHTd4QcDqehoYGWqkYDd4jmcDha5tHHUdrZ2YkQ2rlzJ3X//cGDB5pPirRjsVgikcjPzy87O7uioqLP8IXZ2dl79+4tKirCj5f74HK56NeGm45nO5lSvLy8EEKVlZUIodra2uDg4LFjxxYXF7e1tSUlJQ24yEsvvYQQGvBkdrjmzJmzefPmqqoq6vkV+jVN9zkoW1tbaXnLr45tHNCmTZtaW1szMzOpZ6Yjq02vDaSFXiNUqVQm0licWYa8kqD9KBWJRAihAwcOaF7q3rx5c7jxSyQSMzOziooKqiQ9PT0rK+vq1av4S9qfUqlEvzbcdDwnyfR///d/EUL4orW8vFylUq1fv97d3R3/lmnARVxdXW1sbK5cuUJLAAkJCR4eHiUlJVTJlClTLCwsfvjhB6qkuLhYqVTOmjVr9KvTsY39Xbhw4fjx49HR0ZMnT8YlW7duHVltem0gLYaM0NzcfMS3HYqKikiSnD179uirGiX8mzddepLSe5TiTialpaXDirapqWnlypWaJVVVVWq12snJCSFEkuT27dvLy8vz8vL6nC9rwo3Fd11Nx7OaTLu6unp7e0mSfPToUWZm5s6dO+3s7N5//32EkLOzM0KosLCwu7u7qqpK8+6PjY3No0eP7t+/397ezmQyo6Kivv32W6lUWl9f39vb297ejs9tRwBfRmn2jyMIIiIiIjc3NysrSy6Xl5eXr1u3zsHBISwsbHRNR0hrG7WQy+Vr166dPn36Bx98gBDq7u7+4YcfSktLddxifZKFXhtIiyEjlEgkzc3NeXl5KpWqoaHhwYMHmov3b3tvb29LS0tPT09ZWVl4eLizs/Pbb789gqoKCgpo7BrF4/Hc3d3r6up02SA0HqUEQaxZs+bkyZMHDx6Uy+Vqtbqurg73vQ8NDbW3tx/w56p8Pv/KlStXr16Vy+UqlaqkpOStt97i8/mbN29GCFVWVu7bt++LL75gsViaHfhSUlI0K8GNxdejJsQwz7kQHU/zc3Nz+z/K53A448ePX79+PdVJhSTJ7du329jYWFlZhYSE4E6RYrG4trb29u3bLi4uXC735Zdfxp0/Pv74Yy8vL4IgCIKYMWNGRkZGUlISvnwYP358TU1NVlaWtbU1QmjcuHE//vgjFYOdnR1+Nqpp69atmp1Oent7k5OTx48fz2KxrK2tg4OD7969iydRa3FycsL9QlJTU/FtdVdX12vXru3du9fS0hIhZG9vf/z48ezsbPx/2Nra+uTJk4O1MTw8HM/G5/OXLFmSnp6OOz/yeLzAwMA+RyS2aNEiHbfYzp07NWvT3sCMjAzcHLwZP//8c6FQiBBycXH56aefRnkkkDo/pdUSIUmSTU1N8+fPJwjCzc3tvffe27p1K0JIIpHgY6nP0RIWFsZisRwdHc3NzYVCYVBQUE1NzciqunjxokAgSExM1KWlunx3pFIpi8VSKBT4I11H6ZA78enTp9u3b3d2djY3NxeJREuXLq2oqCBJMjg4GCEUExMzYLSBgYFubm4WFhYcDkcsFoeGhlJdVsrLywdMU8nJyZo1+Pv7Ozo64tMp7aBrFABDMPxv8/FvHA25RkyX705VVZW5uflgvTUNT61Wz5079/Dhw/qovLGxkSCIlJQUXWaGrlEAmCKTfV+RRCKJj4+Pj4+nfpRpRGq1Oi8vr729PTQ0VB/1x8bGTp8+Hf+GxaRAMgXgeRAZGRkSEhIaGmr0d5oUFRWdPXu2oKBAe9fXkdm/f39paenFixdxb2iTAskUgKFFRUVlZma2tbW5ubmdOXPG2OEMbNeuXVKplOptbSwLFiw4fvw49aYCGp07d+7p06dFRUX4MYapMTd2AAA8A3bv3t2nV7lp8vPz8/PzM3YU+rJ48eLFixcbO4pBwZkpAADQAJIpAADQAJIpAADQAJIpAADQAJIpAADQgEEa5NX/ur+JAwAA6GWYLGegrlEjGxwGAIQQHl0Gv8UGAJNloDNTAEZs+fLlSLehvAEwIrhnCgAANIBkCgAANIBkCgAANIBkCgAANIBkCgAANIBkCgAANIBkCgAANIBkCgAANIBkCgAANIBkCgAANIBkCgAANIBkCgAANIBkCgAANIBkCgAANIBkCgAANIBkCgAANIBkCgAANIBkCgAANIBkCgAANIBkCgAANIBkCgAANIBkCgAANIBkCgAANIBkCgAANIBkCgAANIBkCgAANIBkCgAANIBkCgAANIBkCgAANIBkCgAANIBkCgAANIBkCgAANDA3dgAA9FVcXPyf//yH+vjzzz8jhD7//HOqZNq0ab///e+NEBkAg2OQJGnsGAD4jfz8/ICAADMzMyaTiRDChyiDwUAI9fb2qtXqr7766i9/+YuRowTgtyCZApOjUqns7OzkcvmAU4VCYUNDA5vNNnBUAGgH90yByWGxWK+//vqA6VLLJACMC5IpMEWvv/66UqnsX65SqVauXGn4eAAYElzmA1PU29v70ksvPXnypE+5SCR6/PgxvpcKgEmBgxKYIiaTuXr16j6X82w2++2334ZMCkwTHJfARPW/0lcqla+//rqx4gFAO7jMB6Zr/Pjx1dXV1Ed3d/eamhojxgOAFnBmCkzXG2+8wWKx8N9sNvutt94ybjwAaAFnpsB0VVdXjx8/nvp49+7dCRMmGDEeALSAM1NguiQSybRp0xgMBoPBmDZtGmRSYMogmQKT9uabb5qZmZmZmb355pvGjgUAbeAyH5i0R48eOTk5kST58OFDR0dHY4cDwKCe/2S6f//+mzdvGjsKMHJFRUUIoVdeecXIcYBRmDNnzubNm40dhX49/5f5N2/e/O6774wdxfPpu+++M8C2dXZ2dnFx0fdatKirqztz5owRA3jWfffddy/CCc0L8T7T2bNnnz592thRPIdCQkIQQvrets3NzQghGxsbva5Fi5ycnBUrVsAhNGL4OHnuvRDJFDzTjJhGAdDd83+ZDwAABgDJFAAAaADJFAAAaADJFAAAaADJlB4XL160tLT86quvTLzOAcXHx3t6egqFQg6HI5FItm3b1tHRob/VGaxdxlJYWBgZGXn27Fl3d3f8W9jVq1drzuDn5ycQCMzMzCZPnnz79m2jBJmUlOTh4cHlcvl8voeHR3R0tOagW1oOifPnzyclJanVaqOEbcogmdJDH799MNjvKa5evbpx48b79+83Njbu3r07NTVVr31Znu/fiXz44YdpaWlRUVFLly79+eefxWKxra1tVlbWhQsXqHmuXLly+vTpgICAioqKmTNnGiXOa9eu/e1vf6utrX3y5ElCQkJSUtKyZcuoqVoOicDAQIIgFixY0NraapTITRf5vFu2bNmyZctor1ahUMyZM8f069SFv79/T08P9XH58uUIodra2iEX1NO2pQtd2/PUqVM6flP27NkzYcKErq4uqkQsFh8/fpzJZDo6Ora2tlLlBQUFixcvHn1sIxYcHKwZJ86Vjx49wh+HPCSkUumcOXNUKpUu6zLx44QucGY6QocPH5bJZKZfpy7y8/PNzMyoj3Z2dgghhUJh+EjoZeDtWV1dHR0dHRcXRxCEZrmvr294eHh9ff2WLVsMFsyQcnNzNePE7z2gruWHPCRiY2NLS0tTU1MNFO6zAJLp/7l27Zqnp6elpSVBEF5eXpcvX6YmHTt2zNvbmyAIPp/v6uqakJAQHh4eERFRU1PDYDAkEsn169ednZ0ZDMbHH3+MEJo0aRKDwWAymbNmzcLH37Zt23DN//jHPwZbl/Y6EUIkSe7fv3/SpEkcDsfa2jooKOjOnTt40sGDB/l8Po/HO3fu3MKFC4VC4bhx406ePDmyTVFfX8/lct3c3Ea+NQfXp13aI09LSyMIYsyYMWvXrnVwcCAIwtfXt7i4GE+VSqVsNnvs2LH444YNG/h8PoPBaGxsRP22J0Lo0qVLQqFw165d+mgXjpYkycDAwP6TEhMTJ0yYcOjQocLCwgGXHc3OVavVMTExzs7OXC536tSp+Dx6uKqqqqysrAb72W7/Q8La2nrevHmpqankc33TZniMe2JsADpeYpw+fTo2Nra5ubmpqWn27Nm2tra4/MCBAwihPXv2NDU1NTc3f/bZZ6tWrSJJcunSpWKxmFr84cOHCKH09HSSJHt6elxdXZ2dnTUvlN5///0DBw5oX5eWOkmSjImJYbPZx44da21tLSsrmzlzpp2d3ePHj/HUHTt2IIS++eabtrY2mUw2d+5cPp+vVCqHu7k6OzsFAoFUKtVl5pFdvvVpl/bIw8LC+Hx+ZWVld3d3RUWFj4+PQCCgrjdXrVplb29P1ZycnIwQamhowB/7bM/8/HyBQBAfHz/cgHW8zHd3d/f09OxTKBaL7927R5LkjRs3mEymq6trR0cH2e8yfzQ7d8uWLRwO58yZMy0tLVFRUUwm89atWzo2TalU1tXVpaenczicY8eODTjPYIdEZGQkQqikpGTItbwgl/mQTAewe/duhJBMJlMqlVZWVvPnz6cm9fT04P/G2hMfTsE5OTn4Y2dnp7Ozc1tbm5Z1aa9ToVBYWFiEhoZSU7///nuEEJUa8PeNuguWkZGBEKqurh5Ww3E9EyZMkMvlusxMYzIdLPKwsDBLS0tq2Vu3biGE4uLi8MdhJdMR0yWZdnR0MBiMgICAPuVUMiVJMiIiAiG0ceNG8rfJdDQ7t6uri8fjUcsqFAoOh7N+/Xodm2Zvb48QsrW1/eijjwb71zvYIfHll18ihI4ePTrkWl6QZAqX+QPA4w6p1eqysrLW1tZXX32VmmRmZrZp06Yha/jrX/9qaWlJ3VHKysoKCgoSCoVa1qW9woqKio6ODm9vb6rEx8eHzWZT17x94EGSVSrVkKFqys3NzcnJuXz5skAgGNaCNNIeube3N4/Hoy6BTQf+d8jj8bTMk5iYOHHixIyMjOvXr2uWj2bn3r17V6FQTJkyBU/icrljx47Vffs8fPhQJpOdOHHiyJEjM2bM6H+LWcshgRv75MkTHdf13INk+n8uXLjwyiuviEQiDoezbds2XIh73llZWQ23NgsLi7///e83btzApxiffPKJVCrVvi7tcDcUCwsLzUIrK6v29vbhxjaY7OzsvXv3FhUVubq60lWnPnA4nIaGBmNH0Vd3dzdCiMPhaJmHIIjMzEwGg/HOO+90dXVR5aPZuZ2dnQihnTt3Mn714MED3R8eslgskUjk5+eXnZ1dUVGBr5Mo2g8JLpeLfm04QJBMsdra2uDg4LFjxxYXF7e1tSUlJeHyl156CSGEH2gMl1QqZbFYBw4c+Pbbb52cnMRisfZ1aYcTep9vV2tr67hx40YQW3/p6elZWVlXr17FTTZZKpWKxlbTCGeWIa8w8DuSq6qqEhISqMLR7FyRSIQQom7HYyN4eahEIjEzM6uoqKBKhjwklEol+rXhAEEyxcrLy1Uq1fr1693d3QmCYDAYuNzV1dXGxubKlSsjqHPcuHHLly8/c+ZMdHR0eHj4kOvSbsqUKRYWFj/88ANVUlxcrFQqZ82aNYLYNJEkuX379vLy8ry8vD4nRyaoqKiIJMnZs2fjj+bm5sO9laEnY8aMYTAYbW1tQ86ZkJDg4eFRUlJClYxm5zo5OREEUVpaOqxom5qaVq5cqVlSVVWlVqudnJyQzocEbiy+6woQJFPM2dkZIVRYWNjd3V1VVUXdq+JwOFFRUd9++61UKq2vr+/t7W1vb6+srEQI2djYPHr06P79++3t7YN9nyMiInp6elpaWv74xz8OuS7tdRIEERERkZubm5WVJZfLy8vL161b5+DgEBYWNsq2V1ZW7tu374svvmCxWAwNKSkpo6yZLr29vS0tLT09PWVlZeHh4c7Ozm+//TaeJJFImpub8/LyVCpVQ0PDgwcPNBfssz0LCgr01zWKx+O5u7vX1dUNOSe+2NfsxTmanUsQxJo1a06ePHnw4EG5XK5Wq+vq6n755ReEUGhoqL29/YA/V+Xz+VeuXLl69apcLlepVCUlJW+99Rafz8cji+h4SODGenl5DRnki8IYT70MSscnidu3b7exsbGysgoJCcFdIMViMe6C8/HHH3t5eREEQRDEjBkzMjIySJK8ffu2i4sLl8t9+eWXd+7ciXs78ni8wMBAzWrnz59/6NAhHdelvc7e3t7k5OTx48ezWCxra+vg4OC7d+/iCjMyMvDTgPHjx9fU1Hz++ef4YZeLi8tPP/2kveHl5eUDHhjJycl0bVtN6enpmu0aMvKwsDAWi+Xo6Ghubi4UCoOCgmpqaqjampqa5s+fTxCEm5vbe++9t3XrVoSQRCLBO05zez5+/PjixYsCgSAxMXFYAZM6d43CN3YUCgX+mJubi+/t2NnZ4Sf4mrZu3arZNWo0O/fp06fbt293dnY2NzcXiURLly6tqKggSTI4OBghFBMTM2C0gYGBbm5uFhYWHA5HLBaHhoaWl5fjSToeEv7+/o6Ojr29vUNumRfkaT4kUzByBti2YWFhNjY2el3FkHRMplVVVebm5oP11jQ8tVo9d+7cw4cP66PyxsZGgiBSUlJ0mfkF+Q7CZT4wdc/KC4okEkl8fHx8fLxe37mlI7VanZeX197eHhoaqo/6Y2Njp0+frtlHBUAyfc7duXOHMTg9fdNeWJGRkSEhIaGhobo8idKroqKis2fPFhQUaO/6OjL79+8vLS29ePEi7iUNMEimzzkPDw8tFybZ2dnGDlCbqKiozMzMtrY2Nze3Z2Ww5V27dkml0j179hg3jAULFhw/fpx6cQGNzp079/Tp06KiImtra9orf6bB6KTAdO3evbtPN/Jngp+fn5+fn7Gj0JfFixcvXrzY2FGYIjgzBQAAGkAyBQAAGkAyBQAAGkAyBQAAGkAyBQAAGrwQT/PPnDmj4/tEwAi8INv2BWmmnmgOffq8eiGS6ezZs99//31jR/EcwgMKPPfb9ubNm6mpqSMbWwmgX4+T594LkUzx2/CMHcVz6PTp0wihF2HbpqamvgjN1BN8nDz34J4pAADQAJIpAADQAJIpAADQAJIpAADQAJIpAADQAJKpTk6cOMFgMHx9fUdTycWLFy0tLb/66iu6otJTnWCUCgsLIyMjz5496+7ujt8bu3r1as0Z/Pz8BAKBmZnZ5MmTBxyjyQCSkpI8PDy4XC6fz/fw8IiOjsYDm2Px8fGenp5CoZDD4Ugkkm3btlFvvD5//nxSUtKz8sZuQ4JkqpMTJ06IxeKbN29WV1ePuBKSJGkMSX91gtH48MMP09LSoqKili5d+vPPP4vFYltb26ysrAsXLlDzXLly5fTp0wEBARUVFTNnzjRKnNeuXfvb3/5WW1v75MmThISEpKQkzX71V69e3bhx4/379xsbG3fv3p2amhoSEoInBQYGEgSxYMGC1tZWo0RusiCZDq2pqamysjIuLg4hdPToUd0X7Orq0jyZ9ff3b2trCwgIGE0w+qjTlPVpr4lUNZi9e/dmZ2fn5OQIBAKqMC0tjclkhoWFGf31+5rYbPaGDRtEIpGFhUVISEhQUNDXX3+NhzVFCFlYWODRtwQCwfLly4ODgy9duvTw4UM8ddOmTdOmTVu0aFFPT4/xWmByIJkOLScnx9/fH/9DxsOl6bjg4cOHZTIZvcHoo05TRmN79b3pqquro6Oj4+LiCILQLPf19Q0PD6+vr9+yZYv+1j5cubm5mnE6OjoihKhr+fz8fM3BqO3s7BBCCoWCKomNjS0tLU1NTTVQuM8CSKZDO3HixJIlSwQCgZ+f3/37969du9Z/nmPHjnl7exMEwefzXV1dExISwsPDIyIiampqGAyGRCK5fv26s7Mzg8HAYztPmjSJwWAwmcxZs2bhY3Tbtm2WlpYEQfzjH/9ACF27ds3T0xOXeHl5Xb58GSGkvU6EEEmS+/fvnzRpEofDsba2DgoKunPnDp508OBBPp/P4/HOnTu3cOFCoVA4bty4kydPGmADaolKKpWy2WxqdI0NGzbw+XwGg9HY2Ni/vWlpaQRBjBkzZu3atQ4ODgRB+Pr6FhcXj6AqhNClS5eEQuGuXbvoamZaWhpJkoGBgf0nJSYmTpgw4dChQ4WFhcPdREPuOLVaHRMT4+zszOVyp06dOrKfvVZVVVlZWbm4uAw4tb6+nsvlurm5USXW1tbz5s1LTU2FG03/n97HPzW2UQ4z++DBA5FI1NPTQ5LksWPHEELvvvtun3nwT4/37NnT1NTU3Nz82WefrVq1iiTJpUuXisViajZ8lZSenk6SZE9Pj6urq7OzM64Ze//99w8cOID/Pn36dGxsbHNzc1NT0+zZs21tbXG5ljpJkoyJiWGz2ceOHWttbS0rK5s5c6adnd3jx4/x1B07diCEvvnmm7a2NplMNnfuXD6fr1QqR7xxdNy22qNatWqVvb09NXNycjJCqKGhYcD2hoWF8fn8ysrK7u7uiooKHx8fgUBQW1s7gqry8/MFAkF8fPyQ8es41LO7u7unp2efQrFYfO/ePZIkb9y4wWQyXV1dOzo6SJIsKChYvHgxNdtodtyWLVs4HM6ZM2daWlqioqKYTOatW7eGjBZTKpV1dXXp6ekcDmewQao7OzsFAoFUKu1THhkZiRAqKSkZci0w1DNACKETJ0785S9/wZc8gYGBHA7n9OnTXV1d1AwqlSouLm7+/PkffPCBjY2NtbX1u+++6+Pjo71aMzOzTZs21dbW5ubm4hKFQnH27Nl33nkHf1y2bNmHH35obW1tY2MTGBjY1NTU0NCgvc6urq79+/cvWbLkjTfesLS09PLy+vTTTxsbGz///HPN2Xx9fYVCoUgkCg0N7ezsrK2tHe42GRYdo9Kdubk5PoPz9PQ8ePBge3t7ZmbmCOrx9/eXy+XR0dEjC6OPzs7Oe/fu4ibXEAAAH2hJREFUicXiwWaYM2fO+++/f//+/Q8++KDPpNHsuO7u7oMHDwYHBy9dutTKymrnzp0sFkv3DeLk5DRu3LjY2Nh9+/atWLFiwHl2797t4OCQmJjYp3z8+PEIofLych3X9dyDZDoEfI2P/xYKhX5+fnK5/Ny5c9QMZWVlra2tr776KlWCE+WQNf/1r3+1tLSk7jplZWUFBQUJhcL+c+IBdYfsjFJRUdHR0eHt7U2V+Pj4sNls6kK4DzabjRBSqVRDhjoaw41qWLy9vXk8HnVFbEQymYwkSe3jKicmJk6cODEjI+P69eua5aPZcXfv3lUoFFOmTMGTuFzu2LFjdd8gDx8+lMlkJ06cOHLkyIwZM/rfU87Nzc3Jybl8+bLmIzUMN/bJkyc6ruu5B8lUmx9//LG8vDwgIIAaaB736NR8po9751lZWQ23cgsLi7///e83btz4/vvvEUKffPKJVCqlpl64cOGVV14RiUQcDmfbtm26VIi7qlhYWGgWWllZtbe3Dzc2Guk7Kg6HM+Q5uwF0d3fjYLTMQxBEZmYmg8F45513NC9uRrOJOjs7EUI7d+6kDtEHDx5oPinSjsViiUQiPz+/7OzsioqKPmPBZmdn7927t6ioyNXVtf+yXC4X/dpwgCCZanf8+PHXX39d87ZIc3Mzl8u9cuXK48eP8TwvvfQSQgg/5RguqVTKYrEOHDjw7bffOjk5UReJtbW1wcHBY8eOLS4ubmtrS0pK0qU2nND7fANbW1vHjRs3gtjooteoVCqV0RuI4cwy5NXDnDlzNm/eXFVVlZCQQBWOZhOJRCKEEHWrHbt58+Zw45dIJGZmZhUVFVRJenp6VlbW1atX8RHen1KpRL82HCBIplqQJJmdnb1hwwbNQmtr65CQELVafeLECVzi6upqY2Nz5cqVEawCv2j1zJkz0dHR4eHhVHl5eblKpVq/fr27uztBEDq+433KlCkWFhY//PADVVJcXKxUKmfNmjWC2OgyZFTm5uYjvtVQVFREkuTs2bNHX9UojRkzhsFg6NKTNCEhwcPDo6SkhCoZzY5zcnIiCKK0tHRY0TY1Na1cuVKzpKqqSq1WOzk5IYRIkty+fXt5eXleXl6f82VNuLH29vbDWvVzDJLpoG7cuCEUCv/whz/0KV+3bh3SuNLncDhRUVHffvutVCqtr6/v7e1tb2+vrKxECNnY2Dx69Oj+/fvt7e2DfckjIiJ6enpaWlr++Mc/UoXOzs4IocLCwu7u7qqqKs17Z1rqJAgiIiIiNzc3KytLLpeXl5evW7fOwcEhLCyMhs0xUkNGJZFImpub8/LyVCpVQ0PDgwcPNBfv397e3t6Wlpaenp6ysrLw8HBnZ+e33357BFUVFBTQ2DWKx+O5u7vX1dXpskEyMzM1e3GOZscRBLFmzZqTJ08ePHhQLper1eq6ujrc9z40NNTe3n7An6vy+fwrV65cvXpVLperVKqSkpK33nqLz+dv3rwZIVRZWblv374vvviCxWIxNKSkpGhWghvr5eU1ZJAvCsN1HDCSkXXLePfdd/l8vrm5+bRp027fvk2VJyQkODg44E3n6OiYkZGByz/++GMvLy+CIAiCmDFjBi6/ffu2i4sLl8t9+eWXd+7cibtA8ni8wMBAzXXNnz//0KFDfQLYvn27jY2NlZVVSEgI7kYqFotra2u119nb25ucnDx+/HgWi2VtbR0cHHz37l1cYUZGBn5iMH78+Jqams8//xw/7HJxcfnpp5+Gu30wHbetlqhIkmxqapo/fz5BEG5ubu+9997WrVsRQhKJBHd40mzv48ePw8LCWCyWo6Ojubm5UCgMCgqqqakZWVUXL14UCASJiYlDxq9j1yh800ahUOCPubm5+L6NnZ3dxo0b+8y8detWza5Ro9lxT58+3b59u7Ozs7m5uUgkWrp0aUVFBUmSwcHBCKGYmJgBow0MDHRzc7OwsOBwOGKxODQ0tLy8HE8a7AF9cnKyZg3+/v6Ojo69vb1DbpkXpGsUJFMwcobftvg3joZcI6lzMq2qqjI3Nx+st6bhqdXquXPnHj58WB+VNzY2EgSRkpKiy8wvyHcQLvPBM8Zk31ckkUji4+Pj4+OpH2UakVqtzsvLa29vDw0N1Uf9sbGx06dP1+x/AiCZAkCbyMjIkJCQ0NBQo7/TpKio6OzZswUFBdq7vo7M/v37S0tLL168iHtAAwySKXhmREVFZWZmtrW1ubm5nTlzxtjhDGzXrl1SqXTPnj3GDWPBggXHjx+n3lRAo3Pnzj19+rSoqMja2pr2yp9pL8RQz+D5sHv37j69yk2Tn5+fn5+fsaPQl8WLFy9evNjYUZgiODMFAAAaQDIFAAAaQDIFAAAaQDIFAAAavBAPoOrq6nJycowdxXMI/6Dwud+2+L0hz30z9aeurs4UXkajd8b+1YDeaY65CAAwihfhF1AMEoZwAaZt+fLlCE4MgcmDe6YAAEADSKYAAEADSKYAAEADSKYAAEADSKYAAEADSKYAAEADSKYAAEADSKYAAEADSKYAAEADSKYAAEADSKYAAEADSKYAAEADSKYAAEADSKYAAEADSKYAAEADSKYAAEADSKYAAEADSKYAAEADSKYAAEADSKYAAEADSKYAAEADSKYAAEADSKYAAEADSKYAAEADSKYAAEADSKYAAEADSKYAAEADSKYAAEADSKYAAEADSKYAAEADSKYAAEADSKYAAEADSKYAAEADBkmSxo4BgN84fvz44cOHe3t78cd79+4hhNzc3PBHJpP57rvvrlq1ymjxATAQSKbA5JSVlU2bNk3LDP/5z3+mTp1qsHgA0AUkU2CKPDw87t69O+AkiURSVVVl4HgAGBLcMwWmaPXq1SwWq385i8Vas2aN4eMBYEhwZgpM0c8//yyRSAY8OKuqqiQSieFDAkA7ODMFpsjd3X3mzJkMBkOzkMFgeHt7QyYFpgmSKTBRb775ppmZmWaJmZnZm2++aax4ANAOLvOBiZLJZA4ODlQHKYQQk8l89OiRvb29EaMCYDBwZgpM1JgxY+bNm0ednJqZmb3yyiuQSYHJgmQKTNfq1as1r5xWr15txGAA0A4u84HpksvlIpFIqVQihFgslkwms7KyMnZQAAwMzkyB6RIKha+99pq5ubm5ufmiRYsgkwJTBskUmLQ33nhDrVar1Wr4MT4wcXCZD0xad3e3nZ0dSZKNjY1cLtfY4QAwKEimv5GTk7NixQpjRwHAM+DUqVPLly83dhQmxNzYAZiiU6dOGTsEo1mxYkV4ePicOXOMHcj/V1paymAwtL9HargOHDiAEHr//fdprPOFAucc/UEyHcCL/P92xYoVc+bMMaktsGTJEoSQuTmdx+rp06fRi72jRwmSaX+QTIGpozeNAqAn8DQfAABoAMkUAABoAMkUAABoAMkUAABoAMl0tP76178KBAIGg1FaWmrsWP5PUlKSh4cHl8vl8/keHh7R0dFyuVyva7x48aKlpeVXX32l17UYUWFhYWRk5NmzZ93d3RkMBoPB6PPWFT8/P4FAYGZmNnny5Nu3bxslSO37PT4+3tPTUygUcjgciUSybdu2jo4OPOn8+fNJSUlqtdooYT8/SKAB9zAd7lInT55ECJWUlOgjpBHw9/dPSUmRyWTt7e05OTksFuvPf/6zjssihE6dOjXcNebn5wuFwvPnzw93QWNZtmzZsmXLdJw5JiYmICBALpfjj2Kx2NbWFiGUn5+vOVtBQcHixYtpDnQ4tO/3efPmZWRkNDU1yeXyU6dOsVis1157jZqampo6b968lpYWHdc1suPk+QZnps8hNpu9YcMGkUhkYWEREhISFBT09ddf//LLL/pbo7+/f1tbW0BAgP5WgXV1dfn6+up7LZr27t2bnZ2dk5MjEAiowrS0NCaTGRYW1tbWZshgtNO+3y0sLMLCwmxsbAQCwfLly4ODgy9duvTw4UM8ddOmTdOmTVu0aFFPT4/xWvBsg2RKgz5DFRldbm4uQRDUR0dHR4QQdU33TDt8+LBMJjPY6qqrq6Ojo+Pi4jS3J0LI19c3PDy8vr5+y5YtBgtmSNr3e35+vuYwMHZ2dgghhUJBlcTGxpaWlqamphoo3OcOJNORIEkyOTl54sSJHA7H0tJy69atmlPVanVMTIyzszOXy506dSq+dXDw4EE+n8/j8c6dO7dw4UKhUDhu3Dh8fwD797///bvf/Y7H4wmFQi8vL3y3a8CqhquqqsrKysrFxWV0jR7U9evXnZ2dGQzGxx9/jIZqaVpaGkEQY8aMWbt2rYODA0EQvr6+xcXFeKpUKmWz2WPHjsUfN2zYwOfzGQxGY2MjQig8PDwiIqKmpobBYOBh9S5duiQUCnft2qWnpqWlpZEkGRgY2H9SYmLihAkTDh06VFhYOOCyJEnu379/0qRJHA7H2to6KCjozp07eNKQB4MB9nt9fT2Xy3Vzc6NKrK2t582bl5qaSsL7OkbGuHcZTI2O90x37NjBYDD+53/+p6WlRaFQZGRkII17plu2bOFwOGfOnGlpaYmKimIymbdu3cJLIYS++eabtrY2mUw2d+5cPp+vVCpJkuzo6BAKhUlJSV1dXY8fP16yZElDQ4OWqnShVCrr6urS09M5HM6xY8d0XAqN6F4YvlpMT0/HH7W0lCTJsLAwPp9fWVnZ3d1dUVHh4+MjEAhqa2vx1FWrVtnb21M1JycnI4Tw1iBJcunSpWKxmJqan58vEAji4+OHG7CO90zd3d09PT37FIrF4nv37pEkeePGDSaT6erq2tHRQfa7ZxoTE8Nms48dO9ba2lpWVjZz5kw7O7vHjx/jqdo3kb73e2dnp0AgkEqlfcojIyORbnf/R3acPN8gmf6GLslUoVDweDzNW/uaD6C6urp4PF5oaCg1M4fDWb9+Pfnr96erqwtPwim4urqaJMkff/wR9XugoaUqXeDhkmxtbT/66CPqWzokGpPpgC0lSTIsLMzS0pJa9tatWwihuLg4/HFYyXTEdEmmHR0dDAYjICCgTzmVTEmSjIiIQAht3LiR/G0yVSgUFhYW1L4jSfL7779HCFF5X8smMsB+37Fjx4QJE6hHapQvv/wSIXT06NEh1wLJtD+4zB+26upqhUKxYMGCAafevXtXoVBMmTIFf+RyuWPHjqWu7zSx2WyEkEqlQgi5u7uPGTPmjTfeiI2NvX///nCrGtDDhw9lMtmJEyeOHDkyY8YMQ95q7EOzpf15e3vzeDzd22UwMpmMJEkej6dlnsTExIkTJ2ZkZFy/fl2zvKKioqOjw9vbmyrx8fFhs9nUDY0+NDeRvvd7bm5uTk7O5cuXNR+pYbixT5480XFdQBMk02Grq6tDCIlEogGndnZ2IoR27tzJ+NWDBw80b/MPiMvlXr169eWXX961a5e7u3toaGhXV9fIqqKwWCyRSOTn55ednV1RUbF79+5hNNKwOBxOQ0ODsaPoq7u7GyHE4XC0zEMQRGZmJoPx/9q715imzjcA4G+hhUOhlSKXcRcoGxNwBi8BhpmGhMSRcZGxNZvLlJgV4+wYSBgXGeOqg4DBQAwb4YMY7gR0A2OU1IToyBZhdBAuYxbjOuQi0AIFSnv+H07Wf4fQlsMpBXl+33rO24fnnLd96DnnPeelxcXFyeVy9fKZmRmEkJWVlWZja2trmUym8+8atN9ra2uvXLkiFAr37dv36nuJx28TGw42CorphhEXTJeWltZcSxTZkpISzd//jx8/1hnW19f3zp07EokkJSWlrq6uqKiIdKhVuFyuqalpX1/fRt+4NRQKxczMjIuLi7ETWY2oLDqHsgcFBSUmJg4PD+fk5KgXEtNVrSqdem6m4fr9+vXr1dXVHR0dTk5Oa76FmLsQZjQgB4rphvn5+ZmYmDx8+HDNta6urhiGbfRuKIlE0t/fjxCys7MrKCgICAjo7+8nF2pqauqTTz7RXDI8PKxUKl1dXTcUZ8sIhUIcxwMDA4mXdDp9vRMCW8ze3p5Go+kzkjQnJ8fHx6e7u1u9xM/Pz8rK6rffflMv6erqWl5ePnTokM5ohuh3HMdTUlJEIlFLS8uq38uaiI0lzrqCjYJiumF2dnYxMTGNjY2VlZVSqbS3t7eiokK9FsOws2fP1tTUlJeXS6VSpVL5/PlznQPmJRJJfHz8wMDA8vJyd3f36OhoYGAguVCWlpb37t3r6OiQSqUKhaK7u/vzzz+3tLRMTEykYOMpolKppqenV1ZWent7ExIS3Nzczpw5Q6zicrkvX75saWlRKBQTExOjo6Oab7SxsZFIJGKxWCaTKRSK9vZ2ww2NYjKZnp6exFkd7YiDfc1RnBiGJSUlNTc3V1dXS6VSkUh0/vx5R0dHPp+vT7T1+p3H4zk4OKx5u6r2fu/v7//+++9/+OEHBoNB01BUVKQZhNhYf39/nUmCNRj8EteOoufQKJlMdu7cub1791pZWYWEhGRmZiKEXFxcfv/9dxzHl5aWUlJS3Nzc6HQ6UXn7+vrKysqIs/ve3t4jIyMVFRVsNhsh5O7uPjQ0JBaLg4ODORyOqampk5NTenr6ysrKeqF0phcREeHh4WFlZWVubu7l5cXj8UQikZ57AG38Ku3169eJkaFMJjMiIkL7luI4zufzGQyGs7MznU5ns9lRUVEjIyPqaFNTUydOnMAwzMPD4+LFi8QYXi6XS4ydevLkibu7u4WFRUhIyNjYWFtbG4vFys3N3VDCuN5DowQCAYPBWFhYIF42Nzd7eXkhhGxtbYkr+JqSk5M1h0apVKrCwkJvb28Gg8HhcKKjowcHB4lVOnfRev0eHR2NEMrMzFwzWy39LhKJ1vz6FxYWakYIDw93dnZWqVQ69wyJz8lrD4rpf5C7N/91sgVfEuKmRoP+CZ30LKbDw8N0Ol3/UbqGplQqjx07VllZaYjgk5OTGIYVFRXp0xiK6avgMB8YwU55QBGXy83Ozs7Ozt4ON+MqlcqWlhaZTMbj8QwRPysr6+DBgwKBwBDBdwMopjvMwMAAbX0G+prtZqmpqbGxsTwez+jPNBEKhU1NTe3t7dqHvpJTXFzc09PT1tbGYDAoD75LQDHdYXx8fLQcaNTW1ho7QR3S0tKqqqpmZ2c9PDwaGxuNnY5e8vLyBAJBQUGBcdMIDQ29deuW+sEFFGptbV1aWhIKhRwOh/LguwfM+wi2VH5+/na+fWA9YWFhYWFhxs7CUCIjIyMjI42dxY4Hv0wBAIACUEwBAIACUEwBAIACUEwBAIACcAFqDfX19cZOwZhIPFNjxyHum9zlHQ2oRcNhigIN9fX1H3/8sbGzAGAHqKur++ijj4ydxTYCv0zXsJv/wdBotN3wJYmNjUUINTQ0GDuRnWq7TSK5HcA5UwAAoAAUUwAAoAAUUwAAoAAUUwAAoAAUUwAAoAAUUwAAoAAUU8o0NTV5enpqPl3UzMzM3t7++PHjhYWF09PTxk4QUOb+/fupqamaPf7ZZ59pNggLC2OxWKampr6+vmtO2bTFFhcXfXx8MjIyiJe3b9++evXqTnlE904BxZQyMTExf/31l5eX1549e3AcV6lU4+Pj9fX1Hh4eKSkpvr6+mnNVgp3r22+/LS0tTUtLU/f43r17q6urf/75Z3Wbe/fuNTQ0fPDBB319fQEBAUbMlpCenj44OKh+GRERgWFYaGjozMyMEbN6zUAxNRQajWZtbX38+PGqqqr6+voXL16Eh4cb/WntRieXy4ODg7dbKP1duXKltra2vr6exWKpF5aWlpqYmPD5/O3Zv48ePfrjjz9WLfzqq6/eeeed999/f2VlxShZvX6gmG6FDz/88MyZM+Pj4zdu3DB2LkZWWVk5Pj6+3ULp6c8//7x8+fJ3332HYZjm8uDg4ISEhL///vvSpUtbmY8+5HJ5cnLytWvXXl2VlZXV09Oz5ipAAhTTLULMC9/e3k68VCqVmZmZbm5uFhYWBw4cIGZFLS8vt7S0ZDKZra2tJ0+eZLPZLi4uNTU16iAPHz48evQok8lks9n+/v5SqXS9UAaF43hxcfHbb79tbm7O4XCioqIGBgaIVQKBwMzMTD21xoULFywtLWk02uTkJEIoISEhKSlpZGSERqNxudzS0lIMw+zt7ePj4x0dHTEMCw4O7urqIhEKIXT37l02m52Xl2e4DS8tLcVxPCIi4tVVubm5b7755o8//nj//v0136tlp+ns9810cXp6+oULF+zs7F5dxeFw3nvvvWvXru3m+6epZPgJUHeSzU/1rD5nugpR+FxdXYmXly5dMjc3b2xsnJ6eTktLMzEx+fXXX3EcT09PRwg9ePBgdnZ2fHz82LFjlpaWy8vLOI7Pzc2x2eyrV6/K5fKxsbFTp05NTExoCUUO0mMK38zMTDMzs5s3b87MzPT29gYEBNja2o6NjRFrP/30UwcHB3XjwsJChBCRKo7jMTExXl5e6rV8Pt/S0rK/v39xcbGvr+/IkSMsFuvZs2ckQv30008sFis7O1ufzdRzqudVPD099+/fv2qhl5fX06dPcRx/9OiRiYnJvn375ubmcBxvb2+PjIxUN9O+07T0O76JLu7s7IyIiMBxfGJiAiGUnp6+qkFqaipCqLu7e6O7Qp/PyW4Dv0y3CIvFotFoMpkMIbS4uFheXh4dHR0TE2NtbZ2RkcFgMKqqqtSNg4OD2Wy2nZ0dj8ebn59/9uwZQkgsFkulUl9fXwzDHBwcmpqabG1tdYainFwuLy4uPnXq1OnTp/fs2ePv73/jxo3JycmKigpyAel0OvF7bf/+/eXl5TKZjFz+4eHhUqn08uXL5NLQaX5+/unTp15eXus1CAoK+vrrr8Vi8TfffLNqlZ47bc1+J93Fcrk8ISGhvLxcSxtvb2+EkEgk0hkN6ATFdIvMz8/jOM5msxFCg4ODCwsLfn5+xCoLC4s33nhDfdCnyczMDCGkUCgQQp6envb29qdPn87KyhKLxUQD/UNRpa+vb25u7vDhw+olR44cMTMzUx+eb8bhw4eZTKZB8ydtfHwcx3Ht0yzn5ua+9dZbZWVlnZ2dmss3utM0+510F6elpX3xxRfOzs5a2hCb8+LFC53RgE5QTLfI0NAQQsjHxwchND8/jxDKyMhQj0gdHR1dWFjQHsHCwqKjoyMkJCQvL8/T05PH48nlcnKhNoMYTGNlZaW50NramvjRvXnm5ubEMel2s7i4iBAyNzfX0gbDsKqqKhqNFhcXJ5fL1cs3s9PIdXFnZ6dIJDp37pz2ZhYWFujfTQObBMV0i9y9exchdPLkSYQQcTWgpKRE84SLPs+39/X1vXPnjkQiSUlJqaurKyoqIh2KNGtra4TQqiowMzPj4uKy+eAKhYKqUJQj6o7Oge5BQUGJiYnDw8M5OTnqhZvZaeS6uLKy8sGDByYmJkT9JYLk5eXRaDTN8c7Ly8vqTQObBMV0K4yNjZWUlLi4uMTFxSGEXF1dMQzr6enZUBCJRNLf348QsrOzKygoCAgI6O/vJxdqM/z8/KysrDS/kF1dXcvLy4cOHSJe0ul04viUBKFQiON4YGDg5kNRzt7enkaj6TOSNCcnx8fHp7u7W71E507TglwXV1VVaRZfzQtQmmcbiM1xcHDYUHCwJiim1MNxfG5uTqVSER/iurq6d99919TUtKWlhThnimHY2bNna2pqysvLpVKpUql8/vz5P//8oz2sRCKJj48fGBhYXl7u7u4eHR0NDAwkF2ozMAxLSkpqbm6urq6WSqUikej8+fOOjo58Pp9owOVyX7582dLSolAoJiYmRkdHNd9uY2MjkUjEYrFMJiMKpUqlmp6eXllZ6e3tTUhIcHNzI4aRbTRUe3u7QYdGMZlMT09PYvIo7YiDfVNTU80l2nea9mjrdTGPx3NwcNjM7arE5vj7+5OOAP7PACMEdrDNDI26ffv2gQMHmEymmZmZiYkJ+vcmqKNHj2ZnZ09NTWk2XlpaSklJcXNzo9PpdnZ2MTExfX19ZWVlxAUBb2/vkZGRiooKovi6u7sPDQ2JxeLg4GAOh2Nqaurk5JSenr6ysrJeKNJ7AOkx5EWlUhUWFnp7ezMYDA6HEx0dPTg4qF47NTV14sQJDMM8PDwuXryYnJyMEOJyucSApydPnri7u1tYWISEhIyNjfH5fAaD4ezsTKfT2Wx2VFTUyMgIuVBtbW0sFis3N1efzSQ3NEogEDAYjIWFBeJlc3MzcXHf1tb2yy+/XNU4OTlZc2iUlp2mvd/x9bs4OjoaIZSZmakz8/WGRoWHhzs7OxP/+DdEn8/JbgPF9D82P850p9viLwmfz7exsdmyP6dGrpgODw/T6fSbN28aIiUSlErlsWPHKisryb19cnISw7CioiIS74Vi+io4zAdGtoOeXcTlcrOzs7Ozs+fm5oydC1IqlS0tLTKZjMfjkYuQlZV18OBBgUBAbWK7FhRTADYgNTU1NjaWx+MZ/ZkmQqGwqampvb1d+9DX9RQXF/f09LS1tTEYDMpz252gmAKjSUtLq6qqmp2d9fDwaGxsNHY6+srLyxMIBAUFBcZNIzQ09NatW+pnF2xIa2vr0tKSUCjkcDiUJ7Zr0Y2dANi98vPz8/PzjZ0FGWFhYWFhYcbOgrzIyMjIyEhjZ/G6gV+mAABAASimAABAASimAABAASimAABAAbgAtYbY2Fhjp2BMJSUlDQ0Nxs7CsH755Re06zsaUIuGw4wFGh4/flxcXGzsLADYARITE4OCgoydxTYCxRQAACgA50wBAIACUEwBAIACUEwBAIACUEwBAIAC/wMzI7cIlfGCDgAAAABJRU5ErkJggg==",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "execution_count": 94,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "## Generate a image of the structure of the model\n",
        "from tensorflow.keras.utils import plot_model\n",
        "from IPython.display import Image\n",
        "plot_model(fine_model, to_file='convnet.png', show_shapes=True,show_layer_names=True)\n",
        "Image(filename='convnet.png')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-N6Rtvg1e0Fj"
      },
      "source": [
        "#### FineTune-Model Training"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V5e2dtjxe0Fj",
        "outputId": "59333aa2-fb27-42a3-f27d-7455c47ba6cf"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/15\n",
            "598/598 [==============================] - 70s 109ms/step - loss: 0.4396 - categorical_accuracy: 0.8847 - val_loss: 0.6365 - val_categorical_accuracy: 0.8134\n",
            "Epoch 2/15\n",
            "598/598 [==============================] - 64s 106ms/step - loss: 0.2231 - categorical_accuracy: 0.9369 - val_loss: 0.5800 - val_categorical_accuracy: 0.8226\n",
            "Epoch 3/15\n",
            "598/598 [==============================] - 64s 106ms/step - loss: 0.1857 - categorical_accuracy: 0.9445 - val_loss: 0.7333 - val_categorical_accuracy: 0.8008\n",
            "Epoch 4/15\n",
            "598/598 [==============================] - 65s 109ms/step - loss: 0.1646 - categorical_accuracy: 0.9517 - val_loss: 0.7252 - val_categorical_accuracy: 0.8042\n",
            "Epoch 5/15\n",
            "598/598 [==============================] - 64s 106ms/step - loss: 0.1390 - categorical_accuracy: 0.9578 - val_loss: 0.6833 - val_categorical_accuracy: 0.8159\n",
            "Epoch 6/15\n",
            "598/598 [==============================] - 64s 106ms/step - loss: 0.1202 - categorical_accuracy: 0.9640 - val_loss: 0.7640 - val_categorical_accuracy: 0.8092\n",
            "Epoch 7/15\n",
            "598/598 [==============================] - 64s 106ms/step - loss: 0.1087 - categorical_accuracy: 0.9692 - val_loss: 0.7180 - val_categorical_accuracy: 0.8172\n",
            "Epoch 8/15\n",
            "598/598 [==============================] - 64s 106ms/step - loss: 0.1002 - categorical_accuracy: 0.9702 - val_loss: 0.7721 - val_categorical_accuracy: 0.8214\n",
            "Epoch 9/15\n",
            "598/598 [==============================] - 64s 106ms/step - loss: 0.0967 - categorical_accuracy: 0.9718 - val_loss: 0.6984 - val_categorical_accuracy: 0.8306\n",
            "Epoch 10/15\n",
            "598/598 [==============================] - 62s 103ms/step - loss: 0.0894 - categorical_accuracy: 0.9740 - val_loss: 0.8081 - val_categorical_accuracy: 0.8218\n",
            "Epoch 11/15\n",
            "598/598 [==============================] - 61s 101ms/step - loss: 0.0863 - categorical_accuracy: 0.9742 - val_loss: 0.7611 - val_categorical_accuracy: 0.8214\n",
            "Epoch 12/15\n",
            "598/598 [==============================] - 63s 106ms/step - loss: 0.0713 - categorical_accuracy: 0.9783 - val_loss: 0.7450 - val_categorical_accuracy: 0.8281\n",
            "Epoch 13/15\n",
            "598/598 [==============================] - 64s 107ms/step - loss: 0.0719 - categorical_accuracy: 0.9781 - val_loss: 0.7323 - val_categorical_accuracy: 0.8298\n",
            "Epoch 14/15\n",
            "598/598 [==============================] - 64s 107ms/step - loss: 0.0742 - categorical_accuracy: 0.9761 - val_loss: 0.6952 - val_categorical_accuracy: 0.8411\n",
            "Epoch 15/15\n",
            "598/598 [==============================] - 64s 107ms/step - loss: 0.0643 - categorical_accuracy: 0.9817 - val_loss: 0.7470 - val_categorical_accuracy: 0.8264\n"
          ]
        }
      ],
      "source": [
        "# train model\n",
        "train_history = fine_model.fit(train_dataset_augmented_concat, validation_data=valid_dataset, epochs=TRAIN_EPOCHS, verbose=1, callbacks=[EARLY_STOPPING])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k6ahsz1-e0Fj"
      },
      "source": [
        "**Results (1. Epoche):**\n",
        "Results of various test for hyperparameters and unfreezing!\n",
        "```\n",
        "EfficientNetB0-Retrain=0: \n",
        "- affect_net(AHNS), train_dataset, EfficientNetB0-Retrain=0, learning_rate=0.0001:\n",
        "    - 299/299 [==============================] - 102s 323ms/step - loss: 1.2411 - categorical_accuracy: 0.4644 - val_loss: 0.9989 - val_categorical_accuracy: 0.5958\n",
        "- affect_net(AHNS), train_dataset_augmented, EfficientNetB0-Retrain=0, learning_rate=0.0001:\n",
        "    - 299/299 [==============================] - 179s 593ms/step - loss: 1.0738 - categorical_accuracy: 0.5483 - val_loss: 0.9336 - val_categorical_accuracy: 0.6294\n",
        "- affect_net(AHNS), train_dataset_augmented_concat, EfficientNetB0-Retrain=0, learning_rate=0.0001:\n",
        "    - 598/598 [==============================] - 629s 1s/step - loss: 0.9592 - categorical_accuracy: 0.6033 - val_loss: 0.8582 - val_categorical_accuracy: 0.6637\n",
        "\n",
        "\n",
        "EfficientNetB0-Retrain=-5:\n",
        "- affect_net(AHNS), train_dataset, EfficientNetB0-Retrain=-5, learning_rate=0.0001:\n",
        "    - 299/299 [==============================] - 137s 440ms/step - loss: 1.2833 - categorical_accuracy: 0.4548 - val_loss: 1.0154 - val_categorical_accuracy: 0.5983\n",
        "- affect_net(AHNS), train_dataset_augmented, EfficientNetB0-Retrain=-5, learning_rate=0.0001:\n",
        "    - 299/299 [==============================] - 165s 548ms/step - loss: 1.0715 - categorical_accuracy: 0.5464 - val_loss: 0.9584 - val_categorical_accuracy: 0.6029\n",
        "- affect_net(AHNS), train_dataset_augmented_concat, EfficientNetB0-Retrain=-5, learning_rate=0.0001:\n",
        "    - 598/598 [==============================] - 351s 578ms/step - loss: 1.2941 - categorical_accuracy: 0.4383 - val_loss: 0.9781 - val_categorical_accuracy: 0.6197\n",
        "\n",
        "\n",
        "EfficientNetB0-Retrain=-61:\n",
        "- affect_net(AHNS), train_dataset, EfficientNetB0-Retrain=-61, learning_rate=0.0001:\n",
        "    - 299/299 [==============================] - 168s 560ms/step - loss: 0.9523 - categorical_accuracy: 0.6122 - val_loss: 0.7561 - val_categorical_accuracy: 0.7065\n",
        "- affect_net(AHNS), train_dataset_augmented, EfficientNetB0-Retrain=-61, learning_rate=0.0001:\n",
        "    - 299/299 [==============================] - 168s 554ms/step - loss: 0.8521 - categorical_accuracy: 0.6637 - val_loss: 0.7043 - val_categorical_accuracy: 0.7216\n",
        "- affect_net(AHNS), train_dataset_augmented_concat, EfficientNetB0-Retrain=-61, learning_rate=0.0001:\n",
        "    - 598/598 [==============================] - 173s 287ms/step - loss: 0.7085 - categorical_accuracy: 0.7314 - val_loss: 0.6284 - val_categorical_accuracy: 0.7518\n",
        "\n",
        "\n",
        "learning_rate=0.0005:\n",
        "- affect_net(AHNS), train_dataset, EfficientNetB0-Retrain=-61, learning_rate=0.0005:\n",
        "    - 299/299 [==============================] - 40s 108ms/step - loss: 1.0199 - categorical_accuracy: 0.5646 - val_loss: 0.7106 - val_categorical_accuracy: 0.7304\n",
        "- affect_net(AHNS), train_dataset_augmented, EfficientNetB0-Retrain=-61, learning_rate=0.0005:\n",
        "    - 299/299 [==============================] - 45s 131ms/step - loss: 0.7204 - categorical_accuracy: 0.7374 - val_loss: 0.6749 - val_categorical_accuracy: 0.7556\n",
        "- affect_net(AHNS), train_dataset_augmented_concat, EfficientNetB0-Retrain=-61, learning_rate=0.0005:\n",
        "    - 598/598 [==============================] - 73s 113ms/step - loss: 0.5910 - categorical_accuracy: 0.7965 - val_loss: 0.5991 - val_categorical_accuracy: 0.7820\n",
        "\n",
        "learning_rate=0.001:\n",
        "- affect_net(AHNS), train_dataset, EfficientNetB0-Retrain=-61, learning_rate=0.001:\n",
        "    - 299/299 [==============================] - 94s 297ms/step - loss: 0.9832 - categorical_accuracy: 0.5901 - val_loss: 0.6620 - val_categorical_accuracy: 0.7438\n",
        "- affect_net(AHNS), train_dataset_augmented, EfficientNetB0-Retrain=-61, learning_rate=0.001:\n",
        "    - 299/299 [==============================] - 173s 572ms/step - loss: 0.7029 - categorical_accuracy: 0.7333 - val_loss: 0.6457 - val_categorical_accuracy: 0.7568\n",
        "- affect_net(AHNS), train_dataset_augmented_concat, EfficientNetB0-Retrain=-61, learning_rate=0.001:\n",
        "    - 598/598 [==============================] - 1400s 2s/step - loss: 0.5652 - categorical_accuracy: 0.7893 - val_loss: 0.6146 - val_categorical_accuracy: 0.7803\n",
        "\n",
        "\n",
        "EfficientNetB0-Retrain=-76:\n",
        "- affect_net(AHNS), train_dataset_augmented_concat, EfficientNetB0-Retrain=-76, learning_rate=0.0005:\n",
        "    Epoch 1/8\n",
        "    598/598 [==============================] - 211s 344ms/step - loss: 0.8448 - categorical_accuracy: 0.6507 - val_loss: 0.5615 - val_categorical_accuracy: 0.7912\n",
        "    Epoch 2/8\n",
        "    598/598 [==============================] - 99s 165ms/step - loss: 0.5277 - categorical_accuracy: 0.8058 - val_loss: 0.4868 - val_categorical_accuracy: 0.8151\n",
        "    Epoch 3/8\n",
        "    598/598 [==============================] - 97s 162ms/step - loss: 0.4071 - categorical_accuracy: 0.8512 - val_loss: 0.5343 - val_categorical_accuracy: 0.8101\n",
        "    Epoch 4/8\n",
        "    598/598 [==============================] - 85s 141ms/step - loss: 0.3009 - categorical_accuracy: 0.8970 - val_loss: 0.5622 - val_categorical_accuracy: 0.8172\n",
        "    Epoch 5/8\n",
        "    598/598 [==============================] - 87s 145ms/step - loss: 0.2517 - categorical_accuracy: 0.9135 - val_loss: 0.5850 - val_categorical_accuracy: 0.8075\n",
        "    Epoch 6/8\n",
        "    598/598 [==============================] - 79s 132ms/step - loss: 0.2007 - categorical_accuracy: 0.9352 - val_loss: 0.7140 - val_categorical_accuracy: 0.7929\n",
        "    Epoch 7/8\n",
        "    598/598 [==============================] - 91s 153ms/step - loss: 0.1737 - categorical_accuracy: 0.9431 - val_loss: 0.7241 - val_categorical_accuracy: 0.7954\n",
        "    Epoch 8/8\n",
        "    598/598 [==============================] - 77s 128ms/step - loss: 0.1490 - categorical_accuracy: 0.9513 - val_loss: 0.7408 - val_categorical_accuracy: 0.8038\n",
        "\n",
        "EfficientNetB0-Retrain=-91:\n",
        "- affect_net(AHNS), train_dataset_augmented_concat, EfficientNetB0-Retrain=-91, learning_rate=0.0005:\n",
        "    Output exceeds the size limit. Open the full output data in a text editor\n",
        "    Epoch 1/15\n",
        "    598/598 [==============================] - 71s 103ms/step - loss: 0.8329 - categorical_accuracy: 0.6609 - val_loss: 0.5869 - val_categorical_accuracy: 0.7761\n",
        "    Epoch 2/15\n",
        "    598/598 [==============================] - 60s 100ms/step - loss: 0.5114 - categorical_accuracy: 0.8133 - val_loss: 0.4585 - val_categorical_accuracy: 0.8327\n",
        "    Epoch 3/15\n",
        "    598/598 [==============================] - 60s 100ms/step - loss: 0.3840 - categorical_accuracy: 0.8661 - val_loss: 0.4844 - val_categorical_accuracy: 0.8277\n",
        "    Epoch 4/15\n",
        "    598/598 [==============================] - 60s 100ms/step - loss: 0.2942 - categorical_accuracy: 0.9001 - val_loss: 0.5688 - val_categorical_accuracy: 0.8084\n",
        "    Epoch 5/15\n",
        "    598/598 [==============================] - 60s 100ms/step - loss: 0.2344 - categorical_accuracy: 0.9212 - val_loss: 0.5606 - val_categorical_accuracy: 0.8130\n",
        "    Epoch 6/15\n",
        "    598/598 [==============================] - 60s 100ms/step - loss: 0.1878 - categorical_accuracy: 0.9386 - val_loss: 0.6160 - val_categorical_accuracy: 0.8235\n",
        "    Epoch 7/15\n",
        "    598/598 [==============================] - 61s 102ms/step - loss: 0.1696 - categorical_accuracy: 0.9450 - val_loss: 0.6213 - val_categorical_accuracy: 0.8252\n",
        "    Epoch 8/15\n",
        "    598/598 [==============================] - 60s 100ms/step - loss: 0.1539 - categorical_accuracy: 0.9494 - val_loss: 0.5868 - val_categorical_accuracy: 0.8390\n",
        "    Epoch 9/15\n",
        "    598/598 [==============================] - 60s 100ms/step - loss: 0.1266 - categorical_accuracy: 0.9601 - val_loss: 0.6409 - val_categorical_accuracy: 0.8298\n",
        "    Epoch 10/15\n",
        "    598/598 [==============================] - 60s 100ms/step - loss: 0.1147 - categorical_accuracy: 0.9625 - val_loss: 0.7411 - val_categorical_accuracy: 0.8197\n",
        "    Epoch 11/15\n",
        "    598/598 [==============================] - 60s 100ms/step - loss: 0.1042 - categorical_accuracy: 0.9678 - val_loss: 0.6427 - val_categorical_accuracy: 0.8386\n",
        "    Epoch 12/15\n",
        "    598/598 [==============================] - 60s 100ms/step - loss: 0.0862 - categorical_accuracy: 0.9732 - val_loss: 0.7274 - val_categorical_accuracy: 0.8289\n",
        "    Epoch 13/15\n",
        "    ...\n",
        "    Epoch 15/15\n",
        "    597/598 [============================>.] - ETA: 0s - loss: 0.0858 - categorical_accuracy: 0.9735Restoring model weights from the end of the best epoch: 8.\n",
        "    598/598 [==============================] - 60s 100ms/step - loss: 0.0858 - categorical_accuracy: 0.9735 - val_loss: 0.7710 - val_categorical_accuracy: 0.8193\n",
        "    Epoch 15: early stopping\n",
        "\n",
        "EfficientNetB0-Retrain=-104:\n",
        "- affect_net(AHNS), train_dataset_augmented_concat, EfficientNetB0-Retrain=-104, learning_rate=0.0005:\n",
        "    Epoch 1/15\n",
        "    598/598 [==============================] - 70s 108ms/step - loss: 0.8042 - categorical_accuracy: 0.6799 - val_loss: 0.5259 - val_categorical_accuracy: 0.7899\n",
        "    Epoch 2/15\n",
        "    598/598 [==============================] - 63s 105ms/step - loss: 0.5000 - categorical_accuracy: 0.8219 - val_loss: 0.4824 - val_categorical_accuracy: 0.8222\n",
        "    Epoch 3/15\n",
        "    598/598 [==============================] - 63s 105ms/step - loss: 0.3654 - categorical_accuracy: 0.8712 - val_loss: 0.5288 - val_categorical_accuracy: 0.8130\n",
        "    Epoch 4/15\n",
        "    598/598 [==============================] - 63s 105ms/step - loss: 0.2952 - categorical_accuracy: 0.8988 - val_loss: 0.4686 - val_categorical_accuracy: 0.8369\n",
        "    Epoch 5/15\n",
        "    598/598 [==============================] - 63s 105ms/step - loss: 0.2320 - categorical_accuracy: 0.9222 - val_loss: 0.5502 - val_categorical_accuracy: 0.8302\n",
        "    Epoch 6/15\n",
        "    598/598 [==============================] - 63s 105ms/step - loss: 0.2021 - categorical_accuracy: 0.9340 - val_loss: 0.5946 - val_categorical_accuracy: 0.8289\n",
        "    Epoch 7/15\n",
        "    598/598 [==============================] - 63s 105ms/step - loss: 0.1763 - categorical_accuracy: 0.9436 - val_loss: 0.6082 - val_categorical_accuracy: 0.8176\n",
        "    Epoch 8/15\n",
        "    598/598 [==============================] - 63s 105ms/step - loss: 0.1428 - categorical_accuracy: 0.9560 - val_loss: 0.6586 - val_categorical_accuracy: 0.8122\n",
        "    Epoch 9/15\n",
        "    598/598 [==============================] - 63s 105ms/step - loss: 0.1330 - categorical_accuracy: 0.9583 - val_loss: 0.6423 - val_categorical_accuracy: 0.8319\n",
        "    Epoch 10/15\n",
        "    598/598 [==============================] - 63s 105ms/step - loss: 0.1141 - categorical_accuracy: 0.9633 - val_loss: 0.6430 - val_categorical_accuracy: 0.8331\n",
        "    Epoch 11/15\n",
        "    598/598 [==============================] - 63s 105ms/step - loss: 0.1055 - categorical_accuracy: 0.9667 - val_loss: 0.6348 - val_categorical_accuracy: 0.8373\n",
        "    Epoch 12/15\n",
        "    598/598 [==============================] - 63s 105ms/step - loss: 0.0957 - categorical_accuracy: 0.9693 - val_loss: 0.7768 - val_categorical_accuracy: 0.8218\n",
        "    Epoch 13/15\n",
        "    ...\n",
        "    Epoch 14/15\n",
        "    598/598 [==============================] - 64s 107ms/step - loss: 0.0841 - categorical_accuracy: 0.9734 - val_loss: 0.7251 - val_categorical_accuracy: 0.8252\n",
        "    Epoch 15/15\n",
        "    598/598 [==============================] - 64s 106ms/step - loss: 0.0776 - categorical_accuracy: 0.9754 - val_loss: 0.7069 - val_categorical_accuracy: 0.8340\n",
        "    \n",
        "    --> 83_affAHNS_retrain104_lr0005_es3.h5\n",
        "```"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YLPDf9LPe0Fj"
      },
      "source": [
        "#### FineTune-Model Evaluation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "P57pujuue0Fk",
        "outputId": "abec4b72-df8a-41cc-d2a6-019266f3c043"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Plot the train- and validation-accuracy of last training\n",
        "plt.plot(train_history.history['categorical_accuracy'])\n",
        "plt.plot(train_history.history['val_categorical_accuracy'])\n",
        "plt.title('model accuracy')\n",
        "plt.ylabel('accuracy')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'val'], loc='upper left')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "AZhL3AR5e0Fk",
        "outputId": "36c3e692-4edc-4596-b739-3d675a6c1952"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Plot the train- and validation-loss of last training\n",
        "plt.plot(train_history.history['loss'])\n",
        "plt.plot(train_history.history['val_loss'])\n",
        "plt.title('model loss')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'val'], loc='upper left')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l80S-9u4e0Fk",
        "outputId": "45242949-6105-4fb0-ba43-1f85cc0ebb26"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "94/94 - 5s - loss: 0.7836 - categorical_accuracy: 0.8136 - 5s/epoch - 53ms/step\n"
          ]
        }
      ],
      "source": [
        "# evaluate training with test dataset\n",
        "loss_test, acc_test = fine_model.evaluate(test_dataset, verbose=2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E5qkTtlUe0Fk"
      },
      "source": [
        "#### Save Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6XQ4BAgue0Fl"
      },
      "outputs": [],
      "source": [
        "## save model \n",
        "# fine_model.save(model_folder + \"NEW_83_affAHNS_retrain104_lr0005_es3.h5\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "URycYWr1e0Fl"
      },
      "outputs": [],
      "source": [
        "## Load a saved model\n",
        "fine_model = tf.keras.models.load_model(model_folder + \"83_affAHNS_retrain104_lr0005_es3.h5\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z0Rc0z9NID_b"
      },
      "source": [
        "---\n",
        "## Demo - Eigene Bilder Klassifizieren\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Sx6UxKr2K85D"
      },
      "source": [
        "\n",
        "### 1. Bild Generieren (GColab Only)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SYy0pFJTOxjs"
      },
      "outputs": [],
      "source": [
        "# define the image to be categorized (image.jpg = without crop)\n",
        "# demo_image = \"image_crop.jpg\"\n",
        "demo_image = \"image.jpg\"\n",
        "## GDrive demo-image folder\n",
        "# demo_image_path = root_directory + \"demo/\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0wD_UUL-ZDo_"
      },
      "outputs": [],
      "source": [
        "from IPython.display import display, Javascript\n",
        "from google.colab.output import eval_js\n",
        "from base64 import b64decode\n",
        "\n",
        "\n",
        "# get webcam access and make a photo for classification demo\n",
        "def take_photo(filename='image.jpg', quality=0.8):\n",
        "  js = Javascript('''\n",
        "    async function takePhoto(quality) {\n",
        "      const div = document.createElement('div');\n",
        "      const capture = document.createElement('button');\n",
        "      capture.textContent = 'Capture';\n",
        "      div.appendChild(capture);\n",
        "\n",
        "      const video = document.createElement('video');\n",
        "      video.style.display = 'block';\n",
        "      const stream = await navigator.mediaDevices.getUserMedia({video: true});\n",
        "\n",
        "      document.body.appendChild(div);\n",
        "      div.appendChild(video);\n",
        "      video.srcObject = stream;\n",
        "      await video.play();\n",
        "\n",
        "      // Resize the output to fit the video element.\n",
        "      google.colab.output.setIframeHeight(document.documentElement.scrollHeight, true);\n",
        "\n",
        "      // Wait for Capture to be clicked.\n",
        "      await new Promise((resolve) => capture.onclick = resolve);\n",
        "\n",
        "      const canvas = document.createElement('canvas');\n",
        "      canvas.width = video.videoWidth;\n",
        "      canvas.height = video.videoHeight;\n",
        "      canvas.getContext('2d').drawImage(video, 0, 0);\n",
        "      stream.getVideoTracks()[0].stop();\n",
        "      div.remove();\n",
        "      return canvas.toDataURL('image/jpeg', quality);\n",
        "    }\n",
        "    ''')\n",
        "  display(js)\n",
        "  data = eval_js('takePhoto({})'.format(quality))\n",
        "  binary = b64decode(data.split(',')[1])\n",
        "  with open(filename, 'wb') as f:\n",
        "    f.write(binary)\n",
        "  return filename"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 515
        },
        "id": "CiuGnZYEZFuv",
        "outputId": "13193943-c38f-4e28-a303-1dda3b481db9"
      },
      "outputs": [],
      "source": [
        "from IPython.display import Image\n",
        "try:\n",
        "  filename = take_photo()\n",
        "  print('Saved to {}'.format(filename))\n",
        "  \n",
        "  # Show the image which was just taken.\n",
        "  display(Image(filename))\n",
        "except Exception as err:\n",
        "  # Errors will be thrown if the user does not have a webcam or if they do not\n",
        "  # grant the page permission to access it.\n",
        "  print(str(err))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sUxLkSIeZIW8",
        "outputId": "c1ad25de-fda3-4d33-fd20-5eccf782b468"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "640 480\n"
          ]
        }
      ],
      "source": [
        "from PIL import Image\n",
        " \n",
        "im = Image.open(\"image.jpg\")\n",
        " \n",
        "width, height = im.size\n",
        "print(width, height)\n",
        " \n",
        "# left, upper combo gives the upper left corner coordinates\n",
        "# right, lower combo gives the lower right corner coordinates\n",
        "def crop_image(im: Image, left, upper, right, lower):\n",
        "   im2 = im.crop((left, upper, right, lower))\n",
        "   im2.show()\n",
        "   return im2\n",
        "\n",
        "\n",
        "def resize_image(im:Image, width, height):\n",
        "   im1 = im.resize((width, height))\n",
        "   im1.show()\n",
        "   return im1\n",
        "\n",
        "def save_image(im:Image, filename):\n",
        "   im.save(filename)\n",
        "\n",
        "## Set the pixels of the croped Image\n",
        "cropped = crop_image(im, 80, 0, 560, 480)\n",
        "save_image(cropped, \"image_crop.jpg\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D8pqBVM2bZWs"
      },
      "source": [
        "### 2. Bild Wählen"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kIwZtr_we0Fo"
      },
      "outputs": [],
      "source": [
        "demo_image_path = root_directory + \"demo/\"\n",
        "## Raw webcam image\n",
        "demo_image = \"image.jpg\"\n",
        "\n",
        "## Croped webcam image\n",
        "demo_image = \"image_crop.jpg\"\n",
        "\n",
        "## Custom test images:\n",
        "#demo_image = demo_image_path + \"imageHappy.jpg\"\n",
        "#demo_image = demo_image_path + \"imageSad.jpg\"\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CL6kiMpdOJNP"
      },
      "outputs": [],
      "source": [
        "# read demo image file\n",
        "tf_image = tf.keras.utils.load_img(demo_image, target_size=(IMG_HEIGHT, IMG_WIDTH))\n",
        "input_arr = tf.keras.preprocessing.image.img_to_array(tf_image)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        },
        "id": "w7U_zipPOcnh",
        "outputId": "bcc3091e-f63e-4586-9882-5381d2789031"
      },
      "outputs": [],
      "source": [
        "# display image\n",
        "tf_image"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_KPyghfYe0Fp"
      },
      "source": [
        "### 3. Bild Klassifizieren"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_tiG2Gtne0Fp"
      },
      "outputs": [],
      "source": [
        "## Model wählen:\n",
        "# MODEL = baseLine_model\n",
        "#MODEL = base_model_classifier\n",
        "MODEL = fine_model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "beSlwi9KOeeh",
        "outputId": "365f8a97-0f5b-494d-82dd-30374640c282"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1/1 [==============================] - 0s 25ms/step\n"
          ]
        }
      ],
      "source": [
        "# convert image to np.array\n",
        "input_batch = np.array([input_arr])\n",
        "\n",
        "# predict classification of the image\n",
        "prediction = MODEL.predict(input_batch)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LECAnMLSOeWL",
        "outputId": "5f0bb61f-02e5-4bd0-ab48-c8ccfa895aa8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Predicted Class:  ANGER\n"
          ]
        }
      ],
      "source": [
        "print(\"Predicted Class: \", CLASSES[np.argmax(prediction)])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i4BjofU-e0Fq"
      },
      "source": [
        "### 4. Metriken Auswerten"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5JJ8NSKZQ_XE",
        "outputId": "57ff74a2-0532-421a-c7ae-909481bcccb6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "ANGER :  0.9953057 <--- Predicted Mood\n",
            "HAPPY :  0.00033384998\n",
            "NEUTRAL :  0.0006059106\n",
            "SAD :  0.0037544717\n"
          ]
        }
      ],
      "source": [
        "# get all prediction values\n",
        "pred = []\n",
        "for i,value in enumerate(prediction[0]):\n",
        "    if i == np.argmax(prediction):\n",
        "        print(CLASSES[i], \": \", value, \"<--- Predicted Mood\")\n",
        "        pred.append(value)\n",
        "    else:\n",
        "         print(CLASSES[i], \": \", value)\n",
        "         pred.append(value)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "NCVmZo63RCt1",
        "outputId": "b1908893-e732-4c86-857d-35ad22c6a1da"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<BarContainer object of 4 artists>"
            ]
          },
          "execution_count": 163,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# plot predicted values\n",
        "plt.bar(CLASSES, pred)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GmvOdcIjbvOl"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
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