{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UNprBgz6jHw4"
      },
      "source": [
        "# Eye Diseases Classification"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uAS6Q4dgjsNP"
      },
      "source": [
        "# 1. Data Loading\n",
        "## 1.1. Import Libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "t76fL_Oti-R1",
        "outputId": "cffc7ecf-581d-4fb9-9d46-078360515f55"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: tensorflow in c:\\users\\fasta\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (2.17.0)\n",
            "Requirement already satisfied: tensorflow-intel==2.17.0 in c:\\users\\fasta\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from tensorflow) (2.17.0)\n",
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            "Requirement already satisfied: gast!=0.5.0,!=0.5.1,!=0.5.2,>=0.2.1 in c:\\users\\fasta\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from tensorflow-intel==2.17.0->tensorflow) (0.6.0)\n",
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            "Requirement already satisfied: ml-dtypes<0.5.0,>=0.3.1 in c:\\users\\fasta\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from tensorflow-intel==2.17.0->tensorflow) (0.4.1)\n",
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            "Requirement already satisfied: packaging in c:\\users\\fasta\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from tensorflow-intel==2.17.0->tensorflow) (24.1)\n",
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            "Requirement already satisfied: setuptools in c:\\users\\fasta\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from tensorflow-intel==2.17.0->tensorflow) (75.1.0)\n",
            "Requirement already satisfied: six>=1.12.0 in c:\\users\\fasta\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from tensorflow-intel==2.17.0->tensorflow) (1.16.0)\n",
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            "Requirement already satisfied: wrapt>=1.11.0 in c:\\users\\fasta\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from tensorflow-intel==2.17.0->tensorflow) (1.16.0)\n",
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            "Requirement already satisfied: tensorboard<2.18,>=2.17 in c:\\users\\fasta\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from tensorflow-intel==2.17.0->tensorflow) (2.17.1)\n",
            "Requirement already satisfied: keras>=3.2.0 in c:\\users\\fasta\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from tensorflow-intel==2.17.0->tensorflow) (3.5.0)\n",
            "Requirement already satisfied: numpy<2.0.0,>=1.26.0 in c:\\users\\fasta\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from tensorflow-intel==2.17.0->tensorflow) (1.26.4)\n",
            "Requirement already satisfied: wheel<1.0,>=0.23.0 in c:\\users\\fasta\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from astunparse>=1.6.0->tensorflow-intel==2.17.0->tensorflow) (0.44.0)\n",
            "Requirement already satisfied: rich in c:\\users\\fasta\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from keras>=3.2.0->tensorflow-intel==2.17.0->tensorflow) (13.8.1)\n",
            "Requirement already satisfied: namex in c:\\users\\fasta\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from keras>=3.2.0->tensorflow-intel==2.17.0->tensorflow) (0.0.8)\n",
            "Requirement already satisfied: optree in c:\\users\\fasta\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from keras>=3.2.0->tensorflow-intel==2.17.0->tensorflow) (0.12.1)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in c:\\users\\fasta\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from requests<3,>=2.21.0->tensorflow-intel==2.17.0->tensorflow) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in c:\\users\\fasta\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from requests<3,>=2.21.0->tensorflow-intel==2.17.0->tensorflow) (3.10)\n",
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            "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\fasta\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from requests<3,>=2.21.0->tensorflow-intel==2.17.0->tensorflow) (2024.8.30)\n",
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            "Requirement already satisfied: tensorboard-data-server<0.8.0,>=0.7.0 in c:\\users\\fasta\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from tensorboard<2.18,>=2.17->tensorflow-intel==2.17.0->tensorflow) (0.7.2)\n",
            "Requirement already satisfied: werkzeug>=1.0.1 in c:\\users\\fasta\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from tensorboard<2.18,>=2.17->tensorflow-intel==2.17.0->tensorflow) (3.0.4)\n",
            "Requirement already satisfied: MarkupSafe>=2.1.1 in c:\\users\\fasta\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from werkzeug>=1.0.1->tensorboard<2.18,>=2.17->tensorflow-intel==2.17.0->tensorflow) (2.1.5)\n",
            "Requirement already satisfied: markdown-it-py>=2.2.0 in c:\\users\\fasta\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from rich->keras>=3.2.0->tensorflow-intel==2.17.0->tensorflow) (3.0.0)\n",
            "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in c:\\users\\fasta\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from rich->keras>=3.2.0->tensorflow-intel==2.17.0->tensorflow) (2.18.0)\n",
            "Requirement already satisfied: mdurl~=0.1 in c:\\users\\fasta\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from markdown-it-py>=2.2.0->rich->keras>=3.2.0->tensorflow-intel==2.17.0->tensorflow) (0.1.2)\n"
          ]
        }
      ],
      "source": [
        "!pip install --upgrade tensorflow\n",
        "import os, shutil\n",
        "import zipfile\n",
        "import random\n",
        "from random import sample\n",
        "import shutil\n",
        "from shutil import copyfile\n",
        "import pathlib\n",
        "from pathlib import Path\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import time\n",
        "from tqdm.notebook import tqdm as tq\n",
        "\n",
        "%matplotlib inline\n",
        "import matplotlib.image as mpimg\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from matplotlib.image import imread\n",
        "\n",
        "import cv2\n",
        "from PIL import Image\n",
        "import skimage\n",
        "from skimage import io\n",
        "from skimage.transform import resize\n",
        "from skimage.transform import rotate, AffineTransform, warp\n",
        "from skimage import img_as_ubyte\n",
        "from skimage.exposure import adjust_gamma\n",
        "from skimage.util import random_noise\n",
        "\n",
        "import keras\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import confusion_matrix, classification_report\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras import Model, layers\n",
        "from tensorflow.keras.preprocessing import image\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator, img_to_array, load_img\n",
        "from tensorflow.keras.optimizers import Adam, RMSprop, SGD\n",
        "from tensorflow.keras.layers import InputLayer, Conv2D, SeparableConv2D, MaxPooling2D, MaxPool2D, Dense, Flatten, Dropout, BatchNormalization\n",
        "from tensorflow.keras.models import Sequential, Model\n",
        "\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "from tensorflow.keras.callbacks import ModelCheckpoint, Callback, EarlyStopping, ReduceLROnPlateau\n",
        "\n",
        "import warnings\n",
        "warnings.simplefilter(action='ignore', category=FutureWarning)\n",
        "\n",
        "import logging\n",
        "logging.getLogger('tensorflow').setLevel(logging.ERROR)\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xUrc_yH59cRR",
        "outputId": "1cbb6125-b211-4ba6-89ad-af297a957c75"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2.17.0\n"
          ]
        }
      ],
      "source": [
        "import tensorflow as tf\n",
        "print(tf.__version__)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rssopwBQk0Qe"
      },
      "source": [
        "## 1.2. Load Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "JW9D2kKPkt6W",
        "outputId": "56debfd5-0a4c-4893-eac6-61f73d396e47"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "'kaggle' is not recognized as an internal or external command,\n",
            "operable program or batch file.\n"
          ]
        }
      ],
      "source": [
        "# Download eye disease dataset from kaggle\n",
        "!kaggle datasets download -d gunavenkatdoddi/eye-diseases-classification"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RTmPLR6Ku7Gh"
      },
      "source": [
        "# 2. Exploratory Data Analysis"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 474
        },
        "id": "Nvi9MX1zny9U",
        "outputId": "137832cb-f670-46fe-bf0b-3857c4372569"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "base_dir = r\"C:\\Users\\fasta\\Downloads\\classification data\\dataset\"\n",
        "\n",
        "label_counts = {}\n",
        "\n",
        "for label_name in os.listdir(base_dir):\n",
        "    label_path = os.path.join(base_dir, label_name)\n",
        "\n",
        "    if os.path.isdir(label_path):\n",
        "        num_images = len([f for f in os.listdir(label_path) if os.path.isfile(os.path.join(label_path, f))])\n",
        "        label_counts[label_name] = num_images\n",
        "\n",
        "df = pd.DataFrame(list(label_counts.items()), columns=['Label', 'Count'])\n",
        "\n",
        "labels = df['Label'].tolist()\n",
        "counts = df['Count'].tolist()\n",
        "\n",
        "colors = plt.cm.viridis(np.linspace(0, 1, len(labels)))\n",
        "\n",
        "plt.figure(figsize=(10, 6))\n",
        "bars = plt.bar(labels, counts, color=colors)\n",
        "\n",
        "plt.xlabel('Labels')\n",
        "plt.ylabel('Number of Images')\n",
        "plt.title('Distribution of Eye Diseases')\n",
        "plt.xticks(rotation=45, ha='right')\n",
        "\n",
        "for bar in bars:\n",
        "    yval = bar.get_height()\n",
        "    plt.text(bar.get_x() + bar.get_width() / 2, yval, int(yval), ha='center', va='bottom')\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "riIpS01gqLuB"
      },
      "outputs": [],
      "source": [
        "def display_sample_img(data_dir, title):\n",
        "  labels = [label for label in os.listdir(data_dir)\n",
        "            if os.path.isdir(os.path.join(data_dir, label))\n",
        "            and label not in ['train', 'test']]\n",
        "\n",
        "  plt.figure(figsize=(12, 6))\n",
        "  plt.suptitle(title, fontsize=16)\n",
        "\n",
        "  for idx, label in enumerate(labels):\n",
        "      label_path = os.path.join(data_dir, label)\n",
        "\n",
        "      if len(os.listdir(label_path)) > 0:\n",
        "          images = os.listdir(label_path)\n",
        "          random_image = random.choice(images)\n",
        "\n",
        "          img_path = os.path.join(label_path, random_image)\n",
        "          img = mpimg.imread(img_path)\n",
        "\n",
        "          plt.subplot(2, len(labels) // 2, idx + 1)\n",
        "          plt.imshow(img)\n",
        "          plt.axis('off')\n",
        "          plt.title(f\"{label}\")\n",
        "\n",
        "  plt.tight_layout()\n",
        "  plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hwUA4KQQZqgR"
      },
      "source": [
        "# 3. Modeling"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pZ1fnYVKZsd7"
      },
      "source": [
        "## 3.1. Data Splitting"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NaKM8mdoOmJ7",
        "outputId": "cb22a746-5c95-4095-b88b-ee54c641ac51"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Series([], dtype: int64)\n"
          ]
        }
      ],
      "source": [
        "dataset_dir = '/content/eye-disease/dataset'\n",
        "desired_labels = ['cataract', 'diabetic_retinopathy', 'glaucoma', 'normal']\n",
        "\n",
        "file_name = []\n",
        "labels = []\n",
        "full_path = []\n",
        "\n",
        "for path, subdirs, files in os.walk(dataset_dir):\n",
        "  for name in files:\n",
        "    label = os.path.basename(path)\n",
        "    if label in desired_labels:\n",
        "      full_path.append(os.path.join(path, name))\n",
        "      labels.append(label)\n",
        "      file_name.append(name)\n",
        "\n",
        "df = pd.DataFrame({\"path\": full_path, \"file_name\": file_name, \"labels\": labels})\n",
        "print(df.groupby(['labels']).size())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "ituyOQr5ap-d"
      },
      "outputs": [
        {
          "ename": "ValueError",
          "evalue": "With n_samples=0, test_size=0.2 and train_size=None, the resulting train set will be empty. Adjust any of the aforementioned parameters.",
          "output_type": "error",
          "traceback": [
            "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "Cell \u001b[1;32mIn[17], line 5\u001b[0m\n\u001b[0;32m      2\u001b[0m y \u001b[38;5;241m=\u001b[39m df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlabels\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[0;32m      3\u001b[0m \u001b[38;5;28mlen\u001b[39m(df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpath\u001b[39m\u001b[38;5;124m'\u001b[39m]) \u001b[38;5;241m==\u001b[39m \u001b[38;5;28mlen\u001b[39m(df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlabels\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[1;32m----> 5\u001b[0m X_train, X_test, y_train, y_test \u001b[38;5;241m=\u001b[39m \u001b[43mtrain_test_split\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m      6\u001b[0m \u001b[43m    \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtest_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0.2\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrandom_state\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m42\u001b[39;49m\n\u001b[0;32m      7\u001b[0m \u001b[43m)\u001b[49m\n\u001b[0;32m      9\u001b[0m df_train \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mDataFrame({\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpath\u001b[39m\u001b[38;5;124m'\u001b[39m: X_train, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlabels\u001b[39m\u001b[38;5;124m'\u001b[39m: y_train, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mset\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtrain\u001b[39m\u001b[38;5;124m'\u001b[39m})\n\u001b[0;32m     10\u001b[0m df_test \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mDataFrame({\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpath\u001b[39m\u001b[38;5;124m'\u001b[39m: X_test, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlabels\u001b[39m\u001b[38;5;124m'\u001b[39m: y_test, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mset\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtest\u001b[39m\u001b[38;5;124m'\u001b[39m})\n",
            "File \u001b[1;32mc:\\Users\\fasta\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\utils\\_param_validation.py:213\u001b[0m, in \u001b[0;36mvalidate_params.<locals>.decorator.<locals>.wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    207\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m    208\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m config_context(\n\u001b[0;32m    209\u001b[0m         skip_parameter_validation\u001b[38;5;241m=\u001b[39m(\n\u001b[0;32m    210\u001b[0m             prefer_skip_nested_validation \u001b[38;5;129;01mor\u001b[39;00m global_skip_validation\n\u001b[0;32m    211\u001b[0m         )\n\u001b[0;32m    212\u001b[0m     ):\n\u001b[1;32m--> 213\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    214\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m InvalidParameterError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m    215\u001b[0m     \u001b[38;5;66;03m# When the function is just a wrapper around an estimator, we allow\u001b[39;00m\n\u001b[0;32m    216\u001b[0m     \u001b[38;5;66;03m# the function to delegate validation to the estimator, but we replace\u001b[39;00m\n\u001b[0;32m    217\u001b[0m     \u001b[38;5;66;03m# the name of the estimator by the name of the function in the error\u001b[39;00m\n\u001b[0;32m    218\u001b[0m     \u001b[38;5;66;03m# message to avoid confusion.\u001b[39;00m\n\u001b[0;32m    219\u001b[0m     msg \u001b[38;5;241m=\u001b[39m re\u001b[38;5;241m.\u001b[39msub(\n\u001b[0;32m    220\u001b[0m         \u001b[38;5;124mr\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mparameter of \u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124mw+ must be\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m    221\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mparameter of \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfunc\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__qualname__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m must be\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m    222\u001b[0m         \u001b[38;5;28mstr\u001b[39m(e),\n\u001b[0;32m    223\u001b[0m     )\n",
            "File \u001b[1;32mc:\\Users\\fasta\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\model_selection\\_split.py:2785\u001b[0m, in \u001b[0;36mtrain_test_split\u001b[1;34m(test_size, train_size, random_state, shuffle, stratify, *arrays)\u001b[0m\n\u001b[0;32m   2782\u001b[0m arrays \u001b[38;5;241m=\u001b[39m indexable(\u001b[38;5;241m*\u001b[39marrays)\n\u001b[0;32m   2784\u001b[0m n_samples \u001b[38;5;241m=\u001b[39m _num_samples(arrays[\u001b[38;5;241m0\u001b[39m])\n\u001b[1;32m-> 2785\u001b[0m n_train, n_test \u001b[38;5;241m=\u001b[39m \u001b[43m_validate_shuffle_split\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   2786\u001b[0m \u001b[43m    \u001b[49m\u001b[43mn_samples\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtest_size\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrain_size\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdefault_test_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0.25\u001b[39;49m\n\u001b[0;32m   2787\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   2789\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m shuffle \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mFalse\u001b[39;00m:\n\u001b[0;32m   2790\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m stratify \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
            "File \u001b[1;32mc:\\Users\\fasta\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\model_selection\\_split.py:2415\u001b[0m, in \u001b[0;36m_validate_shuffle_split\u001b[1;34m(n_samples, test_size, train_size, default_test_size)\u001b[0m\n\u001b[0;32m   2412\u001b[0m n_train, n_test \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mint\u001b[39m(n_train), \u001b[38;5;28mint\u001b[39m(n_test)\n\u001b[0;32m   2414\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m n_train \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m-> 2415\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m   2416\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mWith n_samples=\u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m, test_size=\u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m and train_size=\u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m, the \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   2417\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mresulting train set will be empty. Adjust any of the \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   2418\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124maforementioned parameters.\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(n_samples, test_size, train_size)\n\u001b[0;32m   2419\u001b[0m     )\n\u001b[0;32m   2421\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m n_train, n_test\n",
            "\u001b[1;31mValueError\u001b[0m: With n_samples=0, test_size=0.2 and train_size=None, the resulting train set will be empty. Adjust any of the aforementioned parameters."
          ]
        }
      ],
      "source": [
        "X = df['path']\n",
        "y = df['labels']\n",
        "len(df['path']) == len(df['labels'])\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(\n",
        "    X, y, test_size=0.2, random_state=42\n",
        ")\n",
        "\n",
        "df_train = pd.DataFrame({'path': X_train, 'labels': y_train, 'set': 'train'})\n",
        "df_test = pd.DataFrame({'path': X_test, 'labels': y_test, 'set': 'test'})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-ziFsLkAbTs0",
        "outputId": "852eaf3f-9cba-4cb8-d130-1abc79756716"
      },
      "outputs": [
        {
          "ename": "NameError",
          "evalue": "name 'df_train' is not defined",
          "output_type": "error",
          "traceback": [
            "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "Cell \u001b[1;32mIn[3], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtrain size\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;28mlen\u001b[39m(\u001b[43mdf_train\u001b[49m))\n\u001b[0;32m      2\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtest size\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;28mlen\u001b[39m(df_test))\n",
            "\u001b[1;31mNameError\u001b[0m: name 'df_train' is not defined"
          ]
        }
      ],
      "source": [
        "print('train size', len(df_train))\n",
        "print('test size', len(df_test))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BlnNIF8Gbd9_",
        "outputId": "eda6b3d1-5dea-4e90-e3f0-cd144da29cce"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The data has been moved and separated into train and test.\n"
          ]
        }
      ],
      "source": [
        "datasource_path = \"/content/eye-disease/dataset\"\n",
        "dataset_path = \"/content/eye-disease/dataset\"\n",
        "\n",
        "def move_files(df, source_path, target_path):\n",
        "    for index, row in df.iterrows():\n",
        "        file_path = row['path']\n",
        "        label = row['labels']\n",
        "        file_set = row['set']\n",
        "\n",
        "        dest_dir = os.path.join(target_path, file_set, label)\n",
        "        if not os.path.exists(dest_dir):\n",
        "            os.makedirs(dest_dir)\n",
        "\n",
        "        destination_file_name = os.path.basename(file_path)\n",
        "        file_dest = os.path.join(dest_dir, destination_file_name)\n",
        "\n",
        "        if not os.path.exists(file_dest):\n",
        "            shutil.copy2(file_path, file_dest)\n",
        "\n",
        "move_files(df_train, datasource_path, dataset_path)\n",
        "move_files(df_test, datasource_path, dataset_path)\n",
        "\n",
        "print(\"The data has been moved and separated into train and test.\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kq9YjYsyeCvZ"
      },
      "source": [
        "## 3.2. Data Augmentation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t_KJOZ93csdh",
        "outputId": "a1e8a33f-672c-4706-90cf-ade00ac09e1a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Training Image Counts: {'Normal': 859, 'Diabetic Retinopathy': 848, 'Glaucoma': 824, 'Cataract': 842}\n",
            "Testing Image Counts: {'Normal': 215, 'Diabetic Retinopathy': 250, 'Glaucoma': 183, 'Cataract': 196}\n"
          ]
        }
      ],
      "source": [
        "TRAIN_DIR = '/content/eye-disease/dataset/train'\n",
        "TEST_DIR = '/content/eye-disease/dataset/test'\n",
        "\n",
        "train_normal = os.path.join(TRAIN_DIR, 'normal')\n",
        "train_diabetic_retinopathy = os.path.join(TRAIN_DIR, 'diabetic_retinopathy')\n",
        "train_glaucoma = os.path.join(TRAIN_DIR, 'glaucoma')\n",
        "train_cataract = os.path.join(TRAIN_DIR, 'cataract')\n",
        "\n",
        "test_normal = os.path.join(TEST_DIR, 'normal')\n",
        "test_diabetic_retinopathy = os.path.join(TEST_DIR, 'diabetic_retinopathy')\n",
        "test_glaucoma = os.path.join(TEST_DIR, 'glaucoma')\n",
        "test_cataract = os.path.join(TEST_DIR, 'cataract')\n",
        "\n",
        "def count_images(directory):\n",
        "    return len(os.listdir(directory))\n",
        "\n",
        "train_counts = {\n",
        "    'Normal': count_images(train_normal),\n",
        "    'Diabetic Retinopathy': count_images(train_diabetic_retinopathy),\n",
        "    'Glaucoma': count_images(train_glaucoma),\n",
        "    'Cataract': count_images(train_cataract),\n",
        "}\n",
        "\n",
        "test_counts = {\n",
        "    'Normal': count_images(test_normal),\n",
        "    'Diabetic Retinopathy': count_images(test_diabetic_retinopathy),\n",
        "    'Glaucoma': count_images(test_glaucoma),\n",
        "    'Cataract': count_images(test_cataract),\n",
        "}\n",
        "\n",
        "print(\"Training Image Counts:\", train_counts)\n",
        "print(\"Testing Image Counts:\", test_counts)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FyoPRjn0eTa7",
        "outputId": "e2a4537b-f01d-48ff-c50b-c507098da431"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Found 3373 images belonging to 4 classes.\n",
            "Found 844 images belonging to 4 classes.\n"
          ]
        }
      ],
      "source": [
        "BATCH_SIZE = 32\n",
        "IMG_SIZE = (224, 224)\n",
        "\n",
        "train_datagen = ImageDataGenerator(\n",
        "    rescale=1./255,\n",
        "    rotation_range=10,\n",
        "    horizontal_flip=True,\n",
        "    zoom_range=0.1,\n",
        "    brightness_range=[0.8, 1.2],\n",
        "    fill_mode='nearest',\n",
        ")\n",
        "\n",
        "test_datagen = ImageDataGenerator(\n",
        "    rescale=1./255\n",
        ")\n",
        "\n",
        "train_generator = train_datagen.flow_from_directory(\n",
        "    TRAIN_DIR,\n",
        "    class_mode='categorical',\n",
        "    color_mode=\"rgb\",\n",
        "    target_size=IMG_SIZE,\n",
        "    batch_size=BATCH_SIZE,\n",
        "    shuffle=True,\n",
        ")\n",
        "\n",
        "test_generator = test_datagen.flow_from_directory(\n",
        "    TEST_DIR,\n",
        "    batch_size=BATCH_SIZE,\n",
        "    target_size=IMG_SIZE,\n",
        "    class_mode='categorical',\n",
        "    color_mode=\"rgb\",\n",
        "    shuffle=False,\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NDPIg2KGeaxk",
        "outputId": "a2a151f2-615b-4899-b3ce-16d44b74c4b2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "classes:  {'cataract': 0, 'diabetic_retinopathy': 1, 'glaucoma': 2, 'normal': 3}\n"
          ]
        }
      ],
      "source": [
        "print(\"classes: \", train_generator.class_indices)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gbL02w_mfM8d"
      },
      "source": [
        "## 3.3. Model Architecture"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "WUVOtbp6edww"
      },
      "outputs": [],
      "source": [
        "from tensorflow.keras.applications import MobileNetV2, EfficientNetB0, ResNet50\n",
        "from tensorflow.keras.layers import Input, Dense, Dropout, AveragePooling2D, GlobalAveragePooling2D, BatchNormalization, Conv2D, MaxPooling2D\n",
        "from tensorflow.keras.callbacks import LearningRateScheduler, EarlyStopping, ModelCheckpoint\n",
        "from tensorflow.keras.models import Model, Sequential\n",
        "from tensorflow.keras.optimizers import Adamax, Adam\n",
        "from tensorflow.keras import regularizers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HSSrMca9fcmJ",
        "outputId": "d139d98f-8a8b-4518-c5b8-b85bd67808b9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/keras-applications/efficientnetb0_notop.h5\n",
            "\u001b[1m16705208/16705208\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0us/step\n"
          ]
        }
      ],
      "source": [
        "input_shape = (224, 224, 3)\n",
        "classes = 4\n",
        "\n",
        "base_model = EfficientNetB0(weights='imagenet', include_top=False, input_shape=input_shape)\n",
        "base_model.trainable = True\n",
        "\n",
        "model = Sequential([\n",
        "    base_model,\n",
        "    BatchNormalization(),\n",
        "    Conv2D(512, (3, 3), activation='relu', padding='same'),\n",
        "    GlobalAveragePooling2D(),\n",
        "    Dense(256, activation='relu'),\n",
        "    BatchNormalization(),\n",
        "    Dropout(0.4),\n",
        "    Dense(classes, activation='softmax')\n",
        "])\n",
        "\n",
        "optimizer = tf.keras.optimizers.Adam(learning_rate=1e-3, clipnorm=1.0)\n",
        "model.compile(optimizer=optimizer, loss='categorical_crossentropy', metrics=['accuracy'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v6WruQoHELAn"
      },
      "source": [
        "## 3.4. Model Training"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "0e-nKEmCgYx-"
      },
      "outputs": [],
      "source": [
        "def cyclic_lr(epoch, lr):\n",
        "    base_lr = 1e-5\n",
        "    max_lr = 1e-3\n",
        "    step_size = 2000\n",
        "    cycle = np.floor(1 + epoch / (2 * step_size))\n",
        "    x = np.abs(epoch / step_size - 2 * cycle + 1)\n",
        "    lr = base_lr + (max_lr - base_lr) * max(0, (1 - x))\n",
        "    return lr"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "lUEbYClYrZ2w"
      },
      "outputs": [],
      "source": [
        "class stopTraining(tf.keras.callbacks.Callback):\n",
        "  def on_epoch_end(self, epoch, logs={}):\n",
        "    val_accuracy = logs.get('val_accuracy')\n",
        "    val_loss = logs.get('val_loss')\n",
        "\n",
        "    if val_accuracy is not None and val_loss is not None:\n",
        "      if val_accuracy >= 0.93 and val_loss < 0.13:\n",
        "        print('Akurasi di atas 93%, stop training')\n",
        "        self.model.stop_training = True\n",
        "\n",
        "stop_train = stopTraining()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "jNfgcfVqiG0R"
      },
      "outputs": [],
      "source": [
        "callbacks = [\n",
        "    stop_train,\n",
        "    LearningRateScheduler(cyclic_lr),\n",
        "    EarlyStopping(monitor='val_loss', patience=30, restore_best_weights=True),\n",
        "    ModelCheckpoint(filepath='best_model_1.keras', monitor='val_loss', save_best_only=True, verbose=1, mode='min')\n",
        "]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5ZC1VpYFrmTk",
        "outputId": "a875309d-7097-475d-e90a-86010a32c9ea"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Class Weights: {0: 1.0014845605700713, 1: 0.9943985849056604, 2: 1.0233616504854368, 3: 0.9816647264260768}\n"
          ]
        }
      ],
      "source": [
        "count_normal = len(os.listdir(train_normal))\n",
        "count_diabetic_retinopathy = len(os.listdir(train_diabetic_retinopathy))\n",
        "count_glaucoma = len(os.listdir(train_glaucoma))\n",
        "count_cataract = len(os.listdir(train_cataract))\n",
        "\n",
        "total_images = (count_normal + count_diabetic_retinopathy + count_glaucoma + count_cataract)\n",
        "\n",
        "weight_normal = (1 / count_normal) * total_images / 4\n",
        "weight_diabetic_retinopathy = (1 / count_diabetic_retinopathy) * total_images / 4\n",
        "weight_glaucoma = (1 / count_glaucoma) * total_images / 4\n",
        "weight_cataract = (1 / count_cataract) * total_images / 4\n",
        "\n",
        "class_weights = {\n",
        "    0: weight_cataract,\n",
        "    1: weight_diabetic_retinopathy,\n",
        "    2: weight_glaucoma,\n",
        "    3: weight_normal\n",
        "}\n",
        "\n",
        "print(\"Class Weights:\", class_weights)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gUWhFCrwr018",
        "outputId": "c8b7859e-2b5b-407f-ddf4-e06914b0338b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/150\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1s/step - accuracy: 0.4509 - loss: 1.3880\n",
            "Epoch 1: val_loss improved from inf to 1.38577, saving model to best_model_1.keras\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m257s\u001b[0m 1s/step - accuracy: 0.4521 - loss: 1.3851 - val_accuracy: 0.2547 - val_loss: 1.3858 - learning_rate: 1.0000e-05\n",
            "Epoch 2/150\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 570ms/step - accuracy: 0.7345 - loss: 0.7217\n",
            "Epoch 2: val_loss did not improve from 1.38577\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m120s\u001b[0m 624ms/step - accuracy: 0.7345 - loss: 0.7216 - val_accuracy: 0.3175 - val_loss: 1.4048 - learning_rate: 1.0495e-05\n",
            "Epoch 3/150\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 578ms/step - accuracy: 0.7574 - loss: 0.6503\n",
            "Epoch 3: val_loss did not improve from 1.38577\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m70s\u001b[0m 623ms/step - accuracy: 0.7576 - loss: 0.6500 - val_accuracy: 0.1754 - val_loss: 1.6777 - learning_rate: 1.0990e-05\n",
            "Epoch 4/150\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 592ms/step - accuracy: 0.7987 - loss: 0.5247\n",
            "Epoch 4: val_loss did not improve from 1.38577\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m85s\u001b[0m 648ms/step - accuracy: 0.7987 - loss: 0.5248 - val_accuracy: 0.2903 - val_loss: 1.6853 - learning_rate: 1.1485e-05\n",
            "Epoch 5/150\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 587ms/step - accuracy: 0.8177 - loss: 0.4836\n",
            "Epoch 5: val_loss did not improve from 1.38577\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m71s\u001b[0m 636ms/step - accuracy: 0.8178 - loss: 0.4834 - val_accuracy: 0.3791 - val_loss: 1.8033 - learning_rate: 1.1980e-05\n",
            "Epoch 6/150\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 582ms/step - accuracy: 0.8431 - loss: 0.4367\n",
            "Epoch 6: val_loss improved from 1.38577 to 1.23385, saving model to best_model_1.keras\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m73s\u001b[0m 650ms/step - accuracy: 0.8431 - loss: 0.4366 - val_accuracy: 0.5735 - val_loss: 1.2339 - learning_rate: 1.2475e-05\n",
            "Epoch 7/150\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 564ms/step - accuracy: 0.8514 - loss: 0.3922\n",
            "Epoch 7: val_loss improved from 1.23385 to 0.68671, saving model to best_model_1.keras\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m82s\u001b[0m 631ms/step - accuracy: 0.8514 - loss: 0.3922 - val_accuracy: 0.7476 - val_loss: 0.6867 - learning_rate: 1.2970e-05\n",
            "Epoch 8/150\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 587ms/step - accuracy: 0.8555 - loss: 0.3718\n",
            "Epoch 8: val_loss improved from 0.68671 to 0.59789, saving model to best_model_1.keras\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m74s\u001b[0m 669ms/step - accuracy: 0.8555 - loss: 0.3717 - val_accuracy: 0.8152 - val_loss: 0.5979 - learning_rate: 1.3465e-05\n",
            "Epoch 9/150\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 580ms/step - accuracy: 0.8820 - loss: 0.3319\n",
            "Epoch 9: val_loss improved from 0.59789 to 0.43301, saving model to best_model_1.keras\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m80s\u001b[0m 644ms/step - accuracy: 0.8820 - loss: 0.3320 - val_accuracy: 0.8519 - val_loss: 0.4330 - learning_rate: 1.3960e-05\n",
            "Epoch 10/150\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 576ms/step - accuracy: 0.8916 - loss: 0.3001\n",
            "Epoch 10: val_loss improved from 0.43301 to 0.41191, saving model to best_model_1.keras\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m87s\u001b[0m 682ms/step - accuracy: 0.8916 - loss: 0.3001 - val_accuracy: 0.8614 - val_loss: 0.4119 - learning_rate: 1.4455e-05\n",
            "Epoch 11/150\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 573ms/step - accuracy: 0.8845 - loss: 0.3285\n",
            "Epoch 11: val_loss did not improve from 0.41191\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m75s\u001b[0m 626ms/step - accuracy: 0.8845 - loss: 0.3283 - val_accuracy: 0.8578 - val_loss: 0.4521 - learning_rate: 1.4950e-05\n",
            "Epoch 12/150\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 582ms/step - accuracy: 0.8970 - loss: 0.2901\n",
            "Epoch 12: val_loss improved from 0.41191 to 0.39919, saving model to best_model_1.keras\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m73s\u001b[0m 660ms/step - accuracy: 0.8970 - loss: 0.2900 - val_accuracy: 0.8720 - val_loss: 0.3992 - learning_rate: 1.5445e-05\n",
            "Epoch 13/150\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 576ms/step - accuracy: 0.9040 - loss: 0.2635\n",
            "Epoch 13: val_loss did not improve from 0.39919\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m85s\u001b[0m 674ms/step - accuracy: 0.9040 - loss: 0.2635 - val_accuracy: 0.8590 - val_loss: 0.4479 - learning_rate: 1.5940e-05\n",
            "Epoch 14/150\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 590ms/step - accuracy: 0.9050 - loss: 0.2668\n",
            "Epoch 14: val_loss did not improve from 0.39919\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m71s\u001b[0m 636ms/step - accuracy: 0.9051 - loss: 0.2668 - val_accuracy: 0.8578 - val_loss: 0.4422 - learning_rate: 1.6435e-05\n",
            "Epoch 15/150\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 571ms/step - accuracy: 0.9128 - loss: 0.2375\n",
            "Epoch 15: val_loss did not improve from 0.39919\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m83s\u001b[0m 626ms/step - accuracy: 0.9128 - loss: 0.2375 - val_accuracy: 0.8732 - val_loss: 0.4272 - learning_rate: 1.6930e-05\n",
            "Epoch 16/150\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 574ms/step - accuracy: 0.9214 - loss: 0.2189\n",
            "Epoch 16: val_loss improved from 0.39919 to 0.39584, saving model to best_model_1.keras\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m87s\u001b[0m 687ms/step - accuracy: 0.9214 - loss: 0.2188 - val_accuracy: 0.8815 - val_loss: 0.3958 - learning_rate: 1.7425e-05\n",
            "Epoch 17/150\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 577ms/step - accuracy: 0.9191 - loss: 0.2133\n",
            "Epoch 17: val_loss improved from 0.39584 to 0.38287, saving model to best_model_1.keras\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m80s\u001b[0m 675ms/step - accuracy: 0.9191 - loss: 0.2133 - val_accuracy: 0.8886 - val_loss: 0.3829 - learning_rate: 1.7920e-05\n",
            "Epoch 18/150\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 566ms/step - accuracy: 0.9320 - loss: 0.1879\n",
            "Epoch 18: val_loss did not improve from 0.38287\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m71s\u001b[0m 623ms/step - accuracy: 0.9320 - loss: 0.1879 - val_accuracy: 0.8851 - val_loss: 0.3878 - learning_rate: 1.8415e-05\n",
            "Epoch 19/150\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 584ms/step - accuracy: 0.9373 - loss: 0.1768\n",
            "Epoch 19: val_loss improved from 0.38287 to 0.37155, saving model to best_model_1.keras\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m72s\u001b[0m 648ms/step - accuracy: 0.9373 - loss: 0.1768 - val_accuracy: 0.8957 - val_loss: 0.3716 - learning_rate: 1.8910e-05\n",
            "Epoch 20/150\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 586ms/step - accuracy: 0.9444 - loss: 0.1498\n",
            "Epoch 20: val_loss did not improve from 0.37155\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m81s\u001b[0m 635ms/step - accuracy: 0.9443 - loss: 0.1499 - val_accuracy: 0.8815 - val_loss: 0.3925 - learning_rate: 1.9405e-05\n",
            "Epoch 21/150\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 564ms/step - accuracy: 0.9367 - loss: 0.1575\n",
            "Epoch 21: val_loss did not improve from 0.37155\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m82s\u001b[0m 621ms/step - accuracy: 0.9367 - loss: 0.1576 - val_accuracy: 0.8863 - val_loss: 0.4085 - learning_rate: 1.9900e-05\n",
            "Epoch 22/150\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 579ms/step - accuracy: 0.9406 - loss: 0.1591\n",
            "Epoch 22: val_loss did not improve from 0.37155\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m81s\u001b[0m 627ms/step - accuracy: 0.9406 - loss: 0.1591 - val_accuracy: 0.8863 - val_loss: 0.3896 - learning_rate: 2.0395e-05\n",
            "Epoch 23/150\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 583ms/step - accuracy: 0.9527 - loss: 0.1391\n",
            "Epoch 23: val_loss did not improve from 0.37155\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m70s\u001b[0m 629ms/step - accuracy: 0.9527 - loss: 0.1391 - val_accuracy: 0.8886 - val_loss: 0.3772 - learning_rate: 2.0890e-05\n",
            "Epoch 24/150\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 571ms/step - accuracy: 0.9556 - loss: 0.1234\n",
            "Epoch 24: val_loss improved from 0.37155 to 0.36033, saving model to best_model_1.keras\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m88s\u001b[0m 680ms/step - accuracy: 0.9555 - loss: 0.1235 - val_accuracy: 0.8934 - val_loss: 0.3603 - learning_rate: 2.1385e-05\n",
            "Epoch 25/150\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 574ms/step - accuracy: 0.9531 - loss: 0.1218\n",
            "Epoch 25: val_loss did not improve from 0.36033\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m72s\u001b[0m 629ms/step - accuracy: 0.9531 - loss: 0.1218 - val_accuracy: 0.8922 - val_loss: 0.3609 - learning_rate: 2.1880e-05\n",
            "Epoch 26/150\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 577ms/step - accuracy: 0.9593 - loss: 0.1163\n",
            "Epoch 26: val_loss did not improve from 0.36033\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m75s\u001b[0m 674ms/step - accuracy: 0.9593 - loss: 0.1163 - val_accuracy: 0.8839 - val_loss: 0.4173 - learning_rate: 2.2375e-05\n",
            "Epoch 27/150\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 579ms/step - accuracy: 0.9558 - loss: 0.1191\n",
            "Epoch 27: val_loss did not improve from 0.36033\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m76s\u001b[0m 676ms/step - accuracy: 0.9558 - loss: 0.1191 - val_accuracy: 0.8886 - val_loss: 0.4140 - learning_rate: 2.2870e-05\n",
            "Epoch 28/150\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 571ms/step - accuracy: 0.9622 - loss: 0.1007\n",
            "Epoch 28: val_loss did not improve from 0.36033\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m77s\u001b[0m 625ms/step - accuracy: 0.9622 - loss: 0.1009 - val_accuracy: 0.8993 - val_loss: 0.3820 - learning_rate: 2.3365e-05\n",
            "Epoch 29/150\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 588ms/step - accuracy: 0.9626 - loss: 0.1021\n",
            "Epoch 29: val_loss did not improve from 0.36033\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m71s\u001b[0m 635ms/step - accuracy: 0.9625 - loss: 0.1021 - val_accuracy: 0.8910 - val_loss: 0.4019 - learning_rate: 2.3860e-05\n",
            "Epoch 30/150\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 574ms/step - accuracy: 0.9618 - loss: 0.1011\n",
            "Epoch 30: val_loss did not improve from 0.36033\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m76s\u001b[0m 671ms/step - accuracy: 0.9617 - loss: 0.1012 - val_accuracy: 0.9028 - val_loss: 0.4333 - learning_rate: 2.4355e-05\n",
            "Epoch 31/150\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 583ms/step - accuracy: 0.9691 - loss: 0.0853\n",
            "Epoch 31: val_loss did not improve from 0.36033\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m71s\u001b[0m 630ms/step - accuracy: 0.9691 - loss: 0.0855 - val_accuracy: 0.9040 - val_loss: 0.4072 - learning_rate: 2.4850e-05\n",
            "Epoch 32/150\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 569ms/step - accuracy: 0.9686 - loss: 0.0979\n",
            "Epoch 32: val_loss did not improve from 0.36033\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m82s\u001b[0m 621ms/step - accuracy: 0.9686 - loss: 0.0980 - val_accuracy: 0.9017 - val_loss: 0.3653 - learning_rate: 2.5345e-05\n",
            "Epoch 33/150\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 571ms/step - accuracy: 0.9681 - loss: 0.0832\n",
            "Epoch 33: val_loss did not improve from 0.36033\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m82s\u001b[0m 627ms/step - accuracy: 0.9681 - loss: 0.0832 - val_accuracy: 0.9040 - val_loss: 0.3904 - learning_rate: 2.5840e-05\n",
            "Epoch 34/150\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 586ms/step - accuracy: 0.9747 - loss: 0.0707\n",
            "Epoch 34: val_loss did not improve from 0.36033\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m71s\u001b[0m 632ms/step - accuracy: 0.9747 - loss: 0.0708 - val_accuracy: 0.9028 - val_loss: 0.4473 - learning_rate: 2.6335e-05\n",
            "Epoch 35/150\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 581ms/step - accuracy: 0.9723 - loss: 0.0739\n",
            "Epoch 35: val_loss did not improve from 0.36033\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m71s\u001b[0m 630ms/step - accuracy: 0.9723 - loss: 0.0740 - val_accuracy: 0.9017 - val_loss: 0.4492 - learning_rate: 2.6830e-05\n",
            "Epoch 36/150\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 583ms/step - accuracy: 0.9697 - loss: 0.0761\n",
            "Epoch 36: val_loss did not improve from 0.36033\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m83s\u001b[0m 636ms/step - accuracy: 0.9697 - loss: 0.0761 - val_accuracy: 0.9076 - val_loss: 0.3912 - learning_rate: 2.7325e-05\n",
            "Epoch 37/150\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 592ms/step - accuracy: 0.9772 - loss: 0.0611\n",
            "Epoch 37: val_loss did not improve from 0.36033\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m71s\u001b[0m 639ms/step - accuracy: 0.9771 - loss: 0.0612 - val_accuracy: 0.8934 - val_loss: 0.4424 - learning_rate: 2.7820e-05\n",
            "Epoch 38/150\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 593ms/step - accuracy: 0.9738 - loss: 0.0851\n",
            "Epoch 38: val_loss did not improve from 0.36033\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m73s\u001b[0m 645ms/step - accuracy: 0.9739 - loss: 0.0850 - val_accuracy: 0.9005 - val_loss: 0.3865 - learning_rate: 2.8315e-05\n",
            "Epoch 39/150\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 573ms/step - accuracy: 0.9789 - loss: 0.0570\n",
            "Epoch 39: val_loss improved from 0.36033 to 0.33830, saving model to best_model_1.keras\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m76s\u001b[0m 680ms/step - accuracy: 0.9789 - loss: 0.0571 - val_accuracy: 0.9064 - val_loss: 0.3383 - learning_rate: 2.8810e-05\n",
            "Epoch 40/150\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 584ms/step - accuracy: 0.9757 - loss: 0.0688\n",
            "Epoch 40: val_loss did not improve from 0.33830\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m72s\u001b[0m 640ms/step - accuracy: 0.9757 - loss: 0.0687 - val_accuracy: 0.8969 - val_loss: 0.4041 - learning_rate: 2.9305e-05\n",
            "Epoch 41/150\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 582ms/step - accuracy: 0.9788 - loss: 0.0604\n",
            "Epoch 41: val_loss did not improve from 0.33830\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m80s\u001b[0m 630ms/step - accuracy: 0.9788 - loss: 0.0604 - val_accuracy: 0.9171 - val_loss: 0.3441 - learning_rate: 2.9800e-05\n",
            "Epoch 42/150\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 596ms/step - accuracy: 0.9820 - loss: 0.0543\n",
            "Epoch 42: val_loss did not improve from 0.33830\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m84s\u001b[0m 645ms/step - accuracy: 0.9820 - loss: 0.0543 - val_accuracy: 0.9159 - val_loss: 0.3516 - learning_rate: 3.0295e-05\n",
            "Epoch 43/150\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 576ms/step - accuracy: 0.9806 - loss: 0.0579\n",
            "Epoch 43: val_loss did not improve from 0.33830\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m71s\u001b[0m 630ms/step - accuracy: 0.9806 - loss: 0.0579 - val_accuracy: 0.9088 - val_loss: 0.3640 - learning_rate: 3.0790e-05\n",
            "Epoch 44/150\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 595ms/step - accuracy: 0.9776 - loss: 0.0588\n",
            "Epoch 44: val_loss did not improve from 0.33830\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m72s\u001b[0m 644ms/step - accuracy: 0.9777 - loss: 0.0588 - val_accuracy: 0.9028 - val_loss: 0.3948 - learning_rate: 3.1285e-05\n",
            "Epoch 45/150\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 578ms/step - accuracy: 0.9767 - loss: 0.0627\n",
            "Epoch 45: val_loss did not improve from 0.33830\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m72s\u001b[0m 634ms/step - accuracy: 0.9767 - loss: 0.0626 - val_accuracy: 0.9017 - val_loss: 0.3989 - learning_rate: 3.1780e-05\n",
            "Epoch 46/150\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 601ms/step - accuracy: 0.9793 - loss: 0.0556\n",
            "Epoch 46: val_loss did not improve from 0.33830\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m83s\u001b[0m 646ms/step - accuracy: 0.9792 - loss: 0.0556 - val_accuracy: 0.9088 - val_loss: 0.3990 - learning_rate: 3.2275e-05\n",
            "Epoch 47/150\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 579ms/step - accuracy: 0.9813 - loss: 0.0499\n",
            "Epoch 47: val_loss did not improve from 0.33830\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m70s\u001b[0m 626ms/step - accuracy: 0.9813 - loss: 0.0500 - val_accuracy: 0.9159 - val_loss: 0.3882 - learning_rate: 3.2770e-05\n",
            "Epoch 48/150\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 589ms/step - accuracy: 0.9898 - loss: 0.0252\n",
            "Epoch 48: val_loss did not improve from 0.33830\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m84s\u001b[0m 645ms/step - accuracy: 0.9898 - loss: 0.0252 - val_accuracy: 0.9111 - val_loss: 0.4260 - learning_rate: 3.3265e-05\n",
            "Epoch 49/150\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 582ms/step - accuracy: 0.9856 - loss: 0.0366\n",
            "Epoch 49: val_loss did not improve from 0.33830\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m70s\u001b[0m 628ms/step - accuracy: 0.9856 - loss: 0.0367 - val_accuracy: 0.9159 - val_loss: 0.3669 - learning_rate: 3.3760e-05\n",
            "Epoch 50/150\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 597ms/step - accuracy: 0.9886 - loss: 0.0360\n",
            "Epoch 50: val_loss did not improve from 0.33830\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m72s\u001b[0m 648ms/step - accuracy: 0.9886 - loss: 0.0361 - val_accuracy: 0.9076 - val_loss: 0.4154 - learning_rate: 3.4255e-05\n",
            "Epoch 51/150\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 569ms/step - accuracy: 0.9853 - loss: 0.0427\n",
            "Epoch 51: val_loss did not improve from 0.33830\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m81s\u001b[0m 626ms/step - accuracy: 0.9853 - loss: 0.0427 - val_accuracy: 0.9218 - val_loss: 0.3836 - learning_rate: 3.4750e-05\n",
            "Epoch 52/150\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 582ms/step - accuracy: 0.9871 - loss: 0.0348\n",
            "Epoch 52: val_loss did not improve from 0.33830\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m81s\u001b[0m 626ms/step - accuracy: 0.9871 - loss: 0.0348 - val_accuracy: 0.9159 - val_loss: 0.3792 - learning_rate: 3.5245e-05\n",
            "Epoch 53/150\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 578ms/step - accuracy: 0.9817 - loss: 0.0538\n",
            "Epoch 53: val_loss did not improve from 0.33830\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m82s\u001b[0m 627ms/step - accuracy: 0.9817 - loss: 0.0537 - val_accuracy: 0.9159 - val_loss: 0.3780 - learning_rate: 3.5740e-05\n",
            "Epoch 54/150\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 585ms/step - accuracy: 0.9871 - loss: 0.0438\n",
            "Epoch 54: val_loss did not improve from 0.33830\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m73s\u001b[0m 642ms/step - accuracy: 0.9871 - loss: 0.0438 - val_accuracy: 0.9159 - val_loss: 0.3710 - learning_rate: 3.6235e-05\n",
            "Epoch 55/150\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 566ms/step - accuracy: 0.9856 - loss: 0.0459\n",
            "Epoch 55: val_loss did not improve from 0.33830\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m79s\u001b[0m 621ms/step - accuracy: 0.9856 - loss: 0.0459 - val_accuracy: 0.9182 - val_loss: 0.3449 - learning_rate: 3.6730e-05\n",
            "Epoch 56/150\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 591ms/step - accuracy: 0.9839 - loss: 0.0495\n",
            "Epoch 56: val_loss did not improve from 0.33830\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m83s\u001b[0m 640ms/step - accuracy: 0.9839 - loss: 0.0494 - val_accuracy: 0.9159 - val_loss: 0.3635 - learning_rate: 3.7225e-05\n",
            "Epoch 57/150\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 575ms/step - accuracy: 0.9852 - loss: 0.0396\n",
            "Epoch 57: val_loss did not improve from 0.33830\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m71s\u001b[0m 628ms/step - accuracy: 0.9852 - loss: 0.0397 - val_accuracy: 0.9218 - val_loss: 0.3460 - learning_rate: 3.7720e-05\n",
            "Epoch 58/150\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 587ms/step - accuracy: 0.9885 - loss: 0.0354\n",
            "Epoch 58: val_loss did not improve from 0.33830\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m74s\u001b[0m 665ms/step - accuracy: 0.9885 - loss: 0.0355 - val_accuracy: 0.9182 - val_loss: 0.3730 - learning_rate: 3.8215e-05\n",
            "Epoch 59/150\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 627ms/step - accuracy: 0.9923 - loss: 0.0230\n",
            "Epoch 59: val_loss did not improve from 0.33830\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m89s\u001b[0m 725ms/step - accuracy: 0.9923 - loss: 0.0231 - val_accuracy: 0.9017 - val_loss: 0.4407 - learning_rate: 3.8710e-05\n",
            "Epoch 60/150\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 771ms/step - accuracy: 0.9894 - loss: 0.0309\n",
            "Epoch 60: val_loss did not improve from 0.33830\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m95s\u001b[0m 869ms/step - accuracy: 0.9893 - loss: 0.0309 - val_accuracy: 0.9135 - val_loss: 0.4197 - learning_rate: 3.9205e-05\n",
            "Epoch 61/150\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 703ms/step - accuracy: 0.9864 - loss: 0.0332\n",
            "Epoch 61: val_loss did not improve from 0.33830\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m136s\u001b[0m 801ms/step - accuracy: 0.9864 - loss: 0.0332 - val_accuracy: 0.9230 - val_loss: 0.4008 - learning_rate: 3.9700e-05\n",
            "Epoch 62/150\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 608ms/step - accuracy: 0.9875 - loss: 0.0339\n",
            "Epoch 62: val_loss did not improve from 0.33830\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m78s\u001b[0m 705ms/step - accuracy: 0.9875 - loss: 0.0340 - val_accuracy: 0.9100 - val_loss: 0.3765 - learning_rate: 4.0195e-05\n",
            "Epoch 63/150\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 581ms/step - accuracy: 0.9920 - loss: 0.0263\n",
            "Epoch 63: val_loss did not improve from 0.33830\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m70s\u001b[0m 628ms/step - accuracy: 0.9920 - loss: 0.0264 - val_accuracy: 0.9171 - val_loss: 0.3575 - learning_rate: 4.0690e-05\n",
            "Epoch 64/150\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 592ms/step - accuracy: 0.9922 - loss: 0.0303\n",
            "Epoch 64: val_loss did not improve from 0.33830\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m85s\u001b[0m 649ms/step - accuracy: 0.9922 - loss: 0.0303 - val_accuracy: 0.9135 - val_loss: 0.3871 - learning_rate: 4.1185e-05\n",
            "Epoch 65/150\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 581ms/step - accuracy: 0.9911 - loss: 0.0247\n",
            "Epoch 65: val_loss did not improve from 0.33830\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m81s\u001b[0m 641ms/step - accuracy: 0.9911 - loss: 0.0248 - val_accuracy: 0.9159 - val_loss: 0.3837 - learning_rate: 4.1680e-05\n",
            "Epoch 66/150\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 593ms/step - accuracy: 0.9906 - loss: 0.0367\n",
            "Epoch 66: val_loss did not improve from 0.33830\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m82s\u001b[0m 639ms/step - accuracy: 0.9906 - loss: 0.0366 - val_accuracy: 0.9194 - val_loss: 0.4326 - learning_rate: 4.2175e-05\n",
            "Epoch 67/150\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 595ms/step - accuracy: 0.9866 - loss: 0.0372\n",
            "Epoch 67: val_loss did not improve from 0.33830\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m73s\u001b[0m 653ms/step - accuracy: 0.9866 - loss: 0.0372 - val_accuracy: 0.9289 - val_loss: 0.4084 - learning_rate: 4.2670e-05\n",
            "Epoch 68/150\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 610ms/step - accuracy: 0.9875 - loss: 0.0333\n",
            "Epoch 68: val_loss did not improve from 0.33830\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m73s\u001b[0m 659ms/step - accuracy: 0.9876 - loss: 0.0333 - val_accuracy: 0.9135 - val_loss: 0.3669 - learning_rate: 4.3165e-05\n",
            "Epoch 69/150\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 589ms/step - accuracy: 0.9910 - loss: 0.0344\n",
            "Epoch 69: val_loss did not improve from 0.33830\n",
            "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m71s\u001b[0m 637ms/step - accuracy: 0.9910 - loss: 0.0343 - val_accuracy: 0.9182 - val_loss: 0.3470 - learning_rate: 4.3660e-05\n"
          ]
        }
      ],
      "source": [
        "history = model.fit(train_generator,\n",
        "                    epochs = 150,\n",
        "                    validation_data=test_generator,\n",
        "                    class_weight = class_weights,\n",
        "                    callbacks=callbacks\n",
        "                  )"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bwbUtnN2EQH9"
      },
      "source": [
        "## 3.5. Model Evalation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vf2dRNBDsbdK",
        "outputId": "77ad9ac0-4133-4e03-caf7-3938fa3a98c5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 187ms/step - accuracy: 0.9074 - loss: 0.3438\n",
            "Modev Evaluation\n",
            "Final Training Accuracy: 0.99\n",
            "Final Validation Accuracy: 0.92\n",
            "Test Loss: 0.34, Test Accuracy: 0.91\n"
          ]
        }
      ],
      "source": [
        "test_generator.reset()\n",
        "\n",
        "train_acc = history.history['accuracy'][-1]\n",
        "val_acc = history.history['val_accuracy'][-1]\n",
        "test_loss, test_accuracy = model.evaluate(test_generator)\n",
        "\n",
        "print('Modev Evaluation')\n",
        "print(f\"Final Training Accuracy: {train_acc:.2f}\")\n",
        "print(f\"Final Validation Accuracy: {val_acc:.2f}\")\n",
        "print(f'Test Loss: {test_loss:.2f}, Test Accuracy: {test_accuracy:.2f}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 512
        },
        "id": "rOa8-ml5U9vf",
        "outputId": "78541996-1674-4200-fcc2-589b3041ace5"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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33jjjmPr06UNBQQHvvvuue5vT6Sz3RWm/fv0IDAzknXfeYdasWVx//fX4+/ufMvbly5ezbNmycsfcvXt3fH19eeutt4od7/XXXy+xr81mK9Er8/XXX5OUlFRsW9HaQ2Upw96nTx+cTif//e9/i21/7bXXsFgsZZ6vdjqfffYZDRo04J577uHGG28sdhsxYgTBwcHu4Xo33HADhmEwduzYEscpev/9+vXDarUybty4Er0+x39GDRs2LDZfDeCDDz44aY9TaUr73N96660Sx7jhhhtYs2YN33333UnjLnLHHXcwd+5cXn/9dcLDwyvscxaRqkk9TiIip3DJJZdQs2ZNBg0axIMPPojFYuHTTz89r8OZTmfMmDHMnTuXSy+9lHvvvdd9Ad6yZUtWr159ytc2bdqUhg0bMmLECJKSkggNDeXbb78t01yZk+nbty+XXnopTzzxBDt37qR58+ZMnz693PN/goOD6devn3ue04kloq+99lqmT59O//79ueaaa0hMTOS9996jefPmZGZmlutcRetRvfDCC1x77bX06dOHv/76i1mzZpXombn22msZN24cQ4YM4ZJLLmHdunV8/vnnxXqqwEwWatSowXvvvUdISAhBQUF06tSp1Pk7ffv2pVu3bjz11FPs3LmTNm3aMHfuXL7//nsefvjhYoUgztS+ffv45ZdfShSgKGK32+nVqxdff/01b775Jt26deOOO+7gzTffZOvWrVx99dW4XC5+++03unXrxrBhw2jUqBFPPfUUzz77LF26dOH666/Hbrfzxx9/EBsb614P6a677uKee+7hhhtuoEePHqxZs4Y5c+aU+GxP5dprr+XTTz8lLCyM5s2bs2zZMubPn1+i/Pq///1vvvnmG2666SbuvPNO2rdvz5EjR/jhhx947733aNOmjXvf2267jccee4zvvvuOe++91+MLE4uId1OPk4jIKYSHh/O///2P2rVr8/TTT/PKK6/Qo0cPXnrpJU+H5ta+fXtmzZpFzZo1eeaZZ5g4cSLjxo3jqquuKtZDUxpfX19+/PFH2rZtywsvvMDYsWNp3Lgxn3zyyRnHY7Va+eGHH7j99tv57LPPeOqpp4iLi+Pjjz8u97GKkqXatWtz5ZVXFvvb4MGDGT9+PGvWrOHBBx9kzpw5fPbZZ+71hcrrueeeY+zYsfz111/8+9//Zvv27cydO9fdc1TkySef5NFHH2XOnDk89NBDrFq1ip9++on4+Phi+/n6+vLxxx9js9m45557GDBgAL/++mup5y76zB5++GH+97//8fDDD7Nx40ZefvllJkyYcEbv50RffvklLper2HDHE/Xt25fDhw+7eysnT57Myy+/TGJiIv/+978ZP348OTk5xdajGjduHJMmTSInJ4ennnqKUaNGsWvXLq666ir3PnfffTePP/44ixYt4tFHHyUxMZF58+aV+GxP5Y033mDgwIF8/vnnPProo+zfv5/58+eXKEISHBzMb7/9xr333svMmTN58MEHeeedd2jSpIl7Md0i0dHR7rWm7rjjjjLHIiLVk8Xwpp9NRUSkwvTr148NGzawdetWT4ci4rX69+/PunXryjQnUESqN/U4iYhUATk5OcWeb926lZkzZ3LFFVd4JiCRSmD//v389NNP6m0SkTJRj5OISBVQu3ZtBg8eTIMGDdi1axfvvvsueXl5/PXXXyXWJhKp7hITE1myZAkfffQRf/zxB9u3bycmJsbTYYmIl1NxCBGRKuDqq6/miy++4MCBA9jtdjp37sz48eOVNImU4tdff2XIkCHUrVuXjz/+WEmTiJSJepxEREREREROQ3OcRERERERETkOJk4iIiIiIyGlUuzlOLpeLffv2ERISgsVi8XQ4IiIiIiLiIYZhkJGRQWxsLFbrqfuUql3itG/fvhKLFIqIiIiISPW1Z8+eEotkn6jaJU4hISGA+eGEhoZ6OBpwOBzMnTuXnj174uvr6+lw5DhqG++m9vFeahvvpvbxbmof76W28W5n2j7p6enEx8e7c4RTqXaJU9HwvNDQUK9JnAIDAwkNDdU/Qi+jtvFuah/vpbbxbmof76b28V5qG+92tu1Tlik8Kg4hIiIiIiJyGkqcRERERERETkOJk4iIiIiIyGlUuzlOZWEYBgUFBTidznN+LofDgY+PD7m5ueflfFJ2VbVtbDYbPj4+KscvIiIiUg5KnE6Qn5/P/v37yc7OPi/nMwyDmJgY9uzZowtZL1OV2yYwMJDatWvj5+fn6VBEREREKgUlTsdxuVwkJiZis9mIjY3Fz8/vnF8wu1wuMjMzCQ4OPu2iW3J+VcW2MQyD/Px8Dh06RGJiIo0bN64y701ERETkXFLidJz8/HxcLhfx8fEEBgael3O6XC7y8/Px9/fXBayXqaptExAQgK+vL7t27XK/PxERERE5tapzNViBqtJFskhp9B0XERERKR9dPYmIiIiIiJyGEicREREREZHT8GjitGjRIvr27UtsbCwWi4UZM2ac9jULFy6kXbt22O12GjVqxJQpU855nNVVQkICr7/+uqfDEBERERHxOI8mTllZWbRp04a33367TPsnJiZyzTXX0K1bN1avXs3DDz/MXXfdxZw5c85xpN7NYrGc8jZmzJgzOu4ff/zBv/71rwqJ8YsvvsBms3H//fdXyPFERERERM4nj1bV6927N7179y7z/u+99x7169fn1VdfBaBZs2YsXryY1157jV69ep2rML3e/v373Y+nTZvGqFGj2Lx5s3tbcHCw+7FhGDidTnx8Tt/0kZGRFRbjxIkTeeyxx3j//fd59dVXPVrJLT8/X+sXiYiIiEi5VKpy5MuWLaN79+7FtvXq1YuHH374pK/Jy8sjLy/P/Tw9PR0Ah8OBw+Eotq/D4cAwDFwuFy6XCzATjRyHs4LeQUmGYZCT78SW5yixZlSAr61M60hFRUW5H4eEhGCxWNzbFi5cyFVXXcX//vc/Ro0axbp165g9ezbx8fE8+uijLF++nKysLJo1a8bzzz9f7PNt0KABDz30EA899BAANpuN999/n5kzZzJ37lzi4uJ4+eWX+cc//nHK+BITE1m6dClff/01v/zyC9988w233XZbsX0mTZrEa6+9xrZt26hVqxbXX389b731FgCpqak88cQTfP/996SlpdGoUSPGjx/Ptddey9ixY/n+++9ZtWqV+1hvvPEGb7zxBjt27ABgyJAhpKam0qFDB9555x3sdjvbt2/n008/5a233mLz5s0EBQXRrVs3XnvtNfdnZxgGf//9N8899xy//fYbhmHQtm1bJk2aRFJSEj169GDXrl3ExMS4z/3II4+watUqfv3119O2mye5XC4Mw8DhcGCz2Twdzhkp+vd74r9j8Ty1jXdT+3g3tY/3Utt4tzNtn/LsX6kSpwMHDhAdHV1sW3R0NOnp6eTk5BAQEFDiNS+88AJjx44tsX3u3Lkl1mry8fEhJiaGzMxM8vPzAcjJd9J5wu8V+C7KbtnwiwnwK99FbW5uLoZhuBPE7OxsAB5//HGeffZZEhISqFGjBnv37qVbt2488cQT2O12vvzyS6677jpWrFhBfHw8YF5c5+bmuo8FMHbsWMaOHcuoUaP44IMPuOOOO1i7di01a9Y8aUzvv/8+PXv2xGKxcP311/Phhx9y7bXXuv8+ceJEnn76aUaPHk337t1JT09n+fLlpKen43K5uPrqq8nIyHD3OG7atIm8vDzS09PJy8vD6XQWizE3NxeXy1UsSf75558JCAjg22+/BcwEOiMjg8cff5zGjRtz6NAhnnrqKe644w6+/vprAPbt28c111zDZZddxvfff09ISAjLly8nNTWVtm3bkpCQwEcffcSDDz7oPs/nn3/O2LFji8XjjfLz88nJyWHRokUUFBR4OpyzMm/ePE+HICehtvFuah/vpvbxXmob71be9im6Vi6LSpU4nYmRI0cyfPhw9/P09HTi4+Pp2bMnoaGhxfbNzc1lz549BAcHu4eS+eR77qIyJDSEQL/yNZG/vz8Wi8X93oqSw2effZbrrrvOvV+9evW49NJL3c8vvPBCZs2axcKFC93zkKxWK/7+/sU+pyFDhnDnnXcC8PLLL/P+++/z999/c/XVV5caj8vl4ssvv+SNN94gNDSUwYMH88wzz3D48GHq168PwIQJExg+fDiPPfaY+3VXXHEFYCa4K1euZMOGDVxwwQUAtG7d2r2f3W7HZrMVi7Fowdqibb6+vgQFBTFlypRiQ/Tuu+++YrGGhYXRqVMnrFYrwcHBvPjii4SGhvLVV1+5X9euXTv3/nfddRcff/wxTz/9NADTp08nLy+PgQMHEhQUVOrn4S1yc3MJCAiga9eulXYBXIfDwbx58+jRowe+vr6eDkeOo7bxbmof76b28V5qG+92pu1Tnh+7K1XiFBMTw8GDB4ttO3jwIKGhoaX2NoF5YW2320ts9/X1LfGhOp1OLBYLVqvVvUBokN2XjePO3fwpl8tFRnoGIaEhJRYlLetQveMVHePE+44dOxY7fmZmJmPGjOGnn35i//79FBQUkJOTw549e4rtV/R5FGnTpo37eUhICKGhoaSkpJx0QdV58+aRlZXFtddei9VqJSoqih49ejBlyhSeffZZkpOT2bdvH927dy/1GGvXrqVOnTo0bdq01OMXfT4nxnz8NovFQqtWrUokCCtXrmTMmDGsWbOGo0ePuodn7t27l+bNm7NmzRo6d+6Mn59fqbENGTKEZ555hhUrVnDxxRfzySefcPPNNxMSElJqrN7EarVisVhK/XdQ2VSF91BVqW28m9rHu6l9vFdlbRuXyyA918HRbAdHs/NJy3YQ6GejSUwINQLP79xvwzDIzCsg2O5T7mvd0ylv+5Rn30qVOHXu3JmZM2cW2zZv3jw6d+58zs5psVjK3etTHi6XiwI/G4F+PidNPirCiT0gI0aMYN68ebzyyis0atSIgIAAbrzxRvcQxZM58ctlsVjcCUdpJk6cyJEjR4olti6Xi7Vr1zJ27NiTJrxFTvd3q9WKYRjFtpU2VvXE95+VlUWvXr3o1asXn3/+OZGRkezevZtevXq5P4PTnTsqKoq+ffsyefJk6tev7+6xExEREc/Zn5bD8h1H+PtAOqH+vkQG2wkP9iMi2E5EiJ3wID/8fb13fq9hGKTnFpCane9OclKz88nMLaDAZeAsvB3/+NhzFw6nQXqOg9ScotcWJko5Dk64ZHKLCfWnSUwITWNCaFJ4axQVjN3n9J9TfoGrRKxHsx2kZjsKHxc9P3afmu2gwGWwbkxPQvwrTxLq0cQpMzOTbdu2uZ8nJiayevVqatWqRd26dRk5ciRJSUl88sknANxzzz3897//5bHHHuPOO+/k559/5quvvuKnn37y1FuotJYsWcLgwYPp378/YLbFzp07K/Qchw8f5vvvv+fLL7+kRYsW7u1Op5PLLruMuXPncvXVV5OQkMCCBQvo1q1biWO0bt2avXv3smXLFvdQveNFRkZy4MABDMNw/2KxevXq08a2adMmDh8+zIsvvuie0/Xnn3+WOPeUKVNwOByl9lqCOVxvwIAB1KlTh4YNGxYb/igiIpXPrsNZzNlwALuPjRqBvtQM9Ct2fy5+IS8Pl8sgKTWH7YcyCfTzoUVsKEH2SvU7eIUrSpR+33GY33ccZufh089ZCbH7uJOoiGA7MWH+xNbwJ65GYOF9ABHBdqzWim/rXIeTTQcy2LAvjY370jmYnntc0uEgLceB03WSDKcCBPnZqBHoR80gX1KzHew9msOB9FwOpOfy65ZD7v1sVgsNIoJoEhNCfK1AMnMLiiViRYlRVv6ZF1FLzXYocSqrP//8s9jFctFcpEGDBjFlyhT279/P7t273X+vX78+P/30E4888ghvvPEGderU4aOPPqrWpcjPVOPGjZk+fTp9+/bFYrHwzDPPnLLn6Ex8+umnhIeHc/PNN5f4n0yfPn2YOHEiV199NWPGjOGee+4hKiqK3r17k5GRwZIlS3jggQe4/PLL6dq1KzfccAMTJkygUaNGbNq0CYvFwtVXX80VV1zBoUOHeOmll7jxxhuZPXs2s2bNKjF/7UR169bFz8+Pt956i3vuuYf169fz7LPPFtvn/vvv56233mLAgAE8+eSThIWF8fvvv9OxY0eaNGkCmFUdQ0NDee655xg3blyFfn4iInL+HEzP5c0FW5n2xx4KTnHR6muzEBbgR81AX2oE+lKr8MLbvBXv1YgIsRNyhomWw+li19FMtiVnsi05g23JmWxNzmTHoaxi1X4tFmgUGUzrOjVoEx9G6zo1aBoT4tU9KmfrdImS1QIt48JoXSeMnHwXh7PySMnM43BmPimZeTicBhl5BWTkFZCYknXS8/jaLNQOCyC2hj+xNQKICbFz6IAFy/oDRIQEuJOPmoEn78FKy3awYb+ZIG3Yl86GfWlsP5RVpsQowNdGzUBfwgLN71uIvw8+Nis+Vgs2iwWb1YKPrfDeasVqOfY81N+38DtqvrZmkJn41wjww8+n+AinjFwHWw5msOlABpsPZLBpfwabDqSTnlvA1sLv3elYLRAWcOIPDcf+nZiP/Y7FdJrPzVt5NHG64oorSgyzOt6UKVNKfc1ff/11DqOqHiZMmMCdd97JJZdcQkREBI8//niFV4KbNGkS/fv3L/V/GDfccAN33HEHKSkpDBo0iNzcXF577TVGjBhBREQEN954o3vfb7/9lhEjRjBgwACysrJo1KgRL774ImCu5fXOO+8wfvx4nn32WW644QZGjBjBBx98cMrYIiMjmTJlCk8++SRvvvkm7dq145VXXilWWj08PJzvv/+ecePGcfnll2Oz2Wjbtm2xXiWr1crgwYMZP348AwcOPNuPTEREzrO0bAfvLdrO5CWJ5DrMHxA7NwinRqBvsV/Xj2Y7yC8wh0GlZJoX4mXh52MlIsiPsEA/84L3uJtPKY8LnC427LIxYsUCHM7Sr5H8bFYSIgLJyC1gf1qu++L221V7AfOCv0lMiJlM1QmjYWQweQUusvOdZOcXFN47yckvICvfSc5x28vaz2H3sZoXxwG+1AgqvDg/oXeuvHO1XS6Dw1n5HEzPLbzlcSA9l+TC5wfS8ziYnsuRrOLTCooSpYsbhHNxg1pclFCL0JP0YhQNgzs+kUrJzGN/Wi77UnPYl5pDUmEPjMNpsPtINruPHJ+Y2fg6cW2J4/r7WqkRcOz9+/ta2Zqcyd6jOaXGER7kR/PYUFrEhlG3VmCxhKLoOOcrqQjx96V9vVq0r1fLvc0wDA6k57KpMJE6kJZDWIAZ44k9sTUD/Qjx9zknvXPexmKcKnOpgtLT0wkLCyMtLa3UqnqJiYnUr1//vFUaKyqbHRoaek7nOEn5lbVthg4dyqFDh/jhhx/OY3RnxxPf9YrmcDiYOXMmffr0qZSTdKsytY13U/uYcvKdTF6ayHsLt5Oea1bQbVe3Bo9d3ZSLG4Sf9DVHC+dspGU7OJKdz5GsfFKKLsAz8jicdezx2QxhAgj0s9EwMpjGUcE0jDLvG0UFU7dWID428/9LyRm5rN2Txtq9qaxNSmPt3rQSiYWn+PlYCfX3pSzX0y4DUrPzT9nbV6Q8idKZKnC6OJiRdyyZSs1hz+Es1m/bjT20Fqk5Be6haqeLuU7NAFoUJklF99Ghdo8O+ayKzvS/bafKDU5UvQfFipyFtLQ01q1bx9SpUytV0iQicjoul8H+9FxSs/NpEBFc7jUFixiG+Yu9OaTqCMt3HCYjr4C4GgHE1jg2BCqu8BZbI4CoELs7KTgXHE4XX/6xhzcXbOVQhtlr1CQ6hH/3asJVzaJOeTEb4GcjwM+Msyxy8p1mz0aWOTHfdcKk/gKXC5dhUOA8tr3A6eTA1vXces0V1A0POe2v+FEh/nRv7k/35uY6l4ZhsPdoDmv3prE2KZW1e9LYm5pNoK8PAX42At03HwL9bAT42QjyM/8W4GvDVsZegxyHmUSmZhXOd8kpPvnf4TTIL3CVuWeuiMUCEcF2okPtxIT6ExXqT3SIPzFhdvfj+FoB53xejI/N6v5eFjEvzHfSp09H94V5UXW44+f9HM3OJyvPSUJEIC1qhxEWWH1/oKhqlDiJnKGiBYPvueceevTo4elwRETKxTAMDmXkkZiSxc6ULHYU3iemZLHzcBZ5BeawNYsF6tUKLKy0FUqzwopb9cKDSlxkn5go/b7jMPvTckuce9MBcz5FaWxWCzGh/sTVDKBZTAgtYsNoHhvKBdEhJeZmlIfLZfDj2n1MmLeFXYVzYurUDODRnhfwjzZxZU4YyiPAz0Z8rUDiawWW+TUOh4OZKeuIqxFwRkOfLBaL+5zXtK5d7tdXBMMwyMp3kpqdT0ZuwUkruZ2oZpAvEcF2fM9h4lzRLBYLIf6+hPj7lqudpXJS4iRyhlR6XETOlmGYvQz5BS7yClyF987ijx0usgrnoOTkOwvnpBybp1I0NyXX4SzTBarT5WJ7ko2nVv1CZt7JF3n3tVkIsvuQmu1g5+Fsdh7OZs6GY2sp+vtaaRxlJlH1I4LYlpxZaqLka7PQNr5G4bCqcKJC7Ow7fj5J4f2+1Fz2p+XgcJpV45JSc1iReKTYcRpHhRTOCwl1J1TBhRXlShtalXT02LGTUnPc7zci2M6DVzXi1g51zyoZk9JZLBaC7T7uthGpKvSNFhEROUfyCpzsPpxN4nE9OTsOmfdpOWaxgXNYdfgULEABFgvE1QigfkSQ+5YQEUSDiCDiagTgY7NyKCPPrLR1IL3wPoMtBzPIdbhYl5TGuqS0Ykc+MVFqV7dmiaF+jaNLXyjc6TILLySl5rDrcJa7Etn6pDTScwvYuD+djfvT+WblsdfUrRVIgdPFgfTc036WIf4+3HN5Q4ZcmnBO12gUkapJ/9UQEZFKq8DpIrmwl6GolyI120GtID+iQ+1Eh/q7b2f767fDWbwqWU6+k6y8ArIdxx6n5xaw+3DhsLfDWSQdzSlXYuRjtWD3seLnY8XuY8Pua8XuYyXAz4dA38K5KXbz8YnzVQL8bGWahO90Otm2cR039uxCg+jQ01buigyxExli57LGEceO4TLYdTjLnUjtSMkiITzwpIlSWdmsFnd7tatbk/4XmtuL5u1s2JfOxn1phWWd0zmQnlus4tmJ5aOPzaUKIK6GP3VqBla68sci4j2UOImIiFdzOF2s3pPKpgMZ7mFYRcOvDqTnlnmhyGC7D1GFE86jQ/2JCrGDhcJSzIXJz/FJUeHQuKLn+c4zW+su2O7j7smpX9ibkxARRHiQH/bCBMmvMFk6F/NsTuRwOJh5cC2No4PxPcMkwma10CAymAaRwfRude7n0Rw/b+fqljHu7Yczzd4wfz8bdc7hgqUiIqDESUREvIxhGGw+mMHirSks2ZbCisQjpyzr7GO1ULuGP7FhAcTVDKBWoB9HsvI5cNxaMJl5BebtUAE7Dp18wcuysFktJSqTmdXJfAi2m8UAGkQEUT8imISIQCKDVXb4XAkPtnNJI7unwxCRakKJk4iIVBjDMFieeIQ5ey0c/n030WGBRAT7ER5sJzLYTmiAT6lJRFJqDku2prB4WwpLtx8uUcI4PMiPtvE1qFOzcNhV0X1hL8Ppemoy8wpITs8tXEwzz51UWS2WEiWZA094XJQUBRXu52ezKhESEamGlDiJiMhZyy9w8eOafUxaksiGfemAjZl7NpXYz9dmITzITkSIH+FBdkIDfFmflEZiSvFeoABfGx3r1+KyRhFc2iiCpjGnX8/mVILtPgQXDi0TERE5E0qcxO2KK66gbdu2vP766wAkJCTw8MMP8/DDD5/0NRaLhe+++45+/fqd1bkr6jgicn4dycrn89938cnvu9yLifr7WmkWWkBUdAxHsh0czsznUGYeGbkFOJwGBwp7fo5ns5qV2C5tGM6ljSK4sG5NlYkWERGvosSpCujbty8Oh4PZs2eX+Ntvv/1G165dWbNmDa1bty7Xcf/44w+CgoIqKkwAxowZw4wZM1i9enWx7fv376dmzZoVeq6TycnJIS4uDqvVSlJSEna7xseLlNfWgxlMWpLI9FVJ7oVSo0PtDOycwE3tarNs4Xz69GmLr6+v+zW5DidHsvJJycxzJ1Op2fk0iAimU4NahPj7nux0IiIiHqfEqQoYOnQoN9xwA3v37qVOnTrF/jZ58mQuuuiicidNAJGRkRUV4mnFxMScfqcK8u2339KiRQsMw2DGjBnccsst5+3cJzIMA6fTiY+P/ilKxUjOyOXPnUf5Y+cRdh/OdldrK1biupTHx8/lKa3wQaCfD1YLLNqawsTFiSzacsh9zlZxYQy9rD59WtXGz8eKw+EoNTZ/X5u7NLSIiEhlo3EQp2MYkJ91bm+O7NK3l2UJeODaa68lMjKSKVOmFNuemZnJ119/zdChQzl8+DADBgwgLi6OwMBAWrVqxRdffHHK4yYkJLiH7QFs3bqVrl274u/vT/PmzZk3b16J1zz++ONccMEFBAYG0qBBA5555hn3RdSUKVMYO3Ysa9aswWKxYLFY3DFbLBZmzJjhPs66deu48sorCQgIIDw8nH/9619kZma6/z548GD69evHK6+8Qu3atQkPD+f+++8/6QXb8SZOnMg///lP/vnPfzJx4sQSf9+wYQPXXnstNWrUID4+nssvv5zt27e7/z5p0iRatGiB3W6ndu3aDBs2DICdO3disViK9aalpqZisVhYuHAhAAsXLsRisTBr1izat2+P3W5n8eLFbN++neuuu47o6GiCg4Pp0KED8+fPLxZXXl4ejz/+OPHx8djtdho1asTEiRMxDINGjRrxyiuvFNt/9erVWCwWtm3bdtrPRDyvrCW1j2cYBjsOZTLtj92M+HoNV7z8Cx2fX8B9n69i8pKdLNiUzKz1B/h+9T6++nMvn/2+m4mLE3ln4XZen7+V/8zexLj/beTpGesZ/tUa7vlsFYMmreCm95ZxzZuL6fbKQjqNX0CrMXNp+ORMLnh6FoMmrWDRlkNYLNCrRTRf/V9nfhh2Kf0ujNPQOhERqdL0M/fpOLJhfOw5O7wVqHGyPz65D/xOP1TOx8eHgQMHMmXKFJ566il3taevv/4ap9PJgAEDyMzMpH379jz++OOEhoby008/cccdd9CwYUM6dux42nO4XC6uv/56oqOjWb58OWlpaaXOfQoJCWHKlCnExsaybt067r77bkJCQnjssce45ZZbWL9+PbNnz3YnBWFhYSWOkZWVRa9evejcuTN//PEHycnJ3HXXXQwbNqxYcvjLL79Qu3ZtfvnlF7Zt28Ytt9xC27Ztufvuu0/6PrZv386yZcuYPn06hmHwyCOPsGvXLurVqwdAUlISXbt25YorrmD+/PlYrVbWrFlDQUEBAO+++y7Dhw/nxRdfpHfv3qSlpbFkyZLTfn4neuKJJ3jllVdo0KABNWvWZM+ePfTp04fnn38eu93OJ598Qt++fdm8eTN169YFYODAgSxbtow333yTNm3akJiYSEpKChaLhTvvvJPJkyczYsQI9zkmT55M165dadSoUbnjk3Mv1+Hkz51HWbzNLLm9fl8afjYrNQP9qBHoS41A38LHftQsfBwW6EuNAF92H8nmj51H+HPnUQ5n5Rc7rsUCTaJD6JBQiyYxIRiGQV6By33LL3CRV+A87rGLPIeTHIezcHFXJzn5BWQVrl2UnV/gXsDV4TQI8rNxc4d4hlxSn7rhgR745ERERDxDiVMVceedd/Lyyy/z66+/csUVVwDmhfMNN9xAWFgYYWFhxS6qH3jgAebMmcNXX31VpsRp/vz5bNq0iTlz5hAbayaS48ePp3fv3sX2e/rpp92PExISGDFiBF9++SWPPfYYAQEBBAcH4+Pjc8qheVOnTiU3N5dPPvnEPcfqv//9L3379uU///kP0dHRANSsWZP//ve/2Gw2mjZtyjXXXMOCBQtOmThNmjSJ3r17u+dT9erVi8mTJzNmzBgA3n77bcLCwvjyyy+x2Wykp6fTrl07rFbzl/TnnnuORx99lIceesh9zA4dOpz28zvRuHHj6NGjh/t5rVq1aNOmjfv5s88+y3fffccPP/zAsGHD2LJlC1999RXz5s2je/fuADRo0MC9/+DBgxk1ahQrVqygY8eOOBwOpk6dWqIXSjzH6TJYn5TmTpT+3HWU/ILiC6rmFbhKLZxwKn4+VtrWqcFFCTXpkFCLdnVrEhZYcXOFihKv7MIkKjzIToDfmS2aKiIiUpkpcTod30Cz5+cccblcpGdkEBoS4r44L3buMmratCmXXHIJkyZN4oorrmDbtm389ttvjBs3DgCn08n48eP56quvSEpKIj8/n7y8PAIDy3aOv//+m/j4eHfSBNC5c+cS+02bNo0333yT7du3k5mZSUFBAaGhoWV+H0XnatOmTbHCFJdeeikul4vNmze7E6cWLVpgsx27gKtduzbr1q076XGdTicff/wxb7zxhnvbP//5T0aMGMGoUaOwWq2sXr2aLl264Ovri8tV/KI2OTmZffv2cdVVV5Xr/ZTmoosuKvY8MzOTMWPG8NNPP7F//34KCgrIyclh9+7dgDnszmazcfnll5d6vNjYWK655homTZpEx44d+fHHH8nLy+Omm24661jlzOQVONmWnMmq3aks2ZrC0u0ppOcWFNsnJtSfSxtFcFnjcDok1MIwIDXbwdHsfI5m57sfp2Y7SM3O52jhfXiwnQ4JteiQUJNWdcKw+5y7RMZiseDva8Pf10atIL9zdh4RERFvp8TpdCyWMg2XO2MuF/g6zXOcmDiV09ChQ3nggQd4++23mTx5Mg0bNnRfaL/88su88cYbvP7667Rq1YqgoCAefvhh8vPzT3PUslu2bBm33347Y8eOpVevXu6em1dffbXCznG846t1gXmBd2Kyc7w5c+aQlJRUohiE0+lkwYIF9OjRg4CAk09aP9XfAHfiaxw3N+1kc65OrFY4YsQI5s2bxyuvvEKjRo0ICAjgxhtvdLfP6c4NcNddd3HHHXfw2muvMXnyZG655ZYyJ8Ziys4vwO5jO+1iqsdzuQySUnPYdCCDzQfSC+8z2JGSVWLeUoi/D50bhHNZY3NtogYRQSUWUo2vVSFvRURERCqYEqcq5Oabb+ahhx5i6tSpfPLJJ9x7773ui7IlS5Zw3XXX8c9//hMwe7q2bNlC8+bNy3TsZs2asWfPHvbv30/t2rUB+P3334vts3TpUurVq8dTTz3l3rZr165i+/j5+eF0Ok97rilTppCVleVOMJYsWYLVaqVJkyZlirc0EydO5NZbby0WH8Dzzz/PxIkT6dGjB61bt+bjjz/G4XAU680Cc/5WQkICCxYsoFu3biWOX1SFcP/+/Vx44YUAJcqun8ySJUsYPHgw/fv3B8weqJ07d7r/3qpVK1wuF7/++qt7qN6J+vTpQ1BQEO+++y6zZ89m0aJFZTp3dZaT7+T3xMP8uvkQi7YeYschcxHWID8bIf6+hPj7FN583feh/j4E233Yl5bL5gPpbDmYSWZeQanHDwvwpUVsKJcWLuLaMjYUH5sKKIiIiFRGSpyqkODgYG655RZGjhxJeno6gwcPdv+tcePGfPPNNyxdupSaNWsyYcIEDh48WObEqXv37lxwwQUMGjSIl19+mfT09BIJSOPGjdm9ezdffvklHTp04KeffuK7774rtk9CQgKJiYmsXr2aOnXqEBISUmIdpdtvv53Ro0czaNAgxowZw6FDh3jggQe444473MP0yuvQoUP8+OOP/PDDD7Rs2bLY3wYOHEj//v05cuQIw4YN46233uLWW2/l8ccfx2azsX79ei6++GKaNGnCmDFjuOeee4iKiqJ3795kZGSwZMkSHnjgAQICArj44ot58cUXqV+/PsnJycXmfJ1K48aNmT59On379sVisfDMM88U6z1LSEhg0KBB3Hnnne7iELt27SI5OZmbb74ZAJvNxuDBgxk5ciSNGzcudShldWcYBluTM1m05RC/bjnE8sQjJeYZAWTlO8nKd3IgvWzH9bNZaRgVTNOYEJrEhNA0JoSmMaFEh9pL9CiJiIhI5aTEqYoZOnQoEydOpE+fPsXmIz399NPs2LGDXr16ERgYyL/+9S/69etHWlpamY5rtVr57rvvGDp0KB07diQhIYE333yTq6++2r3PP/7xDx555BGGDRtGXl4e11xzDc8884y78ALADTfcwPTp0+nWrRupqalMnjy5WIIHEBgYyJw5c3jooYfo0KEDgYGB3HDDDUyYMOGMP5eiQhOlzU+66qqrCAgI4LPPPuPBBx/k559/5t///jfdunXDZrPRtm1bunTpAsCgQYPIzc3ltddeY8SIEURERHDjjTe6jzVp0iSGDh1K+/btadKkCS+99BI9e/Y8bXwTJkzgzjvv5JJLLiEiIoLHH3+c9PTiV+3vvvsuTz75JPfddx+HDx+mbt26PPnkk8X2GTp0KOPHj2fIkCFn8jFVSem5DhZvTXH3Ku1PK154ITbMn8ubRHL5BZF0rB+OYRhk5BYU3hykF94fvy0zr4DwYD+axITSLCaEhIggfNWTJCIiUqVZDKOMiwVVEenp6YSFhZGWllaiaEFubi6JiYnUr18ff3//8xKPy+UiPT2d0NDQksUhxKMqY9v89ttvXHXVVezZs+eUvXOe+K5XNIfDwcyZM+nTp0+J+W65Die/bErm+9X7+HlzcrFeJbuPlU4NwunaOIIrmkTSMDJYvUIV7FRtI56n9vFuah/vpbbxbmfaPqfKDU6kHieRKiAvL49Dhw4xZswYbrrppjMe0liZOV0Gv+84zIy/kpi9/gAZx807ahAZxBUXRHF5k0g61a+Fv6/KaYuIiEj5KHESqQK++OILhg4dStu2bfnkk088HU65GYZBem4B+1JzOJqVT2jAsQVgA/1sJ+0RMgxYl5TG/9Yl8+PafRzKyHP/LTbMn75tY7muTRzNaoeoV0lERETOihInkSpg8ODBJeaKeZMCp7mw677UXPal5pBUeNvnvuWetDKdn81KWKAvNQN9qRHoR83ChMrXZmHOahvJvy9371sj0Jc+rWpzXZtYOiTUwlqOsuIiIiIip6LESUQqTK7DSWJKFtuSM9manMn25Ey2JmewMyWbfOfJ19gqEh7kR80gPzJyHRzNdpBf4CLf6eJQRl6x3qRjLPj7WunRPIbr2sTS9YJI/Hwqx3w0ERERqVyUOJWimtXLkGqoIr7jDqeLWesPsHFfOtuSM9iWnMnuI9m4TnJoP5uV2jX8iQ0LIK5mALE1Aoir4U9sDfNxbFgAAX7H5h4ZhkGOw8nRbAdHs/JJzXaQmpPP0WwHqVn5pGbnkXdwByNuvZIawadfIFhERETkbChxOk5RBY7s7GwCAnQhJlVXdnY2wBlXBdp0IJ1Hv1rDhn0lFzoK9fehcXQIjSKDaRQVTKPoYBpFBhNXI6BcQ+csFguBfj4E+vkQV6Pkv0ezes52guz6z5iIiIice7riOI7NZqNGjRokJycD5npC53pCucvlIj8/n9zc3EpT8rq6qIptYxgG2dnZJCcnU6NGDWy28lWXK3C6eH/RDl6fvwWH06BGoC99W8fSuDA5ahQdTGSwFn0VERGpEnLTYeVk2L8WLn8MIpt4OiKPUuJ0gpiYGAB38nSuGYZBTk4OAQEButj0MlW5bWrUqOH+rpfVtuRMHv16DWv2pALQvVk0469vSVRI5VwHSkRERE4i8xAsfw/++BBy08xtm2fBP96EVjd6NjYPUuJ0AovFQu3atYmKisLhcJzz8zkcDhYtWkTXrl21mJqXqapt4+vrW66eJpfLYNKSRF6es5m8Ahch/j6M7tuCG9rFVbmEUkREpFo7uguWvgV/fQoFuea2iAsgoBbs+R2+HQq7f4dez4OP3bOxeoASp5Ow2WzlHsZ0pucpKCjA39+/Sl2cVwVqG9h1OIt/f72WFTuPANClcQQv3dia2mGaAygick4Yp69AWiU5csHmB946ND77CNblH9JqzwrIbA8163g6oop1cCMseR3WfQOG09wW2w66DIcm1wAG/DIefnvF7IVKWgk3TYGa9TwY9PmnxEmkitt6MINZ6w8w/++D5Be4qB8RRP2IIBIigmhQ+LhWkF+x3iOXy+Dz5bt4YdYmsvOdBPnZeOqa5gzoGK9eJhGRc8GRA/NG47NyClf51MTquxSa9oG6ncHHz9PRVTzDgIMbYOsc2DIX9q4AnwCIagbRzSGqxbH7oHDPxZm+D5a9DSunYMvPpAFgvH8p9BoPbW+Dyv7/xN3LYfFrsGXWsW0NroDLhkP9rsXf31XPQHwn+O5fsG8VvN8Vrv8ALuh13sP2FCVOIlWMYRhs2JfOrPX7mb3+ANsPZRX7+6YDGSVeE+Lv406iEiKC+HPnURZvSwGgU/1avHJTG+JrBZ6X+EVEqp39a2H63XBoExYg2HkAVrxn3vxCoGE38+K0cU8IjvJcnIYBWSmQtsd8HhYPQRFlTx7ysyFxEWyZDVvnQfre4n93ZEHSn+bteMHRENUcoluYt3qXQM2Es347p5SyzeyBWfMluMypG0ZUS9IyMqiRswu+vw/WfQ1936gcvS6GAZnJkLzB7F1K3gj718DB9YU7WKBZX7jsEYhrd/LjXNAT/m8RfD3Y7HWaerOZZHV7CmxlTCsMAw5vMxPnFv3O8o2dX0qcRKoAl8vgrz2pzF6/n9kbDrDnSI77b742C5c1iqB3y9pEhPiRmJJNYkomO1OySUzJYl9aDhm5BazZm8aavWnu1/n7Wnn86qYM6pxQrjLiIuLFXE6sK96j7a45WOcthcCaYA8F/9AT7sPM+4AaYKtCQ5UNo2J7CM72eC4XLHsLFjxrXpwHR1Nw9cus+nMFF4Uexrp9PmQdgr9/MG9gDp8qSqIim1bs+3E5IfMgpO6GtL3H3fYce+w8YTFyH38IjYMa8RBWx0ymwuoce2yxwLYFsGUO7Pzt2LwZMHuYGlxuvpdG3c2/HdxgXtQf3Ghe5B/dacaUeRB2/GK+zuoL17wK7QdV3Hsvsu8vswdm4w9A4cKE9S6Fyx6hoN7lLJr5P66plYht0UtmPO9cDFeNgo7/Ams5pngYBuz9E/7+3nwc1dzsYYtsCr5nORw+PwuSNx1Lkg6uNz/T7MMl97X6Qptb4dKHIKJx2Y5foy4MmQ1zn4YV78PiCbD3D7hhIoREl9zf6TB/HNi9rPD2O2SnABZosNP870wlocRJpJLKdThZnniEBX8fZM6GAxxMP/Y/M39fK1dcEEXvVjF0axpFqP/JL3xyHU52HTaTqaKkyjDg3isa0iAy+Hy8FRE5H1xO+H4YtjVTqQew4rfTv8ZmN39hbnUTNO4FvpWwiuaB9WbPwPrp5oVjnYvM4W91L4Y6HcBejv/O5abDnhXHLv72rTKTg4uGQJsB5bsATNsL391jJhNgziP5x5sYfmHs3w7OPn2w2mzmhfzWOWbisX+1ec59q2DhC+X5FCqQxewBAsg8YCY7R7abt7IIq2t+pxr3gvpdSiYJkU2A6489z8songTs/cN8/z8+aCZZvcaXvafjZAzDbIffJhxLzgAu6G32wNTtZD53ODAsNlydH8TWoh/88CDsWgyznzDnBl33X3Oo4akc2mx+H9d9bSaFJ7JYoVaDwkSqZeFwxeZQs745/8swzGT6+GS2KLlNLdyWnXKSk1vMYxf13EU1N4felZbsnI6PH/R5yfxsfnjQ/Pze7wI3Tobarc122lWYKO39Ewpyir/eZjf/LWalKHESkXPjYHouv2xKZsGmZJZsSyE73+n+W7DdhyubRtG7ZQyXN4kk0K9s/7z9fW00iQmhSUzIuQpbRDytMGlizVQMi41tkb1o0LARNkemmQzkpkFeuvm46D4/w+xd+PtH82YPhWb/MEsR1+9avl/Xz7eju2D9N+bFbPLG4n9L/NW8AVhs5kVeUSIVf3Hxi8j0fceSpN3LzAv1E4s3pGw2L5znjzU/mw5DIfbCU8e37hv4abj5ufsGwtUvQruBZu/M8RV9rVao0968dXsSMg7A1rlmErVjIeRnnvFHdFK+gSV7jdyP60Bo7LFqagV55md0st6ptD3mPvGdzGTpgqvL30tmD4H4DuYNzMRh0cvwy/Nmb0fKZvNiPbDWmb3f3cth7lPmhT6Y34lWN5o9MNEtTv668IYw6EdYNQXmjTaHF77XBbqOMIeuHT8vLS2p8Pv4NRxYd2y7bxA0vcaMvainLfuwOYzt8LZjvYxgtktwFKTvL9nrV5qgqBPmihX2ZvlV8LD7ljdATGuYdgcc+hs+vtbcfuK/k4Ca5r+vuhebQy1rt6mUVfmUOIl4MZfLYM3eVHeytGFferG/R4XY6dYkil4to7m0UQR2Hy++kBERzzguacJiw9n/AzYm+pJwZR9sp6oY6nKaF3LrvoZ135rzUVZ/Zt6Co80LplY3mkPHvGGCfFYKbPjOjHfP8mPbbX7mULBWN5kXu3tWFCZCv0PabrNHZ99f8Ps75v61GkBkMzi4zhyydqKa9c0Lv7oXQ1x7M6H6Y5LZI/LXp+Ytrj1cNBRaXl+8RyU3DWb+G9ZOM5/HtYfrPzTjKouQGDPBajcQnAUlf8U/axbwCyp7e/rYoVZ981YawwBXQcUO97RYChdibQrf/Z+ZQH50FQz4snyLs2YcMBOetV+az3384cI74JIHyj5nyWqFi+40e89+etQssLDwBdgwA3q/CEcSzSR51xLcw/6sPtCoh/lvp0lv8/MuUmwe0oZjwxUPbQZH9nE9VBYIqV08oS1KcGvEm0MnzzSRPBMRjeHuBfC/4cc+zxp1C3+QKLxFXOC9FRPLQYmTiJc5mJ7L7zsOs2hLCgs3J3M4K9/9N4sFWtepwVVNo7iyaRQtYkNV5U5ETu6EpIkbJ2JccC0kzjz9a602iGll3q4aY67hsvYr2DjDnG/y+zvmrVZDMylp1tf8hf58/jcpLxM2zzTj2v7zsTLKWMxhYK1uNuM6fihQTCuzVwjMXpGiJKqoR+nIDvMG5rCpmON6pOpebCYvx4tuYSZJe5bDHx/Bxu/NSfNJK2HOk9D2dvPiOisZpv+fmaxZrNBlhJkAnGlSYfMBm5ePFLBYzt0cueb/MBO2L24z2+uj7uYcmwt6nvp1Bfmw/F349aXCHjsLXPhPuPKZMxuyBhAWBwO+gA3TYeZjZs/LJ9cV36fepWay1LzfyZMai8WMISQaGl55bLvLab7HrENmUhRS2/sqLfoFQf/3oOu/zR8LwuI8HdE5ocRJxMOKEqXfdxxh+Y7D7EgpXgUvxO5DlwsiuLJpNFc0iSQiuPJ1bYtUSjmp5sX4tvnmr8SXP2b+ons+OXLMOSQBNcv/WpcTvr8f1nzhTppo0b/4ULCyslrNXpZ6l0Dvl2D7ArNnZ9NMc27Lry+at9A60LiHOSSrfteKHxYE5oXv8ec/vtcl9kIziWtxPYTWPv2xwuqYF7OtbjSf56SaQ7ZStphzVep0MIeKnY7Fciyx6vWC2eu0crLZY/X72+YNC2BAjXpmCee6F5/Bm5diYlrB3T/DVwNh91KzwluPcWavUWkJ/Nb5MPtxcxgcQNxF5jyduPZnH4vFYvbCNugGs0ea38+o5tD6JnP72fy3w2oze3XKWrzBUywWiGjk6SjOKY8nTm+//TYvv/wyBw4coE2bNrz11lt07Nix1H0dDgcvvPACH3/8MUlJSTRp0oT//Oc/XH311ec5apEzd7pEyWKBFrGhdG4QTremUXRIqIWvrfJ3b4t4PcMwL5i3FE7E373suB4MzJ6Wa183h1+dS2l7zfNvnQs7fjUTp7KUCT7eyZKmiuDjZw4xatL7WI/P+m/NIVPpe82EYeVkc+hTQpdjFeDOpmSzy2W2x7qvzXbIOXrsb7UamMlSq5vO/sIyoIaZ+DXucebHCI40Fw299CGzmtyfE832xIA2t0Hv/5jVC6ViBEfCwO9h5ghY9THMe8YcYnrt68eKmRzZAXOeMr+rAEGR0H2sWdCjooePBdaC69+Hfu9WiaFpUpxHE6dp06YxfPhw3nvvPTp16sTrr79Or1692Lx5M1FRJdcpePrpp/nss8/48MMPadq0KXPmzKF///4sXbqUCy88zURMEQ/KL3AxY3USkxYnllhHqShRurh+OBc3CKdD/VqEBVSh8r9SdRxYZ97cBQRKKSiQl45PbhpX52bjk1TvuBLFJ4zDD4kpXlzAMMzjlTrBvPDmyC5ZLrtEGe3j78PM+6J9fQNK/grtyIWdi49VLUvdVfzvEReYF/27lpqVvL4ZYu7X5+WKu/h1Oc1ejqKELXlDyX2KylE3uMJMoOpffvIhcecyaTqRPRha32zeHDmQ+NuxzzJtD2ybZ97AnDd0QU+zF8e/Rsm2PLEymmGYZZSPn2NVxBvnWJ3IaissiNDT7HnKOlQxPRtSko+fuZ5SdAuzt2fNF2av0vUfwF+fwdK3wJlv9hx3usfsPfYPO7cxKWmqkjyaOE2YMIG7776bIUOGAPDee+/x008/MWnSJJ544okS+3/66ac89dRT9OnTB4B7772X+fPn8+qrr/LZZ5+d19hFyiIrr4Av/9jDR7/tYH+auXaFEiWpdNL3w/zRxya0n4YFsAMkrzdvpbH6mNW5QmKPJUz5JRdnLiHnSFmjLv2cxydXvoFwYK2ZkBWx+RXvJSma9O50mHMifnvFnPy8eyn0/wDqdT6zWHKOHlvbZtu84j0oFivU6XisZLPFCkveMBOIHQvNW2w7M4Fqem3xC7TzmTSdyDfgWKLQ5xVI/rswiZprzv859Ld5O+nrA4u3T1662QNYpKiqX+ubzDby5qp+J6pR17zJuWOxQKf/M3/s+Hqw+WPEm8f9qN6gm9nbV54CEiIn8FjilJ+fz8qVKxk5cqR7m9VqpXv37ixbtqzU1+Tl5eHvX3wNiYCAABYvXnzS8+Tl5ZGXd6xsY3q6WZXM4XDgOJNx3hWsKAZviEWKO5u2OZqdz2e/7+GT33eTmmO+PirEzpBL6nFT+7gSiZLav/z0b+fULHuWQ8oWjEY9Sk5mLytnPtYV72Fd/CqW/CwMLBj1LoHACLCHYBzXW2DYQ825IP6hOGyBLFv+B5e0rIdv1gFIS8KSvhfS92JJS4KMfVhcBeav8CdULTMCwyE0DiMsHiO0DoTFmfehdTB8A7DkZ7h7tiy5aeYaL3mFz4/r9bIct528DCyGy6zulXOkRPJlBMdgNOqBq1EPjPpdwe+4dX2O/351eQxLwuXYfrgPS+oujCl9cF3yMK4u/y7bBPj8LCxbZ2Nd/y2WHT+bn0FRDP41MBpeiatRT4wGV5acPN73v9DlMazL38G6+jMs+1bBV3dghDfC2flBjJY3gsWG7X8PYl03DaOwep5xwbUl5jSdt387tRpDp8bQaRjkpGLZ8TPWbfPgyA6zrYrapihxdWSbt8wD7kMYNj+MRj1xtbzB/C77FF4DOF3mrQrSf9vOUt3LYMgcfL76J5bDWzHC6uLs8RzGBb1LlnsvJ7WNdzvT9inP/hbDMIxyHb2C7Nu3j7i4OJYuXUrnzsd+sXvsscf49ddfWb58eYnX3HbbbaxZs4YZM2bQsGFDFixYwHXXXYfT6SyWHB1vzJgxjB07tsT2qVOnEhh4DiatSrWWmgc/77ey7KCFfJc5dCTC36B7rIsOkQY+6rmXcywseyfN9n1NdIa5VogLG/trtGNnxFWkBDcr85CmqLQ1tEr6nOA88yL2SGBD1tW5g9SgBmcfpOHC35FKgOMwAflHcdgCyfELJ8evFk7rOSh+Yhj4uHLxcWbj68zB15mNjzMHH1cOWfYY0gLqlWuol48zh1Z7P6XuEfNHu6OB9VlZ7x6y/EsWI7AYBUSmb6DO0WXUTluJj+u4H/L84zgQdiEHQ9twNKgRhqVsPSh+jnQaHJpL/ZT5+DnNpCPHtxYZ/rFEZazHhZWVCfexr2bp84W9jcUowMeZU6xtfJ3ZWDA4FNyMAp+g0x9E5AQ2Zy7hWVtICW6Ky+plFejEq2RnZ3PbbbeRlpZGaOiph2BXqsTp0KFD3H333fz4449YLBYaNmxI9+7dmTRpEjk5pa9lUFqPU3x8PCkpKaf9cM4Hh8PBvHnz6NGjB76nWk9DzrvytM32Q1l8uDiRH9bsx+E0/0k1rx3C/3WpT68W0disXjj+/nzJSsH6xwdg9cF1yYPHfjE+S/q3c4LD27D9+gLWv78HwLD6QERTLMcNlTPCG+NqNxhX61tPPr7/yA5s857Gum2u+ZqgKJxXjsJodbM5ZKwMqkvbWP7+HtvMR7HkpmL4BuLs/izGhQMBA8veP7Bs+Bbr399jyT7sfo1Rsz6uFtfjanGDOaTobORlYP3rY6zL38WSedA8flFPU7PrTvqy6tI+lZXax3upbbzbmbZPeno6ERERZUqcPDZULyIiApvNxsGDB4ttP3jwIDExpQ8riYyMZMaMGeTm5nL48GFiY2N54oknaNDg5L+A2u127PaSv2D6+vp61Zfe2+KpyvILXFgt4FPGSnUna5ttyZnM23iQeRsP8NeeVIp+gri4QS3uvaIRXRtHVO81lvIyYNnb5qTcwtXtbZt+hBs+gpiWFXaa8/pvp2hB0L1/mItPnqwwQWkT3c+VtCT49T/mBGjDCVig1U1Yuo00q40dWG9W9Vr7FZbDW7HNewrbL8+Zk+o7DDXLN4NZHW3xhBKTqC2XP47PGRZBqPL/XWt9oznHacY9WBIX4TPrUdg4HVL3mGv1FAmKLCxkcBOWuPbYLBYqZHaOby3o8gh0vs+c07RhBpaOd+PT9Jqyvbyqt08lp/bxXmob71be9inPvh5LnPz8/Gjfvj0LFiygX79+ALhcLhYsWMCwYcNO+Vp/f3/i4uJwOBx8++233HzzzechYqns8gtcvLNwG+8s3I7NYqF1nTDa1avJhfE1aFev5mnXR3K6DFbvOcrcjQeZt+FgiTLi3ZtFc1+3hrSrewbrrVQljlz4c5I5ib7ol/aYVpBx0JwY/mE3uGo0XHyf91cdcuRA0iqzDPLuZbBnhTkvoyx8g8xEqkZdcwHMVjcWXyH+bGUfMROd5R+As7BX/YKrzUUcj09MY1rCta+ZpXfXTjPbJnmjuc7MX5+aVb4u6G1uz9hnvqbhlXD1fyDyLHtEqoOwOLjje3OdngXjYNcSc7tfiFlCvNWNZgW8c5lI+9ih/WDzJiIi54xHq+oNHz6cQYMGcdFFF9GxY0def/11srKy3FX2Bg4cSFxcHC+88AIAy5cvJykpibZt25KUlMSYMWNwuVw89thjnnwbUgn8tfsoT3y7js0Hj1XtWp54hOWJxyaJx9cKoF3dY4lUo4gA8p2wYFMyv2w+zIJNB0nJzHfv72uz0LlhBD2aR9O9WRS1wwLO63s6pwyj/OV9nQVmtbGFL5pliAHCG8GVT0Oz68wk6ocHYMssmPuUuUZNv3e9a3Xx7CNm9a/dy2DXMtj3F7hOmDTqF1xYTjm0RBluctOPLcbpyDJvGfvNY859BtrcavbynE1Vp7xM+P0ds2eoKImrewl0H33qBTX9Q6Hj3dDhLtj9O/zxEWz8HpJWmjcwF+a8+gVo0sc7yzt7K6vVXHCzwRVmz1/di80k1rcK/TdBREQ8mzjdcsstHDp0iFGjRnHgwAHatm3L7NmziY6OBmD37t1Yj/tFOjc3l6effpodO3YQHBxMnz59+PTTT6lRo4aH3oF4u+z8Al6du4VJSxIxDAgP8mPMP1rQJCaEv3YfZdWuVP7ac5StyZnsOZLDniM5fL/a/NXd7mPFcNnIX7HafbwQfx+6NYmiR/NormgSSYh/FeqqT91jLmK57hs4tMlMeqKbmyufR7c0H4fFl7ygNgz4+0f4+dljpYNDYuGKJ8yelqJf2oMjYcAXsHIKzHkSEn+Fdy+Bvq+fn3LJLhdkJZe+RlDqbvO+tFLXwdFQt3Ph7WLzszhV70FBfmGltzSzzHbiIrM35+hOWPG+eUvoAhfdaZaS9jnNpOX8LNj7p5ns7F5mDhMsHPpITCuz965R97InOhaLObysXmfIfNHsddqx0IzpkmG62D8bMa3McsciIlIleTRxAhg2bNhJh+YtXLiw2PPLL7+cjRs3noeopCpYvDWFkd+tZc8Rswfg+gvjeOba5tQMMi9UL4gO4ZYO5roa6bkO1uxJ5a/dqazafZS/dqeSluMALNQO86dn82h6NI+hY/1a+FWl0njZR2DjDFj7tbkuzfHca658e2ybPRSimhUmUy3MkslL/2suDAoQUBMuG272bJR2AW6xwEVDzIv06XeZPTpfDzbXeen9n9MvKJqV4k4gbHv/5PKU/fjse/n0SUNeujkX6MTeo9JENDETpKJEqWZC+XpffPzAJxyCws3nsRdC5wdgx8/wx0TYMht2/mbegqOh3UBziFVYHXP/zEOw53fzfe5aCvvXFM5dOk6tBtDtKWhx/dkNdwyOhC7DzZuIiIickscTJ5GKlpbt4PmZG/nqT3OV+bgaATzfvyVXNIk66WtC/X3p0jiSLo0jATAMgy370/j5118ZekMX/PyqUCnT/CzYPMtcTHPbfHNtmyL1LjPnZCRcBkd2wMEN5nyYgxvN3qS8dHPY2Z4Tql76BkHn+80ei7Ksxh7RCIbOM4f1LZ4Aa6aac0Ou/+DYcDPDgKOJ5pC53cvMROLwVvchrEANgNILapbOYjV7w8LqnHCLN+9rxJ+b1eStVrNXqFF3s2dv1cew8mPIPAiLXobfXjWTyfQkc7X7E4XGHUvk6l0Ckc28f36YiIhIFaPESaqU2ev388z3GziUkYfFAgMvrse/r25KsL18X3WLxUKDyCA2BVI1KuO5XGaPx5ppsOknc+5NkZhW0Ooms+pXUa8HQERjuKDXsecF+WbicnAjJG8w71N3mfM6ujwKwSdPTEtl84WrnoFGV8H0/zOPNbk3tBtkDpnb/buZWJwoshnU60xB7EWs2LCDjh064uNzmhplvkHmewupff6q3Z1MjXhz3lfXx2DT/8xhfDt/M4cuFolqXtjrdYl5XyPec/GKiIgIoMRJqohDGXmM+n49s9abi3U2iAzipRtac1FCLQ9H5mFZKeYclj8nm4lJkRr1zGSp1U0Q1bRsx/LxM4fnRbcAbqq4GOtdAvcuhpmPmcUlVk4+9jerL8S1OzbHKL6jOTwQMBwODu2ZidHwSqiMZWF9/KDl9eYteRNs/xnCG5qFJwKr+fdWRETECylxkkpv+Y7DDPviLw5l5GGzWrj38oYMu7IR/r4VslJK5WMYZtnsPz4y5y85CysB2sOg9c3mrU4H76qa5h8G178PTfvApplmGey6nc35QdWhWEFU07InsCIiIuIRSpyk0nK5DD74bQcvz9mM02XQOCqY129tS4vYczBHpTLIy4C1X5lDvw6uP7a9dluzBHbLGyp2HaFzofl15k1ERETEyyhxkkopLdvBo1+vYf7f5hyY/hfG8Xz/lgT6VcOv9MGN8OdEc/5SfuE6VT7+0PJG6HCnucCpiIiIiJyVaniVKZXdur1p3Dd1JXuO5OBnszLmHy0Y0DHec0Uc0pLMggvN/wEhMefvvAX5MPdpc12gIuGNzPWB2gzQPBkRERGRCqTESSoNwzCYumI3Y3/YSL7TRXytAN65rT2t6nhwaN76b+F/j5gLnf78HPR6Di6849zPH0rdY65/lPSn+bzptebaSfUv9665SyIiIiJVhBInqRSy8wt46rv1fPdXEgDdm0Xz6k1tCAv0UDW13LRjVeAA/EIgLw1+eMBcH6nvG+YipefC1nkw/W7IOWoWVej/ATS5+tycS0REREQAcw1JEa+2LTmTfm8v4bu/krBZLYzs3ZQPB7b3XNK0axm8e5mZNFms5no8/94GPZ8DnwBIXATvXAJL3wKXs+LO63KavVqf32gmTbXbwv8tUtIkIiIich6ox0m8VoHTxfRVSYz5cQPZ+U6iQuy8NeBCOjUI90xATgcsfAEWvwaGy1wL6foPzAVKAS55AJpeAz88aC5oOvdpWD8drvtv4dpHZyEzGb4daiZlAB3ugl7jwcd+dscVERERkTJR4iReJy3bwRd/7OaTpTvZl5YLQOcG4bwxoC1RIf6eCSplqzk8bt9f5vM2t0Hv/4B/aPH9ajWAQT/Cqk9g7jOwbxW83xUuGw5dR5xZorNrKXw9BDIPgG+QOQywdQUuQCsiIiIip6XESbxGYkoWk5ck8s3KvWTnm0PcwoP8GNqlPv/q0gAf21mOLM04AL+/YyY1NjtEN4eo5mZvUFRziGwKvickZoaBddUUmD8KHNngXwP6vg4t+p/8PBYLtB8EjXvCzBGw6X+w6CXY+L3Z+xTfsWzxGgYsfRPmjwXDCRFN4JZPIbLJGX4AIiIiInKmlDiJRxmGwbIdh5m0OJEFm5IxDHN705gQ7ry0Pv9oG4u/r+3sTnJ4u5mArJ4Kzvxj2zMPwPafjz23WKFWw8KEqgWW8MZ03PEmttWFvUz1L4d+70JYXNnOG1obbvnMTJhmjoCUzTCxp5k4+dcwe6vsoSfchxXeh8DS/8Lmn8xjtboZrn0N7MFn91mIiIiIyBlR4iQekVfg5Mc1+5m0OJGN+9Pd27s1iWToZQ24tFH42a/LtH+tOR9p4wxzThJAnY5w2SMQFAnJG8zFYw9uMB/nHIXDW83bxu/xAWoDhs0Py1Wj4eL7wFrOXi+LBVr0g/pdzTlPqz+HPcvL/nqbH1z9ork2k8qMi4iIiHiMEic5r3IdTqYu3817v24nOSMPAH9fKze0q8OQS+vTKOose1QMw5wTtHgCbJt/bHvjnmbCVLfzsQQkvkPx12UcOJZMJW/EOLCelCwnNW59B986F55dXIG1oN87ZvJ1eBvkpUNu+gn3acWfB4ZDn5ch9izPLSIiIiJnTYmTnBf5BS6+XrmHtxZs40C6WfAhOtTOwM4J3NaxLjWD/M7uBIYBW2bDbxNg7wpzm8UKLa6Hyx6GmFanfr3FYg6tC60NjboDUOBwsHTmTPpEtzy72I4X09K8iYiIiEilosRJzqkCp4sZq/fxxoIt7DmSA0DtMH8euLIxN7avg59PBS0lNn80LHnDfGyzw4W3m+XBz9UitCIiIiJSrShxknPC5TL437r9vD5/CzsOZQEQEWzn/m4NGdCx7tkXfDheyjZY9rb5uPMwuORBCImuuOOLiIiISLWnxEkqlGEYzN14kNfmbWHTgQwAagT6cs/lDRnYuR6BfufgK7dgLLgKoHEv6PV8xR9fRERERKo9JU5SYVbtPsqYHzawdm8aACF2H+7u2oAhlyYQ4u97bk66ezn8/YM5n6nH2HNzDhERERGp9pQ4SYWYuW4/D09bTX6Bi0A/G0MuTeDuLg2oEXiWRR9OxTBg3jPm4wv/CVHNzt25RERERKRaU+IkZ23i4kSe+2kjhgHdm0Xz4g2tiAi2n/sT//2juSaSbyBc8eS5P5+IiIiIVFtKnOSMuVwGz8/8m4mLEwG44+J6jPlHC2zW87BQq9MB88eYjzsPM8uIi4iIiIicI0qc5IzkOpw8+tUaflq3H4Anejfl/7o2wGI5D0kTwMopcGQ7BEXCpQ+en3OKiIiISLWlxEnKLTU7n7s/+ZM/dh7F12bhlZvacF3buPMXQG46LHzRfHzFE2APOX/nFhEREZFqSYmTlMueI9kMnryC7YeyCLH78P7A9lzSMOL8BrHkDchOgfBG0G7Q+T23iIiIiFRLSpykzNYnpTFkyh8cysijdpg/k4d0oGlM6PkNIn3fscVuu48B2zkqcy4iIiIichwlTlImCzcnc9/nq8jOd9I0JoTJQzpQOyzg/Afyy/NQkAPxF0PTa8//+UVERESkWlLiJKf11Z97GDl9HU6XwSUNw3nvjvaEnqsFbU/l4AZYPdV83PNZOF+FKERERESk2lPiJKe0ZFsKj3+7FsOAfm1jeenGNvj5WD0TzLzRYLig+XUQ39EzMYiIiIhItaTESU7qcGYej0xbjWHATe3r8NKNrc9fufET7VgI2+aB1QeuGu2ZGERERESk2vJQ14F4O8MwePzbtSRn5NEoKphx17X0XNLkcsHcZ8zHFw2F8IaeiUNEREREqi0lTlKqT5btYv7fyfjZrLx564UE+Nk8F8z6b+DAWrCHwuWPeS4OEREREam2lDhJCX/vT+f5mX8DMLJPU5rHnueS48dz5MKCcebjyx6GoPO8ZpSIiIiICEqc5AQ5+U4e/OIv8gtcXNk0isGXJHg2oBUfQNoeCImFTvd6NhYRERERqbaUOEkxz/20ka3JmUSG2HnZk8UgAAry4LdXzcdXPg1+gZ6LRURERESqNSVO4jZ7/X4+X74biwVeu7kt4cF2zwa0awnkpkJwNLS51bOxiIiIiEi1psRJANiXmsPj364D4F9dG3BZYy+YS7Rljnl/QS+werA4hYiIiIhUe0qcBKfL4JFpq0nLcdC6ThiP9mji6ZDAMGDzLPPxBVd7NhYRERERqfaUOAnv/LKN5YlHCPKz8eatF+Ln4wVfi0ObIXUX2OzQ4ApPRyMiIiIi1ZzHr5DffvttEhIS8Pf3p1OnTqxYseKU+7/++us0adKEgIAA4uPjeeSRR8jNzT1P0VY9K3cd5fUFWwEYd11LEiKCPBxRoS2FvU31u4Kfl8QkIiIiItWWRxOnadOmMXz4cEaPHs2qVato06YNvXr1Ijk5udT9p06dyhNPPMHo0aP5+++/mThxItOmTePJJ588z5FXDWk5Dh784i+cLoPr2sZyfbs4T4d0zPHzm0REREREPMyjidOECRO4++67GTJkCM2bN+e9994jMDCQSZMmlbr/0qVLufTSS7nttttISEigZ8+eDBgw4LS9VFKSYRg89d06klJziK8VwHP9Wnq29Pjxso/AnuXmY81vEhEREREv4OOpE+fn57Ny5UpGjhzp3ma1WunevTvLli0r9TWXXHIJn332GStWrKBjx47s2LGDmTNncscdd5z0PHl5eeTl5bmfp6enA+BwOHA4HBX0bs5cUQznO5avVybxv7X78bFamHBjK/xt5z+Gk7FsmoWP4cKIakFBUAx4KC5PtY2UjdrHe6ltvJvax7upfbyX2sa7nWn7lGd/i2EYRrmOXkH27dtHXFwcS5cupXPnzu7tjz32GL/++ivLly8v9XVvvvkmI0aMwDAMCgoKuOeee3j33XdPep4xY8YwduzYEtunTp1KYGD1XFA1KQteW2fDYVi4tq6THnEe+QqcVPvEt6mTupwt0X35O/YmT4cjIiIiIlVUdnY2t912G2lpaYSGhp5yX4/1OJ2JhQsXMn78eN555x06derEtm3beOihh3j22Wd55plnSn3NyJEjGT58uPt5eno68fHx9OzZ87QfzvngcDiYN28ePXr0wNfX95yfLzOvgOvf/R2HkU3XxuG8+s92WK1eMkQPwOnA57VhADTofT/14y7yWCjnu22kfNQ+3ktt493UPt5N7eO91Dbe7Uzbp2g0Wll4LHGKiIjAZrNx8ODBYtsPHjxITExMqa955plnuOOOO7jrrrsAaNWqFVlZWfzrX//iqaeewmotOWXLbrdjt9tLbPf19fWqL/35iMcwDEZ/s57Ew9nUDvPn9VvbYbf7ndNzltveZZCXDoER+NTt6BUL33rbd0WKU/t4L7WNd1P7eDe1j/dS23i38rZPefb1WHEIPz8/2rdvz4IFC9zbXC4XCxYsKDZ073jZ2dklkiObzbyw9tCIw0pl6ord/LBmHzarhbcGXEitIC9LmgA2zzbvL+jlFUmTiIiIiAh4eKje8OHDGTRoEBdddBEdO3bk9ddfJysriyFDhgAwcOBA4uLieOGFFwDo27cvEyZM4MILL3QP1XvmmWfo27evO4GS0q1PSmPsjxsBePzqJlyUUMvDEZ3EluMSJxERERERL+HRxOmWW27h0KFDjBo1igMHDtC2bVtmz55NdHQ0ALt37y7Ww/T0009jsVh4+umnSUpKIjIykr59+/L888976i1UCum5Du6fuor8Ahfdm0Vxd5cGng6pdCnb4Mh2sPpCg26ejkZERERExM3jxSGGDRvGsGHDSv3bwoULiz338fFh9OjRjB49+jxEVjUYhsHj36xl1+Fs4moE8MpNbbxnvaYTbZll3idcBv6eL9whIiIiIlLEowvgyrn38dKdzFp/AF+bhf/ediE1Ar1wXlORLXPMey16KyIiIiJeRolTFbZmTyrPz/wbgJG9m3Fh3ZoejugUco7CrqXmY81vEhEREREvo8SpikrLdnDf56twOA2ubhHDkEsTPB3SqW1bAIYTIptCrfqejkZEREREpBglTlWQYRiM+GYNSak51K0VyH9ubO2985qKuIfpqbdJRERERLyPEqcqaOLiROZtPIifzco7t7cjLMDLF2lzFsDWuebjC3p7NhYRERERkVIocapiVu46youzNgHwzLXNaBkX5uGIymDvCshNhYCaUKeDp6MRERERESlBiVMVklfgZPhXqylwGVzbujb/vLiep0Mqm82FZcgb9QCbxyvki4iIiIiUoMSpCpm8ZCe7DmcTGWLnhetbef+8piJF85uaqAy5iIiIiHgnJU5VxKGMPP778zYAHr+6KSH+Xj6vqciRHZCyGaw+0PAqT0cjIiIiIlIqJU5VxCtzNpOZV0CbOmFcf2Gcp8Mpu6LeprqdIaCGR0MRERERETkZJU5VwPqkNL5auQeAUX1bYLVWkiF6AFtmm/cXaJieiIiIiHgvJU6VnGEYjP1xA4YB17WNpX29mp4Oqexy02HnEvOxEicRERER8WJKnCq5/63dzx87jxLga+OJ3k09HU75bP8ZXA4IbwQRjTwdjYiIiIjISSlxqsRy8p3uNZvuubwhtcMCPBxROWmYnoiIiIhUEkqcKrEPFu0gKTWHuBoB/KtrA0+HUz4uJ2ydaz5W4iQiIiIiXk6JUyW1Py2H937dDsATvZsS4GfzcETllLQSsg+DPQzqXuzpaERERERETkmJUyX1n1mbyHE46ZBQk2tb1/Z0OOW3eZZ53+gqsFWSNadEREREpNpS4lQJrdx1lBmr92GxwOi+LbBYKlH58SJF6zc16e3ZOEREREREykCJUyXjchmM+3EDADe3j6dlXJiHIzoDqbsheQNYrNCou6ejERERERE5LSVOlcz0v5JYszeNYLsPI3o18XQ4Z2bnYvO+TgcIrOXZWEREREREykCJUyWSmVfAS7PN8uMPXNmIyBC7hyM6Q6m7zfvISpr4iYiIiEi1o8SpEnnnl20kZ+RRLzyQwZcmeDqcM5e217wPi/dsHCIiIiIiZaTEqZLYcySbjxYnAvD0Nc2x+1Sy8uPHcydOdTwbh4iIiIhIGSlxqiTGz/yb/AIXlzWKoHuzKE+Hc3aKEqfQOM/GISIiIiJSRkqcKoFdh7OYtf4ANquFZ65tXjnLjxcxDEhPMh+rx0lEREREKgklTpXAtuRMAJrGhNAkJsTD0ZylnKPgyDYfq8dJRERERCoJJU6VwJ4jZqJRp2aAhyOpAEXD9IIiwdffs7GIiIiIiJSREqdKYO/RHADiawZ6OJIKoMIQIiIiIlIJKXGqBPYcNXuc4mtVocRJw/REREREpBJR4lQJ7DlS2ONUqwoM1UvXGk4iIiIiUvkocaoE9h4tmuNUhXqcNFRPRERERCoRJU5eLi3HQXpuAVDFikOEaaieiIiIiFQeSpy8XFFFvYhgPwL9fDwcTQVIK1rDSUP1RERERKTyUOLk5YqG6cVVhWF6zgLI2Gc+1lA9EREREalElDh5uWOlyKvAML3MA2C4wOoLQVGejkZEREREpMyUOHm5oqF6VasUeSxY9dUTERERkcpDV69ebo8WvxURERER8TglTl7uWCnyKjBUT4mTiIiIiFRSSpy8mGEYxy1+qx4nERERERFPUeLkxQ5n5ZPjcGKxQGwNf0+Hc/bcc5y0hpOIiIiIVC5ekTi9/fbbJCQk4O/vT6dOnVixYsVJ973iiiuwWCwlbtdcc815jPj8KCoMER3ij93H5uFoKkB6UY+T1nASERERkcrF44nTtGnTGD58OKNHj2bVqlW0adOGXr16kZycXOr+06dPZ//+/e7b+vXrsdls3HTTTec58nPPXYq8VhWY3wQaqiciIiIilZbHE6cJEyZw9913M2TIEJo3b857771HYGAgkyZNKnX/WrVqERMT477NmzePwMDAKpk47SksDFElKurlZ0HOUfNxmIbqiYiIiEjl4uPJk+fn57Ny5UpGjhzp3ma1WunevTvLli0r0zEmTpzIrbfeSlBQUKl/z8vLIy8vz/08PT0dAIfDgcPhOIvoK0ZRDKXFsislC4DaYXaviPWsHN6FL2DYQyiwBUIleD+nahvxPLWP91LbeDe1j3dT+3gvtY13O9P2Kc/+FsMwjHIdvQLt27ePuLg4li5dSufOnd3bH3vsMX799VeWL19+ytevWLGCTp06sXz5cjp27FjqPmPGjGHs2LEltk+dOpXAQO/uyXl3o5VNaVYGNHRycZTHmqlCRKav45LtL5PuX4dfmo33dDgiIiIiImRnZ3PbbbeRlpZGaGjoKff1aI/T2Zo4cSKtWrU6adIEMHLkSIYPH+5+np6eTnx8PD179jzth3M+OBwO5s2bR48ePfD19S32t9e2LAayuebyTnSqX8szAVYQy+ojsB2C45rSp08fT4dTJqdqG/E8tY/3Utt4N7WPd1P7eC+1jXc70/YpGo1WFh5NnCIiIrDZbBw8eLDY9oMHDxITE3PK12ZlZfHll18ybty4U+5nt9ux2+0ltvv6+nrVl/7EeFwug32puQAkRIZ4VaxnJHM/ANYa8Vgr2Xvxtu+KFKf28V5qG++m9vFuah/vpbbxbuVtn/Ls69HiEH5+frRv354FCxa4t7lcLhYsWFBs6F5pvv76a/Ly8vjnP/95rsP0iOSMPPKdLmxWCzGhVWgNJxWGEBEREZFKyOND9YYPH86gQYO46KKL6NixI6+//jpZWVkMGTIEgIEDBxIXF8cLL7xQ7HUTJ06kX79+hIeHeyLsc66ool5sDX98bB4vfnj20rSGk4iIiIhUXh5PnG655RYOHTrEqFGjOHDgAG3btmX27NlER0cDsHv3bqzW4onD5s2bWbx4MXPnzvVEyOdF0eK3VaIUOWgNJxERERGp1DyeOAEMGzaMYcOGlfq3hQsXltjWpEkTPFgM8LzYc8Rc/LZOzSqw+K1hQHqS+ThUQ/VEREREpPKpAmPAqqa9VWnx2+zDUJALWCA01tPRiIiIiIiUmxInL1U0xym+VhVInNL2mPfB0eBTssKhiIiIiIi3U+LkparUUL20wmF6qqgnIiIiIpWUEicvVOB0cSDdXMOpavQ4qTCEiIiIiFRuSpy80P60XJwuAz8fK5HBVWBoW9FQvVAlTiIiIiJSOSlx8kJFpcjr1AzAarV4OJoKUFRRTz1OIiIiIlJJKXHyQkWFIepUhYp6oKF6IiIiIlLpKXHyQnuPmoUh4qtCYQhQcQgRERERqfSUOHmhoqF6VaIwhNMBGfvNx2Hxno1FREREROQMKXHyQnuOVqFS5On7AANsdgiM8HQ0IiIiIiJnRImTF9pbtPhtVZjjVFQYIjQWrPq6iYiIiEjlpCtZL5PrcHIwPQ+oIkP1VBhCRERERKoAJU5eJinVHKYX5GejZqCvh6OpAEVrOClxEhEREZFKTImTlzm2hlMgFksVWMMpTWs4iYiIiEjlp8TJy7hLkdeqAoUhQEP1RERERKRKUOLkZarc4rfu4hBKnERERESk8lLi5GX2HqlCpchBc5xEREREpEpQ4uRl3KXIq0JFvbwMyE0zH4fFeTYWEREREZGzoMTJyxQtflsl1nAqKgzhHwb2EM/GIiIiIiJyFpQ4eZGsvAKOZOUDUKcqFIdwF4aI92wcIiIiIiJnSYmTFymqqBcW4EuofxVawylUw/REREREpHJT4uRFitZwqjKlyNO1hpOIiIiIVA1KnLxIUSnyKjG/CbSGk4iIiIhUGUqcvMieKleKXImTiIiIiFQN5U6cEhISGDduHLt37z4X8VRrVaoUOShxEhEREZEqo9yJ08MPP8z06dNp0KABPXr04MsvvyQvL+9cxFbtVKlS5C6X5jiJiIiISJVxRonT6tWrWbFiBc2aNeOBBx6gdu3aDBs2jFWrVp2LGKsFwzDYW1gcokoM1ctOAWc+YIGQ2p6ORkRERETkrJzxHKd27drx5ptvsm/fPkaPHs1HH31Ehw4daNu2LZMmTcIwjIqMs8pLzy0gI68AgDpVocepqBR5SG2wVYHS6iIiIiJSrfmc6QsdDgffffcdkydPZt68eVx88cUMHTqUvXv38uSTTzJ//nymTp1akbFWaUVrOEUE2wnws3k4mgrgnt+kNZxEREREpPIrd+K0atUqJk+ezBdffIHVamXgwIG89tprNG3a1L1P//796dChQ4UGWtW55zdVlTWc0jS/SURERESqjnInTh06dKBHjx68++679OvXD1/fksOw6tevz6233lohAVYXRT1OVWKYHqiinoiIiIhUKeVOnHbs2EG9evVOuU9QUBCTJ08+46Cqo6TUoop6VaTHKb0wcQpV4iQiIiIilV+5i0MkJyezfPnyEtuXL1/On3/+WSFBVUfHhuqpx0lERERExNuUO3G6//772bNnT4ntSUlJ3H///RUSVHV0bKheFelxUuIkIiIiIlVIuROnjRs30q5duxLbL7zwQjZu3FghQVU3hnH8UL0q0ONUkAeZB83HSpxEREREpAood+Jkt9s5ePBgie379+/Hx+eMq5tXaxkOyHW4sFggtkYV6HFK32fe+/hDYLhnYxERERERqQDlTpx69uzJyJEjSUtLc29LTU3lySefpEePHhUaXHVxJM+8jwn1x8/njNck9h5Fw/RC48Bi8WwsIiIiIiIVoNxdRK+88gpdu3alXr16XHjhhQCsXr2a6OhoPv300woPsDo4nGcmF1VimB5AutZwEhEREZGqpdyJU1xcHGvXruXzzz9nzZo1BAQEMGTIEAYMGFDqmk5yekU9TnWqzOK3hcVDwuI9G4eIiIiISAU5o0lJQUFB/Otf/6roWKqtw7lVrMcprajHKc6zcYiIiIiIVJAzruawceNGdu/eTX5+frHt//jHP846qOrmcFGPk0qRi4iIiIh4pXJXItixYwdt2rShZcuWXHPNNfTr149+/frRv39/+vfvX+4A3n77bRISEvD396dTp06sWLHilPunpqZy//33U7t2bex2OxdccAEzZ84s93m9yZGiOU5a/FZERERExCuVO3F66KGHqF+/PsnJyQQGBrJhwwYWLVrERRddxMKFC8t1rGnTpjF8+HBGjx7NqlWraNOmDb169SI5ObnU/fPz8+nRowc7d+7km2++YfPmzXz44YfExVXeIWFOl8HRwh6nKpM4FRWHCFXiJCIiIiJVQ7mH6i1btoyff/6ZiIgIrFYrVquVyy67jBdeeIEHH3yQv/76q8zHmjBhAnfffTdDhgwB4L333uOnn35i0qRJPPHEEyX2nzRpEkeOHGHp0qXuQhQJCQmnPEdeXh55eXnu5+np6QA4HA4cDkeZYz1Xko5k4jQs+FgthAfYvCKms5Kbjm9e4WccGAWV+P0UtUWlb5MqSu3jvdQ23k3t493UPt5LbePdzrR9yrO/xTAMozwHr1mzJqtWraJ+/fo0bNiQjz76iG7durF9+3ZatWpFdnZ2mY6Tn59PYGAg33zzDf369XNvHzRoEKmpqXz//fclXtOnTx9q1apFYGAg33//PZGRkdx22208/vjj2Gy2Us8zZswYxo4dW2L71KlTCQz0fA/P9nR4c4MP4XaDUe2cng7nrIXk7OHKTU+RbwtiVut3PR2OiIiIiMhJZWdnc9ttt5GWlkZoaOgp9y13j1PLli1Zs2YN9evXp1OnTrz00kv4+fnxwQcf0KBBgzIfJyUlBafTSXR0dLHt0dHRbNq0qdTX7Nixg59//pnbb7+dmTNnsm3bNu677z4cDgejR48u9TUjR45k+PDh7ufp6enEx8fTs2fP034458M3f+6BDX/TOLYWffp08HQ4Z82ybR5sAt+IBvTp08fT4ZwVh8PBvHnz6NGjh0rteyG1j/dS23g3tY93U/t4L7WNdzvT9ikajVYW5U6cnn76abKysgAYN24c1157LV26dCE8PJxp06aV93Dl4nK5iIqK4oMPPsBms9G+fXuSkpJ4+eWXT5o42e127HZ7ie2+vr5e8aXfn25WJaxbK9Ar4jlrmfsBsNSIrxrvB+/5rkjp1D7eS23j3dQ+3k3t473UNt6tvO1Tnn3LnTj16tXL/bhRo0Zs2rSJI0eOULNmTSwWS5mPExERgc1m4+DBg8W2Hzx4kJiYmFJfU7t2bXx9fYsNy2vWrBkHDhwgPz8fPz+/cr4bz9uTmgNAXI0qUorcXRii8hbsEBERERE5Ubmq6jkcDnx8fFi/fn2x7bVq1SpX0gTg5+dH+/btWbBggXuby+ViwYIFdO7cudTXXHrppWzbtg2Xy+XetmXLFmrXrl0pkyaApKNm4qQ1nEREREREvFe5EidfX1/q1q2L01kxRQyGDx/Ohx9+yMcff8zff//NvffeS1ZWlrvK3sCBAxk5cqR7/3vvvZcjR47w0EMPsWXLFn766SfGjx/P/fffXyHxeMLewsQpXomTiIiIiIjXKvdQvaeeeoonn3ySTz/9lFq1ap3VyW+55RYOHTrEqFGjOHDgAG3btmX27NnughG7d+/Gaj2W28XHxzNnzhweeeQRWrduTVxcHA899BCPP/74WcXhKQ6niwPpuQDEKXESEREREfFa5U6c/vvf/7Jt2zZiY2OpV68eQUFBxf6+atWqch1v2LBhDBs2rNS/lbagbufOnfn999/LdQ5vtT81F5cBvhaDyODKOdSwGJcT0veZj5U4iYiIiEgVUu7E6fg1l+Ts7DlqrnlVy59yzxHzSpnJ4HKAxQbBpRf4EBERERGpjMqdOJ2s7LeU354jhYmTvVxrEHuvoop6IbXBVu6vloiIiIiI1ypXcQipWO4ep5LLTFVOaXvMew3TExEREZEqptzdAlar9ZTDyiqq4l51EBlsp3VcKLF+Rz0dSsVIK+xxCtMaTiIiIiJStZQ7cfruu++KPXc4HPz11198/PHHjB07tsICqw4GX1qf2zvWYebMmZ4OpWKoop6IiIiIVFHlTpyuu+66EttuvPFGWrRowbRp0xg6dGiFBCaVkHuoXrxn4xARERERqWAVNsfp4osvZsGCBRV1OKmMikqRh8Z6Ng4RERERkQpWIYlTTk4Ob775JnFxmttSrWUcMO9Dans2DhERERGRClbuoXo1a9YsVhzCMAwyMjIIDAzks88+q9DgpBJxOSHzoPlYiZOIiIiIVDHlTpxee+21YomT1WolMjKSTp06UbNmzQoNTiqRrENgOMFiheAoT0cjIiIiIlKhyp04DR48+ByEIZVe0fym4Giw2jwbi4iIiIhIBSv3HKfJkyfz9ddfl9j+9ddf8/HHH1dIUFIJaX6TiIiIiFRh5U6cXnjhBSIiIkpsj4qKYvz48RUSlFRCGYU9TkqcRERERKQKKnfitHv3burXr19ie7169di9e3eFBCWVUPp+8z5UiZOIiIiIVD3lTpyioqJYu3Ztie1r1qwhPDy8QoKSSkhD9URERESkCit34jRgwAAefPBBfvnlF5xOJ06nk59//pmHHnqIW2+99VzEKJWBhuqJiIiISBVW7qp6zz77LDt37uSqq67Cx8d8ucvlYuDAgZrjVJ0V9ThpqJ6IiIiIVEHlTpz8/PyYNm0azz33HKtXryYgIIBWrVpRr169cxGfVBbp6nESERERkaqr3IlTkcaNG9O4ceOKjEUqK0cO5Kaaj5U4iYiIiEgVVO45TjfccAP/+c9/Smx/6aWXuOmmmyokKKlkMgor6vkEgH+YZ2MRERERETkHyp04LVq0iD59+pTY3rt3bxYtWlQhQUklc/z8JovFs7GIiIiIiJwD5U6cMjMz8fPzK7Hd19eX9PT0CglKKhnNbxIRERGRKq7ciVOrVq2YNm1aie1ffvklzZs3r5CgpJIpGqqnxElEREREqqhyF4d45plnuP7669m+fTtXXnklAAsWLGDq1Kl88803FR6gVALuxW9jPBuHiIiIiMg5Uu7EqW/fvsyYMYPx48fzzTffEBAQQJs2bfj555+pVavWuYhRvF3RUL3QWM/GISIiIiJyjpxROfJrrrmGa665BoD09HS++OILRowYwcqVK3E6nRUaoFQC7h4nDdUTERERkaqp3HOciixatIhBgwYRGxvLq6++ypVXXsnvv/9ekbFJZZGh4hAiIiIiUrWVq8fpwIEDTJkyhYkTJ5Kens7NN99MXl4eM2bMUGGI6sowipcjFxERERGpgsrc49S3b1+aNGnC2rVref3119m3bx9vvfXWuYxNKoOco1CQaz4OVnEIEREREamaytzjNGvWLB588EHuvfdeGjdufC5jksqkqLcpoBb4+ns2FhERERGRc6TMPU6LFy8mIyOD9u3b06lTJ/773/+SkpJyLmOTykDzm0RERESkGihz4nTxxRfz4Ycfsn//fv7v//6PL7/8ktjYWFwuF/PmzSMjI+NcxineSvObRERERKQaKHdVvaCgIO68804WL17MunXrePTRR3nxxReJioriH//4x7mIUbxZ+n7zXovfioiIiEgVdsblyAGaNGnCSy+9xN69e/niiy8qKiapTNxD9bT4rYiIiIhUXWeVOBWx2Wz069ePH374oSIOJ5WJe/Fb9TiJiIiISNVVIYmTVGPphT1OoepxEhEREZGqS4mTnB13j5OKQ4iIiIhI1aXESc6cswCyks3HSpxEREREpApT4iRnLisZDBdYbBAU6eloRERERETOGSVOcuaOL0Vu1VdJRERERKour7jaffvtt0lISMDf359OnTqxYsWKk+47ZcoULBZLsZu/v/95jFbcMooSJw3TExEREZGqzeOJ07Rp0xg+fDijR49m1apVtGnThl69epGcnHzS14SGhrJ//373bdeuXecxYnHL0OK3IiIiIlI9eDxxmjBhAnfffTdDhgyhefPmvPfeewQGBjJp0qSTvsZisRATE+O+RUdHn8eIxU2lyEVERESkmvDx5Mnz8/NZuXIlI0eOdG+zWq10796dZcuWnfR1mZmZ1KtXD5fLRbt27Rg/fjwtWrQodd+8vDzy8vLcz9PT0wFwOBw4HI4KeidnrigGb4ilvGzp+7ACzsAoXJUw/tOpzG1THah9vJfaxrupfbyb2sd7qW2825m2T3n2txiGYZTr6BVo3759xMXFsXTpUjp37uze/thjj/Hrr7+yfPnyEq9ZtmwZW7dupXXr1qSlpfHKK6+waNEiNmzYQJ06dUrsP2bMGMaOHVti+9SpUwkMDKzYN1TNdN72H6IyNrCq7r/YE36Zp8MRERERESmX7OxsbrvtNtLS0ggNDT3lvpUucTqRw+GgWbNmDBgwgGeffbbE30vrcYqPjyclJeW0H8754HA4mDdvHj169MDX19fT4ZSLz/uXYknZTMFt0zHqd/V0OBWuMrdNdaD28V5qG++m9vFuah/vpbbxbmfaPunp6URERJQpcfLoUL2IiAhsNhsHDx4stv3gwYPExJSt4ICvry8XXngh27ZtK/Xvdrsdu91e6uu86UvvbfGUScYBAHxq1oHKFns5VMq2qUbUPt5LbePd1D7eTe3jvdQ23q287VOefT1aHMLPz4/27duzYMEC9zaXy8WCBQuK9UCditPpZN26ddSurZLY51V+FuSlmY9VjlxEREREqjiP9jgBDB8+nEGDBnHRRRfRsWNHXn/9dbKyshgyZAgAAwcOJC4ujhdeeAGAcePGcfHFF9OoUSNSU1N5+eWX2bVrF3fddZcn30b1U9jbhG8Q2EM8G4uIiIiIyDnm8cTplltu4dChQ4waNYoDBw7Qtm1bZs+e7S4xvnv3bqzWYx1jR48e5e677+bAgQPUrFmT9u3bs3TpUpo3b+6pt1A9Fa3hFFobLBbPxiIiIiIico55PHECGDZsGMOGDSv1bwsXLiz2/LXXXuO11147D1HJKaUXLX6rYXoiIiIiUvV5fAFcqaQyChe/VeIkIiIiItWAEic5M0VznELKVv1QRERERKQyU+IkZya9sMcpNNazcYiIiIiInAdKnOTMqMdJRERERKoRJU5yZtxznNTjJCIiIiJVnxInKT/DONbjFKriECIiIiJS9SlxkvLLPgLOfPNxsIbqiYiIiEjVp8RJyq9o8dvACPDx82wsIiIiIiLngRInKb8MLX4rIiIiItWLEicpv6LESfObRERERKSaUOIk5Zde1OOk+U0iIiIiUj0ocZLyUylyEREREalmlDhJ+WnxWxERERGpZpQ4SfmlF/Y4harHSURERESqByVOUn7qcRIRERGRakaJk5SP0wFZh8zHmuMkIiIiItWEEicpn8yDgAFWXwgM93Q0IiIiIiLnhRInKZ/jS5Fb9fURERERkepBV75SPkWL34Zo8VsRERERqT6UOEn5ZGjxWxERERGpfpQ4SfmoFLmIiIiIVENKnKR8VIpcRERERKohJU5SPhmFPU4qRS4iIiIi1YgSJykf9TiJiIiISDWkxEnKp6gcueY4iYiIiEg1osRJyi4vA/IzzMfqcRIRERGRakSJk5Rd0TA9vxCwh3g2FhERERGR80iJk5Rd0RpOoVr8VkRERESqFyVOUnbpWvxWRERERKonJU5SdipFLiIiIiLVlBInKTuVIhcRERGRakqJk5RdemGPk0qRi4iIiEg1o8RJyk49TiIiIiJSTSlxkrIrqqqnOU4iIiIiUs0ocZKycbnU4yQiIiIi1ZYSJymb7MPgcgAWJU4iIiIiUu0ocZKyKRqmFxQJNl/PxiIiIiIicp4pcZKyydDityIiIiJSfSlxkrIpSpxUilxEREREqiElTlI26epxEhEREZHqyysSp7fffpuEhAT8/f3p1KkTK1asKNPrvvzySywWC/369Tu3AQpkFC5+q1LkIiIiIlINeTxxmjZtGsOHD2f06NGsWrWKNm3a0KtXL5KTk0/5up07dzJixAi6dOlyniKt5lSKXERERESqMY8nThMmTODuu+9myJAhNG/enPfee4/AwEAmTZp00tc4nU5uv/12xo4dS4MGDc5jtNVYuuY4iYiIiEj15ePJk+fn57Ny5UpGjhzp3ma1WunevTvLli076evGjRtHVFQUQ4cO5bfffjvlOfLy8sjLy3M/T09PB8DhcOBwOM7yHZy9ohi8IZZT8cnYjwVwBESAl8daUSpL21RXah/vpbbxbmof76b28V5qG+92pu1Tnv09mjilpKTgdDqJjo4utj06OppNmzaV+prFixczceJEVq9eXaZzvPDCC4wdO7bE9rlz5xIYGFjumM+VefPmeTqEk7K6HPTNTgFg/vIN5Pvs9nBE55c3t42ofbyZ2sa7qX28m9rHe6ltvFt52yc7O7vM+3o0cSqvjIwM7rjjDj788EMiIiLK9JqRI0cyfPhw9/P09HTi4+Pp2bMnoaGh5yrUMnM4HMybN48ePXrg6+ulC8um7YE1YNj86N73ZrBYPB3ReVEp2qYaU/t4L7WNd1P7eDe1j/dS23i3M22fotFoZeHRxCkiIgKbzcbBgweLbT948CAxMSWLEGzfvp2dO3fSt29f9zaXywWAj48PmzdvpmHDhsVeY7f/f3v3HhxVYf5//LOby+YKSQi5cQu3kgASNBGM6LcqEUS/3oqWdmiNcaqDJBabdgaxleBoG7SWUvvDUC9oR6xYnGLVHxdDBBwdELkJQoio3ASSgAIJCSRL9nz/WLOw5LK5kXOSvF8zmT17ztmzT3xYJ5855zzrkMPhaHCsgIAAS/2jt1o9XqqPS5Js4XEKCAw0uZjOZ+negP5YGL2xNvpjbfTHuuiNtbW2P63Z19ThEIGBgUpNTVVRUZFnncvlUlFRkdLT0xvsn5SUpF27dmnHjh2enzvuuEM33nijduzYoQEDBnRm+T1H/ZffMoocAAAAPZTpl+rl5uYqMzNTaWlpGjdunBYuXKiqqiplZWVJku677z7169dP+fn5CgoK0ujRo71eHxERIUkN1qMDVfLltwAAAOjZTA9O06ZN0/HjxzV37lyVlpZq7NixWr16tWdgxKFDh2S3mz41vWer+OHLbxlFDgAAgB7K9OAkSTk5OcrJyWl02/r165t97WuvvdbxBcEbX34LAACAHo5TOfCNe5wAAADQwxGc4Bv3OAEAAKCHIziheYYhVfwQnLjHCQAAAD0UwQnNq6mQnFXuZc44AQAAoIciOKF5pbvcj+EJUmCoubUAAAAAJiE4oXmHP3U/Dhhnbh0AAACAiQhOaN7hze7HAePNrQMAAAAwEcEJTTOMi844EZwAAADQcxGc0LTvvpLOnpT8g6S4K8yuBgAAADANwQlNO7TJ/ZhwleQfaG4tAAAAgIkITmgagyEAAAAASQQnNIfBEAAAAIAkghOaUv29dKLEvUxwAgAAQA9HcELjvt3ifuwzTArtY24tAAAAgMkITmgcY8gBAAAAD4ITGsdgCAAAAMCD4ISG6s5LR7a6lznjBAAAABCc0IiyLyRnteToLUWPMLsaAAAAwHQEJzTkGUN+tWTnnwgAAADAX8VoiMEQAAAAgBeCExrynHFiMAQAAAAgEZxwqYqj0ulDks0u9Us1uxoAAADAEghO8FZ/til2lOQIN7cWAAAAwCIITvDmuUyP+5sAAACAegQneGMwBAAAANAAwQkXOM9Kxz53LzMYAgAAAPAgOOGCozskl1MKi5UiBpldDQAAAGAZBCdc4LlMb5xks5lbCwAAAGAhBCdcwP1NAAAAQKMITnAzjIuC0zXm1gIAAABYDMEJbt9/I1V/J/k5pPgxZlcDAAAAWArBCW71Z5sSrpT8HebWAgAAAFgMwQluFw+GAAAAAOCF4AS3w5vdjwyGAAAAABogOHUVhz6Vvvv68hz77CmpvNi9zBknAAAAoAGCU1dQtlt69RbpzZ9dnuMf2SLJkCIHS2Exl+c9AAAAgC6M4NQV7H5HMlzSiS+lmjMdf3wu0wMAAACaRXDqCorfvbB8cn/HH5/BEAAAAECzCE5Wd/xL6fjeC8+//6Zjj++qk77d4l7mjBMAAADQKIKT1e19z/t5Rwen8j1S7RkpMFyKSe7YYwMAAADdBMHJ6op/CE69B7ofO3qyXv1lev3TJLtfxx4bAAAA6CYsEZwWLVqkxMREBQUFafz48dq8eXOT+/7nP/9RWlqaIiIiFBoaqrFjx+r111/vxGo70anD0tHtkmzSNTPc677v4HucGAwBAAAA+GR6cHrrrbeUm5urvLw8bdu2TSkpKZo8ebLKy8sb3T8qKkq///3vtXHjRu3cuVNZWVnKysrSmjVrOrnyTrD3fffjoGulAde4lzv6Uj0GQwAAAAA+mR6cFixYoAcffFBZWVkaOXKkFi9erJCQEC1ZsqTR/W+44QbdfffdSk5O1tChQzVr1iyNGTNGH3/8cSdX3gn2/DBNL/l2KWqwe7nyqFRb3THHryyTTh6QZHNfqgcAAACgUf5mvnltba22bt2qOXPmeNbZ7XZlZGRo48aNPl9vGIY+/PBDlZSU6Jlnnml0n5qaGtXU1HieV1RUSJKcTqecTmc7f4P2q6+hQS1nyuV/aKNskpzDp0gB4fIPipDt3Ck5j++TYka2+71tBzbKX5IRk6zzfiGSBf57WEmTvYEl0B/rojfWRn+sjf5YF72xtrb2pzX7mxqcTpw4obq6OsXGxnqtj42N1d69e5t4lXT69Gn169dPNTU18vPz0wsvvKCbb7650X3z8/P15JNPNlj/wQcfKCQkpH2/QAcqLCz0ej7oxDqNlaGTIUP00cefS/pc/2OPUqROafvat3Usov1niEYeWabhkg644rRz5cp2H6+7urQ3sBb6Y130xtroj7XRH+uiN9bW2v5UV7f8Si5Tg1NbhYeHa8eOHTpz5oyKioqUm5urIUOG6IYbbmiw75w5c5Sbm+t5XlFRoQEDBmjSpEnq1atXJ1bdOKfTqcLCQt18880KCAjwrPd78zVJUq/x03Xrtbe619X+V9r9jVIHR8qVfmu739vvn/9PkjTg2nvU/4r2H6+7aao3sAb6Y130xtroj7XRH+uiN9bW1v7UX43WEqYGp+joaPn5+amsrMxrfVlZmeLi4pp8nd1u17BhwyRJY8eOVXFxsfLz8xsNTg6HQw6Ho8H6gIAAS/2j96rn7CnpwEeSJL9Rd8mvfn20+3f2O3Xgwrq2cp6Tju2QJPknpksW+m9hNVb7twJv9Me66I210R9roz/WRW+srbX9ac2+pg6HCAwMVGpqqoqKijzrXC6XioqKlJ6e3uLjuFwur/uYurwv10iu81LfZE9YkiRFDXE/dsRkvWOfS3W1UmhfKXJw+48HAAAAdGOmX6qXm5urzMxMpaWlady4cVq4cKGqqqqUlZUlSbrvvvvUr18/5efnS3Lfs5SWlqahQ4eqpqZGK1eu1Ouvv66CggIzf42OVXzRNL2LRQ11P3bEdzl9+5n7sf84yWZr//EAAACAbsz04DRt2jQdP35cc+fOVWlpqcaOHavVq1d7BkYcOnRIdvuFE2NVVVWaOXOmvv32WwUHByspKUlLly7VtGnTzPoVOlZtlfTVWvfyyDu8t9Wfcar4VnKelQKC2/4+Zbvdj/EpbT8GAAAA0EOYHpwkKScnRzk5OY1uW79+vdfzp59+Wk8//XQnVGWSr9ZK589JkYlS7GjvbSFRkqO3VHPa/f1LMcltf5/yH4JTbPvHmgMAAADdnelfgItLFL/nfky+veEldDbbhS/Cbc99Tq466XiJe7kDvg8KAAAA6O4ITlZyvsY9GEKSku9ofJ8+9fc5tSM4ff+N+6yWf7D7zBYAAACAZhGcrGT/R1JNhRQWJ/Vr4gtu6+9z+u7rtr9P/f1NMUmS3a/txwEAAAB6CIKTlez5r/sx+X8lexOt6YiR5OXF7seYUW0/BgAAANCDEJyswnVeKlnpXr50DPnFPMGpHSPJGQwBAAAAtArBySJshzdJ1d9JwZHSoOua3rH+u5xOH5ac59r2ZmV73I8MhgAAAABahOBkEba9/9+9MOI2ya+ZKfGh0VJguCRDOnWw9W9UW33hMr9YLtUDAAAAWoLgZAWGS/aS993LzV2mJ7V/JPnxvZIMKSRaCotp/esBAACAHojgZAGR1ftlqzwmBYZJQ27w/YL2jCQv/+EyPe5vAgAAAFqM4GQB8ae2uBeGT5ICgny/oD0jybm/CQAAAGg1gpPZDEPxp38ITr4u06vXnpHk9RP1CE4AAABAixGczHa8WGE1ZTL8HO4zTi3RnuBUf8aJwRAAAABAixGcTGbf6x4KYQy5UXKEtexFF48kP1/b8jerOiFVlbuX+ya1okoAAACgZyM4mcxe4h5D7kr635a/KCxGCgiVDFfrRpLXD4aITGx5SAMAAABAcDLVd1/LVr5bLtllDJ/c8tfZbG27XM8zGILL9AAAAIDWIDiZqfg9SdKJ8GQpOLJ1r+3ThuBUPxiCUeQAAABAqxCczFRTIcM/WMd6p7X+tW0ZSc4ocgAAAKBNCE5mmjhX53+zV4ejrmv9a1t7qZ7LJZUXu5cJTgAAAECrEJzMFhiqOj9H61/X2uB06qDkrJL8AqU+Q1v/fgAAAEAPRnDqqupHkp86JNU5fe9fP1EveoTkF3D56gIAAAC6IYJTVxUeJ/kHS0adOzz54vniWy7TAwAAAFqL4NRVtXYkeTmDIQAAAIC2Ijh1Za0ZSV4fnGL5DicAAACgtQhOXVlLR5Kfr5FO7HMvc8YJAAAAaDWCU1fW0kv1TnzpvhcqqLfUK+Hy1wUAAAB0MwSnrqylwcnzxbej3PdGAQAAAGgVglNX5hlJflCqO9/0fuW73Y8xyZe/JgAAAKAbIjh1ZeHxkn+Q5DovnW5mJDmjyAEAAIB2ITh1ZXa7FDnYvdzc5XrlF12qBwAAAKDVCE5dnec+p/2Nbz97Uqo44l7mUj0AAACgTQhOXV0fHyPJy/e6H3v1l4IjOqUkAAAAoLshOHV1vibr1Q+G4P4mAAAAoM0ITl2dr+DkGUVOcAIAAADaiuDU1dWPJD95QHLVNdxePxgilsEQAAAAQFsRnLq6Xv0kP4fkckqnD3tvMwzOOAEAAAAdgODU1dntUmSie/nSy/Uqjkg1pyWbnxQ9vNNLAwAAALoLglN30NR9TvVnm6KHS/6Ozq0JAAAA6EYITt1Bnx/uc/rukuBUP1GPy/QAAACAdiE4dQdRg92PTZ1xYhQ5AAAA0C4Ep+6gqUv1yovdjzFM1AMAAADawxLBadGiRUpMTFRQUJDGjx+vzZs3N7nvSy+9pOuvv16RkZGKjIxURkZGs/v3CJ6R5PsvjCSvc0onStzLnHECAAAA2sX04PTWW28pNzdXeXl52rZtm1JSUjR58mSVl5c3uv/69ev185//XOvWrdPGjRs1YMAATZo0SUeOHOnkyi2kd3/JHiDV1bon6UnSd1+7nweGSb0HmlsfAAAA0MWZHpwWLFigBx98UFlZWRo5cqQWL16skJAQLVmypNH933jjDc2cOVNjx45VUlKSXn75ZblcLhUVFXVy5RZi92s4ktwzGCLZPbIcAAAAQJv5m/nmtbW12rp1q+bMmeNZZ7fblZGRoY0bN7boGNXV1XI6nYqKimp0e01NjWpqajzPKyoqJElOp1NOp7Md1XeM+hraW4tfZKLs3+1T3fF9cg2YIPuxL+QnyRU9QnUW+D27oo7qDS4P+mNd9Mba6I+10R/rojfW1tb+tGZ/U4PTiRMnVFdXp9jYWK/1sbGx2rt3b4uOMXv2bCUkJCgjI6PR7fn5+XryyScbrP/ggw8UEhLS+qIvk8LCwna9fvRpu4ZK2r+1SLtLYzTum3WKl7T7uPTNypUdUmNP1d7e4PKiP9ZFb6yN/lgb/bEuemNtre1PdXV1i/c1NTi11/z587Vs2TKtX79eQUFBje4zZ84c5ebmep5XVFR47ovq1atXZ5XaJKfTqcLCQt18880KCAho83HsW45Ja9ZoSIQ06NZb5b/oCUlS8g1TlZR4fQdV27N0VG9wedAf66I31kZ/rI3+WBe9sba29qf+arSWMDU4RUdHy8/PT2VlZV7ry8rKFBcX1+xrn3vuOc2fP19r167VmDFjmtzP4XDI4XA0WB8QEGCpf/TtrqfvMEmS/eQB2V3npFMHJUn+CSmShX7Prshq/1bgjf5YF72xNvpjbfTHuuiNtbW2P63Z19SpAYGBgUpNTfUa7FA/6CE9Pb3J1z377LN66qmntHr1aqWlpXVGqdZ38Ujy+u9vCouVQvuYVxMAAADQTZh+qV5ubq4yMzOVlpamcePGaeHChaqqqlJWVpYk6b777lO/fv2Un58vSXrmmWc0d+5c/etf/1JiYqJKS0slSWFhYQoLCzPt9zBd7wGS3V86f076aq17XQzf3wQAAAB0BNOD07Rp03T8+HHNnTtXpaWlGjt2rFavXu0ZGHHo0CHZLxqnXVBQoNraWt1zzz1ex8nLy9O8efM6s3Rr8fOXIgZJ338tFb/vXhc7ytyaAAAAgG7C9OAkSTk5OcrJyWl02/r1672eHzhw4PIX1FVFDXEHJ893OHHGCQAAAOgIfDNqd9JnqPfzWIITAAAA0BEITt1J1JCLntik6BGmlQIAAAB0JwSn7uTi4BQ1RAq0zhf8AgAAAF0Zwak7uTg4cZkeAAAA0GEITt1JxEDJ5udejmGiHgAAANBRCE7diV+AFDnIvcwZJwAAAKDDWGIcOTrQjb+XvlwjDZ9kdiUAAABAt0Fw6m6uuMf9AwAAAKDDcKkeAAAAAPhAcAIAAAAAHwhOAAAAAOADwQkAAAAAfCA4AQAAAIAPBCcAAAAA8IHgBAAAAAA+EJwAAAAAwAeCEwAAAAD4QHACAAAAAB8ITgAAAADgA8EJAAAAAHwgOAEAAACADwQnAAAAAPCB4AQAAAAAPhCcAAAAAMAHghMAAAAA+EBwAgAAAAAf/M0uoLMZhiFJqqioMLkSN6fTqerqalVUVCggIMDscnARemNt9Me66I210R9roz/WRW+sra39qc8E9RmhOT0uOFVWVkqSBgwYYHIlAAAAAKygsrJSvXv3bnYfm9GSeNWNuFwuHT16VOHh4bLZbGaXo4qKCg0YMECHDx9Wr169zC4HF6E31kZ/rIveWBv9sTb6Y130xtra2h/DMFRZWamEhATZ7c3fxdTjzjjZ7Xb179/f7DIa6NWrFx9Ci6I31kZ/rIveWBv9sTb6Y130xtra0h9fZ5rqMRwCAAAAAHwgOAEAAACADwQnkzkcDuXl5cnhcJhdCi5Bb6yN/lgXvbE2+mNt9Me66I21dUZ/etxwCAAAAABoLc44AQAAAIAPBCcAAAAA8IHgBAAAAAA+EJwAAAAAwAeCk4kWLVqkxMREBQUFafz48dq8ebPZJfVIH330kW6//XYlJCTIZrPpnXfe8dpuGIbmzp2r+Ph4BQcHKyMjQ/v27TOn2B4mPz9fV199tcLDwxUTE6O77rpLJSUlXvucO3dO2dnZ6tOnj8LCwjR16lSVlZWZVHHPUlBQoDFjxni+bDA9PV2rVq3ybKc31jF//nzZbDY9+uijnnX0xzzz5s2TzWbz+klKSvJspzfmOnLkiH7xi1+oT58+Cg4O1hVXXKEtW7Z4tvN3gXkSExMbfHZsNpuys7MlXf7PDsHJJG+99ZZyc3OVl5enbdu2KSUlRZMnT1Z5ebnZpfU4VVVVSklJ0aJFixrd/uyzz+r555/X4sWL9emnnyo0NFSTJ0/WuXPnOrnSnmfDhg3Kzs7Wpk2bVFhYKKfTqUmTJqmqqsqzz29+8xu99957Wr58uTZs2KCjR4/qJz/5iYlV9xz9+/fX/PnztXXrVm3ZskU33XST7rzzTu3evVsSvbGKzz77TP/4xz80ZswYr/X0x1yjRo3SsWPHPD8ff/yxZxu9Mc/Jkyc1YcIEBQQEaNWqVdqzZ4/+8pe/KDIy0rMPfxeY57PPPvP63BQWFkqS7r33Xkmd8NkxYIpx48YZ2dnZnud1dXVGQkKCkZ+fb2JVkGSsWLHC89zlchlxcXHGn//8Z8+6U6dOGQ6Hw3jzzTdNqLBnKy8vNyQZGzZsMAzD3YuAgABj+fLlnn2Ki4sNScbGjRvNKrNHi4yMNF5++WV6YxGVlZXG8OHDjcLCQuPHP/6xMWvWLMMw+OyYLS8vz0hJSWl0G70x1+zZs43rrruuye38XWAts2bNMoYOHWq4XK5O+exwxskEtbW12rp1qzIyMjzr7Ha7MjIytHHjRhMrw6X279+v0tJSr1717t1b48ePp1cmOH36tCQpKipKkrR161Y5nU6v/iQlJWngwIH0p5PV1dVp2bJlqqqqUnp6Or2xiOzsbN12221efZD47FjBvn37lJCQoCFDhmj69Ok6dOiQJHpjtnfffVdpaWm69957FRMToyuvvFIvvfSSZzt/F1hHbW2tli5dqgceeEA2m61TPjsEJxOcOHFCdXV1io2N9VofGxur0tJSk6pCY+r7Qa/M53K59Oijj2rChAkaPXq0JHd/AgMDFRER4bUv/ek8u3btUlhYmBwOh2bMmKEVK1Zo5MiR9MYCli1bpm3btik/P7/BNvpjrvHjx+u1117T6tWrVVBQoP379+v6669XZWUlvTHZN998o4KCAg0fPlxr1qzRww8/rF//+tf65z//KYm/C6zknXfe0alTp3T//fdL6pz/r/l3yFEA4DLLzs7WF1984XUfAMw3YsQI7dixQ6dPn9bbb7+tzMxMbdiwweyyerzDhw9r1qxZKiwsVFBQkNnl4BJTpkzxLI8ZM0bjx4/XoEGD9O9//1vBwcEmVgaXy6W0tDT96U9/kiRdeeWV+uKLL7R48WJlZmaaXB0u9sorr2jKlClKSEjotPfkjJMJoqOj5efn12DKR1lZmeLi4kyqCo2p7we9MldOTo7ef/99rVu3Tv379/esj4uLU21trU6dOuW1P/3pPIGBgRo2bJhSU1OVn5+vlJQU/e1vf6M3Jtu6davKy8t11VVXyd/fX/7+/tqwYYOef/55+fv7KzY2lv5YSEREhH70ox/pq6++4rNjsvj4eI0cOdJrXXJysudSSv4usIaDBw9q7dq1+tWvfuVZ1xmfHYKTCQIDA5WamqqioiLPOpfLpaKiIqWnp5tYGS41ePBgxcXFefWqoqJCn376Kb3qBIZhKCcnRytWrNCHH36owYMHe21PTU1VQECAV39KSkp06NAh+mMSl8ulmpoaemOyiRMnateuXdqxY4fnJy0tTdOnT/cs0x/rOHPmjL7++mvFx8fz2THZhAkTGnztxZdffqlBgwZJ4u8Cq3j11VcVExOj2267zbOuUz47HTJiAq22bNkyw+FwGK+99pqxZ88e46GHHjIiIiKM0tJSs0vrcSorK43t27cb27dvNyQZCxYsMLZv324cPHjQMAzDmD9/vhEREWH897//NXbu3GnceeedxuDBg42zZ8+aXHn39/DDDxu9e/c21q9fbxw7dszzU11d7dlnxowZxsCBA40PP/zQ2LJli5Genm6kp6ebWHXP8dhjjxkbNmww9u/fb+zcudN47LHHDJvNZnzwwQeGYdAbq7l4qp5h0B8z/fa3vzXWr19v7N+/3/jkk0+MjIwMIzo62igvLzcMg96YafPmzYa/v7/xxz/+0di3b5/xxhtvGCEhIcbSpUs9+/B3gbnq6uqMgQMHGrNnz26w7XJ/dghOJvr73/9uDBw40AgMDDTGjRtnbNq0yeySeqR169YZkhr8ZGZmGobhHj36xBNPGLGxsYbD4TAmTpxolJSUmFt0D9FYXyQZr776qmefs2fPGjNnzjQiIyONkJAQ4+677zaOHTtmXtE9yAMPPGAMGjTICAwMNPr27WtMnDjRE5oMg95YzaXBif6YZ9q0aUZ8fLwRGBho9OvXz5g2bZrx1VdfebbTG3O99957xujRow2Hw2EkJSUZL774otd2/i4w15o1awxJjf43v9yfHZthGEbHnLsCAAAAgO6Je5wAAAAAwAeCEwAAAAD4QHACAAAAAB8ITgAAAADgA8EJAAAAAHwgOAEAAACADwQnAAAAAPCB4AQAAAAAPhCcAABohs1m0zvvvGN2GQAAkxGcAACWdf/998tmszX4ueWWW8wuDQDQw/ibXQAAAM255ZZb9Oqrr3qtczgcJlUDAOipOOMEALA0h8OhuLg4r5/IyEhJ7svoCgoKNGXKFAUHB2vIkCF6++23vV6/a9cu3XTTTQoODlafPn300EMP6cyZM177LFmyRKNGjZLD4VB8fLxycnK8tp84cUJ33323QkJCNHz4cL377ruebSdPntT06dPVt29fBQcHa/jw4Q2CHgCg6yM4AQC6tCeeeEJTp07V559/runTp+tnP/uZiouLJUlVVVWaPHmyIiMj9dlnn2n58uVau3atVzAqKChQdna2HnroIe3atUvvvvuuhg0b5vUeTz75pH76059q586duvXWWzV9+nR9//33nvffs2ePVq1apeLiYhUUFCg6Orrz/gMAADqFzTAMw+wiAABozP3336+lS5cqKCjIa/3jjz+uxx9/XDabTTNmzFBBQYFn2zXXXKOrrrpKL7zwgl566SXNnj1bhw8fVmhoqCRp5cqVuv3223X06FHFxsaqX79+ysrK0tNPP91oDTabTX/4wx/01FNPSXKHsbCwMK1atUq33HKL7rjjDkVHR2vJkiWX6b8CAMAKuMcJAGBpN954o1cwkqSoqCjPcnp6ute29PR07dixQ5JUXFyslJQUT2iSpAkTJsjlcqmkpEQ2m01Hjx7VxIkTm61hzJgxnuXQ0FD16tVL5eXlkqSHH35YU6dO1bZt2zRp0iTddddduvbaa9v0uwIArIvgBACwtNDQ0AaXznWU4ODgFu0XEBDg9dxms8nlckmSpkyZooMHD2rlypUqLCzUxIkTlZ2dreeee67D6wUAmId7nAAAXdqmTZsaPE9OTpYkJScn6/PPP1dVVZVn+yeffCK73a4RI0YoPDxciYmJKioqalcNffv2VWZmppYuXaqFCxfqxRdfbNfxAADWwxknAICl1dTUqLS01Gudv7+/ZwDD8uXLlZaWpuuuu05vvPGGNm/erFdeeUWSNH36dOXl5SkzM1Pz5s3T8ePH9cgjj+iXv/ylYmNjJUnz5s3TjBkzFBMToylTpqiyslKffPKJHnnkkRbVN3fuXKWmpmrUqFGqqanR+++/7wluAIDug+AEALC01atXKz4+3mvdiBEjtHfvXknuiXfLli3TzJkzFR8frzfffFMjR46UJIWEhGjNmjWaNWuWrr76aoWEhGjq1KlasGCB51iZmZk6d+6c/vrXv+p3v/udoqOjdc8997S4vsDAQM2ZM0cHDhxQcHCwrr/+ei1btqwDfnMAgJUwVQ8A0GXZbDatWLFCd911l9mlAAC6Oe5xAgAAAAAfCE4AAAAA4AP3OAEAuiyuNgcAdBbOOAEAAACADwQnAAAAAPCB4AQAAAAAPhCcAAAAAMAHghMAAAAA+EBwAgAAAAAfCE4AAAAA4APBCQAAAAB8+D+plzioM890KAAAAABJRU5ErkJggg==",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(10,6))\n",
        "plt.plot(history.history['accuracy'], label='Train Accuracy')\n",
        "plt.plot(history.history['val_accuracy'], label='Validation Accuracy')\n",
        "plt.title('Training and Validation Accuracy')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.legend()\n",
        "plt.grid(True)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "id": "W0ObZLx7VKwE",
        "outputId": "e59900f8-25fc-48b3-91aa-b2b31809873b"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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dtm3buuZnNW/enO7du9OhQwciIyP5888/+frrrxk9enSpvh4RkbNRciUi4kaqVKnCTz/9xIMPPsh//vMfIiIiuPHGG7nsssvo06eP1eEB0KFDB2bPns1DDz3Ek08+Se3atZk4cSIbN248bzVDHx8ffvzxR+69916ee+45/P39ufLKKxk9ejRt2rQpVjxeXl788MMP3H///Xz66afYbDauuOIKXnrpJdq1a1ekYw0bNoxp06YRExPDpZdemue+kSNHEh8fzzvvvMPcuXNp3rw5n376KV999RWLFi0qctzPPPMM/v7+TJ48mYULF9KxY0fmzZvHgAED8uz3+OOPk5yczLRp05g+fTrt27dn5syZPPbYY3n28/HxYerUqYwdO5Y777yTzMxMPvroowKTq9zf2bhx45g+fTofffQRsbGxvPDCCzz44INFfi3nM2fOnAIXHY6NjaVly5YsWrSIxx57jKlTp5KYmEiTJk346KOPGDlypGvfG2+8kXfffZe33nqLhIQEoqOjGTJkCOPHj3cNJ7z33nv54YcfmDdvHunp6dStW5dnnnmGhx9+uNRfk4hIQWxOdzodKiIiFdbgwYNVBltERCo1zbkSEZEiS01NzXN769atzJo1i+7du1sTkIiIiBtQz5WIiBRZTEwMI0eOpH79+uzevZu3336b9PR0Vq9enW/tJhERkcpCc65ERKTI+vbty+eff058fDx+fn506tSJZ599VomViIhUauq5EhERERERKQWacyUiIiIiIlIKlFyJiIiIiIiUAs25KkB2djYHDhwgJCQEm81mdTgiIiIiImIRp9PJqVOnqFGjhmtdvbNRclWAAwcOULt2bavDEBERERERN7F3715q1ap1zn2UXBUgJCQEML/A0NBQS2NxOBzMmzeP3r174+PjY2kskp/ax32pbdyb2se9qX3cl9rGval93Ftx2ycxMZHatWu7coRzUXJVgNyhgKGhoW6RXAUGBhIaGqo/Ujek9nFfahv3pvZxb2of96W2cW9qH/dW0vYpzHQhFbQQEREREREpBUquRERERERESoGSKxERERERkVKgOVciIiIiUiFkZWXhcDisDuOsHA4H3t7epKWlkZWVZXU48g9nax+73Y63t3epLMGk5EpERERE3F5SUhL79u3D6XRaHcpZOZ1OoqOj2bt3r9ZKdUPnap/AwEBiYmLw9fUt0XMouRIRERERt5aVlcW+ffsIDAwkKirKbROX7OxskpKSCA4OPu9is1L+Cmofp9NJRkYGR44cYefOnTRq1KhEbafkSkRERETcmsPhwOl0EhUVRUBAgNXhnFV2djYZGRn4+/sruXJDZ2ufgIAAfHx82L17t+v+4lKri4iIiEiF4K49VlLxlVYyrORKRERERESkFCi5EhERERERKQVKrkREREREKojY2FgmTZpkdRhyFkquRERERERKmc1mO+fP+PHji3XcP/74g9tvv71EsXXv3p3777+/RMeQgqlaoIiIiIhIKTt48KDr+vTp0xk3bhybN292bQsODnZddzqdZGVl4e19/q/mUVFRpRuolCr1XImIiIhIheJ0OknJyLTkp7CLGEdHR7t+wsLCsNlsrtubNm0iJCSE2bNn06FDB/z8/Pj111/Zvn07gwYNonr16gQHB3PhhRcyf/78PMf957BAm83G+++/z5VXXklgYCCNGjXihx9+KNHv95tvvqFFixb4+fkRGxvLSy+9lOf+t956i0aNGuHv70/16tW55pprXPd9/fXXtGrVioCAAKpUqULPnj1JTk4uUTwViXquRERERKRCSXVk0XzcXEue+++JfQj0LZ2v0I899hgvvvgi9evXJyIigr1799K/f3/+7//+Dz8/Pz7++GMGDhzI5s2bqVOnzlmPM2HCBJ5//nleeOEFXn/9dYYNG8bu3buJjIwsckwrV67kuuuuY/z48QwZMoSlS5dy9913U6VKFUaOHMmff/7JvffeyyeffELnzp05fvw4v/zyC2B664YOHcrzzz/PlVdeyalTp/jll18KnZB6AiVXIiIiIiIWmDhxIr169XLdjoyMpE2bNq7bTz/9NN999x0//PADo0ePPutxRo4cydChQwF49tlnee2111ixYgV9+/Ytckwvv/wyl112GU8++SQAjRs35u+//+aFF15g5MiR7Nmzh6CgIC6//HJCQkKoW7cu7dq1A0xylZmZyVVXXUXdunUBaNWqVZFjqMiUXEnpiF8P4XXAP9TqSERERMTDBfjY+XtiH8ueu7RccMEFeW4nJSUxfvx4Zs6c6UpUUlNT2bNnzzmP07p1a9f1oKAgQkNDOXz4cLFi2rhxI4MGDcqzrUuXLkyaNImsrCx69epF3bp1qV+/Pn379qVv376uIYlt2rThsssuo1WrVvTp04fevXtzzTXXEBERUaxYKiLNuZKS2zIXJneB2Y9aHYmIiIhUAjabjUBfb0t+bDZbqb2OoKCgPLcfeughvvvuO5599ll++eUX1qxZQ6tWrcjIyDjncXx8fPL9frKzs0stzjOFhISwatUqPv/8c2JiYhg3bhxt2rQhISEBu91OXFwcs2fPpnnz5rz++us0adKEnTt3lkks7kjJlZTcHx+Yy0PrrY1DREREpAL77bffGDlyJFdeeSWtWrUiOjqaXbt2lWsMzZo147fffssXV+PGjbHbTa+dt7c3PXv25Pnnn+evv/5i165d/Pzzz4BJ7Lp06cKECRNYvXo1vr6+fPfdd+X6GqykYYFSMkmHYVtOFZvUE9bGIiIiIlKBNWrUiG+//ZaBAwdis9l48skny6wH6siRI6xZsybPtpiYGB588EEuvPBCnn76aYYMGcKyZct44403eOuttwD46aef2LFjB926dSMiIoJZs2aRnZ1NkyZNWL58OQsWLKB3795Uq1aN5cuXc+TIEZo1a1Ymr8EdKbmSkvnrS3Bmmespx6yNRURERKQCe/nll7n55pvp3LkzVatW5dFHHyUxMbFMnmvatGlMmzYtz7ann36a//znP3z55ZeMGzeOp59+mpiYGCZOnMjIkSMBCA8P59tvv2X8+PGkpaXRqFEjPv/8c1q0aMHGjRtZsmQJkyZNIjExkbp16/LSSy/Rr1+/MnkN7kjJlRSf0wlrzvijdKSAIxV8AqyLSURERMTNjBw50pWcAHTv3r3A8uSxsbGu4XW57rnnnjy3/zlMsKDjJCQknDOeRYsWnfP+q6++mquvvrrA+y6++OKzPr5Zs2bMmTPnnMf2dJpzJcUX/xcc3gB2X7DlVM5JOW5tTCIiIiIiFlFyJcW35nNz2aQ/BFYx1zU0UEREREQqKSVXUjxZDlj3lbne9gYlVyIiIiJS6Sm5kuLZGgcpRyGoGjS47HRylaphgSIiIiJSOVmaXC1ZsoSBAwdSo0YNbDYbM2bMOOf+I0eOxGaz5ftp0aKFa5/x48fnu79p06Zl/EoqobU5hSxaXwd2bwiMNLc150pEREREKilLk6vk5GTatGnDm2++Waj9X331VQ4ePOj62bt3L5GRkVx77bV59mvRokWe/X799deyCL/ySjkOm3MqwbQZai5dyZWGBYqIiIhI5WRpKfZ+/foVqe59WFgYYWFhrtszZszgxIkTjBo1Ks9+3t7eREdHl1qc8g/rv4FsB0S3guiWZpvmXImIiIhIJVeh17n64IMP6NmzJ3Xr1s2zfevWrdSoUQN/f386derEc889R506dc56nPT0dNLT0123cxdrczgcOByOsgm+kHKf3+o4zmRf/RleQFarIWTnxOXlF44dyE4+SpYbxVrW3LF9xFDbuDe1j3tT+7ivyto2DocDp9NJdnY22dnZVodzVrlrTuXGKu7lXO2TnZ2N0+nE4XBgt9vz3FeUvzebs6CVxyxgs9n47rvvGDx4cKH2P3DgAHXq1GHatGlcd911ru2zZ88mKSmJJk2acPDgQSZMmMD+/ftZv349ISEhBR5r/PjxTJgwId/2adOmERgYWKzX46mC0/Zz2caxZGNnbstXyfAJBaD2sV9pv+ddDoe0ZFnDRyyOUkRERDxJ7qik2rVr4+vra3U44oEyMjLYu3cv8fHxZGZm5rkvJSWFG264gZMnTxIaGnrO41TY5Oq5557jpZde4sCBA+f8I0tISKBu3bq8/PLL3HLLLQXuU1DPVe3atTl69Oh5f4FlzeFwEBcXR69evfDx8bE0FgCvnydiX/Ya2Y36kHXdZ67ttm1xeE8firN6KzJvXWhhhOXL3dpHTlPbuDe1j3tT+7ivyto2aWlp7N27l9jYWPz9/a0O56ycTienTp0iJCQEm81WKse89NJLadOmDa+88goA9evX57777uO+++4762PsdjvffPNNob9Xl/Vx3MW52ictLY1du3ZRu3btfO+xxMREqlatWqjkqkIOC3Q6nXz44YfcdNNN5z17ER4eTuPGjdm2bdtZ9/Hz88PPzy/fdh8fH7f54HKLWLKzYL1Z28qr3TC8zownpDoAtrQE6+O0gFu0jxRIbePe1D7uTe3jvipb22RlZWGz2fDy8sLLy31XEsodamaz2Rg0aBAOh4M5c+bk2++XX36hW7durF27ltatW5/3uLmvHeCPP/4gKCjovL+Hovyuxo8fz4wZM1izZk2e7QcPHiQiIqJMf+dTpkzh/vvvJyEhocyeI9eZ7fPP1+Tl5YXNZivwb6sof2vu++48h8WLF7Nt27az9kSdKSkpie3btxMTE1MOkXm4HYvg1EHwD4fGffPeFxhhLlXQQkRERIRbbrmFuLg49u3bl+++jz76iAsuuKBQidU/RUVFldu0lejo6AI7IOTsLE2ukpKSWLNmjStL3rlzJ2vWrGHPnj0AjB07luHDh+d73AcffEDHjh1p2bJlvvseeughFi9ezK5du1i6dClXXnkldrudoUOHlulrqRTWfm4uW10D3v/4Q8utFuhIAUdq+cYlIiIilYvTCRnJ1vwUckbN5ZdfTlRUFFOmTMmzPSkpia+++opbbrmFY8eOMXToUGrWrElgYCCtWrXi888/P+dxY2NjmTRpkuv21q1b6datG/7+/jRv3py4uLh8j3n00Udp3LgxgYGB1K9fnyeffNJVpGHKlClMmDCBtWvXutaIzY35n+vQrlu3jksvvZSAgACqVKnC7bffTlJSkuv+kSNHMnjwYF588UViYmKoUqUK99xzT4kKsOzZs4dBgwYRHBxMaGgo1113HYcOHXLdv3btWnr06EFISAihoaF06NCBP//8E4Ddu3czcOBAIiIiCAoKolWrVsybN6/YsRSGpcMC//zzT3r06OG6PWbMGABGjBjBlClTOHjwoCvRynXy5Em++eYbXn311QKPuW/fPoYOHcqxY8eIiori4osv5vfffycqKqrsXkhlkJYIG38y19vckP9+v1Dw8obsTLMOVljN8o1PREREKg9HCjxbw5rnfvwA+Aaddzdvb2+GDx/OlClTeOKJJ1xzfL766iuysrIYOnQoSUlJdOjQgUcffZTQ0FBmzpzJTTfdRIMGDbjooovO+xzZ2dlcddVVVK9eneXLl3Py5Enuv//+fPuFhIQwZcoUatSowbp167jtttsICQnhkUceYciQIaxfv545c+Ywf/58gDxLH+VKTk6mT58+dOrUiT/++IPDhw9z6623Mnr06DwJ5MKFC4mJiWHhwoVs27aNIUOG0LZtW2677bbzvp6CXl9uYrV48WIyMzO55557GDJkCIsWLQJg2LBhtGvXjrfffhu73c6aNWtcw/juueceMjIyWLJkCUFBQaxfvz5fJcDSZmly1b17d85VT+OfmT6Yxk5JSTnrY7744ovSCE3+6e8ZkJkKVRtDzfb577fZTO9V0iEzNFDJlYiIiFRyN998My+88AKLFy+me/fugBkSePXVV7vWb33ooYdc+//73/9m7ty5fPnll4VKrubPn8+mTZuYO3cuNWqYZPPZZ5/Nt47sf/7zH9f12NhYHnroIb744gseeeQRAgICCA4OPu86sdOmTSMtLY2PP/6YoCCTXL7xxhsMHDiQ//3vf1SvbubfR0RE8MYbb2C322natCkDBgxgwYIFxUquFixYwLp169i5cye1a9cG4OOPP6ZFixb88ccfXHjhhezZs4eHH36Ypk2bAtCoUSPX4/fs2cPVV19Nq1atXK89d8mlslIhC1qIBdbkdFG3GWoSqYIERJ5OrkRERETKik+g6UGy6rkLqWnTpnTu3JkPP/yQ7t27s23bNn755RcmTpwImEIdzz77LF9++SX79+8nIyOD9PT0Qs+p2rhxI7Vr13YlVgCdOnXKt9/06dN57bXX2L59O0lJSWRmZha5IvbGjRtp06aNK7EC6NKlC9nZ2WzevNmVXLVo0SJP71BMTAzr1q0r0nOd+Zy1a9d2JVYAzZs3Jzw8nI0bN3LhhRcyZswYbr31Vj755BN69uzJtddeS4MGDQC49957ueuuu5g3bx49e/bkyiuvJDY2tlixFFaFLGgh5ez4TtizFLBB6yFn3y933pWSKxERESlLNpsZmmfFTxFLrN9yyy188803nDp1io8++ogGDRpwySWXAPDCCy/w6quv8uijj7Jw4ULWrFlDnz59yMjIKLVf1bJlyxg2bBj9+/fnp59+YvXq1TzxxBOl+hxn+mdlPZvNVqYLKo8fP54NGzYwYMAAfv75Z5o3b853330HwK233sqOHTu46aabWLduHRdddBHvvvtumcUCSq6kMNbmDLWs3/3cw/0CI81l6okyD0lERESkIrjuuuvw8vJi2rRpfPzxx9x8882u+Ve//fYbgwYN4sYbb6RNmzbUr1+fLVu2FPrYzZo1Y+/evRw8eNC17ffff8+zz9KlS6lbty5PPPEEF1xwAY0aNWL37t159vH19SUrK+u8z7V27VqSk5Nd23777Te8vLxo0qRJoWMuitzXt3fvXte2v//+m4SEBJo3b+7a1rhxYx544AHmzZvHVVddxUcffeS6r3bt2tx55518++23jBkzhqlTp5ZJrLmUXMm5ZWefrhLYtoBCFmfKTa7UcyUiIiICQHBwMEOGDGHs2LEcPHiQkSNHuu5r1KgRcXFxLF26lI0bN3LHHXfkqYR3Pj179qRx48aMGDGCtWvX8ssvv/DEE0/k2adRo0bs2bOHL774gu3bt/Paa6+5enZyxcbGuqp2Hz16lPT09HzPNWzYMPz9/RkxYgTr169n4cKF/Pvf/+amm25yDQksrqysLFcF8dyfjRs30rNnT1q1asWwYcNYtWoVK1asYPjw4VxyySVccMEFpKamMnr0aBYtWsTu3bv57bff+OOPP2jWrBkA999/P3PnzmXnzp2sWrWKRYsWlVkimEvJlZzbnqWQsBt8Q6Dp5efeV8MCRURERPK55ZZbOHHiBH369MkzP+o///kP7du3p0+fPnTv3p3o6GgGDx5c6ON6eXnx3XffkZqaykUXXcStt97K//3f/+XZ54orruCBBx5g9OjRtG3blqVLl/Lkk0/m2efqq6+mb9++9OjRg6ioqALLwQcGBjJ37lyOHz/OhRdeyDXXXMNll13GG2+8UbRfRgGSkpJo165dnp+BAwdis9n4/vvviYiIoFu3bvTs2ZP69eszffp0AOx2O8eOHWP48OE0btyY6667jn79+jFhwgTAJG333HMPzZo1o2/fvjRq1IgXX3yxxPGei815rnJ9lVRiYiJhYWGcPHmyyJP9SpvD4WDWrFn079/fmpXYZ9wDaz6FdjfCoDfPve+yN2Hu49DyGrjmg/KJz2KWt4+cldrGval93Jvax31V1rZJS0tj586d1KtXD39/f6vDOavs7GwSExMJDQ3Fy0t9GO7mXO1zrvdYUXIDtbqcXUayKcEOBa9t9U8BGhYoIiIiIpWXSrFLfk4nJO435dczkiC8LtTJX9YzHw0LFBEREZFKTMlVZZdyHA5tgMMb4fDfOT8bIf2MBdbaDIXCdG3nJleqFigiIiIilZCSq8roj/dh448miUo6S0UaL2+o0ghqtIOOdxTuuKoWKCIiIiKVmJKryibpCMx8MO+28LpQrTlUb24uqzWDKg3B269ox85NrhwpkJECvoVfwVxERETkfFSHTcpKab23lFxVNkc2msvQmnDdJxDVBPyCS+fYfqGmxys7E1KPK7kSERGRUmG32wHIyMggICDA4mjEE6WkpACUuAqnkqvK5shmcxndGmp1KN1j22xm3lXSITOXK6xW6R5fREREKiVvb28CAwM5cuQIPj4+blvmPDs7m4yMDNLS0tw2xsqsoPZxOp2kpKRw+PBhwsPDXYl8cSm5qmyObjGXUY3L5vgBkTnJleZdiYiISOmw2WzExMSwc+dOdu/ebXU4Z+V0OklNTSUgIACbzWZ1OPIP52qf8PBwoqOjS/wcSq4qmyObzGXVJmVzfJVjFxERkTLg6+tLo0aNyMjIsDqUs3I4HCxZsoRu3bpVqkWeK4qztY+Pj0+Je6xyKbmqbI7k9lyVVXKVU9RC5dhFRESklHl5eeHv7291GGdlt9vJzMzE399fyZUbKo/20WDQyiTtJCTFm+tVG5XNc6gcu4iIiIhUUkquKpPcXquQGPAPK5vn0LBAEREREamklFxVJkdzKgVWLaNiFnBGcnW87J5DRERERMQNKbmqTHLLsJfVfCtQz5WIiIiIVFpKriqT3DLsZdlzFaA5VyIiIiJSOSm5qkxcPVdNy+45NCxQRERERCopJVeVhSMVEnIW3SvTYYG5pdiVXImIiIhI5aLkqrI4tg2c2eAfDkFRZfc8ucmVIwUyUsrueURERERE3IySq8rizGIWNlvZPY9fKHjlrE2t3isRERERqUSUXFUW5VHMAkzipnlXIiIiIlIJKbmqLMqjDHsuVQwUERERkUpIyVVl4eq5KofkSmtdiYiIiEglpOSqMsjKNAUtAKLKeFggnFEx8ETZP5eIiIiIiJtQclUZJOyGrAzwDoCwOmX/fOq5EhEREZFKSMlVZZA736pqI/AqhyYP1JwrEREREal8lFxVBkfLsZgFqOdKRERERColJVeVgavnqryTK5ViFxEREZHKQ8lVZeAqw14OxSxApdhFREREpFJScuXpnE44utVcV8+ViIiIiEiZUXLl6RIPQMYpsNkhsn75PKerFLuSKxERERGpPJRcebrcYhaR9cHbt3yeMze5cqRARkr5PKeIiIiIiMWUXHm6I1vMZXlVCgTwCwUvb3NdvVciIiIiUkkoufJ05V2GHcBm07wrEREREal0lFx5utyeq/IqZpFLa12JiIiISCWj5MrTHdlkLsurDHsulWMXERERkUpGyZUnSzkOKUfN9arlnFzlFrXQsEARERERqSSUXHmy3MWDw2qDb1D5PnfusEAVtBARERGRSkLJlSfLLWZR3r1WcEbPlYYFioiIiEjloOTKk1lRhj2XClqIiIiISCWj5MqTWdpzpVLsIiIiIlK5KLnyZK6eq6bl/9zquRIRERGRSkbJlafKSIaTe8x1K4YFBqhaoIiIiIhULkquPNXRreYysOrp4hLlKfc5VS1QRERERCoJS5OrJUuWMHDgQGrUqIHNZmPGjBnn3H/RokXYbLZ8P/Hx8Xn2e/PNN4mNjcXf35+OHTuyYsWKMnwVbiq3DLsVvVZweligIwUyUqyJQURERESkHFmaXCUnJ9OmTRvefPPNIj1u8+bNHDx40PVTrVo1133Tp09nzJgxPPXUU6xatYo2bdrQp08fDh8+XNrhuzcri1kA+IWAl7e5rt4rEREREakEvK188n79+tGvX78iP65atWqEh4cXeN/LL7/MbbfdxqhRowCYPHkyM2fO5MMPP+Sxxx4r8DHp6emkp6e7bicmJgLgcDhwOBxFjq805T5/UeOwH96EF5AV2ZBsi16Dd0AktuTDOBIPQWB1S2Ioa8VtHyl7ahv3pvZxb2of96W2cW9qH/dW3PYpyv6WJlfF1bZtW9LT02nZsiXjx4+nS5cuAGRkZLBy5UrGjh3r2tfLy4uePXuybNmysx7vueeeY8KECfm2z5s3j8DAwNJ/AcUQFxdXpP0v3bWKEGD5jgSOHJ1VNkGdR48sX0KBPxbN4UjoPktiKC9FbR8pP2ob96b2cW9qH/eltnFvah/3VtT2SUkp/BSXCpVcxcTEMHnyZC644ALS09N5//336d69O8uXL6d9+/YcPXqUrKwsqlfP20tSvXp1Nm3adNbjjh07ljFjxrhuJyYmUrt2bXr37k1oaGiZvZ7CcDgcxMXF0atXL3x8fAr3oCwH3mtvBuDC/jdCaM0yjPDs7Mcmw559XNSqIc4W/S2JoawVq32kXKht3Jvax72pfdyX2sa9qX3cW3HbJ3dUW2FUqOSqSZMmNGlyukBD586d2b59O6+88gqffPJJsY/r5+eHn59fvu0+Pj5u84dRpFgSdkB2JvgG4xNZF2y2sg3ubIKqAuCdfhLc5PdYVtzpvSJ5qW3cm9rHval93Jfaxr2pfdxbUdunKPtW+FLsF110Edu2bQOgatWq2O12Dh06lGefQ4cOER0dbUV41jhyRjELqxIrOF0xUAUtRERERKQSqPDJ1Zo1a4iJiQHA19eXDh06sGDBAtf92dnZLFiwgE6dOlkVYvk7anEZ9ly5yVXKMWvjEBEREREpB5YOC0xKSnL1OgHs3LmTNWvWEBkZSZ06dRg7diz79+/n448/BmDSpEnUq1ePFi1akJaWxvvvv8/PP//MvHnzXMcYM2YMI0aM4IILLuCiiy5i0qRJJCcnu6oHVgpHLC7Dnit3IWElVyIiIiJSCViaXP3555/06NHDdTu3qMSIESOYMmUKBw8eZM+ePa77MzIyePDBB9m/fz+BgYG0bt2a+fPn5znGkCFDOHLkCOPGjSM+Pp62bdsyZ86cfEUuPJrVCwjncvVcaVigiIiIiHg+S5Or7t2743Q6z3r/lClT8tx+5JFHeOSRR8573NGjRzN69OiShlcxZWfD0a3melV3Sa7UcyUiIiIinq/Cz7mSfzi5FzJTwe4LEbHWxhKQOyxQPVciIiIi4vmUXHmao1vMZWQDsFtcaV9zrkRERESkElFy5Wlc860sLmYBp4cFZqZCRuFXthYRERERqYiUXHkaVxn2ptbGAeAXAl45vWda60pEREREPJySK09zJGdYoNVl2MEsYKyiFiIiIiJSSSi58iROJxzZZK5bXYY9l8qxi4iIiEgloeTKkyQfgbQEwAZVGlodjaGeKxERERGpJJRceZLcYhYRdcEnwNpYcgVEmEv1XImIiIiIh1Ny5Ulyi1lYvXjwmXJ7rlTQQkREREQ8nJIrT5JbzMIdyrDn0rBAEREREakklFx5ErfsudJCwiIiIiJSOSi58iSunit3Sq7UcyUiIiIilYOSK0+RlginDpjr7rDGVS6VYhcRERGRSkLJlac4utVcBkdDQLiloeQRkDssUMmViIiIiHg2b6sDkBLKzoat8+DXV8xtdypmAZpzJSIiIiKVhpKriir9FKyZBsvfgePbzTabF7S8xtq4/il3WGBmKmSkgG+gtfGIiIiIiJQRJVcVzfGdsOJdWP0ppCeabX5h0GE4XHibWUDYnfiFgJcPZDvMWldKrkRERETEQym5qgicTmy7f4U/3oPNswCn2V6lEXS8A9oMBb9gS0M8K5vNDA1MOmSGBobVsjoiEREREZEyoeTKnWVlYlvzGd03vYj3mr2ntze4DP51NzS4FLwqQE2SwCo5yZWKWoiIiIiI51Jy5c5sXtiXvUZY2l6cPoHY2gw1PVXutI5VYWitKxERERGpBJRcuTMvL7K6PszmPxbS+Pr/wyc0yuqIiicgwlyq50pEREREPJiSKzfnbHkN2/YE0tid1q4qKvVciYiIiEglUAEm7EiFl5tcparnSkREREQ8l5IrKXtaSFhEREREKgElV1L2NCxQRERERCoBJVdS9lzJlYYFioiIiIjnUnIlZc81LFDJlYiIiIh4LiVXUvYCNOdKRERERDyfkispe7nDAjNTISPF2lhERERERMqIkispe34h4OVjrqscu4iIiIh4KCVXUvZsNpVjFxERERGPp+RKyofKsYuIiIiIh1NyJeVD5dhFRERExMMpuZLyERBhLpVciYiIiIiHUnIl5UPDAkVERETEwym5kvKRm1ypWqCIiIiIeCglV1I+1HMlIiIiIh5OyZWUD5ViFxEREREPp+RKyoeqBYqIiIiIh1NyJeXD1XOl5EpEREREPJOSKykfARoWKCIiIiKeTcmVlI/cYYGZqZCRYm0sIiIiIiJlQMmVlA+/EPDyMddVjl1EREREPJCSKykfNpsqBoqIiIiIR1NyJeVHa12JiIiIiAdTciXlR+XYRURERMSDKbmS8qNy7CIiIiLiwZRcSflROXYRERER8WCWJldLlixh4MCB1KhRA5vNxowZM865/7fffkuvXr2IiooiNDSUTp06MXfu3Dz7jB8/HpvNluenadOmZfgqpNByhwWqWqCIiIiIeCBLk6vk5GTatGnDm2++Waj9lyxZQq9evZg1axYrV66kR48eDBw4kNWrV+fZr0WLFhw8eND18+uvv5ZF+FJUKmghIiIiIh7M28on79evH/369Sv0/pMmTcpz+9lnn+X777/nxx9/pF27dq7t3t7eREdHl1aYUlpUil1EREREPJilyVVJZWdnc+rUKSIjI/Ns37p1KzVq1MDf359OnTrx3HPPUadOnbMeJz09nfT0dNftxMREABwOBw6Ho2yCL6Tc57c6jtJg8w3DG3AmHyPTA14PeFb7eBq1jXtT+7g3tY/7Utu4N7WPeytu+xRlf5vT6XQW6ehlxGaz8d133zF48OBCP+b555/nv//9L5s2baJatWoAzJ49m6SkJJo0acLBgweZMGEC+/fvZ/369YSEhBR4nPHjxzNhwoR826dNm0ZgYGCxXo/kF568g0u2jCfFJ5K4lpOsDkdERERE5LxSUlK44YYbOHnyJKGhoefct8ImV9OmTeO2227j+++/p2fPnmfdLyEhgbp16/Lyyy9zyy23FLhPQT1XtWvX5ujRo+f9BZY1h8NBXFwcvXr1wsfHx9JYSuzELnzeugCndwCZj+61OppS4VHt42HUNu5N7ePe1D7uS23j3tQ+7q247ZOYmEjVqlULlVxVyGGBX3zxBbfeeitfffXVORMrgPDwcBo3bsy2bdvOuo+fnx9+fn75tvv4+LjNH4Y7xVJsodUBsGWm4uN0gK/n9Ap6RPt4KLWNe1P7uDe1j/tS27g3tY97K2r7FGXfCrfO1eeff86oUaP4/PPPGTBgwHn3T0pKYvv27cTExJRDdHJOfiHglfPmVFELEREREfEwliZXSUlJrFmzhjVr1gCwc+dO1qxZw549ewAYO3Ysw4cPd+0/bdo0hg8fzksvvUTHjh2Jj48nPj6ekydPuvZ56KGHWLx4Mbt27WLp0qVceeWV2O12hg4dWq6vTQpgs6kcu4iIiIh4LEuTqz///JN27dq5yqiPGTOGdu3aMW7cOAAOHjzoSrQA3n33XTIzM7nnnnuIiYlx/dx3332uffbt28fQoUNp0qQJ1113HVWqVOH3338nKiqqfF+cFCyoqrlMOWptHCIiIiIipczSOVfdu3fnXPU0pkyZkuf2okWLznvML774ooRRSZlyrXV13No4RERERERKWYWbcyUVXGBOz1Wyeq5ERERExLMouZLypTlXIiIiIuKhlFxJ+dKcKxERERHxUEqupHyp50pEREREPJSSKylfuclVspIrEREREfEsSq6kfKnnSkREREQ8lJIrKV+acyUiIiIiHkrJlZSv3J6r1BOQnWVtLCIiIiIipUjJlZSv3OTKmQ2pCZaGIiIiIiJSmpRcSfmy+4B/mLmueVciIiIi4kGUXEn5cxW10LwrEREREfEcSq6k/AXmFrVQz5WIiIiIeA4lV1L+XGtdqedKRERERDyHkispf0Fa60pEREREPI+SKyl/WkhYRERERDyQkispf5pzJSIiIiIeSMmVlD/NuRIRERERD6TkSspfkHquRERERMTzKLmS8qc5VyIiIiLigZRcSflTciUiIiIiHkjJlZS/3OTKkQIZKdbGIiIiIiJSSpRcSfnzCwG7r7mu3isRERER8RBKrqT82WxnDA1UxUARERER8QxKrsQaWutKRERERDyMkiuxRmCkuUxWciUiIiIinkHJlVhDa12JiIiIiIdRciXW0JwrEREREfEwSq7EGppzJSIiIiIeRsmVWMM150o9VyIiIiLiGZRciTVcc66OWxuHiIiIiEgpUXIl1tCcKxERERHxMEqu3Nz0P/fx7S4v9p5IsTqU0qU5VyIiIiLiYZRcubnP/9jL4oNebDmUZHUopcvVc3UcsrOsjUVEREREpBQouXJzNcICADh4Ms3iSEpZbkELnJCaYGUkIiIiIiKlQsmVm6sR7g/AgQQPS67sPuAfZq5r3pWIiIiIeAAlV26uRlhOcuVpPVegeVciIiIi4lGUXLm5mJzkyuOGBcLpeVda60pEREREPICSKzdXI9zMudqfkGpxJGUgSD1XIiIiIuI5lFy5udxhgYdPpePIyrY4mlKWW9RCc65ERERExAMouXJzVYJ88bY5cToh3tOGBrrmXB23Ng4RERERkVKg5MrNeXnZCPcz1w942tBAzbkSEREREQ+i5KoCiPB1AnDgpIclV5pzJSIiIiIeRMlVBRCR03O1/4SHJVe5PVeacyUiIiIiHkDJVQXgSq48bSFhzbkSEREREQ+i5KoCiPTLGRbocXOucqoFas6ViIiIiHgAJVcVQISvufS45Cp3zlVmKmSkWBuLiIiIiEgJKbmqACLO6LlyOp0WR1OKfIPBnpM5at6ViIiIiFRwSq4qgPCc/CM5I4vE1ExrgylNNtsZ865UMVBEREREKjZLk6slS5YwcOBAatSogc1mY8aMGed9zKJFi2jfvj1+fn40bNiQKVOm5NvnzTffJDY2Fn9/fzp27MiKFStKP/hy5GuHyCAfAPYleNjwOddaV0quRERERKRiszS5Sk5Opk2bNrz55puF2n/nzp0MGDCAHj16sGbNGu6//35uvfVW5s6d69pn+vTpjBkzhqeeeopVq1bRpk0b+vTpw+HDh8vqZZSLGmEBABzwtIqBQbnl2JVciYiIiEjF5m3lk/fr149+/foVev/JkydTr149XnrpJQCaNWvGr7/+yiuvvEKfPn0AePnll7ntttsYNWqU6zEzZ87kww8/5LHHHiv9F1FOaoT7s/5AoucVtXANC9ScKxERERGp2CxNropq2bJl9OzZM8+2Pn36cP/99wOQkZHBypUrGTt2rOt+Ly8vevbsybJly8563PT0dNLT0123ExMTAXA4HDgcjlJ8BUWX+/zRIWbi1d7jyZbHVJq8/COwA1mnjpBdAV9Xblt4Upt4CrWNe1P7uDe1j/tS27g3tY97K277FGX/CpVcxcfHU7169TzbqlevTmJiIqmpqZw4cYKsrKwC99m0adNZj/vcc88xYcKEfNvnzZtHYGBg6QRfQonxuwE7KzfuYFbWNqvDKTWN44/RDNi7eTVrU2dZHU6xxcXFWR2CnIXaxr2pfdyb2sd9qW3cm9rHvRW1fVJSCl/zoEIlV2Vl7NixjBkzxnU7MTGR2rVr07t3b0JDQy2MzGTKcXFxdL+oNTN2b4DACPr372hpTKXJa2U8HPyWOlWDqNm/v9XhFFlu+/Tq1QsfHx+rw5EzqG3cm9rHval93Jfaxr2pfdxbcdsnd1RbYVSo5Co6OppDhw7l2Xbo0CFCQ0MJCAjAbrdjt9sL3Cc6Ovqsx/Xz88PPzy/fdh8fH7f5w6gdGQzAgZNpbhNTqQgxvYxeqcfxqsCvy53eK5KX2sa9qX3cm9rHfalt3Jvax70VtX2Ksm+FWueqU6dOLFiwIM+2uLg4OnXqBICvry8dOnTIs092djYLFixw7VNR1Qz3B+DwqXQyMrMtjqYUBapaoIiIiIh4BkuTq6SkJNasWcOaNWsAU2p9zZo17NmzBzDD9YYPH+7a/84772THjh088sgjbNq0ibfeeosvv/ySBx54wLXPmDFjeO+995g6dSobN27krrvuIjk52VU9sKKKDPLF19sLpxMOJXpQOfagnGqByaoWKCIiIiIVm6XDAv/880969Ojhup0772nEiBFMmTKFgwcPuhItgHr16jFz5kweeOABXn31VWrVqsX777/vKsMOMGTIEI4cOcK4ceOIj4+nbdu2zJkzJ1+Ri4rGZrNRMzyAnUeT2Z+QSu1I9yi0UWK5PVepJyA7C7zs1sYjIiIiIlJMliZX3bt3x+l0nvX+KVOmFPiY1atXn/O4o0ePZvTo0SUNz+3UCPdn59Fkz1rrKiAy54rTJFi5PVkiIiIiIhVMhZpzVdnVCAsAYP8JD0qu7N7gH26ua2igiIiIiFRgxUqu9u7dy759+1y3V6xYwf3338+7775baoFJfjUjTHJ14KQHJVdwurdKRS1EREREpAIrVnJ1ww03sHDhQsAs7NurVy9WrFjBE088wcSJE0s1QDmtRnhOz1WCBxW0gDMqBqrnSkREREQqrmIlV+vXr+eiiy4C4Msvv6Rly5YsXbqUzz77rMB5UlI6auYkVx415wogUD1XIiIiIlLxFSu5cjgcrkV358+fzxVXXAFA06ZNOXjwYOlFJ3nUOCO5OlchkAonMKeoRbKSKxERERGpuIqVXLVo0YLJkyfzyy+/EBcXR9++fQE4cOAAVapUKdUA5bSYMLOQcEpGFidTHRZHU4o050pEREREPECxkqv//e9/vPPOO3Tv3p2hQ4fSpk0bAH744QfXcEEpff4+dqoG+wKwz5MqBmrOlYiIiIh4gGKtc9W9e3eOHj1KYmIiERERru233347gYEesritm6oZHsDRpAwOJKTSsmaY1eGUDs25EhEREREPUKyeq9TUVNLT012J1e7du5k0aRKbN2+mWrVqpRqg5FXDE4ta5PZcaZ0rEREREanAipVcDRo0iI8//hiAhIQEOnbsyEsvvcTgwYN5++23SzVAycuVXJ30oHLsQbnDAo9bG4eIiIiISAkUK7latWoVXbt2BeDrr7+mevXq7N69m48//pjXXnutVAOUvE6vdeWBPVcpR8GTqiCKiIiISKVSrOQqJSWFkJAQAObNm8dVV12Fl5cX//rXv9i9e3epBih51Qw3FQM9a1hgzpyrzDRwpFgbi4iIiIhIMRUruWrYsCEzZsxg7969zJ07l969ewNw+PBhQkNDSzVAyatmuCkYst+TqgX6BoHdrJumeVciIiIiUlEVK7kaN24cDz30ELGxsVx00UV06tQJML1Y7dq1K9UAJa8aOT1Xh0+lk56ZZXE0pcRm01pXIiIiIlLhFasU+zXXXMPFF1/MwYMHXWtcAVx22WVceeWVpRac5BcZ5IuftxfpmdkcOplOnSoeUvo+MBIS9yu5EhEREZEKq1jJFUB0dDTR0dHs27cPgFq1amkB4XJgs9moGR7AjqPJ7E9I9aDkSj1XIiIiIlKxFWtYYHZ2NhMnTiQsLIy6detSt25dwsPDefrpp8nOzi7tGOUftNaViIiIiIj7KVbP1RNPPMEHH3zAf//7X7p06QLAr7/+yvjx40lLS+P//u//SjVIySt33pVHlWPXnCsRERERqeCKlVxNnTqV999/nyuuuMK1rXXr1tSsWZO7775byVUZy60Y6JE9VynquRIRERGRiqlYwwKPHz9O06ZN821v2rQpx48fL3FQcm4e2XPlSq70/hERERGRiqlYyVWbNm1444038m1/4403aN26dYmDknOrqTlXIiIiIiJup1jDAp9//nkGDBjA/PnzXWtcLVu2jL179zJr1qxSDVDyO13QIg2n04nNZrM4olKgOVciIiIiUsEVq+fqkksuYcuWLVx55ZUkJCSQkJDAVVddxYYNG/jkk09KO0b5h+gwMyww1ZFFQorD4mhKieZciYiIiEgFV+x1rmrUqJGvcMXatWv54IMPePfdd0scmJydv4+dqBA/jpxKZ39CKhFBvlaHVHK561ylJkBWJtiL/dYUEREREbFEsXquxHq5QwM9pqhFQETOFSeknrA0FBERERGR4lByVUHVzKkY6DFFLezepxMszbsSERERkQpIyVUFVSPMgysGat6ViIiIiFRARZrYctVVV53z/oSEhJLEIkVwZsVAjxFYFY5tU8+ViIiIiFRIRUquwsLCznv/8OHDSxSQFE7NCA+bcwVa60pEREREKrQiJVcfffRRWcUhRVTT0wpaAATlDgs8bm0cIiIiIiLFoDlXFVTusMAjp9JJz8yyOJpSkluOXXOuRERERKQCUnJVQUUE+uDvY5ov/qSHzLtyFbTQnCsRERERqXiUXFVQNpvN89a6CsrpudKcKxERERGpgJRcVWA1Pa1ioHquRERERKQCU3JVgbmKWpzwkJ4rJVciIiIiUoEpuarATq915YHJldNpbSwiIiIiIkWk5KoCcyVXJz0kucqdc5WZBhnJ1sYiIiIiIlJESq4qsBrh/oAHFbTwCQRv85o0NFBEREREKholVxVYzTOGBTo9YRidzaa1rkRERESkwlJyVYFFh/ljs0GaI5sTKQ6rwykdgZHmMuW4tXGIiIiIiBSRkqsKzM/bTlSwH+BBFQO11pWIiIiIVFBKrio4j1tIWOXYRURERKSCUnJVwdX0uHLsmnMlIiIiIhWTkqsKLrdioOckV+q5EhEREZGKSclVBVfT49a6ykmukpVciYiIiEjFouSqgjs95yrN4khKiXquRERERKSCUnJVwbmSK0+pFqg5VyIiIiJSQSm5quByhwUeTUonzZFlcTSlQD1XIiIiIlJBuUVy9eabbxIbG4u/vz8dO3ZkxYoVZ923e/fu2Gy2fD8DBgxw7TNy5Mh89/ft27c8Xkq5Cw/0IcDHDkD8SQ8YGpi7zlXqCcjKtDYWEREREZEisDy5mj59OmPGjOGpp55i1apVtGnThj59+nD48OEC9//22285ePCg62f9+vXY7XauvfbaPPv17ds3z36ff/55ebyccmez2TyrYmBABGAz11NPWBqKiIiIiEhRWJ5cvfzyy9x2222MGjWK5s2bM3nyZAIDA/nwww8L3D8yMpLo6GjXT1xcHIGBgfmSKz8/vzz7RURElMfLsUTNiEDAQxYS9rLnJFho3pWIiIiIVCjeVj55RkYGK1euZOzYsa5tXl5e9OzZk2XLlhXqGB988AHXX389QUFBebYvWrSIatWqERERwaWXXsozzzxDlSpVCjxGeno66enprtuJiYkAOBwOHA5HUV9Wqcp9/nPFERPqC8De48mWx1savAMjsaUeJzPxEM6IhlaHc06FaR+xhtrGval93Jvax32pbdyb2se9Fbd9irK/zel0Oot09FJ04MABatasydKlS+nUqZNr+yOPPMLixYtZvnz5OR+/YsUKOnbsyPLly7noootc27/44gsCAwOpV68e27dv5/HHHyc4OJhly5Zht9vzHWf8+PFMmDAh3/Zp06YRGBhYgldYPubuszFrr52OUdnc0DDb6nBK7OItz1AleQsrYkdzMOKi8z9ARERERKSMpKSkcMMNN3Dy5ElCQ0PPua+lPVcl9cEHH9CqVas8iRXA9ddf77reqlUrWrduTYMGDVi0aBGXXXZZvuOMHTuWMWPGuG4nJiZSu3Ztevfufd5fYFlzOBzExcXRq1cvfHx8CtwnffUBZu1djz20Kv37X1DOEZY+e/IXsGULHZrWJbtDf6vDOafCtI9YQ23j3tQ+7k3t477UNu5N7ePeits+uaPaCsPS5Kpq1arY7XYOHTqUZ/uhQ4eIjo4+52OTk5P54osvmDhx4nmfp379+lStWpVt27YVmFz5+fnh5+eXb7uPj4/b/GGcK5baVYIBiD+Z7jbxlkhwFAD29ATsFeT1uNN7RfJS27g3tY97U/u4L7WNe1P7uLeitk9R9rW0oIWvry8dOnRgwYIFrm3Z2dksWLAgzzDBgnz11Vekp6dz4403nvd59u3bx7Fjx4iJiSlxzO6oVkTOQsIJqVg4yrP0aK0rEREREamALK8WOGbMGN577z2mTp3Kxo0bueuuu0hOTmbUqFEADB8+PE/Bi1wffPABgwcPzlekIikpiYcffpjff/+dXbt2sWDBAgYNGkTDhg3p06dPubym8lY91B+bDdIzszmenGF1OCWXu9ZVsqoFioiIiEjFYfmcqyFDhnDkyBHGjRtHfHw8bdu2Zc6cOVSvXh2APXv24OWVNwfcvHkzv/76K/Pmzct3PLvdzl9//cXUqVNJSEigRo0a9O7dm6effrrAoX+ewNfbi2ohfhxKTGd/QipVgiv461TPlYiIiIhUQJYnVwCjR49m9OjRBd63aNGifNuaNGly1uFvAQEBzJ07tzTDqxBqhAdwKDGdAwmptK4VbnU4JROY03Olda5EREREpAKxfFiglI4a4bnzrtIsjqQUBEaay5Tj1sYhIiIiIlIESq48RK2c5OpAQqrFkZSCM+dceUKBDhERERGpFJRceYg6Vcxix0u3H6v4FQNz51xlpUNGsrWxiIiIiIgUkpIrD9G/ZQyBvnY2HkxkydYKPlfJNwi8TU+c5l2JiIiISEWh5MpDRAT5MvSiOgC8vWibxdGUAlUMFBEREZEKRsmVB7m1az187DZ+33Gc1XtOWB1OyQTlJFfJSq5EREREpGJQcuVBYsICGNy2JgCTF2+3OJoSUs+ViIiIiFQwSq48zB2X1Mdmg7kbDrHt8Cmrwym+oChzeXSztXGIiIiIiBSSkisP07BaCL2aVQfgncU7LI6mBJr0M5d/fghpidbGIiIiIiJSCEquPNCd3RsAMGPN/oq77lWzQRDVFNJOwop3rI5GREREROS8lFx5oPZ1IvhX/UgcWU4++HWn1eEUj5cXdHvYXF/2JqRX4CGOIiIiIlIpKLnyUHd1bwjA5yv2cCI5w+JoiqnFlVClIaSegD/etzoaEREREZFzUnLlobo1qkrzmFBSMrL4eNluq8MpHi/76d6rpa9DepK18YiIiIiInIOSKw9ls9m4K2fu1ZSlO0nJyLQ4omJqeQ1E1jcl2f/80OpoRERERETOSsmVB+vXMpq6VQI5keLgyz/2Wh1O8di9oetD5vrS1yAjxdp4RERERETOQsmVB/O2e3Fb1/oAvPfLThxZ2RZHVEytr4PwupB8BFZ+ZHU0IiIiIiIFUnLl4a7pUIuqwX7sT0jlx7UHrA6neOw+0C2n9+q3V8FRQcvLi4iIiIhHU3Ll4fx97Nx8cSwAkxdvJzvbaW1AxdX6egirA0mHYNXHVkcjIiIiIpKPkqtK4MZ/1SXEz5sth5L4edNhq8MpHm9f6PqAuf7rK+BIszYeEREREZF/UHJVCYT6+zDsX3UBeGvRNpzOCtp71XYYhNaCUwdh9SdWRyMiIiIikoeSq0ri5i6x+Hp7sWpPAn/sOmF1OMXj7QcX32+u//oKZKZbGo6IiIiIyJmUXFUS1UL9uaZDLcDMvaqw2t0EITGQuB/WfGZ1NCIiIiIiLkquKpHbu9bHywY/bzrMxoOJVodTPD7+0OV+c/2XlyEzw9JwRERERERyKbmqRGKrBtGvVQwA71Tk3qsOIyC4OpzcC399YXU0IiIiIiKAkqtK565LGgDww9oDbD10yuJoisknALrcZ64veRGyHNbGIyIiIiKCkqtKp2XNMPq0qE62E56dtdHqcIqvwygIioKE3fDXl1ZHIyIiIiKi5KoyeqxfM7y9bCzcfIRftx61Opzi8Q2Ezvea60tegKxMa+MRERERkUpPyVUlVK9qEDfmrHv1zMy/ycquoOteXXAzBFaBEzth/ddWRyMiIiIilZySq0rqvssaEervzab4U3yzap/V4RSPXzB0Gm2uL3vT2lhEREREpNJTclVJRQT58u9LGwHw4tzNpGRU0GF17W4yl/F/QdIRa2MRERERkUpNyVUlNrxzXWpHBnD4VDrvLtlhdTjFExwF1VuZ6zsXWxuLiIiIiFRqSq4qMT9vO4/2bQrAO4t3cCgxzeKIiqn+JeZyxyJLwxARERGRyk3JVSU3oFUM7euEk+rI4uV5W6wOp3jq9zCXOxaBs4IW5xARERGRCk/JVSVns9l4YkBzAL5cuZeNBxMtjqgY6nYCLx84uReOV9DhjSIiIiJS4Sm5EjrUjWBAqxicOQsLOyta749vENTuaK5raKCIiIiIWETJlQDwaN+m+Nq9+GXrURZtqYBV9+p3N5dKrkRERETEIkquBIA6VQIZ0dksLPzszI1kZmVbHFER5SZXO5dAdpaloYiIiIhI5aTkSlxG92hEeKAPWw8n8eWfFWxh4RrtwC8U0hLg4FqroxERERGRSkjJlbiEBfpwb87Cwi/HbSYpvQItLGz3htiu5rqGBoqIiIiIBZRcSR43/qsusVUCOZqUweRF260Op2hc864WWhqGiIiIiFROSq4kD19vLx7rZxYWfu+XHRw8mWpxREXQIGe9qz2/Q0aKtbGIiIiISKWj5Ery6dMimotiI0nPzOaFuZutDqfwqjSE0JqQlQF7f7c6GhERERGpZJRcST5mYeFmAHy7aj/r9p20OKJCstlUkl1ERERELKPkSgrUpnY4g9rWAOCpH9aTnV1BFhZWciUiIiIiFlFyJWf1WL+mBPraWbUnge9W77c6nMKpd4m5PPgXJB+zNhYRERERqVSUXMlZxYQFMPrShgA8N3sTp9IcFkdUCCHVoVpzwAm7llgdjYiIiIhUIkqu5Jxuubge9aoGcTQpndcWbLU6nMLR0EARERERsYCSKzknP2874wY2B+Cj33ax7fApiyMqBCVXIiIiImIBt0iu3nzzTWJjY/H396djx46sWLHirPtOmTIFm82W58ff3z/PPk6nk3HjxhETE0NAQAA9e/Zk69YK0uvihno0qUbPZtXIzHYy/oe/cTrdvLhF3c7g5Q0ndsHxnVZHIyIiIiKVhOXJ1fTp0xkzZgxPPfUUq1atok2bNvTp04fDhw+f9TGhoaEcPHjQ9bN79+489z///PO89tprTJ48meXLlxMUFESfPn1IS0sr65fjsZ68vDm+3l78uu0oczfEWx3OufmFQK0LzfWdi62NRUREREQqDcuTq5dffpnbbruNUaNG0bx5cyZPnkxgYCAffvjhWR9js9mIjo52/VSvXt11n9PpZNKkSfznP/9h0KBBtG7dmo8//pgDBw4wY8aMcnhFnqlulSDu6FYfgKd/2khqRpbFEZ2HhgaKiIiISDnztvLJMzIyWLlyJWPHjnVt8/LyomfPnixbtuysj0tKSqJu3bpkZ2fTvn17nn32WVq0aAHAzp07iY+Pp2fPnq79w8LC6NixI8uWLeP666/Pd7z09HTS09NdtxMTEwFwOBw4HNZWyMt9fqvjALitS12+XrmP/QmpvPnzFu67rKHVIZ2Vrc7FeAPOHYvJzEgHW9mcR3Cn9pG81DbuTe3j3tQ+7ktt497UPu6tuO1TlP0tTa6OHj1KVlZWnp4ngOrVq7Np06YCH9OkSRM+/PBDWrduzcmTJ3nxxRfp3LkzGzZsoFatWsTHx7uO8c9j5t73T8899xwTJkzIt33evHkEBgYW56WVuri4OKtDAKBvdRsfnbQzefF2Ik9uoYr/+R9jBZszk/5e/ninHue3byZzMjC2TJ/PXdpH8lPbuDe1j3tT+7gvtY17U/u4t6K2T0pKSqH3tTS5Ko5OnTrRqVMn1+3OnTvTrFkz3nnnHZ5++uliHXPs2LGMGTPGdTsxMZHatWvTu3dvQkNDSxxzSTgcDuLi4ujVqxc+Pj6WxgLQz+lk05SVLNtxnGVpNXjrqrZWh3RWXklfwLZ5dK2ZRXan/mXyHO7WPnKa2sa9qX3cm9rHfalt3Jvax70Vt31yR7UVhqXJVdWqVbHb7Rw6dCjP9kOHDhEdHV2oY/j4+NCuXTu2bdsG4HrcoUOHiImJyXPMtm3bFngMPz8//Pz8Cjy2u/xhuFMsEwa1pN+rvxC38TBLdyZwSeMoq0MqWMNLYds87Lt/wd5tzPn3LwF3ah/JS23j3tQ+7k3t477UNu5N7ePeito+RdnX0oIWvr6+dOjQgQULFri2ZWdns2DBgjy9U+eSlZXFunXrXIlUvXr1iI6OznPMxMREli9fXuhjyrk1rh7CyM6xAEz4YQMZmdnWBnQ29XuYy93LwKFKkSIiIiJStiyvFjhmzBjee+89pk6dysaNG7nrrrtITk5m1KhRAAwfPjxPwYuJEycyb948duzYwapVq7jxxhvZvXs3t956K2AqCd5///0888wz/PDDD6xbt47hw4dTo0YNBg8ebMVL9Ej39WxE1WA/dhxN5qPf3HQtqagmEBwNmamwd7nV0YiIiIiIh7N8ztWQIUM4cuQI48aNIz4+nrZt2zJnzhxXQYo9e/bg5XU6Bzxx4gS33XYb8fHxRERE0KFDB5YuXUrz5s1d+zzyyCMkJydz++23k5CQwMUXX8ycOXPyLTYsxRfq78OjfZvw8Nd/8dqCrQxuV5PqoW72+7XZTEn2v74wJdnrX2J1RCIiIiLiwSzvuQIYPXo0u3fvJj09neXLl9OxY0fXfYsWLWLKlCmu26+88opr3/j4eGbOnEm7du3yHM9mszFx4kTi4+NJS0tj/vz5NG7cuLxeTqVxdftatKsTTnJGFs/N2mh1OAXTelciIiIiUk7cIrmSisnLy8bEK1pis8GMNQdYsfO41SHll9tbdWA1pJ6wNhYRERER8WhKrqREWtUK4/oLawNw69Q/mLuh4LXELBNaA6o2AZyw8xeroxERERERD6bkSkrssb7NaFcnnMS0TO74ZCUTf/zbvSoIamigiIiIiJQDJVdSYmGBPky/vRO3da0HwIe/7eTayUvZe7zwq1mXqbJKro7vwP7JQFrvnQLZWaV7bBEpf9lZkH4KTsXDse0Qvw4OroVsNzpZJCIibs3yaoHiGXy9vXhiQHMuqleFh75ay9p9Jxnw2i+8cG0b+rQo3ILQZSa2C9jscHw7JOyB8DolP+bBv+DTq/FKPkw9IGvxs9B7YsmPu2MxzBwDvsEQURfC656+DK9rYvdxs6qMIhXNHx/Ams8gI/n0jyMFMs+yHt4Ft8DlL5dvjCIiUiEpuZJS1at5dWbeezGjp61mzd4E7vhkJTd3qcdj/Zri621RR6l/GNTsAPtWmOSl/U0lO96u3+Dz6yE9EWdYbWwn92Jf+ipUbwFthhT/uAdWwxc3QEaSuX1wTcH7BUefTrhqtIMWg83cMhE5vw3fmRMY52QD3yDwCYTkI/DnB1CnE7S+tuTPv+d32LMMOt4JPgElP56IFF9GCqQl6H+olColV1LqakUE8uUdnXhh7ibe+2UnH/62k5W7j/PGDe2pHRloTVD1u+ckV4tKllxtmgVfjzJnuOt0JvPaT9j56f00PvQj/PBvqNIAal1Q9OMe3wGfXWsSq3rdzBevE7tNT1vC7pzru839SfHmZ+9yWPclzH3cfPFreRU0HwzBUcV/fSKe7OhW+H60uX7BzebvxTfodCKVe+kTYNbJA/j5GVjyAvx4H8S0gagSLOuxdwVMvQKy0uHwJrhy8unnEZHylZkBH/UzJzIb9YGuD0Kdjud9mMj5KLmSMuF2wwTrd4clz5vkKjsLvOxFP8bqz0wC5cyCJv3hmg8BbzbGXE3DsEy8tsw2PU+3LYSwmoU/btJh+OQqc4Y8uhUM+Qz8Q/Pv53RCynFI2GWSreM7YNt8cxZ8z1LzM/sRk5y1uAqaDYTAyKK/TpHiykyHdV+ZJCS6ldXR5JWRDNNvMico6l4M/V4AeyH+BXYfa3qbdv0CX42AWxeAbzFOEh3fCZ8PNYkVmMXNa10AF91W9GOJSMn9Nun0CJGtc81PbFeTZNXvrhMfueLXQWhNfZ8oAhW0kDKVO0ywbe281QQdWeU8QbzWheATBClH4c2LTKKU5Sj84397Db6/2yRWbYfBdZ+cHtJj8yLriregWgtIOgRfDDVDDQoj/RR8dg2c2GmG+Q37puDECswHfVAVM8Sx5VXQ7SG4eQ48sAF6/x/UaA/ObJNA/ngvvNgIPrsO1n4BaYmFf60ixXF4E7x3GXx/D0zuCj89YE4GuAOnE368H45shODq5sRIYRIrMCdirv4AgqrB4b9h9sNFf/7UBJh2nfn8iW4N3R832+eMNb1ZIlK+Dm+Cxc+b672ehnY3gZePOYnyyWB4/zLYNFPFbH6dBJMvhlfbwK+vgCPV6ogqBCVXUuZyhwmeWU1w2HvLOXzqLJPHy4K3L1zxGviHw7FtJlF6rT2seA8c54jD6YS4cRD3pLnd+d8w6M38X8z8QmDo5xBYxVQXm3GXeey5ZGbA9BvN/oFV4MZvIaR60V9bWC3oPBpuXwj3roHLxkH1VpCdac7EfXcHvNTEvNbzxSRSVE6nKRDx7iVwaJ0pxoIT/vwQXu8AK6dY/wXlzw/MEFqbHa75qOh/ZyHV4er3weYFqz+FNZ8X/rFZDvhyOBzdAiE14IbpcMkjZkhids59pw4VLR6xljPn/f3jfSZxloolO8uMQsl2QOO+Of/X34D71pgh+d4BsH+lGYkyuQv89RVkZVoddflb8znMf8pcT0+E+ePh9Qtg7XTrP9PdnJIrKRe5wwTfvakDIX7erNh1nIGv/8rK3SfKL4hW18AD66HXRHMW+uQemPUQvNoalr4O6Ul598/KhB9Gw2+vmtu9JkLvZ84+VCCiLgz51Jz9+nvG6bNiBcnONgnejkWmR23YV1C1YclfY2Q9M6Thrl/hnhVwyWNQpZGphDbrITMsKfloyZ9HBCD5GHwxzBSIyEyDBpfCv1fBiJ8gqhmkHjdfQN+/zHxZscL+laaHCKDneFM9tDjqX2KGCIJ5vYc3nv8xTqfpwdu52Pyd3zDdTJy32cyXuapN4NRBM4+zKD3pYp0sB/x0v2nXlVPMKAGdtKpYVrxn5mD7hsCAl0//Tw+rBf3+B/evg4vHgF+o6a3+9lZ44wLT3uc6GetJts43338AOo2GwZPN0MDEffDd7fBed1MgTAqk5ErKVe8W0cwY3YVG1YI5lJjO9e8u45Pfd+Msr39OfiHQ5T64/y8z5yK0lhnKN+8/MKmlSYhSE8wH6FcjzFlqmxdc8YZ53PnU7Xy6ZPOiZ2HDjIL3i3vSzE3x8obrPjZD/UpbVBPoMdYkWX2eA7svbJkNb3eGbQtK//mkbG2ZC1+NMsnKgomw7E1zZnHLPNj3p5mDl5pQfl/0diwy76XNM817q8+zZlhrSHWo1xXu/MVs8w2BA6vMkMEf7jUJWXlJOQ5fjoCsDGh6uTlDXRJdH4T6PczJii9H5D8h80+/TYLVn5jPkGs/gpjWp+/zC4HrPzO/n92/QdxTJYtNyl5aohneuXIKYDOf339/b3pGS0PSEfWElbUTu2HBBHO998SC50cHR0HPp0yS1eM/EBBphu7/eJ/5nvDz/5m18DzV/pWmRz07E1pdZ4ZNth0K/15pRsb4hpgRNx9fYaYeHN5kdcRuR8mVlLsGUcF8d08X+reKxpHl5MkZ63noq79Ic5TjQrw+AdDxdrh3tUmcIutD6glY+H/wSkt471LY9BPY/cz8qqJUGGw/HP51t7n+3Z3mQ+hMS1+HZW+Y64PehEY9S+c1nY2XF3S6G277GaKammTy06tg7hOmAIG4v4N/mX92G741X+x+eclUiZxxJ0y71vQMvdYO/lcXnq4KLzYxXwDKYihLZgbMexI+HmyqVlZtbIo8dLrHvNdy2X3Mtn+vhNbXA05YNRVeb2+GEZb1wtvZ2fDt7XByr/n7HvxWySeoe9nhqvcgJAaOboaZD549md0wwwyjAej7X2jcJ/8+VRvBlW+b67+/Ceu+Lll8UnZO7oMP+8L2n01FyaGfQ8+cL+lzHjd/oyWxZ7kZRfFKS70PyorTaRIkRwrU7QLtR557/4BwuORhM+Klz7Om5yb5iCmO9UoL+OZW2GdRj3xZObbdJEyOZFPUY9Cbpz/XfQLMCab71sBFt5uTC1vnwtudzO9Vw5tdlFyJJYL9vHnzhvY83r8pXjb4ZtU+rn57KXuPF7IQRGnx9jWJ0+g/zaT1as0h4xQc3mDOztz4DTS7vOjH7fU0NLgMMlPNULzcD521000vGZhhhm2uL73Xcj7RrUwlwwtvNbeXvWG+lB/ZXH4xlJfMdGybfqL5/unYdi6p2MN2Uk/AlzeZYXexXc3QtItuh5bXmF6UmDYQVtsMOwNztjEp3nwB+KifOVNbWo5ugw96wdLXACd0GAW3L87bI/NPIdXhqndg1Byo3tKsKTNzDN4f9SIieXvpxfZPv7wI2+LA29/0DvuHlc5xg6PMZ4XNbir+rf4k/z77/jRzHQEuugM63nH24zUbCBc/YK7/8G849HfpxCml58Aa0/N6eIMpiDJqFjTpZ04eNO5rKkB+Per8PZlnc3ij6RFzpJj/P9/cAjPuLv7xpGBrpsGOheYzYeBreU8GnYtvkGnr+9bCtVOg9r/M5+y6r+D9S817Y93X5sRTRZZ02Jx4zS28M+RT8x3pn4KqQv8X4O7lZkSAM9uc9HutnTnxp/lYSq7EOjabjdu7NeDTWzoSGeTLhgOJDHzjV37ZeqT8g/GymzlZd/4G108zvU83zzbDm4rD7m0qklVpBIn7Yfows0bW9zk9Wv+6GzrfW3rxF5ZvIAx4Ca7/3Ax1iF8H71xiJmdX5AQETPx7lpu5EC82xvubkTQ6PBPvaVeZ4WsrpxS+iqO7yM42vZ8ndkF4HRjyCXR/zPxju+YDGD4D7lhizqw+cQCeOARjNsKV75r5AvtWmMp9ZxueWlhOJ6z6GN7pakoXB0SYJQMGTip8WfK6nUwi1u8F8AvDFv8XF295GtuGb0oWW0G2/wwLnzXXL3+l9MvCx3aBS3NOksx6GOLXn77vxG6zyHhmmlk7p+9z5z9ej/9AvUvMl+vpN0LaydKNN9fhTab6V0X7O7DS5jnwUX9zwqJac9NLW6Oduc9mg8Fvmx6NY9vMXLyifo6e3AefXm1OOtS6ELo9YoaRrvkM3ulmEjspuVOHTG8/mBNUxZnjbPeBFlfCLXPh9kXQ5gYzJHr/nyYhntQKFr9gercqmvRTZq3NE7tyKhd/bYYun0vVhmZo86g5UPMC09u1YGJOsZByHInkhmzOcpvsUnEkJiYSFhbGyZMnCQ09S1nscuJwOJg1axb9+/fHx8fH0ljK0v6EVO76dCV/7TuJlw0e6tOEuy5pgM3N15k4b/sc226GGKYlnN7W8hoztKiwZ83Kyql488V9x0Jzu8kAuOJ1U+69Ijm+A/760pScP7HTtdkZEsMBe21qJG/A5kg2GwMioP0Is7ZQWC2LAi6CJS+YRWztfnDLPKjRtvCPPbELvr7F/OMH6DDSzL0ryhpN2dmwZY7pqdqzzGyr1w2ufMcUZiiupCNk/zQGr00/4MSG7YrXS7a495lO7jNfSlOOmba+4rXSOe4/ZWfD50Ng6zyo0tB82XJmwwe94cgmU7Hz5tnn/4KSK/moOdGRuM/8LQ75tHQ/I9ISzUmGk3uhzVCzgPE5uO3/nuxs07bJh80Q57REwHlGUnPG9TO3edkhpq0ZIlrY/yvL34U5j5p2rd8DrptacA/o7mUwZYBZqmPQW9BuWOGOn3LcDDU8utkUN7l5jllLaNdv8O1t5sScl48pxPKvu13vB7dtG3f25XAzPy6mDdz6c+GXYjifpMPmxN0f75v3I+C0+7E7vDM1h72OT2Tt0nmespSZYT7Ltv9sKhffEgdVGhTtGM6cYd8/PWD+XpoPNt9zCur5slhx/36KkhsouSqAkitrpDmyeOr7DUz/cy8AfVpU5/lr2hAW4L6vu1Dts32hOTPpzDJjmG/4yn0+cLKz4fe3zNyQbIeZS9L1QTPUJdyN/ymknoAN35lhlnt/P73dJwiaD4I2Q3DU/Bez5syl/6Vd8Fn3Bax4BxL2mP1sdjMcq+OdUOdf7rlY5PafzeLSOM28wOIkH1kOM4/w10nmOFFNTY9q9RbnfpwjzQx5W/oGHNtqtnn5mN6azveWypd+R0Y6+969nnpHfzYb+j1/7uFzhZGZYYZC7v/TfIm6eR74+Jc41rNKPmZ68xL3m4W7U0+YkxUhMaaHoyiLiYOZSP5hX1OA49InzVp2peXH+3IKMeS48l1oM+Ssu1v2v8fpNAV3jm42X1aTjpjL5MPmi2zyUfNZWlzB0abnsW4XiL3YzBn8599/dpYZvv37W+Z2++Gmqpz9HL+HJS/Cz0+b+Vi3LYRqTc8dR0aKWU9p73JTov+WeXk/c1OOmx6ATT+Z2w17ml6y4GqV6ntBqfj7BzO02mY3J0HONYy5uDIzTPK2/G1XZVSnTxC2LveaQjq+QaX/nKUhO9vM3f1runnvjvgJapWgwNbfP8DXN5vvE416myHZuWuCugklVxZRcmWtz1fs4anvN5CRlU21ED+eGdyS3i2irQ6rQIVun81zTEWwbg+ffZFgKx1caybnHt1yelv1lubDsXFfqHWBOfNrtUMbzEKGf39vvoCCGUJTv7s5G990gOufWL62yc4yvTC/v20WiswV0wY63mWGe5TlF/GiSNhrel9Sj5svdle8XrLjbV9o5gAlHTLzDfr8H1xwS/4vlSnHTbGJFe+cHtriFwYXjDKJT0l6q/7B4XAwa+ZMLvdbjn15zpfYy56CrmOKd0CnE2Y/amL3DzPDJSNiSy3es9qzHKb0N3MwwHxBGTW7aL2MZ1o51ZT3xmbmfDa8rOQxbv8ZPrnSXG82EDb+aNYju2PJWc9QW/K/J+kwfD/aTJI/J5s5wx5cLacnyZbzXradfk/n3s69npFsPudyPzdyBUWZKq+xXU3CFV7HFELZPNPcf9lTZk7c+U7AZGeb+So7Fp4ePni2XuKsTDNUfMscE//Nc6Fas/z75a6nNfdxM8w0qBpcORlH3W6V7ntBsaWegDc7ms++rg+aandlLHPHLyR+fR+RKTlzSoOjzYmptje4x//RM8WNM8vN2OxmqYhGvUp+zG3z4YsbzZzzuhfDDV8Uvge/HCi5soiSK+ut2ZvAmOlr2HHUDOe6vHUM469oQdVgP4sjy8uj2icjBf54z8wN27fCdO3nCojMSbT6mLWMAsLzPz7tJJzcb87in9yXc7nfzCNp1Nv0KPkFFy+2fX+aM8NbZp/eVr0ltB4Cra6F0Jh8Dzln28Svh+WTzYTkzJx1S3wCzZC3hj3NF9rI+sWLtaQy003vxYFVpdv7knTELG69Lc7cbnq5SdoCI+H4TlPaffWn5h8imGUKOt1tkrsy+Mfoap9+/fD57SVY/F9zR9eHzBeRovQmHtpgvoDuWGRuD50OTfqWesxntfT1nEI1NjNns2n/kh3vh3+bOW4BEWaeWkTd4h/rzOGAF95m1vGZegXs/tUMk7slrsCe9HL/bNs0y7zulKNmGGzTARASbRKooGqmkERwlLkMrFq8YV2OVPNZsvs32PUr7Pvj9N9/Li8fc9bd7mcqOba8uvDHTzoMb3cxvWxnG5LqdJr1g1Z/ak503DTDzEc8l0N/mzk9h02xk6x/jWZmanv6XX5Fxf+/U9a+H22KzlRpBHf+Wi4n0HJPHA2on4n3zxMhIaeoULUW0Pvp0jlhUpDsbLNY+tGt5uSD6ycy56eKOamS+9n6+9sw5zFzvSjDWQtj9zJTpCU90Sw1M+xrE4MbUHJlESVX7iHNkcWrC7by7pIdZGU7iQj04amBLRjUtobbzMXy2PZJOW7OPm2ZYy7PnGBvs5szvZH1zkim9psqV+fiEwjNrjDrZcR2O//QMqcTdi4xVd92Lsl9cpOkXXz/6UnlZ1Gotkk+BqumwB8fmrkuZ4qsn5No9TTDh8prWMdPD5iz1f7hcMfi0u19+ecw0NBaULMdbJp5OpmObgWd74MWg889DKqE8rXPb6+as6hg5pf0efb8CVbyUTPsceUUE7/dD3pNgH/dVWZxF8jpNF/gQmuWzhcnRxp81BcOrDZDOUfNLv4Xk9zhgOF14a6l5gTHyf0wuYs5q99ptOnJ/GcI5fXZlp5kEuNVU83t6i3NXI3qzcvuOXNlppshXLt+M8nm3hXmZFBApCm1XudfRT/mjkVmmQKcpqpkq2vy3r/gafOZZvMy8+qaDijccR2pJoH/430AEv1rEdTsUuyhNUxFzpAYk3iGRJtktLTmFFVkOxbBx4PM9VFzzp/ElpI8fzu2bNNmi58/Pfe6wWUmyTrf8OyiOL4Tvr/HnDg4F7uvSbICInOSdafpzev6YOnFkuvAajO0PfW46c29aYZ5r1pMyZVFlFy5l3X7TvLIN3+x8WAiAJc2rcYzg1tSI9z6cbyVon2yMs28gC1zzEK2R89Rut0/3BSKCK1p5puE1jTDpf76Eo6fUXY7tJaZ79HmhvxVm5xO81xLXjxdjMHL2/RSXfyAWRuoEIrUNk4nHFpvEsltC0zxhtxhXmD+IdXtbBKtRn0gqnGhYiiyNZ+b8e/YYNhXpTNEoyAHVptx8cd3nN7WsKeZG1DvknKZg1Zg+6x4D2blzDNqP8JU+itoGE1mBqx413xhSc9J/JsPMssblMdQwPKQsNeUvT91EGp3NF9MilKMBMxw0E8Gm+sjfspb/XTTLPhiqLk+7Ot877Vy+Wzb96cp3HB8B2CDzqPNXDNvi0YoZGbAoXVmaYPgasU/zs/PmGI0viHmBEnu0Mvl78DsR8z1ga9BhxFFP/bGn3B+fw+2M4sk5WMzwx1Dqpshab5B5nfq7WdOQORe9/Y3n23e/qb3MrCKmYMW2cB95gUXV0YyvNXJ9BpdeBsMeLHcnrrAv52U4+Z/2op3zYktmxe0HWZ66UNKMO0hd+jovCdNtT6fIGh1tXn9Kcdyfo6by3/20oL53fR/oew+8w9vPL0mYmQDGP695fO5lVxZRMmV+3FkZfPO4u28tmAbGVnZBPt581i/ptxwUR28vKzrxaqU7XN8J2yNM2ejXElULXN5tt4dp9MMwVkzDdZ/e/oLMZjyw22GmupCOxbCLy+b9WTAfBFoPxy63GvmQhRBidom/ZTpLds2H7bOh5N78t5fswO0u8kMGSqtOXTx6+D9nuYf4CWPQY+xpXPcs0k/Zb4EZqaZdbNK8yxqIZy1fdZMM2dgndnQ6joziT/3LHxu4j33idPJenRrs0hvbJdyjb9cHPrb9GClnTRJ/fWfFb438Z/DAQv6cjnrETNHLbCKWYbijOG1ZfrZlpVp1sNZ/D9TnCK0pqleWK9b6T6PVbIyYepA2LPUDO29Jc70Dn99M+CEHk/AJY8U+/COE/vY8O2LtIqtij3liCkzfupgTgGQwyUr+AFmdEJELEQ1MclW1cY51xuV3npxZW3O42Zh7tBacM/v5Trn55x/O8d3wPwJ8PcMc9s7wKx32fHO8xdB+aeT+8ywx9yKv3W7mEV/I+sVvH9GyhkJ1zHz/7p2x7I/mXZ8h+lBTNhj2mPED0WvRliKlFxZRMmV+9p2+BSPfP0Xq/YkANCxXiT/vbo19apaU4lH7VMMjjTYPAvWfm6SlzPnduXyDYYLb4F/3VPsYQSl1jZOp1nDZtt8k1TuXJy3eEHzwdDuRtOzVdx/UqkJ8G53U0q+YU9TUdLqUv1l7Jzts/5b06ORnWnmhl3zoWmDM+dVBVc3w1naDHW/SeKlafcy0/uUmWbOdA96s3Dvs4KGA/6TI80k9IfWmcTmphmu32WZfbYd32EKRuz7w9xueY1J/AIiSu853MHJ/TD5YnMSqlFv877NyjCLuPd/sURfaM/ZNtlZZrhsUrxJupIOmSGFmWlmsePMM37+efvUQTNf51xDvIOjzYmYix8o/jqQZSntpBl6+cf7gLPAXtmyVqi/nT3LzTDPfStOb6vfwwxpbtjr3J//Tqc5CTXnMTOnydvfFF7peKf7/t84ud8kWMe2mmGrN30H0S0tCaU8kisNypUKpWG1EL66szMfL9vF83M2s3zncfpOWsK9lzXilovr4e/jwV+yPIWPP7S8yvycOmQm4K6ZZsZ/B0SYyn0db3efL1s2mzljW7WR+ceXdNiUrV31iRkiuXaa+YlsYJKsNkMLLLBxVq6FgndCWB33WAPNai2vMonrl8NNKerJXc0/5dx5VZ3uMVUF3agCVZmp2wmunQJfDDMLywZFmXll57J94emy64PePHshGR9/uPYjU5ly5xJTibM0y7+fKXde2uzHzPAlvzCzoHnra8vm+awWltMbN+06sxYamKGr/Z4v254CL3vOHKzqUISPIRen0yRZRzabROvoZlNF9sgWk7Dl/mxfYHruez9TqlVEi83phPXfmBMwOetN0Wl0uSdWhVanoym/v/s3U1hi8yzTA7Vjoflf0vEOU13wn59xp+LNiZMtc8ztWhea3v1CDpe3TFhNM3f00yvNKI0pA8znWOvr3adKbylSciUVjt3Lxqgu9ejZrDpjv13Hr9uO8sLczXzxxx4e79eMvi2j3abghZxHSHUzz6fTaDPEIbBK0eeVlLfgaqdj3veHqeq24TszTG3BBDPUrlEv848xONr0vjizzBllZ5ZJps7ctud3UwXR7msWKXWTikqWa9LXlAb+4obT8/w8bV5VYTXpBwNfNVXmfptk3oOd7il437REU3UPzHDA8/UuVG1k5lx8fw8sfNaUJK/TsXTiTk8yPb1b48xcxtzhtXUvNomHO6+lVxoa94Eu95liLbFdzdpi7t7LarOZZCm0BjTokfe+tJMm4Vr7uZnns/4bs8zIJY+YIjRFnaflSDXH+PND875tdY0Zbl3U9eGObYeZD54eHleloUnc63cv2nHKm81miiXFXgwndpv5WKs+Mf9LZj9i/pe0u9EM246INb+rWQ+ZQjR2X+g+1qw7WFGKlwRHmbmfn11reux+vM+8xovuMCNVPOh/XwVpEZH8akcG8sktFzFjzX7+N3sze4+nctdnq+hYL5InL29Oy5oVZGy4mH8yFe2Lls0GtS8yP33/a8bQr/rELGq8Zc7pM4uF1f8FqNm+TEKtsBr0gBE/wsqPTI9g7MVWR2Sd9jeZtccWTDBn54OioPV1+feLG2fmWYXXhZ7jC3fstsPMsLV1X5n17u78BbyLMdTa6TQ9HtviTEK1e6mZvJ/LOwC6P2ZOTrh7klFaek4wy0VENas4X4LPxj/MrHlY6wIzF3bmQ+ZL8vynTK9qv+fzJ2QFSdhrhu2t+tgMm8y16DkzD69xX+gw0gyRPtf7JDPdLJD+y0tmiKPdz/S8drnPuqIoxRVR11Tt7D7WJK/L3zG99b+/ZXq2qrcwRZfAzDO98p3yqahZ2gLCzZyrPz4wrytxHyx8Bn592SSS/7r77HPGKpAK/pculZ3NZuPKdrXo0yKayYt38M7i7SzfeZyBb/zKdR1q82CfxlQL8bwuZ3EzfsHmH0O7G83wmTWfmjO62Q4zOdzLnnPpdfq2l/fp600HmOp4kl/ulzkx81ySDsPyt82aZQGR0Kjn6fu3LzSJKJx7OOA/2Www4GVTve/ETrOI8eD3C/fY1AQztGlrnJmXeHJv3vsj6pme3Ia9cpY0cPOe6dJms5nlDTxNTBuz+PFfX5iE/ugWMzew2RVmGYV/nixzOs3aYiveybv0Q1ht02sRUsMkW7t/NUPkNs8yxQ/aDzefq//szdqxyPRWHdtmbje41Mxls7BQQqnwC4aLbjOLvG//2fytb5tvEisvb7MOYLeHynSZjDLnE2Aqg3a8AzbMgKWvmqGCK941SXezK0yPXK0OVkdabEquxCME+nozpldjhlxYm+fnbOL7NQeY/udeZq47yD09GjKqS6zmY0n5iGpshq71mmh1JOJpbDbzxTXlqOll+vIm07NX64KiDwf8J/9QuOYD+KA3/P09trrdgKjT96cnmV6pw3/DkU3m8vAmOHUg73G8/U0S1bCXSaoq+pddOTsvLzP8uUl/WPRf8+V44w8mGej6oOmhzF2KY8W7rkWQATNMsuOdZshrbu9UmyHm5NSqqaYnLHEfLHrWLDDeuC90GGUS1bgnzfsfTGGbvs9Bi6vKZQmJcuPlZU6cNOpphmL+/b0pjBLT2urISo/dx8y5bHWNGT689HXz3vl7hvmp28W8hxr1qXDzkJVciUepGR7Aq9e3Y3inWCb+9Ddr9ybwvzmbmLZit+ZjiUjF5+UFg94ypZS3/2zmL9w81wwfKupwwH+q2cFUHYt7EnvcE7SI7IF9+qdwdJMpo3w2kQ1y1oDrZb4QVbbeqcouIBz6/df0MM162JSg//lpkySlnTy9CL1PoCk7ftHtUK1ZwceKamyGx136JGzMGRK8+7fTvVkuNnOcS5+oOOXhi6tqo7IrNOMObDYzP65+dzi0wSRZ674y7b77N6jaBG5fePalXtyQkivxSB3qRvDdXZ3NfKw5m1zzsTrVr8IbN7SjSnAFG48tIpLL2xeu+8SspXRgFUzpb+ZjAQx6o/DDAQvSaTTsWIRt+wIaHp4Nh8+4L7g6RDWFas3NmjzVmpv1jzz9y60UTnRLGDUL1n1tyoznJuQRsSYRajvMJGKF4eNvejVaX2t6s1ZOMVVZU09ATFuzwLjmqHqe6i1MsZtLnzRDSP/8yMxHq0CJFSi5Eg/m5WXjqva16Nvy9HysZTuOce07y/jklo7UDA+wOkQRkeLxC4ZhX8GHfU7PO7nw1pIvxOvlBVe9S/bc/7D7wGHqdOiDPaal6WnwoGpeUkZsNpMQNe5jhvZF1j//uk3nE9UY+j5r1rU7uc8UPKgsBVEqq7CaZmh915zqiBVMxRrEKFIMufOxZt7blRph/uw4kszVby1l66FzLJQoIuLugqqaxTirNoYa7UxlulI6btbA1/mr9giyL7gZYrsosZKi8Q816wI2LsX5Mj7+ULWhEqvKxD/U9FxVMEqupNJoWC2Yr+/qTMNqwcQnpnHtO8tYvafinREREXEJrwP3rIBbfy7ZcEARESkVSq6kUqkRHsBXd3SiTe1wElIcDHt/OUu2HLE6LBGR4rPZKlw1LRERT6VPY6l0IoJ8mXZrR7o2qkpKRha3TP2Dn/46cP4HioiIiIicg5IrqZSC/Lx5f8QFDGgdgyPLyb8/X80nv++2OiwRERERqcCUXEml5edt57Xr23Hjv+rgdMKTM9bz2oKtOJ1Oq0MTERERkQpIyZVUanYvG08Pasm9lzUC4OW4LUz48W+ys5VgiYiIiEjRaJ0rqfRsNhtjejUmMtCH8T/+zZSluziWnEHPZtVITHWQkOIgIefyZKqDk6kZrm2JqQ6q+9mhdjyXt62F3ctm9csREREREYsouRLJMbJLPcIDfXnoq7X8uPYAP64tXJGLPZk27vvyL15ZsI3butXn6va18PfROhwiIiIilY2SK5EzDG5Xk8ggX95atA0bNsIDfQgL8CEs0IfwAF/CAnwID/QhPGebN05e+XYJvx/zZ9exFJ74bj2vxG3l5otjufFfdQn197H6JYmIiIhIOVFyJfIP3RpH0a1xVKH2dTgc9Kvt5H+juvLtmnjeW7KDAyfTeH7OZt5auJ1h/6rDLV3qUS3Uv4yjFhERERGrqaCFSCkI9PVmVJd6LH6kBy9f14bG1YNJSs/kncU7uPh/C3nsm7/YcSTJ6jBFREREpAwpuRIpRT52L65qX4s593XjgxEXcGFsBBlZ2Xzxx156vryYp75fT0JKhtVhioiIiEgZUHIlUga8vGxc1qw6X93Zma/v7MRlTauR7YSpy3bT48VFfPL7brJU7l1ERETEoyi5EiljF8RG8sHIC5l2W0eaVA/hRIqDJ2es5/LXf2X5jmNWhyciIiIipUTJlUg56dygKjPvvZiJg1oQFuDDxoOJDHn3d0ZPW8WBhFSrwxMRERGRElK1QJFy5G33YninWC5vXYOX4zYzbfkefvrrIPM3HuLu7g25vVv9s66RdSrNwfYjyWw/nMS2I0nsPJJMg2pB3N29IUF++lMWERERsZpbfCN78803eeGFF4iPj6dNmza8/vrrXHTRRQXu+9577/Hxxx+zfv16ADp06MCzzz6bZ/+RI0cyderUPI/r06cPc+bMKbsXIVIEkUG+PDO4FUMvqsOEH/9mxc7jvBy3hS//3MujfZsSGeTLtsNJbD+S5Lo8lJie/0Ab4Ps1B3j+mtZ0blC1/F+IiIiIiLhYnlxNnz6dMWPGMHnyZDp27MikSZPo06cPmzdvplq1avn2X7RoEUOHDqVz5874+/vzv//9j969e7NhwwZq1qzp2q9v37589NFHrtt+fn7l8npEiqJFjTCm3/4vfvrrIM/O2si+E6n8+/PVZ92/WogfDaKCaVAtiJrhgXz6+272nUjlhveWc+O/6jC2XzP1YomIiIhYxPJvYS+//DK33XYbo0aNAmDy5MnMnDmTDz/8kMceeyzf/p999lme2++//z7ffPMNCxYsYPjw4a7tfn5+REdHl23wIqXAZrMxsE0NLmtWjcmLtjNtxR5C/X2oHxVMw2rBNIgKomG1YOpHBRMW4JPnsTd1qstzszby2fI9fPr7HhZtPsLzV7emc0P1YomIiIiUN0uTq4yMDFauXMnYsWNd27y8vOjZsyfLli0r1DFSUlJwOBxERkbm2b5o0SKqVatGREQEl156Kc888wxVqlQp8Bjp6emkp58ecpWYmAiAw+HA4XAU9WWVqtzntzoOKVhpto+PDf7doz7/7lH/vM+Xy88Lxl/elN7Nonh8xgbTi/X+cm64qBYP925McCXuxdLfjntT+7g3tY/7Utu4N7WPeytu+xRlf5vT6bRssZ0DBw5Qs2ZNli5dSqdOnVzbH3nkERYvXszy5cvPe4y7776buXPnsmHDBvz9/QH44osvCAwMpF69emzfvp3HH3+c4OBgli1bht2ev1jA+PHjmTBhQr7t06ZNIzAwsASvUKT8pGXBD7u9+O2QKQIa6efk+gbZNAnTeloiIiIixZWSksINN9zAyZMnCQ0NPee+Ffq09n//+1+++OILFi1a5EqsAK6//nrX9VatWtG6dWsaNGjAokWLuOyyy/IdZ+zYsYwZM8Z1OzExkdq1a9O7d+/z/gLLmsPhIC4ujl69euHj43P+B0i5crf2uQpYtuMYj3+3gX0Jabz1t53rL6zFo30qXy+Wu7WN5KX2cW9qH/eltnFvah/3Vtz2yR3VVhiWftuqWrUqdrudQ4cO5dl+6NCh886XevHFF/nvf//L/Pnzad269Tn3rV+/PlWrVmXbtm0FJld+fn4FFrzw8fFxmz8Md4pF8nOn9unWJJq5D1Tlv7M38cnvu/nij338svUY913WiMHtauLrXbmWt3OntpH81D7uTe3jvtQ27k3t496K2j5F2dfSb1m+vr506NCBBQsWuLZlZ2ezYMGCPMME/+n555/n6aefZs6cOVxwwQXnfZ59+/Zx7NgxYmJiSiVuEXcX5OfN04NbMu22jtSKCGB/QiqPfPMXPV5cxCfLdpHmyLI6RBERERGPY/kp7DFjxvDee+8xdepUNm7cyF133UVycrKreuDw4cPzFLz43//+x5NPPsmHH35IbGws8fHxxMfHk5SUBEBSUhIPP/wwv//+O7t27WLBggUMGjSIhg0b0qdPH0teo4hVOjeoyrwHuvFE/2ZEhfixPyGVJ7/fQNfnF/L+LztIyci0OkQRERERj2H5JIwhQ4Zw5MgRxo0bR3x8PG3btmXOnDlUr14dgD179uDldToHfPvtt8nIyOCaa67Jc5ynnnqK8ePHY7fb+euvv5g6dSoJCQnUqFGD3r178/TTT2utK6mUAn29ua1bfW7qVJcv/9zL5EXbOXAyjWdmbuTNhdu45eJ6DO8cS6i/hi+IiIiIlITlyRXA6NGjGT16dIH3LVq0KM/tXbt2nfNYAQEBzJ07t5QiE/Ec/j52hneK5foL6zBj9X7eXLSN3cdSeHHeFt5ZsoORnWMZ1aUekUG+eR7nyMom1ZFFWkYWqY6cn4wsakYEUC3E/yzPJiIiIlL5uEVyJSLlx9fbi+surM1V7Wvy018HeXPhNrYeTuL1n7fx/i87iQrxy5NMZWYXXMrdx27jjm4NuKdHQwJ88y9xICIiIlLZWD7nSkSs4W33YnC7msy9vxuTb2xPixqhpDqy2HM8hSOn0jmVnpknsfKyQZCvnarBvlQP9cOR5eSNhdvo+fJi4v4+dI5nEhEREakc1HMlUsl5edno2zKGPi2i2XAgkfTMbAJ87AT42s2ljx1/Xy987V7YbDYAnE4n8/4+xMQf/2Z/Qiq3ffwnlzWtxvgrWlA7Ugtvi4iISOWk5EpEALDZbLSsGVboffu0iKZro6o5wwl3sGDTYX7ddpR7ejTkjkvq4+etoYIiIiJSuWhYoIgUW6CvN4/2bcrs+7rRuUEV0jOzeTluC31eWcLiLUesDk9ERESkXCm5EpESa1gtmM9u7chrQ9tRLcSPXcdSGPHhCu7+bCUHT6ZaHZ6IiIhIudCwQBEpFTabjSva1KBHkygmzd/KlKW7mLUunp83HaZLg6p0bliVLg2r0KR6iGvuloiIiIgnUXIlIqUqxN+HJy9vzjUdavHkjPX8ufsECzYdZsGmwwBUCfKlU4MqdGlYlS4NqlKnigpgiIiIiGdQciUiZaJZTChf3dmJDQcS+W3bUX7bfow/dh7nWHIGP/11kJ/+OghArYiAnJ6tKlzSOIrwQN/zHFlERETEPSm5EpEyk1uBsGXNMO64pAEZmdms3nOC37YfY+m2o6zZm8C+E6lM/3Mv0//ci4/dxiWNo7iibU16NqtGoK8+okRERKTi0DcXESk3vt5edKxfhY71qzCmV2OS0zNZses4S7cdZcmWo2w+dIr5Gw8zf+NhAn3t9GpenUFta9C1URQ+dtXfEREREfem5EpELBPk502PJtXo0aQaTwyALYdO8cOaA3y/dj97j6fy/ZoDfL/mAOGBPvRvFcOgNjW4MDYSL6+zF8RwOp2kZGSRkJzO8XRwZGXj41OOL0pEREQqLSVXIuI2GlcP4aE+TXiwd2PW7E3g+zUH+OmvgxxNSmfa8j1MW76HmDB/LoyNJNWRRXJ6JsnpmSTl/CSnZ5GckYnTmXtEb55Zs4DaEQHUqxpEbNUg6lcNol7VYGKrBlIjLOCciZqIiIhIUSi5EhG3Y7PZaFcngnZ1IvjPgGb8vuM436/Zz5z18Rw8mcYPaw8U4hjghZOsbNh1LIVdx1Jgc96Fjf28vahbJZB6VYNoXyeCPi2iia0aVFYvS0RERDyckisRcWvedi8ublSVixtV5enBLVm0+Qh7jicT5OdNsJ83Qb7ep6/72Qn2N9e9yWbWrNlc0PVS9iZksPNoMruOJbPjSDI7jyax53gK6ZnZbDmUxJZDSczdcIjnZm+iSfUQ+rSMpk+L6jSPCdWaXCIiIlJoSq5EpMLw97HTt2V0ofZ1OBzYbFA91J9aVULo1KBKnvszs7I5kJDGjqNJbDucxKLNR/h9xzE2HzrF5kOneG3BVmpHBtCneTR9WkbTvk4Edg0hFBERkXNQciUilZK33Ys6VQKpUyWQ7k2qcWvX+pxMcbBg0yHmrI9nydYj7D2eyvu/7uT9X3dSNdiXXs1Nj1bnBlXx9Vb1QhEREclLyZWISI6wQB+ual+Lq9rXIjUji8VbjjBvQzzzNx7iaFIGn6/Yw+cr9hDi702vZtXp0zKaSxpH4e9jtzp0ERERcQNKrkREChDga4Yg9m0ZjSMrm993HGPuhnjmbjjEkVPpfLt6P9+u3k+gr50eTarRt2U0PZpWI9hPH6siIiKVlb4FiIich4/di66NoujaKIqJV7Rk1Z4TzF4fz5z18exPSGXmuoPMXHcQX28vujWKol/LaHo2q05YoBbYEhERqUyUXImIFIGXl40LYiO5IDaS/wxoxrr9J12J1s6jyczfeIj5Gw9h97JRr2oQDaOCaVAtiIbVgmkYFUKDakEE+uqjV0RExBPpP7yISDHZbDZa1wqnda1wHunThC2Hkpi9/iBz1sezKf4U2w6bSoRsyPu4muEBNKgWTIMok3TVCA8g1N+HsAAfQgO8CQvwwc+7ePO4srKdZGRm4+/jpTLyIiIi5UzJlYhIKbDZbDSJDqFJdAj392xM/Mk0thzKSbCOmCRr++EkjiVnsD8hlf0JqSzZcuSsx/Pz9spJtnKSLn9vAnztpDuySXVkmZ+MLNLyXM8mIysbgFB/75wELvcniAbVgqkTGYiPXZUORUREyoKSKxGRMhAd5k90mD/dGkfl2X4iOcOVbOX+HE1K52Sqg8RUB6fSM3E6IT0zm8On0jl8Kr1Yz5+YlsnqPQms3pOQZ7u3l426VQJNwlUtmPpVg6ifk3yFB/oW9+WKiIgISq5ERMpVRJAvFwZFcmFsZIH3Z2c7OZWeSWKqwyRcaSbpSkzNJNWRhb+PF/4+dgJ87AT4mkv/M64H+NjxttvYn5DK9sPJbD+SdPrncDKpjiy2H0lm+5Fk+PtQnueODPLNSbZMwlWvahANooKoExmkdb1EREQKQcmViIgb8fKyEZYzFLB2CY7TNNqHptGhebZlZzuJT0zLSbTMcMWdR5PZcSSZgyfTOJ6cwfHkDP7cfSLP4+xeNmqE+xMZ6Osaphge6OOKMzwg7/aYMH/CAnw050tERCodJVciIpWEl5eNGuEB1AgPoGujvMMVk9MzTaJ1NJkdZyRdO44kkZyRxd7jqew9nlro5wrx96Z2RCB1IgOpUyWQ2hEB1I4MpHZkILUiAjizH8zpdJKUnklCioMTKRn5LrOynQxoHZMvWRQREXE3Sq5ERIQgP29a1gzj/9u78/CoyvNv4N8z+5JkMknIZBIICRLZQXYD+LqQsr5WlFLtS9uIbfmBwWKxLeqr4Fq4bLVqa0Oxiu0PKxZfoWoBjWz+StkXAYEYJEDMSsgyk9mX5/3jhIGBsA+ZA/l+Ls81Z57zZOaeua+jc/uc8zx9syxR7UII1Dl9qGhwo8ktX6rY7AmgqfUesWZPAE1u/+l2dwAnXX44vUEcqHbgQLXjnPeSJMCWqIcIqPHc3g1o9gQQDIsLxveHdYdxV890PHzHTRhynksqiYiI4o3FFRERnZckSbAlGWBLMlzy33j8IXzb6EZFoxvHT7pxvMGDikY3KhrcON7ghtsfQo3DB0AC4I/8nV6jgtWkQ7JJG3lMNunQ4PLhswO1WHeoDusO1WFojhUP39Edd/ToxEsPiYhIUVhcERFRTBl1auTZEpFnSzznmBACDS4/jtQ5sOHfmzH2jlHoZDHCatLBoD3/2l7l9S4s/uIb/L+dldh+tBHT3tmOnhmJmHnHTZjYzw4Np5cnIiIF4H+NiIio3UiShNQEPW7pkoybLQK97ImwW4wXLKwAIDfNjAX39cf/zL0T//W/usGsU+NQjROzl+3BnS9vwH9vOQZvINROn4KIiKhtHLkiIqLrhi3JgCcm9MLDd3THf285irc3HUVFgwdPr9yP1z7/GsNyU2BonZ7eoFHDoFVFpqs3aFXQt05Xr9OooJYkqFUSJEmeEVEtSVCp5DaVBKgkCVq1vJhzilkHk07NyxCJiOiCWFwREdF1x2LSYtZdefjJqG74x44KLP7iCCqbPFi1r+aavadOo0KKSYcU87mb1ayDPcmArqnyjIgXG4m7ECEETrT4UOfwoYvVBItJG8NPQURE1xKLKyIium4ZdWoUjsjB/xmejXWH6lDT7IU3EII3EIY3GILHH4Iv2Po8EIInEII3EII/GEZIyGt/hYVAKOoRkf1AKIwmdwC+YBj+YBg1Di9qHN6LxpXRWmjJmxnZKa37KWYkGDSodXhR2eTBt41uVDZ6Wvc9qGz04NsmD/zBcOS1slNM6JdlQb/OFvTLsqBvpoUFFxGRQrG4IiKi655WrcLYPhnX5LWFEPAEQpFFlhtcfjS6/TjZIj82uOT9qmYPjtW74fQFI0XY1vKGc15PJQEXmXkeKgmwmnQ46fLjeOssi//aVx053jXVhL5ZFvTPkguuPiy4iIgUgcUVERHRBUiSBJNOA5NOg85W0wX7CiHQ6A7g2EkXjje4cbTejWMNLhw/6caxBjdOOH0IC0CrlmC3GJGVbERnqxFZ1lP78iLLGRYDtGoVmt0B7K9qxt5vm7G/shn7KptxvMGNYyfl7V97Txdc6Yl69MhIRF56InpkJMgzNqYnINEQv6LLFwyhttmHGocXLn8Qg7pYWQQS0Q2NxRUREVGMSJIUuQ9rYLb1nOMuXxAtviDSEvRQqy4+OYbFpMXI7mkY2T0t0tbk9mN/pQN7K5siBVdFgwd1Th/qnD78T1l91GtkJRuRZ0tAD1sislNNEAIIhsIIhgUCIYFgKIxAWCAUDiMYktsCwSAqjqtwqKQMJr0WBq0aeq0KBk3rY2TSEBW8wTBqm72obpZH62od8n6tw4sGlz8qFrVKwtAcKwp62VDQy4acNPMVftOXJhwWcHqDcPmDSDFfeLp/IqJYYHFFRETUTsx6Dcz6q/tPb7JJh1F5aRiVd7rgcnoDKKtrQVmtE6U1LSirc+LrWidqHT5UNsn3dG0oPXGZ76TChuryq4oVkBeHzrAYoJIklNe7sOVIA7YcacAL/zqI7ukJGN0rHQW9bBiUbb1owXlqZLC62YPqJi/qnD40uv1ocvvR6A5EHuW2AJo9AYTOuAYzxayD3WKA3WJAhsUAu8XY+twYaWMBRkRXg8UVERHRdS7RoMWgbCsGnTVa1uwO4Os6J0prnCirdaKyyQO1SoJGpYJGLT9q1VJkX6OSoFGrICGMr8u+QWZ2DgIhAV8w3DpRyOnJQryBMHzBEHRquXjKSDLAliQXKKeeZyQZkGzSRqawP37Sjc8P1mLtoVpsPdKAw3UtOFzXgj9vPIIUsw539OiEgl42GHVqVDd5Ud3sQVXrY3Wz/OgNhNv6Ci5Iq5YQCInIPXNfVTnO23dojhVzx/XEkJyUy34fIiIWV0RERDcoi0mLoTkpGHqZhUIgEMAqfxkmTOgJrTZ290hlp5rw0KhcPDQqFw5vABtLT2DtwVqsLz2BBpcfH+6qxIe7Ki/6OmkJOtgtRtiS9Eg26WA1aVsfz9g3a2E16ZBs0kKnVsHhCaLaIY94VTV7UNPsRVWTFzVntHkDYWw/2ojvLdqMcX0yMHd8T+Re40sXlSAclgtog1bFtdyIrhKLKyIiImp3SQYt7h6QibsHZCIYCmPHsUasPViLL76uh0olRS7fy0w+feleZrI8OnYll+5ZTFpYTFr0zEhq87gQAlXNXvxhbRn+saMCa76qwecHazF1eDZ+PjoPqQn6q/3IVyUUFmjxBlHvdKPeK8d7NbyBEDYdrsfq/fLnbHIHIEmASauGUaeBWS8vuG3SqWHWa6L2k01y4Woxni5gTxW5FqMWGrUq8j6BUBgNLj9OOH2nt5bo/RZvECqVvHC3JEmQgMhC3nKbvK9WSTDr1UjQa5Fo0CBBr0FC62OiQdPapkWCXoNuncy8xJPigsUVERERxZVGrcKt3VJxa7dU/N+J8YlBkiRkJRuxcHJ/TBuZi4WrD2J96Qn8dfMxfLirEjPvvAkPjcy9pB/sQgh82+jB7oomHKhyIBAKQwIgSYgUD5AACacKB3nfGwih2ROAwyvfL+bwBCPPW3xBnK6nNPhj6QYMyUnBsJwUDMmxom+WBdozipq2uHxBbCg9gdX7q7H+UB1c/tBZcQMufwgufwj1LVfyLcoSDRpYjFq4/aFzJjVpL2adGuP72XHfwCzc2i0VqkuYQOZKhcMCdU4fKpvcOF7fgu11EoJfVsOol0dNtRr58ludWgWdRgWtWt6MOjXSEnTQa1gE3khYXBERERGdoUdGIpZMG4b/HK7Hi6sO4qsqB15aU4qlm4/hsTE9cO/ArKgf6x5/CHu/bcLuiibsOtaI3RVNOOH0XZPYDFoVgsEQGt0BlByoRcmB2kj7wC5WDM2VC66B2ckw6zVodgfw+cFarN5fgy/KTkQtUG23GDC2TwbG9c1An8wkeALywtsuXwieQBDus/bdvhBafHLBd+YkIk2eABpdfji8QQCA0xuEs3UfkGeJTEvQoVOiHmkJenRK0KNT4uktQa+BgFyUCoHIQt5nPw+Gw3C1xtDilWfedHgDkf0Wn/y+DS4/mj0BfLDzW3yw81tkWgy4Z2AW7huYhTxb4mV/5+GwOL3Q91mLf1c2yZeV+kNn3guoxt+/2XfJr28xauXvIkGP9KTo7yc90dD6vemQbNJd0iyjFF8sroiIiIjaMKJ7Gj6eNQor91Tid5+WoqrZi8eWf4m3/l2OHwzrgrK6Fuw63oiD1c6oWQkBQKOS0CczCf07y0WOgEDrPwiHRWsxAQjIBYQQAgatGklGrby1jv4kGeVL7ZIMWiQZNVCJMD76ZBW6DBiB3RUObD/aiB3HGtDkDmDzkZPYfOQkALmg6ZZmRnm9C8EzYstJNWFcXzvG9c1A/yxLVJF4tWuiBUNhOLzByGyNZr0anRL0sJp013Tk6GxCCOw41ogPd32LT/ZWo6rZi+IN36B4wzfol2XBvQOz8N1bMpF21qWep4ooebbNFnxd60RZrTzpiicQOs+7ydQqCRlJBtgteriaG2BNSUMwDPhCYQSCYQRC8uYPhuEPCQRCYbj9QQRCAs0eeaTycN2FhwtVkjzjZapZj9QEHVIT9Eg165B2xn5qa/GalqiDSXd5P/NDYYHqZrmIrGhwo6LRg6omD0JhAbVKglYtnZ4QRyVBrZagVakixwxaNZJNOiQbta2XisqXi1qM2ouOqt5IWFwRERERnYdKJeG+QZ0xoZ8dSzYdxZ/WH8aBagee/udXUf3SE/XyjI1dkzEoW75M71rc8xMIhKFRAQO7JGNYt074r9vlouCbEy3YdrQB28sbsP1oY2uRIP9Y75mRiHF95RGqHrbEazZphUatiqzzFk+SJEUmcpl/dx+sO1SHD3dVYkNpHfa1rg334qqDuP3mThjc1YryehfKap0oq2uB2992EaVTqyKLfZ+9+HeW1YiMJAM0apU8GcyqVZgwYchFJ4MRQi6szrz/rM5x1j1pTh/qnF40ugMIC6C+xY/6Fj9Qe/HvwaxTI+2M0cK0RJ28n6iHSadGVZO3tYhyo6JBLqSC4au7l+98EvTy/yywmrVI1GsREiKy3l4wJI9KBkMCgTPW2wuGw8hJNWNl0chrEtO1wuKKiIiI6CIMWjVm3nET7h/aBX9afxj7q5rR227BoK7JGJhtRabFELeZ9lQqCXm2ROTZEjF1eFcAQFWTB19VOdA9PaFDzHh4PgatGhP62TGhnx0nW3z4ZG81PtxdiS8rmrDuUB3WHaqL6q9Tq9Ctkxl5tkTcnJ4gP9oSkJ1iipqoIxYkSZJHeky6i16uGAyF0eD242RL6+byob7Fj5MtvsjzE63P61t88AbC8v1zJ904dtJ9yTFp1fK9h11STOhsNaKz1QSdWtVaBMnFUCgsF0GhkJDbw2GEwgIuXwhNngCaWy8VbXLL9wsKgchlm5VNnsv6jhymwGX1VwIWV0RERESXKMWsw1P/u3e8w7iozGQjMpON8Q5DUVIT9CgckYPCETn45kQLVu6uxPEGN7qlJeBmm1xI5aTGvoiKBY1ahfREA9ITDRftK4SAyx/CCadcaNW3PsqjY37Ut/jg8gVhtxjRJcWILlZTpJiyJRliel9XKCzg8ATk+/LcfjS7A3D6glBL8vp62rPW2NO0Xmp46tj1ONkHiysiIiIi6lBu6pSAx8b0iHcY14QkSfI09XpN3Ect1SoJVrMOVrMOuegYI6iKKM3feOMN5OTkwGAwYPjw4di2bdsF+y9fvhw9e/aEwWBAv379sGrVqqjjQgjMmzcPdrsdRqMRBQUFKCsru5YfgYiIiIiIOri4F1fvv/8+5syZg/nz52PXrl0YMGAAxo4di7q6ujb7/+c//8EPfvAD/OQnP8Hu3bsxadIkTJo0Cfv374/0eemll/D6669j0aJF2Lp1K8xmM8aOHQuv19teH4uIiIiIiDqYuBdXr7zyCn72s59h2rRp6N27NxYtWgSTyYS33367zf6vvfYaxo0bh1/96lfo1asXnn/+eQwaNAh//OMfAcijVq+++iqeeuop3HPPPejfvz/+9re/oaqqCitXrmzHT0ZERERERB1JXO+58vv92LlzJ5544olIm0qlQkFBATZv3tzm32zevBlz5syJahs7dmykcCovL0dNTQ0KCgoixy0WC4YPH47NmzfjgQceOOc1fT4ffL7Ti/05HA4AQCAQQCAQ31lKTr1/vOOgtjE/ysXcKBvzo2zMj3IxN8rG/CjblebncvrHtbiqr69HKBSCzWaLarfZbDh06FCbf1NTU9Nm/5qamsjxU23n63O2BQsW4Nlnnz2n/bPPPoPJZLq0D3ONlZSUxDsEugDmR7mYG2VjfpSN+VEu5kbZmB9lu9z8uN2XPp09ZwsE8MQTT0SNhjkcDnTp0gVjxoxBUlJSHCOTK+WSkhJ85zvfuehidNT+mB/lYm6UjflRNuZHuZgbZWN+lO1K83PqqrZLEdfiKi0tDWq1GrW10ctM19bWIiMjo82/ycjIuGD/U4+1tbWw2+1RfW655ZY2X1Ov10Ov15/TrtVqFXNiKCkWOhfzo1zMjbIxP8rG/CgXc6NszI+yXW5+LqdvXCe00Ol0GDx4MNauXRtpC4fDWLt2LfLz89v8m/z8/Kj+gDy0d6p/bm4uMjIyovo4HA5s3br1vK9JRERERER0teJ+WeCcOXNQWFiIIUOGYNiwYXj11Vfhcrkwbdo0AMCPf/xjZGVlYcGCBQCA2bNn4/bbb8fLL7+MiRMnYtmyZdixYwcWL14MQF447dFHH8ULL7yAvLw85Obm4umnn0ZmZiYmTZoUr49JREREREQ3uLgXV/fffz9OnDiBefPmoaamBrfccgvWrFkTmZDi+PHjUKlOD7CNGDECf//73/HUU0/hySefRF5eHlauXIm+fftG+vz617+Gy+XC9OnT0dTUhFGjRmHNmjUwGAzt/vmIiIiIiKhjiHtxBQCzZs3CrFmz2jy2YcOGc9qmTJmCKVOmnPf1JEnCc889h+eeey5WIRIREREREV1Q3BcRJiIiIiIiuhGwuCIiIiIiIooBFldEREREREQxwOKKiIiIiIgoBlhcERERERERxQCLKyIiIiIiohhgcUVERERERBQDiljnSmmEEAAAh8MR50iAQCAAt9sNh8MBrVYb73DoLMyPcjE3ysb8KBvzo1zMjbIxP8p2pfk5VROcqhEuhMVVG5xOJwCgS5cucY6EiIiIiIiUwOl0wmKxXLCPJC6lBOtgwuEwqqqqkJiYCEmS4hqLw+FAly5dUFFRgaSkpLjGQudifpSLuVE25kfZmB/lYm6UjflRtivNjxACTqcTmZmZUKkufFcVR67aoFKp0Llz53iHESUpKYknqYIxP8rF3Cgb86NszI9yMTfKxvwo25Xk52IjVqdwQgsiIiIiIqIYYHFFREREREQUAyyuFE6v12P+/PnQ6/XxDoXawPwoF3OjbMyPsjE/ysXcKBvzo2ztkR9OaEFERERERBQDHLkiIiIiIiKKARZXREREREREMcDiioiIiIiIKAZYXBEREREREcUAiyuFe+ONN5CTkwODwYDhw4dj27Zt8Q6pQ/riiy9w9913IzMzE5IkYeXKlVHHhRCYN28e7HY7jEYjCgoKUFZWFp9gO5gFCxZg6NChSExMRHp6OiZNmoTS0tKoPl6vF0VFRUhNTUVCQgImT56M2traOEXccRQXF6N///6RxRrz8/OxevXqyHHmRVkWLlwISZLw6KOPRtqYo/h55plnIElS1NazZ8/IceYmviorK/HDH/4QqampMBqN6NevH3bs2BE5zt8F8ZOTk3POuSNJEoqKigBc+3OHxZWCvf/++5gzZw7mz5+PXbt2YcCAARg7dizq6uriHVqH43K5MGDAALzxxhttHn/ppZfw+uuvY9GiRdi6dSvMZjPGjh0Lr9fbzpF2PBs3bkRRURG2bNmCkpISBAIBjBkzBi6XK9LnF7/4BT7++GMsX74cGzduRFVVFe677744Rt0xdO7cGQsXLsTOnTuxY8cO3HXXXbjnnnvw1VdfAWBelGT79u3485//jP79+0e1M0fx1adPH1RXV0e2f//735FjzE38NDY2YuTIkdBqtVi9ejUOHDiAl19+GVarNdKHvwviZ/v27VHnTUlJCQBgypQpANrh3BGkWMOGDRNFRUWR56FQSGRmZooFCxbEMSoCIFasWBF5Hg6HRUZGhvjtb38baWtqahJ6vV689957cYiwY6urqxMAxMaNG4UQci60Wq1Yvnx5pM/BgwcFALF58+Z4hdlhWa1W8Ze//IV5URCn0yny8vJESUmJuP3228Xs2bOFEDx34m3+/PliwIABbR5jbuJr7ty5YtSoUec9zt8FyjJ79mxx0003iXA43C7nDkeuFMrv92Pnzp0oKCiItKlUKhQUFGDz5s1xjIzOVl5ejpqamqhcWSwWDB8+nLmKg+bmZgBASkoKAGDnzp0IBAJR+enZsyeys7OZn3YUCoWwbNkyuFwu5OfnMy8KUlRUhIkTJ0blAuC5owRlZWXIzMxEt27dMHXqVBw/fhwAcxNvH330EYYMGYIpU6YgPT0dAwcOxJtvvhk5zt8FyuH3+7F06VI89NBDkCSpXc4dFlcKVV9fj1AoBJvNFtVus9lQU1MTp6ioLafywVzFXzgcxqOPPoqRI0eib9++AOT86HQ6JCcnR/VlftrHvn37kJCQAL1ejxkzZmDFihXo3bs386IQy5Ytw65du7BgwYJzjjFH8TV8+HC88847WLNmDYqLi1FeXo7bbrsNTqeTuYmzI0eOoLi4GHl5efj0008xc+ZM/PznP8df//pXAPxdoCQrV65EU1MTHnzwQQDt8+81TUxehYhIAYqKirB///6o+xIovnr06IE9e/agubkZH3zwAQoLC7Fx48Z4h0UAKioqMHv2bJSUlMBgMMQ7HDrL+PHjI/v9+/fH8OHD0bVrV/zjH/+A0WiMY2QUDocxZMgQ/OY3vwEADBw4EPv378eiRYtQWFgY5+joTG+99RbGjx+PzMzMdntPjlwpVFpaGtRq9Tmzl9TW1iIjIyNOUVFbTuWDuYqvWbNm4ZNPPsH69evRuXPnSHtGRgb8fj+ampqi+jM/7UOn06F79+4YPHgwFixYgAEDBuC1115jXhRg586dqKurw6BBg6DRaKDRaLBx40a8/vrr0Gg0sNlszJGCJCcn4+abb8bhw4d5/sSZ3W5H7969o9p69eoVuWyTvwuU4dixY/j888/x05/+NNLWHucOiyuF0ul0GDx4MNauXRtpC4fDWLt2LfLz8+MYGZ0tNzcXGRkZUblyOBzYunUrc9UOhBCYNWsWVqxYgXXr1iE3Nzfq+ODBg6HVaqPyU1paiuPHjzM/cRAOh+Hz+ZgXBRg9ejT27duHPXv2RLYhQ4Zg6tSpkX3mSDlaWlrwzTffwG638/yJs5EjR56z5MfXX3+Nrl27AuDvAqVYsmQJ0tPTMXHixEhbu5w7MZkWg66JZcuWCb1eL9555x1x4MABMX36dJGcnCxqamriHVqH43Q6xe7du8Xu3bsFAPHKK6+I3bt3i2PHjgkhhFi4cKFITk4W//znP8XevXvFPffcI3Jzc4XH44lz5De+mTNnCovFIjZs2CCqq6sjm9vtjvSZMWOGyM7OFuvWrRM7duwQ+fn5Ij8/P45RdwyPP/642LhxoygvLxd79+4Vjz/+uJAkSXz22WdCCOZFic6cLVAI5iieHnvsMbFhwwZRXl4uNm3aJAoKCkRaWpqoq6sTQjA38bRt2zah0WjEiy++KMrKysS7774rTCaTWLp0aaQPfxfEVygUEtnZ2WLu3LnnHLvW5w6LK4X7wx/+ILKzs4VOpxPDhg0TW7ZsiXdIHdL69esFgHO2wsJCIYQ87erTTz8tbDab0Ov1YvTo0aK0tDS+QXcQbeUFgFiyZEmkj8fjEQ8//LCwWq3CZDKJe++9V1RXV8cv6A7ioYceEl27dhU6nU506tRJjB49OlJYCcG8KNHZxRVzFD/333+/sNvtQqfTiaysLHH//feLw4cPR44zN/H18ccfi759+wq9Xi969uwpFi9eHHWcvwvi69NPPxUA2vzOr/W5IwkhRGzGwIiIiIiIiDou3nNFREREREQUAyyuiIiIiIiIYoDFFRERERERUQywuCIiIiIiIooBFldEREREREQxwOKKiIiIiIgoBlhcERERERERxQCLKyIiIiIiohhgcUVERHSVJEnCypUr4x0GERHFGYsrIiK6rj344IOQJOmcbdy4cfEOjYiIOhhNvAMgIiK6WuPGjcOSJUui2vR6fZyiISKijoojV0REdN3T6/XIyMiI2qxWKwD5kr3i4mKMHz8eRqMR3bp1wwcffBD19/v27cNdd90Fo9GI1NRUTJ8+HS0tLVF93n77bfTp0wd6vR52ux2zZs2KOl5fX497770XJpMJeXl5+OijjyLHGhsbMXXqVHTq1AlGoxF5eXnnFINERHT9Y3FFREQ3vKeffhqTJ0/Gl19+ialTp+KBBx7AwYMHAQAulwtjx46F1WrF9u3bsXz5cnz++edRxVNxcTGKioowffp07Nu3Dx999BG6d+8e9R7PPvssvv/972Pv3r2YMGECpk6dioaGhsj7HzhwAKtXr8bBgwdRXFyMtLS09vsCiIioXUhCCBHvIIiIiK7Ugw8+iKVLl8JgMES1P/nkk3jyySchSRJmzJiB4uLiyLFbb70VgwYNwp/+9Ce8+eabmDt3LioqKmA2mwEAq1atwt13342qqirYbDZkZWVh2rRpeOGFF9qMQZIkPPXUU3j++ecByAVbQkICVq9ejXHjxuG73/0u0tLS8Pbbb1+jb4GIiJSA91wREdF1784774wqngAgJSUlsp+fnx91LD8/H3v27AEAHDx4EAMGDIgUVgAwcuRIhMNhlJaWQpIkVFVVYfTo0ReMoX///pF9s9mMpKQk1NXVAQBmzpyJyZMnY9euXRgzZgwmTZqEESNGXNFnJSIi5WJxRURE1z2z2XzOZXqxYjQaL6mfVquNei5JEsLhMABg/PjxOHbsGFatWoWSkhKMHj0aRUVF+N3vfhfzeImIKH54zxUREd3wtmzZcs7zXr16AQB69eqFL7/8Ei6XK3J806ZNUKlU6NGjBxITE5GTk4O1a9deVQydOnVCYWEhli5dildffRWLFy++qtcjIiLl4cgVERFd93w+H2pqaqLaNBpNZNKI5cuXY8iQIRg1ahTeffddbNu2DW+99RYAYOrUqZg/fz4KCwvxzDPP4MSJE3jkkUfwox/9CDabDQDwzDPPYMaMGUhPT8f48ePhdDqxadMmPPLII5cU37x58zB48GD06dMHPp8Pn3zySaS4IyKiGweLKyIiuu6tWbMGdrs9qq1Hjx44dOgQAHkmv2XLluHhhx+G3W7He++9h969ewMATCYTPv30U8yePRtDhw6FyWTC5MmT8corr0Req7CwEF6vF7///e/xy1/+Emlpafje9753yfHpdDo88cQTOHr0KIxGI2677TYsW7YsBp+ciIiUhLMFEhHRDU2SJKxYsQKTJk2KdyhERHSD4z1XREREREREMcDiioiIiIiIKAZ4zxUREd3QePU7ERG1F45cERERERERxQCLKyIiIiIiohhgcUVERERERBQDLK6IiIiIiIhigMUVERERERFRDLC4IiIiIiIiigEWV0RERERERDHA4oqIiIiIiCgG/j+PBJb/f0L8TwAAAABJRU5ErkJggg==",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(10,6))\n",
        "plt.plot(history.history['loss'], label='Train Loss')\n",
        "plt.plot(history.history['val_loss'], label='Validation Loss')\n",
        "plt.title('Training and Validation Loss')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.grid(True)\n",
        "plt.legend(loc='upper right')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 855
        },
        "id": "SHJgPzJfVW7n",
        "outputId": "4eda8694-379b-420a-faf7-b7e3f89b81fa"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "                      precision    recall  f1-score   support\n",
            "\n",
            "            cataract     0.9278    0.9184    0.9231       196\n",
            "diabetic_retinopathy     0.9756    0.9600    0.9677       250\n",
            "            glaucoma     0.9074    0.8033    0.8522       183\n",
            "              normal     0.8182    0.9209    0.8665       215\n",
            "\n",
            "            accuracy                         0.9064       844\n",
            "           macro avg     0.9073    0.9006    0.9024       844\n",
            "        weighted avg     0.9096    0.9064    0.9065       844\n",
            "\n"
          ]
        },
        {
          "data": {
            "image/png": 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A6D/gC3zu1wfz5wRiQJ/P8OrVSywLXpOv+CB1Uq/kylTvNm0S0b/iOH6f0CHQP/5e0FHoEIi0UnYO/+RrC6VhydcZjT9bXyy383LHwGK5ndLGSi4RERERiQ57comIiIhEqCy3GhQHJrlEREREIsQkl4iIiIhER+pJLntyiYiIiEh0WMklIiIiEiFWcomIiIhIfGTFdCmCoKAg1K9fH6amprCxsUH37t0RExOjMScjIwMjR46EpaUlTExM4OPjg8ePH2vMiY+PR6dOnWBkZAQbGxtMnDgROTk5RYqFSS4RERERFYvw8HCMHDkSp0+fxsGDB5GdnY127drh5cuX6jnjxo3D77//jh07diA8PBwPHz5Ez5491cdzc3PRqVMnZGVl4dSpU9i4cSM2bNiAr7/+ukixcDMIomLCzSC0BzeDICoYN4PQHqWxGYR5nx+L5XZebOn7r6/79OlT2NjYIDw8HC1atEBycjKsra3x008/oVevXgCA69evw9XVFREREWjUqBH+/PNPdO7cGQ8fPkT58uUBACEhIZg8eTKePn0KfX39Qt03K7lEREREIlRc2/pmZmYiJSVF45KZmVmoGJKTkwEAFhYWAIDz588jOzsbbdq0Uc9xcXFB1apVERERAQCIiIiAm5ubOsEFgPbt2yMlJQVXrlwp9ONnkktERERE7xUUFASlUqlxCQoK+uj18vLy8NVXX6Fp06aoXbs2AODRo0fQ19eHubm5xtzy5cvj0aNH6jlvJ7hvjr85VlhcXYGIiIhIhIprdYWpU6di/PjxGmMKheKj1xs5ciT+/vtvnDhxoljiKComuUREREQiVFxJrkKhKFRS+7ZRo0Zh7969OHbsGCpXrqwet7W1RVZWFl68eKFRzX38+DFsbW3VcyIjIzVu783qC2/mFAbbFYiIiIioWKhUKowaNQq//vorDh8+DAcHB43jn376KfT09BAWFqYei4mJQXx8PBo3bgwAaNy4MS5fvownT56o5xw8eBBmZmaoWbNmoWNhJZeIiIhIjATYC2LkyJH46aefsHv3bpiamqp7aJVKJQwNDaFUKjF48GCMHz8eFhYWMDMzw+jRo9G4cWM0atQIANCuXTvUrFkT/fr1w8KFC/Ho0SNMnz4dI0eOLFJFmUkuERERkQgJsePZqlWrAACenp4a4+vXr8eAAQMAAN999x3kcjl8fHyQmZmJ9u3bIzg4WD1XR0cHe/fuxfDhw9G4cWMYGxvD398fs2fPLlIsXCeXqJhwnVztwXVyiQrGdXK1R2msk2s9cFux3M7T9b2L5XZKG3tyiYiIiEh02K5AREREJEJCtCtoEya5RERERGIk7RyX7QpEREREJD6s5BIRERGJENsViIiIiEh0pJ7ksl2BiIiIiESHlVwiIiIiEZJ6JZdJLhEREZEIST3JZbsCEREREYkOK7lEREREYiTtQi6TXCIiIiIxknq7ApNcIiIiIhGSepLLnlwiIiIiEh1WcomIiIhESOqVXCa5RERERGIk7RyX7QpEREREJD6s5BIRERGJENsViIiIiEh0pJ7ksl2BiIiIiESHlVwiIiIiEZJ6JZdJLhEREZEIST3JZbsCEREREYkOK7lEREREYiTtQi6TXKLicnWht9Ah0D/K1R8ldAj0j+dnVwgdAr1FLlMJHQKVIqm3KzDJJSIiIhIhqSe57MklIiIiItFhJZeIiIhIhCReyGWSS0RERCRGbFcgIiIiIhIZVnKJiIiIREjihVwmuURERERixHYFIiIiIiKRYSWXiIiISIQkXshlkktEREQkRnK5tLNctisQERERkeiwkktEREQkQmxXICIiIiLRkfrqCkxyiYiIiERI4jkue3KJiIiISHxYySUiIiISIbYrEBEREZHoSD3JZbsCEREREYkOK7lEREREIiTxQi6TXCIiIiIxYrsCEREREZHIsJJLREREJEISL+QyySUiIiISI6m3KzDJJSIiIhIhiee47MklIiIiIvFhJZeIiIhIhNiuQERERESiI/Ecl+0KRERERCQ+rOQSERERiRDbFYiIiIhIdCSe47JdgYiIiIjEh5VcIiIiIhFiuwIRERERiY7Ec1y2KxARERGR+LCSS0RERCRCbFcgIiIiItGReI7LJJeIiIhIjKReyWVPLhERERGJDiu5RERERCLESq6IyGQy/Pbbb0W6jqenJ7766iv19/b29li6dGmhr79hwwaYm5sX6T4La+bMmahTp06J3Pb/Qlvjep+SfI2IiIi0lUxWPJey6l8luREREdDR0UGnTp2KfN2iJpGl7ezZs/jyyy9L/X4LStADAgIQFhZW6rG8TVvjeh9t//nSVufPncXoEcPQxrMZPGo543DYIaFDEqWAQe1w4seJeHJiEeLCgrB9yRA42dm8d/5vK4bjVdQKdPF01xivYlsOu5YPQ+KpJYgLC8L8r7pDR0dUNQutwN8L7ZGbm4tVK5aha8c2aNqgDrp1aoe1q4OhUqmEDo202L/6XzE0NBSjR4/GsWPH8PDhw+KOSVDW1tYwMjISOgwAgImJCSwtLUvktrOzs//1dUsyLhLGq1fpcHZ2xtTpgUKHImrN61ZHyLZjaNl/EToPXwFdXR3sXTUKRgb6+eaO7tMKBf39lstl2LV8OPT1dNFqwGIM+Xoz+nZtiK+HF73oQB/G3wvtsXH9Wvyy42dMmjodO379A6O/moBNG0Kx7acfhQ5Nq8lksmK5lFVFTnLT0tKwbds2DB8+HJ06dcKGDRvyzfn9999Rv359GBgYwMrKCj169ADwujUgLi4O48aN03jiCvr4e+nSpbC3t1d/f/bsWbRt2xZWVlZQKpVo2bIlLly4UKTYX758if79+8PExAQVKlTA4sWL8815txK4ZMkSuLm5wdjYGFWqVMGIESOQlpaW73q//fYbnJycYGBggPbt2+PevXsax3fv3o26devCwMAAjo6OmDVrFnJyctT3CQA9evSATCZTf1/Q87Ju3TrUqlULCoUCFSpUwKhRowr12GUyGVatWoWuXbvC2NgY8+bNK7a4BgwYgO7du2PRokWoUKECLC0tMXLkSI1E+vnz5+jfvz/KlSsHIyMjdOzYETdv3lQff9NS8KHn8datW+jWrRvKly8PExMT1K9fH4cO/bey8r6frzf++usvuLq6wsTEBB06dEBCQgIA4NixY9DT08OjR4805n/11Vdo3rx5oZ7fsq5Z85YYNXYcvNq0FToUUes2Khg//n4G124/wuUbD/Bl4I+oWsECn9SsojHPvUYljO3XGsNm5v8D3qaxK1wdbTFo2kZcuvEAB05exezgPzD08xbQ09UprYciCfy90B6XLkahpWdrNGvhiYqVKqFN2/Zo2Lgprvx9WejQtJoQ7QrHjh1Dly5dULFixQI/DR4wYEC+JLpDhw4ac5KSktCnTx+YmZnB3NwcgwcPLjD3+pgiJ7nbt2+Hi4sLnJ2d0bdvX6xbt07j44I//vgDPXr0gLe3N6KiohAWFoYGDRoAAHbt2oXKlStj9uzZSEhIUCcZhZGamgp/f3+cOHECp0+fhpOTE7y9vZGamlro25g4cSLCw8Oxe/duHDhwAEePHv1ooiyXy7F8+XJcuXIFGzduxOHDhzFp0iSNOenp6Zg3bx42bdqEkydP4sWLF/D19VUfP378OPr374+xY8fi6tWrWL16NTZs2KBONM+ePQsAWL9+PRISEtTfv2vVqlUYOXIkvvzyS1y+fBl79uxB9erVC/34Z86ciR49euDy5csYNGhQscUFAEeOHMGtW7dw5MgRbNy4ERs2bNB4AzRgwACcO3cOe/bsQUREBFQqFby9vTUS4Y89j2lpafD29kZYWBiioqLQoUMHdOnSBfHx8QA+/POVnp6ORYsWYfPmzTh27Bji4+MREBAAAGjRogUcHR2xefNm9fzs7Gxs2bIFgwYNKvTzS1RUZiYGAIDnyenqMUMDPWwIGoCvvtmOx4n5/39r6O6Av2Mf4knSf48dPHUNSlND1KxWoeSDJhKAe51PcDbyNOLu3gEA3Ii5juioC2jSTBqFiLLk5cuX8PDwwMqVK987502h6c1l69atGsf79OmDK1eu4ODBg9i7dy+OHTv2r1pJi7y6QmhoKPr27asOMjk5GeHh4fD09AQAzJs3D76+vpg1a5b6Oh4eHgAACwsL6OjowNTUFLa2tkW639atW2t8v2bNGpibmyM8PBydO3f+6PXT0tIQGhqKH3/8EV5eXgCAjRs3onLlyh+83rsnpc2dOxfDhg1DcHCwejw7OxsrVqxAw4YN1bfr6uqKyMhINGjQALNmzcKUKVPg7+8PAHB0dMScOXMwadIkBAYGwtraGgBgbm7+wedl7ty5mDBhAsaOHaseq1+//kcf+xv/+c9/MHDgQPX3gwYNKpa4AKBcuXJYsWIFdHR04OLigk6dOiEsLAxDhgzBzZs3sWfPHpw8eRJNmjQBAGzZsgVVqlTBb7/9hs8++6xQz6OHh4f6ZwkA5syZg19//RV79uzBqFGjPvjzlZ2djZCQEFSrVg0AMGrUKMyePVt9fPDgwVi/fj0mTpwI4PWnERkZGfj8888LfLyZmZnIzMzUGFPpKKBQKD74PBG9IZPJ8G1AL5yKuoWrt/77hmzhBB+cjr6DvUcLrlCVtzTDk3eS3ydJKa+PWZkBMSUXM5FQBgwagpdpaejVvRPkOjrIy83FiNFfoWOnLkKHptWEaDXo2LEjOnbs+ME5CoXivXnFtWvXsH//fpw9exb16tUDAHz//ffw9vbGokWLULFixULHUqRKbkxMDCIjI+Hn5wcA0NXVRe/evREaGqqec/HiRXUSWZweP36MIUOGwMnJCUqlEmZmZkhLS1NX8T7m1q1byMrKUidQwOuk29nZ+YPXO3ToELy8vFCpUiWYmpqiX79+SExMRHr6fysvurq6Gsmmi4sLzM3Nce3aNQBAdHQ0Zs+eDRMTE/VlyJAhSEhI0LidD3ny5AkePnz4Pz23b35Y3iiOuN6oVasWdHT++1FphQoV8OTJEwCvf2B1dXU1nntLS0s4OzurnyPg489jWloaAgIC4OrqCnNzc5iYmODatWuF+hkwMjJSJ7jvxge8rjTHxsbi9OnTAF63T3z++ecwNjYu8PaCgoKgVCo1Lt8uCPpoHERvLJ36OWpVr4D+U9arxzq1dINngxqY+O0vAkZGpH0O/vUn9u/bi7lB32LLzzsxc04Qfty4Dnv3/CZ0aFqtuNoVMjMzkZKSonF5t9BTFEePHoWNjQ2cnZ0xfPhwJCYmqo9FRETA3NxcI2dp06YN5HI5zpw5U6T7KVIlNzQ0FDk5ORpZtEqlgkKhwIoVK6BUKmFoaFikAIDXLQHvniH57olR/v7+SExMxLJly2BnZweFQoHGjRsjKyuryPdXWHfv3kXnzp0xfPhwzJs3DxYWFjhx4gQGDx6MrKysQp+glpaWhlmzZqFnz575jhkYGBTqNv7N8/qudxO24ojrDT09PY3vZTIZ8vLyih7kBwQEBODgwYNYtGgRqlevDkNDQ/Tq1atQPwMFxff2z5yNjQ26dOmC9evXw8HBAX/++SeOHj363tubOnUqxo8frzGm0mEVlwrnu8mfwbt5bbQZvBQPnrxQj3vWrwHHylZ4dOxbjflbF32Bk1G30H7IMjxOTEG92nYax20szAAAj5+llHjsREJY/t0i+A/6Au07vj7BsrpTDSQkPMT60DXo3LW7sMFJQFBQkMYn9AAQGBiImTNnFvm2OnTogJ49e8LBwQG3bt3C//3f/6Fjx47qlbsePXoEGxvNVWd0dXVhYWGR79yZjyl0kpuTk4NNmzZh8eLFaNeuncax7t27Y+vWrRg2bBjc3d0RFham8bH42/T19ZGbm6sxZm1tjUePHkGlUqlL6xcvXtSYc/LkSQQHB8Pb2xsAcO/ePTx79qyw4aNatWrQ09PDmTNnULVqVQCvT4a6ceMGWrZsWeB1zp8/j7y8PCxevBhy+eui9/bt2/PNy8nJwblz59S9xzExMXjx4gVcXV0BAHXr1kVMTMwH+2f19PTyPS9vMzU1hb29PcLCwtCqVavCPeiPKI64CsPV1RU5OTk4c+aMul0hMTERMTExqFmzpnrex57HkydPYsCAAeoTGdPS0nD37l2N+yro56uwvvjiC/j5+aFy5cqoVq0amjZt+t65CkX+1oSMnH91tyQx303+DF1be6DdkGWIe5iocWzR+gNY/+spjbHzv0zDpMU78Uf43wCAM5fuYPLg9rAuZ4Knz1+fiOHVyAXJqa9w7XbR/gAQlRUZGa/Uf4ff0NHRgaqYiyliIy+mdoWCCjv/tj3v7XNt3Nzc4O7ujmrVquHo0aPF3glQ6CR37969eP78OQYPHgylUqlxzMfHB6GhoRg2bBgCAwPh5eWFatWqwdfXFzk5Odi3bx8mT54M4HVf67Fjx+Dr6wuFQgErKyt4enri6dOnWLhwIXr16oX9+/fjzz//hJmZmfo+nJycsHnzZtSrVw8pKSmYOHFikaqbJiYmGDx4MCZOnAhLS0vY2Nhg2rRp+X5p3la9enVkZ2fj+++/R5cuXXDy5EmEhITkm6enp4fRo0dj+fLl0NXVxahRo9CoUSN1svb111+jc+fOqFq1Knr16gW5XI7o6Gj8/fffmDt3rvp5CQsLQ9OmTaFQKFCuXLl89zNz5kwMGzYMNjY26NixI1JTU3Hy5EmMHj260M/D24orro9xcnJCt27dMGTIEKxevRqmpqaYMmUKKlWqhG7duqnnfex5dHJywq5du9ClSxfIZDLMmDEjX7W4oJ+vwmrfvj3MzMwwd+5cjX5dKUh/+VKj7ePB/fu4fu0alEolKhSh/4k+bOnUz9G7Yz18Nm4N0l5moLylKQAgOS0DGZnZeJyYWuDJZvcSnqsT4kMR13Dt9iOEzvXHtGW/obylGQJHdsbq7ceQlc13WsWJvxfao3nLVlj3w2rY2laAYzUnxFy/ii2bN6Brt/yfRNJ/FVdLbkGFneLi6OgIKysrxMbGwsvLC7a2thrthMDrIlhSUlKRz+cqdE9uaGgo2rRpky/BBV4nuefOncOlS5fg6emJHTt2YM+ePahTpw5at26NyMhI9dzZs2fj7t27qFatmvrEJldXVwQHB2PlypXw8PBAZGSk+sz3t+//+fPnqFu3Lvr164cxY8bkK2d/zLfffovmzZujS5cuaNOmDZo1a4ZPP/30vfM9PDywZMkSLFiwALVr18aWLVsQFJS/79LIyAiTJ0/Gf/7zHzRt2hQmJibYtm2b+nj79u2xd+9eHDhwAPXr10ejRo3w3Xffwc7uvx85Ll68GAcPHkSVKlXwySefFBiPv78/li5diuDgYNSqVQudO3fWWIarqIorrsJYv349Pv30U3Tu3BmNGzeGSqXCvn37NNoIPvY8LlmyBOXKlUOTJk3QpUsXtG/fHnXr1tW4n4J+vgpLLpdjwIAByM3NRf/+/f/1Yy2Lrlz5G717dUfvXt0BAIsWBqF3r+4IXrFc2MBEZujnLWBuaoSDa7/C3UNB6kuvdnU/fuV/5OWp4DN2FXLz8nB0wwSsm9cfP+2NxOxVf5Rg5NLE3wvtMXHKdHi1bYdv5s/GZz06YemSb9Gz1+cYPmqM0KFptbKwTu79+/eRmJiIChVerw7TuHFjvHjxAufPn1fPOXz4MPLy8jTO7SkMmYrbhZAW2LBhA7766iu8ePFC0DgGDx6Mp0+fYs+ePUW+LtsVtEe5+oVbP5pK3vOzK4QOgd6SncOP97WFqUHJ71LYPrhoJ2q9z18jCp9cpqWlITY2FgDwySefYMmSJWjVqhUsLCxgYWGBWbNmwcfHB7a2trh16xYmTZqE1NRUXL58WV0t7tixIx4/foyQkBBkZ2dj4MCBqFevHn766acixV3kJcSIxCg5ORmXL1/GTz/99K8SXCIiIm0jF2CzsnPnzmmcO/Sml9ff3x+rVq3CpUuXsHHjRrx48QIVK1ZEu3btMGfOHI12iC1btmDUqFHw8vKCXC6Hj48Pli8v+icoTHJFYMuWLRg6dGiBx+zs7HDlypVSjqjs6datGyIjIzFs2DC0bcvdjYiIqOwTYp1cT0/PfCtmve2vv/766G1YWFgUuWpbELYriEBqaioeP35c4DE9PT2NHlsqOWxX0B5sV9AebFfQLmxX0B6l0a7gHRL58UmFsG9Yg2K5ndLGSq4ImJqawtTUVOgwiIiISIsIUMjVKkxyiYiIiERIBmlnuSVfKyciIiIiKmWs5BIRERGJkBCrK2gTJrlEREREIiTE6grahO0KRERERCQ6rOQSERERiZDEC7lMcomIiIjESC7xLJdJLhEREZEISTzHZU8uEREREYkPK7lEREREIiT11RWY5BIRERGJkMRzXLYrEBEREZH4sJJLREREJEJcXYGIiIiIREfaKS7bFYiIiIhIhFjJJSIiIhIhrq5ARERERKIjl3aOy3YFIiIiIhIfVnKJiIiIRIjtCkREREQkOhLPcZnkEhEREYmR1Cu57MklIiIiItFhJZeIiIhIhKS+ugKTXCIiIiIRYrsCEREREZHIsJJLREREJELSruMyySUiIiISJTnbFYiIiIiIxIWVXCIiIiIRknghl0kuERERkRhxdQUiIiIiIpFhJZeIiIhIhCReyGWSS0RERCRGUl9dgUkuERERkQhJPMdlTy4RERERiQ8ruUREREQiJPXVFZjkEhWT7Jw8oUOgfyRFrhA6BPpHtzVnhA6B3hLqV0foEOgfpgaKEr8PqX9cL/XHT0REREQixEouERERkQixXYGIiIiIREcu7RyX7QpEREREJD6s5BIRERGJkNQruUxyiYiIiERI6j25bFcgIiIiItFhJZeIiIhIhNiuQERERESiI/FuBSa5RERERGIkl3iWy55cIiIiIhIdVnKJiIiIREjqlUwmuUREREQiJPFuBckn+UREREQkQqzkEhEREYmQ1E88Y5JLREREJEISz3HZrkBERERE4sNKLhEREZEIccczIiIiIhIdqffksl2BiIiIiESHlVwiIiIiEZJ4IZdJLhEREZEYsSeXiIiIiERHBmlnuezJJSIiIiLRYSWXiIiISITYrkBEREREoiP1JJftCkREREQkOkxyiYiIiERIJpMVy6Uojh07hi5duqBixYqQyWT47bffNI6rVCp8/fXXqFChAgwNDdGmTRvcvHlTY05SUhL69OkDMzMzmJubY/DgwUhLSyvy42eSS0RERCRCclnxXIri5cuX8PDwwMqVKws8vnDhQixfvhwhISE4c+YMjI2N0b59e2RkZKjn9OnTB1euXMHBgwexd+9eHDt2DF9++WWRHz97comIiIioWHTs2BEdO3Ys8JhKpcLSpUsxffp0dOvWDQCwadMmlC9fHr/99ht8fX1x7do17N+/H2fPnkW9evUAAN9//z28vb2xaNEiVKxYsdCxsJJLREREJEIyWfFcMjMzkZKSonHJzMwscjx37tzBo0eP0KZNG/WYUqlEw4YNERERAQCIiIiAubm5OsEFgDZt2kAul+PMmTNFuj8muUREREQiJJfJiuUSFBQEpVKpcQkKCipyPI8ePQIAlC9fXmO8fPny6mOPHj2CjY2NxnFdXV1YWFio5xQW2xWIiIiI6L2mTp2K8ePHa4wpFAqBoik8JrlEREREIlRc6+QqFIpiSWptbW0BAI8fP0aFChXU448fP0adOnXUc548eaJxvZycHCQlJamvX1hsVyAiIiISoeLqyS0uDg4OsLW1RVhYmHosJSUFZ86cQePGjQEAjRs3xosXL3D+/Hn1nMOHDyMvLw8NGzYs0v2xkktEREQkQnKU/pZnaWlpiI2NVX9/584dXLx4ERYWFqhatSq++uorzJ07F05OTnBwcMCMGTNQsWJFdO/eHQDg6uqKDh06YMiQIQgJCUF2djZGjRoFX1/fIq2sADDJJSIiIqJicu7cObRq1Ur9/ZteXn9/f2zYsAGTJk3Cy5cv8eWXX+LFixdo1qwZ9u/fDwMDA/V1tmzZglGjRsHLywtyuRw+Pj5Yvnx5kWORqVQq1f/+kIgoNSNP6BDoH7o67MTSFt1/KNqSP1SyQv3qCB0C/aNyuZI/cSv41N1iuZ0RTeyL5XZKGyu5RERERCJUXCeelVUsd0hUQftJF5W9vT2WLl1aLPEQERERFSdWcktYREQEmjVrhg4dOuCPP/4o0nXt7e3x1Vdf4auvviqZ4IgA5ObmYs2qFfjzj9+RmPgMVtY26NK1OwZ/ORyy4jytlgol9IfVCDt0AHfv3IbCwAAedT7BV+MCYO/gKHRoolK7gik++6QCnKyNYWmsj5l/3kDEnecFzh3T0h6dapVHyIk4/Hrp9WL07hVN8W33mgXOH/3L37jx5GWJxS52P21cixNHwxAfdwcKhQI13ergy5FfoYqdg8a8K5ejsS5kOa5fuQy5XAfVajhjwdIQKN7q7ZQ6ucT/D2eSW8JCQ0MxevRohIaG4uHDh0U+M5CopG1cvxa/7PgZs+YEwbGaE65e/Ruzv/4/mJiYwrdPP6HDk5zz5yLR268PatV2Q25OLr5ftgTDvxyMXbv/gKGRkdDhiYaBnhy3n6Xjr2tPEdixxnvnNXEoB5fyJniWlqUxfvVRGnzXX9AY829YGXUqmTHB/R9dijqHrj6+cKlZC7m5uQhdtRyTxg7Duq2/wtDw9e/AlcvRmPrVcPj5D8boCVOho6ODWzdvQCbnB9Rvk3iOy3aFkpSWloZt27Zh+PDh6NSpEzZs2JBvzu+//4769evDwMAAVlZW6NGjBwDA09MTcXFxGDduHGQymbqiNnPmTPWCyW8sXboU9vb26u/Pnj2Ltm3bwsrKCkqlEi1btsSFC5r/GX9Mamoq+vTpA2NjY1SoUAHfffcdPD09P1hVXrJkCdzc3GBsbIwqVapgxIgRSEtLUx8vTOwAsG7dOtSqVQsKhQIVKlTAqFGj1Mfi4+PRrVs3mJiYwMzMDJ9//jkeP36c7z7WrVuHqlWrwsTEBCNGjEBubi4WLlwIW1tb2NjYYN68eUWKXcwuXYxCS8/WaNbCExUrVUKbtu3RsHFTXPn7stChSVLw6lB0694T1as7wdnFBbPnfYOEhIe4evWK0KGJyrn4ZGyMvI9T76neAoClsR5GNLfHgoO3kJOneY52Tp4Kz19lqy8pmTlobF8OB64/LenQRe+bpSHo0Lkb7B2ro5qTMybNmIMnjxJw8/pV9ZxVSxeix+f/gV//wbB3rI4qdg7wbNMe+vr6AkZO2oZJbgnavn07XFxc4OzsjL59+2LdunV4ezGLP/74Az169IC3tzeioqIQFhaGBg0aAAB27dqFypUrY/bs2UhISEBCQkKh7zc1NRX+/v44ceIETp8+DScnJ3h7eyM1NbXQtzF+/HicPHkSe/bswcGDB3H8+PGPJspyuRzLly/HlStXsHHjRhw+fBiTJk0q9H0CwKpVqzBy5Eh8+eWXuHz5Mvbs2YPq1asDAPLy8tCtWzckJSUhPDwcBw8exO3bt9G7d2+N27h16xb+/PNP7N+/H1u3bkVoaCg6deqE+/fvIzw8HAsWLMD06dNx5sx/z/oujtjLKvc6n+Bs5GnE3b0DALgRcx3RURfQpFlzgSMjAEhLe/17q1QqBY5EWmQAJnlVwy8XHyLu+auPzm9sbw5TA10cuP6s5IOTmJf/FBxMzV7/DjxPSsS1K5dhXs4Co4f0g09HT4wbPhCXLxatmCMFcpmsWC5lFdsVSlBoaCj69u0LAOjQoQOSk5MRHh4OT09PAMC8efPg6+uLWbNmqa/j4eEBALCwsICOjg5MTU2LvI1d69atNb5fs2YNzM3NER4ejs6dO3/0+qmpqdi4cSN++ukneHl5AQDWr1//0VaLt6u89vb2mDt3LoYNG4bg4OBCxz537lxMmDABY8eOVY/Vr18fABAWFobLly/jzp07qFKlCgBg06ZNqFWrFs6ePauel5eXh3Xr1sHU1BQ1a9ZEq1atEBMTg3379kEul8PZ2RkLFizAkSNH1LunFEfsZdWAQUPwMi0Nvbp3glxHB3m5uRgx+it07NRF6NAkLy8vD99+Mx91PqmL6k7v/0idit/ndSsiVwX8dunxxycDaO9qg/P3kvHsZdbHJ1Oh5eXlYeXShajt/gkcqjkBABIe3gcAbFy7CsPGTEA1J2cc/PN3TBw9BGu37ELlqnZChqxVynB+WiyY5JaQmJgYREZG4tdffwUA6Orqonfv3ggNDVUnuRcvXsSQIUOK/b4fP36M6dOn4+jRo3jy5Alyc3ORnp6O+Pj4Ql3/9u3byM7OVleVgddVJGdn5w9e79ChQwgKCsL169eRkpKCnJwcZGRkID09HUaF6CV88uQJHj58qE6s33Xt2jVUqVJFneACQM2aNWFubo5r166pk1x7e3uYmpqq55QvXx46OjqQv9WrVb58eY29sYsae2ZmJjIzMzXGslR6xbK3d2k7+Nef2L9vL+YGfYtq1Z0Qc/0alnwbBGtrG3Tu2l3o8CQtaO4sxMbexIZNPwkdiqRUtzZCd/fyGLn970LNtzLWx6dVlJh/4GYJRyY9y7+dh7u3YrFszQb1mOqf1pHOPXqhQ+fuAAAnZ1dcOHsG+/f+hi9GjC3glkiK2K5QQkJDQ5GTk4OKFStCV1cXurq6WLVqFXbu3Ink5GQAgKGhYZFvVy6X4939O7KzszW+9/f3x8WLF7Fs2TKcOnUKFy9ehKWlJbKySq7CcPfuXXTu3Bnu7u7YuXMnzp8/j5UrVwKA+n4/Fvu/eT4Koqenp/G9TCYrcCwvL6/Qsb8rKCgISqVS47L422+KJf7Stvy7RfAf9AXad+yE6k410KlLN/j19cf60DVChyZpQfNm41j4UaxdtxHli/hpDv1v3CqYwdxQDz/2/wT7hjXAvmENYGumwJAmVbGxb51889u5WCE1IwcRd1+UeqxitnzRfJw+eQyLg9fC2ua/vwMWVlYAADv7ahrz7ewd8eRR4Vv7pEBeTJeyipXcEpCTk4NNmzZh8eLFaNeuncax7t27Y+vWrRg2bBjc3d0RFhaGgQMHFng7+vr6yM3N1RiztrbGo0ePoFKp1CejXbx4UWPOyZMnERwcDG9vbwDAvXv38OxZ4fvEHB0doaenh7Nnz6Jq1aoAgOTkZNy4cQMtWrQo8Drnz59HXl4eFi9erK6Ybt++vUixm5qawt7eHmFhYRpbAr7h6uqKe/fu4d69e+pq7tWrV/HixQvUrFnwUj6FUZjY3zV16lT1VoVvZKn03jNbu2VkvNKocgOAjo4OVHncwU0IKpUK38yfg8NhB7F2/WZUqlzl41eiYnUo5hku3E/WGJvf2QVhN54VeGJZOxdrHLrxDLl53EC0OKhUKny/OAgnwg9jycpQVKhYWeO4bYVKsLS2wf34uxrj9+/FoX7jpqUYqfaT+jKQTHJLwN69e/H8+XMMHjw438kiPj4+CA0NxbBhwxAYGAgvLy9Uq1YNvr6+yMnJwb59+zB58mQArz92P3bsGHx9faFQKGBlZQVPT088ffoUCxcuRK9evbB//378+eefMDMzU9+Hk5MTNm/ejHr16iElJQUTJ04sUpXU1NQU/v7+mDhxIiwsLGBjY4PAwEDI5fL3/sJUr14d2dnZ+P7779GlSxecPHkSISEhGnMKE/vMmTMxbNgw2NjYoGPHjkhNTcXJkycxevRotGnTBm5ubujTpw+WLl2KnJwcjBgxAi1btkS9evUK/fj+TezvUigU+VoTyuq2vs1btsK6H1bD1rYCHKs5Ieb6VWzZvAFdu/UUOjRJmj93Fv7ctxdLlwfD2NgYz569TqpMTEw19nan/42BrhwVlf99Pm1NFXC0NEJqZg6epmUhNTNHY35OngrP07Nx/0WGxnidSmaooDTA/qtPQMVj+bfzEHbgT8xZuAxGxsZISnxdpDE2NoHCwAAymQy9+/hj4w+r4OhUA9WdXHBg3x7Ex91B4PzFAkevXaSd4pbtKrTWCg0NRZs2bQo8G9rHxwfnzp3DpUuX4OnpiR07dmDPnj2oU6cOWrdujcjISPXc2bNn4+7du6hWrRqsra0BvK5mBgcHY+XKlfDw8EBkZCQCAgLy3f/z589Rt25d9OvXD2PGjIGNjU2RHsOSJUvQuHFjdO7cGW3atEHTpk3h6ur63j+yHh4eWLJkCRYsWIDatWtjy5YtCAoK0phTmNj9/f2xdOlSBAcHo1atWujcuTNu3nzd5yaTybB7926UK1cOLVq0QJs2beDo6Iht27YV6bH9m9jFbOKU6fBq2w7fzJ+Nz3p0wtIl36Jnr88xfNQYoUOTpB3btiItNRVfDOyHNp7N1Je/9u8TOjRRqWFjjFW93bCqtxsAYFgzO6zq7Yb+DSp/5JqaOrha40pCKu69k/zSv7dn13a8TEvF+BGD8Fmn1urLkUN/qef4+PaDn/9grFr6Lb7s1wsXzp3BwmWrUZGffNBbZKp3mySJCvDy5UtUqlQJixcvxuDBg4UORyuV1UquGOnq8P27tuj+w5mPT6JSE+pXR+gQ6B+Vy5X8ico/nr9fLLfT99OivfnTFmxXoAJFRUXh+vXraNCgAZKTkzF79mwAQLdu3QSOjIiIiApD6u0KTHLpvRYtWoSYmBjo6+vj008/xfHjx2H1z1mtRERERNqMSS4V6JNPPsH58+eFDoOIiIj+JYkvrsAkl4iIiEiMpL6EGM/OICIiIiLRYSWXiIiISISkXslkkktEREQkQmxXICIiIiISGVZyiYiIiERI2nVcJrlEREREoiT1dgUmuUREREQiJPWeVKk/fiIiIiISIVZyiYiIiESI7QpEREREJDrSTnHZrkBEREREIsRKLhEREZEISbxbgUkuERERkRjJJd6wwHYFIiIiIhIdVnKJiIiIRIjtCkREREQkOjK2KxARERERiQsruUREREQixHYFIiIiIhIdqa+uwCSXiIiISISkXsllTy4RERERiQ4ruUREREQiJPVKLpNcIiIiIhHiEmJERERERCLDSi4RERGRCMmlXchlkktEREQkRmxXICIiIiISGVZyiYiIiESIqysQERERkeiwXYGIiIiISGRYySUiIiISIa6uQERERESiI/V2BSa5RERERCIk9RPP2JNLRERERKLDSi4RERGRCEm8kMskl4iIiEiM5BLvV2C7AhERERGJDiu5RMVELvW1WrSIxIsXWmXbwHpCh0BvqTZ0u9Ah0D8SN/mV+H1I/b9CJrlEREREYiTxLJftCkREREQkOqzkEhEREYkQN4MgIiIiItGR+vkJbFcgIiIiItFhJZeIiIhIhCReyGWSS0RERCRKEs9ymeQSERERiZDUTzxjTy4RERERiQ4ruUREREQiJPXVFZjkEhEREYmQxHNctisQERERUfGYOXMmZDKZxsXFxUV9PCMjAyNHjoSlpSVMTEzg4+ODx48fl0gsTHKJiIiIxEhWTJciqlWrFhISEtSXEydOqI+NGzcOv//+O3bs2IHw8HA8fPgQPXv2/PeP8QPYrkBEREQkQkKtrqCrqwtbW9t848nJyQgNDcVPP/2E1q1bAwDWr18PV1dXnD59Go0aNSrWOFjJJSIiIqL3yszMREpKisYlMzPzvfNv3ryJihUrwtHREX369EF8fDwA4Pz588jOzkabNm3Uc11cXFC1alVEREQUe9xMcomIiIhESCYrnktQUBCUSqXGJSgoqMD7bNiwITZs2ID9+/dj1apVuHPnDpo3b47U1FQ8evQI+vr6MDc317hO+fLl8ejRo2J//GxXICIiIhKh4mpWmDp1KsaPH68xplAoCpzbsWNH9dfu7u5o2LAh7OzssH37dhgaGhZTRIXDSi4RERERvZdCoYCZmZnG5X1J7rvMzc1Ro0YNxMbGwtbWFllZWXjx4oXGnMePHxfYw/u/YpJLREREJEYCra7wtrS0NNy6dQsVKlTAp59+Cj09PYSFhamPx8TEID4+Ho0bN/7f7qgAbFcgIiIiEiEhVlcICAhAly5dYGdnh4cPHyIwMBA6Ojrw8/ODUqnE4MGDMX78eFhYWMDMzAyjR49G48aNi31lBYBJLhEREZEoCbGt7/379+Hn54fExERYW1ujWbNmOH36NKytrQEA3333HeRyOXx8fJCZmYn27dsjODi4RGKRqVQqVYncMpHEvMzir5K20JFLfTNL7ZGRnSt0CPSWakO3Cx0C/SNxk1+J38fl+2nFcjtulU2K5XZKGyu5RERERCIk9bf7THKJiIiIxEjiWS5XVyAiIiIi0WEll4iIiEiEhFhdQZswySUiIiISISFWV9AmbFcgIiIiItFhJZeIiIhIhCReyGWSS0RERCRKEs9y2a5ARERERKLDSi4RERGRCHF1BSIiIiISHamvrsAkl4iIiEiEJJ7jsieXiIiIiMSHlVwiIiIiMZJ4KZdJLhEREZEISf3EM7YrEBEREZHoMMmlYiGTyfDbb78JHUaxsre3x9KlS4UOg4iI6F+RyYrnUlYxyS1jIiIioKOjg06dOhX5ukzaqCAhwd+jrpuLxqVnl45ChyVZ58+dxegRw9DGsxk8ajnjcNghoUOSjKjz5zBhzAh0atsSDevURPhhzef+SNhBjB72Bdq2bIyGdWrixvVrAkUqPo2drbFlXAtcWdYNiZv84F23ksZxazMDrBjSEFeWdcO9Hz7D9gBPOJY30ZhjozTAqqGNcHV5d8T/8BkOz26PLvUql+bD0DqyYrqUVUxyy5jQ0FCMHj0ax44dw8OHD4UOR1BZWVlChyAa1ao74cCR4+pL6KafhA5Jsl69SoezszOmTg8UOhTJefUqHU41nDFx6oz3HH8Fj0/qYtTYCaUcmfgZKXRxJf45Jm06X+DxzV81h521CfouPY5WM/bj3rOX2DW5NYz0ddRzgr9shOq2Zui79Bia/98+/HHuPkJHNYWbXbnSehikZZjkliFpaWnYtm0bhg8fjk6dOmHDhg355vz++++oX78+DAwMYGVlhR49egAAPD09ERcXh3HjxkEmk0H2z+cPM2fORJ06dTRuY+nSpbC3t1d/f/bsWbRt2xZWVlZQKpVo2bIlLly4UKTYPT09MWbMGEyaNAkWFhawtbXFzJkzNebEx8ejW7duMDExgZmZGT7//HM8fvxYffxNrGvXroWDgwMMDAwAvG6VWL16NTp37gwjIyO4uroiIiICsbGx8PT0hLGxMZo0aYJbt26pb+vWrVvo1q0bypcvDxMTE9SvXx+HDkm3YqajowMrK2v1pVw5/lEQSrPmLTFq7Dh4tWkrdCiS06RZCwwbNRaerdsUeNy7c1d8MXQE6jdsXMqRiV/YpQTM33kZf5y/n+9YNVtT1K9uhYCNZxF1Jwmxj1IRsPEsDPR10LOxnXpefScr/HDwBi7cTkLc05dYvOcKktOz4WEv4f/PJF7KZZJbhmzfvh0uLi5wdnZG3759sW7dOqhUKvXxP/74Az169IC3tzeioqIQFhaGBg0aAAB27dqFypUrY/bs2UhISEBCQkKh7zc1NRX+/v44ceIETp8+DScnJ3h7eyM1NbVI8W/cuBHGxsY4c+YMFi5ciNmzZ+PgwYMAgLy8PHTr1g1JSUkIDw/HwYMHcfv2bfTu3VvjNmJjY7Fz507s2rULFy9eVI/PmTMH/fv3x8WLF+Hi4oL//Oc/GDp0KKZOnYpz585BpVJh1KhR6vlpaWnw9vZGWFgYoqKi0KFDB3Tp0gXx8fFFekxiER8fh3atm6NLhzaYNjkACQnS/pSAiLSHvu7rVCUzO089plIBWdm5aFTDWj129uYzdG9UFebG+pDJgB4Nq0Khp4OT156UeszaQlZM/8oqLiFWhoSGhqJv374AgA4dOiA5ORnh4eHw9PQEAMybNw++vr6YNWuW+joeHh4AAAsLC+jo6MDU1BS2trZFut/WrVtrfL9mzRqYm5sjPDwcnTt3LvTtuLu7IzDw9UewTk5OWLFiBcLCwtC2bVuEhYXh8uXLuHPnDqpUqQIA2LRpE2rVqoWzZ8+ifv36AF63KGzatAnW1tYatz1w4EB8/vnnAIDJkyejcePGmDFjBtq3bw8AGDt2LAYOHKie7+HhoX5ugNdJ8q+//oo9e/ZoJMNS4ObmgVlzgmBn74Bnz55gzaqVGOzfFzt+3QNjY5OP3wARUQm6mZCCe89eYsZnHhi/PhLpmbkY3sEZlSyNUd7cUD1v0MqTCB3ZFLdW+SA7Jw+vsnLQf9lx3HmSJmD0JCRWcsuImJgYREZGws/PDwCgq6uL3r17IzQ0VD3n4sWL8PLyKvb7fvz4MYYMGQInJycolUqYmZkhLS2tyFVPd3d3je8rVKiAJ09ev8O+du0aqlSpok5wAaBmzZowNzfHtWv/PbnDzs4uX4L77m2XL18eAODm5qYxlpGRgZSUFACvK7kBAQFwdXWFubk5TExMcO3atUI/pszMTKSkpGhcMjMzC3VdbdO0eQu0bd8BNZyd0aRpc3wfvAZpqSk4+Nd+oUMjIkJOrgr+y4+jmq0pbof0wv21n6GZa3kcjH6IvLz/fpr5fz7uUBrpocc3h+EV+BeC98dg3cimcK2sFDB6YUl9dQVWcsuI0NBQ5OTkoGLFiuoxlUoFhUKBFStWQKlUwtDQ8AO3UDC5XK7R8gAA2dnZGt/7+/sjMTERy5Ytg52dHRQKBRo3blzkE7/09PQ0vpfJZMjLy3vP7IIZGxt/9Lbf9BsXNPbm/gICAnDw4EEsWrQI1atXh6GhIXr16lXoxxQUFKRRMQeAqdO/xrQZMwv9WLSVqZkZqtrZ4158nNChEBEBAKLvPofnjP0wNdSDvq4ciamZOBDYFhfvJAEA7G1MMKRtDTSZ+gdiHrwuZly59wKNna0xuI0TAjacEzJ8wZTh/LRYsJJbBuTk5GDTpk1YvHgxLl68qL5ER0ejYsWK2Lp1K4DX1cywsLD33o6+vj5yc3M1xqytrfHo0SONRPftXlcAOHnyJMaMGQNvb2/UqlULCoUCz549K74HCMDV1RX37t3DvXv31GNXr17FixcvULNmzWK9L+D1YxowYAB69OgBNzc32Nra4u7du4W+/tSpU5GcnKxxCZg0tdjjFEJ6+kvcv3cPVgVUzImIhJT6KhuJqZlwLG+COg4W2HfhAQDA8J9VFt6p2SA3TwV5WS5F/q8kfuIZK7llwN69e/H8+XMMHjwYSqXmxy4+Pj4IDQ3FsGHDEBgYCC8vL1SrVg2+vr7IycnBvn37MHnyZACv18k9duwYfH19oVAoYGVlBU9PTzx9+hQLFy5Er169sH//fvz5558wMzNT34eTkxM2b96MevXqISUlBRMnTvxXVeMPadOmDdzc3NCnTx8sXboUOTk5GDFiBFq2bIl69eoV630Brx/Trl270KVLF8hkMsyYMaNIVWWFQgGFQqEx9jJL9Z7Z2u27RQvQomUrVKhYEU+fPkHIyhWQ68jRoWPh+62p+KS/fKnRNvPg/n1cv3YNSqUSFd76JIeKX3r6S9x/67l/+OABbly/BjOlErYVKiI5+QUeJyTg6dPXbVZxcXcBAJZWVrC04pvC/4WxQhcOb617W9XaBLWrmuP5yyw8SExH1/pVkJiaifuJL1Gzijnm96mLfecf4OjfjwC87tu99SgViwfUR+DPUUhKy4J33crwrGULvyXhQj0sEhgruWVAaGgo2rRpky/BBV4nuefOncOlS5fg6emJHTt2YM+ePahTpw5at26NyMhI9dzZs2fj7t27qFatmrqv1dXVFcHBwVi5ciU8PDwQGRmJgICAfPf//Plz1K1bF/369cOYMWNgY2NTrI9RJpNh9+7dKFeuHFq0aIE2bdrA0dER27ZtK9b7eWPJkiUoV64cmjRpgi5duqB9+/aoW7duidyXtnv8+DGmTp6AHl06YvKEcVCam2Pjlm0oZ2EhdGiSdOXK3+jdqzt69+oOAFi0MAi9e3VH8IrlwgYmAdeuXEE/Xx/08/UBACxdvAD9fH2wJngFAOD40SPo5+uD8aOHAwCmT56Afr4+2LWjZP6fkpI6DhYIn9sR4XNfb0Qzr09dhM/tiKk9X59bYWtuiFVDG+H0gk4I6vsptp+6iyHBp9TXz8lVwXfxUSSmZmLLuJY4Nq8jejezx8g1p3HoUuFXExIbqa+uIFO925BJRP9KWa3kipGOvOz+pyw2Gdm5H59Epaba0O1Ch0D/SNzkV+L3EZ9UPCdEV7VQfHySFmIll4iIiIhEhz25RERERCIk9c+0mOQSERERiZCUF5YA2K5ARERERCLESi4RERGRKEm7lMskl4iIiEiE2K5ARERERCQyrOQSERERiZDEC7lMcomIiIjESOrtCkxyiYiIiESoLG/JWxzYk0tEREREosNKLhEREZEYSbuQyySXiIiISIwknuOyXYGIiIiIxIeVXCIiIiIR4uoKRERERCQ6XF2BiIiIiEhkWMklIiIiEiNpF3KZ5BIRERGJkcRzXCa5RERERGIk9RPP2JNLRERERKLDSi4RERGRCEl9dQUmuUREREQixHYFIiIiIiKRYZJLRERERKLDdgUiIiIiEWK7AhERERGRyLCSS0RERCRCXF2BiIiIiESH7QpERERERCLDSi4RERGRCEm8kMskl4iIiEiUJJ7lMsklIiIiEiGpn3jGnlwiIiIiEh1WcomIiIhESOqrKzDJJSIiIhIhiee4bFcgIiIiIvFhJZeIiIhIjCReymWSS0RERCRCXF2BiIiIiEhkWMklIiIiEiGpr64gU6lUKqGDICLtkJmZiaCgIEydOhUKhULocCSNr4X24GuhPfhaUFEwySUitZSUFCiVSiQnJ8PMzEzocCSNr4X24GuhPfhaUFGwJ5eIiIiIRIdJLhERERGJDpNcIiIiIhIdJrlEpKZQKBAYGMgTOrQAXwvtwddCe/C1oKLgiWdEREREJDqs5BIRERGR6DDJJSIiIiLRYZJLRERERKLDJJeIiIiIRIdJLpGEHTt2DDk5OfnGc3JycOzYMQEikq7AwEDExcUJHQYRkWhwdQUiCdPR0UFCQgJsbGw0xhMTE2FjY4Pc3FyBIpOeOnXq4O+//0bLli0xePBg+Pj4cJkkAf3yyy/Yvn074uPjkZWVpXHswoULAkUlDZcuXSr0XHd39xKMhMo6XaEDICLhqFQqyGSyfOOJiYkwNjYWICLpunjxIqKiorB+/XqMHTsWI0eOhK+vLwYNGoT69esLHZ6kLF++HNOmTcOAAQOwe/duDBw4ELdu3cLZs2cxcuRIocMTvTp16kAmk+F9Nbg3x2QyGd+I0wexkkskQT179gQA7N69Gx06dNCoGObm5uLSpUtwdnbG/v37hQpR0rKzs/H7779j/fr1+Ouvv+Di4oLBgwdjwIABUCqVQocnei4uLggMDISfnx9MTU0RHR0NR0dHfP3110hKSsKKFSuEDlHUitK2Y2dnV4KRUFnHSi6RBL1JlFQqFUxNTWFoaKg+pq+vj0aNGmHIkCFChSd5KpUK2dnZyMrKgkqlQrly5bBixQrMmDEDP/zwA3r37i10iKIWHx+PJk2aAAAMDQ2RmpoKAOjXrx8aNWrEJLeEMXGl4sIkl0iC1q9fDwCwt7fHxIkTYWRkJHBEBADnz5/H+vXrsXXrVigUCvTv3x8rV65E9erVAQDff/89xowZwyS3hNna2iIpKQl2dnaoWrUqTp8+DQ8PD9y5c+e9H6FTybp69WqB/dFdu3YVKCIqC9iuQCRhd+7cQU5ODpycnDTGb968CT09Pdjb2wsTmAS5ubnh+vXraNeuHYYMGYIuXbpAR0dHY86zZ89gY2ODvLw8gaKUhi+++AJVqlRBYGAgVq5ciYkTJ6Jp06Y4d+4cevbsidDQUKFDlIzbt2+jR48euHz5skaf7ptzCdiTSx/CJJdIwlq2bIlBgwbB399fY/zHH3/E2rVrcfToUWECk6A5c+Zg0KBBqFSpktChSF5eXh7y8vKgq/v6w86ff/4Zp06dgpOTE4YOHQp9fX2BI5SON2/21q5dCwcHB0RGRiIxMRETJkzAokWL0Lx5c6FDJC3GJJdIwszMzHDhwgX1x+FvxMbGol69enjx4oUwgRERAbCyssLhw4fh7u4OpVKJyMhIODs74/Dhw5gwYQKioqKEDpG0GHtyiSRMJpOpT6p5W3JyMj8GLGW5ubnYsGEDwsLC8OTJk3wtCYcPHxYoMmnKyMjApUuXCnwt2AdaenJzc2FqagrgdcL78OFDODs7w87ODjExMQJHR9qOSS6RhLVo0QJBQUHYunWruv8zNzcXQUFBaNasmcDRScvYsWOxYcMGdOrUCbVr1y5w/WIqHfv370f//v3x7NmzfMe4Nmvpql27NqKjo+Hg4ICGDRti4cKF0NfXx5o1a+Do6Ch0eKTl2K5AJGFXr15FixYtYG5uru5tO378OFJSUnD48GHUrl1b4Ailw8rKCps2bYK3t7fQoUiek5MT2rVrh6+//hrly5cXOhxJ++uvv/Dy5Uv07NkTsbGx6Ny5M27cuAFLS0ts27YNrVu3FjpE0mJMcokk7uHDh1ixYgWio6NhaGgId3d3jBo1ChYWFkKHJikVK1bE0aNHUaNGDaFDkTwzMzNERUWhWrVqQodCBUhKSkK5cuX4aQd9FJNcIiItsHjxYty+fRsrVqzgH2+BDRo0CE2bNsXgwYOFDoWI/gdMcokI6enpBS607u7uLlBE0vBme+U3Dh8+DAsLC9SqVQt6enoax3bt2lWaoUlaeno6PvvsM1hbW8PNzS3fazFmzBiBIpOejIwMfP/99zhy5EiBJwFeuHBBoMioLOCJZ0QS9vTpUwwcOBB//vlngcd5gk3JerO98hs9evQQKBJ629atW3HgwAEYGBjg6NGjGpV1mUzGJLcUDR48GAcOHECvXr3QoEEDfspBRcJKLpGE9enTB3FxcVi6dCk8PT3x66+/4vHjx5g7dy4WL16MTp06CR0iUamztbXFmDFjMGXKFMjlcqHDkTSlUol9+/ahadOmQodCZRB/e4kk7PDhw1iyZAnq1asHuVwOOzs79O3bFwsXLkRQUJDQ4UlK69atC9x8IyUlhWeQl7KsrCz07t2bCa4WqFSpknqdXKKi4m8wkYS9fPkSNjY2AIBy5crh6dOnAAA3Nzf2upWyo0eP5uuJBl73JB4/flyAiKTL398f27ZtEzoMwusTMidPnoy4uDihQ6EyiD25RBLm7OyMmJgY2Nvbw8PDA6tXr4a9vT1CQkJQoUIFocOThEuXLqm/vnr1Kh49eqT+Pjc3F/v370elSpWECE2ycnNzsXDhQvz1119wd3fPd+LZkiVLBIpMeurVq4eMjAw4OjrCyMgo32uRlJQkUGRUFjDJJZKwsWPHIiEhAQAQGBiIDh06YMuWLdDX18eGDRuEDU4i6tSpA5lMBplMVmBbgqGhIb7//nsBIpOuy5cv45NPPgEA/P333xrHeOJT6fLz88ODBw8wf/58lC9fns8/FQlPPCMitfT0dFy/fh1Vq1aFlZWV0OFIQlxcHFQqFRwdHREZGQlra2v1MX19fdjY2Ki3XCaSGiMjI0RERMDDw0PoUKgMYiWXSKKys7Ph4uKCvXv3wtXVFcDrPyh169YVODJpsbOzA4B863+Sdrh//z4AoHLlygJHIk0uLi549eqV0GFQGcUkl0ii9PT0kJGRIXQY9I6rV68WuDFH165dBYpIevLy8tTL6KWlpQEATE1NMWHCBEybNo2rLpSib775BhMmTMC8efMK3JjDzMxMoMioLGC7ApGEzZ8/Hzdu3MDatWuhq8v3vEK6ffs2evTogcuXL0Mmk+HNf81vehC5MUfpmTp1KkJDQzFr1iz1+qwnTpzAzJkzMWTIEMybN0/gCKXjzRuKd3txVSoVZDIZfy/og5jkEklYjx49EBYWBhMTE7i5ucHY2FjjOLeSLT1dunSBjo4O1q5dCwcHB0RGRiIxMRETJkzAokWL0Lx5c6FDlIyKFSsiJCQkX/V89+7dGDFiBB48eCBQZNITHh7+weMtW7YspUioLGLphkjCzM3N4ePjI3QYBCAiIgKHDx+GlZUV5HI55HI5mjVrhqCgIIwZMwZRUVFChygZSUlJcHFxyTfu4uLCJatKUXZ2NmbPno2QkBA4OTkJHQ6VQUxyiSRs/fr1QodA/8jNzVXv7GRlZYWHDx/C2dkZdnZ2iImJETg6afHw8MCKFSuwfPlyjfEVK1bwLP9SpKenp7GONFFRMcklItICtWvXRnR0NBwcHNCwYUMsXLgQ+vr6WLNmDRwdHYUOT1IWLlyITp064dChQ2jcuDGA15X2e/fuYd++fQJHJy19+/ZFaGgovvnmG6FDoTKIPblEEvfLL79g+/btBZ7Rz619S89ff/2Fly9fomfPnoiNjUXnzp1x48YNWFpaYtu2bQVuFEEl58GDBwgODsb169cBAK6urhgxYgQqVqwocGTSMnr0aGzatAlOTk749NNP8503wN3n6EOY5BJJ2PLlyzFt2jQMGDAAa9aswcCBA3Hr1i2cPXsWI0eO5FnkAktKSkK5cuW4yxNJVqtWrd57TCaT4fDhw6UYDZU1THKJJMzFxQWBgYHw8/ODqakpoqOj4ejoiK+//hpJSUlYsWKF0CFK0r179wAAVapUETgSaVq/fj1MTEzw2WefaYzv2LED6enp8Pf3FygyIioKrmhNJGHx8fFo0qQJAMDQ0BCpqakAgH79+mHr1q1ChiY5OTk5mDFjBpRKJezt7WFvbw+lUonp06cjOztb6PAkJSgoqMBtrW1sbDB//nwBIiLg9e5zb3agIyoMJrlEEmZra6teEqlq1ao4ffo0AODOnTvghzyla/To0VizZg0WLlyIqKgoREVFYeHChQgNDcWYMWOEDk9S4uPj4eDgkG/czs4O8fHxAkQkXXl5eZg9ezaUSiXs7OxgZ2cHc3NzzJkzh1th00dxdQUiCWvdujX27NmDTz75BAMHDsS4cePwyy+/4Ny5c+jZs6fQ4UnKTz/9hJ9//hkdO3ZUj7m7u6NKlSrw8/PDqlWrBIxOWmxsbHDp0iXY29trjEdHR8PS0lKYoCRq2rRp6tUV3t19LiMjg+cN0AcxySWSsDVr1qirISNHjoSlpSVOnTqFrl27YujQoQJHJy0KhSJfUgUADg4O0NfXL/2AJMzPzw9jxoyBqakpWrRoAeD1zltjx46Fr6+vwNFJy8aNG7F27VqN3efc3d1RqVIljBgxgkkufRBPPCOSsPj4eFSpUqXAfeHv3buHqlWrChSZ9MyePRvXr1/H+vXroVAoAACZmZkYPHgwnJycEBgYKHCE0pGVlYV+/fphx44d0NV9XQvKy8tD//79ERISwjcdpcjAwACXLl1CjRo1NMZjYmJQp04dvHr1SqDIqCxgkkskYTo6OkhISICNjY3GeGJiImxsbJCbmytQZNLTo0cPhIWFQaFQqHfVio6ORlZWFry8vDTm7tq1S4gQJefGjRuIjo6GoaEh3NzcYGdnJ3RIktOwYUM0bNgw3+5zo0ePxtmzZ9XnERAVhO0KRBKmUqkKXIM1LS0NBgYGAkQkXebm5vDx8dEY4xJiwqpRo0a+CiKVLu4+R/8LVnKJJGj8+PEAgGXLlmHIkCEwMjJSH8vNzcWZM2ego6ODkydPChUikWAGDRr0wePr1q0rpUgIAB4+fIiVK1dy9zkqMlZyiSQoKioKwOtK7uXLlzV6DPX19eHh4YGAgAChwpO0p0+fIiYmBgDg7OwMa2trgSOSnufPn2t8n52djb///hsvXrzg9soCqFixIk8wo3+FlVwiCRs4cCCWLVsGMzMzoUORvJcvX2L06NHYtGmTesULHR0d9O/fH99//71GtZ1KX15eHoYPH45q1aph0qRJQocjKS9evEBkZCSePHmSb23c/v37CxQVlQVMcomItMDQoUNx6NAhrFixQmM90DFjxqBt27ZcJ1cLxMTEwNPTEwkJCUKHIhm///47+vTpg7S0NJiZmWmcQyCTydSb2RAVhEkukcSdO3cO27dvR3x8PLKysjSO8Sz+0mNlZYVffvkFnp6eGuNHjhzB559/jqdPnwoTGKnt27cP/v7+fC1KUY0aNeDt7Y358+fz0wwqMvbkEknYzz//jP79+6N9+/Y4cOAA2rVrhxs3buDx48fo0aOH0OFJSnp6OsqXL59v3MbGBunp6QJEJF1vTsx8Q6VSISEhAX/88Qf8/f0FikqaHjx4gDFjxjDBpX+FlVwiCXN3d8fQoUMxcuRImJqaIjo6Gg4ODhg6dCgqVKiAWbNmCR2iZHh5ecHS0hKbNm1SL9/26tUr+Pv7IykpCYcOHRI4Qulo1aqVxvdyuRzW1tZo3bo1Bg0apN4ggkpez5494evri88//1zoUKgMYpJLJGHGxsa4cuUK7O3tYWlpiaNHj8LNzQ3Xrl1D69at2XtYiv7++2+0b98emZmZGptBGBgY4K+//kKtWrUEjpCo9IWGhmL27NkYOHAg3NzcoKenp3H87e1+id7Ft6NEElauXDmkpqYCACpVqoS///4bbm5uePHiBT8iL2W1a9fGzZs3sWXLFvV6oH5+fujTpw8MDQ0Fjo5IGEOGDAHwetvrd8lkMu7KSB/EJJdIwlq0aIGDBw/Czc0Nn332GcaOHYvDhw/j4MGD+baSpZJnZGSk/qNOwvrll1/ee0LmhQsXBIpKet5dMoyoKJjkEknYihUrkJGRAQCYNm0a9PT0cOrUKfj4+GD69OkCRyc9N2/exJEjRwpcD/Trr78WKCrpWb58OaZNm4YBAwZg9+7dGDhwIG7duoWzZ89i5MiRQodHRIXEnlwiIi3www8/YPjw4bCysoKtrW2+9UBZPSw9Li4uCAwMhJ+fn/qETEdHR3z99ddISkrCihUrhA6RiAqBSS6RhO3btw86Ojpo3769xviBAweQm5uLjh07ChSZ9NjZ2WHEiBGYPHmy0KFInpGREa5duwY7OzvY2Njg4MGD8PDwwM2bN9GoUSMkJiYKHSIRFYJc6ACISDhTpkwp8MSNvLw8TJkyRYCIpOv58+f47LPPhA6DANja2qp30qpatSpOnz4NALhz5w5YFyIqO5jkEknYzZs3UbNmzXzjLi4uiI2NFSAi6frss89w4MABocMgAK1bt8aePXsAAAMHDsS4cePQtm1b9O7dm5ukEJUhPPGMSMKUSiVu374Ne3t7jfHY2FgYGxsLE5REVa9eHTNmzMDp06cLXA90zJgxAkUmPWvWrFGf+Ddy5EhYWlri1KlT6Nq1K4YOHSpwdOKXkpJS6LlmZmYlGAmVdezJJZKwoUOHIiIiAr/++iuqVasG4HWC6+Pjg/r162Pt2rUCRygdDg4O7z0mk8lw+/btUoyGSDhyuVzjxMsP4Tq59CFMcokkLDk5GR06dMC5c+dQuXJlAMD9+/fRvHlz7Nq1C+bm5sIGSFRKLl26VOi57u7uJRgJhYeHq7++e/cupkyZggEDBqBx48YAgIiICGzcuBFBQUHw9/cXKkwqA5jkEkmcSqXCwYMHER0dDUNDQ7i7u6NFixZCh0VUqt5UDz/2J5G7bJUuLy8vfPHFF/Dz89MY/+mnn7BmzRocPXpUmMCoTGCSS0QkkPHjx2POnDkwNjbG+PHjPzh3yZIlpRSVNMXFxRV6rp2dXQlGQm8zMjJCdHQ0nJycNMZv3LiBOnXqcPtx+iCeeEZEJJCoqChkZ2ervybhMHHVTlWqVMEPP/yAhQsXaoyvXbsWVapUESgqKitYySUiInrLm+XD3iWTyWBgYIDq1at/8ERBKj779u2Dj48PqlevjoYNGwIAIiMjcfPmTezcuRPe3t4CR0jajEkuEZEWGDRoEJYtWwZTU1ON8ZcvX2L06NFYt26dQJFJz/v6c9+MyWQyNGvWDL/99hvKlSsnUJTSce/ePaxatQrXr18HALi6umLYsGGs5NJHMcklItICOjo6SEhIgI2Njcb4s2fPYGtri5ycHIEik56wsDBMmzYN8+bNQ4MGDQC8rh7OmDED06dPh1KpxNChQ9GwYUOEhoYKHC0RvQ97cokkhguta5eUlBSoVCqoVCqkpqbCwMBAfSw3Nxf79u3Ll/hSyRo7dizWrFmDJk2aqMe8vLxgYGCAL7/8EleuXMHSpUsxaNAgAaOUjuPHj2P16tW4ffs2duzYgUqVKmHz5s1wcHBAs2bNhA6PtBiTXCKJMTc350LrWuTN6yGTyVCjRo18x2UyGWbNmiVAZNJ169atAt/gmZmZqTflcHJywrNnz0o7NMnZuXMn+vXrhz59+uDChQvIzMwE8HqN7/nz52Pfvn0CR0jajEkukcQcOXJE/fXHFlqnknfkyBGoVCq0bt0aO3fuhIWFhfqYvr4+7OzsULFiRQEjlJ5PP/0UEydOxKZNm2BtbQ0AePr0KSZNmoT69esDAG7evMme0FIwd+5chISEoH///vj555/V402bNsXcuXMFjIzKAvbkEkkYF1rXHnFxcahatWqhq+xUcmJiYtCtWzfcuXNHncjeu3cPjo6O2L17N2rUqIHffvsNqamp6Nevn8DRipuRkRGuXr0Ke3t7mJqaIjo6Go6Ojrh9+zZq1qyJjIwMoUMkLcZKLpGERUREICQkJN94vXr18MUXXwgQkXTZ2dmx91BLODs74+rVqzhw4ABu3LihHmvbti3kcjkAoHv37gJGKB22traIjY2Fvb29xviJEyfg6OgoTFBUZsiFDoCIhPNmofV3caH10rdz5060b98ehoaGBfYeUumSy+Xo0KEDxowZgzFjxqB9+/bqBJdKz5AhQzB27FicOXMGMpkMDx8+xJYtWxAQEIDhw4cLHR5pObYrEEkYF1rXHp988gnGjRuH/v37a3wsGxUVhY4dO+LRo0dCh0hU6lQqFebPn4+goCD1Fr4KhQIBAQGYM2eOwNGRtmOSSyRxXGhdO7D3kOj9srKyEBsbi7S0NNSsWRMmJiZCh0RlAHtyiSSuSpUq/DhcC7D3kCi/t3cCrFmzpnqcOwFSYbDBiEjijh8/jr59+6JJkyZ48OABAGDz5s04ceKEwJFJC3sPifLbuHEjXr16lW/81atX2LRpkwARUVnCSi6RhHGhde0xZcoU5OXlwcvLC+np6WjRooW693D06NFChyd63AlQu3AnQCoO7MklkjCe7KQdcnNzcfLkSbi7u8PIyIi9hwKQy+XcCVCLfOz1eLMT4LRp00oxKiprWMklkrCYmBi0aNEi37hSqcSLFy9KPyCJ0tHRQbt27XDt2jWYm5tr9B5S6eBOgNqFOwFScWCSSyRhPNlJe9SuXRu3b9+Gg4OD0KFIUsuWLdVfz549G0uWLNHYCbBr165wc3PDmjVr4O/vL0SIkvLm9bhz5w53AqR/jSeeEUkYT3bSHnPnzkVAQAD27t2LhIQEpKSkaFyo9ERERKBevXr5xuvVq4fIyEgBIpKuw4cP45dffsk3vmPHDmzcuFGAiKgsYU8ukYRxoXXt8fZuWm9XrVQqFWQyGftAS5GzszO6deuGhQsXaoxPmjQJu3fvRkxMjECRSU+NGjWwevVqtGrVSmM8PDwcX375JV8L+iAmuUTEhda1QHh4+AePv/1xOpUs7gSoPQwMDHD9+vV8LVV3796Fq6trgcuLEb3BnlwiCeNC69qjsEnsiBEjMHv2bFhZWZVwRNLl7e2NGzduaOwE2KVLF+4EKAAbGxtcunQpX5IbHR0NS0tLYYKiMoOVXCIJ09HRQUJCQr71Jp89ewZbW1vk5OQIFBm9j5mZGS5evMgTA0kSJk+ejG3btmH9+vXqlWDCw8MxaNAg9OrVC4sWLRI4QtJmrOQSSRAXWi+7WJcoHcePH8fq1atx+/Zt7NixA5UqVcLmzZvh4OCAZs2aCR2eZMyZMwd3796Fl5cXdHVfpyx5eXno378/tyOnj2KSSyRB5ubmkMlkkMlkqFGjRr7jbxZaJ5Ii7gSoPfT19bFt2zbMmTMH0dHRMDQ0hJubG+zs7IQOjcoAtisQSVB4eDgXWi+j3t6ZjkoGdwIkEgdWcokkiAutE70fdwIU1vjx4zFnzhwYGxtj/PjxH5y7ZMmSUoqKyiImuUQSdvjwYZiYmOCzzz7TGN+xYwfS09O5sxNJEncCFFZUVBSys7PVX78P35zTxzDJJZKwoKAgrF69Ot+4jY0NvvzySya5Wqhv374wMzMTOgxRe7MT4Lp169Q7AUZERCAgIAAzZswQOjzRO3LkSIFfExUVe3KJJIwLrWuP9evXs6quJbgTIJE4MMklkrCqVatixYoV6Nq1q8b47t27MXLkSNy/f1+gyKSH25dqH+4EKIyePXsWeu6uXbtKMBIq6+Qfn0JEYuXn54cxY8bgyJEjyM3NRW5uLg4fPoyxY8fC19dX6PAkJT4+Hg4ODvnG7ezsEB8fL0BE0jVo0CCkpqZCX18fNWvWRIMGDWBiYoKXL19i0KBBQocnekqlUn0xMzNDWFgYzp07pz5+/vx5hIWFQalUChgllQWs5BJJWFZWFvr164cdO3bkW2g9JCQE+vr6AkcoHayqaw/uBKg9Jk+ejKSkJISEhEBHRwfA6w1rRowYATMzM3z77bcCR0jajEkuEeHGjRtcaF1g3L5UeG92AixXrhxu3rwJa2tr9bHc3Fz8/vvvmDJlCh4+fChglNJibW2NEydOwNnZWWM8JiYGTZo0QWJiokCRUVnA1RWICDVq1Chw5zMqPdy+VHjcCVD75OTk4Pr16/mS3OvXryMvL0+gqKisYJJLJDFcaF07cftS4R05coQ7AWqZgQMHYvDgwbh16xYaNGgAADhz5gy++eYbDBw4UODoSNsxySWSGC60rt1YVRcOdwLUPosWLYKtrS0WL16MhIQEAECFChUwceJETJgwQeDoSNuxJ5eISCCsqmsnrlmsnVJSUgCAm6FQobGSS0QkkMJW1al0cSdA7ZKTk4OjR4/i1q1b+M9//gMAePjwIczMzLh2MX0QK7lEEsOF1ok+jDsBao+4uDh06NAB8fHxyMzMxI0bN+Do6IixY8ciMzMTISEhQodIWoybQRBJDBda105vNiB4FzcgKH02Nja4dOlSvvHo6GhYWloKEJF0jR07FvXq1cPz589haGioHu/RowfCwsIEjIzKAlZyiSSMC61rD25AoD24ZrH2sLS0xKlTp+Ds7AxTU1NER0fD0dERd+/eRc2aNZGeni50iKTF2JNLJGHr1q3DiRMn1Aku8DrZGj9+PJo0acIktxS82YBApVIhNTUVBgYG6mO5ubnYt29fvsSXShbXLNYeeXl5yM3NzTd+//59mJqaChARlSVMcokkjAutC48bEGgfrlmsPdq1a4elS5dizZo1AF7/PqSlpSEwMBDe3t4CR0faju0KRBI2fvx4bNq0Cf/3f/+Xb6H1fv36cdmqUhAeHs4NCIje4969e+jQoQNUKhVu3ryJevXq4ebNm7CyssKxY8f4KQd9EJNcIgnLy8vDokWLsGzZMo2F1seOHYsJEyZotDFQyYqLi+MGBALimsXaKycnB9u2bUN0dDTS0tJQt25d9OnTR+NENKKCMMklIgBcaF0bHD9+HKtXr8bt27exY8cOVKpUCZs3b4aDgwOaNWsmdHii1qpVK/z6668wNzdHq1at3jtPJpPh8OHDpRiZdGVnZ8PFxQV79+6Fq6ur0OFQGcSeXCKJ40Lr2mHnzp3o168f+vTpgwsXLiAzMxMAkJycjPnz52Pfvn0CRyhuR44cKfBrEo6enh4yMjKEDoPKMK6TSyRhcXFxcHNzQ7du3TBy5Eg8ffoUALBgwQIEBAQIHJ20zJ07FyEhIfjhhx+gp6enHm/atCkuXLggYGREwhk5ciQWLFjAJfToX2Ell0jC3iy0/u4i9z169MCQIUMEjEx6YmJi1Guyvk2pVOLFixelH5DEcCdA7XT27FmEhYXhwIEDcHNzg7GxscZxvhb0IUxyiSTs+PHjOHXqFPT19TXG7e3t8eDBA4GikiZbW1vExsbm20r2xIkTcHR0FCYoCXl7hz+VSoVff/0VSqUS9erVA/B6J8AXL14UKRmm/525uTl8fHyEDoPKKCa5RBLGhda1x5AhQzB27FisW7cOMpkMDx8+REREBAICAjBjxgyhwxO99evXq7+ePHkyPv/88/fuBEil5+3XhaiouLoCkYT17t0bSqUSa9asgampKS5dugRra2t069YNVatW5R+YUqRSqTB//nwEBQWptypVKBQICAjAnDlzBI5OWqytrXHixIl8m6TExMSgSZMmSExMFCgy6cjLy8O3336LPXv2ICsrC15eXggMDOSyYVQkTHKJJIwLrWufrKwsxMbGIi0tDTVr1uQKFwIoV64cNmzYgG7dummM7969GwMGDMDz588Fikw65syZg5kzZ6JNmzYwNDTEX3/9BT8/P6xbt07o0KgMYZJLJHFcaF373Lt3DwBQpUoVgSORJu4EKDwnJycEBARg6NChAIBDhw6hU6dOePXqFeRyLgxFhcMkl0iiuNC6dsnJycGsWbOwfPlypKWlAQBMTEwwevRoBAYGaiwrRiWLOwEKT6FQIDY2VuONnoGBAWJjY1G5cmUBI6OyhCeeEUkUF1rXLqNHj8auXbuwcOFCNG7cGAAQERGBmTNnIjExEatWrRI4QumQy+WYNGkSJk2axJ0ABZKTkwMDAwONMT09PWRnZwsUEZVFrOQSSdj8+fNx48YNrF27Frq6fM8rJKVSiZ9//hkdO3bUGN+3bx/8/PyQnJwsUGTS9O5OgKamptwJsBTJ5XJ07NgRCoVCPfb777+jdevWGmvlcp1c+hD+VSOSMC60rj0UCkW+NXIBwMHBId86xlSy4uLi0KFDB8THxyMzMxNt27aFqakpFixYgMzMTISEhAgdouj5+/vnG+vbt68AkVBZxiSXSMK40Lr2GDVqFObMmYP169erq1eZmZmYN28eRo0aJXB00sKdAIXH5QupODDJJZIw/iER1ru7Zx06dAiVK1eGh4cHACA6Olq9RiiVHu4ESCQOTHKJJIgLrWuHt7eSBZCvqs4lxITBnQCJxIEnnhFJEBdaJ3o/7gRIJA5McokkiAutE70fdwIkEgcmuUQSxIXWtdMvv/yC7du3Iz4+HllZWRrHLly4IFBU0sSdAInKPpZsiCSIC61rn+XLl2PgwIEoX748oqKi0KBBA1haWuL27dv51s6lkpOdnY1q1arh5s2b6NOnDxYuXIjg4GB88cUXTHCJyhieeEYkQSqVCgMGDNBYaD0jIwPDhg3jQusCCQ4Oxpo1a+Dn54cNGzZg0qRJcHR0xNdff42kpCShw5MM7gRIJB5sVyCSoIEDBxZqHk+wKT1GRka4du0a7OzsYGNjg4MHD8LDwwM3b95Eo0aNkJiYKHSIksGdAInEgb+9RBLE5FX72NraIikpCXZ2dqhatSpOnz4NDw8P3LlzB6xFlC7uBEgkDkxyiYi0QOvWrbFnzx588sknGDhwIMaNG4dffvkF586dy7dpBJUs7gRIJA5sVyAi0gJ5eXnIy8tTfzz+888/49SpU3BycsLQoUPz7b5FREQfxiSXiIgI3AmQSGzYrkBEJJBLly6hdu3akMvluHTp0gfnuru7l1JU0jVv3jyNnQCXLVuGJ0+ecCdAojKKlVwiIoHI5XI8evQINjY2kMvlkMlkBZ5kJpPJkJubK0CE0sKdAInEhUkuEZFA4uLiULVqVchkMsTFxX1wrp2dXSlFJV3cCZBIXNiuQEQkkLcTVyaxwuNOgETiwiSXiEgge/bsKfTcrl27lmAkBHAnQCKxYbsCEZFA3u3zfLcnVyaTqb9mT27J406AROLCJJeISAscOnQIkydPxvz589G4cWMAQEREBKZPn4758+ejbdu2AkdIRFS2MMklItICtWvXRkhICJo1a6Yxfvz4cXz55Ze4du2aQJEREZVNXBOFiEgL3Lp1C+bm5vnGlUol7t69W+rxEBGVdazkEhFpgRYtWsDAwACbN29G+fLlAQCPHz9G//79kZGRgfDwcIEjJCIqW5jkEhFpgdjYWPTo0QM3btxQr9N67949ODk54bfffkP16tUFjpCIqGxhkktEpCVUKhUOHjyI69evAwBcXV3Rpk0bjVUWiIiocJjkEhEREZHo8MQzIiIiIhIdJrlEREREJDpMcomIiIhIdJjkEhEREZHo6AodABGRVKWkpBR6rpmZWQlGQkQkPlxdgYhIIHK5vNDLg+Xm5pZwNERE4sJKLhGRQI4cOaL++u7du5gyZQoGDBiAxo0bAwAiIiKwceNGBAUFCRUiEVGZxUouEZEW8PLywhdffAE/Pz+N8Z9++glr1qzB0aNHhQmMiKiMYpJLRKQFjIyMEB0dDScnJ43xGzduoE6dOkhPTxcoMiKisomrKxARaYEqVarghx9+yDe+du1aVKlSRYCIiIjKNlZyiYi0wL59++Dj44Pq1aujYcOGAIDIyEjcvHkTO3fuhLe3t8AREhGVLUxyiYi0xL1797Bq1Spcv34dAODq6ophw4axkktE9C8wySUiIiIi0WFPLhGRljh+/Dj69u2LJk2a4MGDBwCAzZs348SJEwJHRkRU9jDJJSLSAjt37kT79u1haGiICxcuIDMzEwCQnJyM+fPnCxwdEVHZwySXiEgLzJ07FyEhIfjhhx+gp6enHm/atCkuXLggYGRERGUTk1wiIi0QExODFi1a5BtXKpV48eJF6QdERFTGMcklItICtra2iI2NzTd+4sQJODo6ChAREVHZxiSXiEgLDBkyBGPHjsWZM2cgk8nw8OFDbNmyBQEBARg+fLjQ4RERlTm6QgdARETAlClTkJeXBy8vL6Snp6NFixZQKBQICAjA6NGjhQ6PiKjM4Tq5RERaJCsrC7GxsUhLS0PNmjVhYmIidEhERGUS2xWIiLTAoEGDkJqaCn19fdSsWRMNGjSAiYkJXr58iUGDBgkdHhFRmcNKLhGRFtDR0UFCQgJsbGw0xp89ewZbW1vk5OQIFBkRUdnEnlwiIgGlpKRApVJBpVIhNTUVBgYG6mO5ubnYt29fvsSXiIg+jkkuEZGAzM3NIZPJIJPJUKNGjXzHZTIZZs2aJUBkRERlG9sViIgEFB4eDpVKhdatW2Pnzp2wsLBQH9PX14ednR0qVqwoYIRERGUTk1wiIi0QFxeHqlWrQiaTCR0KEZEocHUFIiItcPjwYfzyyy/5xnfs2IGNGzcKEBERUdnGJJeISAsEBQXBysoq37iNjQ3mz58vQERERGUbk1wiIi0QHx8PBweHfON2dnaIj48XICIiorKNSS4RkRawsbHBpUuX8o1HR0fD0tJSgIiIiMo2JrlERFrAz88PY8aMwZEjR5Cbm4vc3FwcPnwYY8eOha+vr9DhERGVOVxdgYhIC2RlZaFfv37YsWMHdHVfL2Gel5eH/v37IyQkBPr6+gJHSERUtjDJJSLSIjdu3EB0dDQMDQ3h5uYGOzs7oUMiIiqTmOQSERERkehwW18iIoGMHz8ec+bMgbGxMcaPH//BuUuWLCmlqIiIxIFJLhGRQKKiopCdna3++n24CxoRUdGxXYGIiIiIRIdLiBERERGR6LBdgYhIID179iz03F27dpVgJERE4sNKLhGRQJRKpfpiZmaGsLAwnDt3Tn38/PnzCAsLg1KpFDBKIqKyiT25RERaYPLkyUhKSkJISAh0dHQAALm5uRgxYgTMzMzw7bffChwhEVHZwiSXiEgLWFtb48SJE3B2dtYYj4mJQZMmTZCYmChQZEREZRPbFYiItEBOTg6uX7+eb/z69evIy8sTICIiorKNJ54REWmBgQMHYvDgwbh16xYaNGgAADhz5gy++eYbDBw4UODoiIjKHrYrEBFpgby8PCxatAjLli1DQkICAKBChQoYO3YsJkyYoO7TJSKiwmGSS0SkZVJSUgAAZmZmAkdCRFR2sSeXiEhL5OTk4NChQ9i6dat6K9+HDx8iLS1N4MiIiMoeVnKJiLRAXFwcOnTogPj4eGRmZuLGjRtwdHTE2LFjkZmZiZCQEKFDJCIqU1jJJSLSAmPHjkW9evXw/PlzGBoaqsd79OiBsLAwASMjIiqbuLoCEZEWOH78OE6dOgV9fX2NcXt7ezx48ECgqIiIyi5WcomItEBeXh5yc3Pzjd+/fx+mpqYCREREVLYxySUi0gLt2rXD0qVL1d/LZDKkpaUhMDAQ3t7ewgVGRFRG8cQzIiItcO/ePXTo0AEqlQo3b95EvXr1cPPmTVhZWeHYsWOwsbEROkQiojKFSS4RkZbIycnBtm3bEB0djbS0NNStWxd9+vTROBGNiIgKh0kuEZHAsrOz4eLigr1798LV1VXocIiIRIE9uUREAtPT00NGRobQYRARiQqTXCIiLTBy5EgsWLAAOTk5QodCRCQKbFcgItICbzZ9MDExgZubG4yNjTWO79q1S6DIiIjKJm4GQUSkBczNzeHj4yN0GEREosFKLhERERGJDntyiYgElJeXhwULFqBp06aoX78+pkyZglevXgkdFhFRmcckl4hIQPPmzcP//d//wcTEBJUqVcKyZcswcuRIocMiIirz2K5ARCQgJycnBAQEYOjQoQCAQ4cOoVOnTnj16hXkctYhiIj+LSa5REQCUigUiI2NRZUqVdRjBgYGiI2NReXKlQWMjIiobGOZgIhIQDk5OTAwMNAY09PTQ3Z2tkARERGJA5cQIyISkEqlwoABA6BQKNRjGRkZGDZsmMZauVwnl4ioaJjkEhEJyN/fP99Y3759BYiEiEhc2JNLRERERKLDnlwiIiIiEh0muUREREQkOkxyiYiIiEh0mOQSERERkegwySUiIiIi0WGSS0RERESiwySXiIiIiESHSS4RERERiQ6TXCIiIiISnf8HKlerxSFKGJgAAAAASUVORK5CYII=",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "test_generator.reset()\n",
        "\n",
        "preds_1 = model.predict(test_generator, verbose=0)\n",
        "preds_1 = preds_1.argmax(axis=1)\n",
        "\n",
        "labels = ['cataract', 'diabetic_retinopathy', 'glaucoma', 'normal']\n",
        "cm = pd.DataFrame(data=confusion_matrix(test_generator.classes, preds_1, labels=range(len(labels))),\n",
        "                  index=[f\"Actual {label}\" for label in labels],\n",
        "                  columns=[f\"Predicted {label}\" for label in labels])\n",
        "sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\")\n",
        "\n",
        "print(\"\\n\")\n",
        "print(classification_report(y_true=test_generator.classes, y_pred=preds_1, target_names=labels, digits=4))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WFn0erAObLL5"
      },
      "source": [
        "# 4. Model Convertion\n",
        "## 4.1. Saved Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3KFhtemzVxJE",
        "outputId": "01a9fa0f-d446-42f7-c3cb-ca92b983a4b5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Saved artifact at './saved_model_1/eye_diseases_model_1'. The following endpoints are available:\n",
            "\n",
            "* Endpoint 'serve'\n",
            "  args_0 (POSITIONAL_ONLY): TensorSpec(shape=(None, 224, 224, 3), dtype=tf.float32, name='keras_tensor_238')\n",
            "Output Type:\n",
            "  TensorSpec(shape=(None, 4), dtype=tf.float32, name=None)\n",
            "Captures:\n",
            "  134377406630896: TensorSpec(shape=(1, 1, 1, 3), dtype=tf.float32, name=None)\n",
            "  134371365148928: TensorSpec(shape=(1, 1, 1, 3), dtype=tf.float32, name=None)\n",
            "  134371365071408: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371365069120: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371352475280: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371365068768: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371365068064: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371352477040: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371351085296: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371351087232: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371351086528: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371351087056: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371351093568: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371351097088: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371351093040: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371351099552: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371351098848: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371350694720: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371350692256: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371350692608: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371350693136: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371350695952: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371350694896: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371350700528: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371350697888: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371350698240: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371350704224: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371351056928: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371351059040: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371350707920: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371351054640: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371352471408: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371351058336: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371351067488: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371349811216: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371349812448: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371349811568: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371349817024: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371349814384: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371349814736: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371349818256: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371349817200: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371349822832: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371349820192: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371349820544: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371350202320: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371349809104: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371350707216: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371350204256: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371350204608: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371350207248: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371350207600: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371350202672: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371350210064: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371350210240: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371350206368: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371350214816: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371350212176: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371350212528: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371350055216: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371350055920: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371350059792: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371350057152: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371350057504: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371350063488: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371350054864: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371350068064: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371350065424: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371350065776: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371350155104: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371350158624: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371350154576: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371350161088: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371350161264: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371350154224: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371350165840: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371350163200: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371350163552: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371350167248: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371350234560: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371350235616: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371350234736: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371350235088: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371350239840: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371350234208: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
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            "  134371348729168: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
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            "  134371348626272: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
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            "  134371348626976: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371348776560: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371348777264: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
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            "  134371348786944: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371348784304: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371348784656: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371348789408: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371348859712: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371348857248: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371348857600: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371348858128: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371348864640: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371348868160: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371348864112: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371348870624: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371348870800: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371349021264: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371349021968: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371348872384: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371349021440: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371349027424: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371349022320: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371349032000: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371349029360: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371349029712: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371349034464: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371349086624: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371349088912: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371349036752: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371349086272: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371349093312: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371349096832: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371349092784: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371349099296: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371349099472: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371349102288: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371349235840: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371349100880: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371349101936: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371349239712: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371349234608: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371349244288: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371349241648: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371349242000: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371349247984: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371349351056: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371349348592: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371349350000: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371349349472: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371349355984: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371349359504: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371349355456: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371349361968: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371349362144: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371349414304: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371349416592: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371349364432: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371349413952: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371349417296: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371349416240: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371349421872: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371349419232: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371349419584: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371349424336: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371349422048: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371349428912: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371349426272: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371349426624: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371349532336: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371349535856: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371349531808: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371349538320: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371349538496: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371349531456: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371349543072: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371349540432: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371349540784: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371009890960: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371009891664: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371009895536: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371009892896: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371009893248: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371009898000: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371009888672: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371009902576: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371009899936: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371009900288: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371009989616: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371009993136: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371009989088: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371009995600: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371009995776: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371009988736: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371010000352: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371009997712: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371009998064: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371010070480: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371010071184: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371010075056: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371010072416: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371010072768: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371010077520: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371010069072: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371010082096: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371010079456: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371010079808: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371010185520: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371010189040: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371010184992: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371010191504: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371010191680: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371010184640: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371010196256: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371010193616: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371010193968: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371010299152: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371010299856: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371010303728: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371010301088: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371010301440: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371010306192: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371010298096: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371010310768: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371010308128: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371010308480: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371010414192: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371010417712: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371010413664: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371010420176: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371010420352: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371010413312: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371010424928: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371010422288: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371010422640: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371010426160: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371010511088: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371010513376: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371010427568: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371010511792: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134370736124704: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134370338251776: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371364876560: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134370736117136: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371352594208: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134370338257936: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371364879904: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134370338248960: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134370736115728: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134370338341280: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134370338248256: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134370736122768: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371364870400: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134370338355888: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "Model disimpan dalam format SavedModel di: ./saved_model_1/eye_diseases_model_1\n"
          ]
        }
      ],
      "source": [
        "saved_model_path = './saved_model_1/eye_diseases_model_1'\n",
        "model.export(saved_model_path)\n",
        "print(f\"Model disimpan dalam format SavedModel di: {saved_model_path}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tRY1o4Y-a42B"
      },
      "source": [
        "## 4.2. TF-Lite"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Tw-wljlwaKC5",
        "outputId": "387e1ce0-d0ea-42f3-92da-a8827124ac12"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Saved artifact at '/tmp/tmp6cgcm_pb'. The following endpoints are available:\n",
            "\n",
            "* Endpoint 'serve'\n",
            "  args_0 (POSITIONAL_ONLY): TensorSpec(shape=(None, 224, 224, 3), dtype=tf.float32, name='keras_tensor_238')\n",
            "Output Type:\n",
            "  TensorSpec(shape=(None, 4), dtype=tf.float32, name=None)\n",
            "Captures:\n",
            "  134377406630896: TensorSpec(shape=(1, 1, 1, 3), dtype=tf.float32, name=None)\n",
            "  134371365148928: TensorSpec(shape=(1, 1, 1, 3), dtype=tf.float32, name=None)\n",
            "  134371365071408: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371365069120: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371352475280: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371365068768: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371365068064: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371352477040: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371351085296: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371351087232: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371351086528: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371351087056: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
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            "  134371010189040: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371010184992: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371010191504: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371010191680: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371010184640: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371010196256: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371010193616: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371010193968: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371010299152: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371010299856: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371010303728: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371010301088: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371010301440: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371010306192: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371010298096: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371010310768: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371010308128: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371010308480: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371010414192: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371010417712: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371010413664: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371010420176: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371010420352: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371010413312: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371010424928: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371010422288: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371010422640: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371010426160: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371010511088: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371010513376: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371010427568: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371010511792: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134370736124704: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134370338251776: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371364876560: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134370736117136: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371352594208: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134370338257936: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371364879904: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134370338248960: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134370736115728: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134370338341280: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134370338248256: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134370736122768: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134371364870400: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "  134370338355888: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
            "Model disimpan dalam format TF-Lite di: ./saved_model_1/tflite_model/eye_diseases_model_1.tflite\n",
            "Labels disimpan dalam file: ./saved_model_1/tflite_model/label.txt\n"
          ]
        }
      ],
      "source": [
        "converter = tf.lite.TFLiteConverter.from_saved_model(saved_model_path)\n",
        "tflite_model = converter.convert()\n",
        "\n",
        "tflite_model_dir = './saved_model_1/tflite_model/'\n",
        "os.makedirs(tflite_model_dir, exist_ok=True)\n",
        "\n",
        "converter = tf.lite.TFLiteConverter.from_keras_model(model)\n",
        "tflite_model = converter.convert()\n",
        "\n",
        "tflite_model_path = os.path.join(tflite_model_dir, 'eye_diseases_model_1.tflite')\n",
        "with open(tflite_model_path, 'wb') as f:\n",
        "    f.write(tflite_model)\n",
        "\n",
        "labels = ['cataract', 'diabetic_retinopathy', 'glaucoma', 'normal']\n",
        "\n",
        "label_file_path = os.path.join(tflite_model_dir, 'label.txt')\n",
        "with open(label_file_path, 'w') as f:\n",
        "    for label in labels:\n",
        "        f.write(f\"{label}\\n\")\n",
        "\n",
        "print(f\"Model disimpan dalam format TF-Lite di: {tflite_model_path}\")\n",
        "print(f\"Labels disimpan dalam file: {label_file_path}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fbNlr3CqbTOS"
      },
      "source": [
        "## 4.3. Tensorflow.js"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SqnohkRbbGxd",
        "outputId": "ccb7737c-3bb5-46bf-fe81-3714e100721e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Collecting tensorflowjs\n",
            "  Downloading tensorflowjs-4.21.0-py3-none-any.whl.metadata (3.2 kB)\n",
            "Requirement already satisfied: flax>=0.7.2 in /usr/local/lib/python3.10/dist-packages (from tensorflowjs) (0.8.4)\n",
            "Requirement already satisfied: importlib_resources>=5.9.0 in /usr/local/lib/python3.10/dist-packages (from tensorflowjs) (6.4.5)\n",
            "Requirement already satisfied: jax>=0.4.13 in /usr/local/lib/python3.10/dist-packages (from tensorflowjs) (0.4.26)\n",
            "Requirement already satisfied: jaxlib>=0.4.13 in /usr/local/lib/python3.10/dist-packages (from tensorflowjs) (0.4.26+cuda12.cudnn89)\n",
            "Requirement already satisfied: tensorflow<3,>=2.13.0 in /usr/local/lib/python3.10/dist-packages (from tensorflowjs) (2.17.0)\n",
            "Requirement already satisfied: tf-keras>=2.13.0 in /usr/local/lib/python3.10/dist-packages (from tensorflowjs) (2.17.0)\n",
            "Collecting tensorflow-decision-forests>=1.5.0 (from tensorflowjs)\n",
            "  Downloading tensorflow_decision_forests-1.10.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (6.0 kB)\n",
            "Requirement already satisfied: six<2,>=1.16.0 in /usr/local/lib/python3.10/dist-packages (from tensorflowjs) (1.16.0)\n",
            "Requirement already satisfied: tensorflow-hub>=0.16.1 in /usr/local/lib/python3.10/dist-packages (from tensorflowjs) (0.16.1)\n",
            "Collecting packaging~=23.1 (from tensorflowjs)\n",
            "  Downloading packaging-23.2-py3-none-any.whl.metadata (3.2 kB)\n",
            "Requirement already satisfied: numpy>=1.22 in /usr/local/lib/python3.10/dist-packages (from flax>=0.7.2->tensorflowjs) (1.26.4)\n",
            "Requirement already satisfied: msgpack in /usr/local/lib/python3.10/dist-packages (from flax>=0.7.2->tensorflowjs) (1.0.8)\n",
            "Requirement already satisfied: optax in /usr/local/lib/python3.10/dist-packages (from flax>=0.7.2->tensorflowjs) (0.2.2)\n",
            "Requirement already satisfied: orbax-checkpoint in /usr/local/lib/python3.10/dist-packages (from flax>=0.7.2->tensorflowjs) (0.6.4)\n",
            "Requirement already satisfied: tensorstore in /usr/local/lib/python3.10/dist-packages (from flax>=0.7.2->tensorflowjs) (0.1.65)\n",
            "Requirement already satisfied: rich>=11.1 in /usr/local/lib/python3.10/dist-packages (from flax>=0.7.2->tensorflowjs) (13.8.1)\n",
            "Requirement already satisfied: typing-extensions>=4.2 in /usr/local/lib/python3.10/dist-packages (from flax>=0.7.2->tensorflowjs) (4.12.2)\n",
            "Requirement already satisfied: PyYAML>=5.4.1 in /usr/local/lib/python3.10/dist-packages (from flax>=0.7.2->tensorflowjs) (6.0.2)\n",
            "Requirement already satisfied: ml-dtypes>=0.2.0 in /usr/local/lib/python3.10/dist-packages (from jax>=0.4.13->tensorflowjs) (0.4.1)\n",
            "Requirement already satisfied: opt-einsum in /usr/local/lib/python3.10/dist-packages (from jax>=0.4.13->tensorflowjs) (3.3.0)\n",
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            "Requirement already satisfied: absl-py>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow<3,>=2.13.0->tensorflowjs) (1.4.0)\n",
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            "Requirement already satisfied: gast!=0.5.0,!=0.5.1,!=0.5.2,>=0.2.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow<3,>=2.13.0->tensorflowjs) (0.6.0)\n",
            "Requirement already satisfied: google-pasta>=0.1.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow<3,>=2.13.0->tensorflowjs) (0.2.0)\n",
            "Requirement already satisfied: h5py>=3.10.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow<3,>=2.13.0->tensorflowjs) (3.11.0)\n",
            "Requirement already satisfied: libclang>=13.0.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow<3,>=2.13.0->tensorflowjs) (18.1.1)\n",
            "Requirement already satisfied: protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3 in /usr/local/lib/python3.10/dist-packages (from tensorflow<3,>=2.13.0->tensorflowjs) (3.20.3)\n",
            "Requirement already satisfied: requests<3,>=2.21.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow<3,>=2.13.0->tensorflowjs) (2.32.3)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (from tensorflow<3,>=2.13.0->tensorflowjs) (71.0.4)\n",
            "Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow<3,>=2.13.0->tensorflowjs) (2.4.0)\n",
            "Requirement already satisfied: wrapt>=1.11.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow<3,>=2.13.0->tensorflowjs) (1.16.0)\n",
            "Requirement already satisfied: grpcio<2.0,>=1.24.3 in /usr/local/lib/python3.10/dist-packages (from tensorflow<3,>=2.13.0->tensorflowjs) (1.64.1)\n",
            "Requirement already satisfied: tensorboard<2.18,>=2.17 in /usr/local/lib/python3.10/dist-packages (from tensorflow<3,>=2.13.0->tensorflowjs) (2.17.0)\n",
            "Requirement already satisfied: keras>=3.2.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow<3,>=2.13.0->tensorflowjs) (3.4.1)\n",
            "Requirement already satisfied: tensorflow-io-gcs-filesystem>=0.23.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow<3,>=2.13.0->tensorflowjs) (0.37.1)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from tensorflow-decision-forests>=1.5.0->tensorflowjs) (2.1.4)\n",
            "Requirement already satisfied: wheel in /usr/local/lib/python3.10/dist-packages (from tensorflow-decision-forests>=1.5.0->tensorflowjs) (0.44.0)\n",
            "Collecting wurlitzer (from tensorflow-decision-forests>=1.5.0->tensorflowjs)\n",
            "  Downloading wurlitzer-3.1.1-py3-none-any.whl.metadata (2.5 kB)\n",
            "Collecting ydf (from tensorflow-decision-forests>=1.5.0->tensorflowjs)\n",
            "  Downloading ydf-0.7.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (3.8 kB)\n",
            "Requirement already satisfied: namex in /usr/local/lib/python3.10/dist-packages (from keras>=3.2.0->tensorflow<3,>=2.13.0->tensorflowjs) (0.0.8)\n",
            "Requirement already satisfied: optree in /usr/local/lib/python3.10/dist-packages (from keras>=3.2.0->tensorflow<3,>=2.13.0->tensorflowjs) (0.12.1)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorflow<3,>=2.13.0->tensorflowjs) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorflow<3,>=2.13.0->tensorflowjs) (3.10)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorflow<3,>=2.13.0->tensorflowjs) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorflow<3,>=2.13.0->tensorflowjs) (2024.8.30)\n",
            "Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.10/dist-packages (from rich>=11.1->flax>=0.7.2->tensorflowjs) (3.0.0)\n",
            "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /usr/local/lib/python3.10/dist-packages (from rich>=11.1->flax>=0.7.2->tensorflowjs) (2.18.0)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.18,>=2.17->tensorflow<3,>=2.13.0->tensorflowjs) (3.7)\n",
            "Requirement already satisfied: tensorboard-data-server<0.8.0,>=0.7.0 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.18,>=2.17->tensorflow<3,>=2.13.0->tensorflowjs) (0.7.2)\n",
            "Requirement already satisfied: werkzeug>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.18,>=2.17->tensorflow<3,>=2.13.0->tensorflowjs) (3.0.4)\n",
            "Requirement already satisfied: chex>=0.1.86 in /usr/local/lib/python3.10/dist-packages (from optax->flax>=0.7.2->tensorflowjs) (0.1.86)\n",
            "Requirement already satisfied: etils[epath,epy] in /usr/local/lib/python3.10/dist-packages (from orbax-checkpoint->flax>=0.7.2->tensorflowjs) (1.9.4)\n",
            "Requirement already satisfied: nest_asyncio in /usr/local/lib/python3.10/dist-packages (from orbax-checkpoint->flax>=0.7.2->tensorflowjs) (1.6.0)\n",
            "Requirement already satisfied: humanize in /usr/local/lib/python3.10/dist-packages (from orbax-checkpoint->flax>=0.7.2->tensorflowjs) (4.10.0)\n",
            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas->tensorflow-decision-forests>=1.5.0->tensorflowjs) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->tensorflow-decision-forests>=1.5.0->tensorflowjs) (2024.2)\n",
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            "Requirement already satisfied: MarkupSafe>=2.1.1 in /usr/local/lib/python3.10/dist-packages (from werkzeug>=1.0.1->tensorboard<2.18,>=2.17->tensorflow<3,>=2.13.0->tensorflowjs) (2.1.5)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from etils[epath,epy]->orbax-checkpoint->flax>=0.7.2->tensorflowjs) (2024.6.1)\n",
            "Requirement already satisfied: zipp in /usr/local/lib/python3.10/dist-packages (from etils[epath,epy]->orbax-checkpoint->flax>=0.7.2->tensorflowjs) (3.20.2)\n",
            "Downloading tensorflowjs-4.21.0-py3-none-any.whl (89 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m89.1/89.1 kB\u001b[0m \u001b[31m3.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading packaging-23.2-py3-none-any.whl (53 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m53.0/53.0 kB\u001b[0m \u001b[31m3.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading tensorflow_decision_forests-1.10.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (15.5 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m15.5/15.5 MB\u001b[0m \u001b[31m9.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading wurlitzer-3.1.1-py3-none-any.whl (8.6 kB)\n",
            "Downloading ydf-0.7.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (9.2 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m9.2/9.2 MB\u001b[0m \u001b[31m107.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: ydf, wurlitzer, packaging, tensorflow-decision-forests, tensorflowjs\n",
            "  Attempting uninstall: packaging\n",
            "    Found existing installation: packaging 24.1\n",
            "    Uninstalling packaging-24.1:\n",
            "      Successfully uninstalled packaging-24.1\n",
            "Successfully installed packaging-23.2 tensorflow-decision-forests-1.10.0 tensorflowjs-4.21.0 wurlitzer-3.1.1 ydf-0.7.0\n"
          ]
        }
      ],
      "source": [
        "!pip install tensorflowjs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zvO6PpCUbYds",
        "outputId": "9ed2a32f-cd51-4286-eebc-2a89e31f9583"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "failed to lookup keras version from the file,\n",
            "    this is likely a weight only file\n",
            "weight normalization/count with shape () and dtype int64 was auto converted to the type int32\n"
          ]
        }
      ],
      "source": [
        "import tensorflowjs as tfjs\n",
        "\n",
        "tfjs_model_dir = './saved_model_1/tfjs_model/'\n",
        "os.makedirs(tfjs_model_dir, exist_ok=True)\n",
        "\n",
        "tfjs_model_path = os.path.join(tfjs_model_dir, 'model')\n",
        "tfjs.converters.save_keras_model(model, tfjs_model_path)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4Fo5gyjvbfXE",
        "outputId": "8fb3914c-7371-4b2f-f5ae-e300909ff975"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Folder './saved_model_1' telah dikompres menjadi './eye_saved_model_1.zip'.\n"
          ]
        }
      ],
      "source": [
        "folder_to_zip = './saved_model_1'\n",
        "zip_file_path = './eye_saved_model_1.zip'\n",
        "\n",
        "shutil.make_archive(base_name=zip_file_path.replace('.zip', ''), format='zip', root_dir=os.path.dirname(folder_to_zip), base_dir=os.path.basename(folder_to_zip))\n",
        "\n",
        "print(f\"Folder '{folder_to_zip}' telah dikompres menjadi '{zip_file_path}'.\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "9nOiIJ1tbvRW",
        "outputId": "b39ea4a9-0197-4304-aaae-1750c3c30876"
      },
      "outputs": [
        {
          "data": {
            "application/javascript": "\n    async function download(id, filename, size) {\n      if (!google.colab.kernel.accessAllowed) {\n        return;\n      }\n      const div = document.createElement('div');\n      const label = document.createElement('label');\n      label.textContent = `Downloading \"${filename}\": `;\n      div.appendChild(label);\n      const progress = document.createElement('progress');\n      progress.max = size;\n      div.appendChild(progress);\n      document.body.appendChild(div);\n\n      const buffers = [];\n      let downloaded = 0;\n\n      const channel = await google.colab.kernel.comms.open(id);\n      // Send a message to notify the kernel that we're ready.\n      channel.send({})\n\n      for await (const message of channel.messages) {\n        // Send a message to notify the kernel that we're ready.\n        channel.send({})\n        if (message.buffers) {\n          for (const buffer of message.buffers) {\n            buffers.push(buffer);\n            downloaded += buffer.byteLength;\n            progress.value = downloaded;\n          }\n        }\n      }\n      const blob = new Blob(buffers, {type: 'application/binary'});\n      const a = document.createElement('a');\n      a.href = window.URL.createObjectURL(blob);\n      a.download = filename;\n      div.appendChild(a);\n      a.click();\n      div.remove();\n    }\n  ",
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/javascript": "download(\"download_aebbcf4a-0ac0-477f-be00-7a44bcd73d37\", \"eye_saved_model_1.zip\", 149189880)",
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from google.colab import files\n",
        "\n",
        "zip_file_path = './eye_saved_model_1.zip'\n",
        "files.download(zip_file_path)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sWwT-Rl3b2GU"
      },
      "source": [
        "# 5. Inference with Saved Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z1SYPWCkuGco",
        "outputId": "e0e8688d-7559-4247-ccb1-a1664b30278a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "File telah diekstrak ke folder: eye_saved_model\n"
          ]
        }
      ],
      "source": [
        "import zipfile\n",
        "import os\n",
        "\n",
        "zip_file_path = 'eye_saved_model_1.zip'\n",
        "output_folder = 'eye_saved_model'\n",
        "\n",
        "os.makedirs(output_folder, exist_ok=True)\n",
        "\n",
        "with zipfile.ZipFile(zip_file_path, 'r') as zip_ref:\n",
        "    zip_ref.extractall(output_folder)\n",
        "\n",
        "print(f\"File telah diekstrak ke folder: {output_folder}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "8rmX6Oi0b0eg"
      },
      "outputs": [],
      "source": [
        "def predict_images(folder_path):\n",
        "    model_path = './eye_saved_model/saved_model_1/eye_diseases_model_1'\n",
        "    model = tf.saved_model.load(model_path)\n",
        "    infer = model.signatures['serving_default']\n",
        "\n",
        "    labels = ['cataract', 'diabetic_retinopathy', 'glaucoma', 'normal']\n",
        "    custom_labels = {\n",
        "        'c': 'cataract',\n",
        "        'dr': 'diabetic retinopathy',\n",
        "        'h': 'healthy',\n",
        "        'g': 'glaucoma'\n",
        "    }\n",
        "\n",
        "    images = []\n",
        "    titles = []\n",
        "    actual_labels = []\n",
        "\n",
        "    for filename in os.listdir(folder_path):\n",
        "        file_path = os.path.join(folder_path, filename)\n",
        "        if filename.lower().endswith(('.png', '.jpg', '.jpeg', '.bmp', '.gif')):\n",
        "            image = Image.open(file_path)\n",
        "            image = image.resize((224, 224))\n",
        "            image_array = np.array(image).astype(np.float32) / 255.0\n",
        "            image_array = np.expand_dims(image_array, axis=0)\n",
        "\n",
        "            predictions = infer(tf.convert_to_tensor(image_array, dtype=tf.float32))['output_0']\n",
        "            predicted_index = np.argmax(predictions.numpy(), axis=1)[0]\n",
        "            predicted_probability = np.max(predictions.numpy()) * 100\n",
        "            predicted_label = labels[predicted_index]\n",
        "\n",
        "            actual_label = filename.split('_')[-1].split('.')[0]\n",
        "            actual_label = custom_labels.get(actual_label, actual_label)\n",
        "\n",
        "            title = f\"actual: {actual_label}\\n predicted: {predicted_label} ({predicted_probability:.2f}%)\"\n",
        "            titles.append(title)\n",
        "            images.append(image)\n",
        "\n",
        "    num_images = len(images)\n",
        "    num_columns = 3\n",
        "    num_rows = (num_images + num_columns - 1) // num_columns\n",
        "\n",
        "    plt.figure(figsize=(13, num_rows * 5))\n",
        "\n",
        "    for i in range(num_images):\n",
        "        plt.subplot(num_rows, num_columns, i + 1)\n",
        "        plt.imshow(images[i])\n",
        "        plt.axis('off')\n",
        "        plt.title(titles[i])\n",
        "\n",
        "    plt.suptitle(\"Prediction\", y=0.85)\n",
        "    plt.subplots_adjust(top=0.75, wspace=0.3, hspace=0.4)\n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0FBHNK5_chyI",
        "outputId": "fb1cfd64-5aac-4caa-ae76-a4552103015e"
      },
      "outputs": [
        {
          "ename": "NameError",
          "evalue": "name 'os' is not defined",
          "output_type": "error",
          "traceback": [
            "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "Cell \u001b[1;32mIn[2], line 3\u001b[0m\n\u001b[0;32m      1\u001b[0m folder_path \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m/content/new_eye_data\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m----> 3\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[43mos\u001b[49m\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mexists(folder_path):\n\u001b[0;32m      4\u001b[0m     shutil\u001b[38;5;241m.\u001b[39mrmtree(folder_path)\n\u001b[0;32m      5\u001b[0m     \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFolder \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfolder_path\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m telah dihapus.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
            "\u001b[1;31mNameError\u001b[0m: name 'os' is not defined"
          ]
        }
      ],
      "source": [
        "folder_path = '/content/new_eye_data'\n",
        "\n",
        "if os.path.exists(folder_path):\n",
        "    shutil.rmtree(folder_path)\n",
        "    print(f\"Folder '{folder_path}' telah dihapus.\")\n",
        "else:\n",
        "    print(f\"Folder '{folder_path}' tidak ditemukan.\")\n",
        "\n",
        "zip_file_path = '/content/new eye data.zip'\n",
        "extract_to = '/content/new_eye_data'\n",
        "\n",
        "os.makedirs(extract_to, exist_ok=True)\n",
        "\n",
        "with zipfile.ZipFile(zip_file_path, 'r') as zip_ref:\n",
        "    zip_ref.extractall(extract_to)\n",
        "\n",
        "print(f\"Folder {extract_to} telah dibuat\")\n",
        "print(f\"File diekstrak ke {extract_to}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 71,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 570
        },
        "id": "-ifwJRpyrimj",
        "outputId": "77b6c5e2-24b2-41fe-ddb4-5bebd7598808"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1300x1000 with 6 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "folder_path = '/content/new_eye_data/new eye data'\n",
        "predict_images(folder_path)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3hj2dknMrt3M"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "gpuType": "T4",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
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